diff --git a/bladex/__init__.py b/bladex/__init__.py
index a035827..e527a59 100644
--- a/bladex/__init__.py
+++ b/bladex/__init__.py
@@ -1,16 +1,18 @@
 """
 BladeX init
 """
-__all__ = ['profilebase', 'nacaprofile', 'customprofile', 'reversepropeller', 'blade', 'shaft', 'propeller', 'deform', 'params', 'ndinterpolator']
+__all__ = ['ProfileInterface', 'NacaProfile', 'CustomProfile',
+        'ReversePropeller', 'Blade', 'Shaft', 'Propeller', 'Deformation',
+        'ParamFile', 'RBF', 'reconstruct_f', 'scipy_bspline']
 
 from .meta import *
-from .profilebase import ProfileBase
-from .nacaprofile import NacaProfile
-from .customprofile import CustomProfile
+from .profile import ProfileInterface
+from .profile import NacaProfile
+from .profile import CustomProfile
 from .blade import Blade
 from .shaft import Shaft
 from .propeller import Propeller
 from .deform import Deformation
 from .params import ParamFile
-from .ndinterpolator import RBF, reconstruct_f, scipy_bspline 
+from .ndinterpolator import RBF, reconstruct_f, scipy_bspline
 from .reversepropeller import ReversePropeller
diff --git a/bladex/blade.py b/bladex/blade.py
index 488f144..e516cb5 100644
--- a/bladex/blade.py
+++ b/bladex/blade.py
@@ -479,7 +479,7 @@ def plot(self, elev=None, azim=None, ax=None, outfile=None):
         >>> blade_2.rotate(rot_angle_deg=72)
 
         >>> fig = plt.figure()
-        >>> ax = fig.gca(projection=Axes3D.name)
+        >>> ax = fig.add_subplot(projection='3d')
         >>> blade_1.plot(ax=ax)
         >>> blade_2.plot(ax=ax)
 
@@ -504,7 +504,7 @@ def plot(self, elev=None, azim=None, ax=None, outfile=None):
             ax = ax
         else:
             fig = plt.figure()
-            ax = fig.gca(projection=Axes3D.name)
+            ax = fig.add_subplot(projection='3d')
         ax.set_aspect('auto')
 
         for i in range(self.n_sections):
diff --git a/bladex/profile/__init__.py b/bladex/profile/__init__.py
new file mode 100644
index 0000000..3b44d32
--- /dev/null
+++ b/bladex/profile/__init__.py
@@ -0,0 +1,8 @@
+"""
+Profile init
+"""
+__all__ = ['ProfileInterface', 'NacaProfile', 'CustomProfile']
+
+from .profileinterface import ProfileInterface
+from .nacaprofile import NacaProfile
+from .customprofile import CustomProfile
diff --git a/bladex/customprofile.py b/bladex/profile/customprofile.py
similarity index 51%
rename from bladex/customprofile.py
rename to bladex/profile/customprofile.py
index 23c0e97..301d3ce 100644
--- a/bladex/customprofile.py
+++ b/bladex/profile/customprofile.py
@@ -1,6 +1,6 @@
 """
-Derived module from profilebase.py to provide the airfoil coordinates for a general
-profile. Input data can be:
+Derived module from profilebase.py to provide the airfoil coordinates for a
+general profile. Input data can be:
     - the coordinates arrays;
     - the chord percentages, the associated nondimensional camber and thickness,
       the real values of chord lengths, camber and thickness associated to the
@@ -8,15 +8,14 @@
 """
 
 import numpy as np
-from .profilebase import ProfileBase
-from scipy.optimize import newton
+from .profileinterface import ProfileInterface
 
 
-class CustomProfile(ProfileBase):
+class CustomProfile(ProfileInterface):
     """
-    Provide custom profile, given the airfoil coordinates or the airfoil parameters,
-    i.e. , chord percentages and length, nondimensional and maximum camber,
-    nondimensional and maximum thickness.
+    Provide custom profile, given the airfoil coordinates or the airfoil
+    parameters, i.e. , chord percentages and length, nondimensional and
+    maximum camber, nondimensional and maximum thickness.
 
     If coordinates are directly given as input:
 
@@ -31,101 +30,73 @@ class CustomProfile(ProfileBase):
 
     If section parameters are given as input:
 
-    :param numpy.ndarray chord_perc: 1D array that contains the chord percentages
-        of an airfoil section for which camber and thickness are measured
-    :param numpy.ndarray camber_perc: 1D array that contains the camber percentage
-       of an airfoil section at all considered chord percentages. The percentage is
-       taken with respect to the section maximum camber
+    :param numpy.ndarray chord_perc: 1D array that contains the chord
+        percentages of an airfoil section for which camber and thickness are
+        measured
+    :param numpy.ndarray camber_perc: 1D array that contains the camber
+        percentage of an airfoil section at all considered chord percentages.
+        The percentage is taken with respect to the section maximum camber
     :param numpy.ndarray thickness_perc: 1D array that contains the thickness
        percentage of an airfoil section at all considered chord percentages.
        The percentage is with respect to the section maximum thickness
-    :param float chord_len: the length of the chord line of a certain airfoil section
     :param float camber_max: the maximum camber at a certain airfoil section
-    :param float thickness_max: the maximum thickness at a certain airfoil section
+    :param float chord_len: the chord length at a certain airfoil section
+    :param float thickness_max: the maximum thickness at a certain airfoil
+    section
     """
     def __init__(self, **kwargs):
         super(CustomProfile, self).__init__()
 
-        if all([key in ['xup', 'yup', 'xdown', 'ydown'] for key in kwargs]):
-            self.xup_coordinates = kwargs['xup']
-            self.yup_coordinates = kwargs['yup']
-            self.xdown_coordinates = kwargs['xdown']
-            self.ydown_coordinates = kwargs['ydown']
+        if set(kwargs.keys()) == set(
+                ['xup', 'yup', 'xdown', 'ydown']):
+            self._xup_coordinates = kwargs['xup']
+            self._yup_coordinates = kwargs['yup']
+            self._xdown_coordinates = kwargs['xdown']
+            self._ydown_coordinates = kwargs['ydown']
             self._check_coordinates()
+            self.generate_parameters(convention='british')
 
         elif set(kwargs.keys()) == set([
-                'chord_perc', 'camber_perc', 'thickness_perc', 'chord_len',
-                'camber_max', 'thickness_max'
+                'chord_perc', 'camber_perc', 'thickness_perc',
+                'camber_max', 'thickness_max' , 'chord_len'
         ]):
 
-            self.chord_percentage = kwargs['chord_perc']
-            self.camber_percentage = kwargs['camber_perc']
-            self.thickness_percentage = kwargs['thickness_perc']
-            self.chord_len = kwargs['chord_len']
-            self.camber_max = kwargs['camber_max']
-            self.thickness_max = kwargs['thickness_max']
+            self._chord_percentage = kwargs['chord_perc']
+            self._camber_percentage = kwargs['camber_perc']
+            self._thickness_percentage = kwargs['thickness_perc']
+            self._camber_max = kwargs['camber_max']
+            self._thickness_max = kwargs['thickness_max']
+            self._chord_length = kwargs['chord_len']
             self._check_parameters()
+            self.generate_coordinates(convention='british')
 
         else:
             raise RuntimeError(
                 """Input arguments should be the section coordinates
-                (xup, yup, xdown, ydown) and chord_len (optional)
-                or the section parameters (camber_perc, thickness_perc,
-                camber_max, thickness_max, chord_perc, chord_len).""")
+                (xup, yup, xdown, ydown) or the section parameters
+                (camber_perc, thickness_perc,
+                camber_max, thickness_max, chord_perc).""")
 
+    def generate_parameters(self, convention='british'):
+        return super().generate_parameters(convention)
 
-    def _check_parameters(self):
+    def generate_coordinates(self, convention='british'):
+        if convention == 'british':
+            self._compute_coordinates_british_convention()
+        elif convention == 'american':
+            self._compute_coordinates_american_convention()
+
+    def _compute_coordinates_british_convention(self):
         """
-        Private method that checks whether the airfoil parameters defined
-        are provided correctly.
-        In particular, the chord, camber and thickness percentages are
-        consistent and have the same length.
+        Compute the coordinates of points on upper and lower profile according
+        to the British convention.
         """
-
-        if self.chord_percentage is None:
-            raise ValueError('object "chord_perc" refers to an empty array.')
-        if self.camber_percentage is None:
-            raise ValueError('object "camber_perc" refers to an empty array.')
-        if self.thickness_percentage is None:
-            raise ValueError(
-                'object "thickness_perc" refers to an empty array.')
-        if self.chord_len is None:
-            raise ValueError('object "chord_len" refers to an empty array.')
-        if self.camber_max is None:
-            raise ValueError('object "camber_max" refers to an empty array.')
-        if self.thickness_max is None:
-            raise ValueError('object "thickness_max" refers to an empty array.')
-
-        if not isinstance(self.chord_percentage, np.ndarray):
-            self.chord_percentage = np.asarray(self.chord_percentage,
-                                               dtype=float)
-        if not isinstance(self.camber_percentage, np.ndarray):
-            self.camber_percentage = np.asarray(self.camber_percentage,
-                                                dtype=float)
-        if not isinstance(self.thickness_percentage, np.ndarray):
-            self.thickness_percentage = np.asarray(self.thickness_percentage,
-                                                   dtype=float)
-        if not isinstance(self.chord_len, np.ndarray):
-            self.chord_len = np.asarray(self.chord_len, dtype=float)
-        if not isinstance(self.camber_max, np.ndarray):
-            self.camber_max = np.asarray(self.camber_max, dtype=float)
-        if not isinstance(self.thickness_max, np.ndarray):
-            self.thickness_max = np.asarray(self.thickness_max, dtype=float)
-        if self.chord_len < 0:
-            raise ValueError('chord_len must be positive.')
-        if self.camber_max < 0:
-            raise ValueError('camber_max must be positive.')
-        if self.thickness_max < 0:
-            raise ValueError('thickness_max must be positive.')
-
-        # Therefore the arrays camber_percentage and thickness_percentage
-        # should have the same length of chord_percentage, equal to n_pos,
-        # which is the number of cuts along the chord line
-        if self.camber_percentage.shape != self.chord_percentage.shape:
-            raise ValueError('camber_perc and chord_perc must have same shape.')
-        if self.thickness_percentage.shape != self.chord_percentage.shape:
-            raise ValueError(
-                'thickness_perc and chord_perc must have same shape.')
+        self._xup_coordinates = self.chord_percentage*self.chord_length
+        self._xdown_coordinates = self._xup_coordinates.copy()
+        self._yup_coordinates = (self.camber_percentage*self.camber_max +
+                self.thickness_max/2*self.thickness_percentage)
+        self._ydown_coordinates = (self.camber_percentage*self.camber_max -
+                self.thickness_max/2*self.thickness_percentage)
 
     def _compute_orth_camber_coordinates(self):
         """
@@ -140,14 +111,15 @@ def _compute_orth_camber_coordinates(self):
         m = np.zeros(n_pos)
         for i in range(1, n_pos, 1):
             m[i] = (self.camber_percentage[i]-
-                self.camber_percentage[i-1])/(self.chord_percentage[i]-
-                    self.chord_percentage[i-1])*self.camber_max/self.chord_len
+                    self.camber_percentage[i-1])*self.camber_max/(
+                    self.chord_percentage[i]-
+                    self.chord_percentage[i-1])/self.chord_length
 
         m_angle = np.arctan(m)
 
-        xup_tmp = (self.chord_percentage*self.chord_len -
+        xup_tmp = (self.chord_percentage*self.chord_length -
                 self.thickness_percentage*np.sin(m_angle)*self.thickness_max/2)
-        xdown_tmp = (self.chord_percentage*self.chord_len +
+        xdown_tmp = (self.chord_percentage*self.chord_length +
                 self.thickness_percentage*np.sin(m_angle)*self.thickness_max/2)
         yup_tmp = (self.camber_percentage*self.camber_max +
                 self.thickness_max/2*self.thickness_percentage*np.cos(m_angle))
@@ -160,94 +132,68 @@ def _compute_orth_camber_coordinates(self):
 
         return [xup_tmp, xdown_tmp, yup_tmp, ydown_tmp]
 
-
-
-    def generate_coordinates(self):
+    def _compute_coordinates_american_convention(self):
         """
-        Method that generates the coordinates of a general airfoil
-        profile, starting from the chord percentages and the related
-        nondimensional camber and thickness, the maximum values of thickness
-        and camber.
+        Compute the coordinates of points on upper and lower profile according
+        to the American convention.
         """
+        [self._xup_coordinates, self._xdown_coordinates, self._yup_coordinates,
+            self._ydown_coordinates] = self._compute_orth_camber_coordinates()
 
-        [self.xup_coordinates, self.xdown_coordinates, self.yup_coordinates,
-            self.ydown_coordinates] = self._compute_orth_camber_coordinates()
-
-        self.ydown_coordinates = self.ydown_curve(
-                self.chord_len*(self.chord_percentage).reshape(-1,1)).reshape(
-                        self.chord_percentage.shape)
-        self.xup_coordinates = self.chord_percentage * self.chord_len
-        self.xdown_coordinates = self.xup_coordinates.copy()
-        self.yup_coordinates = (2*self.camber_max*self.camber_percentage -
+        self._ydown_coordinates = self.ydown_curve(
+                (self.chord_percentage*self.chord_length).reshape(-1,1)
+                    ).reshape(self.chord_percentage.shape)
+        self._yup_coordinates = (2*self.camber_max*self.camber_percentage -
                 self.ydown_coordinates)
 
-        self.yup_coordinates[0] = 0
-        self.yup_coordinates[-1] = 0
-        self.ydown_coordinates[0] = 0
-        self.ydown_coordinates[-1] = 0
-
-
-    def adimensionalize(self):
-        """
-        Rescale coordinates of upper and lower profiles of section such that
-        coordinates on x axis are between 0 and 1.
-        """
-        factor = abs(self.xup_coordinates[-1]-self.xup_coordinates[0])
-        self.yup_coordinates *= 1/factor
-        self.xdown_coordinates *= 1/factor
-        self.ydown_coordinates *= 1/factor
-        self.xup_coordinates *= 1/factor
-
-    def generate_parameters(self):
+    def _check_parameters(self):
         """
-        Method that generates the parameters of a general airfoil profile
-        (chord length, chord percentages, camber max, thickness max, camber and
-        thickness percentages), starting from the upper and lower
-        coordinates of the section profile.
+        Private method that checks whether the airfoil parameters defined
+        are provided correctly.
+        In particular, the chord, camber and thickness percentages are
+        consistent and have the same length.
         """
-        n_pos = self.xup_coordinates.shape[0]
 
-        self.chord_len = abs(np.max(self.xup_coordinates)-
-                np.min(self.xup_coordinates))
-        self.chord_percentage = self.xup_coordinates/self.chord_len
-        camber = (self.yup_coordinates + self.ydown_coordinates)/2
-        self.camber_max = abs(np.max(camber)-np.min(camber))
-        self.camber_percentage = camber/self.camber_max
+        if self._chord_percentage is None:
+            raise ValueError('object "chord_perc" refers to an empty array.')
+        if self._camber_percentage is None:
+            raise ValueError('object "camber_perc" refers to an empty array.')
+        if self._thickness_percentage is None:
+            raise ValueError(
+                'object "thickness_perc" refers to an empty array.')
+        if self._camber_max is None:
+            raise ValueError('object "camber_max" refers to an empty array.')
+        if self._thickness_max is None:
+            raise ValueError('object "thickness_max" refers to an empty array.')
+        if self._chord_length is None:
+            raise ValueError('object "chord_length" refers to an empty array.')
 
-        n_pos = self.chord_percentage.shape[0]
-        m = np.zeros(n_pos)
-        for i in range(1, n_pos, 1):
-            m[i] = (self.camber_percentage[i]-
-                self.camber_percentage[i-1])/(self.chord_percentage[i]-
-                    self.chord_percentage[i-1])*self.camber_max/self.chord_len
-        m_angle = np.arctan(m)
+        if not isinstance(self._chord_percentage, np.ndarray):
+            self._chord_percentage = np.asarray(self._chord_percentage,
+                                               dtype=float)
+        if not isinstance(self._camber_percentage, np.ndarray):
+            self._camber_percentage = np.asarray(self._camber_percentage,
+                                                dtype=float)
+        if not isinstance(self.thickness_percentage, np.ndarray):
+            self._thickness_percentage = np.asarray(self.thickness_percentage,
+                                                   dtype=float)
+        if not isinstance(self.camber_max, np.ndarray):
+            self._camber_max = np.asarray(self._camber_max, dtype=float)
+        if not isinstance(self.thickness_max, np.ndarray):
+            self._thickness_max = np.asarray(self._thickness_max, dtype=float)
+        if self._camber_max < 0:
+            raise ValueError('camber_max must be positive.')
+        if self._thickness_max < 0:
+            raise ValueError('thickness_max must be positive.')
 
-        # generating temporary profile coordinates orthogonal to the camber
-        # line
-        ind_horizontal_camber = (np.sin(m_angle)==0)
-        def eq_to_solve(x):
-            spline_curve = self.ydown_curve(x.reshape(-1,1)).reshape(
-                    x.shape[0],)
-            line_orth_camber = (camber[~ind_horizontal_camber] +
-                    np.cos(m_angle[~ind_horizontal_camber])/
-                    np.sin(m_angle[~ind_horizontal_camber])*(self.chord_len*
-                        self.chord_percentage[~ind_horizontal_camber]-x))
-            return spline_curve - line_orth_camber
-
-        xdown_tmp = self.xdown_coordinates.copy()
-        xdown_tmp[~ind_horizontal_camber] = newton(eq_to_solve,
-                xdown_tmp[~ind_horizontal_camber])
-        xup_tmp = 2*self.chord_len*self.chord_percentage - xdown_tmp
-        ydown_tmp = self.ydown_curve(xdown_tmp.reshape(-1,1)).reshape(
-                xdown_tmp.shape[0],)
-        yup_tmp = 2*self.camber_max*self.camber_percentage - ydown_tmp
-        if xup_tmp[1]<self.xup_coordinates[0]:
-            xup_tmp[1], xdown_tmp[1] = xup_tmp[2], xdown_tmp[2]
-            yup_tmp[1], ydown_tmp[1] = yup_tmp[2], ydown_tmp[2]
-
-        thickness = np.sqrt((xup_tmp-xdown_tmp)**2 + (yup_tmp-ydown_tmp)**2)
-        self.thickness_max = np.max(thickness)
-        self.thickness_percentage = thickness/self.thickness_max
+        # Therefore the arrays camber_percentage and thickness_percentage
+        # should have the same length of chord_percentage, equal to n_pos,
+        # which is the number of cuts along the chord line
+        if self._camber_percentage.shape != self._chord_percentage.shape:
+            raise ValueError('camber_perc and chord_perc must have same shape.')
+        if self._thickness_percentage.shape != self._chord_percentage.shape:
+            raise ValueError(
+                'thickness_perc and chord_perc must have same shape.')
 
     def _check_coordinates(self):
         """
@@ -301,12 +247,23 @@ def _check_coordinates(self):
         # The condition yup_coordinates >= ydown_coordinates must be satisfied
         # element-wise to the whole elements in the mentioned arrays.
         if not all(
-                np.greater_equal(self.yup_coordinates, self.ydown_coordinates)):
+                np.greater_equal(self.yup_coordinates[1:-1],
+                    self.ydown_coordinates[1:-1])):
             raise ValueError('yup is not >= ydown elementwise.')
 
-        if not self.xdown_coordinates[0] == self.xup_coordinates[0]:
+        if not np.isclose(self.xdown_coordinates[0], self.xup_coordinates[0],
+                atol=1e-6):
             raise ValueError('(xdown[0]=xup[0]) not satisfied.')
-        if not np.allclose(self.ydown_coordinates[0], self.yup_coordinates[0]):
-            raise ValueError('(ydown[0]=yup[0]) not satisfied.')
-        if not self.xdown_coordinates[-1] == self.xup_coordinates[-1]:
+
+        if not np.isclose(self.xdown_coordinates[-1], self.xup_coordinates[-1],
+                atol=1e-6):
             raise ValueError('(xdown[-1]=xup[-1]) not satisfied.')
+
+        if not np.isclose(self.ydown_coordinates[0], self.yup_coordinates[0],
+                atol=1e-6):
+            raise ValueError('(ydown[0]=yup[0]) not satisfied.')
+
+        if not np.isclose(self.ydown_coordinates[-1], self.yup_coordinates[-1],
+                atol=1e-6):
+            raise ValueError('(ydown[-1]=yup[-1]) not satisfied.')
+
diff --git a/bladex/nacaprofile.py b/bladex/profile/nacaprofile.py
similarity index 95%
rename from bladex/nacaprofile.py
rename to bladex/profile/nacaprofile.py
index 7e39c23..726d4db 100644
--- a/bladex/nacaprofile.py
+++ b/bladex/profile/nacaprofile.py
@@ -1,12 +1,12 @@
 """
-Derived module from profilebase.py to provide the airfoil coordinates for standard
-Naca profiles.
+Derived module from profilebase.py to provide the airfoil coordinates for
+standard Naca profiles.
 """
 from scipy.interpolate import splev, splrep
 import numpy as np
-from .profilebase import ProfileBase
+from .profileinterface import ProfileInterface
 
-class NacaProfile(ProfileBase):
+class NacaProfile(ProfileInterface):
     """
     Generate 4- and 5-digit NACA profiles.
 
@@ -24,29 +24,29 @@ class NacaProfile(ProfileBase):
 
         - P/10: indicates the location of the maximum camber measured from the
           leading edge. The location is normalized by the chord length.
-        
+
         - TT/100: the maximum thickness as fraction of the chord length.
 
     The profile 00TT refers to a symmetrical NACA airfoil.
 
     The NACA five-digit series describes more complex airfoil shapes.
     Its format is: LPSTT, where:
-        
+
         - L: the theoretical optimum lift coefficient at ideal
           angle-of-attack = 0.15*L
-        
+
         - P: the x-coordinate of the point of maximum camber
           (max camber at x = 0.05*P)
-        
+
         - S: indicates whether the camber is simple (S=0) or reflex (S=1)
           TT/100: the maximum thickness in percent of chord, as in a four-digit
           NACA airfoil code
 
     References:
-    
+
     - Moran, Jack (2003). An introduction to theoretical and computational
       aerodynamics. Dover. p. 7. ISBN 0-486-42879-6.
-    
+
     - Abbott, Ira (1959). Theory of Wing Sections: Including a Summary of
       Airfoil Data. New York: Dover Publications. p. 115. ISBN 978-0486605869.
 
@@ -68,7 +68,8 @@ def __init__(self, digits, n_points=240, cosine_spacing=True):
         self.n_points = n_points
         self.cosine_spacing = cosine_spacing
         self._check_args()
-        self._generate_coordinates()
+        self.generate_coordinates()
+        self.generate_parameters(convention='british')
 
     def _check_args(self):
         """
@@ -84,7 +85,10 @@ def _check_args(self):
         if self.n_points < 0:
             raise ValueError('n_points must be positive.')
 
-    def _generate_coordinates(self):
+    def generate_parameters(self, convention='british'):
+        return super().generate_parameters(convention)
+
+    def generate_coordinates(self):
         """
         Private method that generates the coordinates of the NACA 4 or 5 digits
         airfoil profile. The method assumes a zero-thickness trailing edge, and
@@ -214,4 +218,5 @@ def _generate_coordinates(self):
                     self.ydown_coordinates = yc - yt
 
         else:
-            raise Exception
\ No newline at end of file
+            raise Exception
+
diff --git a/bladex/profilebase.py b/bladex/profile/profileinterface.py
similarity index 79%
rename from bladex/profilebase.py
rename to bladex/profile/profileinterface.py
index a595a47..fcd9bf3 100644
--- a/bladex/profilebase.py
+++ b/bladex/profile/profileinterface.py
@@ -2,13 +2,14 @@
 Base module that provides essential tools and transformations on airfoils.
 """
 
+from abc import ABC, abstractmethod
 import numpy as np
 import matplotlib.pyplot as plt
-from .ndinterpolator import reconstruct_f
+from scipy.optimize import newton
+from ..ndinterpolator import reconstruct_f
 from scipy.interpolate import RBFInterpolator
 
-
-class ProfileBase(object):
+class ProfileInterface(ABC):
     """
     Base sectional profile of the propeller blade.
 
@@ -37,16 +38,167 @@ class ProfileBase(object):
     :param numpy.ndarray trailing_edge: 2D coordinates of the airfoil's
         trailing edge. Default values are zeros
     """
+    @abstractmethod
+    def generate_parameters(self, convention='british'):
+        """
+        Abstract method that generates the airfoil parameters based on the
+        given coordinates.
 
-    def __init__(self):
-        self.xup_coordinates = None
-        self.xdown_coordinates = None
-        self.yup_coordinates = None
-        self.ydown_coordinates = None
-        self.chord_line = None
-        self.camber_line = None
-        self.leading_edge = np.zeros(2)
-        self.trailing_edge = np.zeros(2)
+        The method generates the airfoil's chord length, chord percentages,
+        maximum camber, camber percentages, maximum thickness, thickness
+        percentages.
+
+        :param str convention: convention of the airfoil coordinates. Default
+            value is 'british'
+        """
+        self._update_edges()
+        # compute chord parameters
+        self._chord_length = np.linalg.norm(self.leading_edge -
+                self.trailing_edge)
+        self._chord_percentage = (self.xup_coordinates - np.min(
+            self.xup_coordinates))/self._chord_length
+        # compute camber parameters
+        _camber = (self.yup_coordinates + self.ydown_coordinates)/2
+        self._camber_max = abs(np.max(_camber))
+        if self._camber_max == 0:
+            self._camber_percentage = np.zeros(self.xup_coordinates.shape[0])
+        elif self.camber_max != 0:
+            self._camber_percentage = _camber/self._camber_max
+        # compute thickness parameters
+        if convention == 'british' or self._camber_max==0:
+            _thickness = abs(self.yup_coordinates - self.ydown_coordinates)
+        elif convention == 'american':
+            _thickness = self._compute_thickness_american()
+        self._thickness_max = np.max(_thickness)
+        if self._thickness_max == 0:
+            self._thickness_percentage = np.zeros(self.xup_coordinates.shape[0])
+        elif self._thickness_max != 0:
+            self._thickness_percentage = _thickness/self._thickness_max
+
+    @abstractmethod
+    def generate_coordinates(self):
+        """
+        Abstract method that generates the airfoil coordinates based on the
+        given parameters.
+
+        The method generates the airfoil's upper and lower surfaces
+        coordinates. The method is called automatically when the airfoil
+        parameters are inserted by the user.
+
+        :param str convention: convention of the airfoil coordinates. Default
+            value is 'british'
+        """
+        pass
+
+    @property
+    def xup_coordinates(self):
+        """
+        X-coordinates of the upper surface of the airfoil.
+        """
+        return self._xup_coordinates
+
+    @xup_coordinates.setter
+    def xup_coordinates(self, xup_coordinates):
+        self._xup_coordinates = xup_coordinates
+
+    @property
+    def xdown_coordinates(self):
+        """
+        X-coordinates of the lower surface of the airfoil.
+        """
+        return self._xdown_coordinates
+
+    @xdown_coordinates.setter
+    def xdown_coordinates(self, xdown_coordinates):
+        self._xdown_coordinates = xdown_coordinates
+
+    @property
+    def yup_coordinates(self):
+        """
+        Y-coordinates of the upper surface of the airfoil.
+        """
+        return self._yup_coordinates
+
+    @yup_coordinates.setter
+    def yup_coordinates(self, yup_coordinates):
+        self._yup_coordinates = yup_coordinates
+
+    @property
+    def ydown_coordinates(self):
+        """
+        Y-coordinates of the lower surface of the airfoil.
+        """
+        return self._ydown_coordinates
+
+    @ydown_coordinates.setter
+    def ydown_coordinates(self, ydown_coordinates):
+        self._ydown_coordinates = ydown_coordinates
+
+    @property
+    def chord_length(self):
+        """
+        Chord length of the airfoil.
+        """
+        return self._chord_length
+
+    @chord_length.setter
+    def chord_length(self, chord_length):
+        self._chord_length = chord_length
+
+    @property
+    def chord_percentage(self):
+        """
+        Chord percentages of the airfoil.
+        """
+        return self._chord_percentage
+
+    @chord_percentage.setter
+    def chord_percentage(self, chord_percentage):
+        self._chord_percentage = chord_percentage
+
+    @property
+    def camber_max(self):
+        """
+        Maximum camber of the airfoil.
+        """
+        return self._camber_max
+
+    @camber_max.setter
+    def camber_max(self, camber_max):
+        self._camber_max = camber_max
+
+    @property
+    def camber_percentage(self):
+        """
+        Camber percentages of the airfoil.
+        """
+        return self._camber_percentage
+
+    @camber_percentage.setter
+    def camber_percentage(self, camber_percentage):
+        self._camber_percentage = camber_percentage
+
+    @property
+    def thickness_max(self):
+        """
+        Maximum thickness of the airfoil.
+        """
+        return self._thickness_max
+
+    @thickness_max.setter
+    def thickness_max(self, thickness_max):
+        self._thickness_max = thickness_max
+
+    @property
+    def thickness_percentage(self):
+        """
+        Thickness percentages of the airfoil.
+        """
+        return self._thickness_percentage
+
+    @thickness_percentage.setter
+    def thickness_percentage(self, thickness_percentage):
+        self._thickness_percentage = thickness_percentage
 
     def _update_edges(self):
         """
@@ -59,6 +211,8 @@ def _update_edges(self):
         trailing edge, hence both the leading and the trailing edges are always
         unique.
         """
+        self.leading_edge = np.zeros(2)
+        self.trailing_edge = np.zeros(2)
         if np.fabs(self.xup_coordinates[0] - self.xdown_coordinates[0]) > 1e-4:
             raise ValueError('Airfoils must have xup_coordinates[0] '\
                             'almost equal to xdown_coordinates[0]')
@@ -224,7 +378,8 @@ def deform_camber_line(self, percent_change, n_interpolated_points=None):
         The percentage of change is defined as follows:
 
         .. math::
-            \\frac{\\text{new magnitude of max camber - old magnitude of maximum \
+            \\frac{\\text{new magnitude of max camber - old magnitude of
+            maximum \
             camber}}{\\text{old magnitude of maximum camber}} * 100
 
         A positive percentage means the new camber is larger than the max
@@ -322,17 +477,44 @@ def reference_point(self):
         ]
         return np.asarray(reference_point)
 
-    @property
-    def chord_length(self):
-        """
-        Measure the l2-norm (Euclidean distance) between the leading edge
-        and the trailing edge.
-
-        :return: chord length
-        :rtype: float
-        """
-        self._update_edges()
-        return np.linalg.norm(self.leading_edge - self.trailing_edge)
+    def _compute_thickness_american(self):
+        """
+        Compute the thickness of the airfoil using the American standard
+        definition.
+        """
+        n_pos = self.xup_coordinates.shape[0]
+        m = np.zeros(n_pos)
+        for i in range(1, n_pos, 1):
+            m[i] = (self._camber_percentage[i]-
+                self._camber_percentage[i-1])/(self._chord_percentage[i]-
+                self._chord_percentage[i-1])*self._camber_max/self._chord_length
+        m_angle = np.arctan(m)
+
+        # generating temporary profile coordinates orthogonal to the camber
+        # line
+        camber = self._camber_max*self._camber_percentage
+        ind_horizontal_camber = np.sin(m_angle)==0
+        def eq_to_solve(x):
+            spline_curve = self.ydown_curve(x.reshape(-1,1)).reshape(
+                        x.shape[0],)
+            line_orth_camber = (camber[~ind_horizontal_camber] +
+                np.cos(m_angle[~ind_horizontal_camber])/
+                np.sin(m_angle[~ind_horizontal_camber])*(
+                    self._chord_percentage[~ind_horizontal_camber]
+                    *self._chord_length-x))
+            return spline_curve - line_orth_camber
+
+        xdown_tmp = self.xdown_coordinates.copy()
+        xdown_tmp[~ind_horizontal_camber] = newton(eq_to_solve,
+            xdown_tmp[~ind_horizontal_camber])
+        xup_tmp = 2*self._chord_percentage*self._chord_length - xdown_tmp
+        ydown_tmp = self.ydown_curve(xdown_tmp.reshape(-1,1)).reshape(
+            xdown_tmp.shape[0],)
+        yup_tmp = 2*self._camber_max*self._camber_percentage - ydown_tmp
+        if xup_tmp[1]<self.xup_coordinates[0]:
+            xup_tmp[1], xdown_tmp[1] = xup_tmp[2], xdown_tmp[2]
+            yup_tmp[1], ydown_tmp[1] = yup_tmp[2], ydown_tmp[2]
+        return np.sqrt((xup_tmp-xdown_tmp)**2 + (yup_tmp-ydown_tmp)**2)
 
     def max_thickness(self, n_interpolated_points=None):
         """
@@ -355,7 +537,7 @@ def max_thickness(self, n_interpolated_points=None):
             Phillips, Warren F. (2010). Mechanics of Flight (2nd ed.). \
                 Wiley & Sons. p. 27. ISBN 978-0-470-53975-0.
 
-            Bertin, John J.; Cummings, Russel M. (2009). Pearson Prentice Hall, \
+            Bertin, John J.; Cummings, Russel M. (2009). Pearson Prentice Hall,\
                 ed. Aerodynamics for Engineers (5th ed.). \
                 p. 199. ISBN 978-0-13-227268-1.
 
