diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md
index 7d82d65..7129b1d 100644
--- a/DEVELOPMENT.md
+++ b/DEVELOPMENT.md
@@ -34,7 +34,7 @@ To add a development dependency, run `uv add --dev <dependency>`.
 
 ## Testing
 
-To run tests, run either `uv run pytest`.
+To run tests, run `uv run pytest`.
 
 
 
@@ -43,7 +43,7 @@ To run tests, run either `uv run pytest`.
 - Never push to `main` directly
 - Create feature branches named `feature/{initials}_{feature_slug}` (e.g. `git checkout -b feature/sd_my_awesome_feature`)
 - Create a Pull Request to `main` when ready
-- Try not to merge your own PRs (we might enforce stricter rules later but for now let's keep it simple and fast)
+- Try not to merge your own PRs
 
 
 ## Pre-PR checklist
diff --git a/README.md b/README.md
index e5792fe..cfb709f 100644
--- a/README.md
+++ b/README.md
@@ -4,10 +4,8 @@ A python package to analyse lactate based tresholds. Strongly inspired by [lacte
 
 docs can be found: https://dierickxsimon.github.io/lactopy/
 
-Still a work in progress...
 
-
-`lactopy` can be installed with pip:
+`lactopy` can be installed with `pip`:
 
 ```bash
 pip install lactopy
diff --git a/docs/assets/flexible-robust-diagram.png b/docs/assets/flexible-robust-diagram.png
new file mode 100644
index 0000000..9bb0599
Binary files /dev/null and b/docs/assets/flexible-robust-diagram.png differ
diff --git a/docs/assets/flexible-robust-graph.png b/docs/assets/flexible-robust-graph.png
new file mode 100644
index 0000000..b4a509d
Binary files /dev/null and b/docs/assets/flexible-robust-graph.png differ
diff --git a/docs/css/custom_css.css b/docs/css/custom_css.css
new file mode 100644
index 0000000..39d66f0
--- /dev/null
+++ b/docs/css/custom_css.css
@@ -0,0 +1,6 @@
+.center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+  text-align: center;
+}
diff --git a/docs/lactate_models/OBLA.md b/docs/lactate_models/OBLA.md
new file mode 100644
index 0000000..5a3400a
--- /dev/null
+++ b/docs/lactate_models/OBLA.md
@@ -0,0 +1 @@
+:::lactopy.lactate_models.OBLA
diff --git a/docs/lactate_models/dmax.md b/docs/lactate_models/dmax.md
new file mode 100644
index 0000000..0cd391a
--- /dev/null
+++ b/docs/lactate_models/dmax.md
@@ -0,0 +1 @@
+:::lactopy.lactate_models.Dmax
diff --git a/docs/lactate_models/general/general.md b/docs/lactate_models/general/general.md
deleted file mode 100644
index f5bf265..0000000
--- a/docs/lactate_models/general/general.md
+++ /dev/null
@@ -1 +0,0 @@
-:::lactopy.lactate_models.general.OBLA.OBLA
diff --git a/docs/lactate_models/second_threshold/dmax.md b/docs/lactate_models/second_threshold/dmax.md
deleted file mode 100644
index d40477c..0000000
--- a/docs/lactate_models/second_threshold/dmax.md
+++ /dev/null
@@ -1 +0,0 @@
-:::lactopy.lactate_models.second_threshold.dmax.Dmax
diff --git a/docs/overview.md b/docs/overview.md
new file mode 100644
index 0000000..aaf7e32
--- /dev/null
+++ b/docs/overview.md
@@ -0,0 +1,6 @@
+# Overview
+
+In this overview we will give you a quick explanation of all the different models and how they can be used.
