From 26ce4835775e609ce2e44a0a4ae64c143ac23ce6 Mon Sep 17 00:00:00 2001
From: dtgaebe <86246113+dtgaebe@users.noreply.github.com>
Date: Mon, 26 Jun 2023 13:34:37 -0700
Subject: [PATCH 01/20] Initial setup of utils functions"

---
 wecopttool/__init__.py  |  1 +
 wecopttool/utilities.py | 65 +++++++++++++++++++++++++++++++++++++++++
 2 files changed, 66 insertions(+)
 create mode 100644 wecopttool/utilities.py

diff --git a/wecopttool/__init__.py b/wecopttool/__init__.py
index d36607f3..88ba9cb4 100644
--- a/wecopttool/__init__.py
+++ b/wecopttool/__init__.py
@@ -33,6 +33,7 @@
 from wecopttool import waves
 from wecopttool import hydrostatics
 from wecopttool import pto
+from wecopttool import utilities
 
 try:
   from wecopttool import geom
diff --git a/wecopttool/utilities.py b/wecopttool/utilities.py
new file mode 100644
index 00000000..dceb67d3
--- /dev/null
+++ b/wecopttool/utilities.py
@@ -0,0 +1,65 @@
+"""Functions that are useful for WEC analysis and design.
+"""
+
+
+from __future__ import annotations
+
+
+__all__ = [
+    "intrinsic_impedance"
+    "natural_frequency",
+    "plot_bem_results"
+]
+
+
+from typing import Optional, Union
+import logging
+from pathlib import Path
+
+import numpy as np
+from xarray import DataArray
+from numpy.typing import ArrayLike
+# from autograd.numpy import ndarray
+from xarray import DataArray, Dataset
+
+# from wecopttool.core import linear_hydrodynamics
+
+
+# logger
+_log = logging.getLogger(__name__)
+
+def natural_frequency(impedance: DataArray, freq: ArrayLike
+                      ) -> tuple[ArrayLike, int]:
+    """Find the natural frequency based on the lowest magnitude impedance.
+
+    Parameters
+    ----------
+    impedance: DataArray
+        Complex intrinsic impedance matrix. 
+        Dimensions: omega, radiating_dofs, influenced_dofs
+    freq: list[float]
+        Frequencies.
+
+    Returns
+    -------
+    f_n: float
+        Natural frequencies.
+    ind: int
+        Index of natural frequencies.
+
+    Examples
+    --------
+    import capytaine as cpy
+    
+    hydrostatic_stiffness = lupa_fb.hydrostatic_stiffness
+    hydro_data = wecopttool.linear_hydrodynamics(bem_data, inertia_matrix, hydrostatic_stiffness)
+    hydro_data = wecopttool.check_linear_damping(hydro_data)
+    impedance = wecopttool.hydrodynamic_impedance(hydro_data)
+    """
+    #TODO: Only calculate for the dofs that have natural restoring force,
+    #heave, roll, pitch
+    np.diag(np.abs(impedance).argmin(dim = 'omega'))
+    ind = np.argmin(np.abs(impedance), axis=0)
+    f_n = freq[ind]
+
+    return f_n, ind

From 779e1cc849436ab41b826c0d005741cbc999ff70 Mon Sep 17 00:00:00 2001
From: dtgaebe <86246113+dtgaebe@users.noreply.github.com>
Date: Mon, 26 Jun 2023 15:52:27 -0700
Subject: [PATCH 02/20] Updated notation and doc string (added example use) of
 impedance calculation in core

---
 wecopttool/core.py | 24 ++++++++++++++++++++++--
 1 file changed, 22 insertions(+), 2 deletions(-)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index cb92b1f9..161c4d50 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -2273,13 +2273,33 @@ def hydrodynamic_impedance(hydro_data: Dataset) -> Dataset:
     hydro_data
         Dataset with linear hydrodynamic coefficients produced by
         :py:func:`wecopttool.linear_hydrodynamics`.
+
+    
+    Examples
+    ----------
+    The :py:fun:`wecopttool.run_bem` function only returns the
+        boundary elemet results for the given :py:meth:`capytaine`
+        floating body :python:`fb` and the frequency vector :python:`meth`
+
+    >>> bem_data = wot.run_bem(fb, freq)
+
+    The user needs to define the inertial and hydrostatic stiffness properties
+    :python:`inertia_matrix` and :python:`hydrostatic_stiffness` to use 
+    :py:func:`wecopttool.linear_hydrodynamics`, followed by the check 
+    :py:func:`wecopttool.check_linear_damping`.
+
+    >>> hydro_data = wot.linear_hydrodynamics(bem_data, 
+                                              inertia_matrix,
+                                              hydrostatic_stiffness)
+    >>> hydro_data = wot.check_linear_damping(hydro_data)
+    >>> impedance = wot.hydrodynamic_impedance(hydro_data)
     """
 
-    Zi = (hydro_data['inertia_matrix'] \
+    intrinsic_impedance = (hydro_data['inertia_matrix'] \
         + hydro_data['added_mass'])*1j*hydro_data['omega'] \
             + hydro_data['radiation_damping'] + hydro_data['friction'] \
                 + hydro_data['hydrostatic_stiffness']/1j/hydro_data['omega']
-    return Zi
+    return intrinsic_impedance
 
 
 def atleast_2d(array: ArrayLike) -> ndarray:

From 33ac6bcf70da7bd8eb1411c3109ae6e2bc1977ac Mon Sep 17 00:00:00 2001
From: dtgaebe <86246113+dtgaebe@users.noreply.github.com>
Date: Mon, 26 Jun 2023 15:53:21 -0700
Subject: [PATCH 03/20] added functions for plotting hydrodynamic coefficients
 and for plotting bode graph of impedance

---
 wecopttool/utilities.py | 81 ++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 79 insertions(+), 2 deletions(-)

diff --git a/wecopttool/utilities.py b/wecopttool/utilities.py
index dceb67d3..5b29072c 100644
--- a/wecopttool/utilities.py
+++ b/wecopttool/utilities.py
@@ -21,6 +21,7 @@
 from numpy.typing import ArrayLike
 # from autograd.numpy import ndarray
 from xarray import DataArray, Dataset
+import matplotlib.pyplot as plt
 
 # from wecopttool.core import linear_hydrodynamics
 
@@ -58,8 +59,84 @@ def natural_frequency(impedance: DataArray, freq: ArrayLike
     """
     #TODO: Only calculate for the dofs that have natural restoring force,
     #heave, roll, pitch
-    np.diag(np.abs(impedance).argmin(dim = 'omega'))
-    ind = np.argmin(np.abs(impedance), axis=0)
+
+    ind = np.diag(np.abs(impedance).argmin(dim = 'omega'))
     f_n = freq[ind]
 
     return f_n, ind
+
+
+def plot_hydrodynamic_coefficients(bem_data):
+    """Plots hydrodynamic coefficients (added mass, radiation damping,
+       and wave excitation)based on BEM data.
+
+
+    Parameters
+    ----------
+    bem_data
+        Linear hydrodynamic coefficients obtained using the boundary
+        element method (BEM) code Capytaine, with sign convention
+        corrected.
+
+    
+    """
+    radiating_dofs = bem_data.radiating_dof.values
+    influenced_dofs = bem_data.influenced_dof.values
+
+    # plots
+    fig_am, ax_am = plt.subplots(len(radiating_dofs), len(influenced_dofs),
+                                tight_layout=True, sharex=True, 
+                                figsize=(3*len(radiating_dofs), 3*len(influenced_dofs)), squeeze=False)
+    fig_rd, ax_rd = plt.subplots(len(radiating_dofs), len(influenced_dofs),
+                                tight_layout=True, sharex=True, 
+                               figsize=(3*len(radiating_dofs), 3*len(influenced_dofs)), squeeze=False)
+    fig_ex, ax_ex = plt.subplots(len(influenced_dofs), 1,
+                                tight_layout=True, sharex=True, 
+                                figsize=(3, 3*len(radiating_dofs)), squeeze=False)
+
+    # plot titles
+    fig_am.suptitle('Added Mass Coefficients', fontweight='bold')
+    fig_rd.suptitle('Radiation Damping Coefficients', fontweight='bold')
+    fig_ex.suptitle('Wave Excitation Coefficients', fontweight='bold')
+
+    sp_idx = 0
+    for i, rdof in enumerate(radiating_dofs):
+        for j, idof in enumerate(influenced_dofs):
+            sp_idx += 1
+            if i == 0:
+                np.abs(bem_data.diffraction_force.sel(influenced_dof=idof)).plot(
+                    ax=ax_ex[j,0], linestyle='dashed', label='Diffraction force')
+                np.abs(bem_data.Froude_Krylov_force.sel(influenced_dof=idof)).plot(
+                    ax=ax_ex[j,0], linestyle='dashdot', label='Froude-Krylov force')
+                ex_handles, ex_labels = ax_ex[j,0].get_legend_handles_labels()
+                ax_ex[j,0].set_title(f'{idof}')
+                ax_ex[j,0].set_xlabel('')
+                ax_ex[j,0].set_ylabel('')
+            if j <= i:
+                bem_data.added_mass.sel(
+                    radiating_dof=rdof, influenced_dof=idof).plot(ax=ax_am[i, j])
+                bem_data.radiation_damping.sel(
+                    radiating_dof=rdof, influenced_dof=idof).plot(ax=ax_rd[i, j])
+                if i == len(radiating_dofs)-1:
+                    ax_am[i, j].set_xlabel(f'$\omega$', fontsize=10)
+                    ax_rd[i, j].set_xlabel(f'$\omega$', fontsize=10)
+                    ax_ex[j, 0].set_xlabel(f'$\omega$', fontsize=10)
+                else:
+                    ax_am[i, j].set_xlabel('')
+                    ax_rd[i, j].set_xlabel('')
+                if j == 0:
+                    ax_am[i, j].set_ylabel(f'{rdof}', fontsize=10)
+                    ax_rd[i, j].set_ylabel(f'{rdof}', fontsize=10)
+                else:
+                    ax_am[i, j].set_ylabel('')
+                    ax_rd[i, j].set_ylabel('')
+                if j == i:
+                    ax_am[i, j].set_title(f'{idof}', fontsize=10)
+                    ax_rd[i, j].set_title(f'{idof}', fontsize=10)
+                else:
+                    ax_am[i, j].set_title('')
+                    ax_rd[i, j].set_title('')
+            else:
+                fig_am.delaxes(ax_am[i, j])
+                fig_rd.delaxes(ax_rd[i, j])
+    fig_ex.legend(ex_handles, ex_labels, loc=(0.08, 0), ncol=2, frameon=False)

From 2f5ab58a0105912ba24d7d32b4b106efb5ca9c70 Mon Sep 17 00:00:00 2001
From: dtgaebe <86246113+dtgaebe@users.noreply.github.com>
Date: Mon, 26 Jun 2023 16:01:06 -0700
Subject: [PATCH 04/20] added impedance bode plot function

---
 wecopttool/utilities.py | 65 +++++++++++++++++++++++++++++++++--------
 1 file changed, 53 insertions(+), 12 deletions(-)

diff --git a/wecopttool/utilities.py b/wecopttool/utilities.py
index 5b29072c..1e908d66 100644
--- a/wecopttool/utilities.py
+++ b/wecopttool/utilities.py
@@ -8,7 +8,8 @@
 __all__ = [
     "intrinsic_impedance"
     "natural_frequency",
-    "plot_bem_results"
+    "plot_bem_results",
+    "plot_bode_intrinsic_impedance"
 ]
 
 
@@ -36,7 +37,8 @@ def natural_frequency(impedance: DataArray, freq: ArrayLike
     Parameters
     ----------
     impedance: DataArray
-        Complex intrinsic impedance matrix. 
+        Complex intrinsic impedance matrix produced by
+        :py:func:`wecopttool.hydrodynamic_impedance`.
         Dimensions: omega, radiating_dofs, influenced_dofs
     freq: list[float]
         Frequencies.
@@ -47,18 +49,11 @@ def natural_frequency(impedance: DataArray, freq: ArrayLike
         Natural frequencies.
     ind: int
         Index of natural frequencies.
-
-    Examples
-    --------
-    import capytaine as cpy
-    
-    hydrostatic_stiffness = lupa_fb.hydrostatic_stiffness
-    hydro_data = wecopttool.linear_hydrodynamics(bem_data, inertia_matrix, hydrostatic_stiffness)
-    hydro_data = wecopttool.check_linear_damping(hydro_data)
-    impedance = wecopttool.hydrodynamic_impedance(hydro_data)
     """
+
     #TODO: Only calculate for the dofs that have natural restoring force,
-    #heave, roll, pitch
+    #heave, roll, pitch, but issue is that dofs might not necessairly be called\
+    #  'Heave', 'Roll', 'Pitch', otherwise can print warning that results for other dofs are meaningless
 
     ind = np.diag(np.abs(impedance).argmin(dim = 'omega'))
     f_n = freq[ind]
@@ -140,3 +135,49 @@ def plot_hydrodynamic_coefficients(bem_data):
                 fig_am.delaxes(ax_am[i, j])
                 fig_rd.delaxes(ax_rd[i, j])
     fig_ex.legend(ex_handles, ex_labels, loc=(0.08, 0), ncol=2, frameon=False)
+
+
+def plot_bode_intrinsic_impedance(impedance: DataArray):
+    """Find the natural frequency based on the lowest magnitude impedance.
+
+    Parameters
+    ----------
+    impedance: DataArray
+        Complex intrinsic impedance matrix produced by
+        :py:func:`wecopttool.hydrodynamic_impedance`.
+        Dimensions: omega, radiating_dofs, influenced_dofs
+
+
+    """
+    radiating_dofs = impedance.radiating_dof.values
+    influenced_dofs = impedance.influenced_dof.values
+    mag = 20.0 * np.log10(np.abs(impedance))
+    phase = np.rad2deg(np.unwrap(np.angle(impedance)))
+    freq = impedance.omega.values   #TODO: freq units?!
+    fig, axes = plt.subplots(2*len(radiating_dofs), len(influenced_dofs),
+                                tight_layout=True, sharex=True, 
+                                figsize=(3*len(radiating_dofs), 3*len(influenced_dofs)), squeeze=False)
+    fig.suptitle('Impedance Bode Plots \n Mag (dB), Phase (deg)', fontweight='bold')
+
+    sp_idx = 0
+    for i, rdof in enumerate(radiating_dofs):
+        for j, idof in enumerate(influenced_dofs):
+            sp_idx += 1
+            axes[2*i, j].semilogx(freq, mag[i, j, :])    # Bode magnitude plot
+            axes[2*i+1, j].semilogx(freq, phase[i, j, :])    # Bode phase plot
+            axes[2*i, j].grid(True, which = 'both')
+            axes[2*i+1, j].grid(True, which = 'both')
+
+            if i == len(radiating_dofs)-1:
+                axes[2*i+1, j].set_xlabel(f'Frequency (Hz)', fontsize=10)
+            else:
+                axes[i, j].set_xlabel('')
+            if j == 0:
+                axes[2*i, j].set_ylabel(f'{rdof} \n Mag. (dB)', fontsize=10)
+                axes[2*i+1, j].set_ylabel(f'Phase. (deg)', fontsize=10)
+            else:
+                axes[i, j].set_ylabel('')
+            if i == 0:
+                axes[i, j].set_title(f'{idof}', fontsize=10)
+            else:
+                axes[i, j].set_title('')

From ab43675f7ca0955fc0cad999675387cf5313e41f Mon Sep 17 00:00:00 2001
From: dtgaebe <86246113+dtgaebe@users.noreply.github.com>
Date: Thu, 29 Jun 2023 11:26:56 -0700
Subject: [PATCH 05/20] latest version before draft pr

---
 wecopttool/utilities.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/wecopttool/utilities.py b/wecopttool/utilities.py
index 1e908d66..72ec6e73 100644
--- a/wecopttool/utilities.py
+++ b/wecopttool/utilities.py
@@ -51,14 +51,15 @@ def natural_frequency(impedance: DataArray, freq: ArrayLike
         Index of natural frequencies.
     """
 
-    #TODO: Only calculate for the dofs that have natural restoring force,
-    #heave, roll, pitch, but issue is that dofs might not necessairly be called\
-    #  'Heave', 'Roll', 'Pitch', otherwise can print warning that results for other dofs are meaningless
-
-    ind = np.diag(np.abs(impedance).argmin(dim = 'omega'))
-    f_n = freq[ind]
-
-    return f_n, ind
+    restoring_dofs = ['Heave','Roll','Pitch']
+    indeces = [np.abs(impedance.loc[rdof,idof]).argmin(dim = 'omega') 
+                for rdof in impedance.radiating_dof 
+                for idof in impedance.influenced_dof 
+                if rdof == idof  #considering modes to be independent
+                and any([df in str(rdof.values) for df in restoring_dofs])]
+    f_n = [freq[indx.values] for indx in indeces]
+
+    return f_n, indeces
 
 
 def plot_hydrodynamic_coefficients(bem_data):

From 08103cad773af2f41cff9fecd467a6e41bf593ea Mon Sep 17 00:00:00 2001
From: dtgaebe <86246113+dtgaebe@users.noreply.github.com>
Date: Thu, 6 Jul 2023 10:52:55 -0700
Subject: [PATCH 06/20] adding zero freq function

---
 wecopttool/utilities.py | 23 +++++++++++++++++------
 1 file changed, 17 insertions(+), 6 deletions(-)

diff --git a/wecopttool/utilities.py b/wecopttool/utilities.py
index 72ec6e73..6fa3ed26 100644
--- a/wecopttool/utilities.py
+++ b/wecopttool/utilities.py
@@ -9,7 +9,8 @@
     "intrinsic_impedance"
     "natural_frequency",
     "plot_bem_results",
-    "plot_bode_intrinsic_impedance"
+    "plot_bode_intrinsic_impedance",
+    "add_zerofreq_to_xr"
 ]
 
 
@@ -21,7 +22,7 @@
 from xarray import DataArray
 from numpy.typing import ArrayLike
 # from autograd.numpy import ndarray
-from xarray import DataArray, Dataset
+from xarray import DataArray, concat
 import matplotlib.pyplot as plt
 
 # from wecopttool.core import linear_hydrodynamics
@@ -138,8 +139,8 @@ def plot_hydrodynamic_coefficients(bem_data):
     fig_ex.legend(ex_handles, ex_labels, loc=(0.08, 0), ncol=2, frameon=False)
 
 
-def plot_bode_intrinsic_impedance(impedance: DataArray):
-    """Find the natural frequency based on the lowest magnitude impedance.
+def plot_bode_impedance(impedance: DataArray, title: str):
+    """Plot Bode graph from wecoptool impedance data array.
 
     Parameters
     ----------
@@ -154,11 +155,11 @@ def plot_bode_intrinsic_impedance(impedance: DataArray):
     influenced_dofs = impedance.influenced_dof.values
     mag = 20.0 * np.log10(np.abs(impedance))
     phase = np.rad2deg(np.unwrap(np.angle(impedance)))
-    freq = impedance.omega.values   #TODO: freq units?!
+    freq = impedance.omega.values/2/np.pi   
     fig, axes = plt.subplots(2*len(radiating_dofs), len(influenced_dofs),
                                 tight_layout=True, sharex=True, 
                                 figsize=(3*len(radiating_dofs), 3*len(influenced_dofs)), squeeze=False)
-    fig.suptitle('Impedance Bode Plots \n Mag (dB), Phase (deg)', fontweight='bold')
+    fig.suptitle(title + ' Bode Plots \n Mag (dB), Phase (deg)', fontweight='bold')
 
     sp_idx = 0
     for i, rdof in enumerate(radiating_dofs):
@@ -182,3 +183,13 @@ def plot_bode_intrinsic_impedance(impedance: DataArray):
                 axes[i, j].set_title(f'{idof}', fontsize=10)
             else:
                 axes[i, j].set_title('')
+
+def add_zerofreq_to_xr(data):
+    """Add a zero-frequency component to an :python:`xarray.Dataset`.  
+      Frequency variable must be called :python:`omega`.    """    
+    if not np.isclose(data.coords['omega'][0].values, 0):
+        tmp = data.isel(omega=0).copy(deep=True) * 0   
+        tmp['omega'] = tmp['omega'] * 0                      
+        data = concat([tmp, data], dim='omega')
+    return data
+

From 9dfa37a91b19de80cb0302b1f21afc82a233f59b Mon Sep 17 00:00:00 2001
From: dtgaebe <86246113+dtgaebe@users.noreply.github.com>
Date: Wed, 2 Aug 2023 13:29:42 -0700
Subject: [PATCH 07/20] changes to pto.py that Im unaware what they were

---
 wecopttool/pto.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wecopttool/pto.py b/wecopttool/pto.py
index 2d6ba3e0..a73bbc7c 100644
--- a/wecopttool/pto.py
+++ b/wecopttool/pto.py
@@ -60,7 +60,7 @@ def __init__(self,
         loss: Optional[TLOSS] = None,
         names: Optional[list[str]] = None,
     ) -> None:
-        """Create a PTO object.
+        """Create a PTO object
 
         The :py:class:`wecopttool.pto.PTO` class describes the
         kinematics, control logic, impedance and/or non-linear power

From 7d12b97c823c3937a280b99f76fc9be140460c14 Mon Sep 17 00:00:00 2001
From: dtgaebe <86246113+dtgaebe@users.noreply.github.com>
Date: Tue, 24 Oct 2023 22:13:29 -0700
Subject: [PATCH 08/20] slight updates

---
 examples/tutorial_1_WaveBot.ipynb | 686 +++++++++++++++++++++++++++++-
 wecopttool/utilities.py           |  16 +-
 2 files changed, 691 insertions(+), 11 deletions(-)