@@ -550,7 +732,6 @@ def scale(self, factor, translate=True):
             self.yup_coordinates *= factor
             self.ydown_coordinates *= factor
 
-
     def plot(self,
              profile=True,
              chord_line=False,
@@ -619,3 +800,4 @@ def plot(self,
             plt.savefig(outfile)
         else:
             plt.show()
+
diff --git a/tests/test_blade.py b/tests/test_blade.py
index eaed000..f24f8a2 100644
--- a/tests/test_blade.py
+++ b/tests/test_blade.py
@@ -450,7 +450,7 @@ def test_plot_ax_multi(self):
         blade_2 = create_sample_blade_custom()
         blade_2.apply_transformations()
         fig = plt.figure()
-        ax = fig.gca(projection=Axes3D.name)
+        ax = fig.add_subplot(projection='3d')
         blade_1.plot(ax=ax)
         blade_2.plot(ax=ax)
         plt.close()
@@ -755,7 +755,7 @@ def test_solid_errors_exception(self):
     def test_generate_solid(self):
         blade = create_sample_blade_NACA()
         blade.apply_transformations()
-        blade_solid = blade.generate_solid(max_deg=2, display=False, 
+        blade_solid = blade.generate_solid(max_deg=2, display=False,
                                                  errors=None)
         self.assertIsInstance(blade_solid, TopoDS_Solid)
 
diff --git a/tests/test_package.py b/tests/test_package.py
index 111494a..ec36a65 100644
--- a/tests/test_package.py
+++ b/tests/test_package.py
@@ -6,10 +6,6 @@
 import numpy as np
 
 class TestPackage(TestCase):
-    def test_import_blade_1(self):
-        from bladex import ProfileBase
-        profile = ProfileBase()
-
     def test_import_blade_2(self):
         from bladex import CustomProfile
         vec = np.array([1.2, 2.4])
diff --git a/tests/test_profilebase.py b/tests/test_profilebase.py
deleted file mode 100644
index 0d21b4f..0000000
--- a/tests/test_profilebase.py
+++ /dev/null
@@ -1,549 +0,0 @@
-import os
-from unittest import TestCase
-import bladex.profilebase as pb
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-def create_sample_profile():
-    """
-    xup_coordinates, yup_coordinates, xdown_coordinates, ydown_coordinates
-    """
-    profile = pb.ProfileBase()
-    profile.xup_coordinates = np.linspace(-1.0, 1.0, 5)
-    profile.yup_coordinates = np.array([0.0, 0.75, 1.0, 0.75, 0.0])
-    profile.xdown_coordinates = np.linspace(-1.0, 1.0, 5)
-    profile.ydown_coordinates = np.zeros(5)
-    return profile
-
-
-def create_sample_profile_2():
-    """
-    xup_coordinates != xdown_coordinates elementwise
-    """
-    profile = pb.ProfileBase()
-    profile.xup_coordinates = np.linspace(-1.0, 1.0, 5)
-    profile.yup_coordinates = np.array([0.0, 0.75, 1.0, 0.75, 0.0])
-    profile.xdown_coordinates = np.linspace(-1.0, 1.0, 5)
-    profile.xdown_coordinates[2] = 0.1
-    profile.ydown_coordinates = np.zeros(5)
-    return profile
-
-
-def create_sample_profile_3():
-    """
-    Coordinates of unit circle (i.e. x^2+y^2=1)
-    """
-    profile = pb.ProfileBase()
-    profile.xup_coordinates = np.linspace(-1.0, 1.0, num=250)
-    profile.yup_coordinates = np.sqrt(1 - np.power(profile.xup_coordinates, 2))
-    profile.xdown_coordinates = np.linspace(-1.0, 1.0, num=250)
-    profile.ydown_coordinates = -np.sqrt(
-        1 - np.power(profile.xup_coordinates, 2))
-    return profile
-
-
-class TestProfileBase(TestCase):
-    def test_xup_init(self):
-        profile = pb.ProfileBase()
-        assert profile.xup_coordinates is None
-
-    def test_yup_init(self):
-        profile = pb.ProfileBase()
-        assert profile.yup_coordinates is None
-
-    def test_xdown_init(self):
-        profile = pb.ProfileBase()
-        assert profile.xdown_coordinates is None
-
-    def test_ydown_init(self):
-        profile = pb.ProfileBase()
-        assert profile.ydown_coordinates is None
-
-    def test_chord_init(self):
-        profile = pb.ProfileBase()
-        assert profile.chord_line is None
-
-    def test_camber_init(self):
-        profile = pb.ProfileBase()
-        assert profile.camber_line is None
-
-    def test_leading_edge_size(self):
-        profile = pb.ProfileBase()
-        assert profile.leading_edge.size == 2
-
-    def test_trailing_edge_size(self):
-        profile = pb.ProfileBase()
-        assert profile.trailing_edge.size == 2
-
-    def test_leading_edge_init(self):
-        profile = pb.ProfileBase()
-        np.testing.assert_equal(profile.leading_edge, np.zeros(2))
-
-    def test_trailing_edge_init(self):
-        profile = pb.ProfileBase()
-        np.testing.assert_equal(profile.trailing_edge, np.zeros(2))
-
-    def test_update_edges_exceptions(self):
-        profile = create_sample_profile()
-        profile.xdown_coordinates[0] = 0.1
-        with self.assertRaises(ValueError):
-            profile._update_edges()
-
-    def test_update_edges_exceptions_2(self):
-        profile = create_sample_profile()
-        profile.xdown_coordinates[-1] = 0.1
-        with self.assertRaises(ValueError):
-            profile._update_edges()
-
-    def test_update_edges_leading_edge(self):
-        profile = create_sample_profile()
-        profile.yup_coordinates[-1] = 1.0
-        profile._update_edges()
-        np.testing.assert_equal(profile.leading_edge, np.array([-1.0, 0.0]))
-
-    def test_update_edges_trailing_edge(self):
-        profile = create_sample_profile()
-        profile._update_edges()
-        np.testing.assert_equal(profile.trailing_edge, np.array([1.0, 0.0]))
-
-    def test_update_edges_trailing_edge_finite(self):
-        profile = create_sample_profile()
-        profile.yup_coordinates[-1] = 1.0
-        profile._update_edges()
-        np.testing.assert_equal(profile.trailing_edge, np.array([1.0, 0.5]))
-
-    def test_interpolate_crds_exceptions_1(self):
-        profile = pb.ProfileBase()
-        with self.assertRaises(TypeError):
-            profile.interpolate_coordinates(num=500.5, radius=1.0)
-
-    def test_interpolate_crds_exceptions_2(self):
-        profile = pb.ProfileBase()
-        with self.assertRaises(ValueError):
-            profile.interpolate_coordinates(num=-500, radius=1.0)
-
-    def test_interpolate_crds_exceptions_3(self):
-        profile = pb.ProfileBase()
-        with self.assertRaises(ValueError):
-            profile.interpolate_coordinates(num=500, radius=-1.0)
-
-    def test_interpolate_coordinates_xup(self):
-        profile = create_sample_profile_3()
-        xup_actual = profile.interpolate_coordinates(num=500, radius=10)[0]
-        xup_expected = np.load('tests/test_datasets/interp_xup_unit_circle.npy')
-        np.testing.assert_almost_equal(xup_actual, xup_expected)
-
-    def test_interpolate_coordinates_yup(self):
-        profile = create_sample_profile_3()
-        yup_actual = profile.interpolate_coordinates(num=500, radius=10)[2]
-        yup_expected = np.load('tests/test_datasets/interp_yup_unit_circle.npy')
-        np.testing.assert_almost_equal(yup_actual, yup_expected, decimal=5)
-
-    def test_interpolate_coordinates_xdown(self):
-        profile = create_sample_profile_3()
-        xdown_actual = profile.interpolate_coordinates(num=500, radius=10)[1]
-        xdown_expected = np.load(
-            'tests/test_datasets/interp_xdown_unit_circle.npy')
-        np.testing.assert_almost_equal(xdown_actual, xdown_expected)
-
-    def test_interpolate_coordinates_ydown(self):
-        profile = create_sample_profile_3()
-        ydown_actual = profile.interpolate_coordinates(num=500, radius=10)[3]
-        ydown_expected = np.load(
-            'tests/test_datasets/interp_ydown_unit_circle.npy')
-        np.testing.assert_almost_equal(ydown_actual, ydown_expected, decimal=5)
-
-    def test_chord_line_shape(self):
-        profile = create_sample_profile()
-        profile.compute_chord_line(n_interpolated_points=None)
-        assert profile.chord_line.shape == (2, 5)
-
-    def test_chord_line(self):
-        profile = create_sample_profile()
-        profile.compute_chord_line(n_interpolated_points=None)
-        expected_chord = np.array([np.linspace(-1, 1, 5), np.zeros(5)])
-        np.testing.assert_equal(profile.chord_line, expected_chord)
-
-    def test_chord_line_interpolated(self):
-        # Interpolation by keyword
-        profile = create_sample_profile()
-        profile.compute_chord_line(n_interpolated_points=500)
-        assert profile.chord_line.shape == (2, 500)
-
-    def test_chord_line_interpolated_2(self):
-        # Interpolation because x_up != x_down elementwise
-        profile = create_sample_profile_2()
-        profile.compute_chord_line(n_interpolated_points=None)
-        assert profile.chord_line.shape == (2, 500)
-
-    def test_camber_line_shape(self):
-        profile = create_sample_profile()
-        profile.compute_camber_line(n_interpolated_points=None)
-        assert profile.camber_line.shape == (2, 5)
-
-    def test_camber_line(self):
-        profile = create_sample_profile()
-        profile.compute_camber_line(n_interpolated_points=None)
-        expected_camber = np.array(
-            [np.linspace(-1, 1, 5),
-             np.array([0, 0.375, 0.5, 0.375, 0])])
-        np.testing.assert_equal(profile.camber_line, expected_camber)
-
-    def test_camber_line_interpolated(self):
-        profile = create_sample_profile()
-        profile.compute_camber_line(n_interpolated_points=500)
-        assert profile.camber_line.shape == (2, 500)
-
-    def test_camber_line_interpolated_2(self):
-        profile = create_sample_profile_2()
-        profile.compute_camber_line(n_interpolated_points=None)
-        assert profile.camber_line.shape == (2, 500)
-
-    def test_deform_camber_exceptions(self):
-        profile = create_sample_profile()
-        with self.assertRaises(TypeError):
-            profile.deform_camber_line()
-
-    def test_deform_camber(self):
-        profile = create_sample_profile()
-        profile.deform_camber_line(
-            percent_change=100, n_interpolated_points=None)
-        expected_deformed_camber = np.array(
-            [np.linspace(-1.0, 1.0, 5),
-             np.array([0.0, 0.75, 1.0, 0.75, 0.0])])
-        np.testing.assert_equal(profile.camber_line, expected_deformed_camber)
-
-    def test_deform_camber_yup(self):
-        profile = create_sample_profile()
-        profile.deform_camber_line(
-            percent_change=100, n_interpolated_points=None)
-        expected_yup_coordinates = np.array([0.0, 1.125, 1.5, 1.125, 0.0])
-        np.testing.assert_equal(profile.yup_coordinates,
-                                expected_yup_coordinates)
-
-    def test_deform_camber_ydown(self):
-        profile = create_sample_profile()
-        profile.deform_camber_line(
-            percent_change=100, n_interpolated_points=None)
-        expected_ydown_coordinates = np.array([0.0, 0.375, 0.5, 0.375, 0.0])
-        np.testing.assert_equal(profile.ydown_coordinates,
-                                expected_ydown_coordinates)
-
-    def test_deform_camber_interpolated(self):
-        profile = create_sample_profile()
-        profile.deform_camber_line(percent_change=50, n_interpolated_points=500)
-        assert profile.camber_line[1].size == 500
-
-    def test_deform_camber_interpolated_2(self):
-        profile = create_sample_profile_2()
-        profile.deform_camber_line(
-            percent_change=50, n_interpolated_points=None)
-        assert profile.camber_line[1].size == 500
-
-    def test_reference_point_shape(self):
-        profile = create_sample_profile()
-        assert profile.reference_point.size == 2
-
-    def test_reference_point(self):
-        profile = create_sample_profile()
-        np.testing.assert_equal(profile.reference_point, np.zeros(2))
-
-    def test_chord_length(self):
-        profile = create_sample_profile()
-        assert profile.chord_length == 2.0
-
-    def test_max_thickness(self):
-        profile = create_sample_profile()
-        thickness = profile.max_thickness(n_interpolated_points=None)
-        assert thickness == 1.0
-
-    def test_max_thickness_interpolated(self):
-        profile = create_sample_profile()
-        thickness = profile.max_thickness(n_interpolated_points=500)
-        np.testing.assert_almost_equal(thickness, 1.0, decimal=4)
-
-    def test_max_thickness_interpolated_2(self):
-        profile = create_sample_profile_2()
-        thickness = profile.max_thickness(n_interpolated_points=None)
-        np.testing.assert_almost_equal(thickness, 1.0, decimal=4)
-
-    def test_max_camber(self):
-        profile = create_sample_profile()
-        camber_max = profile.max_camber(n_interpolated_points=None)
-        assert camber_max == 0.5
-
-    def test_max_camber_interpolated(self):
-        profile = create_sample_profile()
-        camber_max = profile.max_camber(n_interpolated_points=500)
-        np.testing.assert_almost_equal(camber_max, 0.5, decimal=5)
-
-    def test_max_camber_interpolated_2(self):
-        profile = create_sample_profile_2()
-        camber_max = profile.max_camber(n_interpolated_points=None)
-        np.testing.assert_almost_equal(camber_max, 0.5, decimal=5)
-
-    def test_max_camber_negative(self):
-        profile = create_sample_profile()
-        profile.ydown_coordinates = np.array([0.0, -0.75, -1.0, -0.75, 0.0])
-        profile.yup_coordinates = np.zeros(5)
-        camber_max = profile.max_camber(n_interpolated_points=None)
-        assert camber_max == -0.5
-
-    def test_rotate_exceptions(self):
-        profile = pb.ProfileBase()
-        with self.assertRaises(ValueError):
-            profile.rotate(rad_angle=None, deg_angle=None)
-
-    def test_rotate_exceptions_2(self):
-        profile = pb.ProfileBase()
-        with self.assertRaises(ValueError):
-            profile.rotate(rad_angle=np.pi, deg_angle=180)
-
-    def test_rotate_in_degrees_xup(self):
-        profile = create_sample_profile()
-        profile.rotate(deg_angle=135)
-        rotated_xup_coordinates = np.array(
-            [0.70710678, -0.1767767, -0.70710678, -0.88388348, -0.70710678])
-        np.testing.assert_almost_equal(profile.xup_coordinates,
-                                       rotated_xup_coordinates)
-
-    def test_rotate_in_degrees_yup(self):
-        profile = create_sample_profile()
-        profile.rotate(deg_angle=135)
-        rotated_yup_coordinates = np.array(
-            [-0.70710678, -0.88388348, -0.70710678, -0.1767767, 0.70710678])
-        np.testing.assert_almost_equal(profile.yup_coordinates,
-                                       rotated_yup_coordinates)
-
-    def test_rotate_in_degrees_xdown(self):
-        profile = create_sample_profile()
-        profile.rotate(deg_angle=135)
-        rotated_xdown_coordinates = np.array(
-            [0.70710678, 0.35355339, 0.0, -0.35355339, -0.70710678])
-        np.testing.assert_almost_equal(profile.xdown_coordinates,
-                                       rotated_xdown_coordinates)
-
-    def test_rotate_in_degrees_ydown(self):
-        profile = create_sample_profile()
-        profile.rotate(deg_angle=135)
-        rotated_ydown_coordinates = np.array(
-            [-0.70710678, -0.35355339, 0.0, 0.35355339, 0.70710678])
-        np.testing.assert_almost_equal(profile.ydown_coordinates,
-                                       rotated_ydown_coordinates)
-
-    def test_rotate_in_radians_xup(self):
-        profile = create_sample_profile()
-        profile.rotate(rad_angle=3 * np.pi / 4)
-        rotated_xup_coordinates = np.array(
-            [0.70710678, -0.1767767, -0.70710678, -0.88388348, -0.70710678])
-        np.testing.assert_almost_equal(profile.xup_coordinates,
-                                       rotated_xup_coordinates)
-
-    def test_rotate_in_radians_yup(self):
-        profile = create_sample_profile()
-        profile.rotate(rad_angle=3 * np.pi / 4)
-        rotated_yup_coordinates = np.array(
-            [-0.70710678, -0.88388348, -0.70710678, -0.1767767, 0.70710678])
-        np.testing.assert_almost_equal(profile.yup_coordinates,
-                                       rotated_yup_coordinates)
-
-    def test_rotate_in_radians_xdown(self):
-        profile = create_sample_profile()
-        profile.rotate(rad_angle=3 * np.pi / 4)
-        rotated_xdown_coordinates = np.array(
-            [0.70710678, 0.35355339, 0.0, -0.35355339, -0.70710678])
-        np.testing.assert_almost_equal(profile.xdown_coordinates,
-                                       rotated_xdown_coordinates)
-
-    def test_rotate_in_radians_ydown(self):
-        profile = create_sample_profile()
-        profile.rotate(rad_angle=3 * np.pi / 4)
-        rotated_ydown_coordinates = np.array(
-            [-0.70710678, -0.35355339, 0.0, 0.35355339, 0.70710678])
-        np.testing.assert_almost_equal(profile.ydown_coordinates,
-                                       rotated_ydown_coordinates)
-
-    def test_translate_exceptions(self):
-        profile = pb.ProfileBase()
-        with self.assertRaises(TypeError):
-            profile.translate()
-
-    def test_translate_xup(self):
-        profile = create_sample_profile()
-        profile.translate(translation=[1.0, 2.0])
-        translated_xup_coordinates = np.array([0.0, 0.5, 1.0, 1.5, 2.0])
-        np.testing.assert_equal(profile.xup_coordinates,
-                                translated_xup_coordinates)
-
-    def test_translate_yup(self):
-        profile = create_sample_profile()
-        profile.translate(translation=[1.0, 2.0])
-        translated_yup_coordinates = np.array([2.0, 2.75, 3.0, 2.75, 2.0])
-        np.testing.assert_equal(profile.yup_coordinates,
-                                translated_yup_coordinates)
-
-    def test_translate_xdown(self):
-        profile = create_sample_profile()
-        profile.translate(translation=[1.0, 2.0])
-        translated_xdown_coordinates = np.array([0.0, 0.5, 1.0, 1.5, 2.0])
-        np.testing.assert_equal(profile.xdown_coordinates,
-                                translated_xdown_coordinates)
-
-    def test_translate_ydown(self):
-        profile = create_sample_profile()
-        profile.translate(translation=[1.0, 2.0])
-        translated_ydown_coordinates = np.array([2.0, 2.0, 2.0, 2.0, 2.0])
-        np.testing.assert_equal(profile.ydown_coordinates,
-                                translated_ydown_coordinates)
-
-    def test_reflect_xup(self):
-        profile = create_sample_profile()
-        profile.reflect()
-        flipped_xup_coordinates = np.array([1.0, 0.5, 0.0, -0.5, -1.0])
-        np.testing.assert_equal(profile.xup_coordinates,
-                                flipped_xup_coordinates)
-
-    def test_reflect_yup(self):
-        profile = create_sample_profile()
-        profile.reflect()
-        flipped_yup_coordinates = np.array([0.0, -0.75, -1.0, -0.75, 0.0])
-        np.testing.assert_equal(profile.yup_coordinates,
-                                flipped_yup_coordinates)
-
-    def test_reflect_xdown(self):
-        profile = create_sample_profile()
-        profile.reflect()
-        flipped_xdown_coordinates = np.array([1.0, 0.5, 0.0, -0.5, -1.0])
-        np.testing.assert_equal(profile.xdown_coordinates,
-                                flipped_xdown_coordinates)
-
-    def test_reflect_ydown(self):
-        profile = create_sample_profile()
-        profile.reflect()
-        flipped_ydown_coordinates = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
-        np.testing.assert_equal(profile.ydown_coordinates,
-                                flipped_ydown_coordinates)
-
-    def test_scale_exceptions(self):
-        profile = pb.ProfileBase()
-        with self.assertRaises(TypeError):
-            profile.scale()
-
-    def test_scale_xup(self):
-        profile = create_sample_profile()
-        profile.scale(factor=1.5)
-        scaled_xup_coordinates = np.array([-1.5, -0.75, 0.0, 0.75, 1.5])
-        np.testing.assert_equal(profile.xup_coordinates, scaled_xup_coordinates)
-
-    def test_scale_yup(self):
-        profile = create_sample_profile()
-        profile.scale(factor=1.5)
-        scaled_yup_coordinates = np.array([0.0, 1.125, 1.5, 1.125, 0.0])
-        np.testing.assert_equal(profile.yup_coordinates, scaled_yup_coordinates)
-
-    def test_scale_xdown(self):
-        profile = create_sample_profile()
-        profile.scale(factor=1.5)
-        scaled_xdown_coordinates = np.array([-1.5, -0.75, 0.0, 0.75, 1.5])
-        np.testing.assert_equal(profile.xdown_coordinates,
-                                scaled_xdown_coordinates)
-
-    def test_scale_ydown(self):
-        profile = create_sample_profile()
-        profile.scale(factor=1.5)
-        scaled_ydown_coordinates = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
-        np.testing.assert_equal(profile.ydown_coordinates,
-                                scaled_ydown_coordinates)
-
-    def test_plot_exceptions(self):
-        profile = create_sample_profile()
-        with self.assertRaises(ValueError):
-            profile.plot(outfile=1.2)
-
-    def test_plot_no_coordinates(self):
-        profile = create_sample_profile()
-        profile.xup_coordinates = None
-        with self.assertRaises(ValueError):
-            profile.plot()
-
-    def test_plot_profile(self):
-        profile = create_sample_profile()
-        profile.plot(
-            profile=True,
-            chord_line=False,
-            camber_line=False,
-            ref_point=False,
-            outfile=None)
-        plt.close()
-
-    def test_plot_chord_exception(self):
-        profile = create_sample_profile()
-        with self.assertRaises(ValueError):
-            profile.plot(
-                profile=True,
-                chord_line=True,
-                camber_line=False,
-                ref_point=False,
-                outfile=None)
-
-    def test_plot_chord_line(self):
-        profile = create_sample_profile()
-        profile.compute_chord_line()
-        profile.plot(
-            profile=True,
-            chord_line=True,
-            camber_line=False,
-            ref_point=False,
-            outfile=None)
-        plt.close()
-
-    def test_plot_camber_exception(self):
-        profile = create_sample_profile()
-        with self.assertRaises(ValueError):
-            profile.plot(
-                profile=True,
-                chord_line=False,
-                camber_line=True,
-                ref_point=False,
-                outfile=None)
-
-    def test_plot_camber_line(self):
-        profile = create_sample_profile()
-        profile.compute_camber_line()
-        profile.plot(
-            profile=True,
-            chord_line=False,
-            camber_line=True,
-            ref_point=False,
-            outfile=None)
-        plt.close()
-
-    def test_plot_ref_point(self):
-        profile = create_sample_profile()
-        profile.plot(
-            profile=True,
-            chord_line=False,
-            camber_line=False,
-            ref_point=True,
-            outfile=None)
-        plt.close()
-
-    def test_plot_all(self):
-        profile = create_sample_profile()
-        profile.compute_camber_line()
-        profile.compute_chord_line()
-        profile.plot(
-            profile=True,
-            chord_line=True,
-            camber_line=True,
-            ref_point=True,
-            outfile=None)
-        plt.close()
-
-    def test_plot_save(self):
-        profile = create_sample_profile()
-        profile.plot(outfile='tests/test_datasets/test_plot.png')
-        plt.close()
-        self.assertTrue(os.path.isfile('tests/test_datasets/test_plot.png'))
-        self.addCleanup(os.remove, 'tests/test_datasets/test_plot.png')
diff --git a/tests/test_profileinterface.py b/tests/test_profileinterface.py
new file mode 100644
index 0000000..1216855
--- /dev/null
+++ b/tests/test_profileinterface.py
@@ -0,0 +1,10 @@
+import os
+from unittest import TestCase
+from bladex import ProfileInterface
+import numpy as np
+import matplotlib.pyplot as plt
+
+class TestProfileInterface(TestCase):
+    def test_instantiation(self):
+        with self.assertRaises(TypeError):
+            ProfileInterface()
\ No newline at end of file
diff --git a/tests/test_profiles.py b/tests/test_profiles.py
index 9b8122c..1b590c8 100644
--- a/tests/test_profiles.py
+++ b/tests/test_profiles.py
@@ -1,7 +1,7 @@
 from unittest import TestCase
 import numpy as np
 
-from  bladex import CustomProfile, NacaProfile, ProfileBase
+from  bladex import CustomProfile, NacaProfile, ProfileInterface
 
 def create_custom_profile():
     """
@@ -16,7 +16,7 @@ def create_custom_profile():
 
 class TestCustomProfile(TestCase):
     def test_inheritance_custom(self):
-        self.assertTrue(issubclass(CustomProfile, ProfileBase))
+        self.assertTrue(issubclass(CustomProfile, ProfileInterface))
 
     def test_xup_member(self):
         profile = create_custom_profile()
@@ -124,7 +124,7 @@ def test_trailing_edge_x(self):
 
 class TestNacaProfile(TestCase):
     def test_inheritance_naca(self):
-        self.assertTrue(issubclass(NacaProfile, ProfileBase))
+        self.assertTrue(issubclass(NacaProfile, ProfileInterface))
 
     def test_digits_member(self):
         profile = NacaProfile(digits='0012', n_points=240)
diff --git a/tutorials/README.md b/tutorials/README.md
index 7de340a..45d94f3 100644
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -14,5 +14,13 @@ Here we show how to prepare a blade 3D sectional profiles by applying all the tr
 #### [Tutorial 4](https://github.com/mathLab/BladeX/blob/master/tutorials/tutorial-4-deform_blade.ipynb)
 Here we show how to deform a blade and its parametric curves by using a parameter file.
 
