From e3e62a6c3ffa065dbf27e47662fc55cc23e694e5 Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Fri, 15 Mar 2024 15:18:27 +0000
Subject: [PATCH 01/31] Added sky fraction metric and added remove_monopole to
 totalpowermetric

---
 rubin_sim/maf/metrics/summary_metrics.py | 38 +++++++++++++++++++++---
 1 file changed, 34 insertions(+), 4 deletions(-)

diff --git a/rubin_sim/maf/metrics/summary_metrics.py b/rubin_sim/maf/metrics/summary_metrics.py
index eac413698..9682c73cd 100644
--- a/rubin_sim/maf/metrics/summary_metrics.py
+++ b/rubin_sim/maf/metrics/summary_metrics.py
@@ -205,31 +205,61 @@ def run(self, data_slice, slice_point=None):
             return result
 
 
+class FskyMetric(BaseMetric):
+    """Calculate fraction of a desired footprint got covered.
+        To be applied as a summary metric to any healpix slicer.
+        Pixels with hp.UNSEEN or np.nan will be ignored, as well
+        as values falling outside of a ]min_val, max_val[ range.
+    Parameters
+    ----------
+    min_val, max_val : `float`
+        Minimum and maximum values for valid pixels (excluded)
+    """
+
+    def __init__(self, min_val=-np.inf, max_val=np.inf, **kwargs):
+        super().__init__(**kwargs)
+        self.min_val = min_val
+        self.mask_val = hp.UNSEEN
+        self.max_val = max_val
+
+    def run(self, data_slice, slice_point=None):
+        valid = data_slice["metricdata"] > self.min_val
+        valid &= data_slice["metricdata"] < self.max_val
+        valid &= data_slice["metricdata"] != self.mask_val
+        npix = data_slice["metricdata"].size
+        result = np.sum(valid) / npix
+        return result
+
+
 class TotalPowerMetric(BaseMetric):
     """
     Calculate the total power in the angular power spectrum between lmin/lmax.
     """
 
     def __init__(
-        self, col="metricdata", lmin=100.0, lmax=300.0, remove_dipole=True, mask_val=np.nan, **kwargs
+        self, col="metricdata", lmin=100.0, lmax=300.0, remove_monopole=True, remove_dipole=True, mask_val=np.nan, **kwargs
     ):
         self.lmin = lmin
         self.lmax = lmax
+        self.remove_monopole = remove_monopole
         self.remove_dipole = remove_dipole
         super(TotalPowerMetric, self).__init__(col=col, mask_val=mask_val, **kwargs)
 
     def run(self, data_slice, slice_point=None):
         # Calculate the power spectrum.
+        data = data_slice[self.colname]
+        if self.remove_monopole:
+            data = hp.remove_monopole(data, verbose=False, bad=self.mask_val)
         if self.remove_dipole:
-            cl = hp.anafast(hp.remove_dipole(data_slice[self.colname], verbose=False))
-        else:
-            cl = hp.anafast(data_slice[self.colname])
+            data = hp.remove_dipole(data, verbose=False, bad=self.mask_val)
+        cl = hp.anafast(data)
         ell = np.arange(np.size(cl))
         condition = np.where((ell <= self.lmax) & (ell >= self.lmin))[0]
         totalpower = np.sum(cl[condition] * (2 * ell[condition] + 1))
         return totalpower
 
 
+
 class StaticProbesFoMEmulatorMetricSimple(BaseMetric):
     """This calculates the Figure of Merit for the combined
     static probes (3x2pt, i.e., Weak Lensing, LSS, Clustering).

From 550815dc147eefc317f9c0415a607d3ce67640f8 Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Fri, 15 Mar 2024 15:19:00 +0000
Subject: [PATCH 02/31] Added metric for linear combination of depth
 fluctuations

---
 rubin_sim/maf/metrics/uniformity_metrics.py | 91 +++++++++++++++++++++
 1 file changed, 91 insertions(+)
 create mode 100644 rubin_sim/maf/metrics/uniformity_metrics.py

diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
new file mode 100644
index 000000000..73e93dbc5
--- /dev/null
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -0,0 +1,91 @@
+__all__ = "LinearMultibandModelMetric"
+
+import numpy as np
+from rubin_sim.maf.metrics.base_metric import BaseMetric
+from rubin_sim.maf.metrics.exgal_m5 import ExgalM5
+import healpy as hp
+
+
+class LinearMultibandModelMetric(BaseMetric):
+    """ """
+
+    def __init__(
+        self,
+        model_dict,
+        metric_name="LinearMultibandModel",
+        m5_col="fiveSigmaDepth",
+        filter_col="filter",
+        post_processing_fn=lambda x : x,
+        units="mag",
+        extinction_cut=0.2,
+        min_depth_cut={'i': 25.0},
+        max_depth_cut={},
+        mean_depth={"u": 0.0, "g": 0.0, "r": 0.0, "i": 0.0, "z": 0.0, "y": 0.0},
+        n_filters=6,
+        **kwargs,
+    ):
+        """
+        Args:
+
+        """
+        self.n_filters = n_filters
+        self.extinction_cut = extinction_cut
+        self.min_depth_cut = min_depth_cut
+        self.max_depth_cut = max_depth_cut
+        if "cst" in model_dict:
+            self.cst = model_dict["cst"]
+        else:
+            self.cst = 0.0
+        lsst_filters = ["u", "g", "r", "i", "z", "y"]
+        self.bands = [x for x in model_dict.keys() if x in lsst_filters]
+        self.model_arr = np.array([model_dict[x] for x in self.bands])[:, None]
+        self.mean_depth = mean_depth
+        self.m5_col = m5_col
+        self.filter_col = filter_col
+        self.post_processing_fn = post_processing_fn
+        self.exgal_m5 = ExgalM5(m5_col=m5_col, units=units)
+
+        self.exgal_m5 = ExgalM5(
+            m5_col=m5_col,
+            filter_col=filter_col,
+            units=units,
+            metric_name=metric_name+"ExgalM5",
+        )
+        super().__init__(
+            col=[m5_col, filter_col], metric_name=metric_name, units=units, maps=self.exgal_m5.maps, **kwargs
+        )
+
+    def run(self, data_slice, slice_point=None):
+        """ """
+
+        if slice_point["ebv"] > self.extinction_cut:
+            return self.badval
+
+        n_filters = len(set(data_slice[self.filter_col]))
+        if n_filters < self.n_filters:
+            return self.badval
+
+        # add extinction_cut and depth cuts? and n_filters cuts
+        depths = np.vstack(
+            [
+                self.exgal_m5.run(data_slice[data_slice[self.filter_col] == lsst_filter], slice_point) - self.mean_depth[lsst_filter]
+                for lsst_filter in self.bands
+            ]
+        ).T
+        assert depths.shape[0] >= 1
+
+        if np.any(depths == self.badval):
+            return self.badval
+        for i, lsst_filter in enumerate(self.bands):
+            if lsst_filter in self.min_depth_cut and depths[0, i] < self.min_depth_cut[lsst_filter] - self.mean_depth[lsst_filter]:
+                return self.badval
+            if lsst_filter in self.max_depth_cut and depths[0, i] > self.max_depth_cut[lsst_filter] - self.mean_depth[lsst_filter]:
+                return self.badval
+
+        val = self.post_processing_fn(
+            self.cst + np.dot(depths, self.model_arr)
+        )
+
+        return val
+
+

From dcb8b30f51a09a96551c6bb243464e19544482b5 Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Wed, 20 Mar 2024 16:14:05 +0000
Subject: [PATCH 03/31] Removed unnecessary fsky metric since
 AreaThresholdMetric does the job

---
 rubin_sim/maf/metrics/summary_metrics.py | 26 ------------------------
 1 file changed, 26 deletions(-)

diff --git a/rubin_sim/maf/metrics/summary_metrics.py b/rubin_sim/maf/metrics/summary_metrics.py
index 9682c73cd..3db791c2f 100644
--- a/rubin_sim/maf/metrics/summary_metrics.py
+++ b/rubin_sim/maf/metrics/summary_metrics.py
@@ -205,32 +205,6 @@ def run(self, data_slice, slice_point=None):
             return result
 
 
-class FskyMetric(BaseMetric):
-    """Calculate fraction of a desired footprint got covered.
-        To be applied as a summary metric to any healpix slicer.
-        Pixels with hp.UNSEEN or np.nan will be ignored, as well
-        as values falling outside of a ]min_val, max_val[ range.
-    Parameters
-    ----------
-    min_val, max_val : `float`
-        Minimum and maximum values for valid pixels (excluded)
-    """
-
-    def __init__(self, min_val=-np.inf, max_val=np.inf, **kwargs):
-        super().__init__(**kwargs)
-        self.min_val = min_val
-        self.mask_val = hp.UNSEEN
-        self.max_val = max_val
-
-    def run(self, data_slice, slice_point=None):
-        valid = data_slice["metricdata"] > self.min_val
-        valid &= data_slice["metricdata"] < self.max_val
-        valid &= data_slice["metricdata"] != self.mask_val
-        npix = data_slice["metricdata"].size
-        result = np.sum(valid) / npix
-        return result
-
-
 class TotalPowerMetric(BaseMetric):
     """
     Calculate the total power in the angular power spectrum between lmin/lmax.

From 69fb816132e1ffa9edb813c2397c07c220fc849c Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Wed, 20 Mar 2024 16:14:25 +0000
Subject: [PATCH 04/31] fixed unit and logical operator

---
 rubin_sim/maf/metrics/area_summary_metrics.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/rubin_sim/maf/metrics/area_summary_metrics.py b/rubin_sim/maf/metrics/area_summary_metrics.py
index 1890953c8..12e4a643b 100644
--- a/rubin_sim/maf/metrics/area_summary_metrics.py
+++ b/rubin_sim/maf/metrics/area_summary_metrics.py
@@ -96,7 +96,7 @@ def __init__(
         self.lower_threshold = lower_threshold
         self.mask_val = np.nan  # Include so all values get passed
         self.col = col
-        self.units = "degrees"
+        self.units = "square degrees"
 
     def run(self, data_slice, slice_point=None):
         # find out what nside we have
@@ -113,7 +113,7 @@ def run(self, data_slice, slice_point=None):
             npix = len(
                 np.where(
                     (data_slice[self.col] > self.lower_threshold)
-                    and (data_slice[self.col] < self.upper_threshold)
+                    & (data_slice[self.col] < self.upper_threshold)
                 )[0]
             )
         area = pix_area * npix

From f349b2d272b41a2965ee3b106d4a9f4339040b3a Mon Sep 17 00:00:00 2001
From: Boris Leistedt <boris.leistedt@gmail.com>
Date: Wed, 20 Mar 2024 22:08:55 +0000
Subject: [PATCH 05/31] Added prototype of nested metric, untested for now

---
 rubin_sim/maf/metrics/uniformity_metrics.py | 112 +++++++++++++++++++-
 1 file changed, 111 insertions(+), 1 deletion(-)

diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
index 73e93dbc5..0f4bf81fb 100644
--- a/rubin_sim/maf/metrics/uniformity_metrics.py
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -68,10 +68,13 @@ def run(self, data_slice, slice_point=None):
         # add extinction_cut and depth cuts? and n_filters cuts
         depths = np.vstack(
             [
-                self.exgal_m5.run(data_slice[data_slice[self.filter_col] == lsst_filter], slice_point) - self.mean_depth[lsst_filter]
+                self.exgal_m5.run(data_slice[data_slice[self.filter_col] == lsst_filter], slice_point)
                 for lsst_filter in self.bands
             ]
         ).T
+        for i, lsst_filter in enumerate(self.bands):
+            if lsst_filter in self.mean_depth:
+                depths[i] -= self.mean_depth[lsst_filter]
         assert depths.shape[0] >= 1
 
         if np.any(depths == self.badval):
@@ -89,3 +92,110 @@ def run(self, data_slice, slice_point=None):
         return val
 
 
+
+
+class NestedLinearMultibandModelMetric(BaseMetric):
+    """ """
+
+    def __init__(
+        self,
+        dict_of_model_dicts,
+        metric_name="NestedLinearMultibandModelMetric",
+        m5_col="fiveSigmaDepth",
+        filter_col="filter",
+        post_processing_fn=lambda x : x,
+        units="mag",
+        extinction_cut=0.2,
+        n_filters=6,
+        min_depth_cut={},
+        max_depth_cut={},
+        mean_depth={},
+        **kwargs,
+    ):
+
+        """
+        Args:
+            data_slice (ndarray): Values passed to metric by the slicer,
+                which the metric will use to calculate metric values
+                at each slice_point.
+            slice_point (Dict): Dictionary of slice_point metadata passed
+                to each metric.
+        """
+        self.dict_of_model_dicts = dict_of_model_dicts
+        self.n_filters = n_filters
+        self.extinction_cut = extinction_cut
+        self.min_depth_cut = min_depth_cut
+        self.max_depth_cut = max_depth_cut
+        self.mean_depth = mean_depth
+        self.m5_col = m5_col
+        self.badval = np.nan
+        self.filter_col = filter_col
+        self.post_processing_fn = post_processing_fn
+
+        self.exgal_m5 = ExgalM5(
+            m5_col=m5_col,
+            filter_col=filter_col,
+            units=units,
+            metric_name=metric_name+"_ExgalM5",
+        )
+        super().__init__(
+            col=[m5_col, filter_col], metric_name=metric_name, units=units, maps=self.exgal_m5.maps, **kwargs
+        )
+        # magic line so MAF knows we're returning something complicated
+        self.metric_dtype = "object"
+
+    def run(self, data_slice, slice_point=None):
+        "Returns a dictionary containing the values of the model evaluated at the data_slice"
+
+        n_bins = len(self.dict_of_model_dicts)
+        bad_val_arr = np.repeat(self.badval, n_bins)
+
+        # apply extinction and n_filters cut
+        if slice_point["ebv"] > self.extinction_cut:
+            return bad_val_arr
+        n_filters = len(set(data_slice[self.filter_col]))
+        if n_filters < self.n_filters:
+            return bad_val_arr
+
+        lsst_filters = ["u", "g", "r", "i", "z", "y"]
+
+        # initialize dictionary of outputs
+        names = self.dict_of_model_dicts.keys()
+        types = [float]*n_bins
+        result_dict = np.zeros(1, dtype=list(zip(names, types)))
+
+        for binname, model_dict in self.dict_of_model_dicts.items():
+
+            # put together a vector of depths
+            depths = np.vstack(
+                [
+                    self.exgal_m5.run(data_slice[data_slice[self.filter_col] == lsst_filter], slice_point)
+                    for lsst_filter in lsst_filters
+                ]
+            ).T
+
+            # if there are depth cuts, apply them
+            for i, lsst_filter in enumerate(lsst_filters):
+                if lsst_filter in self.min_depth_cut and depths[0, i] < self.min_depth_cut[lsst_filter]:
+                    depths[0, i] = self.badval
+                if lsst_filter in self.max_depth_cut and depths[0, i] > self.max_depth_cut[lsst_filter]:
+                    depths[0, i] = self.badval
+
+            # if provided, subtract the mean
+            for i, lsst_filter in enumerate(self.bands):
+                if lsst_filter in self.mean_depth:
+                    depths[i] -= self.mean_depth[lsst_filter]
+
+            # now assemble the results
+            model_arr = np.array([model_dict[key] if key in model_dict else 0.0 for x in lsst_filters])
+            cst = model_dict['cst'] if 'cst' in model_dict else 0.0
+
+            result_dict[binname] = self.post_processing_fn(
+                cst + np.dot(depths, model_arr)
+            )
+
+        # returns dictionary where each key of the original input dict_of_model_dicts
+        # contains a scalar value resulting from a linear combination of the six depths
+        #
+        return result_dict
+

From fb0b41f4af5f09830d2b0581a827209fa2d8e59e Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Thu, 21 Mar 2024 17:52:19 +0000
Subject: [PATCH 06/31] working summary metric

---
 rubin_sim/maf/metrics/summary_metrics.py | 110 +++++++++++++++++++++++
 1 file changed, 110 insertions(+)

diff --git a/rubin_sim/maf/metrics/summary_metrics.py b/rubin_sim/maf/metrics/summary_metrics.py
index 3db791c2f..de3302571 100644
--- a/rubin_sim/maf/metrics/summary_metrics.py
+++ b/rubin_sim/maf/metrics/summary_metrics.py
@@ -329,3 +329,113 @@ def run(self, data_slice, slice_point=None):
         f = interpolate.interp2d(areas, depths, fom_arr, bounds_error=False)
         fom = f(area, median_depth)[0]
         return fom
+
+
+class TomographicClusteringSigma8bias(BaseMetric):
+    """Calculate fraction of a desired footprint got covered.
+        To be applied as a summary metric to any healpix slicer.
+        Pixels with hp.UNSEEN or np.nan will be ignored, as well
+        as values falling outside of a ]min_val, max_val[ range.
+    Parameters
+    ----------
+    min_val, max_val : `float`
+        Minimum and maximum values for valid pixels (excluded)
+    """
+
+    def __init__(self, density_tomography_model, percentage_uncorrected=0.1, lmin=10, badval=np.nan, **kwargs):
+        super().__init__(**kwargs)
+
+        # initialize dictionary of outputs
+        n_bins = len(density_tomography_model['lmax'])
+        names = [str(i) for i in range(n_bins)]
+        types = [float] * n_bins
+        self.badval = badval
+        self.mask_val = badval
+        # self.mask_val = np.zeros(1, dtype=list(zip(names, types)))
+        # for i in range(n_bins):
+        #    self.mask_val[str(i)] = badval
+        # self.mask_val = (np.repeat(badval, len(density_tomography_model['lmax'])), ) # get whole array passed
+        # print(type(self.mask_val), self.mask_val.shape)
+        self.percentage_uncorrected = percentage_uncorrected
+        self.lmin = lmin
+        self.density_tomography_model = density_tomography_model
+        self.totalPowerMetrics = [
+            TotalPowerMetric(col='metricdata', lmin=lmin, lmax=lmax, metric_name='TotalPower_bin', mask_val=hp.UNSEEN)
+            for lmax in density_tomography_model['lmax']
+        ]
+        self.areaThresholdMetric = AreaThresholdMetric(
+            col='metricdata', metric_name='FootprintFraction_bin', lower_threshold=hp.UNSEEN, upper_threshold=np.inf
+        )
+
+    def run(self, data_slice, slice_point=None):
+        # need to define an array of bad values for the masked pixels
+        badval_arr = np.repeat(self.badval, len(self.density_tomography_model['lmax']))
+        # converts the input recarray to an array
+        data_slice_list = [badval_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()]
+        data_slice_arr = np.asarray(data_slice_list, dtype=float).T  # should be (nbins, npix)
+        data_slice_arr[~np.isfinite(data_slice_arr)] = hp.UNSEEN # need to work with TotalPowerMetric and healpix
+
+        fskys = np.array([
+            self.areaThresholdMetric.run(np.core.records.fromrecords(x, dtype=[('metricdata', float)])) / 42000
+            for x in data_slice_arr])  # sky fraction
+        spuriousdensitypowers = np.array([
+            self.totalPowerMetrics[i].run(np.core.records.fromrecords(x, dtype=[('metricdata', float)]))
+            for i, x in enumerate(data_slice_arr)]) / fskys
+        # some gymnastics needed to convert each slice into a recarray.
+        # this could probably be avoided if recarrays were returned by the original nested/vector metric,
+        # except that we would need to manipulate the names in various places, which I wanted to avoid,
+        # so for now the main metric returns an array per healpix pixel (not a recarray) and puts together
+        # healpix maps which we need to convert to a recarray to pass to AreaThresholdMetric and TotalPowerMetric
+        def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, percentage_uncorrected):
+            # solve for multiplicative sigma8^2 term between
+            # measured angular power spectra (spurious measured Cells times percentage_uncorrected)
+            # and model ones (polynomial model from CCL).
+            n_bins = model_cells['lmax'].size
+            assert len(spurious_powers) == n_bins
+            assert len(fskys) == n_bins
+            assert model_cells['poly1d_coefs_loglog'].shape[0] == n_bins
+            totalvar_mod = np.zeros((n_bins, 1))
+            totalvar_obs = np.zeros((n_bins, 1))
+            totalvar_var = np.zeros((n_bins, 1))
+            transfers = np.zeros((n_bins, 1))
+            # loop over tomographic bins
+            sigma8square_model = model_cells['sigma8square_model']  # hardcoded; assumed CCL cosmology
+            for i in range(n_bins):
+                # get model Cells from polynomial model (in log log space)
+                ells = np.arange(lmin, model_cells['lmax'][i])
+                polynomial_model = np.poly1d(model_cells['poly1d_coefs_loglog'][i, :])
+                cells_model = np.exp(polynomial_model(np.log(ells)))
+
+                # model variance is sum of cells x (2l+1)
+                totalvar_mod[i, 0] = np.sum(cells_model * (2 * ells + 1))
+
+                # observations is spurious power  noiseless model
+                totalvar_obs[i, 0] = totalvar_mod[i, 0] + spurious_powers[i] * percentage_uncorrected
+
+                # simple model variance of cell baased on Gaussian covariance
+                cells_var = 2 * cells_model ** 2 / (2 * ells + 1) / fskys[i]
+                totalvar_var[i, 0] = np.sum(cells_var * (2 * ells + 1) ** 2)
+
+            # model assumed sigma8 = 0.8 (add CCL cosmology here? or how I obtained them + documentation
+
+            results_fractional_spurious_power = totalvar_obs / totalvar_mod - 1.0
+
+            transfers = totalvar_mod / sigma8square_model  # model Cell variance divided by sigma8^2, which is the common normalization
+
+            # model ratio: formula for posterior distribution on unknown multiplicative factor in multivariate Gaussian likelihood
+            FOT = np.sum(transfers[:, 0] * totalvar_obs[:, 0] / totalvar_var[:, 0])
+            FTT = np.sum(transfers[:, 0] * transfers[:, 0] / totalvar_var[:, 0])
+            # mean and stddev of multiplicative factor
+            sigma8squared_fit = FOT / FTT
+            sigma8squared_error = FTT ** -0.5
+
+            return sigma8squared_fit, sigma8squared_error, sigma8square_model
+
+        sigma8squared_fit, sigma8squared_error, sigma8square_model = solve_for_multiplicative_factor(
+            spuriousdensitypowers, self.density_tomography_model, fskys, self.lmin, self.percentage_uncorrected)
+
+        results_sigma8_squared_bias = (sigma8squared_fit - sigma8square_model) / sigma8squared_error
+
+        # TODO: turn into bias on sigma 8 instead
+
+        return results_sigma8_squared_bias
\ No newline at end of file

From c3d20bb8bcca3a779d08c20d61c3ad0406397fd4 Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Thu, 21 Mar 2024 17:55:00 +0000
Subject: [PATCH 07/31] Nested metric now working and returning arrays working
 with the sigma8 summary metric

---
 rubin_sim/maf/metrics/uniformity_metrics.py | 31 +++++++++++----------
 1 file changed, 16 insertions(+), 15 deletions(-)

diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
index 0f4bf81fb..b29cb8dc9 100644
--- a/rubin_sim/maf/metrics/uniformity_metrics.py
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -99,7 +99,7 @@ class NestedLinearMultibandModelMetric(BaseMetric):
 
     def __init__(
         self,
-        dict_of_model_dicts,
+        arr_of_model_dicts,
         metric_name="NestedLinearMultibandModelMetric",
         m5_col="fiveSigmaDepth",
         filter_col="filter",
@@ -107,6 +107,7 @@ def __init__(
         units="mag",
         extinction_cut=0.2,
         n_filters=6,
+        badval=np.nan,
         min_depth_cut={},
         max_depth_cut={},
         mean_depth={},
@@ -121,33 +122,34 @@ def __init__(
             slice_point (Dict): Dictionary of slice_point metadata passed
                 to each metric.
         """
-        self.dict_of_model_dicts = dict_of_model_dicts
+        self.arr_of_model_dicts = arr_of_model_dicts
         self.n_filters = n_filters
         self.extinction_cut = extinction_cut
         self.min_depth_cut = min_depth_cut
         self.max_depth_cut = max_depth_cut
         self.mean_depth = mean_depth
         self.m5_col = m5_col
-        self.badval = np.nan
+        self.badval = badval
         self.filter_col = filter_col
         self.post_processing_fn = post_processing_fn
 
         self.exgal_m5 = ExgalM5(
             m5_col=m5_col,
             filter_col=filter_col,
+            badval=self.badval,
             units=units,
             metric_name=metric_name+"_ExgalM5",
         )
         super().__init__(
-            col=[m5_col, filter_col], metric_name=metric_name, units=units, maps=self.exgal_m5.maps, **kwargs
+            col=[m5_col, filter_col], badval=self.badval, metric_name=metric_name, units=units, maps=self.exgal_m5.maps, **kwargs
         )
         # magic line so MAF knows we're returning something complicated
         self.metric_dtype = "object"
 
     def run(self, data_slice, slice_point=None):
-        "Returns a dictionary containing the values of the model evaluated at the data_slice"
+        "Returns an array containing the values of the model evaluated at the data_slice"
 
-        n_bins = len(self.dict_of_model_dicts)
+        n_bins = len(self.arr_of_model_dicts)
         bad_val_arr = np.repeat(self.badval, n_bins)
 
         # apply extinction and n_filters cut
@@ -160,11 +162,10 @@ def run(self, data_slice, slice_point=None):
         lsst_filters = ["u", "g", "r", "i", "z", "y"]
 
         # initialize dictionary of outputs
-        names = self.dict_of_model_dicts.keys()
-        types = [float]*n_bins
-        result_dict = np.zeros(1, dtype=list(zip(names, types)))
+        #types = [float]*n_bins
+        result_arr = np.zeros((n_bins,), dtype=float)
 
-        for binname, model_dict in self.dict_of_model_dicts.items():
+        for binnumber, model_dict in enumerate(self.arr_of_model_dicts):
 
             # put together a vector of depths
             depths = np.vstack(
@@ -182,20 +183,20 @@ def run(self, data_slice, slice_point=None):
                     depths[0, i] = self.badval
 
             # if provided, subtract the mean
-            for i, lsst_filter in enumerate(self.bands):
+            for i, lsst_filter in enumerate(lsst_filters):
                 if lsst_filter in self.mean_depth:
                     depths[i] -= self.mean_depth[lsst_filter]
 
             # now assemble the results
-            model_arr = np.array([model_dict[key] if key in model_dict else 0.0 for x in lsst_filters])
+            model_arr = np.array([model_dict[x] if x in model_dict else 0.0 for x in lsst_filters])
             cst = model_dict['cst'] if 'cst' in model_dict else 0.0
 
-            result_dict[binname] = self.post_processing_fn(
+            result_arr[binnumber] = self.post_processing_fn(
                 cst + np.dot(depths, model_arr)
             )
 
-        # returns dictionary where each key of the original input dict_of_model_dicts
+        # returns array where each element of the original input arr_of_model_dicts
         # contains a scalar value resulting from a linear combination of the six depths
         #
-        return result_dict
+        return result_arr
 

From 7b51a43b03dd315a865c3091ce1c113508fedb54 Mon Sep 17 00:00:00 2001
From: Boris Leistedt <boris.leistedt@gmail.com>
Date: Thu, 28 Mar 2024 16:47:41 +0000
Subject: [PATCH 08/31] Added doc and reformated with black

---
 rubin_sim/maf/metrics/summary_metrics.py    | 182 ++++++++++++++------
 rubin_sim/maf/metrics/uniformity_metrics.py | 149 ++++++++++++----
 2 files changed, 247 insertions(+), 84 deletions(-)

diff --git a/rubin_sim/maf/metrics/summary_metrics.py b/rubin_sim/maf/metrics/summary_metrics.py
index de3302571..94d7445ce 100644
--- a/rubin_sim/maf/metrics/summary_metrics.py
+++ b/rubin_sim/maf/metrics/summary_metrics.py
@@ -211,7 +211,14 @@ class TotalPowerMetric(BaseMetric):
     """
 
     def __init__(
-        self, col="metricdata", lmin=100.0, lmax=300.0, remove_monopole=True, remove_dipole=True, mask_val=np.nan, **kwargs
+        self,
+        col="metricdata",
+        lmin=100.0,
+        lmax=300.0,
+        remove_monopole=True,
+        remove_dipole=True,
+        mask_val=np.nan,
+        **kwargs,
     ):
         self.lmin = lmin
         self.lmax = lmax
@@ -233,7 +240,6 @@ def run(self, data_slice, slice_point=None):
         return totalpower
 
 
-
 class StaticProbesFoMEmulatorMetricSimple(BaseMetric):
     """This calculates the Figure of Merit for the combined
     static probes (3x2pt, i.e., Weak Lensing, LSS, Clustering).
@@ -332,110 +338,176 @@ def run(self, data_slice, slice_point=None):
 
 
 class TomographicClusteringSigma8bias(BaseMetric):
-    """Calculate fraction of a desired footprint got covered.
-        To be applied as a summary metric to any healpix slicer.
-        Pixels with hp.UNSEEN or np.nan will be ignored, as well
-        as values falling outside of a ]min_val, max_val[ range.
-    Parameters
-    ----------
-    min_val, max_val : `float`
-        Minimum and maximum values for valid pixels (excluded)
     """
+    Compute bias on sigma8 due to spurious contamination of density maps.
+    This is a summary metric to be interfaced with NestedLinearMultibandModelMetric.
+    NestedLinearMultibandModelMetric converts 6-band depth maps into a set of maps (e.g tomographic redshift bins)
+    which describes spurious density fluctuations in each bin.
+    This summary metric multiplies the maps by the parameter power_multiplier,
+    which can be used to describe the fraction of power uncorrected by systematics mitigation schemes,
+    computes the total power (via angular power spectra with lmin-lmax limits)
+    and then infers sigma8^2 via a model of the angular power spectra.
+    By taking sigma8_obs minus sigma8_model divided by the uncertainty, one derives a bias.
+    """
+
+    def __init__(
+        self,
+        density_tomography_model,
+        power_multiplier=0.1,
+        lmin=10,
+        badval=np.nan,
+        convert_to_sigma8=True,
+        **kwargs,
+    ):
+        """
+        Parameters
+        ----------
+        density_tomography_model: dict
+            dictionary containing models calculated for fiducial N(z)s and Cells:
+                lmax: numpy.array of int, of shape (Nbins, )
+                    lmax corresponding to kmax of 0.05
+                poly1d_coefs_loglog: numpy.array of float, of shape (Nbins, )
+                    polynomial fits to log(C_ell) vs log(ell) computed for CCL
+                sigma8square_model (float)
+                    value of sigma8^2 used as fiducal model for CCL
+        power_multiplier: `float`, optional
+            fraction of power (variance) which is uncorrected and thus biases sigma8
+        lmin: `int`, optional
+            lmin for the analysis
+        convert_to_sigma8: `str`, optional
+            Convert the bias to sigma8 instead of sigma8^2 (via change of variables for the uncertainty)
+        badval: `float`, optional
+            value to return for bad pixels (e.g. pixels not passing cuts)
+
+        Returns (with the method 'run')
+        -------
+        result: `float`
+            value of sigma8 bias calculated from this model: (sigma8^2_obs - sigma^2_model) / error on sigma8^2_obs
+            if convert_to_sigma8=Then then it is about sigma8 instead of sigma8^2.
 
-    def __init__(self, density_tomography_model, percentage_uncorrected=0.1, lmin=10, badval=np.nan, **kwargs):
+        """
         super().__init__(**kwargs)
 
-        # initialize dictionary of outputs
-        n_bins = len(density_tomography_model['lmax'])
-        names = [str(i) for i in range(n_bins)]
-        types = [float] * n_bins
+        self.convert_to_sigma8 = convert_to_sigma8
         self.badval = badval
         self.mask_val = badval
-        # self.mask_val = np.zeros(1, dtype=list(zip(names, types)))
-        # for i in range(n_bins):
-        #    self.mask_val[str(i)] = badval
-        # self.mask_val = (np.repeat(badval, len(density_tomography_model['lmax'])), ) # get whole array passed
-        # print(type(self.mask_val), self.mask_val.shape)
-        self.percentage_uncorrected = percentage_uncorrected
+        self.power_multiplier = power_multiplier
         self.lmin = lmin
         self.density_tomography_model = density_tomography_model
+        # to compute angular power spectra and total power, initialize an array of metrics, with the right lmin and lmax.
         self.totalPowerMetrics = [
-            TotalPowerMetric(col='metricdata', lmin=lmin, lmax=lmax, metric_name='TotalPower_bin', mask_val=hp.UNSEEN)
-            for lmax in density_tomography_model['lmax']
+            TotalPowerMetric(
+                col="metricdata", lmin=lmin, lmax=lmax, metric_name="TotalPower_bin", mask_val=hp.UNSEEN
+            )
+            for lmax in density_tomography_model["lmax"]
         ]
         self.areaThresholdMetric = AreaThresholdMetric(
-            col='metricdata', metric_name='FootprintFraction_bin', lower_threshold=hp.UNSEEN, upper_threshold=np.inf
+            col="metricdata",
+            metric_name="FootprintFraction_bin",
+            lower_threshold=hp.UNSEEN,
+            upper_threshold=np.inf,
         )
 
     def run(self, data_slice, slice_point=None):
+
         # need to define an array of bad values for the masked pixels
-        badval_arr = np.repeat(self.badval, len(self.density_tomography_model['lmax']))
+        badval_arr = np.repeat(self.badval, len(self.density_tomography_model["lmax"]))
         # converts the input recarray to an array
-        data_slice_list = [badval_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()]
+        data_slice_list = [
+            badval_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()
+        ]
         data_slice_arr = np.asarray(data_slice_list, dtype=float).T  # should be (nbins, npix)
-        data_slice_arr[~np.isfinite(data_slice_arr)] = hp.UNSEEN # need to work with TotalPowerMetric and healpix
-
-        fskys = np.array([
-            self.areaThresholdMetric.run(np.core.records.fromrecords(x, dtype=[('metricdata', float)])) / 42000
-            for x in data_slice_arr])  # sky fraction
-        spuriousdensitypowers = np.array([
-            self.totalPowerMetrics[i].run(np.core.records.fromrecords(x, dtype=[('metricdata', float)]))
-            for i, x in enumerate(data_slice_arr)]) / fskys
+        data_slice_arr[~np.isfinite(data_slice_arr)] = (
+            hp.UNSEEN
+        )  # need to work with TotalPowerMetric and healpix
+
+        # measure valid sky fractions and total power (via angular power spectra) in each bin.
+        fskys = np.array(
+            [
+                self.areaThresholdMetric.run(np.core.records.fromrecords(x, dtype=[("metricdata", float)]))
+                / 42000
+                for x in data_slice_arr
+            ]
+        )  # sky fraction
+        spuriousdensitypowers = (
+            np.array(
+                [
+                    self.totalPowerMetrics[i].run(
+                        np.core.records.fromrecords(x, dtype=[("metricdata", float)])
+                    )
+                    for i, x in enumerate(data_slice_arr)
+                ]
+            )
+            / fskys
+        )
         # some gymnastics needed to convert each slice into a recarray.
         # this could probably be avoided if recarrays were returned by the original nested/vector metric,
         # except that we would need to manipulate the names in various places, which I wanted to avoid,
         # so for now the main metric returns an array per healpix pixel (not a recarray) and puts together
         # healpix maps which we need to convert to a recarray to pass to AreaThresholdMetric and TotalPowerMetric
-        def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, percentage_uncorrected):
+
+        def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, power_multiplier):
+            """
+            Infer multiplicative factor sigma8^2 (and uncertainty) from the model Cells and observed total powers
+            since it os a Gaussian posterior distribution.
+            """
             # solve for multiplicative sigma8^2 term between
-            # measured angular power spectra (spurious measured Cells times percentage_uncorrected)
+            # measured angular power spectra (spurious measured Cells times power_multiplier)
             # and model ones (polynomial model from CCL).
-            n_bins = model_cells['lmax'].size
+            n_bins = model_cells["lmax"].size
             assert len(spurious_powers) == n_bins
             assert len(fskys) == n_bins
-            assert model_cells['poly1d_coefs_loglog'].shape[0] == n_bins
+            assert model_cells["poly1d_coefs_loglog"].shape[0] == n_bins
             totalvar_mod = np.zeros((n_bins, 1))
             totalvar_obs = np.zeros((n_bins, 1))
             totalvar_var = np.zeros((n_bins, 1))
-            transfers = np.zeros((n_bins, 1))
             # loop over tomographic bins
-            sigma8square_model = model_cells['sigma8square_model']  # hardcoded; assumed CCL cosmology
+            sigma8square_model = model_cells["sigma8square_model"]  # hardcoded; assumed CCL cosmology
             for i in range(n_bins):
                 # get model Cells from polynomial model (in log log space)
-                ells = np.arange(lmin, model_cells['lmax'][i])
-                polynomial_model = np.poly1d(model_cells['poly1d_coefs_loglog'][i, :])
+                ells = np.arange(lmin, model_cells["lmax"][i])
+                polynomial_model = np.poly1d(model_cells["poly1d_coefs_loglog"][i, :])
                 cells_model = np.exp(polynomial_model(np.log(ells)))
 
                 # model variance is sum of cells x (2l+1)
                 totalvar_mod[i, 0] = np.sum(cells_model * (2 * ells + 1))
 
                 # observations is spurious power  noiseless model
-                totalvar_obs[i, 0] = totalvar_mod[i, 0] + spurious_powers[i] * percentage_uncorrected
+                totalvar_obs[i, 0] = totalvar_mod[i, 0] + spurious_powers[i] * power_multiplier
 
