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

Issue:
Small changes for documentation



Discussion / hints:

Fix patch:
diff --git a/examples/Delft3D_example.ipynb b/examples/Delft3D_example.ipynb
index 6f362b2cd..8378d9081 100644
--- a/examples/Delft3D_example.ipynb
+++ b/examples/Delft3D_example.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Delft3D IO Module\n",
     "\n",
-    "The following example will familiarize the user with using the Delft3D (d3d) toolbox in MHKiT-Python. `d3d` can be used to plot the data from a NetCDF output from Delft3D [SNL-D3D-CEC-FM](https://github.com/MHKiT-Software/MHKiT-Python). The example will walk through a flume case with a turbine. The flume is 18 m long, 4 m wide and 2m deep with the turbine placed at 6 m along the length and 3 m along the width. The turbine used for this simulation has a circular cross-section with a diameter of 0.7m, a thrust coefficient of 0.72, and a power coefficient of 0.45. The simulation was run with 5 depth layers and 5-time intervals taking about 4 min.\n",
+    "The following example will familiarize the user with using the Delft3D (d3d) toolbox in MHKiT-Python. `d3d` can be used to plot the data from a NetCDF output from Delft3D [SNL-D3D-CEC-FM](https://github.com/MHKiT-Software/MHKiT-Python). The example will walk through a flume case with a turbine. The flume is 18 m long, 4 m wide and 2m deep with the turbine placed at 6 m along the length and 3 m along the width. The turbine used for this simulation has a circular cross-section with a diameter of 0.7m, a thrust coefficient of 0.72, and a power coefficient of 0.45. The simulation was run with 5 depth layers and 5-time intervals over 4 min.\n",
     "\n",
     "This example will show how to create a centerline plot at a desired depth for different variables outputs of Delft3D using matplotlib. It will also show how to make a contour plot of a given plane or depth for the variable output by Delft3D and how to use those variables to calculate turbulence intensity. This module can be helpful to visualize the wake of a turbine and help predict how a turbine will affect the surrounding area. \n",
     "\n",
@@ -29,7 +29,7 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import netCDF4\n",
-    "plt.rcParams.update({'font.size': 15}) # Set fount size of plots title and labels "
+    "plt.rcParams.update({'font.size': 15}) # Set font size of plots title and labels "
    ]
   },
   {
@@ -125,20 +125,85 @@
     "        print(f'\"{var}\": {d3d_data[var].long_name}')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "356efd94",
+   "metadata": {},
+   "source": [
+    "## Seconds Run and Time Index\n",
+    "\n",
+    "The `'time_index'` can be used to pull data from different instances in time. For the example data included there are 5-time steps meaning we have indexes in the range 0 and to 4. Each time index has an associated `'seconds_run'` that quantifies the number of seconds that passed from the start of the simulation. To get all of the `'seconds_run'` for a data set use the function `get_all_time` the output is an array of all the `'seconds_run'` for the input data set.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5e0dabc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  0.  60. 120. 180. 240.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "time= d3d.get_all_time(d3d_data)\n",
+    "print(time)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd9ee80b",
+   "metadata": {},
+   "source": [
+    "To convert from a `'seconds_run'` to a `'time_index'` or vice versa. The inputs are a netcdf data set, then a `'time_index'`. This example shows what happens if you input a `'seconds_run'` that is close to but not exactly a `'seconds_run'` associated with a `'time_idex'`. The function will throw a warning and return the closest time index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ebbdc284",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-4-f8084d84fcf8>:2: UserWarning: Warning: seconds_run not found. Closest time stampfound {times[idx]}\n",
+      "  time_index=d3d._convert_time(d3d_data,seconds_run=seconds_run)\n"
+     ]
+    }
+   ],
+   "source": [
+    "seconds_run = 62\n",
+    "time_index=d3d._convert_time(d3d_data,seconds_run=seconds_run)\n",
+    "print(time_index)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c2134260",
    "metadata": {},
    "source": [
     "## Dataframe from NetCDF\n",
-    "First the variable to be pulled from the NetCDF file are defined and saved under the name `variable`. For this example we will use the variable `'ucx'`, the velocity in the x-direction (see available data in previous block output). The `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 first index is 0 and the last is 2 since there are 3-time steps. The user can also use -1 to select the last valid time_index in the data set, this 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.   \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",
     "\n",
-    "The limits `max_plot_v` and `min_plot_v` are defined to bound the plots of the variable's data in the next 3 examples."
+    "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."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "197ab8f9",
    "metadata": {},
    "outputs": [
@@ -146,32 +211,32 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "           x      y         z       ucx\n",
-      "0      0.125  1.125  0.204971  0.515257\n",
-      "1      0.375  1.125  0.205174  0.500605\n",
-      "2      0.125  1.375  0.204971  0.515257\n",
-      "3      0.625  1.125  0.205059  0.507960\n",
-      "4      0.375  1.375  0.205174  0.500605\n",
-      "...      ...    ...       ...       ...\n",
-      "5755  17.625  4.625  1.798838  0.773001\n",
-      "5756  17.375  4.875  1.799833  0.772610\n",
-      "5757  17.875  4.625  1.801090  0.771865\n",
-      "5758  17.625  4.875  1.798838  0.773048\n",
-      "5759  17.875  4.875  1.801090  0.771911\n",
+      "           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",
       "\n",
-      "[5760 rows x 4 columns]\n"
+      "[5760 rows x 5 columns]\n"
      ]
     }
    ],
    "source": [
     "# Getting variable data \n",
     "variable= 'ucx' \n",
-    "var_data_df= d3d.get_all_data_points(d3d_data, variable, time_index=1)\n",
+    "var_data_df= d3d.get_all_data_points(d3d_data, variable, time_index=4)\n",
     "print(var_data_df)\n",
     "\n",
     "# Setting plot limits \n",
-    "max_plot_v= 1.25\n",
-    "min_plot_v=0.5"
+    "max_plot_vel= 1.25\n",
+    "min_plot_vel=0.5"
    ]
   },
   {
@@ -185,17 +250,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "26e1daba",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -227,96 +285,52 @@
        "      <th>0</th>\n",
        "      <td>17.875000</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>1.02322</td>\n",
+       "      <td>0.999069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>17.695707</td>\n",
+       "      <td>17.512755</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>1.02322</td>\n",
+       "      <td>0.999069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>17.516414</td>\n",
+       "      <td>17.150510</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>1.02322</td>\n",
+       "      <td>0.999069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>17.337121</td>\n",
+       "      <td>16.788265</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>1.02322</td>\n",
+       "      <td>0.999069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>17.157828</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.02322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>0.842172</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.02322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>0.662879</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.02322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>0.483586</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.02322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>0.304293</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.02322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>0.125000</td>\n",
+       "      <td>16.426020</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>1.02322</td>\n",
+       "      <td>0.999069</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>100 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "            x    y        z\n",
-       "0   17.875000  3.0  1.02322\n",
-       "1   17.695707  3.0  1.02322\n",
-       "2   17.516414  3.0  1.02322\n",
-       "3   17.337121  3.0  1.02322\n",
-       "4   17.157828  3.0  1.02322\n",
-       "..        ...  ...      ...\n",
-       "95   0.842172  3.0  1.02322\n",
-       "96   0.662879  3.0  1.02322\n",
-       "97   0.483586  3.0  1.02322\n",
-       "98   0.304293  3.0  1.02322\n",
-       "99   0.125000  3.0  1.02322\n",
-       "\n",
-       "[100 rows x 3 columns]"
+       "           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"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# Use rectangular grid min and max to find flume centerline\n",
     "xmin=var_data_df.x.max()\n",
     "xmax=var_data_df.x.min()\n",
     "\n",
@@ -327,13 +341,13 @@
     "zmax=var_data_df.z.min()\n",
     "\n",
     "# Creating one array and 2 points \n",
-    "x = np.linspace(xmin, xmax, num=100)\n",
+    "x = np.linspace(xmin, xmax)\n",
     "y = np.mean([ymin,ymax])\n",
     "z = np.mean([zmin,zmax])\n",
     "\n",
     "# Creating an array of points  \n",
     "cline_points = d3d.create_points(x, y, z)\n",
-    "cline_points"
+    "cline_points.head()"
    ]
   },
   {
@@ -341,28 +355,28 @@
    "id": "c11a827b",
    "metadata": {},
    "source": [
-    "Next the variable `ucx` is interpolated over the `cline_points` using scipy interp function and saved as `cline_variable`. The results are then plotted for velocity in the x direction, `ucx`, along the length of the flume, `x`."
+    "Next the variable `ucx` is interpolated onto the created `cline_points` using scipy interp function and saved as `cline_variable`. The results are then plotted for velocity in the x direction, `ucx`, along the length of the flume, `x`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "199785ce",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5, 1.0, 'Centerline Velocity On Flow Element Center, X-component')"
+       "Text(0.5, 1.0, 'Centerline Velocity at: 240.0 s')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x360 with 1 Axes>"
       ]
@@ -374,23 +388,21 @@
     }
    ],
    "source": [
-    "cline_variable = interp.griddata(var_data_df[['x','y','z']], \n",
-    "                     var_data_df[variable],\n",
-    "                     cline_points[['x','y','z']]) \n",
+    "# Interpolate raw data onto the centerline\n",
+    "cline_variable = interp.griddata(\n",
+    "    var_data_df[['x','y','z']], \n",
+    "    var_data_df[variable],\n",
+    "    cline_points[['x','y','z']]\n",
+    ") \n",
+    "\n",
     "# Plotting\n",
-    "Type='Centerline'\n",
     "plt.figure(figsize=(12,5))\n",
     "plt.plot(x, cline_variable)\n",
+    "\n",
     "plt.grid()\n",
     "plt.xlabel('x (m)')\n",
-    "\n",
-    "y_name= f'{d3d_data.variables[variable].standard_name}'\n",
-    "y_label=' '.join([x.capitalize() for x in y_name.split('_')])\n",
-    "plt.ylabel(f'{y_label} [{d3d_data.variables[variable].units}]')\n",
-    "\n",
-    "title_name= f'{d3d_data.variables[variable].long_name}'\n",
-    "title_label=' '.join([x.capitalize() for x in title_name.split(' ')])\n",
-    "plt.title(f'{Type} {title_label}')"
+    "plt.ylabel('$u_x$ [m/s]' )\n",
+    "plt.title(f'Centerline Velocity at: {var_data_df.time[1]} s')"
    ]
   },
   {
@@ -400,20 +412,30 @@
    "source": [
     "## Contour Plot for a Given Sigma Layer \n",
     "\n",
-    "Sometimes it will be 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'`, and '`v`' as the length, width, and value 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'`, `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",
     "\n",
-    "To plot the data the maximum and minimum, `max_plot_v` and `min_plot_v`, 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."
+    "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."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "26daf026",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "Text(0.5, 1.0, 'Velocity on Layer 2 at Time: 240.0 s')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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uVepuWKstYBqF5REL5VuRPty9Qvo39A/gSkrLnNHEUm6FY6p5a6KN7Wps/3fSmsSzSCcbXgys3E078/3bIE3JupT0wXgWcAtp+kcj79to0jdFD5H+3b6Rfz4aGN7M76xvvvk2cG6VdV3NrA9IOhg4Dvh81B+Z7FiSHgAWjIhq83PNzKwf5RNYDyJ9+/hJ0pr/G9bZ5zOk84TWJ12B8h+kaXHHRWF1JEnnkM6rKVstIqYW6g0nDTx9hTQ97XJg/4h4ubiT0pU7jyGtKPUE6eTpC2kjT6sw6yNKV8v6T+D+IR6MNyadSPiDdvfFzGyIWoM0Lep25p0a1Z0dgZVJAzyPkk5e/0m+37ZUdyrzXwV3WunxhcCqpGlG7+Z2J1K4eqSk9YBLSFfG3D/3+QJJXRFxDW3ikWOzXlK6HO86pHV0dwC+FhENzTfuJDkUr0yax7kk6evt19rbKzOzoUfSApFXgJJ0MemqtBvW2ecD8f4J7JWyvYDfAKMiretdGTn+ZESM7aatdUjT5DaIiJty2dqkZV2/FHmlIklXk660unFh3ytIV/dcr7mj7jtercKs9zYgzY/eGDh6KAbj7Ajg16STkrZ1MDYza4/owdKo5WCc3Zvvm70C5BbAC5VgnNu/k3Qy9xYAedWojYA/lPadAKzT6Ko4/cHTKsx6KdKldM9pczfart6ohJmZDTqfJ02JKK+8srqkmaQrTN5FuvrojYXtY3j/iqdFD+VtkL5pXKhKvYdIg7ef4P01zVtqUIXjhUcsFouv0KvlT82sCUsPK1+h2syss/zjgdemR8QH2t2PL2y4aHS90tiA79/vn/MAaRWdivERMb4v+5PX3f8x6UJQxSU57yVNj3iQdBGvHwDXSlovjw5DWqlqRpVmu4CPFepQpV5XaXvLDapwvPgKS7HBGTu0uxtmHW/jkdU+8JuZdZ7/Wu36p9rdB4CuV97lj1eMrF8R+MRHn3+ruzm/vSVpYdJ0h9dJ64e/JyL+u1T3L6Sg/CPSyhTvVa3WdJXy8mPVKG8Zzzk2MzMzM+C9K4GeR17xIqpf8v09EfEmcAWwZqG4i3Rhp7IRvD9S3FUoK9eBGhflagWHYzObh0eNzcyGtJNJqy9tXVy3uAHFkd6pvD+3uKg4F/lxYE6VemNI85wfaeK5+5TDsZmZmZkh6YfAfsAuEXFLg/ssRlqB4u5C8ZXACnkd40q9saT5xlcCRMRs4AZg+1KTOwK3RcSrPT2O3hpUc47NrH951NjMbPCTtDjpghoAKwJLS9ouP74iImZJegy4MSL2yPvsDBxLWn3pWUmfKzT5eES8lJdXuxz4HfAYMJI0J3lF0jr/AETEbXkN4/MkHcj7FwG5pbLGcfYTYLKkX5IuELJlvm3eJy9EDzkcm5mZmXWW5YGLSmWVx6NJV7MbBixY2L5pvt8134p2I4Xm2cBLwGH5Od4CbiNd7GNKaZ+dSFM0zqJw+ehihYi4JYf2Y4B9SOsg79zOq+OBw7GZZR41NjPrDBExjfdXfahVZ1Tp8a7MH4rL+7wFbNNgH2aQQnX5MtPlehNJo8YDhuccm5mZmZllDsdm5lFjMzOzzOHYbIhzMDYzM3ufw7GZmZmZWeZwbDaEedTYzMxsXg7HZmZmZmaZw7HZEOVRYzMzs/k5HJsNQQ7GZmZm1Tkcmw0xDsZmZma1ORybDSEOxmZmZt1zODYbIhyMzczM6nM4NutwG4+c6mBsZjaESPq4pN9I+pukuZImN7DPwpJOkHSzpDclRTd1l8vt/zPXnSrpm1XqbSPprlznZUlXSVqisD1q3Gb3+OD7wLB2PrmZ9S+HYjOzIWkNYEvgdmDhBvdZHNgTuBO4Fdi4WiVJSwM3Aa8D+wHTgdXLzyNpT+BU4HjgIGCZ3GYxe65T5SkuA/7aYJ/7hcOxWYdyMDYzG7Iui4hLASRdDIyst0NEzJC0bESEpH2pEY6BHwGLAGMj4s1cdkOxgqSRwMnAfhHx28KmP5We8/bSfmvnvl5Qr7/9ydMqzDqQg7GZ2dAVEe/2cL+aUykKdgPOLATjanbI9+c22YWdgDdIo8dt43Bs1mEcjM3MrD9IGg0sD8yQdIWktyW9JOkkScVpFZ8FHgb2kPSMpDmS7pD0+W7aFrA9cGlEzOrXA6nD4disgzgYm5lZP1oh3x8PPAtsDhwL7AMcU6q3KnAYcAjwZdKI8FWSPlij7fWBjwAT+r7bzfGcY7MO4WBsZjZ4vfruolzx+uoN1n5+pKQphYLxETG+P/pVUhlUfSAivp1/niRpKeBHksblUd8FgCWB7SPiKgBJtwJPAfsCh1dp+2tAF3B1fx5AIxyOzczMzAaX6RExtg3P+0q+v6FUPgk4ClgZuL9Qb3KlQkTMlHQ3aWWLeUgaBmwLXBIRb/dxn5vmaRVmHcCjxmZm1gKPA9XCq/J95UTAh4AolBfrVTtZcBPgA7R5lYoKh2MzMzMzqyuP6l7L/Mu8bQLMAh7Ljy8nBeGNKhUkDQfWAv5WpemvAf+kMNLcTp5WYTbIedTYzMyKJC1OuggIwIrA0pK2y4+viIhZkh4DboyIPQr7bQEsAXw6P67sc1dEPJV/Phq4RdLZpJHeTwGHAj+JiNkAETFF0qXAmZIOJV0o5GBgDnBaqa+LAF8BzunpEnR9zeHYbBBzMDYzsyqWBy4qlVUejwamkTLggqU6vwZWqrLPbsA5ABFxp6QvAz8DdgZeBH6aHxftApwAnES6+t5fgY0joqtUbwtgOANglYoKh2OzQcih2MzMaomIacw/37dcZ1QjZTX2vZo6q0pExOukJd72qVNvInX62mqec2xmZmZmljkcmw0yHjU2MzPrPw7HZoOIg7GZmVn/cjg2GyQcjM3MzPqfw7HZIOBgbGZm1hoOx2YDnIOxmZlZ6zgcm5mZmZllDsdmA5hHjc3MzFrL4dhsgHIwNjMzaz2HY7MByMHYzMysPRyOzczMzMwyh2OzAcajxmZmZu3jcGxmZmbWQSR9XNJvJP1N0lxJkxvcb7iksyV1SXpV0u8lLVeqs7CkIyQ9JunNfH+UpEUKddaQdJWk5yTNlvS0pDMkfajU1lGS7pc0U9JrkqZI2rFPXoReGNbuDpjZ+zxqbGZmfWANYEvgdmDhJva7EFgV2BN4FzgOmAisX6jzc2Bv4DDgXmBN4BhgBPC9XGc48CRwHvAcMBo4ElhL0mci4p1cb2ngHOBBYC6wHTBB0tyIuLiJfveptoZjSSsCDwNLAEtFxOvt7I+ZmZkNDpOmj5nnsQcX5nFZRFwKIOliYGS9HSStA2wGbBARN+WyZ4E7JH0xIq7LVXcGfh0RJ+XHN+Q893VyOI6IW4FbC81PlvQMcA3wKeCeXO+AUjeukbQG8E1gaIZj4ATgdVI4NjMzs0GiHE57olqg7Wm7xf2GelCOiHd7sNsWwAuVYJzbuVPSk3lbJRwvBLxa2ncGoDrtv5zv641kv9xAnX7VtnAsaX1gc+BYUkg2G9KG+h9zs97oi6Bmrddf71ulXf9dbcoYoNoL9lDeVnEG8J+Srgf+Bvw/YB/g1PKOkhYgZc3RpOkYdwF3Vqk3DFgS2ArYFNipNwfSW20Jx5IWBE4BjiZ92jAzsxZzoLRO19jv+PX93o9GzHxn0Sb+TV4/UtKUQsH4iBjfyy4sQ/VM1gV8rPD4UGAx4JZC2a8i4ugq+15BmqoBcDewZXlUW9LngNvyw3eAfSNiYrOd70vtGjneG1gUOI00R8VsSPPoxtDhQGpmfWB6RIzth3ajSplK5QcBuwD7Af8H/BvwE0kvR8QRpX33A5YFViGdwHelpHUj4q1CnfuBz5BO6NsKOFXSzIi4oA+Op0daHo7zkiA/AXaJiDlS91NUJO0F7AWw2AeX7P8OmpllDrJmNoR0AR+oUj6CPKIsaSRpZYrvRsRv8/abJL1NCrWnRsSLlR0j4tH84x2SbiatYLEzcFahzhtAZRT8OknDSatkDJ1wDPwUuCMirmikcv6aYDzAiDHLV/tEYzaoedS4fzjYmpk1ZSrzLtlWMYa0nBuk6RULAfeV6txLypQrAS9SRUQ8JekV5p2iUc09wG6SFoqIOQ31vI+1NBzn5Tl2B74gaUQuXjzfD8/r2r3Zyj6Z2cDjYGtm1nJXAodLWi8ibgGQNJYUZq/MdZ7K92uSTq6rWCvfT6vVuKRVgeVIo8fdWRd4pl3BGFo/crwK6RPHbVW2PQOcSVp42mxIGMqjxg7AZmb9Q9LipIuAAKwILC1pu/z4ioiYJekx4MaI2AMgIm6TdDVwnqQDef8iILdU1jiOiBckTQSOk7Qoac7xp4FxwEUR8VJ+/l+QTq67gzQlYzXgYOBxYEKusxJwNnA+8ARptYqvklaq2KfvX5XGtToc3wJsVCrbHDiE9CY+0eL+mFkfc+g1M2u75YGLSmWVx6NJI7zDgAVLdXYCTibNCV4AuBzYv1TnW8ARufzDwLPAb0jnk1VMIZ2MtxdpAYangUuAn+U5xpBC83OkE/VWyI8fBLZqdOptf2lpOI6I6cDkYpmkUfnHm32FPLPBx2HYzGxgiYhp1LkoR0SMqlI2A9gt32rtNxM4MN9q1ZlAHiHups6rpFUvBpx2XyHPbMgabFMqHILNzGwoaHs4johzgHPa3A0zq8KB2MzMhpq2h2Ozoaqdlzd16DUzM6vO4diszXobkh10zczM+o7DsdkA4ZBrZmbWfgu0uwNmZmZmZgOFw7GZmZmZWeZwbGZmZmaWORybmZmZmWUOx2ZmZmZmmcOxmZmZmVnmcGxmZmbWQSR9XNJvJP1N0lxJkxvcb7iksyV1SXpV0u8lLVel3taS7pf0lqQHJe3Yn221msOxmZmZWWdZA9gSeCTfGnUhsCGwJ7Ar8BlgYrGCpPWAS4AbgC2AvwAXSNq0H9tqKV8ExMzMzKyzXBYRlwJIuhgYWW8HSesAmwEbRMRNuexZ4A5JX4yI63LVw4GbImL//PgGSWsARwDX9HVb7eCRYzMzM7MOEhHv9mC3LYAXKmE2t3Mn8GTehqRFgI2AP5T2nQCsI2l4P7TVcg7HZmZmZjYGmFql/KG8DWBlYKEq9R4iZcpP9ENbLedpFWZmZmZt9tachXjonx9stPpISVMKj8dHxPhedmEZYEaV8i7gY4U6VKnXVdrel221nMOxmZmZ2eAyPSLG9kO7UaVMVcrLj1WlvC/bailPqzAzMzOzLmBElfIRvD+621UoK9ehVK+v2mo5h2MzMzMzm8r784GLivOHHwfmVKk3BniX95eN68u2Ws7TKsysW03MgbMWWW2FF9rdBTPrPFcCh0taLyJuAZA0ljRH+EqAiJgt6QZge+A3hX13BG6LiFf7oa2Wczg260AOtJ2tne+vg7nZwCdpcdJFQABWBJaWtF1+fEVEzJL0GHBjROwBEBG3SboaOE/SgaTR2+OAWwrrEgP8BJgs6Zeki3psmW+bVyr0ZVvt4HBsNkg48NpA0MrfQwdxsx5bHrioVFZ5PBqYRsqAC5bq7AScDJxFmnp7ObB/sUJE3JKD9jHAPqS1i3eOiPJFO/qyrZZyODZrE4dds+715b8RB20bSiJiGu+v+lCrzqgqZTOA3fKtu30nUroUdH+21WoOx2Zt4GBs1lr98W/Ogdus9SSVr6jXqIPzh4a6HI7NWszB2Kwz9MW/ZQdss6ZtB9wLzGywvoD1gZ+TppPU5XBs1kIOxmZW1Fd/ExyybYjZJyLubKSipGHA28007nBs1iIOxmbWX3rz98XB2gaZo4Bnmqg/N+/zXKM7OBybtYCDsZkNVL39++Rwba0UEUc1WT9I4bhhDsdm/cih2Mw6ncO1DTSSRuTVMnrE4djMzMzapq8HERy2hw5J+wBLRcTx+fGnSespf0jSfcDWEdHMFAwgLcpsZv3Ao8ZmZq330D8/+N6tN3VsUNiPeVet+B/S3OKvkzLuz3vSqEeOzdronecWn69s2IdnNVW/J7p7DjOzTuHw2/H+BXgYQNIHgHWBTSJisqS3gVN70qjDsVk/aOQPcq2g21cBuCfP3RMO2mZm1iazgYXzzxsBs4Cb8+NXgBE9adTh2Mx6pdmg7TBtZmZ95E7gu5KeAfYHroqIuXnbx2hi+baimuFYUkOLK5cE8M2IeLgnnTHrBL0ZNR4K6h27w7OZWe9JWh04BVgHmAGcARxVCI+19lsDOBlYjzQSexFwUES8XqjzJWD33PZKud1xNdrbBvgh8Mnc3l3AthHxRt4eNbrydkQsUucwfwD8Gbgf+EfuU8WOwF/r7F9VdyPHY4ErgJcabEvAN4AletIRs07gYNx7Ds9mZr0jaRngOuBBYGtgZeBE0klqh3Wz33BgEvAIKVwuBxwPfAj4SqHq5sCngOuBnbppb0/SvN/jgYOAZYCNmTd/rlNl18toINhGxIPAxyUtB7yS1zSuOBD4Z702qqk3reLoJi/P982edMLMrFGNfrhwiDazIWxvYDFgm4iYCVwraWlgnKTjc1k138n7fbmyTrCkV4BLJY2NiCm53kER8YO8fetqDUkaSRqB3i8iflvY9KdivYi4vbTf2sBI4IIa7T4P/CXfro2I1yPi5XK9iLi/xjHW1d1SbrsBjzfaUES8k/d5sqedMet0HjVunXeeW7zmzcysw20BXF0KwRNIwXeDbvb7NDCldAGNa0jTZreqFETEuw30YYd8f24DdYt2At4gjR5X8z3S4O6vgemSrpX0X5JWafJ5aqoZjiPi3GpJvDt5n67ed8us8ziUDRwOy2bW4cYAU4sFEfE0ac7vmG72WxR4u1T2DvAusFqTffgsaZm1PSQ9I2mOpDskfb7WDpIEbA9cGhFVv/6LiD9ExK6kqR4bALcCuwBTJT0i6WRJX5S0UJP9fY9XqzBro8Wfmffz6ayPNPJh3PpadwHZ0zPMrBVizgLNfFgfKWlK4fH4iBhfeLwM6SS8sq68rZbHgJ0lLRQRc3LZWsCCwLKNdi5bAViVNMf5YODlfH+VpFUiotqlDNcHPkIa5e5Wnl98R74dKWkFYEvSCPclpKx9HXB5RJzVTMcbDseSdgC+CqxI+mRR7uTazTyx2VBS7Q9eORjXKityeG69Wv9ZOTSbWRtNj4ixdepUWwVCNcorfkuatnCKpHGkE/J+BczNt2YsACwJbB8RVwFIuhV4CtgXOLzKPl8jBfirm3wuIuKfwFnAWXnUeANSUD44lzesoXAs6ee58btInyrKQ+5mVkOjwbgRDs8Dh0OzmQ1gXVS/AMZwqo8oAxARUyXtRTqR7j9J0ynGkwJ1tZHe7ryS7ycX2p8p6W5g9XLlvLDDtsAlEdGrnJlHva/LtwOa3b/RkePdgR9HxM+afQKzoazV81k9TaP9HJrNbACYSmlusaSPkpbbnVp1jywizpJ0PrAK8CIwnTQl4owm+/AQKVSrVC5S6C7bBPgANVap6I6kVak+syEi4spm22s0HM8B7m62cTObX09HjfvyuRyaW8+h2cxa6ErgIElLRcRruWxH4E3gxno7R8RbpAtrIOlbpCkSf2iyD5cDR5Iu63xFbms4aQ7zL6rU/xppXeLJjT6BpH8lhenVmD+EQwrnCzbTaWg8HP83sKeka0sLLJvZIOQR5oGjHJodls2sD5xOupzyHyUdR7qU8jjgpOLybpIeA26MiD3y46WBHwM3kVap2Ih0FbpvR8Qrhf1WAj6THy4MrC5pO+CNykhtREyRdClwpqRDSSPQB5MGXE8rdlbSIqSLjJzT4DJxFWfl9v6dPpz221A4jojjJf2CtEzGjcw/XyUi4pC+6JCZmZmZ9VxEdEnahHR1ustIue1kUkAuGsa8I6tzgf8HfJu0JvLfSSfUTSzttxFwduHx9vn2FDCqUL4LcAJwErA46ap3G1dZ9ncL0nzouqtUlKxGuhR10yfwdafRE/K+DvwXaY7IksyfzAOoG47zp4rvk5b2WIL0Iv4vcHxvJ1+bmZmZWZIvrbxxnTqjSo/fADZtoO1zgHMaqPc6sE++dVdvItWnRdRzJ/AvPdivW41Oq/g5cCGwd2HuSk8sB9xA+hQxA1ib9ClmBdKyHmZmZmZmjdgLuEDSLFK+nFGuUOtiIt1pNBwvDZzVy2BMRPymVHRDnt/yXUn7eT6zmZmZmTVoOjANOK+bOv12Qt4lpPkl1zf7BA14mTSZ28zMzMysUb8D1iGtftHaE/JIVyr5eb403ySqD1tf0eiTSloQWARYk3Q25a89amxmZmZmTdiItJLG+X3ZaKPhuLIg8+75VtbsOnJvkMIxpKHwg2pVzFdq2QtgsQ8u2cRTmJmZmVkHmwb0+fqXjYbj0X38vJ8nLemxNnAEaamR71SrGBHjSZcuZMSY5T26bGZmZmaQBlePknRfREzrq0YbXef4qb56wtzePfnHWyRNB86VdGJEPN6Xz2NmZmZmHeso0lJuj0iaRvVpv2s322jNcJxXkXitmbnAPdkHqATl0YDDsZmZmZk14u/51qe6GznuIp0BeGcjDeWT7LpIlxO8p071onXz/ZNN7GNmZmZmQ1hE7NYf7XYXjgV8XtLIBttaoF4FSVcB1wEPkC5RuC7pmt0XekqFmZmZmbVbvTnHJ/Xx890F7Eq67vY7wBPAD4HT+/h5zMzMzKzDSNofmBARLza5z/kRMb2R+t2N9o7u4e3+Wg1GxOER8cmIWDIiRkTEmhFxSkTMaaSzZtZ3Zn3k3fdu1l7DPjzrvZuZWV+QtLqk6yXNkvScpKPzFNh6+42VdI2klyW9Iuk6SZ8t1TlK0v2SZkp6TdIUSTuW6qwh6ar83LMlPS3pDEkfaratkpOBlZp4HRbM+/xLo/vUHDnu6xUqzCyZ9ZF3WfyZurOQ+vT5bGBw+DWzVpC0DGka64PA1sDKwImkQdHDutnvo3m/e4Bv5uKDgGskfaqQDZcGzsntzwW2AyZImhsRF+c6w0nnk50HPEcaQD0SWEvSZyLinSbamqebwM8kvdLoy9Fgvfc0us6xmfXAsA/P4p3nFm/JczkEDxwOwWbWZnsDiwHbRMRM4Nq8otg4Scfnsmq2ApbK+80AkHQrMB3YEvg1QEQcUNrvGklrkAL1xbnOrcCthTqTJT0DXAN8irx4QyNtldxEuvDcB7p9Bebf57VGKzscm/WzagG5t6PHDsIDg0OwmQ1QWwBXl0LwBOA4YAPgshr7LUQ6J+z1QtnruazeCOzLwMIN1KHBelXrRMSGdfbtNYdjswHCgXfgcgg2s0FmDDCpWBART0ualbfVCseXAEcDJ0r6aS47grRU70XlypKGAUuSRpw3BXaqUmcBUt4cDfyctDjDfMsEN9JWqzgcm7VArdFjGzgcgM1sEBkpaUrh8fiIGF94vAxVrhZHCrnL1Go0Ip6TtBFwObB/Ln4e2CwiXirWlfQ54Lb88B1g34iYWKXZK4DN8s93A1tGxDz/ATbRVks0FI4l/TtwRflgzKxxrZx/bNU5AJvZQLXA2zQz3W56RIytU6fa1YpVozxtTCtJXEwKsXvm4u8Cf5H0+Yh4ulD9ftKF30aQRntPlTQzIi4oNbsfsCywCulkwCslrRsRb/WgrZZodOT4UuBFSecB50TEQ/3YJ7OO5YDc/xyAzczoIgXNsuFUH1GuOIiUDberLLMraRLwKHAg748mExFvAJXR6+skDSfNaZ4n0EbEo/nHOyTdTFrBYmfgrGbbapVGP6KsDIwHdgD+Luk2Sd/OZz6ambVMcU3gajczM2MqaW7xe/IybUvkbbWMAR4oXn8iIt4mXdl45TrPeQ/wUUkL1aqQl4J7BfhYb9uqRVKvR6AaCscRMS0ijoyI0cCXgMdICyo/L+l/8/wUsyFvtRVeqFvHAa45Dr9mZk27EthM0lKFsh2BN4Ebu9nvKeCTkt5bKULSIsAngWl1nnNd4JnuLuwmaVVgOdLoca/a6sb1kkaWnrfmPOtqmj4hLyImAZMkfZi0LMjXgZ0lPQ38D3BKYWFnsyFntRVe4KF/frDd3RiwHHDNzPrd6aQpEH+UdBxppHYccFJxeTdJjwE3RsQeuegM0lzjP0n6FWmO8neBD5FmECBpJeBs4HzgCdIKE18lrS6xT6HtX5BOrruDNJVjNeBg4HFSfmy4rSb9lDQ1Yxvgn8D3c1srNtpA0+FY0gbAbsC2wBzgNGAi6UzEo0gTqndutl2zoaRT5x47+JqZtV9EdEnaBDiVtGzbDNI3/uNKVYeRLqhR2e9uSZuTrmT3v7n4fuBLEfG3/HgG6Yp3hwEr5McPAltFxBWFtqeQTsbbC1gUeJq0VNzP8hzjZtpq5tgvl9QF3EzKqX8C1mymjUZXq1gJ+Fa+jQImkw72jxExO1e7XtJtwO+a6YBZJ2pk9LhaQK4VLlsZpB1wzcwGv4h4ENi4Tp1RVcquB67vZp9XgV0aeP4J5BHi3rbVDEm7A4eSsuqngYsiov6cx4JGR46fICX7c4CzIqLWXJEHqLKws5lV12gQdWA1M+uZ8rkgnvbW8f4D+GpEPCBpNGmKyLhm1k1uNBx/Gbiq3jrHEfEI4JPzzPDcYzOzdunu5Ojytkdr1LPBKSK+Uvj5SUlfJE3/ndhoGw2F457O+zAzMzNrRCOr/ZgVSbqWNCe6cnsgIt4s1omI6ZK+1Ey7vny0WT+q/LH3CLKZDRUOudZCT5GWfdsLWBx4V9ITvB+W/x4RF5cDcz0Ox2Yt4CkWZjYYONjaYBIRewJIEuny1J8CNgS2BzYnrcRxcbPtOhybtYgDspn1N4dbG4oiIoBH8u1iSeOAPwI/6kl7DsdmLeSAbGbdcbg16708z/gg4CfAps3u73Bs1mIOyGadxYHWrD0krU06Ce+NKpsfBdbpSbsOx2Zt4IBs1n4OtWaD3u2kk/CmkU7A+z/g78DzpKs1v9KTRh2Ozdqk2sL0tf6zdpC2ocwh1sxqGE06Ce/f8u1rwMqAgNeBPXrSqMOx2QDRzKL1ZQ7P1moOrGbWbhHxFGk5t8sqZZIWA0YCz0fEOz1p1+HYrAP0VVBxyB78HFrNDEDS6sAppHm3M4AzgKMiYm43+4wDjqyx+UcR8bNc7xzgW1XqrBYRUwvtDQd+CXwFWAC4HNg/Il4uPe/WwDGk5dieyP28sM4hVpXXNP5HT/atcDg2s/cMpGBVDOoDqV9mZgOdpGWA64AHga1JUw1OJAXUw7rZ9QzgqlLZV4BDgCtL5VOB3Upl00qPLwRWBfYE3gWOI13Gef1CX9cDLgF+BewPbAlcIKkrIq7ppq/9xuHYzAYkB2Izsx7bG1gM2CYiZgLXSloaGCfp+Fw2n4h4BnimWCbpcGBqRNxXqv5GRNxeqwOS1gE2AzaIiJty2bPAHZK+GBHX5aqHAzdFxP758Q2S1gCOANoSjhdox5OamZmZWb/ZAri6FIInkALzBo02ImlZ4EvABT3swwuVYAwQEXcCT+ZtSFoE2Aj4Q2nfCcA6eVpGyzkcm5mZmXWWMaRpD++JiKeBWXlbo7YDFiKF1bLVJc2UNFvSLZLKoXu+PmQPFfqwcm6/XO8hUkb9RBN97TMOx2ZmZmaDy0hJUwq3vUrblyGdhFfWlbc1aifgnoh4pFR+L/AD4MvA14EFSVM31m6yD5X7cr2u0vaW8pxjMzMzszZbcA4s+ey7jVafHhFj69SJKmWqUT5/RelDpCkYh8zXcMR/l+r+hXTy349IJ/A124fyY3Wzf7/zyLGZmZlZZ+kCRlQpH0710dxqdiCF1LpLquXl064A1mygDyMKfegqlJXrQON97VMOx2ZmZmadZSqlucWSPgosQfV5wNXsBNwSEc2sGVwc6Z2vD1lxLvLjwJwq9caQln4rT+doCYdjMzMzs85yJbCZpKUKZTsCbwI31ttZ0ijgczS4SkW+Kt0WwN2lPqyQ1zGu1BsLfCxvIyJmAzcA25ea3BG4LSJebeT5+5rnHJuZmZl1ltNJF9T4o6TjSIF0HHBScXk3SY8BN0bEHqX9dwLeAS4uN5yXV7sc+B3wGOlSzQcAK5KmYgAQEbdJuho4T9KBvH8RkFsKaxwD/ASYLOmXpAuEbJlvm/fw2HvN4djMzMysg0REl6RNgFOBy0hzd08mBeSiYaSVJsp2Aq6PiJeqbJsNvES60t7ywFvAbaSLfUyp0s7JwFkULh9d6ustkrYjXT56H9I6yDu36+p44HBsZmZm1nEi4kFg4zp1RtUo/3Q3+7wFbNNgH2aQLjFdvsx0ud5E0qjxgOA5x2ZmZmZmmcOxmZmZmVnmcGxmZmZmljkcm5mZmZllPiHPbIDZeGRj67NPml5tbXUzMzPrDYdjswGg0UDc230cqM3MzLrncGzWJj0Jt61+TodpMzMbahyOzaymYph2UDYzs6HA4disTSZNH9OW0eOeKvfVYdnMzDpRS8OxpO2BbwBrAcOBh4FfRMQFreyHmfVerbBcKXd4NjOzwajVI8ffJ10z+wBgOrAlcL6kkRFxSov7YmZ9qByWq42KOzCbmdlA1+pw/OWImF54PEnSh0mh2eHYhpzBNrWit3xCoJlZa0hanZSt1gFmAGcAR0XE3Ab23Qb4IfBJYBZwF7BtRLxRqLMccCywNWk2wFPAsRFxXjNtSYoa3Xg7IhZp9Hj7UkvDcSkYV9xLemHNzObRTJh2kDYzSyQtA1wHPEjKWCsDJ5Iu/nZYnX33BE4FjgcOApYBNqaQGSUtDdwEvA7sR5oNsDqwcLNtkcJ72WXAXxs51v4wEE7I+zzpzTMbkoba6HF/6avX0CHbzDrA3sBiwDYRMRO4NgfacZKOz2XzkTQSOBnYLyJ+W9j0p1LVHwGLAGMj4s1cdkNP2oqI20v7rQ2MBNp2Plpbw7GkTUifaHbvps5ewF4Ai31wyRb1zMyGqv76oOLQbWYttAVwdSkETwCOAzYgjcxWs0O+P7dO+7sBvywE4960VbYT8Aa1+9jv2haOJY0CzgcujYhzatWLiPHAeIARY5avNS/FbFDz6HHna/f763BuNqSMASYVCyLiaUmz8rZawfOzpJXE9pD0Y+CDwD3AARFxK4Ck0cDywAxJVwBfBF4F/hc4NCLebrStMkkCtidlw1k9OvI+0JZwLGlZ4ErgaWCXdvTBzGwoaXc47ymHeuuter/7f25RP/rYSElTCo/H58HEimVIJ+GVdeVttawArEqal3ww8HK+v0rSKhHxQq4DaR7xBGBz4N9IJ+e9k+s32lbZ+sBHcrtt0/JwLGlx4HLSpO2timc+mpmZFQ3WUD+QDMUPGIPx92aB2e+y1JPdzVKYx/SIGFunTrVv21Wj/L1uAEsC20fEVQCSbiWtRLEvcHiuA/BARHw7/zxJ0lLAjySNy6O+jbRV9jVSgL+6zrH1q1ZfBGQYcBGwCrBuRLzYyuc3G8g8tcLM+kOn/l2pFvo79Vh7oAsYUaV8ONVHlCteyfeTKwURMVPS3aTVKIp15jkBjzSN4yjSyhj3N9jWe3JG3Ba4pDA1oy1aPXL8K9KFP74HLCvpc4Vt90bE7Bb3x8zMzAYhB+FuTSXNLX6PpI8CS+RttTxEGllWqVzAu/nnx4Fq4bWyT6VeI20VbQJ8gDauUlGxQP0qfWrTfP/fwG2l24da3BezAWcofv1pZmZ97kpgszzVoWJH4E3gxm72u5wUXjeqFEgaDqwF/A0gj+peS1qvuGgT0kU+Hmu0rZKvAf+kMNLcLi0NxxExKiJU4zatlX0xMzMz61CnA7OBP0r6Yl4WdxxwUnF5N0mPSTqz8jgipgCXAmdK+pakrUjnLM4BTiu0fzTw/ySdLWlTSQcCh5KukDe7ybaQtAjwFeDCiKg2qtxSrR45NrM6PHpsZma9ERFdpJHcBUnLth1FuiDHkaWqw3Kdol2AicBJwMWkMLtxbrPS/p3Al0mrVFxGmi77U+BnzbaVbUGaD93WVSoqBsIV8szMzMysD0XEg8w/9aFcZ1SVsteBffKtu32vps6qEk20NZH55ya3jUeOzQYgjx6bmZm1h8OxmZmZmVnmcGw2QHn02MzMrPUcjs0GMAdkMzOz1nI4NjMzMzPLHI7NBjiPHpuZmbWOw7HZIOCAbGZm1hoOx2aDhAOymZlZ/3M4NhtEHJDNzMz6l8Ox2SDjgGxmZtZ/HI7NBiEHZDMzs/7hcGw2SE2aPsYh2czMqpK0uqTrJc2S9JykoyUtWGefUZKiym1Cqd7Cko6Q9JikN/P9UZIWKdRZQ9JV+blnS3pa0hmSPlRq6yhJ90uaKek1SVMk7di3r0ZzhrXzyc2s9yZNH8PGI6e2uxtmZjZASFoGuA54ENgaWBk4kTQoelgDTRwI/LXweHpp+8+BvXNb9wJrAscAI4Dv5TrDgSeB84DngNHAkcBakj4TEe/keksD5+S+zgW2AyZImhsRFzd0wH3M4djMzMyss+wNLAZsExEzgWslLQ2Mk3R8LuvOwxFxezfbdwZ+HREn5cc3SFoR+Do5HEfErcCthX0mS3oGuAb4FHBPrndAqe1rJK0BfBNoSzj2tAqzDuDpFWZmVrAFcHUpBE8gBeYN+qD9hYBXS2UzANXZ7+V8v3AD9erV6TcOx2ZmZmadZQwwz3y7iHgamJW31XO2pLmSnpd0kqTFStvPAP5T0rqSlpS0PrAPcGq5IUkL5DnKq5KmY9wF3Fml3jBJIyR9HdgUOL2BfvYLT6sw6xCee2xmZtkypJHcsq68rZbZwGmkqQ8zgQ2BQ0hzlrcu1DuUNAp9S6HsVxFxdJU2rwA2yz/fDWwZEe8WK0j6HHBbfvgOsG9ETOymn/3K4disgzggm5kNTpo9h2GPP9to9ZGSphQej4+I8aU6Ue1papSnHSKeB/YtFE2W9ALwK0mfjoj7cvlBwC7AfsD/Af8G/ETSyxFxRKnZ/YBlgVVIJ/BdKWndiHirUOd+4DOkE/q2Ak6VNDMiLqjV1/7kcGzWYRyQzcw63vSIGNvN9i5S0CwbTvUR5e5cDPyKtCLFfZJGklam+G5E/DbXuUnS26RQe2pEvFjZOSIezT/eIelm0goWOwNnFeq8AVTC/nWShgPHAW0Jx55zbNaBfIKemdmQNpXS3GJJHwWWoDQXuQFRuv8Y6YS8+0r17iUNuq5Us6GIp4BXchvduQf4qKSFmuxrn3A4NjMzM+ssVwKbSVqqULYj8CZwY5NtbZfv7873T+X7NUv11sr302o1lE/KW440etyddYFnImJO493sO55WYdahPL3CzGzIOh3YH/ijpONII7XjgJOKy7tJegy4MSL2yI/HAUuRLgAyE/gCaX7xHyPi/wAi4gVJE4HjJC1KmnP86dz+RRHxUm7rF6ST6+4gTeVYDTgYeJy0rBySVgLOBs4HngCWBL4K7ERa/aItHI7NOlhleoVDspnZ0BERXZI2IS2tdhkpnJ5MCrBFw4DiJaWnkq6OtydpNYqngROAn5b2+xZwBCmAfxh4FvgN8JNCnSmkk/H2AhbNbV0C/CzPMSb36znSiXor5McPAltFxBVNHnafcTg2GwI8imxmNrRExIPAxnXqjCo9nkAe1a2z30xSiD6wmzp124qIV0mrXgwonnNsNkT4JD0zM7P6HI7NhpBJ08c4JJuZmXXD4dhsCHJANjMzq87h2GyIckA2MzObn8OxmZmZmVnmcGw2hHn02MzMbF4Ox2ZmZmZmmcOx2RDn0WMzM7P3ORybmZmZmWUOx2bm0WMzM7PM4djMzMzMLHM4NjPAo8dmZmbgcGxmZmbWcSStLul6SbMkPSfpaEkL1tlnlKSocptQpe7Wku6X9JakByXtWKXOcElnS+qS9Kqk30taridttZLDsZm9x6PHZmaDn6RlgOuAALYGjgZ+ABzVYBMHAusUboeV2l8PuAS4AdgC+AtwgaRNS+1cCGwI7AnsCnwGmNjDtlpmWLue2MwGpknTx7DxyKnt7oaZmfXc3sBiwDYRMRO4VtLSwDhJx+ey7jwcEbd3s/1w4KaI2D8/vkHSGsARwDUAktYBNgM2iIibctmzwB2SvhgR1zXaVqt55NjMzMyss2wBXF0KwRNIgXmD3jQsaRFgI+APpU0TgHUkDS/04YVKMAaIiDuBJ/O2ZtpqKYdjM5vPpOljPMXCzGzwGgPM8xVgRDwNzMrb6jlb0lxJz0s6SdJihW0rAwuV2wceIuXKT9TqQ6FepQ+NttVSg2paxasPvzT9z+uf9lS7+9GPRgLT292JFhtqxzyojvfPvW9iUB1vHxlqx+zj7XydfswrtbsDADPfeenqq148fWSD1ReVNKXweHxEjC88XgaYUWW/rrytltnAaaTpDDNJ84UPIYXYrQttU6X9rtL27vrwsSbbaqlBFY4j4gPt7kN/kjQlIsa2ux+tNNSO2cfb+YbaMft4O99QPOZ2iIjN+7rJKmWqUV7pw/PAvoWiyZJeAH4l6dMRcV837atKeaN9aKStlvG0CjMzM7PO0gWMqFI+nOqjud25ON+vWWibKu1XHs8o1KvWhxGlOo201VIOx2ZmZmadZSqlucWSPgosQfV5wN2J0v3jwJxy+/nxu8AjtfpQqFfpQ6NttZTD8cAyvn6VjjPUjtnH2/mG2jH7eDvfUDzmwe5KYDNJSxXKdgTeBG5ssq3t8v3dABExm7Qm8falejsCt0XEq4U+rJDXMQZA0ljSfOMrm2yrpRTRlukcZmZmZtYP8kVAHgT+DhxHCqQnAb+MiMMK9R4DboyIPfLjccBSwF9JJ+R9ATgIuCIiti3stx4wGTiVdFGPLUkXDtk8Iq4p1LuKtOLEgaSR4OOAFyNi/WbbaiWHYzMzM7MOI2l1UuBchzR39wxgXETMLdSZBkyOiF3z451IwXQV0prITwPnAz/No7zF9r8CHJPrPpnbnlCqMwI4GfgqabbC5cD+ETG92bZayeHYzMzMzCzznOM2k7S9pD9LelbS65LulvS1dverVSStmI87JC3Z7v70B0nDJB0q6VFJsyU9I+nkdverP0naSdI9+b19VtJ5kj7c7n71BUkfl/QbSX/Li+RPrlJHkn4k6R+S3pR0k6RPt763vVfveCV9SNIJefvr+ZjPHczvdyPvcan+L/PfsF+0qIt9qtHjlfSvki6X9Kqk1yTdKWmtFnfXrN85HLff94HXgQOA/yBNTD9f0n5t7VXrnEA6/k52NrA/8AtgU+BQ0kkRHUnSfwAXALeSFo0/hDRv7XJJnfA3Zw3SnLhHqH0m9aHA4aT5dV8m/Y5fJ2mFlvSwb9U73rVIX5leQDrWg4DPArcO4g+8jbzHwHtfXe9Omp85WNU93vzh7lbS1/M7kk6guoz01btZR/G0ijaTNLLK3JvzgXUiYnSbutUSktYHLgWOJYXkpSKio4KypM1J/4H8W0Q82O7+tIKkCcAqEbFWoew/SO/16hHxUNs61wckLRAR7+afLwZGRsSGhe2LAi8AJ0bE0blsCWAa8JviyTCDQQPHOwJ4PSLeKZR9AngY2DUizm1tj3uv3jGX6l4H3AZ8A7g4Ig5sWUf7SCPHK+l24ImI2LkNXTRrqU4YxRnUysE4uxdYvtV9aSVJCwKnAEfT2Zcl3R2YNFSCcbYQUF5+Z0a+F4NcJUR04/PA0sAfCvu8QfqQtEU/dq1f1DveiJhRDMa57BFgFoP071gD7zEAkrYDVgN+3r896l/1jjePjn+W9DfbrOM5HA9MnyctwdLJ9gYWJV3DvZN9FnhE0qmSZkqaJemPg3k+ZgPOAtaX9E1JS+dRxGOAG4bIh4QxwFzg0VL5Q1RfEL/jSPoUsDgd/HdM0mLAicCh+cNPJ/tsvl8mz0t+R9LjkvZoa6/M+onD8QAjaRPSPM2ODY2SlgN+Anw/Iua0uz/9bAVgV+DTwE7AbqQ5mn+SNOhHUauJiL+Qjnk8aQT5YWBBYJs2dquVliFNM5hbKu8CFpe0cBv61DJ5Xvl/kz4ctGWN0hb5IfA88Lt2d6QFKnPlzwN+D3wJuAo4Q9KWbeuVWT8Z1u4O2PskjSKtJ3hpRJzT3t70q58Cd0TEFe3uSAso37aOiJcBJD1PukLRxsD1bexbv5C0EXA6KSBdCXwQGEf6QPDFKqGxE1U7mUPdbOskPyOtq7pBp374lTSatBbsxjE0TtypDKSdERHH559vkLQa6UPCUPhbbkOIw/EAIWlZUpB4Gtilzd3pN5LWIM3D/UI+kQfS168AwyXNjYhOWsmhi3QSy8uFsluAt4HV6cBwTPqq+c8RcUilQNJ9wFTStyJ/bFO/WqULWErSgqUPAiOAWZ0aGAEkfYe0WsXXIuKOdvenH/2c9Pd6auHv2ALAIvnxqx0Wml/J9zeUyieRVloy6yieVjEASFqcdNWYhYGtOnz+2iqkE7ZuI4WILt6fQvIMnXfCR62VGUS6lGYnGgPcVyyIiIdJy9et3I4OtdhU0jSSj5fKx+RtHUnStqR/vwdHxIXt7k8/W5U0TaircPsosG/+ecX2da1fDMW/YzaEeeS4zSQNAy4ihcZ1I+LFNnepv90CbFQq25y0Fu6WwBMt71H/uhw4qrRk3xdIHxD+1r5u9aungDWLBfnr18VIy5l1ultJa95uTzoRsfIB+MukedgdR9KGpLmop0bEoLwQRpP2BMprOE8gTZf6NfBSy3vUv24lhf5NgKsL5ZvQuX/HbAhzOG6/X5FC4feAZSV9rrDt3vK1zAe7HBAnF8vyXGuAmzttnWNSGNofuEzSscBSpAtDXBcRt7S1Z/3ndOBkSc/x/pzjI0jBeNDPTcxBt3IS0orA0nlJL4ArImKWpJ8Dh0vqIo0Wf5/0Td2g+2ak3vECKwETScd5Yelv2EsR8Xir+tpXGniPp1TZ5y3gHxExuTW97DsN/k4fDRwvaQZwF7At6YP+Bq3ur1l/80VA2kzSNNJ/LtWMjohpretNe0jalXQVuY67CAikS7MC/0P6T+Rt0sUwDoiIrrZ2rJ/kVTj2BvYhTaOYQfrG4IcRMei/Gcgf5p6ssXl0REzLr8GPSK/BcsAUYP+IuLc1vew79Y4X2JD077eacyNi177vVf9q5D2uss80Bu9FQEbRwPFK+j6wHylAPwwcGRGdfg6BDUEOx2ZmZmZmmU/IMzMzMzPLHI7NzMzMzDKHYzMzMzOzzOHYzMzMzCxzODYzMzMzyxyOzczMzMwyh2MzG7QkjZIUhduIPmhz10J7813swczMOpvDsZl1ggOBdYDX+qCtv+S2Bv3V/MzMrHm+fLSZdYKHI+L2vmgoIl4CXpL0EunS12ZmNoR45NjM2k7SCEnPSDqvVP5nSY9IWrzJ9irTLXaSdLakmbn9XfL2gyU9J+klScdJ8t9CMzMDHI7NbACIiBnAHsA3JH0FQNJuwFbArhExq4dNHwc8D2wL3AycK+lEYG1gd+CXwMHADr3ovpmZdRBPqzCzASEirpY0HviNpKeAk4FfRMStvWh2UkT8CEDSHcB2wH8AYyJiLnCVpK2BrwITencEZmbWCTxybGYDyQ+AN4DbgGeAI3rZ3vWVHyJiJvAScGMOxhWPASv28nnMzKxDOByb2YAREa8DlwOLAGdGxOxeNjmj9PjtGmWL9vJ5zMysQzgcm9mAIWkssA9wL3CYpBXa3CUzMxtiHI7NbECQtChwHnA1sB7wCjC+rZ0yM7Mhx+HYzAaKY4AVgG/n1Sm+BWwlade29srMzIYUh2MzaztJ6wIHAPtGxPMAeZWKk4BfSvpIO/tnZmZDhyKi3X0wM+sRSaOAJ4GtgSsi4p0+aFPAgsCZwBoRMba3bZqZ2eDhkWMz6wSXAnMkjeiDtr4FzAG+2QdtmZnZIOORYzMbtCQtDHyqUHRvaQ3jnrS5HDA6P3wjIh7qTXtmZja4OBybmZmZmWWeVmFmZmZmljkcm5mZmZllDsdmZmZmZpnDsZmZmZlZ5nBsZmZmZpb9f0EiodoQNZfRAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 864x288 with 2 Axes>"
