From 5afae976efd0e65ea8f72dd3920f563f68270345 Mon Sep 17 00:00:00 2001
From: AlexanderJuestel <alexander.juestel@gmail.com>
Date: Sat, 27 Jan 2024 16:26:59 +0100
Subject: [PATCH] EditFiles

---
 docs/source/index.rst                         |   1 +
 ..._Adding_Deviation_to_Borehole_Object.ipynb | 101 +++----
 docs/source/tutorials.rst                     |  14 +
 pyborehole/borehole.py                        | 285 +++++++++++-------
 pyborehole/design.py                          |   1 +
 pyborehole/deviation.py                       |  98 ++++--
 pyborehole/logs.py                            |   4 +-
 7 files changed, 311 insertions(+), 193 deletions(-)
 create mode 100644 docs/source/tutorials.rst

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 55a719d..4be2aed 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -13,6 +13,7 @@ Welcome to PyBorehole's documentation!
    about
    installation
    contributing
+   tutorials
    modules
 
 
diff --git a/docs/source/notebooks/02_Adding_Deviation_to_Borehole_Object.ipynb b/docs/source/notebooks/02_Adding_Deviation_to_Borehole_Object.ipynb
index 9863b40..df64307 100644
--- a/docs/source/notebooks/02_Adding_Deviation_to_Borehole_Object.ipynb
+++ b/docs/source/notebooks/02_Adding_Deviation_to_Borehole_Object.ipynb
@@ -270,7 +270,7 @@
     {
      "data": {
       "text/plain": [
-       "<pyborehole.deviation.Deviation at 0x1e94cef4a60>"
+       "<pyborehole.deviation.Deviation at 0x2aa0c119090>"
       ]
      },
      "execution_count": 6,
@@ -1664,8 +1664,8 @@
        "      <th>True Vertical Depth</th>\n",
        "      <th>Northing_rel</th>\n",
        "      <th>Easting_rel</th>\n",
-       "      <th>Northing</th>\n",
        "      <th>Easting</th>\n",
+       "      <th>Northing</th>\n",
        "      <th>True Vertical Depth Below Sea Level</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1675,8 +1675,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.634992e+06</td>\n",
        "      <td>310805.244563</td>\n",
+       "      <td>5.634992e+06</td>\n",
        "      <td>136.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1684,8 +1684,8 @@
        "      <td>10.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.634992e+06</td>\n",
        "      <td>310805.244563</td>\n",
+       "      <td>5.634992e+06</td>\n",
        "      <td>126.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1693,8 +1693,8 @@
        "      <td>20.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.634992e+06</td>\n",
        "      <td>310805.244563</td>\n",
+       "      <td>5.634992e+06</td>\n",
        "      <td>116.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1702,8 +1702,8 @@
        "      <td>30.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.634992e+06</td>\n",
        "      <td>310805.244563</td>\n",
+       "      <td>5.634992e+06</td>\n",
        "      <td>106.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1711,8 +1711,8 @@
        "      <td>40.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.634992e+06</td>\n",
        "      <td>310805.244563</td>\n",
+       "      <td>5.634992e+06</td>\n",
        "      <td>96.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1720,19 +1720,19 @@
        "</div>"
       ],
       "text/plain": [
-       "   True Vertical Depth  Northing_rel  Easting_rel      Northing   \n",
-       "0                  0.0           0.0          0.0  5.634992e+06  \\\n",
-       "1                 10.0           0.0          0.0  5.634992e+06   \n",
-       "2                 20.0           0.0          0.0  5.634992e+06   \n",
-       "3                 30.0           0.0          0.0  5.634992e+06   \n",
-       "4                 40.0           0.0          0.0  5.634992e+06   \n",
+       "   True Vertical Depth  Northing_rel  Easting_rel        Easting   \n",
+       "0                  0.0           0.0          0.0  310805.244563  \\\n",
+       "1                 10.0           0.0          0.0  310805.244563   \n",
+       "2                 20.0           0.0          0.0  310805.244563   \n",
+       "3                 30.0           0.0          0.0  310805.244563   \n",
+       "4                 40.0           0.0          0.0  310805.244563   \n",
        "\n",
-       "         Easting  True Vertical Depth Below Sea Level  \n",
-       "0  310805.244563                                136.0  \n",
-       "1  310805.244563                                126.0  \n",
-       "2  310805.244563                                116.0  \n",
-       "3  310805.244563                                106.0  \n",
-       "4  310805.244563                                 96.0  "
+       "       Northing  True Vertical Depth Below Sea Level  \n",
+       "0  5.634992e+06                                136.0  \n",
+       "1  5.634992e+06                                126.0  \n",
+       "2  5.634992e+06                                116.0  \n",
+       "3  5.634992e+06                                106.0  \n",
+       "4  5.634992e+06                                 96.0  "
       ]
      },
      "execution_count": 37,
@@ -1777,8 +1777,8 @@
        "      <th>True Vertical Depth</th>\n",
        "      <th>Northing_rel</th>\n",
        "      <th>Easting_rel</th>\n",
-       "      <th>Northing</th>\n",
        "      <th>Easting</th>\n",
+       "      <th>Northing</th>\n",
        "      <th>True Vertical Depth Below Sea Level</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1788,8 +1788,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.634992e+06</td>\n",