+#### [Tutorial 5](https://github.com/mathLab/BladeX/blob/master/tutorials/tutorial-5-foils-customprofile.ipynb)
+Here we show how to initialize a blade starting from its parameters, either using the british convention or the american
+one for the thickness generation.
+
+#### [Tutorial 6](https://github.com/mathLab/BladeX/blob/master/tutorials/tutorial-6-blades-customprofile.ipynb)
+Here we show how to deform a blade by deforming only its local (thickness, camber) parameters, and/or its global (rake,
+skew, etc) parameters.
+
 #### More to come...
-We plan to add more tutorials but the time is often against us. If you want to contribute with a notebook on a feature not covered yet we will be very happy and give you support on editing!
\ No newline at end of file
+We plan to add more tutorials but the time is often against us. If you want to contribute with a notebook on a feature not covered yet we will be very happy and give you support on editing!
diff --git a/tutorials/tutorial-1-generate_foils.ipynb b/tutorials/tutorial-1-generate_foils.ipynb
index c1b7aba..72c38cb 100644
--- a/tutorials/tutorial-1-generate_foils.ipynb
+++ b/tutorials/tutorial-1-generate_foils.ipynb
@@ -25,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First of all we import from BladeX the `NacaProfile` and `CustomProfile` classes, which are inherited from the `ProfileBase` class containing the methods responsible for computing all the basic attributes and geometric features of the foil.\n",
+    "First of all we import from BladeX the `NacaProfile` and `CustomProfile` classes, which are inherited from the `ProfileBase` abstract class containing the methods responsible for computing all the basic attributes and geometric features of the foil.\n",
     "We also import `numpy` and we set `matplotlib` for the notebook."
    ]
   },
@@ -429,7 +429,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/tutorial-2-transform_foils.ipynb b/tutorials/tutorial-2-transform_foils.ipynb
index 753f695..0a32fa6 100644
--- a/tutorials/tutorial-2-transform_foils.ipynb
+++ b/tutorials/tutorial-2-transform_foils.ipynb
@@ -25,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First of all we import from BladeX the `NacaProfile` class for generating the NACA foils that we shall operate on. Moreover we import the `ProfileBase` class which contains the necessary methods for computing all the basic attributes and geometric features of the foil.\n",
+    "First of all we import from BladeX the `NacaProfile` class for generating the NACA foils that we shall operate on.\n",
     "Finally we import `numpy` and we set `matplotlib` for the notebook."
    ]
   },
@@ -37,7 +37,7 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from bladex import NacaProfile, ProfileBase"
+    "from bladex import NacaProfile"
    ]
   },
   {
@@ -52,24 +52,490 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAPGothic' (ipagp.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstQurn' (KacstQurn.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerifBoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-Regular.otf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstLetter' (KacstLetter.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic' (ume-tgo4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-DemiItalic.otf) italic normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Courier GPL&GNU' (BPG_Courier_GPL&GNU.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'RaghuMalayalam' (RaghuMalayalamSans.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-ExtraLight Italic.ttf) italic normal 200 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Muol' (KhmerOS_muol.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic C4' (ume-tgc4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Vemana2000' (Vemana2000.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Devanagari' (Lohit-Devanagari.ttf) normal normal 400 normal>) = 10.05\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Mincho S3' (ume-pms3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Bold-Italic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstOffice' (KacstOffice.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'rsfs10' (rsfs10.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Light.ttf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Oblique.otf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-Bold.otf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-ExtraBoldItalic.ttf) italic normal 800 normal>) = 11.43\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Z003' (Z003-MediumItalic.otf) italic normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumGothic' (NanumGothicBold.ttf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Devanagari' (Samyak-Devanagari.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-Demi.otf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS75F.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-RegularOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist-Oblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume UI Gothic' (ume-ugo4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Hebrew' (NotoSansHebrew-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Bengali' (Lohit-Bengali.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-Roman.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai UI' (NotoSansThaiUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Mincho' (ume-tmo3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nanum Brush Script' (NanumBrush.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Stam Sefarad CLM' (StamSefaradCLM.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Malayalam' (Samyak-Malayalam.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS76F.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Muol Pali' (KhmerOS_muolpali.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil' (NotoSansTamil-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume UI Gothic O5' (ume-ugo5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Pothana2000' (Pothana2000.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstOne' (KacstOne.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil UI' (NotoSansTamilUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAMincho' (ipam.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'OpenSymbol' (opens___.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic O5' (ume-tgo5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Thai' (NotoSerifThai-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'eufm10' (eufm10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS System' (KhmerOS_sys.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai' (NotoSansThai-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstArt' (KacstArt.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS' (KhmerOS.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter-Bold.ttf) normal normal 700 normal>) = 10.335\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstFarsi' (KacstFarsi.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Kannada' (Lohit-Kannada.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Unikurd Web' (Unikuweb.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic S4' (ume-tgs4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao UI' (NotoSansLaoUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-Book.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-Oblique.otf) oblique normal 400 condensed>) = 11.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Light-Italic.ttf) italic normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-Oblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'D050000L' (D050000L.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Oriya' (Lohit-Oriya.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Freehand' (KhmerOS_freehand.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Assamese' (Lohit-Assamese.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'AR PL UKai CN' (ukai.ttc) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstDecorative' (KacstDecorative.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-Medium.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Punjabi' (Lohit-Punjabi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer UI' (NotoSansKhmerUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer UI' (NotoSansKhmerUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed-BoldOblique.ttf) oblique normal 700 condensed>) = 1.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumGothic' (NanumGothicExtraBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-MediumOblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'VL PGothic' (VL-PGothic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-Light.otf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Glaho GPL&GNU' (BPG_Glaho_GPL&GNU_2011.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Georgian' (NotoSansGeorgian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-DemiOblique.otf) oblique normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans Narrow' (PTN57F.ttf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstScreen' (KacstScreen.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic S5' (ume-tgs5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstTitleL' (KacstTitleL.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumMyeongjo' (NanumMyeongjoBold.ttf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Glaho GPL&GNU' (BPG_Glaho_GPL&GNU.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstTitle' (KacstTitle.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari' (NotoSansDevanagari-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Rachana' (Rachana.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Ethiopic' (NotoSansEthiopic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-Medium.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstNaskh' (KacstNaskh.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PakType Naskh Basic' (PakTypeNaskhBasic.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMonoBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumMyeongjo' (NanumMyeongjoExtraBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PakType Tehreer' (PakType_Tehreer.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-ExtraLight.ttf) normal normal 200 normal>) = 0.24\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstPoster' (KacstPoster.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Fasthand' (KhmerOS_fasthand.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao' (NotoSansLao-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic' (ume-pgo4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-Italic.ttf) italic normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-LightItalic.otf) italic normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-LightOblique.ttf) oblique normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Light.ttf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam CLM' (MiriamCLM-Book.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-ExtraLight.ttf) normal normal 200 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Stam Ashkenaz CLM' (StamAshkenazCLM.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Siemreap' (KhmerOS_siemreap.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-SemiboldItalic.ttf) italic normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-MediumItalic.ttf) italic normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nuosu SIL' (NuosuSIL.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAGothic' (ipag.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nafees Web Naskh' (NafeesWeb.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic O5' (ume-pgo5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Content' (KhmerOS_content.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic S5' (ume-pgs5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Saab' (Saab.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam CLM' (MiriamCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic C5' (ume-tgc5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSansOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil UI' (NotoSansTamilUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Ethiopic' (NotoSansEthiopic-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMonoOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Bokor' (KhmerOS_bokor.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kalyani' (Kalyani.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Lao' (NotoSerifLao-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstDigital' (KacstDigital.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed-Bold.ttf) normal normal 700 condensed>) = 0.5349999999999999\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Marathi' (Lohit-Marathi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Madan2' (madan.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari UI' (NotoSansDevanagariUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'VL Gothic' (VL-Gothic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-Demi.otf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-BookOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Georgian' (NotoSerifGeorgian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Abyssinica SIL' (AbyssinicaSIL-R.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Metal Chrieng' (KhmerOS_metalchrieng.ttf) normal normal 400 normal>) = 10.05\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumMyeongjo' (NanumMyeongjo.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao' (NotoSansLao-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'esint10' (esint10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-Oblique.otf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-MediumOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAPMincho' (ipamp.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Armenian' (NotoSerifArmenian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Mincho' (ume-pmo3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Gujarati' (Lohit-Gujarati.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Telugu' (Lohit-Telugu.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Tamil' (Samyak-Tamil.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Light.otf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Lao' (NotoSerifLao-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed.ttf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil' (NotoSansTamil-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS55F.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'WenQuanYi Zen Hei' (wqy-zenhei.ttc) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer' (NotoSansKhmer-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Hebrew' (NotoSansHebrew-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Gubbi' (Gubbi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-Light.ttf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Malayalam' (Lohit-Malayalam.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Armenian' (NotoSansArmenian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Armenian' (NotoSansArmenian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-LightItalic.ttf) italic normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS56F.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed-Oblique.ttf) oblique normal 400 condensed>) = 1.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed.ttf) normal normal 400 condensed>) = 0.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Navilu' (Navilu.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nanum Pen Script' (NanumPen.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Georgian' (NotoSansGeorgian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Muol Light' (KhmerOS_muollight.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-Medium.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-BoldOblique.otf) oblique normal 700 condensed>) = 11.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-BoldOblique.otf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans Narrow' (PTN77F.ttf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Thai' (NotoSerifThai-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'LKLUG' (lklug.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Nepali' (Lohit-Nepali.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-BdIta.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo-Oblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'AR PL UMing CN' (uming.ttc) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Battambang' (KhmerOS_battambang.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSans.ttf) normal normal 400 normal>) = 10.05\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic S4' (ume-pgs4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-ExtraBold.ttf) normal normal 800 normal>) = 10.43\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Padauk' (Padauk.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai' (NotoSansThai-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai UI' (NotoSansThaiUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Tamil' (Lohit-Tamil.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer' (NotoSansKhmer-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Meera' (Meera.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Dyuthi' (Dyuthi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Light_Oblique.otf) oblique normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerifItalic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Regular-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'AnjaliOldLipi' (AnjaliOldLipi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tibetan Machine Uni' (TibMachUni-1.901b.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'wasy10' (wasy10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Chveulebrivi GPL&GNU' (BPG_Chveulebrivi_GPL&GNU.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerif.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSansBoldOblique.ttf) oblique normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-MediumOblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic C4' (ume-pgc4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'WenQuanYi Micro Hei' (wqy-microhei.ttc) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstBook' (KacstBook.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Oriya' (Samyak-Oriya.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'msam10' (msam10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Mincho S3' (ume-tms3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao UI' (NotoSansLaoUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Semibold.ttf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-Book.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-Medium.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Georgian' (NotoSerifGeorgian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Bold_Oblique.otf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumGothic' (NanumGothic.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-Roman.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'kacstPen' (kacstPen.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari' (NotoSansDevanagari-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Armenian' (NotoSerifArmenian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Gujarati' (Samyak-Gujarati.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Padauk' (Padauk-bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSansBold.ttf) normal normal 600 normal>) = 10.24\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-BookOblique.otf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari UI' (NotoSansDevanagariUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'stmary10' (stmary10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic C5' (ume-pgc5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PakType Naqsh' (PakType_Naqsh.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Jomolhari' (Jomolhari-alpha3c-0605331.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMonoBoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Suruma' (Suruma.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerifBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'msbm10' (msbm10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMono.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/u/a/aivagnes/anaconda3/lib/python3.8/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n"
+     ]
+    },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f70d5cc8438>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f70cdeeaa20>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -104,19 +570,21 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f70cde76978>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Chord length after scaling:  2.0\n"
+      "Chord length after scaling:  1.0\n"
      ]
     }
    ],
@@ -141,12 +609,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f70d5cc8128>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -171,22 +641,26 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f70cde13cc0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f70cd8eeda0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -222,12 +696,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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vtgm+/zcnVqfg1zf209k8sd1KI4FaAAy77E88FEIYEnsSTEDe3VrN959+EYfZzH3XXMa8kqI9lg0k8wf1BYPcedF55Ke5DqClGhMBTQg0Rsylt1ixOQWP/Mq3U5DZAO4cHbf/w0UkJPn1jRM7QV00UIvQ2dAZ973BSmJEMPGE4OlVa/jly68zOSebe6+6lML0tD2WjcXj/OKl16np7Oan552lbSxzmKIJgcaIsbsUrrjVxqZPojsFmQ2mqFLPd+530t4Q53e3eIiEJ6ZbadRfi9FWlpK7pFCMSDlxpoZUKfnLOx/wt3c/ZFFlBfdcegEuy9CLwpCIGv79kndYWd/At08/ScskehijCYHGqHDqFWaKp+j47699REJDN/LTFxi5+dcOtqyM8pfveybkVpcRfw0Ga3lKZSfSiCASi/HLl1/n2dVrueSI2fz43DMwGfbu8fPQsk94Y9MWrl14FGfPPHS8pDSGjyYEGqOCohNc9yM7XS0qz/11z1tYLjzHzOe+b2P56xH+c7d/yKmk8UKNh4hHOjFY9jyfPhihTIw1Ak8wxPefeZH3tm3nphOP4+aTj9+n7//L6zbyv+UrOXvmNL5wzFEHyFKNiYrmJKwxakxfYOSEi0y89K8Ax55noqhy6H+vc6+30N2msviRIJl5CudePzECt2LhNgD05rx9lEwgxPiPCHr8fm575iWaenv58blncNKUoSOFB7OyroE/vfUuC0qLufXUE7WoYY3RGREIIc4SQlQJIaqFELcPcf4kIUS/EGJN8vHTVOtqHFx87jY7FrvgX3d49zj1I4TgC7fbOPpMI//9tZ9lr0yM1NWxUCsAenNq++8mpobGLz6ird/Dtx5/ltb+xL7BqYhAfXcPv3j5dUoy3Pz4vDP3GFOgcXgxYiEQQuiA+4GzgenA1UKIoSYc35dSzk0+fjHMuhoHCc50hc9/307V6hhLn95zA68ogpvvcTJ1voG/3OZlzfvjP8XymRDk7qNkAkVvR43vHj9xIKjv7uFbTzyLJxTmnksv5MjifU9n9QWC/Oj5VzDpdfzyonO1gLFRJq6q+MJhWrxetnR10eTxjLdJKTMaI4IFQLWUskZKGQEeBy48AHU1JignXGxi2lEGHvutn76uPbuKGk2C7/3VSWGlnj98o3/c9zFICIGC3pSaC6XQ21BjB14ItrZ38O0nniOuqvz+iot220NgKCKxOHe8+Bo9Pj//d+G55DgdB8DSQ5O4quKPROgOBGjyeNjW3c269nbWdnSwtbeXNr+fQCyGPzpxPMr2xWgIQQHQOOh9U/LYriwUQqwVQrwmhJgxzLoaBxFCCL78czvhoOShX+496ZzVoXD7P1y4c3Tcc1M/9VXjF30cC7ejM2WmvA+xorMh43teGB8L1je1JLKHGg388cpLKM/K3GedhJvo22xsaeW2s09jal7OAbD04EdKSSQepy8UotXno6a3lw2dnazt6KCqp4d6j4eOQABvNEos6fRg1evJt9uZlZXFlIyhk/pNREZDCIZaadp1cng1UCKlnAP8GXh+GHUTBYW4UQixUgixsrOzc39tHTVWv7qNJ+54h57mg2f4dyDJL9dz6detLF8c5qPX9r4GkJap8MN/uTBZBHd/uZ/2hvGJPlYjvegM7pTLK3obamzoILqxYF1TCz947iUybDb+eOUlFOwlUGww/1u+cscOYydqew0PiZSSYDRKdzCY6OX39LCuo4MNnZ3U9PXR6vMRiEbRC4FZr9/RcClCkG42U5aWxpzsbKZmZpJrt2M4yNZeRkMImoDBE5SFQMvgAlJKj5TSl3z9KmAQQmSmUnfQNR6QUs6XUs7PysoaBbNHRtOmLrrqPTz2o3d47q4PaanqnlCukBOB879spWKWngd/7tvrFBFAVqGOH/zLRSwi+dWX+ujtOPBiEI/2oTOkpVxe0dkBiYyPfXbVdU0t/PC5l8h2OPjdFReR5bCnVG9p1TYeWvYJp0+fou0wlkRKSSgWozsYpNHjoaq7mzXt7Wzu7qa+v5/OQIC4qpJmNlNgt5Nnt5NmMhGTkkAsRiwex22xUJGezuzsbMrS0kg3mw/qdN2jYfkKoFIIUSaEMAJXAS8OLiCEyBVJHzUhxILkfbtTqTtRueC7Cznja/MwWvS0VHXz3F0f8uQd77Llw0bi0YmdT+dAodMLbrrLQTggefDn3n0KZeEkPbf9w0V/t8pdX+rH139gU1HEo30oxvSUyyt6G8CYLxivbWzmh88mROC3l1+I22ZLqd6mljbuWfwWswry+PZph+/mMgPTO81e746e/qauLur7++kOBhFAltVKqcvFtIwMZmdnk2O3o0pJq99Pq8+HLxrFbTZTmZ7OrOxsSlwuXCYTyiHynY44jkBKGRNC3AK8DuiAB6WUG4UQNyXP/w24DPiaECIGBIGrZKJVGLLuSG06UFQeXUDJ7Bw+eHQ9m99vpKfZy1v/+JRlT2xi5imlzDy5BKtrzyH+hwOFk/RcfquNR3/jZ9krYY47b+/fx6TZBr5zn4t7vtrPPV/t54cPpmG2HpgfmxrtHeaIICkEMT+YxmaUuraxmR899zLZTge/u/yiIfcVHopOr487XnyVLLuNn11wNkb9wTVVsb/IZK/dH4ngi0TwR6NE1c86FBa9nnSzGavBgM1gSEzzCIEqJf3hMG1+P/3hMKqU6IXAbTaTZjbjMBoPaSEVB+N0xvz58+XKlSvH24ydaNrcxdsPfoq3M4jdbcbXE0LRCSqOymf2aWXkVKQf0v9Ie0ONS352TR8tdXHueTEdd86+G6Xlr4e599seZh1r4Lt/cWEwju13J9UYte/MI73sa6SX3ZRSHX/Xu7Sv+yb58/+H2Tlz1G1a19TCD599iRyXg99elroIhKMxvv3kczT19vLnqy+jJCP1dY+Djbiq4otGEw1/NEogGkVNtmlGnQ5bssG3Jh+De/CqlHjCYXpDoZ0a/7RDuPEXQqySUs7f9bgWWTxKFE7L5OpfnsyK56tYs3g7ZoeBnHI39Wvb2fZxM1klLmadVkbl0QXojYdH72wARSf42t0OfnBJL3+5zcsP/+VC0e39B3b0mSa+8gs7D/zYx5+/4+Gbv3eiN4zdj1JNev8o+tTm3mFgjQDkGLiQVrV18OPnEyOB4YiAlJI/vLmUbe0d/OLCcw45EYjG43iTvX1fNEoo9pmXmVWvJ8NiwW4wYDMaMQ6xYCulxBuJ0BMM0pds/HWHUc9/T2hCMIoYTHqOvXIGk44u4O1/raF+bTtlR+SQM8nN1mVNvP2vNXz4+Eamn1DMzFNKcWalNtd7KJBXpue6H9t54Ec+XvpXkAtv3HfDdvJlFsJBePhOH3+5zcstv3HsU0D2l4EIYaGkHmQ1IBrxmHdUbanv7uEHz76E02LhnksvSFkEIJGCesBDaGFF2ajaNR5E43F8kQje5CMcT6y/KUJgMxhIt9t39Pr3tFgrpcQfjdIbCtEbChFTVXRJb5/0w7jxH4wmBGNAdmkal99xAp++Vs3KF7bSUtXDwium4cq2seHtOta8XsOni7dTOjuHWaeVUTQjC6Ec+v+IJ11iZt0HUZ76k58ZxxiYNHvf/vpnfcFCJCx57Ld+jCa48U4Hyhh8V58JQeprOgPrCWq0b9TsaO338P2nX8SgU7jn0gvITNE7CGBFXQP/eP8jTphccdB6CMVUdUeP3xuJ7OjxK0JgNxrJtFpxGI1YknP7eyMYi9ETDNIbChGJxxGAy2zGbTbjPIQWekcDTQjGCJ1eYf75k6mYn8/Sf6/hnYfWkVORzonXzmbRNTPZuLSeje/WUfe7dlw5NmadUsrURcWYbKkFMx2MDASaVa+N8ufveLjruXSs9n07rl3wZSuRoOSZ+wMYzIIbfmof9R6cjCdiHYRiSrmOYkjs5BWP9o+KDV0+P99/+gUi8Ri/v+LiYe0U1tTbx52vvEFphpvvnXnqQdPDVaXEH4ngiUTwhMMEBzX8NoMBt92Ow2jEajCk9Jki8fiOxn/gWk6jcYcL6MHs4jmWaEIwxqTn2bn4B8dR9WEjy57YxFM/e5dZp5Vz9CVTmH9BJdUrWtnwdi0fPLaRj5/ZQuXRBcw4uYTssrSD5sc8HGxOhVt+6+Tnn+/jwZ/7+Po9jpQ+56W3WImEJS/9M4jRKPj87bZR/X52jAh0wxACnRmhmEZlROALh/nBsy/RFwjym8svpCwz9ahUfzjCT194FUUIfnHhOVgME7czIaUkHI/jCYfxJHv+qpQIwGYwkDeo4U+1xx5T1cS0TzCIL5nWwWYwUOhwkG42H3TBXeOBJgQHACEEUxcVU3pELsuf2cK6N2uo/qSZ466eweSFBUw5tpCOuj42vl3H1uXNbH6/gcxiJzNOKmHywkKMlon7w94fphxp4LJbrDz1pwDT5hs49cp9b5QuhODq79iIhuHVh4MYzYIrvz16aywyGdAuhhlaoxhcxEcoBLF4nP97+XUaenq565LzmJqbegoIVUruem0JTb193HPZheS6nCOyZSyIJ6d7Bhr/SHKe36TT4bZYcBqNOIzGYfXWZdLjpzsYpD8cRgJmnY48ux232YxJrzVtw0H7tg4gZpuRE6+dzbTji3n3kXUs+dtqNr3bwIlfmEV2aRrZN8zluKtnsPWjJjYsrefdR9bz4RObmHx0AdNPOrRGCRd91UrV6igP/dJH6XQ9FbP2LXZCCK79oY1ISPL83wMYzHDJ10ZHDBKJcEHK4QUD6gxpqLH9nxqSUnLvW++xqr6R755xSkpZRAfz0LLlfFxTxzdOOYG5RRMnTVc4Hqc/6Zbpi0SQJKZ7HEYjOTYbTqNxvxrrQDRKTzBIT3LRVy8EmVYrGRZLSusGGkOjCcE4kF2WxqU/OZ5N79Tz8dObefwn7zDrtDKOumAKJpuBmaeUMePkUjpq+9i4tJ6tHzez6b0GMktciVHCMYUYLQf3n07RCW75jZMfXtrLH77p4VfPpuNM33ePUAjBl35uJxqRPHVvAKNJcN4NI9/YRojE9zl8IXATj3Tv930fX7Ga1zZs4pqj53HWzGnDqvvu1moeXb6Ks2dO54I5ox/HMByklASiUfrDYfoHzfWbdDqyrFZcJhM2o3G/Fmij8Tg9oRA9wSDBWCyx6