From d10bc2c97dc2669326b9244696b6086acf64f92c Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Mon, 26 May 2025 21:58:01 +0200
Subject: [PATCH 01/19] bug fix for when taking the derivative two times in a
 row

---
 src/lactopy/base/adaptors/cubicAdaptors.py | 6 ++++--
 src/lactopy/base/adaptors/polyAdapter.py   | 6 ++++--
 2 files changed, 8 insertions(+), 4 deletions(-)

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/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

From d4890a6ad140480b8691ffff4801f51250da3f3e Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Mon, 26 May 2025 21:59:03 +0200
Subject: [PATCH 02/19] add dmax plot for base

---
 src/lactopy/lactate_models/dmax.py | 12 ++++++-
 src/lactopy/plots/Dmax.py          | 50 ++++++++++++++++++++++++++++++
 src/lactopy/plots/base.py          |  1 +
 3 files changed, 62 insertions(+), 1 deletion(-)
 create mode 100644 src/lactopy/plots/Dmax.py

diff --git a/src/lactopy/lactate_models/dmax.py b/src/lactopy/lactate_models/dmax.py
index bc2e0d7..730f8be 100644
--- a/src/lactopy/lactate_models/dmax.py
+++ b/src/lactopy/lactate_models/dmax.py
@@ -1,4 +1,5 @@
 from lactopy.base import BaseModel
+from lactopy.plots.Dmax import DmaxPlot
 
 
 class Dmax(BaseModel):
@@ -6,6 +7,15 @@ class Dmax(BaseModel):
     Dmax model for lactate threshold estimation.
     """
 
+    def __init__(self):
+        super().__init__()
+        self.plot = DmaxPlot(self)
+
+    def fit(self, *args, **kwargs):
+        rv = super().fit(*args, **kwargs)
+        self.dxdt_model = self.model.dxdt()
+        return rv
+
     def predict(self) -> float:
         """
         Predicts intensity for the Dmax method.
@@ -18,4 +28,4 @@ def predict(self) -> float:
         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)
+        return self.dxdt_model.predict_inverse(dxdt_first_last_value)
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..78e53fe 100644
--- a/src/lactopy/plots/base.py
+++ b/src/lactopy/plots/base.py
@@ -31,6 +31,7 @@ def plot_fit(self):
             color="blue",
             label="Data",
         )
+        print(self.base_lactate_model.model.predict(X))
         plt.plot(
             X, self.base_lactate_model.model.predict(X), color="red", label="Model"
         )

From 15991b6a842d9e799ecf3357a4ae67819626b8a7 Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Mon, 26 May 2025 22:04:39 +0200
Subject: [PATCH 03/19] add docs

---
 docs/quickstart.ipynb     | 211 +++++++++++++++++++++++++++-----------
 src/lactopy/plots/base.py |   1 -
 2 files changed, 153 insertions(+), 59 deletions(-)

diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index 782856c..555ba3f 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -2,19 +2,20 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "150ece86",
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "\n",
-    "from lactopy.lactate_models.general.OBLA import OBLA"
+    "from lactopy.lactate_models.OBLA import OBLA\n",
+    "from lactopy.lactate_models.dmax import Dmax\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "e0dd4c2e",
    "metadata": {},
    "outputs": [],
@@ -24,14 +25,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "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 +63,15 @@
        "  }\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 +83,7 @@
        "  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 +91,7 @@
        "  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 +101,7 @@
        "  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 +117,14 @@
        "\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 +132,28 @@
        "  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 +171,14 @@
        "\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 +191,13 @@
        "  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 +205,13 @@
        "  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 +220,12 @@
        "  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 +233,79 @@
        "  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 +315,18 @@
        "  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 +414,7 @@
        "\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 +429,31 @@
        "  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": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -465,17 +466,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "77b4e630",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.96291728 0.97901208 0.9952331  1.01161534 1.02819383 1.04500355\n",
+      " 1.06207951 1.07945673 1.09717019 1.11525492 1.13374591 1.15267816\n",
+      " 1.17208669 1.1920065  1.21247259 1.23351997 1.25518364 1.2774986\n",
+      " 1.30049987 1.32422245 1.34870133 1.37397153 1.40006805 1.4270259\n",
+      " 1.45488008 1.48366559 1.51341745 1.54417064 1.57596019 1.60882109\n",
+      " 1.64278836 1.67789698 1.71418198 1.75167834 1.79042109 1.83044522\n",
+      " 1.87178574 1.91447765 1.95855596 2.00405567 2.05101179 2.09945932\n",
+      " 2.14943327 2.20096864 2.25410043 2.30886366 2.36529332 2.42342443\n",
+      " 2.48329198 2.54493098 2.60837643 2.67366335 2.74082673 2.80990158\n",
+      " 2.88092291 2.95392571 3.028945   3.10601578 3.18517305 3.26645182\n",
+      " 3.34988709 3.43551388 3.52336717 3.61348198 3.70589332 3.80063618\n",
+      " 3.89774558 3.99725651 4.09920398 4.203623   4.31054857 4.4200157\n",
+      " 4.53205939 4.64671465 4.76401647 4.88399987 5.00669985 5.13215142\n",
+      " 5.26038958 5.39144933 5.52536568 5.66217363 5.8019082  5.94460437\n",
+      " 6.09029717 6.23902159 6.39081264 6.54570532 6.70373464 6.8649356\n",
+      " 7.02934321 7.19699247 7.36791839 7.54215597 7.71974022 7.90070613\n",
+      " 8.08508873 8.27292301 8.46424397 8.65908662]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "<Axes: title={'center': 'Lactate Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -497,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "279f619a",
    "metadata": {},
    "outputs": [
@@ -507,7 +531,7 @@
        "np.float64(145.93695290778828)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -519,17 +543,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "6fcd47b1",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.96291728 0.97901208 0.9952331  1.01161534 1.02819383 1.04500355\n",
+      " 1.06207951 1.07945673 1.09717019 1.11525492 1.13374591 1.15267816\n",
+      " 1.17208669 1.1920065  1.21247259 1.23351997 1.25518364 1.2774986\n",
+      " 1.30049987 1.32422245 1.34870133 1.37397153 1.40006805 1.4270259\n",
+      " 1.45488008 1.48366559 1.51341745 1.54417064 1.57596019 1.60882109\n",
+      " 1.64278836 1.67789698 1.71418198 1.75167834 1.79042109 1.83044522\n",
+      " 1.87178574 1.91447765 1.95855596 2.00405567 2.05101179 2.09945932\n",
+      " 2.14943327 2.20096864 2.25410043 2.30886366 2.36529332 2.42342443\n",
+      " 2.48329198 2.54493098 2.60837643 2.67366335 2.74082673 2.80990158\n",
+      " 2.88092291 2.95392571 3.028945   3.10601578 3.18517305 3.26645182\n",
+      " 3.34988709 3.43551388 3.52336717 3.61348198 3.70589332 3.80063618\n",
+      " 3.89774558 3.99725651 4.09920398 4.203623   4.31054857 4.4200157\n",
+      " 4.53205939 4.64671465 4.76401647 4.88399987 5.00669985 5.13215142\n",
+      " 5.26038958 5.39144933 5.52536568 5.66217363 5.8019082  5.94460437\n",
+      " 6.09029717 6.23902159 6.39081264 6.54570532 6.70373464 6.8649356\n",
+      " 7.02934321 7.19699247 7.36791839 7.54215597 7.71974022 7.90070613\n",
+      " 8.08508873 8.27292301 8.46424397 8.65908662]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "<Axes: title={'center': 'Lactate Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -550,11 +597,59 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "e65a3ee0",
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d06c8293",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.96291728 0.97901208 0.9952331  1.01161534 1.02819383 1.04500355\n",
+      " 1.06207951 1.07945673 1.09717019 1.11525492 1.13374591 1.15267816\n",
+      " 1.17208669 1.1920065  1.21247259 1.23351997 1.25518364 1.2774986\n",
+      " 1.30049987 1.32422245 1.34870133 1.37397153 1.40006805 1.4270259\n",
+      " 1.45488008 1.48366559 1.51341745 1.54417064 1.57596019 1.60882109\n",
+      " 1.64278836 1.67789698 1.71418198 1.75167834 1.79042109 1.83044522\n",
+      " 1.87178574 1.91447765 1.95855596 2.00405567 2.05101179 2.09945932\n",
+      " 2.14943327 2.20096864 2.25410043 2.30886366 2.36529332 2.42342443\n",
+      " 2.48329198 2.54493098 2.60837643 2.67366335 2.74082673 2.80990158\n",
+      " 2.88092291 2.95392571 3.028945   3.10601578 3.18517305 3.26645182\n",
+      " 3.34988709 3.43551388 3.52336717 3.61348198 3.70589332 3.80063618\n",
+      " 3.89774558 3.99725651 4.09920398 4.203623   4.31054857 4.4200157\n",
+      " 4.53205939 4.64671465 4.76401647 4.88399987 5.00669985 5.13215142\n",
+      " 5.26038958 5.39144933 5.52536568 5.66217363 5.8019082  5.94460437\n",
+      " 6.09029717 6.23902159 6.39081264 6.54570532 6.70373464 6.8649356\n",
+      " 7.02934321 7.19699247 7.36791839 7.54215597 7.71974022 7.90070613\n",
+      " 8.08508873 8.27292301 8.46424397 8.65908662]\n"
+     ]
+    },
+    {
+     "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": [
-    "# plot of all different models"
+    "dmax_model = Dmax()\n",
+    "dmax_model.fit(df['watt'], df['lactate'])\n",
+    "dmax_model.plot.plot_predictions()\n"
    ]
   }
  ],
diff --git a/src/lactopy/plots/base.py b/src/lactopy/plots/base.py
index 78e53fe..8c35735 100644
--- a/src/lactopy/plots/base.py
+++ b/src/lactopy/plots/base.py
@@ -31,7 +31,6 @@ def plot_fit(self):
             color="blue",
             label="Data",
         )
-        print(self.base_lactate_model.model.predict(X))
         plt.plot(
             X, self.base_lactate_model.model.predict(X), color="red", label="Model"
         )

From 916075d295291c500bbb41e4f60e9436a4b47eed Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Thu, 29 May 2025 20:10:13 +0200
Subject: [PATCH 04/19] clean up imports

---
 docs/quickstart.ipynb                         | 125 ++----------------
 src/lactopy/lactate_models/__init__.py        |   3 +
 src/lactopy/lactate_models/bsln.py            |   2 +-
 .../lactate_models/{OBLA.py => obla.py}       |   0
 tests/test_obla.py                            |   6 +-
 5 files changed, 18 insertions(+), 118 deletions(-)
 rename src/lactopy/lactate_models/{OBLA.py => obla.py} (100%)

diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index 555ba3f..5bcaa1f 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -2,20 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "150ece86",
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "\n",
-    "from lactopy.lactate_models.OBLA import OBLA\n",
-    "from lactopy.lactate_models.dmax import Dmax\n"
+    "from lactopy.lactate_models import OBLA"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "e0dd4c2e",
    "metadata": {},
    "outputs": [],
@@ -25,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "7f657183",
    "metadata": {},
    "outputs": [
@@ -453,7 +452,7 @@
        "OBLA()"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -466,40 +465,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "77b4e630",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.96291728 0.97901208 0.9952331  1.01161534 1.02819383 1.04500355\n",
-      " 1.06207951 1.07945673 1.09717019 1.11525492 1.13374591 1.15267816\n",
-      " 1.17208669 1.1920065  1.21247259 1.23351997 1.25518364 1.2774986\n",
-      " 1.30049987 1.32422245 1.34870133 1.37397153 1.40006805 1.4270259\n",
-      " 1.45488008 1.48366559 1.51341745 1.54417064 1.57596019 1.60882109\n",
-      " 1.64278836 1.67789698 1.71418198 1.75167834 1.79042109 1.83044522\n",
-      " 1.87178574 1.91447765 1.95855596 2.00405567 2.05101179 2.09945932\n",
-      " 2.14943327 2.20096864 2.25410043 2.30886366 2.36529332 2.42342443\n",
-      " 2.48329198 2.54493098 2.60837643 2.67366335 2.74082673 2.80990158\n",
-      " 2.88092291 2.95392571 3.028945   3.10601578 3.18517305 3.26645182\n",
-      " 3.34988709 3.43551388 3.52336717 3.61348198 3.70589332 3.80063618\n",
-      " 3.89774558 3.99725651 4.09920398 4.203623   4.31054857 4.4200157\n",
-      " 4.53205939 4.64671465 4.76401647 4.88399987 5.00669985 5.13215142\n",
-      " 5.26038958 5.39144933 5.52536568 5.66217363 5.8019082  5.94460437\n",
-      " 6.09029717 6.23902159 6.39081264 6.54570532 6.70373464 6.8649356\n",
-      " 7.02934321 7.19699247 7.36791839 7.54215597 7.71974022 7.90070613\n",
-      " 8.08508873 8.27292301 8.46424397 8.65908662]\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "<Axes: title={'center': 'Lactate Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -521,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "279f619a",
    "metadata": {},
    "outputs": [
@@ -531,7 +507,7 @@
        "np.float64(145.93695290778828)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -543,40 +519,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "6fcd47b1",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.96291728 0.97901208 0.9952331  1.01161534 1.02819383 1.04500355\n",
-      " 1.06207951 1.07945673 1.09717019 1.11525492 1.13374591 1.15267816\n",
-      " 1.17208669 1.1920065  1.21247259 1.23351997 1.25518364 1.2774986\n",
-      " 1.30049987 1.32422245 1.34870133 1.37397153 1.40006805 1.4270259\n",
-      " 1.45488008 1.48366559 1.51341745 1.54417064 1.57596019 1.60882109\n",
-      " 1.64278836 1.67789698 1.71418198 1.75167834 1.79042109 1.83044522\n",
-      " 1.87178574 1.91447765 1.95855596 2.00405567 2.05101179 2.09945932\n",
-      " 2.14943327 2.20096864 2.25410043 2.30886366 2.36529332 2.42342443\n",
-      " 2.48329198 2.54493098 2.60837643 2.67366335 2.74082673 2.80990158\n",
-      " 2.88092291 2.95392571 3.028945   3.10601578 3.18517305 3.26645182\n",
-      " 3.34988709 3.43551388 3.52336717 3.61348198 3.70589332 3.80063618\n",
-      " 3.89774558 3.99725651 4.09920398 4.203623   4.31054857 4.4200157\n",
-      " 4.53205939 4.64671465 4.76401647 4.88399987 5.00669985 5.13215142\n",
-      " 5.26038958 5.39144933 5.52536568 5.66217363 5.8019082  5.94460437\n",
-      " 6.09029717 6.23902159 6.39081264 6.54570532 6.70373464 6.8649356\n",
-      " 7.02934321 7.19699247 7.36791839 7.54215597 7.71974022 7.90070613\n",
-      " 8.08508873 8.27292301 8.46424397 8.65908662]\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "<Axes: title={'center': 'Lactate Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -595,62 +548,6 @@
     "# Plot the prediction at 4 mmol\n",
     "obla.plot.plot_predictions(4)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "d06c8293",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.96291728 0.97901208 0.9952331  1.01161534 1.02819383 1.04500355\n",
-      " 1.06207951 1.07945673 1.09717019 1.11525492 1.13374591 1.15267816\n",
-      " 1.17208669 1.1920065  1.21247259 1.23351997 1.25518364 1.2774986\n",
-      " 1.30049987 1.32422245 1.34870133 1.37397153 1.40006805 1.4270259\n",
-      " 1.45488008 1.48366559 1.51341745 1.54417064 1.57596019 1.60882109\n",
-      " 1.64278836 1.67789698 1.71418198 1.75167834 1.79042109 1.83044522\n",
-      " 1.87178574 1.91447765 1.95855596 2.00405567 2.05101179 2.09945932\n",
-      " 2.14943327 2.20096864 2.25410043 2.30886366 2.36529332 2.42342443\n",
-      " 2.48329198 2.54493098 2.60837643 2.67366335 2.74082673 2.80990158\n",
-      " 2.88092291 2.95392571 3.028945   3.10601578 3.18517305 3.26645182\n",
-      " 3.34988709 3.43551388 3.52336717 3.61348198 3.70589332 3.80063618\n",
-      " 3.89774558 3.99725651 4.09920398 4.203623   4.31054857 4.4200157\n",
-      " 4.53205939 4.64671465 4.76401647 4.88399987 5.00669985 5.13215142\n",
-      " 5.26038958 5.39144933 5.52536568 5.66217363 5.8019082  5.94460437\n",
-      " 6.09029717 6.23902159 6.39081264 6.54570532 6.70373464 6.8649356\n",
-      " 7.02934321 7.19699247 7.36791839 7.54215597 7.71974022 7.90070613\n",
-      " 8.08508873 8.27292301 8.46424397 8.65908662]\n"
-     ]
-    },
-    {
-     "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": [
-    "dmax_model = Dmax()\n",
-    "dmax_model.fit(df['watt'], df['lactate'])\n",
-    "dmax_model.plot.plot_predictions()\n"
-   ]
   }
  ],
  "metadata": {
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/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/tests/test_obla.py b/tests/test_obla.py
index 2ee261a..9506f9a 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(

From bad41411c19e03febfd4e601622e245bf8beaca0 Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Thu, 29 May 2025 22:10:24 +0200
Subject: [PATCH 05/19] inital start of docs

---
 README.md                                    |   4 +-
 docs/assets/flexible-robust-diagram.png      | Bin 0 -> 24643 bytes
 docs/assets/flexible-robust-graph.png        | Bin 0 -> 81196 bytes
 docs/css/custom_css.css                      |   6 ++
 docs/lactate_models/general/general.md       |   2 +-
 docs/lactate_models/second_threshold/dmax.md |   2 +-
 docs/overview.md                             |   6 ++
 docs/quickstart.ipynb                        |  93 +++++++++++++++++--
 mkdocs.yml                                   |   6 ++
 9 files changed, 105 insertions(+), 14 deletions(-)
 create mode 100644 docs/assets/flexible-robust-diagram.png
 create mode 100644 docs/assets/flexible-robust-graph.png
 create mode 100644 docs/css/custom_css.css
 create mode 100644 docs/overview.md

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 0000000000000000000000000000000000000000..9bb05991e44c195f1a2d72b6a32cce8e44d6906e
GIT binary patch
literal 24643
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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/general/general.md b/docs/lactate_models/general/general.md
index f5bf265..5a3400a 100644
--- a/docs/lactate_models/general/general.md
+++ b/docs/lactate_models/general/general.md
@@ -1 +1 @@
-:::lactopy.lactate_models.general.OBLA.OBLA
+:::lactopy.lactate_models.OBLA
diff --git a/docs/lactate_models/second_threshold/dmax.md b/docs/lactate_models/second_threshold/dmax.md
index d40477c..0cd391a 100644
--- a/docs/lactate_models/second_threshold/dmax.md
+++ b/docs/lactate_models/second_threshold/dmax.md
@@ -1 +1 @@
-:::lactopy.lactate_models.second_threshold.dmax.Dmax
+:::lactopy.lactate_models.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 5bcaa1f..b2fe5fb 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -9,7 +9,7 @@
    "source": [
     "import pandas as pd\n",
     "\n",
-    "from lactopy.lactate_models import OBLA"
+    "from lactopy import OBLA"
    ]
   },
   {
@@ -460,7 +460,7 @@
    "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')"
    ]
   },
   {
@@ -481,7 +481,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -497,17 +497,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "id": "279f619a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "np.float64(145.93695290778828)"
+       "np.float64(147.14747308909196)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -519,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "id": "6fcd47b1",
    "metadata": {},
    "outputs": [
@@ -529,13 +529,13 @@
        "<Axes: title={'center': 'Lactate Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -548,6 +548,81 @@
     "# Plot the prediction at 4 mmol\n",
     "obla.plot.plot_predictions(4)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "acff8813",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lactopy.lactate_models import Dmax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "65aa368c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+      ]
+     },
+     "execution_count": 18,
+     "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": 19,
+   "id": "7ef327c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+      ]
+     },
+     "execution_count": 19,
+     "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.plot.plot_predictions()"
+   ]
   }
  ],
  "metadata": {
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

From bf4b20fdd4b1de5ca79e63053be15637e4988fa3 Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Thu, 29 May 2025 22:10:54 +0200
Subject: [PATCH 06/19] impl of mod dmax

---
 src/lactopy/__init__.py            |  3 +--
 src/lactopy/base/base.py           |  2 +-
 src/lactopy/lactate_models/dmax.py | 39 +++++++++++++++++++++++++-----
 src/lactopy/plots/base.py          | 12 +++++++--
 tests/test_obla.py                 | 18 ++++++++------
 5 files changed, 56 insertions(+), 18 deletions(-)

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/base.py b/src/lactopy/base/base.py
index d552a89..f8ca91c 100644
--- a/src/lactopy/base/base.py
+++ b/src/lactopy/base/base.py
@@ -42,7 +42,7 @@ 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):
diff --git a/src/lactopy/lactate_models/dmax.py b/src/lactopy/lactate_models/dmax.py
index 730f8be..fb55491 100644
--- a/src/lactopy/lactate_models/dmax.py
+++ b/src/lactopy/lactate_models/dmax.py
@@ -1,3 +1,7 @@
+from numpy.typing import ArrayLike
+import copy
+
+
 from lactopy.base import BaseModel
 from lactopy.plots.Dmax import DmaxPlot
 
@@ -11,8 +15,34 @@ def __init__(self):
         super().__init__()
         self.plot = DmaxPlot(self)
 
-    def fit(self, *args, **kwargs):
-        rv = super().fit(*args, **kwargs)
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        impl: str = "normal",
+        threshold_above_baseline=0.4,
+        *args,
+        **kwargs,
+    ):
+        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}")
+
+        rv = super().fit(X, y, *args, **kwargs)
         self.dxdt_model = self.model.dxdt()
         return rv
 
@@ -24,8 +54,5 @@ def predict(self) -> float:
             float:
                 Predicted intensity.
         """
-
-        dxdt_first_last_value = (self.y.max() - self.y.min()) / (
-            self.X.max() - self.X.min()
-        )
+        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/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 9506f9a..37c6727 100644
--- a/tests/test_obla.py
+++ b/tests/test_obla.py
@@ -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"])

From 71a16122aa6458d9620cd549c07303f8e55cba0a Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Fri, 30 May 2025 09:33:51 +0200
Subject: [PATCH 07/19] update docs

---
 DEVELOPMENT.md                                |  4 +-
 .../{general/general.md => OBLA.md}           |  0
 .../{second_threshold => }/dmax.md            |  0
 src/lactopy/base/base.py                      |  7 +--
 src/lactopy/lactate_models/dmax.py            | 47 +++++++++++++++++--
 5 files changed, 48 insertions(+), 10 deletions(-)
 rename docs/lactate_models/{general/general.md => OBLA.md} (100%)
 rename docs/lactate_models/{second_threshold => }/dmax.md (100%)

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/docs/lactate_models/general/general.md b/docs/lactate_models/OBLA.md
similarity index 100%
rename from docs/lactate_models/general/general.md
rename to docs/lactate_models/OBLA.md
diff --git a/docs/lactate_models/second_threshold/dmax.md b/docs/lactate_models/dmax.md
similarity index 100%
rename from docs/lactate_models/second_threshold/dmax.md
rename to docs/lactate_models/dmax.md
diff --git a/src/lactopy/base/base.py b/src/lactopy/base/base.py
index f8ca91c..65e39e3 100644
--- a/src/lactopy/base/base.py
+++ b/src/lactopy/base/base.py
@@ -1,8 +1,9 @@
+from numpy.typing import ArrayLike
+from typing import Union
+
 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.plots.base import Plot
@@ -28,7 +29,7 @@ def _validate_lactate_test(self, X: ArrayLike, y: ArrayLike):
         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="4th_poly"):
         """
         Fit the model to the training data.
 
diff --git a/src/lactopy/lactate_models/dmax.py b/src/lactopy/lactate_models/dmax.py
index fb55491..81f2e41 100644
--- a/src/lactopy/lactate_models/dmax.py
+++ b/src/lactopy/lactate_models/dmax.py
@@ -9,9 +9,23 @@
 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)
 
@@ -21,9 +35,33 @@ def fit(
         y: ArrayLike,
         impl: str = "normal",
         threshold_above_baseline=0.4,
-        *args,
         **kwargs,
     ):
+        """
+        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
@@ -42,17 +80,16 @@ def fit(
             case _:
                 raise ValueError(f"Unknown implementation: {impl}")
 
-        rv = super().fit(X, y, *args, **kwargs)
+        super().fit(X, y, **kwargs)
         self.dxdt_model = self.model.dxdt()
-        return rv
+        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[-1] - self.y[0]) / (self.X[-1] - self.X[0])
         return self.dxdt_model.predict_inverse(dxdt_first_last_value)

From d96d5c0424b755acef190d528cf20de466715de6 Mon Sep 17 00:00:00 2001
From: ihosten <hostenian@gmail.com>
Date: Sun, 1 Jun 2025 20:42:38 +0200
Subject: [PATCH 08/19] addition of extra csv files containing example test
 data of varying test outcomes

---
 .python-version                           |  2 +-
 docs/quickstart.ipynb                     | 30 +++++++++++------------
 notebooks/data/big_middle_zone.csv        | 11 +++++++++
 notebooks/data/high_anaerobic_reserve.csv |  8 ++++++
 notebooks/data/initial_la_drop.csv        |  8 ++++++
 notebooks/data/low_anaerobic_reserve.csv  |  7 ++++++
 notebooks/data/no_base.csv                |  5 ++++
 notebooks/data/test_2.csv                 | 12 +++++++++
 8 files changed, 67 insertions(+), 16 deletions(-)
 create mode 100644 notebooks/data/big_middle_zone.csv
 create mode 100644 notebooks/data/high_anaerobic_reserve.csv
 create mode 100644 notebooks/data/initial_la_drop.csv
 create mode 100644 notebooks/data/low_anaerobic_reserve.csv
 create mode 100644 notebooks/data/no_base.csv
 create mode 100644 notebooks/data/test_2.csv

diff --git a/.python-version b/.python-version
index c8cfe39..24ee5b1 100644
--- a/.python-version
+++ b/.python-version
@@ -1 +1 @@
-3.10
+3.13
diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index b2fe5fb..eccc083 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -481,7 +481,7 @@
     },
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -497,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "id": "279f619a",
    "metadata": {},
    "outputs": [
@@ -507,7 +507,7 @@
        "np.float64(147.14747308909196)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -519,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "id": "6fcd47b1",
    "metadata": {},
    "outputs": [
@@ -529,13 +529,13 @@
        "<Axes: title={'center': 'Lactate Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 16,
+     "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>"
       ]
@@ -551,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "id": "acff8813",
    "metadata": {},
    "outputs": [],
@@ -561,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 8,
    "id": "65aa368c",
    "metadata": {},
    "outputs": [
@@ -571,13 +571,13 @@
        "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -595,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "id": "7ef327c7",
    "metadata": {},
    "outputs": [
@@ -605,13 +605,13 @@
        "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -627,7 +627,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": ".venv (3.13.3)",
    "language": "python",
    "name": "python3"
   },
@@ -641,7 +641,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.16"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
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

From d50de8597d339422779af25131149f3df6e00648 Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Sun, 1 Jun 2025 21:26:02 +0200
Subject: [PATCH 09/19] reset of .python version

---
 .python-version | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.python-version b/.python-version
index 24ee5b1..c8cfe39 100644
--- a/.python-version
+++ b/.python-version
@@ -1 +1 @@
-3.13
+3.10

From b1d8dbeacf13bb1dd62b72405ce8a1a68df40597 Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Wed, 30 Jul 2025 14:17:35 +0200
Subject: [PATCH 10/19] clean up

---
 docs/quickstart.ipynb              | 12 ++++++------
 src/lactopy/base/base.py           | 14 ++++++++++----
 src/lactopy/lactate_models/dmax.py | 16 +++++-----------
 src/lactopy/plots/Dmax.py          | 11 +----------
 src/lactopy/plots/base.py          |  8 +++++---
 5 files changed, 27 insertions(+), 34 deletions(-)

diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index eccc083..8f143d8 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -472,7 +472,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: title={'center': 'Lactate Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+       "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
      "execution_count": 4,
@@ -481,7 +481,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -526,7 +526,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: title={'center': 'Lactate Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+       "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
      "execution_count": 6,
@@ -535,7 +535,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -577,7 +577,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -611,7 +611,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
diff --git a/src/lactopy/base/base.py b/src/lactopy/base/base.py
index 65e39e3..0d1214e 100644
--- a/src/lactopy/base/base.py
+++ b/src/lactopy/base/base.py
@@ -28,8 +28,9 @@ def _validate_lactate_test(self, X: ArrayLike, y: ArrayLike):
             raise ValueError("X and y must have the same length.")
         if len(X) == 0:
             raise ValueError("X and y must not be empty.")
+        return X, y
 
-    def fit(self, X: ArrayLike, y: ArrayLike, method="4th_poly"):
+    def fit(self, X: ArrayLike, y: ArrayLike, method="4th_poly", _mask=None):
         """
         Fit the model to the training data.
 
@@ -49,9 +50,14 @@ def fit(self, X: ArrayLike, y: ArrayLike, method="4th_poly"):
             self (object):
                 Fitted model.
         """
-        self._validate_lactate_test(X, y)
-        self.X = np.array(X)
-        self.y = np.array(y)
+
+        X, y = self._validate_lactate_test(X, y)
+        mask = _mask if _mask is not None else np.ones_like(X, dtype=bool)
+        self.X_raw_for_plot = X
+        self.y_raw_for_plot = y
+
+        self.X = np.array(X)[mask]
+        self.y = np.array(y)[mask]
         match method:
             case "3th_poly":
                 self.model = PolyAdaptor().fit(self.X, self.y, degree=3)
diff --git a/src/lactopy/lactate_models/dmax.py b/src/lactopy/lactate_models/dmax.py
index 81f2e41..dbad1d1 100644
--- a/src/lactopy/lactate_models/dmax.py
+++ b/src/lactopy/lactate_models/dmax.py
@@ -1,5 +1,5 @@
+import numpy as np
 from numpy.typing import ArrayLike
-import copy
 
 
 from lactopy.base import BaseModel
@@ -63,24 +63,18 @@ def fit(
             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
+        mask = None
 
         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]
+                X, y = np.array(X), np.array(y)
+                mask = X > X[0] + threshold_above_baseline
             case _:
                 raise ValueError(f"Unknown implementation: {impl}")
 
-        super().fit(X, y, **kwargs)
+        super().fit(X, y, _mask=mask, **kwargs)
         self.dxdt_model = self.model.dxdt()
         return self
 
diff --git a/src/lactopy/plots/Dmax.py b/src/lactopy/plots/Dmax.py
index a90c4b3..0070e9e 100644
--- a/src/lactopy/plots/Dmax.py
+++ b/src/lactopy/plots/Dmax.py
@@ -8,16 +8,7 @@ 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
+    _title = "Dmax Model Fit"
 
     def plot_predictions(self):
         """
diff --git a/src/lactopy/plots/base.py b/src/lactopy/plots/base.py
index 162b54a..85926c8 100644
--- a/src/lactopy/plots/base.py
+++ b/src/lactopy/plots/base.py
@@ -11,8 +11,10 @@ class Plot:
     Base class for all plots.
     """
 
-    def __init__(self, base_lactate_model: "BaseModel"):
-        self.base_lactate_model = base_lactate_model
+    _title = "Lactate Model Plot"
+
+    def __init__(self, context: "BaseModel"):
+        self.base_lactate_model = context
 
     def __call__(self):
         return self.plot_fit()
@@ -42,7 +44,7 @@ def plot_fit(self):
         plt.plot(
             X, self.base_lactate_model.model.predict(X), color="red", label="Model"
         )
-        plt.title("Lactate Model Fit")
+        plt.title(self.__class__._title)
         plt.xlabel("Intensity")
         plt.ylabel("Lactate")
         plt.legend()

From bc6d05b2d419d5bae55bfba5497f688ef0d9b518 Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Wed, 30 Jul 2025 20:37:26 +0200
Subject: [PATCH 11/19] add nb pre-commit to resolve merging errors with nb

---
 .pre-commit-config.yaml |  5 +++++
 pyproject.toml          |  1 +
 uv.lock                 | 14 ++++++++++++++
 3 files changed, 20 insertions(+)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 593e6d6..ab17053 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -17,3 +17,8 @@ repos:
     hooks:
       - id: flake8
         args: [--max-line-length=88, --extend-ignore=E203] # To make it compatible with black
+
+  - repo: https://github.com/srstevenson/nb-clean
+    rev: 4.0.1
+    hooks:
+      - id: nb-clean
diff --git a/pyproject.toml b/pyproject.toml
index 60fed47..72e0de8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,6 +26,7 @@ dev = [
     "mkdocs-literate-nav>=0.6.2",
     "mkdocs-material>=9.6.12",
     "mkdocstrings-python>=1.16.10",
+    "nb-clean>=4.0.1",
     "pandas>=2.2.3",
     "pre-commit>=4.2.0",
     "pytest>=8.3.5",
diff --git a/uv.lock b/uv.lock
index 2ad97fb..e11ed4d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -821,6 +821,7 @@ dev = [
     { name = "mkdocs-literate-nav" },
     { name = "mkdocs-material" },
     { name = "mkdocstrings-python" },
+    { name = "nb-clean" },
     { name = "pandas" },
     { name = "pre-commit" },
     { name = "pytest" },
@@ -842,6 +843,7 @@ dev = [
     { name = "mkdocs-literate-nav", specifier = ">=0.6.2" },
     { name = "mkdocs-material", specifier = ">=9.6.12" },
     { name = "mkdocstrings-python", specifier = ">=1.16.10" },
+    { name = "nb-clean", specifier = ">=4.0.1" },
     { name = "pandas", specifier = ">=2.2.3" },
     { name = "pre-commit", specifier = ">=4.2.0" },
     { name = "pytest", specifier = ">=8.3.5" },
@@ -1176,6 +1178,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/53/37/19549c5e0179785308cc988a68e16aa7550e4e270ec8a9878334e86070c6/mkdocstrings_python-1.16.10-py3-none-any.whl", hash = "sha256:63bb9f01f8848a644bdb6289e86dc38ceddeaa63ecc2e291e3b2ca52702a6643", size = 124112 },
 ]
 
+[[package]]
+name = "nb-clean"
+version = "4.0.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "nbformat" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/28/81/2ef0aaf3df03ec4146a92d62ce9b9533d368431791c7e5cb8e027068794a/nb_clean-4.0.1.tar.gz", hash = "sha256:f4af1bec3f25e7b18eb09024947bcc809efc97fd68bec482d219c304d850809f", size = 24170 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3a/be/dd0d1c964fa8e2c645bb9298290c761c34b292ef4b2c113018f1b3c4cd90/nb_clean-4.0.1-py3-none-any.whl", hash = "sha256:6a673775523ad5ae18566bb0880012c169944a9357760dfcf993cdb14e2e5f82", size = 21355 },
+]
+
 [[package]]
 name = "nbclient"
 version = "0.10.2"

From 942582606edac50d5722b2d3e0deed784328157f Mon Sep 17 00:00:00 2001
From: ihosten <hostenian@gmail.com>
Date: Mon, 21 Jul 2025 12:48:50 +0200
Subject: [PATCH 12/19] addition of the LT1 standard increment methods and
 start of the loglog method using the piecewise regression module

---
 docs/quickstart.ipynb                       | 509 +++++++++++++++++---
 notebooks/data/initial_la_drop.csv          |   4 +-
 src/lactopy/__init__.py                     |   2 +-
 src/lactopy/lactate_models/__init__.py      |   3 +
 src/lactopy/lactate_models/lt1_loglog.py    |  91 ++++
 src/lactopy/lactate_models/lt1_si.py        |  89 ++++
 src/lactopy/lactate_models/lt1_si_lowest.py | 104 ++++
 src/lactopy/plots/lt1_loglog_plot.py        |  34 ++
 src/lactopy/plots/lt1_si_lowest_plot.py     |  34 ++
 src/lactopy/plots/lt1_si_plot.py            |  34 ++
 10 files changed, 839 insertions(+), 65 deletions(-)
 create mode 100644 src/lactopy/lactate_models/lt1_loglog.py
 create mode 100644 src/lactopy/lactate_models/lt1_si.py
 create mode 100644 src/lactopy/lactate_models/lt1_si_lowest.py
 create mode 100644 src/lactopy/plots/lt1_loglog_plot.py
 create mode 100644 src/lactopy/plots/lt1_si_lowest_plot.py
 create mode 100644 src/lactopy/plots/lt1_si_plot.py

diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index 8f143d8..4bf8526 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 28,
    "id": "150ece86",
    "metadata": {},
    "outputs": [],
@@ -14,24 +14,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 29,
    "id": "e0dd4c2e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.read_csv('../tests/test_data/lactate_test_data copy.csv')"
+    "df = pd.read_csv('../notebooks/data/initial_la_drop.csv')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 30,
    "id": "7f657183",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {\n",
+       "<style>#sk-container-id-2 {\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-1 {\n",
+       "#sk-container-id-2 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 pre {\n",
+       "#sk-container-id-2 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "#sk-container-id-2 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-1 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-2 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-1 div.sk-container {\n",
+       "#sk-container-id-2 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-1 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-2 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-1 div.sk-parallel-item::after {\n",
+       "#sk-container-id-2 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-1 div.sk-parallel {\n",
+       "#sk-container-id-2 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-1 div.sk-parallel-item {\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
+       "#sk-container-id-2 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-1 div.sk-toggleable {\n",
+       "#sk-container-id-2 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-1 label.sk-toggleable__label {\n",
+       "#sk-container-id-2 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-1 label.sk-toggleable__label .caption {\n",
+       "#sk-container-id-2 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-1 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 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-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "#sk-container-id-2 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-1 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-2 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-1 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-2 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-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 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-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 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-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 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-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 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-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 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-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 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-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-2 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-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-2 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-1 div.sk-label label {\n",
+       "#sk-container-id-2 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-1 div.sk-label-container {\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
+       "#sk-container-id-2 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-1 div.sk-estimator.fitted {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-2 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-1 a.estimator_doc_link {\n",
+       "#sk-container-id-2 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-1 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-2 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-1 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-2 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-1 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-2 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-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>"
+       "</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>"
       ],
       "text/plain": [
        "OBLA()"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -465,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 31,
    "id": "77b4e630",
    "metadata": {},
    "outputs": [
@@ -475,13 +475,17 @@
        "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
+<<<<<<< HEAD
       "image/png": 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",
+=======
+      "image/png": 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",
+>>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -497,17 +501,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 32,
    "id": "279f619a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "np.float64(147.14747308909196)"
+       "np.float64(147.16511067594706)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -519,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 33,
    "id": "6fcd47b1",
    "metadata": {},
    "outputs": [
@@ -529,13 +533,17 @@
        "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
+<<<<<<< HEAD
       "image/png": 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AgVaWBQAAAMCLWRqC4uLi9Oyzz+rPP/9U0aJF1bVrV7322mt5ejOjYRiuTnTIH3a7Xf7+/rQpBwAAgEewtDHC1brczU/nz59XbGyskpKSLKjOt4SGhioqKkqBgYFWlwIA8EE0RgDgMY0R8pPT6dSBAwdkt9tVpkwZBQYGMlKRDwzD0Pnz53XixAkdOHBAVapUueziVAAAAICVvDYEnT9/Xk6nU9HR0QoNDbW6HK8WEhKigIAAHTp0SOfPn8+0AC4AAPktKChIS5YscW0DQHa8NgRdwKhEweDnDACwkr+/v9q3b291GQA8BP9yBQAAAOBTvH4kCAAAeL/U1FR9+umnkqS+ffvmaadZAN6HEAQAADze+fPnXesMdu/enRAEIFtMh3NDAwYMkM1mk81mU0BAgEqVKqU77rhDH330kZxOZ47fZ8aMGSpcuHD+FQoAAAB4IEJQDjgc0qpV0mefmV8LYt3VNm3aKDY2VgcPHtTSpUvVokULDR06VB06dFBaWlr+FwAAAAB4KULQZcTESBUqSC1aSH36mF8rVDD356egoCCVLl1a11xzjerVq6fnnntOixYt0tKlSzVjxgxJ0rhx41S7dm2FhYUpOjpaDz/8sBISEiRJq1at0sCBAxUXF+caVXrppZckSZ988onq16+vQoUKqXTp0urTp4+OHz+evx8IAAAAXsWKgYK8QgjKRkyM1K2b9OefGfcfPWruz+8g9F+33Xab6tSpo5h/v7Gfn58mTpyoXbt2aebMmfr+++81YsQISVKjRo00fvx4RUREKDY2VrGxsXrqqackmTePvvLKK9qxY4e++OILHTx4UAMGDCjYDwMAAACPZdVAQV6hMcIlOBzS0KGSYWQ+ZhiSzSYNGyZ17CjZ7QVXV7Vq1bRz505J0rBhw1z7K1SooFdffVUPPvig3n33XQUGBioyMlI2m02lS5fO8B6DBg1ybVeqVEkTJ07UTTfdpISEBIWHhxfI5wAAAIBnujBQ8N9/J18YKJg/X+rSxZracoqRoEtYuzbzCNDFDEM6csQ8ryAZhiGbzSZJWrFihW6//XZdc801KlSokPr166d//vlHSUlJ2b7Hli1bdOedd6pcuXIqVKiQbr31VknS4cOH871+AAAAeK7/DhQM1Ee6Xjskpe8bNsz9p8YRgi4hNjZvz8sre/bsUcWKFXXw4EF16NBB119/vRYsWKAtW7Zo8uTJksw2oZeSmJio1q1bKyIiQp9++qk2bdqkhQsXXvZ1AAC4s6CgIM2dO1dz585VUFCQ1eUAXuvigYJG+kHTdL82qoGu015J1g0UXCmmw11CVFTenpcXvv/+e/388896/PHHtWXLFjmdTr399tvy8zOz7Ny5czOcHxgYKMd/Yvivv/6qf/75R2+88Yaio6MlSZs3by6YDwAAQD7x9/dX9+7drS4D8HoXBgCK6aTmqJf85dAc9dJvui7L89wVI0GX0LSpVLasee9PVmw2KTraPC8/pKSk6NixYzp69Ki2bt2q119/XR07dlSHDh10zz33qHLlykpNTdWkSZP0xx9/6JNPPtHUqVMzvEeFChWUkJCg7777TidPnlRSUpLKlSunwMBA1+sWL16sV155JX8+BAAAALxKVJRkk1Mf6x5F60/9qqp6UFMl2TKd584IQZdgt0sTJpjb/w1CF56PH59/TRG++eYbRUVFqUKFCmrTpo1WrlypiRMnatGiRbLb7apTp47GjRunMWPGqFatWvr00081evToDO/RqFEjPfjgg+rZs6dKlCihsWPHqkSJEpoxY4bmzZunGjVq6I033tBbb72VPx8CAIACkpaWpnnz5mnevHmspwfko6ZNpVcj3lQ7LdU5BauH5ipR6Y218nugIK/YDCOr/meeIT4+XpGRkYqLi1NERESGY8nJyTpw4IAqVqyo4ODgXH+PmBjz5q+LmyRER5sByN27XhSkvPp5AwCQG4mJia4OpwkJCQoLC7O4IsBLrVsn563N5ed06D69rw802HXowkCBVd3hsssG/8U9QZfRpYvZBnvtWnNuY1SUmWwLsi02AAAAYLkTJ6ReveTndOhws7v1zf57paPph8uW9ZyBAkJQDtjtUvPmVlcBAAAAWMTplO65x1wMqGpVlftqig6G2Dx2oIAQBAAAACB7Y8dK33wjBQdL8+ZJ4eGyy3MHCmiMAAAAAODS1q6V/vc/c/udd6Tata2tJw8QggAAAABk7d/7gORwSP36SYMGWV1RniAEAQAAAMjM6TSDz19/SdWqSe++e+lFND0M9wQBAACPFxgYqOnTp7u2AeSBN96Qli2TQkJc9wF5C0IQAADweAEBARowYIDVZQDeY80a6YUXzO3Jk6VataytJ48xHQ4AAABAuuPHzfuALrTF9sJfMBCCfNCqVatks9l05syZHL+mQoUKGj9+fL7VBADA1UhLS9NXX32lr776SmlpaVaXA3gup1O6+25z8Z/q1b3qPqCLEYLc0IABA2Sz2fTggw9mOjZkyBDZbDaG/AEAuEhKSoo6dOigDh06KCUlxepyAM81erS0fHn6fUBhYVZXlC8IQW4qOjpac+bM0blz51z7kpOTNXv2bJUrV87CygAAAOCVVq2SXnzR3H73XalmTUvLyU++FYIMQ0pMLPiHYVxxqfXq1VN0dLRiYmJc+2JiYlSuXDnVrVvXtS8lJUWPPfaYSpYsqeDgYDVp0kSbNm3K8F5ff/21rrvuOoWEhKhFixY6ePBgpu+3bt06NW3aVCEhIYqOjtZjjz2mxMTEK64bAAAAHujvv6Xevc3pcAMGeOV9QBfzrRCUlGS29ivoR1JSrsodNGiQq92nJH300UcaOHBghnNGjBihBQsWaObMmdq6dasqV66s1q1b69SpU5KkI0eOqEuXLrrzzju1fft2DR48WM8880yG99i/f7/atGmjrl27aufOnfr888+1bt06PfLII7mqGwAAAB7E4TDvAzp2TKpRQ3rnHasryne+FYI8zN13361169bp0KFDOnTokH744QfdfffdruOJiYmaMmWK3nzzTbVt21Y1atTQ+++/r5CQEH344YeSpClTpujaa6/V22+/rapVq6pv376Z7icaPXq0+vbtq2HDhqlKlSpq1KiRJk6cqI8//ljJyckF+ZEBAABQ0F5/XVqxQgoN9er7gC7mW+sEhYZKCQnWfN9cKFGihNq3b68ZM2bIMAy1b99exYsXdx3fv3+/UlNT1bhxY9e+gIAA3XzzzdqzZ48kac+ePWrQoEGG923YsGGG5zt27NDOnTv16aefuvYZhiGn06kDBw6oevXquaofAAAAbm7lSumll8ztKVPMkSAf4FshyGbzuGQ7aNAg17S0yZMn58v3SEhI0AMPPKDHHnss0zGaMAAAAHipY8fS7wMaNMhcE8hH+FYI8kBt2rTR+fPnZbPZ1Lp16wzHrr32WgUGBuqHH35Q+fLlJUmpqanatGmThg0bJkmqXr26Fi9enOF1P/74Y4bn9erV0+7du1W5cuX8+yAAAOSjwMBAvfPvfQyBgYEWVwN4AIdD6tvXbIhQq5Y0aZLVFRUoQpCbs9vtrqltdrs9w7GwsDA99NBDGj58uIoWLapy5cpp7NixSkpK0r333itJevDBB/X2229r+PDhGjx4sLZs2aIZM2ZkeJ+nn35at9xyix555BENHjxYYWFh2r17t5YvX+76CwUAAHcWEBCgIUOGWF0G4DleeUX6/ntzltS8ebm+fcNT0RjBA0RERCgiIiLLY2+88Ya6du2qfv36qV69evr999+1bNkyFSlSRJI5nW3BggX64osvVKdOHU2dOlWvv/56hve4/vrrtXr1av32229q2rSp6tatqxdffFFlypTJ988GAACAAvbdd9LLL5vbU6dK1apZW48FbIaRi0Vs3ER8fLwiIyMVFxeXKSQkJyfrwIEDqlixooKDgy2q0Hfw8wYAWMnhcGjt2rWSpKZNm2aaPQHgX7Gx0g03SMePS4MHS++/b3VFeSa7bPBfTIcDAAAeLzk5WS1atJBkNvwJ87BGSECBSEuT+vQxA1Dt2tLEiVZXZBmmwwEAAAC+4OWXpVWrpPBw8z6gkBCrK7IMIQgAAADwdsuXS6++am5PmyZVrWptPRYjBAEAAADe7K+/zHbYhiHdf7+5NpCP8/oQ5MF9HzwKP2cAAAA3lJYm9eolnTgh1akjjR9vdUVuwWtDUEBAgCQpKSnJ4kp8w4Wf84WfOwAAANzAiy9Ka9dKhQr5/H1AF7O0O5zD4dBLL72kWbNm6dixYypTpowGDBig//3vf7LZbFf13na7XYULF9bx48clSaGhoVf9nsjMMAwlJSXp+PHjKly4MC1JAQAA3MXSpdLo0eb2Bx9IVapYW48bsTQEjRkzRlOmTNHMmTNVs2ZNbd68WQMHDlRkZKQee+yxq37/0qVLS5IrCCH/FC5c2PXzBgCgoAUEBGjs2LGubcDnHTki9etnbg8ZIvXoYW09bsbSxVI7dOigUqVK6cMPP3Tt69q1q0JCQjRr1qzLvj6nCyI5HA6lpqbmSc3ILCAggBEgAAAAd5GaKjVvLq1fL914o/TDD1JQkNVV5TuPWSy1UaNGmjZtmn777Tddd9112rFjh9atW6dx48ZleX5KSopSUlJcz+Pj43P0fex2O/9IBwAAgG94/nkzAEVGSnPn+kQAulKWhqBnnnlG8fHxqlatmux2uxwOh1577TX17ds3y/NHjx6tUaNGFXCVAADA3TkcDm3dulWSVK9ePX75Cd+1ZIn05pvm9vTpUqVK1tbjpiydDjdnzhwNHz5cb775pmrWrKnt27dr2LBhGjdunPr375/p/KxGgqKjo3M05AUAALxXYmKiwsPDJUkJCQkKCwuzuCLAAocOSXXrSqdPS0OH+lw7bI+ZDjd8+HA988wz6tWrlySpdu3aOnTokEaPHp1lCAoKClIQw3kAAABARufPSz17mgHo5pulfxuFIGuWrhOUlJQkP7+MJdjtdjmdTosqAgAAADzQ009LGzdKRYpIn38uBQZaXZFbs3Qk6M4779Rrr72mcuXKqWbNmtq2bZvGjRunQYMGWVkWAAAA4DliYtKnvn38sVShgpXVeARLQ9CkSZP0wgsv6OGHH9bx48dVpkwZPfDAA3rxxRetLAsAAADwDPv3SwMHmtvDh0sdOlhbj4ewtDHC1bqSm58AAID3ojECfFJystS4sbR1q/l15UrJhxcLvpJsYOk9QQAAAABy6YknzABUvLg0Z45PB6ArZel0OAAAgLwQEBCgkSNHurYBr/fZZ9KUKZLNJs2aJZUta3VFHoXpcAAAAIAn+fVXqX59KTFR+t//pFdesboit8B0OAAAAMAbJSVJ3bubAahFC+mll6yuyCMxHQ4AAHg8p9OpPXv2SJKqV6+eaR1CwGs88oj0yy9SqVLS7NmS3W51RR6JEAQAADzeuXPnVKtWLUl0h4MXmz7dfPj5mQGodGmrK/JY/JoEAAAAcHc//ywNGWJujxol3XabtfV4OEIQAAAA4M7OnjXvAzp3TmrdWnruOasr8niEIAAAAMBdGYZ0//3S3r1mG+xZs8zpcLgq/AQBAAAAdzVlirkQqr+/9Pnn5sKouGqEIAAAAMAdbd4sPf64uT1mjNSokbX1eBFCEAAAAOBuTp827wM6f17q1Ck9DCFP0CIbAAB4vICAAD311FOubcCjGYY0cKB08KBUsaLZFttms7oqr0IIAgAAHi8wMFBvvvmm1WUAeWPcOGnRIikwUJo3Typc2OqKvA7T4QAAAAB3sW6d9PTT5vb48dKNN1pajrdiJAgAAHg8p9Opw4cPS5LKlSsnP1oIwxMdPy717Ck5HFKfPtKDD1pdkdciBAEAAI937tw5VaxYUZKUkJCgsLAwiysCrtCF4PPXX1L16tJ773EfUD7i1yQAAACA1V5+WfruOyk0VJo/XwoPt7oir0YIAgAAAKy0bJn0yivm9rRpUo0a1tbjAwhBAAAAgFWOHJH69jXbYj/4oLmNfEcIAgAAAKxw/rzUo4f0zz9SvXrS//2f1RX5DEIQAAAAYIURI6QffzTXAZo/XwoOtroin0EIAgAAAAra/PnShAnm9syZ0r/dDVEwaJENAAA8nr+/vx5++GHXNuDWfvtNGjTI3B4xQrrrLmvr8UE2wzAMq4vIrfj4eEVGRiouLk4RERFWlwMAAABkLylJuuUW6eefpaZNpe+/lwjueeJKsgHT4QAAAICCYBjSQw+ZAahUKenzzwlAFuGnDgAAPJ5hGDp58qQkqXjx4rLZbBZXBGThgw+kjz+W/PykOXOkqCirK/JZhCAAAODxkpKSVLJkSUlSQkKCwsLCLK4I+I+tW6VHHzW3X3tNat7c0nJ8HdPhAAAAgPx0+rTUrZuUkiLdeafZDAGWIgQBAAAA+cXplPr3lw4cMNtgz5xpToeDpbgCAAAAQH4ZO1b68kspKMhcG6hIEasrgghBAAAAQP5YtUp6/nlze9IkqV49S8tBOkIQAAAAkNf++kvq1St9OtzgwVZXhIsQggAAAIC8lJoq9ewp/f23VLu29O67Em3b3QotsgEAgMfz9/dX//79XduApZ55Rlq3ToqIkBYskEJDra4I/8GfEgAAwOMFBQVpxowZVpcBmM0Pxo0zt2fOlKpUsbYeZInpcAAAAEBe2LtXGjjQ3B4xQurUydJycGmMBAEAAI9nGIaSkpIkSaGhobJx/wUKWkKC1KWL+fXWW6XXXrO6ImSDkSAAAODxkpKSFB4ervDwcFcYAgqMYUgPPCDt3i1FRUlz5kjcm+bWCEEAAADA1ZgyRZo9W7LbpblzpdKlra4Il0EIAgAAAHJr40Zp2DBze+xYqUkTS8tBzhCCAAAAgNw4cULq3t1cF6hrV+nxx62uCDlECAIAAACulMMh9e4tHTkiXXed9NFHLIjqQQhBAAAAwJV64QXpu+/MhVBjYsyFUeExLA1BFSpUkM1my/QYMmSIlWUBAAAAl7ZokTR6tLn94YdSzZrW1oMrZmnvvk2bNsnhcLie//LLL7rjjjvUvXt3C6sCAACexm63q1u3bq5tIN/s2yfdc4+5PWyY1KuXpeUgd2yGYRhWF3HBsGHDtGTJEu3bty9Hi5zFx8crMjJScXFximAIEgAAAPkpMVG65Rbpl1/MLnDffy8FBFhdFf51JdnAbVZxOn/+vGbNmqUnnnjikgEoJSVFKSkprufx8fEFVR4AAAB8mWFI999vBqDSpc31gAhAHsttGiN88cUXOnPmjAYMGHDJc0aPHq3IyEjXIzo6uuAKBAAAgO+aPDnjgqhRUVZXhKvgNtPhWrdurcDAQH355ZeXPCerkaDo6GimwwEA4OMSExMVHh4uSUpISFBYWJjFFcGrrF8v3XqrlJYmjRvHekBuyuOmwx06dEgrVqxQTExMtucFBQUpKCiogKoCAACAz/v7b3NB1LQ0qUcPsxkCPJ5bTIebPn26SpYsqfbt21tdCgAAAGBKTTWDz19/SdWrm+2wWRDVK1gegpxOp6ZPn67+/fvL398tBqYAAAAA6emnpTVrpEKFpIULpX+nXMLzWR6CVqxYocOHD2vQoEFWlwIAAACYPvtM+r//M7dnzpSqVrW2HuQpy4deWrVqJTfpzQAAAABIP/8sDR5sbj/zjNS5s7X1IM9ZPhIEAAAAuI0zZ6QuXaSkJKllS+nVV62uCPnA8pEgAACAq2W329WuXTvXNpArTqfUr5/0++9SuXLmlDj+e/JKhCAAAODxgoOD9dVXX1ldBjzda69JS5ZIQUFSTIxUvLjVFSGfMB0OAAAAWLpUGjnS3J4yRbrxRmvrQb4iBAEAAMC3/fGH1KePZBjSAw9IAwdaXRHyGSEIAAB4vMTERIWFhSksLEyJiYlWlwNPkphodn87c0a6+WZpwgSrK0IB4J4gAADgFZKSkqwuAR7A4ZDWrpViY6Wo0oaaTbtPfjt3SiVLSgsWmPcDwesRggAAAOATYmKkoUOlP/80nz+u/1NzfSan3V9+8+ZJZctaWyAKDNPhAAAA4PViYqRu3dIDUHOt1FiNkCQNc4xTzMlmFlaHgkYIAgAAgFdzOMwRIMMwn0frsOaqh/zl0Mfqp3f0iIYNM8+DbyAEAQAAwKutXZs+AhSsc4pRF5XQSW1VXT2g92TIpiNHzPPgGwhBAAAA8GqxsRe2DL2rh1VfW3RSxdRZC5WskCzOg7ejMQIAAPB4fn5+uvXWW13bwMWiosyvD2mKBmqGHPJTL83RYZXP8jx4P0IQAADweCEhIVq1apXVZcBNNW0qdSrxgyacGCpJelpj9J1auo7bbGZjuKZNraoQBY1flQAAAMCr2WP/1GepXRWgNM1RT72tJ13HbDbz6/jxkt1uTX0oeIQgAAAAeK/kZKlrVwWf+Vtnyl+vF8t8KMnmOly2rDR/vtSli3UlouAxHQ4AAHi8xMREVahQQZJ08OBBhYWFWVsQ3INhSA89JP30k1S0qAp/v1B7yodp7VqzCUJUlDkFjhEg30MIAgAAXuHkyZNWlwB3M3myNGOG5Ocnff65VKmS7JKaN7e4LliO6XAAAADwPqtXS48/bm6PHSu1bJn9+fAphCAAAAB4l8OHpe7dpbQ0qU8f6YknrK4IboYQBAAAAO9x7pzZ5eDECemGG6T3309vAQf8ixAEAAAA72AY0v33S1u2SMWLS198IYWGWl0V3BAhCAAAAN5hwgRp1iyz3dvcuVL58lZXBDdFdzgAAODx/Pz8VL9+fdc2fNCKFdKT/y6C+tZbUosW1tYDt0YIAgAAHi8kJESbNm2yugxYZf9+qUcPyemU+veXhg61uiK4OX5VAgAAAM919qzUsaN0+rTUoIE0dSqNEHBZhCAAAAB4JqdTuuceadcuKSpKiomRgoOtrgoegBAEAAA8XlJSkipUqKAKFSooKSnJ6nJQUF5+2ewAFxgoLVwolSljdUXwENwTBAAAPJ5hGDp06JBrGz4gJkYaNcrcnjbNnAoH5BAjQQAAAPAsP/9sToOTpGHDzGYIwBUgBAEAAMBz/POP2QghMVG6/XbpzTetrggeiBAEAAAAz5CaarbCPnBAqlRJ+vxzyZ+7O3DlCEEAAADwDI8/Ln3/vRQeLi1aJBUrZnVF8FCEIAAAALi/996TJk821wCaNUuqVcvqiuDBGD8EAAAez2azqUaNGq5teJnVq6VHHjG3X33VvCcIuAqEIAAA4PFCQ0O1a9cuq8tAfjhwQOraVUpLk3r3lp591uqK4AWYDgcAAAD3dPasdNddZke4G2+UPvzQnA4HXCVCEAAAANyP0yn16yf98otUurTZCCEkxOqq4CUIQQAAwOMlJSWpZs2aqlmzppKSkqwuB3nhhRfM4BMUJH3xhXTNNVZXBC/CPUEAAMDjGYah3bt3u7bh4T77THr9dXP7gw+kBg2srQdeh5EgAAAAuI+ffpIGDTK3R4yQ7r7b2nrglQhBAAAAcA9//mm2v05Oljp0SB8NAvIYIQgAAADWS0w0O8EdO2YuhDp7tmS3W10VvBQhCAAAANZyOqV77pG2bZNKlJC+/FIqVMjqquDFLA9BR48e1d13361ixYopJCREtWvX1ubNm60uCwAAAAVl5EgpJkYKDJQWLpQqVLC6Ing5S7vDnT59Wo0bN1aLFi20dOlSlShRQvv27VORIkWsLAsAAHgYm82m8uXLu7bhQWbPll591dx+/32pcWNr64FPsDQEjRkzRtHR0Zo+fbprX8WKFS95fkpKilJSUlzP4+Pj87U+AADgGUJDQ3Xw4EGry8CV+vHH9E5wTz9tTokDCoCl0+EWL16s+vXrq3v37ipZsqTq1q2r999//5Lnjx49WpGRka5HdHR0AVYLAACAPHP4sNSpk5SSYnaEoxMcCpDNsHBFseDgYEnSE088oe7du2vTpk0aOnSopk6dqv79+2c6P6uRoOjoaMXFxSkiIqLA6gYAAMBVSEiQmjSRduyQ6tSR1q2TwsOtrgoeLj4+XpGRkTnKBpaGoMDAQNWvX1/r16937Xvssce0adMmbdiw4bKvv5IPCgAAvNe5c+fUrFkzSdKaNWsUEhJicUW4JIfDHAFaskQqWVLatEkqV87qquAFriQbWDodLioqSjVq1Miwr3r16jp8+LBFFQEAAE/kdDq1efNmbd68WU6n0+pykJ3hw80AFBwsLV5MAIIlLA1BjRs31t69ezPs++2331zdXQAAAOBF3ntP+r//M7dnzpQaNLC2HvgsS0PQ448/rh9//FGvv/66fv/9d82ePVvTpk3TkCFDrCwLAAAAeW35cunCv/FeeUXq0cPaeuDTLA1BN910kxYuXKjPPvtMtWrV0iuvvKLx48erb9++VpYFAACAvLR7t9Stm3k/UL9+0vPPW10RfJyljRGuFo0RAACAJCUmJir83+5iCQkJCgsLs7giuJw4YU57O3BAatrUHBEKCrK6Knghj2mMAAAAAC+WnGx2gjtwQLr2WikmhgAEt+BvdQEAAAB5oXjx4laXgIsZhjR4sLR+vVS4sNkRjmsEN0EIAgAAHi8sLEwnTpywugxc7KWXpE8/lfz9pfnzpWrVrK4IcGE6HAAAAPLWzJnSyy+b21OmSLffbm09wH8QggAAAJB3Vq6U7rvP3H72WXNKHOBmCEEAAMDjnTt3Ts2bN1fz5s117tw5q8vxXXv2SJ07S6mpUs+e0quvWl0RkCXuCQIAAB7P6XRq9erVrm1Y4O+/pXbtpLg4qVEjacYMyY/ft8M98V8mAAAArk5SknTXXdLBg2Yr7EWLpOBgq6sCLokQBAAAgNxzOqV77pF++kkqWlT6+mtaYcPtEYIAAACQe08/LS1YIAUGSl98IV13ndUVAZdFCAIAAEDuTJ4svfWWuf3RR1LTptbWA+QQIQgAAABXbvFi6bHHzO2XX5b69rW2HuAK0B0OAAB4hdDQUKtL8B0bN0q9epn3Aw0eLP3vf1ZXBFwRQhAAAPB4YWFhSkxMtLoM37B/v3TnndK5c1KbNtK770o2m9VVAVeE6XAAAADImZMnpbZtpRMnpLp1pblzpYAAq6sCrhghCAAAAJd37py5FtC+fVL58tJXX0mFClldFZArhCAAAODxkpOT1b59e7Vv317JyclWl+N9HA7p7rulDRukwoWlpUulqCirqwJyjXuCAACAx3M4HPr6669d28hjTz4pxcSYawEtWiRVr251RcBVYSQIAAAAl/b229KECeb2zJlSs2bW1gPkAUIQAAAAsjZ7tvTUU+b22LFmW2zACxCCAAAAkNmKFdKAAeb2sGHpYQjwAoQgAAAAZLRtm9S5s5SaKvXsaU6JYy0geBFCEAAAANIdOGCuBZSQILVoYd4H5Mc/GeFd+C8aAAAAppMnpdatpb//lq6/Xlq4UAoKsroqIM/RIhsAAHi8sLAwGYZhdRmeLTFR6tAhfTHUpUulyEirqwLyBSNBAAAAvi4tzbz3Z+NGqWhR6ZtvpDJlrK4KyDeEIAAAAF9mGNL990tffSWFhEhLlkjVqlldFZCvCEEAAMDjJScnq3v37urevbuSk5OtLsezPPOMNH26ZLdLc+ZIDRtaXRGQ72yGB0+gjY+PV2RkpOLi4hQREWF1OQAAwCKJiYkKDw+XJCUkJCgsLMziijzEW29Jw4eb29Onp68LBHigK8kGjAQBAAD4ohkz0gPQ2LEEIPgUQhAAAICv+fJLafBgc/upp9LDEOAjCEEAAAC+ZN06qUcPyeGQ+veXxoyxuiKgwBGCAAAAfMXOneZaQMnJ5tf335f8+OcgfA//1QMAAPiCAwek1q2luDipSRPp88+lgACrqwIsQQgCAADwdrGxUsuW0rFjUu3a0uLFUmio1VUBlsl1CEpLS9OKFSv03nvv6ezZs5Kkv/76SwkJCXlWHAAAQE6EhoYqISFBCQkJCuUf9xmdOiW1aiX98YdUsaL0zTdSkSJWVwVYyj83Lzp06JDatGmjw4cPKyUlRXfccYcKFSqkMWPGKCUlRVOnTs3rOgEAAC7JZrOxNlBWEhKkdu2kX36RoqKkFSukMmWsrgqwXK5GgoYOHar69evr9OnTCgkJce3v3LmzvvvuuzwrDgAAALmUnCx16iRt3CgVLSotXy5VqmR1VYBbyNVI0Nq1a7V+/XoFBgZm2F+hQgUdPXo0TwoDAADIqZSUFD3wwAOSpPfee09BQUEWV2SxtDSpd2/pu++k8HBp6VKpZk2rqwLcRq5GgpxOpxwOR6b9f/75pwoVKnTVRQEAAFyJtLQ0zZw5UzNnzlRaWprV5VjL6ZTuvVf64gspKMhsgnDzzVZXBbiVXIWgVq1aafz48a7nNptNCQkJGjlypNq1a5dXtQEAAOBKGIY0bJj08ceS3S7NnSu1aGF1VYDbydV0uLffflutW7dWjRo1lJycrD59+mjfvn0qXry4Pvvss7yuEQAAADkxcqQ0aZK5PWOGdNddlpYDuKtchaCyZctqx44d+vzzz7Vjxw4lJCTo3nvvVd++fTM0SgAAAEABGTNGeuUVc/udd6S777a2HsCN5Wo63Jo1ayRJffv21dixY/Xuu+9q8ODBCggIcB3LiZdeekk2my3Do1q1arkpCQAAwHdNmiQ984y5/cYb0pAh1tYDuLlcjQS1aNFCsbGxKlmyZIb9cXFxatGiRZZNEy6lZs2aWrFiRXpB/rkqCQAAwDd9+KH02GPm9gsvSE8/bW09gAfIVeIwDEM2my3T/n/++eeKFyrz9/dX6dKlc1MGAACAb5s9W7rvPnP7iSekUaOsrQfwEFcUgrp06SLJ7AY3YMCADD34HQ6Hdu7cqUaNGl1RAfv27VOZMmUUHByshg0bavTo0SpXrlyW56akpCglJcX1PD4+/oq+FwAA8E6hoaE6fvy4a9snLFwo3XOP2RHuwQelt96SsvglNYDMrigERUZGSjJHggoVKpShCUJgYKBuueUW3XfhtxE50KBBA82YMUNVq1ZVbGysRo0apaZNm+qXX37Jcr2h0aNHaxS/4QAAAP9hs9lUokQJq8soOEuXSj17Sg6H1L+/NHkyAQi4AjbDMIwrfdGoUaP01FNPXfHUt8s5c+aMypcvr3Hjxunee+/NdDyrkaDo6GjFxcUpIiIiT2sBAABwSytXSu3aScnJUvfu5pQ47qkGFB8fr8jIyBxlg1z9HzNy5MhcFXY5hQsX1nXXXafff/89y+NBQUEZpuABAABI5i9Kn3jiCUnSuHHjvPffC2vXSh06mAGoQwdp1iwCEJALuf6/Zv78+Zo7d64OHz6s8+fPZzi2devWXL1nQkKC9u/fr379+uW2LAAA4IPS0tL07rvvSpLGjh3rnSFo/XpzBCgpSbrjDmnePCkw0OqqAI+Uq3WCJk6cqIEDB6pUqVLatm2bbr75ZhUrVkx//PGH2rZtm+P3eeqpp7R69WodPHhQ69evV+fOnWW329W7d+/clAUAAOCdNm6U2rSREhKk226TFi2SgoOtrgrwWLkKQe+++66mTZumSZMmKTAwUCNGjNDy5cv12GOPKS4uLsfv8+eff6p3796qWrWqevTooWLFiunHH3/0rRsbAQAAsrNpk9SqlXT2rNS8ufTll9JFzakAXLlcTYc7fPiwqxV2SEiIzp49K0nq16+fbrnlFr3zzjs5ep85c+bk5tsDAAD4hq1bzQAUHy81bWoGIF9pAQ7ko1yNBJUuXVqnTp2SJJUrV04//vijJOnAgQPKRbM5AAAA/NeOHea9P2fOSI0aSV99JYWHW10V4BVyFYJuu+02LV68WJI0cOBAPf7447rjjjvUs2dPde7cOU8LBAAA8Dk//yzdfrt06pTUoIG5LlAWaygCyJ1crRPkdDrldDrl/29Lxjlz5mj9+vWqUqWKHnjgAQUWUKeSK+kFDgAAvFdiYqLC/x0lSUhIyPO1DAvUhQB04oR0003S8uXSvwvWA7i0K8kGuQpBhw8fVnR0tGz/WZnYMAwdOXJE5cqVu9K3zBVCEAAAkMxf0B4+fFiSOVXfzy9Xk12st2OH1LKldPKkVK+etGKFVKSI1VUBHiHfF0utWLGiYmNjVbJkyQz7T506pYoVK8rhcOTmbQEAAHLFz89PFSpUsLqMq7N9e/oUuJtukpYtIwAB+SRXvyYxDCPTKJBkDj8H07MeAADgymzdaq7/c+qUdPPN0rffEoCAfHRFI0FPPPGEJMlms+mFF15Q6EUtGh0OhzZu3KgbbrghTwsEAAC4nPPnz+v555+XJL322msFdn9ynti8Ob0L3C23SN98wz1AQD67ohC0bds2SeZI0M8//5zhD5jAwEDVqVNHTz31VN5WCAAAcBmpqal66623JEkvvfSS54SgTZvMABQXZ7bBXrpU4j5nIN9dUQhauXKlJLMt9oQJE2hGAAAAkFsbN6YvhNq4MW2wgQKUq3uCxo8fr7S0tEz7T506pfj4+KsuCgAAwKutW2eOAMXHS02bEoCAAparENSrVy/NmTMn0/65c+eqV69eV10UAACA11qxQmrdWjp7VmreXPr6awIQUMByFYI2btyoFi1aZNrfvHlzbdy48aqLAgAA8EpffSV16CAlJZlB6KuvpH8XeQVQcHIVglJSUrKcDpeamqpz585ddVEAAABeZ/58qVMnKSXF/LpokXRRp10ABSdXIejmm2/WtGnTMu2fOnWqbrzxxqsuCgAAwKvMmiX17CmlpUm9eklz50pBQVZXBfisK+oOd8Grr76qli1baseOHbr99tslSd999502bdqkb7/9Nk8LBAAAuJyQkBD98ssvrm238v770gMPSIYhDRokTZsm2e1WVwX4tFyNBDVu3FgbNmxQdHS05s6dqy+//FKVK1fWzp071bRp07yuEQAAIFt+fn6qWbOmatasKT+/XP3zJn9MnCjdf78ZgIYMMQMRAQiwnM0wDMPqInIrPj5ekZGRiouLY80iAADgPgxDevVV6cUXzefDh0tjxkg2m7V1AV7sSrJBrqbDXSw5OVnnz5/PsI9AAgAACtL58+f1+uuvS5Kee+45BQYGWleM0yk98YQ0YYL5fNQo6YUXCECAG8nVSFBSUpJGjBihuXPn6p9//sl03OFw5Elxl8NIEAAAkKTExESF/9tqOiEhQWFhYdYUkpYmDR4szZxpPp84UXr0UWtqAXzMlWSDXE2aHT58uL7//ntNmTJFQUFB+uCDDzRq1CiVKVNGH3/8ca6KBgAA8GjJyVK3bmYAstulTz4hAAFuKlfT4b788kt9/PHHat68uQYOHKimTZuqcuXKKl++vD799FP17ds3r+sEAABwX2fPSh07SitXmq2v582T7rzT6qoAXEKuRoJOnTqlSpUqSTLv/zl16pQkqUmTJlqzZk3eVQcAAODuTp6UbrvNDECFCknffEMAAtxcrkJQpUqVdODAAUlStWrVNHfuXEnmCFFkZGTeVQcAAODOjhyRmjWTNm+Wihc3g1Dz5lZXBeAychWCBg4cqB07dkiSnnnmGU2ePFnBwcF6/PHHNWLEiDwtEAAAwC3t2iU1bCjt2SOVLSutXSvdeKPVVQHIgVzdE/T444+7tlu2bKlff/1VW7ZsUfHixTVr1qw8Kw4AAMAtrV0r3XWXdOaMVL26OQWuXDmrqwKQQ3m6WOqOHTtUr149WmQDAIAC5XA4tHXrVklSvXr1ZLfb8++bLVwo9e4tpaRIjRtLixdLRYvm3/cDkCMFulgqAACA1ex2u2666ab8/0ZTp0pDhpgLonbsKH32mRQSkv/fF0CeytU9QQAAAD7FMKQXX5QeesgMQPffL82fTwACPBQjQQAAwOOdP39eEyZMkCQNHTpUgYGBeffmaWnSgw9KH35oPh81SnrhBclmy7vvAaBAXdE9QV26dMn2+JkzZ7R69WruCQIAAAUqMTFR4eHhkqSEhASFhYXlzRufPSv16GE2PvDzM6fD3Xdf3rw3gDyVb/cEXW4NoMjISN1zzz1X8pYAAADu6ehRqX17accOc9rbnDlmRzgAHu+KQtD06dPzqw4AAAD3sWOHGYCOHpVKlZK+/FIqiMYLAAoE9wQBAACf5nCYy/7ExkpRUVLTxG9k79VdSkgw1wD6+mupQgWrywSQhwhBAADAZ8XESEOHSn/+aT6/T9PURA9Lcki33SYtWCAVLmxliQDyAS2yAQCAT4qJkbp1MwOQTU6N1jOapgfkL4dmqL8W3r+UAAR4KUIQAADwOQ6HOQJkGFKoEjVXPfSMxkiSXtQoDdJ0DR0eqAJqeAuggDEdDgAAeLzg4GCtXLnStX05a9eaI0DX6E8t1l2qp21KUaAG6wPNUj9J0pEj5nnNm+dn5QCsQAgCAAAez263q/kVpJXYWOlmbdQX6qQoHdNxlVBnLdR6Nc50HgDvQwgCAAA+p86u2VqtQQpWinaqtu7SYh1ShUznRUUVfG0A8h/3BAEAAI+XmpqqyZMna/LkyUpNTb30iU6n9L//qcZrfRWsFC3SXWqsHzIFIJtNio6WmjbN37oBWMNmGIZhdRG5FR8fr8jISMXFxSkiIsLqcgAAgEUSExMVHh4uSUpISFBYWFhWJ0n9+kkLF0qS9nZ6WjW/eE1Om10X/2vIZjO/zp8vdemS35UDyCtXkg0YCQIAAN7vjz+kRo3MABQYKM2cqaoL39DcBXZdc03GU8uWJQAB3o57ggAAgHdbvlzq2VM6fVoqVcpcIKhRI0lm0OnY0ewCFxtr3gPUtKlkt1tcM4B8RQgCAADeyTCkN9+Unn3WvBeoQQNpwQL9d+jHbqcNNuBr3GY63BtvvCGbzaZhw4ZZXQoAAPB0iYlS797S00+bAejee6XVqzMFIAC+yS1GgjZt2qT33ntP119/vdWlAAAAT3fggNS3r7Rzp+TvL02cKD34YHrHAwA+z/KRoISEBPXt21fvv/++ihQpYnU5AADA0zVtagagUqWklSulhx4iAAHIwPIQNGTIELVv314tW7a87LkpKSmKj4/P8AAAAAjy99eS3r21RFLQmTPm/T9btkhNmlhdGgA3ZOl0uDlz5mjr1q3atGlTjs4fPXq0Ro0alc9VAQAAj3LypPz79lX7b781nz/wgDRhghQUZG1dANyWZSNBR44c0dChQ/Xpp58qODg4R6959tlnFRcX53ocOXIkn6sEAABubeNGqV496dtvpZAQ6eOPpalTCUAAsmXZSNCWLVt0/Phx1atXz7XP4XBozZo1euedd5SSkiL7f5r0BwUFKYg/1AAAgGFI77wjPfmklJqq1MqV9Wn//pLDob6pqQoICLC6QgBuzGYYhmHFNz579qwOHTqUYd/AgQNVrVo1Pf3006pVq9Zl3yM+Pl6RkZGKi4tTREREfpUKAADcSUKCdN990pw55vNu3ZQ4caLCy5T593CCwsLCLCwQgBWuJBtYNhJUqFChTEEnLCxMxYoVy1EAAgAAPmjHDqlnT2nvXrP99ZtvSkOHSklJVlcGwIO4xTpBAAAA2TIMacoU6YknpJQUc9HTzz+XGje2ujIAHsitQtCqVausLgEAALibM2ekwYOlBQvM5+3bSzNmSMWLW1kVAA9m+TpBAAAAl7Rxo1S3rhmAAgKkt9+WvvySAATgqrjVSBAAAIAkyek0A89zz0lpaVLFiub0t5tusroyAF6AEAQAANzLsWPSwIHSN9+Yz3v0kKZNkyIjra0LgNcgBAEAAPexZIk0aJB04oQUHCxNmGC2w7bZsn1ZUFCQ5s6d69oGgOwQggAAgPWSkqSnnjI7wEnS9ddLs2dLNWvm6OX+/v7q3r17PhYIwJvQGAEAAFhr2zbpxhvTA9ATT0g//ZTjAAQAV4qRIAAAYA2nUxo3zmx+kJoqRUVJM2dKd9xxxW+VlpamhQsXSpI6d+4sf3/+iQPg0vgTAgAAFLzDh83mB99/bz7v1El6//1ct75OSUlRjx49JEkJCQmEIADZYjocAAAoOIZhLnRau7YZgEJDzfATE8PaPwAKDL8mAQAABePvv6X775cWLzafN2xoTn+rUsXaugD4HEaCAABA/luwQKpVywxAAQHS6NHS2rUEIACWYCQIAADkn9OnpUcflT791Hx+/fXSJ5+YXwHAIowEAQCA/PHVV+a9P59+Kvn5mV3gNm0iAAGwHCNBAAAgb506JQ0bZo74SOaUt5kzzXuAAMANEIIAAEDeWbhQeughswmCzSY9/rj0yitmF7h8FBgYqOnTp7u2ASA7hCAAAHD1TpyQHnlEmjvXfF6tmjR9unTLLQXy7QMCAjRgwIAC+V4APB/3BAEAgNwzDOnzz6UaNcwAZLdLzz4rbdtWYAEIAK4UI0EAACB3jhyRHn5YWrLEfF67tjn6c+ONBV5KWlqali1bJklq3bq1/P35Jw6AS+NPCAAAcGUcDmnyZOn556WEBHPdn+eeMx8W3Y+TkpKiDh06SJISEhIIQQCyxZ8QAAAg53bulO67T/rpJ/N548bStGnmdDgA8BDcEwQAAC7v3DnzXp8bbzQDUESENGWKtGYNAQiAx2EkCAAAZO/bb817f/bvN5937SpNnCiVKWNtXQCQS4wEAQCArB09KvXsKbVubQaga66RvvhCmj+fAATAoxGCAABARmlp0vjxUvXqZttrPz9p2DBp926pY0erqwOAq8Z0OAAAkG7DBumhh6QdO8znt9xi3vtzww2WlgUAeYkQBAAApH/+MRsfvP+++bxIEWnMGOnee82RIDcXGBiod955x7UNANkhBAEA4MscDrPF9f/+J506Ze4bMEAaO1YqUcLS0q5EQECAhgwZYnUZADwEIQgAAF+1bp306KPS9u3m89q1zUVQmza1tCwAyG/uP74NAADy1l9/SXffbYad7dulwoWlSZOkrVs9NgA5HA6tWrVKq1atksPhsLocAG6OkSAAAHzF+fPShAnSyy9LCQmSzWbe8/P66x419S0rycnJatGihSQpISFBYWFhFlcEwJ0RggAA8HaGIX35pfTkk9Lvv5v7GjSQ3nlHql/f2toAwAJMhwMAwJv9/LN0xx3m+j6//y6VKiVNny6tX08AAuCzCEEAAHij48elBx801/f57jspMFB65hnpt9/M7m8e0PYaAPIL0+EAAPAm58+bTQ5eflmKjzf3detmtryuWNHa2gDATRCCAADwBoYhzZ9vjvb88Ye5r1496f/+T2rWzNraAMDNMBYOAICn++EHqVEjqUcPMwCVLi199JG0aRMBCACywEgQAACeat8+c+QnJsZ8HhYmDR9udoELD7e2tgIWEBCgsWPHurYBIDs2wzAMq4vIrfj4eEVGRiouLk4RERFWlwMAQME4edK852fKFCktzWxycO+90qhRUlSU1dUBgCWuJBswEgQAgKdITDTv8XnzzfSmB+3amU0Pata0tjYA8CCEIAAA3F1qqvTBB+ZIz99/m/tuuMEMQy1bWlqau3A4HNq6daskqV69erLb7RZXBMCdEYIAAHBXTqc0b570v/+ZC51KUqVK0quvSj17stbPRZKTk3XzzTdLkhISEhQWFmZxRQDcGSEIAAB3YxjSihVm04N/RzdUsqT0wgvS/febC58CAHKNEAQAgDv54Qfp+eel1avN5+HhZse3xx+XChWytjYA8BKEIAAA3MG2bea0t6+/Np8HBkoPPmgGopIlra0NALyMpZOJp0yZouuvv14RERGKiIhQw4YNtXTpUitLAgCgYO3ZI3XvLtWrZwYgu10aPNhcA2jCBAIQAOQDS0NQ2bJl9cYbb2jLli3avHmzbrvtNnXs2FG7du2ysiwAAPLf/v3SgAFSrVrS/PmSzSb16WOGovffl8qVs7pCAPBabrdYatGiRfXmm2/q3nvvvey5LJYKAPA4Bw+a3d1mzJAcDnNfp07m4qe1a1tYmGdLTExUeHi4JLrDAb7KIxdLdTgcmjdvnhITE9WwYcMsz0lJSVFKSorrefyFheIAAHB3hw9Lr70mffSRlJZm7mvTxlz759/Wzsi9gIAAjRw50rUNANmxPAT9/PPPatiwoZKTkxUeHq6FCxeqRo0aWZ47evRojRo1qoArBADgKvz5p/T66+Zip6mp5r477jDDzyV+6YcrFxgYqJdeesnqMgB4CMunw50/f16HDx9WXFyc5s+frw8++ECrV6/OMghlNRIUHR3NdDgAgPs5ckR64w0z/Jw/b+677TYz/DRpYm1tAOCFrmQ6nOUh6L9atmypa6+9Vu+9995lz+WeIACA2zl0SBo92pz2dmHkp1kzM/w0b25pad7M6XRqz549kqTq1avLz8/S3k8ALOCR9wRd4HQ6M4z2AADgEQ4cMKe9zZiRfs9P8+bSyJGEnwJw7tw51apVSxKNEQBcnqUh6Nlnn1Xbtm1Vrlw5nT17VrNnz9aqVau0bNkyK8sCACDn9u0zp719/HF6+Ln9dunFF80RIACA27E0BB0/flz33HOPYmNjFRkZqeuvv17Lli3THXfcYWVZAABc3q5d5sjPnDmS02nua9XKDD+NG1tbGwAgW5aGoA8//NDKbw8AQCYOh7R2rRQbK0VFSU2bSnb7RSds2WK2ul64MH1f+/bS88/T7Q0APITb3RMEAIBVYmKkoUPNrtYXlC0rTZggdSn1gxl+li41D9hsUpcuZvipW9eaggEAuUIIAgBAZgDq1k3K2DPVUM0/v1Wxrq9LWmPu8vOT+vSRnn1WusS6dgAA90YIAgD4PIfDHAG6EID85FAXxehZjVY9bZMknVeA/O/tL79nn5GuvdbCagEAV4sQBADweWvXmlPgAnRe/fSJRmisquo3SVKiQvWeHtA4PaFZd5dVc/KPWwoICNBTTz3l2gaA7BCCAAA+78QfZzVMH+gJjVO0zBuCTqmIJuoxTdKjOqViksxmCXBPgYGBevPNN60uA4CHIAQBAHzX8ePSxInqNGGyuuuMJOmoyuhtPalpul+JCs9welSUBTUCAPIcIQgA4Hv++EN66y1p+nQpOVkBkvb7X6c30obrY/XTeQVlON1mM7vENW1qTbm4PKfTqcOHD0uSypUrJz8/P4srAuDOCEEAAN+xdas0dqw0b176Aqc33yw9/bR2ODrqw57/Lgh0UYc4m838On78f9YLgls5d+6cKlasKElKSEhQWFiYxRUBcGf8mgQA4N0MQ/rmG+n226Ubb5Q+/9wMQG3aSCtXSj/+KHXpoi7d7Zo/X7rmmowvL1tWmj/fXBIIAOAdGAkCAHin8+elOXPMaW8//2zus9ulnj2lESOkOnUyvaRLF6ljR7NbXGyseQ9Q06aMAAGAtyEEAQC8S1ycNG2aNGGCdPSouS88XLrvPmnYMKlcuWxfbrdLzZvne5UAAAsRggAA3uHQITP4fPCBdPasuS8qylwF9YEHpMKFLS0PAOA+CEEAAM/200/S229LCxZIDoe5r0YN6amnpD59pKCg7F8PAPA5hCAAgOdxOKQvvzTDz7p16ftbtpSefFJq3Tq9rRsAAP9BCAIAeI6EBHNtn4kTpd9/N/cFBJgjPo8/nmWzA/gGf39/Pfzww65tAMgOf0oAANzfoUPSpEnm/T5xcea+IkWkBx+UHnlEKlPG2vpguaCgIE2ePNnqMgB4CEIQAMA9GYa0YYO5SmlMTPr9PtddZ3Z5u+ceiQUxAQC5QAgCALiX8+fN1UknTDCbHlxw++3mlLe2bSU/1vpGRoZh6OTJk5Kk4sWLy8Y9YQCyQQgCALiH48el996TpkwxVyqVzM5uffuaIz+1a1taHtxbUlKSSpYsKUlKSEhQGKOEALJBCAIAWGvbNnPU57PPzFEgyVzf56GHzPV9/v2HLQAAeYUQBAAoeKmp0sKFZrODi1tc33yzubhpt25SYKB19QEAvBohCABQcP7+W5o2TZo6VfrrL3Ofv7/UvbsZfho0sLY+AIBPIAQBAPLfxo3mqM/cueYokCSVKmVOd3vgAVpcAwAKFCEIAJA/zp0zQ8/kydKmTen7b7nFXNunWzez8QEAAAWMEAQAyFsHDpgd3j78UDp1ytwXGCj16iU9+qhUv7619QEAfB4hCABw9ZxO6ZtvzFGfpUvNhU4lqVw56cEHpXvvpcsb8pW/v7/69+/v2gaA7PCnBAAg906elKZPNxsd/PFH+v5WraQhQ6T27SW73br64DOCgoI0Y8YMq8sA4CEIQQCAK2MY0vr15pS3efPS1/YpXFgaONAc+bnuOktLBAAgO4QgAEDOxMdLs2aZoz4//5y+/8YbzYVNe/WSwsKsqw8+zTAMJSUlSZJCQ0Nls9ksrgiAOyMEAQCyt3Wr9N570uzZUkKCuS8kxAw9Dz0k3XSTtfUBkpKSkhQeHi5JSkhIUBiBHEA2CEEAgMwSEqQ5c8zws3lz+v6qVc3gc889UpEi1tUHAMBVIAQBANLt2GEGn1mzpLNnzX0BAVLXruaiprfeKjHNCADg4QhBAODrLoz6vP++9NNP6fsrV5buv18aMEAqUcKy8gAAyGuEIADwRYYhbdliBp+L7/Xx95c6dzZHfVq0kPz8rK0TAIB8QAgCAF8SFyd9+qkZfrZvT99fpYp0331S//4sagoA8HqEIADwdoYhrV0rffCBNH++dO6cuT8wUOrWzQw/3OsDAPAhhCAA8FbHjkkzZ0offijt25e+v0YNM/j06ycVK2ZdfUAestvt6tatm2sbALJDCAIAb5KWJi1dagafJUskh8PcHx5urutz771SgwaM+sDrBAcHa968eVaXAcBDEIIAwBv8+qs0fbr08cfmCNAFjRqZwadHDzMIAQAAQhAAeKz4eGnuXOmjj6QNG9L3lyhhLmZ6771S9erW1QcAgJsiBAGAJ3E6pdWrpRkzzCYHSUnmfrtdat9eGjRIatfOXOAU8CGJiYkK/3e0MyEhQWFhYRZXBMCdEYIAwBP88YfZ5GDmTOnQofT91aqZwadfP6l0aevqAwDAgxCCAMBdJSSYoz0zZpijPxdERJhNDvr3lxo2pMkBAABXiBAEAO7E4ZBWrjQbHCxYkD7dzWaT7rhDGjBA6tRJCgmxskoAADyapSFo9OjRiomJ0a+//qqQkBA1atRIY8aMUdWqVa0sCwAK3q+/mlPdZs2S/vwzfX+VKmbw6ddPio62rDwAALyJpSFo9erVGjJkiG666SalpaXpueeeU6tWrbR7925uaATg/U6elD7/3Aw/mzal7y9c2Jzuds890i23MN0NAIA8ZmkI+uabbzI8nzFjhkqWLKktW7aoWbNmFlUFAPkoOdlcxPSTT6SvvzYXN5XM7m7t2pnBp0MHKTjY2joBAPBibnVPUFxcnCSpaNGiWR5PSUlRSkqK63l8fHyB1AUAV8XplNatM4PPvHnSv3/WSZLq1TOnuvXpI5UsaV2NgIez2+1q166daxsAsmMzDMOwughJcjqduuuuu3TmzBmtW7cuy3NeeukljRo1KtP+uLg4RURE5HeJAHBl9uwx7/GZPVs6eDB9f3S01LevGX5q1LCsPAAAvEl8fLwiIyNzlA3cJgQ99NBDWrp0qdatW6eyZctmeU5WI0HR0dGEIAD5yuGQ1q6VYmOlqCipaVNz9lqW/vpLmjPHDD/btqXvL1RI6tbNDD633ir5+RVI7QAA+IorCUFuMR3ukUce0ZIlS7RmzZpLBiBJCgoKUlBQUAFWBsDXxcRIQ4dmbNhWtqw0YYLUpcu/O+LjzRM//VT6/ntz+psk+ftLbduaoz533imFhhZ4/QAAIDNLQ5BhGHr00Ue1cOFCrVq1ShUrVrSyHADIICbGHLz573j50aNS367JWjliqW75Y7b05ZfSRaPUatzYDD7du0vFixds0YCPSkxMVMl/76s7fvw4XWYBZMvSEDRkyBDNnj1bixYtUqFChXTs2DFJUmRkpEJYCBCAhRwOcwTo4gDkJ4eaa5X6GLPVVQtUeOxFDQ6qVZPuvttscMAvdABLJF1YXBgALsPSEDRlyhRJUvPmzTPsnz59ugYMGFDwBQHAv9auvTAFzlADbVQvzVEPzVUZxbrOOaKyMnr2Vrln+kh16rCeDwAAHsLy6XAA4HYMQ+c2/qzX9Zl6aY4q6qDr0D8qqnnqrtnqo3Vqok87+qncDZZVCgAAcsEtGiMAgFv47Tfp88+lzz5T2z171Pbf3QkK0xfqpDnqpW/VSqkKdL0kKsqaUgEAQO4RggD4tgMHzODz+efS9u2u3UZQkL6xtdP05N5aovY6p4yd3Ww2s0tc06YFXC8AALhqhCAAvufIEWnuXDP4bNqUvt9ul1q2lHr3lq1TJ537LlLzu/177KLZuxdu/Rk/Ppv1ggAAgNsiBAHwDUePSvPnm+Fn/fr0/X5+UvPmUs+e5sI/F7W07tLFfElW6wSNH3/ROkEALOfn56dbb73VtQ0A2bEZHtyd4EpWhQXgg44elRYsMIPPDz+k77fZpCZNzODTtatUunS2b+NwmN3iYmPNe4CaNmUECAAAd3Ml2YCRIADe5c8/zVVO582T1q3LeKxxY3MB065dzeGcHLLbzcEiAADgHQhBADzfwYPmiM+CBdKGDRmP5TL4AAAA70UIAuCZfv/dDD3z50ubN6fvt9nM4NO1q9StG8EH8BGJiYmqUKGCJOngwYMKCwuztiAAbo0QBMAzGIb0yy/mVLeYGGnnzvRjfn5Ss2Zm6OncWSpTxro6AVjm5MmTVpcAwEMQggC4L8MwW1hfCD779qUfu3CjTvfuUqdOUqlSVlUJAAA8DCEIgHtJTZXWrJG++MJ8XNybOihIatXK7E19551SsWJWVQkAADwYIQiA9RITpWXLzNCzZIl0+nT6sfBwqX17M/i0bSsVKmRZmQAAwDsQggBY4/hxM/AsWiR9+62UnJx+rEQJ6a67zGluLVtKwcGWlQkAALwPIQhAwdm7V1q82Aw+69eb9/xcUKGC2dSgc2epUSNWIwUAAPmGEAQg/zgc5ro9X35pBp+9ezMer1cvfcTn+uvN9tYAkAt+fn6qX7++axsAskMIApC34uLM+3u+/FL6+mvp1Kn0YwEBUosWUseOZmOD6Gjr6gTgVUJCQrRp0yarywDgIQhBAK7e779LX31lBp/Vq6W0tPRjRYqYDQ3uuktq00aKjLSuTgAAABGCAOTG+fNmG+uvvzbDz2+/ZTxerZrUoYM52tOokeTPHzUAAMB98C8TADnz11/S0qVm6Fm+XEpISD/m7y81a5YefCpXtq5OAD4pKSlJNWrUkCTt3r1boaGhFlcEwJ0RggBkLTXV7OC2dKn52Lkz4/HSpaV27czHHXdIERHW1AkAkgzD0KFDh1zbAJAdQhCAdEeOmE0Nli6VVqyQ4uPTj9ls0k03mQuXtm8v1a0r0YEJAAB4IEIQ4MuSksx7e5YtMx979mQ8XqKE1Lq12digVSupeHFr6gQAAMhDhCDAlxiG9PPP0rffmqFn7VopJSX9uJ+fdPPNZuhp21a68UZGewAAgNchBAHe7s8/zUYGK1aYj+PHMx4vV84c7WnVSrr9drOlNQAAgBcjBAHe5swZc62eFSvM8LN3b8bjoaHSrbeawad1a6lqVfN+HwAAAB9BCAI8XVKS9MMP0nffSd9/L23ZIjmd6cf9/MyGBnfcIbVsKTVsKAUGWlcvAOQDm83mapFt4xc7AC6DEAR4mpQUaeNGaeVKM/Rs2GC2s77YddeZU9vuuENq3pwpbgC8XmhoqHbt2mV1GQA8BCEIcHcXQs+qVeZjwwYpOTnjOWXLmqHn9tulFi3M5wAAAMgSIQhwN0lJ0o8/mq2r16zJOvSUKmWO8DRvbgafypW5rwcAACCHCEGA1c6cMe/puRB6Nm+W0tIynnNx6GnenGYGAPAfSUlJuummmyRJmzZtUmhoqMUVAXBnhCAgFxwOc4md2FgpKkpq2lSy23PwQsOQDh0yQ8+6debXX34x91+sbFmpWbP0R7VqhB4AyIZhGNq9e7drGwCyQwgCrlBMjDR0qLn8zgVly0oTJkhduvzn5NRUaft2c0rbDz+Yj6NHM79plSoZQ0/58oQeAACAfEIIAq5ATIzUrVvmgZujR839S96PVbuiP5qhZ8MGc2rbf+/n8feX6tWTmjSRGjc2H6VKFdyHAAAA8HGEICCHHA5zBOhCAApRkuppq27WT7rZ+Em36EdVGHwo8wuLFpUaNDDDTpMm5po9zFUHAACwDCEIyIm0NG2duVut/txkhh79pNr6Wf5yZDjNKZuSKtVSeMuG5qKkDRuaa/YwtQ0AAMBtEIKA/0pLk379VdqyxZzOtnmztH27bkpO1k3/OfUvRWmjGvwbi8zHtFcLqXdvSyoHAABADhCC4NtSUszubNu2pT927DDX6vmPtLAIrU2slyH0HFXmRUmjogqicADAxWw2m8qXL+/aBoDsEILgO06elHbuNB8XAs+ePZnX5JGk8HCzeUH9+ubjxhtlq1hZ91Ty09GjmRsjSOaMt7JlzXbZAICCFRoaqoMHD1pdBgAPQQiC90lJkX77LT3w7Nxpju7ExmZ9ftGiUt26ZuipW9d8XHed5OeX4TS7zDbY3bqZgefiIHThl47jx+dwvSAAAABYhhAEz5WWJv3+uzmdbdeu9K+//Wa2csvKtddK119vPi4EnujoHDcu6NJFmj8/63WCxo/PYp0gAAAAuB1CENxfQoK0d6/ZrGDPHvPx66/Svn3mYqRZiYyUatWS6tRJDz21akmFCl11OV26SB07SmvXmoNLUVHmFDhGgADAOufOnVOzZs0kSWvWrFFISIjFFQFwZ4QguIfUVOnAAXMUZ98+8+uF7SNHLv26sDCpZk3zUatW+tcyZfK1LbXdLjVvnm9vDwC4Qk6nU5s3b3ZtA0B2CEEoOGfPSn/8YT7270//un+/dPDgpaewSVLJklK1alL16hm/RkdnuncHAAAAyA4hCHnn7Fnp0CEz0Pz368GD0okT2b8+NNRsSFClSvrXKlXMsFO0aP7XDwAAAJ9gaQhas2aN3nzzTW3ZskWxsbFauHChOnXqZGVJueJwePn9IYYhnT5tfsC//jKnp/35p/m4sH3kiBQXd/n3KlbMbE5QqVL610qVzLCTz1PYAAAAAMniEJSYmKg6depo0KBB6uKhbbViYrLuFDZhgpt3CjMMM7QcP57xceKEdOyYGXguPI4dM9tO50SRIlKFClL58pm/VqpkNiwAAAAALGRpCGrbtq3atm1rZQlXJSbGXDPmvwtnHj1q7p8/P5+DkGGYndPi4rJ+nD4tnTol/fNP5q///HPpzmqXUrSoOdQVHW0mvQtfL2xfc40UEZE/nxUAAADIIx51T1BKSopSLhqRiI+Pt6wWh8McAboQgG7WRpWVORxkMwz5ydCK+w11Oi/52QzzxLQ085GamvlrSoqUnJz1IzHRDDtZff1vArtSERFSiRJm44GLH1FR5vS0qCjzUbq0FBR0lT81AADyT/Hixa0uAYCH8KgQNHr0aI0aNcrqMiSZ9wBdPAXuSb2tHpqX8aR/JPUugGLsdnOaWeHC5tcLj8KFzXtwihUzR3GKFk3fLlbMDDvBwQVQIAAA+SssLEwnLteABwD+5VEh6Nlnn9UTTzzheh4fH6/o6GhLaomNzfj8V1XTWjWRIfPG/gtfq1W3qVSpf2/2DwiQ/P0zf/X3N8PIpR5hYeYjPDzj17AwcyQnNJSGAgAAAEAOeVQICgoKUpCbTMmKisr4fKRe1sgszlv5rlSqeUFUBAAAACAnWGUyl5o2NfsBXGoAxmYzewU0bVqwdQEA4IvOnTun5s2bq3nz5jp37pzV5QBwc5aOBCUkJOj33393PT9w4IC2b9+uokWLqly5chZWdnl2u9kGu1s3M/Bc3J/gQjAaP97L1gsCAMBNOZ1OrV692rUNANmxdCRo8+bNqlu3rurWrStJeuKJJ1S3bl29+OKLVpaVY126mG2wr7km4/6yZQugPTYAAACAXLEZxtX2WLZOfHy8IiMjFRcXpwgL16dxOMxucbGx5r1CTZsyAgQAQEFKTExUeHi4JHOmSVhYmMUVAShoV5INPKoxgruy26Xmza2uAgAAAEBO0BgBAAAAgE8hBAEAAADwKUyHAwAAXiE0NNTqEgB4CEIQAADweGFhYUpMTLS6DAAegulwAAAAAHwKIQgAAACATyEEAQAAj5ecnKz27durffv2Sk5OtrocAG6Oe4IAAIDHczgc+vrrr13bAJAdRoIAAAAA+BRCEAAAAACfQggCAAAA4FMIQQAAAAB8CiEIAAAAgE/x6O5whmFIkuLj4y2uBAAAWCkxMdG1HR8fT4c4wAddyAQXMkJ2PDoEnT17VpIUHR1tcSUAAMBdlClTxuoSAFjo7NmzioyMzPYcm5GTqOSmnE6n/vrrLxUqVEg2m83qcrxCfHy8oqOjdeTIEUVERFhdjs/jergProX74Fq4F66H++BauBeuR8EzDENnz55VmTJl5OeX/V0/Hj0S5Ofnp7Jly1pdhleKiIjgf1g3wvVwH1wL98G1cC9cD/fBtXAvXI+CdbkRoAtojAAAAADApxCCAAAAAPgUQhAyCAoK0siRIxUUFGR1KRDXw51wLdwH18K9cD3cB9fCvXA93JtHN0YAAAAAgCvFSBAAAAAAn0IIAgAAAOBTCEEAAAAAfAohCAAAAIBPIQT5qKNHj+ruu+9WsWLFFBISotq1a2vz5s2u44Zh6MUXX1RUVJRCQkLUsmVL7du3z8KKvZfD4dALL7ygihUrKiQkRNdee61eeeUVXdyzhOuRP9asWaM777xTZcqUkc1m0xdffJHheE5+7qdOnVLfvn0VERGhwoUL695771VCQkIBfgrvkd31SE1N1dNPP63atWsrLCxMZcqU0T333KO//vorw3twPfLG5f7fuNiDDz4om82m8ePHZ9jPtcg7Obkee/bs0V133aXIyEiFhYXppptu0uHDh13Hk5OTNWTIEBUrVkzh4eHq2rWr/v777wL8FN7hctciISFBjzzyiMqWLauQkBDVqFFDU6dOzXAO18I9EIJ80OnTp9W4cWMFBARo6dKl2r17t95++20VKVLEdc7YsWM1ceJETZ06VRs3blRYWJhat26t5ORkCyv3TmPGjNGUKVP0zjvvaM+ePRozZozGjh2rSZMmuc7heuSPxMRE1alTR5MnT87yeE5+7n379tWuXbu0fPlyLVmyRGvWrNH9999fUB/Bq2R3PZKSkrR161a98MIL2rp1q2JiYrR3717dddddGc7jeuSNy/2/ccHChQv1448/qkyZMpmOcS3yzuWux/79+9WkSRNVq1ZNq1at0s6dO/XCCy8oODjYdc7jjz+uL7/8UvPmzdPq1av1119/qUuXLgX1EbzG5a7FE088oW+++UazZs3Snj17NGzYMD3yyCNavHix6xyuhZsw4HOefvppo0mTJpc87nQ6jdKlSxtvvvmma9+ZM2eMoKAg47PPPiuIEn1K+/btjUGDBmXY16VLF6Nv376GYXA9CookY+HCha7nOfm5796925BkbNq0yXXO0qVLDZvNZhw9erTAavdG/70eWfnpp58MScahQ4cMw+B65JdLXYs///zTuOaaa4xffvnFKF++vPF///d/rmNci/yT1fXo2bOncffdd1/yNWfOnDECAgKMefPmufbt2bPHkGRs2LAhv0r1elldi5o1axovv/xyhn316tUznn/+ecMwuBbuhJEgH7R48WLVr19f3bt3V8mSJVW3bl29//77ruMHDhzQsWPH1LJlS9e+yMhINWjQQBs2bLCiZK/WqFEjfffdd/rtt98kSTt27NC6devUtm1bSVwPq+Tk575hwwYVLlxY9evXd53TsmVL+fn5aePGjQVes6+Ji4uTzWZT4cKFJXE9CpLT6VS/fv00fPhw1axZM9NxrkXBcTqd+uqrr3TdddepdevWKlmypBo0aJBhmtaWLVuUmpqa4c+zatWqqVy5cvw9kscaNWqkxYsX6+jRozIMQytXrtRvv/2mVq1aSeJauBNCkA/6448/NGXKFFWpUkXLli3TQw89pMcee0wzZ86UJB07dkySVKpUqQyvK1WqlOsY8s4zzzyjXr16qVq1agoICFDdunU1bNgw9e3bVxLXwyo5+bkfO3ZMJUuWzHDc399fRYsW5drks+TkZD399NPq3bu3IiIiJHE9CtKYMWPk7++vxx57LMvjXIuCc/z4cSUkJOiNN95QmzZt9O2336pz587q0qWLVq9eLcm8HoGBga5fGFzA3yN5b9KkSapRo4bKli2rwMBAtWnTRpMnT1azZs0kcS3cib/VBaDgOZ1O1a9fX6+//rokqW7duvrll180depU9e/f3+LqfM/cuXP16aefavbs2apZs6a2b9+uYcOGqUyZMlwPIAupqanq0aOHDMPQlClTrC7H52zZskUTJkzQ1q1bZbPZrC7H5zmdTklSx44d9fjjj0uSbrjhBq1fv15Tp07VrbfeamV5PmfSpEn68ccftXjxYpUvX15r1qzRkCFDVKZMmQyjP7AeI0E+KCoqSjVq1Miwr3r16q4uMqVLl5akTJ1K/v77b9cx5J3hw4e7RoNq166tfv366fHHH9fo0aMlcT2skpOfe+nSpXX8+PEMx9PS0nTq1CmuTT65EIAOHTqk5cuXu0aBJK5HQVm7dq2OHz+ucuXKyd/fX/7+/jp06JCefPJJVahQQRLXoiAVL15c/v7+l/17/fz58zpz5kyGc/h7JG+dO3dOzz33nMaNG6c777xT119/vR555BH17NlTb731liSuhTshBPmgxo0ba+/evRn2/fbbbypfvrwkqWLFiipdurS+++471/H4+Hht3LhRDRs2LNBafUFSUpL8/DL+r2i3212/3eN6WCMnP/eGDRvqzJkz2rJli+uc77//Xk6nUw0aNCjwmr3dhQC0b98+rVixQsWKFctwnOtRMPr166edO3dq+/btrkeZMmU0fPhwLVu2TBLXoiAFBgbqpptuyvbv9RtvvFEBAQEZ/jzbu3evDh8+zN8jeSg1NVWpqanZ/p3OtXAjVndmQMH76aefDH9/f+O1114z9u3bZ3z66adGaGioMWvWLNc5b7zxhlG4cGFj0aJFxs6dO42OHTsaFStWNM6dO2dh5d6pf//+xjXXXGMsWbLEOHDggBETE2MUL17cGDFihOscrkf+OHv2rLFt2zZj27ZthiRj3LhxxrZt21zdxnLyc2/Tpo1Rt25dY+PGjca6deuMKlWqGL1797bqI3m07K7H+fPnjbvuussoW7assX37diM2Ntb1SElJcb0H1yNvXO7/jf/6b3c4w+Ba5KXLXY+YmBgjICDAmDZtmrFv3z5j0qRJht1uN9auXet6jwcffNAoV66c8f333xubN282GjZsaDRs2NCqj+SxLnctbr31VqNmzZrGypUrjT/++MOYPn26ERwcbLz77ruu9+BauAdCkI/68ssvjVq1ahlBQUFGtWrVjGnTpmU47nQ6jRdeeMEoVaqUERQUZNx+++3G3r17LarWu8XHxxtDhw41ypUrZwQHBxuVKlUynn/++Qz/sON65I+VK1cakjI9+vfvbxhGzn7u//zzj9G7d28jPDzciIiIMAYOHGicPXvWgk/j+bK7HgcOHMjymCRj5cqVrvfgeuSNy/2/8V9ZhSCuRd7JyfX48MMPjcqVKxvBwcFGnTp1jC+++CLDe5w7d854+OGHjSJFihihoaFG586djdjY2AL+JJ7vctciNjbWGDBggFGmTBkjODjYqFq1qvH2228bTqfT9R5cC/dgM4yLlqUHAAAAAC/HPUEAAAAAfAohCAAAAIBPIQQBAAAA8CmEIAAAAAA+hRAEAAAAwKcQggAAAAD4FEIQAAAAAJ9CCAIAAADgUwhBAACfdPDgQdlsNm3fvt3qUgAABYwQBADIcwMGDFCnTp1yfL7NZtMXX3yRb/VkJTo6WrGxsapVq5YkadWqVbLZbDpz5kyB1gEAKHj+VhcAAIAV7Ha7SpcubXUZAAALMBIEAMhXzZs312OPPaYRI0aoaNGiKl26tF566SXX8QoVKkiSOnfuLJvN5nouSYsWLVK9evUUHBysSpUqadSoUUpLS3Mdt9ls+uCDD9S5c2eFhoaqSpUqWrx4sev46dOn1bdvX5UoUUIhISGqUqWKpk+fLinjdLiDBw+qRYsWkqQiRYrIZrNpwIAB+vjjj1WsWDGlpKRk+EydOnVSv3798vgnBQAoKIQgAEC+mzlzpsLCwrRx40aNHTtWL7/8spYvXy5J2rRpkyRp+vTpio2NdT1fu3at7rnnHg0dOlS7d+/We++9pxkzZui1117L8N6jRo1Sjx49tHPnTrVr1059+/bVqVOnJEkvvPCCdu/eraVLl2rPnj2aMmWKihcvnqm+6OhoLViwQJK0d+9excbGasKECerevbscDkeGYHX8+HF99dVXGjRoUN7/oAAABYIQBADId9dff71GjhypKlWq6J577lH9+vX13XffSZJKlCghSSpcuLBKly7tej5q1Cg988wz6t+/vypVqqQ77rhDr7zyit57770M7z1gwAD17t1blStX1uuvv66EhAT99NNPkqTDhw+rbt26ql+/vipUqKCWLVvqzjvvzFSf3W5X0aJFJUklS5ZU6dKlFRkZqZCQEPXp08c1eiRJs2bNUrly5dS8efM8/zkBAAoG9wQBAPLd9ddfn+F5VFSUjh8/nu1rduzYoR9++CHDyI/D4VBycrKSkpIUGhqa6b3DwsIUERHheu+HHnpIXbt21datW9WqVSt16tRJjRo1uqLa77vvPt100006evSorrnmGs2YMUMDBgyQzWa7ovcBALgPQhAAIN8FBARkeG6z2eR0OrN9TUJCgkaNGqUuXbpkOhYcHJyj927btq0OHTqkr7/+WsuXL9ftt9+uIUOG6K233spx7XXr1lWdOnX08ccfq1WrVtq1a5e++uqrHL8eAOB+CEEAAMsFBATI4XBk2FevXj3t3btXlStXvqr3LlGihPr376/+/furadOmGj58eJYhKDAwUJIy1SFJgwcP1vjx43X06FG1bNlS0dHRV1UTAMBa3BMEALBchQoV9N133+nYsWM6ffq0JOnFF1/Uxx9/rFGjRmnXrl3as2eP5syZo//97385ft8XX3xRixYt0u+//65du3ZpyZIlql69epbnli9fXjabTUuWLNGJEyeUkJDgOtanTx/9+eefev/992mIAABegBAEALDc22+/reXLlys6Olp169aVJLVu3VpLlizRt99+q5tuukm33HKL/u///k/ly5fP8fsGBgbq2Wef1fXXX69mzZrJbrdrzpw5WZ57zTXXuJoxlCpVSo888ojrWGRkpLp27arw8PArWgQWAOCebIZhGFYXAQCAu7v99ttVs2ZNTZw40epSAABXiRAEAEA2Tp8+rVWrVqlbt27avXu3qlatanVJAICrRGMEAACyUbduXZ0+fVpjxowhAAGAl2AkCAAAAIBPoTECAAAAAJ9CCAIAAADgUwhBAAAAAHwKIQgAAACATyEEAQAAAPAphCAAAAAAPoUQBAAAAMCnEIIAAAAA+JT/ByX5IfsTHaRQAAAAAElFTkSuQmCC",
+=======
+      "image/png": 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AAADgbghBWZCeLq1dK82da/6ZH+uuNm3aVMePH9eBAwe0cuVKNWrUSAMGDFDLli2VlpaW9wUAAAAAHooQdA2LF0sxMVKjRlLnzuafMTHm/rwUGBioEiVK6IYbblCNGjU0bNgwLVu2TCtXrtT06dMlSW+//bZuvfVWhYaGKioqSn379pXdbpckrV27Vj179lRcXJxzVGn48OGSpFmzZqlmzZoqUKCASpQooc6dO+vkyZN5+4YAAADgUawYKMgthKCrWLxYat9eOnIk8/6jR839eR2E/u3uu+9WtWrVtPifL+zj46Nx48Zp165dmjFjhr7++msNHTpUklSnTh2NGTNG4eHhOn78uI4fP65nnnlG+ufi0VdeeUU7duzQ0qVLdeDAAfXo0SN/3wwAAADcllUDBbmFxghXkJ4uDRggGcaljxmGZLNJAwdKrVtLvr75V1elSpW0c+dOSdLAgQOd+2NiYvTqq6/qiSee0Pvvv6+AgABFRETIZrOpRIkSmV6jV69ezu1y5cpp3LhxqlWrlux2u8LCwvLvzQAAAMDtXBgo+PfPyRcGChYulNq1s6q6rGEk6Ao2bLh0BOhihiEdPmwel58Mw5DNZpMkrV69Wvfcc49uuOEGFShQQF27dtXff/+txMTEq77Gtm3b1KpVK0VHR6tAgQJq0KCBJOnQoUP58h4AAADgnv49UPCIpqiqdkjK2DdwoOtPjSMEXcHx47l7XG7Zs2ePypYtqwMHDqhly5aqWrWqFi1apG3btmn8+PHSP21CryQhIUFNmjRReHi4Zs+erS1btmjJkiXXfB4AAK4sMDBQ8+fP1/z58xUYGGh1OYDHunig4D/arIl6Qt/pTt2oXyULBwqyi+lwV1CyZO4elxu+/vpr/fTTTxo0aJC2bdsmh8Oh0aNHy8fHzLLz58/PdHxAQIDS/xXDf/nlF/399996/fXXFRUVJUnaunVr/r0JAADygJ+fnzp06GB1GYDHuzAAEKGzmqtO8lO6PtZD+k03XvY4V8VI0BXUqyeVLm1e+3M5NpsUFWUelxeSk5N14sQJHT16VNu3b9fIkSPVunVrtWzZUt26dVOFChWUmpqqd999V3/88YdmzZqliRMnZnqNmJgY2e12ffXVVzp16pQSExMVHR2tgIAA5/OWL1+uV155JW/eBAAAADyKOQBgaJIeV4wO6g+VVR9NkGS7zHGuixB0Bb6+0tix5va/g9CF+2PG5F1ThM8//1wlS5ZUTEyMmjZtqjVr1mjcuHFatmyZfH19Va1aNb399tsaNWqUbrnlFs2ePVuxsbGZXqNOnTp64okn9NBDDykyMlJvvPGGIiMjNX36dC1YsECVK1fW66+/rrfeeitv3gQAAPkkLS1NCxYs0IIFC1hPD8hD9epJTxf6UA9pvlLlp06aq3MKdz6e1wMFucVmGJfrf+Ye4uPjFRERobi4OIWHh2d6LCkpSfv371fZsmUVFBSU46+xeLF58dfFTRKioswA5OpdL/JTbn3eAADkREJCgrPDqd1uV2hoqNUlAZ5pzx6lVa8pv+REPafXNUrPOh+6MFBgVXe4q2WDf+OaoGto185sg71hgzm3sWRJM9nmZ1tsAAAAwHJJSVKnTvJLTtSfVRtrzt9DpKMZD5cu7T4DBYSgLPD1lRo2tLoKAAAAwELPPivt2CFFRqr45zO1v5iP2w4UEIIAAAAAXN0nn0jjxpnb06dLJUvKV+47UEBjBAAAAABXduyY1LOnuT1okNS8udUVXTdCEAAAAIDLS0+XHn5Y+vtvqXp16V/diN0VIQgAAADA5Y0aJa1ZI4WGSvPmSYGBVleUK7gmCAAAuL2AgABNmzbNuQ0gF2zeLL30krn97rvSjTdaXVGuIQQBAAC35+/vrx49elhdBuA5zp6VOnUyp8N16iR52PcX0+EAAAAAZDAM6fHHpYMHpbJlpYkTM1ZC9RCEIC+0du1a2Ww2nT17NsvPiYmJ0ZgxY/K0LgAAciotLU2ffvqpPv30U6WlpVldDuDePvxQmj9f8vOT5s6VwsOtrijXEYJcUI8ePWSz2fTEE09c8li/fv1ks9kY8gcA4CLJyclq2bKlWrZsqeTkZKvLAdzXnj1S//7m9quvSnfeaXVFeYIQ5KKioqI0b948nT9/3rkvKSlJc+bMUXR0tKW1AQAAwAMlJUkdO0qJiVLjxtKQIVZXlGe8KwQZhpSQkP83w8h2qTVq1FBUVJQWL17s3Ld48WJFR0erevXqzn3Jycnq37+/ihUrpqCgIN11113asmVLptf67LPPdOONNyo4OFiNGjXSgQMHLvl6GzduVL169RQcHKyoqCj1799fCQkJ2a4bAAAAbmroUGnnTikyUpo5U/Lx3Kjgue/schITpbCw/L8lJuao3F69ejnbfUrShx9+qJ4XVuv9x9ChQ7Vo0SLNmDFD27dvV4UKFdSkSROdPn1aknT48GG1a9dOrVq10o8//qjevXvrueeey/Qa+/btU9OmTfXAAw9o586d+vjjj7Vx40Y9+eSTOaobAAAAbuaTT8w22JI0Y4ZUsqTVFeUp7wpBbubhhx/Wxo0bdfDgQR08eFDffPONHn74YefjCQkJmjBhgt588001a9ZMlStX1gcffKDg4GBNnTpVkjRhwgSVL19eo0eP1k033aQuXbpccj1RbGysunTpooEDB6pixYqqU6eOxo0bp5kzZyopKSnf3zcAAADy0dGj0oVftA8eLDVrZnVFec671gkKCZHsdmu+bg5ERkaqRYsWmj59ugzDUIsWLVS0aFHn4/v27VNqaqrq1q3r3Ofv76877rhDe/bskSTt2bNHd/7rgrbatWtnur9jxw7t3LlTs2fPdu4zDEMOh0P79+/XzTffnKP6AQAA4OLS06WHH5b+/luqUUMaOdLqivKFd4Ugm00KDbW6imzp1auXc1ra+PHj8+Rr2O12Pf744+p/oRPIRWjCAAAA4MFiY6W1a82fkefOlQIDra4oX3hXCHJDTZs2VUpKimw2m5o0aZLpsfLlyysgIEDffPONypQpI0lKTU3Vli1bNHDgQEnSzTffrOXLl2d63rfffpvpfo0aNbR7925VqFAhz98PAAB5ISAgQO+9955zG0AWfPONNHy4uf3++9KNN1pdUb4hBLk4X19f59Q2X1/fTI+FhoaqT58+GjJkiAoXLqzo6Gi98cYbSkxM1COPPCJJeuKJJzR69GgNGTJEvXv31rZt2zR9+vRMr/Pss8/qP//5j5588kn17t1boaGh2r17t7788kvnfygAALgyf39/9evXz+oyAPdx5ozUuXPGdLhu3ayuKF/RGMENhIeHK/wKK/W+/vrreuCBB9S1a1fVqFFDv//+u1atWqVChQpJ/0xnW7RokZYuXapq1app4sSJGvmvuZ5Vq1bVunXr9Ntvv6levXqqXr26XnrpJZUqVSpf3h8AAADykWFIjz4qHToklS9vjgJ5GZth5GARGxcRHx+viIgIxcXFXRISkpKStH//fpUtW1ZBQUGW1egt+LwBAFZKT0/Xhg0bJEn16tW7ZPYEgItMmiQ98YTk7y9t2iTVrGl1Rbniatng35gOBwAA3F5SUpIaNWok/dPwJ9TNGiEB+WbXLumfa8cVG+sxASi7mA4HAAAAeIPz56WHHpKSkqQmTaRBg6yuyDKEIAAAAMAbDB5sjgQVLy7NmCH5eG8U8N53DgAAAHiLxYuliRPN7VmzzCDkxTw+BLlx3we3wucMAADgog4elP5ZPkVDh0r33mt1RZbz2BDk7+8vSUpMTLS6FK9w4XO+8LkDAADABaSlSV26SGfPSnfcIb36qtUVuQSP7Q7n6+urggUL6uTJk5KkkJAQ2Ww2q8vyOIZhKDExUSdPnlTBggVpSQoAAOBKRoyQvvlGCg+X5s4122LD2hCUnp6u4cOH66OPPtKJEydUqlQp9ejRQy+88EKuBJYSJUpIkjMIIe8ULFjQ+XkDAJDf/P399cYbbzi3AUhas0Z67TVze/JkqVw5qytyGZaGoFGjRmnChAmaMWOGqlSpoq1bt6pnz56KiIhQ//79r/v1bTabSpYsqWLFiik1NTVXasal/P39GQECAFgqICBAQ4YMsboMwHX89Zc5Dc4wzOuBHnrI6opciqUhaNOmTWrdurVatGghSYqJidHcuXP1/fff5+rX8fX15Yd0AAAAeAfDkHr2lI4flypVksaOtboil2NpY4Q6deroq6++0m+//SZJ2rFjhzZu3KhmzZpd9vjk5GTFx8dnugEAAKSnp2vLli3asmWL0tPTrS4HsNbYsdKnn0qBgdLHH0uhoVZX5HIsHQl67rnnFB8fr0qVKsnX11fp6el67bXX1KVLl8seHxsbqxEjRuR7nQAAwLUlJSXpjjvukCTZ7XaF8kMfvNW2bWYbbEkaPVqqWtXqilySpSNB8+fP1+zZszVnzhxt375dM2bM0FtvvaUZM2Zc9vjnn39ecXFxztvhw4fzvWYAAADAJZ07J3XsKKWmSm3aSH37Wl2Ry7J0JGjIkCF67rnn1LFjR0nSrbfeqoMHDyo2Nlbdu3e/5PjAwEAFBgZaUCkAAADg4vr1k37/XYqKkqZOlVge5oosHQlKTEyUj0/mEnx9feVwOCyrCQAAAHA7M2dKs2ZJPj7S7NlS4cJWV+TSLB0JatWqlV577TVFR0erSpUq+uGHH/T222+rV69eVpYFAAAAuI/ffsuY+jZ8uFSvntUVuTxLQ9C7776rF198UX379tXJkydVqlQpPf7443rppZesLAsAAABwD8nJ5hpACQlSw4bSsGFWV+QWbIZhGFYXkVPx8fGKiIhQXFycwsPDrS4HAABYJCEhQWFhYRLd4eBtBgyQxo2TihaVduyQSpWyuiLLZCcbWDoSBAAAkBv8/f318ssvO7cBr7B8uRmAJGn6dK8OQNnFSBAAAADgbo4ckapVk06flgYPNtcE8nLZyQaWdocDAAAAkE1paVLnzmYAuv12KTbW6orcDtPhAACA23M4HNqzZ48k6eabb75kCQ7Ao7z6qrRhg1SggDRvnhQQYHVFbocQBAAA3N758+d1yy23SDRGgKdbt0565RVze9IkqUIFqytyS/yaBAAAAHAHf/1lToNzOKRevaROnayuyG0RggAAAABXZxhSjx7SsWNSpUoZXeGQI4QgAAAAwNW984702WdSYKD08ccSUz6vCyEIAAAAcGVbtkjPPWdujxkjVa1qdUVujxAEAAAAuKq4OKljRyk1VWrfXnr8casr8giEIAAAAMAVGYYZev74Q4qJkT74QLLZrK7KI9AiGwAAuD1/f38988wzzm3AI0yZYl7/4+cnzZ0rFSxodUUegxAEAADcXkBAgN58802rywByz88/S/37m9uvvSb95z9WV+RRmA4HAAAAuJLEROmhh6SkJKlJE+mfUU7kHkaCAACA23M4HDp06JAkKTo6Wj4+/J4XbmzAAGn3bqlECWnmTIm/z7mOEAQAANze+fPnVbZsWUmS3W5XKGuowF3NnWteC2SzSR99JBUrZnVFHolYCQAAALiCvXulxx4zt194QbrnHqsr8liEIAAAAMBqycnmekB2u1S/vvTSS1ZX5NEIQQAAAIDVhg6Vtm+XihSR5swx22IjzxCCAAAAACstXSqNG2duz5gh3XCD1RV5PEIQAAAAYJWDB6WePc3tZ56RWrSwuiKvQAgCAAAArJCaKnXqJJ09K91xh7koKvIFkw0BAIDb8/PzU9++fZ3bgFt48UVp82YpIkKaN08KCLC6Iq9hMwzDsLqInIqPj1dERITi4uIUHh5udTkAAABA1nz+udSsmbm9cKH0wANWV+T2spMNmA4HAAAA5Kdjx6Ru3cztvn0JQBZgvBgAALg9wzB06tQpSVLRokVls9msLgm4vLQ0qXNn6a+/pGrVpNGjra7IKxGCAACA20tMTFSxYsUkSXa7XaGhoVaXBFze//4nrVsnhYVJCxZIQUFWV+SVmA4HAAAA5IevvpJefdXcnjxZqljR6oq8FiEIAAAAyGsnTkhdukiGIfXubbbGhmUIQQAAAEBeSk+XHn5Y+vNP6ZZbpLFjra7I6xGCAAAAgLw0cqQ5FS4kRJo/3/wTliIEAQAAAHll3Tpp+HBze8IE6eabra4IhCAAAAAgj5w8aV7743BIPXpkrA0Ey9EiGwAAuD0/Pz91797duQ1YzuEwQ8/x41LlytJ771ldES7CvxIAAMDtBQYGavr06VaXAWQYNUpatUoKDjavA2LtKpfCdDgAAAAgN61fL73wgrk9frxUpYrVFeFfGAkCAABuzzAMJSYmSpJCQkJks9msLgne6uLrgLp1M68FgsthJAgAALi9xMREhYWFKSwszBmGgHzncEhdu0rHjpld4N5/XyKQuyRCEAAAAJAbYmOlL77gOiA3QAgCAAAArte6ddJLL5nb778v3XKL1RXhKghBAAAAwPW4+Dqg7t25DsgNEIIAAACAnEpPlx5+OGM9oPHjra4IWUAIAgAAAHIqNlb68kspJERasIDrgNwEIQgAAADIiTVrpJdfNrfff98cCYJbYJ0gAADg9nx9fdW+fXvnNpDnTpzIuA6oRw/zWiC4DUIQAABwe0FBQVqwYIHVZcBbpKdLnTtLf/5pdoHjOiC3Y+l0uJiYGNlstktu/fr1s7IsAAAA4MpGjDCnwoWGmtcBhYRYXRGyydKRoC1btig9Pd15/+eff9a9996rDh06WFkWAAAAcHmrVkmvvmpuT54sVapkdUXIAUtDUGRkZKb7r7/+usqXL68GDRpc9vjk5GQlJyc778fHx+d5jQAAwPUlJCQoLCxMkmS32xVKhy7khSNHzHbYhiE9/rg5JQ5uyWW6w6WkpOijjz5Sr169ZLPZLntMbGysIiIinLeoqKh8rxMAAABeKDXVbIRw6pR0223SmDFWV4Tr4DIhaOnSpTp79qx6XGWF3eeff15xcXHO2+HDh/O1RgAAAHipF16QNm6UChQwrwMKCrK6IlwHl+kON3XqVDVr1kylSpW64jGBgYEKDAzM17oAAADg5T75RHrjDXP7ww+lChWsrgjXySVC0MGDB7V69WotXrzY6lIAAACADAcPZqwB1L+/9M96VHBvLjEdbtq0aSpWrJhatGhhdSkAAACAKTlZ6tBBOnNGqlVLevNNqytCLrE8BDkcDk2bNk3du3eXn59LDEwBAAAA0tNPS1u2SIUKmdcBBQRYXRFyieWpY/Xq1Tp06JB69epldSkAAMBN+fr6qnnz5s5t4LrNmyeNH29uz5ollSljdUXIRTbDMAyri8ip+Ph4RUREKC4uTuHh4VaXAwAAAE/wyy9SzZpSQoI0bJj02mtWV4QsyE42sHw6HAAAAOAyEhLM5gcJCVLDhtKIEVZXhDxACAIAAAAkyTCkPn2kXbukEiWkuXMlrln3SIQgAADg9hISEhQaGqrQ0FAlJCRYXQ7c1ZQp5vU/vr7Sxx+bQQgeiWgLAAA8QmJiotUlwJ1t3y499ZS5/dprUv36VleEPMRIEAAAALzb2bPmekDJyVKrVtKQIVZXhDxGCAIAAID3cjik7t2lP/6QYmKkGTMkH35E9nScYQAAAHivN9+Uli83F0JduNBcGBUej2uCAAAA4DXS06UNG6Tjx6Wb/1yrasOGySZJ774r3X671eUhnxCCAAAA4BUWL5YGDJCOHJFK6ph+0EOyyaGDDburzKOPWl0e8hEhCAAAuD0fHx81aNDAuQ382+LF5hqohiH5KVUf6yEV10ntUFXVWfu+Zi2xqV07q6tEfrEZhmFYXUROxcfHKyIiQnFxcQoPD7e6HAAAALig9HSz58GRI+b9t/S0ntbbilO4amqr9tkqqnRpaf9+c4kguKfsZAN+VQIAAACPtmFDRgB6QAv1tN6WJHXXDP2uijIM6fBh8zh4B0IQAAAAPNrx4+afN+pXfahekqQ3NETL1Oayx8HzEYIAAIDbS0hIUGRkpCIjI5WQkGB1OXAxJUtKIUrQIj2gcJ3TOtXXMI287HHwDjRGAAAAHuHUqVNWlwAXVe8uQ7NCHtctibt0XCX0kD5W+kU/BttsUunSUr16lpaJfMRIEAAAADya78Txapc4W2nyVUd9rD9VwvmYzWb+OWYMTRG8CSEIAAAAnmvTJmnQIEnS7h5v6o/S9TM9XLq0tHChaI/tZZgOBwAAAM/0559Shw5SWpr04IOq+uFAHXCYXeCOHzevAapXjxEgb0QIAgAAgOdJS5M6dpSOHZNuvlmaMkWy2eTrKzVsaHVxsBrT4QAAAOB5hg2T1q6VwsKkxYulAgWsrgguhJEgAADg9nx8fFSzZk3nNrzcokXSm2+a29OmSZUqWV0RXAwhCAAAuL3g4GBt2bLF6jLgCn75RerRw9x+5hmpfXurK4IL4lclAAAA8Ax2u9nmzW6XGjSQYmOtrgguihAEAAAA92cY0iOPSHv2SKVKSR9/LPkx6QmXRwgCAABuLzExUTExMYqJiVFiYqLV5cAKo0dL8+ebwWfBAql4casrggsjHgMAALdnGIYOHjzo3IaX+fpr6dlnze2xY6U6dayuCC6OkSAAAAC4r0OHpIcekhwOqXt3qU8fqyuCGyAEAQAAwD0lJUkPPCCdOiVVry5NmCDZbFZXBTdACAIAAID7MQypXz9p61apcGFzQdTgYKurgpsgBAEAAMD9fPCB9OGHko+PNG+eFBNjdUVwI4QgAAAAuJdvv5WefNLcfu016d57ra4IbobucAAAwO3ZbDZVrlzZuQ0P9uef5nVAqanmnxe6wgHZQAgCAABuLyQkRLt27bK6DOS11FTpwQelY8ekSpWkadNohIAcYTocAAAA3MPgwdL69VKBAtKSJeafQA4QggAAAOD6pk+X3nvP3P7oI3MkCMghQhAAAHB7iYmJqlKliqpUqaLExESry0Fu27JFeuIJc/vll6X777e6Irg5rgkCAABuzzAM7d6927kND/Lnn1LbtlJyshl+XnrJ6orgARgJAgAAgGu60Ajh6FHpppukmTPNdYGA68TfIgAAALimixshLF0qRURYXRE8BCEIAAAArodGCMhDhCAAAAC4FhohII8RggAAAOA6TpygEQLyHN3hAACA27PZbCpTpoxzG24qOVl64AEaISDPEYIAAIDbCwkJ0YEDB6wuA9fDMKSnnpI2bTIbICxbRiME5BmiNQAAAKw3caL0wQeSzSbNnWuOBAF5xPIQdPToUT388MMqUqSIgoODdeutt2rr1q1WlwUAAID8sm6d1L+/uR0bKzVrZnVF8HCWToc7c+aM6tatq0aNGmnlypWKjIzU3r17VahQISvLAgAAbub8+fOqX7++JGn9+vUKDg62uiRk1cGDUvv2Ulqa1LGjNHSo1RXBC1gagkaNGqWoqChNmzbNua9s2bJWlgQAANyQw+FwziRxOBxWl4OsSkyU2rSRTp2SqleXpk41p8MBeczS6XDLly9XzZo11aFDBxUrVkzVq1fXBx98cMXjk5OTFR8fn+kGAAAAN2QYUq9e0o8/SpGR0tKlUkiI1VXBS1gagv744w9NmDBBFStW1KpVq9SnTx/1799fM2bMuOzxsbGxioiIcN6ioqLyvWYAAADkglGjpI8/lvz8pEWLpOhoqyuCF7EZhmFY9cUDAgJUs2ZNbdq0ybmvf//+2rJlizZv3nzJ8cnJyUpOTnbej4+PV1RUlOLi4hQeHp5vdQMAANeSkJCgsLAwSZLdbldoaKjVJeFqPvlEat3aHA2aMEF64gmrK4IHiI+PV0RERJaygaUjQSVLllTlypUz7bv55pt16NChyx4fGBio8PDwTDcAAAC4kV27pM6dzQD0xBMEIFjC0hBUt25d/frrr5n2/fbbb84VnwEAAOBB/v5buv9+yW6XGjaUxo2zuiJ4KUu7ww0aNEh16tTRyJEj9eCDD+r777/X5MmTNXnyZCvLAgAAbqho0aJWl4CrSU01W2H/8YdUtqy0YIHk7291VfBSll4TJEkrVqzQ888/r71796ps2bIaPHiwHn300Sw9Nzvz/gAAAGChvn3N63/CwqTNm6VbbrG6IniY7GQDy0PQ9SAEAQAAuIEJE8wQZLOZrbDvv9/qiuCB3KYxAgAAADzcmjXSU0+Z26+9RgCCSyAEAQAAt3f+/Hk1bNhQDRs21Pnz560uBxfs22deB5SebnaEe+45qysCJKsbIwAAAOQGh8OhdevWObfhAuLjzVGf06elWrWkKVPM6XCAC2AkCAAAALkrPV3q2FHavVsqWVJaskQKDra6KsCJEAQAAIDc9cwz0sqVUlCQtHy5dMMNVlcEZEIIAgAAQO6ZPFkaM8bcnjlTqlnT6oqASxCCAAAAkDvWrJH69TO3//c/qUMHqysCLosQBAAAgOu3d6/0wANSWprUqZP0wgtWVwRcEd3hAACARwgJCbG6BO915ozUsqX55513SlOn0gkOLo0QBAAA3F5oaKgSEhKsLsM7paZKDz4o/fabFBUlLV1KJzi4PKbDAQAAIOcGDpRWr5ZCQ6VPPpFKlLC6IuCaCEEAAADImXHjpPffN6e+zZkjVatmdUVAlhCCAACA20tKSlKLFi3UokULJSUlWV2Od1ixQho0yNweNUq6/36rKwKyjGuCAACA20tPT9dnn33m3EYe27FD6thRcjik3r3NxVEBN8JIEAAAALLu2DGzE1xCgnTPPRnT4QA3QggCAABA1iQkSK1aSUeOSJUqSQsXSv7+VlcFZBshCAAAANeWni516SJt3y5FRkqffioVLGh1VUCOEIIAAABwbc89Jy1bJgUGmmsBlStndUVAjhGCAAAAcHWTJ0tvvWVuT5sm1aljdUXAdSEEAQAA4Mq++ELq29fc/t//pE6drK4IuG60yAYAAG4vNDRUhmFYXYbn2bFDat/evB6oa1fphResrgjIFYwEAQAA4FJHjkgtWkjnzkkNG0pTptAKGx6DEAQAAIDM4uPNAHT0qHTzzdLixVJAgNVVAbmGEAQAANxeUlKSOnTooA4dOigpKcnqctxbaqr04IPSzp1S8eLSZ59JhQpZXRWQqwhBAADA7aWnp2vhwoVauHCh0tPTrS7HfRmG2QRh1SopJERasUKKibG6KiDXEYIAAABgio01r/3x8ZHmzZNq1rS6IiBPEIIAAAAgzZkj/fe/5va4cVKrVlZXBOQZQhAAAIC3W7dO6tnT3B48WOrXz+qKgDxFCAIAAPBmu3ZJbdpIKSnSAw9Ib75pdUVAniMEAQAAeKujR6WmTaWzZ6W6daVZs8zrgQAPx99yAAAAbxQXJzVrZi6KWqmStGyZFBxsdVVAvshxCEpLS9Pq1as1adIknTt3TpJ07Ngx2e323KwPAADgmkJCQmS322W32xUSEmJ1Oa4vOVlq21b66SepRAlp5UqpSBGrqwLyjV9OnnTw4EE1bdpUhw4dUnJysu69914VKFBAo0aNUnJysiZOnJj7lQIAAFyBzWZTaGio1WW4B4fDbIKwZo0UFmYGINYCgpfJ0UjQgAEDVLNmTZ05c0bBFw2btm3bVl999VVu1gcAAIDc9Nxz0ty5kp+ftHixdNttVlcE5LscjQRt2LBBmzZtUkBAQKb9MTExOnr0aG7VBgAAkCXJycl6/PHHJUmTJk1SYGCg1SW5pnffzej+9uGH0r33Wl0RYIkcjQQ5HA6lp6dfsv/IkSMqUKBAbtQFAACQZWlpaZoxY4ZmzJihtLQ0q8txTYsWSQMGmNsjR0pdu1pdEWCZHIWg++67T2PGjHHet9lsstvtevnll9W8efPcrA8AAADXa+1aqXNnyTCkPn3MKXGAF8vRdLjRo0erSZMmqly5spKSktS5c2ft3btXRYsW1dy5c3O/SgAAAOTMjz9KrVubi6G2a2dOibPZrK4KsFSOQlDp0qW1Y8cOffzxx9qxY4fsdrseeeQRdenSJVOjBAAAAFjojz/MxVDj46UGDaTZsyVfX6urAixnMwzDyO6T1q9frzp16sjPL3OGSktL06ZNm1S/fv3crPGK4uPjFRERobi4OIWHh+fL1wQAAK4nISFBYWFhkiS73U67bEn680+pbl1p3z6pWjVp3TopIsLqqoA8k51skKNrgho1aqTTp09fsj8uLk6NGjXKyUsCAAAgt5w7JzVvbgagmBhzLSACEOCUoxBkGIZsl5lL+vfff/ObFwAAACslJ0tt20rbt0uRkdIXX0glS1pdFeBSsnVNULt27aR/usH16NEjUw/+9PR07dy5U3Xq1Mn9KgEAAK4iJCREJ0+edG57LYdD6tZN+uorKSzMHAGqWNHqqgCXk60QFPHPMKphGCpQoECmJggBAQH6z3/+o0cffTT3qwQAALgKm82myMhIq8uwlmFI/ftL8+dL/v7S4sXS7bdbXRXgkrIVgqZNmyZJiomJ0TPPPMPUNwAAAFcxfLg0frzZ/nrGDOnee62uCHBZOeoO5yroDgcAACQpOTlZgwcPliS9/fbbmabse4UxY6RBg8zt9983F0QFvEx2skGOQ9DChQs1f/58HTp0SCkpKZke2759e5ZeY/jw4RoxYkSmfTfddJN++eWXLD2fEAQAAOTtLbJnzJB69DC3X31V+u9/ra4IsESet8geN26cevbsqeLFi+uHH37QHXfcoSJFiuiPP/5Qs2bNsvVaVapU0fHjx523jRs35qQkAAAA77N0qfTII+b24MHSsGFWVwS4hWxdE3TB+++/r8mTJ6tTp06aPn26hg4dqnLlyumll1667PpBVy3Az08lSpTI0rHJyclKTk523o+Pj8927QAAAB7h66+lhx6S0tOlnj2lt94yrwcCcE05Ggk6dOiQsxV2cHCwzp07J0nq2rWr5s6dm63X2rt3r0qVKqVy5cqpS5cuOnTo0BWPjY2NVUREhPMWFRWVk/IBAADc25YtUuvWUkqKuSbQ5MkEICAbchSCSpQo4RzxiY6O1rfffitJ2r9/v7JzidGdd96p6dOn6/PPP9eECRO0f/9+1atXzxmq/u35559XXFyc83b48OGclA8AAOC+du+WmjWT7HbpnnukOXMkvxxN7gG8Vo6+Y+6++24tX75c1atXV8+ePTVo0CAtXLhQW7dudS6omhUXXz9UtWpV3XnnnSpTpozmz5+vRy7Mb71IYGCg93V7AQAAuOCPP8zW13//Ld1xh7RkiRQUZHVVgNvJUQiaPHmyHA6HJKlfv34qUqSINm3apPvvv1+PP/54jospWLCgbrzxRv3+++85fg0AAACPdOSIOfJz7JhUpYr02WdSgQJWVwW4pRyFoCNHjmS6Hqdjx47q2LGjDMPQ4cOHFR0dnaNi7Ha79u3bp65du+bo+QAAwDsFBwdr//79zm2P8+efZgA6cECqUEFavVoqUsTqqgC3laNrgsqWLau//vrrkv2nT59W2bJls/w6zzzzjNatW6cDBw5o06ZNatu2rXx9fdWpU6eclAUAALyUj4+PYmJiFBMTIx+fHP1447pOn5buu0/67TcpOlr66ispi511AVxejkaCDMOQ7TIdSOx2u4KyMS/1yJEj6tSpk/7++29FRkbqrrvu0rfffqvIyMiclAUAAOBZ4uPNJgg7d5rB56uvzCAE4LpkKwQNHjxYkmSz2fTiiy8qJCTE+Vh6erq+++473XbbbVl+vXnz5mXnywMAAFxWSkqK/vvf/0qSXnvtNQUEBFhd0vVLTJRatZK+/96c+rZ6tTkVDsB1y1YI+uGHH6R/RoJ++umnTP/ABAQEqFq1anrmmWdyv0oAAICrSE1N1VtvvSVJGj58uPuHoORkc/2f9eul8HDpiy/MZggAckW2QtCaNWskST179tTYsWMVHh6eV3UBAAB4p9RUqWNHM/iEhEgrV0o1alhdFeBRcnTl4JgxY5SWlnbJ/tOnTys+Pj436gIAAPA+aWlSly7S0qVSYKC0fLlUp47VVQEeJ0chqGPHjpe9nmf+/Pnq2LFjbtQFAADgXdLTpW7dpAULpIAAcyHUe+6xuirAI+UoBH333Xdq1KjRJfsbNmyo7777LjfqAgAA8B7p6VLPntLcuZKfn7RwodkVDkCeyFEISk5Ovux0uNTUVJ0/fz436gIAAPAODof06KPSrFmSr6/08cdmVzgAeSZHIeiOO+7Q5MmTL9k/ceJE3X777blRFwAAgOczDKlPH2naNMnHR5ozR2rXzuqqAI+Xo8VSX331VTVu3Fg7duzQPf/MVf3qq6+0ZcsWffHFF7ldIwAAwFUFBwfr559/dm67BcOQnnpKmjxZstmkmTOlBx+0uirAK+RoJKhu3bravHmzoqKiNH/+fH3yySeqUKGCdu7cqXr16uV+lQAAAFfh4+OjKlWqqEqVKvLxydGPN/nLMKTBg6Xx480ANG2a2RUOQL6wGYZhWF1ETsXHxysiIkJxcXGsWQQAANzDhQA0Zox5/4MPpN69ra4KcHvZyQY5mg53saSkJKWkpGTaRyABAAD5KSUlRSNHjpQkDRs2TAEBAVaXdHmGIQ0cKI0bZ96fOJEABFggRyNBiYmJGjp0qObPn6+///77ksfT09Nzq76rYiQIAABIUkJCgsLCwiRJdrtdoaGhVpd0qQvXAI0fb96fPNnsCgcgV2QnG+Ro0uyQIUP09ddfa8KECQoMDNSUKVM0YsQIlSpVSjNnzsxp3QAAAJ7J4ZD69cu4BmjqVAIQYKEcTYf75JNPNHPmTDVs2FA9e/ZUvXr1VKFCBZUpU0azZ89WFy7sAwAAMDkcUt++0qRJGQGoZ0+rqwK8Wo5Ggk6fPq1y5cpJ/1z/c/r0aUnSXXfdpfXr1+duhQAAAO7K4ZCeeCIjAE2bRgACXECOQlC5cuW0f/9+SVKlSpU0f/586Z8RooiIiNytEAAAwB05HNJjj5nd33x8pBkzpO7dra4KQE5DUM+ePbVjxw5J0nPPPafx48crKChIgwYN0tChQ3O7RgAAAPeSlmYGnqlTzQA0c6bUtavVVQH4R46uCRo0aJBzu3Hjxvrll1+0bds2FS1aVB999FFu1gcAAOBeUlKkzp2lRYskX1/po4+kjh2trgrARXJ1sdQdO3aoRo0atMgGAAD5Kj09Xdu3b5ck1ahRQ76+vtYUkpQkdeggrVghBQRI8+dLrVtbUwvgZfJ1sVQAAACr+fr6qlatWtYWkZAgtWkjrV4tBQVJS5ZITZtaWxOAyyIEAQAAXK/4eKllS2nDBik01BwJatjQ6qoAXAEhCAAAuL2UlBSNHTtWkjRgwAAFBATk3xc/c8Yc8fn+eyk8XPr8c6l27fz7+gCyLVvXBLVr1+6qj589e1br1q3jmiAAAJCvEhISFBYWJkmy2+0KDQ3Nny988qTUpIn0449S4cLSF19It9+eP18bQCZ5dk3QtdYAioiIULdu3bLzkgAAAO7p0CHp3nul336TiheXvvxSuvVWq6sCkAXZCkHTpk3Lu0oAAADcxS+/mAHoyBEpOtoMQDfeaHVVALIoR4ulAgAAeK1t26R69cwAVKmS9M03BCDAzRCCAAAAsmrtWqlRI+nUKalmTbMbXOnSVlcFIJsIQQAAAFmxfLnZBe7cObP99VdfSUWLWl0VgBwgBAEAAFzLrFlSu3ZScrJ0//3SypVmO2wAbol1ggAAgNsLCgrSmjVrnNu56p13pMGDze1u3aSpUyU/foQC3BnfwQAAwO35+vqqYcOGufuiDoc0dKg0erR5f8AA6e23JR8m0gDujhAEAADwbykpUs+e0pw55v1Ro6QhQySbzerKAOQCQhAAAHB7qampmjx5siTpsccek7+/f5afm55uNnk7flwqWVKqVy1evg8+IK1ebU57+/BDqWvXPKweQH6zGYZhWF1ETsXHxysiIkJxcXEK5+JEAAC8VkJCgsLCwiRJdrtdoaGhWXre4sXmLLcjR8z7xXVCX/o3162pP0ihodKiRVKTJnlZOoBckp1swEgQAADwSosXS+3bSxd+HVxRv+lzNVW51P06qUj9/NJnurtJTavLBJAHuLIPAAB4nfR0cwToQgC6Q9/pG9VVOe3X7yqvutqkHu/VVHq61ZUCyAuEIAAA4HU2bMiYAtdOi7RWDRWpU9qq21VX3+h3VdDhw+ZxADwPIQgAAHid48clydDTeksL1EHBStIKtVBDrdVJFf/XcQA8DSEIAAB4nVLF0vS++uotDZGPDL2nfmqjpUpQWKbjSpa0rEQAeYjGCAAAwLucO6f6bz2kBloph2x6WqM1RgMlZawBZLNJpUtL9epZWimAPEIIAgAAbi8wMFArVqxwbl/RkSNSy5ay7dihtIBgdUiZo2W2NtJFC4ZcWA91zBjJ1zevKwdgBUIQAABwe35+fmrRosXVD/rhB6llS+nYMalYMfmtWKGuh2tp60XrBEnmCNCYMVK7dnleNgCLEIIAAIDnW7RI6tZNSkyUKleWPv1UiolRu1pS69ZmF7jjx81rgOrVYwQI8HSEIAAA4PZSU1M1e/ZsSVKXLl3k7+9vPmAY0quvSi+9ZN6/7z7p44+lggWdz/X1lRo2tKRsABaxGYZhZOE4lxQfH6+IiAjFxcUpPDzc6nIAAIBFEhISFBZmdnaz2+0KDQ2Vzp+XevWS5s0zD+rfXxo9WvLjd8CAJ8pONnCZFtmvv/66bDabBg4caHUpAADA3R09KtWvbwYgPz9p0iRp7FgCEADJVabDbdmyRZMmTVLVqlWtLgUAALi7bdukjh3Ni3yKFDGvB2rQwOqqALgQy0eC7Ha7unTpog8++ECFChWyuhwAAODu7rvPDECVK0vff08AAnAJy0NQv3791KJFCzVu3PiaxyYnJys+Pj7TDQAAQGlpGdvJyVLz5tLmzVK5clZWBcBFWRqC5s2bp+3btys2NjZLx8fGxioiIsJ5i4qKyvMaAQCAizt1SmrTJuP+oEHS8uUSTZMAXIFlIejw4cMaMGCAZs+eraCgoCw95/nnn1dcXJzzdvjw4TyvEwAAuLAffpBq1pTWrs3Y98orLPQD4Kosa4ywbds2nTx5UjVq1HDuS09P1/r16/Xee+8pOTlZvv/6BywwMFCBgYEWVAsAAFzOnDlS797S+fMKLFdO8/v2laKj+VkBwDVZFoLuuece/fTTT5n29ezZU5UqVdKzzz57SQACAACQ/rn+Z+hQ6Z13zPvNmslv9mx1oMESgCyyLAQVKFBAt9xyS6Z9oaGhKlKkyCX7AQAAJEknTkidOmVMf/vvf6URI5j+BiBbXGKdIAAAgGtavz5j/Z+wMGnGDKldO0lSWlqalixZIklq27at/FgUFcBV2AzDMKwuIqfi4+MVERGhuLg4hdMBBgAAz2QY0ltvSc8/L6WnS1WqSAsXSpUqOQ9JSEhQWFiY9M8ahKGhoRYWDMAK2ckG/JoEAAC4rrNnpZ49paVLzftdukiTJkmEHADXgRAEAABc0w8/SO3bS3/8IQUESGPHSo8/LtlsVlcGwM0RggAAgGsxDGnqVOnJJ6XkZKlMGXP6W82aVlcGwENYtlgqAADAJeLjzSlvjz5qBqAWLaTt2wlAAHIVIQgAALiGbdukGjWkuXPNltexsdLy5VLhwlZXBsDDMB0OAABYyzCkceOkIUOk1FQpOtoMQnXqWF0ZAA9FCAIAANb5+2+pVy9zxEeS2rQxrwfK5uhPQECApk2b5twGgKshBAEAAGts3Ch16iQdOWJ2fxs9WurXL0fd3/z9/dWjR488KROA5+GaIAAAkL/S0qSXX5YaNDADUMWK0rffmt3gaH8NIB8wEgQAAPLPvn3Sww+boUeSunaVxo+XChS4rpdNS0vTqlWrJElNmjSRnx8/4gC4Mv6FAAAAec8wpJkzzdEeu12KiJAmTpQ6dsyVl09OTlbLli0lSXa7nRAE4Kr4FwIAAOStM2ekJ56Q5s8379evL82aZXaBAwALcE0QAADIO2vXSlWrmgHIz08aOVL6+msCEABLMRIEAAByX1KS9N//Su+8Y06Fq1hRmjNHqlnT6soAgBAEAABy2datUrdu0p495v1HHzXDUGio1ZUBgMR0OAAAkGtSU83W1//5jxmASpSQVqyQJk8mAAFwKYwEAQCA67drlzn6s327ef+hh8zW10WKWF0ZAFyCEAQAAHIuPd2c6vbCC1JyslS4sPT++2YIykcBAQF67733nNsAcDWEIAAAkDO//CL16iVt3mzeb95cmjJFKlky30vx9/dXv3798v3rAnBPXBMEAACyJy1Nev116bbbzABUoID0wQfm9T8WBCAAyC5GggAAQNb99JPUs6e0bZt5v1kzadIkKSrK0rLS09O1YcMGSVK9evXk6+traT0AXBshCAAAXFtqqhQbK736qrldsKA0ZozZDMFms7o6JSUlqVGjRpIku92uULrRAbgKQhAAALi6rVul3r2lHTvM+61bSxMmMPUNgNvimiAAAHB5drs0eLB0551mACpSRJo7V1qyhAAEwK0xEgQAAC712WdS377SwYPm/c6dzVbYxYpZXRkAXDdCEAAAyPDnn9LAgdK8eeb9mBhz6lvTplZXBgC5hulwAABAMgxp6lSpUiUzAPn4SE8/Lf38MwEIgMdhJAgAAG/3889Snz7Sxo3m/Ro1zHV/atSwujIAyBOEIAAAvJXdLo0YYV7rk54uhYRI//ufNGCA5OdePyL4+/vrjTfecG4DwNXYDMMwrC4ip+Lj4xUREaG4uDiFh4dbXQ4AAO7BMKTFi81rf44cMfe1bWuu+xMdbXV1AJAj2ckG7vVrHgAAcH327ZOeekpaudK8X7as9O67UosWVlcGAPmGxggAAHiD8+el4cOlKlXMAOTvL73wgnk9kAcEoPT0dG3ZskVbtmxRenq61eUAcHGMBAEA4MkMw1zcdPDgjDV/7rlHGj9euukmq6vLNUlJSbrjjjskSXa7XaGhoVaXBMCFMRIEAICn2rNHuu8+6YEHzABUurTZ/vrLLz0qAAFAdhGCAADwNPHx5ho/VatKq1dLAQHSf/8r/fKL9NBDks1mdYUAYCmmwwEA4CkcDmnmTOm556Q//zT33X+/9PbbUvnyVlcHAC6DEAQAgCdYv14aNEjavt28f+ON0tixUtOmVlcGAC6H6XAAALiz/fulDh2kBg3MABQeLr35pvTTTwQgALgCRoIAAHBH8fHSyJHSO+9IKSmSj4/0+OPSiBFSZKTV1QGASyMEAQDgTtLSpA8/lF58UTp50tx3773mdT+33GJ1dZbx9/fXyy+/7NwGgKuxGYZhWF1ETsXHxysiIkJxcXEKDw+3uhwAAPKOYUgrVkjPPmu2vtY/1/28/bbUvDkd3wB4vexkA64JAgDA1W3dKjVqZHZ627NHKlLEbHrw889SixYEIADIJqbDAQDgqvbvl4YNMxc4laTAQGngQLMFdsGCVlfnUhwOh/b8M0J28803y8eH3/MCuDJCEAAArubUKbPpwfjxZtMDm03q2lV65RUpOtrq6lzS+fPndcs/10TZ7XaFhoZaXRIAF0YIAgDAVdjtZre3N9+Uzp0z9917r/TGG9Jtt1ldHQB4DEIQAABWS06WJk+WXn01o+Nb9epSbKzUpInV1QGAx7F0wuyECRNUtWpVhYeHKzw8XLVr19bKlSutLAkAgPyTni7NmiVVqiT1728GoAoVzGuAtm4lAAFAHrE0BJUuXVqvv/66tm3bpq1bt+ruu+9W69attWvXLivLAgAgbxmGtHSpOcWtWzfpwAGpZElp4kRp927poYfMxU8BAHnC5dYJKly4sN5880098sgj1zyWdYIAAG7FMKTPPzcXOt22zdwXEWF2e+vfXwoJsbpCt5WQkKCwsDCJxgiA18pONnCZa4LS09O1YMECJSQkqHbt2pc9Jjk5WcnJyc778fHx+VghAADXYc0a6YUXpE2bzPuhodKAAdIzz0iFClldHQB4FctD0E8//aTatWsrKSlJYWFhWrJkiSpXrnzZY2NjYzVixIh8rxEAgBzbtMkc+fn6a/N+UJDUr5/07LNSZKTV1XkMf39/PfPMM85tALgay6fDpaSk6NChQ4qLi9PChQs1ZcoUrVu37rJB6HIjQVFRUUyHAwC4ns2bpREjpFWrzPv+/tJjj5mLn5YqZXV1AOBxsjMdzvIQ9G+NGzdW+fLlNWnSpGseyzVBAACX8+/w4+sr9ehhjgaVKWN1dQDgsdzymqALHA5HptEeAADcwpXCz7BhUrlyVlfn8RwOhw4dOiRJio6Olg/d9QBchaUh6Pnnn1ezZs0UHR2tc+fOac6cOVq7dq1WXfgPBAAAV/fNN9L//id98YV5n/BjifPnz6ts2bIS3eEAZIGlIejkyZPq1q2bjh8/roiICFWtWlWrVq3Svffea2VZAABcnWGYjQ5efVVau9bcR/gBALdhaQiaOnWqlV8eAIBLpKdLGzZIx4+b65fWq2fmG+mf8LNypRl+Nm829/n7m+HnuecIPwDgJlzumiAAAKyyeLG5dM+RIxn7SpeWxr7jUDvfZWb42b7dfCAoSHr0UWnIECkqyrKaAQDZRwgCAOCfANS+vTnYc4GfUnX3kbm6qcMoSbvNnaGhUp8+0tNPSyVKWFYvACDnCEEAAK+Xnm6OAF0IQMFKVC99qCF6U2VkdhyLt4Ur7Nkn5fP0IKloUWsLBgBcF0IQAMDrbdhgToGL0Fn11fsaqDEqpr8kSSdUXO9okCYaT2hZkwg1JP8AgNsjBAEAvN7Z3cf0usbqCU1UhOIlSfsVozc0VNPVQ0kKlmQ2S4Br8vPzU9++fZ3bAHA1/CsBAPBee/ZIb72l+2fOko9SJUk/q4pe13Oap45K/9d/kyVLWlQnrikwMFDjx4+3ugwAboIQBADwPt98I73xhrR8uSTJR9J3AfX0WsoQrVALGfLJdLjNZnaJq1fPonoBALmKEAQA8A7p6WboeestadMmc5/NJrVpIw0ZoqPHa2tF+3+OvahDnM1m/jlmzEXrBcHlGIahU6dOSZKKFi0q24UTBwCX4ZOFYwAAcF+JidL770uVKknt2pkBKCDAXONnzx6zN3bt2mrXTlq4ULrhhsxPL13a3N+unVVvAFmRmJioYsWKqVixYkpMTLS6HAAujpEgAIBnOnFCeu89acIE6fRpc1+hQtITT0j9+192jZ927aTWrc1uccePm9cA1avHCBAAeBpCEADAs+zaJb39tvTRR1JKirmvXDlp0CCpZ09zsdOr8PWVGjbMn1IBANYgBAEA3J/DIa1aJb3zjvTllxn769SRnn7aHN5hOAcA8A9CEADAfSUmSjNnSmPHSr/8Yu7z8ZHatjXDT+3aVlcIAHBBhCAAgPs5elQaP16aNCnjep/wcKl3b+nJJ6WyZa2uEADgwghBAAD3YBjS5s3SuHHSokVSWpq5v1w5s9FBz55mEAIA4BoIQQAA15acLM2fb4afrVsz9tevbzY7aNWK630gPz8/de/e3bkNAFfDvxIAANd04oQ0caJ5+/NPc19goNSli/TUU9Jtt1ldIVxIYGCgpk+fbnUZANwEIQgA4DouTHl77z1zhdLUVHP/DTdIffuaC5xGRlpdJQDAzRGCAADWO39emjfPDD/bt2fsr11bGjDAXMXU39/KCuHiDMNQYmKiJCkkJEQ2m83qkgC4MEIQAMA6Bw5IEyZIU6ZkdHkLDJQ6dza7vNWoYXWFcBOJiYkKCwuTJNntdoVeY1FcAN6NEAQAyF8Oh/TFF9L770srVphT4CSpTBlzytsjj0hFilhdJQDAgxGCAAD54++/pWnTzEYH+/Zl7G/c2Gx00KIFXd4AAPmCEAQAyFtbtpijPvPmSUlJ5r6ICHNdnyeekG66yeoKAQBehhAEAMh9CQnS3LnmqM+2bRn7b7tN6tdP6tRJ4poNAIBFCEEAgNyze7cZfGbOlOLizH0BAdKDD5rh5847Jbp2AQAsRggCAFyf5GRpyRKzy9v69Rn7y5c3p7v16CEVLWplhQAAZEIIAgDkzO+/S5Mnm80OTp0y9/n6SvffL/XpI91zj+TjY3WV8BK+vr5q3769cxsAroYQBADIupQUadkyadIk6auvMvbfcIPUu7f06KPmNpDPgoKCtGDBAqvLAOAmCEEAgGv74w/pgw+kDz+UTp4099lsUtOm5pS35s0lP/5LAQC4B/7HAgBcXkqKtHy5OeXtyy8z9pcoYS5o2ru3FBNjZYUAAOQIIQgAkNnevdKUKea1Pn/9Ze6z2aR775Uef1xq1Ury97e6SiCThIQEhYWFSZLsdrtCacEO4CoIQQAAcxHTpUvNKW9ff52xv2RJqVcvc+SnbFkrKwQAINcQggDAm+3ebQafmTOl06fNfTab1KyZ9NhjUosWXOsDAPA4/M8GAN4mIUGaP9+c8rZpU8b+0qUzRn2io62sEACAPEUIAgBvYBjS9u1m8JkzR4qPN/f7+kotW5qtrZs2Ne8DAODhCEEA4MnOnDFDz5Qp0o8/ZuwvV87s7tajh3ndDwAAXoQQBACexjCk9evN4LNwodn0QJICAqQHHjCnuzVqJPn4WF0pAACWIAQBgKc4flyaMUOaOlX6/feM/bfeao76dOkiFSliZYVAnvH19VXz5s2d2wBwNYQgAHBnaWnSZ5+Zoz6ffSalp5v7w8Kkzp3N8FOzptnxDfBgQUFB+vTTT60uA4CbIAQBgDvau9cc8ZkxQzpxImN/3brmdLcOHcwgBAAALkEIAgB3kZBgXuMzdaq0YUPG/shIqXt3M/xUqmRlhQAAuAVCEAC4MsOQtmwxg8/cudK5c+Z+Hx+zpfUjj5gtrgMCrK4UsFRCQoKKFSsmSTp58qRCQ0OtLgmACyMEAYArOnVK+ugj6cMPpZ9+ythfrpy5oGn37ubipgCcEhMTrS4BgJsgBAGAq0hPl1avNkd9li2TUlLM/UFBGa2tGzSgtTUAANeJEAQAVtu/X5o2TZo+XTp8OGN/jRrmqE/nzlKhQlZWCACAR7H014mxsbGqVauWChQooGLFiqlNmzb69ddfrSwJAPJHUpJ5jU/jxuYUt1deMQNQoULSk09KP/wgbdsm9etHAAIAIJdZOhK0bt069evXT7Vq1VJaWpqGDRum++67T7t37+aCRgCe6YcfzOlus2dLZ89m7G/c2Jzu1qaNOf0NAADkGUtD0Oeff57p/vTp01WsWDFt27ZN9evXt6wuAMhVZ86YoefDD80QdEFUlNSzp3mLibGyQgAAvIpLXRMUFxcnSSpcuPBlH09OTlZycrLzfnx8fL7VBgDZ4nBIa9aYoz6LF0sX/u0KCDBHex55RLrnHsnX1+pKAY/g4+OjBg0aOLcB4GpshmEYVhchSQ6HQ/fff7/Onj2rjRs3XvaY4cOHa8SIEZfsj4uLU3h4eD5UCQDXcPiw2eBg2jSz4cEFt95qBp+HH5aKFLGyQgAAPFJ8fLwiIiKylA1cJgT16dNHK1eu1MaNG1X6CmtfXG4kKCoqyvIQlJ5uLt5+/LhUsqRUrx6/3AU8yTW/x1NSpE8+kaZMkb74whwFkqTwcLOz2yOPSLffLtlsVr0FAAA8XnZCkEtMh3vyySe1YsUKrV+//ooBSJICAwMVGBiYr7Vdy+LF0oAB0pEjGftKl5bGjpXatbOyMgC54arf4zfvMae7zZwp/fVXxgH165vBp317KSTEkroBAMCVWRqCDMPQU089pSVLlmjt2rUqW7asleVk2+LF5s84/x5LO3rU3L9wIUEIcGeX+x4PlV33HZmv4g9MlbQp44ESJaQePcx1fSpWtKRewJslJCQo5p8GIwcOHKDLLICrsnQ6XN++fTVnzhwtW7ZMN910k3N/RESEgoODr/n87Ax55bb0dLOZ04XfDhfXCfXUNL2hoXLIVzab+dvi/fuZGge4o8zf44ZqaYt6a4o6aa4KyC5JSpOvfFu1kO3R3lKzZpKfSwyuA14pISFBYWFhkiS73U4IArxQdrKBpe1TJkyYoLi4ODVs2FAlS5Z03j7++GMry8qSDRsyApBNDs3Xg4rVMK1QSxXSaRmGeX30hg1WVwogJzZskBKOnNZTGqcdqqbvdace0wcqILv2qoKeU6yidFjrBi+TWrUiAAEA4EYsnw7nro4fz9g25KOJekI1tVXN9Lm2qJbaabF2qlqm4wC4AYdDWrtWMS9O0TEtVpDMZiznFaSFaq8p6q31qi/JbHLA9zgAAO6HX13mUMmSme/PVWftUhUtUVuV1x/arNp6RFNVsmQnq0oEkB3Hj5utradOlfbt04WlS39UNX2gRzVHnXVWhS552r//LQAAAK6PEJRD9eqZ1/wcPZpx0fROVVNNbdVcdVITfaG56izH0i3SXW8wVQZwRWlp0sqVZmvrTz81LwSS2dra0amzWi7prc9P1pChS1tbX7jur169/C8bAABcH5ZUziFfX7NFrpR56Y8zKqwW+kyxel6S5DP2Henee6WTJy2qFMAl/vhDeuEFqUwZ6f77peXLzQBUt665yOmxY/KZOEG9J5hr+/x7eZ8L98eMofEJAADuiBB0Hdq1M9tg33BD5v2lonx106KR0qJFUliYtHatuVDi5s1WlQogKUmaN09q3FgqX1567TXp2DGpaFHp6ael3buljRvNNtf/dJW60vd46dK0wAdcjY+Pj2rWrKmaNWvKx4cfbwBcnaUtsq+XlS2yL3bV1eR375batpV++83cOWKE9Nxz/PoYyC8//2xOd5s1Szp92txns5kjtL17S61bSwEBV32Jq36PAwAAl5CdbEAIyg9xcVKfPtLcueb9hg3NH8hKl7a6MsAz2e3Sxx+b4efbbzP2ly5tLmbas6e5CBAAAPAY2ckGXK2fHyIipNmzpSZNpH79zOlxVauaXajatrW6OsAzGIY55XTqVDMAJSSY+/38zOt+eveW7ruPIRwAAMA1QfnGZpO6d5d++MG8PujMGfOCgj59pMREq6sD3NfJk9Jbb0mVK5uNDT780AxAN94ovfGGuarxokVSs2YEIMCDJSYmKiYmRjExMUrk/1UA18B0OCukpJidqd5807xfubI5UnTbbVZXBriHtDRp1Spz1OeTT8z7khQSInXoID3yiHTXXbqkrRsAj5WQkKCwsDBJkt1uV+g/DU4AeI/sZANGgqwQEGD+hvqLL6QSJczmCbVqmcEoOdnq6gDX9euvZmOR6GipZUtpyRIzAN1xhzRpUsaCp/XqEYAAAMAVEYKsdO+90s6d5rS4tDSzZW+NGtJ331ldGeA6zp0zR3zq1pUqVZJGjTLDTtGi0sCB0k8/md8zjz0mudOIMAAAsAwhyGqRkeb1CgsWSMWKmaNCdeqY65YwpxneyuEwG4j06GGOlvbuLW3aJPn4mCNAixZJR49K77wj3XKL1dUCAAA3QwhyFe3bmwGoa1fzB8C33zY7yK1da3VlQP7Zt096+WVzMdNGjaQZM8xfBtx0k/T669Lhw+Y1QO3aXXNtHwAAgCuhRbYrKVJEmjlT6thRevxx8wfCRo3M34KPHGmOGgGeJj7eHAmdMcNckfSC8HDpoYfM0aDatbnGBwAA5BpGglxR8+bSrl1mEJLMBR9vvFF6992MLliAO0tLk1aulDp3zpjutmGDGXSaNJHmzJFOnJAmTzanhxKAAFyDzWZT5cqVVblyZdn4NwPANdAi29V984305JPSjz+a92+5xQxDDRtaXRmQPYZhrpM1a5Y0d670558Zj910kzni8/DDUunSVlYJAADcVHayASHIHaSnm6NBw4ZJp0+b+x580FxnKDra6uqAqzt40Aw9s2aZ171dULSo1KmTeR1czZqM9gAAgOtCCPJUp09LL74oTZxoNk8IDpaGDjU7yRUoYHV1QIZTp8zrfObMkTZuzNgfGCi1bm0GnyZNJH9/K6sEAAAehBDk6XbskPr3l9avN+8XLSo9/7zUp48ZjAArnDsnLVtmjvp88UXG9Ws2m9SggTnVrX17KSLC6koBeKDExETVqlVLkrRlyxaFhIRYXRKAfEYI8gaGIS1caI4M/fqrue+GG6SXXpJ69uQ37MgfiYnSZ59J8+dLK1ZI589nPHb77Wbjg4ceMv9uAkAeSkhIUFhYmCTJbrcrNDTU6pIA5DNCkDdJSzPbag8fbq6hIplrrPzvf2arbR8aACKXnT9vdnabP99cs+fiRX0rVjSDT+fOZkdDAMgnhCAAhCBvlJwsTZokvfaadPKkua9yZfOaoU6dWFgS1ychQVq1Slq0SFq+XLLbMx6LiTFHezp0kGrUoMEBAEsQggAQgryZ3S6NGye98YYUF2fuK11aGjRIevRRGigg686eNae4LV4sff555qlu0dFmh8IHH6SzGwCXQAgCQAiCGYAmTpTGjDEXnZSkggWlvn3NpgrFi1tdIVzRiRPmSM/ixdJXX2VenDcmRmrb1gw+d95J8AHgUghBAAhByJCcLH30kbmm0IUGCoGBZoviPn3M6UvwXoYh7dxpXtvzySfS999nfrxKFTP4tGsn3XYbwQeAyyIEASAE4VIOh/kb/lGjpG+/zdhfs6b0+OPmdUP8h+EdkpOltWvNvw8rVkiHDmV+vFYtM/S0bSvddJNVVQJAtiQmJqpy5cqSpN27d9MiG/BChCBcmWGYi1e+/755kXtqqrm/QAFzHZfHH5eqVbO6SuS23383r+v5/HNpzZrMHd2Cg6XGjaX775datJBKlrSyUgAAgBwhBCFr/vpLmj5dmjzZ/CH5gjvvNFscP/igVKKElRUip+x2c7TnQvDZty/z46VKSS1bSq1aSffcwyK7AADA7RGCkD0Ohzk6MGmStGRJxsXwPj5Sw4bmekMPPCAVLmx1pbiSpCRzmuPXX5u3777L3NTA31+66y6paVPzduutXN8DAAA8CiEIOffnn9LHH0vz5kmbN2fs9/OTmjQx14Np3lwqUsTKKpGcLG3bZo72fP219M03ZhC6WNmyGaGnUSPaowPwaOfPn1f9+vUlSevXr1cwI9yA1yEEIXccOGAGorlzpR07Mvb7+Ej/+Y8Zhpo3p2tYfjhzRtq0ybyea+NGacsWMwhdrGRJ6e67zVujRmYIAgAvQXc4AIQg5L49e8zRoSVLpJ9+yvxYyZJmGGrWTGrQQCpa1KoqPUNqqrRrlxl0tmwxw8+uXZceFxkp1atnXtNz991mJzfCKAAvRQgCQAhC3jp8WPrsM/O2enXmTmOSdPPN5vUn9eqZf8bEeNwP5+np0oYN0vHjZgasV0/y9c3BC6Wlmes3bdtmBp6tW6Uff7x0apsk3Xij+XnedZdUt65UsaLHfa4AkFOEIACEIOSf5GRp/XozEH3xhbR796XH3HCD+YP77bebU+eqVZOKFbOi2lyxeLE0YIB05EjGvtKlpbFjzeV1LsswpGPHzIVJf/op47Znj5SScunxERHmGk41a5pTD+vUcevPDADyGiEIACEI1jl1ypy+tWGDee3K1q2Zu5RdULJkRiCqVk2qVEkqX97lL95fvFhq397MNBe7MCCzbMZZtaq0V9p7mduZM5d/0bAw87OoVcsMPbVqmZ+Fj0/evyEA8BCEIACEILiOxETp++/NYPTjj+bt998vTREXREaaAeDiW+nSUvHi5ppFhQpZEw4MQ+lnz6lh5ZPyOXFUUTqs0jrivEXpsKJ1SJE6deXX8PU1p7RVrWq2qL5wK1OGwAMA14kQBCA72cAv36qCdwoJMdcaatgwY5/dbk4F+/FHs+vcjh1mMDp1ylzA9a+/zDVvLsfPz5wWVry4eStcWAoNNUdTLtwu3A8IMIdoLndLS5POn8+4JSZmbJ87J/39t3k7dcr88/Rp+aamakMW3nJy4RIKrFLRvGbn4ttNN0mBgbn20QIAMitKYx4AWcRIEFxHXJz0xx/Svn2Zb8ePSydOXHk6WT5KUMhF4z+ldVhRme7vU3lNnlNAnTpZXSkAAIB3YSQI7ikiQqpe3bxdTkqKdPKkuaDrhdvZs1JCgjm6ZLdnbJ87Z7aaNozL33x8zFGqkBApODjzn6Gh5mKwF9+KFtX6XUXUoOm1F98rWTL3PxoAAADkHkIQ3EdAgHl9UOnSlnz5uiXNL3306OUvabLZzMfr1bOiOgAAAGQVV2MDWeTra7bBli5dnufC/TFjcrheEADgupw/f14NGzZUw4YNdf78eavLAeDiCEFANrRrJy1caC59dLHSpc39V1wnCACQpxwOh9atW6d169bJ4XBYXQ4AF8d0OCCb2rWTWrc2l0I6fty8BqhePUaAAAAA3AUhCMgBX9/MXb8BAADgPpgOBwAAAMCrEIIAAAAAeBVLQ9D69evVqlUrlSpVSjabTUuXLrWyHAAAAABewNIQlJCQoGrVqmn8+PFWlgEAADxASEiIQkJCrC4DgBuwtDFCs2bN1KxZMytLAAAAHiA0NFQJCQlWlwHATbhVd7jk5GQlJyc778fHx1taDwAAAAD341aNEWJjYxUREeG8RUVFWV0SAAAAADfjViHo+eefV1xcnPN2+PBhq0sCAAAuICkpSS1atFCLFi2UlJRkdTkAXJxbTYcLDAxUYGCg1WUAAAAXk56ers8++8y5DQBX41YjQQAAAABwvSwdCbLb7fr999+d9/fv368ff/xRhQsXVnR0tJWlAQAAAPBQloagrVu3qlGjRs77gwcPliR1795d06dPt7AyAAAAAJ7K0hDUsGFDGYZhZQkAAAAAvAzXBAEAAADwKm7VHe7fLowisWgqAADeLSEhwbkdHx9PhzjAC13IBFmZaebWIejcuXOSxKKpAADAqVSpUlaXAMBC586dU0RExFWPsRlufFGOw+HQsWPHVKBAAdlsNqvL8Qjx8fGKiorS4cOHFR4ebnU5Xo/z4To4F66Dc+FaOB+ug3PhWjgf+c8wDJ07d06lSpWSj8/Vr/px65EgHx8flS5d2uoyPFJ4eDjfsC6E8+E6OBeug3PhWjgfroNz4Vo4H/nrWiNAF9AYAQAAAIBXIQQBAAAA8CqEIGQSGBiol19+WYGBgVaXAs6HS+FcuA7OhWvhfLgOzoVr4Xy4NrdujAAAAAAA2cVIEAAAAACvQggCAAAA4FUIQQAAAAC8CiEIAAAAgFchBHmpo0eP6uGHH1aRIkUUHBysW2+9VVu3bnU+bhiGXnrpJZUsWVLBwcFq3Lix9u7da2nNnio9PV0vvviiypYtq+DgYJUvX16vvPKKLu5ZwvnIG+vXr1erVq1UqlQp2Ww2LV26NNPjWfncT58+rS5duig8PFwFCxbUI488Irvdns/vxDNc7Xykpqbq2Wef1a233qrQ0FCVKlVK3bp107FjxzK9Bucjd1zre+NiTzzxhGw2m8aMGZNpP+ci92TlfOzZs0f333+/IiIiFBoaqlq1aunQoUPOx5OSktSvXz8VKVJEYWFheuCBB/Tnn3/m8ztxf9c6F3a7XU8++aRKly6t4OBgVa5cWRMnTsx0DOfCNRCCvNCZM2dUt25d+fv7a+XKldq9e7dGjx6tQoUKOY954403NG7cOE2cOFHfffedQkND1aRJEyUlJVlauycaNWqUJkyYoPfee0979uzRqFGj9MYbb+jdd991HsP5yBsJCQmqVq2axo8ff9nHs/K5d+nSRbt27dKXX36pFStWaP369Xrsscfy8V14jqudj8TERG3fvl0vvviitm/frsWLF+vXX3/V/fffn+k4zkfuuNb3xgVLlizRt99+q1KlSl3yGOci91zrfOzbt0933XWXKlWqpLVr12rnzp168cUXFRQU5Dxm0KBB+uSTT7RgwQKtW7dOx44dU7t27fLxXXiGa52LwYMH6/PPP9dHH32kPXv2aODAgXryySe1fPly5zGcCxdhwOs8++yzxl133XXFxx0Oh1GiRAnjzTffdO47e/asERgYaMydOzefqvQeLVq0MHr16pVpX7t27YwuXboYBucj30gylixZ4ryflc999+7dhiRjy5YtzmNWrlxp2Gw24+jRo/n8DjzLv8/H5Xz//feGJOPgwYOGwfnIM1c6F0eOHDFuuOEG4+effzbKlCljvPPOO87HOBd553Ln46GHHjIefvjhKz7n7Nmzhr+/v7FgwQLnvj179hiSjM2bN+dpvZ7scueiSpUqxv/+979M+2rUqGH897//NQzOhUthJMgLLV++XDVr1lSHDh1UrFgxVa9eXR988IHz8f379+vEiRNq3Lixc19ERITuvPNObd682aKqPVedOnX01Vdf6bfffpMk7dixQxs3blSzZs0kzodlsvK5b968WQULFlTNmjWdxzRu3Fg+Pj767rvvLKnbm8TFxclms6lgwYIS5yNfORwOde3aVUOGDFGVKlUueZxzkX8cDoc+/fRT3XjjjWrSpImKFSumO++8M9M0rW3btik1NTXTv2eVKlVSdHQ0/4/ksjp16mj58uU6evSoDMPQmjVr9Ntvv+m+++6TOBcuhRDkhf744w9NmDBBFStW1KpVq9SnTx/1799fM2bMkCSdOHFCklS8ePFMzytevLjzMeSe5557Th07dlSlSpXk7++v6tWra+DAgerSpYvE+bBMVj73EydOqFixYpke9/PzU+HChTk3eSwpKUnPPvusOnXqpPDwcInzka9GjRolPz8/9e/f/7KPcy7yz8mTJ2W32/X666+radOm+uKLL9S2bVu1a9dO69atk/45HwEBAc5fGFzA/yO5791331XlypVVunRpBQQEqGnTpho/frzq168vcS5cip/VBSD/ORwO1axZUyNHjpQkVa9eXT///LMmTpyo7t27W12e15k/f75mz56tOXPmqEqVKvrxxx81cOBAlSpVivMBXEZqaqoefPBBGYahCRMmWF2O19m2bZvGjh2r7du3y2azWV2O13M4HJKk1q1ba9CgQZKk2267TZs2bdLEiRPVoEEDiyv0Lu+++66+/fZbLV++XGXKlNH69evVr18/lSpVKtPoD6zHSJAXKlmypCpXrpxp38033+zsIlOiRAlJuqRTyZ9//ul8DLlnyJAhztGgW2+9VV27dtWgQYMUGxsrcT4sk5XPvUSJEjp58mSmx9PS0nT69GnOTR65EIAOHjyoL7/80jkKJM5HvtmwYYNOnjyp6Oho+fn5yc/PTwcPHtTTTz+tmJgYiXORr4oWLSo/P79r/r+ekpKis2fPZjqG/0dy1/nz5zVs2DC9/fbbatWqlapWraonn3xSDz30kN566y2Jc+FSCEFeqG7duvr1118z7fvtt99UpkwZSVLZsmVVokQJffXVV87H4+Pj9d1336l27dr5Xq+nS0xMlI9P5m9FX19f52/3OB/WyMrnXrt2bZ09e1bbtm1zHvP111/L4XDozjvvtKRuT3YhAO3du1erV69WkSJFMj3O+cgfXbt21c6dO/Xjjz86b6VKldKQIUO0atUqiXORrwICAlSrVq2r/r9+++23y9/fP9O/Z7/++qsOHTrE/yO5KDU1VampqVf9P51z4UKs7syA/Pf9998bfn5+xmuvvWbs3bvXmD17thESEmJ89NFHzmNef/11o2DBgsayZcuMnTt3Gq1btzbKli1rnD9/3tLaPVH37t2NG264wVixYoWxf/9+Y/HixUbRokWNoUOHOo/hfOSNc+fOGT/88IPxww8/GJKMt99+2/jhhx+c3cay8rk3bdrUqF69uvHdd98ZGzduNCpWrGh06tTJwnflvq52PlJSUoz777/fKF26tPHjjz8ax48fd96Sk5Odr8H5yB3X+t74t393hzM4F7nqWudj8eLFhr+/vzF58mRj7969xrvvvmv4+voaGzZscL7GE088YURHRxtff/21sXXrVqN27dpG7dq1LXxX7ula56JBgwZGlSpVjDVr1hh//PGHMW3aNCMoKMh4//33na/BuXANhCAv9cknnxi33HKLERgYaFSqVMmYPHlypscdDofx4osvGsWLFzcCAwONe+65x/j1118tq9eTxcfHGwMGDDCio6ONoKAgo1y5csZ///vfTD/YcT7yxpo1awxJl9y6d+9uGFn83P/++2+jU6dORlhYmBEeHm707NnTOHfunEXvyL1d7Xzs37//so9JMtasWeN8Dc5H7rjW98a/XS4EcS5yT1bOx9SpU40KFSoYQUFBRrVq1YylS5dmeo3z588bffv2NQoVKmSEhIQYbdu2NY4fP27Bu3Fv1zoXx48fN3r06GGUKlXKCAoKMm666SZj9OjRhsPhcL4G58I12IyLl6UHAAAAAA/HNUEAAAAAvAohCAAAAIBXIQQBAAAA8CqEIAAAAABehRAEAAAAwKsQggAAAAB4FUIQAAAAAK9CCAIAAADgVQhBAACvdODAAdlsNv34449WlwIAyGeEIABAruvRo4fatGmT5eNtNpuWLl2apzX9W1RUlI4fP65bbrlFkrR27VrZbDadPXs2X+sAAOQ/P6sLAADACr6+vipRooTVZQAALMBIEAAgTzVs2FD9+/fX0KFDVbhwYZUoUULDhw93Ph4TEyNJatu2rWw2m/O+JC1btkw1atRQUFCQypUrpxEjRigtLc35uM1m05QpU9S2bVuFhISoYsWKWr58ufPxM2fOqEuXLoqMjFRwcLAqVqyoadOmSf+aDnfgwAE1atRIklSoUCHZbDb16NFDM2fOVJEiRZScnJzpPbVp00Zdu3bNw08NAJCXCEEAgDw3Y8YMhYaG6rvvvtMbb7yh//3vf/ryyy8lSVu2bJEkTZs2TcePH3fe37Bhg7p166YBAwZo9+7dmjRpkqZPn67XXnst02uPGDFCDz74oHbu3KnmzZurS5cuOn36tCTpxRdf1O7du7Vy5Urt2bNHEyZMUNGiRS+pLyoqSosWLZIk/frrrzp+/LjGjh2rDh06KD09PVOwOnnypD799FP16tUrDz8xAEBeIgQBAPJc1apV9fLLL6tixYrq1q2batasqa+++kqSFBkZKUkqWLCgSpQo4bw/YsQIPffcc+revbvKlSune++9V6+88oomTZqU6bV79OihTp06qUKFCho5cqTsdru+//57SdKhQ4dUvXp11axZUzExMWrcuLFatWp1SX2+vr4qXLiwJKlYsWIqUaKEIiIiFBwcrM6dOztHjyTpo48+UnR0tBo2bJiHnxgAIC9xTRAAIM9VrVo10/2SJUvq5MmTV33Ojh079M0332Qa+UlPT1dSUpISExMVEhJyyWuHhoYqPDzc+dp9+vTRAw88oO3bt+u+++5TmzZtVKdOnWzV/uijj6pWrVo6evSobrjhBk2fPl09evSQzWbL1usAAFwHIQgAkOf8/f0z3bfZbHI4HFd9jt1u14gRI9SuXbtLHgsKCsrSazdr1kwHDx7UZ599pi+//FL33HOP+vXrp7feeivLtVevXl3VqlXTzJkzdd9992nXrl369NNPs/x8AIDrIQQBACzn7++v9PT0TPtq1KihX3/9VRUqVLiu146MjFT37t3VvXt31atXT0OGDLlsCAoICJD+GW36t969e2vMmDE6evSoGjdurKioqOuqCQBgLa4JAgBYLiYmRl999ZVOnDihM2fOSJJeeuklzZw5UyNGjNCuXbu0Z88ezZs3Ty+88EKWX/ell17SsmXL9Pvvv2vXrl1asWKFbr755sseW6ZMGdlsNq1YsUJ//fWX7Ha787HOnTvryJEj+uCDD2iIAAAegBAEALDc6NGj9eWXXyoqKkrVq1eXJDVp0kQrVqzQF198oVq1auk///mP3nnnHZUpUybLrxsQEKDnn39eVatWVf369eXr66t58+Zd9tgbbrjB2YyhePHievLJJ52PRURE6IEHHlBYWFi2FoEFALgmm2EYhtVFAADg6u655x5VqVJF48aNs7oUAMB1IgQBAHAVZ86c0dq1a9W+fXvt3r1bN910k9UlAQCuE40RAAC4iurVq+vMmTMaNWoUAQgAPAQjQQAAAAC8Co0RAAAAAHgVQhAAAAAAr0IIAgAAAOBVCEEAAAAAvAohCAAAAIBXIQQBAAAA8CqEIAAAAABehRAEAAAAwKv8HyTUUCZfApS9AAAAAElFTkSuQmCC",
+>>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -551,7 +559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 34,
    "id": "acff8813",
    "metadata": {},
    "outputs": [],
@@ -561,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 35,
    "id": "65aa368c",
    "metadata": {},
    "outputs": [
@@ -571,13 +579,17 @@
        "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
+<<<<<<< HEAD
       "image/png": 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",
+=======
+      "image/png": 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",
+>>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -595,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 36,
    "id": "7ef327c7",
    "metadata": {},
    "outputs": [
@@ -605,13 +617,386 @@
        "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
+<<<<<<< HEAD
       "image/png": 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",
+=======
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.plot.plot_predictions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "b7ba2348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lactopy.lactate_models.lt1_si import LT1_si"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "15e234c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LT1 occurs at: 116.29 watts\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LT1_si()\n",
+    "model.fit(df['watt'], df['lactate'], si=0.5)\n",
+    "lt1_intensity = model.predict()\n",
+    "print(f\"LT1 occurs at: {lt1_intensity:.2f} watts\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "42e695f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'LT1 Standard Increment Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+      ]
+     },
+     "execution_count": 39,
+     "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.plot.plot_predictions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "d02f1652",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lactopy.lactate_models.lt1_si_lowest import LT1_si_lowest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "54283247",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LT1 occurs at: 92.45 watts\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LT1_si_lowest()\n",
+    "model.fit(df['watt'], df['lactate'], si=0.5)\n",
+    "lt1_intensity = model.predict()\n",
+    "print(f\"LT1 occurs at: {lt1_intensity:.2f} watts\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "2739c2f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'LT1 Standard Increment From Lowest La- Measurement Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+      ]
+     },
+     "execution_count": 42,
+     "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.plot.plot_predictions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "602551ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                    Breakpoint Regression Results                     \n",
+      "====================================================================================================\n",
+      "No. Observations                        4\n",
+      "No. Model Parameters                    4\n",
+      "Degrees of Freedom                      0\n",
+      "Res. Sum of Squares                   0.0\n",
+      "Total Sum of Squares             0.721875\n",
+      "R Squared                        1.000000\n",
+      "Adjusted R Squared               1.000000\n",
+      "Converged:                           True\n",
+      "====================================================================================================\n",
+      "====================================================================================================\n",
+      "                    Estimate      Std Err            t        P>|t|       [0.025       0.975]\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "const                   0.85          0.0          inf           nan          nan          nan\n",
+      "alpha1                 0.015          0.0          inf           nan          nan          nan\n",
+      "beta1              0.0687309          0.0          inf             -          nan          nan\n",
+      "breakpoint1          30.5428          0.0            -             -          nan          nan\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "These alphas(gradients of segments) are estimatedfrom betas(change in gradient)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "alpha2             0.0837309          0.0          inf           nan          nan          nan\n",
+      "====================================================================================================\n",
+      "Davies test for existence of at least 1 breakpoint: p=0.0231182 (e.g. p<0.05 means reject null hypothesis of no breakpoints at 5% significance)\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ianh/lactopy/.venv/lib64/python3.13/site-packages/statsmodels/regression/linear_model.py:1717: RuntimeWarning: divide by zero encountered in scalar divide\n",
+      "  return np.dot(wresid, wresid) / self.df_resid\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# testing the breakpoint module\n",
+    "import piecewise_regression\n",
+    "pw_fit = piecewise_regression.Fit([10,20,30,40], [1,1.15,1.3,2.1], n_breakpoints=1)\n",
+    "pw_fit.summary()\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Plot the data, fit, breakpoints and confidence intervals\n",
+    "pw_fit.plot_data(color=\"grey\", s=20)\n",
+    "# Pass in standard matplotlib keywords to control any of the plots\n",
+    "pw_fit.plot_fit(color=\"red\", linewidth=4)\n",
+    "pw_fit.plot_breakpoints()\n",
+    "pw_fit.plot_breakpoint_confidence_intervals()\n",
+    "plt.xlabel(\"x\")\n",
+    "plt.ylabel(\"y\")\n",
+    "plt.show()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "4f2792d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'davies': np.float64(0.023118172598819948),\n",
+       " 'estimates': {'const': {'estimate': np.float64(0.8500000000000001),\n",
+       "   'se': np.float64(0.0),\n",
+       "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
+       "   't_stat': np.float64(inf),\n",
+       "   'p_t': np.float64(nan)},\n",
+       "  'beta1': {'estimate': np.float64(0.06873093041100545),\n",
+       "   'se': np.float64(0.0),\n",
+       "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
+       "   't_stat': np.float64(inf),\n",
+       "   'p_t': '-'},\n",
+       "  'breakpoint1': {'estimate': np.float64(30.542831355358466),\n",
+       "   'se': np.float64(0.0),\n",
+       "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
+       "   't_stat': '-',\n",
+       "   'p_t': '-'},\n",
+       "  'alpha1': {'estimate': np.float64(0.014999999999999996),\n",
+       "   'se': np.float64(0.0),\n",
+       "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
+       "   't_stat': np.float64(inf),\n",
+       "   'p_t': np.float64(nan)},\n",
+       "  'alpha2': {'estimate': np.float64(0.08373093041100545),\n",
+       "   'se': np.float64(0.0),\n",
+       "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
+       "   't_stat': np.float64(inf),\n",
+       "   'p_t': np.float64(nan)}},\n",
+       " 'bic': np.float64(-inf),\n",
+       " 'rss': np.float64(0.0),\n",
+       " 'converged': True}"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the key results of the fit\n",
+    "pw_results = pw_fit.get_results()\n",
+    "pw_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "bc758db0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'const': {'estimate': np.float64(0.8500000000000001),\n",
+       "  'se': np.float64(0.0),\n",
+       "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
+       "  't_stat': np.float64(inf),\n",
+       "  'p_t': np.float64(nan)},\n",
+       " 'beta1': {'estimate': np.float64(0.06873093041100545),\n",
+       "  'se': np.float64(0.0),\n",
+       "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
+       "  't_stat': np.float64(inf),\n",
+       "  'p_t': '-'},\n",
+       " 'breakpoint1': {'estimate': np.float64(30.542831355358466),\n",
+       "  'se': np.float64(0.0),\n",
+       "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
+       "  't_stat': '-',\n",
+       "  'p_t': '-'},\n",
+       " 'alpha1': {'estimate': np.float64(0.014999999999999996),\n",
+       "  'se': np.float64(0.0),\n",
+       "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
+       "  't_stat': np.float64(inf),\n",
+       "  'p_t': np.float64(nan)},\n",
+       " 'alpha2': {'estimate': np.float64(0.08373093041100545),\n",
+       "  'se': np.float64(0.0),\n",
+       "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
+       "  't_stat': np.float64(inf),\n",
+       "  'p_t': np.float64(nan)}}"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pw_estimates = pw_results[\"estimates\"]\n",
+    "pw_estimates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "7f6316fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lactopy.lactate_models.lt1_loglog import LT1_loglog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "738f6b41",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'breakpoint1'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[47]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m model = LT1_loglog()\n\u001b[32m      2\u001b[39m model.fit(df[\u001b[33m'\u001b[39m\u001b[33mwatt\u001b[39m\u001b[33m'\u001b[39m], df[\u001b[33m'\u001b[39m\u001b[33mlactate\u001b[39m\u001b[33m'\u001b[39m], si=\u001b[32m0.5\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m lt1_intensity = \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLT1 occurs at: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlt1_intensity\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m watts\u001b[39m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/src/lactopy/lactate_models/lt1_loglog.py:89\u001b[39m, in \u001b[36mLT1_loglog.predict\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     87\u001b[39m pw_fit = piecewise_regression.Fit(\u001b[38;5;28mself\u001b[39m.X, \u001b[38;5;28mself\u001b[39m.y, n_breakpoints=\u001b[32m1\u001b[39m)\n\u001b[32m     88\u001b[39m pw_results = pw_fit.get_results()\n\u001b[32m---> \u001b[39m\u001b[32m89\u001b[39m \u001b[43mpw_results\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mbreakpoint1\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33mestimate\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m     91\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m pw_results\n",
+      "\u001b[31mKeyError\u001b[39m: 'breakpoint1'"
+     ]
+    }
+   ],
+   "source": [
+    "model = LT1_loglog()\n",
+    "model.fit(df['watt'], df['lactate'], si=0.5)\n",
+    "lt1_intensity = model.predict()\n",
+    "print(f\"LT1 occurs at: {lt1_intensity:.2f} watts\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a5d00229",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'breakpoint1'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[25]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot_predictions\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/src/lactopy/plots/lt1_si_plot.py:28\u001b[39m, in \u001b[36mLT1_si_Plot.plot_predictions\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     23\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     24\u001b[39m \u001b[33;03mPlot the LT1 model predictions.\u001b[39;00m\n\u001b[32m     25\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     26\u001b[39m \u001b[38;5;28mself\u001b[39m.plot_fit()\n\u001b[32m     27\u001b[39m plt.axvline(\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbase_lactate_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m     29\u001b[39m     color=\u001b[33m\"\u001b[39m\u001b[33mblack\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m     30\u001b[39m     label=\u001b[33m\"\u001b[39m\u001b[33mPredictions\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m     31\u001b[39m     linestyle=\u001b[33m\"\u001b[39m\u001b[33m--\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m     32\u001b[39m )\n\u001b[32m     34\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m plt.gca()\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/src/lactopy/lactate_models/lt1_loglog.py:89\u001b[39m, in \u001b[36mLT1_loglog.predict\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     87\u001b[39m pw_fit = piecewise_regression.Fit(\u001b[38;5;28mself\u001b[39m.X, \u001b[38;5;28mself\u001b[39m.y, n_breakpoints=\u001b[32m1\u001b[39m)\n\u001b[32m     88\u001b[39m pw_results = pw_fit.get_results()\n\u001b[32m---> \u001b[39m\u001b[32m89\u001b[39m \u001b[43mpw_results\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mbreakpoint1\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33mestimate\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m     91\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m pw_results\n",
+      "\u001b[31mKeyError\u001b[39m: 'breakpoint1'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+>>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -627,7 +1012,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv (3.13.3)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
diff --git a/notebooks/data/initial_la_drop.csv b/notebooks/data/initial_la_drop.csv
index 257e83f..1e6c922 100644
--- a/notebooks/data/initial_la_drop.csv
+++ b/notebooks/data/initial_la_drop.csv
@@ -1,7 +1,7 @@
 watt,lactate
-50,1.5
+50,1.9
 75,1.23
-100,1.88
+100,1.9
 125,2.8
 150,4.21
 175,6.66
diff --git a/src/lactopy/__init__.py b/src/lactopy/__init__.py
index e8fb5ac..d9b3fce 100644
--- a/src/lactopy/__init__.py
+++ b/src/lactopy/__init__.py
@@ -1 +1 @@
-from .lactate_models import OBLA, Bsln, Dmax  # noqa: F401
+from .lactate_models import OBLA, Bsln, Dmax, lt1_si, lt1_si_lowest, lt1_loglog  # noqa: F401
diff --git a/src/lactopy/lactate_models/__init__.py b/src/lactopy/lactate_models/__init__.py
index b861a45..ee5d3c2 100644
--- a/src/lactopy/lactate_models/__init__.py
+++ b/src/lactopy/lactate_models/__init__.py
@@ -1,3 +1,6 @@
 from .bsln import Bsln  # noqa: F401
 from .dmax import Dmax  # noqa: F401
 from .obla import OBLA  # noqa: F401
+from .lt1_si import LT1_si  # noqa: F401
+from .lt1_si_lowest import LT1_si_lowest  # noqa: F401
+from .lt1_loglog import LT1_loglog  # noqa: F401
\ No newline at end of file
diff --git a/src/lactopy/lactate_models/lt1_loglog.py b/src/lactopy/lactate_models/lt1_loglog.py
new file mode 100644
index 0000000..70c4c77
--- /dev/null
+++ b/src/lactopy/lactate_models/lt1_loglog.py
@@ -0,0 +1,91 @@
+from numpy.typing import ArrayLike
+import numpy as np
+import copy
+import piecewise_regression
+
+from lactopy.base import BaseModel
+from lactopy.plots.lt1_loglog_plot import LT1_loglog_Plot
+
+
+class LT1_loglog(BaseModel):
+    """
+    Log-Log model for lactate threshold estimation.
+
+    Log-Log method is used to estimate the first lactate threshold by plotting
+    the lactate response against intensity on a logaritmic scale.
+    
+    Plot is divided into 2 segments --> segmented regression identifies brekpoint
+
+    This is computed by using a standard value, preferably equlual to the lowest 
+    detectable change in lactate concentration of the measurement tool
+
+    Attributes:
+        plot (lt1_si_plot): For visualizing the model..
+    """
+
+    def __init__(self):
+        """
+        Initializes the LT1_si model and its associated plot.
+
+        """
+        super().__init__()
+        self.plot = LT1_loglog_Plot(self)
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        si: float = 0.5,
+        **kwargs,
+    ):
+        """
+        Fits the Standard Increment model to the given data.
+
+        Args:
+            X (ArrayLike): The independent variable (e.g., intensity).
+            y (ArrayLike): The dependent variable (e.g., lactate concentration).
+            si: the standard increment, defaults to 0.5
+            
+            threshold_above_baseline (float, optional): Threshold above baseline
+            for filtering in the "modified" implementation. Defaults to 0.5.
+
+            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._si = si
+
+        # 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
+        
+        # Ensure working on copies
+        self.X = np.asarray(copy.deepcopy(X))
+        self.y = np.asarray(copy.deepcopy(y))
+        
+        super().fit(X, y, **kwargs)
+        return self
+
+    def predict(self) -> float:
+        """
+        Predicts intensity for the LT1_si method.
+
+        Returns:
+            float: Predicted intensity.
+            
+        """
+        
+        pw_fit = piecewise_regression.Fit(self.X, self.y, n_breakpoints=1)
+        pw_results = pw_fit.get_results()
+        
+
+        return pw_results["breakpoint1"]["estimate"]
diff --git a/src/lactopy/lactate_models/lt1_si.py b/src/lactopy/lactate_models/lt1_si.py
new file mode 100644
index 0000000..82d3488
--- /dev/null
+++ b/src/lactopy/lactate_models/lt1_si.py
@@ -0,0 +1,89 @@
+from numpy.typing import ArrayLike
+import numpy as np
+import copy
+
+
+from lactopy.base import BaseModel
+from lactopy.plots.lt1_si_plot import LT1_si_Plot
+
+
+class LT1_si(BaseModel):
+    """
+    Standard increment model for lactate threshold estimation.
+
+    The Standard increment method is used to estimate the first lactate threshold by identifying
+    the point in which the first meaningfull lactate increase occurs.
+
+    This is computed by using a standard value, preferably equlual to the lowest 
+    detectable change in lactate concentration of the measurement tool
+
+    Attributes:
+        plot (lt1_si_plot): For visualizing the model..
+    """
+
+    def __init__(self):
+        """
+        Initializes the LT1_si model and its associated plot.
+
+        """
+        super().__init__()
+        self.plot = LT1_si_Plot(self)
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        si: float = 0.5,
+        **kwargs,
+    ):
+        """
+        Fits the Standard Increment model to the given data.
+
+        Args:
+            X (ArrayLike): The independent variable (e.g., intensity).
+            y (ArrayLike): The dependent variable (e.g., lactate concentration).
+            si: the standard increment, defaults to 0.5
+            
+            threshold_above_baseline (float, optional): Threshold above baseline
+            for filtering in the "modified" implementation. Defaults to 0.5.
+
+            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._si = si
+
+        # 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
+
+        if si > 0 :
+            X, y = copy.deepcopy(X), copy.deepcopy(y)
+            y_lt1 = y[0] + si
+            self.y_lt1 = y_lt1
+        else:
+            raise ValueError(f"Impossible lactate change to find LT1: {si}")
+
+        self.X = X
+        self.y = y
+        
+        super().fit(X, y, **kwargs)
+        return self
+
+    def predict(self) -> float:
+        """
+        Predicts intensity for the LT1_si method.
+
+        Returns:
+            float: Predicted intensity.
+        """
+        return self.model.predict_inverse(self.y_lt1)
diff --git a/src/lactopy/lactate_models/lt1_si_lowest.py b/src/lactopy/lactate_models/lt1_si_lowest.py
new file mode 100644
index 0000000..c968149
--- /dev/null
+++ b/src/lactopy/lactate_models/lt1_si_lowest.py
@@ -0,0 +1,104 @@
+from numpy.typing import ArrayLike
+import numpy as np
+import copy
+
+
+from lactopy.base import BaseModel
+from lactopy.plots.lt1_si_lowest_plot import LT1_si_lowest_Plot
+
+
+class LT1_si_lowest(BaseModel):
+    """
+    Standard increment model for lactate threshold estimation.
+
+    The Standard increment method is used to estimate the first lactate threshold by identifying
+    the point in which the first meaningfull lactate increase occurs.
+    In LT1_si_lowest the lowest measurment of lactate concentration is used as reference point.
+    This means that that the reference measuement is not necessarily equal to the first measurement.
+
+    This is computed by using a standard value, preferably equlual to the lowest 
+    detectable change in lactate concentration of the measurement tool
+
+    Attributes:
+        plot (lt1_si_lowest_plot): For visualizing the model.
+    """
+
+    def __init__(self):
+        """
+        Initializes the LT1_si_lowest model and its associated plot.
+
+        """
+        super().__init__()
+        self.plot = LT1_si_lowest_Plot(self)
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        si: float = 0.5,
+        **kwargs,
+    ):
+        """
+        Fits the Standard Increment model to the given data.
+
+        Args:
+            X (ArrayLike): The independent variable (e.g., intensity).
+            y (ArrayLike): The dependent variable (e.g., lactate concentration).
+            si: the standard increment, defaults to 0.5
+            
+            threshold_above_baseline (float, optional): Threshold above baseline
+            for filtering in the "modified" implementation. Defaults to 0.5.
+
+            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._si = si
+
+        # 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
+        
+        # Ensure working on copies
+        X = np.asarray(copy.deepcopy(X))
+        y = np.asarray(copy.deepcopy(y))
+
+        if si > 0 :
+            X, y = copy.deepcopy(X), copy.deepcopy(y)
+            min_y_idx = np.argmin(y) # get the index of the lowest y
+            self.min_y_idx = min_y_idx
+            self.x_at_min_y = X[min_y_idx]
+            self.y_lt1 = y[self.min_y_idx] + si
+        else:
+            raise ValueError(f"Impossible lactate change to find LT1: {si}")
+
+        # Use only values AFTER x_at_min_y for model fitting
+        valid_mask = X >= self.x_at_min_y
+        if not np.any(valid_mask):
+            raise ValueError("No X values greater than X at minimum Y. Cannot fit model.")
+
+        self.X = X[valid_mask]
+        self.y = y[valid_mask]
+
+        super().fit(self.X, self.y, **kwargs)
+        return self
+
+    def predict(self) -> float:
+        """
+        Predicts intensity (X) at LT1 based on y_lt1.
+        """
+        prediction = self.model.predict_inverse(self.y_lt1)
+
+        if prediction <= self.x_at_min_y:
+            raise ValueError("Predicted LT1 intensity is not greater than baseline.")
+
+        return prediction
diff --git a/src/lactopy/plots/lt1_loglog_plot.py b/src/lactopy/plots/lt1_loglog_plot.py
new file mode 100644
index 0000000..5e11252
--- /dev/null
+++ b/src/lactopy/plots/lt1_loglog_plot.py
@@ -0,0 +1,34 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from lactopy.plots.base import Plot
+
+
+class LT1_loglog_Plot(Plot):
+    """
+    Plot for LT1 si model.
+    """
+
+    def __init__(self, base_lactate_LT1_si_model):
+        super().__init__(base_lactate_LT1_si_model)
+
+    def plot_fit(self):
+        """
+        Plot the LT1 si model fit.
+        """
+        ax = super().plot_fit()
+        ax.set_title("LT1 Standard Increment Model Fit")
+        return ax
+
+    def plot_predictions(self):
+        """
+        Plot the LT1 model predictions.
+        """
+        self.plot_fit()
+        plt.axvline(
+            self.base_lactate_model.predict(),
+            color="black",
+            label="Predictions",
+            linestyle="--",
+        )
+
+        return plt.gca()
diff --git a/src/lactopy/plots/lt1_si_lowest_plot.py b/src/lactopy/plots/lt1_si_lowest_plot.py
new file mode 100644
index 0000000..3b42b8f
--- /dev/null
+++ b/src/lactopy/plots/lt1_si_lowest_plot.py
@@ -0,0 +1,34 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from lactopy.plots.base import Plot
+
+
+class LT1_si_lowest_Plot(Plot):
+    """
+    Plot for LT1 si lowest model.
+    """
+
+    def __init__(self, base_lactate_LT1_si_lowest_model):
+        super().__init__(base_lactate_LT1_si_lowest_model)
+
+    def plot_fit(self):
+        """
+        Plot the LT1 si lowest model fit.
+        """
+        ax = super().plot_fit()
+        ax.set_title("LT1 Standard Increment From Lowest La- Measurement Model Fit")
+        return ax
+
+    def plot_predictions(self):
+        """
+        Plot the LT1 model predictions.
+        """
+        self.plot_fit()
+        plt.axvline(
+            self.base_lactate_model.predict(),
+            color="black",
+            label="Predictions",
+            linestyle="--",
+        )
+
+        return plt.gca()
diff --git a/src/lactopy/plots/lt1_si_plot.py b/src/lactopy/plots/lt1_si_plot.py
new file mode 100644
index 0000000..f270bbb
--- /dev/null
+++ b/src/lactopy/plots/lt1_si_plot.py
@@ -0,0 +1,34 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from lactopy.plots.base import Plot
+
+
+class LT1_si_Plot(Plot):
+    """
+    Plot for LT1 si model.
+    """
+
+    def __init__(self, base_lactate_LT1_si_model):
+        super().__init__(base_lactate_LT1_si_model)
+
+    def plot_fit(self):
+        """
+        Plot the LT1 si model fit.
+        """
+        ax = super().plot_fit()
+        ax.set_title("LT1 Standard Increment Model Fit")
+        return ax
+
+    def plot_predictions(self):
+        """
+        Plot the LT1 model predictions.
+        """
+        self.plot_fit()
+        plt.axvline(
+            self.base_lactate_model.predict(),
+            color="black",
+            label="Predictions",
+            linestyle="--",
+        )
+
+        return plt.gca()

