Adds export functions for decorators (fixes #36)

Author: dobener

examples/SiO2_Si Mueller Matrix/SiO2_Si Mueller Matrix.ipynb

@@ -46,7 +46,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2c9b20db144043b9b3a4576021bb5c7b",
+       "model_id": "7869aa4a5e2a4d669019f040ba1222e9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -78,20 +78,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 5,
    "id": "dc1ca09c-b0a6-4ce5-8715-fed86f1da48f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h2>Fit Statistics</h2><table><tr><td>fitting method</td><td>leastsq</td><td></td></tr><tr><td># function evals</td><td>98</td><td></td></tr><tr><td># data points</td><td>22336</td><td></td></tr><tr><td># variables</td><td>7</td><td></td></tr><tr><td>chi-square</td><td> 4.15193667</td><td></td></tr><tr><td>reduced chi-square</td><td> 1.8594e-04</td><td></td></tr><tr><td>Akaike info crit.</td><td>-191860.730</td><td></td></tr><tr><td>Bayesian info crit.</td><td>-191804.632</td><td></td></tr></table><h2>Variables</h2><table><tr><th> name </th><th> value </th><th> standard error </th><th> relative error </th><th> initial value </th><th> min </th><th> max </th><th> vary </th></tr><tr><td> SiO2_n0 </td><td>  1.46444761 </td><td>  7.0599e-04 </td><td> (0.05%) </td><td> 1.452 </td><td> -100.000000 </td><td>  100.000000 </td><td> True </td></tr><tr><td> SiO2_n1 </td><td> -0.13383336 </td><td>  0.58947808 </td><td> (440.46%) </td><td> 36.0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> SiO2_n2 </td><td>  15.9280047 </td><td>  0.28218991 </td><td> (1.77%) </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> SiO2_k0 </td><td>  0.01718283 </td><td>  6.4320e-04 </td><td> (3.74%) </td><td> 0 </td><td> -100.000000 </td><td>  100.000000 </td><td> True </td></tr><tr><td> SiO2_k1 </td><td> -24.0903682 </td><td>  1.03845809 </td><td> (4.31%) </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> SiO2_k2 </td><td>  9.17505029 </td><td>  0.41326998 </td><td> (4.50%) </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> SiO2_d </td><td>  104.067145 </td><td>  0.10335035 </td><td> (0.10%) </td><td> 120 </td><td>  0.00000000 </td><td>  40000.0000 </td><td> True </td></tr></table><h2>Correlations (unreported correlations are < 0.100)</h2><table><tr><td>SiO2_k1</td><td>SiO2_k2</td><td>-0.9764</td></tr><tr><td>SiO2_k0</td><td>SiO2_k1</td><td>-0.9640</td></tr><tr><td>SiO2_n0</td><td>SiO2_d</td><td>-0.9557</td></tr><tr><td>SiO2_n1</td><td>SiO2_n2</td><td>-0.9513</td></tr><tr><td>SiO2_k0</td><td>SiO2_d</td><td>0.9464</td></tr><tr><td>SiO2_n0</td><td>SiO2_k0</td><td>-0.9044</td></tr><tr><td>SiO2_k0</td><td>SiO2_k2</td><td>0.8935</td></tr><tr><td>SiO2_k1</td><td>SiO2_d</td><td>-0.8400</td></tr><tr><td>SiO2_n0</td><td>SiO2_k1</td><td>0.8028</td></tr><tr><td>SiO2_k2</td><td>SiO2_d</td><td>0.7319</td></tr><tr><td>SiO2_n0</td><td>SiO2_k2</td><td>-0.6995</td></tr><tr><td>SiO2_n1</td><td>SiO2_d</td><td>-0.2971</td></tr><tr><td>SiO2_n1</td><td>SiO2_k0</td><td>-0.2812</td></tr><tr><td>SiO2_n1</td><td>SiO2_k1</td><td>0.2496</td></tr><tr><td>SiO2_n1</td><td>SiO2_k2</td><td>-0.2175</td></tr><tr><td>SiO2_n0</td><td>SiO2_n2</td><td>0.2059</td></tr></table>"
+       "<h2>Fit Statistics</h2><table><tr><td>fitting method</td><td>leastsq</td><td></td></tr><tr><td># function evals</td><td>107</td><td></td></tr><tr><td># data points</td><td>22336</td><td></td></tr><tr><td># variables</td><td>7</td><td></td></tr><tr><td>chi-square</td><td> 3.00724141</td><td></td></tr><tr><td>reduced chi-square</td><td> 1.3468e-04</td><td></td></tr><tr><td>Akaike info crit.</td><td>-199065.245</td><td></td></tr><tr><td>Bayesian info crit.