        "      <td>310805.244563</td>\n",
+       "      <td>5.634992e+06</td>\n",
        "      <td>136.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1797,8 +1797,8 @@
        "      <td>10.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.634992e+06</td>\n",
        "      <td>310805.244563</td>\n",
+       "      <td>5.634992e+06</td>\n",
        "      <td>126.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1806,8 +1806,8 @@
        "      <td>20.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.634992e+06</td>\n",
        "      <td>310805.244563</td>\n",
+       "      <td>5.634992e+06</td>\n",
        "      <td>116.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1815,8 +1815,8 @@
        "      <td>30.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.634992e+06</td>\n",
        "      <td>310805.244563</td>\n",
+       "      <td>5.634992e+06</td>\n",
        "      <td>106.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1824,8 +1824,8 @@
        "      <td>40.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.634992e+06</td>\n",
        "      <td>310805.244563</td>\n",
+       "      <td>5.634992e+06</td>\n",
        "      <td>96.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1833,19 +1833,19 @@
        "</div>"
       ],
       "text/plain": [
-       "   True Vertical Depth  Northing_rel  Easting_rel      Northing   \n",
-       "0                  0.0           0.0          0.0  5.634992e+06  \\\n",
-       "1                 10.0           0.0          0.0  5.634992e+06   \n",
-       "2                 20.0           0.0          0.0  5.634992e+06   \n",
-       "3                 30.0           0.0          0.0  5.634992e+06   \n",
-       "4                 40.0           0.0          0.0  5.634992e+06   \n",
+       "   True Vertical Depth  Northing_rel  Easting_rel        Easting   \n",
+       "0                  0.0           0.0          0.0  310805.244563  \\\n",
+       "1                 10.0           0.0          0.0  310805.244563   \n",
+       "2                 20.0           0.0          0.0  310805.244563   \n",
+       "3                 30.0           0.0          0.0  310805.244563   \n",
+       "4                 40.0           0.0          0.0  310805.244563   \n",
        "\n",
-       "         Easting  True Vertical Depth Below Sea Level  \n",
-       "0  310805.244563                                136.0  \n",
-       "1  310805.244563                                126.0  \n",
-       "2  310805.244563                                116.0  \n",
-       "3  310805.244563                                106.0  \n",
-       "4  310805.244563                                 96.0  "
+       "       Northing  True Vertical Depth Below Sea Level  \n",
+       "0  5.634992e+06                                136.0  \n",
+       "1  5.634992e+06                                126.0  \n",
+       "2  5.634992e+06                                116.0  \n",
+       "3  5.634992e+06                                106.0  \n",
+       "4  5.634992e+06                                 96.0  "
       ]
      },
      "execution_count": 38,
@@ -2259,21 +2259,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 56,
    "id": "adbde116-98d0-4050-945f-50749714713d",
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 640x480 with 1 Axes>,\n",
-       " <Axes3D: xlabel='Easting', ylabel='Northing', zlabel='TVD'>)"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": 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",
@@ -2286,7 +2275,7 @@
     }
    ],
    "source": [
-    "borehole.deviation.plot_deviation_3d(relative=True)"
+    "fig, ax = borehole.deviation.plot_deviation_3d(relative=True)"
    ]
   },
   {
@@ -2331,7 +2320,7 @@
        "</td></tr> </table>"
       ],
       "text/plain": [
-       "PolyData (0x1e950334e20)\n",
+       "PolyData (0x2aa0e54e860)\n",
        "  N Cells:    220\n",
        "  N Points:   800\n",
        "  N Strips:   220\n",
@@ -2353,7 +2342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "id": "c146360f-0cfa-45ea-bb0a-1eb5acc53404",
    "metadata": {},
    "outputs": [
@@ -2392,7 +2381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "id": "e993bfd1-aa94-4bb8-bf6f-5976d332ec5c",
    "metadata": {},
    "outputs": [
@@ -2421,7 +2410,7 @@
        "</td></tr> </table>"
       ],
       "text/plain": [
-       "PolyData (0x1e950b77340)\n",
+       "PolyData (0x2aa0fdf8d00)\n",
        "  N Cells:    220\n",
        "  N Points:   800\n",
        "  N Strips:   220\n",
@@ -2431,7 +2420,7 @@
        "  N Arrays:   2"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2443,7 +2432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "id": "0ba73c74-4050-4a14-9f36-2083f06c6e80",
    "metadata": {},
    "outputs": [
diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst
new file mode 100644
index 0000000..8083e7f
--- /dev/null
+++ b/docs/source/tutorials.rst
@@ -0,0 +1,14 @@
+.. _tutorials_ref:
+
+Tutorials
+=========
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Examples
+
+   notebooks/01_Creating_a_new_Borehole_Object
+   notebooks/02_Adding_Deviation_to_Borehole_Object
+   notebooks/03_Adding_Casing_Scheme
+   notebooks/04_Adding_Well_Logs
+   notebooks/05_Calculating_Properties_from_Well_Logs
\ No newline at end of file
diff --git a/pyborehole/borehole.py b/pyborehole/borehole.py
index e2107ed..ddfdee2 100644
--- a/pyborehole/borehole.py
+++ b/pyborehole/borehole.py
@@ -259,47 +259,47 @@ def init_properties(self,
 