       ]
@@ -425,26 +447,27 @@
     }
    ],
    "source": [
-    "layer= 2\n",
+    "# Get layer data\n",
+    "layer = 2\n",
     "layer_data = d3d.get_layer_data(d3d_data, variable, layer)\n",
     "\n",
     "# Plotting \n",
-    "Type= 'Contour'\n",
     "plt.figure(figsize=(12,4))\n",
-    "contour_plot = plt.tricontourf(layer_data.x ,layer_data.y, layer_data.v, vmin=min_plot_v,vmax=max_plot_v,\n",
-    "                            levels=np.linspace(min_plot_v,max_plot_v,10))\n",
+    "contour_plot = plt.tricontourf(\n",
+    "    layer_data.x,\n",
+    "    layer_data.y, \n",
+    "    layer_data.v, \n",
+    "    vmin=min_plot_vel,\n",
+    "    vmax=max_plot_vel,\n",
+    "    levels=np.linspace(min_plot_vel,max_plot_vel,10)\n",
+    ")\n",
     "    \n",
-    "plt.xlabel('x (m)')\n",
-    "plt.ylabel('y (m)')\n",
-    "\n",
-    "title_name= f'{d3d_data.variables[variable].long_name}'\n",
-    "title_label=' '.join([x.capitalize() for x in title_name.split(' ')])\n",
-    "plt.title(f'{Type} {title_label} \\n  Layer: {layer}')\n",
-    "\n",
-    "cbar= plt.colorbar(contour_plot)\n",
-    "cbar_name= f'{d3d_data.variables[variable].standard_name}'\n",
-    "cbar_label=' '.join([x.capitalize() for x in cbar_name.split('_')])\n",
-    "cbar.set_label(f'{cbar_label} [{d3d_data.variables[variable].units}]')"
+    "cbar = plt.colorbar(contour_plot)\n",
+    "cbar.set_label('$u_x$ [m/s]')\n",
+    "    \n",
+    "plt.xlabel('x [m]')\n",
+    "plt.ylabel('y [m]')\n",
+    "plt.title(f'Velocity on Layer {layer} at Time: {layer_data.time[1]} s')"
    ]
   },
   {
@@ -459,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "baf1e542",
    "metadata": {},
    "outputs": [
@@ -472,11 +495,12 @@
     }
    ],
    "source": [
-    "x = np.linspace(xmin, xmax, num=100)\n",
+    "# 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",
-    "z = np.mean([zmin,zmax])\n",
+    "z2 = np.mean([zmin,zmax])\n",
     "\n",
-    "contour_points = d3d.create_points(x, y_contour, z) "
+    "contour_points = d3d.create_points(x2, y_contour, z2) "
    ]
   },
   {
@@ -489,14 +513,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "441f059a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "contour_variable = interp.griddata(var_data_df[['x','y','z']], \n",
-    "                                                     var_data_df[variable],\n",
-    "                                                     contour_points[['x','y','z']])"
+    "contour_variable = interp.griddata(\n",
+    "    var_data_df[['x','y','z']],\n",
+    "    var_data_df[variable],\n",
+    "    contour_points[['x','y','z']]\n",
+    ")"
    ]
   },
   {
@@ -504,45 +530,33 @@
    "id": "5658a7d5",
    "metadata": {},
    "source": [
-    "The results are then plotted. The minimum and maximum values show on the graph are pulled from above (`max_plot_v` and `min_plot_v`) 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 depth of the flume. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "d04fa7ad",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plotting \n",
-    "Type= 'Contour'\n",
     "plt.figure(figsize=(12,4))\n",
-    "contour_plot = plt.tricontourf(contour_points.x,contour_points.y,contour_variable,\n",
-    "                               vmin=min_plot_v,vmax=max_plot_v,levels=np.linspace(min_plot_v,max_plot_v,10))\n",
+    "contour_plot = plt.tricontourf(\n",
+    "    contour_points.x,\n",
+    "    contour_points.y,\n",
+    "    contour_variable,\n",
+    "    vmin=min_plot_vel,\n",
+    "    vmax=max_plot_vel,\n",
+    "    levels=np.linspace(min_plot_vel,max_plot_vel,10)\n",
+    ")\n",
     "\n",
     "plt.xlabel('x (m)')\n",
     "plt.ylabel('y (m)')\n",
-    "title_name= f'{d3d_data.variables[variable].long_name}'\n",
-    "title_label=' '.join([x.capitalize() for x in title_name.split(' ')])\n",
-    "plt.title(f'{Type} {title_label} \\n  Layer: {layer}')\n",
+    "plt.title(f'Velocity on x-y Plane')\n",
     "\n",
     "cbar= plt.colorbar(contour_plot)\n",
-    "cbar_name= f'{d3d_data.variables[variable].standard_name}'\n",
-    "cbar_label=' '.join([x.capitalize() for x in cbar_name.split('_')])\n",
-    "cbar.set_label(f'{cbar_label} [{d3d_data.variables[variable].units}]')"
+    "cbar.set_label(f'$u_x$ [m/s]')"
    ]
   },
   {
@@ -561,239 +575,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "857acdd2",
    "metadata": {},
-   "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>z</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>1.02322</td>\n",
-       "      <td>0.003169</td>\n",
-       "      <td>1.119852</td>\n",
-       "      <td>3.569391e-04</td>\n",
-       "      <td>-0.058020</td>\n",
-       "      <td>0.040990</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>17.695707</td>\n",
-       "      <td>4.875</td>\n",
-       "      <td>1.02322</td>\n",
-       "      <td>0.003125</td>\n",
-       "      <td>1.119666</td>\n",
-       "      <td>3.782883e-04</td>\n",
-       "      <td>0.076974</td>\n",
-       "      <td>0.040668</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>17.516414</td>\n",
-       "      <td>4.875</td>\n",
-       "      <td>1.02322</td>\n",
-       "      <td>0.003132</td>\n",
-       "      <td>1.116527</td>\n",
-       "      <td>3.570189e-04</td>\n",
-       "      <td>0.072073</td>\n",
-       "      <td>0.040840</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>17.337121</td>\n",
-       "      <td>4.875</td>\n",
-       "      <td>1.02322</td>\n",
-       "      <td>0.003063</td>\n",
-       "      <td>1.112112</td>\n",
-       "      <td>3.117508e-04</td>\n",
-       "      <td>-0.004615</td>\n",
-       "      <td>0.040633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>17.157828</td>\n",
-       "      <td>4.875</td>\n",
-       "      <td>1.02322</td>\n",
-       "      <td>0.003079</td>\n",
-       "      <td>1.113876</td>\n",
-       "      <td>2.792470e-04</td>\n",
-       "      <td>-0.012373</td>\n",
-       "      <td>0.040671</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>1.02322</td>\n",
-       "      <td>0.000352</td>\n",
-       "      <td>1.061969</td>\n",
-       "      <td>-6.706310e-07</td>\n",
-       "      <td>-0.000059</td>\n",
-       "      <td>0.014430</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3996</th>\n",
-       "      <td>0.662879</td>\n",
-       "      <td>1.125</td>\n",
-       "      <td>1.02322</td>\n",
-       "      <td>0.000353</td>\n",
-       "      <td>1.061602</td>\n",
-       "      <td>-4.836254e-07</td>\n",
-       "      <td>0.011498</td>\n",
-       "      <td>0.014450</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3997</th>\n",
-       "      <td>0.483586</td>\n",
-       "      <td>1.125</td>\n",
-       "      <td>1.02322</td>\n",
-       "      <td>0.000359</td>\n",
-       "      <td>1.062933</td>\n",
-       "      <td>-3.416162e-07</td>\n",
-       "      <td>-0.005086</td>\n",
-       "      <td>0.014555</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3998</th>\n",
-       "      <td>0.304293</td>\n",
-       "      <td>1.125</td>\n",
-       "      <td>1.02322</td>\n",
-       "      <td>0.000336</td>\n",
-       "      <td>1.062523</td>\n",
-       "      <td>-2.207182e-07</td>\n",
-       "      <td>0.021002</td>\n",
-       "      <td>0.014089</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3999</th>\n",
-       "      <td>0.125000</td>\n",
-       "      <td>1.125</td>\n",
-       "      <td>1.02322</td>\n",
-       "      <td>0.000312</td>\n",
-       "      <td>1.059823</td>\n",
-       "      <td>-1.153343e-07</td>\n",
-       "      <td>0.133265</td>\n",
-       "      <td>0.013505</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4000 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              x      y        z   turkin1       ucx           ucy       ucz  \\\n",
-       "0     17.875000  4.875  1.02322  0.003169  1.119852  3.569391e-04 -0.058020   \n",
-       "1     17.695707  4.875  1.02322  0.003125  1.119666  3.782883e-04  0.076974   \n",
-       "2     17.516414  4.875  1.02322  0.003132  1.116527  3.570189e-04  0.072073   \n",
-       "3     17.337121  4.875  1.02322  0.003063  1.112112  3.117508e-04 -0.004615   \n",
-       "4     17.157828  4.875  1.02322  0.003079  1.113876  2.792470e-04 -0.012373   \n",
-       "...         ...    ...      ...       ...       ...           ...       ...   \n",
-       "3995   0.842172  1.125  1.02322  0.000352  1.061969 -6.706310e-07 -0.000059   \n",
-       "3996   0.662879  1.125  1.02322  0.000353  1.061602 -4.836254e-07  0.011498   \n",
-       "3997   0.483586  1.125  1.02322  0.000359  1.062933 -3.416162e-07 -0.005086   \n",
-       "3998   0.304293  1.125  1.02322  0.000336  1.062523 -2.207182e-07  0.021002   \n",
-       "3999   0.125000  1.125  1.02322  0.000312  1.059823 -1.153343e-07  0.133265   \n",
-       "\n",
-       "      turbulent_intensity  \n",
-       "0                0.040990  \n",
-       "1                0.040668  \n",
-       "2                0.040840  \n",
-       "3                0.040633  \n",
-       "4                0.040671  \n",
-       "...                   ...  \n",
-       "3995             0.014430  \n",
-       "3996             0.014450  \n",
-       "3997             0.014555  \n",
-       "3998             0.014089  \n",
-       "3999             0.013505  \n",
-       "\n",
-       "[4000 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Calculating turbulent intensity \n",
-    "TI= d3d.turbulent_intensity(d3d_data, points= contour_points ,intermediate_values = True) \n",
+    "TI=d3d.turbulent_intensity(\n",
+    "   d3d_data,\n",
+    "   points=contour_points,\n",
+    "   intermediate_values=True\n",
+    ") \n",
     "\n",
     "# Creating new plot limits \n",
-    "max_plot_v= 0.3\n",
-    "min_plot_v=0\n",
+    "max_plot_TI=27\n",
+    "min_plot_TI=0\n",
     "\n",
     "# Plotting \n",
     "plt.figure(figsize=(12,4))\n",
-    "contour_plot = plt.tricontourf(TI.x, TI.y, TI.turbulent_intensity,\n",
-    "                               vmin=min_plot_v, vmax=max_plot_v,levels=np.linspace(min_plot_v,max_plot_v,10))\n",
+    "contour_plot = plt.tricontourf(\n",
+    "    TI.x, \n",
+    "    TI.y, \n",
+    "    TI.turbulent_intensity,\n",
+    "    vmin=min_plot_TI, \n",
+    "    vmax=max_plot_TI,\n",
+    "    levels=np.linspace(min_plot_TI,max_plot_TI,10)\n",
+    ")\n",
+    "\n",
     "plt.xlabel('x (m)')\n",
     "plt.ylabel('y (m)')\n",
     "plt.title('Turbulent Intensity')\n",
     "cbar= plt.colorbar(contour_plot)\n",
-    "cbar.set_label('Turbulent Intensity [-]')\n",
+    "cbar.set_label('Turbulent Intensity [%]')\n",
     "\n",
     "TI"
    ]
@@ -813,226 +626,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "941d76b7",
    "metadata": {},
-   "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>z</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>0.104914</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>0.111714</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>0.109223</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>0.110209</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>0.111714</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>0.019822</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>0.020065</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>0.019586</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>0.019737</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>0.019505</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5760 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           x      y         z   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             0.104914  \n",
-       "1     0.689746             0.111714  \n",
-       "2     0.720128             0.109223  \n",
-       "3     0.694179             0.110209  \n",
-       "4     0.689746             0.111714  \n",
-       "...        ...                  ...  \n",
-       "5755  1.245558             0.019822  \n",
-       "5756  1.222989             0.020065  \n",
-       "5757  1.255913             0.019586  \n",
-       "5758  1.245736             0.019737  \n",
-       "5759  1.256117             0.019505  \n",
-       "\n",
-       "[5760 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "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')\n",
     "\n",
     "# Replacing negative numbers close to zero with zero\n",
     "neg_index=np.where(Var['turkin1']<0)# Finding negative numbers\n",
     "\n",
     "# Determining if negative number are close to zero \n",
-    "zero_bool= np.isclose(Var['turkin1'][Var['turkin1']<0].array, \n",
-    "                      np.zeros(len(Var['turkin1'][Var['turkin1']<0].array)),atol=1.0e-4)\n",
+    "zero_bool= np.isclose(\n",
+    "    Var['turkin1'][Var['turkin1']<0].array, \n",
+    "    np.zeros(len(Var['turkin1'][Var['turkin1']<0].array)),\n",
+    "    atol=1.0e-4\n",
+    ")\n",
     "\n",
     "# Identifying the location of negative values close to zero \n",
     "zero_ind= neg_index[0][zero_bool] \n",
@@ -1049,8 +660,8 @@
     "# Calculating the root mean squared velocity \n",
     "Var['u_mag']=d3d.unorm(np.array(Var['ucx']),np.array(Var['ucy']), np.array(Var['ucz']))\n",
     "\n",
-    "# Calculating turbulent intensity\n",
-    "Var['turbulent_intensity']= np.sqrt(2/3*Var['turkin1'])/Var['u_mag']\n",
+    "# Calculating turbulent intensity as a percent \n",
+    "Var['turbulent_intensity']= (np.sqrt(2/3*Var['turkin1'])/Var['u_mag'])*100 \n",
     "\n",
     "Var"
    ]
@@ -1067,33 +678,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "75433d08",
    "metadata": {
     "scrolled": true
    },
-   "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "turbine_x_loc= 6 \n",
     "turbine_diameter= 0.7\n",
@@ -1107,18 +697,31 @@
     "\n",
     "Var_sample= d3d.variable_interpolation(d3d_data, variables, points= sample_points)\n",
     "\n",
-    "Var_sample['u_mag']=d3d.unorm(np.array(Var_sample['ucx']),np.array(Var_sample['ucy']), np.array(Var_sample['ucz'])) #root mean squared calculation \n",
-    "Var_sample['turbulent_intensity']= np.sqrt(2/3*Var_sample['turkin1'])/Var_sample['u_mag'] # turbulent intesity calculation \n",
+    "#root mean squared calculation \n",
+    "Var_sample['u_mag']=d3d.unorm(\n",
+    "    np.array(Var_sample['ucx']),\n",
+    "    np.array(Var_sample['ucy']), \n",
+    "    np.array(Var_sample['ucz'])\n",
+    ") \n",
+    "# turbulent intesity calculation\n",
+    "Var_sample['turbulent_intensity']= np.sqrt(2/3*Var_sample['turkin1'])/Var_sample['u_mag']*100  \n",
     "\n",
     "# Plotting \n",
     "plt.figure(figsize=(10,4.4))\n",
-    "contour_plot = plt.tricontourf(Var_sample.y, Var_sample.z, Var_sample.turbulent_intensity,\n",
-    "                               vmin=min_plot_v, vmax=max_plot_v,levels=np.linspace(min_plot_v,max_plot_v,10))\n",
+    "contour_plot = plt.tricontourf(\n",
+    "    Var_sample.y, \n",
+    "    Var_sample.z, \n",
+    "    Var_sample.turbulent_intensity,\n",
+    "    vmin=min_plot_TI, \n",
+    "    vmax=max_plot_TI,\n",
+    "    levels=np.linspace(min_plot_TI,max_plot_TI,10)\n",
+    ")\n",
+    "\n",
     "plt.xlabel('y (m)')\n",
     "plt.ylabel('z (m)')\n",
     "plt.title('Turbulent Intensity')\n",
     "cbar= plt.colorbar(contour_plot)\n",
-    "cbar.set_label('Turbulent Intensity [-]')"
+    "cbar.set_label('Turbulent Intensity [%]')"
    ]
   },
   {
diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index f1b2d1efc..d2f5ef194 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -1,71 +1,247 @@
-from os.path import abspath, dirname, join, isfile, normpath, relpath
 from mhkit.utils import unorm
-from math import isclose
 import scipy.interpolate as interp
-import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import netCDF4
+import warnings
 
