From 2f94e03a390b92f88952c89ba15b727bcd953bc3 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Thu, 24 Feb 2022 17:06:49 -0800
Subject: [PATCH 01/38] initial implementation of HCGA fit

---
 orbitize/gaia.py    | 149 ++++++++++++++++++++++++++++++++++++++++++++
 orbitize/sampler.py |   2 +-
 2 files changed, 150 insertions(+), 1 deletion(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 72959c93..0582dc91 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -4,6 +4,9 @@
 with contextlib.redirect_stdout(None):
     from astroquery.gaia import Gaia
 from astropy import units as u
+import astropy.io.fits as fits
+import astropy.time as time
+from astropy.io.ascii import read
 
 class GaiaLogProb(object):
     """
@@ -160,3 +163,149 @@ def compute_lnlike(
         return lnlike
 
 
+
+class HGCALogProb(object):
+    """
+    Class to compute the log probability of an orbit with respect to the proper
+    motion anomalies derived jointly from Gaia and Hipparcos using the HGCA catalogs 
+    produced by Brandt (2018, 2021). With this class, both Gaia and Hipparcos
+    data will be considered. Do not need to pass in the Hipparcos class into System!
+
+    Required auxiliary data:
+      * HCGA of acceleration (either DR2 or EDR3)
+      * Hipparcos IAD file (see orbitize.hipparcos for more info)
+      * Gaia Observation Forecast Tool (GOST) CSV output (https://gaia.esac.esa.int/gost/). 
+
+    For GOST, after entering the target name and resolving its coordinates,
+    use 2014-07-26T00:00:00 as the start date. For the end date, use
+    2016-05-23T00:00:00 for DR2 and 2017-05-28T00:00:00 for EDR3. 
+
+    Args:
+        hip_id (int): the Hipparcos source ID of the object you're fitting.
+        hcga_filepath (str): path to HCGA catalog FITS file
+        hiplogprob (orbitize.hipparcos.HipLogProb): object containing
+            all info relevant to Hipparcos IAD fitting
+        gost_filepath (str): path to CSV file outputted by GOST
+
+    Written: Jason Wang, 2022
+    """
+    def __init__(self, hip_id, hcga_filepath, hiplogprob, gost_filepath):
+
+        # grab the entry from the HCGA
+        with fits.open(hcga_filepath) as hdulist:
+            hgtable = hdulist[1].data
+        entry = hgtable[np.where(hgtable['hip_id'] == hip_id)]
+        # check we matched on a single target. mainly check if we typed hip id number incorrectly
+        if len(entry) != 0:
+            raise ValueError("HIP {0} encountered {1} matches. Expected 1 match.".format(hip_id, len(entry)))
+        self.hgca_entry = entry
+
+        # grav the relevant values
+        hip_pm = np.array([entry['pmra_hip'][0], entry['pmdec_hip'][0]])
+        hip_pm_err = np.array([entry['pmra_hip_error'][0], entry['pmdec_hip_error'][0]])
+
+        hg_pm = np.array([entry['pmra_hg'][0], entry['pmdec_hg'][0]])
+        hg_pm_err = np.array([entry['pmra_hg_error'][0], entry['pmdec_hg_error'][0]])
+
+        gaia_pm = np.array([entry['pmra_gaia'][0], entry['pmdec_gaia'][0]])
+        gaia_pm_err = np.array([entry['pmra_gaia_error'][0], entry['pmdec_gaia_error'][0]])
+
+        # the PMa and their error bars. 
+        # TODO: there are covariances to be used, but they are not being used here. 
+        self.hip_hg_dpm = hip_pm - hg_pm
+        self.hip_hg_dpm_err = np.sqrt(hip_pm_err**2 + hg_pm_err**2)
+        self.gaia_hg_dpm = gaia_pm - hg_pm
+        self.gaia_hg_dpm_err = np.sqrt(gaia_pm_err**2 + hg_pm_err**2)
+
+        # save the hipparcos object for use later
+        self.hiplogprob = hiplogprob
+        self.hipparcos_epoch = hiplogprob.epochs # in decimal year
+
+        # read in the GOST file to get the estimated Gaia epochs
+        gost_dat = read(gost_filepath)
+        self.gaia_epoch = time.Time(gost_dat).decimalyear # in decimal year
+
+    
+    def _save(self, hf):
+        """
+        Saves the current object to an hdf5 file
+
+        Args:
+            hf (h5py._hl.files.File): a currently open hdf5 file in which
+                to save the object.
+        """
+        # TODO: save stuff here if needed
+        self.hiplogprob._save(hf)
+
+    def compute_lnlike(
+        self, raoff_model, deoff_model, samples, param_idx
+    ):
+        """
+        Computes the log likelihood of an orbit model with respect to a single 
+        Gaia astrometric point. This is added to the likelihoods calculated with 
+        respect to other data types in ``sampler._logl()``. 
+
+        Args:
+            raoff_model (np.array of float): NxM primary RA
+                offsets from the barycenter incurred from orbital motion of 
+                companions (i.e. not from parallactic motion), where N is the
+                number of epochs of combined from Hipparcos and Gaia and M is the 
+                number of orbits being tested, and raoff_model[0,:] are position
+                predictions at the Hipparcos epoch, and raoff_model[1,:] are
+                position predictions at the Gaia epoch
+            deoff_model (np.array of float): NxM primary decl
+                offsets from the barycenter incurred from orbital motion of 
+                companions (i.e. not from parallactic motion), where N is the
+                number of epochs of combined from Hipparcos and Gaia and M is the  
+                number of orbits being tested, and deoff_model[0,:] are position
+                predictions at the Hipparcos epoch, and deoff_model[1,:] are
+                position predictions at the Gaia epoch
+            samples (np.array of float): R-dimensional array of fitting 
+                parameters, where R is the number of parameters being fit. Must 
+                be in the same order documented in ``System``. 
+            param_idx: a dictionary matching fitting parameter labels to their
+                indices in an array of fitting parameters (generally 
+                set to System.basis.param_idx).
+                
+        Returns:
+            np.array of float: array of length M, where M is the number of input 
+                orbits, representing the log likelihood of each orbit with 
+                respect to the Gaia position measurement.
+        """
+        # find the split between hipparcos and gaia data
+        gaia_index = len(self.hipparcos_epoch)
+
+        # Begin forward modeling the data of the HCGA: linear motion during the Hip
+        # and Gaia missions, and the linear motion of the star between the two missions 
+
+        # fit linear motion in RA/Dec to the star during the Hipparcos epoch
+        model_ra_hip = raoff_model[:gaia_index]
+        model_hip_pmra = np.polyfit(self.hipparcos_epoch, model_ra_hip, 1)[0] # mas/yr
+        model_dec_hip = deoff_model[:gaia_index]
+        model_hip_pmdec = np.polyfit(self.hipparcos_epoch, model_dec_hip, 1)[0] # mas/yr
+        model_hip_pm = np.array([model_hip_pmra, model_hip_pmdec])
+
+        # fit linear motion in RA/Dec to the star in Gaia epoch
+        model_ra_gaia = raoff_model[gaia_index:]
+        model_gaia_pmra = np.polyfit(self.gaia_epoch, model_ra_gaia, 1)[0]  # mas/yr
+        model_dec_gaia = deoff_model[gaia_index:]
+        model_gaia_pmdec = np.polyfit(self.gaia_epoch, model_dec_gaia, 1)[0] # mas/yr
+        model_gaia_pm = np.array([model_gaia_pmra, model_gaia_pmdec])
+
+        # compute the PM difference betwen Hipparcos and Gaia positions. 
+        hg_dt = np.mean(self.gaia_epoch) - np.mean(self.hipparcos_epoch)
+        model_hg_pmra = (np.mean(model_ra_gaia) - np.mean(model_ra_hip))/hg_dt
+        model_hg_pmdec = (np.mean(model_dec_gaia) - np.mean(model_dec_hip))/hg_dt
+        model_hg_pm = np.array([model_hg_pmra, model_hg_pmdec]) 
+
+        # take the difference between the linear motion measured during a mission, and the
+        # linear motion of the star between missions. These are our observables we compare
+        # to the data. Each variable contains both RA and Dec. 
+        model_hip_hg_dpm = model_hip_pm - model_hg_pm
+        model_gaia_hg_dpm = model_gaia_pm - model_hg_pm
+        
+        chi2 = (model_hip_hg_dpm - self.hip_hg_dpm)**2/(self.hip_hg_dpm_err)**2
+        chi2 += (model_gaia_hg_dpm - self.gaia_hg_dpm)**2/(self.gaia_hg_dpm_err)**2
+        lnlike = -0.5 * np.sum(chi2)
+
+        return lnlike
\ No newline at end of file
diff --git a/orbitize/sampler.py b/orbitize/sampler.py
index d400e9ef..26ebbc6a 100644
--- a/orbitize/sampler.py
+++ b/orbitize/sampler.py
@@ -111,7 +111,7 @@ def _logl(self, params):
             if self.system.gaia is not None:
 
                 gaiahip_epochs = Time(
-                    [self.system.gaia.hipparcos_epoch, self.system.gaia.gaia_epoch], 
+                    np.append(self.system.gaia.hipparcos_epoch, self.system.gaia.gaia_epoch), 
                     format='decimalyear'
                 ).mjd
 

From 059c3860972c29f5afcc27c02fdb31367f17b447 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Thu, 24 Feb 2022 17:13:43 -0800
Subject: [PATCH 02/38] Fix typos

---
 orbitize/gaia.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 0582dc91..a479f439 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -172,7 +172,7 @@ class HGCALogProb(object):
     data will be considered. Do not need to pass in the Hipparcos class into System!
 
     Required auxiliary data:
-      * HCGA of acceleration (either DR2 or EDR3)
+      * HGCA of acceleration (either DR2 or EDR3)
       * Hipparcos IAD file (see orbitize.hipparcos for more info)
       * Gaia Observation Forecast Tool (GOST) CSV output (https://gaia.esac.esa.int/gost/). 
 
@@ -182,7 +182,7 @@ class HGCALogProb(object):
 
     Args:
         hip_id (int): the Hipparcos source ID of the object you're fitting.
-        hcga_filepath (str): path to HCGA catalog FITS file
+        hcga_filepath (str): path to HGCA catalog FITS file
         hiplogprob (orbitize.hipparcos.HipLogProb): object containing
             all info relevant to Hipparcos IAD fitting
         gost_filepath (str): path to CSV file outputted by GOST
@@ -191,7 +191,7 @@ class HGCALogProb(object):
     """
     def __init__(self, hip_id, hcga_filepath, hiplogprob, gost_filepath):
 
-        # grab the entry from the HCGA
+        # grab the entry from the HGCA
         with fits.open(hcga_filepath) as hdulist:
             hgtable = hdulist[1].data
         entry = hgtable[np.where(hgtable['hip_id'] == hip_id)]
@@ -275,7 +275,7 @@ def compute_lnlike(
         # find the split between hipparcos and gaia data
         gaia_index = len(self.hipparcos_epoch)
 
-        # Begin forward modeling the data of the HCGA: linear motion during the Hip
+        # Begin forward modeling the data of the HGCA: linear motion during the Hip
         # and Gaia missions, and the linear motion of the star between the two missions 
 
         # fit linear motion in RA/Dec to the star during the Hipparcos epoch

From 7bf87d5e185b26fa00b25b24a55c7a22580a37c2 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Fri, 25 Feb 2022 11:21:15 -0800
Subject: [PATCH 03/38] bug fix and store less data in object

---
 orbitize/gaia.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index a479f439..a1d409b8 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -196,9 +196,9 @@ def __init__(self, hip_id, hcga_filepath, hiplogprob, gost_filepath):
             hgtable = hdulist[1].data
         entry = hgtable[np.where(hgtable['hip_id'] == hip_id)]
         # check we matched on a single target. mainly check if we typed hip id number incorrectly
-        if len(entry) != 0:
+        if len(entry) != 1:
             raise ValueError("HIP {0} encountered {1} matches. Expected 1 match.".format(hip_id, len(entry)))
-        self.hgca_entry = entry
+        #  self.hgca_entry = entry
 
         # grav the relevant values
         hip_pm = np.array([entry['pmra_hip'][0], entry['pmdec_hip'][0]])
@@ -218,12 +218,12 @@ def __init__(self, hip_id, hcga_filepath, hiplogprob, gost_filepath):
         self.gaia_hg_dpm_err = np.sqrt(gaia_pm_err**2 + hg_pm_err**2)
 
         # save the hipparcos object for use later
-        self.hiplogprob = hiplogprob
+        #  self.hiplogprob = hiplogprob
         self.hipparcos_epoch = hiplogprob.epochs # in decimal year
 
         # read in the GOST file to get the estimated Gaia epochs
         gost_dat = read(gost_filepath)
-        self.gaia_epoch = time.Time(gost_dat).decimalyear # in decimal year
+        self.gaia_epoch = time.Time(gost_dat["ObservationTimeAtGaia[UTC]"]).decimalyear # in decimal year
 
     
     def _save(self, hf):
@@ -235,7 +235,8 @@ def _save(self, hf):
                 to save the object.
         """
         # TODO: save stuff here if needed
-        self.hiplogprob._save(hf)
+        #  self.hiplogprob._save(hf)
+        pass
 
     def compute_lnlike(
         self, raoff_model, deoff_model, samples, param_idx

From ce957cedc6cd92839c735f40f4edd6f2fecefe19 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Wed, 9 Mar 2022 14:53:15 -0800
Subject: [PATCH 04/38] fix indexing bug in measureing pm

---
 orbitize/gaia.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index a1d409b8..fb837c47 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -281,16 +281,16 @@ def compute_lnlike(
 
         # fit linear motion in RA/Dec to the star during the Hipparcos epoch
         model_ra_hip = raoff_model[:gaia_index]
-        model_hip_pmra = np.polyfit(self.hipparcos_epoch, model_ra_hip, 1)[0] # mas/yr
+        model_hip_pmra = np.polyfit(self.hipparcos_epoch, model_ra_hip, 1)[0,0] # mas/yr (get slope from polyfit)
         model_dec_hip = deoff_model[:gaia_index]
-        model_hip_pmdec = np.polyfit(self.hipparcos_epoch, model_dec_hip, 1)[0] # mas/yr
+        model_hip_pmdec = np.polyfit(self.hipparcos_epoch, model_dec_hip, 1)[0,0] # mas/yr
         model_hip_pm = np.array([model_hip_pmra, model_hip_pmdec])
 
         # fit linear motion in RA/Dec to the star in Gaia epoch
         model_ra_gaia = raoff_model[gaia_index:]
-        model_gaia_pmra = np.polyfit(self.gaia_epoch, model_ra_gaia, 1)[0]  # mas/yr
+        model_gaia_pmra = np.polyfit(self.gaia_epoch, model_ra_gaia, 1)[0,0]  # mas/yr
         model_dec_gaia = deoff_model[gaia_index:]
-        model_gaia_pmdec = np.polyfit(self.gaia_epoch, model_dec_gaia, 1)[0] # mas/yr
+        model_gaia_pmdec = np.polyfit(self.gaia_epoch, model_dec_gaia, 1)[0,0] # mas/yr
         model_gaia_pm = np.array([model_gaia_pmra, model_gaia_pmdec])
 
         # compute the PM difference betwen Hipparcos and Gaia positions. 

From 6f0a9ce171f84244819df1878fd8f755478f385f Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Thu, 7 Apr 2022 13:37:03 -0700
Subject: [PATCH 05/38] added autodownload of HGCA

---
 orbitize/gaia.py | 23 ++++++++++++++++++++---
 1 file changed, 20 insertions(+), 3 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index fb837c47..21c54308 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -1,5 +1,7 @@
+import os
 import numpy as np
 import contextlib
+import requests
 
 with contextlib.redirect_stdout(None):
     from astroquery.gaia import Gaia
@@ -7,6 +9,7 @@
 import astropy.io.fits as fits
 import astropy.time as time
 from astropy.io.ascii import read
+from orbitize import DATADIR
 
 class GaiaLogProb(object):
     """
@@ -182,17 +185,31 @@ class HGCALogProb(object):
 
     Args:
         hip_id (int): the Hipparcos source ID of the object you're fitting.
-        hcga_filepath (str): path to HGCA catalog FITS file
         hiplogprob (orbitize.hipparcos.HipLogProb): object containing
             all info relevant to Hipparcos IAD fitting
         gost_filepath (str): path to CSV file outputted by GOST
+        hgca_filepath (str): path to HGCA catalog FITS file. 
+            If None, will download and store in orbitize.DATADIR
 
     Written: Jason Wang, 2022
     """
-    def __init__(self, hip_id, hcga_filepath, hiplogprob, gost_filepath):
+    def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
+
+        # use default HGCA catalog if not supplied
+        if hgca_filepath is None:
+            # check orbitize.DATAIDR and download if needed
+            hgca_filepath = os.path.join(DATADIR, "HGCA_vEDR3.fits")
+            if not os.path.exists(hgca_filepath):
+                hgca_url = 'http://physics.ucsb.edu/~tbrandt/HGCA_vEDR3.fits'
+                print("No HGCA catalog found. Downloading HGCA vEDR3 from {0} and storing into {1}.".format(hgca_url, hgca_filepath))
+                hgca_file = requests.get(hgca_url)
+                with open(hgca_filepath, 'wb') as f:
+                    f.write(hgca_file.content)
+            else:
+                print("Using HGCA catalog stored in {0}".format(hgca_filepath))
 
         # grab the entry from the HGCA
-        with fits.open(hcga_filepath) as hdulist:
+        with fits.open(hgca_filepath) as hdulist:
             hgtable = hdulist[1].data
         entry = hgtable[np.where(hgtable['hip_id'] == hip_id)]
         # check we matched on a single target. mainly check if we typed hip id number incorrectly

From 5ffc5c01cd5318bce2b17392bec12578f8f5dcb4 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Thu, 7 Apr 2022 13:37:24 -0700
Subject: [PATCH 06/38] separate gaia from hipparcos in lnprob

---
 orbitize/sampler.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/orbitize/sampler.py b/orbitize/sampler.py
index 26ebbc6a..c69c5113 100644
--- a/orbitize/sampler.py
+++ b/orbitize/sampler.py
@@ -108,22 +108,22 @@ def _logl(self, params):
                 raoff_model[:,0,:], deoff_model[:,0,:], params, self.system.param_idx
             )
 
-            if self.system.gaia is not None:
+        if self.system.gaia is not None:
 
-                gaiahip_epochs = Time(
-                    np.append(self.system.gaia.hipparcos_epoch, self.system.gaia.gaia_epoch), 
-                    format='decimalyear'
-                ).mjd
+            gaiahip_epochs = Time(
+                np.append(self.system.gaia.hipparcos_epoch, self.system.gaia.gaia_epoch), 
+                format='decimalyear'
+            ).mjd
 
-                # compute Ra/Dec predictions at the Gaia epoch
-                raoff_model, deoff_model, _ = self.system.compute_all_orbits(
-                    params, epochs=gaiahip_epochs
-                ) 
+            # compute Ra/Dec predictions at the Gaia epoch
+            raoff_model, deoff_model, _ = self.system.compute_all_orbits(
+                params, epochs=gaiahip_epochs
+            ) 
 
-                # select body 0 raoff/deoff predictions & feed into Gaia module lnlike fn
-                lnlikes_sum += self.system.gaia.compute_lnlike(
-                    raoff_model[:,0,:], deoff_model[:,0,:], params, self.system.param_idx
-                )
+            # select body 0 raoff/deoff predictions & feed into Gaia module lnlike fn
+            lnlikes_sum += self.system.gaia.compute_lnlike(
+                raoff_model[:,0,:], deoff_model[:,0,:], params, self.system.param_idx
+            )
 
         return lnlikes_sum
 

From 8570c7d301dab135681722ee858322808d3cb23b Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Fri, 26 May 2023 16:58:49 -0500
Subject: [PATCH 07/38] begin working on abscissa fitting

---
 orbitize/gaia.py | 52 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 21c54308..5beec64c 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -10,6 +10,7 @@
 import astropy.time as time
 from astropy.io.ascii import read
 from orbitize import DATADIR
+from astropy.coordinates import get_body_barycentric_posvel
 
 class GaiaLogProb(object):
     """
@@ -234,14 +235,65 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         self.gaia_hg_dpm = gaia_pm - hg_pm
         self.gaia_hg_dpm_err = np.sqrt(gaia_pm_err**2 + hg_pm_err**2)
 
+        # save gaia best fit values
+        self.gaia_plx0 = entry['parallax_gaia']
+        self.gaia_alpha0 = entry['gaia_ra']
+        self.gaia_delta0 = entry['gaia_dec']
+        self.gaia_pm_ra0 = entry['pmra_gaia']
+        self.gaia_pm_dec0 = entry['pmdec_gaia']
+
         # save the hipparcos object for use later
         #  self.hiplogprob = hiplogprob
         self.hipparcos_epoch = hiplogprob.epochs # in decimal year
+        self.hipparcos_cos_phi = hiplogprob.cos_phi
+        self.hipparcos_sin_phi = hiplogprob.sin_phi
+        self.hipparcos_plx0 = hiplogprob.plx0
+        self.hipparcos_alpha0 = hiplogprob.alpha0
+        self.hipparcos_delta0 = hiplogprob.delta0
+        self.hipparcos_pm_ra0 = hiplogprob.pm_ra0
+        self.hipparcos_pm_dec0 = hiplogprob.pm_dec0
 
         # read in the GOST file to get the estimated Gaia epochs
         gost_dat = read(gost_filepath)
         self.gaia_epoch = time.Time(gost_dat["ObservationTimeAtGaia[UTC]"]).decimalyear # in decimal year
+        self.gaia_scan_theta = gost_dat["scanAngle[rad]"]
+
+        # reconstruct the model 5 parameter RA/Dec curves for both hipparcos and gaia
+        # first for Hipparcos
+        self.hip_bary_pos, _ = get_body_barycentric_posvel('earth', self.hipparcos_epoch)
+
+        # reconstruct ephemeris of star given van Leeuwen best-fit (Nielsen+ 2020 Eqs 1-2) [mas]
+        self.hip_changein_alpha_st = (
+            self.hipparcos_plx0 * (
+                self.hip_bary_pos.x.value * np.sin(np.radians(self.hipparcos_alpha0)) - 
+                self.hip_bary_pos.y.value * np.cos(np.radians(self.hipparcos_alpha0))
+            ) + (self.hipparcos_epoch - 1991.25) * self.hipparcos_pm_ra0
+        )
+        self.hip_changein_delta = (
+            hiplogprob.plx0 * (
+                self.hip_bary_pos.x.value * np.cos(np.radians(self.hipparcos_alpha0)) * np.sin(np.radians(self.hipparcos_delta0)) + 
+                self.hip_bary_pos.y.value * np.sin(np.radians(self.hipparcos_alpha0)) * np.sin(np.radians(self.hipparcos_delta0)) - 
+                self.hip_bary_pos.z.value * np.cos(np.radians(self.hipparcos_delta0))
+            ) + (self.hipparcos_epoch - 1991.25) * self.hipparcos_pm_dec0
+        )
+
+        # now for Gaia
+        self.gaia_bary_pos, _ = get_body_barycentric_posvel('earth', self.gaia_epoch)
 
+        self.gaia_changein_alpha_st = (
+            entry['parallax_gaia'] * (
+                self.gaia_bary_pos.x.value * np.sin(np.radians(entry['gaia_ra'])) - 
+                self.gaia_bary_pos.y.value * np.cos(np.radians(entry['gaia_ra']))
+            ) + (self.gaia_epoch - entry['epoch_ra_gaia']) * entry['pmra_gaia']
+        )
+
+        self.gaia_changein_delta = (
+            entry['parallax_gaia'] * (
+                self.gaia_bary_pos.x.value * np.cos(np.radians(entry['gaia_ra'])) * np.sin(np.radians(entry['gaia_dec'])) + 
+                self.gaia_bary_pos.y.value * np.sin(np.radians(entry['gaia_ra'])) * np.sin(np.radians(entry['gaia_dec'])) - 
+                self.gaia_bary_pos.z.value * np.cos(np.radians(entry['gaia_dec']))
+            ) + (self.gaia_epoch - entry['epoch_dec_gaia']) * entry['pmdec_gaia']
+        )
     
     def _save(self, hf):
         """

From 49e6d1340b275abaaaf8beb71df2443a778ece1c Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Fri, 26 May 2023 17:02:42 -0500
Subject: [PATCH 08/38] adding more fields to be saved

---
 orbitize/gaia.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 5beec64c..b63c3d9e 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -281,18 +281,18 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         self.gaia_bary_pos, _ = get_body_barycentric_posvel('earth', self.gaia_epoch)
 
         self.gaia_changein_alpha_st = (
-            entry['parallax_gaia'] * (
-                self.gaia_bary_pos.x.value * np.sin(np.radians(entry['gaia_ra'])) - 
-                self.gaia_bary_pos.y.value * np.cos(np.radians(entry['gaia_ra']))
-            ) + (self.gaia_epoch - entry['epoch_ra_gaia']) * entry['pmra_gaia']
+            self.gaia_plx0 * (
+                self.gaia_bary_pos.x.value * np.sin(np.radians(self.gaia_alpha0)) - 
+                self.gaia_bary_pos.y.value * np.cos(np.radians(self.gaia_alpha0))
+            ) + (self.gaia_epoch - entry['epoch_ra_gaia']) * self.gaia_pm_ra0
         )
 
         self.gaia_changein_delta = (
-            entry['parallax_gaia'] * (
-                self.gaia_bary_pos.x.value * np.cos(np.radians(entry['gaia_ra'])) * np.sin(np.radians(entry['gaia_dec'])) + 
-                self.gaia_bary_pos.y.value * np.sin(np.radians(entry['gaia_ra'])) * np.sin(np.radians(entry['gaia_dec'])) - 
-                self.gaia_bary_pos.z.value * np.cos(np.radians(entry['gaia_dec']))
-            ) + (self.gaia_epoch - entry['epoch_dec_gaia']) * entry['pmdec_gaia']
+            self.gaia_plx0 * (
+                self.gaia_bary_pos.x.value * np.cos(np.radians(self.gaia_alpha0)) * np.sin(np.radians(self.gaia_delta0)) + 
+                self.gaia_bary_pos.y.value * np.sin(np.radians(self.gaia_alpha0)) * np.sin(np.radians(self.gaia_delta0)) - 
+                self.gaia_bary_pos.z.value * np.cos(np.radians(self.gaia_delta0))
+            ) + (self.gaia_epoch - entry['epoch_dec_gaia']) * self.gaia_pm_dec0
         )
     
     def _save(self, hf):

From 5524a1ddb0f9bf4c4592b47397a1f9ea6607f54f Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Sat, 17 Jun 2023 11:56:03 -0500
Subject: [PATCH 09/38] include plx in HGCA fit

---
 orbitize/gaia.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index b63c3d9e..c4b3f6eb 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -347,6 +347,7 @@ def compute_lnlike(
 
         # Begin forward modeling the data of the HGCA: linear motion during the Hip
         # and Gaia missions, and the linear motion of the star between the two missions 
+        plx = samples[param_idx['plx']]
 
         # fit linear motion in RA/Dec to the star during the Hipparcos epoch
         model_ra_hip = raoff_model[:gaia_index]

From cdc6b633c55acc8e8864f1f3ae9913b4049dabee Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Fri, 23 Jun 2023 17:03:39 -0500
Subject: [PATCH 10/38] some half-thought-out code

---
 orbitize/gaia.py | 29 ++++++++++++++++++++++++++---
 1 file changed, 26 insertions(+), 3 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index c4b3f6eb..46821d70 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -161,7 +161,7 @@ def compute_lnlike(
 
         delta_chi2 = ((delta_model - dec_data) / delta_unc)**2
 
-        chi2 = alpha_chi2 + delta_chi2
+        chi2 =   + delta_chi2
         lnlike = -0.5 * chi2 
 
         return lnlike
@@ -351,9 +351,32 @@ def compute_lnlike(
 
         # fit linear motion in RA/Dec to the star during the Hipparcos epoch
         model_ra_hip = raoff_model[:gaia_index]
-        model_hip_pmra = np.polyfit(self.hipparcos_epoch, model_ra_hip, 1)[0,0] # mas/yr (get slope from polyfit)
+        # model_hip_pmra = np.polyfit(self.hipparcos_epoch, model_ra_hip, 1)[0,0] # mas/yr (get slope from polyfit)
         model_dec_hip = deoff_model[:gaia_index]
-        model_hip_pmdec = np.polyfit(self.hipparcos_epoch, model_dec_hip, 1)[0,0] # mas/yr
+        
+        def optimize_pm(fitparams):
+            guess_pm_ra, guess_pm_dec = fitparams
+            guess_hip_changein_alpha_st = (
+                plx * (
+                    self.hip_bary_pos.x.value * np.sin(np.radians(self.hipparcos_alpha0)) - 
+                    self.hip_bary_pos.y.value * np.cos(np.radians(self.hipparcos_alpha0))
+                ) + (self.hipparcos_epoch - 1991.25) * guess_pm_ra
+            )
+            guess_hip_changein_delta = (
+                plx * (
+                    self.hip_bary_pos.x.value * np.cos(np.radians(self.hipparcos_alpha0)) * np.sin(np.radians(self.hipparcos_delta0)) + 
+                    self.hip_bary_pos.y.value * np.sin(np.radians(self.hipparcos_alpha0)) * np.sin(np.radians(self.hipparcos_delta0)) - 
+                    self.hip_bary_pos.z.value * np.cos(np.radians(self.hipparcos_delta0))
+                ) + (self.hipparcos_epoch - 1991.25) * guess_pm_dec
+            )
+
+            guess_hip_changein_alpha_st += model_ra_hip
+            guess_hip_changein_delta += model_dec_hip
+
+
+
+        
+        
         model_hip_pm = np.array([model_hip_pmra, model_hip_pmdec])
 
         # fit linear motion in RA/Dec to the star in Gaia epoch

From 1b422f1dca9701c98e5fb941cb189cd3bcebc9ca Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Mon, 26 Jun 2023 17:01:59 -0500
Subject: [PATCH 11/38] draft hgca v2 lnlike, not tested

---
 orbitize/gaia.py | 145 +++++++++++++++++++++++++++--------------------
 1 file changed, 83 insertions(+), 62 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 46821d70..4be4c168 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -9,8 +9,10 @@
 import astropy.io.fits as fits
 import astropy.time as time
 from astropy.io.ascii import read
-from orbitize import DATADIR
 from astropy.coordinates import get_body_barycentric_posvel
+import scipy.optimize
+
+from orbitize import DATADIR
 
 class GaiaLogProb(object):
     """
@@ -219,21 +221,14 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         #  self.hgca_entry = entry
 
         # grav the relevant values
-        hip_pm = np.array([entry['pmra_hip'][0], entry['pmdec_hip'][0]])
-        hip_pm_err = np.array([entry['pmra_hip_error'][0], entry['pmdec_hip_error'][0]])
+        self.hip_pm = np.array([entry['pmra_hip'][0], entry['pmdec_hip'][0]])
+        self.hip_pm_err = np.array([entry['pmra_hip_error'][0], entry['pmdec_hip_error'][0]])
 
-        hg_pm = np.array([entry['pmra_hg'][0], entry['pmdec_hg'][0]])
-        hg_pm_err = np.array([entry['pmra_hg_error'][0], entry['pmdec_hg_error'][0]])
+        self.hg_pm = np.array([entry['pmra_hg'][0], entry['pmdec_hg'][0]])
+        self.hg_pm_err = np.array([entry['pmra_hg_error'][0], entry['pmdec_hg_error'][0]])
 
-        gaia_pm = np.array([entry['pmra_gaia'][0], entry['pmdec_gaia'][0]])
-        gaia_pm_err = np.array([entry['pmra_gaia_error'][0], entry['pmdec_gaia_error'][0]])
-
-        # the PMa and their error bars. 
-        # TODO: there are covariances to be used, but they are not being used here. 
-        self.hip_hg_dpm = hip_pm - hg_pm
-        self.hip_hg_dpm_err = np.sqrt(hip_pm_err**2 + hg_pm_err**2)
-        self.gaia_hg_dpm = gaia_pm - hg_pm
-        self.gaia_hg_dpm_err = np.sqrt(gaia_pm_err**2 + hg_pm_err**2)
+        self.gaia_pm = np.array([entry['pmra_gaia'][0], entry['pmdec_gaia'][0]])
+        self.gaia_pm_err = np.array([entry['pmra_gaia_error'][0], entry['pmdec_gaia_error'][0]])
 
         # save gaia best fit values
         self.gaia_plx0 = entry['parallax_gaia']
@@ -241,6 +236,8 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         self.gaia_delta0 = entry['gaia_dec']
         self.gaia_pm_ra0 = entry['pmra_gaia']
         self.gaia_pm_dec0 = entry['pmdec_gaia']
+        self.gaia_epoch_ra = entry['epoch_ra_gaia']
+        self.gaia_epoch_dec = entry['epoch_dec_gaia']
 
         # save the hipparcos object for use later
         #  self.hiplogprob = hiplogprob
@@ -250,13 +247,20 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         self.hipparcos_plx0 = hiplogprob.plx0
         self.hipparcos_alpha0 = hiplogprob.alpha0
         self.hipparcos_delta0 = hiplogprob.delta0
-        self.hipparcos_pm_ra0 = hiplogprob.pm_ra0
-        self.hipparcos_pm_dec0 = hiplogprob.pm_dec0
+        self.hipparcos_pm_ra0 = entry['pmra_hip']
+        self.hipparcos_pm_dec0 = entry['pmdec_hip']
+        self.hipparcos_epoch_ra = entry['epoch_ra_hip']
+        self.hipparcos_epoch_dec = entry['epoch_dec_hip']
+        self.hippaarcos_errs = hiplogprob.eps
 
         # read in the GOST file to get the estimated Gaia epochs
         gost_dat = read(gost_filepath)
         self.gaia_epoch = time.Time(gost_dat["ObservationTimeAtGaia[UTC]"]).decimalyear # in decimal year
-        self.gaia_scan_theta = gost_dat["scanAngle[rad]"]
+        gaia_scan_theta = gost_dat["scanAngle[rad]"]
+        gaia_scan_phi = gaia_scan_theta + np.pi/2
+        self.gaia_cos_phi = np.cos(gaia_scan_phi)
+        self.gaia_sin_phi = np.sin(gaia_scan_phi)
+
 
         # reconstruct the model 5 parameter RA/Dec curves for both hipparcos and gaia
         # first for Hipparcos
@@ -267,24 +271,24 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
             self.hipparcos_plx0 * (
                 self.hip_bary_pos.x.value * np.sin(np.radians(self.hipparcos_alpha0)) - 
                 self.hip_bary_pos.y.value * np.cos(np.radians(self.hipparcos_alpha0))
-            ) + (self.hipparcos_epoch - 1991.25) * self.hipparcos_pm_ra0
-        )
+            ) + (self.hipparcos_epoch - self.hipparcos_epoch_ra) * self.hipparcos_pm_ra0
+        ) + hiplogprob.R * hiplogprob.cos_phi
         self.hip_changein_delta = (
             hiplogprob.plx0 * (
                 self.hip_bary_pos.x.value * np.cos(np.radians(self.hipparcos_alpha0)) * np.sin(np.radians(self.hipparcos_delta0)) + 
                 self.hip_bary_pos.y.value * np.sin(np.radians(self.hipparcos_alpha0)) * np.sin(np.radians(self.hipparcos_delta0)) - 
                 self.hip_bary_pos.z.value * np.cos(np.radians(self.hipparcos_delta0))
-            ) + (self.hipparcos_epoch - 1991.25) * self.hipparcos_pm_dec0
-        )
+            ) + (self.hipparcos_epoch - self.hipparcos_epoch_dec) * self.hipparcos_pm_dec0
+        ) + hiplogprob.R * hiplogprob.sin_phi
 
-        # now for Gaia
+        # now for Gaia, we don't have the Gaia residuals, we assume the data is perfect
         self.gaia_bary_pos, _ = get_body_barycentric_posvel('earth', self.gaia_epoch)
 
         self.gaia_changein_alpha_st = (
             self.gaia_plx0 * (
                 self.gaia_bary_pos.x.value * np.sin(np.radians(self.gaia_alpha0)) - 
                 self.gaia_bary_pos.y.value * np.cos(np.radians(self.gaia_alpha0))
-            ) + (self.gaia_epoch - entry['epoch_ra_gaia']) * self.gaia_pm_ra0
+            ) + (self.gaia_epoch - self.gaia_epoch_ra) * self.gaia_pm_ra0
         )
 
         self.gaia_changein_delta = (
@@ -292,9 +296,11 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
                 self.gaia_bary_pos.x.value * np.cos(np.radians(self.gaia_alpha0)) * np.sin(np.radians(self.gaia_delta0)) + 
                 self.gaia_bary_pos.y.value * np.sin(np.radians(self.gaia_alpha0)) * np.sin(np.radians(self.gaia_delta0)) - 
                 self.gaia_bary_pos.z.value * np.cos(np.radians(self.gaia_delta0))
-            ) + (self.gaia_epoch - entry['epoch_dec_gaia']) * self.gaia_pm_dec0
+            ) + (self.gaia_epoch - self.gaia_epoch_dec) * self.gaia_pm_dec0
         )
     
+        self.deg2mas = u.degree.to(u.mas)
+
     def _save(self, hf):
         """
         Saves the current object to an hdf5 file
@@ -349,57 +355,72 @@ def compute_lnlike(
         # and Gaia missions, and the linear motion of the star between the two missions 
         plx = samples[param_idx['plx']]
 
-        # fit linear motion in RA/Dec to the star during the Hipparcos epoch
         model_ra_hip = raoff_model[:gaia_index]
-        # model_hip_pmra = np.polyfit(self.hipparcos_epoch, model_ra_hip, 1)[0,0] # mas/yr (get slope from polyfit)
         model_dec_hip = deoff_model[:gaia_index]
+        model_ra_gaia = raoff_model[gaia_index:]
+        model_dec_gaia = deoff_model[gaia_index:]
+
         
-        def optimize_pm(fitparams):
-            guess_pm_ra, guess_pm_dec = fitparams
+        def lsqr_astrom(fitparams, epochs, epoch_ref_ra, epoch_ref_dec, data_changein_ra, data_changein_dec, 
+                        raoff_planet, decoff_planet, sin_phi, cos_phi, ra0, dec0, errs):
+            guess_ra_off, guess_dec_off, guess_pm_ra, guess_pm_dec = fitparams
             guess_hip_changein_alpha_st = (
                 plx * (
-                    self.hip_bary_pos.x.value * np.sin(np.radians(self.hipparcos_alpha0)) - 
-                    self.hip_bary_pos.y.value * np.cos(np.radians(self.hipparcos_alpha0))
-                ) + (self.hipparcos_epoch - 1991.25) * guess_pm_ra
+                    self.hip_bary_pos.x.value * np.sin(np.radians(ra0)) - 
+                    self.hip_bary_pos.y.value * np.cos(np.radians(ra0))
+                ) + (epochs - epoch_ref_ra) * guess_pm_ra
             )
             guess_hip_changein_delta = (
                 plx * (
-                    self.hip_bary_pos.x.value * np.cos(np.radians(self.hipparcos_alpha0)) * np.sin(np.radians(self.hipparcos_delta0)) + 
-                    self.hip_bary_pos.y.value * np.sin(np.radians(self.hipparcos_alpha0)) * np.sin(np.radians(self.hipparcos_delta0)) - 
-                    self.hip_bary_pos.z.value * np.cos(np.radians(self.hipparcos_delta0))
-                ) + (self.hipparcos_epoch - 1991.25) * guess_pm_dec
+                    self.hip_bary_pos.x.value * np.cos(np.radians(ra0)) * np.sin(np.radians(dec0)) + 
+                    self.hip_bary_pos.y.value * np.sin(np.radians(ra0)) * np.sin(np.radians(dec0)) - 
+                    self.hip_bary_pos.z.value * np.cos(np.radians(dec0))
+                ) + (epochs - epoch_ref_dec) * guess_pm_dec
             )
 
-            guess_hip_changein_alpha_st += model_ra_hip
-            guess_hip_changein_delta += model_dec_hip
-
-
-
+            guess_hip_changein_alpha_st += raoff_planet + guess_ra_off
+            guess_hip_changein_delta += decoff_planet + guess_dec_off
+
+            # calculate distance between line and expected measurement (Nielsen+ 2020 Eq 6) [mas]
+            dists = np.abs(
+                (self.alpha_abs_st - data_changein_ra) * cos_phi + 
+                (self.delta_abs - data_changein_dec) * sin_phi
+            ) / errs
+
+            return dists
+
+        hip_init_guess = [0, 0, self.hipparcos_pm_ra0, self.hipparcos_pm_dec0]
+        hip_args = (self.hipparcos_epoch, self.hipparcos_epoch_ra, self.hipparcos_epoch_dec, 
+                    self.hip_changein_alpha_st, self.hip_changein_delta, 
+                    model_ra_hip, model_dec_hip,
+                    self.hipparcos_sin_phi, self.hipparcos_cos_phi, self.hipparcos_alpha0, 
+                    self.hipparcos_delta0, self.hippaarcos_errs)
+        hip_fit, _ = scipy.optimize.leastsq(lsqr_astrom, hip_init_guess, args=hip_args)
+        model_hip_pos_offset = hip_fit[0:2]
+        model_hip_pm = hip_fit[2:4]
         
-        
-        model_hip_pm = np.array([model_hip_pmra, model_hip_pmdec])
-
-        # fit linear motion in RA/Dec to the star in Gaia epoch
-        model_ra_gaia = raoff_model[gaia_index:]
-        model_gaia_pmra = np.polyfit(self.gaia_epoch, model_ra_gaia, 1)[0,0]  # mas/yr
-        model_dec_gaia = deoff_model[gaia_index:]
-        model_gaia_pmdec = np.polyfit(self.gaia_epoch, model_dec_gaia, 1)[0,0] # mas/yr
-        model_gaia_pm = np.array([model_gaia_pmra, model_gaia_pmdec])
-
-        # compute the PM difference betwen Hipparcos and Gaia positions. 
-        hg_dt = np.mean(self.gaia_epoch) - np.mean(self.hipparcos_epoch)
-        model_hg_pmra = (np.mean(model_ra_gaia) - np.mean(model_ra_hip))/hg_dt
-        model_hg_pmdec = (np.mean(model_dec_gaia) - np.mean(model_dec_hip))/hg_dt
+        gaia_init_guess = [0, 0, self.gaia_pm_ra0, self.gaia_pm_dec0]
+        gaia_args = (self.gaia_epoch, self.gaia_epoch_ra, self.gaia_epoch_dec, 
+                     self.gaia_changein_alpha_st, self.gaia_changein_delta, 
+                     model_ra_gaia, model_dec_gaia,
+                     self.gaia_sin_phi, self.gaia_cos_phi, self.gaia_alpha0, 
+                     self.gaia_delta0, 1)
+        gaia_fit, _ = scipy.optimize.leastsq(lsqr_astrom, gaia_init_guess, args=gaia_args)
+        model_gaia_pos_offset = gaia_fit[0:2]
+        model_gaia_pm = gaia_fit[2:4]
+
+        hg_dalpha_st = (self.hipparcos_alpha0 * np.cos(np.radians(self.hipparcos_delta0)) +  - self.gaia_alpha0 * np.cos(np.radians(self.gaia_delta0))) * self.deg2mas
+        hg_dalpha_st += model_hip_pos_offset[0]- model_gaia_pos_offset[0]
+        model_hg_pmra = hg_dalpha_st/(self.gaia_epoch_ra - self.hipparcos_epoch_ra)
+
+        hg_ddelta = (self.hipparcos_delta0- self.gaia_alpha0 * np.cos(np.radians(self.gaia_delta0))) * self.deg2mas
+        hg_ddelta += model_hip_pos_offset[0]  - model_gaia_pos_offset[0]
+        model_hg_pmdec = hg_ddelta/(self.gaia_epoch_ra - self.hipparcos_epoch_dec)
         model_hg_pm = np.array([model_hg_pmra, model_hg_pmdec]) 
 
-        # take the difference between the linear motion measured during a mission, and the
-        # linear motion of the star between missions. These are our observables we compare
-        # to the data. Each variable contains both RA and Dec. 
-        model_hip_hg_dpm = model_hip_pm - model_hg_pm
-        model_gaia_hg_dpm = model_gaia_pm - model_hg_pm
-        
-        chi2 = (model_hip_hg_dpm - self.hip_hg_dpm)**2/(self.hip_hg_dpm_err)**2
-        chi2 += (model_gaia_hg_dpm - self.gaia_hg_dpm)**2/(self.gaia_hg_dpm_err)**2
+        chi2 = (model_hip_pm - self.hip_pm)**2/(self.hip_pm_err)**2 + (model_gaia_pm - self.gaia_pm)**2/(self.gaia_pm_err)**2
+        chi2 += (model_hg_pm - self.hg_pm)**2/(self.hg_pm)**2
+
         lnlike = -0.5 * np.sum(chi2)
 
         return lnlike
\ No newline at end of file

From 5240659a9beacc7c3de7a09fb6102720f27fa3b4 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Tue, 15 Aug 2023 11:13:49 -0700
Subject: [PATCH 12/38] fix criterion in selecting fitting secondary mass

---
 orbitize/system.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/orbitize/system.py b/orbitize/system.py
index 7c299b87..4f1dddd9 100644
--- a/orbitize/system.py
+++ b/orbitize/system.py
@@ -135,8 +135,10 @@ def __init__(self, num_secondary_bodies, data_table, stellar_or_system_mass,
         self.track_planet_perturbs = (
             self.fit_secondary_mass and 
             (
-                (len(self.radec[0]) + len(self.seppa[0] > 0) or
-                (self.num_secondary_bodies > 1)
+                ((len(self.radec[0]) + len(self.seppa[0]) > 0) or
+                (self.num_secondary_bodies > 1) or
+                (hipparcos_IAD is not None) or 
+                (gaia is not None)
                 )
             )
         )

From 5890a89b6940a9ba3ca20fbeb8c319faa4976d91 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Tue, 15 Aug 2023 13:46:19 -0700
Subject: [PATCH 13/38] fixed some bugs but this is wrong

---
 orbitize/gaia.py | 69 ++++++++++++++++++++++++++----------------------
 1 file changed, 37 insertions(+), 32 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 4be4c168..8e6d1c32 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -231,13 +231,13 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         self.gaia_pm_err = np.array([entry['pmra_gaia_error'][0], entry['pmdec_gaia_error'][0]])
 
         # save gaia best fit values
-        self.gaia_plx0 = entry['parallax_gaia']
-        self.gaia_alpha0 = entry['gaia_ra']
-        self.gaia_delta0 = entry['gaia_dec']
-        self.gaia_pm_ra0 = entry['pmra_gaia']
-        self.gaia_pm_dec0 = entry['pmdec_gaia']
-        self.gaia_epoch_ra = entry['epoch_ra_gaia']
-        self.gaia_epoch_dec = entry['epoch_dec_gaia']
+        self.gaia_plx0 = entry['parallax_gaia'][0]
+        self.gaia_alpha0 = entry['gaia_ra'][0]
+        self.gaia_delta0 = entry['gaia_dec'][0]
+        self.gaia_pm_ra0 = entry['pmra_gaia'][0]
+        self.gaia_pm_dec0 = entry['pmdec_gaia'][0]
+        self.gaia_epoch_ra = entry['epoch_ra_gaia'][0]
+        self.gaia_epoch_dec = entry['epoch_dec_gaia'][0]
 
         # save the hipparcos object for use later
         #  self.hiplogprob = hiplogprob
@@ -247,10 +247,10 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         self.hipparcos_plx0 = hiplogprob.plx0
         self.hipparcos_alpha0 = hiplogprob.alpha0
         self.hipparcos_delta0 = hiplogprob.delta0
-        self.hipparcos_pm_ra0 = entry['pmra_hip']
-        self.hipparcos_pm_dec0 = entry['pmdec_hip']
-        self.hipparcos_epoch_ra = entry['epoch_ra_hip']
-        self.hipparcos_epoch_dec = entry['epoch_dec_hip']
+        self.hipparcos_pm_ra0 = entry['pmra_hip'][0]
+        self.hipparcos_pm_dec0 = entry['pmdec_hip'][0]
+        self.hipparcos_epoch_ra = entry['epoch_ra_hip'][0]
+        self.hipparcos_epoch_dec = entry['epoch_dec_hip'][0]
         self.hippaarcos_errs = hiplogprob.eps
 
         # read in the GOST file to get the estimated Gaia epochs
@@ -264,7 +264,7 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
 
         # reconstruct the model 5 parameter RA/Dec curves for both hipparcos and gaia
         # first for Hipparcos
-        self.hip_bary_pos, _ = get_body_barycentric_posvel('earth', self.hipparcos_epoch)
+        self.hip_bary_pos, _ = get_body_barycentric_posvel('earth', time.Time(self.hipparcos_epoch, format="decimalyear"))
 
         # reconstruct ephemeris of star given van Leeuwen best-fit (Nielsen+ 2020 Eqs 1-2) [mas]
         self.hip_changein_alpha_st = (
@@ -282,7 +282,7 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         ) + hiplogprob.R * hiplogprob.sin_phi
 
         # now for Gaia, we don't have the Gaia residuals, we assume the data is perfect
-        self.gaia_bary_pos, _ = get_body_barycentric_posvel('earth', self.gaia_epoch)
+        self.gaia_bary_pos, _ = get_body_barycentric_posvel('earth', time.Time(self.gaia_epoch, format="decimalyear"))
 
         self.gaia_changein_alpha_st = (
             self.gaia_plx0 * (
@@ -355,26 +355,27 @@ def compute_lnlike(
         # and Gaia missions, and the linear motion of the star between the two missions 
         plx = samples[param_idx['plx']]
 
-        model_ra_hip = raoff_model[:gaia_index]
-        model_dec_hip = deoff_model[:gaia_index]
-        model_ra_gaia = raoff_model[gaia_index:]
-        model_dec_gaia = deoff_model[gaia_index:]
+        # for now, think about only 1 model at a time to not break our brains
+        model_ra_hip = raoff_model[:gaia_index, 0]
+        model_dec_hip = deoff_model[:gaia_index, 0]
+        model_ra_gaia = raoff_model[gaia_index:, 0]
+        model_dec_gaia = deoff_model[gaia_index:, 0]
 
         
-        def lsqr_astrom(fitparams, epochs, epoch_ref_ra, epoch_ref_dec, data_changein_ra, data_changein_dec, 
+        def lsqr_astrom(fitparams, epochs, epoch_ref_ra, epoch_ref_dec, bary_pos, data_changein_ra, data_changein_dec, 
                         raoff_planet, decoff_planet, sin_phi, cos_phi, ra0, dec0, errs):
             guess_ra_off, guess_dec_off, guess_pm_ra, guess_pm_dec = fitparams
             guess_hip_changein_alpha_st = (
                 plx * (
-                    self.hip_bary_pos.x.value * np.sin(np.radians(ra0)) - 
-                    self.hip_bary_pos.y.value * np.cos(np.radians(ra0))
+                    bary_pos.x.value * np.sin(np.radians(ra0)) - 
+                    bary_pos.y.value * np.cos(np.radians(ra0))
                 ) + (epochs - epoch_ref_ra) * guess_pm_ra
             )
             guess_hip_changein_delta = (
                 plx * (
-                    self.hip_bary_pos.x.value * np.cos(np.radians(ra0)) * np.sin(np.radians(dec0)) + 
-                    self.hip_bary_pos.y.value * np.sin(np.radians(ra0)) * np.sin(np.radians(dec0)) - 
-                    self.hip_bary_pos.z.value * np.cos(np.radians(dec0))
+                    bary_pos.x.value * np.cos(np.radians(ra0)) * np.sin(np.radians(dec0)) + 
+                    bary_pos.y.value * np.sin(np.radians(ra0)) * np.sin(np.radians(dec0)) - 
+                    bary_pos.z.value * np.cos(np.radians(dec0))
                 ) + (epochs - epoch_ref_dec) * guess_pm_dec
             )
 
@@ -383,14 +384,15 @@ def lsqr_astrom(fitparams, epochs, epoch_ref_ra, epoch_ref_dec, data_changein_ra
 
             # calculate distance between line and expected measurement (Nielsen+ 2020 Eq 6) [mas]
             dists = np.abs(
-                (self.alpha_abs_st - data_changein_ra) * cos_phi + 
-                (self.delta_abs - data_changein_dec) * sin_phi
+                (guess_hip_changein_alpha_st - data_changein_ra) * cos_phi + 
+                (guess_hip_changein_delta - data_changein_dec) * sin_phi
             ) / errs
 
             return dists
 
-        hip_init_guess = [0, 0, self.hipparcos_pm_ra0, self.hipparcos_pm_dec0]
-        hip_args = (self.hipparcos_epoch, self.hipparcos_epoch_ra, self.hipparcos_epoch_dec, 
+        hip_init_guess = [0., 0., self.hipparcos_pm_ra0, self.hipparcos_pm_dec0]
+        hip_args = (self.hipparcos_epoch, self.hipparcos_epoch_ra, self.hipparcos_epoch_dec,
+                    self.hip_bary_pos,
                     self.hip_changein_alpha_st, self.hip_changein_delta, 
                     model_ra_hip, model_dec_hip,
                     self.hipparcos_sin_phi, self.hipparcos_cos_phi, self.hipparcos_alpha0, 
@@ -401,6 +403,7 @@ def lsqr_astrom(fitparams, epochs, epoch_ref_ra, epoch_ref_dec, data_changein_ra
         
         gaia_init_guess = [0, 0, self.gaia_pm_ra0, self.gaia_pm_dec0]
         gaia_args = (self.gaia_epoch, self.gaia_epoch_ra, self.gaia_epoch_dec, 
+                     self.gaia_bary_pos,
                      self.gaia_changein_alpha_st, self.gaia_changein_delta, 
                      model_ra_gaia, model_dec_gaia,
                      self.gaia_sin_phi, self.gaia_cos_phi, self.gaia_alpha0, 
@@ -409,18 +412,20 @@ def lsqr_astrom(fitparams, epochs, epoch_ref_ra, epoch_ref_dec, data_changein_ra
         model_gaia_pos_offset = gaia_fit[0:2]
         model_gaia_pm = gaia_fit[2:4]
 
-        hg_dalpha_st = (self.hipparcos_alpha0 * np.cos(np.radians(self.hipparcos_delta0)) +  - self.gaia_alpha0 * np.cos(np.radians(self.gaia_delta0))) * self.deg2mas
+        hg_dalpha_st = -(self.hipparcos_alpha0 * np.cos(np.radians(self.hipparcos_delta0))  - self.gaia_alpha0 * np.cos(np.radians(self.gaia_delta0))) * self.deg2mas
         hg_dalpha_st += model_hip_pos_offset[0]- model_gaia_pos_offset[0]
         model_hg_pmra = hg_dalpha_st/(self.gaia_epoch_ra - self.hipparcos_epoch_ra)
 
-        hg_ddelta = (self.hipparcos_delta0- self.gaia_alpha0 * np.cos(np.radians(self.gaia_delta0))) * self.deg2mas
-        hg_ddelta += model_hip_pos_offset[0]  - model_gaia_pos_offset[0]
+        hg_ddelta = -(self.hipparcos_delta0 - self.gaia_delta0) * self.deg2mas
+        hg_ddelta += model_hip_pos_offset[1]  - model_gaia_pos_offset[1]
         model_hg_pmdec = hg_ddelta/(self.gaia_epoch_ra - self.hipparcos_epoch_dec)
         model_hg_pm = np.array([model_hg_pmra, model_hg_pmdec]) 
 
         chi2 = (model_hip_pm - self.hip_pm)**2/(self.hip_pm_err)**2 + (model_gaia_pm - self.gaia_pm)**2/(self.gaia_pm_err)**2
-        chi2 += (model_hg_pm - self.hg_pm)**2/(self.hg_pm)**2
+        chi2 += (model_hg_pm - self.hg_pm)**2/(self.hg_pm_err)**2
 
         lnlike = -0.5 * np.sum(chi2)
 
-        return lnlike
\ No newline at end of file
+        return lnlike
+    
+

From 9eaf8881a32d1e28fd3de701dd9a61936a16543d Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Tue, 15 Aug 2023 14:40:04 -0700
Subject: [PATCH 14/38] simplified version but may be working

---
 orbitize/gaia.py | 123 +++++++++++++++++------------------------------
 1 file changed, 43 insertions(+), 80 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 8e6d1c32..c0d6400d 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -230,6 +230,15 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         self.gaia_pm = np.array([entry['pmra_gaia'][0], entry['pmdec_gaia'][0]])
         self.gaia_pm_err = np.array([entry['pmra_gaia_error'][0], entry['pmdec_gaia_error'][0]])
 
+
+        # the PMa and their error bars. 
+        # TODO: there are covariances to be used, but they are not being used here. 
+        self.hip_hg_dpm = self.hip_pm - self.hg_pm
+        self.hip_hg_dpm_err = np.sqrt(self.hip_pm_err**2 + self.hg_pm_err**2)
+        self.gaia_hg_dpm = self.gaia_pm - self.hg_pm
+        self.gaia_hg_dpm_err = np.sqrt(self.gaia_pm_err**2 + self.hg_pm_err**2)
+
+
         # save gaia best fit values
         self.gaia_plx0 = entry['parallax_gaia'][0]
         self.gaia_alpha0 = entry['gaia_ra'][0]
@@ -262,45 +271,6 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         self.gaia_sin_phi = np.sin(gaia_scan_phi)
 
 
-        # reconstruct the model 5 parameter RA/Dec curves for both hipparcos and gaia
-        # first for Hipparcos
-        self.hip_bary_pos, _ = get_body_barycentric_posvel('earth', time.Time(self.hipparcos_epoch, format="decimalyear"))
-
-        # reconstruct ephemeris of star given van Leeuwen best-fit (Nielsen+ 2020 Eqs 1-2) [mas]
-        self.hip_changein_alpha_st = (
-            self.hipparcos_plx0 * (
-                self.hip_bary_pos.x.value * np.sin(np.radians(self.hipparcos_alpha0)) - 
-                self.hip_bary_pos.y.value * np.cos(np.radians(self.hipparcos_alpha0))
-            ) + (self.hipparcos_epoch - self.hipparcos_epoch_ra) * self.hipparcos_pm_ra0
-        ) + hiplogprob.R * hiplogprob.cos_phi
-        self.hip_changein_delta = (
-            hiplogprob.plx0 * (
-                self.hip_bary_pos.x.value * np.cos(np.radians(self.hipparcos_alpha0)) * np.sin(np.radians(self.hipparcos_delta0)) + 
-                self.hip_bary_pos.y.value * np.sin(np.radians(self.hipparcos_alpha0)) * np.sin(np.radians(self.hipparcos_delta0)) - 
-                self.hip_bary_pos.z.value * np.cos(np.radians(self.hipparcos_delta0))
-            ) + (self.hipparcos_epoch - self.hipparcos_epoch_dec) * self.hipparcos_pm_dec0
-        ) + hiplogprob.R * hiplogprob.sin_phi
-
-        # now for Gaia, we don't have the Gaia residuals, we assume the data is perfect
-        self.gaia_bary_pos, _ = get_body_barycentric_posvel('earth', time.Time(self.gaia_epoch, format="decimalyear"))
-
-        self.gaia_changein_alpha_st = (
-            self.gaia_plx0 * (
-                self.gaia_bary_pos.x.value * np.sin(np.radians(self.gaia_alpha0)) - 
-                self.gaia_bary_pos.y.value * np.cos(np.radians(self.gaia_alpha0))
-            ) + (self.gaia_epoch - self.gaia_epoch_ra) * self.gaia_pm_ra0
-        )
-
-        self.gaia_changein_delta = (
-            self.gaia_plx0 * (
-                self.gaia_bary_pos.x.value * np.cos(np.radians(self.gaia_alpha0)) * np.sin(np.radians(self.gaia_delta0)) + 
-                self.gaia_bary_pos.y.value * np.sin(np.radians(self.gaia_alpha0)) * np.sin(np.radians(self.gaia_delta0)) - 
-                self.gaia_bary_pos.z.value * np.cos(np.radians(self.gaia_delta0))
-            ) + (self.gaia_epoch - self.gaia_epoch_dec) * self.gaia_pm_dec0
-        )
-    
-        self.deg2mas = u.degree.to(u.mas)
-
     def _save(self, hf):
         """
         Saves the current object to an hdf5 file
@@ -362,70 +332,63 @@ def compute_lnlike(
         model_dec_gaia = deoff_model[gaia_index:, 0]
 
         
-        def lsqr_astrom(fitparams, epochs, epoch_ref_ra, epoch_ref_dec, bary_pos, data_changein_ra, data_changein_dec, 
-                        raoff_planet, decoff_planet, sin_phi, cos_phi, ra0, dec0, errs):
+        def lsqr_linear_fit(fitparams, epochs, epoch_ref_ra, epoch_ref_dec, 
+                        raoff_planet, decoff_planet, sin_phi, cos_phi, pm_ra0, pm_dec0, errs):
             guess_ra_off, guess_dec_off, guess_pm_ra, guess_pm_dec = fitparams
-            guess_hip_changein_alpha_st = (
-                plx * (
-                    bary_pos.x.value * np.sin(np.radians(ra0)) - 
-                    bary_pos.y.value * np.cos(np.radians(ra0))
-                ) + (epochs - epoch_ref_ra) * guess_pm_ra
-            )
-            guess_hip_changein_delta = (
-                plx * (
-                    bary_pos.x.value * np.cos(np.radians(ra0)) * np.sin(np.radians(dec0)) + 
-                    bary_pos.y.value * np.sin(np.radians(ra0)) * np.sin(np.radians(dec0)) - 
-                    bary_pos.z.value * np.cos(np.radians(dec0))
-                ) + (epochs - epoch_ref_dec) * guess_pm_dec
-            )
+            
+            # combine proper motion and orbital motion, fit for inferred lienar motion
+            data_changein_alpha_st = (epochs - epoch_ref_ra) * pm_ra0
+            data_changein_delta = (epochs - epoch_ref_dec) * pm_dec0
 
-            guess_hip_changein_alpha_st += raoff_planet + guess_ra_off
-            guess_hip_changein_delta += decoff_planet + guess_dec_off
+            data_changein_alpha_st += raoff_planet 
+            data_changein_delta += decoff_planet
+
+            guess_changein_alpha_st = (epochs - epoch_ref_ra) * (guess_pm_ra + pm_ra0) + guess_ra_off
+            guess_changein_delta = (epochs - epoch_ref_dec) * (guess_pm_dec + pm_dec0) + guess_dec_off
 
             # calculate distance between line and expected measurement (Nielsen+ 2020 Eq 6) [mas]
             dists = np.abs(
-                (guess_hip_changein_alpha_st - data_changein_ra) * cos_phi + 
-                (guess_hip_changein_delta - data_changein_dec) * sin_phi
+                (guess_changein_alpha_st - data_changein_alpha_st) * cos_phi + 
+                (guess_changein_delta - data_changein_delta) * sin_phi
             ) / errs
 
             return dists
 
-        hip_init_guess = [0., 0., self.hipparcos_pm_ra0, self.hipparcos_pm_dec0]
+        hip_init_guess = [0., 0., 0, 0]
         hip_args = (self.hipparcos_epoch, self.hipparcos_epoch_ra, self.hipparcos_epoch_dec,
-                    self.hip_bary_pos,
-                    self.hip_changein_alpha_st, self.hip_changein_delta, 
                     model_ra_hip, model_dec_hip,
-                    self.hipparcos_sin_phi, self.hipparcos_cos_phi, self.hipparcos_alpha0, 
-                    self.hipparcos_delta0, self.hippaarcos_errs)
-        hip_fit, _ = scipy.optimize.leastsq(lsqr_astrom, hip_init_guess, args=hip_args)
+                    self.hipparcos_sin_phi, self.hipparcos_cos_phi, self.hipparcos_pm_ra0, 
+                    self.hipparcos_pm_dec0, self.hippaarcos_errs)
+        hip_fit, _ = scipy.optimize.leastsq(lsqr_linear_fit, hip_init_guess, args=hip_args)
         model_hip_pos_offset = hip_fit[0:2]
         model_hip_pm = hip_fit[2:4]
         
-        gaia_init_guess = [0, 0, self.gaia_pm_ra0, self.gaia_pm_dec0]
+        gaia_init_guess = [0, 0, 0, 0]
         gaia_args = (self.gaia_epoch, self.gaia_epoch_ra, self.gaia_epoch_dec, 
-                     self.gaia_bary_pos,
-                     self.gaia_changein_alpha_st, self.gaia_changein_delta, 
                      model_ra_gaia, model_dec_gaia,
-                     self.gaia_sin_phi, self.gaia_cos_phi, self.gaia_alpha0, 
-                     self.gaia_delta0, 1)
-        gaia_fit, _ = scipy.optimize.leastsq(lsqr_astrom, gaia_init_guess, args=gaia_args)
+                     self.gaia_sin_phi, self.gaia_cos_phi, self.gaia_pm_ra0, 
+                     self.gaia_pm_dec0, 1)
+        gaia_fit, _ = scipy.optimize.leastsq(lsqr_linear_fit, gaia_init_guess, args=gaia_args)
         model_gaia_pos_offset = gaia_fit[0:2]
         model_gaia_pm = gaia_fit[2:4]
 
-        hg_dalpha_st = -(self.hipparcos_alpha0 * np.cos(np.radians(self.hipparcos_delta0))  - self.gaia_alpha0 * np.cos(np.radians(self.gaia_delta0))) * self.deg2mas
-        hg_dalpha_st += model_hip_pos_offset[0]- model_gaia_pos_offset[0]
+        # compute the change in position between hipparcos and gaia
+        hg_dalpha_st = model_gaia_pos_offset[0] - model_hip_pos_offset[0]
         model_hg_pmra = hg_dalpha_st/(self.gaia_epoch_ra - self.hipparcos_epoch_ra)
 
-        hg_ddelta = -(self.hipparcos_delta0 - self.gaia_delta0) * self.deg2mas
-        hg_ddelta += model_hip_pos_offset[1]  - model_gaia_pos_offset[1]
-        model_hg_pmdec = hg_ddelta/(self.gaia_epoch_ra - self.hipparcos_epoch_dec)
+        hg_ddelta =  model_gaia_pos_offset[1] - model_hip_pos_offset[1]
+        model_hg_pmdec = hg_ddelta/(self.gaia_epoch_dec - self.hipparcos_epoch_dec)
         model_hg_pm = np.array([model_hg_pmra, model_hg_pmdec]) 
 
-        chi2 = (model_hip_pm - self.hip_pm)**2/(self.hip_pm_err)**2 + (model_gaia_pm - self.gaia_pm)**2/(self.gaia_pm_err)**2
-        chi2 += (model_hg_pm - self.hg_pm)**2/(self.hg_pm_err)**2
-
+        # take the difference between the linear motion measured during a mission, and the
+        # linear motion of the star between missions. These are our observables we compare
+        # to the data. Each variable contains both RA and Dec. 
+        model_hip_hg_dpm = model_hip_pm - model_hg_pm
+        model_gaia_hg_dpm = model_gaia_pm - model_hg_pm
+        
+        chi2 = (model_hip_hg_dpm - self.hip_hg_dpm)**2/(self.hip_hg_dpm_err)**2
+        chi2 += (model_gaia_hg_dpm - self.gaia_hg_dpm)**2/(self.gaia_hg_dpm_err)**2
         lnlike = -0.5 * np.sum(chi2)
 
         return lnlike
-    
-
+    
\ No newline at end of file

From f34e7b81a5d3c92da1f0b3fd67222e3eb070ed6e Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Tue, 22 Aug 2023 12:22:42 -0500
Subject: [PATCH 15/38] add ra/dec covariances in hgca fit

---
 orbitize/gaia.py | 28 +++++++++++++++++++++++-----
 1 file changed, 23 insertions(+), 5 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index c0d6400d..914d5bd9 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -13,6 +13,7 @@
 import scipy.optimize
 
 from orbitize import DATADIR
+import orbitize.lnlike
 
 class GaiaLogProb(object):
     """
@@ -223,20 +224,34 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         # grav the relevant values
         self.hip_pm = np.array([entry['pmra_hip'][0], entry['pmdec_hip'][0]])
         self.hip_pm_err = np.array([entry['pmra_hip_error'][0], entry['pmdec_hip_error'][0]])
+        hip_radec_cov = entry['pmra_pmdec_hip'][0] * entry['pmra_hip_error'][0] * entry['pmdec_hip_error'][0]
+        self.hip_pm_cov = np.array([[entry['pmra_hip_error'][0]**2, hip_radec_cov], [hip_radec_cov, entry['pmdec_hip_error'][0]**2]])
 
         self.hg_pm = np.array([entry['pmra_hg'][0], entry['pmdec_hg'][0]])
         self.hg_pm_err = np.array([entry['pmra_hg_error'][0], entry['pmdec_hg_error'][0]])
+        hg_radec_cov = entry['pmra_pmdec_hg'][0] * entry['pmra_hg_error'][0] * entry['pmdec_hg_error'][0]
+        self.hg_pm_cov = np.array([[entry['pmra_hg_error'][0]**2, hg_radec_cov], [hg_radec_cov, entry['pmdec_hg_error'][0]**2]])
 
         self.gaia_pm = np.array([entry['pmra_gaia'][0], entry['pmdec_gaia'][0]])
         self.gaia_pm_err = np.array([entry['pmra_gaia_error'][0], entry['pmdec_gaia_error'][0]])
+        gaia_radec_cov = entry['pmra_pmdec_gaia'][0] * entry['pmra_gaia_error'][0] * entry['pmdec_gaia_error'][0]
+        self.gaia_pm_cov = np.array([[entry['pmra_gaia_error'][0]**2, gaia_radec_cov], [gaia_radec_cov, entry['pmdec_gaia_error'][0]**2]])
 
 
-        # the PMa and their error bars. 
-        # TODO: there are covariances to be used, but they are not being used here. 
+        # the PMa and their error bars.  
         self.hip_hg_dpm = self.hip_pm - self.hg_pm
         self.hip_hg_dpm_err = np.sqrt(self.hip_pm_err**2 + self.hg_pm_err**2)
+        self.hip_hg_dpm_cov = self.hip_pm_cov + self.hg_pm_cov
+        self.hip_hg_dpm_radec_corr = self.hip_hg_dpm_cov[0][1]
         self.gaia_hg_dpm = self.gaia_pm - self.hg_pm
         self.gaia_hg_dpm_err = np.sqrt(self.gaia_pm_err**2 + self.hg_pm_err**2)
+        self.gaia_hg_dpm_cov = self.gaia_pm_cov + self.hg_pm_cov
+        self.gaia_hg_dpm_radec_corr = self.gaia_hg_dpm_cov[0][1]
+
+        # condense together into orbitize "observations"
+        self.dpm_data = np.array([self.hip_hg_dpm, self.gaia_hg_dpm])
+        self.dpm_err = np.array([self.hip_hg_dpm_err, self.gaia_hg_dpm_err])
+        self.dpm_corr = np.array([self.hip_hg_dpm_radec_corr, self.gaia_hg_dpm_radec_corr])
 
 
         # save gaia best fit values
@@ -385,9 +400,12 @@ def lsqr_linear_fit(fitparams, epochs, epoch_ref_ra, epoch_ref_dec,
         # to the data. Each variable contains both RA and Dec. 
         model_hip_hg_dpm = model_hip_pm - model_hg_pm
         model_gaia_hg_dpm = model_gaia_pm - model_hg_pm
-        
-        chi2 = (model_hip_hg_dpm - self.hip_hg_dpm)**2/(self.hip_hg_dpm_err)**2
-        chi2 += (model_gaia_hg_dpm - self.gaia_hg_dpm)**2/(self.gaia_hg_dpm_err)**2
+
+        # chi2 = (model_hip_hg_dpm - self.hip_hg_dpm)**2/(self.hip_hg_dpm_err)**2
+        # chi2 += (model_gaia_hg_dpm - self.gaia_hg_dpm)**2/(self.gaia_hg_dpm_err)**2
+
+        chi2 = orbitize.lnlike.chi2_lnlike(self.dpm_data, self.dpm_err, self.dpm_corr, np.array([model_hip_hg_dpm, model_gaia_hg_dpm]), np.zeros(self.dpm_data.shape), [])
+
         lnlike = -0.5 * np.sum(chi2)
 
         return lnlike

From a8528e14e52728c73fb9fc6ced24c176f77f3973 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Tue, 22 Aug 2023 12:22:54 -0500
Subject: [PATCH 16/38] add hgca test