</td><td>-199009.148</td><td></td></tr></table><h2>Variables</h2><table><tr><th> name </th><th> value </th><th> standard error </th><th> relative error </th><th> initial value </th><th> min </th><th> max </th><th> vary </th></tr><tr><td> SiO2_n0 </td><td>  1.45271092 </td><td>  5.7283e-04 </td><td> (0.04%) </td><td> 1.452 </td><td> -100.000000 </td><td>  100.000000 </td><td> True </td></tr><tr><td> SiO2_n1 </td><td>  30.7219120 </td><td>  0.55071825 </td><td> (1.79%) </td><td> 36.0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> SiO2_n2 </td><td>  3.41046425 </td><td>  0.25306938 </td><td> (7.42%) </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> SiO2_k0 </td><td>  0.00393829 </td><td>  5.7854e-04 </td><td> (14.69%) </td><td> 0 </td><td> -100.000000 </td><td>  100.000000 </td><td> True </td></tr><tr><td> SiO2_k1 </td><td> -6.96288298 </td><td>  0.91853048 </td><td> (13.19%) </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> SiO2_k2 </td><td>  3.16088017 </td><td>  0.36345646 </td><td> (11.50%) </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> SiO2_d </td><td>  103.560409 </td><td>  0.08979888 </td><td> (0.09%) </td><td> 120 </td><td>  0.00000000 </td><td>  40000.0000 </td><td> True </td></tr></table><h2>Correlations (unreported correlations are < 0.100)</h2><table><tr><td>SiO2_k1</td><td>SiO2_k2</td><td>-0.9755</td></tr><tr><td>SiO2_k0</td><td>SiO2_k1</td><td>-0.9639</td></tr><tr><td>SiO2_n1</td><td>SiO2_n2</td><td>-0.9509</td></tr><tr><td>SiO2_k0</td><td>SiO2_d</td><td>0.9506</td></tr><tr><td>SiO2_n0</td><td>SiO2_d</td><td>-0.9496</td></tr><tr><td>SiO2_n0</td><td>SiO2_k0</td><td>-0.9026</td></tr><tr><td>SiO2_k0</td><td>SiO2_k2</td><td>0.8907</td></tr><tr><td>SiO2_k1</td><td>SiO2_d</td><td>-0.8461</td></tr><tr><td>SiO2_n0</td><td>SiO2_k1</td><td>0.8035</td></tr><tr><td>SiO2_k2</td><td>SiO2_d</td><td>0.7348</td></tr><tr><td>SiO2_n0</td><td>SiO2_k2</td><td>-0.6978</td></tr><tr><td>SiO2_n1</td><td>SiO2_d</td><td>-0.4581</td></tr><tr><td>SiO2_n1</td><td>SiO2_k0</td><td>-0.4355</td></tr><tr><td>SiO2_n1</td><td>SiO2_k1</td><td>0.3877</td></tr><tr><td>SiO2_n1</td><td>SiO2_k2</td><td>-0.3367</td></tr><tr><td>SiO2_n2</td><td>SiO2_d</td><td>0.2265</td></tr><tr><td>SiO2_n2</td><td>SiO2_k0</td><td>0.2153</td></tr><tr><td>SiO2_n2</td><td>SiO2_k1</td><td>-0.1916</td></tr><tr><td>SiO2_n2</td><td>SiO2_k2</td><td>0.1664</td></tr><tr><td>SiO2_n0</td><td>SiO2_n1</td><td>0.1663</td></tr></table>"
       ],
       "text/plain": [
-       "<lmfit.minimizer.MinimizerResult at 0x24dde4b87c8>"
+       "<lmfit.minimizer.MinimizerResult at 0x28891ca4670>"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -102,14 +102,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "71878507-d611-4507-a011-f5b9029001e9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9d6bd609f0ba43879bb29f8d83292817",
+       "model_id": "1508bca945114e18b0f11ad3af3c608b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -131,17 +131,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "86384356-dfdb-4033-946f-976620006609",
+   "id": "7de31826-3e98-4ade-9359-01ae9239b849",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python (elli)",
    "language": "python",
-   "name": "python3"
+   "name": "myenv"
   },
   "language_info": {
    "codemirror_mode": {

examples/TiO2 Fit/TiO2 Multilayerfit.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 24,
    "id": "complimentary-longer",
    "metadata": {},
    "outputs": [],
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 25,
    "id": "elect-disorder",
    "metadata": {},
    "outputs": [],
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 26,
    "id": "9f600c98-dbc9-4772-9d5f-8ed74c997b91",
    "metadata": {},
    "outputs": [],
@@ -68,14 +68,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 27,
    "id": "f432f126-b688-426a-8b94-abedd44f0e78",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "efc67653182646d9899bb1729821c731",
+       "model_id": "2361aeeab1924bd48c742d28f932f7aa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -127,20 +127,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 28,
    "id": "822af8b9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h2>Fit Statistics</h2><table><tr><td>fitting method</td><td>leastsq</td><td></td></tr><tr><td># function evals</td><td>31</td><td></td></tr><tr><td># data points</td><td>1852</td><td></td></tr><tr><td># variables</td><td>4</td><td></td></tr><tr><td>chi-square</td><td> 0.04559962</td><td></td></tr><tr><td>reduced chi-square</td><td> 2.4675e-05</td><td></td></tr><tr><td>Akaike info crit.</td><td>-19645.1968</td><td></td></tr><tr><td>Bayesian info crit.