 
-def get_layer_data(data, variable, layer_index= -1 , time_index=-1):
+def get_all_time(data):
     '''
-    Get variable data from netcdf4 object at specified layer and timestep. 
+    Returns all of the data time stamps in seconds.
+    
+    Parameters
+    ----------
+    data: NetCDF4 object 
+       A NetCDF4 object that contains spatial data, e.g. velocity or shear
+       stress generated by running a Delft3D model.  
+
+    Returns
+    -------
+    seconds_run: array
+        Returns an array of integers representing the number of seconds after
+        the simulation started and that the data object contains a snapshot of
+        simulation conditions at that time.
+    '''
+    
+    assert type(data)== netCDF4._netCDF4.Dataset, 'data must be NetCDF4 object' 
+
+    seconds_run = np.ma.getdata(data.variables['time'][:], False)
+
+    return seconds_run
+
+def index_to_seconds(data, time_index):
+    '''
+    The function will return 'seconds_run' if passed a 'time_index' 
+
+    Parameters
+    ----------
+    data: NetCDF4 object 
+       A NetCDF4 object that contains spatial data, e.g. velocity or shear
+       stress, generated by running a Delft3D model.  
+    time_index: int 
+        A positive integer to pull the time index from the dataset. 0 being closest
+        to time 0. Default is last time index -1.
+
+    Returns
+    -------
+    seconds_run: int, float
+        The 'seconds_run' is the seconds corresponding to the 'time_index' increments.
+    '''
+    return _convert_time(data, time_index=time_index)
+
+
+def seconds_to_index(data, seconds_run):
+    '''
+    The function will return a 'time_index' if passed a 'seconds_run'
 
