Repository: MHKiT-Software/MHKiT-Python (language: python)

Issue:
Delft3D z calculation 
### Describe the bug:
Add a clear description of what the refence of z is. 

### The issue:
 In the code **[d3d.py](https://github.com/MHKiT-Software/MHKiT-Python/blob/master/mhkit/river/io/d3d.py)** two function output the z location of data points ( `get_layer_data`  and  `get_all_data_points` ).  The refence for this output needs to be clarified whether its the free surface or the zero water level. 




Discussion / hints:
Did we switch this to a standard output?

Do we still want to add a new/ custom output?
Z is still calculated as "the depth from the zero-water level at the surface" 

`d3d.py` lines 218-227
```
   if dimensions== 2:
        if layer_dim == 'FlowLink_xu FlowLink_yu': 
            z = [bottom_depth_wdim]-water_level_wdim
        else:
            z = [bottom_depth]-water_level
    else:
        if layer_dim == 'FlowLink_xu FlowLink_yu': 
            z = [bottom_depth_wdim*layer_percentages[layer_index]]-water_level_wdim 
        else:
            z = [bottom_depth*layer_percentages[layer_index]]-water_level
```

yes, I think instead of outputting `z` we want to output `depth` and move this to it's own function `depth_zero_water_level` 
From our discussion
- Currently get layer data returns x,y,z,variable
- Adjust to:
  - No custom z
  - no surface reference option in the function
  - Output water depth and water level always
  - water level is a constant in z-direction
  - water depth is variable by % of layer