diff --git a/examples/tutorial_1_WaveBot.ipynb b/examples/tutorial_1_WaveBot.ipynb
index 34c0cd99..c4029aa0 100644
--- a/examples/tutorial_1_WaveBot.ipynb
+++ b/examples/tutorial_1_WaveBot.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,9 +128,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fb.show_matplotlib()\n",
     "_ = wb.plot_cross_section(show=True)  # specific to WaveBot"
@@ -150,9 +171,160 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=8.796, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.97e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.111, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.43e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.425, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.94e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.739, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.50e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.053, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.10e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.367, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.73e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.681, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.40e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.996, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.10e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.310, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.82e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.624, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.56e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.938, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.32e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.252, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.11e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.566, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.90e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.881, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.72e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.195, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.54e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.509, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.38e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.823, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.23e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.137, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.08e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.451, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.95e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.765, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.83e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.080, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.71e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.394, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.60e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.708, depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.50e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=8.796, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.97e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.111, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.43e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.425, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.94e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.739, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.50e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.053, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.10e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.367, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.73e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.681, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.40e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.996, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.10e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.310, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.82e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.624, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.56e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.938, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.32e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.252, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.11e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.566, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.90e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.881, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.72e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.195, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.54e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.509, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.38e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.823, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.23e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.137, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.08e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.451, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.95e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.765, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.83e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.080, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.71e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.394, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.60e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.708, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.50e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n"
+     ]
+    }
+   ],
    "source": [
     "f1 = 0.05\n",
     "nfreq = 50\n",
@@ -161,6 +333,506 @@
     "bem_data = wot.run_bem(fb, freq)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "XX TODO: Illustration if intrinsic impedance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:wecopttool.core:Linear damping for DOF \"Heave\" has negative or close to zero terms. Shifting up via linear friction of 44.31820709090029 N/(m/s).\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.7150000000000001, <xarray.DataArray ()>\n",
+       "array(1890.73905495)\n",
+       "Coordinates:\n",
+       "    g            float64 9.81\n",
+       "    rho          float64 1.025e+03\n",
+       "    body_name    <U16 'WaveBot_immersed'\n",
+       "    water_depth  float64 inf, 'f_n = 0.65')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\dtgaebe\\anaconda3\\envs\\wecopttool_dev\\lib\\site-packages\\IPython\\core\\events.py:89: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
+      "  func(*args, **kwargs)\n",
+      "c:\\Users\\dtgaebe\\anaconda3\\envs\\wecopttool_dev\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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gACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAv9s787Doir7N4DfMwMMOyoomwjuirggau5LCZrlkqmZpriU+WpvmZXm2yKVmZqppaVZbr9cM5d8zY1y3xXFfVcEQVzYd4aZ5/cHcV4m9kdgQO7PdXEB5zznme/MOTP3nJ2IpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISEqxA2T58uVQqVRQqVQYM2aM0bhvv/1WGdeiRQujcUeOHFHG9ezZs3SqLgX79+9X6sr5UavVsLOzQ4sWLfDhhx8iPj7+iR5jwYIFCAoKQlBQULGnWblypVJP9+7dpR87LCxMeeytW7eWePrcdZSkfhlhYWGl8pz/qXv37kq/K1euLLV+TcnLy0t5Tk+b3MtB7vekjY0NmjRpgrfeeguRkZFl8thl+br+8zmpVCpYWFigTp06CAwMxPXr1/Nt7+XlJf2Y+/fvV97/oaGhT/YECmFW3IZdu3ZV/j506JDRuNz/X7x4EXFxcahevToA4ODBg8q4Ll26SBdaHoQQSE5OxoULF3DhwgXs27cPJ06ckO5vwYIFuHv3LgCU+YfwP4WFheGzzz4DAAQGBmLAgAHl+vhEpUEIgdTUVFy7dg3Xrl3D9u3bcfnyZVhbW5u6tCei0+kQERGB//u//8OmTZtw4MAB+Pn5lVr/+/fvV97/Xl5eaNWqVan1nVux10AaNGgAV1dXAMDNmzdx//59ZVzuABFC4MiRI8r/lSFAPD09IYSAXq/H9u3blW8hJ0+exOXLl01cXfnS6XTIysrCqFGjIISAEKLMw8/Ly0t5rP3795fpY1HlIYSAwWDAiRMnlMC4e/dupV5G7ty5AyEEbt++jXbt2gEAUlJSMHXqVBNXJqdE+0ByB0BOaFy7dg0PHz6ERqNB7969jcbp9XocPXoUAGBmZob27dsDAJYuXYrnnnsOtWvXho2NDSwsLFC7dm0MHToU58+fVx5j8uTJyurc2rVrjWr58ssvlXGLFi1Shp87dw7Dhw9H7dq1YWFhgRo1aqB3797466+/in4x1Gq88MILqFmzpjIsNTXVqM2ZM2cwdOhQuLu7K/0/99xz2LRpk9ImZ/NPztoHYLwaKyP3JqXp06fj22+/RePGjWFlZYVmzZphzZo1Stvu3bujR48eyv+rVq1Sph01ahQAYNSoUcqwrVu3Yvz48XB2doZWq8W9e/cK3ISVe7PQsWPHEBgYCEdHR1SrVg3PP/88bt26ZVT39u3b0a1bN1SvXh1mZmZwdHREq1atMHbsWMTFxQEofBNWXFwcPvroI7Ro0QI2NjawsrJCgwYNMH78eKnX8Z/P4ciRIxg8eDBsbW1Rq1YtTJ06FTqdDseOHUPnzp1hbW2Nhg0bYv78+RBC5Ds/goKC8O2336JRo0bQarVo1KgRfvjhhzyP+/DhQ7z33nto0qQJrKysYGNjg7Zt2+LHH3806hsAYmNjMXbsWDg6OsLGxgb+/v5G743c0tPTMXr0aLRq1Qo1a9aEhYUFbGxs0KJFC3z66adISUkxap97E8mpU6fg7+8PGxsbuLu7Y/z48UhOTjZqr9frsWTJEnTu3BnVqlWDhYUF3N3d0b9/f6MvksnJyfjss8+M5lXz5s0xa9YsZGZmlng+5dTarl07+Pj4KMP++Z68efMmxo4dCy8vL1hYWMDe3h4dO3bETz/99ESvK5D9hWrBggVo164d7OzsoNVq0bhxY3z44YdITEyUek4AULduXUyZMkX5//jx40VOk5SUhE8++QQ+Pj6wtrZW3vsff/yxUS0qlUpZ+wCA0aNH59mUe/ToUfTu3RtOTk4wMzND9erV0axZM4wYMQK3b98u/hMRJbBo0SIBQAAQEydOFEIIsXTpUgFAtGnTRixcuFAAEB06dBBCCHH69Gml/TPPPKP0079/f2X4P39sbW3F9evXhRBCXLlyRRneu3dvo1qaNGkiAAgrKysRFxcnhBDi999/F+bm5vn2q1KpxOLFi5Xp9+3bp4zz9PQUQgih1+vFzp07hUqlEgBEw4YNhU6nU6bZvHlzgf0DEO+//74QQogVK1YU2Kaolzz3tN26dct3ePXq1fPt98iRI0IIIbp161bgYwcGBgohhAgMDFSGOTk5GbW5c+eO0eNNnz5dqSN33/nV0bRpU5GVlaXMfzMzswJruXHjhhBCiDt37uT7nO/cuSM8PDzyndbBwaHQ1/Gfta5YsSLf4TVr1szTd79+/YSlpWWe4WvXrs13fuTXBwAxe/Zspf2tW7eEq6trga/F0KFDlbYZGRnCz88vTxt7e3tha2ubZzmKi4srdHkLCAgwel1yhltZWQmtVpun/bhx44xq6dmzZ4F9nz17VgghRExMjPD29i6wXdeuXUVGRkah8yv3cpDz/AwGgzh16pSwsbERAESNGjVETEyMMs2xY8eMXpN//gwePFgYDAap1zU9Pb3Q91LTpk1FbGxsoc8p9+ud897KsXHjRmW4jY1NnvY5n0tCCPHo0SPRuHHjAmtp3LixePz4cZ7H++fPihUrRERERKGvWXBwcJHPSam12C2FEOfPn1cepEWLFkIIIUaMGCEAiHfffVecO3dOABDm5uYiNTVVzJs3T2mf8+EqhBC7d+8Wp0+fFo8fPxY6nU7ExMSIjz/+WGk7efJkpW2XLl0EAKHRaMT9+/eFEEKcPHlSaZvzgZiamqq8kb28vMSpU6dERkaGuHbtmvLCW1lZiUePHgkhjAMkv58aNWqI8+fPK3WkpqYafdB+//33IjExUezdu1fY29srw0+ePKlM4+npmWehLEpxAkSj0Yh169aJhIQEMWXKFGX4m2++qbTP/fxyXqPccgdItWrVxLZt20RycrK4evWqSElJKVaA+Pj4iKtXr4p79+6Jpk2bKsOPHTsmhBDim2++UYZt2LBBZGZmiocPH4qjR4+KTz/9VJmfBQVI3759leHt27cXZ86cESkpKeLy5cvi888/L/K1LE6AdOjQQURGRopjx44Zzf/u3buLBw8eiHXr1inDevXqle/80Gq14r///a9ISkoSK1euNPqAzvmAefHFFwUAYWZmJjZu3ChSU1PFgwcPxODBg5X227dvF0IIsXz5cmVYvXr1xPnz50VsbKz417/+lecDVojsD7o1a9aIW7duiaSkJJGZmSlu3rwpWrVqpbTNvSzn7mPo0KHi0aNH4tixY0qYWFpaKh+6ueehi4uL2L59u0hKShLh4eFi4cKF4vbt20IIId566y2l3aJFi0RiYqKIj48Xb7/9ttHwwvwzQP75o9VqxZ9//mk0Te7QmjZtmoiPjxchISFGXzx+/fVXqdd17ty5Rn3HxMSIlJQUMXv27Hw/1wqSX4Dcvn1btG3bVhnes2fPPO1zB8iECROU4QEBAeLevXsiMjLSKNwnTJigtJ8+fXq+y74QQmzatEkZ9/XXX4v09HQRGxsrQkJCxFdffSUuXLhQ5HNSai12S5H9baBGjRoCyP5GHxsbq3xIbt68WRgMBuVb6b59+8SAAQOUQn///Xeln3PnzomhQ4cKDw8PYWFhkWdByb228csvvyjDv/nmGyGE8cKa8607ODi40IUv5+e3334TQhQdIACEm5ubCA8Pz9O/r6+v0evyzjvvKOM+/vhjZXhZBchLL72kDL9w4UK+H3AlCZD8PoyLEyC55+l7772nDF+3bp0QQoitW7cqw7p27Sq++OIL8euvvyprmDnyC5C0tDSjtZewsLBiv4b51VpQgOzcuVMZXqtWLWX4nj17hBDZH845wxo3bpzv6/Pqq68aPW6HDh2Ucdu2bcvzXAr6eeutt4QQQgwdOlQZtnDhQqXf5ORko35yW7ZsmejcubOoXr26UKvVefpev3690jZnmFqtNvoGnfvbeU64d+7cWRm2cuXKAl9rd3f3Ip/fiy++WOj8KipAgOy1hTNnzgghhLhx44Yy3MnJSVnzFUKI+fPnK+Nee+01qde1U6dORdbj4+NT6HPK/XoX9GNtbS1OnTqVp33uAMn9+p47d04ZfvbsWWV47dq1leGFBUjuaXx9fcWnn34q1qxZI86fP698cSiuEu0DUalU6NSpE5D9KmP9+vXKdv4uXboYjT948CAOHz6cZ7q7d++iY8eOWL9+PSIiIvLdNpqWlqb8PWjQIOWIrl9++QU6nQ7r168HAPj4+KBjx44AgAcPHhTrOTx+/DjPsJyd6AaDAXfu3EHnzp0BAFFRUZg3b16e/j09PY2mz324XXHreBJNmzZV/raxsVH+Tk9Pl+pP9uiPouro378/3nvvPVhbW+PgwYP45JNPMGTIEDRq1Ah+fn6IiooqsO+YmBhkZWUBAOzs7PK85qWlQYMGyt9WVlbK33Xr1gUAaLVaZVhBr+8/a8v9/8OHD42eS2Fyls3cy6iHh4fyt42NDZycnPJM980332Ds2LE4fPgw4uLiYDAY8rTJ/Z7K4eLiory3cvrPkfNco6OjlWHNmzcvsPbiLPf5vfcKI/4+sOL+/ft4+eWXAQCJiYn4/PPP8zxm7dq1odFolP/ze0+W9HUti+eUw8zMDLVr18Zrr72G06dPo02bNoW2L+jzR+azp1WrVpg7dy6qVauGs2fP4vPPP8fw4cPRokULNGrUCBcvXiz28yjxiYS5D+edPXs2gOwPkpwZkDN++fLlyovr7e0NR0dHAMDWrVuVnXrPPvssIiMjIYTAtm3b8n08S0tLjBgxAgAQGhqKr7/+Wul33LhxSjtnZ2fl7169eikLX+4fg8GAN998s8DnlrNj8ZVXXlGGXb16NU//uXeOA9k7gfOro6yO1Tc3Ny/yMUry2LKHRBanjrlz5yI2NhanTp3Cr7/+iokTJwLIPhgh54MgP46OjjAzyz7KPCkpCeHh4VI1FiXnMYo7PD//XB5y/1+rVi2j52JnZ4eMjIx8l8+cA0Vyf5hFREQof6ekpOT7gbV69Wrl72+//RapqakQQmDgwIGF1p17/gH5z0MXFxfl78I+WHKWe5VKhaioqHyfX84BNSXl4uKiHPwB5P+evHfvHvR6vfJ/fu/Jkr6uufs/duxYvs+psC9B+ck5CivnMN5ffvnF6ItYQQr6/JH97Hnvvffw6NEjhIaGYtOmTfjoo4+g0Whw8+ZNvP/++8V+PiUOkNxHYuVe+8iREyC5n2Tu8bnfmDlHi9y6dQszZswo8DFzB8X06dMBZH9bzAkWAOjUqZNy9NSePXswd+5cxMTEICMjA1evXsXs2bONvm3mRwiBu3fvYsOGDcqwnEOXO3bsqITg2bNnsWTJEiQnJ+PAgQNGJ6n17dtX+TunPYAyPZknP7kf+8aNG3mOxCkPBw4cwMyZM3Hp0iV4eXlhwIABRuejFBYKlpaW6NOnj/L/q6++itDQUKSlpeH69euFLi/lbfPmzfjjjz+QnJyMVatW4dixYwCyl9HOnTvD0tJSOUIxKSkJY8aMQVhYmPIhsmrVKnTq1Ek55D0gIEDpe/78+bhw4QLi4+PxwQcf5Lsmk/s9ZWtrC5VKhd9//x1//PHHEz+33CH04YcfYufOnUhOTkZkZCQWL16MO3fuAABeeuklANnvocDAQFy5cgU6nQ7R0dH47bff0Lt3b/zyyy9SNTx48MDoPZbznmzQoIHy4fv48WNMnz4dCQkJCA0Nxfz585X2/fr1A1Dy1zXnOQHAxIkTERISgoyMDMTExGDHjh0YPHgwvvrqK6nnVFI5zwEApk6diqioKNy/f9/o8N/cbXK//y9evGj0/C5fvoyPP/4Yp06dgouLC/r27YtBgwYpa9sl+rJWog1eQgidTqccEZHzs3r16kLHr1mzRhl/+/ZtYW1tnWc7YKNGjfLd9p+jY8eORu1HjhyZp822bdvy3aeS+ydHcfaBWFpaipCQEGWa3377rdBt2ZMmTTKq59///neeNvk9t9yKsw8k9z6JgnZAp6Wl5Xt0UM720Nz7QPbt21doHQXtA8l9REl+21xz77/K7ydnO3RBzyEsLKzMj8LK/Rxy77PKPTxnWO5t0rlfn4K2/8+aNUtpf/v27SL3E+TMh4KOFrK2tjZ67+SYNWtWnrZqtVrUr18/3+ef3/Mp6HUpyVFYzZo1K/T5/XNb/D8VZx+IWq0W//3vf5Vpjhw5ku/nSc7PwIEDizwKq6DXNT09XXTv3r3QenK/NwqSu33u5aqo9rnnz8OHD0XDhg0LrKNhw4bKAUJCGB8B+8/HP3ToUKHP6b333iuyxhwlXgMxMzNDhw4djIb9cw3jn+Nz9ikA2duWd+zYgfbt28Pa2hqurq54//338d133xX6uLnXQvL7H8j+9h8SEoKRI0eiTp06MDc3h4ODA5o2bYqRI0carVkUxNzcHHXq1MGrr76KY8eOoXXr1sq4l19+GceOHcPgwYPh4uICMzMzODg4oHv37li/fr3Rtx4g++zz4cOHw9nZudwvPWFpaYlff/0V7dq1g62tbbk+dg4/Pz+8/vrraN68OWrUqAGNRgM7Ozu0b98eS5cuxVtvvVXo9J6enggNDcV//vMf+Pj4wMrKCpaWlqhfv77RZkZTe/3117F48WI0atQIFhYWaNCgAb7//nujb4d169ZFaGgopkyZAm9vb1haWsLKygr16tVD3759sXjxYmVZs7CwwJ49ezBmzBhUr14dVlZW6NGjBw4cOGB0jlKO999/H59//jm8vLyg1WrRsmVLbNmyxeh9J8vCwgK7du3CDz/8gE6dOsHBwQHm5uZwdXVF3759lc0mNWrUwIkTJ/DFF1/A19cXNjY20Gq18PT0hL+/P7755hs8//zzUjWYmZnB1dUV/fv3x19//YUXX3xRGdexY0ecPXsWo0aNgoeHB8zNzWFra4tnnnkGixcvxsaNG5X3XklfV61Wi+DgYCxcuBAdOnSAvb29cs5a165dMWPGDAQGBko9p5KqWbMmTp06hf/85z/K8qPVatG0aVNMmzYNp06dMtpE5+fnhx9++AENGzaEhYWFUV/16tXDW2+9hdatW8PJyQkajQbW1tZo1aoVZs2ahVmzZhW7LtXfiUf01Bk1ahTi4+PzXAts//796NGjB+Li4lCtWjWpvleuXInRo0cDyN6sWt6XqiGqCHg1XiIiksIAoSrv6NGj6Nq1K6ysrODh4YG3337b6KCD1atXo02bNrCzs4OLiwuGDRtmdOmIefPmYcmSJUZ9njlzBiqVSrksREJCAsaNG4datWrB3t4ezz77LM6dO1c+T5CojDBAqEq7cOECevXqhYEDB+L8+fPYsGEDDh8+bLR/JjMzE1988QXOnTuHrVu34s6dO9i1a5dyKOe4ceOMrkUGAGvXrkWHDh1Qr149CCHwwgsvIDo6Gjt27EBISAhat26N5557DrGxseX9lIlKT7F3txNVMoGBgUKj0QgbGxujn5zrXMXFxYkRI0YYXftJCCEOHTok1Gq1SEtLy7ffnEvpJCUlCSGEOHPmjFCpVMrZ8nq9Xri7u4vvv/9eCCHEX3/9Jezt7UV6erpRP/Xr1xc//vhjaT9tonLDNRB6qvXo0QOhoaFGPz///LMyPiQkBCtXroStra3y06tXL+WqBED2eT/9+/eHp6cn7OzslCsG5xwv7+vriyZNmmDdunUAss9/efjwIYYMGaI8RnJyMhwdHY0e586dO3muXkxUmRT/dFuiSsjGxibPCaT37t1T/s65OsHbb7+dZ9o6deogJSUFAQEBCAgIwOrVq1GzZk2Eh4ejV69eRpfhGT58ONauXYsPP/wQa9euRa9evZTDKg0GA1xdXfO9j4XsUWBEFQEDhKq01q1b49KlSwVepeDChQt4/PgxZs2apVw/6fTp03naDRs2DB9//DFCQkLw22+/YfHixUaPER0dDTMzsye6TSlRRcNNWFSlTZ06FceOHcPEiRMRGhqKGzduYNu2bfj3v/8NIHstxMLCAgsXLsTt27exbds2fPHFF3n6qVu3Ljp27IixY8ciKysL/fv3V8b17NkTHTp0wIABA7B7926EhYXh6NGj+Pjjj/MNI6LKggFCVVqLFi1w4MAB3LhxA126dIGvry8++eQT5XpLNWvWxMqVK7Fx40Z4e3tj1qxZmDt3br59DR8+HOfOncPAgQONruyrUqmwY8cOdO3aFWPGjEGjRo0wdOhQhIWFGV0Aj6iy4ZnoREQkhWsgREQkhQFCRERSGCBERCSFAUJERFIYIEREJIUBQkREUngmehkyGAyIioqCnZ1dud+RkIgKJ4RAUlIS3NzcoFbzu7QMBkgZioqKUi5/QUQVU0REBGrXrm3qMiolBkgZsrOzA5C9gNrb2xfYTqfTYc+ePQgICIC5ufkTtSusTUHjSjrclMqqpiftt6TTl9Y8l5nfBY2riPMbKLu6YmNjUbduXeV9SiXHAClDOZut7O3tiwwQa2tr2NvbF/lhUlS7wtoUNK6kw02prGp60n5LOn1pzXOZ+V3QuIo4v4GynecAuHn5CTBAKoD7Cem4m2zqKqiiy8zMxLfffgsAeOedd2BhYWHiiqiq456jCmDJwduYd8EMgStP4+jNx+DlySg/Op0OU6ZMwZQpU5Rvz0SmxDUQExNCQG8A1BA4eisWR2+dQCuPapjQvT56NnWGWs3Va8pmZmaGwMBA5W8iU+NSaGIqlQoz+nujsT4Mty3qYmNIJEIj4jHulxA0crbFv7rXR98WbjDTcGWxqtNqtVi5cqWpyyBS8FOpgnC0BKa/2BSHpz6Lf3WvDzutGa4/SMa7G86h+9z9+OVYGNJ1elOXSUSkYIBUMDXttJjauwkOf/gsPujVGI42FrgXl4ZPfr+EHvMO4c9IFZLSs0xdJhERA6SicrAyx8QeDXB46rP4rF8zuFezwuPkTPw3XINu3xzE17uv4nFyhqnLpHKUkpKCatWqoVq1akhJSTF1OUQMkIrOykKDwI5e2P9Bd8we2AzOVgJJ6Vn4ft8tdJ69F0HbLiEyPs3UZVI5SUhIQEJCgqnLIALAneiVhrlGjYG+7rCIOgeLun5YeigM5+4lYOXRMKw+fhcDfN0xvlt9eFbXmrpUKiNWVla4fv268jeRqTFAKhm1CgjwdkafFu44cjMGP+y/iaO3YvBbyD1sOnMP/k1robnG1FVSWVCr1WjYsKGpyyBSMEAqKZVKhc4NndC5oRPOhsfhh/23EHz5AfZcfog9MMPx1BBMfLYBOtRz5KUaiKhMMECeAr51quOnkW1w/UESfth7A9vOReHIrRgcuRUD3zrVMKF7AzzXpJapy6QnpNPpsHTpUgDAuHHjKtT1qqhqYoA8RRo52+HrQc3RQh2BW+Z1sfFMJM6Gx+ON/zuNRs62GNelLtS8SkqllZmZibfeegsAMGrUKAYImRwD5CnkaAmM6NMUk/wbY/mRO/jl2F1cf5CM93+7AEetBok1IzC0nScszbmzpDLRaDQYNGiQ8jeRqTFAnmI5JyWO71Yfq4/fxbLDtxGTokPQf69g0b7beL1LXQx/pg4s+VlUKVhaWmLjxo2mLoNIwfNAqoCckxL3T+6Kl730cHOwxOPkDMzaeRWdZu3FvD9vIJkXdyWiEqo0ayAJCQnYsmULDh06hLCwMKSmpqJmzZrw9fVFr1690LFjR1OXWOFZWWjQ1VXgi1GdsePSIyzefxO3HqVg8YE7MFdrcM3sKsZ3bwC3ajzHgIiKVuHXQO7fv4833ngDrq6u+Pzzz5GSkoJWrVrhueeeQ+3atbFv3z74+/vD29sbGzZsMHW5lYK5Ro1BfrUR/G43LHmtNZq720NnUOH/joej65x9eH/jOdx8yDtcVTSpqalwd3eHu7s7UlNTTV0OUcVfA2nZsiVGjhyJkydPwsfHJ982aWlp2Lp1K+bNm4eIiAi8//775Vxl5aRWq9DbxxXPNnLEgnW7cDajJo7djlVOSgxoWgstKvwSUnUIIRAVFaX8TWRqFX4N5NKlS5g7d26B4QFkX9bh1VdfxYkTJ5Qb7hQlKSkJkyZNgqenJ6ysrNCxY0ecOnVKGS+EQFBQENzc3GBlZYXu3bvj0qVLT/x8KiKVSoXG1QT+b3QbbJnQEf7ezhAC2H35Ib4+b4YJa0NxNTrR1GVWeZaWljh79izOnj0LS0tLU5dDVPEDpGbNmmXS/vXXX0dwcDB++eUXXLhwAQEBAejZsyciIyMBAHPmzMG8efOwaNEinDp1Ci4uLvD390dSUlKJn0NlknNS4u5JXdG3hQtUEAi+8hDPf3sI/153FrcecdOWqWg0GrRq1QqtWrXiYbxUIVT4AMktJiZG+TsiIgKffvopPvjgAxw6dKhE/aSlpWHTpk2YM2cOunbtigYNGiAoKAh169bF4sWLIYTAggUL8NFHH2HgwIHw8fHBqlWrkJqairVr15b206qQGrvYYd7gFviwpR7PN8teI/nvuSj4zzuAyb+G4m4MLydOVNVVigC5cOECvLy8UKtWLTRp0gShoaFo27Yt5s+fj6VLl6JHjx7YunVrsfvLysqCXq/PsxnAysoKhw8fxp07dxAdHY2AgABlnFarRbdu3XD06NHSelqVgos18N3Qltjxdhf4ezvDIIDNZyLx7DcHMPW387gXx5255UWn02HlypVYuXIldDoed02mVyl2kU6ZMgXNmzfH6tWrsXr1arz44ovo06cPfv75ZwDAv//9b8yaNQsDBgwoVn92dnbo0KEDvvjiCzRt2hTOzs5Yt24dTpw4gYYNGyI6OhoA4OzsbDSds7Mz7t69W2C/GRkZyMj4302eEhOz9xvodLpC3/A544r6UChOu8LaFDSuOMMb1rTCD6+2xIXIBHz71y0cuPEYG05HYPPZexjiVxvju9WFi33Zb5cv7mtV3v2WdHqZeZ6ZmYnRo0cDAAYMGAAbGxup+V3QuLJ6bZ9UWc9zkqcSleBwDicnJ+zduxctWrRAcnIy7O3tcfLkSbRp0wYAcPXqVbRv3x7x8fHF7vPWrVsYM2YMDh48CI1Gg9atW6NRo0Y4c+YMfv75Z3Tq1AlRUVFwdXVVpnnjjTcQERGBXbt25dtnUFAQPvvsszzD165dC2tr65I96QruThKwI0KN6wnZK7FmKoFOLgI93QywtzBxcU+pzMxMzJ49GwAwdepUWFjwhX4SqampGDZsGBISEmBvb2/qciqlShEgarUa0dHRqFUr+4qydnZ2OHfuHOrVqwcAePDgAdzc3KDX60vcd0pKChITE+Hq6opXXnkFycnJWLhwIerXr48zZ87A19dXadu/f39Uq1YNq1atyrev/NZAPDw88Pjx40IXUJ1Oh+DgYPj7+xd6gbzitCusTUHjSjo8txN3YrHgr5s4fTceAGBlrsZrz9TB6529UMOm9D/givtalXe/JZ2+tOa5zPwuaFxZvbZPqqzqiomJgaurKwPkCVSKTVgA8tzTorTucWFjYwMbGxvExcVh9+7dmDNnDurWrQsXFxcEBwcrAZKZmYkDBw4o3wDzo9VqodXmvSOgubl5sRb80mxXWJuCxpV0OAB0buSMTg1r4dCNx/gm+DrORcTjp8NhWHsyAmM618XrXerBwar0P4yK+1qVd78lnb605rnM/C5oXFm9tk+qtOuqiM+xsqk0ATJq1Cjlwzk9PR3jx4+HjY0NABh96y+u3bt3QwiBxo0b4+bNm/jggw/QuHFjjB49GiqVCpMmTcLMmTPRsGFDNGzYEDNnzoS1tTWGDRtWqs/raaBSqdC1UU10aeiEvVcfYl7wdVyKSsTCvTex8mgY3uhSD6M7ecHOkm9YoqdJpQiQf54c+Nprr+VpM3LkyBL1mZCQgGnTpuHevXuoUaMGXn75ZXz55ZfKt5IpU6YgLS0NEyZMQFxcHJ555hns2bMHdnZ28k/kKadSqfBcU2c826QWdl+KxvzgG7j2IAnzgq9j+ZE7eLNrfQR29IS1RaVY7Cqc1NRUtGzZEgBw7ty5p26/WmmISc7AkgO30MjZDoPbeJi6nKdepXgnr1ixotT7HDJkCIYMGVLgeJVKhaCgIAQFBZX6Yz/tVKrsS6QEeLtg+4X7WPDnddx+lILZu65i2eHbGN+tPl5rz/uRlJQQAjdv3lT+pv9JSNXhp0O3seLIHaRk6uHqYIl+rdygNeMyVpYqRYBQ5aRWq9CvpRv6+Ljg99AofPvXDYTHpmLGH1ew4kgYpj7fBH1buPKe7cVkaWmJw4cPK38TkJyRhRWH72DpodtISs8CADR3d8B7AY1goakUp7lVauUeIPHx8fjtt99w69YtfPDBB6hRowbOnDkDZ2dnuLu752k/cODAYve9efPm0iyVSomZRo2X/WqjXys3bAq5h+/+uoHI+DS8ve4sVh0NwycveqOVRzVTl1nhaTQadOrUydRlVAhpmXr8cjwMi/ffQlxq9vkcjZ3t8K5/I/Rq5swvJeWkXAPk/Pnz6NmzJxwcHBAWFoY33ngDNWrUwJYtW3D37l383//9X55pHBwclL+FENiyZQscHByUc0BCQkIQHx9foqAh0zDXqDG0XR30b+WOnw/dxg/7byHkbhwGfH8EA33d8UHvxnB14L1IqGAZWXpsOBWBhXtv4lFS9sEz9Zxs8E7Phujbwg1qNYOjPJVrgEyePBmjRo3CnDlzjHZGP//88wUe3ZR7/8fUqVMxZMgQLFmyRLmYnF6vx4QJE3gcdyViZaHBv59riCFtPTBn1zVsOnMPm89GYufFaLzZrR7e7FofVhbcdv1PWVlZ2LJlCwDgpZdegplZ1dkCbRDAlrNR+HbvLUTGpwEAale3wtvPNcRAX3eYcXOVSZTrEnjq1Cn8+OOPeYa7u7srlw8pzPLly3H48GGjK5FqNBpMnjwZHTt2xNdff12q9VLZcra3xDdDWiKwoye+2H4Zp8LisODPG9hwKgJTezdBv5b8RplbRkaGcuBHcnJylQgQIUT2OUYXNLh3/CIAoJadFv9+tgFeaVsHFmYMDlMq1yXQ0tJSuT5UbteuXSvWZdizsrJw5coVNG7c2Gj4lStXYDAYSq1OKl8talfDr292wI4L0Zi54woi49MwaUMoVh4Nw6d9vdG6TnVTl1ghqNVqdOvWTfn7aXcxMgFf7byCIzdjAKhgqzXDxB4NMLqTF4/gqyDKNUD69++Pzz//HL/++iuA7MM9w8PD8eGHH+Lll18ucvrRo0djzJgxuHnzJtq3bw8AOH78OGbNmqVcZI4qJ5VKhRdauOK5prWw7PAd/LDvJkIj4jHwh6Po38oNU3s3qfL3areyssL+/ftNXUaZi4xPw9zd17DlbPa9ecw1KnSqpcfswM5wrmZj4uoot3INkLlz56JPnz6oVasW0tLS0K1bN0RHR6NDhw748ssvizW9i4sL5s+fj/v37wMAXF1dMWXKFLz33ntlXT6VA0tzDSb2aIDBfrUxd881bAy5h99Do7D7UjTGdamHMR3rmLpEKiM6vQFLD97Gd3/dQEZW9haFfi3d8O5z9XH+2L4yubYaPZlyDRB7e3scPnwYe/fuxZkzZ2AwGNC6dWv07NmzWNOr1WpMmTIFU6ZMUTaFcef506mWvSXmDGqJkR288Pn2yzh5Jxbf7b2JDaci0NNZhed5It1TJeRuLD7ZegmX72e/r9vVrYFPXvBG89oO0Ol0OG/i+ih/5RogYWFh8PLywrPPPotnn332ifpicFQNPu4O2DCuPXZfisaXO64gIjYNa5I0uLniNGa/3BJeTlVnk0ZaWho6dOgAADh27BisrCr/Jr3YlEx8teMKNobcAwA4WJljel9vvOTrznM5KoFy3RNXr149dO7cGT/++CNiY2OLNU3v3r2LdRfApKQkzJ49G99///2TlkkVTM6lUf6c3A0fBDSEhVrgxJ049FpwEEsO3EKWvmocQGEwGHDu3DmcO3eu0h80IoTAbyH38Nw3+5XweKWNB/a+1w0DW9dmeFQS5boGcvr0aaxbtw4zZszAO++8g169euG1115Dv3798r0MOgAMHjwYQ4YMgZ2dHfr164c2bdrAzc0NlpaWiIuLw+XLl3H48GHs2LEDL774Ig/lfYppzTQY16UuLB9dwV+JtXD0Vixm7byK/56LwuyXW8DH3aHoTioxS0tL7NmzR/m7srpyPxHTt13CyTvZXyKbuNjhy5eaw8+TR9tVNuUaIK1bt0br1q0xZ84c7N+/H2vXrsWbb76J119/HS+//DKWL1+eZ5qxY8dixIgR+O2337Bhwwb89NNPyp0HVSoVvL290atXL4SEhOQ5vJeeTk6WwMqX/PD7+QeY8ccVXIpKRP/vj+CNLvUwqWfDp/YQT41GA39/f1OXIU0IgZ8O3cbsXdegNwhYmqvxbs9GGNO5Lsx5ImClZJIzkVQqFXr06IEePXrgX//6F8aOHYtVq1blGyAAYGFhgWHDhilnqyckJCAtLQ2Ojo68KUwVpVKpMLiNB7o1ronPtl3GHxfuY8mBW9h9KRpfDWyO9vUcTV0i5RKfmolPfr+E/56LAgD0buaCT/p6w72KH5pd2Zkk9iMiIjBnzhy0atUKbdu2hY2NDRYtWlTs6R0cHODi4sLwINSys8T3w1tj6Qg/ONtrcedxCoYuPY5pmy8gIU1n6vJKVVZWFv744w/88ccfyMrKMnU5xfbfc1HoOe8A/nsuCmZqFb7o3wyLX2vN8HgKlOsayNKlS7FmzRocOXIEjRs3xvDhw7F161Z4eXmVZxn0FApo5oL29R0xa+dVrD0RjnUnw/HXlQf4YoAPejVzMXV5pSIjIwMvvvgigMpxKZOEVB0++f0itv291tGwli1mvdyC+zqeIuW6BH7xxRcYOnQovv32W7Rq1ao8H5qqAHtLc8x8qTn6tXTDtM0XcOdxCt78JQR9mrtgxoDmlf5ENLVarVyFuqJfyuTE7Ri8uyEUUQnp0KhVmNijAd7q0YDXrnrKlGuAhIeH8/A8KnPt6zli5ztd8O1fN7D04G3suBCNkLtxmP9KK3Ss72Tq8qRZWVnh1KlTpi6jUFl6A7776wYW7bsJgwC8HK2xYKgv7/fylCrXrwM54ZGamoqrV6/i/PnzRj/lJSsrCx9//DHq1q0LKysr1KtXD59//rnRsfVCCAQFBcHNzQ1WVlbo3r07Ll26VG410pOxNNdgau8m+H1iJ9SvaYMHiRkY/vMJfLPnWpU5b6S8RcWnYdhPJ/Dd3uzwGORXG3+83YXh8RQr1zWQR48eYdSoUdi1a1e+4/V6fbnUMXv2bCxZsgSrVq1Cs2bNcPr0aYwePRoODg545513AABz5szBvHnzsHLlSjRq1AgzZsyAv78/rl27ZnQvE6rYfNwd8N9/d8Zn2y5jw+nsGxEduxWDb1/15U7cUrTtXBQ+3nIBielZsNWa4cuXfNC/Vd47jNLTpVzXQCZNmoT4+HgcP34cVlZW2LVrF1atWoWGDRti27Zt0v0GBgaW6NIox44dQ//+/fHCCy/Ay8sLgwYNQkBAAE6fPg0ge+1jwYIF+OijjzBw4ED4+Phg1apVSE1Nxdq1a6XrJNOwtjDD7EEt8N2rvrDVmuH03Tg8v+Agdl28b+rSSiQtLQ2dOnVCp06dkJaWZupyFLN2XsXb684iMT0LLT2qYfu/OzM8qohyXQPZu3cvfv/9d7Rt2xZqtRqenp7w9/eHvb09vvrqK7zwwgtS/bq7u5dop2Lnzp2xZMkSXL9+HY0aNcK5c+dw+PBhLFiwAABw584dREdHIyAgQJlGq9WiW7duOHr0KN588818+83IyEBGRobyf84FH3U6HXS6gg8pzRlXWJvitiusTUHjSjrclJ6kpue9a6KZS3u8u/E8zt9LxPjVZzCsXW1M690YGhik+5WpS2aeZ2ZmKpf1ycjIgJmZmdT8LmiczGu79NAdLDlwCwAwoVs9vNWjHsw16lJdZspqOaxIy3VlpRKi/C5ram9vj/Pnz8PLywteXl5Ys2YNOnXqhDt37qBZs2ZITU0tlzqEEPjPf/6D2bNnQ6PRQK/X48svv8S0adMAAEePHkWnTp0QGRkJNzc3Zbpx48bh7t272L17d779BgUF4bPPPsszfO3atbC2ti6bJ0MllmUAdkSo8VdU9pcOV2uBUQ31cKngs0iv1ys70du2bWt0Z87yJgRwMFqFzWHZNfT31ONZt8p1heTU1FQMGzYMCQkJvDirpHJdA2ncuDGuXbsGLy8vtGrVCj/++CO8vLywZMkSuLq6llsdGzZswOrVq7F27Vo0a9YMoaGhmDRpEtzc3BAYGKi0++cRY0KIQo8imzZtGiZPnqz8n5iYCA8PDwQEBBS6gOp0OgQHB8Pf37/QkyOL066wNgWNK+lwUyqtmvoBOHTzMT747SLup2Ri/iUL9K+jw6fDn4OFRckP9y1pXbLzvG/fvsXup6TjiltTYpoOH/9+GTvDHgAA3ujshSm9GhX5nGWV1XIYExNTan1VVeUaIJMmTVJuBDV9+nT06tULa9asgYWFBVauXFnk9Lk/nHNTqVSwtLREgwYN0L9/f9SoUaPQfj744AN8+OGHGDp0KACgefPmuHv3Lr766isEBgbCxSX7xLPo6GijYHv48CGcnZ0L7Fer1eZ7UUhzc/NiLfil2a6wNgWNK+lwUyqNmp5t6opdk2pg8q+hOHTjMTbc1gA7b2LGS82lr81U0rpKa57LzO+CxhXW/m5MCgKXn0RYTCrM1CpM6d0Yb3SpVy6H55f2cljRlunKqFwDZPjw4crfvr6+CAsLw9WrV1GnTh04ORV9fP7Zs2dx5swZ6PV6NG7cGEII3LhxAxqNBk2aNMEPP/yA9957D4cPH4a3t3eB/aSmpubZZ6LRaJTDeOvWrQsXFxcEBwfD19cXAJCZmYkDBw5g9uzZMk+dKqiadlqsGt0OP+y7gW+Cr2PD6Xu4G5uKxcP9UL2CnXio1+tx6NAhAECXLl3KfRNWaEQ8xq48hZiUTLhXs8L3w1vzEN0qziSnhWZmZuLatWuwsLBA69atixUeQPY91Xv27ImoqCiEhITgzJkziIyMhL+/P1599VVERkaia9euePfddwvtp2/fvvjyyy/xxx9/ICwsDFu2bMG8efPw0ksvAcheo5k0aRJmzpyJLVu24OLFixg1ahSsra2VCzrS00OtVuHNrnXxehMDbLQaHL8di/7fH8H1B0mmLs1Ienq6chHS9PT0cn3sQzce4dWlxxGTkgkfd3tsmdiR4UHlGyCpqakYO3YsrK2t0axZM4SHhwMA3n77bcyaNavI6b/++mt88cUXRvsT7O3tERQUhDlz5sDa2hqffvopQkJCCu1n4cKFGDRoECZMmICmTZvi/fffx5tvvokvvvhCaTNlyhRMmjQJEyZMQJs2bRAZGYk9e/bwHJCnmE91gY1vPAOPGlYIj03FwB+OYu/VB6YuS5Fz+wJvb+9yvaJD8OUHGLvyNNJ0enRp6IT14zqgll3lvR8JlZ5yDZBp06bh3Llz2L9/v9ENcXr27IkNGzYUOX1CQgIePnyYZ/ijR4+UQ2arVauGzMzMQvuxs7PDggULcPfuXaSlpeHWrVuYMWOG0c5TlUqFoKAg3L9/H+np6Thw4AB8fHyK+1SpkmrobIvfJ3ZGu7o1kJyRhbGrTuOng7dRjgcrFsja2hqXLl3CpUuXyu2ovu3nozB+dQgy9Qb0buaCnwPbwFZbsS/iSOWnXANk69atWLRoETp37mz0Dcrb2xu3bt0qcvr+/ftjzJgx2LJlC+7du4fIyEhs2bIFY8eOxYABAwAAJ0+eRKNGZXdECD39athYYPXYZzC0rQeEAL7ccQUf/HYeGVnlc6WEiuK/56Lw9rqz0BsEBvq6Y9EwX2jNns6bdZGccr+USa1atfIMT0lJKdYq+Y8//oh3330XQ4cOVe6HYGZmhsDAQMyfPx8A0KRJE/z888+lWzhVORZmanw1sDkaOdthxh+X8VvIPYQ9TsGPI/zgaJv/7ZefJv89F4V31p+FQQCD/Wpj9sstoFbzQqhkrFzXQNq2bYs//vhD+T8nNH766Sd06NChyOltbW3x008/ISYmRjkiKyYmBkuXLoWNjQ0AoFWrVrxUPJUKlUqFMZ3rYsXodrCzzL4EyuAfjyEq3jSXEUlLS4O/vz/8/f3L9FImuy49wKQNoQwPKlK5roF89dVX6N27Ny5fvoysrCx8++23uHTpEo4dO4YDBw4Uux9bW1u0aNGiDCsl+p9ujWpiy4ROGLnsBG4/SsHgJcew5vVn4OVkU651GAwG/Pnnn8rfZeFSnArLT5yH3iAwiOFBRSjXAOnYsSOOHDmCuXPnon79+tizZw9at26NY8eOoXnz5sXq49SpU9i4cSPCw8Pz7CzfvHlzWZRNhAa1bLHxXx0x/KfjCItJxeAfj2H12GfQ2KX8jsrTarVYvXq18ndpO3IrBsuvqZElBPq1dGN4UJHK/XCK5s2bY9WqVVLTrl+/HiNHjkRAQACCg4MREBCAGzduIDo6WjmHg6isuFezwq/jO2DkspO4Gp2EV5Yew6rR7dCynM6HMDMzMzoZtzQdufkY49ecRZZQwb9pLXwzpCU0DA8qQrnsA0lMTCzWT1FmzpyJ+fPnY/v27bCwsMC3336LK1euYMiQIahTp045PBOq6mrZWWL9uPZo6VEN8ak6DP/5BE7crtzXVDpy8zHGrjqFdJ0B3tUMmD+khfSlXKhqKZelpFq1aqhevXqBPznji3Lr1i3lku9arVY5euvdd9/F0qVLy/ppEAEAqllbYM3rz6B9vexzRUYuP4kD1x+V+ePmXI331KlTpXbztbPh8Up4dG/khLGNDdDyvuVUTOWyCWvfvn3K30II9OnTBz///DPc3Ut205kaNWogKSn78hLu7u64ePEimjdvjvj4+HK7FDwRANhqzbBydDtMWHMGe68+xL/WhmJEfRX6lOFjpqeno127dgCA5ORk5cjDJ7Hgr5vZ4dG4JhYNbYm/9uR/t1Ci/JRLgHTr1s3of41Gg/bt26NevXol6qdLly4IDg5G8+bNMWTIELzzzjvYu3cvgoOD8dxzz5VmyURFsjTX4McRfnh3Qyi2n7+PVTfU6HIzBj2aupTJ46lUKnh6eip/P6mYdODo7VioVMAX/X245kElVqmuSbBo0SLlInLTpk2Dubk5Dh8+jIEDB+KTTz4xcXVUFZlr1Ph2qC8MBgN2XHyAietCsfaN9mVyoUFra2uEhYWVWn8nHmYHRucGTvCoYc079FGJVaoAyX2fD7VajSlTpmDKlCkmrIgI0KhVmPNyc9yMuI/rCcDoFSexcXwHNKhVcS+8qTcInHiUvRYzpI2Hiauhyspk66zleTVRorKmNVPj9cYGtHC3R1yqDiOWnTTZGevFcfjmY8RnqlDNyhwBzQq+SRpRYcplDWTgwIFG/6enp2P8+PF5dgIWdCJgcW+cU1pHphDJ0GqAn0a0xrBlp3DrUQpGLDuBjeM7okYp3ZgqPT1duYvm+vXrja5oXVIbQyIBAP1auvICiSStXALEwcHB6P/XXnutRNMLIeDp6YnAwEDlDoFEFVENGwv839hnMGjxUdx6lILRK09h7evPwKYULoGu1+vx+++/K3/LiknOwN5r2YcdD/Yr2ZGQRLmVS4CsWLHiiaY/ceIEli9fjm+//RZ169bFmDFjMHz48GKdO0JU3tyrWeGXse0weMkxnIuIx/jVIVg+qu0Tn5xnYWGhnO+U+941JXH9QRI+2nIBOr1AHRuBJuV4KRZ6+lSK4/batm2LxYsX4/79+5g8eTK2bNmC2rVrY+jQoQgODjZ1eUR5NKhlhxWj28HaQoNDNx7jqx1Xn7hPc3NzvPHGG3jjjTdgbm5eomnTdXp8tfMK+nx7CKfC4mBlrsYLdcrmgoxUdVSKAMlhaWmJ1157DX/99RcuXryIhw8fonfv3oiNjS1RP15eXlCpVHl+Jk6cCCB7k1lQUBDc3NxgZWWF7t2749KlS2XxlOgp1sqjGuYNaQUAWH7kDn4PjTRJHUIIvLfxHH48cBtZBoEAb2fsfLsTmlQz/V0WqXKrVAECAPfu3cOMGTPg7++Pa9eu4YMPPjC6R3pxnDp1Cvfv31d+ctZiBg8eDACYM2cO5s2bh0WLFuHUqVNwcXGBv7+/chY8UXH19nHBhO71AQBTN53HlftFX/OtIAaDQbmlbUku577tfDT+OH8fZmoVfhzhh6Uj28C9mpV0HUQ5KkWAZGZmYsOGDQgICEDDhg1x5swZLFiwABEREZg1axbMzEq2K6dmzZpwcXFRfrZv34769eujW7duEEJgwYIF+OijjzBw4ED4+Phg1apVSE1Nxdq1a8voGdLT7L2AxujS0AnpOgPGrw5BQqrcCXtpaWnw8fGBj49PsW8oFZ8BfLb9CgDg7ecaolezsjlLnqqmSnEioaurK+zs7BAYGIgffvhBuS1ucnKyUbuSrokA2eG0evVqTJ48GSqVCrdv30Z0dDQCAgKUNlqtFt26dcPRo0fx5ptvFthXRkYGMjIylP9zrjCs0+kKPcs3Z1xRZwIXp11hbQoaV9LhplRWNT1pv0VN/80gH7y0+DjuxqTi7fVn8P0rPsV6vNz96nQ6ODk5Gf1f2ONmZGZizS01ktKz0KK2Pd7oVCdP+9zTVcT5DZT9PCd5KiFEhd8Qqlb/b0UpvxMQhRBQqVRShzb++uuvGDZsGMLDw+Hm5oajR4+iU6dOiIyMhJubm9Ju3LhxuHv3Lnbv3l1gX0FBQfjss8/yDF+7di2sra1LXBs9XSKSgW8vaqATKvSurcfzHmX71jt4X4VNYRqYqwU+aKGHM7daGUlNTcWwYcOQkJAg9eWTKskaSO6r+Za2ZcuW4fnnnzcKCyBvUOWEVGGmTZuGyZMnK/8nJibCw8MDAQEBhS6gOp0OwcHB8Pf3L/TomuK0K6xNQeNKOtyUyqqmJ+23uNPXbBiJqZsvYdc9DTxs9Xh3SM8nmucFjY+KT8OH3x0BYMCUgEYY1alukdNVxPkNlF1dMTGV+z4uFUGlCJB/Xs23tNy9exd//vmn0RnwLi7Z24ijo6Ph6uqqDH/48CGcnQu/5INWq833VqPm5ubFWvBLs11hbQoaV9LhplRWNT1pv0VN/0o7L1yMSsYvx+9izU01AtP0qF2MtdOi+v3n+Bk7zyFNZ0A9O4GRHbxKtCxUxPkNlH5dFfE5VjaVYid6WVmxYgVq1aql3KQKAOrWrQsXFxej80syMzNx4MABdOzY0RRl0lPmkxe94e1qh9QsFaZtuYTibkVOT0/H8OHDMXz4cOWq1PkJvvwAwZcfwEytwuB6et7XnMpMlQ0Qg8GAFStWIDAw0OgoLpVKhUmTJmHmzJnYsmULLl68iFGjRsHa2hrDhg0zYcX0tLAwU2PuoOYwVwkcuhmDX47fLdZ0er0ea9euxdq1awvc35eamYWgbdnnLI3p5Ak37nqjMlQpNmGVhT///BPh4eEYM2ZMnnFTpkxBWloaJkyYgLi4ODzzzDPYs2cP7Ox42QcqHQ1r2aKvpwGbwzSYueMKOtZ3QoNatoVOY2Fhgfnz5yt/5+fbv24gMj4N7tWsMLF7Pez/82ap106Uo8oGSEBAQIGbDlQqFYKCghAUFFS+RVGV0sVF4IHGEUduxWDyr6HY9K+OhV4vy9zcHJMmTSpw/LXoJCw7dAcA8Hn/ZrC2qLJvbyonVXYTFpGpqVXArIHN4GBljvP3ErDwrxvSfRkMAv/ZckG5VMlzTXmPDyp7DBAiE3Kxt8SXL2WfVLho302E3I0rsK3BYEBYWBjCwsLyXMrktzORCLkbB2sLDYL6NSvTmolyMECITOzFFm54ydcdBgG8uyEUCWn5nyGdlpaGunXrom7dukaXMknWAXP2XAcATPZvBDde54rKCQOEqAII6tcM7tWsEB6big82nitw/5y1tXWeqxpsvatGQloWmrraY1RHr3KoligbA4SoAnCwMsfi11rDQqPGnssPsPTg7TxtbGxskJKSgpSUFOV20Mdvx+LUIzVUKmDmSz4we8KbVhGVBJc2ogqiRe1q+LSvNwBgzu5rOHG78EttJKTp8Om2ywCAV9vWhm8d3qGTyhcDhKgCGf5MHbzk6w69QeCtdWfxKCkj33YpGVkYveIk7sSkwt5c4L2eDcu5UqIqfB4IUUWkUqnw5Us+uBSVgOsPkjHp1/MY+vctPDIyMvDWW29BbxBI8RuJM+FJcLAyw5sN02Fvxes6UfljgBBVMNYWZlj8mh/6LTyMk2FxiHqkQbjNbXSpZ4+ff/4ZAODh8DzsbG2wbKQfIs8fMXHFVFVxExZRBVS/pi2+GdIS5hoV7qWosOCvmxi45ARq9QhEtS4jYKk1x7JRbdGytoOpS6UqjGsgRBVUbx9X7H/PDgt/24uH5i44ejsGqnaDYa9RYenINmhfz5F31SOTYoAQVWC17LTo4CzQp48vsoQaJ+7EoKadFs3cuOZBpscAIaokLM3VaFZDBSCzWHfIJCprDBCiSiI1NRW1atUCACQnJysnExKZCgOkDOVcjiIxMbHQdjqdDqmpqUhMTCzy/thFtSusTUHjSjrclMqqpiftt6TTy8zzzMxMZXhiYiL0er3U/C5oXEWc30DZ1ZWUlAQAxb4jJOXFAClDOQuoh4eHiSuhp42bm5upS3hqJCUlwcGB+5RkqATjt8wYDAZERUXBzs6uyO3Vbdu2xalTp4rsszjtCmtT0Lj8hicmJsLDwwMRERGwt7cvsrbyUtzXqrz7Len0pTXPZeZ3fuMq6vwGymaeCyHg5+eH69evQ63mGQ0yuAZShtRqNWrXrl2sthqNplhv2uK0K6xNQeMKm8be3r5CfaAU97Uq735LOn1pzXOZ+V3YuIo2v4Gym+cWFhYMjyfAV66CmDhxYqm1K6xNQeOK+/gVQVnV+qT9lnT60prnMvO7JI9fEVTUeV7VcRMWFSgxMREODg5ISEiocN9IqfRxflNJcQ2ECqTVajF9+nRotVpTl0LlgPObSoprIEREJIVrIEREJIUBQkREUhggREQkhQFCRERSGCBERCSFAUJERFIYIEREJIUBQkREUhggREQkhQFCRERSGCBERCSFAUJERFIYIEREJIV3JCxDJbmlLRGVLyEEkpKS4ObmxrsSSmKAlKGoqCh4eHiYugwiKkRERESxbz1NxhggZcjOzg5A9gJa2B3edDod9uzZg4CAAJibmz9Ru8LaFDSupMNNqaxqetJ+Szp9ac1zmfld0LiKOL+BsqsrNjYWdevWVd6nVHIMkDKUs9nK3t6+yACxtraGvb19kR8mRbUrrE1B40o63JTKqqYn7bek05fWPJeZ3wWNq4jzGyjbeQ6Am5efAAOEiMpFlt4AtUoFtTr7A1sIgSyDgP7vnyyDgEoFmKlVMIjs9jq9gE6nQ0w6cC8uDfbWBhhE9rS5GQSgUgE5g800KjjZ8ta8ZY0BQkQlkpyRhRsPknD9QRIeJmaghq0FbLVmuBeXhntxqXicnIn41EykZOiRmpmF1Ew9UjKykJKph0oF2FqYIcsgkJGlh6HYN9Q2w+dnDxW7xpYe1fD7xE5Sz4+KjwFCRAVKStfh9N04nA2Px7mIeFx/kIT7CenS/QkBJGVklWgalQowVwmo1BpkZBmUYWrV/9ZkVCoVhBBQq1TKWgyVPQYIERm5n5CO7RfvYv/VRzgTHoesfFYTnGy1aOJiB1cHS8Sl6pCcoYNbNSvUqWGNmnZaVLe2gI3WDNYWGliZa2CjNYO9pRkMIjuUzDVqaM3U0JppYKZR/b1pK7tvvSE7CMzUKmjUKmRlZWHHjh3o06cXzMzMuM+iAmGAEFUxIXdjMfOPK6irUeH5XPsSjt2OwZIralw7ftBo05KnozX8PKvD16MavN3s0aCmHRys5Xdm17ST3zfB8KhYGCBEVUhcSiYmrDmDB4kZCIEGsWtC8fGL3vhh/y38FnIPORenaFe3Bvq2dEP3RjXhUcPatEVThcUAIaoihBCYtvkCHiRmwNlOi8fJ6dh77RH2XjsAIHu/QsdaBnz2alc0cHEwcbVUGZj8/H2dToeIiAhcu3YNsbGxpi6H6Km18fQ97LoUDXONCj++5ovJzfWo52QDAKhf0wbrX2+HIfUM8HTkGgcVj0nWQJKTk7FmzRqsW7cOJ0+eREZGhjKudu3aCAgIwLhx49C2bVtTlEf01LnzOAVB/70EAHgvoDGaudnjrg3w+4T2uBCVDD+v6lALA3ZcNHGhVKmU+xrI/Pnz4eXlhZ9++gnPPvssNm/ejNDQUFy7dg3Hjh3D9OnTkZWVBX9/f/Tu3Rs3btwo7xKJnipxqZkYu+oUUjP16FDPEeO61FPGWZpr0LGBE7RmGhNWSJVVua+BHD16FPv27UPz5s3zHd+uXTuMGTMGS5YswbJly3DgwAE0bNiwnKskejpk6oFxq8/i9qMUuDlYYsHQVlCrVdDrTV0ZPQ3KPUA2btxYrHZarRYTJkwo42qInl5ZegNW3VDjYlwCHKzMsWpMOzjbW5q6LHqKmHwnOhGVjZm7ruNiXPYJez8HtkFDZ151lkqXSQ/jfemll/I9MUilUsHS0hINGjTAsGHD0LhxYxNUR1R5Hbn5GL8cD4cKAvMGN0dbrxqmLomeQiZdA3FwcMDevXtx5swZJUjOnj2LvXv3IisrCxs2bEDLli1x5MgRU5ZJVKmkZeoxbfMFAEBnZ4EAb2cTV0RPK5Ougbi4uGDYsGFYtGiRcktJg8GAd955B3Z2dli/fj3Gjx+PqVOn4vDhw6YslajSWPDndYTHpsLFXosX66SYuhx6ipl0DWTZsmWYNGmS0f2I1Wo1/v3vf2Pp0qVQqVR46623cPEiD04nKo4L9xLw06HbAIDP+nnDkteaoDJk0gDJysrC1atX8wy/evUq9H8fZ2hpackLqBEVg05vwNRN52EQQN+Wbni2cU1Tl0RPOZN+PxkxYgTGjh2L//znP2jbti1UKhVOnjyJmTNnYuTIkQCAAwcOoFmzZqYsk6hS2HzmHi7fT0Q1a3NM7+tt6nKoCjDpGsj8+fMxadIkzJkzB127dkWXLl0wZ84cvPvuu5g3bx4AICAgAOvXry/Vxw0KCoJKpTL6cXFxUcYLIRAUFAQ3NzdYWVmhe/fuuHTpUqnWQFSahBBYefQuAOBf3erzdq5ULky6BqLRaPDRRx/ho48+QmJiIgDA3t7eqE2dOnXK5LGbNWuGP//806iWHHPmzMG8efOwcuVKNGrUCDNmzIC/vz+uXbsGOzseS08Vz+m78bhyPxGW5mq80tbD1OVQFWHyEwmzsrLw559/Yt26dcq+jqioKCQnJ5fp45qZmcHFxUX5qVkze3uxEAILFizARx99hIEDB8LHxwerVq1Camoq1q5dW6Y1EclafSIcADCglTuqWVuYuBqqKky6BnL37l307t0b4eHhyMjIgL+/P+zs7DBnzhykp6djyZIlZfbYN27cgJubG7RaLZ555hnMnDkT9erVw507dxAdHY2AgAClrVarRbdu3XD06FG8+eabBfaZkZFhdGXhnLUqnU4HnU5X4HQ54wprU9x2hbUpaFxJh5tSWdX0pP2WdPrSmuc6nQ7xGcDuSw8BAMPb1c4zTXGXhYo4v4Gyn+ckTyWEyHvD43IyYMAA2NnZYdmyZXB0dMS5c+dQr149HDhwAK+//nqZXYl3586dSE1NRaNGjfDgwQPMmDEDV69exaVLl3Dt2jV06tQJkZGRcHNzU6YZN24c7t69i927dxfYb1BQED777LM8w9euXQtra95jgcrGjnA1dkeqUd9O4G0fXiWxuFJTUzFs2DAkJCTk2XROxWPSNZDDhw/jyJEjsLAwXuX29PREZGRkmT3u888/r/zdvHlzdOjQAfXr18eqVavQvn17AHnvvSyEKPJw4mnTpmHy5MnK/4mJifDw8EBAQEChC6hOp0NwcDD8/f1hbl7wvaaL066wNgWNK+lwUyqrmp6035JOX1rzPCUtAx99vR8A8E6flnjex6VY0+Y3riLOb6Ds6oqJiSm1vqoqkwaIwWBQzvfI7d69e+W6s9rGxgbNmzfHjRs3MGDAAABAdHQ0XF1dlTYPHz6Es3Phl4TQarXQavMe/WJubl6sBb802xXWpqBxJR1uSmVV05P2W9Lpn3Se/xkahWSdCs72Wjzfwh3mmry7NUu6LFTE+Q2Ufl0V8TlWNibdie7v748FCxYo/6tUKiQnJ2P69Ono06dPudWRkZGBK1euwNXVFXXr1oWLiwuCg4OV8ZmZmThw4AA6duxYbjURFccvJyIAAMPaeuQbHkRlyaRrIPPnz0ePHj3g7e2N9PR0DBs2DDdu3ICTkxPWrVtXZo/7/vvvo2/fvqhTpw4ePnyIGTNmIDExEYGBgVCpVJg0aRJmzpyJhg0bomHDhpg5cyasra0xbNiwMquJqKRuP0rGuXsJ0KgEXmnjbupyqAoyaYC4ubkhNDQU69atw5kzZ2AwGDB27FgMHz4cVlZWZfa49+7dw6uvvorHjx+jZs2aaN++PY4fPw5PT08AwJQpU5CWloYJEyYgLi4OzzzzDPbs2cNzQKhC2XftEQCgvr2AI08cJBMw+aXWrKysMGbMGIwZM6bcHrOoM9tVKhWCgoIQFBRUPgURSdh3NfvQXe9qJjuQkqq4cg+Qbdu2Fbttv379yrASosorOSMLJ+5kH0XUrDoDhEyj3AMk5yinHCqVCv88FSXncNn8jtAiIuDwjcfQ6QU8a1ijllWiqcuhKqrcD9swGAzKz549e9CqVSvs3LkT8fHxSEhIwM6dO9G6dWvs2rWrvEsjqjRyNl91b+xk4kqoKjPpPpBJkyZhyZIl6Ny5szKsV69esLa2xrhx43DlyhUTVkdUMQkhsO/a3wHSqCYSr982cUVUVZn0wPFbt27BwcEhz3AHBweEhYWVf0FElcClqEQ8TMqAtYUGbb2qm7ocqsJMGiBt27bFpEmTcP/+fWVYdHQ03nvvPbRr186ElRFVXDmbrzo1cILWjCcPkumYdOlbvnw5Hj58CE9PTzRo0AANGjRAnTp1cP/+fSxbtsyUpRFVWHv/3nz1bJNaJq6EqjqT7gNp0KABzp8/j+DgYFy9ehVCCHh7e6Nnz568DzpRPmKSMxAaEQ8A6NGYAUKmZfITCVUqFQICAozuv0FE+Tt44xGEALxd7eHiYMl7WpBJlfsmrJLc3zwiIgJHjhwpw2qIKpe9V7MvX9KjSU0TV0JkggBZvHgxmjRpgtmzZ+d7mG5CQgJ27NiBYcOGwc/PD7GxseVdIlGFlKU34OD17ADh/g+qCMp9E9aBAwewfft2LFy4EP/5z39gY2MDZ2dnWFpaIi4uDtHR0ahZsyZGjx6NixcvolYtvlGIAOB8ZAIS0nRwsDJHKw8evkumZ5J9IC+++CJefPFFxMTE4PDhwwgLC0NaWhqcnJzg6+sLX19fqNU8PJEotyM3HgMAOjVwhEbNg0zI9Ey6E93R0RH9+/c3ZQlElcbhmzkBwsuXUMXAr/lElUBKRhbOhMcBADozQKiCYIAU4YcffkDdunVhaWkJPz8/HDp0yNQlURV0+m4cdHqB2tWtUKeGtanLIQLAACnUhg0bMGnSJHz00Uc4e/YsunTpgueffx7h4eGmLo2qmKO3so9G7NzAiSfZUoXBACnEvHnzMHbsWLz++uto2rQpFixYAA8PDyxevNjUpVEVc/RW9s2juP+DKhIGSAEyMzMREhKS5wz5gIAAHD161ERVUVWUmAlcfZAMgAFCFYvJL2WSn99//x0JCQkYOXKkyWp4/Pgx9Ho9nJ2djYY7OzsjOjo632kyMjKQkZGh/J+YmH2nOJ1OV+glJ3LGFXVZiuK0K6xNQeNKOtyUyqqmJ+23pNOXZJ7fSMzeZOXtagc7C5XRNDLzu6BxFXF+A2U/z0meSvzzfrIVQJMmTXDjxg2T3tI2KioK7u7uOHr0KDp06KAM//LLL/HLL7/g6tWreaYJCgrCZ599lmf42rVrYW3NHZ8kZ+1NNU48UuNZNwP6expMXc5TIzU1FcOGDUNCQgLs7e1NXU6lVCHXQPL7cC5vTk5O0Gg0edY2Hj58mGetJMe0adMwefJk5f/ExER4eHggICCg0AVUp9MhODgY/v7+MDc3f6J2hbUpaFxJh5tSWdX0pP2WdPrits/MzMT0kH0AgNf826DLPzZhyczvgsZVxPkNlF1dMTExpdZXVVUhA6QisLCwgJ+fH4KDg/HSSy8pw4ODgws8+VGr1UKr1eYZbm5uXqwFvzTbFdamoHElHW5KZVXTk/Zb0umLan/ncQriM1Uw16jQoX4tmJtrStxPScdVxPkNlH5dFfE5VjYm34l+6NAhvPbaa+jQoQMiIyMBAL/88gsOHz5s4sqAyZMn4+eff8by5ctx5coVvPvuuwgPD8f48eNNXRpVETlHX/nVqQYri/zDg8hUTBogmzZtQq9evWBlZYWzZ88qO6CTkpIwc+ZMU5YGAHjllVewYMECfP7552jVqhUOHjyIHTt2wNPT09SlURVx5O/zPzrWdzRxJUR5mTRAZsyYgSVLluCnn34yWp3s2LEjzpw5Y8LK/mfChAkICwtDRkYGQkJC0LVrV1OXRFWE3iBw/E52gHRigFAFZNIAuXbtWr4fyPb29oiPjy//gogqkPP34pGUngUrjUAzNx4lRBWPSQPE1dUVN2/ezDP88OHDqFevngkqIqo49lx+AABo5CB4+XaqkEwaIG+++SbeeecdnDhxAiqVClFRUVizZg3ef/99TJgwwZSlEZmUwSCw9Wz2QSWtnSrcqVpEAEx8GO+UKVOQkJCAHj16ID09HV27doVWq8X777+Pt956y5SlEZnU8dsxuJ+QDntLMzSrnmXqcojyZfLzQL788kt89NFHuHz5MgwGA7y9vWFra2vqsohMatOZ7LWPPs1dYK4OM20xRAUw+XkgAGBtbY02bdqgSZMm+PPPP3HlyhVTl0RkMqmZWdh58T4A4KVWbiauhqhgJg2QIUOGYNGiRQCAtLQ0tG3bFkOGDEGLFi2wadMmU5ZGZDK7L0UjNVMPT0dr+Ho4mLocogKZNEAOHjyILl26AAC2bNkCg8GA+Ph4fPfdd5gxY4YpSyMymc1/b74a6FubN4+iCs2kAZKQkIAaNWoAAHbt2oWXX34Z1tbWeOGFF3Djxg1TlkZkEtEJ6Th88zEA4CVfdxNXQ1Q4kwaIh4cHjh07hpSUFOzatUu5eVNcXBwsLS1NWRqRSWwNjYQQQDuvGqjjyFsAUMVm0qOwJk2ahOHDh8PW1haenp7o3r07gOxNW82bNzdlaUTlTgiBzWfuAQAGtubaB1V8Jg2QCRMm4JlnnkF4eDj8/f2hVmevENWrV4/7QKjKuRSViOsPkmFhpkafFq6mLoeoSCY/D8TPzw9+fn5Gw1544QUTVUNkOjk7zwO8nWFvyXtVUMVn8gC5d+8etm3bhvDwcGRmZhqNmzdvnomqIipfGVl6bDuXHSAvt65t4mqIisekAfLXX3+hX79+qFu3Lq5duwYfHx+EhYVBCIHWrVubsjSicrX0wG08Ts6Es70WXRo6FT0BUQVg0qOwpk2bhvfeew8XL16EpaUlNm3ahIiICHTr1g2DBw82ZWlE5SY8JhWL9mVflfqjF7xhpqkQF4ggKpJJl9QrV64gMDAQAGBmZoa0tDTY2tri888/x+zZs8vscb28vKBSqYx+PvzwQ6M24eHh6Nu3L2xsbODk5IS33347zyY2oiclhMD0bReRkWVApwaO6Mud51SJmHQTlo2NjXIbWzc3N9y6dQvNmjUDADx+/LhMH/vzzz/HG2+8ofyf+wKOer0eL7zwAmrWrInDhw8jJiYGgYGBEEJg4cKFZVoXVS3BVx5i37VHsNCo8UV/H555TpWKSQOkffv2OHLkCLy9vfHCCy/gvffew4ULF7B582a0b9++TB/bzs4OLi4u+Y7bs2cPLl++jIiICLi5ZV/M7ptvvsGoUaPw5Zdfwt6ed4ejJ5ehB7764yoA4M1u9VCvJq9CTZWLSQNk3rx5SE5OBgAEBQUhOTkZGzZsQIMGDTB//vwyfezZs2fjiy++gIeHBwYPHowPPvgAFhYWAIBjx47Bx8dHCQ8A6NWrl3Jf9B49euTbZ0ZGhrJGBQCJiYkAAJ1OB51OV2AtOeMKa1PcdoW1KWhcSYebUlnV9KT9lnR6nU6HXffUiE7MQO3qVhjX2bNE86w440s6riLOb6Ds5znJUwkhqtztzubPn4/WrVujevXqOHnyJKZNm4b+/fvj559/BgCMGzcOYWFh2LNnj9F0Wq0WK1euxKuvvppvv0FBQfjss8/yDF+7di2srXlZCvqfqFTg6/MaGIQK45ro0ax6lXsbmlxqaiqGDRuGhIQEblWQZPLzQAAgMzMTDx8+hMFgMBpep06dYvdR0Id3bqdOnUKbNm3w7rvvKsNatGiB6tWrY9CgQZg9ezYcHR0BIN9t0UKIQrdRT5s2DZMnT1b+T0xMhIeHBwICAgpdQHU6HYKDg+Hv7w9z84JPICtOu8LaFDSupMNNqaxqetJ+SzK9wSDw6s8nYRAJ6NnECR8ML/iQ9aL6lZnfBY2riPMbKLu6YmJiSq2vqsqkAXL9+nWMHTsWR48eNRqe80Gt1+uL3ddbb72FoUOHFtrGy8sr3+E5+1tu3rwJR0dHuLi44MSJE0Zt4uLioNPp4OzsXGD/Wq0WWq02z3Bzc/NiLfil2a6wNgWNK+lwUyqrmp6036KmNxgEPvvjIs5EJMBCLfDpi96lMs9l5ndB4yri/AZKv66K+BwrG5MGyOjRo2FmZobt27fD1dX1iY5AcXJygpOT3AlYZ8+eBQC4umYfQtmhQwd8+eWXuH//vjJsz5490Gq1eS67QlRcBoPAp9suYvXxcKhUwCv1DHB14FWnqfIyaYCEhoYiJCQETZo0KbfHPHbsGI4fP44ePXrAwcEBp06dwrvvvot+/fopm8wCAgLg7e2NESNG4Ouvv0ZsbCzef/99vPHGG9xWSlL+GR6zX/KB9n6oqcsieiImPZHQ29u7zM/3+CetVosNGzage/fu8Pb2xqeffoo33ngD69atU9poNBr88ccfsLS0RKdOnTBkyBAMGDAAc+fOLdda6enwz/CYO6glXvLlvc6p8iv3NZCcQ1uB7ENpp0yZgpkzZ6J58+Z5tkmWxbf91q1b4/jx40W2q1OnDrZv317qj09VS37h8bJfbR5CSk+Fcg+QatWqGe3rEELgueeeM2ojsxOdqKJJycjC9G2X8FvIPaPwIHpalHuA7Nu3r7wfkqjc7b/2EB9tuYjI+DSGBz21yj1AunXrVt4PSVRu4lIzMWv3JeXmULWrW2HWwBbozEu001PIJDvRU1NTMXHiRLi7u6NWrVoYNmxYue9MJypNQgicfazC898dxeYzkVCpgDGd6mL3pK4MD3pqmeQw3unTp2PlypUYPnw4LC0tsW7dOvzrX//Cxo0bTVEO0RM5fy8eC4KvY+8NDYBMNKxli9mDWqB1neqmLo2oTJkkQDZv3oxly5YpZ46/9tpr6NSpE/R6PTQajSlKIioRnd6AnRejsfLIHZwJjwcAaFQCE7rXx1vPNYLWjMsxPf1MEiARERHo0qWL8n+7du1gZmaGqKgoeHh4mKIkomJ5lJSBdSfDsebEXTxIzL7ysrlGhT4+LmiKCIx9tgHMGR5URZgkQPR6vXLpdKUQMzNkZWWZohyiQiVnZOH4rRjsuHAf28/fR6Y++6KfNe20eO0ZT7z6jAeqW2qwY0eEiSslKl8mCRAhBEaNGmV04cH09HSMHz8eNjY2yrDNmzebojyq4gwGgcv3E3Hg+iMcvP4IZ8LjoNP/73LrvnWqYVRHLzzv4woLs+zjUHhiIFVFJgmQnPug5/baa6+ZoBIiQG8QuBuTipOPVPhz43kcvRWLmJRMozZ1alijayMnDPLzQCuPaqYplKiCMUmArFixwhQPS1Vclt6A8NhU3HiYjBsPkv7+nYxbj5KRkWUAoAEQDQCwsdCgQ30ndGvkhK6NasLT0abQvomqogpxQymiJ5Wu0yM2JRMxyZl4nJKBmORMxCRnICYlE1Hxabj5MBm3H6Uo+y/+SWumhrNWjz5+9dC9iTNa16mubJ4iovwxQKjcGQwCGVkGpOv0SNPpka7TI11nQJpOjwydHulZf/+f+b+/0/9ul5qhw5VbamxfG4rYVF12SCRnIimjeAdgWJlr0KCWLRrWskUDZ1s0qmWHhs62cLY1x+5dO9HHvyFvNERUTAyQCmDpoTvYeVWNrbFnoFapkbO7Nud29Tn/GwwGPHqkxm+PQqBSq/HP29kLARiEAY8fq7HhwWng74tW5jQzCANiYtRYc/8UVCqVUb9xsRr8EnUye7jSXiA2VoNl4cchoILeIGAQAnqDgF4IGAwCBgGj4f/7nR0U+nyGPzk18PBhnqHmGhUcbbRwtLWAo60WTjYWcLS1QE077d+hYQf3alZQq/PeuIw7wYlKjgFSAVyMTMTFODUQV5zLuaiB+KLu5azG9YTYgqdPjMtnuApIis9/eHJiPsNLh7lGBUszDSwtNLA0V8PSTAMrCw0szTTQmqthZa6Bpbnm799qmGtUiAy7hfa+PnB2sIKjrRaONtmBYW9p9kR3tSSikmGAVABD29aGQ1oUmjdvDrO/T0JT4e8Pwly/9Ho9Lpw/jxYtW8BMkz3rcj4vc37r9QacCw1Fq1atlL6yx6ugz8pCaGgofH19jR5Hr8/C2bNn4evrC3Oz//WblaXH2bNn0LaNH7Tm5lCpAI1aBY1KBbVaBY1aBbVKBfXfw9Wq7GHFGa4118DSTA0zTcn2M+h0OuzYcRN92nlwUxORiTFAKoCO9R0Rf02gT5vahX4o6nQ6WEWfQx9f9wLb6XQ6mEeeRZ+Wrnna6HQ6qO+dRZ/mLkbjdDodRLjA8z55h+vvCjzXpBY/rIkoDx5mQkREUrgGUoZydnLnvo1vfnQ6HVJTU5GYmFjkGkhR7QprU9C4kg43pbKq6Un7Len0pTXPZeZ3QeMq4vwGyq6upKQkAMhzMAoVHwOkDOUsoLxAJFHFlZSUBAcHB1OXUSmpBOO3zBgMBkRFRcHOzq7Io4Patm2LU6dOFdlncdoV1qagcfkNT0xMhIeHByIiImBvb19kbeWluK9Vefdb0ulLa57LzO/8xlXU+Q2UzTwXQsDPzw/Xr1+HWs2t+TK4BlKG1Go1atcu3n2wNRpNsd60xWlXWJuCxhU2jb29fYX6QCnua1Xe/ZZ0+tKa5zLzu7BxFW1+A2U3zy0sLBgeT4CvXAUxceLEUmtXWJuCxhX38SuCsqr1Sfst6fSlNc9l5ndJHr8iqKjzvKrjJiwqUGJiIhwcHJCQkFDhvpFS6eP8ppLiGggVSKvVYvr06Ub3baGnF+c3lRTXQIiISArXQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4RKxUsvvYTq1atj0KBBpi6Fysj27dv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+      "text/plain": [
+       "<Figure size 300x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hd = wot.linear_hydrodynamics(bem_data, mass, stiffness)\n",
+    "hd = wot.check_linear_damping(hd)\n",
+    "intrinsic_impedance = wot.hydrodynamic_impedance(hd)\n",
+    "fig, axes = wot.utilities.plot_bode_impedance(intrinsic_impedance,'WaveBot Intrinsic Impedance',True)\n",
+    "natural_freq, _ = wot.utilities.natural_frequency(intrinsic_impedance, freq)\n",
+    "axes[0,0].axvline(natural_freq,color = 'k', linestyle = ':')\n",
+    "axes[0,0].text(x=natural_freq[0]*1.1, y =np.min(abs(intrinsic_impedance))*1.2, s = f'f_n = {natural_freq[0]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<xarray.DataArray ()>\n",
+       " array(12)\n",
+       " Coordinates:\n",
+       "     g               float64 9.81\n",
+       "     rho             float64 1.025e+03\n",
+       "     body_name       <U16 'WaveBot_immersed'\n",
+       "     water_depth     float64 inf\n",
+       "     radiating_dof   <U5 'Heave'\n",
+       "     influenced_dof  <U5 'Heave']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "restoring_dofs = ['Heave','Roll','Pitch']\n",
+    "indeces = [np.abs(intrinsic_impedance.loc[rdof,idof]).argmin(dim = 'omega') \n",
+    "            for rdof in intrinsic_impedance.radiating_dof \n",
+    "            for idof in intrinsic_impedance.influenced_dof \n",
+    "            if rdof == idof  #considering modes to be independent\n",
+    "            and any([df in str(rdof.values) for df in restoring_dofs])]\n",
+    "\n",
+    "indeces = [np.abs(intrinsic_impedance.loc[rdof,idof]).argmin(dim = 'omega') \n",
+    "            for rdof in intrinsic_impedance.radiating_dof \n",
+    "            for idof in intrinsic_impedance.influenced_dof \n",
+    "            if rdof == idof  #considering modes to be independent\n",
+    "            and any([df in str(rdof.values) for df in restoring_dofs])]\n",
+    "\n",
+    "indeces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (radiating_dof: 1, influenced_dof: 1)&gt;\n",
+       "array([[12]], dtype=int64)\n",
+       "Coordinates:\n",
+       "    g               float64 9.81\n",
+       "    rho             float64 1.025e+03\n",
+       "    body_name       &lt;U16 &#x27;WaveBot_immersed&#x27;\n",
+       "    water_depth     float64 inf\n",
+       "  * radiating_dof   (radiating_dof) object &#x27;Heave&#x27;\n",
+       "  * influenced_dof  (influenced_dof) object &#x27;Heave&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>radiating_dof</span>: 1</li><li><span class='xr-has-index'>influenced_dof</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2853045c-8949-44f3-a29b-675535919f75' class='xr-array-in' type='checkbox' checked><label for='section-2853045c-8949-44f3-a29b-675535919f75' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>12</span></div><div class='xr-array-data'><pre>array([[12]], dtype=int64)</pre></div></div></li><li class='xr-section-item'><input id='section-b5ac71ed-d79e-463f-84ff-f5c9420c8dc3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b5ac71ed-d79e-463f-84ff-f5c9420c8dc3' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>g</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.81</div><input id='attrs-f2184404-5425-4afb-9477-6790f51e298e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2184404-5425-4afb-9477-6790f51e298e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceead050-702f-4e86-853f-387c8f9f5948' class='xr-var-data-in' type='checkbox'><label for='data-ceead050-702f-4e86-853f-387c8f9f5948' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(9.81)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.025e+03</div><input id='attrs-8f0ff17c-d9ff-4b95-b498-61bdba659dcc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f0ff17c-d9ff-4b95-b498-61bdba659dcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63430f93-9844-41eb-98d5-31453ab21211' class='xr-var-data-in' type='checkbox'><label for='data-63430f93-9844-41eb-98d5-31453ab21211' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1025.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>body_name</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U16</div><div class='xr-var-preview xr-preview'>&#x27;WaveBot_immersed&#x27;</div><input id='attrs-023b3684-23bc-4a46-83cb-727217708701' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-023b3684-23bc-4a46-83cb-727217708701' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25957ae5-0ca2-42d1-ac61-be3f83b2872f' class='xr-var-data-in' type='checkbox'><label for='data-25957ae5-0ca2-42d1-ac61-be3f83b2872f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;WaveBot_immersed&#x27;, dtype=&#x27;&lt;U16&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_depth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>inf</div><input id='attrs-610610b7-bfd7-4de5-9e39-f5df6ecac1f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-610610b7-bfd7-4de5-9e39-f5df6ecac1f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad7d806a-b4f6-4bea-bc8a-c1f48aee3cf0' class='xr-var-data-in' type='checkbox'><label for='data-ad7d806a-b4f6-4bea-bc8a-c1f48aee3cf0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(inf)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>radiating_dof</span></div><div class='xr-var-dims'>(radiating_dof)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Heave&#x27;</div><input id='attrs-2ddcae70-5030-4a69-9b31-a8db4940e1b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ddcae70-5030-4a69-9b31-a8db4940e1b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25a446b7-7398-443c-a5a1-978985e9c286' class='xr-var-data-in' type='checkbox'><label for='data-25a446b7-7398-443c-a5a1-978985e9c286' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Heave&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>influenced_dof</span></div><div class='xr-var-dims'>(influenced_dof)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Heave&#x27;</div><input id='attrs-cc07ff99-2349-413b-8312-80065aa29a7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc07ff99-2349-413b-8312-80065aa29a7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58f08ac5-18e9-48c9-a22b-3964be49a61b' class='xr-var-data-in' type='checkbox'><label for='data-58f08ac5-18e9-48c9-a22b-3964be49a61b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Heave&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c58b885-dcd3-49d2-b63b-0886b23d3c6a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5c58b885-dcd3-49d2-b63b-0886b23d3c6a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (radiating_dof: 1, influenced_dof: 1)>\n",
+       "array([[12]], dtype=int64)\n",
+       "Coordinates:\n",
+       "    g               float64 9.81\n",
+       "    rho             float64 1.025e+03\n",
+       "    body_name       <U16 'WaveBot_immersed'\n",
+       "    water_depth     float64 inf\n",
+       "  * radiating_dof   (radiating_dof) object 'Heave'\n",
+       "  * influenced_dof  (influenced_dof) object 'Heave'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indx = np.abs(intrinsic_impedance).argmin(dim = 'omega') \n",
+    "in_sel = indx.sel(influenced_dof  = 'Heave', radiating_dof   = 'Heave')\n",
+    "indx"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/wecopttool/utilities.py b/wecopttool/utilities.py
index 6fa3ed26..fee8ae7c 100644
--- a/wecopttool/utilities.py
+++ b/wecopttool/utilities.py
@@ -33,7 +33,8 @@
 
