Anselmoo · Anselmoo · Feb 20, 2023 · Feb 17, 2023 · Feb 17, 2023 · Feb 18, 2023
@@ -10,6 +10,27 @@
 
 ---
 
+## v0.16.0
+
+---
+
+- Add [Cumulative Distribution Function][38] to the `SpectraFit` package
+- Refactor the `model.py` of `SpectraFit` package
+
+## v0.15.1
+
+---
+
+- Maintenance of the `SpectraFit` package
+
+## v0.15.0
+
+---
+
+- Add `plugins` to the `SpectraFit` package for working with [RIXS][36] data
+- `pikle`-file converter and visualizer for [RIXS][36] data
+- Simplify the `SpectraFit` continous deployment by using [build][37]
+
 ## v0.14.0
 
 ---
@@ -343,3 +364,6 @@
 [33]: https://github.com/conda-forge/spectrafit-feedstock
 [34]: https://fishshell.com
 [35]: https://githubnext.com/projects/copilot-labs/
+[36]: https://en.wikipedia.org/wiki/Resonant_inelastic_X-ray_scattering
+[37]: https://github.com/pypa/build
+[38]: https://en.wikipedia.org/wiki/Cumulative_distribution_function
@@ -67,23 +67,31 @@
 
 Here is a list of implemented models of `spectrafit`:
 
-::: spectrafit.models.gaussian
+::: spectrafit.models.DistributionModels.gaussian
 
-::: spectrafit.models.lorentzian
+::: spectrafit.models.DistributionModels.lorentzian
 
-::: spectrafit.models.pseudovoigt
+::: spectrafit.models.DistributionModels.pseudovoigt
 
-::: spectrafit.models.voigt
+::: spectrafit.models.DistributionModels.voigt
 
-::: spectrafit.models.exponential
+::: spectrafit.models.DistributionModels.exponential
 
-::: spectrafit.models.power
+::: spectrafit.models.DistributionModels.power
 
-::: spectrafit.models.linear
+::: spectrafit.models.DistributionModels.linear
 
-::: spectrafit.models.constant
+::: spectrafit.models.DistributionModels.constant
 
-::: spectrafit.models.step
+::: spectrafit.models.DistributionModels.erf
+
+::: spectrafit.models.DistributionModels.heaviside
+
+::: spectrafit.models.DistributionModels.atan
+
+::: spectrafit.models.DistributionModels.log
+
+::: spectrafit.models.DistributionModels.c_gaussian
 
 ## Important constans for the models
 

@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,20 +53,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "##  Warning: uncertainties could not be estimated:\n",
-      "'MinimizerResult' object has no attribute 'covar': Covariance Matrix could not be calculated.\n",
-      "\n",
-      "Error: Cannot determine Confidence Intervals without sensible uncertainty estimates -> No confidence interval could be calculated!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "initial_model = [\n",
     "    {\n",
@@ -127,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,22 +127,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[PosixPath('fit_example9_3.csv'),\n",
-       " PosixPath('act_example9_3.csv'),\n",
-       " PosixPath('org_example9_3.csv')]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -184,7 +160,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9 (main, Dec 21 2022, 15:55:14) [Clang 14.0.0 (clang-1400.0.29.202)]"
+   "version": "3.10.9"
   },
   "orig_nbformat": 4,
   "vscode": {

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,40 +71,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"100%\"\n",
-       "            height=\"650\"\n",
-       "            src=\"http://127.0.0.1:8050/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x2a0381390>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "_app.app_run()"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![_](https://github.com/Anselmoo/spectrafit/raw/main/docs/examples/images/Figure_9_4.png)"
+    "![_](https://github.com/Anselmoo/spectrafit/blob/9f46aac202a6edfe8d063ecd46fadd3c14a6e28d/docs/examples/images/Figure_9_4.png?raw=true)"
    ]
   }
  ],

@@ -88,7 +88,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9 (main, Dec 21 2022, 15:55:14) [Clang 14.0.0 (clang-1400.0.29.202)]"
+   "version": "3.10.9"
   },
   "orig_nbformat": 4,
   "vscode": {

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,17 +64,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "##  Warning: uncertainties could not be estimated:\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "initial_model = [\n",
     "    {\n",
@@ -121,7 +113,7 @@
     "        }\n",
     "    },\n",
     "]\n",
-    "spn.solver_model(initial_model=initial_model, show_plot=False, show_metric=False)"
+    "spn.solver_model(initial_model=initial_model, show_plot=True, show_metric=False)"
    ]
   },
   {
@@ -131,8 +123,16 @@
    "source": [
     "#### Note:\n",
     "\n",
-    "Currently, the global fitting routine has no plot function. However, the\n",
-    "metric function can be used to plot the fit errors."
+    "Currently, the global fitting routine provides each spectrum as its own.\n",
+    "This also allows to use other color schemes."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://github.com/Anselmoo/spectrafit/raw/main/docs/examples/images/Figure_9_6.png)"
    ]
   }
  ],

@@ -153,6 +153,7 @@ nav:
           - Export Results: examples/example9_3.ipynb
           - RIXS Map Visualization: examples/example9_4.ipynb
           - Using Converters as Module: examples/example9_5.ipynb
+          - RIXS Global-Fitting in Jupyter: examples/example9_6.ipynb
   - Documentation:
       - Models: doc/models.md
       - Expression: doc/expression.md