         Parameters
         __________
-            address : str
+            address : str, default: ``None``
                 Address of the Borehole, e.g. ``address='Am Kraftwerk 17, 52249 Eschweiler, Deutschland'``.
-            location : tuple
+            location : tuple, default: ``None``
                 Coordinates tuple representing the location of the Borehole, e.g. ``location=(6.313031, 50.835676)``.
-            year : int
+            year : int, default: ``None``
                 Year the borehole was drilled, e.g. ``year=2024``.
-            crs : Union[str, pyproj.crs.crs.CRS]
+            crs : Union[str, pyproj.crs.crs.CRS], default: ``None``
                 Coordinate Reference System of the coordinates, e.g. ``crs='EPSG:4326'``.
-            altitude_above_sea_level : Union[int, float]
+            altitude_above_sea_level : Union[int, float], default: ``None``
                 Altitude above sea level, e.g. ``altitude_above_sea_level=136``.
-            altitude_above_kb : Union[int, float]
+            altitude_above_kb : Union[int, float], default: ``None``
                 Altitude above KB, e.g. ``altitude_above_kb=140``.
-            borehole_id : Union[str, int, float]
+            borehole_id : Union[str, int, float], default: ``None``
                 Unique identifier for this borehole, e.g. ``borehole_id='DABO123456'``.
-            borehole_type : str
+            borehole_type : str, default: ``None``
                 Borehole type, e.g. ``borehole_type='exploration'``.
-            md : Union[int, float]
+            md : Union[int, float], default: ``None``
                 Measured depth of the borehole, e.g. ``md=100``.
-            tvd : Union[int, float]
+            tvd : Union[int, float], default: ``None``
                 True vertical depth of the borehole, e.g. ``tvd=95``.
-            depth_unit : str
+            depth_unit : str, default: ``None``
                 Unit for the depth values, e.g. ``depth_values='m'``.
-            vertical : bool, default is ``True``
+            vertical : bool, default: ``True``
                 Variable to state if the borehole is vertical (True) or deviated (False), e.g. ``vertical=True``.
-            contractee : str
+            contractee : str, default: ``None``
                 Contractee of the drilling operation, e.g. ``contractee='Fraunhofer IEG'``.
-            drilling_contractor : str
+            drilling_contractor : str, default: ``None``
                 Drilling contractor who performed the drilling, e.g. ``drilling_contractor='RWE BOWA'``.
-            logging_contractor : str
+            logging_contractor : str, default: ``None``
                 Logging contractor who performed the logging, e.g. ``logging_contractor='DMT GmbH'``.
-            field : str
+            field : str, default: ``None``
                 Name of the field the well was drilled in, e.g. ``field='Erdwärme Aachen'``.
-            project : str
+            project : str, default: ``None``
                 Name of the project the borehole was drilled for, e.g. ``project='DGE Rollout'``.
-            start_drilling : str
+            start_drilling : str, default: ``None``
                 Start date of the drilling operation, e.g. ``start_drilling='2023-10-18'``.
-            end_drilling : str
+            end_drilling : str, default: ``None``
                 End date of the drilling operation, e.g. ``end_drilling='2023-10-28'``.
-            start_logging : str
+            start_logging : str, default: ``None``
                 Start date of the logging operation, e.g. ``start_logging='2023-10-18'``.
-            end_logging : str
+            end_logging : str, default: ``None``
                 End date of the logging operation, e.g. ``end_logging='2023-10-28'``.
 
         Raises
@@ -314,17 +314,21 @@ def init_properties(self,
             >>> borehole = Borehole(name='Weisweiler R1')
             >>> borehole.init_properties(address='Am Kraftwerk 17, 52249 Eschweiler, Deutschland', location=(6.313031, 50.835676), crs='EPSG:4326', altitude_above_sea_level=136, borehole_id='DABO123456')
             >>> borehole.df
-                                                    Value
-                ID                                  DABO123456
-                Name                                RWE EB1
-                Address                             Am Kraftwerk 17, 52249 Eschweiler, Germany
-                Location                            POINT (6.313031 50.835676)
-                X                                   6.313031
-                Y                                   50.835676
-                Coordinate Reference System         EPSG:4326
-                Coordinate Reference System PyProj  EPSG:4326
-                Altitude above sea level            136
-                Altitude above KB                   None
+
+            =====================================  =========================================
+            Index                                  Value
+            =====================================  =========================================
+            ID                                     DABO123456
+            Name                                   RWE EB1
+            Address                                Am Kraftwerk 17, 52249 Eschweiler, Germany
+            Location                               POINT (6.313031 50.835676)
+            X                                      6.313031
+            Y                                      50.835676
+            Coordinate Reference System            EPSG:4326
+            Coordinate Reference System PyProj     EPSG:4326
+            Altitude above sea level               136
+            Altitude above KB                      None
+            =====================================  =========================================
 