Gsy4bZYcJlMWuM/ChzcrclBjKIIZp5SSvn8PJY/vZm1b9SwdVkTCy6ZyvQTS1AUQU55Ojnl6TtGCRvfqefdh9ex7ImNVB5TyIyTSsguTRvvj7LfONIVvv0nJz+7po/7vuPh9n/sO74AEt/dV+90EAnD/+7xYzQJzvjcvqeX9kpSCBjmZvQ6cw6R7u37dcuPa+r41wcfc/KUSq4/9uhh1a3v7uE3r7/F9LxcvnHKCePSGA4EZA00/rFkBK/dYKDA4cBlMmHezykaVUr6QyG6QyE84cT6jdVgoMjhIN1iQa8t+o4qmhCMM1aniZNvmMvMU8v44NENvPvwOja8Xceia2ZSOC2RYthkNTDr1DJmnlJKe00fG9+pY+uyJja9U09miYtpxxcz+ZgCzPaDb6OR8pkGvvgTO//4iY+n/hzgym+lNtWj0wtu+Y2DWETy7//zIXRw+lX7LwaKPjGqUGN7T5u9K3pTLvFIF1KNppy+GqC5t4+7XltCZXYW3z3jlGE15IFIhJ+/tBizwcBPzzvzgKaPGEjF0DcoGlcRAqfJhCv52N9GWkpJMBajKxCgNxQiLiUGRSHHZsOdnPrRGBu0b3aCkFXi4qLbj2X7ylaWPbGJF369jPJ5uRx75Qxc2YnGUQhBbkU6uRXpLLpqJls/amLz+w28/9/1fPj4RsqPzGXa8cUUzsgaE1/7seKUyy1Ur43x/N8ClE7Tc/SZqXnu6A2CW//o5A/f8PDgz3wg4fSr908MFH0aMHxX0MRuZpJYuAODJbU5+lA0ys9eWowiBHecfxYmQ+o/w8TeAksTi8PDjDPYX+KquqPx9yS9fPSDArLs+znlM0BMVekJBukeNPWTZjaTYbFowV4HCE0IJhBCCCYdlU/pnBzWvL6dVS9vo27tUuacUc68cyt3ijEw2QzMOq2MWaeV0Vnfz5YPGqha1kT1Jy3Y3WamHlfM1OOLdojIROeLP7HTVB3jL7d5yCpIo3xmar1rg1Hw7T87+eM3PTz4cx9SwhnXDF8MFJ0JobMM2xVUb0rsDhYLt6csBPe+9S51Xd386pLzh+3l8/yn63inqpovLTqGucWFw6o7HAYa/97k1IwE9IqC22Ih3WTCPsIGWkqJLxKhKxikLxRCkkgIV+R0km42a1M/BxhNCCYgeqOO+edPZtqiYj5+ejOfvlbNpnfrmXdeJbNOLdstV1FWiYusklkce8V0ate0s/n9Bla9vJWVL20lf0oG004opmJ+HgbTxP1zG02C/3efi59c0ctvb/bwy6fSUkpOBwkx+NafnPzxVg///kViZLA/awY6QzrxSM+w6gzsbzyw3/G+WLplG0s2VfGFY+ZzVGnxsO61qaWNv723jIUVpVx51JHDqpsKUko8yTw8A9M+BkUh02ol3WzGlmJQ194YCPjqDgYJx+PohCDDaiXTYsE6geMfDnW07KMHAV0N/Xz05CYaNnRiz7Bw9CVTmbywcK/TP76eIFs+bGTz+w14OgIYzHoqjy5g2vFFEzoTan1VjDuu7qWgXM9P/5OGyZK6ndGI5I+3eli9NML1P7EPWwxaVl0PSPLnPZRyHalGqH33GNKKb8Bdcctey7Z7vNz4n8cpcbv5w5UXD8tv3hcKc+N/HkdRFP72uSuwm1MPfNsbA3l4BqJx48kkbOlmM+nJBG4j/V+RyXWFAZ9/SCwoZyQFRkv1cODYU/ZRTQgOIho3dfLRk5vorOsno8jJwsunUTwre68/VCklrVt72Px+A9WftBCLxHHl2JhybCGTFxZOyKmjlW+H+f3XPSw408Q3fz+83EKxiOSP3/Kw6u0In/u+bViupR2bfkKw5yNKFr05LHsbl1+MwVJE7uw/7bFMXFX53tMvsK29k79/4cphpY+QUvLLV97gg+oa7r3yklHZczic3KGrJxgkoqo75uXTRzEPTzgWoyvZ+4+pKnpFIcNiIcNi2W9vIo2RoaWhPgQomp5F4U9PoHpFCx8/vZmXf7+cgmmZLLx8GjnlQ++sJYQgf0oG+VMyOP5zs6he0cLWZY188lwVnzxXRV6lm8nHFjJpQT5m28TwOpp/iomrvmPjsd/6eTRP4fO3pb4gqjcKvvVHJ/ff5uV/9/jxeyRX3GpNqVdrsBThi7yIGg+i6FIfTRhtkwl71u21zItr1rOuqYXvnnHKsEQA4PWNW3h3azU3LDpmRCKgSklfKER3MIg3EgGSeXgsllHLwzPQ++8KBPAk7+EymcjQfP4nNKMiBEKIs4B7Sewy9k8p5d27nP8ccFvyrQ/4mpRybfJcHeAF4kBsKLXS+AyhCCqPLqB8Xh4bl9ax4sWtPP2L9yk9IpcFF00hq2TPjYzRomf6CcVMP6EYb3eArR81U7WskXcfXsf7/9tA6ZwcphxbSMmcHHT68V2sO/9LFrpb47zy7yCuTIXzv5R6z15vFHzjtw4sdsHzfwsQ8Khc92P7PkcWBmsiqjcaaMDkmJLy/Yz2Svwdi1FjviH3M+jwennww+XMLynmzBlTU74uJJLJ3bf0PeYWFXDl/COGVXeAYDRKV7L3H5cSYzIVQ4bFMmTO/v0hEo/TFQjQHQwSVVUMijLq99AYO0YsBCIRm38/cDqJzehXCCFelFJuGlSsFjhRStkrhDgbeAAYHEFzspSya6S2HE7o9AqzTy9n6qJi1i2pYc3i7Tx5x7uUz8tjwcVTyCjcuzeKI8PKvPMqOfLcSXTW91P1YRPbljdRs6oVk81A5dGJHEjjtZ4ghOC6H9rx9Ege/Y0fp1vhxItTTxGt6ARf+YUdm0Pw8oNBAj7JV+907HVzG6M9Edkb8W4ephBMStTzV2N2zd3pnJSSP7/9HqqU3HraicP6LqPxOHe+8gYGnY7bzzpt2Ll4+sJhOv1+fNHomLhkDmzy0hkI7Jj7dxqNFCUjibXe/8HDaIwIFgDVUsoaACHE48CFwA4hkFIuG1T+Y2Ds/N4OM4wWPfMvmMysU8tY88Z21r5eQ83qViYdlc+Ci6aQnu/Ya30hRCLPUWkax101nYYNnWxdlohP2PB2Ha4cG5MXFjLl2AO/nqDoBDf/2oGvT+WBH3uxuQTzT0l9kVQIwTXfs2FzCp74Y4CgT/LNPzgxmoZuoAzWYoTORti7CQcXpXwfo20yABHftt2E4MPqWj7aXseNJxxL3jBdRR/56BO2dXTy8wvOTjleIKaqdAUCdAUCRFQVo6JQ4HCQMYrRuFFVpTsQoCsYJBKPo08GfWVaLFqyt4OU0firFQCNg943sXNvf1e+BLw26L0E3hBCSODvUsoHhqokhLgRuBGguHh4bneHAyabgaMvnsqc08v5dPF21i2pYfuKFiYvLGT+hZNJy9l3Q6LoFErn5FA6J4dIMMr2la1ULWtixQtVrHi+iuyyNCqPKWDSgnzs6SNM6ZAiBqPg//3ZyZ3X9/PHWz38vz85OfLk4YnBRTfZsDoV/v0LH3d/uZ//d58Tu2v3RlEIBZNjGmHvpiGutGf05jyEzk7Et22n45FYnL+/9yGlGW4uPXLOsK5Z1dbBEys+5cwZUzluUvk+y4djMTqSAiABu9FI4Sj2zKWU+KJRugKBHX7/doOBfLudNM3z56BnxF5DQojLgTOllF9Ovv8CsEBK+Y0hyp4M/AVYJKXsTh7Ll1K2CCGygSXAN6SU7+3tnoer19BwCHrCrH61mg1v1xGPxqk8poAjz6sko2D4aYq93UG2LW9m2/Jmuur7QUD+5Awqjy6g4qg8LI7RcWXcG36Pyq9u6Ke+KjZsMRjgw5dC/PWHXnKLddz2dxdZhbvPXXdX/57+xkcpPeH9YS0YN6+6DoFC/rx/7zj29Ko1/O3dD7n7kvOZP4yYgUgszs3/exJvKMy/rrt6r66iwWiUdr+fnlAIAbgtFrKt1lHbk2Ag6rcrECCU9Pt3WyxkWq1ayoeDkDFzHxVCLAR+JqU8M/n+BwBSyrt2KTcbeA44W0q5dQ/X+hngk1L+dm/31IQgdfx9IdYs3s6GpXXEwnHKjsxl3nmVe/Qy2he9rb6EKHzcTF+bD0UnKJyRlVjAPjJ3TPdOGBCDhqoY395PMdi0PMLvv+FBb4Tv/dW1W/rrQPcHtK39Orlz/4bVvTDl63ZW/RJ/+2JKjn8fIQSeYIhrH/wv0/JyuOuS84dl40MfLue/y1fyy4vO5Zjy0iHL+CMR2vx++sNhFCHItFjIttlGbWE2GI3SGQjQEwqhSonVYCDLYiHdYtF6/wcxYykEemArcCrQDKwArpFSbhxUphh4G7h28HqBEMIGKFJKb/L1EuAXUsrFe7unJgTDJ+SLsG5JDeverCXsj1I0I4t551eSPyVjv6YOpJR0NXioTo4UvN1BdHqFkjk5VB5TQOmcnN0ioEeDwSODr9/jYOHZqS8gD9C8Pcavb+zH06Pyzd/vLChqLEDd+8fjKvo8GZO+nfI1+5ueoHvrryg+9g305hwe/OBjHvtkFX//wlWUZ6W+29i2jk6+/r+nOHXaZG4767TdzgeiUVp9PvrDYXRCkG2zkWW1jsr8/4DrZ0cggC8S2THCyLJatajfQ4QxDSgTQpwD/JGE++iDUso7hRA3AUgp/yaE+CdwKVCfrBKTUs4XQpSTGCVAYr3iUSnlnfu6nyYE+08kGGPD0jrWLN5O0BMmr9LNkedVUjJ774Fpe0NKSfv2XrZ+3Ez1Jy0EPWEMZh1lR+RReXQBRTOzRtUd1e9R+e3NHqpWRfnij4cfQQzQ16nym6/1U7splrjGoPxELatvQI35KFzwZMrXC/atpnX19eTOuZ+4bT6f++cjzC8t5qfnnZXyNaLxOLc8+hS9gSD/vPZqnJbPRC4Ui9Hq89EbCu0QgGyrdVR8/4daYM60WskcJYHRmDhokcUaOxGLxNn8XgOrX6vG1x3EXehg7pkVTD6mAJ1h/3vyqipp3tJF9cfNbF/VStgfxWQzUDE/n0lH5VEwLRNFN/LGJRKS/Pk7Hla+FeGSm61c9o3UgsYGEwokrrF6aYTzbrBw9XdtKIqgr+Fheqp/T9HCVzBYUnNwi0c91L9/PO6KW3mpZRYPf/QJf//ClVRkZaZsz38+WsHDH33CLy48h2MryoCEOLT4fHQHgyhCkGW1kmOzjUoDHYzF6PD76QkGdywwZ2uun4c0mhBoDEk8prJteTNrFm+nu9GD1WXasffBSPc3iMdUGjd0sPXjZmo/bSMWjmO2Gyk7MpdJR+VTMC1zRCOFeEzyzzt8vPNMiFOvMPPFn9j3GicwFGpc8vCvfLzxvxBHn2nk5l87EbKFxo/OwV1xK2klN6R8rfoPz8ToPIKvv1/JjPxc/u+ic1OuW9PZxdf+9xQnTq7gh+ecgSolHX4/bX4/UkoyrVZybbYRb8Q+kPWzPRDAEw4jgIzk9M9E3vReY3TQUkxoDIlOrzD1uCKmHFtI06Yu1izezvJnt7Dq5W1MXVTEnDPLU3I93dO1S+fmUjo3l1gkTsP6DqpXtFD9SQub32vAZDPsEIXC6cOfPtLpBTf+0k5apsLzfw/Q3hjn1j84saelfh1FJ/jij+1kF+r43z1+etr7+O5f8jA5ZuDrWDIsITDaK+nr3YgnVMQlw3AXjcXj/Ob1t3GYTXz9pOPpDYVo9nqJxOO4TCYKHI4R5+aRUtIbCtHh9xOIxdAnI39Ha31B4+BGGxFo7EZ3s4e1i2uo+qgJNa5SdkQus08vp2Dq/i0s70osEqdxYyfVn7RQt6aNSDCGyZoQhfJ5eRROzxx2yux3nwvxz596yczX8d2/OikoH37Dufz1MPd/30NGro6v/Wwp+uAvKFzwNEZ7ZUr1e7bfS3fdQ9zd8G3++cUvpPxdPbp8JQ9+uJwfnnM6RdmZeCMRzHo9hQ4HTtPIXHPjqkp3MEhHIEAkHsek0+3Y8Uvz/jn80KaGNIaNvy/Ehrfq2LC0jpAvQnq+g1mnljLl2CKMltEZTMajSVFY0ULt6oQo6AwKRTOyKDsil5I5OdjSUvMM2ro6yu9u6ScWhVt+6+CIE4ffiG79NMpvb+5HSsnnvvIdZi6aSuaUH6ZUd/u2JxCNv2Kz41ece1Rq00J1Xd187X9PcmRJMZcdMy+RJDDZUx+J6MZUlQ6/n85AgLiU2AwGcmw2bf7/MEcTAo39JhaJs215M+vfqqWzrh+DWc/U44qYdWrpPlNYDId4TKVlazd1n7ZRu7oNb3cQgJzydEqPyKHsiFzcBY69NmSdzXF+9/V+6rfEueiriUVknX54DV9bfZxf39hPV0uYy6//Pefd+nMU3b6T3j363tMcE/s/7JW/ILvown2Wj6sqtzz6NK39Hv7fuadT4HJS5HSOKBYgGo/TEQjQGQigSonLZCLHZsNunBiZZTXGF00INEaMlJL2mj7Wv1lL9YoW1JhK4fRMZp1aRuncnFHxBhp8r+4mD3WftlP7aRsdtX0AOLOslM7NpWRONvmTM4aMVYiEJA/90sfSp0NMX2DgG79zkpY1PNs8vSq/ubGF6vUmLrlxG5d9e+E+93344oMP8+PSP5FR+kXcFd/c5+f754fLeeKTVVx7/DGcP2sGaSPorUficdr9/h0pJtLNZnJtNm0BWGMnNCHQGFUCnjCb3q1n49J6fD1BbGlmph5fxLTji8ckOZ2/N0Td2jZqP22naWMn8ZiK3qgjf0oGxbOyKZ6ZRVqefaeG9N3nQjz4cy8Wu+DmXzuZfdzwesXhoMrvv/oW6z6Zy+lXG7nuR849ji62tXfytf89yZ/nvUZ6Wim5c+7b83VjMT5uaOSuFxczu6iAOy86d7+9gcKxGO1+P91JF9AMi4Ucm03b+EVjSDQh0BgT1LhK3Zp2Nr3XQMO6dqSEwumZTD+hhLIjc8ckujgajtG8pZuG9R00buigr80PgCPDQtGsbEpmZVMwLROT1UDj1hj3fttD8/Y4Z1xj5prv2Ye1/aWv430e+cVa3n/zSo482cg3fufEbN29/mOfrOJfH3zMQ6fWofrWU3zc0MHxPcEgdX393L9kKd1eP//+4jWk21Lfa2GAUCxGm8+3I8fQgACkmv1TSkkoHCIYDBKJRAhHIkQi4UHPYaLRKPF4nLiqosbjqFIlHleJq3FUVUUIgRACRSiJZ0UgBl4LgaLTYdDr0esN6PX65Gt98rVhp9cmkynxMJpQNC+mMUMTAo0xx9cTZMsHjWx6rwFvVwCTzcCUY4uYdnwRGUXOMVuk9HT6aVjfScOGDpo2dRENxRCKIHdSOsUzs8mbksWbz+hZ/EiIvFIdN//awaQ5qU2ZSClpXnk1H74+nxcevY7ymXq+91cXroydG6vbnnmRbp+f35zoo7fmPkpP+AhFb935Ol4vHYEAy6qqeX7VWn5w9umcOm3ysD5rMBqlze+nNykAWVbrTjmGYrEY/Z5++j39eLyexGuvB7/fTyDgxx8I4A/6CQQCqKqa8n0VRUGn6FAUBUWnJBprCapUkVIiVbnTaykT7/cHgyEhDGajGaPJiNlowmQyYzKZsJgtWC0WrFYbVos1+dqafG3FMAp7LB/KaEKgccCQqqRpcxeb3q2nZlUralziLnAweWEhlccU4Mwcfg84VeIxlfbtvdSv76BxfQed9f0AWBxGHDkZfPqxnZZGJ2de6+KSm20pjQ6CPctpXXMjje2/4sFfLyA9W+G2B1zklSZ636qUXHDfA5w5YxrXz4zQseE7FMx/DJNzOpBYwK3t68MXjRIJR/j5869wdFkpd5x/VsqNlj8apS2ZY0gBzFIS7u2iuzvx6OrpprevF3/Av1tdi9mCzWbDZrVhtVqxWW3YLFZsNhsWswWj0YTJaBz0bMRkNGEwGnY0/vvTuKqqSiweIxZLPKLR6JDvo9Eo4XBiFDLwHAqHiYTDhAYdC4dCBEJBwslNcIZCr9NjsVqwWWzYbTYcdgd2uwOH3YHDbk++TzybjIefB5UmBBrjQtAbZvuKFrZ+1Ezrth6AxD7JCwuoOCp/zFNYB/pDNG7spGF9J40bOgh6E/vo+vw2IvE0TriqgEWXZO8zmK1t3bcI9i4nbH2R338z0Xh8768uKucaaOnr59oH/8t3Tj+ZU8tNNC2/hKzpd+LIPY9QLEZ1by/ReJxCh4M7nn+Vdo+Hf153NenWvQtiXI3T0tVFZygERhOxaJS6LRvZtGYF0UGNYZrTRYY7g/R0Ny6nizSnC5fThdPpxOVwYTzEPIbi8TjBYHDHyCYQDBIIBggGAwSCAQKBAP5AAJ/fh8/nxev3EY/Hd7uOQW/YIQ4upwuXK400V+K7czkTry1myyElFpoQaIw7nk4/Wz9uZutHzfS2eBGKIH9KBuXz8ig/Mhe7e2w3u5GqpKuhn/r1HWxZ1k5fSx9CSCQ6imZkUjE/h+KZ2Tizdm+go4EGmj65FEvGIlT7b7jnq/30dqh8534X3pwm7njxNf589aVMzcmg9t2jSSu+AVPRjVT39iKAivR0nv90Hf/+cDk/Oe9MTpw8abd79Hv6aWhqpKGpgb5ggMziMrLyCggHg1RvXIuvs50sdwZZmVlkuDPJzMjAne7GaDi0GvrRRkpJMBTE6/Pi8/nw+rx4k88+nxePz7tjOm1XwTAajLhcLtKcabicTtJcabjT3bjTM8hId2OxHFxCoQmBxoRBSkl3o4dtn7RQu6qV3lYfANllaZTPy6PsyFzcoxifsCf8/RGe/3MzVR91kObqw2RM9LLTcu0JT6RZ2RRM/cxFta/+IXq2/4Hs6XcRN5zFr27oo6UmzjG39vCc502evukG0qwW6j88A4PrKDqyvolBUahIT6e2s4tvPfEcx1eW8+NzzwQSDf+2mmq2126nobGePk8/uUWlTDviKNzZOcSjEXShELkOJ9lZmeh1mifQWKJKlYA/QJ+nj77+fvoHP3v66e/vx+f37VTHbDInhcFNRvJ54OF0OlHExFr41oRAY8LS2+KlZnUbNataP4sXyLZSPHOgMc4ctUjmoWhvjPPYb32se7ef/MJ+Kqd5CPb1Eo+q6AwKBVMyKD0il9IjsvHUfI1ooIaC+Y8RjhRw91f6qdkUpe+UZbx07wXoFIWGT64mhJVo2d1Uut2Eo1Fu+m8ipfXtJxxNQ10122qq6ezqBMButzPriKPILZuEYjRhVBRy7HYytDQQE45INEJvXy89vT109/TQ0/vZo7e/d6cFeIPeQEZGBtkZWWRlfvbIcGdg0I9PfIcmBBoHBb6eILWr26hf30Hz5i5ikTiKTpBX6aZ4VjaF07PILHaOavDaAFWro/z31z6q18YoKIdTLg5jt/TSsL6D/vbEImxWiRV35mIKKnuYctp9hEJmvn1lE/21Rn76bzeT5uuoW3UjIu6lZMFjKAJue+JZ1rd2cAR+9AEPer2esuIyJpVPIr+sgpCiI5TMA5Rrt+M2mw+q6QaNBHE1Tn9/f0Ikenvo7umms6uTzu5O+vr6kCTaWiEE6WnpZA0IREYmOdk55GTljPl6jiYEGgcd8Wic1m09NKzvoGFDJ92NHgAMZj15lW7yp2SQP8VNdmnaiPZQGIyUkuWvR3j2fj+N2+Lklui46KtWps+L0LC+ndrVbbTX9AKQltPPzFOPYXWsn1d/kYYj5uJL/xBkRW/DJP00ym/wyPJV1MR1lMV9nFpRwsxpM6kon0R/NEZnIEBUVbHo9eTabKQdxAIQV+PEojGisWjCKygeQ42rxOJxVDWeiEcYeKjxRDxC8lwseXy3cvE4qpTIAbfUQQ91l/c7HurOZSHR8JKMeRBCIHY9BjviHxAkR2GJuAhF0aHT6dApSuJZp0seS7jTJt4r6HR6dDoFRdGhT5YbHC8B4PF56Ovro6unm+6eLjq6Ounu7iYWjyXsROBOd5Obk0NOdi652bnk5eSSlpY2alNMY71D2VnAvSR2KPunlPLuXc6L5PlzgADwRSnl6lTqDoUmBIcn/r4QLVu6adnaTUtVNz3NXgB0BoXssjSyy9LIKUsnq8yFK9s2okZVVSWr3orwzF8C1G+O4coUnHK5hVOvNGM2Rdnw5mts/agdT08+Qi+osQbxrFiIPdfEV279Kj19fv615WQajE6OLczhB+efiyoEXf4APdEIEnAkN4JxHuBEcFJKwpEwgUCAUDhMOBxKuGyGQjtcOBPHE+cGykRjyYY+GiMWi+70fn9jBvbGjmA15bOgtVQfilBg4CuVJMQk+dmllCAlKjJ5TiIZJB7JY6pUUZOCNdCbH5XPhUgE0xkSQjEQQDfgbhuNRolGozvKK4qCw27H6XCRnpbOwqOOobiweP/uPYZ7FutI7Fl8OtBEYs/iq6WUmwaVOQf4BgkhOBq4V0p5dCp1h0ITAg1IuKa2bu2hpaqbtu29dNX3E48lGiSTzUB2aRpZpS7cBU7cBQ7S8+y7RTqrqiTUFyHQE048ehOvg71hwr4oYW+U1poIzVVhPB1RBHHsToHDrWAxd6PGm4jG3YRiDvTpepq9kzj76heQVhe+iBuXTj/kph9CUdDp9egMegxmE0azGYPZhMFixmQxY7bbMDtsmO129MY9zyerUiUcCiddKYMEBrtUBvwJd8oBl8rk62AgSFzd3Z1yMIpQEkFdpmQwl9GIwWBAbzDs6OUa9AYMBj16nX6X44mI4c96z4N71Imec+KYbqdetl6nQxl0biJFGKtqMqI6KQzxQaOXgXOJ92pilJMcFUUHRDOaEM5YLEokGk0eGxDSwaKaeD0gzJFIhGgsyuB2+oyTT+fE407cr88xlhvTLACqpZQ1yRs9DlwIDG7MLwQekYlP87EQIk0IkQeUplBXQ2NILA5TwvV0Xh6QSD3RuqWX5g3dtFf30rHBQ/WSVuJhiRqVyKhEJ3QIKZBRiAbiRAMx9tiZFWC06THZDaTZ9Lgr9fi8Cv3dEm+tikOXRbEtn0ydAWfRZiKnNDM/PQRUYtRHcNnjGG1m4kYH0piGzWRKbAKvStRYjHgsRjwaIxoOEwmG8Pf1EQmGkbtG/CoCVa8QFZKgGsUXDdMf8tMX8BIIBthTZ05RlB0Rt1arlUx35o4oXFvy2Ww2Y0pG7poH0jyYTBj0WoTuYBQlGU09To5bcTVOJByhu7cHd1r6qF9/ND5WAdA46H0TiV7/vsoUpFgXACHEjcCNAMXF+zcsisfUUd1EXWNsUOMqYV+MsDfRIw95IgR6Puu1B5O99kDvZ734YG+YYF+EPY3gFb3AYNMhTaDqVFRFRUkTWLL1CINAZxRY3CZsGSbsmWbs2RbsmRYsDiMGkx6DSYfepENEBMFlIbxvR1DaVNCHiJ9xJ1WeFVSkfxsVhbCvhd6mGiYdmUdxoRlFSGKqQqffRl2fg/Z+QTgSTUzDRMJEwpEdeX9UqWJQdFh0Rix6Ixa9AYvOiNVgwmEwk6boScNKodmKtGQhDTp0VjMmlx2bOw2nOwObLRE9bNL2Hjhk0Ck6LBYLhZaCMbn+aAjBUP9pu/4c91QmlbqJg1I+ADwAiamh4Rg4wD/PW0Lr+l50RgWjRY/JYcCSbsTqNmFyGDCYdejNOgwW/Wev9/RsSZTbccykQ9Er6AwCnUFBMSgoOnFI/BDVuEo8ohKPqsSjknhURY0NPpZ8ROJEg3FioTjRUJxYeIjXwTixsEokECXsTTb2ySmYxHOMaCC2V3v0Zh3W5N/Nkm4ir8C203urO/E68T7x2mjV7/S3UFWJryeItzOApyuApzOAtztAsD9MwBOmZWs3wVVh1HjiX80cMpLTlUZmrwNFKoTMYbqL2sg89SfY0rax9EdHkVfUQlZZOpBPJCZ48Jdr6Oju4dRvzWf6JDMVOR7yHF56nFDVrqepz4rJ7sTkNmIym7FZrMkcOolcOrYdvXcbRqMRIQSxcAR/v4dAXz/+Pg/+nj683b2E+toJ1bfTq9dhd6fjyEjHlZOFKycLoyW1jX00Dl9GQwiagKJB7wuBlhTLGFOoO2qUHZdNNBDD3xUm5IkQ7IvQ15hwC9QZEw03QiDVRGMn4yNfINIZld0EQqdXBr0WO0QDQCgCIZLPSsKLIbEAxmfvk+dEsnxikSsROfvZI7kIFt/l/cDrZDlVTUybxJMNuxobaNTVHcdGbZ1MgN70mYianQZMdgPWdBPpRTZMDsPOD3vi2ew0JBr2dCOW9ESjPlIUReDMtOLMtLKnPpYaV+n9wEv7Y7341gURRrCdasF5lg19iaRp2fdB3c6KR75Mb89KWj7JYOHZXbS1GhEYuPDGs/H1+2nc2ErxaVeSUZFPzFNFZu8a3PZ2UMIY3NMxuo9E6FKLqtabjLiyM3FlZ+44JqUk6PHi7epJPLp7aN1aQ/PmbQBYnA7SchOi4MrNxrwf2U41Dm1GY7FYT2LB91SgmcSC7zVSyo2DypwL3MJni8V/klIuSKXuUIzGYnE8qtJd66V9Yx/tm5OPTX14WoM7yphcBjLK7LhLHaQV2nAWWHFkW7CkGYhHZKKnG4oTDcWIhVXUqLpLgzqo95zsNSfKyJ0b3WiisU1kbfyswd7pmJr4Ow1+P9CgJ4RhkEAMFguFHSOTwcc+e09CkAYEakC4jMljAwK207FBQjbwWi8SjXxyxDR49DRwXGfcv+RlBxqpSnrf9tLyry4CVWEMGTqyr3STeZGTpo09bHypHvg/Cua8T9WSr5A+5VL++qvbOSv7Ki77YxnWyRtpb3gTc9HPaNjYRdDjQadTMDvsZJUUklFcgM0eJtqzirh3GyhGDOlHYHTPQ+hHJ82GGo/j6+6lr72T/vZO+tu7iCc9USwOO+kFubgL8kjLzUKnbV5z2DDW7qPnAH8k4QL6oJTyTiHETQBSyr8l3UfvA84i4T56vZRy5Z7q7ut+Y+k1FOgN07Gln7aNfXRVe+jc1k9XtRd/Z2hHGZ1RIaPcQWalk8wKJ1mVTtxldtJL7FjTxzaJmsbYIWOS7sUeWh7sIlQXwVxiJO1iB916HzUftlP9TivB3gilx37MrAv/jcF2IzMv/DWtH7fztTOu4oI5X+D6t67H3/RvlFg/2CdjLzqfj196j1f/8gRHnTEfl9uGlBKjxUxGUT6ubBs2fTUEt4Fiwph5NIb0IxDK6K5KSlXF39tPX1sHva3t9LV2oMbjCEXBlZNJen4uGYX5WNPGLl24xvijBZSNkGBfJCkMHrqqPXRt89C13UNvvX9Hbx3A7DLgLnWQXmLHXWrf6dmRa0FRtB/ZREONqHS91E/rQ92Em6OIbEF/foiqmlY6tybSWNuyzFScmMuU00OEvVdjz5xH5WkvEKyOsu7L2/jVqlu45rs3cNXPr6Ov4WXCLa9ic1Vgyj0dQ/psHvvVQzx257+59a+3MXPRDLrqm+htaSMejSGEwJHpwun04rC0YHVZMOUcj94xZcwaZTUWp7+jk57mNnqb2/D3JdN1O+1kFheSWVyAIytDE4VDjLF0Hz0ssKQZKZqfSdH8zJ2OR0Nxeuq89NT66K330VPvo7fOR8u6Hja90rjTOoPerCOtyJaYZsq34Cqw4SqwJh82nHmWMdnRS2NovM0B6h/swP9GEOEHLyGqelvpaPNg2KajaH4mc68opeLEXLKnpYGMsPnVk9AZbJQt+if+TRG2frMRs82MxW7F6080pvGoh+bqJ5hy3L2E25YgZZQrb7+Wte+s4h+33cefVzzEjJOPQ1VVPB1dyca4labtcSAHvUHidC0jLetTsqaeiNmVP+qfXdHrSM/PJT0/F46CsD9Ad2MLXQ1NNG2sonHDFoxWC5lF+WSWFJKWm42YQH79GqOLJgQjxGDWkTM1jZypabudi0dV+pv99NQlRaLOR2+DH09LgNb1Pfi7dtlgQ4A9y7yTMNizLThyzNizLNiTz1a3UeupDYNgX4TuWi/dNV46t/bTvq4ffRXkx9Mw6Qz0RHy0mftxHGVl3lGTKJqfSc70tN1cjZtX30WobxOTTnkK3wob239QjyFDz5T7i8m8OIvO5g4Awv5GpIxjLryQSPtSIu3vIGNBbv3rbXzzmC9x/y2/4Y7n7kFRFNJys0nLzYZ5s4kEQ/S2tNHd1EpvczM9XXFqNr+HzWUko7gcd2EBzqyMMWmQTTYr+VMnkT91EtFwhJ6mFroammnbXkdL1XYMZjPZZUVkl5fgyHRr/3+HGJoQjCE6g4K71IG7dOiUytFgDE9rkP5mP/3NAfpbAonnZj/tm/vY9lYL0eDuEaCKQcGeacKeY8GeZcaRY8GWmXSdHOxGmfSyMTsP7eCgaCiOpzWApyXxHXpaAvTU+uiuSTT+gZ6E4OqFQpk9izJ7Fnp0qCXgutjOjAvKsWXsfW3H37mStk33klHxBYLvzqfh903YppqZfG8Rhgw9+ZOKaN7aAECobxMmRyk6gwNzwXmE294k2r2cdJeXL9xxA/+87S+sfP1jjjpr4U73MFrM5FSUklNRilRVPB2tdG1bQW+7h4b1W2hYX4XOYCA9P4f0/FzcBbmY7bZR/z4NJuMOO+KxGD3NbXTU1NNStZ3mzduwOOxkl5eQXV6C1TX26cI1xh5tjWCCE/ZF8XWE8HYE8XWE8CWfP3ufOObvDu/RzVPoBJY04w4XTLPL+Jl7pj0RT2G0GTA59IOOGzDa9TtiJBIPBb1ZN2aiIqUkFooT9seI+GNEfFHCvhjB3jD+7vBnaSB6Eu99HSE8LYEdDf1gHLkWMsodZJQ7cOfZsTUaiS6PIoOStBPs5H85E/vM1Dx01FiQza+cQDzqx7LmOXpfVUk7yU7FnQXoLIne+cM/fYDn732cp7reYMurx2B2VjLp5Md2fK5o93IinR8iLEX86OqXCYckf/7k3+gN++6LxfwN+JvexNMVwhcsoL9HEA4kvNusLgfpBXm483Nx5WahS3Hz+v0hFo7Q2dBER009fa2J0Y8zK4PcyeVklxZp3kcHAdoawUHKQKOcUb73npcaVwn1Rwn0JiNv9/TcE8bTEiDs/yyYKx4eXsIwnUnZIQ4DbqGKPhlAN9hlNemuqugEUoIaU1FjSZfauPzM3TaqJhp+f2yfsRsGiw5rRiJYzJFjpuAIN658K848K858a/K1BYNFT6QzStt/e+h4qpdIOIL7NAd5N2RimzK8AKuWtb8k5NmK/qO/0rtcpfDrWeRdn5GI60hSNKWYeCxO4+b1hD3byJz0+R3nhBAYM49B6O2EW5fw478t5DsXPs/rD77EuV+9eJ/319uKcVZ+AbPrA9J7V1NUmo60n0B/d5ye5jZatlTTvGkrik6HKycr6Rqai9U1uh5AepORvMpy8irLCfsDdNQ20Latlq0frmD78k/JLi8mt7Jcmzo6CNGE4BBB0Sk7ommHSywSJ+KL7Rzh64sR8cUSEcHJRyIqOBEZPPh4LBhHVRPBa2o8GecQl6hx9bNYB0Bv0ifiEfQKikEkEowl4xSMtsQIxGhN5PYx2vQ73g98roEI4X0Rbo1Sd28bnc/3IeOSjLNc5F+fgaV8+N+Nt+Nj2jfdj9x4CfEt85lyXz6uhfbdyhVNLQFg28evk2EBR87xu5UxpM1E6O3Q/CJ3PXoK99/xHGdcfx6GvSSVG0AoBky5J6NzlBNuWQy9L5KdfQwF049Hjav0t3XS05LwAKpZsYaaFWCyWhKjhYJc0vNy0JtGL9e9yWalaOZUCmdMwdPZTevW7bTX1NO6tQZbuovcynJyKkowmDR36oMBbWpI45Ah1Bih9aFuul7qAyDz/DTyvpiBuWj/GsBwh5eNzx+PGgli2/I8FXdMwpg1dKMd8ge5Mudszvq8m4Unr2LO5dv3GAsQD3XgrX6CSNBPbWMZR19y5bDskvEQ4ba3iXk2o7MVY8o/F0X/WbRwyOent6Ut4Y3U0p4IJBMCV3YmGUX5ZBQVjMncfiwSSYwSttbg7e5F0enILi8mf2oljozRT5SmMXy0OAKNQ5ZgTZiWB7voXuxB6AVZF6eRd20Gprz9m7OWMhFYVr/0xzDrETJ0j1By1QU7TQUNxY0zr8bt3spNdx9D6bH377VsPOKh9t37yMo1Yik8E2P6rGHbGOvfQLjtbYTOhLngPHTWwt3LqSqezm56mtvoaWrB19MHJNJOZBQXkFmUPyaeSL7uXlqqqmmvqUeNxXFmZZA/rZKskkIUneYiPV5oawQahxy+TUHaHu6m500vikmQ+zk3uZ9377HXngqhpgh1v2rDU/sJuqv/S1rOFyg948KU6hZMslO3Tk96yb7n/XVGJ619R9LRtJTZ+jeQMQ/GzGNTnlsXQmBIm4ViziHU/DLB+icxZp+AwT1vp2skIocTeYbKjpxFyOenu7GF7sYWmjdtpWnDFgwmE+7CPDKKC3Dn54zKoq89I53Jxx5F+bw5tFXX0lJVzZb3Pma72UTe5Aryp1Rg0nIeTRi0EYHGQYVUJf0f+mj9Tw/elQF0doXsK9LJ/ZwbQ/r+92vUqKT90R6a/94J+iiGr1yPsAaYcf5H6IyulK5x31fOZ8lj/TzR/ipm2+7rCLsSDUf40rTLueXOo5k+14TeNR1T3hkk9mtKHRkPE2p9nbh3G3rXDEy5p6WUoiIWidLT3Ep3Yws9TS3EIlGEopCen0NWSREZxQUYRmldQUpJb0tiYbu7sQWEILO4gMIZU3ZKoKcxtmgjAo2DGjWi0v2qh7b/dhOsiWDM0VP07WyyL05DZx/ZVEPfhz4aft9OqDZC2kl2zJf8i87abUw65pmURSASaMFlX4+UJTRtbWbSEVP2WcdgMnLyVWfxq68+xUNrfwz9nyKjXsyFFyB0qXs2JaaGzifS9RHRro9QI72YCy/cad1gKPRGA9llxWSXFSeinNu76Gpspqu+iZ6mVsRHCul5OWSVFiZFYf8XfoUQuAvycBfkEfT6aKnaTtvW7XTVN+HMyqBwxhQyiwu06OVxQhMCjQlNzBOn4+le2h/vIdoVxzrZRPn/5eM+w4liGJmLYrA2TMMf2un/wI+pyEDlHwoxzqxmy2t/IqPi87gKTkv5Wl3bHiE7PwBA/YaalIQA4PTrzuG5ex/nvVd7OPvzZxNueZ1g3eOYiy5GSVGEINHQmrKORTFlEG5ZTLDuf1iKLkExZaRUX1EU0vKyScvLpuKouXi7euisb6KrrpGqD1cglq0kLSkKmcWFGMz7LwoWh52K+XMonTOdtupamjZuZdM7yzA7bBROn0zupDItJuEAo00NaUxIgvVhOp7opfOFPtSgxHmMjbxr3TiPHtmm9ADR7hgtD3bR8VQvilkh/8uZ5FyVDroYW149iVi4h+kXfIzemJbS9dR4mA3PzcHknMaPrvZx9pcv5Eu/viVle76x4HqsThu/fvM+Yv4GQk0vIoQOc9HF6Cy5w/588WAbocbnkTKOpehidNb9z1UkpcTX3UtnXSOd9Y2EvH4QgvS8bLLKihOiMMLpI6mqdDU007SxCk9nN3qjkbwpFRRMq8RkHZ203BoJtKkhjQnPwPx/++O99H/kR+jBfYaTvGszsE4e+S5bMU+c1oe7aX+sBzUiybowjcKvZ2FwJ34GLWt/S7BvIxUnP5GyCAB0b/8v0WArpcf9leJpj1G3sWZYdh13yUk8+n8P0t3SSUZ+MdbSqwk2Pkuw/gnMBeehd1QM63o6Sy6W0qsJNjxNsOEpzIXno7eXD+saAyQyo7pxZLopmzcbX08vnXVNdNY1sPXDFWz7aBXuglyyy0rIKMpHl0Kk9G73UBSySovIKi2iv6NrR9K7po1V5E4qo2jWVCyOfa+5aOw/2ohAY9yJeeN0vdBH+5O9hJuiGDL1ZF+WRtYl6RgzR95XiQdU2h/tofWRbuI+FfcZTgpuysRS+tn0RqBnHZtfPRl32WWUHff3lK8t1Sgbnj8SgyWHKWct4U833c2qNz7hkdrnUr5G45Y6vj7vOr527//j7C8nPJTUmJ9Q43OooQ5MOadgcM9N+XoDqLEAocZnUEOdmPLPwuCaPuxr7AkpJd7uHjprG+iobSQSCKLodWQU5pNdXoK7IHdEbqJBr4/GDVto21aLlJLssmKKZ03Dlp76dJnG7mgjAo0JR7AmTPsTPXS93I8alNjnWCj8ehbpp4x8/h8SAtPxRC9tj/YQ64uTdoKdwpuzdhtdSDVK3bKb0ZszKJp/17Du0b39MSL+BooX/BYhBKUzK3jrv4vp6+glLTu1IKrCKSVkFmazdumqHUKg6G1YSq4k1Pwy4fa3UKP9GLNPGNa0mKK3Yim+glDTi4RbXgM1hiF99rA+354QQuDMzMCZmUH5/Ln0t3fSUduQmEKqa0RnMJBVUkhWeTHp+5HC2uKwM3nhfErmzKBpYxUtVdvpqKkns7iA4tnTcWS6R+VzaCQYkRAIIdzAE0ApUAdcIaXs3aVMEfAIkAuowANSynuT534GfAXoTBb/oZTy1ZHYpDGxUSMqvUu9dDzdh3dVAGEUZJzpJOeqdGzTRmc+ONIZpe1/PXQ804fqV3EtslHw5Szss4e+ftuGPxDsXU/Fif9Db0q9gVFjAVrW/gprxjycBWcAUDIjMQVTv7GGtOx5KV1HCMHcU+bz8UvvE4/H0SV70kIx7EhlHe1ZiRr1YM4/C6GkvpAqdCbMRRcTan6JcNsSgFETg8H2D6TTnnT0kfS1tCdEob6RtupaDGYTWaVFZJcV48zOHJaYmawWKo6aS/GsaTRv3kbz5q10NTSTnp9L8expiRTeGiNmpCOC24G3pJR3CyFuT76/bZcyMeA7UsrVQggHsEoIsURKuSl5/g9Syt+O0A6NCU6oKULns310vtBHrDeOqdBA4TezyLowbUT+/4MJ1oVp+18PXS/1I2MS9+lO8q/f+/pCsHcjrevvIb30MtKKzxvW/do33Uc02Er5CQ/taNwGhKBuYw1zTk5NCADmnDyPNx95lZq126g8cuqO40IoGHNOQRhcRDreJdjgw1J4IWIfrqGDEYoec8H5iZHBGInBAIqi4C7Mw12YhxqbT3dzK53J5HQtW6ox2awJUSgvxu5OT1kUDGYTpUfMpHDGFFqqqmnaWMXaxUtJy8umdO5MXDlZY/J5DhdG+gu8EDgp+fph4B12EQIpZSvQmnztFUJsBgqATWgc0siYpO8DHx1PJxZ/USD9BDvZl6UnvH9GYdtOqUr6l/lpf7yH/mV+hEGQeYGLvGv3nWNIqjHqlt2MzphG8VH3DOu+0UAbbRv/SFrxBdizj9lxPD3HjSsrnboN24d1vekLEykmtq3cvJMQQDJ7acZ8FIOTUMurBOqfwFJ8GYoh9XxBQtFjLrzggIjBAIpel5geKikkFo3S3dBMR21DIqJ5YxVWl5OcisS+Bqnuq6A3GiieNY2CaZW0bq2hYd1m1rz2Nun5uZQeMRNnVmrusho7M1IhyEk29EgpW4UQex2nCSFKgSOA5YMO3yKEuBZYSWLk0LuHujcCNwIUFxeP0GyNsSTSEaXzuT46nusj2hHDkK2n4MZMsi5Kw5gzOv7hcV+czpf6aX+il3BDBEOmnoKvZZJ9STqGjNT+rVvX/4ZAzxrKT3gYvXl4DUjjyh8g1RgFR/xst3OlM8qpH6bnUGZhNs5MF9tWV+2xjN45GbPOQqjpOYL1j2MpvhxlGN5Nn4nBC4Tb3kToLOidlcOyc3/RGww7NruJhsN01jXRvr2O2tXrqV29HlduNjkVJWSVFKFPIRurTq+ncPpk8iaX07Klmsb1W/j0lTdxF+ZResRMHBnaGsJw2KfXkBDiTRLz+7vyI+BhKWXaoLK9UsohV8iEEHbgXeBOKeWzyWM5QBeJLVX+D8iTUt6wL6M1r6GJh1QlnuV+Op7uo/c9L8TBtdBG9uXppC2yI/Sj0PuXEt+6IJ3P99HzhiexwDzbQs7V6cNeYPZ1fETVG+fgLrtiWF5CAP1Nr1O99Ary5/yIvNnf3+38P79/H4sffJEn2l/bMd+fCj+76Hv0tHbzp+UP7rVcPNhGsPEZBDrMxZeiMw9vWkSqUYINT6GGOjAXXYreVjSs+qNJ0Oujo6ae9u11BD0+FJ2OjKJ8cipKSS/IRUlxkTkWjdKyeRuNG6qIRSJkFBdQOncmdnfa2H6Ag4z99hqSUu4xvFII0S6EyEuOBvKAjj2UMwDPAP8bEIHktdsHlfkH8PK+7NGYWER7Y3S90E/HswnXT326jrwvZJB1SRrmwtHJUxPtidH1Sj+dz/cRqo2gWBILzFmXpmOfMfwF5li4l9oPvoLRVkzxguEtT8WjPho++Q5m11RyZnxryDIlM8qIBMO01bRQUJl6IzvpiCk8/fajREJhjHuJ3NVZcrGWXJWIE6h/EkvxJegseSnfRygGLIUXE6h/nFDT81hKrkRnHp9FV4vDTsmcGRTPno63q4f27XU7vI8MZlMiBUZFCY6MvW92ozcYKJ49nfypk2jatI2mjVWsanid7PJiSo+YpcUh7IORTg29CFwH3J18fmHXAiLx1/sXsFlK+ftdzuUNTC0BFwMbRmiPxgFAqhLPigCdz/XRu9SLjEocR1oTrp8nO1CMI88Xo0ZU+j/00/VqP33vepExsM+2UPbTPNynO9DZ9s9HXUpJw/JvEwm0MvWsN9ANY54doPnTXxDxNzLlzNdRdEMLXemsRABY/caaYQlBweRi1Hicjvo2CqeU7LWsYsrAUnIVwYanCNY/hbnoQvS2vdcZjNBbsBRfSrDuMUINz2IpuwbF4Ey5/mgjhMCZlYEzK4OKo+bS09xG+/a6HfskW10OsstLyanY+3qC3mikdO4MCqZV0rhhC82bttJZ10T+lApK5swYUWqMQ5mRCsHdwJNCiC8BDcDlAEKIfOCfUspzgOOALwDrhRBrkvUG3ETvEULMJTE1VAd8dYT2aIwhkfYonS/20flCP5GWKDqnQvZlaWRfmr5fu3/tilQl3lUBuhd76HnTQ9yrok/XkXOVm6yL0kblHt3b/0tv/XMUHHEHtszUvXoA+lvepLPq72RN+epOC8S7Ujy1FCEEdRtrOPaiE1O+fl55IhVEa23LPoUAQDG6sJRcRajxGUKNz2EuvBC9vSzl+ykGJ+YBMWh8HkvpVQhl9HYx218UnY7M4gIyiwsS+yTXN9K+vZ66T9dT9+l60vJyyK0sI7O4YI97NBtMRsrnzaZg6iTq1mykeUs1bdV1iV3Vpk/erwjoQxktslhjr6hRSd97Xjqf70t4/qjgXGAl6+I00k9yoJhG1vuXUhKoCtP9Wj/dr3uIdsRQrArpJzvIOMuJ62jbqKwvAIT6t7L5lROxZc6j8rQXEErqo4poqItNLx+L3uhm2jlLUfR7n5K6ac7nKJ5exg8f+2XK9+jr6OXasov4ym++wfk3X5ZyPRkLEmx4GjXSPWwxAIj5agk1PofOXo658MIJu99w0OujfXsd7dV1hHx+dAYDOcl9ku0Ze3dF9fd5qF21ju7GZowWM6VHzCR3Utlhl+1UiyzWGBbB2jCdz/fR9Uo/sZ44hmw9+TdkkHnByOf+pZQEt4XpectLzxIPoboIQg+u4+xkfNtF2gl2dJbR/YHGoz62v/sFFL2F0kUPDEsEpJTUf/RN4uFeKk95Zp8iAFA8rZSmqvph2ejKSsNss9Be1zasekJvwVJyGcH6pwk1vYC58IJh5RbS28sw5pxEpH0pkc4PMGXvvt/yRMDisFM6dyYlc2bQ19ZB27Za2qoT00e2dBe5k8rIrijBaN49bsSW5mTmqYvob++kZtVati5bSdPGKsrnz8VdmDdhxe9AoQmBxg7iQZWeJR46n+/DtyaI0EPa8Q6yLk7DtdCG0O3/j0VKSWBzKNH4v+kh3BgFBRxHWsn9nJv0Ux0Y0sbm31FKSf3HtxLybKXy1OcwDjMbZ8fm++hveoXCeXdidae2pWRWUS5r3l6FlHJYu445M114uvuGZR+A0CXFoOFpQk0vYi64AL0jdTEwpB+BGu4m2v0JitGNIW3GsG04UAghSM/LIT0vh1g4QkddImBt+4o11KxaR0ZRPrmTynAX5O7W43flZDH37FPpbmyhZuVaNrz1Pun5uVQsmIst7fDNY6QJwWHOgEtm10uJqRnVr2IuMVJ0azaZ57lS9snf07X9G0L0vOmh5y0vkZYo6MB5lI286zJIP8mxI/PnWNJZ9Q96654mf+5PcOadNKy63rb3aVp9B2nF55M97esp18sqyibkD+Lv82FPT31B2pnhwtPdPywbBxA6C5bipBg0D08MhBCYck9BRvoIty1BMbmH5Yk0XuhNRvKnTCJ/yiT8vf20VdfSvr2OrvomjBYzOZPKyK0sw+r87G8gkrujuQvzaNm8jfq1G1n5wuvkT6mg9IiZI9qA52BFE4LDlHBLlK5X++l6uZ9wQwTFLHCf7iTrojTscy37PVRWQyqeFX563/PR956PaGcMoQfnMTYKvpJJ2on2Mev5D4WvcwVNq36Iq+Ascmf+v2HVjQRaqHn/ekyOckoX/mVY30lmYcIds7OxfVhC4HC78PZ4hmXnYBJicDnBhqcSYjCMNQMhdJgLzyNQ+19CTS9hLfv8sFJZjDe2dBcVR82lbN5sehpbaKuupXHDFhrXbyYtL5u8yRVkFhfsyIqqKAqFM6aQU1FK3afrk4ntGig9YgZ5UyalHMNwKKAJwWFEPKDS85aHrpf68a5M7KblmG8l//oM3Kftv0tmpCtG/wc+et/z4vnYjxqSKFYF10Ib6Sc6SDvRjt4xsu0k94dosJOa967DYM2n9Li/IUTqP2w1FmD7O59HjQWYfPrL6IzDc63MKsoBoKu5g7LZk1KuZ0+z01bbPKx77YrQmRNiUP9kYpqo6GL0ttSi8YXOgrngAoL1jxFqeQVz0aXD+t4mAoqikFlSSGZJIeFAkLbqWtq21rD53Y8wmE3kTiojb3I5luQowWA2UblwPvlTJ1H9yRqql39Ky5btVCyYi7tg4o+KRgNNCA5xBnz+u17up/ctD2pIYio0UPC1TDLPdWHKH/7C78Bib997icbfvyEEgDFXT+YFaaSdYMc53zoq8QT7ixqPUPPetcTCPUw963X0ptRSQgNIqVL74Y0EuldTceJ/saRN3XelXdgxImgaMsZyj+gNeuKx+LDvtysJMbgsIQaNz2MpvhSdtSClujpLDqbcUwm3vkGkcxmm7EUjtme8MFktlMyeTvGsafQ2t9G6dTuNyY1v0vJyyJ9SQUZRPopOhy09jdlnnJhYP1ixhvVL3iOzpJCKBUdgth08I6P9QROCQ5RgXZiul/vpfrWfSFsMnV0h42wXmee7sM8Z/tRPtC+GZ7mf/mV++j/2E+2MAWCbaabg5izST7BjqTRNGO+LxpW34etYRtmif2F1zxlW3ebVP6Wv4SUK5/1q2BlJBxjYi6CvY8jUWXtEZ9CjjoIQAAi9FXPJ5QTrnyDY+CyW4stT3vrSkDaLeKCFaPdydJZc9I7URzUTESHEjqyo4UCQtm01tG6tYdM7ywaNEiqwOO2J9YOCXJo2bqV+7UZ6mlspmTODwumTR7TZzkRGE4JDiEhXjJ4lHrpf60/00pVEvp+iW3NIP9GOYk69hy5jEt+GIP0f+elf5sO/KQQSdE4F19E2XAvtuI6zYcyaeJuMd279F11bHyR3xrdxl6Xujz9Qt33Tn8ma8hWyp9283zbodDpsaXa8PcNb+NXpdcSioyMEkNzgpviyhBg0PIOl5PKU00mYck9FDXcSalmMtezzw0pwN5ExWS2JtBazptHT0k5r1RCjhOICimdPI7usmOpPPqV21Trat9dRecy8Q3IPBE0IDnJi3ji9S710L/bg+SQR8GWdYqLoW9lknO1MuaGWUhKqj+BdGaD/Yz+eT/zEfSooYJ9poeDGTFwL7dhmmEfkRjrWeNs/pOGT7+MsOIP8uT8ZVt3e+hdo+OS7OAvOoGj+3SMe3TjcLrzd+7/wO1ooBieW4isI1j9OqOFpLCVXopj2nW11YB+DQO1/CDW/iqX0SoQ4dHrEQlHIKMwjozCPsD9A67Za2rYlRglGq4W8yRXkTy5n5qmL6GpsZvvyT1m7eCk5FSWUz5+L0TLyfbQnCpoQHISoYZW+D310L/bQ954PGUnM++ffkEHGWa6UUjFIKQk3RfGs9ONdGcCzIkC0KzHdY8zR4z7diWuhDecCG3rnwfHjD/saqHn3WkyOMsoX/XNYQWP9LW9S+8GXsGUeRfnxDyGUkf80nG7nsD2AIsEwRsvop3lQjK4dC8jBhqcSYmDc97qJYnRhyjudcPPLRDo/OqjXC/aGyWaldO4MSmZPo7u5lZbN1dSv2UDDuk1klRSSP62SeReeSeP6LTRu2EJXQwvl82aTN6ViwkyHjgRNCA4SZFziWRmge3E/vW95iftU9G4d2ZemkXGWC9tM8z7/IcMtiYbfszKAd6WfSFui4Tdk6HDMt+Gcb8V5lA1TkeGg++dOePlcg6pGmHLSY+iMqQcH+To+Yvs7n8fsmsqkU55EZ0htk5R94XA76e/sG1adcCiMyTI2fuyKyY25JLGAHKwfEIN9f08G5xTivrrEeoGtOGUPpIMRoShkFhWQWVRAoN9Ly5ZttFUnMqLa3WnkT63kiHNPo2blWrZ9vIqO2gYmHzsfq2v8EvaNBpoQTGCklPg3hhJJ2N7oJ9oVR7EpuE9xkHG2E+f8PefhkaokuD2Mb20Q75oAvrVBws1RAPRpOhzzreR90Ypzvg1zmfGga/gHI6WkbtnXCfZuYNIpT2J2pb7ZSqBnLdvevgKjrYDKU59DP4rz4A63k8ZhppmIBCN7TUE9UnSmzB3eRDtGBilkYDXlnkI82Ey45TV0ZdciUkizcbBjdTmYdPSRlB05i/aaelo2V7N12Qr0RiO5lWWk52fTuH4LK194nZK5MyiaOfWgjT3QhGCCMdD49yzx0POml0hrFGEQpC2yk3G2k7RFQy/6xoMq/o1BfGuCeNcG8K0LEveqQKLHb59jJedqN86jrFgqTKOyTeREoX3jH+itf5aCI36GK7mJfCoE+6vY9uYl6I0uJp/2AgbL6O57a0uz4+/3DatOYmpobCNbdebsRArqhqeTYnAVyj4Cx4RiwFxwbiJTaevrEzo53WijMxjInzKJvMkV9Ld30rKlmqZNW0FK0vJyUONx6lavp7O2kSnHHYUj8+DbHU0TggmAlBL/pkGNf0s0GY1rp+CmTNJPcuwUkCWlJNwcxb8xhG9DEN/aAIEtIWRipgdLuRH36U4ccy3Y51gxFR58Uz2p0t/0Os2f/oL00kv3uFHMUAT7trB1yfmg6Kg87QWMtsJRt83qsBH0BIaVbyjkD2JLG94eCfuDzpKHpehigg3PEGp4BkvJFQjd3gVIZ87BmLWISMe7xPo3YkibOeZ2TiSEEKTlZpOWm03YH6Bl63Zaq2qIhkIYLWZCPh+rX15C4cyplM6dsccU2RORg8fSQ4w9N/42Cr6aSfqJjh2LtNGeGH3ve/FtCOHfFMS/MUSsL+FiKEwC+3QzuV/ISDT8s63oXQfH4u5ICfZtoeaDL2Nxz6J04X0pN7YJETgPhMKU01/G7KwYE/usDhuqqhIJhjFZU/Mw8XT3k1cx+qI0FDprYWIP48bnCTY9j6XoEoSydy8zg3seMW814fal6GwlKU0rHYqYbFbKjphFyezpdNY30bxpK96uHoSi0LRhCx21DUw74RjSckZ3lDlWjEgIhBBu4AmglMTGMlcMtfm8EKIO8AJxIDaQDzvV+ocKUkr8m0P0LvHSvcTzWeN/tI2CGxM9fyQEtobofK4P/6Ygvg0hIq2JuX0UsJSbSDvRjn2GBdsMM5ZJ5mHt1XuoEA12Uv325Sg6M5NOenSfUxsD7CYCrsljZqPFkZhHD3j8wxICp/vALTzq7WWY8s8i3PIqoeaXMRdesFcXUSEE5vyzCNQ8TLj1DcxFlxyyo81UUHQ6cspLyCkvwdPRRdOmrXTWNxLxB1j72ttklhQyZdEC9IaJF28zmJGOCG4H3pJS3i2EuD35/rY9lD1ZStk1gvoHJVJNZPfsXeql920v4ebPGv+cy9PQZ+gJN0bpXeql+W+dOzx5AEwFBuwzzdiuSsc23YJtmhmd9eBcjBpN1HiI7e9eQzTUwZQzXsWY4ubrwb7NbF1yPkLomHz6y8NaVN4fLI6E91HAGyA9d99++9FIlIDHjyPzwKZDNrimQTxMuP0twq1vYMo7a6+Nu2JMw5h9ApH2t4n1rceQPvsAWjtxcWZnMj07k5A/QPPGKpq3VNNV30R3YwtTjz+a7LKJ6201UiG4EDgp+fph4B2G15CPtP6ERI1KvKv89L7tpfcdL9GuOEIP5lITzgVW1JDEtzZI/4f+RAUFzCVG7HOt2CabsU4xYZ1qxpCuzdztyoCHkL/zE8pPeDjl7SaDvRvZ+uaFB0wEAKzOhBAEfYGUyg8EnzkzDnxefIN7LjIeJNK1DKGYMeactFcxMKTPJe6tJtzxDjp76bjudzzRMNusVCw4gtIjZ1G7ej2tVduxOOzjbdZeGWlLkzOw+byUslUIsafYawm8IYSQwN+llA8Ms/6EJx5U6XvXS9cr/XhXBVBDEhRQjIkfk4xBsDpM2CywTjaTcbYT62Qz1ilmLBWmUd+R61Cldd2vd+wtkF5yUUp1fJ0rqH77UhS9jcmnvXhARADA6khMVwU8/pTK93f1ARzQqaHBGDKPQcZDRHtXI3RmjFkL91hWCIEp7wwCtQNTRJce1lNEQ6HT65m04Agqjpo74b+bfQqBEOJNYKhMVT8axn2Ok1K2JBv6JUKILVLK94ZRHyHEjcCNAMXFE2eIJVXJ2nOriXTEEnI3gA7MpUaslWYs5SYs5UYsFSbMhcYJnaJhItNT+zSt6+4io/wacmd+J6U6ntalbH/ncxgs2VSe9gIm+743hR8tLANC4E1tRNDd3AlARsH49IeEEBhzTkKqocTIQGfB4J67x/KK0YUx6/jEFJFnS2KKSWM3JroIQApCIKU8bU/nhBDtQoi8ZG8+Dxgy566UsiX53CGEeA5YALwHpFQ/WfcB4AFIbF6/L7sPFEIRoICpyIB9rpW042xYJ5sxFxhHbdN1DfB1LKdu2c3Ys4+l+Jg/pvTj6mt4ObGxjHMSk099DoM1tcybo4U1uUYQTFEIOhoTexVnF+eMmU37YqCnL5NrBuhMe23gDelziPVvItL+Dnp7GUJ36OTfOZwY6XzEi8B1ydfXAS/sWkAIYRNCOAZeA2cAG1KtfzAw95VK5jw/iYqf5ZNxugtLiUkTgVEk7K1j+zvXYLQVUHHS/1D24e8O0F3zONvfuxarezZTznjlgIsAfDYiCHpTmxrqbOxAp9eRljO+AUlC6DAXnIdiLSTcspiYv2EvZRVMeacj40HCHe8fQCs1RpORCsHdwOlCiG3A6cn3CCHyhRCvJsvkAB8IIdYCnwCvSCkX762+hsYAsUgf1UuvRMook05+Er1p341k++a/UPfhV3HkHEflaS+kVGcsGMgZFA5GUirf2dBGZmE2ugmQ814oeiyFF6KY0gk1vUA81LnHsjpzNgb3kcT61hEPjGx3NY3xYUSLxVLKbuDUIY63AOckX9cAQ+4Msqf6GhoAajzM9nc+R9i7ncpTn93nIq+UKk2rfkzH5vtJKz6fskX/RBnHqQqDKeE7Ho2kKARNHTu2uJwICJ0Zc9ElibQSjc9iKb16j95BxqxjiXm2Em5bgqXsC4dUuurDAc1VRWNCIqVK3Yc34Wv/gNJj/4oj94S9llfjIWrfv56OzfeTPfUmyo9/eFxFAEBvTAhBLBxNqXzr9mZySifWHrmKwYm56BKkGiHU+CwyHhqynFCMmHJPQQ13E+1de4Ct1BgpmhBoTEiaV/8kkUjuyJ/jLrt8r2Vj4R62vXkRvfXPUzjvTgrn3z2svQjGCiEEeqOBaApC4Ov10tfRQ9GUA+fVlCo6cxbmwgtRw70Em15AqrGhy9kr0NlKiHQuQ8ZSWyDXmBhoQqAx4Wjf/FfaN91H1pQbyZl+617Lhn31VC0+E3/XKsqOf5Cc6bdMKHc9gyk1IWjamkhXXThl4rhGD0ZvK8aUfxZqoIlw62Kk3N1xTwiBKedkUCOEO5eNg5Ua+4smBBoTit7652la+QPSis7b53aR/q5VbFl8OtFQO5WnPY+79NIDaGlqGEyGlNYIGrcMCMHEGxEMYHBNw5h1PDFPFZGOocOAFFMGhvQjEgvHoT16g2tMMDQh0JgweNuXUfvBjdiyjqZsH1tN9tQ+TdUb56AoJqac+TqOnOMOoKWpYzAaUxsRVDVgMBkn3BrBrhgyjsKQPpdoz0qiPWuGLGPMWgg6E+H2pUOOHDQmHpoQaEwIgn1b2P7OVRjtxUw6+TGUPeyAJaVK85pfJvYXzjiSqecsxZI2cSNaDSYD0VAqI4I68icVTgjX0b2RiD4+GZ29nHD728R8dbuX0ZkxZS1CDTQR91YfeCM1ho0mBBrjTsTfyLa3LkHozFSe+swe/f7jUT81711H2/rfkFHxeSpPewGDOfMAWzs89EY90ejeRwRSSqo/3Ur5nAOTA2mkCKFgzj8XxZRJqPkl4uFdkwqDPm0Wwugm3PkBUqrjYKXGcNCEQGNciQY72frmRahRH5WnPLPHXEARfzNVb5xFX8NLFM77JSUL70PRGQ+wtcNHCLHPxeue1i76OnqYdMTY7Y0w2gidEXPRRQhhINT43G5eQkIomLIWISM9xPo3jpOVGqmiCYHGuBGPeKh++1Ii/mYmnfIEVvesIcv5Oj5m82snE/bWMunkJ8iZ/o0J5Rk0Uqo/3QpAxRFTxtmS4ZGIMbgQGQsM6Vaqc0xCMecl3EnV1GIpNMYHTQg0xgU1FqT6nasJ9G6k4sT/YM/ePeWxlJKOLQ9Q9ca56PRWpp75Bq7CM8fB2rFl+6dVKIpC+exJ423KsNFZ8jDnn40abCHc+sZOi8NCCIzZxyNjPqK9a8bPSI19ogmBxgFHqlFq3r8eX/uHlB33d1wFp+9WRo0FqPvwqzSu+B6u/NOYes47WNKnj4O1Y0/1p1spmFKM2Tb0AvlER++cjDFrETHPZqJdH+98zlaEzlZKpOuTPUYla4w/mhBoHFCkVKn76Bb6m16jaMFvcJddtluZsLeWLYvPoKf2SfLm/JCKkx9Db0w78MYeAKSUbFu1hUkH2bTQrhgyFqB3TSfStYyoZ8tO54zZi0ANEe35dJys09gX2l6IGgcMKSVNK39AT83j5M/5MdlTvrJbmf7mJdR+8GVAMumUp4YcLRxKNG9rpL+zlxnHHdz7/gohMOWejhrpJ9zyOooxHZ05kUBPZ85BZ68g0rMKg/tIRAppxDUOLNqIQOOA0brubjq2/I3sqTeTO+u7O52TaoyWNXdS/fblGG1FTDvnnUNeBAA2LVsHwPRjD24hgETqanPh+QidmVDjCzt5EhkzF4Ia1tYKJiiaEGgcENo2/J7WdXeTUfE5CuffuZPXTyTQwtYlF9C6/h4yKq5h6llvYHKUj6O1o8e+Ims3frAWV1Y6BZVFB8iisUXR2zAXXoSMBwk2v4SUcQB0lhx0tjIiPSuR8dTScmscODQh0Bhz2jf/heZPf0566WWUHPNnhPjs366/+U02v7yIQPenlB77N0qP/QuK3jqO1o4usUgMvcGwx/Mbl61jxqLZh5Q7rM6SgynvdNRAE5H2d3YcN2YdA/EQ0b4142abxtCMSAiEEG4hxBIhxLbkc/oQZaYIIdYMeniEEN9KnvuZEKJ50LlzRmKPxsSjs+qfiSRyxRdQdtzfd+QPkmqUptV3UP32pRgsuUw7910yKq4eZ2tHn2g4umODml3pbOqgo76NGYfAtNCuGFzTMbjnEe1dQ7RvPQA6Sz46WwnR7pVaXMEEY6QjgtuBt6SUlcBbyfc7IaWsklLOlVLOBeYBAeC5QUX+MHBeSvnqrvU1Dl66qv9DwyffwVVwFmWL/oVQEr4JEX8jVW+cQ/vGP5JZ+UWmnv0WZtfBE1U7HKKRyB6FYN07qwGYuWjuAbTowGHMPgGdrZhw21vEgy0AGDKORsaDxPo3j7N1GoMZqRBcCDycfP0wcNE+yp8KbJdS1o/wvhoTnO6aJ6n/6Bs4806h/MSHd6SD6Kl9mk0vHUewdxNli/5FyTH37jHB3KFANBTBaBo6FcbqJctJz3FTOqviAFt1YEjkJDoPobcTanoRNepDZy1EMWcn1gq0zKQThpEKQY6UshUg+Zy9j/JXAY/tcuwWIcQ6IcSDQ00tDSCEuFEIsVIIsbKzc88baWuMP731z1G37Ks4chZRcdL/UHRmYpE+aj/4CrUffAmzazLTzn1vyBiCQ41oOIp+CCGIx+OseXslR56+4JBaH9gVobdgLrwQGY8Qan4RUDG45yMjvcR9NeNtnkaSfQqBEOJNIcSGIR4XDudGQggjcAHw1KDDfwUqgLlAK/C7PdWXUj4gpZwvpZyflZU1nFtrHED6Gl+l5v0vY8tcQMXJj6PorXjbP2Dzy4voqXuGvDk/ZMqZizE7D81e8GBUVSUWjQ05NVS9ugpvj4cjTlswDpYdWHTmLEz5Z6AGW4m0v4feORmhtxPtWTXepmkk2WdAmZTytD2dE0K0CyHypJStQog8YG9bEp0NrJZStg+69o7XQoh/AC+nZrbGRKSv8RVq3rsOq3sOlac8hVCMNK2+g/aN92JylDH1zDewZc0fbzMPGAMb0hiMuwvB6iWfIIRg7imHx/dhcE5FDbQQ7V2NYs3H4D6SSMd7xIPt6Cw5423eYc9Ip4ZeBK5Lvr4OeGEvZa9ml2mhpHgMcDGwYYT2aIwTvQ0vsf3daxMicNqzRIKtbFl8amJBeNK1TDv3/cNKBIAdG9IYzbtPDa1espzK+dNwZrgOtFnjhjHnRBRLHuHW19FZi0AxEO1dPd5maTByIbgbOF0IsQ04PfkeIUS+EGKHB5AQwpo8/+wu9e8RQqwXQqwDTga+PUJ7NMaB3voXqHnvi9gyjmDSyU/Tte0hNr98PFF/MxUnPUrJwj+hM9jH28wDTtCXiKy1OG07He9p7Wbris3MP/Po8TBr3BBCh7ngfBB6wi2L0TmmEPNsRcaD423aYc+Icg1JKbtJeALterwFOGfQ+wCQMUS5L4zk/hrjT0/ds9R+8GVsmfMpnH8X1Usvw9+1krSi8yg++g8YLPvyHzh0CXj8AFgdOwfILX/lA6SUHHvhieNh1riiGByYC84l1PA0OoMDZIxo/yaM7nnjbdphjZZ0TmO/6al9mtoPb8SWeRTOvJPZ+vrZKAYbZcc/SHrJJYe0N0wq+HcIwc4jgo9eeI+CyiKKppWOg1Xjj95WgjHrOCKdHyIMTqK9azGkH3nY/7+MJ1qKCY39orvmSWo//ArW9Nmo8SCt6+7CVXgGM85fjrv0Uu1HDQQHhMD1mRB4ezysf+9TjrnghMP6OzJkHI3OXo6MehOupIGm8TbpsEYTAo1h013zOHXLvorRVkSgdwMRfyNlxz9I+Qn/OayngnZlqBHBJ68uIx6Lc+xFJ4yXWRMCIQTm/LMQBgeA5ko6zmhCoDEsuqr/Q92HN6EoZiK+etKKzmHG+Z9oo4AhCHiTQuD8bI3goxfeJbMw+6DfiGY0EDpLYvEYQdy3HTUa2GcdjbFBEwKNlGnb+CfqP7oFkChGFxUnPUbFiY9gsGgBfkMR6B8QgoTHlKerj1VvLGfRJSdroplEZ8nFkFwoDrctGWdrDl+0xWKNlKj98Gv01DwKQNbkGyk48qcJrw+NPRLw+FEUBbPNDMAHzy4lHotz8tVnjLNlEwtj9gnE/fXobMXjbcphiyYEGvukfdN99NQ8is7gYtIpT2HPPrz83/eX/q4+HG4nipIYeC997A1KZpRTNnvSOFs2sRBCYC2/drzNOKzRhEBjn2RNvZmwr57CI/8PRW8eb3MOGvq7+nBlJfIotlQ3UfXJJq77v6+Os1UaGrujCYHGPlEUheIFvxlvMw46+jv7cGUlUki888QShBCceOUeU3dpaIwb2mKxhsYY4UmOCFRVZemjrzPrhCPILNDcazUmHpoQaGiMEf1dfTgz01jz1kra61o54/rzxtskDY0h0YRAQ2MMiEVj+Hq9uDLTWPzgizgzXSy84PjxNktDY0g0IdDQGAM8Xf0A6I16PnllGad94RwMe9iyUkNjvNGEQENjDOjv7AWgdv121HhcmxbSmNBoQqChMQZ0t3QBsP7dT5l7ynzyKwrH2SINjT2jCYGGxhjQ05YQgv7OXs768rC299bQOOCMSAiEEJcLITYKIVQhxB73IRRCnCWEqBJCVAshbh903C2EWCKE2JZ8Th+JPRoaE4WBEUFWUQ5Hn3vsOFujobF3Rjoi2ABcAry3pwJCCB1wP4nN66cDVwshpidP3w68JaWsBN5KvtfQOOipXVcNwPk3X4pOr8VtakxsRiQEUsrNUsqqfRRbAFRLKWuklBHgcWBgrHwh8HDy9cPARSOxR0NjorB11WaEIjj9unPH2xQNjX1yILoqBUDjoPdNwEDWshwpZSuAlLJVCLHHsEshxI3AjQDFxVqWQo2JTcGkIvLKCrC57ONtiobGPtmnEAgh3gRyhzj1IynlCyncY6jE6zKFejtXkPIB4AGA+fPnD7u+hsaB5M7X/jjeJmhopMw+hUBKOdIsWU1A0aD3hUBL8nW7ECIvORrIAzpGeC8NDQ0NjWFyINxHVwCVQogyIYQRuAp4MXnuReC65OvrgFRGGBoaGhoao8hI3UcvFkI0AQuBV4QQryeP5wshXgWQUsaAW4DXgc3Ak1LKjclL3A2cLoTYBpyefK+hoaGhcQARUh580+3z58+XK1euHG8zNDQ0NA4qhBCrpJS7xXxpkcUaGhoahzmaEGhoaGgc5mhCoKGhoXGYowmBhoaGxmHOQblYLIToBOr3s3om0DWK5owWml3DQ7NreGh2DZ+JattI7CqRUmbtevCgFIKRIIRYOdSq+Xij2TU8NLuGh2bX8Jmoto2FXdrUkIaGhsZhjiYEGhoaGoc5h6MQPDDeBuwBza7hodk1PDS7hs9EtW3U7Trs1gg0NDQ0NHbmcBwRaGhoaGgMQhMCDQ0NjcOcQ1IIhBCXCyE2CiFUIcQe3ayEEGcJIaqEENVCiNsHHXcLIZYIIbYln9NHya59XlcIMUUIsWbQwyOE+Fby3M+EEM2Dzp1zoOxKlqsTQqxP3nvlcOuPhV1CiCIhxFIhxObk3/zWQedG9fva0//LoPNCCPGn5Pl1QogjU607xnZ9LmnPOiHEMiHEnEHnhvybHiC7ThJC9A/6+/w01bpjbNf3Btm0QQgRF0K4k+fG5PsSQjwohOgQQmzYw/mx/d+SUh5yD2AaMAV4B5i/hzI6YDtQDhiBtcD05Ll7gNuTr28Hfj1Kdg3rukkb20gEgQD8DPjuGHxfKdkF1AGZI/1co2kXkAccmXztALYO+jv+//bOJUSOIozjv//BHKISX+RhVPSQkyDoIcQoPlDEXYjRWzxoxIDkkINX8ZizuakHoxBFzMUoIST4PASUBHVxjRJf8eKyywZi8HFRD38PVaPFbE93z2zX7JqpHwxTXV1f1X+++rqrq7p3uzN/1cVLUmYaOEF4K9824HRb28y6tgNXx/RUT1ddn45J133AsVFsc+rqK78D+HgM/roHuAP4esD+rLF1Sc4IbJ+1/V1Dsa3Aj7Z/sv0XcBjYGfftBA7F9CHg0Y6kDVvvA8A526P+FXVblvt7V8xfthdsz8T074R3XmzuqP2UunhJ9b7uwCngKoU377WxzabL9qe2L8bNU4S3BOZmOb95Rf3Vx+PAWx21PRDbJ4Ffaopkja1LciBoyWbg52R7jv9OIBtsL0A40QDrO2pz2Hp3sTQI98Wp4WtdLcEMocvA+5K+kPTMCPa5dAEg6WbgduB0kt2Vv+ripalMG9uculL2EK4sewzq03HpulPSrKQTkm4d0janLiStBR4G3k6yc/mriayx1fjO4tWKpA+BjRW7nrfd5pWXqshb9rO0dbqGrGcN8AjwXJL9MrCfoHM/8ALw9Bh13WV7XtJ64ANJ38YrmZHp0F9XEA7YZ23/FrNH9ldVExV5/fEyqEyWWGtoc2lB6X7CQHB3kt15nw6ha4aw7PlHvH/zLrClpW1OXT12AJ/YTq/Uc/mriayx9b8dCGw/uMwq5oAbk+0bgPmYXpS0yfZCnH6d70KXpGHqnQJmbC8mdf+blvQKcGycumzPx+/zkt4hTEtPssL+knQZYRB40/aRpO6R/VVBXbw0lVnTwjanLiTdBhwEpmxf6OXX9Gl2XcmAje3jkl6SdF0b25y6EpbMyDP6q4mssTXJS0OfAVsk3RKvvncBR+O+o8DumN4NtJlhtGGYepesTcaTYY/HgMonDHLoknS5pCt7aeChpP0V85ckAa8CZ20f6NvXpb/q4iXV+2R8wmMb8Gtc0mpjm02XpJuAI8ATtr9P8uv6dBy6Nsb+Q9JWwvnoQhvbnLqinnXAvSQxl9lfTeSNra7vfq+GD+GgnwP+BBaB92L+9cDxpNw04SmTc4QlpV7+tcBHwA/x+5qOdFXWW6FrLeGAWNdn/wZwBvgqdvamcekiPJUwGz/frBZ/EZY5HH3yZfxM5/BXVbwAe4G9MS3gxbj/DMkTa4NirSM/Nek6CFxM/PN5U5+OSde+2O4s4Sb29tXgr7j9FHC4zy6bvwgXfQvA34Rz155xxlb5FxOFQqEw4Uzy0lChUCgUKANBoVAoTDxlICgUCoUJpwwEhUKhMOGUgaBQKBQmnDIQFAqFwoRTBoJCoVCYcP4BQe2lXJRbWf0AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f70cd9105f8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -258,7 +734,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/tutorial-5-foils-customprofile.ipynb b/tutorials/tutorial-5-foils-customprofile.ipynb
new file mode 100644
index 0000000..7549bde
--- /dev/null
+++ b/tutorials/tutorial-5-foils-customprofile.ipynb
@@ -0,0 +1,804 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1c3cf134",
+   "metadata": {},
+   "source": [
+    "# BladeX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbbc9387",
+   "metadata": {},
+   "source": [
+    "## Tutorial 5: Prepare a blade 2D sectional profile using CustomProfile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dda573f2",
+   "metadata": {},
+   "source": [
+    "In this tutorial we present the generation of different `CustomProfile` sections, starting both from the coordinates definition and the parameters definition.\n",
+    "In the first part, two different conventions for the thickness calculation are tested taken into account:\n",
+    "- the british convention, where the thickness is measured on the orthogonal direction w.r.t. the chord line;\n",
+    "- the american convention, where the thickness is measured on the orthogonal direction w.r.t. the camber line.\n",
+    "Moreover, a second part is considered to test the deformation of parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07a6abdf",
+   "metadata": {},
+   "source": [
+    "First of all we import all the useful classes from `BladeX`, together with other utilities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8e143620",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "from bladex import CustomProfile, NacaProfile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4c4e70d",
+   "metadata": {},
+   "source": [
+    "### Part 1: creation of different section using either the coordinates or the parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8afa590e",
+   "metadata": {},
+   "source": [
+    "We create a section with `NacaProfile`, from which we will extract the coordinates and parameters. By default, within the class the parameters are generated using the british convention. Then, we generate a `CustomProfile` section using the coordinates and another using the parameters. The parameters are also printed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "adae547b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameters (British convention):\n",
+      "Chord length: 1.0\n",
+      "Chord percentage: [0.         0.02904817 0.06233258 0.09649612 0.13115634 0.16614077\n",
+      " 0.20134615 0.23670127 0.2721528  0.30765872 0.34318492 0.37870306\n",
+      " 0.4139692  0.44887493 0.48375021 0.51858868 0.55338477 0.58813361\n",
+      " 0.6228308  0.65747236 0.69205453 0.72657375 0.76102644 0.79540903\n",
+      " 0.82971775 0.86394861 0.89809728 0.932159   0.96612852 1.        ]\n",
+      "Thickness max: 0.11984156347903108\n",
+      "Thickness percentage: [0.00000000e+00 4.96261940e-01 6.68783145e-01 7.82477388e-01\n",
+      " 8.62832486e-01 9.19991221e-01 9.59371400e-01 9.84365536e-01\n",
+      " 9.97325460e-01 1.00000000e+00 9.93756161e-01 9.79701518e-01\n",
+      " 9.58774960e-01 9.31918157e-01 8.99816460e-01 8.63013268e-01\n",
+      " 8.21967086e-01 7.77062351e-01 7.28617424e-01 6.76890613e-01\n",
+      " 6.22084813e-01 5.64351132e-01 5.03791784e-01 4.40462439e-01\n",
+      " 3.74374155e-01 3.05495009e-01 2.33751484e-01 1.59029678e-01\n",
+      " 8.11763745e-02 2.75484733e-16]\n",
+      "Camber max: 0.03997886114414057\n",
+      "Camber percentage: [0.         0.1650694  0.31526768 0.45059484 0.57105089 0.67663582\n",
+      " 0.76734964 0.84319233 0.90416391 0.95026438 0.98149372 0.99785195\n",
+      " 1.         0.99405155 0.98149372 0.9623265  0.9365499  0.90416391\n",
+      " 0.86516854 0.81956378 0.76734964 0.70852611 0.64309319 0.57105089\n",
+      " 0.49239921 0.40713814 0.31526768 0.21678784 0.11169861 0.        ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "section = NacaProfile(digits='4412', n_points=30)\n",
+    "\n",
+    "# Define CustomProfile from coordinates\n",
+    "section_coords = CustomProfile(xup=section.xup_coordinates,\n",
+    "        yup=section.yup_coordinates,\n",
+    "        xdown=section.xdown_coordinates,\n",
+    "        ydown=section.ydown_coordinates)\n",
+    "\n",
+    "# Define CustomProfile from parameters\n",
+    "section_params = CustomProfile(chord_perc=section.chord_percentage,\n",
+    "        chord_len=section.chord_length,\n",
+    "        thickness_max=section.thickness_max,\n",
+    "        camber_max=section.camber_max,\n",
+    "        thickness_perc=section.thickness_percentage,\n",
+    "        camber_perc=section.camber_percentage)\n",
+    "\n",
+    "print('Parameters (British convention):')\n",
+    "print('Chord length: {}'.format(section.chord_length))\n",
+    "print('Chord percentage: {}'.format(section.chord_percentage))\n",
+    "print('Thickness max: {}'.format(section.thickness_max))\n",
+    "print('Thickness percentage: {}'.format(section.thickness_percentage))\n",
+    "print('Camber max: {}'.format(section.camber_max))\n",
+    "print('Camber percentage: {}'.format(section.camber_percentage))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0906a06a",
+   "metadata": {},
+   "source": [
+    "Then the same thing is done using the american convention. When printing the parameters, there is a little difference in the thickness between the two conventions, as we expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f2d92267",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameters (American convention):\n",
+      "Chord length: 1.0\n",
+      "Chord percentage: [0.         0.02904817 0.06233258 0.09649612 0.13115634 0.16614077\n",
+      " 0.20134615 0.23670127 0.2721528  0.30765872 0.34318492 0.37870306\n",
+      " 0.4139692  0.44887493 0.48375021 0.51858868 0.55338477 0.58813361\n",
+      " 0.6228308  0.65747236 0.69205453 0.72657375 0.76102644 0.79540903\n",
+      " 0.82971775 0.86394861 0.89809728 0.932159   0.96612852 1.        ]\n",
+      "Thickness max: 0.12022845170420489\n",
+      "Thickness percentage: [4.03999691e-16 4.73341009e-01 6.73939922e-01 7.86959769e-01\n",
+      " 8.69699145e-01 9.26697643e-01 9.64968132e-01 9.88237455e-01\n",
+      " 9.99238891e-01 1.00000000e+00 9.92102816e-01 9.76807599e-01\n",
+      " 9.55369251e-01 9.28381990e-01 8.96168256e-01 8.59352548e-01\n",
+      " 8.18397327e-01 7.73685641e-01 7.25527556e-01 6.74167564e-01\n",
+      " 6.19790048e-01 5.62523793e-01 5.02445715e-01 4.39583990e-01\n",
+      " 3.73920703e-01 3.05394298e-01 2.33900792e-01 1.59299766e-01\n",
+      " 8.13976137e-02 6.34856657e-16]\n",
+      "Camber max: 0.03997886114414057\n",
+      "Camber percentage: [0.         0.1650694  0.31526768 0.45059484 0.57105089 0.67663582\n",
+      " 0.76734964 0.84319233 0.90416391 0.95026438 0.98149372 0.99785195\n",
+      " 1.         0.99405155 0.98149372 0.9623265  0.9365499  0.90416391\n",
+      " 0.86516854 0.81956378 0.76734964 0.70852611 0.64309319 0.57105089\n",
+      " 0.49239921 0.40713814 0.31526768 0.21678784 0.11169861 0.        ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "section.generate_parameters(convention='american')\n",
+    "\n",
+    "# Define CustomProfile from parameters (american convention)\n",
+    "section_american_coords = CustomProfile(chord_perc=section.chord_percentage,\n",
+    "        chord_len=section.chord_length,\n",
+    "        thickness_max=section.thickness_max,\n",
+    "        camber_max=section.camber_max,\n",
+    "        thickness_perc=section.thickness_percentage,\n",
+    "        camber_perc=section.camber_percentage)\n",
+    "\n",
+    "# Define CustomProfile from coordinates calculated from the previously generated parameters (american convention)\n",
+    "section_american_coords.generate_coordinates(convention='american')\n",
+    "section_american_params = CustomProfile(xup=section_american_coords.xup_coordinates,\n",
+    "        yup=section_american_coords.yup_coordinates,\n",
+    "        xdown=section_american_coords.xdown_coordinates,\n",
+    "        ydown=section_american_coords.ydown_coordinates)\n",
+    "\n",
+    "print('Parameters (American convention):')\n",
+    "print('Chord length: {}'.format(section.chord_length))\n",
+    "print('Chord percentage: {}'.format(section.chord_percentage))\n",
+    "print('Thickness max: {}'.format(section.thickness_max))\n",
+    "print('Thickness percentage: {}'.format(section.thickness_percentage))\n",
+    "print('Camber max: {}'.format(section.camber_max))\n",
+    "print('Camber percentage: {}'.format(section.camber_percentage))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bef8d74",
+   "metadata": {},
+   "source": [
+    "Then, the four sections are visualized in a plot: the results obtained are exactly the same using all the different initializations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "33e0d85e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAPGothic' (ipagp.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstQurn' (KacstQurn.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerifBoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-Regular.otf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstLetter' (KacstLetter.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic' (ume-tgo4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-DemiItalic.otf) italic normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Courier GPL&GNU' (BPG_Courier_GPL&GNU.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'RaghuMalayalam' (RaghuMalayalamSans.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-ExtraLight Italic.ttf) italic normal 200 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Muol' (KhmerOS_muol.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic C4' (ume-tgc4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Vemana2000' (Vemana2000.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Devanagari' (Lohit-Devanagari.ttf) normal normal 400 normal>) = 10.05\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Mincho S3' (ume-pms3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Bold-Italic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstOffice' (KacstOffice.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'rsfs10' (rsfs10.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Light.ttf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Oblique.otf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-Bold.otf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-ExtraBoldItalic.ttf) italic normal 800 normal>) = 11.43\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Z003' (Z003-MediumItalic.otf) italic normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumGothic' (NanumGothicBold.ttf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Devanagari' (Samyak-Devanagari.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-Demi.otf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS75F.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-RegularOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist-Oblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume UI Gothic' (ume-ugo4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Hebrew' (NotoSansHebrew-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Bengali' (Lohit-Bengali.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-Roman.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai UI' (NotoSansThaiUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Mincho' (ume-tmo3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nanum Brush Script' (NanumBrush.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Stam Sefarad CLM' (StamSefaradCLM.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Malayalam' (Samyak-Malayalam.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS76F.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Muol Pali' (KhmerOS_muolpali.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil' (NotoSansTamil-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume UI Gothic O5' (ume-ugo5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Pothana2000' (Pothana2000.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstOne' (KacstOne.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil UI' (NotoSansTamilUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAMincho' (ipam.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'OpenSymbol' (opens___.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic O5' (ume-tgo5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Thai' (NotoSerifThai-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'eufm10' (eufm10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS System' (KhmerOS_sys.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai' (NotoSansThai-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstArt' (KacstArt.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS' (KhmerOS.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter-Bold.ttf) normal normal 700 normal>) = 10.335\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstFarsi' (KacstFarsi.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Kannada' (Lohit-Kannada.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Unikurd Web' (Unikuweb.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic S4' (ume-tgs4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao UI' (NotoSansLaoUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-Book.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-Oblique.otf) oblique normal 400 condensed>) = 11.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Light-Italic.ttf) italic normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-Oblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'D050000L' (D050000L.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Oriya' (Lohit-Oriya.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Freehand' (KhmerOS_freehand.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Assamese' (Lohit-Assamese.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'AR PL UKai CN' (ukai.ttc) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstDecorative' (KacstDecorative.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-Medium.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Punjabi' (Lohit-Punjabi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer UI' (NotoSansKhmerUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer UI' (NotoSansKhmerUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed-BoldOblique.ttf) oblique normal 700 condensed>) = 1.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumGothic' (NanumGothicExtraBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-MediumOblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'VL PGothic' (VL-PGothic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-Light.otf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Glaho GPL&GNU' (BPG_Glaho_GPL&GNU_2011.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Georgian' (NotoSansGeorgian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-DemiOblique.otf) oblique normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans Narrow' (PTN57F.ttf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstScreen' (KacstScreen.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic S5' (ume-tgs5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstTitleL' (KacstTitleL.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumMyeongjo' (NanumMyeongjoBold.ttf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Glaho GPL&GNU' (BPG_Glaho_GPL&GNU.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstTitle' (KacstTitle.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari' (NotoSansDevanagari-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Rachana' (Rachana.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Ethiopic' (NotoSansEthiopic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-Medium.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstNaskh' (KacstNaskh.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PakType Naskh Basic' (PakTypeNaskhBasic.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMonoBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumMyeongjo' (NanumMyeongjoExtraBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PakType Tehreer' (PakType_Tehreer.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-ExtraLight.ttf) normal normal 200 normal>) = 0.24\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstPoster' (KacstPoster.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Fasthand' (KhmerOS_fasthand.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao' (NotoSansLao-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic' (ume-pgo4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-Italic.ttf) italic normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-LightItalic.otf) italic normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-LightOblique.ttf) oblique normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Light.ttf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam CLM' (MiriamCLM-Book.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-ExtraLight.ttf) normal normal 200 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Stam Ashkenaz CLM' (StamAshkenazCLM.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Siemreap' (KhmerOS_siemreap.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-SemiboldItalic.ttf) italic normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-MediumItalic.ttf) italic normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nuosu SIL' (NuosuSIL.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAGothic' (ipag.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nafees Web Naskh' (NafeesWeb.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic O5' (ume-pgo5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Content' (KhmerOS_content.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic S5' (ume-pgs5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Saab' (Saab.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam CLM' (MiriamCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic C5' (ume-tgc5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSansOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil UI' (NotoSansTamilUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Ethiopic' (NotoSansEthiopic-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMonoOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Bokor' (KhmerOS_bokor.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kalyani' (Kalyani.