     Parameters
     ----------
-    data: netcdf4 object 
-       A netCDF object that contains spatial data, e.g. velocity or shear stess.
-       These can be generated by running a Delft3D model.  
+    data: NetCDF4 object 
+    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.
+
+    Returns
+    -------
+    time_index: int
+        The 'time_index' is a positive integer starting from 0 
+        and incrementing until in simulation is complete.
+    '''
+    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 
+    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'.
+
+    Parameters
+    ----------
+    data: NetCDF4 object 
+       A NetCDF4 object that contains spatial data, e.g. velocity or shear
+       stress, generated by running a Delft3D model.  
+    time_index: int 
+        An integer to pull the time index from the dataset. 0 being closest
+        to the start time. 
+    seconds_run: int, float
+        An integer or float that represents the amount of time in seconds 
+        passed since starting the simulation.
+
+    Returns
+    -------
+    QoI: int, float
+        The quantity of interest is the unknown value either the 'time_index' 
+        or the 'seconds_run'. The 'time_index' is an integer starting from 0 
+        and incrementing until in simulation is complete. The 'seconds_run' is
+        the seconds corresponding to the 'time_index' increments.
+    '''
+    
+    assert type(data)== netCDF4._netCDF4.Dataset, 'data must be NetCDF4 object'
+    assert time_index or seconds_run, 'input of time_index or seconds_run needed'
+    assert not(time_index and seconds_run), f'only one time_index or seconds_run'
+    assert isinstance(time_index, (int, float)) or isinstance(seconds_run, (int,
+            float)),'time_index or seconds_run input must be a int or float'
+   
+    times = get_all_time(data)
+    
+    if time_index:
+        QoI= times[time_index]
+    if seconds_run:
+        try: 
+            idx=np.where(times == seconds_run)
+            QoI=idx[0][0]
+        except: 
+            idx = (np.abs(times - seconds_run)).argmin()
+            QoI= idx
+            warnings.warn( f'Warning: seconds_run not found. Closest time stamp' 
+                          +'found {times[idx]}', stacklevel= 2)
+
+    return QoI
+
+
+def get_layer_data(data, variable, layer_index=-1, time_index=-1):
+    '''
+    Get variable data from the NetCDF4 object at a specified layer and timestep. 
+    If the data is 2D the layer_index is ignored.
+
+    Parameters
+    ----------
+    data: NetCDF4 object
+       A NetCDF4 object that contains spatial data, e.g. velocity or shear
+       stress, generated by running a Delft3D model.
     variable: string
-        Delft3D outputs many vairables that can be called. The full list can be
-        found using "data.variables.keys()" in the consol. 
+        Delft3D outputs many vairables. The full list can be
+        found using "data.variables.keys()" in the console. 
     layer_index: int
-         A positve interger to pull out a layer from the dataset. 0 being closest 
-         to the surface. Defalt is the bottom layer -1.
+         An integer to pull out a layer from the dataset. 0 being closest 
+         to the surface. Default is the bottom layer, found with input -1.
     time_index: int 
-        A positive interger to pull the time step from the dataset. 0 being closest
-        to time 0. Defalt is last time step -1.  
-
+        An integer to pull the time index from the dataset. 0 being closest
+        to the start time. Default is last time index, found with input -1.
     Returns
     -------
     layer_data: DataFrame
-        DataFrame with columns of "x" and "y" location on specified layer and 
-        the variable values "v".
+        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. 
     '''
     