         .. versionadded:: 0.0.1
         """
@@ -599,13 +603,18 @@ def init_properties(self,
 
         self.has_properties = True
 
-    def create_df(self):
+    def create_df(self) -> pd.DataFrame:
         """Create DataFrame from Borehole Object Attributes.
 
         Returns
         _______
             df : pd.DataFrame
-                Borehole Object DataFrame containing the Borehole Metadata.
+                Borehole Object DataFrame containing the Borehole Metadata. Data columns are as follows:
+
+                ======= =====================
+                Index   Index of each entry
+                Value   Value of each entry
+                ======= =====================
 
         Examples
         ________
@@ -615,17 +624,22 @@ def create_df(self):
             >>> borehole.init_properties(address='Am Kraftwerk 17, 52249 Eschweiler, Deutschland', location=(6.313031, 50.835676), crs='EPSG:4326', altitude_above_sea_level=136, borehole_id='DABO123456')
             >>> borehole.create_df()
             >>> borehole.df
-                                                Value
-            ID                                  DABO123456
-            Name                                Weisweiler R1
-            Address                             Am Kraftwerk 17, 52249 Eschweiler, Germany
-            Location                            POINT (6.313031 50.835676)
-            X                                   6.313031
-            Y                                   50.835676
-            Coordinate Reference System         EPSG:4326
-            Coordinate Reference System PyProj  EPSG:4326
-            Altitude above sea level            136
-            Altitude above KB                   None
+
+            =====================================  =========================================
+            Index                                  Value
+            =====================================  =========================================
+            ID                                     DABO123456
+            Name                                   RWE EB1
+            Address                                Am Kraftwerk 17, 52249 Eschweiler, Germany
+            Location                               POINT (6.313031 50.835676)
+            X                                      6.313031
+            Y                                      50.835676
+            Coordinate Reference System            EPSG:4326
+            Coordinate Reference System PyProj     EPSG:4326
+            Altitude above sea level               136
+            Altitude above KB                      None
+            ...                                    ...
+            =====================================  =========================================
 
         See Also
         ________
@@ -676,13 +690,18 @@ def create_df(self):
 
         return df
 
-    def create_properties_df(self):
+    def create_properties_df(self) -> pd.DataFrame:
         """Create Properties DataFrame from Borehole Object Attributes.
 
         Returns
         _______
             df : pd.DataFrame
-                DataFrame containing the Borehole Properties.
+                DataFrame containing the Borehole Properties. Data columns are as follows:
+
+                ======= =====================
+                Index   Index of each entry
+                Value   Value of each entry
+                ======= =====================
 
         Examples
         ________
@@ -692,17 +711,21 @@ def create_properties_df(self):
             >>> borehole.init_properties(address='Am Kraftwerk 17, 52249 Eschweiler, Deutschland', location=(6.313031, 50.835676), crs='EPSG:4326', altitude_above_sea_level=136, borehole_id='DABO123456')
             >>> borehole.create_properties_df()
             >>> borehole.properties
-                                                Value
-            ID                                  True
-            Name                                True
-            Address                             True
-            Location                            True
-            X                                   True
-            Y                                   True
-            Coordinate Reference System         True
-            Coordinate Reference System PyProj  True
-            Altitude above sea level            True
-            Altitude above KB                   False
+
+            =====================================  =========================================
+            Index                                  Value
+            =====================================  =========================================
+            ID                                     True
+            Name                                   True
+            Address                                True
+            Location                               True
+            X                                      True
+            Y                                      True
+            Coordinate Reference System            True
+            Coordinate Reference System PyProj     True
+            Altitude above sea level               True
+            Altitude above KB                      False
+            =====================================  =========================================
 
         See Also
         ________
@@ -777,18 +800,22 @@ def update_df(self,
             >>> borehole.create_df()
             >>> borehole.update_df(data_dict={'Date': 2023})
             >>> borehole.df
-                                                Value
-            ID                                  DABO123456
-            Name                                Weisweiler R1
-            Address                             Am Kraftwerk 17, 52249 Eschweiler, Germany
-            Location                            POINT (6.313031 50.835676)
-            X                                   6.313031
-            Y                                   50.835676
-            Coordinate Reference System         EPSG:4326
-            Coordinate Reference System PyProj  EPSG:4326
-            Altitude above sea level            136
-            Altitude above KB                   None
-            Data                                2023
+
+            =====================================  =========================================
+            Index                                  Value
+            =====================================  =========================================
+            ID                                     DABO123456
+            Name                                   Weisweiler R1
+            Address                                Am Kraftwerk 17, 52249 Eschweiler, Germany
+            Location                               POINT (6.313031 50.835676)
+            X                                      6.313031
+            Y                                      50.835676
+            Coordinate Reference System            EPSG:4326
+            Coordinate Reference System PyProj     EPSG:4326
+            Altitude above sea level               136
+            Altitude above KB                      None
+            Data                                   2023
+            =====================================  =========================================
 