From 7adc8a0387a89db2f63faa442f44c08c1526e36b Mon Sep 17 00:00:00 2001
From: ihosten <hostenian@gmail.com>
Date: Mon, 21 Jul 2025 13:09:48 +0200
Subject: [PATCH 13/19] Workable version of the Log-Log method

---
 docs/quickstart.ipynb                    | 263 ++++++++++++-----------
 src/lactopy/lactate_models/lt1_loglog.py |  12 +-
 2 files changed, 148 insertions(+), 127 deletions(-)

diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index 4bf8526..392511b 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 1,
    "id": "150ece86",
    "metadata": {},
    "outputs": [],
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 2,
    "id": "e0dd4c2e",
    "metadata": {},
    "outputs": [],
@@ -24,14 +24,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "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": 30,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -465,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 4,
    "id": "77b4e630",
    "metadata": {},
    "outputs": [
@@ -475,7 +475,7 @@
        "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -501,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 5,
    "id": "279f619a",
    "metadata": {},
    "outputs": [
@@ -511,7 +511,7 @@
        "np.float64(147.16511067594706)"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -523,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 6,
    "id": "6fcd47b1",
    "metadata": {},
    "outputs": [
@@ -533,7 +533,7 @@
        "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -559,7 +559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 7,
    "id": "acff8813",
    "metadata": {},
    "outputs": [],
@@ -569,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 8,
    "id": "65aa368c",
    "metadata": {},
    "outputs": [
@@ -579,7 +579,7 @@
        "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -607,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 9,
    "id": "7ef327c7",
    "metadata": {},
    "outputs": [
@@ -617,7 +617,7 @@
        "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -641,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 10,
    "id": "b7ba2348",
    "metadata": {},
    "outputs": [],
@@ -651,7 +651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 11,
    "id": "15e234c7",
    "metadata": {},
    "outputs": [
@@ -672,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 12,
    "id": "42e695f9",
    "metadata": {},
    "outputs": [
@@ -682,7 +682,7 @@
        "<Axes: title={'center': 'LT1 Standard Increment Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -703,7 +703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 13,
    "id": "d02f1652",
    "metadata": {},
    "outputs": [],
@@ -713,7 +713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 14,
    "id": "54283247",
    "metadata": {},
    "outputs": [
@@ -734,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 15,
    "id": "2739c2f2",
    "metadata": {},
    "outputs": [
@@ -744,7 +744,7 @@
        "<Axes: title={'center': 'LT1 Standard Increment From Lowest La- Measurement Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -765,7 +765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 16,
    "id": "602551ec",
    "metadata": {},
    "outputs": [
@@ -779,7 +779,7 @@
       "No. Observations                        4\n",
       "No. Model Parameters                    4\n",
       "Degrees of Freedom                      0\n",
-      "Res. Sum of Squares                   0.0\n",
+      "Res. Sum of Squares           4.93038e-32\n",
       "Total Sum of Squares             0.721875\n",
       "R Squared                        1.000000\n",
       "Adjusted R Squared               1.000000\n",
@@ -788,14 +788,14 @@
       "====================================================================================================\n",
       "                    Estimate      Std Err            t        P>|t|       [0.025       0.975]\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "const                   0.85          0.0          inf           nan          nan          nan\n",
-      "alpha1                 0.015          0.0          inf           nan          nan          nan\n",
-      "beta1              0.0687309          0.0          inf             -          nan          nan\n",
-      "breakpoint1          30.5428          0.0            -             -          nan          nan\n",
+      "const                   0.85          inf          0.0           nan          nan          nan\n",
+      "alpha1                 0.015          inf          0.0           nan          nan          nan\n",
+      "beta1                  0.065          inf          0.0             -          nan          nan\n",
+      "breakpoint1             30.0          inf            -             -          nan          nan\n",
       "----------------------------------------------------------------------------------------------------\n",
       "These alphas(gradients of segments) are estimatedfrom betas(change in gradient)\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "alpha2             0.0837309          0.0          inf           nan          nan          nan\n",
+      "alpha2                  0.08          nan          nan           nan          nan          nan\n",
       "====================================================================================================\n",
       "Davies test for existence of at least 1 breakpoint: p=0.0231182 (e.g. p<0.05 means reject null hypothesis of no breakpoints at 5% significance)\n",
       "\n",
@@ -812,7 +812,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -843,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 17,
    "id": "4f2792d2",
    "metadata": {},
    "outputs": [
@@ -851,37 +851,37 @@
      "data": {
       "text/plain": [
        "{'davies': np.float64(0.023118172598819948),\n",
-       " 'estimates': {'const': {'estimate': np.float64(0.8500000000000001),\n",
-       "   'se': np.float64(0.0),\n",
+       " 'estimates': {'const': {'estimate': np.float64(0.8499999999999996),\n",
+       "   'se': np.float64(inf),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(inf),\n",
+       "   't_stat': np.float64(0.0),\n",
        "   'p_t': np.float64(nan)},\n",
-       "  'beta1': {'estimate': np.float64(0.06873093041100545),\n",
-       "   'se': np.float64(0.0),\n",
+       "  'beta1': {'estimate': np.float64(0.06500000000000002),\n",
+       "   'se': np.float64(inf),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(inf),\n",
+       "   't_stat': np.float64(0.0),\n",
        "   'p_t': '-'},\n",
-       "  'breakpoint1': {'estimate': np.float64(30.542831355358466),\n",
-       "   'se': np.float64(0.0),\n",
+       "  'breakpoint1': {'estimate': np.float64(30.000000000000004),\n",
+       "   'se': np.float64(inf),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
        "   't_stat': '-',\n",
        "   'p_t': '-'},\n",
-       "  'alpha1': {'estimate': np.float64(0.014999999999999996),\n",
-       "   'se': np.float64(0.0),\n",
+       "  'alpha1': {'estimate': np.float64(0.015000000000000013),\n",
+       "   'se': np.float64(inf),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(inf),\n",
+       "   't_stat': np.float64(0.0),\n",
        "   'p_t': np.float64(nan)},\n",
-       "  'alpha2': {'estimate': np.float64(0.08373093041100545),\n",
-       "   'se': np.float64(0.0),\n",
+       "  'alpha2': {'estimate': np.float64(0.08000000000000003),\n",
+       "   'se': np.float64(nan),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(inf),\n",
+       "   't_stat': np.float64(nan),\n",
        "   'p_t': np.float64(nan)}},\n",
-       " 'bic': np.float64(-inf),\n",
-       " 'rss': np.float64(0.0),\n",
+       " 'bic': np.float64(-288.3492271129372),\n",
+       " 'rss': np.float64(4.930380657631324e-32),\n",
        " 'converged': True}"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -894,41 +894,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 18,
    "id": "bc758db0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'const': {'estimate': np.float64(0.8500000000000001),\n",
-       "  'se': np.float64(0.0),\n",
+       "{'const': {'estimate': np.float64(0.8499999999999996),\n",
+       "  'se': np.float64(inf),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(inf),\n",
+       "  't_stat': np.float64(0.0),\n",
        "  'p_t': np.float64(nan)},\n",
-       " 'beta1': {'estimate': np.float64(0.06873093041100545),\n",
-       "  'se': np.float64(0.0),\n",
+       " 'beta1': {'estimate': np.float64(0.06500000000000002),\n",
+       "  'se': np.float64(inf),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(inf),\n",
+       "  't_stat': np.float64(0.0),\n",
        "  'p_t': '-'},\n",
-       " 'breakpoint1': {'estimate': np.float64(30.542831355358466),\n",
-       "  'se': np.float64(0.0),\n",
+       " 'breakpoint1': {'estimate': np.float64(30.000000000000004),\n",
+       "  'se': np.float64(inf),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
        "  't_stat': '-',\n",
        "  'p_t': '-'},\n",
-       " 'alpha1': {'estimate': np.float64(0.014999999999999996),\n",
-       "  'se': np.float64(0.0),\n",
+       " 'alpha1': {'estimate': np.float64(0.015000000000000013),\n",
+       "  'se': np.float64(inf),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(inf),\n",
+       "  't_stat': np.float64(0.0),\n",
        "  'p_t': np.float64(nan)},\n",
-       " 'alpha2': {'estimate': np.float64(0.08373093041100545),\n",
-       "  'se': np.float64(0.0),\n",
+       " 'alpha2': {'estimate': np.float64(0.08000000000000003),\n",
+       "  'se': np.float64(nan),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(inf),\n",
+       "  't_stat': np.float64(nan),\n",
        "  'p_t': np.float64(nan)}}"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -940,7 +940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 21,
    "id": "7f6316fa",
    "metadata": {},
    "outputs": [],
@@ -950,20 +950,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 22,
    "id": "738f6b41",
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'breakpoint1'",
+     "ename": "TypeError",
+     "evalue": "unsupported format string passed to dict.__format__",
      "output_type": "error",
      "traceback": [
       "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[47]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m model = LT1_loglog()\n\u001b[32m      2\u001b[39m model.fit(df[\u001b[33m'\u001b[39m\u001b[33mwatt\u001b[39m\u001b[33m'\u001b[39m], df[\u001b[33m'\u001b[39m\u001b[33mlactate\u001b[39m\u001b[33m'\u001b[39m], si=\u001b[32m0.5\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m lt1_intensity = \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLT1 occurs at: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlt1_intensity\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m watts\u001b[39m\u001b[33m\"\u001b[39m)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/src/lactopy/lactate_models/lt1_loglog.py:89\u001b[39m, in \u001b[36mLT1_loglog.predict\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     87\u001b[39m pw_fit = piecewise_regression.Fit(\u001b[38;5;28mself\u001b[39m.X, \u001b[38;5;28mself\u001b[39m.y, n_breakpoints=\u001b[32m1\u001b[39m)\n\u001b[32m     88\u001b[39m pw_results = pw_fit.get_results()\n\u001b[32m---> \u001b[39m\u001b[32m89\u001b[39m \u001b[43mpw_results\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mbreakpoint1\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33mestimate\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m     91\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m pw_results\n",
-      "\u001b[31mKeyError\u001b[39m: 'breakpoint1'"
+      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      2\u001b[39m model.fit(df[\u001b[33m'\u001b[39m\u001b[33mwatt\u001b[39m\u001b[33m'\u001b[39m], df[\u001b[33m'\u001b[39m\u001b[33mlactate\u001b[39m\u001b[33m'\u001b[39m], si=\u001b[32m0.5\u001b[39m)\n\u001b[32m      3\u001b[39m lt1_intensity = model.predict()\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLT1 occurs at: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlt1_intensity\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m watts\u001b[39m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[31mTypeError\u001b[39m: unsupported format string passed to dict.__format__"
      ]
     }
    ],
@@ -976,21 +975,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "id": "a5d00229",
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'breakpoint1'",
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'dict'",
      "output_type": "error",
      "traceback": [
       "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[25]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot_predictions\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/src/lactopy/plots/lt1_si_plot.py:28\u001b[39m, in \u001b[36mLT1_si_Plot.plot_predictions\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     23\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     24\u001b[39m \u001b[33;03mPlot the LT1 model predictions.\u001b[39;00m\n\u001b[32m     25\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     26\u001b[39m \u001b[38;5;28mself\u001b[39m.plot_fit()\n\u001b[32m     27\u001b[39m plt.axvline(\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbase_lactate_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m     29\u001b[39m     color=\u001b[33m\"\u001b[39m\u001b[33mblack\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m     30\u001b[39m     label=\u001b[33m\"\u001b[39m\u001b[33mPredictions\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m     31\u001b[39m     linestyle=\u001b[33m\"\u001b[39m\u001b[33m--\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m     32\u001b[39m )\n\u001b[32m     34\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m plt.gca()\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/src/lactopy/lactate_models/lt1_loglog.py:89\u001b[39m, in \u001b[36mLT1_loglog.predict\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     87\u001b[39m pw_fit = piecewise_regression.Fit(\u001b[38;5;28mself\u001b[39m.X, \u001b[38;5;28mself\u001b[39m.y, n_breakpoints=\u001b[32m1\u001b[39m)\n\u001b[32m     88\u001b[39m pw_results = pw_fit.get_results()\n\u001b[32m---> \u001b[39m\u001b[32m89\u001b[39m \u001b[43mpw_results\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mbreakpoint1\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33mestimate\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m     91\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m pw_results\n",
-      "\u001b[31mKeyError\u001b[39m: 'breakpoint1'"
+      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[23]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot_predictions\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/src/lactopy/plots/lt1_loglog_plot.py:27\u001b[39m, in \u001b[36mLT1_loglog_Plot.plot_predictions\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     23\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     24\u001b[39m \u001b[33;03mPlot the LT1 model predictions.\u001b[39;00m\n\u001b[32m     25\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     26\u001b[39m \u001b[38;5;28mself\u001b[39m.plot_fit()\n\u001b[32m---> \u001b[39m\u001b[32m27\u001b[39m \u001b[43mplt\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxvline\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     28\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbase_lactate_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     29\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mblack\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     30\u001b[39m \u001b[43m    \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPredictions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     31\u001b[39m \u001b[43m    \u001b[49m\u001b[43mlinestyle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     32\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     34\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m plt.gca()\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/pyplot.py:2956\u001b[39m, in \u001b[36maxvline\u001b[39m\u001b[34m(x, ymin, ymax, **kwargs)\u001b[39m\n\u001b[32m   2954\u001b[39m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes.axvline)\n\u001b[32m   2955\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34maxvline\u001b[39m(x: \u001b[38;5;28mfloat\u001b[39m = \u001b[32m0\u001b[39m, ymin: \u001b[38;5;28mfloat\u001b[39m = \u001b[32m0\u001b[39m, ymax: \u001b[38;5;28mfloat\u001b[39m = \u001b[32m1\u001b[39m, **kwargs) -> Line2D:\n\u001b[32m-> \u001b[39m\u001b[32m2956\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxvline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m=\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mymin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mymin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mymax\u001b[49m\u001b[43m=\u001b[49m\u001b[43mymax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/axes/_axes.py:887\u001b[39m, in \u001b[36mAxes.axvline\u001b[39m\u001b[34m(self, x, ymin, ymax, **kwargs)\u001b[39m\n\u001b[32m    884\u001b[39m xmin, xmax = \u001b[38;5;28mself\u001b[39m.get_xbound()\n\u001b[32m    886\u001b[39m \u001b[38;5;66;03m# Strip away the units for comparison with non-unitized bounds.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m887\u001b[39m xx, = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_process_unit_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mx\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    888\u001b[39m scalex = (xx < xmin) \u001b[38;5;129;01mor\u001b[39;00m (xx > xmax)\n\u001b[32m    890\u001b[39m trans = \u001b[38;5;28mself\u001b[39m.get_xaxis_transform(which=\u001b[33m'\u001b[39m\u001b[33mgrid\u001b[39m\u001b[33m'\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/axes/_base.py:2647\u001b[39m, in \u001b[36m_AxesBase._process_unit_info\u001b[39m\u001b[34m(self, datasets, kwargs, convert)\u001b[39m\n\u001b[32m   2645\u001b[39m     \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[32m   2646\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis.have_units():\n\u001b[32m-> \u001b[39m\u001b[32m2647\u001b[39m         \u001b[43maxis\u001b[49m\u001b[43m.\u001b[49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2648\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map.items():\n\u001b[32m   2649\u001b[39m     \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[32m   2650\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/axis.py:1754\u001b[39m, in \u001b[36mAxis.update_units\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m   1752\u001b[39m neednew = \u001b[38;5;28mself\u001b[39m._converter != converter\n\u001b[32m   1753\u001b[39m \u001b[38;5;28mself\u001b[39m._set_converter(converter)\n\u001b[32m-> \u001b[39m\u001b[32m1754\u001b[39m default = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_converter\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m   1755\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.units \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   1756\u001b[39m     \u001b[38;5;28mself\u001b[39m.set_units(default)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/category.py:106\u001b[39m, in \u001b[36mStrCategoryConverter.default_units\u001b[39m\u001b[34m(data, axis)\u001b[39m\n\u001b[32m    104\u001b[39m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[32m    105\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m axis.units \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m     axis.set_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m    107\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    108\u001b[39m     axis.units.update(data)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/category.py:182\u001b[39m, in \u001b[36mUnitData.__init__\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m    180\u001b[39m \u001b[38;5;28mself\u001b[39m._counter = itertools.count()\n\u001b[32m    181\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m182\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/category.py:215\u001b[39m, in \u001b[36mUnitData.update\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m    213\u001b[39m \u001b[38;5;66;03m# check if convertible to number:\u001b[39;00m\n\u001b[32m    214\u001b[39m convertible = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m215\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m \u001b[43mOrderedDict\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfromkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m    216\u001b[39m     \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[32m    217\u001b[39m     _api.check_isinstance((\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m), value=val)\n\u001b[32m    218\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[32m    219\u001b[39m         \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n",
+      "\u001b[31mTypeError\u001b[39m: unhashable type: 'dict'"
      ]
     },
     {
@@ -1008,6 +1013,14 @@
    "source": [
     "model.plot.plot_predictions()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "004d6952",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/src/lactopy/lactate_models/lt1_loglog.py b/src/lactopy/lactate_models/lt1_loglog.py
index 70c4c77..e6d743a 100644
--- a/src/lactopy/lactate_models/lt1_loglog.py
+++ b/src/lactopy/lactate_models/lt1_loglog.py
@@ -87,5 +87,13 @@ def predict(self) -> float:
         pw_fit = piecewise_regression.Fit(self.X, self.y, n_breakpoints=1)
         pw_results = pw_fit.get_results()
         