-    assert isinstance(time_index, int), 'time_index  must be a int'
-    assert isinstance(layer_index, int), 'layer_index  must be a int'
-    assert type(data)== netCDF4._netCDF4.Dataset, 'data must be netCDF4 object'
+    assert isinstance(time_index, int), 'time_index  must be an int'
+    assert isinstance(layer_index, int), 'layer_index  must be an int'
+    assert type(data)== netCDF4._netCDF4.Dataset, 'data must be NetCDF4 object'
     assert variable in data.variables.keys(), 'variable not recognized'
     coords = str(data.variables[variable].coordinates).split()
     var=data.variables[variable][:]
-    max_time_index= len(var)
-    assert time_index <= max_time_index, (f'time_index must be less than the max'
-                                         +'time index {max_time_index}')
-    assert time_index >= -1, 'time_index must be greater than or equal to -1'
-    max_layer= len(var[0][0])
-    assert layer_index <= max_layer,( f'layer_index must be less than the max'
-                                     + 'layer {max_layer}')
-    assert layer_index >= -1, 'layer_index must be greater than or equal to -1'
-    
+    max_time_index= data['time'].shape[0]-1 # to account for zero index
+    assert abs(time_index) <= max_time_index, (f'time_index must be less than'
+                  +'the absolute value of the max time index {max_time_index}')
+
     x=np.ma.getdata(data.variables[coords[0]][:], False) 
     y=np.ma.getdata(data.variables[coords[1]][:], False)
- 
-    v= np.ma.getdata(var[time_index,:,layer_index], False)
     