 def natural_frequency(impedance: DataArray, freq: ArrayLike
                       ) -> tuple[ArrayLike, int]:
-    """Find the natural frequency based on the lowest magnitude impedance.
+    """Find the natural frequency based on the lowest magnitude impedance,
+       for restoring degrees of freedom (Heave, Roll, Pitch).
 
     Parameters
     ----------
@@ -139,7 +140,10 @@ def plot_hydrodynamic_coefficients(bem_data):
     fig_ex.legend(ex_handles, ex_labels, loc=(0.08, 0), ncol=2, frameon=False)
 
 
-def plot_bode_impedance(impedance: DataArray, title: str):
+def plot_bode_impedance(impedance: DataArray, 
+                        title: Optional[str]= None,
+                        plot_natural_freq: Optional[bool] = False,
+):
     """Plot Bode graph from wecoptool impedance data array.
 
     Parameters
@@ -159,7 +163,8 @@ def plot_bode_impedance(impedance: DataArray, title: str):
     fig, axes = plt.subplots(2*len(radiating_dofs), len(influenced_dofs),
                                 tight_layout=True, sharex=True, 
                                 figsize=(3*len(radiating_dofs), 3*len(influenced_dofs)), squeeze=False)
-    fig.suptitle(title + ' Bode Plots \n Mag (dB), Phase (deg)', fontweight='bold')
+    fig.suptitle(title + ' Bode Plots', fontweight='bold')
+    fn, fn_indx = natural_frequency(impedance=impedance,freq=freq)
 
     sp_idx = 0
     for i, rdof in enumerate(radiating_dofs):
@@ -169,7 +174,9 @@ def plot_bode_impedance(impedance: DataArray, title: str):
             axes[2*i+1, j].semilogx(freq, phase[i, j, :])    # Bode phase plot
             axes[2*i, j].grid(True, which = 'both')
             axes[2*i+1, j].grid(True, which = 'both')
-
+            # if i == j and plot_natural_freq:
+            #     axes[2*i, j].axvline(freq[fn_indx.sel(radiating_dofs=rdof,
+            #                                         influenced_dofs=idof)])
             if i == len(radiating_dofs)-1:
                 axes[2*i+1, j].set_xlabel(f'Frequency (Hz)', fontsize=10)
             else:
@@ -183,6 +190,7 @@ def plot_bode_impedance(impedance: DataArray, title: str):
                 axes[i, j].set_title(f'{idof}', fontsize=10)
             else:
                 axes[i, j].set_title('')
+    return fig, axes
 
 def add_zerofreq_to_xr(data):
     """Add a zero-frequency component to an :python:`xarray.Dataset`.  

From a5c743a9df0d32974bde45f2a686de8d9fea9e8b Mon Sep 17 00:00:00 2001
From: dtgaebe <86246113+dtgaebe@users.noreply.github.com>
Date: Tue, 31 Oct 2023 10:44:32 -0700
Subject: [PATCH 09/20] Drafted calculation of power flows and visualization
 with sankey diagram

---
 wecopttool/utilities.py | 117 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 117 insertions(+)

diff --git a/wecopttool/utilities.py b/wecopttool/utilities.py
index fee8ae7c..fee49c5a 100644
--- a/wecopttool/utilities.py
+++ b/wecopttool/utilities.py
@@ -24,6 +24,7 @@
 # from autograd.numpy import ndarray
 from xarray import DataArray, concat
 import matplotlib.pyplot as plt
+from matplotlib.sankey import Sankey
 
 # from wecopttool.core import linear_hydrodynamics
 
@@ -201,3 +202,119 @@ def add_zerofreq_to_xr(data):
         data = concat([tmp, data], dim='omega')
     return data
 
+
+def calculate_power_flows(wec, pto, results, waves, intrinsic_impedance):
+    wec_fdom, _ = wec.post_process(results, waves)
+    x_wec, x_opt = wec.decompose_state(results.x)
+
+    #power quntities from solver
+    P_mech = pto.mechanical_average_power(wec, x_wec, x_opt, waves)
+    P_elec = pto.average_power(wec, x_wec, x_opt, waves)
+
+    #compute analytical power flows
+    Fex_FD = wec_fdom.force.sel(type=['Froude_Krylov', 'diffraction']).sum('type')
+    Rad_res = np.real(intrinsic_impedance.squeeze())
+    Vel_FD = wec_fdom.vel
+
+    P_max, P_e, P_r = [], [], []
+
+    #This solution only works if the radiation resistance matrix Rad_res is invertible
+    # TODO In the future we might want to add an entirely unconstrained solve for optimized mechanical power
+    for om in Rad_res.omega.values:    #use frequency vector from intrinsic impedance because it does not contain zero freq
+        #Eq. 6.69
+        Fe_FD_t = np.atleast_2d(Fex_FD.sel(omega = om))    #Dofs are row vector, which is transposed in standard convention
+        Fe_FD = np.transpose(Fe_FD_t)
+        R_inv = np.linalg.inv(np.atleast_2d(Rad_res.sel(omega= om)))
+        P_max.append((1/8)*(Fe_FD_t@R_inv)@np.conj(Fe_FD)) 
+        #Eq.6.57
+        U_FD_t = np.atleast_2d(Vel_FD.sel(omega = om))
+        U_FD = np.transpose(U_FD_t)
+        R = np.atleast_2d(Rad_res.sel(omega= om))
+        P_r.append((1/2)*(U_FD_t@R)@np.conj(U_FD))
+        #Eq. 6.56 (replaced pinv(Fe)*U with U'*conj(Fe) as suggested in subsequent paragraph)
+        P_e.append((1/4)*(Fe_FD_t@np.conj(U_FD) + U_FD_t@np.conj(Fe_FD)))
+
+    power_flows = {'Optimal Excitation' : 2* np.sum(np.real(P_max)),    #6.68 positive because the only inflow
+                'Radiated': -1*np.sum(np.real(P_r)), #negative because "out"flow
+                'Actual Excitation': -1*np.sum(np.real(P_e)), #negative because "out"flow
+                'Electrical (solver)': P_elec,  #solver determins sign
+                'Mechanical (solver)': P_mech, #solver determins sign
+                }
+
+    power_flows['Absorbed'] =  power_flows['Actual Excitation'] - power_flows['Radiated']
+    power_flows['Unused Potential'] =  -1*power_flows['Optimal Excitation'] - power_flows['Actual Excitation']
+    power_flows['PTO Loss'] = power_flows['Mechanical (solver)'] -  power_flows['Electrical (solver)']
+
+    return power_flows
+
+
+def plot_power_flow(power_flows):
+        fig = plt.figure(figsize = [8,4])
+        ax = fig.add_subplot(1, 1, 1,)
+        plt.viridis()
+        sankey = Sankey(ax=ax, 
+                        scale= 1/power_flows['Optimal Excitation'],
+                        offset= 0,
+                        format = '%.1f',
+                        shoulder = 0.02,
+                        tolerance=1e-03*power_flows['Optimal Excitation'],
+                        unit = 'W'
+        )
+
+        sankey.add(flows=[power_flows['Optimal Excitation'],
+                        power_flows['Unused Potential'],
+                        power_flows['Actual Excitation']], 
+                labels = ['Optimal Excitation', 
+                        'Unused Potential ', 
+                        'Excited'], 
+                orientations=[0, -1,  -0],#arrow directions,
+                pathlengths = [0.2,0.3,0.2],
+                trunklength = 1.0,
+                edgecolor = 'None',
+                facecolor = (0.253935, 0.265254, 0.529983, 1.0) #viridis(0.2)
+        )
+
+        sankey.add(flows=[-1*(power_flows['Absorbed'] + power_flows['Radiated']),
+                        power_flows['Radiated'],
+                        power_flows['Absorbed'],
+                        ], 
+                labels = ['Excited', 
+                        'Radiated', 
+                        ''], 
+                prior= (0),
+                connect=(2,0),
+                orientations=[0, -1,  -0],#arrow directions,
+                pathlengths = [0.2,0.3,0.2],
+                trunklength = 1.0,
+                edgecolor = 'None', 
+                facecolor = (0.127568, 0.566949, 0.550556, 1.0) #viridis (0.5)
+        )
+
+        sankey.add(flows=[-1*(power_flows['Mechanical (solver)']),
+                        power_flows['PTO Loss'],
+                        power_flows['Electrical (solver)'],
+                        ], 
+                labels = ['Mechanical', 
+                        'PTO-Loss' , 
+                        'Electrical'], 
+                prior= (1),
+                connect=(2,0),
+                orientations=[0, -1,  -0],#arrow directions,
+                pathlengths = [.2,0.3,0.2],
+                trunklength = 1.0,
+                edgecolor = 'None',
+                facecolor = (0.741388, 0.873449, 0.149561, 1.0) #viridis(0.9)
+        )
+
+
+        diagrams = sankey.finish()
+        for diagram in diagrams:
+            for text in diagram.texts:
+                text.set_fontsize(10)
+        #remove text label from last entries
+        for diagram in diagrams[0:2]:
+                diagram.texts[2].set_text('')
+
+
+        plt.axis("off") 
+        plt.show()

From dadb917f378c09eeb37c8532a080f3101581f76b Mon Sep 17 00:00:00 2001
From: dtgaebe <86246113+dtgaebe@users.noreply.github.com>
Date: Tue, 31 Oct 2023 13:06:55 -0700
Subject: [PATCH 10/20] Added intrinsic impedance bode plot and sankey diagrams

---
 examples/tutorial_1_WaveBot.ipynb | 738 +++---------------------------
 1 file changed, 76 insertions(+), 662 deletions(-)