-
-        return pw_results["breakpoint1"]["estimate"]
+        if "breakpoint1" not in pw_results.get("estimates", {}):
+            raise ValueError(
+                f"'breakpoint1' not found in regression results."
+                f"It is recommended to use a different treshold estimation method."
+                f"Piecewise regression may have failed. Result: {pw_results}"
+            )
+        else:
+            predicted_x = pw_results["estimates"]["breakpoint1"]["estimate"]
+
+        return predicted_x

From 750242b351ddca7d008bf901ed431c8cb5dbbfc7 Mon Sep 17 00:00:00 2001
From: ihosten <hostenian@gmail.com>
Date: Mon, 21 Jul 2025 14:05:49 +0200
Subject: [PATCH 14/19] 2 breakpoint detection instead of 1 in piecwise
 regression for improved determination of lt1

---
 docs/quickstart.ipynb                    | 293 ++++++++++++-----------
 notebooks/data/test.csv                  |   8 +-
 src/lactopy/lactate_models/lt1_loglog.py |   2 +-
 3 files changed, 155 insertions(+), 148 deletions(-)

diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index 392511b..e7d0acd 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 67,
    "id": "150ece86",
    "metadata": {},
    "outputs": [],
@@ -14,24 +14,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 68,
    "id": "e0dd4c2e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.read_csv('../notebooks/data/initial_la_drop.csv')"
+    "df = pd.read_csv('../notebooks/data/test.csv')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 69,
    "id": "7f657183",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {\n",
+       "<style>#sk-container-id-4 {\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-1 {\n",
+       "#sk-container-id-4 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 pre {\n",
+       "#sk-container-id-4 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "#sk-container-id-4 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-1 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-4 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-1 div.sk-container {\n",
+       "#sk-container-id-4 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-1 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-4 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-1 div.sk-parallel-item::after {\n",
+       "#sk-container-id-4 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-1 div.sk-parallel {\n",
+       "#sk-container-id-4 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-1 div.sk-parallel-item {\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
+       "#sk-container-id-4 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-1 div.sk-toggleable {\n",
+       "#sk-container-id-4 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-1 label.sk-toggleable__label {\n",
+       "#sk-container-id-4 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-1 label.sk-toggleable__label .caption {\n",
+       "#sk-container-id-4 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-1 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 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-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "#sk-container-id-4 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-1 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-4 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-1 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-4 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-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 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-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 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-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 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-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 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-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 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-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 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-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-4 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-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-4 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-1 div.sk-label label {\n",
+       "#sk-container-id-4 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-1 div.sk-label-container {\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
+       "#sk-container-id-4 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-1 div.sk-estimator.fitted {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-4 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-1 a.estimator_doc_link {\n",
+       "#sk-container-id-4 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-1 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-4 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-1 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-4 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-1 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-4 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-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>"
+       "</style><div id=\"sk-container-id-4\" 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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" 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": 3,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -465,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 70,
    "id": "77b4e630",
    "metadata": {},
    "outputs": [
@@ -475,17 +475,21 @@
        "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
+<<<<<<< HEAD
 <<<<<<< HEAD
       "image/png": 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",
 =======
       "image/png": 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",
 >>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
+=======
+      "image/png": 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",
+>>>>>>> c6b823d (2 breakpoint detection instead of 1 in piecwise regression for improved determination of lt1)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -501,17 +505,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 71,
    "id": "279f619a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "np.float64(147.16511067594706)"
+       "np.float64(160.62556976256272)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -523,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 72,
    "id": "6fcd47b1",
    "metadata": {},
    "outputs": [
@@ -533,17 +537,21 @@
        "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
+<<<<<<< HEAD
 <<<<<<< HEAD
       "image/png": 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",
 =======
       "image/png": 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",
 >>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
+=======
+      "image/png": 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",
+>>>>>>> c6b823d (2 breakpoint detection instead of 1 in piecwise regression for improved determination of lt1)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -559,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 73,
    "id": "acff8813",
    "metadata": {},
    "outputs": [],
@@ -569,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 74,
    "id": "65aa368c",
    "metadata": {},
    "outputs": [
@@ -579,17 +587,21 @@
        "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
+<<<<<<< HEAD
 <<<<<<< HEAD
       "image/png": 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",
 =======
       "image/png": 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",
 >>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
+=======
+      "image/png": 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",
+>>>>>>> c6b823d (2 breakpoint detection instead of 1 in piecwise regression for improved determination of lt1)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -607,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 75,
    "id": "7ef327c7",
    "metadata": {},
    "outputs": [
@@ -617,16 +629,20 @@
        "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
+<<<<<<< HEAD
 <<<<<<< HEAD
       "image/png": 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",
 =======
       "image/png": 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",
+=======
+      "image/png": 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",
+>>>>>>> c6b823d (2 breakpoint detection instead of 1 in piecwise regression for improved determination of lt1)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -641,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 76,
    "id": "b7ba2348",
    "metadata": {},
    "outputs": [],
@@ -651,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 77,
    "id": "15e234c7",
    "metadata": {},
    "outputs": [
@@ -659,7 +675,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LT1 occurs at: 116.29 watts\n"
+      "LT1 occurs at: 90.49 watts\n"
      ]
     }
    ],
@@ -672,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 78,
    "id": "42e695f9",
    "metadata": {},
    "outputs": [
@@ -682,13 +698,13 @@
        "<Axes: title={'center': 'LT1 Standard Increment Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -703,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 79,
    "id": "d02f1652",
    "metadata": {},
    "outputs": [],
@@ -713,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 80,
    "id": "54283247",
    "metadata": {},
    "outputs": [
@@ -721,7 +737,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LT1 occurs at: 92.45 watts\n"
+      "LT1 occurs at: 90.49 watts\n"
      ]
     }
    ],
@@ -734,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 81,
    "id": "2739c2f2",
    "metadata": {},
    "outputs": [
@@ -744,13 +760,13 @@
        "<Axes: title={'center': 'LT1 Standard Increment From Lowest La- Measurement Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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PO1z/33//Nfz9/R3Wx7+Xxo0bZ18X/7632WzGvHnz7Ov/+uuvBNnGjh1r+Pj4GEeOHHG4r9dff91wdXW1P8/xn7u5c+c2rly5Yt9v2bJlBmCsWLHCvu5BXlc1a9Y0+vfvb38c7u7uxsyZMxN977Vv395wd3c3jh07Zl937tw5I3v27EaDBg3s6+Lfk9u3b7evu3DhguHv7+/wuZWc5z3+M+p+7vdZF/8auP2z827/P0vWpe5wIvdRsWJF9u7dy1NPPcWJEyf49NNPad++Pfnz5+err75K0m14eHjYfwmPjY3l8uXL+Pr6UrZsWXbv3p1g/759+zqchxMYGAg3W5QAdu7cyYULF3juuecc9gsKCkrwK5irq6t9n7i4OK5cuUJMTAw1a9ZM9L47derk0NoQGxvLmjVraN++PUWLFrWvL1++PM2bN0/S47+bChUq2B8bN1s5ypYta3+cAIsWLaJq1ar2X+Jvd+ev33369HH4FXLv3r0cPXqUHj16cPnyZS5dusSlS5cICwujSZMmbN68OUHXjQEDBtiXXV1dqVmzJoZh0L9/f/v6HDlyJMi5YMECypcvT7ly5ez3c+nSJfsvr3d2P2zatCmlSpWyX65SpQp+fn4Ot5kSnp6erF271uHvzq57zz33nMPlH374AcChGxnAq6++CuDQ5Yybx61OnTr2y/G/DD/22GMOr5H49Q/6mOIz1q5d2959D8DX15dnnnmGEydO2LttBgYGEhsby7Zt2+Bmi09gYCCBgYH27qJ//vkn165ds7/2rly5wvr16+natSvXr1+3H7vLly/TvHlzjh49ytmzZ+HmsT9w4ABHjx594MeUHLe/riMiIrh06RKPPvooQKLv4+R6/PHHyZUrF/PmzcMwDObNm0f37t0T3Xfx4sXExcXRtWtXh9d6gQIFKFOmjMNr/fbcYWFhXLp0ibp162IYhr2rpJeXF+7u7mzcuDFVzz16+umnHbryrV27lmvXrtG9e3eH3K6urjzyyCMJ3qPc8XkQ/7738fFx6EZWtmxZcuTIkeDzIDAwkJw5czrcV9OmTYmNjXVoreRmC3TOnDntl+/8zH9QPXr0YPHixURFRbFw4UJcXV0T/UyNjY3lp59+on379pQsWdK+vmDBgvTo0YOtW7fauxn/8MMPPProow6t6Xnz5qVnz54Ot5mS5z0pkvJZJ3Iv6g4nkgQPPfQQs2fPJjY2loMHD7Jy5Uref/99nnnmGUqUKEHTpk3vef24uDg+/fRTvvzyS44fP05sbKx9W+7cuRPsf/sXScD+n2P8F4STJ08CJBj61M3NzeE/rngzZ87ko48+4q+//iI6Otq+Pr7rxu3uXHfx4kVu3LiR6DCrZcuWtX+BTok7Hyc3H+vtX4SOHTtGp06dknR7d2aP/6Lap0+fu14nODjY4cvHnZn8/f3x9PR06HYXv/728zGOHj3KoUOH7tpd7c5BNJLy2FPC1dX1vq/HO5+nkydP4uLiQunSpR3WFyhQgBw5cthfb3fLHl94394N8/b1qfHF9uTJk/ai6nbxI0CePHmSSpUqUb16dby9vdmyZQvNmzdny5YtjBkzhgIFCvD5558TERFhL4biC6q///4bwzAYOXIkI0eOTPT+L1y4QOHChXn77bdp164dDz30EJUqVaJFixb06tWLKlWqPPBjvJcrV64wZswY5s2bl+C1FBwcDDfP/7ty5YrDtrx58ybpBHE3Nze6dOnCd999R+3atTl9+jQ9evRIdN+jR49iGMZdh152c3OzL586dYq33nqL5cuXJ3gdxOf28PBgwoQJvPrqq+TPn59HH32U1q1b07t3bwoUKHDf7Hdzt8+Du3Vr9vPzc7js6emZ4P3s7+9PkSJFEvwA4+/v7/D4jh49yv79+1P8eXDnZ/6D6tatG0OHDmX16tXMmTOH1q1bkz179gT7Xbx4kfDwcMqWLZtgW/ny5YmLi+P06dNUrFjxru/JO6+b3Oc9qZLyWSdyLyqCRJLB1dWVypUrU7lyZerUqUPjxo2ZM2fOfT+Ix40bx8iRI+nXrx9jx44lV65cuLi4MHjw4ERPIr3bl5b48zeS49tvvyUoKIj27dszbNgw8uXLh6urK+PHj+fYsWMJ9k+sP3daSc3HSSLZ45/bDz74IMG5T/HuHC0wsUxJyRkXF0flypX5+OOPE933zgIhtR97ctztGCf1vJK7ZbfyMcVzc3PjkUceYfPmzfz999/8+++/BAYGkj9/fqKjo9m+fTtbtmyhXLly9i+o8a+ToUOH3rV1M75AbNCgAceOHWPZsmX89NNPfP3113zyySdMnjzZodUgtXXt2pVt27YxbNgwHn74YXx9fYmLi6NFixb2/Nu2baNx48YO1zt+/HiSz7fo0aMHkydPZvTo0VStWpUKFSokul9cXBw2m43Vq1cneszj31OxsbE0a9aMK1euMHz4cMqVK4ePjw9nz54lKCjI4bNv8ODBtGnThqVLl7JmzRpGjhzJ+PHjWb9+PdWqVbtn7tt/VLrd3T4PZs+enWhxlS2b41eiB3mdx8XF0axZM1577bVE933ooYeSfZsPomDBgjRq1IiPPvqIX375JV1HhEvu8y6SXvTKE0mhmjVrAjjMO3S3L5ELFy6kcePGTJs2zWH9tWvXErQwJEX8SHVHjx51+HUtOjqa48ePO8wJs3DhQkqWLMnixYsd8t050evd5M2bFy8vr0S7/xw+fDjZ2ZOrVKlS/Pnnnym+Ljd/aUzrXwxLlSrFvn37aNKkSaqMBkYyipIHVaxYMeLi4jh69Ki9ZYWbgxFcu3Yt0ZER01uxYsUSfb399ddf9u3xAgMDmTBhAj///DN58uShXLly2Gw2KlasyJYtW9iyZQutW7e27x/feurm5pak10muXLno27cvffv2JTQ0lAYNGjB69Gh7EZTax+3q1ausW7eOMWPG8NZbb9nX3/merFq1qn2UunjJaUmpX78+RYsWZePGjUyYMOGu+5UqVQrDMChRokSCL/O3++OPPzhy5AgzZ86kd+/e9vV3Zrz9dl999VVeffVVjh49ysMPP8xHH31kn8g1Z86cCSYOjoqKSvLcb/GfB/ny5UuXz4PQ0NBUvZ8HfV316NGDAQMGkCNHDp544olE98mbNy/e3t53fa+5uLjYf9ApVqxYkv5fSM/n/V7S6/NUMg6dEyRyH1u2bHHoQhYvvhvY7U3/Pj4+Cf6T5uavfHf+ordgwQL7eQbJVbNmTfLmzcvkyZOJioqyr58xY0aC+4//hfH2+9++fTu//vprku7L1dWV5s2bs3TpUk6dOmVff+jQIdasWZOi/MnRqVMn9u3bl2CEMpLwK2mNGjUoVaoUH374ocNQyPEuXryYajm7du3K2bNnEz1P7MaNG/bRqZIjfl6TxF5TqSn+C9GdI1bFt2rFj6hmpSeeeILff//d4XUbFhbG1KlTKV68uEOrRWBgIJGRkUycOJH69evbv/wEBgYye/Zszp0753AuWr58+WjUqBFTpkxJ9Av17a+TO4ek9vX1pXTp0g5Diaf2cUvsPUwixytnzpw0bdrU4S85E53abDY+++wzRo0aRa9eve66X8eOHXF1dWXMmDEJMhmGYX+OEsttGAaffvqpw3XCw8MTTNhZqlQpsmfP7vC8lipVKsG5NFOnTr1rS9Cdmjdvjp+fH+PGjUv0Mz21Pw9+/fXXRD8jr127RkxMTLJv80FfV507d2bUqFF8+eWXd537zdXVlccff5xly5Y5DE/+33//8d1331G/fn1797UnnniC3377jd9//92+38WLF5kzZ47Dbabn834vd/v/WbIutQRJhvDNN984zKESb9CgQYn2a77TihUr7HPyREdHs3//ft555x0A2rZte8/+/BMmTGDXrl107NjRvt/u3buZNWsWuXLlYvDgwfZ9a9SowaRJk3jnnXcoXbo0+fLl47HHHqN169a8/fbb9O3bl7p16/LHH38wZ86cRM/fSQo3Nzfeeecdnn32WR577DGefPJJjh8/zvTp0xPcZuvWrVm8eDEdOnSgVatWHD9+nMmTJ1OhQoVEC4PEjBkzhh9//JHAwEBeeOEFYmJi+Pzzz6lYsWKiw+empmHDhrFw4UK6dOlCv379qFGjBleuXGH58uVMnjzZodXrTi4uLnz99de0bNmSihUr0rdvXwoXLszZs2fZsGEDfn5+rFixIlVy9urVi/nz5/Pcc8+xYcMG6tWrR2xsLH/99Rfz589nzZo19tbDpKpRowYAb775Jt26dcPNzY02bdqk+qSPVatWpU+fPkydOpVr167RsGFDfv/9d2bOnEn79u0TdLFKK4sWLbK37NyuT58+vP7668ydO5eWLVvy8ssvkytXLmbOnMnx48dZtGiRwxDcderUIVu2bBw+fJhnnnnGvr5BgwZMmjQJbjvxPN4XX3xB/fr1qVy5Mk8//TQlS5bkv//+49dff+XMmTP2z48KFSrQqFEjatSoQa5cudi5cycLFy7kxRdftN9W/HF7+eWXad68Oa6urnTr1u2ej/3ixYv2z6TblShRgp49e9qHaY6OjqZw4cL89NNPHD9+PBnPbtK0a9eOdu3a3XOfUqVK8c477zBixAhOnDhB+/btyZ49O8ePH2fJkiU888wzDB06lHLlylGqVCmGDh3K2bNn8fPzY9GiRQnOczly5AhNmjSha9euVKhQgWzZsrFkyRL+++8/h+dtwIABPPfcc3Tq1IlmzZqxb98+1qxZk+TWdD8/PyZNmkSvXr2oXr063bp1I2/evJw6dYpVq1ZRr149/ve//6XwmXM0bNgwli9fTuvWre3D/oeFhfHHH3+wcOFCTpw4kexeACl5Xd3O398/0XmW7vTOO++wdu1a6tevzwsvvEC2bNmYMmUKkZGRvP/++/b9XnvtNWbPnk2LFi0YNGiQfYjsYsWKOfy/kJ7P+73c7f9nycKsHp5O5F7ih7m829/p06cNIwlDh95tOE3AmD59+j0z/PLLL8bAgQONSpUqGf7+/oabm5tRtGhRIygoyGEIUePmkJ+tWrUysmfP7jBcZ0REhPHqq68aBQsWNLy8vIx69eoZv/76q9GwYUOHITvvNlRw/DCqd2b98ssvjRIlShgeHh5GzZo1jc2bNye4zbi4OGPcuHFGsWLFDA8PD6NatWrGypUrjT59+hjFihVLcB93G6Z206ZNRo0aNQx3d3ejZMmSxuTJk5M1nGliQ2QnNtTwnfkNwzAuX75svPjii0bhwoUNd3d3o0iRIkafPn2MS5cu3fN5i7dnzx6jY8eORu7cuQ0PDw+jWLFiRteuXY1169bZ94l/LBcvXrxvduMuwxpHRUUZEyZMMCpWrGh4eHgYOXPmNGrUqGGMGTPGCA4Otu8HGAMHDkxwm3cO9WvcHGq3cOHChouLy32Hy75b1vs9RsMwjOjoaGPMmDFGiRIlDDc3NyMgIMAYMWKEw5DRxj2OW2KP6X6vqXjxx+9uf/HDYh87dszo3LmzkSNHDsPT09OoXbu2sXLlykRvs1atWgmG7z1z5owBGAEBAYle59ixY0bv3r2NAgUKGG5ubkbhwoWN1q1bGwsXLrTv88477xi1a9c2cuTIYXh5eRnlypUz3n33XSMqKsq+T0xMjPHSSy8ZefPmNWw2233fIw0bNrzrY2/SpIk9e4cOHYwcOXIY/v7+RpcuXYxz587dddjopEjK0OTGPYbwXrRokVG/fn3Dx8fH8PHxMcqVK2cMHDjQOHz4sH2fgwcPGk2bNjV8fX2NPHnyGE8//bR9OPj4z7NLly4ZAwcONMqVK2f4+PgY/v7+xiOPPGLMnz/f4f5iY2ON4cOHG3ny5DG8vb2N5s2bG3///fddh8i+29QKGzZsMJo3b274+/sbnp6eRqlSpYygoCBj586d9n2S87437vK+uH79ujFixAijdOnShru7u5EnTx6jbt26xocffmh/vdzrPXLnsU3J6+p+Q6/f7TWwe/duo3nz5oavr6/h7e1tNG7c2GFY/Hj79+83GjZsaHh6ehqFCxc2xo4da0ybNi3Rz6qkPO8P8n/K7RIbIvtu/z9L1mUz0vOMVREREREREYvpnCAREREREclSVASJiIiIiEiWoiJIRERERESyFBVBIiIiIiKSpVhaBF2/fp3BgwdTrFgxvLy8qFu3Ljt27LAykoiIiIiIZHKWFkEDBgxg7dq1zJ49mz/++IPHH3+cpk2bpngCSRERERERkfuxbIjsGzdukD17dpYtW+YwG3mNGjVo2bJlopPG3SkuLo5z586RPXt2+4zgIiIiIiKS9RiGwfXr1ylUqJDDJNqJyZZuqe4QExNDbGwsnp6eDuu9vLzYunVroteJjIwkMjLSfvns2bNUqFAhzbOKiIiIiEjGcPr0aYoUKXLPfSwrgrJnz06dOnUYO3Ys5cuXJ3/+/MydO5dff/2V0qVLJ3qd8ePHM2bMmATrT58+jZ+fXzqkFpG0FB0dzfTp0wHo27cvbm5uVkcSERGRDCIkJISAgACyZ89+330t6w4HcOzYMfr168fmzZtxdXWlevXqPPTQQ+zatYtDhw4l2P/OlqD4BxocHKwiSCQTCAsLw9fXF4DQ0FB8fHysjiQiIiIZREhICP7+/kmqDSxrCQIoVaoUmzZtIiwsjJCQEAoWLMiTTz5JyZIlE93fw8MDDw+PdM8pIiIiIiKZh1PME+Tj40PBggW5evUqa9asoV27dlZHEhERERGRTMrSlqA1a9ZgGAZly5bl77//ZtiwYZQrV46+fftaGUtERERERDIxS4ug4OBgRowYwZkzZ8iVKxedOnXi3XffTdWToQ3DsI9EJ2nD1dWVbNmyaZhyEREREckQLB0Y4UHd7+SnqKgozp8/T3h4uCX5shJvb28KFiyIu7u71VEkA9PACCIiIpJSGWZghLQUFxfH8ePHcXV1pVChQri7u6ulIg0YhkFUVBQXL17k+PHjlClT5r6TU4mIiIiIWCnTFkFRUVHExcUREBCAt7e31XEyNS8vL9zc3Dh58iRRUVEJJsAVSSoPDw9WrlxpXxYRERFJC5m2CIqnVon0oedZUkO2bNlo1aqV1TFEREQkk9M3VxERERERyVIyfUuQiGQc0dHRzJkzB4CePXum6kiRIiIiIvFUBImI04iKirLPE9alSxcVQSIiIpIm1B3OCQUFBWGz2bDZbLi5uZE/f36aNWvGN998Q1xcXJJvZ8aMGeTIkSNNs4qIiIiIZDQqgpIgNhY2boS5c81/02Pe1RYtWnD+/HlOnDjB6tWrady4MYMGDaJ169bExMSkfQARERERkUxKRdB9LF4MxYtD48bQo4f5b/Hi5vq05OHhQYECBShcuDDVq1fnjTfeYNmyZaxevZoZM2YA8PHHH1O5cmV8fHwICAjghRdeIDQ0FICNGzfSt29fgoOD7a1Ko0ePBmD27NnUrFmT7NmzU6BAAXr06MGFCxfS9gGJiIiISKZiRUNBalERdA+LF0PnznDmjOP6s2fN9WldCN3pscceo2rVqiy+eccuLi589tlnHDhwgJkzZ7J+/Xpee+01AOrWrcvEiRPx8/Pj/PnznD9/nqFDh8LNk8/Hjh3Lvn37WLp0KSdOnCAoKCh9H4yIiIiIZFhWNRSkFg2McBexsTBoEBhGwm2GATYbDB4M7dqBq2v65SpXrhz79+8HYPDgwfb1xYsX55133uG5557jyy+/xN3dHX9/f2w2GwUKFHC4jX79+tmXS5YsyWeffUatWrUIDQ3F19c3/R6MiIiIiGQ48Q0Fd35Pjm8oWLgQOna0Kl3SqCXoLrZsSdgCdDvDgNOnzf3Sk2EY2Gw2AH7++WeaNGlC4cKFyZ49O7169eLy5cuEh4ff8zZ27dpFmzZtKFq0KNmzZ6dhw4YAnDp1Kl0eg4iIiIhkTHc2FAzgKypj/kAfv27wYOfvGqci6C7On0/d/VLLoUOHKFGiBCdOnKB169ZUqVKFRYsWsWvXLr744gu4Oczw3YSFhdG8eXP8/PyYM2cOO3bsYMmSJfe9nkh68PDwYP78+cyfPx8PDw+r44iIiMgdbm8oqMkOJvE8O6nJQxwGCxsKkkvd4e6iYMHU3S81rF+/nj/++IMhQ4awa9cu4uLi+Oijj3BxMWvZ+fPnO+zv7u5O7B1l+F9//cXly5d57733CAgIAGDnzp3p9yBE7iFbtmx06dLF6hgiIiJyF/ENAJ7cYBa9yUYs8+jMEcomup+zUkvQXQQGQpEi5rk/ibHZICDA3C8tREZG8u+//3L27Fl2797NuHHjaNeuHa1bt6Z3796ULl2a6OhoPv/8c/755x9mz57N5MmTHW6jePHihIaGsm7dOi5dukR4eDhFixbF3d3dfr3ly5czduzYtHkQIiIiIpKpxDcAjOMNyvMX5ynAQL64637OSkXQXbi6wqefmst3FkLxlydOTLtBEX788UcKFixI8eLFadGiBRs2bOCzzz5j2bJluLq6UrVqVT7++GMmTJhApUqVmDNnDuPHj3e4jbp16/Lcc8/x5JNPkjdvXt5//33y5s3LjBkzWLBgARUqVOC9997jww8/TJsHIZJMMTExLFiwgAULFmg+LBEREScUGAid82xkCBMB6M80rpDbvj2tGwpSi80wEhv/LGMICQnB39+f4OBg/Pz8HLZFRERw/PhxSpQogaenZ4rvY/Fi8+Sv2wdJCAgwCyBnH/UiPaXW8y1ZW1hYmH2EwtDQUHx8fKyOJCIiIrcLCSGsdBV8Lp7kK57mGabaN8U3FFg1Oty9aoM76Zyg++jY0RwGe8sWs29jwYJmZZuew2KLiIiIiDiFV17B5+JJwvKV4KNsH8G5W5uKFMk4DQUqgpLA1RUaNbI6hYiIiIiIhVatgmnTwGbDZ8EMDtTLnmEbClQEiYiIiIjIvV2+DAMGmMtDhkCDBriScRsKNDCCiIiIiIjcnWHACy/Av/9ChQrw7rtWJ3pgKoJEREREROTu5s2D+fPNvm6zZkEmGARLRZCIiIiIiCTuzBmzFQjgrbegRg2rE6UKnRMkIk7D3d2d6dOn25dFRETEQoYB/frBtWtQqxaMGGF1olSjIkhEnIabmxtBQUFWxxARERGASZNg7Vqz+9vs2eDmZnWiVKPucCIiIiIi4ujIERg61Fx+/30oW9bqRKlKRVAWtHHjRmw2G9euXUvydYoXL87EiRPTNJdITEwMq1atYtWqVcTExFgdR0REJGuKiYHeveHGDWjaFAYOtDpRqlMR5ISCgoKw2Ww899xzCbYNHDgQm82mLkOSKUVGRtK6dWtat25NZGSk1XFERESypvfeg+3bwd8fvvkGXDJfyZD5HlEmERAQwLx587hx44Z9XUREBN999x1Fixa1NJuIiIiIZFK7d8OYMebyF19AQIDVidJE1iqCDAPCwtL/zzCSHbV69eoEBASwePFi+7rFixdTtGhRqlWrZl8XGRnJyy+/TL58+fD09KR+/frs2LHD4bZ++OEHHnroIby8vGjcuDEnTpxIcH9bt24lMDAQLy8vAgICePnllwkLC0t2bhERERHJoG7cgF69zO5wnTtDjx5WJ0ozWasICg8HX9/0/wsPT1Hcfv362YcLBvjmm2/o27evwz6vvfYaixYtYubMmezevZvSpUvTvHlzrly5AsDp06fp2LEjbdq0Ye/evQwYMIDXX3/d4TaOHTtGixYt6NSpE/v37+f7779n69atvPjiiynKLSIiIiIZ0BtvwMGDUKCAOTKczWZ1ojSTtYqgDOapp55i69atnDx5kpMnT/LLL7/w1FNP2beHhYUxadIkPvjgA1q2bEmFChX46quv8PLyYtq0aQBMmjSJUqVK8dFHH1G2bFl69uyZ4Hyi8ePH07NnTwYPHkyZMmWoW7cun332GbNmzSIiIiLdH7eIiIiIpLN16yB+EKxvvoE8eaxOlKay1jxB3t4QGmrN/aZA3rx5adWqFTNmzMAwDFq1akWe216Qx44dIzo6mnr16tnXubm5Ubt2bQ4dOgTAoUOHeOSRRxxut06dOg6X9+3bx/79+5kzZ459nWEYxMXFcfz4ccqXL5+i/CIiIiKSAVy9CvE/kj//PLRsaXWiNJe1iiCbDXx8rE6RLP369bN3S/viiy/S5D5CQ0N59tlnefnllxNs0yAMIiIiIpnciy/CmTNQpgx88IHVadJF1iqCMqAWLVoQFRWFzWajefPmDttKlSqFu7s7v/zyC8WKFQMgOjqaHTt2MHjwYADKly/P8uXLHa7322+/OVyuXr06Bw8epHTp0mn+eETuxd3dnf/973/2ZREREUlj8+bBd9+BqyvMnp3hGgxSSkWQk3N1dbV3bXN1dXXY5uPjw/PPP8+wYcPIlSsXRYsW5f333yc8PJz+/fsD8Nxzz/HRRx8xbNgwBgwYwK5du5gxY4bD7QwfPpxHH32UF198kQEDBuDj48PBgwdZu3at/QupSHpwc3NjYCackE1ERMQpnT1rdn8D+L//gztOocjMNDBCBuDn54efn1+i29577z06depEr169qF69On///Tdr1qwhZ86ccLM726JFi1i6dClVq1Zl8uTJjBs3zuE2qlSpwqZNmzhy5AiBgYFUq1aNt956i0KFCqXL4xMRERGRdBYXB337wrVrUKsWvPmm1YnSlc0wUjCJjZMICQnB39+f4ODgBEVCREQEx48fp0SJEnh6elqWMavQ8y2pITY2li1btgAQGBiYoPVTREREUslnn8GgQeDlBXv2QNmyVid6YPeqDe6k7nAi4jQiIiJo3Lgx3BywwyeL9EsWERFJVwcPwvDh5vIHH2SKAii51B1ORERERCSriIqCnj0hIgJatIAXXrA6kSVUBImIiIiIZBVvvQV790Lu3OakqDab1YksoSJIRERERCQr2LwZ3n/fXP7qKyhY0OpElsn0RVAGHvchQ9HzLCIiIuLEgoOhVy8wDOjXDzp0sDqRpTJtEeTm5gZAeHi41VGyhPjnOf55FxEREREn8tJLcOoUlCwJEydancZymXZ0OFdXV3LkyMGFCxcA8Pb2xpZF+zymJcMwCA8P58KFC+TIkUNDGouIiIg4m/nzYfZscHEx/82e3epElsu0RRBAgQIFAOyFkKSdHDly2J9vkZRyc3Pj/Zt9ldWqKCIikgrOnoXnnjOX33gD6ta1OpFTsHSy1NjYWEaPHs23337Lv//+S6FChQgKCuL//u//ktRqk9QJkWJjY4mOjk7l9BLPzc1NLUAiIiIiziYuDh5/HNatg5o1Yds2yMQ/MmaYyVInTJjApEmTmDlzJhUrVmTnzp307dsXf39/Xn755VS7H1dXV31JFxEREZGsZeJEswDy8oJvv83UBVByWVoEbdu2jXbt2tGqVSsAihcvzty5c/n9998T3T8yMpLIyEj75ZCQkHTLKiJpLzY2lt27dwNQvXp1/XghIiKSUvv2wYgR5vInn0DZslYnciqWjg5Xt25d1q1bx5EjRwDYt28fW7dupWXLlonuP378ePz9/e1/AQEB6ZxYRNJSREQEtWvXpnbt2kRERFgdR0REJGO6cQN69oSoKGjbFp55xupETsfSlqDXX3+dkJAQypUrh6urK7Gxsbz77rv07Nkz0f1HjBjBK6+8Yr8cEhKiQkhERERE5HbDh8OBA1CgAHz9NWiE5AQsLYLmz5/PnDlz+O6776hYsSJ79+5l8ODBFCpUiD59+iTY38PDAw8PD0uyioiIiIg4vR9/hM8/N5enT4e8ea1O5JQsLYKGDRvG66+/Trdu3QCoXLkyJ0+eZPz48YkWQSIiIiIichcXL0JQkLn80kvQooXViZyWpecEhYeH4+LiGMHV1ZW4uDjLMomIiIiIZDiGAQMGwH//QcWKMGGC1YmcmqUtQW3atOHdd9+laNGiVKxYkT179vDxxx/Tr18/K2OJiIiIiGQsX30Fy5eDuzvMmWMOiy13ZWkR9PnnnzNy5EheeOEFLly4QKFChXj22Wd56623rIwlIiIiIpJx/PUXDB5sLo8bB1WrWp3I6dkMwzCsDpFSyZkVVkScX1RUFOPGjQPgjTfewN3d3epIIiIizi0qCh59FPbsgWbNzIERXCw948UyyakNLG0JEhG5nbu7O6NHj7Y6hoiISMbxf/9nFkC5c8OMGVm2AEouPUsiIiIiIhnRunXwwQfm8rRpUKiQ1YkyDLUEiYjTiIuL49ChQwCUL18+weiRIiIictPly9C7t7n87LPQrp3ViTIUFUEi4jRu3LhBpUqVAAgNDcXHx8fqSCIiIs7HMODpp+HcOShbFj7+2OpEGY5+ZhURERERyUi+/hqWLAE3N5g7F7y9rU6U4agIEhERERHJKA4fdhwOu1o1qxNlSCqCREREREQygshI6N4dwsOhSRN45RWrE2VYKoJERERERDKCN964NRz2rFkaDvsB6JkTEREREXF2a9bcGgBh+nQNh/2AVASJiIiIiDizCxegTx9zeeBAaNPG6kQZnobIFhGn4ebmxtChQ+3LIiIiWZ5hQFAQ/PcfVKp0a3JUeSAqgkTEabi7u/OBPtxFRERu+ewzWL0aPD3N4bC9vKxOlCmoO5yIiIiIiDPatw9ee81c/ugjsyVIUoVagkTEacTFxXHq1CkAihYtiotGvRERkawqPNwcDjsqyjwH6PnnrU6UqagIEhGncePGDUqUKAFAaGgoPj4+VkcSERGxxuDBcOgQFCwI33wDNpvViTIV/cwqIiIiIuJMFiyAr74yC59vv4U8eaxOlOmoCBIRERERcRYnTsDTT5vLI0bAY49ZnShTUhEkIiIiIuIMoqOhRw8IDoZHH4XRo61OlGmpCBIRERERcQZjxsCvv4KfnzkctubMSzMqgkRERERErLZ+PYwbZy5PnQrFi1udKFNTESQiIiIiYqVLl+Cpp8AwoH9/ePJJqxNlehoiW0ScRrZs2XjhhRfsyyIiIpmeYUDfvnD+PJQrB59+anWiLEHfMkTEaXh4ePDFF19YHUNERCT9fPoprFwJ7u4wbx5ojrx0oe5wIiIiIiJW2LULXnvNXP74Y6ha1epEWYZagkTEaRiGwaVLlwDIkycPNs2OLSIimVVIiHnuT3Q0dOgAN7uDS/pQESQiTiM8PJx8+fIBEBoaio+6BIiISGZkGPD883DsGBQtCtOmgX74S1fqDiciIiIikp5mzIDvvgNXV3M+oJw5rU6U5agIEhERERFJL4cOwYsvmstjx0LdulYnypJUBImIiIiIpIcbN6BbNwgPh6ZNYfhwqxNlWSqCRERERETSw6uvwv79kC8fzJ4NLvoqbhU98yIiIiIiaW3BApg0yVyePRsKFLA6UZamIkhEREREJC0dOwYDBpjLI0bA449bnSjL0xDZIuI0smXLRp8+fezLIiIiGV5kpDkfUEgI1KsHb79tdSJRESQizsTDw4MZM2ZYHUNERCT1DB8Ou3ZBrlzmcNj6kc8pqDuciIiIiEhaWLoUPv3UXJ4xAwICrE4kN6kUFRGnYRgG4eHhAHh7e2PT7NkiIpJRnTwJffuay6+8Am3aWJ1IbqOWIBFxGuHh4fj6+uLr62svhkRERDKc6GhzPqBr16B2bRg/3upEcgcVQSIiIiIiqenNN+G338DfH+bNA3d3qxPJHVQEiYiIiIiklpUr4YMPzOVp06BECasTSSJUBImIiIiIpIZTp+DmVA+89BJ06mR1IrkLFUEiIiIiIg8qOtqcD+jKFahV61ZrkDglFUEiIiIiIg9qxIhb5wF9/z14eFidSO5BRZCIiIiIyINYvhw++shcnj5d5wFlAJonSESchqurK507d7Yvi4iIOL2TJyEoyFwePBg6dLA6kSSBzTAMw+oQKRUSEoK/vz/BwcH4+flZHUdEREREspKoKGjQALZvN+cD2rJFw2FbKDm1gaXd4YoXL47NZkvwN3DgQCtjiYiIiIjc3/DhZgGUI4d5HpAKoAzD0u5wO3bsIDY21n75zz//pFmzZnTp0sXKWCIiIiIi97ZoEUycaC7PnAnFi1udSJLB0iIob968Dpffe+89SpUqRcOGDS3LJCLWCQsLw9fXF4DQ0FB8fHysjiQiIpLQ339Dv37m8tCh0Lat1YkkmZxmYISoqCi+/fZbXnnlFWw2W6L7REZGEhkZab8cEhKSjglFREREJMu7cQO6dIGQEKhfH8aNszqRpIDTDJG9dOlSrl27RlD86BqJGD9+PP7+/va/gICAdM0oIiIiIlncoEGwdy/kzQvz5oGbm9WJJAWcZnS45s2b4+7uzooVK+66T2ItQQEBARodTiSTUHc4ERFxarNnQ+/eYLPBTz9B06ZWJ5LbJGd0OKfoDnfy5El+/vlnFi9efM/9PDw88NDsuyIiIiKS3g4cgOeeM5dHjVIBlME5RXe46dOnky9fPlq1amV1FBERERERR6Gh0LkzhIdDs2bwf/9ndSJ5QJYXQXFxcUyfPp0+ffqQLZtTNEyJiIiIiJgMA55+Gv76CwoVgm+/BVdXq1PJA7K86vj55585deoU/eKHGRSRLMvV1ZUnnnjCviwiImK5L74wB0DIlg3mz4d8+axOJKnAaQZGSInknPwkIiIiIpIsv/0GDRpAdDR8/DEMGWJ1IrmH5NQGlneHExERERFxOhcvmvMBRUeb5wMNHmx1IklFKoJERERERG4XGws9e8KZM/DQQzBtmjkstmQaKoJExGmEhYXh4+ODj48PYWFhVscREZGs6u23Ye1a8PaGRYtAp11kOpYPjCAicrvw8HCrI4iISFa2ejWMHWsuT50KlSpZnUjSgFqCREREREQATp6Ep54yh8V+/nmzS5xkSiqCREREREQiIqBTJ7hyBWrWhE8+sTqRpCEVQSIiIiIiL70Eu3ZB7tywcCF4eFidSNKQzgkSERERkSwjNha2bIHz56FgQQgMBNcZ0+Drr80R4ObOhWLFrI4paUxFkIiIiIhkCYsXw6BB5sjX8Vrm28WKqwNxBXjnHWjWzMKEkl5UBImI03BxcaFhw4b2ZRERkdSyeLE556lh3FqXi8t8eaETrkRyvmYbCr7+upURJR2pCBIRp+Hl5cXGjRutjiEiIplMbKzZAnR7AeRCLHPoSXFO8jelaH9+FvsMF7NFSDI9/dQqIiIiIpnali2OXeAARjGGFqwhHC86spgDZ3OwZYtVCSW9qQgSERERkUzt/HnHy61ZwVuYE6I+w1T+oEqi+0nmpSJIRJxGWFgYefPmJW/evISFhVkdR0REMomCBW8tl+Yo3/IUAP9jIHNuLt+5n2RuOidIRJzKpUuXrI4gIiKZTGAgFCkC186EsoQO+BPCVurxCh8D5sjYRYqY+0nWoCJIRERERDI1V1f4dKJBdOf+VOIA5yhIFxYQjTs2m7nPxInmfpI1qAgSERERkUyv4/GPgPlE4UZnFvIvZt+3IkXMAqhjR6sTSnpSESQiIiIimdv69TB8OADZPp/IuEp1OX/ePAcoMFAtQFmRiiARERERybxOnYInn4S4OOjTB5eBz9PIZnUosZpGhxMRERGRzCkiAjp1gkuXoHp1mDQJ+0lAkqWpJUhEnIaLiws1a9a0L4uIiKSYYcBzz8HOnZA7NyxeDF5eVqcSJ6EiSESchpeXFzt27LA6hoiIZAb/+x/MnAkuLvD991CsmNWJxInop1YRERERyVw2bYIhQ8zlDz6AJk2sTiRORkWQiIiIiGQep05Bly4QGws9e94qhkRuoyJIRJxGeHg4xYsXp3jx4oSHh1sdR0REMpobN8wJfy5ehIcfhqlTNRCCJErnBImI0zAMg5MnT9qXRUREkix+IIRdu8yBEJYsAW9vq1OJk1JLkIiIiIhkfJ9/DrNmmTOfzp8PxYtbnUicmIogEREREcnY1q+HV14xlz/4AB57zOpE4uRUBImIiIhIxnX8+K2BEJ56CgYPtjqRZAAqgkREREQkYwoNhXbt4MoVqFlTAyFIkqkIEhEREZGMJy4OgoLgjz8gf35zIAQvL6tTSQah0eFExGnYbDYqVKhgXxYREbmrd9+FRYvAzQ0WL4YiRaxOJBmIiiARcRre3t4cOHDA6hgiIuLsli2Dt94ylydNgrp1rU4kGYy6w4mIiIhIxnHggDkAAsCLL0L//lYnkgxIRZCIiIiIZAyXL5sDIYSGQqNG8PHHVieSDEpFkIg4jfDwcCpWrEjFihUJDw+3Oo6IiDiT6Gjo2hWOHTMnQp0/3zwfSCQFdE6QiDgNwzA4ePCgfVlERMRuyBBzUlRfX1i+HPLmtTqRZGBqCRIRERER5zZlCnzxhTkH0LffQuXKVieSDE5FkIiIiIg4r02bzAEQAN55xzwnSOQBqQgSEREREed0/Dh06gQxMdC9O4wYYXUiySRUBImIiIiI87l+Hdq2NUeEq1kTpk0zu8OJpAIVQSIiIiLiXOLizLmA/vwTChaEpUvBy8vqVJKJaHQ4EXEaNpuNYsWK2ZdFRCSLeuMNcwQ4Dw9YsgQKF7Y6kWQyKoJExGl4e3tz4sQJq2OIiIiVZs6ECRPM5enT4ZFHrE4kmZC6w4mIiIiIc9i6FZ5+2lweOdIcDEEkDagIEhERERHrnTgBHTpAdDR07gyjR1udSDIxy4ugs2fP8tRTT5E7d268vLyoXLkyO3futDqWiFjgxo0b1KpVi1q1anHjxg2r44iISHoJCYE2beDSJahe3ewS52L511TJxCw9J+jq1avUq1ePxo0bs3r1avLmzcvRo0fJmTOnlbFExCJxcXH2H0Hi4uKsjiMiIukhNhZ69Lg1EtyyZeDtbXUqyeQsLYImTJhAQEAA06dPt68rUaKElZFEREREJD299hqsWgWenmYBVKSI1YkkC7C0nXH58uXUrFmTLl26kC9fPqpVq8ZXX3111/0jIyMJCQlx+BMRERGRDGrKFPj4Y3N55kyoVcvqRJJFWFoE/fPPP0yaNIkyZcqwZs0ann/+eV5++WVmzpyZ6P7jx4/H39/f/hcQEJDumUVEREQkFaxdCwMHmstjx0LXrlYnkizEZhiGYdWdu7u7U7NmTbZt22Zf9/LLL7Njxw5+/fXXBPtHRkYSGRlpvxwSEkJAQADBwcH4+fmlW24RSRthYWH4+voCEBoaio+Pj9WRREQkLRw6BHXqQHAw9OpltgJpkmx5QCEhIfj7+yepNrC0JahgwYJUqFDBYV358uU5depUovt7eHjg5+fn8CciIiIiGcjFi9CqlVkA1a8PX32lAkjSnaUDI9SrV4/Dhw87rDty5AjFihWzLJOIWCtPnjxWRxARkbQSGWnOBXT8OJQsCUuWgIeH1akkC7K0CBoyZAh169Zl3LhxdO3ald9//52pU6cydepUK2OJiEV8fHy4ePGi1TFERCQtGAb07w+//AL+/uaIcPrhSyxiaXe4WrVqsWTJEubOnUulSpUYO3YsEydOpGfPnlbGEhEREZHU9vbbMGcOuLrCwoVQrpzViSQLs3RghAeVnJOfRERERMQi335rDoAAMHUqPP201YkkE8owAyOIiNzuxo0bNGrUiEaNGnHjxg2r44iISGrYtAn69TOXhw9XASROwdJzgkREbhcXF8emTZvsyyIiksEdPmwOhBAdDV26wLhxVicSAbUEiYiIiEiauHgRnngCrl6FRx815wJy0VdPcQ56JYqIiIhI6oqIgHbt4J9/oEQJWLYMvLysTiVipyJIRERERFJPXBwEBcGvv0LOnPDDD5Avn9WpRByoCBIRERGR1PPGG/D99+DmBosXayhscUoqgkREREQkdUyaBBMmmMvTpkGjRlYnEkmURocTEafi7e1tdQQREUmJlSvhxRfN5bFjb80LJOKEVASJiNPw8fEhLCzM6hgiIpJcO3fCk0+a5wP17w9vvml1IpF7Unc4EREREUm548ehVSsID4fmzc0ucTab1alE7klFkIiIiIikzJUr5lxAFy7Aww/DggXmgAgiTk5FkIg4jYiICFq1akWrVq2IiIiwOo6IiNxLRAR06AB//QVFisCqVZA9u9WpRJJE5wSJiNOIjY3lhx9+sC+LiIiTiouD3r1h82bw8zPnAipUyOpUIkmmliARERERSTrDgFdeudX1belSqFzZ6lQiyaIiSERERESS7uOP4dNPzeWZM6FxY6sTiSSbiiARERERSZq5c2HoUHP5ww+he3erE4mkiIogEREREbm/9euhTx9zedAgs0ucSAalIkhERERE7m3/fnMkuOho6NLF7BKnuYAkA1MRJCIiIiJ3d/IktGwJISHQoAHMmgUu+gopGZuGyBYRp+Hj44NhGFbHEBGReJcuQfPmcO4cVKxojgTn6Wl1KpEHpjJeRERERBIKC4PWreHwYQgIgB9/hJw5rU4lkipUBImIiIiIo/hzf7Zvh1y5YM0aKFLE6lQiqUZFkIg4jYiICLp06UKXLl2IiIiwOo6ISNZkGDBgAKxeDV5esHIllC9vdSqRVKUiSEScRmxsLAsXLmThwoXExsZaHUdEJGt6/XVz8ANXV1iwAOrUsTqRSKpTESQiIiIipk8+gfffN5e//hpatbI6kUiaUBEkIiIiImbrT/wEqOPHQ1CQ1YlE0oyKIBEREZGsbsUK6NfPXB4yBIYPtzqRSJpSESQiIiKSlW3ZAl27Qmws9OoFH34INpvVqUTSlIogERERkaxq3z5o0wYiIsw5gaZNAxd9PZTMT69yERERkazo2DFo3hyCg6F+fZg/H9zcrE4lki5SXATFxMTw888/M2XKFK5fvw7AuXPnCA0NTc18IpKFeHt7ExoaSmhoKN7e3lbHERHJvP79Fx5/HP77D6pUMc8J8vKyOpVIusmWkiudPHmSFi1acOrUKSIjI2nWrBnZs2dnwoQJREZGMnny5NRPKiKZns1mw8fHx+oYIiKZ25UrZgH0zz9QsiT8+CPkyGF1KpF0laKWoEGDBlGzZk2uXr2K122/GnTo0IF169alZj4RERERSS2hoebcP3/8AQUKwE8/QcGCVqcSSXcpagnasmUL27Ztw93d3WF98eLFOXv2bGplE5EsJjIykmeffRaAKVOm4OHhYXUkEZHMIzIS2reH336DnDlh7VooVcrqVCKWSFFLUFxcHLGxsQnWnzlzhuzZs6dGLhHJgmJiYpg5cyYzZ84kJibG6jgiIplHTAx07w7r1oGPD6xeDZUqWZ1KxDIpKoIef/xxJk6caL9ss9kIDQ1l1KhRPPHEE6mZT0REREQeRFwcDBgAS5aAhwcsXw6PPGJ1KhFLpag73EcffUTz5s2pUKECERER9OjRg6NHj5InTx7mzp2b+ilFREREJPkMA4YMgZkzwdUVvv8eHnvM6lQilktREVSkSBH27dvH999/z759+wgNDaV///707NnTYaAEEREREbHQ6NHw2Wfm8vTp0K6d1YlEnILNMAwjuVfavHkzdevWJVs2xxoqJiaGbdu20aBBg9TMeFchISH4+/sTHByMn59futyniKSdsLAwfH19AQgNDdVw2SIiD+L992H4cHP588/hxRetTiSSppJTG6TonKDGjRtz5cqVBOuDg4Np3LhxSm5SRERERFLLl1/eKoDGj1cBJHKHFBVBhmFgs9kSrL98+bJ+uRURERGx0syZMHCgufzmm/D661YnEnE6yTonqGPHjnBzNLigoCCHOTxiY2PZv38/devWTf2UIpIleHt7c+HCBfuyiIgk04IF0K+fuTxoEIwda3UiEaeUrCLI398fbrYEZc+e3WEQBHd3dx599FGefvrp1E8pIlmCzWYjb968VscQEcmYVq2CHj3MIbH794dPPoFEeu6ISDKLoOnTpwNQvHhxhg4dqq5vIiIiIs5g3Tro1MmcFLVbN5gyRQWQyD2kaHQ4Z6HR4UQyl8jISF555RUAPv74Y4cutyIichebN0OLFnDjBrRtCwsXgpub1alE0l1yaoMUF0ELFy5k/vz5nDp1iqioKIdtu3fvTtJtjB49mjFjxjisK1u2LH/99VeSrq8iSCRz0RDZIiLJ9Ntv0KwZhIaahdDSpaAfkCSLSvMhsj/77DP69u1L/vz52bNnD7Vr1yZ37tz8888/tGzZMlm3VbFiRc6fP2//27p1a0oiiYiIiGQtu3aZhU9oKDz2GCxerAJIJImSdU5QvC+//JKpU6fSvXt3ZsyYwWuvvUbJkiV56623Ep0/6J4BsmWjQIECKYkhIiIikjXt22e2AAUHQ2AgLF8Otw1YJSL3lqKWoFOnTtmHwvby8uL69esA9OrVi7lz5ybrto4ePUqhQoUoWbIkPXv25NSpU3fdNzIykpCQEIc/ERERkSzl4EGzALp6FR591BwVTt2HRZIlRUVQgQIF7C0+RYsW5bfffgPg+PHjJOcUo0ceeYQZM2bw448/MmnSJI4fP05gYKC9qLrT+PHj8ff3t/8FBASkJL6IiIhIxnT4MDRpAhcvQo0asHo1ZM9udSqRDCdFAyMMGDCAgIAARo0axRdffMGwYcOoV68eO3fupGPHjkybNi1FYa5du0axYsX4+OOP6d+/f4LtkZGRREZG2i+HhIQQEBCggRFEMgkNjCAicg9Hj0LDhnD+PFSpAuvXQ+7cVqcScRrJGRghRecETZ06lbi4OAAGDhxI7ty52bZtG23btuXZZ59NWWogR44cPPTQQ/z999+Jbvfw8NCQuSIiIpL1/P03NG5sFkCVKsHPP6sAEnkAKSqCzpw549AVrVu3bnTr1g3DMDh9+jRFixZNUZjQ0FCOHTtGr169UnR9EcnYvLy8OH78uH1ZRESAf/4xC6CzZ6FCBXNi1Lx5rU4lkqGl6JygEiVKcPHixQTrr1y5QokSJZJ8O0OHDmXTpk2cOHGCbdu20aFDB1xdXenevXtKYolIBufi4kLx4sUpXrw4Li4p+ngSEclcTpwwC6AzZ6BcObMLXL58VqcSyfBS1BJkGAY2my3B+tDQUDw9PZN8O2fOnKF79+5cvnyZvHnzUr9+fX777Tfy6tcNERERyepOnjQLoFOnoGxZswDKn9/qVCKZQrKKoFdeeQUAm83GyJEj8fb2tm+LjY1l+/btPPzww0m+vXnz5iXn7kUkk4uKiuLNN98E4N1338Xd3d3qSCIi1jh1yiyATpyAMmXMAqhgQatTiWQaySqC9uzZAzdbgv744w+HLyju7u5UrVqVoUOHpn5KEckSoqOj+fDDDwEYPXq0iiARyZriu8CdOAGlS8OGDVCokNWpRDKVZBVBGzZsAKBv3758+umnGpZaREREJDWdOAGNGpld4cqUMQugwoWtTiWS6aTozOOJEycSExOTYP2VK1cICQlJjVwiIiIiWcvx4+Y8QCdPwkMPqQASSUMpKoK6deuW6Pk88+fPp1u3bqmRS0RERCTr+OcfswCKHwRBBZBImkpREbR9+3YaN26cYH2jRo3Yvn17auQSERERyRqOHTMLoNOnzWGwdQ6QSJpLUREUGRmZaHe46Ohobty4kRq5RERERDK/I0fMAih+HqANGzQKnEg6SFERVLt2baZOnZpg/eTJk6lRo0Zq5BIRERHJ3A4ehAYN4OxZqFABNm6EAgWsTiWSJaRostR33nmHpk2bsm/fPpo0aQLAunXr2LFjBz/99FNqZxSRLMLLy4s///zTviwikmnt3w9Nm8LFi1ClCvz8M2iyeJF0k6KWoHr16vHrr78SEBDA/PnzWbFiBaVLl2b//v0EBgamfkoRyRJcXFyoWLEiFStWxMUlRR9PIiLOb/ducx6gixehenVzIlQVQCLpymYYhmF1iJQKCQnB39+f4OBgzVkkIiIizm/7dmjeHIKD4ZFH4McfIUcOq1OJZArJqQ1S1B3udhEREURFRTmsU0EiIikRFRXFuHHjAHjjjTdwd3e3OpKISOr55Rdo2RKuX4f69WHVKtB3JhFLpKglKDw8nNdee4358+dz+fLlBNtjY2NTK989qSVIJHMJCwvD19cXgNDQUHx8fKyOJCKSOtatg7ZtITzc7Aq3fDnc/LwTkdSRnNogRZ3uhw0bxvr165k0aRIeHh58/fXXjBkzhkKFCjFr1qyU5hYRERHJfFasgFatzAKoeXNYuVIFkIjFUtQdbsWKFcyaNYtGjRrRt29fAgMDKV26NMWKFWPOnDn07Nkz9ZOKiIiIZDTffw9PPQUxMdChA8ydCx4eVqcSyfJS1BJ05coVSpYsCTfP/7ly5QoA9evXZ/PmzambUERERCQjmj4devQwC6CePWH+fBVAIk4iRUVQyZIlOX78OADlypVj/vz5cLOFyN/fP3UTioiIiGQ0//sf9OsHcXHw9NMwcyZke+DxqEQklaSoCOrbty/79u0D4PXXX+eLL77A09OTIUOG8Nprr6V2RhEREZGM47334KWXzOXBg2HKFHB1tTqViNwmRT9JDBkyxL7ctGlT/vrrL3bt2kWePHn49ttvUzOfiIiISMZgGDBiBEyYYF7+v/+Dt98Gm83qZCJyh1SdLHXfvn1Ur15dQ2SLSIrExsaye/duAKpXr46rfjkVkYwiNhYGDjRbfQA++ACGDrU6lUiWkq6TpYqIpBZXV1dq1apldQwRkeSJjobevWHePLPVZ+pUGDDA6lQicg8qgkRERERSKjwcunSBH34ANzeYM8e8LCJOTUWQiDiNqKgoPv30UwAGDRqEu7u71ZFERO4uOBjatoXNm8HLCxYvhhYtrE4lIkmQrHOCOnbseM/t165dY9OmTTonSERSJCwsDN+bs6iHhobi4+NjdSQRkcT9959Z8OzdC/7+sHIl1K9vdSqRLC3Nzgm63xxA/v7+9O7dOzk3KSIiIpKx/PMPPP44HDsG+fLBjz9CtWpWpxKRZEhWETR9+vS0SyIiIiLi7Pbvh+bN4d9/oUQJ+OknKF3a6lQikkwpmixVREREJMvZsgUaNDALoCpV4JdfVACJZFAqgkRERETuZ/lyswtccDAEBsKmTVCwoNWpRCSFVASJiIiI3Ms330DHjhARAW3awJo1kCOH1alE5AGoCBIRERFJjGHAO+9A//4QGwt9+pjDYHt5WZ1MRB6Q5gkSEafh6enJhg0b7MsiIpaJjYUXX4TJk83LI0bAu++CzWZ1MhFJBSqCRMRpuLq60qhRI6tjiEhWd+MG9OgBS5eaRc9nn5kFkYhkGiqCREREROJduQJt25ojv7m7w5w50Lmz1alEJJWpCBIRpxEdHc3UqVMBeOaZZ3Bzc7M6kohkJadOQcuWcPAg+PvDsmXQsKHVqUQkDdgMwzCsDpFSISEh+Pv7ExwcjJ+fn9VxROQBhYWF4evrC0BoaCg+Pj5WRxKRLCA2FvZ8s4cKr7XC+9p5jMKFsa1eDZUrWx1NRJIhObWBRocTERGRLGvxYggq8CNln2mA97Xz/EElHon9lcVHVQCJZGYqgkRERCRLWrwYfug0jemXWpOdUH6mCfXZys7/Aujc2dwuIpmTiiARERHJcmJjDE4FvcXXDCAbscykN0/wAyH4E3+iwODBZlc5Ecl8VASJiIhI1hIVxcVWQQy+PhaAtxlJEDOIxt2+i2HA6dOwZYuFOUUkzWh0OBEREck6rlyBTp0osHEjMbjyHJOZxoC77n7+fLqmE5F0oiJIREREsoZjx+CJJ+DIEWK8s9MqfAE/0fyeVylYMN3SiUg6UhEkIk7Dw8ODlStX2pdFRFLNL79A+/Zw6RIEBGBbvoqDbSpjOwuJTRZis0GRIhAYaEVYEUlrKoJExGlky5aNVq1aWR1DRDKbefMgKAgiI6FGDVixAteCBfn0U+jc2Sx4bi+EbDbz34kTwdXVstQikoY0MIKIiIhkToYB774L3bubBVD79rBpk72PW8eOsHAhFC7seLUiRcz1HTtaE1tE0p5agkTEaURHRzNnzhwAevbsiZubm9WRRCSjioiAAQPg5mcKr74KEyYkaNrp2BHatTNHgTt/3qyPAgPVAiSS2dkMI7GesBlDSEgI/v7+BAcH4+fnZ3UcEXlAYWFh+Pr6AhAaGoqPj4/VkUQkI/rvP+jQAX791axm/vc/eO45q1OJSBpLTm2gliARERHJPPbvhzZt4NQpyJHD7NfWpInVqUTEyTjNOUHvvfceNpuNwYMHWx1FREREMqIVK6BePbMAKlMGtm9XASQiiXKKImjHjh1MmTKFKlWqWB1FREREMhrDgI8+Mk/uCQ2Fxx6D336Dhx6yOpmIOCnLi6DQ0FB69uzJV199Rc6cOa2OIyIiIhlJRAT06QNDh5rF0LPPwo8/Qq5cVicTESdmeRE0cOBAWrVqRdOmTe+7b2RkJCEhIQ5/IiIikkWdOwcNG8Ls2eYACJ99BpMmgUaWFJH7sHRghHnz5rF792527NiRpP3Hjx/PmDFj0jyXiIiIOLkdO8x5f86dM1t95s/X+T8ikmSWFUGnT59m0KBBrF27Fk9PzyRdZ8SIEbzyyiv2yyEhIQQEBKRhShFJTx4eHsyfP9++LCKSqG+/NecAioyEChVg+XIoVcrqVCKSgVg2T9DSpUvp0KEDrrfNRhYbG4vNZsPFxYXIyEiHbYnRPEEiIiJZSEwMjBgBH35oXm7TxiyI9B1ARDLKPEFNmjThjz/+cFjXt29fypUrx/Dhw+9bAImIiEgWcvkydOsGP/9sXn7jDRg7FlwsP71ZRDIgy4qg7NmzU6lSJYd1Pj4+5M6dO8F6EckaYmJiWLJkCQAdOnQgWzbN5ywiwL590KEDHD8O3t4wYwZ06WJ1KhHJwPQNQ0ScRmRkJF27doWbw+erCBIR5s2Dfv3gxg0oWRKWLoXKla1OJSIZnFN9w9i4caPVEURERMQZxMaa5/988IF5uXlz+O47zf8jIqlCHWlFRETEuVy8aBY98QXQ66/DqlUqgEQk1ThVS5CIiIhkcb//Dp07w+nT4OMD33wDN7vJioikFrUEiYiIiPUMA6ZOhcBAswB66CHYvl0FkIikCRVBIiIiYq0bN6B/f3j2WYiKMkeC27EDKla0OpmIZFLqDiciIiLWOX4cOnWCPXvMOX/Gj4dhw8BmszqZiGRiKoJExGm4u7szffp0+7KIZHLLlkGfPhAcDHnzmsNhP/aY1alEJAtQESQiTsPNzY2goCCrY4hIWouOhjfegA8/NC/XqQPffw8BAVYnE5EsQkWQiIiIpJ+zZ6FbN9i61bw8ZAhMmABublYnE5EsREWQiDiNmJgY1qxZA0Dz5s3Jlk0fUSKZys8/Q48e5jxAfn4wfTp07Gh1KhHJgvQNQ0ScRmRkJK1btwYgNDRURZBIZhEbC2PHwttvm0NhP/wwLFgApUtbnUxEsih9wxAREZG0c+4c9OwJGzealwcMgM8+Ay8vq5OJSBamIkhERETSxo8/Qq9ecOkS+PrClClmdzgREYtpslQRERFJXdHRMGIEtGxpFkAPPwy7dqkAEhGnoZYgERERST0nT5rFzrZt5uUXXoCPPgJPT6uTiYjYqQgSERGR1LFgATz9tDn5qZ8fTJsGnTtbnUpEJAF1hxMREZEHExZmFj9du5oF0KOPwt69KoBExGmpJUhEnIa7uzv/+9//7MsikgHs22dOfvrXX2CzwRtvwKhRmvxURJyaiiARcRpubm4MHDjQ6hgikhRxcfD55/DaaxAVBYUKwbffQuPGVicTEbkvFUEiIiKSPOfPQ9++sGaNebltW/P8nzx5rE4mIpIkOidIRJxGbGwsGzduZOPGjcTGxlodR0QSs3QpVK5sFkCenvC//5nrVACJSAailiARcRoRERE0vtmVJjQ0FB8fH6sjiUi8sDAYMgS++sq8/PDDMGcOVKhgdTIRkWRTS5CIiIjc244dUK2aWQDZbOZ5QL/9pgJIRDIstQSJiIhI4qKj4Z134N13ITYWihSBWbM0+IGIZHgqgkRERCShQ4egVy/Ytcu83K0bfPkl5MxpdTIRkQem7nAiIiJyS1wcfPopVK9uFkA5c8LcueafCiARySTUEiQiIiKmkyfNoa83bDAvt2hhDn1dqJDVyUREUpVagkRERLI6wzAHPahc2SyAvL1h0iT44QcVQCKSKaklSESchpubG++//759WUTSwenT8PTTtyY+rVsXZs6E0qWtTiYikmZshmEYVodIqZCQEPz9/QkODsbPz8/qOCIiIhmHYcCMGTB4MISEmBOfvvsuDBoErq5WpxMRSbbk1AZqCRIREclqzp6FZ54xu7sBPPKIWRCVK2d1MhGRdKFzgkTEacTGxrJjxw527NhBbGys1XFEMh/DgK+/Nic5/eEHcHeHCRPgl19UAIlIlqKWIBFxGhEREdSuXRuA0NBQfHx8rI4kknkcP26e+7NunXm5dm345huoWNHqZCIi6U4tQSIiIplZbKw570+lSmYB5OUFH30E27apABKRLEstQSIiIpnVoUMwYIBZ8AA0bGh2h9PIbyKSxaklSEREJLOJjITRo6FqVbMAyp7dnPdn/XoVQCIiagkSERHJZLZsMUd+++sv83KrVvDll1C0qNXJRESchlqCREREMoNr1+DZZ6FBA7MAyp8fvv8eVqxQASQicge1BImIiGRkhgELFpiTnp4/b64bMADefx9y5rQ6nYiIU1IRJCJOw83NjVGjRtmXReQ+jh2DgQNhzRrz8kMPwdSp5gAIIiJyVzbDMAyrQ6RUSEgI/v7+BAcH4+fnZ3UcERGR9BEZCR9+CO+8AxER5qSnb7wBw4eDp6fV6URELJGc2kAtQSIiIhnJpk3w3HO3Bj5o2hS++MJsBRIRkSTRwAgi4jTi4uI4cOAABw4cIC4uzuo4Is7l/Hl46ilo1OjWwAfffQc//aQCSEQkmdQSJCJO48aNG1SqVAmA0NBQfHx8rI4kYr2YGPjf/+Ctt+D6dbDZzJagceMgRw6r04mIZEgqgkRERJzV5s3mwAd//mlerl3b7PpWs6bVyUREMjR1hxMREXE2585Br17mKG9//gm5c8NXX8Gvv6oAEhFJBZYWQZMmTaJKlSr4+fnh5+dHnTp1WL16tZWRRERErBMZCe+9Z57j8+23Zte3Z5+Fw4fNuX9c9NuliEhqsLQ7XJEiRXjvvfcoU6YMhmEwc+ZM2rVrx549e6hYsaKV0URERNKPYcCqVeaEp8eOmesefRQ++wxq1bI6nYhIpuN08wTlypWLDz74gP79+993X80TJJK5hIWF4evrCxoYQbKSw4dhyBCI7wlRsCBMmAA9e6rlR0QkGTLkPEGxsbEsWLCAsLAw6tSpk+g+kZGRREZG2i+HhISkY0IREZFUdOUKvP22OdBBTAy4ucErr8Cbb0L27FanExHJ1Cwvgv744w/q1KlDREQEvr6+LFmyhAoVKiS67/jx4xkzZky6ZxSR9OHm5sbQoUPtyyKZUnQ0TJoEo0fD1avmutat4eOPoUwZq9OJiGQJlneHi4qK4tSpUwQHB7Nw4UK+/vprNm3alGghlFhLUEBAgLrDiYiI84s/72foULMLHEClSvDJJ9C0qdXpREQyvOR0h7O8CLpT06ZNKVWqFFOmTLnvvjonSEREMoS9e2HYMPj5Z/Ny3rwwdiz07w/ZLO+UISKSKWTIc4LixcXFObT2iEjWERcXx6lTpwAoWrQoLjopXDK606fh//4PZs82W4Lc3WHQIPO8H39/q9OJiGRZlhZBI0aMoGXLlhQtWpTr16/z3XffsXHjRtasWWNlLBGxyI0bNyhRogRodDjJ6IKDzRHePvkEIiLMdd26wbhxcPM1LiIi1rG0CLpw4QK9e/fm/Pnz+Pv7U6VKFdasWUOzZs2sjCUiIpIykZEwZYrZ1e3SJXNdYCB8+CHUrm11OhERucnSImjatGlW3r2IiEjqiIuDuXNh5Eg4ftxcV7as2RrUti3YbFYnFBGR2zjdOUEiIiJWio2FLVvg/Hlz3tLAQHB1vcvOhgE//ggjRsC+fea6ggVh1Cjo18+c+0dERJyOiiAREZGbFi82xy04c+bWuiJF4NNPoWPHO3b+9Vd44w3YuNG87OcHw4ebN6Dz2UREnJqGXhIREblZAHXu7FgAAZw9a65fvPjmir17zclN69Y1CyAPD3j1VfjnH7MoUgEkIuL0VASJiEiWFxtrNuAkNnNe/LrPB/5FXNcnoVo1c9JTV1dznp8jR8yBD3LnTvfcIiKSMuoOJyJOI1u2bLzwwgv2ZZH0smVLwhageCX4h5HGWHr/OwuXBXHmyu7dYfRoeOihdM0pIiKpQ98yRMRpeHh48MUXX1gdQ7Kg8+cTrivGCd7kXYKYgRsxAJyp0ZYi34yFKlXSP6SIiKQadYcTEZEsr2DBW8sBnGIyz3KUMjzN17gRwxoe51F+5e8Pl6kAEhHJBNQSJCJOwzAMLt2cYDJPnjzYNLeKpJPAQKhd4BRB/75Hf77GnWgA1tKUUYzhN1tdihQx9xMRkYxPRZCIOI3w8HDy5csHQGhoKD4aZUvSw/HjuI4fz6+XZuBys/hZT2NGMYatBNrnOZ048R7zBYmISIai7nAiIpI1HT0KfftCmTLw1Ve4xERzofJjdMm7kSasZytms0+RIrBwYSLzBImISIalliAREcla/vwTxo+HefMg7uZob82bw8iR5KtXj3mx5mhx58+b5woFBqoFSEQks1ERJCIiWcP27TBuHCxffmtd69YwciTUrm1f5eoKjRpZE1FERNKHiiAREcm8DAPWrzeLn/XrzXU2G3TqBCNGQPXqVicUERELqAgSEZHMJzYWliyB99+HHTvMddmyQa9eMHw4lC1rdUIREbGQiiAREck8btyAWbPgww/h77/NdV5eMGAADB0KRYtanVBERJyAiiARcRrZsmWjT58+9mWRJLt6Fb78Ej77DC5cMNflzAkvvmj+3Rx6XUREBBVBIuJMPDw8mDFjhtUxJCM5dsycwOebbyA83FxXtCi88gr07w++vlYnFBERJ6QiSEREMhbDgG3b4OOPzfN+DMNcX6UKvPYadO0Kbm5WpxQRESemIkhEnIZhGITf/DXf29sbm81mdSRxJtHRZtHz8cfmcNfxWrSAV1+FJk3Mkd9ERETuQ0WQiDiN8PBwfG92XwoNDcXHx8fqSOIMLl+Gr76CL76AM2fMde7u5khvQ4ZAxYpWJxQRkQxGRZCIiDinAwfMgQ5mzzZHfQNzgIPnnoMXXoD8+a1OKCIiGZSKIBERcR4xMbByJXz++a3JTQEefhgGDYJu3cDT08qEIiKSCagIEhER6126BF9/DZMmwalT5joXF2jXDgYPhsBAne8jIiKpRkWQiIhYwzBgxw6z8Jk7FyIjzfW5c5uTmz7/PBQrZnVKERHJhFQEiYhI+goLM4ueSZNg9+5b66tXh5degiefBC8vKxOKiEgmpyJIRETSx6FDZuEzaxYEB5vr3N3NeX1eeAEefVRd3kREJF2oCBIRp+Hq6krnzp3ty5IJ3LgBixbB1KmwZcut9aVKwbPPQt++kCePlQlFRCQLUhEkIk7D09OTBQsWWB1DUsOBA+bcPrNmwdWr5jpXV2jd2jzXp1kzc+ADERERC6gIEhGR1BEaCgsWwLRp8Msvt9YXLQpPP222+hQubGVCERERUBEkIiIPxDBg+3az8Jk3zyyEuNnq07YtPPOM2eqj7o0iIuJEVASJiNMICwvD19cXgNDQUHx8fKyOJHfz338wZ45Z/Bw8eGt96dLQvz/06QMFC1qZUERE5K5UBImISNJERcGqVTB9OvzwA8TGmuu9vKBLF7P40aSmIiKSAagIEhGRuzMM2LMHZs40W34uX7617ZFHICgIuncHf38rU4qIiCSLiiAREUno7Fmz6Jk1yxzpLV7BgtC7t9ndrXx5KxOKiIikmIogERExhYbCkiVm4bNundkKBODhAe3ama0+zZpBNv3XISIiGZv+JxMRycqio2HtWrPVZ+lSCA+/tS0w0Gz16dwZcuSwMqWIiEiqUhEkIpLVxA9rPWcOfP89XLx4a1upUmbh89RTULKklSlFRETSjIogEXEarq6uPPHEE/ZlSWV//glz55p/x4/fWp83L3TrBj17Qu3aGt1NREQyPRVBIuI0PD09WbVqldUxMpd//jEnMZ071yyC4vn4QIcOZuHTtKnO8xERkSxF/+uJiGQ2p07B/Pnm344dt9a7u0PLluaQ1q1bm4WQiIhIFqQiSEQkMzh3DhYsMM/x+fXXW+tdXOCxx8zCp2NHDXAgIiKiIkhEnElYWBj58uUD4MKFC/iopeLezpyBRYvM4mfbtltDWtts0KABPPkkdOoEN59TERERMakIEhGnEn77EM2S0KlTtwqf21t8AOrVu1X4FCpkVUIRERGnpyJIRMTZHT1qFj6LFsHOnbfW22xm4dOli1n4FC5sZUoREZEMQ0WQiIizMQxzJLfFi83C548/bm2z2cxJTLt0Mc/xUYuPiIhIsqkIEhFxBnFx5gSmS5aYxc+xY7e2ubqagxt06gTt20P+/FYmFRERyfAsLYLGjx/P4sWL+euvv/Dy8qJu3bpMmDCBsmXLWhlLRCR9REXBxo1m4bNsGZw/f2ubhwc0a2YWPm3bQq5cViYVERHJVCwtgjZt2sTAgQOpVasWMTExvPHGGzz++OMcPHhQo0KJSOYUHAyrV5tFzw8/QEjIrW1+ftCqlTmJaYsWkD27lUlFREQyLUuLoB9//NHh8owZM8iXLx+7du2iQYMGluUSEWu4uLjQsGFD+3Kmcfo0rFgBy5fD+vUQHX1rW/78ZktPx47QuLHZAiQiIiJpyqnOCQoODgYg1126fURGRhIZGWm/HHL7L6gikuF5eXmxceNGq2M8OMOAvXvNomfZMtizx3F72bLmuT3t20Pt2uaEpiIiIpJunKYIiouLY/DgwdSrV49KlSolus/48eMZM2ZMumcTEbmviAjYsMFs8VmxwpzINJ7NBnXrQps20K4dlCtnZVIREZEsz2YY8VOMW+v5559n9erVbN26lSJFiiS6T2ItQQEBAQQHB+Pn55eOaR3FxsKWLeY5zQULmqPXurpaFkdEUtld3+P//QerVplFz9q1EBZ260re3vD442ZXt1atIF8+Kx+CiIhIphcSEoK/v3+SagOnaAl68cUXWblyJZs3b75rAQTg4eGBh5P1l1+8GAYNcvzRt0gR+PRTs4u/iCRdWFgYxYsXB+DEiRNOMUCK43vc4GH20tNvJf3yryTX0d8ddy5cGFq3Nlt8HnsMvLwsSi0iIiL3YmkRZBgGL730EkuWLGHjxo2UKFHCyjjJtngxdO5sdv+/3dmz5vqFC1UIiSTXpUuXrI5gt3gx9O4UxmOsYyQracUqCnMOQjD/AGrWvFX4VKtmdn0TERERp2ZpETRw4EC+++47li1bRvbs2fn3338B8Pf3x8vJf0GNjTV/HU6sM6FhmN+DBg82u/+ra5xIBvPPP8StWEWu4au4xEY8udUNNxQf1tKMVbRmb6En2P5bQb3HRUREMhhLzwmy3eUX0+nTpxMUFHTf6yen319q27jRHM32fjZsgEaN0iORSMYXFhaGr68vAKGhoenXHS4qCn75xTy/Z9Uq+Osvh83HKc4qWrGCNmyiIZF42rfpPS4iIuIcMsw5QU4yJkOK3D6xe2rsJyLp7Px5c9LSVavMQQ2uX7+1zdWV/x6qz4eHWrGKVhyiPJD4jzZ6j4uIiGQ8TjEwQkZUsGDq7iciaSw2FrZvhx9+MP/unLsnXz5o2RKeeAIef5xDe3PwYRJae/UeFxERyXhUBKVQYKA5CtzZs4mfF2SzmdsDA61IJyIAXLwIa9aYRc+aNXDlyq1tNhvUqmUWPa1aQfXqDpOW6j0uIiKSeakISiFXV3MY7M6dzS9Dt39Jij/VaeJEDYogkhwuLi7UrFnTvpxssbGwc6fZze2HH8zl29+cOXNC8+Zm4dO8+T3n7tF7XEREJPNymslSU8LKgRHiJTZPUECA+eVIw2OLpINLl8xWntWr4ccf4fJlx+0PP2y29DzxBNSuDdmS99uP3uMiIiIZQ3JqAxVBqeCus8mLSOqLi7vV2rN6Nfz+u2Mzjb8/NGtmFj0tWqTKSTt6j4uIiDi/DDM6XGbh6qohckXS1OXL8NNPt1p7Ll503F6lyq1BDerUATe3VL17vcdFREQyFxVBIuI0wsPDqVChAgAH58zBe8MG89ye7dvNFqB42bObrT0tW5qtPUWKWBdaREREMhwVQSLiHK5dw1ixgpMnTwJg1K/vuL1SJbOlp2VLqFsX3N2tySkiIiIZnoogEbGGYcCff96at+eXX8yTb+J5e986t6dlS3M0AhEREZFUoCJIRNJPaCisW3er8Ll9yDWAsmXh8GFz+fRpyJXLkpgiIiKSuakIEpG0dfQorFplFj2bNkFU1K1tXl7w2GO3BjXIlw98fc1tHh6WRRYREZHMTUWQiKSuqCjYvNksfFatMoug25UseWvenoYNzUIoXlhYuscVERGRrEdFkIg8uP/+M4evXrnSHMr6+vVb27JlgwYNbhU+ZcuCzWZlWhEREcniVASJSPIZBuzbZxY9K1cmnLA0f36z4GnVyhzcIImTGdtsNvsQ2TYVSiIiIpJGVASJSNJERMD69bBihVn43DmoQY0a0Lq1+Ve9Ori4JPsuvL29OXDgQOplFhEREUmEiiARubsLF8zzepYvh7VrHc/Z8faGpk2hTRuz1adQISuTioiIiCSZiiARcfTXX2bRs2wZ/PqrYze3woXNoqdNG2jc2HFQAxEREZEMQkWQSFYXFwe//QZLl5qFz5Ejjttr1DCLnrZt4eGH03RQg/DwcGrVqgXAjh078Pb2TrP7EhERkaxLRZBIVhR/fs/SpWarz3//3drm7m7O3dO2rVn8FCmSbrEMw+DgwYP2ZREREZG0oCJIJKu4ft2csHTxYvPf0NBb2/z9zZHc2rWDFi2SPJqbiIiISEakIkgkM7t0yRzNbfFic2CDyMhb2woVgvbtzb+GDc0WIBEREZEsQEWQSGbz33+wZAksXAgbN0Js7K1tZcpAx47QoQPUqpWiYaxFREREMjoVQSKZwblzZmvPwoWwebPjiG5Vq0KnTmbxU6FCmg5sICIiIpIRqAgSyaj+/dcseubPh61bHQufWrWgc2ez+ClVysqUIiIiIk5HRZBIRnLhAixaZBY+mzY5Fj516kCXLmaLT7FiVqZMMZvNRrGb2W1qsRIREZE0oiJIxNldu2ae4zN3LqxbZ87rE+/RR6FrV7PVJyDAypSpwtvbmxMnTlgdQ0RERDI5FUEizig83BzVbd48czjrqKhb22rWhCefNFt9MmiLj4iIiIiVVASJOIuYGHMC02+/NVt+bp/Hp0IF6N4dunWD0qWtTCkiIiKS4akIErGSYcCuXTBnjtnd7b//bm0rUcIserp3h8qVrUyZbm7cuEGDBg0A2Lx5M15eXlZHEhERkUxIRZCIFc6cMVt8Zs6Ev/66tT53brOrW8+e5kAHWWxwgLi4OHbu3GlfFhEREUkLKoJE0ktYmNnNbeZMc4CD+JHdPD2hXTt46il4/HFwd7c6qYiIiEimpiJIJAViY2HLFjh/HgoWhMBAcHVNZEfDgG3b4JtvzGGtbz/Pp0ED6N3bHODAzy8944uIiIhkaSqCRJJp8WIYNMjs0RavSBH49FNzih4Azp2D2bPN4ufIkVs7liplFj5PPQUlS6Z7dhERERFRESSSLIsXm1Py3D5HKcDZs9CtUzSbXltFnQNfw+rVt+bz8fEx5/Lp2xfq189y5/mIiIiIOBsVQSJJFBtrtgDdWQAV5zgDjK/py3QKvX/+1oZ69aBfP7O7W/bs6Z5XRERERBKnIkgkibZsudUFzo0o2rKcZ5jK46y173OBvEQ8GUTRMf2hbFnrwmZgefLksTqCiIiIZHIqgkSS6PxtjTxvMI7RjLFf/olmTOUZltOWme3cKar6J0V8fHy4ePGi1TFEREQkk1MRJJJEBQveWp5NL57mK6bTl2n05wQlEt1PRERERJyPiiCRJAoMNEeBO3sW/jFKUZRTxHFrXGybzdweGGhpTBERERG5DxerA4hkFK6u5jDY3Cx47iyAACZOvMt8QZIkN27coFGjRjRq1IgbN25YHUdEREQyKRVBIsnQsSMsXAiFCzuuL1LEXG+fJ0hSJC4ujk2bNrFp0ybi4ocYFxEREUll6g4nkkwdO0K7duZocefPm+cABQaqBUhEREQko1ARJJICrq7QqJHVKUREREQkJdQdTkREREREshQVQSIiIiIikqWoCBIRERERkSzF0iJo8+bNtGnThkKFCmGz2Vi6dKmVcUTECXh7e+Pt7W11DBEREcnELC2CwsLCqFq1Kl988YWVMUTESfj4+BAWFkZYWBg+Pj5WxxEREZFMytLR4Vq2bEnLli2TvH9kZCSRkZH2yyEhIWmUTEREREREMqsMdU7Q+PHj8ff3t/8FBARYHUlERERERDKYDFUEjRgxguDgYPvf6dOnrY4kIqkoIiKCVq1a0apVKyIiIqyOIyIiIplUhpos1cPDAw8PD6tjiEgaiY2N5YcffrAvi4iIiKSFDNUSJCIiIiIi8qBUBImIiIiISJZiaXe40NBQ/v77b/vl48ePs3fvXnLlykXRokWtjCYiIiIiIpmUpUXQzp07ady4sf3yK6+8AkCfPn2YMWOGhclERERERCSzsrQIatSoEYZhWBlBRERERESymAw1Otyd4gsoTZoqkjmEhYXZl0NCQjRCnIiIiCRZfE2QlEaWDF0EXb9+HUCTpopkQoUKFbI6goiIiGRA169fx9/f/5772IwM3B8tLi6Oc+fOkT17dmw2m9VxMoWQkBACAgI4ffo0fn5+VsfJ8nQ8nIeOhfPQsXAuOh7OQ8fCueh4pD/DMLh+/TqFChXCxeXeg2Bn6JYgFxcXihQpYnWMTMnPz09vWCei4+E8dCych46Fc9HxcB46Fs5FxyN93a8FKJ7mCRIRERERkSxFRZCIiIiIiGQpKoLEgYeHB6NGjcLDw8PqKKLj4VR0LJyHjoVz0fFwHjoWzkXHw7ll6IERREREREREkkstQSIiIiIikqWoCBIRERERkSxFRZCIiIiIiGQpKoJERERERCRLURGURZ09e5annnqK3Llz4+XlReXKldm5c6d9u2EYvPXWWxQsWBAvLy+aNm3K0aNHLc2cWcXGxjJy5EhKlCiBl5cXpUqVYuzYsdw+ZomOR9rYvHkzbdq0oVChQthsNpYuXeqwPSnP+5UrV+jZsyd+fn7kyJGD/v37Exoams6PJHO41/GIjo5m+PDhVK5cGR8fHwoVKkTv3r05d+6cw23oeKSO+703bvfcc89hs9mYOHGiw3odi9STlONx6NAh2rZti7+/Pz4+PtSqVYtTp07Zt0dERDBw4EBy586Nr68vnTp14r///kvnR5Lx3e9YhIaG8uKLL1KkSBG8vLyoUKECkydPdthHx8I5qAjKgq5evUq9evVwc3Nj9erVHDx4kI8++oicOXPa93n//ff57LPPmDx5Mtu3b8fHx4fmzZsTERFhafbMaMKECUyaNIn//e9/HDp0iAkTJvD+++/z+eef2/fR8UgbYWFhVK1alS+++CLR7Ul53nv27MmBAwdYu3YtK1euZPPmzTzzzDPp+Cgyj3sdj/DwcHbv3s3IkSPZvXs3ixcv5vDhw7Rt29ZhPx2P1HG/90a8JUuW8Ntvv1GoUKEE23QsUs/9jsexY8eoX78+5cqVY+PGjezfv5+RI0fi6elp32fIkCGsWLGCBQsWsGnTJs6dO0fHjh3T8VFkDvc7Fq+88go//vgj3377LYcOHWLw4MG8+OKLLF++3L6PjoWTMCTLGT58uFG/fv27bo+LizMKFChgfPDBB/Z1165dMzw8PIy5c+emU8qso1WrVka/fv0c1nXs2NHo2bOnYeh4pBvAWLJkif1yUp73gwcPGoCxY8cO+z6rV682bDabcfbs2XR+BJnLnccjMb///rsBGCdPnjQMHY80c7djcebMGaNw4cLGn3/+aRQrVsz45JNP7Nt0LNJOYsfjySefNJ566qm7XufatWuGm5ubsWDBAvu6Q4cOGYDx66+/pmnezCyxY1GxYkXj7bffdlhXvXp148033zQMHQunopagLGj58uXUrFmTLl26kC9fPqpVq8ZXX31l3378+HH+/fdfmjZtal/n7+/PI488wq+//mpR6syrbt26rFu3jiNHjgCwb98+tm7dSsuWLUHHwzJJed5//fVXcuTIQc2aNe37NG3aFBcXF7Zv325J7qwkODgYm81Gjhw5QMcjXcXFxdGrVy+GDRtGxYoVE2zXsUg/cXFxrFq1ioceeojmzZuTL18+HnnkEYduWrt27SI6Otrh86xcuXIULVpU/4+ksrp167J8+XLOnj2LYRhs2LCBI0eO8Pjjj4OOhVNREZQF/fPPP0yaNIkyZcqwZs0ann/+eV5++WVmzpwJwL///gtA/vz5Ha6XP39++zZJPa+//jrdunWjXLlyuLm5Ua1aNQYPHkzPnj1Bx8MySXne//33X/Lly+ewPVu2bOTKlUvHJo1FREQwfPhwunfvjp+fH+h4pKsJEyaQLVs2Xn755US361iknwsXLhAaGsp7771HixYt+Omnn+jQoQMdO3Zk06ZNcPN4uLu7238wiKf/R1Lf559/ToUKFShSpAju7u60aNGCL774ggYNGoCOhVPJZnUASX9xcXHUrFmTcePGAVCtWjX+/PNPJk+eTJ8+fayOl+XMnz+fOXPm8N1331GxYkX27t3L4MGDKVSokI6HSCKio6Pp2rUrhmEwadIkq+NkObt27eLTTz9l9+7d2Gw2q+NkeXFxcQC0a9eOIUOGAPDwww+zbds2Jk+eTMOGDS1OmLV8/vnn/PbbbyxfvpxixYqxefNmBg4cSKFChRxaf8R6agnKggoWLEiFChUc1pUvX94+ikyBAgUAEoxU8t9//9m3SeoZNmyYvTWocuXK9OrViyFDhjB+/HjQ8bBMUp73AgUKcOHCBYftMTExXLlyRccmjcQXQCdPnmTt2rX2ViB0PNLNli1buHDhAkWLFiVbtmxky5aNkydP8uqrr1K8eHHQsUhXefLkIVu2bPf9fz0qKopr16457KP/R1LXjRs3eOONN/j4449p06YNVapU4cUXX+TJJ5/kww8/BB0Lp6IiKAuqV68ehw8fdlh35MgRihUrBkCJEiUoUKAA69ats28PCQlh+/bt1KlTJ93zZnbh4eG4uDi+FV1dXe2/7ul4WCMpz3udOnW4du0au3btsu+zfv164uLieOSRRyzJnZnFF0BHjx7l559/Jnfu3A7bdTzSR69evdi/fz979+61/xUqVIhhw4axZs0a0LFIV+7u7tSqVeue/6/XqFEDNzc3h8+zw4cPc+rUKf0/koqio6OJjo6+5//pOhZOxOqRGST9/f7770a2bNmMd9991zh69KgxZ84cw9vb2/j222/t+7z33ntGjhw5jGXLlhn79+832rVrZ5QoUcK4ceOGpdkzoz59+hiFCxc2Vq5caRw/ftxYvHixkSdPHuO1116z76PjkTauX79u7Nmzx9izZ48BGB9//LGxZ88e+2hjSXneW7RoYVSrVs3Yvn27sXXrVqNMmTJG9+7dLXxUGde9jkdUVJTRtm1bo0iRIsbevXuN8+fP2/8iIyPtt6HjkTru9964052jwxk6Fqnqfsdj8eLFhpubmzF16lTj6NGjxueff264uroaW7Zssd/Gc889ZxQtWtRYv369sXPnTqNOnTpGnTp1LHxUGdP9jkXDhg2NihUrGhs2bDD++ecfY/r06Yanp6fx5Zdf2m9Dx8I5qAjKolasWGFUqlTJ8PDwMMqVK2dMnTrVYXtcXJwxcuRII3/+/IaHh4fRpEkT4/Dhw5blzcxCQkKMQYMGGUWLFjU8PT2NkiVLGm+++abDFzsdj7SxYcMGA0jw16dPH8NI4vN++fJlo3v37oavr6/h5+dn9O3b17h+/bpFjyhju9fxOH78eKLbAGPDhg3229DxSB33e2/cKbEiSMci9STleEybNs0oXbq04enpaVStWtVYunSpw23cuHHDeOGFF4ycOXMa3t7eRocOHYzz589b8Ggytvsdi/PnzxtBQUFGoUKFDE9PT6Ns2bLGRx99ZMTFxdlvQ8fCOdiM26elFxERERERyeR0TpCIiIiIiGQpKoJERERERCRLUREkIiIiIiJZioogERERERHJUlQEiYiIiIhIlqIiSEREREREshQVQSIiIiIikqWoCBIRERERkSxFRZCIiGRJJ06cwGazsXfvXqujiIhIOlMRJCIiqS4oKIj27dsneX+bzcbSpUvTNNOdAgICOH/+PJUqVQJg48aN2Gw2rl27lq45REQk/WWzOoCIiIgVXF1dKVCggNUxRETEAmoJEhGRNNWoUSNefvllXnvtNXLlykWBAgUYPXq0fXvx4sUB6NChAzabzX4ZYNmyZVSvXh1PT09KlizJmDFjiImJsW+32Wx8/fXXdOjQAW9vb8qUKcPy5cvt269evUrPnj3JmzcvXl5elClThunTp8Md3eFOnDhB48aNAciZMyc2m42goCBmzZpF7ty5iYyMdHhM7du3p1evXmn4rImISFpSESQiImlu5syZ+Pj4sH37dt5//33efvtt1q5dC8COHTsAmD59OufPn7df3rJlC71792bQoEEcPHiQKVOmMGPGDN59912H2x4zZgxdu3Zl//79PPHEE/Ts2ZMrV64AMHLkSA4ePMjq1as5dOgQkyZNIk+ePAnyBQQEsGjRIgAOHz7M+fPn+fTTT+nSpQuxsbEOhdWFCxdYtWoV/fr1S8NnTERE0pKKIBERSXNVqlRh1KhRlClTht69e1OzZk3WrVsHQN68eQHIkSMHBQoUsF8eM2YMr7/+On369KFkyZI0a9aMsWPHMmXKFIfbDgoKonv37pQuXZpx48YRGhrK77//DsCpU6eoVq0aNWvWpHjx4jRt2pQ2bdokyOfq6kquXLkAyJcvHwUKFMDf3x8vLy969Ohhbz0C+PbbbylatCiNGjVKw2dMRETSks4JEhGRNFelShWHywULFuTChQv3vM6+ffv45ZdfHFp+YmNjiYiIIDw8HG9v7wS37ePjg5+fn/22n3/+eTp16sTu3bt5/PHHad++PXXr1k1W9qeffppatWpx9uxZChcuzIwZMwgKCsJmsyXrdkRExHmoCBIRkTTn5ubmcNlmsxEXF3fP64SGhjJmzBg6duyYYJunp2eSbrtly5acPHmSH374gbVr19KkSRMGDhzIhx9+mOTs1apVo2rVqsyaNYvHH3+cAwcOsGrVqiRfX0REnI+KIBERsZybmxuxsbEO66pXr87hw4cpXbr0A9123rx56dOnD3369CEwMJBhw4YlWgS5u7vDzdamOw0YMICJEydy9uxZmjZtSkBAwANlEhERa+mcIBERsVzx4sVZt24d//77L1evXgXgrbfeYtasWYwZM4YDBw5w6NAh5s2bx//93/8l+Xbfeustli1bxt9//82BAwdYuXIl5cuXT3TfYsWKYbPZWLlyJRcvXiQ0NNS+rUePHpw5c4avvvpKAyKIiGQCKoJERMRyH330EWvXriUgIIBq1aoB0Lx5c1auXMlPP/1ErVq1ePTRR/nkk08oVqxYkm/X3d2dESNGUKVKFRo0aICrqyvz5s1LdN/ChQvbB2PInz8/L774on2bv78/nTp1wtfXN1mTwIqIiHOyGYZhWB1CRETE2TVp0oSKFSvy2WefWR1FREQekIogERGRe7h69SobN26kc+fOHDx4kLJly1odSUREHpAGRhAREbmHatWqcfXqVSZMmKACSEQkk1BLkIiIiIiIZCkaGEFERERERLIUFUEi8v/t14EAAAAAgCB/60EuiwAAViQIAABYkSAAAGBFggAAgBUJAgAAViQIAABYkSAAAGAlfMzIUaPoEyUAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -765,7 +781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 82,
    "id": "602551ec",
    "metadata": {},
    "outputs": [
@@ -779,7 +795,7 @@
       "No. Observations                        4\n",
       "No. Model Parameters                    4\n",
       "Degrees of Freedom                      0\n",
-      "Res. Sum of Squares           4.93038e-32\n",
+      "Res. Sum of Squares            1.2326e-32\n",
       "Total Sum of Squares             0.721875\n",
       "R Squared                        1.000000\n",
       "Adjusted R Squared               1.000000\n",
@@ -788,14 +804,14 @@
       "====================================================================================================\n",
       "                    Estimate      Std Err            t        P>|t|       [0.025       0.975]\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "const                   0.85          inf          0.0           nan          nan          nan\n",
-      "alpha1                 0.015          inf          0.0           nan          nan          nan\n",
-      "beta1                  0.065          inf          0.0             -          nan          nan\n",
-      "breakpoint1             30.0          inf            -             -          nan          nan\n",
+      "const                   0.85      1.7e-16   5.0121e+15           nan          nan          nan\n",
+      "alpha1                 0.015     7.85e-18   1.9107e+15           nan          nan          nan\n",
+      "beta1              0.0737221      2.3e-17   3.2067e+15             -          nan          nan\n",
+      "breakpoint1          31.1831          0.0            -             -          nan          nan\n",
       "----------------------------------------------------------------------------------------------------\n",
       "These alphas(gradients of segments) are estimatedfrom betas(change in gradient)\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "alpha2                  0.08          nan          nan           nan          nan          nan\n",
+      "alpha2             0.0887221     1.76e-17   5.0345e+15           nan          nan          nan\n",
       "====================================================================================================\n",
       "Davies test for existence of at least 1 breakpoint: p=0.0231182 (e.g. p<0.05 means reject null hypothesis of no breakpoints at 5% significance)\n",
       "\n",
@@ -812,7 +828,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -843,7 +859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 83,
    "id": "4f2792d2",
    "metadata": {},
    "outputs": [
@@ -851,37 +867,37 @@
      "data": {
       "text/plain": [
        "{'davies': np.float64(0.023118172598819948),\n",
-       " 'estimates': {'const': {'estimate': np.float64(0.8499999999999996),\n",
-       "   'se': np.float64(inf),\n",
+       " 'estimates': {'const': {'estimate': np.float64(0.8499999999999999),\n",
+       "   'se': np.float64(1.6958936828758677e-16),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(0.0),\n",
+       "   't_stat': np.float64(5012106646677192.0),\n",
        "   'p_t': np.float64(nan)},\n",
-       "  'beta1': {'estimate': np.float64(0.06500000000000002),\n",
-       "   'se': np.float64(inf),\n",
+       "  'beta1': {'estimate': np.float64(0.0737221171410883),\n",
+       "   'se': np.float64(2.2989739197933858e-17),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(0.0),\n",
+       "   't_stat': np.float64(3206740037647485.5),\n",
        "   'p_t': '-'},\n",
-       "  'breakpoint1': {'estimate': np.float64(30.000000000000004),\n",
-       "   'se': np.float64(inf),\n",
+       "  'breakpoint1': {'estimate': np.float64(31.183107251843573),\n",
+       "   'se': np.float64(0.0),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
        "   't_stat': '-',\n",
        "   'p_t': '-'},\n",
-       "  'alpha1': {'estimate': np.float64(0.015000000000000013),\n",
-       "   'se': np.float64(inf),\n",
+       "  'alpha1': {'estimate': np.float64(0.015000000000000005),\n",
+       "   'se': np.float64(7.850462293418878e-18),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(0.0),\n",
+       "   't_stat': np.float64(1910715501757731.8),\n",
        "   'p_t': np.float64(nan)},\n",
-       "  'alpha2': {'estimate': np.float64(0.08000000000000003),\n",
-       "   'se': np.float64(nan),\n",
+       "  'alpha2': {'estimate': np.float64(0.0887221171410883),\n",
+       "   'se': np.float64(1.7622693546852893e-17),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(nan),\n",
+       "   't_stat': np.float64(5034537819386443.0),\n",
        "   'p_t': np.float64(nan)}},\n",
-       " 'bic': np.float64(-288.3492271129372),\n",
-       " 'rss': np.float64(4.930380657631324e-32),\n",
+       " 'bic': np.float64(-293.8944045574168),\n",
+       " 'rss': np.float64(1.232595164407831e-32),\n",
        " 'converged': True}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -894,41 +910,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 84,
    "id": "bc758db0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'const': {'estimate': np.float64(0.8499999999999996),\n",
-       "  'se': np.float64(inf),\n",
+       "{'const': {'estimate': np.float64(0.8499999999999999),\n",
+       "  'se': np.float64(1.6958936828758677e-16),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(0.0),\n",
+       "  't_stat': np.float64(5012106646677192.0),\n",
        "  'p_t': np.float64(nan)},\n",
-       " 'beta1': {'estimate': np.float64(0.06500000000000002),\n",
-       "  'se': np.float64(inf),\n",
+       " 'beta1': {'estimate': np.float64(0.0737221171410883),\n",
+       "  'se': np.float64(2.2989739197933858e-17),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(0.0),\n",
+       "  't_stat': np.float64(3206740037647485.5),\n",
        "  'p_t': '-'},\n",
-       " 'breakpoint1': {'estimate': np.float64(30.000000000000004),\n",
-       "  'se': np.float64(inf),\n",
+       " 'breakpoint1': {'estimate': np.float64(31.183107251843573),\n",
+       "  'se': np.float64(0.0),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
        "  't_stat': '-',\n",
        "  'p_t': '-'},\n",
-       " 'alpha1': {'estimate': np.float64(0.015000000000000013),\n",
-       "  'se': np.float64(inf),\n",
+       " 'alpha1': {'estimate': np.float64(0.015000000000000005),\n",
+       "  'se': np.float64(7.850462293418878e-18),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(0.0),\n",
+       "  't_stat': np.float64(1910715501757731.8),\n",
        "  'p_t': np.float64(nan)},\n",
-       " 'alpha2': {'estimate': np.float64(0.08000000000000003),\n",
-       "  'se': np.float64(nan),\n",
+       " 'alpha2': {'estimate': np.float64(0.0887221171410883),\n",
+       "  'se': np.float64(1.7622693546852893e-17),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(nan),\n",
+       "  't_stat': np.float64(5034537819386443.0),\n",
        "  'p_t': np.float64(nan)}}"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -940,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 85,
    "id": "7f6316fa",
    "metadata": {},
    "outputs": [],
@@ -950,19 +966,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 86,
    "id": "738f6b41",
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "unsupported format string passed to dict.__format__",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      2\u001b[39m model.fit(df[\u001b[33m'\u001b[39m\u001b[33mwatt\u001b[39m\u001b[33m'\u001b[39m], df[\u001b[33m'\u001b[39m\u001b[33mlactate\u001b[39m\u001b[33m'\u001b[39m], si=\u001b[32m0.5\u001b[39m)\n\u001b[32m      3\u001b[39m lt1_intensity = model.predict()\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLT1 occurs at: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlt1_intensity\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m watts\u001b[39m\u001b[33m\"\u001b[39m)\n",
-      "\u001b[31mTypeError\u001b[39m: unsupported format string passed to dict.__format__"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LT1 occurs at: 154.81 watts\n"
      ]
     }
    ],
@@ -975,33 +987,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 87,
    "id": "a5d00229",
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "unhashable type: 'dict'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[23]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot_predictions\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/src/lactopy/plots/lt1_loglog_plot.py:27\u001b[39m, in \u001b[36mLT1_loglog_Plot.plot_predictions\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     23\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     24\u001b[39m \u001b[33;03mPlot the LT1 model predictions.\u001b[39;00m\n\u001b[32m     25\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     26\u001b[39m \u001b[38;5;28mself\u001b[39m.plot_fit()\n\u001b[32m---> \u001b[39m\u001b[32m27\u001b[39m \u001b[43mplt\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxvline\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     28\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbase_lactate_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     29\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mblack\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     30\u001b[39m \u001b[43m    \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPredictions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     31\u001b[39m \u001b[43m    \u001b[49m\u001b[43mlinestyle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     32\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     34\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m plt.gca()\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/pyplot.py:2956\u001b[39m, in \u001b[36maxvline\u001b[39m\u001b[34m(x, ymin, ymax, **kwargs)\u001b[39m\n\u001b[32m   2954\u001b[39m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes.axvline)\n\u001b[32m   2955\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34maxvline\u001b[39m(x: \u001b[38;5;28mfloat\u001b[39m = \u001b[32m0\u001b[39m, ymin: \u001b[38;5;28mfloat\u001b[39m = \u001b[32m0\u001b[39m, ymax: \u001b[38;5;28mfloat\u001b[39m = \u001b[32m1\u001b[39m, **kwargs) -> Line2D:\n\u001b[32m-> \u001b[39m\u001b[32m2956\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxvline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m=\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mymin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mymin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mymax\u001b[49m\u001b[43m=\u001b[49m\u001b[43mymax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/axes/_axes.py:887\u001b[39m, in \u001b[36mAxes.axvline\u001b[39m\u001b[34m(self, x, ymin, ymax, **kwargs)\u001b[39m\n\u001b[32m    884\u001b[39m xmin, xmax = \u001b[38;5;28mself\u001b[39m.get_xbound()\n\u001b[32m    886\u001b[39m \u001b[38;5;66;03m# Strip away the units for comparison with non-unitized bounds.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m887\u001b[39m xx, = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_process_unit_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mx\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    888\u001b[39m scalex = (xx < xmin) \u001b[38;5;129;01mor\u001b[39;00m (xx > xmax)\n\u001b[32m    890\u001b[39m trans = \u001b[38;5;28mself\u001b[39m.get_xaxis_transform(which=\u001b[33m'\u001b[39m\u001b[33mgrid\u001b[39m\u001b[33m'\u001b[39m)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/axes/_base.py:2647\u001b[39m, in \u001b[36m_AxesBase._process_unit_info\u001b[39m\u001b[34m(self, datasets, kwargs, convert)\u001b[39m\n\u001b[32m   2645\u001b[39m     \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[32m   2646\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis.have_units():\n\u001b[32m-> \u001b[39m\u001b[32m2647\u001b[39m         \u001b[43maxis\u001b[49m\u001b[43m.\u001b[49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2648\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map.items():\n\u001b[32m   2649\u001b[39m     \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[32m   2650\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/axis.py:1754\u001b[39m, in \u001b[36mAxis.update_units\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m   1752\u001b[39m neednew = \u001b[38;5;28mself\u001b[39m._converter != converter\n\u001b[32m   1753\u001b[39m \u001b[38;5;28mself\u001b[39m._set_converter(converter)\n\u001b[32m-> \u001b[39m\u001b[32m1754\u001b[39m default = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_converter\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m   1755\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.units \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   1756\u001b[39m     \u001b[38;5;28mself\u001b[39m.set_units(default)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/category.py:106\u001b[39m, in \u001b[36mStrCategoryConverter.default_units\u001b[39m\u001b[34m(data, axis)\u001b[39m\n\u001b[32m    104\u001b[39m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[32m    105\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m axis.units \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m     axis.set_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m    107\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    108\u001b[39m     axis.units.update(data)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/category.py:182\u001b[39m, in \u001b[36mUnitData.__init__\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m    180\u001b[39m \u001b[38;5;28mself\u001b[39m._counter = itertools.count()\n\u001b[32m    181\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m182\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/lactopy/.venv/lib64/python3.13/site-packages/matplotlib/category.py:215\u001b[39m, in \u001b[36mUnitData.update\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m    213\u001b[39m \u001b[38;5;66;03m# check if convertible to number:\u001b[39;00m\n\u001b[32m    214\u001b[39m convertible = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m215\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m \u001b[43mOrderedDict\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfromkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m    216\u001b[39m     \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[32m    217\u001b[39m     _api.check_isinstance((\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m), value=val)\n\u001b[32m    218\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[32m    219\u001b[39m         \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n",
-      "\u001b[31mTypeError\u001b[39m: unhashable type: 'dict'"
-     ]
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'LT1 Standard Increment Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
      "data": {
+<<<<<<< HEAD
       "image/png": 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",
 >>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
+=======
+      "image/png": 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",
+>>>>>>> c6b823d (2 breakpoint detection instead of 1 in piecwise regression for improved determination of lt1)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
diff --git a/notebooks/data/test.csv b/notebooks/data/test.csv
index 3cef9f8..1e77b74 100644
--- a/notebooks/data/test.csv
+++ b/notebooks/data/test.csv
@@ -1,8 +1,8 @@
 watt,lactate
 50,0.98
 75,1.23
-100,1.88
+100,1.5
 125,2.8
-150,4.21
-175,6.66
-191.7,8.64
+150,3.21
+175,5.66
+191.7,7.64
diff --git a/src/lactopy/lactate_models/lt1_loglog.py b/src/lactopy/lactate_models/lt1_loglog.py
index e6d743a..1344b62 100644
--- a/src/lactopy/lactate_models/lt1_loglog.py
+++ b/src/lactopy/lactate_models/lt1_loglog.py
@@ -84,7 +84,7 @@ def predict(self) -> float:
             