Fix patch:
diff --git a/examples/Delft3D_example.ipynb b/examples/Delft3D_example.ipynb
index 8378d9081..a87de112b 100644
--- a/examples/Delft3D_example.ipynb
+++ b/examples/Delft3D_example.ipynb
@@ -196,7 +196,7 @@
    "metadata": {},
    "source": [
     "## Dataframe from NetCDF\n",
-    "For this example we will create a DataFrame from the D3d NetCDF object using the variable `'ucx'`, the velocity in the x-direction (see available data in previous block output). MHKiT's `d3d` function `get_all_data_points` will pull all the raw data from the NetCDF file for the specified variable at a specific time index in a dataframe `var_data_df`. The `time_index` can be used to pull data from a different instances in time (the last valid time_index, `time_index`=-1, is the default setting). If an integer outside of the valid range is entered the code will output an error with the max valid output listed.  The output data from  `get_all_data_points` will be the position (x, y, z) in meters, the value of vairable in meters per second and the run time of the simulation in seconds or `'x'`, `'y'`, `'z'`, '`ucx`', and `'time'`. The time run in simulation varies based on the 'time_step'. \n",
+    "For this example we will create a DataFrame from the D3d NetCDF object using the variable `'ucx'`, the velocity in the x-direction (see available data in previous block output). MHKiT's `d3d` function `get_all_data_points` will pull all the raw data from the NetCDF file for the specified variable at a specific time index in a dataframe `var_data_df`. The `time_index` can be used to pull data from a different instances in time (the last valid time_index, `time_index`=-1, is the default setting). If an integer outside of the valid range is entered the code will output an error with the max valid output listed.  The output data from  `get_all_data_points` will be the position (x, y, waterdepth) in meters, the value of vairable in meters per second and the run time of the simulation in seconds or `'x'`, `'y'`, `'waterdepth'`, `'waterlevel'`, '`ucx`', and `'time'`. The time run in simulation varies based on the 'time_step'. \n",
     "\n",
     "The limits `max_plot_vel` and `min_plot_vel` are defined to bound the plots of the variable's data in the next 3 example plots."
    ]
@@ -211,20 +211,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "           x      y         z       ucx   time\n",
-      "0      0.125  1.125  0.204638  0.717881  240.0\n",
-      "1      0.375  1.125  0.199927  0.689645  240.0\n",
-      "2      0.125  1.375  0.204638  0.717881  240.0\n",
-      "3      0.625  1.125  0.199497  0.694164  240.0\n",
-      "4      0.375  1.375  0.199927  0.689645  240.0\n",
-      "...      ...    ...       ...       ...    ...\n",
-      "5755  17.625  4.625  1.800595  1.245117  240.0\n",
-      "5756  17.375  4.875  1.800572  1.222989  240.0\n",
-      "5757  17.875  4.625  1.800020  1.255866  240.0\n",
-      "5758  17.625  4.875  1.800596  1.245292  240.0\n",
-      "5759  17.875  4.875  1.800019  1.256067  240.0\n",
+      "           x      y  waterdepth  waterlevel       ucx   time\n",
+      "0      0.125  1.125    0.199485   -0.005154  0.717881  240.0\n",
+      "1      0.375  1.125    0.200008    0.000081  0.689645  240.0\n",
+      "2      0.125  1.375    0.199485   -0.005154  0.717881  240.0\n",
+      "3      0.625  1.125    0.200056    0.000559  0.694164  240.0\n",
+      "4      0.375  1.375    0.200008    0.000081  0.689645  240.0\n",
+      "...      ...    ...         ...         ...       ...    ...\n",
+      "5755  17.625  4.625    1.794649   -0.005946  1.245117  240.0\n",
+      "5756  17.375  4.875    1.794848   -0.005724  1.222989  240.0\n",
+      "5757  17.875  4.625    1.799824   -0.000196  1.255866  240.0\n",
+      "5758  17.625  4.875    1.794638   -0.005958  1.245292  240.0\n",
+      "5759  17.875  4.875    1.799828   -0.000191  1.256067  240.0\n",
       "\n",
-      "[5760 rows x 5 columns]\n"
+      "[5760 rows x 6 columns]\n"
      ]
     }
    ],
@@ -245,7 +245,7 @@
    "metadata": {},
    "source": [
     "## Plotting a Line Plot Between Two Points \n",
-    "To view the maximum wake effect of the turbine we can plot a velocity down the centerline of the flume. To do this we will create an array between two points down the center of the flume. We will then interpolate the grided data results onto our centerline array. Since the flume is retangular the centerline points are found by taking the average of the maximum and minimum value for the width, `y`, and height, `z`, of the flume. We will then input one array, `x`, and two points, `y` and `z` into the function `create_points` to create our centerline array to interpolate over. The function `create_points` will then output a dataframe, `cline_points`, with keys `'x'`, `'y'`, and `'z'`."
+    "To view the maximum wake effect of the turbine we can plot a velocity down the centerline of the flume. To do this we will create an array between two points down the center of the flume. We will then interpolate the grided data results onto our centerline array. Since the flume is retangular the centerline points are found by taking the average of the maximum and minimum value for the width, `y`, and the `waterdepth`, of the flume. We will then input one array, `x`, and two points, `y` and `waterdepth` into the function `create_points` to create our centerline array to interpolate over. The function `create_points` will then output a dataframe, `cline_points`, with keys `'x'`, `'y'`, and `'waterdepth'`."
    ]
   },
   {
@@ -277,7 +277,7 @@
        "      <th></th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
-       "      <th>z</th>\n",
+       "      <th>waterdepth</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -285,43 +285,43 @@
        "      <th>0</th>\n",
        "      <td>17.875000</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>0.999069</td>\n",
+       "      <td>1.000931</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>17.512755</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>0.999069</td>\n",
+       "      <td>1.000931</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>17.150510</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>0.999069</td>\n",
+       "      <td>1.000931</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>16.788265</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>0.999069</td>\n",
+       "      <td>1.000931</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>16.426020</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>0.999069</td>\n",
+       "      <td>1.000931</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "           x    y         z\n",
-       "0  17.875000  3.0  0.999069\n",
-       "1  17.512755  3.0  0.999069\n",
-       "2  17.150510  3.0  0.999069\n",
-       "3  16.788265  3.0  0.999069\n",
-       "4  16.426020  3.0  0.999069"
+       "           x    y  waterdepth\n",
+       "0  17.875000  3.0    1.000931\n",
+       "1  17.512755  3.0    1.000931\n",
+       "2  17.150510  3.0    1.000931\n",
+       "3  16.788265  3.0    1.000931\n",
+       "4  16.426020  3.0    1.000931"
       ]
      },
      "execution_count": 6,
@@ -337,16 +337,16 @@
     "ymin=var_data_df.y.max()\n",
     "ymax=var_data_df.y.min()\n",
     "\n",
-    "zmin=var_data_df.z.max()\n",
-    "zmax=var_data_df.z.min()\n",
+    "waterdepth_min=var_data_df.waterdepth.max()\n",
+    "waterdepth_max=var_data_df.waterdepth.min()\n",
     "\n",
     "# Creating one array and 2 points \n",
     "x = np.linspace(xmin, xmax)\n",
     "y = np.mean([ymin,ymax])\n",
-    "z = np.mean([zmin,zmax])\n",
+    "waterdepth = np.mean([waterdepth_min,waterdepth_max])\n",
     "\n",
     "# Creating an array of points  \n",
-    "cline_points = d3d.create_points(x, y, z)\n",
+    "cline_points = d3d.create_points(x, y, waterdepth)\n",
     "cline_points.head()"
    ]
   },
@@ -376,7 +376,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x360 with 1 Axes>"
       ]
@@ -390,9 +390,9 @@
    "source": [
     "# Interpolate raw data onto the centerline\n",
     "cline_variable = interp.griddata(\n",
-    "    var_data_df[['x','y','z']], \n",
+    "    var_data_df[['x','y','waterdepth']], \n",
     "    var_data_df[variable],\n",
-    "    cline_points[['x','y','z']]\n",
+    "    cline_points[['x','y','waterdepth']]\n",
     ") \n",
     "\n",
     "# Plotting\n",
@@ -412,7 +412,7 @@
    "source": [
     "## Contour Plot for a Given Sigma Layer \n",
     "\n",
-    "Sometimes it is useful to plot only the raw data of a given layer. Using `get_layer_data` a single layer of raw data will be retrieved from the NetCDF object. The `d3d` function ,`get_layer_data` takes 3 inputs, the NetCDF object (`d3d_data`), a variable name as a string (`variable`), and the layer to retrieve (`layer`) as an integer. The variable is set to the velocity in the x direction, `'ucx'`, and the layer of data to plot as 2. Since there are 5 sigma layers in this example layer 2 is approximately the middle layer.  `layer` works as an index that begins at begins at 0 and ends at 4.  The `get_layer_data` then outputs a dataframe `layer_data` with the keys `'x'`, `'y'`, `z`, '`v`', and `'time'` as the length, width, depth, value, and run time of simulation of the specified variable in this case velocity in the x direction, 'ucx'.  \n",
+    "Sometimes it is useful to plot only the raw data of a given layer. Using `get_layer_data` a single layer of raw data will be retrieved from the NetCDF object. The `d3d` function ,`get_layer_data` takes 3 inputs, the NetCDF object (`d3d_data`), a variable name as a string (`variable`), and the layer to retrieve (`layer`) as an integer. The variable is set to the velocity in the x direction, `'ucx'`, and the layer of data to plot as 2. Since there are 5 sigma layers in this example layer 2 is approximately the middle layer.  `layer` works as an index that begins at begins at 0 and ends at 4.  The `get_layer_data` then outputs a dataframe `layer_data` with the keys `'x'`, `'y'`, `'waterdepth'`, `'waterlevel'`, `'v'`, and `'time'` as the length, width, waterdepth, value, and run time of simulation of the specified variable in this case velocity in the x direction, 'ucx'.  \n",
     "\n",
     "To plot the data the maximum and minimum, `max_plot_vel` and `min_plot_vel`, values are pulled from above to limit bounds of the color bar to show the value of the specified variable, in this case it's velocity in the x direction. The type of plot is also defined as a string 'contour' to add to the title of the plot."
    ]
@@ -477,7 +477,7 @@
    "source": [
     "## Plotting a Contour Plot of a given Plane \n",
     "\n",
-    "If you wanted to look at a contour plot of a specific depth you can create a contour plot over given points. Expanding on the centerline array points we can create a grid using the same `create_points` function by inputting two arrays along the length of the flume, `x`  , and width of the flume, `y_contour` , and one point for the depth we want to look at `z`.  `create_points` then outputs a dataframe `contour_points` of points to calculate the contour values over with the keys `'x'`, `'y'`, and `'z'`."