         See Also
         ________
@@ -829,9 +856,9 @@ def update_value(self,
                 Borehole object attribute provided as str, e.g. ``attribute='name'``.
             value : Union[int, float, str]
                 Value of the attribute to be updated, e.g. ``value='RWE EB2'``.
-            crs : Union[str, pyproj.crs.crs.CRS]
+            crs : Union[str, pyproj.crs.crs.CRS], default: ``None``
                 Coordinate Reference System of the coordinates, e.g. ``crs='EPSG:4326'``.
-            transform_coordinates : bool
+            transform_coordinates : bool, default: ``None``
                 Boolean value to transform the coordinates if a new Coordinate Reference System is provided, e.g. ``transform_coordinates=True``.
 
         Raises
@@ -973,18 +1000,25 @@ def update_value(self,
         self.properties.loc[df_indices_dict[attribute], 'Value'] = True
 
     def to_gdf(self,
-               crs: Union[str, pyproj.crs.crs.CRS] = None):
+               crs: Union[str, pyproj.crs.crs.CRS] = None) -> gpd.GeoDataFrame:
         """Create GeoDataFrame from Borehole Object DataFrame.
 
         Parameters
         __________
-            crs : Union[str, pyproj.crs.crs.CRS]
+            crs : Union[str, pyproj.crs.crs.CRS], default: ``None``
                 Coordinate Reference System of the coordinates, e.g. ``crs='EPSG:4326'``.
 
         Returns
         _______
             gpd.GeoDataFrame
-                GeoDataFrame of the Borehole Data Object DataFrame.
+                GeoDataFrame of the Borehole Data Object DataFrame. Data columns are as follows:
+
+                ========== =========================
+                Index      Index of each entry
+                ID         ID of each entry
+                Name       Name of each entry
+                geometry   Geometry of each entry
+                ========== =========================
 
         Raises
         ______
@@ -1001,8 +1035,12 @@ def to_gdf(self,
             >>> borehole.init_properties(address='Am Kraftwerk 17, 52249 Eschweiler, Deutschland', location=(6.313031, 50.835676), crs='EPSG:4326', altitude_above_sea_level=136, borehole_id='DABO123456')
             >>> borehole.create_df()
             >>> borehole.to_gdf()
-                ID         Name    geometry
-            0   DABO123456 RWE EB1 POINT (6.31303 50.83568)
+
+            =======  ============ ========== ==========================
+            Index    ID           Name       geometry
+            =======  ============ ========== ==========================
+            0        DABO123456   RWE EB1    POINT (6.31303 50.83568)
+            =======  ============ ========== ==========================
 
         See Also
         ________
@@ -1042,7 +1080,7 @@ def add_deviation(self,
                       md_column: str = 'MD',
                       dip_column: str = 'DIP',
                       azimuth_column: str = 'AZI',
-                      add_origin: bool = True):
+                      add_origin: bool = True) -> Deviation:
         """Add deviation to the Borehole Object.
 
         Parameters
@@ -1064,7 +1102,7 @@ def add_deviation(self,
 
         Returns
         _______
-            WellDeviation
+            Deviation
                 Well Deviation Object.
 
         Raises
@@ -1083,10 +1121,14 @@ def add_deviation(self,
             >>> borehole = Borehole(name='Weisweiler R1')
             >>> borehole.add_deviation(path='Deviation.csv', delimiter=';', md_column='MD', dip_column='DIP', azimuth_column='AZI')
             >>> borehole.deviation.deviation_df
-                Measured Depth  Inclination  Azimuth
-            0   0.05            0.0          0.0
-            1   0.10            0.0          0.0
-            2   0.15            0.0          0.0
+
+            ======  ================  =============  =========
+            Index   Measured Depth    Inclination    Azimuth
+            ======  ================  =============  =========
+            0       0.05              0.0            0.0
+            1       0.10              0.0            0.0
+            2       0.15              0.0            0.0
+            ======  ================  =============  =========
 
         See Also
         ________
@@ -1150,7 +1192,7 @@ def add_deviation(self,
 
     def add_well_logs(self,
                       path: str,
-                      nodata: Union[int, float] = -9999):
+                      nodata: Union[int, float] = -9999) -> Union[LASLogs, DLISLogs]:
         """Add Well Logs to the Borehole Object.
 
         Parameters
@@ -1162,7 +1204,7 @@ def add_well_logs(self,
 
         Returns
         _______
-            WellLogs
+            LASLogs or DLISLogs
                 Well Logs Object.
 