                 # simple model variance of cell baased on Gaussian covariance
-                cells_var = 2 * cells_model ** 2 / (2 * ells + 1) / fskys[i]
+                cells_var = 2 * cells_model**2 / (2 * ells + 1) / fskys[i]
                 totalvar_var[i, 0] = np.sum(cells_var * (2 * ells + 1) ** 2)
 
             # model assumed sigma8 = 0.8 (add CCL cosmology here? or how I obtained them + documentation
-
             results_fractional_spurious_power = totalvar_obs / totalvar_mod - 1.0
-
-            transfers = totalvar_mod / sigma8square_model  # model Cell variance divided by sigma8^2, which is the common normalization
+            transfers = (
+                totalvar_mod / sigma8square_model
+            )  # model Cell variance divided by sigma8^2, which is the common normalization
 
             # model ratio: formula for posterior distribution on unknown multiplicative factor in multivariate Gaussian likelihood
             FOT = np.sum(transfers[:, 0] * totalvar_obs[:, 0] / totalvar_var[:, 0])
             FTT = np.sum(transfers[:, 0] * transfers[:, 0] / totalvar_var[:, 0])
             # mean and stddev of multiplicative factor
-            sigma8squared_fit = FOT / FTT
-            sigma8squared_error = FTT ** -0.5
+            sigma8square_fit = FOT / FTT
+            sigma8square_error = FTT**-0.5
 
-            return sigma8squared_fit, sigma8squared_error, sigma8square_model
+            return sigma8square_fit, sigma8square_error, sigma8square_model
 
-        sigma8squared_fit, sigma8squared_error, sigma8square_model = solve_for_multiplicative_factor(
-            spuriousdensitypowers, self.density_tomography_model, fskys, self.lmin, self.percentage_uncorrected)
+        # solve for the gaussian posterior distribution on sigma8^2
+        sigma8square_fit, sigma8square_error, sigma8square_model = solve_for_multiplicative_factor(
+            spuriousdensitypowers, self.density_tomography_model, fskys, self.lmin, self.power_multiplier
+        )
 
-        results_sigma8_squared_bias = (sigma8squared_fit - sigma8square_model) / sigma8squared_error
+        results_sigma8_square_bias = (sigma8square_fit - sigma8square_model) / sigma8square_error
 
-        # TODO: turn into bias on sigma 8 instead
+        # turn result into bias on sigma8, via change of variable and simple propagation of uncertainty.
+        sigma8_fit = sigma8square_fit**0.5
+        sigma8_model = sigma8square_model**0.5
+        sigma8_error = 0.5 * sigma8square_error * sigma8_fit / sigma8square_fit
+        results_sigma8_bias = (sigma8_fit - sigma8_model) / sigma8_error
 
-        return results_sigma8_squared_bias
\ No newline at end of file
+        if self.convert_to_sigma8:
+            return results_sigma8_bias
+        else:
+            return results_sigma8_square_bias
diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
index b29cb8dc9..ef37cf355 100644
--- a/rubin_sim/maf/metrics/uniformity_metrics.py
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -7,7 +7,11 @@
 
 
 class LinearMultibandModelMetric(BaseMetric):
-    """ """
+    """
+    Calculates a single linear combination of depths.
+    This is useful for calculating density or redshift fluctuations from depth fluctuations on the sky,
+    for a single redshift bins (thus it is a single metric). For multiple bins, see NestedLinearMultibandModelMetric.
+    """
 
     def __init__(
         self,
@@ -15,17 +19,58 @@ def __init__(
         metric_name="LinearMultibandModel",
         m5_col="fiveSigmaDepth",
         filter_col="filter",
-        post_processing_fn=lambda x : x,
+        post_processing_fn=lambda x: x,
         units="mag",
         extinction_cut=0.2,
-        min_depth_cut={'i': 25.0},
+        min_depth_cut={"i": 25.0},
         max_depth_cut={},
         mean_depth={"u": 0.0, "g": 0.0, "r": 0.0, "i": 0.0, "z": 0.0, "y": 0.0},
         n_filters=6,
         **kwargs,
     ):
         """
-        Args:
+        Parameters
+        ----------
+        arr_of_model_dicts: dict
+            linear coefficients to be applied to the M5 depth values
+            Keys should be filter names + 'cst' if there is a constant offset.
+        post_processing_fn: lambda
+            post processing function to apply to the linear combination of inputs
+        metric_name: `str`, optional
+            metric name
+        m5_col: `str`, optional
+            column name for the m5 depth (almost always 'fiveSigmaDepth')
+        filter_col: `str`, optional ('filter')
+            column name for the filter (almost always 'filter')
+        units: `str`, optional
+            column name for the units (depends on the inputs, so almost always 'mag')
+        extinction_cut: `float`, optional
+            sky cut on extinction (0.2 by default)
+        n_filters: `int`, optional
+            sky cut on the number of filters required (6 by default)
+        badval: `float`, optional
+            value to return for bad pixels (e.g. pixels not passing cuts)
+        min_depth_cut: `dict`, optional
+            Cut the areas of low depths, based on cuts in this dict.
+            Keys should be filter names. Default is no cuts.
+        max_depth_cut: `dict`, optional
+            Cut the areas of high depths, based on cuts in this dict.
+            Keys should be filter names. Default is no cuts.
+        mean_depth: `dict`, optional
+            Mean depths, which will be subtracted from the inputs before applying model.
+            Keys should be filter names. Default is zero in each band.
+            In a lot of cases, instead of feeding mean_depths, one can remove the monopole
+            in the constructed healpix maps.
+
+        Returns (with the method 'run')
+        -------
+        result: `float`
+            Linear combination of the M5 depths (minus mean depth, if provided).
+            result = np.sum([
+                    model_dict[band] * M5_depth[band]
+                    for band in ["u", "g", "r", "i", "z", "y"]
+                    ])
+            if post_processing_fn is provided: result => post_processing_fn(result)
 
         """
         self.n_filters = n_filters
@@ -49,7 +94,7 @@ def __init__(
             m5_col=m5_col,
             filter_col=filter_col,
             units=units,
-            metric_name=metric_name+"ExgalM5",
+            metric_name=metric_name + "ExgalM5",
         )
         super().__init__(
             col=[m5_col, filter_col], metric_name=metric_name, units=units, maps=self.exgal_m5.maps, **kwargs
@@ -80,22 +125,28 @@ def run(self, data_slice, slice_point=None):
         if np.any(depths == self.badval):
             return self.badval
         for i, lsst_filter in enumerate(self.bands):
-            if lsst_filter in self.min_depth_cut and depths[0, i] < self.min_depth_cut[lsst_filter] - self.mean_depth[lsst_filter]:
+            if (
+                lsst_filter in self.min_depth_cut
+                and depths[0, i] < self.min_depth_cut[lsst_filter] - self.mean_depth[lsst_filter]
+            ):
                 return self.badval
-            if lsst_filter in self.max_depth_cut and depths[0, i] > self.max_depth_cut[lsst_filter] - self.mean_depth[lsst_filter]:
+            if (
+                lsst_filter in self.max_depth_cut
+                and depths[0, i] > self.max_depth_cut[lsst_filter] - self.mean_depth[lsst_filter]
+            ):
                 return self.badval
 
-        val = self.post_processing_fn(
-            self.cst + np.dot(depths, self.model_arr)
-        )
+        val = self.post_processing_fn(self.cst + np.dot(depths, self.model_arr))
 
         return val
 
 
-
-
 class NestedLinearMultibandModelMetric(BaseMetric):
-    """ """
+    """
+    Calculates multiple linear combination of depths.
+    This is useful for calculating density or redshift fluctuations from depth fluctuations on the sky,
+    for multiple redshift bins (thus it is a nested metric). For a single bin, see LinearMultibandModelMetric.
+    """
 
     def __init__(
         self,
@@ -103,7 +154,7 @@ def __init__(
         metric_name="NestedLinearMultibandModelMetric",
         m5_col="fiveSigmaDepth",
         filter_col="filter",
-        post_processing_fn=lambda x : x,
+        post_processing_fn=lambda x: x,
         units="mag",
         extinction_cut=0.2,
         n_filters=6,
@@ -113,14 +164,52 @@ def __init__(
         mean_depth={},
         **kwargs,
     ):
-
         """
-        Args:
-            data_slice (ndarray): Values passed to metric by the slicer,
-                which the metric will use to calculate metric values
-                at each slice_point.
-            slice_point (Dict): Dictionary of slice_point metadata passed
-                to each metric.
+        Parameters
+        ----------
+        arr_of_model_dicts: list of dicts
+            array of linear coefficients to be applied to the M5 depth values
+            Keys should be filter names + 'cst' if there is a constant offset.
+        post_processing_fn: lambda
+            post processing function to apply to the linear combination of inputs
+        metric_name: `str`, optional
+            metric name
+        m5_col: `str`, optional
+            column name for the m5 depth (almost always 'fiveSigmaDepth')
+        filter_col: `str`, optional ('filter')
+            column name for the filter (almost always 'filter')
+        units: `str`, optional
+            column name for the units (depends on the inputs, so almost always 'mag')
+        extinction_cut: `float`, optional
+            sky cut on extinction (0.2 by default)
+        n_filters: `int`, optional
+            sky cut on the number of filters required (6 by default)
+        badval: `float`, optional
+            value to return for bad pixels (e.g. pixels not passing cuts)
+        min_depth_cut: `dict`, optional
+            Cut the areas of low depths, based on cuts in this dict.
+            Keys should be filter names. Default is no cuts.
+        max_depth_cut: `dict`, optional
+            Cut the areas of high depths, based on cuts in this dict.
+            Keys should be filter names. Default is no cuts.
+        mean_depth: `dict`, optional
+            Mean depths, which will be subtracted from the inputs before applying model.
+            Keys should be filter names. Default is zero in each band.
+            In a lot of cases, instead of feeding mean_depths, one can remove the monopole
+            in the constructed healpix maps.
+
+        Returns  (with the method 'run')
+        -------
+        result: list of `float`
+            List is the same size as arr_of_model_dicts.
+            For each element, return a linear combination of the M5 depths (minus mean depth, if provided).
+            for i, model_dict in enumerate(arr_of_model_dicts):
+                result[i] = np.sum([
+                    model_dict[band] * M5_depth[band]
+                    for band in ["u", "g", "r", "i", "z", "y"]
+                    ])
+            if post_processing_fn is provided: result[i] => post_processing_fn(result[i])
+
         """
         self.arr_of_model_dicts = arr_of_model_dicts
         self.n_filters = n_filters
@@ -138,10 +227,15 @@ def __init__(
             filter_col=filter_col,
             badval=self.badval,
             units=units,
-            metric_name=metric_name+"_ExgalM5",
+            metric_name=metric_name + "_ExgalM5",
         )
         super().__init__(
-            col=[m5_col, filter_col], badval=self.badval, metric_name=metric_name, units=units, maps=self.exgal_m5.maps, **kwargs
+            col=[m5_col, filter_col],
+            badval=self.badval,
+            metric_name=metric_name,
+            units=units,
+            maps=self.exgal_m5.maps,
+            **kwargs,
         )
         # magic line so MAF knows we're returning something complicated
         self.metric_dtype = "object"
@@ -162,7 +256,7 @@ def run(self, data_slice, slice_point=None):
         lsst_filters = ["u", "g", "r", "i", "z", "y"]
 
         # initialize dictionary of outputs
-        #types = [float]*n_bins
+        # types = [float]*n_bins
         result_arr = np.zeros((n_bins,), dtype=float)
 
         for binnumber, model_dict in enumerate(self.arr_of_model_dicts):
@@ -189,14 +283,11 @@ def run(self, data_slice, slice_point=None):
 
             # now assemble the results
             model_arr = np.array([model_dict[x] if x in model_dict else 0.0 for x in lsst_filters])
-            cst = model_dict['cst'] if 'cst' in model_dict else 0.0
+            cst = model_dict["cst"] if "cst" in model_dict else 0.0
 
-            result_arr[binnumber] = self.post_processing_fn(
-                cst + np.dot(depths, model_arr)
-            )
+            result_arr[binnumber] = self.post_processing_fn(cst + np.dot(depths, model_arr))
 
         # returns array where each element of the original input arr_of_model_dicts
         # contains a scalar value resulting from a linear combination of the six depths
         #
         return result_arr
-

From 30c7c5464897c6e86e0ba7a77553e633bef11c17 Mon Sep 17 00:00:00 2001
From: Lynne Jones <ljones.uw@gmail.com>
Date: Fri, 29 Mar 2024 15:47:30 -0700
Subject: [PATCH 09/31] Reformatting and minor changes

---
 rubin_sim/maf/metrics/__init__.py             |    2 +
 .../maf/metrics/cosmology_summary_metrics.py  |  357 ++++++
 rubin_sim/maf/metrics/summary_metrics.py      |  312 -----
 rubin_sim/maf/metrics/tomography_models.py    | 1048 +++++++++++++++++
 rubin_sim/maf/metrics/uniformity_metrics.py   |  310 ++---
 tests/maf/test_cosmology_summaries.py         |   20 +
 tests/maf/test_summarymetrics.py              |    8 -
 7 files changed, 1588 insertions(+), 469 deletions(-)
 create mode 100644 rubin_sim/maf/metrics/cosmology_summary_metrics.py
 create mode 100644 rubin_sim/maf/metrics/tomography_models.py
 create mode 100644 tests/maf/test_cosmology_summaries.py

diff --git a/rubin_sim/maf/metrics/__init__.py b/rubin_sim/maf/metrics/__init__.py
index 3c39eb153..9281c95b1 100644
--- a/rubin_sim/maf/metrics/__init__.py
+++ b/rubin_sim/maf/metrics/__init__.py
@@ -6,6 +6,7 @@
 from .cadence_metrics import *
 from .calibration_metrics import *
 from .chip_vendor_metric import *
+from .cosmology_summary_metrics import *
 from .coverage_metric import *
 from .crowding_metric import *
 from .cumulative_metric import *
@@ -40,6 +41,7 @@
 from .surfb_metric import *
 from .technical_metrics import *
 from .tgaps import *
+from .tomography_models import *
 from .transient_metrics import *
 from .use_metrics import *
 from .vector_metrics import *
diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
new file mode 100644
index 000000000..97a6ea783
--- /dev/null
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -0,0 +1,357 @@
+__all__ = (
+    "TotalPowerMetric",
+    "StaticProbesFoMEmulatorMetricSimple",
+    "TomographicClusteringSigma8bias",
+)
+
+import warnings
+
+import healpy as hp
+import numpy as np
+from scipy import interpolate
+
+from .area_summary_metrics import AreaThresholdMetric
+from .base_metric import BaseMetric
+
+# Cosmology-related summary metrics.
+# These generally calculate a FoM for various DESC metrics.
+
+
+class TotalPowerMetric(BaseMetric):
+    """Calculate the total power in the angular power spectrum,
+    between lmin/lmax.
+
+    Parameters
+    ----------
+    lmin : `float`, optional
+        Minimum ell value to include when calculating total power.
+    lmax : `float`, optional
+        Maximum ell value to include when calculating total power.
+    remove_monopole : `bool`, optional
+        Flag to remove monopole when calculating total power.
+    remove_dipole : `bool`, optional
+        Flag  to remove dipole when calculating total power.
+    col : `str`, optional
+        The column name to operate on.
+        For summary metrics, this is almost always `metricdata`.
+    mask_val : `float` or np.nan, optional
+        The mask value to apply to the metric values when passed.
+        If this attribute exists, the metric_values will be passed
+        using metric_values.filled(mask_val).
+        If mask_val is `None` for a metric, metric_values will be passed
+        using metric_values.compressed().
+    """
+
+    def __init__(
+        self,
+        lmin=100.0,
+        lmax=300.0,
+        remove_monopole=True,
+        remove_dipole=True,
+        col="metricdata",
+        mask_val=np.nan,
+        **kwargs,
+    ):
+        self.lmin = lmin
+        self.lmax = lmax
+        self.remove_monopole = remove_monopole
+        self.remove_dipole = remove_dipole
+        super().__init__(col=col, mask_val=mask_val, **kwargs)
+
+    def run(self, data_slice, slice_point=None):
+        # Calculate the power spectrum.
+        data = data_slice[self.colname]
+        if self.remove_monopole:
+            data = hp.remove_monopole(data, verbose=False, bad=self.mask_val)
+        if self.remove_dipole:
+            data = hp.remove_dipole(data, verbose=False, bad=self.mask_val)
+        cl = hp.anafast(data)
+        ell = np.arange(np.size(cl))
+        condition = np.where((ell <= self.lmax) & (ell >= self.lmin))[0]
+        totalpower = np.sum(cl[condition] * (2 * ell[condition] + 1))
+        return totalpower
+
+
+class StaticProbesFoMEmulatorMetricSimple(BaseMetric):
+    """Calculate the FoM for the combined static probes
+    (3x2pt, i.e. Weak Lensing, LSS, Clustering).
+
+    Parameters
+    ----------
+    year : `int`, optional
+        The year of the survey to calculate FoM.
+        This calibrates expected depth and area.
+
+    Returns
+    -------
+    result : `float`
+        The simple 3x2pt FoM emulator value, for the
+        years where the correlation between area/depth and value is defined.
+
+    Notes
+    -----
+    This FoM is purely statistical and does not factor in systematics.
+    The implementation here is simpler than in
+    `rubin_sim.maf.mafContrib.StaticProbesFoMEmulatorMetric`, and that
+    more sophisticated version should replace this metric.
+
+    This version of the emulator was used to generate the results in
+    https://ui.adsabs.harvard.edu/abs/2018arXiv181200515L/abstract
+
+    Note that this is truly a summary metric and should be run on the
+    output of Exgalm5_with_cuts.
+    """
+
+    def __init__(self, year=10, **kwargs):
+        self.year = year
+        super().__init__(col="metricdata", mask_val=-666, **kwargs)
+
+    def run(self, data_slice, slice_point=None):
+        # derive nside from length of data slice
+        nside = hp.npix2nside(len(data_slice))
+        pix_area = hp.nside2pixarea(nside, degrees=True)
+
+        # Chop off any outliers (and also the masked value)
+        good_pix = np.where(data_slice[self.col] > 0)[0]
+
+        # Calculate area and med depth from
+        area = pix_area * np.size(good_pix)
+        median_depth = np.median(data_slice[self.col][good_pix])
+
+        # FoM is calculated at the following values
+        if self.year == 1:
+            areas = [7500, 13000, 16000]
+            depths = [24.9, 25.2, 25.5]
+            fom_arr = [
+                [1.212257e02, 1.462689e02, 1.744913e02],
+                [1.930906e02, 2.365094e02, 2.849131e02],
+                [2.316956e02, 2.851547e02, 3.445717e02],
+            ]
+        elif self.year == 3:
+            areas = [10000, 15000, 20000]
+            depths = [25.5, 25.8, 26.1]
+            fom_arr = [
+                [1.710645e02, 2.246047e02, 2.431472e02],
+                [2.445209e02, 3.250737e02, 3.516395e02],
+                [3.173144e02, 4.249317e02, 4.595133e02],
+            ]
+
+        elif self.year == 6:
+            areas = [10000, 15000, 2000]
+            depths = [25.9, 26.1, 26.3]
+            fom_arr = [
+                [2.346060e02, 2.414678e02, 2.852043e02],
+                [3.402318e02, 3.493120e02, 4.148814e02],
+                [4.452766e02, 4.565497e02, 5.436992e02],
+            ]
+
+        elif self.year == 10:
+            areas = [10000, 15000, 20000]
+            depths = [26.3, 26.5, 26.7]
+            fom_arr = [
+                [2.887266e02, 2.953230e02, 3.361616e02],
+                [4.200093e02, 4.292111e02, 4.905306e02],
+                [5.504419e02, 5.624697e02, 6.441837e02],
+            ]
+        else:
+            warnings.warn("FoMEmulator is not defined for this year")
+            return self.badval
+
+        # Interpolate FoM to the actual values for this sim
+        areas = [[i] * 3 for i in areas]
+        depths = [depths] * 3
+        f = interpolate.interp2d(areas, depths, fom_arr, bounds_error=False)
+        fom = f(area, median_depth)[0]
+        return fom
+
+
+class TomographicClusteringSigma8bias(BaseMetric):
+    """Compute bias on sigma8 due to spurious contamination of density maps.
+    Run as summary metric on NestedLinearMultibandModelMetric.
+
+    Parameters
+    ----------
+    density_tomograph_model : `dict`
+        dictionary containing models calculated for fiducial N(z)s and Cells:
+        lmax : numpy.array of int, of shape (Nbins, )
+            lmax corresponding to kmax of 0.05
+        poly1d_coefs_loglog : numpy.array of float, of shape (Nbins, )
+            polynomial fits to log(C_ell) vs log(ell) computed for CCL
+        sigma8square_model (float)
+            value of sigma8^2 used as fiducal model for CCL
+    power_multiplier : `float`, optional
+        fraction of power (variance) which is uncorrected
+        and thus biases sigma8
+    lmin : `int`, optional
+        lmin for the analysis
+    convert_to_sigma8 : `str`, optional
+        Convert the bias to sigma8 instead of sigma8^2
+        (via change of variables for the uncertainty)
+
+    Returns
+    -------
+    result : `float`
+        Value of sigma8 bias calculated from this model:
+        (sigma8^2_obs - sigma^2_model) / error on sigma8^2_obs
+        if `convert_to_sigma8` is True,
+        then it is about sigma8 instead of sigma8^2.
+
+    Notes
+    -----
+    This is a summary metric to be run on the results
+    of the NestedLinearMultibandModelMetric.
+
+    NestedLinearMultibandModelMetric converts 6-band depth maps into
+    a set of maps (e.g tomographic redshift bins) which describe
+    spurious density fluctuations in each bin.
+
+    This summary metric multiplies the maps by the parameter power_multiplier,
+    which can be used to describe the fraction of power uncorrected by
+    systematics mitigation schemes, computes the total power
+    (via angular power spectra with lmin-lmax limits)
+    and then infers sigma8^2 via a model of the angular power spectra.
+    By taking sigma8_obs minus sigma8_model divided by the uncertainty,
+    one derives a bias.
+    """
+
+    def __init__(
+        self,
+        density_tomography_model,
+        power_multiplier=0.1,
+        lmin=10,
+        convert_to_sigma8=True,
+        **kwargs,
+    ):
+        super().__init__(col="metricdata", **kwargs)
+        # Set mask_val, so that we receive metric_values.filled(mask_val)
+        self.mask_val = hp.UNSEEN
+
+        self.convert_to_sigma8 = convert_to_sigma8
+
+        self.power_multiplier = power_multiplier
+        self.lmin = lmin
+        self.density_tomography_model = density_tomography_model
+        # to compute angular power spectra and total power,
+        # initialize an array of metrics, with the right lmin and lmax.
+        self.totalPowerMetrics = [
+            TotalPowerMetric(lmin=lmin, lmax=lmax, mask_val=self.mask_val)
+            for lmax in density_tomography_model["lmax"]
+        ]
+        self.areaThresholdMetric = AreaThresholdMetric(
+            lower_threshold=hp.UNSEEN,
+            upper_threshold=np.inf,
+            mask_val=self.mask_val,
+        )
+
+    def run(self, data_slice, slice_point=None):
+        # need to define an array of bad values for the masked pixels
+        badval_arr = np.repeat(self.badval, len(self.density_tomography_model["lmax"]))
+        # converts the input recarray to an array
+        data_slice_list = [
+            badval_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()
+        ]
+        # should be (nbins, npix)
+        data_slice_arr = np.asarray(data_slice_list, dtype=float).T
+        data_slice_arr[~np.isfinite(data_slice_arr)] = (
+            hp.UNSEEN
+        )  # need to work with TotalPowerMetric and healpix
+
+        # measure valid sky fractions and total power
+        # (via angular power spectra) in each bin.
+        # The original metric returns an array at each slice_point (of the
+        # original slicer) -- so there is a bit of "rearrangement" that
+        # has to happen to be able to pass a np.array with right dtype
+        # (i.e. dtype = [("metricdata", float)]) to each call to
+        # the AreaThresholdMetric and TotalPowerMetric `run` methods.
+        totalsky = 42000
+        fskys = np.array(
+            [
+                self.areaThresholdMetric.run(np.core.records.fromrecords(x, dtype=[("metricdata", float)]))
+                / totalsky
+                for x in data_slice_arr
+            ]
+        )  # sky fraction
+        spuriousdensitypowers = (
+            np.array(
+                [
+                    self.totalPowerMetrics[i].run(
+                        np.core.records.fromrecords(x, dtype=[("metricdata", float)])
+                    )
+                    for i, x in enumerate(data_slice_arr)
+                ]
+            )
+            / fskys
+        )
+
+        def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, power_multiplier):
+            """
+            Infer multiplicative factor sigma8^2 (and uncertainty)
+            from the model Cells and observed total powers
+            since it os a Gaussian posterior distribution.
+            """
+            # solve for multiplicative sigma8^2 term between
+            # measured angular power spectra
+            # (spurious measured Cells times power_multiplier)
+            # and model ones (polynomial model from CCL).
+            n_bins = model_cells["lmax"].size
+            assert len(spurious_powers) == n_bins
+            assert len(fskys) == n_bins
+            assert model_cells["poly1d_coefs_loglog"].shape[0] == n_bins
+            totalvar_mod = np.zeros((n_bins, 1))
+            totalvar_obs = np.zeros((n_bins, 1))
+            totalvar_var = np.zeros((n_bins, 1))
+            # loop over tomographic bins
+            # hardcoded; assumed CCL cosmology
+            sigma8square_model = model_cells["sigma8square_model"]
+            for i in range(n_bins):
+                # get model Cells from polynomial model (in log log space)
+                ells = np.arange(lmin, model_cells["lmax"][i])
+                polynomial_model = np.poly1d(model_cells["poly1d_coefs_loglog"][i, :])
+                cells_model = np.exp(polynomial_model(np.log(ells)))
+
+                # model variance is sum of cells x (2l+1)
+                totalvar_mod[i, 0] = np.sum(cells_model * (2 * ells + 1))
+
+                # observations is spurious power  noiseless model
+                totalvar_obs[i, 0] = totalvar_mod[i, 0] + spurious_powers[i] * power_multiplier
+
+                # simple model variance of cell baased on Gaussian covariance
+                cells_var = 2 * cells_model**2 / (2 * ells + 1) / fskys[i]
+                totalvar_var[i, 0] = np.sum(cells_var * (2 * ells + 1) ** 2)
+
+            # model assumed sigma8 = 0.8
+            # (add CCL cosmology here? or how I obtained them + documentation)
+            # results_fractional_spurious_power =
+            # totalvar_obs / totalvar_mod - 1.0
+
+            # model Cell variance divided by sigma8^2,
+            # which is the common normalization
+            transfers = totalvar_mod / sigma8square_model
+
+            # model ratio: formula for posterior distribution on unknown
+            # multiplicative factor in multivariate Gaussian likelihood
+            FOT = np.sum(transfers[:, 0] * totalvar_obs[:, 0] / totalvar_var[:, 0])
+            FTT = np.sum(transfers[:, 0] * transfers[:, 0] / totalvar_var[:, 0])
+            # mean and stddev of multiplicative factor
+            sigma8square_fit = FOT / FTT
+            sigma8square_error = FTT**-0.5
+
+            return sigma8square_fit, sigma8square_error, sigma8square_model
+
+        # solve for the gaussian posterior distribution on sigma8^2
+        sigma8square_fit, sigma8square_error, sigma8square_model = solve_for_multiplicative_factor(
+            spuriousdensitypowers, self.density_tomography_model, fskys, self.lmin, self.power_multiplier
+        )
+
+        results_sigma8_square_bias = (sigma8square_fit - sigma8square_model) / sigma8square_error
+        if not self.convert_to_sigma8:
+            return results_sigma8_square_bias
+
+        else:
+            # turn result into bias on sigma8,
+            # via change of variable and simple propagation of uncertainty.
+            sigma8_fit = sigma8square_fit**0.5
+            sigma8_model = sigma8square_model**0.5
+            sigma8_error = 0.5 * sigma8square_error * sigma8_fit / sigma8square_fit
+            results_sigma8_bias = (sigma8_fit - sigma8_model) / sigma8_error
+            return results_sigma8_bias
diff --git a/rubin_sim/maf/metrics/summary_metrics.py b/rubin_sim/maf/metrics/summary_metrics.py
index 94d7445ce..a8c205ead 100644
--- a/rubin_sim/maf/metrics/summary_metrics.py
+++ b/rubin_sim/maf/metrics/summary_metrics.py
@@ -5,15 +5,11 @@
     "IdentityMetric",
     "NormalizeMetric",
     "ZeropointMetric",
-    "TotalPowerMetric",
-    "StaticProbesFoMEmulatorMetricSimple",
 )
 