+    "If you wanted to look at a contour plot of a specific waterdepth you can create a contour plot over given points. Expanding on the centerline array points we can create a grid using the same `create_points` function by inputting two arrays along the length of the flume, `x`  , and width of the flume, `y_contour` , and one point for the `waterdepth` we want to look at.  `create_points` then outputs a dataframe `contour_points` of points to calculate the contour values over with the keys `'x'`, `'y'`, and `'waterdepth'`."
    ]
   },
   {
@@ -492,15 +492,135 @@
      "text": [
       "[0, 1, 2]\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>waterdepth</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.875000</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.000931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>17.695707</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.000931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17.516414</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.000931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17.337121</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.000931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.157828</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.000931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3995</th>\n",
+       "      <td>0.842172</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>1.000931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3996</th>\n",
+       "      <td>0.662879</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>1.000931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3997</th>\n",
+       "      <td>0.483586</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>1.000931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3998</th>\n",
+       "      <td>0.304293</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>1.000931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3999</th>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>1.000931</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4000 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              x      y  waterdepth\n",
+       "0     17.875000  4.875    1.000931\n",
+       "1     17.695707  4.875    1.000931\n",
+       "2     17.516414  4.875    1.000931\n",
+       "3     17.337121  4.875    1.000931\n",
+       "4     17.157828  4.875    1.000931\n",
+       "...         ...    ...         ...\n",
+       "3995   0.842172  1.125    1.000931\n",
+       "3996   0.662879  1.125    1.000931\n",
+       "3997   0.483586  1.125    1.000931\n",
+       "3998   0.304293  1.125    1.000931\n",
+       "3999   0.125000  1.125    1.000931\n",
+       "\n",
+       "[4000 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "# Create x-y plane at z level midpoint\n",
     "x2 = np.linspace(xmin, xmax, num=100)\n",
     "y_contour = np.linspace(ymin, ymax, num=40)\n",
-    "z2 = np.mean([zmin,zmax])\n",
+    "z2 = np.mean([waterdepth_min,waterdepth_max])\n",
     "\n",
-    "contour_points = d3d.create_points(x2, y_contour, z2) "
+    "contour_points = d3d.create_points(x2, y_contour, z2) \n",
+    "contour_points"
    ]
   },
   {
@@ -519,9 +639,9 @@
    "outputs": [],
    "source": [
     "contour_variable = interp.griddata(\n",
-    "    var_data_df[['x','y','z']],\n",
+    "    var_data_df[['x','y','waterdepth']],\n",
     "    var_data_df[variable],\n",
-    "    contour_points[['x','y','z']]\n",
+    "    contour_points[['x','y','waterdepth']]\n",
     ")"
    ]
   },
@@ -530,15 +650,28 @@
    "id": "5658a7d5",
    "metadata": {},
    "source": [
-    "The results are then plotted. The minimum and maximum values show on the graph are pulled from above (`max_plot_vel` and `min_plot_vel`) as in the previous example. The contour plot of the velocity is then output at the center depth of the flume. "
+    "The results are then plotted. The minimum and maximum values show on the graph are pulled from above (`max_plot_vel` and `min_plot_vel`) as in the previous example. The contour plot of the velocity is then output at the center waterdepth of the flume. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "d04fa7ad",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fue5kSd+oUm87SXfnOq9Iuk7SwoXj0cljVo8vvhcMauWbm1ljOBibmQ1oqwNbA3cA89d4zmBgH+Au4HZg02qVJA0BbgVeBw4EpgGrld9H0j7A6cBJwKHAYrnNYvZct8pbXAX8vcY+N4TDsZmZmVl7uSoirgSQdCkwvLsTImK6pMUjIiQdQCfhGDgSWABYOyLezGU3FytIGg6cBhwYEb8rHPpL6T3vKJ23Tu7rRd31t5E8rcKszXjU2MxsYIuIOT08r9OpFAV7AucUgnE1X8vPv6+zCzsDb5BGj1vG4disjTgYm5lZo0haEVgKmC7pGklvS3pZ0qmSitMqPgc8CuwtaaqkdyTdKWm9LtoWsCNwZUTMbOiFdMPh2KxNOBibmVmDLZOfTwKeA7YEfgLsD/y4VG8V4CjgcODLpBHh6yQt3UnbGwIfBcb0frfr4znHZmZmZi322pwFueb11Wqs/cJwSZMKBaMjYnQj+lVSGVR9KCK+lb8eJ2lR4EhJo/Ko7zzAIsCOEXEdgKTbgWeAA4Cjq7S9C9ABXN/IC6iFw7FZG/CosZnZgDItItZuwfu+mp9vLpWPA44FVgIeLNQbX6kQETMk3UNa2eIDJA0Ctgcui4i3e7nPdfO0CrN+zsHYzMya5EmgWnhVfq7cCPgIEIXyYr1qNwtuBixJi1epqHA4NuvHHIzNzKxZ8qjujXx4mbfNgJnAE/n11aQgvEmlgqShwFrAA1Wa3gX4N4WR5lbytAozMzOzNiJpMGkTEIDlgCGSdsivr4mImZKeAG6JiL0L520FLAyskV9Xzrk7Ip7JXx8HTJB0Hmmk91PAEcDxETELICImSboSOEfSEaSNQg4D3gF+U+rrAsBXgPN7ugRdb3M4NuunPGpsZmadWAq4pFRWeb0iMIWUAect1fktsEKVc/YEzgeIiLskfRn4KbAr8BJwQn5dtDtwMnAqafe9vwObRkRHqd5WwFD6wCoVFQ7HZv2Qg7GZmXUmIqbw4fm+5Tojainr5Nzr6WZViYh4nbTE2/7d1LuCbvrabJ5zbNbPOBibmZk1jsOxWT/iYGxmZtZYDsdm/YSDsZmZWeM5HJv1Aw7GZmZmzeFwbNbHORibmZk1j8OxWR/mYGxmZtZcDsdmZmZmZpnDsVkf5VFjMzOz5nM4NjMzMzPLHI7N+iCPGpuZmbWGw7FZH+NgbGZm1joOx2ZmZmZmmcOxmZmZWRuR9HFJZ0l6QNJsSeNrPG+opPMkdUh6TdKfJC1RqjO/pGMkPSHpzfx8rKQFCnVWl3SdpOclzZL0rKSzJX2k1Naxkh6UNEPSfyRNkrRTr3wT5sKgVnfAzN7nKRVmZtYLVge2Bu4A5q/jvIuBVYB9gDnAicAVwIaFOj8D9gOOAu4D1gR+DAwDvpfrDAWeBi4AngdWBH4ErCXpsxHxbq43BDgfeBiYDewAjJE0OyIuraPfvaql4VjScsCjwMLAohHxeiv7Y2ZmZtYGroqIKwEkXQoM7+4ESesCWwAbRcStuew54E5JX4yIsbnqrsBvI+LU/PrmnOd2I4fjiLgduL3Q/HhJU4EbgE8B9+Z6B5e6cYOk1YFvAAMzHAMnA6+TwrHZgOZRYzOzro2bNrLL4/49mkTEnB6cthXwYiUY53bukvR0PlYJx/MBr5XOnQ6om/Zfyc/djWS/UkOdhmpZOJa0IbAl8BNSSDYzM+tTugtj1reUf14Oy3UZCVT7hj2Sj1WcDXxb0k3AA8BngP2B08snSpqHlDVXJE3HuBu4q0q9QcAiwDbA5sDOc3Mhc6sl4VjSvMCvgeNInzbMBjT/Arf+xqHR+oPa/p3e1PB+1GLGuwvW8d/VTcMlTSoUjI6I0XPZhcWonsk6gI8VXh8BLARMKJSdERHHVTn3GtJUDYB7gK3Lo9qSPg9MzC/fBQ6IiCvq7XxvatXI8X7AgsBvSHNUzMyswOHTzLowLSLWbkC7UaVMpfJDgd2BA4F/AJ8Gjpf0SkQcUzr3QGBxYGXSDXzXSlo/It4q1HkQ+Czphr5tgNMlzYiIi3rhenqk6eE4LwlyPLB7RLwjdT1FRdK+wL4ACy29SOM7aNZkHjXuXxxazaxNdQBLVikfRh5RljSctDLFdyPid/n4rZLeJoXa0yPipcqJEfF4/vJOSbeRVrDYFTi3UOcNoDIKPlbSUNIqGQMnHAMnAHdGxDW1VM5/JhgNMGzkUtU+0ZiZdcph1sysJpP54JJtFSNJy7lBml4xH3B/qc59pEy5AvASVUTEM5Je5YNTNKq5F9hT0nwR8U5NPe9lTQ3HeXmOvYAvSBqWiwfn56F5Xbs3m9knM+v7HHDNzBruWuBoSRtExAQASWuTwuy1uc4z+XlN0s11FWvl5ymdNS5pFWAJ0uhxV9YHprYqGEPzR45XJn3imFjl2FTgHNLC02YDwkCfUuHQa2bW+yQNJm0CArAcMETSDvn1NRExU9ITwC0RsTdAREyUdD1wgaRDeH8TkAmVNY4j4kVJVwAnSlqQNOd4DWAUcElEvJzf/+ekm+vuJE3JWBU4DHgSGJPrrACcB1wIPEVareKrpJUq9u/970rtmh2OJwCblMq2BA4n/RCfanJ/zKxBHHzNzFpmKeCSUlnl9YqkEd5BwLylOjsDp5HmBM8DXA0cVKrzTeCYXL4s8BxwFul+sopJpJvx9iUtwPAscBnw0zzHGFJofp50o94y+fXDwDa1Tr1tlKaG44iYBowvlkkakb+8zTvkmfVPDsJmZn1HREyhm005ImJElbLpwJ750dl5M4BD8qOzOmPII8Rd1HmNtOpFn9PqHfLMrA9y2DUzs4Gq5eE4Is4Hzm9xN8xaYty0kU2dd+zQa2Zm1rWWh2Ozga5aYK01MDvsmpmZ9S6HY7M+yKHXzMysNeZpdQfMzMzMzPoKh2MzMzMzs8zh2MzMzMwsczg2MzMzM8scjs3MzMzMModjMzMzM7PM4djMzMzMLHM4NjMzM2sjkj4u6SxJD0iaLWl8jecNlXSepA5Jr0n6k6QlqtTbVtKDkt6S9LCknRrZVrM5HJuZmZm1l9WBrYHH8qNWFwMbA/sAewCfBa4oVpC0AXAZcDOwFfA34CJJmzewrabyDnlmZmZm7eWqiLgSQNKlwPDuTpC0LrAFsFFE3JrLngPulPTFiBibqx4N3BoRB+XXN0taHTgGuKG322oFjxybmZmZtZGImNOD07YCXqyE2dzOXcDT+RiSFgA2Af5cOncMsK6koQ1oq+kcjs3MzMxsJDC5Svkj+RjASsB8Veo9QsqUn2hAW03naRVmZmZmLfbWO/PxyL+XrrX6cEmTCq9HR8TouezCYsD0KuUdwMcKdahSr6N0vDfbajqHYzMzM7P+ZVpErN2AdqNKmaqUl1+rSnlvttVUnlZhZmZmZh3AsCrlw3h/dLejUFauQ6leb7XVdA7HZmZmZjaZ9+cDFxXnDz8JvFOl3khgDu8vG9ebbTWdp1WYWV3qmBNnvWDVZV5sdRfMbGC4Fjha0gYRMQFA0tqkOcLXAkTELEk3AzsCZxXO3QmYGBGvNaCtpnM4NmtzDrP9W6t/fg7nZv2PpMGkTUAAlgOGSNohv74mImZKegK4JSL2BoiIiZKuBy6QdAhp9PZEYEJhXWKA44Hxkn5B2tRj6/zYslKhN9tqBYdjs36k1UHJBp5m/JtzADfrdUsBl5TKKq9XBKaQMuC8pTo7A6cB55Km3l4NHFSsEBETctD+MbA/ae3iXSOivGlHb7bVVA7HZn2Eg68NVI34t+/AbQNZREzh/VUfOqszokrZdGDP/Ojq3CsobQXdyLaazeHYrIUciM0ao7f+23LINutbJJV31KvVYflDQ7ccjs2azIHYrP9wyDbrc3YA7gNm1FhfwIbAz0jTSbrlcGzWJA7FZgNXb/z374Bt9p79I+KuWipKGgS8XU/jDsdmTeBgbGZza25/jzhcW5s4FphaR/3Z+Zznaz3B4diswRyMzawv6OnvIodq60si4tg66wcpHNfM4disgRyMzay/68nvMQdqayVJw/JqGT3icGzWIA7GZjZQOVBbM0jaH1g0Ik7Kr9cgraf8EUn3A9tGRD1TMACHYzMzM+sDvDKI9cCBwK8Kr39Fmlt8CHA4aYWK3ett1OHYzMzM2ob/ajeg/BfwKICkJYH1gc0iYrykt4HTe9Kow7FZA9Tzy/nd5wc3sCeNMWjZma3ugpmZ2Sxg/vz1JsBM4Lb8+lVgWE8adTg2a5H+GIor6um7g7SZmTXIXcB3JU0FDgKui4jZ+djHqGP5tqKaw7Gk/wbWAZYBFiQl8seA2yOioydvbjZQ9edgXC8HaTOz5pO0GvBrYF1gOnA2cGwhPHZ23urAacAGpJHYS4BDI+L1Qp0vAXvltlfI7Y7qpL3tgB8Cn8zt3Q1sHxFv5OPRSVfejogFurnMHwB/BR4E/pX7VLET8Pduzq+qy3As6WPA/sBuwNLAHNI3eBZpqHowMEfSLaRv+sURMacnHTEbCAZSKO6Jrr4/Ds5mZrWRtBgwFngY2BZYCTgFmAc4qovzhgLjSIOfOwFLACcBHwG+Uqi6JfAp4CZg5y7a24c07/ck4FBgMWBTPpg/161y6lXUEGwj4mHg45KWAF7NaxpXHAL8u7s2quk0HEs6mxSKJwDHAbcDDxU/cUgaDnwW2IJ04aMk7R0RE3rSGbN24JtBGqOWDxYO0GZmAOwHLARsFxEzgBslDSHltJNyWTXfyed9ubJOsKRXgSslrR0Rk3K9QyPiB/n4ttUayhnxNODAiPhd4dBfivUi4o7SeesAw4GLOmn3BeBv+XFjRLweEa+U60XEg51cY7e6Gjl+CxgZEc90ViEipgHXAtdK+j6wI7BcTztj1s48atx4Hnk2MwNgK+D6UggeA5wIbEQama1mDWBSaQONG4AAtgEmAdQ4S+Br+fn3Nfc62Rl4o4s+fg/YGvgtMEzSbeSwHBGP1/leVc3T2YGIOKCrYFyl/pyIuDgiLu6Njpm1Ewfj1nv3+cGdPszM2sxIYHKxICKeJc35HdnFeQsCb5fK3iVNq121zj58jrTM2t6Spkp6R9Kdktbr7ARJIg20XhkRVUc0IuLPEbEHaarHRqSZDbsDkyU9Juk0SV+UNF+d/X2PV6sw60WeUtE/dRaQPdpsZs0S78xTz4f14ZImFV6PjojRhdeLke4RK+vIxzrzBLCrpPki4p1cthYwL7B4rZ3LlgFWIc1xPgx4JT9fJ2nliKi2W8uGwEdJo9xdyvOL78yPH0lahjSivA1wGSlrjwWujohz6+l4PatVrAN8lTRtYsEqfdypnjc2Gyi6+2U3eOqH/4Az86O+r7UvcGg2sz5qWkSs3U2daqtAqJPyit+Rpi38WtIo0g15ZwCz86Me8wCLADtGxHUAkm4HngEOAI6ucs4upAB/fZ3vRUT8GzgXODePGm9ECsqH5fKa1RSOJR1MusvxReApPjzkbmY9UC0Yd1VejYN08zk0m1kf10H1DTCGUn1EGYCImCxpX9KNdN8mTacYTQrU9e7L/Wp+Hl9of4ake4DVypUlDQK2By6LiLnKmXnUe2x+HFzv+bWOHP8A+CXw/dIyGWbWA/WE35625dDcfNVCswOzmbXAZEpziyUtDyxMaS5yWUScK+lCYGXgJWAaaUrE2XX24RFSqFapXKTQXbYZsCSdrFLRFUmr0PnMhmvrba/WcLwA6S5AB2OzOlQLS70ZjLvi6Rp9Q/nfgMOymTXBtcChkhaNiP/ksp2AN4Fbujs5It4ibayBpG+Spkj8uc4+XA38iLSt8zW5raGkOcw/r1J/F9K6xONrfYO8Qd1FpJsFyyEcUjift55OQ+3h+HxgO9LwtJn1U4OnzuOA3GLvPj/YAdnMGu1M0nbKl0s6kbSV8ijg1OLybpKeAG6JiL3z6yHA/wG3klap2IQ0e+BbEfFq4bwVSPtcAMwPrCZpB+CNykhtREySdCVwjqQjSCPQhwHvAL8pdlbSAqRNRs6vczO5c3N7/490M2GvTPutNRwfDpye7/obx4fnq0RE/LY3OmRmjeWA3HoOyGbWSBHRIWkz0u50V5Fy22mkgFw0iA+OrM4GPgN8i7QZyD9JN9RdUTpvE+C8wusd8+MZYEShfHfgZOBU0q7Kfwc2jYiOUntbkeZDd7tKRcmqpK2o676Bryu1huNNSbvlLZq/LgvSYsxdyp8qvk9a2mNh0jfxD8BJczv52sxq54DcepXpFg7JZtYIeWvlapmtWGdE6fUbwOY1tH0+aVZBd/VeB/bPj67qXUH1aRHduQv4rx6c16VaJz+eQVpHbnVggYiYp/SodT7HEsDNwD6kTwnnkobvT62z32Y2l5o199m65k1IzMx6bF9gX0m7SVpW0uDyoyeN1jpyvCzwnYh4pCdvUhERZ5WKbs7zW74r6UDf8GfWXB5B7hs8zcLMrEemAVOAC7qo07Ab8sYCn6YxN+S9QprMbWZmZmZWqz8C65JWv2j6DXm/As6UtBDVb8irzG2piaR5ScvDrUm6m/K3HjU2MzMzszpsQlpJ48