</td><td>-19623.1007</td><td></td></tr></table><h2>Variables</h2><table><tr><th> name </th><th> value </th><th> standard error </th><th> relative error </th><th> initial value </th><th> min </th><th> max </th><th> vary </th></tr><tr><td> SiO2_n0 </td><td>  1.45200000 </td><td>  0.00000000 </td><td> (0.00%) </td><td> 1.452 </td><td> -100.000000 </td><td>  100.000000 </td><td> False </td></tr><tr><td> SiO2_n1 </td><td>  36.0000000 </td><td>  0.00000000 </td><td> (0.00%) </td><td> 36.0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> SiO2_n2 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> SiO2_k0 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -100.000000 </td><td>  100.000000 </td><td> False </td></tr><tr><td> SiO2_k1 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> SiO2_k2 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> SiO2_d </td><td>  276.360000 </td><td>  0.00000000 </td><td> (0.00%) </td><td> 276.36 </td><td>  0.00000000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> TiO2_n0 </td><td>  2.23183168 </td><td>  0.00494640 </td><td> (0.22%) </td><td> 2.236 </td><td> -100.000000 </td><td>  100.000000 </td><td> True </td></tr><tr><td> TiO2_n1 </td><td>  449.068377 </td><td>  23.2018459 </td><td> (5.17%) </td><td> 451 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> TiO2_n2 </td><td>  199.772645 </td><td>  26.9846271 </td><td> (13.51%) </td><td> 251 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> TiO2_k0 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -100.000000 </td><td>  100.000000 </td><td> False </td></tr><tr><td> TiO2_k1 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> TiO2_k2 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> TiO2_d </td><td>  24.8772291 </td><td>  0.01058302 </td><td> (0.04%) </td><td> 20 </td><td>  0.00000000 </td><td>  40000.0000 </td><td> True </td></tr></table><h2>Correlations (unreported correlations are < 0.100)</h2><table><tr><td>TiO2_n0</td><td>TiO2_n1</td><td>-0.9910</td></tr><tr><td>TiO2_n1</td><td>TiO2_n2</td><td>-0.9879</td></tr><tr><td>TiO2_n0</td><td>TiO2_n2</td><td>0.9640</td></tr></table>"
+       "<h2>Fit Statistics</h2><table><tr><td>fitting method</td><td>leastsq</td><td></td></tr><tr><td># function evals</td><td>31</td><td></td></tr><tr><td># data points</td><td>1852</td><td></td></tr><tr><td># variables</td><td>4</td><td></td></tr><tr><td>chi-square</td><td> 0.04559962</td><td></td></tr><tr><td>reduced chi-square</td><td> 2.4675e-05</td><td></td></tr><tr><td>Akaike info crit.</td><td>-19645.1968</td><td></td></tr><tr><td>Bayesian info crit.</td><td>-19623.1007</td><td></td></tr></table><h2>Variables</h2><table><tr><th> name </th><th> value </th><th> standard error </th><th> relative error </th><th> initial value </th><th> min </th><th> max </th><th> vary </th></tr><tr><td> SiO2_n0 </td><td>  1.45200000 </td><td>  0.00000000 </td><td> (0.00%) </td><td> 1.452 </td><td> -100.000000 </td><td>  100.000000 </td><td> False </td></tr><tr><td> SiO2_n1 </td><td>  36.0000000 </td><td>  0.00000000 </td><td> (0.00%) </td><td> 36.0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> SiO2_n2 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> SiO2_k0 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -100.000000 </td><td>  100.000000 </td><td> False </td></tr><tr><td> SiO2_k1 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> SiO2_k2 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> SiO2_d </td><td>  276.360000 </td><td>  0.00000000 </td><td> (0.00%) </td><td> 276.36 </td><td>  0.00000000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> TiO2_n0 </td><td>  2.23183197 </td><td>  0.00494641 </td><td> (0.22%) </td><td> 2.236 </td><td> -100.000000 </td><td>  100.000000 </td><td> True </td></tr><tr><td> TiO2_n1 </td><td>  449.066997 </td><td>  23.2018577 </td><td> (5.17%) </td><td> 451 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> TiO2_n2 </td><td>  199.774273 </td><td>  26.9846794 </td><td> (13.51%) </td><td> 251 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> True </td></tr><tr><td> TiO2_k0 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -100.000000 </td><td>  100.000000 </td><td> False </td></tr><tr><td> TiO2_k1 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> TiO2_k2 </td><td>  0.00000000 </td><td>  0.00000000 </td><td>  </td><td> 0 </td><td> -40000.0000 </td><td>  40000.0000 </td><td> False </td></tr><tr><td> TiO2_d </td><td>  24.8772290 </td><td>  0.01058302 </td><td> (0.04%) </td><td> 20 </td><td>  0.00000000 </td><td>  40000.0000 </td><td> True </td></tr></table><h2>Correlations (unreported correlations are < 0.100)</h2><table><tr><td>TiO2_n0</td><td>TiO2_n1</td><td>-0.9910</td></tr><tr><td>TiO2_n1</td><td>TiO2_n2</td><td>-0.9879</td></tr><tr><td>TiO2_n0</td><td>TiO2_n2</td><td>0.9640</td></tr></table>"