---
 .../example_data/gaia_edr3_betpic_epochs.csv  | 45 +++++++++++++++++++
 tests/test_gaia.py                            | 45 +++++++++++++++++--
 2 files changed, 87 insertions(+), 3 deletions(-)
 create mode 100644 orbitize/example_data/gaia_edr3_betpic_epochs.csv

diff --git a/orbitize/example_data/gaia_edr3_betpic_epochs.csv b/orbitize/example_data/gaia_edr3_betpic_epochs.csv
new file mode 100644
index 00000000..cd087df9
--- /dev/null
+++ b/orbitize/example_data/gaia_edr3_betpic_epochs.csv
@@ -0,0 +1,45 @@
+Target, ra[rad], dec[rad], ra[h:m:s], dec[d:m:s], ObservationTimeAtGaia[UTC], CcdRow[1-7], zetaFieldAngle[rad], scanAngle[rad], Fov[FovP=preceding/FovF=following], parallaxFactorAlongScan, parallaxFactorAcrossScan, ObservationTimeAtBarycentre[BarycentricJulianDateInTCB]
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2014-09-24T03:17:43.690,1,0.003366090442748648,-2.300433958482974,FoVP,-0.7177768121570808,0.716584607472628,2456924.6385198794
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2014-09-24T05:04:17.895,6,-0.0017592745186114803,-2.2989421430856525,FoVF,-0.7179415637921202,0.7163662372846105,2456924.712528852
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2014-10-01T18:42:06.590,7,-0.00650994606000498,2.541359236566953,FoVP,0.7116794191056144,0.7166374323350725,2456932.280652986
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2014-10-01T20:28:40.824,5,-0.001445323169629156,2.543117372208244,FoVF,0.7112554019851087,0.7169776440973757,2456932.3546622447
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2014-11-03T08:05:49.312,4,7.379734901303453E-4,-1.6391612276689718,FoVF,-0.6803572545190627,0.7088631454338691,2456964.839525243
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2014-12-07T01:43:30.212,7,-0.004849959376093157,-2.5453289312449967,FoVF,0.6523701313098679,0.7030559574218078,2456998.5744054615
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-01-05T23:43:47.075,2,0.0025747885979415836,-0.4942730752652883,FoVP,-0.6582104476275792,0.7000839976813767,2457028.491161975
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-01-06T01:30:21.281,6,-0.0020271807631072774,-0.49115917840781126,FoVF,-0.6594673585899907,0.6989435391249444,2457028.565168211
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-02-07T11:22:03.024,7,-0.006295401625795344,-1.4403312052347343,FoVP,0.6844806580297031,0.7039768380081998,2457060.975515688
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-02-07T13:08:37.264,5,-0.0012850130662692158,-1.438006139309293,FoVF,0.683776941405708,0.7047219521301625,2457061.0495214257
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-03-12T10:53:44.404,4,-0.0016724966096404376,0.6827836603917707,FoVP,-0.7079503473251454,0.7058933185346745,2457093.9550098255
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-06-07T09:46:48.222,2,0.0032231934804026075,0.6243368912655289,FoVP,0.6755480884910476,0.720672913448407,2457180.906924771
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-07-16T20:48:42.265,3,9.159206573612685E-4,2.8086879399064,FoVP,-0.6868433075451905,0.7281972252904217,2457220.366807382
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-07-16T22:35:16.471,7,-0.004261568960156839,2.8081985368817692,FoVF,-0.6856104978194572,0.7294175121952486,2457220.4408154115
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-08-10T02:42:59.981,1,0.0036191819871523073,1.6824763736679587,FoVP,0.7085412300520944,0.7247153497310905,2457244.6132780225
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-09-15T14:01:23.521,3,0.001089927452839157,-2.444198739257349,FoVP,-0.7196839438339331,0.7206183765561472,2457281.0852944436
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-09-15T15:47:57.737,7,-0.004085851094577186,-2.4430036981963634,FoVF,-0.7196627408057288,0.7206192439872735,2457281.1593035525
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-11-16T01:25:49.514,3,8.854859632097954E-4,-1.4264296688090004,FoVP,-0.6684715550056647,0.7086359784904921,2457342.5619674516
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-11-16T03:12:23.738,7,-0.003773896187501599,-1.4235221893706327,FoVF,-0.6695598930166724,0.7075315446893528,2457342.6359756514
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-12-20T07:03:04.009,3,9.588368448122845E-4,-2.303779872316881,FoVP,0.6488519167675069,0.7064083940105257,2457376.796352539
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2015-12-20T08:49:38.184,1,0.005480454883411049,-2.3005608291804354,FoVF,0.6475441389228678,0.7076150554246801,2457376.8703589914
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-01-19T06:48:28.296,1,0.00455958460866644,-0.2506064763631727,FoVP,-0.6711781552129196,0.6994346336977754,2457406.785930979
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-01-19T08:35:02.483,5,-1.6103865792494655E-4,-0.2476878009395681,FoVF,-0.6723150392499362,0.6983982869510355,2457406.8599365856
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-02-19T18:28:00.555,6,-0.004873371904800672,-1.2381780932616115,FoVP,0.6934653764733474,0.7070101491885915,2457438.271051817
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-02-19T20:14:34.781,4,2.6074977073553676E-4,-1.23621070452396,FoVF,0.6929968714204228,0.7075225391539418,2457438.3450571797
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-03-24T17:54:55.881,6,-0.0043337270022286226,0.8991683120726217,FoVP,-0.7074817657423257,0.7082054154742482,2457472.247171013
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-04-19T05:44:58.009,4,-0.0014381446312868695,-0.22886248328921097,FoVP,0.6963362021808351,0.712819874024422,2457497.739628123
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-04-19T07:31:32.219,2,0.003886499388137549,-0.2288081754393124,FoVF,0.6971805758323766,0.7119662135302238,2457497.813633586
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-05-27T16:50:32.073,5,-0.0029145249998517703,1.948752602913419,FoVP,-0.6680580667308067,0.725043819579978,2457536.201254772
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-06-18T22:47:24.786,7,-0.005962641445618194,0.8409130362544286,FoVP,0.6714549790241314,0.7239052814778538,2457558.4490316375
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-06-19T00:33:58.986,5,-8.288178662300212E-4,0.8400634884096743,FoVF,0.6729927004193484,0.722489770277802,2457558.5230387584
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-07-28T05:37:36.385,1,0.005979846420325473,3.021466380293874,FoVF,-0.6961663851608516,0.725506680122301,2457597.7343020677
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-07-28T09:51:16.403,7,-0.0064117397384628435,3.0209332244477936,FoVP,-0.6937088110107983,0.7279934284451889,2457597.9104628544
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-08-22T09:43:34.594,6,-0.005021369192567612,1.891477111963893,FoVP,0.7163779836170526,0.721693043409109,2457622.9056568476
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-09-26T22:53:20.282,2,0.004663702203171653,-2.2467618758998045,FoVF,-0.7127789642106954,0.7211462908866082,2457658.4550358946
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-10-28T02:48:28.158,3,6.303049850276892E-4,3.0113148092982573,FoVP,0.6912829527953595,0.7107404199561433,2457689.619063065
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-11-27T20:32:15.113,1,0.004541520064988245,-1.207663399140569,FoVP,-0.6588548698686335,0.7090410364005234,2457720.3582387106
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2016-11-27T22:18:49.298,5,-1.748119107680908E-5,-1.2045518070063896,FoVF,-0.6600772355459219,0.7078414863572481,2457720.432246054
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2017-01-01T02:09:11.754,7,-0.006902628486465427,-2.079797953973847,FoVP,0.6550633131402046,0.7064251343794189,2457754.5922265993
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2017-01-01T03:55:46.012,6,-0.002282603205303411,-2.07671056686794,FoVF,0.653834682789045,0.7075793064419984,2457754.6662335903
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2017-01-31T15:38:55.842,2,0.004129546721164321,-0.007922543428096887,FoVF,-0.6861814540849321,0.6996909360179947,2457785.154084105
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2017-04-06T20:18:02.634,2,0.003134500531205524,1.1100328332672587,FoVP,-0.7041159475800817,0.7099434017490356,2457850.346244692
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2017-04-06T22:04:36.843,6,-0.0022166543522921323,1.1103871962777563,FoVF,-0.7034680010111511,0.7105601458702862,2457850.420249916
+bet Pic,1.5153157780177544,-0.8912787619608181,05:47:17.088,-51:03:59.441,2017-05-01T09:56:47.276,6,-0.0030387516834771058,-0.018759200967237467,FoVF,0.686165682370106,0.7161488905344787,2457874.914289276
\ No newline at end of file
diff --git a/tests/test_gaia.py b/tests/test_gaia.py
index ba592b8a..1fd348ee 100644
--- a/tests/test_gaia.py
+++ b/tests/test_gaia.py
@@ -1,7 +1,7 @@
 import numpy as np
 import os
 from orbitize import DATADIR
-from orbitize import hipparcos, gaia, basis, system, read_input
+from orbitize import hipparcos, gaia, basis, system, read_input, sampler
 
 def test_dr2_edr3():
     """
@@ -198,8 +198,47 @@ def test_orbit_calculation():
 
     assert np.isclose(np.exp(lnlike), 1)
 
+def test_hgca():
+    """
+    Tests that the HGCA module works for beta Pic b.
+
+    Checks can download HGCA catalog if not available, and checks GRAVITY Collaboration et al. 2020 
+    orbital parameters against the data
+    """
+    iad_filepath = os.path.join(DATADIR, "HIP027321.d")
+    gost_filepath = os.path.join(DATADIR, "gaia_edr3_betpic_epochs.csv")
+    astrometry_filepath = os.path.join(DATADIR, 'betaPic.csv')
+
+    hipparcos_lnprob = hipparcos.HipparcosLogProb(iad_filepath, 107412, 1)
+    hcga_lnprob = gaia.HGCALogProb(107412, hipparcos_lnprob, gost_filepath)
+
+    # test a few things were read in correctly
+    assert(np.all(np.isfinite(hcga_lnprob.hip_pm)))
+    assert(len(hcga_lnprob.hipparcos_epoch) > 0)
+    assert(len(hcga_lnprob.gaia_epoch) > 0)
+
+    # Initialize System object which stores data & sets priors
+    data_table = read_input.read_file(astrometry_filepath)
+    this_system = system.System(
+        1, data_table, 1.75 ,
+        51.44, mass_err=0.05, plx_err=0.12, tau_ref_epoch=55000,
+        fit_secondary_mass=True, gaia=hcga_lnprob
+    )
+    
+    # create sampler just for lnlike function
+    this_sampler = sampler.MCMC(this_system, 1, 100 , 1)
+
+    # params from GRAVITY Collaboration et al. 2020 fit to HGCA DR2 
+    params = np.array([[ 1.04e+01,  1.44e-01,  1.5534e+00,  3.5325e+00, 5.5670e-01,  1.80968e-01,  5.1456e+01, 1.367e-02,  1.783]])
+
+    chi2 = this_sampler._logl(params)
+
+    assert(np.isfinite(chi2))
+
+
 if __name__ == '__main__':
-    test_dr2_edr3()
+    # test_dr2_edr3()
     # test_system_setup()
     # test_valueerror()
-    # test_orbit_calculation()
\ No newline at end of file
+    # test_orbit_calculation()
+    test_hgca()
\ No newline at end of file

From 965f13da388bc9f36609abd52997ced21a3df765 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Wed, 23 Aug 2023 14:34:14 -0500
Subject: [PATCH 17/38] fix typos in hgca test

---
 tests/test_gaia.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tests/test_gaia.py b/tests/test_gaia.py
index 1fd348ee..09c98d8b 100644
--- a/tests/test_gaia.py
+++ b/tests/test_gaia.py
@@ -209,20 +209,20 @@ def test_hgca():
     gost_filepath = os.path.join(DATADIR, "gaia_edr3_betpic_epochs.csv")
     astrometry_filepath = os.path.join(DATADIR, 'betaPic.csv')
 
-    hipparcos_lnprob = hipparcos.HipparcosLogProb(iad_filepath, 107412, 1)
-    hcga_lnprob = gaia.HGCALogProb(107412, hipparcos_lnprob, gost_filepath)
+    hipparcos_lnprob = hipparcos.HipparcosLogProb(iad_filepath, 27321, 1)
+    hgca_lnprob = gaia.HGCALogProb(27321, hipparcos_lnprob, gost_filepath)
 
     # test a few things were read in correctly
-    assert(np.all(np.isfinite(hcga_lnprob.hip_pm)))
-    assert(len(hcga_lnprob.hipparcos_epoch) > 0)
-    assert(len(hcga_lnprob.gaia_epoch) > 0)
+    assert(np.all(np.isfinite(hgca_lnprob.hip_pm)))
+    assert(len(hgca_lnprob.hipparcos_epoch) > 0)
+    assert(len(hgca_lnprob.gaia_epoch) > 0)
 
     # Initialize System object which stores data & sets priors
     data_table = read_input.read_file(astrometry_filepath)
     this_system = system.System(
         1, data_table, 1.75 ,
         51.44, mass_err=0.05, plx_err=0.12, tau_ref_epoch=55000,
-        fit_secondary_mass=True, gaia=hcga_lnprob
+        fit_secondary_mass=True, gaia=hgca_lnprob
     )
     
     # create sampler just for lnlike function

From 253919b2d0f5ccc721b6f7592e425bbccdefc301 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Wed, 23 Aug 2023 14:35:11 -0500
Subject: [PATCH 18/38] add hgca tutorial

---
 docs/tutorials/HGCA_tutorial.ipynb | 142 +++++++++++++++++++++++++++++
 1 file changed, 142 insertions(+)
 create mode 100644 docs/tutorials/HGCA_tutorial.ipynb

diff --git a/docs/tutorials/HGCA_tutorial.ipynb b/docs/tutorials/HGCA_tutorial.ipynb
new file mode 100644
index 00000000..9f9bbc90
--- /dev/null
+++ b/docs/tutorials/HGCA_tutorial.ipynb
@@ -0,0 +1,142 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting the Hipparcos-Gaia Catalog of Acclerations (HGCA)\n",
+    "\n",
+    "Jason Wang (2023)\n",
+    "\n",
+    "We will demonstrate how to fit the stellar absolute astrometry from the [Hipparcos-Gaia Catalog of Accelerations (HGCA; Brandt 2021)](https://ui.adsabs.harvard.edu/abs/2021ApJS..254...42B/abstract) to help constrain the mass and orbital parameters of the companion. This is an alternative to fitting the Hipparcos IAD and Gaia astrometry. We encourage users to try both to see how similar or different the results are, as the two methods utilize the same underlying data. The implementation of fitting the HGCA here is based on [Brandt et al. 2019](https://ui.adsabs.harvard.edu/abs/2019AJ....158..140B/abstract), but utilizes the DR3 version of the HGCA. \n",
+    "\n",
+    "\n",
+    "## Obtain the necessary data\n",
+    "\n",
+    "While `orbitize!` will automatically download the HGCA catalog, you need to obtain two other data files to reconstruct the Hipparcos and Gaia observations for your star. These files tell us when and in what orientation did Hipparcos and Gaia take data of the star for the forward modeling that happens in `orbitize!`. \n",
+    "\n",
+    "  1. The Hipparcos IAD file of the star. Follow the section in the [Hipparocs IAD tutorial](https://orbitize.readthedocs.io/en/latest/tutorials/Hipparcos_IAD.html#Part-1:-Obtaining-the-IAD) on how to obtain this file for your star of interest.\n",
+    "  2. The anticipated Gaia epochs and scan directions in a CSV file that is obtained from the [Gaia Observation Forecast Tool (GOST)](https://gaia.esac.esa.int/gost/). For GOST, after entering the target name and resolving its coordinates, use 2014-07-26T00:00:00 as the start date. For the end date, use 2016-05-23T00:00:00 for DR2 and 2017-05-28T00:00:00 for EDR3. You probably will be fitting the EDR3. The output CSV file is all you need. \n",
+    "\n",
+    "## Setup the `HGCALogProb` Object\n",
+    "\n",
+    "The user just needs to setup the `orbitize.gaia.HGCALogProb` object, which will handle the rest of the HGCA modeling under the hood. To setup the `HGCALogProb` object, we also need to create an `orbitize.hipparcos.HipparcosLogProb` instance, but it will not actually be used in the fitting. It is simply used to handle reading in the Hipparcos IAD to maximize code reuse. \n",
+    "\n",
+    "In the example below, we setup the HGCA fitting for beta Pictoris (HIP 27321).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from orbitize import DATADIR, hipparcos, gaia\n",
+    "\n",
+    "# the necessary input data for beta Pic is part of the orbitize! example data!\n",
+    "iad_filepath = os.path.join(DATADIR, \"HIP027321.d\")\n",
+    "gost_filepath = os.path.join(DATADIR, \"gaia_edr3_betpic_epochs.csv\")\n",
+    "\n",
+    "hipparcos_lnprob = hipparcos.HipparcosLogProb(iad_filepath, 27321, 1) # we're just going to assume one planet for simplicity\n",
+    "hgca_lnprob = gaia.HGCALogProb(27321, hipparcos_lnprob, gost_filepath)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup orbit fit\n",
+    "\n",
+    "After you do this step, the rest of the orbit fit setup is basically the same as a standard `orbitize!` orbit fit. The only differences is that you need to pass the `HGCALogProb` object into the system class (but not the `HipparcosLogProb` because the Hipparcos data is already being handled in HGCA). We also recommend you explicitly set up the system to fit for the dynamical mass of the companions in the system (this should happen automatically with the inclusion of HGCA data, but we recommend being explicit so it is clear what you are fitting for).\n",
+    "\n",
+    "We will also note that that the default prior on the companion mass is a log-uniform prior between 1e-6 and 2 solar masses. If you want a more restricted mass, now is also the time to adjust that. We present one example here, but refer to the Modifying Priors tutorial for further details. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from orbitize import read_input, system, priors\n",
+    "import astropy.units as u\n",
+    "\n",
+    "# read in relative astrometry\n",
+    "astrometry_filepath = os.path.join(DATADIR, 'betaPic.csv')\n",
+    "data_table = read_input.read_file(astrometry_filepath)\n",
+    "\n",
+    "# set up the system, passing in hgca_lnprob and setting it fit dynamical mass\n",
+    "stellar_mass = 1.75\n",
+    "stellar_mass_err = 0.05\n",
+    "plx = 51.44\n",
+    "plx_err = 0.12\n",
+    "\n",
+    "this_system = system.System(\n",
+    "    1, data_table, stellar_mass, plx, \n",
+    "    mass_err=stellar_mass_err, plx_err=plx_err,\n",
+    "    fit_secondary_mass=True, gaia=hgca_lnprob\n",
+    ")\n",
+    "\n",
+    "# adjust the prior on mass to be log uniform between 1 and 50 Jupiter masses\n",
+    "mjup = u.Mjup.to(u.Msun)\n",
+    "this_system.sys_priors[this_system.param_idx['m1']] = priors.LogUniformPrior(mjup, 50*mjup)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run orbitize! \n",
+    "\n",
+    "From here onwards, everything is the same as an regular `orbitize` fit, so we refer to reader to the other tutorials. We recommend using the MCMC sampler, since the orbits are generally too constrained for OFTI. To pick the right number of temperatures, walkers, steps for MCMC, check out papers that performed similar fits (e.g., Section 3.1 of [GRAVITY Collaboration et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020A%26A...633A.110G/abstract) and Section 3.1 of [Hinkley et al. (2023)](https://ui.adsabs.harvard.edu/abs/2023A%26A...671L...5H/abstract))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from orbitize import sampler\n",
+    "\n",
+    "# MCMC parameters\n",
+    "# for demonstration purposes only. You will need to increase these likely\n",
+    "n_temps = 2\n",
+    "n_walkers = 50\n",
+    "n_threads = 1\n",
+    "burn_steps = 1\n",
+    "total_orbits = 100 * n_walkers\n",
+    "\n",
+    "# create the sampler, run it, and save posteriors\n",
+    "this_sampler = sampler.MCMC(this_system,n_temps,n_walkers,n_threads)\n",
+    "\n",
+    "this_sampler.run_sampler(total_orbits, burn_steps=burn_steps, thin=10)\n",
+    "\n",
+    "this_sampler.results.save_results(\"demo_hgca.hdf5\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.1"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 25a1978b69f713ed798bef6edf96463f5ce24831 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Wed, 23 Aug 2023 15:03:41 -0500
Subject: [PATCH 19/38] small tutorail changes for docs

---
 docs/tutorials.rst                 | 1 +
 docs/tutorials/HGCA_tutorial.ipynb | 4 +++-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/docs/tutorials.rst b/docs/tutorials.rst
index 4a1acd01..a001d4b6 100644
--- a/docs/tutorials.rst
+++ b/docs/tutorials.rst
@@ -55,6 +55,7 @@ us if you are still confused).
    tutorials/Using_nonOrbitize_Posteriors_as_Priors.ipynb
    tutorials/Changing_bases_tutorial.ipynb
    tutorials/Hipparcos_IAD.ipynb
+   tutorials/HGCA_tutorial.ipynb
 
 
 
diff --git a/docs/tutorials/HGCA_tutorial.ipynb b/docs/tutorials/HGCA_tutorial.ipynb
index 9f9bbc90..e86c845f 100644
--- a/docs/tutorials/HGCA_tutorial.ipynb
+++ b/docs/tutorials/HGCA_tutorial.ipynb
@@ -38,7 +38,9 @@
     "iad_filepath = os.path.join(DATADIR, \"HIP027321.d\")\n",
     "gost_filepath = os.path.join(DATADIR, \"gaia_edr3_betpic_epochs.csv\")\n",
     "\n",
-    "hipparcos_lnprob = hipparcos.HipparcosLogProb(iad_filepath, 27321, 1) # we're just going to assume one planet for simplicity\n",
+    "# Create the HGCA and helper Hipparcos object\n",
+    "# we're just going to assume one planet for simplicity\n",
+    "hipparcos_lnprob = hipparcos.HipparcosLogProb(iad_filepath, 27321, 1) \n",
     "hgca_lnprob = gaia.HGCALogProb(27321, hipparcos_lnprob, gost_filepath)\n"
    ]
   },

From 545c77d2a775e9a4fbdb4ddf845ea0da6e518057 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Mon, 28 Aug 2023 12:04:53 -0500
Subject: [PATCH 20/38] forgot corr norm

---
 orbitize/gaia.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 914d5bd9..1c679009 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -242,11 +242,11 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         self.hip_hg_dpm = self.hip_pm - self.hg_pm
         self.hip_hg_dpm_err = np.sqrt(self.hip_pm_err**2 + self.hg_pm_err**2)
         self.hip_hg_dpm_cov = self.hip_pm_cov + self.hg_pm_cov
-        self.hip_hg_dpm_radec_corr = self.hip_hg_dpm_cov[0][1]
+        self.hip_hg_dpm_radec_corr = self.hip_hg_dpm_cov[0][1]/np.sqrt(self.hip_hg_dpm_cov[0][0] * self.hip_hg_dpm_cov[1][1])
         self.gaia_hg_dpm = self.gaia_pm - self.hg_pm
         self.gaia_hg_dpm_err = np.sqrt(self.gaia_pm_err**2 + self.hg_pm_err**2)
         self.gaia_hg_dpm_cov = self.gaia_pm_cov + self.hg_pm_cov
-        self.gaia_hg_dpm_radec_corr = self.gaia_hg_dpm_cov[0][1]
+        self.gaia_hg_dpm_radec_corr = self.gaia_hg_dpm_cov[0][1]/np.sqrt(self.gaia_hg_dpm_cov[0][0] * self.gaia_hg_dpm_cov[1][1])
 
         # condense together into orbitize "observations"
         self.dpm_data = np.array([self.hip_hg_dpm, self.gaia_hg_dpm])

From 5d2de787ae8c193f28f1321b17536273091dabf9 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Tue, 29 Aug 2023 17:08:07 -0500
Subject: [PATCH 21/38] HGCA saves to results. cleaned up unused fields

---
 orbitize/gaia.py    | 80 ++++++++++++++++++---------------------------
 orbitize/results.py | 16 +++++++++
 tests/test_gaia.py  | 13 +++++++-
 3 files changed, 60 insertions(+), 49 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 1c679009..8faa82a9 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -220,70 +220,55 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         if len(entry) != 1:
             raise ValueError("HIP {0} encountered {1} matches. Expected 1 match.".format(hip_id, len(entry)))
         #  self.hgca_entry = entry
+        self.hip_id = hip_id
 
-        # grav the relevant values
+        # grab the relevant PM and uncertainties from HGCA
         self.hip_pm = np.array([entry['pmra_hip'][0], entry['pmdec_hip'][0]])
         self.hip_pm_err = np.array([entry['pmra_hip_error'][0], entry['pmdec_hip_error'][0]])
         hip_radec_cov = entry['pmra_pmdec_hip'][0] * entry['pmra_hip_error'][0] * entry['pmdec_hip_error'][0]
-        self.hip_pm_cov = np.array([[entry['pmra_hip_error'][0]**2, hip_radec_cov], [hip_radec_cov, entry['pmdec_hip_error'][0]**2]])
 
         self.hg_pm = np.array([entry['pmra_hg'][0], entry['pmdec_hg'][0]])
         self.hg_pm_err = np.array([entry['pmra_hg_error'][0], entry['pmdec_hg_error'][0]])
         hg_radec_cov = entry['pmra_pmdec_hg'][0] * entry['pmra_hg_error'][0] * entry['pmdec_hg_error'][0]
-        self.hg_pm_cov = np.array([[entry['pmra_hg_error'][0]**2, hg_radec_cov], [hg_radec_cov, entry['pmdec_hg_error'][0]**2]])
 
         self.gaia_pm = np.array([entry['pmra_gaia'][0], entry['pmdec_gaia'][0]])
         self.gaia_pm_err = np.array([entry['pmra_gaia_error'][0], entry['pmdec_gaia_error'][0]])
         gaia_radec_cov = entry['pmra_pmdec_gaia'][0] * entry['pmra_gaia_error'][0] * entry['pmdec_gaia_error'][0]
-        self.gaia_pm_cov = np.array([[entry['pmra_gaia_error'][0]**2, gaia_radec_cov], [gaia_radec_cov, entry['pmdec_gaia_error'][0]**2]])
 
 
-        # the PMa and their error bars.  
+        # compute the differential PMs by subtracting Hip and Gaia from HG. Also propogate errors
         self.hip_hg_dpm = self.hip_pm - self.hg_pm
         self.hip_hg_dpm_err = np.sqrt(self.hip_pm_err**2 + self.hg_pm_err**2)
-        self.hip_hg_dpm_cov = self.hip_pm_cov + self.hg_pm_cov
-        self.hip_hg_dpm_radec_corr = self.hip_hg_dpm_cov[0][1]/np.sqrt(self.hip_hg_dpm_cov[0][0] * self.hip_hg_dpm_cov[1][1])
+        self.hip_hg_dpm_radec_corr = (hip_radec_cov + hg_radec_cov)/(self.hip_hg_dpm_err[0] * self.hip_hg_dpm_err[1])
         self.gaia_hg_dpm = self.gaia_pm - self.hg_pm
         self.gaia_hg_dpm_err = np.sqrt(self.gaia_pm_err**2 + self.hg_pm_err**2)
-        self.gaia_hg_dpm_cov = self.gaia_pm_cov + self.hg_pm_cov
-        self.gaia_hg_dpm_radec_corr = self.gaia_hg_dpm_cov[0][1]/np.sqrt(self.gaia_hg_dpm_cov[0][0] * self.gaia_hg_dpm_cov[1][1])
+        self.gaia_hg_dpm_radec_corr = (gaia_radec_cov + hg_radec_cov)/(self.gaia_hg_dpm_err[0] * self.gaia_hg_dpm_err[1])
 
         # condense together into orbitize "observations"
         self.dpm_data = np.array([self.hip_hg_dpm, self.gaia_hg_dpm])
         self.dpm_err = np.array([self.hip_hg_dpm_err, self.gaia_hg_dpm_err])
         self.dpm_corr = np.array([self.hip_hg_dpm_radec_corr, self.gaia_hg_dpm_radec_corr])
-
-
-        # save gaia best fit values
-        self.gaia_plx0 = entry['parallax_gaia'][0]
-        self.gaia_alpha0 = entry['gaia_ra'][0]
-        self.gaia_delta0 = entry['gaia_dec'][0]
-        self.gaia_pm_ra0 = entry['pmra_gaia'][0]
-        self.gaia_pm_dec0 = entry['pmdec_gaia'][0]
+        
+        # grab reference epochs for Gaia from HGCA so we can forward model it
         self.gaia_epoch_ra = entry['epoch_ra_gaia'][0]
         self.gaia_epoch_dec = entry['epoch_dec_gaia'][0]
+        # read in the GOST file to get the estimated Gaia epochs and scan angles
+        gost_dat = read(gost_filepath, converters={'*':[int, float, bytes]})
+        self.gaia_epoch = time.Time(gost_dat["ObservationTimeAtGaia[UTC]"]).decimalyear # in decimal year
+        gaia_scan_theta = gost_dat["scanAngle[rad]"]
+        gaia_scan_phi = gaia_scan_theta + np.pi/2
+        self.gaia_cos_phi = np.cos(gaia_scan_phi)
+        self.gaia_sin_phi = np.sin(gaia_scan_phi)
+        self.gost_filepath = gost_filepath # save for saving
 
-        # save the hipparcos object for use later
-        #  self.hiplogprob = hiplogprob
+        # save the same infor from Hipparcos. we also have the errors on the Hipparcos data
         self.hipparcos_epoch = hiplogprob.epochs # in decimal year
         self.hipparcos_cos_phi = hiplogprob.cos_phi
         self.hipparcos_sin_phi = hiplogprob.sin_phi
-        self.hipparcos_plx0 = hiplogprob.plx0
-        self.hipparcos_alpha0 = hiplogprob.alpha0
-        self.hipparcos_delta0 = hiplogprob.delta0
-        self.hipparcos_pm_ra0 = entry['pmra_hip'][0]
-        self.hipparcos_pm_dec0 = entry['pmdec_hip'][0]
         self.hipparcos_epoch_ra = entry['epoch_ra_hip'][0]
         self.hipparcos_epoch_dec = entry['epoch_dec_hip'][0]
         self.hippaarcos_errs = hiplogprob.eps
-
-        # read in the GOST file to get the estimated Gaia epochs
-        gost_dat = read(gost_filepath)
-        self.gaia_epoch = time.Time(gost_dat["ObservationTimeAtGaia[UTC]"]).decimalyear # in decimal year
-        gaia_scan_theta = gost_dat["scanAngle[rad]"]
-        gaia_scan_phi = gaia_scan_theta + np.pi/2
-        self.gaia_cos_phi = np.cos(gaia_scan_phi)
-        self.gaia_sin_phi = np.sin(gaia_scan_phi)
+        self.hiplogprob = hiplogprob # save for saving
 
 
     def _save(self, hf):
@@ -294,9 +279,13 @@ def _save(self, hf):
             hf (h5py._hl.files.File): a currently open hdf5 file in which
                 to save the object.
         """
-        # TODO: save stuff here if needed
-        #  self.hiplogprob._save(hf)
-        pass
+        # save hipparocs IAD using exiting code
+        self.hiplogprob._save(hf)
+
+        # save Gaia GOST file. avoid unicode!!
+        gost_dat = read(self.gost_filepath, converters={'*':[int, float, bytes]})
+        hf.create_dataset("Gaia_GOST", data=gost_dat)
+
 
     def compute_lnlike(
         self, raoff_model, deoff_model, samples, param_idx
@@ -348,18 +337,15 @@ def compute_lnlike(
 
         
         def lsqr_linear_fit(fitparams, epochs, epoch_ref_ra, epoch_ref_dec, 
-                        raoff_planet, decoff_planet, sin_phi, cos_phi, pm_ra0, pm_dec0, errs):
+                        raoff_planet, decoff_planet, sin_phi, cos_phi, errs):
             guess_ra_off, guess_dec_off, guess_pm_ra, guess_pm_dec = fitparams
             
-            # combine proper motion and orbital motion, fit for inferred lienar motion
-            data_changein_alpha_st = (epochs - epoch_ref_ra) * pm_ra0
-            data_changein_delta = (epochs - epoch_ref_dec) * pm_dec0
-
-            data_changein_alpha_st += raoff_planet 
-            data_changein_delta += decoff_planet
+            # orbital motion, fit for inferred lienar motion
+            data_changein_alpha_st = raoff_planet 
+            data_changein_delta = decoff_planet
 
-            guess_changein_alpha_st = (epochs - epoch_ref_ra) * (guess_pm_ra + pm_ra0) + guess_ra_off
-            guess_changein_delta = (epochs - epoch_ref_dec) * (guess_pm_dec + pm_dec0) + guess_dec_off
+            guess_changein_alpha_st = (epochs - epoch_ref_ra) * (guess_pm_ra) + guess_ra_off
+            guess_changein_delta = (epochs - epoch_ref_dec) * (guess_pm_dec) + guess_dec_off
 
             # calculate distance between line and expected measurement (Nielsen+ 2020 Eq 6) [mas]
             dists = np.abs(
@@ -372,8 +358,7 @@ def lsqr_linear_fit(fitparams, epochs, epoch_ref_ra, epoch_ref_dec,
         hip_init_guess = [0., 0., 0, 0]
         hip_args = (self.hipparcos_epoch, self.hipparcos_epoch_ra, self.hipparcos_epoch_dec,
                     model_ra_hip, model_dec_hip,
-                    self.hipparcos_sin_phi, self.hipparcos_cos_phi, self.hipparcos_pm_ra0, 
-                    self.hipparcos_pm_dec0, self.hippaarcos_errs)
+                    self.hipparcos_sin_phi, self.hipparcos_cos_phi, self.hippaarcos_errs)
         hip_fit, _ = scipy.optimize.leastsq(lsqr_linear_fit, hip_init_guess, args=hip_args)
         model_hip_pos_offset = hip_fit[0:2]
         model_hip_pm = hip_fit[2:4]
@@ -381,8 +366,7 @@ def lsqr_linear_fit(fitparams, epochs, epoch_ref_ra, epoch_ref_dec,
         gaia_init_guess = [0, 0, 0, 0]
         gaia_args = (self.gaia_epoch, self.gaia_epoch_ra, self.gaia_epoch_dec, 
                      model_ra_gaia, model_dec_gaia,
-                     self.gaia_sin_phi, self.gaia_cos_phi, self.gaia_pm_ra0, 
-                     self.gaia_pm_dec0, 1)
+                     self.gaia_sin_phi, self.gaia_cos_phi, 1)
         gaia_fit, _ = scipy.optimize.leastsq(lsqr_linear_fit, gaia_init_guess, args=gaia_args)
         model_gaia_pos_offset = gaia_fit[0:2]
         model_gaia_pm = gaia_fit[2:4]
diff --git a/orbitize/results.py b/orbitize/results.py
index fe68c8a9..d13c7a76 100644
--- a/orbitize/results.py
+++ b/orbitize/results.py
@@ -209,16 +209,32 @@ def load_results(self, filename, append=False):
             )
 
             os.system('rm {}'.format(tmpfile))
+
+            # load Gaia data
             try:
                 gaia_num = int(hf.attrs['gaia_num'])
                 dr = str(hf.attrs['dr'])
                 gaia = orbitize.gaia.GaiaLogProb(gaia_num, hipparcos_IAD, dr)
             except KeyError:
                 gaia = None
+
+            # alternatively load HGCA. Note requires hipparcos_IAD attribute
+            gaiagost_data = hf.get("Gaia_GOST")
+            if gaiagost_data is not None:
+                
+                tmpfile = 'thisisprettyhackysorrylmao'
+                tmptbl = table.Table(np.array(gaiagost_data))
+                tmptbl.write(tmpfile, format="ascii", overwrite=True)
+
+                gaia = orbitize.gaia.HGCALogProb(int(hip_num), hipparcos_IAD, tmpfile)
+                hipparcos_IAD = None # HGCA handles hipparocs, so don't want to pass Hipparcos also into the system
+
+                os.system('rm {}'.format(tmpfile))
         else:
             hipparcos_IAD = None
             gaia = None
 
+
         try:
             fitting_basis = np.str(hf.attrs['fitting_basis'])
         except KeyError:
diff --git a/tests/test_gaia.py b/tests/test_gaia.py
index 09c98d8b..c8ceba50 100644
--- a/tests/test_gaia.py
+++ b/tests/test_gaia.py
@@ -1,7 +1,7 @@
 import numpy as np
 import os
 from orbitize import DATADIR
-from orbitize import hipparcos, gaia, basis, system, read_input, sampler
+from orbitize import hipparcos, gaia, basis, system, read_input, sampler, results
 
 def test_dr2_edr3():
     """
@@ -236,6 +236,17 @@ def test_hgca():
     assert(np.isfinite(chi2))
 
 
+    # briefly run sampler and save result
+    this_sampler.run_sampler(100, 0)
+    this_sampler.results.save_results("hgca_test.hdf5")
+
+    # check that the data stays the same
+    res = results.Results()
+    res.load_results("hgca_test.hdf5")
+    new_hgca_lnprob = res.system.gaia
+    assert(isinstance(new_hgca_lnprob, gaia.HGCALogProb))
+    assert(new_hgca_lnprob.hip_hg_dpm_radec_corr == hgca_lnprob.hip_hg_dpm_radec_corr)
+
 if __name__ == '__main__':
     # test_dr2_edr3()
     # test_system_setup()

From c59a9a84826d33456cac952014089793cd632d9a Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Wed, 30 Aug 2023 17:20:16 -0500
Subject: [PATCH 22/38] speed up hgca fit using linear solver

---
 orbitize/gaia.py | 64 ++++++++++++++++++++++--------------------------
 1 file changed, 29 insertions(+), 35 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 8faa82a9..75629a02 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -10,7 +10,7 @@
 import astropy.time as time
 from astropy.io.ascii import read
 from astropy.coordinates import get_body_barycentric_posvel
-import scipy.optimize
+import numpy.linalg
 
 from orbitize import DATADIR
 import orbitize.lnlike
@@ -255,7 +255,7 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         # read in the GOST file to get the estimated Gaia epochs and scan angles
         gost_dat = read(gost_filepath, converters={'*':[int, float, bytes]})
         self.gaia_epoch = time.Time(gost_dat["ObservationTimeAtGaia[UTC]"]).decimalyear # in decimal year
-        gaia_scan_theta = gost_dat["scanAngle[rad]"]
+        gaia_scan_theta = np.array(gost_dat["scanAngle[rad]"])
         gaia_scan_phi = gaia_scan_theta + np.pi/2
         self.gaia_cos_phi = np.cos(gaia_scan_phi)
         self.gaia_sin_phi = np.sin(gaia_scan_phi)
@@ -327,47 +327,22 @@ def compute_lnlike(
 
         # Begin forward modeling the data of the HGCA: linear motion during the Hip
         # and Gaia missions, and the linear motion of the star between the two missions 
-        plx = samples[param_idx['plx']]
-
         # for now, think about only 1 model at a time to not break our brains
         model_ra_hip = raoff_model[:gaia_index, 0]
         model_dec_hip = deoff_model[:gaia_index, 0]
         model_ra_gaia = raoff_model[gaia_index:, 0]
         model_dec_gaia = deoff_model[gaia_index:, 0]
 
-        
-        def lsqr_linear_fit(fitparams, epochs, epoch_ref_ra, epoch_ref_dec, 
-                        raoff_planet, decoff_planet, sin_phi, cos_phi, errs):
-            guess_ra_off, guess_dec_off, guess_pm_ra, guess_pm_dec = fitparams
-            
-            # orbital motion, fit for inferred lienar motion
-            data_changein_alpha_st = raoff_planet 
-            data_changein_delta = decoff_planet
-
-            guess_changein_alpha_st = (epochs - epoch_ref_ra) * (guess_pm_ra) + guess_ra_off
-            guess_changein_delta = (epochs - epoch_ref_dec) * (guess_pm_dec) + guess_dec_off
-
-            # calculate distance between line and expected measurement (Nielsen+ 2020 Eq 6) [mas]
-            dists = np.abs(
-                (guess_changein_alpha_st - data_changein_alpha_st) * cos_phi + 
-                (guess_changein_delta - data_changein_delta) * sin_phi
-            ) / errs
-
-            return dists
-
-        hip_init_guess = [0., 0., 0, 0]
-        hip_args = (self.hipparcos_epoch, self.hipparcos_epoch_ra, self.hipparcos_epoch_dec,
-                    model_ra_hip, model_dec_hip,
-                    self.hipparcos_sin_phi, self.hipparcos_cos_phi, self.hippaarcos_errs)
-        hip_fit, _ = scipy.optimize.leastsq(lsqr_linear_fit, hip_init_guess, args=hip_args)
+        hip_fit = self._linear_pm_fit(self.hipparcos_epoch, model_ra_hip, model_dec_hip, 
+                                      self.hipparcos_epoch_ra, self.hipparcos_epoch_dec, 
+                                      self.hipparcos_sin_phi, self.hipparcos_cos_phi, self.hippaarcos_errs)
         model_hip_pos_offset = hip_fit[0:2]
         model_hip_pm = hip_fit[2:4]
-        
-        gaia_init_guess = [0, 0, 0, 0]
-        gaia_args = (self.gaia_epoch, self.gaia_epoch_ra, self.gaia_epoch_dec, 
-                     model_ra_gaia, model_dec_gaia,
-                     self.gaia_sin_phi, self.gaia_cos_phi, 1)
-        gaia_fit, _ = scipy.optimize.leastsq(lsqr_linear_fit, gaia_init_guess, args=gaia_args)
+
+
+        gaia_fit = self._linear_pm_fit(self.gaia_epoch, model_ra_gaia, model_dec_gaia,
+                                       self.gaia_epoch_ra, self.gaia_epoch_dec, 
+                                       self.gaia_sin_phi, self.gaia_cos_phi, 1)
         model_gaia_pos_offset = gaia_fit[0:2]
         model_gaia_pm = gaia_fit[2:4]
 
@@ -393,4 +368,23 @@ def lsqr_linear_fit(fitparams, epochs, epoch_ref_ra, epoch_ref_dec,
         lnlike = -0.5 * np.sum(chi2)
 
         return lnlike
+    
+    def _linear_pm_fit(self, epochs, raoff_planet, decoff_planet, epoch_ref_ra, epoch_ref_dec, sin_phi, cos_phi, errs):
+        """
+        Performs a linear leastsq fit to determine the inferred proper motion given the stellar orbit around the barycenter
+        """
+        # Sovle y = A * x
+        # construct A matrix
+        A_pmra = cos_phi * (epochs - epoch_ref_ra) / errs
+        A_raoff = cos_phi / errs
+        A_pmdec = sin_phi * (epochs - epoch_ref_dec) / errs
+        A_decoff = sin_phi / errs
+        A_matrix = np.vstack((A_raoff, A_decoff, A_pmra, A_pmdec)).T
+
+        # construct y matrix
+        y_vec = (raoff_planet * cos_phi + decoff_planet * sin_phi)/errs
+
+        x, res, _, _ = numpy.linalg.lstsq(A_matrix, y_vec, rcond=None)
+        
+        return x
     
\ No newline at end of file

From 088e2215e7f79ac714b855abf83651cae6816101 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Tue, 5 Sep 2023 15:03:46 -0700
Subject: [PATCH 23/38] fix likelihood bug

---
 orbitize/gaia.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 75629a02..081c007d 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -253,7 +253,7 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         self.gaia_epoch_ra = entry['epoch_ra_gaia'][0]
         self.gaia_epoch_dec = entry['epoch_dec_gaia'][0]
         # read in the GOST file to get the estimated Gaia epochs and scan angles
-        gost_dat = read(gost_filepath, converters={'*':[int, float, bytes]})
+        gost_dat = read(gost_filepath, converters={'*':[int, float, bytes]}, delimiter=",")
         self.gaia_epoch = time.Time(gost_dat["ObservationTimeAtGaia[UTC]"]).decimalyear # in decimal year
         gaia_scan_theta = np.array(gost_dat["scanAngle[rad]"])
         gaia_scan_phi = gaia_scan_theta + np.pi/2
@@ -363,11 +363,9 @@ def compute_lnlike(
         # chi2 = (model_hip_hg_dpm - self.hip_hg_dpm)**2/(self.hip_hg_dpm_err)**2
         # chi2 += (model_gaia_hg_dpm - self.gaia_hg_dpm)**2/(self.gaia_hg_dpm_err)**2
 
-        chi2 = orbitize.lnlike.chi2_lnlike(self.dpm_data, self.dpm_err, self.dpm_corr, np.array([model_hip_hg_dpm, model_gaia_hg_dpm]), np.zeros(self.dpm_data.shape), [])
+        lnlike = orbitize.lnlike.chi2_lnlike(self.dpm_data, self.dpm_err, self.dpm_corr, np.array([model_hip_hg_dpm, model_gaia_hg_dpm]), np.zeros(self.dpm_data.shape), [])
 
-        lnlike = -0.5 * np.sum(chi2)
-
-        return lnlike
+        return np.sum(lnlike)
     
     def _linear_pm_fit(self, epochs, raoff_planet, decoff_planet, epoch_ref_ra, epoch_ref_dec, sin_phi, cos_phi, errs):
         """
@@ -387,4 +385,5 @@ def _linear_pm_fit(self, epochs, raoff_planet, decoff_planet, epoch_ref_ra, epoc
         x, res, _, _ = numpy.linalg.lstsq(A_matrix, y_vec, rcond=None)
         
         return x
+
     
\ No newline at end of file

From 62a3b96ed7236af317c6b8d8f757960ed24f0759 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Mon, 11 Sep 2023 13:14:23 -0500
Subject: [PATCH 24/38] fix bugs causing test to crash

---
 orbitize/gaia.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 5033afea..da15052d 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -226,7 +226,7 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
                 print("Using HGCA catalog stored in {0}".format(hgca_filepath))
 
         # grab the entry from the HGCA
-        with fits.open(hgca_filepath) as hdulist:
+        with fits.open(hgca_filepath, ignore_missing_simple=True) as hdulist:
             hgtable = hdulist[1].data
         entry = hgtable[np.where(hgtable['hip_id'] == hip_id)]
         # check we matched on a single target. mainly check if we typed hip id number incorrectly
@@ -266,7 +266,7 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
         self.gaia_epoch_ra = entry['epoch_ra_gaia'][0]
         self.gaia_epoch_dec = entry['epoch_dec_gaia'][0]
         # read in the GOST file to get the estimated Gaia epochs and scan angles
-        gost_dat = read(gost_filepath, converters={'*':[int, float, bytes]}, delimiter=",")
+        gost_dat = read(gost_filepath, converters={'*':[int, float, bytes]})
         self.gaia_epoch = time.Time(gost_dat["ObservationTimeAtGaia[UTC]"]).decimalyear # in decimal year
         gaia_scan_theta = np.array(gost_dat["scanAngle[rad]"])
         gaia_scan_phi = gaia_scan_theta + np.pi/2

From b1b48b3fb9a2726ea79ec064ba9d344c74128420 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Mon, 11 Sep 2023 13:14:35 -0500
Subject: [PATCH 25/38] udpate hgca tutorial discribing differences with IAD

---
 docs/tutorials/HGCA_tutorial.ipynb | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/docs/tutorials/HGCA_tutorial.ipynb b/docs/tutorials/HGCA_tutorial.ipynb
index e86c845f..9f8ce073 100644
--- a/docs/tutorials/HGCA_tutorial.ipynb
+++ b/docs/tutorials/HGCA_tutorial.ipynb
@@ -8,7 +8,15 @@
     "\n",
     "Jason Wang (2023)\n",
     "\n",
-    "We will demonstrate how to fit the stellar absolute astrometry from the [Hipparcos-Gaia Catalog of Accelerations (HGCA; Brandt 2021)](https://ui.adsabs.harvard.edu/abs/2021ApJS..254...42B/abstract) to help constrain the mass and orbital parameters of the companion. This is an alternative to fitting the Hipparcos IAD and Gaia astrometry. We encourage users to try both to see how similar or different the results are, as the two methods utilize the same underlying data. The implementation of fitting the HGCA here is based on [Brandt et al. 2019](https://ui.adsabs.harvard.edu/abs/2019AJ....158..140B/abstract), but utilizes the DR3 version of the HGCA. \n",
+    "We will demonstrate how to fit the stellar absolute astrometry from the [Hipparcos-Gaia Catalog of Accelerations (HGCA; Brandt 2021)](https://ui.adsabs.harvard.edu/abs/2021ApJS..254...42B/abstract) to help constrain the mass and orbital parameters of the companion. The implementation of fitting the HGCA here is based on [Brandt et al. 2019](https://ui.adsabs.harvard.edu/abs/2019AJ....158..140B/abstract), but utilizes the DR3 version of the HGCA. \n",
+    "\n",
+    "## Difference to fitting IAD directly\n",
+    "\n",
+    "This method is an alternative to fitting the Hipparcos IAD and Gaia astrometry. Instead of fitting the individual epochs of Hipparcos data, which also includes needing to fit for the position, proper motion, and parallax of the star, we are only fitting for two differential proper motions: the proper motion difference between Hipparcos and the change in position between Hipparcos and Gaia; the proper motion difference between Gaia and the change in position between Hipparcos and Gaia. This simplifies the fit, but also ignores any detectable curvature in the stellar astrometry seen by Hipparcos. For planet companion cases, the acceleration should be well within the noise of the Hipparcos measurements. \n",
+    "\n",
+    "The benefit of using this technique is that the errors should be more robust. The HGCA catalog inflates the error bars from the Hipparcos and Gaia measurements on a global scale to match the true observed scatter in the data. Additionally, there are bad epochs in the Hipparcos IAD that may not be removed. \n",
+    "\n",
+    "We encourage users to try both to see how similar or different the results are, as the two methods utilize the same underlying data. \n",
     "\n",
     "\n",
     "## Obtain the necessary data\n",

From 082cc7c6680657dd49d7f3cfc4ddcff3a57af6c5 Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Mon, 11 Sep 2023 14:15:51 -0500
Subject: [PATCH 26/38] fix HGCA catalog URL

---
 orbitize/gaia.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index da15052d..019dfb5e 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -217,7 +217,7 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
             # check orbitize.DATAIDR and download if needed
             hgca_filepath = os.path.join(DATADIR, "HGCA_vEDR3.fits")
             if not os.path.exists(hgca_filepath):
-                hgca_url = 'http://physics.ucsb.edu/~tbrandt/HGCA_vEDR3.fits'
+                hgca_url = 'https://web.physics.ucsb.edu/~tbrandt/HGCA_vEDR3.fits'
                 print("No HGCA catalog found. Downloading HGCA vEDR3 from {0} and storing into {1}.".format(hgca_url, hgca_filepath))
                 hgca_file = requests.get(hgca_url)
                 with open(hgca_filepath, 'wb') as f:

From 8519cff65f628dcca498c9969d9ac261e147169d Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Mon, 11 Sep 2023 15:00:41 -0500
Subject: [PATCH 27/38] turn off ssl certificate verification for HGCA

---
 orbitize/gaia.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 019dfb5e..41ae074b 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -219,7 +219,7 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
             if not os.path.exists(hgca_filepath):
                 hgca_url = 'https://web.physics.ucsb.edu/~tbrandt/HGCA_vEDR3.fits'
                 print("No HGCA catalog found. Downloading HGCA vEDR3 from {0} and storing into {1}.".format(hgca_url, hgca_filepath))
-                hgca_file = requests.get(hgca_url)
+                hgca_file = requests.get(hgca_url, verify=False)
                 with open(hgca_filepath, 'wb') as f:
                     f.write(hgca_file.content)
             else:

From ddca876ee662e890c7b3392f230a507d3b455626 Mon Sep 17 00:00:00 2001
From: wbalmer <williamobalmer@gmail.com>
Date: Mon, 2 Oct 2023 14:07:07 -0400
Subject: [PATCH 28/38] Plot function for PM_RA and PM_DEC vs Epoch when using
 HGCA

---
 orbitize/plot.py    | 269 +++++++++++++++++++++++++++++++++++++++++++-
 orbitize/results.py |  24 +++-
 2 files changed, 290 insertions(+), 3 deletions(-)

diff --git a/orbitize/plot.py b/orbitize/plot.py
index 3b0ba6c2..414c7aa9 100644
--- a/orbitize/plot.py
+++ b/orbitize/plot.py
@@ -11,6 +11,7 @@
 import matplotlib.pyplot as plt
 from matplotlib.collections import LineCollection
 import matplotlib.colors as colors
+from matplotlib.ticker import FormatStrFormatter
 
 from erfa import ErfaWarning
 
@@ -333,7 +334,7 @@ def plot_orbits(results, object_to_plot=1, start_mjd=51544.,
                 fig = plt.figure(figsize=(14, 6))
                 ax = plt.subplot2grid((2, 14), (0, 0), rowspan=2, colspan=6)
         else:
-            plt.set_current_figure(fig)
+            plt.figure(fig.number)
             if rv_time_series:
                 ax = plt.subplot2grid((3, 14), (0, 0), rowspan=2, colspan=6)
             else:
@@ -669,4 +670,268 @@ def plot_orbits(results, object_to_plot=1, start_mjd=51544.,
         ax2.locator_params(axis='x', nbins=6)
         ax2.locator_params(axis='y', nbins=6)
 
-    return fig
\ No newline at end of file
+    return fig
+
+def plot_propermotion(results, system, object_to_plot=1, start_mjd=44239.,
+                      periods_to_plot=1, end_year=2030.0, alpha = 0.05,
+                      num_orbits_to_plot=100, num_epochs_to_plot=100,
+                      show_colorbar=True, cmap=cmap,
+                      cbar_param=None,
+                      # fig=None
+                     ):
+    """
+    Plots the proper motion of a host star as induced by a companion for
+    one orbital period for a select number of fitted orbits
+    for a given object, with line segments colored according to a given
+    parameter (most informative is usually mass of companion)
+
+    Args:
+        system (object): orbitize.system object with a HGCALogProb passed to system.gaia
+        object_to_plot (int): which object to plot (default: 1)
+        start_mjd (float): MJD in which to start plotting orbits (default: 51544,
+            the year 2000)
+        periods_to_plot (int): number of periods to plot (default: 1)
+        end_year (float): decimal year specifying when to stop plotting orbit
+            tracks in the Sep/PA panels (default: 2025.0).
+        alpha (float): transparency of lines (default: 0.05)
+        num_orbits_to_plot (int): number of orbits to plot (default: 100)
+        num_epochs_to_plot (int): number of points to plot per orbit (default: 100)
+        show_colorbar (Boolean): Displays colorbar to the right of the plot [True]
+        cmap (matplotlib.cm.ColorMap): color map to use for making orbit tracks
+            (default: modified Purples_r)
+        cbar_param (string): options are the following: 'sma1', 'ecc1', 'inc1', 'aop1',
+            'pan1', 'tau1', 'plx', 'm0', 'm1', etc. Number can be switched out. Default is None.
+        fig (matplotlib.pyplot.Figure): optionally include a predefined Figure object to plot the orbit on.
+            Most users will not need this keyword.
+
+    Return:
+        ``matplotlib.pyplot.Figure``: the orbit plot if input is valid, ``None`` otherwise
+
+
+    (written): William Balmer (2023), based on plot_orbits by Sarah Blunt and Henry Ngo
+
+    """
+
+    if Time(start_mjd, format='mjd').decimalyear >= end_year:
+        raise ValueError('start_mjd keyword date must be less than end_year keyword date.')
+
+    if object_to_plot > results.num_secondary_bodies:
+        raise ValueError("Only {0} secondary bodies being fit. Requested to plot body {1} which is out of range".format(results.num_secondary_bodies, object_to_plot))
+
+    if object_to_plot == 0:
+        raise ValueError("Plotting the primary's orbit is currently unsupported. Stay tuned.")
+
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore', ErfaWarning)
+
+        data = results.data[results.data['object'] == object_to_plot]
+        possible_cbar_params = [
+            'sma',
+            'ecc',
+            'inc',
+            'aop'
+            'pan',
+            'tau',
+            'plx',
+            'm0',
+            'm1'
+        ]
+
+        if cbar_param is None:
+            pass
+        elif cbar_param[0:3] in possible_cbar_params:
+            index = results.param_idx[cbar_param]
+        else:
+            raise Exception(
+                "Invalid input; acceptable inputs include 'Epoch [year]', 'plx', 'sma1', 'ecc1', 'inc1', 'aop1', 'pan1', 'tau1', 'sma2', 'ecc2', ...)"
+            )
+        # Select random indices for plotted orbit
+        num_orbits = len(results.post[:, 0])
+        if num_orbits_to_plot > num_orbits:
+            num_orbits_to_plot = num_orbits
+        choose = np.random.randint(0, high=num_orbits, size=num_orbits_to_plot)
+
+        # Get posteriors from random indices
+        standard_post = []
+        if results.sampler_name == 'MCMC':
+            # Convert the randomly chosen posteriors to standard keplerian set
+            for i in np.arange(num_orbits_to_plot):
+                orb_ind = choose[i]
+                param_set = np.copy(results.post[orb_ind])
+                standard_post.append(results.basis.to_standard_basis(param_set))
+        else: # For OFTI, posteriors are already converted
+            for i in np.arange(num_orbits_to_plot):
+                orb_ind = choose[i]
+                standard_post.append(results.post[orb_ind])
+
+        standard_post = np.array(standard_post)
+
+        sma = standard_post[:, results.standard_param_idx['sma{}'.format(object_to_plot)]]
+        ecc = standard_post[:, results.standard_param_idx['ecc{}'.format(object_to_plot)]]
+        inc = standard_post[:, results.standard_param_idx['inc{}'.format(object_to_plot)]]
+        aop = standard_post[:, results.standard_param_idx['aop{}'.format(object_to_plot)]]
+        pan = standard_post[:, results.standard_param_idx['pan{}'.format(object_to_plot)]]
+        tau = standard_post[:, results.standard_param_idx['tau{}'.format(object_to_plot)]]
+        plx = standard_post[:, results.standard_param_idx['plx']]
+
+        # Then, get the other parameters
+        if 'mtot' in results.labels:
+            mtot = standard_post[:, results.standard_param_idx['mtot']]
+        elif 'm0' in results.labels:
+            m0 = standard_post[:, results.standard_param_idx['m0']]
+            m1 = standard_post[:, results.standard_param_idx['m{}'.format(object_to_plot)]]
+            mtot = m0 + m1
+
+        raoff = np.zeros((num_orbits_to_plot, num_epochs_to_plot))
+        deoff = np.zeros((num_orbits_to_plot, num_epochs_to_plot))
+        vz_star = np.zeros((num_orbits_to_plot, num_epochs_to_plot))
+        epochs = np.zeros((num_orbits_to_plot, num_epochs_to_plot))
+
+        # Loop through each orbit to plot and calcualte ra/dec offsets for all points in orbit
+        # Need this loops since epochs[] vary for each orbit, unless we want to just plot the same time period for all orbits
+        for i in np.arange(num_orbits_to_plot):
+            # Compute period (from Kepler's third law)
+            period = np.sqrt(4*np.pi**2.0*(sma*u.AU)**3/(consts.G*(mtot*u.Msun)))
+            period = period.to(u.day).value
+
+            # Create an epochs array to plot num_epochs_to_plot points over one orbital period
+            epochs[i, :] = np.linspace(start_mjd, float(
+                start_mjd+(period[i]*periods_to_plot)), num_epochs_to_plot)
+
+            # Calculate ra/dec offsets for all epochs of this orbit
+            raoff0, deoff0, _ = kepler.calc_orbit(
+                epochs[i, :], sma[i], ecc[i], inc[i], aop[i], pan[i],
+                tau[i], plx[i], mtot[i], tau_ref_epoch=results.tau_ref_epoch
+            )
+
+            raoff[i, :] = raoff0
+            deoff[i, :] = deoff0
+
+        # Create a linearly increasing colormap for our range of epochs
+        if cbar_param is not None:
+            cbar_param_arr = results.post[:, index]
+            norm = mpl.colors.Normalize(vmin=np.min(cbar_param_arr),
+                                        vmax=np.max(cbar_param_arr))
+
+        elif cbar_param is None:
+
+            norm = mpl.colors.Normalize()
+
+        # Create figure for orbit plots
+        fig, axs = plt.subplots(1, 2, figsize=(8,4), facecolor='white')
+
+        # Plot each orbit (each segment between two points coloured using colormap)
+        for i in np.arange(num_orbits_to_plot):
+            epoch_in_yr = Time(epochs[i,:], format='mjd').decimalyear
+            # masses (in same units, solar)
+            m_b = standard_post[:, results.param_idx['m1']][i]
+            m_a = standard_post[:, results.param_idx['m0']][i]
+            # dt
+            timestep = epoch_in_yr[1]-epoch_in_yr[0]
+            # dra/dt and ddec/dt
+            ddec_b = np.gradient(deoff[i,:], timestep) # in mas/yr
+            dec_b_radian = deoff[i,:]*(2.7777778e-7)*(0.017453293) # mas -> deg -> radian
+            ra_b = raoff[i,:]
+            rastar_b = ra_b*np.cos(dec_b_radian) # in mas
+            drastar_b = np.gradient(rastar_b, timestep) # in mas/yr
+
+            # convert to dRA^star_star (lol) and dDec_star
+            mass_ratio_ = (-1*m_b/(m_a+m_b))
+            ddec_a = ddec_b * mass_ratio_
+            drastar_a = drastar_b * mass_ratio_
+
+            if cbar_param is not None:
+                color = cmap(norm(standard_post[:, results.param_idx[cbar_param]][i]))
+            else:
+                color = 'k'
+
+            axs[0].plot(epoch_in_yr,drastar_a+system.gaia.hg_pm[0],
+                        color=color,
+                        alpha=alpha,
+                        zorder=0
+                    )
+            axs[1].plot(epoch_in_yr,ddec_a+system.gaia.hg_pm[1],
+                        color=color,
+                        alpha=alpha,
+                        zorder=0
+                    )
+
+
+
+    axs[0].set_xlim(1980,2030)
+    axs[0].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+    axs[1].set_xlabel('Epoch')
+
+    axs[0].set_ylabel(r'$\mu_\alpha^*$ [mas/yr]')
+
+    axs[0].errorbar(np.nanmedian(system.gaia.hipparcos_epoch),
+                    system.gaia.hip_pm[0],
+                    yerr=system.gaia.hip_pm_err[0],
+                    zorder=30,
+                    mec='k',
+                    fmt='s', color='cornflowerblue')
+
+    hgca_epoch = (system.gaia.gaia_epoch_ra+np.nanmedian(system.gaia.hipparcos_epoch))/2
+    hgca_epoch_err = (system.gaia.gaia_epoch_ra-np.nanmedian(system.gaia.hipparcos_epoch))/2
+
+    axs[0].errorbar(hgca_epoch,
+                    system.gaia.hg_pm[0],
+                    xerr=hgca_epoch_err,
+                    yerr=system.gaia.hg_pm_err[0],
+                    zorder=30,
+                    mec='k',
+                    fmt='^', color='#6280D6')
+
+
+    axs[0].errorbar(system.gaia.gaia_epoch_ra,
+                    system.gaia.gaia_pm[0],
+                    yerr=system.gaia.gaia_pm_err[0],
+                    zorder=30,
+                    mec='k',
+                    fmt='o', color='#5f61b4')
+
+
+    axs[1].set_xlim(1980,2030)
+    axs[1].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+
+    axs[1].errorbar(np.nanmedian(system.gaia.hipparcos_epoch),
+                    system.gaia.hip_pm[1],
+                    yerr=system.gaia.hip_pm_err[1],
+                    zorder=30,
+                    mec='k',
+                    fmt='s', color='cornflowerblue', label='Hip.')
+
+    axs[1].errorbar(hgca_epoch,
+                    system.gaia.hg_pm[1],
+                    xerr=hgca_epoch_err,
+                    yerr=system.gaia.hg_pm_err[1],
+                    zorder=30,
+                    mec='k',
+                    fmt='^', color='#6280D6', label='H-G')
+
+    axs[1].errorbar(system.gaia.gaia_epoch_ra,
+                    system.gaia.gaia_pm[1],
+                    yerr=system.gaia.gaia_pm_err[1],
+                    zorder=30,
+                    mec='k',
+                    fmt='o', color='#5f61b4', label='Gaia')
+
+    axs[1].set_ylabel(r'$\mu_\delta$ [mas/yr]')
+    axs[1].set_xlabel('Epoch')
+    axs[0].set_xlabel('Epoch')
+
+    cbar_ax = fig.add_axes([1.03, 0.15, 0.03, 0.80])
+
+    cbar = mpl.colorbar.ColorbarBase(
+    cbar_ax, cmap=cmap, norm=norm, orientation='vertical',
+        label=cbar_param
+    )
+
+    axs[0].set_rasterization_zorder(1)
+    axs[1].set_rasterization_zorder(1)
+
+    axs[1].legend()
+
+    plt.tight_layout()
+
+    return fig
diff --git a/orbitize/results.py b/orbitize/results.py
index d1e7accf..dee48b8d 100644
--- a/orbitize/results.py
+++ b/orbitize/results.py
@@ -354,5 +354,27 @@ def plot_orbits(self, object_to_plot=1, start_mjd=51544.,
             plot_errorbars=plot_errorbars, fig=fig
         )
 
+    def plot_propermotion(self,
+                          # system,
+                          object_to_plot=1, start_mjd=44239.,
+                          periods_to_plot=1, end_year=2030.0, alpha = 0.05,
+                          num_orbits_to_plot=100, num_epochs_to_plot=100,
+                          show_colorbar=True,
+                          cmap=orbitize.plot.cmap,
+                          cbar_param=None,
+                    # fig=None
+        ):
+        """
+        Wrapper for orbitize.plot.plot_propermotion
+        """
 
-
+        return orbitize.plot.plot_propermotion(self, self.system,
+                        object_to_plot=object_to_plot, start_mjd=start_mjd,
+                        periods_to_plot=periods_to_plot, end_year=end_year,
+                        alpha = alpha, num_orbits_to_plot=num_orbits_to_plot,
+                        num_epochs_to_plot=num_epochs_to_plot,
+                        show_colorbar=show_colorbar,
+                        cmap=cmap,
+                        cbar_param=cbar_param,
+                        # fig=fig
+                        )

From 076ea94dae48d2d7e713028b22a541790310f38b Mon Sep 17 00:00:00 2001
From: wbalmer <williamobalmer@gmail.com>
Date: Mon, 2 Oct 2023 14:22:12 -0400
Subject: [PATCH 29/38] Updated tutorial with plot function.

---
 docs/tutorials/HGCA_tutorial.ipynb | 96 ++++++++++++++++++++++++++----
 1 file changed, 85 insertions(+), 11 deletions(-)

diff --git a/docs/tutorials/HGCA_tutorial.ipynb b/docs/tutorials/HGCA_tutorial.ipynb
index 9f8ce073..af0020e1 100644
--- a/docs/tutorials/HGCA_tutorial.ipynb
+++ b/docs/tutorials/HGCA_tutorial.ipynb
@@ -35,9 +35,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using HGCA catalog stored in /Users/wbalmer/orbitize/orbitize/example_data/HGCA_vEDR3.fits\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "from orbitize import DATADIR, hipparcos, gaia\n",
@@ -65,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,9 +112,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting Burn in\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/wbalmer/orbitize/orbitize/priors.py:463: RuntimeWarning: invalid value encountered in log\n",
+      "  lnprob = np.log(np.sin(element_array)/normalization)\n",
+      "/Users/wbalmer/orbitize/orbitize/priors.py:354: RuntimeWarning: invalid value encountered in log\n",
+      "  lnprob = -np.log((element_array*normalizer))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Burn in complete. Sampling posterior now.\n",
+      "100/100 steps completed\n",
+      "Run complete\n"
+     ]
+    }
+   ],
    "source": [
     "from orbitize import sampler\n",
     "\n",
@@ -125,13 +161,52 @@
     "\n",
     "this_sampler.results.save_results(\"demo_hgca.hdf5\")\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot proper motions from the orbit fit over the HGCA observations\n",
+    "\n",
+    "William Balmer (2023)\n",
+    "\n",
+    "Now, you can plot the result of your fit using the  `orbitize.plot.plot_propermotion` function directly, or it's wrapper within the `sampler.results` object. The function is similar to `orbitize.plot.plot_orbits` that you may already be familiar with. These plots show the H-G proper motion (with a large x-axis error bar) in addition to the two \"real\" proper motion measurements from Hipparchos and Gaia."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/wbalmer/orbitize/orbitize/plot.py:935: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "  plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure = this_sampler.results.plot_propermotion(cbar_param='m1', alpha=0.1, periods_to_plot=3)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "exog",
    "language": "python",
-   "name": "python3"
+   "name": "exog"
   },
   "language_info": {
    "codemirror_mode": {
@@ -143,10 +218,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  },
-  "orig_nbformat": 4
+   "version": "3.10.5"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From 248ea9643dd73384507a621946f5fd9640f89361 Mon Sep 17 00:00:00 2001
From: wbalmer <williamobalmer@gmail.com>
Date: Mon, 2 Oct 2023 19:08:53 -0400
Subject: [PATCH 30/38] apply caution as per Jason's warning

---
 orbitize/plot.py | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/orbitize/plot.py b/orbitize/plot.py
index 414c7aa9..f69f268d 100644
--- a/orbitize/plot.py
+++ b/orbitize/plot.py
@@ -676,7 +676,7 @@ def plot_propermotion(results, system, object_to_plot=1, start_mjd=44239.,
                       periods_to_plot=1, end_year=2030.0, alpha = 0.05,
                       num_orbits_to_plot=100, num_epochs_to_plot=100,
                       show_colorbar=True, cmap=cmap,
-                      cbar_param=None,
+                      cbar_param=None, tight_layout=False,
                       # fig=None
                      ):
     """
@@ -685,6 +685,11 @@ def plot_propermotion(results, system, object_to_plot=1, start_mjd=44239.,
     for a given object, with line segments colored according to a given
     parameter (most informative is usually mass of companion)
 
+    Important Note: These plotted trajectories aren't what are fitting in the
+    likelihood evaluation for the HGCA runs. The implementation forward models
+    the Hip/Gaia measurements per epoch and infers the differential proper motions.
+    This plot is given only for the purposes of an approximate visualization.
+
     Args:
         system (object): orbitize.system object with a HGCALogProb passed to system.gaia
         object_to_plot (int): which object to plot (default: 1)
@@ -701,6 +706,7 @@ def plot_propermotion(results, system, object_to_plot=1, start_mjd=44239.,
             (default: modified Purples_r)
         cbar_param (string): options are the following: 'sma1', 'ecc1', 'inc1', 'aop1',
             'pan1', 'tau1', 'plx', 'm0', 'm1', etc. Number can be switched out. Default is None.
+        tight_layout (bool): apply plt.tight_layout function?
         fig (matplotlib.pyplot.Figure): optionally include a predefined Figure object to plot the orbit on.
             Most users will not need this keyword.
 
@@ -932,6 +938,13 @@ def plot_propermotion(results, system, object_to_plot=1, start_mjd=44239.,
 
     axs[1].legend()
 
-    plt.tight_layout()
+    notestring = "Important Note: These plotted trajectories aren't what are fitting in the \n"+
+    "likelihood evaluation for the HGCA runs. The implementation forward models \n"+
+    "the Hip/Gaia measurements per epoch and infers the differential proper motions. \n"+
+    "This plot is given only for the purposes of an approximate visualization."
+    print(notestring)
+
+    if tight_layout:
+        plt.tight_layout()
 
     return fig

From 40bf993c4591db24f5e38d79db7f7f33642e95bb Mon Sep 17 00:00:00 2001
From: wbalmer <williamobalmer@gmail.com>
Date: Tue, 3 Oct 2023 12:19:12 -0400
Subject: [PATCH 31/38] correct error in string handling when printing warning
 (oops!)