LebLSekWOA44BjS8cqWxHWM2z9BikcQxoKP7Szinmnln0BFlp6kTrewszMzMza2BSg1+ek1RqON+nl912PtKTHOsAxpKVGvlOtYkSMJm1dyLCRS3l02czMzMwgDa4eK+n+iJjSW43WFI4jotvdVOoREffmLydImgb8XtIpEfFkb76PmZmZmbWtY0lLuT0maQrVp/2uU2+jnYZjSYvk9enqUtqqsBaVoLwi4HBs1kReqaJv8EoVZmY98s/86FVdjRw/K+kM4LzuRnTztn//D/gecCNwfB19WD8/P13HOWY2lxyM+wYHYzOznomIPRvRblfh+EukkHukpAeA20npfBowCxhGGu1dC9gIeJO0lMbpnTUo6TrSzX0PkbYoXJ+0Z/fFnlJh1jwOxn2Dg7GZWd/TaTiOiHuArSWtDHwD2AzYi/dXmQB4lrRP9l7AXyPinW7e725gD9K+2+8CTwE/BM7sWffNrF4Oxn2Dg7GZWf0kHQSMiYiX6jznwoiYVkv9bvePjYjHI+LoiFgvIhYibQG9HLBgRIyIiN0i4rIagjG5nU9GxCIRMSwi1oyIX9dyrpnNPQfjvsHB2MwaTdJqkm6SNFPS85KOy/tMdHfe2pJukPSKpFcljZX0uVKdYyU9KGmGpP9ImiRpp1Kd1SVdl997lqRnJZ0t6SP1tlVyGrBCHd+HefM5/1XrObUu5faeiOio9xyzgWLVZV7kkX8v/d7rQcvO5N3nP7i1+8yPzmHw1G4/l/YKh+G+wWHYzJpJ0mKkaawPA9sCKwGnkAZFj+rivOXzefeSZg1AWi7tBkmfiohnctkQ4Pzc/mxgB2CMpNkRcWmuM5R0P9kFwPOkqbg/AtaS9NmIeLeOtj7QTeCnkl6t9dtRY7331B2OzWzu9XZAdgjuOxyEzawP2A9YCNguImYAN0oaAoySdFIuq2YbYNF83nQASbeT7jfbGvgtQEQcXDrvBkmrkwL1pbnO7aT71SrGS5oK3AB8irxaWS1tldxK2nhuyS6/Ax8+p+aV1ByOzVqkEmg7C8kOvH2bQ7CZ9WFbAdeXQvAY4ETSIgpXdXLefKR7wopL+b6ey7obgX0FmL+GOtRYr2qdiNi4m3PnmsOxWYs5BPddDsBm1k+NBMYVCyLiWUkz87HOwvFlwHHAKZJOyGXHAB3AJeXKkgYBi5BGnDcHdq5SZx5S3lwR+BlpcYa7etJWszgcmzVYtXnH1vc4CJtZPzJc0qTC69ERMbrwejGq7BZHCrmLddZoRDwvaRPgauCgXPwCsEVEvFysK+nzwMT88l3ggIi4okqz1wBb5K/vAbaOiA+MCtXRVlPUFI4l/T/gmvLFmFltHJD7BgdgM+ur5nm782l2VUyLiLW7qRNVytRJeTqYVpK4lBRi98nF3wX+Jmm9iHi2UP1B4LOkfS+2AU6XNCMiLio1eyCwOLAy6WbAayWtHxFv9aCtpqh15PhK4CVJFwDnR8QjDeyTWVtyQG4OB2AzMzpIQbNsKNVHlCsOJWXDHSrL7EoaBzwOHML7o8lExBtAZfR6rKShpDnNHwi0EfF4/vJOSbeRVrDYFTi33raapdaPKCsBo4GvAf+UNFHSt/Kdj2ZWIwe3uTNo2ZndPszMjMmkucXvycu0LZyPdWYk8FBx/4mIeJu0s/FK3bznvcDykubrrEJeCu5V4GNz21ZnJM31KFRN4TgipkTEjyJiRdK20k+QFlR+QdIf8vwUM7O55uBrZjbXrgW2kLRooWwn4E3gli7Oewb4pKT3VoqQtADwSWBKN++5PjC1q43dJK1C2kzu6bltqws3SRpeet9O51lX05NNQMYB4yQtS1oWZDdgV0nPAr8Cfl1Y2NlswClvBFJWCXkDbYqFw62ZWdOcSZoCcbmkE0kjtaOAU4vLu0l6ArglIvbORWeT5hr/RdIZpDnK3wU+QppBgKQVgPOAC4GnSCtMfJW0usT+hbZ/Trq57k7SVI5VgcOAJ0n5sea26nQCaWrGdsC/ge/ntpartYG6w7GkjYA9ge2Bd4DfAFeQ7kQ8ljShetd62zUbaNohJDvwmpn1PRHRIWkz4HTSsm3TSX/xH1WqOoi0oUblvHskbUnaye4PufhB4EsR8UB+PZ20491RwDL59cPANhFxTaHtSaSb8fYFFgSeJS0V99M8x7ietuq59qsldQC3kXLqX4A162mj1tUqVgC+mR8jgPGki708ImblajdJmgj8sZ4OmLWj7kaPi+oNmLWG6WK7nZ3jcGtm1p4i4mFg027qjKhSdhNwUxfnvAbsXsP7jyGPEM9tW/WQtBdwBCmrrgFcEhEv1tNGrSPHT5GS/fnAuRHR2VyRh6iysLOZ9Z6eBFqHYDMbKFZdpq4cxOPdV7H+5X+Ar0bEQ5JWJE0RGVXPusm1huMvA9d1t85xRDwG+OY8MzMz65F6w61ZUUR8pfD105K+SJr+e0WtbdQUjns678NsIKtnaoWZWTty0LVGknQjaU505fFQRLxZrBMR0yR9qZ52vX20WQM5IJtZu3DQtT7oGdKyb/sCg4E5kp7i/bD8z4i4tByYu+NwbNZgDshm1tc46Fo7iIh9ACSJtD31p4CNgR2BLUkrcVxab7sOx2ZN4IBsZr3J4dbsfRERwGP5camkUcDlwJE9ac/h2KxJHJDNrMLh1qxx8jzjQ4Hjgc3rPd/h2KyJiv9DdFA2618caM36FknrkG7Ce6PK4ceBdXvSrsOxWYs4KJs1ngOtWVu7g3QT3hTSDXj/AP4JvEDarfnVnjTqcGzWB9TyP3AHaBsIHGbNrA4rkm7C+3R+7AKsBAh4Hdi7J406HJv1E/WEBgdpaxSHVzPrKyLiGdJybldVyiQtBAwHXoiId3vSrsOxWRvqzQDjoN23OJyaWS0krQb8mjTvdjpwNnBsRMzu4pxRwI86OXxkRPw01zsf+GaVOqtGxORCe0OBXwBfAeYBrgYOiohXSu+7LfBj0nJsT+V+XtzNJVaV1zT+V0/OrXA4NrMuOYyZmfUvkhYDxgIPA9uSphqcQgqoR3Vx6tnAdaWyrwCHA9eWyicDe5bKppReXwysAuwDzAFOJG3jvGGhrxsAlwFnAAcBWwMXSeqIiBu66GvDOBybmZmZtZf9gIWA7SJiBnCjpCHAKEkn5bIPiYipwNRimaSjgckRcX+p+hsRcUdnHZC0LrAFsFFE3JrLngPulPTFiBibqx4N3BoRB+XXN0taHTgGaEk4nqcVb2pmZmZmDbMVcH0pBI8hBeaNam1E0uLAl4CLetiHFyvBGCAi7gKezseQtACwCfDn0rljgHXztIymczg2MzMzay8jSdMe3hMRzwIz87Fa7QDMRwqrZatJmiFplqQJksqh+0N9yB4p9GGl3H653iOkjPqJOvraaxyOzczMzPqX4ZImFR77lo4vRroJr6wjH6vVzsC9EfFYqfw+4AfAl4HdgHlJUzfWqbMPledyvY7S8abynGMzMzOzFpv3HVjkuTm1Vp8WEWt3UyeqlKmT8g9XlD5CmoJx+Icajvhlqe7fSDf/HUm6ga/ePpRfq4vzG84jx2ZmZmbtpQMYVqV8KNVHc6v5GimkdrukWl4+7RpgzRr6MKzQh45CWbkO1N7XXuVwbGZmZtZeJlOaWyxpeWBhqs8DrmZnYEJE1LNmcHGk90N9yIpzkZ8E3qlSbyRp6bfydI6mcDg2MzMzay/XAltIWrRQthPwJnBLdydLGgF8nhpXqci70m0F3FPqwzJ5HeNKvbWBj+VjRMQs4GZgx1KTOwETI+K1Wt6/t3nOsZmZmVl7OZO0ocblkk4kBdJRwKnF5d0kPQHcEhF7l87fGXgXuLTccF5e7Wrgj8ATpK2aDwaWI03FACAiJkq6HrhA0iG8vwnIhMIaxwDHA+Ml/YK0QcjW+bFlD699rjkcm5mZmbWRiOiQtBlwOnAVae7uaaSAXDSItNJE2c7ATRHxcpVjs4CXSTvtLQW8BUwkbfYxqUo7pwHnUtg+utTXCZJ2IG0fvT9pHeRdW7U7Hjgcm5mZmbWdiHgY2LSbOiM6KV+ji3PeArarsQ/TSVtMl7eZLte7gjRq3Cd4zrGZmZmZWeZwbGZmZmaWORybmZmZmWUOx2ZmZmZmmcOxmZmZmVnm1SrM+pBNh9e6cVEyblq1zYfMzMyspxyOzfqAekNxPec5QJuZmdXO4disxXoajHu7fYdoMzMzh2Ozlmp0MK5HT/viUG1mZu2kqeFY0o7A14G1gKHAo8DPI+KiZvbDzHpPtVDtwGxmZv1Vs0eOv0/aM/tgYBqwNXChpOER8esm98XMGqS7UWiHZzMz66uaHY6/HBHTCq/HSVqWFJodjm3AGTdtZJ+aWtEs9Vyzg7SZWf0krUbKVusC04GzgWMjYnYN524H/BD4JDATuBvYPiLeKNRZAvgJsC1pNsAzwE8i4oJ62pIUnXTj7YhYoNbr7U1NDcelYFxxH+kba2b2IT358OBAbWYDmaTFgLHAw6SMtRJwCml/i6O6OXcf4HTgJOBQYDFgUwqZUdIQ4FbgdeBA0myA1YD5622LFN7LrgL+Xsu1NkJfuCFvPdIPz8ysV/T2aLzDtpn1M/sBCwHbRcQM4MYcaEdJOimXfYik4cBpwIER8bvCob+Uqh4JLACsHRFv5rKbe9JWRNxROm8dYDjQsvvRWhqOJW1G+kSzVxd19gX2BVho6UWa1DOz5hmoUyv6k0b/fBy+zayXbQVcXwrBY4ATgY1II7PVfC0//76b9vcEflEIxnPTVtnOwBt03seGa1k4ljQCuBC4MiLO76xeRIwGRgMMG7lUZ/NSzMz6rb764cih3azfGgmMKxZExLOSZuZjnQXPz5FWEttb0v8BSwP3AgdHxO0AklYElgKmS7oG+CLwGvAH4IiIeLvWtsokCdiRlA1n9ujKe0FLwrGkxYFrgWeB3VvRB7O+xKPH1he1+79Jh/+Bobt/x39tUj962XBJkwqvR+fBxIrFSDfhlXXkY51ZBliFNC/5MOCV/HydpJUj4sVcB9I84jHAlsCnSTfnvZvr19pW2YbAR3O7LdP0cCxpMHA1adL2NsU7H83MzJql3cN/O6nng0x//bnOM2sOiz7d1SyFD5gWEWt3U6faX9vVSfl73QAWAXaMiOsAJN1OWoniAODoXAfgoYj4Vv56nKRFgSMljcqjvrW0VbYLKcBf3821NVSzNwEZBFwCrAysHxEvNfP9zfoyjx6bmVXn34116wCGVSkfSvUR5YpX8/P4SkFEzJB0D2k1imKdD9yAR5rGcSxpZYwHa2zrPTkjbg9cVpia0RLNHjk+g7Txx/eAxSV9vnDsvoiY1eT+mJmZmbWbyaS5xe+RtDywcD7WmUdII8sqlQuYk79+EqgWXivnVOrV0lbRZsCStHCViop5uq/SqzbPz78EJpYeH2lyX8zMzMza0bXAFnmqQ8VOwJvALV2cdzUpvG5SKZA0FFgLeAAgj+reSFqvuGgz0iYfT9TaVskuwL8pjDS3SlPDcUSMiAh18pjSzL6Y9UW+QcjMzHrBmcAs4HJJX8zL4o4CTi0u7ybpCUnnVF5HxCTgSuAcSd+UtA3pnsV3gN8U2j8O+Iyk8yRtLukQ4AjSDnmz6mwLSQsAXwEujohqo8pN1eyRYzMzMzNroIjoII3kzktatu1Y0oYcPypVHZTrFO0OXAGcClxKCrOb5jYr7d8FfJm0SsVVpOmyJwA/rbetbCvSfOiWrlJR0Rd2yDOzAt+YZ2ZmcysiHubDUx/KdUZUKXsd2D8/ujr3erpZVaKOtq7gw3OTW8Yjx2ZmZmZmmcOxWR/kucdmZmat4XBs1kc5IJuZmTWfw7GZmZmZWeZwbNaHefTYzMysuRyOzfo4B2QzM7PmcTg26wcckM3MzJrD4disn3BANjMzazyHYzMzMzOzzOHYrB/x6LGZmVljORyb9TMOyGZmZo3jcGzWDzkgm5lZVyStJukmSTMlPS/pOEnzdnPOCElR5TGmVG9+ScdIekLSm/n5WEkLFOqsLum6/N6zJD0r6WxJHym1daykByXNkPQfSZMk7dS73436DGrlm5tZz42bNpJNh09udTfMzKyPkbQYMBZ4GNgWWAk4hTQoelQNTRwC/L3welrp+M+A/XJb9wFrAj8GhgHfy3WGAk8DFwDPAysCPwLWkvTZiHg31xsCnJ/7OhvYARgjaXZEXFrTBfcyh2OzfswB2czMqtgPWAjYLiJmADdKGgKMknRSLuvKoxFxRxfHdwV+GxGn5tc3S1oO2I0cjiPiduD2wjnjJU0FbgA+Bdyb6x1cavsGSasD3wBaEo49rcKsn/MUCzMzK9kKuL4UgseQAvNGvdD+fMBrpbLpgLo575X8PH8N9bqr0zAOx2ZtwAHZzMwKRgIf+LNiRDwLzMzHunOepNmSXpB0qqSFSsfPBr4taX1Ji0jaENgfOL3ckKR58hzlVUjTMe4G7qpSb5CkYZJ2AzYHzqyhnw3haRVmZmZm7WUx0khuWUc+1plZwG9IUx9mABsDh5PmLG9bqHcEaRR6QqHsjIg4rkqb1wBb5K/vAbaOiDnFCpI+D0zML98FDoiIK7roZ0M5HJu1Cc8/NjPrvzTrHQY9+Vyt1YdLmlR4PToiRpfqRLW36aQ8nRDxAnBAoWi8pBeBMyStERH35/JDgd2BA4F/AJ8Gjpf0SkQcU2r2QGBxYGXSDXzXSlo/It4q1HkQ+Czphr5tgNMlzYiIizrrayM5HJu1EQdkM7MBYVpErN3F8Q5S0CwbSvUR5a5cCpxBWpHifknDSStTfDcifpfr3CrpbVKoPT0iXqqcHBGP5y/vlHQbaQWLXYFzC3XeACphf6ykocCJQEvCseccm7UZzz82MxvwJlOaWyxpeWBhSnORaxCl54+Rbsi7v1TvPtKg6wqdNhTxDPBqbqMr9wLLS5qvzr72CodjMzMzs/ZyLbCFpEULZTsBbwK31NnWDvn5nvz8TH5es1Rvrfw8pbOG8k15S5BGj7uyPjA1It6pvZu9x9MqzNqQp1eYmQ1oZwIHAZdLOpE0UjsKOLW4vJukJ4BbImLv/HoUsChpA5AZwBdI84svj4h/AETEi5KuAE6UtCBpzvEauf1LIuLl3NbPSTfX3UmayrEqcBjwJGlZOSStAJwHXAg8BSwCfBXYmbT6RUs4HJu1KQdkM7OBKSI6JG1GWlrtKlI4PY0UYIsGAcUtpSeTdsfbh7QaxbPAycAJpfO+CRxDCuDLAs8BZwHHF+pMIt2Mty+wYG7rMuCneY4xuV/Pk27UWya/fhjYJiKuqfOye43DsVkbc0A2MxuYIuJhYNNu6owovR5DHtXt5rwZpBB9SBd1um0rIl4jrXrRp3jOsVmb8w16ZmZmtXM4NhsAHJDNzMxq43BsNkA4IJuZmXXP4djMzMzMLHM4NhtAPHpsZmbWNYdjswHGAdnMzKxzDsdmZmZmZpnDsdkA5NFjMzOz6hyOzczMzMwyh2OzAcqjx2ZmZh/mcGxmZmZmljkcmw1gHj02MzP7IIdjMzMzszYjaTVJN0maKel5ScdJmrebc0ZIiiqPMVXqbivpQUlvSXpY0k5V6gyVdJ6kDkmvSfqTpCV60lYzORybDXAePTYzay+SFgPGAgFsCxwH/AA4tsYmDgHWLTyOKrW/AXAZcDOwFfA34CJJm5fauRjYGNgH2AP4LHBFD9tqmkGtemMzMzMza4j9gIWA7SJiBnCjpCHAKEkn5bKuPBoRd3Rx/Gjg1og4KL++WdLqwDHADQCS1gW2ADaKiFtz2XPAnZK+GBFja22r2TxybGYePTYzay9bAdeXQvAYUmDeaG4alrQAsAnw59KhMcC6koYW+vBiJRgDRMRdwNP5WD1tNZXDsZkBDshmZm1kJDC5WBARzwIz87HunCdptqQXJJ0qaaHCsZWA+crtA4+QcuUnOutDoV6lD7W21VT9alrFa4++PO2vG/7mmVb3o4GGA9Na3YkmG2jX3Kev96+932Sfvt4GGWjX7Ottf+1+zSu0ugMAM959+frrXjpzeI3VF5Q0qfB6dESMLrxeDJhe5byOfKwzs4DfkKYzzCDNFz6cFGK3LbRNlfY7Sse76sPH6myrqfpVOI6IJVvdh0aSNCki1m51P5ppoF2zr7f9DbRr9vW2v4F4za0QEVv2dpNVytRJeaUPLwAHFIrGS3oROEPSGhFxfxftq0p5rX2opa2m8bQKMzMzs/bSAQyrUj6U6qO5Xbk0P69ZaJsq7VdeTy/Uq9aHYaU6tbTVVA7HZmZmZu1lMqW5xZKWBxam+jzgrkTp+UngnXL7+fUc4LHO+lCoV+lDrW01lcNx3zK6+yptZ6Bds6+3/Q20a/b1tr+BeM393bXAFpIWLZTtBLwJ3FJnWzvk53sAImIWaU3iHUv1dgImRsRrhT4sk9cxBkDS2qT5xtfW2VZTKaIl0znMzMzMrAHyJiAPA/8ETiQF0lOBX0TEUYV6TwC3RMTe+fUoYFHg76Qb8r4AHApcExHbF87bABgPnE7a1GNr0sYhW0bEDYV615FWnDiENBJ8IvBSRGxYb1vN5HBsZmZm1mYkrUYKnOuS5u6eDYyKiNmFOlOA8RGxR369MymYrkxaE/lZ4ELghDzKW2z/K8CPc92nc9tjSnWGAacBXyXNVrgaOCgiptXbVjM5HJuZmZmZZZ5z3GKSdpT0V0nPSXpd0j2Sdml1v5pF0nL5ukPSIq3uTyNIGiTpCEmPS5olaaqk01rdr0aStLOke/PP9jlJF0hattX96g2SPi7pLEkP5EXyx1epI0lHSvqXpDcl3Sppjeb3du51d72SPiLp5Hz89XzNv+/PP+9afsal+r/Iv8N+3qQu9qpar1fSf0u6WtJrkv4j6S5JazW5u2YN53Dcet8HXgcOBv6HNDH9QkkHtrRXzXMy6frb2XnAQcDPgc2BI0g3RbQlSf8DXATcTlo0/nDSvLWrJbXD75zVSXPiHqPzO6mPAI4mza/7Munf+FhJyzSlh72ru+tdi/Qn04tI13oo8Dng9n78gbeWnzHw3p+u9yLNz+yvur3e/OHudtKf53ci3UB1FelP72ZtxdMqWkzS8Cpzby4E1o2IFVvUraaQtCFwJfATUkheNCLaKihL2pL0P5BPR8TDre5PM0gaA6wcEWsVyv6H9LNeLSIeaVnneoGkeSJiTv76UmB4RGxcOL4g8CJwSkQcl8sWBqYAZxVvhukParjeYcDrEfFuoewTwKPAHhHx++b2eO51d82lumOBicDXgUsj4pCmdbSX1HK9ku4AnoqIXVvQRbOmaodRnH6tHIyz+4Clmt2XZpI0L/Br4Djae1vSvYBxAyUYZ/MB5eV3pudn0c9VQkQX1gOGAH8unPMG6UPSVg3sWkN0d70RMb0YjHPZY8BM+unvsRp+xgBI2gFYFfhZY3vUWN1dbx4d/xzpd7ZZ23M47pvWIy3B0s72AxYk7eHezj4HPCbpdEkzJM2UdHl/no9Zg3OBDSV9Q9KQPIr4Y+DmAfIhYSQwG3i8VP4I1RfEbzuSPgUMpo1/j0laCDgFOCJ/+Glnn8vPi+V5ye9KelLS3i3tlVmDOBz3MZI2I83TbNvQKGkJ4Hjg+xHxTqv702DLAHsAawA7A3uS5mj+RVK/H0WtJiL+Rrrm0aQR5EeBeYHtWtitZlqMNM1gdqm8Axgsaf4W9Klp8rzyX5I+HLRkjdIm+SHwAvDHVnekCSpz5S8A/gR8CbgOOFvS1i3rlVmDDGp1B+x9kkaQ1hO8MiLOb21vGuoE4M6IuKbVHWkC5ce2EfEKgKQXSDsUbQrc1MK+NYSkTYAzSQHpWmBpYBTpA8EXq4TGdlTtZg51cayd/JS0rupG7frhV9KKpLVgN42BceNOZSDt7Ig4KX99s6RVSR8SBsLvchtAHI77CEmLk4LEs8DuLe5Ow0hanTQP9wv5Rh5If34FGCppdkS000oOHaSbWF4plE0A3gZWow3DMelPzX+NiMMrBZLuByaT/ipyeYv61SwdwKKS5i19EBgGzGzXwAgg6Tuk1Sp2iYg7W92fBvoZ6ff15MLvsXmABfLr19osNL+an28ulY8jrbRk1lY8raIPkDSYtGvM/MA2bT5/bWXSDVsTSSGig/enkEyl/W746GxlBpG20mxHI4H7iwUR8Shp+bqVWtGhJptMmkby8VL5yHysLUnanvTf72ERcXGr+9Ngq5CmCXUUHssDB+Svl2td1xpiIP4eswHMI8ctJmkQcAkpNK4fES+1uEuNNgHYpFS2JWkt3K2Bp5reo8a6Gji2tGTfF0gfEB5oXbca6hlgzWJB/vPrQqTlzNrd7aQ1b3ck3YhY+QD8ZdI87LYjaWPSXNTTI6JfboRRp32A8hrOY0jTpX4LvNz0HjXW7aTQvxlwfaF8M9r395gNYA7HrXcGKRR+D1hc0ucLx+4r72Xe3+WAOL5YludaA9zWbusck8LQQcBVkn4CLEraGGJsRExoac8a50zgNEnP8/6c42NIwbjfz03MQbdyE9JywJC8pBfANRExU9LPgKMldZBGi79P+ktdv/vLSHfXC6wAXEG6zotLv8Nejognm9XX3lLDz3hSlXPeAv4VEeOb08veU+O/6eOAkyRNB+4Gtid90N+o2f01azRvAtJikqaQ/udSzYoRMaV5vWkNSXuQdpFru01AIG3NCvyK9D+Rt0mbYRwcER0t7ViD5FU49gP2J02jmE76i8EPI6Lf/2Ugf5h7upPDK0bElPw9OJL0PVgCmAQcFBH3NaeXvae76wU2Jv33W83vI2KP3u9VY9XyM65yzhT67yYgI6jheiV9HziQFKAfBX4UEe1+D4ENQA7HZmZmZmaZb8gzMzMzM8scjs3MzMzMModjMzMzM7PM4djMzMzMLHM4NjMzMzPLHI7NzMzMzDKHYzNrC5LWktQhaUgvtPUbSef0Rr/MzKx/8TrHZtYWJF0L3B8RP+yFtkaQdnz7ZEQ8MbftmZlZ/+FwbGb9nqSVgceAT0TE473U5ljggYj4QW+0Z2Zm/YOnVZhZnyBpmKSpki4olf9V0mOSBndx+jeBfxSDsaSNJYWkzSRdKekNSY9L2lzSvJJOljRN0nN5W9yyy4DdJPn3pJnZAOJf+mbWJ0TEdGBv4OuSvgIgaU9gG2CPiJjZxembAbd3cuwsYALwVeAZ4FLgdGBRYNf8+hRJny+ddzuwNPDfPbgcMzPrpwa1ugNmZhURcb2k0cBZkp4BTgN+HhGdBV8kCfgM8MdOqvwhIk7OdacCDwGrRMSmuWwssBMpPN9ROO8hYDawDvDAXF2YmZn1Gx45NrO+5gfAG8BEYCpwTDf1FwMWAKZ1cvymwteVm+vGVQoiYg7wFLBc8aSIeBeYDixTY7/NzKwNOBybWZ8SEa8DV5MC7zkRMaubUxbMz53Vm15o++1yWfZ2oZ2iWZ2Um5lZm3I4NrM+RdLawP7AfcBRkrobuX0lPw9rQHeGAa82oF0zM+ujHI7NrM+QtCBwAXA9sAEpmI7u6pw8svwssGIv92VJYDBpiTgzMxsgHI7NrC/5MWmO77fy6hTfBLaRtEc35/0dWKuX+7I2EHS+CoaZmbUhh2Mz6xMkrQ8cDBwQES8A5FUqTgV+IemjXZx+ObCRpIV6sUtbArdExCvd1jQzs7bhHfLMrN+TND9pZYvvRsQlvdDevKQ1kY+IiM6WiDMzszbkkWMz6/fyKhQnA9/rpSZ3BN4ExvRSe2Zm1k94ExAzaxenA4MlDY2I1+ayLQF757WOzcxsAPG0CjMzMzOzzNMqzMzMzMwyh2MzMzMzs8zh2MzMzMwsczg2MzMzM8scjs3MzMzMsv8P2P5P/KFkLBYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plotting \n",