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Lao' (NotoSerifLao-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstDigital' (KacstDigital.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed-Bold.ttf) normal normal 700 condensed>) = 0.5349999999999999\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Marathi' (Lohit-Marathi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Madan2' (madan.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari UI' (NotoSansDevanagariUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'VL Gothic' (VL-Gothic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-Demi.otf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-BookOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Georgian' (NotoSerifGeorgian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Abyssinica SIL' (AbyssinicaSIL-R.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Metal Chrieng' (KhmerOS_metalchrieng.ttf) normal normal 400 normal>) = 10.05\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumMyeongjo' (NanumMyeongjo.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao' (NotoSansLao-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'esint10' (esint10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-Oblique.otf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-MediumOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAPMincho' (ipamp.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Armenian' (NotoSerifArmenian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Mincho' (ume-pmo3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Gujarati' (Lohit-Gujarati.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Telugu' (Lohit-Telugu.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Tamil' (Samyak-Tamil.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Light.otf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Lao' (NotoSerifLao-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed.ttf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil' (NotoSansTamil-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS55F.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'WenQuanYi Zen Hei' (wqy-zenhei.ttc) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer' (NotoSansKhmer-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Hebrew' (NotoSansHebrew-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Gubbi' (Gubbi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-Light.ttf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Malayalam' (Lohit-Malayalam.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Armenian' (NotoSansArmenian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Armenian' (NotoSansArmenian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-LightItalic.ttf) italic normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS56F.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed-Oblique.ttf) oblique normal 400 condensed>) = 1.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed.ttf) normal normal 400 condensed>) = 0.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Navilu' (Navilu.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nanum Pen Script' (NanumPen.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Georgian' (NotoSansGeorgian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Muol Light' (KhmerOS_muollight.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-Medium.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-BoldOblique.otf) oblique normal 700 condensed>) = 11.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-BoldOblique.otf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans Narrow' (PTN77F.ttf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Thai' (NotoSerifThai-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'LKLUG' (lklug.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Nepali' (Lohit-Nepali.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-BdIta.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo-Oblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'AR PL UMing CN' (uming.ttc) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Battambang' (KhmerOS_battambang.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSans.ttf) normal normal 400 normal>) = 10.05\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic S4' (ume-pgs4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-ExtraBold.ttf) normal normal 800 normal>) = 10.43\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Padauk' (Padauk.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai' (NotoSansThai-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai UI' (NotoSansThaiUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Tamil' (Lohit-Tamil.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer' (NotoSansKhmer-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Meera' (Meera.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Dyuthi' (Dyuthi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Light_Oblique.otf) oblique normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerifItalic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Regular-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'AnjaliOldLipi' (AnjaliOldLipi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tibetan Machine Uni' (TibMachUni-1.901b.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'wasy10' (wasy10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Chveulebrivi GPL&GNU' (BPG_Chveulebrivi_GPL&GNU.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerif.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSansBoldOblique.ttf) oblique normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-MediumOblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic C4' (ume-pgc4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'WenQuanYi Micro Hei' (wqy-microhei.ttc) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstBook' (KacstBook.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Oriya' (Samyak-Oriya.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'msam10' (msam10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Mincho S3' (ume-tms3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao UI' (NotoSansLaoUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Semibold.ttf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-Book.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-Medium.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Georgian' (NotoSerifGeorgian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Bold_Oblique.otf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumGothic' (NanumGothic.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-Roman.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'kacstPen' (kacstPen.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari' (NotoSansDevanagari-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Armenian' (NotoSerifArmenian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Gujarati' (Samyak-Gujarati.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Padauk' (Padauk-bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSansBold.ttf) normal normal 600 normal>) = 10.24\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-BookOblique.otf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari UI' (NotoSansDevanagariUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'stmary10' (stmary10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic C5' (ume-pgc5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PakType Naqsh' (PakType_Naqsh.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Jomolhari' (Jomolhari-alpha3c-0605331.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMonoBoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Suruma' (Suruma.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerifBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'msbm10' (msbm10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMono.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/u/a/aivagnes/anaconda3/lib/python3.8/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(section.xup_coordinates, section.yup_coordinates, 'k', marker='d', label='NACAProfile')\n",
+    "plt.plot(section.xdown_coordinates, section.ydown_coordinates, 'k', marker='d')\n",
+    "plt.plot(section_coords.xup_coordinates, section_coords.yup_coordinates, 'r', marker='.', markevery=3,\n",
+    "         label='CustomProfile (from coords, british convention)')\n",
+    "plt.plot(section_coords.xdown_coordinates, section_coords.ydown_coordinates, 'r', marker='.', markevery=3)\n",
+    "plt.plot(section_params.xup_coordinates, section_params.yup_coordinates, 'b', marker='*', markevery=6,\n",
+    "         label='CustomProfile (from parameters, british convention)')\n",
+    "plt.plot(section_params.xdown_coordinates, section_params.ydown_coordinates, 'b', marker='*', markevery=6)\n",
+    "plt.plot(section_american_coords.xup_coordinates, section_american_coords.yup_coordinates, 'g', marker='^', markevery=9,\n",
+    "         label='CustomProfile (from coords, american convention)')\n",
+    "plt.plot(section_american_coords.xdown_coordinates, section_american_coords.ydown_coordinates, 'g', marker='^', markevery=9)\n",
+    "plt.plot(section_american_params.xup_coordinates, section_american_params.yup_coordinates, 'm', marker='s', markevery=12,\n",
+    "         label='CustomProfile (from parameters, american convention)')\n",
+    "plt.plot(section_american_params.xdown_coordinates, section_american_params.ydown_coordinates, 'm', marker='s', markevery=12)\n",
+    "plt.axis('equal')\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f3f850c",
+   "metadata": {},
+   "source": [
+    "### Part 2: deformation of the section by modifying the parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8b8aae9",
+   "metadata": {},
+   "source": [
+    "Here, the deformation rates of the thickness and camber are defined and then a new `CustomProfile` section is generated considering as parameters the previous ones multiplied by the deformation rates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "eab3f7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thickness_max_rate = 2\n",
+    "camber_max_rate = 0.5\n",
+    "\n",
+    "section_deformed = CustomProfile(chord_perc=section.chord_percentage,\n",
+    "        chord_len=section.chord_length,\n",
+    "        thickness_max=section.thickness_max*thickness_max_rate,\n",
+    "        camber_max=section.camber_max*camber_max_rate,\n",
+    "        thickness_perc=section.thickness_percentage,\n",
+    "        camber_perc=section.camber_percentage)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c421d0fc",
+   "metadata": {},
+   "source": [
+    "Finally, the original section and the deformed one are plotted to see how the deformation acts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "62e30804",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(section.xup_coordinates, section.yup_coordinates, 'k', label='Original')\n",
+    "plt.plot(section.xdown_coordinates, section.ydown_coordinates, 'k')\n",
+    "plt.plot(section_deformed.xup_coordinates, section_deformed.yup_coordinates, 'b', label='Deformed')\n",
+    "plt.plot(section_deformed.xdown_coordinates, section_deformed.ydown_coordinates, 'b')\n",
+    "plt.axis('equal')\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/tutorial-6-blades-customprofile.ipynb b/tutorials/tutorial-6-blades-customprofile.ipynb
new file mode 100644
index 0000000..6cdc63f
--- /dev/null
+++ b/tutorials/tutorial-6-blades-customprofile.ipynb
@@ -0,0 +1,661 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c2ec2fd3",
+   "metadata": {},
+   "source": [
+    "# BladeX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad979fa2",
+   "metadata": {},
+   "source": [
+    "## Tutorial 6: Generate and deform a blade modifying the parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0ab2647",
+   "metadata": {},
+   "source": [
+    "The goal of this tutorial is to show how to generate a blade starting from its parameters, related both to the sections (camber, thickness) and to the whole blade (chord lengths, pitch, rake, skew angles)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f21ab8e",
+   "metadata": {},
+   "source": [
+    "We start from some useful imports, as usual."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4362678f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from bladex import Blade, CustomProfile, NacaProfile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45d4e8f4",
+   "metadata": {},
+   "source": [
+    "Then, we create the blade using `NacaProfile` sections for the sake of simplicity, and then we extract and deform the parameters from it. The blade is created and visualized also initializing all other global parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aa8f973f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAPGothic' (ipagp.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstQurn' (KacstQurn.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerifBoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-Regular.otf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstLetter' (KacstLetter.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic' (ume-tgo4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-DemiItalic.otf) italic normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Courier GPL&GNU' (BPG_Courier_GPL&GNU.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'RaghuMalayalam' (RaghuMalayalamSans.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-ExtraLight Italic.ttf) italic normal 200 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Muol' (KhmerOS_muol.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic C4' (ume-tgc4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Vemana2000' (Vemana2000.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Devanagari' (Lohit-Devanagari.ttf) normal normal 400 normal>) = 10.05\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Mincho S3' (ume-pms3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Bold-Italic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstOffice' (KacstOffice.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'rsfs10' (rsfs10.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Light.ttf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Oblique.otf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-Bold.otf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-ExtraBoldItalic.ttf) italic normal 800 normal>) = 11.43\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Z003' (Z003-MediumItalic.otf) italic normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumGothic' (NanumGothicBold.ttf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Devanagari' (Samyak-Devanagari.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-Demi.otf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS75F.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-RegularOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist-Oblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume UI Gothic' (ume-ugo4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Hebrew' (NotoSansHebrew-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Bengali' (Lohit-Bengali.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-Roman.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai UI' (NotoSansThaiUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Mincho' (ume-tmo3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nanum Brush Script' (NanumBrush.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Stam Sefarad CLM' (StamSefaradCLM.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Malayalam' (Samyak-Malayalam.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS76F.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Muol Pali' (KhmerOS_muolpali.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil' (NotoSansTamil-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume UI Gothic O5' (ume-ugo5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Pothana2000' (Pothana2000.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstOne' (KacstOne.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil UI' (NotoSansTamilUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAMincho' (ipam.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'OpenSymbol' (opens___.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic O5' (ume-tgo5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Thai' (NotoSerifThai-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'eufm10' (eufm10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS System' (KhmerOS_sys.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai' (NotoSansThai-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstArt' (KacstArt.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS' (KhmerOS.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter-Bold.ttf) normal normal 700 normal>) = 10.335\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstFarsi' (KacstFarsi.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Kannada' (Lohit-Kannada.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Unikurd Web' (Unikuweb.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic S4' (ume-tgs4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao UI' (NotoSansLaoUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-Book.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-Oblique.otf) oblique normal 400 condensed>) = 11.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Light-Italic.ttf) italic normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-Oblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'D050000L' (D050000L.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Oriya' (Lohit-Oriya.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Freehand' (KhmerOS_freehand.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Assamese' (Lohit-Assamese.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'AR PL UKai CN' (ukai.ttc) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstDecorative' (KacstDecorative.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-Medium.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Punjabi' (Lohit-Punjabi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer UI' (NotoSansKhmerUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer UI' (NotoSansKhmerUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed-BoldOblique.ttf) oblique normal 700 condensed>) = 1.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumGothic' (NanumGothicExtraBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-MediumOblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'VL PGothic' (VL-PGothic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-Light.otf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Glaho GPL&GNU' (BPG_Glaho_GPL&GNU_2011.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgTypewriter' (TlwgTypewriter-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Georgian' (NotoSansGeorgian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-DemiOblique.otf) oblique normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans Narrow' (PTN57F.ttf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIX' (STIX-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstScreen' (KacstScreen.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic S5' (ume-tgs5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstTitleL' (KacstTitleL.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumMyeongjo' (NanumMyeongjoBold.ttf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Glaho GPL&GNU' (BPG_Glaho_GPL&GNU.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstTitle' (KacstTitle.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari' (NotoSansDevanagari-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Rachana' (Rachana.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Ethiopic' (NotoSansEthiopic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-Medium.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstNaskh' (KacstNaskh.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Loma' (Loma-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PakType Naskh Basic' (PakTypeNaskhBasic.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMonoBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumMyeongjo' (NanumMyeongjoExtraBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PakType Tehreer' (PakType_Tehreer.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-ExtraLight.ttf) normal normal 200 normal>) = 0.24\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstPoster' (KacstPoster.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Fasthand' (KhmerOS_fasthand.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao' (NotoSansLao-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic' (ume-pgo4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-Italic.ttf) italic normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-LightItalic.otf) italic normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-LightOblique.ttf) oblique normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Light.ttf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Caladea' (Caladea-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam CLM' (MiriamCLM-Book.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-ExtraLight.ttf) normal normal 200 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Stam Ashkenaz CLM' (StamAshkenazCLM.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Siemreap' (KhmerOS_siemreap.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-SemiboldItalic.ttf) italic normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-MediumItalic.ttf) italic normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nuosu SIL' (NuosuSIL.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAGothic' (ipag.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nafees Web Naskh' (NafeesWeb.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic O5' (ume-pgo5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Content' (KhmerOS_content.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic S5' (ume-pgs5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Saab' (Saab.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam CLM' (MiriamCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Gothic C5' (ume-tgc5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSansOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typist' (TlwgTypist-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil UI' (NotoSansTamilUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Ethiopic' (NotoSansEthiopic-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMonoOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Bokor' (KhmerOS_bokor.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kalyani' (Kalyani.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Lao' (NotoSerifLao-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstDigital' (KacstDigital.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed-Bold.ttf) normal normal 700 condensed>) = 0.5349999999999999\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Marathi' (Lohit-Marathi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Madan2' (madan.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari UI' (NotoSansDevanagariUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'VL Gothic' (VL-Gothic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Bookman' (URWBookman-Demi.otf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-BookOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Georgian' (NotoSerifGeorgian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Abyssinica SIL' (AbyssinicaSIL-R.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Metal Chrieng' (KhmerOS_metalchrieng.ttf) normal normal 400 normal>) = 10.05\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumMyeongjo' (NanumMyeongjo.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao' (NotoSansLao-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'esint10' (esint10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-Oblique.otf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Simple CLM' (SimpleCLM-MediumOblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'IPAPMincho' (ipamp.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Armenian' (NotoSerifArmenian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Mincho' (ume-pmo3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Gujarati' (Lohit-Gujarati.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Telugu' (Lohit-Telugu.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Tamil' (Samyak-Tamil.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Waree' (Waree-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Light.otf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Lao' (NotoSerifLao-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerifCondensed.ttf) normal normal 400 condensed>) = 10.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Tamil' (NotoSansTamil-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS55F.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'WenQuanYi Zen Hei' (wqy-zenhei.ttc) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer' (NotoSansKhmer-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Hebrew' (NotoSansHebrew-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Gubbi' (Gubbi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-Light.ttf) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Malayalam' (Lohit-Malayalam.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Armenian' (NotoSansArmenian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Armenian' (NotoSansArmenian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-LightItalic.ttf) italic normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans' (PTS56F.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed-Oblique.ttf) oblique normal 400 condensed>) = 1.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-BoldItalic.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSansCondensed.ttf) normal normal 400 condensed>) = 0.25\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Navilu' (Navilu.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nanum Pen Script' (NanumPen.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Georgian' (NotoSansGeorgian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Muol Light' (KhmerOS_muollight.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'David CLM' (DavidCLM-Medium.