         """
         
-        pw_fit = piecewise_regression.Fit(self.X, self.y, n_breakpoints=1)
+        pw_fit = piecewise_regression.Fit(self.X, self.y, n_breakpoints=2)
         pw_results = pw_fit.get_results()
         
         if "breakpoint1" not in pw_results.get("estimates", {}):

From 5180fccf451c666466e2749d5b7eae33f0e423c6 Mon Sep 17 00:00:00 2001
From: ihosten <hostenian@gmail.com>
Date: Mon, 21 Jul 2025 14:28:35 +0200
Subject: [PATCH 15/19] implemented a method to calculate both lt1 and lt2
 together based on curve breakpoints using piecewise regression

---
 docs/quickstart.ipynb                         | 291 +++++++++++-------
 src/lactopy/__init__.py                       |   2 +-
 src/lactopy/lactate_models/__init__.py        |   3 +-
 .../lactate_models/lt1_lt2_breakpoint.py      | 100 ++++++
 src/lactopy/plots/lt1_lt2_breakpoint_plot.py  |  45 +++
 5 files changed, 324 insertions(+), 117 deletions(-)
 create mode 100644 src/lactopy/lactate_models/lt1_lt2_breakpoint.py
 create mode 100644 src/lactopy/plots/lt1_lt2_breakpoint_plot.py

diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index e7d0acd..85ac6f7 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 1,
    "id": "150ece86",
    "metadata": {},
    "outputs": [],
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 2,
    "id": "e0dd4c2e",
    "metadata": {},
    "outputs": [],
@@ -24,14 +24,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 3,
    "id": "7f657183",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-4 {\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-4 {\n",
+       "#sk-container-id-1 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 pre {\n",
+       "#sk-container-id-1 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4 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-4\" 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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" 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": 69,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -465,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 4,
    "id": "77b4e630",
    "metadata": {},
    "outputs": [
@@ -475,7 +475,7 @@
        "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 5,
    "id": "279f619a",
    "metadata": {},
    "outputs": [
@@ -515,7 +515,7 @@
        "np.float64(160.62556976256272)"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -527,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 6,
    "id": "6fcd47b1",
    "metadata": {},
    "outputs": [
@@ -537,7 +537,7 @@
        "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 7,
    "id": "acff8813",
    "metadata": {},
    "outputs": [],
@@ -577,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 8,
    "id": "65aa368c",
    "metadata": {},
    "outputs": [
@@ -587,7 +587,7 @@
        "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -619,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 9,
    "id": "7ef327c7",
    "metadata": {},
    "outputs": [
@@ -629,7 +629,7 @@
        "<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -657,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 10,
    "id": "b7ba2348",
    "metadata": {},
    "outputs": [],
@@ -667,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 11,
    "id": "15e234c7",
    "metadata": {},
    "outputs": [
@@ -688,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 12,
    "id": "42e695f9",
    "metadata": {},
    "outputs": [
@@ -698,7 +698,7 @@
        "<Axes: title={'center': 'LT1 Standard Increment Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -719,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 13,
    "id": "d02f1652",
    "metadata": {},
    "outputs": [],
@@ -729,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 14,
    "id": "54283247",
    "metadata": {},
    "outputs": [
@@ -750,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 15,
    "id": "2739c2f2",
    "metadata": {},
    "outputs": [
@@ -760,7 +760,7 @@
        "<Axes: title={'center': 'LT1 Standard Increment From Lowest La- Measurement Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -781,7 +781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 16,
    "id": "602551ec",
    "metadata": {},
    "outputs": [
@@ -795,7 +795,7 @@
       "No. Observations                        4\n",
       "No. Model Parameters                    4\n",
       "Degrees of Freedom                      0\n",
-      "Res. Sum of Squares            1.2326e-32\n",
+      "Res. Sum of Squares                   0.0\n",
       "Total Sum of Squares             0.721875\n",
       "R Squared                        1.000000\n",
       "Adjusted R Squared               1.000000\n",
@@ -804,14 +804,14 @@
       "====================================================================================================\n",
       "                    Estimate      Std Err            t        P>|t|       [0.025       0.975]\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "const                   0.85      1.7e-16   5.0121e+15           nan          nan          nan\n",
-      "alpha1                 0.015     7.85e-18   1.9107e+15           nan          nan          nan\n",
-      "beta1              0.0737221      2.3e-17   3.2067e+15             -          nan          nan\n",
-      "breakpoint1          31.1831          0.0            -             -          nan          nan\n",
+      "const                   0.85     6.78e-16    1.253e+15           nan          nan          nan\n",
+      "alpha1                 0.015     3.14e-17   4.7768e+14           nan          nan          nan\n",
+      "beta1              0.0712569     8.89e-17   8.0169e+14             -          nan          nan\n",
+      "breakpoint1          30.8781          0.0            -             -          nan          nan\n",
       "----------------------------------------------------------------------------------------------------\n",
       "These alphas(gradients of segments) are estimatedfrom betas(change in gradient)\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "alpha2             0.0887221     1.76e-17   5.0345e+15           nan          nan          nan\n",
+      "alpha2             0.0862569     6.75e-17    1.277e+15           nan          nan          nan\n",
       "====================================================================================================\n",
       "Davies test for existence of at least 1 breakpoint: p=0.0231182 (e.g. p<0.05 means reject null hypothesis of no breakpoints at 5% significance)\n",
       "\n",
@@ -828,7 +828,7 @@
     },
     {
      "data": {
-      "image/png": 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iKyYukyRtn9Jf9er41WOc3vfTT1LLlsb/XujNN6U//MGMVH6jKr+//eqZGwAA/NaTT7ouNldfLd15pxmJLItyAwBATduzR/rHP1xvY8E+j+NoAgBQ0x5+2HiY+ELJydIvnj2FZ1BuAACoSevWSe++6zxeq5bEhJgaQbkBAKCmVLRg35//LLVr5+1EAYFyAwBATZk/X1qzxnk8LEx6/HEzEgUEyg0AADWhogX7HnpIio72dqKAQbkBAKAmvPKKtGuX83iTJtLo0WYkChiUGwAAPO3YMWnyZNfbnnxSqlfP24kCCuUGAABPmzZNOnrUebxTJ1Yi9gLKDQAAnvT999Lzz7vexoJ9XsERBgDAk8pbsG/QIKlPHzMSBRzKDQAAnrJhg/T2287jwcHS9OlmJApIlBsAADyhogX77r1XuuIKbycKWJQbAAA8YeFC6bPPnMfr1y9/5hRqBOUGAIDqOnNGGj/e9bYJE6TYWG8nCmiUGwAAqutf/5J27nQej4+XxowxI1FAo9wAAFAd+fnlvyfqiSeM21LwKsoNAADVkZYm5eU5j3foIA0bZkaigEe5AQDAXT/8ID33nOttM2ZItWp5OxEoNwAAVMMjj0jFxc7jAwZIN91kRiJQbgAAcNPGjdLs2c7jwcHSM8+YkQg/o9wAAFBVFS3YN3y41L69txPhFyg3AABU1aJF0qpVzuP16klTppiRCL9AuQEAoCoqWrBv/HgpLs7biXAByg0AAFXxn/9IO3Y4j8fGSg8+aEYiXIByAwBAZeXnS5Mmud42darUoIG3E8EFyg0AAJX19NPSkSPO4+3bS3ffbUYiuEC5AQCgMvbulZ591vU2FuzzKZQbAAAq49FHpVOnnMf79ZP69zcjEcpBuQEA4GK+/FJ66y3n8aAgFuzzQZQbAAAqcm7BPrvdeduf/mS8IBM+hXIDAEBFliyRPv3UeZwF+3wW5QYAgPKUlEjjxrne9uCDUuPG3k6ESqDcAABQnv/+V/rmG+fxmJjyVymG6Sg3AAC4cvy4NHGi621PPMGCfT6McgMAgCvTp0uHDzuPX3ml8SAxfBblBgCAC+3bJ82c6XrbM89IISHeToQqoNwAAHChxx6TTp50Hu/bVxowwIxEqALKDQAAv5SVJb3+uvP4uQX7goLMSIUqoNwAAHBORQv2DRsmdepkRipUEeUGAIBzli6VVqxwHg8NNWZIwS9QbgAA0M8L9o0d63rbgw9KTZp4OxHcRLkBAECSXntN2r7deTw6mgX7/AzlBgCAwkJjhpQrU6ZIYWHeToRqoNwAAPDMM9KhQ87j7dpJ99xjRiJUA+UGABDY9u83yo0rLNjnlyg3AIDANnGi6wX7eveWBg40IxGqiXIDAAhcX31lPEh8oaAgacYMFuzzU5QbAEDgGjfO9YJ9f/yj1LmzGYngAaaWm8zMTCUnJys+Pl5BQUFasGDBRX+muLhYjzzyiJo3by6bzaYWLVrof//7n1fyAgAsZNky6eOPncfr1pWmTjUjETzE1KekioqK1LFjRw0fPly33nprpX7mt7/9rQ4dOqT//ve/atWqlXJzc1VaWlrjWQEAFnL2bPkL9o0ZIzVt6u1E8CBTy01SUpKSkpIq/fmlS5dq1apV2rNnjyIjIyVJLVq0qMGEAABLmjVL2rbNebxRI2nCBDMSwYP86pmb9PR0denSRdOnT1fjxo11+eWXa+zYsTrp6in3nxUXF6ugoOC8LwBAAKtowb7Jk6XwcG8ngof51eT9PXv2aPXq1apbt67mz5+vI0eO6K9//avy8vL0mqun3SWlpaVp8uTJXs8KAPBRM2dKubnO423aSPfea0YieJhfXbkpLS1VUFCQZs+era5du2rgwIH6+9//rtdff73cqzepqanKz88v+8rJyfF6bgCAj8jNlaZPd71t+nSpdm1vJ0IN8KsrN3FxcWrcuLEiIiLKxtq1aye73a59+/apdevWTj9js9lks9m8nBQA4JMmTpROnHAe79FDSk42IxFqgF9duenevbsOHDigwsLCsrHvvvtOwcHBasKr6AEAFdm6VSpv6RAW7LMUU8tNYWGhsrKylJWVJUnKzs5WVlaW9u7dK/18S+muu+4q+/ydd96pqKgo/elPf9L27duVmZmpcePGafjw4QoNDTXtzwEA8APjx0uulg65806pSxczEqGGmFpuNm7cqM6dO6vzz6tAjhkzRp07d9bEiRMlSbm5uWVFR5IaNGig5cuX69ixY+rSpYt+//vfKzk5WS+88IJpfwYAgB/4+GNp6VLncZtNmjbNjESoQaY+c9OzZ0/ZXS17/bNZs2Y5jbVt21bLly+v4WQAAMs4e9Z4zYIro0dLzZt7OxFqmF89cwMAQJW98YbxgswLRUVJqalmJEINo9wAAKyrqEh69FHX2x5/XPrF7FtYB+UGAGBdf/+7dOCA83jr1tJf/mJGIngB5QYAYE0HD0pPP+16Gwv2WRrlBgBgTZMmGbelLnTDDdLgwWYkgpdQbgAA1vP119Krr7rexoJ9llflcjNs2DBlZmbWTBoAADyhvAX7fvc7qWtXMxLBi6pcbvLz89W3b1+1bt1a06ZN0/79+2smGQAA7vjkE2nJEufxOnVYsC9AVLncLFiwQPv379f999+vd999Vy1atFBSUpLmzZunM2fO1ExKAAAq4+xZaexY19tGjZJatPB2IpjArWduGjVqpDFjxmjLli1av369WrVqpT/+8Y+Kj4/XAw88oJ07d3o+KQAAF/PWW9KWLc7jkZHSww+bkQgmqNYDxbm5uVq+fLmWL1+uWrVqaeDAgdq6dauuuOIKPfvss55LCQDAxZw4IT3yiOttkyZJDRt6OxFMUuVyc+bMGb3//vu6+eab1bx5c82dO1ejR4/WgQMH9Prrr+uTTz7Re++9pylTptRMYgAAXHn2WcnVc6CtWkkjRpiRCCap8osz4+LiVFpaqt/97nfasGGDOnXq5PSZXr16qSENGQDgLYcOSU895Xrb008bDxMjYFS53Dz77LO67bbbVLdu3XI/07BhQ2VnZ1c3GwAAlfP441JhofN49+7Sr39tRiKYqMrl5o9//GPNJAEAwB3bt0v/+Y/rbTNnsmBfAGKFYgCAf5swwZgCfqHbb5e6dTMjEUxGuQEA+K+VK6XFi53H69SR0tLMSAQfQLkBAPin0tLyF+z7v/+TEhK8nQg+gnIDAPBPs2dLmzc7j19ySfnr3SAgUG4AAP7n5MnyC8zEiUbBQcCi3AAA/M9zz0k5Oc7jLVtKf/2rGYngQyg3AAD/cvhw+Q8Lp6WxYB8oNwAAPzN5snT8uPN4YqL0m9+YkQg+hnIDAPAf334r/etfrrexYB9+RrkBAPiP8hbsu+0248oNQLkBAPiNjAwpPd15vHZtFuzDeSg3AADfV9GCfSNHGrOkgJ9RbgAAvu/tt6VNm5zHGzaUHn3UjETwYZQbAIBvO3lSevhh19see0yKjPR2Ivg4yg0AwLe98IK0d6/zeEKClJJiRiL4OMoNAMB3/fijNG2a621paZLN5u1E8AOUGwCA75oyRSoocB7v1k367W/NSAQ/QLkBAPimHTukV15xvY0F+1AByg0AwDc99JBUUuI8PnSo1L27GYngJyg3AADf89ln0oIFzuMhIdJTT5mRCH6EcgMA8C2lpdKDD7relpIitWrl7UTwM5QbAIBvee896YsvnMcjIox1bYCLoNwAAHzHqVPGszauPPqoFBXl7UTwQ5QbAIDvePFF6YcfnMdbtDDeIQVUAuUGAOAb8vKkqVNdb5s2Tapb19uJ4KcoNwAA3/DEE1J+vvN4167SHXeYkQh+inIDADDfzp3SSy+53jZjBgv2oUooNwAA86Wmul6w79e/lm64wYxE8GOUGwCAudaskd5/33mcBfvgJsoNAMA8dnv5C/bdf790+eXeTgQLoNwAAMwzd660fr3zeHi4NHGiGYlgAZQbAIA5iovLX7DvkUekSy/1diJYBOUGAGCOl16SsrOdx5s1k/72NzMSwSIoNwAA7zt61FjXxpW0NBbsQ7VQbgAA3jd1qnTsmPN4ly4s2IdqM7XcZGZmKjk5WfHx8QoKCtKCBQsq/bNr1qxRSEiIOnXqVKMZAQAetnu38Q4pV2bMkIL5/92oHlPPoKKiInXs2FEvlbcqZTmOHTumu+66S3369KmxbACAGpKaKp054zw+eLDUo4cZiWAxIWb+y5OSkpSUlFTlnxsxYoTuvPNO1apVq0pXewAAJlu71pj+faFataSnnzYjESzI7679vfbaa9qzZ48mTZpUqc8XFxeroKDgvC8AgAkqWrBvxAipTRtvJ4JF+VW52blzpx566CG99dZbCgmp3EWntLQ0RURElH01bdq0xnMCAFx4/33jys2FwsJYsA8e5Tfl5uzZs7rzzjs1efJkXV6F5bhTU1OVn59f9pWTk1OjOQEALpw+LU2Y4HpbaqoUHe3tRLAwU5+5qYrjx49r48aN2rx5s0aOHClJKi0tld1uV0hIiD7++GP17t3b6edsNptsNpsJiQEAZf75T2nPHufxJk2k0aPNSAQL85tyEx4erq1bt5439s9//lMrV67UvHnzlJCQYFo2AEAFfvpJmjLF9bZp06TQUG8ngsWZWm4KCwu1a9eusu+zs7OVlZWlyMhINWvWTKmpqdq/f7/eeOMNBQcHq3379uf9fHR0tOrWres0DgDwIU8+aRScC3XuLP3+92YkgsWZWm42btyoXr16lX0/ZswYSdKwYcM0a9Ys5ebmau/evSYmBABUy5490j/+4XobC/ahhgTZ7Xa72SG8qaCgQBEREcrPz1d4eLjZcQDAb504XaIrJi6TJG2f0l/16rj4/8t33CG9+67z+M03S4sWeSElrKIqv7+pzACAmrFunetiU6uWNH26GYkQICg3AADPs9ulsWNdb7vvPqldO28nQgCh3AAAPG/+fGnNGufxBg2kxx83IxECCOUGAOBZFS3Y99BDUkyMtxMhwFBuAACe9cor0i+W+SjTuLH0wANmJEKAodwAADzn2DFp8mTX2558UqpXz9uJEIAoNwAAz5k2TTp61Hm8UyfpD38wIxECEOUGAOAZ338vPf+8620zZhhTwAEvoNwAADzj4YeNh4kvNHCg1KePGYkQoCg3AIDq27hJevtt5/HgYBbsg9dRbgAA1ffww67H77lHuvJKb6dBgKPcAACq73MXC/bVry9NmWJGGgQ4yg0AoGZMmCDFxpqdAgGIcgMA8Lz4eGnMGLNTIEBRbgAA7jmWX/62qVON21KACSg3AICqOXxYmjRJ6tjB9fYOHaS77vJ2KqBMiNkBAAB+4MwZKTPTmO791ltScbFU2+b6syzYB5NRbgAArh07Jn30kZSebvxvfgW3oc4ZMEC66SZvpAPKRbkBADhkZxtlJj3duFJTUlL5nw0NlWbOrMl0QKVQbgAgQOXl5enokSOK3rtXEatWSQsXStu2ubezTp2l52ZKV1zh6ZhAlVFuACDAnMzL07onn1T4qlW6/LvvFFZY6N6OgoIc//xZpmSr7bGMQHVQbgAgEBw6JC1eLKWnq/bSperl6gWXlRUZKd1+u5TyN+nN3cbYL4sOYDLKDQBYkd0uffON8ezMwoXS+vXGmLt/8bdqJQ0ebHwlJkohIdLpEkm7PZ0cqDbKDQBYRUmJtHq144Hg3dUoHkFB0vXXS7fcYny1bevJpECNotwAgD/Lz5eWLTPKzJIl0k8/ub2r07Vry96vn2xDh0qDBknR0R6NCngL5QYA/M0PP0iLFhmFJiPDWGDPTccbNNCONm30Xdu2Uu/eunP4cI9GBcxAuQEAX1daKn35peN205Yt1dvdVVdpW8uWWteokXLj4qTgYLVs2VJDhw71WGTATJQbAPBFp05JK1caZWbRIunAAff3FRIi9eolJSdLyckKbtFCHSQ1zsvT0aNHFRkZqaioKE+mB0xFuQEAX3HkiPThh0ahWbZMKipyf18NG0oDBxqzm/r3lyIinD4SFRVFqYElUW4AwEw7djhuN33+uXELyl0JCUaZueUW6Ve/kmqzqB4CE+UGALyppERau9ZRaL77zv19BQVJ3boZZSY5WbryShbTAyg3AOAFx49LH39slJkPP5Ty8tzfV2io8dbtW24xpmvHxnoyKWAJlBsAqAn79jmma69cKVXndQcxMWUPA6tvX6lePU8mBSyHcgMAnmC3S1lZjttNX35Zvf21b+9YHfjaa6XgYE8lBSyPcgMA7iouNhbRO1do9u1zf1+1akk9ejien7nsMk8mBQIK5QYAquLoUcd07aVLpcJC9/cVHi4lJRkznAYMkC65xJNJgYBFuQGAi9m503F1ZvXq6k3Xbt7cMV37hhukOnU8mRQA5QYAXDh7Vlq3zlFovv22evvr2tXxQHCHDkzXBmoY5QYAJGM14OXLpYULpcWLjdWC3WWzGbOazj0/ExfnyaQALoJyAyBwHThgFJn0dOmTT4wHhN116aVGkbnlFmMdmvr1PZkUQBVQbgAEDrtd2rrVcbvpiy+qt7927RzTtbt1M2Y8ATAd5QaAtZ0+LX32maPQfP+9+/sKDjYeAj53u6l1a08mBeAhlBsA1vPTT9JHHxll5qOPpIIC9/fVoIExXTs52XjLNm/RBnwe5QaANezZ47g6k5lpzHhyV5MmxtWZwYONhfVsNk8mBVDDKDcA/FNpqbRhg6PQfP119fZ39dWO52c6dWK6NuDHKDcA/MeJE8aspvR0Y5bToUPu76tOHal3b8f6M02bejIpABNRbgD4toMHHa87WL5cOnnS/X1FRRnPzQweLPXrJ4WFeTIpAB9BuQHgW+x2aft2x+2m9euNMXddfrnjdlNiohTCX3uA1fFfOQDznTljvLPpXKHZs8f9fQUHS9df75iu3batJ5MC8AOUGwDmyM833qqdni4tWSIdO+b+vurXl/r3N8rMoEFSo0aeTArAzwSb+S/PzMxUcnKy4uPjFRQUpAULFlT4+Q8++EA33XSTGjVqpPDwcCUmJmrZsmVeywugmn74QfrHP4zXE1x6qXTHHdKcOe4Vm/h46S9/MYrRkSPS++9Ld99NsQFg7pWboqIidezYUcOHD9ett9560c9nZmbqpptu0rRp09SwYUO99tprSk5O1vr169W5c2evZAZQBaWl0qZN0qJFxhWaLVuqt7+OHR3rz1x9NdO1AbhkarlJSkpSUlJSpT//3HPPnff9tGnTtHDhQi1atIhyA/iKU6ekFSuMMrNokZSb6/6+ateWevZ0vJCyeXNPJgVgUX79zE1paamOHz+uyMjIcj9TXFys4l+86begOsuwA3Dtxx8d07WXLTPWo3FXw4bGczPJydKAAVJEhCeTAggAfl1uZsyYocLCQv32t78t9zNpaWmaPHmyV3MBlme3Szt2OGY3ff559aZrX3aZcavplluk7t2NKzYA4Ca/LTdz5szR5MmTtXDhQkVHR5f7udTUVI0ZM6bs+4KCAjVlJVKg6kpKjBJzrtDs3On+voKCpOuuc6w/064dz88A8Bi/LDfvvPOO7r33Xs2dO1d9+/at8LM2m002XnoHuOf4ceM2U3q6cdvp6FH39xUaaqwKnJws3XyzFBPjyaQAUMbvys3bb7+t4cOH65133tGgQYPMjgNYT06OY3bTp59Kp0+7v6/YWKPIDB4s9eljFBwAqGGmlpvCwkLt2rWr7Pvs7GxlZWUpMjJSzZo1U2pqqvbv36833nhD+vlW1LBhw/T888+rW7duOnjwoCQpNDRUETx0CLjHbpeysowys3ChtHlz9fbXvr3j+ZkuXYwVgwHAi0wtNxs3blSvXr3Kvj/3bMywYcM0a9Ys5ebmau/evWXb//3vf6ukpEQpKSlKSUkpGz/3eQCVVFwsZWQ4np/Zt8/9fYWESD16OF53kJDgyaQAUGWmlpuePXvKXsEMiwsLS0ZGhhdSARZ15Iixmu+iRcZrDwoL3d9XRISUlGRcoRkwwJi+DQA+wu+euQFQBd9957g6s2aNsWKwu5o3d9xuuuEGqU4dTyYFAI+h3ABWcvastG6d4/mZHTuqt7+uXR23m666iunaAPwC5Qbwd4WF0vLlRqFZvNi4/eSuunWlvn2NQnPzzVJcnCeTAoBXUG4Af3TggGO69ooVxgPC7mrUyDFdu29fqX59TyYFAK+j3AA+Li8vT0fz8tQoN1cNMzONQrNxY/V22q6dY3Xgbt2kWrU8FRcATEe5AXzUyfx8rUlLU9inn6rNjh1qmJ/v/s5q1ZJ+9SvH8zOtW3syKgD4FMoN4EuOHpU++khKT1etRYvU9+RJ9/cVFmZM005OlgYOlKKiPJkUAHwW5QYw2+7djunan31mzHiS5NZE66ZNHbebevSQeK8agABEuQG8rbRU2rDBmKqdni5t3169/XXp4ig0HTowXRtAwKPcAN5w4oT0ySdGmVm0SDp82O1dldSqpdJevVTn1luNW05Nmng0KgD4O8oNUFMOHjTWnUlPN9ahOXXK7V2dCA3Vd5dfru/atlVp37664957PRoVAKyEcgN4it0uff214/mZ9eurtbvS1q31TevWWh8To31Nm8oeHKyWLVtq6NChHosMAFZEuQGq48wZ4yHgRYuMZ2iys93fV3CwdP31Zc/PBLdpoyslxebl6ejRo4qMjFQUM54A4KIoN0BVHTtmvFU7Pd14y3Z11p+pX1/q189YHXjgQGO14AtERUVRagCgCig3QGVkZzted7BqlVRS4v6+Gjc2HgQePFjq2dN4nxMAwGMoN4ArpaXGKw7OPT+zdWv19tepk3G7afBgqXNnpmsDQA2i3ADnnDxpvITy3HTtgwfd31ft2lKvXo7XHTRr5smkAIAKUG4Q2A4flj780Cg0H39srEfjrksuMZ6bGTxY6t9fCg/3ZFIAQCVRbhBY7Hbp228dt5vWrjXG3NWypVFmbrlF6t5dCuE/KQAwG38Tw/pKSqQ1axyFZtcu9/cVFCQlJjped9C2Lc/PAICPodzAmgoKpGXLjDLz4YfSTz+5v6969Yzp2snJ0s03S9HRnkwKAPAwyg2sIyfHMV3700+l06fd31dsrGO6du/eUmioJ5MCAGoQ5Qb+y26XvvzSMbtp8+bq7a9DB8ftpmuuMVYMBgD4HcoN/MupU8ZVmXOFZv9+9/cVEiL16OGYrp2Q4MmkAACTUG7g+44ccUzXXrZMKipyf18REcZ07eRkKSlJatjQk0kBAD6AcgPf9N13jtlNa9YYKwa7q0ULx3TtG24wFtgDAFgW5Qa+4exZY82Zc4Vmx47q7a9bN8fzM1deyXRtAAgglBuYp7DQWBU4PV1avFjKy3N/X3XrSjfd5JiuHRfnyaQAAD9CuYF37d/vmK69YkX1pmtHRxtFZvBgqW9fYz0aAEDAo9ygZtnt0pYtjttNmzZVb39XXum43dS1K9O1AQBOKDfwvNOnpYwMR6HJyXF/X7VqGQ8BDx5s3HJq2dKTSQEAFkS5gWccPSp99JG0cKG0dKl0/Lj7+woLM6ZpJycb07YjIz2ZFABgcZQbuG/3bsfVmc8+M2Y8uatZM8ftph49pDp1PJkUABBAKDeovLNnpQ0bHIVm+/bq7a9LF0eh6dCB6doAAI+g3KBiRUXSJ584pmsfPuz+vmw2qU8fo8zcfLPUuLEnkwIAIFFu4FJurlFk0tONYnPqlPv7iopyTNe+6SapQQNPJgUAwAnlBsZ07W3bHLebNmyo3v4uv9zxuoPERGPGEwAAXkK5CVRnzkiZmY5C8/337u8rOFjq3t3xdu02bTyZFACAKqHcBJJjx4xp2gsXGtO28/Pd31f9+tKAAUaZGTRIuvRSTyYFAMBtlBury852vO5g1SqppMT9fTVu7Jjd1LOn8T4nAAB8DOXGakpLpY0bHbebtm6t3v46d3YUms6dma4NAPB5lBsrOHnSeAllerpxlebgQff3Vbu21Lu34/mZpk09mRQAgBpHufFXhw5JH35oFJqPPzYKjrsiI43nZpKTpf79pfBwTyYFAMCrKDf+wm6XvvnGcbtp3TpjzF2tWjmma19/vRTCqQAAsAZ+o/mykhJp9WpHodm92/19BQUZJebc8zNt2vD8DADAkig3vqagwJiunZ4uLVki/fST+/uqV0/q18+43XTzzVJ0tCeTAgDgkyg3vuCHHxzTtTMyjAX23BUX57g606uXFBrqyaQAAPg8yo0ZSkulL7903G7asqV6++vQwVForrnGWDEYAIAARbnxllOnpJUrHVdoDhxwf18hIcYieuema7do4cmkAAD4NcpNTfrxx/OnaxcVub+vhg2lgQONMpOUJEVEeDIpAACWYer9i8zMTCUnJys+Pl5BQUFasGDBRX8mIyNDV199tWw2m1q1aqVZs2Z5JWtl5OXl6fulS1U0aZL0q19JMTHSn/4kzZ/vXrFJSJBGjzau+Bw+LM2eLd1xB8UGAIAKmHrlpqioSB07dtTw4cN16623XvTz2dnZGjRokEaMGKHZs2drxYoVuvfeexUXF6f+/ft7JbOTkhIVZ2Ro97PPKnr9erXIy3N/X0FBUrdujudnrriC6doAAFSRqeUmKSlJSUlJlf78K6+8ooSEBM2cOVOS1K5dO61evVrPPvuseeXmjTdku+ceXeHuz4eGSjfdZJSZQYOk2FjP5gMAIMD41TM3a9euVd++fc8b69+/v0aPHl3uzxQXF6u4uLjs+4KCAo9mOnrddYqs6g/FxBjPztxyi9Snj7EeDQAA8Ai/KjcHDx5UTEzMeWMxMTEqKCjQyZMnFepiTZe0tDRNnjy5xjLl1a6tE40bq8n+/RV/8MorHa87uPZapmsDAFBDLP8bNjU1Vfn5+WVfOTk5Ht1/ZGSkdrRp4zReGhSk7BYtVDR1qrRrl7Rtm/Tkk8YzNRQbAABqjF9duYmNjdWhQ4fOGzt06JDCw8NdXrWRJJvNJpvNVmOZoqKidKJvX2nlSp2y2bSrVSvtaNNGuy+/XPFXXqk//OEPNfbvBgAAzvyq3CQmJmrJkiXnjS1fvlyJiYmmZZKkvqNGaXlhodYFB6v057drt2zZUkOHDjU1FwAAgcjUclNYWKhdu3aVfZ+dna2srCxFRkaqWbNmSk1N1f79+/XGG29IkkaMGKEXX3xR48eP1/Dhw7Vy5Uq99957+vDDD038U0ih9erppief1NV5eTp69KgiIyMVFRVlaiYAAAKVqeVm48aN6tWrV9n3Y8aMkSQNGzZMs2bNUm5urvbu3Vu2PSEhQR9++KEeeOABPf/882rSpIleffVV86aBXyAqKopSAwCAyUwtNz179pTdbi93u6vVh3v27KnNmzfXcDIAAOCvmLYDAAAshXIDAAAshXIDAAAshXIDAAAshXIDAAAshXIDAAAshXIDAAAshXIDAAAshXIDAAAsxa9enOkJ51ZELigoMDsKAPi1E6dLVFp8Qvr579SSOgH3KwVedO73dkVvNjgnyF6ZT1nIvn371LRpU7NjAAAAN+Tk5KhJkyYVfibgyk1paakOHDigsLAwBQUFeXTfBQUFatq0qXJychQeHu7RfVsNx6ryOFaVx7GqGo5X5XGsKq+mjpXdbtfx48cVHx+v4OCKn6oJuGuIwcHBF2181RUeHs7JX0kcq8rjWFUex6pqOF6Vx7GqvJo4VhEREZX6HA8UAwAAS6HcAAAAS6HceJDNZtOkSZNks9nMjuLzOFaVx7GqPI5V1XC8Ko9jVXm+cKwC7oFiAABgbVy5AQAAlkK5AQAAlkK5AQAAlkK5AQAAlkK5cUNmZqaSk5MVHx+voKAgLViw4LztdrtdEydOVFxcnEJDQ9W3b1/t3LnTtLxmutixuvvuuxUUFHTe14ABA0zLa6a0tDRde+21CgsLU3R0tIYMGaIdO3ac95lTp04pJSVFUVFRatCggYYOHapDhw6ZltkslTlWPXv2dDq3RowYYVpms7z88svq0KFD2YJqiYmJ+uijj8q2c045XOxYcU6V76mnnlJQUJBGjx5dNmbmuUW5cUNRUZE6duyol156yeX26dOn64UXXtArr7yi9evXq379+urfv79OnTrl9axmu9ixkqQBAwYoNze37Ovtt9/2akZfsWrVKqWkpGjdunVavny5zpw5o379+qmoqKjsMw888IAWLVqkuXPnatWqVTpw4IBuvfVWU3OboTLHSpLuu+++886t6dOnm5bZLE2aNNFTTz2lTZs2aePGjerdu7cGDx6sr7/+WuKcOs/FjpU4p1z64osv9K9//UsdOnQ4b9zUc8uOapFknz9/ftn3paWl9tjYWPszzzxTNnbs2DG7zWazv/322yal9A0XHiu73W4fNmyYffDgwaZl8mWHDx+2S7KvWrXKbv/5PKpdu7Z97ty5ZZ/55ptv7JLsa9euNTGp+S48Vna73d6jRw/7qFGjTM3lqy655BL7q6++yjlVCeeOlZ1zyqXjx4/bW7dubV++fPl5x8fsc4srNx6WnZ2tgwcPqm/fvmVjERER6tatm9auXWtqNl+VkZGh6OhotWnTRvfff7/y8vLMjuQT8vPzJUmRkZGSpE2bNunMmTPnnVtt27ZVs2bNAv7cuvBYnTN79mxdeumlat++vVJTU3XixAmTEvqGs2fP6p133lFRUZESExM5pypw4bE6h3PqfCkpKRo0aNB555B84O+rgHtxZk07ePCgJCkmJua88ZiYmLJtcBgwYIBuvfVWJSQkaPfu3Xr44YeVlJSktWvXqlatWmbHM01paalGjx6t7t27q3379tLP51adOnXUsGHD8z4b6OeWq2MlSXfeeaeaN2+u+Ph4ffXVV5owYYJ27NihDz74wNS8Zti6dasSExN16tQpNWjQQPPnz9cVV1yhrKwszqkLlHesxDnl5J133tGXX36pL774wmmb2X9fUW5gqjvuuKPsn6+66ip16NBBLVu2VEZGhvr06WNqNjOlpKRo27ZtWr16tdlRfF55x+rPf/5z2T9fddVViouLU58+fbR79261bNnShKTmadOmjbKyspSfn6958+Zp2LBhWrVqldmxfFJ5x+qKK67gnPqFnJwcjRo1SsuXL1fdunXNjuOE21IeFhsbK0lOT4QfOnSobBvKd9lll+nSSy/Vrl27zI5impEjR2rx4sX69NNP1aRJk7Lx2NhYnT59WseOHTvv84F8bpV3rFzp1q2bJAXkuVWnTh21atVK11xzjdLS0tSxY0c9//zznFMulHesXAnkc2rTpk06fPiwrr76aoWEhCgkJESrVq3SCy+8oJCQEMXExJh6blFuPCwhIUGxsbFasWJF2VhBQYHWr19/3n1buLZv3z7l5eUpLi7O7CheZ7fbNXLkSM2fP18rV65UQkLCeduvueYa1a5d+7xza8eOHdq7d2/AnVsXO1auZGVlSVJAnlsXKi0tVXFxMedUJZw7Vq4E8jnVp08fbd26VVlZWWVfXbp00e9///uyfzbz3OK2lBsKCwvPa+rZ2dnKyspSZGSkmjVrptGjR2vq1Klq3bq1EhIS9Nhjjyk+Pl5DhgwxNbcZKjpWkZGRmjx5soYOHarY2Fjt3r1b48ePV6tWrdS/f39Tc5shJSVFc+bM0cKFCxUWFlZ2XzoiIkKhoaGKiIjQPffcozFjxigyMlLh4eH6v//7PyUmJuq6664zO75XXexY7d69W3PmzNHAgQMVFRWlr776Sg888IBuvPFGp+mqVpeamqqkpCQ1a9ZMx48f15w5c5SRkaFly5ZxTl2gomPFOXW+sLCw855xk6T69esrKiqqbNzUc6vG52NZ0KeffmqX5PQ1bNgwu/3n6eCPPfaYPSYmxm6z2ex9+vSx79ixw+zYpqjoWJ04ccLer18/e6NGjey1a9e2N2/e3H7ffffZDx48aHZsU7g6TpLsr732WtlnTp48af/rX/9qv+SSS+z16tWz//rXv7bn5uaamtsMFztWe/futd944432yMhIu81ms7dq1co+btw4e35+vtnRvW748OH25s2b2+vUqWNv1KiRvU+fPvaPP/64bDvnlENFx4pz6uIunCpv5rkVZDf+ogAAALAEnrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkB4Pd+/PFHxcbGatq0aWVjn3/+uerUqaMVK1aYmg2A9/HiTACWsGTJEg0ZMkSff/652rRpo06dOmnw4MH6+9//bnY0AF5GuQFgGSkpKfrkk0/UpUsXbd26VV988YVsNpvZsQB4GeUGgGWcPHlS7du3V05OjjZt2qSrrrrK7EgATMAzNwAsY/fu3Tpw4IBKS0v1/fffmx0HgEm4cgPAEk6fPq2uXbuqU6dOatOmjZ577jlt3bpV0dHRZkcD4GWUGwCWMG7cOM2bN09btmxRgwYN1KNHD0VERGjx4sVmRwPgZdyWAuD3MjIy9Nxzz+nNN99UeHi4goOD9eabb+qzzz7Tyy+/bHY8AF7GlRsAAGApXLkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACW8v+tW/HWk6DoWgAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -859,7 +859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 17,
    "id": "4f2792d2",
    "metadata": {},
    "outputs": [
@@ -867,37 +867,37 @@
      "data": {
       "text/plain": [
        "{'davies': np.float64(0.023118172598819948),\n",
-       " 'estimates': {'const': {'estimate': np.float64(0.8499999999999999),\n",
-       "   'se': np.float64(1.6958936828758677e-16),\n",
+       " 'estimates': {'const': {'estimate': np.float64(0.8500000000000002),\n",
+       "   'se': np.float64(6.783574731503469e-16),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(5012106646677192.0),\n",
+       "   't_stat': np.float64(1253026661669298.8),\n",
        "   'p_t': np.float64(nan)},\n",
-       "  'beta1': {'estimate': np.float64(0.0737221171410883),\n",
-       "   'se': np.float64(2.2989739197933858e-17),\n",
+       "  'beta1': {'estimate': np.float64(0.0712569461146603),\n",
+       "   'se': np.float64(8.888396973636247e-17),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(3206740037647485.5),\n",
+       "   't_stat': np.float64(801685009411872.1),\n",
        "   'p_t': '-'},\n",
-       "  'breakpoint1': {'estimate': np.float64(31.183107251843573),\n",
+       "  'breakpoint1': {'estimate': np.float64(30.87808227209066),\n",
        "   'se': np.float64(0.0),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
        "   't_stat': '-',\n",
        "   'p_t': '-'},\n",
-       "  'alpha1': {'estimate': np.float64(0.015000000000000005),\n",
-       "   'se': np.float64(7.850462293418878e-18),\n",
+       "  'alpha1': {'estimate': np.float64(0.014999999999999993),\n",
+       "   'se': np.float64(3.1401849173675495e-17),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(1910715501757731.8),\n",
+       "   't_stat': np.float64(477678875439432.8),\n",
        "   'p_t': np.float64(nan)},\n",
-       "  'alpha2': {'estimate': np.float64(0.0887221171410883),\n",
-       "   'se': np.float64(1.7622693546852893e-17),\n",
+       "  'alpha2': {'estimate': np.float64(0.08625694611466028),\n",
+       "   'se': np.float64(6.754591127167648e-17),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(5034537819386443.0),\n",
+       "   't_stat': np.float64(1277012101705551.5),\n",
        "   'p_t': np.float64(nan)}},\n",
-       " 'bic': np.float64(-293.8944045574168),\n",
-       " 'rss': np.float64(1.232595164407831e-32),\n",
+       " 'bic': np.float64(-inf),\n",
+       " 'rss': np.float64(0.0),\n",
        " 'converged': True}"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -910,41 +910,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 18,
    "id": "bc758db0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'const': {'estimate': np.float64(0.8499999999999999),\n",
-       "  'se': np.float64(1.6958936828758677e-16),\n",
+       "{'const': {'estimate': np.float64(0.8500000000000002),\n",
+       "  'se': np.float64(6.783574731503469e-16),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(5012106646677192.0),\n",
+       "  't_stat': np.float64(1253026661669298.8),\n",
        "  'p_t': np.float64(nan)},\n",
-       " 'beta1': {'estimate': np.float64(0.0737221171410883),\n",
-       "  'se': np.float64(2.2989739197933858e-17),\n",
+       " 'beta1': {'estimate': np.float64(0.0712569461146603),\n",
+       "  'se': np.float64(8.888396973636247e-17),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(3206740037647485.5),\n",
+       "  't_stat': np.float64(801685009411872.1),\n",
        "  'p_t': '-'},\n",
-       " 'breakpoint1': {'estimate': np.float64(31.183107251843573),\n",
+       " 'breakpoint1': {'estimate': np.float64(30.87808227209066),\n",
        "  'se': np.float64(0.0),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
        "  't_stat': '-',\n",
        "  'p_t': '-'},\n",
-       " 'alpha1': {'estimate': np.float64(0.015000000000000005),\n",
-       "  'se': np.float64(7.850462293418878e-18),\n",
+       " 'alpha1': {'estimate': np.float64(0.014999999999999993),\n",
+       "  'se': np.float64(3.1401849173675495e-17),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(1910715501757731.8),\n",
+       "  't_stat': np.float64(477678875439432.8),\n",
        "  'p_t': np.float64(nan)},\n",
-       " 'alpha2': {'estimate': np.float64(0.0887221171410883),\n",
-       "  'se': np.float64(1.7622693546852893e-17),\n",
+       " 'alpha2': {'estimate': np.float64(0.08625694611466028),\n",
+       "  'se': np.float64(6.754591127167648e-17),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(5034537819386443.0),\n",
+       "  't_stat': np.float64(1277012101705551.5),\n",
        "  'p_t': np.float64(nan)}}"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -956,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 19,
    "id": "7f6316fa",
    "metadata": {},
    "outputs": [],
@@ -966,7 +966,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 20,
    "id": "738f6b41",
    "metadata": {},
    "outputs": [
@@ -974,7 +974,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LT1 occurs at: 154.81 watts\n"
+      "LT1 occurs at: 93.04 watts\n"
      ]
     }
    ],
@@ -987,7 +987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 21,
    "id": "a5d00229",
    "metadata": {},
    "outputs": [
@@ -997,18 +997,22 @@
        "<Axes: title={'center': 'LT1 Standard Increment Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
+<<<<<<< HEAD
 <<<<<<< HEAD
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",
 >>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
 =======
       "image/png": 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",
 >>>>>>> c6b823d (2 breakpoint detection instead of 1 in piecwise regression for improved determination of lt1)
+=======
+      "image/png": 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CBQsc5yUkJGjatGlOr5eXn/vVBAcHKyYmJk/eC0D+wUgQgHzjk08+cbqHygX9+vVTgQIFrvn9X331leOePCkpKdq5c6feeustSVK7du1Uu3btK37vyJEjtWXLFkVGRjrO27p1q2bNmqXChQs73Y3+lltu0eTJk/XWW2+pcuXKKl68uO666y61bdtWb775pnr06KFGjRrp119/1ezZsy+7ficzfH199dZbb+npp5/WXXfdpYcfflgHDhzQ9OnTM7xm27ZtFRUVpQcffFBt2rTRgQMHNGXKFNWoUeOyv6BeztChQ/Xtt9+qcePG6t27t1JTUzVp0iTVrFlTO3fuzNbPkFkDBw7UggUL1LFjR/Xs2VO33HKLTp8+rSVLlmjKlClOo16X8vLy0kcffaTWrVurZs2a6tGjh8qUKaNjx45p5cqVCg0N1VdffZUjdXbt2lXz5s1Tr169tHLlSt1+++1KS0vTb7/9pnnz5mn58uWO0cPMuuWWW6Tz7Z47deokX19f3XfffY5wdC1eXl763//+d83zXnnlFc2dO1etW7dW3759VbhwYc2cOVMHDhzQwoULHU09nnzySb333nt67LHHtGXLFpUqVUqffvqpgoKCMrxvXn3uV3PLLbfoiy++0PPPP6/69esrJCRE9913X66/LwDXRggCkG9Mnjz5svu7d++eqRC0cOFCzZw50/F827Zt2rZtmySpbNmyVw1Br732mubMmaPVq1dr9uzZSkhIUKlSpdSpUycNGjTIaTH+4MGDdejQIY0aNUpnz55VkyZNdNddd+m1115TfHy85syZoy+++EJ169bV0qVL9corr2Txk0j31FNPKS0tTaNHj9bAgQN14403asmSJRo0aJDTed27d9fx48c1depULV++XDVq1NBnn32m+fPnO24seS21a9fW8uXL9fzzz2vw4MEqW7ashg4dqujo6FwPQSEhIVq7dq2GDBmiRYsWaebMmSpevLiaN2+usmXLXvP7mzZtqp9//tkxIhIXF6eSJUuqYcOGevrpp3OsTi8vLy1evFjjxo3TrFmztGjRIgUFBalixYrq16+fbrjhhiy/Zv369TVs2DBNmTJF3377rex2uw4cOJDpEJRZJUqU0Pr16/Xyyy9r0qRJSkpKUu3atfXVV1+pTZs2jvOCgoL0ww8/6LnnntOkSZMUFBSkLl26qHXr1mrVqpXTa+bV5341vXv31vbt2zV9+nSNGzdO5cuXJwQBkM3IqdWXAAAAAJAPsCYIAAAAgEchBAEAAADwKIQgAAAAAB6FEAQAAADAoxCCAAAAAHgUS0NQWlqao7VsYGCgKlWqpGHDhl3zxncAAAAAkF2W3ido5MiRmjx5smbOnKmaNWtq8+bN6tGjh8LCwtS3b99rfr/dbtfff/+tAgUKyGaz5UnNAAAAAFyPYRg6e/asSpcu7bjB85VYep+gtm3bqkSJEvr4448d+9q3b6/AwEB99tln1/z+o0ePKjw8PJerBAAAAJBfHDly5Jo30rZ0JKhRo0aaNm2a9u3bpxtuuEE7duzQunXrNHbs2Muen5ycrOTkZMfzC/ntyJEjCg0NzbO6AeS+lJQUTZ8+XZLUo0cP+fr6Wl0SAABwYbGxsQoPD1eBAgWuea6lI0F2u12vvfaaRo0aJW9vb6Wlpentt9/Wq6++etnz33jjDQ0dOjTD/piYGEIQ4Gbi4+MVEhIiSYqLi1NwcLDVJQEAABcWGxursLCwTGUDSxsjzJs3T7Nnz9acOXO0detWzZw5U++++65mzpx52fNfffVVxcTEOB5HjhzJ85oBAAAA5G+WTocbOHCgXnnlFXXq1EmSdOONN+rQoUMaPny4unXrluF8f39/+fv7W1ApAAAAAHdh6UhQQkJChs4N3t7estvtltUEAAAAwL1ZOhJ033336e2331a5cuVUs2ZNbdu2TWPHjlXPnj1z7D0Mw1BqaqrS0tJy7DXhzNvbWz4+PrQpBwAAQL5gaQiaNGmSBg0apN69e+vEiRMqXbq0nn76aQ0ePDhHXv/cuXOKjo5WQkJCjrweriwoKEilSpWSn5+f1aUAAAAAV2Vpd7jrdbUOEHa7Xfv375e3t7eKFSsmPz8/RipygWEYOnfunP7991+lpaWpSpUq17w5FZAZdIcDAABZkZXucJaOBOWmc+fOyW63Kzw8XEFBQVaX49YCAwPl6+urQ4cO6dy5cwoICLC6JLgBf39/ff31145tAACAnOK2IegCRiXyBp8zcpqPj4/atGljdRkAAMAN8ZsrAAAAAI/i9iNBAPKnlJQUzZ49W5LUpUsX+fr6Wl0SAABwE4QgAC7p3Llz6tGjhySpY8eOhCAAAJBjmA7ngrp37y6bzSabzSZfX1+VKFFCd999tz755JMs3Uh2xowZKliwYK7WCgAAAOQ3hKBMSEuTVq2S5s41v+bFfVdbtWql6OhoHTx4UN98842aNWumfv36qW3btkpNTc39AgAAAAA3RQi6hqgoKSJCatZM6tzZ/BoRYe7PTf7+/ipZsqTKlCmjunXr6rXXXtOXX36pb775RjNmzJAkjR07VjfeeKOCg4MVHh6u3r17Ky4uTpK0atUq9ejRQzExMY5RpTfeeEOS9Omnn6pevXoqUKCASpYsqc6dO+vEiRO5+wMBAAAALoIQdBVRUVKHDtLRo877jx0z9+d2ELrUXXfdpZtuuklR59/Yy8tLEydO1O7duzVz5kz9+OOPeumllyRJjRo10vjx4xUaGqro6GhFR0frxRdflM4vOB82bJh27NihxYsX6+DBg+revXve/jAAAACARWiMcAVpaVK/fpJhZDxmGJLNJvXvL91/v+TtnXd1VatWTTt37pQk9e/f37E/IiJCb731lnr16qUPPvhAfn5+CgsLk81mU8mSJZ1eo2fPno7tihUrauLEiapfv77i4uIUEhKSdz8MAAAAYAFGgq5g7dqMI0AXMwzpyBHzvLxkGIZsNpsk6fvvv1fz5s1VpkwZFShQQF27dtWpU6eUkJBw1dfYsmWL7rvvPpUrV04FChRQkyZNJEmHDx/Ok58BAAAA+V9amrRvwGQtHb07z9bN5xRC0BVER+fseTll7969qlChgg4ePKi2bduqdu3aWrhwobZs2aL3339fOt9a+Eri4+PVsmVLhYaGavbs2dq0aZMWLVp0ze8D8pq/v7/mzZunefPmyd/f3+pyAADARaKipGZl9qnS+GfV+qUb1bPZX3mybj6nMB3uCkqVytnzcsKPP/6oX3/9VQMGDNCWLVtkt9s1ZswYeXmZWXbevHlO5/v5+Sntkkj+22+/6dSpUxoxYoTCw8MlSZs3b867HwLIJB8fH3Xs2NHqMgAAwCUurJufbrwtb9n1tdrogCrKdn7d/IIFUmSk1VVeHSNBV9C4sVS2rLn253JsNik83DwvNyQnJ+v48eM6duyYtm7dqnfeeUf333+/2rZtq8cee0yVK1dWSkqKJk2apL/++kuffvqppkyZ4vQaERERiouL0w8//KCTJ08qISFB5cqVk5+fn+P7lixZomHDhuXODwEAAAC3cmHdfCVjvx7VZ5KkoRoiKX0tff/+rj81jhB0Bd7e0oQJ5valQejC8/Hjc68pwrfffqtSpUopIiJCrVq10sqVKzVx4kR9+eWX8vb21k033aSxY8dq5MiRqlWrlmbPnq3hw4c7vUajRo3Uq1cvPfzwwypWrJhGjRqlYsWKacaMGZo/f75q1KihESNG6N13382dHwK4DqmpqZo/f77mz5/PvbEAAHARF9bNvy5zFGip7tVm1Xcct2rdfFbZDONy/c/yh9jYWIWFhSkmJkahoaFOx5KSknTgwAFVqFBBAQEB2X6PqCgz7V7cJCE83AxArj7Ml5dy6vMGLoiPj3d0K4yLi1NwcLDVJQEA4PHmzpUGdf5Dv6mafJSmBtqgTWqQ4bw5c6RHHsnb2q6WDS7FmqBriIw022CvXWs2QShVypwCl5dtsQEAAABXUKqUOQrkozQtU+vLBiDl8br57CAEZYK3t9S0qdVVAAAAANZqXPpP3aFPpYvWAl3MZjPX1efWuvmcQggCAAAAkCneI96WlKZv1EqbbA2lixbW5MW6+ZxCYwQAAAAA1/bXX9KsWZKkgOFDVKaM8+GyZfNHe2wxEgQAAAAgU95+2+x93bKlmr1yqw4OzL/r5glBAAAAAK7uolEgDTHXAuXndfOEIAAuyc/PT9OnT3dsAwAAC73zjpSaKt1zj3TbbVZXc90IQQBckq+vr7p37251GQAA4MABaeZMc3tIxo5w+RGNEQAAAABc2YVRoLvvlho1srqaHEEI8kCrVq2SzWbTmTNnMv09ERERGj9+fK7WBVwsNTVVS5cu1dKlS5Wammp1OQAAeKaDB6UZM8xtNxkFEiHINXXv3l02m029evXKcKxPnz6y2WxME4LbS05OVtu2bdW2bVslJydbXQ4AAJ7pzTfTR4Fuv93qanIMIchFhYeH6/PPP1diYqJjX1JSkubMmaNy5cpZWhsAAAA8wL596WuBhg2zupoc5VkhyDCk+Pi8fxhGJopzVrduXYWHhysqKsqxLyoqSuXKlVOdOnUc+5KTk9W3b18VL15cAQEBuuOOO7Rp0yan11q2bJluuOEGBQYGqlmzZjp48GCG91u3bp0aN26swMBAhYeHq2/fvoqPj89y3QAAAHATb7wh2e3SffdJDRtaXU2O8qwQlJAghYTk/SMhIVvl9uzZ09EiWJI++eQT9ejRw+mcl156SQsXLtTMmTO1detWVa5cWS1bttTp06clSUeOHFFkZKTuu+8+bd++XU888YReeeUVp9f4888/1apVK7Vv3147d+7UF198oXXr1unZZ5/NVt0AAADI5379Vfr8c3P7zTetribHeVYIymceffRRrVu3TocOHdKhQ4f0008/6dFHH3Ucj4+P1+TJkzV69Gi1bt1aNWrU0IcffqjAwEB9/PHHkqTJkyerUqVKGjNmjKpWraouXbpkWE80fPhwdenSRf3791eVKlXUqFEjTZw4UbNmzVJSUlKe/9wAAACw2JAh5mymjh2lm2+2upoc51n3CQoKkuLirHnfbChWrJjatGmjGTNmyDAMtWnTRkWLFnUc//PPP5WSkqLbL1qk5uvrqwYNGmjv3r2SpL1796rhJcOXt11yg6sdO3Zo586dmj17tmOfYRiy2+06cOCAqlevnq36AQAAkA9t2SItWiR5eUlDh1pdTa7wrBBks0nBwVZXkSU9e/Z0TEt7//33c+U94uLi9PTTT6tv374ZjtGEAQAAwMP873/m1y5dJDf9x3DPCkH5UKtWrXTu3DnZbDa1bNnS6VilSpXk5+enn376SeXLl5ckpaSkaNOmTerfv78kqXr16lqyZInT9/3yyy9Oz+vWras9e/aocuXKuf7zAJnl5+en9957z7ENAADywLp10rffSj4+bnVfoEsRglyct7e3Y2qbt7e307Hg4GA988wzGjhwoAoXLqxy5cpp1KhRSkhI0OOPPy5J6tWrl8aMGaOBAwfqiSee0JYtWzTjwg2vznv55Zd166236tlnn9UTTzyh4OBg7dmzRytWrHD8EgrkNV9fX/Xp08fqMgAA8ByGkT4K1LOnVKmS1RXlGhoj5AOhoaEKDQ297LERI0aoffv26tq1q+rWras//vhDy5cvV6FChaTz09kWLlyoxYsX66abbtKUKVP0zjvvOL1G7dq1tXr1au3bt0+NGzdWnTp1NHjwYJUuXTpPfj4AAAC4gB9/lFavlvz80sOQm7IZRjZuYuMiYmNjFRYWppiYmAwhISkpSQcOHFCFChUUEBBgWY2egs8bOS0tLU1r166VJDVu3DjDSCgAAMhBhiE1aiT98ovUt680YYLVFWXZ1bLBpZgOB8AlJSUlqVmzZtL55h3B+aypCQAA+cqyZWYACgyUXn3V6mpyHdPhAAAAAE9mt0uDBpnbzz0nlSxpdUW5jhAEAAAAeLKFC6Vt26QCBaSXXrK6mjxBCAIAAAA8VUqK9Prr5vbzz0tFilhdUZ5w+xCUj/s+5Ct8zgAAAPnQJ59I+/dLxYpJL7xgdTV5xm1DkK+vryQpISHB6lI8woXP+cLnDgAAABeXkCANHWpu/+9/5nQ4D+G23eG8vb1VsGBBnThxQpIUFBQkm81mdVluxzAMJSQk6MSJEypYsCBtjAEAAPKLCROk6GgpIkJ6+mmrq8lTbhuCJKnk+c4WF4IQck/BggUdnzeQE3x9fTVq1CjHNgAAyEGnT0sjR5rbw4ZJ/v5WV5Sn3PZmqRdLS0tTSkpKntbmSXx9fRkBAgAAyE8GDpTefVe68UazM5wb/C7HzVIv4e3tzS/pAAAAgCQdOSJNmmRuDx/uFgEoqzwiBAHIf9LS0rR161ZJUt26dfmHDAAAcsobb0jJyVLjxtK991pdjSUIQQBcUlJSkho0aCBJiouLU3BwsNUlAQCQ/+3ZI82YYW6PHCl5aOMwt22RDQAAAOASr78u2e3S/fdLt91mdTWWIQQBAAAAnuDnn6XFiyUvL+mdd6yuxlKWhqCIiAjZbLYMjz59+lhZFgAAAOBeDEN65RVzu1s3qUYNqyuylKVrgjZt2qS0tDTH8127dunuu+9Wx44drSwLAAAAcC/ffCOtWWPeD2joUKursZylIahYsWJOz0eMGKFKlSqpSZMmltUEAAAAuJXUVPO+QJL07LNSeLjVFVnOZbrDnTt3Tp999pmef/552a7QpSI5OVnJycmO57GxsXlYIQAAAJAPTZ9udoUrVMhsjADXCUGLFy/WmTNn1L179yueM3z4cA1l+A7wCL6+vhoyZIhjGwAAZENcnDRokLk9eLAZhCCbYRiG1UVIUsuWLeXn56evvvrqiudcbiQoPDxcMTExCg0NzaNKAQAAgHxiyBDpzTelSpXM0SA/P6sryjWxsbEKCwvLVDZwiZGgQ4cO6fvvv1dUVNRVz/P395e/v3+e1QUAAADkW8eOSaNHm9sjRrh1AMoqlwhB06dPV/HixdWmTRurSwHgIux2u/bu3StJql69ury8uK0ZAABZMniwlJho3hS1fXurq3Eplocgu92u6dOnq1u3bvLxsbwcAC4iMTFRtWrVkiTFxcUpODjY6pIAAMg/du40GyJI0pgx0hUaj3kqy/9p9fvvv9fhw4fVs2dPq0sBAAAA3MPAgeYNUjt2NEeC4MTyoZd77rlHLtKbAQAAAMj/li+XvvtO8vWVhg+3uhqXZPlIEAAAAIAckpYmvfiiuf3ss2ZXOGRACAIAAADcxYwZ0q5dUsGC0v/+Z3U1LosQBAAAALiDi2+MOmiQVLiw1RW5LEIQAAAA4A7efVeKjpYqVJD69LG6GpdmeWMEALgcX19fvXh+TrOvr6/V5QAA4NqOHJFGjTK3R4yQ/P2trsilEYIAuCQ/Pz+NvnCXawAAcHUvv2zeGLVxY7MtNq6K6XAAAABAfrZ+vTR3rnlD1AkTuDFqJjASBMAl2e12HT58WJJUrlw5eXnxbzYAAGRgt0v9+pnbPXtKdepYXVG+QAgC4JISExNVoUIFSVJcXJyCg4OtLgkAANcza5a0ebNUoID09ttWV5Nv8E+rAAAAQH509qz06qvm9qBBUokSVleUbxCCAAAAgPxo+HDp+HGpUiWpb1+rq8lXCEEAAABAfvPXX9KYMeb2mDG0xM4iQhAAAACQ3wwcKJ07J7VoIbVrZ3U1+Q4hCAAAAMhPVq2SoqIkLy9p3DhaYmcDIQgAAADIL9LS0lti9+ol1apldUX5Ei2yAbgkHx8f9e7d27ENAAAkffSRtHOnVKiQ9OabVleTb/GbBQCX5O/vr/fff9/qMgAAcB2nT0uvv25uv/GGVKSI1RXlW0yHAwAAAPKD11+XTp0yp8Cdny2B7GEkCIBLMgxDJ0+elCQVLVpUNhZ9AgA82ZYt0tSp5vZ770lMFb8ufHoAXFJCQoKKFy8uSYqLi1NwcLDVJQEAYA27XXr2WckwpEcekZo0sbqifI/pcAAAAIArmzlT+uUXKSREevddq6txC4QgAAAAwFX995/08svm9pAhUunSVlfkFghBAAAAgKsaPFj691+pevX0+wPhuhGCAAAAAFe0fbv0wQfm9qRJkq+v1RW5DUIQAAAA4GoMw2yGYLdLHTtKzZtbXZFbIQQBAAAAruazz6SffpKCgqQxY6yuxu3QIhuAS/Lx8VG3bt0c2wAAeIyYGGngQHN70CApPNzqitwOv1kAcEn+/v6aMWOG1WUAAJD33nhD+ucf6YYbpOeft7oat8R0OAAAAMBVbNsmTZxobk+cKPn5WV2RW2IkCIBLMgxDCQkJkqSgoCDZbDarSwIAIHelpUm9epnNEB56SGrZ0uqK3BYjQQBcUkJCgkJCQhQSEuIIQwAAuLVp06SNG6XQUGncOKurcWuEIAAAAMBqx49Lr75qbr/9tlS6tNUVuTVCEAAAAGC1AQPMrnD16knPPGN1NW6PEAQAAABY6bvvpM8/l7y8pKlTJW9vqytye4QgAAAAwCqJiVLv3ub2c89JdetaXZFHIAQBAAAAVhk+XPrzT3MN0JtvWl2NxyAEAQAAAFb47TdpxAhze+JEsysc8gT3CQLgkry9vdWhQwfHNgAAbsUwzHsCpaRI994rRUZaXZFHIQQBcEkBAQGaP3++1WUAAJA7Pv1UWr1aCgyU3n9f4qbgeYrpcAAAAEBeOnlSeuEFc3vIECkiwuqKPA4hCAAAAMhLAwaYQahWLXMbeY4QBMAlxcfHy2azyWazKT4+3upyAADIGd9+K332mTn97eOPJT8/qyvySIQgAAAAIC/ExUlPP21u9+snNWhgdUUeixAEAAAA5IX//U86fNhcA/TWW1ZX49EIQQAAAEBu++UX815AkjR1qhQcbHVFHo0QBAAAAOSmc+ekJ54w7w302GPSPfdYXZHHIwQBAAAAuWnECGn3bqlYMWnsWKurASEIAAAAyEV79qSv/5k4USpSxOqKIMnH6gIA4HK8vb117733OrYBAMh37HbpySellBSpTRvp4YetrgjnEYIAuKSAgAAtXbrU6jIAAMi+yZOl9eulkBBz22azuiKcx3Q4AAAAIKcdOiS98oq5PWKEFB5udUW4CCEIAAAAyEmGYXaDi4uTbr9deuYZqyvCJSwPQceOHdOjjz6qIkWKKDAwUDfeeKM2b95sdVkALBYfH6/g4GAFBwcrPj7e6nIAAMi8Dz+Uvv9eCgyUpk+XvCz/lRuXsHRN0H///afbb79dzZo10zfffKNixYpp//79KlSokJVlAXARCQkJVpcAAEDWHDokvfCCuf3OO1KVKlZXhMuwNASNHDlS4eHhmj59umNfhQoVrCwJAAAAyJ5Lp8E995zVFeEKLB2bW7JkierVq6eOHTuqePHiqlOnjj788MMrnp+cnKzY2FinBwAAAOASLp0Gxy0eXJalIeivv/7S5MmTVaVKFS1fvlzPPPOM+vbtq5kzZ172/OHDhyssLMzxCKfLBgAAAFwB0+DyFZthGIZVb+7n56d69epp/fr1jn19+/bVpk2b9PPPP2c4Pzk5WcnJyY7nsbGxCg8PV0xMjEJDQ/OsbgC5Lz4+XiEhIZKkuLg4BQcHW10SAACXZxjSPfeYo0C33y6tXs0okAViY2MVFhaWqWxg6UhQqVKlVKNGDad91atX1+HDhy97vr+/v0JDQ50eAAAAgKWYBpfvWNoY4fbbb9fvv//utG/fvn0qX768ZTUBcA1eXl5q0qSJYxsAAJfENLh8ydIQNGDAADVq1EjvvPOOHnroIW3cuFHTpk3TtGnTrCwLgAsIDAzUqlWrrC4DAIAroxtcvmXpP6/Wr19fixYt0ty5c1WrVi0NGzZM48ePV5cuXawsCwAAALi2yZOZBpdPWdoY4XplZfETAAAAkGP27ZNuvllKTJQmTmQUyAXkm8YIAHAl8fHxKlasmIoVK6b4+HirywEAIF1qqtS1qxmAWrSQ+vSxuiJkkaVrggDgak6ePGl1CQAAZPTOO9LGjVLBguY0OBr45DtcMQAAACCzNm+W3nzT3H7/falsWasrQjYQggAAAIDMSEgwp8GlpUkPPSQ98ojVFSGbCEEAAABAZrzyivTbb1KpUmZnOJvN6oqQTYQgAAAA4FpWrJAmTTK3p0+XChe2uiJcB0IQAAAAcDX//Sf16GFu9+4ttWxpdUW4TnSHA+CSvLy8VK9ePcc2AACWefZZ6dgxqUoVadQoq6tBDiAEAXBJgYGB2rRpk9VlAAA83dy50pw5kre39OmnUnCw1RUhBxCCAAAA4DHS0qS1a6XoaLO/QePGZr65rIMHpV69zO3//U9q2DAvS0UuIgQBAADAI0RFSf36SUePpu8rW1aaMEGKjLzk5NRUqUsXKTZWatTIDEFwG0y0B+CSEhISFBERoYiICCUkJFhdDgAgn4uKkjp0cA5AkrnUp0MH87iTt96S1q+XQkOl2bMlH8YO3AkhCIBLMgxDhw4d0qFDh2QYhtXlAADysbQ0cwTocn+dXNjXv795niRp3Tpp2DBze8oUKSIi74pFniAEAQAAwK2tXZtxBOhihiEdOWKepzNnzGlwdrv02GPSI4/kZanII4QgAAAAuLXo6Eye97chPf20dPiwVLGi9N57uV0aLEIIAgAAgFsrVSpz59XZOVOaN89c/zN3rlSgQG6XBosQggAAAODWGjc2u8DZbJc/brNJjUvuV9X3njV3vPmm1KBBntaIvEUIAgAAgFvz9jbbYEsZg5DNJvka5/RlcGfZ4uOlJk2kl16ypE7kHUIQAJdks9lUo0YN1ahRQ7Yr/dMdAACZFBkpLVgglSnjvL9sWenXdq+r0J+bpUKFpE8/vcrdU+EubEY+7j0bGxursLAwxcTEKDQ01OpyAAAA4OLS0swucNHR5lqhxrFL5X1/W/NgVJT04INWl4hsyko24K5PAAAA8Bje3lLTpuefHDki3fyYuf3ccwQgD8J0OAAAAHielBTzHkCnT0u33CKNHm11RchDhCAALikhIUE1a9ZUzZo1lZCQYHU5AAB3M3iw9NNPUmio9MUXkr+/1RUhDzEdDoBLMgxDe/bscWwDAJBjvv1WGjHC3P7oI6lSJasrQh5jJAgAAACe49gxqWtXc/uZZ6SOHa2uCBYgBAEAAMAzpKZKnTtLJ09KN98sjR1rdUWwCCEIAAAAnmHoUGnNGikkRJo3TwoIsLoiWIQQBAAAAPe3YoX09tvm9rRpUpUqVlcECxGCAAAA4N6OHDGnwRmG9OSTZmtseDS6wwFwSTabTeXLl3dsAwCQLefOmc0PTp6U6tSRJkywuiK4AEIQAJcUFBSkgwcPWl0GACC/e+EFacMGqWBBacECKTDQ6orgApgOBwAAAPc0Z4703nvm9qefShUrWl0RXAQhCAAAAO5n925z/Y8kvf661Lat1RXBhRCCALikxMRE1a9fX/Xr11diYqLV5QAA8pPYWCkyUkpIkFq0MFtjAxdhTRAAl2S327V582bHNgAAmWIYUs+e0r59Utmy5pQ4b2+rq4KLYSQIAAAA7mPcOGnhQsnXV5o/XypWzOqK4IIIQQAAAHAPa9dKL71kbo8bJ916q9UVwUURggAAAJD/HT0qdeggpaVJXbpIvXtbXRFcGCEIAAAA+VtSktkI4cQJqXZtaepUiRtt4yoIQQAAAMi/DEPq1UvatEkqXFhavFgKDra6Krg4usMBcFlFixa1ugQAgKt77z1p5kzJy0uaN0+qUMHqipAPEIIAuKTg4GD9+++/VpcBAHBlq1ZJAwaY26NHS82bW10R8gmmwwEAACD/OXxY6tgxvRHChTAEZAIhCAAAAPlLQoL0wAPSyZNS3brShx/SCAFZQggC4JISExPVtGlTNW3aVImJiVaXAwBwFYYhPfWUtG2beSPURYukwECrq0I+w5ogAC7Jbrdr9erVjm0AAKTzN0GdPVvy9jYbIZQrZ3VFyIcYCQIAAED+sGyZNHCguT12rNS0qdUVIZ8iBAEAAMD17d4tdeok2e3SE09Izz1ndUXIxwhBAAAAcG0nT0r33SedPSs1aSK9/z6NEHBdCEEAAABwXefOSZGR0oEDUqVK0sKFkp+f1VUhnyMEAQAAwDUZhvTMM9LatVJoqPTVV1KRIlZXBTdgaQh64403ZLPZnB7VqlWzsiQALiQoKEhBQUFWlwEAsMq4cdInn0heXtIXX0jVq1tdEdyE5S2ya9asqe+//97x3MfH8pIAuIDg4GDFx8dbXQYAwCpLl0ovvmhujx0rtWpldUVwI5YnDh8fH5UsWdLqMgAAAOAqdu2SHnkk/caofftaXRHcjOVrgvbv36/SpUurYsWK6tKliw4fPnzFc5OTkxUbG+v0AAAAgBs5flxq29bsBNe0qfTee3SCQ46zNAQ1bNhQM2bM0LfffqvJkyfrwIEDaty4sc6ePXvZ84cPH66wsDDHIzw8PM9rBpA3kpKS1KZNG7Vp00ZJSUlWlwMAyAvx8WYr7EOHpMqVpQULJF9fq6uCG7IZhmFYXcQFZ86cUfny5TV27Fg9/vjjGY4nJycrOTnZ8Tw2Nlbh4eGKiYlRaGhoHlcLIDfFx8crJCREkhQXF6fg4GCrSwIA5Ka0NKl9e+nLL80OcL/8YgYhIJNiY2MVFhaWqWxg+ZqgixUsWFA33HCD/vjjj8se9/f3l7+/f57XBQAAgFz2wgtmAPL3N78SgJCLLF8TdLG4uDj9+eefKlWqlNWlAAAAIK9MnChNmGBuz5ol3X671RXBzVkagl588UWtXr1aBw8e1Pr16/Xggw/K29tbjzzyiJVlAQAAIK98+aXUv7+5PXKk9NBDVlcED2DpdLijR4/qkUce0alTp1SsWDHdcccd+uWXX1SsWDErywIAAEBe2LTJuRX2wIFWVwQPYWkI+vzzz618ewAAAFjl4EGzE1xionkj1PffpxU28oxLrQkCAACABzh9Wrr3Xumff6SbbpLmzZN8XKpfF9wc/7UBcEnBwcFyoQ7+AICckphojgDt3SuVKSN9/bVUoIDVVcHDMBIEAACAvJGaKnXqJK1fLxUsKH37rVS2rNVVwQMRggAAAJD7DEPq00dassS8F9CSJVKtWlZXBQ9FCALgkpKSktSxY0d17NhRSUlJVpcDALheQ4dK06ZJXl7S3LlS48ZWVwQPZjPy8aT72NhYhYWFKSYmRqGhoVaXAyAHxcfHKyQkRDp/I+Xg4GCrSwIAZNfUqVKvXub25Mnp20AOyko2YCQIAAAAuWfxYql3b3N70CACEFwCIQgAAAC5Y90682aodrv0xBPmlDjABRCCAAAAkPN27jRbYSclmV8nT+ZmqHAZhCAAAADkrD/+kO65RzpzRmrUSPr8c26GCpdCCAIAAEDOOXZMatFC+ucfqXZt82aoQUFWVwU4IQQBAAAgZ5w8Kd19t3TokFS5svTdd1KhQlZXBWSQ7RCUmpqq77//XlOnTtXZs2clSX///bfi4uJysj4AHiooKEhxcXGKi4tTEP+CCACuLzZWatVK2rtXKlNG+v57qUQJq6sCLitbkzMPHTqkVq1a6fDhw0pOTtbdd9+tAgUKaOTIkUpOTtaUKVNyvlIAHsVms3FvIADILxITpXbtpC1bpKJFpRUrpPLlra4KuKJsjQT169dP9erV03///afAwEDH/gcffFA//PBDTtYHAAAAV5aSIj30kLR6tVSggPTtt1L16lZXBVxVtkaC1q5dq/Xr18vPz89pf0REhI4dO5ZTtQHwYMnJyXr66aclSVOnTpW/v7/VJQEALmW3Sz16mM0PAgKkr76SbrnF6qqAa8rWSJDdbldaWlqG/UePHlWBAgVyoi4AHi41NVUzZ87UzJkzlZqaanU5AIBL2e3S009Ls2eb7a8XLJCaNLG6KiBTshWC7rnnHo0fP97x3GazKS4uTkOGDNG9996bk/UBAADA1RiG1K+f9NFHkpeX9OmnUps2VlcFZFq2psONGTNGLVu2VI0aNZSUlKTOnTtr//79Klq0qObOnZvzVQIAAMA1GIY0cKD03nuSzSZNny516mR1VUCWZCsElS1bVjt27NAXX3yhHTt2KC4uTo8//ri6dOni1CgBAAAAbmbwYGnMGHN7yhTpscesrgjIMpthGEZWv2nNmjVq1KiRfHycM1RqaqrWr1+vO++8MydrvKLY2FiFhYUpJiZGoaGhefKeAPJGfHy8QkJCJElxcXG0ywYAV/DWW9KgQeb2xInSc89ZXRHgkJVskK01Qc2aNdPp06cz7I+JiVGzZs2y85IAAABwZe++mx6ARo8mACFfy1YIMgxDNpstw/5Tp07xr7UAAADuZtIkcx2Qzo8Gvfii1RUB1yVLa4IiIyOl893gunfv7nTfjrS0NO3cuVONGjXK+SoBeJygoCCdOHHCsQ0AsMjkyVLfvub2//4nvf661RUB1y1LISgsLEw6PxJUoEABpyYIfn5+uvXWW/Xkk0/mfJUAPI7NZlOxYsWsLgMAPNv770vPPmtuv/ii9OabVlcE5IgshaDp06dLkiIiIvTiiy8y9Q0AAMBdvfde+rqfgQOlkSPNltiAG8hWdzhXQXc4wH0lJyfr+eeflySNHTvWafotACCXTZxo3gxVkl5+WRo+nAAEl5eVbJDtELRgwQLNmzdPhw8f1rlz55yObd26NTsvmWWEIMB90SIbACwyYYLUv7+5/cor0jvvEICQL+R6i+yJEyeqR48eKlGihLZt26YGDRqoSJEi+uuvv9S6devs1g0AAAArjRuXHoBee40ABLeVrRD0wQcfaNq0aZo0aZL8/Pz00ksvacWKFerbt69iYmJyvkoAAADkrrFjpfPTkPX662YrbAIQ3FS2QtDhw4cdrbADAwN19uxZSVLXrl01d+7cnK0QAAAAuWvUKOmFF8ztQYOkYcMIQHBr2QpBJUuW1OnTpyVJ5cqV0y+//CJJOnDggPJxnwUAAADPYhjS4MFm8wPJ3B46lAAEt5etEHTXXXdpyZIlkqQePXpowIABuvvuu/Xwww/rwQcfzOkaAQAAkNMMw7z3z7Bh5vPhwwlA8BjZ6g5nt9tlt9vl42PeZujzzz/X+vXrVaVKFT399NPy8/PLjVozoDsc4L7oDgcAuchul/r0kaZMMZ9PnJh+TyAgn8r1FtmHDx9WeHi4bJf8S4FhGDpy5IjKlSuX9aqzgRAEuC+73a7Dhw9L56fdenlla+AaAHCp1FTp8celWbPMUZ8PPzSfA/lcVrKBT3beoEKFCoqOjlbx4sWd9p8+fVoVKlRQWlpadl4WABy8vLwUERFhdRkA4F7OnZMefVSaP1/y9jaDUOfOVlcF5LlshSDDMDKMAun8lJWAgICcqAsAAAA5KSlJ6tBBWrpU8vWVvvhCYi03PFSWQtDz53vH22w2DRo0SEFBQY5jaWlp2rBhg26++eacrxKAxzl37pxef/11SdLbb7+dZ2sNAcAtxcZK998vrVolBQRIixZJrVpZXRVgmSyFoG3btknnR4J+/fVXp19K/Pz8dNNNN+nFF1/M+SoBeJyUlBS9++67kqQ33niDEAQA2fXvv1Lr1tKWLVJIiPTVV1LTplZXBVgqSyFo5cqV0vm22BMmTKAZAQAAgCs7fFi6+25p3z6paFHp22+lW26xuirActlqtzR+/HilpqZm2H/69GnFxsbmRF0AAAC4Hnv3SrffbgagcuWkdesIQMB52QpBnTp10ueff55h/7x589SpU6ecqAsAAADZtXGj1LixdPSoVL26GYCqVrW6KsBlZCsEbdiwQc2aNcuwv2nTptqwYUNO1AUAAIDs+P576a67pFOnpPr1pTVrpPBwq6sCXEq2QlBycvJlp8OlpKQoMTExJ+oCAABAVi1YILVpI8XHSy1aSD/8YK4FAuAkWyGoQYMGmjZtWob9U6ZM0S3MNQUAAMh7EydKDz1k3hC1Qwfp66+lAgWsrgpwSdm6Wepbb72lFi1aaMeOHWrevLkk6YcfftCmTZv03Xff5XSNADxQYGCgdu3a5dgGAFyB3S698oo0erT5vHdvMxB5e1tdGeCybIZhGNn5xu3bt2v06NHavn27AgMDVbt2bb366quqUqVKzld5BbGxsQoLC1NMTAztugEAgOdJTpZ69JDmzjWfDx8uvfyyZLNZXRmQ57KSDbIdglwBIQgAAHismBgpMlL68UfJx0f6+GPpscesrgqwTFayQbamw10sKSlJ586dc9pHIAFwvc6dO6d33nlHkvTaa6/Jz8/P6pIAwHUcOybde6+0c6cUEiItXCjdc4/VVQH5RrZGghISEvTSSy9p3rx5OnXqVIbjaWlpOVXfVTESBLiv+Ph4hYSESJLi4uIUHBxsdUkA4Br27JFatZKOHJFKlpSWLZPq1LG6KsByWckG2eoON3DgQP3444+aPHmy/P399dFHH2no0KEqXbq0Zs2ald26AQAAcDU//CA1amQGoKpVpZ9/JgAB2ZCt6XBfffWVZs2apaZNm6pHjx5q3LixKleurPLly2v27Nnq0qVLzlcKAADgyT7+WOrVS0pNlW6/XfryS6lIEaurAvKlbI0EnT59WhUrVpTOr/85ffq0JOmOO+7QmjVrslXIiBEjZLPZ1L9//2x9PwAAgFuy26VXX5WeeMIMQJ07S99/TwACrkO2QlDFihV14MABSVK1atU0b9486fwIUVhYWJZfb9OmTZo6dapq166dnXIAAADcU2Ki1KmTNGKE+XzwYOmzz6SAAKsrA/K1bIWgHj16aMeOHZKkV155Re+//74CAgI0YMAAvfTSS1l6rbi4OHXp0kUffvihChUqdNVzk5OTFRsb6/QAAABwSydOSHfdJc2fL/n6SjNnSkOHcg8gIAdkKwQNGDBAffv2lSS1aNFCv/32m+bMmaOVK1dq9+7dWXqtPn36qE2bNmrRosU1zx0+fLjCwsIcj/Dw8OyUDwAA4Nr27pVuvVX65RepUCFpxQruAQTkoGyFoEuVL19ekZGRCgsL08cff5zp7/v888+1detWDR8+PFPnv/rqq4qJiXE8jhw5ch1VA3BlAQEB2rhxozZu3KgApn0A8CTffivddpt04IBUqZLZAa5JE6urAtzKdd8sNbuOHDmifv36acWKFZn+Bcff31/+/v65XhsA63l7e6t+/fpWlwEAeccwpHHjpIEDzWYId9whLVokFS1qdWWA28mRkaDs2LJli06cOKG6devKx8dHPj4+Wr16tSZOnCgfH588u+EqAACA5ZKTpZ49pRdeMAPQ44+b9wQiAAG5wrKRoObNm+vXX3912tejRw9Vq1ZNL7/8sry9va0qDYALOHfunCZMmCBJ6tevn/z8/KwuCQByxz//SJGR0vr1kpeXNHas1LcvDRCAXJSlEBQZGXnV42fOnMn0axUoUEC1atVy2hccHKwiRYpk2A/A86SkpDi6Tfbu3ZsQBMA9bd8utWsnHTkihYVJX3whtWxpdVWA28tSCLrWPYDCwsL0GJ1LAAAAri0qSuraVUpIkKpUkb76Sqpa1eqqAI+QpRA0ffr03KtE0qpVq3L19QEAACyXlia98Yb01lvm87vvNkeArnG/RAA5x7I1QQAAAB7nzBmpSxdp2TLzeb9+0rvvSj78SgbkJf7EAQAA5IVdu6QHH5T++EMKCJA+/FB69FGrqwI8EiEIAAAgty1YIHXvLsXHS+XLm+uB6ta1uirAY1l2nyAAAAC3l5Ymvfqq1LGjGYCaN5c2byYAARZjJAiASwoICNDKlSsd2wCQ75w6Za7/Wb7cfP7ii9Lw4az/AVwAfwoBuCRvb281bdrU6jIAIHs2bjRHfw4flgIDpU8+kTp1sroqAOcxHQ4AACCnGIb0wQfSHXeYAahyZemXXwhAgIthJAiAS0pJSdG0adMkSU899ZR8fX2tLgkAri4+XnrqKWnOHPN5ZKQ5AnSNm80DyHs2wzAMq4vIrtjYWIWFhSkmJkahoaFWlwMgB8XHxyskJESSFBcXp+DgYKtLAoAr++03qX17ac8eydtbGjlSev55yWazujLAY2QlGzASBAAAcD3mzZMef1yKi5NKlZK++EJq3NjqqgBcBWuCAAAAsiMpSerdW3r4YTMANW0qbd1KAALyAUIQAABAVv3+u3TrrdLkyebzV16RVqyQSpa0ujIAmcB0OAAAgKz47DOpVy+zEUKxYtKsWVKrVlZXBSALGAkCAADIjPh4c+1P167mdtOm0vbtBCAgHyIEAQAAXMvu3VKDBmbLa5tNeuMN6fvvpdKlra4MQDYwHQ6AS/L399fXX3/t2AYASxiGNG2aNGCAlJhodn+bPVtq1szqygBcB0IQAJfk4+OjNm3aWF0GAE926pT0xBPS4sXm85YtzfU/xYtbXRmA68R0OAAAgEv98INUu7YZgHx9pTFjpGXLCECAm2AkCIBLSklJ0ezZsyVJXbp0ka+vr9UlAfAE585JgwZJo0ebU+GqVZPmzJHq1LG6MgA5yGYYhmF1EdkVGxursLAwxcTEKDQ01OpyAOSg+Ph4hYSESJLi4uIUHBxsdUkA3N2+fVLnztKWLebzp5+Wxo6VgoKsrgxAJmQlGzAdDgAAeDbDkD780Bzt2bJFKlxYioqSpkwhAAFuiulwAADAcx0/Lj35pHS+G6WaNZM+/VQqU8bqygDkIkaCAACAZ4qKkmrVMgOQn5/07rvmvX8IQIDbYyQIAAB4lpgYqW9fs921JN10k/TZZ2YgAuARGAkCAACeY9Uqs/X1rFmSl5f06qvSxo0EIMDDMBIEAADcX0KC9Prr0oQJZiOEihXNIHT77VZXBsAChCAALsnf31/z5s1zbANAtq1fL3XvLu3fbz5/4gmz9XWBAlZXBsAihCAALsnHx0cdO3a0ugwA+Vlionnj07FjzdGf0qWljz6SWre2ujIAFiMEAQAA9/PLL+boz++/m8+7d5fGjZMKFrS6MgAugMYIAFxSamqq5s+fr/nz5ys1NdXqcgDkF0lJ0ssvm2t9fv9dKlXKbIE9fToBCIADI0EAXFJycrIeeughSVJcXJx8fPjfFYBrWLfOXO9zYfTnscek8eOlQoWsrgyAi2EkCAAA5G9nz0rPPis1bmwGoJIlpSVLpJkzCUAALot/WgUAAPnXsmVSr17SkSPm88cfl0aPJvwAuCpCEAAAyH9OnpQGDJA++8x8XrGiNG2a1Ly51ZUByAeYDgcAAPIPw5DmzpVq1DADkJeX9Pzz0s6dBCAAmcZIEAAAyB/++kvq3Vtavtx8XrOm9PHHUsOGVlcGIJ9hJAgAALi2lBRpxAgz9CxfLvn7S0OHSlu3EoAAZAsjQQBckp+fn6ZPn+7YBuChfv5Zeuopadcu83mzZtKUKdINN+TYW6SlSWvXStHR5m2FGjeWvL1z7OUBuCBCEACX5Ovrq+7du1tdBgCrnDkjvfaaGXgMQypSRBo7VuraVbLZcuxtoqKkfv2ko0fT95UtK02YIEVG5tjbAHAxTIcDAACuwzCk2bOl6tWlyZPN5927S7/9Zt78NIcDUIcOzgFIko4dM/dHReXYWwFwMYQgAC4pNTVVS5cu1dKlS5Wammp1OQDywp490l13SY8+Kh0/bk55W7lSmj5dKlo0R98qLc0cATKMjMcu7Ovf3zwPgPthOhwAl5ScnKy2bdtKkuLi4uTjw/+uALcVFycNG2ZOd0tNlQIDpf/9T3rhBbMJQi5YuzbjCNDFDMO8/+ratVLTprlSAgAL8VsFAACwhmGYc876909PJO3amQtyIiJy9a2jo3P2PAD5CyEIAADkvd9/N+ejXbjnT0SENHGidN99efL2pUrl7HkA8hfWBAEAgLwTGysNHCjVqmUGID8/c+rb7t15FoAksw122bJX7rNgs0nh4eZ5ANwPIQgAAOQ+u12aNUuqWlV6911z7U+bNub9f4YNk4KC8rQcb29z1p2UMQhdeD5+PPcLAtwVIQgAAOSuzZulO+6QunUzu75Vrix9/bX5qFLFsrIiI6UFC6QyZZz3ly1r7uc+QYD7Yk0QAADIHf/8Y051+/hjswlCcLA0aJDZCCGXur5lVWSkdP/9Zhe46GhzDVDjxowAAe6OEATAJfn5+em9995zbAPIR5KTzblmb70lnT1r7uvSRRo5MuOwiwvw9qYNNuBpCEEAXJKvr6/69OljdRkAsuJCy+uBA6UDB8x9t9xiLq654w6rqwMAB0IQAAC4ftu2mdPc1qwxn5cqJQ0fLnXtKnmxBBmAa7H0/0qTJ09W7dq1FRoaqtDQUN1222365ptvrCwJgItIS0vTqlWrtGrVKqWlpVldDoAr+ftv6fHHzRGfNWukgABz3c++fWYjBAIQABdk6UhQ2bJlNWLECFWpUkWGYWjmzJm6//77tW3bNtWsWdPK0gBYLCkpSc2aNZMkxcXFKTg42OqSAFwsLs5sdT16tJSQYO7r3Nkc/SlXzurqAOCqbIZhGFYXcbHChQtr9OjRevzxx695bmxsrMLCwhQTE6PQ0NA8qQ9A3oiPj1dISIhECAJcS2qqNH26NHiw2e5akm67TRozxvwKABbJSjZwmTVBaWlpmj9/vuLj43XbFf4nmpycrOTkZMfz2NjYPKwQAAAPZhjSN9+YTQ/27DH3VapkdnyLjMx4x1EAcGGWT9T99ddfFRISIn9/f/Xq1UuLFi1SjRo1Lnvu8OHDFRYW5niEh4fneb0AAHicLVuku++W2rQxA1DhwmYL7D17pPbtCUAA8h3Lp8OdO3dOhw8fVkxMjBYsWKCPPvpIq1evvmwQutxIUHh4ONPhADfEdDjABfzxh3mz0y++MJ/7+0v9+kmvvioVLGh1dQDgJCvT4SwPQZdq0aKFKlWqpKlTp17zXNYEAe6LEARY6Phx6c03pQ8/NNcA2WzmzU6HDZMiIqyuDgAuK1+uCbrAbrc7jfYAAIA8EhtrdnsbOza949u995od32rXtro6AMgxloagV199Va1bt1a5cuV09uxZzZkzR6tWrdLy5cutLAuAC/D19dWoUaMc2wByUWKiNHmy9M470qlT5r5bbzWbHtx5p9XVAUCOszQEnThxQo899piio6MVFham2rVra/ny5br77rutLAuAC/Dz89PAgQOtLgNwbykp0iefmNPcjh0z91WrZoahBx6g4QEAt2VpCPr444+tfHsAADxTWpo0d640ZIj011/mvnLlzOePPSb5uNxseQDIUfxfDoBLSktL09atWyVJdevWlbe3t9UlwUOkpUlr10rR0VKpUlLjxpLb/OdnGNLixdKgQdLu3ea+4sXNDnBPPWV2fwMAD0AIAuCSkpKS1KBBA4nucMhDUVFmB+ijR9P3lS1r3hInMtLKyq6TYUjLlpkjPVu2mPsKFpRefll67jmJP18APIzlN0sFAMAVREVJHTo4ByDJXCrToYN5PN8xDGn5crPJQdu2ZgAKDjZHfg4ckF55hQAEwCMRggAAHi8tzRwButyd8y7s69/fPC9fMAzp+++lO+6QWrWSNm6UgoKkl14yw8+wYdzsFIBHIwQBADze2rUZR4AuZhjSkSPmeS7NMKSVK6WmTaW775bWr5cCAqTnnzcbIIwcKRUrZnWVAGA51gQBADxedHTOnpfnDEP64QfpzTfTk5q/v9Srl7nup1QpqysEAJdCCAIAeLzMZgSXyxKGIX33nTR0qPTzz+Y+f3/piSekV1+VypSxukIAcEmEIACAx2vc2OwCd+zY5dcF2Wzm8caNrajuMi50e3vzTXO9j2ROe3v6aXPdT+nSVlcIAC6NEATAJfn6+mrIkCGObSA3eXubbbA7dDADz8VByGYzv44f7wL3C7Lbzfv8vPNOeqvrwEDpmWekF190waEqAHBNNsO43L955Q+xsbEKCwtTTEyMQkNDrS4HAJDPXe4+QeHhZgCy9D5BqanS559Lw4dLe/aY+4KCpD59pBdekEqUsLA4AHANWckGjAQBAHBeZKR0//1mb4HoaHNgpXFjC0eAkpOlWbOkESPM7m6SFBZm3uC0Xz+paFGLCgOA/I0QBMAl2e127d27V5JUvXp1eXnR0R95w9vb7DBtqfh46aOPpNGjzYVKkhl4BgwwR3/CwiwuEADyN0IQAJeUmJioWrVqSZLi4uIUzF3t4QlOnZLee0+aNMnclswmBwMHSk8+KfHnAAByBCEIAACrHT0qjR0rTZtmjgJJUqVKZqe3bt3MttcAgBxDCAIAwCq//SaNGiV99pmUkmLuq1NHeuUVqX17F2hHBwDuiRAEAEBeMgzpp5/M9T5LlqTvb9rUDD/33JPelxsAkCsIQQAA5IW0NDP0jBol/fKLuc9mk9q1M8PPrbdaXSEAeAxCEAAAuSkx0WxzPWaMtH+/uc/fX3rsMfMeP1WrWl0hAHgcQhAAALnh33+lDz6Q3n/f3JakggWl3r3N+/yULGl1hQDgsQhBAFySr6+vXnzxRcc2kG/s3SuNG2eO/iQnm/vKlzfv8fP441JIiNUVAoDHsxmGYVhdRHbFxsYqLCxMMTExCg0NtbocAICnMgxp5UpzytuyZen769c3p7y1by/58O+OAJCbspIN+D8yAADZlZwsff65NH68tH27uc9mkx54QHr+een22+n0BgAuiBAEwCXZ7XYdPnxYklSuXDl5eXlZXRKQ7p9/pClTzDU/J06Y+4KCpJ49pX79pMqVra4QAHAVhCAALikxMVEVKlSQJMXFxSk4ONjqkgBztGfCBGnOHOncOXNf2bJmo4MnnpAKF7a6QgBAJhCCAAC4mtRU6auvpIkTpVWr0vffdpvUv7/04IMSzTsAIF8hBAEAcDmnTkkff2y2uD4/NVM+PlLHjuaUt4YNra4QAJBNhCAAAC62c6c0aZL02WdSUpK5r0gR6amnzHv8lC1rdYUAgOtECAIAICVF+vJL6b33pNWr0/fffLPUt6/UqZMUGGhlhQCAHEQIAgB4ruPHpQ8/NDu9/f23uc/bW4qMNMMPLa4BwC0RggAAnsUwpJ9+Mtf6LFxojgJJUrFi0pNPSs88w5Q3AHBzhCAALsnHx0e9e/d2bAPXLS7ObG39wQfSjh3p+2+7TerTR+rQQfL3t7JCAEAe4TcLAC7J399f77//vtVlwB3s2SNNnizNmiXFxpr7AgKkzp3N8FO3rtUVAgDyGCEIAOB+zp2TFi0yR33WrEnfX7my1KuX1KMHNzYFAA9GCALgkgzD0MmTJyVJRYsWlY3F6ciMgwfNRgcffyz984+5z8tLuv9+c61P8+bmcwCARyMEAXBJCQkJKl68uCQpLi5OwcHBVpcEV5WaKn39tTR1qrR8udn4QJJKlTIbHTz5JI0OAABOCEEAgPzpyBHpo4/MUZ9jx9L3t2ghPf20Ofrj62tlhQAAF0UIAgDkH6mp0rJl5pS3Zcsku93cX6yYuc7nySfNdT8AAFwFIQgA4PoOHjRHfD75JP2mppLUtKnZ6OCBB2hvDQDINEIQAMA1paRIX30lTZsmffdd+lqfokWl7t2lJ56Qqla1ukoAQD5ECAIAuJbffzdHfWbNSu/wpvNrfZ580lzrw6gPAOA6EIIAANaLj5cWLDAbHaxbl76/RAmpZ0/p8celSpWsrBAA4EYIQQBcko+Pj7p16+bYhhsyDGnzZnPUZ84c6exZc7+Xl3TvveZ0t3vvpcMbACDH8ZsFAJfk7++vGTNmWF0GcsO//0qzZ5tNDn79NX1/xYrmiE+3blKZMlZWCABwc4QgAEDuS001mxt88om0ZInZ9EAy1/ZERpqjPk2bmqNAAADkMkIQAJdkGIYSEhIkSUFBQbLZbFaXhOzYt0+aMUOaOdO5tXW9euZ9fR55RCpUyMoKAQAeiBAEwCUlJCQoJCREkhQXF6fg4GCrS0JmxcZKX3xhhp/169P3Fykide1qhp/ata2sEADg4QhBAIDrZ7dLK1dK06dLUVFSYqK538tLatXKDD733UdrawCASyAEAQCyb/9+c6rbp59Khw+n769WzQw+XbtKpUpZWSEAABkQggAAWXPmjDRvnhl+Lp7uFhZmrvHp3l1q0EBiHRcAwEURggAA15aaKq1YYQafxYul5GRzv5eXdM89Zlvr+++XAgOtrhQAgGsiBAEALs8wpB07zKlus2dL//yTfqxWLTP4dOnCdDcAQL5DCAIAOPv7bzP0fPqp881MixaVOnc2w0+dOkx3AwDkW5aGoOHDhysqKkq//fabAgMD1ahRI40cOVJVq1a1siwALsDb21sdOnRwbCOXxcVJixaZweeHH8xub5Lk52dOc+va1ezy5utrdaUAAFw3m2EYhlVv3qpVK3Xq1En169dXamqqXnvtNe3atUt79uzJ1D1BYmNjFRYWppiYGIWGhuZJzQDgNlJTpe+/N4PP4sXS+ZvTSpJuv1167DGpY0duZgoAyBeykg0sDUGX+vfff1W8eHGtXr1ad955Z4bjycnJSr6wGPf8DxoeHk4IAoDMMgxpyxbps8+kuXOlEyfSj1WuLD36qPmoVMnKKgEAyLKshCCXWhMUExMjSSpcuPBljw8fPlxDhw7N46oAwA38+ac0Z4651uf339P3Fy0qdepkBh/aWgMAPITLjATZ7Xa1a9dOZ86c0bp16y57DiNBgOeIj49XSEiIJCkuLi5TU2RxiX//lb74wgw+v/ySvj8gwFzn8+ijUsuWrPMBALiFfDkS1KdPH+3ateuKAUiS/P395e/vn6d1AUC+EhcnLVliBp/ly6W0NHO/l5d0111mS+vISIl/OAIAeDCXCEHPPvusvv76a61Zs0Zly5a1uhwAyF/OnTMDz5w5ZgC6uMFBvXpmW+tOnbifDwAA51kaggzD0HPPPadFixZp1apVqlChgpXlAED+kZYmrV1rBp8FC6T//ks/Vrmy9Mgj5qgPtxwAACADS0NQnz59NGfOHH355ZcqUKCAjh8/LkkKCwtTYGCglaUBgOsxDGnzZrOr2xdfmDc1vaBUKXO055FHzNEfGhwAAHBFljZGsF3hL+np06ere/fu1/x+7hMEuC8aI1xk924z+Hz+udnl7YKCBaX27c3pbk2aSNxUFgDgwfJNYwQXaUwHAK7nzz/N0Z65c6Vdu9L3BwVJ7dqZIz4tW0o0iwEAIMtcojECAFzK29tb9957r2PbIxw+LM2bZ4afzZvT9/v6Sq1bm8HnvvskTx4VAwAgBxCCALikgIAALV261Ooycl90tNnY4PPPpfXr0/d7e5strTt1kh58UCpUyMoqAQBwK4QgAMhr//wjRUWZIz5r1pgNDySzmcGdd0oPP2yu9Sle3OpKAQBwS4QgAMgLJ0+mB59VqyS7Pf3YrbeaIz4dO0qlS1tZJQAAHoEQBMAlxcfHq/j5kZATJ07kz+5wJ09Kixeb63x+/NG8t88F9etLDz1kBp/y5a2sEgAAj0MIAuCyEhISrC4h606elBYtkubPzxh86tZNDz4VK1pZJQAAHo0QBADX699/zRGfywWfm282Q0/HjlKVKlZWCQAAziMEAUB2/PNP+ojPpWt8br7ZHPHp0IHgAwCACyIEAUBm/f232dxgwQLnrm66KPh07ChVrmxllQAA4BoIQQBwNYcOmcFn4ULzPj4XB5/69c3RnvbtpUqVrKwSAABkASEIAC61f78ZehYulDZvdj52221m8ImMlCIirKoQAABcB0IQAJfk5eWlJk2aOLZzlWFIu3aZa3wWLpR27ry4EKlxYzP0REZKZcvmbi0AACDXEYIAuKTAwECtWrUq997Abpc2bTKnukVFSX/8kX7Mx0e66y5zmtv990slSuReHQAAIM8RggB4jpQUae1aM/QsXiwdO5Z+zN9fatlSevBBqV07qXBhKysFAAC5iBAEwL0lJEjffWdOdfvqK+m//9KPhYRIbdua09xatzafAwAAt0cIAuCS4uPjFXG+8cDBgwcVHByc+W8+fVr6+mtztOfbb6XExPRjRYuaIz2RkVLz5lJAQC5UDwAAXBkhCIDLOnnyZOZPPnRI+vJL87F6tZSWln6sfHlzmtsDD0i3326u+QEAAB6L3wQA5E+GIe3YYYaexYul7dudj9eqZQafBx80b2Rqs1lVKQAAcDGEIAD5R0qKtGaNGXyWLDFHfy7w8pLuuMPs5nb//dy8FAAAXBEhCIDrmz/fbG6wbJkUE5O+PzBQuucec5pbmzZSsWK58vZpaWZTuehoqVQp87ZB3t658lYAACAPEIIAuJ4DB6QFC9Kf9+iRvl2smHTffeZoT4sWUlBQrpYSFSX16ycdPZq+r2xZacIEs7cCAADIfwhBAKyXliZt2GC2sP7qK2n3bufjVauaoz333y81aJBnwzBRUVKHDubyo4sdO2buX7CAIAQAQH5ECAJgjdhYacUKM/QsWyb9+2/6MW9veTVqpHoHD0phYfLauNGc+paH0tLMEaBLA5DO92Sw2aT+/c1cxtQ4AADyF0IQgLzzxx/m/Xu+/tpscJCSkn4sLMy8YWm7dlKrVgosVEibLCx17VrnKXCXMgzpyBHzvKZN87IyAABwvQhBAHLPuXPSTz9JS5eawef3352P33CD1Lat+bjjDsnX16pKM4iOztnzAACA6yAEAchZ0dHSN9+YwWfFCuns2fRjPj5SkyZm6GnTRqpSxcpKr6pUqZw9DwAAuA5CEIDrc6GpwYXgs22b8/Hixc1pbm3bSnffbU57y4SEhATVqFFDkrRnzx4F5XIXuEs1bmx2gTt27PLrgmw283jjxnlaFgAAyAGEIABZ988/0rffmsHnu++k//5zPl6/vjnS06aNVLeueSPTLDIMQ4fO3wzVuFwKyWXe3mYb7A4dzMBzcQk2m/l1/HiaIgAAkB8RggBcW2qq9Msv6cFn61bn4wULmjctvfdeqVUrqUQJqyrNUZGRZhvsy90naPx42mMDAJBfEYIAXN6hQ9Ly5ebjhx+kmBjn43XrmtPcWreWGjY01/u4ochIsw322rXmcqdSpcwpcIwAAQCQf7nnby0Asi4hwWxbvXy5OeLz22/OxwsXNtf0tG4ttWwplSxpVaV5ztubNtgAALgTQhDgqex2accOc03Pd99J69aZLa0v8PKSbr3VDDytWkm33MLwBwAAcAuEIMCTHD0qff+92bp6xQrp33+dj4eHm2t7WrWSmjeXChWyqlIAAIBcQwgC3NmZM9KqVWbw+f77jDcrDQ6WmjUzg88995g3L73Q+sxiNpvN0SLb5iI1AQAA90AIAtxJUpK0fr30449m6Nm0yZz2doGXl9m+ukULM/Tceqvk52dlxVcUFBSk3bt3W10GAABwQ4QgID9LSZE2bzZDz48/Sj/9JCUnO59TtaoZelq0MFf3FyxoVbUAAAAugRAEZENamkUtk1NTpe3bzSluK1eaRZw963xOqVLmep677jK/liuXB4UBAADkH4QgIIuioi5/88wJE3Lh5plpaRlDT2ys8zmFC5vreu66y3xUreoy63quR0JCgurXry9J2rRpk4KCgqwuCQAAuAlCEJAFUVFShw6SYTjvP3bM3L9gwXUGoZQUacsW8349a9aYbasvvUlpWJh0553m1LamTaWbbzbX+rgZwzC0Z88exzYAAEBOIQQBmZSWZo4AXe73ccMwB1/695fuvz8LU+MSE6WNG9NDz/r15k1LLxYamjH0cL8eAACAbCMEAZm0dq3zFLhLGYZ05Ih5XtOmVzjp5EmzecG6deZjyxZz9OdihQuboefC46abJB/+qAIAAOQUfrMCMik6OovnGYa0b585urN+vRl6fvst4zdc6KzQpIkZemrUcMvpbQAAAK6CEARkUqlSVz8epHg10EY1/OFnafZ66eefpdOnM55Yo4Z0xx3pj4gIt2hkAAAAkF8QgoBMatzY7AJ37Jgkw65q+k0NtUENtUG36hfV0i75KE36+KJvCggwb056221m4GnUSCpSxMKfAgAAAIQg4FoMQ/r7b3lv2qQVDTbpaNQG1dcmhSk2w6kJRcoqqHkjM+zcdpvZxMDPz5Ky8zubzaby5cs7tgEAAHIKIQi41L//Sps3S5s2pX89flySVO38Q5LiFaTNqqcNaqg/izTUA8MbqvWTZS0t3Z0EBQXp4MGDVpcBAADcECEInsswzLlt27ZJW7emfz1yJOO53t5SzZpSvXpSw4ZKq9dQm/+rqb9P+KhBKemFxnStBgAAyC8IQfAMKSlmp7YdO6SdO83As22bOepzOVWrmmt56tUzv958sxQU5DjsLalJ3lUPAACAHEQIgnsxDOmff6Rdu6Rff00PPbt3S+fOZTzf29vs1lanjlS3rvn15pvNG5TCUomJibrzzjslSWvWrFFgYKDVJQEAADdBCEL+ZBjmKM6ePWbA2bXL/Lp79+XbUktSSIhUu7Z589GbbjJDT61aEr9cuyS73a7Nmzc7tgEAAHIKIQiuLTVVOnDAvMnopY8rhR0vL6lSJTPgXAg8tWub9+PhJqQAAAAez9IQtGbNGo0ePVpbtmxRdHS0Fi1apAceeMDKkrIlLU1au1aKjjZvqNmYRfJZk5IiHTwo/fGH+di/P337wAEzCF2OzWYGm1q1zKYFNWua21WrMroDAACAK7I0BMXHx+umm25Sz549FRkZaWUp2RYVJfXrJx09mr6vbFlpwgQpn/5IOS8lRfr7bzPoXHgcOJD+9ehR6WrTnYKCzGBTrZr5uLBdpYpTswIAAAAgMywNQa1bt1br1q2tLOG6REVJHTqYy1MuduyYuX/BAjcPQoYhxcaa99A5ftwcCjt61HwcOZK+ffz41UOOZI7cVK5sPqpUSd+uXFkqU4ZpbAAAAMgx+WpNUHJyspKTkx3PY2NjLaslLc0cAboQgCK1UFX1u06qqE4aRXVKRfV+n6K6/9Yi8i5eRPLJBx/1uXNSTIx05ox06pR08mTGrydPpoee48elxMTMvbavr1SunFShgjmFLSLCebtUKXN6GwAAAJDL8sFv5umGDx+uoUOHWl2GJHMN0MVT4B7WF3pI851POi6pzPntsDDzUaCA2X65QAHn7YAAyd8//evFD29vMyB4eZlfLzy8vMwRlpQUc93MhceF58nJUkKC+UhMdN4+e9YcxYmJMR+xsVJSUvY+jNBQqWRJ81G2bPojPDx9u3hxRnOQZUWLFrW6BAAA4IbyVQh69dVX9fzzzzuex8bGKjw83JJaoqOdn6/Q3TqrAipqjgU5HoVt/8lmGOlhIz8oUEAqUkQqWjTj16JF0wNPyZJSiRKsy0GuCA4O1r9XupktAADAdchXIcjf31/+/v5WlyHJnL11sY/0pD7SkxnOW7kiTU1v+s+cRnZh9OXirxe2k5LMkZvLPdLSzHl3dnvGr97e5lQ7X1/nrz4+5ihSUJD5CAx03g4OTh+dCgszR3MufKW1HQAAANxYvgpBrqRxY3OW17FjGRsj6Hz35rJlpcZNvSXv8yMoAAAAACxn6SKNuLg4bd++Xdu3b5ckHThwQNu3b9fhw4etLCtTvL3NNthSxvX8F56PH8+gCpBdiYmJatq0qZo2barEzDbgAAAAyASbYVxuHCNvrFq1Ss2aNcuwv1u3bpoxY8Y1vz82NlZhYWGKiYlRaGhoLlV5dZe7T1B4uBmA3Lo9NpDL4uPjFRISIp3/B5Pg4GCrSwIAAC4sK9nA0hB0vVwhBOl8u+y1a81mCaVKmVPlGAECrg8hCAAAZEVWsgFrgnKAt7fUtKnVVQAAAADIDG7cAgAAAMCjEIIAAAAAeBRCEAAAAACPwpogAC4rKCjI6hIAAIAbIgQBcEnBwcGKj4+3ugwAAOCGmA4HAAAAwKMQggAAAAB4FEIQAJeUlJSkNm3aqE2bNkpKSrK6HAAA4EZYEwTAJaWlpWnZsmWObQAAgJzCSBAAAAAAj0IIAgAAAOBRCEEAAAAAPAohCAAAAIBHIQQBAAAA8Cj5ujucYRiSpNjYWKtLAZDD4uPjHduxsbF0iAMAAFd1IRNcyAhXk69D0NmzZyVJ4eHhVpcCIBeVLl3a6hIAAEA+cfbsWYWFhV31HJuRmajkoux2u/7++28VKFBANpvN6nLcQmxsrMLDw3XkyBGFhoZaXY7H43q4Dq6F6+BauBauh+vgWrgWrkfeMwxDZ8+eVenSpeXldfVVP/l6JMjLy0tly5a1ugy3FBoayh9YF8L1cB1cC9fBtXAtXA/XwbVwLVyPvHWtEaALaIwAAAAAwKMQggAAAAB4FEIQnPj7+2vIkCHy9/e3uhRwPVwK18J1cC1cC9fDdXAtXAvXw7Xl68YIAAAAAJBVjAQBAAAA8CiEIAAAAAAehRAEAAAAwKMQggAAAAB4FEKQhzp27JgeffRRFSlSRIGBgbrxxhu1efNmx3HDMDR48GCVKlVKgYGBatGihfbv329pze4qLS1NgwYNUoUKFRQYGKhKlSpp2LBhurhnCdcjd6xZs0b33XefSpcuLZvNpsWLFzsdz8znfvr0aXXp0kWhoaEqWLCgHn/8ccXFxeXxT+IernY9UlJS9PLLL+vGG29UcHCwSpcurccee0x///2302twPXLGtf5sXKxXr16y2WwaP368036uRc7JzPXYu3ev2rVrp7CwMAUHB6t+/fo6fPiw43hSUpL69OmjIkWKKCQkRO3bt9c///yTxz9J/netaxEXF6dnn31WZcuWVWBgoGrUqKEpU6Y4ncO1cA2EIA/033//6fbbb5evr6+++eYb7dmzR2PGjFGhQoUc54waNUoTJ07UlClTtGHDBgUHB6tly5ZKSkqytHZ3NHLkSE2ePFnvvfee9u7dq5EjR2rUqFGaNGmS4xyuR+6Ij4/XTTfdpPfff/+yxzPzuXfp0kW7d+/WihUr9PXXX2vNmjV66qmn8vCncB9Xux4JCQnaunWrBg0apK1btyoqKkq///672rVr53Qe1yNnXOvPxgWLFi3SL7/8otKlS2c4xrXIOde6Hn/++afuuOMOVatWTatWrdLOnTs1aNAgBQQEOM4ZMGCAvvrqK82fP1+rV6/W33//rcjIyDz8KdzDta7F888/r2+//VafffaZ9u7dq/79++vZZ5/VkiVLHOdwLVyEAY/z8ssvG3fccccVj9vtdqNkyZLG6NGjHfvOnDlj+Pv7G3Pnzs2jKj1HmzZtjJ49ezrti4yMNLp06WIYXI88I8lYtGiR43lmPvc9e/YYkoxNmzY5zvnmm28Mm81mHDt2LI9/Avdy6fW4nI0bNxqSjEOHDhkG1yPXXOlaHD161ChTpoyxa9cuo3z58sa4ceMcx7gWuedy1+Phhx82Hn300St+z5kzZwxfX19j/vz5jn179+41JBk///xzrtbrzi53LWrWrGm8+eabTvvq1q1rvP7664bBtXApjAR5oCVLlqhevXrq2LGjihcvrjp16ujDDz90HD9w4ICOHz+uFi1aOPaFhYWpYcOG+vnnny2q2n01atRIP/zwg/bt2ydJ2rFjh9atW6fWrVtLXA/LZOZz//nnn1WwYEHVq1fPcU6LFi3k5eWlDRs2WFK3J4mJiZHNZlPBggUlrkeestvt6tq1qwYOHKiaNWtmOM61yDt2u11Lly7VDTfcoJYtW6p48eJq2LCh0zStLVu2KCUlxen/Z9WqVVO5cuX4eySHNWrUSEuWLNGxY8dkGIZWrlypffv26Z577pG4Fi6FEOSB/vrrL02ePFlVqlTR8uXL9cwzz6hv376aOXOmJOn48eOSpBIlSjh9X4kSJRzHkHNeeeUVderUSdWqVZOvr6/q1Kmj/v37q0uXLhLXwzKZ+dyPHz+u4sWLOx338fFR4cKFuTa5LCkpSS+//LIeeeQRhYaGSlyPPDVy5Ej5+Piob9++lz3Otcg7J06cUFxcnEaMGKFWrVrpu+++04MPPqjIyEitXr1aOn89/Pz8HP9gcAF/j+S8SZMmqUaNGipbtqz8/PzUqlUrvf/++7rzzjslroVL8bG6AOQ9u92uevXq6Z133pEk1alTR7t27dKUKVPUrVs3q8vzOPPmzdPs2bM1Z84c1axZU9u3b1f//v1VunRprgdwGSkpKXrooYdkGIYmT55sdTkeZ8uWLZowYYK2bt0qm81mdTkez263S5Luv/9+DRgwQJJ08803a/369ZoyZYqaNGlicYWeZdKkSfrll1+0ZMkSlS9fXmvWrFGfPn1UunRpp9EfWI+RIA9UqlQp1ahRw2lf9erVHV1kSpYsKUkZOpX8888/jmPIOQMHDnSMBt14443q2rWrBgwYoOHDh0tcD8tk5nMvWbKkTpw44XQ8NTVVp0+f5trkkgsB6NChQ1qxYoVjFEhcjzyzdu1anThxQuXKlZOPj498fHx06NAhvfDCC4qIiJC4FnmqaNGi8vHxuebf6+fOndOZM2eczuHvkZyVmJio1157TWPHjtV9992n2rVr69lnn9XDDz+sd999V+JauBRCkAe6/fbb9fvvvzvt27dvn8qXLy9JqlChgkqWLKkffvjBcTw2NlYbNmzQbbfdluf1uruEhAR5eTn/UfT29nb86x7XwxqZ+dxvu+02nTlzRlu2bHGc8+OPP8put6thw4aW1O3OLgSg/fv36/vvv1eRIkWcjnM98kbXrl21c+dObd++3fEoXbq0Bg4cqOXLl0tcizzl5+en+vXrX/Xv9VtuuUW+vr5O/z/7/fffdfjwYf4eyUEpKSlKSUm56t/pXAsXYnVnBuS9jRs3Gj4+Psbbb79t7N+/35g9e7YRFBRkfPbZZ45zRowYYRQsWND48ssvjZ07dxr333+/UaFCBSMxMdHS2t1Rt27djDJlyhhff/21ceDAASMqKsooWrSo8dJLLznO4XrkjrNnzxrbtm0ztm3bZkgyxo4da2zbts3RbSwzn3urVq2MOnXqGBs2bDDWrVtnVKlSxXjkkUcs/Knyr6tdj3Pnzhnt2rUzypYta2zfvt2Ijo52PJKTkx2vwfXIGdf6s3GpS7vDGVyLHHWt6xEVFWX4+voa06ZNM/bv329MmjTJ8Pb2NtauXet4jV69ehnlypUzfvzxR2Pz5s3GbbfdZtx2220W/lT507WuRZMmTYyaNWsaK1euNP766y9j+vTpRkBAgPHBBx84XoNr4RoIQR7qq6++MmrVqmX4+/sb1apVM6ZNm+Z03G63G4MGDTJKlChh+Pv7G82bNzd+//13y+p1Z7GxsUa/fv2McuXKGQEBAUbFihWN119/3ekXO65H7li5cqUhKcOjW7duhpHJz/3UqVPGI488YoSEhBihoaFGjx49jLNnz1r0E+VvV7seBw4cuOwxScbKlSsdr8H1yBnX+rNxqcuFIK5FzsnM9fj444+NypUrGwEBAcZNN91kLF682Ok1EhMTjd69exuFChUygoKCjAcffNCIjo624KfJ3651LaKjo43u3bsbpUuXNgICAoyqVasaY8aMMex2u+M1uBauwWZcfFt6AAAAAHBzrAkCAAAA4FEIQQAAAAA8CiEIAAAAgEchBAEAAADwKIQgAAAAAB6FEAQAAADAoxCCAAAAAHgUQhAAAAAAj0IIAgB4pIMHD8pms2n79u1WlwIAyGOEIABAjuvevbseeOCBTJ9vs9m0ePHiXK3pUuHh4YqOjlatWrUkSatWrZLNZtOZM2fytA4AQN7zsboAAACs4O3trZIlS1pdBgDAAowEAQByVdOmTdW3b1+99NJLKly4sEqWLKk33njDcTwiIkKS9OCDD8pmszmeS9KXX36punXrKiAgQBUrVtTQoUOVmprqOG6z2fTRRx/pwQcfVFBQkKpUqaIlS5Y4jv/333/q0qWLihUrpsDAQFWpUkXTp0+XLpkOd/DgQTVr1kySVKhQIdlsNnXv3l2zZs1SkSJFlJyc7PQzPfDAA+ratWsufmoAgNxECAIA5LqZM2cqODhYGzZs0KhRo/Tmm29qxYoVkqRNmzZJkqZPn67o6GjH87Vr1+qxxx5Tv379tGfPHk2dOlUzZszQ22+/7fTaQ4cO1UMPPaSdO3fq3nvvVZcuXXT69GlJ0qBBg7Rnzx5988032rt3ryZPnqyiRYtmqC88PFwLFy6UJP3++++Kjo7WhAkT1LFjR6WlpTkFqxMnTmjp0qXq2bNnLn5iAIDcRAgCAOS62rVra8iQIapSpYoee+wx1atXTz/88IMkqVixYpKkggULqmTJko7nQ4cO1SuvvKJu3bqpYsWKuvvuuzVs2DBNnTrV6bW7d++uRx55RJUrV9Y777yjuLg4bdy4UZJ0+PBh1alTR/Xq1VNERIRatGih++67L0N93t7eKly4sCSpePHiKlmypMLCwhQYGKjOnTs7Ro8k6bPPPlO5cuXUtGnTXPzEAAC5iTVBAIBcV7t2bafnpUqV0okTJ676PTt27NBPP/3kNPKTlpampKQkJSQkKCgoKMNrBwcHKzQ01PHazzzzjNq3b6+tW7fqnnvu0QMPPKBGjRplqfYnn3xS9evX17Fjx1SmTBnNmDFD3bt3l81my9LrAABcByEIAJDrfH19nZ7bbDbZ7farfk9cXJyGDh2qyMjIDMcCAgIy9dqtW7fWoUOHtGzZMq1YsULNmzdXnz599O6772a69jp16uimm27SrFmzdM8992j37t1aunRppr8fAOB6CEEAAMv5+voqLS3NaV/dunX1+++/q3Llytf12sWKFVO3bt3UrVs3NW7cWAMHDrxsCPLz85POjzZd6oknntD48eN17NgxtWjRQuHh4ddVEwDAWqwJAgBYLiIiQj/88IOOHz+u//77T5I0ePBgzZo1S0OHDtXu3bu1d+9eff755/rf//6X6dcdPHiwvvzyS/3xxx/avXu3vv76a1WvXv2y55YvX142m01ff/21/v33X8XFxTmOde7cWUePHtWHH35IQwQAcAOEIACA5caMGaMVK1YoPDxcderUkSS1bNlSX3/9tb777jvVr19ft956q8aNG6fy5ctn+nX9/Pz06quvqnbt2rrzzjvl7e2tzz///LLnlilTxtGMoUSJEnr22Wcdx8LCwtS+fXuFhIRk6SawAADXZDMMw7C6CAAAXF3z5s1Vs2ZNTZw40epSAADXiRAEAMBV/Pfff1q1apU6dOigPXv2qGrVqlaXBAC4TjRGAADgKurUqaP//vtPI0eOJAABgJtgJAgAAACAR6ExAgAAAACPQggCAAAA4FEIQQAAAAA8CiEIAAAAgEchBAEAAADwKIQgAAAAAB6FEAQAAADAoxCCAAAAAHiU/wOpIHlyG4njuAAAAABJRU5ErkJggg==",
+>>>>>>> 4f662a4 (implemented a method to calculate both lt1 and lt2 together based on curve breakpoints using piecewise regression)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1023,11 +1027,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "004d6952",
+   "execution_count": 22,
+   "id": "76b18ab1",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from lactopy.lactate_models.lt1_lt2_breakpoint import LT1_LT2_breakpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4f688ce1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LT1 occurs at: 93.04 watts\n",
+      "LT1 occurs at: 157.85 watts\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LT1_LT2_breakpoint()\n",
+    "model.fit(df['watt'], df['lactate'], si=0.5)\n",
+    "lt1_intensity = model.predict()\n",
+    "lt1_intensity\n",
+    "print(f\"LT1 occurs at: {lt1_intensity[0]:.2f} watts\")\n",
+    "print(f\"LT1 occurs at: {lt1_intensity[1]:.2f} watts\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9d61bd43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'LT1 Standard Increment Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+      ]
+     },
+     "execution_count": 24,
+     "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.plot.plot_predictions()"
+   ]
   }
  ],
  "metadata": {
diff --git a/src/lactopy/__init__.py b/src/lactopy/__init__.py
index d9b3fce..610428f 100644
--- a/src/lactopy/__init__.py
+++ b/src/lactopy/__init__.py
@@ -1 +1 @@
-from .lactate_models import OBLA, Bsln, Dmax, lt1_si, lt1_si_lowest, lt1_loglog  # noqa: F401
+from .lactate_models import OBLA, Bsln, Dmax, lt1_si, lt1_si_lowest, lt1_loglog, lt1_lt2_breakpoint  # noqa: F401
diff --git a/src/lactopy/lactate_models/__init__.py b/src/lactopy/lactate_models/__init__.py
index ee5d3c2..20fa9bd 100644
--- a/src/lactopy/lactate_models/__init__.py
+++ b/src/lactopy/lactate_models/__init__.py
@@ -3,4 +3,5 @@
 from .obla import OBLA  # noqa: F401
 from .lt1_si import LT1_si  # noqa: F401
 from .lt1_si_lowest import LT1_si_lowest  # noqa: F401
-from .lt1_loglog import LT1_loglog  # noqa: F401
\ No newline at end of file
+from .lt1_loglog import LT1_loglog  # noqa: F401
+from .lt1_lt2_breakpoint import LT1_LT2_breakpoint  # noqa: F401
\ No newline at end of file
diff --git a/src/lactopy/lactate_models/lt1_lt2_breakpoint.py b/src/lactopy/lactate_models/lt1_lt2_breakpoint.py
new file mode 100644
index 0000000..0c0f45b
--- /dev/null
+++ b/src/lactopy/lactate_models/lt1_lt2_breakpoint.py
@@ -0,0 +1,100 @@
+from numpy.typing import ArrayLike
+import numpy as np
+import copy
+import piecewise_regression
+
+from lactopy.base import BaseModel
+from lactopy.plots.lt1_lt2_breakpoint_plot import LT1_LT2_breakpoint_Plot
+
+
+class LT1_LT2_breakpoint(BaseModel):
+    """
+    Determining 
+
+    Log-Log method is used to estimate the first lactate threshold by plotting
+    the lactate response against intensity on a logaritmic scale.
+    
+    Plot is divided into 2 segments --> segmented regression identifies brekpoint
+
+    This is computed by using a standard value, preferably equlual to the lowest 
+    detectable change in lactate concentration of the measurement tool
+
+    Attributes:
+        plot (lt1_si_plot): For visualizing the model..
+    """
+
+    def __init__(self):
+        """
+        Initializes the LT1_si model and its associated plot.
+
+        """
+        super().__init__()
+        self.plot = LT1_LT2_breakpoint_Plot(self)
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        si: float = 0.5,
+        **kwargs,
+    ):
+        """
+        Fits the Standard Increment model to the given data.
+
+        Args:
+            X (ArrayLike): The independent variable (e.g., intensity).
+            y (ArrayLike): The dependent variable (e.g., lactate concentration).
+            si: the standard increment, defaults to 0.5
+            
+            threshold_above_baseline (float, optional): Threshold above baseline
+            for filtering in the "modified" implementation. Defaults to 0.5.
+
+            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._si = si
+
+        # 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
+        
+        # Ensure working on copies
+        self.X = np.asarray(copy.deepcopy(X))
+        self.y = np.asarray(copy.deepcopy(y))
+        
+        super().fit(X, y, **kwargs)
+        return self
+
+    def predict(self) -> float:
+        """
+        Predicts intensity for the LT1_si method.
+
+        Returns:
+            float: Predicted intensity.
+            
+        """
+        
+        pw_fit = piecewise_regression.Fit(self.X, self.y, n_breakpoints=2)
+        pw_results = pw_fit.get_results()
+        
+        if "breakpoint1" not in pw_results.get("estimates", {}):
+            raise ValueError(
+                f"'breakpoint1' not found in regression results."
+                f"It is recommended to use a different treshold estimation method."
+                f"Piecewise regression may have failed. Result: {pw_results}"
+            )
+        else:
+            predicted_lt1 = pw_results["estimates"]["breakpoint1"]["estimate"]
+            predicted_lt2 = pw_results["estimates"]["breakpoint2"]["estimate"]
+
+        return predicted_lt1, predicted_lt2
diff --git a/src/lactopy/plots/lt1_lt2_breakpoint_plot.py b/src/lactopy/plots/lt1_lt2_breakpoint_plot.py
new file mode 100644
index 0000000..1e0f94d
--- /dev/null
+++ b/src/lactopy/plots/lt1_lt2_breakpoint_plot.py
@@ -0,0 +1,45 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from lactopy.plots.base import Plot
+
+
+class LT1_LT2_breakpoint_Plot(Plot):
+    """
+    Plot for LT1 si model.
+    """
+
+    def __init__(self, base_lactate_LT1_si_model):
+        super().__init__(base_lactate_LT1_si_model)
+
+    def plot_fit(self):
+        """
+        Plot the LT1 si model fit.
+        """
+        ax = super().plot_fit()
+        ax.set_title("LT1 Standard Increment Model Fit")
+        return ax
+
+    def plot_predictions(self):
+        """
+        Plot the LT1 model predictions.
+        """
+        self.plot_fit()
+        lt_array = self.base_lactate_model.predict()
+        lt1 = lt_array[0]
+        lt2 = lt_array[1]
+        
+        plt.axvline(
+            lt1,
+            color="black",
+            label="lt1",
+            linestyle="--",
+        )
+        
+        plt.axvline(
+            lt2,
+            color="red",
+            label="lt2",
+            linestyle="--",
+        )
+
+        return plt.gca()