     "plt.figure(figsize=(12,4))\n",
@@ -566,7 +699,7 @@
    "source": [
     "## Contour Plot of Turbulent Intensity \n",
     "\n",
-    "Turbulent Intensity is the ratio of the magnitude of turbulent velocity to total velocity. The function `turbulent_intensity` takes the inputs of the NetCDF object, the points to caculate  over, the time index, and an optional boolian input to output `intermediate_values` used to calculate turbulent intensity. The fuction then pulls variables `'ucx'`, `'ucy'`, `'ucz'`, and `'turkin1'` as the velocity in the x, y, z and turbulence kinetic energy respectively. The function then calculates and outputs the turbulent intensity, `turbulent_intensity`, for any given time_index in the data_frame `TI`. The `TI` dataframe also includes the `x`,`y`, and `z` location. If the `intermediate_values` bollian is equal to true the turbulent kinetic energy `'turkin1'`, and velocity in the `'ucx'`, `'ucy'`, and `'ucz'` direction are also included.\n",
+    "Turbulent Intensity is the ratio of the magnitude of turbulent velocity to total velocity. The function `turbulent_intensity` takes the inputs of the NetCDF object, the points to caculate  over, the time index, and an optional boolian input to output `intermediate_values` used to calculate turbulent intensity. The fuction then pulls variables `'ucx'`, `'ucy'`, `'ucz'`, and `'turkin1'` as the velocity in the x, y, waterdepth and turbulence kinetic energy respectively. The function then calculates and outputs the turbulent intensity, `turbulent_intensity`, for any given time_index in the data_frame `TI`. The `TI` dataframe also includes the `x`,`y`, and `waterdepth` location. If the `intermediate_values` bollian is equal to true the turbulent kinetic energy `'turkin1'`, and velocity in the `'ucx'`, `'ucy'`, and `'ucz'` direction are also included.\n",
     "\n",
     "In this example it is calculating the turbulent intensity over the same contour_points used above, however it can also calculate over 'cells', the coordinate system for the raw velocity data, or 'faces', the coordinate system for the raw turbulence data. If nothing is specified for `points`, `'cells'` is the default coordinate system.    \n",
     "\n",
@@ -575,10 +708,222 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "857acdd2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "points provided\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>waterdepth</th>\n",
+       "      <th>turkin1</th>\n",
+       "      <th>ucx</th>\n",
+       "      <th>ucy</th>\n",
+       "      <th>ucz</th>\n",
+       "      <th>turbulent_intensity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.875000</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.000931</td>\n",
+       "      <td>0.003148</td>\n",
+       "      <td>1.115198</td>\n",
+       "      <td>3.600628e-04</td>\n",
+       "      <td>-0.059917</td>\n",
+       "      <td>4.101755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>17.695707</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.000931</td>\n",
+       "      <td>0.003091</td>\n",
+       "      <td>1.115302</td>\n",
+       "      <td>3.816327e-04</td>\n",
+       "      <td>0.079242</td>\n",
+       "      <td>4.060148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17.516414</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.000931</td>\n",
+       "      <td>0.003096</td>\n",
+       "      <td>1.112163</td>\n",
+       "      <td>3.603633e-04</td>\n",
+       "      <td>0.074341</td>\n",
+       "      <td>4.075892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17.337121</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.000931</td>\n",
+       "      <td>0.003013</td>\n",
+       "      <td>1.108294</td>\n",
+       "      <td>3.147688e-04</td>\n",
+       "      <td>-0.004829</td>\n",
+       "      <td>4.043806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.157828</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.000931</td>\n",
+       "      <td>0.003029</td>\n",
+       "      <td>1.110058</td>\n",
+       "      <td>2.822649e-04</td>\n",
+       "      <td>-0.012586</td>\n",
+       "      <td>4.047740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3995</th>\n",
+       "      <td>0.842172</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>1.000931</td>\n",
+       "      <td>0.000392</td>\n",
+       "      <td>1.058136</td>\n",
+       "      <td>-6.727795e-07</td>\n",
+       "      <td>-0.000070</td>\n",
+       "      <td>1.527464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3996</th>\n",
+       "      <td>0.662879</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>1.000931</td>\n",
+       "      <td>0.000393</td>\n",
+       "      <td>1.057769</td>\n",
+       "      <td>-4.857739e-07</td>\n",
+       "      <td>0.011486</td>\n",
+       "      <td>1.529358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3997</th>\n",
+       "      <td>0.483586</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>1.000931</td>\n",
+       "      <td>0.000399</td>\n",
+       "      <td>1.059054</td>\n",
+       "      <td>-3.423436e-07</td>\n",
+       "      <td>-0.005860</td>\n",
+       "      <td>1.540513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3998</th>\n",
+       "      <td>0.304293</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>1.000931</td>\n",
+       "      <td>0.000374</td>\n",
+       "      <td>1.059019</td>\n",
+       "      <td>-2.209735e-07</td>\n",
+       "      <td>0.023087</td>\n",
+       "      <td>1.491256</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3999</th>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>1.000931</td>\n",
+       "      <td>0.000350</td>\n",
+       "      <td>1.056319</td>\n",
+       "      <td>-1.155897e-07</td>\n",
+       "      <td>0.135350</td>\n",
+       "      <td>1.434725</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4000 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              x      y  waterdepth   turkin1       ucx           ucy  \\\n",
+       "0     17.875000  4.875    1.000931  0.003148  1.115198  3.600628e-04   \n",
+       "1     17.695707  4.875    1.000931  0.003091  1.115302  3.816327e-04   \n",
+       "2     17.516414  4.875    1.000931  0.003096  1.112163  3.603633e-04   \n",
+       "3     17.337121  4.875    1.000931  0.003013  1.108294  3.147688e-04   \n",
+       "4     17.157828  4.875    1.000931  0.003029  1.110058  2.822649e-04   \n",
+       "...         ...    ...         ...       ...       ...           ...   \n",
+       "3995   0.842172  1.125    1.000931  0.000392  1.058136 -6.727795e-07   \n",
+       "3996   0.662879  1.125    1.000931  0.000393  1.057769 -4.857739e-07   \n",
+       "3997   0.483586  1.125    1.000931  0.000399  1.059054 -3.423436e-07   \n",
+       "3998   0.304293  1.125    1.000931  0.000374  1.059019 -2.209735e-07   \n",
+       "3999   0.125000  1.125    1.000931  0.000350  1.056319 -1.155897e-07   \n",
+       "\n",
+       "           ucz  turbulent_intensity  \n",
+       "0    -0.059917             4.101755  \n",
+       "1     0.079242             4.060148  \n",
+       "2     0.074341             4.075892  \n",
+       "3    -0.004829             4.043806  \n",
+       "4    -0.012586             4.047740  \n",
+       "...        ...                  ...  \n",
+       "3995 -0.000070             1.527464  \n",
+       "3996  0.011486             1.529358  \n",
+       "3997 -0.005860             1.540513  \n",
+       "3998  0.023087             1.491256  \n",
+       "3999  0.135350             1.434725  \n",
+       "\n",
+       "[4000 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Calculating turbulent intensity \n",
     "TI=d3d.turbulent_intensity(\n",
@@ -617,23 +962,230 @@
    "metadata": {},
    "source": [
     "## Comparing Face Data to Cell Data \n",
-    "In Delft3D there is a staggered grid where some variables are stored on the faces and some are stored on the cells. The `d3d.variable_interpolation` function allows you to interpolate face data onto the cell locations or vice versa. For this example, we input the variable names, `'ucx'`, `'ucy'`,`'ucz'`, and `'turkin1'`, which are pulled from the NetCDF object (`d3d_data`) and are to be interpolated on to the coordinate system `'faces'`. The output is a data frame, `Var`, with the interpolated data. \n",
+    "In Delft3D there is a staggered grid where some variables are stored on the faces and some are stored on the cells. The `d3d.variable_interpolation` function allows you to linearly interpolate face data onto the cell locations or vice versa. For this example, we input the variable names, `'ucx'`, `'ucy'`,`'ucz'`, and `'turkin1'`, which are pulled from the NetCDF object (`d3d_data`) and are to be interpolated on to the coordinate system `'faces'`. The output is a data frame, `Var`, with the interpolated data. \n",
     "\n",
     "Near the boundaries linear linterpolate will sometimes return negative values. When calculating turbulent intensity negative turbulent kinetic energy values (`turkin1`) will have and invalid answered. To filter out any negative values the index of where the negative numbers are located are calculated as `neg_index`. If the value is close to 0 (greater then -1.0e-4) the values is replaced with zero, if not it is replaced with nan. `zero_bool` determines if the negative number is close to 0. `zero_ind` is the location of the negative numbers to be replaced with zero and `non_zero_bool` are the locations of the numbers to replace with nan. To calculate turbulent intensity the magnitude of the velocity is calculated with `'ucx'`, `'ucy'`, `'ucz'` and saved as `u_mag`.Turbulent intensity is then calculated with `u_mag` and `turkin1` and saved as `turbulent_intensity`. \n",
     "\n",
+    "Linear interpolate can also leave nan values near the edges of data. If you want fill these in using nearest interpolate set the values edges to equal `'nearest'` otherwis this option is dufalted to `'none'`. \n",
+    "\n",