diff --git a/examples/tutorial_1_WaveBot.ipynb b/examples/tutorial_1_WaveBot.ipynb
index c4029aa0..066d84e0 100644
--- a/examples/tutorial_1_WaveBot.ipynb
+++ b/examples/tutorial_1_WaveBot.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,30 +128,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fb.show_matplotlib()\n",
     "_ = wb.plot_cross_section(show=True)  # specific to WaveBot"
@@ -171,160 +150,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=8.796, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.97e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.111, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.43e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.425, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.94e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.739, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.50e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.053, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.10e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.367, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.73e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.681, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.40e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.996, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.10e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.310, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.82e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.624, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.56e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.938, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.32e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.252, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.11e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.566, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.90e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.881, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.72e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.195, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.54e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.509, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.38e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.823, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.23e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.137, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.08e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.451, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.95e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.765, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.83e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.080, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.71e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.394, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.60e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.708, depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.50e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=8.796, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.97e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.111, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.43e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.425, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.94e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.739, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.50e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.053, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.10e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.367, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.73e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.681, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.40e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.996, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.10e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.310, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.82e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.624, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.56e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.938, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.32e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.252, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.11e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.566, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.90e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.881, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.72e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.195, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.54e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.509, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.38e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.823, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.23e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.137, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.08e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.451, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.95e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.765, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.83e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.080, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.71e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.394, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.60e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.708, depth=inf, radiating_dof=Heave, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.50e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "f1 = 0.05\n",
     "nfreq = 50\n",
@@ -337,58 +165,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "XX TODO: Illustration if intrinsic impedance"
+    "Next, we use the utilities class `wecopttool.utilities`, which has functions that help you analyze and design WECs, but are not needed for the core function of WecOptTool.\n",
+    "For example, we calculate the WEC's intrinsic impedance with it's hydrodynamic coefficients and inherent inertial properties.\n",
+    "\n",
+    "The intrinsic impedance tells us how a hydrodynamic body responds to different excitation frequencies. For oscillating systems we are intersted in both, the amplitude of the resulting velocity as well as the phase between velocity and excitation force. Bode plots are useful tool to visualize the frequency response function.\n",
+    "\n",
+    "Lastly, we also calculate the natural frequency of the hydrodynamic body. At this frequency of excitation the floating body would be naturally at resonance. The natural frequency reveals itself as a trough in the intrinsic impedance for restoring degrees of freedom (heave, roll, pitch).\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:wecopttool.core:Linear damping for DOF \"Heave\" has negative or close to zero terms. Shifting up via linear friction of 44.31820709090029 N/(m/s).\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.7150000000000001, <xarray.DataArray ()>\n",
-       "array(1890.73905495)\n",
-       "Coordinates:\n",
-       "    g            float64 9.81\n",
-       "    rho          float64 1.025e+03\n",
-       "    body_name    <U16 'WaveBot_immersed'\n",
-       "    water_depth  float64 inf, 'f_n = 0.65')"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\dtgaebe\\anaconda3\\envs\\wecopttool_dev\\lib\\site-packages\\IPython\\core\\events.py:89: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
-      "  func(*args, **kwargs)\n",
-      "c:\\Users\\dtgaebe\\anaconda3\\envs\\wecopttool_dev\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
-      "  fig.canvas.print_figure(bytes_io, **kw)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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gACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAkAiIAAkAgJAIiAAJAICQCIgACQCAv9s787Doir7N4DfMwMMOyoomwjuirggau5LCZrlkqmZpriU+WpvmZXm2yKVmZqppaVZbr9cM5d8zY1y3xXFfVcEQVzYd4aZ5/cHcV4m9kdgQO7PdXEB5zznme/MOTP3nJ2IpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4SIiKQwQIiISEqxA2T58uVQqVRQqVQYM2aM0bhvv/1WGdeiRQujcUeOHFHG9ezZs3SqLgX79+9X6sr5UavVsLOzQ4sWLfDhhx8iPj7+iR5jwYIFCAoKQlBQULGnWblypVJP9+7dpR87LCxMeeytW7eWePrcdZSkfhlhYWGl8pz/qXv37kq/K1euLLV+TcnLy0t5Tk+b3MtB7vekjY0NmjRpgrfeeguRkZFl8thl+br+8zmpVCpYWFigTp06CAwMxPXr1/Nt7+XlJf2Y+/fvV97/oaGhT/YECmFW3IZdu3ZV/j506JDRuNz/X7x4EXFxcahevToA4ODBg8q4Ll26SBdaHoQQSE5OxoULF3DhwgXs27cPJ06ckO5vwYIFuHv3LgCU+YfwP4WFheGzzz4DAAQGBmLAgAHl+vhEpUEIgdTUVFy7dg3Xrl3D9u3bcfnyZVhbW5u6tCei0+kQERGB//u//8OmTZtw4MAB+Pn5lVr/+/fvV97/Xl5eaNWqVan1nVux10AaNGgAV1dXAMDNmzdx//59ZVzuABFC4MiRI8r/lSFAPD09IYSAXq/H9u3blW8hJ0+exOXLl01cXfnS6XTIysrCqFGjIISAEKLMw8/Ly0t5rP3795fpY1HlIYSAwWDAiRMnlMC4e/dupV5G7ty5AyEEbt++jXbt2gEAUlJSMHXqVBNXJqdE+0ByB0BOaFy7dg0PHz6ERqNB7969jcbp9XocPXoUAGBmZob27dsDAJYuXYrnnnsOtWvXho2NDSwsLFC7dm0MHToU58+fVx5j8uTJyurc2rVrjWr58ssvlXGLFi1Shp87dw7Dhw9H7dq1YWFhgRo1aqB3797466+/in4x1Gq88MILqFmzpjIsNTXVqM2ZM2cwdOhQuLu7K/0/99xz2LRpk9ImZ/NPztoHYLwaKyP3JqXp06fj22+/RePGjWFlZYVmzZphzZo1Stvu3bujR48eyv+rVq1Sph01ahQAYNSoUcqwrVu3Yvz48XB2doZWq8W9e/cK3ISVe7PQsWPHEBgYCEdHR1SrVg3PP/88bt26ZVT39u3b0a1bN1SvXh1mZmZwdHREq1atMHbsWMTFxQEofBNWXFwcPvroI7Ro0QI2NjawsrJCgwYNMH78eKnX8Z/P4ciRIxg8eDBsbW1Rq1YtTJ06FTqdDseOHUPnzp1hbW2Nhg0bYv78+RBC5Ds/goKC8O2336JRo0bQarVo1KgRfvjhhzyP+/DhQ7z33nto0qQJrKysYGNjg7Zt2+LHH3806hsAYmNjMXbsWDg6OsLGxgb+/v5G743c0tPTMXr0aLRq1Qo1a9aEhYUFbGxs0KJFC3z66adISUkxap97E8mpU6fg7+8PGxsbuLu7Y/z48UhOTjZqr9frsWTJEnTu3BnVqlWDhYUF3N3d0b9/f6MvksnJyfjss8+M5lXz5s0xa9YsZGZmlng+5dTarl07+Pj4KMP++Z68efMmxo4dCy8vL1hYWMDe3h4dO3bETz/99ESvK5D9hWrBggVo164d7OzsoNVq0bhxY3z44YdITEyUek4AULduXUyZMkX5//jx40VOk5SUhE8++QQ+Pj6wtrZW3vsff/yxUS0qlUpZ+wCA0aNH59mUe/ToUfTu3RtOTk4wMzND9erV0axZM4wYMQK3b98u/hMRJbBo0SIBQAAQEydOFEIIsXTpUgFAtGnTRixcuFAAEB06dBBCCHH69Gml/TPPPKP0079/f2X4P39sbW3F9evXhRBCXLlyRRneu3dvo1qaNGkiAAgrKysRFxcnhBDi999/F+bm5vn2q1KpxOLFi5Xp9+3bp4zz9PQUQgih1+vFzp07hUqlEgBEw4YNhU6nU6bZvHlzgf0DEO+//74QQogVK1YU2Kaolzz3tN26dct3ePXq1fPt98iRI0IIIbp161bgYwcGBgohhAgMDFSGOTk5GbW5c+eO0eNNnz5dqSN33/nV0bRpU5GVlaXMfzMzswJruXHjhhBCiDt37uT7nO/cuSM8PDzyndbBwaHQ1/Gfta5YsSLf4TVr1szTd79+/YSlpWWe4WvXrs13fuTXBwAxe/Zspf2tW7eEq6trga/F0KFDlbYZGRnCz88vTxt7e3tha2ubZzmKi4srdHkLCAgwel1yhltZWQmtVpun/bhx44xq6dmzZ4F9nz17VgghRExMjPD29i6wXdeuXUVGRkah8yv3cpDz/AwGgzh16pSwsbERAESNGjVETEyMMs2xY8eMXpN//gwePFgYDAap1zU9Pb3Q91LTpk1FbGxsoc8p9+ud897KsXHjRmW4jY1NnvY5n0tCCPHo0SPRuHHjAmtp3LixePz4cZ7H++fPihUrRERERKGvWXBwcJHPSam12C2FEOfPn1cepEWLFkIIIUaMGCEAiHfffVecO3dOABDm5uYiNTVVzJs3T2mf8+EqhBC7d+8Wp0+fFo8fPxY6nU7ExMSIjz/+WGk7efJkpW2XLl0EAKHRaMT9+/eFEEKcPHlSaZvzgZiamqq8kb28vMSpU6dERkaGuHbtmvLCW1lZiUePHgkhjAMkv58aNWqI8+fPK3WkpqYafdB+//33IjExUezdu1fY29srw0+ePKlM4+npmWehLEpxAkSj0Yh169aJhIQEMWXKFGX4m2++qbTP/fxyXqPccgdItWrVxLZt20RycrK4evWqSElJKVaA+Pj4iKtXr4p79+6Jpk2bKsOPHTsmhBDim2++UYZt2LBBZGZmiocPH4qjR4+KTz/9VJmfBQVI3759leHt27cXZ86cESkpKeLy5cvi888/L/K1LE6AdOjQQURGRopjx44Zzf/u3buLBw8eiHXr1inDevXqle/80Gq14r///a9ISkoSK1euNPqAzvmAefHFFwUAYWZmJjZu3ChSU1PFgwcPxODBg5X227dvF0IIsXz5cmVYvXr1xPnz50VsbKz417/+lecDVojsD7o1a9aIW7duiaSkJJGZmSlu3rwpWrVqpbTNvSzn7mPo0KHi0aNH4tixY0qYWFpaKh+6ueehi4uL2L59u0hKShLh4eFi4cKF4vbt20IIId566y2l3aJFi0RiYqKIj48Xb7/9ttHwwvwzQP75o9VqxZ9//mk0Te7QmjZtmoiPjxchISFGXzx+/fVXqdd17ty5Rn3HxMSIlJQUMXv27Hw/1wqSX4Dcvn1btG3bVhnes2fPPO1zB8iECROU4QEBAeLevXsiMjLSKNwnTJigtJ8+fXq+y74QQmzatEkZ9/XXX4v09HQRGxsrQkJCxFdffSUuXLhQ5HNSai12S5H9baBGjRoCyP5GHxsbq3xIbt68WRgMBuVb6b59+8SAAQOUQn///Xeln3PnzomhQ4cKDw8PYWFhkWdByb228csvvyjDv/nmGyGE8cKa8607ODi40IUv5+e3334TQhQdIACEm5ubCA8Pz9O/r6+v0evyzjvvKOM+/vhjZXhZBchLL72kDL9w4UK+H3AlCZD8PoyLEyC55+l7772nDF+3bp0QQoitW7cqw7p27Sq++OIL8euvvyprmDnyC5C0tDSjtZewsLBiv4b51VpQgOzcuVMZXqtWLWX4nj17hBDZH845wxo3bpzv6/Pqq68aPW6HDh2Ucdu2bcvzXAr6eeutt4QQQgwdOlQZtnDhQqXf5ORko35yW7ZsmejcubOoXr26UKvVefpev3690jZnmFqtNvoGnfvbeU64d+7cWRm2cuXKAl9rd3f3Ip/fiy++WOj8KipAgOy1hTNnzgghhLhx44Yy3MnJSVnzFUKI+fPnK+Nee+01qde1U6dORdbj4+NT6HPK/XoX9GNtbS1OnTqVp33uAMn9+p47d04ZfvbsWWV47dq1leGFBUjuaXx9fcWnn34q1qxZI86fP698cSiuEu0DUalU6NSpE5D9KmP9+vXKdv4uXboYjT948CAOHz6cZ7q7d++iY8eOWL9+PSIiIvLdNpqWlqb8PWjQIOWIrl9++QU6nQ7r168HAPj4+KBjx44AgAcPHhTrOTx+/DjPsJyd6AaDAXfu3EHnzp0BAFFRUZg3b16e/j09PY2mz324XXHreBJNmzZV/raxsVH+Tk9Pl+pP9uiPouro378/3nvvPVhbW+PgwYP45JNPMGTIEDRq1Ah+fn6IiooqsO+YmBhkZWUBAOzs7PK85qWlQYMGyt9WVlbK33Xr1gUAaLVaZVhBr+8/a8v9/8OHD42eS2Fyls3cy6iHh4fyt42NDZycnPJM980332Ds2LE4fPgw4uLiYDAY8rTJ/Z7K4eLiory3cvrPkfNco6OjlWHNmzcvsPbiLPf5vfcKI/4+sOL+/ft4+eWXAQCJiYn4/PPP8zxm7dq1odFolP/ze0+W9HUti+eUw8zMDLVr18Zrr72G06dPo02bNoW2L+jzR+azp1WrVpg7dy6qVauGs2fP4vPPP8fw4cPRokULNGrUCBcvXiz28yjxiYS5D+edPXs2gOwPkpwZkDN++fLlyovr7e0NR0dHAMDWrVuVnXrPPvssIiMjIYTAtm3b8n08S0tLjBgxAgAQGhqKr7/+Wul33LhxSjtnZ2fl7169eikLX+4fg8GAN998s8DnlrNj8ZVXXlGGXb16NU//uXeOA9k7gfOro6yO1Tc3Ny/yMUry2LKHRBanjrlz5yI2NhanTp3Cr7/+iokTJwLIPhgh54MgP46OjjAzyz7KPCkpCeHh4VI1FiXnMYo7PD//XB5y/1+rVi2j52JnZ4eMjIx8l8+cA0Vyf5hFREQof6ekpOT7gbV69Wrl72+//RapqakQQmDgwIGF1p17/gH5z0MXFxfl78I+WHKWe5VKhaioqHyfX84BNSXl4uKiHPwB5P+evHfvHvR6vfJ/fu/Jkr6uufs/duxYvs+psC9B+ck5CivnMN5ffvnF6ItYQQr6/JH97Hnvvffw6NEjhIaGYtOmTfjoo4+g0Whw8+ZNvP/++8V+PiUOkNxHYuVe+8iREyC5n2Tu8bnfmDlHi9y6dQszZswo8DFzB8X06dMBZH9bzAkWAOjUqZNy9NSePXswd+5cxMTEICMjA1evXsXs2bONvm3mRwiBu3fvYsOGDcqwnEOXO3bsqITg2bNnsWTJEiQnJ+PAgQNGJ6n17dtX+TunPYAyPZknP7kf+8aNG3mOxCkPBw4cwMyZM3Hp0iV4eXlhwIABRuejFBYKlpaW6NOnj/L/q6++itDQUKSlpeH69euFLi/lbfPmzfjjjz+QnJyMVatW4dixYwCyl9HOnTvD0tJSOUIxKSkJY8aMQVhYmPIhsmrVKnTq1Ek55D0gIEDpe/78+bhw4QLi4+PxwQcf5Lsmk/s9ZWtrC5VKhd9//x1//PHHEz+33CH04YcfYufOnUhOTkZkZCQWL16MO3fuAABeeuklANnvocDAQFy5cgU6nQ7R0dH47bff0Lt3b/zyyy9SNTx48MDoPZbznmzQoIHy4fv48WNMnz4dCQkJCA0Nxfz585X2/fr1A1Dy1zXnOQHAxIkTERISgoyMDMTExGDHjh0YPHgwvvrqK6nnVFI5zwEApk6diqioKNy/f9/o8N/cbXK//y9evGj0/C5fvoyPP/4Yp06dgouLC/r27YtBgwYpa9sl+rJWog1eQgidTqccEZHzs3r16kLHr1mzRhl/+/ZtYW1tnWc7YKNGjfLd9p+jY8eORu1HjhyZp822bdvy3aeS+ydHcfaBWFpaipCQEGWa3377rdBt2ZMmTTKq59///neeNvk9t9yKsw8k9z6JgnZAp6Wl5Xt0UM720Nz7QPbt21doHQXtA8l9REl+21xz77/K7ydnO3RBzyEsLKzMj8LK/Rxy77PKPTxnWO5t0rlfn4K2/8+aNUtpf/v27SL3E+TMh4KOFrK2tjZ67+SYNWtWnrZqtVrUr18/3+ef3/Mp6HUpyVFYzZo1K/T5/XNb/D8VZx+IWq0W//3vf5Vpjhw5ku/nSc7PwIEDizwKq6DXNT09XXTv3r3QenK/NwqSu33u5aqo9rnnz8OHD0XDhg0LrKNhw4bKAUJCGB8B+8/HP3ToUKHP6b333iuyxhwlXgMxMzNDhw4djIb9cw3jn+Nz9ikA2duWd+zYgfbt28Pa2hqurq54//338d133xX6uLnXQvL7H8j+9h8SEoKRI0eiTp06MDc3h4ODA5o2bYqRI0carVkUxNzcHHXq1MGrr76KY8eOoXXr1sq4l19+GceOHcPgwYPh4uICMzMzODg4oHv37li/fr3Rtx4g++zz4cOHw9nZudwvPWFpaYlff/0V7dq1g62tbbk+dg4/Pz+8/vrraN68OWrUqAGNRgM7Ozu0b98eS5cuxVtvvVXo9J6enggNDcV//vMf+Pj4wMrKCpaWlqhfv77RZkZTe/3117F48WI0atQIFhYWaNCgAb7//nujb4d169ZFaGgopkyZAm9vb1haWsLKygr16tVD3759sXjxYmVZs7CwwJ49ezBmzBhUr14dVlZW6NGjBw4cOGB0jlKO999/H59//jm8vLyg1WrRsmVLbNmyxeh9J8vCwgK7du3CDz/8gE6dOsHBwQHm5uZwdXVF3759lc0mNWrUwIkTJ/DFF1/A19cXNjY20Gq18PT0hL+/P7755hs8//zzUjWYmZnB1dUV/fv3x19//YUXX3xRGdexY0ecPXsWo0aNgoeHB8zNzWFra4tnnnkGixcvxsaNG5X3XklfV61Wi+DgYCxcuBAdOnSAvb29cs5a165dMWPGDAQGBko9p5KqWbMmTp06hf/85z/K8qPVatG0aVNMmzYNp06dMtpE5+fnhx9++AENGzaEhYWFUV/16tXDW2+9hdatW8PJyQkajQbW1tZo1aoVZs2ahVmzZhW7LtXfiUf01Bk1ahTi4+PzXAts//796NGjB+Li4lCtWjWpvleuXInRo0cDyN6sWt6XqiGqCHg1XiIiksIAoSrv6NGj6Nq1K6ysrODh4YG3337b6KCD1atXo02bNrCzs4OLiwuGDRtmdOmIefPmYcmSJUZ9njlzBiqVSrksREJCAsaNG4datWrB3t4ezz77LM6dO1c+T5CojDBAqEq7cOECevXqhYEDB+L8+fPYsGEDDh8+bLR/JjMzE1988QXOnTuHrVu34s6dO9i1a5dyKOe4ceOMrkUGAGvXrkWHDh1Qr149CCHwwgsvIDo6Gjt27EBISAhat26N5557DrGxseX9lIlKT7F3txNVMoGBgUKj0QgbGxujn5zrXMXFxYkRI0YYXftJCCEOHTok1Gq1SEtLy7ffnEvpJCUlCSGEOHPmjFCpVMrZ8nq9Xri7u4vvv/9eCCHEX3/9Jezt7UV6erpRP/Xr1xc//vhjaT9tonLDNRB6qvXo0QOhoaFGPz///LMyPiQkBCtXroStra3y06tXL+WqBED2eT/9+/eHp6cn7OzslCsG5xwv7+vriyZNmmDdunUAss9/efjwIYYMGaI8RnJyMhwdHY0e586dO3muXkxUmRT/dFuiSsjGxibPCaT37t1T/s65OsHbb7+dZ9o6deogJSUFAQEBCAgIwOrVq1GzZk2Eh4ejV69eRpfhGT58ONauXYsPP/wQa9euRa9evZTDKg0GA1xdXfO9j4XsUWBEFQEDhKq01q1b49KlSwVepeDChQt4/PgxZs2apVw/6fTp03naDRs2DB9//DFCQkLw22+/YfHixUaPER0dDTMzsye6TSlRRcNNWFSlTZ06FceOHcPEiRMRGhqKGzduYNu2bfj3v/8NIHstxMLCAgsXLsTt27exbds2fPHFF3n6qVu3Ljp27IixY8ciKysL/fv3V8b17NkTHTp0wIABA7B7926EhYXh6NGj+Pjjj/MNI6LKggFCVVqLFi1w4MAB3LhxA126dIGvry8++eQT5XpLNWvWxMqVK7Fx40Z4e3tj1qxZmDt3br59DR8+HOfOncPAgQONruyrUqmwY8cOdO3aFWPGjEGjRo0wdOhQhIWFGV0Aj6iy4ZnoREQkhWsgREQkhQFCRERSGCBERCSFAUJERFIYIEREJIUBQkREUngmehkyGAyIioqCnZ1dud+RkIgKJ4RAUlIS3NzcoFbzu7QMBkgZioqKUi5/QUQVU0REBGrXrm3qMiolBkgZsrOzA5C9gNrb2xfYTqfTYc+ePQgICIC5ufkTtSusTUHjSjrclMqqpiftt6TTl9Y8l5nfBY2riPMbKLu6YmNjUbduXeV9SiXHAClDOZut7O3tiwwQa2tr2NvbF/lhUlS7wtoUNK6kw02prGp60n5LOn1pzXOZ+V3QuIo4v4GynecAuHn5CTBAKoD7Cem4m2zqKqiiy8zMxLfffgsAeOedd2BhYWHiiqiq456jCmDJwduYd8EMgStP4+jNx+DlySg/Op0OU6ZMwZQpU5Rvz0SmxDUQExNCQG8A1BA4eisWR2+dQCuPapjQvT56NnWGWs3Va8pmZmaGwMBA5W8iU+NSaGIqlQoz+nujsT4Mty3qYmNIJEIj4jHulxA0crbFv7rXR98WbjDTcGWxqtNqtVi5cqWpyyBS8FOpgnC0BKa/2BSHpz6Lf3WvDzutGa4/SMa7G86h+9z9+OVYGNJ1elOXSUSkYIBUMDXttJjauwkOf/gsPujVGI42FrgXl4ZPfr+EHvMO4c9IFZLSs0xdJhERA6SicrAyx8QeDXB46rP4rF8zuFezwuPkTPw3XINu3xzE17uv4nFyhqnLpHKUkpKCatWqoVq1akhJSTF1OUQMkIrOykKDwI5e2P9Bd8we2AzOVgJJ6Vn4ft8tdJ69F0HbLiEyPs3UZVI5SUhIQEJCgqnLIALAneiVhrlGjYG+7rCIOgeLun5YeigM5+4lYOXRMKw+fhcDfN0xvlt9eFbXmrpUKiNWVla4fv268jeRqTFAKhm1CgjwdkafFu44cjMGP+y/iaO3YvBbyD1sOnMP/k1robnG1FVSWVCr1WjYsKGpyyBSMEAqKZVKhc4NndC5oRPOhsfhh/23EHz5AfZcfog9MMPx1BBMfLYBOtRz5KUaiKhMMECeAr51quOnkW1w/UESfth7A9vOReHIrRgcuRUD3zrVMKF7AzzXpJapy6QnpNPpsHTpUgDAuHHjKtT1qqhqYoA8RRo52+HrQc3RQh2BW+Z1sfFMJM6Gx+ON/zuNRs62GNelLtS8SkqllZmZibfeegsAMGrUKAYImRwD5CnkaAmM6NMUk/wbY/mRO/jl2F1cf5CM93+7AEetBok1IzC0nScszbmzpDLRaDQYNGiQ8jeRqTFAnmI5JyWO71Yfq4/fxbLDtxGTokPQf69g0b7beL1LXQx/pg4s+VlUKVhaWmLjxo2mLoNIwfNAqoCckxL3T+6Kl730cHOwxOPkDMzaeRWdZu3FvD9vIJkXdyWiEqo0ayAJCQnYsmULDh06hLCwMKSmpqJmzZrw9fVFr1690LFjR1OXWOFZWWjQ1VXgi1GdsePSIyzefxO3HqVg8YE7MFdrcM3sKsZ3bwC3ajzHgIiKVuHXQO7fv4833ngDrq6u+Pzzz5GSkoJWrVrhueeeQ+3atbFv3z74+/vD29sbGzZsMHW5lYK5Ro1BfrUR/G43LHmtNZq720NnUOH/joej65x9eH/jOdx8yDtcVTSpqalwd3eHu7s7UlNTTV0OUcVfA2nZsiVGjhyJkydPwsfHJ982aWlp2Lp1K+bNm4eIiAi8//775Vxl5aRWq9DbxxXPNnLEgnW7cDajJo7djlVOSgxoWgstKvwSUnUIIRAVFaX8TWRqFX4N5NKlS5g7d26B4QFkX9bh1VdfxYkTJ5Qb7hQlKSkJkyZNgqenJ6ysrNCxY0ecOnVKGS+EQFBQENzc3GBlZYXu3bvj0qVLT/x8KiKVSoXG1QT+b3QbbJnQEf7ezhAC2H35Ib4+b4YJa0NxNTrR1GVWeZaWljh79izOnj0LS0tLU5dDVPEDpGbNmmXS/vXXX0dwcDB++eUXXLhwAQEBAejZsyciIyMBAHPmzMG8efOwaNEinDp1Ci4uLvD390dSUlKJn0NlknNS4u5JXdG3hQtUEAi+8hDPf3sI/153FrcecdOWqWg0GrRq1QqtWrXiYbxUIVT4AMktJiZG+TsiIgKffvopPvjgAxw6dKhE/aSlpWHTpk2YM2cOunbtigYNGiAoKAh169bF4sWLIYTAggUL8NFHH2HgwIHw8fHBqlWrkJqairVr15b206qQGrvYYd7gFviwpR7PN8teI/nvuSj4zzuAyb+G4m4MLydOVNVVigC5cOECvLy8UKtWLTRp0gShoaFo27Yt5s+fj6VLl6JHjx7YunVrsfvLysqCXq/PsxnAysoKhw8fxp07dxAdHY2AgABlnFarRbdu3XD06NHSelqVgos18N3Qltjxdhf4ezvDIIDNZyLx7DcHMPW387gXx5255UWn02HlypVYuXIldDoed02mVyl2kU6ZMgXNmzfH6tWrsXr1arz44ovo06cPfv75ZwDAv//9b8yaNQsDBgwoVn92dnbo0KEDvvjiCzRt2hTOzs5Yt24dTpw4gYYNGyI6OhoA4OzsbDSds7Mz7t69W2C/GRkZyMj4302eEhOz9xvodLpC3/A544r6UChOu8LaFDSuOMMb1rTCD6+2xIXIBHz71y0cuPEYG05HYPPZexjiVxvju9WFi33Zb5cv7mtV3v2WdHqZeZ6ZmYnRo0cDAAYMGAAbGxup+V3QuLJ6bZ9UWc9zkqcSleBwDicnJ+zduxctWrRAcnIy7O3tcfLkSbRp0wYAcPXqVbRv3x7x8fHF7vPWrVsYM2YMDh48CI1Gg9atW6NRo0Y4c+YMfv75Z3Tq1AlRUVFwdXVVpnnjjTcQERGBXbt25dtnUFAQPvvsszzD165dC2tr65I96QruThKwI0KN6wnZK7FmKoFOLgI93QywtzBxcU+pzMxMzJ49GwAwdepUWFjwhX4SqampGDZsGBISEmBvb2/qciqlShEgarUa0dHRqFUr+4qydnZ2OHfuHOrVqwcAePDgAdzc3KDX60vcd0pKChITE+Hq6opXXnkFycnJWLhwIerXr48zZ87A19dXadu/f39Uq1YNq1atyrev/NZAPDw88Pjx40IXUJ1Oh+DgYPj7+xd6gbzitCusTUHjSjo8txN3YrHgr5s4fTceAGBlrsZrz9TB6529UMOm9D/givtalXe/JZ2+tOa5zPwuaFxZvbZPqqzqiomJgaurKwPkCVSKTVgA8tzTorTucWFjYwMbGxvExcVh9+7dmDNnDurWrQsXFxcEBwcrAZKZmYkDBw4o3wDzo9VqodXmvSOgubl5sRb80mxXWJuCxpV0OAB0buSMTg1r4dCNx/gm+DrORcTjp8NhWHsyAmM618XrXerBwar0P4yK+1qVd78lnb605rnM/C5oXFm9tk+qtOuqiM+xsqk0ATJq1Cjlwzk9PR3jx4+HjY0NABh96y+u3bt3QwiBxo0b4+bNm/jggw/QuHFjjB49GiqVCpMmTcLMmTPRsGFDNGzYEDNnzoS1tTWGDRtWqs/raaBSqdC1UU10aeiEvVcfYl7wdVyKSsTCvTex8mgY3uhSD6M7ecHOkm9YoqdJpQiQf54c+Nprr+VpM3LkyBL1mZCQgGnTpuHevXuoUaMGXn75ZXz55ZfKt5IpU6YgLS0NEyZMQFxcHJ555hns2bMHdnZ28k/kKadSqfBcU2c826QWdl+KxvzgG7j2IAnzgq9j+ZE7eLNrfQR29IS1RaVY7Cqc1NRUtGzZEgBw7ty5p26/WmmISc7AkgO30MjZDoPbeJi6nKdepXgnr1ixotT7HDJkCIYMGVLgeJVKhaCgIAQFBZX6Yz/tVKrsS6QEeLtg+4X7WPDnddx+lILZu65i2eHbGN+tPl5rz/uRlJQQAjdv3lT+pv9JSNXhp0O3seLIHaRk6uHqYIl+rdygNeMyVpYqRYBQ5aRWq9CvpRv6+Ljg99AofPvXDYTHpmLGH1ew4kgYpj7fBH1buPKe7cVkaWmJw4cPK38TkJyRhRWH72DpodtISs8CADR3d8B7AY1goakUp7lVauUeIPHx8fjtt99w69YtfPDBB6hRowbOnDkDZ2dnuLu752k/cODAYve9efPm0iyVSomZRo2X/WqjXys3bAq5h+/+uoHI+DS8ve4sVh0NwycveqOVRzVTl1nhaTQadOrUydRlVAhpmXr8cjwMi/ffQlxq9vkcjZ3t8K5/I/Rq5swvJeWkXAPk/Pnz6NmzJxwcHBAWFoY33ngDNWrUwJYtW3D37l383//9X55pHBwclL+FENiyZQscHByUc0BCQkIQHx9foqAh0zDXqDG0XR30b+WOnw/dxg/7byHkbhwGfH8EA33d8UHvxnB14L1IqGAZWXpsOBWBhXtv4lFS9sEz9Zxs8E7Phujbwg1qNYOjPJVrgEyePBmjRo3CnDlzjHZGP//88wUe3ZR7/8fUqVMxZMgQLFmyRLmYnF6vx4QJE3gcdyViZaHBv59riCFtPTBn1zVsOnMPm89GYufFaLzZrR7e7FofVhbcdv1PWVlZ2LJlCwDgpZdegplZ1dkCbRDAlrNR+HbvLUTGpwEAale3wtvPNcRAX3eYcXOVSZTrEnjq1Cn8+OOPeYa7u7srlw8pzPLly3H48GGjK5FqNBpMnjwZHTt2xNdff12q9VLZcra3xDdDWiKwoye+2H4Zp8LisODPG9hwKgJTezdBv5b8RplbRkaGcuBHcnJylQgQIUT2OUYXNLh3/CIAoJadFv9+tgFeaVsHFmYMDlMq1yXQ0tJSuT5UbteuXSvWZdizsrJw5coVNG7c2Gj4lStXYDAYSq1OKl8talfDr292wI4L0Zi54woi49MwaUMoVh4Nw6d9vdG6TnVTl1ghqNVqdOvWTfn7aXcxMgFf7byCIzdjAKhgqzXDxB4NMLqTF4/gqyDKNUD69++Pzz//HL/++iuA7MM9w8PD8eGHH+Lll18ucvrRo0djzJgxuHnzJtq3bw8AOH78OGbNmqVcZI4qJ5VKhRdauOK5prWw7PAd/LDvJkIj4jHwh6Po38oNU3s3qfL3areyssL+/ftNXUaZi4xPw9zd17DlbPa9ecw1KnSqpcfswM5wrmZj4uoot3INkLlz56JPnz6oVasW0tLS0K1bN0RHR6NDhw748ssvizW9i4sL5s+fj/v37wMAXF1dMWXKFLz33ntlXT6VA0tzDSb2aIDBfrUxd881bAy5h99Do7D7UjTGdamHMR3rmLpEKiM6vQFLD97Gd3/dQEZW9haFfi3d8O5z9XH+2L4yubYaPZlyDRB7e3scPnwYe/fuxZkzZ2AwGNC6dWv07NmzWNOr1WpMmTIFU6ZMUTaFcef506mWvSXmDGqJkR288Pn2yzh5Jxbf7b2JDaci0NNZhed5It1TJeRuLD7ZegmX72e/r9vVrYFPXvBG89oO0Ol0OG/i+ih/5RogYWFh8PLywrPPPotnn332ifpicFQNPu4O2DCuPXZfisaXO64gIjYNa5I0uLniNGa/3BJeTlVnk0ZaWho6dOgAADh27BisrCr/Jr3YlEx8teMKNobcAwA4WJljel9vvOTrznM5KoFy3RNXr149dO7cGT/++CNiY2OLNU3v3r2LdRfApKQkzJ49G99///2TlkkVTM6lUf6c3A0fBDSEhVrgxJ049FpwEEsO3EKWvmocQGEwGHDu3DmcO3eu0h80IoTAbyH38Nw3+5XweKWNB/a+1w0DW9dmeFQS5boGcvr0aaxbtw4zZszAO++8g169euG1115Dv3798r0MOgAMHjwYQ4YMgZ2dHfr164c2bdrAzc0NlpaWiIuLw+XLl3H48GHs2LEDL774Ig/lfYppzTQY16UuLB9dwV+JtXD0Vixm7byK/56LwuyXW8DH3aHoTioxS0tL7NmzR/m7srpyPxHTt13CyTvZXyKbuNjhy5eaw8+TR9tVNuUaIK1bt0br1q0xZ84c7N+/H2vXrsWbb76J119/HS+//DKWL1+eZ5qxY8dixIgR+O2337Bhwwb89NNPyp0HVSoVvL290atXL4SEhOQ5vJeeTk6WwMqX/PD7+QeY8ccVXIpKRP/vj+CNLvUwqWfDp/YQT41GA39/f1OXIU0IgZ8O3cbsXdegNwhYmqvxbs9GGNO5Lsx5ImClZJIzkVQqFXr06IEePXrgX//6F8aOHYtVq1blGyAAYGFhgWHDhilnqyckJCAtLQ2Ojo68KUwVpVKpMLiNB7o1ronPtl3GHxfuY8mBW9h9KRpfDWyO9vUcTV0i5RKfmolPfr+E/56LAgD0buaCT/p6w72KH5pd2Zkk9iMiIjBnzhy0atUKbdu2hY2NDRYtWlTs6R0cHODi4sLwINSys8T3w1tj6Qg/ONtrcedxCoYuPY5pmy8gIU1n6vJKVVZWFv744w/88ccfyMrKMnU5xfbfc1HoOe8A/nsuCmZqFb7o3wyLX2vN8HgKlOsayNKlS7FmzRocOXIEjRs3xvDhw7F161Z4eXmVZxn0FApo5oL29R0xa+dVrD0RjnUnw/HXlQf4YoAPejVzMXV5pSIjIwMvvvgigMpxKZOEVB0++f0itv291tGwli1mvdyC+zqeIuW6BH7xxRcYOnQovv32W7Rq1ao8H5qqAHtLc8x8qTn6tXTDtM0XcOdxCt78JQR9mrtgxoDmlf5ENLVarVyFuqJfyuTE7Ri8uyEUUQnp0KhVmNijAd7q0YDXrnrKlGuAhIeH8/A8KnPt6zli5ztd8O1fN7D04G3suBCNkLtxmP9KK3Ss72Tq8qRZWVnh1KlTpi6jUFl6A7776wYW7bsJgwC8HK2xYKgv7/fylCrXrwM54ZGamoqrV6/i/PnzRj/lJSsrCx9//DHq1q0LKysr1KtXD59//rnRsfVCCAQFBcHNzQ1WVlbo3r07Ll26VG410pOxNNdgau8m+H1iJ9SvaYMHiRkY/vMJfLPnWpU5b6S8RcWnYdhPJ/Dd3uzwGORXG3+83YXh8RQr1zWQR48eYdSoUdi1a1e+4/V6fbnUMXv2bCxZsgSrVq1Cs2bNcPr0aYwePRoODg545513AABz5szBvHnzsHLlSjRq1AgzZsyAv78/rl27ZnQvE6rYfNwd8N9/d8Zn2y5jw+nsGxEduxWDb1/15U7cUrTtXBQ+3nIBielZsNWa4cuXfNC/Vd47jNLTpVzXQCZNmoT4+HgcP34cVlZW2LVrF1atWoWGDRti27Zt0v0GBgaW6NIox44dQ//+/fHCCy/Ay8sLgwYNQkBAAE6fPg0ge+1jwYIF+OijjzBw4ED4+Phg1apVSE1Nxdq1a6XrJNOwtjDD7EEt8N2rvrDVmuH03Tg8v+Agdl28b+rSSiQtLQ2dOnVCp06dkJaWZupyFLN2XsXb684iMT0LLT2qYfu/OzM8qohyXQPZu3cvfv/9d7Rt2xZqtRqenp7w9/eHvb09vvrqK7zwwgtS/bq7u5dop2Lnzp2xZMkSXL9+HY0aNcK5c+dw+PBhLFiwAABw584dREdHIyAgQJlGq9WiW7duOHr0KN588818+83IyEBGRobyf84FH3U6HXS6gg8pzRlXWJvitiusTUHjSjrclJ6kpue9a6KZS3u8u/E8zt9LxPjVZzCsXW1M690YGhik+5WpS2aeZ2ZmKpf1ycjIgJmZmdT8LmiczGu79NAdLDlwCwAwoVs9vNWjHsw16lJdZspqOaxIy3VlpRKi/C5ram9vj/Pnz8PLywteXl5Ys2YNOnXqhDt37qBZs2ZITU0tlzqEEPjPf/6D2bNnQ6PRQK/X48svv8S0adMAAEePHkWnTp0QGRkJNzc3Zbpx48bh7t272L17d779BgUF4bPPPsszfO3atbC2ti6bJ0MllmUAdkSo8VdU9pcOV2uBUQ31cKngs0iv1ys70du2bWt0Z87yJgRwMFqFzWHZNfT31ONZt8p1heTU1FQMGzYMCQkJvDirpHJdA2ncuDGuXbsGLy8vtGrVCj/++CO8vLywZMkSuLq6llsdGzZswOrVq7F27Vo0a9YMoaGhmDRpEtzc3BAYGKi0++cRY0KIQo8imzZtGiZPnqz8n5iYCA8PDwQEBBS6gOp0OgQHB8Pf37/QkyOL066wNgWNK+lwUyqtmvoBOHTzMT747SLup2Ri/iUL9K+jw6fDn4OFRckP9y1pXbLzvG/fvsXup6TjiltTYpoOH/9+GTvDHgAA3ujshSm9GhX5nGWV1XIYExNTan1VVeUaIJMmTVJuBDV9+nT06tULa9asgYWFBVauXFnk9Lk/nHNTqVSwtLREgwYN0L9/f9SoUaPQfj744AN8+OGHGDp0KACgefPmuHv3Lr766isEBgbCxSX7xLPo6GijYHv48CGcnZ0L7Fer1eZ7UUhzc/NiLfil2a6wNgWNK+lwUyqNmp5t6opdk2pg8q+hOHTjMTbc1gA7b2LGS82lr81U0rpKa57LzO+CxhXW/m5MCgKXn0RYTCrM1CpM6d0Yb3SpVy6H55f2cljRlunKqFwDZPjw4crfvr6+CAsLw9WrV1GnTh04ORV9fP7Zs2dx5swZ6PV6NG7cGEII3LhxAxqNBk2aNMEPP/yA9957D4cPH4a3t3eB/aSmpubZZ6LRaJTDeOvWrQsXFxcEBwfD19cXAJCZmYkDBw5g9uzZMk+dKqiadlqsGt0OP+y7gW+Cr2PD6Xu4G5uKxcP9UL2CnXio1+tx6NAhAECXLl3KfRNWaEQ8xq48hZiUTLhXs8L3w1vzEN0qziSnhWZmZuLatWuwsLBA69atixUeQPY91Xv27ImoqCiEhITgzJkziIyMhL+/P1599VVERkaia9euePfddwvtp2/fvvjyyy/xxx9/ICwsDFu2bMG8efPw0ksvAcheo5k0aRJmzpyJLVu24OLFixg1ahSsra2VCzrS00OtVuHNrnXxehMDbLQaHL8di/7fH8H1B0mmLs1Ienq6chHS9PT0cn3sQzce4dWlxxGTkgkfd3tsmdiR4UHlGyCpqakYO3YsrK2t0axZM4SHhwMA3n77bcyaNavI6b/++mt88cUXRvsT7O3tERQUhDlz5sDa2hqffvopQkJCCu1n4cKFGDRoECZMmICmTZvi/fffx5tvvokvvvhCaTNlyhRMmjQJEyZMQJs2bRAZGYk9e/bwHJCnmE91gY1vPAOPGlYIj03FwB+OYu/VB6YuS5Fz+wJvb+9yvaJD8OUHGLvyNNJ0enRp6IT14zqgll3lvR8JlZ5yDZBp06bh3Llz2L9/v9ENcXr27IkNGzYUOX1CQgIePnyYZ/ijR4+UQ2arVauGzMzMQvuxs7PDggULcPfuXaSlpeHWrVuYMWOG0c5TlUqFoKAg3L9/H+np6Thw4AB8fHyK+1SpkmrobIvfJ3ZGu7o1kJyRhbGrTuOng7dRjgcrFsja2hqXLl3CpUuXyu2ovu3nozB+dQgy9Qb0buaCnwPbwFZbsS/iSOWnXANk69atWLRoETp37mz0Dcrb2xu3bt0qcvr+/ftjzJgx2LJlC+7du4fIyEhs2bIFY8eOxYABAwAAJ0+eRKNGZXdECD39athYYPXYZzC0rQeEAL7ccQUf/HYeGVnlc6WEiuK/56Lw9rqz0BsEBvq6Y9EwX2jNns6bdZGccr+USa1atfIMT0lJKdYq+Y8//oh3330XQ4cOVe6HYGZmhsDAQMyfPx8A0KRJE/z888+lWzhVORZmanw1sDkaOdthxh+X8VvIPYQ9TsGPI/zgaJv/7ZefJv89F4V31p+FQQCD/Wpj9sstoFbzQqhkrFzXQNq2bYs//vhD+T8nNH766Sd06NChyOltbW3x008/ISYmRjkiKyYmBkuXLoWNjQ0AoFWrVrxUPJUKlUqFMZ3rYsXodrCzzL4EyuAfjyEq3jSXEUlLS4O/vz/8/f3L9FImuy49wKQNoQwPKlK5roF89dVX6N27Ny5fvoysrCx8++23uHTpEo4dO4YDBw4Uux9bW1u0aNGiDCsl+p9ujWpiy4ROGLnsBG4/SsHgJcew5vVn4OVkU651GAwG/Pnnn8rfZeFSnArLT5yH3iAwiOFBRSjXAOnYsSOOHDmCuXPnon79+tizZw9at26NY8eOoXnz5sXq49SpU9i4cSPCw8Pz7CzfvHlzWZRNhAa1bLHxXx0x/KfjCItJxeAfj2H12GfQ2KX8jsrTarVYvXq18ndpO3IrBsuvqZElBPq1dGN4UJHK/XCK5s2bY9WqVVLTrl+/HiNHjkRAQACCg4MREBCAGzduIDo6WjmHg6isuFezwq/jO2DkspO4Gp2EV5Yew6rR7dCynM6HMDMzMzoZtzQdufkY49ecRZZQwb9pLXwzpCU0DA8qQrnsA0lMTCzWT1FmzpyJ+fPnY/v27bCwsMC3336LK1euYMiQIahTp045PBOq6mrZWWL9uPZo6VEN8ak6DP/5BE7crtzXVDpy8zHGrjqFdJ0B3tUMmD+khfSlXKhqKZelpFq1aqhevXqBPznji3Lr1i3lku9arVY5euvdd9/F0qVLy/ppEAEAqllbYM3rz6B9vexzRUYuP4kD1x+V+ePmXI331KlTpXbztbPh8Up4dG/khLGNDdDyvuVUTOWyCWvfvn3K30II9OnTBz///DPc3Ut205kaNWogKSn78hLu7u64ePEimjdvjvj4+HK7FDwRANhqzbBydDtMWHMGe68+xL/WhmJEfRX6lOFjpqeno127dgCA5ORk5cjDJ7Hgr5vZ4dG4JhYNbYm/9uR/t1Ci/JRLgHTr1s3of41Gg/bt26NevXol6qdLly4IDg5G8+bNMWTIELzzzjvYu3cvgoOD8dxzz5VmyURFsjTX4McRfnh3Qyi2n7+PVTfU6HIzBj2aupTJ46lUKnh6eip/P6mYdODo7VioVMAX/X245kElVqmuSbBo0SLlInLTpk2Dubk5Dh8+jIEDB+KTTz4xcXVUFZlr1Ph2qC8MBgN2XHyAietCsfaN9mVyoUFra2uEhYWVWn8nHmYHRucGTvCoYc079FGJVaoAyX2fD7VajSlTpmDKlCkmrIgI0KhVmPNyc9yMuI/rCcDoFSexcXwHNKhVcS+8qTcInHiUvRYzpI2Hiauhyspk66zleTVRorKmNVPj9cYGtHC3R1yqDiOWnTTZGevFcfjmY8RnqlDNyhwBzQq+SRpRYcplDWTgwIFG/6enp2P8+PF5dgIWdCJgcW+cU1pHphDJ0GqAn0a0xrBlp3DrUQpGLDuBjeM7okYp3ZgqPT1duYvm+vXrja5oXVIbQyIBAP1auvICiSStXALEwcHB6P/XXnutRNMLIeDp6YnAwEDlDoFEFVENGwv839hnMGjxUdx6lILRK09h7evPwKYULoGu1+vx+++/K3/LiknOwN5r2YcdD/Yr2ZGQRLmVS4CsWLHiiaY/ceIEli9fjm+//RZ169bFmDFjMHz48GKdO0JU3tyrWeGXse0weMkxnIuIx/jVIVg+qu0Tn5xnYWGhnO+U+941JXH9QRI+2nIBOr1AHRuBJuV4KRZ6+lSK4/batm2LxYsX4/79+5g8eTK2bNmC2rVrY+jQoQgODjZ1eUR5NKhlhxWj28HaQoNDNx7jqx1Xn7hPc3NzvPHGG3jjjTdgbm5eomnTdXp8tfMK+nx7CKfC4mBlrsYLdcrmgoxUdVSKAMlhaWmJ1157DX/99RcuXryIhw8fonfv3oiNjS1RP15eXlCpVHl+Jk6cCCB7k1lQUBDc3NxgZWWF7t2749KlS2XxlOgp1sqjGuYNaQUAWH7kDn4PjTRJHUIIvLfxHH48cBtZBoEAb2fsfLsTmlQz/V0WqXKrVAECAPfu3cOMGTPg7++Pa9eu4YMPPjC6R3pxnDp1Cvfv31d+ctZiBg8eDACYM2cO5s2bh0WLFuHUqVNwcXGBv7+/chY8UXH19nHBhO71AQBTN53HlftFX/OtIAaDQbmlbUku577tfDT+OH8fZmoVfhzhh6Uj28C9mpV0HUQ5KkWAZGZmYsOGDQgICEDDhg1x5swZLFiwABEREZg1axbMzEq2K6dmzZpwcXFRfrZv34769eujW7duEEJgwYIF+OijjzBw4ED4+Phg1apVSE1Nxdq1a8voGdLT7L2AxujS0AnpOgPGrw5BQqrcCXtpaWnw8fGBj49PsW8oFZ8BfLb9CgDg7ecaolezsjlLnqqmSnEioaurK+zs7BAYGIgffvhBuS1ucnKyUbuSrokA2eG0evVqTJ48GSqVCrdv30Z0dDQCAgKUNlqtFt26dcPRo0fx5ptvFthXRkYGMjIylP9zrjCs0+kKPcs3Z1xRZwIXp11hbQoaV9LhplRWNT1pv0VN/80gH7y0+DjuxqTi7fVn8P0rPsV6vNz96nQ6ODk5Gf1f2ONmZGZizS01ktKz0KK2Pd7oVCdP+9zTVcT5DZT9PCd5KiFEhd8Qqlb/b0UpvxMQhRBQqVRShzb++uuvGDZsGMLDw+Hm5oajR4+iU6dOiIyMhJubm9Ju3LhxuHv3Lnbv3l1gX0FBQfjss8/yDF+7di2sra1LXBs9XSKSgW8vaqATKvSurcfzHmX71jt4X4VNYRqYqwU+aKGHM7daGUlNTcWwYcOQkJAg9eWTKskaSO6r+Za2ZcuW4fnnnzcKCyBvUOWEVGGmTZuGyZMnK/8nJibCw8MDAQEBhS6gOp0OwcHB8Pf3L/TomuK0K6xNQeNKOtyUyqqmJ+23uNPXbBiJqZsvYdc9DTxs9Xh3SM8nmucFjY+KT8OH3x0BYMCUgEYY1alukdNVxPkNlF1dMTGV+z4uFUGlCJB/Xs23tNy9exd//vmn0RnwLi7Z24ijo6Ph6uqqDH/48CGcnQu/5INWq833VqPm5ubFWvBLs11hbQoaV9LhplRWNT1pv0VN/0o7L1yMSsYvx+9izU01AtP0qF2MtdOi+v3n+Bk7zyFNZ0A9O4GRHbxKtCxUxPkNlH5dFfE5VjaVYid6WVmxYgVq1aql3KQKAOrWrQsXFxej80syMzNx4MABdOzY0RRl0lPmkxe94e1qh9QsFaZtuYTibkVOT0/H8OHDMXz4cOWq1PkJvvwAwZcfwEytwuB6et7XnMpMlQ0Qg8GAFStWIDAw0OgoLpVKhUmTJmHmzJnYsmULLl68iFGjRsHa2hrDhg0zYcX0tLAwU2PuoOYwVwkcuhmDX47fLdZ0er0ea9euxdq1awvc35eamYWgbdnnLI3p5Ak37nqjMlQpNmGVhT///BPh4eEYM2ZMnnFTpkxBWloaJkyYgLi4ODzzzDPYs2cP7Ox42QcqHQ1r2aKvpwGbwzSYueMKOtZ3QoNatoVOY2Fhgfnz5yt/5+fbv24gMj4N7tWsMLF7Pez/82ap106Uo8oGSEBAQIGbDlQqFYKCghAUFFS+RVGV0sVF4IHGEUduxWDyr6HY9K+OhV4vy9zcHJMmTSpw/LXoJCw7dAcA8Hn/ZrC2qLJvbyonVXYTFpGpqVXArIHN4GBljvP3ErDwrxvSfRkMAv/ZckG5VMlzTXmPDyp7DBAiE3Kxt8SXL2WfVLho302E3I0rsK3BYEBYWBjCwsLyXMrktzORCLkbB2sLDYL6NSvTmolyMECITOzFFm54ydcdBgG8uyEUCWn5nyGdlpaGunXrom7dukaXMknWAXP2XAcATPZvBDde54rKCQOEqAII6tcM7tWsEB6big82nitw/5y1tXWeqxpsvatGQloWmrraY1RHr3KoligbA4SoAnCwMsfi11rDQqPGnssPsPTg7TxtbGxskJKSgpSUFOV20Mdvx+LUIzVUKmDmSz4we8KbVhGVBJc2ogqiRe1q+LSvNwBgzu5rOHG78EttJKTp8Om2ywCAV9vWhm8d3qGTyhcDhKgCGf5MHbzk6w69QeCtdWfxKCkj33YpGVkYveIk7sSkwt5c4L2eDcu5UqIqfB4IUUWkUqnw5Us+uBSVgOsPkjHp1/MY+vctPDIyMvDWW29BbxBI8RuJM+FJcLAyw5sN02Fvxes6UfljgBBVMNYWZlj8mh/6LTyMk2FxiHqkQbjNbXSpZ4+ff/4ZAODh8DzsbG2wbKQfIs8fMXHFVFVxExZRBVS/pi2+GdIS5hoV7qWosOCvmxi45ARq9QhEtS4jYKk1x7JRbdGytoOpS6UqjGsgRBVUbx9X7H/PDgt/24uH5i44ejsGqnaDYa9RYenINmhfz5F31SOTYoAQVWC17LTo4CzQp48vsoQaJ+7EoKadFs3cuOZBpscAIaokLM3VaFZDBSCzWHfIJCprDBCiSiI1NRW1atUCACQnJysnExKZCgOkDOVcjiIxMbHQdjqdDqmpqUhMTCzy/thFtSusTUHjSjrclMqqpiftt6TTy8zzzMxMZXhiYiL0er3U/C5oXEWc30DZ1ZWUlAQAxb4jJOXFAClDOQuoh4eHiSuhp42bm5upS3hqJCUlwcGB+5RkqATjt8wYDAZERUXBzs6uyO3Vbdu2xalTp4rsszjtCmtT0Lj8hicmJsLDwwMRERGwt7cvsrbyUtzXqrz7Len0pTXPZeZ3fuMq6vwGymaeCyHg5+eH69evQ63mGQ0yuAZShtRqNWrXrl2sthqNplhv2uK0K6xNQeMKm8be3r5CfaAU97Uq735LOn1pzXOZ+V3YuIo2v4Gym+cWFhYMjyfAV66CmDhxYqm1K6xNQeOK+/gVQVnV+qT9lnT60prnMvO7JI9fEVTUeV7VcRMWFSgxMREODg5ISEiocN9IqfRxflNJcQ2ECqTVajF9+nRotVpTl0LlgPObSoprIEREJIVrIEREJIUBQkREUhggREQkhQFCRERSGCBERCSFAUJERFIYIEREJIUBQkREUhggREQkhQFCRERSGCBERCSFAUJERFIYIEREJIV3JCxDJbmlLRGVLyEEkpKS4ObmxrsSSmKAlKGoqCh4eHiYugwiKkRERESxbz1NxhggZcjOzg5A9gJa2B3edDod9uzZg4CAAJibmz9Ru8LaFDSupMNNqaxqetJ+Szp9ac1zmfld0LiKOL+BsqsrNjYWdevWVd6nVHIMkDKUs9nK3t6+yACxtraGvb19kR8mRbUrrE1B40o63JTKqqYn7bek05fWPJeZ3wWNq4jzGyjbeQ6Am5efAAOEiMpFlt4AtUoFtTr7A1sIgSyDgP7vnyyDgEoFmKlVMIjs9jq9gE6nQ0w6cC8uDfbWBhhE9rS5GQSgUgE5g800KjjZ8ta8ZY0BQkQlkpyRhRsPknD9QRIeJmaghq0FbLVmuBeXhntxqXicnIn41EykZOiRmpmF1Ew9UjKykJKph0oF2FqYIcsgkJGlh6HYN9Q2w+dnDxW7xpYe1fD7xE5Sz4+KjwFCRAVKStfh9N04nA2Px7mIeFx/kIT7CenS/QkBJGVklWgalQowVwmo1BpkZBmUYWrV/9ZkVCoVhBBQq1TKWgyVPQYIERm5n5CO7RfvYv/VRzgTHoesfFYTnGy1aOJiB1cHS8Sl6pCcoYNbNSvUqWGNmnZaVLe2gI3WDNYWGliZa2CjNYO9pRkMIjuUzDVqaM3U0JppYKZR/b1pK7tvvSE7CMzUKmjUKmRlZWHHjh3o06cXzMzMuM+iAmGAEFUxIXdjMfOPK6irUeH5XPsSjt2OwZIralw7ftBo05KnozX8PKvD16MavN3s0aCmHRys5Xdm17ST3zfB8KhYGCBEVUhcSiYmrDmDB4kZCIEGsWtC8fGL3vhh/y38FnIPORenaFe3Bvq2dEP3RjXhUcPatEVThcUAIaoihBCYtvkCHiRmwNlOi8fJ6dh77RH2XjsAIHu/QsdaBnz2alc0cHEwcbVUGZj8/H2dToeIiAhcu3YNsbGxpi6H6Km18fQ97LoUDXONCj++5ovJzfWo52QDAKhf0wbrX2+HIfUM8HTkGgcVj0nWQJKTk7FmzRqsW7cOJ0+eREZGhjKudu3aCAgIwLhx49C2bVtTlEf01LnzOAVB/70EAHgvoDGaudnjrg3w+4T2uBCVDD+v6lALA3ZcNHGhVKmU+xrI/Pnz4eXlhZ9++gnPPvssNm/ejNDQUFy7dg3Hjh3D9OnTkZWVBX9/f/Tu3Rs3btwo7xKJnipxqZkYu+oUUjP16FDPEeO61FPGWZpr0LGBE7RmGhNWSJVVua+BHD16FPv27UPz5s3zHd+uXTuMGTMGS5YswbJly3DgwAE0bNiwnKskejpk6oFxq8/i9qMUuDlYYsHQVlCrVdDrTV0ZPQ3KPUA2btxYrHZarRYTJkwo42qInl5ZegNW3VDjYlwCHKzMsWpMOzjbW5q6LHqKmHwnOhGVjZm7ruNiXPYJez8HtkFDZ151lkqXSQ/jfemll/I9MUilUsHS0hINGjTAsGHD0LhxYxNUR1R5Hbn5GL8cD4cKAvMGN0dbrxqmLomeQiZdA3FwcMDevXtx5swZJUjOnj2LvXv3IisrCxs2bEDLli1x5MgRU5ZJVKmkZeoxbfMFAEBnZ4EAb2cTV0RPK5Ougbi4uGDYsGFYtGiRcktJg8GAd955B3Z2dli/fj3Gjx+PqVOn4vDhw6YslajSWPDndYTHpsLFXosX66SYuhx6ipl0DWTZsmWYNGmS0f2I1Wo1/v3vf2Pp0qVQqVR46623cPEiD04nKo4L9xLw06HbAIDP+nnDkteaoDJk0gDJysrC1atX8wy/evUq9H8fZ2hpackLqBEVg05vwNRN52EQQN+Wbni2cU1Tl0RPOZN+PxkxYgTGjh2L//znP2jbti1UKhVOnjyJmTNnYuTIkQCAAwcOoFmzZqYsk6hS2HzmHi7fT0Q1a3NM7+tt6nKoCjDpGsj8+fMxadIkzJkzB127dkWXLl0wZ84cvPvuu5g3bx4AICAgAOvXry/Vxw0KCoJKpTL6cXFxUcYLIRAUFAQ3NzdYWVmhe/fuuHTpUqnWQFSahBBYefQuAOBf3erzdq5ULky6BqLRaPDRRx/ho48+QmJiIgDA3t7eqE2dOnXK5LGbNWuGP//806iWHHPmzMG8efOwcuVKNGrUCDNmzIC/vz+uXbsGOzseS08Vz+m78bhyPxGW5mq80tbD1OVQFWHyEwmzsrLw559/Yt26dcq+jqioKCQnJ5fp45qZmcHFxUX5qVkze3uxEAILFizARx99hIEDB8LHxwerVq1Camoq1q5dW6Y1EclafSIcADCglTuqWVuYuBqqKky6BnL37l307t0b4eHhyMjIgL+/P+zs7DBnzhykp6djyZIlZfbYN27cgJubG7RaLZ555hnMnDkT9erVw507dxAdHY2AgAClrVarRbdu3XD06FG8+eabBfaZkZFhdGXhnLUqnU4HnU5X4HQ54wprU9x2hbUpaFxJh5tSWdX0pP2WdPrSmuc6nQ7xGcDuSw8BAMPb1c4zTXGXhYo4v4Gyn+ckTyWEyHvD43IyYMAA2NnZYdmyZXB0dMS5c+dQr149HDhwAK+//nqZXYl3586dSE1NRaNGjfDgwQPMmDEDV69exaVLl3Dt2jV06tQJkZGRcHNzU6YZN24c7t69i927dxfYb1BQED777LM8w9euXQtra95jgcrGjnA1dkeqUd9O4G0fXiWxuFJTUzFs2DAkJCTk2XROxWPSNZDDhw/jyJEjsLAwXuX29PREZGRkmT3u888/r/zdvHlzdOjQAfXr18eqVavQvn17AHnvvSyEKPJw4mnTpmHy5MnK/4mJifDw8EBAQEChC6hOp0NwcDD8/f1hbl7wvaaL066wNgWNK+lwUyqrmp6035JOX1rzPCUtAx99vR8A8E6flnjex6VY0+Y3riLOb6Ds6oqJiSm1vqoqkwaIwWBQzvfI7d69e+W6s9rGxgbNmzfHjRs3MGDAAABAdHQ0XF1dlTYPHz6Es3Phl4TQarXQavMe/WJubl6sBb802xXWpqBxJR1uSmVV05P2W9Lpn3Se/xkahWSdCs72Wjzfwh3mmry7NUu6LFTE+Q2Ufl0V8TlWNibdie7v748FCxYo/6tUKiQnJ2P69Ono06dPudWRkZGBK1euwNXVFXXr1oWLiwuCg4OV8ZmZmThw4AA6duxYbjURFccvJyIAAMPaeuQbHkRlyaRrIPPnz0ePHj3g7e2N9PR0DBs2DDdu3ICTkxPWrVtXZo/7/vvvo2/fvqhTpw4ePnyIGTNmIDExEYGBgVCpVJg0aRJmzpyJhg0bomHDhpg5cyasra0xbNiwMquJqKRuP0rGuXsJ0KgEXmnjbupyqAoyaYC4ubkhNDQU69atw5kzZ2AwGDB27FgMHz4cVlZWZfa49+7dw6uvvorHjx+jZs2aaN++PY4fPw5PT08AwJQpU5CWloYJEyYgLi4OzzzzDPbs2cNzQKhC2XftEQCgvr2AI08cJBMw+aXWrKysMGbMGIwZM6bcHrOoM9tVKhWCgoIQFBRUPgURSdh3NfvQXe9qJjuQkqq4cg+Qbdu2Fbttv379yrASosorOSMLJ+5kH0XUrDoDhEyj3AMk5yinHCqVCv88FSXncNn8jtAiIuDwjcfQ6QU8a1ijllWiqcuhKqrcD9swGAzKz549e9CqVSvs3LkT8fHxSEhIwM6dO9G6dWvs2rWrvEsjqjRyNl91b+xk4kqoKjPpPpBJkyZhyZIl6Ny5szKsV69esLa2xrhx43DlyhUTVkdUMQkhsO/a3wHSqCYSr982cUVUVZn0wPFbt27BwcEhz3AHBweEhYWVf0FElcClqEQ8TMqAtYUGbb2qm7ocqsJMGiBt27bFpEmTcP/+fWVYdHQ03nvvPbRr186ElRFVXDmbrzo1cILWjCcPkumYdOlbvnw5Hj58CE9PTzRo0AANGjRAnTp1cP/+fSxbtsyUpRFVWHv/3nz1bJNaJq6EqjqT7gNp0KABzp8/j+DgYFy9ehVCCHh7e6Nnz568DzpRPmKSMxAaEQ8A6NGYAUKmZfITCVUqFQICAozuv0FE+Tt44xGEALxd7eHiYMl7WpBJlfsmrJLc3zwiIgJHjhwpw2qIKpe9V7MvX9KjSU0TV0JkggBZvHgxmjRpgtmzZ+d7mG5CQgJ27NiBYcOGwc/PD7GxseVdIlGFlKU34OD17ADh/g+qCMp9E9aBAwewfft2LFy4EP/5z39gY2MDZ2dnWFpaIi4uDtHR0ahZsyZGjx6NixcvolYtvlGIAOB8ZAIS0nRwsDJHKw8evkumZ5J9IC+++CJefPFFxMTE4PDhwwgLC0NaWhqcnJzg6+sLX19fqNU8PJEotyM3HgMAOjVwhEbNg0zI9Ey6E93R0RH9+/c3ZQlElcbhmzkBwsuXUMXAr/lElUBKRhbOhMcBADozQKiCYIAU4YcffkDdunVhaWkJPz8/HDp0yNQlURV0+m4cdHqB2tWtUKeGtanLIQLAACnUhg0bMGnSJHz00Uc4e/YsunTpgueffx7h4eGmLo2qmKO3so9G7NzAiSfZUoXBACnEvHnzMHbsWLz++uto2rQpFixYAA8PDyxevNjUpVEVc/RW9s2juP+DKhIGSAEyMzMREhKS5wz5gIAAHD161ERVUVWUmAlcfZAMgAFCFYvJL2WSn99//x0JCQkYOXKkyWp4/Pgx9Ho9nJ2djYY7OzsjOjo632kyMjKQkZGh/J+YmH2nOJ1OV+glJ3LGFXVZiuK0K6xNQeNKOtyUyqqmJ+23pNOXZJ7fSMzeZOXtagc7C5XRNDLzu6BxFXF+A2U/z0meSvzzfrIVQJMmTXDjxg2T3tI2KioK7u7uOHr0KDp06KAM//LLL/HLL7/g6tWreaYJCgrCZ599lmf42rVrYW3NHZ8kZ+1NNU48UuNZNwP6expMXc5TIzU1FcOGDUNCQgLs7e1NXU6lVCHXQPL7cC5vTk5O0Gg0edY2Hj58mGetJMe0adMwefJk5f/ExER4eHggICCg0AVUp9MhODgY/v7+MDc3f6J2hbUpaFxJh5tSWdX0pP2WdPrits/MzMT0kH0AgNf826DLPzZhyczvgsZVxPkNlF1dMTExpdZXVVUhA6QisLCwgJ+fH4KDg/HSSy8pw4ODgws8+VGr1UKr1eYZbm5uXqwFvzTbFdamoHElHW5KZVXTk/Zb0umLan/ncQriM1Uw16jQoX4tmJtrStxPScdVxPkNlH5dFfE5VjYm34l+6NAhvPbaa+jQoQMiIyMBAL/88gsOHz5s4sqAyZMn4+eff8by5ctx5coVvPvuuwgPD8f48eNNXRpVETlHX/nVqQYri/zDg8hUTBogmzZtQq9evWBlZYWzZ88qO6CTkpIwc+ZMU5YGAHjllVewYMECfP7552jVqhUOHjyIHTt2wNPT09SlURVx5O/zPzrWdzRxJUR5mTRAZsyYgSVLluCnn34yWp3s2LEjzpw5Y8LK/mfChAkICwtDRkYGQkJC0LVrV1OXRFWE3iBw/E52gHRigFAFZNIAuXbtWr4fyPb29oiPjy//gogqkPP34pGUngUrjUAzNx4lRBWPSQPE1dUVN2/ezDP88OHDqFevngkqIqo49lx+AABo5CB4+XaqkEwaIG+++SbeeecdnDhxAiqVClFRUVizZg3ef/99TJgwwZSlEZmUwSCw9Wz2QSWtnSrcqVpEAEx8GO+UKVOQkJCAHj16ID09HV27doVWq8X777+Pt956y5SlEZnU8dsxuJ+QDntLMzSrnmXqcojyZfLzQL788kt89NFHuHz5MgwGA7y9vWFra2vqsohMatOZ7LWPPs1dYK4OM20xRAUw+XkgAGBtbY02bdqgSZMm+PPPP3HlyhVTl0RkMqmZWdh58T4A4KVWbiauhqhgJg2QIUOGYNGiRQCAtLQ0tG3bFkOGDEGLFi2wadMmU5ZGZDK7L0UjNVMPT0dr+Ho4mLocogKZNEAOHjyILl26AAC2bNkCg8GA+Ph4fPfdd5gxY4YpSyMymc1/b74a6FubN4+iCs2kAZKQkIAaNWoAAHbt2oWXX34Z1tbWeOGFF3Djxg1TlkZkEtEJ6Th88zEA4CVfdxNXQ1Q4kwaIh4cHjh07hpSUFOzatUu5eVNcXBwsLS1NWRqRSWwNjYQQQDuvGqjjyFsAUMVm0qOwJk2ahOHDh8PW1haenp7o3r07gOxNW82bNzdlaUTlTgiBzWfuAQAGtubaB1V8Jg2QCRMm4JlnnkF4eDj8/f2hVmevENWrV4/7QKjKuRSViOsPkmFhpkafFq6mLoeoSCY/D8TPzw9+fn5Gw1544QUTVUNkOjk7zwO8nWFvyXtVUMVn8gC5d+8etm3bhvDwcGRmZhqNmzdvnomqIipfGVl6bDuXHSAvt65t4mqIisekAfLXX3+hX79+qFu3Lq5duwYfHx+EhYVBCIHWrVubsjSicrX0wG08Ts6Es70WXRo6FT0BUQVg0qOwpk2bhvfeew8XL16EpaUlNm3ahIiICHTr1g2DBw82ZWlE5SY8JhWL9mVflfqjF7xhpqkQF4ggKpJJl9QrV64gMDAQAGBmZoa0tDTY2tri888/x+zZs8vscb28vKBSqYx+PvzwQ6M24eHh6Nu3L2xsbODk5IS33347zyY2oiclhMD0bReRkWVApwaO6Mud51SJmHQTlo2NjXIbWzc3N9y6dQvNmjUDADx+/LhMH/vzzz/HG2+8ofyf+wKOer0eL7zwAmrWrInDhw8jJiYGgYGBEEJg4cKFZVoXVS3BVx5i37VHsNCo8UV/H555TpWKSQOkffv2OHLkCLy9vfHCCy/gvffew4ULF7B582a0b9++TB/bzs4OLi4u+Y7bs2cPLl++jIiICLi5ZV/M7ptvvsGoUaPw5Zdfwt6ed4ejJ5ehB7764yoA4M1u9VCvJq9CTZWLSQNk3rx5SE5OBgAEBQUhOTkZGzZsQIMGDTB//vwyfezZs2fjiy++gIeHBwYPHowPPvgAFhYWAIBjx47Bx8dHCQ8A6NWrl3Jf9B49euTbZ0ZGhrJGBQCJiYkAAJ1OB51OV2AtOeMKa1PcdoW1KWhcSYebUlnV9KT9lnR6nU6HXffUiE7MQO3qVhjX2bNE86w440s6riLOb6Ds5znJUwkhqtztzubPn4/WrVujevXqOHnyJKZNm4b+/fvj559/BgCMGzcOYWFh2LNnj9F0Wq0WK1euxKuvvppvv0FBQfjss8/yDF+7di2srXlZCvqfqFTg6/MaGIQK45ro0ax6lXsbmlxqaiqGDRuGhIQEblWQZPLzQAAgMzMTDx8+hMFgMBpep06dYvdR0Id3bqdOnUKbNm3w7rvvKsNatGiB6tWrY9CgQZg9ezYcHR0BIN9t0UKIQrdRT5s2DZMnT1b+T0xMhIeHBwICAgpdQHU6HYKDg+Hv7w9z84JPICtOu8LaFDSupMNNqaxqetJ+SzK9wSDw6s8nYRAJ6NnECR8ML/iQ9aL6lZnfBY2riPMbKLu6YmJiSq2vqsqkAXL9+nWMHTsWR48eNRqe80Gt1+uL3ddbb72FoUOHFtrGy8sr3+E5+1tu3rwJR0dHuLi44MSJE0Zt4uLioNPp4OzsXGD/Wq0WWq02z3Bzc/NiLfil2a6wNgWNK+lwUyqrmp6036KmNxgEPvvjIs5EJMBCLfDpi96lMs9l5ndB4yri/AZKv66K+BwrG5MGyOjRo2FmZobt27fD1dX1iY5AcXJygpOT3AlYZ8+eBQC4umYfQtmhQwd8+eWXuH//vjJsz5490Gq1eS67QlRcBoPAp9suYvXxcKhUwCv1DHB14FWnqfIyaYCEhoYiJCQETZo0KbfHPHbsGI4fP44ePXrAwcEBp06dwrvvvot+/fopm8wCAgLg7e2NESNG4Ouvv0ZsbCzef/99vPHGG9xWSlL+GR6zX/KB9n6oqcsieiImPZHQ29u7zM/3+CetVosNGzage/fu8Pb2xqeffoo33ngD69atU9poNBr88ccfsLS0RKdOnTBkyBAMGDAAc+fOLdda6enwz/CYO6glXvLlvc6p8iv3NZCcQ1uB7ENpp0yZgpkzZ6J58+Z5tkmWxbf91q1b4/jx40W2q1OnDrZv317qj09VS37h8bJfbR5CSk+Fcg+QatWqGe3rEELgueeeM2ojsxOdqKJJycjC9G2X8FvIPaPwIHpalHuA7Nu3r7wfkqjc7b/2EB9tuYjI+DSGBz21yj1AunXrVt4PSVRu4lIzMWv3JeXmULWrW2HWwBbozEu001PIJDvRU1NTMXHiRLi7u6NWrVoYNmxYue9MJypNQgicfazC898dxeYzkVCpgDGd6mL3pK4MD3pqmeQw3unTp2PlypUYPnw4LC0tsW7dOvzrX//Cxo0bTVEO0RM5fy8eC4KvY+8NDYBMNKxli9mDWqB1neqmLo2oTJkkQDZv3oxly5YpZ46/9tpr6NSpE/R6PTQajSlKIioRnd6AnRejsfLIHZwJjwcAaFQCE7rXx1vPNYLWjMsxPf1MEiARERHo0qWL8n+7du1gZmaGqKgoeHh4mKIkomJ5lJSBdSfDsebEXTxIzL7ysrlGhT4+LmiKCIx9tgHMGR5URZgkQPR6vXLpdKUQMzNkZWWZohyiQiVnZOH4rRjsuHAf28/fR6Y++6KfNe20eO0ZT7z6jAeqW2qwY0eEiSslKl8mCRAhBEaNGmV04cH09HSMHz8eNjY2yrDNmzebojyq4gwGgcv3E3Hg+iMcvP4IZ8LjoNP/73LrvnWqYVRHLzzv4woLs+zjUHhiIFVFJgmQnPug5/baa6+ZoBIiQG8QuBuTipOPVPhz43kcvRWLmJRMozZ1alijayMnDPLzQCuPaqYplKiCMUmArFixwhQPS1Vclt6A8NhU3HiYjBsPkv7+nYxbj5KRkWUAoAEQDQCwsdCgQ30ndGvkhK6NasLT0abQvomqogpxQymiJ5Wu0yM2JRMxyZl4nJKBmORMxCRnICYlE1Hxabj5MBm3H6Uo+y/+SWumhrNWjz5+9dC9iTNa16mubJ4iovwxQKjcGQwCGVkGpOv0SNPpka7TI11nQJpOjwydHulZf/+f+b+/0/9ul5qhw5VbamxfG4rYVF12SCRnIimjeAdgWJlr0KCWLRrWskUDZ1s0qmWHhs62cLY1x+5dO9HHvyFvNERUTAyQCmDpoTvYeVWNrbFnoFapkbO7Nud29Tn/GwwGPHqkxm+PQqBSq/HP29kLARiEAY8fq7HhwWng74tW5jQzCANiYtRYc/8UVCqVUb9xsRr8EnUye7jSXiA2VoNl4cchoILeIGAQAnqDgF4IGAwCBgGj4f/7nR0U+nyGPzk18PBhnqHmGhUcbbRwtLWAo60WTjYWcLS1QE077d+hYQf3alZQq/PeuIw7wYlKjgFSAVyMTMTFODUQV5zLuaiB+KLu5azG9YTYgqdPjMtnuApIis9/eHJiPsNLh7lGBUszDSwtNLA0V8PSTAMrCw0szTTQmqthZa6Bpbnm799qmGtUiAy7hfa+PnB2sIKjrRaONtmBYW9p9kR3tSSikmGAVABD29aGQ1oUmjdvDrO/T0JT4e8Pwly/9Ho9Lpw/jxYtW8BMkz3rcj4vc37r9QacCw1Fq1atlL6yx6ugz8pCaGgofH19jR5Hr8/C2bNn4evrC3Oz//WblaXH2bNn0LaNH7Tm5lCpAI1aBY1KBbVaBY1aBbVKBfXfw9Wq7GHFGa4118DSTA0zTcn2M+h0OuzYcRN92nlwUxORiTFAKoCO9R0Rf02gT5vahX4o6nQ6WEWfQx9f9wLb6XQ6mEeeRZ+Wrnna6HQ6qO+dRZ/mLkbjdDodRLjA8z55h+vvCjzXpBY/rIkoDx5mQkREUrgGUoZydnLnvo1vfnQ6HVJTU5GYmFjkGkhR7QprU9C4kg43pbKq6Un7Len0pTXPZeZ3QeMq4vwGyq6upKQkAMhzMAoVHwOkDOUsoLxAJFHFlZSUBAcHB1OXUSmpBOO3zBgMBkRFRcHOzq7Io4Patm2LU6dOFdlncdoV1qagcfkNT0xMhIeHByIiImBvb19kbeWluK9Vefdb0ulLa57LzO/8xlXU+Q2UzTwXQsDPzw/Xr1+HWs2t+TK4BlKG1Go1atcu3n2wNRpNsd60xWlXWJuCxhU2jb29fYX6QCnua1Xe/ZZ0+tKa5zLzu7BxFW1+A2U3zy0sLBgeT4CvXAUxceLEUmtXWJuCxhX38SuCsqr1Sfst6fSlNc9l5ndJHr8iqKjzvKrjJiwqUGJiIhwcHJCQkFDhvpFS6eP8ppLiGggVSKvVYvr06Ub3baGnF+c3lRTXQIiISArXQIiISAoDhIiIpDBAiIhICgOEiIikMECIiEgKA4RKxUsvvYTq1atj0KBBpi6Fysj27dvRuHFjNGzYED///LOpy6EKgIfxUqnYt28fkpOTsWrVKvz222+mLodKWVZWFry9vbFv3z7Y29ujdevWOHHiBGrUqGHq0siEuAZCpaJHjx6ws7MzdRlURk6ePIlmzZrB3d0ddnZ26NOnD3bv3m3qssjEGCBVwMGDB9G3b1+4ublBpVJh69atedr88MMPqFu3LiwtLeHn54dDhw6Vf6FUZp50GYiKioK7u7vyf+3atREZGVkepVMFxgCpAlJSUtCyZUssWrQo3/EbNmzApEmT8NFHH+Hs2bPo0qULnn/+eYSHhytt/Pz84OPjk+cnKiqqvJ4GPYEnXQby29Jd1C0KqAoQVKUAEFu2bDEa1q5dOzF+/HijYU2aNBEffvhhifret2+fePnll5+0RCpjMsvAkSNHxIABA5Rxb7/9tlizZk2Z10oVG9dAqrjMzEyEhIQgICDAaHhAQACOHj1qoqqoPBVnGWjXrh0uXryIyMhIJCUlYceOHejVq5cpyqUKhDeUquIeP34MvV4PZ2dno+HOzs6Ijo4udj+9evXCmTNnkJKSgtq1a2PLli1o27ZtaZdLZaA4y4CZmRm++eYb9OjRAwaDAVOmTIGjo6MpyqUKhAFCAPJuzxZClGgbN4/IqfyKWgb69euHfv36lXdZVIFxE1YV5+TkBI1Gk2dt4+HDh3m+kdLTicsAyWKAVHEWFhbw8/NDcHCw0fDg4GB07NjRRFVReeIyQLK4CasKSE5Oxs2bN5X/79y5g9DQUNSoUQN16tTB5MmTMWLECLRp0wYdOnTA0qVLER4ejvHjx5uwaipNXAaoTJj6MDAqe/v27RMA8vwEBgYqbb7//nvh6ekpLCwsROvWrcWBAwdMVzCVOi4DVBZ4LSwiIpLCfSBERCSFAUJERFIYIEREJIUBQkREUhggREQkhQFCRERSGCBERCSFAUJERFIYIEQVUGZmJho0aIAjR46Uar/bt2+Hr68vDAZDqfZLVRMDhMrcqFGjoFKp8vzkvjYTGVu6dCk8PT3RqVMnZVhB9zIfNWoUBgwYUKx+X3zxRahUKqxdu7aUKqWqjAFC5aJ37964f/++0U/dunXztMvMzDRBdRXPwoUL8frrr5dJ36NHj8bChQvLpG+qWhggVC60Wi1cXFyMfjQaDbp374633noLkydPhpOTE/z9/QEAly9fRp8+fWBrawtnZ2eMGDECjx8/VvpLSUnByJEjYWtrC1dXV3zzzTfo3r07Jk2apLTJ7xt7tWrVsHLlSuX/yMhIvPLKK6hevTocHR3Rv39/hIWFKeNzvt3PnTsXrq6ucHR0xMSJE6HT6ZQ2GRkZmDJlCjw8PKDVatGwYUMsW7YMQgg0aNAAc+fONarh4sWLUKvVuHXrVr6v1ZkzZ3Dz5k288MILJXyVgbCwsHzX9rp376606devH06ePInbt2+XuH+i3BggZHKrVq2CmZkZjhw5gh9//BH3799Ht27d0KpVK5w+fRq7du3CgwcPMGTIEGWaDz74APv27cOWLVuwZ88e7N+/HyEhISV63NTUVPTo0QO2trY4ePAgDh8+DFtbW/Tu3dtoTWjfvn24desW9u3bh1WrVmHlypVGITRy5EisX78e3333Ha5cuYIlS5bA1tYWKpUKY8aMwYoVK4wed/ny5ejSpQvq16+fb10HDx5Eo0aNYG9vX6LnAwAeHh5Ga3lnz56Fo6MjunbtqrTx9PRErVq1cOjQoRL3T2TExFcDpiogMDBQaDQaYWNjo/wMGjRICCFEt27dRKtWrYzaf/LJJyIgIMBoWEREhAAgrl27JpKSkoSFhYVYv369Mj4mJkZYWVmJd955RxkGQGzZssWoHwcHB7FixQohhBDLli0TjRs3FgaDQRmfkZEhrKysxO7du5XaPT09RVZWltJm8ODB4pVXXhFCCHHt2jUBQAQHB+f73KOiooRGoxEnTpwQQgiRmZkpatasKVauXFng6/XOO++IZ599Ns9wAMLS0tLodbSxsRFmZmaif//+edqnpaWJZ555Rrz44otCr9cbjfP19RVBQUEF1kBUHLyhFJWLHj16YPHixcr/NjY2yt9t2rQxahsSEoJ9+/bB1tY2Tz+3bt1CWloaMjMz0aFDB2V4jRo10Lhx4xLVFBISgps3b8LOzs5oeHp6utHmpWbNmkGj0Sj/u7q64sKFCwCA0NBQaDQadOvWLd/HcHV1xQsvvIDly5ejXbt22L59O9LT0zF48OAC60pLS4OlpWW+4+bPn4+ePXsaDZs6dSr0en2etmPHjkVSUhKCg4OhVhtvbLCyskJqamqBNRAVBwOEyoWNjQ0aNGhQ4LjcDAYD+vbti9mzZ+dp6+rqihs3bhTrMVUqFcQ/bneTe9+FwWCAn58f1qxZk2famjVrKn+bm5vn6TfnMFgrK6si63j99dcxYsQIzJ8/HytWrMArr7wCa2vrAts7OTkpAfVPLi4ueV5HOzs7xMfHGw2bMWMGdu3ahZMnT+YJSACIjY01eo5EMhggVOG0bt0amzZtgpeXF8zM8i6iDRo0gLm5OY4fP446deoAAOLi4nD9+nWjNYGaNWvi/v37yv83btww+tbdunVrbNiwAbVq1ZLa3wAAzZs3h8FgwIEDB/KsGeTo06cPbGxssHjxYuzcuRMHDx4stE9fX18sXrwYQgioVKoS17Rp0yZ8/vnn2LlzZ777WXLWsHx9fUvcN1Fu3IlOFc7EiRMRGxuLV199VTlaaM+ePRgzZgz0ej1sbW0xduxYfPDBB/jrr79w8eJFjBo1Ks9mmmeffRaLFi3CmTNncPr0aYwfP95obWL48OFwcnJC//79cejQIdy5cwcHDhzAO++8g3v37hWrVi8vLwQGBmLMmDHYunUr7ty5g/379+PXX39V2mg0GowaNQrTpk1DgwYNjDa95adHjx5ISUnBpUuXSvCqZbt48SJGjhyJqVOnolmzZoiOjkZ0dDRiY2OVNsePH4dWqy2yDqKiMECownFzc8ORI0eg1+vRq1cv+Pj44J133oGDg4MSEl9//TW6du2Kfv36oWfPnujcuTP8/PyM+vnmm2/g4eGBrl27YtiwYXj//feNNh1ZW1vj4MGDqFOnDgYOHIimTZtizJgxSEtLK9EayeLFizFo0CBMmDABTZo0wRtvvIGUlBSjNmPHjkVmZibGjBlTZH+Ojo4YOHBgvpvWinL69GmkpqZixowZcHV1VX4GDhyotFm3bh2GDx9e6GY0ouLgPdHpqdG9e3e0atUKCxYsMHUpeRw5cgTdu3fHvXv34OzsXGT7CxcuoGfPnvnu5H8Sjx49QpMmTXD69Ol8T+QkKgmugRCVoYyMDNy8eROffPIJhgwZUqzwALL3rcyZM8fopMbScOfOHfzwww8MDyoV3IlOVIbWrVuHsWPHolWrVvjll19KNG1gYGCp19OuXTu0a9eu1PulqombsIiISAo3YRERkRQGCBERSWGAEBGRFAYIERFJYYAQEZEUBggREUlhgBARkRQGCBERSWGAEBGRlP8H1Tb048VEkiYAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 300x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "hd = wot.linear_hydrodynamics(bem_data, mass, stiffness)\n",
     "hd = wot.check_linear_damping(hd)\n",
@@ -396,441 +185,7 @@
     "fig, axes = wot.utilities.plot_bode_impedance(intrinsic_impedance,'WaveBot Intrinsic Impedance',True)\n",
     "natural_freq, _ = wot.utilities.natural_frequency(intrinsic_impedance, freq)\n",
     "axes[0,0].axvline(natural_freq,color = 'k', linestyle = ':')\n",
-    "axes[0,0].text(x=natural_freq[0]*1.1, y =np.min(abs(intrinsic_impedance))*1.2, s = f'f_n = {natural_freq[0]}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<xarray.DataArray ()>\n",
-       " array(12)\n",
-       " Coordinates:\n",
-       "     g               float64 9.81\n",
-       "     rho             float64 1.025e+03\n",
-       "     body_name       <U16 'WaveBot_immersed'\n",
-       "     water_depth     float64 inf\n",
-       "     radiating_dof   <U5 'Heave'\n",
-       "     influenced_dof  <U5 'Heave']"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "restoring_dofs = ['Heave','Roll','Pitch']\n",
-    "indeces = [np.abs(intrinsic_impedance.loc[rdof,idof]).argmin(dim = 'omega') \n",
-    "            for rdof in intrinsic_impedance.radiating_dof \n",
-    "            for idof in intrinsic_impedance.influenced_dof \n",
-    "            if rdof == idof  #considering modes to be independent\n",
-    "            and any([df in str(rdof.values) for df in restoring_dofs])]\n",
-    "\n",
-    "indeces = [np.abs(intrinsic_impedance.loc[rdof,idof]).argmin(dim = 'omega') \n",
-    "            for rdof in intrinsic_impedance.radiating_dof \n",
-    "            for idof in intrinsic_impedance.influenced_dof \n",
-    "            if rdof == idof  #considering modes to be independent\n",
-    "            and any([df in str(rdof.values) for df in restoring_dofs])]\n",
-    "\n",
-    "indeces"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (radiating_dof: 1, influenced_dof: 1)&gt;\n",
-       "array([[12]], dtype=int64)\n",
-       "Coordinates:\n",
-       "    g               float64 9.81\n",
-       "    rho             float64 1.025e+03\n",
-       "    body_name       &lt;U16 &#x27;WaveBot_immersed&#x27;\n",
-       "    water_depth     float64 inf\n",
-       "  * radiating_dof   (radiating_dof) object &#x27;Heave&#x27;\n",
-       "  * influenced_dof  (influenced_dof) object &#x27;Heave&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>radiating_dof</span>: 1</li><li><span class='xr-has-index'>influenced_dof</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2853045c-8949-44f3-a29b-675535919f75' class='xr-array-in' type='checkbox' checked><label for='section-2853045c-8949-44f3-a29b-675535919f75' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>12</span></div><div class='xr-array-data'><pre>array([[12]], dtype=int64)</pre></div></div></li><li class='xr-section-item'><input id='section-b5ac71ed-d79e-463f-84ff-f5c9420c8dc3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b5ac71ed-d79e-463f-84ff-f5c9420c8dc3' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>g</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.81</div><input id='attrs-f2184404-5425-4afb-9477-6790f51e298e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2184404-5425-4afb-9477-6790f51e298e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceead050-702f-4e86-853f-387c8f9f5948' class='xr-var-data-in' type='checkbox'><label for='data-ceead050-702f-4e86-853f-387c8f9f5948' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(9.81)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.025e+03</div><input id='attrs-8f0ff17c-d9ff-4b95-b498-61bdba659dcc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f0ff17c-d9ff-4b95-b498-61bdba659dcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63430f93-9844-41eb-98d5-31453ab21211' class='xr-var-data-in' type='checkbox'><label for='data-63430f93-9844-41eb-98d5-31453ab21211' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1025.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>body_name</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U16</div><div class='xr-var-preview xr-preview'>&#x27;WaveBot_immersed&#x27;</div><input id='attrs-023b3684-23bc-4a46-83cb-727217708701' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-023b3684-23bc-4a46-83cb-727217708701' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25957ae5-0ca2-42d1-ac61-be3f83b2872f' class='xr-var-data-in' type='checkbox'><label for='data-25957ae5-0ca2-42d1-ac61-be3f83b2872f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;WaveBot_immersed&#x27;, dtype=&#x27;&lt;U16&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_depth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>inf</div><input id='attrs-610610b7-bfd7-4de5-9e39-f5df6ecac1f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-610610b7-bfd7-4de5-9e39-f5df6ecac1f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad7d806a-b4f6-4bea-bc8a-c1f48aee3cf0' class='xr-var-data-in' type='checkbox'><label for='data-ad7d806a-b4f6-4bea-bc8a-c1f48aee3cf0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(inf)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>radiating_dof</span></div><div class='xr-var-dims'>(radiating_dof)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Heave&#x27;</div><input id='attrs-2ddcae70-5030-4a69-9b31-a8db4940e1b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ddcae70-5030-4a69-9b31-a8db4940e1b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25a446b7-7398-443c-a5a1-978985e9c286' class='xr-var-data-in' type='checkbox'><label for='data-25a446b7-7398-443c-a5a1-978985e9c286' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Heave&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>influenced_dof</span></div><div class='xr-var-dims'>(influenced_dof)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Heave&#x27;</div><input id='attrs-cc07ff99-2349-413b-8312-80065aa29a7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc07ff99-2349-413b-8312-80065aa29a7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58f08ac5-18e9-48c9-a22b-3964be49a61b' class='xr-var-data-in' type='checkbox'><label for='data-58f08ac5-18e9-48c9-a22b-3964be49a61b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Heave&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c58b885-dcd3-49d2-b63b-0886b23d3c6a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5c58b885-dcd3-49d2-b63b-0886b23d3c6a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (radiating_dof: 1, influenced_dof: 1)>\n",
-       "array([[12]], dtype=int64)\n",
-       "Coordinates:\n",
-       "    g               float64 9.81\n",
-       "    rho             float64 1.025e+03\n",
-       "    body_name       <U16 'WaveBot_immersed'\n",
-       "    water_depth     float64 inf\n",
-       "  * radiating_dof   (radiating_dof) object 'Heave'\n",
-       "  * influenced_dof  (influenced_dof) object 'Heave'"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "indx = np.abs(intrinsic_impedance).argmin(dim = 'omega') \n",
-    "in_sel = indx.sel(influenced_dof  = 'Heave', radiating_dof   = 'Heave')\n",
-    "indx"
+    "axes[0,0].text(x=natural_freq[0]*1.1, y =axes[0,0].get_ylim()[1]*0.9, s = f'$f_n$ = {natural_freq[0]} Hz')"
    ]
   },
   {
@@ -1088,6 +443,36 @@
     "pto_tdom"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lastly, we will visualize the average power at different stages and how the power flows through the system.\n",
+    "\n",
+    "On the very left of the power flow Sankey diagram we start with the *Optimal Excitation*, which is only determined by the incident wave and the hydrodynamic damping and friction. \n",
+    "In order for the actual *Excited* power to equal the *Optimal Excitation* the absorbed *Mechanical* power would need to equal the *Radiated* power (power that is radiated away from the WEC). In this case the *Unused Potential* would be zero.\n",
+    "In other words, you can never convert more than 50% of the incident wave energy into kinetic energy. For more information on this we refer to Johannes Falnes Book - Ocean Waves and Oscillating System, specifically Chapter 6.\n",
+    "\n",
+    "However, the optimal 50% absorption is an overly optimistic goal considering that real world systems are likely constrained in their oscillation velocity amplitude and phase, due to limitations in stroke and applicable force.\n",
+    "\n",
+    "\n",
+    "The PTO force constraint used in this optimization also stopped us from absorbing the maximal potential wave energy. It is notable that we absorbed approximately 3/4 of the maximal absorbable power (*Mechanical* / (1/2 *Optimal Excitation*)), with relatively little *Radiated* power (about 1/2 of the absorbed power). To also absorb the last 1/4 of the potential wave power, we would need to increase the *Radiated* power three times!!\n",
+    "\n",
+    "\n",
+    "A more important question than \"How to achieve optimal absorption?\" is \"How do we optimize useable output power?\", e.g. *Electrical* power. For this optimization case we used a loss-less PTO that has no impedance in itself. Therefore, the *Electrical* power equals the absorbed *Mechanical* power.\n",
+    "We'll show in the following sections that this a poor assumption and that the powerflow looks fundamentally different when taking the PTO dynamics into account!!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p_flows = wot.utilities.calculate_power_flows(wec, pto, results, waves, intrinsic_impedance)\n",
+    "wot.utilities.plot_power_flow(p_flows)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1286,7 +671,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The attentive user might have noticed that the amplitude of the position, force and power signals is about half the magnitude of the signals we plotted in the first part of the tutorial. We can see that optimizing for electrical power requires optimal state trajectories with smaller amplitudes. For most WECs the electrical power is the usable form of power, thus the WEC should be designed for electrical power and we can avoid over-designing, which would results from expecting the forces associated with the optimal trajectories for mechanical power maximisation."
+    "The attentive user might have noticed that the amplitude of the position, force and power signals is about half the magnitude of the signals we plotted in the first part of the tutorial. We can see that optimizing for electrical power requires optimal state trajectories with smaller amplitudes. For most WECs the electrical power is the usable form of power, thus the WEC should be designed for electrical power and we can avoid over-designing, which would results from expecting the forces associated with the optimal trajectories for mechanical power maximisation.\n",
+    "\n",
+    "The Sankey power flow diagrams confirm this observation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p_flows_2 = wot.utilities.calculate_power_flows(wec_2, pto_2, results_2, waves, intrinsic_impedance)\n",
+    "wot.utilities.plot_power_flow(p_flows_2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another very interesting observation can be drawn from the power flow diagram for the unconstrained case, but optimized for electrical power (below). Although we absorbed 40% more kinetic energy we only converted 13% more *Electrical* power. For the case of the WaveBot increasing the range of applicablt force does not seem to be a good design decision.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p_flows_2_nocon = wot.utilities.calculate_power_flows(wec_2_nocon, pto_2, results_2_nocon, waves, intrinsic_impedance)\n",
+    "wot.utilities.plot_power_flow(p_flows_2_nocon)"
    ]
   },
   {