---
 orbitize/plot.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/orbitize/plot.py b/orbitize/plot.py
index f69f268d..21aabea2 100644
--- a/orbitize/plot.py
+++ b/orbitize/plot.py
@@ -938,11 +938,10 @@ def plot_propermotion(results, system, object_to_plot=1, start_mjd=44239.,
 
     axs[1].legend()
 
-    notestring = "Important Note: These plotted trajectories aren't what are fitting in the \n"+
-    "likelihood evaluation for the HGCA runs. The implementation forward models \n"+
-    "the Hip/Gaia measurements per epoch and infers the differential proper motions. \n"+
-    "This plot is given only for the purposes of an approximate visualization."
-    print(notestring)
+    print("Important Note: These plotted trajectories aren't what are fitting in the \n",
+    "likelihood evaluation for the HGCA runs. The implementation forward models \n",
+    "the Hip/Gaia measurements per epoch and infers the differential proper motions. \n",
+    "This plot is given only for the purposes of an approximate visualization.")
 
     if tight_layout:
         plt.tight_layout()

From 46451c8d4ae8107dfc9f273c201cb523066774b4 Mon Sep 17 00:00:00 2001
From: wbalmer <williamobalmer@gmail.com>
Date: Tue, 3 Oct 2023 12:33:15 -0400
Subject: [PATCH 32/38] Update caution text in print and tuto nb as per Jason's
 explanation

---
 docs/tutorials/HGCA_tutorial.ipynb | 20 ++++++++++++--------
 orbitize/plot.py                   |  8 ++++----
 2 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/docs/tutorials/HGCA_tutorial.ipynb b/docs/tutorials/HGCA_tutorial.ipynb
index af0020e1..65539b24 100644
--- a/docs/tutorials/HGCA_tutorial.ipynb
+++ b/docs/tutorials/HGCA_tutorial.ipynb
@@ -126,10 +126,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/wbalmer/orbitize/orbitize/priors.py:463: RuntimeWarning: invalid value encountered in log\n",
-      "  lnprob = np.log(np.sin(element_array)/normalization)\n",
       "/Users/wbalmer/orbitize/orbitize/priors.py:354: RuntimeWarning: invalid value encountered in log\n",
-      "  lnprob = -np.log((element_array*normalizer))\n"
+      "  lnprob = -np.log((element_array*normalizer))\n",
+      "/Users/wbalmer/orbitize/orbitize/priors.py:463: RuntimeWarning: invalid value encountered in log\n",
+      "  lnprob = np.log(np.sin(element_array)/normalization)\n"
      ]
     },
     {
@@ -170,7 +170,9 @@
     "\n",
     "William Balmer (2023)\n",
     "\n",
-    "Now, you can plot the result of your fit using the  `orbitize.plot.plot_propermotion` function directly, or it's wrapper within the `sampler.results` object. The function is similar to `orbitize.plot.plot_orbits` that you may already be familiar with. These plots show the H-G proper motion (with a large x-axis error bar) in addition to the two \"real\" proper motion measurements from Hipparchos and Gaia."
+    "Now, you can plot the result of your fit using the  `orbitize.plot.plot_propermotion` function directly, or it's wrapper within the `sampler.results` object. The function is similar to `orbitize.plot.plot_orbits` that you may already be familiar with. These plots show the H-G proper motion (with a large x-axis error bar) in addition to the two \"real\" proper motion measurements from Hipparchos and Gaia.\n",
+    "\n",
+    "Note that the HGCALogprob fits for the time-averaged proper motions, and not the instantaneous proper motions that are shown in this visualization. This is a subtle point, but important, because the data points show over the orbits here are not exactly what is driving the MCMC solver towards the most likely orbits."
    ]
   },
   {
@@ -179,16 +181,18 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/Users/wbalmer/orbitize/orbitize/plot.py:935: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
-      "  plt.tight_layout()\n"
+      "Important Note of Caution: the orbitize! implementation of the HGCA \n",
+      " fits for the time-averaged proper motions, and not the instantaneous proper \n",
+      " motions that are being plotted here. This plot is provided only for the \n",
+      " purpose of an approximate check on the fit.\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x288 with 3 Axes>"
       ]
diff --git a/orbitize/plot.py b/orbitize/plot.py
index 21aabea2..ec68bf71 100644
--- a/orbitize/plot.py
+++ b/orbitize/plot.py
@@ -938,10 +938,10 @@ def plot_propermotion(results, system, object_to_plot=1, start_mjd=44239.,
 
     axs[1].legend()
 
-    print("Important Note: These plotted trajectories aren't what are fitting in the \n",
-    "likelihood evaluation for the HGCA runs. The implementation forward models \n",
-    "the Hip/Gaia measurements per epoch and infers the differential proper motions. \n",
-    "This plot is given only for the purposes of an approximate visualization.")
+    print("Important Note of Caution: the orbitize! implementation of the HGCA \n",
+    "fits for the time-averaged proper motions, and not the instantaneous proper \n",
+    "motions that are being plotted here. This plot is provided only for the \n",
+    "purpose of an approximate check on the fit.")
 
     if tight_layout:
         plt.tight_layout()

From 5e842ce76c00d9a9844ab063b98128657078bbaf Mon Sep 17 00:00:00 2001
From: wbalmer <williamobalmer@gmail.com>
Date: Mon, 8 Jan 2024 13:45:41 -0500
Subject: [PATCH 33/38] update hgca edr3 to stable vizier url, safer fits.open

---
 orbitize/gaia.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 41ae074b..4272dac8 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -217,7 +217,7 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
             # check orbitize.DATAIDR and download if needed
             hgca_filepath = os.path.join(DATADIR, "HGCA_vEDR3.fits")
             if not os.path.exists(hgca_filepath):
-                hgca_url = 'https://web.physics.ucsb.edu/~tbrandt/HGCA_vEDR3.fits'
+                hgca_url = 'https://cdsarc.cds.unistra.fr/ftp/J/ApJS/254/42/HGCA_vEDR3.fits'
                 print("No HGCA catalog found. Downloading HGCA vEDR3 from {0} and storing into {1}.".format(hgca_url, hgca_filepath))
                 hgca_file = requests.get(hgca_url, verify=False)
                 with open(hgca_filepath, 'wb') as f:
@@ -226,7 +226,7 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
                 print("Using HGCA catalog stored in {0}".format(hgca_filepath))
 
         # grab the entry from the HGCA
-        with fits.open(hgca_filepath, ignore_missing_simple=True) as hdulist:
+        with fits.open(hgca_filepath, ignore_missing_simple=True, , ignore_missing_end=True) as hdulist:
             hgtable = hdulist[1].data
         entry = hgtable[np.where(hgtable['hip_id'] == hip_id)]
         # check we matched on a single target. mainly check if we typed hip id number incorrectly

From 4caf29b6ca1a5715da5119252adf2d136b0590be Mon Sep 17 00:00:00 2001
From: Jason Wang <jason.wang@outlook.com>
Date: Mon, 8 Jan 2024 14:00:00 -0600
Subject: [PATCH 34/38] fix syntax issue

---
 orbitize/gaia.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/orbitize/gaia.py b/orbitize/gaia.py
index 4272dac8..ac313732 100644
--- a/orbitize/gaia.py
+++ b/orbitize/gaia.py
@@ -226,7 +226,7 @@ def __init__(self, hip_id, hiplogprob, gost_filepath, hgca_filepath=None):
                 print("Using HGCA catalog stored in {0}".format(hgca_filepath))
 
         # grab the entry from the HGCA
-        with fits.open(hgca_filepath, ignore_missing_simple=True, , ignore_missing_end=True) as hdulist:
+        with fits.open(hgca_filepath, ignore_missing_simple=True, ignore_missing_end=True) as hdulist:
             hgtable = hdulist[1].data
         entry = hgtable[np.where(hgtable['hip_id'] == hip_id)]
         # check we matched on a single target. mainly check if we typed hip id number incorrectly

From d5fe676dd1a36a8ce5f5dea0ca8d0762e983788f Mon Sep 17 00:00:00 2001
From: Sarah Blunt <sblunt@caltech.edu>
Date: Thu, 18 Jan 2024 16:53:49 -0800
Subject: [PATCH 35/38] pep8 cleanup because I decided I care about that lol

---
 tests/test_gaia.py | 223 +++++++++++++++++++++++++++------------------
 1 file changed, 132 insertions(+), 91 deletions(-)

diff --git a/tests/test_gaia.py b/tests/test_gaia.py
index bc6031db..65815eba 100644
--- a/tests/test_gaia.py
+++ b/tests/test_gaia.py
@@ -3,79 +3,90 @@
 from orbitize import DATADIR
 from orbitize import hipparcos, gaia, basis, system, read_input, sampler, results
 
+
 def test_dr2_edr3():
     """
     Test that both DR2 and eDR3 retrieval gives ballpark similar values for
     beta Pic
     """
-    hip_num = '027321' # beta Pic
+    hip_num = "027321"  # beta Pic
     edr3_num = 4792774797545800832
     dr2_number = 4792774797545105664
 
     num_secondary_bodies = 1
-    path_to_iad_file = '{}HIP{}.d'.format(DATADIR, hip_num)
+    path_to_iad_file = "{}HIP{}.d".format(DATADIR, hip_num)
 
     myHip = hipparcos.HipparcosLogProb(path_to_iad_file, hip_num, num_secondary_bodies)
 
-    dr3Gaia = gaia.GaiaLogProb(
-        edr3_num, myHip, dr='edr3'
-    )
-    dr2Gaia = gaia.GaiaLogProb(
-        dr2_number, myHip, dr='dr2'
-    )
+    dr3Gaia = gaia.GaiaLogProb(edr3_num, myHip, dr="edr3")
+    dr2Gaia = gaia.GaiaLogProb(dr2_number, myHip, dr="dr2")
 
-    assert np.isclose(dr2Gaia.ra, dr3Gaia.ra, atol=0.1) # abs tolerance in degrees
+    assert np.isclose(dr2Gaia.ra, dr3Gaia.ra, atol=0.1)  # abs tolerance in degrees
 
 
 def test_system_setup():
     """
     Test that a System object with Hipparcos and Gaia is initialized correctly
     """
-    hip_num = '027321' # beta Pic
+    hip_num = "027321"  # beta Pic
     edr3_num = 4792774797545800832
     num_secondary_bodies = 1
-    path_to_iad_file = '{}HIP{}.d'.format(DATADIR, hip_num)
+    path_to_iad_file = "{}HIP{}.d".format(DATADIR, hip_num)
 
     myHip = hipparcos.HipparcosLogProb(path_to_iad_file, hip_num, num_secondary_bodies)
-    myGaia = gaia.GaiaLogProb(
-        edr3_num, myHip, dr='edr3'
-    )
+    myGaia = gaia.GaiaLogProb(edr3_num, myHip, dr="edr3")
 
-    input_file = os.path.join(DATADIR, 'betaPic.csv')
+    input_file = os.path.join(DATADIR, "betaPic.csv")
     plx = 51.5
 
     num_secondary_bodies = 1
     data_table = read_input.read_file(input_file)
 
     betaPic_system = system.System(
-        num_secondary_bodies, data_table, 1.75, plx, hipparcos_IAD=myHip, 
-        gaia=myGaia, fit_secondary_mass=True, mass_err=0.01, 
-        plx_err=0.01
+        num_secondary_bodies,
+        data_table,
+        1.75,
+        plx,
+        hipparcos_IAD=myHip,
+        gaia=myGaia,
+        fit_secondary_mass=True,
+        mass_err=0.01,
+        plx_err=0.01,
     )
 
     assert betaPic_system.labels == [
-       'sma1', 'ecc1', 'inc1', 'aop1', 'pan1', 'tau1', 'plx', 'pm_ra', 'pm_dec', 
-       'alpha0', 'delta0', 'm1', 'm0'
-   ]
+        "sma1",
+        "ecc1",
+        "inc1",
+        "aop1",
+        "pan1",
+        "tau1",
+        "plx",
+        "pm_ra",
+        "pm_dec",
+        "alpha0",
+        "delta0",
+        "m1",
+        "m0",
+    ]
 
     assert betaPic_system.fit_secondary_mass
     assert betaPic_system.track_planet_perturbs
 
+
 def test_valueerror():
     """
     Check that if I don't say dr2 or edr3, I get a value error
     """
-    hip_num = '027321' # beta Pic
+    hip_num = "027321"  # beta Pic
     edr3_num = 4792774797545800832
     num_secondary_bodies = 1
-    path_to_iad_file = '{}HIP{}.d'.format(DATADIR, hip_num)
+    path_to_iad_file = "{}HIP{}.d".format(DATADIR, hip_num)
 
     myHip = hipparcos.HipparcosLogProb(path_to_iad_file, hip_num, num_secondary_bodies)
     try:
-        myGaia = gaia.GaiaLogProb(
-            edr3_num, myHip, dr='dr3'
-        )
-        assert False, 'Test failed!'
+        myGaia = gaia.GaiaLogProb(edr3_num, myHip, dr="dr3")
+        assert False, "Test failed!"
     except ValueError:
         pass
 
@@ -104,70 +115,77 @@ def test_orbit_calculation():
     pm_a = 0
     pm_d = 0
 
-    plx = 100 # [mas]
+    plx = 100  # [mas]
     m0 = 1
     m1 = 1
     a0 = 0
     d0 = 0
 
-    hip_num = '027321' # beta Pic
+    hip_num = "027321"  # beta Pic
     edr3_num = 4792774797545800832
     num_secondary_bodies = 1
-    path_to_iad_file = '{}HIP{}.d'.format(DATADIR, hip_num)
+    path_to_iad_file = "{}HIP{}.d".format(DATADIR, hip_num)
 
     myHip = hipparcos.HipparcosLogProb(path_to_iad_file, hip_num, num_secondary_bodies)
-    myGaia = gaia.GaiaLogProb(
-        edr3_num, myHip, dr='edr3'
-    )
+    myGaia = gaia.GaiaLogProb(edr3_num, myHip, dr="edr3")
 
     param_idx = {
-        'sma1':0, 'ecc1':1, 'inc1':2, 'aop1':3,'pan1':4, 'tau1':5, 'plx':6,
-        'm0':7, 'm1':8, 'alpha0':9, 'delta0':10, 'pm_ra':11, 'pm_dec':12
+        "sma1": 0,
+        "ecc1": 1,
+        "inc1": 2,
+        "aop1": 3,
+        "pan1": 4,
+        "tau1": 5,
+        "plx": 6,
+        "m0": 7,
+        "m1": 8,
+        "alpha0": 9,
+        "delta0": 10,
+        "pm_ra": 11,
+        "pm_dec": 12,
     }
 
-
     # Case 1: only proper motion explains Gaia-Hip offset
-    pm_a = 100 # [mas/yr]
-    pm_d = 100 # [mas/yr]
-    sma = 1e-17 
+    pm_a = 100  # [mas/yr]
+    pm_d = 100  # [mas/yr]
+    sma = 1e-17
 
-    raoff  = np.zeros((2, 1))
+    raoff = np.zeros((2, 1))
     deoff = np.zeros((2, 1))
 
     myGaia.ra = myHip.alpha0 + (
-        myGaia.mas2deg * pm_a * (myGaia.gaia_epoch - myGaia.hipparcos_epoch) / 
-        np.cos(np.radians(myHip.delta0)) 
+        myGaia.mas2deg
+        * pm_a
+        * (myGaia.gaia_epoch - myGaia.hipparcos_epoch)
+        / np.cos(np.radians(myHip.delta0))
     )
     myGaia.dec = myHip.delta0 + (
         myGaia.mas2deg * pm_d * (myGaia.gaia_epoch - myGaia.hipparcos_epoch)
     )
     test_samples = [sma, ecc, inc, aop, pan, tau, plx, m0, m1, a0, d0, pm_a, pm_d]
 
-    lnlike = myGaia.compute_lnlike(
-        raoff, deoff, test_samples, param_idx
-    )
+    lnlike = myGaia.compute_lnlike(raoff, deoff, test_samples, param_idx)
 
     assert np.isclose(np.exp(lnlike), 1)
 
     # Case 2: only H0 offset explains Gaia-Hip offset
-    test_samples[param_idx['pm_dec']] = 0
-    test_samples[param_idx['pm_ra']] = 0
-    a0 = 100; d0 = 100
-    test_samples[param_idx['alpha0']] = a0 # [mas]
-    test_samples[param_idx['delta0']] = d0 # [mas]
-
-    myGaia.ra = myHip.alpha0 + myGaia.mas2deg * a0 / np.cos(np.radians(myHip.delta0)) 
+    test_samples[param_idx["pm_dec"]] = 0
+    test_samples[param_idx["pm_ra"]] = 0
+    a0 = 100
+    d0 = 100
+    test_samples[param_idx["alpha0"]] = a0  # [mas]
+    test_samples[param_idx["delta0"]] = d0  # [mas]
+
+    myGaia.ra = myHip.alpha0 + myGaia.mas2deg * a0 / np.cos(np.radians(myHip.delta0))
     myGaia.dec = myHip.delta0 + myGaia.mas2deg * d0
 
-    lnlike = myGaia.compute_lnlike(
-        raoff, deoff, test_samples, param_idx
-    )
+    lnlike = myGaia.compute_lnlike(raoff, deoff, test_samples, param_idx)
 
     assert np.isclose(np.exp(lnlike), 1)
 
     # Case 3: only orbital motion explains Gaia-Hip offset
-    test_samples[param_idx['alpha0']] = 0
-    test_samples[param_idx['delta0']] = 0
+    test_samples[param_idx["alpha0"]] = 0
+    test_samples[param_idx["delta0"]] = 0
 
     mas2arcsec = 1e-3
     deg2arcsec = 3600
@@ -175,65 +193,83 @@ def test_orbit_calculation():
     myGaia.ra = myHip.alpha0
     myGaia.dec = myHip.delta0 + 1
 
-    sma = 2 * (myGaia.dec - myHip.delta0) * deg2arcsec * (plx * mas2arcsec) # [au]
-    per = 2 * (myGaia.gaia_epoch - myGaia.hipparcos_epoch) # [yr]
+    sma = 2 * (myGaia.dec - myHip.delta0) * deg2arcsec * (plx * mas2arcsec)  # [au]
+    per = 2 * (myGaia.gaia_epoch - myGaia.hipparcos_epoch)  # [yr]
     mtot = sma**3 / per**2
 
-    test_samples[param_idx['sma1']] = sma
-    test_samples[param_idx['m0']] = mtot/2
-    test_samples[param_idx['m1']] = mtot/2
+    test_samples[param_idx["sma1"]] = sma
+    test_samples[param_idx["m0"]] = mtot / 2
+    test_samples[param_idx["m1"]] = mtot / 2
 
     # passes through peri (+sma decl for e=0 orbits) at Hipparcos epoch
     # -> @ Gaia epoch, primary should be at +sma decl
     tau = basis.tp_to_tau(myGaia.hipparcos_epoch, 58849, per)
-    test_samples[param_idx['tau1']] = tau
+    test_samples[param_idx["tau1"]] = tau
 
     # choose sma and mass so that Hipparcos/Gaia difference is only due to orbit.
-    deoff[1,:] = (myGaia.dec - myHip.delta0) / myGaia.mas2deg
-    deoff[0,:] = 0
-    lnlike = myGaia.compute_lnlike(
-        raoff, deoff, test_samples, param_idx
-    )
+    deoff[1, :] = (myGaia.dec - myHip.delta0) / myGaia.mas2deg
+    deoff[0, :] = 0
+    lnlike = myGaia.compute_lnlike(raoff, deoff, test_samples, param_idx)
 
     assert np.isclose(np.exp(lnlike), 1)
 
+
 def test_hgca():
     """
     Tests that the HGCA module works for beta Pic b.
 
-    Checks can download HGCA catalog if not available, and checks GRAVITY Collaboration et al. 2020 
+    Checks can download HGCA catalog if not available, and checks GRAVITY Collaboration et al. 2020
     orbital parameters against the data
     """
     iad_filepath = os.path.join(DATADIR, "HIP027321.d")
     gost_filepath = os.path.join(DATADIR, "gaia_edr3_betpic_epochs.csv")
-    astrometry_filepath = os.path.join(DATADIR, 'betaPic.csv')
+    astrometry_filepath = os.path.join(DATADIR, "betaPic.csv")
 
     hipparcos_lnprob = hipparcos.HipparcosLogProb(iad_filepath, 27321, 1)
     hgca_lnprob = gaia.HGCALogProb(27321, hipparcos_lnprob, gost_filepath)
 
     # test a few things were read in correctly
-    assert(np.all(np.isfinite(hgca_lnprob.hip_pm)))
-    assert(len(hgca_lnprob.hipparcos_epoch) > 0)
-    assert(len(hgca_lnprob.gaia_epoch) > 0)
+    assert np.all(np.isfinite(hgca_lnprob.hip_pm))
+    assert len(hgca_lnprob.hipparcos_epoch) > 0
+    assert len(hgca_lnprob.gaia_epoch) > 0
 
     # Initialize System object which stores data & sets priors
     data_table = read_input.read_file(astrometry_filepath)
     this_system = system.System(
-        1, data_table, 1.75 ,
-        51.44, mass_err=0.05, plx_err=0.12, tau_ref_epoch=55000,
-        fit_secondary_mass=True, gaia=hgca_lnprob
+        1,
+        data_table,
+        1.75,
+        51.44,
+        mass_err=0.05,
+        plx_err=0.12,
+        tau_ref_epoch=55000,
+        fit_secondary_mass=True,
+        gaia=hgca_lnprob,
     )
-    
-    # create sampler just for lnlike function
-    this_sampler = sampler.MCMC(this_system, 1, 100 , 1)
 
-    # params from GRAVITY Collaboration et al. 2020 fit to HGCA DR2 
-    params = np.array([[ 1.04e+01,  1.44e-01,  1.5534e+00,  3.5325e+00, 5.5670e-01,  1.80968e-01,  5.1456e+01, 1.367e-02,  1.783]])
+    # create sampler just for lnlike function
+    this_sampler = sampler.MCMC(this_system, 1, 100, 1)
+
+    # params from GRAVITY Collaboration et al. 2020 fit to HGCA DR2
+    params = np.array(
+        [
+            [
+                1.04e01,
+                1.44e-01,
+                1.5534e00,
+                3.5325e00,
+                5.5670e-01,
+                1.80968e-01,
+                5.1456e01,
+                1.367e-02,
+                1.783,
+            ]
+        ]
+    )
 
     chi2 = this_sampler._logl(params)
 
-    assert(np.isfinite(chi2))
-
+    assert np.isfinite(chi2)
 
     # briefly run sampler and save result
     this_sampler.run_sampler(100, 0)
@@ -243,30 +279,35 @@ def test_hgca():
     res = results.Results()
     res.load_results("hgca_test.hdf5")
     new_hgca_lnprob = res.system.gaia
-    assert(isinstance(new_hgca_lnprob, gaia.HGCALogProb))
-    assert(new_hgca_lnprob.hip_hg_dpm_radec_corr == hgca_lnprob.hip_hg_dpm_radec_corr)
+    assert isinstance(new_hgca_lnprob, gaia.HGCALogProb)
+    assert new_hgca_lnprob.hip_hg_dpm_radec_corr == hgca_lnprob.hip_hg_dpm_radec_corr
+
 
 def test_nointernet():
     """
     Test that the internet-less object setup works
     """
-    hip_num = '027321' # beta Pic
+    hip_num = "027321"  # beta Pic
     dr2_number = 4792774797545105664
 
     num_secondary_bodies = 1
-    path_to_iad_file = '{}HIP{}.d'.format(DATADIR, hip_num)
+    path_to_iad_file = "{}HIP{}.d".format(DATADIR, hip_num)
 
     myHip = hipparcos.HipparcosLogProb(path_to_iad_file, hip_num, num_secondary_bodies)
 
-    dr3Gaia = gaia.GaiaLogProb(
-        dr2_number, myHip, dr='dr2', query=False, gaia_data = {'ra':0, 'dec':0, 'ra_error':0, 'dec_error':0}
+    _ = gaia.GaiaLogProb(
+        dr2_number,
+        myHip,
+        dr="dr2",
+        query=False,
+        gaia_data={"ra": 0, "dec": 0, "ra_error": 0, "dec_error": 0},
     )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test_nointernet()
     # test_dr2_edr3()
     # test_system_setup()
     # test_valueerror()
     # test_orbit_calculation()
-    test_hgca()
\ No newline at end of file
+    test_hgca()

From 886ea2a416fa3a6be02d787b9009bc72a15e4a2d Mon Sep 17 00:00:00 2001
From: Sarah Blunt <sblunt@caltech.edu>
Date: Thu, 18 Jan 2024 16:58:23 -0800
Subject: [PATCH 36/38] remove test-generated file after test is complete

---
 tests/test_gaia.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/test_gaia.py b/tests/test_gaia.py
index 65815eba..70dbce85 100644
--- a/tests/test_gaia.py
+++ b/tests/test_gaia.py
@@ -282,6 +282,8 @@ def test_hgca():
     assert isinstance(new_hgca_lnprob, gaia.HGCALogProb)
     assert new_hgca_lnprob.hip_hg_dpm_radec_corr == hgca_lnprob.hip_hg_dpm_radec_corr
 
+    os.remove("hgca_test.hdf5")
+
 
 def test_nointernet():
     """

From 1aabcfe6e096e3bdd1f7311287b25412419a2680 Mon Sep 17 00:00:00 2001
From: Sarah Blunt <sblunt@caltech.edu>
Date: Thu, 18 Jan 2024 17:05:32 -0800
Subject: [PATCH 37/38] run hgca tutorial

---
 docs/tutorials/HGCA_tutorial.ipynb | 45 ++++++++++++++++++------------
 1 file changed, 27 insertions(+), 18 deletions(-)