     "The data can be called, as shown, with `Var`.  We now have `'ucx'` and  `'turkin1'` on the same grid so then can be compared and used in calculations. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "941d76b7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>waterdepth</th>\n",
+       "      <th>turkin1</th>\n",
+       "      <th>ucx</th>\n",
+       "      <th>ucy</th>\n",
+       "      <th>ucz</th>\n",
+       "      <th>u_mag</th>\n",
+       "      <th>turbulent_intensity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.125</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>0.199485</td>\n",
+       "      <td>0.008562</td>\n",
+       "      <td>0.717881</td>\n",
+       "      <td>-1.531373e-07</td>\n",
+       "      <td>0.056840</td>\n",
+       "      <td>0.720128</td>\n",
+       "      <td>10.491394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.375</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>0.200008</td>\n",
+       "      <td>0.008906</td>\n",
+       "      <td>0.689645</td>\n",
+       "      <td>-3.833700e-07</td>\n",
+       "      <td>-0.011832</td>\n",
+       "      <td>0.689746</td>\n",
+       "      <td>11.171384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.125</td>\n",
+       "      <td>1.375</td>\n",
+       "      <td>0.199485</td>\n",
+       "      <td>0.009280</td>\n",
+       "      <td>0.717881</td>\n",
+       "      <td>-4.313024e-07</td>\n",
+       "      <td>0.056840</td>\n",
+       "      <td>0.720128</td>\n",
+       "      <td>10.922269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.625</td>\n",
+       "      <td>1.125</td>\n",
+       "      <td>0.200056</td>\n",
+       "      <td>0.008780</td>\n",
+       "      <td>0.694164</td>\n",
+       "      <td>-6.750081e-07</td>\n",
+       "      <td>0.004593</td>\n",
+       "      <td>0.694179</td>\n",
+       "      <td>11.020928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.375</td>\n",
+       "      <td>1.375</td>\n",
+       "      <td>0.200008</td>\n",
+       "      <td>0.008906</td>\n",
+       "      <td>0.689645</td>\n",
+       "      <td>-1.064686e-06</td>\n",
+       "      <td>-0.011832</td>\n",
+       "      <td>0.689746</td>\n",
+       "      <td>11.171384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5755</th>\n",
+       "      <td>17.625</td>\n",
+       "      <td>4.625</td>\n",
+       "      <td>1.794649</td>\n",
+       "      <td>0.000914</td>\n",
+       "      <td>1.245117</td>\n",
+       "      <td>9.389975e-04</td>\n",
+       "      <td>0.033098</td>\n",
+       "      <td>1.245558</td>\n",
+       "      <td>1.982237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5756</th>\n",
+       "      <td>17.375</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.794848</td>\n",
+       "      <td>0.000903</td>\n",
+       "      <td>1.222989</td>\n",
+       "      <td>2.199022e-04</td>\n",
+       "      <td>0.000277</td>\n",
+       "      <td>1.222989</td>\n",
+       "      <td>2.006523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5757</th>\n",
+       "      <td>17.875</td>\n",
+       "      <td>4.625</td>\n",
+       "      <td>1.799824</td>\n",
+       "      <td>0.000908</td>\n",
+       "      <td>1.255866</td>\n",
+       "      <td>8.894604e-04</td>\n",
+       "      <td>0.010782</td>\n",
+       "      <td>1.255913</td>\n",
+       "      <td>1.958613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5758</th>\n",
+       "      <td>17.625</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.794638</td>\n",
+       "      <td>0.000907</td>\n",
+       "      <td>1.245292</td>\n",
+       "      <td>2.690449e-04</td>\n",
+       "      <td>0.033248</td>\n",
+       "      <td>1.245736</td>\n",
+       "      <td>1.973690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5759</th>\n",
+       "      <td>17.875</td>\n",
+       "      <td>4.875</td>\n",
+       "      <td>1.799828</td>\n",
+       "      <td>0.000900</td>\n",
+       "      <td>1.256067</td>\n",
+       "      <td>2.501023e-04</td>\n",
+       "      <td>0.011193</td>\n",
+       "      <td>1.256117</td>\n",
+       "      <td>1.950496</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5760 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           x      y  waterdepth   turkin1       ucx           ucy       ucz  \\\n",
+       "0      0.125  1.125    0.199485  0.008562  0.717881 -1.531373e-07  0.056840   \n",
+       "1      0.375  1.125    0.200008  0.008906  0.689645 -3.833700e-07 -0.011832   \n",
+       "2      0.125  1.375    0.199485  0.009280  0.717881 -4.313024e-07  0.056840   \n",
+       "3      0.625  1.125    0.200056  0.008780  0.694164 -6.750081e-07  0.004593   \n",
+       "4      0.375  1.375    0.200008  0.008906  0.689645 -1.064686e-06 -0.011832   \n",
+       "...      ...    ...         ...       ...       ...           ...       ...   \n",
+       "5755  17.625  4.625    1.794649  0.000914  1.245117  9.389975e-04  0.033098   \n",
+       "5756  17.375  4.875    1.794848  0.000903  1.222989  2.199022e-04  0.000277   \n",
+       "5757  17.875  4.625    1.799824  0.000908  1.255866  8.894604e-04  0.010782   \n",
+       "5758  17.625  4.875    1.794638  0.000907  1.245292  2.690449e-04  0.033248   \n",
+       "5759  17.875  4.875    1.799828  0.000900  1.256067  2.501023e-04  0.011193   \n",
+       "\n",
+       "         u_mag  turbulent_intensity  \n",
+       "0     0.720128            10.491394  \n",
+       "1     0.689746            11.171384  \n",
+       "2     0.720128            10.922269  \n",
+       "3     0.694179            11.020928  \n",
+       "4     0.689746            11.171384  \n",
+       "...        ...                  ...  \n",
+       "5755  1.245558             1.982237  \n",
+       "5756  1.222989             2.006523  \n",
+       "5757  1.255913             1.958613  \n",
+       "5758  1.245736             1.973690  \n",
+       "5759  1.256117             1.950496  \n",
+       "\n",
+       "[5760 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "variables= ['turkin1', 'ucx', 'ucy', 'ucz']\n",
     "\n",
-    "Var= d3d.variable_interpolation(d3d_data, variables, points='faces')\n",
+    "Var= d3d.variable_interpolation(d3d_data, variables, points='faces', edges = 'nearest')\n",
     "\n",
     "# Replacing negative numbers close to zero with zero\n",
     "neg_index=np.where(Var['turkin1']<0)# Finding negative numbers\n",
@@ -671,31 +1223,52 @@
    "id": "947e8188",
    "metadata": {},
    "source": [
-    "When plotting data using `d3d.variable_interpolation`it is helpful to define points to interpolate onto as the `'cell'` and `'face'` grids contain a 3 dimensional grid of points. In this next example we will use the `d3d.variable_interpolation` function to plot a contour plot of tubulent intesity normal to turbine (y-z cross section) `n` turbine diameters downstream of the turbine.\n",
+    "When plotting data using `d3d.variable_interpolation`it is helpful to define points to interpolate onto as the `'cell'` and `'face'` grids contain a 3 dimensional grid of points. In this next example we will use the `d3d.variable_interpolation` function to plot a contour plot of tubulent intesity normal to turbine (y-waterdepth cross section) `n` turbine diameters downstream of the turbine.\n",
     "\n",
-    "As stated in the intro, the turbine is located at 6m along the length `turbine_x_loc` with a diameter of 0.7m  (`turbine_diameter`). Similar to the previous example, we input the variable names `variables` `'ucx'`, `'ucy'`,`'ucz'`, and `'turkin1'`, which are pulled from the NetCDF object (`d3d_data`). Unlike the previous example, the points are defined as `sample_points` created using `create_points` from `x_sample`, `y_sample`, and `z_sample` with `x_sample` at a constant point of 1 (`N`) turbine diameters downstream of the turbine (6.7m) and `y_sample` and `z_sample` being arrays between the minimum and maximum values for that flume dimension. The interpolated data is the saved from `d3d.variable_interpolation` as `Var_sample`and used to calculate turbulent intensity as in the previous example. The data is then plotted along the y and z axis."
+    "As stated in the intro, the turbine is located at 6m along the length `turbine_x_loc` with a diameter of 0.7m  (`turbine_diameter`). Similar to the previous example, we input the variable names `variables` `'ucx'`, `'ucy'`,`'ucz'`, and `'turkin1'`, which are pulled from the NetCDF object (`d3d_data`). Unlike the previous example, the points are defined as `sample_points` created using `create_points` from `x_sample`, `y_sample`, and `waterdepth_sample` with `x_sample` at a constant point of 1 (`N`) turbine diameters downstream of the turbine (6.7m) and `y_sample` and `waterdepth_sample` being arrays between the minimum and maximum values for that flume dimension. The interpolated data is the saved from `d3d.variable_interpolation` as `Var_sample`and used to calculate turbulent intensity as in the previous example. The data is then plotted along the y and waterdepth axis."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "75433d08",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 1, 2]\n",
+      "points provided\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x316.8 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "turbine_x_loc= 6 \n",
     "turbine_diameter= 0.7\n",
     "N=1\n",
     "x_sample = turbine_x_loc+N*turbine_diameter\n",
     "y_samples = np.linspace(ymin, ymax, num=40)\n",
-    "z_samples = np.linspace(zmin,zmax, num=256)\n",
+    "waterdepth_samples = np.linspace(waterdepth_min,waterdepth_max, num=256)\n",
     "\n",
     "variables= ['turkin1', 'ucx', 'ucy', 'ucz']\n",
-    "sample_points = d3d.create_points(x_sample, y_samples, z_samples) \n",
+    "sample_points = d3d.create_points(x_sample, y_samples, waterdepth_samples) \n",
     "\n",
-    "Var_sample= d3d.variable_interpolation(d3d_data, variables, points= sample_points)\n",
+    "Var_sample= d3d.variable_interpolation(d3d_data, variables, points= sample_points, edges = 'nearest')\n",
     "\n",
     "#root mean squared calculation \n",
     "Var_sample['u_mag']=d3d.unorm(\n",
@@ -710,7 +1283,7 @@
     "plt.figure(figsize=(10,4.4))\n",
     "contour_plot = plt.tricontourf(\n",
     "    Var_sample.y, \n",
-    "    Var_sample.z, \n",
+    "    Var_sample.waterdepth, \n",
     "    Var_sample.turbulent_intensity,\n",
     "    vmin=min_plot_TI, \n",
     "    vmax=max_plot_TI,\n",
@@ -731,6 +1304,14 @@
    "source": [
     "This sample data used in this example is not spatial or temporally resolved and is for demonstration purposes only. In this example there are 5 sigma layers. The low level of discretization can be seen in in the sharp edges in the turbulent intensity plot above around the turbine."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c8f6be8-c9fe-4803-88dc-cf2914555496",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index d2f5ef194..0ac411155 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -8,7 +8,8 @@
 