         Raises
@@ -1181,19 +1223,23 @@ def add_well_logs(self,
             >>> borehole = Borehole(name='Weisweiler R1')
             >>> borehole.add_well_logs(path='Well_logs.las')
             >>> borehole.logs.well_header
-                mnemonic   unit   value               descr
-            0   STRT       M      100.0               Log Start Depth
-            1   STOP       M      0.05                Log Stop Depth
-            2   STEP       M      -0.05               Log Increment
-            3   NULL              -999.25             Null Value
-            4   COMP              RWE Power           Company Name
-            5   WELL              EB 1                Well Name
-            6   FLD               KW Weisweiler       Field Name
-            7   LOC                                   Location
-            8   PROV                                  Province
-            9   SRVC                                  Service Company
-            10  DATE              26-Oct-2023         Date
-            11  UWI                                   Unique Well ID
+
+            =======  ========== ====== ================    =================
+            Index    mnemonic   unit   value               descr
+            =======  ========== ====== ================    =================
+            0        STRT       M      100.0               Log Start Depth
+            1        STOP       M      0.05                Log Stop Depth
+            2        STEP       M      -0.05               Log Increment
+            3        NULL              -999.25             Null Value
+            4        COMP              RWE Power           Company Name
+            5        WELL              EB 1                Well Name
+            6        FLD               KW Weisweiler       Field Name
+            7        LOC                                   Location
+            8        PROV                                  Province
+            9        SRVC                                  Service Company
+            10       DATE              26-Oct-2023         Date
+            11       UWI                                   Unique Well ID
+            =======  ========== ====== ================    =================
 
         See Also
         ________
@@ -1240,7 +1286,7 @@ def add_well_logs(self,
     def add_well_tops(self,
                       path: str,
                       delimiter: str = ',',
-                      unit: str = 'm'):
+                      unit: str = 'm') -> WellTops:
         """Add Well Tops to the Borehole Object.
 
         Parameters
@@ -1249,7 +1295,7 @@ def add_well_tops(self,
                 Path to the well top file, e.g. ``path='Well_Tops.csv'``.
             delimiter : str, default: ``','``
                 Delimiter for the well top file, e.g. ``delimiter=','``.
-            unit : str
+            unit : str, default: ``'m'``
                 Unit of the depth measurements, e.g. ``unit='m'``.
 
         Returns
@@ -1271,11 +1317,15 @@ def add_well_tops(self,
             >>> borehole = Borehole(name='Weisweiler R1')
             >>> borehole.add_well_tops(path='Well_Tops.csv', delimiter=';')
             >>> borehole.well_tops.df
-                Top              MD
-            0   Infill           3.0
-            1   Base Quaternary  9.5
-            2   Sand 1           28.5
-            3   Clay             32.0
+
+            =======  =================  ======
+            Index    Top                MD
+            =======  =================  ======
+            0        Infill             3.0
+            1        Base Quaternary    9.5
+            2        Sand 1             28.5
+            3        Clay               32.0
+            =======  =================  ======
 
         See Also
         ________
@@ -1313,7 +1363,7 @@ def add_well_tops(self,
 
     def add_litholog(self,
                      path: str,
-                     delimiter: str = ','):
+                     delimiter: str = ',') -> LithoLog:
         """Add LithoLog to the Borehole Object.
 
         Parameters
@@ -1342,11 +1392,15 @@ def add_litholog(self,
             >>> borehole = Borehole(name='Weisweiler R1')
             >>> borehole.add_litholog(path='LithoLog.csv', delimiter=';')
             >>> borehole.litholog.df
-                Top              MD
-            0   Infill           3.0
-            1   Base Quaternary  9.5
-            2   Sand 1           28.5
-            3   Clay             32.0
+
+            =======  =================  ======
+            Index    Top                MD
+            =======  =================  ======
+            0        Infill             3.0
+            1        Base Quaternary    9.5
+            2        Sand 1             28.5
+            3        Clay               32.0
+            =======  =================  ======
 
         See Also
         ________
@@ -1377,7 +1431,7 @@ def add_litholog(self,
         self.has_litholog = True
         self.df.loc['Litholog', 'Value'] = self.has_litholog
 
-    def add_well_design(self):
+    def add_well_design(self) -> WellDesign:
         """Add Well Design object to Borehole Object.
 