-import warnings
 
 import healpy as hp
 import numpy as np
-from scipy import interpolate
 
 from .base_metric import BaseMetric
 
@@ -203,311 +199,3 @@ def run(self, data_slice, slice_point=None):
             return result[0]
         else:
             return result
-
-
-class TotalPowerMetric(BaseMetric):
-    """
-    Calculate the total power in the angular power spectrum between lmin/lmax.
-    """
-
-    def __init__(
-        self,
-        col="metricdata",
-        lmin=100.0,
-        lmax=300.0,
-        remove_monopole=True,
-        remove_dipole=True,
-        mask_val=np.nan,
-        **kwargs,
-    ):
-        self.lmin = lmin
-        self.lmax = lmax
-        self.remove_monopole = remove_monopole
-        self.remove_dipole = remove_dipole
-        super(TotalPowerMetric, self).__init__(col=col, mask_val=mask_val, **kwargs)
-
-    def run(self, data_slice, slice_point=None):
-        # Calculate the power spectrum.
-        data = data_slice[self.colname]
-        if self.remove_monopole:
-            data = hp.remove_monopole(data, verbose=False, bad=self.mask_val)
-        if self.remove_dipole:
-            data = hp.remove_dipole(data, verbose=False, bad=self.mask_val)
-        cl = hp.anafast(data)
-        ell = np.arange(np.size(cl))
-        condition = np.where((ell <= self.lmax) & (ell >= self.lmin))[0]
-        totalpower = np.sum(cl[condition] * (2 * ell[condition] + 1))
-        return totalpower
-
-
-class StaticProbesFoMEmulatorMetricSimple(BaseMetric):
-    """This calculates the Figure of Merit for the combined
-    static probes (3x2pt, i.e., Weak Lensing, LSS, Clustering).
-    This FoM is purely statistical and does not factor in systematics.
-
-    This version of the emulator was used to generate the results in
-    https://ui.adsabs.harvard.edu/abs/2018arXiv181200515L/abstract
-
-    A newer version is being created. This version has been renamed
-    Simple in anticipation of the newer, more sophisticated metric
-    replacing it.
-
-    Note that this is truly a summary metric and should be run on the output of
-    Exgalm5_with_cuts.
-    """
-
-    def __init__(self, nside=128, year=10, col=None, **kwargs):
-        """
-        Args:
-            nside (int): healpix resolution
-            year (int): year of the FoM emulated values,
-                can be one of [1, 3, 6, 10]
-            col (str): column name of metric data.
-        """
-        self.nside = nside
-        super().__init__(col=col, **kwargs)
-        if col is None:
-            self.col = "metricdata"
-        self.year = year
-
-    def run(self, data_slice, slice_point=None):
-        """
-        Args:
-            data_slice (ndarray): Values passed to metric by the slicer,
-                which the metric will use to calculate metric values
-                at each slice_point.
-            slice_point (Dict): Dictionary of slice_point metadata passed
-                to each metric.
-        Returns:
-             float: Interpolated static-probe statistical Figure-of-Merit.
-        Raises:
-             ValueError: If year is not one of the 4 for which a FoM is calculated
-        """
-        # Chop off any outliers
-        good_pix = np.where(data_slice[self.col] > 0)[0]
-
-        # Calculate area and med depth from
-        area = hp.nside2pixarea(self.nside, degrees=True) * np.size(good_pix)
-        median_depth = np.median(data_slice[self.col][good_pix])
-
-        # FoM is calculated at the following values
-        if self.year == 1:
-            areas = [7500, 13000, 16000]
-            depths = [24.9, 25.2, 25.5]
-            fom_arr = [
-                [1.212257e02, 1.462689e02, 1.744913e02],
-                [1.930906e02, 2.365094e02, 2.849131e02],
-                [2.316956e02, 2.851547e02, 3.445717e02],
-            ]
-        elif self.year == 3:
-            areas = [10000, 15000, 20000]
-            depths = [25.5, 25.8, 26.1]
-            fom_arr = [
-                [1.710645e02, 2.246047e02, 2.431472e02],
-                [2.445209e02, 3.250737e02, 3.516395e02],
-                [3.173144e02, 4.249317e02, 4.595133e02],
-            ]
-
-        elif self.year == 6:
-            areas = [10000, 15000, 2000]
-            depths = [25.9, 26.1, 26.3]
-            fom_arr = [
-                [2.346060e02, 2.414678e02, 2.852043e02],
-                [3.402318e02, 3.493120e02, 4.148814e02],
-                [4.452766e02, 4.565497e02, 5.436992e02],
-            ]
-
-        elif self.year == 10:
-            areas = [10000, 15000, 20000]
-            depths = [26.3, 26.5, 26.7]
-            fom_arr = [
-                [2.887266e02, 2.953230e02, 3.361616e02],
-                [4.200093e02, 4.292111e02, 4.905306e02],
-                [5.504419e02, 5.624697e02, 6.441837e02],
-            ]
-        else:
-            warnings.warn("FoMEmulator is not defined for this year")
-            return self.badval
-
-        # Interpolate FoM to the actual values for this sim
-        areas = [[i] * 3 for i in areas]
-        depths = [depths] * 3
-        f = interpolate.interp2d(areas, depths, fom_arr, bounds_error=False)
-        fom = f(area, median_depth)[0]
-        return fom
-
-
-class TomographicClusteringSigma8bias(BaseMetric):
-    """
-    Compute bias on sigma8 due to spurious contamination of density maps.
-    This is a summary metric to be interfaced with NestedLinearMultibandModelMetric.
-    NestedLinearMultibandModelMetric converts 6-band depth maps into a set of maps (e.g tomographic redshift bins)
-    which describes spurious density fluctuations in each bin.
-    This summary metric multiplies the maps by the parameter power_multiplier,
-    which can be used to describe the fraction of power uncorrected by systematics mitigation schemes,
-    computes the total power (via angular power spectra with lmin-lmax limits)
-    and then infers sigma8^2 via a model of the angular power spectra.
-    By taking sigma8_obs minus sigma8_model divided by the uncertainty, one derives a bias.
-    """
-
-    def __init__(
-        self,
-        density_tomography_model,
-        power_multiplier=0.1,
-        lmin=10,
-        badval=np.nan,
-        convert_to_sigma8=True,
-        **kwargs,
-    ):
-        """
-        Parameters
-        ----------
-        density_tomography_model: dict
-            dictionary containing models calculated for fiducial N(z)s and Cells:
-                lmax: numpy.array of int, of shape (Nbins, )
-                    lmax corresponding to kmax of 0.05
-                poly1d_coefs_loglog: numpy.array of float, of shape (Nbins, )
-                    polynomial fits to log(C_ell) vs log(ell) computed for CCL
-                sigma8square_model (float)
-                    value of sigma8^2 used as fiducal model for CCL
-        power_multiplier: `float`, optional
-            fraction of power (variance) which is uncorrected and thus biases sigma8
-        lmin: `int`, optional
-            lmin for the analysis
-        convert_to_sigma8: `str`, optional
-            Convert the bias to sigma8 instead of sigma8^2 (via change of variables for the uncertainty)
-        badval: `float`, optional
-            value to return for bad pixels (e.g. pixels not passing cuts)
-
-        Returns (with the method 'run')
-        -------
-        result: `float`
-            value of sigma8 bias calculated from this model: (sigma8^2_obs - sigma^2_model) / error on sigma8^2_obs
-            if convert_to_sigma8=Then then it is about sigma8 instead of sigma8^2.
-
-        """
-        super().__init__(**kwargs)
-
-        self.convert_to_sigma8 = convert_to_sigma8
-        self.badval = badval
-        self.mask_val = badval
-        self.power_multiplier = power_multiplier
-        self.lmin = lmin
-        self.density_tomography_model = density_tomography_model
-        # to compute angular power spectra and total power, initialize an array of metrics, with the right lmin and lmax.
-        self.totalPowerMetrics = [
-            TotalPowerMetric(
-                col="metricdata", lmin=lmin, lmax=lmax, metric_name="TotalPower_bin", mask_val=hp.UNSEEN
-            )
-            for lmax in density_tomography_model["lmax"]
-        ]
-        self.areaThresholdMetric = AreaThresholdMetric(
-            col="metricdata",
-            metric_name="FootprintFraction_bin",
-            lower_threshold=hp.UNSEEN,
-            upper_threshold=np.inf,
-        )
-
-    def run(self, data_slice, slice_point=None):
-
-        # need to define an array of bad values for the masked pixels
-        badval_arr = np.repeat(self.badval, len(self.density_tomography_model["lmax"]))
-        # converts the input recarray to an array
-        data_slice_list = [
-            badval_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()
-        ]
-        data_slice_arr = np.asarray(data_slice_list, dtype=float).T  # should be (nbins, npix)
-        data_slice_arr[~np.isfinite(data_slice_arr)] = (
-            hp.UNSEEN
-        )  # need to work with TotalPowerMetric and healpix
-
-        # measure valid sky fractions and total power (via angular power spectra) in each bin.
-        fskys = np.array(
-            [
-                self.areaThresholdMetric.run(np.core.records.fromrecords(x, dtype=[("metricdata", float)]))
-                / 42000
-                for x in data_slice_arr
-            ]
-        )  # sky fraction
-        spuriousdensitypowers = (
-            np.array(
-                [
-                    self.totalPowerMetrics[i].run(
-                        np.core.records.fromrecords(x, dtype=[("metricdata", float)])
-                    )
-                    for i, x in enumerate(data_slice_arr)
-                ]
-            )
-            / fskys
-        )
-        # some gymnastics needed to convert each slice into a recarray.
-        # this could probably be avoided if recarrays were returned by the original nested/vector metric,
-        # except that we would need to manipulate the names in various places, which I wanted to avoid,
-        # so for now the main metric returns an array per healpix pixel (not a recarray) and puts together
-        # healpix maps which we need to convert to a recarray to pass to AreaThresholdMetric and TotalPowerMetric
-
-        def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, power_multiplier):
-            """
-            Infer multiplicative factor sigma8^2 (and uncertainty) from the model Cells and observed total powers
-            since it os a Gaussian posterior distribution.
-            """
-            # solve for multiplicative sigma8^2 term between
-            # measured angular power spectra (spurious measured Cells times power_multiplier)
-            # and model ones (polynomial model from CCL).
-            n_bins = model_cells["lmax"].size
-            assert len(spurious_powers) == n_bins
-            assert len(fskys) == n_bins
-            assert model_cells["poly1d_coefs_loglog"].shape[0] == n_bins
-            totalvar_mod = np.zeros((n_bins, 1))
-            totalvar_obs = np.zeros((n_bins, 1))
-            totalvar_var = np.zeros((n_bins, 1))
-            # loop over tomographic bins
-            sigma8square_model = model_cells["sigma8square_model"]  # hardcoded; assumed CCL cosmology
-            for i in range(n_bins):
-                # get model Cells from polynomial model (in log log space)
-                ells = np.arange(lmin, model_cells["lmax"][i])
-                polynomial_model = np.poly1d(model_cells["poly1d_coefs_loglog"][i, :])
-                cells_model = np.exp(polynomial_model(np.log(ells)))
-
-                # model variance is sum of cells x (2l+1)
-                totalvar_mod[i, 0] = np.sum(cells_model * (2 * ells + 1))
-
-                # observations is spurious power  noiseless model
-                totalvar_obs[i, 0] = totalvar_mod[i, 0] + spurious_powers[i] * power_multiplier
-
-                # simple model variance of cell baased on Gaussian covariance
-                cells_var = 2 * cells_model**2 / (2 * ells + 1) / fskys[i]
-                totalvar_var[i, 0] = np.sum(cells_var * (2 * ells + 1) ** 2)
-
-            # model assumed sigma8 = 0.8 (add CCL cosmology here? or how I obtained them + documentation
-            results_fractional_spurious_power = totalvar_obs / totalvar_mod - 1.0
-            transfers = (
-                totalvar_mod / sigma8square_model
-            )  # model Cell variance divided by sigma8^2, which is the common normalization
-
-            # model ratio: formula for posterior distribution on unknown multiplicative factor in multivariate Gaussian likelihood
-            FOT = np.sum(transfers[:, 0] * totalvar_obs[:, 0] / totalvar_var[:, 0])
-            FTT = np.sum(transfers[:, 0] * transfers[:, 0] / totalvar_var[:, 0])
-            # mean and stddev of multiplicative factor
-            sigma8square_fit = FOT / FTT
-            sigma8square_error = FTT**-0.5
-
-            return sigma8square_fit, sigma8square_error, sigma8square_model
-
-        # solve for the gaussian posterior distribution on sigma8^2
-        sigma8square_fit, sigma8square_error, sigma8square_model = solve_for_multiplicative_factor(
-            spuriousdensitypowers, self.density_tomography_model, fskys, self.lmin, self.power_multiplier
-        )
-
-        results_sigma8_square_bias = (sigma8square_fit - sigma8square_model) / sigma8square_error
-
-        # turn result into bias on sigma8, via change of variable and simple propagation of uncertainty.
-        sigma8_fit = sigma8square_fit**0.5
-        sigma8_model = sigma8square_model**0.5
-        sigma8_error = 0.5 * sigma8square_error * sigma8_fit / sigma8square_fit
-        results_sigma8_bias = (sigma8_fit - sigma8_model) / sigma8_error
-
-        if self.convert_to_sigma8:
-            return results_sigma8_bias
-        else:
-            return results_sigma8_square_bias
diff --git a/rubin_sim/maf/metrics/tomography_models.py b/rubin_sim/maf/metrics/tomography_models.py
new file mode 100644
index 000000000..97d21f276
--- /dev/null
+++ b/rubin_sim/maf/metrics/tomography_models.py
@@ -0,0 +1,1048 @@
+__all__ = ("DENSITY_TOMOGRAPHY_MODEL",)
+
+import numpy as np
+
+"""A dictionary of TOMOGRAPHY models for use with the
+cosmology summary metrics (TomographicClusteringSigma8bias).
+
+This dictionary is derived from work shown in 
+https://github.com/ixkael/ObsStrat/blob/
+meanz_uniformity_maf/code/meanz_uniformity/
+romanrubinmock_for_sigma8tomoghy.ipynb
+"""
+
+# The dictionary DENSITY_TOMOGRAPHY_MODEL contains the current model.
+# It is intended for use with various DESC cosmology metrics, in
+# particular the TomographicClusteringSigma8bias metric.
+#
+# The first set of keys are the years (year1, ..., year10),
+# since this would change typical depth and galaxy catalog cuts.
+# In what follows we have 5 tomographic bins.
+#
+# The second nested dictionary has the following:
+# `sigma8square_model` :
+# The fiducial sigma8^2 value used in CCL for the theory predictions.
+# `poly1d_coefs_loglog` :
+# a polynomial (5th degree) describing the angular power spectra
+# (in log log space) in the 5 tomographic bins considered,
+# thus has shape (5, 6)
+# `lmax` :
+# the lmax limits to sum the Cells over when calculating sigma8
+# for each tomographic bin. thus is it of shape (5, )
+# `dlogN_dm5` :
+# the derivatives of logN wrt m5 calculated in  Qianjun & Jeff's simulations.
+# It is an array of 5 dictionaries (5 = the tomographic bins)
+#
+# Each dictionary must have keys that are the lsst bands.
+# If some are missing they are ignored in the linear model.
+# These badnpasses are the ones which will be fed to
+# NestedLinearMultibandModelMetric.
+# Everything else above is going into the modeling.
+#
+# The notebook I used to make this dictionary is
+# https://github.com/ixkael/ObsStrat/blob/meanz_uniformity_maf/code/
+# meanz_uniformity/romanrubinmock_for_sigma8tomography.ipynb
+
+
+DENSITY_TOMOGRAPHY_MODEL = {
+    "year1": {
+        "sigma8square_model": 0.8**2.0,
+        "poly1d_coefs_loglog": np.array(
+            [
+                [
+                    -7.94546823e-04,
+                    2.62494452e-02,
+                    -2.27597655e-01,
+                    4.67266497e-01,
+                    3.38850627e-01,
+                    -1.14120412e01,
+                ],
+                [
+                    3.98978657e-04,
+                    6.69012313e-03,
+                    -1.35005872e-01,
+                    3.72800783e-01,
+                    3.97123211e-01,
+                    -1.29908869e01,
+                ],
+                [
+                    1.03940293e-03,
+                    -5.18759967e-03,
+                    -6.85178825e-02,
+                    2.69468206e-01,
+                    4.60031701e-01,
+                    -1.41217016e01,
+                ],
+                [
+                    1.42986845e-03,
+                    -1.27862974e-02,
+                    -2.26263084e-02,
+                    1.85536763e-01,
+                    5.12886349e-01,
+                    -1.48861030e01,
+                ],
+                [
+                    1.73667255e-03,
+                    -1.89601890e-02,
+                    1.80214246e-02,
+                    9.50637447e-02,
+                    5.58980246e-01,
+                    -1.55053201e01,
+                ],
+            ]
+        ),
+        "lmax": np.array([64, 94, 124, 146, 164]),
+        "dlogN_dm5": [
+            {
+                "cst": 0.0,
+                "u": 0.043962974958662145,
+                "g": 0.043962974958662145,
+                "r": 0.043962974958662145,
+                "i": 0.043962974958662145,
+                "z": 0.043962974958662145,
+                "y": 0.043962974958662145,
+                "ugrizy": 0.043962974958662145,
+            },
+            {
+                "cst": 0.0,
+                "u": 0.034752650841693544,
+                "g": 0.034752650841693544,
+                "r": 0.034752650841693544,
+                "i": 0.034752650841693544,
+                "z": 0.034752650841693544,
+                "y": 0.034752650841693544,
+                "ugrizy": 0.034752650841693544,
+            },
+            {
+                "cst": 0.0,
+                "u": 0.033787326976188484,
+                "g": 0.033787326976188484,
+                "r": 0.033787326976188484,
+                "i": 0.033787326976188484,
+                "z": 0.033787326976188484,
+                "y": 0.033787326976188484,
+                "ugrizy": 0.033787326976188484,
+            },
+            {
+                "cst": 0.0,
+                "u": -0.058749555322422424,
+                "g": -0.058749555322422424,
+                "r": -0.058749555322422424,
+                "i": -0.058749555322422424,
+                "z": -0.058749555322422424,
+                "y": -0.058749555322422424,
+                "ugrizy": -0.058749555322422424,
+            },
+            {
+                "cst": 0.0,
+                "u": -0.08739685057046752,
+                "g": -0.08739685057046752,
+                "r": -0.08739685057046752,
+                "i": -0.08739685057046752,
+                "z": -0.08739685057046752,
+                "y": -0.08739685057046752,
+                "ugrizy": -0.08739685057046752,
+            },
+        ],
+    },
+    "year2": {
+        "sigma8square_model": 0.8**2.0,
+        "poly1d_coefs_loglog": np.array(
+            [
+                [
+                    -7.72116403e-04,
+                    2.59654127e-02,
+                    -2.26957718e-01,
+                    4.69432713e-01,
+                    3.39213385e-01,
+                    -1.13989048e01,
+                ],
+                [
+                    4.22801028e-04,
+                    6.36607807e-03,
+                    -1.34108644e-01,
+                    3.74841312e-01,
+                    3.96986022e-01,
+                    -1.29600665e01,
+                ],
+                [
+                    1.07354523e-03,
+                    -5.73388904e-03,
+                    -6.60135335e-02,
+                    2.67377905e-01,
+                    4.62220942e-01,
+                    -1.40738990e01,
+                ],
+                [
+                    1.46948747e-03,
+                    -1.34766061e-02,
+                    -1.89609170e-02,
+                    1.80340598e-01,
+                    5.17052663e-01,
+                    -1.48679098e01,
+                ],
+                [
+                    1.79954923e-03,
+                    -1.99827467e-02,
+                    2.28427111e-02,
+                    9.06204330e-02,
+                    5.69080814e-01,
+                    -1.55353679e01,
+                ],
+            ]
+        ),
+        "lmax": np.array([64, 95, 123, 147, 166]),
+        "dlogN_dm5": [
+            {
+                "cst": 0.0,
+                "u": 0.055492947394854004,
+                "g": 0.055492947394854004,
+                "r": 0.055492947394854004,
+                "i": 0.055492947394854004,
+                "z": 0.055492947394854004,
+                "y": 0.055492947394854004,
+                "ugrizy": 0.055492947394854004,
+            },
+            {
+                "cst": 0.0,
+                "u": 0.029608214159172044,
+                "g": 0.029608214159172044,
+                "r": 0.029608214159172044,
+                "i": 0.029608214159172044,
+                "z": 0.029608214159172044,
+                "y": 0.029608214159172044,
+                "ugrizy": 0.029608214159172044,
+            },
+            {
+                "cst": 0.0,
+                "u": 0.01704700362928206,
+                "g": 0.01704700362928206,
+                "r": 0.01704700362928206,
+                "i": 0.01704700362928206,
+                "z": 0.01704700362928206,
+                "y": 0.01704700362928206,
+                "ugrizy": 0.01704700362928206,
+            },
+            {
+                "cst": 0.0,
+                "u": -0.061409368487154156,
+                "g": -0.061409368487154156,
+                "r": -0.061409368487154156,
+                "i": -0.061409368487154156,
+                "z": -0.061409368487154156,
+                "y": -0.061409368487154156,
+                "ugrizy": -0.061409368487154156,
+            },
+            {
+                "cst": 0.0,
+                "u": -0.050114709867968066,
+                "g": -0.050114709867968066,
+                "r": -0.050114709867968066,
+                "i": -0.050114709867968066,
+                "z": -0.050114709867968066,
+                "y": -0.050114709867968066,
+                "ugrizy": -0.050114709867968066,
+            },
+        ],
+    },
+    "year3": {
+        "sigma8square_model": 0.8**2.0,
+        "poly1d_coefs_loglog": np.array(
+            [
+                [
+                    -7.47335451e-04,
+                    2.55922442e-02,
+                    -2.25366760e-01,
+                    4.68243951e-01,
+                    3.39807736e-01,
+                    -1.14004586e01,
+                ],
+                [
+                    4.37952430e-04,
+                    6.13359097e-03,
+                    -1.33135431e-01,
+                    3.74413364e-01,
+                    3.97397465e-01,
+                    -1.29291594e01,
+                ],
+                [
+                    1.08534795e-03,
+                    -5.92068724e-03,
+                    -6.51754697e-02,
+                    2.66734891e-01,
+                    4.63243671e-01,
+                    -1.40474513e01,
+                ],
+                [
+                    1.47812824e-03,
+                    -1.35845536e-02,
+                    -1.88487898e-02,
+                    1.82317694e-01,
+                    5.17486035e-01,
+                    -1.48384185e01,
+                ],
+                [
+                    1.82831353e-03,
+                    -2.03855872e-02,
+                    2.40741011e-02,
+                    9.27502779e-02,
+                    5.73850795e-01,
+                    -1.55657849e01,
+                ],
+            ]
+        ),
+        "lmax": np.array([64, 94, 123, 147, 168]),
+        "dlogN_dm5": [
+            {
+                "cst": 0.0,
+                "u": 0.04571336657551873,
+                "g": 0.04571336657551873,
+                "r": 0.04571336657551873,
+                "i": 0.04571336657551873,
+                "z": 0.04571336657551873,
+                "y": 0.04571336657551873,
+                "ugrizy": 0.04571336657551873,
+            },
+            {
+                "cst": 0.0,
+                "u": 0.03578384485292078,
+                "g": 0.03578384485292078,
+                "r": 0.03578384485292078,
+                "i": 0.03578384485292078,
+                "z": 0.03578384485292078,
+                "y": 0.03578384485292078,
+                "ugrizy": 0.03578384485292078,
+            },
+            {
+                "cst": 0.0,
+                "u": 0.023135003462698395,
+                "g": 0.023135003462698395,
+                "r": 0.023135003462698395,
+                "i": 0.023135003462698395,
+                "z": 0.023135003462698395,
+                "y": 0.023135003462698395,
+                "ugrizy": 0.023135003462698395,
+            },
+            {
+                "cst": 0.0,
+                "u": -0.05756589965508176,
+                "g": -0.05756589965508176,
+                "r": -0.05756589965508176,
+                "i": -0.05756589965508176,
+                "z": -0.05756589965508176,
+                "y": -0.05756589965508176,
+                "ugrizy": -0.05756589965508176,
+            },
+            {
+                "cst": 0.0,
+                "u": -0.025917249340435374,
+                "g": -0.025917249340435374,
+                "r": -0.025917249340435374,
+                "i": -0.025917249340435374,
+                "z": -0.025917249340435374,
+                "y": -0.025917249340435374,
+                "ugrizy": -0.025917249340435374,
+            },
+        ],
+    },
+    "year4": {
+        "sigma8square_model": 0.8**2.0,
+        "poly1d_coefs_loglog": np.array(
+            [
+                [
+                    -7.41568743e-04,
+                    2.55297105e-02,
+                    -2.25339653e-01,
+                    4.69342516e-01,
+                    3.39385437e-01,
+                    -1.13829013e01,
+                ],
+                [
+                    4.47724999e-04,
+                    5.99613130e-03,
+                    -1.32666973e-01,
+                    3.74708889e-01,
+                    3.97389350e-01,
+                    -1.29064781e01,
+                ],
+                [
+                    1.09566923e-03,
+                    -6.07450334e-03,
+                    -6.45513085e-02,
+                    2.66522400e-01,
+                    4.63653700e-01,
+                    -1.40182124e01,
+                ],
+                [
+                    1.48898237e-03,
+                    -1.37812174e-02,
+                    -1.77616026e-02,
+                    1.80633787e-01,
+                    5.19149830e-01,
+                    -1.48277616e01,
+                ],
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+            {
+                "cst": 0.0,
+                "u": 0.029973080589983655,
+                "g": 0.029973080589983655,
+                "r": 0.029973080589983655,
+                "i": 0.029973080589983655,
+                "z": 0.029973080589983655,
+                "y": 0.029973080589983655,
+                "ugrizy": 0.029973080589983655,
+            },
+            {
+                "cst": 0.0,
+                "u": 0.02262813226603151,
+                "g": 0.02262813226603151,
+                "r": 0.02262813226603151,
+                "i": 0.02262813226603151,
+                "z": 0.02262813226603151,
+                "y": 0.02262813226603151,
+                "ugrizy": 0.02262813226603151,
+            },
+            {
+                "cst": 0.0,
+                "u": -0.046678413257774214,
+                "g": -0.046678413257774214,
+                "r": -0.046678413257774214,
+                "i": -0.046678413257774214,
+                "z": -0.046678413257774214,
+                "y": -0.046678413257774214,
+                "ugrizy": -0.046678413257774214,
+            },
+            {
+                "cst": 0.0,
+                "u": -0.025182273951223154,
+                "g": -0.025182273951223154,
+                "r": -0.025182273951223154,
+                "i": -0.025182273951223154,
+                "z": -0.025182273951223154,
+                "y": -0.025182273951223154,
+                "ugrizy": -0.025182273951223154,
+            },
+        ],
+    },
+}
diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
index ef37cf355..0366ff483 100644
--- a/rubin_sim/maf/metrics/uniformity_metrics.py
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -1,82 +1,93 @@
-__all__ = "LinearMultibandModelMetric"
+__all__ = ("SingleLinearMultibandModelMetric", "NestedLinearMultibandModelMetric")
 
 import numpy as np
-from rubin_sim.maf.metrics.base_metric import BaseMetric
-from rubin_sim.maf.metrics.exgal_m5 import ExgalM5
-import healpy as hp
 
-
-class LinearMultibandModelMetric(BaseMetric):
-    """
-    Calculates a single linear combination of depths.
-    This is useful for calculating density or redshift fluctuations from depth fluctuations on the sky,
-    for a single redshift bins (thus it is a single metric). For multiple bins, see NestedLinearMultibandModelMetric.
+from .base_metric import BaseMetric
+from .exgal_m5 import ExgalM5
+
+
+class SingleLinearMultibandModelMetric(BaseMetric):
+    """Calculate a single linear combination of depths.
+
+    This is useful for calculating density or redshift fluctuations
+    from depth fluctuations on the sky,
+    for a single redshift bins (thus it is a single metric).
+    For multiple bins, see NestedLinearMultibandModelMetric.
+
+    Parameters
+    ----------
+    arr_of_model_dicts : `dict`
+        Linear coefficients to be applied to the M5 depth values.
+        Keys should be filter names + 'cst' if there is a constant offset.
+    post_processing_fn : lambda
+        Post processing function to apply to the linear combination of inputs.
+        Default `lambda x: x` simply returns the output.
+    extinction_cut : `float`, optional
+        E(B-V) cut on extinction (0.2 by default).
+    n_filters : `int`, optional
+        Cut on the number of filters required (6 by default).
+    min_depth_cut : `dict`, optional
+        Cut the areas of low depths, based on cuts in this dict.
+        Keys should be filter names. Default is no cuts.
+    max_depth_cut : `dict`, optional
+        Cut the areas of high depths, based on cuts in this dict.
+        Keys should be filter names. Default is no cuts.
+    mean_depth : `dict`, optional
+        Mean depths, which will be subtracted from the inputs
+        before applying model.
+        Keys should be filter names. Default is zero in each band.
+        In a lot of cases, instead of feeding mean_depths,
+        one can remove the monopole in the constructed healpix maps.
+    m5_col : `str`, optional
+        Column name for the m5 depth.
+    filter_col : `str`, optional
+        Column name for the filter.
+    units : `str`, optional
+        Label for "units" in the output, for use in plots.
+    badval : `float`, optional
+        Value to return for metric failure.
+        Specified here so that exgalm5 uses the same value.
+
+    Returns
+    -------
+    result : `float`
+        Linear combination of the M5 depths (minus mean depth, if provided).
+        result = np.sum([
+                model_dict[band] * M5_depth[band]
+                for band in ["u", "g", "r", "i", "z", "y"]
+                ])
+        if post_processing_fn is provided:
+        result => post_processing_fn(result)
     """
 
     def __init__(
         self,
         model_dict,
-        metric_name="LinearMultibandModel",
-        m5_col="fiveSigmaDepth",
-        filter_col="filter",
         post_processing_fn=lambda x: x,
-        units="mag",
         extinction_cut=0.2,
-        min_depth_cut={"i": 25.0},
-        max_depth_cut={},
-        mean_depth={"u": 0.0, "g": 0.0, "r": 0.0, "i": 0.0, "z": 0.0, "y": 0.0},
         n_filters=6,
+        min_depth_cut=None,
+        max_depth_cut=None,
+        mean_depth=None,
+        m5_col="fiveSigmaDepth",
+        filter_col="filter",
+        units="mag",
+        badval=np.nan,
         **kwargs,
     ):
-        """
-        Parameters
-        ----------
-        arr_of_model_dicts: dict
-            linear coefficients to be applied to the M5 depth values
-            Keys should be filter names + 'cst' if there is a constant offset.
-        post_processing_fn: lambda
-            post processing function to apply to the linear combination of inputs
-        metric_name: `str`, optional
-            metric name
-        m5_col: `str`, optional
-            column name for the m5 depth (almost always 'fiveSigmaDepth')
-        filter_col: `str`, optional ('filter')
-            column name for the filter (almost always 'filter')
-        units: `str`, optional
-            column name for the units (depends on the inputs, so almost always 'mag')
-        extinction_cut: `float`, optional
-            sky cut on extinction (0.2 by default)
-        n_filters: `int`, optional
-            sky cut on the number of filters required (6 by default)
-        badval: `float`, optional
-            value to return for bad pixels (e.g. pixels not passing cuts)
-        min_depth_cut: `dict`, optional
-            Cut the areas of low depths, based on cuts in this dict.
-            Keys should be filter names. Default is no cuts.
-        max_depth_cut: `dict`, optional
-            Cut the areas of high depths, based on cuts in this dict.
-            Keys should be filter names. Default is no cuts.
-        mean_depth: `dict`, optional
-            Mean depths, which will be subtracted from the inputs before applying model.
-            Keys should be filter names. Default is zero in each band.
-            In a lot of cases, instead of feeding mean_depths, one can remove the monopole
-            in the constructed healpix maps.
-
-        Returns (with the method 'run')
-        -------
-        result: `float`
-            Linear combination of the M5 depths (minus mean depth, if provided).
-            result = np.sum([
-                    model_dict[band] * M5_depth[band]
-                    for band in ["u", "g", "r", "i", "z", "y"]
-                    ])
-            if post_processing_fn is provided: result => post_processing_fn(result)
-
-        """
-        self.n_filters = n_filters
-        self.extinction_cut = extinction_cut
+        if min_depth_cut is None:
+            min_depth_cut = {}
         self.min_depth_cut = min_depth_cut
+        if max_depth_cut is None:
+            max_depth_cut = {}
         self.max_depth_cut = max_depth_cut
+        if mean_depth is None:
+            # Set mean_depth to 0 if not specified
+            mean_depth = dict([(f, 0) for f in "ugrizy"])
+        self.mean_depth = mean_depth
+
+        self.n_filters = n_filters
+        self.extinction_cut = extinction_cut
         if "cst" in model_dict:
             self.cst = model_dict["cst"]
         else:
@@ -84,20 +95,14 @@ def __init__(
         lsst_filters = ["u", "g", "r", "i", "z", "y"]
         self.bands = [x for x in model_dict.keys() if x in lsst_filters]
         self.model_arr = np.array([model_dict[x] for x in self.bands])[:, None]
-        self.mean_depth = mean_depth
+
         self.m5_col = m5_col
         self.filter_col = filter_col
         self.post_processing_fn = post_processing_fn
-        self.exgal_m5 = ExgalM5(m5_col=m5_col, units=units)
 
-        self.exgal_m5 = ExgalM5(
-            m5_col=m5_col,
-            filter_col=filter_col,
-            units=units,
-            metric_name=metric_name + "ExgalM5",
-        )
+        self.exgal_m5 = ExgalM5(m5_col=m5_col, filter_col=filter_col, badval=badval)
         super().__init__(
-            col=[m5_col, filter_col], metric_name=metric_name, units=units, maps=self.exgal_m5.maps, **kwargs
+            col=[m5_col, filter_col], units=units, maps=self.exgal_m5.maps, badval=badval, **kwargs
         )
 
     def run(self, data_slice, slice_point=None):
@@ -120,10 +125,14 @@ def run(self, data_slice, slice_point=None):
         for i, lsst_filter in enumerate(self.bands):
             if lsst_filter in self.mean_depth:
                 depths[i] -= self.mean_depth[lsst_filter]
-        assert depths.shape[0] >= 1
+        # Don't raise Exceptions in the middle of metrics -
+        # assert depths.shape[0] >= 1
+        if depths.shape[0] < 1:
+            return self.badval
 
         if np.any(depths == self.badval):
             return self.badval
+
         for i, lsst_filter in enumerate(self.bands):
             if (
                 lsst_filter in self.min_depth_cut
@@ -137,127 +146,130 @@ def run(self, data_slice, slice_point=None):
                 return self.badval
 
         val = self.post_processing_fn(self.cst + np.dot(depths, self.model_arr))
-
         return val
 
 
 class NestedLinearMultibandModelMetric(BaseMetric):
-    """
-    Calculates multiple linear combination of depths.
-    This is useful for calculating density or redshift fluctuations from depth fluctuations on the sky,
-    for multiple redshift bins (thus it is a nested metric). For a single bin, see LinearMultibandModelMetric.
+    """Calculate multiple linear combinations of depths.
+
+    This is useful for calculating density or redshift fluctuations
+    from depth fluctuations on the sky,
+    for multiple redshift bins (thus it is a nested metric).
+    For a single bin, see LinearMultibandModelMetric.
+
+    Parameters
+    ----------
+    arr_of_model_dicts : `list` [ `dicts` ]
+        Array of linear coefficients to be applied to the M5 depth values.
+        Keys should be filter names + 'cst' if there is a constant offset.
+    post_processing_fn : lambda
+        Post processing function to apply to the linear combination of inputs.
+        Default `lambda x: x` simply returns the output.
+    extinction_cut : `float`, optional
+        E(B-V) cut on extinction (0.2 by default).
+    n_filters : `int`, optional
+        Cut on the number of filters required (6 by default).
+    min_depth_cut : `dict`, optional
+        Cut the areas of low depths, based on cuts in this dict.
+        Keys should be filter names. Default is no cuts.
+    max_depth_cut : `dict`, optional
+        Cut the areas of high depths, based on cuts in this dict.
+        Keys should be filter names. Default is no cuts.
+    mean_depth : `dict`, optional
+        Mean depths, which will be subtracted from the inputs
+        before applying model.
+        Keys should be filter names. Default is zero in each band.
+        In a lot of cases, instead of feeding mean_depths,
+        one can remove the monopole in the constructed healpix maps.
+    m5_col : `str`, optional
+        Column name for the m5 depth.
+    filter_col : `str`, optional
+        Column name for the filter.
+    units : `str`, optional
+        Label for "units" in the output, for use in plots.
+    badval : `float`, optional
+        Value to return for metric failure.
+        Specified here so that exgalm5 uses the same value.
+
+    Returns
+    -------
+    result : `list` [ `float` ]
+        List is the same size as arr_of_model_dicts.
+        For each element, return a linear combination of the M5 depths
+        (minus mean depth, if provided).
+        for i, model_dict in enumerate(arr_of_model_dicts):
+            result[i] = np.sum([
+                model_dict[band] * M5_depth[band]
+                for band in ["u", "g", "r", "i", "z", "y"]
+                ])
+        if post_processing_fn is provided:
+        result[i] => post_processing_fn(result[i])
     """
 
     def __init__(
         self,
         arr_of_model_dicts,
-        metric_name="NestedLinearMultibandModelMetric",
-        m5_col="fiveSigmaDepth",
-        filter_col="filter",
         post_processing_fn=lambda x: x,
-        units="mag",
         extinction_cut=0.2,
         n_filters=6,
+        min_depth_cut=None,
+        max_depth_cut=None,
+        mean_depth=None,
+        m5_col="fiveSigmaDepth",
+        filter_col="filter",
+        units="mag",
         badval=np.nan,
-        min_depth_cut={},
-        max_depth_cut={},
-        mean_depth={},
         **kwargs,
     ):
-        """
-        Parameters
-        ----------
-        arr_of_model_dicts: list of dicts
-            array of linear coefficients to be applied to the M5 depth values
-            Keys should be filter names + 'cst' if there is a constant offset.
-        post_processing_fn: lambda
-            post processing function to apply to the linear combination of inputs
-        metric_name: `str`, optional
-            metric name
-        m5_col: `str`, optional
-            column name for the m5 depth (almost always 'fiveSigmaDepth')
-        filter_col: `str`, optional ('filter')
-            column name for the filter (almost always 'filter')
-        units: `str`, optional
-            column name for the units (depends on the inputs, so almost always 'mag')
-        extinction_cut: `float`, optional
-            sky cut on extinction (0.2 by default)
-        n_filters: `int`, optional
-            sky cut on the number of filters required (6 by default)
-        badval: `float`, optional
-            value to return for bad pixels (e.g. pixels not passing cuts)
-        min_depth_cut: `dict`, optional
-            Cut the areas of low depths, based on cuts in this dict.
-            Keys should be filter names. Default is no cuts.
-        max_depth_cut: `dict`, optional
-            Cut the areas of high depths, based on cuts in this dict.
-            Keys should be filter names. Default is no cuts.
-        mean_depth: `dict`, optional
-            Mean depths, which will be subtracted from the inputs before applying model.
-            Keys should be filter names. Default is zero in each band.
-            In a lot of cases, instead of feeding mean_depths, one can remove the monopole
-            in the constructed healpix maps.
-
-        Returns  (with the method 'run')
-        -------
-        result: list of `float`
-            List is the same size as arr_of_model_dicts.
-            For each element, return a linear combination of the M5 depths (minus mean depth, if provided).
-            for i, model_dict in enumerate(arr_of_model_dicts):
-                result[i] = np.sum([
-                    model_dict[band] * M5_depth[band]
-                    for band in ["u", "g", "r", "i", "z", "y"]
-                    ])
-            if post_processing_fn is provided: result[i] => post_processing_fn(result[i])
-
-        """
         self.arr_of_model_dicts = arr_of_model_dicts
         self.n_filters = n_filters
         self.extinction_cut = extinction_cut
+        if min_depth_cut is None:
+            min_depth_cut = {}
         self.min_depth_cut = min_depth_cut
+        if max_depth_cut is None:
+            max_depth_cut = {}
         self.max_depth_cut = max_depth_cut
+        if mean_depth is None:
+            mean_depth = {}
         self.mean_depth = mean_depth
+
         self.m5_col = m5_col
         self.badval = badval
         self.filter_col = filter_col
         self.post_processing_fn = post_processing_fn
 
+        self.n_bins = len(self.arr_of_model_dicts)
+        self.bad_val_arr = np.repeat(self.badval, self.n_bins)
+
         self.exgal_m5 = ExgalM5(
             m5_col=m5_col,
             filter_col=filter_col,
             badval=self.badval,
-            units=units,
-            metric_name=metric_name + "_ExgalM5",
         )
         super().__init__(
             col=[m5_col, filter_col],
             badval=self.badval,
-            metric_name=metric_name,
             units=units,
             maps=self.exgal_m5.maps,
+            metric_dtype="object",
             **kwargs,
         )
-        # magic line so MAF knows we're returning something complicated
-        self.metric_dtype = "object"
 
     def run(self, data_slice, slice_point=None):
-        "Returns an array containing the values of the model evaluated at the data_slice"
-
-        n_bins = len(self.arr_of_model_dicts)
-        bad_val_arr = np.repeat(self.badval, n_bins)
-
         # apply extinction and n_filters cut
         if slice_point["ebv"] > self.extinction_cut:
-            return bad_val_arr
+            return self.bad_val_arr
+
         n_filters = len(set(data_slice[self.filter_col]))
         if n_filters < self.n_filters:
-            return bad_val_arr
+            return self.bad_val_arr
 
         lsst_filters = ["u", "g", "r", "i", "z", "y"]
 
         # initialize dictionary of outputs
         # types = [float]*n_bins
-        result_arr = np.zeros((n_bins,), dtype=float)
+        result_arr = np.zeros((self.n_bins,), dtype=float)
 
         for binnumber, model_dict in enumerate(self.arr_of_model_dicts):
 
@@ -287,7 +299,7 @@ def run(self, data_slice, slice_point=None):
 
             result_arr[binnumber] = self.post_processing_fn(cst + np.dot(depths, model_arr))
 
-        # returns array where each element of the original input arr_of_model_dicts
-        # contains a scalar value resulting from a linear combination of the six depths
-        #
+        # returns array where each element of the original
+        # input arr_of_model_dicts contains a scalar value resulting
+        # from a linear combination of the six depths
         return result_arr
diff --git a/tests/maf/test_cosmology_summaries.py b/tests/maf/test_cosmology_summaries.py
new file mode 100644
index 000000000..3d53629f2
--- /dev/null
+++ b/tests/maf/test_cosmology_summaries.py
@@ -0,0 +1,20 @@
+import unittest
+
+import healpy as hp
+import numpy as np
+
+import rubin_sim.maf.metrics as metrics
+
+
+class TestCosmologySummaryMetrics(unittest.TestCase):
+
+    def test_total_power_metric(self):
+        nside = 64
+        data = np.ones(hp.nside2npix(nside), dtype=list(zip(["testcol"], ["float"])))
+        metric = metrics.TotalPowerMetric(col="testcol")
+        result = metric.run(data)
+        np.testing.assert_equal(result, 0.0)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/maf/test_summarymetrics.py b/tests/maf/test_summarymetrics.py
index 3ae2750cb..9ea65be34 100644
--- a/tests/maf/test_summarymetrics.py
+++ b/tests/maf/test_summarymetrics.py
@@ -77,13 +77,5 @@ def test_zeropoint_metric(self):
         result = metric.run(data)
         np.testing.assert_equal(result, np.ones(10, float) + 5.5)
 
-    def test_total_power_metric(self):
-        nside = 128
-        data = np.ones(12 * nside**2, dtype=list(zip(["testcol"], ["float"])))
-        metric = metrics.TotalPowerMetric(col="testcol")
-        result = metric.run(data)
-        np.testing.assert_equal(result, 0.0)
-
-
 if __name__ == "__main__":
     unittest.main()

From 0d6645268b2ea1f6b7fc5a2a3a678d83b7d1fc90 Mon Sep 17 00:00:00 2001
From: Lynne Jones <ljones.uw@gmail.com>
Date: Fri, 29 Mar 2024 16:18:54 -0700
Subject: [PATCH 10/31] Make sure we import the uniformity metrics, add  to end
 of metric class names.

---
 rubin_sim/maf/metrics/__init__.py                  | 1 +
 rubin_sim/maf/metrics/cosmology_summary_metrics.py | 4 ++--
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/rubin_sim/maf/metrics/__init__.py b/rubin_sim/maf/metrics/__init__.py
index 9281c95b1..f0683ff42 100644
--- a/rubin_sim/maf/metrics/__init__.py
+++ b/rubin_sim/maf/metrics/__init__.py
@@ -44,6 +44,7 @@
 from .tomography_models import *
 from .transient_metrics import *
 from .use_metrics import *
+from .uniformity_metrics import *
 from .vector_metrics import *
 from .visit_groups_metric import *
 from .weak_lensing_systematics_metric import *
diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 97a6ea783..3be3bbe8b 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -1,7 +1,7 @@
 __all__ = (
     "TotalPowerMetric",
     "StaticProbesFoMEmulatorMetricSimple",
-    "TomographicClusteringSigma8bias",
+    "TomographicClusteringSigma8biasMetric",
 )
 
 import warnings
@@ -165,7 +165,7 @@ def run(self, data_slice, slice_point=None):
         return fom
 
 
-class TomographicClusteringSigma8bias(BaseMetric):
+class TomographicClusteringSigma8biasMetric(BaseMetric):
     """Compute bias on sigma8 due to spurious contamination of density maps.
     Run as summary metric on NestedLinearMultibandModelMetric.
 