From 17b523748c96381d52dc09ab56c4ab90dce1520d Mon Sep 17 00:00:00 2001
From: dtgaebe <dtgaebe@sandia.gov>
Date: Wed, 29 Nov 2023 10:35:29 -0800
Subject: [PATCH 11/20] updated to latest wot version and removed natural
 frequency calc

---
 examples/tutorial_1_WaveBot.ipynb | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/examples/tutorial_1_WaveBot.ipynb b/examples/tutorial_1_WaveBot.ipynb
index 9ab640cb..a1141b60 100644
--- a/examples/tutorial_1_WaveBot.ipynb
+++ b/examples/tutorial_1_WaveBot.ipynb
@@ -173,7 +173,7 @@
     "\n",
     "The intrinsic impedance tells us how a hydrodynamic body responds to different excitation frequencies. For oscillating systems we are intersted in both, the amplitude of the resulting velocity as well as the phase between velocity and excitation force. Bode plots are useful tool to visualize the frequency response function.\n",
     "\n",
-    "Lastly, we also calculate the natural frequency of the hydrodynamic body. At this frequency of excitation the floating body would be naturally at resonance. The natural frequency reveals itself as a trough in the intrinsic impedance for restoring degrees of freedom (heave, roll, pitch).\n"
+    "The natural frequency reveals itself as a trough in the intrinsic impedance for restoring degrees of freedom (heave, roll, pitch).\n"
    ]
   },
   {
@@ -182,13 +182,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hd = wot.linear_hydrodynamics(bem_data, mass, stiffness)\n",
+    "hd = wot.add_linear_friction(bem_data, friction = None) #we're not actually adding friction\n",
     "hd = wot.check_linear_damping(hd)\n",
+    "\n",
     "intrinsic_impedance = wot.hydrodynamic_impedance(hd)\n",
-    "fig, axes = wot.utilities.plot_bode_impedance(intrinsic_impedance,'WaveBot Intrinsic Impedance',True)\n",
-    "natural_freq, _ = wot.utilities.natural_frequency(intrinsic_impedance, freq)\n",
-    "axes[0,0].axvline(natural_freq,color = 'k', linestyle = ':')\n",
-    "axes[0,0].text(x=natural_freq[0]*1.1, y =axes[0,0].get_ylim()[1]*0.9, s = f'$f_n$ = {natural_freq[0]} Hz')"
+    "fig, axes = wot.utilities.plot_bode_impedance(intrinsic_impedance,'WaveBot Intrinsic Impedance',True)"
    ]
   },
   {
@@ -951,7 +949,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.6"
   },
   "vscode": {
    "interpreter": {

From 13346699bdaa9b6be96b0c6189cc23ce5514ff42 Mon Sep 17 00:00:00 2001
From: dtgaebe <dtgaebe@sandia.gov>
Date: Wed, 29 Nov 2023 10:41:47 -0800
Subject: [PATCH 12/20] Added bem plot function from utilities

---
 examples/tutorial_2_AquaHarmonics.ipynb | 20 ++------------------
 1 file changed, 2 insertions(+), 18 deletions(-)

diff --git a/examples/tutorial_2_AquaHarmonics.ipynb b/examples/tutorial_2_AquaHarmonics.ipynb
index 36a95c44..275c9ad7 100644
--- a/examples/tutorial_2_AquaHarmonics.ipynb
+++ b/examples/tutorial_2_AquaHarmonics.ipynb
@@ -143,23 +143,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig, axes = plt.subplots(3,1)\n",
-    "bem_data['added_mass'].plot(ax = axes[0])\n",
-    "bem_data['radiation_damping'].plot(ax = axes[1])\n",
-    "axes[2].plot(bem_data['omega'],np.abs(np.squeeze(bem_data['diffraction_force'].values)), color = 'orange')\n",
-    "axes[2].set_ylabel('abs(diffraction_force)', color = 'orange')\n",
-    "axes[2].tick_params(axis ='y', labelcolor = 'orange')\n",
-    "ax2r = axes[2].twinx()\n",
-    "ax2r.plot(bem_data['omega'], np.abs(np.squeeze(bem_data['Froude_Krylov_force'].values)), color = 'blue')\n",
-    "ax2r.set_ylabel('abs(FK_force)', color = 'blue')\n",
-    "ax2r.tick_params(axis ='y', labelcolor = 'blue')\n",
-    "\n",
-    "for axi in axes:\n",
-    "    axi.set_title('')\n",
-    "    axi.label_outer()\n",
-    "    axi.grid()\n",
-    "    \n",
-    "axes[-1].set_xlabel('Frequency [rad/s]')"
+    "wot.utilities.plot_hydrodynamic_coefficients(bem_data)"
    ]
   },
   {
@@ -915,7 +899,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.6"
   },
   "vscode": {
    "interpreter": {

From 39b58292ebf8695b62a81c6210365906249f7aa3 Mon Sep 17 00:00:00 2001
From: dtgaebe <dtgaebe@sandia.gov>
Date: Wed, 29 Nov 2023 10:51:41 -0800
Subject: [PATCH 13/20] replaced bem plot code with utilities function

---
 examples/tutorial_3_LUPA.ipynb | 63 ++--------------------------------
 1 file changed, 3 insertions(+), 60 deletions(-)

diff --git a/examples/tutorial_3_LUPA.ipynb b/examples/tutorial_3_LUPA.ipynb
index 10c9dc55..832acb2e 100644
--- a/examples/tutorial_3_LUPA.ipynb
+++ b/examples/tutorial_3_LUPA.ipynb
@@ -380,65 +380,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# plot coefficients\n",
-    "radiating_dofs = bem_data.radiating_dof.values\n",
-    "influenced_dofs = bem_data.influenced_dof.values\n",
-    "\n",
-    "# plots\n",
-    "fig_am, ax_am = plt.subplots(len(radiating_dofs), len(influenced_dofs),\n",
-    "                             tight_layout=True, sharex=True)\n",
-    "fig_rd, ax_rd = plt.subplots(len(radiating_dofs), len(influenced_dofs),\n",
-    "                             tight_layout=True, sharex=True)\n",
-    "fig_ex, ax_ex = plt.subplots(len(influenced_dofs), 1,\n",
-    "                             tight_layout=True, sharex=True, figsize=(4, 6))\n",
-    "\n",
-    "# plot titles\n",
-    "fig_am.suptitle('Added Mass Coefficients', fontweight='bold')\n",
-    "fig_rd.suptitle('Radiation Damping Coefficients', fontweight='bold')\n",
-    "fig_ex.suptitle('Wave Excitation Coefficients', fontweight='bold')\n",
-    "\n",
-    "# subplotting across 4DOF\n",
-    "sp_idx = 0\n",
-    "for i, rdof in enumerate(radiating_dofs):\n",
-    "    for j, idof in enumerate(influenced_dofs):\n",
-    "        sp_idx += 1\n",
-    "        if i == 0:\n",
-    "            np.abs(bem_data.diffraction_force.sel(influenced_dof=idof)).plot(\n",
-    "                ax=ax_ex[j], linestyle='dashed', label='Diffraction force')\n",
-    "            np.abs(bem_data.Froude_Krylov_force.sel(influenced_dof=idof)).plot(\n",
-    "                ax=ax_ex[j], linestyle='dashdot', label='Froude-Krylov force')\n",
-    "            ex_handles, ex_labels = ax_ex[j].get_legend_handles_labels()\n",
-    "            ax_ex[j].set_title(f'{idof}')\n",
-    "            ax_ex[j].set_xlabel('')\n",
-    "            ax_ex[j].set_ylabel('')\n",
-    "        if j <= i:\n",
-    "            bem_data.added_mass.sel(\n",
-    "                radiating_dof=rdof, influenced_dof=idof).plot(ax=ax_am[i, j])\n",
-    "            bem_data.radiation_damping.sel(\n",
-    "                radiating_dof=rdof, influenced_dof=idof).plot(ax=ax_rd[i, j])\n",
-    "            if i == len(radiating_dofs)-1:\n",
-    "                ax_am[i, j].set_xlabel(f'$\\omega$', fontsize=10)\n",
-    "                ax_rd[i, j].set_xlabel(f'$\\omega$', fontsize=10)\n",
-    "                ax_ex[j].set_xlabel(f'$\\omega$', fontsize=10)\n",
-    "            else:\n",
-    "                ax_am[i, j].set_xlabel('')\n",
-    "                ax_rd[i, j].set_xlabel('')\n",
-    "            if j == 0:\n",
-    "                ax_am[i, j].set_ylabel(f'{rdof}', fontsize=10)\n",
-    "                ax_rd[i, j].set_ylabel(f'{rdof}', fontsize=10)\n",
-    "            else:\n",
-    "                ax_am[i, j].set_ylabel('')\n",
-    "                ax_rd[i, j].set_ylabel('')\n",
-    "            if j == i:\n",
-    "                ax_am[i, j].set_title(f'{idof}', fontsize=10)\n",
-    "                ax_rd[i, j].set_title(f'{idof}', fontsize=10)\n",
-    "            else:\n",
-    "                ax_am[i, j].set_title('')\n",
-    "                ax_rd[i, j].set_title('')\n",
-    "        else:\n",
-    "            fig_am.delaxes(ax_am[i, j])\n",
-    "            fig_rd.delaxes(ax_rd[i, j])\n",
-    "fig_ex.legend(ex_handles, ex_labels, loc=(0.08, 0), ncol=2, frameon=False)"
+    "wot.utilities.plot_hydrodynamic_coefficients(bem_data)"
    ]
   },
   {
@@ -1218,6 +1160,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "influenced_dofs = bem_data.influenced_dof.values\n",
     "## Forces\n",
     "fig_res2, ax_res2 = plt.subplots(len(influenced_dofs), 1, sharex=True, figsize=(8, 10))\n",
     "for j, idof in enumerate(influenced_dofs):\n",
@@ -1475,7 +1418,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.6"
   },
   "vscode": {
    "interpreter": {

From 01e1a85f241ff2f7e228115efe12bdde5d35993e Mon Sep 17 00:00:00 2001
From: dtgaebe <dtgaebe@sandia.gov>
Date: Wed, 29 Nov 2023 11:30:35 -0800
Subject: [PATCH 14/20] updated the sankey power flow calculation in order to
 use the utility function

---
 examples/tutorial_4_Pioneer.ipynb | 53 +++++++++----------------------
 1 file changed, 15 insertions(+), 38 deletions(-)

diff --git a/examples/tutorial_4_Pioneer.ipynb b/examples/tutorial_4_Pioneer.ipynb
index 0fd03425..e73a3bc6 100644
--- a/examples/tutorial_4_Pioneer.ipynb
+++ b/examples/tutorial_4_Pioneer.ipynb
@@ -759,17 +759,24 @@
     "Vel = wec_fdom.vel\n",
     "\n",
     "P_max_absorbable = (np.abs(Fex)**2/(8*Rad_res) ).squeeze().sum('omega').item() # after Falnes Eq. 6.10\n",
-    "P_opt_excitation = 2*P_max_absorbable # after Falnes Eq. 6.10\n",
     "P_radiated = ((1/2)*(Rad_res * np.abs(Vel)**2 ).squeeze().sum('omega').item()) # after Falnes Eq. 6.4\n",
     "P_excited= (1/4)*(Fex*np.conjugate(Vel) + np.conjugate(Fex)*Vel ).squeeze().sum('omega').item() # after Falnes Eq. 6.3\n",
-    "P_absorbed = P_excited - P_radiated # after Falnes Eq. 6.2 absorbed by WEC-PTO (difference between actual excitation power and actual radiated power needs to be absorbed by PTO)"
+    "\n",
+    "power_flows = {'Optimal Excitation' : 2* (np.real(P_max_absorbable)),    #positive because the only inflow\n",
+    "            'Radiated': -1*(np.real(P_radiated)), #negative because \"out\"flow\n",
+    "            'Actual Excitation': -1*(np.real(P_excited)), #negative because \"out\"flow\n",
+    "}\n",
+    "power_flows['Unused Potential'] =  -1*power_flows['Optimal Excitation'] - power_flows['Actual Excitation']\n",
+    "power_flows['Absorbed'] =  power_flows['Actual Excitation'] - power_flows['Radiated']  # after Falnes Eq. 6.2 \n",
+    "#absorbed by WEC-PTO (difference between actual excitation power and actual radiated power needs to be absorbed by PTO)\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We also calculate the power dissipated due to flywheel friction and make sure that the absorbed power (calculated as the difference between excited and radiated power) matches the sum of mechanical power captured by the PTO and the power dissipated due to flywheel friction. We use a relative tolerance of 1%."
+    "We also calculate the power dissipated due to flywheel friction and make sure that the absorbed power (calculated as the difference between excited and radiated power) matches the sum of mechanical power captured by the PTO and the power dissipated due to flywheel friction. The utilities function to plot the Sankey diagram lumps the dissipated power due to flywheel friction into the PTO loss."
    ]
   },
   {
@@ -792,7 +799,9 @@
     "fw_fric_power = fw_friction_power(wec, x_wec, x_opt, waves_regular, nsubsteps)\n",
     "avg_fw_fric_power = np.mean(fw_fric_power)\n",
     "\n",
-    "assert(np.isclose(P_absorbed, -1*(avg_mech_power -avg_fw_fric_power), rtol=0.01)) # assert that solver solution matches"
+    "power_flows['Electrical (solver)']= avg_elec_power  #solver determins sign\n",
+    "power_flows['Mechanical (solver)']= avg_mech_power - avg_fw_fric_power #solver determins sign, friction is dissipated\n",
+    "power_flows['PTO Loss'] = power_flows['Mechanical (solver)'] -  power_flows['Electrical (solver)'] "
    ]
   },
   {
@@ -801,39 +810,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from matplotlib.sankey import Sankey\n",
-    "\n",
-    "P_PTO_loss = avg_mech_power - avg_elec_power  \n",
-    "P_unused = P_opt_excitation - P_excited # Difference between the theoretical optimum excitation, if the WEC velocity would be in resonance with the excitation force\n",
-    "\n",
-    "Power_flows = [P_opt_excitation, P_PTO_loss, -1*avg_fw_fric_power, -1*P_radiated, -1*P_unused, avg_elec_power, ]\n",
-    "\n",
-    "fig = plt.figure(figsize = [6,4])\n",
-    "ax = fig.add_subplot(1, 1, 1,)\n",
-    "sankey = Sankey(ax=ax, \n",
-    "                scale=0.5/P_max_absorbable,\n",
-    "                offset= 0,\n",
-    "                format = '%.2f W',shoulder = 0.02)\n",
-    "\n",
-    "sankey.add(flows=Power_flows, \n",
-    "           labels = ['Optimal Excitation \\n $ 2 \\\\frac{\\left | F_e \\\\right |^2}{8Re(Z_i)} = 2*P_{mech}^{max}$ ', \n",
-    "                      'PTO-Loss \\n $ P_{mech} - P_{elec}$', \n",
-    "                      'Flywheel friction',\n",
-    "                      'Radiated \\n $ \\\\frac{1}{2} Re(Z_i) \\left | U \\\\right |^2  $ ', \n",
-    "                      'Unused Potential \\n(neither absorbed nor radiated)', \n",
-    "                      'Electrical'], \n",
-    "           orientations=[0, -1, -1,-1, -1, 0], # arrow directions\n",
-    "           pathlengths = [0.4,0.2,0.6,0.6,0.7,0.5,],\n",
-    "           trunklength = 1.5,\n",
-    "           edgecolor = '#2b8cbe',\n",
-    "           facecolor = '#2b8cbe' )\n",
-    "\n",
-    "diagrams = sankey.finish()\n",
-    "for diagram in diagrams:\n",
-    "    for text in diagram.texts:\n",
-    "        text.set_fontsize(10);\n",
-    "plt.axis(\"off\") \n",
-    "plt.show()"
+    "wot.utilities.plot_power_flow(power_flows)"
    ]
   },
   {
@@ -972,7 +949,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

From d0d6a8431b66f2365a8cf83fb27acb6b05a71a4f Mon Sep 17 00:00:00 2001
From: dtgaebe <dtgaebe@sandia.gov>
Date: Thu, 7 Dec 2023 16:05:28 -0800
Subject: [PATCH 15/20] Adjusted tut1 to waves realization notation

---
 examples/tutorial_1_WaveBot.ipynb | 6 +++---
 wecopttool/core.py                | 3 ++-
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/examples/tutorial_1_WaveBot.ipynb b/examples/tutorial_1_WaveBot.ipynb
index 28bb686e..cbea5044 100644
--- a/examples/tutorial_1_WaveBot.ipynb
+++ b/examples/tutorial_1_WaveBot.ipynb
@@ -468,7 +468,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "p_flows = wot.utilities.calculate_power_flows(wec, pto, results, waves, intrinsic_impedance)\n",
+    "p_flows = wot.utilities.calculate_power_flows(wec, pto, results[0], waves.sel(realization=0), intrinsic_impedance)\n",
     "wot.utilities.plot_power_flow(p_flows)"
    ]
   },
@@ -677,7 +677,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "p_flows_2 = wot.utilities.calculate_power_flows(wec_2, pto_2, results_2, waves, intrinsic_impedance)\n",
+    "p_flows_2 = wot.utilities.calculate_power_flows(wec_2, pto_2, results_2[0], waves.sel(realization = 0), intrinsic_impedance)\n",
     "wot.utilities.plot_power_flow(p_flows_2)"
    ]
   },
@@ -694,7 +694,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "p_flows_2_nocon = wot.utilities.calculate_power_flows(wec_2_nocon, pto_2, results_2_nocon, waves, intrinsic_impedance)\n",
+    "p_flows_2_nocon = wot.utilities.calculate_power_flows(wec_2_nocon, pto_2, results_2_nocon[0], waves.sel(realization = 0), intrinsic_impedance)\n",
     "wot.utilities.plot_power_flow(p_flows_2_nocon)"
    ]
   },
diff --git a/wecopttool/core.py b/wecopttool/core.py
index caeebd05..37847cd4 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -2270,7 +2270,8 @@ def hydrodynamic_impedance(hydro_data: Dataset) -> Dataset:
         + hydro_data['added_mass'])*1j*hydro_data['omega'] \
             + hydro_data['radiation_damping'] + hydro_data['friction'] \
                 + hydro_data['hydrostatic_stiffness']/1j/hydro_data['omega']
-    return Zi.transpose('omega', 'radiating_dof', 'influenced_dof')
+    return intrinsic_impedance.transpose('omega', 'radiating_dof', 'influenced_dof')
+
 
 
 def atleast_2d(array: ArrayLike) -> ndarray:

From a2d6861aeab3f51d90ff5d43633017c3b37294a8 Mon Sep 17 00:00:00 2001
From: dtgaebe <dtgaebe@sandia.gov>
Date: Mon, 11 Dec 2023 11:47:29 -0800
Subject: [PATCH 16/20] deleted empty cell

---
 examples/tutorial_4_Pioneer.ipynb | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/examples/tutorial_4_Pioneer.ipynb b/examples/tutorial_4_Pioneer.ipynb
index ae548897..455fbedb 100644
--- a/examples/tutorial_4_Pioneer.ipynb
+++ b/examples/tutorial_4_Pioneer.ipynb
@@ -935,13 +935,6 @@
     "    axi.label_outer()\n",
     "    axi.autoscale(axis='x', tight=True)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 0eeda29920e75ef606ffd67a7d7a78dad130ef3f Mon Sep 17 00:00:00 2001
From: dtgaebe <dtgaebe@sandia.gov>
Date: Mon, 11 Dec 2023 13:41:56 -0800
Subject: [PATCH 17/20] format changes

---
 examples/tutorial_1_WaveBot.ipynb |   2 +-
 wecopttool/utilities.py           | 382 ++++++++++++++++++------------
 2 files changed, 230 insertions(+), 154 deletions(-)

diff --git a/examples/tutorial_1_WaveBot.ipynb b/examples/tutorial_1_WaveBot.ipynb
index cbea5044..a4b0948c 100644
--- a/examples/tutorial_1_WaveBot.ipynb
+++ b/examples/tutorial_1_WaveBot.ipynb
@@ -186,7 +186,7 @@
     "hd = wot.check_linear_damping(hd)\n",
     "\n",
     "intrinsic_impedance = wot.hydrodynamic_impedance(hd)\n",
-    "fig, axes = wot.utilities.plot_bode_impedance(intrinsic_impedance,'WaveBot Intrinsic Impedance',True)"
+    "fig, axes = wot.utilities.plot_bode_impedance(intrinsic_impedance,'WaveBot Intrinsic Impedance')"
    ]
   },
   {
diff --git a/wecopttool/utilities.py b/wecopttool/utilities.py
index fee49c5a..7bbf6262 100644
--- a/wecopttool/utilities.py
+++ b/wecopttool/utilities.py
@@ -6,11 +6,12 @@
 
 
 __all__ = [
-    "intrinsic_impedance"
-    "natural_frequency",
-    "plot_bem_results",
-    "plot_bode_intrinsic_impedance",
-    "add_zerofreq_to_xr"
+    "plot_hydrodynamic_coefficients",
+    "plot_bode_impedance",
+    "calculate_power_flows",
+    "plot_power_flow",
+    # "add_zerofreq_to_xr",
+    # "natural_frequency",
 ]
 
 
@@ -26,48 +27,17 @@
 import matplotlib.pyplot as plt
 from matplotlib.sankey import Sankey
 
-# from wecopttool.core import linear_hydrodynamics
 
 
 # logger
 _log = logging.getLogger(__name__)
 
-def natural_frequency(impedance: DataArray, freq: ArrayLike
-                      ) -> tuple[ArrayLike, int]:
-    """Find the natural frequency based on the lowest magnitude impedance,
-       for restoring degrees of freedom (Heave, Roll, Pitch).
 
-    Parameters
-    ----------
-    impedance: DataArray
-        Complex intrinsic impedance matrix produced by
-        :py:func:`wecopttool.hydrodynamic_impedance`.
-        Dimensions: omega, radiating_dofs, influenced_dofs
-    freq: list[float]
-        Frequencies.
-
-    Returns
-    -------
-    f_n: float
-        Natural frequencies.
-    ind: int
-        Index of natural frequencies.
-    """
-
-    restoring_dofs = ['Heave','Roll','Pitch']
-    indeces = [np.abs(impedance.loc[rdof,idof]).argmin(dim = 'omega') 
-                for rdof in impedance.radiating_dof 
-                for idof in impedance.influenced_dof 
-                if rdof == idof  #considering modes to be independent
-                and any([df in str(rdof.values) for df in restoring_dofs])]
-    f_n = [freq[indx.values] for indx in indeces]
-
-    return f_n, indeces
 
 
-def plot_hydrodynamic_coefficients(bem_data):
+def plot_hydrodynamic_coefficients(bem_data)-> None:
     """Plots hydrodynamic coefficients (added mass, radiation damping,
-       and wave excitation)based on BEM data.
+       and wave excitation) based on BEM data.
 
 
     Parameters
@@ -76,22 +46,36 @@ def plot_hydrodynamic_coefficients(bem_data):
         Linear hydrodynamic coefficients obtained using the boundary
         element method (BEM) code Capytaine, with sign convention
         corrected.
-
     
     """
     radiating_dofs = bem_data.radiating_dof.values
     influenced_dofs = bem_data.influenced_dof.values
 
     # plots
-    fig_am, ax_am = plt.subplots(len(radiating_dofs), len(influenced_dofs),
-                                tight_layout=True, sharex=True, 
-                                figsize=(3*len(radiating_dofs), 3*len(influenced_dofs)), squeeze=False)
-    fig_rd, ax_rd = plt.subplots(len(radiating_dofs), len(influenced_dofs),
-                                tight_layout=True, sharex=True, 
-                               figsize=(3*len(radiating_dofs), 3*len(influenced_dofs)), squeeze=False)
-    fig_ex, ax_ex = plt.subplots(len(influenced_dofs), 1,
-                                tight_layout=True, sharex=True, 
-                                figsize=(3, 3*len(radiating_dofs)), squeeze=False)
+    fig_am, ax_am = plt.subplots(
+        len(radiating_dofs), 
+        len(influenced_dofs),
+        tight_layout=True, 
+        sharex=True, 
+        figsize=(3*len(radiating_dofs),3*len(influenced_dofs)),
+        squeeze=False
+        )
+    fig_rd, ax_rd = plt.subplots(
+        len(radiating_dofs),
+        len(influenced_dofs),
+        tight_layout=True,
+        sharex=True, 
+        figsize=(3*len(radiating_dofs), 3*len(influenced_dofs)),
+        squeeze=False
+        )
+    fig_ex, ax_ex = plt.subplots(
+        len(influenced_dofs),
+        1,
+        tight_layout=True, 
+        sharex=True, 
+        figsize=(3, 3*len(radiating_dofs)), 
+        squeeze=False
+        )
 
     # plot titles
     fig_am.suptitle('Added Mass Coefficients', fontweight='bold')
@@ -143,17 +127,18 @@ def plot_hydrodynamic_coefficients(bem_data):
 
 def plot_bode_impedance(impedance: DataArray, 
                         title: Optional[str]= None,
-                        plot_natural_freq: Optional[bool] = False,
-):
+                        #plot_natural_freq: Optional[bool] = False,
+)-> None:
     """Plot Bode graph from wecoptool impedance data array.
 