         Returns
@@ -1397,8 +1451,11 @@ def add_well_design(self):
             >>> borehole = Borehole(name='Weisweiler R1')
             >>> borehole.add_well_design()
             >>> borehole.well_design
-            Pipes: {}
-            Cements: {}
+
+            ========= ====
+            Pipes:    {}
+            Cements:  {}
+            ========= ====
 
         .. versionadded:: 0.0.1
         """
diff --git a/pyborehole/design.py b/pyborehole/design.py
index c1e58f7..b088c6c 100644
--- a/pyborehole/design.py
+++ b/pyborehole/design.py
@@ -23,6 +23,7 @@ class WellDesign:
         >>> borehole.add_well_design()
         >>>
 
+    .. versionadded:: 0.0.1
     """
 
     def __init__(self,
diff --git a/pyborehole/deviation.py b/pyborehole/deviation.py
index 85657fd..c56cb9b 100644
--- a/pyborehole/deviation.py
+++ b/pyborehole/deviation.py
@@ -1,6 +1,7 @@
 import pandas as pd
 import numpy as np
-from typing import Union
+from typing import Union, Tuple
+import matplotlib
 import matplotlib.pyplot as plt
 from matplotlib.collections import LineCollection
 
@@ -17,13 +18,13 @@ class Deviation:
         step : float, default: ``5``
                 Step for resampling the deviation data, e.g. ``step=5``.
         md_column : str, default: ``'MD'``
-                Column containing the measured depths.
+                Column containing the measured depths, e.g. ``md_column='MD'``.
         dip_column : str, default: ``'DIP'``
-            Column containing the dip values.
+            Column containing the dip values, e.g. ``dip_column='DIP'``.
         azimuth_column : str, default: ``'AZI'``
-            Column containing the azimuth values.
+            Column containing the azimuth values, e.g. ``azimtuh_column='AZI'``.
         add_origin: bool, default: ``True``
-                Boolean value to add the location of the borehole to survey DataFrames.
+                Boolean value to add the location of the borehole to survey DataFrames, e.g. ``add_origin=True``.
 
     Returns
     _______
@@ -42,17 +43,21 @@ class Deviation:
         >>> borehole.init_properties(address='Am Kraftwerk 17, 52249 Eschweiler, Deutschland', location=(6.313031, 50.835676), crs='EPSG:4326', altitude_above_sea_level=136, borehole_id='DABO123456')
         >>> borehole.add_deviation(path='Deviation.csv', delimiter=';', md_column='MD', dip_column='DIP', azimuth_column='AZI')
         >>> borehole.deviation.deviation_df
-            Measured Depth  Inclination  Azimuth
-        0   0.05            0.0          0.0
-        1   0.10            0.0          0.0
-        2   0.15            0.0          0.0
+
+        ======= ================ ============= =========
+        Index   Measured Depth   Inclination   Azimuth
+        ======= ================ ============= =========
+        0       0.05             0.0           0.0
+        1       0.10             0.0           0.0
+        2       0.15             0.0           0.0
+        ======= ================ ============= =========
 
     See Also
     ________
-        add_litholog : Add LithoLog to the Borehole Object.
-        add_well_design : Add Well Design to the Borehole Object.
-        add_well_logs : Add Well Logs to the Borehole Object.
-        add_well_tops : Add Well Tops to the Borehole Object.
+        pyborehole.borehole.Borehole.add_litholog : Add LithoLog to the Borehole Object.
+        pyborehole.borehole.Borehole.add_well_design : Add Well Design to the Borehole Object.
+        pyborehole.borehole.Borehole.add_well_logs : Add Well Logs to the Borehole Object.
+        pyborehole.borehole.Borehole.add_well_tops : Add Well Tops to the Borehole Object.
 
     .. versionadded:: 0.0.1
     """
@@ -261,9 +266,33 @@ def add_origin_to_desurveying(self,
         ________
             >>> from pyborehole.borehole import Borehole
             >>> borehole = Borehole(name='Weisweiler R1')
-            >>> borehole.init_properties(address='Am Kraftwerk 17, 52249 Eschweiler, Deutschland', location=(6.313031, 50.835676), crs='EPSG:4326', altitude_above_sea_level=136, borehole_id='DABO123456')
+            >>> borehole.init_properties(address='Am Kraftwerk 17, 52249 Eschweiler, Deutschland', location=(310805, 5634992), crs='EPSG:25832', altitude_above_sea_level=136, borehole_id='DABO123456')
             >>> borehole.add_deviation(path='Deviation.csv', delimiter=';', md_column='MD', dip_column='DIP', azimuth_column='AZI')
-            >>> borehole.deviation.add_origin_to_desurveying(x=100, y=100, z=000)
+            >>> borehole.deviation.desurveyed_df.head()
+
+            =======   ===================  ============  ===========
+            Index     True Vertical Depth  Northing_rel  Easting_rel
+            =======   ===================  ============  ===========
+            0         0.0                  0.0           0.0
+            1         5.0                  0.0           0.0
+            2         10.0                 0.0           0.0
+            3         15.0                 0.0           0.0
+            4         20.0                 0.0           0.0
+            =======   ===================  ============  ===========
+
+            >>> borehole.deviation.add_origin_to_desurveying()
+            >>> borehole.deviation.desurveyed_df.head()
+
+            =======  =====================  ==============  ============= ========== ========== ======================================
+            Index    True Vertical Depth    Northing_rel    Easting_rel   Easting    Northing   True Vertical Depth Below Sea Level
+            =======  =====================  ==============  ============= ========== ========== ======================================
+            0        0.0                    0.0             0.0           310805     5634992    136
+            1        5.0                    0.0             0.0           310805     5634992    131
+            2        10.0                   0.0             0.0           310805     5634992    126
+            3        15.0                   0.0             0.0           310805     5634992    121
+            4        20.0                   0.0             0.0           310805     5634992    116
+            =======  =====================  ==============  ============= ========== ========== ======================================
+
 