diff --git a/docs/tutorials/HGCA_tutorial.ipynb b/docs/tutorials/HGCA_tutorial.ipynb
index 65539b24..cf52098e 100644
--- a/docs/tutorials/HGCA_tutorial.ipynb
+++ b/docs/tutorials/HGCA_tutorial.ipynb
@@ -42,7 +42,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Using HGCA catalog stored in /Users/wbalmer/orbitize/orbitize/example_data/HGCA_vEDR3.fits\n"
+      "Using HGCA catalog stored in /home/sblunt/Projects/orbitize/orbitize/example_data/HGCA_vEDR3.fits\n"
      ]
     }
    ],
@@ -56,8 +56,8 @@
     "\n",
     "# Create the HGCA and helper Hipparcos object\n",
     "# we're just going to assume one planet for simplicity\n",
-    "hipparcos_lnprob = hipparcos.HipparcosLogProb(iad_filepath, 27321, 1) \n",
-    "hgca_lnprob = gaia.HGCALogProb(27321, hipparcos_lnprob, gost_filepath)\n"
+    "hipparcos_lnprob = hipparcos.HipparcosLogProb(iad_filepath, 27321, 1)\n",
+    "hgca_lnprob = gaia.HGCALogProb(27321, hipparcos_lnprob, gost_filepath)"
    ]
   },
   {
@@ -81,7 +81,7 @@
     "import astropy.units as u\n",
     "\n",
     "# read in relative astrometry\n",
-    "astrometry_filepath = os.path.join(DATADIR, 'betaPic.csv')\n",
+    "astrometry_filepath = os.path.join(DATADIR, \"betaPic.csv\")\n",
     "data_table = read_input.read_file(astrometry_filepath)\n",
     "\n",
     "# set up the system, passing in hgca_lnprob and setting it fit dynamical mass\n",
@@ -91,14 +91,21 @@
     "plx_err = 0.12\n",
     "\n",
     "this_system = system.System(\n",
-    "    1, data_table, stellar_mass, plx, \n",
-    "    mass_err=stellar_mass_err, plx_err=plx_err,\n",
-    "    fit_secondary_mass=True, gaia=hgca_lnprob\n",
+    "    1,\n",
+    "    data_table,\n",
+    "    stellar_mass,\n",
+    "    plx,\n",
+    "    mass_err=stellar_mass_err,\n",
+    "    plx_err=plx_err,\n",
+    "    fit_secondary_mass=True,\n",
+    "    gaia=hgca_lnprob,\n",
     ")\n",
     "\n",
     "# adjust the prior on mass to be log uniform between 1 and 50 Jupiter masses\n",
     "mjup = u.Mjup.to(u.Msun)\n",
-    "this_system.sys_priors[this_system.param_idx['m1']] = priors.LogUniformPrior(mjup, 50*mjup)"
+    "this_system.sys_priors[this_system.param_idx[\"m1\"]] = priors.LogUniformPrior(\n",
+    "    mjup, 50 * mjup\n",
+    ")"
    ]
   },
   {
@@ -126,9 +133,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/wbalmer/orbitize/orbitize/priors.py:354: RuntimeWarning: invalid value encountered in log\n",
+      "/home/sblunt/Projects/orbitize/orbitize/priors.py:354: RuntimeWarning: invalid value encountered in log\n",
       "  lnprob = -np.log((element_array*normalizer))\n",
-      "/Users/wbalmer/orbitize/orbitize/priors.py:463: RuntimeWarning: invalid value encountered in log\n",
+      "/home/sblunt/Projects/orbitize/orbitize/priors.py:463: RuntimeWarning: invalid value encountered in log\n",
       "  lnprob = np.log(np.sin(element_array)/normalization)\n"
      ]
     },
@@ -155,11 +162,11 @@
     "total_orbits = 100 * n_walkers\n",
     "\n",
     "# create the sampler, run it, and save posteriors\n",
-    "this_sampler = sampler.MCMC(this_system,n_temps,n_walkers,n_threads)\n",
+    "this_sampler = sampler.MCMC(this_system, n_temps, n_walkers, n_threads)\n",
     "\n",
     "this_sampler.run_sampler(total_orbits, burn_steps=burn_steps, thin=10)\n",
     "\n",
-    "this_sampler.results.save_results(\"demo_hgca.hdf5\")\n"
+    "this_sampler.results.save_results(\"demo_hgca.hdf5\")"
    ]
   },
   {
@@ -192,9 +199,9 @@
     },
     {
      "data": {
-      "image/png": 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8884jn8/z5JNPLmmdK0nqSp6Haavc/GUFVlOALSRa21nTq8nsxJM2J4JoTUlJSUk53ZHI5Oh+F+Piiy9m9+7d7NmzhyAI2LFjx5xi59u3b+fee+8F4P777+d1r3sdQgj27NlDFEUAPP/88/zwhz9k06ZNS1rnSpJaDOdh2io3c/q0MJz73vFiYauc+ruaSR4LW1qny+mkNbtSjoT0wSIlJWUlONaXEsMwuP3227nsssuI45hrrrmGCy64gJtvvplt27axfft2rr32Wt797nezefNm+vr62LFjB6BKGN12222YpommafzFX/wFAwMDAPOu85jtwzFb80nM4axy6n0JrJb4mpkt3WamNXM1xzZ32yeCoE5JSUlJSTkeD5mXX345l19++Yxpt956a+d/x3H4/Oc/P2e5d7/73bz73e9e8jqPFakreR4WFjjq72rWC0yS+Svld4uv1UJlcs+dnmYmp6SknEj88aee548/9fyKrS+fz894/alPfYobbrhhwfk/85nP8LKXvYwLLriAiy66iPe9732Uy+UVG0/KAkiOT4Xrk5zUYjgPs0vVtDlxxNdc9bXatQwXyuSG1R9bSkpKyonCgw8+yMc+9jG+8pWvMDQ0RBzH3HvvvRw6dCjtmZxyQpBaDGdxOIEDq5tIsVB8YZvVHVt7DCemoE5JSUlpUy2Pctf/voqDBw8e923/4R/+If/7f//vTlaprutcc801nHvuucd9LKcjqcFwcVKL4SwOJ3AON/140B7bwsklq5eZfCKL1pSUlJRuvv7AHbzw7Hf5w/91G3/+iT876vW5rsuWLVs6rycnJxfMGn3qqafYunXrUW8zZfkoT3J6H1qMVRGGP/rRj3jb297Wef3ss89y66238uu//uudaVJKPvjBD/LAAw+QzWb51Kc+dVy+TEsROKsVY7jYCa3E13EazCyWIqjT72NKSspq0B1PWC2Psusb9/OGq/6au+5+Hz3nvItCaQ0A/+97jqw1XiaT4Yknnui8/tSnPnXYWndtvv/97/Pud7+bWq3G//pf/2vGfTHl2JDehxZnVVzJ5557Lk888QRPPPEEjz/+ONlsliuuuGLGPF/5ylfYvXs3u3fv5s477+T973//cRnbiSxwpkXr4ca29MElydLb/Cx9bPO/n1oMU5ZLerqkHAu+/sAdbL7oTfSvP5/NL9vO1/7p/xyzbcVxzJYtW9iyZQs333wzABdccAHf+c53ALjwwgt54okneOMb34jrusdsHCndyKP8PfVZdVfyI488wjnnnMOZZ858Utu5cydXXXUVQghe9apXUS6XOXDgAOvXrz+m41mKwGnPd7zdyou5khcbWzt+si0I564ABAJdF8vet8UEtXJznx5fqpSUlBOLtiXwwIED/M9f+wL/z3U7AXjpa67ln/7qTdz/6Y+wbt26Fd+uruszLIkAv/M7v8OHPvQhdu7cyfDwMEAqCo8j6W1ocVZdGO7YsYN3vOMdc6Yv1DT62AtD9XexGMPVqMnXrmG4EAuNTUpJHE+LwXatQ9UtZXq6QCCRRJESlssRiIsJ5dUU1CknP+nFPGUl+MP/dRvnvOznyRYGAcgWBjn7wu0rFmu4FC6//HLGxsZ44xvfSBzH9PT08NKXvpTLLrvsuGz/tGaFPGSnOqsqDIMg4Mtf/jIf/ehHj3gdd955J3feeSegmlsfLYuJr+75jnch6cNlS8PseoHqRZJI4jjpvK++E9PvqemiK+NKdt6TUolHTVtcIC4mlNMi1ykpKavNo49+m+9++5t89//eO2O6Gbz6qNZbr9dnvH7Pe97De97zngXnv/rqq7n66quPapspR0aqCxdnVYXhV77yFbZu3cratWvnvLfUptHXXXcd1113HQDbtm076jEtLr5Wr/TKQvUV23SPTUpJksgut7HoiLK2JbC9m90t65Jk+v+2lTGRAkPXDntcFqqvOD226flWqzNLSkrK6c23H/sPYDoZ5UiTTVJOTiQsqd/x6c6q1jH827/923ndyKCaTH/6059GSsm3vvUtSqXSMXcjw1LctdPzHW+WbjFMiOOkSxhOC0LD0NB1rWMFbK9PCIGmaRjG9K9yNUMSS6IoWXCfF4vLbK+/vQ8pKSkpq8n/+54zU1GYkrIAq2YxbDQaPPzww/yf/zOdEXbHHXcAcP3113P55ZfzwAMPsHnzZrLZLPfcc88xH9NiWb/d7x1vYbhU8SWlihHUdQ0QnVjBpbiDu9ejllGCsO2OllJimvqc9SyeeJK2xUtJSUlJWX3Se9DirJowzOVyTExMzJh2/fXXd/4XQvDJT37yuI5pKQKn/f7xPreWOrZ23KCu0/orWiJx+QihLIxxrIRhHEsgniMOU4thSkpKSsoJz2nUveRoWPWs5BOJpQic9vvHXxgubs1su3uVO9zouIyPBiUOBZoGYajEoRAJhqHNEXuLC2o4XepApaSkpKSceKQWw8VJeyV3cSILnMVOZhVTmHSSQFZCFHbTjj/UNNFlPZw5tsUFddr9JCUlJSUl5UTmlBOGUZQcxdLzCxwlhKbXu1ICpx2/t6SRHaa4dXt8UqqCqu2YwpVG17WWpRCiKO4ck6UKajVvqgxTUlJWl4/+0Tf56B99c0XXeejQId75zndy9tln84pXvIJXv/rVfPGLX1xw/v3793PllVeu6BhSFqftVTvS39OBU0oYJomkUfMJw5hmM1h2T+OFBE696lOv+nNOiqM5SaRUY63X/CXPv9B034/wvLATE3gsy8Houoau60jZLWyXVvtxpQS150UEQXz0K0pJSUlZAaSUvOlNb+Knf/qnefbZZ3n88cfZsWMH+/btW3CZDRs2cP/99x/HUaZI2uXcjvz3dOCUEoZt767bDJCJJEmWZz1crFRNW4ysRCJF0nLFykQSL8HKOV+pGiklUZwogRZLNE0lm7TfO1a0y97EcUIUxSTJ0rqZrFRh69kW3JTTgdPkipxyzPnsZz/Hn/5/7+R3f+e/sHnzS/jsZz931Ov8l3/5FyzLmpFAeeaZZ/Krv/qrPPfcc/zUT/0UW7duZevWrfzHf6hais899xwvfelLO//PN0/KCnOU1sLTxWJ4SiWftD+yMIjRM5oSX8vYw+6uIG26BUgcJWCvTLHm7vVGUYJuHF6jz9cxJEkkSaxqFbbL0bTP22PZYaRtmWwXwY6iGNte/ECvRFu8JFGPbcvU/CkpKSl89rOf44Mf/C1ecfENrF17PocOPc0HP/hbALzrXe884vU+9dRTbN26dd731qxZw8MPP4zjOOzevZt3vOMd7Nq1a9nzpKwMp4m2OypOKWEIEk0XxJ6yKC0mtuYsvYD4AuVCbccvrkSvX2VlA6EJJawW/Shmiql2bcE4ThCo2MO2QFT7cmw7jChxqJMkUaeY9hKWao3tyEVrZzud7Ou0i0pKSsrS+P3fv5VXXHwD69e/DID161/GKy6+gd///VuPShjO5gMf+ADf+MY3sCyLr371q9xwww088cQT6LrOj3/84znzh2G46DwpK0SqDBfl1HIlA5ZtkEiWIVammU9otF2+pq0rt2+Xpe+oXMmJRGhKXC3XlawsdSozWNNEp8Vdt0v3eJz7atsaMqGTEX04VqLIdfdnmsTpF/x48rGPfYwLLriAl770pbzjHe/A8zweeeQRtm7dypYtW/gv/+W/8Mwzz8y77Ec/+lE2b97Mueeeyz//8z8f55GnpCj27NnN2rXnz5i2du357Nmz+6jWe8EFF/Cd73yn8/qTn/wkjzzyCGNjY3zsYx9j7dq1fPe732XXrl0EQTBn+aXMk7IyyKP8PR045YShSr6QxLGK31uqCFms5IrRKhIdR8nKdPGQIGi7ZDmsOJw9tnarO+U+bglGIZBJd4zk8TmFNU2g6UqULpYRviKxmcm0uTFJn/yOGyMjI3ziE59g165dPPnkk8RxzI4dO3j/+9/PZz/7WZ544gne+c538pGPfGTOsk8//TQ7duzgqaee4sEHH+RXfuVXiOM0eSjl+HPWWS/i0KGnZ0w7dOhpzjrrRUe13te97nV4nsdf/uVfdqY1m00AKpUK69evR9M0/uZv/mbec38p86QcPSr5JI0xXIxTTBgqwaDrWseJOp/VMIkTwllZrQtlJLdPBN3QEK0afosJnMCPFj2BZEu4aa1kkcMlU3SPLQhi/Nb6u0vS6Lp23C2GbQxDdUJZzEq7EqJVJhJdU0J4uRbhlKMjiiJc1yWKIprNJhs2bEAIQbVaBdTNbcOGDXOW27lzJ29/+9uxbZuzzjqLzZs389hjjx3v4aekcMstN/P4t2/nwIHvkSQRBw58j8e/fTu33HLzUa1XCMGXvvQl/vVf/5WzzjqLV77ylVx99dX80R/9Eb/yK7/Cvffey0UXXcQPf/hDcrncnOWXMk/KCpGaDBfllIoxVB1J2n2CF1b49apPHCWU+rOzYvLmtxi2p+m66IozhPnOkjCIaVR97IxBNm8vMuCWiG0JzoXo3ofKZIM4kvQO5tB1rdWJRNUtjKPp/T1ewlDKltVQm85Stqz5T6sVsRhKiaFraMwv+lOODUNDQ3zoQx/ijDPOIJPJcOmll3LppZdy1113cfnll5PJZCgWi3zrW9+as+zIyAivetWrOq+Hh4cZGRk5nsNPSQGmE0x+/Td+l4nxFzj77Bfx8Y//yYrEF65fv54dO3bM+973vve9zv9/9Ed/BMCmTZt48sknAXjRi1407zwpKavBKWUxFKi4M1XgWSNulXHpRsrp8jBhEM1dx2GSGXRdZTq3YxHnEzjtdUbh4d2q3QkYRldiy0LzgnI3d7uLNU25j7WugtbtpJbjZfJub8c0NXRdEMeLlwk60rG1M5KVEE0thkfKkRz/qakpdu7cyZ49e9i/fz+NRoPPfOYzfOxjH+OBBx5g3759vPe97+U3f/M3j3hcd955J9u2bWPbtm1MTIwf16fzZFb8cMrSOdmO3bve9U5+8398jj/86Dd45pkfrmjSyXI5ndyTJwTy+LiSH3zwQc4991w2b97MbbfdNud93/d529vexubNm7nkkkt47rnnAHj44Yd5xStewYUXXsgrXvEK/uVf/qWzzGtf+1rOPfdctmzZwpYtWxgdHV2RQzIfp5bFUBMkUqKbOnGYkCDnxKF1x/KFQYztmMDhCkjTUXCGoeF3rWO+ZdqCMG4Vf16wA0lX0rCmC8JQLjh/+4T0vYg4AUMXJK3SOYmUmEKbJQyPf6aupmmYJvh+RBgmWJaYdxxHU+RatoRg5xilmclHRHwESTtf/epXOeussxgcHATgzW9+M//3//5fvvvd73LJJZcA8La3vY03vOENc5YdGhpi7969ndf79u1jaGhoznzXXXcd1113HQAv37K1Izh0/dg/v4ah6uSTzVrHfFsLEUcJQjs2XYuOJVEUE4YJ2ay5rO9iGMYzeq4fT37nxlcf923Oh2wJFU1bmWoXKYtzrHV4HMd84AMf4OGHH2Z4eJiLL76Y7du3c/7500lPd999N729vTzzzDPs2LGDG2+8kfvuu4+BgQH+4R/+gQ0bNvDkk09y2WWXzfCufPazn2Xbtm3Hdgc4xSyGoISRYWhoumhly8o574OKGUxm9PtVf+d+N2UnXlFrJ6DEC1sMk0R2yuQslm0smBacoGIf56N98YiiBE2AYeqd+oVIOX0z6WQmH1+L4bQFU0PTNJJk4QLUR3Pti7uEodZOQEkzk5dFO3FpuZxxxhl861vfotlsIqXkkUce4fzzz6dSqXRKazz88MOcd955c5bdvn07O3bswPd99uzZw+7du3nlK1+56DZ9P8L3VSzwsT6d1cPX6oYn+P7J2dGn/dks59jFcULgRyeVpTHl1OBYWwwfe+wxNm/ezNlnn41lWbz97W9n586dM+bZuXMnV199NQBXXnkljzzyCFJKXv7yl3fitC+44AJc18X3l9YdbSU5pSyGSZzgexG5nIWuawRB1MlMDoOYwIswbR1QwjDwppNE6hUPw9IQGXPGOrvPg3YGbhwlnfXMRiYS3dJbbuwEWGA+pn3JbcEZRQmGOXd+KZVLXCZKRJqWPiPRo21h0NqZybp2HIXhzCdd09Tx/WlLz+ynYCGO/OYrW/53IQSi9UiTSLnAEU6ZD5U8tfzlLrnkEq688kq2bt2KYRi8/OUv57rrrmN4eJi3vOUtaJpGb28vf/3Xfw3Al7/8ZXbt2sWtt97KBRdcwFvf+lbOP/98DMPgk5/8JLp++E9NyraF+Pi42qbFTYKmrc4Z1c6YPNlojzlJJIt8rF0L0Vpm5cZwMlvcukOLFp7n5Ds3TkSO9WEcGRlh48aNndfDw8M8+uijC85jGAalUomJiQkGBgY68/z93/89W7duxbancxXe+973ous6b3nLW7jpppuO2Tl/agnDBLxGQL5gqyxiRMdCUi97gHI3A5iWTuBFKm5PSgIvIgwFhWJm7oq7jn270LUl1BWw+4LUyWBuJZREUcKC6SddF4JOyZcFrF9xLFtCUlkLdV0j9MNOLJ/oCEzR6qKy1CN29My+IGuawDAEYaha9ZlzhK444guclLJjKdTSzORlkyTKKmYss/B7m1tuuYVbbrllxrQrrriCK664Ys6827dvZ/v27Z3Xv/u7v8vv/u7vLnlbsmUJV/8f0XCXRbe4WTWkevg76TgCi2F7zpUQO47jMDExQX9//0koDmXX34XHLqVkYmICx3GOy6hOVVYisXhsbGyGO7c7BGaleOqpp7jxxht56KGHOtM++9nPMjQ0RK1W4y1veQt/8zd/w1VXXbWi221zSglDUC3iNKGEltBEJ9avTRwpV43ecQsnKLHC/GeMnHb5grI0hkHcuYB3P+m1Y+CEpm6+C7mGmWdThqERzpOwMp0so9ynmiY6ruoomplJrWmiE3fXXvZYXyjbWcnd6LpOHCur4ewYoqNpiydnXTtVssvJeCc9/rSLogsx9/M6IRFgWTqBHx5XS8lqhSZ0f2dPNtojXp6oXrn9HR4eZt++fYyNjR31uo437RAGWPx76TgOw8PDx2FUpzhHecoNDg4etl3hUmKq2/MMDw8TRRGVSoX+/v7O/FdccQWf/vSnOeecc2YsA1AoFHjnO9/JY489lgrD5SBahZ/1ltWuHWyfJAm1coiTMZRFUagbgRDKtavN88TWttS1MWYIyvaFbWbBZdESpoEX47shSSJx5g3M7rK06RrSj+ckoMSxJE6U5U1Gat/aojaJY+VW7bKiSSlp1Hx0Q8O2jWPWL3l63+e6QNodUdoB/YYxbTU8qvFIOi7k9nbaFt+Tz1JwfOm2Fp4MVlatfV63wiOONUcSJ3cstt8ew0kh3lt0W1uX+l1s7+9KWEhN0+Sss846+hWtAm7r/gCQyZgn1ed+UnIcssAvvvhidu/ezZ49exgaGmLHjh187nOfmzHP9u3buffee3n1q1/N/fffz+te9zqEEJTLZX7u536O2267jde85jWd+aMoolwuMzAwQBiG/OM//iP/7b/9t2O2D6uWfFIul7nyyit5yUtewnnnncc3v/nNGe9LKfm1X/s1Nm/ezMte9rIZ7YYWQsqWQV60xZmGZLp8SuDFeG5Iox6oi6+udUrAzNJ/C6LpYsE4ufZFri2MEimpVz08N6RZn9XiSMoZIqm7s0r3MYjjGLrcfwLRKYqdRMkMpaVpgihM8NyIRj2YtxzPSrJwwo4ar9YS5jO/iEfuHpSz3C261hbIJ77QWU2kbGe8t8T0SWJl7dQYPcbbaZ+fmiaQrH59zJPNatjtNVjqsesI8ZNsX1ea7kYFi5X5SlkZVDLnkf8uhmEY3H777Vx22WWcd955vPWtb+WCCy7g5ptv5stf/jIA1157LRMTE2zevJk//dM/7ZS0uf3223nmmWe49dZbZ5Sl8X2fyy67jJe97GVs2bKFoaEhfumXfumYHaNVsxh+8IMf5A1veAP3338/QRB02ge1+cpXvsLu3bvZvXs3jz76KO9///vnBHDOR7dGMQwNzxNEYUwUx/heSBInJFLge2ErXjDuWKHm+9BnBwW3BWccJxiaPmOZaQtaa54wacUFagR+RCZrdhJNZm+rbcGMogTTUhY25Y5tZx2r1nmiVdZANzSiOJmReNHuPgLK4uK5Uaccz7FkobI0hqERBDOthjPbCS7fldxePo4TJOr4hEHcEuzp0/Z8tK2F7e44JxMq+//Yjrm9enV8YpL4+CegdO/jySQMu+OqkyReegJKx2R4mpeckqAbyiqexPIU9eGdflx++eVcfvnlM6bdeuutnf8dx+Hzn//8nOVuuukmbrrppnnX+fjjj6/sIA/DqlgMK5UK//Zv/8a1114LgGVZ9PT0zJhn586dXHXVVQgheNWrXkW5XObAgQOLrrvbmieEEoX1ekC9FuC6kXpClVCZ8oiTeIareV5L4KwYQ5iOH5ydvt6JMRTTXVKSRJIrqhQU3z+8Ba9twYS2lScBVCeXdjHrGYkm4dwM0ziSGKbAtDTCIDqmQmCxG1g7K7nbang03U9UyZ6YZiPAc0N8LyIIIppuoKa1kolSppm2Fra645xkVomjqXu5VKbFjfqmr4p4nuFKPv6bP1I68XHK679ka3T3ET6JdPCKImXLByLSgv3Hk+NR4PpkZ1WE4Z49exgcHOS9730vL3/5y3nf+95Ho9GYMc98Kd9LbaMVhQmeFxIEEWEQEYUxuiawbA3H0ckVHUDi+THVqSaNqt95ap0dzyRnObKklIRhTCLnFgruuFY1ZTFMElUD0TA0DFMnCuLOOmBaJIVBTBioYq9xS3B2rIWti8bsAGVd15BJMkO0tmsYapqGZihr5uye0CvJtCt5/qf9ttUwSZSg89wQ2bnrLe8LFgQRbjMk9GOEJrAdA8cxyWYsTFPHNFUJH88LaTaDk8ZdeiyQUuK5YSfxSlkLWzee1v8nC0LQCfU4VnTOY1i1G/SM68xJdPOR0xeBI7ZIny4329mcCOfd6cixdiWfCqyKMIyiiO985zu8//3v5z//8z/J5XLzto1ZCt1ttMYnxvG8GM8NiGOJ7RgIIPSU5UzXVAmbTNZC1wRBMyTwYsYP1vC8sBM8PYeu+2gUJoReRLPmE4eqbmI7NqRd7Llj1euyQJqWriyIcdI5uYJAWbgaVY9G1etY/5QVM+mIPKEqVquhaNMWwySZGZfStgzphobeshCFi1gpj4aFkk+6UWJXMjXepFnzadT81rKHX2/b0hXHCc1mgNsMQKjjqErgqHgwoStXo9AEtq1jmKpwebMZ4vvRafWUBypGtVb2cOsB9apHHCdomnKzt62Fmnby1LUXQnDM75dd4kZrCejjfc50Nnc89vcY0M52VzfPxXegXZO08//pyKzzTrJwk4OUlUGSWgyXwqrcIYaHhxkeHu600rryyivnJJcsp43Wrl272LVrF329/ZimwLIMslkTyzIwTIMkURYUhSAKYrxGSLPu43shtapHsx602rnNsrDNijFsBwvLROL7SrD4XtR5j44VMKJS9qhXXcIg7sQNti14UZTgNkMa9emq5m0LZBhMZyd3YvJmmUwCP6ZWcalXvc7FRCZJl8tQ1Tycm/yxcixmMQTwvQi35pPEqih4HEl8L1QxNa0OKVGUEIYxQRATBFHrb4zrhlSrHr4foem6SsBpxVG2f0F1RFEZ0Cpu0TA1ZCJpNgPqrc+1vd4wjDvCO0mO3bFZDXwvpFp2SWKJnTEJ/Ih61T9prYWgHq44xkWuuzRZJwHleGRCz4d2HGIqV5IZcdWth9YltVyUTHcvOk210MzzrpVIdzI+FZxkyKP8PR1YFWG4bt06Nm7cyI9+9COATnutbrZv386nP/1ppJR861vfolQqsX79+sOut10TKpHTYkXXBUjw/biToew1AzRDYDkG2ZylOqa4EQLw3HCGOGxd9qZft1vq6RqBrzKGu+KoO+3oahWfOI6JI6hVPZJYxQqGXe5k0V5f+wIZJQhNCUMVp6h1tt12GQshCIOYKIo7JXHaWdbtC6xp6CRJgm6oDijRbLG7YsjDWguTROI2AizHIFfKoJsaEqjXfFwv7BTBTpKkk02uaVono7lRD4iihHzeJps1MQ0Ny1JleNq/mYx6ABBCYFk6lqVj2wb5gk3GMUGqOpAzuqUkbTGaEASxEo6hOqbtjjIn080Z1Pnk1kMMQ6PYlyGTM5FAo+oR+vGcYugnA93dgY51rKygJW7a2zvOH39HYGnHpzzPStFl9JrOIl+SxbD10Z5kQnglmU9Un0Qf/cnJ0arC0+TzWbUcqD//8z/nXe96F0EQcPbZZ3PPPfdwxx13AHD99ddz+eWX88ADD7B582ay2Sz33HPPouuMgpixkSpeI6R3MEv/2nyr2HXLCherfsO6GUMru9ewdDI5myRJ8N2QQo9D4Eed+LjZdALVDYFbizEtY/pkkTOFm9NyWQug2QhUEesg7liq2oH1Scvdp8am4YcRmZzZmdblSUYI8LwIXdfIFRwMQ3QEWLuMjWFpCF92MpiVxXLlP2op5xcabYE+NVZndKSGmTEo9mWIowTH0UFC4EUUCs4M13v38p4bIgStWozToliIuZ+J3soSn11I28mYqvWhHxHHEscxZmyj7fZq/+0WhO1xtS/a843zRMJvtXfM5OxOSRrD0qlNuYweqNLbn6HRCPGbIT39udUe7pKZzmI/dtuQytA8Y4PHXay0now0TSWQnSx0ew2mi3QvYTkkQmho4vR1Jc/ebSHmmZiy4qRHeHFWTRhu2bJlTvXw66+/vvO/EIJPfvKTy1qnYerYjkEma9KoBUhZ6yQmyCQhlpJG1QMNrIyJjBKqFY84jskVnE6tQU3T8P0IIQyQM61iSaKe6k3LIIo8VQexXcW/9TcKYxIpsRyDOExU3FsCYRh3LHgyaZeeURdG3TSIgogkER3R2C2IOkVkpYojs2yDOHSJAyiWMrhuiAhFqyWdjkAQRxLT0gmDY+Or6a7B1RZWSSLx/YjyWIOJQ3VMUwMpOfhCmZ6+jMr6DBPqnktPb7bjYu9ep+dFxImKEdU1QRiqxJxG1VfxhbMEu2GofW33Z+7GNNtJOBG+r4Rm+5iqwztXlLYTNtrdQuK4qzamJjoxeycSvhuit5KcpJQEftQ6nyUv7B5n//M6+YJNsS/LxGh9tYe7KO14nuVYoY5mW9Pftfa0Y7a5+cfQ+tsex0lT5Lrr+tj5Ti1NGap5NU7ONoArQPfDvvorTvu6jseD0/VBZDmcPFHoS8BsuRadrEnPQJZ6JWBytIFlmyRxQr3s0az5OBmTYm+GvrV5DFMn8GIsRwmUWtnDyRgdy1w8y7aftErG2I5BEsfTBbKhc7GLIpUtbJp6y0qYKGtVy60ZBnErYQJyRQdd17AsJWCSRLX0a69TzhamraxlTQPPjWjWfUzbQNc1mg0lbKfrHMYYpt5xna40UqqLehhNx+5JCeXROr4b0tOfYeisPvIFB8vUCSOJbevkSg5BEFMtu3PWGQSqjpxl6eiaUOOPVUJF4Ef4nVjRafR5ioN3Y7USVqJQxTAeDtHKrjQMDdPUW8tqnTqSUaTcz0Grq8uJcJFpW8PtjBK9vhdRK3toQuBkLYQOmazF4PoCvf1ZMpljX9vyaGk/ZEwLw2O4MdltMGwFbaxC8km3VfpEOK+WgjJ0dlvqxZIsMm0j7WkthrpCGKB1Dp6mh+J40s4uPtLf04FTShiCSkQI3JBM1sKwBG4jYOJQDa8ZUqs0MSyNXI+Daepk8zaZnAVS4jdDnKxJo+4TxwmOYyKl7Ljo2rStGLqhKzdtFHXFGLasesl0fSonYxCGEbqpoekaQZDgNSOVOCHplG/RdEGCRCay1QN42i0zw03TEoxuPUDTAAleM8DJKvHbFkftBIy2e3kl4wzbJXuCICaMYmQiiSOJrgm8hk8USXIFi2zOJo4ScgWbTecOogGT400sRyeTs6iVXcJwWqiFYUwUxpim3hEElqXjBxG1iquskd5cYdcWc9FhMvos28Aw9Y6bf6koK6ESiu0YRsNQzRPbIjFcZZHou6GKsbQNojCmOuWCAMvWqNd9egdy9K3JYWctAj+mZzC7KuNcLt31SJNjaFaSMNNwLFbj/iwRdCWbnSR3oHZ8ZhvBEsculRpvWxhPlv1dSU6M8y4lZS6nlDBsizbPjahXXLI5GytrUCv71KZcZALZgqMEk6ZKBOiGQLd0Nc0QyESVj9E0geMYSJQVq7ONVrJI24rUHdjftvi1rXyapuLc2u5V3dBAgOeHBC13cxgmnWxmXddJEuX+jWdnE7f+TRJJ1Kp7mCvYaIaG1wgxTSVUg5bwiSO1zVrNJ/AjKlMu9brfKQ4d+NGyM5ZVgemkI4SEaLlqE0nghVSmXA6N1IjjCK8RMTVWp1kLkFIJ1mKPQxwl1Csexd4MSKiMux2XbdA6RpZtqESaVsHcoKkKkxuWsvr5Xjhn3LqhtTKdF94fy9LRdA2/1ZP6SGiL0LY1UdenLYlhK4Fl9tiSRHVn8byQZiOgXvc7v426j9sqrROGyx9XEisLtNWKn6xXPBIpyeRtylMeiYS1wyXiRBK3Epa8xlyr64lIEk8Lw+Wcp90Z60uh25WctDpQrEa5mpkJHMd180fMjPhM1P+LjX362IouC+mxGN2JTfd5BydfRvrJiGQ6TCUtV7Mwp1QDniROWhnFKuHCMDWyeYcDz5epVXz6BnNIVC3Cdms6w1TJEJZtYOg6oYypVwMKpQyapmGZqstJEMRYlq4KB8fKDazrgiCUHRdl4EedelSdDFvTwNBDAj9GN1SB7VolAinRWzX+4rh109YESaSewMtllyiM0Izp4s3t9SexErbZvInfjFThaCnRDUGjEVKedNENFdcYhTGmreO5EUhIkESzYg51XcUl6oa2YMJNHMtW0oyavy2IBOC6EYapU55wmRqvk8tbSCJ6+7PkSw5CqBJByh0rmZpskslb5Ao2biPErQfQamnXjgFM4gSZKMGlCcgVbCQCGcX4XoTQNExT61xYVfs9FY/YXsds2ut3W11TVMjAkcdxqQSlLkGRJK3ai3EnDjFqWVbbaPp05mv72EZRggzlrPVqGKb6PBYrBwTgZAx8N8LzQjI5izAICdyQgXUFCqUMgZfQbIb09GfxasGC6zuR6O4ktJzrcb3iAVDqW6JltB3vBtSrHq4bUuzJLGeoR4+kkxAHJ5fFsLsuphBi0Z6/3bF1M/f3JIipXEm6zjuglaG9aqM5bUiP8eKcUsJQlSQRVCZcNB361uSIg4goUlamIFLiLQpi9FZhZL1laRNCw7R0fE9TXTZc1dvYMPRWCRkl5nw3xLQNyuNNmnWfWEpqdR/D0HEbqgizk7U6iQq6oSyLAnXZsx2TWsXFcyP6jTyGLvCkJAhVRnISCdxmSNJJjEkwh4rUqz6+G7asgQmFkoOmadgZA99V1rqg3TMYKBTsjjvZtg0ErXIubeHV6kaSJCru0fcj8OlYAQ1T77i0ZwvCdk/mJJGdmD/PDalONlUmOKoXdb7HodQ7fXMOvIiim2Fkb4XyRJOBwTyBH1Mpu+SLNtmcPcNyI6HlWtewsiaeFxIjOu7yMEw64rDjTo4SLGvh3quapsSh74UEwcIicrmopBS9I6JdN1DljAQ4tomTMQ6btNJeLm6FA0StuE2gE+9omHNFYuApUQ5K1AhDnRMHXyij6RqlvgyGoVPscZgYb9BshupcPsFpJ2V1OEYXcynljDi5dtvJxcTNsRiHaDtwTiaBIGcZDJch4oXoEkYny/6uIBJmPCR2hxGcaMltpwynU6DgUXBqCUNNiZ84UXFWTs5iarKJ5ZjkizZJBJ4b4PmhcnkkYBoami7wg4iBTI7Ai5BxQrOqklBAZbLGsaRe80mkKh7sNnxVBy+MMWsBa9YWMUydWtUnShJyebvjrk4SiWPqaIYSLpoucJtBKwFF1dfrZCJrgqAZqbZ6GgSBZHSkjp01yBZtwkBZNPNFtc+WYzA51sDKmNiOge0YHZe2rmtEMkHS6oISxB1hqOreBWQLNo5jdgRiFCkLl9eKrTQMleltWTNFiWzFNrYDpicO1HCyJhvO7GFspIadMQEx4yJnOQb9a/OMH6oxuq9CqcdG01qCRlMp2oaptxJ/QkzTwG/F8JkYIDQkMbWKh5M1W4WtwTBaMYlSEoURvt/KzO7K9lN/p61xiakThnHHWrpi56AQqkd1LHEcE8NsJcYk7UzzhZczjFaJJNVam6gtEIOEKArBVaLdtPSu0kcSy9GYHG/geRE9/Rn8ZojvJfQMZDHN1jnsGGQcg3rV78SdntiILsvSsXOxdbbRvWVx/DNl216G2WM5kWmLamaIm2kL4OEegmC6Niuclrqw9TAwnzBkwetEytGT6sLFObWEYSt4N5uzCfyIiQM1wigmmzUplJxO6Ra3qfont8WDZevEYazck45O4IHrhp1i1CoLWYm+JJYkUYLtmGQyJtWK2xEzCWDaOr4boWmhKrAMoCmhlrENpAxB02nUfHwvwGxlIxuGhq5r+BKiSI0tSST5gs3EoTqGmcW2DaQJuq4SNbRAIwhiZS3TWuV6bAPfV2JK07VOjT/D1GYkoIR+rKyA7RZ7mkCzlFXKMGN8P2712k1afXc1TMvouJoDX8Up6pqmMq2jmKEze2lWA5JY0reuQBTE1Ks+lmPQrheIhDXDPex++iB7n62QK5qqs0nDw7A0glAV946TBEsTBK5ywYPA0AW6Y+I1VaxeJme1CmTHnXHFscRthjjOQifJ9EU3iiVhPSCbNTuWUCUKVJKLkzWX9eTerr8Yhip5yW7F/amYN5Wwo8oJLeweDoMY3w3JFmzlTjY0HKcVw9gSyeqzFQRuRBRG4KsHDc3UkUhqZR/d0sgXnE7MmtbaPxkn2PmFDs6JxYykr2O3EfV3nsza42256c6MPhZCOEmSFW2HOM+hW5q4mQ4xPOlc5yuJ7H4aQB2D6W5MqTI8VpyGp9qyOaWEIagvl+XoCGB8tE4mqzpklPoyREmCXw2RSUy9HlAqOchWfKHXUIkASZRgGJpq5dYMW5nJoUr0aH1XwzDCzpiUBnIcOFClVnUJggjL1LBMCwm4zYC6baC1RKPbCDqiwLZ1XDegMqUSZBJJJxNXtix3pqWRRJDJW3AIGnUP29aJophM1kQKKE81yeVtDENQq3jkSw5OVonRwIuwsyaGoQShpOU+DlUJmyCIMFqZ0m26g/Ydx+y0UgvDuCVKVBFt2zJo1Hzcekixx6FScwmjmCiJmRxv4rS6kcRRguuG6KYSqO1C0X0DWQbXFJgab2DqAidj4lgGWcdEMzUajQBNCPJ5i8gNsTMWwlDLZhwTr+F3egBnW5ZDvZU5rOuCwI9UcpCuTWdzS6bFKeqvaWq4bkijHuA40yVc3GZAGERIVG/mpRa59tyIMEyU5bbLRa32fbr3c9AKZWiL0RnraAREYYwo2jOmt7vBmKZGGCb4fsjURBPdEJhBgmkb2BmTRkUlGJX6nZag7vpuALquUyicLMJQ/e24J5cg1JYrMDoapVvcoB4OjpflRrbdW10mw5W+eSVJwthYg0LBJpu1VnTdM4Xh4jGD7Xqv3Rb00+1m3V1Iv0257Knr+0lQTurk5jQ72Y6AU0sYtq6vuqYhbEHoRa1sXUDTcRyDg89XcN2QwAtptlq0WbZBrexSr3hkshYIqeodZg100yD0VNygaeoIXdBshBiWTjZrknEMylMucazcrnGckMur4P92IWG/qTpOmKZGIASBF2GZBpUpj94BFaOnt6x77czlKEkIA0mj7mNYOs1GgGUGxEmCZgiEjKhWfAxdEEWS0I3wPCVMMlmTRiMgkZJCKaPKtLSshWGgLIlJLGe4laM4IYmVa9vssmi1BYxl6fhepIpXTzYZ21/FyVrEMqFZjyiUMtSrARqCgbX5jvu5XguQscTOzjzV+tfmqdc9VbzaUqVkfD8in3HQdUEUShp1D98PEYYgaeVLaEIgNI3AUy5lo+VajUSClFqrXmFCGCVkTJ3FnrxNU8f3QjRdifY4Tlq9p0WrNeHM5YXWjifs7ooiOlnJlq3PG7eoYiBbfaxbsYRJknQEH6g6jIEfkclbM7YbBKqMT7foSaIEKROiVniEnbUxDF0dbwFoWudBBlDWXV2VT0qWUa5nNeluGaZeLy7UZpeWWqqQFDPVTcdieDwtN9OhDyvvyo4iVR7L96IVE4bzHbtOyOBh7r3d700ve3rdrOeztqrvv6r4sJLhLSkzOd0eQo6EU0oYStlKpGgGJAnESUyl3MRyNDRNZ836IiM/mcJvKHff1ESTUo8KzpetMhXFHgfD1qlOuUwerNMzqEROFMY0G4GygrUsiZZjkMlbTIw1qJVdisVMq0exjmkYxFEIGh0rnKYrK1aj5mNYygVbqwdYhta5QOi6wNA16tWwlfyh09OXJQ5jKtUGUZBgmMoSGfghE2Nxq1ewxG2E2I5JrmhhGBq1WoBhGhSKdktYMCPOUNOUyypsFeluu7PbtDNtVQJK6yYpYfJQjTBIyJU0Duyt4DYDBtZlqYy59K3Jk823rF2ajpMx8ZoBgR/NEKJCF+TzNs2Gj+/7TI3GZAs2YaTGZzsG9VqA64YUejMkLbe3kzGJwwSvGZLN2/ieysrWNI0woNP5Iw4SDF2b01llNqapt2IrE9VVRbZFurIoOpnpmM12VxSVRd6VRayp0IN2UfPD0Y4lbLc7VHGOytLpNVU9Qqd1427XbUySpPXQoKtgdQGBG3WswyJjgqYxNdGgOuVTGrDxvJAgtDEtlcHuexG6oZKP5qsFecIhOCKt0C2oliYMW5vrmq3dpu143UBmb+dY1G1sF7gPwnjFXMrzxWcuxwTYOeYnU7LNCjFbVHcaEEhJ6CfY9kJLphwNLcdDyiKcUsIwCGL2vTCluoGYOuWpBvVaSLPh4WQcMnkL01FWId3QqEw0MXShYtQsHSmVK7WnmKNvbZ69uyeQkw3WritQryn3ZSZrqUxjIVqt6UwkkvGDVYrFjOqUEks0HXJ5BxGruMP2zdmwdHRNU/GCUiNwQ8y8g+dFaloC6ECSYGQshJSEfsTkeJPRAzU0DYo9DpZlkCBJ4ph8IUsYqCzqTN6iUQuVJTGMaNS8VukTnaBVK69tPdQ0QRgmgOzUZWy7O+NWuRhQwscwNHw/ZHK0jm2brNvYg5SS6ngT2zGYHHWpVVyGX9Q34zOxHYPAj6hXfbIFJVKCIKLZ8Gg0fEaeK2O0Srhksha5kk02b6EbKi7RMoVyCbc+M13XsB2D0I+wLWUF9v2o4y5v15CMwoRq1SObtdANMZ2gMg9Wu4RNJ2NdWfHiKOm4u2Yv2y0UfT8kChNs2yDwY4QWt6yKSvDNJ07abuG29TCKlAvbyZidbQWBqt9oO8YMC0K7VqJAWXNzRUslNnkRhR5VWDwMIoIgQtOUBT2JEzI5S8WxuieBMGSuu225Fryl3ADmnacdrHyc7iDzWd5WestJu66jVNdJx1mBWMN54zNb2zusxXDm/gpOv5v17P1NklavdyHww5A8qTJMWT1OKWGYJBLPDWi6EUXbAE1HsxKafkIiAp7fM8HkaINkf50oSij02Lh+RNY2MA1lkWk2QpyqR7GUwTA0DrxQBikp9mSVhS1OVJaJ6nDXKg8jePYHE5imQd+aAkIkSCnIF0zcRkjkheimQSIljaqP0ATZnEW9FuC7Mf0D6qbvuSGNllUydCN0S2dytE7ghlQmG4ReSD5vkc1b2LaB50UEYUJlokm95mFnDDac2YNpGrjNABKoVj2aTR8NjSRRrsqe3iyGpRNGRseFrXo5R52iwpouMCwlbjRNJTcceqGK2wzoX5cnk7WYGmuQz1v0rStwcF8ZBFSnvNZx0VpZtSppollXhZ1N22BivM6+PRPK9Y9ECg3D1qk1A2p1nyhSBZs1BIWSg+/HODmLgbUFAIyWFTAKY5ysRSZj4msaURiRJEqIF0rqGKiSPC23cCvGc7ZI1FrZ7G4z4M8+8Ti6ofGbv7atUx9yIWGnCvRKVTg9a5LJWjTqvupj3apNGbaOpaaJjuBs0209bNZ9ZbW01b61y9aoDOSZVki/GRJHcSs+VkdoOn4jQCawdkMJwxC4zQhNqLCH0I+wHWM6XvIk8FJNuySXlwAiuyTVUjtwCGYmfAi6Mm6PI10GtBVXSnEs0VoJcr4XzYipPVLmjc8US2gpOCvpQn2+p6cynG0x1HUN3zs5Qj1OVo7/N/vk49QShnGCSGDt2jxrNhSIvZBsziGKo1av2DyeG1GbaPLCMxNkiw7Zko3t6PT252hMNnjhJxP0D+ZIYonbDJg6WCWJYuQm0Uls0DQNy9GplH0aDQ8pJYEb8Mz3DzEw1GxZtgyavRYju6cQEgq9Nk7GJtdvE4QxeqslX63cJBkqksmYuE3lOg2aEXGUcGDPJLVGQKno0L8mj6npjB2sYNkmvmPgZEyGzunDbwQ88yOPqUM1/vNbL1AqOaoEDZJif1aVkxmrk8iEZiPk0P4q+YLD4IYipV4Hw9A79RrnK6pcr3rsf76M70Xk8iahF/HC2CTVyQZ9AzlkHJPP2eQKNtWKx6GDdZysSTZnYpk6hq6RcQySOKFZcxkfqVEqZVg7XCSJoTzhomswMVpX4tQX+K3uLM26z9RUUyWe5Bw2nFlizdoinhcidKEEpKbhOAaRobreeK1ak5at3MCZjBJqnczeVpxlO+tXCOUCrrdc6u1e00ArdnRhYRIEqiWiYeqMHarRrPn09efQTZ04Ut1tVH9tEJoSpZatz2j7px44JIauLAZhFBMFMYi5rukkbschKeur6hKjusnYGZ1sK1M7m9ewLINazaNeUeWZajUPmUC9fnJ0PoGZ+mK5WmlJuhDmGCHbyS7HK1O2s5lOjOHiJV+WSxQnaIaGqavM/5VAdonqGYjDy7zZ73WSi04jZovqtkU3kzVp1oNOZYqUFUYe/1JUJyOnlDD03YjqpMvAYI7Qi8nmbYSEekNALLEti3zWJGMVaFY9XDcg9ANcN6I20CT0EprVAN+NkElCveqzf2+VKIaBdQWSGNyaBxoYhkG14iOFSiBwsha1qofUJHbWJpuzcKtNAPJ9qg3fwX1lclWHbK+tOqmYOuMjNYSEyBsgCCO8WkAUx4yPVjkwUsOydPoGs+QLDs8+dYhaxWfdRtVur17zydZsqmWPWtlDJNCseEwcbGA6Gvmcg++F9A3kGFiTx86auG7A2IEaEsnEoRqVySa9A1lKPRmyOXuOEGnWA8YP1BBC0DugRKaV0ZgYbSAjSbMWUit72FmLRKoi3T29TqvLi4muQbMW4vshE4dqvPDMJHbWZPisXirjHkJIDu0rM7G/SragPi/Pj1m/sUh2nYnpGISBpFZzCfyQ53dPcGhvjWKvQ84NCWPVD9o0VW0/lbyj3O/tO46mCTIZE9PUW4W9W/Ua/YggEB2BiFS1MFUXmZYwbGWpL4RKUJHsfvoQ9UlXlQtqBC1xKdB01TYvbHW2sRzV7s/JmFi2jpMxsB2zY/1UHVwigkBln8/JWnbDTiiA6eigCZpljySB4sB0MXGtVa/R1DWKpQwJcHB/DYGg1Hdyuak6BqUlqIcZsyzJYCjpTm7pXvj4iZV5EmBY2Xp2SSI7bRx9T4UZWNbRXf4X8uwv1tptzn61SkSdTkx74VsWw9Z1zLFNGjUfz4vI51NheCxILYaLc0oJQ5BMjjYojzbo35BXLdSCBDNnYllax3oVNENIIPFjgjDh0EiFsX11so5G70CWoY0lsr0OB18oM3awSmWyyaGDNUq9WTI9Do1awNholdBX/YK9hup9XJ1oommCNcM9mKaOW/Yp9lv0r83jZAz058v4zYhmxadWd9E1nVrFJ4lj+gbz1Cse4wdrlCeaHBypEEUxTsYgX8ow+kKN0ZEqxb4MpmlgZ03KUxWefrxKo+bhezG5vEUmZ1HsMQkj1Sf5wEgVUxesO6NALmdhORZhGFHsyTCwJk+jFlCdcvEaIT0DuY5LVDc0PDdk/GANhKDQ5+DVA/IFBzTI5iz61+WIg5iR5ypUqwGmqTF0Zg+bXjRIHMeMPDdFbdJDb5XoqU55TI03cbImzapHrerhuTEyiZBSw3N9PDcm8CLGRir09mdYM1zCsnV03aCnx271Oo6YOFilkbEolBwM2yQM4xlJFe0YQJB4XgSogtNtt7FlqQzyMFTubrcZqHqKrULZSaufdrvLynx4XsDoSJn9P5miPOUydGZvx2raqPrIWLVe1A1BtmCRLzlowgRUkfPAj/GaEXHcJPBD+gdznRI27eSH2X18PTckDhMMu5WNHcU06z7FngyWaXQSijRdI/CV5dm0NZrNCMcx0A2BTE6GGmlzx7jcy/mSbgALia+jrCXYjj9tf47tUIL555296cVLviyHJFEVBwxH9X+vVVX/9KMXhgtYNBe1AM4u7KwWmD5mrXVrAv0w3YJOalqx0G3iWCXLGaaG0LVODd2Uled0s04fCaeUMJQS3HqA7/oEYUw2bxGEMVbdQBMCyzKJ44RGxcPJ2mAEGFLQuzaLkOA2IiYnm+zbO8XmwjqGzugFAXt/PE5jyqNQsClXPGoVHyurI4SGFBA3QnUB0zQmR+vohiCTt8llTHLSZHK0lU1saWRzJj/67gHyfRnOPH8AxzFxPZ+J8TqNqs/EaJ2psRpeM8A0daqTPj/+zggI1eNY0zRG9kwS+KFymVoGuqmjG6rETnWiyfBZvWiBQb3cpFpukLF0Jg5q1B0fhE4cR7jVABGjkmeihGrTbdVVtMjlLQI/VlY3CYU+h+pEk0zOIle0ObivAkKgCR03CMjkTdb1ZdA01c0giRPG99fx6xHZogVIxg81OLivguXoFEoWvhvSqPq49VbdPl1j6mCERKps4zgkCWNcN8K0DUBiOyalvixrhwoU+7KUD9X5yQ9GOetFA6zZUGwltiREobK4+V5EECX4XkCloj6/bM4ilzMxze74ygSvEfCFL97Px//sI0xMvMDdd2/mwzf+Dm9729vnPdd8L2T/s5Ps+eEYYZiw8UX99PVn2LunTOhFOBmTTDGD1wipV32qEy5xMolpGRRKDv3rC+SLDoalqw43fkS9EeL7NQyr1UZRqELm7ZhI3w3xmiGWpR4M4kRSm3IxDJ18q7evEoYAEs+NSCQEXoxp6QwM5gFo1P3j8n08WmZbVZZ9QV+iK1lbQHgczQ0kjiVxkiBQyUtRq4XkfMXNZ+dwdN5doRtYkrTjhrVW/3YNP4jJH+2KFxDVi7UUnG0xbBuEZx8zlQB3+ILwJysqzHJ6n5JEtlpegmXpBMHSssfbFS9SlsZ0GaqUw7EkYTg5ObnoPJqm0dPTc7TjOSo0TcPOGkR+hNsIMXMGxV4bTWhIKdj/QpnGlAua4MwXZVSrOz3BtnWEJigUJVPlJvtfqOA2Q3r7cximRiZnK6GmaUxWVVeJYk+W3v4MfpiQydcxNI18b5Na1SOIYirPV7AdnTiBYp9NZdLHdwNMQ0c3VQcWGUkCP2Lk2Qr7kwl8PyYKlAtbJgmFko1AgiZoTLpYOZsEyaF9VcIwwjA0in0GGcdEQ9CIVPLI2IE6cRhRnmxgWzr5gTyZnMrIti2T2mRMrexhmXrHOohQ3VCmmgEH9sbYlspkzuZMxvbXcesepf4c1Sm30wc5SWKqFZ98j83ZL1mL1wgYO1jjh989gF8L6V+fI9+T4YVnJnj+mXEqUw2yORuv5jN20EXTNAbX5VW5GCmpjLtErcxgpCR0Bb7hE/lqv5rCozKhEld6+7MUe228Gvzkh2NMjjdYN1QiX3TIZCyyOXVzaWcbN2oeblO5tCtTqotLNmdgWyr28e+/8Hl+7/dvZtsrb2Dt2vM5dOhpbvyd3yVJJNdd/94Z51kSJ1TGm4zsKYOAdWf04Dcjnn7uAEJT/bB9LyRpxQFmMyphyGsGNCoeI89NsW/PJJom2Hzhenr6Mji2iQY06x6er+oh9vXnWqJYnduNqk8SSZxekwQ6Vs6BwdzM7GcpCXwVL9m+QZdayVQAxdLJUeAalItRaIJO78VFF+j6d0mu527rlZzz3pGQJErgtIuuw3Tx+O7+3rMHLWYpw5W6fUWt0krtsdiWTqMRHnXZmoXsmYvmkkige7MCZJIs85id3LTjM2Haoqs76jtsWzqeF+F7Kjb+cOtIhWHKsWBJwnDDhg1s2LDhsBfKOI554YUXVmxgR4IQkkzGwHZyeF5EY8JDhgmBF0OrGHAcxpiG4OCISipxsjZeKyasb20W3ciBrhHFCWMHayrrk4TKuMuPvQOU1ubZfP4gAo1G3cd3IzK2iWHpWJZBqc8hDBMOulNITaNc9Uh0yBVNwiCi6Qb0rs/RnPJ48tsvMPJ8mUYjoFhy0Frt65IkoVC0KA0UqE26quB2xcNxTGIvRLcgSQROVlcu2nKTZs2nWQ8I3RgnoxNJjcALsfMmYcNnrOzhuyG2pVyQuV4HNIHp6PQO5FRSQtXj4L4KYRBT7C2QL2aUuzKMVUkUIZgcbRCGEc1aoFxkpsaatYOdvtCBF1IZb1DqyzC4ocjkWJ161aU80SCOVFHtKIyRIqF3MIuTsQm9GMsWrF1foFYOaJSbSCmIgpigGWEYEtcPkXFCLCH0lbVONwR9g3mGNvUwNdrg4HNlevqz5As2jmOhW8qNqxsGGcemWBSgqWxp3wuplj3iRJ0fH/2jj7LtlTewfv3LAFi//mVse+UNfPS2j/Luq38R2zY6ySJuI2D/C2XVYWQgy+TBBm7DI5u3lWvb1MiXMvT0Z5BSxQV6boRb9cnnHfXaC4mDmPJ4g/0/mcTK6uTyGUxLwwti4ihmRJtUtQcdA12X5IoZ+tbkyLR6gNemPHJZg1yrS4quT7ucAy9CNzSiWJLLmTPiJE+KG+wscSRYfq27JZermed4LFWHzkccJ61SQtPrbYcIhFE8XTNzgTGsdJu4Th3Vlgi0bINGI8Q7ymLXUsp5heViLQWVKJp5PsaxxJTMe8yieY7ZyU631bRt0VX7C6alSnD5QUwmu/A6osOEuaQszPEwGD744IN88IMfJI5j3ve+9/Hbv/3bM973fZ+rrrqKxx9/nP7+fu677z42bdrEww8/zG//9m8TBAGWZfEnf/InvO51rwPg8ccf5z3veQ+u63L55Zfz8Y9//Jhdy5ckDM877zz+8z//87DzvPzlL1+RAR0NgR9zaF9VXZQtVWvQcXRyWdU72W36BEJ1xzBMQaIb2I5OdSoiaMZYjo7jmEihkS/aNOoBtaqPnTHQbY3R/VWCMGFwfYHBDUX6MnnchsfEoSaRHxIGCWECtq2zZm2BXI9DLEEzBGGUMDnZIGzGNJsBURBx8LkKoR9R7MnSvzaP5Ri4jYBszmRwuEipL08iY4J6AFKo7OhIp1DMcLA8hVcL0YRBrmipZJZAleupVxO0Vhs6MzbRNI216zOAxtjBKtWqT9OLsQwDEklt0iOXt9BN6OnNYNgGbt3nwNQUuaKNYZsUixaaFPStzaPrklrV59D+GjnNxq0HjEUVfC+iPNagZyDL0Fl9TIzWePLxvezfU2V0b5W+wTzrNhQYea7CwNoCud4MWhJT6nXoX1dE1wSHDlSYGNHJOKoQ8/iBBlEUdvoZe80AEUuCIML1Yp4rT3Bof431G0sMb+7Dzlv0rsmhme2SL5I4Cltuqlgl86zNs2ZtAbcZMjXRAAuef/4n/NTPnD/jfFq79nwe/uefEAYRSSKxLNVqsVb2qE65WBmTalkJtGIpQ09/llzJYWBdnkbF54UfTxAGqpOB5Zg4GYNib5b+dRqarqvWjGUPXYNMxiaMEnRLR4sSsqUMhZKN2wipVz2mRpsMvUhnON9Hkkgmx5toQjA4VAKm2/W1i2K3u7FUypPoRo4gbM7YtyiKOp6AE8HavyAdZbj8q/liN4D52pLN3vRyMoMDP1KdiXQN09DnLKdpqnh91LKEdccczsjFmDb8rghJnICYthhaloEmVOIUhxEei7KQK7n99gLvz9bicZy0RObceMJ2bOZ8x+xkpltUd0rVdLnMza5uVfMRxwmJlBinyPE4bkh5zF3JcRzzgQ98gIcffpjh4WEuvvhitm/fzvnnT99f7r77bnp7e3nmmWfYsWMHN954I/fddx8DAwP8wz/8Axs2bODJJ5/ksssuY2RkBID3v//9/NVf/RWXXHIJl19+OQ8++CBvfOMbj8k+LEkYfvOb31yRebrZtGkThUIBXdcxDINdu3bNeF9KyQc/+EEeeOABstksn/rUp9i6deth16kbGpteMki9rNyPtYrP/j1V8iWbnjVZ+jf04rkJJAlRDIVeC01o5IsO1djF9yN61uRV1mjWpNibY9+ecdyar0qNOAZuLWD/njKhl5DvtVmzoUgUSBKZUJlsUj5Ux3EM8jmHYk+GRjNiYF2OA89N4tcCojhBd6BaDrFzFj2DWTac0UOMqk9nWQb5gsOZLxrEyZkkMuHAnimcnImm6QhDY+3GEpZjgkjo680RtuvfrdHwmiFjI2VcN8C2DZI4Iol1pNBZM1RkcLjAc7snGD9QY+9zU2zUJaZlcHB/FRnHrB0qsbYnQxDGTO6rMHqwip21MITAtAwGhwoYpgpg1y2D3jU5piYbVCaahH7C1ESDgXUFhKnxvX9/jmd+NIZAYDs6Z57fj5mzyZZsBoYL+F5CY8rF0ZUVM0kkumWALogRrN/UQ6E/S23KI/RVCZpCj4MQglrZZWK0wdiBKvWyz54fjjHyXJkzz+0nDNZy7kVD5LO2uggk6svqNkPqZZ/nfzyBZU/R05+jWHSwsxZnnfUiDh16umMxBDh06Gk2bjybej2kWFTJHJ4bMXGoThCo8UyONLEtjVJ/lt7+HJoh+NF/7kfGkC86DGwoUurPYjuGKtTdimdsVH08V+K7EaXeLEPn9Ks6h4agWvZUl5esBQLK4w2yRZt80WZq3MXzfPymz9AZvZ02jO2bjOeGqruNoxPHkpdtOXdea//+/ft5xSteAZwY1v7ZTDt3Ww7LrmSQw4m1mQknh78BzO7cMd/9YjmZwX5LGOZy9oLF1FVMq+z0+u7UT5xVC3Ap418qcSuRqhvNUGLrSGnXeZw/+WTx5JkZjnSp1nO4Y9Zu23m4+U422nsRt2vHalpnmq5pqnTXAu7+JJkucJ9yYvHYY4+xefNmzj77bADe/va3s3PnzhnCcOfOnfzBH/wBAFdeeSU33HADUsoZBrYLLrgA13XxfZ/JyUmq1SqvetWrALjqqqv40pe+tLrC0HEcpJTs27ePjRs3LjjPcvna177GwMDAvO995StfYffu3ezevZtHH32U97///Tz66KOHXZ+mCbI9DvmeLE7WJI4invneKM2Ky9jzIX4tizQE2azNmuEcfYMFRvfXVes0W8f3Eg48V6FvTRbdzFDssxhOetj/QpmxAw3svEnsJ7hugO+F+IcikAK34TN0Vi9C1xifcKlNubgVn43n9GOYPvuenaQy0WT47H6iOKZedsk6MfmhPN9Jhnkh1NlmHQANrIxB5Eu8ekhl3MWrBsgooVB06F2TIVfKkslaJIOQxJKeDXlCL8LfFwJCJXf0Z8kmGUQru2/8QJ1D++uMHpikb7BIT3+ObMHmuR+N8pOnx1m/scjAhgKhLwm8kEP7yuR7M6wbLlIre4ztr5Ekkg1n9SIRaBqgCTYMFxne1IfnhtiWQbWimsD3DGT5yfcP8NwPJ8g4JoW+DD0DWc67aAP7ni3TN5jnzLP7ObC3gldXZYMmDtUo9SrX9ZmbB9B0DTtjcdYFOepVn4mxGs2KTxzGCF1nYLjIwFCRtRtLHHhhispkk3rF59knx9j/XJlnnhjlpa/ayOBwL4ahLGmJlJhZg7rrM/p8jYMjVQY3FOlfV+B//MaH+L3f//0ZMYbffvTP+R+//tuMHqrQqDtYlk59ssGBF6aIEFQO1fAbPoNr+ykUbaqTLvWaR7E/y5mbByj0Op0bZxjEVKdcGlOqP7Rpa2SyFsawTt9gnlzJQdPUOHVTta4TAsrjar8KRUc9wFQ9Duwtk83bqj1g6+beTlTxmgGGqamHBUNf0Nq/bdu2zsPYiWDtX4wjqYG8qGFgns4d88+zNCEiE4mQiwsXw9A62fDtNoxi9jbEyrWJi6NkjmXJaGWtHymHG9vhLJ5SpRxPhwp0W20Ps852kpgS1Ce/S7k7fKDbottJFDIAn1Z1hNnLygXd+CmLc6xdySMjIzN00vDw8Bzt0j2PYRiUSiUmJiZm6KG///u/Z+vWrdi2zcjICMPDwzPW2bYkHguWnJUshODyyy/n+9///jEbTDc7d+7kqquuQgjBq171KsrlMgcOHGD9+vULLqPpAiEFzWoTr2mQLVqc8eJ+yuN1Qi9CaCqGb/xQFa/pM7RpgI2bTcJmiET1v61WPKbG6hi2jmZoGJbO4FAP9bKP1AVh7FMdb7LhjBJO3iTyQ6bG6q0YO43+gSxjYUz5UJPRkSlIBJGvXJhnnNPH2HiNyYM11p/Vw4YzenniSY0oTGj6LlEcE0YSHTBsQRQLcnkVAxn6CdlilkKPQ3m8QRBLqpMejYqHnTVA0yiUHKpTHvmiQ2kgS7ZoY+g6zUqTF3ZPUhn1CZplSn1ZNpzZw0suXM++vRUSCZGfYNsmazfmaZZDxkdq9A5kWDvUi1sNiJMEQ2iEjYA4UgW+z9zcTy6vajSuHS5hGjpIyejzUxzaW2VgXY4zzxukVvHpX5cnCcGvBRT6HZIwwa+HmIauahLmM/QOZMkUIwbW5dEkjO2v4dgGhY0Z+tfkcByD8YN1qhMuYRSja4J1QyXOOKef6niTSrnB3j0VyhMNRvZMUa96vGTrBi7YOkTvYBbXjWjUAnp6lXXPrQcEbkRlvMmVv/A2dEPjpt/7n0xMvMCZZ57D7930+7zupy4nDiXNhk+zIRgdqXJotA6aoDpaR9M0ms2A558ZRyLY+KJ+1g6V8MOYcKIJUsWH1io+SMjlLPrW58nkLZpVj5zuUOjNdM7h7nZhSRzTqPn0rckzsL6A1/AZ26+EX6kvQ2XKxbQMcgVLJdrUA4SmYZqCqBmSyZodS/7evXsXfKhbrrX/eNJt1VvS9bzjel683EzXrHMntK2TSx5py4ojFi+vIoSKP2xnKs8X5riSNrEkTjDNmZ1OdEMj8TjqBJSFspKBRe7AMzPN23GJC29n+pid7C7l2SEMUct63D2t2808u9C1lK38nVPEcnq8OVphODY2xrZt2zqvr7vuOq677rqjHNVMnnrqKW688UYeeuihFV3vUllWuZqtW7fy7W9/m4svvvioNyyE4NJLL0UIwS//8i/PObDzqe6RkZHDCkOBQBcQRAlBrcno/iqWrRN6CU5GwzQNNC1Ctmq/PbVrBNvUaTY8hIRcyaIy5TF5qEEYqexgU9dIZKKsToAfCypTDV740Sgv3nYGYRTjeTEjz04Qx2DYOoWiTX2izo++e4ieviylvgx+IoiTmMqYi5TQvyaHbmqYrVZs5f11REZgW6oWoTANIhliZXXMrIa7L6BWdbEcg1o5AK2VVGDpGLaGKXR6+nIIKakDMpKYmsb6s/owzQE2vXQtT31zH/ufneSF3ROUxxqsPaPIho0llbHrBui64MALNQQQuAH7n/exMhYbN/chhKA60aRedvEjKPQ6BEFCUnFxmyHjE4e49pd+id983+8TNi3CIG51kEmQQC5jMXmwxuRojcAP2L9nSmUpZy16ig6+G/DsUzUS0RKPJZv6ZJMDeyYp9DggNDI5E13TEEIS1APcRoDrRUoIRQm5vM3QGSWESKhOuEweqvKfX/fZ+8MJ1g6XGDqrl56BLJatE8WqP3TgJqq7jR/xpv/nCkbHXgQIbvjli5Qbd9JDknTqq+kCTF1Qrwc06wG9fVmSOALT5sxz+in0ZmjWApIkwW34VCZcAi/CyZoU+hxCYqYmGux/YYrAjyj0ZqhUPXRdufeEhChJcByDyQN1AAbO6UNoUK14ZAsWpXU5cjmLJJb4bqgyNwP18FHqy1CrB4iWBcKyLOI44Y1vfCPffvQ7JHHSaXvY5kis/ccDSVc80DLrCi7Fwjjbldy9bPu95WyzXXtvKTdsTROIWJ23MpEIfeYyYpn7uxBJkpBI9dDcTTveMIoSLGv5Imu+/s5tOrpw3uVmzjMtphc/1tNueImmrVxXmOPN7PMuiWXn82i3FGx/XvMlmCg38soVPz+dWIlyNYODg3NC37oZGhpi7969ndf79u1jaGho3nmGh4eJoohKpUJ/f39n/iuuuIJPf/rTnHPOOZ359+3bd9h1riTLEoaPPvoon/nMZ9i0aRO5XK4T6/O9731v2Rv+xje+wdDQEKOjo7z+9a/nJS95CT/90z+97PXceeed3HnnnQBMTk3w/E/G8D1J6AfUaz5xLCCOMByLvsEMkRdTq/qYGZMD+8vICOrlJqZlsO6MHiKgGYQ09vpUpprYtqGCtDWdbM5QgqAZsOeZKepeTC5vU696GLYBSYLvRxBJapM+uq1RnmpiPqdjF0wmpmpM7q+jGRo/+cEYUmj4+hmEQcyhQzU0S6ALQc9Anh99bz9T400kArfm06z6aI6B4ZjoWQOBRNc1XD+iPuZhWgbjB6pMHqwhDA0nY3Jwb4XnW63/nKyBsHXsHhsvjJicbDB6oIKTsyj0Z/H9mFzBwjB1mrWQMAgRCQhD54WfjNO/NoffiKhXPPxYFWh+9gcHMHUDAdx7/yf47vef4BN/+Qkuu/jdJEhqVY/kxxNopsbUWBO34lKrh+SKFgJJnEhcL6FaOUAcQRxDYkie/8kEpqah64JmU3X6yOQsLFPFIgJEYUIYhkR+gudFhEEEGtiWpbqXCEnTi6jVIyYnmuzdM8mPnzpI35ocPX1ZVYNSV6U8GlWfMFDtE6fGmwgh+No//gg0iDzVc1hzdNxqwNREQxWVrgU0an5rH2IG1xfYv6+Mtr+KlBK3FuDVA4QGmYKjCqBXfHVji2Lcut96MDCVtTqWkEiiJCaKYryaSpjJF22+/50RmvUApKTUl0GYuhqTpiFjie8FJKGqk6kJQbMZkUiJpanYPBIYKJ7Jn3/0b9l81vkLf5kW4WMf+xh33XUXQgguvPBC7rnnHmzb5qabbuLzn/88uq7z/ve/n1/7tV+bs+y9997LRz7yEQBuuukmrr766kW3pwoft/5vTWsXjF7spiiW4opdwJXc3bt3OfeQREr0Jd6tVZ9s1RFHJawYs95fGZdXJ7FBm18YxlECR56YPC9tcXO4G3C3MNS0JX5e0OkiFCetFpInI7POuyhOyHRbdAUIoaEJ5i2w377vnqzCeNU5xq7kiy++mN27d7Nnzx6GhobYsWMHn/vc52bMs337du69915e/epXc//99/O6170OIQTlcpmf+7mf47bbbuM1r3lNZ/7169dTLBb51re+xSWXXMKnP/1pfvVXf/WY7cOyhOGv/Mqv8FM/9VP09fUd9YbbanfNmjVcccUVPPbYYzOE4VJUN8w0456x4cXY+SwD6w08L6Za8ahPNqlXJVEUoxs6ZkEnBnrX5tA11UXDa+iq5pwuGFyfw3agPOkThjEJqpaUYRpYGQPbj4jzNlEQ0agH9K/LYtp5dFtHSonvx1QOVJBInKyJndHRDdW9pFxugpBkiiZxAogEKSSBH1LecA5SgIwTRqXg2YMGgiyaLoidBGlJKonOs/v1mf1JBcRhFi0SICBw8urGGQo0Acm4RI6DpkkM0wTZjyz2keQlYaDKQAgXZemoq5uVpmsEWozUWj1+Q4FxUE2XepGYRG2jpi5OvjvBv+36Z9549V/z4Kev5dz/+ivki4NUDL0TPzMqdSInRtqqXIZE3RRUYXABRkvDtFtjxa1fAyIS8CV6rKOFohW0DwkSbJCmsoy0b4KaLZAZSbguJg4TfCSerlHRBCOBhjGhY9qaKoECSPJEWoJsSmqRS8mRGLnpC3UiVdyfH0QqG1AIXNcnSUBIiW7raBmDuh9hmhoylIRJQqYvo9z5xrTAiaIEz5Vk+zJkcva01aB1swiCkOq4i53TyJYsdEOnPuUiTAM7YxCiEVQDJSSlJAxDAl+51nRNxcPFUYxpW+gZU2U6aoJnnnua3/notawZWI9tZzg0tpeXvexlS36oGxkZ4ROf+ARPP/00mUyGt771rezYsQMpJXv37uWHP/whmqYxOjo6Z9nJyUluueUWdu3ahRCCV7ziFWzfvp3e3t7DblO5F2X7NAeWkmk8d9kF5+1sp/162gomWttcqnUhSZR/T1+GWNE0gSaEsuCas7YjgBWoRtIphTKrrWPbqnmkdfAOZzFUbxwmxrA1g0pgUZm1ITFLuWNPH7MEeZJ2Rek+75IkgVkW3XZLwfkShKSUaTby0SBXrgzUQhiGwe23385ll11GHMdcc801XHDBBdx8881s27aN7du3c+211/Lud7+bzZs309fXx44dOwC4/fbbeeaZZ7j11lu59dZbAXjooYdYs2YNf/EXf9EpV/PGN77xmCWewDKF4ejoKL/wC7/A1q1bueaaa7jsssuO6IvZaDRUrb5CgUajwUMPPcTNN988Y57t27dz++238/a3v51HH32UUql0WDcygGFqZB2NfM6ikBdksibr1hfZv2ecqbEmYSOi0GdjaBoDAzlyJYfaZJPEC/EaIUkU01PMUczb9JQ8RkdqTI7WCeo+a4dKWLpGU2hkMxahLkiCiOpBl56BLNmMCVKSsU1qB+r09WQwMqoGYO+aLIMbMkyN1sjkTPp6MjSbMb29DsKdHrtMJJFUIkJIiZMxVQs4NyQRykIoWheRtvtF0zRI1Hu6qXWuOjKRGK26X0mshJzREgnqiRMMU8f3QkIvUr1KTR1aWX+5gkUcJYRhjKDllrRVl5XAj1VAdKJaWH3zkXt50Zafp3/9+Wy+6Od54lt/wxve8jvQKp+SxIkSKBIMS8fJmqqMS6Ria3RDlRASQnQ6Hyi3HCBVHbPAi1SogKl1lpFSksSytX8ahpEQRwlCE8qyKFWWru9FSKncNYapt1ptaWTzFu0bVBKrYuMNXVAo2Wx71RnEMsFvqjJExaLDnh+MMj7eYGqsTlB2MXsMzjhngKFNPcSyFcJQDwmDiMGBHEPn9JPJWliOjm6oc6FR8UAICj2qbmU3cZyw//kyA6Usazf2YDsmYwerhOtDMjkLTROEkbKYRV5ArR5i6AKrJQClpiwM9ZpPvuBgO6pjimFo7Pjbv2+VadIwdI1fvPrt/MM//MOyvrdRFOG6LqZp0mw22bBhAzfddBOf+9znOjFRa9asmbPcP//zP/P617++80D5+te/ngcffJB3vOMdC25LZZ+qz781YVljFYI5LvPZdIvI2cuCEutLvYcs1ZI5G13XOg9JM8cgSOTRK8P2w9Lsft+apqHpgmiRY7QQi+XtLOYKF3R1ZNHaWedL2/bJbjXsdiXP+/m0RPV8CULtY3YyCuLTicsvv5zLL798xrS20AMVvvP5z39+znI33XQTN91007zr3LZtG08++eRRjater5PPL97zaFnC8CMf+Qj/83/+Tx566CHuuecebrjhBt761rdy7bXXdnzhS+HQoUNcccUVgLrZvPOd7+QNb3gDd9xxBwDXX389l19+OQ888ACbN28mm81yzz33LGHNAqlpVGoBAvC8BMvRyPfliNFpNnxEEzRLo94MCBGgC5x8Bqlp1OsBEkGu18HIWvSuz1F3lcuwGST092SQGpiWyeShGp4b4voR8ZSLljUxhGCq7OL7McVe1SKuWvbZ/0IZP4gIA1VfsNFQxZWzeRM0Qa6Q4azyXrwgwDYNojCCSKPXzjK4vsih56aQQL7XIYnByZmEcYJjmRR6HSoTAYEfIhDUQpdMXpVpKfZm6F+rejBPjtXx6iGGrZMrOOQLJiKRVCZD9j4/QWWsSZygijT32vT35tEsg7F9FdyqT7EvQ39vEcPUSISgkLNoVHz2Pr+P73/7S7zpV5TIuPA117LzL/47N7zjKnpL/TTrHkIXquWgDNhwZi+lQZuxgw2atUD1XB7M0TOYJfBDwjDByZgEfkySSDIZnSiU1Os+kwfr+F6ElTEoFh0l7IQgiRJV2kFq1KdCalPKHZwtqcSYg3unODRSJQpjBtbmKfXkMHWdnkKOjeeo+MlEQGXCxT9zDaWBAuWqB4DvRqrMTdNnquwSxjGTlSYRgoG+HGvO6KG4Jo/bDPHGm9SbKlaz0vCpP3kQy9QwbQOhCYTQsByDXMnG9WZe8MMkpjrWwG1E5HsdxkZrTB1qEsYxxaJD1Dq/gyBW5ZMEOBmLbMlC6LrqyaypdndhlBBFMVogiOOEKJR84e+/zGWv/3kK+TyhpmHbNmeeeeaSv7NDQ0N86EMf4owzziCTyXDppZdy6aWX8o53vIP77ruPL37xiwwODvKJT3yCF73oRTOWXSheeNFvc5c/dTqfYVH/8PSyiyFn9qudLRSXE+c3HSu3PEtOp2RNq69y97hXwq6RxCoDeL4EE8PQDtsL/GhY0BU+bTDslFyZdj0vbd1aq4fySWs17FLV7ePfHZfabik4X4JQu1NPmnhy5MhkJb5ZJyfnn3/+kkqTLbtXshCCdevWsW7dOgzDYGpqiiuvvJLXv/71/PEf//GS1nH22Wfz3e9+d87066+/fsZ2PvnJTy5zcJAtWUReQugFaDrIJMEwdc44q4fnn5kgdBNyBRPHtlR9Oy8maPpYtkEYqVZngR+y5sxeMtkCjbJL5MU0pjxG902RzTv09WdVF5N6QBQl6H5MdaJJ30AeGapg7lzRxMqootFhGHe6gRT7c3g1H10TWLaB5gnQQcYxWgzZkkk2n2NqrMnkaINsxiRJVE9jy7GI/Agna+BPujSDBN8N8OuBsix6IVEYE9TVsahJSRzElPoz9PblGPcqTB6oUxlt0NOfId+TYWCoiOcGRF5Mvapatsk4plkJWLexRP/aPGGPTeBF1MYb6I6JldExehwE8OC/72Dzy7aTLQwCkC0Mcs5FP8/ffv4urrriBmp1H8c2MA0Nrx5QmXBp1gMqE01VrkNI6hWdKEgIvJAojrFtUyVW+CGJTIiCBNvSMYSkWg9UEotl0DvgkO/NYZkGmiGQsSTjGMR5i8mDDRpVl0Jvlnzexi3ZjB9Q3VH8gYBMzqI61aA22aR/fQGE6rTSqLhUJppkCjZWq5SI64ckQczUWJOmHzC5r6YSPgYzBM2IqUMNwiCiMlpHAPmeHGEY4foB1UqC9COiVluvTMkmV3DIZM1OJmbgqgcFKSWGrVP7SYPQD9ENjWJ/niROqE261OseoRuj64JCb5ZEJlTLLiR0EhjcRkCtEZAEMbapBKMExkcPcs11P8/ms87jZ3/m55ftTpmammLnzp3s2bOHnp4efuEXfoHPfOYz+L6P4zjs2rWLL3zhC1xzzTX8+7//+/K+ty2644XHJ8aA6Rpvy1VJQluC27n1N4mTGdbbaW0nkVJ0Cg0LMVM0tl+3y4cIZgrNbmZvo7MFKdFakQZJIjuuaLEcpXQY4mRuqZo2uqYRBOERrbfd0m0hUaYJMW+dxO496i650h3XuRR0XSNOYuJYqlCNk4huV3LcEimzOxPJrrCE7gShJGFO+ZqU5XGq90r+0z/903mnSymp1+tLWseyhOHHP/5xPv3pTzMwMMD73vc+/uRP/gTTNEmShBe96EVLFobHCk2AkzHwkojqWIDmGMSxJAlies8sUpnIMDXZ7HQgCMOYfMnBrfpkijamKXDdkKmxBu6Ux+aXrmVif4V1QzpJEtN0IzQ81p+xkTXDBUxdozzRwM7olEpZEAmlUoZc1mLTiwfQDEGlnKXYl+G7//cFMlmLzeeuYXK8wW9+5G1MlQ/O2Yf+gQ089IV/Y2y8wQu7JwgDycBAjoENefrW5qlMevSuy1GbdLFsnWrZo96I0IWkUfERqNIxSZRgOsryZmdNNp3psG59gf3PTtFwA7IZE7tgkctZnLG5D9s0iOOIF35SIQkjlcWdMTn3wnVEfkQcJpQnG5QrHn39eTZu6iVYX2Lf6DM8+f1dPPnoZ2bsR7B5C+s39uBMuWw8u0ToxoyPNugdyIIuyOUsTMtgYEOBfEH9Xyu7reLMBkmskjTiWPX9zeYNiqUMQoOx/VXGRmoEQYwhoK9fuWU1XSB0jSRIaGz02P98GbcZsmZtnlJPhmy2zIEXpnCbEQNr8uSKDoahMTCYJ1+0iaJEibwwwinYGLpodVuJmDxYo1C0iaYiRAKm0OjpyVIoOSDAK3vkchbF3qzKpnV03EZIMwoga9KXt7AMHbcR0JxoElR1MnlTJa/UAjQNDEOnMqbOz56+PIVeBxB4jQDXC7B1nXyfg5MxMC0DvX1DlNMuqVCPyToWxYKNEAJT19F0+B+/9tv85q/eyDcf+zd2/tP9PPXUU3z4wx9esrX/q1/9KmeddRaDg+oB4M1vfjP/8R//wfDwMG9+85sBuOKKK3jve987Z9mhoSG+/vWvd17v27eP1772tXPm644X3vryVyAl1CoumoYq6E5bK6lYysPRtjYethh2y/1bLXvYGaMTetEt/qIwXlivCFQ3nJbAP9x2qmUPy9bJ5u0572uaaJXSms62FULguSGWpWNay35+76DKnSwgDA2tc+4sNM9CLFbeUWjzt8XrvimrY9aaf5nJNkK0rIZJgpQnl9Ww2zKdxOrBoNui2/63XZKnnSDUjslcrlU65fTiwx/+ML/1W7+FYcy9biTJ0jwEy7riTE5O8oUvfGGOC0rTNP7xH/9xOas6JkgESQSmpkKT/HpAlCQ0KwGxlHi1QHXWiqV69EJSr/lkiw7ZooWuC7I5B68Zs/+FMqEf4nmqhd3ghhLhoSpT4x7P/nCcYm+WbN7ErRq4jRDLatJsRGgCdF1ndKRGrmRRm3JbWWaS8QM1fiRHMDMGU+WDvO+TE3P24a4P9DM16ROG6kl44kCVUT8kkZKpSRffjajVfGQi6enJoOs6jiMJ44Q4jpGxbO1vQkZCo+Yxtj+ifNBicGOJ3vUFMm5Io+yy/5kpDhkVSmtyoEnqkwGGIQgiga7rNKseL+weY91wiYQEy7FIxhuMH6qiC+gfzHPH//c5nn5sL/kem1o1BAmbLxxgdG+VidEaYZioDiflAEPXyRZtms2IOInRY51Ga18sO8YPYoQQWJa6SSckGC0XvxACw9QxLJ3hcwboHcxzcG+F2pTL2KE6a4dL2FlVwgVDx3QMMnmLA8+VqVU8cgWLNRuK+EHCxIEK+/dVOftcC9M2mRytsf6MHjRdw8maTI42KPVnMWwdWuUxmnUPK6NTa4aYtkbPQJ5zLlpPz2COZtnDzRj09mSxswaBF6FpgoH1xU5mtR/EWLbOhrxF5MeMjVSZPKRibbM5E80ySIKEnsEcfWtyOFkLKSWBGyGFRWEgQ7HPIfQlmg62baKJaetS3OqmkZiQE4I1g3klalC9V7VWH9bNLzmboe+pJK7lWPvPOOMMvvWtb9FsNslkMjzyyCNs27aNYrHI1772Nc466yz+9V//lRe/+MVzlr3sssv48Ic/zNTUFKCCqT/60Y8u+n1OEpW4FAbxtCVusdqEs2LfDhv3J5VHASDwok69uK68LtWm0NDIZKaFaVtQRlFCEMSYptZxS8+3rba7MPBjsrPCe9rj1XWVLNO2GkopCYKYKJKYR5E1nCQLJyq0hceRCEM4vLu+0+95nlI8ME98p1i+i0/TWlbDky3WsCuEIZ7Hkjz7uLYThBaKiU1ZJqe2wZCtW7fypje9qdPZqpu77rprSetYcku8V73qVdxyyy0LznPeeectaYPHEl1XCSdBoJEtOsRRosSeY5EIMB0TdI2etTmEoVEqOUyM1tESiZ0xyeQdMjmD4kCGPT8YY2K0jpSgCQ3DgNJAjlrFZ2qsrlqs1UJVnkZAvRrhuh6ZjE0Sx6pFXBiCEEwerONkbFSMWETNPbz7JvAClWyRgEBSr4U06h69+SKFXoe+gSy+F1Dqc/DDGDs2cOshk7FE1wBL4AiDTM6k1J/F9UOCRkTS6r4RRTFW1mLDORYTI1WmDtbQDYGTtxCGRmWigd8M0SyNg3srWKZB/5osnqfa4Bm6YN9PJojCmGbNo1kL2HzROiZ37aPpBowdsPEaIbEEO2+iGyrj08oa6IZOEgcEXoxpqBu+ZelksgZoqqyGnVFu1jCUBKH6DBMJjUZARqrez3bG4ozN/YyP1jm0t8zzu8colrIMbCiQyRrIRKDpDsMv7mffTyapTrpkcgZ9Axka1SaV8Sa7nzzAug0l6mWbJJasP7NXlaKp+sRBTHEwoyyWXszYgRqaLhkfqRC4kcoilIKpAzWqky6aoaHpQKudoqYLsjmVVZxplTSq1nxGD9aQsSQBzLxJ6IUEsSSDoHdDHsPSsTMWQkIcxKrHcl5g5yxMQyOOVMKJbsy0khi6StaQCViOciGbQuD7MY1GyGc+dxdf+ofP01Pq4y1vegcXXPBt/vIv/3LJ1v5LLrmEK6+8kq1bt2IYBi9/+cu57rrrcF2Xd73rXXzsYx8jn893Ljy7du3ijjvu4K677qKvr4/f+73f69Q/vfnmm5dU2UBGsiXepq1OS72mz2wrt4Alj2lXdVvwdS+btOL+9JZ3oVkPyOYtrJYFT9MEvh8RhvFhY766k2AWsmAqMUjHatiVh73EPZ5nu52M1/lFX6dkzRFkJrddyQvRPhyJlHRvfVrXz4wdFcs1GTIzq1ueRHUNu/cyilvdb7qYLnYtZiQILdbbO2UJSHnKxxjec889nZqIs3n88ceXtI4lCcNPf/rTfOADH+DFL34xb3jDG3jDG97AunXrlj7S44SmCfKtYsl+0SYMYkIvptifIYlB65GMHqhhZlTtuFrVUy6cIMIwDYSQmLaBrmm8/GfO4tsPP8uhkTK5Pps163toNn28ekA2Y5Et2Bi6xtShuupCUXFpVAN0Q8OxTWWBqwYUe7OdOnMD64uEQcTU3uph96PRCEGAkTXI9WUJgoQoUidzruC0xJUglhBHEsMwKJQMbNtAM3R0Q1N9gHuySJlg+RaTbo16I0AIieWYWC1rmJ21cN0mg+uLnPHiXkZH6ux/fpLJcRddN9A0yeREg56+LNmsjevG9K3JEbgxE6NNapOqj+/aoSJ7f6LcqvUpn+JAViWAFC0Mw0RoKlM2W7BUCZtY0jOYI1tw6O3LoJsawgjRNcjllRs0jk08LyCOdJyMymSWUpUBals78qUM6zf2cHBvhalDdfY/N0Whx6F3IEeu4NCbybJmQ5HnfzxBddKlb02BbNHh2ScPUp10qVYD0DQmR+sMtGINkRK3EWBmTExTw/ciDEun3vBoVFzQBMWS6sYyNdbAsE2Gz+4lk7OIY2W587yARjMkmwVaAfaRGzJ6oEoUQaFgUerLKje6bWAIrdN9px0baBYdzIxBIsG0VB9VoWk4GaMjToBO5ncURiBUmIRULcH5/vcf54LzX06lWuH//MW9nHP2JnRd53Of/6vWd2bp1v5bbrllzsOhbdv80z/905x5t23bNuPp9JprruGaa65Z0nZQh4y4JcyUgEvm1LprT5tPlE1brIAFjGFSyk6xYJgWcO1loygh8CImvAjZEk9hmDC4Vpn92hnfnhuhG+3lxJyxdQuvOEo6Luv2GNo7rOuCMIpbpW+OPvu0U8Owy5rW3uf22BHz18pblCW4ktX25lsQaDUB7LjtOTIJ3M5Q7o7PPNHpfjhol+zqppOM07L2tj8fqfKIThoBfCLSDm84lTn33HMB9XD+h3/4hzz//PNEUbSsutNLEoZ/+Zd/CcAPf/hDvvKVr/Ce97yHSqXCf/2v/5U3vOENvOY1r0HXV79/pWHq5PMWhiaI/ZgwSpgKXSxbCUHb1mlUA9auV+Lg+WcnqYxVMS2dXMFUGXKJxDR11m0ssXFzDyPPT6Ch4eRMdEtjcKhIJqesVms3l2iWfTa9dA17fzKJ540jEPheRN/6HGEzodBn4zU8KtWQQhgTBXGnSPNCFIoWTt6mNmVCWENbk8NwDHw3Il9yKPVliOKEnr48rh+QzZqQQHmsjrB0kihRLtoYhA66qausXzciyivxIpOEXN5h/RklxvZXEUIQ+LB+Uy+1ike2YDM11iTXl0HGUKt59A7kkLoglzM57+VD/ORHB6lMuLxk2wY0Q6N/TZHevhx7dk/QuzZLruCwZrikPo9I0rc2R09/lsCPyIYWmYyJkzPJ5CzQBFGSYLbcue2gfk0DP4jJ5i3Vfq7ugwA7Y3QukE7WpNSXpVnzGR2pUJ1yObSvSibnYmcsckWbobN6MAzw3Igzz+nDtDV+/MRB3KZqI6dbOnGcsGa4B9PSaVZ9sjmTXE+GMKrQO5An0RJ0x8DK6Gy+cC2Fngy1skehxyFbsLGddu1DieXqTI03mBj1aTYCQjdC13XWDvWgmxqZvMOadXnsjIltq7IybjPCq6tEKCdroZsaXjMkShIsUyNouVR1XYkNmSSd+nkAQZBgGoJMxiKbNzFMgwce/hK33va7nHPOZr7/9C42bhpgw7qZZZ9OBGv/fLQzfZFK+Kp8DCVsgiDqXOCNVrkjaMe+iY5o6a5lOKf2ngTZcuVJCVHcSjLRoFEPaLoBSaLKRvX0ZSFRD5OeG+BkrM62JZIwTDpJKJ4XdgSRaWotkajGpmoWzt1XQSvWEDGdcMPReb1mZ7xOH7f22HQlPI7EYgidUIb5aCfnzC7D0w4sbJfM6syvLR4TOh9tq2EcS06AW9DSkO1yPQtbdIUmVP1UXSMIlYdpuccmZQFOcYthm3e96138yZ/8CRdeeOGy214uK8bwJS95CS95yUv4jd/4DVzX5Wtf+xqf//zn+c3f/M3Dtog5njTqIY2Ki9uMEDoEXohlaggdklg9XTamVF3C9RvyHNozhVv3qJY9nJxJXE9ACvoaEVbWQNN1dCTl8TpRJJGxJJtXHTKmDtWIQ0nQCMnlHHIFB8sRNCYDJg7UyeVttAQSqUop+0GMk1Mt7w6HYRpkCzYylrh1D8s2KPVlGTtUV+7ynLJqxiTIWMU51Sc96lUP27EwbI2mG2JYOsVcBsNUT52hgCRU3TTyPQ6FXod8wQGpgrgb1QAZmdi2idRM4lgQeiH9Qzka1ZDgQJXedQV0TWftcJHJ0QpRGGPbOlPjLlJKNr64nxd2T9Cs+hT785RKDl4jxM4aWLYq0mzoOnbGJJu3cDImmZzZsQpZtkEm23V8BCSEWLayklm2TrMRAgInM/MO62RMegdzNGsBlakG9bKH3+qFXNNV3GS90iAKYzKOyYYzexh5dpLyeI1s1iAKE9ZuKJLP20wequPkLQoFm/EEMjmD8IDEEDqFPod80cFt+GgSSn0OgRviNgKSOMFthHiNgP8/e38ebFl213ein7XWns94x5yzqlQpCUlUuQCBgHbbAmRECyiDLdPYbePGyHL7Mby2HzyiHzQgIIyNw3YITDusB5Jx246ShY0lC56wmQzGliWBhJALSVWqOaebdzjTntde6/2x9j733sybWZlZWaKQ8hdxI4d79jn7nLOH7/r9vsN0L8NYJ4haWe8zWosZrSZOMT4rnOl6LanyGs9TDsS0mdtVqdGZi7rzPEmDoMw11oCSCqkEUnrtn054ky4qGmHZPDbA9xW+r/jZn3Mq30cf/UPe//5f4q+/9S3M5zPOnz/Pb/3Wb71kFnVHlm2Bl3Wq3s6Ds2q5qL6vlsbmUopD+bnyYMewrXRR0WjDqM2mtrh7ROgr6sp1nXRjuXJpzu5OSpQErKzF9AchcRy4cyStmM/KJTDsRCJNY/GVUzB77WffNIa6Nk456zsfy+Zq8vcB+xZgmQe8jwhfwCi53dRrPT87ULi/bw1CHq0efr7qbFNuVJ1R86Htlttfa9EC3Fbnb+lr+MckQ7lbuxzV0e2qW0gcFAi5z+yzvLOfa7XfjP+cr42NDR5++OHb2vaWgOF73vMevu7rvo7BYMA/+Af/gN/7vd/jB3/wB/npn/7p23rxO11KSYYrMbNZ7gjPApK+657U2qK1ZTgIKcqGxbxidaNPECmKRUFV1QhPktcV6byiNwpRShEEChl4KOW4d0GsSPoB2aJ0gKfnAIXnW8ZrkRtF47z+Ttw3prcS0Z+V1LUTuyghaIxlZXyCn/3Oa3kAqysn2Do/pSw1VelADQh6KwmzvYLHPnGZ+SxntlNSZDVgSXo+2aJESoHBkCQhQimmuxkY6I8jVtYS+sMQY535c6PNMvkjHjhFrvI95ns5yhOki5KN4z3OP76HsIpeD7YuLegNQmTsky4qhJL0V9yYfnd7QWNwdkCjiLoyrhunJFWtUZ67kTvDaneTV63ZtD0Qk3D1glhKuew8OJNqRRhaylKjKnFopOq2F/SGIckgoNzQ5IuKsnRArdYNa8f7XHhyj8l2QVMbokHI9nMznn16gtYWz5eM1hJmuzmLeUFV1Ez3cow17Jyfks1yhmsRaVZx6aMT6rJhvJZQN5a6qFGeO2aiOOD4PWPC0Ge4EuF5ilobN5b2JL4vydMGEEShh9aNS1KRgiBSTmmpG7xQkcS++7wClz7TP0LZCjBfFCjlQEnHV+usPF796lfxile8ku/+rv8ntS75ki/5opfcou6aEiyFC133sK4dgIsi1QIegTGO53cIFBzRMTw4Ml12D2m7Tq0vXroosMYyGEasbvSWRungjsXBKGKykzKfOxU6tABIOMCqfOmSkjwX6ViWLq7R81xU49Wm21ffo7oM5brRh0bTt1Nd6pCUjiNprePzKiWX/EhMaxLfeuUZY1vl4n7C0JFdqucXhiOuGqPvb9upaw8Cw+Wvbrm6TqsDlbe+/We7OpueQwbfV5WUAl3vA926bhyv+a4i+W7dZL3tbW/jLW95C1/zNV9DGO7fMzoXiRvVLQHDH/uxH+Mv/IW/wH/+z/+ZX/3VX+X7vu/7+Jt/82/y3/7bf7v1vX4RSkjBeC3hwlN7SCVQnuL42YgyrbHSYOuG9RNDp9QLFMPVPhvHB8x3clbWBgw3Eoq0ZDbdpUgrxusJ47WYJPEZr/eZzUrCwKOuDKubfS49NSEIJbrU1NYyXu+7DmWgWq6XwvNd/Nv6yT7rbQxfURh+5if+DXVe83vCKby/YPEZTt47YnKloC41eV7TFJqyqBFC4XkQxYp8UVIVGj8QKAlBHNAbhJRpjR94Li9XKo6fGSKkSxpZO95ndb1PNi9ZTAt6/YDdrZTSl8TGdem8wGBCnyKv8ENFcblGbArCgU+RVQzXY4yxzCc5vieptWbvcsa996/ie5JL52esnxpwZWvGykaf6SRlesUBqqZxyS5BqJb2HwLwvI4fdphcfeg7Fa31hd1XmIaR53h8uXaJL0eNYoQgSvxlykpVanTVoOuG/jDm2ce32b08RwYS0Vh2txbsXZyx9dyM3jB2j08bslnFYq/ASJhOCprGEsc+ceSTSUm0EjLcSOj1AzzPI+z5jMcJQeQhpSDLnKI6jn2sdTGEtXajxbjnAE1vGOIHnjOnrpo2MlAgtcD3Jb7vOTAtxA27KXXrlaikxGAPWXl0i7owjPm7f+8fsL29zbd/+7e/ZBZ1R1c3bhQtMGTZ3dpXEAsHuuumBSH2EJg5KjykUxW3/3DgSQl0aSlyTdIPl+P9Lo2nq14vYDbJnf1M4KGbLorRum6hVge8CB2PrxsNKyWvHa1eZcjdfcdFYXmh7Y2m5RJaa6/pqi45htKB7SzT1/VOFFIQBGr5mXe8z+cba3ac0IMjULvPfDi0/cFx9+1U12nt+JMv9RKwFJUcNebrLJAORhdKpV7AMuFudfW5zjHs6l3vehef/OQnqev6kF/oHQeG3cjpl37pl3jrW9/K13/91183vuWPohptiCPP5f36AaZ2oMc2UM5zp1LuS0IlXUybsAxGMQjBfJJxzyvWKJOAyW7ObFpSZBV+7OGFPlpr5ytnDHtbC46dHuKHijKvyecVNpCMxzFR7FMuauKBR57VWJOia+04aIFPEHt4fsPGiQGeknz8KXeziBOPMPJZPybIMs1oHKILj2xWIj3JcJygT1qKvMIaSxgFRLGzZEl6AWnPRyhBNAhJ+gGn710ljAKee3qP+axgvJLgee61XIazRzor6fdD9xkZgx945GlIPWxAztnbydg4PmSvVSmHsUeR1uRRhW0ayrzm9GuOYUzD/NGCjZM9itTQGwUYG3Pl0ozdKwuUlXjKiWJc59QghOt+dWrMrq6+2TijVzcKaxqDlO4YjGKfdFGS5zW9XnDDm5QfqCWvU+vGCYc817mstxZsnhpS64Z8XrGYZqxu9IhC5ca9ZcPqsQFpXuL5gqTvc+8r19k4PkBJyebpISpwI90w9DDW0liz7IB4nqKu9PKGFYQenufG/2HkUZQ181nB+mafMPKoq4Y0rfADhVJq+d5tC2DUdWZJxhiMtniBExR4UlI1+wbA3aLut3/7t/mN3/h11tfXX1KLumuqm7dZUHJ/lKar5hpum1KCuj6srt3vQF276LCWJQBqjBP8KCXJ0gpda6KodwhAab0PbprGkvRC5tOcumyQnkBIJwKT7XeVLpzQyu2bbO2EnPr06oizo+5RsnW8tm1H/7Y/wvaY64Dp1ZY0QkBda4pcUyXaqehb787umDPGnTNloamlcb6NNwm85PI7ODAJOIAMD3EMW6+f271pd51W1/l8abcNOx5sZ5V01CndAULVTr4abfBbz8y79QLr84Rj+OEPf5hPfepTt7XtLfWlT506xd/4G3+Dd7/73bzpTW+iLMubNkz8bJQxhvm0QEqJbjknfuC1wEe47oESbjxlLCCJYp+kF7C3kwOuQzMaxwSxx/aVlLpqiAYBVSseWFnvY6xl67k5o7WeI5tXNY1uMKbBV65bs7I2wArLc5/ZASHZPDlEKkljYTEv8T2P/ijBawGLUBIlnWVMEHn4oY8MfMLEjRKlL9k8PaC/ElPVmqjvEw9CkkHIcDUhDD3COKA/iPCVwlhYXe8Rxz6zvYLFwgFMcCOm3sDZ+WSLEimccEdKwXglJoxc1N58t8APHbE/S2v80Kcsa3RtmF7OEBJWjyesnRzQ1Ia9KwXWuDjA8WYPP/S58OTEiUpCD8+Xy1W98iQIsST+A2DFNaNka50yr1N3djcOKR3H0GUcNzd9jHieIu4FHD8z5gseOslwHAGSjZMjPF+wfWnB3taCwUpCFAc0DYzWI8qyhsbFF55+2Tq+rxivxWycGLK+2SNOfOcj2HE+F1XLeWoVrvX+PkrljrsgUCSJT15o9vYyrLEuC1pJdN1gjOsSdmN0IcR1b8paGyxtnrYQy86T6xra5aLul3/5l3nrW/86o9GIqqpu+nP7bFfT7nfX/dNVw95uynQvpyoO2z0d7Mx14HAfaLjHHLKMMXaZNJLNCtJZgZCC3d3MnZuBdCDqAKCxxi6vKf1BSBD6zOcFpjFu0aMkgS9RQjLZcTSEg/vXjWvn04L5LN/flyO4hMsc9KPEG7dQuuXcdeKXDuxa66gYZevdKD0IQkUUefjtdUApidfSIpIkcNfQVljTgdvnwyj7yuSrxDTiaHWt49Xd3vtdKsDbc+WlWgctZ5ojzK27OugD6bUcym67u3W3bqa+8iu/kkcfffS2tr0lYPiv//W/5o1vfCO/8iu/wng8Znd3l7//9//+bb3wi1FSCCY7GcZa6ly35HVB3AsRxlLrZrmabAwYrTGNYbASYY1hupvieR6ep1jf6GO0pdYGGQjKTGN1Q9wPiXuOY6ikizDL5zVoqEtDWdXEUYAXeighWCxKFNBrxRaeFORZSd043l3H96nymjzTruvjScAShoIwDPEjH2EEUS9k40SfujColp2sa9PesCzCczdD5Ut0myCycWJA0xgmkwJwxO75tMRap76e7hVYIAg9dN0QJT5eKNk83adpGva2U5J+2FrFGKTnoULBpeemgGAwSlBSkQwjptOMomoYjmPCKGA4itnbWpDn1ZJTZLTBaBwAkgIlu+4EWHEtcakjqXemvwdvlE5gISlLfcvKyjD2Ga3GnLhnFaEsUewxXE1YzCo+88ktwsTHaKf8DUJFnWsMlnjoMRyFlJl2ymHPAbEkcZ1br43Rq8qaNK1awAZlqZcAr/vxPEmvHxJGiuleznTuFjW9nr8Eh1qbdjuWgO+oatqOmpL74HqZnNCYQ4u6b/iGb3AdxpfQou7qsu0kVbQK46pqsLrBNM2RC4GuwzLdzUnnJXAYaBzuGNoDXUPR3qQN2bxEKunSKFpVaNedbIxlbzujzGqUEozGoeMkpqWzGWocKA8CiWxVzVo3+yILAbreN9OulwsFdyxMd3PydB+oy3YRYA4shm61Oisex72Ty//Lcxed6fuK4TDCb0fxNyrfV8SJj+crqkpTVfq6x2JXBwUlyzr4vV5VUogjR/83W90irGleysDQ/dl9L9fzmOw+HtMKUHTLCb/bMXzh1dGSbvfnj0t98IMf5KGHHuKVr3wlDz74IA888AAPPvjgTW17S6PkJEn4qq/6Kh577DF+67d+C4Aoim59j1+kkp6k0RaMRZcGT3lI5SGV83+zpUF5AinchdALvFagEjrj6WnB+skRCIHwBEHkIaRAWMF8lhP1XPfRj51liURijaWsNH3hXneyVzAchzTGUJYNge9RFM7oOoi81nRb0dTGjaEE7c3IsJgX+H6M1sb5EyrlwKMUVIWmNwgYbyQ89dgu2aJkOE7wA+UMkxuDCjywljDyqeuGGNc1nM9KirxiOilRnqLIa/qjkN4g4MqlBWVe0xuEDnx0BtNS0BtElPOKQEnKrPX1CxV14fh94/WEoqiZ7hZsnhpw+dkJZVq3+Z+G1Y2EvSsL5tOS9c1mCeCMcYkmQkjkMufUHurQdGWtdXYySqK1vUZ5GEY+Wt/cSPnqinsBJ8+OuXJpws6ljLgfsNgreO6TOzzzim2KeYkQsJhWZKkD04NRHz/0yWYZcf9wjIXvu85RIetWdNCgPLfvZV1TFvrA+92v0SimrgyLWYmvJNb6BIGHaQxFUeN7aomXr8ef6jJXu44hdF0U1zF697vfza/8yq/wvd/7vYzHY5qmeUkt6q4ugeuW4Lm4uKqssUIghKQ4wiBeKYk1ddu1PcBray/kB6/nbirf8eTc55TOChptGIxi11FsR56dEjrPayxurCqEIIoDgrBkPnOcXXDXGIHj4+nGtue/XVqqVLVueYYuzcVvFxFN61Fa5PW+Y0FLGzBtok3H77vZ6qxQDn4+zl/TdfuiyEe11BLPc1ZIz/udiJYuoQ1lWlKWmiS5vsOCE4Vc3THc/9yveX4pbs9T8cD+uZi8W7e9+azVEhmKFhgevY9d19hNTBwdwNniH/bALK+iJtyt5yn70u4o38n6wAc+cNvb3hIw/Nmf/Vne/va389xzz/HQQw/xwQ9+kK/4iq/g13/91297B+5kWWNRnltll3VNLH03YtIGL3Crdgktv65AKKdmwzhBwWJSEPiSOPZai4AGT0nC2GOvaZjs1AgBQeB85sLYRwlBOisZrSWuS1A2BKGPCjyqyjDeSGi0Zb6XsxL2KfOapOcEEX7gRr5COs6cqVwsnK4brLYIzxLEPrpVKFtgMEzwfUG2qJCeU/ZaLKYBvwUNUeyyhk3jzHR7/YA8r8jzGolF4RTCURKAgKKNcAO3ivU9hfIU/UFENi+wQpDNa3rSdUTmeznZvOTkvSsYY9m9PMNX7oZYZBVFVmM0jDcSLJC39jKNtVRlTdP6LiiF81Fb3ojtEcBw/+Yt24v+QYJ5N1LOs9rx9sKbP6Q9X5EMAk7ds8ZiWlFXGj9SFLrmiU9cZrgak7SpJVXVIBGM12KqwnV7o/ja15LSCU2UkmRZRZFVS5WqkFwTTyaEQAYeG5uSSxcmpGlFEHiEkdfyKCv3/XQxeNcBhqaxIK7tQLhxMgRhtCQduxg3n6/92q+96c/qs12Hu6SgW6Dj+ZKyrI+88Tdtd1Upia47Q+xrkWHXCSyLetldms+cP+ZwFLa8QrMUnxhjqUrddmMdeFGepD+MmO5lpGlFFPnLLpDnKYx16umy0m7hNXLpOt2UoK4abOJAUl03S5Pj7th2ViU3L8goW+/EzsKp28a2YhzTLlSFcKDw4PkTBB55Vi2Vyc9Xfqjwa+dsUBSaKLr+OSfac7arLnXiKNDWiV9eCKjrYvJeqobX+8IbR+nx/SNMLduSUrRxoR7YEl0bwhaHG9P6ZX6e8OXuaH2efGZXRxffSt3SKPntb387H/7wh7nnnnv4jd/4DT760Y8yHo9v+8XvdDXaEEYuWq1M60MXBs/z8AJF01iC0P1ZFhrVJoXE/YAy18wmBYNRhOe7TpxSkjByyt90UfLskxNH6lYKGUjGm32kEGxfnJHnLpvZCsGpMyOaUrfcP4+iqhxxXklnGSElAscxEQj80ENK6VJbSk2WVsg24s8ai26admwJ/WFEkbmbY1M3NNrQWLMcIwYtOOpGV0k/IPQVZe5Ahmq5hn5rf4JxI2khhfPNC9woe+VYQlU1BD0f5QmyeUk+r6jK2oktYo84VCymJRbB8VND/MDjyvlpyxGDlVHsXjevieMAa6y7gVuDkHIJZByw4ZA32kGweBAYXj029n1nXVIW+pY5WVHss7reY/VYn9F6gvI8mtKNFXVlKLIS0wptkILhSo+mNviBv+SHXl1dZ2UwCFFSki4qyrJZgpaDP90NOorcYqMxliytWMxKB9IDRdM06OrGebYO7MhrgHXXNTSNZWdnl//yX/4rv/Hrv8F8Pl92/V+KZa2lyOrWCw9oFw9B4GEM1+GVuj6gU+E2COk+l+75ls9tLE1tWEwLZz7eGNKsJox8VHtMmsa4xzVmOdbuvquOLxqGjgucpfW+WKN9Gb+lg6Tzypllp5XzoGwFV7RK4av3rTkw0u2SSer6xl000xiKrD7UbdO664y4z69sF39xfO3iIoo9B2JvsvtkLfi+8xRtdHPDMXTLeNnfdvn/R4ySr5uWcvN10PD6pVjLdYoxGHtjX0IhOpNr19Soq/3jpTNRj6LrA8u7dW05Bol9QT+fD3VLwDCKouXouCxLvuALvuC2VS8vRnX+eFhLVRmEakdw7QkYxj6mcSkSAnexxFq8wKl7BZbdywuCwHMX9sbixR5aa4arjjfnK0GY+FRlzfblBXHss3q8h7UQxD79lYgoVBw7NaKqGrS1DEYRQrjO42gtaflIgqo0fP19KV97ekLU97FCoGtLnmuyeYmSkigKnGhFG5QvscBwxY2bi4XjUuWLyvFPlLPd8AM3Au8Iy2HoE0ZOqFHXDaJ9Ps9z1hpCuu6JUgLdGvRaa4l6vjMRrgwn7hkxXIkYjCN6wxBjDcY0WCReIEj6Af1RzNrxAbvbmQOQRcP4WB+sZW83RwqBEnKprHU3FHeimYOs9Lb2py6dQk8uweHVJ2jYdi3K4tZGK56v6I8j4tinNwg5dnqI8ATD9YjNMwPA8SCLTBOEHuP1mLpsiHre83Y1PE8xHMcEgUdZ1E6scANeXxz7NNrStJ3vLHMcRc9T10SrXV1OsS2ONB1WSvCud/0cf/pP/ym+/uvfxN/5iR/nscce40d+5Edu9mP6rJcQgqKoQFiaxh2zwrpuvbX2yHGyixJr00Nae5+jOirWQl7UywMsnRfoNl0H3DHRaENjLFXdLDm/SkkHOg4AOpfLbdqF2j5vTAgHUOrSxTi6WEuzLyAS7lzrQGc3Ku7AovM6lwe8Ba9/Q+oAXZzsg4SDqTjOA08SRUcfs0GgQLpUoFupoPV3rVpz8KNKXvUduDH+dUbJBwQXL6SkdAuEFyLcedHKdte7tmN7A0PurnMMAt+TLWfVWSo5UOgtF/l36ybLgjUv7OfzoW7pqDp9+jSTyYRv+qZv4s/8mT/Dn/2zf/YFtSvvdJnGMJvkICTGNEjRXmRaUUMce9jWKFcp5z9YtrYhcT/ED32m29mSg4SEOJDEUcBgtcd4JaI/DDl2ckhvHLJYVGxdmtEbBnQKyl4cIKViMcvxfEVZaAarCcawVAE6M2mXttBow2AYMRwnrKxFLU/SLI18lbJYAUYb92VZiPsBnnKdKCkF6bxquSpiKdTwfLnsGCrP8QaTXkAU+RjrzL6V5wCy9BXWuJuS0aY1xDUo5TFYicGXrB0fsbLeJxlECCkxDWAl090UXbm7m/IVZ1+xTl1rZtMcJQQn7xsThB7FomAxL7DWWV6EkfsuirbL5xS4hzsJV0eYLY2Ij7hRqrYTu+9nd/MVxT7rx4dobVjZ7BElPotZwWg1dhSDvKJIK3qJTxgpjLUkg6NNpq8uKQXDUUivF1Lmmslufl1w6HuOB9Y0huEoXo4tfd8lxdTV9d+bMdZ5317nhvvTP/3T/Op//C3OnDnLb/7mb/DqV7/6JdXtv7pct891vpra0K73XJdbCuojulst3kJ6B4DhVZPkjjNo2ii8IFAUaeW+06RdOIbOFqSuGrdw9JwoQgjhhADNfnatUpIoCSiK2i26sMubtWncdxLEnks+yl0Gum15fbo1vbe4xynvQC5ua/bpph43BjnudcQhkOFseBpqrfFuAArBgVLfU5Q3qe53qSfu8+imE2Whj+ymOAP7AyIgY448RuGAWOUFdmX2owVfenfx7p1Z21nVXP8WvBSgNM6qxvlNupF/GHp/LFJe7tYfz7oljuEv/uIvAvAjP/IjfNVXfRXT6ZSv+7qve1F27HbK8xRFpjHGEIS+U8NaN56xwOqms5rRbcB93TSYukF5gqQXEMYe092cqqyXq2chHJcojH12hyHzvYyzr1hnNEroJyHbl2YIFWPsFGEdz04AF5+eEYUKbSEM3di4LBqUqqhLZ2qstUU3mlj5jFYSvI0e8SSjzDVVUTmlcNtxMAiaVhXleYq475PNHfcsWxQo3wMhnO2Mkk5pWDrg2Y1ogsAj6Suniqxq4p4TmQic/12+qKjqhiYtUZ7EU4L+KGQxLfHGMUHkiOe6aJCexFrD5WenWGuoSsNgpNg8OaDXjxA4/8DNYwP6w4imsexeSanz2nUvWhWvhXYs4nhfB+8ZB60dunIken1k/FUQetR1Q5HX9K6TDnLkceMrVjd7+I8pJILBKOLKhQWLWUU0jJg8vk1RaE69LEEi8DxBcAsjHCEEw3GE1g1FqdnbzRmOomW+b1fdIqVLzAhDrwUfos1SrqnKhji59obglLTXjpI7gnoUhvT6PYQQVFVFFEUvqW7/1SVwQoJGN9jGLW504RZ4nq8o8orJbkbSC5bgxFj3GciWF2iN2Sebt3fkLqbNaHf8CCWp25xd2QpDhHDd9tm0ZDCOWuV8KwTwFXVet8emxRoYjSK2S7cY2tjsu66gkuhat5QRkL4kz2qMdapZ3dTUhWawGkNrQu6pFozCUoglhWy7f/tm5VdX05hrOkdaG4r2nIxi/4bd7Y76kKV126G+MeBoJ9Tu85Ru27Kol+bsB+sgd7nDQNdT13f/9ULHdfvWNZ3l0UuHa9i9tc4+6UajZHkAKPu+anO6a0bj+Hm/o7t1g/o8GQe/kLrto+tP/+k/zcMPP0wQ3Dj397NZfqjYONln48SAfj+kzGqXpVo7U9z+Stzmq7obgdGWuiW5J72QwThCVw3T7YxsVjqbmHZUkvQDxpsDilzTVA5MYsEPPDZODOkPA6qidokVAva2ZkgliEIfKyRRHCAE5HNnSTEYxDR1g6ndqtHzJcqTJMOYMy9fIYxC/Eg6+5PWOqPIXBavENAbJOhGs5iV1JWzVbHWLsnnnu++Wq0bBxjaTlvSgsGiaDCNXZpe+4FHYwzp3Klv+/0QT0ninjODthaiJARhKYuaOFJki5LdrQVeoBCYZYJHbxiycWLI6vE+cT9ibb2HpwTT3ZT5rCROnIef33qkSeFygKuqWUaYLTNxpTjEibpR17C7STXtyPxWqtcPGa/GlFXNxpkhRVZz6ZkpQei8DWks480ErS1RL7zldAUhBEkvpN8PsFgmexlZtm9P0jSGutL0+z7KU6StdUkXYSaEizI0xlyj3OzGj1Ie7rga48ZOjTacPnOGoljw8MMP82f+zJ/h8ccff0l1+68p4Tpo1rhRqVJquYgJQmcEXuQurnDZGbIsj/WD1kb7Zs1uBD3ZSUlTZxRflxrpSZc/vXRkdveOutZUhXZem1hM+zy6blqfSdvetD36LUe5LPTSM7AunajED7wlfUI3mvksZ7qTMZ8VrTDLdeCU77rRTWtx1O4KVa3RtTmya+i6n/aQ8bm11iXuSEscB9ccq402LOYls0neik4c79ol89zcOPngceZ57jpVt3zng3UQGLaU4et68XWA8U7ct5fWNS+1cXI7Wep260YATyq3qGh0JyACELesUL9bh+sux/D565aA4Uc+8hG++Zu/mS/+4i/mwQcfXP7cbjVNwxd90RfxDd/wDdf8rixL/uf/+X/m3LlzvO51r+Opp5563udzY1R3A0lGUdthcYbMok2m8NpsXWstVeEAlVQS5UuGqzFGWGbznLxVD3cduCjyGa8naN3aygRuBdfUtjXJDtFVQ11rjLBMdgqUlPQGPnWuCROFH3lU2qWgCOlMfI11+QZB5BTKValZ3+wThB7ZrMLikh+kkizmzjQZY4l6Cj9w2cZSgt+OiqLWPkIq6WK+6s7w141x4jhw+dCtl5nnO2FCd0OoSo3vKfxQIdtOlfA8jG4IQ0mRajAwGCfs7eQsFhVxL6A3iIiSgKrShJGPFzigq2tNf+zUvXnqfCPDnrcEeFIKx5VRYmmnoVtCuzWWMDg8AluKKa7DuwpCbz8H9hbKDxQbJ4cYbemPYuIk4MLTE2Z7JfO9HC+QrKz2HIG/f3uLIdXGNI7HboGSzksmezlVpSlLjbWCOA6IQq/lSh5+f17rmdhxR7vqRtPdWKoD1XleY6yzL3rve/8dq6sr/OAP/hBve9uPsr6+znvf+97beh+frfJ816nTtV5SJSzuuypyzWyvWILDLgtYtSPZQ8CwxSplUbfRes4XLi80aVrS70cupcM41NK0Y2ZhLWVZO3DTioLKUpNnNbNJgdaOIyuVs3ZCsDSv9jwXNSdkB5zcTb4qGur2etQ0ht3tFKON8x/19n0nwQGBLC0pc70UJF19Y1p2ng4I7TqrpCj0rwEe1tpWCOOETVXZkC5KlzkuoLwJYNiNkg9Wl4hy9fbL7t2hVJobdC/l0bzQW62O42mal9bNvNuTgznWNyqphOOq14Y48V/wmP1uQeeleds/nwd1S8Dwf/lf/he+/du/nX/zb/4N//7f//vlz+3W29/+dl71qlcd+buf+7mfY2Vlhccff5y/9bf+Ft///d9/U8+pPEldGnoDp+ZNp6UzqW5XtVHsu5gwpaAxmNogfSd6GAwjkl7AbLegzCt83yeKfXp9p0Adrbhx6pWLC6yFIq8IE48g9BmMY9fFyBzXpiprhKeIEh9tDAKJ57mbW9IPHE+paXWUwqmcDY5LmAzC1pLGJap4viKMFEo6I900rRBCMliNmE9LpKcYjmLixD+kxPZ91RLcbWvi66ww+oPQAdxF4Yj4uabWhuFK4qwr8nqplK6rhij2KHONHzrrHC9QrB1LKIqK+V6O33ERBZS5JhkEBIEinZaUhSbuBwxGEaZu8EKPINwHr+D835SnCENvKQpaegAesaLuRCgHk1AOVhCqpdDmVmrzxJAg9khnFadevkaRlTz1+DbprCAZ+Cickji5TWAolWw7BoLxSkJvEFLrhuleTppWLspRSqLYa4+vIxI+fLm0ZemqWXYMxbJLWFcapZwK1fMkH/nIR3jzm/88X/7lX8r3fM/3cP78eb7kS77ktt7HZ6OMNmSLmrrS1LVd2vwY4ygHTvBgSPpO6Z4tKupaI9quqZDO5N1YN4prGrN0LfADSdILaJqGLK2Iey7SsGkMTa3ZubxAVxqBM63vtq2rhqQX0usH1HXjFmqGZYxc0gvQtSHP6qVHYNPYlv7hhGNV6c4nz1fE/ZCyqMlyZ4a9mBYUWb3kNtaVRiBIEncdcq95GBx2x0FHq6gq3dJHJL4vD3ULTWOWRutR4hMnzm3BGkue1a0xt+NKat39mNZ7dB9gHRwldyWEs72x5trz7qBg7PlGu116yZ0o1XV6X0Jdw+79dznWN1N5USOEoN8LMM2+mv1u3V51nevb/fl8qFsChhsbGzz88MPcd9993HPPPcuf26nnnnuOX/qlX+Itb3nLkb9/73vfy1/9q38VgDe/+c382q/92vOu/Kx1iRpN4zKSpZLMpyVVoen3AhcGH8nlirSuNVobQt93CkBPsXqsT6MbdOMI8HHsO1UxblW8stEjm1dMr2TowrB2vE/gKXrjCKUk852MpjaMVhJOnBmyfmJIvx+gm2YJ1GSrSKxNg1QCazrVrQOOIBxoyDXprCQZhPSHIYHv43tO+NEYw2AYg2rVkYOA/vCw2bhTF3OAiO+EOGHoLuB7u6674fsuUqxL7ihTB0iVkgShx+qxBIslm+dYKRwf0vMIo9DF98WK9eMDAPJFTX8QkfSjdlxVMBhGbJwZuJth4rdjwv3RnbO0cJyvXj8kin38wLuuJ6EQXHecDI5LeTtdwzDy2Tw1oihqRqtuPy5+ZhehJMfOjIkHjm7g+bc3ytm343D7nCQBa2sJSS9wI6PGUFV6KbKoqmtvAL6vQIhDN1/dWGrt0ijKlq8ZRv4hv7puUfev//V7+Lf/9hc5d+7cC1rUvdglpFh6kFZF7cbIwtE33Fdul4bInQgkTx2VQ7Rc1fkkZz7JyRaVs7eRzmLJOs0HjvnrRBsu09qia6dEdqNi161stHELOeG+l7CNMzSNXcbzSSmW4qHJXra0adFVw2JWsJiU2MYBxe59RLE7TquyYbZXYNsxcjZ3492q5ex5viII3blTVc0h5f2+L6CjFLgFlXJqbm2pWlFIWTgrrt2tdKlgr9tkHStwnpuFM6HP84qqatof180uipqi0BRlvRTGXX099jxn9VNVzaHfderarmt4IzwkW5XRnQBznQjlpQQMO+6ovQlgaO0+yA7C/bz36ibH/XfriHqBY+SXUvf5xaxbEp+87W1v4y1veQtf8zVfQxjuk/s749xbqf/9f//f+cmf/Enm8/mRvz9//jxnzpxxO+l5jEYjdnZ2WF9fv+5zWix+6/BvgbjnuoZ+6DFYiRAC/MDHCsBadJvEEYQKP5TUlWF9s0++qJjt5ASRU4N6SrUjG8Ha8QGz3bzt1El6o4heEtC/HDpeY+vZd/K+VTZPDlChRzYv2b48pz9wXTitGxdbVzbE7Sqw61Er5YBc3A+ZbTsF6+bxIXletwkJjpQvQ8VwNV7yhKRwq2OtbTsu68ZLTnzQ3dzyTC9/V2vDcDMmW1Rtlqul1w+Y7Dr+ExrGqxEi8BiMayZbKYNhyLkvPMaVCwsGg4DAlwxGkRNICMF8XrK6kdDrh2xdmCKwrG72SBcew5V4SYQ/OPrq+DMHbWnUDbBXpyq/nggF3IW0yF302NUijxvV2Zet8fTjOxSFZv3kkJ3tlF7P5/jJIaOViPWTw5t+rmv32/15UJTsOoSOspCmzsQW68BhllX0y2BpV9NVJ07pPrc0LalKQxQ6usRRYoNuUWfajk4Yhi9pjqGUkrjn0wB12bgOnmy5ajhObJnX5GnVdlJdLrTOjUvDqV230JeCMq9b2xmFFE7E09iGomjoDQN8JZ33aV5hrCFIvJaS4jp8de34uEHbyfU8RVVqZ7ZduK4iwrKYFQSBz2JaMNnN23F1Q2/gtwtWi/RgPimQElbWV9i5ki27w/1hQNIPmE1yEhE6UUjktUDL2W3pxvEkO1pB1YE03AjZdQoF+aImihR5O/7u0lzquqHYdR35pO8WglHkL4U+VWWW9A7ouiSu69ktxLophKOCyLbT3QncHE2lE6LUdbMEelXRRlc+HzBk3+j7hZZSYpnPfiee71Zr6akp5LJrLHA51s93XSpL99mFLSAMAg8pXGoOyYu/75+z9XkC7l5I3VLH8F3vehcf+9jH+MAHPrAcI7///e+/5Rd9//vfz+bm5h0ZZb3jHe/gta99La997WvZ2dmhsW7UZBtnixInHuvH+ihPtXwXCHwP067ojbaoQOG33n3SU5y8Z4XBSkRvGDEYRm7M1Io7+v2A8XqCsQ3JsIuRk/QGEeHAp78Sc/bcGifvGTrekJKMViOEEiwmOcOVGIQgX1QY03L4FMuLf5SE6NopA/ujkKQfMl7vOR9CY7DWUFYNQeBi087ev8bxUyOC2ENrQ1XW5Jkz1C1ylwYynxZUpXZqyBYojVcTPCVJeq6r1F3s3Y0iwFpBlPj0VxIa3bBxcojyJb1hzMaJEa946ARn719n/dSQstRM93J0o6nymjDwSPoB68cGDNeSVikqOXnvCsdPDZeeg105I9frk9KPqhuJUMB11oQQVLfYNewNQo6fGlIVhmNnh/RHERsnh6yfGNEfRS/o5uJyf8U1q84upSEMfaJIEYQeceRENIt5SZHXZGm1/KnbzmCe1Y6rhCCJfeK203rUqK5b1L373Y/w3vf+O/b29vi3//bf3vZ7efHLddaSxMdiSeelGzNqpzT22u57EHqOrlEZjHF8S62dWMMPJEHk4/mSvOXVpfOKPKuY7xUUWUW/HxLGHkZbZtOC+awin5c0WmMbgS412aJcptYYYx0v19hWeS8pKwe0LDBeiUgGAZcuzMkWJWnmkoOC0HOWI6UmneWk88rRMhSYpoEWwMQ9l0Y0n7puvlLSxbw1FiUFUWuXdOXSnPk0ZzErSOcF010nwgkjj7rNT49in7gfkM1Ldq+k2FbQU+cV6bygzGrCwFE4ev2QpBe5a0jlRtGyjaL0PHd9DEOPKPKcz+sBcVtZ6mXcpZSOR1trN7Yu8orJXs7O1pxp271N5+W++vqq2geGd2Zc2sXy/VF1DXVtmOzmZGnVLu7d9KajRFx3O93Q6MYJl1rBJIAfelS3SJG5W/tlcd3aF/JzM/WBD3yAV77ylZw7d46/+3f/7jW/v56GYmdnh6/6qq+i3+/zXd/1XYe2ef3rX88rX/lKHnroIR566CG2trZe6Mdx3bqljuGHP/zhO2Jx8Tu/8zu8733v45d/+ZcpioLZbMZf/st/mX/xL/7F8jGnTp3i2Wef5fTp02itmU6nrK2tXfNcb33rW3nrW98KwBe+5k+4FaoSSCNRnsfa8SHBgSgxKaTzKQN0ZduOonJqRtwKb7waU6UDR3QP3Y1W1w1YCEKf/jih1pZRq2K2xhJEHmHgkzcVx8+MWxK7xvfdOHZtrUdjDPfcv8ajH73AZC8nDF16gDGtF5kS9AYBVet3FoQKL/QJIvc4cKOouqwRw8h5rgWKYydHjFZjpntZO3bylouipA9F5vwOez3nhxWEHoNR2PK0HGDR2jjLEyUJYtcp9dtOldaWwSjg1L0rBJHjHQ7GEadftoJ8ThEE7iaSzSv80G0bRT69fki2qPb5XYlPfxC15t76wCr+1skbHald66O7Aa7b4rqGWjc3reTzPMnZl626EeS84uTLVtk41iMZhIzX4lvax6Oqi7nqqlNrugxd3UaUKeLYJ2+TecLI4+r7pLVOpeweVx8aKR5V73rXu/jkJz9JXTu+0nQ65f3vf/9tdfs/W+V8A937zxcVg1GMsW503HkWCsHSIqpLKimyGs/38HwP2xi8JGipBRWe77jG80VBVWkCX6GUo1ykC/d727Q+n77z6fQiRRA60N00ZmkjZIwhigK0bsgWVdsJlxw7OebJx64w3UvpxSFCQJZWBKFCa0XWlASxT5rVGCOoK8No3PJidUMY+uxsLegP7XIBVB8wvp7tZqSLitE4IUo8Gi2pKo2xlspXVHWDNWbZoaJNNGraBed4vbf0aZzu5ozWEqQUJD0ftStZTAvG4+i6wgghxTJtyPlu2uXoW7YUgCwt2ZlV6FrjeRK/NSZXbaczW5T4gQP1B8/dzgD8TgG5/cSk69v9vJhV1W5hGoYeuhXXWeviHa+Xk+xSaLqULAf0u8VkEDhv3KrSTjB0t265XuyGYdM0fOd3fif/8T/+R06fPs2XfumX8vDDD/PqV796+ZiDGopHHnmE7//+7+fd7343URTxYz/2Y3ziE5/gE5/4xDXP/S//5b/kta997Yv7BrjFjuFXfuVX8uijj77gF/2Jn/gJnnvuOZ566ikeeeQRvvqrv/oQKAR4+OGH+fmf/3kAfuEXfoGv/uqvvqmTWlcNSoBsR7KqJfxL2XHsWPIPtdE01uD7+yeYaoUQtBeoKAmcula7k9N5BAr6g4Ag9h1JvG5cxyeSeL6Htm3EHCy7C8PVxBHia0sYuRX6sZMDvMBvveskvufSUQCsdvvaxYAFYYDAvZemcqMra90IqROcdCNW31cEgfvp9Z3Pm+9Lp5xuVcph6OP7ss3NVUtjZaWc8rNTO3aGvALLYCVmtJIsSfVhEjBaiQkTn9FqjPIVo7WEuBcQRt5SEdmBQK9Vi17dFRCI21J7KeW6gtcToex3DW9thd0fxU6hLOD4qRHDlR7jtQTPf+EX4i7mqqtu7O/Gwm7QdHAsV7cj0O777H46vtHyOQ54xB1VH/7wh/nIRz7Cz//8z/Oud72Le++9l3e+850v+P28WGWhfU9OKNE0TiFsmtYkWQpapQkWkF77mRjHnatKjR84M2qtDVESIBBks2LJoXPKYUORV3ieaO1BLL1RhBDOEN/zJTuXU4qsxvflfnqJgKZ2QCfs/EYB2uN8vBLRaDciDkO/FcBYgsAHIRHWdbK01m5MLtyouGqtsLruvjHGKcwXFdmiZOviDGMcPUMqSGcVum4YriQkSUCZ1+xsLai1QSin6o7iwPlotgbpYegxHMdIKdi5PHehALjzpT8IKMuGxaI6+nvZvyy4Ef6BY7MDYOmiYL6XMZ+m1GWDBDwlluRCL3DXubrSpPPyGiPqLsLxTpWUDiB/truGnTuA8t3iJkl8pHIj4s7W6KhtOm60W4DsZ2cbY92IX0CeXZv8c7deGvWhD32Ic+fO8bKXvYwgCPjWb/3Waxwgrqeh6PV6/Mk/+SeXCXN/VHVLwPCDH/wgDz30EK985St58MEHeeCBB16QXc3V9UM/9EO8733vA+A7vuM72NnZ4dy5c/zDf/gPj2zHXl2yXRlba5fEddNawkivU8C6TksU+9RFg0AQJ0HbahFtXJwl7vkk/cBZtrR5wsa4qDIvUBhrXK6pEBRZRZD4rjPZjomryqmJpXRil6Tvo5Rk58oCC6xv9ljZ6NMbBkSxT5govMDD990NThvrukTW2XP4vmgtbiy6BVTGgrD2uitPcBfv/iCkP4zx22zT7gLpBa67oNouydKmA9GOKN32bTaDUyq3vJgiq6jrhuFKhKdcZy5KfE6eGREnfmuX42w6ytYs3PPUfnevtZIAWuWgWYKbm63nE6G4ZAa1VFbebPm+Yn1zQC8JaVqBUH9484bZN6ouwQNYWqp0i5buJtCBO99X11UhKtnlRjv+W2eSfL1R98FF3UvJ8PdGJYQE6xYxyndZ2I02NLWhKjTprGDr/JTJlYy60kSJG9fq0iw9MGkFIFK6sd58UjKfFpjGEkYB872cyXaG8h2toqkNtjFE7fkcJyF13ZDOCiZ7GdPdjMWsRCqBaX1wHB9RLnm6TdPQG0b0+gHT7Yzpbkrgd+e2RCnHw/PazuN06kbLnqfAQF5owHXZn3til51Lc4q8Zj4tmO8VCCno9SOSfkClazdG1w1R4iG9TnDnxsqdFZSzyBLk87IFoV7LWXQdVtOa/g+GEcoTTPey5/lyDn5PnTDIpdRsX1w4dbN2190oCSiymmxW0EJ+GgNh4kRw6axc5k8Dh86HO1FdfvJnGxh2U5jgwCIubC2EjDWU5bU5047PaghDtQSEqr0mNO13FEc+Ranv2Lj986ostFYFt//zPHVQHwEuMe78+fPXfcxBDcXz1bd/+7fz0EMP8WM/9mMvqhDmllogH/jAB+74Drz+9a/n9a9/PQA/+qM/uvz/KIp4z3vec0vP5UCMG332hw7UFVlNMgjxPLn0FZNK0BuFrGz08aIcPxRLuwrfa4PLPYmQDiQpJZf+Wt3fVeBTlZokCagqw3gYsX58gPRk2/qHqOdW5dpaPOWxutknm5cgIOlHbsTiK+rKEAQeurWT8QOPIHKg0nWqlBupxT5R7LUcPRfrZX25tH253nEilURI65SXplUySmcvUZUlwhMtqbyL0HNj9f04q65D4J5LtXFeypP0+gFxL+DKhTl+mw18cAQVxT612jeq7i6EB20pmvbaeKsRT/silGY/K/gq0OO3sYR11aDim3t+z5fEic9wvUe+KOkPQqL4zhi5d1neB1WdB33e1AFgFwSKFKdC9LzDry+VgNqNPF3deIHQLeruu+8+wjDkscce48EHH+TjH//4HXlfL0a1uBCE4/6mk4wsLTFYitYAfDbJyeYlWjeEiU8Qu5zjrHBWMkK29kdt/Fzc95lMMwbjmF4v4OKTezw13156kU4up+xcSVk/1md1o09VaXa35sxnOfFeQJwE5FmJQbTuAgYZCKLYI01LlzLTuHzrtY0el57a5ZnP7HJ/6BFEvjPDHgZcuTzn8nMTmsaZyl+6MGVlI8EiyNOKom6Y7qRuNH1qRJqWZHPHd4wSvxUquVSY3jhY8omjxCeIFNYYmspgA0eX8XxJ1POZ7WWtcEm4BWnoUZeabFHRbzulg2HEdOIiLPuDw52Lq2MqwU0Eisx5Zqbzgt2tBUVZuXzokWa4EtIbBOw+4157PE7o4h690CU0pYuKXiuGUUpQ46YWyrszixgpP/siFN0alXeL6e76rDyxP7FphYFB2x2s24bCPvXFqe+FsaSLiijySHo+eV6TZTX9W0h4uluuXiigunLlyqFx7kE624tZ//Jf/ktOnTrFfD7nz//5P8///X//33zbt33bi/JatwQMX8oqRtgXJDSNQUlFEHt0oznPb4Fh6xzveR7D1ZioHzjFWEssD1rSP7STKimWkWR1Z/tiLFHokZY1fqioG9fdG67FBLGPH3oEvsT3vaWTv8WNUzZP9AkD1XJIWvsNY2ga0XbAHCfR8xS9YcjmyZF7X61XWhB6JEmANfs2NDdzoetyXsMDwethqFjMWXY+6qpxI6G2S+iu/Z3FSvdaEPeCpSccQKAkx06PMMZcw0vyfLW0d9G6gdpdIDt+ILQj5fa5b/c777h6VzfDZMuH6qw/bvazipKAlVXD8ZODOwYK4UDMlTHLz8H92y75VV11KsSqakiuUiF2n7NuO8BCqhsC66sXdd/wDd/wkrarAee7N59U7FxOkcIy3c5YLArm04q6NsSJRxIramOZ7Gb4qRvX5VlNuigx2hB4krpxo2Tnk2nbBYlhvpchlSBAMt1x4+I8zckWEVmvYuOUZDiOCMOAK9sLjHYLqqKoEdJdM3Td4LcG1r4vyPOagXG+i4HvsX5iyGxS8OxTe9z3inWU9AgTRZlpnt3bIe6FVLlm6/yEuOv4TUp2t1L6w4DVtR7GNOxeXrCYFgxGCWDZu7xACBejGPd8gsR3nfm26zwYRxjjxEu6BR6YhjgJnAo6qxBStob4ljytCFqT/MEgIFuUTCc5vX54CARefU/VdeOEQe1n8cSjl3ny8R1nyyLhkqd49uldNo4NnBCm1Fy5OGfjxAA/cKpl6UknymnB4dLo2xjU7YdzHSopBaK5c2rnmynTGNTBNKL2wzMNLec7oGncuDnLHBfba/O7u+quEY2gXQC7xkQQKPL8LjD8o6iNjQ0+8pGPXPf3nT6iq+eee45Tp04d+Zjn01BcvQ3AYDDgL/2lv8SHPvShP1pg+MVf/MX83u/93gt+zIteAsJIOa8w6VbxceIxGEXAftSSUvsdwq6D6HmuPaE8SdzzHFgxLmZMtpFk9gDrXfkuLm7teJ/6uTmm0UixbxdSlc4/TF2DdjrLGtNNrx1fybioLwv0hhFVWRP3nW8esFw568qQ9EMq7XhUDEIX2XXESv6aj0ccXi15nmp9FB0AdipPRdhz9hVxEpC2sW3WipYi5D6rqxMV3L9vfBEXYh8EKSXQtW3Nc7s851u/CbiuoeOKuee5VmQShI6r52xabm4t1HVuozi4o7mkHfDr/OyU6sZmR/u7+Z3lxxH75+xK3EhJSXFDYHj1ou6lbldTlZpLF6ZLKkOpG4qqoTGCsnCJRmUhuVJUzCcVKMGxEwOsFVRF6URbsUccuMz0/ihmbTPBD33iXshir6RpNL1B7GyU9nKMbti5ABef2UMpyb0vd9/RymbC9taCPKtYPdYjzzXGaHotyCJxMZRh7FNkNVpbPOU6wqNxgrWWve2Ui6HHaDXhyuUpi1nhFM1lgx97TK7kPP3pHUzjjL13dzN6/QB9n+bKpTkIgR95nDs9YLyW8NTju0z3UhZ7Odms5PjZEaHvsTsp2Lk8Z+1YH6UERaEpM5eL3jSGIHLXpyKrUYEijN1CcTEtSeelS/Vpu4Z7ezlFURO3CyNjnOl2WToAaqxd+i/OJikf/k9PcuGpCYGv6A0D4iTCDwSzvYzJdooXKnqDhMmVBZcv9Dh+Zkx/ELb50YbFrCRblIxW4iWn+2qrptut/cSkWxehdJ28ph0Nd7GdSf/G0ZjNVZzhbgppjF2mnkjpJiWLufPbVV0Ag9ctyPejLjs+snOWkJSlZrEob+8D+TyuF1t88qVf+qU89thjPPnkk5w6dYpHHnmEf/Wv/tWhx3Qaiq/4iq+4KQ2F1prJZML6+jp1XfP+97+fN7zhDS/ae7ipu+Qf/uEf3pBLaK1lOp3esZ16ITUcJQTBHN100WyuY+VGoq6lJBB0+KEzQZXt2NQYiJOAqtRk88oZ7fouXs5xwpy3lCcFTWvlMFqNKNIaayGI1JIrV9fNUiTQfeXL8aGSjvvYdvKqsqHXpjhI6QjjSW8/51RIufSw8kOPMFatxYs4lHZy47o2h9SNWuul+EG0Wc9h7OMHCpvaJRlc+fJQR+tW66DBcwdiTNNmvbYiodt7XokQpk12ubYj4Cw33GccBOqmbgrL0e5Nf7Y3V91LN62PmRuvd/FY4pqOZ6dCPEpZvVRlN3bZ1b66/tgs6q4q01iEdSun+STH1I6rNVqNUEIAMWvH+zz9qSvYxlKUDXs7C8IgIBn5jEc9srwmywrHM24MzzyxB9aSDEPSaYEw0O/FNJXGNpY4CTl2esDl8zOee2KbE/eMGI6cqCrpe5jGLEVldWXIqVCeomkpGFHkU6QOtPqeApwYw5MSawSXL86Y7i6wSKLII0oc3aVcVAxXQhfLeHxIFPk8+tHzpLOS+aRkZ3vBYBQihULUhs0zI6LEZ3W9j9Wgq5oirRmdjPFCxeVLM5q6oSo0ZVozm2RI6TjFUgiSQUC+qEmnJWGsSHqhS21Ka9dBjXwGo5C9vZytizNWVntt/vR+skrguxGwaQzblxd84sPPkk0qNk8MOfeqDQy4LPgG0qx0gLkxTK4s0KVPOq/Ym2ScuWeVlfWe68AGDiDlWe1se+j4oXI5MVGes9CR6tYVxo6+sr8wvbquTmUxxn3feVotL01Sipu+BnaWRlcb23fn+sHXVcqFGoCLbtTtdMiBUAcgrbUY64BhVWrms5LqOrY/d+s6ZW+OJ/hCyvM8/vE//se88Y1vpGka/tpf+2u85jWv4Yd+6Id47Wtfy8MPP8x3fMd38Ff+yl/h3LlzrK6u8sgjjyy3v/fee5nNZlRVxb/7d/+O//Af/gP33HMPb3zjG6nrmqZpeMMb3sBf/+t//cV7DzfzoE9+8pPP+xh1I0fiz2J5nmS0loAxLoatsXiBakUp7saslMBTajmG7C4Goj35pOyI/2apbD0IHF250WetDYNRjBKSumqWPmJeq2S+2sy1294BFe26h7W7uXtKtfmrTsEnDmwshUAqRVM7S4jxesJi6vhGy27m85QQ12KvDngcTEbBHhh5tqDDEZ3lLXkNXvv6rj1qjUX67nPvcn9d5+x2n7fj6dnrjor8QKGzuvW3e/5jtVNo32mhxsGuaTcCB6eG11x7ke/GSmV5BDBss7CNtddVTF9vUff444/z4IMPvqQWdQdLeZLeMGS6nVKXmiD0nPo19F0WsjbEScDKap/7X3Oc2U7KM0/tEiUBSS9kZSNhVQjOP7PLzpWK46fdiDLLnFH79oUZXugRJj66ibGNYWsvR9cNx04NuXxhzjOPb7N+3DkHBLHzQ5xdyWiExUpBlZnWHkq2iSwW6QmKokIAs2nKYl6ihCEILLN5yWzP0B+FbJ4cEkY+i0mOUhAGPlVR4QUSGUrOvGIdU7nko+3LM6ZXcmTgJhWVNayu99g8OeLYvWPSSc5iViHEnGTotws4ifQEfuiOt92tOZW2jNcSeqMQFQgWi5w0dZ1IiyXLCrKyAgG60uxu56TzkuEwYLzeoz+MEarlAgvL3k7KzuUpn/rYFnlasnmyz2AUc/K+MdNZie95lEUNV0B4EiEhnxcOUBrIL2pmOwUbJwesrMZO1AdUuWY2ywlCRTOOCEJ/Kf47WI5iI1uKjH9TVJS6tlTsm94bY8nnJVXZYLEtn1vS6yueeXyXpjEMxi4Hvj8Il5Qct21zjY1UV9Y6bmfHrYbO3cG9prfkhdsl5zOO/XYfDXXdkKVtnKsQVJWhKBqgIgydFVOcXCteuVvPX5+N9JI3velNvOlNbzr0fzeroeg8Da+u3/3d371j+/d8dVPA8KU8cjpYHZdvMAiJewFFUTuj6zbztzPNlVLiBYrNM6PlzVZrN47rVsZR4pOM9uPP+uMY3RpcSimdFYZ2ylovkvRHEUVeL3k/nu+sIjpxgLUcMjcOIjfG9QKnWjbtJLZLKLh6Zdp12UzL3Ynilht5FbH5RqkCbhxx+ErmvLBKl+pgcIkShd4ff7TJIh2geaFjHSFY2rUsRRfdSPU2E8o7EYq7UB89KvJ9RSmd/9fzAUPbWYuEL85ip7POOCg62d/dw/vdjfurStPrXSVAaTu5N+oaX29Rd5Bj+FJZ1B2sPKv46H95Zpnbm/QDgjjAn+YopUjnFVuXFiAg1ZpZmzfdW43Jipqnn54glWC6l3P5woydnQVh4LN+cuji3bTB1iVPPL7DcDXG8xXFrGIxLRDSUtWG2aLgM4/vunzlWCHbyYJuLEYb0mlJkVWsbPZY2+ihfM+pxBHoNkKuLhuaWrN1foaQUFcWpWA6qzDakqelSzGRkqTvc/H8jLx0Wde+khSZZrzep6gaZpOcvVlBZSxFqbl4cU7SCxmtxaR5xc5e5rpcjSEvG6aLCqlc7rQxhisXUi5dXHDlSkrc91nMSqpcszcpQMBkN3edVAnSkyipKPKK6W7B+WdnYJwIyArLYq9gtpuTLgqqrCIeBCwWFcqfc+HSzMWLbvbojyNU4mPyiioz5IUmnWls09AYg/QUWxfnDNZiVtZd1GZd6dY9AYLII06cl6vvqSUQdBY0pk2DciWVu7YrrxUMXscOpmsQCOFScYpcuxH+rKKqXHe+yDVN7UzCwzhwHVdPUmvHUxdKtLxSjyD2iKIApECpboKB+zw8SOKu++kEUHlZ40lF0g8wjaFuO7BCSVTrCFE3hrKoMZbl/UB5ji8dhT5CCupaM90rPktn5OdGWbhpk+rP5/qccsi07apSSOdVWFUaY/bb+MsLQruy9H1nW1O1/BEh3UrYtOpjdaCj43nugtSNITxPobyu2+VACeyLAtzYw/H2Om6fbLtlXefRDzykcipYr3Yjkho3wtjnnu2PN5zNgdu3rqN3Kwf5Pgf68HM6vqUT1CynHa0nmbHgeaLttMoX3EFzn0H7dyloGvd/Rtx+x7B7roPWNUcBJb/tjl4vRq+rTnx0qyrpm6nlcUgXN3YYbB/16Qa+OjLBRbWWKS5C8Oh9vd6i7qXOMRQI/FgS9xRZppGALmvqHIQpmeym6JUea5s9aAym1Pieh9WC4diN5PZ2coyG/ijCWkuShEShhMbnlQ9sUuR6GTs2HASMhgG9nlsY7e2mmNLS1BXbF+fYuqE/itg8M0AJB+LW1no8+8QusysZwlriXkA6K6hqQ5m6RWlV1EghqMuGOBT0V51JutE12awhnWREkcKLArSvUMZis5qmbPBjH53VnPvC4ySjgAvP7LGYlNRFg0LQHwY0lUE0hpNnhjSNYe9Kxt5OyupKxHCcUJUNvi+I+wG9XkidN4xWnem2SELqwMM2Ft9XnDwxpBjHFFnBcJzQH0VcuTSlyAwCQ55qZnsFe9sLsnlFkVcUWQ3GUIgKXWiiJGQhGjzPIwB80/K3G0vgCYJhj+HQUKSVWzQbgfIFpjaYynVro15AXRjStMBiGQw7jrhtVfui5QBHhLHr8DnxS+vUgDuP1AFRkLue7S/6hHCPuXJ+jkFjPcX49JBGNzzzmR0WO+57CfshTakp8hp9wES+W7gHkUd/GDEaBCj/8HmoQ0mcBPQHLjEpz2snhJyVhLFHELpjLfHk0lO2c4bwlSQMwiWVqa4ahJQ0xraceCc2s+PPjpDmc6ru4sLnrc8tYIjrcC1vtFcdANbuk5C7clYvLkxeSoGScmnGjGBpntwRf22rHu1OTtueqLbad9nvXssPXWZr07QAryU/gwMfymu7Pq0XoWmBoLOXUAf22b0X6QlMa6rbWZOYq9DUjS4THai7Wr3r+4q8rtv9MsvxR0cAFy0g9PwXDpSEbGPNaD/71hzcqbLFNTyfm37e9nu9GeuaqmyIkxsBw45feOeBoWk5pNbsv46U+56OR32Bnq8oCo3W5pAQxn1ewpkzf5aUlp+tUp5kZewyt/e2U6LEx5OSomqYXJohG0EI6MwBwjDy2TjmcqWLecX68SHjQUy6XrB1OWU2yVg/1uPY8T6f+v1L0A/oxyHHT0ZOGCEUXqBYO9fD9xWXLswRxjCdllhtmO3WKAHZTo5QkjKVJIlhbT1hPstp8prz5xcI5TxLA19R1oYo9hmuxSRzB/aGg4hkEDGd5KSTOVhDEMQMBhEWy2wnR1cGISy9xCdPa6pKE5aK4yfGDF4VcuW5GVcup6TTipV15yNaLWp6w4hT946dF2ONi7tr49QGgxhhnX2Xo7s07M5zwlASDQIGw4i1Y33ytObZz+yiJOxdTPGERxTbNhKv4NnHrzC9uMALFH6o8BQMhxFIJyYbr/cAS7aoKNOKJy5dorHuMxkfS+j1YqwRxK3p92KSg4QgjjB1w/alBWdftsqxU0PyLKYsKsYriQtHgn0uLlAVDkiZusHzLHHiE8YhXuCBsS3/dv9a40XOiNstzA3T7QwlBYNBSBDv51TXpcGToMuGC5/ZJUw8jt+7ymgctc4G7tyrG4vVFl1pZlcyfE9x+uXrxK0YaWc7JY594taHM2rFPvPIpz8Il9dYz3PpN+DeW9Dx4q1FN26SVRaaMHK0IwvLsfPdulsvRn1OAUOwhzowQroEkQ7QLYPfD9xDHY9s3/plKYpou4ZulGWW/7bNfkcxjJyh7kEAsu8paAl8SaPdxcuGoBQ0dt/YWClvCRqlaEcfQhwaDbrR87Viim7MfLsdw4PP6fuKzFboDhB5nRVKG9cmu07rnVEH0gJgpcQSAAvRtPt2reXMzVYHMK/XNewuulXVEJrrW9c07SLhToOt/e9dok2zXIB0ytvrVZdLW1/lZ+hU2BDdpNL6j1PpqmExyV1ntV2seIEi8RVZ4pPUmt5qzHy3IF1UVLWmXIStL55m4/iA9RMDxBXB7k62JO2XhUZIB5DSRUp/xfmPFlnNbK+gzEo2T40R1jJf1JRphbVdBJxgulcQD0OstVzY2qGpG/a2M+ra0BuGxH2fdAae7yE9wep6QtQPAY/SFhw/M3QxZ1oTBgojFYtpTtxzHqV13eAliibXZLMSgVPrznZz1o716fcDTp9bZ7Sa8PTj21x4ds7Zl3t4XsDWhRkyUKxsJPTjgMmVFD/yCEKf+V5G0YpF5tMC31esrMUuVrEXoLWlzHXr2yrYem6Krg2jlYgiM1zc3eP8E7uk84Ko5VumWU2da3KvwY8Ug5WIe1+5QRj6FHlBntfsXc7cmH9RMN3OEZ5g8/iA0VrikkBWYva2c7K6oN8LydOaxx69zKl7G46fGiCky8oerfSc32JWo9vuWW8UEngS3eAy4tOKxcypdOPYJ+67zxQcAKuKmqqoQTgKx2xSEAQeeVoRt563zzy2zYWndpEWNyKOnXVYMS3xJfjrPaR055sSDQ3gHbDBUp5tTekbPE/SG4QEgUeR1xSZJs9qylIvr00uIlPh+y428ODCT2t76MrvxuTOvsq0YQR36xbLfnY4hn/c63nvKGma0uv1WCwW9Pv9z8Y+veDqAIw4QoUrhEs3OfAfzvi5blAS57/VWgMoJagqF3knpVO2atMQxk7wEUX+kkSMdSfxPqBzINEPFOmiRDcGL1DO5qFpgHZ0XGuchY3rVnZO0p7nOSFKu/8WZxa9qMulMu+gmOTmxCf7+3aw/HYE0vloHYyss9YSRD5GG/w7kM150NJLSmfvU9fNgX27Fgjf/HO7m1pVm+uOi/0WGN7IuqZpDYrvdB3MRa7aEebNgM8gcDFYdW04mNZsjOMgBtHnHjBUnqMcLOYlpjbIRUVuS6firjReEHDynhGzXsTuTkqzMGxdnGOtxfcFl56ZYA3ISNJbiclzTZlVfOaTGVVacer+MUVesXVxxsnTY06eXSUIZuRpRVnWKE9hdUOUBOxsLShmNdZAbxhz4syI9WMDgtjnyrMTPvXxixR5zXAlQljpQOe8wBjQpcYK513X1A3yUxYVeJR5jdECPwbbCjN6o4j5pEBXBr/nu/GqETSVpsidS8IOC+K0ZnWjzysfOMHjj17m8jNz1o/36fV8Ll+cUyYhZ1+7ymySc+GZCYHvRDZlXjFe6TFs7buEhOFKjG0ss0nB7uWFG7Vq56sXBgrP9zhxNqIoCnqrAafOrXDizAp5WvOZT1xgvlsQJz66qtGlZvfyAtMYFvMSawxRHNAfxaxuJlSlYbKzYLZXMNnJ6I8ijp8Zceq+MdsXFxR5RYQPQnHx6V10UTFcS5hPSoq8YbwSMxyHNNpSlU5FnWOXlKDBOMbohqLQ5GnN7hWX3hLGnjPiT3wQLh1rb3vBYppjWtuwKBkw21nw2CcuUxea+161ycu+4Bgn7hshgd1LKduX52TTkqjXkPQjhJA0WlPmmrrQxMOQi0/OHDdZga5s650pXCa0J0nTgnReURY1Yeixut533o1Hgjx3LeymCUK0PMeqodaG8C4wvK26yzF8/nreO8re3h7vete7OHfuHF/3dV93ze9/8zd/k9e//vUvxr7ddnWj4k5lDPsjVyEP+8VJ4cBCXTeHejb7voBOXRyGLtC8qQ1R5PzIZpP8mj6Pi5RjaRAdBM40tir1csSgtcUPuhGiA19R5EyrhXAdTaUEWrv97iBKGPnLbp7bt2vVyM/nY3jwvXXVjc2733d/dkAmCBVxm636Qut6XctWsPyCV3NSSpR0+32UQvmgdc1RwNBRB/Z9I+9UHYy/6zzguu5w+wjg+t9fR3w/WB0t4WpT8RvVT/7kT/KGN7yBJ554gre85S386I/+KCdPnrz1N/Qil5TOp82WTgGc72oWexnGCvK0RHqK4VpE0g/xIgW5szuZ7GRUhSYexBRFTZrWhP0Ai2G6UzDbTWm0ZfViCkpSZQ1PfGqbne20FV9JZlsZaZpT5hXl3FnGnLhvRBL5pKV2wK4y7O3N2dvNGK73GSqBbKMdZaBIL1ZUuWY+KQgjRTQMUVKwdWlBVRqU5wCumCuCWNEbxqRpzWxWsJiVrR+rQAmX/qG1IctK8qyiembGysaC/ihi9fiQ3e0Fzz45IRn4GCCbzfjYBxt644i93YJsnhP3I3p9nyDy6a1EpPOK+TSnrJzAwmCZzHIuPTmlrGqmuxkrG31ODEOqxjKflfh+gAHCnseF52YIzyMexviBJMs19bRChZnLh8fZdqRp6Xh0/QDTQH8lAWNJ5yVblxdcem5G1FMMV/tIaZnNKnqDABkotDVUjSUeBuzspEz2UnyvFWlIRx3RTYNt3LnkKYlSAtXGinqR4h0/9wdYY/hzX/8yAKLIw098snlJXjXMrqQMxhEXzs/4Lx/4FNsXF9z76nVe/tAJ1o8NqLVhvluQZRVGwHxesH1ZowJJbxgzWAnpr0QI6TjjpoH5vGI+K8iziq0LM+JB4NTmSlK2MaoroxhrBbtXUvZ2MoLYI7xq4d1NcJrGYI2lKBzPWFeaWlui2P+co5DcrZdGPS8w/LVf+zX+2T/7Z/y1v/bX2NraYnNz89Dv3/e+9/FjP/ZjfMu3fAu//du/zb/4F//iRdvZ5yvZMor3T5bDwhPYtzhIeoHj0rVxcdApdFvlrGFJUl52zkKPPN0ftXYpKLDPf/E8Rzh2gg03pvZbjtgSpJr9TFwnelGtGrgmjPx9S4SS/Y6htXihu+AtX1/enE1NV4e7cgc+t7Yb2hzg40A3XretEeudugBd+zwd97P7+wupbgSsG9NyDa/t/HXWNXXVXKNQfrH4hfvdwv3El8NeZjfePvAVWV4fSpfpnse7BRC7u7vLU089xcte9jJ+/Md/nB/+4R/mn/7Tf3qL7+bFryDyOH5mzKVLM8ef6/v0RxG6apjvZBRlzqc/augNfLzI5f6evn8NDOxuZaTT3HG0WgDu+R5hKOmNQxoNtGKE4WbkVN+ZJl3UxIliOE4wWrPzzJyt8xNMYzl2asTGsQGf/oOLTLdz4l5IkeaUecO9X7DGYlpQFg3WQCZKJwxJPPK5dsC1ceNlYyz9sRPHoQTTKxnZosAPJf0komka8qJy37Gx5FlNVTeEPZ9j0RApoapyrlxaMN/LGa3G3PfyDZ57apfzT+5Ra814JWE2LdjZWhDHAWHgYeoGXXX8audlmC1KTNsd9zzFeDXhwhN7TC6nqEhx/OyIpO/z1Kd2CGKfsB8ilCCbV+xenFKXLhP6zLlV8lmJFIK0qNm5sCDq+xw7O2b9RJ8gkORFw+7WgulOTpnVrK17JMOIva05kysZs52csB8wXklcvF5Wkc8rdGU4ocZsnhwgpBvnY6HWDUZblJAYz3mY1rpBNwLVNDS1WtKGhK84dmboOoxpzWwnZbpTUFc1ypNsnB3y2McusbeTkox9zt6/Tn8UM93LSCeVEy71A1Y2+7zs1Zs0tWX70oxsVlMXmsD3iYcBYeDjhYrNU0Pm85LdrQWm0uxcTpnt5TTWZUfHsYd3FvwNdy031pLPK0pVE8bekl7TNM66rK4bpJBLbrZBOOV0UR0RoHC3nq/uTpKfv54XGH7Zl30Z73znO3n22WevAYUAWZbxjne8g49+9KPXxL58tktIwWAUUqROSHGwO2VNly7h/jM8QN4VQjAYRW6EcmDbTihwUBQxGMeHOmvLY+zA0RYl/qGDLww9stRlmS73Q8p9crFyIGEwjFrjY3fx7vb94H5GB/b7UDD8TdjVdM9x1InhewqtDUnf5bkaY5zPou+9IO/Ca1+fw/t74B/yFoHu9cr5LnZcw2tj+jrrmqLQeP5hpXXTclLvJDDc7xbue0lKca2ZNVyfX+kHCrLaxZsFbt/KUi9FULeyL7/9279NXdccP378lqxq/tE/+kf87M/+LEIIHnjgAd71rncRRS6Z53u+53t45zvfyWKxOHLbn/iJn+Dnfu7nUErxUz/1U7zxjW+84WvVpcaXMIwUc40zQUayup7wzOo2tvWfm01LppdTBisxeiVm/VifjeMDhOesaoJAIbFYrTl2asxkN8ULHB/Om5WY2rJxfMRgFPLEo5cpc82xk0NO37dCNs05//gO/ZWIJA5Y2+hx/xce49KTU3ae3UNX1mXY7hYcPz3kqcd2aeqGY8f7rK0mSGWZTyqEsuxup1RpQzZNEVIy3hxwzxdssLqW8vgfXObZT21z6mUrUFv6/YjxZo9ASLRumG7nzPcy9i47o+vVQczlywum84JsVuIrQT9yCU/PPb2LbeDBLztF4Ptk84rV9YTdKymz3YzpdsraRo/xWgKNy1AeDJ25ti40g17IJBRsnBpy/MSI6U6K1ZrVjR66bBisxDz92LYDyyeHHL9nhZNnx1x+dsJ4s0eRakLfo1hUFNOCsh8QrsWsbfRZ2+wzHEfsXV4w2cm4cmFGqBT33b/G1sU5l89P2XpuRhB6jDciwsCjTEsuPbWHUoKXvWKTIPIYDCOscalJujHouqEuNVXVpkEhURLe++//DT/zM3+HnZ1n+Pl/do7/8wf/P/yVv/KXWcwKnn18hwvPTPACSTmvOP/4DhK47+VrvPw1x0gnOUWmWV2LOX5mRJgctoo6fe8Ki1nB5EpGOi1o0gYrJF6okNYgG0PgS4T0OXF6wOpGQlVoZrMStEEaS9waWDsvTIUxLIWFfqCQnsuNrkpNEHiHFrFlqR3//Qi3grt1g+qsIO7WDet5geGrXvUqgOsmnwyHQ5599lm+6Zu+iQ996EN3du9usVyCyeFRK3Rm5zdWu0opsFcBCN2YNv5NtcfT4dGkOAjMDrxeByq78gJHLnZG0gci4bRxnEZ5wCdQSZRSy+2713Xv75o3fMtA6nrjWt+X5Pn+OVNVDcZCFN1cUsit1hG48Lqg9VarU4e7rqE90vg2in2ytKIsNVHkwLZplYx3ulvYdawdj7MDwYevT8/3vjvwV9eGIKAF7g1JcmvqxL/39/4eAGfOnOFbv/Vb+cqv/Mqb2u78+fP81E/9FI8++ihxHPMt3/ItPPLII/yv/+v/ykc+8hH29vauu+2jjz7KI488wn//7/+dCxcu8IY3vIFPf/rTNwSleVrzyY9dRHnSdVp8gScEYeKBEGycGGA2Ldtbc9JZhalrPvMHW+TzkuNnx/SGIUKCJwXpvKRc1DRtF7k/jqCxpPOKpm85//QumycG7nWzmmcfu0Jv5FS8yhcoH7Yvztg4OeTY6REIyeMfu0A6KxivxVw6P3XdpVnlYjEjn9k0w1MKpGA4jDg7jvGV4pnHtrhyYcHOxRmNrhEIRus9VjYTpJLueeYls72cKA44+4pVTowjeEJQFZrLs5I48Yj7AaZumO5m/P7WgtE4JhyGhKFPvih4+tO7vOwLNkBYJrs5cT9gPsnY25rzybJmvJK4lJPYdybW45iLz0y4+MyEIPDYON5H64bnnthjsrtguJq4wIjGMp8VyEDRWMfPm+2mbTSbZbGoGazGHDs1wBiY7GbsbqVEPZ+g51Nt9rECVjZ6hLGPkqA1nDjj0R9HXHx6wu52ymxnwXijx9rJIVXZkP/BFum0ZPP4gKgXMBg69bFqM+6NASWkW/SVmve85z38nZ/8cb70dd/NsWOv5vLlR/l/fe//we7llK//um8iX1QEnuMmPvXpbS48PUF4Lvhg+7kpxlp6owjpSbYuzFC+chn2/uFjtj+OUEow2cnY3V5QPlXTGEtZafKsYryasH58wMpmwHxWEvYC+r0AYyzTieNAep50Xd3WSkcKQAoGq7Ezt64bwquEKU1j2NtOwd4dJd9q3eUYPn+9YNb62972Nv7JP/knvPOd73wJcg0Pj5I7U9Pr1SGlr8BxV5QjNzfaXCNKOPhUB2/sovWX6bqMXYaywZJlNapN6ahrF0ElASH2O4kHn/tGwE9K4UZl3Lw109Vgtiu/NbrugEdRaMe/9F64d+HVr+/qGlXQNYD6hbyGlAJ5g66h57mw+qpNFJFSkGXVkjJwp+qQb5rojMoF+hY/085vsks66PhG4U0KTx544AEefPDB5c/KygoPPPAA3/M933PT+6C1Js9zfN8nyzJOnjxJ0zR83/d9H//qX/0rfvEXf/HI7d773vfyrd/6rYRhyH333ce5c+f40Ic+xFd8xVdc97VMY3j2sV2MMYw3Iia7GU3ZsLM1Q3keSkoW05w8rThx3wpx36f4vctcfnbS2qMEeL4i7nkIqWiM4eJTe2SLCs9TnL5vTLEomc8KeolPMStZzAvCxOfK5QVPP75DkVaoUIGUbF2cw0cvcN+rN7HamTPnixrpSXTdsJgUeNLFyV14cofFvCKKFFE/pM61U9D6Es/zGa/FLlrPk8x2cqpCs9h1YgzlC7zAY7K7YKozdKEJYo+6ds8xHMRMd3MmO7nz5DSGxjq3g1GgCAKPcl7w3OM7zHZSeoOQMPAxyp3LTW0oKjfa9KQiHvroyuL7kgtP7rK7nXHinjHpvGZyZYvJXkaRaWY7OxgEq+sxOq+otWWnMkTJnJknme0VbBsXmxknAZiY0WqEnykWRUG+XdNcatg7P8eL3ETEaEvU8+mPAxaznDz3WF1P8ELF9sU52xcWbF9KGa5G9EbOuiafl/QGEUEbUyqVRPmeE8353TUEfupn/hFf+rrv5sQJ19A4ceJBvvR1380/+sf/gC+45yuZXElpKs2x+8acf2pCVTWMRzHT7YxPppdYO9YD6/wnXQSdC0hQXstf9K5tIhRpzWJeUmSOvxiGzkYpnZUs0pLFvMJTEjuApraUWenEh91139Jmx1uEEly5OANc4lac+Pjh/iJdV4b5NL8jgsDPp7LcbRjeTN3SUXX1zeWBBx7g53/+5/mBH/iBF2v/XlDZA3/p/AdvnAzi/jSdN2ELKH1ftt0ki+cdfLzAHpGJdLVfYGf1EniS+azACEGeVcS9gMjzKcoaWgX10mpHiGUHbXkgHwEmDnYU4fn1vFJC0xw2uYYWeAhn6WGMoSw1YahctNaLQGM51C1r/+xG9rfrZXiwDnMNLUJc+5xdfGGR1yBc/F/S8++oIrnjFjoO5/7IHK6+QN1YfAKtAKUFhmXhxsjBTd4Y/tN/+k98/OMf5+Mf/ziPPPIIjz76KL/8y7980+fuqVOn+N7v/V7Onj1LHMd87dd+LV/7tV/L29/+dh5++GFOnDhx3W3Pnz/Pl3/5ly//ffr0ac6fP3/D15OeREWS+ZUCgyVMPMqsIc9rd1M2hvmkwDaW3kqMrhqCRCE9SV3WVJ5gspMS+IqVE32alrYxnxR86qPnl4bhdamZFDVe5FGmNeP2mNm5klLllVscSoluDFsXZ0hfEAbKXQtCj8vnZyhARc4cPmqM63IKg1EetW5orKVuGgRQziqKpkbiEQYCrxfgRR66qrl0fgYWVCAw2o1Jd3cWeNJxJHVjKSIDvqSsDZNW/La2kVCbhsVe7gQmsU9Vt1F6uz6DldCZyluXE12mDdY6A+1VMwBjSGc5WxemlKVhsSj59B8+wd//mR/ir3zj32Jjc5O93Zwo8Ulzj8leDhb8yGdvN8M0lmJWYqUg6QfU2mIl6FpTVtr9XRiK0iUPNVNLuagdNWc1RCAIQo9kFCCkpTaWlY0+0hNMdnK2zi8IdnIGqxF5XnL81Cq9nu+M+bXBNPvXQOkJlBA8d/4JXv81rz50TB079mr+48WnWKQF83mOzmuap+HCU3sUeUWjY/AEXiCYzwumkxypnKI4iLx9lTmgpAPwSrr0lKpw/oJNo9GtgE16isWixFpI04q8aOj1POoD41/bgsGmdpzJRlsMFmMbhIWiakBDGAhQEqU8PL/1PRSCILrLMbzluosMn7duCRhefXP5i3/xL/Ka17zmJQsMu3tsY66OHbve490DmsYglXLRRjiA1pmQHlK6CrEENfbA81+tvN3PYnbpKbpy48okcbF9XSZyByKX+8ONO4biSIBxc+/xKJ8/L1BUdeOC2e1BMc6d7Bhe5/+5FlC/sNc53DU8Cmw6zqZHnjle6Z0GhQe5hd2/pTxId7gKHT/Pm/Z91+HU2n1Hvd7Nj5FXV1d5/etfv+zq/+7v/i7nzp276e339vZ473vfy5NPPsl4POYv/IW/wD//5/+c97znPfzmb/7mTT/Pjeod73gH73jHOwAo6gX3v/oY55+ZkE1LmtrQG0WYpkEYgbZO9RoNFSdPj0kGAQjL7sUeg7WQJIk4/5kdl1ZhBP2+T9L30aWhMYYrz04ZrvWIQ+Xiz4rGtd2MizBTgEQQRq6bPFwLUdKjXFSIJCD0FYMTfdd58ySrG3133ghntKxwEWbxICIZRvi+wJOSReI6hDLwGIxCti7MiWKPpjGU85K80OxtpWRpSdNAJBQiUfieJPIk0hqGox7DYUjUU0wnJY2BwSCgql3k2vHTI6pKM93OWGQ1SkG8ElNnDUHoMxjGTLdTdNVga8N4LaYsGoSV9Ps+a+sJP/vIO/jMs5/kP/zOL/AX3/S/YY2lNwiIAkXgu2ujH3lOqT0vscKwutZntNoj6gX0xzFRJNt8X3cN1pVLbKlrzd6lBVpbhqsxKhAgFL0kQJ0cca+EdFZw+dkpe5cWPPPEDum8Ip2UBFGAOdGweXaD8UqM9BxX29QOXGlt0LXh9KmXcfnyo8uOIcDly49y5vTLOHP/GlHgEwQS3VjOPzlhMHYpKqNxwplzayT9kEYbsrSkqhqUkvT7IdHAx1SWqtZUlUtrGYwTpAd1WdNoSzKKHHhUgv4goi4002mBtobRMG69FTlyzGOtxWiLqTV1YykKQ1PXKJzYrGksZVkhrF3SKu7W3brTdUvA8Oqby2OPPcaP//iPvxj7dUdqCTTMfofseU+jdpxpDAh3d6DLKa6Facn/nSkzyw7XwTo4wu6eE5zKNwo9amlIkqA1N+1GwZ3J9TXIcIn8rr4GLGPxboFQeyP1r9/auORZjRQOGB70GLyTdRS/7mpA/UKr6xo2N+ga+r7CxvaGsXK3WzfqFh5WLt0cFSAIFCkwb018o1tIP/j0pz/NK17xiuW/oyji4x//+E1v/6u/+qvcd999bGxsAPDn/tyf44d/+IfJ83wJMLMs49y5czz++OOHtj116hTPPvvs8t/PPffckUK1t771rbz1rW8F4JXnXsOgH3L/y9fYu5Jz8dkJZV0jrcATUE4KytKgS2cP5czmfTZODlk7OWC0EhEGknlaY43BC3x8X+Ld77OyGpNNS3RtOHnPGK0Nu9upEx1Jl02d9EMkYKxAWWgqwZmXj9i+lDLfy+gNIsbjmOxMTdR1r4zldycDbG6RYeuFWkh8o5YOBbaJXN55LfBnksqLUVZiseiogQiq2EV0GmvZaq8Rnq/whKSpLeKyE8/JwQo60lRFszRlJwVVSoKoh/WHVJFGaIGauUWpyASqEBjVp6o1j29L/LlC1wPyXkgQejz5zIT/36+/j6/7tnfygX/+HZz7H/43eqtn2UXRTCyN6rvFTi3wLkvK0qdpEvzLHsGexPMtUuXLiMrOwF11kaONoNEuIUVM969hQpbLc1AgsIzR8YDiZevkaY2uG3YFPPO0x0e2F/QG1SH7rv1rhuXL3/j/4Ffe8/f58q/c5xh++L/9ND/wfT8AtUEBG8eGbF2e4QeS8caIk2dG3PvKdUBQZjVh4nHq7ArKl+SLmrLQoB1NYSAi53lZuJg7KySDUczqZp8k8ZnOSpRyqSrzeUXQD1BSsL7RX5raX7cEy2tW55IQR57rJjaGyXa29GHd20lv4uy9W8uydzmGN1O3BAyvvrm8/OUvv6Wbyx9Vdby1m8E31lgaIBA482rRmV3LNlnDYK08BDJtR1xYik+6rhzLf7t9cBwVgTjQ7bMu9cJe25lbjpKvt7NLIHVooxu+vxtxFz3fA5x6Oo79/fHMHfTKuj7H8AhAfQdea5+cfv0M5Zsdx95KHfQtPChcOvgd3+q77HhNVdUsRVE3W3/jb/wNPvOZz3Dq1CkefPBBnnzySd70pjeRZRlJkjzv9mfPnuWDH/wgWZYRxzG/9mu/xt/+23+b7/7u714+pt/vXwMKAR5++GH+0l/6S/ztv/23uXDhAo899hhf9mVfdsPXsxaCUUQMrB8fgjKcf3bmfER7nrOciiWT3Zzf/Z2nWNvsu8zdxjDfzZltpwSBpD8M2LuSMb0yxQiLpxR+5Ij8Oqu5cn7OcCNBKIW1Dckw4Mr5uVsQeQpT1tgKptsp5twqJ+4b8cSjV6jqhvmiYOVEn+E4ZufyjMVejW25xY22COmy0p23qVu8daNDIQVN7fLIdW3c72h9OH3ltmnPiQ4UGdPmrQuoSo1SkiDyUJ4kS+tlupOtDVpX7fHirlldnGIQetSNbfl5juvcZDWmMZg21em3fuX/y7k/8U2snXg15/7Ew3zkd36er374/40XuM/I4uy0pHRZxF1HTQjHtbOAMvKQwMoCjTBI4V7TNKZd+LIEkO4S2ixziDsFfxA6Y+p8UVJXDVVeQ3s9TQbhgWvx/hn1wGu/EWvg1977diZ7z3LP2fv5of/j/+Trvvphnn1sm/m0RPpTnnj0Cum0IE4CksQn6UU0WpPOaibbU56rd5EI/MBDSCizmqo0eB70V2LCyCNbVIBgtJYwkykzIM1ql5RzWVG0BuW+L6G6lnp0VHVTDt0uKMOgE5815IuKMPapKpfkc7dure5Okp+/bumOePXNpSgKvvALv/Cmby5dFUXBn/pTf4qyLNFa8+Y3v5m3ve1thx5TliXf9m3fxu/+7u+ytrbGu9/9bu69995b2d0DnMFulCxuCJwceHMrdOeBJdpcTYtSrapUu+xjz9sHd0cdaQfNp0W7AlQtUGlc2Il7TQtYsRx9Xr3/NzqGhbj+699om+spkzsbFIAo9pb50He8rnljdjmyh31AfSdqyTXUna/htRnKL0YdVB8fzETu6qg9eH5+aJu+o81Ni066+o3f+A0AnnnmGX7/93+f97///eR5zkMPPYSUkk9+8pM33P51r3sdb37zm/niL/5iPM/ji77oi5bdvaPqfe97Hx/5yEf40R/9UV7zmtfwLd/yLbz61a/G8zx+5md+5nltcqQSRL4iGrjx/kP/w33Uv/UUl56bID1JtBIy6PlYH3qDBGGhzjTDccDqyRE7lxesbfYYjGK2V1Me/+8XmW1nyAjqtEZLN+rL04psUaK1cXzA0lKkFXWuSQYBSrjvMM1qtp6Z0F+JaMqGS5fmYCwvf+AEumrwPA/bZNyzuIgXutxg5QmEFcRRQNwLsFUDynF8e/0A0Zqt9xKPqO/yk8PQo640WxemKCU5e26Ni0/PqQpnapxlJdQwGicoJen1A9ZODtjZq6nTksVujvQ9dKnR1hBFPrUVFGmBaGB9OGC4lrjJh3LnwvblBVvP7RFEAb1Bwz/5yHt5+G++D4AH/oe38N7/6xv5tjd8M6++/xwXn5tiBQx7AXE/JEw8PvPxHZJRwMbJAUZAXVVLP1djjAOhUhD4Hr4vme8VWOMSlRZpgWmBcVG0oM+21wPlxvHGWEplWFQ5Tz15hclOhvEl4bEe97/6OMfPruzTeGhtoITgf/zGr+Vla26U/PX/o+PAPvfEHlsX5lRFjW0MW+enLGYlg6Ez/X784xfdNVIKhHAuBWWuKUvdciFdjnGtodpymdGj9YQw9KnKmiuXnbiw1g2+LxFCYoxhkVb0koD5TN74ei32XTTA5TUjIPNcHGs6K6h1QxQ5V4W73a9brZufrn0+1y3dXa6+uXzsYx/j93//92/65tJVGIb8+q//Ov1+n7qu+ZN/8k/yP/1P/9MhgvrP/dzPsbKywuOPP84jjzzC93//9/Pud7/7Vnb3WqBxg+PBqUfNMrPWrYDFMj2lGycL0bTq5H0Q1TUMD+K6q8GXMZZQedfc/U1jQeyrVq96AxxsGV7bUez2neXz3gzkuZ4tjJRO6Qeui1YU9YsGoq7RXSwnrHfGy/BgdV1D162xt2QIfTu1n4m8/zl3x9OyxBHj9JvYLd93fpPRbcbgnT17lrNnz/K2t72NX/iFXwC4rvfg1fW2t73tmgXcwTr4PA8//DAPP/zw8t8/8AM/cEtcZD9QhJGHbSDs+Xie4BUPbjDfTUlnJfEwojeMGIx6vPqLj1Nkmk/+3kXyCws2T40ZrsT0RhGDlYSN0yOKsuYz2UWEEfR6PlVl6a8FjFYT5tsZXuizc2XOYBwyGIdUhevKIgVeIJlPcqJhhB95rB7rgbXsXckw1mAbg9ENQaiY6hzbGMJBgjFuZOkHCj9UqNAnStz3tn5qSDar8AJJFASsrCeo1goljHuEkULXhhP3jOmNI3xPki0q5pOci8/sUeea/maCkop8UTMex4zODLnwzJSq1CTDiNmVlLgf4MUe82mM1g3CWow1LrZNwGgzQWO5/MyEXs/nP/zXd3Pui/4sycBRBpLBBvc/9Gf59Q//W77kK3+Q3jAg8BVxEjFej2iMYbAaceLeMWfuXyOMA4pSUxRuhK+1wVcCrCAKfYR0Y/DeMCIZhK7b2HK4y1Izn5VURY0uG+qiQQgn8hECsrTm5D0jPvpfn2brwpz5tGAyz7l/eIy1YwNsOxVwvrXumuv7VwDLcMWFSS5mBV4g8cMIJd1J5weKwZqLOlTKATDTdj5lSzER0plKl6XjkQahR1Vp6sp1h8OhR38UUpWGPKswOJP2MPJdd1QpxuP4yPPWWLvcd2Nc1nLTOM/bpm5Q0k0HNAYhJMNxSBAqgsgJje7W3brTdVt3l+7m8o3f+I3L/7vZmwu4m3+Xu1zXNXV9LQB573vfy4/8yI8A8OY3v5nv+q7vuk21qthXJHP9huFBgcJBv0Ip9keBXSKJrs2SOwMcCThdl8r9ommc35dUAnPVgsUcEKhcA/xoO4rXe2cd8LV2yTe8mXIdzKOfeDSKMGbf4ueo0esLrauf8SAmul4384WUlGLpG2mMy599MaOkuu8b9lNurukGd3O0ZVluggFL0gscV+4OimReihno1kJjLSbXzA0kw4Aw9OmtxlSFpkwrlBD4sc/lyynj1YTV9Zjzz0x47Pcv0RvHaG2oioaN4wPWN/vsrCVk0xIHBqBIK868YgOsu0bEPZ/drQXKU6wfG7CYlUvPuagXEIQ+eV5hGsP6yYG78ccB6ydcF266l+LHIdMrC4SEtRMjBBD0fJd17ilW1mKMgdP3r7L17IymsS5iczPBSEjnBX7gMVyLMdotaqWSICTD9YRT965w9tw6H//Qs0x2ctaOeWxdmOGHit4XbLB5esyV8xPyeYUXKPK0ZnMlwTYWP4yIk4B8VmGVe+5s4UzTy6phOiv4vY/9Ho/+4e/x8d/554e+D/OqL6Y3DAj3AobDENMAUjDfLmiMIeoFJL2QMPEZriYI6TwhJ5McJQVB5DEeRqSTEikl/XHUdu+dqCJIfPrjmJXNPkVWA7alBRQuCcUagjgg6vl8xSjgv/3KZ7hyacHWhTllrVk7PmDt+OCa4+iBrzh76N87l+b4gU9/GLK7s6D36DarJwY88LqzvOpLTtHrh0tqSdM4wUxVauqqwTTG5RRXDfN5QVVoosR3I3hrqRvL5pkRxhi2Ls2RQrCy1mOxKPGTgLX13pJXbkwLBpsjxiNin/6kqwY/9PA8wWJWkQwCxmsJ2bx0PqaD4Nrt79b1yzrNwd26cd0xctWt3lyapuFLvuRLePzxx/nO7/xOXve61x36/fnz5zlz5ozbSc9jNBqxs7PD+vr6Le6ZpelGokfw+IDlSs3ZxYC1+4yVrpPXqUuVcsCw6Tgy7AOZQw2hA8BAa8cpEi2lGlrQZyzWuEi8o4DKNXxAcfTvb5WsdrXP4sHqLor7vMwXAUBdDf4OIMODgPpOlmxtd4w1LXC7s/6MXXXCJSn3P8MjQei1uPCmynELP/dvBtZaskXF5EqKsQ60VUWDzhvGG32asmb72SkbZ8Zk84oiq8krzcrxAcJaFtMc6TvD4+lOhpCC8UaCJ6Xr+kjIJwXPfHKL42dX3WOMuxHvbqUcOzlwdxAliWOFCgxR7CGFZbbIiEKf+169ia4ayqJh9cQAodrovUhy8YkJ+bygN4woFrWLMAPGKzFCSvzAQ0rJbJLiB33C2Kc/CEnnJUVeIYxgMAqWhutBqFpw53H/PStIJfjEh89TN4Yg8qjzms/8wRXWTw+4/1XH+MyjW0x2S+fPOS/Rbazn6kaPKPGdB6OA3jiiyWv6oxAVevzw9/4TTp4dsXspZTGvWN/sU9eajZND8qKmzGvC431A4IcejbX0+yG+p0jnBXleEyc+ySBkMAwx1pDlNVWpmU5cN3X9ZJ/eIKKpDXWtmc8qqspgjW67fYayMnhKsHKsT75wuc5dopWvfO571TrTScbupZT/+h8fJwx97ikbBqPIJQzJA4vt9houcGbndVEjVmJ2Li8o8prxMZeYYxrLbFLQ6wWuc4gFAX7o4QdeyxfVbrLUWCRQprUDhsB0p2DruSlRP3B5zZ5k68KENNVsbPZaqxvhhDsWkLThBiz3sbsOGOPU842xqMZQasgWFXHfdwtIIRisxCT9z/1rwZ2uO914+FysPzJ3TKUUH/vYx5hMJnzzN38zn/jEJ/jCL/zCW36egxYX2ztXjniEQNg2Pu469gCdqajbr/1MSmAJILpxtMtadv5Z3TjZcq2SdmlEql1+qu+r5YWmOzC7EbWnrsN7E0f7JB58jWWHrXPAvwms0wGV64kxut8dfOydLMER5MkDo+Tu9e/ka3ddQ6sdZ+nFGCl3x9LBGMVrRshtXY0Ll/95twDQtRvR1k1DmdV4StLUmjgOGG7EoA3nH9tBlzWnXrbO9uUZdWkYrSX0xyE7l1OmO6kb3RqDti7VYv3MCKsNu9sZfs9jtltw7LThzLk1/vAjBeP1hMWsZj4vnYVJ5DMcxVR1w2Lm4vT80CNPa44FHipwFjaLvRzbdvfWT47IU00+KwmPu47S1nMpddGQ9CJWN3uOM1hrBzYCgfIUcZvfXsw1UeSyhHe3U+pSc+/LTzDZzVlMC4LIZ+34gHtescali3Ow4K8nXHl6wpOfuEzc8zhzboXs90uyRUVRuIxtT0nKXDNcSShzjamckKGY14xGCWdfuUKaNmTTHF00zqB74JNeLGkaw3xaUeQN070cL1Ds7aZkWU3SC1yOsu+sY4qiXpq668YQhQojLVWhaQzkucZSEicBcS9EdnGcib+MuisLJ9hAwGAlJu6HZPOCbObygZuzK5y9kvKpj1/iwlN7fPyDT2EbOHF2jL80qLeHJzECLj83YdZ+hpefnVGVNUHoU2Q1ZVpT5DWz3Yww9A9dG+0BwJbnmqQf4nmCbF6Ttt3DRmuqwrJ1fobwJMdOjsiLCiUFYqPnBHbL0QjtBaCdGNHGZLavKZRA4NGEDUHko6sGqQQrGz0W05K4HzBcia8x2r5bN1F3geHz1h+5bfp4POarvuqr+MAHPnAIGHYWF6dPn0ZrzXQ6ZW1t7ZrtD1pcfPEXfckRr+BYg0IKxBEHRDdChgNChase1okXluNkJamb5oicYnFoG3DAUAjpHO8NIPY7ccZYGgOBPBqAddcOe+Df1z7o1mPkbmRZ09WLoUje34Grm2UWl/9ytTL5zr72QfuaOz1SttZ1hrvXcTfGGzy/OGybcOff7R/vCiOPP/EV93DpuQlPP75DFPtMtjNGmzHj9R4nTo+YTwpmk4IvPNajSCvQlvFGzOaZFVaPD/j0xy6wmJZEvRBPAo2PlILTL18n+93zlJlLMrr83IxjZ0aEscfeVkFvEJBNSwTSbetBOIiYXJlTVDX9cUiRNizmJf1RSBD5ZPMCawWer7DGsnqsx5XaUBWa8UbCbNenWFSk84zxRkI6LV2yUOjhSemsogA/EJRFTegJgkAhhOPLBaHPypoTiiwmOWHi4UWK4ThECkGvF5BEHv/9Ixd44g8u8arXnqE3jJhNC1CgjKAqG3YvpfihR28Qks1LikXFdJJhjeH4mRXqsuGTv3+B+aTg1P2rxIlPnPicODOiqRvyUegAk4EqrQk8hRco1+UUFoTACz2quqLSDbrQCCWJAkl/GJH0Q2ptmM9KsqwmCBVh6GOahrqWBIFTnPuBRxA11LXjeg4Cxepmj7LQLKYFK7s9PE8x3U258NSMZz4z4d5XpKwVPVaP9xBCXsU5dH+vC4P05P+/vTcPsvQq7/s/57zrXXufRTPaZ4QWRiJiJBZjXAgLYcUZF0thHMqAJaJicRSHckwqxqTkUIDjDRywKwqCSBgQhV3x+FeALGKblG0s5BFY2BCFEYyQZtFoer/ru57fH+d933t7eu/pnunpPp+qrpm+/d73Pfe995z7nGf5PggHGtMdpCUZGKnil1xsR+ALhzCLCDmOrXOuRa+vebuV4vkO5YqDZUnqg5CqATqtgMZUh5mpDp2O9oCeOd2gUnXZedkArq/zy8tVTxclZWtD7tnsRylFGOnQtQrQxS7dGM93iEPdw94tWXQ6Ec1GeL6n5kVNXg9gWJoLYhieOXMGx3EYHByk0+nw9a9/nQ984ANzjjl06BAPPvggr3jFK/iTP/kTbrvttjWF/pTKvnD7/fQZPRkTnUtn2zIz9jKjT6lCMiI/3rK05ykSPeFslRWI9I8uD9dGkaJUyhapLKScDyM3vuxFWosIKYpzL8ZacvL6X89izMmhXGcEZ01O1bOp+0PwyxSurv66Qr/HvTZ16xdSzlMRrEzPDpbLzzy7+gQwm/8Cy5JUah4Dw2UGBtu6N3Q3wi87WFIyuKPCnitHOHlskuePTZIoheVKfM9naMhn72WDhJ2YZ58+w8xMG8d1iIOYSsVBKRjZUeOZ/9umstMjjRUnfjhJuewyHqX4ZRspBDPjLYZ3VlAIanUfZQuaZ7rU6x67Lq/TacRMn2kztKNCqiyaE03qYxW63Rjbs6kM+ti2VjeQWW5xnCpsz6JSd7PIQxY9yCa56zugUoIo0e0pfYckTokCnc9Wqbla7LkZ0W7HVAdKxJ2EkR01BocrtBoBP/7BJM89PcGefUO8cLLJ9KkWQ7vKlB2bKIwYP9WgNliiXNFVw42JDo5v47o2ey4f4v/+4wmmDfnEXwAAVv5JREFUJ9rsv2k3caLTL/ScFQwO+diWTbvdxfZshkdLuL4u4imVHYTMDR5otxMCJyKJU6xMTHp2tqs3xXFKGCXESUouqeO5FoNDJfxM4zXPx+u2Q+LIwi/ZeL6N61UoVRz8ikNjOmBq4vvMTLR56snn2XHJIHGYMrKrMu8zFUcJpaqj2/ZVXKIooTrosfuKQXZdVqdWLxHFeu1PYt29xC85hbZi0I30+1p1F8jxrZBennLmdJPhqTpJHHHixw3iJEElYPs2oFtaur635IY0lz+LorQwEBuzAbYtaZ6cIUlS/IqjJZHWWXt1W2AMw2W5IIbhqVOneMc73kGS6PZrb3nLW/jZn/1ZPvShD3Hw4EEOHTrE3XffzS/+4i+yb98+hoeHefjhh9d4tZ5Xrz+5Pw/76XwzUciYLDRd83BEbqwUXsMoKYo0FiJPMLZtHZ7Ov/vzw5NEL7hikbldpBAWOYwLeRVFVoG/upxAKZeu/l0qzHzOCIHqywBWc/60fj2TF760fu+U0l7DONF6lediHOah6UK3TS1QhTxvHIt7TQ2ZnZyF5P2SQ6MRYLkWftXRxqEQjOyqEEUhz59o4LiSku8gHBBIkljxohfvpNPo0pgJsKSk0dDtyWYaXbySRFnQmg4Z21OnNRtguxaDY2U6zQhh6X7UzZku9aGSNlQrHidmp0jTlGqtRKmc8OOnJojCBN+zmAqzCmVdhIrnWpQqDqSCyoCv5XDilMZMl24nJgwS3IqLkJI41B1CHEuCsGg3IyxHUCq5pFFKGMb4FZeBLAzcaoeoVAsft7NUGL/s8qKb9yKF5MSxKbySw+jeCs8dnWRmssvOXTVSKQnaMSptgdJrk0r1OhR0IhC6w0eaKFrNLuWqi+c6JJEOEVcqDkkYM3umw469deoDJaJY4XkWltQhcWlrz6klE/ySjWMLanUPkERRqtf9OM0iJoqgG9FuRczOBrTbupe141i4roVtC9JUZJEX7Tm1LN09SAm45l/s5un/+zw/+O5pnj06wXd3HNfGpiUoVVwduhU6vzjs6OuUKw6N6Q5BJ2Z0b51y1aVU8rSnUuRSM3ZRdGKXJUmSEkUpnmcvWvgVRSm2Y7FzT42J8RZDozEqTpmd0j29d+ypEwaCINDhd4ToOScyQes0UURRQhjFJJGOUqWxLoJxfIskTqkOeDglB8+1qdW88zUltwZq9Y6U7cgFMQxvvPFGvvOd78x7/Dd/8zeL//u+z5e//OVzvlZeQJL2fRjysF9/i7JCcT8PZZ5lmGgR1l44WevJaU3DXJz27JJnLUXTK2DpaZJoUeskUcUOe0HODvkuaLXOfW0rRQhR6OudzUbmF+prs+SuLRe83Si0Yah0z+hUkdAz+FdLvsHQxTz6hVmWXPbembDxMihFqxkSRQnlms/46SZCSEZGy6QJ+svbluy5apgffOc0QTvELdlIpT363XZEZcBl12VDdH8wjmMJhndWCDsJcRiTpIrKYJmZ8Tb2eAuv7KIaHYZGqzoMqRIiFN1ORNDWRhxKIV1BFKQolTKyq8YLz84webrJ7ssHEZnGZBSlSGnh+jblqkdjpkOp4lKuOZx4ZoZuK2D8dAPfd6hWXRIFQRBpg1aBtKDTjHFcizRWhdcw7EY6JGkJ0gSGRrSCgGPrDkVeySWNU2561eUkScr48w0GdlUYGq1y5uQMwyNlPN+hFYVEITQmu1iuBUJi2bowo93qIIWgPuLTmg0IOiEjOwZ0Fyh0lKTVjugGEUM7yggpsB2B5zk4rpVVUIMUkjSNiOMYKW2SGKRUOI7E83qGld4E6nnUautuNKAIgiQT7AbLkbi+1EVdQmDbFlKClDa1IclNP3EZZ05NM3W6yw+/dxqvZBMrxWVXz009as52mZpooUSZZ34wrj27rkWzEXLmTEMbm0obq1YW5g26cVEQZ0kolR0as93inHkKeKpSuu2QINTGfzsTtPaqLs3JDs8fn+b0yQbDu3ShkedZurtOESmRiExbU0i0tI+vc8eTWI8z6CS4dZty3dNi5a5lwqJrwdy0ZbngOYYbiQ4H9z0gekZhLt6sPUii/xBgfi6CzhvrydbkBmWS6NZVZ+9Cck9kv5ad/le7itIsyVo3V1kklNxzGS76GmW261ztR10LLy9c5LGhFckZi9SeFNfNC3M2agw9r2Fu2MlVF6P05xXm41xImmZBzo6nq3n7im1PFKYIBeWqgyIliRNKNV9LmDQjolhRH/DZfcUAp388Q6nkEsXa6JNSEHUTLFsyOFhiZryJV3GpXVLGkhIZRQwMlrCUYmaqS5ykhEFCkgp2Xz6g5/NEB5EoojCmORsQtGPKZYduoA1FlSpGLqlx4oeTTE3ogowgSEiCGOlaWgPRs6gOlpBKMHJJhfEX2iRxysxUB2tMEqUprmsTdBL8kiRNdGtMqUvokVLr7AkpaTUCGs1A6+7VHdIYwijRrSs7MZEXFS3i9t20myRRdJoxrqcNv8nxNlfuH6U526XTDUlLLklbv45yxSfoxpz40RTdbsylV43QbAWMn+kysqNGEqdZv2ebqdNTOI5kYKRCY7yDdHv9v/P8OcuWRJGlK8pL2mjM9T3jqO9zL/IogcJzbYSK8UsOtZouQAqChDDztAJEcUqKwvcd6nUPx7HYsetanj06wZN/9xzt2Q6z421mh9vElw4ydskAUihSpT2G5YrH4HCZzkwHy4GhkTLVqkel4hXTMYoTbZBKEL5DqxmQpopy3Z+3Vmuvts5dBL3uS0tSrbpUKh71ukuys8borionn5mmNdnB3SHxB0uUSja1AR/Lsoo0pjyKBbrLycxkW9+zju50Ux3UbQvzKMfsVHsjp+CWxNiFy7OlY1dpqj12MsvrS1U6zyic17eypxEz51w92ZreY3nLqW43zsLKvT/mEjWW1esXmp1Yt4dK0r6q1WVeRzaUhYtPevlJq6FXgLJwQQ6w7r2Di2uf/UCRCKpZSQ7kOY8he+/zkHreNmyl9BuFObryeaX3bO79N2vV2egWaI3pjg6jOVrk2HIktitpN0LCbkx9uMTYJYNUai5+ySbqpkyNt6nUPKJIt3kbGC0hpWD6TIeR0Qr1AR+EJApidl05yFCmbxg0A1qNgCRR1EfKKAWRSAmThInxWaYmWgwMl1CpoN2OCKOU2oCWDJk83dASNI7ecCSpjgY4jkUUK/yKQ32gTLniIi1JGie6mKMdk2TGZ5KkmRdL4Jdsut04WzskzWbA9HSXOE4ZGa1QKnk6ZCoUlarW95uaaGM7FlJKylWPoR0VhkZKOL6F7zs0ZwI6nQDPd4jClKAdM3GygZIwtENr4z339DhRN2Hs0jr1AZ92o0sYqSLU3Wl1SeOU4V11wm5c5EBbtkB/pntRjiTR6SiOY2ehYRvfd4pcQce1svVXKwOJTDmi201ACWxHd3YZGiwxOFSiXHG0NzBOmZrqcPy5GY4fn2Fios2Nr7yMwZ1lklgx9UKL9kyX545O0Gp0dHgbiLsJ9ZqnWwi2Yyolj9FddUZ2VBgcLjM0on9Gx6oMDpcYGqmwY1eN+qDP0Kj+/9jO6ryfctXDdiTStqjUfC69bJDRsQojYxUGhyuM7Khy5Yt28C9edTlju2vMTnY5c2KGdjMgCpLCKIyihDjW61AQxMxOd+i0datDx7EZHq1QKrk4tlWs362sb7phc/HII4/wohe9iH379vGxj31s3t+DIODnf/7n2bdvHy972ct45plnAJiYmOA1r3kN1WqVX/7lX57znCeeeIIDBw6wb98+7r333g0NiW95w1DvZDMPFHPbky3UzHwxJ11/nmGObUs81ypCn2GQFEZimqisqlCHoM/WHNRh5t5OeyGWMt56x7AmqyI3vhYSuk7TdEMFoPu9Zb3XtlBF98YqkebGYS+0nBYpBkuRa1P2H7c6o3Bh76BxGPYhAEvQagZMnmli2VqTMApSXN+l0wxxbEltuEy56uBWXD2nLAi7EbPTbYSVbeYSRXWoQhTExKHW7KtUPCxpUS65DI7qrhhSQNgOCRoRQ0MVhsfKlHwPadk0pju0GhGpLUlFyuSZJpNnGjSaAX7NI4kTuu0AC4FSAs+zddcRzyIOIqJQt9zzKw5eycESEpWASvRnqdEMaLe6Wj7FsXE9m8ZMwNRkW3v30pRKxaZa0YUPjmchBVo2y5GM7KySJtrDVKq6ulK55uGWHAYGK4zurGABMxNt6iMlgm5Cc6ZNpxEhEOy6YgTLlpz88TSzMx0sWzC6ewBSRbcZMDPbJeykzE4GVAd8du6pEUUpQRj31jBBIbSvVEocJQhpzctVziMudvaeep6NX3IolRzKZVfnEFpiTp6uJSUl36VW9RgdqTIyUtJtOxNFsxFSqpW44rqd2BWH6ck2UxMtJseb/Oj7Z/jxD8f50f87Q6cTUhnyaE136LYC3IqLX7FxvLk5gzLTnE2z/EfXsfGyTic5cZzSbodMTrZ54YUmQZDguhajo+Wsz7XQvZH7KFc8rrt5D5ddPUzQjjj21DjP/Wic06dmaTYDwjAmTdPCQGy3IkgUpZLL4HCJUlaUkxOGsdbkNKwCnfd/Lj/LkSQJ73vf+/ja177G97//fb74xS/y/e9/f84x/Z3d/v2///dF8a3v+/yX//Jf+J3f+Z15533Pe97D//gf/4OjR49y9OhRHnnkkfW5JQuw5ULJ/V+4OodQkcR6oqkidCx1LsdCxRxyrjHWf8zZsjVAsYC5rk6Yhl6oJDc40lRhyVzzUPXCyLZc2hjInZeFy3DhJMP+yuWVhiPzxVn3ke4tjBvZ8aTv6sW1Fhtb3hN4vfUMF7uWEGS79YQ0FUWeYP/7X4TCivCxfny1RmE/Kg8hm/jGHJI4JWzrrhwqSYmCiDBMmHqhTbnm4Nc8nauWKjzPYWikwuxUC5kopCWYmelSHyrjuBadZkippr15U2e6jOwc0HIvnhakHtlV5YXjs0yON7BSQbsZkiQpl1w2xAvHZ0glOFKvGZWaRxykhGFKYzrA8Wy9nrgO01MBMYpumKAsXYna6SR0OjFCdqk2fOysFV3Y0BtIYUHZ9pkebzH+fJM41bmTSarodHVF78jOKiXfxnIs0kxKxbZ1Ll+SKJQgk4LxmR5v66rVsoNl6wKObjdm5+XDREHK+PNNLrlSUak4nPpxC6UUIwMeozsrNKfaBG0t2D15usXojhpSSGamWsRJShToFnX14TIDgxWCKKLdDKkN+Dr6IQUIvRaFoRZornrWkpvffoTQBnWaFeXlYvvQm3sqKx5wXEm57OlCjTAmDBOuv3k3z/3gDLOTLRqzAeWBiPEzTRKhiIIUyxbE04qJU1N0w5ShYVtv5oOYmZkulkUx53MPXrejcz2tWNFqBli29tCl2YY6CBN836ZScRGAbVuEQVK04YyzsHS+rioFOy4dwPIkzx4d57kfTTMz3WFsZ52BkTJSSLphRKcREjRChsfK1AdL+OX5re+aM915jxmWQc2vH1hvHn/8cfbt28dVV10FwFvf+lYOHz7M9ddfXxyzWGe3SqXCq171Kp5++uk55zx16hSzs7NF2+C3v/3t/Nmf/Rk/8zM/syGvYUsZhrqgQ1ebJpHO0UiSLGwrdO5O7iFa9lwLfHYWEoYWQmQFLroSz/PtOV5JnYeYFAUiSvXy5yxLZu36Fh5DsfvOfl/QyyTWblPkSdX9xlfuQdxYY2z5xyxLG4ZaMHqddWvmXTs37HKNQ5UlzYs5xn1ObjgvZDyu/JoLPbj217DVsG2dvzsz3tZfmFLiWILmbBtpVajUPZJYd5+oDZWoz5RoNgOiMCEMem3EBkfKhO2I6ck2lQGfbjvUbeccm3LZJY4Thscq1IcqPP/cLPVhm9qwz+SZFoOjZYZ2VHjm6UmiKKVac/FtCwZ9olhHASplLfMyuqvK5PMNom6ClIKwkzA4aNHqRMSR1jycPNNCSAvHFswGHfzQpRtElOslpBC0OyG1mo9E4Vcs6jWfdjNgYMDPih8EKRCFCQqwHAuVhqhszg6NlZkebzEz2WHHrpr2yPkW7VZIEiVcc9MuGo0fc/LYDOWah+VYxEmMcGxmptqUyg6VQY+gE9KcCRC63oZuOyZVHd2SsOJQHdChU8txiaKYTl9PdYEgVSlBECHl6ls3yqyyOYpTHLe/lalYcLPqeTZKudo47A5w2f5RnjrSpdMM8VwLSylsBKOXVem0El54bpqZiQ4ChV9yiZSi0QyJzzYWlKLb0aHycmb0RbHOH6xUnGIzWZI6HN7tRHr+dmNkcQ8EcSyL/GGFKFKShkarCCl4/tlpOu2I5mxXV6m7VlbdrIulRndWcb35RmEcp7pneMV0Ptls9HdtA9i7dy/f+ta3Fj1mJZ3dTpw4wd69e+ec88SJExswes3WMgwVBEFMGOmqwTzHRVoS2fdlvhS9FMP51tbZsjXFdc9aVPqvUQg2F8eozFDUIRO9IC1Wlbzw+c8e02LjXY7c+JprGOovvI3KL+xnqSFLqb0HGxrS7kN7DvNiIV0xritMk+LvuXyKlIt7nFdxRaDnSeh/zJB54gHXtxkYKSMsQW2wpJPzU1CpwPMtLQMTpZSrLrZl0Q4D3U0ke58Ghyu0ZkNO/Xiaas0ljRKmzrSpDpYYGavSDmImx9uM7ipz/IcOtYESA6NlWs0AFPhVh85sN6vULdNoRIzsqOhq5W5EHCl8TzA0UuavXhgiDBJ+anAcWwhGd9ZwZ7tMPz8LUkvYjI6WKVVcWrMBfslCJTa1ukuSxIyO6JBvYzLAdrVHsNOO6HZjXNfWMjiORRTEuhrYkjpfMUlJ4hTPc6gO+HRbIe2Wbp+WpCnC6tJqhFx69Si7Lx1geqKNX/Hwqw4zpyMcV3Di2Az1AZfqgI90JLW6h2PZ7LysThTGpEqQCoXtOZQrHpajox2u5xZhVyn1JjkIEpJY6bw7Syy68V0Mx5EksRa37vcaLob2NDoMj5TZ/+JdPPv/xum0Q2anO+zYXcP1bDzLRfgxrmNjozfx9QGPkeEyl1xSyzQds1Qjpb2FLTfCdS1KJRek9hJGYYzrOUgh6AYRQurNdJLqjb5uWSkJA/27ZUvd0USArrShENseGavieQ7PPzdFmijdacezsR2JV3K182ARw3p2qk2SKAZHyqu7udscxbl7DM+cOcPBgweL3/ubbGwVtpRhKIQOP8RBTJxCybZRiSp6ja4kpDGvK8hZi9rZsjVCiqIR+kILYO5Ryqvqcs273EiNo3RRc6CX77hElfBZtTKrMVbyJvZ5aCrX1NroNksr8Rjm4zvf5Ma/lMy5D+tdHb2gw9DYhQXddkSjGTF2SZ36YInmbEC56pEkKbOzXcoVF9vREiaOI7FcG68kSaf0wi8UtBshQyMV6nWfOIqRrqQyWKIx28G2dV6eGzsc/9Ek5aqD7dg4rtYrLFU9kiCmNRPqNAOR4rg2s9MdgtClPuBxfLpNY6ZNtepgZ54uFSQEHd2yLIl1rm6UgO9YqCilNubj+DYlXxsXSoLj2jiuw8CQNrpaMyGVmovtOow/3yToRAgpiMMEr+SQRGmx4XUsQZz0qmHLNZc0VnRaIZZjZV1ELDqNkDRV7Lp0gOnxNq1GW2svokijlNmpFr4vGBqrEp+YodWKSOOAsbEa7W7E8R9OIGUJv6K/MnKR6mrNxS9rOR0hBGmoBZkdx8JxZXHsasgNqihKcZyVKxM4rs0llw0ydkmN556eoDXbpRskWO2IF8JZwm5MqlJSBY5tUan7OJ6FZVkkCUVKkGVJOu0Iy7LxS5bugJIVqbVbYZFu5KQ6zzOKU0qOo/MlfZ33KADft3syaKK38U4U2HaWYzliEccJp0/M0m4EDI6UcTxby2jFC7/2OE5pzgRUai6ev6W+wi8KxsbGOHLkyKJ/z7u25Rw/fpw9e/YseMxynd36jz9+/PiS51xPtlTxSR7iU0pkele95GUp9O56Jejw7MK7Cr1A9BlisGzxh23LIsydxEmvGrloELzEQJY5fy/Ze227oH6ZhDx3buO9hf3esqVEGi8suZG4IZI5fcVIJr1wPlGY0Gx0GBorU617qFgbQ+WKh0ih2w6xHEkUahFiIcAra2MxiRIQ2rsTdEL95e9YpImiUveIo0wuxrGoj5RIU8XsRIdKzUHYFpZrMThSYdflw1xyxQC7Lx+ASOHVHRwpaE2HZI4lZqa62ohwLITUOcrdTkQURExPtXV1stDt7fKCMyEEpaqLsCRprGjPBvi+zchYVasnJArH04ZGuexg2VlLzaxAqtuJaMx0icIE6VjEYVzkPnu+g+1axGlKHKa6pZ3vkCQJs5Ntdl8xjOPZtGZDHE93UsGT+BUH23OpVl0GhkskQURrpktCgluxac4GJHGMkDrMi9KC1zrNIqWdeVCVSnF9e80pFjmOY2Weu5UXoOluOT57rhzC92zazYiwFWLbEr9kM7Sjol97JvlTqTpUKh7VqlekLgTdmMZsl2Yj0DI0kSIMY5JYV7hLKWg2AmamO8RxQhwrXWhUdimVdZeUvM1d/49WMUgyIXwdmcjTVoZGKuzaoyWBThybIujGxXfDQrdwZkJL2AwYb+GayL+/1/qzHLfccgtHjx7l2LFjhGHIww8/zKFDh+Yck3d2A1bU2W337t3U63Uee+wxlFI89NBD/NzP/dw53Yel2FKGIehuBXGcImWmBZbd61SpFZse+URe+G+5bI0qfl9OYDqXRdGhSS0/YVty2WJi0T/2xT40fQbGWsjlcsIwKTyHG+2pK85uDCJzCxZDZClbnRglBVGaEMYptidJ0oRGo0sSpZlsTILnOdQHSji2TZKClBZRmNDOesnajqPz1GwLWwpmp7XX0JaS+lCZKIoZ2lHG8ywaM23SJCFVCY7vUK6VtFdPSgbHqkCqw7dBQtiO6HRiEqVba0opiMKUOIUwiEkjrX3oe47+XSWAKrq3pKR0miG51Eua6FQT19WGpu1KlNLn9X0H33fwfLsolBJCECeKmek23W4EZJvXFKIoJgWkKxGWYGqijetbutNLI8DxJLWhMp7jAJJuKyAMUobGaoRRSpzoTh6OJUmVwpaCsKOLgOJEV82mKlsvE4W0JeWy7jaiVE+JYS32oS4QlMRZd6mVYNva03jplaP4Ax7ddkhzNsCvOlhSUBvwiboxUTeiVPdA6DW5G0RFhyrLlkVbPKUgCiNazYBGo0uj0aWZV2iHCa5jUa7oSupcgUIzN8LTUzvIBKzzoqHsd9e1qA6U2XnpIEmccvxHk3SDcMFNaRzGtBoB1bq3ojC74SwUG24Z2rbNJz/5Se644w6uu+463vKWt3DDDTfwoQ99iD//8z8H4O6772ZiYoJ9+/bxe7/3e3Mkba644gre//738z//5/9k7969RUXzH/7hH/Kud72Lffv2cfXVV29Y4QlswVCyLi7RUgFqFcbg3BOd5RGcc42zZGv6Ju5SFr/n2rRlWBSurGSxXMmOOz9CG4/Ln3Oha7iuXXgLdWX1BtPnCBVzH9o2zP0SWdw7sF2xHQuUIOiERGEMmVc7CFOkYxF2YqQtcGyB51oIBd2Si1+xCLsxYRgjhPbe2a7EdgRIXRjm+DbheJugG2PZAq9sgbQY2V2hMdVldiZkcEdCGCWUMvkUv+SSJAqv4oDQXj3bFliOziFzHVnk5pYqLhaCuJvQCBLqQyVUkpLEmeQF4HkOKJiZ6ZKkaC+nUkRRjJASy9YhRse1dS5ylCIsHSb3yw7NmS6WI/F9m25Hqy44tgIpkLakNdVBCQGJ3ox6vkNzusvEC1qL0S05REHK5ftHCKOUTiukNdNhcKSC42qjI2hFxEFMEOocTt93EVJRKll4noUtwSvZVKoetmtp4XApC63Xs8Ooq8VxLLpdXZm+0nXJzdoaju6ocqIZMX56lqizg7HdA7QbAY2pLtK2qQ/6eCWPUlkXbyRpCqnWqY3CFMezsB2LJBGoKCFv1SmEpFZ18XyHas1bMD0p/+7IIzHasJ/rUMiF8As5Hkvglx32XDHIc8emOP70JLsuH9St8zLiMGbidAuAweHSmu6pYeOrkgHuvPNO7rzzzjmPrbSzW65peDYHDx7kn//5n9dtjEuxpTyGAlH02eyvZiv+vsL1SbB0H+E832Q1hmeuw5Z3BtAC+kvkDhZjzqv9Fj0AWHsoGfTrcV0tbXF+6cnsbDvLMCOXstBs05uwAJYl8WsucZQiEbiug+/buI7MulRoCSglIM2k3FxfUqp6ICAKY5SARCVMj7cQUuCVdJ9b29L9gafONLUQvdJ9rv2SQ22kQtiOSMKEJNTt2SSSci3P7dPi0UIKSjUf17N1p4y0Z9yXqi7SgjhJCIKE+mBJ5x8qBUiSSBserm+TJglCKqIgIVGKKEywLC3unCYKx9FVr7pnri6IsjPRfJXovLpK1UUisR2LStmllnmTXEdQH/CxLaENPk/SnGozMFLCcyw6sxHVQZ/BsTJ+xWF2qku3FYJQeL7N7EyHdlt7z0Z31YkTiINUp69km2fHkbiejRAyM3pUIegvRF+6zFo+A7ZO/4nClXsNLVtSqniMXFLH82xarYhTz87g+BZKCRrTbSxLUKo6OLbUvaA9R98v19afA9+hXvfxfZty2cnCxK4OGZdshoYruI41p9lBP3nIP29ikCtHpGm/82Kuhm6uUOFXPHZdOkCSpJw5PsupH0/RmO4yNd7i5LMzBEHM4EgJ23gLDRvI1v90rWVdEkA26Rcy2uZ05uj/+zLXEoJCukDrD65sKEsddlb04qJgToHPNrWFetXvfY9dmKFsSmxbUqm5dGZDWg0d9h0cLmVdQzxaswGtlu6PrEpgCYHr6JyxxmSXJE7xs5BqY6qLY2vpFMe1dM6eYzF+cpahHVXiLPfPch12DLtMPj9LqxlQq5eIQ0UcxNSGS9oLpxS1usf48038ikO3HfGWd/404+Mn572GHTv28qd//L+xLYlfsSm1PVzXIghTXE9iJ5Ik1V6uMIyJgoQ4TIrq1iRO8XxtdAkJYTfGBhQ2SindL9qxUA40Z3U1tm1buJ6N61m0m3kRiI1KQ0Z3VpmZ6KKUNoIbM12kJXBtG0eC61nYvjaQklTn91WqPl7VplKPmTjVJMjEupNYkUQprq2LN5RQpFnOXBgmuiDIOncvuOtaOmdzhRXKti2xHcmOXVWOj3i0p0NOPTtFY2Y3SZIQRwrbkpTKurI63+QLIUjShKAbZ72Yc4+elrEKw4Q00R5c17UKKa3+ArW8eC+K0sJI7o8G6PSmhW+IrmQm06B0Gd2tcw5TBRMvNAGo1FyGRsrGKDxHTE738mz5T9icfMEVrlJSCGK1eNJzfzh5NQufgr72eCsLIQopIFGLK9r0ewwvkljk3GEu7zXdiswJJatlipC2IZYt8TyHZhrQng2RtsTzdVg37MR4nkUaa5mQbifS/YKDWHuMag5BN8avJLjKIghikjSlZAvCMKJU86gMapmb1myX5kyIJaE26DIwXKVS9ejMhAwMlum0u3TaAcM7a7RnA6I4xSu7JFGKcLQ8y/j4Sd71qYl5r+HT7xtBCosoirEti+HRClEUE4cxpXKZQKksBC2xLEWrERDHMZZlZ0VhKa7v6BC11N6zMIhxfbuQ3bBti7x9b9iNKWUFODoM2iWOFF7JptsKGLukpr1PZ9p4VZ2rGHYTqqM+QWbMeL7NnqsGCDoRU2NN4jjCVZL6SImpF5qkic5djKIEJRSWpaMgCoFKdUFKkqRUqy6L9YBf1eegr0LZtpcXu89zEwdGq9TqZQQShGTyRIMYvU66FQfH1d1WPF9HSdJUEYc67Ou4VmHk5WkD3W6E61g4jvb05VJfrqv6QsaqkLxRaEMwbySw3LiF0FXmUaR7V1drJVSaUqq4WvBbSlOBvC6sQ7XfNlint1Qoed0Qy3928p1m/4HLfl6ywwtNq9UMaRnD6WLcBV2MY15XhLioPL3nG9vRciCtZqjzcmUv/7tS83W+oGUhEFhZIYESUK35hHFKt6PzBMMg1tp/tqDT0W3NhnfVqA16zEx2icOYsUvq+L6LY0l2Xz5ItxMSBDHNmYA0hXLdw7Z0LmOaKhzPJgnSXiPzRYiTlG4nRKVQqrkopYtTEDq0WK75RFGCY1m0m12CKDN+pOhJ8di6+MMvOYRBQhjoamCVprp3tGMhLUGQVWfrIhttlERxrAtnPJsoUozurpHGKZZlMbprgE4jAqFQic5/TBNF2E2xXcnwrhrNyS5RJ8GxLbySXeRnBZ1YF9tkHjMrE/PX4W89JlifDV/uKcw1RZfDtiXliktlwMOvuriu5IXTs7Rmukhbe28tWxtanu8W4fo4TuaJ2idxojvQZMZeGCZZ3mNMpxPRbAZ645Hkig69Npu5puFKi/nsTJNSSEFtwENaWjjbKznGKFwvFBveEm8rsOUNw1VEehd8zmJPkjIPha584UtRWtKiP5S6mrEs8/eLZSPT7y3b7sZhHnAyzEUppVveCZHJ0Qhk5oFK05T6oIdKUuIgLrQ3fd/B8ywGR8pYUhd0WI7OOU6RkAqCdoSwJJ7vMLZnkIEhj6teNMbo7jrS0sbC7suHkVIyM9HRsjBRiudalGquNkDDBK9sz+uIsxBJFNNqREhLUK35pFkIMi9EqdY9kijBr9q6S1KQ4HhWEemQUusQponC9XTf4cZUQJooLNEzvlzPJuzEvWIG20Lauh9zGieUKi5xmFCuOFQGfC7fP8K1N+8CoZid6hKH2rBJ4oTWjK6S3nXpAEmaMjvV0a37KrpbihBa7UGIPg+eANfXBRtW1hVEsbaCuLORUuDYukI5N8CWQqcK2AwNl/EciV9xaTciqnWP+mAJz3O16LXnFKFgHU6WlMtOJnSvDbokVlnbRd3utNcyVBaeQJ2fbRc/uddy1fqNWU6l7+sKer/soFJF0I2Xf7JhRZyHouQtwTYwDPstpxWbhssekYcJ+heqZReCtKc7qK2BFYRRlznnRRuCPctbdrG+jHOh95K3Zzh9KaIgIezGoFKSKCVOFCq7T2miKNc8HFvSaYckmbwIgGPblCsetYESnW5MqWRTHfApl11sxyIKEqpVF9+1KJdd9l49SrnmIUC3ULN1149a3ac53SFsRbi+pQXzSw62Z9NpR3glF2mJzGBYnDBI6LS0ZE657haGbhLrL/tavaSLEpBU6yVsV+K4tg4pKt09JK/ITRJFpe7T7gQIIShVbOJM589xrazPt74PVmbcKFKiMKFS00U5Qai44ppRBoYr1AZLVGoek6dbWejdRVgWcZLglmx2XlJDWBbNmS4q1P2Xpa3LSUoVfT9VmlUfK+3Zs21dcLLen2XH7W0SliNvaFAd8LFsG7+svYeNmQAk1Ad9LRPT997l53VcO+tslBm3mae2WtX5oY5j4fs2pZJLqeQUbf/ONgTXlNouwPPtovVfnh8adOOiiYJhHVDn+LMN2PKGIbBqq2Olh+sJ3C9ds/ixeb5KLm+gcm/ZcobfCsZxsdoTeve2TWbaQojttQtdDUIKmrMBrZkAYenuQnkGhi6ecPHKDkE3IU4UcVYimueI1QdKJHFC0Il1Lp4n8VyLctVBWAK35BAnWo9QWFmVbdoz0HdcNoAQgstfNMKey4dIooRq3cd3bYJuhBDg+NroXAqVKtpNLVNVrng6vBumpIU3UGK7FmE3wnYEYRAjJcRBbqjo7iWWLYnDhFLFwS+5upq25BQdlfKexHFmcEpLt6OzpM6FkwgsyyIOE5JU6fCzlOzcW6c+UubqG8ZwSzaWLZBS4dgWrudQrrqEUUJjJqBS8/BK+vXHYUoca4+g7O8bn87d5KyXgZiLhCdJumxIOQ/nlqse5aqDtCTDO6rUh0uM7a7hlhxdnZ4Z3ErpFniO0wv7JnFKEOhiHt2aU0vx6FQgfYzugjV/8q61qK6XCt97sl/SIeRux3gN1w9jGS7HtkhcOBeh1aWeolXumdNreDF0cUhRbqIfS1XPg7jY9Vcy9lwP4SIyEPOhagmHDeousskRWc15mgJi+QT17YTjWgyOVui2Q7yKQ3s20MZfCiiF41pU6yXazWlQLqnSYdBc6qVc0+HCokOIgDjSbe3iKKFc8eh2It2eUmYbvD4t0PpAiV1XDDK2ewDXc2g3AyxbUhn0mJhs6Z7iCGxPMrZjD59+3/x2Vrt2X0qlrmV2BkbKWehRj19KC2XpCuRS2SbO9O7yEHMUZflumcHnuBbddoSLfr7t5Dp7mcfQ0xvOONJfXCpReK5NmoU+tQdSt81Loqzy2bGoD5UZ2VljdqrD5HibTjOkORNSHy5hOxZe2c7Czy5DO2s0pnX1c7cdkKZaxFoKQULPUMpWN2B9N62OY5HEKWFWub3YfBFCaM9eyaFc9Zid7lKpe9QHy7xwchbCCNvTnll9z3pFJ6DX824QFx68fkMwTnrtUKVW8l5g/VfnJNPTj7S0HFDYjUhinRtpOAcULFFXasjYFoZhXvCx8hzDlU9qKytC6VUbL0zaZwTmh6WpWr5N3wp23nmN80VF7i1bgVG9ZcneOKWW3yBsRwZGSnRaAWkCbQKirFWYENojVK17nDklUIkuAoljnbjvODaeb1PybeJQV3lKxyZJs36+iTYywq5up2eVHSD/wsjCeL6FnWkPlqoOjUYXmaZYWUVqu6XbqiVJyv/3pW/wg++d5p/tK7EswTtvhf0HdnHy2WleONVAKjJNRHSMRuqwdTtKsWyJ69t0myGdVlAUpWg9Q6uYG25mGEZBglJ6MbNtizDIPIR5pWzmTUtThe1ZBJ2YcsVBWgIVaEHqNIU4TPDLupil7Fr4ZRfblsy2QqYm2+y8tJ4ZPBbDYzUsy0IoRaXi0GwoZme6+r2wtKA2UZIJOIOw8o3e+q9KrmfT6UQ69F2yF10XdZ6foFL3CToRrdkuQScmimKwJK7vFL2co0zA2rZ1NXK3ozvI5Ll+OVIKSNAeV6u3kZtnGK7ZY7iwMe2XdMV9px1SrS/toTYsz7aOUq2QC7L9eO6553jNa17D9ddfzw033MAnPvGJeccopbj33nvZt28fN954I9/+9rfXfkGV592s8OWuYlLbtlVI1yxm4OQ9Mfv/ngsbLyCcvyBLjv0itCkKb5lSRVP67Ub+1Zmm2/ceLIkiq6jVc6zTiui2Y2TWJcIvOZQqHl7Jze6jNgi050dQGfSRWa7Y4M4yXll779JUYbt2JoOSgEoRzPX8SCmpVF2qdb+oLlW6DoFqzdXeQlcQRwlSWji2RZqmulDCtQuNOyHAr2qjtDHdJQxiPN+mOuhRHfAyg8TG9V267RjXs7Fd3bfX7hOcl5bE8XSbPyG1fI1l6wq43GtoO5IwMwx13+RM9dCxGRqrUK5q8etCAiULn6epLmzxyy5BEKNUQn2wTLcbY1uC2qBPazai3dS6kdW6Wxi6jucU90xIQarSrHBGf77XOxIgpcD3bNJUew4Xw7IldiZJ41c8VObdc2yJ62ij37b1uq1lZzKjsBuhAD/r99yPLoDSbQvzsQDzwslrtAt7Wv9i/nX9sk4byDcCBsNGckE8hrZt87u/+7vcfPPNNBoNXvrSl3L77bdz/fXXF8d87Wtf4+jRoxw9epRvfetbvOc97+Fb3/rWOV13pYbhnGm5bFmwXiC08ZfOa5GUprpPpszyXsIk0frZWU7UQi2V5jy/kEFY/LjcyFqv8MV5QegiAh1Wu4jGvZ4IHfJbzQZhW6F0cUe7HeH5NkHmyRkaq2jPoKcNtqAbZ51CEiCripUwPFqhWvV0B4t6iXYrpNPqauNS6Fy0MExJUwFCzpn4aQqWq0OXaZaTJxS4Hvhlm+dPNOh2IhzHQUilhbabei2wHUkS697B1brP8GiJ1mzI1HgLkNQGSiSRAqU3jZYldGeVJGFgtEx9sMTMVGdOcQRAueLSVqFukyZ661kS90KhKoU4G7Pr2dSHSsRxqgsoal5hdERhrCtuhdLGasmlPuRRG/Lwqx5hGBO0A5IULrmkxqkfT9Oc7TI8WsXxIqSU1EfKeL41R8JDe2dz7diNmdd5F6koylIH7PkFQHlKgVeycV0JqaI6XCLoRAipK8ttxybIDC3L1tIwCvA9e9H1Ng8n55uIOe1RcxRrc7koFl3DXU8XoXQ7EU5fX+blquIN8zEew+W5IF9Hu3fv5uabbwagVqtx3XXXceLEiTnHHD58mLe//e0IIXj5y1/O9PQ0p06dOqfrrnT3uppdbt5D03Es3UGhD6V64S3b0gr6+kkU28PljKL873ml2sKDWPFwNw2C3gTdtoZhX+vF5TYI2xMdDkWB59nk0clq3QOy6lPfJg5jbcBBsVGT0kJIyZUv2sHuy4ep1HTP2VLVw/HsQoswTVOSJEFavS/ZXCPQsS2Ugk47wpKCgWFdxauNUoskTqjUXaJuWngmFWBnIeY0UUhb4PkulbpHFMX4JV0tGwZx1iZNVx6Xyi47Lx3ALzm4vk1twJ/XH1hXIuuiEz1l8irttMi7VEoRZcaO0C47XNcutA1zIWYthaIrloOuFt2u1UsMDldwLElzNmBmqotlCYbHqkhbt7yrDfqUKz479w4wuqtGqazzO/MNtKI3nzcyOyIvygm6MXE833OoJXx0P+raYIkgSEiTFMvWhp1b0ukDUab9GARx4SlcyoEgpcg29tm8tcQ8bTu1Rp9h3j5vMXL5mlYjKBwG+WbJsArSc/zZBlzwb6NnnnmG73znO7zsZS+b8/iJEye49NJLi9/37t07z3hciv4JVhv0qS1TPTj3yQv+d0GkFFTrPtW6l4Vl8upI3RoJwMnEZntj6p11ucWzVHF1s/alPIYXo13VN+btahgKcw+WREGhDWdnoU6nZOOX3KKCs1LzSGJt4KSJKrqAWBZFYQDoHDzPtbEdG2nrkKvnOYVH0JKyCOXlz/Gygo4wiLNuIrrCNwoTylXdrzmJUsIowrXzjZ/A9W3txUt1YYsQumdxmmgjpVr1iDM5GpUVkoAuPMnz9PqrX/tJU1VIo+Q5ynGcnceW2JbUMj/0GS3Zaawshy6OUzxPF2ZYmRHYbOjimpGxGkpJJs80mHihSX3Yp1RyqA/4SAnlqovqC7snqerrB6wjADrvemPll4QQOtybGYcLVSq7mcRNfdBHSsHxZ6aIsrzOgSEtLB5FCfktKpWcJSMz+XXzIps8NzjN3sectTahWs6R5TgWpYpLkiiaswFhEJvQ8mpRqph3a/3ZDlzQ4pNms8mb3vQmPv7xj1Ov19d0jvvvv5/7778fgPGJMwsek+tSrZTVzmnHsbIClJQkUQihF19QRQ7iQucXcvlqXCFE8cWx+Hj7PJEXDfmYt2dF8tmYWzCfvDCpUvdQDZ3bZ50VNixVXYSUdNsRwgIpdNcKkeuMxinS1Z4/yxaUfIdOK8R2JNLWbci0eLQO/4I2vhRgObovrp3oHsvdTlwYcpW6xZmTNu12gO3ajOxwsGb05s/KOlgkaYpSumOLsCQ7Lx3Ay4zG2emONhzRIc801YLcttPrumEvsDorpbR0TaTFl2V2rVRqLb3E1qFkHU7Xxm4+v2yntwY6mV7iwFBJn0eAX/GwHZsoUUycnkWlsHvPICAY2z1AGMW6a0uRH51V5WZVujr3TmTX3bhQck5uHAaZgaSUmtNPORcEl9Ji7+WDPHN0giROqdV9KjWPxmxAEieUym4mSbOy8UopSdIEpTKpnkjNMwbX+sqXWwtdT3s0281Q62OahWNVaIm0Cz2Kzc8FMwyjKOJNb3oTb3vb23jjG9847+979uzhueeeK34/fvw4e/bsmXfcPffcwz333APAzf/ipeszuDVMNt0/U7flynN8zm6HVEz67J918xJdhGuDWO97cBGymg3CtkRk9ybXi1N9lf/Z4l4qOfglm5mpLp5v6RBvoorUizhOdeeQ7NvAcSwqVZduJyYgIlV5rq8gkT2vgEB7GR3HIoq14WHbFuWqSxqnoASDQ2UmzrQolx2Gd5S5MxzH8x3SpKK1CMMUmYW2CwFlx8J1dbVxtxNjOy6e7xAGMZ1Ydz3JQ8MLzY1c/cC2JUmcYNuSbpiSWrprhisyryLocHNfeDKXeMlzJkEbGsM7KkSBLnyRUjA8ViIOdR5hbdAvwt2psmg3A/JewfnokijNpHPyXNn1l6pZDG0cOgSBrjCP47RocWfbFq7rEIYJO/YOZl5arWHZzbyMtZpHKQsrr/yaejOeZlXuQJZfbhVe09W+eKVU1ilm+edZlq7I77YjI1+zFoxluCwX5FOllOLuu+/muuuu4/3vf/+Cxxw6dIiHHnoIpRSPPfYYAwMD7N69+7yMTyz6y9L0JyPnbZEWOnFhFK2b+Ovcfy8G8qFuZ8Mwf8O29T1YhHztlkLokKhSmeExv2JzeIc2xGxXa9flHr/cmwZ94tVS59zpjiI2tpNLlMTEUUq3ExVGQ7sT0elEpKk2Lv2SrSMAltS9j6sOIzsq1Ad8KlWfgeEynmehSAsh5rwFZhj2BJSFEPgVF9vRXk3HtbBsXegiMsNOLVKtn4dwdb5g75i8iMXOpHpsRyIWCOk6ru6m0S9AbVkik2+xEFJQqfjsvKTO0GgZmfUNBvBL2vizHV35nXeiiRMdi00KQ6l37vOF59l4voNAi4R32hFBEGM7giCIaMx0qA2VqQ56eJ4NqaLkO5TK7qqv1R9OLqTHkjw/NT9mvV7Z4mMoVbRWp8Gw3lyQT9Xf/d3f8bnPfY4DBw7wkpe8BICPfOQjPPvsswC8+93v5s477+SrX/0q+/bto1wu89nPfvb8DXCNkzrfyefewsVPmy2c62YQXISGRWHMXoRjXyfWe4OwlUiSFJXqOSKz/FxtLOm/K3q7/tqALgpxfRvX097BNFG6ICzrDFLkGsosNSPLK7vk0kE67ZigG4HSRQhJolvwuY6VffGqooADtMcminVf4mrdI+hqrUTPd3QlsZBEYUwUag3BvDuJ0xfmHB6rkCZarSA3zhSA0nl7C0kY9b8G6WodxqJgJkmxHEsXv2TFLAt1ZCpXvXn3ek5oNNNl9Ssujemu/j3TYPVLDu1mXPRdTmP9LiSF9E2+Ob4wTplcViiJtVGuQ+paRmh2usvwaAXL7hnkued2Lej8ysw5KEXhke5pEa7WY8ianmdYPWcXCxnmc0EMw1e96lXLJnEKIfjUpz51nkY0/9prJW+svpLzrpen6OJcS4y3LEcuVXG+ifj93/99Pv3pTyOE4MCBA3z2s5/l7rvv5siRIziOw6233sp//+//HceZH5p78MEH+fCHPwzABz/4Qd7xjncsfbG+jhKyrw/tQnPTcSx27K4VLcyiKCFNU52vlyqSONV6g9l5XM8qPHOWZTE47NBt6+4jvu+QKoXjJEVuctIXMgRteMWhDif6ZYck85jlBSoUnUaUbsuXeQv7N4uWJRkYyqpl842kUiiVomJtKJy9uZxjGEqJtLWgtciMt573cK6RspLuSnloNDcM82K5NNHeT5nJwtQGvCJPMklUIbekx5CelTqz9Fu8UVi27IVYU0UYlOm0Qp2fqlTWAccqOp2sBV2dLIrGBcXX2Ro9houJWxvWGWUiySthWyQonMtkW9cd3Nmh5G1sFJl70J8CsPnvwYkTJ/iDP/gDjhw5wj//8z+TJAkPP/wwb3vb23jqqaf4p3/6JzqdDp/+9KfnPXdycpL77ruPb33rWzz++OPcd999TE1NLXk9BUXOlZBkxlxP3ufsphqlqodfcgqvV5JqqRiAOEqyEKg2BKysj3B+rONYVGoeUmgpk7xFY+GlPMsjr4Wje4Vh5YqrvWqWzmuTUhtnYRATJ+B51oIhy7yQJn+uEIIkySqpFyjK6mmf6sfLFS3A7Wc5ctrQ7ZNPKRSTl7zVc0Kj+bG6uETOydvLjXRtyFrEcZLJ3/RL1OTi1psjb1ZKUYhXt5oBnXZUvGerKUhc7NypUnN6vivW7jEUa3iewbARbAvDcLVslLGSL0S2Lee1Wzqn8+ZCt8nFsxWybYnnO9vaMLQsiecvLqa72YjjmE6nQxzHtNttLrnkEu68887CkLr11ls5fvz4vOf9xV/8BbfffjvDw8MMDQ1x++2388gjjyx9sT6PmUrB9W0c18L1Fy4U6O9CIYQABZbUWnfdrnbB9XvgqnUfx7OKSt38+TrPT3uDRJErN/daQuhKade3ChmpNFU4tlVoH9qWoFz1GBwpUyq7C37Ocw9fIVtj9wS1Fys86X88l63xSg6OZxct4vIcxcILtfSdLl5/fwUzaA9oLgCdd2HpFa9ob2wYJtljIPI4P0vr8Z1PRJaPqb28CZ5vUx/wFkz1WS1yzvtH9v+1nSv30hrOA3mB0Fp/tgEXxzfSFqF/V72e1WS5YXEx5U7k+Zjbmbw/68XAnj17+NVf/VUuu+wydu/ezcDAAK973euKv0dRxOc+9zle//rXz3vuWjRJpRRF5w/tkZKUq14h+nz2+lx8Sffl1RVt4bKD++ec41h4ZxmZOrycZvl+qjjHguFr16Zc84traxkbiWXrkKxfchgZq1Ktzc/p678e5MUm2vhV6GpiK8tVzsmPWUzovlLzcNzeJiMvwNHXWd7gyEOj/bmbTlbY0F9IZ2WeVtuxCsO2yL2U+YvaHN5C0K/LsiSua1OpudQG/HUbW3HPMoOh30BcfSh5XYZkWAHGLlye7f3NvAR5tdd6r28bkU/W645i3k7DxjA1NcXhw4c5duwYJ0+epNVq8cd//MfF39/73vfy6le/mp/8yZ9c8zXuv/9+Dh48yMGDB5mYnOjlytHL8VtsPhYev6wKOc/7yqtzpTVXOmqhPrdK9QSks0qQzIu28DXzIos8fOo4FtKWoHQlte3M1zBdaMxFBbbsFUTkXUpyite1TIcc0W8grzCUPH88+om5JmC/jmp+/TRNdRWwACHzwiB9Q9Rm8hjmRmvWRu/sbjLnijbURc8oVAunASzHSqVqDOdGHvY3AtdLYyyJRSiVnXXdXeZUa97qurCskNqAX7T9MhjWm//9v/83V155JWNjYziOwxvf+Ea++c1vAnDfffdx5swZfu/3fm/B565Gk/TIkSMcOXKEsbFRnSieecrmGzdzF2idi9j7ghZ91bGVqqf7C591fN7jvDijQht1WVhVqaVlV4Sg0AlUCixHe5Bct+eNXGr1KAo80t4XTqnsUqn5WiT7LI+hECtvoZmkqqiUXekaZlmi8PgBeL7WXPS8fsNQjzlJFK6ndR1zCZ7VXOt8kW/EXc/GLznrrvvXL1Gm1NpkvU3hyXlEYVrirQBjGC7BRuS/5V9I603/4mwwrDeXXXYZjz32GO12G6UUf/mXf8l1113Hpz/9af7iL/6CL37xi4t6s+644w4effRRpqammJqa4tFHH+WOO+5Y5op93iu1Mq3OooBC6UrRXBh7TpXqWcfnXkIdek173+pKdzBa6pqFMaSVpIsCDdvteaaWk6TKjdmid7qV6RpaohhX/rOSiEAhF9PnMVzpsiCEmFPBbNkW5er8dpxSyiK0nXd9knJumG2zrEX5Wuu69oJSPeeKXs/RRrhaW65gL/y8Oe7Z1sa0xFsJW8swNPPKYNgQXvayl/HmN7+Zm2++mQMHDpCmKffccw/vfve7OX36NK94xSt4yUtewm/+5m8CcOTIEd71rncBMDw8zG/8xm9wyy23cMstt/ChD32I4eHhZa+Ze+LyKtflkFK3ZcvXbsua38e2nzzHVWveJTr3ty+8q5apFO0PbyulBbVlZtCt9Osjr7Q+O+rbP7a8D/BKc3JFcR+0l3E1BkduWOb/X4h8HHGcZDqRQveazsLI+nWt+JIbSl4YtVhu5nqQpx7oFois+nvIeAwNmw0jm24wGFbEfffdx3333TfnsTiOFzz24MGDc6Rr7rrrLu66664VX6sozNDx3b6K17l5ef1IKYj6QqiWJYmTtJBxmX8NgeNIokjHh2zHIonSvhBuL6y61BgtW2rNQylIgShKe51WlvmyL8LZZ1Ut5MVZuUC3bcsVG3iWlLp/sRKrtjakFEhHIpawbPOCjlyqxnGkvk0KyOu5N5GVYzu96vONIP9s5eHk1UaEltuAGNaX8+H1e+SRR/h3/+7fkSQJ73rXu/iP//E/zvl7EAS8/e1v54knnmBkZIQvfelLXHHFFQB89KMf5YEHHsCyLP7gD/6giK5cccUV1Go1LMvCtm2OHDmyYeM3hqHBYNicCLSAcqpWFNvIQ615pxEpBTLNW1Qu/Bzb7hWIpImkHYW9NnrLFFH0Chu08LOQEolCpUmRE7jcl31euLJQnUh/r/XVFJZJSxAHSdExZjUIISiX3WWrLx1HaoNIKbpKkab5a9g8hSc51fr6h5D7kTLXoEzXZBQbqZrzzAbbhUmS8L73vY+vf/3r7N27l1tuuYVDhw5x/fXXF8c88MADDA0N8fTTT/Pwww/zgQ98gC996Ut8//vf5+GHH+Z73/seJ0+e5Kd/+qf5wQ9+gJUtYH/913/N6Ojoxr4AtloouY9tkgpgMGxp8jDrnPRFIRb2GGaemyhMilZzuQhxfyHH2eRyLHk+YP+xS31h98vN5L9bfWNYSaFDng/YL7PTf+0Fe64vg2VJlNJh6LUoFejim6XvWTG2XKS70PLYXN7C84UW/tZFP6svPjFh5POKOsefZXj88cfZt28fV111Fa7r8ta3vpXDhw/POebw4cNF96c3v/nN/OVf/iVKKQ4fPsxb3/pWPM/jyiuvZN++fTz++OPr8rJXw5Y1DA0Gw8WNyIpDONujohRhN5pnuGjBZ/3/PA9Oa82xpJFTXK/P0Ms7nizrMRSiL2ws5hRqrLRYJL9mcc5zpF8Say1VuKu5Z0BxD7ZzrpyUgFLa27yKG1DkZW7Hm3YhUBsvV7MS3db+Y2zbZmBggImJiSWfK4Tgda97HS996Uu5//771+NuLIoJJRsMhk2JFJI0jQv5mbPRIea5j0tLksRpYaDlxQeFsbcCXcE8d06soNK/34DK5WSkpYs/VuQxPFt/cR0UC3IDWam1aZtqT6skTVOUWv4eyOL+koWvt5+RY0lJmiSZ4bD6cNX2u2MXjnONJp45c4aDBw8Wv99zzz3cc8895ziq5fnbv/1b9uzZwwsvvMDtt9/Otddey6tf/eoNudaWMwy34ZpkMGxJhIA00Zn5YiH7ZoG5btvaMLTP8txFcbJsyC7XFcyFtcUKaje00TlX3MyytJGwIo+hyA3D1RctLIWV3Ye1VuPqftMsWrjTT67FCHkRxZoueXHTl8+5muIGI1VzAThHy3BsbGzJwo+V6Lbmx+zdu5c4jpmZmWFkZGTJ5+b/7tixgze84Q08/vjjG2YYmlCywWDYlEirJ1gtV/jF6TgWrmfN8dblrcv6xawXQ8heWHQl2qD9f86PdT0bz7dXZOj1dypZTvNwNbiejevZazY48nu2onCy6NOc3Kb2TV5oJIRArUIEeTuH3y8EeXOac/lZjltuuYWjR49y7NgxwjDk4Ycf5tChQ3OOOXToEA8++CAAf/Inf8Jtt92GEIJDhw7x8MMPEwQBx44d4+jRo9x66620Wi0ajQYArVaLRx99lBe/+MXrfXsKtpzH0GAwbA36BbOllEW18FJYtqRkz+8ApD1g6bLh5DwkyEqF6PvO1Z/fuFLNQSlFYYyup8dwPdq/WZYgTpa/Zz1P2fYV2i8E2LNuiukK389co3M73rMLxgZXptq2zSc/+UnuuOMOkiThrrvu4oYbbuBDH/oQBw8e5NChQ9x999384i/+Ivv27WN4eJiHH34YgBtuuIG3vOUtXH/99di2zac+9Sksy+L06dO84Q1vALRE2L/+1/96wb706/YaNuzMmwhToWwwXHzYds/iONcvTsvSodEkVdhLhEalJVBhrp24Ao+f6P3nXLxzSbr5JEukFJDoMHfxXixAfwh1I7o6XQzk712eY7lSwzCXNTJsLe68807uvPPOOY/l4v8Avu/z5S9/ecHn/vqv/zq//uu/Puexq666iieffHL9B7oI28IwNBgMFx/9Fb7SEiQLa2mviLzdW5oolFzcCJNFJfTKCkGK7idrHxpSCBI2pgXnuZB3glmuCCXv3rJiL+sWpL9XtG5nuHzhTl6RbC3SStKwAag+aSjDomzpT+Rm24EbDIaVs9wX5mqnt2XJZfX5tAag1k5cyfmLEOI5rDVnNXXZVEgpi9DoYuQeMsHmM27PF/2eY9te/p7R9/ftes8uFBudY7gVuCCG4V133cWOHTsWTZ5USnHvvfeyb98+brzxRr797W+f5xEaDIatxkoKKnTBi87RW1koObcM1z6uvHrZ2oQGgpTa05pXai9ELo2D3L6b8f7XnXunk2Rp3TttTJv8wvPOBgtcbwUuiGH4zne+k0ceeWTRv3/ta1/j6NGjHD16lPvvv5/3vOc953F0BoNhs1CpeVRr3rrlYVnW8p1QylWXcsVZ0fl6HsO1j8n1bKo1D/sci0U2ipV0QlFqe4dEz/4crMQ7vZ1zMi8sxjJcjgsyk1/96lczPDy86N8PHz7M29/+doQQvPzlL2d6eppTp06dxxEaDIYLjuhV7a4Xy0nXpKlCWmJORfSSQ+zLLTsX1tKh5HyxUrmf7WzknP056N2zhb2GabrydAXDOqJApef2sx3YlKvRSlrKGAyG7UT+DXpuO/aiOGARr2EuH7JSI2cdIsmbnv57tpBxqDILZzsbhsAckWsgyzVc+HOWKpNfeKHY6JZ4W4FNaRiuhvvvv5+DBw9y8OBBxsfPXOjhGAyGdWJ5b8q5iTfHcTpvoV+tfIgQAsu21tR67mLCsuSieXNKKRxHYtubMxR+vnBsOedz0J+fOe+epWrbtg+84JhI8rJsytVsJS1lcu655x6OHDnCkSNHGB0dK3ZgtiOxnV6/VIPBYAC9HuTenKTPmxPHKYrV5335vr2pQ8HrRZ4311+IEicpqVL4vrNiUe+tiuvZ80TFi89Z3z1LsntmvIXnH935xHgMl2NTzuRDhw7x0EMPoZTiscceY2BggN27d6/ouVJKaoM+fsmhVHapDfpmAhoMFyXz5+16rcs9b05KHCfESUqSplhSbnnv31qRUmBJSZKmRFFCkqQkiblnS6G1IOfeszhJsaQw98ywabkgAte/8Au/wDe+8Q3Gx8fZu3cv9913H1EUAfDud7+bO++8k69+9avs27ePcrnMZz/72VWd/+xWWgaD4eJmI5z+ti1JUkWapJnQsNj2Xq/lsG2JSMgMHB0OtZboJGNY7J6Zz9mFYps4/c6JC2IYfvGLX1zy70IIPvWpT52n0RgMhu2IEALbEii58hZmhizfsO+emVSd5cnvWZLqDYi5ZxcIHUu+0KPY9JiWeAaDYVNyvr47hfF6rRpzz1ZPvhExXEi2T57guWAMQ4PBcNHQv6Ybp4vBYFg1xi5cFmMYGgyGTcncNmMXcCAGg2HLoJbpYW3YpFXJBoPBYDAYDIbzj/EYGgyGiwiz2zcYDGtEmdqTlWAMQ4PBcBFgYskGg+HcUJhQ8kowoWSDwbDpEf0rlUk4NBgMhg1jS3kMzfeFwbC1KFVcpKUFgRPfwfNtXM8mjhKjBWcwGFaPcRguyxYzDM0XhcGwlXC93hJVKjvZ/0znCIPBsDaMjuHymNXVYDAYDAaDwQBsMY+hwWAwGAwGw0L8xB03Mj7+1XM6x+jo6DqNZvNiDEODwWAwGAxbnkceeeRCD+GiwISSDQaDwWAwGAyAMQwNBoPBYDAYDBnGMDQYDAaDwWAwAMYwNBgMBoPBYDBkGMPQYDAYDAaDwQCAUFtI7bFarXLttdde6GEsypkzZxgbG7vQw1gSM8ZzZ7OPD+Cpp56i2Wxe6GEsymafy3BxvM+bfYybfXyw+ce42eey4eJjS8nVXHvttRw5cuRCD2NRDh48uKnHB2aM68FmHx/oMW5mNvtchovnfd7MY9zs44PNP8bNPpcNFx8mlGwwGAwGg8FgAIxhaDAYDAaDwWDI2FKG4T333HOhh7Akm318YMa4Hmz28cHmH+NmHx+YMa4Hm318sPnHuNnHZ7j42FLFJwaDwWAwGAyGtbOlPIYGg8FgMBgMhrWzqQ3Du+66ix07dvDiF7+4eOzJJ5/kFa94BQcOHOBf/at/xezsLABRFPGOd7yDAwcOcN111/HRj360eM4jjzzCi170Ivbt28fHPvaxCzbGMAz5pV/6JQ4cOMBNN93EN77xjeI5TzzxBAcOHGDfvn3ce++9rJcj97nnnuM1r3kN119/PTfccAOf+MQnAJicnOT2229n//793H777UxNTQGglOLee+9l37593HjjjXz7298uzvXggw+yf/9+9u/fz4MPPrgu41vLGJ966ile8YpX4Hkev/M7vzPnXBvxXq92fJ///Oe58cYbOXDgAK985St58sknN3R8axnj4cOHufHGG3nJS17CwYMH+du//dviXBv1Pm/2+bzZ5zJs/vm82efyWsZ4vufzxTCXDVsctYn5P//n/6gnnnhC3XDDDcVjBw8eVN/4xjeUUko98MAD6oMf/KBSSqnPf/7z6ud//ueVUkq1Wi11+eWXq2PHjqk4jtVVV12lfvjDH6ogCNSNN96ovve9712QMX7yk59U73znO5VSSp0+fVrdfPPNKkkSpZRSt9xyi/r7v/97laapev3rX6+++tWvrsv4Tp48qZ544gmllFKzs7Nq//796nvf+576D//hP6iPfvSjSimlPvrRj6pf+7VfU0op9ZWvfEW9/vWvV2maqr//+79Xt956q1JKqYmJCXXllVeqiYkJNTk5qa688ko1OTl5QcZ4+vRp9fjjj6v/9J/+k/rt3/7t4jwb9V6vdnx/93d/V9ybr371q8U93MjP4mrH2Gg0VJqmSimlnnzySfWiF71IKbWx7/Nmn8+bfS4rtfnn82afy2sZ4/mezxfDXDZsbTa1YaiUUseOHZuzUNfr9WISPPvss+q6665TSin1hS98Qf3sz/6siqJIjY+Pq/3796uJiQn1zW9+U73uda8rnv+Rj3xEfeQjH7kgY3zve9+rHnrooeK42267TX3rW99SJ0+eLCZz/lruueeedR1jzqFDh9Sjjz6qrrnmGnXy5EmllF6IrrnmGqWUUvfcc4/6whe+UByfH3f2mM4+7nyOMec//+f/POfL5Hy816sZn1JKTU5OqksuueS8jm+1Y/zmN7+prr32WqXU/M/eer/Pm30+X0xzWanNP583+1xezRiVujDzebPOZcPWZVOHkhfihhtu4PDhwwB8+ctf5rnnngPgzW9+M5VKhd27d3PZZZfxq7/6qwwPD3PixAkuvfTS4vl79+7lxIkTF2SMN910E3/+539OHMccO3aMJ554gueee44TJ06wd+/eDR/jM888w3e+8x1e9rKXcfr0aXbv3g3Arl27OH36NMCi9+t83ceVjHExzscYVzu+Bx54gJ/5mZ85b+NbzRj/1//6X1x77bX8y3/5L/nMZz5zXseYs9nn82ady7D55/Nmn8trGeP5ns8X01w2bB0uOsPwM5/5DH/4h3/IS1/6UhqNBq7rAvD4449jWRYnT57k2LFj/O7v/i4/+tGPNtUY77rrLvbu3cvBgwf5lV/5FV75yldiWdZ5GVOz2eRNb3oTH//4x6nX63P+JoRACHFexrEUm32Mqx3fX//1X/PAAw/wW7/1W5tyjG94wxt46qmn+LM/+zN+4zd+47yNsZ/NPp8341yGrTdXLgSbfT5fbHPZsHW46AzDa6+9lkcffZQnnniCX/iFX+Dqq68G4Atf+AKvf/3rcRyHHTt28BM/8RMcOXKEPXv2FLt8gOPHj7Nnz54LMkbbtvn93/99/vEf/5HDhw8zPT3NNddcw549ezh+/PiGjTGKIt70pjfxtre9jTe+8Y0A7Ny5k1OnTgFw6tQpduzYAbDo/dro+7iaMS7GRo5xteP77ne/y7ve9S4OHz7MyMjIho9vLWPMefWrX82PfvQjxsfHz/t82ezzebPNZdj883mzz+W1jPF8z+eLcS4btg4XnWH4wgsvAJCmKR/+8Id597vfDcBll13GX/3VXwHQarV47LHHuPbaa7nllls4evQox44dIwxDHn74YQ4dOnRBxthut2m1WgB8/etfx7Ztrr/+enbv3k29Xuexxx5DKcVDDz3Ez/3cz63LWJRS3H333Vx33XW8//3vLx4/dOhQUaX24IMPFtc7dOgQDz30EEopHnvsMQYGBti9ezd33HEHjz76KFNTU0xNTfHoo49yxx13XJAxLsZGvderHd+zzz7LG9/4Rj73uc9xzTXXbPj41jLGp59+uqiW/fa3v00QBIyMjGzo+7wQm30+b6a5DJt/Pm/2ubyWMZ7v+XyxzmXDFuJCJDaulLe+9a1q165dyrZttWfPHvXpT39affzjH1f79+9X+/fvVx/4wAeKxPBGo6He/OY3q+uvv15dd9116r/+1/9anOcrX/mK2r9/v7rqqqvUhz/84Qs2xmPHjqlrrrlGXXvtteq1r32teuaZZ4rz/MM//IO64YYb1FVXXaXe9773Fc85V/7mb/5GAerAgQPqpptuUjfddJP6yle+osbHx9Vtt92m9u3bp1772teqiYkJpZRSaZqq9773veqqq65SL37xi9U//MM/FOd64IEH1NVXX62uvvpq9ZnPfGZdxreWMZ46dUrt2bNH1Wo1NTAwoPbs2aNmZmaUUhvzXq92fHfffbcaHBwsjn3pS19anGujPourHePHPvYxdf3116ubbrpJvfzlL1d/8zd/U5xro97nzT6fN/tcVmrzz+fNPpfXMsbzPZ8vhrls2NqYzicGg8FgMBgMBuAiDCUbDAaDwWAwGDYGYxgaDAaDwWAwGABjGBoMBoPBYDAYMoxhaDAYDAaDwWAAjGFoMBgMBoPBYMgwhqFhRViWxUte8pLi52Mf+9i6nfuZZ57hxS9+8bqdz2AwLI2ZzwaDYTHsCz0Aw8VBqVTiH//xHy/0MAwGwzpg5rPBYFgM4zE0nBNXXHEFv/Zrv8aBAwe49dZbefrppwHtNbjtttu48cYbee1rX8uzzz4LwOnTp3nDG97ATTfdxE033cQ3v/lNAJIk4d/8m3/DDTfcwOte9zo6nc4Fe00Gw3bFzGeDwWAMQ8OK6HQ6c0JPX/rSl4q/DQwM8E//9E/88i//Mr/yK78CwL/9t/+Wd7zjHXz3u9/lbW97G/feey8A9957Lz/1Uz/Fk08+ybe//W1uuOEGAI4ePcr73vc+vve97zE4OMif/umfnvfXaDBsF8x8NhgMi2E6nxhWRLVapdlsznv8iiuu4K/+6q+46qqriKKIXbt2MTExwejoKKdOncJxHKIoYvfu3YyPjzM2Nsbx48fxPK84xzPPPMPtt9/O0aNHAfit3/otoijigx/84Hl7fQbDdsLMZ4PBsBjGY2g4Z4QQC/5/NfR/sViWRRzH5zwug8Gwesx8Nhi2N8YwNJwzeRjqS1/6Eq94xSsAeOUrX8nDDz8MwOc//3l+8id/EoDXvva1/NEf/RGg85BmZmYuwIgNBsNimPlsMGxvTFWyYUXkOUk5r3/96wuJi6mpKW688UY8z+OLX/wiAP/tv/03fumXfonf/u3fZmxsjM9+9rMAfOITn+Cee+7hgQcewLIs/uiP/ojdu3ef99djMGxnzHw2GAyLYXIMDefEFVdcwZEjRxgdHb3QQzEYDOeImc8Gg8GEkg0Gg8FgMBgMgPEYGgwGg8FgMBgyjMfQYDAYDAaDwQAYw9BgMBgMBoPBkGEMQ4PBYDAYDAYDYAxDg8FgMBgMBkOGMQwNBoPBYDAYDIAxDA0Gg8FgMBgMGf8/mJ6xEdJ4hbEAAAAASUVORK5CYII=\n",