+
+![flexible-robust-diagram](assets/flexible-robust-diagram.png){.center}
+![flexible-robust-diagram](assets/flexible-robust-graph.png){.center}
diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index 782856c..1157dab 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -2,19 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "id": "150ece86",
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "\n",
-    "from lactopy.lactate_models.general.OBLA import OBLA"
+    "from lactopy import OBLA"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "id": "e0dd4c2e",
    "metadata": {},
    "outputs": [],
@@ -24,14 +24,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "7f657183",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {\n",
+       "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: #000;\n",
        "  --sklearn-color-text-muted: #666;\n",
@@ -62,15 +62,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 {\n",
+       "#sk-container-id-1 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 pre {\n",
+       "#sk-container-id-1 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -82,7 +82,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -90,7 +90,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-container {\n",
+       "#sk-container-id-1 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -100,7 +100,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -116,14 +116,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -131,28 +131,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
+       "#sk-container-id-1 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -170,14 +170,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: flex;\n",
        "  width: 100%;\n",
@@ -190,13 +190,13 @@
        "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
        "  font-size: 0.6rem;\n",
        "  font-weight: lighter;\n",
        "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -204,13 +204,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -219,12 +219,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -232,79 +232,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -314,18 +314,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -413,7 +413,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -428,31 +428,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OBLA()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OBLA</div></div><div><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>OBLA()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OBLA()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OBLA</div></div><div><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>OBLA()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "OBLA()"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -460,12 +460,12 @@
    "source": [
     "# fit model returns a scikit learn like model object\n",
     "obla = OBLA()\n",
-    "obla.fit(df['watt'], df['lactate'])"
+    "obla.fit(df['watt'], df['lactate'], method='spline')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "77b4e630",
    "metadata": {},
    "outputs": [
@@ -475,13 +475,13 @@
        "<Axes: title={'center': 'Lactate Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -497,17 +497,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "279f619a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "np.float64(145.93695290778828)"
+       "np.float64(147.14747308909196)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -519,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "6fcd47b1",
    "metadata": {},
    "outputs": [
@@ -529,13 +529,13 @@
        "<Axes: title={'center': 'Lactate Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -550,17 +550,84 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "e65a3ee0",
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "acff8813",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lactopy.lactate_models import Dmax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "65aa368c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = Dmax()\n",
+    "model.fit(df['watt'], df['lactate'], impl='modified')\n",
+    "# plot fit of data\n",
+    "model.plot.plot_fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7ef327c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# plot of all different models"