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans Narrow' (NimbusSansNarrow-BoldOblique.otf) oblique normal 700 condensed>) = 11.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-BoldOblique.otf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Garuda' (Garuda-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PT Sans Narrow' (PTN77F.ttf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Thai' (NotoSerifThai-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'LKLUG' (lklug.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Nepali' (Lohit-Nepali.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-BdIta.otf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo-Oblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'AR PL UMing CN' (uming.ttc) normal normal 300 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Khmer OS Battambang' (KhmerOS_battambang.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSans.ttf) normal normal 400 normal>) = 10.05\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic S4' (ume-pgs4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-ExtraBold.ttf) normal normal 800 normal>) = 10.43\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Padauk' (Padauk.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai' (NotoSansThai-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Umpush' (Umpush-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Thai UI' (NotoSansThaiUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Lohit Tamil' (Lohit-Tamil.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Kinnari' (Kinnari-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Khmer' (NotoSansKhmer-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Meera' (Meera.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Dyuthi' (Dyuthi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Light_Oblique.otf) oblique normal 300 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerifItalic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Overpass' (Overpass-Regular-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'AnjaliOldLipi' (AnjaliOldLipi.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tibetan Machine Uni' (TibMachUni-1.901b.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'wasy10' (wasy10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'BPG Chveulebrivi GPL&GNU' (BPG_Chveulebrivi_GPL&GNU.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerif.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans UI' (NotoSansUI-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSansBoldOblique.ttf) oblique normal 600 normal>) = 11.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Frank Ruehl CLM' (FrankRuehlCLM-MediumOblique.ttf) oblique normal 500 normal>) = 11.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic C4' (ume-pgc4.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'WenQuanYi Micro Hei' (wqy-microhei.ttc) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'KacstBook' (KacstBook.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Oriya' (Samyak-Oriya.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'msam10' (msam10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume Mincho S3' (ume-tms3.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Norasi' (Norasi-Italic.ttf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Mono PS' (NimbusMonoPS-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Lao UI' (NotoSansLaoUI-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Sawasdee' (Sawasdee-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Open Sans' (OpenSans-Semibold.ttf) normal normal 600 normal>) = 10.24\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Miriam Mono CLM' (MiriamMonoCLM-Book.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Keter YG' (KeterYG-Medium.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Georgian' (NotoSerifGeorgian-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Sans' (NimbusSans-Bold.otf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Neue' (ComicNeue_Bold_Oblique.otf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'NanumGothic' (NanumGothic.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Cantarell' (Cantarell-Regular.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'TlwgMono' (TlwgMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'P052' (P052-Roman.otf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'kacstPen' (kacstPen.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari' (NotoSansDevanagari-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif Armenian' (NotoSerifArmenian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Samyak Gujarati' (Samyak-Gujarati.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Padauk' (Padauk-bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSans' (FreeSansBold.ttf) normal normal 600 normal>) = 10.24\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'C059' (C059-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans' (NotoSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'URW Gothic' (URWGothic-BookOblique.otf) oblique normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Tlwg Typo' (TlwgTypo-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Sans Devanagari UI' (NotoSansDevanagariUI-Bold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'stmary10' (stmary10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Ume P Gothic C5' (ume-pgc5.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'PakType Naqsh' (PakType_Naqsh.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Purisa' (Purisa-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Jomolhari' (Jomolhari-alpha3c-0605331.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMonoBoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Suruma' (Suruma.ttf) normal normal 500 normal>) = 10.145\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Nimbus Roman' (NimbusRoman-Italic.otf) italic normal 400 normal>) = 11.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Carlito' (Carlito-Regular.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeSerif' (FreeSerifBold.ttf) normal normal 700 normal>) = 10.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Noto Serif' (NotoSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'msbm10' (msbm10.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'FreeMono' (FreeMono.ttf) normal normal 400 normal>) = 10.05\n",
+      "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Hadasim CLM' (HadasimCLM-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
+      "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/u/a/aivagnes/anaconda3/lib/python3.8/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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qM/CefzcfBkpYveHc2B5ripBt/k0mE4WFhYRCoYjprmdggEsLVbxUY+G2tpSPvDFPtBHFaBBF8SNvvDwUHymikNuPIfxLrKysRKvVsm7dukkx/2SJwufzUVFRgdFoZM2aNRHZcCxvBGXHh2je/yWqzn2I+ky8n7qHwJIbQRWPeOBAzI4TLaRgkFBPL8HeHkK9ZiS3C8nnQ/L7AQFVVibKnBxUOTkkJydHQmtT3gAvNR3jtYoOluvcHDt2bFii76MUYUz1Zp8BguwVBCHrRDSRBZhjfYCJ8JEhClkbUVlZSWpqKq2trSxcuDCSUJoMJkMUVquVmpqaU6aSx8pQRtFzlLj3H0TVuhtRl4H3gp8TWPrZaScoo4G89ZFEkUBDI/5jx/BXVhJoaCDQ0gJDtALjQUhIQJWTgzInm6SsLLb0VKFy34guPZni4uKIAtLpdKLT6YYlRs9k4giFQlNOlsbY9/NF4Fbg/hN/vhCrhaPFGU8UI7URLpcLj8cz5qTwaBANUUiSxPHjx7FYLKMea7pEIfTVoX3vAdSNOxETkvGe9yMCy/8N1AlTXnMyEJ1O/Lt2kfDmLrprahAHBgBQpKaiWVRC/Nlno8rPR5mZgTIjA4VejxAXh6DRIIkioa4ugp2dBDu7CHV2hv/e3oF/717u8Af4VXYtlfrlfNnxZVZnrY44Wcm+Eo2NjXi93pNS8+TkCUVqs43pRBRTLn8LwtOEE5epgiB0AD8hTBDPCoLwZaANuH5Ki08DZzRRjJwUXllZiUKhoKSkZMokARMThey8rdfrx3TennLlxN6G6r0H0VQ+Bxo9vrP+G//q22LekDUaJJ8Pz+7duHe+hnfvXggEUBqNxJ9zNnHr1xO3ciXKzMyJE5yAYv581PPnn/J/L1f08Msdb3DB+i7eG3yDr7//dQoMBVxTeA2X5l+KQW+I+ErIiWibzUZlZWWkWSs5ORmTyTRuInA6N2O0CIVCU2rm8/v9Uy6XS5J08xj/9akpLRgjnLFEMVSGbTabaWpqoqysDLPZPO0LZLyb3GazUV1dPeG2ZtIRhcuM8oOHUB55HAQFbblX0ZJzDSpdOkm9NpKThai7PieLQGMTzh3bcf9rJ5LDgSItDf311yFt2Ei3QU/+kiUxOU5IlPjbh51oU0r40ebPsXf/OQxmDrK9eTsPlT/En6r+xOUFl/PVJV8lQZUwrMtzqAeHrOEQBGGYB8dQwp6N0utUk5kulyvqxPpHBWccUYwmw/b5fBEZdl9fH6FQaFrHGI0oJEmipaUFs9nMqlWrIsKiyawxKryDKPf9EeWBP0HQh6PoaqpSLqdo9Xms0GgiNvbNzc2n2OFNJxSXQiG87+7G8cwz+I8cAY2GhM3no7vyKuLWrEZQKnE4HAgdHVM+xkj89cN26swufnnNIhSCgEah4dKCS7m04FJqbbVsb97OjuYdlPeX88DGB8jUZg57/chmrUAggM1miygg1Wr1sPzGTHtjTrU8KudiPk44o4hiqDZCNqDNyckZJsNWKpUxb+gKBAJUVFSQkJAQ9ZCfCSOKoA/l4b+i3PMQgsdKcNHV1GVfiys+i2WLF0dcroaayg41XOns7EQURUwmE8FgMOqnm+jx4H7xRRz/eJpQVxfK7CwSv34n2iuvRBmFYfBU8XZ9P79/p4VLytK4uDTtlP9flLSI76/+PptzNvPj/T/m9ndu528X/I3k+LF1IWq1epgCUibVjo4OBgcHCQQCdHR0zJg58XQiisk6pp3pOGOIYqg2oquri/b29lEnhSsUimlHFEMvqIGBASorK1mwYEFEWRftGqOehyShqHkB1bu/QLC3Ic47D+fGb3O4RyQrM4tlJ6zwRnvt0FC8sLAw0rzV3d3NoUOH0Gg0kSfu0B4MAHFwEOezz+F85hnEgQE0y5Zh+sY3iD9vE8IYF3us9vi76vr49o4aFmcZ+OnlxePesBszN/KHTX/g9ndu5+cHf85vz/lt1McZOhvE6/VSXV0NQFNT03APjuTkmEjqp5rMnNt6zACGJizlSeEqlWpMGbZSqZw2UcjHbW1tpbu7OzJPdDIYTUchdB5E9eaPUHQdQkwvI3DTs1gMS6irq5uSP4XcvBUfH8/atWvxer2n9mCoVMS99hre7TuQXC7izzkHwxduJW758qiOMV1l6RP7Ovn1rmaWZBv4n5uWoNVMfGMVm4q5ffHtPFLxCLW2WhYlLZrSsdVqNbm5uRGnbjkaG2lOLNv5TxZTLY/ORRQxxshJ4TU1NRQWFkamSY0GhUIxbdMZedCP7B0xlafGsK3HYBeqt3+Gsno7kj6DwGUPEVpyA80trfQ3N8esaSw+Pp7s7Gyys7MJWq3Y/vo3vM/vwO3z4125EuGG60lYsQLVDG4xZNjcAX76aj276vq5sCSVe68uIUEd/ed41byr+EPFH3i/+/0pEcXIzlFBEMY0J5ZdrCZrTjzViGIuRxFDyOP75Ce72WxmxYoVEz7ZlUrlMGPUyWJwcJDKykrUajWLFy+e8joKhQKCXpQf/Abl3odBDBE86z8JbbyTgBBHxbFydDpdRMkZK4TsdpxP/R3ns88ieb1oP30Rxi9/GUV+fkRK3dzcPCwxGEvzXUmSeL22j3tfa2TQE+S/zsvhslzorTqCZ2AAr8uBFBIRQyG6uzqpcQ+SmJGJMT0TnSkJ4cRnoVfrMWgM2HxT65aeqHN0NBcrm802KXPiuYjiJGadKEZOCq+srMRgMEQtw56qfkGSJDo6Oujo6GDZsmWUl5dP5fTlxUhoe4fMvfehcnURKrmC4AV3gykfh8NBRcUB5s+fP6mcx0QQBwdx/P0fOJ95BsnjIeEEQagLCyM/M9Slaaj5rsPhQKfTRaopckVnsiXGhu4B/uf5D7A113ERVvJEK76/WXl+nNf0HN4X+btSo8GYlkFiegaS2sgycx4pBVMb/jzZioRKpRrVnFgemix7cCQnJ0fK1HPJzJOYVaIYqo3o7++nvr7+FGn0RJhKjiIYDFJVVYVSqZyyPX8E1mZUb/yAtOZdeA3z8N+8FWneJgC6urpoaWmZ0pyQsSA6nehf/Rfd3/kuktNJwoWfwnjbbagXLBj3dSPNd10uF1arNVJuTkxMJD4+fkLS9TqdlH+4l73v7kXZ08giKbztM2ZkkT6vlKTsXEyZ2ehTUkgwmojXG1CoVEiSxOFDh1i0oJCB3h4GzT3hPy292Hu6sfccpoh44p5bwsGWNoo3pGNMi15EN113q9HMieUytdvtjkwi8/l8E5bKR8LlckXVxfxRwqwQhZyLkO3CmpqacDgcU9q7TzaiCD/hKygoKCAnJ+eU84r6iRrwoNz7MMoPHwGlmoEN36Ul42IWzVuCKIqRwTPRjiScCKLHg/Ofz+J86kkMA4PEnbcJ4+23oymevA/F0Elbcqv4wMAA3d3dWK1WDh06FHmaGo1GxGCQliMHqHjvXcx1VQiSSFClRzF/JRvPX8/CpUuJ149v8RYKhVAolRhS0jCkpEHZ0sj/WTwW/u2Nf2O1cx0XBpOoed9M9e5eshYaKd6YRl6ZCYVyfBKIpQ3eaLNPnU4n5eXl1NXV4ff7I4nRpKSkCROjbrd7LkcxWcgWdTU1NRiNRjo6OkhLS2P16tVT2jdPJqLo6Oigra1t1Ce8XLWI5hwUTbtQvX4Xgr2VUNlnCF5wN+6ABqm3N+KXmZGRMart3lgY6+ekQADXjh0M/t9fEK1W4s86i/ZN57Ly2mujWjcayPb1sr9jYWFhuP+ispy2A3vpb6hB8vtwKPU0Ji4nd+U6br1sPblJ0T9Zx/ps3UE339/3ffySn9uu+CLzjPNwD/pp3N9H/T4L7z7RhDZRzTk3zydzgXHM9WfSBk/24NBoNKxYsWLYXJD29vaIObFcURkZoTqdzjNv6xE2w+lAkoIIQioQD3RGWyOfUaIYalHn9Xoxm80sX748qknhYyEaogiFQlRXVyNJ0phP+Khmhzq6Ub35Q5S1LyEmFxG4eRvSvLAvhGCz4fF4OHTo0KT9MkeDJIp43tzFwB//SKizE82qVSQ++CBxy5dxfAbbzCVRpLPyGOVv/oueumpEQUmDdj5NaaUUlxRyda5AQZoRtX+QQEAVdZlxNKIY9A/y3b3fpdZWyy/W/YJ5xnkAaI0all2YzZILsuiqHeDgy+288Vgda6/KZ9HZGWOuP1tTy0fOBZH1LUMTx0MTo/LWZboQBOE/gdsACagAvihJ0lQz+X8BrkcQAsAewAD8EUH4RTRkMSNEMTRhKYflLpeLBQsWTIskYOKth9PppKKigry8vHGHDo81lwMAMYTi8N9QvXsPiEGCm+4itP4/Iq3fkiTR3d3NwMAAGzdunFaDGoDvyBHsD/2OQHU16oVFmB56iPizNs5oL0PQ76dxz7tUvPk6gUErLrWBo0kbsOcs59qNRfxkeSbGeNUwtWhHR0ekzJicnHxK/8VQjLyRG+wN/HD/D+lx93D32rs5L+e8U16jUAjklplIn2/g/X80sf/5NrSJGvKXnKo/mSkH7mgg61tSU8OJWL/fj81mo6uri9/97nccOXIEg8FAYmIiS5YsmdJ5CoKQA3wdKJMkySMIwrPATYQ9NSe7mALIRJL6EYT/AP6BJN2NIFQgST+PZomYE8VQbYTT6aSyspK8vLyYhWLjRRRyMnE0RedIjEU4Qm8Fqn/9N4ruI4iF5xO4+AFIOllZCAaDVFRUoFAoSElJmRZJBDs6cDzyBzxvv40yPZ2kn/wY7aWXjqqkjFUTlM/t4tBrO6natRPJ66QnLp1j6Z8mb8Uabl+dw8b5w233R1OLyv0XDQ0NxMXFRYhjqFpUPt+gGOTZpmd5rOoxjBojD5/zMMtTxxeDaeKVnPdvRez8Qw17t7aQU5KIUj38ZptpopiMalWj0ZCRkUFGRgZ/+tOfuOmmm0hJSeH+++/nnnvuoXBIZWqSUAEJQjgK0AJdU1wnDrAiCOuAK4FvIwh6IOqqQEyJYmgzV2dnJ52dnZH8QGdn57SFUjD6DR4KhaitrSUQCESdTDxlnYAb5Xu/RLn/T5CQROCq/0Es+8ww+3s5MVpYWIjBYKCpqWlK70F0OrE+9hiDf/8HglqN8St3oL/lFhTTjEzGg7Xfymtbd2A/vBtlyE9LQj7dxZeypHQej25eMsxmfzyMLDPK1QJZLSo3tSUkJNDsbeahdx6i1l7LOVnn8L1V3yMpLjp1qlKlYNVlebzxWB0dNXYKlg3f2s00UUzHBs/n8/HFL35xXOHgRJAkqVMQhF8R9p/wAK9LkvT6FJfzAr8D/gzsRJIqEISVQG+0C8SMKOSkZSAQoKqqCo1GM6wUOV2hlIyRTWEul4vy8nJycnLIy8uL+qk7lCiE4++g3vntcLJy+S0EN/8YEoZf0N3d3Rw/fjxCfC6Xa9K9EpIo4nzxRawP/56Q1UrC5Zdj+o+vokwdX0sgq0AnG1H4giLvHDvOsZ0voW85hFIK0pm4kNSNn+bWc1eQrgnQ19cXNUmMhoSEBBISEiLGNA6Hg6quKp46+hRH3EcwqUx8p/Q7XLHwiklXg1Lzw5WDwT7fKf83056U0yEiue9kOhAEIYmw+3YhYAeeEwThc5IkPTWJRVRACpLUiyC8iiQ9d+L78UA5cGm0S8WMKARBYGBggKqqqlEbrGLVozFUOt3T00Nzc/O4MzzGgkKhQHL3o3r7LpSVzyImLyBwy/NI+WcN+zl5KrnX6x0WrUzWj8JbWUn/fffjq6wkbvky0n/3O4SFRTF/KnoDIT5otvHWkWZcB9+keKAaoyTizV/G6su38O+rSiJbC6vVGtNjtzhaeKLuCd7seBO1Qs3lyZdza9mteAY8HDlyBCBShoyPj4+8d4PBMKoi1z3gByBOd+plOtPJzOlEFB6PZ9Lai1FwIXBckiQLgCAI24GzgOiJAlKBSwnnN36NIPQT3m74ACXQQTjJOSFiuvXo7+8f08shVkQB4Yukuro64lMx6YYfSSKl622S3/wjgn+Q4FnfInT2N0E1PPSXS5+jTSWPVs8Rstmw/v73OLbvQJmSQtovfoH+issjuZzoT3lsUnL5grzXZOXN2n4O1bSwpO8gZY5aFAIkL9vAp66/nuQxVKLTzXtIksQhyyGeaXyGPT17iFfGc13hdWyK20RXcxcVhyro6+tjYGAAn+/UyEBGfHx8RDkqS68t+1xIgkh28all0pneekxn/RiRWBuwQRAELeGtx6eAg5NcwwfUAUHgEGFyiCN8309K6BFToli4cOGYN0+siMLtduN2u0/xqYgag12oXvsOhY2v409fBlc+jJRedsqPyaa6Y5U+J4ooJFHEsX071od/j+h0kvi5z5H0lTtQnEjq9vT0hAcEp6SQlJQ0blg+2nvsc/p5u76ft+v7+bDFhsbn4BzXUW6yVaMQoHjT+ay6bAuG1OhVr5PBgG+Ane07ef7487Q6WskSsvhcwudIcabQ824PbwXfAkCn05GamkpWVhZGo5GEhIRINOHxeHA4HFgsFhwOBz6fj+bmZqqqqiLHWZdWirphEGlpEkLCyc/oTM1RxCrpLEnSPkEQtgKHCd/oR4DHJrmIDdiDICQCLyJJw3MSghD1nnPWJNxKpXLYBOipQB7TFhcXx7x58yb3YklEceQJVG//DCSRrqVfQ1p3B6npw+v0Q52uxjPwHY8ofDW19N1zD76KCuLXrCb1ru+hWVgUWb+hoQGHw0F2djZ2u53W1lYUCsW4TVySJFFvdvFuQz/v1PdT3uUAYL5e4guKChK69iEgUbLpfFZetgV9ytR6KMZDUAxywHyAV1tfZXf3bhR+BWvFtZzjPgevzYsHD64kF0uXLiUlJTzXY8WKFVGtPazbs95M+143xgQf+UlpeN/oxPt2F+pSE5pVqShztDM+d3Q6OZAYksVPCJvrTg2CoECSRGAl8D0E4etAI2ENxX8BecAXolkqpkQx3oejUqmmHFHIlngul4u1a9dy8ODkIjDB2ozqX99C0bYHcd65BC75Nda+AIkMP99gMEhlZSVxcXETOl2NtvUQnU6sf/gDg8/8E6XJRNo996C//LLI5xIIBCgvL8doNLJixQqCwWCkicvv90e0Cg6HA71ej85o4rhLxQvVfu7ac5CewfBWZUmWgTvPyqbQfIiO914j6POycOO5rLryWoxp0Y8viCbHIkoiVdYq3mh/gzc738TutTM/MJ+rfFch9IfJ0phuZO25a1m4cGFElDQwMEBPT0/U5yJ3ew62Q/d7drS6BJZ/Jg23OIi9y0lKtwZ9rZ1AhQ1FRjyKZaBImdmIYioRyxk19ChMEiBJ7yAIC4D/Bd4G1hHe2nwt2qVmNaKYClF4PB7Ky8tJT0+npKTklDr9uBBDKA88inL3/aDUELjst4jLPguCgMLaNOxGl7X9UxlLKEkSrjffpP+BBwn19WG8/jqS7rwTpfHk3lourS5YsICMjIxTSEYeFByIS+SY28o7Ry0cbG/GH5LQKCSWZ8bz+VU5XFSWyUDlfg4+/xjHB+wUrFjDus/cSFJ2brQf6SnvYyQkSaLaVs2ujl283fk2vZ5edJKOTWzCZDbhd/vR6XQsXruYsrKyUedqTvap6nMHOfhyO00H+kjN13H+rUVojRogB2lRePCTzWzFX2klsdZD0i5wuy3oz9ZPyZRmIkw1ovB6vbFIZMYWgqAG3iUcWfwI+B2S9J+TWeKMJgqLxUJ9ff0p7lDyWuPu6/vqUL3yTRRdhwgVXUzwkl+C4WRCb2hEIFdPohFqjXx9oKuL/nvvw/3ee2hKSsj47W+IX7p02M/K64/Wc+IJhDjYOsCeZhvvN1lpsXoAyDXFc93KLM4tSkZlPc68vBxajh3mjfsewmvtw5RbwDlf/ArzlkTnZDURQlKIiv4K3ul8h3e73qXX04tKUHGW8Syu9F6Jo91BMBgkIy+DFZ9awYIFC8a9kaIlClGUaDrQx5GdHfjcQZZekMWyi7JRqk4+zQVBQKfToSvUQWEewfN99D9Vg26Pm/pAOZ6k6NSik/o8puFFccY0hJ3cepwD3AdsB7IJV0B2AF/lxEzTiTBrW4/JjOATRZHGxkYGBwcj7tsj1xqz4hAKoPzwEZQf/Bo0BgJXP4pYumWYcEpeQxZqeTyeyVdPQiG0b75JxyuvgiCQ/N//ReLNNyMMIS9Jkoa9D7U6POG7rtfJ+439vN/Yx6G2AfwhiTiVgjX5idy4OptzFiRRkHzSLHZv2xGO/HMX7RVHMKSms/YLX0FfMB+bzcb+/ftJTEwkJSVlwlkYI+ENejk6eJRnDz/Le93vYffZ0Sg0rEtfxy3pt0ArtB5tZVA5SGlpKatWrYraEmAiopBEidYKG8fe6GSg10tagZ4LbysgOWdiS0KVIY7+jSpy3hfIb4kn7rz5DAwMDFOLyvmeqY5AmGoy84xyt5K3HqAG7kSS5KahWxCEHwFXAY9Gs9Rp98wcCa/XS3l5OSkpKWN2mI4VnQjmKlSvfANFTzmh0qsJXnQv6Ea/sEOhEK2treTm5g7b0kQDX1UVlp/9HGNtLQnnnUfq9+5CNUKFNzQfkb2gjJcqLexptrK32UqfM5xrmJ+SwA2rszlnfhKr8xOJH2El5/e4OfzyDirfeBWVRsO6a29myYWXoFKHiVMeojN0FoacFE1JSRl11qfVa2VPzx7e736ffb378Ik+tCotZ2WexblZ55LjyaH8cDmtna3Ex8ezYcMGVqxYMemLfyyiCAZEmg/3U/1uD4MWL4np8Zz3+QXkL02a1O8gpJRQrEpCfNuCyi2dohaVG7Zk8ZNMHNGOQBBFcUp2AWdURAFyVPE6ghCPIOQCfsJl03uRpNMj4Z4u+vr6qKurY9GiRaPue2WcElGE/Cj3/A7lnt9CvInAZ/6CWHLFmK+XfRTT0tKYP8q0q7Eguj3Y/vAHBv7xD5QpKdj+/d8p/I+vnnKBd1rsbHu/nM6AnmO9Fo73tQKQrFNz1vxkzl6QwoZCEykJytFzBKJIw9732LftaTyOQdJKl7L581/GNEqicmRno8/nw2q1npz1qdcxEDdAtbeafZZ9VNvCztXpCel8KuNTLE1YyiVLLqG5oZn9b+2nvK8cg8HA5s2bWbp06ZT3/yOJYtDipWGfhYYDFvzuECm5WjZ9LkwQCsXkn/iSJKHQqhEByTc8ukxISCAnJyfiLSE3tcnTyIZuU8aKGkKh0JR8Ts84dytJEhGE+cAWwhGEEhCBTATh60jSa9EsM2tbj/Egh+h2uz0qM5uhEYXQW4Hq5a+jMFeFvSIuuhe0o7d8y/6cvb29FBYWTipn4v5gD32/+AXBri4M111H8je+TmdlJYIg4PAGOdRmZ99xK+/Vm2ns8yIBWs0AawpM3LAqh7MWJFOcro/cFGMJrvpaj/PBP/5Kb1MD6fMXcsnXv02n3YE20RTVecbFxaFL0VEVqmKvZy8fHv8Qm9+GgEC+Jp9rM6/lvNzzWJmzkr6+Pqqqqnjq8aew2+0kJydzySWXsGjRomnLoyVJIuSXaNhvoflQP73NDgQF5C1OYtHZ6WTMn56PpyiKSM1O0ChQJI99vYw03ZVbxId6Z8rRxtAIbDpW/WcMUQiC8kTU8CPCOgwnsIMwUVwItEa71GnxzBx6gfh8PsrLyzGZTKxZsyaqi0epVCIGvCjf+xPKPQ9BQjKBax9HLB5bui6XPjUaDWvXro2IfCZCyGaj/5e/wvnKK6gLC8n+618JlC3lvTY7z9f5+U3lfqq6BhElUCmgOEnFVzcVcFZRGstyjGhU0SXEfC4nB55/lup33iRBb+C8L36F4o3nIigUdB07Nm5+JygGqbHVsK93H/vM+6ix1iAiYlAbWJ+xno0ZG1mfsR6jyhgxmH355Zdpb2/H5/ORlpbGVVddRVFR0bTr/wFfiI4aO/X7ezE3e5BCVoxp8ay8NIcFa1JPVDJOoi8QpMkToN0foNcfwhEK4RLDc0U1CgGNIJAXp6YoXkNRgoaUE9uz+G6RUM0gcRvTEdTRJx1HtoiPjMD0ej1JSUn4fL6PfjLzJNKBl4BlgAVJehFBuAjIBGqjWWBWiWJktUJWP5aUlER+cdFA62gm+f3/h6q/htDi6whedM8pTVxDIXtU5OfnR+zwJpJgS5KEa+dO+h54ENHhwPaZW3hz9WXs2++k5sV3ECVQCrAyX8Ht5xSQGrKyIi+RJaWTy3dIkkT9nt3s2/oPfE4Hizd/mjVbridOO/bFJkkSna5O9pv3c8B8gEOWQzgDTgQEypLK+MKiL7A+Yz2lSaWoFMPVjL29vezdu5fBwUFSUlIoLS0lJSUFm81GQ0NDREY9maep3xOko2aAtgobnXUDhAIicTol2UsSWLZpHql54fZzUZKodvvYM+jhoNNDucuHJTA8qktQCCQoBJSCgF+U8IoSviEkmaxSsEBSkKm0smqll5vPLo36PEdDXFxcZKiQbIFns9mwWq0MDAxEoo1oE8VnmLuV/MF1Et5yNANrEIR2YCHhXEVUmNWth0qlIhgMolQqaW5upr+/f1z14ykIBVDufZji93+FGG+aMIqAsUufYxGFJEl01LfSf8+9GI/tpyVtHvdv+hKtYhZxR3pZnmvk/20qZO28JLydNaxduYjy8vIpuW5bO9v54O9/paehlowFCzn7P79Hav68U35OEASsPisV5goOmQ9xwHKAHndYzJSRkMHm7M2sz1jPmvQ1GDWn9kXIW7v3338fq9VKRkYGF154ITqdDpfLRWFhIaIoRlybjh8/HgnJU1JSTplKBuCy+2mvttNeZaO3yYEYkkgwqilal0rB0iSkBBeBYIDkPB0fOjz8y+Zil92F+QQxzItTc7YxgTJtHEXxGvLj1GRqlMSNeIpLkkRPIESjx0+D20ddywANTi9vJmVi4RBl1ZeQmnolaamfISHh1M9uMpAt8AwGA06nk9zcXILB4LBE8VBv0dGu9zNq63Gy6vEbwAE8BDwIPAn8lrA8PCrMekTh9XqpqqpCr9dPauaFYKlB9fKdKHrKGci7iMFzfkT6vLEHx4iiSENDQ0TNOTIpF9FBhETqep0cbrNzqNVK4q5/ccOh59FIIo+v3EL3BVdyXWEKqwtMLM0evpV4tz1IeXn5pPQXAAGflwPPP8eRnS+hiU9g0623U3L2eZGZFxDupTjad5RDfYfY076HrsawZ4lBbWBV2ipuWXgLa9PXkqcfv7W+s7OT3bt309XVRVJSEldeeSULFy5EEATMZvOwz2PoHAw5JG9paYmYxaoDBpzd0FXnwNrpBsCYFk/pORnkL00KRw4KuaTr4GVniHfKWzEHQmgVAucmatmcqOUco5YMTXSXniAIZGlUpHV7WPFaL6LFi3pZMpUhO3klC3H2r6K39x/09DyBwbCajIxbSE66IOrfxVgIhUKo1WqMRmPkM5HVs11dXdTW1qLVaiOfmSyycrvd07ZFBBAEwUTYP2IJ4cjgS5Ik7Z3SYpI0dHsRtRpzKGaVKILB8I1VWloavUW/GEK57w8o33sQ4owErvk/euLHz8bLeY/k5GRWrlw57Eayuvwc6xjgw0YzB47307z9HTwBkSxnH9+p2Mqi3kbsi5ajvev7/Hh5McpRMvKSJNHU1ITf72fjxo2TmjrecvQQ7z7xvzj6LBSffR4brvss8QZjhBiO9B3hSN8RGgcakZCIV8YzP24+lxdezobsDRSbilEKE28L7HY7u3fvpqGhAZ1Ox0UXXTSqLdtYJBMXF0dyYho+cxwDx+1U1tjxucLkkJAK8zZoKVyeRs781MiaPlFiZ7+Dpy2DHHJ6UQLnm7RcnWzg/EQtCRM4a4+GYJcb3+4egk2DCIlqtNcXoi5ORDjQS1bSekg6C3+gj76+l7BYttPY+C1Ski9j3rwfolRObkzkUIzWdCarZ2WLf7fbPWwEQlNTE3V1deTmTk0lOwK/A3ZKknSdEG7emvqbGSojHiptnswSE/z8pBYbK5MvN1q1tLRQWloadYgu9DeheuVOFJ0Hw0N2Ln4AdGm0tbUhCAJ5eXmnvMZms1FdXU1JSQlGUzK1vQ7KOwY51jHAsY4BWk8oH5UKgXmJSs5ZmMEFlW+Rvu0JFBo1yd/6LwzXbBnzBpKt8LRaLX19fZx99tlRvRenzcp7T/2FpgN7ScrOZeXNN9Kit3Os/xhH+47SNBh2y4pTxrEkeQmr0laxOm01pUml1FbVUlRUFJU02Ov18uGHH3LkyBGUSiVr165lzZo1oxKr2WzG7XZHGuxEUcLa4aKrbpDOugH62pxIEmgSlGQXJ5Jbmkj2okSUGiL7+MHBQTzxWt7V6HnFI2ENiRTEqblUI3FxgpIl+ZO/aSRJItg4iG+/hVCLEyFeSdzGdDRr0yKJywMHDrB27doRrwvR1f1/dHb+CaNxLcULH0GhmFp59/DhwyxbtixqLYUoiuzbt48HHniAnp4ekpKSePTRR6OdRjfsYhMEwQgcA+ZLZ0jzyIznKAKBQOTGysrKiu6Dl0QUh/6C6u2fgyruFFs6pVJ5iq2eKIp8WNnEhw09DKqTeXjHcap7yvEHw9u0NL2G5bmJXL86h5V5iRQYFXTveZ+Up+/FV1GB9rzzSP3B91FljO76DOH957FjxyLzUfv7+ydUIIpiiMpdr7HnuacIBYO412eyK7eV39Z8HYAEZQJLU5byqdxPsSptFaVJpaincHGLokhlZSXvv/8+Ho+HJUuWcPbZZ0+4X/YOhqjfZ6G7foDuxkH87hAIkJKjZckFWeSUJJKar0ehHP4e09PTsegT+WePnVetTkL+EKuEIHdIbs6O04MkolNMLvsvDvrxV9jwH+lHGvAjGNTEX5CFZlUqQtzEUZQgKMnJvh2NOp3jLXdj6dtBRvoNkzqHyLlMso1doVCwceNGCgsL+dnPfkZZWdl05s3OByzAXwVBWE7YS+IbkiS5JrWKIOQiSR0IQgbhVvXAia8gEJxMVBHzrcfQKMdut1NVVUVRUREZGRk0NTVN3Go+0IH6lW+gaH2P0PxPEbzst8N6NORjdA/6qKvupbLTQXmnnYqOAVyB8HET1L0szjZyy9pcluclsjwnkazEuJNRVzCI5X//jO7Pfyag15N+373oLr103BveYrHQ0NDAkiVLMJ5o9hrLos4f8lNjq+Fw1Xv0v7SHeLOPrhQPe5dYUST1syJ5BZ9JvZYlpiWUJJUMq0xM9JmOho6ODt566y0sFgs5OTls3ryZjDEIz+sM0NPkoKdxkPZaGx57ELCiTVSTV5ZEdomRrCIj8frRyUqUJHYPuPm/XjsfOrzoFAKfTU/k39ITKYhXR9rFW1pa6O/vp6+vL7KPHy0pKnmCBOoG8FfZCLU4AVAW6NFckIW6xISgnHy5NjX1anp6n8Jm2zVlopiq+YxcHp3s5PoRUAGrgDtP+FL8DriLsB4iOoQ/6BuBXwPfJqzIDBEmCdnl6peTOaGYQxY29fT0sHLlyojN2biNYZKEouKfqN78AUgigUt/jbj8c0hAu9VNVbeD6i4H1d2DVHQOMOANAe2oFAI5eoHNCxLZUJzF0hwjRWk6VGPsh/0NjZh//GP81dUE1qxm3oO/RJkydvJJkiSam5uxWq2sWbNmWD5C7l8xu82U95dT0VdBRV8FdX01lNXrWNpsRKEBx6fyWHv2+fx72goKjAUoBEVkmzYd3YLT6WT37t3U1NRgMBi4/PLLT5Gj+z1BepsdEXKwdYe3Xuo4BabcOLKXxrN4/TwS0+PHPZeAKPGi1cGfe+w0egNkqJV8JzeZG1ONGFUnn/Zyu7jT6USj0WAymYYlRQ0GAykJJgwWBWKjk2CLA0RQJGmIOycD9bJklEkTP4nHI05BEFAImskolGMG2WB4mugAOiRJkge3biVMFJOBALx/4u+HCZdH4wk7XCUwybRCzIlC3mrExcWdMnh4TKJw96H613+jrH8VZ8Za3ir5CQe7E6k5fIjaXicObzgKUSsFitJ0nDs/kVydxMr8ZLB3smrFsshTfixIgQD2v/4N26OPojAYSL7/PhpSU8clCTkfkZCQwOrVq1EoFHiDXmpttVT2V/Ku+V1++vJPMXvC1QONQsNa/0JuOrAAweahcONGLvjc7SQYTj03t9tNe3s7JpMJk8k07tNrZEQRCoU4cuQIe/bsQRRF1q9fz/r161Gr1fjcQczHw8TQ2+zA2uUGCZQqgbR5elZekkNmkZGUXB1mSy8+nw9Txti5D58osbVvkMd67HT5gyxK0PDLwnQuT9KjHkd6LUda8fHxZGVlkaFKItAwgG+PDamnFz/g10qESuJIWJKKrig16if4RBHzwOA+XO5qcnO/EdV6sUQshv9IktQjCEK7IAglkiTVEbbBq57kMgJh/QTAW4SduP2c3HZMatJ3zImioqKCrKysUROWSqXyFN/Eg2/8k6VHfowQcvDL4Gd5rPUyxNZBEtROFmUauHxJBkuyjZRlGViYrkejUmC1WqmtrSUuYGXphnUTVh38DY2Yf/Qj/DU16C65hNS7votkMCAeOjTma1wuF0eOHUFIFWhVtrLt0Daq+qtoGmgidOJJlaJKYWXmSlakr6BEuwDraweoeXcXxrR0Nn/n2+SN0QIuC81ycnLo6+ujsbGR+Pj4yDTy8XQl7e3t7Nq1i/7+fubPn8+GNWfjs6o4/EoX5mYHth4PSKBQCaQV6Fl+YTaZRUZS83XDWrdljBVFyATxP902egMhVuni+Wl+KuclRteNKQVElBYfniMdBBoHkAbCOSVlVgKqTZmoSxIJmZTY7Xa6+vsZPHA8MnE9OTl53M9gvG2B01lOY+O3iY8vJCP9pgnPM9ZwOp2xiCgA7gT+fqLi0Qx8cZKvTwZuQhD+CPwBsAEKwtsOJYLQiSRFvZWJOVGsWrVqTMYfLaJQNr5Gj5jI4xn3oc9fxq8z9SzKNFCQrB21NOn3+6mvr0eSJFatWjV+K3MwiP3xx7H98X9QGAyk//pX6C+8EAgnq4YKrkJiiFZHKzXWGg53HqbcXE5XsAtfW5jYDGoDZcll3Fp6K0tSlrA4ZTEtVS0sXryYrqpjvPPIo3gGBlh56VWs+8yNqONGv9Db29vp7Oxk1apVKBSKyPm73W76+/sj80mSkpJISUmJuIu73W7effddampq0MbpKU7eQLDOwBt7wnJ9lUZBWoGeFZ9OJmO+gdQ83SlDc6JBUJJ4vt/BI102Ov1B1ujjebAwnY2GhPE/a0lC7PMSbHYQbHZganEgiOBXK1DN06M+OxPVAgOKIRJuJeGkaHp6OkMnrtfW1iI4e8kQrCRLVnQhOwqvFcHvBDGIJAkscvmJc8xHTCkilFpKKGUhvbYXaO94GI0mk5LiR1AqZ99Axuv1TieJGYEkSUeBNdNYwg48jiS5EYSHCG814k586Qn3fUSNGU1mnnKwUezwln7hd6jUGn6smvjDlZOj+fn5WK3WcS9cf3Mzlh/+CF9VFbpPX0Tq976H8oQQJigGaR5oZq9jL+8deo9aWy31tnq8ofDcEY2goTS5lI2pGylLKqMspWxUYVODu4pdj/6O44f2k5o/jyv+83ukFy4Y9Xzk0YqBQCBisze0lCzb2Ofl5REKhbDZbHR39XL0g3o62jvpc7+JKIXQuvLQ9uTj1sWRPi+B4o3pZMzXk5ytnXAC+HiQJIlddje/6uynyRtgqTaOnxekcY5xbIIQnQGCLQ6Cxx0EjzuRHOGoQZESh3ehBvUCI8lLsxGi6HdROLpIanmb1LY9KDv3o3CetNETBRUBjRFJY0ChiUchiSS5bKj79iIEwzkXCVDFK1DEzaPHsAJHYSpTvV+n68d5ukYdjkACsAxBcADthLceIcKO3n7C1Y+oMevKzJFVD1XCxGGaJEm0t7fT1dUVEVBZLJbRfzYUYuCpv2N75BEErRbDvT+lbW0ub/e9Tl1DHfX2epoHmgmI4c8pYTCBYlMxVxVehd6lZ6FxIecvPR+1auwSpSRJ1H3wLkef/DNSMMiG6z7LysuuRjlG6TcQCHD06NFIb8VoZCpJEs5+H5Y2F5ZWJ31tTnrNFhz6eoIaB3EhE1n6hRjzEshcYCRnftqEuY2JIJ/DUaeXBzr6Oej0Uhin5uH5GVySNEqFwhsi2BZOQAZbnIiWMLEKCUpU8wyo5htQFRpQJGowNzVhMiWMSxKCvRV17Quo6l9BaQk7b4v6TEI56/FnrURMK0VMmo+kz8Dr89Pf34/VasXlchEKBcjJthLsfxqF5RiJ3gTSQnkYu3uRbK9x4e8v4tyiFK5ZnsG5RcmoJ0GiU3X4jpWpboyQB3yLsHQ7l/C2w0r4njcBu4AfRLvYjEQUY2EqdnihUIiqqioUCgVr166N5DlG69M4Xvsh7p89QELVcZqXpvKXyzTUO34eTuUApjgTJaYSbi6+meKkYrwtXq7adBVeT3h+R37RyaaxseDos/DO3x6ltfwIxuxcLrjtP8gtKh7z52UvzqKiItLTT/pJ+FwBupsH6G93Y2l30tfmwucKk6giTiSU3ok9uQWNJo7SomVsOHs9RqMxEm1YLBYaGxtJSEiI9GRMdg5qb1DkfwZDvGXuJFWl5Kf5qdyQZkQll5F9IYLtLoKtTkKtDkIn8h+oBFR5OjRLssLEkHlq1DHmTeN3oq57CVXls6i6woZLoazVeDf9gND8CxGTi05xI4Pw3I+cnBzS0jR09+ygt3cr5j4zkIyQ9gVI3oI6NQuDwYCnb5BbK6y8WN7LOw39pOjUfGljHreszRl1OzsS0xn+cwaRRRPwb4R9Mm8mrMV4G1hB2JeifDKLnZbu0Wjhdrs5duwYubm5w1SYo60jhUJYvnonCc4Af7xSRdtZSRSZFvJpUzELTQspTiomNT512C9xT+cerP1W6urqWLJkybjTxiRJouqdN/jg6SeQRJFzP/clSM8hMWPs+ZKy52dpyWL8AwIVVZ30tTmxtDkZ7DsxXlGAxPR48spMpObrcCv72H/kfRwOB8uWLePcc8/l+PHjkfNWKpWRNumhMuLRchtjPRVdIZFHe2z8X2/4HL6alcS/Z5rQ+kVCDYN42pyE2pwniUEpoMzREnd2Bqp5BpQ52gm3EyNvGIW5GvWxJ1DX7EAIuAglF+E75y4CpdcgGccn52BwEJv9bfr7dzI4uA8QUShKKJz3LZKSPkUwKGGz2SIO5jqdjptKU7h9w3L2d7j5x8FOfvlmM+809PP76xejixv/sp+qF8VMjxCYJHxIkhdByAPcSNIfT3y/BkEIAucC/4x2sTOWKGT/w9Fu4NE6PwWlkvgf/jdxOXncW7QGjXL8SogkSZGBMxOZ5QxazLz1f3+ko7qC3LKlbP7SV0hMz6SysvKULYTfG6S/w0V9eRuWNgeSO56a56oiVWudSUNavoHiDekkZceTkqdHE6/E6XTy9ttvU19fT2pqKldcccWEbuAR01mdblhuQ/7sEhISIpWUuLg4JEniRauTX3b00xsIcaFKwR2DIUrKXQRfMeM4sZUYRgwFepQ5ukl5Psifr4IQqtoXUB/5G6quA0iqeIIlV+Jfegti9upRIwcZPl8XdvtubPZ3cDgOIklBNJpssrK+SELCBZh7BVJSwoObNBoi08TlpGh/fz/1dbXog0G+s9bEhwU6fv1uJ3e9UMfvbxhfVj1VY12PxzPqaMTTBIHwVTcAZCEIa4FuwvmJdSf+jBpn3NZDbokeGBgY1VgXxjbqXXbBjVGdYygUitzk43WwSqJI5duvs+eZJ0EQOP8Ld7B480WR9xj0SnTVD3J8YJD+Dhf9HU4GLN4IKSQY1aTmJbBgZRqp+XpS8/QR45ahfTHHjh3jvffeIxgMcs4557BmzZphT7Ro55yOFm309/dTXV1No1/i7wojVYJAmQ/uq/exrCt8fL/GjSpXh7rMhCpfjzJ74ohhPAhuK5n1T5K56xWUbjOiqQDveT8msOQGiDeN+hpRDOB0lTNgfx/7wPt4PA0AxMXln+gIvQidbjGCIDA4OIhC0TX6sQUBvV6PXq+noKAgQp7rJStXL1CxvaGfvZVNLJ+fRULC6Ina6bhbnTGmNSd1Ei8AJYRnerQAi4B3gO9NZrkzKqLw+/2Ul5eTmJg4prHudCFvZ/Ly8nC5XGOSxKDFzK4//4HOmkryFi9n1ZW34vdoOfBSK9ZOF/1dLjyDAWAQAH1SHEnZCcRl+MlekETpykJ0pvHT7larlTfeeIPOzk7y8vK46KKLpiv9jUByh9B0BonrVPJc0MBWE5j8Ej9s8HFpn59gigL7MiVCdgJ5KxdE2sOnA8Heiubgo6ir/ok+6MObeza+i39JqHAzCKd+zl5vB4ODexkY2MvA4D5E0YUgqNDrV5CX+01MpvOIj593ynUwmWTjUPI8T0pme2M1Dn/4YeT1eklMTIyY9ch9SB8Lq34ZYXnqfQjCrwmb1bQhSRNbu43ArBLFeDf+wMAAlZWVLFy4cFjSL5aQdQrydqatre1Uaz5PgH3bX6Fq17NIEiRmX05fTwmvPdoBgEIpYMrUkrsoiaDKSU5RCoWl2fiCbioqKlgzgTEwhC/EvXv3smfPHtRqNRdffDGLFy8e8/OZcM5pUCTU6yHU5SbU6SbU6SJo9/NijprfF2tw6AVu9qq4M8lA8jUGMKjweDwcP34cp7OP3kO2qHIbY0HR34hm38Ooap8HhYpA6WeoNX2KtMXnDlMpBgJWBgcPMOjYx+Dgfny+8Geq0WSRknIxiYnnYDSsRaUavxI2laqE2x/i0ffbSNaq2bS0kHi1cpiD+dCxjgqFYkpEcYa5W4URHnK8nrANnvLE95TASyN8KsbFrG49xkJ7ezsdHR3D+kJiCbn3xGw2D8tHyLmOoWFm88Fyyl97EoUqH2PW5aTmZ5OcrT3xpSMxPSGicKyrqyMlRYttsI/m5uaobO27urp49dVXsVgsFBcXs2nTJrTak7M0J7pAJVFC7PeGSaH7BDn0ekA8YTegV9M4L4F71yZwTCGyShvH3fPSKNUOj27kxiWTyURmZiZ2u33M3MZYEKzNxO39DaraF0AVT2DVbfjX3I6kz8RdVYUoOrDZDjDoOMDg4IHIdkKp1GMwrCEz43MYjRuIjy+Y1HUzWaJos3r49o4aanud/O66xZGxCCMdzIca07jdbrxeb6SiFI3niGzwc0bg5PCfLwIbgPOBA4ARKOBkH0hUOK12/aFQiOrqaiRJYt26ddN2fh7rGJWVlajV6lPyEaMRRfHGlfi9/03JWWuI103c7t3Z2UkwGJxwgFAgEGD37t0cOHAAnU7HNddcw4IFC8Ju1aFQJDkbDAYjTzQBAbHfR0JbCBr6cVrN4UpE4MT2U6NAmaUlbn0aymwtnsx4HnE4eaJ3gESVwP25aXwmZXy3a0EQhhnOjsxtjLS3VygUCINdaPb+BnXVc6DU4F/7FQJrvoJfLeBwHMHR9gRuzwfU1rUBEoIQh8GwgpTkOzEa16LTlSEIU7/0oq0uOLxB/vZhB4/v60CjUvDw9Ys5b+HY0Z5sTCNJUqSC1N/fT2VlJaIoYjKZxo26zqjhPyc9Li4lrKcwA28gSTsRhP9hkkY4p4UoJEmKzBTNzs4mL298O7fx1hnvdR6PJ1JeHc11aLTqiVKlYPlFGyc8djAYxGKxoNVqJ5SSt7W18eqrr2Kz2Vi+fDmbNm0iPv5kt6ZSqUQKigR63QS6XQR63Ii9HkSzFwISiYCkCkCmFs3yZJRZWpTZWhQpcZFtyWt2F79o7cEcCHFjmpH/yknGpJo88Q6tpOTn5xMMBiOVlOPVRyjqeZGsthcRkPAuvRbzojUMhJpwNP07Xm/zic81HkGYT1rabSQnb0CvW4pCEb0L2HiQfD5Czc0oOjtxHTuG6HIhBYIISiVCQjyK5GSsCSa2DSTwTKUVpy/EpWVp/Nen5pNhjE6qKT88ZP9M2eZ/aEUpPj5+2DQyiK1fphDeHhwEOiVJGntIzdiQ96pOwl2jAlB04nvzGWGWMxFmfeuhUCgwm800NjayePFiTCbTlI4zWjQwFHI+YrxjTOTEPRY8Hg9Hjx5Fp9ORlZU15nv2+/28/fbbHD58GJPJxA033EB+fj6SOxiWPfd6CPWGtw4hiyc8bQHCkUJGAprlKYipahoH2shfvoA4oyHSHyI/0Tp9AX7a1sfbA24WJWj4/YJMVuqjE15FIw5SqVSkJSeS1bqduCO/RfC5sGTl0pCnxBv/FvS8hUKhQ69fQWrqFRj0K9HpllBZWUNmRvGkRWAjEervx7f/AL5Dh/BVVBBsaQFRREm4y2ksbEHgvLQcEpcuRu3Mw3asjrQNl6OIYqBRKBQ6ZauhUqmGTSOT9StyUtRqtXLgwIHoLR4nxjeAGsJbhcnjZNXjecKy7b8Cv0EQvg5UAHWTWW5WIwpZu9Da2jpm6TNayBWUkUQxdMjPRA7fUyEK2WqvrKwMu90+5utbWlp49ZVXGRgcYEX+YtYZS1G852fAXIHkOiljFwxqVBkJqIsSUWYmoMrUokgORwoul4vqigrmL59PUkpypJFNFEW8gQB/73fySM8AEnBXbgq3ZiRGVJXRYiyiCASsOB1HoX4H6UdfI97lpt+kpnFxIv7kRPT6FaRplxES5+MYNDI44CTgTwApBY1GnJZCMdjVhefNN3G/9TaBqrC0WzAaiVu6lITzz8dhMkFmBvEFC9hrDvB28yD7m/tRej0Uqf1clhpiQ9BC7vFG/EcPIL71GvEK+Le7n+HT8y/j0vxLSUsY+4aORpkp9+bk5uYiiiJHjx6loaGBF198kbfeeot77rmHjRsnjkxHgxAe/Xc5cA/hbcN00At0IEkeBOEKwkOKWybrmTlrROH3+6moqEAQBJYsWTItkoDRb3JZ7i17RU6U8JosUXR2dtLW1saqVatISEhgYGBg1J6NV/53GxX9DRjFBK4IrCaz3oSosiOkhQlBlZGAMj0BZaYWhXb0X4HVelIxKrctyxdvucPND1rMVLn9nGeI54e5yeRoVAiiiBhFQnQkRNGHy12Ly1mB01WJy1WBytrKwiYXKfYAbp2WlnOuggVXscCwgri4ERYCOQzr/qyurmZwcJDW1lbS09MxGo0TnpPodOJ5801cr7yC/+gxANSlpRi/cgfxZ52FuqSEEAKVXYP860grh+o91O9pQQIyDBouWzuPi0vTWJlnRDGEoCRJwtndysHDL2HQVvOnqj/xaNWjrEtfx/9b8v8oNp0qv5+KDd6qVavYuHEjN998MxdddNGU5pYOwUPAd4Cp96ufLJX9DPgs0I4k+YDjU1luVrYeg4ODVFRUUFRURE9Pz5TC/ZEYqcmQ8xE5OTmjmu6OhmiJQpIk6urq8Hq9rF27NnIRjFa2FAQBlVLF8sSFbCxegyZDhyo9AWVyfNRaha6urkgr+tCqgyck8lBnP//XbSNFreSRoiwuNoX3x6IoRj6PUCgU2Z6MvOAlKYjHcxyXqwr7wD4CgQY6u1qQpHCUE08KxR0SaccHkdQJeM6/i9CKL5GiHD9kHyp0ys/P5/DhwxgMBnp6eqivr49Y2w+tpEiShL+yEtf2HXjefBPJ60VVWIjxq19F++mLUGZnc7zfw74WOx9uq+FAqx2HL4RCgJLUeP7fpgI2FSVTlnnqMOah52XInsfm7DvZDHQ4O9jZtpPtx7fzpbe+xNeWfo2bFg73rZhqr4eco5jsfJcR53sFYJYk6ZAgCOdPeaGTF2YdcCuC8BLh1nM3YUn3pPw3Zzyi6OjooL29PVI6tFgsk24MGw1Db3LZCKasrGxSgqWxFJ5DIU8lT0xMZPny5cP7F8Ygmk9/8aqwP8OJ7Hy0Ibg8BsDlcrFq1aphF+sHA25+cLyXNl+Am9IS+W5+KokjLOjUanWEMCRJIhj04fW24HbX4vHWhv/01CGKJ7o+BS0aTTGZGf+GXldGcnsL2j2PILj7CSy7Bf/Z30EaY45rNEhLS4tM4JJl1dXV1YhuN8mVVcS99RZiczOCVov24ovRXnUl5twi3mgdYP8BOwda27GcmPyekxjHp0vTOGt+EpnCAJnJxinpbXL1udxWdhs3FN3A/Yfv5+GKh5lnmMeGzA2Rn5lq92iMkplnA1cJgnAZ4SSkURCEpyRJ+twU19MC/07YP9NHWEuhQxCWIUneaBeZEaIQBIFgMEhNTQ2iKA57Co/Waj4VyOvI3pyTmjg25DzHiyhk1+2xpoCN9nqZIOQ9erQkIW+bEhISWLZsWeR1g8EQ97ZZeNYyyLx4NU+X5rLeeGplKyR68bgbcblqcLlqcblrcbvrI6SgUCSQkFBCSvIWdLrF6PWL6etTolZryFHaiH/t+yi7DhLKWo3nM08iZiyN6rzHwtAchRxtJPh8mF59FedzW5EcDgL5+bRfcyNHS9ZT79dR9bYLs/MgEJ78vr7AxLp5JtbPM5GXdNKEprnZMW3PB6PGyE/X/ZTLXr6Md7reGUYU040opgNJkr7HCXn1iYjiv6dBEgC3nPhlqAjf72ogbjIkATNEFHJVICsri/z8/GE3y2jmNVOBIAg0NTURHx8fVT5iNIy39ZCrJkuXLh3Tj1OhUAwjvfBTPBj5v2ghDyzKysoaVsZ90+bkh8d76Q+EuCMriW/kphAnCPj9ZlzuetyuOlzuOtzuejye48hlE6XSgE63iIz069Hpy9DpykiIL0CShIhmQ5IkQu4asuofR9v4HFJ8Ep6Lf0Nw8XWjyq0ni6FEEezpxfHkE7heeBHJ78eydB1vLr6AV8jA6glCrUhSvJviJIGL89Ssm5fE8sKMMfUKU33ij8T+3v24gi6ydcOb76YTUZxBOoow5JA5vLcMEjawOf0SbkmSIoOHR9sGTMWTYiS8Xi+9vb1kZGREjGCmgtGIQpIk2tra6OnpmbCrdGhEId+Ak4kiICzSkaXrsvTbGgjx01YzL/U7KI4L8UBWE/mhcppqGnB7GggGByKv12gy0ekWkZz8KXS6UnTaEuLickc9B0E4QWCSxMDeJyjZ/yAavw3f0lvwbPxvpPhEFKKEIEz/RpQkCWt7D72P/hnDW/9CkuDt/NU8U7SZTn0aefHxnJOXyOo8I6vzTeQnxUciUZvNRm9vL/X19eh0uohKVE6AT7ed2xv08lT9Uzxe9zgLExdy/YLrh/3/dCKKGPllAiBJ0juEG7hOO2YkmTlZ38zJQC5PpqWlkZycPG3LsqFEIYpiRCkaTZQiE8VUSaK/vz/SSi+HrDvadvPzHgMOKZ7PSM9xtXcHqq4gZoUWrbaI5OQL0WoXotOWoNUuRKUa20NjVNjbCL34TbJ69hJKX4Lv4qcQs1aiHvI+hiZG5Rtmos8iKEo0mF1UdA5S29hFxitbSa57D50k8nrBOg6fexUFiwr5r3wjK3KMpBlGJ+CheoWhuQ1ZHZmcnIzX653S733AN8BLrS/xbOOz9Hn7uCj3Ir698tskqIZ7a06VKGLhwH2mYsZyFLEmCtkOr7u7m9WrV9Pd3T3t6slQovD7/Rw9epT09HQKCqLrPRhKFEONcqNBR0cH3d3drFq1alip2BHwk8wA9yfuYrExF23C79Bqi9BoxhZ2RQUxiPLAY6jfexBJUODb/FNCq78EJ4YPDa2QDNVrDK2kyO9RHsBU0eWgsstBeecg1d1OQj4fVzW/z80Nb5Hg99K9dhNxX/gyt6xcyG3qqalEh7aMy5PFe3t7I7NMRkYbI+EL+djXu4832t9gd/duAmKA1Wmr+em6n7IydeXoH9UUtx5nZFNYjDDrEm6VSnWKZf9EGPqkl70aYrGFkYnC4XBQUVHBwoULo1bWSZJEQkICzc3N2Gw2UlNTSUtLQ6sd385ekiTq6+vx+/2nVDYAPj//U3x2PqiE86fxzoZD6DmGZud/ozRXMph1DqqrfguJY88EHUkaFoePyq5BKrsGqep2UtXjxOYO52I0SoFFGTq+pWph3Tv/JK6vl7izNtJx/mbWX7MlZu8BwtdOeno6FouFgoICgFOijZSUFHxqH/vN+9nTs4d9vfvwhDyYNCauLryaq+ZdRVFi0QRHmlpzo9/vj4kD95mIGYsoxsJkb3CvN+xnmZmZOSwxOjKROBUoFAoGBgZoa2tj2bJlUT8N5EauhIQE1q1bh8/no6+vj4aGBrxeL0lJSaSlnWp+GwqFqKiowGAwUFxcPEYeQYjdLyXgRv3+r1AdfBS/2oR504Mkrv/cmM5SkiRhcfqp7naEJ7Od+NPsCJcoFQIUpmg5d0EyizN1LM7UU2hrx/P7hwkcK0dVVITp7h8Rv34dLQcOxOpdnAJRFPG7nAx0ttPXdhxLRwuW3nbcgwOIfn/496OQSNHAZxLnk5E1j+KilWQlLiJFlz9j5wVTI5iPAmY9opgMUcj5iNLSUpKTh9fzlUrlqJPTo4UkSfT19eF0OtmwYcO4nZ9DMTQfIZNAXFwcOTk55OTkDLOjq6urQ6fTkZqaisFgoKamhry8PLKyxvbZjBUUrR+gee2/Udhb6My8CPFTd5OSM//k+xAl2qweansd1PQ4qe1xUtPjoN8VdicXgMJULevmJbEky0BZloHSLD06zQlzF5sN2yOPMPj8CyhMJgzf/S4JV1yOcKJsHesh3JIo0ttUT8vRQ9Qd2MNeqzX8fQEGtQEc2iD+JDDpU0jXpZMsmMAZxGXrZ7CyiQ8PVgKg0sRhTM8nTpfHWTffSEru1FopTjm/M8dUd0ZwxhLF0EE5CQmnDnKZztZDbj0PhUJkZ2dHRRKyPmKipOVIOzqXy0VHR0d4cI9Wi9frjbQjz8iF5XOgfufnqI89SdCYz9GlP0W76jq63QKv7uug3uykvtdJg9mF98Skd5VCoChdx7lFKZRlGSjL1FOSeZIUhn0OoRDOHTuwP/IHRJcLw803Y7rjdhQGQySnYbfbgbBYTZIklEplVF4bpxxLkuhrbebYe6/TeugAIacHUQE9SR66SrxYU0JkFRazKnMNF6WtpCypbFSv1GAwSEdzE21VFfS1NDHY1oO1cx8vP7Sc5GwthStTWLA6lQTj5KfIjzzfjytZnHFEIYoiNTU1hEKhiD3/aJhq56fX6+Xo0aPk5OSg0WhwOCYuKUdLEiMhCAIejwe73c7GjRtRqVT09YVNblwuF0lJSaSmpkZclaYLb92b6N74Dkp3L+8mXctD3qtoq9bSf+BY5GeStGqK03XcuDqbkkw9izIMzE/Tooli7oWvugbrvffir64mbs1qkr/7XTQLTg48UijC4x6bmppYvnw5arV6TK+Nsd5vUAxS21XOkd07GTxSh9rmJ6SQ6Ejz0FUcJHlRESlk8JXFl7AkdcmEJsoQzm3MKy5hXnFJuPfD6aSzpYf2ygHsLR4Ov9rBsTc6WXJBFks2Ty3ai5W240zFrOcoxhNcyfmIjIyMCSsPU4koZLs9eStjsVgmJBs5HzFZpaVcpbFYLKxevToStWRnZ5OdnY0oithstsjs0YSEhEgkMpWE2JEXfsfZdffTIObwncBPqDIvpChNz7n5eorTdSxM11OcoSNVp5n0U090OLD/8X9wPPssypRkUu/5BdpLLjllne7ubjo6OoZVcmSiH1pGDoVCkd/dQGCAals11bZqGpuOojraQ0FnPEpRwJkcQjonk/lrNnBB7mqKEotQCkoOHjzIirQVU7oxBUHAYDCwaKmBRUvDUU97k5mqXWaOvdZFd7uZxEVB/H7/pBoXzyh3qxnAaYkoRktCyuMCF0XhOQmTjyi6urpobW0dZrc30RpTJQlRFKmvrycUCrFy5cpRL2iFQhEp7cmuUhaLhYqKCkRRjJCGwTC+Q5WMxGVXss9lpjb7Wr6ggfPXLUM9vQ7G8Hm99Ra2Bx4k1N+P4frrMf3HV1GMIiqSSXHlypWjdk4qFAqCUpCGwQYq+yupslZRZa2i09VJujWO5U2JFFsSkFQ6jKuKWX3hVRQXjV6+jGWIr1armb8oh/mLcnjtf2oY6PBiLJFOqaQYjcZxj+l0Os8kq/6Y44zYenR0dESeRKPlI6JdZzRIkkRDQwNOp3NYzwmMTxSjJS2jQTAYpKKiApPJxLx5pzpIj4ahrlLz5s0jEAjQ19dHa2srTqeTxMTEiMBsrK3YvIJ51Lg/y1KlkpKSkmnfSEGzGev99+N5513UJSWk/fY3xC0+dR6GJEk0NzfjdDpZseLkUz4knRz6XG2tptpaTb29nuCJLtW0+DRWBRdwcVUmYmsfcXoDi6+6mEWbLiBOF64+jbfVi2UuwOMIcOz1TnqbnRStT0GvV7Js2TICgQA2m42uri5qa2vR6/WkpKSQnJx8SrQRS3erMxGzvvUY2rEpD+6VPScno4aLJqIIBoOUl5ej1+sjM0snWmOq+QggYu83b948MjIyon7dSKjVarKyssjKyoo4RVssFpqamoiLiyMtLY3U1NRIE5xMTklJSVGLxcaCJIo4t2/H9ruHIRTE9I1vYLzlswijRAly+30wFCSxMJE3Ot6g1lZLjbWGOnsd7qAbAK1Ky6KkRdxcfDNlyWXk+ZJpfPl1Wo4eJM6YyIobP8/izRehjosfVewFnOLsNV2IIYmepkGaD/XTUm5FEiXKNmWw6PwUWlrD561Wq4dNW3c6nRHdhiRJEedyo9EYE7GVEJ7q9QSQSbhx5zFJkn433fcaC5w2c12fz8exY8cmpYQciokiCnl+R0FBwZgTt0YSxXRIYmBgIFLKnaq931jnONQp2u1209fXR1VVFcFgEJPJRH9/P/PmzZtwsthECLS10//zn+M7dIj4detI/uEPUI/wGg2KQY4PHqfWVsuHTR/S7m+n3deOuzp8c8Up4lhoWshlBZdRllxGaXIpBYYClIIS94CdA88/y653d6GOj2fdtTex7MLLUA/p+h0p9pJ/J0MVovL3JksaHmeAnoZBOmsH6KwdwOcOoo5XsnBtGqXnZmBMi8fhcIz6wJJzG7KHZiAQwGq10tnZyb/+9S/+/ve/Ex8fT19fH6mpqZM6r6EfL/BfkiQdFgTBABwSBOENSZKqp7pgrHBaiCIUCnHw4MGo8xGjQaFQjEkUsj/FRPNEhxLFVPMRAL29vbS0tLBixYqot05ThVarJT8/n/z8fAYGBigvL0er1dLa2orNZotsUSbjsCSFQjiefgb7H/8AKjUpP/4xuquvYtA/SKP5EA0DDTTYw1/NgycnwWsUGopNxVyWeRklSSWUJpVSaCxEpRh+7FAwyNE3XuTgi9sIBfws+dQlrL7qWhIM42sYZCKQvTZCoRDNzc3odLpIQnS88qt70I/5uJPeZge9zQ7sPZ7weWuV5C4ykb8kiexFiaiGjEuMloDUanVkjGFJSQl2u52XXnqJa6+9lltuuYXbb7994g9+BCRJ6iY89g9JkhyCINQAOcDHkyjGu8k6Ojrwer2cffbZ00r+KJXKUbce7e3tdHV1ReVPIW+DplPZaG1txWq1smrVqqhFW7GA3W6npqaGFStWYDAYkCSJgYEB+vr6OH78OGq1OiIrH4+8Aq2tWH7yEwLlFQyuKWHvZ5dSpXiLppf/F4vXEvm5pLgkik3FXD//etR2NavzVrNmwRqUwvjbxfaqct5/6i/Ye7rIX7aSs266laSsyUc+ci7E7/ezdOnSYb83URSRRIlBi4++Nhd9bW4sLU4c/eFWAZVGQVqBnsIVyWQuNJKSq0MxhtvYVBrClEolGRkZXHjhhfz4xz+OidhMEIR5hCeR75v2YjHArEUUcj7C7/ej1Wqn/eQd2Xgmrx8IBE6Z3TkW5KgkGAxOev8r6z0EQRiWxJsNmM1mjh8/zsqVKyNkKAgCJpMJk8lEUVERHo+Hvr4+ampq8Pv9pKSkkJaWRmJiYoQIP3jyPtIf2YZfKfHXKxW8t7gRTX87hcZC1mSsoSixiKLEIhaaFpISnxIpX89fMn/CnhiXzcoHT/+NpgMfkpieyWXfvIuC5aum9H7lXIgkSZSVlYXb0f0ife1OzC1OzC0OLC0OfO5whBmnU5FWoGPBumQy5htJydGhirIpLRZeFNNNtAqCoAe2Ad+UJGlwWovFCLNCFHI+Ii0tjdLSUvbv308oFJqWAenQX4bf7+fYsWOkpKRE7U8hRw9ut5u6urpIyB7NRSLb46Wmpp5izDPTaG9vx2w2TxjBJCQkkJeXR15eXqTrsrOzM9J1mZaWhjonn67SNNr//TIuL1jC1xMXkK3LPmXrAOHcSHl5+Zg+IzJEUaTqrdfZt+1pxGCQtVtuYMVlV6FST81MWZKk8CAijwKdkMq+HS2YW5xYu1xIIkhIxKf7URV4EDQuApIbV8CL3R+guVlJfFc8BoOBpKQksrOzycnJGfchdbq9KARBUBMmib9LkrR92gvGCDO+9ZBFTiUlJZEkj5yInKZTMRCuX5eXl1NUVBS1h+JQP8sNGzZEqgoNDQ3odLpIVWG0G1G+YebPnz9jM1LHOufGxkY8Hs+Y2oyxIHddytn7wcFB+vr6QL8Q/Z2/4NzUVNKS08bcCjocDiorK4c5go+G/o423vnro5ibG8hbvJxzP/9lEjMmbzQb8IawtDuxtDhoruzG1ScS8EjAACqNguS8BDLXSAyGuunp66DP5wVbeExicnIypuQMVCoVoihGpp41NTVFHg4pKiNLsxex9NyVKNMSTmncmypRTLeHRwjfOP8H1EiS9JtpLRZjzGhEIdvbj5wpGosWcTj5ZF+6dGnUbD6ysiEPpk1OTo70ZlgsFo4cOYJCoYiYqGi12kheYPHixWPa480ERFGkqqqKuLg4li5dOq0IRhAEEhMTSUxMZMGCBXi9Xvr6+qirq8Pn85GcnExqamqk89Vms1FXV8fy5cvHJJJQMMjhl7dz+OUdaBK0fOr2O1m44ZyozlMMSdh63PS1ObG0OrG0ObH3eiJzrhJMSvIXp5A+z4DC4KG5szZ8rm0+4uPjWVC0gMLCQvLy8sa9Bvx+P93d3bQ1t9J6tBF/ox13XQNCWhzq9amoFydFktun2Vj380CFIAhHT3zv+5IkvTrdhaeLGSEKSZKora09xd4+ctBp+mZKkkRLSwt+v5+NGzdGLbWdKGk51CilsLAQn8+HxWKhrq4Op9OJJEksWrQopnZnE0Emw7S0NPLzY98iHR8fHxm5GAqFIsYwdXV1Ee+QFStWjEkSlpZm3vrzH7B2trNwwzmc/dkvjFnNkESJwT4vfe0u+tqc9LU76e90EzoxSzVOqyI1X0f+0iScITO5xanMX1hAY2Mj+/e/SWdnJyqVipKSEsrKysjPz4/66a/RaCgoKKCgoIBzN29CdAbwVVnxHenD/3InUr8f9bnpeL1eEhISJk0YsXC3kiTpfSY56m+2MGMRRXJyMmlpaaM+VabjxC0/XRUKBVqtNupKw1SMb+X2cf8Jj4OsrKyI8CkateR04fV6KS8vp6CgYFoCrmihVCojEVRnZyft7e1kZGREkrayrFyn0yGGQhx6cSuHX3kerTGRS7/xXeatWB1Zaygp9He66D/xZ8B7wmJPrSAlV0fJxnRS8/SkFegxpMQRCoU4duwYCzIycbvd/O1vf8NisZCYmMgFF1zA0qVLY2IOo9CriV+XjjIpHudzTUgdHpxOJzabLWIXID/MohF7nWEDimOOGctRZGRkxNwOz+fzcfTo0YiJzb59+8adPwrTE1HJzlpqtTqi7JTnVAwMDGA2myNO4PINNt0JaDJk091FixbFVMAVDeSS71C1rGzO09TUhLWjjY7db+A091B81iY2XH8rHodA/Ydm+jtdWDtdWLvcBP0nXMFVAknZWhasSiUlV0dqvg5ThhaFcvjvIhAIcOzYMVQqFW+//Tbd3d0kJSVx+eWXU1paGpPKkhQQCbY58TcOEKi1Iw74UZg0BDcYaWpqYuXKlcTFxY0p9pIJY+S5zEm4p4hY+2bK08aGirQmGlQ8HZLw+/2Ul5eTkZFxyuSxoaVIIJLXOHYs3M49NK8xlXyC1Wqlvr6epUuXzupTSh5A5PF4WL58+bCbIS4ujsyMTNr3fkjdzm0oVXEkL/gMPR0FPPvzqkhOQRWnIDlbx8J1aaTk6kjJ0WHKTEAxQRu73+9n79699PT00Nrail6v55JLLmHJkiXTIghx0E+w00Ww3UWg3Umo2w2iBEoB9XwjCZ/KwZUh0dDcyIoVKyLRykix19DBSqOJvWLtwH2m4bQoMydLFD09PTQ3N0emjY1cZ7Ttx3RIwuVyRUYgRiPHHdrQ5ff7I63jHo8nsgUba0bFaO9VTgDPpv+iPGZBoVCwZMmSUT+vbT9/iP62fSjUC1DGX4SCJDLyE9CnqlFo/fiVDjR6SEvTkpqail4/9qi/oXC5XLz88st0dHSgUCg455xzWLt27aQFbFJAJNjtDhNDh5NQpwtxMKwiRSmgytISvyEd1TwD6nw9gkaJ1WqloWE4SYyG0aTlQ702mpqa5vwoYn5QlSqqHIX8hBsYGBj1whlLxj0dObb8NF+8ePGUnhAajSbiOSEnB7u7u6mtrY3oF1JSUkYtDbe2ttLf38+qVatiUjqOFqIoRrw8CwsLx/y8ll9yKd11RZSdv5mkTB3q+FMjOb/fT39/P8ePH8flcmEymUhLSyMpKWnUyK+hoYGdO3fi8XgoKSnhggsuiOpzlyQJsd8XJoUTX6FetzwDCYVJgypPjypXhypHjzIzAUE1/EYOk0TDhCQxEkOjDUmSuO+++8jPz5/VcvlsQ5hAbjplLWowGBwzaujq6sLn81FYWDju6ysrK4mPjx+zbbq8vJzCwsJhF9ZUp3XJ59XZ2cmyZcti/jSX9QsWi4X+/v5IZ6JsVFNfX08gEKCsrGxWn0xyh21aWlrUw52jhSiK2O12LBYLNpuN+Pj4iKxcFEXefPNNampqMBqNXHLJJcybN2/stRx+gl3uk6TQ5ULynWAFjQJVjm7Yl0I/fjQyVZIYCkmS+M1vfkNVVRVPPfXUdMj9jKx0DMUZufWQRxLm5+eTk5Mz5s+NjCimOohnvOHAscJQ/UJRUVGkC7SysjKSMY+Fj8RkICta8/LypjWBeywM1ahAeIvR19fH22+/TU1NDYFAgCVLlnDhhRcOSwKL7iDBLhehbjfBLhfBLjeS48QWQgBlRgKaJcmocnQos3UoU6OfFA+xI4lHHnmEI0eO8M9//nNWI8DTgTOOKGTn7Wgmk8uNYdPJR4w1HHimodVqycrKwmw2U1hYiEajoaWlJeKlKYfrMxVdyN6h0eZhYgGlUklFRQXV1dXodDrOP+88NEEVtTsPkxhIQOdWo7QGkQYCkdcokuNQFxhQZWtR5uhQZWoR1FP/TGJFEo899hjvv/8+27Ztm9VmwNOFGa16jIWxiGKyTlfybI+pksRYw4FnA7LJzfz5JxusZKMam82GxWKhvr4evV4fyWvE6oJ0uVyUl5fH3DtjPBxvPM6/dv4Ll9vFIl0BZ2uXoHgriOQOEB6wHSRkEBnQBQllK4kvSCS5OJMEU+yqPrEiib/+9a+8/vrr7NixI2bl8DMdZ0QyU+4O9Hq9rFmzJuowTqFQEAgEYjYceLYg906UlZWd4pcx0kvT6XRiNptpa2sbJoiaavetbLCzdOnSWav7H3zpfd6q2UOiqOWqwBrSgokoNQqUxYmoMrQoMxPCkUKckjROmvPUNNcRCAQina9j+VaGQl58ITdaTfKpBz+BWJAEwFNPPcULL7zAiy++OKGNwccJM5bMlNu3R4Pcsbly5cqIRFnuPZiMF4Q8nUuj0URuoGguAnk48GzrFIYee9myZZP24/B6vVgsFiwWS1Q30FjHXr58+Ywb7AyFrbGXD3fvIduUwcL1i0nIMkadUwgGg/T399PX18fg4CBGo5HU1NRI5ajS3sp/1NbTIeSxe2kqOdpTt1GxIol//vOfPPHEE7z88suxvm7O+GTmjBGFKIoEAoFR/8/n81FRUUFpaWnY32D+/Ekl00YmLWUHa4vFgiRJpKWlkZ6ePuqN2NHRQU9PD8uWLZv1sFGuqixfvnzax5ZvIIvFgsPhmLAM2dvbS2trKytWrJj19y0rOqd7o45WOYpLhO9a+1iZ4OMnpRejGjHnI1YksX37dh577DFeeeWVmRBWzRHFaAgGg3z44YcIgsDSpUuj7sSMJmnp9/sxm81YLJaIYUt6ejp6vZ6Ghgb8fj9lZWUz1p8x1nm3tLRgt9tZtmxZzI89sgyZkJAQaZXXaDR0dHTQ29vL8uXLZz07P5MEJUdYfX19+Hw+UlJSSE1NjYjbYkUSL730Eg8//DCvvPLKTOV05ojilAVP2Mc1NDSwadOmqH+BQ3X3cpPORJCfur29vfT19aHT6SgqKprRasJIyJ20cufpTB93aKu8fAMpFIpJteLHCt3d3ZEIaqYrA6FQKLJFGRgYQKPR4Ha7WbVq1bS2CTt37uTBBx/k1VdfPWX+bQwxRxQjv1ddXR0JIc8+++yo1pqO0lLuwMzJySE+Ph6z2YzdbsdoNEaqCTMVXcjTy41G47iKx5mAJEnU19dHPCYsFkvkqTvSEm8mIEcxK1asmNXoDcK5mNraWtLS0rDb7cPmwU6GNHbt2sXPf/5zXnnllQmt/6aJTy5RSJI0bNq43+/n6NGjEXv+vXv3ctZZZ0W1zlRJQq4uLFq0aJgmQ+7+lPe6cqielpYWsyefLGbKycmZto3+ZCETskajYeHChZHPTH7qWiyWSGJwJsiyra2N/v7+GdlmTQRZgj+0V0b2Fenr68Pr9UZ0KrI5z2jYvXs3P/zhD3nllVdmo8V/jiggfMOWl5dTXFwcYeY9e/ZMSBRTVVoCEd+IaKoLTqczkgyVS5Dp6elTLn/JdnmzKWaSIUcx8qSysTCSLOXBQtFWjsbC8ePHGRwcZOnSpbPeJDUaSYxEKBSK6FTsdnvE+jAlJSWSQ/nggw/47ne/y8svvzxbJD9HFGazmcbGRpYtWzasbj8eUUxHaTl0OPCyZcsmHSHICTKz2UwoFCI1NZX09HR0Ol1U5yHrFGbbLg9OumFlZmaOK30fDUPzGpIkRXoyon3fsgze6/XOer8KREcSIyHrVPr6+ujr62PXrl10dnbywQcf8Prrr8+Io9gY+GQTRU1NDTabbdRk1t69e1m/fv0pF9R0jWbk4cCxMDqRZ4CazeZIy3h6evqY+3s5ipltnQKcdDqfN2/etLsY5VZ5i8WCx+OZMFSX8yHy5z6buRiYGkmMhjfffJOf/exnpKen09XVxdNPP01paWkMz3RMfHKJIhQK0dDQQEFBwagX14EDB1ixYsUwAplOPmIqw4EnA7ll3Gw2Mzg4SGJiIunp6RGL/87OTrq7u2clwz8SHo+HY8eOUVxcHPPM/MhQfWSrvFzVEQRh1pvaIHYkUV5ezh133MG2bdsoKirC7/ejUChmq5z8ySUKIOI1ORoOHz5MaWlp5Mk7HZKI1XDgaDFUt2C1WiNOR9Ot108FTqeTioqKWdnqjCZ4CgQCGI1GFi1a9JElierqar70pS/x7LPPsmjRohieYdSYI4qx1j927BgLFixAr9cPcwqa7HZhpoYDRwO5uiCKIlqtdpjPxHSTgtHAbrdTW1t7WqTooihGrP9kfctQcdtMk0asSKKuro5bb72Vp59+msWLF8fwDCeFM54oTlsTvezEPZ3KxmwOBx4J2fAlOTk5Mo1d9pmwWCxUVFRMKCefDobKome7OUmurMjvHcL5nP7+flpaWnA6nTPaKh8rkmhqauLWW2/lySefPJ0k8ZHAjEYUcmfnaKipqYkMmplKZUN2il66dOms5wTkxGFeXt6406H8fn+kgjJUTm4wGKb1xO3u7qajoyMmPSOThWynP54j1tBWeZvNNuH0tckgViTR2trKTTfdxF/+8hdWr1498QtmFmd8RHFaiELufejt7SUrK4v09PSoL3h5GLGcPJvtMpxsvDvZxKEsJzebzZEnbnp6+riin9HQ1tZGX18fy5Ytm/W+jWAwyNGjRyOeoNFALkHKpdfptMrLU8umSxIdHR3ccMMNPProo6xfv37K68QQc0QxkiiGJi09Hk9E6KRQKCJ7+7FC6dM5HBiIjBScaAbnRJCfuLKc3GAwkJ6ePq5CUpIkmpubcblc07awnwoCgUDEnnA6CWN5hOHQpr1oWuVjRRLd3d1cd911PPzww5x77rlTXifG+GQTxUiD3fGMb71eL2azGbPZHNnbZ2RkRJ46p2s4sAyz2czx48dZvnx5THMCciXBbDbT399PfHx8xHRXjrJkYx9RFE+LTkGW3xcWFsa052Fkq/xY09diRRI9PT1cd911/PrXv2bz5s2xeAuxwhxRyNHDZERUsqLTbDYTDAbR6/XYbLZJtaTHEu3t7ZjN5ikpPScLl8sVaZOXm5lsNht6vX5Sxj6xgtfr5dixYxQVFc2oE5goihFJudVqjRCmSqWiubl52iRhsVi49tpruffee/n0pz8dwzOPCeaIYjqelhDeT7a0tKDVavH7/aSmppKRkTErJThJkmhsbMTr9bJ48eJZD/fdbjdHjx5FkiTUavWk5eTThSzkKikpmdDoOJaQJAm3201rays9PT3o9frItnQqZWCr1cpnPvMZfvKTn3D55ZfPwBlPG3NE4ff7p9yzcfz4cQYGBli6dGnEZ1P2l3C5XJEqwky0TMvDkOPi4oZ1YM4W5JyA3H06WTn5dOF2uzl27Niovp6zgaHbDUEQIrksr9cbmb4mV8zGg91u59prr+W73/0uW7ZsmZ2Tnzw+2UTxxS9+keTkZLZs2cLKlSujfiIPHQ5cXFw8hqHqcEn1VKsIo0FOmqalpc1mY1AEcrg/1KF7KCaSk08XstpzuknbqWK8nIT83i0WCwMDA5FW+eTk5FOqQIODg1x33XV84xvf4Prrr5/NtzBZfLKJwul08sorr7Bt2zZqa2vZvHkzW7ZsYe3atWNe0OMNBx4LI6sI07lx5Jt0tuTgIyEnbaMN9yVJwm63YzabsVqt6HS6SDJ0KuVT2cNjNl26h2IyicuRiWC5VV6n06FWq7n++uu54447+OxnPzsj57pz506+8Y1vEAqFuO2227jrrrumutQnmyiGwuPxsHPnTrZt28bRo0fZtGkTW7ZsYePGjZEM92SHA496wiNunGhKjzLkJ+npkIND+AlYVVU15Sf5UHv/vr6+ScvJBwYGqKmpmZJDeCww3eqGrIr99re/zdGjR1m/fj133333mEOXp4NQKERxcTFvvPEGubm5rF27lqeffpqysrKpLDdHFKPB5/PxxhtvsHXrVg4cOMBZZ53F/PnzaWxs5N57741ZuDvyiZOQkBC5cUY+bWXF3+nom4CTN0ksb1KPxxOpoMgl57ESgvLxT0eL/NDjT7e64fF4uPnmm7n00ktJS0tj586d/N///V/M+2727t3L3XffzWuvvQbAfffdB8D3vve9qSw3RxQTIRAI8L3vfY+nn36a9PR0VqxYwdVXX835558fU3nyyKetRqOJkIbVaqWtrY3ly5fPevcnnNRozGT36VA5uc/nixjTGI1GrFYrjY2Np6X7FWJHEj6fj1tuuYXLL7+cr371qzOagN66dSs7d+7kz3/+MwBPPvkk+/bt45FHHpnKcmc8UZz2yaqBQAC/3x8Z5PPee+/x3HPP8cMf/jBCGp/61KemLXISBAGDwYDBYGDBggURvcK+ffsIhULjTlafSXR1ddHV1cWqVatmVKOh0WjIyckhJycnUj1qa2vDbrcjiiKLFi06LTM0Y0USfr+fL3zhC1x00UUzThLAqF3Rs10Zm02cdqLQarU8/PDDkX9v3ryZzZs3EwqF2LNnD9u2beNnP/sZpaWlbNmyhU9/+tMxCc1lXUZSUhLz58+nr68v0vGZnp5Oenr6jIfgcmPbypUrZ9WEVqVSkZGREZHRFxQUYLVaaW5unlROZ7qIFUkEAgG+/OUvc9ZZZ/HNb35zVm7Y3Nxc2tvbI//u6OiYdRPl2cRp33pEA1EUOXDgAFu3buX1119nwYIFXH311VxyySVTymfIE8y1Wu0pakfZsVlWhcpt4rHMW8hCLp/Pd1r8JSEcyciOXHK+Jho5eawgk8R02+SDwSB33HEHpaWl/OhHP5q1p3owGKS4uJhdu3aRk5PD2rVr+cc//jHVdvUzPhT5SBDFUMiGKc899xw7d+4kJyeHq6++mssuuyyqSkUgEODYsWNkZmZOOME8EAhgsVjo7e3F5/NFSGM6qlDZS1ShUJwW6zggYj68fPnycaMGeXvW19eHQqGY9oBkGbEiiVAoxNe+9jXy8vL4+c9/Puuf5auvvso3v/lNQqEQX/rSl/jBD34w1aXmiGImIUkSlZWVbN26lVdffZWUlBS2bNnC5ZdfPmpfgmyZN5aQaTwEg8GIMtLtdkdUodEOCIYwyVVUVGAwGGZ9IJCMoaMNJxPJDB2QHAwGI8nQyZJmrEhCFEW++c1vkpSUxAMPPHBaorIYYo4oZgtyh+XWrVt5+eWX0ev1XHXVVVx55ZWkp6fT3t5OZ2dnTCTJ8iAds9mMw+EgKSmJjIyMcSXFwWCQY8eOkZ6eHrWQLJaQJfFOp3PabepD5eRDSXMiOXksSeI73/kOKpWKhx566KNOEjBHFKcHsnfDtm3beP755/H7/dhstoh5aiyf5KIoRuTUAwMDJCYmkpGRMcwCTp4alpeXN6mp7bGCnBORBzTH8v2PJicfr1U8FiTxwx/+EK/Xyx//+MePA0nAHFGcfjz11FP87ne/48orr+TNN99EFEWuuOIKrrnmGnJzc2N600iSFJGS22w2DAYDJpOJ9vZ2iouLZ7RNe7xzqquriwxJnsntzmhyclncFgt/T0mS+OlPf0pfXx//+7//O+vjCmcQc0RxunHw4EFKS0vR6XRIkkR3dzfbt29n+/btuN1urrjiCq6++mrmz58fc9Lo6emhrq4OtVodKTtOtQdjqudQU1ODSqWa9Q5YWeDW1tZGT09PJNJKT0+fUilUkiTuu+8+Wltb+dvf/vZxIgmYI4ozG2azmR07drBt2zZsNhuXXXYZV199dUyqEfIYAVkSPlQVGhcXF1GFzpTISW6T12q1MSfBaDF0uyFJUtRy8pGQJIlf//rXVFdX89RTT826V+gsYI4oPiro7+/nhRdeYPv27XR3d3PxxRezZcuWKekc+vv7aWhoGLNvYqiLlUqlivkcELm6kpiYOO6g4pnEeDmJ8eTkIwlNkiR+//vfc+DAAZ555pnToh6dBcwRxUcRdrudl156ie3bt3P8+HEuuugitmzZwvLlyyckjd7eXlpbW1mxYkVUIiW5cctsNiMIQkQVOtW9fCgUory8nJSUlNPipQGTS1yO9M0cOg9EEAQeffRR3nnnHbZu3Torowm+/e1v89JLL6HRaFiwYAF//etfZ6OTeI4oPupwOBy8+uqrbN26lbq6Oi644AK2bNnCmjVrTiGNjo4Oent7h6kdJwOfzxchjVAoFDEYjlayLpdgpzLNPFaYTnVj6DyQhx9+mMbGRtxuN2+88casJYJff/11LrjgAlQqFd/97ncBeOCBB2b6sHNE8XGC7KmxdetWysvLI54a69at46WXXmLBggUsWbIkJom2kcODZK/QsfwyZeu83NzccYcSzSRiVQIFePzxx3n22WdZu3Ytb731Fn/7299YsmRJjM40OuzYsYOtW7fy97//faYPNUcUH1fInhrPPfccu3btIjc3lx/96Eece+65MU+2BYPBCGl4PJ5TVKGynf68efNOyygDiC1JPPPMMzz55JO8/PLLkWSnPLx6NnHllVdy44038rnPfW6mD/XJJopo93sxtBSbdXzjG99ArVZz4YUXsn37dvbs2cO6deu4+uqrOe+882K+r5ZVob29vTidThITE7HZbBQXF8d05sZkEEuS2L59O//7v//Lyy+/PGN+nRdeeCE9PT2nfP+ee+7h6quvjvz94MGDbN++fTYI6pNNFNHs92JsKTbr6O7uHhbqB4NBdu/ezdatW9m9ezcrV67k6quv5oILLoj5MGG3283hw4fRarX4fD5MJhPp6ekzMhh4LMSSJF566SUefvhhXnnlldNiRSjj8ccf509/+hO7du2anKWBJMHUSOWTTRRDMdZ+L8aWYmcUQqEQH3zwAdu2beOtt96irKyMLVu2cNFFF03bU0OeubFo0SJMJhOiKEZUkTabDaPRGDEYnilxUixJYufOnTz44IO8+uqrk5rpGmvs3LmTb33rW7z77ruTi9BCIVAqwecDhwMm5/l6xhPFrClX/vKXv3DjjTee8v3Ozs5hTVK5ubns27dvtk5rRqFUKtm0aRObNm2KeGo899xz3H///RQVFbFlyxYuvvjiSbtdu1wuysvLWbx4cWRymkKhIDk5meTkZCRJYmBgALPZTGNjIzqdjoyMDFJSUmKWP7Hb7TEjiV27dnH//fefdpIA+NrXvobP5+Oiiy4CYMOGDfzpT38a/0UySTgc8NnPwn/8B1xyySyc7exh2ldNtPs9lUrFLbfccsrPfVIsxRQKBevXr2f9+vWIosjRo0d57rnn+M1vfkNeXl7EU2OiztZo7PQFQcBkMmEymZAkCYfDEfHllM1opqMKtdvt1NbWxoQkdu/ezU9/+lNeeeWVKTuvxxKNjY2Tf5FSCX4/XHcdXHzxSZIwm+E0JZdjjWkTxZtvvjnu/z/++OO8/PLL7Nq1a1QC+KRZikGYNFatWsWqVau49957I54aV155JWlpaVx99dVcccUVpzxdZVn4smXLonbcEgQBo9GI0WikqKgoIiU/cuRIRBWanp4eddI1liTxwQcf8P3vf5+XX375tMxQmTbuuSccPZhMUFcH8fHw6U/Diy/C1q0wMBCOMEaJpD9qmNEcRTT7vRhbin2kMdJTw2AwRDw1qqqqCAQCnHvuuTHz8nS73REpeTSq0FiSxL59+/jWt77Fiy++eFr8OWKCZ5+FG26Anh7IzISf/AT+8hf4whegqCic3HS5wmQyPs74EHpGiaKoqAifzxdR1cn7va6uLm677TZeffVVIKaWYh8bSJJEU1MT27Zt44knnsBut3P77bfzuc99jszMzJhvz2QHK7PZjCiKEds/OekaS5I4fPgw//Ef/8ELL7xw2npRpoVgEORcz3PPwZ13wt69UFgIDQ2wcGH4/y6/HM49FyYu93+yiWIm8dxzz3H33XdTU1PD/v37WbNmzag/N2/ePAwGA0qlEpVKxcGDB2f5TKeH/fv3c+edd/KHP/yB3bt38/zzzyNJEldccQVbtmyJuacGnFSF9vb2EggEMBgM2Gw2Vq9ePW2SKC8v54477mDbtm0UFRXF6IxPE15/PbzV+MEP4NVX4R//gNJS6OqCO+6A/Hz4wx+iWWmOKGYKskHtHXfcwa9+9atxieLgwYNnRKJsKvD5fLhcrki+QvbU2LZtGzt27MDj8UQ8NWbCh7Ovr4+amhp0Ol1ESp6eno7BYJj0saqrq/nSl77Ec889R0lJSUzPc1Yg3yuCADt2wB//CG+8Ef7ePfeEtyJ//3s4snjnnXBEER3OeKL4yDb2l5aWnu5TmBXExcUNaz8XBIHs7GzuvPNOvva1r2GxWNixYwff+ta3sNvtEU+NsabATwZ2u53GxkbWrl1LfHw8oVCIvr4+WltbcTqdJCcnk5GRMaFXJkBtbS1f+tKXePrppz+aJAHDxVQXXBBOWtrtkJgYjioEAc47D44cmQxJfCTwkY0oZJx//vnjRhSFhYWRluU77riD22+/fZbPcPYge2ps27aNnp4eLrnkErZs2UJpaemklZoT5SREUYwYDA8ODmIymSIGwyOP1djYyOc+9zmefPJJli9fPq33OBn86le/4tvf/jYWiyV2EeUdd4DNFq5otLXBD38IK1aAnHx/990wWUwOZ3xEcUYTRTQajYmIoquri+zsbMxmMxdddBG///3v2bRp04ye95kA2VNj27ZttLa2cuGFF3LNNddEZdM/2cSl3B5uNpux2+0RVajRaKS7u5ubb76Zv/zlL6xevTpWb29CtLe3c9ttt1FbW8uhQ4emThSiCEM/r6amcDLz2DG4+27QaqGkBDo7w9WOL3xhKkc544nijN56TKTRiAayJiM9PZ1rrrmG/fv3fyKIwmQy8fnPf57Pf/7zOBwOXnnlFX77299SX18f8dRYvXr1KaQxleqGQqEgJSWFlJSUiCq0ubmZW2+9lUAgwFe/+tVZ3yr+53/+Jw8++GDkgTIlDCWJ118Pi6eWLAlXPEpKwmXR5mZ46CE4eBDGeFh9HPCx8DofCy6XC4fDEfn766+/PuueBmcCDAYDN910E8899xx79+7lnHPO4bHHHmPjxo185zvfYc+ePYRCIQ4ePEhVVdW0SqCyKjQzMxODwcCPf/xjBgcH+epXvxrjdzU2XnzxRXJycqa/zZFJ4tZbw/qIb34TfvrTcA4CwhWO/v7w3z/GJAFneEQxHnbs2MGdd96JxWLh8ssvZ8WKFbz22mvDNBq9vb1cc801QFjY9dnPfpZLPmYa/MlCq9Vy7bXXcu211+L1ennjjTd48sknueOOOwgGgzzwwAPT7gfp6enhxhtv5Le//S2bN2+O0ZkPx3jb0nvvvZfXX399aguP7AD95S9BrYbHHw8nMHftCm89kpLC1Y0YDMz+SECSpPG+PrF49tlnpbKyMkkQBOnAgQNj/ty//vUvqbi4WFqwYIF03333zeIZxg779u2TVq5cKT3xxBPSbbfdJi1evFj60pe+JL3wwguSzWaTXC5X1F8tLS3SqlWrpNdee+20vJfy8nIpLS1NKigokAoKCiSlUinl5eVJ3d3d0S3Q1TX83++/L0n9/ZJ0552S9I1vSFJrqyQtWCBJX/yiJNXWxuq0J7oPT/vXHFGMgerqaqm2tlY677zzxiSKYDAozZ8/X2pqapJ8Pp+0bNkyqaqqapbPdPro7OyU2tvbI/8OBALSrl27pK985SvS4sWLpc9//vPS1q1bJavVOi5JtLW1SWvXrpVeeeWV0/huhqOgoECyWCzR/XBtrSRdcokk+XySFAic/P7AgCRdd50kyZ/RrbdK0t13x/I0TzsRTPT1kd16zDSiSb7t37+foqIi5s+fD8BNN93ECy+88JEx3ZExsglPpVJxwQUXcMEFFwzz1Lj77rtZvHgxW7Zs4cILLxzmqWG327nhhhv4wQ9+wGWXXTbbbyE2CATCZU+lMvwlw2iEvDy4/npYvjxcHv3zn0/feZ4GzBHFNPBx9tKQMdJTY//+/WzdupX77ruPoqIirrnmGs466yz+7d/+jf/6r/+aXpVhBtDS0hL9Dy9ZEq5m7N4NmzcPz1f8+Mdh5WVDQ7jK8fEbQjQuPlnvdgSi0WmMB+kT4qUhQ6FQsGHDBjZs2IAoihw5coStW7fyrW99ix/+8Idcd911p/sUJ48334QPPwwTwvXXhx2q6uvDRCEIJ8nCZIKPsVhvInyiiWK6Oo1PopeGDIVCwerVq1m9ejX33HPPR5cgVarwtuJf/4LBwXCPhssF69eHFZdDyeITjE80UUwXa9eupaGhgePHj5OTk8MzzzzDP/7xj9N9WrOO2TLynRGcf374z1tvDf+5ZUu4BLp1a9i1at26TzxJwMdccDUd7Nixg9zcXPbu3cvll1/OxRdfDIQl4XKyTqVS8cgjj3DxxRdTWlrKDTfc8Ik03PnIY+gWcuNGuOaaMDk89RQcPnz6zusMwhnd6/Fxh9Vq5cYbb6SlpYV58+bx7LPPkpSUdMrPfdQ9NT6SqKwMe0x85SvhqsfM4owPWeaI4jTiO9/5DsnJydx1113cf//92Gy2UedcftQ9NT6yGOpkNbM444ni47316OkJW6mPT4anDS+88AK3ntgb33rrrTz//POn94TmMByfsBLoePh4E8UDD8DXv34yGXWGEUZvb29kylhWVhZms3nUnxMEgU9/+tOsXr2axx57bDZPcQ5zAD7uVY+bboJHHgn/3esNewVccMGs1sPH02pEiw8++GCYp8aiRYs+Ea3yv//973nkkUdQqVRcfvnlPPjgg6f7lD6x+HgTRWlp2CfgyBH4/vdh2bJZn+A0nlYjIyMjMru0u7t7zEnkn0RPjbfffpsXXniB8vJy4uLixoy25jA7+HhuPeQtRkJCuA7+rW9BcXF4K5KfP/prQqGwUcks4qqrruLxxx8HwoOSRlODflI9Nf7nf/6Hu+66K+IXOhaJ/v/27i+kzjqO4/j7O3GtRhAYQVoXHbE/ghjUPAsEqRshL2YEtrHZ8GJjwWwEu5BgFw2iq6Bmgy6WQRFJXYwtysbJpLlIOxgY2y7MJJht6LbEtlbZ0U8Xv8c/O+oeWXmOf74veDjPec7vOXx/yPk+3/N7fv6Oy421nSiOHoUTJ6C8PMzPh9lkMN0m+hBSUHDzkmc50NLSQiqVoqysjFQqRUv0+w9z52qMjIxQXV1NZWUlVVVV1NXVrYs1NQYGBuju7iaZTFJTU0M6nc53SOva2vzqkcnAkSPQ3h4mzRw7Fo4VFs4mg+kBzuPHobUVEgk4eBC2bMlZmEVFRXR2ds47XlxcPPPjSIlEgv7+/pzFlEu3Gr/JZDKMjY3R09NDOp2moaGBoaGh1TtVfLWL+T/01efyZamxUdq5U7pyJSw0kkxKmYw0OTm//fi4dP261NEh7dkjjY2F46dPh8eJiZvbnz8vDQ8vaxf+q7jFdKamptTc3KzS0lJVVFSor68vD1HeWm1trbq6umaeJxIJjY6O5i+g5ZX39SbitrwH8L9vsElQKyiZc+wTQX20vyF6NMHT0WudgsOCXwR3CjYLzgkei9om55y3XbAj2i/Ie3+z/6BQAPwMJICNQD9QntXmWaCDMNFnK9Cb77gX6Mc+4HC0/zBwgWiCoG+539beGIX0F9IppF/nHB0BNkevT49YPgq8AnwJNACPAz8AhUh/AG8CBzCrB94DnorOu4D0cfRekwCYbVhBNXEVMChpSNIE0A5kj5JuAz5Q0APcY2b35zrQGG1AwszOEvqwW1HWcLm3NscosknNmGUnxavAJuArpKuYTQLfMTtt/R+gHvgNeBnpW8x2AW8B92JWQrgap5B+z0EvlqqEcPWdNgwkl9CmBLi0vKEtXZTkduU7DhesvYpiMbOVxLRrhBL9U8w+AmqBHqRrmD0JNAL3Ae8jfR2dsx14DbNi4ACwG+jF7G3M7s5JP+ItVNlkX4mX0sa5GeujoliI9CfwEmZ3ECqDTuASZnuBHYTS9wzwBPBTdFY1sBe4i1Di70c6i1kSmMxxDxYzDDw45/kDwMXbaOPcjPVTUSxG+hvpG6Q2pEHC4N/rSB8Co8ALAJhtI4xPXCR8qLqBJsxKkXqRbuSpB9nSQJmZPWRmGwlV0MmsNieBFy3YCoxLWjFfO9zKs34risVIc1fH7WS2UmgGPo/2J5EOYXYIeAezJqT5EwLyQFLGzPYDpwh3QNoknTOzfdHr7wJfEO58DAI3gKZ8xetWh7j1KNw0sxrgR+AR4FXgM8KA6HNAK9L3eYzOuWXlieJ2mD0DPE+4v/8GcIYwSu/cmuSJwjkXywcznXOxPFE452J5onDOxfJE4ZyL5YnCORfLE4VzLpYnCudcLE8UzrlY/wLITdulaajv/QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create sections with NACAProfile\n",
+    "n_sections = 10\n",
+    "sections = np.asarray([NacaProfile(digits='4412', n_points=50) for i in range(n_sections)])\n",
+    "\n",
+    "# Define blade parameters\n",
+    "radii = np.arange(1.0, 11.0, 1.0)\n",
+    "chord_lengths = np.array([0.05, 2.5, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7])\n",
+    "pitch = np.arange(1., 11.)\n",
+    "rake = np.arange(0.1, 1.1, 0.1)\n",
+    "skew_angles = np.arange(1., 21, 2.)\n",
+    "\n",
+    "# Create the Blade\n",
+    "blade = Blade(sections=sections,\n",
+    "        radii=radii,\n",
+    "        chord_lengths=chord_lengths,\n",
+    "        pitch=pitch,\n",
+    "        rake=rake,\n",
+    "        skew_angles=skew_angles)\n",
+    "\n",
+    "# Tranform coordinates from planar to cylindrical coordinates and reflect the blade\n",
+    "blade.apply_transformations(reflect=True)\n",
+    "\n",
+    "# Plot the sections\n",
+    "blade.plot(elev=None, azim=None, outfile=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61aabd42",
+   "metadata": {},
+   "source": [
+    "Now the deformation rates for different parameters are defined. Then, new deformed `CustomProfile` sections are generated using the deformed parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "78b71d18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define rates of deformation for some section parameters\n",
+    "def_thickness = 1.2\n",
+    "def_camber = 1.1\n",
+    "\n",
+    "# Generate a deformed blade with modified parameters\n",
+    "sections_deformed = []\n",
+    "for sec in sections:\n",
+    "    sec_deformed = CustomProfile(chord_perc=sec.chord_percentage,\n",
+    "            chord_len=sec.chord_length,\n",
+    "            thickness_max=sec.thickness_max*def_thickness,\n",
+    "            camber_max=sec.camber_max*def_camber,\n",
+    "            thickness_perc=sec.thickness_percentage,\n",
+    "            camber_perc=sec.camber_percentage)\n",
+    "    sections_deformed.append(sec_deformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9ba9d1e",
+   "metadata": {},
+   "source": [
+    "Finally, a deformed blade is defined starting from the above defined sections and from other deformed global parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "76d78504",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define rates of deformation for some blade parameters\n",
+    "def_pitch = 1.2\n",
+    "def_chord_len = 0.6\n",
+    "\n",
+    "blade_deformed = Blade(sections=sections_deformed,\n",
+    "        radii=radii,\n",
+    "        chord_lengths=chord_lengths*def_chord_len,\n",
+    "        pitch=pitch*def_pitch,\n",
+    "        rake=rake,\n",
+    "        skew_angles=skew_angles)\n",
+    "\n",
+    "# Tranform coordinates from planar to cylindrical coordinates and reflect the blade\n",
+    "blade_deformed.apply_transformations(reflect=True)\n",
+    "\n",
+    "# Plot the sections\n",
+    "blade_deformed.plot(elev=None, azim=None, outfile=None)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}