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4L8gaRyGEEEKIHUhz+x60TThgg9tgweR77Xvf+96Atj3XX389Bx54IN/61rcYP348mUyG008/nXw+v+0OUoga27Y5+OCDB6zfj+OYBQsWbHT9/vTp0wfsDzB//vz6/p/61Kd46aWXWLx4cf02duxYrrrqKp544on6GJtba+C9IBlHIYQQQgixzbz22mv85Cc/4ec//zmFQoELL7yQz3zmM/zoRz/a1ocmtqmhmaoKmzfmrFmzOPfccznkkEM49NBDuf322ymXy5x//vkAnHPOOYwbN465c+cCcPnll/PhD3+Yr3/96xx33HE89NBDvPDCC9x3330AjBgxghEjRgx4DsuyaG9vZ8899wTeWa2BbUECRyGEEEIIsVU8/vjj5HK5AduiKNrkY1zX5fvf/z7jxo0D4I477uC4447j61//urTzEdvcGWecwfr165kzZw4dHR0ceOCBzJs3r14AZ8WKFQP6hh5++OE8+OCDXHvttVxzzTVMmTKFRx99lH333Xeznvftag1sCxI4CiGEEEKIreKII47gnnvuGbDt2Wef5eyzz97oYyZMmFAPGiGZphfHMUuWLJHAcSc21MVxNsell17KpZdeOuh9Tz755AbbTjvtNE477bR3PP6yZcs22PZ2tQa2BQkchRBCCCHEVpHNZpk8efKAbatWrdpGRyO2Z0NdHEdsPgkchRBCCCF2IH0df9+s7dvaihUrWLNmTX3t1jPPPIOu6/X1XkKI4UECRyGEEEKIHUBbWxvpdIYnH/i3je6TTmdoa2t7D4/q7aVSKc4991z+8z//k0KhwGWXXcbpp59en6b6yCOPMHv27I02XBdCvDckcBRCCCGE2AFMmDCBV199ha6uLvxQccvPCgB84ZRGbDNpNN7W1rbNejhuzOTJkznllFM49thj6enp4fjjj+fuu++u35/P51myZMk2PEKxLQynNY4iIYGjEEIIIcQOYsKECUyYMAEvULS90AvA+97XgmNpQ/7c3/ve9wbd/pGPfKT+Yf28887jvPPO22CfSy65hEsuuWTQx2/sMWLHJoHj8COBoxBCCCHEDsaxNL79mdZtfRhCbDnF5rZcfOfjii2iv/0uQgghhBBCCCF2ZhI4CiGEEEKIbeL6669n8eLF2/owxDCkhvAmtoxMVRVCCCGEEEIMKypWqHgI1jgOwZg7CwkchRBCCCGEEMOKUsltKMYVW0amqgohhBBCCCGE2CQJHIUQQgghhBBCbJJMVRVCCCGE2MF4fsxn5i4B4O7Ze+LYkisQ2xfp4zj8SOAohBBCCCGEGF5kkeOwI5efhBBCCCGEEEJskgSOQgghhBA7oNWvP82j95zMggW/fk+e77zzzuOkk07aYPuTTz6Jpmn09fVt8vF/+tOfOOOMMxgzZgyO47Drrrty/PHH8/Of/1ymF+6E+hOOQ3ETW0YCRyGEEEKIHYxSihd/ewc965Zw3XVzhn3g9b//+78cdthhlEolHnjgAV555RXmzZvHySefzLXXXks+n9/WhyjETm+7DxxvvvlmNE3jiiuuqG9zXZfPfvazjBgxglwux6mnnsq6des2OY5Sijlz5jBmzBjS6TQzZszgH//4xxAfvRBCCCHEu+P58Qa3X/7fE6xf9Wf2OexcXnj+WX7xi3kb7DNclMtlLrzwQo477jh+8YtfcNRRR7Hbbrux1157ceGFF/LnP/+ZpqambX2Y4j3WXxxnKG5iy2zXxXGef/55vvWtb7H//vsP2H7llVfyi1/8gocffpimpiYuvfRSTjnlFP74xz9udKyvfvWrfPOb3+SBBx5g0qRJfOlLX2LmzJn87W9/I5VKDfWpCCGEEEJskf7qqf2UUjx+/zWM3OUADp15NZ2rFnPBJbM5/sJd0TStvt/91+31Xh/qoH71q1/R3d3N1VdfvdF93nzcYucgtXGGn+0241gqlfjkJz/Jt7/9bVpaWurb8/k8999/P7feeisf/ehHOfjgg/nud7/L008/zTPPPDPoWEopbr/9dq699lpOPPFE9t9/f77//e+zZs0aHn300Y0eg+d5FAqFATchhBBCiG1p9et/ZP2qP/O+j3wWTdN430c+y/pVf2b16xu/gL61PP744+RyuQG3Y445ZpOP+fvf/w7AnnvuWd/2/PPPDxjj8ccfH9LjFsORGsKb2BLbbcbxs5/9LMcddxwzZszgpptuqm9ftGgRQRAwY8aM+rapU6cyYcIEFi5cyGGHHbbBWEuXLqWjo2PAY5qampg2bRoLFy7kzDPPHPQY5s6dy5e//OWteFZCCCGEEJvn7tlvBFxKKT70ofMZPf4Axu3+AQDG7f4BRo8/gJ4l9/O//3XBkGbvjjjiCO65554B25599lnOPvtsAHK5XH372Wefzb333jvoOPvvvz+LFy8GYMqUKYRhODQHLIYtyTgOP9tl4PjQQw/x4osv8vzzz29wX0dHB7Zt09zcPGD76NGj6ejoGHS8/u2jR49+x48BmD17NrNmzap/XSgUGD9+/Ds9DSGEEEKId82x35hA9sQTT/DC889y1Nn31QNETdM44MOf5Vc/vJjfPflrZs6cOWTHks1mmTx58oBtq1atqv+7PxgEaGxsBJLAEGDJkiX1C/yO42wwjhBi29ruAseVK1dy+eWXM3/+/G2+9tBxHBzH2abHIIQQQggB/YX+rqOxdQKpTAtda/5Wvy+VaaGxdQJz5lzHUUcdtc3WDA4WDB511FG0trZyyy238Mgjj2yDoxLD0VAVspHiOFtuuwscFy1aRGdnJwcddFB9WxRF/P73v+fOO+/kiSeewPd9+vr6BmQd161bR3t7+6Bj9m9ft24dY8aMGfCYAw88cEjOQwghhBBia/J9n5WrVlHoWc1j95026D6rVsX4vj+sLnzncjn+67/+izPOOIPjjjuOyy67jClTplAqlZg3bx4AhmFs46MUQmx3geORRx7Jyy+/PGDb+eefz9SpU/nCF77A+PHjsSyLBQsWcOqppwLJ1IcVK1Ywffr0QcecNGkS7e3tLFiwoB4oFgoFnn32WS655JIhPR8hhBBCiK3BcRyefWYh69evxw9i5n5nOQCzL9gV20qms44aNWpYBY39Tj75ZJ5++mluueUWzjnnHHp6emhqauKQQw7hoYce4vjjj9/WhyjeY7LGcfjZ7qqqNjQ0sO+++w64ZbNZRowYwb777ktTUxMXXnghs2bN4re//S2LFi3i/PPPZ/r06QMK40ydOrU+HaK/D+RNN93EY489xssvv8w555zD2LFjOemkk7bRmQohxNApFotcccUV7LrrrqTTaQ4//PAB68bPO+88NE0bcDv66KPfdty77rqLiRMnkkqlmDZtGs8999xQnoYQ4i3Gjx/PQQcdxPvedxBtY/embezevO99B3HQQcltl112GbLn/t73vjdoNfqPfOQjKKU2qD/xVocccggPP/ww69atIwgCurq6mDdvHmeccYa049gJqVgN2W1zbe7ftocffpipU6eSSqXYb7/9+OUvfzng/uuvv56pU6eSzWZpaWlhxowZPPvsswP2mThx4gZ/h2+++ebNPvatabvLOL4Tt912G7quc+qpp+J5HjNnzuTuu+8esM+SJUvI5/P1r6+++mrK5TIXX3wxfX19fPCDH2TevHnbfB2lEEIMhf/3//4ff/nLX/jBD37A2LFj+eEPf8iMGTP429/+xrhx4wA4+uij+e53v1t/zNtlKX784x8za9Ys7r33XqZNm8btt9/OzJkzWbJkCaNGjRrS8xFCDOTY+rDp0yjE9mxz/7Y9/fTTnHXWWcydO5fjjz+eBx98kJNOOokXX3yRfffdF4A99tiDO++8k912241qtcptt93GUUcdxWuvvcbIkSPrY91www1cdNFF9a8bGhqG/oQ3QVOyQnSrKRQKNDU10dnZzciRrdv6cIQQO5j+3zH5fL5ejXBLVKtVGhoa+N///V+OO+64+vaDDz6YY445hptuuonzzjuPvr6+Tfayfatp06bx/ve/nzvvvBOAOI4ZP348n/vc5/jiF7846GM8z8PzvPrX/dWp163rYtSoEVt2gkJsx1zXZenSpUyaNEkuXr8Lm3odt9bvUjE0+t+f++/7PZlM7u0fsJkqlRIXXvwvrFy5csD7v7Gil5v7t+2MM86gXC4P6D162GGHceCBB260/Uz/Of/617/myCOPBJKM4xVXXMEVV1zxbk53q9rupqoKIYR4d8IwJIqiDT5MpdNp/vCHP9S/fvLJJxk1ahR77rknl1xyCd3d3Rsd0/d9Fi1aNKAfrq7rzJgxg4ULF270cXPnzqWpqal+k5ZGQgghABSqXll1q95Icmbjx48f8Pdn7ty5GxzDlvxtW7hw4YD9AWbOnLnR/X3f57777qOpqYkDDjhgwH0333wzI0aM4H3vex9f+9rXtnk/0x1yqqoQQoiNa2hoYPr06dx4443stddejB49mv/+7/9m4cKF9VL5Rx99NKeccgqTJk3i9ddf55prruGYY45h4cKFg1Y37OrqIoqiQfvhvvrqqxs9FumHK4QQYjBDXRxnsIzjW23J37aOjo531Bv+8ccf58wzz6RSqTBmzBjmz59PW1tb/f7LLruMgw46iNbWVp5++mlmz57N2rVrufXWWzfrfLcmCRyFEGIn9IMf/IALLriAcePGYRgGBx10EGeddRaLFi0C4Mwzz6zvu99++7H//vuz++678+STT9an0WwN0g9XCCHEYIa6j2NjY+M2nap8xBFHsHjxYrq6uvj2t7/N6aefzrPPPltfN/nmi6r7778/tm3z6U9/mrlz526zv5syVVUIIXZCu+++O7/73e8olUqsXLmS5557jiAI2G233Qbdf7fddqOtrY3XXntt0Pvb2towDIN169YN2L6pHrpCCCHEcLYlf9va29vf0f7ZbJbJkydz2GGHcf/992OaJvfff/9Gj2XatGmEYciyZcu27GS2AgkchRBiJ5bNZhkzZgy9vb088cQTnHjiiYPut2rVKrq7uxkzZsyg99u2zcEHH8yCBQvq2+I4ZsGCBRvtoSuEEEIMZ1vyt2369OkD9geYP3/+2/4tjON4QLG4t1q8eDG6rm/TKuUyVVUIIXZCTzzxBEop9txzT1577TWuuuoqpk6dyvnnn0+pVOLLX/4yp556Ku3t7bz++utcffXVTJ48mZkzZ9bHOPLIIzn55JO59NJLgWRazbnnnsshhxzCoYceyu233065XOb888/fVqcpxE7L80I+fckTAHzrnpk4jnzkE9uZIZqqurkLJ9/ub9s555zDuHHj6sV1Lr/8cj784Q/z9a9/neOOO46HHnqIF154gfvuuw+AcrnMV77yFT7+8Y8zZswYurq6uOuuu1i9ejWnnXYakBTYefbZZzniiCNoaGhg4cKFXHnllZx99tm0tLRsxRdj88hvESGE2Anl83lmz57NqlWraG1t5dRTT+UrX/kKlmURhiEvvfQSDzzwAH19fYwdO5ajjjqKG2+8ccC6itdff52urq7612eccQbr169nzpw5dHR0cOCBBzJv3rwNigQIIYQQb2eoi+O8U2/3t23FihXo+huTOA8//HAefPBBrr32Wq655hqmTJnCo48+Wu/haBgGr776Kg888ABdXV2MGDGC97///Tz11FPss88+QLL+/6GHHuL666/H8zwmTZrElVdeOWDd47YgfRy3IunjKIQYSjtD77H+c5Q+jmJntbX6OFYqHqeceiuVai/XXjOTI4/8yKAVkYej733ve1xxxRX09fVt8RjSx3H71f/+fOvu35JOb/0+jtVqiU9/5gh5/7eArHEUQgghhNiB/OxnP2Pq1D14Yt41PPW7rzFz5gx22213fvaznw35c3d0dHD55ZczefJkUqkUo0eP5gMf+AD33HMPlUrlHY1xxhln8Pe//32Ij1QIsblkqqoQQgghxA7iZz/7GZ/4xCfYZfz7Ofa4y2hu2ZW+3uW8/PJP+MQnPsFPf/pTTjnllCF57n/+85984AMfoLm5mf/4j/9gv/32w3EcXn75Ze677z7GjRvHxz/+8bcdJ51Ok06nh+QYxfZjqNtxiM0nGUchhBBCiB1AFEVceeUsdhn/fo746LWMHDUVy0ozctRUjvjotewy/v1ceeXniaJoSJ7/M5/5DKZp8sILL3D66aez1157sdtuu3HiiSfyi1/8ghNOOAGAW2+9lf32249sNsv48eP5zGc+Q6lUqo/zve99j+bm5vrXr7/+OieeeCKjR48ml8vx/ve/n1//+tdDcg5i+Ohf4zgUN7FlJHAUQgghhNgBPPXUU6xYsZz99jsdTRv4EU/TdPbd7zRWrFjGU089tdWfu7u7m1/96ld89rOfJZvNDrqPpmkA6LrON7/5Tf7617/ywAMP8Jvf/Iarr756o2OXSiWOPfZYFixYwJ/+9CeOPvpoTjjhBFasWLHVz0MMIxI5DjsSOAohhBBC7ADWrl0LQHPLroPe31Lb3r/f1vTaa6/VW/y8WVtbG7lcjlwuxxe+8AUArrjiCo444ggmTpzIRz/6UW666SZ+8pOfbHTsAw44gE9/+tPsu+++TJkyhRtvvJHdd9+dxx57bKufhxg+1BDexJaRwFEIIYQQYgcwZswYAPp6lw96f29te/9+74XnnnuOxYsXs88++9Sbm//617/myCOPZNy4cTQ0NPCpT32K7u7ujRbPKZVK/Pu//zt77bUXzc3N5HI5XnnlFck4CvEek8BRCCGEEGIH8KEPfYgJE3bl5Zd/glLxgPuUivnLyw8zYcJEPvShD2315548eTKaprFkyZIB23fbbTcmT55cL3azbNkyjj/+ePbff3/+53/+h0WLFnHXXXcB4Pv+oGP/+7//O4888gj/8R//wVNPPcXixYvZb7/9Nrq/2DEks0rVENy29ZltvyRwFEIIIYTYARiGwW233cqqlc/z29/cRGfnKwRBhc7OV/jtb25i1crnue22rw9JP8cRI0bwsY99jDvvvJNyubzR/RYtWkQcx3z961/nsMMOY4899mDNmjWbHPuPf/wj5513HieffDL77bcf7e3tLFu2bCufgRh2ZK7qsCOBoxBCCCHEDuKUU07hpz/9KagO/u8XV/HgD0/n/35xFRqdQ9qKA+Duu+8mDEMOOeQQfvzjH/PKK6+wZMkSfvjDH/Lqq69iGAaTJ08mCALuuOMO/vnPf/KDH/yAe++9d5PjTpkyhZ/97GcsXryYP//5z/zrv/4rcRxv8jFCiK1P+jgKIYQQQuxATjnlFI4++jhOOfVWKtVerr1mJkce+ZEhyTS+2e67786f/vQn/uM//oPZs2ezatUqHMdh77335t///d/5zGc+QyaT4dZbb+WWW25h9uzZ/Mu//Atz587lnHPO2ei4t956KxdccAGHH344bW1tfOELX6BQKAzpuYhtT/o4Dj+akldvqykUCjQ1NdHZ2c3Ika3b+nCEEDuY/t8x+XyexsbGbX04Q6L/HNet62LUqBHb+nCEeM+5rsvSpUuZNGkSqVRqi8fxvJBPX/IEAN+6ZyaOs3PlCjb1Ou4Mv0u3Z/3vz523/5p0evDWLu9GtVrm0itmyPu/BXau3yJCCCGEEDsBxzH53neO29aHIcQWG6qWi5Iy23KyxlEIIYQQQgghxCZJxlEIIYQQQggxrMgax+FHAkchhBBCCCHEsCJTVYcfmaoqhBBCCDHMSFbk3ZHXT4itTwJHIYQQQohhor9lhu/72/hItm+VSgUAy7K28ZGILaWG8Ca2jExVFUIIIYQYJkzTJJPJsH79eizLQtflGv/mUEpRqVTo7Oykubl5yHtXiiE0VFGeRI5bTAJHIYQQQohhQtM0xowZw9KlS1m+fPm2PpztVnNzM+3t7dv6MMS7IMVxhh8JHIUQQgghhhHbtpkyZYpMV91ClmVJpnEHIMVxhh8JHIUQQgghhhld10mlUtv6MIQQok4mzgshhBBCCCGE2CTJOAohhBBCCCGGFVnjOPxI4CiEEEIIIYQYVhRDtMZx6w+505CpqkIIIYQQQgixEXfddRcTJ04klUoxbdo0nnvuuU3u//DDDzN16lRSqRT77bcfv/zlLwfcf/311zN16lSy2SwtLS3MmDGDZ599dsA+PT09fPKTn6SxsZHm5mYuvPBCSqXSVj+3zSGBoxBCCCGEEEIM4sc//jGzZs3iuuuu48UXX+SAAw5g5syZdHZ2Drr/008/zVlnncWFF17In/70J0466SROOukk/vKXv9T32WOPPbjzzjt5+eWX+cMf/sDEiRM56qijWL9+fX2fT37yk/z1r39l/vz5PP744/z+97/n4osvHvLz3RRNyUTfraZQKNDU1ERnZzcjR7Zu68MRQuxg+n/H5PN5Ghsbt/XhDIn+c1y3rotRo0Zs68MRQuyAdobfpduz/vfnazfPI53KbvXxq26Zq7549Dt+/6dNm8b73/9+7rzzTgDiOGb8+PF87nOf44tf/OIG+59xxhmUy2Uef/zx+rbDDjuMAw88kHvvvXfQ5+g/51//+tcceeSRvPLKK+y99948//zzHHLIIQDMmzePY489llWrVjF27NgtOfV3TTKOQgghhBBCiGFFDeENkmDtzTfP8zY4Bt/3WbRoETNmzKhv03WdGTNmsHDhwkGPe+HChQP2B5g5c+ZG9/d9n/vuu4+mpiYOOOCA+hjNzc31oBFgxowZ6Lq+wZTW95IEjkIIIYQQQojhZYgjx/Hjx9PU1FS/zZ07d4ND6OrqIooiRo8ePWD76NGj6ejoGPSwOzo63tH+jz/+OLlcjlQqxW233cb8+fNpa2urjzFq1KgB+5umSWtr60af970ggaMQQuyEisUiV1xxBbvuuivpdJrDDz+c559/vn6/Uoo5c+YwZswY0uk0M2bM4B//+Mfbjru5BQSEEEKIwQx1xnHlypXk8/n6bfbs2e/RmSWOOOIIFi9ezNNPP83RRx/N6aefvtF1k8OFBI5CCLET+n//7/8xf/58fvCDH/Dyyy9z1FFHMWPGDFavXg3AV7/6Vb75zW9y77338uyzz5LNZpk5cyau6250zM0tICCEEEJsK42NjQNujuNssE9bWxuGYbBu3boB29etW0d7e/ug47a3t7+j/bPZLJMnT+awww7j/vvvxzRN7r///voYb/3bGYYhPT09G33e94IEjkIIsZOpVqv8z//8D1/96lf5l3/5FyZPnsz111/P5MmTueeee1BKcfvtt3Pttddy4oknsv/++/P973+fNWvW8Oijj2503FtvvZWLLrqI888/n7333pt7772XTCbDd77znY0+xvO8DdaZCCGEECiFGoLb5jSHtG2bgw8+mAULFtS3xXHMggULmD59+qCPmT59+oD9AebPn7/R/d88bv86y+nTp9PX18eiRYvq9//mN78hjmOmTZv2jo9/a5PAUQghdjJhGBJFEalUasD2dDrNH/7wB5YuXUpHR8eAxf1NTU1MmzZtk4v7N7eAAMDcuXMHrDEZP378uzw7IYQQO4L+GG8obptj1qxZfPvb3+aBBx7glVde4ZJLLqFcLnP++ecDcM455wyY5nr55Zczb948vv71r/Pqq69y/fXX88ILL3DppZcCUC6Xueaaa3jmmWdYvnw5ixYt4oILLmD16tWcdtppAOy1114cffTRXHTRRTz33HP88Y9/5NJLL+XMM8/cZhVVQQJHIYTY6TQ0NDB9+nRuvPFG1qxZQxRF/PCHP2ThwoWsXbu2vvB+c4oBbEkBAYDZs2cPWGOycuXKd3l2QgghxNZzxhln8J//+Z/MmTOHAw88kMWLFzNv3rz637sVK1awdu3a+v6HH344Dz74IPfddx8HHHAAP/3pT3n00UfZd999ATAMg1dffZVTTz2VPfbYgxNOOIHu7m6eeuop9tlnn/o4P/rRj5g6dSpHHnkkxx57LB/84Ae577773tuTfwtzmz77Frjnnnu45557WLZsGQD77LMPc+bM4ZhjjgHAdV0+//nP89BDD+F5HjNnzuTuu+/e4MPMmymluO666/j2t79NX18fH/jAB7jnnnuYMmXKe3FKQgjxnvvBD37ABRdcwLhx4zAMg4MOOoizzjprwLSY94LjOIOuKxFCCLFzq08tHYJxN9ell15azxi+1ZNPPrnBttNOO62ePXyrVCrFz372s7d9ztbWVh588MHNOs6htt1lHHfZZRduvvlmFi1axAsvvMBHP/pRTjzxRP76178CcOWVV/Lzn/+chx9+mN/97nesWbOGU045ZZNjbkkRCCGE2J7tvvvu/O53v6NUKrFy5Uqee+45giBgt912qy+835xiAFtSQEAIIYQQ24/tLnA84YQTOPbYY5kyZQp77LEHX/nKV8jlcjzzzDPk83nuv/9+br31Vj760Y9y8MEH893vfpenn36aZ555ZtDxtrQIBEhRByHE9i+bzTJmzBh6e3t54oknOPHEE5k0aRLt7e0DFvcXCgWeffbZjS7u35ICAkIIIcTGDJc1juIN213g+GZRFPHQQw9RLpeZPn06ixYtIgiCAcUZpk6dyoQJEzZanGFLikD0k6IOQojt1RNPPMG8efNYunQp8+fP54gjjmDq1Kmcf/75aJrGFVdcwU033cRjjz3Gyy+/zDnnnMPYsWM56aST6mMceeSR3HnnnfWv366AgBBCCCG2X9vdGkeAl19+menTp+O6LrlcjkceeYS9996bxYsXY9s2zc3NA/bfVHGGLSkC0W/27NnMmjWr/nWhUJDgUQixXehvdrxq1SpaW1s59dRT+cpXvoJlWQBcffXVlMtlLr74Yvr6+vjgBz/IvHnzBlRiff311+nq6qp/fcYZZ7B+/XrmzJlDR0cHBx544IACAkIIIcQ7NlTpQUk5brHtMnDcc889Wbx4Mfl8np/+9Kece+65/O53v3vPj0OKOgghtlenn346p59++kbv1zSNG264gRtuuGGj+/QXKXuzTRUQEEIIId4piRuHn+1yqqpt20yePJmDDz6YuXPncsABB/CNb3yD9vZ2fN+nr69vwP6bKs6wJUUghBBCCCGEEGJnsl0Gjm8VxzGe53HwwQdjWdaA4gxLlixhxYoVGy3OsCVFIIQQQgghhBBDR4rjDD/b3VTV2bNnc8wxxzBhwgSKxSIPPvggTz75JE888QRNTU1ceOGFzJo1i9bWVhobG/nc5z7H9OnTOeyww+pjTJ06lblz53LyyScPKAIxZcoUJk2axJe+9KUNikAIIYQQQggh3iuqdhuKccWW2O4Cx87OTs455xzWrl1LU1MT+++/P0888QQf+9jHALjtttvQdZ1TTz0Vz/OYOXMmd99994AxlixZQj6fr3/9TopACCGEEEIIId4bssZx+NGUkpdvaykUCjQ1NdHZ2c3Ika3b+nCEEDuY/t8x+XyexsbGbX04Q6L/HNet62LUqBHb+nCEEDugneF36fas//25Yc7jpFLZrT6+65aZc8Px8v5vgR1ijaMQQgghhBBCiKEjgaMQQgghhBBCiE3a7tY4CiGE2HlEUYymaei6ttmPVUoRRwrD3LJrpFEYoxsamrb5zy2EEOLdUbX/DcW4YstI4CiEEGJYCoOIcslHNzQaGjevWJlSilLRI44U2QYHczODR88NcasBtmOQztib9VghhBDvnhTHGX5kqqoQQohhyfciAOJIEceb95c+CmPiKHlMGESb/dxuNRhwDEIIId5jaghvYotI4CiEEGJYit90WTiK4s167JuvKPcHkFt8HJsZtAohhBA7IpmqKoQQYlh6c7eoOFJgvfPHvjnYi+LNCzrfGijGsdqiNZZCCCG2nKxxHH4kcBRCCDEsqTcFcJub9Xtz0Nk/jueFhEGEaRrohoZSoGlgWcZGH/vW4xDbP88NCaMYXQdN0zBNHdM03v6BQoj3lKxxHH4kcBRCCDHsKKUG/HHf3ODtzYGmUlCtBoRBRBBGVCoBUS2LaFk62ayDbb8ROLz1uaIoxkICix1BtRpQrfpEoSKqvc+6rtGQs6UI0k4ijhWFvLutD0O8A8nfgSHIOErkuMUkcBRCCDHsvPXv+uZON33zBwPfj4hRBH6MYeok3TUUGuB5EUp5NDY69azTW59bPmTsGKrVgELeRVHLMmsxKq4FEgUPTdNIpTdjPrTY7sRxTG9vlaobbOtDEWK7JIGjEEKIYefdThftf7jvRwRBRBQrbMfAcUwsy8AwNMIgplTyCPyIajUgk9EwDH2D55biONs/3wsplzwUkEqZZLM2pqmjFBQLLlU3pFBwsWwdw5Ds8o6qkHcJghhD1iwLsUUkcBRCCDHsVCs+ugpwUiaxUnjVENsxSaUtlFJUSj62Y2LZg3/IV0oRBhFRFBOFMbqhYVkGqZSJYSQFxQ1DJ4pjKmUfz43QtIBMxqqXTdB1jTje/FYgw1UcvzHtS9O0nabgj+9HlCs+YaRwHIPGRgddT74HNA0aGh2CMCIMFfm8R3Nz+h29Nv3TqftfU13X0LSd4zXdHpVKHq4XocFm94UV24ascRx+JHAUQggx7JQKPrFXRR+RIQhCKgUfTYP2cU141RDfS24tbdlBH69ihe9H6KaGQmEYOrZt1IPGfo5jEoUxrhsShjHVakh/zKAbSeC4IxTHCWpB9Jvpuo5l6Tt0sBPHCtcN8L0I2zLIZu160NhP13Wam9P0dFeTILPkvW1gEUUxwVv6g2pacnFiZwnItydhGFEu+0ByoSAMt/+f6Z2BrHEcfqSPoxBCiOGn9oddARrJB/GwNqU0DKNNPDDh+REKiCIwTQMNBhTA6WcYepLJTJn4fkgUxYRhEmD1BxhKbb/TVZVKAuj+oFHT3siKxXGM520YUO5IfD/Ed0M0XSOVNrHt5Hr5Wz84mqZBQ6ODBlSrIb4fbnTMgUFj/+up1V7rcIOAUmx7hYKLUuDYBhkpgiTEFtuijONjjz222Y/52Mc+Rjqd3pKnE0IIsZMxzSSgsy0DZehUKwGxSoKdIIhRSqFpWv2/bxZFcTJNNYxJZUwCX2FZxkYza7ZtEIYxpmkkmblQYRoaoOrTVVWsYDvMJAVBTFwrLJSs7UyC4ThWBEGEUqoe6Lw1G7u9SzLIAWGsSKeTta3VakAUxcSxwjR10mmr/n2RSpm4VQPPT7JTg33PhGFcv3BhGHq9lYtSijCMk6nR0Ruvt3jvKJVMK3/rNOxy2cf3Y5miuh1SDNFU1a0/5E5jiwLHk046abP21zSNf/zjH+y2225b8nRCCLFD2dKLbzsV7U3/0TRMQ0+KmcSAUgR+hGUbqFihGQM/3LtukKxNVDGWbYJKMk4bfSpNw3FMwjCip6dKFCbFM+I4CQY0IJ2xMMy3D6x8L6RU9GrTFpNjtlPmBtMj3wtJgLRh0AjJejzbNuqBZRBEO9y6R88NqFQCQBGFBn19VYJgYHa1c22RdMYilbawTINU2iIIkkys54WkUm9UWe0PDmFg0AgDp6m+eVqwBI9vL661xnk7/T9baGBbxoCfrSCIcKvBgCBD0zQMEyrlpIJqNpcURBJiS9x111187Wtfo6OjgwMOOIA77riDQw89dKP7P/zww3zpS19i2bJlTJkyhVtuuYVjjz0WgCAIuPbaa/nlL3/JP//5T5qampgxYwY333wzY8eOrY8xceJEli9fPmDcuXPn8sUvfnFoTvId2OI1jh0dHYwaNeod7dvQ0LClTyOEEDucLb341tbWNjQHNBzVPgAqxYBFFZap43vJ9sBPqqXqb/ps3p8x7M8gGoYOVhJgDpad7BeGEYW8i+9GVCs+pmlgO2aS1YwUpZJHk6ljGHq9yEySfYqJoxhN04jjiELe2+BytmXqjBjd8J5+aA3DiHIpQNPBcTZc2wnUg9sgSDK5vh9i2+YOETz6fsT6rjKVsodtmwRelFyAMHVSKRNNg86OMq7r09sLmbSFricVdS3HwElZlEoeTu17AKgFjaoeJPbrz96iwDCTyrz9mUet9pxicP3TfnVdH3QqeT+34rN+fRm3GuC5ARoahm2QTps0t2TRtKRlTxwpwiAmipPX3nMD0CCXdchmZYrq9ma4FMf58Y9/zKxZs7j33nuZNm0at99+OzNnzmTJkiWDxkJPP/00Z511FnPnzuX444/nwQcf5KSTTuLFF19k3333pVKp8OKLL/KlL32JAw44gN7eXi6//HI+/vGP88ILLwwY64YbbuCiiy6qf72tY6otChzPPffczZp2evbZZ9PY2LglTyWEEDskufi2aar+X4VB/1pDBVqSxfHdsB48vnm6oO+FxKp/jaJGOm1SqU01jaIYwxhYDCaOYwoFj0rZB03DdkyiOEbXNFSsMCwN14uwAoO+3iq6nvR+jGOFoeu1aq06biUk31dF05LsZK7RIQoVvhcRhDHd64q0jsphmkNbjCaKYnwvwnWDenZMxQrfi7Hs5MP5m7Of/cGj76v6ekjH2fi03uHA95NCRqEfJ4FgZmBGNwgiOtYUKBU9dENL0tZardBGrPDcELfqE4ch1WpIOm3geSGmZaBrGm4lpFz2yeUcbMugsSlNXPv+AeqBYBwnmW/fD9/4IOollVo1TUM3NMIwQtN2vGnAW0M94H4bxWKVzrUlCvkqYRTXZx6EZZ+uzojOjiItI7M0Nabr7XaUUlQqAaWij0Lh2CZhGEsGeLujGJqJpZs35q233spFF13E+eefD8C9997LL37xC77zne8Mmv37xje+wdFHH81VV10FwI033sj8+fO58847uffee2lqamL+/PkDHnPnnXdy6KGHsmLFCiZMmFDf3tDQQHt7++ae4JDZosDxu9/97mbtf88992zJ0wghxA5JLr69PfVG5FifZpqsNQRd0zAtnTBI1psl2UUd34uSYNJLvjZMHdM00LUQP4zo667Wp7dlsjZxrOjpruLVCqHkMjZ2ymDd2phCLQi0LR3XjaiUPXINaXwvxLSN2gfQJPtULPpUK0nVVydl0dCUwrIM0s3Jc6xfV8T1Qpb+vYvWtiyZnE1mCLIfUZS0FumfYmvWMqSaVsuo9lZA08g1ODS1vPH9p2nJtFXPi4Dkw3x/EZmh0N8ipb/VSf8aUqVULdNrYL7lA36l4lPscwnDCPWWWj6GqZPNOeQabVw3pHNdkd6eCr4XkcvZYOp4XoCTsojCmJ58Bd9LMlKjR+eSYMTSSWccVBhRqQT45ZBC3kVBkoWtBX66rteDwGrFJ4qSb1TD0NB1vb5+sj8IN4xkHa7jWDtEJndriWtVjyG5wGNZgwfWPd0V1qzopVhwiRXJz07axnYMymWPaldAX4+P64Zo42DEqBzpdPJau25AKm0Rq5hYQXdXmRFt2fqFJzH8DXXGsVAoDNjuOA6O4wzY5vs+ixYtYvbs2fVtuq4zY8YMFi5cOOj4CxcuZNasWQO2zZw5k0cffXSjx5TP59E0jebm5gHbb775Zm688UYmTJjAv/7rv3LllVdimtuuKca7euYgCDj66KO59957mTJlytY6JiGE2KFt6cW3t/6R25EFfphkiEgyOJBkzmIV19trqNrF6DB8o8hLGEVoRvLBwDT0WmAZke+tYFoGtm0S1tb1haHC80N0TaOpOYVp6LjVgDhKprlaloHnh1RLyboqtxrR2JzCsnSaW1JYlkml5FEueQA0N6VpacvgeyFRpKiUfdIZi7ZROZb/owvPC1i7qo+m5gx2OmlCb9lJoLQ11kD2B85KQSZrYZoGjmMSxzE968tAMu222OcSBBEtIzL1IKg/ePT9sJ4J2trZmThWeLXWGBsTBFEyddFIsr+GoVHMe5SLXn0fTU+qoJpWcrEgCmPWryuwanlIFCtcLyLwAwzdwPeT7HBjYwrbNigWPNxKEjS0jcph2gZp06JU9OnrLpPJ2ji2iWlq9PR4lAou3XbyXqXSZj3bmFSqTS4cpGqFd/qFYYznhfXvySCMiCNFJmsP60zueynJNL4x7ffNr0sUxQS1tiiv/KUDt+JjWiYjxzQwojWD7ZhUKgGpKKaxMU3V8nCrET1dJeyUBQoUMWGkaGh0aGywKRQ9wlDRtb6EYxt0rS9uu5MX79wQJxzHjx8/YPN1113H9ddfP2BbV1cXURQxevToAdtHjx7Nq6++OujwHR0dg+7f0dEx6P6u6/KFL3yBs846a8BF4ssuu4yDDjqI1tZWnn76aWbPns3atWu59dZb38lZDol3FThalsVLL720tY5FCCF2KnLxbeMK+SpEFgpFU0uaWCl0TSOO3lhfGMcK10uChUolQNeTKqK6lnwwdT0NsxxQLiUZCQdIZ2zcSkBPTxnN1HAci+a2LIauUa0kRTT02ofZajUkjpJCJ24te1XMu2QbHOJIgaXwvBDb1tF1g0zOJvAjnJSJ5yYBWKXsYxgaqYxNueShGTq93SWaWzOYtfWSnhti2QaWrZPvdeldXyKOwUmb2I5JS0uGdG7wDGUUxrXMV0y5nKxpzGRtjFoxIYAwiDENA7PBwNA18n0ubjkgr7tkG2xs26gXxrEso17cRde1rTbF0vdCXDes98Q0LR1d19F1rX4DcN0Qt+ITVCJ6uksUe5Og3XFMGppTNDSmBgRgcRzT3VmmXPYolTzKZQ/fj2lpSZNOW6AllTRtJ8ky9fVWCfyIbM6iUvIxbZ3AjSiXXEoFHwzIpm3QNAxDwy0n7/GYsY1oegrHMetTogFsx9ggwDZNHdO06wVbXDekUgmSQKbB2Skzj74X4tWyfMmFiYBKOcQt1wpJOcn3ei7roGoZ8r//bR2lggtoNLfY6LqGUuAHIVGYFC9KZ0x0TWHoAZVKyKql3TSPyCTZyaxN06gc6ayDnbLoXFukZ32ZwI/QzR23BY1451auXDkgUHtrtvG9EAQBp59+OkqpDWZovjlruf/++2PbNp/+9KeZO3fuNjlWeJeBIyRTqO6//35uvvnmrXE8Qgix05CLbxvXP92yVPDx3Aiv6gM6saawTbO+3sxOmbX1fBFhpEg5BkqBZRoYuo5hJNVR4yjGMHVSWYtqNaBY9NB1jewYB7fi16fNGbqGZSdr3qIgyW7GCnINdtIHzjFZ31Gk2pjC0AEFKcdmxKgsQS275bkhqbSF5yZr8dZ3FNE1jbbROTwvxnMDojBCIwkywjCma12B3vUVDFOrT4uslCIqJY/erhINTWlGjMpi2xamlayT9KoBbjUJdqvVABXFqEjDrQSoWNWnm/YHOamURSpjoRk6xT4Xt+JjmBphEJFKWxjGG8V/+ouWvNtKq1GtJYbvhoRBhGkZ5BodbGfgx48wSAKBKEymjVYqPr1dSYVb3dDJ5HRs2yQKY4oFD9NMqpr6frI2MZNzCKOYSiVI1py6Ac1tGZqa0vhuiO8l6x59PyKbtTH05PXrWF1FEWPqBoaZrF/tLJawbR2NJOvp+SGGnkx77n/e/rYPmyroYtUqfxqmQbnk4XshhViRzdk7zVq7OFa4lYDgTX0xOzuK9HSVURGksyaWnVys6OuqsCaIyDQ6FApVCnkXQzcYM6GJdCr5vvf9pLJqtRpiGKDipPhTyrbwg5hYKXq6y6TSyfpUatnzKEp+B0RhnPRllYYM2welNui5urXGBWhsbHzbZSBtbW0YhsG6desGbF+3bt1G1x62t7e/o/37g8bly5fzm9/85m2PZdq0aYRhyLJly9hzzz03ue9QedeBYxiGfOc73+HXv/41Bx98MNlsdsD92zKdKoQQw51cfNsITSOMFH4QgwalSoBt6lTKBqEZoxkajmMkAZimo+uQtkyclEnaNglqAYhp6gRuSBQq3IrPqqXd9KyvUip56DpEboBpm2imTiZtkck5lIpJsRzT0okihWXrGIZBW1uGfJ9LEEV0rysQBjHZhhQTd2utF+XoX/fmVgM0DQo9ZUp5F93Q2WViMy0jTLq6ypQLHtVqQK4pRaXisX5tERWDUgbt7VlyTSn8akix4LF+bYFy0aens0T7uGYyOXvAhynd0HFSZn3dThBERIGiVHDJ5GxCv9YawkmClUzWJvRDSgWfSsEjnbOJIoXtGAOKi/RPWe3PSG4utxbYetUQ3wvQDSMJ7koelm1ip0w0koxeGCaFi3RDx3cDPDekpTWDbmikUkmvxTCIk+O0dQI/ptDnEkVR0mJDTz5gNjWn6e2pEEewZlkfvaky2UaHjlV9VIoBVlrHcQxs26JrXZEwSCqlOq0muYYMrhviVUKCIMC0koBPKQ1Nh8ZGO2nVYhmk0xZO6u1fF03TyGQsDEOjVPIIgqherTWV2rHXPcaxolz0iGsXeZy0RW9XmUJfNbmw4Rg0t2Rw0gZuJSQKPUpFl/VdJQp5j+aWNOMmNjJydEOyzlnB2lV5uteXqJQ9TNMgmzEp9imiIMZzQ6quj26ZZLIBpq4olVNUyj7FgottmbS2ZUDTKJVL2/jVEdsL27Y5+OCDWbBgQb0iehzHLFiwgEsvvXTQx0yfPp0FCxZwxRVX1LfNnz+f6dOn17/uDxr/8Y9/8Nvf/pYRI0a87bEsXrwYXdffcWG9ofCuA8e//OUvHHTQQQD8/e9/H3CfzOUXQohNk4tvg9N0HdsxSacMNF0nm0mCG8fSSWcsKmWf1csKydRQx0TTNRqaU8SNKXQ0NC3JRnSuzdOxso9SsUoq4xDGMUEYoymwDKM2PTHAtg3iMMbzw2QKo2Um/eUMDd+LyeaSFg3tY226u0qsXtGH60aYlkm5EpBKWdgpk3TGploJcN2ANUt7CYOQYt4lnbMp9FXJ5lLYhk5V18n3VfEqPtWqV6v6Ck4KujtLqBhyjSkam3XKfVVWLOvFNjUKvVXGTmzCsW1My6ClLZmWp0iKjCRFg5J1fypWFHqqxLHCcsz6tFNdT9Y6Rn5EpNWmjtYyZ2EYk0olwaPn9a8djTeZWXsr3w3oWJ2nkHep1or1pFM2pq2jAbqpk0rFuBUfTaeeYdVMqJb9pMItSYDbNiqHUSt81J85jVVM6CuiOMZ1Q4Iwolz2Cf0Yt+rh2Mn+KKiUPfJ9FeIoxkol00rzPRVWvuaSyto4jk62MY1b8SkGLpat46QMdENLghNdw/NjSn0uHWaR0WNBRRpaKzQ0vvOpYklbjyQz7Psxupa8R07KHNJCRNuSW0nWC2u6RiZrUyq6dK0rYZgGo8Y0MmJUlkLepdTn4nsRMQona7B2ddJvJ45jnJSF74ZYjknXuiJ9PRUqpYAoiImDCBXFOI6VrIU2dLxAEZSquNWAtK3jVSPCIKahyUFv0mhvb8aydZYtrWzrl0e8A6pWOGsoxt0cs2bN4txzz+WQQw7h0EMP5fbbb6dcLterrJ5zzjmMGzeOuXPnAnD55Zfz4Q9/mK9//escd9xxPPTQQ7zwwgvcd999QBI0fuITn+DFF1/k8ccfJ4qi+vrH1tZWbNtm4cKFPPvssxxxxBE0NDSwcOFCrrzySs4++2xaWlq24quxed71b6vf/va3W+M4hBBip7QtLr5FUcT111/PD3/4Qzo6Ohg7diznnXce1157bf05zzvvPB544IEBj5s5cybz5s3b5Nib2yR5YxobbHJZBydlYtkmPV0REKOhUeytsm5Nkd6uEp4fYVkmmq7INaQZt2tTEhTFMT0dRXrXV6iWPSKg6kVkczatI7KMHJ2hu7NCuejipC3SGZvIjygXXCrlAMMMGDO+CV03cJ2AONbw/IimpmSdWzbngOYTBiF9PRVQSQGWTC4JRpYu6aNa8YhiRa4hhW7oVEsBKoaGxjSm5dPTWWFpVxnL0Mk1Opi6Tm9XlSCIWLakC8s0qJR9Aj8inbYolAOqVZ/X/tZJ+7hGJkwZiWaAZVtYtoFpJu+dk0qmnVZKXq3vXUjjm6ZG+m6YTNQzNHQtqbqqlMKrBDgZi0o5wLIMLNsgDKNaIaGkIM3b6euusPL1bvJ9LpViFd9T+F5A6Ec0taZpbs2SbbRxKyGWo5NrSGFaBk0tKUoFj8AL0XWdbM4mm7Wp1oJI00rWgAZ+hFdJMpSxSjLPfjmk2FelWgqSICVtMnpMAyoGv+rT1VXFcnRGjMzh2Ab/eKUTzUjarWSyNipSlAsuhT4Xx0kuAMQKgjDGdX3iMEJpGtVqMiV69NgmKmWfctEjnbVrbT5q7T5qmdr+7K+mgUaSsVQKDF3HthWBF2HaOm41JAiSYH1Hatnhe2F9emoma+N6AWtX5olihWmA70Ys/3sXlZJPoc+tVzYul5LqxHEQYlsGa1YUaGlNoaHR1+sSeBGOrZNpzdUq3CYzD7INKcoVj3SDTW9XhdiPWLOyF8M0aWxJo4hJpS3KJY9M1iaV2jGD9R2NGqKpqps75hlnnMH69euZM2cOHR0dHHjggcybN69eAGfFihUDCpwdfvjhPPjgg1x77bVcc801TJkyhUcffZR9990XgNWrV/PYY48BcOCBBw54rt/+9rd85CMfwXEcHnroIa6//no8z2PSpElceeWVG1Rr3VJhGLJmzZoBrT/eiXf9k3Puuedy4YUX8i//8i/vdighhNjpbIuLb7fccgv33HMPDzzwAPvssw8vvPAC559/Pk1NTVx22WX1/Y4++ugBFWDfbjH+5jZJ3pTAj7Bbk+mjaMl0xb7eKp3rSoTVgMAPsG0LJ2WDCYEXke9OpsE1NNhEKKolnziIsGwL2zHQzOQDZkPOJnCTKa62Y5FKmTQ1OZTLPpVuHz8IyVoOQSUk12Jh2Tb5vIfrBkmVz6KHaehkMxaVosfyv3fS1ZBm7IQmGhodqhUfFcdYtsWIZocwUBimhqZptTYEAbmszWt9VbrXF3EMi8Zmh0yDTVzy6Fqdr0+XrZQ8MhmHtjE50lmLatnFr0as/Gcv1bJP77hGTNtkxKgcYyc2YZpJAKLrGtkGh0rJB5V8kPe9MFmz54VJ2ffWNHGkKBWqGKGBpoOmKex0UtglDONkzaWu4fshgZ9si6KkH146Yw+YalksuKxZ0Udfd4VCvkoqbWJEEWExIp02iENFpeLjBbXANDAIw5gxYxvpWlcmjhWpjE1Ta5psrdCQ70fEYUxY6/dXqa2X8/2IMFYEbkhfdyXJsGox6XSKttFZ0hmHVMpk/dqYVNoELanCWyp6tI7M4lVC7JSOW47o6ymilCKXc5KiOKaOpWsEYQgxxJGG5/oUe6t0rSnQ01liwsQW+rrK5Boc0plk/atp6ZiWUV+DOpj+Nh2gqJYitFphILfqk05bpNL2dj99NYrieqGppB1GxOqlfRRLPtW8S2NLBqJk2m61EiaZ+qxNT1cJv/a9OXJ0I8W+Kr5XZn2HQUOjg2boOLbBiJGNNDQ65JrSyevphpRKLpqu09SUpqkpxZplvZRKHoQ+RhGyWYtSwaNS8UmlTKqeZBzF5rn00ks3OjX1ySef3GDbaaedxmmnnTbo/hMnTnzb4PWggw7imWee2ezjfKf++te/ctBBBxFFb99L9c3edeCYz+eZMWMGu+66K+effz7nnnsu48aNe7fDCiHETmFbXHx7+umnOfHEEznuuOOA5I/Yf//3f/Pcc88N2M9xnM1qPLy5TZIBPM/D895otdDfcqS7s0zsGTQ0Z8jmLOIgwvd81q0oojSFoes0NqfINtg0tWYo9FTp7iyRX1/mtRV9uJWAbINFW3sTzSMzxDHYloHjGFRKHsWCj2lppNMWgR/RsTZZ72ZaBrmcQxQovGqIYXjEelJsJ/AiVq3vRank+W3TpLtUolLxKfRWWb86abVhpgyyaYsxE5rp6axSdX0yaRt0iIKYStEjn68ShxFBNQbDp2tdmWopqbzZ2JZBt3X6eisEfkxJedi9Bk41oqkljZ1K1g3me6rke6qMHNdEtRTQ01Vm7Phmxk1sBpL1d7ZjJOvyTK0+bTSqBWGZrE3n6gJRCBgRkZeccyqIyTQ6KAVhoKi6yZTDYq9L6Ef1NWu6kRSHSWccdB1Wr+hj/doChT6X1hFpUo6JChRt7TmctE1Dc7LerFSo4rsRtpcUnHl9yXrslEU6azOqvYGGxlTy/ZfSk96LtfYM+d4KoR8SBjFuLTvc21UGXRFHGiPHNDFiRAZDSwoMdXeXCaKYbIODpkGp4FLsq5JtcBg3oYmudWUqVRfd0Mg2pEnnDIo9VXw/REUKTSl0PSYOk+IqpaJPHMbkuyt0LOtj1LhG2kblGD2uiVxjiihMXpvQ03EyyXpb3dBrWZNalkMlU4U9L8QwQdN04ijGrUS4lRDdcElnbDIZC9MytssgMrlYoZIg2tZZ8VoP69cWKearjGzPYRga2SaHfE+ZTKOJZRhUKj5+GJNusBg1qoGmlgxrV/TRVS3TtaZAx4qYEaNz7HfIOEa1N9Dcksa0kyJZvV1lyiWfaikg22CjAsg1pZKWOJUAwzAoFlyCQKGpmEopoFyRNY5CbIl3HTg++uijrF+/nh/84Ac88MADXHfddcyYMYMLL7yQE088EcuytsZxCiHEDmlbXHw7/PDDue+++/j73//OHnvswZ///Gf+8Ic/bLCe8sknn2TUqFG0tLTw0Y9+lJtuummjC/i3pEkywNy5c/nyl7+8wfZK0YXAxPciin0mhk7yod71CaMYFSl6e0qMHJXD0HXaRmdpG51j6T+6Wb++hOsGxLHCtMtYKYOW9gYgmfJYyLt4rg+aTaXo44ch1WqIaRhousJyLJqaHBqaU3iVANcPKOY9oigm1+Cg6zoNTQ7Vss+Y8c2Uyx4dawrke6vk+1zSGYuW1gzVaoSKY9C0pJ9iFFMueHSuKlKteIQxjGzL0NNbYeVrXSiSLOCoMY20jc6wflUe343IZJJsSbHPpVK0aR2dwzIMurtKaLpGx6o+mlpSZIopqgWP7s4SY3ZpZsToDCiNdMbGThkEfkQp7xIGEemMje+FmLXA0nJM0jmDUj5p1RGGMY0tKYJQEfoh3etL+OUQyzZJZy28akChxyX0Y3RTw3V9OlcVqJaTD+olw00qqpZDbMegZUQG3/Xxyl6yIDOOqZRC1neUsGutGMZObK4HeJmsXV/7aBg6fhzipCyCUKHFyTrKfG+5VrUzpLUtjV0rWpNO25QKLoGfrHPUNDBMnUrZR9OT9Y+rl/eRTls0tqTI5Ww8N2L9mkI9yxn4Yb31i2HqmIZOQ6NDMe8TuEmQHrgBlbyH5wZM2W8Mo9obCWrrQlHgVZOKr6lMMpX4zcIwxveTKrJJ4Z+ISiUgjpKCMpWyXyugk1Qd3V6CSM8Nk3WNmkY6Y7FmVR8r/tlNKe/R1p6loTlNY6PDmpV5giDCMHQC16djdRHPC2hqStPclCbwI5rasgRhhFvxiVRIteix4h89NDenickAyTrKfJ8LQLbRRsUQqoiGxhSNTSmCIKpP/873VQmqQTKdWG1elkVsI0Pcx3FH1r8EZmOq1eoWjbtVJnmPHDmSWbNmMWvWLF588UW++93v8qlPfYpcLsfZZ5/NZz7zGelRJoQQg9gWF9+++MUvUigUmDp1KoZhEEURX/nKV/jkJz9Z3+foo4/mlFNOYdKkSbz++utcc801HHPMMSxcuBDD2HCt25Y0SQaYPXv2gDUbhUKB8ePHU857aIGZNPNuTIqrlEs+SimclEmp6BO4Ed3rK8QxlAoeqYxJttFm/MRmOtaUccse+b4quqmhGUmxmTVLK4ShoqHZhiCi4saUiy6aCaER49gGZc9DhQrDMDF0RSXv0ddZolj2cVIGo8c0ksmYpDMWqazNmFQTDc0Ory/pplhyKfd5uG5IxqmgmyajxzZgGDqaAt8PyfdWKZdcbNOgO4jwA59qNcK0dNA0MjmTvzyzmmLepWlEhnG7teAHMcW8i1/18aoBucY0o8c10tVZJvRDCr0VLMeiWvIpF3361ldoakmRa0ozclyOTC6DWw2olv2kn56mYUcGtp0UZ9E0jVTGItvg0LW2iF8N6SwXUTpJwFcKkumYdtKuxNChWg2pFjziOKS7o0yl7FHp88g2OFitKfq6K4RBjOVk6OlM7tf0pNKtbmgEfoQiCRSbmlMUuqv4lYDRuzQlvRYbHCzbwK0EVKoBvhdiWRr5Pg+vEmCZJlEc0joyh2nqxJGi2OfiV0PCOCaVtqmWXdJZm/VrS1imTtX1KJaTSrGeHzFmlyb6usoEboRp6Fg5k7amZJ1cueBh6Ek/x3TWqrX60ChXdFQcE0SKfD758FXq8xmzazN7HdBOrimNVw2Sqb1BRCkfJdVhMxa2k7zWpqmj1BvBYCZr09SSplr1qZSCeg/IwI+wHSPpy2npSb9Pa8uq3A61OIrr7WFSGYtCvsKSl9biVUKaWtK0j20iimJWr8gThhGptEkqa9PdVcXOmjSNzNA2Ik2+r0qx4NHbUwZg1LgcxElQWi15LPrDCkaNaSDX5KAZGlFMEpgriOI4ubDTnKKpKUWp4GOYJp1r+igX/eTnq8EmljaO2wUVb34hm3c67o7ub3/7G2eeeSaTJk0a9P61a9duUFfhndiqq4PXrl3L/PnzmT9/PoZhcOyxx/Lyyy+z995789WvfpUrr7xyaz6dEELsEN7ri28/+clP+NGPfsSDDz7IPvvsw+LFi7niiisYO3Ys5557LgBnnnlmff/99tuP/fffn913350nn3ySI488cqsdi+M4g66d9N2QUuChSJqBVyyPaslDodOQdcjlUvh+QFBbc9fdWSRVm9ZoWhYtI9PkTaiWQgp9LmGg0PQChqEzYlQOwpjG5gyuG2ClNXrXV0lZGs1taQI/mc7Zvb4IsYahgR8ka+2qBcV6VaTU59LcnCWbszFr2bwJk1pZ/vf1eJpHpZhM18vkNErFpMm55wasXZ1HtyCdM3ELAX09FXQNdMeksTlNy6gMr/11PaViFV3XGT+ljdZROfLd5aTiaa06qm5q2LbDLhNtutaVkp6XPVXMERlyLSm8os/y7jKObdLTlWO3qSMZMTqHZZt4bohf9Ql9g6aWNKmsRbng41YDUmkL2zboXleir6uM6wWAhmXq5JpT+H5MoaePSjUgk7JpanEoFpN1gSpUpBttGprS5FpSlCsBtqOTythJ0OtH6JrCdiwCL8L3Q3QTWkZkiSNFV0eRxtY0vh/R0pYl15hCacnawRgNTVN0dpSToCyMMGyDES0pMo6VTItV4HkBhYKLW/UJQ8WIUTn6uitUqx5uOUJDI9eQIoxiHMdgyeK1OCmDdNpm9NgmWsc00LO2SEl3SadNwlBhpYykqIsdoRsOucYUfb1ViBSGZSTZaxWz7BWftct7GTuxhUmTR9I4IlULHmPiCCrFZDqlnTJJpfvbniSl/X0/CWYzGYdUysb3w6Rwjh/huiGGrmHHBmEQUyXAsg3sWiZyuKhWAqhlaKM4ZPHCVXiVkGyjw4Q9RlDJu5SKPipWtLTlaGhN07Gqj3LRI5dzGDEyi46OZUfke/uoVgJsyyDyFZOnjiKbs/nnP9aT76my/B/dOI6B4Zi0jEzT1JpBI8ncZrM2Ohr5bhffDyn3VvHcCF3T0W3QgHgoegOKrU8yjlts3333Zdq0aVxyySWD3r948WK+/e1vb/a47zpwDIKAxx57jO9+97v86le/Yv/99+eKK67gX//1X+uNLB955BEuuOACCRyFEGIT3u7i24UXXrhVnueqq67ii1/8Yj043G+//Vi+fDlz586tB45vtdtuu9HW1sZrr702aOC4JU2SN6WxJY2ubHRNh0jR3VWkt7eKk7FIN9o4pkHbqBwKnZiIwI8JvJBCl4tfDZIshKmRaksThiGVQlJUJZWzyfdWME0Npbk0tqbB1LHzfpJR0wxaxqQo9LpUSj7lqk+p6KJDUv20xSHWtCTbVumjsSmVtOJwTHw3wNI1LF0j0iGOFKEbEWVj8n1lOlcX8VyfTC6FZRsUI48ojol1yNkGhqFY83ovlYJHjMauk1sYMSpLtewR+hG2qRMZOsTJNMdKNcC2dEaNbaDQl7S+KJU8giikbWQO3TEo9Fbwl/dRKSRFSRpbUjS2pnGrSSsD349wshZ+EFIueLhlHydtkWm0k+xXNUBFIalUKmlZ4kUU+1yiWKGjkWt0SNeyr1GUprHFYcwuLYBGFIKug5O10HQdy9JBh56OCtVqgGHoSZuGvEdc6+vXuapAqtbvz3IMco1p7JSRZPFqlTqrFR/DMrFsnVzWIZdzSKctfDcgiKKkomvJJeWYrF7Wm0wZNRWGDk2tWTI5B01XvPZyJ2hgmCbjJjWTa87glQP6eisopbAMHUNXFHpd/CAk3ZCmsdkkChUYOsVeF88LsNIWVtpAxRrFgsvfX17L2qU95JrTjBidI5O1AWo9I6m9FhpO2iKVNtEMPZlKq+mk0ia6pqHpGikn+bdhasSRStp46DGmaeBWfX7zmz/S2dnBLruM44iPfnibLg3yvTApYKRp2CmDZ3+3lErJw7ZNJu7ZSqXPpVDwsAydxvYMuq6zelkP3evL2LZJtsEmDhVBHJDPV0g3JN+DjQ1pWlpTtQJCIZOmjKSnq8TqFXn6equkUBT6NCrlgOaWNON3bwWlUSq6lPIu+Z5K0oszVCgUBiZRpIhDmaoqdmwf+MAHWLJkyUbvb2ho2KLaCu86cBwzZgxxHHPWWWfx3HPPbVBWFuCII46gubn53T6VEELscDbn4tvWChwrlcqA0uEAhmEQb2L+1qpVq+ju7mbMmDGD3r8lTZI3xU4bOIZDOpW0hyjmPXw3RNc11i3tAy2mY5lB04gMI8c00Doim0xL1XVilWQUVAy5Jgtdt3BskzUr8hS6ypiaxlo3JJNxKJVcWkZkaWxJE3ohlbJHseiRzVk0Nacol5OgsVoJaWpNk2tIYZgaPd1l3FJIvq+Cn7Ixa0VQQi+kqTWLU/Hpy7toWtKOolLyCcMY2zHJpK0koPEiMg1Jf8PQi1i3soiuKyI/BhNKRZ98X5WWtjSmbRD6EUGg8KoepaJPuRKQ90OsLp1Mg00mY9PXVaFcUFQLPrmcQyZrE8WKvi6X3u4qURCTylqMHtdIOmsTBCHr1yYFiQq9VXSSi/FtYxowbZPmERncakgYJC08Ai/CtgwMWyedtam6IaWuMoEXYuqQa0ozfo+RrF7ajenoZHI2pmWSyTmEYYTnhrSOyjBqXA4VKvK9VTJpjTBWlItJZrhU8NBMyDWkqZR8GltSdK4uAooISOcctNBHUxa+GeCbyWsTa1Ct+Di2gd2SJQxDSl1Jq5RSOWbsLo20jMiCFrFiSS+ZjE2u0aZ5dJZQQdf6cjJFmmQ6dK4xhe+GOGmbYsHFdyMKPREpx8TWk8AvjCJ68xWKeQ3TMLBtHS3W6V1XxrR0bMsg05gUcMrmUvXv7zhWSasOLanAatg6dtoklbZIpwxMy6y1IDFA0wiCpC2Khsb/PfE4N/7HHNasXVkfb9zY8dx0w1xOOeVkbCcpyqMnvUDQ9SQQHarprfGbqqg6KZOXnltJsaeKbuhM2nMElXxIseBhmjqpnE3oRZSKVfp6qliWQSZnkU5ZKKUo5gM0wyCTMxk3sZHdp4wi9CO6O8t4VR+3EuBWQ0xbp3lkhihI+oE2tibB6JqleSxbp1rxqZR8XDdA06GhOYVpakm7Dx2q1Z0g5bQDULX/DcW4O7pvfOMbm7x/991336Kq7u86cLzttts47bTTSKVSG92nubmZpUuXvtunEkKIHc62uPh2wgkn8JWvfIUJEyawzz778Kc//Ylbb72VCy64AIBSqcSXv/xlTj31VNrb23n99de5+uqrmTx5MjNnzqyPc+SRR3LyySfXA8O3a5K8OVb8o4e0XiaTdShVfYqFCqEXE7gh5YKL0sCxDXq7KwR+RBDEhF6EYSTr8NxCSBhGFHoAdKqFKqEXoSno6y5hGAZ5VcZaa7Ium2f0Lk1ksg757gpRpPCrJnbKJpW2MUyDTJNF4Cn8IET3oZL3CcIIzXbw4whDT4If0zFpaE6hkcFaW6RU8ulYlqdc9ojDmLbROdat6qNc8NA00FMGbj5Zv6dQqFARa6AFGn1dVVy3A9MwaGxJ0zwiQ67BwXc14jgidAPKJRelNAp9VWzLwE6bxKGir7tKT1eFhqYUI8flaGixKRd93EqE3xtS6nOxUjojRzeRbbEhUmiahpVK+t5Vyz6VoocirlUyVZSqSTY33WCTa0phOQZ+NaSv18Ur+5imTn5dmSUvrKRjVQFd12huy+BXA8rlANPQMTSNVM4G2ySKIhzHJPB8gnJAHEMUxkRxSFBWVIs+6YzJ08/+ngcfvYd/PeWzvG//afhhRGNjMjVRR8et+KBrFPsqlEo+tmXiODrlUoim6RS7SzSOyNDbU6K7s0hvR4k4jmkd3YCTStG3vky1kvQTLPW6BGFIQ1OKfF8FW9fxgxivElAoVHCLAZ4bQaRQmiLStWTtXcUDX6HpSWCbzZioWEepmFSPybpleayUSVOtGqimIEYRRzG6BigIY4Wma0mm065laDU96VdYa+fy9KLfMPebV7PLLu/n2OMup7llV/p6l/PySz/hgos+RceKO/jYkcdi2wZOysKyk2nNmmagG0kvTt3UMPSkZYtm6MlUaUOvtwbRaus6dV2vBZyb7ilbKfv1Kaqv/W0dnauLxHHM+N2aiQJFIV8lVuBYRhLEl4LkfUol71NLS5Zy0cereESaQjc1Ro5pZMLEEbX2JgbjJtn0dZd5/ZX1uNUQ0DAApUGuOYWGorujiG5olAsurhsRRRFtYxoZMbaJhiYHyzAIo5iudSW8yN3s30liG0iuAg7NuDsR13V56aWX6OzsHHCBWNM0TjjhhM0aa4sDxzlz5nDiiSfyqU99akuHEEKInd7mXHzrb1Xxbt1xxx186Utf4jOf+QydnZ2MHTuWT3/608yZMwdIso8vvfQSDzzwAH19fYwdO5ajjjqKG2+8ccB6xNdff52urq7612/XJHlzLH91PblsFkPX8fyYwEumNqo4xi36KA3KhoGT8ajkPZb/rQs3SBrAW46VtFCIwTR1DANCLyKutdEIA0UYeahYEavkw3LnyjwNTTboOlEAuq5AB9MyaR2RQdN1ymUfpWLCMFm3pps6gRdi6Rpr+z88axphqHAcHc0w8Mo+fXkPz0vW+61bVaBa8AjjmJaWDG7JJ4wUugZ22qRUSip1anotk1PyiIHedSV6mx0aW7M4GYvAjYhiDUMzcF2PahCjwrgWaIBhaARRRKm3Qm9niREjG7DTJn6pSqHgohs6qbSNV/QxUjamqZHNWcmU0EpAb75C5MXYtk4UKgIvSDK/PVVUTNILrxJQLFTxKj5eJUrW3WWSqZzVUjL119AUStOwHCMJWgww9WQaaxzHRJEiCKMkYIwUQRgSxxAHUdJLMlb8bOFdrOt9nR/++G7sytgkADU10o6FaRqEQUilHBD6yfRDpRShH6OimCCMsdPJR50oSC48BFGMYxk4WQvD1NFIgqKgGuD6IVFMUg23tt3QNTQ0VBwTExN5Cj+OUCHEKkJP2RiGlvS4dEMMPekRaNkalqVjmEYSHKJhmAamoWPWXo/kgCFWMbECrda6I+nbkTyvYWigacRE3PXw1xi3y/s54shr0bRk1sDIUVM54shr+c2Cm/jq7TdiFHbB0JNKxJqeZEJNx0z6TNoapqZjOAaGodXeEz256RqGrqFbSbbSsCwMHXRTq9+vG8m/DUND03WiKHkddEuje12JNSvyKKVoak2T76rQ29WV9F9M1y4QeCrp8+gHWIZOnLbIr+sELckmpxyLXGMaI1L0rivSHdd+DqoB6zuKFIs+bsVHV8n3iFKKVauK+F5IFCXvnWkb2E7Su7Wp0SZl6oRuiBf4VCo+oRvgF4PN/p0k3ntJG5uhGXdnMW/ePM4555wBf6v7aZr23vVxXLVqFccccwy2bXPCCSfw8Y9/nCOPPBLbtrd0SCGE2Glsy4tvDQ0N3H777dx+++2D3p9Op3niiSfedpxly5ZtsG1TTZI3h+uGqNBHNzWiIEKFMbapgwZxrKGZYBqg/Ihy3qXHS3ro6aaG0jWIkyb1jm1h2zphpIhjBbpOXOtjGIURKIXnRRBrdKcNTFMDXUOFgKXh6Dodr3VBbd0ZGsSahq4lBWMMw6DqBknvP6WwDSM5DstAaTHKj1G6hg6EmLjVWoVSTSVZxkARRxG5BhvLtij0VihVoiSQdDx0XSOIkmm3hqnXgwrT0jFrH+KV0ojDGM8PCGqvg1LUW4FomoaKkq81XQEaCpWsHyVZi6kZSdsL0zTRDIVSGqYOhm4QhRGhH+FHMcSAlkxojeOkv2FS+jbJZtm2QRBGhGFyDsmzaWiGgaElF/pVrFAqTuKyECIAFRMlb0fSD5HkPDvLS1jX+zr7HHYuf33mAf7wuycZ27gX9fyXrtXOkXoAqFQMKrkoYOj92xTEECXxGIaZvJf942iAH4KKo9pB90/z1JPTrb2H/RO8o1gRRnHS7qPqYaZ1NE1HaeD5IQRAJcmW6LqOoSVj6loSzCWBYfLBTdd0altqZx2jlEryqRqAjgLW9C0hX+rkAx++uh409tM0nf32P43/+8VVPPnEb2lv2qP/xUfXNGJN1V4uHb3/rPXaK6bV1l4mO0NtD60WeCbvX+2u/tdaB10z6sccBCG+H6Fi0I3kwkIUKXRLx9ANTLP2XsWKMPkxBJWcu24lYzoZG9vWsQ0zCax1DV0HP4Kq69Uv8jhWUiCKKE7WvJZ9quUAP4iS49WTQHXU6Bx/XpdUUQ6CkKiWaUmKKJXf/peQEDuAz33uc5x22mnMmTNniy7ivtUWB47f+c53iOOYP/7xj/z85z/niiuuYO3atXzsYx/jxBNP5Pjjj6e1tfVdH6AQQuyI5OLbplUKVbwoqDWsT/rCGbW2BJqmofsaUfJpHMMgyU5FCqVBHCTTAC1Dw7RrLQ+Ulnwgj0mmgoYqCWKiJLOlatMENU0nVkm2yTL1JOAL+5u3KwxLSz4sx7UPz8mn6eT/TANNJVM+UYogit8ITBREKukLGEdxfU2pipPsYLUQEoVJ4GcenxQf8rfSa6k28u/4LdvD2m1zvLmmpwK82r/7wxrtTfdtbGzjLf+tj6cUf73/O4zc5QAOnXk1nasW81f3j0z65GeHZL3exnP+G9KBjZWi2dwPVhtbWayoBdU15Zfz8DI0t+w66P4tte1eXE4K5cTJ95tSMVqUfN/GqCS7SX92843Xsb//ZHIp4I1ylv2tC7SkHCmangSysUp6akZERCFQu1BhGBr52vRWpSVXDzSSgF5pOhrJhY0kJq6t8TRqQayh1aYgJ2tAMTXQTfp/zIjjpHgQgA4qfuP4NS05H0USrPd2FDFqhYfM2s+2ZiYBcqQ8hNgZrFu3jlmzZm2VoBHe5RpHXdf50Ic+xIc+9CG++tWv8sorr/Dzn/+cb33rW1x88cUceuihfPzjH+ess84a8obWQgixPZGLb5vmlXy0MPnQGiuVZD2qEbX8FYYJlmOgGUkWLopiVKwwdI0gSD6YYuvEtayQAkwj2R+SAFOFMUF/1kjTkimSYVwLHBVBLXLTFEn2R4coSj6/apqGYemEoULFEbptQJw0P0+qNsZouo6ha0RRTByoegsAXdcIDYVhAZpOlPQcIfCTrOhW7ZO1HVv9+h9Zv+rPHHX2fWiaxvs+8ll+9cOLWf36H9ll8ge39eG95zK5kQD09S5n5KipG9zf27scgH0O3J09JowjjpJpwmgaKkrWqgZ+RBQrolAlwWMUo/Tke1zVArYoqmXmoiTIVLWpu/3BomkYhDHEUUS1EuBVk/WphqFhWxoYSfY1jMJaVJxkzYmT6bi6BkZ/RtfQ0QwNPVK1qw1JgKtiRWxQD2xVGEOYPD6Ma6Ft/1WQ5BQxNA1NV2gquZAU+BEYScbVtsxk2roJhq7jR1vrsowYSqp2sW0oxt1ZfOITn+DJJ59k99133yrjbdW/T3vttRd77bUXV199NevXr+exxx7jscceA+Df//3ft+ZTCSHEdk8uvm1crjmNHidZk5Dkw2AcQ+An0yUNSwc7yWtpKsZAR09pSRP32odQxzFBT9Yc6oaWTH0zk6lsuqGhqZggJAn0VJxU5gyjpHS/SjKKSoFl61iWmeRfdA1NgW5oWLZBEMYQJlPy0EjGAFBJARtiRRwm46O0+riWaRDXpj8aVnIefpA0F1HPPZNMXzQ1lB+DDjo6mqbQbTM5D8tANxSEyQdjTA3b0UlnbJRSeEGyppMoxq3ERHEEQVzL8IBpGWhWMgXRMA0cXRGhEYdJ24dIKQwUYazVMr5gOwZWysRA4VZjAj8mDIKkx6IBtm1BrfJnttEmnbExHYtMxkLXNZRRm5qoAxrJ+rgYiMALk6IzYRgTx0klxfkLbmXULgcwbvcPADBu9w8wctz+vPx/X2PqERl0K2lf0dycJtvooNCoFjxMK1m/l83ZaCo5Nkhec8PUSaUdLDt5fTVdo1r0cb0I0zHINaZoG5OjaUQG4phyOcT3Q1Dguz49HWWqXrKWNp2zk0xaDMQK0zaIY0W54uFWQ0pFH98Pif0QN4iJ/AgtToof6Sr5/kzZJrqlESsNTana66NhmjrZBpt0g4NlG1iGhr7PQSz65VhefuknA9Y4QpLN+8tLDzOqbRwfO+ZjRBG1ixjJ66lBvcCOhp4EWLXMndIUqGS6KJpWW0MKhm1iWwZKB0PTQUG55OJ5IcU+j7Wre6gWApSCTM6isTWbBJe1wC5WSZsOzw2J4xjHschmTUzbwrB1MqlamxYz+UGrlD2KFR+vGhEHEX6k0GKFFsdoWrJuNfAiokAl64z9KPn5TOnompGEnHE9T0oQxcRh8j2laq8BybfbG+tLxfAmxXHetTvvvJPTTjuNp556iv3222+Dtj2XXXbZZo33rgPHarWKUopMJgPA8uXLeeSRR9h777258MILt1r5eCGE2NHJxbc3TNhrFEZsodDJZC0yDRZosHp5X9KuQdcwtKS/XRiE6MrAtpMP45GmEcVJ3z6lIA6TSo22ZYCuY1qgDANTT9ajBWHSoF1FimrJRUVg2MkUOts2cDIGlmGRyiQtA1wvxjI0NEMRRkmiRLP0pECHH6GbWlJkx48IgwhN02kekaGp2aHQ56JpGqmMiR/UPrzrGqW8h++FxERJkKjXWqSEyZj9mVcVJYVUMDVsw8AyDSKSTKWKFIVCMn4SvCaFXay0RlxW+JEi8nx0SyeMIlRFYZo6dsrCzFhEKkYpjVTWxLZMiBWVSoCfpGSxzGSNXhjG6KYiZWkoHKJAkUrpGI4GgQmGRipjo5samayJnU6m/EahIgg8/DBZxxjFisAL8WvrJFVcK78fw+vLX2Tl2lfr2UZIsrzvO+JSfvXDi1nT8xfGte5FseJT7Kli2xrprEOuwQZdkWvMJIF8FEMUo2vJWjtD09At0EwwbA2vEhBGEYZVWzMbRfhlHz9tYhgQBQGaiomDiEpfBdOE1lyaTKODX43w/QjPTQoBeW4StZiGhuPoGJpDGFtJD07PJ/TBdwPiICmwVPUDgihEr0ImbWNaBiqOCANFHOmEYUS+r4pjG6TSFnbG5LzTLudr93yR3y64iX33P42Wll3p7V3OX156mFWrnufaWf+JmTIxSS5cxJFK1qFqOqaZ3GxHx7SSwElp1N6bWnGe/imhClRELVMIXuTjuSG+F1EpefR0V9AMk0yTSa7Voak5je9FBEHy8+RVQ1wvmart5GyyGYtcYwonY5JK20k2VKmkiJMX4nsKn2S6d6rBwDZ1UikbJ22gQgiiEDRIOw62Y6L0mHTGorE5jWWbxHHyMxBFMVqsiAKFHwSEXkSl7FMqeHiVgErVx3dDTFvBI+/97zUh3mv//d//za9+9StSqRRPPvnkgGn+mqa994HjiSeeyCmnnMK//du/0dfXx6GHHopt23R1dXHrrbdyySWXvNunEEKIHZZcfBtc04gUhBbFvEex6qE7oBkm6UYHJ+uQzhlkMzZu3iXf7Sbrmkwj+RDqhui6jpO1kqIwTpLJ0ZQiiCLcEDQtJjS0eqXVTCYpcmM5JnEtoLNTJoapYegGtpPM5zNMnYaUiWVp+NUQtGS9opVKqmRqWY1MQxIUlgsuoa/INtmMGJkh05jCTtmkczYjxuQIwuRDrq5rdK7IJ9VF46RYj2npeF5EtRygawrLNogjhVsN8CphctymhkJHQyXnE8aga/V1oWi1NZ6GTjpjoRkRvlsrEhNGWKaJMkCzNIIwSjJdjolhaUk2TjNw9FrRkrSOZVtUKx5eWSMmaYkA4KSS/nxKAUZyPnEYEUc6xdjFquj1IjkqVoRxf2YxJtYgjkgqi2oKE4Vh6Dz5wo9pbBlPKtNC15q/1b8vUpkWGlrG84c//w+nf/j/Q4s1wjCg7ELgJZmo9vHNSXElRT17FmgKLY6wHDtZaxlDpc8n8EMiFWNaJp4fJhVg/RDVU2vL4ke4bkDsJ2toLUcnbZu4lYgwDImi5Pun6kf1qaBxnMyNjuOYIFBJtheAKOnZqYOuKUI/wvOTFjGuV03q5RhJARi9P7Ota8navTiZwtmS3odzTr6Gxxb8F//3i6vqr0trczsX/+t17DLyIFYs602mX+skxWwApWmoMCaMo3pWrnZ37f+9sQ43DlVS8TZO0vxRlLyuQZAUo4nCWg9KXSOdMwl6Y7o6S0Sxqk1npX4Otm1gWBqxAeWKT6HsEasSppZMjVUKwijJIsYqRtd1bFMn1DSqbkC54ieVYXUNyzGpeD4VN8A0NcJI4brJ+mfdqH0YVv3/ietTWY20Rc4yyLYqWmJFGERUqlIcZ3ugGKKqqlt/yGHr//v//j++/OUv88UvfnGD/s1b4l0Hji+++CK33XYbAD/96U9pb2/nT3/6E//zP//DnDlzJHAUQohNkItvg3O9ABXpuH4SAPT1ekRhFd+PkmDATtHYbNI0qpF0a5bIj7Fsk2JflUJPBc3QaB6RIZW2QFc4aQcVxVSKfhJURHGtb52O5STBYRjEeEGY9AWsVU5VtdRdgIZF0rMuilSy3tFPKoOm0xZhnLSu0DQN34swDJ1cY5p0LvkzW3VjyqUipm2CG7J+bQnDNshkbXq6OuitdGM5BgqFqwK0WMcLAsqBR1NjKy3tY1ARlEs+gRuhohCvklSKjMKQlGMl/SNrgVgchdgpOykIUit1ogO+GyYVKL2QKIxrLRoMND1Zu6abBlYt4xhGCk0li9LSOQfT0kmndSqpkDCIiEOVVKqMkoIpoRdiGmCYJlqtooph6knWNCI5Ak0RKQ0MA90w0IB0GizbwrYNdMCtuhTKPRQK63nsvtMG/wbJtqFrMYaVHHPkK6IgpNwTscrtYcToDKPGJL05lUqm6KazNnam1kNRKdINFg1mCssxqRZ9dEMj15yiZWQOXQPXDSj0uYDCSNs4GZOG1gymptWnQ0IyJTSMk3WtURjjekmw6VVDwiDAD+Kkz2gYE4URfhijQoUfhklvUj8k8hWhSqrURkqhRbXp19SmWGpJhdhYxew55XCu2m0aKzpeoer20drSxuTd98c0TLT+skT14LN2pBpopomBkaxvrFWZjWsVaOuFcWJFRH8AmFSO9f2YKEqyjfXMJEnV3VI5BC2s94DUDQPd0DA0HSdjYZoQxuCXQ7QwTqZnaxoKDSOpe4Nu6NgZE8sysCwNx7HfOCZdw7JMnNr7pmpTDG3HxDBr56pUPfgFahlT/Y2quZqGTa1KLMl03VRVpqqKnYPv+5xxxhlbJWiErRA4VioVGhoaAPjVr37FKaecgq7rHHbYYSxfvvxdH6AQQuzI5OLb4MI4yTzkmtPJ+jFLp5ivYqJhGAqv7NO5KpkGajsm2VYbK23hBCGpwAKStY35vIfp6GiGSSpj05JziKOolhWDUl+FMAbd1ElnTBqMNJgk/fhMg3LJo1IOCPwoCQBqU+xCX5GyDVINDnbaJKiE5HtdYqWwbYNU1iSVcjBraxKLJZ9iyUNFPlYqqeZhmjqGXeXzc87A9zfekNxx0nz7jv+ldeRo0qUAt+ITexFRS5Q0o4/jpFVBrV+i6q8wGyeVJsMIQjfJUlqOQcbSsXyT0E8yUFGg0BVEWjJ1VYUaSkvWehqmjuNYmGbSiN73krWalm3jBUkvTMPQUHGEbZtoVjIlNQ4i7IyD7RjoGQfNVKDX+hkaBpauSGUssjm7VmE2ye7FUUQUZrnpmm9TKPYl6zGN5BhsO6lEW8q76HGGlNGIWwrwq8mUz2oxoFryiaoh+fVVUqaBbZk0tKRpbEkxYnQjhqkn2dZ0Eni4bkDkKxpyDk0tKcZNaiXXlMJ3A1Yt7yVlG9iOSVNrhua2DIah86b+2W+yYQ4jCEKCIMYr+1SqPuW8R1/Bo1x28apJb1LPj3DdsNYbNCJwwyTgDEPCoDbVGoUOBHGMFkdEXoCmwZjWKTiWiWbqeCUf0opU2sRJJesikwxmbT1j/wq/WkuQqL89Tf+xRjFxFOP7ydToahCiPC3JTqqYwI/qU7I1gyRgx0jGM5KgNtmuY1tJD0VT1+tldS07WV9qmgaapuoXbQxDw7KSbUlrDoNUxsKxTJyUQTpjJ/1Sg6R3KkAqY2I7Vv/p1CSB6Jvpen97kdr6zVpYrWkaxeLW6YkrhpYUx3n3zj33XH784x9zzTXXbJXx3nXgOHnyZB599FFOPvlknnjiCa688koAOjs7aWxsfNcH+FZz587lZz/7Ga+++irpdJrDDz+cW265hT333LO+j+u6fP7zn+ehhx7C8zxmzpzJ3XffvclStEoprrvuOr797W/T19fHBz7wAe655x6mTJmy1c9BCCH6ycW3waXTFk3NOXSgpTWNbVusWtaNW/DRbB0/iImjCPP/b+/e46Mo7/2Bf55nbrubewgkgICoVFAUKGqMYrWaY1B7Kki9HatIrVQrKKb1QsvF2zlUT1W0ovzsUaqnUi3nKLW1xYNRezOiIAhURaUotpJwzW0vc31+fzwzkywJISRZcuH7fr22dWdnZ5+Z3Qzzne/zfJ+IP36qIALOORxLg2UZsiubJbNxTsKFY7qASEDhQlaP9OejEwxQOWBDA+OAI2TZ/kh2BMITyClQoUcsJJvk1CBayoRteeBcjvnLztbBVQUK5BhHAQY9wsPxenKcpIqcbNm11U46EI4LAQamKti1axcsK4Vzpi9FfslXWh2HupqP8eYzNyBlNSEWPQrZ2YYcFykEGutMOJ4L25JZK9sWcp5IVxYS4VzWyXRsGynLhZOywTUFRkSBEtGhGgJ2yoKregC4nGqEy2IprisQb7Sg6ypigwwYhoJk0oXnuf78kJ7MNgqGrDwDriP7RnIOpDwXQuWwHQ92o4ArbChcQDU4FE1DTq6AHovCYwJNjZbs1ikYwIRfWJOhuHgwjho2DIah+tOpAJomj6vnygI8qaQMhpnrIpVw4NoO6vcmsfPLBiSaLOzalYDleBgZGYTskQWI5cipbhjT4LoOEg0WLNuBnZLj51IJBXV7E6jbk4Rl2kgmbCiagqLiHOQNkEHjodB1/xIrPxYu8zwPluWiqclEY52JxoYEkkkbqaSDZNJCMu4ilbJgJi2kki5sy/GDOcD1bAhPhSoEPEduK266EElZrEepl1NMyMJKHLoWBGscqioDNX9eDAh/TkvPk+Mtg66pwpNjfV3Xg207EB4DUzhUTYWiKTAMBbEcA3pEhR6RWUJFVaDrTHbZVlUAHhRNhaZxGLoKPapCUxVZiViVRZZ0XYOhKRCMwUpaaGy04NgeDEPOJapHFOgRGRyGF/kCMqiMaOGyjl7/7z97iwfjkL5L0kP8aZAyst0jhOu6eOCBB/Dqq6/i5JNPblUc56GHHjqk7XU5cFywYAH+7d/+DbfeeivOPfdclJWVAZAXQBMmTOjq5lv54x//iJtuugmnnnoqHMfBj370I5x//vn44IMPkJWVBQC49dZb8corr2DFihXIy8vDrFmzcMkll+Cvf/3rAbf7wAMP4NFHH8UzzzyDkSNHYv78+aioqMAHH3yASORQZncihJCOO9w33/qKxr1JOAkGParCdWTF02SjDHyKj8pDQ10Sjg3k5msYflwRolEVibiDHdgHCIFotgGuMJhxG3pEFpHxXA/JlINkgwnHcSFcAVVTEYtpgMblmLOkDdVRYLkywNQNFbkFURgRDVbCBvNceLYNJhhisQjgT96elx/B0GG5yM43sHNHI5oaLGgqR0Tl4ApHdo4ip+awXSi6AstyoUcUNMRlMJJf8hUUDR934OPRkED9viRiWQbyC2KymmhBDIkmC6bl+OPlHIBlIdFkQuEcXOMwEzaScQvxhhSSCRWKqiASk9k2zxVwoips04YrANt0YZk2VF2B4wBQOTzGkIhb8Jgu553kDGqWBs8ViBVwKFyVBW2YHNuoaH4BGiHHSLqOA8WVGS/H8uBZFhptB8l6G0xhUBUFmh4UbdGgaQpURUDT5OdZSdlxUtNkV2LGAE1RkFNgYMRXipCbH4URlYWTrKSN+j1J1HyxD59+uBO7v2yEY7uob0iiblcTBg7Okd+j5cBKMCgag+IwmJ6AYaiIZslCNoCcziGSpaNgYAz5hVn+fJ1dxzlHJMIRiWgoKsqGEIVIJm3Emywk4xYs25XtMx2YpoNU0ka80UIi6cBM2jBTJuykA8uWRXQcx4PrF6RxbQ/ChQwALVm4B/48iuHnM78aThCLhQGY8MccemAKh8IYdMOQN0iiCrKyDGTlajCiBoyIimhMQzRLR1ZMhxbRZOVWJmBEVEQMDYrC4Lkyswm/CI7sispbVTRVVQ7NkJnFWEz1s7rNGVHOOTRdkVlMrZu6mHK7e7ZDMksgMwMSj5y4EZs2bQrjsc2bN6e91pn5cLscOH7rW9/CpEmTsGPHDowb1/yP3nnnnYepU6d2dfOtrFq1Ku35L37xCwwaNAjr1q3D1772NdTX1+Opp57C8uXLce655wIAli1bhjFjxuDtt9/G6aef3mqbQggsXrwY8+bNw8UXXwwAePbZZ1FcXIyVK1fiiiuuaLMtpmnCNJsnkW1ooK4PhJBD0/Lm23nnnZfxm299hSxKw+CZHpyIBzPlIBJVkFeUjYLCGFSFw7ZcGDENnHHkF8ZQVKzJ5UNcDBgUg6brcgyXJ6CpClSDw9AVNCUcNO5NoGFfAhCAHtHgOgLC85CIW+E4Ssfx4DmyIEpE19HgxRFv5IhGNGTlR5CVawB+BVbbkmMmNV3H0ccNRLzBkvM1Oh7MpAVF44gV6cgrisHzgHhDCq4j0Fic06HjkVuQhVhuBApXwDVAcA6uANmFUUT88YoCAslGC1lZGlRNRX5RFIwx1O1NonFvQnb38+RUI67twfE8OCkbrhuBmbTR1GQhFRewkh64yqFHVESzDEBlaKgzYZsOoMjxiAJyCgdF5dA0jixdRXZBBK4tx/lFYiqycyOyCqvlwrXlJPGcC6gKh/DkpO9qREEsokNRZSVZznhaZk8IWajFEzJTlZVtILswiqyYDiOiwohoaUHdwCG5OOrYQow8oRifbtqB7Z/uQWODhS+21yGRcDB4RC4KinP87KWc+iK3IIKc3CiGHF0AxuW0LynTgaYpyMrWuy1obAtjDLGYjpg/jYrjyO6iVsqFaduwTVlEyLJdWCkHpunCsuTUJcmkBcuU2VbHceC4cgylvAEgu3Y6tg3XFn6Xv+Yxf8H/ya7DDJwr4JyDc3kDQDdUaBENWVk6svMMGFEd2Tk6IoYKTVfBOIfK5VymnMv9kO/Tmqu3+tPYqKqca9VzPdmOoKsvk91JEwkLUTD5fRrNl6WeP58kP8RML+k/qKtq173xxhvdur1umcexpKQEe/fuxf/93//BsponVa2pqcHo0a0nqe1O9fX1ABBOlL1u3TrYto3y8vJwndGjR2P48OGorq5uM3Dctm0bampq0t6Tl5eH0tJSVFdXHzBwXLRoEe6+++7u3B1CyBHmcN986yuGHVOAnFg2XEcgJy+CXTsaoakcA4fkIBrTkZ0bQXa+AZUzeIJD1XU59122Dsf2kJUdgaqryMo1oOuKrIAKyIqmKseebAMDS/KQPyCK7DzZq8S2HezZHYeVsMEZw55dcdTvSUAIIBJRoemyOqkAw6CjcpCbE4HtCPzzs71INMSxO2EimbBhRHQUDcpGwaAsNNUnkWxSYCZsOfbPkV2HAAbDkFmYjhhYnI2hR+XBdT0oCoMR1cAVLouFRDXY/nx5WsRDstGBYsjxiJwBisKRlRNBJEuHbihwXAHX8QAmYJsuGvYlkYzbyDUtJBptpOIpJOMuFIUhElHAGQd0D6oix65Fc3RYcRtckVOl5OTGcPTxA5CTq2PvriQYYygZmovsXFl4hkFO9+A4HnL8Y+04HhIJO60Sqew+LMK74IrKoeuKfERlYGXbMijyPOEXnDGhaXIcoqxAyhDN0nHUyEIUleRg6DF7se2DWtT+sx71++JwbQ81nzdAj6qIZcmCN9lGBEcdW4hITHZlTSVtaJ4ix6AexqCFsSArp8DvQAXX9eTYR0fOyWhZrlzmeBDBeFt/bGIq6Xdr9QvxeH6JUwEB07Rl8G4LOK6MIIUnwJic01Q3AE3TYBiya2nEUMH84kUC8rtRFA7NUBHxu5pyhUEIf45Iv3KvrittFuFgDOC89W/dtuXfAmNo9bdAASMhvU+XA8e///3vmDp1KjZt2gTGWBjFByd++Q9kZniehzlz5uDMM8/E2LFjAchgVdd15Ofnp61bXFyMmpqaNrcTLN9/DGR77wGAuXPnorKyMnze0NCAYcOGdWZXCCFHsJKSEpSUlKQtO+2003qoNb1Ddm4EBXnZ0HQFtu0hmiUnlJddGRmMmIbBwwvgmi7ijRZSSQuJBhMN++KIxiJy3kFDBhRZWTo4Y0glZPEUVVdgpRwIAFqLDAfnHNGIjljMQG6eLJSyZ2cTUnELnHMMP64Iu3c2IJmw/ekjZDe7AUVyLKbtCjQ1mOCKC6ZyJJM2cgdko2gox+4djbBSsviLosoMaf6AGHbWf9ah45FfGMOIYwbI7qWW7L4ZydLlnI2enOQ9lbSx658NiEY12d1PCDi2gMo4jNwI8gujiGQbMFNyvCaYnPeSCaD2ywaYKRuRiBzr+eXWvajbmwRXGCIxDdFYAVQFiGZFYKYccAaYpoPCoiwMKM5B4aAs2JYHuByaoaB4aF56N6j9hpSpKkdOjg7HUeG6zdVmWFA0qI2gIRgvqmmKzMyZTjhlhmW5fgCphO+NRDWMGluMggFRfP73faj9Zx0a9yWgKnJeRNUPcrLyZREjAH5AKq9bdKPnK28qijwWLQ9fkD30PJnddT2EvwHbktNmhNNpCOFnEtGcaWyxLcZYOP6P+YE3h/xbAJddW4NMqOfJ6TIUP9OocB5Og6GqMtA+VJbfNVjTlYxmdknfJDw0Z6i7ebukc7ocON5yyy0YOXIkqqqqMHLkSLzzzjvYs2cPfvCDH+CnP/1pd7TxgG666SZs3rwZf/nLXzL6OQdiGAYMgwZYE0K67oMPPsD27dvTem0AwDe/+c0ealHPUnUN2XkRRGMaEnELuXkDUTQ0B54rYKYcqCpHotFCLEdHvh5F/b4EahtMWKaHrDx/jjk/AwIA0SwdEDKb5FiurOrI0Dz/G5rrJQRLGGPIK4hBaZFBGTqiEE0NKezzM5E5+QaycnUMG1WIeKOJpoYUHEeOnczJi0KLKEjGTURiBnLyFHBVbn3YyHwwwZG/PatDx4OBQdMVGIaKpnrZzdW1PWTnGmGgpHIOfXgBbFsGUa4fXAkhUDAwhkhMD2/wJhI2XNfzgwqGWLaORKMFQ5dFTIpLcrF3VxOSTTbMlI3cgigGFGeDcYbdXzaCqwy5+VFwzuXchP6k9lxhyMoxOjR2JsiwqarsFuz6XRltWwY/qiqzXPtvq2VmznH8bpmO57/PlWPmdPm6EAJ5RVkYwQE9wmElcyFcD/mFMeT607Uoql/tFTIDJgRkN1q15wPHtgTBHudKm20UQvhzL3poOZiLc3k82wrQOvJ9BRlPMyWzm8E2Y1lyTOOhcvzpSxhrUUiIENKrdfkvtbq6Gq+//jqKior8f4A4Jk2ahEWLFuHmm2/G+vXru6OdrcyaNQu/+93v8Kc//QlHHXVUuLykpASWZaGuri4t61hbW9vqjn7L9wTrDB48OO0948ePz0j7CSEE6NleG72ZAFA0JAfCE9AiGvSIiqLiHOyqaYQeUWGlHGhCjumLxjSZOcrSACFQUJSNrFwDmp+dCkSz5Vg6M2XL96gy4AnWCeaIYy0urDVdCT8PkN0nBw8vAFM4UgkbmqEiGtOhahxGTIcelYVjPFfATFhIxYFUygLjHJFs2aU0EtHgOkB2roacPFlxs67m4zaPQ8vluq6CMYas3Aia6lPwXIHGuhQiUQ2ekN1OGWMoHJgFMMCx5G11PaKmBQuMMWRl6Ugm5TQQcl5KjgGD5HyYtu0iJxpB4aBs7N0ZR/3uBCzLwd5dCcCVrwsTKCxSkJVrIJalw7JcKClHBmyHGAS0DiBlRQzHkcEPY3I8ZBDktiQzXYoMakwZOAZBJCDAuCKr3+ZFMTJqYO/ORqQSDlRDhWW6UDUFuQXyO5BZzCDb2HcDGZm1lcfM9SukyrGiAp4nu4bK48nC7r0HE5yX5DhGBYwBtu1BUeX4Xse2oOkcuq52KHPoeQJmShaoUTXKNpIDoKqqvU6Xz4yu64al5IuKivDll1/i+OOPx4gRI7Bly5YuN3B/QgjMnj0bL730Et58802MHDky7fWJEydC0zRUVVVh2rRpAIAtW7Zg+/btYdGJ/Y0cORIlJSWoqqoKA8WGhgasWbPmiJ1DjRByePRkr43eTFeZrICZcmBEOCJRDZoux6Ul4hb0iPznK8iuCAFk5xooLM5BTn4E2dkGIlGt9Xb9aQQitotEkwXHcQHI9bwWF8ctxbJ1WZzSE4jEZPCZkx+Fosg5JBWF+ZkThqLiHHAux0d6wkMq6fpj9DSoigI1i0PhCur3JZFMWCgcUIBINIY3n7nhgMciEo1h8ODisF2cM2TnRZBoMuFYHlKJ5gqRkZgWVp5sL2Mmi7JocBw5N2LQJRI6EG2xXvHQXMSydOyuaUCi0YIAwBhHVp4GM+UgJz8CrnBEohyO7cLzGFStc2PTmgNI4c8zKLtfyq6SAoAXricDHiAYHyeE391RYTBTsuqoTLZ5MBlDNKoiElMx5OhC1O2JI9FowbWF7F4LmWlMJW2ZbfSLu/R1QQCpqrJKqet6frdg4QeUzevJYwq0PJ4AwoCzZTERmWXUoSgsDLY9T8Dyi/KoGg9vBLQVlHqeQCJhwXPleFajDwfpJLOoOE7v0+W/1rFjx+L999/HyJEjUVpaigceeAC6ruPJJ5/EMccc0x1tTHPTTTdh+fLl+M1vfoOcnJxwDGJeXh6i0Sjy8vJw3XXXobKyEoWFhcjNzcXs2bNRVlaWVhhn9OjRWLRoEaZOnQrGGObMmYP77rsPo0aNCqfjGDJkCKZMmdLt+0AIIYGe6rXR2w0amgvDkFNxaP4k7IzJ7EQ0piMZt2BEVVlYRXhyLGOOnFNRVflBi84EgYHnZ2SUoNAMWnfbY4whlp0+LEHXFbAcw+8yyKFBQDdUqJoMxIyoJqdPMG0wziFc/0KFy4vyeKOJeNyFrhbgrb+sRW1NLZg//Uc0psH1/AI2AAYPLsaor6T/e8o5Q3ZuBJbptMiSKYecKZPHof0gKSc/gqxcHYlGE6bpQNPkcRdC+IGCA8ZYOEG70sWgKwh4AB7eGGienkGEyw5EN+QUH44jg08I+T0n4rbMtubIiri2n0VOJm3Y/lg7VeOIRrVOlanvzWR2UQmDSBlINh9PGSAebCusVfdh3c8y2rYL25LZXlnNVXZBVdTmsZBBZVXblhlQ7ncnp2wjIX1HlwPHefPmIR6PAwDuuecefOMb38BZZ52FAQMG4IUXXuhyA/f3xBNPAADOOeectOXLli3DtddeCwB4+OGHwTnHtGnTYJomKioq8Pjjj6etv2XLlrAiKwDcfvvtiMfjmDlzJurq6jBp0iSsWrWK5nAkhGTU4e610VfoEQ05+VFwzppHafnXl5quQAiZWbNSLhzLk11TNSWc+40d5GKUMZkZCy5yFYWndcc7GE1TYPtjJbNz04NKeYEug8tsEQkzNnLz8r+jMQ0N+1JwbA8DCopRVFAMxuX4wJaFYSLR5nGabR4nQz0s3So558jOiyK7xbJk3IKZcpBoah6Xa0TVbq1E2hxESsGxDL7n4JgGP46WU0MEwU3QddWxm8dQhisDYdCoaQoiUbXfBY0tBd1+FQXQtJYZRaT9RoPjyVjz8Wzv7yIYcyqrwHqwHVcWcrI9OGgdkSoKQzSW2alOSD/Qi+ZxXLJkCf7zP/8TNTU1GDduHH72s5+1W8RuxYoVmD9/Pj777DOMGjUK999/Py688EIAgG3bmDdvHn7/+9/j73//O/Ly8lBeXo6f/OQnGDJkSLiNvXv3Yvbs2fjtb38bxjWPPPIIsrOzD/SxGdflf20qKirC/z7uuOPw0UcfYe/evSgoKMjIybcj6eVIJIIlS5ZgyZIlHd4OYwz33HMP7rnnni63kRBCOupw99roK8Jqjy36zfEW/6bIDKMBMynnMPQcD5Fcw88qdSz4U/1J5W3bhRFRwyxWR96rqByMpWcsD7QPbW3PMDRkxXQ0NZp+dVBPdqP1K31y5cDVRXuLqF/V1UzJLp5cYW12D+5O8ljKwKejgnkFRSTorinHoHqenHszyIh1dGqU/iQIJLtLcxVYNewaG2SMGeRvhHM592d/DtBJ9+gtQxxfeOEFVFZWYunSpSgtLcXixYtRUVGBLVu2YNCgQa3Wf+utt3DllVdi0aJF+MY3voHly5djypQpeO+99zB27FgkEgm89957mD9/PsaNG4d9+/bhlltuwTe/+U2sXbs23M5VV12FHTt2YPXq1bBtGzNmzMDMmTOxfPnyrh6CTmOCOvp2m4aGBuTl5WHnzj0YOLCwp5tDCOkDXn31VcTjcVxyySX49NNP8Y1vfAMff/xx2Gvj3HPPDdcNzjH19fXIzc3twVZnTrCPH2z8DKNGH4V4oxlml/IKoqjflwzXLSjKQjJhIdFkQTMURKNy/KOi8FZZwLZ4nkBjfQoAkJMXQVNDSo6VzDE61N0y0WSFQWdXAibXlWP5+mrgIoTMLClq2xU7CeltjoRzaV8WfD83fftZGHqs27dvWgks+eU1Hf7+S0tLceqpp+Kxxx4DIKe/GTZsGGbPno0777yz1fqXX3454vE4fve734XLTj/9dIwfPx5Lly5t8zPeffddnHbaafj8888xfPhwfPjhhzjhhBPw7rvv4pRTTgEArFq1ChdeeCH+8Y9/pGUmD6du6d+SSqWwceNG7Ny5U44naOFILSVPCCEdcbh7bfQVQQAij0EwT0YbmbuIBst0IbzmLoeK2rHjxnlzd9WgMAqQPkVHezRDkRPSWzJ47Oz3JbM0nXprr8CYnCqEEEK6VYZTjg0NDWmL25pmz7IsrFu3DnPnzg2Xcc5RXl6O6urqNjdfXV2dNs87IP+tX7ly5QGbVF9fD8ZYOCNEdXU18vPzw6ARAMrLy8E5x5o1azB16tSD7mYmdDlwXLVqFa655hrs3r271WuMsSO2lDwhhHQU3XxrjSlIn6n8ADiXXQyDOfyA9quJ7k83VDi2lVYcpaMBoOpn2DxPIBG3oKoKjAhViCSEkL5g2LBhac8XLlyIu+66K23Z7t274bouiouL05YXFxfjo48+anO7NTU1ba4fFPTcXyqVwh133IErr7wyzIDW1NS06garqioKCwsPuJ3Docv/ws2ePRuXXnopFixY0OogEUIIad+qVatw9dVXY8+ePa1eO5JvvgXBW0diOCOqwnHkxO2Ms0Oq6qlpSph1DJ4fShsjUQ2JuAXHlmO5KHAkhJC+4Ysvvkjrqrp/tvFwsG0bl112GYQQYQHQ3qzLo+5ra2tRWVlJQSMhhHTC7Nmzcdlll2HHjh3+vHXNjyM1aAwwpE9OzljbSUhF4YhlG7JgTtahV2qMxnQ5TjGmHXKFUk1XEMvS5VQY1F2TEEK6jfBExh4AkJubm/ZoK3AsKiqCoiiora1NW15bW4uSkpI2211SUtKh9YOg8fPPP8fq1avTgtiSkhLs3LkzbX3HcbB3794Dfu7h0OXA8Vvf+hbefPPNbmgKIYQceejmW/dQVY5oTOvUHIKcy8xhZyci13Q5t2SmK4oSQsiRJBjimIlHR+m6jokTJ6Kqqipc5nkeqqqqUFZW1uZ7ysrK0tYHgNWrV6etHwSNn3zyCV577TUMGDCg1Tbq6uqwbt26cNnrr78Oz/NQWlra8R3oZl3uU/PYY4/h0ksvxZ///GecdNJJ0LT0fzhvvvnmrn4EIYT0W8HNt2OPPbanm9I77Z88bDE9ByGEEJJplZWVmD59Ok455RScdtppWLx4MeLxOGbMmAEAuOaaazB06FAsWrQIAHDLLbfg7LPPxoMPPoiLLroIzz//PNauXYsnn3wSgAwav/Wtb+G9997D7373O7iuG45bLCwshK7rGDNmDCZPnozrr78eS5cuhW3bmDVrFq644ooeq6gKdEPg+Ktf/Qr/93//h0gkgjfffHO/bkWMAkdCCGkH3Xw7sP3HNwbzIlLcSAgh5HC5/PLLsWvXLixYsAA1NTUYP348Vq1aFfYU2r59Ozhv7u1yxhlnYPny5Zg3bx5+9KMfYdSoUVi5ciXGjh0LAPjnP/+Jl19+GQAwfvz4tM964403cM455wAAnnvuOcyaNQvnnXceOOeYNm0aHn300czvcDu6PI9jSUkJbr75Ztx5551pB+1IRPM4EkIO1VNPPYUbbrgBkUgEAwYMaHXz7e9//3v4/EiYeyzYx48/2o5jjxuKZNyGZTkA5LyN9XsT4byOBUVZPdlUQkgfdSScS/uy4Pu58bJlGZvH8Ylfz6DvvxO6nHG0LAuXX375ER80EkJIZ/z4xz/G3XffTTff2tJGSdW0eR0JIYT0WwICXcxvHXC7pHO6fJUyffp0vPDCC93RFkIIOeLQzbcDYwwdmsuREEIIIZnX5SsV13XxwAMP4Oyzz8bs2bNRWVmZ9iCEEHJgPXHzzXVdzJ8/HyNHjkQ0GsWxxx6Le++9N+3OrhACCxYswODBgxGNRlFeXo5PPvnkoNtesmQJjj76aEQiEZSWluKdd97pUls7Mo8jIYSQ/qc3VFUl6brcVXXTpk2YMGECAGDz5s1przH6F58QQtoV3Hx79dVXcfLJJ7cqjvPQQw91+2fef//9eOKJJ/DMM8/gxBNPxNq1azFjxgzk5eWFxXgeeOABPProo3jmmWcwcuRIzJ8/HxUVFfjggw8QiUTa3O4LL7yAyspKLF26FKWlpVi8eDEqKiqwZcsWDBo06JDbyRiDoqTf31RUDtf1Dn2nCSGEENIlXQ4c33jjje5oByGEHJF64ubbW2+9hYsvvhgXXXQRAODoo4/Gr371qzA7KITA4sWLMW/ePFx88cUAgGeffRbFxcVYuXIlrrjiija3+9BDD+H6668PS5QvXboUr7zyCp5++mnceeedbb7HNE2Yphk+b2hoAABEY3IiZj2iwvMEVE2Ry7N0MCaXE0II6cc8IR+Z2C7plE79y7tx40aMHTu2w2Ny/va3v+H444+HqtI/9IQQ0lJP3Hw744wz8OSTT+Ljjz/GV77yFbz//vv4y1/+EmY3t23bhpqaGpSXl4fvycvLQ2lpKaqrq9sMHC3Lwrp16zB37txwGecc5eXlqK6uPmBbFi1ahLvvvrvVct2QgSJjDNEsvcU2GWLZxqHvNCGEkL5FZKY4DvVV7bxOjXGcMGEC9uzZ0+H1y8rKsH379s58FCGEkG5255134oorrsDo0aOhaRomTJiAOXPm4KqrrgKAcCLiYI6qQHFxcfja/nbv3g3XdQ/pPQAwd+5c1NfXh48vvviiK7tGCCGEkAzpVApQCIH58+cjFuvY3CqWZXXmYwghpF/qbK+N7vLrX/8azz33HJYvX44TTzwRGzZswJw5czBkyBBMnz692z6nIwzDgGFQBpEQQkg64QmIDHQrzcQ2jxSdChy/9rWvYcuWLR1ev6ysDNFotDMfRQgh/c6ECRNQU1ODgQMHdmj9srIybNiwAUVFRd3y+bfddluYdQSAk046CZ9//jkWLVqE6dOno6SkBABQW1uLwYMHh++rra3F+PHj29xmUVERFEVBbW1t2vLa2tpwe4QQQgjpuzoVOL755pvd3AxCCDly9HSvjUQi0SrbqSgKPE9WKx05ciRKSkpQVVUVBooNDQ1Ys2YNbrzxxja3qes6Jk6ciKqqKkyZMgUA4HkeqqqqMGvWrG5tPyGEkCOA8B+Z2C7pFKpWQwghh1lP99r413/9V/z7v/87hg8fjhNPPBHr16/HQw89hO985zsAZEGaOXPm4L777sOoUaPC6TiGDBkSBoUAcN5552Hq1KlhYFhZWYnp06fjlFNOwWmnnYbFixcjHo+HVVYJIYQQ0ndR4EgIIYdZZ3ttBFNVdNXPfvYzzJ8/H9///vexc+dODBkyBN/73vewYMGCcJ3bb78d8XgcM2fORF1dHSZNmoRVq1alzeG4detW7N69O3x++eWXY9euXViwYAFqamowfvx4rFq1qlXBHEIIIeRgRIaqqmakUusRggk6et2moaEBeXl52LlzDwYOLOzp5hBC+pngHFNfX4/c3Nyebk5GBPtYW7sbgwYN6OnmEEL6oSPhXNqXBd/Pd7/xJHSt+2ukWHYS//W7mfT9d0KnpuMghBBCCCGEEHLkoMCREEIIIYQQQki7aIwjIYQQQgghpFcRQj4ysV3SOZRxJISQXmD16tXhtBubN2/Ga6+9hpqamh5uFSGEENJDgsgxEw/SKYccOP75z38GAPz1r39td71du3Z1rkWEEHIE+uEPfwhd1/Hoo4/i0ksvxbJly3D++efjsssu67ZqqoQQQkhfITyRsQfpnEMOHP/whz+guroar7zySrvr3XjjjZgzZw7++c9/hssee+yxQ28hIYQcAXRdBwAsX74c7733Hp577jls3LgRF1xwQThPIiGEEEJITzmkwPHuu++G4zg499xz4bou7rnnngOue9FFF+Gll17Csccei3POOQeTJk3CihUrutxgQgjpjwYNGoRbb70VtbW14Lz51Dxjxgxs3ry5B1tGCCGEEHKIxXEWLlyIn//857j33nuRn5+P7373uwdcd9GiRXj33XcxaNAgbNy4EQsWLMA3vvGNLjeYEEL6o2effRYvvPACnn/+eUybNg3f+MY3cMIJJ2DDhg1QFKWnm0cIIYQcVlQcp/c55K6qjuPghz/8IVzXbXe9oUOHIplMAgBOPvlkvPTSS3jwwQc710pCCOnnBgwYgO9///soLS3FL3/5S9TX1+OJJ57Ahx9+iOeff76nm0cIIYQcXlQcp9c55Ok4brzxRgDA9773vXbXe+ihhzB58mR87Wtfw7hx4/Dll18iEol0rpWEENJPffHFFxg2bFjasvz8fNxxxx091CJCCCGEkNYyNo/jhAkTsG7dOrz66qvYuHEjsrKy8PLLL2fq4wghpE8aMWIECgsLMW7cOIwfPz58WJaFRx99FM8880xPN5EQQgghpOuBY1t3ywOxWAxTp07F1KlTu/oxhBDSL23btg3r16/Hhg0bsH79evz617/Gl19+CQDIzc3t4dYRQgghPSNTU2fQdBydd8hjHPc3YsQIFBUV4bzzzsMPfvAD/Pd//zc2bdqEdevWYfr06d3RRkII6bdGjBiBKVOm4K677sJvfvMbfPHFF/jLX/6CY489Fk888URPN48QQgjpEQIZGuLYibYsWbIERx99NCKRCEpLS/HOO++0u/6KFSswevRoRCIRnHTSSfj973+f9vqLL76I888/HwMGDABjDBs2bGi1jXPOOQeMsbTHDTfc0InWd58uB47btm3Df/3Xf+Gss87Cp59+ih/96EcYP348TjvtNOqaSgghnVBWVoZHHnkEP/3pT3u6KYQQQkjP6CXFcV544QVUVlZi4cKFeO+99zBu3DhUVFRg586dba7/1ltv4corr8R1112H9evXY8qUKZgyZUra1FrxeByTJk3C/fff3+5nX3/99dixY0f4eOCBBw6p7d2ty11VR4wYEd4xD1RXV2P69OntzvNICCEEsCwLuq63Wj5q1Cj87W9/64EWEUIIIf1fQ0ND2nPDMGAYRqv1HnroIVx//fWYMWMGAGDp0qV45ZVX8PTTT+POO+9stf4jjzyCyZMn47bbbgMA3HvvvVi9ejUee+wxLF26FABw9dVXAwA+++yzdtsYi8VQUlJyyPuWKV3OOLaF7pYTQkjHZGdnY/z48ZgxYwYeeeQR/OlPf8Knn36Kn/3sZygvL+/p5hFCCCH90rBhw5CXlxc+Fi1a1Gody7Kwbt26tH+POecoLy9HdXV1m9utrq5u9e93RUXFAddvz3PPPYeioiKMHTsWc+fORSKROORtdKcuZxzpbjkhhHTe66+/jvfffx/vv/8+nnvuOcydOxepVAoAMHnyZCxYsAAnnXQSTjrpJAwZMqSHW0sIIYQcHsKTj0xsF5AFPlsWoWsr27h79264rovi4uK05cXFxfjoo4/a3H5NTU2b69fU1BxSO//t3/4NI0aMwJAhQ7Bx40bccccd2LJlC1588cVD2k536nLgmJ2djRNOOAETJkzA+PHjMWHCBAwZMoTulhNCSAdMmjQJkyZNCp97noctW7Zgw4YN2LBhA9555x38/Oc/x86dO7Fv374ebCkhhBBy+AghIA5xPGJHtwvIyuW9uXr5zJkzw/8+6aSTMHjwYJx33nnYunUrjj322B5pU5cDx0O5Wz569OguN5gQQvozzjnGjBmDMWPG4MorrwyX19bW9mCrCCGEkCNPUVERFEVp9W9wbW3tAccelpSUHNL6HVVaWgoA+PTTT/tu4Hgod8td1+3qxxFCyBGpuLi41UB+QgghpN/qRAXUDm+3g3Rdx8SJE1FVVRUWAvU8D1VVVZg1a1ab7ykrK0NVVRXmzJkTLlu9ejXKysq60upwyo7Bgwd3aTtd0e3FcYK75VdeeSXuv/9+rFq1Cjt27AgntO4Of/rTn/Cv//qvGDJkCBhjWLlyZdrrQggsWLAAgwcPRjQaRXl5OT755JODbvdQ52ghhBBCCCGE9F+VlZX4+c9/jmeeeQYffvghbrzxRsTj8bDK6jXXXIO5c+eG699yyy1YtWoVHnzwQXz00Ue46667sHbt2rRAc+/evdiwYQM++OADAAiTbsE4yK1bt+Lee+/FunXr8Nlnn+Hll1/GNddcg6997Ws4+eSTD+Pep8tIVdW27D9ItCvi8TjGjRuHJUuWtPn6Aw88gEcffRRLly7FmjVrkJWVhYqKirALbVsOdY6W9ijKYTushBBCCCGE9Du9ZBpHXH755fjpT3+KBQsWYPz48diwYQNWrVoVxjbbt2/Hjh07wvXPOOMMLF++HE8++STGjRuH//mf/8HKlSsxduzYcJ2XX34ZEyZMwEUXXQQAuOKKKzBhwoRwug5d1/Haa6/h/PPPx+jRo/GDH/wA06ZNw29/+9suHtWuYSITo04PI8YYXnrppTB9LITAkCFD8IMf/AA//OEPAQD19fUoLi7GL37xC1xxxRVtbqe0tBSnnnoqHnvsMQAyDT1s2DDMnj27zTla2tLQ0IC8vDzs2bMPhYX5Xd43QghpKTjH1NfX9+oB/V0R7GNt7W4MGjSgp5tDCOmHjoRzaV8WfD/fPutn0NVot2/fcpL45Z9n0/ffCf0uNbZt2zbU1NSkVXTNy8tDaWnpAedP6cwcLQBgmiYaGhrSHoQQQgghhJCuCaqqZuJBOqffBY5B3+BDmT+lvTla2ptzZdGiRWkThw4bNqyLrSeEEEIIIYT0mr6qJNTvAsfDae7cuaivrw8fX3zxRU83iRBCCCGEEEK6XZen4+htgjlSamtr08rV1tbWYvz48W2+pzNztACAYRgwDKPrjSaEEEIIIYSEesFsHGQ//S7jOHLkSJSUlKCqqipc1tDQgDVr1hxw/pSWc7QEgjlaujrnCiGEEEIIIYT0dX0y49jU1IRPP/00fL5t2zZs2LABhYWFGD58OObMmYP77rsPo0aNwsiRIzF//nwMGTIkrLwKAOeddx6mTp0azqlSWVmJ6dOn45RTTsFpp52GxYsXp83RQgghhBBCCDk8hCcgvO5PD2Zim0eKPplxXLt2LSZMmIAJEyYAkEHfhAkTsGDBAgDA7bffjtmzZ2PmzJk49dRT0dTUhFWrViESiYTb2Lp1K3bv3h0+P9gcLYQQ0l8cffTRYIy1etx0000AgHPOOafVazfccEO72xRCYMGCBRg8eDCi0SjKy8vxySefHI7dIYQQ0g9RVdXep8/P49ib0DyOhJBM6q65x3bt2gXXdcPnmzdvxr/8y7/gjTfewDnnnINzzjkHX/nKV3DPPfeE68RisXY/8/7778eiRYvwzDPPhD09Nm3ahA8++CDtpt3B0DyOhJBMo3kce7fg+7mi9OGMzeP4/Jpb6fvvhD7ZVbW3cxwPnifAOevppvRp8q4QwjtDitInE+S9xv7Hk3OZSSJHnoEDB6Y9/8lPfoJjjz0WZ599drgsFou1WxysJSEEFi9ejHnz5uHiiy8GADz77LMoLi7GypUrccUVVxzwvaZpwjTN8DnNh0sIIQQAIPxHJrZLOoWuxDPAcVxYlgPLcikd3kmO48I0HViWA9t2Ydvyuet6Pd20Psl1PZim2+p4Oo5Hv9FOksfUgdfHx0pYloVf/vKX+M53vpN2I+G5555DUVERxo4di7lz5yKRSBxwG9u2bUNNTQ3Ky8vDZXl5eSgtLUV1dXW7n0/z4RJCCCF9A2UcMyDINHqeB9P0oKoKVJVi9I4QQsC23fBiPLiQDTJltu3CdQU0jVO2rIMcx4XjBAE3Q3DYhBBwHBeuy6BpCmXIO8jz5O8wCLgdx4OuKz3cqs5buXIl6urqcO2114bL/u3f/g0jRozAkCFDsHHjRtxxxx3YsmULXnzxxTa3UVNTAwCtxoQXFxeHrx3I3LlzUVlZGT5vaGig4JEQQggVx+mFKHDMAF1XoetqeHHpOHIsEQWP7RNCpGVpWwbc8jh6cF0PnufBtgU0TaHg8SBkoC2DRkXhUNXmgNtxvDDjaFkudJ2Cx4PxPAHLcsLnqsr7fBfqp556ChdccAGGDBkSLps5c2b43yeddBIGDx6M8847D1u3bsWxxx7brZ9P8+ESQgghfUPfvuLpxThnMAw1vKiUWR/3IO86stl20G2SQdfVtECbMZkV03V5r0NewFNX4Pa4rhcGjaqqtAq0VZXDMIJl8nj29W6XmRQE2ADAOYdhqFDVvn3z4vPPP8drr72G7373u+2uV1paCgBp0yC1FIyFrK2tTVteW1vb4XGShBBCCOndKHDMME1rzpoFGR7SmiwoJI9Ne5kvzpkfPLKw6yoFj60F3SmB5kxjWxhj0HUKHjtCHk/h38ToH12lly1bhkGDBuGiiy5qd70NGzYAAAYPHtzm6yNHjkRJSQmqqqrCZQ0NDVizZg3Kysq6rb2EEEKOHHKYUmYepHMocDwMWna5lGPKKHhsyfNaduc9eHdJGTwq4Xttm45nS0FADTRnatvTVvBIwXg6x2kOqPtLF2nP87Bs2TJMnz4dqto8amHr1q249957sW7dOnz22Wd4+eWXcc011+BrX/saTj755HC90aNH46WXXgIgf0Nz5szBfffdh5dffhmbNm3CNddcgyFDhmDKlCmHe9cIIYT0C5mKGukap7NojONhoqoKhJDdB23bBWOsW8eTCSHSCsr0lrFqntc80eqBpn8IghzOD5wZ2x/nMiCShXQ82DYOGiAdart6SlCt03X97zN4gQFM/g80jYfddtt6f3OX344dkyB4DILGYMxjbzouPUXe2JA3J/pTEaHXXnsN27dvx3e+85205bqu47XXXsPixYsRj8cxbNgwTJs2DfPmzUtbb8uWLaivrw+f33777YjH45g5cybq6uowadIkrFq16pDmcCSEEEJCnoBgGQjyqGdVp1HgeBhpmgwePc87pGIknieQStpwbA+cAwAD44DCORSNw7bdVl1gg8Cqpy50bduFZbXOrioKh6bxMGvTPB2E7P53KILxo0EBGMbYQQPPYJqUlt0xGWNQFOaPA+y5LoiyCm/r7zJoqfAEzJT8HQByjGI0qkJROYyIBs7ZfkHOoe1LEDyaphtmLftLdq0rWt7Y6OuFcFo6//zz28wsDxs2DH/84x8P+v7938sYwz333IN77rmn29pICCGEkN6DAsfDTNM4LEuEWZ3m4iRtc2wXibgdlg6WcZj877hlwXE8RLM0KEpzkBBkH+U8iK5fVObwBZCplB0WEQFkEBtMpxEUbDFNF7rG4QkRBnydCVAUhUOI5uJDjKHNi3shBJJJOy0oa9kux5EBl2nKbG00qh224xVU6mzZ5Tb4zhiTvSpcx0UibkHhDFA5bMuBZXlwbBe6ocK2XMSydLhecxa1M0FOc+bRCbsB9+WpJrqq5TyXh3pjIxOCccCEEEIIIYcbBY4Z0LJc//462iVQCAEz6cBMyW1xhSES1cJAwrQcOAmZObNSDrJyDBiGCsYYGPwusf70FcGk75GIekjdOTsjCFaBYFoSpcW8lnIsY5Dxa4pbAIBYTOvSVCWqysOg9EDdgJNJOdl9cPxbHnPXlUWLgvkjPQ84HEm24Pu3LTfMKgaVTgEWZkUt04aZcsEZg6oyZOdqgBBIJB24jgfLdP1996CoDEZEhaZ1/k+7q92A+4v0qXS6nnmVU8nIGyWMHXoXact00FhvdqkNhBBCSF+RqUI2VMah8yhwzIBEwkI8biEW09q8MDxYl0DP85BosuA68petRxQYET8oZDKgcJMCmq7CMh04tou6vQmomoJIi0yZwjkYANMP1BJNJhSVIxLRwotXReXdNhedzNjJQDcS0VplqoKKqLquIpmwYVkOhAAsywPndnhx3plMX3vdgC3L8bORMpO4f5CqKHL/DUNNu7jPFNlGmS0Mzl3Bx1kpB/EmM+x6m0rYcFwPwhPgnCGWrcMTMsDUNQUuZ1BUDst00NRkIuIHncFNhAMJtn+gY90yk9vRbsD9TZAB7uy+u66HVMqBmbJhmS4EmscgB39/mq4gYmjQDeWAf4NCCCTjNmzLpfH8hBBCjhwUOfY6FDhmQDJhY9/eBJoaOGJZBgxDAVeau0UKL+i2KbsoCghwJgM4x3GRjNt+8AIYUQ3MZrBtmZ3zhECiyYJpOlA1jmhMh2O7cGwXnuMhFbeg+RnNVEoGTAICriODISOiwra9MKh1HA8m/K6NKk+bPuRQJRImGutMcEUGQMIT0A0VekRBNKqH6wkhwPzKqPX7UmiqT8F1BVRNBrWKymHoKoyoAk1TOxRICiGPl2W6EJ4HM+kXhmEMyWRzZlNR2t+WDCI7tfsHJbO/zWMYhRBwbReOI28eeMGJTAikUg4STaYskCMAParCMFQkEjYScRueK8e7aoYGK+WgqSklt5NykJ2tYU9MR3ZOBLEsPfxuXc8DRFAYSH5UML5TUThULf0GQstKwM2Zt94RPHp+lli0HODOmoMyzhkY73yRKHkDobmbaqLJ8p8HrwtYKRsCAkKWLEIkqkGPyN9sfV0SDXVJOG7LYw2oCgfjDIrfvlTSQYNIQVHkTZVIVG2uLMzkd5WMW/BcAc459MiRl/klhBBCSO9AgWMG1O9NwLZUCL/7Y3aOgWhMh6oAtuVCM1QoqgIEXTdNF4rKwfwLX0CuZxgqbMWVwQ5jsFIuTMtGKunAEx5UVYNjuvA8F1bSRv2+FEzLgQCgaSpUncMTHhgAzuR4woZ9CbgewBmQna0jEtUQiepQVfkZskwNg6IAwu+y6QoB5gFclYVtGBgUf7yXEEDDvgTq9ibRUJ8CA6CqgJl0ZMBjqIhkaVA1FbEsHbEcA4o/Ri8Rt6H6gZxjy+NgJh3ohoJGIduoqhyRmIZoVIMeCcZyIsy8mkmZiXGDYAxCdv0UQDIuvw/TdOA6LqyoAa4koRkqVJWDcw6uyOCCK9z//0OvsOp5Aq4/D6Xs6tpc4VYIGbTb/nQOaeM9Hbl+EPBwAEzhMhPpeFAVDkDAMDgcVyDemIJrC7ieB9d1kIzbiMdtmEkHlmnBsgWE68GxPNm1WeeIRCPIyVMRjUUQy9URixkwYhpiMR2aroIpgOf5NxBMgHH5W1EUWQwGAFzHg+vK46ppClRNAWcA62Q1Wtd18ec//xk7duzA4MGDcdZZZ0HpQLRupmzYfvGgtICxHYxBfrfB9xx8536w3Fb7gy7ElunAc7y04LO+LoGmeguJuAUh0scbarqKRNJCImGDMQ7NYIhEDESjKlSNAeCwUg5sS4619YQ8xq7nwrE92Q3dE1BVBYqiQFEAx/LCG0jRbA1mKtGh/SaEEEL6OpGhqqodvYYgrVHgmAGW6aCoSJUZB8eTgaTpoHFfEq7rQdflRaDws0YQDLYjwBRAU1WoKkNWji67WzKGeIMJz++j1lBnwUxZ4BxwEg5M00FDfRKJuA3LcWAlXdi2A1VTYEQ1RCIq4I97FGBwXQ/JhOkHh3JKB91QEI1p0AwVwnXheQzJhAUr5fhBJ8BUDs7kexSVQ9EUuLaDZJMNKHIfFD+wbNxnyW6ynCOZTKK+LoGsnAjMhI1dNY2Ixy0wAFk5EWRla8jKjUBVZDDpOB6slA0GBtuWwRj2AICAoijQDDk+0UzYSDTafrYWUDQFmsERjepQVC6zegxoajRhJmxoqoJU3Pan75EX51xlUP0uwkFmiQHgKgPAwjFoXGHNXXpVJSw+5PpjSNMrtAKuJ+C5HlwXzd0TgeZMcxBoCk9mkQwFmsqRTLmo2x1HU2MKru1CCAaVA6Yt4LgOAAbHcpFKWmFXX8vyYFo2hCu3CyEr7jLIgFbxA2I1okFTObJzDOQVRFBYFEVWThRZOREoGodwZXEg7ndf1vx9DboxCyHCjKiqNGcmwwyf0jLTJwMizgHG/YDcD75efPFF3HprJbZv/zw8ZsOHj8DDDz+ESy65pM2/J9tykYxb4c2B4DgHAWFLws+mep4I/9t1PLgHGHbMuMy4tgwobUsWIgJj0P3KtImEiX27E/73IgvlqJoGrjBYlot4k4XGeBxNDRYcR45HNSIq7LiDJtXvgm4L2J4HO+nAclw4KQem7cK2BCA82TNAeBBuMP4Rsvu5oSKWbUDTOEwnecDzDiGEEEJIJlHgmAHbPtoJs8FDKm7LLoiuQE5BDJ7tAozBc+JQNAW248K1Hbh+RU/XERCukOOeslRomiYDEzBEohxGTAWgAFxAU+UYtHijDTNlwfEYGDw/O6nAdTwkGkwkG03ougotKscWOqYNzwVsxwbjHE2NKXge4LgemJ/lcxwhX3cAweQyhTNoigLGActyYaYceBBQuALOBVRNhaZzPwMqoGsK9IgcM8g9BtPdCytpy6I+tgdFURDJkmO7dF1FLEtDVnYUWlSDoshuf5qqwPODNK5wmMKDU5dEY10Kru1B1TiMiAx8FduDZTLE60wwhYMrgO14iDeYAGPIyYmAK0wWk7FdWIkkbNOGqivwXAbLdmAl7eZgFTILKrNbLgQ4OBNgjIOpLAxcdF2FasgxqJGoBk1XwRUFqiq7va559y+4/8F7ceftC3HaKWfCMh1YKReuJ8CYrK5bvzeFvXvjMBtN2JYHD4DiR+wMABMCri2DINuRlXRdW2Yq/TgYjAO6rslAmDM4rgXXApKmC7ge3EYTXAB7uYBgABOym2s0piErP4KcHNm1VY/ofgDF5XeuyoBKVRRwVd7Y4IqcBkTTlANm7bDfIs4ZXvn9b/Gd66/GUcNOxYUXzUZ+wQjU7fscmzb9Gt/61rfw/PO/xiVTp/rvZ/BcD8m4Jcf2wS8QFdGg+uMBD1ZcRggBzw0ywJ78b1eEwb7wH44nAMjvPJm0EG8yIQRgGCqSDKjfl4RjubLLr6YgryCCWLYOxhjqG0wkzSa4jotkwoGqyGPmWh4a9iZgJuXv3XFlYAhPdm0FRHiDgoHB8s8Truv6XVsFFM6hKrK4VTxuw9AVWC5lHAkhhBwZaIhj70OBYwZsfusLfKrvhuPILBHgZ2T8C0LH9ABFXuwrjMucFGcyi2gJmYlCMMYJcDwPDLL7INM4DF2RRVBUBY7rj4XUOQxDg6LJi3xVYfAEIDiHyhiYIlviMEBTOJSICithIWW5sJIOXNeF48qCIJ7tgDMGzS+iI9viT/GRcOB5LhgY6ht3I2k1QgiZXXNtB57twfUEFBWIaDnI1gph2U548Q/IQyJkQhCqwsBUDlVVoegMqqpCMxREoyqiMR05+VHoQTVYDsTrTdiWAzCGaJaOaJYhg2yDyyyrpkHVFTAAiaQFM+FAj6hwdAWu68jiM/UmzKQcR6ppClIpF5ZpAwBs00FTg4lkwoJry2BDFvAxkTI9uJYLV8BPH/rfLpffH2McOufguhyvpugcT/z6bmz/8iPcc/dd+PHNjyE7Pwrd0CE8D/EmU2aKk7Y/llGAK4Cqa+Aq989ssotoojGFlOnCsWQ3TU3hAOfgGve79DJ4totU0oZty2q8tunBcR24LgA/iAJk2wWTvw+VA4xxOYelzqBqGhRDQUxXoEZV6FENmsKhGfLGA2cMzOCIqCr0qCKzpYr8zoLuoIwBqqqCczkuT1UVCHj40fw7cdRRp+Lr584DYzJjOXDQaHz93Hl44/X7UHnrD1A64WtQNQ1CCKQSNhgEmKIgElURieky8DbbSB/6ASRjLf67jedgMnuswB9vHGSPbQ/xJlnUCn4X1L21TWhqMOEJD/CAWI4OITxs29KAvbuSqK9LyuJFtgMBBk1VwPwMs/DHQ9q2rHbrODKbzjmHoity3lBFgaYxQOHQOOCy4F9ImfHNzo/AEQJ20oXruGiybDTFKeNICCHkCCGQmaJwndjmkiVL8J//+Z+oqanBuHHj8LOf/QynnXbaAddfsWIF5s+fj88++wyjRo3C/fffjwsvvDB8/cUXX8TSpUuxbt067N27F+vXr8f48ePTtpFKpfCDH/wAzz//PEzTREVFBR5//HEUFxcf+g50EwocM6B2+z7oPNVcNsMPLDzXg0xmMSgAmD+siyn+5aYrAFe+SzCB/btgMzBwlYErCpjCwIP3ewDzAxnO5DrMDzyFfCPgAi4A4coxj1D8cXVc9msUQsBzZKDEGAPTZaEcRVPAITOSniv7mgsBJFJ78cuqH8FxDjw9gKoYuKTsbmRHBwCCNf+dimAkJWDZLoTngjFbdhdlAIcKrjUnrRgHWFBQUgVULgMWI6qCqxyqrkLTAZVrftAmx4jJqUwYVE1eiHueBwgBRVWhKgoURcjPh59ltRyYcQu268J1ZVDhmC5SSReWa8ssnycQXN8LwcJjB7QIUvzg6Ytdm7D9y49w4unT8be3n8HKF1/BiEEny8JHihy/yVUZFGXFdESzIuBZCjxHwLQdmEkbqYTlj+tz5Y0Af9xpwnWax5/6vyfH8WQVVLu5iAuEzDDKLtHyt+AJ2W0SArAA+Vvz5HNw7gd+siqvoilQOQNTVWhRBkVRwfxqvZrqd09W5ffFFdm9VXZzZTAMBaqmAgz48OP12LHjH7jwojlh0Bj+rhnH2JMuxR9euQ0rlr+M8SedCtdyITiT3bqz5PfFNQVcUcAVDsOQmVAjosngVJNdo8OxjIc4zYWZdJA05XjRxoYUbNORbfBkBth1XPzj831o2JuEbTkyewkBJvy/Vy6z7SoXcP2usoD8e2RMyGrHCoPnyDGqHIBwHMRTAsJzAQ/QojqiEQ5dV6BFNRQMiMCyPCQ1B8mkDTPpQThuu/tCCCGE9B+9I3J84YUXUFlZiaVLl6K0tBSLFy9GRUUFtmzZgkGDBrVa/6233sKVV16JRYsW4Rvf+AaWL1+OKVOm4L333sPYsWMBAPF4HJMmTcJll12G66+/vs3PvfXWW/HKK69gxYoVyMvLw6xZs3DJJZfgr3/966HvcjehwDEDGvcloMBrHoflX8PKzKL8by4AMC7H6HEuowHBwot7AH7RFHnRLwBwjUN4HK7nQFgCwvbCC36Fpb8PLdL74Vgv4XdHVYLsjAxwPA+y76InwMDD7CRXuHwvwkRIGJDuTeyE45g4Z/pS5Jd8pdUxqKv5GG8+cwNSJxyH7CEnhM3a3/7NDrQ3zbkDINXO66Gs/Z63jFe8/T5E9R+xjmz44IQQWPvUYgw8ahxOq7gdO/+xAe/+owrFFdeF3Sstf90mALv33wDz29JN7TkUAs1t69Sbbf/RIjm29QvZxTK/YESbbyvwl1f97j188Z6swMsgb6pw+N1xWYssov93woK0L4LiRko4ppJx2cWaBZlQRQmfMya7ADtBZtDx4Nge4DGAC7iOK7uOCw+eI/92ZIZSfjbXZddtVVPAOQfjMiAXfnZXyEo3UIIxoGow9pRBeAKu7SBhuvAcV/79ieAUIHseKLoCrioyMwrA81x4rgfTpIwjIYSQI4Pw5KVxJrZ7KB566CFcf/31mDFjBgBg6dKleOWVV/D000/jzjvvbLX+I488gsmTJ+O2224DANx7771YvXo1HnvsMSxduhQAcPXVVwMAPvvsszY/s76+Hk899RSWL1+Oc889FwCwbNkyjBkzBm+//TZOP/30Q9uJbkKBYwYwt3ncl8xMhK/IAE3I7B9jHrgCuMwNC6fI8Wr+xa0f4HFVAddkFVHh2PAsWZQFHvO73wHuAYaZCeGPU/QzfADCSp7BHRchZBEXz3PDLqTB/womZIAlgj80GUgmTZlpzC/5CoqGj+u+g9dP/HPrX7HrH+/j/G8/CcYYJpxzE/7vlzPxz61/xVHHTerp5h12seyBAIC6fZ9j4KDRrV7ft08Wy7EbNOxGI8CEHw/6v9sWAxLCn7qAn51PXyZvmngt0u0yOGQC8g6LP6TRE0J2KQ1u7Ij9bm4wFnZzBZOZeM7kNhhkQCgACFc0B7BBFprJrukMftDpeeENHAYBz2vedNDsVpjcttwfuYbtHTjDTwghhJCOa2hoSHtuGAYMw0hbZlkW1q1bh7lz54bLOOcoLy9HdXV1m9utrq5GZWVl2rKKigqsXLmyw21bt24dbNtGeXl5uGz06NEYPnw4qqurKXDsT2IFBhQeAWNyXBnzgiImcgwb40xebDJAgPvBmgtP+Bem/jQIXFH8b0hA2LLAh2t7YTdDAGF0KNDi6tnPxvhhYZglBIOco1D2qZQX18H1NRgURQtf86/K/avtIEBl4UsppXlexvbYa9bCzN4ZdqMF8xN94UV9cKUPP2UTPJXdYllQIhTwK3fKTKlMiwbHEH4lzba68bXYH39PmB9MM3+sYnN7RFqWNliJc/jjPHna8WWMhZVGw464TL53/eYHMXDoyRh67JkAgKHHnomBQ0/G+pd/gpKTfwgviOpdQPhfgvCruzI/1cz8uSk1XQc3FCh+qk0AYJ7MZrkQcC00l5ZWAdWfA1B+CMKKvHDkd8gU+Bkw5ndj9f+fyalCwhsdnmyjX9YFwvXnIA2OExNg4a3AICXdHAIJT4RHfqDwkB0pwsaNv8a55zWPcQRkkLdp4wpkR4owuPh48PA1mbVjAASXfWz9RF6YmGdgUIJuuJw134CBnO5Gtrbl99Y85Qxv/lmE4x1F+DsQ4XIIQAGT1XABcL/LNnhwM8X/voDwxpD8DXvNB1Ow5jjUY8GfuF9tNmhE86EM2sHUIHCVBYvUjHTZIYQQQnqfYPqyTGwXAIYNG5a2fOHChbjrrrvSlu3evRuu67YaV1hcXIyPPvqoze3X1NS0uX5NTU2H21hTUwNd15Gfn9+l7XQ3ChwzoPCYfGgwZEbDvxIXYNB0FXpEgRpRwMGg+GPUXNsLx6VxlUHTFcgxgQKM+0Gj48A0hezaZntyYvHgIt6WUzt4QmY+RNDlVbDm8W3+tB9BptHzL+oh/BiMcSiMyaIsXIFwXTh+IMaEvOLlnENRAQ4OHm8APjz4sSgenochxUVgngyyXD+rKbMvzfMdKgoD4xwQDJ7nwHFkUOX44w2BoEciC6u/CgByeJgAPA8Cqt99MQiU/a7AjAEKAw/mTOSQhWX8eRtdPwsk/MBVuPKYwJ9mIri4VzTFDzoZRHixL8fTMfiBMQf+/o/12NXwd5z/zSfDzDNjDBO+Pgv/98uZiKufYVjJyXCC4juuzEAJz4PnyvSuwuXUJ1zl0HQOIyLn24wY3J9zUgWDB8sf/2hbcr7PaMyAEVFlZVkOqKoC1VAhXP/3pXNEDUX21A2mC/GCMEwEYan8Xbryd+d6TFYAduTNDdeV80x6EHKKCb9bp+t6fndND26QYfN/Y8IDzppwFf5Q/Sher7oPJ518KQoKRmDfvs+xaeMK/OOLd/Evp3wf2flReVj9mwnB+1vGS6Ll/4eRfvMjuCchbzD4YWIYn7EWmT75fQU3JoLvlEP+PfD9pmkJ/n5Yi+MkX/PXCfqUQ8jjL794qEx2uVVVmfdnnEEzFEQiGhRdgWoo0FVZXIhx+bcgx5oyuI4AV/2bTYLDSsXxwrqD/90RQgghpH1ffPEFcnNzw+f7ZxtJaxQ4ZkDhwFyoqi6zN6YL13PBOIemy/FXjiMzj1xRoHgAIrLbHBOAEVEA/4JR4XJS8UhE9TNUMkPhpFw4tiMDMduFnXJgpvyCLn5AqGgMelQBZ3JOQ8t04XpyjKMIxmxBQFGaK7syRZHv01U5MbkrA1UIwHHcMMsUydLB9nVs8N2JE4fhK18ZBV1XAeHC8QQcy4VjubLipOXAdeV+uLYL03Lh2C7gCXj+JbpjObBMWclVCMB1Hbi2zDAG8wSCM8Dz4Dl+IOMCXAXA5DQVnAmkkq4fuDIoOgP3qwupisw+McjMomLIi/ZgUnqZwRJ+lkpWgWU86MnIwzkKOeQcjm//4SXkFgxDJFaA3V9+EB6LSKwAuQXD8M7HL+O0snNkUBomdxk8y4bryCDS9QSY58G1XRnkQkBVNRhZKooG5yA7x4DrCTi2TE8VFkVRMCgHOfkR5BVGoUfldC62ZSPeYKKx3oTnODCiepj99sKKn/KYe64skMT8Aj8t52RUNA4mmP+ZDhzbgWkL2KYD23RhO0ElV/k7cy0XpiuDR9cREI6HYaMqUFSSjd9W/Rf+8Mpt4XHJyxmES867FaNHnh5W8fE82Zbm8bl+xhMtMqhpAWXrO5KsObZr0eW0OQfN/Ok8ZLDm30jQGTjj4FwB48LPzHKojPvVf+XfserP7anrMuBTuQJVZ+EYYUWRlX0Vf1ylosuAH0I2RdNUaJHm069/n8NvD4OiMihclcv8sZqGocK0ksDCDv3pEUIIIX1a0BsoE9sFgNzc3LTAsS1FRUVQFAW1tbVpy2tra1FSUtLme0pKSg5p/QNtw7Is1NXVpWUdD3U73Y0CxwwYVJKF/Px8aBENdkpeuOuGClXnMJMWUqZfRVFTkJ1rwIjp0FQGPapC1RQ5PYMnkJ8fgRGVmctU0gZjsniNYzngHDBisqIkANimjaZGC2ZCzkWoawqiWQYUhcGyHKQStqwG6XmwTReqqsCIaVAUBjNlI5V0kEo5SMbl9AJcALH8KPIHRKDrGlzLxe6dcZhxE64rYBhah47FhLLhOPNrJ4bjI11PZrgcT0C4MnBJNqXQuDeFun1J2LYjK08KD4amwfVcpJIOXM8FV1VwIZBosmBaLoQ/H5/nCD+IBITHYLtynkkhBAxDScv6uZYLz//vVMqBovgTwCsKDIMjtzCGWJ6OiKEDENB0FY4f4Hqe8KedkIFlOEegkPvkCQHLthH/+T407KvFy09e2uYx0ZXB+OqZwxGNGX5hFQbFzyq5DmAlbaQaTSTjFpLJFMyEi6ZGE05K7mOiwUQqYSOSpSM3N4IRxw9EbkEUgwbnIJZjpM1taKZUMMaRnRdFLFtmIz3/uLl+pVw5L6QH13Fh+YG7bbtwbE9+b0KEwaaiMGhaFhSNQdN4WIyG+YGm53r+e124rgfHCcbtAq4rMNkZh1t+8B1Ur3kLNTW1yM8txPGjxkPTFAj/+3E9eSPB87OewhP+vItBN1k/i+h393QcB8K/syJEWMsYYEJ271U49IicSkRVAK6oUDUVRoTLiqyGAsWvJgu/eI3CAC+YG1ORvQAihpwzVNUUqLoqu6H7x0UE6fuwj7ispir87r/CcdDYYMHz5Bym0SxNZrj9pCjjDAJ+V2UmK9pyhUHTFOi6AlWVc1c2NqaPxyCEEEL6LQ/7FR/oxu12kK7rmDhxIqqqqjBlyhT5ds9DVVUVZs2a1eZ7ysrKUFVVhTlz5oTLVq9ejbKysg5/7sSJE6FpGqqqqjBt2jQAwJYtW7B9+/ZD2k53o8AxA44+tgg5OdlQFQXRLB3ZuQYcx4OZcOB6HhzTwZ59CaiKgoGDs1AyNB+qyqEbKnbVNAIACgZkoXBgFsyUg0TcQrzJgqIwxGI6OGcwstIDN8YYLFPOl2iZDhzLRSoup3IIuoNGs3VoupwywfMEUnEbruP6U3xwuJaLnTuaULcnAcaEnODeUBDN1gFwDBlRiHh9CrbtIrVxJwBZPbUtwXLGGKyk449P9DN7CgfnAh5nUDiHrsaQmxfDkJFyTKiiysxMENgIyGkzACAa1aFwBtf1YKUcpBKWnGTd8WCZNpJJG67rIZpjICtLQ6LJwt7aOFIpGwwMBQOiiGTraNyXQqLRhGW6yMrRkFuYhcFH50M3NCQa5QTwuQUR6LoKI6LKwF9TOvT9bzj9XezcubPFeDmkdcstKhqIoUOPCtfnTAYOAPxMrwfHcpBI2NhTG0e8KYV4Qwpmwka8PoVU0gGEQF5hDCVH52PwsDwUDspu1T7bcpFoksVUIjENhp/h4gqX80W2sT+eJ8fSyoDShWO6sEwXpunAddLPtLYtwBwHXJXdOhVVzqWZZciReK4foAItxmACALIx/JhvAgAMQ0UkpkFRFf/34q/i/4cQLdOKTLbNk92/w6BdBO1u/66kEDJYDqoJa7oCTVP9rCpLu8HA/LGqtu3B8QPhlttnjEHLUvzfBU8L1vf/TM8TaKxLwjA0GcTn6nKsZYvmMtY8Bljxb0yE43lbsJyO3bAhhBBCSPeorKzE9OnTccopp+C0007D4sWLEY/Hwyqr11xzDYYOHYpFixYBAG655RacffbZePDBB3HRRRfh+eefx9q1a/Hkk0+G29y7dy+2b9+OL7/8EoAMCgGZaSwpKUFeXh6uu+46VFZWorCwELm5uZg9ezbKysp6rDAOQIFjRhQPzkXUyAIYkJ1nIH9AFoQQsFIOFIVDUWSXv6a4Bc/1oOkq8gfEYCZtDCjOAWdA/gA5l0Q0SwdTGBSNQ+EM0ZgORZUXlkL42SJXThWgarL/ZHauAVVVZPbHcmXmLaKGmbKA5/ldDf1gEwwoKM6BJwTMlA0r4SDRlEIibsvxcRrHUaMKoaoKooUnwngkijefueGAxyESiWFgURFcx4PbxpztLXGFIRLVoUfUNi/C43HTb4cIuw6q2Tpi2bJIj+t54IzDiKkwTRdWyoGmcQgBjBjlwYzbaGxIhd0dFUVBdl4EisKgGyqKSnKgGyoa62XQaERURKIaNEOFqiphYNcRw4YNazXg+lAEXV9zNAVZOYbsvuq4MtNq2vBcASspM455hTHk5EXSgkAhBCzTRTIuJ9UwIiqisY4VM5JTWciiTLqhpk1p4jgeLMtp7p4adG915dyEtuUCsMP5LIP98Dw/4PSDMw4ZyGZl62HAGBSmCYvUhFVPm4vWHCwwDMjxsizM2jHm110SzQGopnEoCm93O0xhMBQeBtyOLYNo23LheR4s04FlOuBcjl/WdEWOT27BdTwkE/LvR9dV5ORFZJdVQgghhPQJl19+OXbt2oUFCxagpqYG48ePx6pVq8ICONu3bw97AALAGWecgeXLl2PevHn40Y9+hFGjRmHlypXhHI4A8PLLL4eBJwBcccUVANIL9Dz88MPgnGPatGkwTRMVFRV4/PHHD8MeHxgTmeg8fIRqaGhAXl4etn36T+Tk5MqxTaoCz/NkVzc/Wxior0+iqT7ld0eTF6eMwQ9omn+A8bgls2hRDVobWaIgk+I46VVE5Dg9Hs5r1xFCCDQ1WWE3TyvpIpWSmS6uckAIqLoCI6Lioy1bUfPlzjDj4rkeFEVmTvMKoigZXIyjjjpKZg295sAgIIvTcL+raPsX054n0ORnz2IxDRAyo+bYblomzBNAImGBc4bc3IifXfO7UTouGhtMNNan4LkejIgGMMB1RJjpUjUFRlQG8q67f996v1trMP7vECaZ78hxD7uPtgiQVFWBqvJwHduSQbFtp1eQ5X4xHcZZeLMAkN2hs3KNbm1ryza7rgfXEWGBHsfxZHAeBIJhtRr53chMdsvsHvfb3vHj2TxPIwsLHMllPO13HhxP1/VavJdDaydD2FHNQaTTKhMZ/J5dp/mzGWPIzouE32VnBeeY+vr6g47L6KuCfayt3Y1Bgwb0dHMIIf3QkXAu7cuC7+fi4xZBUyLdvn3bTeE3n86l778TKOOYAbkFURQWZh90vVhMl11GE7Y/VQdHNKa1yoQEF6YHCv4YY/4YqPTgQ2YkgwCjOeCRQU/rbnDBtnRdgWnKyqY5+RHkIIKi4mwIAViWCzNlIxm3UVIyFCUlQ6Ey2XWUc4ZYloH8ATGZrQp0w6+Mczney7ZlUZ1oVAuzbJ4/VtKxXSQTlj+FApNFdvYLsDRNQWFRcxpNVquUGTrFL3wSy9L9gK1l8CGDctcVcN3mNgUBZGcCSRFULg27WTYHIIoiC7G03CZjMjuqG6rMFluuzP753ShbTkei+OP6jANkcLtD8LtTVcDwv+TgdxeMSwyeyzGK/u/TE/D8LLkcEijguCIMumRWXlaF5X510aA7acs7evsLAtmWn9uyrZqmdPgGysGomuL//nT5PViOn4kU8gaO0xww6oYCPaJ1OWgkhBBCjiQy4ZCZ7ZLOocCxB2maAk1ToORwGIYKw2j9dQTdUIPsYXtaXninByVyigf532nvCAPIsOikP9ec43hwHC8MjlpehMturwyKJrsDRmMaGGTXxkx2wwsCR8eRwUFYzVThYZdCVVegaLKQiKErzUFEizFwqibHear7dVcMjnNLQddNVW0OhIIgsq1xdfJYph/Plieolt0xW2NQVRaOcWsP5wyGHxgKIfzslvyutTCoOfzkd8KADnx88Ntsndlt5o+QhPCY/5t2045pMI7Ua56Mc7/2yAC8uwLGtrTsoirH5QZ/O3KMcKYCd0IIIaRfo8ix16HAsYdpWpDd89DW9DHBHIaHeuErs0FBgRHujxHz4Hlokd1q3X00wBlgOx5M00YkEhTkaM5YCg8wIoCuKy1ezyxVlYGe61fubDvQlsV1IhE17P7bUe1d4DcH5YCmNRdjCTNsLSaMP5Te30G2MuhS3BmMMT8D1qm395jmoLy5q+7+2cKgy2tbQWHbMteduCOCbKnWsSGlhBBCCCF9BgWOPSwIHIOxWK27qcoU4cGyje0JMmDBvIXA/sVIgCCIDESiGkTCgRCAqvCwIE/AsmS1m/a6DmaCpikHCRy9w9Ku/ceNtj6erQPyYLqHllleykZJLQPzwMF+o8H76HgSQgghhGQeBY49LH3sntsqQAwyjorSvRfEzd0p296upilhl1BPCKj7XZBnql0Ho2kcpsn8sWRe2rixlpmqw92ugx1PcujomBJCCCFHrqBmQia2SzqHqjX0AkGlVNv20n7MQbEPoGsZx87S9eZ2tXQo4y67myxywv12pRe+aXmsKPNECCGEENJ3BUMcM/EgnUOBYy8QFO8I5mUMtBzfmMniHgdulyzssf+UBs0BWs8EZ0GgHWREW7eLftaEEEIIIYR0J7rC7iWCYMiymrNoPR0Icc7CrqAts3vN3VR7pl1BAZL06UYQBt09FdASQgghhBDSX1Hg2EsEgWPL7F5PZ/ZatqtlN9qeDmhlu+RnB4F2y6kxKONICCGEENLHUV/VXoeK4/QSMrunwHFcWJYLw2A9ntkDmrvRep6AbbvgnMPzmidr7ymyGq0bFskJgsae6tZLCCGEEEK6D03j2PtQaqYXaS5G4yKVsiGECLtl9oZ2maYL05TTcKhqzxagaVkkJ5Wyw+lBtB6a+J4QQgghhJD+jALHXkRVeVrhF6A5aOtJuq6GYwqDbqq9pV2MsbCbqgwme75dhPR2Rx99tD/dSfrjpptuAgCkUincdNNNGDBgALKzszFt2jTU1ta2u00hBBYsWIDBgwcjGo2ivLwcn3zyyeHYHUIIIf2QnMc5Mw/SORQ49jKGoYZdLRWFp81T2JMMQw0zjJqm9HgWFJDdUiOR5nbpukLdVAnpgHfffRc7duwIH6tXrwYAXHrppQCAW2+9Fb/97W+xYsUK/PGPf8SXX36JSy65pN1tPvDAA3j00UexdOlSrFmzBllZWaioqEAqlcr4/hBCCCEk82iMYy/DOUN2thHOk9hbqCpHTk7va5emKdA0pde1i5DebODAgWnPf/KTn+DYY4/F2Wefjfr6ejz11FNYvnw5zj33XADAsmXLMGbMGLz99ts4/fTTW21PCIHFixdj3rx5uPjiiwEAzz77LIqLi7Fy5UpcccUVmd8pQggh/Yt38FV61XaPAD2fNiJt6q1BELWLkP7Fsiz88pe/xHe+8x0wxrBu3TrYto3y8vJwndGjR2P48OGorq5ucxvbtm1DTU1N2nvy8vJQWlp6wPcETNNEQ0ND2oMQQgihrqq9T78OHJcsWYKjjz4akUgEpaWleOedd9pdf8WKFRg9ejQikQhOOukk/P73vz9MLSWEkJ6xcuVK1NXV4dprrwUA1NTUQNd15Ofnp61XXFyMmpqaNrcRLC8uLu7wewKLFi1CXl5e+Bg2bFjndoQQQgghGdVvA8cXXngBlZWVWLhwId577z2MGzcOFRUV2LlzZ5vrv/XWW7jyyitx3XXXYf369ZgyZQqmTJmCzZs3H+aWE0LI4fPUU0/hggsuwJAhQ3rk8+fOnYv6+vrw8cUXX/RIOwghhPQulHHsffpt4PjQQw/h+uuvx4wZM3DCCSdg6dKliMViePrpp9tc/5FHHsHkyZNx2223YcyYMbj33nvx1a9+FY899thhbjkhhBwen3/+OV577TV897vfDZeVlJTAsizU1dWlrVtbW4uSkpI2txMs37/yanvvCRiGgdzc3LQHIYQQQnqffhk4WpaFdevWpY234ZyjvLz8gONtqqur09YHgIqKinbH59DYHEJIX7Zs2TIMGjQIF110Ubhs4sSJ0DQNVVVV4bItW7Zg+/btKCsra3M7I0eORElJSdp7GhoasGbNmgO+hxBCCGmXyOCDdEq/DBx3794N13UPabxNTU3NIY/PobE5hJC+yvM8LFu2DNOnT4eqNhfYzsvLw3XXXYfKykq88cYbWLduHWbMmIGysrK0iqqjR4/GSy+9BEAWp5ozZw7uu+8+vPzyy9i0aROuueYaDBkyBFOmTDncu0YIIaQ/oMCx16HpOLpg7ty5qKysDJ83NDRQ8EgI6RNee+01bN++Hd/5zndavfbwww+Dc45p06bBNE1UVFTg8ccfT1tny5YtqK+vD5/ffvvtiMfjmDlzJurq6jBp0iSsWrUKkUgk4/tCCCGk/8nUeEQa49h5/TJwLCoqgqIohzTepqSk5JDH5xiGAcMwut5gQgg5zM4///wD/uMZiUSwZMkSLFmy5IDv3/+9jDHcc889uOeee7q1nYQQQo5QmcoOUtzYaf2yq6qu65g4cWLaeBvP81BVVXXA8TZlZWVp6wPA6tWraXwOIYQQQggh5IjXLzOOAFBZWYnp06fjlFNOwWmnnYbFixcjHo9jxowZAIBrrrkGQ4cOxaJFiwAAt9xyC84++2w8+OCDuOiii/D8889j7dq1ePLJJ3tyNwghhBBCCDniUMKx9+m3gePll1+OXbt2YcGCBaipqcH48eOxatWqsADO9u3bwXlzwvWMM87A8uXLMW/ePPzoRz/CqFGjsHLlSowdO7andoEQQgghhJAjkxDykYntkk7pl11VA7NmzcLnn38O0zSxZs0alJaWhq+9+eab+MUvfpG2/qWXXootW7bANE1s3rwZF154Yac+l36PhBBCCCGEdF4QN2bicaiWLFmCo48+GpFIBKWlpXjnnXfaXX/FihUYPXo0IpEITjrpJPz+97/fb98EFixYgMGDByMajaK8vByffPJJ2jpHH300GGNpj5/85CeH3vhu1K8DR0IIIYQQQkgf1EsixxdeeAGVlZVYuHAh3nvvPYwbNw4VFRXYuXNnm+u/9dZbuPLKK3Hddddh/fr1mDJlCqZMmYLNmzeH6zzwwAN49NFHsXTpUqxZswZZWVmoqKhAKpVK29Y999yDHTt2hI/Zs2cf+nHsRhQ4EkIIIYQQQkgbHnroIVx//fWYMWMGTjjhBCxduhSxWAxPP/10m+s/8sgjmDx5Mm677TaMGTMG9957L7761a/iscceAyCzjYsXL8a8efNw8cUX4+STT8azzz6LL7/8EitXrkzbVk5ODkpKSsJHVlZWpne3XRQ4EkIIIYQQQo4oDQ0NaQ/TNFutY1kW1q1bh/Ly8nAZ5xzl5eWorq5uc7vV1dVp6wNARUVFuP62bdtQU1OTtk5eXh5KS0tbbfMnP/kJBgwYgAkTJuA///M/4ThOp/e3O/Tb4jiEEEIIIYSQvkkIAeF1f+GQYB7iYcOGpS1fuHAh7rrrrrRlu3fvhuu6YXHNQHFxMT766KM2t19TU9Pm+jU1NeHrwbIDrQMAN998M7761a+isLAQb731FubOnYsdO3bgoYce6uCedj8KHAkhhBBCCCG9i8jQ1Bn+Rr/44gvk5uaGiw3DyMSndVplZWX43yeffDJ0Xcf3vvc9LFq0qMfaSl1VCSGEEEIIIb2LyOADQG5ubtqjrWCsqKgIiqKgtrY2bXltbS1KSkrabHZJSUm76wf/fyjbBIDS0lI4joPPPvvsgOtkGgWOhBBCCCGEELIfXdcxceJEVFVVhcs8z0NVVRXKysrafE9ZWVna+gCwevXqcP2RI0eipKQkbZ2GhgasWbPmgNsEgA0bNoBzjkGDBnVll7qEuqoSQgghhBBCepkW6cFu327HVVZWYvr06TjllFNw2mmnYfHixYjH45gxYwYA4JprrsHQoUOxaNEiAMAtt9yCs88+Gw8++CAuuugiPP/881i7di2efPJJAABjDHPmzMF9992HUaNGYeTIkZg/fz6GDBmCKVOmAJAFdtasWYOvf/3ryMnJQXV1NW699VZ8+9vfRkFBQfcdikNEgSMhhBBCCCGkVxEZGuN4iNM44vLLL8euXbuwYMEC1NTUYPz48Vi1alVY3Gb79u3gvLkT5xlnnIHly5dj3rx5+NGPfoRRo0Zh5cqVGDt2bLjO7bffjng8jpkzZ6Kurg6TJk3CqlWrEIlEAMjxls8//zzuuusumKaJkSNH4tZbb00b99gTmBCHevjIgTQ0NCAvLw+7d+/FgAE9dzeAENI/BeeY+vr6tAH9/Umwj7W1uzFo0ICebg4hpB86Es6lfVnw/ZyXMw8qi3T79h2RQlXjffT9dwJlHAkhhBBCCCG9ihACIgM5R8qZdR4VxyGEEEIIIYQQ0i7KOBJCCCGEEEJ6l0wlBinh2GkUOBJCCCGEEEJ6Feqq2vtQV1VCCCGEEEIIIe2ijCMhhBBCCCGkV+kt03GQZpRxJIQQQgghhBDSLso4EkIIIYQQQnoXIZCRnCOlHDuNAkdCCCGEEEJIr0LFcXofChwJIYQQQgghvQtNx9HrUOBICCGEEEII6VWoOE7vQ8VxCCGEEEIIIYS0izKOhBBCCCGEkN5FhP+Tge2SzqCMIyGEEEIIIYSQdlHGkRBCCCGEENKr0BjH3ocyjoQQcgT65z//iW9/+9sYMGAAotEoTjrpJKxduzZ8/dprrwVjLO0xefLkg253yZIlOProoxGJRFBaWop33nknk7tBCCGknxKeyNiDdA5lHAkh5Aizb98+nHnmmfj617+OP/zhDxg4cCA++eQTFBQUpK03efJkLFu2LHxuGEa7233hhRdQWVmJpUuXorS0FIsXL0ZFRQW2bNmCQYMGZWRfCCGEEHJ4UOBICCFHmPvvvx/Dhg1LCwpHjhzZaj3DMFBSUtLh7T700EO4/vrrMWPGDADA0qVL8corr+Dpp5/GnXfe2fWGE0IIIaTHUFdVQgg5wrz88ss45ZRTcOmll2LQoEGYMGECfv7zn7da780338SgQYNw/PHH48Ybb8SePXsOuE3LsrBu3TqUl5eHyzjnKC8vR3V19QHfZ5omGhoa0h6EEEIIRAYfpFMocCSEkCPM3//+dzzxxBMYNWoUXn31Vdx44424+eab8cwzz4TrTJ48Gc8++yyqqqpw//33449//CMuuOACuK7b5jZ3794N13VRXFyctry4uBg1NTUHbMuiRYuQl5cXPoYNG9Y9O0kIIaRPExAQIgMPihw7jbqqEkLIEcbzPJxyyin4j//4DwDAhAkTsHnzZixduhTTp08HAFxxxRXh+ieddBJOPvlkHHvssXjzzTdx3nnndVtb5s6di8rKyvB5Q0MDBY+EEEJIL0SBIyGEHGEGDx6ME044IW3ZmDFj8L//+78HfM8xxxyDoqIifPrpp20GjkVFRVAUBbW1tWnLa2tr2x0naRjGQYvuEEIIOfK4MPvUdo8EFDgSQsgR5swzz8SWLVvSln388ccYMWLEAd/zj3/8A3v27MHgwYPbfF3XdUycOBFVVVWYMmUKAJnZrKqqwqxZs7qt7YQQQvo3XddRUlKCt2seydhnlJSUQNf1jG2/v6LAMQMYYz3dBEIIOaBbb70VZ5xxBv7jP/4Dl112Gd555x08+eSTePLJJwEATU1NuPvuuzFt2jSUlJRg69atuP3223HcccehoqIi3M55552HqVOnhoFhZWUlpk+fjlNOOQWnnXYaFi9ejHg8HlZZJYQQQg4mEolg27ZtsCwrY5+h6zoikUjGtt9fUeCYAYpCgSMhpPc69dRT8dJLL2Hu3Lm45557MHLkSCxevBhXXXUVAEBRFGzcuBHPPPMM6urqMGTIEJx//vm4995707qVbt26Fbt37w6fX3755di1axcWLFiAmpoajB8/HqtWrWpVMKcjdJ3+eSKEkCNVJBKhwK4XYkIIKi3UTRoaGpCXl4f6+nrk5ub2dHMIIf3MkXCOORL2kRDSs+g8Q0jn0HQchBBCCCGEEELaRYEjIYQQQgghhJB2UeBICCGEEEIIIaRdFDgSQgghhBBCCGkXBY6EEEIIIYQQQtpFgSMhhBBCCCGEkHb1ucDxxRdfxPnnn48BAwaAMYYNGza0WieVSuGmm27CgAEDkJ2djWnTpqG2trbd7QohsGDBAgwePBjRaBTl5eX45JNPMrQXhBBCCCGEENJ39LnAMR6PY9KkSbj//vsPuM6tt96K3/72t1ixYgX++Mc/4ssvv8Qll1zS7nYfeOABPProo1i6dCnWrFmDrKwsVFRUIJVKdfcuEEIIIYQQQkifovZ0Aw7V1VdfDQD47LPP2ny9vr4eTz31FJYvX45zzz0XALBs2TKMGTMGb7/9Nk4//fRW7xFCYPHixZg3bx4uvvhiAMCzzz6L4uJirFy5EldccUVmdoYQQgghhBBC+oA+l3E8mHXr1sG2bZSXl4fLRo8ejeHDh6O6urrN92zbtg01NTVp78nLy0NpaekB3wMApmmioaEh7UEIIYQQQggh/U2/Cxxramqg6zry8/PTlhcXF6OmpuaA7wnW6eh7AGDRokXIy8sLH8OGDeta4wkhhBBCCCGkF+rVXVWfe+45fO973wuf/+EPf8BZZ53Vgy1KN3fuXFRWVobP6+vrMXz4cMo8EkIyIji3CCF6uCWZE+wbnUcJIZlyJJxLCcmEXh04fvOb30RpaWn4fOjQoQd9T0lJCSzLQl1dXVrWsba2FiUlJQd8T7DO4MGD094zfvz4A36WYRgwDCN8vnv3bgCgzCMhJKP27NmDvLy8nm5GRuzZswcAnUcJIZnXn8+lhGRCrw4cc3JykJOTc0jvmThxIjRNQ1VVFaZNmwYA2LJlC7Zv346ysrI23zNy5EiUlJSgqqoqDBQbGhqwZs0a3HjjjR3+7MLCQgDA9u3b++WJqKGhAcOGDcMXX3yB3Nzcnm5Ot+vv+wf0/33s7/sX9GoIzjX9EZ1H+7b+vn9A/9/H/r5/wJFxLiUkE3p14NiWvXv3Yvv27fjyyy8ByKAQkFnDkpIS5OXl4brrrkNlZSUKCwuRm5uL2bNno6ysLK2i6ujRo7Fo0SJMnToVjDHMmTMH9913H0aNGoWRI0di/vz5GDJkCKZMmdLhtnEuh4zm5eX125MtAOTm5tL+9XH9fR/7+/4F55r+iM6j/UN/3z+g/+9jf98/oH+fSwnJhD4XOL788suYMWNG+DyYKmPhwoW46667AAAPP/wwOOeYNm0aTNNERUUFHn/88bTtbNmyBfX19eHz22+/HfF4HDNnzkRdXR0mTZqEVatWIRKJZH6nCCGEEEIIIaQX63OB47XXXotrr7223XUikQiWLFmCJUuWHHCd/QdEM8Zwzz334J577umOZhJCCCGEEEJIv0E5+m5kGAYWLlyYVjCnP6H96/v6+z7S/vV9/X0faf/6vv6+j/19/4AjYx8JyQQmqBYxIYQQQgghhJB2UMaREEIIIYQQQki7KHAkhBBCCCGEENIuChwJIYQQQgghhLSLAkdCCCGEEEIIIe2iwLGFP/3pT/jXf/1XDBkyBIwxrFy5Mu312tpaXHvttRgyZAhisRgmT56MTz75JG2dmpoaXH311SgpKUFWVha++tWv4n//93/T1tm7dy+uuuoq5ObmIj8/H9dddx2ampoyvXsAumcft27diqlTp2LgwIHIzc3FZZddhtra2rR1emofFy1ahFNPPRU5OTkYNGgQpkyZgi1btqStk0qlcNNNN2HAgAHIzs7GtGnTWrV/+/btuOiiixCLxTBo0CDcdtttcBwnbZ0333wTX/3qV2EYBo477jj84he/yPTuddv+3XzzzZg4cSIMw8D48ePb/KyNGzfirLPOQiQSwbBhw/DAAw9kardC3bF/77//Pq688koMGzYM0WgUY8aMwSOPPNLqs3ri+wO6Zx/37NmDyZMnY8iQITAMA8OGDcOsWbPQ0NCQtp2e2sf+fi6l8yidR4Heex4F+v+59Eg4jxLSKwkS+v3vfy9+/OMfixdffFEAEC+99FL4mud54vTTTxdnnXWWeOedd8RHH30kZs6cKYYPHy6amprC9f7lX/5FnHrqqWLNmjVi69at4t577xWcc/Hee++F60yePFmMGzdOvP322+LPf/6zOO6448SVV17ZJ/axqalJHHPMMWLq1Kli48aNYuPGjeLiiy8Wp556qnBdt8f3saKiQixbtkxs3rxZbNiwQVx44YWtvqMbbrhBDBs2TFRVVYm1a9eK008/XZxxxhnh647jiLFjx4ry8nKxfv168fvf/14UFRWJuXPnhuv8/e9/F7FYTFRWVooPPvhA/OxnPxOKoohVq1b1+v0TQojZs2eLxx57TFx99dVi3LhxrT6nvr5eFBcXi6uuukps3rxZ/OpXvxLRaFT8v//3/3r9/j311FPi5ptvFm+++abYunWr+O///m8RjUbFz372s3Cdnvr+umsf9+7dKx5//HHx7rvvis8++0y89tpr4vjjj0/7G+vJfezv51I6j9J5VIjeex4Vov+fS4+E8yghvREFjgew/8XAli1bBACxefPmcJnrumLgwIHi5z//ebgsKytLPPvss2nbKiwsDNf54IMPBADx7rvvhq//4Q9/EIwx8c9//jNDe9O2zuzjq6++Kjjnor6+Plynrq5OMMbE6tWrhRC9ax937twpAIg//vGPYVs1TRMrVqwI1/nwww8FAFFdXS2EkBeFnHNRU1MTrvPEE0+I3NxcYZqmEEKI22+/XZx44olpn3X55ZeLioqKTO9Sms7sX0sLFy5s84Ln8ccfFwUFBeH+CiHEHXfcIY4//vju34l2dHX/At///vfF17/+9fB5b/n+hOi+fXzkkUfEUUcdFT7vLfvY38+ldB6V6Dw6rtXy3nIeFaL/n0v7+3mUkN6Cuqp2kGmaAIBIJBIu45zDMAz85S9/CZedccYZeOGFF7B37154nofnn38eqVQK55xzDgCguroa+fn5OOWUU8L3lJeXg3OONWvWHJ6dOYCO7KNpmmCMpU2aG4lEwDkP1+lN+1hfXw8AKCwsBACsW7cOtm2jvLw8XGf06NEYPnw4qqurAcj2n3TSSSguLg7XqaioQENDA/72t7+F67TcRrBOsI3DpTP71xHV1dX42te+Bl3Xw2UVFRXYsmUL9u3b102tP7ju2r/6+vpwG0Dv+f6A7tnHL7/8Ei+++CLOPvvscFlv2seW+vu5lM6jdB4N9JbzKND/z6VH2nmUkJ5CgWMHBSecuXPnYt++fbAsC/fffz/+8Y9/YMeOHeF6v/71r2HbNgYMGADDMPC9730PL730Eo477jgActzOoEGD0ratqioKCwtRU1NzWPdpfx3Zx9NPPx1ZWVm44447kEgkEI/H8cMf/hCu64br9JZ99DwPc+bMwZlnnomxY8eGbdN1Hfn5+WnrFhcXh22rqalJu9gJXg9ea2+dhoYGJJPJTOxOK53dv47oyDHItO7av7feegsvvPACZs6cGS7rDd8f0PV9vPLKKxGLxTB06FDk5ubiv/7rv8LXess+7q+/n0vpPErn0UBvOI8C/f9ceiSeRwnpKRQ4dpCmaXjxxRfx8ccfo7CwELFYDG+88QYuuOACcN58GOfPn4+6ujq89tprWLt2LSorK3HZZZdh06ZNPdj6junIPg4cOBArVqzAb3/7W2RnZyMvLw91dXX46le/mnYceoObbroJmzdvxvPPP9/TTckI2r+D27x5My6++GIsXLgQ559/fje2rnt0dR8ffvhhvPfee/jNb36DrVu3orKysptb2P36+7mUzqN9S3/fP6D/n0uPxPMoIT1F7ekG9CUTJ07Ehg0bUF9fD8uyMHDgQJSWloZdibZu3YrHHnsMmzdvxoknnggAGDduHP785z9jyZIlWLp0KUpKSrBz58607TqOg71796KkpOSw79P+DraPAHD++edj69at2L17N1RVRX5+PkpKSnDMMccAQK/Yx1mzZuF3v/sd/vSnP+Goo44Kl5eUlMCyLNTV1aXdiaytrQ3bVlJSgnfeeSdte0Eltpbr7F9hr7a2Frm5uYhGo5nYpTRd2b+OOND+Ba9lWnfs3wcffIDzzjsPM2fOxLx589Je6+nvD+iefSwpKUFJSQlGjx6NwsJCnHXWWZg/fz4GDx7cK/bxQPr7uZTOo3QeDbbTk+dRoP+fS4/k8yghPaF33drsI/Ly8jBw4EB88sknWLt2LS6++GIAQCKRAIBWd4wVRYHneQCAsrIy1NXVYd26deHrr7/+OjzPQ2lp6WHag4M70D62VFRUhPz8fLz++uvYuXMnvvnNbwLo2X0UQmDWrFl46aWX8Prrr2PkyJFpr0+cOBGapqGqqipctmXLFmzfvh1lZWVh+zdt2pR20bZ69Wrk5ubihBNOCNdpuY1gnWAbmdId+9cRZWVl+NOf/gTbtsNlq1evxvHHH4+CgoKu78gBdNf+/e1vf8PXv/51TJ8+Hf/+7//e6nN66vsDMvcdBueYYIxdT+5jR/X3cymdR+k82hPnUaD/n0vpPEpID+m5ujy9T2Njo1i/fr1Yv369ACAeeughsX79evH5558LIYT49a9/Ld544w2xdetWsXLlSjFixAhxySWXhO+3LEscd9xx4qyzzhJr1qwRn376qfjpT38qGGPilVdeCdebPHmymDBhglizZo34y1/+IkaNGnXYpuPo6j4KIcTTTz8tqqurxaeffir++7//WxQWForKysq0dXpqH2+88UaRl5cn3nzzTbFjx47wkUgkwnVuuOEGMXz4cPH666+LtWvXirKyMlFWVha+HpSRP//888WGDRvEqlWrxMCBA9ssI3/bbbeJDz/8UCxZsuSwlOjujv0TQohPPvlErF+/Xnzve98TX/nKV8LfRFD9r66uThQXF4urr75abN68WTz//PMiFotlvIx8d+zfpk2bxMCBA8W3v/3ttG3s3LkzXKenvr/u2sdXXnlFPP3002LTpk1i27Zt4ne/+50YM2aMOPPMM3vFPvb3cymdR+k8KkTvPY921z725nPpkXAeJaQ3osCxhTfeeEMAaPWYPn26EKK5TLOmaWL48OFi3rx5aWW2hRDi448/FpdccokYNGiQiMVi4uSTT25VUn7Pnj3iyiuvFNnZ2SI3N1fMmDFDNDY29pl9vOOOO0RxcbHQNE2MGjVKPPjgg8LzvF6xj23tGwCxbNmycJ1kMim+//3vi4KCAhGLxcTUqVPFjh070rbz2WefiQsuuEBEo1FRVFQkfvCDHwjbttPWeeONN8T48eOFruvimGOOSfuM3r5/Z599dpvb2bZtW7jO+++/LyZNmiQMwxBDhw4VP/nJT/rE/i1cuLDNbYwYMSLts3ri++uufXz99ddFWVmZyMvLE5FIRIwaNUrccccdYt++fb1iH/v7uZTOo3QeFaL3nke7ax9787n0SDiPEtIbMSGE6FhukhBCCCGEEELIkYjGOBJCCCGEEEIIaRcFjoQQQgghhBBC2kWBIyGEEEIIIYSQdlHgSAghhBBCCCGkXRQ4EkIIIYQQQghpFwWOhBBCCCGEEELaRYEjIYQQQgghhJB2UeBICCGEEEIIIaRdFDgS0gcxxrBy5cqebgYhhPRZdB4lhJBDQ4EjIYfo2muvBWOs1WPy5Mk93TRCCOkT6DxKCCF9j9rTDSCkL5o8eTKWLVuWtswwjB5qDSGE9D10HiWEkL6FMo6EdIJhGCgpKUl7FBQUAJDdn5544glccMEFiEajOOaYY/A///M/ae/ftGkTzj33XESjUQwYMAAzZ85EU1NT2jpPP/00TjzxRBiGgcGDB2PWrFlpr+/evRtTp05FLBbDqFGj8PLLL2d2pwkhpBvReZQQQvoWChwJyYD58+dj2rRpeP/993HVVVfhiiuuwIcffggAiMfjqKioQEFBAd59912sWLECr732WtoFzRNPPIGbbroJM2fOxKZNm/Dyyy/juOOOS/uMu+++G5dddhk2btyICy+8EFdddRX27t17WPeTEEIyhc6jhBDSywhCyCGZPn26UBRFZGVlpT3+/d//XQghBABxww03pL2ntLRU3HjjjUIIIZ588klRUFAgmpqawtdfeeUVwTkXNTU1QgghhgwZIn784x8fsA0AxLx588LnTU1NAoD4wx/+0G37SQghmULnUUII6XtojCMhnfD1r38dTzzxRNqywsLC8L/LysrSXisrK8OGDRsAAB9++CHGjRuHrKys8PUzzzwTnudhy5YtYIzhyy+/xHnnndduG04++eTwv7OyspCbm4udO3d2dpcIIeSwovMoIYT0LRQ4EtIJWVlZrbo8dZdoNNqh9TRNS3vOGIPneZloEiGEdDs6jxJCSN9CYxwJyYC333671fMxY8YAAMaMGYP3338f8Xg8fP2vf/0rOOc4/vjjkZOTg6OPPhpVVVWHtc2EENKb0HmUEEJ6F8o4EtIJpmmipqYmbZmqqigqKgIArFixAqeccgomTZqE5557Du+88w6eeuopAMBVV12FhQsXYvr06bjrrruwa9cuzJ49G1dffTWKi4sBAHfddRduuOEGDBo0CBdccAEaGxvx17/+FbNnzz68O0oIIRlC51FCCOlbKHAkpBNWrVqFwYMHpy07/vjj8dFHHwGQlfqef/55fP/738fgwYPxq1/9CieccAIAIBaL4dVXX8Utt9yCU089FbFYDNOmTcNDDz0Ubmv69OlIpVJ4+OGH8cMf/hBFRUX41re+dfh2kBBCMozOo4QQ0rcwIYTo6UYQ0p8wxvDSSy9hypQpPd0UQgjpk+g8SgghvQ+NcSSEEEIIIYQQ0i4KHAkhhBBCCCGEtIu6qhJCCCGEEEIIaRdlHAkhhBBCCCGEtIsCR0IIIYQQQggh7aLAkRBCCCGEEEJIuyhwJIQQQgghhBDSLgocCSGEEEIIIYS0iwJHQgghhBBCCCHtosCREEIIIYQQQki7KHAkhBBCCCGEENKu/w/U+eBp6qHOHQAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 576x288 with 3 Axes>"
+       "<Figure size 800x400 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -202,15 +209,17 @@
     }
    ],
    "source": [
-    "figure = this_sampler.results.plot_propermotion(cbar_param='m1', alpha=0.1, periods_to_plot=3)"
+    "figure = this_sampler.results.plot_propermotion(\n",
+    "    cbar_param=\"m1\", alpha=0.1, periods_to_plot=3\n",
+    ")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "exog",
+   "display_name": "python3.11",
    "language": "python",
-   "name": "exog"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -222,7 +231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

From 62b03af25531b1c32324f75a4ef234f90aef0b35 Mon Sep 17 00:00:00 2001
From: Sarah Blunt <sblunt@caltech.edu>
Date: Thu, 18 Jan 2024 17:09:46 -0800
Subject: [PATCH 38/38] update v3 changelog

---
 docs/index.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/index.rst b/docs/index.rst
index 1ca2437e..b8bddd11 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -58,6 +58,7 @@ Changelog:
 
 - discuss MCMC autocorrelation in MCMC tutorial (@michaelkmpoon)
 - add time warning if OFTI doesn't accept an orbit in first 60 s (@michaelkmpoon)
+- implementation of Hipparcos-Gaia catalog of accelerations fitting! (@semaphoreP)
 
 **2.2.2 (2023-06-30)**