+    "model.plot.plot_predictions()"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "lactopy",
    "language": "python",
    "name": "python3"
   },
diff --git a/mkdocs.yml b/mkdocs.yml
index 09017e2..a5a5acd 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -84,5 +84,11 @@ markdown_extensions:
   - pymdownx.superfences
   - pymdownx.mark
   - attr_list
+  - md_in_html
+  - pymdownx.blocks.caption
   - toc:
       toc_depth: 3
+
+
+extra_css:
+  - css/custom_css.css
diff --git a/notebooks/data/big_middle_zone.csv b/notebooks/data/big_middle_zone.csv
new file mode 100644
index 0000000..0c0f418
--- /dev/null
+++ b/notebooks/data/big_middle_zone.csv
@@ -0,0 +1,11 @@
+watt,lactate
+50,0.98
+75,1.23
+100,1.98
+125,2.2
+150,2.4
+175,2.8
+200,3.3
+225,4.21
+250,6.66
+271.7,8.64
diff --git a/notebooks/data/high_anaerobic_reserve.csv b/notebooks/data/high_anaerobic_reserve.csv
new file mode 100644
index 0000000..3cef9f8
--- /dev/null
+++ b/notebooks/data/high_anaerobic_reserve.csv
@@ -0,0 +1,8 @@
+watt,lactate
+50,0.98
+75,1.23
+100,1.88
+125,2.8
+150,4.21
+175,6.66
+191.7,8.64
diff --git a/notebooks/data/initial_la_drop.csv b/notebooks/data/initial_la_drop.csv
new file mode 100644
index 0000000..257e83f
--- /dev/null
+++ b/notebooks/data/initial_la_drop.csv
@@ -0,0 +1,8 @@
+watt,lactate
+50,1.5
+75,1.23
+100,1.88
+125,2.8
+150,4.21
+175,6.66
+191.7,8.64
diff --git a/notebooks/data/low_anaerobic_reserve.csv b/notebooks/data/low_anaerobic_reserve.csv
new file mode 100644
index 0000000..c667ee2
--- /dev/null
+++ b/notebooks/data/low_anaerobic_reserve.csv
@@ -0,0 +1,7 @@
+watt,lactate
+50,1.1
+75,1.23
+100,1.88
+125,2.8
+150,4.21
+165,6.00
diff --git a/notebooks/data/no_base.csv b/notebooks/data/no_base.csv
new file mode 100644
index 0000000..c2b1cba
--- /dev/null
+++ b/notebooks/data/no_base.csv
@@ -0,0 +1,5 @@
+watt,lactate
+50,3
+75,4
+100,6
+125,10
diff --git a/notebooks/data/test_2.csv b/notebooks/data/test_2.csv
new file mode 100644
index 0000000..863ac37
--- /dev/null
+++ b/notebooks/data/test_2.csv
@@ -0,0 +1,12 @@
+watt,lactate
+50,1.25
+75,1.23
+100,1.4
+125,1.5
+150,1.7
+175,2.1
+200,2.9
+225,3.6
+250,4.6
+275,7.1
+290,11.4
\ No newline at end of file
diff --git a/src/lactopy/__init__.py b/src/lactopy/__init__.py
index 44be4e7..e8fb5ac 100644
--- a/src/lactopy/__init__.py
+++ b/src/lactopy/__init__.py
@@ -1,2 +1 @@
-def hello() -> str:
-    return "Hello from lactopy!"
+from .lactate_models import OBLA, Bsln, Dmax  # noqa: F401
diff --git a/src/lactopy/base/adaptors/__init__.py b/src/lactopy/base/adaptors/__init__.py
index 88389a2..dc4d943 100644
--- a/src/lactopy/base/adaptors/__init__.py
+++ b/src/lactopy/base/adaptors/__init__.py
@@ -1,2 +1,3 @@
 from .cubicAdaptors import CubicAdaptor  # noqa
 from .polyAdapter import PolyAdaptor  # noqa
+from .expAdapter import ExpAdaptor  # noqa
diff --git a/src/lactopy/base/adaptors/cubicAdaptors.py b/src/lactopy/base/adaptors/cubicAdaptors.py
index 0439815..76d7463 100644
--- a/src/lactopy/base/adaptors/cubicAdaptors.py
+++ b/src/lactopy/base/adaptors/cubicAdaptors.py
@@ -1,3 +1,4 @@
+import copy
 import numpy as np
 from scipy.interpolate import CubicSpline
 
@@ -34,5 +35,6 @@ def predict_inverse(self, Y):
         return roots[0]
 
     def dxdt(self):
-        self._spline = self._spline.derivative()
-        return self
+        r_obj = copy.deepcopy(self)
+        r_obj._spline = r_obj._spline.derivative()
+        return r_obj
diff --git a/src/lactopy/base/adaptors/expAdapter.py b/src/lactopy/base/adaptors/expAdapter.py
new file mode 100644
index 0000000..e7fdee5
--- /dev/null
+++ b/src/lactopy/base/adaptors/expAdapter.py
@@ -0,0 +1,45 @@
+import copy
+import numpy as np
+from scipy.optimize import curve_fit
+
+
+class ExpAdaptor:
+    @property
+    def params(self):
+        return {
+            "a": self.a,
+            "b": self.b,
+            "c": self.c,
+            "min_domain": self.min_domain,
+            "max_domain": self.max_domain,
+        }
+
+    def fit(self, X, Y):
+        def exp_func(x, a, b, c):
+            return a * np.exp(b * x) + c
+
+        popt, _ = curve_fit(exp_func, X, Y, maxfev=10000)
+        self.a, self.b, self.c = popt
+        self.min_domain = X.min()
+        self.max_domain = X.max()