 def get_all_time(data):
     '''
-    Returns all of the data time stamps in seconds.
+    Returns all of the time stamps from a D3D simulation passed to the function 
+    as a NetCDF object (data)
     
     Parameters
     ----------
@@ -30,6 +31,7 @@ def get_all_time(data):
 
     return seconds_run
 
+
 def index_to_seconds(data, time_index):
     '''
     The function will return 'seconds_run' if passed a 'time_index' 
@@ -53,13 +55,14 @@ def index_to_seconds(data, time_index):
 
 def seconds_to_index(data, seconds_run):
     '''
-    The function will return a 'time_index' if passed a 'seconds_run'
-
+    The function will return the nearest 'time_index' in the data if passed an
+    integer number of 'seconds_run'
+    
     Parameters
     ----------
     data: NetCDF4 object 
-    A NetCDF4 object that contains spatial data, e.g. velocity or shear
-    stress, generated by running a Delft3D model.  
+        A NetCDF4 object that contains spatial data, e.g. velocity or shear
+        stress, generated by running a Delft3D model.  
     seconds_run: int, float
         A positive integer or float that represents the amount of time in seconds 
         passed since starting the simulation.
@@ -72,12 +75,13 @@ def seconds_to_index(data, seconds_run):
     '''
     return _convert_time(data, seconds_run=seconds_run)
 
+
 def _convert_time(data, time_index=None, seconds_run=None):
     '''
-    Converts an index to seconds or seconds to a index. The user 
+    Converts a time index to seconds or seconds to a time index. The user 
     must specify 'time_index' or 'seconds_run' (Not both). The function 
-    will returns 'seconds_run' if passed a 'time_index' or will return the closest
-    'time_index' if passed a number of 'seconds_run'.
+    will returns 'seconds_run' if passed a 'time_index' or will return the 
+    closest 'time_index' if passed a number of 'seconds_run'.
 
     Parameters
     ----------
@@ -145,9 +149,10 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1):
     Returns
     -------
     layer_data: DataFrame
-        DataFrame with columns of "x", "y", and "z" location on specified layer, 
-        the variable values "v", the "time" the simulation has run. The value
-        "z" is the depth from the zero-water level at the surface. 
+        DataFrame with columns of "x", "y", "waterdepth", and "waterlevel" location
+        of the specified layer, variable values "v", and the "time" the 
+        simulation has run. The waterdepth is measured from the water surface and the
+        "waterlevel" is the water level diffrencein meters from the zero water level. 
     '''
     
     assert isinstance(time_index, int), 'time_index  must be an int'
@@ -163,6 +168,7 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1):
     x=np.ma.getdata(data.variables[coords[0]][:], False) 
     y=np.ma.getdata(data.variables[coords[1]][:], False)
     
+    
     if type(var[0][0]) == np.ma.core.MaskedArray: 
         max_layer= len(var[0][0])
         
@@ -176,7 +182,7 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1):
         dimensions= 2
         v= np.ma.getdata(var[time_index,:], False)
         
-    #depth 
+    #waterdepth 
     cords_to_layers= {'FlowElem_xcc FlowElem_ycc':{'name':'laydim', 
                                     'coords':data.variables['LayCoord_cc'][:]},
                        'FlowLink_xu FlowLink_yu': {'name':'wdim', 
@@ -187,7 +193,7 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1):
     cord_sys= cords_to_layers[layer_dim]['coords']
     layer_percentages= np.ma.getdata(cord_sys, False) #accumulative
     bottom_depth=np.ma.getdata(data.variables['waterdepth'][time_index, :], False)
-    water_level= np.ma.getdata(data.variables['s1'][time_index, :], False)
+    waterlevel= np.ma.getdata(data.variables['s1'][time_index, :], False)
     if layer_dim == 'FlowLink_xu FlowLink_yu':
         #interpolate 
         coords = str(data.variables['waterdepth'].coordinates).split()
@@ -202,7 +208,7 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1):
         
         bottom_depth_wdim = interp.griddata(points_laydim, bottom_depth,
                                             points_wdim)
-        water_level_wdim = interp.griddata(points_laydim, water_level,
+        water_level_wdim= interp.griddata(points_laydim, waterlevel,
                                             points_wdim)
         
         idx_bd= np.where(np.isnan(bottom_depth_wdim))
@@ -210,33 +216,36 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1):
         for i in idx_bd: 
             bottom_depth_wdim[i]= interp.griddata(points_laydim, bottom_depth,
                                               points_wdim[i], method='nearest')
-            water_level_wdim[i]= interp.griddata(points_laydim, water_level,
+            water_level_wdim[i]= interp.griddata(points_laydim, waterlevel,
                                               points_wdim[i], method='nearest')
- 
-    depth=[]
+
+    
+    waterdepth=[]
     
     if dimensions== 2:
         if layer_dim == 'FlowLink_xu FlowLink_yu': 
-            z = [bottom_depth_wdim]-water_level_wdim
+            z = [bottom_depth_wdim]
+            waterlevel=water_level_wdim
         else:
-            z = [bottom_depth]-water_level
+            z = [bottom_depth]
     else:
         if layer_dim == 'FlowLink_xu FlowLink_yu': 
-            z = [bottom_depth_wdim*layer_percentages[layer_index]]-water_level_wdim 
+            z = [bottom_depth_wdim*layer_percentages[layer_index]]
+            waterlevel=water_level_wdim
         else:
-            z = [bottom_depth*layer_percentages[layer_index]]-water_level
-    depth=np.append(depth, z)
+            z = [bottom_depth*layer_percentages[layer_index]]
+    waterdepth=np.append(waterdepth, z)
 
     time= np.ma.getdata(data.variables['time'][time_index], False)*np.ones(len(x))
 
-    layer= np.array([ [x_i, y_i, z_i, v_i, t_i] for x_i, y_i, z_i, v_i, t_i in
-                     zip(x, y, depth, v, time)]) 
-    layer_data = pd.DataFrame(layer, columns=['x', 'y', 'z', 'v', 'time'])
+    layer= np.array([ [x_i, y_i, d_i, w_i, v_i, t_i] for x_i, y_i, d_i, w_i, v_i, t_i in
+                     zip(x, y, waterdepth, waterlevel, v, time)]) 
+    layer_data = pd.DataFrame(layer, columns=['x', 'y', 'waterdepth','waterlevel', 'v', 'time'])
 
     return layer_data
 
 
-def create_points(x, y, z):
+def create_points(x, y, waterdepth):
     '''
     Turns three coordinate inputs into a single output DataFrame of points. 
     In any order the three inputs can consist of 3 points, 2 points and 1 array,
@@ -249,13 +258,13 @@ def create_points(x, y, z):
         x values to create points.
     y: float, array or int
         y values to create points.
-    z: float, array or int
-        z values to create points.
+    waterdepth: float, array or int
+        waterdepth values to create points.
 
     Returns
     -------
     points: DateFrame 
-        DataFrame with columns x, y and z points. 
+        DataFrame with columns x, y and waterdepth points. 
         
     Example 
     -------
@@ -264,10 +273,10 @@ def create_points(x, y, z):
     
     x=np.array([1,2])
     y=np.array([3,4,5])
-    z= 6
-    d3d.create_points(x,y,z)
+    waterdepth= 6
+    d3d.create_points(x,y,waterdepth)
     
-       x    y    z
+       x    y    waterdepth
     0  1.0  3.0  6.0
     1  2.0  3.0  6.0
     2  1.0  4.0  6.0
@@ -280,15 +289,15 @@ def create_points(x, y, z):
                                                      +' or array')
     assert isinstance(y, (int, float, np.ndarray)), ('y must be a int, float'
                                                      +' or array')
-    assert isinstance(z, (int, float, np.ndarray)), ('z must be a int, float'
+    assert isinstance(waterdepth, (int, float, np.ndarray)), ('waterdepth must be a int, float'
                                                      +' or array')
     
     directions = {0:{'name':  'x',
                      'values': x},
                   1:{'name':  'y',
                      'values': y},
-                  2:{'name':  'z',
-                     'values': z}}
+                  2:{'name':  'waterdepth',
+                     'values': waterdepth}}
 
     for i in directions:
         try:
@@ -322,11 +331,11 @@ def create_points(x, y, z):
                     
         x_new = directions[0]['values']
         y_new = directions[1]['values']
-        z_new = directions[2]['values']
+        depth_new = directions[2]['values']
             
-        request= np.array([ [x_i, y_i, z_i] for x_i, y_i, z_i in zip(x_new, 
-                                                             y_new, z_new)]) 
-        points= pd.DataFrame(request, columns=[ 'x', 'y', 'z'])
+        request= np.array([ [x_i, y_i, depth_i] for x_i, y_i, depth_i in zip(x_new, 
+                                                             y_new, depth_new)]) 
+        points= pd.DataFrame(request, columns=[ 'x', 'y', 'waterdepth'])
         
     elif N_points == 1: 
         # treat as plane
@@ -354,7 +363,7 @@ def create_points(x, y, z):
     return points 
 
 
-def variable_interpolation(data, variables, points='cells'):
+def variable_interpolation(data, variables, points='cells', edges= 'none'):
     '''
     Interpolate multiple variables from the Delft3D onto the same points. 
 