         See Also
         ________
@@ -298,10 +327,10 @@ def add_origin_to_desurveying(self,
             z = self._borehole.altitude_above_sea_level
 
         # Adding the X coordinate
-        self.desurveyed_df['Northing'] = self.desurveyed_df['Northing_rel'] + y
+        self.desurveyed_df['Easting'] = self.desurveyed_df['Easting_rel'] + x
 
         # Adding the Y coordinate
-        self.desurveyed_df['Easting'] = self.desurveyed_df['Easting_rel'] + x
+        self.desurveyed_df['Northing'] = self.desurveyed_df['Northing_rel'] + y
 
         # Adding the Z coordinate
         self.desurveyed_df['True Vertical Depth Below Sea Level'] = z - self.desurveyed_df['True Vertical Depth']
@@ -322,7 +351,7 @@ def plot_deviation_polar_plot(self,
                                   c: np.ndarray = None,
                                   vmin: Union[float, int] = None,
                                   vmax: Union[float, int] = None,
-                                  cmap: str = 'viridis'):
+                                  cmap: str = 'viridis') -> Tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]:
         """Add polar plot representing the deviation of a borehole.
 
         Parameters
@@ -336,6 +365,13 @@ def plot_deviation_polar_plot(self,
             cmap : str, default: ``'viridis'``
                 Name of the colormap to be used, e.g. ``cmap='viridis'``.
 
+        Returns
+        _______
+            fig : matplotlib.figure.Figure
+                Matplotlib figure.
+            ax : matplotlib.axes.Axes
+                Matplotlib axis.
+
         Raises
         ______
             TypeError
@@ -423,7 +459,7 @@ def plot_deviation_3d(self,
                           elev: Union[float, int] = 45,
                           azim: Union[float, int] = 45,
                           roll: Union[float, int] = 0,
-                          relative: bool = False):
+                          relative: bool = False) -> Tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]:
         """Create 3D Deviation Plot.
 
         Parameters
@@ -439,7 +475,7 @@ def plot_deviation_3d(self,
 
         Returns
         _______
-            fig : matplotlib.figure
+            fig : matplotlib.figure.Figure
                 Matplotlib figure.
             ax : matplotlib.axes.Axes
                 Matplotlib axis.
@@ -529,7 +565,7 @@ def get_borehole_tube(self,
 
         Returns
         _______
-            tube : pv.Tube
+            tube : pv.core.pointset.PolyData
                 PyVista Tube of the borehole.
 
         Raises
@@ -545,6 +581,26 @@ def get_borehole_tube(self,
             >>> borehole.add_deviation(path='Deviation.csv', delimiter=';', md_column='MD', dip_column='DIP', azimuth_column='AZI')
             >>> borehole.deviation.get_borehole_tube(radius=10)
 
+            ==========  =======================
+            PolyData	Information
+            ==========  =======================
+            N Cells     220
+            N Points    800
+            N Strips    220
+            X Bounds    3.108e+05, 3.108e+05
+            Y Bounds    5.635e+06, 5.635e+06
+            Z Bounds    -1.000e+02, 0.000e+00
+            N Arrays    2
+            ==========  =======================
+
+            =============  ========= =========  ========  ============  ============
+            Name           Field     Type       N Comp	  Min	        Max
+            =============  ========= =========  ========  ============  ============
+            TubeNormals    Points    float32	3         -1.000e+00    1.000e+00
+            TVD	           Points    float64	1         -1.000e+02    0.000e+00
+            =============  ========= =========  ========  ============  ============
+
+
         See Also
         ________
             add_origin_to_desurveying : Add origin to desurveying.
diff --git a/pyborehole/logs.py b/pyborehole/logs.py
index a8ee077..79e6ecd 100644
--- a/pyborehole/logs.py
+++ b/pyborehole/logs.py
@@ -176,11 +176,11 @@ def plot_well_logs(self,
         __________
 
         tracks : Union[str, list]
-            Name/s of the logs to be plotted, e.g. ``tracks='SGR'`` or ``tracks=['SGR', 'K'].
+            Name/s of the logs to be plotted, e.g. ``tracks='SGR'`` or ``tracks=['SGR', 'K']``.
         depth_column : str
             Name of the column holding the depths, e.g. ``depth_column='MD'``.
         colors : Union[str, list]
-            Colors of the logs, e.g. ``colors='black'`` or ``colors=['black', 'blue'].
+            Colors of the logs, e.g. ``colors='black'`` or ``colors=['black', 'blue']``.
         add_well_tops : bool, default = False
             Boolean to add well tops to the plot.
         add_well_design : bool, default = False