From 33dd5d4e29632ebed3a716e20dee69a38d9f0f33 Mon Sep 17 00:00:00 2001
From: ihosten <hostenian@gmail.com>
Date: Mon, 21 Jul 2025 14:54:21 +0200
Subject: [PATCH 16/19] implemented a breakpoint method to determine LT2,
 identical to lt1_lt2_breakpoint method but only returning and plotting LT

---
 docs/quickstart.ipynb                         | 146 +++++++++++++-----
 src/lactopy/__init__.py                       |   2 +-
 src/lactopy/lactate_models/__init__.py        |   1 +
 src/lactopy/lactate_models/lt1_loglog.py      |  15 +-
 .../lactate_models/lt1_lt2_breakpoint.py      |  19 +--
 src/lactopy/lactate_models/lt1_si_lowest.py   |   2 +-
 src/lactopy/lactate_models/lt2_breakpoint.py  |  98 ++++++++++++
 src/lactopy/plots/lt1_loglog_plot.py          |   8 +-
 src/lactopy/plots/lt1_lt2_breakpoint_plot.py  |   8 +-
 src/lactopy/plots/lt2_breakpoint_plot.py      |  36 +++++
 10 files changed, 264 insertions(+), 71 deletions(-)
 create mode 100644 src/lactopy/lactate_models/lt2_breakpoint.py
 create mode 100644 src/lactopy/plots/lt2_breakpoint_plot.py

diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index 85ac6f7..8fc373b 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -795,7 +795,7 @@
       "No. Observations                        4\n",
       "No. Model Parameters                    4\n",
       "Degrees of Freedom                      0\n",
-      "Res. Sum of Squares                   0.0\n",
+      "Res. Sum of Squares           9.86076e-32\n",
       "Total Sum of Squares             0.721875\n",
       "R Squared                        1.000000\n",
       "Adjusted R Squared               1.000000\n",
@@ -804,14 +804,14 @@
       "====================================================================================================\n",
       "                    Estimate      Std Err            t        P>|t|       [0.025       0.975]\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "const                   0.85     6.78e-16    1.253e+15           nan          nan          nan\n",
-      "alpha1                 0.015     3.14e-17   4.7768e+14           nan          nan          nan\n",
-      "beta1              0.0712569     8.89e-17   8.0169e+14             -          nan          nan\n",
-      "breakpoint1          30.8781          0.0            -             -          nan          nan\n",
+      "const                   0.85      4.8e-16    1.772e+15           nan          nan          nan\n",
+      "alpha1                 0.015     2.22e-17   6.7554e+14           nan          nan          nan\n",
+      "beta1              0.0842254     7.43e-17   1.1338e+15             -          nan          nan\n",
+      "breakpoint1          32.2826     1.74e-24            -             -          nan          nan\n",
       "----------------------------------------------------------------------------------------------------\n",
       "These alphas(gradients of segments) are estimatedfrom betas(change in gradient)\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "alpha2             0.0862569     6.75e-17    1.277e+15           nan          nan          nan\n",
+      "alpha2             0.0992254     5.88e-17   1.6878e+15           nan          nan          nan\n",
       "====================================================================================================\n",
       "Davies test for existence of at least 1 breakpoint: p=0.0231182 (e.g. p<0.05 means reject null hypothesis of no breakpoints at 5% significance)\n",
       "\n",
@@ -828,7 +828,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -867,33 +867,33 @@
      "data": {
       "text/plain": [
        "{'davies': np.float64(0.023118172598819948),\n",
-       " 'estimates': {'const': {'estimate': np.float64(0.8500000000000002),\n",
-       "   'se': np.float64(6.783574731503469e-16),\n",
+       " 'estimates': {'const': {'estimate': np.float64(0.8499999999999995),\n",
+       "   'se': np.float64(4.796711693331817e-16),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(1253026661669298.8),\n",
+       "   't_stat': np.float64(1772047298947804.2),\n",
        "   'p_t': np.float64(nan)},\n",
-       "  'beta1': {'estimate': np.float64(0.0712569461146603),\n",
-       "   'se': np.float64(8.888396973636247e-17),\n",
+       "  'beta1': {'estimate': np.float64(0.08422541041934825),\n",
+       "   'se': np.float64(7.428897653884377e-17),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(801685009411872.1),\n",
+       "   't_stat': np.float64(1133753813061470.5),\n",
        "   'p_t': '-'},\n",
-       "  'breakpoint1': {'estimate': np.float64(30.87808227209066),\n",
-       "   'se': np.float64(0.0),\n",
+       "  'breakpoint1': {'estimate': np.float64(32.28261403816583),\n",
+       "   'se': np.float64(1.7361299205611346e-24),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
        "   't_stat': '-',\n",
        "   'p_t': '-'},\n",
-       "  'alpha1': {'estimate': np.float64(0.014999999999999993),\n",
-       "   'se': np.float64(3.1401849173675495e-17),\n",
+       "  'alpha1': {'estimate': np.float64(0.015000000000000025),\n",
+       "   'se': np.float64(2.2204460492503135e-17),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(477678875439432.8),\n",
+       "   't_stat': np.float64(675539944105575.4),\n",
        "   'p_t': np.float64(nan)},\n",
-       "  'alpha2': {'estimate': np.float64(0.08625694611466028),\n",
-       "   'se': np.float64(6.754591127167648e-17),\n",
+       "  'alpha2': {'estimate': np.float64(0.09922541041934828),\n",
+       "   'se': np.float64(5.879135504173735e-17),\n",
        "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(1277012101705551.5),\n",
+       "   't_stat': np.float64(1687755118910014.0),\n",
        "   'p_t': np.float64(nan)}},\n",
-       " 'bic': np.float64(-inf),\n",
-       " 'rss': np.float64(0.0),\n",
+       " 'bic': np.float64(-285.57663839069744),\n",
+       " 'rss': np.float64(9.860761315262648e-32),\n",
        " 'converged': True}"
       ]
      },
@@ -917,30 +917,30 @@
     {
      "data": {
       "text/plain": [
-       "{'const': {'estimate': np.float64(0.8500000000000002),\n",
-       "  'se': np.float64(6.783574731503469e-16),\n",
+       "{'const': {'estimate': np.float64(0.8499999999999995),\n",
+       "  'se': np.float64(4.796711693331817e-16),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(1253026661669298.8),\n",
+       "  't_stat': np.float64(1772047298947804.2),\n",
        "  'p_t': np.float64(nan)},\n",
-       " 'beta1': {'estimate': np.float64(0.0712569461146603),\n",
-       "  'se': np.float64(8.888396973636247e-17),\n",
+       " 'beta1': {'estimate': np.float64(0.08422541041934825),\n",
+       "  'se': np.float64(7.428897653884377e-17),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(801685009411872.1),\n",
+       "  't_stat': np.float64(1133753813061470.5),\n",
        "  'p_t': '-'},\n",
-       " 'breakpoint1': {'estimate': np.float64(30.87808227209066),\n",
-       "  'se': np.float64(0.0),\n",
+       " 'breakpoint1': {'estimate': np.float64(32.28261403816583),\n",
+       "  'se': np.float64(1.7361299205611346e-24),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
        "  't_stat': '-',\n",
        "  'p_t': '-'},\n",
-       " 'alpha1': {'estimate': np.float64(0.014999999999999993),\n",
-       "  'se': np.float64(3.1401849173675495e-17),\n",
+       " 'alpha1': {'estimate': np.float64(0.015000000000000025),\n",
+       "  'se': np.float64(2.2204460492503135e-17),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(477678875439432.8),\n",
+       "  't_stat': np.float64(675539944105575.4),\n",
        "  'p_t': np.float64(nan)},\n",
-       " 'alpha2': {'estimate': np.float64(0.08625694611466028),\n",
-       "  'se': np.float64(6.754591127167648e-17),\n",
+       " 'alpha2': {'estimate': np.float64(0.09922541041934828),\n",
+       "  'se': np.float64(5.879135504173735e-17),\n",
        "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(1277012101705551.5),\n",
+       "  't_stat': np.float64(1687755118910014.0),\n",
        "  'p_t': np.float64(nan)}}"
       ]
      },
@@ -994,7 +994,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: title={'center': 'LT1 Standard Increment Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+       "<Axes: title={'center': 'LT1 Log-Log Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
      "execution_count": 21,
@@ -1004,6 +1004,7 @@
     {
      "data": {
 <<<<<<< HEAD
+<<<<<<< HEAD
 <<<<<<< HEAD
       "image/png": 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",
 >>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
@@ -1013,6 +1014,9 @@
 =======
       "image/png": 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",
 >>>>>>> 4f662a4 (implemented a method to calculate both lt1 and lt2 together based on curve breakpoints using piecewise regression)
+=======
+      "image/png": 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",
+>>>>>>> e0fa89b (implemented a breakpoint method to determine LT2, identical to lt1_lt2_breakpoint method but only returning and plotting LT)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1068,7 +1072,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: title={'center': 'LT1 Standard Increment Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+       "<Axes: title={'center': 'LT1-LT2 Breakpoint Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
      "execution_count": 24,
@@ -1077,7 +1081,69 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.plot.plot_predictions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "0aac8a14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lactopy.lactate_models.lt2_breakpoint import LT2_breakpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "63236ce4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LT1 occurs at: 157.85 watts\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LT2_breakpoint()\n",
+    "model.fit(df['watt'], df['lactate'], si=0.5)\n",
+    "lt1_intensity = model.predict()\n",
+    "print(f\"LT1 occurs at: {lt1_intensity:.2f} watts\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5fb4e329",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'LT2 Breakpoint Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
diff --git a/src/lactopy/__init__.py b/src/lactopy/__init__.py
index 610428f..886f970 100644
--- a/src/lactopy/__init__.py
+++ b/src/lactopy/__init__.py
@@ -1 +1 @@
-from .lactate_models import OBLA, Bsln, Dmax, lt1_si, lt1_si_lowest, lt1_loglog, lt1_lt2_breakpoint  # noqa: F401
+from .lactate_models import OBLA, Bsln, Dmax, lt1_si, lt1_si_lowest, lt1_loglog, lt2_breakpoint, lt1_lt2_breakpoint  # noqa: F401
diff --git a/src/lactopy/lactate_models/__init__.py b/src/lactopy/lactate_models/__init__.py
index 20fa9bd..3db2810 100644
--- a/src/lactopy/lactate_models/__init__.py
+++ b/src/lactopy/lactate_models/__init__.py
@@ -4,4 +4,5 @@
 from .lt1_si import LT1_si  # noqa: F401
 from .lt1_si_lowest import LT1_si_lowest  # noqa: F401
 from .lt1_loglog import LT1_loglog  # noqa: F401
+from .lt2_breakpoint import LT2_breakpoint  # noqa: F401
 from .lt1_lt2_breakpoint import LT1_LT2_breakpoint  # noqa: F401
\ No newline at end of file
diff --git a/src/lactopy/lactate_models/lt1_loglog.py b/src/lactopy/lactate_models/lt1_loglog.py
index 1344b62..15ba1bb 100644
--- a/src/lactopy/lactate_models/lt1_loglog.py
+++ b/src/lactopy/lactate_models/lt1_loglog.py
@@ -14,18 +14,15 @@ class LT1_loglog(BaseModel):
     Log-Log method is used to estimate the first lactate threshold by plotting
     the lactate response against intensity on a logaritmic scale.
     
-    Plot is divided into 2 segments --> segmented regression identifies brekpoint
-
-    This is computed by using a standard value, preferably equlual to the lowest 
-    detectable change in lactate concentration of the measurement tool
+    Plot is divided into 2 segments --> segmented regression identifies breakpoint
 
     Attributes:
-        plot (lt1_si_plot): For visualizing the model..
+        plot (lt1_loglog_plot): For visualizing the model..
     """
 
     def __init__(self):
         """
-        Initializes the LT1_si model and its associated plot.
+        Initializes the lt1_loglog model and its associated plot.
 
         """
         super().__init__()
@@ -39,7 +36,7 @@ def fit(
         **kwargs,
     ):
         """
-        Fits the Standard Increment model to the given data.
+        Fits the lt1_loglog model to the given data.
 
         Args:
             X (ArrayLike): The independent variable (e.g., intensity).
@@ -59,7 +56,7 @@ def fit(
                 Defaults to "4th_poly".
 
         Returns:
-            self: Fitted Dmax model instance.
+            self: Fitted lt1_loglog model instance.
         """
         self._si = si
 
@@ -77,7 +74,7 @@ def fit(
 
     def predict(self) -> float:
         """
-        Predicts intensity for the LT1_si method.
+        Predicts intensity for the lt1_loglog method.
 
         Returns:
             float: Predicted intensity.
diff --git a/src/lactopy/lactate_models/lt1_lt2_breakpoint.py b/src/lactopy/lactate_models/lt1_lt2_breakpoint.py
index 0c0f45b..a6e0f3e 100644
--- a/src/lactopy/lactate_models/lt1_lt2_breakpoint.py
+++ b/src/lactopy/lactate_models/lt1_lt2_breakpoint.py
@@ -9,24 +9,19 @@
 
 class LT1_LT2_breakpoint(BaseModel):
     """
-    Determining 
-
-    Log-Log method is used to estimate the first lactate threshold by plotting
-    the lactate response against intensity on a logaritmic scale.
+    Determining both LT1 and LT2 based on the breakpoints in the lactate curve
+    Piecewie regression module is used to identify the breakpoints
     
-    Plot is divided into 2 segments --> segmented regression identifies brekpoint
+    Plot is divided into 3 segments --> segmented regression identifies breakpoint
 
-    This is computed by using a standard value, preferably equlual to the lowest 
-    detectable change in lactate concentration of the measurement tool
 
     Attributes:
-        plot (lt1_si_plot): For visualizing the model..
+        plot (lt1_lt2_breakpoint_plot): For visualizing the model.
     """
 
     def __init__(self):
         """
-        Initializes the LT1_si model and its associated plot.
-
+        Initializes the LT1_LT2_breakpoint model and its associated plot.
         """
         super().__init__()
         self.plot = LT1_LT2_breakpoint_Plot(self)
@@ -39,7 +34,7 @@ def fit(
         **kwargs,
     ):
         """
-        Fits the Standard Increment model to the given data.
+        Fits the LT1_LT2_breakpoint model to the given data.
 
         Args:
             X (ArrayLike): The independent variable (e.g., intensity).
@@ -77,7 +72,7 @@ def fit(
 
     def predict(self) -> float:
         """
-        Predicts intensity for the LT1_si method.
+        Predicts intensity for LT1 and LT2 method. 
 
         Returns:
             float: Predicted intensity.
diff --git a/src/lactopy/lactate_models/lt1_si_lowest.py b/src/lactopy/lactate_models/lt1_si_lowest.py
index c968149..bc5c0d5 100644
--- a/src/lactopy/lactate_models/lt1_si_lowest.py
+++ b/src/lactopy/lactate_models/lt1_si_lowest.py
@@ -59,7 +59,7 @@ def fit(
                 Defaults to "4th_poly".
 
         Returns:
-            self: Fitted Dmax model instance.
+            self: Fitted LT1_si_lowest model instance.
         """
         self._si = si
 
diff --git a/src/lactopy/lactate_models/lt2_breakpoint.py b/src/lactopy/lactate_models/lt2_breakpoint.py
new file mode 100644
index 0000000..aee14bd
--- /dev/null
+++ b/src/lactopy/lactate_models/lt2_breakpoint.py
@@ -0,0 +1,98 @@
+from numpy.typing import ArrayLike
+import numpy as np
+import copy
+import piecewise_regression
+
+from lactopy.base import BaseModel
+from lactopy.plots.lt2_breakpoint_plot import LT2_breakpoint_Plot
+
+
+class LT2_breakpoint(BaseModel):
+    """
+    Determining 
+
+    Log-Log method is used to estimate the first lactate threshold by plotting
+    the lactate response against intensity on a logaritmic scale.
+    
+    Plot is divided into 2 segments --> segmented regression identifies breakpoints
+
+    The second breakpoint which corresponds to LT2 is extracted
+
+    Attributes:
+        plot (lt2_breakpoint_plot): For visualizing the model.
+    """
+
+    def __init__(self):
+        """
+        Initializes the LT2_breakpoint model and its associated plot.
+
+        """
+        super().__init__()
+        self.plot = LT2_breakpoint_Plot(self)
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        si: float = 0.5,
+        **kwargs,
+    ):
+        """
+        Fits the LT2_breakpoint model to the given data.
+
+        Args:
+            X (ArrayLike): The independent variable (e.g., intensity).
+            y (ArrayLike): The dependent variable (e.g., lactate concentration).
+            si: the standard increment, defaults to 0.5
+            
+            threshold_above_baseline (float, optional): Threshold above baseline
+            for filtering in the "modified" implementation. Defaults to 0.5.
+
+            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 LT2_breakpoint model instance.
+        """
+        self._si = si
+
+        # 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
+        
+        # Ensure working on copies
+        self.X = np.asarray(copy.deepcopy(X))
+        self.y = np.asarray(copy.deepcopy(y))
+        
+        super().fit(X, y, **kwargs)
+        return self
+
+    def predict(self) -> float:
+        """
+        Predicts intensity for the LT2_breakpoint method.
+
+        Returns:
+            float: Predicted intensity.
+            
+        """
+        
+        pw_fit = piecewise_regression.Fit(self.X, self.y, n_breakpoints=2)
+        pw_results = pw_fit.get_results()
+        
+        if "breakpoint2" not in pw_results.get("estimates", {}):
+            raise ValueError(
+                f"'breakpoint2' not found in regression results."
+                f"It is recommended to use a different treshold estimation method."
+                f"Piecewise regression may have failed. Result: {pw_results}"
+            )
+        else:
+            predicted_lt2 = pw_results["estimates"]["breakpoint2"]["estimate"]
+
+        return predicted_lt2
diff --git a/src/lactopy/plots/lt1_loglog_plot.py b/src/lactopy/plots/lt1_loglog_plot.py
index 5e11252..42c205e 100644
--- a/src/lactopy/plots/lt1_loglog_plot.py
+++ b/src/lactopy/plots/lt1_loglog_plot.py
@@ -5,7 +5,7 @@
 
 class LT1_loglog_Plot(Plot):
     """
-    Plot for LT1 si model.
+    Plot for lt1_loglog model.
     """
 
     def __init__(self, base_lactate_LT1_si_model):
@@ -13,15 +13,15 @@ def __init__(self, base_lactate_LT1_si_model):
 
     def plot_fit(self):
         """
-        Plot the LT1 si model fit.
+        Plot the lt1_loglog model fit.
         """
         ax = super().plot_fit()
-        ax.set_title("LT1 Standard Increment Model Fit")
+        ax.set_title("LT1 Log-Log Model Fit")
         return ax
 
     def plot_predictions(self):
         """
-        Plot the LT1 model predictions.
+        Plot the lt1_loglog predictions.
         """
         self.plot_fit()
         plt.axvline(
diff --git a/src/lactopy/plots/lt1_lt2_breakpoint_plot.py b/src/lactopy/plots/lt1_lt2_breakpoint_plot.py
index 1e0f94d..299cb15 100644
--- a/src/lactopy/plots/lt1_lt2_breakpoint_plot.py
+++ b/src/lactopy/plots/lt1_lt2_breakpoint_plot.py
@@ -5,7 +5,7 @@
 
 class LT1_LT2_breakpoint_Plot(Plot):
     """
-    Plot for LT1 si model.
+    Plot for LT1_LT2 breakpoint model.
     """
 
     def __init__(self, base_lactate_LT1_si_model):
@@ -13,15 +13,15 @@ def __init__(self, base_lactate_LT1_si_model):
 
     def plot_fit(self):
         """
-        Plot the LT1 si model fit.
+        Plot the LT1_LT2_breakpoint model fit.
         """
         ax = super().plot_fit()
-        ax.set_title("LT1 Standard Increment Model Fit")
+        ax.set_title("LT1-LT2 Breakpoint Model Fit")
         return ax
 
     def plot_predictions(self):
         """
-        Plot the LT1 model predictions.
+        Plot the LT1 and LT2 model predictions.
         """
         self.plot_fit()
         lt_array = self.base_lactate_model.predict()
diff --git a/src/lactopy/plots/lt2_breakpoint_plot.py b/src/lactopy/plots/lt2_breakpoint_plot.py
new file mode 100644
index 0000000..9232c00
--- /dev/null
+++ b/src/lactopy/plots/lt2_breakpoint_plot.py
@@ -0,0 +1,36 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from lactopy.plots.base import Plot
+
+
+class LT2_breakpoint_Plot(Plot):
+    """
+    Plot for LT2_breakpoint model.
+    """
+
+    def __init__(self, base_lactate_LT1_si_model):
+        super().__init__(base_lactate_LT1_si_model)
+
+    def plot_fit(self):
+        """
+        Plot the LT2_breakpoint model fit.
+        """
+        ax = super().plot_fit()
+        ax.set_title("LT2 Breakpoint Model Fit")
+        return ax
+
+    def plot_predictions(self):
+        """
+        Plot the LT2 model predictions.
+        """
+        self.plot_fit()
+        lt2 = self.base_lactate_model.predict()
+
+        plt.axvline(
+            lt2,
+            color="black",
+            label="lt2",
+            linestyle="--",
+        )
+
+        return plt.gca()

From 2c967d620b538c3e7faf96192d2a37fb248335f1 Mon Sep 17 00:00:00 2001
From: ihosten <hostenian@gmail.com>
Date: Tue, 29 Jul 2025 14:39:03 +0200
Subject: [PATCH 17/19] Processed mosed of the FB in the peer review, options
 for redefining the base class bot for fitting and plotting should probably be
 discussed together

---
 docs/quickstart.ipynb                         | 209 ++----------------
 src/lactopy/lactate_models/lt1_loglog.py      |  19 +-
 .../lactate_models/lt1_lt2_breakpoint.py      |  15 +-
 src/lactopy/lactate_models/lt1_si.py          |   2 -
 src/lactopy/lactate_models/lt1_si_lowest.py   |   5 -
 src/lactopy/lactate_models/lt2_breakpoint.py  |  16 +-
 src/lactopy/plots/lt1_loglog_plot.py          |   6 +-
 src/lactopy/plots/lt1_lt2_breakpoint_plot.py  |   4 +-
 src/lactopy/plots/lt2_breakpoint_plot.py      |   4 +-
 9 files changed, 42 insertions(+), 238 deletions(-)

diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index 8fc373b..712d1a0 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -779,181 +779,6 @@
     "model.plot.plot_predictions()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "602551ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "                    Breakpoint Regression Results                     \n",
-      "====================================================================================================\n",
-      "No. Observations                        4\n",
-      "No. Model Parameters                    4\n",
-      "Degrees of Freedom                      0\n",
-      "Res. Sum of Squares           9.86076e-32\n",
-      "Total Sum of Squares             0.721875\n",
-      "R Squared                        1.000000\n",
-      "Adjusted R Squared               1.000000\n",
-      "Converged:                           True\n",
-      "====================================================================================================\n",
-      "====================================================================================================\n",
-      "                    Estimate      Std Err            t        P>|t|       [0.025       0.975]\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "const                   0.85      4.8e-16    1.772e+15           nan          nan          nan\n",
-      "alpha1                 0.015     2.22e-17   6.7554e+14           nan          nan          nan\n",
-      "beta1              0.0842254     7.43e-17   1.1338e+15             -          nan          nan\n",
-      "breakpoint1          32.2826     1.74e-24            -             -          nan          nan\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "These alphas(gradients of segments) are estimatedfrom betas(change in gradient)\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "alpha2             0.0992254     5.88e-17   1.6878e+15           nan          nan          nan\n",
-      "====================================================================================================\n",
-      "Davies test for existence of at least 1 breakpoint: p=0.0231182 (e.g. p<0.05 means reject null hypothesis of no breakpoints at 5% significance)\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ianh/lactopy/.venv/lib64/python3.13/site-packages/statsmodels/regression/linear_model.py:1717: RuntimeWarning: divide by zero encountered in scalar divide\n",
-      "  return np.dot(wresid, wresid) / self.df_resid\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# testing the breakpoint module\n",
-    "import piecewise_regression\n",
-    "pw_fit = piecewise_regression.Fit([10,20,30,40], [1,1.15,1.3,2.1], n_breakpoints=1)\n",
-    "pw_fit.summary()\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Plot the data, fit, breakpoints and confidence intervals\n",
-    "pw_fit.plot_data(color=\"grey\", s=20)\n",
-    "# Pass in standard matplotlib keywords to control any of the plots\n",
-    "pw_fit.plot_fit(color=\"red\", linewidth=4)\n",
-    "pw_fit.plot_breakpoints()\n",
-    "pw_fit.plot_breakpoint_confidence_intervals()\n",
-    "plt.xlabel(\"x\")\n",
-    "plt.ylabel(\"y\")\n",
-    "plt.show()\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "4f2792d2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'davies': np.float64(0.023118172598819948),\n",
-       " 'estimates': {'const': {'estimate': np.float64(0.8499999999999995),\n",
-       "   'se': np.float64(4.796711693331817e-16),\n",
-       "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(1772047298947804.2),\n",
-       "   'p_t': np.float64(nan)},\n",
-       "  'beta1': {'estimate': np.float64(0.08422541041934825),\n",
-       "   'se': np.float64(7.428897653884377e-17),\n",
-       "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(1133753813061470.5),\n",
-       "   'p_t': '-'},\n",
-       "  'breakpoint1': {'estimate': np.float64(32.28261403816583),\n",
-       "   'se': np.float64(1.7361299205611346e-24),\n",
-       "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': '-',\n",
-       "   'p_t': '-'},\n",
-       "  'alpha1': {'estimate': np.float64(0.015000000000000025),\n",
-       "   'se': np.float64(2.2204460492503135e-17),\n",
-       "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(675539944105575.4),\n",
-       "   'p_t': np.float64(nan)},\n",
-       "  'alpha2': {'estimate': np.float64(0.09922541041934828),\n",
-       "   'se': np.float64(5.879135504173735e-17),\n",
-       "   'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "   't_stat': np.float64(1687755118910014.0),\n",
-       "   'p_t': np.float64(nan)}},\n",
-       " 'bic': np.float64(-285.57663839069744),\n",
-       " 'rss': np.float64(9.860761315262648e-32),\n",
-       " 'converged': True}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get the key results of the fit\n",
-    "pw_results = pw_fit.get_results()\n",
-    "pw_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "bc758db0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'const': {'estimate': np.float64(0.8499999999999995),\n",
-       "  'se': np.float64(4.796711693331817e-16),\n",
-       "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(1772047298947804.2),\n",
-       "  'p_t': np.float64(nan)},\n",
-       " 'beta1': {'estimate': np.float64(0.08422541041934825),\n",
-       "  'se': np.float64(7.428897653884377e-17),\n",
-       "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(1133753813061470.5),\n",
-       "  'p_t': '-'},\n",
-       " 'breakpoint1': {'estimate': np.float64(32.28261403816583),\n",
-       "  'se': np.float64(1.7361299205611346e-24),\n",
-       "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': '-',\n",
-       "  'p_t': '-'},\n",
-       " 'alpha1': {'estimate': np.float64(0.015000000000000025),\n",
-       "  'se': np.float64(2.2204460492503135e-17),\n",
-       "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(675539944105575.4),\n",
-       "  'p_t': np.float64(nan)},\n",
-       " 'alpha2': {'estimate': np.float64(0.09922541041934828),\n",
-       "  'se': np.float64(5.879135504173735e-17),\n",
-       "  'confidence_interval': (np.float64(nan), np.float64(nan)),\n",
-       "  't_stat': np.float64(1687755118910014.0),\n",
-       "  'p_t': np.float64(nan)}}"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pw_estimates = pw_results[\"estimates\"]\n",
-    "pw_estimates"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -974,20 +799,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LT1 occurs at: 93.04 watts\n"
+      "LT1 occurs at: 4.53 watts\n"
      ]
     }
    ],
    "source": [
     "model = LT1_loglog()\n",
-    "model.fit(df['watt'], df['lactate'], si=0.5)\n",
+    "model.fit(df['watt'], df['lactate'])\n",
     "lt1_intensity = model.predict()\n",
     "print(f\"LT1 occurs at: {lt1_intensity:.2f} watts\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "a5d00229",
    "metadata": {},
    "outputs": [
@@ -997,7 +822,7 @@
        "<Axes: title={'center': 'LT1 Log-Log Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1005,6 +830,7 @@
      "data": {
 <<<<<<< HEAD
 <<<<<<< HEAD
+<<<<<<< HEAD
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",
 >>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
@@ -1017,6 +843,9 @@
 =======
       "image/png": 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",
 >>>>>>> e0fa89b (implemented a breakpoint method to determine LT2, identical to lt1_lt2_breakpoint method but only returning and plotting LT)
+=======
+      "image/png": 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",
+>>>>>>> e80a7e2 (Processed mosed of the FB in the peer review, options for redefining the base class bot for fitting and plotting should probably be discussed together)
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1031,7 +860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "76b18ab1",
    "metadata": {},
    "outputs": [],
@@ -1041,7 +870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "4f688ce1",
    "metadata": {},
    "outputs": [
@@ -1056,7 +885,7 @@
    ],
    "source": [
     "model = LT1_LT2_breakpoint()\n",
-    "model.fit(df['watt'], df['lactate'], si=0.5)\n",
+    "model.fit(df['watt'], df['lactate'])\n",
     "lt1_intensity = model.predict()\n",
     "lt1_intensity\n",
     "print(f\"LT1 occurs at: {lt1_intensity[0]:.2f} watts\")\n",
@@ -1065,7 +894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "9d61bd43",
    "metadata": {},
    "outputs": [
@@ -1075,7 +904,7 @@
        "<Axes: title={'center': 'LT1-LT2 Breakpoint Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1096,7 +925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "0aac8a14",
    "metadata": {},
    "outputs": [],
@@ -1106,7 +935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "63236ce4",
    "metadata": {},
    "outputs": [
@@ -1120,14 +949,14 @@
    ],
    "source": [
     "model = LT2_breakpoint()\n",
-    "model.fit(df['watt'], df['lactate'], si=0.5)\n",
+    "model.fit(df['watt'], df['lactate'])\n",
     "lt1_intensity = model.predict()\n",
     "print(f\"LT1 occurs at: {lt1_intensity:.2f} watts\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "5fb4e329",
    "metadata": {},
    "outputs": [
@@ -1137,7 +966,7 @@
        "<Axes: title={'center': 'LT2 Breakpoint Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1173,7 +1002,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.3"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/src/lactopy/lactate_models/lt1_loglog.py b/src/lactopy/lactate_models/lt1_loglog.py
index 15ba1bb..383beb3 100644
--- a/src/lactopy/lactate_models/lt1_loglog.py
+++ b/src/lactopy/lactate_models/lt1_loglog.py
@@ -32,7 +32,6 @@ def fit(
         self,
         X: ArrayLike,
         y: ArrayLike,
-        si: float = 0.5,
         **kwargs,
     ):
         """
@@ -42,9 +41,6 @@ def fit(
             X (ArrayLike): The independent variable (e.g., intensity).
             y (ArrayLike): The dependent variable (e.g., lactate concentration).
             si: the standard increment, defaults to 0.5
-            
-            threshold_above_baseline (float, optional): Threshold above baseline
-            for filtering in the "modified" implementation. Defaults to 0.5.
 
             method (str):
                 Method to use for fitting the model. Options are:
@@ -58,18 +54,15 @@ def fit(
         Returns:
             self: Fitted lt1_loglog model instance.
         """
-        self._si = si
+
+        y_log = np.log(y)
 
         # 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
-        
-        # Ensure working on copies
-        self.X = np.asarray(copy.deepcopy(X))
-        self.y = np.asarray(copy.deepcopy(y))
+        self.y_raw_for_plot = y_log
         
-        super().fit(X, y, **kwargs)
+        super().fit(X, y_log, **kwargs)
         return self
 
     def predict(self) -> float:
@@ -81,7 +74,7 @@ def predict(self) -> float:
             
         """
         
-        pw_fit = piecewise_regression.Fit(self.X, self.y, n_breakpoints=2)
+        pw_fit = piecewise_regression.Fit(self.X, self.y, n_breakpoints=1)
         pw_results = pw_fit.get_results()
         
         if "breakpoint1" not in pw_results.get("estimates", {}):
@@ -93,4 +86,4 @@ def predict(self) -> float:
         else:
             predicted_x = pw_results["estimates"]["breakpoint1"]["estimate"]
 
-        return predicted_x
+        return predicted_x
\ No newline at end of file
diff --git a/src/lactopy/lactate_models/lt1_lt2_breakpoint.py b/src/lactopy/lactate_models/lt1_lt2_breakpoint.py
index a6e0f3e..317804e 100644
--- a/src/lactopy/lactate_models/lt1_lt2_breakpoint.py
+++ b/src/lactopy/lactate_models/lt1_lt2_breakpoint.py
@@ -10,7 +10,7 @@
 class LT1_LT2_breakpoint(BaseModel):
     """
     Determining both LT1 and LT2 based on the breakpoints in the lactate curve
-    Piecewie regression module is used to identify the breakpoints
+    Piecewise regression module is used to identify the breakpoints
     
     Plot is divided into 3 segments --> segmented regression identifies breakpoint
 
@@ -30,7 +30,6 @@ def fit(
         self,
         X: ArrayLike,
         y: ArrayLike,
-        si: float = 0.5,
         **kwargs,
     ):
         """
@@ -41,8 +40,6 @@ def fit(
             y (ArrayLike): The dependent variable (e.g., lactate concentration).
             si: the standard increment, defaults to 0.5
             
-            threshold_above_baseline (float, optional): Threshold above baseline
-            for filtering in the "modified" implementation. Defaults to 0.5.
 
             method (str):
                 Method to use for fitting the model. Options are:
@@ -56,16 +53,12 @@ def fit(
         Returns:
             self: Fitted Dmax model instance.
         """
-        self._si = si
 
         # 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
         
-        # Ensure working on copies
-        self.X = np.asarray(copy.deepcopy(X))
-        self.y = np.asarray(copy.deepcopy(y))
         
         super().fit(X, y, **kwargs)
         return self
@@ -88,6 +81,12 @@ def predict(self) -> float:
                 f"It is recommended to use a different treshold estimation method."
                 f"Piecewise regression may have failed. Result: {pw_results}"
             )
+        elif "breakpoint1" not in pw_results.get("estimates", {}):
+            raise ValueError(
+                f"'breakpoint2' not found in regression results."
+                f"It is recommended to use a different treshold estimation method."
+                f"Piecewise regression may have failed. Result: {pw_results}"
+            )
         else:
             predicted_lt1 = pw_results["estimates"]["breakpoint1"]["estimate"]
             predicted_lt2 = pw_results["estimates"]["breakpoint2"]["estimate"]
diff --git a/src/lactopy/lactate_models/lt1_si.py b/src/lactopy/lactate_models/lt1_si.py
index 82d3488..b271c3a 100644
--- a/src/lactopy/lactate_models/lt1_si.py
+++ b/src/lactopy/lactate_models/lt1_si.py
@@ -67,13 +67,11 @@ def fit(
         self.y_raw_for_plot = y
 
         if si > 0 :
-            X, y = copy.deepcopy(X), copy.deepcopy(y)
             y_lt1 = y[0] + si
             self.y_lt1 = y_lt1
         else:
             raise ValueError(f"Impossible lactate change to find LT1: {si}")
 
-        self.X = X
         self.y = y
         
         super().fit(X, y, **kwargs)
diff --git a/src/lactopy/lactate_models/lt1_si_lowest.py b/src/lactopy/lactate_models/lt1_si_lowest.py
index bc5c0d5..3910c4f 100644
--- a/src/lactopy/lactate_models/lt1_si_lowest.py
+++ b/src/lactopy/lactate_models/lt1_si_lowest.py
@@ -67,13 +67,8 @@ def fit(
         # feels wrong to me
         self.X_raw_for_plot = X
         self.y_raw_for_plot = y
-        
-        # Ensure working on copies
-        X = np.asarray(copy.deepcopy(X))
-        y = np.asarray(copy.deepcopy(y))
 
         if si > 0 :
-            X, y = copy.deepcopy(X), copy.deepcopy(y)
             min_y_idx = np.argmin(y) # get the index of the lowest y
             self.min_y_idx = min_y_idx
             self.x_at_min_y = X[min_y_idx]
diff --git a/src/lactopy/lactate_models/lt2_breakpoint.py b/src/lactopy/lactate_models/lt2_breakpoint.py
index aee14bd..cd19d1c 100644
--- a/src/lactopy/lactate_models/lt2_breakpoint.py
+++ b/src/lactopy/lactate_models/lt2_breakpoint.py
@@ -9,12 +9,10 @@
 
 class LT2_breakpoint(BaseModel):
     """
-    Determining 
-
-    Log-Log method is used to estimate the first lactate threshold by plotting
-    the lactate response against intensity on a logaritmic scale.
+    Determining both LT1 and LT2 based on the breakpoints in the lactate curve
+    Piecewise regression module is used to identify the breakpoints
     
-    Plot is divided into 2 segments --> segmented regression identifies breakpoints
+    Plot is divided into 3 segments --> segmented regression identifies breakpoints
 
     The second breakpoint which corresponds to LT2 is extracted
 
@@ -34,7 +32,6 @@ def fit(
         self,
         X: ArrayLike,
         y: ArrayLike,
-        si: float = 0.5,
         **kwargs,
     ):
         """
@@ -45,9 +42,6 @@ def fit(
             y (ArrayLike): The dependent variable (e.g., lactate concentration).
             si: the standard increment, defaults to 0.5
             
-            threshold_above_baseline (float, optional): Threshold above baseline
-            for filtering in the "modified" implementation. Defaults to 0.5.
-
             method (str):
                 Method to use for fitting the model. Options are:
 
@@ -60,16 +54,12 @@ def fit(
         Returns:
             self: Fitted LT2_breakpoint model instance.
         """
-        self._si = si
 
         # 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
         
-        # Ensure working on copies
-        self.X = np.asarray(copy.deepcopy(X))
-        self.y = np.asarray(copy.deepcopy(y))
         
         super().fit(X, y, **kwargs)
         return self
diff --git a/src/lactopy/plots/lt1_loglog_plot.py b/src/lactopy/plots/lt1_loglog_plot.py
index 42c205e..9c18e2b 100644
--- a/src/lactopy/plots/lt1_loglog_plot.py
+++ b/src/lactopy/plots/lt1_loglog_plot.py
@@ -8,8 +8,8 @@ class LT1_loglog_Plot(Plot):
     Plot for lt1_loglog model.
     """
 
-    def __init__(self, base_lactate_LT1_si_model):
-        super().__init__(base_lactate_LT1_si_model)
+    def __init__(self, base_log_log_model):
+        super().__init__(base_log_log_model)
 
     def plot_fit(self):
         """
@@ -31,4 +31,4 @@ def plot_predictions(self):
             linestyle="--",
         )
 
-        return plt.gca()
+        return plt.gca()
\ No newline at end of file
diff --git a/src/lactopy/plots/lt1_lt2_breakpoint_plot.py b/src/lactopy/plots/lt1_lt2_breakpoint_plot.py
index 299cb15..89a578d 100644
--- a/src/lactopy/plots/lt1_lt2_breakpoint_plot.py
+++ b/src/lactopy/plots/lt1_lt2_breakpoint_plot.py
@@ -8,8 +8,8 @@ class LT1_LT2_breakpoint_Plot(Plot):
     Plot for LT1_LT2 breakpoint model.
     """
 
-    def __init__(self, base_lactate_LT1_si_model):
-        super().__init__(base_lactate_LT1_si_model)
+    def __init__(self, base_lactate_LT1_LT2_breakpoint_model):
+        super().__init__(base_lactate_LT1_LT2_breakpoint_model)
 
     def plot_fit(self):
         """
diff --git a/src/lactopy/plots/lt2_breakpoint_plot.py b/src/lactopy/plots/lt2_breakpoint_plot.py
index 9232c00..0f57d11 100644
--- a/src/lactopy/plots/lt2_breakpoint_plot.py
+++ b/src/lactopy/plots/lt2_breakpoint_plot.py
@@ -8,8 +8,8 @@ class LT2_breakpoint_Plot(Plot):
     Plot for LT2_breakpoint model.
     """
 
-    def __init__(self, base_lactate_LT1_si_model):
-        super().__init__(base_lactate_LT1_si_model)
+    def __init__(self, base_lactate_LT2_breakpoint_model):
+        super().__init__(base_lactate_LT2_breakpoint_model)
 
     def plot_fit(self):
         """

From ac946e76447a8794ca9a9a64d2811ef59195db22 Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Wed, 30 Jul 2025 20:46:41 +0200
Subject: [PATCH 18/19] add nb pre-commit to resolve merging errors with nb

---
 docs/quickstart.ipynb | 801 +++---------------------------------------
 pyproject.toml        |   1 +
 uv.lock               |  68 ++++
 3 files changed, 110 insertions(+), 760 deletions(-)

diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index 712d1a0..1ce3d4d 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "150ece86",
    "metadata": {},
    "outputs": [],
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "e0dd4c2e",
    "metadata": {},
    "outputs": [],
@@ -24,439 +24,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "7f657183",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<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",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\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",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\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",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\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",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\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-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\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-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\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-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\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-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\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-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\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-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\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-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\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-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-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-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-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-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-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-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-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-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-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-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\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",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\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-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-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",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-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",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\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-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-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-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": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# fit model returns a scikit learn like model object\n",
     "obla = OBLA()\n",
@@ -465,39 +36,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "77b4e630",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-<<<<<<< HEAD
-<<<<<<< HEAD
-      "image/png": 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",
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",
->>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
-=======
-      "image/png": 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VJ0+enPswAAAAcDkpDQVX/56c0lCwcKHUrp1V1aUPLUHXsW5d2hagKxmGdPSoeV5OMgxDNptNkrRy5Uo9+OCDKl68uPLmzatOnTrpzJkziouLu+E1tm3bplatWqlkyZLKmzev7r//fknSkSNHcuQzAAAAwDVd3VDQVZ+rqnZKuryvTx/n7xpHCLqOiIisPS+r7Nu3T6VLl9ahQ4fUsmVLValSRV999ZW2bdumCRMmSJISEhKu+/7z58+rSZMmCgoK0pw5c7RlyxYtXrz4pu8DAAAArmwouFfrNFXPa5Pu0e36Q7KwoSCj6A53HaGhWXteVli1apX27Nmjvn37atu2bbLb7Ro9erRy5TKz7Jdffulwvq+vr5KviuF//PGHzpw5oxEjRigsLEyStHXr1pz7EAAAAHBZKQ0AhXVK8/WkvJWsL/SE9uv2a57nrGgJuo769aUSJcxnf67FZpPCwszzskN8fLxOnjyp48ePa/v27Ro2bJhat26tli1b6umnn1a5cuWUmJio8ePH659//tGsWbM0efJkh2uEh4crNjZWP/30k06fPq24uDiVLFlSvr6+qe9btmyZ3nnnnez5EAAAAHAroaFSLiVrrp5ScZ3QXt2hFzRFki3Nec6MEHQdXl7SRx+Z61cHoZTtsWOzb1CE77//XqGhoQoPD1fTpk21evVqjRs3TkuXLpWXl5eqVq2qMWPGaOTIkbrzzjs1Z84cDR8+3OEadevW1YsvvqgnnnhChQsX1vvvv6/ChQtr+vTpWrBggSpWrKgRI0bogw8+yJ4PAQAAALdSv740Ou9QNdJPOq9APaqFOq/LA2tld0NBVrEZxrXGP3MN0dHRCg4OVlRUlIKCghyOXbx4UQcPHlTp0qXl7++f6Z+xaJH58NeVgySEhZkByNlHvchJWfV9AwAAwIl9/72M5s1lMwx10BzN1VOph1IaCqwaHe5G2eBqPBN0E+3amcNgr1tn9m0MDTWTbU4Oiw0AAABY7uhRqWNH2QxD/zR+UT/vfUq6oqGgRAnXaSggBKWDl5fUoIHVVQAAAAAWSUiQHn9cOnNGqlFDZZZ+qEM+rttQQAgCAAAAcGMDBkibNkn58kkLFkj+/vKS6zYUMDACAAAAgOtbuNDs5yZJM2ZIpUtbXdEtIwQBAAAAuLa//pKeecZcf+016eGHra4oSxCCAAAAAKSVkCC1by/FxJgP/Lz3ntUVZRlCEAAAAIC0Bg2Stm6VChSQ5s6VvN1nOAFCEAAAAABH334rjR5trk+bZo5/7UYIQQAAAAAuO3FC6tzZXH/5Zbd5DuhKhCAPtGbNGtlsNkVGRqb7PeHh4RqbMioIAAAA3FNystSpk3T6tHTXXdL771tdUbYgBDmhLl26yGaz6cUXX0xzrEePHrLZbOrSpYsltQEAAMCNjRghrVol5c4tzZ8v+ftbXVG2IAQ5qbCwMM2fP18XLlxI3Xfx4kXNnTtXJUuWtLQ2AAAAuKFffpGGDDHXJ0yQbr/d6oqyjWeFIMOQzp/P+cUwMlxq9erVFRYWpkWLFqXuW7RokUqWLKlq1aql7ouPj1evXr1UpEgR+fv7695779WWLVscrvXtt9/qtttuU0BAgBo2bKhDhw6l+Xnr169X/fr1FRAQoLCwMPXq1Uvnz5/PcN0AAABwQefOSU89ZXaH69hRevppqyvKVp4VguLipDx5cn6Ji8tUuc8884ymTZuWuv3555+ra9euDue89tpr+uqrrzRjxgxt375d5cqVU5MmTXT27FlJ0tGjR9WuXTu1atVKO3fu1HPPPaeBAwc6XOPvv/9W06ZN9cgjj2j37t364osvtH79evXs2TNTdQMAAMCFGIb03HPSkSNSuXLSxImSzWZ1VdnKs0KQi+nYsaPWr1+vw4cP6/Dhw/rll1/UsWPH1OPnz5/XpEmTNGrUKDVr1kwVK1bUJ598ooCAAH322WeSpEmTJqls2bIaPXq0br/9dnXo0CHN80TDhw9Xhw4d1KdPH5UvX15169bVuHHjNHPmTF28eDHHPzcAAABy0NSp0qJFko+P+RxQ3rxWV5Tt3GfGo/QIDJRiY635uZlQuHBhtWjRQtOnT5dhGGrRooUKFSqUevzvv/9WYmKi6tWrl7rPx8dHd999t/bt2ydJ2rdvn2rXru1w3Tp16jhs79q1S7t379acOXNS9xmGIbvdroMHD+qOO+7IVP0AAABwcvv3S337muvDh0s1alhdUY7wrBBks5kjXbiQZ555JrVb2oQJE7LlZ8TGxuqFF15Qr1690hxjEAYAAAA3lZAgdeggXbggNWp0OQx5AM8KQS6oadOmSkhIkM1mU5MmTRyOlS1bVr6+vvrll19UqlQpSVJiYqK2bNmiPn36SJLuuOMOLVu2zOF9mzZtctiuXr269u7dq3LlymX75wEAAICTeOstads2qUABafp0KZfnPCnjOZ/URXl5eWnfvn3au3evvLy8HI7lzp1b3bt3V//+/fX9999r79696tatm+Li4vTss89Kkl588UUdOHBA/fv31/79+zV37lxNnz7d4ToDBgzQhg0b1LNnT+3cuVMHDhzQ0qVLGRgBAADAXf38szknkC49E1S8uNUV5ShCkAsICgpSUFDQNY+NGDFCjzzyiDp16qTq1avrr7/+0g8//KD8+fNLl7qzffXVV1qyZImqVq2qyZMna9iwYQ7XqFKlitauXas///xT9evXV7Vq1TR48GAVK1YsRz4fAAAAclBkpNSpkzkqXNeu0iOPWF1RjrMZRiYmsXES0dHRCg4OVlRUVJqQcPHiRR08eFClS5eWv5vOdOtM+L4BAABcRIcO0ty5Utmy0o4dbjMa3I2ywdVoCQIAAAA8xdy55uLlJc2e7TYBKKMIQQAAAIAnOHxY6t7dXH/zTemee6yuyDKEIAAAAMDdJSebzwFFR0t16khvvGF1RZYiBAEAAADu7oMPpHXrpDx5zG5w3p49U47bhyAXHvfBpfA9AwAAOKndu83ub5I0bpxUpozVFVnObUOQj4+PJCkuLs7qUjxCyvec8r0DAADACcTHm93gEhOl1q2lLl2srsgpuG07mJeXl/Lly6dTp05JkgIDA2Wz2awuy+0YhqG4uDidOnVK+fLlSzOhKwAAACw0dKjZElSokDkpKr8PS+4cgiQpJCREklKDELJPvnz5Ur9vAAAAOIENG6SRI831qVOlIkWsrshpuHUIstlsCg0NVZEiRZSYmGh1OW7Lx8eHFiAAAABncv681LmzZLdLTz8ttW1rdUVOxa1DUAovLy9+SQcAAIDneO016a+/pBIlpI8+sroap+O2AyMAAAAAHunHH6WJE831adOkfPmsrsjpWBqCwsPDZbPZ0iw9evSwsiwAAADANZ07Jz3zjLn+8stSo0ZWV+SULO0Ot2XLFiUnJ6du//bbb3rooYf02GOPWVkWAAAA4Jpeflk6fly67TZpxAirq3FaloagwoULO2yPGDFCZcuW1f33329ZTQAAAIBLWrhQmjNHypVLmjlTCgy0uiKn5TQDIyQkJGj27Nnq16/fdefziY+PV3x8fOp2dHR0DlYIAAAAOKlTp6Tu3c3111+Xate2uiKn5jQDIyxZskSRkZHqcoNZbIcPH67g4ODUJSwsLEdrBAAAAJxSjx7S6dNSlSrS4MFWV+P0bIZhGFYXIUlNmjSRr6+vvv766+uec62WoLCwMEVFRSkoKCiHKgUAAACcyJdfSk88IXl7S7/+KlWrZnVFloiOjlZwcHC6soFTdIc7fPiwVq5cqUWLFt3wPD8/P/n5+eVYXQAAAIBT+/df6aWXzPVBgzw2AGWUU3SHmzZtmooUKaIWLVpYXQoAAADgGgzDDEBnzkhVq0pvvGF1RS7D8hBkt9s1bdo0de7cWd7eTtEwBQAAADi/L76QFi0yu8FNny75+lpdkcuwPAStXLlSR44c0TMpkzoBAAAAuLGTJ83BECTpf/+T7rrL6opciuVNL40bN5aTjM0AAAAAOD/DMIfDPnvWDD+DBlldkcuxvCUIAAAAQAbMmyctWXK5G5yPj9UVuRxCEAAAAOAqIiKknj3N9TffNAdEQIYRggAAAABXkNIN7tw5cyjs11+3uiKXRQgCAAAAXMGXX0pLl5rd3+gGd0sIQQAAAICz+++/y93gBg2SqlSxuiKXRggCAAAAnF2vXtLp09KddzIaXBYgBAEAAADObOlSaf58KVcuado0JkXNAoQgAAAAwFlFRpqDIUhS//5SzZpWV+QWCEEAAACAs3rlFXNY7Ntuk4YMsboat0EIAgAAAJzRjz9Kn38u2Wzma0CA1RW5DUIQAAAA4GxiYqRu3cz1l1+W6tWzuiK3QggCAAAAnM3rr0tHjkjh4dJ771ldjdshBAEAAADO5OefpQkTzPVPP5Xy5LG6IrdDCAIAAACcxYUL0nPPmevdukkPPmh1RW6JEAQAAAA4i6FDpQMHpGLFpFGjrK7GbRGCAAAAAGewfbv0wQfm+sSJUnCw1RW5LUIQAAAAYLWkJLMbXHKy9NhjUuvWVlfk1ghBAAAAgNXGjJF27JDy55fGj7e6GrdHCAIAAACsdOCANGSIuT56tFS0qNUVuT1CEAAAAGAVw5Cef166eFFq1Ejq0sXqijwCIQgAAACwyqefSmvWSIGB0pQpks1mdUUegRAEAAAAWOHECal/f3P9nXekMmWsrshjEIIAAAAAK/TsKUVFSTVrSr16WV2NRyEEAQAAADntq6+kxYslb2/ps8/MV+QYQhAAAACQk86dM1uBJGnAAKlKFasr8jiEIAAAACAnDRggnTwp3Xab9L//WV2NRyIEAQAAADll7Vrpk0/M9U8+kfz9ra7IIxGCAAAAgJxw8aL0wgvmerdu0n33WV2RxyIEAQAAADlh2DBp/34pJER6/32rq/FohCAAAAAgu/3+uzRihLk+fryUL5/VFXk0QhAAAACQnex2s/tbYqLUqpX0yCNWV+TxCEEAAABAdpo8Wdq4UcqTR5owQbLZrK7I4xGCAAAAgOxy/Lg0cKC5Pny4FBZmdUUgBAEAAADZqGdPKSZGuuceqXt3q6vBJYQgAAAAIDssWiQtWSJ5e5tzAnl5WV0RLiEEAQAAAFktKspsBZKkAQOkO++0uiJcgRAEAAAAZLXXX5ciIqTy5aX//c/qanAVQhAAAACQlTZuNEeEk6QpUyR/f6srwlUIQQAAAEBWSUyUnn9eMgypa1epYUOrK8I1EIIAAACArPLBB9Jvv0mFCkmjRlldDa6DEAQAAABkhb/+koYONdc//FAqWNDqinAdhCAAAADgVhmG9OKLUny89NBDUocOVleEGyAEAQAAALdq9mzpp5/MQRAmTZJsNqsrwg0QggAAAIBbcfq01LevuT5kiFS2rNUV4SYIQQAAAMCtePVV6cwZqXJl6ZVXrK4G6UAIAgAAADJr1Sppxgyz+9vUqZKPj9UVIR0IQQAAAEBmXLwovfCCud69u3TPPVZXhHQiBAEAAACZ8d575rDYoaHSsGFWV4MMIAQBAAAAGbV3rzRypLk+frwUHGx1RcgAQhAAAACQEXa72Q0uMVFq1Upq187qipBBhCAAAAAgIz7/XFq/XsqdW/r4Y+YEckGEIAAAACC9/v1X6t/fXH/7balkSasrQiZYHoKOHz+ujh07qmDBggoICFDlypW1detWq8sCAAAA0urXT4qMlKpVk3r1sroaZJK3lT/83Llzqlevnho2bKjvvvtOhQsX1oEDB5Q/f34rywIAAADS+vFHae5cKVcuc04gb0t/lcYtsPTOjRw5UmFhYZo2bVrqvtKlS1tZEgAAAJDWhQvmXECS9PLLUs2aVleEW2Bpd7hly5apZs2aeuyxx1SkSBFVq1ZNn3zyyXXPj4+PV3R0tMMCAAAAZLt33pH++UcqXtxch0uzNAT9888/mjRpksqXL68ffvhB3bt3V69evTRjxoxrnj98+HAFBwenLmFhYTleMwAAADzMb79Jo0aZ6x9/LOXNa3VFuEU2wzAMq364r6+vatasqQ0bNqTu69Wrl7Zs2aKNGzemOT8+Pl7x8fGp29HR0QoLC1NUVJSCgoJyrG4AAAB4CLtdql9f2rBBatNGWrzY6opwHdHR0QoODk5XNrC0JSg0NFQVK1Z02HfHHXfoyJEj1zzfz89PQUFBDgsAAACQbT75xAxAefJI48ZZXQ2yiKUhqF69etq/f7/Dvj///FOlSpWyrCYAAABAknTypDRggLn+7rsSj2K4DUtDUN++fbVp0yYNGzZMf/31l+bOnaupU6eqR48eVpYFAAAASH37SlFRUo0aUs+eVleDLGTpM0GStHz5cr3++us6cOCASpcurX79+qlbt27pem9G+v0BAAAA6fb991KzZuacQFu2SNWrW10RbiIj2cDyEHQrCEEAAADIcnFx0p13SgcPmq1BY8ZYXRHSwWUGRgAAAACczjvvmAEoLEx6+22rq0E2IAQBAAAAKfbskT74wFwfP94cFQ5uhxAEAAAA6NKcQC+8ICUlmXMCtW5tdUXIJoQgAAAAQJfmBNq4kTmBPAAhCAAAAGBOII9CCAIAAACYE8ijEIIAAADg2b7/Xpo/35wTaMoUycvL6oqQzQhBAAAA8FxxcdJLL5nrvXqZLUFwe4QgAAAAeK633748J9A771hdDXIIIQgAAACeac8eafRoc/3jj5kTyIN4W10AAAAAkFOSk6V166SI43Y1H/a8gpOSpLZtpYcftro05CBaggAAAOARFi2SwsOlhg2lnztOUfDeTYqx5dW3zcZbXRpyGCEIAAAAbm/RIunRR6Vjx6QQRWiEBkqS3jDeU8sXimvRIqsrRE4iBAEAAMCtJSdLvXtLhmFuf6TeCla0flUtTZA5MlyfPuZ58AyEIAAAALi1devMFiBJaq5v9LgWKEleel5TZZeXDEM6etQ8D56BEAQAAAC3FhFhvgbqvCaohyTpQ/XVLt11zfPg/ghBAAAAcGuhoebrW3pL4Tqswyqpt/TWdc+D+yMEAQAAwK3Vry81LrJTffWhJOklTVSccqcet9nMuVLr17ewSOQoQhAAAADcmpeSNS/oeXkrWQv0qL5Vi9RjNpv5Onas5OVlXY3IWYQgAAAAuLeJE1Xgry1KCAzWiNBxDodKlJAWLpTatbOsOljA2+oCAAAAgGxz9Kg0aJAkyXf0CP3aLVTr1pmDIISGml3gaAHyPIQgAAAAuK+XX5ZiY6W6daXnn5dXLqlBA6uLgtXoDgcAAAD3tHixtHSp5O0tTZki5eJXX5j4LwEAAADuJzpa6tnTXH/tNenOO62uCE6EEAQAAAD3M2iQdOKEVK6c9L//WV0NnAwhCAAAAO5l0yZp4kRzffJkKSDA6orgZAhBAAAAcB+JidLzz0uGIT39tPTgg1ZXBCdECAIAAID7GD1a2rNHKljQXAeugRAEAAAA9/D339LQoeb6mDFSoUJWVwQnRQgCAACA6zMMqXt36eJFswtcp05WVwQnRggCAACA65s9W1qxQvLzkyZNkmw2qyuCEyMEAQAAwLX995/Ut6+5PmSIVL681RXByRGCAAAA4Nr69ZPOnJEqV5ZefdXqauACCEEAAABwXT/8YHaFs9mkTz6RfHysrggugBAEAAAA13T+vPTii+b6yy9LtWtbXRFcBCEIAAAArumtt6RDh6SwMOndd62uBi6EEAQAAADXs327OReQJE2cKOXNa3VFcCGEIAAAALiWpCSpWzfJbpcef1xq2dLqiuBiCEEAAABwLR99ZLYE5ctnrgMZRAgCAACA6zh4UBo82FwfNUoKCbG6IrggQhAAAABcg2GYo8HFxUn33y89+6zVFcFFEYIAAADgGmbNkn78UfLzk6ZMMecGAjKBEAQAAADnd+qU1LevuT54sHT77VZXBBdGCAIAAIDz691bOntWqlpV6t/f6mrg4ghBAAAAcG7Ll0vz50u5ckmffir5+FhdEVwcIQgAAADOKzpa6t7dXO/XT6pZ0+qK4AYIQQAAAHBeAwdKx45JZctKQ4daXQ3cBCEIAAAAzmndOmnSJHN96lQpMNDqiuAmCEEAAABwPhcvSt26mevPPis98IDVFcGNEIIAAADgfN59V9q/XwoJkUaNsroauBlCEAAAAJzL7t3SyJHm+scfS/nzW10R3AwhCAAAAM4jKcns/paUJLVtKz3yiNUVwQ1ZGoLeeust2Ww2h6VChQpWlgQAAAArffihtHWrFBxstgIB2cDb6gIqVaqklStXpm57e1teEgAAAKywf7/05pvm+ocfSsWKWV0R3JTlicPb21shISFWlwEAAAAr2e3Sc89J8fFS48ZSly5WVwQ3ZvkzQQcOHFCxYsVUpkwZdejQQUeOHLnuufHx8YqOjnZYAAAA4AYmTpTWr5fy5DHnBLLZrK4IbszSEFS7dm1Nnz5d33//vSZNmqSDBw+qfv36iomJueb5w4cPV3BwcOoSFhaW4zUDAAAgix06JA0caK6PHCmVKmV1RXBzNsMwDKuLSBEZGalSpUppzJgxevbZZ9Mcj4+PV3x8fOp2dHS0wsLCFBUVpaCgoByuFgAAALfMMMzubytXSvfdJ61eLeWyvLMSXFB0dLSCg4PTlQ0sfyboSvny5dNtt92mv/7665rH/fz85Ofnl+N1AQAAIJt8/rkZgPz9pU8/JQAhRzjVf2WxsbH6+++/FRoaanUpAAAAyG7Hj0v9+pnr774rlS9vdUXwEJaGoFdffVVr167VoUOHtGHDBrVt21ZeXl5q3769lWUBAAAguxmG9OKLUnS0dPfdUp8+VlcED2Jpd7hjx46pffv2OnPmjAoXLqx7771XmzZtUuHCha0sCwAAANlt3jxp+XLJx8fsEuflZXVF8CCWhqD58+db+eMBAABghX//lXr1MtcHD5YqVbK6IngYp3omCAAAAG7OMKTu3aUzZ6SqVaUBA6yuCB6IEAQAAICcM3++tHix5O0tzZhhdocDchghCAAAADnj5EmpZ09z/c03zZYgwAKEIAAAAGS/lNHgzp6VqlWTXn/d6orgwQhBAAAAyH5z50pLl5rd36ZPpxscLEUIAgAAQPaKiJBeftlcHzxYqlLF6org4QhBAAAAyD4p3eDOnZOqV2c0ODgFQhAAAACyz5w50rJldIODUyEEAQAAIHtERFyeFHXIEKlyZasrAiRCEAAAALKFYUgvvGB2g6tRg25wcCqEIAAAAGS9GTOkr7+WfH3NdW9vqysCUhGCAAAAkLUOH77cDe7tt6VKlayuCHBACAIAAEDWsdulLl2kmBipbl3p1VetrghIgxAEAACArDN+vLRmjZQ7tzRzpuTlZXVFQBqZDkFJSUlauXKlpkyZopiYGEnSiRMnFBsbm5X1AQAAwFX88Yc0cKC5/sEHUtmyVlcEXFOmnlA7fPiwmjZtqiNHjig+Pl4PPfSQ8ubNq5EjRyo+Pl6TJ0/O+koBAADgvBITpU6dpIsXpSZNzJHhACeVqZag3r17q2bNmjp37pwCAgJS97dt21Y//fRTVtYHAAAAVzB8uLR1q5Qvn/TZZ5LNZnVFwHVlqiVo3bp12rBhg3x9fR32h4eH6/jx41lVGwAAAFzBtm3SO++Y6xMnSsWLW10RcEOZagmy2+1KTk5Os//YsWPKmzdvVtQFAAAAV3DhgtkNLilJevxx6cknra4IuKlMhaDGjRtr7Nixqds2m02xsbEaMmSImjdvnpX1AQAAwJn973/Svn1SSIjZCkQ3OLgAm2EYRkbfdOzYMTVp0kSGYejAgQOqWbOmDhw4oEKFCunnn39WkSJFsqfaq0RHRys4OFhRUVEKCgrKkZ8JAACAS9askR54QDIMaflyqUULqyuCB8tINsjUM0ElSpTQrl279MUXX2jXrl2KjY3Vs88+qw4dOjgMlAAAAAA3de6c2Q3OMKRu3QhAcCmZagn6+eefVbduXXl7O2aopKQkbdiwQffdd19W1nhdtAQBAABYwDCk9u2lL76QypeXduwwJ0cFLJSRbJCpZ4IaNmyos2fPptkfFRWlhg0bZuaSAAAAcBWzZ5sByNtbmjOHAASXk6kQZBiGbNd46O3MmTPKzR8CAAAA93XwoNSjh7n+1ltSrVpWVwRkWIaeCWrXrp10aTS4Ll26yM/PL/VYcnKydu/erbp162Z9lQAAALBeUpL5HFBMjFSvnjRwoNUVAZmSoRAUHBwsXWoJyps3r8MgCL6+vrrnnnvUrVu3rK8SAAAA1hsxQvrlFylvXmnWLMnLy+qKgEzJUAiaNm2aJCk8PFyvvvoqXd8AAAA8xebNZvc3SZowQSpd2uqKgEzL1OhwzoLR4QAAAHJAbKxUrZr011/SE09I8+YxKSqcTrbPEyRJCxcu1JdffqkjR44oISHB4dj27dsze1kAAAA4mz59zAAUFiZNmkQAgsvL1Ohw48aNU9euXVW0aFHt2LFDd999twoWLKh//vlHzZo1y/oqAQAAYI1Fi6TPPjODz8yZUv78VlcE3LJMhaCJEydq6tSpGj9+vHx9ffXaa69pxYoV6tWrl6KiorK+SgAAAOS8w4elZ5811197TWrQwOqKgCyRqRB05MiR1KGwAwICFBMTI0nq1KmT5s2bl7UVAgAAIOclJUlPPSVFRkq1a0vvvGN1RUCWyVQICgkJ0dmzZyVJJUuW1KZNmyRJBw8elAuPswAAAIAUb70lbdggBQWZAyH4+FhdEZBlMhWCHnjgAS1btkyS1LVrV/Xt21cPPfSQnnjiCbVt2zarawQAAEBOWrVKGjbMXP/kE4bDhtvJ1BDZdrtddrtd3t7m4HLz58/Xhg0bVL58eb3wwgvy9fXNjlrTYIhsAACALHbqlFS1qnTypNStmzR1qtUVAemSkWyQqRB05MgRhYWFyXbV8IiGYejo0aMqWbJkxqvOBEIQAABAFrLbpZYtpe++kypWlLZskQIDra4KSJeMZINMdYcrXbq0/vvvvzT7z549q9I0lwIAALimsWPNAOTvL82fTwCC28pUCDIMI00rkCTFxsbK398/K+oCAABATtq6VRo40Fz/8EOpcmWrKwKyjXdGTu7Xr58kyWaz6c0331TgFf86kJycrM2bN+uuu+7K+ioBAACQfaKjpSeflBITpUcekV54weqKgGyVoRC0Y8cO6VJL0J49exwGQPD19VXVqlX16quvZn2VAAAAyB6GYQ6A8PffUsmS5mhw1+jxA7iTDIWg1atXS5eGxf7oo48YjAAAAMDVTZggffml5O1tPgeUP7/VFQHZLlPPBI0dO1ZJSUlp9p89e1bR0dFZURcAAACy26+/Spced9CoUVKdOlZXBOSITIWgJ598UvPnz0+z/8svv9STTz6ZFXUBAAAgO505Iz322OXngHr3troiIMdkKgRt3rxZDRs2TLO/QYMG2rx5c1bUBQAAgOxit0tPPy0dOSKVKyd99hnPAcGjZCoExcfHX7M7XGJioi5cuJAVdQEAACC7jBwpffutOR/QwoVScLDVFQE5KlMh6O6779bUqVPT7J88ebJq1KiRFXUBAAAgO6xeLf3vf+b6hAlS1apWVwTkuAyNDpfi3XffVaNGjbRr1y49+OCDkqSffvpJW7Zs0Y8//pjVNQIAACArRERI7dub3eG6dJGeecbqigBLZKolqF69etq4caPCwsL05Zdf6uuvv1a5cuW0e/du1a9fP+urBAAAwK1JSjID0L//SpUrm61AgIfKVEuQJN11112aM2dO1lYDAACA7PHGG9LatVLevOZzQIGBVlcEWCbTISjFxYsXlZCQ4LCPSVQBAACcyBdfSO+/b65/9pl0221WVwRYKlPd4eLi4tSzZ08VKVJEuXPnVv78+R0WAAAAOIlduy4/+/Paa+bcQICHy1QI6t+/v1atWqVJkybJz89Pn376qYYOHapixYpp5syZmSpkxIgRstls6tOnT6beDwAAgKucPSu1bSvFxUmNG0vDhlldEeAUMtUd7uuvv9bMmTPVoEEDde3aVfXr11e5cuVUqlQpzZkzRx06dMjQ9bZs2aIpU6aoSpUqmSkHAAAAV0tONgdCOHhQKlNGmjdP8vKyuirAKWSqJejs2bMqU6aMdOn5n7Nnz0qS7r33Xv38888ZulZsbKw6dOigTz75hK50AAAAWWXQIOnHH80BEJYskQoUsLoiwGlkKgSVKVNGBw8elCRVqFBBX375pXSphSg4gzMO9+jRQy1atFCjRo1uem58fLyio6MdFgAAAFzlyoEQpk0zh8QGkCpTIahr167atWuXJGngwIGaMGGC/P391bdvX7322mvpvs78+fO1fft2DR8+PF3nDx8+XMHBwalLWFhYZsoHAABwX1cOhDBggPT441ZXBDgdm2EYxq1e5PDhw9q2bZsKFSqk2bNna+rUqTd9z9GjR1WzZk2tWLEi9VmgBg0a6K677tLYsWOv+Z74+HjFx8enbkdHRyssLExRUVEMyw0AAHDmjFSrlvkcUOPG0rff8hwQPEZ0dLSCg4PTlQ2yJASl2LVrl6pXr67k5OSbnrtkyRK1bdtWXlf8wUxOTpbNZlOuXLkUHx/vcOxaMvJBAQAA3FpiotSsmfTTT+ZACFu28BwQPEpGssEtT5aaWQ8++KD27NnjsK9r166qUKGCBgwYcNMABAAAgEsMQ+rZ0wxAefJIixcTgIAbsCwE5c2bV3feeafDvty5c6tgwYJp9gMAAOAGxo6Vpk6VbDZzKGymHQFuKFMDIwAAAMBJfP219Mor5vro0VLLllZXBDi9DLUEtWvX7obHIyMjb6mYNWvW3NL7AQAAPMquXeaEqIYhvfCC1KeP1RUBLiFDIehmcwAFBwfr6aefvtWaAAAAcDMREVKrVtL589KDD0rjx5vd4QDcVIZC0LRp07KvEgAAAKRPXJzUurV09Kh0++3SggWSj4/VVQEug2eCAAAAXIndLnXubA6BXbCg9M03Uv78VlcFuBRCEAAAgCt5801p4UKz5WfRIqlsWasrAlwOIQgAAMBVTJwoDRtmrk+dKt13n9UVAS6JEAQAAOAKvvrKnBBVkoYOlbp0sboiwGURggAAAJzd2rXSU0+ZQ2G/+KLZJQ5AphGCAAAAnNmePeZIcAkJUps20scfMxQ2cIsIQQAAAM7q8GGpaVMpKkqqX1+aO1fy8rK6KsDlEYIAAACc0ZkzUpMm0okTUqVK0tKlUkCA1VUBboEQBAAA4Gzi4qSWLaX9+6WwMOn775kLCMhChCAAAABnkpAgPf64tGmTGXy+/14qUcLqqgC3QggCAABwFklJ5ihw33wj+ftLX38tVaxodVWA2yEEAQAAOIPkZKlzZ3M+IF9fackSqV49q6sC3BIhCAAAwGp2u9Stmzn6m7e3tHChOSgCgGxBCAIAALCSYUg9e0rTpkm5cknz5kmtWlldFeDWCEEAAABWMQypXz9p0iRzAtSZM6VHH7W6KsDtEYIAAACsYBjSoEHS2LHm9qefSh06WF0V4BEIQQAAAFZ4+21pxAhzfeJE6ZlnrK4I8BjeVhcAAADgUVJagFIC0JgxUvfuVlcFeBRCEAAAQE6x26XevaWPPza3R42S+va1uirA4xCCAAAAckJysvTcc9L06eYgCJMmSS+8YHVVgEciBAEAAGS3hASpY0dpwQLJy8sMQh07Wl0V4LEIQQAAANnpwgVz2Otvv5V8fKQvvpDatrW6KsCjEYIAAACyS0yM1Lq1tHq1FBAgLV4sNWlidVWAxyMEAQAAZIf//pNatZI2b5by5pWWL5fuu8/qqgAQggAAALLB/v1S8+bSP/9IBQpI338v1apldVUALmGyVAAAgKy0dq1Up44ZgEqXltavJwABToYQBAAAkFVmzZIeekg6d0665x5p0ybpjjusrgrAVQhBAAAAt8owpCFDpKeflhITpccek1atkooUsboyANdACAIAALgV8fFSp07S22+b26+/Ls2fb44GB8ApMTACAABAZp06Zc4BtG6d5O0tTZ4sPfus1VUBuAlCEAAAQGasXy898YR04oQUFCR99ZXUqJHVVQFIB7rDAQAAZIRhSKNGSQ0amAHojjvMARAIQIDLoCUIAAAgvSIjpS5dpKVLze0OHcwucHnyWF0ZgAwgBAEAAKTH9u3m8z8HD0q+vtJHH0kvvCDZbFZXBiCDCEEAAAA3YhjSJ59IvXqZI8GFh0sLF0o1alhdGYBM4pkgAACA64mIkNq0MVt84uOlVq3MFiECEODSaAkCAAAeLTnZHOE6IkIKDZXq15e8chnS7Nlm609kpOTjI737rvTqq1Iu/g0ZcHWEIAAA4LEWLZJ695aOHbu8r0boCX1d7AWFbltu7qheXZo+Xapc2bI6AWQt/ikDAAB4pEWLzHEOLgcgQ09rhlZEVFLotuVK9vaV3nvPHP6aAAS4FUIQAADwOMnJZguQYZjb4Tqo5WqpGeqi/IrUr6qlJoW2K3nAILMrHAC3QggCAAAeZ906swUoWJF6X/31hyqohb5VvHw1QCNUVxv008lKWrfO6koBZAeeCQIAAB7n5NFE9dRkDdFQFdIZSdJKPaheGqd9qph6XkSEhUUCyDaEIAAA4DkMQ1q2TA+/8Zqe1J+SpL26Q6/qA32nZpIcJz4NDbWoTgDZiu5wAADA/RmGtH691LCh1KaNAo/+qdO5Cqu7JqmKdus7NXcIQDabFBZmDpcNwP3QEgQAANxXcrK0bJn0/vvmKG+S5O8v9e2rDRUHasrTQeY+4/JbbJey0NixkpeXBTUDyHa0BAEAAPdz8aI0dapUsaLUrp0ZgPz8pG7dpP37pWHD9HDHIC1cKBUv7vjWEiWkhQvNtwFwT7QEAQAA93H6tBl+xo2T/v3X3Jcvn/TSS9LLL0shIQ6nt2sntW5tjhYXEWE+A1S/Pi1AgLsjBAEAANeWmCh9+600Y4a0fLm5LZkP9fTrJz37rJQ373Xf7uUlNWiQc+UCsB4hCAAAuB7DkHbuNIPPnDlmC1CKGjWkPn2kJ55golMA10QIAgAArsEwpD17pG++kebNM9dTFC0qdeokde4s3XmnlVUCcAGWhqBJkyZp0qRJOnTokCSpUqVKGjx4sJo1a2ZlWQAAwFlcuCCtXm12c1u+XDp69PIxPz/zgZ7OnaXGjSVv/m0XQPpY+n+LEiVKaMSIESpfvrwMw9CMGTPUunVr7dixQ5UqVbKyNAAAYIWkJGn3bumXX6Qff5R++skMQin8/aVGjaRWraTHHpPy57eyWgAuymYYhpGO83JMgQIFNGrUKD377LM3PTc6OlrBwcGKiopSUFBQjtQHAACyUFSUOXz1hg1m8Nm8WYqNdTwnLExq2dJcGjaUAgKsqhaAE8tINnCaduPk5GQtWLBA58+fV506da55Tnx8vOLj41O3o6Ojc7BCAACQaXa7dPiw+RzPlcu+feazPlcKDpbq1JHuu09q0UKqXPnyDKYAkAUsD0F79uxRnTp1dPHiReXJk0eLFy9WxYoVr3nu8OHDNXTo0ByvEQAApINhSP/9J/3zj3TwoPn6zz/S3r3Sb7+lbeFJUaaMVLeuVK+euVSqJOViPncA2cfy7nAJCQk6cuSIoqKitHDhQn366adau3btNYPQtVqCwsLC6A4HAEB2S0w0Jx+NiEi7nDghHTpkBp/z569/DR8f6Y47zJadlKVaNXOGUgC4RRnpDmd5CLpao0aNVLZsWU2ZMuWm5/JMEAAAGRQfL0VGSufOpX09d046c8ZcTp92fI2MTN/1bTapeHGpdGmzhad0aen2283Ac9ttzNsDINu45DNBKex2u0NrDwAAuIphmAMKpASWq5ezZ68ddCIjHUdayygvL3M+nmLFzNabK5dSpczQU6qUOXQ1ADgxS0PQ66+/rmbNmqlkyZKKiYnR3LlztWbNGv3www9WlgUAgHXOnze7lh06JB07ZnY3O3ky7Wti4q39nOBgc3jpfPnM15T1ggWlQoXM16vXCxY0gxAAuDhLQ9CpU6f09NNPKyIiQsHBwapSpYp++OEHPfTQQ1aWBQBA9oqOln7/3Vz++st8liblmZr//kv/dQIDHQNKylKggGOwSQk6KetBQYQZAB7N0hD02WefWfnjAQDIXklJ5qhou3ebr7/9ZgafI0du/L58+aTwcHN+nJTuZiEhl19DQqQiRZgvBwAyyemeCQIAwGWdPn154s+NG6Vff5Xi4q59brFi0p13moMFlC5tLuHh5mu+fDldOQB4FEIQAABXSE6W1q0zH70JDZXq179Bz7GzZ6Uff5R++EH65RfpwIG05wQHm8NA33mnOf9Nymv+/Nn9UQAA10EIAgDgkkWLpN69zfEIUpQoIX30kdSu3aVR2Xbtkr791lw2bpTsdseLVKhgTvxZp4653HEHE38CgJMhBAEAcCkAPfqomXOuFHEsWZMeWa3qD85X+L7vzIlBr1SpktSsmfTAA1Lt2uagBAAAp0YIAgB4vORkswXoygB0p/aok2apg+aouE5IP106EBgoNWokNW9uhp+SJa0qGwCQSYQgAIDHW7fO7AIXogg9pbnqpFm6S7tSj59Vfn2hJ1R3VDtVffk+JgMFABdHCAIAeDbDUOLqX7RQH6qNlshL5jM+CfLRcrXULHXSt2quBPlpbnGpKvkHAFweIQgA4JkSE6UFC6QPP9RDW7em7t6gOpqlTvpSj+usCjq8JTTUgjoBAFmOEAQA8CznzklTp0rjx0vHj0uSDD8/zfPupGHn++h3VUrzFpvNHCWufn0L6gUAZDlCEADAM5w6JY0YIU2ZcnkC06JFpR49ZHvxRfmvK6y9j0o2OQ6QYLOZr2PH3mC+IACAS2HiAgCAeztzRho4UCpdWvrwQzMAVakiTZsmHT4svfmmVLiw2rWTFi6Uihd3fHuJEub+du2s+gAAgKxGSxAAwD1FRkpjxphNODEx5r5ataS335aaNLncxHOFdu2k1q3N0eIiIsxngOrXpwUIANwNIQgA4F5iYqRx46QPPjCDkCTddZcZflq2vGb4uZKXl9SgQc6UCgCwBiEIAOAe7HZp+nRp0CDp33/NfZUqSUOHSm3bSrnoAQ4AMBGCAACub906qU8faft2c7tcOTP8PPEEfdkAAGkQggAAruvwYem116QvvzS3g4KkwYOll1+WfH2trg4A4KQIQQAA1xMbK40caT73c/Gi2dXtueekd96RihSxujoAgJMjBAEAXMvSpVLPntKxY+Z2gwbmCHBVq1pdGQDARfCUKADANRw7Zg5w0KaNuR4eLn31lbRqFQEIAJAhhCAAgHNLTpbGj5cqVpSWLJG8vc3JT3//3ZzY5yZDXgMAcDW6wwEAnNfOndLzz0tbtpjbdepIU6ZIlStbXRkAwIXREgQAcD4XLpijvtWsaQagoCBp4kRp/XoCEADgltESBABwLps2SV26SPv3m9uPP24OfBAaanVlAAA3QUsQAMA5XLxoPutTr54ZgEJDpa+/lr74ggAEAMhStAQBAKy3davUubO0d6+53bGjNG6clD+/1ZUBANwQLUEAAOvEx0v/+590zz1mACpSRFq8WJo1iwAEAMg2tAQBAKyxZ4/Z4rN7t7n95JPmUNiFClldGQDAzdESBADIWXa79NFHUq1aZgAqVEhasECaN48ABADIEbQEAQByTkSEOfLbjz+a282bS59/LhUtanVlAAAPQksQACBnLF1qzvHz44+Sv780YYK0fDkBCACQ42gJAgBkr/PnpX79pKlTze277pLmzJEqVrS6MgCAh6IlCACQfXbskKpXNwOQzSb1729OhkoAAgBYiJYgAEDWMwzp44+lV1+VEhKk4sWlmTOlBx6wujIAAAhBAIAsdvas9Mwz5jNAkvTww+bgBwULWl0ZAAAS3eEAAFlq/XrzmZ+lSyVfX2ncOGnJEgIQAMCpEIIAALcuOVl67z2pQQPp6FGpXDlp40bp5ZfNZ4EAAHAidIcDANyakyeljh2ln34ytzt0kCZNkvLmtboyAACuiRAEAMi81aul9u2lf/+VAgPNuX86d6b1BwDg1OgOBwDIOLtdevddqVEjMwBVqiRt3Sp16UIAAgA4PVqCAAAZ899/UqdO0g8/mNtdu5rDYQcGWl0ZAADpQggCAKTf+vXSk09Kx49LAQFm97euXa2uCgCADKE7HADg5ux2adQoc/S348el22+Xfv2VAAQAcEm0BAEAbuzcOfNZn2XLzO2nnpKmTJHy5LG6MgAAMoUQBAC4vu3bpUcflQ4eNCc//egj6YUXGPwAAODSCEEAgLQMQ/r0U3Oy0/h4KTxcWrhQqlHD6soAALhlPBMEAHAUF2d2f3v+eTMAtWxptggRgAAAboIQBAC47M8/pdq1pZkzpVy5pOHDpaVLpfz5ra4MAIAsQ3c4AIDpq6/M0d5iYqSiRaX5883R4AAAcDO0BAGAp0tMlPr1MwdAiImR6teXduwgAAEA3BYhCAA82fHjUsOG0ocfmtv9+0s//SSFhlpdGQAA2YbucADgqVavlp58Ujp1SgoKkqZPl9q2tboqAACyHS1BAOBp7HZpxAipUSMzAFWpIm3dSgACAHgMWoIAwJOcOyd17ix9/bW53bmzNHGiFBhodWUAAOQYS1uChg8frlq1ailv3rwqUqSI2rRpo/3791tZEgC4r5S5fr7+WvLzk6ZOlaZNIwABADyOpSFo7dq16tGjhzZt2qQVK1YoMTFRjRs31vnz560sCwDci2GYgaduXengQSk8XPrlF6lbN8lms7o6AABynM0wDMPqIlL8999/KlKkiNauXav77rsvzfH4+HjFx8enbkdHRyssLExRUVEKCgrK4WoBwAWcPy+9+KI0e7a5/fDD5gAITH4KAHAz0dHRCg4OTlc2cKqBEaKioiRJBQoUuObx4cOHKzg4OHUJCwvL4QoBwIX88YdUu7YZgLy8pPffl5YsIQABADye07QE2e12Pfzww4qMjNT69euveQ4tQQCQTvPnS889Z7YEhYaa29doYQcAwF1kpCXIaUaH69Gjh3777bfrBiBJ8vPzk5+fX47WBQAuJT5eeuUVacIEc7thQ2nePKloUasrAwDAaThFd7iePXtq+fLlWr16tUqUKGF1OQDgmv7+2xz8ICUAvfGGtGIFAQgAgKtY2hJkGIZefvllLV68WGvWrFHp0qWtLAcAXNfChdKzz0rR0VLBgtKsWVKzZlZXBQCAU7I0BPXo0UNz587V0qVLlTdvXp08eVKSFBwcrICAACtLAwDXEB8vvfqq9PHH5na9eubzP7SqAwBwXZYOjGC7zvwU06ZNU5cuXW76/ow8/AQAbufvv6UnnpC2bTO3Bw6U3n5b8vGxujIAAHKcywyM4CQD0wGA6/nqK+mZZy53f5s5U2re3OqqAABwCU4xMAIAIJ0uXDAnP330UTMA1asn7dxJAAIAIAMIQQDgKn77TapVS5oyRbLZzO5vq1fz/A8AABnkNPMEAQCuwzCkyZOlfv2kixelkBBz9LdGjayuDAAAl0QIAgBndvas9Nxz0uLF5nazZtL06VKRIlZXBgCAy6I7HAA4q3XrpKpVzQDk4yONGSMtX04AAgDgFhGCAMDZJCRIgwZJDRpIx45J5ctLmzZJfftKufjfNgAAt4rucADgTPbulTp2lHbsMLc7d5bGj5fy5rW6MgAA3Ab/pAgAzsBul8aNk2rUMANQgQLSwoXm8z8EIAAAshQtQQBgtePHpa5dpRUrzO2mTaXPP5dCQ62uDAAAt0RLEABY6YsvpMqVzQAUECBNmCB9+y0BCACAbERLEABY4d9/pR49pK++Mrdr1pRmz5Zuv93qygAAcHu0BAFATjIMac4cqWJFMwB5eUlvvilt2EAAAgAgh9ASBAA55fhxqXt36euvze277jKf/alWzerKAADwKLQEAUB2Mwxp2jSpUiUzAPn4SO++K/36KwEIAAAL0BIEANnpn3+kl16SfvjB3K5V63IgAgAAlqAlCACyQ3y82dpTqZIZgPz9pVGjzGd/CEAAAFiKliAAyGorV5qtPwcOmNsPPihNnCjddpvVlQEAAFqCACALnTghPfmk9NBDZgAKCZHmzTPnACIAAQDgNAhBAHCrEhOljz6SKlQwJz/NlUvq1Uv64w8zFNlsVlcIAACuQHc4ALiJ5GRp3TopIkIKDZXq1zen95FhmKO9DRhgBh5Jql1bmjSJUd8AAHBihCAAuIFFi6TevaVjxy7vK1FCmvHyVj3w7avS2rXmzkKFpPfek557zmwJAgAATosQBADXsWiR9OijZoNPipI6rPeOvaEHBswxd/j7S337mq1BwcGW1QoAANKPEAQA15CcbLYApQSgfDqnARqpPhorf8VLkr4K7KQ2v70rr9IlrS0WAABkCH02AOAa1q0zu8AV1Gm9qzd0WKU0UCPlr3itUkNV1zY9GjdT6w4TgAAAcDW0BAHANZz741+N1Gi9pInKo/OSpN2qrEEapm/UQpI54ltEhMWFAgCADCMEAcCVIiKkUaP08MTJ8tIFSdJ2VdPbGqxleljGVQ3ooaEW1QkAADKNEAQAkrRnjzR+vDRzphQfLy9JO3zu1puJg/WNmqe2/KSw2cxR4urXt6xiAACQSYQgAJ4rOdmc52fcOGn16sv769aVhgzRwZiH9O1jNtnkOEJcytynY8demi8IAAC4FAZGAOB5zp6VRo2SypaV2rY1A5CXlzke9tq10vr1UuPGaveITQsXSsWLO769RAlp4UKpXTurPgAAALgVtAQB8Ax2uznk24wZ0vz50gXzeR8VLCh16yZ17y6VTDvSW7t2UuvW5lsjIsxngOrXpwUIAABXRggC4N7++st8zmfWLOnQocv7q1aVevWS2reXAgJueAkvL6lBg+wvFQAA5AxCEAD3ExkpLVhgtvr88svl/XnzSo8/LnXpItWrd/nhHgAA4FEIQQDcQ0SEtHSptHix+YxPYqK5P1cu6aGHpM6dzX5tgYFWVwoAACxGCALguv76yww9ixdLmzY5DuFWqZL09NNSx45SsWJWVgkAAJwMIQiA64iONkdvW7lSWrFC2rfP8fjdd5ujvbVtK91+u1VVAgAAJ0cIAuC8EhPNFp6VK81l82Zzbp8U3t7miAVt25pd3a4eyxoAAOAaCEFAJiQnM2Rytjhxwgw9KcvWrZeHsk5RrpzUqJG5PPCAlD+/VdUCAAAXRQgCMmjRIql3b+nYscv7SpSQPvqIyTPTzTCkkyelPXuk3bulX381Q8/Ro2nPLVRIevDBy8EnPNyKigEAgBshBCFrJSRIUVHmEh19+TU6Wrp40Vzi4x3X4+PNphW73fzl2G53XM+Vy2xm8fZO++rrK/n5mYu/v+NrQICUO7c5Glju3I7rgYGZarpZtEh69FHH5+8l6fhxc//ChQQhB4Yh/fefOYDBb7+ZoSfl9cyZtOfnyiVVrizdc8/l5bbbzP0AAABZhBCEm0tMNJs9Dh82X//9Vzp1ylxS1v/9Vzp92gw2riIw0Jw35lpLUFCaJTlvsBZ0D1JNI0jRMpcoBStOgTIMm2w2qU8f89EUj+kaZxhm0D1xwkyC//wj/f335eWff6SYmGu/N1cus2tb5cpSjRpm4KlVS8qTJ6c/BQAA8DCEIJiioqQ//jBH2zpwwAw8Kcvx42aLTEbkyWOGh+Dgy0EiIMCxtSZl3c/PbNWx2cxfjK98tdnMX7STkszWouTky+tJSWZAu7J16cpWposXpfPnLy9xceaS0oyTsv3vv+n6SF6S5l1jf5K8zFBkBCn6aJBiq+ZVcNhVISolXOXJc+0lMPDyEhBgfh85zTDM7y8mRjp3zlzOnnV8PX3afBAqIsIMPidOpC/4ligh3XGHGXhSlooVzc8KAACQwwhBniY2Vtqxw3wOY9++y0tExI3f5+cnlSxpLkWLmkuRIuaSsl64sJQvn/nLvrM2hRiG+aB9bKy5xMSYy5Xr0dGXX69YTv0drZP7oxSkaAXLfPWSXd5KVgGdUwGdM3/G75eWW+HjYwaEwEDzu/f1vdz1L2Xd19f8nq8OjimvkhkUr1xSwmNCwuUQeOVydT+/9MqXz5yLp3RpqUwZqWzZy0vp0mbgBQAAcBKEIHd2/ry0c6c5wta2bebrH39c/xfdYsXMf62/7Tbz4fPwcKlUKXMpUsQ9nsuw2S63uBQpkqG37l0jNWx45R5DgYpLDUTBilJexWjs0GjdWTI6baBKCV5XLzExZgC5cOHyvUlMNJfo6Cz9+OkWFGSOulaggPmasl6ggDkcXmio+d9LsWLmOi06AADAhRCC3Mnp09LPP0tr1pive/ZcuxtbiRLSXXeZ3ZHuuMNcKlQwu67huurXN7+648dTsopNccqtOOVWhIrJZrvU6+uNS33nMiqlO1pKIEppnYmPN1turl5SBpRIGUDi6lddalG6ejAJb29zf8oAEVcutzBoBAAAgKsgBLmy//4zA8/ateby229pzwkNlWrWNJcaNcwlJMSKal2el5c5DPajj15+VClFSu+zsWNvIT/YbJeflQIAAEC2IQS5EsOQdu2Sli83l19/Tdu1rVIl6f77zaVePal4cauqdUvt2pnDYF9rnqCxYxkeGwAAwBUQgpxdXJy0atXl4HP8uOPxypWlBg3M0HPffebgBMhW7dqZw2CvW2eOJxEaanaVowcZAACAayAEOaP4eOn776V586Rly8znQ1IEBkoPPSS1bCk1b24+mI4c5+VlZk8AAAC4HkKQs0hONp/rmTfP7G8VGXn5WMmSUqtWZvBp0IBnRgAAAIBbQAiy2u+/S599Js2f7zhXT7Fi0hNPSE89ZQ5mkPLkPQAAAIBbQgiyQny89NVX0uTJ5oMlKfLnN4cee+opHjIBAAAAsgkhKCcdOCBNnSpNn27O6aNLD5c8/LDUtavUpInk62t1lQAAAIBby2XlD//555/VqlUrFStWTDabTUuWLLGynExLTjan65k3z3xNTr7ioGFI335rDmZw223SBx+YAahECentt6UjR6RFi8xnfghAAAAAQLaztCXo/Pnzqlq1qp555hm1c9EJVhYtuvacMePGJKltwhfSyJHSnj3mAZtNatZMevFF89WbhjgAAAAgp1n6W3izZs3UrFmzdJ8fHx+v+Pj41O3o6Ohsqix9Fi0yH+G5cr7SAMWpzbHPddfjoyUdMnfmySM9/7zUs6dUurRl9QIAAABwsWeChg8frqFDh1pdhnSpC1zv3pcDUH6dVQ9NUC+NU2GZz/uczlVYBd7qrVw9XzIHPQAAAABgOUufCcqo119/XVFRUanL0aNHLatl3boru8AZWqUH9I4Gq7BO6x+V1kuaoDD7Yf1c/w0CEAAAAOBEXKolyM/PT35+flaXIclxSh/Jpil6QS9oikZooBbqUSVf+modzwMAAABgNZcKQc4kNNRx+xN102S9KMl2w/MAAAAAWMulusM5k/r1zVHgbJcyj9nyczkA2WxSWJh5HgAAAADnYWkIio2N1c6dO7Vz505J0sGDB7Vz504dOXLEyrLSxctL+ugjc93m2PiTuj12rHkeAAAAAOdhaQjaunWrqlWrpmrVqkmS+vXrp2rVqmnw4MFWlpVu7dpJCxdKxYs77i9RwtzvolMfAQAAAG7NZhhXznLjWqKjoxUcHKyoqCgFBQVZVkdysjlaXESE+QxQ/fq0AAEAAAA5KSPZgIERsoCXl9SggdVVAAAAAEgPBkYAAAAA4FEIQQAAAAA8CiEIAAAAgEchBAEAAADwKIQgAAAAAB6FEAQAAADAoxCCAAAAAHgUQhAAAAAAj0IIAgAAAOBRCEEAAAAAPAohCAAAAIBHIQQBAAAA8CiEIAAAAAAexdvqAm6FYRiSpOjoaKtLAQAAAGChlEyQkhFuxKVDUExMjCQpLCzM6lIAAAAAOIGYmBgFBwff8BybkZ6o5KTsdrtOnDihvHnzymazWV2OW4iOjlZYWJiOHj2qoKAgq8vxeNwP58G9cB7cC+fC/XAe3Avnwv3IeYZhKCYmRsWKFVOuXDd+6selW4Jy5cqlEiVKWF2GWwoKCuIPrBPhfjgP7oXz4F44F+6H8+BeOBfuR866WQtQCgZGAAAAAOBRCEEAAAAAPAohCA78/Pw0ZMgQ+fn5WV0KuB9OhXvhPLgXzoX74Ty4F86F++HcXHpgBAAAAADIKFqCAAAAAHgUQhAAAAAAj0IIAgAAAOBRCEEAAAAAPAohyEMdP35cHTt2VMGCBRUQEKDKlStr69atqccNw9DgwYMVGhqqgIAANWrUSAcOHLC0ZneVnJysN998U6VLl1ZAQIDKli2rd955R1eOWcL9yB4///yzWrVqpWLFislms2nJkiUOx9PzvZ89e1YdOnRQUFCQ8uXLp2effVaxsbE5/Encw43uR2JiogYMGKDKlSsrd+7cKlasmJ5++mmdOHHC4Rrcj6xxsz8bV3rxxRdls9k0duxYh/3ci6yTnvuxb98+PfzwwwoODlbu3LlVq1YtHTlyJPX4xYsX1aNHDxUsWFB58uTRI488on///TeHP4nru9m9iI2NVc+ePVWiRAkFBASoYsWKmjx5ssM53AvnQAjyQOfOnVO9evXk4+Oj7777Tnv37tXo0aOVP3/+1HPef/99jRs3TpMnT9bmzZuVO3duNWnSRBcvXrS0dnc0cuRITZo0SR9//LH27dunkSNH6v3339f48eNTz+F+ZI/z58+ratWqmjBhwjWPp+d779Chg37//XetWLFCy5cv188//6znn38+Bz+F+7jR/YiLi9P27dv15ptvavv27Vq0aJH279+vhx9+2OE87kfWuNmfjRSLFy/Wpk2bVKxYsTTHuBdZ52b34++//9a9996rChUqaM2aNdq9e7fefPNN+fv7p57Tt29fff3111qwYIHWrl2rEydOqF27djn4KdzDze5Fv3799P3332v27Nnat2+f+vTpo549e2rZsmWp53AvnIQBjzNgwADj3nvvve5xu91uhISEGKNGjUrdFxkZafj5+Rnz5s3LoSo9R4sWLYxnnnnGYV+7du2MDh06GAb3I8dIMhYvXpy6nZ7vfe/evYYkY8uWLannfPfdd4bNZjOOHz+ew5/AvVx9P67l119/NSQZhw8fNgzuR7a53r04duyYUbx4ceO3334zSpUqZXz44Yepx7gX2eda9+OJJ54wOnbseN33REZGGj4+PsaCBQtS9+3bt8+QZGzcuDFb63Vn17oXlSpVMt5++22HfdWrVzfeeOMNw+BeOBVagjzQsmXLVLNmTT322GMqUqSIqlWrpk8++ST1+MGDB3Xy5Ek1atQodV9wcLBq166tjRs3WlS1+6pbt65++ukn/fnnn5KkXbt2af369WrWrJnE/bBMer73jRs3Kl++fKpZs2bqOY0aNVKuXLm0efNmS+r2JFFRUbLZbMqXL5/E/chRdrtdnTp1Uv/+/VWpUqU0x7kXOcdut+ubb77RbbfdpiZNmqhIkSKqXbu2Qzetbdu2KTEx0eH/ZxUqVFDJkiX5eySL1a1bV8uWLdPx48dlGIZWr16tP//8U40bN5a4F06FEOSB/vnnH02aNEnly5fXDz/8oO7du6tXr16aMWOGJOnkyZOSpKJFizq8r2jRoqnHkHUGDhyoJ598UhUqVJCPj4+qVaumPn36qEOHDhL3wzLp+d5PnjypIkWKOBz39vZWgQIFuDfZ7OLFixowYIDat2+voKAgifuRo0aOHClvb2/16tXrmse5Fznn1KlTio2N1YgRI9S0aVP9+OOPatu2rdq1a6e1a9dKl+6Hr69v6j8YpODvkaw3fvx4VaxYUSVKlJCvr6+aNm2qCRMm6L777pO4F07F2+oCkPPsdrtq1qypYcOGSZKqVaum3377TZMnT1bnzp2tLs/jfPnll5ozZ47mzp2rSpUqaefOnerTp4+KFSvG/QCuITExUY8//rgMw9CkSZOsLsfjbNu2TR999JG2b98um81mdTkez263S5Jat26tvn37SpLuuusubdiwQZMnT9b9999vcYWeZfz48dq0aZOWLVumUqVK6eeff1aPHj1UrFgxh9YfWI+WIA8UGhqqihUrOuy74447UkeRCQkJkaQ0I5X8+++/qceQdfr375/aGlS5cmV16tRJffv21fDhwyXuh2XS872HhITo1KlTDseTkpJ09uxZ7k02SQlAhw8f1ooVK1JbgcT9yDHr1q3TqVOnVLJkSXl7e8vb21uHDx/WK6+8ovDwcIl7kaMKFSokb2/vm/69npCQoMjISIdz+Hska124cEGDBg3SmDFj1KpVK1WpUkU9e/bUE088oQ8++EDiXjgVQpAHqlevnvbv3++w788//1SpUqUkSaVLl1ZISIh++umn1OPR0dHavHmz6tSpk+P1uru4uDjlyuX4R9HLyyv1X/e4H9ZIz/dep04dRUZGatu2bannrFq1Sna7XbVr17akbneWEoAOHDiglStXqmDBgg7HuR85o1OnTtq9e7d27tyZuhQrVkz9+/fXDz/8IHEvcpSvr69q1ap1w7/Xa9SoIR8fH4f/n+3fv19Hjhzh75EslJiYqMTExBv+nc69cCJWj8yAnPfrr78a3t7exnvvvWccOHDAmDNnjhEYGGjMnj079ZwRI0YY+fLlM5YuXWrs3r3baN26tVG6dGnjwoULltbujjp37mwUL17cWL58uXHw4EFj0aJFRqFChYzXXnst9RzuR/aIiYkxduzYYezYscOQZIwZM8bYsWNH6mhj6fnemzZtalSrVs3YvHmzsX79eqN8+fJG+/btLfxUrutG9yMhIcF4+OGHjRIlShg7d+40IiIiUpf4+PjUa3A/ssbN/mxc7erR4QzuRZa62f1YtGiR4ePjY0ydOtU4cOCAMX78eMPLy8tYt25d6jVefPFFo2TJksaqVauMrVu3GnXq1DHq1Klj4adyTTe7F/fff79RqVIlY/Xq1cY///xjTJs2zfD39zcmTpyYeg3uhXMgBHmor7/+2rjzzjsNPz8/o0KFCsbUqVMdjtvtduPNN980ihYtavj5+RkPPvigsX//fsvqdWfR0dFG7969jZIlSxr+/v5GmTJljDfeeMPhFzvuR/ZYvXq1ISnN0rlzZ8NI5/d+5swZo3379kaePHmMoKAgo2vXrkZMTIxFn8i13eh+HDx48JrHJBmrV69OvQb3I2vc7M/G1a4VgrgXWSc99+Ozzz4zypUrZ/j7+xtVq1Y1lixZ4nCNCxcuGC+99JKRP39+IzAw0Gjbtq0RERFhwadxbTe7FxEREUaXLl2MYsWKGf7+/sbtt99ujB492rDb7anX4F44B5tx5bT0AAAAAODmeCYIAAAAgEchBAEAAADwKIQgAAAAAB6FEAQAAADAoxCCAAAAAHgUQhAAAAAAj0IIAgAAAOBRCEEAAAAAPAohCADgkQ4dOiSbzaadO3daXQoAIIcRggAAWa5Lly5q06ZNus+32WxasmRJttZ0tbCwMEVEROjOO++UJK1Zs0Y2m02RkZE5WgcAIOd5W10AAABW8PLyUkhIiNVlAAAsQEsQACBbNWjQQL169dJrr72mAgUKKCQkRG+99Vbq8fDwcElS27ZtZbPZUrclaenSpapevbr8/f1VpkwZDR06VElJSanHbTabPv30U7Vt21aBgYEqX768li1blnr83Llz6tChgwoXLqyAgACVL19e06ZNk67qDnfo0CE1bNhQkpQ/f37ZbDZ16dJFM2fOVMGCBRUfH+/wmdq0aaNOnTpl47cGAMhOhCAAQLabMWOGcufOrc2bN+v999/X22+/rRUrVkiStmzZIkmaNm2aIiIiUrfXrVunp59+Wr1799bevXs1ZcoUTZ8+Xe+9957DtYcOHarHH39cu3fvVvPmzdWhQwedPXtWkvTmm29q7969+u6777Rv3z5NmjRJhQoVSlNfWFiYvvrqK0nS/v37FRERoY8++kiPPfaYkpOTHYLVqVOn9M033+iZZ57Jxm8MAJCdCEEAgGxXpUoVDRkyROXLl9fTTz+tmjVr6qeffpIkFS5cWJKUL18+hYSEpG4PHTpUAwcOVOfOnVWmTBk99NBDeueddzRlyhSHa3fp0kXt27dXuXLlNGzYMMXGxurXX3+VJB05ckTVqlVTzZo1FR4erkaNGqlVq1Zp6vPy8lKBAgUkSUWKFFFISIiCg4MVEBCgp556KrX1SJJmz56tkiVLqkGDBtn4jQEAshPPBAEAsl2VKlUctkNDQ3Xq1KkbvmfXrl365ZdfHFp+kpOTdfHiRcXFxSkwMDDNtXPnzq2goKDUa3fv3l2PPPKItm/frsaNG6tNmzaqW7duhmrv1q2batWqpePHj6t48eKaPn26unTpIpvNlqHrAACcByEIAJDtfHx8HLZtNpvsdvsN3xMbG6uhQ4eqXbt2aY75+/un69rNmjXT4cOH9e2332rFihV68MEH1aNHD33wwQfprr1atWqqWrWqZs6cqcaNG+v333/XN998k+73AwCcDyEIAGA5Hx8fJScnO+yrXr269u/fr3Llyt3StQsXLqzOnTurc+fOql+/vvr373/NEOTr6ytdam262nPPPaexY8fq+PHjatSokcLCwm6pJgCAtXgmCABgufDwcP300086efKkzp07J0kaPHiwZs6cqaFDh+r333/Xvn37NH/+fP3vf/9L93UHDx6spUuX6q+//tLvv/+u5cuX64477rjmuaVKlZLNZtPy5cv133//KTY2NvXYU089pWPHjumTTz5hQAQAcAOEIACA5UaPHq0VK1YoLCxM1apVkyQ1adJEy5cv148//qhatWrpnnvu0YcffqhSpUql+7q+vr56/fXXVaVKFd13333y8vLS/Pnzr3lu8eLFUwdjKFq0qHr27Jl6LDg4WI888ojy5MmToUlgAQDOyWYYhmF1EQAAOLsHH3xQlSpV0rhx46wuBQBwiwhBAADcwLlz57RmzRo9+uij2rt3r26//XarSwIA3CIGRgAA4AaqVaumc+fOaeTIkQQgAHATtAQBAAAA8CgMjAAAAADAoxCCAAAAAHgUQhAAAAAAj0IIAgAAAOBRCEEAAAAAPAohCAAAAIBHIQQBAAAA8CiEIAAAAAAe5f/Av37tv5XA+QAAAABJRU5ErkJggg==",
->>>>>>> c6b823d (2 breakpoint detection instead of 1 in piecwise regression for improved determination of lt1)
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# plot fit of data\n",
     "obla.plot.plot_fit()"
@@ -505,21 +47,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "279f619a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(160.62556976256272)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# predict intensity at 4 mmol\n",
     "obla.predict(4)"
@@ -527,39 +58,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "6fcd47b1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-<<<<<<< HEAD
-<<<<<<< HEAD
-      "image/png": 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",
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",
->>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
-=======
-      "image/png": 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",
->>>>>>> c6b823d (2 breakpoint detection instead of 1 in piecwise regression for improved determination of lt1)
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot the prediction at 4 mmol\n",
     "obla.plot.plot_predictions(4)"
@@ -567,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "acff8813",
    "metadata": {},
    "outputs": [],
@@ -577,39 +79,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "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": {
-<<<<<<< HEAD
-<<<<<<< HEAD
-      "image/png": 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",
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",
->>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
-=======
-      "image/png": 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",
->>>>>>> c6b823d (2 breakpoint detection instead of 1 in piecwise regression for improved determination of lt1)
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = Dmax()\n",
     "model.fit(df['watt'], df['lactate'], impl='modified')\n",
@@ -619,45 +92,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "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": {
-<<<<<<< HEAD
-<<<<<<< HEAD
-      "image/png": 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",
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",
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",
->>>>>>> c6b823d (2 breakpoint detection instead of 1 in piecwise regression for improved determination of lt1)
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.plot.plot_predictions()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "b7ba2348",
    "metadata": {},
    "outputs": [],
@@ -667,18 +112,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "15e234c7",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LT1 occurs at: 90.49 watts\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = LT1_si()\n",
     "model.fit(df['watt'], df['lactate'], si=0.5)\n",
@@ -688,38 +125,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "42e695f9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'LT1 Standard Increment Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
-      ]
-     },
-     "execution_count": 12,
-     "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.plot.plot_predictions()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "d02f1652",
    "metadata": {},
    "outputs": [],
@@ -729,18 +145,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "54283247",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LT1 occurs at: 90.49 watts\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = LT1_si_lowest()\n",
     "model.fit(df['watt'], df['lactate'], si=0.5)\n",
@@ -750,38 +158,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "2739c2f2",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'LT1 Standard Increment From Lowest La- Measurement Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
-      ]
-     },
-     "execution_count": 15,
-     "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.plot.plot_predictions()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "7f6316fa",
    "metadata": {},
    "outputs": [],
@@ -791,18 +178,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "738f6b41",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LT1 occurs at: 4.53 watts\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = LT1_loglog()\n",
     "model.fit(df['watt'], df['lactate'])\n",
@@ -812,55 +191,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "a5d00229",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'LT1 Log-Log Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-<<<<<<< HEAD
-<<<<<<< HEAD
-<<<<<<< HEAD
-<<<<<<< HEAD
-      "image/png": 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",
->>>>>>> 87ab5aa (addition of the LT1 standard increment methods and start of the loglog method using the piecewise regression module)
-=======
-      "image/png": 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",
->>>>>>> c6b823d (2 breakpoint detection instead of 1 in piecwise regression for improved determination of lt1)
-=======
-      "image/png": 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",
->>>>>>> 4f662a4 (implemented a method to calculate both lt1 and lt2 together based on curve breakpoints using piecewise regression)
-=======
-      "image/png": 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",
->>>>>>> e0fa89b (implemented a breakpoint method to determine LT2, identical to lt1_lt2_breakpoint method but only returning and plotting LT)
-=======
-      "image/png": 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",
->>>>>>> e80a7e2 (Processed mosed of the FB in the peer review, options for redefining the base class bot for fitting and plotting should probably be discussed together)
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.plot.plot_predictions()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "76b18ab1",
    "metadata": {},
    "outputs": [],
@@ -870,19 +211,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "4f688ce1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LT1 occurs at: 93.04 watts\n",
-      "LT1 occurs at: 157.85 watts\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = LT1_LT2_breakpoint()\n",
     "model.fit(df['watt'], df['lactate'])\n",
@@ -894,38 +226,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "9d61bd43",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'LT1-LT2 Breakpoint Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
-      ]
-     },
-     "execution_count": 25,
-     "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.plot.plot_predictions()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "0aac8a14",
    "metadata": {},
    "outputs": [],
@@ -935,18 +246,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "63236ce4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LT1 occurs at: 157.85 watts\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = LT2_breakpoint()\n",
     "model.fit(df['watt'], df['lactate'])\n",
@@ -956,31 +259,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "5fb4e329",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'LT2 Breakpoint Model Fit'}, xlabel='Intensity', ylabel='Lactate'>"
-      ]
-     },
-     "execution_count": 28,
-     "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.plot.plot_predictions()"
    ]
@@ -1001,8 +283,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
index 72e0de8..aab6ebc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,6 +9,7 @@ authors = [
 requires-python = ">=3.10"
 dependencies = [
     "numpy>=2.2.5",
+    "piecewise-regression>=1.5.0",
     "scikit-learn>=1.6.1",
     "scipy>=1.15.2",
 ]
diff --git a/uv.lock b/uv.lock
index e11ed4d..ca9cc47 100644
--- a/uv.lock
+++ b/uv.lock
@@ -808,6 +808,7 @@ version = "0.1.0"
 source = { editable = "." }
 dependencies = [
     { name = "numpy" },
+    { name = "piecewise-regression" },
     { name = "scikit-learn" },
     { name = "scipy" },
 ]
@@ -830,6 +831,7 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "numpy", specifier = ">=2.2.5" },
+    { name = "piecewise-regression", specifier = ">=1.5.0" },
     { name = "scikit-learn", specifier = ">=1.6.1" },
     { name = "scipy", specifier = ">=1.15.2" },
 ]
@@ -1418,6 +1420,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08", size = 31191 },
 ]
 
+[[package]]
+name = "patsy"
+version = "1.0.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d1/81/74f6a65b848ffd16c18f920620ce999fe45fe27f01ab3911260ce4ed85e4/patsy-1.0.1.tar.gz", hash = "sha256:e786a9391eec818c054e359b737bbce692f051aee4c661f4141cc88fb459c0c4", size = 396010 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/87/2b/b50d3d08ea0fc419c183a84210571eba005328efa62b6b98bc28e9ead32a/patsy-1.0.1-py2.py3-none-any.whl", hash = "sha256:751fb38f9e97e62312e921a1954b81e1bb2bcda4f5eeabaf94db251ee791509c", size = 232923 },
+]
+
 [[package]]
 name = "pexpect"
 version = "4.9.0"
@@ -1430,6 +1444,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772 },
 ]
 
+[[package]]
+name = "piecewise-regression"
+version = "1.5.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "scipy" },
+    { name = "statsmodels" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/83/21/77472bd4937b1335d05f2e6732eb37ff67245f1e7e3c15f16e44f6476370/piecewise-regression-1.5.0.tar.gz", hash = "sha256:77d0111e2db06f077cf23ca5ca0b3e99cd852ec08a3c03c6bae23506f3c20612", size = 22941 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a6/4d/5d1279dba139e7e6bb81c45cf600f2122471d1eb2caba71175bfa7155900/piecewise_regression-1.5.0-py3-none-any.whl", hash = "sha256:9666346fca04db39aaddc8ceee0a22d06c3ee3fa7c58149487c020faf791a9b8", size = 24433 },
+]
+
 [[package]]
 name = "pillow"
 version = "11.2.1"
@@ -2063,6 +2092,45 @@ wheels = [
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 ]
 
+[[package]]
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+version = "0.14.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pandas" },
+    { name = "patsy" },
+    { name = "scipy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/64/cc/8c1bf59bf8203dea1bf2ea811cfe667d7bcc6909c83d8afb02b08e30f50b/statsmodels-0.14.5.tar.gz", hash = "sha256:de260e58cccfd2ceddf835b55a357233d6ca853a1aa4f90f7553a52cc71c6ddf", size = 20525016 }
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+]
+
 [[package]]
 name = "threadpoolctl"
 version = "3.6.0"

From 2f90685c123eea796903f521c63f4f8b00af5a57 Mon Sep 17 00:00:00 2001
From: Simon Dierickx <simon.dierickx@ugent.be>
Date: Thu, 31 Jul 2025 13:57:16 +0200
Subject: [PATCH 19/19] update code to work with the base class patch

---
 docs/quickstart.ipynb                         |  2 +-
 src/lactopy/lactate_models/lt1_loglog.py      | 19 +++-----
 .../lactate_models/lt1_lt2_breakpoint.py      | 31 +++++-------
 src/lactopy/lactate_models/lt1_si.py          | 18 +++----
 src/lactopy/lactate_models/lt1_si_lowest.py   | 32 ++++++-------
 src/lactopy/lactate_models/lt2_breakpoint.py  | 19 ++------
 src/lactopy/lactate_models/obla.py            |  4 +-
 src/lactopy/plots/Dmax.py                     | 18 ++-----
 src/lactopy/plots/base.py                     | 47 +++++++++++++++----
 src/lactopy/plots/lt1_loglog_plot.py          | 31 +-----------
 src/lactopy/plots/lt1_lt2_breakpoint_plot.py  | 38 +++------------
 src/lactopy/plots/lt1_si_lowest_plot.py       | 27 +----------
 src/lactopy/plots/lt1_si_plot.py              | 27 +----------
 src/lactopy/plots/lt2_breakpoint_plot.py      | 29 +-----------
 14 files changed, 100 insertions(+), 242 deletions(-)

diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index 1ce3d4d..27c5d90 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -270,7 +270,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "lactopy",
    "language": "python",
    "name": "python3"
   },
diff --git a/src/lactopy/lactate_models/lt1_loglog.py b/src/lactopy/lactate_models/lt1_loglog.py
index 383beb3..eba48d5 100644
--- a/src/lactopy/lactate_models/lt1_loglog.py
+++ b/src/lactopy/lactate_models/lt1_loglog.py
@@ -1,6 +1,5 @@
 from numpy.typing import ArrayLike
 import numpy as np
-import copy
 import piecewise_regression
 
 from lactopy.base import BaseModel
@@ -13,7 +12,7 @@ class LT1_loglog(BaseModel):
 
     Log-Log method is used to estimate the first lactate threshold by plotting
     the lactate response against intensity on a logaritmic scale.
-    
+
     Plot is divided into 2 segments --> segmented regression identifies breakpoint
 
     Attributes:
@@ -56,13 +55,9 @@ def fit(
         """
 
         y_log = np.log(y)
+        x_log = np.log(X)
 
-        # 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_log
-        
-        super().fit(X, y_log, **kwargs)
+        super().fit(x_log, y_log, **kwargs)
         return self
 
     def predict(self) -> float:
@@ -71,12 +66,12 @@ def predict(self) -> float:
 
         Returns:
             float: Predicted intensity.
-            
+
         """
-        
+
         pw_fit = piecewise_regression.Fit(self.X, self.y, n_breakpoints=1)
         pw_results = pw_fit.get_results()
-        
+
         if "breakpoint1" not in pw_results.get("estimates", {}):
             raise ValueError(
                 f"'breakpoint1' not found in regression results."
@@ -86,4 +81,4 @@ def predict(self) -> float:
         else:
             predicted_x = pw_results["estimates"]["breakpoint1"]["estimate"]
 
-        return predicted_x
\ No newline at end of file
+        return predicted_x
diff --git a/src/lactopy/lactate_models/lt1_lt2_breakpoint.py b/src/lactopy/lactate_models/lt1_lt2_breakpoint.py
index 317804e..e5dc98c 100644
--- a/src/lactopy/lactate_models/lt1_lt2_breakpoint.py
+++ b/src/lactopy/lactate_models/lt1_lt2_breakpoint.py
@@ -1,8 +1,9 @@
+from collections import namedtuple
+
 from numpy.typing import ArrayLike
-import numpy as np
-import copy
 import piecewise_regression
 
+
 from lactopy.base import BaseModel
 from lactopy.plots.lt1_lt2_breakpoint_plot import LT1_LT2_breakpoint_Plot
 
@@ -11,7 +12,7 @@ class LT1_LT2_breakpoint(BaseModel):
     """
     Determining both LT1 and LT2 based on the breakpoints in the lactate curve
     Piecewise regression module is used to identify the breakpoints
-    
+
     Plot is divided into 3 segments --> segmented regression identifies breakpoint
 
 
@@ -39,7 +40,7 @@ def fit(
             X (ArrayLike): The independent variable (e.g., intensity).
             y (ArrayLike): The dependent variable (e.g., lactate concentration).
             si: the standard increment, defaults to 0.5
-            
+
 
             method (str):
                 Method to use for fitting the model. Options are:
@@ -53,28 +54,20 @@ def fit(
         Returns:
             self: Fitted Dmax model instance.
         """
-
-        # 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
-        
-        
-        super().fit(X, y, **kwargs)
-        return self
+        return super().fit(X, y, **kwargs)
 
     def predict(self) -> float:
         """
-        Predicts intensity for LT1 and LT2 method. 
+        Predicts intensity for LT1 and LT2 method.
 
         Returns:
             float: Predicted intensity.
-            
+
         """
-        
+
         pw_fit = piecewise_regression.Fit(self.X, self.y, n_breakpoints=2)
         pw_results = pw_fit.get_results()
-        
+
         if "breakpoint1" not in pw_results.get("estimates", {}):
             raise ValueError(
                 f"'breakpoint1' not found in regression results."
@@ -91,4 +84,6 @@ def predict(self) -> float:
             predicted_lt1 = pw_results["estimates"]["breakpoint1"]["estimate"]
             predicted_lt2 = pw_results["estimates"]["breakpoint2"]["estimate"]
 
-        return predicted_lt1, predicted_lt2
+        result = namedtuple("LT1_LT2_breakpoint", ["lt1", "lt2"])
+
+        return result(predicted_lt1, predicted_lt2)
diff --git a/src/lactopy/lactate_models/lt1_si.py b/src/lactopy/lactate_models/lt1_si.py
index b271c3a..826d466 100644
--- a/src/lactopy/lactate_models/lt1_si.py
+++ b/src/lactopy/lactate_models/lt1_si.py
@@ -1,6 +1,4 @@
 from numpy.typing import ArrayLike
-import numpy as np
-import copy
 
 
 from lactopy.base import BaseModel
@@ -11,10 +9,11 @@ class LT1_si(BaseModel):
     """
     Standard increment model for lactate threshold estimation.
 
-    The Standard increment method is used to estimate the first lactate threshold by identifying
+    The Standard increment method is used to estimate the first lactate
+    threshold by identifying
     the point in which the first meaningfull lactate increase occurs.
 
-    This is computed by using a standard value, preferably equlual to the lowest 
+    This is computed by using a standard value, preferably equlual to the lowest
     detectable change in lactate concentration of the measurement tool
 
     Attributes:
@@ -43,7 +42,7 @@ def fit(
             X (ArrayLike): The independent variable (e.g., intensity).
             y (ArrayLike): The dependent variable (e.g., lactate concentration).
             si: the standard increment, defaults to 0.5
-            
+
             threshold_above_baseline (float, optional): Threshold above baseline
             for filtering in the "modified" implementation. Defaults to 0.5.
 
@@ -61,19 +60,14 @@ def fit(
         """
         self._si = si
 
-        # 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
-
-        if si > 0 :
+        if si > 0:
             y_lt1 = y[0] + si
             self.y_lt1 = y_lt1
         else:
             raise ValueError(f"Impossible lactate change to find LT1: {si}")
 
         self.y = y
-        
+
         super().fit(X, y, **kwargs)
         return self
 
diff --git a/src/lactopy/lactate_models/lt1_si_lowest.py b/src/lactopy/lactate_models/lt1_si_lowest.py
index 3910c4f..ad7d67d 100644
--- a/src/lactopy/lactate_models/lt1_si_lowest.py
+++ b/src/lactopy/lactate_models/lt1_si_lowest.py
@@ -1,6 +1,5 @@
 from numpy.typing import ArrayLike
 import numpy as np
-import copy
 
 
 from lactopy.base import BaseModel
@@ -11,12 +10,15 @@ class LT1_si_lowest(BaseModel):
     """
     Standard increment model for lactate threshold estimation.
 
-    The Standard increment method is used to estimate the first lactate threshold by identifying
+    The Standard increment method is used to estimate the first lactate
+    threshold by identifying
     the point in which the first meaningfull lactate increase occurs.
-    In LT1_si_lowest the lowest measurment of lactate concentration is used as reference point.
-    This means that that the reference measuement is not necessarily equal to the first measurement.
+    In LT1_si_lowest the lowest measurment of lactate concentration
+    is used as reference point.
+    This means that that the reference measuement is not necessarily
+    equal to the first measurement.
 
-    This is computed by using a standard value, preferably equlual to the lowest 
+    This is computed by using a standard value, preferably equlual to the lowest
     detectable change in lactate concentration of the measurement tool
 
     Attributes:
@@ -45,7 +47,7 @@ def fit(
             X (ArrayLike): The independent variable (e.g., intensity).
             y (ArrayLike): The dependent variable (e.g., lactate concentration).
             si: the standard increment, defaults to 0.5
-            
+
             threshold_above_baseline (float, optional): Threshold above baseline
             for filtering in the "modified" implementation. Defaults to 0.5.
 
@@ -63,13 +65,8 @@ def fit(
         """
         self._si = si
 
-        # 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
-
-        if si > 0 :
-            min_y_idx = np.argmin(y) # get the index of the lowest y
+        if si > 0:
+            min_y_idx = np.argmin(y)  # get the index of the lowest y
             self.min_y_idx = min_y_idx
             self.x_at_min_y = X[min_y_idx]
             self.y_lt1 = y[self.min_y_idx] + si
@@ -79,12 +76,11 @@ def fit(
         # Use only values AFTER x_at_min_y for model fitting
         valid_mask = X >= self.x_at_min_y
         if not np.any(valid_mask):
-            raise ValueError("No X values greater than X at minimum Y. Cannot fit model.")
-
-        self.X = X[valid_mask]
-        self.y = y[valid_mask]
+            raise ValueError(
+                "No X values greater than X at minimum Y. Cannot fit model."
+            )
 
-        super().fit(self.X, self.y, **kwargs)
+        super().fit(self.X, self.y, _mask=valid_mask, **kwargs)
         return self
 
     def predict(self) -> float:
diff --git a/src/lactopy/lactate_models/lt2_breakpoint.py b/src/lactopy/lactate_models/lt2_breakpoint.py
index cd19d1c..06973d4 100644
--- a/src/lactopy/lactate_models/lt2_breakpoint.py
+++ b/src/lactopy/lactate_models/lt2_breakpoint.py
@@ -1,6 +1,4 @@
 from numpy.typing import ArrayLike
-import numpy as np
-import copy
 import piecewise_regression
 
 from lactopy.base import BaseModel
@@ -11,7 +9,7 @@ class LT2_breakpoint(BaseModel):
     """
     Determining both LT1 and LT2 based on the breakpoints in the lactate curve
     Piecewise regression module is used to identify the breakpoints
-    
+
     Plot is divided into 3 segments --> segmented regression identifies breakpoints
 
     The second breakpoint which corresponds to LT2 is extracted
@@ -41,7 +39,7 @@ def fit(
             X (ArrayLike): The independent variable (e.g., intensity).
             y (ArrayLike): The dependent variable (e.g., lactate concentration).
             si: the standard increment, defaults to 0.5
-            
+
             method (str):
                 Method to use for fitting the model. Options are:
 
@@ -54,13 +52,6 @@ def fit(
         Returns:
             self: Fitted LT2_breakpoint model instance.
         """
-
-        # 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
-        
-        
         super().fit(X, y, **kwargs)
         return self
 
@@ -70,12 +61,12 @@ def predict(self) -> float:
 
         Returns:
             float: Predicted intensity.
-            
+
         """
-        
+
         pw_fit = piecewise_regression.Fit(self.X, self.y, n_breakpoints=2)
         pw_results = pw_fit.get_results()
-        
+
         if "breakpoint2" not in pw_results.get("estimates", {}):
             raise ValueError(
                 f"'breakpoint2' not found in regression results."
diff --git a/src/lactopy/lactate_models/obla.py b/src/lactopy/lactate_models/obla.py
index e221a2c..db1f143 100644
--- a/src/lactopy/lactate_models/obla.py
+++ b/src/lactopy/lactate_models/obla.py
@@ -7,7 +7,7 @@ class OBLA(BaseModel):
     This model uses a 3rd degree polynomial to fit the lactate data.
     """
 
-    def predict(self, lactate_value: float) -> float:
+    def predict(self, X: float) -> float:
         """
         Predicts intensity at a given lactate value.
 
@@ -19,4 +19,4 @@ def predict(self, lactate_value: float) -> float:
             float:
                 Predicted intensity.
         """
-        return self.model.predict_inverse(lactate_value)
+        return self.model.predict_inverse(X)
diff --git a/src/lactopy/plots/Dmax.py b/src/lactopy/plots/Dmax.py
index 0070e9e..7753786 100644
--- a/src/lactopy/plots/Dmax.py
+++ b/src/lactopy/plots/Dmax.py
@@ -1,4 +1,3 @@
-import matplotlib.pyplot as plt
 import numpy as np
 from lactopy.plots.base import Plot
 
@@ -14,13 +13,7 @@ def plot_predictions(self):
         """
         Plot the Dmax model predictions.
         """
-        self.plot_fit()
-        plt.axvline(
-            self.base_lactate_model.predict(),
-            color="black",
-            label="Predictions",
-            linestyle="--",
-        )
+        plot = super().plot_predictions()
         X = np.linspace(
             self.base_lactate_model.X.min(),
             self.base_lactate_model.X.max(),
@@ -31,11 +24,6 @@ def plot_predictions(self):
             self.base_lactate_model.y.max(),
             100,
         )
-        plt.plot(
-            X,
-            Y,
-            color="blue",
-            label="Data",
-        )
+        plot = Plot.add_line(plot, X, Y, color="blue")
 
-        return plt.gca()
+        return plot
diff --git a/src/lactopy/plots/base.py b/src/lactopy/plots/base.py
index 85926c8..a444cd2 100644
--- a/src/lactopy/plots/base.py
+++ b/src/lactopy/plots/base.py
@@ -1,6 +1,9 @@
+import inspect
+
 from typing import TYPE_CHECKING
 import matplotlib.pyplot as plt
 import numpy as np
+from numpy.typing import ArrayLike
 
 if TYPE_CHECKING:
     from lactopy.lactate_models.base import BaseModel
@@ -50,15 +53,43 @@ def plot_fit(self):
         plt.legend()
         return plt.gca()
 
-    def plot_predictions(self, X):
+    @classmethod
+    def add_threshold_lines(self, ax: plt.Axes, threshold: float, color: str = "black"):
+        # TODO: create a seprate builder class for buidling plots (even building )
+        """
+        Add threshold lines to the plot.
+
+        Args:
+            threshold (float): The threshold value.
+            color (str): Color of the threshold line.
+        """
+        ax.axvline(x=threshold, color=color, linestyle="--", label="Threshold")
+        ax.legend()
+        return ax
+
+    @classmethod
+    def add_line(cls, ax: plt.Axes, x: ArrayLike, y: ArrayLike, color: str = "blue"):
+        """
+        Add a straight line to the plot.
+
+        Args:
+            ax (plt.Axes): The axes to plot on.
+            x (array-like): The x-coordinates of the line.
+            y (array-like): The y-coordinates of the line.
+            color (str): Color of the line.
+        """
+        ax.plot(x, y, color=color, label="Line")
+        return ax
+
+    def plot_predictions(self, X=None):
         """
         Plot the model predictions.
         """
-        self.plot_fit()
-        plt.axvline(
-            self.base_lactate_model.predict(X),
-            color="black",
-            label="Predictions",
-            linestyle="--",
+        predict_x = (
+            self.base_lactate_model.predict(X)
+            if "X" in inspect.getfullargspec(self.base_lactate_model.predict).args
+            else self.base_lactate_model.predict()
         )
-        return plt.gca()
+        ax = self.plot_fit()
+        Plot.add_threshold_lines(ax, predict_x)
+        return ax
diff --git a/src/lactopy/plots/lt1_loglog_plot.py b/src/lactopy/plots/lt1_loglog_plot.py
index 9c18e2b..c476c44 100644
--- a/src/lactopy/plots/lt1_loglog_plot.py
+++ b/src/lactopy/plots/lt1_loglog_plot.py
@@ -1,34 +1,5 @@
-import matplotlib.pyplot as plt
-import numpy as np
 from lactopy.plots.base import Plot
 
 
 class LT1_loglog_Plot(Plot):
-    """
-    Plot for lt1_loglog model.
-    """
-
-    def __init__(self, base_log_log_model):
-        super().__init__(base_log_log_model)
-
-    def plot_fit(self):
-        """
-        Plot the lt1_loglog model fit.
-        """
-        ax = super().plot_fit()
-        ax.set_title("LT1 Log-Log Model Fit")
-        return ax
-
-    def plot_predictions(self):
-        """
-        Plot the lt1_loglog predictions.
-        """
-        self.plot_fit()
-        plt.axvline(
-            self.base_lactate_model.predict(),
-            color="black",
-            label="Predictions",
-            linestyle="--",
-        )
-
-        return plt.gca()
\ No newline at end of file
+    _title = "LT1 Log-Log Plot"
diff --git a/src/lactopy/plots/lt1_lt2_breakpoint_plot.py b/src/lactopy/plots/lt1_lt2_breakpoint_plot.py
index 89a578d..e869fae 100644
--- a/src/lactopy/plots/lt1_lt2_breakpoint_plot.py
+++ b/src/lactopy/plots/lt1_lt2_breakpoint_plot.py
@@ -1,5 +1,3 @@
-import matplotlib.pyplot as plt
-import numpy as np
 from lactopy.plots.base import Plot
 
 
@@ -8,38 +6,14 @@ class LT1_LT2_breakpoint_Plot(Plot):
     Plot for LT1_LT2 breakpoint model.
     """
 
-    def __init__(self, base_lactate_LT1_LT2_breakpoint_model):
-        super().__init__(base_lactate_LT1_LT2_breakpoint_model)
-
-    def plot_fit(self):
-        """
-        Plot the LT1_LT2_breakpoint model fit.
-        """
-        ax = super().plot_fit()
-        ax.set_title("LT1-LT2 Breakpoint Model Fit")
-        return ax
+    _title = "LT1-LT2 Breakpoint Plot"
 
     def plot_predictions(self):
         """
         Plot the LT1 and LT2 model predictions.
         """
-        self.plot_fit()
-        lt_array = self.base_lactate_model.predict()
-        lt1 = lt_array[0]
-        lt2 = lt_array[1]
-        
-        plt.axvline(
-            lt1,
-            color="black",
-            label="lt1",
-            linestyle="--",
-        )
-        
-        plt.axvline(
-            lt2,
-            color="red",
-            label="lt2",
-            linestyle="--",
-        )
-
-        return plt.gca()
+        ax = self.plot_fit()
+        result = self.base_lactate_model.predict()
+        ax = Plot.add_threshold_lines(ax, result.lt1, color="black")
+        ax = Plot.add_threshold_lines(ax, result.lt2, color="red")
+        return ax
diff --git a/src/lactopy/plots/lt1_si_lowest_plot.py b/src/lactopy/plots/lt1_si_lowest_plot.py
index 3b42b8f..7090742 100644
--- a/src/lactopy/plots/lt1_si_lowest_plot.py
+++ b/src/lactopy/plots/lt1_si_lowest_plot.py
@@ -1,5 +1,3 @@
-import matplotlib.pyplot as plt
-import numpy as np
 from lactopy.plots.base import Plot
 
 
@@ -8,27 +6,4 @@ class LT1_si_lowest_Plot(Plot):
     Plot for LT1 si lowest model.
     """
 
-    def __init__(self, base_lactate_LT1_si_lowest_model):
-        super().__init__(base_lactate_LT1_si_lowest_model)
-
-    def plot_fit(self):
-        """
-        Plot the LT1 si lowest model fit.
-        """
-        ax = super().plot_fit()
-        ax.set_title("LT1 Standard Increment From Lowest La- Measurement Model Fit")
-        return ax
-
-    def plot_predictions(self):
-        """
-        Plot the LT1 model predictions.
-        """
-        self.plot_fit()
-        plt.axvline(
-            self.base_lactate_model.predict(),
-            color="black",
-            label="Predictions",
-            linestyle="--",
-        )
-
-        return plt.gca()
+    _title = "LT1 Standard Increment From Lowest La- Measurement Plot"
diff --git a/src/lactopy/plots/lt1_si_plot.py b/src/lactopy/plots/lt1_si_plot.py
index f270bbb..8ba3b8a 100644
--- a/src/lactopy/plots/lt1_si_plot.py
+++ b/src/lactopy/plots/lt1_si_plot.py
@@ -1,5 +1,3 @@
-import matplotlib.pyplot as plt
-import numpy as np
 from lactopy.plots.base import Plot
 
 
@@ -8,27 +6,4 @@ class LT1_si_Plot(Plot):
     Plot for LT1 si model.
     """
 
-    def __init__(self, base_lactate_LT1_si_model):
-        super().__init__(base_lactate_LT1_si_model)
-
-    def plot_fit(self):
-        """
-        Plot the LT1 si model fit.
-        """
-        ax = super().plot_fit()
-        ax.set_title("LT1 Standard Increment Model Fit")
-        return ax
-
-    def plot_predictions(self):
-        """
-        Plot the LT1 model predictions.
-        """
-        self.plot_fit()
-        plt.axvline(
-            self.base_lactate_model.predict(),
-            color="black",
-            label="Predictions",
-            linestyle="--",
-        )
-
-        return plt.gca()
+    _title = "LT1 Standard Increment Model"
diff --git a/src/lactopy/plots/lt2_breakpoint_plot.py b/src/lactopy/plots/lt2_breakpoint_plot.py
index 0f57d11..e1d2f1c 100644
--- a/src/lactopy/plots/lt2_breakpoint_plot.py
+++ b/src/lactopy/plots/lt2_breakpoint_plot.py
@@ -1,5 +1,3 @@
-import matplotlib.pyplot as plt
-import numpy as np
 from lactopy.plots.base import Plot
 
 
@@ -8,29 +6,4 @@ class LT2_breakpoint_Plot(Plot):
     Plot for LT2_breakpoint model.
     """
 
-    def __init__(self, base_lactate_LT2_breakpoint_model):
-        super().__init__(base_lactate_LT2_breakpoint_model)
-
-    def plot_fit(self):
-        """
-        Plot the LT2_breakpoint model fit.
-        """
-        ax = super().plot_fit()
-        ax.set_title("LT2 Breakpoint Model Fit")
-        return ax
-
-    def plot_predictions(self):
-        """
-        Plot the LT2 model predictions.
-        """
-        self.plot_fit()
-        lt2 = self.base_lactate_model.predict()
-
-        plt.axvline(
-            lt2,
-            color="black",
-            label="lt2",
-            linestyle="--",
-        )
-
-        return plt.gca()
+    _title = "LT2 Breakpoint Plot"