From 595377f890e3c60de2367132b053d383824f729b Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Tue, 9 Apr 2024 17:50:39 +0100
Subject: [PATCH 11/31] copied rachel's FOM ratio metric

---
 .../maf/metrics/cosmology_summary_metrics.py  | 93 +++++++++++++++++++
 1 file changed, 93 insertions(+)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 3be3bbe8b..02a8cfb5f 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -355,3 +355,96 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
             sigma8_error = 0.5 * sigma8square_error * sigma8_fit / sigma8square_fit
             results_sigma8_bias = (sigma8_fit - sigma8_model) / sigma8_error
             return results_sigma8_bias
+
+
+class UniformAreaFoMFractionMetric(BaseMetric):
+    """?
+    Run as summary metric on RIZDetectionCoaddExposureTime.
+
+    Parameters
+    ----------
+    ?
+
+    Returns
+    -------
+    ?
+
+    Notes
+    -----
+    ?
+    """
+
+    def __init__(
+            self,
+            **kwargs,
+    ):
+        super().__init__(col="metricdata", **kwargs)
+        # Set mask_val, so that we receive metric_values.filled(mask_val)
+        self.mask_val = hp.UNSEEN
+
+    def run(self, data_slice, slice_point=None):
+
+        def make_clustering_dataset(depth_map, maskval=0, priority_fac=0.9, nside=64):
+            # A utility routine to get a dataset for unsupervised clustering.  Note:
+            # - We want the unmasked regions of the depth map only.
+            # - We assume masked regions are set to `maskval`, and cut 0.1 magnitudes above that.
+            # - We really want it to look at depth fluctuations.  So, we have to rescale the
+            #   RA/dec dimensions to avoid them being prioritized because their values are larger and
+            #   have more variation than depth.  Currently we rescale RA/dec such that their
+            #   standard deviations are 1-priority_fac times the standard deviation of the depth map.
+            #   That's why priority_fac is a tunable parameter; it should be between 0 and 1
+            if priority_fac < 0 or priority_fac >= 1:
+                raise ValueError("priority_fac must lie between 0 and 1")
+            theta, phi = hp.pixelfunc.pix2ang(nside, ipix=np.arange(hp.nside2npix(nside)))
+            # theta is 0 at the north pole, pi/2 at equator, pi at south pole; phi maps to RA
+            ra = np.rad2deg(phi)
+            dec = np.rad2deg(0.5 * np.pi - theta)
+
+            # Make a 3D numpy array containing the unmasked regions, including a rescaling factor to prioritize the depth
+            n_unmasked = len(depth_map[depth_map > 0.1])
+            my_data = np.zeros((n_unmasked, 3))
+            cutval = 0.1 + maskval
+            my_data[:, 0] = ra[depth_map > cutval] * (1 - priority_fac) * np.std(
+                depth_map[depth_map > cutval]) / np.std(ra[depth_map > cutval])
+            my_data[:, 1] = dec[depth_map > cutval] * (1 - priority_fac) * np.std(
+                depth_map[depth_map > cutval]) / np.std(dec[depth_map > cutval])
+            my_data[:, 2] = depth_map[depth_map > cutval]
+            return my_data
+
+            # Check for stripiness
+
+        nside = hp.npix2nside(data_slice.size)
+        self.threebyTwoSummary = StaticProbesFoMEmulatorMetric(nside=nside, metric_name="3x2ptFoM")
+        stripes = has_stripes(data_slice, nside)
+
+        if not stripes:
+            return 1
+        else:
+            # Do the clustering if we got to this point
+            if verbose: print("Verbose mode - Carrying out the clustering exercise for this map")
+            clustering_data = make_clustering_dataset(map)
+            labels = apply_clustering(clustering_data)
+            area_frac, med_val = get_area_stats(data_slice, labels)
+            if verbose:
+                print("Verbose mode - showing original map and clusters identified for this map")
+                show_clusters(data_slice, labels)
+                print("Area fractions", area_frac)
+                print("Median exposure time values", med_val)
+                print("Median exposure time ratio", np.max(med_val)/np.min(med_val))
+                print("Verbose mode - proceeding with area cuts")
+            # Get the FoM without/with cuts.  We want to check the FoM for each area, if we're doing cuts, and
+            # return the higher one. This will typically be for the larger area, but not necessarily, if the smaller area
+            # is deeper.
+            expanded_labels = expand_labels(data_slice, labels)
+            my_hpid_1 = np.where(labels == 1)[0]
+            my_hpid_2 = np.where(labels == 2)[0]
+            data_slice_subset_1 = copy(data_slice)
+            data_slice[my_hpid_2] = hp.UNSEEN
+            data_slice_subset_2 = copy(data_slice)
+            data_slice[my_hpid_1] = hp.UNSEEN
+            fom1 = self.threebyTwoSummary.run(data_slice_subset_1)
+            fom2 = self.threebyTwoSummary.run(data_slice_subset_2)
+            fom = np.max((fom1, fom2))
+            fom_total = self.threebyTwoSummary.run(data_slice)
+            return fom / fom_total
+

From e62e4188ee312700609da9dddac556ec158158a8 Mon Sep 17 00:00:00 2001
From: Renee Hlozek <renee.hlozek@gmail.com>
Date: Wed, 10 Apr 2024 06:18:26 -0700
Subject: [PATCH 12/31] added ancillary code needed for metrics

---
 .../maf/metrics/cosmology_summary_metrics.py  |  135 ++
 .../maf/metrics/uniformity_pkl/bands.pkl      |  Bin 0 -> 49 bytes
 .../maf/metrics/uniformity_pkl/deltas.pkl     |  Bin 0 -> 235 bytes
 .../metrics/uniformity_pkl/densityderiv.pkl   |  Bin 0 -> 2954 bytes
 .../maf/metrics/uniformity_pkl/densityy1.pkl  |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/densityy10.pkl |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/densityy2.pkl  |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/densityy3.pkl  |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/densityy4.pkl  |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/densityy5.pkl  |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/densityy6.pkl  |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/densityy7.pkl  |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/densityy8.pkl  |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/densityy9.pkl  |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/hgb_model.pkl  |  Bin 0 -> 369355 bytes
 .../maf/metrics/uniformity_pkl/maxzs.pkl      |  Bin 0 -> 187 bytes
 .../maf/metrics/uniformity_pkl/meanzderiv.pkl |  Bin 0 -> 2954 bytes
 .../maf/metrics/uniformity_pkl/meanzsy1.pkl   |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/meanzsy10.pkl  |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/meanzsy2.pkl   |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/meanzsy3.pkl   |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/meanzsy4.pkl   |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/meanzsy5.pkl   |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/meanzsy6.pkl   |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/meanzsy7.pkl   |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/meanzsy8.pkl   |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/meanzsy9.pkl   |  Bin 0 -> 3850 bytes
 .../maf/metrics/uniformity_pkl/minzs.pkl      |  Bin 0 -> 187 bytes
 .../uniformity_pkl/simulation_calcs.ipynb     |  633 +++++++++
 .../uniformity_pkl/simulation_photoz_rr.ipynb | 1193 +++++++++++++++++
 .../maf/metrics/uniformity_pkl/years.pkl      |  Bin 0 -> 36 bytes
 31 files changed, 1961 insertions(+)
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/bands.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/deltas.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityderiv.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy1.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy10.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy2.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy3.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy4.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy5.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy6.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy7.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy8.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy9.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/hgb_model.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/maxzs.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzderiv.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy1.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy10.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy2.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy3.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy4.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy5.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy6.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy7.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy8.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy9.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/minzs.pkl
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/simulation_calcs.ipynb
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/simulation_photoz_rr.ipynb
 create mode 100644 rubin_sim/maf/metrics/uniformity_pkl/years.pkl

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 3be3bbe8b..8076be231 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -2,6 +2,7 @@
     "TotalPowerMetric",
     "StaticProbesFoMEmulatorMetricSimple",
     "TomographicClusteringSigma8biasMetric",
+    "TomographicRedshiftFluctuationbiasMetric",
 )
 
 import warnings
@@ -355,3 +356,137 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
             sigma8_error = 0.5 * sigma8square_error * sigma8_fit / sigma8square_fit
             results_sigma8_bias = (sigma8_fit - sigma8_model) / sigma8_error
             return results_sigma8_bias
+
+class UniformMeanzBiasMetricc(BaseMetric):
+
+    """This calculates the bias in the weak lensing power given 
+       the scatter in the redshift of the tomographic sample
+    induced by survey non-uniformity. 
+
+    Parameters
+    ----------
+    year : `int`, optional
+        The year of the survey to calculate the bias.
+        This is used to derive the dm/dz derivative used to translate m5 rms into dz rms.
+
+    Returns
+    -------
+    result : `float` array
+        The ratio of this bias to the desired DESC y1 upper bound on the bias and the y10 
+        DESC SRD requirement. Desired values are less than 1 by Y10.
+
+    Notes
+    -----
+
+    Note that this is truly a summary metric and should be run on the
+    output of Exgalm5_with_cuts.
+    """
+
+    def compute_dzfromdm(zbins, band_ind, year):
+        """ This computes the dm/dz relationship calibrated from simulations
+        by Jeff Newmann.
+
+        Parameters
+        ----------
+        zbins : `int`
+            The number of tomographic bins considered. For now this is zbins < 5
+        filter : `str`
+            The assumed filter band 
+
+        Returns
+        -------
+        dzdminterp : `float` 
+            The interpolated value of the derivative dz/dm
+        meanzinterp : `float` array
+            The meanz in each tomographic bin.
+        
+        """
+        import pandas as pd
+
+        filter_list=["u","g","r","i","z","y"]
+        band_ind =filter_list.index(filter)
+        
+        deriv = pd.read_pickle('uniformity_pkl/meanzderiv.pkl')
+        # pkl file of derivatives with 10 years, 7 bands (ugrizY and combined), 5 bins
+        zvals = pd.read_pickle('uniformity_pkl/meanzsy%i.pkl'%(year+1)) 
+        # pkl file of mean z values for a given year over 5 z bins, 7 bands (ugrizY and combined),
+        # for a fixed delta density index (index 5 assumed below is for zero m_5 shift)
+        meanzinterp = zvals[0:zbins,band_ind,5]
+        dzdminterp = np.abs(deriv[year,band_ind,0:zbins])
+
+        return dzdminterp, meanzinterp
+
+    def use_zbins(meanz_vals, figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0]),  figure_9_width=0.2):
+        """ This computes which redshift bands are within the range 
+         specified in https://arxiv.org/pdf/2305.15406.pdf and can safely be used
+        to compute what Cl bias result from z fluctuations caused by rms variations in the m5.
+
+
+        Parameters
+        ----------
+        meanz_vals : `float` array
+            Array of meanz values to be used.
+        
+        Returns
+        -------
+        use_bins : `boolean` array
+            An array of boolean values of length meanz_vals 
+        
+        """
+        max_z_use = np.max(figure_9_mean_z)+2*figure_9_width
+        use_bins = meanz_vals < max_z_use
+        
+        return use_bins
+
+    def compute_Clbias(meanz_vals,scatter_mean_z_values):
+        """ This computes the Cl bias 
+        that results z fluctuations caused by rms variations in the m5.
+
+        
+
+        Parameters
+        ----------
+        meanz_vals : `float` array
+            Array of meanz values to be used.
+
+        scatter_mean_z_values : `float` array
+            Array of rms values of the z fluctuations
+
+        
+        Returns
+        -------
+        clbiasvals : `float` array
+            An array of values of the clbias
+            
+        mean_z_values_use :  `float` array
+            An array of the meanz values that are within the interpolation range of 2305.15406
+        
+        Notes
+        ------
+        This interpolates from the Figure 9 in https://arxiv.org/pdf/2305.15406.pdf
+
+        """
+        import numpy as np
+        figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0])
+        figure_9_Clbias =np.array([1e-3, 2e-3, 5e-3, 1.1e-2])
+        figure_9_width=0.2
+        figure_9_mean_z_scatter = 0.02
+
+        mzvals= np.array([float(mz) for mz in meanz_vals])
+        sctz = np.array([float(sz)for sz in scatter_mean_z_values])
+        
+        fit_res = np.polyfit(figure_9_mean_z, figure_9_Clbias, 2)
+        poly_fit = np.poly1d(fit_res)
+        use_bins = use_zbins(meanz_vals,figure_9_mean_z, figure_9_width)
+
+        mean_z_values_use = mzvals[use_bins]
+        sctz_use = sctz[use_bins]
+
+        Clbias = poly_fit(mean_z_values_use)
+        rescale_fac =  sctz_use / figure_9_mean_z_scatter
+        Clbias *= rescale_fac
+        fit_res_bias = np.polyfit(mean_z_values_use, Clbias, 1)
+        poly_fit_bias = np.poly1d(fit_res_bias)
+
+        clbiasvals = poly_fit_bias(mean_z_values_use)
+        return clbiasvals, mean_z_values_use
\ No newline at end of file
diff --git a/rubin_sim/maf/metrics/uniformity_pkl/bands.pkl b/rubin_sim/maf/metrics/uniformity_pkl/bands.pkl
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diff --git a/rubin_sim/maf/metrics/uniformity_pkl/deltas.pkl b/rubin_sim/maf/metrics/uniformity_pkl/deltas.pkl
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diff --git a/rubin_sim/maf/metrics/uniformity_pkl/minzs.pkl b/rubin_sim/maf/metrics/uniformity_pkl/minzs.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..07a0d1aef320a84f28e90ce4eea3d08f2fb8cbef
GIT binary patch
literal 187
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zhtXz=pP!%Ce;@!8-V7yEk~*C=X3YeFllBn$roD+V2t0=JU)n<)`oVq*P`Mre>>EvN