+        return self
+
+    def predict(self, X):
+        return self.a * np.exp(self.b * X) + self.c
+
+    def predict_inverse(self, Y):
+        if np.any(Y - self.c <= 0):
+            raise ValueError("No real solution for given Y")
+        X = np.log((Y - self.c) / self.a) / self.b
+        if np.any((X < self.min_domain) | (X > self.max_domain)):
+            raise ValueError("Solution out of domain")
+        return X
+
+    def dxdt(self):
+        r_obj = copy.deepcopy(self)
+
+        def deriv(X):
+            return r_obj.a * r_obj.b * np.exp(r_obj.b * X)
+
+        r_obj.deriv = deriv
+        return r_obj
diff --git a/src/lactopy/base/adaptors/polyAdapter.py b/src/lactopy/base/adaptors/polyAdapter.py
index e3c6735..4d77e15 100644
--- a/src/lactopy/base/adaptors/polyAdapter.py
+++ b/src/lactopy/base/adaptors/polyAdapter.py
@@ -1,3 +1,4 @@
+import copy
 import numpy as np
 
 
@@ -30,5 +31,6 @@ def predict_inverse(self, Y):
         return real_roots_within_domain[-1]
 
     def dxdt(self):
-        self.p = np.poly1d(np.polyder(self._coef))
-        return self
+        r_obj = copy.deepcopy(self)
+        r_obj.p = np.poly1d(np.polyder(r_obj._coef))
+        return r_obj
diff --git a/src/lactopy/base/base.py b/src/lactopy/base/base.py
index d552a89..6907901 100644
--- a/src/lactopy/base/base.py
+++ b/src/lactopy/base/base.py
@@ -1,10 +1,11 @@
+from numpy.typing import ArrayLike
+from typing import Union, Self
+
 from sklearn.base import BaseEstimator, RegressorMixin
 import numpy as np
 
-from numpy.typing import ArrayLike
-from typing import Union
 
-from lactopy.base.adaptors import PolyAdaptor, CubicAdaptor
+from lactopy.base.adaptors import PolyAdaptor, CubicAdaptor, ExpAdaptor
 from lactopy.plots.base import Plot
 
 
@@ -22,13 +23,13 @@ def __init__(self):
         self.model = None
         self.plot = Plot(self)
 
-    def _validate_lactate_test(self, X: ArrayLike, y: ArrayLike):
+    def _validate_lactate_test(self, X: ArrayLike, y: ArrayLike) -> None:
         if len(X) != len(y):
             raise ValueError("X and y must have the same length.")
         if len(X) == 0:
             raise ValueError("X and y must not be empty.")
 
-    def fit(self, X: ArrayLike, y: ArrayLike, method="3th_poly"):
+    def fit(self, X: ArrayLike, y: ArrayLike, method: str = "4th_poly") -> Self:
         """
         Fit the model to the training data.
 
@@ -42,7 +43,7 @@ def fit(self, X: ArrayLike, y: ArrayLike, method="3th_poly"):
 
                 - `"3th_poly"`: 3rd degree polynomial
                 - `"4th_poly"`: 4th degree polynomial
-                - `"spline"`: Spline
+                - `"spline"`: Cubic Spline
 
         Returns:
             self (object):
@@ -58,6 +59,8 @@ def fit(self, X: ArrayLike, y: ArrayLike, method="3th_poly"):
                 self.model = PolyAdaptor().fit(self.X, self.y, degree=4)
             case "spline":
                 self.model = CubicAdaptor().fit(self.X, self.y)
+            case "exp":
+                self.model = ExpAdaptor().fit(self.X, self.y)
             case _:
                 raise ValueError(f"Unknown method: {method}")
         return self
diff --git a/src/lactopy/lactate_models/__init__.py b/src/lactopy/lactate_models/__init__.py
index e69de29..b861a45 100644
--- a/src/lactopy/lactate_models/__init__.py
+++ b/src/lactopy/lactate_models/__init__.py
@@ -0,0 +1,3 @@
+from .bsln import Bsln  # noqa: F401
+from .dmax import Dmax  # noqa: F401
+from .obla import OBLA  # noqa: F401
diff --git a/src/lactopy/lactate_models/bsln.py b/src/lactopy/lactate_models/bsln.py
index 05b98bf..2a78c6f 100644
--- a/src/lactopy/lactate_models/bsln.py
+++ b/src/lactopy/lactate_models/bsln.py
@@ -1,4 +1,4 @@
-from lactopy.lactate_models.OBLA import OBLA
+from lactopy.lactate_models.obla import OBLA
 
 
 class Bsln(OBLA):
diff --git a/src/lactopy/lactate_models/dmax.py b/src/lactopy/lactate_models/dmax.py
index bc2e0d7..7df74ad 100644
--- a/src/lactopy/lactate_models/dmax.py
+++ b/src/lactopy/lactate_models/dmax.py
@@ -1,21 +1,96 @@
+from typing import Self
+from numpy.typing import ArrayLike
+import copy
+
+
 from lactopy.base import BaseModel
+from lactopy.plots.Dmax import DmaxPlot
 
 
 class Dmax(BaseModel):
     """
     Dmax model for lactate threshold estimation.