       ],
       "text/plain": [
-       "<lmfit.minimizer.MinimizerResult at 0x7feb73500af0>"
+       "<lmfit.minimizer.MinimizerResult at 0x275fd719cd0>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,14 +160,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 29,
    "id": "d829a852-1e1e-445b-a6b5-eaaa2ebaf47e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e27f4a5996174e32ba0990957a68437d",
+       "model_id": "53536c7d876341f889d915d3130254d2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -187,14 +187,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 30,
    "id": "cc3cdccd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2e4045c458da4f47b2612a7a53785ed2",
+       "model_id": "705d9b025e8f4dac9e3ca691bc05b3b9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -215,7 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c379e71a",
+   "id": "2a81819c-56b5-4b21-954d-0013f693e946",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -229,9 +229,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "elli",
+   "display_name": "Python (elli)",
    "language": "python",
-   "name": "elli"
+   "name": "myenv"
   },
   "language_info": {
    "codemirror_mode": {

examples/TiO2 Fit/TiO2 Multilayerfit.md

@@ -8,9 +8,9 @@ jupyter:
       format_version: '1.3'
       jupytext_version: 1.11.4
   kernelspec:
-    display_name: Python 3 (ipykernel)
+    display_name: Python (elli)
     language: python
-    name: python3
+    name: myenv
 ---
 
 ```python

src/elli/fitting/decorator.py

@@ -2,6 +2,8 @@
 # Encoding: utf-8
 from abc import ABC, abstractmethod
 from ipywidgets import widgets
+from lmfit import Parameters
+import pandas as pd
 from .params_hist import ParamsHist
 
 
@@ -15,6 +17,50 @@ def __init__(self) -> None:
         self.last_params = ParamsHist()
         self.param_widgets = {}
 
+    @abstractmethod
+    def get_model_data(self,
+                       params:Parameters=None,
+                       append_exp_data=False) -> pd.DataFrame:     
+        """Gets the data from the provided model with the provided parameters.
+        If no parameters are provided, the fitted parameters are used
+        (which default to the initial parameters if no fit has been triggered).
+
+        Args:
+            params (Parameters, optional): 
+                The parameters to calculate the model with. 
+                If not provided, the fitted parameters are used.
+                Defaults to None.
+            append_exp_data (bool, optional):
+                Appends the experimental data if set to True.
+                Defaults to False.
+
+        Returns:
+            pd.DataFrame: The model results
+        """
+        ...
+
+    def to_csv(self,
+              fname:str,
+              params:Parameters=None,
+              append_exp_data:bool=False,
+              *args,
+              **kwargs) -> None:
+        """Saves the current model to csv. This is just a wrapper to
+        pandas Dataframe and any argument to pandas to_csv may be passed
+        as function arguments.
+
+        Args:
+            fname (str): The file name to save the data to.
+            params (Parameters, optional): 
+                The parameters to calculate the model with.
+                If not provided, the fitted parameters are used.
+                Defaults to None.
+            append_exp_data (bool, optional):
+                Appends the experimental data if set to True.
+                Defaults to False.
+        """
+        self.get_model_data(params, append_exp_data).to_csv(fname, *args, **kwargs)
+
     @abstractmethod
     def fit(self, method: str = '') -> None:
         """Execute lmfit with the current fitting parameters