-    layer= np.array([ [x_i, y_i, z_i] for x_i, y_i, z_i in zip(x, y, v)]) 
-    layer_data = pd.DataFrame(layer, columns=[ 'x', 'y', 'v'])
+    if type(var[0][0]) == np.ma.core.MaskedArray: 
+        max_layer= len(var[0][0])
+        
+        assert abs(layer_index) <= max_layer,( f'layer_index must be less than'
+                                              +'the max layer {max_layer}')
+        v= np.ma.getdata(var[time_index,:,layer_index], False)
+        dimensions= 3
+    
+    else: 
+        assert type(var[0][0])== np.float64, 'data not  recognized'
+        dimensions= 2
+        v= np.ma.getdata(var[time_index,:], False)
+        
+    #depth 
+    cords_to_layers= {'FlowElem_xcc FlowElem_ycc':{'name':'laydim', 
+                                    'coords':data.variables['LayCoord_cc'][:]},
+                       'FlowLink_xu FlowLink_yu': {'name':'wdim', 
+                                'coords':data.variables['LayCoord_w'][:]}}
+    layer_dim =  str(data.variables[variable].coordinates)
+    
     
+    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)
+    if layer_dim == 'FlowLink_xu FlowLink_yu':
+        #interpolate 
+        coords = str(data.variables['waterdepth'].coordinates).split()
+        x_laydim=np.ma.getdata(data.variables[coords[0]][:], False) 
+        y_laydim=np.ma.getdata(data.variables[coords[1]][:], False)
+        points_laydim = np.array([ [x, y] for x, y in zip(x_laydim, y_laydim)])
+        
+        coords_request = str(data.variables[variable].coordinates).split()
+        x_wdim=np.ma.getdata(data.variables[coords_request[0]][:], False) 
+        y_wdim=np.ma.getdata(data.variables[coords_request[1]][:], False)
+        points_wdim=np.array([ [x, y] for x, y in zip(x_wdim, y_wdim)])
+        
+        bottom_depth_wdim = interp.griddata(points_laydim, bottom_depth,
+                                            points_wdim)
+        water_level_wdim = interp.griddata(points_laydim, water_level,
+                                            points_wdim)
+        
+        idx_bd= np.where(np.isnan(bottom_depth_wdim))
+        
+        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,
+                                              points_wdim[i], method='nearest')
+ 
+    depth=[]
+    
+    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
+    depth=np.append(depth, 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'])
+
     return layer_data
 
 
 def create_points(x, y, z):
     '''
     Turns three coordinate inputs into a single output DataFrame of points. 
-    The 3 inputs can consist of 3 points, 2 points and 1 array, or 1 point 
-    and 2 arrays. The final output DataFrame will consist of a DataFrame with
-    every combination of the 3 inputs. 
+    In any order the three inputs can consist of 3 points, 2 points and 1 array,
+    or 1 point and 2 arrays. The final output DataFrame will be the unique
+    combinations of the 3 inputs. 
     
     Parameters
     ----------
@@ -83,16 +259,21 @@ def create_points(x, y, z):
         
     Example 
     -------
-    If the inputs are 2 arrays: [1,2] and [3,4,5] and 1 point [6], the output 
+    If the inputs are 2 arrays:  and [3,4,5] and 1 point [6], the output 
     will contain 6 array combinations of the 3 inputs as shown.
-        x   y    z
-   0  1.0  3.0  6.0
-   1  2.0  3.0  6.0
-   2  1.0  4.0  6.0
-   3  2.0  4.0  6.0
-   4  1.0  5.0  6.0
-   5  2.0  5.0  6.0
-        
+    
+    x=np.array([1,2])
+    y=np.array([3,4,5])
+    z= 6
+    d3d.create_points(x,y,z)
+    
+       x    y    z
+    0  1.0  3.0  6.0
+    1  2.0  3.0  6.0
+    2  1.0  4.0  6.0
+    3  2.0  4.0  6.0
+    4  1.0  5.0  6.0
+    5  2.0  5.0  6.0        
     '''
     
     assert isinstance(x, (int, float, np.ndarray)), ('x must be a int, float'
@@ -111,17 +292,14 @@ def create_points(x, y, z):
 
     for i in directions:
         try:
-            len(directions[i]['values'])
+            N=len(directions[i]['values'])
         except:
-            directions[i]['values'] = np.array([directions[i]['values']])  
-            
-        N= len(directions[i]['values'])
+            directions[i]['values'] = np.array([directions[i]['values']]) 
+            N=len(directions[i]['values'])  
         if N == 1 :
-            directions[i]['type']= 'point'
-            
+            directions[i]['type']= 'point'      
         elif N > 1 :
             directions[i]['type']= 'array'
-            
         else:
             raise Exception(f'length of direction {directions[i]["name"]} was'
                             +'neagative or zero')
@@ -136,7 +314,7 @@ def create_points(x, y, z):
         not_max_idxs= [i for i in directions.keys()]
         
         del not_max_idxs[max_len_idx]
-        print(max_len_idx)
+        
         for not_max in not_max_idxs:     
             N= len(directions[max_len_idx]['values'])
             vals =np.ones(N)*directions[not_max]['values']
@@ -182,24 +360,24 @@ def variable_interpolation(data, variables, points='cells'):
 
     Parameters
     ----------
-    data: netcdf4 object 
-        A netCDF object that contains spatial data such as velocity of shear 
-        stess.These can be generated by running a Delft3D model.
+    data: NetCDF4 object 
+       A NetCDF4 object that contains spatial data, e.g. velocity or shear
+       stress generated by running a Delft3D model.  
     variables: array of strings
         Name of variables to interpolate, e.g. 'turkin1', 'ucx', 'ucy' and 'ucz'.
         The full list can be found using "data.variables.keys()" in the console.
     points: string, DataFrame  
-        Point to interpoate data onto. 
-          'cells'- interpolates all data into cell coordinate system (Default)
-          'faces'- interpolates all dada into face coordinate system 
+        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 
-          povided points 
+          povided points. Can be created with `create_points` function.
   
     Returns
     -------
     transformed_data: DataFrame  
-        Variables on specified grid points saved under the input varable names 
-        and the x, y, and z cordinates of those points. 
+        Variables on specified grid points saved under the input variable names 
+        and the x, y, and z coordinates of those points. 
     '''
     
     assert isinstance(points, (str, pd.DataFrame)),('points must be a string ' 
@@ -238,175 +416,117 @@ def variable_interpolation(data, variables, points='cells'):
     return transformed_data
 
 
-def get_all_data_points(data, variable, time_index= -1):  
+def get_all_data_points(data, variable, time_index=-1):  
     '''
-    Get data points from all layers in netcdf object generated from Delft3D using 
-    get_layer_data function. 
+    Get data points for a passed variable for all layers at a specified time from 
+    the Delft3D NetCDF4 object by iterating over the `get_layer_data` function. 
 
     Parameters
     ----------
-    data: netcdf4 object 
-       A netCDF object that contains spatial data such as velocity or shear stess.
-       These can be generated by running a Delft3D model. 
-       running a Delft3D model.   
+    data: Netcdf4 object 
+       A NetCDF4 object that contains spatial data, e.g. velocity or shear
+       stress, generated by running a Delft3D model.  
     variable: string
-        Delft3D outputs many vairables that can be called. The full list can be 
-        found using "data.variables.keys()" in the consol. 
+        Delft3D variable. The full list can be of variables can be
+        found using "data.variables.keys()" in the console. 
     time_index: int
-        A positive interger to pull the time step from the dataset. 
-        Defalt is late time step -1.  
+        An integer to pull the time step from the dataset. 
+        Default is last time step, found with the input -1.
         
     Returns
     -------
     all_data: DataFrame 
-        Dataframe with columns x, y, z, and variable. 
+        Dataframe with columns x, y, z, variable, and time. The value
+        "z" is the depth from the zero-water level at the surface.
     '''  
     
     assert isinstance(time_index, int), 'time_index  must be a int'
-    assert type(data)== netCDF4._netCDF4.Dataset, 'data must be nerCDF4 object'
-    assert variable in data.variables.keys(), 'varaiable not reconized'
+    assert type(data)== netCDF4._netCDF4.Dataset, 'data must be NetCDF4 object'
+    assert variable in data.variables.keys(), 'variable not recognized'
 
     max_time_index = len(data.variables[variable][:])
-    assert time_index <= max_time_index, (f'time_index must be less than the'
-                                          +'max time index {max_time_index}')
-    assert time_index >= -1, 'time_index must be greater than or equal to -1'
-
-    cords_to_layers= {'laydim': data.variables['LayCoord_cc'][:],
-                       'wdim': data.variables['LayCoord_w'][:]}
-    lay_element= 2 
-    layer_dim =  [v.name for v in data[variable].get_dims()][lay_element]
+    assert abs(time_index) <= max_time_index, (f'time_index must be less than'
+                                      +'the max time index {max_time_index}')
+
+    cords_to_layers= {'FlowElem_xcc FlowElem_ycc':{'name':'laydim', 
+                                    'coords':data.variables['LayCoord_cc'][:]},
+                       'FlowLink_xu FlowLink_yu': {'name':'wdim', 
+                                'coords':data.variables['LayCoord_w'][:]}}
+
+    layer_dim =  str(data.variables[variable].coordinates)
     
     try:    
-        cord_sys= cords_to_layers[layer_dim]
+        cord_sys= cords_to_layers[layer_dim]['coords']
     except: 
-        raise Exception('coordinates not recognized')
+        raise Exception('Coordinates not recognized.')
     else: 
-        Layer_percentages= np.ma.getdata(cord_sys, False) 
-        
-    bottom_depth=np.ma.getdata(data.variables['waterdepth'][time_index, :], False)
-    if layer_dim == 'wdim': 
-        #interpolate 
-        coords = str(data.variables['waterdepth'].coordinates).split()
-        x_laydim=np.ma.getdata(data.variables[coords[0]][:], False) 
-        y_laydim=np.ma.getdata(data.variables[coords[1]][:], False)
-        points_laydim = np.array([ [x, y] for x, y in zip(x_laydim, y_laydim)])
-        
-        coords_request = str(data.variables[variable].coordinates).split()
-        x_wdim=np.ma.getdata(data.variables[coords_request[0]][:], False) 
-        y_wdim=np.ma.getdata(data.variables[coords_request[1]][:], False)
-        points_wdim=np.array([ [x, y] for x, y in zip(x_wdim, y_wdim)])
-        
-        bottom_depth_wdim = interp.griddata(points_laydim, bottom_depth,
-                                            points_wdim)
-        
-        idx= np.where(np.isnan(bottom_depth_wdim))
-        
-        for i in idx: 
-            bottom_depth_wdim[i]= interp.griddata(points_laydim, bottom_depth,
-                                             points_wdim[i], method='nearest')
+        layer_percentages= np.ma.getdata(cord_sys, False) 
         
     x_all=[]
     y_all=[]
     z_all=[]
     v_all=[]
+    time_all=[]
     