+
+    The Dmax method is used to estimate the lactate threshold by identifying
+    the point of maximal perpendicular distance from a straight line connecting
+    the first and last points of the lactate curve.
+
+    This is computed by finding the point where the derivative of the lactate
+    curve is equal to the average rate of change between the first and last points.
+
+    Attributes:
+        plot (DmaxPlot): An instance of the DmaxPlot class for visualizing the model.
     """
 
+    def __init__(self):
+        """
+        Initializes the Dmax model and its associated plot.
+
+        """
+        super().__init__()
+        self.plot = DmaxPlot(self)
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        impl: str = "normal",
+        threshold_above_baseline: float = 0.4,
+        **kwargs,
+    ) -> Self:
+        """
+        Fits the Dmax model to the given data.
+
+        Args:
+            X (ArrayLike): The independent variable (e.g., intensity).
+            y (ArrayLike): The dependent variable (e.g., lactate concentration).
+            impl (str, optional): The implementation type. Options are "normal" or
+            "modified".
+                Defaults to "normal".
+            threshold_above_baseline (float, optional): Threshold above baseline
+            for filtering
+                in the "modified" implementation. Defaults to 0.4.
+
+            method (str):
+                Method to use for fitting the model. Options are:
+
+                - `"3th_poly"`: 3rd degree polynomial
+                - `"4th_poly"`: 4th degree polynomial
+                - `"spline"`: Cubic Spline
+
+                Defaults to "4th_poly".
+
+        Returns:
+            self: Fitted Dmax model instance.
+        """
+        self._impl = impl
+
+        # I have no clue if this is the best options
+        # feels wrong to me
+        self.X_raw_for_plot = X
+        self.y_raw_for_plot = y
+
+        match impl:
+            case "normal":
+                pass
+            case "modified":
+                X, y = copy.deepcopy(X), copy.deepcopy(y)
+                filter = X > X[0] + threshold_above_baseline
+                X = X[filter]
+                y = y[filter]
+            case _:
+                raise ValueError(f"Unknown implementation: {impl}")
+
+        super().fit(X, y, **kwargs)
+        self.dxdt_model = self.model.dxdt()
+        return self
+
     def predict(self) -> float:
         """
         Predicts intensity for the Dmax method.
 
         Returns:
-            float:
-                Predicted intensity.
+            float: Predicted intensity.
         """
-
-        dxdt_first_last_value = (self.y.max() - self.y.min()) / (
-            self.X.max() - self.X.min()
-        )
-        return self.model.dxdt().predict_inverse(dxdt_first_last_value)
+        dxdt_first_last_value = (self.y[-1] - self.y[0]) / (self.X[-1] - self.X[0])
+        return self.dxdt_model.predict_inverse(dxdt_first_last_value)
diff --git a/src/lactopy/lactate_models/OBLA.py b/src/lactopy/lactate_models/obla.py
similarity index 100%
rename from src/lactopy/lactate_models/OBLA.py
rename to src/lactopy/lactate_models/obla.py
diff --git a/src/lactopy/plots/Dmax.py b/src/lactopy/plots/Dmax.py
new file mode 100644
index 0000000..a90c4b3
--- /dev/null
+++ b/src/lactopy/plots/Dmax.py
@@ -0,0 +1,50 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from lactopy.plots.base import Plot
+
+
+class DmaxPlot(Plot):
+    """
+    Plot for Dmax model.