     Parameters
     ----------
     impedance: DataArray
-        Complex intrinsic impedance matrix produced by
+        Complex impedance matrix produced by for example by
         :py:func:`wecopttool.hydrodynamic_impedance`.
         Dimensions: omega, radiating_dofs, influenced_dofs
-
+    title: String
+        Title string to be displayed in the plot.
 
     """
     radiating_dofs = impedance.radiating_dof.values
@@ -161,23 +146,24 @@ def plot_bode_impedance(impedance: DataArray,
     mag = 20.0 * np.log10(np.abs(impedance))
     phase = np.rad2deg(np.unwrap(np.angle(impedance)))
     freq = impedance.omega.values/2/np.pi   
-    fig, axes = plt.subplots(2*len(radiating_dofs), len(influenced_dofs),
-                                tight_layout=True, sharex=True, 
-                                figsize=(3*len(radiating_dofs), 3*len(influenced_dofs)), squeeze=False)
+    fig, axes = plt.subplots(
+        2*len(radiating_dofs), 
+        len(influenced_dofs),
+        tight_layout=True, 
+        sharex=True, 
+        figsize=(3*len(radiating_dofs), 3*len(influenced_dofs)), 
+        squeeze=False
+        )
     fig.suptitle(title + ' Bode Plots', fontweight='bold')
-    fn, fn_indx = natural_frequency(impedance=impedance,freq=freq)
 
     sp_idx = 0
     for i, rdof in enumerate(radiating_dofs):
         for j, idof in enumerate(influenced_dofs):
             sp_idx += 1
-            axes[2*i, j].semilogx(freq, mag[i, j, :])    # Bode magnitude plot
-            axes[2*i+1, j].semilogx(freq, phase[i, j, :])    # Bode phase plot
+            axes[2*i, j].semilogx(freq, mag[:, i, j])    # Bode magnitude plot
+            axes[2*i+1, j].semilogx(freq, phase[:, i, j])    # Bode phase plot
             axes[2*i, j].grid(True, which = 'both')
             axes[2*i+1, j].grid(True, which = 'both')
-            # if i == j and plot_natural_freq:
-            #     axes[2*i, j].axvline(freq[fn_indx.sel(radiating_dofs=rdof,
-            #                                         influenced_dofs=idof)])
             if i == len(radiating_dofs)-1:
                 axes[2*i+1, j].set_xlabel(f'Frequency (Hz)', fontsize=10)
             else:
@@ -193,17 +179,37 @@ def plot_bode_impedance(impedance: DataArray,
                 axes[i, j].set_title('')
     return fig, axes
 
-def add_zerofreq_to_xr(data):
-    """Add a zero-frequency component to an :python:`xarray.Dataset`.  
-      Frequency variable must be called :python:`omega`.    """    
-    if not np.isclose(data.coords['omega'][0].values, 0):
-        tmp = data.isel(omega=0).copy(deep=True) * 0   
-        tmp['omega'] = tmp['omega'] * 0                      
-        data = concat([tmp, data], dim='omega')
-    return data
 
 
-def calculate_power_flows(wec, pto, results, waves, intrinsic_impedance):
+
+def calculate_power_flows(wec, 
+                          pto, 
+                          results, 
+                          waves, 
+                          intrinsic_impedance)-> dict[str, float]:
+    """Calculate power flows into a :py:class:`wecopttool.WEC`
+        and through a :py:class:`wecopttool.pto.PTO` based on the results
+        of :py:meth:`wecopttool.WEC.solve` for a single wave realization.
+
+    Parameters
+    ----------
+    wec
+        WEC object of :py:class:`wecopttool.WEC` 
+    pto
+        PTO object of :py:class:`wecopttool.pto.PTO`
+    results
+        Results produced by :py:func:`scipy.optimize.minimize` for a single wave
+        realization.
+    waves
+        :py:class:`xarray.Dataset` with the structure and elements
+        shown by :py:mod:`wecopttool.waves`.
+    intrinsic_impedance: DataArray
+        Complex intrinsic impedance matrix produced by 
+        :py:func:`wecopttool.hydrodynamic_impedance`.
+        Dimensions: omega, radiating_dofs, influenced_dofs
+
+
+    """
     wec_fdom, _ = wec.post_process(results, waves)
     x_wec, x_opt = wec.decompose_state(results.x)
 
@@ -218,11 +224,15 @@ def calculate_power_flows(wec, pto, results, waves, intrinsic_impedance):
 
     P_max, P_e, P_r = [], [], []
 
-    #This solution only works if the radiation resistance matrix Rad_res is invertible
-    # TODO In the future we might want to add an entirely unconstrained solve for optimized mechanical power
-    for om in Rad_res.omega.values:    #use frequency vector from intrinsic impedance because it does not contain zero freq
+    #This solution requires radiation resistance matrix Rad_res to be invertible
+    # TODO In the future we might want to add an entirely unconstrained solve 
+    # for optimized mechanical power
+
+    for om in Rad_res.omega.values:   
+        #use frequency vector from intrinsic impedance (no zero freq)
         #Eq. 6.69
-        Fe_FD_t = np.atleast_2d(Fex_FD.sel(omega = om))    #Dofs are row vector, which is transposed in standard convention
+        #Dofs are row vector, which is transposed in standard convention
+        Fe_FD_t = np.atleast_2d(Fex_FD.sel(omega = om))    
         Fe_FD = np.transpose(Fe_FD_t)
         R_inv = np.linalg.inv(np.atleast_2d(Rad_res.sel(omega= om)))
         P_max.append((1/8)*(Fe_FD_t@R_inv)@np.conj(Fe_FD)) 
@@ -231,90 +241,156 @@ def calculate_power_flows(wec, pto, results, waves, intrinsic_impedance):
         U_FD = np.transpose(U_FD_t)
         R = np.atleast_2d(Rad_res.sel(omega= om))
         P_r.append((1/2)*(U_FD_t@R)@np.conj(U_FD))
-        #Eq. 6.56 (replaced pinv(Fe)*U with U'*conj(Fe) as suggested in subsequent paragraph)
+        #Eq. 6.56 (replaced pinv(Fe)*U with U'*conj(Fe) 
+        # as suggested in subsequent paragraph)
         P_e.append((1/4)*(Fe_FD_t@np.conj(U_FD) + U_FD_t@np.conj(Fe_FD)))
 
-    power_flows = {'Optimal Excitation' : 2* np.sum(np.real(P_max)),    #6.68 positive because the only inflow
-                'Radiated': -1*np.sum(np.real(P_r)), #negative because "out"flow
-                'Actual Excitation': -1*np.sum(np.real(P_e)), #negative because "out"flow
-                'Electrical (solver)': P_elec,  #solver determins sign
-                'Mechanical (solver)': P_mech, #solver determins sign
-                }
-
-    power_flows['Absorbed'] =  power_flows['Actual Excitation'] - power_flows['Radiated']
-    power_flows['Unused Potential'] =  -1*power_flows['Optimal Excitation'] - power_flows['Actual Excitation']
-    power_flows['PTO Loss'] = power_flows['Mechanical (solver)'] -  power_flows['Electrical (solver)']
-
+    power_flows = {
+        'Optimal Excitation' : 2* np.sum(np.real(P_max)),#eq 6.68 positive inflow
+        'Radiated': -1*np.sum(np.real(P_r)), #negative because "out"flow
+        'Actual Excitation': -1*np.sum(np.real(P_e)), #negative because "out"flow
+        'Electrical (solver)': P_elec,  #solver determins sign
+        'Mechanical (solver)': P_mech, #solver determins sign
+                  }
+
+    power_flows['Absorbed'] =  (
+        power_flows['Actual Excitation'] 
+        - power_flows['Radiated']
+            )
+    power_flows['Unused Potential'] =  (
+        -1*power_flows['Optimal Excitation'] 
+        - power_flows['Actual Excitation']
+            )
+    power_flows['PTO Loss'] = (
+        power_flows['Mechanical (solver)'] 
+        -  power_flows['Electrical (solver)']
+            )
     return power_flows
 
 
-def plot_power_flow(power_flows):
-        fig = plt.figure(figsize = [8,4])
-        ax = fig.add_subplot(1, 1, 1,)
-        plt.viridis()
-        sankey = Sankey(ax=ax, 
-                        scale= 1/power_flows['Optimal Excitation'],
-                        offset= 0,
-                        format = '%.1f',
-                        shoulder = 0.02,
-                        tolerance=1e-03*power_flows['Optimal Excitation'],
-                        unit = 'W'
-        )
-
-        sankey.add(flows=[power_flows['Optimal Excitation'],
-                        power_flows['Unused Potential'],
-                        power_flows['Actual Excitation']], 
-                labels = ['Optimal Excitation', 
-                        'Unused Potential ', 
-                        'Excited'], 
-                orientations=[0, -1,  -0],#arrow directions,
-                pathlengths = [0.2,0.3,0.2],
-                trunklength = 1.0,
-                edgecolor = 'None',
-                facecolor = (0.253935, 0.265254, 0.529983, 1.0) #viridis(0.2)
-        )
-
-        sankey.add(flows=[-1*(power_flows['Absorbed'] + power_flows['Radiated']),
-                        power_flows['Radiated'],
-                        power_flows['Absorbed'],
-                        ], 
-                labels = ['Excited', 
-                        'Radiated', 
-                        ''], 
-                prior= (0),
-                connect=(2,0),
-                orientations=[0, -1,  -0],#arrow directions,
-                pathlengths = [0.2,0.3,0.2],
-                trunklength = 1.0,
-                edgecolor = 'None', 
-                facecolor = (0.127568, 0.566949, 0.550556, 1.0) #viridis (0.5)
-        )
-
-        sankey.add(flows=[-1*(power_flows['Mechanical (solver)']),
-                        power_flows['PTO Loss'],
-                        power_flows['Electrical (solver)'],
-                        ], 
-                labels = ['Mechanical', 
-                        'PTO-Loss' , 
-                        'Electrical'], 
-                prior= (1),
-                connect=(2,0),
-                orientations=[0, -1,  -0],#arrow directions,
-                pathlengths = [.2,0.3,0.2],
-                trunklength = 1.0,
-                edgecolor = 'None',
-                facecolor = (0.741388, 0.873449, 0.149561, 1.0) #viridis(0.9)
-        )
+def plot_power_flow(power_flows: dict[str, float])-> None:
+    """Plot power flow through a WEC as Sankey diagram.
 
-
-        diagrams = sankey.finish()
-        for diagram in diagrams:
-            for text in diagram.texts:
-                text.set_fontsize(10)
-        #remove text label from last entries
-        for diagram in diagrams[0:2]:
-                diagram.texts[2].set_text('')
+    Parameters
+    ----------
+    power_flows: dictionary
+        Power flow dictionary produced by for example by
+        :py:func:`wecopttool.utilities.calculate_power_flows`.
+        Required keys: 'Optimal Excitation', 'Radiated', 'Actual Excitation'
+                        'Electrical (solver)', 'Mechanical (solver)',
+                        'Absorbed', 'Unused Potential', 'PTO Loss'
 
 
-        plt.axis("off") 
-        plt.show()
+    """
+    fig = plt.figure(figsize = [8,4])
+    ax = fig.add_subplot(1, 1, 1,)
+    plt.viridis()
+    sankey = Sankey(ax=ax, 
+                    scale= 1/power_flows['Optimal Excitation'],
+                    offset= 0,
+                    format = '%.1f',
+                    shoulder = 0.02,
+                    tolerance=1e-03*power_flows['Optimal Excitation'],
+                    unit = 'W'
+    )
+
+    sankey.add(flows=[power_flows['Optimal Excitation'],
+                    power_flows['Unused Potential'],
+                    power_flows['Actual Excitation']], 
+            labels = ['Optimal Excitation', 
+                    'Unused Potential ', 
+                    'Excited'], 
+            orientations=[0, -1,  -0],#arrow directions,
+            pathlengths = [0.2,0.3,0.2],
+            trunklength = 1.0,
+            edgecolor = 'None',
+            facecolor = (0.253935, 0.265254, 0.529983, 1.0) #viridis(0.2)
+    )
+
+    sankey.add(flows=[
+            -1*(power_flows['Absorbed'] + power_flows['Radiated']),
+            power_flows['Radiated'],
+            power_flows['Absorbed'],
+            ], 
+            labels = ['Excited', 
+                    'Radiated', 
+                    ''], 
+            prior= (0),
+            connect=(2,0),
+            orientations=[0, -1,  -0],#arrow directions,
+            pathlengths = [0.2,0.3,0.2],
+            trunklength = 1.0,
+            edgecolor = 'None', 
+            facecolor = (0.127568, 0.566949, 0.550556, 1.0) #viridis(0.5)
+    )
+
+    sankey.add(flows=[-1*(power_flows['Mechanical (solver)']),
+                    power_flows['PTO Loss'],
+                    power_flows['Electrical (solver)'],
+                    ], 
+            labels = ['Mechanical', 
+                    'PTO-Loss' , 
+                    'Electrical'], 
+            prior= (1),
+            connect=(2,0),
+            orientations=[0, -1,  -0],#arrow directions,
+            pathlengths = [.2,0.3,0.2],
+            trunklength = 1.0,
+            edgecolor = 'None',
+            facecolor = (0.741388, 0.873449, 0.149561, 1.0) #viridis(0.9)
+    )
+
+
+    diagrams = sankey.finish()
+    for diagram in diagrams:
+        for text in diagram.texts:
+            text.set_fontsize(10)
+    #remove text label from last entries
+    for diagram in diagrams[0:2]:
+            diagram.texts[2].set_text('')
+
+
+    plt.axis("off") 
+    plt.show()
+
+
+# def add_zerofreq_to_xr(data):
+#     """Add a zero-frequency component to an :python:`xarray.Dataset`.  
+#       Frequency variable must be called :python:`omega`.    """    
+#     if not np.isclose(data.coords['omega'][0].values, 0):
+#         tmp = data.isel(omega=0).copy(deep=True) * 0   
+#         tmp['omega'] = tmp['omega'] * 0                      
+#         data = concat([tmp, data], dim='omega')
+#     return data
+
+# def natural_frequency(impedance: DataArray, freq: ArrayLike
+#                       ) -> tuple[ArrayLike, int]:
+#     """Find the natural frequency based on the lowest magnitude impedance,
+#        for restoring degrees of freedom (Heave, Roll, Pitch).
+
+#     Parameters
+#     ----------
+#     impedance: DataArray
+#         Complex intrinsic impedance matrix produced by
+#         :py:func:`wecopttool.hydrodynamic_impedance`.
+#         Dimensions: omega, radiating_dofs, influenced_dofs
+#     freq: list[float]
+#         Frequencies.
+
+#     Returns
+#     -------
+#     f_n: float
+#         Natural frequencies.
+#     ind: int
+#         Index of natural frequencies.
+#     """
+
+#     restoring_dofs = ['Heave','Roll','Pitch']
+#     indeces = [np.abs(impedance.loc[rdof,idof]).argmin(dim = 'omega') 
+#                 for rdof in impedance.radiating_dof 
+#                 for idof in impedance.influenced_dof 
+#                 if rdof == idof  #considering modes to be independent
+#                 and any([df in str(rdof.values) for df in restoring_dofs])]
+#     f_n = [freq[indx.values] for indx in indeces]
+
+#     return f_n, indeces
\ No newline at end of file

From c4c7630eecc12fae037605d23f61577c2048b606 Mon Sep 17 00:00:00 2001
From: dtgaebe <dtgaebe@sandia.gov>
Date: Mon, 11 Dec 2023 15:51:48 -0800
Subject: [PATCH 18/20] accounted for wave direction in bem_plot

---
 wecopttool/utilities.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/wecopttool/utilities.py b/wecopttool/utilities.py
index 7bbf6262..3063d82a 100644
--- a/wecopttool/utilities.py
+++ b/wecopttool/utilities.py
@@ -19,12 +19,15 @@
 import logging
 from pathlib import Path
 
-import numpy as np
+import autograd.numpy as np
+from autograd.numpy import ndarray
+
 from xarray import DataArray
 from numpy.typing import ArrayLike
 # from autograd.numpy import ndarray
 from xarray import DataArray, concat
 import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
 from matplotlib.sankey import Sankey
 
 
@@ -35,7 +38,8 @@
 
 
 
-def plot_hydrodynamic_coefficients(bem_data)-> None:
+def plot_hydrodynamic_coefficients(bem_data,
+                                   wave_dir: Optional[float] = 0.0)-> None:
     """Plots hydrodynamic coefficients (added mass, radiation damping,
        and wave excitation) based on BEM data.
 
@@ -46,8 +50,12 @@ def plot_hydrodynamic_coefficients(bem_data)-> None:
         Linear hydrodynamic coefficients obtained using the boundary
         element method (BEM) code Capytaine, with sign convention
         corrected.
+    wave_dir
+        Wave direction(s) to plot the
     
     """
+
+    bem_data = bem_data.sel(wave_direction = wave_dir, method='nearest')
     radiating_dofs = bem_data.radiating_dof.values
     influenced_dofs = bem_data.influenced_dof.values
 
@@ -268,7 +276,7 @@ def calculate_power_flows(wec,
     return power_flows
 
 
-def plot_power_flow(power_flows: dict[str, float])-> None:
+def plot_power_flow(power_flows: dict[str, float])-> Figure:
     """Plot power flow through a WEC as Sankey diagram.
 
     Parameters
@@ -349,10 +357,11 @@ def plot_power_flow(power_flows: dict[str, float])-> None:
     for diagram in diagrams[0:2]:
             diagram.texts[2].set_text('')
 
-
     plt.axis("off") 
     plt.show()
 
+    return fig
+
 
 # def add_zerofreq_to_xr(data):
 #     """Add a zero-frequency component to an :python:`xarray.Dataset`.  

From 4c4f60b7bece6c026529193e6f64ebd4cd1fa94b Mon Sep 17 00:00:00 2001
From: dtgaebe <dtgaebe@sandia.gov>
Date: Tue, 12 Dec 2023 11:33:41 -0800
Subject: [PATCH 19/20] added tests and changes during testing

---
 tests/test_utilities.py | 209 ++++++++++++++++++++++++++++++++++++++++
 wecopttool/utilities.py |  46 +++++----
 2 files changed, 236 insertions(+), 19 deletions(-)
 create mode 100644 tests/test_utilities.py

diff --git a/tests/test_utilities.py b/tests/test_utilities.py
new file mode 100644
index 00000000..c5de9cdc
--- /dev/null
+++ b/tests/test_utilities.py
@@ -0,0 +1,209 @@
+""" Unit tests for functions in the :python:`utilities.py` module.
+"""
+
+import pytest
+import numpy as np
+import xarray as xr
+from matplotlib.pyplot import Figure, Axes
+import wecopttool as wot
+from pytest import approx
+import capytaine as cpy
+
+
+
+# test function in the utilities.py
+
+
+
+@pytest.fixture(scope="module")
+def power_flows():
+    """Dictionary of power flows."""
+    pflows = {'Optimal Excitation': -100,
+                'Radiated': -20,
+                'Actual Excitation': -70,
+                'Electrical (solver)': -40,
+                'Mechanical (solver)': -50,
+                'Absorbed': -50,
+                'Unused Potential': -30,
+                'PTO Loss': -10
+    }
+    return pflows
+
+@pytest.fixture(scope="module")
+def f1():
+    """Fundamental frequency [Hz]."""
+    return 0.1
+
+
+@pytest.fixture(scope="module")
+def nfreq():
+    """Number of frequencies in frequency vector."""
+    return 5
+
+@pytest.fixture(scope="module")
+def ndof():
+    """Number of degrees of freedom."""
+    return 2
+
+@pytest.fixture(scope="module")
+def ndir():
+    """Number of wave directions."""
+    return 3
+
+@pytest.fixture(scope='module')
+def bem_data(f1, nfreq, ndof, ndir):
+    """Synthetic BEM data."""
+    # TODO - start using single BEM solution across entire test suite
+    coords = {
+        'omega': [2*np.pi*(ifreq+1)*f1 for ifreq in range(nfreq)],
+        'influenced_dof': [f'DOF_{idof+1}' for idof in range(ndof)],
+        'radiating_dof': [f'DOF_{idof+1}' for idof in range(ndof)],
+        'wave_direction': [2*np.pi/ndir*idir for idir in range(ndir)],
+    }
+    radiation_dims = ['omega', 'radiating_dof', 'influenced_dof']
+    excitation_dims = ['omega', 'influenced_dof', 'wave_direction']
+    hydrostatics_dims = ['radiating_dof', 'influenced_dof']
+
+    added_mass = np.ones([nfreq, ndof, ndof])
+    radiation_damping = np.ones([nfreq, ndof, ndof])
+    diffraction_force = np.ones([nfreq, ndof, ndir], dtype=complex) + 1j
+    Froude_Krylov_force = np.ones([nfreq, ndof, ndir], dtype=complex) + 1j
+    inertia_matrix = np.ones([ndof, ndof])
+    hydrostatic_stiffness = np.ones([ndof, ndof])
+    
+    data_vars = {
+        'added_mass': (radiation_dims, added_mass),
+        'radiation_damping': (radiation_dims, radiation_damping),
+        'diffraction_force': (excitation_dims, diffraction_force),
+        'Froude_Krylov_force': (excitation_dims, Froude_Krylov_force),
+        'inertia_matrix': (hydrostatics_dims, inertia_matrix),
+        'hydrostatic_stiffness': (hydrostatics_dims, hydrostatic_stiffness)
+    }
+    return xr.Dataset(data_vars=data_vars, coords=coords)
+
+@pytest.fixture(scope='module')
+def intrinsic_impedance(bem_data):
+    bem_data = wot.add_linear_friction(bem_data)
+    intrinsic_impedance = wot.hydrodynamic_impedance(bem_data)
+    return intrinsic_impedance
+
+@pytest.fixture(scope='module')
+def pi_controller_pto():
+    """Basic PTO: proportional-integral (PI) controller, 1DOF, mechanical
+    power."""
+    ndof = 1
+    pto = wot.pto.PTO(ndof=ndof, kinematics=np.eye(ndof),
+                      controller=wot.pto.controller_pi,
+                      names=["PI controller PTO"])
+    return pto
+
+@pytest.fixture(scope='module')
+def regular_wave(f1, nfreq):
+    """Single frequency wave"""
+    wfreq = 0.3
+    wamp = 0.0625
+    wphase = 0
+    wdir = 0
+    waves = wot.waves.regular_wave(f1, nfreq, wfreq, wamp, wphase, wdir)
+    return waves
+
+@pytest.fixture(scope="module")
+def fb():
+    """Capytaine FloatingBody object"""
+    try:
+        import wecopttool.geom as geom
+    except ImportError:
+        pytest.skip(
+            'Skipping integration tests due to missing optional geometry ' +
+            'dependencies. Run `pip install wecopttool[geometry]` to run ' +
+            'these tests.'
+            )
+    mesh_size_factor = 0.5
+    wb = geom.WaveBot()
+    mesh = wb.mesh(mesh_size_factor)
+    fb = cpy.FloatingBody.from_meshio(mesh, name="WaveBot")
+    fb.add_translation_dof(name="Heave")
+    return fb
+
+
+@pytest.fixture(scope="module")
+def wb_bem(f1, nfreq, fb):
+    """Boundary elemement model (Capytaine) results"""
+    freq = wot.frequency(f1, nfreq, False)
+    return wot.run_bem(fb, freq)
+
+@pytest.fixture(scope='class')
+def wb_hydro_impedance(wb_bem):
+    """Intrinsic hydrodynamic impedance"""
+    hd = wot.add_linear_friction(wb_bem)
+    hd = wot.check_linear_damping(hd)
+    Zi = wot.hydrodynamic_impedance(hd)
+    return Zi
+
+
+
+
+def test_plot_hydrodynamic_coefficients(bem_data,ndof):
+    bem_figure_list = wot.utilities.plot_hydrodynamic_coefficients(bem_data)
+    correct_len = ndof*(ndof+1)/2   #using only the subdiagonal elements
+    #added mass
+    fig_am = bem_figure_list[0][0]
+    assert correct_len == len(fig_am.axes)
+    assert isinstance(fig_am,Figure)
+    #radiation damping
+    fig_rd = bem_figure_list[1][0]
+    assert correct_len == len(fig_rd.axes)
+    assert isinstance(fig_rd,Figure)
+    #radiation damping
+    fig_ex = bem_figure_list[2][0]
+    assert ndof == len(fig_ex.axes)
+    assert isinstance(fig_ex,Figure)
+
+def test_plot_bode_impedance(intrinsic_impedance, ndof):
+    fig_Zi, axes_Zi = wot.utilities.plot_bode_impedance(intrinsic_impedance)    
+  
+    assert 2*ndof*ndof == len(fig_Zi.axes)
+    assert isinstance(fig_Zi,Figure)
+    assert all([isinstance(ax, Axes) for ax in np.reshape(axes_Zi,-1)])
+
+
+def test_plot_power_flow(power_flows):
+    fig_sankey, ax_sankey = wot.utilities.plot_power_flow(power_flows)    
+  
+    assert isinstance(fig_sankey, Figure)
+    assert isinstance(ax_sankey, Axes) 
+
+def test_calculate_power_flow(wb_bem,
+                              regular_wave,
+                              pi_controller_pto,
+                              wb_hydro_impedance):
+    """PI controller matches optimal for any regular wave, 
+        thus we check if the radiated power is equal the absorber power
+        and if the Optimal excitation is equal the actual excitation"""
+
+    f_add = {"PTO": pi_controller_pto.force_on_wec}
+    wec = wot.WEC.from_bem(wb_bem, f_add=f_add)
+
+    res = wec.solve(waves=regular_wave,
+                    obj_fun=pi_controller_pto.average_power,
+                    nstate_opt=2,
+                    x_wec_0=1e-1*np.ones(wec.nstate_wec),
+                    x_opt_0=[-1e3, 1e4],
+                    scale_x_wec=1e2,
+                    scale_x_opt=1e-3,
+                    scale_obj=1e-2,
+                    optim_options={'maxiter': 50},
+                    bounds_opt=((-1e4, 0), (0, 2e4),)
+                    )
+
+    pflows = wot.utilities.calculate_power_flows(wec, 
+                          pi_controller_pto, 
+                          res[0], 
+                          regular_wave.sel(realization = 0), 
+                          wb_hydro_impedance)
+
+    assert pflows['Absorbed'] == approx(pflows['Radiated'], rel=1e-4)
+    assert pflows['Optimal Excitation'] == approx(pflows['Actual Excitation'], rel=1e-4)
+
+
+
diff --git a/wecopttool/utilities.py b/wecopttool/utilities.py
index 3063d82a..357a451d 100644
--- a/wecopttool/utilities.py
+++ b/wecopttool/utilities.py
@@ -28,6 +28,8 @@
 from xarray import DataArray, concat
 import matplotlib.pyplot as plt
 from matplotlib.figure import Figure
+from matplotlib.axes import Axes
+
 from matplotlib.sankey import Sankey
 
 
@@ -39,7 +41,8 @@
 
 
 def plot_hydrodynamic_coefficients(bem_data,
-                                   wave_dir: Optional[float] = 0.0)-> None:
+                                   wave_dir: Optional[float] = 0.0
+                                   )-> list(tuple(Figure, Axes)):
     """Plots hydrodynamic coefficients (added mass, radiation damping,
        and wave excitation) based on BEM data.
 
@@ -131,12 +134,12 @@ def plot_hydrodynamic_coefficients(bem_data,
                 fig_am.delaxes(ax_am[i, j])
                 fig_rd.delaxes(ax_rd[i, j])
     fig_ex.legend(ex_handles, ex_labels, loc=(0.08, 0), ncol=2, frameon=False)
-
+    return [(fig_am,ax_am), (fig_rd,ax_rd), (fig_ex,ax_ex)]
 
 def plot_bode_impedance(impedance: DataArray, 
-                        title: Optional[str]= None,
+                        title: Optional[str]= '',
                         #plot_natural_freq: Optional[bool] = False,
-)-> None:
+)-> tuple(Figure, Axes):
     """Plot Bode graph from wecoptool impedance data array.
 
     Parameters
@@ -254,11 +257,11 @@ def calculate_power_flows(wec,
         P_e.append((1/4)*(Fe_FD_t@np.conj(U_FD) + U_FD_t@np.conj(Fe_FD)))
 
     power_flows = {
-        'Optimal Excitation' : 2* np.sum(np.real(P_max)),#eq 6.68 positive inflow
-        'Radiated': -1*np.sum(np.real(P_r)), #negative because "out"flow
-        'Actual Excitation': -1*np.sum(np.real(P_e)), #negative because "out"flow
-        'Electrical (solver)': P_elec,  #solver determins sign
-        'Mechanical (solver)': P_mech, #solver determins sign
+        'Optimal Excitation' : -2* np.sum(np.real(P_max)),#eq 6.68 
+        'Radiated': -1*np.sum(np.real(P_r)), 
+        'Actual Excitation': -1*np.sum(np.real(P_e)), 
+        'Electrical (solver)': P_elec, 
+        'Mechanical (solver)': P_mech, 
                   }
 
     power_flows['Absorbed'] =  (
@@ -266,7 +269,7 @@ def calculate_power_flows(wec,
         - power_flows['Radiated']
             )
     power_flows['Unused Potential'] =  (
-        -1*power_flows['Optimal Excitation'] 
+        power_flows['Optimal Excitation'] 
         - power_flows['Actual Excitation']
             )
     power_flows['PTO Loss'] = (
@@ -276,7 +279,7 @@ def calculate_power_flows(wec,
     return power_flows
 
 
-def plot_power_flow(power_flows: dict[str, float])-> Figure:
+def plot_power_flow(power_flows: dict[str, float])-> tuple(Figure, Axes):
     """Plot power flow through a WEC as Sankey diagram.
 
     Parameters
@@ -290,19 +293,24 @@ def plot_power_flow(power_flows: dict[str, float])-> Figure:
 
 
     """
-    fig = plt.figure(figsize = [8,4])
-    ax = fig.add_subplot(1, 1, 1,)
-    plt.viridis()
+    # fig = plt.figure(figsize = [8,4])
+    # ax = fig.add_subplot(1, 1, 1,)
+    fig, ax = plt.subplots(1, 1,
+        tight_layout=True, 
+        figsize=(8, 4), 
+        )
+
+    # plt.viridis()
     sankey = Sankey(ax=ax, 
-                    scale= 1/power_flows['Optimal Excitation'],
+                    scale= -1/power_flows['Optimal Excitation'],
                     offset= 0,
                     format = '%.1f',
                     shoulder = 0.02,
-                    tolerance=1e-03*power_flows['Optimal Excitation'],
+                    tolerance=-1e-03*power_flows['Optimal Excitation'],
                     unit = 'W'
     )
 
-    sankey.add(flows=[power_flows['Optimal Excitation'],
+    sankey.add(flows=[-1*power_flows['Optimal Excitation'],
                     power_flows['Unused Potential'],
                     power_flows['Actual Excitation']], 
             labels = ['Optimal Excitation', 
@@ -358,9 +366,9 @@ def plot_power_flow(power_flows: dict[str, float])-> Figure:
             diagram.texts[2].set_text('')
 
     plt.axis("off") 
-    plt.show()
+    # plt.show()
 
-    return fig
+    return fig, ax
 
 
 # def add_zerofreq_to_xr(data):

From 1413321d13ddedcc050e86fbb4ea3d2811296959 Mon Sep 17 00:00:00 2001
From: dtgaebe <dtgaebe@sandia.gov>
Date: Tue, 12 Dec 2023 13:54:49 -0800
Subject: [PATCH 20/20] corrected sign convention in tut4

---
 examples/tutorial_4_Pioneer.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/tutorial_4_Pioneer.ipynb b/examples/tutorial_4_Pioneer.ipynb
index 455fbedb..e1ef6055 100644
--- a/examples/tutorial_4_Pioneer.ipynb
+++ b/examples/tutorial_4_Pioneer.ipynb
@@ -772,11 +772,11 @@
     "P_radiated = ((1/2)*(Rad_res * np.abs(Vel)**2 ).squeeze().sum('omega').item()) # after Falnes Eq. 6.4\n",
     "P_excited= (1/4)*(Fex*np.conjugate(Vel) + np.conjugate(Fex)*Vel ).squeeze().sum('omega').item() # after Falnes Eq. 6.3\n",
     "\n",
-    "power_flows = {'Optimal Excitation' : 2* (np.real(P_max_absorbable)),    #positive because the only inflow\n",
-    "            'Radiated': -1*(np.real(P_radiated)), #negative because \"out\"flow\n",
-    "            'Actual Excitation': -1*(np.real(P_excited)), #negative because \"out\"flow\n",
+    "power_flows = {'Optimal Excitation' : -2* (np.real(P_max_absorbable)),    \n",
+    "            'Radiated': -1*(np.real(P_radiated)), \n",
+    "            'Actual Excitation': -1*(np.real(P_excited)), \n",
     "}\n",
-    "power_flows['Unused Potential'] =  -1*power_flows['Optimal Excitation'] - power_flows['Actual Excitation']\n",
+    "power_flows['Unused Potential'] =  power_flows['Optimal Excitation'] - power_flows['Actual Excitation']\n",
     "power_flows['Absorbed'] =  power_flows['Actual Excitation'] - power_flows['Radiated']  # after Falnes Eq. 6.2 \n",
     "#absorbed by WEC-PTO (difference between actual excitation power and actual radiated power needs to be absorbed by PTO)\n",
     "\n"