@@ -370,14 +379,17 @@ def variable_interpolation(data, variables, points='cells'):
         The points to interpolate data onto.
           'cells'- interpolates all data onto the Delft3D cell coordinate system (Default)
           'faces'- interpolates all dada onto the Delft3D face coordinate system 
-          DataFrame of x, y, and z coordinates - Interpolates data onto user 
+          DataFrame of x, y, and waterdepth coordinates - Interpolates data onto user 
           povided points. Can be created with `create_points` function.
+    edges: sting: 'nearest'
+        If edges is set to 'nearest' the code will fill in nan values with nearest 
+        interpolation. Otherwise only linear interpolarion will be used. 
   
     Returns
     -------
     transformed_data: DataFrame  
         Variables on specified grid points saved under the input variable names 
-        and the x, y, and z coordinates of those points. 
+        and the x, y, and waterdepth coordinates of those points. 
     '''
     
     assert isinstance(points, (str, pd.DataFrame)),('points must be a string ' 
@@ -389,30 +401,32 @@ def variable_interpolation(data, variables, points='cells'):
 
     data_raw = {}
     for var in variables:
-        var_data_df = get_all_data_points(data, var,time_index=-1)           
+        var_data_df = get_all_data_points(data, var,time_index=-1)   
+        var_data_df=var_data_df.loc[:,~var_data_df.T.duplicated(keep='first')]        
         data_raw[var] = var_data_df 
     if type(points) == pd.DataFrame:  
         print('points provided')
     elif points=='faces':
-        points = data_raw['ucx'][['x','y','z']]
+        points = data_raw['ucx'][['x','y','waterdepth']]
     elif points=='cells':
-        points = data_raw['turkin1'][['x','y','z']]
+        points = data_raw['turkin1'][['x','y','waterdepth']]
     
     transformed_data= points.copy(deep=True)
     
     for var in variables :    
-        transformed_data[var] = interp.griddata(data_raw[var][['x','y','z']],
-                                        data_raw[var][var], points[['x','y','z']])
-        idx= np.where(np.isnan(transformed_data[var]))
+        transformed_data[var] = interp.griddata(data_raw[var][['x','y','waterdepth']],
+                                        data_raw[var][var], points[['x','y','waterdepth']])
+        if edges == 'nearest' :
+            idx= np.where(np.isnan(transformed_data[var]))
         
-        if len(idx[0]):
-            for i in idx[0]: 
-                transformed_data[var][i]= (interp
-                                          .griddata(data_raw[var][['x','y','z']], 
-                                           data_raw[var][var],
-                                           [points['x'][i],points['y'][i],
-                                            points['z'][i]], method='nearest'))
-            
+            if len(idx[0]):
+                for i in idx[0]: 
+                    transformed_data[var][i]= (interp
+                                              .griddata(data_raw[var][['x','y','waterdepth']], 
+                                                data_raw[var][var],
+                                                [points['x'][i],points['y'][i],
+                                                points['waterdepth'][i]], method='nearest'))
+     
     return transformed_data
 
 
@@ -436,8 +450,10 @@ def get_all_data_points(data, variable, time_index=-1):
     Returns
     -------
     all_data: DataFrame 
-        Dataframe with columns x, y, z, variable, and time. The value
-        "z" is the depth from the zero-water level at the surface.
+        Dataframe with columns x, y, waterdepth, waterlevel, variable, and time.
+        The waterdepth is measured from the water surface and the "waterlevel" is 
+        the water level diffrence in meters from the zero water level.
+ 
     '''  
     
     assert isinstance(time_index, int), 'time_index  must be a int'
@@ -464,7 +480,8 @@ def get_all_data_points(data, variable, time_index=-1):
         
     x_all=[]
     y_all=[]
-    z_all=[]
+    depth_all=[]
+    water_level_all=[]
     v_all=[]
     time_all=[]
     
@@ -474,14 +491,17 @@ def get_all_data_points(data, variable, time_index=-1):
 
         x_all=np.append(x_all, layer_data.x)
         y_all=np.append(y_all, layer_data.y)
-        z_all=np.append(z_all, layer_data.z)
+        depth_all=np.append(depth_all, layer_data.waterdepth)
+        water_level_all=np.append(water_level_all, layer_data.waterlevel)
         v_all=np.append(v_all, layer_data.v)
         time_all= np.append(time_all, layer_data.time)
     
-    known_points = np.array([ [x, y, z, v, time] for x, y, z, v, time in zip(x_all, y_all, 
-                                                                z_all, v_all, time_all)])
+    known_points = np.array([ [x, y, waterdepth, waterlevel, v, time] 
+                    for x, y, waterdepth, waterlevel, v, time in zip(x_all, y_all, 
+                                depth_all, water_level_all, v_all, time_all)])
     
-    all_data= pd.DataFrame(known_points, columns=['x','y','z',f'{variable}', 'time'])
+    all_data= pd.DataFrame(known_points, columns=['x','y','waterdepth', 'waterlevel'
+                                                  ,f'{variable}', 'time'])
 
     return all_data
 
@@ -515,18 +535,18 @@ def turbulent_intensity(data, points='cells', time_index= -1,
     Returns
     -------
       TI_data: Dataframe
-        If intermediate_values is true all values are output 
+        If intermediate_values is true all values are output. 
         If intermediate_values is equal to false only turbulent_intesity and 
-        x, y, and z varibles are output 
+        x, y, and z variables are output.  
             x- position in the x direction 
             y- position in the y direction 
-            z- position in the z direction
+            waterdepth- position in the vertical direction
             turbulet_intesity- turbulent kinetic energy divided by the root
                                 mean squared velocity
             turkin1- turbulent kinetic energy 
             ucx- velocity in the x direction 
             ucy- velocity in the y direction 
-            ucx- velocity in the z direction 
+            ucz- velocity in the vertical direction 
     '''
     
     assert isinstance(points, (str, pd.DataFrame)),('points must a string or'
@@ -553,22 +573,22 @@ def turbulent_intensity(data, points='cells', time_index= -1,
     if type(points) == pd.DataFrame:  
         print('points provided')
     elif points=='faces':
-        points = TI_data_raw['turkin1'].drop(['turkin1'],axis=1)
+        points = TI_data_raw['turkin1'].drop(['waterlevel','turkin1'],axis=1)
     elif points=='cells':
-        points = TI_data_raw['ucx'].drop(['ucx'],axis=1)
+        points = TI_data_raw['ucx'].drop(['waterlevel','ucx'],axis=1)
        
     TI_data = points.copy(deep=True)
 
     for var in TI_vars:    
-        TI_data[var] = interp.griddata(TI_data_raw[var][['x','y','z']],
-                                TI_data_raw[var][var], points[['x','y','z']])
+        TI_data[var] = interp.griddata(TI_data_raw[var][['x','y','waterdepth']],
+                                TI_data_raw[var][var], points[['x','y','waterdepth']])
         idx= np.where(np.isnan(TI_data[var]))
         
         if len(idx[0]):
             for i in idx[0]: 
-                TI_data[var][i]= interp.griddata(TI_data_raw[var][['x','y','z']], 
+                TI_data[var][i]= interp.griddata(TI_data_raw[var][['x','y','waterdepth']], 
                                 TI_data_raw[var][var],
-                                [points['x'][i],points['y'][i], points['z'][i]],
+                                [points['x'][i],points['y'][i], points['waterdepth'][i]],
                                 method='nearest')
 
     u_mag=unorm(np.array(TI_data['ucx']),np.array(TI_data['ucy']), 


Test patch:
diff --git a/mhkit/tests/river/test_io.py b/mhkit/tests/river/test_io.py
index 74f22a447..714711ff1 100644
--- a/mhkit/tests/river/test_io.py
+++ b/mhkit/tests/river/test_io.py
@@ -142,14 +142,14 @@ def test_create_points(self):
     def test_variable_interpolation(self):
         data=self.d3d_flume_data
         variables= ['ucx','turkin1']
-        transformes_data= river.io.d3d.variable_interpolation(data, variables, points= 'faces')
+        transformes_data= river.io.d3d.variable_interpolation(data, variables, points= 'faces', edges='nearest')
         self.assertEqual(np.size(transformes_data['ucx']), np.size(transformes_data['turkin1']))
-        transformes_data= river.io.d3d.variable_interpolation(data, variables, points= 'cells')
+        transformes_data= river.io.d3d.variable_interpolation(data, variables, points= 'cells', edges='nearest')
         self.assertEqual(np.size(transformes_data['ucx']), np.size(transformes_data['turkin1']))        
         x=np.linspace(1, 3, num= 3)
         y=np.linspace(1, 3, num= 3)
-        z=1 
-        points= river.io.d3d.create_points(x,y,z)
+        waterdepth=1 
+        points= river.io.d3d.create_points(x,y,waterdepth)
         transformes_data= river.io.d3d.variable_interpolation(data, variables, points= points)
         self.assertEqual(np.size(transformes_data['ucx']), np.size(transformes_data['turkin1']))
         
@@ -178,10 +178,10 @@ def test_turbulent_intensity(self):
         time_index= -1
         x_test=np.linspace(1, 17, num= 10)
         y_test=np.linspace(3, 3, num= 10)
-        z_test=np.linspace(1, 1, num= 10)
+        waterdepth_test=np.linspace(1, 1, num= 10)
        
-        test_points = np.array([ [x, y, z] for x, y, z in zip(x_test, y_test, z_test)])
-        points= pd.DataFrame(test_points, columns=['x','y','z'])
+        test_points = np.array([ [x, y, waterdepth] for x, y, waterdepth in zip(x_test, y_test, waterdepth_test)])
+        points= pd.DataFrame(test_points, columns=['x','y','waterdepth'])
         
         TI= river.io.d3d.turbulent_intensity(data, points, time_index)
 
@@ -194,15 +194,15 @@ def test_turbulent_intensity(self):
             TI_data= points.copy(deep=True)
         
         for var in TI_vars:    
-            TI_data[var] = interp.griddata(TI_data_raw[var][['x','y','z']],
-                                TI_data_raw[var][var], points[['x','y','z']])
+            TI_data[var] = interp.griddata(TI_data_raw[var][['x','y','waterdepth']],
+                                TI_data_raw[var][var], points[['x','y','waterdepth']])
             idx= np.where(np.isnan(TI_data[var]))
         
             if len(idx[0]):
                 for i in idx[0]: 
-                    TI_data[var][i]= interp.griddata(TI_data_raw[var][['x','y','z']], 
+                    TI_data[var][i]= interp.griddata(TI_data_raw[var][['x','y','waterdepth']], 
                                 TI_data_raw[var][var],
-                                [points['x'][i],points['y'][i], points['z'][i]],
+                                [points['x'][i],points['y'][i], points['waterdepth'][i]],
                                 method='nearest')
         
         u_mag=river.io.d3d.unorm(TI_data['ucx'],TI_data['ucy'], TI_data['ucz'])
@@ -212,15 +212,15 @@ def test_turbulent_intensity(self):
         assert_array_almost_equal(TI.turbulent_intensity, turbulent_intensity_expected, decimal = 2)     
         
         TI = river.io.d3d.turbulent_intensity(data, points='faces')
-        TI_size = np.size(TI)
+        TI_size = np.size(TI['turbulent_intensity'])
         turkin1= river.io.d3d.get_all_data_points(data, 'turkin1',time_index)
-        turkin1_size= np.size(turkin1)
+        turkin1_size= np.size(turkin1['turkin1'])
         self.assertEqual(TI_size, turkin1_size)
         
         TI = river.io.d3d.turbulent_intensity(data, points='cells')
-        TI_size = np.size(TI)
+        TI_size = np.size(TI['turbulent_intensity'])
         ucx= river.io.d3d.get_all_data_points(data, 'ucx',time_index)
-        ucx_size= np.size(ucx)
+        ucx_size= np.size(ucx['ucx'])
         self.assertEqual(TI_size, ucx_size)
 if __name__ == '__main__':
     unittest.main()