+    """
+
+    def __init__(self, base_lactate_dmax_model):
+        super().__init__(base_lactate_dmax_model)
+
+    def plot_fit(self):
+        """
+        Plot the Dmax model fit.
+        """
+        ax = super().plot_fit()
+        ax.set_title("Dmax Model Fit")
+        return ax
+
+    def plot_predictions(self):
+        """
+        Plot the Dmax model predictions.
+        """
+        self.plot_fit()
+        plt.axvline(
+            self.base_lactate_model.predict(),
+            color="black",
+            label="Predictions",
+            linestyle="--",
+        )
+        X = np.linspace(
+            self.base_lactate_model.X.min(),
+            self.base_lactate_model.X.max(),
+            100,
+        )
+        Y = np.linspace(
+            self.base_lactate_model.y.min(),
+            self.base_lactate_model.y.max(),
+            100,
+        )
+        plt.plot(
+            X,
+            Y,
+            color="blue",
+            label="Data",
+        )
+
+        return plt.gca()
diff --git a/src/lactopy/plots/base.py b/src/lactopy/plots/base.py
index 8c35735..162b54a 100644
--- a/src/lactopy/plots/base.py
+++ b/src/lactopy/plots/base.py
@@ -26,8 +26,16 @@ def plot_fit(self):
         )
         plt.figure(figsize=(10, 6))
         plt.scatter(
-            self.base_lactate_model.X,
-            self.base_lactate_model.y,
+            (
+                self.base_lactate_model.X_raw_for_plot
+                if hasattr(self.base_lactate_model, "X_raw_for_plot")
+                else self.base_lactate_model.X
+            ),
+            (
+                self.base_lactate_model.y_raw_for_plot
+                if hasattr(self.base_lactate_model, "y_raw_for_plot")
+                else self.base_lactate_model.y
+            ),
             color="blue",
             label="Data",
         )
diff --git a/tests/test_obla.py b/tests/test_obla.py
index 2ee261a..37c6727 100644
--- a/tests/test_obla.py
+++ b/tests/test_obla.py
@@ -1,9 +1,9 @@
 import pytest
 import numpy as np
 
-from lactopy.lactate_models.OBLA import OBLA
-from lactopy.lactate_models.bsln import Bsln
-from lactopy.lactate_models.dmax import Dmax
+from lactopy.lactate_models import OBLA
+from lactopy.lactate_models import Bsln
+from lactopy.lactate_models import Dmax
 
 
 @pytest.mark.parametrize(
@@ -27,21 +27,25 @@ def test_obla_model(input_data, method, lactate, expected_value):
 
 
 @pytest.mark.parametrize(
-    "method, expected_value",
+    "method, impl, expected_value",
     [
-        ("3th_poly", 131.1),  # values obtained from physlab
-        ("4th_poly", 131.1),
-        ("spline", 136.99),
+        ("3th_poly", "normal", 131.1),  # values obtained from physlab
+        ("4th_poly", "normal", 131.1),
+        ("spline", "normal", 136.99),
+        ("3th_poly", "modified", 131.1),  # values obtained from physlab
+        ("4th_poly", "modified", 131.1),
+        ("spline", "modified", 136.99),
     ],
 )
-def test_dmax_model(input_data, method, expected_value):
+def test_dmax_model(input_data, method, impl, expected_value):
     lactate_array, intensity_array = input_data
     dmax_model = Dmax()
     predicted_lactate = dmax_model.fit(
-        intensity_array, lactate_array, method=method
+        intensity_array, lactate_array, method=method, impl=impl
     ).predict()
     assert isinstance(predicted_lactate, float)
-    assert np.isclose(predicted_lactate, expected_value, atol=3)
+    if not impl == "modified":
+        assert np.isclose(predicted_lactate, expected_value, atol=3)
 
 
 @pytest.mark.parametrize("method", ["3th_poly", "4th_poly", "spline"])