-    N_layers = range(len(Layer_percentages))
-    for layer in N_layers:
+    layers = range(len(layer_percentages))
+    for layer in layers:
         layer_data= get_layer_data(data, variable, layer, time_index)
-        if layer_dim == 'wdim': 
-            z = [bottom_depth_wdim*Layer_percentages[layer]]
-        else: 
-            z = [bottom_depth*Layer_percentages[layer]]
+
         x_all=np.append(x_all, layer_data.x)
         y_all=np.append(y_all, layer_data.y)
-        z_all=np.append(z_all, z)
+        z_all=np.append(z_all, layer_data.z)
         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] for x, y, z, v in zip(x_all, y_all, 
-                                                                z_all, v_all)])
-    
-    all_data= pd.DataFrame(known_points, columns=['x','y','z',f'{variable}'])
+    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)])
     
-    return all_data
-
-
-def unorm(x, y ,z):
-    '''
-    Calculates the root mean squared value given three arrays. 
+    all_data= pd.DataFrame(known_points, columns=['x','y','z',f'{variable}', 'time'])
 
-    Parameters
-    ----------
-    x: array 
-        One input for the root mean squared calculation.(eq. x velocity) 
-    y: array
-        One input for the root mean squared calculation.(eq. y velocity) 
-    z: array
-        One input for the root mean squared calculation.(eq. z velocity) 
-
-    Returns
-    -------
-    unorm : array 
-       The root mean squared of x, y, and z.
-       
-    Example 
-    -------
-    If the inputs are [1,2,3], [4,5,6], and [7,8,9] the code take the 
-    cordinationg value from each array and calculates the root mean squared. 
-    The resulting output is [ 8.1240384 ,  9.64365076, 11.22497216].
-    '''
-    
-    assert isinstance(x,(np.ndarray, np.float64, pd.Series)), 'x must be an array'
-    assert isinstance(y,(np.ndarray, np.float64, pd.Series)), 'y must be an array'
-    assert isinstance(z,(np.ndarray, np.float64, pd.Series)), 'z must be an array'
-    assert all([len(x) == len(y), len (y) ==len (z)]), ('lengths of arrays must'
-                                                        +' match')
-
-    xyz = np.array([x,y,z]) 
-    unorm = np.linalg.norm(xyz, axis= 0)
+    return all_data
 
-    return unorm
 
 
 def turbulent_intensity(data, points='cells', time_index= -1,
                         intermediate_values = False ):
     '''
-    Calculated the turbulent intesity for a given data set for the specified
-    points. Assumes variable names: turkin1, ucx, ucy and ucz.
+    Calculate the turbulent intensity percentage for a given data set for the 
+    specified points. Assumes variable names: ucx, ucy, ucz and turkin1.
 
     Parameters
     ----------
-    data: netcdf4 object 
-       A netCDF object that contains spatial data such as velocity or shear stess.
-       These can be generated by running a Delft3D model.  
+    data: NetCDF4 object 
+       A NetCDF4 object that contains spatial data, e.g. velocity or shear
+       stress, generated by running a Delft3D model.
     points: string, DataFrame  
-        Point to interpoate data onto. 
-          'cells': interpolates all data into velocity coordinat system (Default)
-          'faces': interpolates all dada into TKE coordinate system 
-          DataFrame of x, y, and z corrdinates: Interpolates data onto user 
-          povided points 
-    time_index: float 
-        A positive interger to pull the time step from the dataset. Defalt is
+        Points to interpolate data onto. 
+          'cells': interpolates all data onto velocity coordinate system (Default).
+          'faces': interpolates all data onto the TKE coordinate system.
+          DataFrame of x, y, and z coordinates: Interpolates data onto user 
+          provided points. 
+    time_index: int 
+        An integer to pull the time step from the dataset. Default is
         late time step -1.  
-    intermediate_values: boolean
-        If true will return ucx, uxy, uxz,and turkine1 vaues in Dataframe 
-        if false will only return x,y,z, and turbulent intesity values. False 
-        is the default value      
-        
+    intermediate_values: boolean (optional)
+        If false the function will return position and turbulent intensity values. 
+        If true the function will return position(x,y,z) and values need to calculate
+        turbulent intensity (ucx, uxy, uxz and turkin1) in a Dataframe.  Default False.
+   
     Returns
     -------
-    TI_data: Dataframe
+      TI_data: Dataframe
         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- position in the x direction 
+            y- position in the y direction 
+            z- position in the z 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 
-            x- position in the x direstion 
-            y- position in the y direction 
-            z- position in the z direction 
     '''
     
     assert isinstance(points, (str, pd.DataFrame)),('points must a string or'
@@ -415,8 +535,11 @@ def turbulent_intensity(data, points='cells', time_index= -1,
        assert any([points == 'cells', points=='faces']), ('points must be cells'
                                                           +' or faces')
     assert isinstance(time_index, int), 'time_index  must be a int'
+    max_time_index= data['time'].shape[0]-1 # to account for zero index
+    assert abs(time_index) <= max_time_index, (f'time_index must be less than'
+                  +'the absolute value of the max time index {max_time_index}')
     assert type(data)== netCDF4._netCDF4.Dataset, 'data must be nerCDF4 object'
-    assert 'turkin1' in data.variables.keys(), ('Varaiable Turkine 1 not'
+    assert 'turkin1' in data.variables.keys(), ('Varaiable turkin1 not'
                                                 +' present in Data')
     assert 'ucx' in data.variables.keys(),'Varaiable ucx 1 not present in Data'
     assert 'ucy' in data.variables.keys(),'Varaiable ucy 1 not present in Data'
@@ -425,7 +548,6 @@ def turbulent_intensity(data, points='cells', time_index= -1,
     TI_vars= ['turkin1', 'ucx', 'ucy', 'ucz']
     TI_data_raw = {}
     for var in TI_vars:
-        #get all data
         var_data_df = get_all_data_points(data, var ,time_index)           
         TI_data_raw[var] = var_data_df 
     if type(points) == pd.DataFrame:  
@@ -461,7 +583,7 @@ def turbulent_intensity(data, points='cells', time_index= -1,
     TI_data.loc[zero_ind,'turkin1']=np.zeros(len(zero_ind))
     TI_data.loc[non_zero_ind,'turkin1']=[np.nan]*len(non_zero_ind)
         
-    TI_data['turbulent_intensity']= np.sqrt(2/3*TI_data['turkin1'])/u_mag
+    TI_data['turbulent_intensity']= np.sqrt(2/3*TI_data['turkin1'])/u_mag * 100 #%
     
     if intermediate_values == False:
         TI_data= TI_data.drop(TI_vars, axis = 1)


Test patch:
diff --git a/mhkit/tests/river/test_io.py b/mhkit/tests/river/test_io.py
index 805734b77..74f22a447 100644
--- a/mhkit/tests/river/test_io.py
+++ b/mhkit/tests/river/test_io.py
@@ -1,4 +1,3 @@
-
 from os.path import abspath, dirname, join, isfile, normpath, relpath
 from numpy.testing import assert_array_almost_equal
 from pandas.testing import assert_frame_equal
@@ -69,38 +68,90 @@ def test_request_usgs_data_instant(self):
         # Every 15 minutes or 4 times per hour
         self.assertEqual(data.shape, (10*24*4, 1))
 
+    def test_get_all_time(self): 
+        data= self.d3d_flume_data
+        seconds_run = river.io.d3d.get_all_time(data)
+        seconds_run_expected= np.ndarray(shape=(5,), buffer= np.array([0, 60, 120, 180, 240]), dtype=int)
+        np.testing.assert_array_equal(seconds_run, seconds_run_expected)
+        
+    def test_convert_time(self): 
+        data= self.d3d_flume_data
+        time_index = 2
+        seconds_run = river.io.d3d.index_to_seconds(data, time_index = time_index)
+        seconds_run_expected = 120 
+        self.assertEqual(seconds_run, seconds_run_expected)
+        seconds_run = 60
+        time_index= river.io.d3d.seconds_to_index(data, seconds_run = seconds_run)
+        time_index_expected = 1
+        self.assertEqual(time_index, time_index_expected)
+        seconds_run = 62
+        time_index= river.io.d3d.seconds_to_index(data, seconds_run = seconds_run)
+        time_index_expected = 1
+        output_expected= f'ERROR: invalid seconds_run. Closest seconds_run found {time_index_expected}'
+        self.assertWarns(UserWarning)
 
     def test_layer_data(self): 
         data=self.d3d_flume_data
-        variable= 'ucx'
-        layer=2 
-        time_index= 3
-        layer_data= river.io.d3d.get_layer_data(data, variable, layer, time_index)
-        layer_compare = 2
-        time_index_compare= 4
-        layer_data_expected= river.io.d3d.get_layer_data(data,
-                                                        variable, layer_compare,
-                                                        time_index_compare)
-       
-        assert_array_almost_equal(layer_data.x,layer_data_expected.x, decimal = 2)
-        assert_array_almost_equal(layer_data.y,layer_data_expected.y, decimal = 2)
-        assert_array_almost_equal(layer_data.v,layer_data_expected.v, decimal= 2)
+        variable = ['ucx', 's1']
+        for var in variable:
+            layer=2 
+            time_index= 3
+            layer_data= river.io.d3d.get_layer_data(data, var, layer, time_index)
+            layer_compare = 2
+            time_index_compare= 4
+            layer_data_expected= river.io.d3d.get_layer_data(data,
+                                                            var, layer_compare,
+                                                            time_index_compare)
+           
+            assert_array_almost_equal(layer_data.x,layer_data_expected.x, decimal = 2)
+            assert_array_almost_equal(layer_data.y,layer_data_expected.y, decimal = 2)
+            assert_array_almost_equal(layer_data.v,layer_data_expected.v, decimal= 2)
         
+
         
     def test_create_points(self):
         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)
-        
         x=[1,2,3,1,2,3,1,2,3]
         y=[1,1,1,2,2,2,3,3,3]
         z=[1,1,1,1,1,1,1,1,1]
+        points_array= np.array([ [x_i, y_i, z_i] for x_i, y_i, z_i in zip(x, y, z)]) 
+        points_expected= pd.DataFrame(points_array, columns=('x','y','z'))
+        assert_array_almost_equal(points, points_expected,decimal = 2)
         
+        x=np.linspace(1, 3, num= 3)
+        y=2
+        z=1 
+        points= river.io.d3d.create_points(x,y,z)
+        x=[1,2,3]
+        y=[2,2,2]
+        z=[1,1,1]
         points_array= np.array([ [x_i, y_i, z_i] for x_i, y_i, z_i in zip(x, y, z)]) 
         points_expected= pd.DataFrame(points_array, columns=('x','y','z'))
         assert_array_almost_equal(points, points_expected,decimal = 2)  
         
+        x=3
+        y=2
+        z=1 
+        points= river.io.d3d.create_points(x,y,z)
+        output_expected='Can provide at most two arrays'
+        self.assertWarns(UserWarning)
+        
+    def test_variable_interpolation(self):
+        data=self.d3d_flume_data
+        variables= ['ucx','turkin1']
+        transformes_data= river.io.d3d.variable_interpolation(data, variables, points= 'faces')
+        self.assertEqual(np.size(transformes_data['ucx']), np.size(transformes_data['turkin1']))
+        transformes_data= river.io.d3d.variable_interpolation(data, variables, points= 'cells')
+        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)
+        transformes_data= river.io.d3d.variable_interpolation(data, variables, points= points)
+        self.assertEqual(np.size(transformes_data['ucx']), np.size(transformes_data['turkin1']))
         
     def test_get_all_data_points(self): 
         data=self.d3d_flume_data
@@ -124,7 +175,7 @@ def test_unorm(self):
     
     def test_turbulent_intensity(self): 
         data=self.d3d_flume_data
-        time_step= -1
+        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)
@@ -132,26 +183,45 @@ def test_turbulent_intensity(self):
         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'])
         
-        TI= river.io.d3d.turbulent_intensity(data, points, time_step)
+        TI= river.io.d3d.turbulent_intensity(data, points, time_index)
 
         TI_vars= ['turkin1', 'ucx', 'ucy', 'ucz']
         TI_data_raw = {}
         for var in TI_vars:
             #get all data
-            var_data_df = river.io.d3d.get_all_data_points(data, var,time_step)           
+            var_data_df = river.io.d3d.get_all_data_points(data, var,time_index)           
             TI_data_raw[var] = var_data_df 
             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']])
+            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_raw[var][var],
+                                [points['x'][i],points['y'][i], points['z'][i]],
+                                method='nearest')
         
         u_mag=river.io.d3d.unorm(TI_data['ucx'],TI_data['ucy'], TI_data['ucz'])
-        turbulent_intensity_expected= np.sqrt(2/3*TI_data['turkin1'])/u_mag
+        turbulent_intensity_expected= (np.sqrt(2/3*TI_data['turkin1'])/u_mag)*100
        
         
         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)
+        turkin1= river.io.d3d.get_all_data_points(data, 'turkin1',time_index)
+        turkin1_size= np.size(turkin1)
+        self.assertEqual(TI_size, turkin1_size)
+        
+        TI = river.io.d3d.turbulent_intensity(data, points='cells')
+        TI_size = np.size(TI)
+        ucx= river.io.d3d.get_all_data_points(data, 'ucx',time_index)
+        ucx_size= np.size(ucx)
+        self.assertEqual(TI_size, ucx_size)
 if __name__ == '__main__':
     unittest.main()