literal 0
HcmV?d00001

diff --git a/rubin_sim/maf/metrics/uniformity_pkl/simulation_calcs.ipynb b/rubin_sim/maf/metrics/uniformity_pkl/simulation_calcs.ipynb
new file mode 100644
index 000000000..db13f3bec
--- /dev/null
+++ b/rubin_sim/maf/metrics/uniformity_pkl/simulation_calcs.ipynb
@@ -0,0 +1,633 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a5d40f07-0827-46ab-a814-adbf38bc4f4b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline\n",
+    "import pickle\n",
+    "import pandas\n",
+    "\n",
+    "\n",
+    "# Random forest routine from scikit-learn:\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
+    "# Cross-Validation routines:\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.model_selection import cross_val_predict\n",
+    "from photerr import LsstErrorModel\n",
+    "\n",
+    "from sklearn.experimental import enable_iterative_imputer\n",
+    "from sklearn.impute import IterativeImputer\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0b0733c2-a9df-463f-b158-c93275a6ec79",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5458656c-1776-4823-b654-1505c4108020",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2937e855-3b4a-41e9-ac76-ea8951664d6a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#df_full=pandas.read_pickle('/global/u2/q/qhang/desc/notebooks_for_analysis/Project285/cosmoDC2_pzflow_sample.pkl')\n",
+    "\n",
+    "df_full=pandas.read_pickle('/pscratch/sd/q/qhang/roman-rubin-sims/nonuniform-maf/roman_rubin_2023_v1.1.3_elais-subset.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "cfb5ee3e-c2b6-43c4-b0b4-4ba40fbbfc0c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'g-i')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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eIwwHqGRMbBOQehGZXSljLFmya2cJDCVLdpxlU+UBN4S0jo4ijEXGFsLazc0oVri+HiLlet1BEGCmXHbgUS77Pv/6r81WV32f1WpocqrVSLenKCJgqNUKlgZWh+y0cjnAGYAB1mN/37/b6cT75XJUj9bGq2iBAENZ7Q8sEKwWAElBBcJjbUMC+5LLRcVsWCT2DRBUQKTAR4HjRWV2pYyxZMmulSUwlCyZmmqDNC1ew0iAIbPoMTYYBMABHCFezucddAAoms1IP9/d9Z+pKf9stRrvkynZbLoznpvzz9TrUVdoa8tDZLBTCJt7Pd+mhtPQ9pBav7PjIGNnx79bqThT1es5YDMLEMN2Kc6Ybc4KUCJbbmJiuM8aoEJDjryHTil7DGiSlEUCACrIIjx40bWIUsZYsmTXxhIYSpbMbLQ2iNdx8GYBKmBjcPyqiVFRc7cbYaN63QHK1pZvq1yObW9thVMvl4PZgYkhFZ4UebPI4trfj5BWtRqapoWF6DS/vx/sEen/R0cRxhsMHBRVKv4+2WsAKDPfzuRkZIsRCmSMvA5DxVyZBYDUbSrY1BR/Pq9aJo5Zizs+jtBVAkLJkl0LS2AoWTKz0dlDWhuHnmKAo1zOmR8NBZGGTc2het1Zn62tAAjNprMudJknY6tYdN0P+p5Wy8XTExMOUIpFBytaQ0izxZpNP46pqQjxdLv+XUJt2dR6+pQpOEOPMzERIUEyw6rV+H42XZ6SAJXKcHFHQmxmkaUG64P4WotDIkynNYiGJDWbi3EmezhLzFeyZGaWwFCyZK9P6SasBICBBaFwIqCi23WnvbfnICCX878RD7/8soOUhQUHFoCY9XUPIdVqDpYoVpjP+//099reNltcjNpB1Pnp9RxowTjNzPjftZqH0jSM1miETodijzg/mKzFxahGjWaHY9TvAVrQ+hSLUVIAYEJRSBgpBN4ASDLTzPy4aT6rzWSpnaTp/5pZRvjqYRz5kwABlwl4jGJCL8vYkiV7AnZlii4mS/bITLOHzCL00+9HfSDCRlSEbjT8fX73egGWtrbMvvnNEFevrzsDk8/7dni91YpQ1cqKgxgzf58U/MnJACCDQYAPGKJuN5ifzU0XX6M1Iu2ckBvp/72e73tzM75L+IzsNQAdDA0hQfZPSA8QQ7VrGB8qcJOFpzWFeA2tEGMCPKGn4rOTkxEWUx3Pgzjvhy3S+CD2JPZ5mikTelwBzGTJrpElZihZMrMILeGw9PV796J7vKZ97+xEzZ7dXf/davnnAQkwKxsb0ZW+241Kz1SJJvNMM9OmpiJERYbYzk4Ub1xd9fHMzDgjtLYW6ezT0/55rQXUbEYtIBiYTmeYdSmVQpfEcVE2gMawjA9nqgUnOW5Nt0cXRciNzDDmASZufNx/FPSgG1LR9INkdwGgzlNM8aKYnMtWwPG44paXiblKluwxWwJDyZKZDad9E9ahQCHMCCEwsrHq9XBqOHRCWmtr/reyAVNTERIqFiPkRqiI7b/8stnSUuiFzPy3Oqx+3wEPwuijI69hpOnq6IYolsgxavbY7q5/bm7OP9Pt+nbZl2agkcZuNtxcFgaIUJ/2WAMUlUqhK8pWi9bwG6AUZg6nDRga5bBPcuJavZrPAqSOAwEXGUK6jMAj1VFKlux1lsBQsmRmEcrQFPFOx50+FaYnJjz1vN0OMXM+7/+bDRcbJORzdOTMTbXqGqBWy4HIxERUhsZhT0yEcJq6Q2hw0BNRwHFiwrcJewPQATh0OlGrqFBwVmh6OsJqZhEWwxSwlMvBHmnrD0BbNmsOEEQPtGIx9FawW5OTob8iTGYWoRrCgFrkkhpJZgGa9JwdB1p4D+0Tuiwc/0kg4CKZnMsKPFIdpWTJhiyBoWTX2zR8wv+EvHZ2/IcKz1tbEXYiHZy/0b3QyHR83AHFzExknsF8UFuoUglhNu0sJiaiCjTZYlpleWYmii7mcmZ/+2+73qjbdSE0rAxG09WVFR//4mLUN5qeju0R7qpU/IdwWLcb9X6oTXR4GJWnVWxO2JBGtMyV1meijxjbRxQOQKAKNk1s0SDt7fmxUBzytJBXttL1/n4wUgpKsiDgLEzOeVmdywg8Uh2lZMmGLIGhZNfTlFXgf7Oou6NhpXrdHT0ABMdKVeh22/8eG3OQ0e2GPujwMLQw+Xy0zdCwG7qhmZnoWVapBFAgwwyWirpBCK1v3fIxHB5GrSBAkWbDIdo2c5A1NuYibfRKg4EfB1lfZsNp9MqQHRz4mAgtbmz4a4QRYcZgZJTB4TMAG0J7Gt4jTAYrhGn4bhRoUQCkFbf5LKzQcaGvk5icBw2fXWbg8TjGcxmPO1myjCUwlOx6Git1HB6go9UKhuHoKHp3oQcyc0BAGIhaPKz+i8WotdPp+N/0C1tZCRZla8vfg01CuAyr0us5YOl0zF55xZuxwioNBhHGardDaD01FczN/LyPYXPTQRZhOGVTYKWmpoY1QXxOU+0J4ZHp1et5Jl2l4tugKSygizCdapzMhtt6TE1FvSPV9jDfWoBRWRlCXwAmzbYze311a34Dgk7L5jqOyYHhetDw2XUDBCl9P9kVsgSGkl0/09o52N5eZEXV6+Ewt7cdcMDO0PgUR1woREPUV15xVmdszF+bnx+u0kxrjrU13xYibKpFE6orFgPowLxsb/v2zIJxgsHpdh1w7e/774UF38bRkf+Ppmdvz8dXrQ7X7gGImPn7FH4EBGWZEo4J1ouaRmSLwa7Nz4d+CQDX7QbIIJMN5gTNVqnkQAnAYRaOFQBCVpwyQrBRCooGgzgG7Yt2klPOMjkAMI5TNUeJ9TjeLlsWXbJkJ1i6MpNdP0OrghPTLCcc3va2vz8764wI7NHkZPQS29nx71erkTpPCGhiwl+jS/3hoQMB0t6pFK2VlhFWo3EhDId+plqNekVm/nlE1Ftb0cbDLBij+XnfVrkcYIlwETqjXi/E4fRBm5+P4yWkpo1Y0QSR8t9uR80kM9+39hkDbCmbQwgLjRBzwTni8zhTts34zYLBIdsMh6tp/mTEKdNzFhCj+zALwKdjSEBotF3GLLpkyU6wBIaSXS+DFcIR42TJkAIYHR46aCFTq1j0sBCamfHx4YKM6Fhu3nQQRFYUjM7enoerSNcvFs1u3/aCjGYBYopFT3NvtaJIIsUdt7a8lUepFBWnESLncgGUEGIXCnGshUI0kuV9UuLpXn905PuneSu6pmLx9XochNmwQ1SjHh/378Fe0cgWsMK+6UYPCKXukmaR8f/hYfx9dORzAXME6IJ5ABApYDIbZozOE7pRp67giJBbstF2WbPokiU7xhIYSna9jIdyr+esS7MZGhTCO/W6AwJaXjQa/vrt2w5uKCJIXR7A1Pi4AxZSzAsFD1nRP2xtzQHW8nK03Lhzx7eDc791K8DWK6/4/kl1N/Ptoy0iuwughZOuVHx7VIymwSoi7OVld/DT08GGwczQH80smtN2u6EtYnUPiwVIqlZ9v7AubJfwWL8fWWuEufp9f6/Xi3pJfI/PAYa07pP2PeM3cw7w0hR8LJcbFlPD8pwUusk6dX6ywu5kr7fLmEWXLNkxlsBQsutjrPJxoq1WFA8kFEYBQ/Qs9BRbWoo086Mjr/4MYwIoajajJk+pFCGu7W2zb30rtnl0ZPbMM/H9SsXDTLVaVIFGn8T7ME0TEz4eaugARHA89bp/dm4uxloq+THCQtHAlTkhKw3AQw2latXZr/X1ACK1WoQYNaxF+EsdXy4XGWMAnEolmDdS3aemgtXJ50NYDXhhu4TlSqVgahTYACLRFxEq09BbVnCtQvHj7LxOPYWC3C5zFl2yZBlLYCjZ9TFW+YRhABI4csAFfb/Iqjo8dLAyPe2OHMDT7YYWiNYXCLFhDmByVlf9+0dHkT5fLLpjp+M82z48dK3RM8/4/jc2HKxNTzvYUZ3N7q4DH61LVCxG2v7ERACodjsABewWgIDxHh05YEGvRKNXji2Xi/AcmWdsSxuy6twRDiMVn9CcVvum/QggBa2RWTBrsD6E5jhX2rdslGiXfWg1cNp+mMV2TrpuzuLUU/bUaEtzkOwKWAJDya6XoalptaI2zvh4iJwnJ+PvTifq7RDWIewEiNnedidPVeXVVQcLzWawFysrvg1COUdHDmpgQLa2/P1+3wHQ9naE4WjJ0e87mEEMDRAjPR5GpFTybe3sRNuQjY0AEmiGYFrQUKHRKRajECO6o6kp3y6ABFaJ0BUFGckam5ry9xkbmisteggoUy0Jei3YIGo5UYiRYpZmwQQR0uP4R4l2AUEAFDLRKG7J98xOzzI7yVL2VLJkV9bSnZrsepjWuyFkMznpQAEmZnzcQQz1ZGhSimPe33dH3+3690hf3952B0utoslJB0QwI9WqMyxraw5kZmf99a0tB0+Aoxs3PCRVLkfoaGYm+oUhWNb6PjQ+3d/3z8By0ViVsBc1gqano64RAGdvLwCWFkEEBAHaYIKYz91dP3azACYADxiuXi+E3cpGkS0GEKJkAXMGaB0bC80RhSu16GK2Z1lWtKsgh3AYx6A1h7Tu1HkYHU2/T9lTyZJdWUtgKNnTaeqktNL0wUH0FkMjQ80hdYaACDKw7t1zEFMo+Ov7+77927ddD4SIulyOYo2IlqenIzQ0NhZNUWGPVld9bCsr0SaDmj17e74t6hwhXAYwUP+GTLV8PjLeNLus2XSwRX+0O3eirxqgBTCi9YfyeQdfFHXM5/37WrUaUEjtIzrSowPa2RkuvFgs+py3Wv43wI+sPBgrKnrD4nCOqLtEqI66TGah78m249CaUoAiBcgwR1wrpwmkjwuJpeypZMmupCUwlOzpsqyTwgnCeJBBdv9+ND5FG6OtHjY2AkSMj3slZ8JWg4EzOYAANC0IokslBxn0JwMAUVAQfQ/hr3w+GJutrdjm/r6Pi+au9CkDJBDWm5oKBoX0eLZLNhoi7ImJGHezGRlV7XZoetBGka5Pc1bCZO12pOabRSYY4SfE0YAUdFKE3TieUil0UswZoGpyMlgkQBphObNocaKFGc1C34Pg3Gy4sjXf1fAVIE7DajBOo64vBU2jtEkpeyrZdbcryIgmMJTs6TIFQqTK49i6XQcbhI3q9QjPwHbQUgLHCfNSLruz3972HxgiMtNgl8iWInWeCsyAJMJ0bJvMsM3N0PssLTkI2dryz1QqUbGasbON3V0fy40boQUyC4dcKgXoolo0IutWKxirajWOv92OVP6JCR8/rUYAOhpiGgyioCQiZtLqAV65XIApgCBC84kJPw6z0A5x3mhlAshAXA3wQ1AN0KLSN9XEKcDI8WfT7rU1x0ltOtiGHjNgB9AJ4Eri6WTX1a5wEkECQ8meHlPdBs6T3l/Up6Gx6vi4/261IqQ0ORnvm4UDh71oNCJchojYLNgeChd2u/75W7eGmYaxMd8GRQTbbQdB4+NRJ8gsGrQSukK3s77ujNXUVIC95eUAU7OzEWIqFgMY3Lw5XFOJcXU6vj9Caczd5GQwMb1ehMOoGQQgAghSTbtUGk6xVxCDDqndDqCGxoiK3pqOTysSzisZfGTR0ZBWwUm/79tCtwRzBOtj9vrMMR7YVLc2e33JAMCdWYxJt6vsFGE5s1SLKNn1syucRHA1Rpks2VlMU+eVgVGmgOwt6goRToEp0fYRZB6trDhwQF+0uRmNW3HqhI5WVgIAUMXazEHExoZvc23Nx1IuB/hAKA1b1Gj4e6TPAy6Wlvz41tedzaH2D0xUq+WvV6u+PQBEqeS/SXPv932fOzv+A3gDHADezIbr8iAiHwx8TqiJNDUVxRFhXwAxZqGj0lpOfI4ss8HAxw3zQ/VtQBkAFM0UAAjGi4cvgBB2SRkhZW/0ukGQreE4rS9kFlqkbFq+WTiA00JtVzB8kCzZmeyKJxEkMJTs6TLtMI5TwyHSlZ66PePj3iqD4og4+mLRPwsoaTTcAaNtAbRQH4gQGg8CnHin4/uZmjK7ezeqRh8eBhOimWsIlNHIoMFpNHzfjJXjqdddwK2OGYe9sxPZV9qqw8x/b2/7dmGdpqYC3ADCaHVBOj7zSeiKgomEqtAQUa6A/RIOIwOOitq5XAAXBOGNhm9jfz8YNHRRAB3mB+ZMa0dR4mBsLEAfx02j3GwTVrM4v+jMGDPgEPAFMCTdn+9zHo+zKxw+SJbsTHbFW7AkMJTsattxq3xCXBT9q9cdAJDlRdVnWlUcHESoiu91u75NXqN4IdWhCRGtrUU7C4oJ4iTRLcG+IEbWNPZ+P/qgwUwB2paXI8UdwLWx4W0+ZmaC1RobiyarZu6oAVxoWqgGbebvEf4CaGgoqVDw9wEjrProeN9qRfsNswCKhAy1XYZZ1ERi3wAb5oQ5pZwBoUtqFMFqaZ0h5hoxN6FFaicR+lSBNOcEMMJvzUBjDhCGMzfMASBJrz2OVUX4el1e4fBBsmRntivcgiXdjcmupp200lYNB53YW60Ig9FlnUamnY6DDtLu63VnKNj2zExklrF9nDLd7Qm/kepNlhp9s+hQT6ZXt+sZZhoum5gwe/nlcPr9vqfd37zp+yLdv1x2UEKKupmDCDrVw4pMTTnIAsgxXjM/llzO5wRmZ3fXj5UMMPRUhUIIqqlijeBZV4AwRmYBvrIFHs1CKJ7LhdgccEjokeNHU1SrBYja3w+gCsAB8OzvR60nDX1pFhjCct5DeA3bBZDRDvV8DgCTXfHyHS3PoNfqFQ4fJEt2ZrvCLVgSGEp2Ne2k1GbSwFUwTSim1QoGgmrHxaIDIEDE+nqEXzqd0PW02xHquX8/wmrohxBga1iGekQ49o0Nd/Szs5GRBKNEeKnf930A1tbXo+ErThjmJp8P0DU5GU6+03F90dFRdKcH+NE5nsKMAAQcOdW3ASpkzsFQmfn3AVRjYz4vaILa7WCPCMPRDoQwVbkcqfMAOrMAYs1mlCUYGwsND+J0wB2sDSEu5hSgRZhQe6EpUwYo4W+E1KT0M9dZAJPNSDvNCTxI+OAKOpRkyczsSl63CQwlu3qWXWnjBAkxEfLodCKtfXvbHWy7HanbAJFWK7aNyHl/P8S0ZEJRSZqwG/WH6D6vtW8IsbTbAXLu3vXXbt6MFPJaLTRICwuRuWYWmp9SKYTFg0Fku1Wr/hswRagNBoUSANVqlBZAR4Nuie0D5mBiWi1ny5QhAVzAfCCcpvghcwaAIWyl/doAUwBIwOTeXmyPKuCMDR0QAHhiYjjNHqaO44LpAqwCpMwCjNCCRNlEQA+ZaApYVByt2iyux1GVq7MFP9nPaeGDpC9KluyxWwJDya6e4aTQo+DUeI2QD+LnvT0HLThnHHSlEk6s23UGptuNOjulUsS/tary1lZoeGASaKOBwTyUy6GDQUeDwHp2NtpVjI15Jlq365/f23NwU61Gmw2qM1MSIJfzbQC8treDCSmVPMRGnzB6rlF4kVYXVHcmvES6O0Lw2dkIlyEYptwATAsghDDY3FyE0ShOSXVutkHISbVDhMdKpdBjmYV4eWJiWBgN26Mib4AH4AF2B0BqFoBEwZ2yi2ikYHK0rxkgTMEfYC1buTrLXp4V2CR9UbJkj93yp3/kctjHPvYxe8c73mHVatWWlpbsve99r33jG9848Ttf/OIXLZfLve7nL/7iLx7TqJM9EgMYwCgAANAHNZuRMj4x4YAITQ1M0Nqag4WNDXc29XqEqujXBctSLg+zTQiszdzxq85FKyoThkNcS0HHTieKPxIG29qKTKuJCWeMcjkvplitht5pbS0qZvO9ej2yrLT2DQCj3TZ79dVgw5rN0LhUqyEsZ/zdrofY+n1ns1ZXowCkWYQhzfw3jWUBo/R7o0o2TBearGYznD0sCCwVY1R2R5kYgATVvGu1ACCAIkJniLnZHgwiGXMwWLCJZjF/2oONccLM8T/gDwO08T3NZOM7Z7mus/oiXkuWLNkjsyuz3PjSl75kH/rQh+wd73iHHR0d2c/8zM/Yu971LvuzP/szK+uKfIR94xvfsGnqvZjZ4uLiox5uskdpmq2gdYVwnPW6O3B0MQAcMpbGxjwkNRhEs1FCKoRI0M2oqNcs9DEUBUTIm8s5UMEx41A16wgxdb0elaqpK0SWFlltAAA0TZXKMNvCPl95xWxx0cFSrRaCZUAVgMssqjarADufjyrWMFk4Xhq5zsxEMUnt6aXsjjY/JS1+asq/i/AcJg8tULcb6fdsn3OqrU3Q+NAyhBAWYUQyygA4AAmy0jSTDi0Z8wtLxfVkFsCH4+F8qrZKr0OyDTnHVL3WdH/mbBQrlA2LmT1cenLSGl1OS+flUtuVAUO//du/PfT/pz/9aVtaWrI/+qM/su/7vu878btLS0s2MzNzpv3s7+/bvlSjbameJNmTt+wqGZFxtxthMBzr1laIpwEEm5vuoGmGCoCiDxiia8JIhJA0bXxvzx9qpLK3Wv7d2Vl/bW/Pwdb0dPTJIvWbdhikmrMvQAOOlPRs+pBReJAQ0WDgzFCxGALwbte3DXjb34/0d82mImWe7dCYllBaLuef6XSi2jbZZTA3WeZie9sBFqEnutgjwqa2ELWWAEEwaWSoHR0NhyfVgWj6P9lpCOE1ww8Aa/b68NSoHw2DsS3mkH3ChCE4B8BopW3ONWEtQDXnWkXXXMvsQ8NiZgG6zpOenLRGl9PSebkSdmXCZFlr/s1qc25u7tTPfvd3f7fdvHnTfuiHfsh+93d/98TPfuxjH7Narfbaz507dy5kvMkuyNQx4ryoCQTL8/LLEUra3DR76aXQ+gCcDg/99dXVYIO6Xf9/bW24rYaZvwb4IVX95s3hWjvb2w5szKJoY6HgoTT0NzRgvXUrdEQIs2EtqEOEs221IryjIu1WK6pRE+ZjfMwTGioE2DMzUbl6fT2cdrfrx4czp9o0AE41PRxzrxdzCauF4JxQI6CoUgmmCfEyXerNokI4ocRsqQSy6xBIa1q8NmwFqGlNJRVawyJRMgDww7ybDWevaSVz1QZpuI5WIQi7uWZyuWCstLo1+1PBv4JLs+Eq4MpYnWQKqtjfaZbCb4/eHuS8JHvslhsMrt7dMBgM7Ed/9Edte3vbfu/3fu/Yz33jG9+wL3/5y/a2t73N9vf37b/9t/9mn/zkJ+2LX/zisWzSKGbozp071mw2h0JtyZ6gUd2ZwofdbrAz7bYDGjqg/+VfhqaIEAup2/QsI9Rz967rXUqlACdzcyGwXlrybbz0ko+hWPTPkxmG06xWQ4z8pjf5a9PTkWY/GDg44O983kNca2vOlszO+rar1WBlqtVIW0d3Q6o6rNWNG76/TsdBD81dKWBIFWt0RDA4sEmAvYWFYMsI08HkaFgScLK3FyCm2x0WOrN9s0ilpwQCoTW2TcYZITJqNgEYOE/UAoI9g42ithIgBMCRZV8Ip3G+yNBjPDCAWfaMY+DY9/eDeURgTkYec8D2NT0f0EbKP6yShtNUMH0WZgHAp+yT1kzKWmIrHo+NOi/Zli7JHpm1Wi2r1Wpn8t9XJkym9lM/9VP2x3/8x/b7v//7J37uLW95i73lLW957f93vvOddvfuXfv4xz9+LBianJy0SSj2ZJfTcJQ4OdLXCXF1u/4bAfXmpjtkWk5glYr/dDoORKiLQ8FEgNLRUdQX2tpyBkhZjazBHE1N+ecmJ/17tVpkKnGNKXCpVPx12COYg5kZ397MjNm3vhXMAg9YWKS5OQdqFE5Ux8/73W7onGBUms1gPXZ2Qgg8Oeljpss9YIeCldQLIpMMgbGmkzNGBMUwJZqVhXg6G0pSITgAiSwwWBjAhIap9NhgXfgM4JK+Zfl8HLNZhD/n5gIowExpmFLBG4yTMk/a/FX7vLE9zgnAh2NRRgs7S3YZIb+zao2edMbaddHP6HkB/JqNLsWQ7InalQNDH/7wh+0LX/iCffnLX7bbt2+f+/vf8z3fY5/73OcewciSPRbD2fJDoT/CYLS/IBQDM0JKOkyBmTu9TsfDRSsr/trkpIecWN13u6GX2dgIgKKtJsyC5TELkIAweHo6mCjSywERNIoFuAHoSCvHodK3q1bzH1LNOx1njQYDHyPi63rd7I1vjJRytEcUSgRMsn+z0N389V/7/t/whmGHfnQUYUnV3FAagOayjBcGCLYol4t2KAp6ut2oVwSg0PcpNVCthqYJJ44mR/U1WUDGPMMGAZp0fAADxqDCZ8TTqvNqNoP9AVQxfyraxhECkBWAoH+DaVKQr9d7NrvsuOrVZ22FcJ5tXrRdBUbqoueB86LhVuYhlUy4NHZlzsRgMLAPf/jD9qu/+qv2xS9+0Z577rkH2s7XvvY1u0l7g2RXxzQbB7ag33fneP9+VFduNqMD/e6uOzqt8UNfr0bDwROOVov/8eBC7AuTouCnWPQxUTMI8S6hIHQ1CLNv3AhGolwO5zM9HeJiygSYeTjLLP7H4Wrz1Jde8u3NzUXj2d3dACitlm+3Xo/t0HB1asoZMwodNhoOpCgjQFiH1HkcFxW80T9MTTk4Q5e0uzvMjhDiArBoxexyOUoVACAmJ6MuEfNJe5OVFf8+bB6AC8BLWw90Twi3YV8AfoiUC4XQNdFottfzcwJLBXtDOJJQHK+zPfRUWtWa65Xtw1Ip4OK6NBtmdrDzMD4c52nO/Lws0kXak2akTrKLBmp6DQB2AajM/3VhyK6AXZKr8HT70Ic+ZL/8y79sv/7rv27VatVWV1fNzKxWq9nU36yiX3jhBbt375599rOfNTOzT3ziE/bGN77R3vrWt9rBwYF97nOfs89//vP2+c9//okdR7JzmtZ34QHS7ToIot8YoTGADw4IJ91qDWtPYJOog7O05KJrbW9BderjxI40bF1ejjo709MRxtEeXrVaMCy9no8H54pomLo56FYALNp5Pp+Pwoo7O/F6sxmAbXY2RLqIlnd3A2BsbPjnOh0HQ/PzMb563Y97dtY/Q5o++0ZkTS81tEIHB5HCD9Botx0cwc7BwuRycbydjv8PyCiVAoSp5kibywKACTXxHRgizj0hCVgxADSvMYZCIbLXYNu4ztRZUQOJOUfbRLkDHDrgRlnCweD1onBlgHCYfDbrIM/b/PIszvVJNNR8kozUWeyigNpxoIrtPm4AetpYn/QYLoldGTD0i7/4i2Zm9gM/8ANDr3/605+2H/uxHzMzs5WVFXvllVdee+/g4MA++tGP2r1792xqasre+ta32m/+5m/ae97znsc17GQPYzAvOFF0KQcH0RYDES0hJtLjd3b8dfQzpJQDdgaDYA1WVqJitQqKKbJ4nJXLDoZIbaeOkVlkPwHmOp3Q4BASogFqu+3HOTbmf5Mdt7wc+iecF+CPAo0KEPf3/XhJ6SYMBgPCD0wLxwiwYJu8Dst2506wbmhvlpcj/ET4h/MAcAIE5HLBXBWLsX0E3mSEaUVsMrpgT3AwgKlGI0JUWozRLLLFYGJUP6RMDQCEmkgAGDLJlM3iPcAO15uG5tBdmUXq/0ksg2bCaa2j7OfOyvicxx7FNs+yzyfFSJ1mFwnUjgNVTwKAHmdXIVz5mO1KZpM9TjuPGj3ZBdpg4M653Y4HpvYVW1+PkNbqahQOJNsKBoH6Q/Tv2t72baMVajSC2TBzJzY3F2xCv+/iahgPbH7emY+lJd/u3bvB6jQaPl56h5k5SBkMHAzdvBmhIipAaybS+LizTKVSHEux6OMCyKFBIttrfd2/v7TkQMXM7G/9rah1RLuRjY0oHAkzcudOAKtyOUJlu7uhR6LO0fi4j2NqKnqekVU3GETmWb/vnwPYKTii4CThRVgyqklrXzDON+fRLEKRk5PDQAo2qFQKJwSLhfgZAEOGHg6LecdR47QAQmNjwS6aBdOl2WGEB2GOCKPp71HX+XVySo8qFHURphXGASznZYbOktV3GdiYizjWK2BPfTZZsmtgABTASqHgfxMOqtdDCwNDtLUVq7B799yZAxzQ0hCaaTY9TESNHLPI6IKNqFT89ZmZqPps5mEkHKmGWsxC/wJ7BWBot6PQI2n7lUpsFyaj04ltwQAVi/7a3bvRHgSHDeCDkcHxoh8iqw0BMX9vbEQrEXqnsd1CIdpz7O35OMbHHWgR3jLzytfM1fi4g8aJichaA4AoQ6Tp7cxXtqq1Zo31+wF6mEdACkCL0gKEQelRBqDVfnAAoYODYL60jQnXXlb3QwhNARNjOjiIEJoCPFb+GubNOv8nwdA8Sbuo430QUHXaPk9ibs463rOwX0/6PF/2cOUTsgSGkl0+I4TFKvzoyNmfVstfp7ji4WHUhwF0NBr+HlWWKcYI04QguNWKzCZNx56ZCcc5NRU/ZJQVCg4GWi0XYgNANDOJkBfi5elp/0GHBHOgGph63f8nfEOhRVLwNduMlH8eagpmAAQzM749MshUiA0wIo2f7DWAEkBmft6/PzMTzh7QRiZVLhcNYNk22qOVlVht0oIDETjZejTMJaxEth5zqKJjQKLqrpj33d3QYBHeggHkeFQLBFPIa5Q8IMwFc0f9H7RPZnG+2Q9zovoi3dZZdCjXzQk97PGeR9+TzS48rvbSKKD2IKDrMoXDRtmjDFdeYUCVwFCyy2eqDzGLtPZez9kcBQakLeOEqRSNZqha9fcWFnx7q6vDYTGco1mEb9C0VKuRuVWrRcVnnBwVnPt9d8SDgT/4oGPHx6OAoTrr8fEAc+PjEQo0c9ZJhdNs0yx6fe3vOwtWLAaoIMzGA502IqqtoSI2DBFOhIwy6H30RM2mjx9gurbmr1O4EU2SWYyFMAGOiXMFcBkMAkQA4LTGjjItABo0T7mcj3N7O4orAtLUYKu4PswChDLPhNMYl9Z/0f50fAewhsBbgQ+aIzLSVOxvNtzf7Cwr8CvsUB6LnZfZAJjwXCFr8bg51tcfRFR9Fdi+iwZsT0G4N4GhZJfPyLBCsNtqRY0gtD6aMQQz0m4P60sQFh8cuAOl99bmZuwLJ4jDpojfwkI4ZLPQq6gNBg7SyKgqlTx0BHujKd8ULKSadbMZadrafJR6PoCrWi3mgM+aOWgCNBDSOjryEFWp5Nsic6xYjPAT2U/okPJ5B29HRzF/VMDWStMwOqSr02tNGRj+Hh93EMb8Ex6kOSxaJ5rjwighAGfeW60ASjMz4YR4LZsJxnUA40PWHXqf8fFg3MyGizxqrSDNCAMMq2aMopTqSCi7oIJyQNNZV+BPgUN5LHYeZgPgxA/3F4D3JHvYcNJlPncXDdguc8mEM9rVGm2y62HcTFSGfvlld644VDOvLTQ9HUyD2ev1KLRpKJUixMZr6tympvwz6HEI05lFs1PCZ2xDDUYABspsuD9XuexjBbSQOg5LpM622XQABGgpFPyYb9wI4NPv+2tk2gF2VN+iNH+r5ftXJujw0OcUvQ26Ktg0NE79vh/75GQ4+Xo9wAYC5omJCDuS1dfp+LFsbw9n/qmuig73bBvBNIwdwmvCfzgxmMNcLoCjtragOvTUVABBBN5ooQjtqXidsXQ6cS2o/giGCi0S5zqXi3AZgmq+y75VR5Q1BUJX2KE8Njsrs8F9oAJ57Vl3EhB4lOGky2IXFRp7CjRI6U5LdnkMh9bpOKtBuAsbGzN79dUQBtfr7rBoFkpYaGfHf2AK9vYizJXLuRB4czNW8/2+MzqkTCO0xiHhJGnsqQa7c3joQIiV5/R0FFKkxs2NG1HdmgKBFHwkNEUfL9Lv0TvR5LVU8rG32z62SmVYVNxqeRir240HUS4XHeHNoiZPPu9gk0y8XC7aVVAzaXbW52J3N/bFMZv5PM/OBkCh+nehEEwSonGqgtNnjbYjh4ehKTo8jCrTFDdEK6QODRDDuGF/zKIVizoxvg+oo70HYbhcLsAuzAFFK7XHGKwS1wWtRCjCCUt0dBQp+pyHUcCGsCJgCLaCcXMMyYbtrMwG5wsgq+HS7PdGbUvPw2XU/1wGe0pAYwJDyS6HDQZRU6de91DYzo5nhdFCgdpC6FPQ+Ji5k6aIIjVtxsf9/ZkZ/wx6jqMjB0Tr6/5we+MbI3Os0QhwQD2aZjOYgVF2cBDp5mRQTU5GVevBwB06DMJgEI1SGQ+gAWdMk1iYpHY7ijIqiKDi89SUz1elEvqgg4Ph9PexMdcaHR5GbaTp6WBlqJoNALt5M8Co6nZI3WeciNgrlXA6gAWt70Q7Dao8U3EbIXWnE4wO7UMAWYheEVzz0CWVH7E1bBPMEeAOwAJzowwdIJfVLUAM0Ej7EITru7sxNrNhdk/T7HHEnPOssx0MggVTPRUME9cb718x5/JY7LQ5Qd+n92KWoRsVnuS7qg1LQOh4u+yi8TNYAkPJnryhOSGcsrrqPzs77oC2twMAFQqhOyFNvtHwn7W1cIQ4wFzOX+dmRQysLEEuF2nqMDc0J+V77XYUDNTu82ahDTILDQ5hnWrVx4qWhcati4vBzuDIcfwULdzedjaJOeJYSQfH4TN/9D0jZRyRKICxWPSx0YqkVAqQ1O1GSw4NCcE+1WrDhQYJN46PRzVsQCe1hBqNaAJ740YAEEBcPh/C9sNDH9PRkTNbMCy0NOn1goUxC3YINmZiwve1vR2MDPMC88b5RFjN9lRDBJBF+6NiacJvlHdgDtQRsG8tFnlcHRfGwXxrFhr/p5DZg1s2fENiRBZYjgpP8n2uTbPXVxG/qvYoBPxXQTR+iqW7K9mTM5wQjn9nx0EHoTCqRcMIEOLCccParKyEBgbmAD3N7dvD/avQgwBCCKc880y01KhUzF55JRipw8NgHNCgIK49OBhODddu5oSmKpXISLpzJ8JmACgADVle6GnMopXG/HyEYprNAE0LC36s1WowP7WaHxdhOhgbwk07OwGEer0oYEk9n/n5KNZI6G0wcEYon486PNRagk3SGj/Vqu9/YSEy3qipNDPjY2R/FG0kK0sLPNKrjGuF64UHL4ABpgzx/dhYNKVF50UYFG0SzJJm1gG+YH3QRHH9wNxw3jiXWihStVvqWAmFKpCGRdIsNq7XrAYjhczOZ6PCNyyQsFF6F86ZtpAxC6buqoLS0wT6FyHgv6JAyMws3VnJHr8RGqIuD/V/6DlG2IRMJAVFrVYwO8ViZIetrUWYYWMjih6+/HKsWhADz85G6OjGjejZRdo5OpqNDQdnnU6wMOhnEHhPTUV4CXBBbaDJyXD4mqaNs8Mp8iAeDPxYCBehz8nlXDBOmrymqpdKAcp2djy0xkOt2/WxVasBSgYDnwPSyqm5RPNQ9EewX7AgZv45qlejbQJYTE9HjSWYMNUNdTo+jsXFKFnAMbTbvp1nnokMsFYrxND8UEIAgMqDG+YIpgqgaxZ0PeCIjEStO4RYnfOjWYWI1hFcY5yf7e24ltkeTBAtVxgXmWq6LxX4qrCebZlFaE/Zpuz9lGy04dCPC98oYDIbZuS0NhHfVd3YVTNlwBSon/X9p9yuKMRNdqWNMAChGFpokEKPQyYjidU9YKfZdGdUqwUT0e97aA1j9a7Ay8w/j4AXrcjt26FVMovGpf2+A6fBIBiXXs8dYLkcjVBhc8wiXLW358CEcNzkZFRuho0ADAEgEFBTUVp7aqFb0kyYg4MoKsgx8zM9HUJmHLKZA6Fm09mf/f1ouUGG2cFBgJlm04+BY2M7hYKPlfYoZKHBGBUKwV6h9WEsZH51OnGMAFPCXXwPnQd6KxyV6sIoubCwENtCEwUwHBuL/m6AbTLF9vcj409DhoBQQDUaMA2Rdrs+HnRbhBKZJxgpGAYVYgO6OC7AE+BIQ2/HhcyyK3nNpHwa7CJCLmcJ35ykdwEQsJi6gsJgMzs940vf15DhFQ57ndcSGEr2eE3TjQmHtFr+XrcbbAircaoLI7xtNmPVvrLiTm51NZgKjG0WCs62wGjQWqJQcAaIrChYnVzO33/22ViJHxw4o4Hg95lnoo9Wvx+hFw2PTU0FcwSLYTaczo3D1MrGMF5m0ZF9b2+4OzsOmcKNiJCpG4S2CAYI1oSK0lSqRlxMyAitFPtC/EzIaGPD/79xIwArQGxtLTQ+s7PDAA/wtL3tn6Xth4IDQBh1hzgeMtEAGdRr2tkZrhUzMxM6KbNhQTwhTa475pFq4jgAikECYJkXWD3CnYRDCd8Swm21fBwaRuOcMzfMrYZvcNYaBlM202y0E1Mg9DC6lsvm8C4iXJO1k74/CjABorOg4AoKg83s9IwvfrMI4Pgv03XxiC2BoWSPz3QVgoNoNKI1BiwHtWWmpvxvQkek0NNItF53pwzgUaOCMFWUb9zwfdRqwysgmJODA3/PzLc/N+djKxT8/2YzHCyhIRwv29GQXr3ujAuhLZgF6iSRlUQF5aOjAICDQRRyZP8LC779Tme4Tg8sFeMwi3Agmhs6shMGRKSMrghhebHoQEbFy4DQSiXCRNvbw1ly29sBXGEncGjUTYItQZeDzgjAA4gk64xzgvMBABOepPAhoHdzM0TPZJih00KfpYJrBXJ8BkcxORkVsRGR87eyOQBPjoHsMxwN1yFgidIDZsN6FMakGWfK9ig7pNvmOn5QXcujAB0XYU+q3pLqvHROLlIY/CSB56PK+LpsYPoBLYGhZI/eCFWx+jUb7oGFiJdifrTWIFwBM9Ro+HdJscf5E0rTWL7+vbvr7NHSUlShJrSCI2EVVK0GI4KIGAeOY+x0Qpyr9YkAP9PTIZbd24uso3I52I1sM1FWbQCQfD6ywswiG2t/P6pMb27GcWu48ObNECjjSGnIOhj49wFhs7MR/gPcAYy07g9AY3zc94vDQFeD/osilZOTPsb9/ZgrZe8QpHe7PgayqgA8sHJkESKiBkxMTITwHme1s+PzRFiMY1DdRzY8peUW+n0/dwDptbU4ToBbLhdsIrWoVIhtFun46JHYp+pNCEly3WlGIiwPDmuUE+NYRulazhreuIxFHk8L5zxqO25OHnbflwF4nhQy5BqC5eZ5dNK8X4ZjukBLYCjZozWybbixlL2gOCLs0MpKOMN6PTqyo71AzAr4IaMJjQ66IByCmdmtW5Gt1Gq5gybrDEaAooxLS1GXB22RWehwtreHGSKYkOlp1xSp40Q4nMsFGADoLS35d7e2Qo+EZoYwGkUEAUvUBCIjjD5pHCsPbITQ9XrMweysM0HMEw8vmrOi01H2g1UjYmPqHwF2YDYQL29tDWfC7e8Hw7a25tsulZzJMwuNDtqiubnhkBDnnFDj+HjofggxwroxJ5Rb4DhhogCCAFuAD6BHRdqAGBhIQlCDQQCyUmm4rpFZOBnOP68TAs3WqIINUqfL9ZGtK8TYso5J6+HwubPqWp406DjOTgvnnNfOczzZOVEtzcPOyWUCnqOO5bh5P8ku0zFdgF3dkSe73MaqASdKWjIPFhzU5qY7xM3NYIBIeedBRAiEmkOs5HEgrLwxTY1FrzI3F3ogsq90NQTAoTs6LSYIbeDkYLjK5QgxtdvDBQfRteB4AShknJG+3u9HxWj6guHw0TZR2XpiwksO3L7tn4eBMgsnRviMej04bkJZ6+vD3wEYaYPVfD6qdQPqYIfq9QBN8/M+LsYKO7W4OMxsqR6IOkedTojLAYzocpSBo9Dhzo5va3Y2NEjMb6EQYbhOJ7L9Jicj3IreiOOFseL8k+0FC8f3ZmZCQ0GYjWw2xk7tGhwHGi6YN4TlnEu2w/GSts91y3XC2NQZa3iM/xVQjdK1HOfILxp0XKSNYsLOC0gehLXIsm1cgw/r4C8r8MyazrtZPBNGzd9VOaZzWAJDyR6NaeE4hMEwLoSqaJz50kvDoTBSuHGohK3qdXcm6ETMHERUq8OhAv4mxEJdGqo0w+aQBUSqOkwHeiUypvh/ft7HwP/0uapWA0gAkNC/IFau1dyZayuIpSX/m4KAGxvxP2wLvc4GA98/ISA0PXNzEbYql0NUTKjl6MhB0ORkbEvpcrZF2w0ysqj3U62GoB02hT5eCLsBD8w3D0XOPyCyVgvBMuEjtrW15eOHUQGsImimvxzn6/AwwpE4LwAq7Brj6HZDWExDW8aN8wPUIJrmWuXaJeyHGB6gC2jW2kg4AwpOso9iMbLpACKEULNicgCAZpipg1dTADTqs8eBgUelIXlY0+vT7MFCMQ/KWmhRTvZ11u9eReCppvN+2vxdlWM6hyUwlOziTVcNZEvBCtHCgdU0AKTdjkrSR0fu4HG+jUaIp0lPx6hGPTbmK3lqFvX7oV1BZ8JKHNaAhqg7O15QEGdaKkXTUZyiWWRKdTrDvbFw7DAPxeIwiMHJkDpOqjjsQ6cTxREJM1EOACdLSwjGTf0jqnOPj8fYCLWY+dwtLwc7g06q14sCknqeAEjlsrM8ZrG9YtHDjjTNJfOP5q70WyNs1W77uMpln8+NDbM3vSmA361bw8fabAYTpT3gKKKJDgrGamYmro1KxcdLaEyzx2BkAL2wYhMTkfXH+cABE7Ls9Xy/6L20eB8giOsQgGoWbFnWgWCqVYKZUnE12W76fXVQzA+sY9ZZnQUMnKQhuQzG8+G8oOZhWQvmnc9ehHbmsgLP4+ws83fVjukUS2Ao2cUbNwzZUjg7QgztdhRZZBWMMJkQyWDgLAy9rxCqZq1QcAcPG0A2F20vqCm0v++fpfs8xf3IINPVOvV80AeR3l2pRKr59LR/RwvxUakZej2Xc4ACSzE56eLmcjmYLkACgmvaStDxHWfbbvv7y8sxRhww83XvXoxndjZ6rMHw7O+7fkeLGBJ2ardDB5PLDYe2qKnUbDrLBBNmFszO9LRrvrTjO5+Znx9mTQgXmg1fGwACgCaMF8CEMgHj41HskHNK9hk6Kxw9OhxqCRGeMouMPq3DpMJqXqfStpZBMPNxUAspn/fxAcYZmzJDmhmmtZvUecNsAn4x/Yw6JQUKvH5eMHAZgZDZg4MaBTHnZS34nD4PTvvu0wA81TRciCZx1BxcpWM6gyUwlOzRGO0xeFCwQp+acoexuupgYHs7HiIIpjXlnjDXcUbV5dlZd9RoSej4jm7oxg13MIS6yEibnh4O50xMRGgMvRCOlEawiKup4txoRAiO0IvWjzHzMXY6fkwANhqpKjPE/nggo2WZm/P6Rru7oStqNh0IEG4hDR4AhkiZGkmIt3d2Ym4IvaELMgvn0++HgHl62seuPb4ASP1+FM2EGSOUSbgNXRas4MxMhEvNAmi2WrEvMx/3K69EbSJCq4S0Jif98xRRxBEBUAAYPNRh+8j0AqQgjtY6TwBGAL1ZpNEDdjQ93mwY8NEHD0E6iwFAlWphtCSBOmOcTDYsoWHhUTVjnoYQxoMch7I0ACdNCDiLncR4ZB3/0wI81TgmwsFa7HWUXYVjOoMlMJTsYg0hL20zaIOwseEOkIacCKfX183u3o2spdXV0KCg9zjJYF0AEoOBAwxaLJhFt/dczoGQsh47O+GM0PMgnG61/PM4cbMonGcWmVHUpaEQn1kU34MFaLWimnOz6d+dnw9HrGn4ACTCX9of7eDA546KxhxHoxHiZ3RHN24M1/ipVCLkB6ijAnUu569RHgDhuTIWS0sRYoJVoT7S/fuRxYXO5/Zt39fOjn+XkBpOZm4u5p8HLsduNswi0XsN8EQoAjbLLMJ+2vICvRggqFiM7EauT+1mzrEigjYLoKTO1SzAWLYyON/nujAL4bRm/gHENIyaDbeoqZOGeTguRPE4a8o8SmYgK+o97TiYv2w23nlsFONxXCjsaQGeatxP2gz6Kh/PGS2BoWQXZ4SN6EqODmR93QFQq+WOhTYOPOgoNtjvu0PF2Z/FtF0GoRl6UCGWVa1HtxsZSGSmwQYtLYXTg1HC2aIrqVRCq6J0Mj3AWq14UPJQRngL+Jmbi27vsEUAOWr/sF+KSmrHe9gtUvqpkj01FaEgjvvZZ30sGxv+u92Onm/Fom8HB0OGGwJzavm0275tzmmh4CGxSiXCjZqhhU7p6Cg0Ubu7EUYCaKyshKB4aioYPeYFkEHRRMJkgFjCdWhs0J8BJDXERnq+CrQBOeikVLMFiOH6ADSRJTYYBNumjXrZPuFDQp/K+nAtZu20sMNxTvqsnz3NzquLMXs0dWZ0HGyX5wHndNR+HlYrlDX9zkmhsKdJO5Odw/PUrbrilsBQsoszFfy2WpEWjSPO5SLFm7AUQKhY9NcJQZxnn4TYYC0IsQCUCEFNTjoIghnCgZpFDR/tcE66PSEVdYBkhLH6BxSRuk42GdqQ+XkHLgACWAJCgcvL8TpOvl4PZw/DVir5cZXLPq/Lyz6n9DXjgTU9HVWmx8b8uLvdOA60WgsL0ZdM09vRU7E6RCw9NxegDPALCNSHJiL23V3fztaWA45bt/w479935qpYdPALmEPgjnaIJrKtloMsxOcHBz4fy8sRmiLECCAy83PIddDtBtBFmAt4r1YjowyHm8/7e9vboT0DFCDKJjQJ4wQzCWjKaoSYG7NwNOpYz+J0TgNCoz6rNirUcxyo4bVRYID3H6bOzFnYF/Z12n4eFUtzGsh6EtqZR7Wvp5HpOqMlMJTs4Q39glloOdCAbG2FMLbRCOe8tjbMCL36qju8tbWz7xeH3O0GE0BbDmUGeHASAkNMXSq56Dif95AOLACZU5OTka0E84GTQ79CAT4yyubnozYQ9XAo+IegvFSKPmI3b/p+KxV3ultbURUa7RIOFYDw6quhveEz1Osxc1CBMHxnx8eBpoZCk2aR4QdLhkZKQ3Polg4OotksjApZXOi6OHauAwoitlrRWuXVV6NVyMZGZORtbg6zUVNTwQQRKiOrLpcL1m9317dHFW0E7jhQ2oEQ0iKECJuEYJ/9EBbgb7PQ+LBdWE3mutXyz8zMRPNbBNAIsQmJkU3J9xUwZWsFnYelOYuzOglsZEGNhuz4ro6Zuckex1lYq1Fj0e9nxwGANQvG9bhtXwRLk932WQHC4wAMD3ruz2NPE9N1DktgKNmDW/bG5AGWy0Whufl5s7/8S3faExOhR8GhkInUaAwzNafZ9PRwt3gcMjcyxQ+1AzvMgFmkfN+65WPb3va/YaYQ97K9QsGFyGibzAIAUT+mXPZQG5lfMEMANLLeCKltbUVWGmn5Zg4SqFEEi0UqOE6d+kaEnHDUMCKLi8OVoBF/w7yUShHa2dgIQMVx44Ao4mgWzp6Gqzdvxjln7LA5ZtHahPONnoy+bdq5Hs0X7N70dPRQ6/dDBwaYpS0Jomea1xJ2RYNFGK9QiN5xOHNqT5mF4J+CmwCag4MQypOFR6VrrauEpimXC10WoFJT4XGigGrCsaOAQJb9OImlOQuzdBzoGcV6cB8qWFPQoiL7LEA4i8PW97UYK413OUdsixIE7O84BuphWJpRAE3DRacBhLPu82FYnbNkrj2sPQmm6xJYAkPJHtz0xlShnaa5a4uNlZXhnlS5nDsjNB3UgoFdOMlwloRYCE9p6nO77WOYn48Hcj7vr+dy7sBIjy4Uoujg5KQzRvTq4uFAFhcZQmiQEN3mch7+aTbjgQ2oGAzMvvWtSCHP5eJ4KURIraSNjfgOmhqOER0NIb2trdCzAJK0thIhJm0YSo8wXjNzNgoGBCE0zj2fd5DHvu/fd+A4Pj4cBiJ0pDqgvT0HP5QJWF3192GBdneHgQeaLLYFiKbvGeUYisVI4V9ddYC2vBwAu9fzz83OxnXGWGnzQYaXZu9RWwiQCbMDQJiYeH37F/o5KXvHeUaPZBbnWNPscWZcD3wumyqvjlgF1Gdllo4L9XB9K6jB9LOA3SwYGAUQTnPYOhay8RgD553j4RyRZQozeBoIyB77WZx6FqCZBXOpx5fd1lnZmgdhdbJM20Vpoh40xPoUWwJDyR7MlKpGbwLAwQGurwfbU6+7o7h/P0IwgKjNzcgg0yywUYbTQFRbKETtnW7X2QIeOLThQMjLw7dej1UfD1vCOYNBdG4ns4txUpEaQEfoAN1JLjesycHZU2QQMbSZ/56bc1AwPR3sDNll7baPifAPYa/Z2ejJBfBstUKn0u87A4WT2tuLGkKTkw7UEJvPzDjIIZynD1et6TM76+zKzEw0Q52Z8fHk88GuvPpqaHEAw8vLwyUJarVwuNQEooAk4TeanpKRSDFJRNgAwW7XP4PuiaKNMFjT08HCof+h4z3MEYAHwEtRRwCLWQCodjuAJOfj6Mj3ybWVzwe7xLWPMFyF7bT0YFxm/rmpqWEtCvsAeDG//X6ALYBEFjydtWLwKNaD/7NsTNaJjhJ0n+aw2a8yitzXWpBVyw1wrLx2FhBwHvAxCqBx7nVedTtnYerUzsPqHDf2h9XzPI4wG/u5YmAqgaFkD2bchKR8IwTe3fVVOj9bWxF2uXs3Qh2EGqi3s78/3C/sOOPByOr71q3h8AROBtEwD3ZeW1sLlqXZDKeJdiSX82N55pno1A5LwKoVx7C15cDk9m3fT6PhjpiWEJr+zj75bqUSzBHzyRjMomgirUlo6QGguHHDjwVGjH3QDBVnRDNWGrcSMsPZwaxNTMS2ELfncg7YAEsUQaSpKiEiQAfFNKkaTp2eSsXZm17P54dq4wCLXM5BE6Gu3V1/D+C0shIAotOJcaK/yeWi5hHCb45vf9/3CchSFkbrJAFEOG6zEIATTsznQ+MGuNbtwXDiQDXcwrWkAABHj1YNNguQz+dwzlkmh7nGges+1VljgPoskzMqLHJcWGiUg9PXzuqwlUUyi/0rE8P1lR0399VpzvY84OM4gMaiSMOZ2dpSWT3VKKB2XlZHAYuC24fV8zzqMNvjAluPwBIYSvbgxsOc1gMwIFtbkVLPexsb0b6Apqx0IkcIfVbxNF3TEc/yEF1ejrpE0Ow8ZGGfWMnD6PC5SsVvWor1raxEWjjZQgA/MskOD4cbjZbLoYui+CMPwdnZCLOg8SmXfa5Y5VNMEK1RrRb9regFls9H09mbN6OhrRYqhG1Q1mJxMUDIM88MHxcaG+aBhz8ZapWKb2d7O+pEkdHGg1RLCoyNBWNFCAl2q1CIwo+AH9grstcozaANbHO5yNi6d8+Pl3Ei0CYECAiDoWq3o8kr1ywMzfR0XDOAcsBNVliNI0PDNTVlr1UChz0yizIBsBgwP4T1tCo1jCPskWqa1NnyW8GAMiV8XhknZWuUXQJYZp1UFtQ8qG7kLA6b17OgQJ0nIU+t/H3aYgk7DXyMOq5RAG2UUDwLjrJ6quPYmrOyOjB/x4Hbh9FEPUyY7SyfexyapkdkV2OUyS6nEWbK5SLsgICVejb1ujurbjfYoHbbX0db0moFEDiLaRNUigbCugC4snH/2Vnf7/Ky7xOx7OxssAMcT7sdmpR8PsBXsRgaFxVPm4W4Fn0ILBX773R8nPTrwlnu7kYnd8JS1WqE2HiwEBKj51mvF5lpMBbon9ANDQYhKif81u/78S4sBEsHGAMEDAah3VFWbHLSQSKZgG98o2+n04mQ3O5uhBlx8uPjzp4BBtrtqAiOWJrGvczLxkZkCWpaOnNMhW1CkpQZQIALuCOkiqCfUBwsIJ+ZmorMvHY7qloT3jMb1s0QBiOdn20DQtkPThT2R7+vYImwojoodZpsg+PgNQAVrBHgLRvG0dAPc3wWJ/Ugq/rTHPYo9mDUvgAB3NcYWjVlcEaNYRT4MAsWLQu+GHf2HOh2RoEjvS6y4E+PFTuN1WF/3DtZcHvcMZ9mJ7F2JwGds7I9Dwu2nrAlMJTswU1pbQrlbW3FSr/ZdEam3Q5mg5ulUvHP04eLqs6nGTczmU60hsjlok/U9LT/v77u35mZ8Zv5mWeC+aGQHgzN6mpkBY2Ph+4EISyaJF3RDgbBBuG4zaKhaankoUEeBFSpRsdEOIt2HLBM2XAKTWonJnx8MzNRGHB6Ovq3oX9Bb1QqRQHL5eUo2AhLQzgPkXU+HwUUNzf9s4ScABwweTB8tOrI5cJJv/JKsFiLi/H9Ws1fe+aZyA6jZQgAleNjbFxfmto/Px+Vpdm3WQACwFmlEoU+YW4Q4cKAwdbBkhHq4kEOaCFlXkXOgB00W5RLgLnhmte5UVZGQaiCIWWhdMGB0+L7hE/Zpmpt9PPKXDBPj8NJHbfts2bD4VyZS+4HjjObxTiK6VGGShm0k5iLLJgDDAAisoDiOD3VqGM9S+hIzw81z7jeHvZ8jZqT48DhccdwHNtz1hDpJbUEhpKd33SlAM3f7UaDVB4gm5uR2s1q/vDQHeTWVoTPzE4XTmPaSX5ry0NFCKdxCDAqhAV2dgIo0ZEewTDsFc6O0NvBQYRctCs9q2yyrsgeKpd9LJ2OH3ex6ILiQiF0JJOTzppMT4eomNR5WJTbt2NOCUGyGmWlCGs0MxN93IrF6Bp/dBRZZqWSszn7+16NGoZChbgAMLLgcPoUqARw4njm5oIBgT0jfMNY0b7cv+9hsWLRARkFDgGB6F7QT+GIEMz2ev75nR3/bKXir3PNUesIhpFQ2OGhnweA68ZGFN7kvMOqACwppglQgzVCWE3mGUzTYBDJA4RhqSGlGZUkCygzoewGjlyZglHOFs0IIExZCtUXEcJRzZKyR8p4PG47jj1QAGAWc6KLH5hegAGf13DjqFAf2+b61PT9UcLzUYzVceDoJGbruGPNfib7Pb0PdBsXcb6yx8Jz4Digc1625wrXKMo/6QGc1T72sY/ZO97xDqtWq7a0tGTvfe977Rvf+Map3/vSl75kb3vb26xYLNqb3vQm++QnP/kYRvuUmz4wqPGiD5CdHXesBwchqOah3+tFqGhiwuwNb3DnehbDQROyghVAP0Lj181NBwgIpGEdcB61Wuxzb8+dGAJiWBhSuXkA833CIjRMNQvhMA/Jw0N3vjhMQl8ANATMygZo+jtan14vCliSYcXDCLbj/v0oXglzhD6Eeb99O3Q8jUY0mgWg1WrBUJBKPj4e7B7zXa3G+aeuUrkcIcj790OgzrUwGPj+bt3ycXzbt0VJAdiZzU3/f3ExHtS0P5mZ8f0uLPg2FhaiqCRhVuYdUTsgEOenQBfWktcA8VwrGOGJXM7nt9EIwT997shwMwt2jmPnHAA0cTSIvgHsZnEMaJEAWDAgzCP3HdsCJOFgs6EgfpS5QEf3pJyUAhwdMw4ULZ5mawJACP9q6JHnCmBnlOG8+R73dJa5UAaE+c6Ond+A09OE3KOOlePRc5S9FgBsbDt/wa6aMWSBDq/p5447huO2e9a5uWR2ZZihL33pS/ahD33I3vGOd9jR0ZH9zM/8jL3rXe+yP/uzP7MyWSkZe+mll+w973mPfeADH7DPfe5z9gd/8Af2kz/5k7a4uGjve9/7HvMRPCXGAxV6tddzZ8YN0mq5Y+GhDijSBwmvUdPlrIUWYWnMAiAsLUXtGVKlARmkWZtF7RoceankdX8Im+EUl5ej7YWmqwNaAEk4GI4DXRShEorwUciw34/K0oAfRLhmoVuZmYmeY7ze63loCEaIooAUruSBBohDpNtqOSCq1UIbxUOY8FOjEQ9eWBB0M7AvKyu+jUrFtT5k4NFINp+P+X31Vf8+2XDFYmSNaV+y2dnoaE/16W7Xx1Ov+3cKBQ9f0rGezDZ6iOk80mdtfNzHu7gYIURWujBwzaZfhzS3JWzK9YvDRZxvFitz6mHBVBC+K5WGw16UiqCRK04EZ4ODASjBMimTYRbCcBymhjH0s9oQlvtN6/VoGErv5YdxVg/6fWUPzALIcB0TFuM6PSkEpbW8Ro0p6/DRegEms8BKwcdpocSzaGywLFNyXOhJQ+oaSr1oMMT4jwtr6XE/CNtzhUAQdmXA0G//9m8P/f/pT3/alpaW7I/+6I/s+77v+0Z+55Of/KQ9++yz9olPfMLMzL7927/dXnzxRfv4xz9+LBja39+3fVkhtsjQSRY3OWwQwl0exqQ2E07CSQA0cBjT0/F9nNlpRpE+Hg6zs/46QuVGw50qK/rxcXeI6HrM/AZtt0MfREwevUy16mOdmQmxNAwD4REAFw7m8DCKCAI2CNlRbZqmrITCYIYAW+12MDX88LBUTRItKmDeOAbE6TRUnZ+P1eXMTIRNAB/1ehQdhL0bG4vu9bu74ezJfuv3hxvBAjY7negkv70dIujNzQhbwADdvx9aJeYNNgtWCvBEK5SpqQAJR0d+3ldWImMMVgpnAaAhA65Wi5AXbNdg4GCTDLZGw7dDiJLMLg2HIYqmpQwCa7NgPHEYfB4GkexFqq2jhQLAaraaMgSAGkKmuoKHgcRw7CxQsk5OU+9hXgCU501/zmqOAJRnNQ3VwL7kchEyHQVusuNjzgkL8pns57IOn/viOG2M7vtB08JP0wmdJ/T0qEHFKA0R15WO+0Ez2K6QXZkwWdaazaaZmc2dEGL5yle+Yu9617uGXvvhH/5he/HFF+3wmCrHH/vYx6xWq732c+fOnYsb9FW3bHiMOaRR5+pqABtCPIAf9DewJYifES3PzJx8o3U6/gCv16M5JqGkra0oYkhrDYriIYBGqN1smr38cmg80Iuw6sYZk2kGXY8WaDCI7u2AEvQ6U1MhYG614mEyNeXADD1PvR4PRDQte3uRvQWoQOOirTTI6iIzjWrVMGK0uYAhmpjw7QLKcNKtVmRwFQo+X/PzDghgVaiFRDNaWDGKVObzwUDduxcOR8N1Bwd+PI1GMIEA55deCvZqezsy0QC329uRDQcI7PX8MwDQSsXHjojZLAoXAtYAKmQh7u4GcCWUiP4IwA0Y7HajThbHODsbAAOwT7aedr2HddSMNGWDFEgAcGg1AiBTlgHgoMyRiqL1mabAWl/j/mVORoWDjjNAnoaPAVYa6jmPZceobUtOY2XQcTF/xzEWOPSTBMyAUZhLQPB5j+m40FN27CeFz8yCpUWw/yBzexbTsBaLI5ossyjTzz7FdmWYIbXBYGDPP/+8fe/3fq/9nb/zd4793Orqqi0vLw+9try8bEdHR7a5uWk3b9583XdeeOEFe/7551/7v9VqJUBkFjc52gZ+k02DBgPGZ21tOHxG9tTUlDsXVrC0aRgM3Jmsrvr+ADoImWs1BxFmwdbgyGnToKEjKhO3WqERuX8/HDgCZKpOI3KemYk2EL1epP3TjwvDaRFWMwtqm/oziGl5D6aEVPpKJRiXmzejsjLF5gBisFR0iIdpyxapXFwMsfrBQYAdnHEu58eH0wfQwvTQjX5xMTQwtEJotXzc/b7PK/oWzh1ZWZwPwlrFomeXzc76vglpAYrQD3GNoJHiGsFo6UEWmYKBiYlgw2Zmog0ILNXUlO9zc9OvAeaS8BtMD2wDxzE+HrWeOp1wwLlchMNg3wCvqglSATSCe5gPwkE4Tc0I0/pVZtFTbzCIXm44bsLW3E+a8p09J9THgTlC03RappICKNUlAfbUsZ+npgzfg2XVcCKsGOBxFPPE/J/GWJzGbOjCBOYIYHJc5tRJ+zou9KQ2ipE5z/cv2mDmOBcqtn/KGSHsSoKhn/qpn7I//uM/tt///d8/9bO5zEkc/I3zyL6OTU5O2iQ9qZKFMV9kYh0eRuE8pVbLZU8nx3HT0dssVs7c6IQoaA+BM9OHK+wEgANnrmno+/tR+8YsQAwPMerZIK41C4eFNRr+G4DV6wUwO07TBMsDCKrVoojh3FzohnjokVFH9hNgR6tGo1fZ3o4sOPRLsETaRoJzUyz6d9AoDQYOEMbHox7R2Jhvc3nZz9Fg4MAGNgmBNj3acLKNhmuFYHrW1kLLcnAQ5wZ2gvNDWLBSGQ6vUq18fj5Aa70eocbd3QinEdrM5307Cl7YD+J0DdeWyx5O29nxbXQ6EeIC+FGagKa1MD1USIf1gjmDZSQEyv+E/QAjhBVhdsxCD6LtSnCCGnYmpAlw4RzAolIaQIXUXEeAExrkcj0jojaLEPbERGjZ9P5Wy2pfFDBwLTBezXw6blvZ12FQuN8BoCr6Zj8sKkbZqO2OGsNJbBNgT8NyWlTxPGHAsxadVM2XhuieREaWauNUE/qksg6fgF05MPThD3/YvvCFL9iXv/xlu3379omfvXHjhq3i0P7G1tfXbWxszObn5x/lMJ8+4+F7cDD8G0bm1VdDbEsYCJ0GQtWlJX+NbCacA93gKQZIGnwu558jrMMKn4wyGI9+P4CQWQAP6PzFxahufVoT2K2t0SmwxxmhIyh7ssV4mFSroW/i84OBp7mvrfnc4ERI/56fD2H2zk7MyfZ2pJ1n25cQhkHnhNAXcTRMyGAQAuVeL9pmcD5xCtQT4kGsrTJ6PZ9THtSDgW+HBzvnB4BDiI5xz84Gs0iNJwAJrTZwtBRZBMzh0A4Ogtmjynku5/PFtYPj39oKXRXADGCay0XqP0wNjNf+frQguXHD55RwGkxGuRznlLnL5x1wEio1C7ZQNUI4P8BFNiuKa6tcDhaHcgYAIs4b1wXj5ryxEDELlojyF4Cx40CCjjGbmq/tZlgMaTFITAFVNkTF/mG9VIvEdQsgOgsgOWlfo97jGLmHNEylixhA21k1ROfR2Oj2VUh9Xo3ORbA3hE75m/vvUe3vktmVAUODwcA+/OEP26/+6q/aF7/4RXvuuedO/c473/lO+43f+I2h137nd37H3v72t9s4N1uyk42HCA9pftNtnQeEhsnQrBCa0IrO3EA8mAlV8DorI0IFlYq/vrAQD3k6nJMa3W6/ftx6Y5+1zQd2ViCkn793L/pgFQoxB+12MF84/MlJBwroAYpFd5z9fmhr8nn/TrMZ7FqtFvqohQU/LhwbLJSWDLh3L2oRmQWYhBUAkHzzmxHGWl+P0AOCd5gQHtIwWTzEEUMDYCqV0PgQAoOVWlyMc1qvR1FIakBpCAjtDHoGWCFYR4A1RSabzUjHp1IzQBlWA+AHQFtYiE70MBMwY7mczydgp1BwsEYjXGotMV+EVwGyKjZHW4SzJVuKeeM6AsBRiBJtmgpZGZsyTfo+dhwbBQOon806NtUgcQwAFo5VmUnVyagdlzWFIfoGdME2auFKFWif5IRP2teo99iehqNgy7KsGH8f5zceRPw8Sl+kuq6zMGwnAcDzGCAeQG/2+rpNF7m/S2hXBgx96EMfsl/+5V+2X//1X7dqtfoa41Or1Wzqb9KTX3jhBbt375599rOfNTOzD37wg/bzP//z9vzzz9sHPvAB+8pXvmKf+tSn7Fd+5Vee2HFcOePCZ0XJw2F9fVgsTef1ra3oDt5u+81dLkcdIMS1gBVCWmh5CMsQHiJd+saNyHICUJAVRr2fJ2n9vs8JbUZgQhAST0yYPfecgwFW2oRWyAyjuCN9zsjIoyYPWVXFYmTRKZUPYBgbCxYE8Do2FpornAEtKGgjQeo57TKod1Qux3nAucByAByoGwVDND4ewvlCIdpuIFoGEFE7iu1gtOjo9RzcNJsO/tA6Ucqg3R7WljQa/n+lEj3i2C4Onut1bi70PIivOTaz4fYgXKt6/gAIODTqTWmdoKweirYs2qOO+lKjWBjONWxXqRTnfDAIATNskbaHwVGxTTKvAG+AG1hMdWzsn/tfQ1rZCumAsCwYOs3ZY4BUXmPeAUJZIfkoJ3zSvng/m43HvvXzGr5SIKb30VnAyFmYk/Pog47bz2lg8yyW1Uwxb6OO9yL2d0ntyhzFL/7iL5qZ2Q/8wA8Mvf7pT3/afuzHfszMzFZWVuyVV1557b3nnnvOfuu3fss+8pGP2C/8wi/YrVu37Od+7udSjaGzmmoAoMXJriElvNcLfQsPVbQLpLrncsMF02AXEGLPzjqQ4gFFvy6cBOJfKgOT8WAWTumyGI4aI22+UAitDQ4L9uLevaiurJWny+X4Lg8rwov9fmR5AQjMfB+wHZwHWJqdHQcWGxshIFftFYLmxUXf1vx8PJxh8Or1ABMwGdRlmpqKlG1CajBLOztRKHNzM5q44kRhc8i+oyghfc5gyvg8oHB7O0JigG/E+gAhdFwA81wuWn60WsOC/HY7ShkwBpy/hoSYFw0vEqoFRHKflErRcoXzRFhJq29rCJdyB8w1bOH4eFw3nF969O3theZJtThoymAIcaYAoKxj475n/5qSriwO4z2uiONZnD3/a5iRfTEGnitZJ5wNuZ20L31Pnxknff44U6CjoUSuPT2e05iT4/RBWTA1CoTwXDgObJ41lMVxK9uppQ+0RtVZwO0VtSsDhhA+n2Sf+cxnXvfa93//99tXv/rVRzCia2Bc4KSEUzeFgoqkKdfrztLggMz8JiJri/BKr+cP6/19Z1EoMkiBP7LHOh0viEgbBtLW798P50pFaDQll9WoUgxY3NjwY6YYJGnZZvEgKpUcGAEspqb8eAnBEVaBzUCzo1lRhIgACjs7nrGGpgehOKEnWD6YoImJYLgAEoWCv76z4wwgLFAuF0wN+56acpC0vR1AsFh0oDUxEawMzhz2gyKUZvFAp4kveh6c8tiYV6VeW/PPwaLhqGs1/yHkh9AYsTQhNTQ56+vDadg084WlNIuCjf1+MJwKcOr1EPkD7tGR7e+7Pgmnog4O5wMTBKAGcOlxEUKEgYMxUrbVbFiPok6N8fO32egQjQIg1fDoexrCGmVnEQPrZwAnsGkK0Pgu7CmfzTJgo4AF4E9T95VVZRxqqldiG1mRNcCY99GEse3TmJOsvghGJqttGgVC9FxmwU92G6eF3Bg755rt6YL4PEzWFbQrA4aSPSHjod3rRUo3YAhhNCnFOHYcMA9y9EMAH4rQbWwMV/Tt96Mmz+ZmVGHO583+8i9D74I+6arYYOAA4q/+Kh6+NETd3Y3sMl2ZDQbBXExOuhOtVoPlUfqeUAIPbqoe81ADhKLhmZrybRHmojglNYkmJ/03IBawBHg6Oopzg26ErCaYGMJ/sB9keS0v+/bGxrymEZliOB7CdVxj6JoA1zhJ2KNy2Vu6bGwEezkx4eyY1opB40SjWpzU9PRwUU3Gwb44ZsKJPPgprcBcYzs7fs6WloIFVe2FghMFtuq0cjkHizhTjonfbMcsrhUWEswlbBPMirIJzCuhN81sU8cO0NNxcd2dh3U4TQysoSmzuMZ0zhgLzJzZ6wHHacCC72RDyzBmAG4Nr7G9bEhUhe6MUdvQcBzHMSfHzcdxYahRoIf5gJ3kOXBaKGtUyI1jVXA8KpvsSWS6PSZLYCjZ8aaiafQXOL1m0x0i//OwotgcD1zCL2YRLgMAHR46o/TMM8M3cS7nTgvhM2wTGUBXCQip9fvutEnppjgkISOz4Sw59FWIqwmTTE9HGBKtDG1ANjeDVUJYvLQU2yA9HxExzn95OerZwADBzFUqDjoQO9O4tdPxsVBnanHRQYjWLgIoFwoe8iJVnYcqQmjCqBSDBEy321GfZ23NP48WCqAAw0ixSpgt9re5GXorNDsUwWy3A3jBTpJNNj3t7xP+Qt9EeJAmseosAPoAW0KQsFAcOwU6cThZrRB6Iw3RAQTNhpkuBNuwN4A5HJZZADDuS00SAARwLaiTVKaMOecaOQngZN/Tv7Pv8b86b40EMF4tEcCxjgIcGsJS8Mmc6veYT/4nVJvVEDGv7J85YZ51zk5iTrJAZJS2aRSYGgV6zOJcaFiL7x43PycBLkCfljfIzu15M92uiCUwlOx4wzE0m+E02u1wlghjCW30eu4c7t8fFvg2m5HFQ3ijUvHX6f+Uy0X6+MTE8Rlg1LS5qsZcVqsOQAgfEiLRlHhtCtrtRmbZ3Jx/Do3R+npk1M3MhE7FLHQ06hTm5wOgzs460F1djfo6MBkUzaTQY6EQOjHCMvW6f2d2Nq4JsxAbt9vD4lzaqvDQpmYS1wAhJUCXlk/AWQEAbt0KcFco+LHfvx8hQGonzc1FM18e/ACmdjsYJhwb3evN4jygnwKQA2gJtyHyRQuHxufgwMtNzM2FPgjgB0BjLgixaKkDtFG8hkMn3Md5VefIvMKioL9RsIXTR7fFOTMbzShkryEVF2v4ahTrkGV4RgEBBV86FvatAm0FYseFarL6FsAm7JiCwSzA4Ziz4SgNESkYUG3VacxJFohkwRffy4Ipxg0QUZYoO37mbBQgO0n3kw3JZcOCp+mfrjhASmAo2WgbDCIchRC014sU+XzenUGv58BldzdaYaBpQIBKryg0RIRPCoXoTcaDd2zMnfDTbP1+gKLZ2ejWzkOW9N5CwcEGIUbCNNRPQvi7sBAPyUolShgAWMz8vU4n0vhhk2hHAQCZmAgxe6EQneFhQhDz7u87GIE1mZuL7RB+YmXZbDo7hWPnoU51bwTA+XwI9GFuuA41VAGDBTDgOkN/RDgQ0TcMDewZYVyACfO6uxtzr4CNJrSELXu9EHMj8kegTEkFgCqtQQAdOGIyKM2G6znhGNUB4pTMIpSooTcNX/X7cZ4BS4AuDStxvDhBZZD4nWUsmH+cOHoiDbXx+VGA6jQgwGcZD73huDc0LHyS1oc5yepbsuEmBS4cmwKdLJBQQMV39JgY93HMSRaIjAJfzJ+O8bjv6d8KfE4CZKfpfnTsx4nW9fgVvJ4VNF1SS2Ao2fHGw4ibpdGIh1en4z/clAAmHr6sohE30xoCx8oNyc3Fg5EVPBWhn2br9z3UuLcX4mbakFA+AAeezwdTgqh8a8s/gwB5ft7nsFz299D/wFAcHYUA2CyE3LTdMPPvd7sOwmiwivN85hk/f9vbwaAgUuZ7AFrALg9RiibywAdcUVm5VvOsOjMHTrQ/IZSBZonWOAsLvq3t7bhGAR0a8qFw4f6+AzbGQQkBWrQwPwsLztgB/Hu9uLa3tnxsOCV0LlzHOHCcCOUByPwyC5E4eiHCVowZkJEFADhJQBcsC4wQjBmhUHRoACvmQ6891XtxrOrk1fmyD7M4zxyDhotGhaKygIptZoEAYTxlKgCn6vw5JtX6jHLAWdCkgC0LcAAFVMg/DkjACiqIGwxeL5IeBQZGsUtZ8DUqNKXzlWWrRgGf00JZp7FXzNEoBomSEYB11Wcdp1G6Inb1Rpzs8ZjemDg++lT1eu4YqCmEeJVmrdoRW41qy/QSy+X8oV0oBGNEhePrZJ1O6F1gdHBoMzPOvAFGSX8nRZxMsYkJBxPogMw8vAWgJaRpFhlZhMUAE9SsoV8c9YbMfB+wIWbu0BcWoroz3eVxjDhnwDBOcm7Ow3qUVyBkyMO+VgvWCpE5YK3X83lCY4ZDYl8ASRwa1xrgklU2wnX+RgzO8QEAabXS7cYYOT8AS4ppUvm5VgvAg9aNkBI94wAznEucjOpQzOKYVcuhWYCE4lRPBHjGSanwHECkn1NnCCBhPrOO2yz0JGbDGV1mo0NRyjqcBgQUCKEFYw40DMX1xJizDljDcfyvTFsWZGiYj20eBySU+QJ8co5Gga2sZcNhx4Gv7PdHsVKEn48DPseN4TSwxGdGATC9HjlnWlphlEbpilgCQ8mGjQeRagwIJaAnqdfdgdBV/JvfDKdarweVrytRtm0WKcjlclTq3dvz7fV6V1cg/TC2tRUtG9ADEVbkQT41FaFJWn2srHiorVh0kMHc06B4cdHZDzLQqlX/jYCXYoi5nH9md9fPw9RU9A6rVqOJLg/DGzciVANAXl/3fc7OhrOemxsWlQIU0AHBpqhQGiYon4/w3e6us2ewSjgBHsKwFqRj12r+f6MRLT4IA9KjDpYIDQ/X/tZWhJkQkANQAR0cD4UrCY9pXSTV5ADyKSoK68G9RniU8JWWD4BRBXCpRginpQJYnCaMha78qWxtFmDILD6vImFeU8bn8NCPmXlW8e1xoSjsOBaG+eScI2SnThQMGSDxJN2LMhZcb2axr+NAUza8cxwQUpCgoSOuLwVDJ+mluEYY/2ng4ThWSuf/vHbad7LnCwCW3cZJYbcrZAkMJQvD0UBXax8yHqydTji9XM47km9thQ6CbumaRZY10rsR1/Z6wRZlb7brZKurkS1Gl3jAA06TLDDCN4NBiKQnJ0Of0+lE53fCQ/V6hLHYVi7nc0+LCZwPTAq95chuM/NtUkGahyS9ywqF0DQRjoKRIU2fVeb4eGiSyIAjk4uSAu22f29hIRiaUskB295eaKkADoBErj/6oBEyg1k5OPBtwNbQDmRnJ9LlKUAJOAG4AWRg67hm2T9FK2H3uLZhS8n2wpFq6AH9lDI6sEhcA9yTWpnZLACYhoXQPimwUdZGGQ7V8QA2GQdAju0CUDSNXwHAcUxFNvWd64n3ATM4ftVN6XZGOWCzYaCkY9AwzijQdFJ4h7nhewo4NeNO5+40vZQyahzTacZ5PQ4EXrSNYpC4xjU8yHVyXNjtilgCQ8ncEKZ2Ov4QQuDJjU9Yod2OH03FRjOAQ9EGkVlDW3TjRqTsX3cgZObHv70dIZndXXfmOHpCTnSRJ2up3XbR+WDg7Ak6C5wwrAdAh/AMGpvDwwAGc3PuzBcXHVj1eqHvMQsndP++f5bVME6TByUgF5aF7CzAydpaiIyz2UEHB5HlRmFDZWPy+ai0DFuC0ydbTdu/MB+Ax/n5AIva5yuX8+MmXEimJNmS4+M+Lq3svboazXFhgQj3ofMCbFBQE1YMh0/ojXMOM8J5w2Ci1JGbDc8LgEidGIwLhgMz888CsjhGdEyUR+BzXDtmMae5XDBXXB/8Pk5EzGuwmMomME9mkVWZDbczHhww281qojS8p2zVSZqYLIBiYcjccC61dQzng3Flt8e2TgMxJ4XmON/8HhWGfFSm21fm7yyi8StkCQwlc0MbwapcQ13UWiG7Z2vLwzMwAjhrdBHz8yE6zVq1GtoMNCQvvxyC2etujYaLhGEwaFmiqzGzKFhJGATQsr8f4vNczsHB3p6Dj8PD4TRztEcwPzMzEVo4OIieZOiTaBjKe+iVAET7+8GwzM2F/gaH3O9HaYSxMbNXX422HtPTAa6poQMY4DOwZgAJVuYUcdQicXwP3RD7ZS4IySEip/Fvsejv0ZYDZ1+rRSYewu+1tagwjqOElcJR8JrqqFgMoN8hbR8nSRsT2D6uBQVAHCMLEQ1xqT4GYDM2Fs12s8yMWbAYfJZzqhoW7nHGhkPMMsA4arafZUc05KIhOQVmzCFlDk5imQBF/M24VaA9SpNzWnhH2Q6dI2VFVMNz0vZOeu+kcJ3Om7J4fP9xszAnAZ8rDITMEhhKZhbxemq67Oy4EyRtenfXH/qDgb8GC0FdnE4nHp61mm+HnlpHR8ESUUmZ1S3g6zpkjp3H7t4NIIPuBDGxWTAKgAfAJaFNiiDOzfm5gvEbH3cmY24uwlGEzmA1YKAAQBrSpIozbTr4n5YdODnAEIUeATv1uo+lVovUdrYxGPh+C4VohgpYA5zNzPhY1td9f8vLw3WMAAT0tZuZ8X2igapW47re24smsvv7wSKRzVWpRNo+LJf2F0MjlMtFVhYsEkCT4pAwSRwHoU/S8jsdZ5xgIbiXKJEAuwTghTEhJKNhLq3/YxZgDiDW7UZoEoeKU2ceceyAURw5DhkAlBWwA9i0AKSGkxiPhvbMhgEWDCKf49kxChBhXD+MAbBJVt5xLNVJWVW6Pd0+Qm4NGym4PG57x+1LgZCG6zTcqKwSr6uY/EnYFQc+oyyBoWRxMw4G7jju3h3OfKHgH6tnHvA8oLIpuGNj7ghqNV89z87GAwBx8MqKv0adm2RhgENYHOaY3mN0tG82h0OSCILNotpxvR4sR6Hg267XPZxGTzgc9PJyhJ7QLY2P+2/AGQ9h9GRkHxGeWliIsAzOF8BMI9e1Nd8GqftmPpb5+RDi5/MhIidUxvEQwqKXGE50fn44nV5X2DBfvAdwoXdatRpCa+YAoDUY+FzjGKmGDSMCqzU7GxWpAZPakV5LVbRaPua5ueE6SblchElhOcziGAlHwwhqejmOX0NXzIcyD6NYErbL/zjm45gMs9dnrbHNbANXwKQCB/ah4XQy3GCzCL0CigCdalnWhmuHsZ0kMD6N5WCu9JhGgRAFWydtLwucYHs5Tj43qu8a/ysLBlh7CoHJk7AEhq676YONPmKwPmtr4Zg7nXAIPCC63aiVUipFGIGH5/5+FOl75pnh+Pviom//qleUflTWaARQ2d8PpoJGn4Rq6Ea/vBwPyelpB6LVqocgcbadjoMJgAtOtVz2v1991fuFDQbRUHZlxbc/NeX7oEXHwYGDEZiMcjk0QLS5qFSihxip+TAXZHWRJUSBxLk5H2elEtodUu1x6rBGZuGIOx0/ZtgzQlBmke6O0B8NE/MLmwUbQhIA6fFUuUY0TTjQLHRZlYrPWb8fbUDI/CM7ErDRaAxnBtE4F2enIRicJoyEOmMWK71esEfqJFUzg6aKhYt+Vts8wDhlWY6s2FdBFawO1xQAKOvEzUJXptomDEE2bLICOgACdbPUmCvtWaahstO0LMe9p6HJ45rSnhbiOm5fqv1h7Jp0oiJsZZU0VKpjO6+dNidZDdh5v38FLYGh625Zevfw0FfYd+9G64VuN0DO7m6EBwi14EDMwoH0++4g8nn/XL3u75MhVCg8/ZWmH9bW1nz+ZmYcNH7bt/l5oJ6OWQh+Sf/e3vZzRENbHBLnjFAYbRrKZXfem5vBAu3uOujZ2vL3AQGa0r+/H3qZiQl38DA7FPzjYU8WmAIPs2i8SziOFhWzsw6KAN9a5ZgsM10hUz9pY8PHcONGgDBW9ZSFQF9UqUQ4kYw0rm/Yt62t1zsi2DXadKixoAB8NpsRZjSLUDT7Yk4Z/9RUjAFQUixG0UgNYQFQAAywXZiyOSp2xukCFmF0ACkANOYboKPaGH4AUXyWMBvbImsOEMj2NESEkJ/rlOuC91VDpIy0GseoAPgi0rxPYnowBULKRI0y/ZyGvhCtM/Zs3zWzmDsNg/L/KOYrO+bsGFW7pQBPtWzsV7d/Gvi7wiApgaFk8cDg5l9d9Qc59VyKRQczUPiVSjQEnZ529oCHKJlHrP5xCs2mr4C3t4fTxJOdbP1+1ODZ3HRHT7hkcdHZGS0WCBtB2wiz0OgMBg4WBgN36NQDajbj4Upm39RUvDY7G01UyX5qtXw7N24Ei7K352PCYW9tRU0dzchRXUe16uNrNCKlv1SKUAmiZlgQdZpom8ziwa5MV6EQWY+lUszN4WFo4NAHcf0uLgZQgu1ArwWY07T97W1/7+bNcEyqNaHxLPqkRsPnCkDIPmCQAEiwgRoSYRzcq7yPOJpjzoIOPk/qN6+x+CH8pGnTKtaFJchmVWnWWtZoQ2IWgBLnmXXMozKjAIG6P7NgjEaFqjRbkkw4GMSHsVHOnfHo+TF7fUo/nx0FIHSesyJ5Zd7Y1llAxiiwo+yYMj7Ze1IXPOxLFyJmx5cjOC9DdgktgaHrbtwcY2Ohp6jXo5ZQPu9FFfv9KMTX6Zi9+c2RYVarxY2wtOQObnfXf3DK1IKBNUp2dkOjo5oT2B2qKeNIqY0Dq6LZOoOBnyuyghAIHx6G8B2mBCfCqhw9EqtRAAECYyoxqyMoFoMRIhtsfDzCTFSbJqREtet63X9gI0hxJ3w7GLgDn5hwMDI3F9W6p6Yi44vwDAymWYA8Hvw4AhgcMtCogEzdIBgVreOztxdtQWBBtIlqNuMKp8c9xzwCOuhxNjsbtYbIqKJqOMwbABNxNAJ43ZeGsThmZX/M/LVuNwCkfk6ZCOZPmTINVWrmFt9XB6xZfmaxbcCQAhwFSexbaxkp+BoFPBQQckyn2VkZDXX6ZsOhROY1G7Y6TiCdFVTz+zihtTJwmtGm484yY9r/DTE7zKCeO7NhUMqcMYccrzJaCrROq9l0BRijBIauu/FwaTadEarX42HSanmLBzJvWOmi7yB+r85xYiJaSNTr4Rj29iIckOx8Rld45pCq3dQBAgjNzPjnYUxoF1EuRz8x9BU4TR6WsB6dTgAqs3DGACbq8lQqvs2trXDSs7MeqqKnGmCNUAkZYeq8GS+ABSeJEyD8wQO71YoeaaRdo1U6OvK5oMgkuiuuPzLqYFkKBQf3sF2qxwKcqH6FNh2MnxCfZmIB9JhjgBZNccnA3N8PVsnM99dq+bwrENBzjrPd2YlK15w/wKIKfWEGYYUIiVCbimOApeV/1bNoAU3VBnGMo1gds9ezHJodxXxpuEe/r46VFkCAGgWPmpGmVaUBY2xrlNYHOyujwfsanlUtF2Cf10+rls2YsyAhG5rTUOEoVknBUjbMyXj5jOqT2CZsqjJbHCPb1ew53Tfnjes3mzGY1TZdcsYogaHrbly0aEWmpqLXGBoHmoEWCsEAUKkXnQJd6uk/Vq/HyvpJpoA+Lbay4k6eDvboUlqt4dYVOFsyktDoAAhIs6c6MhWty2UXW8/MuKOt1/17S0uhGQP8qtPFKRWLoTuCQZmcjHCYWaSdUyFbH+Ddrtlf/EWAEzROaGEAP/m8g4iFhchWYvsbG34shI+0iCX70oKWgEfCKuitVJsDs8Q1TFVkVtjaygMwwbkgtbvfj4ayCwvBxsCsrK2FMyU8h5PSSswsQGDaqDMFKMMhsUABFMDG5fPDehwAn1YbVyYH5iErpNYMLQV9aifpbRT8qDPlWZTV07AN1R8BxrBeL8J9WiCU8R4Hhk5jNLKgS8eINkoF7AoE9BizAOK00NcoYAgYHTWvjFFBKAsQdJ1cU1n9FwyjhjFVM3RSiQBlnAC4ev2eNr+XyC7nqJI9emNFxA3DQ7vZdECzvR2iTS1at7zsThLxbL8fjo1VJ9lG1Ce6jr3GHoVRpwcBMIJbQkLaP25+PrQnZFFVqw50qDK+uRkC56Mjf4/iggAsHvYwOoic2S9sH453aWm4Bk2nEw7dLDK/6KtGTSoqYAO+eYgj1GVcMBH5vH+P8FG3G9cq1x99xdQBIXqmLASfpw4SoThS/AEilAYgWw62iiKNAETS9FVjhHMkFN3rOQtLyQnq9MBMUYMJkEH4EVaEDvM4eWUOdEXPtcK5ZF9cI9yzCh4AaBpeginWEIwyHCdZ1tlzHrgecKjqxJWZwvmrhgZQoMwH4Je55jV0UWavDykdx9poGBOtHSBXs/36/eFrVbVRHNNJoa/TTMengELDhNnPcXwsQtg3C12tiaUAlDkAdGrIM3v+FIwx59yDAC9l50bN7yVkhxIYuq6mDxwe5GR6NRr+wG0242ECE6RVdwlF7O5G5WGyeCgYp6uqZA9ve3vOoCwtmT37bDiPXi96lhFeIXzy6qsRloKdoRox4U3qEh0cRHPTw0PXjsGmHBz4NVEqDYex0CeVSv5ABDiZ+XWQz/tYNzcjw43xoSOqVkNnQwinXI7wUrMZ9YZIdacuUKMRYcKdHf8stZIAWrApZqEHAiwWCsMaHMJtzaYzOYSRej3fHgsIGCfuDYAfzgTHgwOnoCn7mZiIhQQMK9smi4/Q0NGRj5W5B+gQosznh7eBw9FQqNbs0bAVq3gFGdy7uorX9HKcoKaAn5ZFpaZgRUM9KtxmHDpW/RzhO9gv3s9mz6n4V8d4EmujxWLJaNRq78qCAPz5PmNRBu1B2lXwWXSCCm407KTXGIBIFw3aYoXjU9bvuDDiSWNVBkxF4IxRgflxrNglswSGrqNlL2KEpIhkb992NqHVCobo6MjfJx15etqdRbfrK2o0JN3uMLBKGWMXa1DYW1sR3uj3A4gsLwdouXUrxM3U+tEyCYCFbMd3GJVcLs43LAYp8Pfu+XcAXGSyHR056KFAIyvGb34znMZgEAUVyV7jQToYRPmGajUcHaEsnDsZX4QAt7YipAUzBtDT1iEwL2bRWw0g1uk4sJqbi2PVlfL4eNQb0qQBmAAyz6hNRCsNgAUMDEAItpWwJplvaIpwptTwgkFStooMTkJICJEJj2qrEBxVuTzcRofGtMo+YFmHyzV4HKOSZV5GOW6zYcDCd1XbwufVSWtoCQcOq60One3ynjJLGso6jrUBkPJ5QBusB6UdOA8ASY6LMWbn5CJAgDJPqlXDFHAAepkf9q/HnA1xjjpnJ4XyFIQBRhUIZcOsl1gykcDQdTRd+R0d+QO83Y5UaAStjUZ0DWd1xPcpNre/H/VlyABi5c/KO9nFGyJYzgk9sVotPx+VimuAWJWiJYLd4H/Axf6+Z3MRhlHGAP1OqeQ/zWa08SBUNTkZIu/NzaglRfgKp0I16sHAP084CWHy2JgzPHt7wUJRzRkwAEArlyOjTUsCoENiPigmahaMJcdcrQZbcHQUzWppl8H8mgULgMMkVR8Gggc9DA0AKZfzYzk8jDIG1M0pFp3lg90h26/fD/EyDBnCV+5FzXADEGUdugp+NXNraipqLrE9QmfZ4zwPo6J2kl5E2Q5e0+0xf6OMeYapAjSpowXMKkvB+LKC5CxrwznVey2XGw7TEvrVtHKOTzPfHgYAAS4Ij+q88X5WuAzQ4fwRckVfxnePY6pOOmejPp/VMrENziWAm/FdYktg6LoaoZRu1x+K6+seTtnacodAVky/76BIKdh+37/DQ4PYOStO6uCsrDzpo3y6Dc3P0lKIoM38vGxuhranWPRSCAACHnb1eoiVZ2aCdVAWYXk5SiLMzQXzB7DK5Xzf9KIzi2rR6G7IfFInRpmAXs9BxdRUaGEqlegqrynjExNmf/3Xvo3l5QAWrD6prI1zZJz5vIOzVis0MTQSJuuHENcb3hB1e5SFmZ2N6xzGiyxL5scsRNkwaIie0Wx0OuE4AXNmAYSyTVxxhDCz9IDDOeLszKK2js5vtiu8MiiE/JhD1Y2cxvqcpoM5TY+jn8tmq2mIb1RBQQ07adFGjPc0DV2Bgjp7mMcswFBgZBbgODsfmuYOeNBQ1XktyyRlQacyMJoxp+dXw4Wwqpzj0/Y96pxlz4smE2gok++xP65bFbdfYktg6Loa2SYqOqVyMA/uXC5CGDRj5XOsxAiL4Hxwepd8FfDUWK/nTh49DmESQi2EgOjU3mxG6GR7288bAmDYPL0GqH6MaJ7zysNud9cLDiKshiUqlx24qKaCVTjhPdLbC4XIxAKM7e0FKzQx4cCt1QpmpN124AHgqdVCewMrMj/vAOvll+OaxIEsLQW7pJqrvT0/JhYDfI7sMdK8AYk4CjL8NJMHtgX9iYYrcE4Ar1LJX6NmFGEunAhOGQdjNtzaQp0lDBIVvlst/zxCbKpgw54gDlbHp+0wRjEcABIVWmffH8UeYTBjqr+BrTaLcYyPH9+k9bRnTJYBYv5Ul8T+lL1g34BZwAZjVXE7yQsaVs2G985ix4WnsqCTeVNgyXXNfpWlYhtnCYVpxAAGTMN9fBc2WZlGs2EgpmEyrUZ+iS2BoetmuurkpuGCxwmgSYHpMYsMIB6A2iyUBzsr+W7X05yTPR7b33cwNDMT7IFZFOfjwU8V8ErF7JVXArSwIjSL8JmZb5OwGanyOzsBoEjrZwVKFWm0L2YuQKagYbsdoblWy68nrqN6PUADD1JWmGQjUqQRR9NsRp2h/X3fJgwSjolK0TA5PJRx9oTVNA0YYFmrBdCnDg/lC8gu6/WiDQqhPrOo74SVSv7+0lI0KVawoL3iYFZZUePI6FiPMzSLGkaANRySAhiuARU/w3Io84LTU40Wn8uaOtRsqAYbxR7pcaO7AdQpS6SidvRaWRvFWMCaURZEmTAYIL6HMx8MQkjPMWg4jOPNgi/GBQt1mjboJAH1ceEpZcH0+3wGLRrJEIw5qyfS64Fzkg2Dsg+2q+dAGTbO1dhYgCKtOK61uVQLdl4B+WO2BIaum3EjsIpvtx24rK2Z3b8fFYUHg0gDptaLrvJ56COGNYtwAQ4l2eMxrdDLqh9xbqXi783OxkMPJoKHKKEvRPT1etSj6fcja4q0bF3pDwbu3G/ciLTzgwNPHUf0S2YatY6ossz+YIO0D1q1GkyQhppu3PDxkeZfLgcwmZuLatWACHQ3xaLZt3+7h4I1TRwRcrsdBSLRzuAkCT1x7zCHiK9JFACglEoOTAlR0g4EFmF5OY6Z9hqIuvkuYm8YJsagmUGEZJgLAIOGzbSkAWyRhvrMhlf9AAcy2NgP9z7nXJ23Ot4s6BmlTcHB8gxRx4lzRhAOuMsCAa51s9dnb2WrbDOHqo9SZ6/nVUPEWbDKGPTYYf4Y56iw3mmi5JPCU/zN9QVbpkwV51G1OzC1MF86j4S3OM4sS6YFRrm+mC/CvISHJyZCr8Y1A8hWYPuw+qnHYAkMXSfjZsARDAaRAQZDxM3N3ziKRiMq/VJZGGdAlVweKimD7PEbjVLJMuJhBRhpNPxzMCg8EGH5cBYapioWg5lZXvZzX60OC+VheXTVCOMyN+fsSj7v1xjaoVwuQkCEnd785tgWYmFWuIj1Dw8duNOSYm4uAEIuN9y/jFT0fj8yv1ZXA2xRH4ZrdnPTx0Tl7XI52CxE5+h1CGNpuGtxcVjv02zG/CBIJ2zV6/l9h9Nh7nEkfE6ZDJzN3p6/RiFFWN5Ox8/zzEw4NRYugFBAgt7jZuGkVbisoRHV1yiro2BKwQSO8zimROeNaxIxeLkc5xpWh7lm29k0cc3oyuqbYMI0pDSqXYY6bAWHGg7kGssek2qERulyThIlsy0FXlyTfFfLGKjGTENaKnhn3IArZYDUWOBwnQJu2L5ZAEcAO9cd/Qhp1My9ynMlCyAvcRYZlsDQdTINh/X77hxarSh2t7vrD38yhchKMgvntLvrrxMm0dUYLEMqsvj47eDAGZpCwR0iXewBBTyg0L9kY/3T0wFqebCScQUrcPNm1Jba3AwBsZkL8LW6MUUhFxbc8c/ORh0kMuE0BLK97fugcvnEhH+G8VE4cn09xn505J9HeFwo+H7Gxnx7Y2NR8RkwwIIAlgud1fT0sOMjpR3ARLiItiMIypU5nZry8ajehL5fuVxosFQjA8vU6/lYYQAoc4GTUYfD4kVD1oQxSKfmOHG6ONJ+P+ZK2ZK9vWEWhOtF2Qq2DZuiIS4YEZgHddKjmBLEz8ytisUJuWT1LhpyUyCgLFk2DMZrKpI2G0755jNm8T/zpd9RJkr/ZmwAGT3mbBjxLIJ0tgX7Rd0pjgMw2O8PN6LlnHEeNETF2BgzAI19AZy5NlRDBeNfLMY++T6LEWWeeM9smLG85JbA0HUyXbVubzuYeeWVSE1eW3MHhLNAg0GV4F7PHe34eDTFhH2YmHAnWa8/4YO8pgabk8u5YyelnUKYABecL7Vyut3Q+PCQhpUYDAIQs1LlmsBBI25mn2ZRiRb92Ph4aI0KhRD0ErI5OormqjhWavtoTaVGIwTh6NlwnjggAH2/Hy07JiZ8G8ViFD0kLLe15eOan/e56vcjZZ6xLC/7d3d3o3zA4mIUTSSpgDT+ublw0uiMAIJkuwHMAIY0fCUcqOJsRPGqKdrZiXlX4S5OCIcHMNFmtbAuOGNKJ5gF04SWyywcs9nojEBYI46H8KBqkLhGVaiLs9ZyARwfTtUsmAkN6yjTAggAUDCPsDjF4smZVBpO00UDwEiZH53j7PiUvYIlU23maYJ0ABjfA5jSGgmQyJi0GXAWOCrbxLgAtoBkFV1r2JTj4ZxwvxNWBxChI2Sbek7wG1fIEhi6TnZ0FNWiBwN/CMMEUGCRFgJc/KzCqSlDLF9XoTzQU3jsyRrna3PTz8/OjmtsCHchOKb6tK7o9vfDuaNxKRTM7tzxhzB6F/qNveENrjHb2fHPIu4dG4uQDW0t0AaZeZhraioExaS4k9o+NuagB9YI/UOrFXQ+QKXXc6CCg1LQoLoPrnnCT5ub/l1S+MmSNPPxvuENsQ9qMiHIxcnv7ARoAeyhydndDRCmlZFxPjAA6K/I4gRYwGIQuqA8AGDTLEATc4fIHbZJGRw0OYQfOe/qvNXhk0GYyw1nKLK9rCAb0KvhHNgQPWazYaetYRU0icrQ8Hlt3cF+AXaIxpWZABjhuEexU7AXjNUs2C2uMf4GKGgoSwXFXOOwgfp8VN0Pc39cyIgwJGAM9gpgS0Yiwn3mhHFkGRjYvCzjl9WAaVhOt0MIbWzMAT73IPtUTR3XqoZlzYYrvF9yS2DouhirjUYjHBA6BrJc8nl3VlQsnpz01+kzxkMBh4dWYWPDHQwOJdmTs14vNDxkcfV6Dk7Gxvw8kdFFS4udHT/P+tCanR1+yFFjiO3CLuHIcQRbWx5OGxsLQTTAYG8vGEZWwWtr4Ziq1XDCrZYDObQlql9B/D897a/Tb6xUChbr4CD0PsWiMznUz1JAT+o84R2AHaUEqJXUagWIq1bN7t4NJ4zwGlCBUzYLfRLOh1AVuhiyzriPJieDfcWx45QI7cHuEU4EGCpDpmESwmgqTObeB1QA6mCJ2LeGy5QJgVFQIKoMEceJY1dwahbp6SrC5tzinJlT1fEwFyzSsk5WHb3Z8QJw9psVD+v3YOLYJ/eShsK4Hzj2LMDIArLjwoYq+gbIscAgFMUxa5hQdU1ZY7+qp9KikAAgnTdYI0KWsFwsBHSMyp7CWhFaZ9yAwASGkl0aGwyiEB6rSaVx0QzoKhd9ETchPaDm5vyiBxDt7TlLkOzyGGCWVSu6HRwyTAMPUh5sh4eRgUaoBCax3/ftINIdDBwIECJrtZypoR0FzpRKxzAwOCFqHvEg39qKhyoMFDobQFsuF01bcUxmEWoDeOTz/jkNgZDpyDHv7DiwIdxbLsexb2xE/71OJypTl8sOKAF3Gtai5pEyMDgSjLnhPb2/ABUsMqgarM1VYXHHx6MsghYEPDwcXo1rmw6AgKaq42gJHyqzBnhSHY7OL9cEf2dZDbahWiyzACMawjELoMcxwzqo8FfDTQp6eI9rDlCmNZRw7Mpu8LpqjwgVAkQZs2qsYI2YL34AiWxTmaSsbgkDkCqDxjniO8qGAU5V1zRKq6ThPNUiqW6J73Bs+h1NcNCwn17XADy9D/kuCwHVZl1iu9yjy9iXv/xl+5Ef+RG7deuW5XI5+7Vf+7UTP//FL37Rcrnc637+4i/+4vEM+DIYqzR0QRqPPjiImjJ0ZzYLB8gDolIZLorXbPoPq2a+l+zymOo2AEQ4ZXp1Vavu4BEdm8WKrlx20DM56awKq752O1pt1Gr+OXRm1apvo1YLBogH5/q6X4Nkl5F9SFZZLhfX2Oys75M2GjjU27d9TDduOOgqlyOcBziikTAAH2dG6jzOAEaG0NPERDBNhH1rtcjiQty8tuZgpFaLYwG8oGcyC1CluhwcDvcdISnGAKtF+EidB842n4/WK2bDDBLgChCBw1cRNXWktM5QoRAsE68jHIfh4T7X8BFjBWTgWNHoaDVwzZTieQEY0v1oKj7V0dk+DBPzqlliWbZHw0dmr2fsVJStwE6z9fg8AI3PA+6U0VLwwtzzo+nm+qzk2awsmuqeCG9rKQhlRkl8UVCn1uvFcx+GV5MI9PrKMnfMB6FlrFCI+5Jzxzg4V6qNUgB2ye1KMUOdTse+67u+y378x3/c3ve+9535e9/4xjdsmg7XZrZI+uzTbjzY0QpRJA9dBjoD7STebsfFzOp2czMQfq8XIZPNzQihJbt8trHh52p/PypKowVDVFwoRJPdfN6bu9KmhX5jq6vBhCDGBljheCsVBxPshzR8HEG5HAzR3Jz/XatFaIFMNLaFaBoNE8UVcQYwDThaQluIwgHtaDlw0rlcHC+Op9kMRofq3AsLfr/geAGU6+sBgAqF6Ic2Px+OHyG2On/uMxYjmi0Gg8B4s6E37i/mvt+PEKTqdAgDUv4CB66OmDHB/BL6pnWHZh8RRkPLwuJIGZys3sYsnLKG2nie6IKLuWGRxfdZvAFotHgfwCVrCjr1f4T8ACNeV22MlgzAicNsagbdqM+MquXDXOkccK3p+xqa020oe8R3mCdlYDREp2APEKKaMI4dsMc22IcyR5oezxxkmSPCthpZGB/3+w+jXIKCqUtsVwoMvfvd77Z3v/vd5/7e0tKSzdAz6BTb39+3fWku2iLz5SqaivzQ/FA9l3ReYtHoLuhAzoWtWSlTU0HNYwcHSSt0We3gwMOXt25FzRwFH6Tda9uH7e24BgC6nGNtuQFAwUHs7ES2FzV6aN+B4yuVQpx740aAA8DMzExkt/Ew5iFOfRMAESAHBgYWAuDAwxmwgqYmn3dAg1MjfGwW2XawpKyAeX1319mpvT0/XlgjnAvd5WFvzPxzCKUZI46n3/fx53LRaHZhIQARIWiydgBqCNUJJ+pxq3aJYoyj9FZsk5U9DAjzgtaLec+2gdDUa0COrv4VOCiDw3izAma21+9HSQWuUa3VpMyGAjBeY5uEHM1iPkbpVgg1KRjhHlFGTZkmQAbXKJ/JhuKU5WLcgHdlehREZgXNHDNgWpMHxsdDV6QtVrLMD8cE8GbuGRPzxHXAvYUAnmPiWLivYF0BTRriZk70+rvkdvm5qwuw7/7u77abN2/aD/3QD9nv/u7vnvjZj33sY1ar1V77uXPnzmMa5QWbXoCsBljR7+xE5+9228Wg29vDqaWzs4HqazW/qElX7XTihiELLdnltPn5eGiiNYFap64QTXcbDQcKdJ2n2Ob8vF8XzWZkFFJYEeBk5g758NCvD/Q7+bwzRgsL/hl0SKTcE1JZXg5Hweu9XqwqYSnNfJ/T07H95eVokEpvL3Q8pOwfHvp1Wq+HRmZ1NWpukb68sxPhAcANc0ZRQCpyj49H/zMAIbqmcjk0Oxpi5n5injhW2KXpad+v1txB+8S8w5SQ1m8Wzp79UVkbzZAKblXHAnuM5gQQpHoeQATOVFf6MIQKBgBrOF4NM2GjxNRZVoq/zYbDU7pNFR+zb4Cn6qMUOKnhyAnTKpiA/VEgxLyxT9UtabgNOw6cYLpd1XWpponPaBgP0K5sk7JeALAs+DaLcih6ffAZxs8CWEGyWbxPPTlNCGBeAJ/o3zQ8d4ntqQZDN2/etP/0n/6Tff7zn7f/8T/+h73lLW+xH/qhH7Ivf/nLx37nhRdesGaz+drP3bt3H+OIL9j0goYu3d6O1GtWxc1mlP+HFVNxKlk2PLCo6Eszy2SX19bWPFyGc8R5NJvxQAf4wBQdHXlGGOmxh4eu4UEYPDvrzI6Zn/+FBX+PoowIL3d3HTjk87H/sTG/fnA05XKEmBBv87mJiRD26s/4uG/3zp3hTBr64fX7vr9XXokHNQ/zatXHyxgANTiSnR3/LkxqoRDjgymjwSxZX/l86KaYU0BRVn+iZQQ0/IODotyFCk4J0xCWILsNfRD3LuJWAJY2GuVexeHpfGrtI0CR6j4AoYxJU7TN4n0VIXPdMIcAHpyiZqZpEUH2ryEcwvma7ajfV8CmQI/w4CgQhKneCoaK+WDMAC9lzjg3ypZpTaGsngkww3e4bvV1BVU6D7wOEOU7KszX9jlsQ/VRowBu9ppE+EwolBA1i4lOZxg0FQrR2JjjBAgjtFcdV1bTdMnsSoXJzmtvectb7C1vectr/7/zne+0u3fv2sc//nH7vu/7vpHfmZyctElovqtsGo82i5t8ayv0FZubw9ToxIQ7PViiw0OvakzhvlzOHcPubqTTr6w8uWNMdro1m7EKB5iQJQgDQlhqejoeuN1uNN4tl/36eMMbfJuIpiuVcKgUKmTbsCdbW68XttKKotdzUMVDn8yxQiEcPFoeAM70tF93OJNi0fdBGw+atm5thbCTPmHsk5Ad176Zj/fgIMJbAJNKxRkkQlY0mK3VYkyEwQCSS0vx8OdZAsBBN6UOgwy7SiXChIuLwejhlNABcb60Lo6GdJhnxoVGhRRxSmccHfk+YQc0bV2dJc6PccNk6Hk1C+esYEA1K7Awuk0NG2UZCOZPHSvPKkCnMiIIjZmbbN2dUaZC51HhL/3cKIaH5yRASEOYKm5mXvVvfgP8FMgQ5gRkMQZqDXE9c5wquNesNsJWXJsKuLjOVcvFooWGxABL2EvGwH3G/QwDDSMIuNX9qd7qEtpTDYZG2fd8z/fY5z73uSc9jEdr3KDcwNCUjUbcRPfvu6OE0my1hh9cWVoeHQU36b17zjpcsSqj19JgLcyGGSIzB7MIowELPDzHx8Pxzs1F+KZQcBaIdixm7tDJMuMBqtdfpeIPzFdfjRpBpVJUhkYUjFMBnKD5wUEB1ra2fL+Li1FNm1Xz0VFomgBkhOoGA79uEX4DIuhzRjjNLABIsRi6ocnJAHCUBcAJU6G60XDWCiDUbocomnIHu7vDzhV93vi4nyN0ezgf1dpwP2odHkJLOD/u+X4/tEGwWINBAFnNflLBtAp3lfFRpshsmNUi1MQzgRAqThhnyTXBfmGuAFoAhfHxaGSrehx1shwjpiAGZ60CaDUFZTBKo7RFGoZiLCqOz7I8jBMROLWgOBdmwzohxq1MCgBVAReghTmFMVPxs36e+UVozRxoE15A+P5+FN0dDPx612KazCEMEdtFx4ceDzZZQ2/KMl5SIGR2DcHQ1772Nbt58+aTHsajNX04HBz4wxkdBw96bhpQvpmzPWRfaIYPqxEyb7hxaP6Z7HKbPkR5QE5OBqAg7IlAd2fHwYOZgwSzYG4GA3fuMDeIqgmlkbHIA7VYDNBNiImaPohlK5VoMYHAmnGsrflYCGuxH1bdMDU82Hd3fX/z88O6FWr74Hw6nWg8DIhgn6WS/1+rRa80ClkOBh5+Q1Q+NxeMFjooQFWpNLwowVkB1gB9OBnCWHyeew7hs66sFdho+ESdO9ulMrkmTLAogpXBUSm7wz4RqcPGqDYGp3p0FCEWdXoU6FQwoqJnZYIAnmbBZGNaC0sFzer8cbrMIdc785ZlijTco+wSpmBJwTjgT8GsCrGZ28PDAKZ8T9msLLum4IFtSjLPa8COeWVfKnLm/DGvWleJ11XTA4jlXi4Wo18lx0cavW7TbFg4TekDQoua4ca5GSVgv0R2pcDQzs6O/dVf/dVr/7/00kv29a9/3ebm5uzZZ5+1F154we7du2ef/exnzczsE5/4hL3xjW+0t771rXZwcGCf+9zn7POf/7x9/vOff1KH8OiNCxiBHboCs2HKns/m875a11iw9p9iW7Oz0a/n7l0X2ia7OtZo+DlUdg+NSacTTV2npwOkjI0FY3Rw4OBpbCyEv6TU8iBdW4vCha3WcKsExPs4uFzOWSmAeC4XJQAA6TxcC4VIK9ewEMegqcDb2wHcYBFmZmIs9DhDC2QWYIFmmFtbDlJo17G/78Dor/4qMiq13cDmpo9haSkcBqyRsiKNRoi0zfx89HoRdqTIJKJdWikQjoNZQNiNk1btjgInQEjW6eP8AEzoRnCuzHmjEUJbQAosjtnwPgjDZbOHqG2mITCcIseHcNssAASmAApgYTYMpLT4IdvOVj3mmlEglg15KfDhWPkfkAGgglUkzAQgYz5gwQAeyoSxL45LWRRlvDRLj+2iy2E8KpLWcBvzTVFP9qVCbcJ7JA+wCJiYiPA694aZX7fFYiQ9KKuFYJ354X0A9yhm7pLZlQJDL774ov3gD/7ga/8///zzZmb2/ve/3z7zmc/YysqKvfLKK6+9f3BwYB/96Eft3r17NjU1ZW9961vtN3/zN+0973nPYx/7YzMuOG4kVqGqacDp8CBAczE5GToRvjMYRGil33f9RKo2fTUNVpC2EgBlmp4uL/v7uqImnZwHvwIgPgswAjAfHkYlanQHPOh5UJNWb+ap/zs7odnZ2/OHsYZdsjoa0sMRPNM2ZnIyChrigBiDikAJhyEMx3lUqxGmwkGZOfhX8epg4HNI6jyMVqHg3yespaJ1BXb7+xG2brV8HNPTEf6g6jS1mwhjAU41/KMGA4cjp3QCQFBDRjhydawcb6MRwljE9tRGYi459+hgNKSnYA7gZhZzAQAilKUlAZSFUVMxOmyWponjgLPMkTIoWm/JLMbOHOh++Rz7Yo5gOtgeoBxGSOsPKWtDuJfPKpvGuBWcwqrwzAbImAX44DrgvmScGgrnutBQNPODuFozPAlrc/8QLuc+pOwKWZIAP2wwCL2a2eki9ktiucFAZyxZ1lqtltVqNWs2m0OFGy+lcVOhD2I1vrrqq9f9fdf6fOtbsZqu190pfPObvg1WOWgksqnYpF0nu9qGY97fd2dVq3nIZ2zMmRQqk+/vO1h59tmoKk2ICRCFyFqr3OLceNg2GrHqNBt2gITAKOZ3eOjXZbMZdXiOjsze/GZnc771rXD49FmjvpFZgJHBwPU96DdwZPwQPkO/ZBagYW3N56XRGBaR4gDRS8GkUn6CFPmZmUivr1ZDJI3j2dwcLl44Nzeccg/zAZCBoSGkqU1gYWo01Vv1KxoK5J5mkUQIREEG2UVshxAj55xaRRoSUsBLer4CXvbJfsyCnVFdkoaOuE6UVWCMPL8I06nGippIADuuO0APc6NZftn9MF6OUUNNhHHZDr9pmcL3aA7MPpXx4trR9i2AQGWbqOnF+2RbwuIA+smYhKEjRAs7aRbzz9+cJ/wGANwsFiHUGYOt7fd9zDwjJiaCNeSeYj6pe3RWMfsjsPP47yvFDCU7xVT0SEoyF3W9HinVMzP+/+amfy+f94cxoZSXXx6O84L6EZ4mu/rGAxvnsbsbQt/ZWbNnnnGQwAME0SQrYRjEpSX/XrcbYSVaU/T7/vr0tF9PZKWRnk9hRlbOtKOAbp+ejhRxHrqwWWNjDjpooEohuEYjQn9USp+dHe4xNj4eFbH39gLkIABfXo4q0pVKsFZkppHFhpOamwuQ1O36Pmq1ACqkugMU0U7Var59wBJAU4+p2YxjJ4VfwxQUxyRbjbHBoGkYRtPsNdNsd3e4HQlhMZw1cwQLZDasDdKmzxynbp/nUTaMp53qAcmanp5tFmo2HPKDYcHpa+o7//MewB9QybwBXvisAjNAFiE3stq0HhFzgVHdXZkkswBnjJ+wLaVJYHdUF8XxIb4n/Mb2qQqvOiDOiwqZkUzoePALlC4YDEJDR1ibBYBZtLM5PIxQcbZdCCwSzJMKwTX8eEktgaGnxXSFoymy0J/ZZpuEGsxipXFw4DoOHpakTHPjp7YbT5fxEOe84swIP/FwBACwksRB1mr+P4AJgED4AvZmd9fDQNoMeGIi6gzh0EqlyHxbXHTAjhME2BNK42FLyA/xs1k8hLn2ceSs5qvVAD4cqxqghXCPWWQ17e56phhVrbkvAGisgBVoMmeNhrO0ZgEUAVuaTo0gGkdMDzYFBrBbhL6ZU0I2ABW0UDh2MvT29qJSONl8aK1UKKxZpMrKaIsLQJnqbhDJIzY2C2ZGQ0Cavs0zTEN8GkZSZofrV5kwkj94Hy0L54OCmmYRyqKQLN/NlgfA0QOcJyZCj6Y6ILapWXFoLBk/zBlAD+CnOh72aRZaPfR82XR/7hsADnMMY0tYmnsOvwBTyDMewMi1xfc4591uvM/9TGIFi2WuEY6BecwybpfYEhh6WoyHHw8ozXrAsVExdHMzVp7EhqHMp6eHV4y5nK9U9YZL9nQZTrBY9PM/Px+akeee85R0fZgTctrcjOrI1aprzyjwqB3iYQJgGmFAWL3DGmg4QBsI6+ockfT8fDzMx8fDidNc1iy2u7MT709POxCijkqhMNw0lgrrrMLRSODg6JfGqplqzzAKiKG3t4fDM4AmgCMM0daW75ceclNT4XgJ43CMiNq1XhJshVk4Lc36AVTAFrAtdIOE2zQkpzobtFGAQeYAJ845MYvXNGSFQyUTEdBDYU6cfbb/FZo1Fmz8z7MNgEGlch2vppQzH5r9pWUQGDPXGnPBtggZsi2zYJA0TGgWjFRWk6TXF9cB4EtZIO4NFVgDTrhmuJ+0LALgBVZqbMz3BTsHyIG1Y340dAfjhWjaLFgzvU9YFGGI6Dl+rj3OO/cF719iUJTA0NNkrLBA9bu7vhLlhmDF2+/7A5c+SAg1teYLDzRSoZM93cYqFWGxOhJWm7u7IZ6v1RwkmwVTsb3todR+398/OHDQAhtBCryZX1cUZzQbBuj5vLNC5XJ0eCfNfmbGx3PvnuuBEKXibGAK7t8f7ppOM1bE1apRAriZ+Wc3Nnwcc3Oxyq5UApTBaG1uhvMky4sO9zs7ccy9nt9bmvkFOCNtvdOJLDVCh4wBzRWOScEODpXQGgJntqXCZtV04OgBBcpoaYiKOYMJMov5A2Sp0J051LAX54EQHTovHCnb5bmlmXIargcQqzZSAZQyJZQAQLfF31yzsFksBgGuXBNsC+CjxSZhqTg+FUUDINALaS0q5o76S4wRcTRjMgvAyTnWMCvjB7wBhgg7Ms+wVAASwrXc5wB1Ms4AsmifVFdnFr0FuWZVwK8MGMymhkm1pMAlZYoSGHqajIcJFykPEijOrS1/7dlng4JGkFkoeIis2fRtdDru3MbSJXItDAewsuLXByEw2rXQCwwgvbvrTn9tzQHP/r7/TSPV7W0HK9PToUli5UqopFyOzC2c0OrqcMd2QkB7exFqY/ULvc8Kf2/P98v3pqZCw0OoCTBAMUe0UPQc29oK58P9s7AQqedanoKK0WYh3kazVCiEBqfTiaSDSiUSERYXw+ExjlwuMv3IKgPkUIaA41OAQqVm9ESMC4YCHRYhn3zej1cBIg4NZsRsWMui22GfhN/MAoxqw1CcolmwKCqwx+HijAlnUS0dx8o1qowZ+jPeQysGmANoEIrjnPHsM4vQIRlQmm2nKevZMCbvwWhqmA/QwviZBwAVoU/+ZpuUldCaPcrIslDgXoVZ49g1HMf8wKgpk8Z8KhAzCxaMhbTqs2CGCJET4iVkrcwcBnBTPRv+5BL6lcs3omQPZtkHGQ+9wSBo+3Z72AGQPo8TwpE0GlGLJbFC18OoJ0JGFGmzu7sOctptF0tD5w8GXnyQukQABVbKOHRq7+CkNzaGdQ1m4ezX14MFwRHwAC6V/PW5ueGMlW7Xv0eaOyv5XM6v6ampcIA4UtLEzQL4AFyy9Xt46JfLzkrB3MACwBqwyiesw3gJXQFWKDJJI2RltTS9H0BFKIMx4fTQrMCwmA2HWNQRKzOgzA/p0WYxb/y028N6G82gwgETCgGUILTG6TNOZSImJ0Ojho4Lh6zsHGBPU9g1xR0HDstEuJXvMQay4/R/zrGm42dr4XCsMDOUldDjY3uMkW20WgHiAAGAYC01oEJyQmmasaa6MxVXUyIB1ofPM2a2rbWRVD/EoobsMBYoaIq4jplXra2ktaTqdT+X2q2eMBuha4TY6J4AiZcwZHZmMPSFL3zB3v3ud9v4+Lh94QtfOPGz//Af/sOHHliyM5oKDrkhWY0eHvpK9+goisexYtJMjK0tB0KsRnlo8WBPdj2MBqRbW/4ge/llZy+WlwMs4zBYSR8d+fs86HDsFAZsNIKa12wiGCEYlbExBwHdbmTOQNfjMKjPMzHhoKjdjpV9vx+ZYmRPEkLSLDgaoSK+NgtnTDiOcAysAtWlb970MbJ90vB3d2N/MD04z2bTe7ohrqUzvYYOVGtiFiAEJgP9C0JvQiZmkRChOhNCdhpSIfxE6IbjZQGlomay7pg3wjrVarA+GE4NFsss3gcsMKdoFGHomBMM0AKIoBCgWcwV1xJzQqiu04ljJ80c5gaQABhkscg2eB2NJA4dYTMLRrNhQMm5IyzIOGDLFAiPjcW1rg2uAZcwcDzHzWKMnBeuEc4fiQGcQ5gzDRUeHPjY0INx7FkDVHLPEP7NVjY3i/lBj0p2GZloHANzpOJs1ZNdMjszGHrve99rq6urtrS0ZO9973uP/Vwul7MeN2WyR29cpJoBwg3Pg8Is9AeIMFnhN5vRyZ62HVfRtJ5Gsgc3WIA///NIaVdRKjVnqNJMUUAcBD/5vDMPZgFWKN6H5gAxMg4INooVc60Wmhaoeg198WBXEAJAgqW6dStW3YQAVERbKvkxUEiORIP19cikZPy06qChrYawcEAInWu1CPcBTra3Y1XNyh2HOTcXwIIwDkydipIBECpmV20XjrjXC7Cj2g6zYDKYJ8AgbMH2doTk0J7AyOEwzYY1ItpUFMCcywVbw3mjpIDZ60X1aIvM4rmFQzYbZitw9GhXcNSAgu3t14MAjBBiVtTNPAG4VVCupqUKNJ0fjRbASZ+9ZsOMVz7vDBIAB+CguhqAF+cH9hE9EWwPQJMWL9wLXDcwtWiSAJR6zQBYmSvuXxYbLALa7bj3CoW4bgl1EwLnnuR60BDjE6o5dJqdGQz15WLSv5M9QYPS5mbFoQAKDg6iAev8fGTXUF9oe9sv3NXVqOT7JIyH4cPY+Lgf49raxYzpOhuVqo+OXKjc77tzbLeDUTQLJgmwcPNm9BejRg61gmiTkc/7Q5sihRTx06J4pO6qhmR+PsTSMFKaTTM1FSUjtLbM2lpoaUg/x7nANBAW7PVCoA1LpWJgdD9TU64jorebOv/x8ajHtbDgnyU0iMCaViNbW779ubloiUK4wSyYi6Oj0F4xhzAPNHwFqB4cRCVrnDyG89O6Rfo+x5qtKUTIQ0M1moWm6feE21QTpMUqu92oN4WWhnERyuT5hdNUoDVqXISDcMJkwKlom7mEEYOdUWYFQ4gMWMxmnGWZPA1ZMi86F7u7UaaB80fqu8793Fyk/5M8ALBljjheNKEcO+cXNlWvCeYf0T7gDdBFdhv1wADY3JMAK8poaF9CAB73FAsAFYxzHok4XEJWyOwCNEOvvvqq3bp1y/JZ9Jzs0Vs2nZ4VNQ9QQl5Q+tVq3IwIpFutCJM9CaOoHunK5zVW84RJkl2cAWp2d/06oZqz6lzIzsH5sBJlFXj/fqyeKeBWKjlAqNWCQVHHUqlEV3X0Q9QwovYQzlGd78SEf081EDs7rltizPW6Ayv0O2QUsc3tbb+OJiYC7A0G7pxIQnj22WB7ADFmEU4qlwNYAKhgeQgFkWmnzoeQIGwE4A/hKUCE+5uSF2y/2fSQJWFF7idW5jBKerwwyzhywi/KGqBxgW1WISzgTgXGMAwATsKXAAmYC7M4HvZnFiAJgATAYZzMk9b6AdhR50oZTRV/o4NDc8NvzfQjhGUW4TkAiVmAGr4Ho1SpBHDS5rgqZiZ7S7VL3BtsAxCrLCu6PM4ldaIATLCGhOQ49xwj51+zLxk/LA9zit9gXjXiUChEWQvKXqgYHwDGPtmeLtovqT00GPqO7/gO+/rXv25vetObLmI8yc5qIHult7mZ19b8oc8NQs8o0mlJm0Rb1O0+uRYbCBJZceiD6DQbGwu9CkXFkl2sUfPmjW+MByBd5lmh4rC3tqIuDyJKMsjov6W9kY6Oois8VaEJQw0GITpl9amCT9rN4ETW1pyJIVxRqfh2b96MkBHjU+e6uurbffZZP96XXw5dDDWEFhejDleh4CE06uLMzPjYp6cdSHEfwooAJmCNmk0/ZhwnmhlW+oQw2DdOk/AICx4cCzocmAZljZgbDb3wP06e7+BstYL0YBBZXVnRNgAHZ6mMA+DXLAAgmVKEPtk354FjUuE4DhRAgx5MM7o0ewoQAntCbSTGSShHGTEAPMwgn+f49bt6XIBTs8iuIuzHsWqGl4YzCacxVtXjAFIAh4AhshQJ/7JQMRsGrhQxRZejGY8aUuT6UX0RgA+mVHWozD/b4To1CzZIw5Vm8Vzg+rvk9tBgKLU2e8zGzaVxa11tdTp+45E9Buqv1WKVBVPEg5jV/ZOwXs8dyfr6MG18FiuXQ2dAobxkF2+5nJ8jqiVzrQGOpqeHKz1T8RjnWq2GFuPw0P+fmfHtbWwE7b646MCdMBMAl9ABLQFIBmClOz4e7T8mJrw8AKtVgNT6erQSgK3RlfbqaoTQZmai/o0uNsxCgEwaNmBncdH/bzQinLG+7mNg5Q5AZFv0fYIRMHOwRMkBwlHa+HUw8Pne2IhCmThCwjuMl+w5MtVgn8wCAKizU20NGVlmEbYBxFD1WrPFcIzKosDUAlyo+aOLMgVamtGVTRUHCGmWF+AL545Imc+rOBtQBasGMIDdIJwDUIdpNBvOtINR41rr9/35ycKU+WT8nEcNS+l8kFlHWBOmi6bZgCattQRw0VYvXHOMC+CKJs1smCXi/iU7TcscoKXS86sgCnaQ+55jV92XHusVsYcGQ8kes7GK0DABjoE4PdohMsUmJqKrd7HoD9z79/3B/eqrT4YVYnVBphsr+vPa9vZwV/KL0B8lG7bDw+hFxoqRmiRoJMgGgwmAISIbCyfcbkdmGrohQmuAFq5xrutGYzgshVakUvEeaghHARqEn2BDm03f940bwwzTzIxvr9Hw9xFGUxJgbS0cpDJcY2P+mfl5ByUULcRJAworlWCDEEmXSiE8R1xMiII+ZJQXIASF+LXdHk7nBvSQ3dPrRcYSq3HCcBMToYfi/qAivdYj0xU8QIV7CrDCmAnV4PBw5JQk0CKJZMIB0BC145yVRcB4zhEKQvMCsACsoCliHlUorCn5gBSKK7Za8VqnE9mFAEKeR4QWATtsA4DP8VMfiM/t7Q1fv4SJuZYBl1oFmoxGGFkAErodDZuyH0T8nHeYLM1YhLGiaCLzzlzALnLNmMX9DtPIvLNvPkPJAJ4FPCMYzxWxhwZDP/3TP21z9ClJ9miNhwMrwX4/6NejI3+YDgZx85G1YTa8ylBdByLQx22Li+GweGic1zjeanVYx5Ds4m1jI1ag09PheHXFurISwNQsas9oh3mYir29OH8HB571Va/HtqamfHs4TITT1EmZnvYQqWbVaGo/dYxUY0KYS1f4gAr2i7O/eze0TFxbhD80TAVTAjijqjaOvdv1EByAY2IitBWazQR7xLOUceVy7rQVKE1PD4dsYG7GxwPYwVihIWGcMEAUwQQcwWTgxNANATC1uStslbILmn6NE9ZU9lzOEzeUpVL9CoAC1oI5gfFSQMR+CFVqz0S+ryADVo7sPY6JuQBkELYCcMLyAC7Q0SiLxPHClinbRdkFjgEQCQji+lOmiYxHxMiMud+P65GwrDbDVU0Rz3oVbJN1piFNRNJmAeI0DV63y1zB9jBmDeMChPU6gCG6AqDogbzH888/P/T/v/k3/8bMzHK5nBWLRXvzm99sP/qjP5pA0kWbCgwJG+BwuEnoTs+qmpBFLucPSsIctE94Ura+HuGAh8lObLdjWzzsk128UfCQFS11bMbGoiL10VFob3q9YAFgiw4OXMMzPe1ZaoSQej2/FufmIswEeGE1zbWP4Hp6Ola5OASubRiTZtObqrbbw5WiYVFx8Di6uTkfx87OcPgJEEZYZnMzjgER6/S0M0SMkzpKfJ8xMid8Dk0HWiDS2qenffyNhoPCiQnf/s5OCGWVVUOLRZ0ddCo4Py0mqSwJ90yjEcJqtDeACFgYwoh8R0NqMD1kaplFOBBASngF5hoAqOcXYMEzCrEwoNEsHDYNZxFBw1SyKEJng/NX1ggAR8q4pnsr2Ol0AuQeHQXzxpgAjzDTmu6vwB19G+UZmC/N9uJ6xjQzmIrVmsUHA6PaK46N7xNihflhEaMLaq4HShZkW9bAjGm4j/0AiLUMRxZ0qWD+EtsDjfBrX/uaffWrX7Ver2dvectbbDAY2F/+5V9aoVCwb/u2b7P/+B//o/3Lf/kv7fd///ftO77jOy56zNfTNPNDBYMI8qCtEdhtbkbmDRczq0ceaCsrT+54qPvysGUayFbh4Z4t5JbsYmwwcHHxc89FSwyEzKqxaLf9HLRa/rmlJf8+zTj3971yNcwGwITeWltbDphwnJ2OAyC2S6o+GhpAGdlQ1aqzWOVy1Jthhc0Dn6xKnGCx6Pun/QzOmzAEqfowDc88E3OgK2R0a+iiBoMIRQGKyKAjHKHaG7NolWEWIYmZmXB0hPYodKcFA80iC4vQXrPpLCzOXlfsgDXYCEAVzpn3yX7Dye7v+5zCtBF2BMDCIvCsUmBAUU0KvLIf0q7ZD13VdZ+E/mEAtZ4S2XoAWUAioFwznLQOlWrdVBdD1hcMiZYpwMHDJjEWtD56zQDyuJYJ8argXEX2XHM81xi7aqdUaK76PVgyXgMYsi8FwjA8nFsVR6uI2yxYO8Aw7+u2mRvuH67nbCr/JbYHAkOwPp/+9Kdt+m+6MLdaLfuJn/gJ+97v/V77wAc+YP/4H/9j+8hHPmL/63/9rwsd8LU1VmI8fA8Pozptux2NHMkmIATFjcoDb3/fgdK9e0/6iC6GxeHBpG0O1taGsxqypoCJFVKy0+3oKFq6aFYSGhvE+zhQHDUp6jgiXt/bc93N1pZ/fnXVnT0ObnfXgQf0O01Lyfwhnb/dDhH9/r5rg5rN4WJ1MKfttt8jMDKwKazcd3eDYZqfd5BELSQzP2b2CxCbmfHv1OvDxRLLZb8eCf/hhBUomIXWBME5TBugQkXfjcZwQ07+BlQqg0XJCRyhhnUIl+PAAVYAgdnZSKxQjSLzxVyoZpHnj2bPmQVTxzHAqsAOcW1oSj77ocifVhOHaTPz7wCkYaIApxQIZcGIzodQHYALUED4jFpVXDM6PsJ1yhoCeBVQAmaUlSNEzMKNuYXx0XAvx6aZjzAx4+PD4E3DvBj3DD5Caydl55lrTLNxCekpQNV5Yn5yuQipEnpWXZeCsEtuucEDpIM988wz9r//9/9+Hevzp3/6p/aud73L7t27Z1/96lftXe96l21S4O+KWqvVslqtZs1m8zXg99hNUxz5abfjYb+z46EiLtjNTXcsOAxSgZtNf/0P/sCF00+raZ2N8xhOJ9nxNjnpafYzM5Ex1GxGrahaza/BYjGADRVsZ2d9jufmwoGYRTYOYtBqNdLq5+fDaVJHCGcL8CBEZhb3Sq/n49rdDX0R4Of/+//8e61WtDZQIbJZPNDpjWYWhelw6Ogh5ueH67hMTfm+AVw4bvQ2tMXASbRa/jkYFxzY+rrPIQ6/13OgNz/v2yuXI6OH7e/sOLgkww52AnZJQykIm3GsPD8QReOYYZ0BPpVKOLutrQBFFFScmQnwoIwg5xCAMjnpxwK4wKniUHd3o60Qzl0ztXj+MW/okBD87+1FRqGGUqmJw5yxgDQLbRhj0tYsMM+AHDIdNeW/VIptw6goONN5oDgi5xL2CL1Ro+FjgXXN5fz+AhShQyI0h5gb1gqmCcCEcUyUHlC9FgBZQQzzzkIky3gWCqEH5LiVDXqCmqHz+O8HYoaazaatr6+/DgxtbGxY629WEzMzM3aQ6r5cjHEhsTJC9d/r+UMSHQH/s6o8PHSW5OjIQwDdrj+8Vlef7PE8ansQIDQ76w/JVitEqMleb/v7ziqur4cwGfCBKPeZZ/xBuLrqzhrWAUaJFHkqRtPSg+3RCgEnTGYOBRVhWHZ3o10HLAIP7NVV3y8allbL6xCRqYMjoqbK6qoDGDLRyL4BOGhvLBwzoYStrXAWMCRk1MHelMvBkE1NRR0wHKdZdFzXjCGcP85+cTHCLIS8mX9AA8wPzhdnT2af9hEjKxAAS2gMNgNHBnhCT6T9xVS0zH4ZHwsTZXPMhkXdgCOOHdaQOZ+a8gUeTAWhMsATPdNUv8O+eFYS0gGEsr+ZmWHgx2JTWRgyyNCHUWMLBh5gopmWKkAHGBDK5FyZDbNUsIecQ73WYd0AS7Oz/n3OD2ntMJcaVgNkmUVYWK8VROKqySLLEN+jc4csQcO5ACplqnjvitgDh8n++T//5/bv//2/t3e84x2Wy+XsD//wD+2jH/3oa33L/vAP/9D+9t/+2xc51utr3OQaEwa5cyPhiKgVga5ibS1oZOqjPAhYeNptMBguVJl0R8cbLIxZrCKp8zMY+PWGg6JpK8zE1laAFICPFp+jdlSp5J/FWY6NRfYZHeoRoyKgJ+yxsxPaonbbv0NaOqnLhJvm50MPhEiUsNn4eNTnUraJXmaEPA4OogFsuezX0exsiL+1JhPVe9G/tNs+nnLZgU6z6T84Hq5D9qeZcGZRb4ZwEqt+Tc9XgSzhEM2Y0no6PD8QeWuiBnoSgAjgRGsumTnI1ca6HB8M0c5OFKtk/rKVkxG385wrFCKjDiDc7UaBTk0LJ+yENonQFjo3gB26xWo19kWtKG0RA8BgvgDThHwBBSrWRkeljBoAiew/almxLXQ2bJfrDyZSE2V2d4db2cDqMH+a9WY2HEaDrWXeAV6qA+Lco5/jOgN4mcXfgCfOE5pWFaVfAXsgMPRLv/RL9pGPfMT+0T/6R3b0N5M/NjZm73//++1nf/Znzczs277t2+y//Jf/cnEjvc7Gxadprlr9lxUZWQxTU34Bf+tbsdLL5925/MmfPNFDuZRWKg2zQTixZKcb9Du1qmAOZmeDMSiVotYQgl0V2kL3A0jGxiJ0ZhbggRU7iwIYEK1YTQ8vwiQsGNBk4IhxHoQiCN/t70d/O7KpWK3DSMCkEJ7BIWmoqd32kBbZOABFnCQhJL6D86jVIoMLPQfhNzJDEfYS5qL/INtF/EuYXMNKhDHNggFTITci6HzembT19Qh/aQXnvb1wgoR+eM6YxXFtbQWLUixGlh2fwxFznIR9EDfDQDCPmnYPgGBbMFKAGMTH1JsCfMGIlMsROuUaaTaHwSKhOIAO+8ZgoWA1CU0hNtaMSuaKhQDaIgAEmVwcO8yULgJgdCilAiAFAALaFZQxL4jmNcNLi2AyP9zX/X4sUjRkZhZzCmja24uwG3OXrRt1ye2BwFClUrH//J//s/3sz/6sffOb37TBYGB/62/9LatIWuDf/bt/96LGeL1Nsz+0eB03Dw94WgHcuhWFvnI5X4murPhDbWUlCtAlC9M6JWYPn+F2nY2HKE6ZIoWAnlwuVvgImglDkF2koAftjVmAiUYjQjg4ZBiMqamorwVTsrAQGZZmURMIESzhiXbb93d0FBoj1Y/QvBWWRvUaR0eRMo0Tv3cvHD1aEkAcYYdSabgX2PS0gzLAIroMqk2rs280PLyH5scsdDwKNmCPYaMAdmTVIYBWJiWX8/HjNBuNCItx3DhyzSJEiE4Ir1538Lm1FcdLmrfW3lG2gmsAQAVzAliBKSScZhbbmJkZ3iYMCecalhBwh+Nm7DB11IBinlScjQ4JIMG+YMtgg2BVOE40XdqORPVkaIM4v2YBtElUIHQKKGGbhDnZJ+PSKABhXLZNWA8mByAH+AFEA6Q0Gw2hPyBSWymxCLpi9lAjrlQq9p3f+Z0XNZZko0yRNg+hft9v+mYzxNI4kFdfHS6Mxerv7l2zP/uzJ300V8N05UfmEw+vpIM73brdKNIIC8CDWTNlzGJOK5UQ/JrFShUgVC4PZ/kQYsBpwebRJZ4WIFTdbTaHm1s2m5HiDmAhxFYuRzsbzRAqFBysEDYpFgOYoYs5PHQQRuYb2WgUbEQci+ND6EwaPIADJwT4QczN3wcH4Rw7nWEWjfHV61F3jDCi6kkID8IiwDANBr54AkABCtkH/cWYj34/9FwAQ4CQamEYB9fA6qofO9cMGXWwVZQnAHABJJgD2DGOd39/uAYWIFHDb6Sro70BpAImmT+AOKwLIAqwgmm6P/9vbw+HFQGjlEYwi3AjY4WV0xR+AJCybmiy0BOpMJxzwqJZSz/osaE5I7zJNQcYAvDw7GNhA3vHtrh/0cWZBVC7xN3pj7OrB9+uoymVzgoK5qfTiQc8KeY82BoN//v//b9oy3GReiEVhj5IK42rYJqumgo6ns1gGgiz4Kx4eAM+cOToY3D2fG5hYbgyNA9qQmaAj0rFHXSn40BIa5vQxwvABKvw6qshvoZB6HajCz0ZmmSzDQa+D2oXkUavGU7cm7T1WFjw+YBJ2t72/VSrfp9ub4fAuVqN41JwYxbhQO5/soYIzxCihL0yC0ZhZye2R7YezpG+WWbupDc2hmsINRp+zHNzwfBNTvoCDPCCcyTso6J3aibBZtP6RIW9aGEAD4SjqFUF88cYKSPAOS4UovAmwIRMQ8AswFP1NJubcc7Q75RKfrw4dOooKcDjPKHPBBiVSnGMAF4YS65V7gMWuCrK5jxntUu0cqE4JONhsaDZkwAfnlOaKE6yAdcJTBgLPRYYyjDCnKElgt3ietcMP8AQx6ZAld+X3BIYuqymF5Aq/klhZRXQakVaMOm3Zv53u+36h9XVYRG12vy8X+SsCs9qaEFI/dXqtNfRCDEkC9vcjKJuiKZhKKamHAyUy35dr6xEiIiMnfv3Y3V9cOCOiv5ghIwKBb8Gt7ejFQZZl7Wa2UsvRSo7OprV1QAy6+vB9nDttlrR2JWMOBgcWkKUSr7/tTUfIw6VMM3YWNRPAsAwLprCchzodABulYq/NzsbTkXr+XCfU4WbkgNTU37PU3aDhAANmR0eRhVlwBVM1PR0sBw4w709Pzfa80w1KSsrIfZlW4SIEFDzeSpVA9rQwwCiVCxPOBKRMI4Y0AtTwrHDjpA4AriiXIJq22AcSYsnbAu7pSUG0BeRrQWQR2DOuQbcazadiss1BAxrD5CCddZ09no9ql9z7QCkyEKE5UI4zrMYVoZFNNlfLDQAkxwfCwe0YloAkh8NpTF2jgXwi2Ca9/hhG08wxf4slsDQZbNsDFfTPrnxeMBsb/trGxsBgorFQPusBup1/8wo9gaBHELRs1itFvunJP511tmgCUj2euO65cGOU+t2Y6VPyANQPzsbaeYIdRFGIwjFAZdKsSDA2ZJtxP3BPYWwmYcz4TDCWhMToekh1FypRHhkZycatuoKmsrK7IfQHSttxtVsurO6dSvqE8EoEM4DtLHoYfsbGxGOwnFpDSd9HnS7UVkZcLmzY3b7tt+7CugI3zEnOE3aT7BIKpd9W4AMwlIAvdu3fbsAXLK+FADcuOHPIoAngGdlJbLaDg58P+h6COlx3jQDCwAGACkWAwwDegB6MOP5fFwvCIwBIKoVMvNt8QwFeCJsRrCf1Y0BTig2SlNUwlNcjwBGDCB0eOjXCeNHWwUI0fAToArwSnalXr/UP9LikYwX3zI2Nlz5HEYLUTjXNfey+hqzOBeYsk0cF9u4xFqiyzuy62oAIR5MPEy4gYhHQ4fyUCeuzMMwl/Mbql6PG19tcXE42wdQdRZjJcWK/DyM0tNogExW+UlXNNp4WJMdgxgUZsUsHvZkorDKv38/Kk8TaiJDslyOrCkcJtf27GzoemZm/DVE29wX9bq/v7AQDpaSACqyrddDy9Lrxb1VKMR4SOHv9bwqN4LqXi/CY7u7Dkq0kSqFF3d2IgxBKA4wAzDQYn7U16nX/TO1mh9HoxHOjqaqCNthGmjoCYPBNgmDzc9HqF2PnTR+2BHOGeCHc61CWpr7sljb3o7zpWwUbTpgugkx7e1Ftl+pFA4YJkSF5WYBQMjoouAebBDgT3VpOG8KN5K1CyhhIcq8q94MgLCwELXfisUI86HF4priWYGgeWpquE8eIb9iMbIzs+UMVCuFJov7Q1kaTe0389/cbxT07PcjW03BIQwR2XgsNNRPcQ1zfQJymR9llC5xyCyBoctkXCxazErjwFzgUNjUVaFaLytwaNGtrShsl9W7QPvz0KhUzg6GzEIIed2BkFk4dpi7ZMfb+rr/XljwB/TGhoOG27d9Hu/fD0GxrsgBAYhxEe9SEZjrWBuYwhKUy+7g+31nZV56yf9nhUxFZ1Ls6XHV7/tvep0BNnI5Hx8sDU6I2jbb2wFIWKnjNGCJAMywDMvLvo2XXoowDSEcxtRuR1YWwOLmTR93sxngg7EoE1at+jyurg5XxkZIq+0hzCJUQ/gKpkvFvABVGCYWcDxzKJXAWPgO7CDZfZOT8SwCHMFoaEkGmBF0VoRKzYZLF/A/+kmzqGrOYhHWCbBkNryYQR9EixmzEBNz3pk7wAN1p2q1KNZoNpwOz7wwx1o8lLIoiMAB6wBctgd7R00owIhZ6KOU9eE8c72je9JCnRy7Jt4ArGCstHI44FOrbx8dxfaYLz1/zNMltfTkvkzGxQLq1voOhBGoNVQuR+VbrRrLjQwAqtdjZWoWsVtSmcfG/DM4qdOMFQU3YbJgCggtJDvdNjejeCOr/FrNAcTWVlQDR1NSqw3rXRqNcKAUpiOMceOGOyxA0vi4Ax40R6pzAww984xvE6exsBAlKdArHRz4uBcXHYSgk2g0ovq1Aoe9PQcGgDMcKAsJiv7h1FVnAxAiG43SBOxDNTsArm7Xxzs+7mM0820SAqTXGM8Q5p7zcHTkcwPzUK/762NjoUkyG2aDCLNjiI65F0ql4RpM1DpS9o2q+YAPzaaDBQdEwuoAFGDOYVAAKIy51QoQTbFI9F65nGu+AD6ErRgvtXMODiLMW636fGVrECF+1orcbEMZK3Q0MEKMSdPX2R7sKNctDBrAVStJ85qGO/ElvV6wX5oeD4gBQDNe5gBNE+E2gLkej9lw2I9Fu4bI2C+M5iW1BIYum3HTgtK5YViVZetMVCpxkfIgzuc9lX51NYqcYQsLwSrpKuKs4l+0CBRQSxYreZi8sxgPrOtsPHgBPGtr7nyo0ky14sHAndrMjF+vaC9YdULls8re2vJtwJiSbUaIqlQye8MbIgzCypdVNGBkenq4w/jWVrRvaLX8M9RaqdX8/d1dBx/UOJqYcNaHEhg3boSTqNdDuHt4GBWGe73QjZAZqqEc9FMABQTV5bJ/j5AhmixAAi1U0OawP0J8/X7UdVLhM2FAvkdYU8XQVPbWtG59bpn5djodP26qcePc798PoMjCjv0Dcs0iXKUNg3lmwvZoxtbiYoSpyMbCcY+Pe/iJ6wyQxZh4NvKsRMytYmCuYTLwAFBknGlRSACP1kHSgoWcezRcMEWMWcsnELIyi2sXn8G1QYFM2B7uEdW3AZ6YE82cI3NNw2KEMwmp8RpzxXWPFo6FDnNwiS2BoctoXIQ4Vy46BJPEqHEaZtEw8/59s29+0y9gHqbqpCcnfVU7ORm1SKCgz2JamXR7O9JaWXVeRztrc1cofuL+KfvMrdGICuCLixEKI9xCnZbNzQgZVSrxwKVFBvcJgGduLvQ3Y2O+7aMjBy1m7tw2NvyHQo1kY+3txZgQNBOaobVIvR5F+NBHoNPDOS4t+XdqNR8nLTII1SwuRmo8BVFZQZPKj5AZ0Idzoo2E2XD6P88CgMfhoZcSIIzLM0XF5ZQ5gOHEQZMdB8Ak9RynS0huczOYatgRxsq84PABEfv70Xj3ueeise/aWjAi1H3a3PT30A2hISJTTPVUZJcp+4EzpyEqoToYC8ANAEmlBYuLPv8qlkYgrmUQmA/VyqDNgtmCgaJWE+ALXQ5FGmFYOA7mnPAb+0EzBEgBdPA5gD7vZTVnqsODGQNYmYXkQjPniEbAFGoqP4wQ9x3sXcomS3ZuUxqTCwl9wPZ2rBxUFwC9yYprfz8aG8IcsVokvswNjq7gPMZqb2kpdXk/j6m4c27Ozwm6iWRuCHZ5+GvdnN3dcCxkksF8cm8cHAToIaysegiy0O7dC7EuzACdt/m8Uv+EVuhQb+ZjazY9VEUWGAwLBQvZTj7vgI26S8oSwASjRSFdXnutwbgAKPb2HDzMzAz3yEL3hzYGpqdSiUw5dESabq6OjJAejWYBgSzS0OYQzgJ4UVGcStLorHZ2IlSn80koTUOd+qyq131/gCSYaEAD7BZVrlmkcY0UCnFeETJTB4o6Q+h8cjl/nX0TStR6OzBsPH8ROGsIzCxAB+EnQpgwV0dHfp2j8wGcaQFMQAoLA1gb9gfTg4RC94sP4TojhEjRUYyQK/sA1BCCpO4TgAqNmFkIxxkLmidCa2ikFBhe8myyy89diX35y1+2H/mRH7Fbt25ZLpezX/u1Xzv1O1/60pfsbW97mxWLRXvTm95kn/zkJx/9QB/WoGsJe0GFb235w5TVIA8r6oogZjOL7BMestPTLh5lNb2/H0XqzqoXwpaWgt2gBxXbTXa8kbGEU5ube9Ijunx2eBjCfwrb7eyYvfxy9C0DpNTrw20gtAjdzEw8qHd2nG1YWRmuw6MVsmFTjo48rLWwEM1ml5f9Or9xw8dImIHFBSUAlpeHs4+opdPvu7NoNPw+ZJWMoHZrKypkk5qfFdYSrsFhwQiQXcUxEC7qduP5ALgiXEI9pVrNx0Pxw8nJ0AyhN1xf93MxNhZMACwG7PP9+8GSkTVmFppE2KLDQ3/ewFax74mJOOfokAA7ZDlRo4pzrCGjXC5qDynzRDbh7Gz0gdvbCyCEpqZcDsBFAVmYDc4f4a6JidA8IeQmLGcWrA7ACzaKYpdsl0WrtijRRTBJAoAbtgubzDxwvfMbYK8lA2BmFJzxP6CFxAN0dpoFhh5Jq3qbBVAklZ/f+CGAGszTeSIQT8AuL0wbYZ1Ox77ru77LfvzHf9ze9773nfr5l156yd7znvfYBz7wAfvc5z5nf/AHf2A/+ZM/aYuLi2f6/hMzbgBE0wjuFOgoLU+NIB5cuqqem4sH3TPPhHPZ2fGH2Orq+ce3tuYPfq33wsoz2fE2NxfF11KPuNONEhE8lA8OQqBLg9K5OXduZITNzrrzaTSCcUFUS2iZqtOseM0iFZoVu7YL4SFPSIeCd2Sa7ez4QgMgBJvBPYpQmzABYuxSye9RKmST5QUTjJNrt0MQTno0eh9YKJzP/LzfhzAbsC8AIqpQT035POzsDIcAX3015hp9FHWH+B8GhjR8GLZWK+r9kCFWqficUnOH8A21gWDGYJmU2SCkBQsFU8K4+X9hwbdHhW90Xlq+gVpNWtiRxRygSENJMHlU+IcVgtkql4PJ0fo7sIAKMEk2AXibRbiPOUDPxFg0MwsdlYI8Fp8KiAA+pPAr66jSC0TdsEbMN+1diDroOeGY+B7XAZ/N+qhssU/NTLukdqXA0Lvf/W5797vffebPf/KTn7Rnn33WPvGJT5iZ2bd/+7fbiy++aB//+MePBUP7+/u2L1WUW49TCwNqVspaa1hol+V+3x9O/H/vnq98qUN086Zf9KwoSD0lhk2Tx/MaD1QejrQvOE9a/nW1ra2ofZLsdCO13cwdU6sVGWcsFmgMur1tdudOMCoIm9tt/y6/ccwavuj3PbRGVg/AqVj077Cynpz0EgCwB4B/xNWFgrOmMDKEkCl7sbgYmhOE0JWKbxNnQWic1TuskxYQBFR0On4PMp7JyQh7r60Fm9Jq+baobYSjI4RHnzUFUbBrhFIIR3Y6AQDMQkzNcZn5fmnXQQkPwpjouKh9A9NACI0MQthsQnKaVWXm493a8rGTnbe9HSwW4BiQoqn+hKE4DkJlmvnGIhQBurIsgBgAwWDgx0zYTlPPVS+DjgpQQiKAaoO0uC7Agcyzfj+qdZOFBtPEcSrgQWDN9zV0BeukKfMaeiPkp9WszeK4OCYdJ/eCCsYJg7IYuMR2pcDQee0rX/mKvetd7xp67Yd/+IftU5/6lB0eHtq4Vs38G/vYxz5m//pf/+vHNUQ3zSJA95DNEND+NRQOY9V0/74/QLSWR7cbGWaEZaBmDw5CRHpeo7os25iaihTgZCcbWXw8QLQCbbKTjfYyCvR5mM/PD4eIzYaFx9vboc9aWgpng/hfa7Doip9QF84EHcf+vjtqdBY4DsIbhN+4rwllTUz49/b23LGXy3F/12pRwJTvwPoCCgAbs7NxnIiezSJ7Dg1MrRahNBgI2C2AJhlLsMyAhcnJ6Iu1txfsC+n/OE70SgjYCwUHd/W6L7YAX3wHIKi1uQgRkSkGK0ZxyomJCDlSTJKMN62dg1OHNRkbGxYDE04EYGnoECCjWVBmcS5hJHHoMEmcP1gW9FJmwywRTBcSB15jDAAYMsBUFwQ41YayHDfb4ZwrqNH0diqNw8KZDYfK2B7ASrPmlJ1iuwAkwDLJC/gfvRevQCaZ2VMOhlZXV215eXnoteXlZTs6OrLNzU27efPm677zwgsv2PPPP//a/61Wy+7cufNoB8oqlSwPjYcTy52cjNVnreY357e+5WzQ3bv+PwBHlfzQwdxErKAeNK0bzUGn4w/IdjuybpKdbsTUzytaTxYZXNPT7njabf/dbAZAIDREnZ563a/XuTn/fqPhYIp6Q7lcpNxzj8DQkkHENc+qmHsR50GYifAS97AW8QO05XKuPZqZ8fEBnmALCS+wuCC8Qn80hMzNZqRus7onA2lhwY9HtwHwaDRiHIi0KazIAgvHTBsINDUAGRwuoUXGPjnpzyaAH/M4NhYtQgCZhUIwUrAjWuGYcJEWBTQL50ztHMTlY2Meuh/leGHSYbOoZaR9zADLZL1xLmCdAEQAKi2lAQuphUEJacLmM79aCgJWTEN0LDS5DslWpNRBNoyGsR2zACroqdgWnyf7DFaMsRCOzOXimgH8KAvG9nURQBgWFo7Ue7bNQv8SC6gv78guyHKZGOXgb27Q7OvY5OSkTT7OYoIq+mOVAXrnAid+TeYRF9y9e7766nadFoeWLxajwi+9hXgoI7DTQmnnsUYjYu39fqo1dF5DoJjswWx/3+z//T8HNKRAw05QtI9MJEDI9LQvAGZmfBsIkamJQugHR4TjhC1AOFwsRj2a/X0HJM1m1PQh1DM9HRoWdEEa+gC4UMCPsNH8fLC+7XYwNzBFhNRgaGgNgTiYz8zNOaB46SUfN44bkDUYOHAolcLB0kNL7+fFxXCMMFaUBVDwVqtFNtfenofM0bXwnCFsyLlCR0UfOgr54UhhKHC6m5vBaNDWBcaLcBM6SdgVwpgKbnkWkvgBqNBsLr6j1wbAl/Aiz1MYQCqmI/ImtEfYEo0ZTBnHyL4A3WYxF4TFACUUe9Q6RWYBvrVuEMfG32TvqWyCffI3oEYZJVgqtsPrAFUFOVrGBQClNY4UTF1Ce6rB0I0bN2w1o4tZX1+3sbExmyem/KSNWLRSojA6XFC6Im21YjXIA5MMFOj8W7diFVapxGqZZpDZkMJ5DSEw4stk57MHBaLJ3AYDDw0jIn7mmSi2iEOHyQHIwACQ6ZXPR/kJnCD1icgqGwyCPSFdH61QLuchNxgQ9D8UY8Txo+2gRcP8vO/v1VeD4aL+FPon6vyQhQbDsLwczhlHy/uwxaWSj+ull6L9hKZMwzIzN4T6cN6zs9FbipYmrZbvt1oNwEM4CxZaAQP1jYpFH8P6erT2IOwGe0RGVbUa4JVCm2Nj/jfABXYEUMmPWWiJqEukAENrEJFCT083ALE2UTWLBqk4fVi2ej3YIkTk2gqDLvUTE34dIWDnPMCQmQ0DLzRwZIlphwCOkzHyPZgu1W8B4NG/AWwAfIBRLU6pYDBbVZvP63MeAG0W21YNEtcXc6lhvEsKhMyecjD0zne+037jN35j6LXf+Z3fsbe//e0j9UJPzJQqZXWKsFNjwmbDDRuhlAm5oA2anQ0tD/F6aqEcHj5cbaCZGWedNjZSjaFkT9ZwsGbOFFE/B/0DD//tbQfwhDIAECpuxYnjFGo1B1xHR9ETbGrKnSL1aGh10WgMOzptf8O9TOalOhgWOyQhdDr+2c3NYD263Uh3h7VhcQQ4aLcdABFqoqcb4Sky3mZmQrtDyJBWI2Tomfln2u0Ie+D8Ec5Wq+GwYbNhCwCXS0uR7n94GPWiCNvzTGMhRygI1svMv0vmG/sDiBFqI7vLLLbL4pJMNq0GTlVxWA1a6QCUKbCJWJpMKTL9OLdUz+Yco3li8cpxIsQHMHLt8eyEPQFQoe1CD8SzHjDB3MDUaF85rnfOBUBFsxo5R2h8qGUFU6fCacoDoFnlPmG+2T7MJdcV1wXzwNgvOSN+pcDQzs6O/dVf/dVr/7/00kv29a9/3ebm5uzZZ5+1F154we7du2ef/exnzczsgx/8oP38z/+8Pf/88/aBD3zAvvKVr9inPvUp+5Vf+ZUndQijjVUBF5LG5aEwqQRaqUQ668GBP8B2d6OoV6nkDzce/nNzUejt7t3QJz2o0dZAb4pkyZ6UbWyEbu1Nb4p7BSbn/v0ISUxMRFbTs89G3ykcEeEPQjc4ClKiqU5MuGluzv9eWAimgUrOsCpsHxaLSsRkMKELRDcDo0trDTQnVLS+c8ePF2YC57y15cwRhVARh5uFPoaQFo4KYTcZZ2ROsTCD+ZmY8GMkOwqtkplv58YNP45WKxz9zk4wMLduBVBtNv099D0AGJ5nhA/JegKIwVRlzwnOGzACI4TDZ1yMlcQUs2DrBoMQL9MHjgUqFaanpwNowcATllIpA+G1SiXAUD7vwB1NF1oaxk0zYoTrjE9rDWmdHoAFYBrAwbHxHRgqalQxdsTjAH3mEFALSCWLTpN7NH0fFpMFRLc73CQWDRrHecntSoGhF1980X7wB3/wtf8ROr///e+3z3zmM7aysmKvvPLKa+8/99xz9lu/9Vv2kY98xH7hF37Bbt26ZT/3cz93eWsMIcBkZQA7BEKHwt/djcJl1JNAY1StxgqQFgMHB6Etovnig9rqqj8wtrdjVZNA0aMxrXuS7Hjb23PQYxYOhUrNmspOp3ozB1GtVqz8Z2f9e+Vy6I7MggWito4yOzA6tVr04yJEQZjr8NAdP0wA1Z5brQBWt2+H46buzdpaHBsOhgrZrNRZzcN8oPnACeN4CcWZRf2gpaXIMOp2fRswMRy/9jQkWwyQxPOGVG/A4vZ2tA7RWjOLi6GhInTFmCmFgEQAEMRn0MzAtgCOCGURmuK7aIYI+QCGYfNoEAuoYC7HxyNjiwUm7Iz2yKPmE9uC9UJbpWL7sbFgg/T8cP0wb7A+7J+wmma7AejRQiFC12PV8iwAZq2HRKiUa4Hrg+MBnMLQAZqyoVH2hV6NMB4gCEaL8OUVsCsFhn7gB37gNQH0KPvMZz7zute+//u/37761a8+wlFdoGlxKjIkEEub+YW2thZ0dj7vq1NWAOgBZmdD6Li5GVkSjUZs60ENqpWbLdmjM5jCBIbOZgCifD7S1GExcbrT0w5eXnnF749bt0LTAQCAycHxw5ZQmqJU8s8gMCYrZ3MzzhkCa5w1YmEYGBwsNXoAW4S/Z2b8e4eHvh/CLZ2O38dkMSGGRl9TLPp+YFrIFgOkmfl30EWx8qeQK8JkvsvKv92OjLKFBd8GwAQGhX1Q1oMQPSCKhRoZrVqzCFE7omS0ORSDBAwxD2gfCRXi6GFKYOlw8Apu2S7HCcggIYRwHbWZeHay+OPZjNCZfbBPgBWAgfpOsGpajoHnMdXVAdEAPNgcBVQKevAXhDARdMNicc51bvEzNLtV0KU6H5g0jk3rFHGfZZ9RCN9Vr5RS65M9sEFVQgP//+29e5TkdXnn/1Rfqrurqquq790zDDMMA8NNboPKeEEEHQFlkZNdMbuHmDXBsNHNIqtZzZ4Ts/yxZDXJmhxFY5bEqFnknCAeL4TAQUBXIQYEUUAuMjAXeqbvVdX3rsvvj+f3mudTNT09PTPdXVXTz/ucPt1V/a2q7/db3+/n8/48z/t5P4mEDh6vvqpkKJu1G5ZwtIhV1IjY4LxnjzZuxX31RAkMpb7k5R2rCwYaF6ovH8Viue/VwoJWoFFK/9pr1igTITG2FHQxp5v8wYO6TVeX6u6Y1BcWrKS/VDIXaRYKkYiSFCY0ND9MeO3t5vjb2KifXyqZBQC6pYYG3Ydk0ryB8BOiDxgVVbmc7gfXTJjqorqLKAH6JATmHDuTHiDaQ+QBF2oiwkx4kBC8iZiEqRQLIyVEe9CtQA4QHSMEJpqTyZQbQJKmCSvdEK4TmaAqj0Uh2iQWjhxbaGjITyieRxzMorOlxUgEBBoRPcSbdh1EqkIRO1GnMKrCApPzJ2KEGx8pNF4seonEQKY4D6H+SKScnJdKSgIxBa20kiACF1YAsg02C3yP+EBBjPhdqYFyMuQ4LhC2ZeILtQW5nLlLT01ZZRg3KDcWN1E2qxqhgwd12+FhvdFONHeLYM6J0NrA/YhWDhMT+hPq3qJRXSy0t+v9MzRkWpLhYb23aGpcKpWXJhP54TGTLdvxg16oo0OjV0y+kK79+y3igfNzmPbq6rIGpBAY9p2qIyZ1BMGItqkYC+9Xxg2MC4kuIMal0ot7fX5etyU6htcTaZGwtBrSFlaoQYImJqxSDa0OJIWoNZomUnQhoejpsdJ5IjcUmBD1QJAeVuOGFgIUkBBxYaxlXCRlRZSEKj/61PG5kDURi3yheeJ74bwQxSJ1iAUEnnEQTeQO7CvnAcIcRi5Dd2uiMKEAurLUPqxQpnVK2Mw2dBJHM8XxILDn/UKRepgp4JhDh+pQ51TjcDJUS2BwELEVZRjWDCvB5ud1EDnlFMv3DwzYSm5sTFeyrGBZGZxIKwg6Hnt6bO3Q06MT94mmNx2G0LAwEtH7BX8eUkqVXjeRiPUiw+OHBqJhZIbIASkd7t2BAStNpxhifl73BXGzSHmVG0SKiTHsMwbJYLFUKCgRCXt4sYhqaLB+bOPj+r6dnaZbyuXMeZsJEPI0M2NCcIhWeGzoS9DpENUKIx7sH3os0mc0zUWbEvoQhZMx4lyOPSRSaJ7YJ84t1XSh+Swap/l5a+kCwSCCEX7noVEi0Q10maEBZVgyTuQnJGoiRj5EzNcJsobkAC1PSGb5LsOqMrYP055hBApiSHowLIXnOBCvQ16wIwi/L+xaSJnyf9K1kHXOPZXQzE2c0zogQiJOhmoLGF+xmgirJRgARSy/j4M2pa6EOLn4Mxmz2IcE9fbq6pcKg2MxTaQXUiisdKwesPfHyI0B2i0NThwM2hMTet+QSkGjArkolczAsaFByenrr1vfMsqtw4KCUEyaz1sn92JRV9lMtpTkE9FYWDB3+PZ2i16RVkEwDJFicmUyI1oQmrcyKVKhBtlCC8IkhqA4mVTCByGk4m5+3irsEFATIQlFvWhlmCTZDwgLUSHGKETpHDP6LXzMwmg3ZDNMvSAUJ+rDsVAqT8SLz4Y04EIdiqcZYxFhE4ELPY24Jth/yCDXDm07wogIEX/2I5HQfWesD0kHYziVeRhwck7ZZ6KQfBb+VxTgQEzYD9KSRBy5Foj6IYJHrxTqjfgMCDARP77/6elyuwXOMdGsOoGToVoBKzFWgKTDwokQkzBWYDMzKmZkYEBTAFOPRPSmQvxHNQaVIcfjHu2tN9YOlPtGoxoBZBJzMrRyCLVFYfk2jr9MYs3NWkI+NGT3aKlkvc64B7u6dGKgc3pzs34G7u9EHugZxiSIzoRmtESsSGsg8CX9Qek8GkHErnwG6bT29vLXdnbqGDA2Zk7RkYilD0mhxeN6jKHXERGNUHSMvqaz04ghn0mUe3RU/+7oMHJHpVOoiWPcol9ZLGYLP85n2D+LzyeC3tZmqSI8oVpaLC3F91YoGGGCLITvE3oEMaFDbCidZzwO016UzIcVVOECF0kDWieR8sazpEFJn4ZWDbOzOm6TgiINRXotTGkitkbnw75znplPSJWSKuR4EONDnCDrlZkFvgM+m30KSVodwclQrYCLFnY/Nmblpl1dFsbEs0PEBjlu2LDJH6kVBofubhNQUsbrk2p9AOdxLBfoyeRYWezfr7+ZcKNRIw/o9dAIIY4+cEAnQ3QpGDcyuZZKpgEaHLQGr7TxIPKRz1tkpL3d0kKQIxYurNZzOfNSCnuPheZ5xaK5VzNZixgZwfl5clKPLyy7RgjMqp8KVSLVRDAgBzxmEgwd6jlmKqQgCAjIiSoRdea8TE6WV0lRvUcEI0zp8Th0kSYKxXdB9I6oCGJiFpdEcIii8B5EWMLzFlo2iJipIcQKshX2EEN3RaqLKAvkBn0XkSIWt4zZEFcIFe/BOeAYGhqUgKbTFsEMU3Ghd1CYziMlSSoMsgixDhv8co+wUCA6hgVEGE2rEzgZqiVwY3EDEbrFDbary246BiW8TajAiMX0RsCdFzMwfDvok+Pl2vUD2q6w2sOvZWam7lZfdQHIDKt0fIMmJpQEUSLPfRi2MzhwwNoaIEql+khEqzuZwNJpvaepgKKHGl42TJQi1hiWdBfVZGibisXyBqPFoqXhpqfNPFDEJrWQIFAmH+oTIxFzvyaSg0kfHkVhmweiA7huh+XYYQshSF8mY4aQRLYovSdNyetx3K6MiIjYPjGBk5rBZyn0QaKhbiVBQdjNghKRMikjflO5RqQHTyTSQw0Net5Dc0eaAFN9FqYy+R4gr5AxRPcQFBoR8xqRcmNesgl4B3EtQFYhdKGNAZopSByRShFLP4YFPaEGi6wD1yPEivQcmrFQc1fjcDJUKwhXJqSiuIExDkulbFIkhSZigxU3CuWQmIhxY83Pm+eQo77ACo1oHqtpx+qCVCV/E9Xp7jbX98r+Vui80OfQp4sJnrTa2Jje09yfTFTZrH0mFWmxmG6fTlvamyhVLqfO1EQ4iAQ0NJQTIT6btAsR4rk5JSi4PRNlWVgwLdLsrJX/E8EYGzOzScggJoCkETGZbGiwcYcoRKivQgsUTtJUhTEGEvmZmbG+XRhmQs7QPkEoiODwObS5mJ0tj6iE1VxhVIhzFe5TqEUKHaBZmHBckByiKpAbFrRcB2gweX2ppCSNdkqInUlbhdV4lLhDthCwh4JoSun5HyJ4CArfRShax4AT7zqif2HvMb4fjin0eIIYsg9NtU81an8P1wtCb6G2tvLIDiWntNvI5zVdNjenAyMrovBmYUXEwMagxKrC4XAcOyBHtL/AgZjJI5HQ+xQDSKIKc3Pm40MfrkjE0l+Tk6pHErG0N6XckKtSybrck+4gIgFBoHAi9Ooh8sIEPTlpiyQiWJAffM0wS5yasvYfaFh4n3C8YbHFMaIZSqU0PUj6jz5vvb1GrMJWJuxz6PvD85zPsHyeSTdME2IXAGEUMYITanFCnx3OKZ9PBIeJnX2ifQrbk84KRdMsUhEaU4UXjr1EciBE+AQRsScqx/6wzxAvDDd5DQtfiCuGkCygiGoRbUS4zncYElGOd3LSonocP8Jt/haxtCH7U3mdQBRrPDrkZKhWwMXIABGLla9ISIFt2qSh7TBnPzenq8ls1gY5wpUiFv4tFvW1DofjxFAo6IIlk1GtUTJpP6mUNfZMJjWKREQm7P1FA1MiLYh8i0UlWqy2s1mLPFE5RHSByZnFkIgZRuImHYrAEcni2yNiKTgWX1QYRSJ6bJGIbkvZOP48TNyUiEO0aN8Qj1u7B/YbwS6icZ5Hq4Q1AboVtHFMvhwDFgVENBoaTE/F+UQaAAGgyTVjJNVyaGOo1gor0NBuhX2/iDLNzJQbZ3Z0mDie94WUEb3n2oEcsW9hmT3kjmgKC2DOddiYm3kDAh2eizC1F+47acQwWgMxC5vNsqDmtaQOQw8pCBWEScQIOt9bnWiHnAzVCogKcVETtubGYyUwPa0XJCK2sMSRi3J+3qpkEglrQzA8XN1jdDhOJoTeTzMzanDa1KRR20RCox9oJ9rbrS/gzIwSASZCUhBTU2Yq2Nysk2tnZzmRYmLr6tJ7n/EgXOFDvogaECmBYFDqj4Mwk6qIpZCoJqM6DbI1N6djT6gPyef1eMLGtBgphoaOra2Wxpqc1PfCZZsUTuiST2QJQhVaEFAOH6bcmLTDylwIZRhxobn11JQJwzmHkBIWmxjYotdDlMz55HjwO6I6DCLB3yFxJFKCeB4vuNA8kfJ7CHChYAtagAYJATZaIeYF0l5h2oooFvvM35Upu5Dksb+cZ84TOjB0U1zrfHe8rg5SZCJOhmoDYWiRSgREjKH/A+JARH6EO2l0SBiUJnvchC0t9pqTGcmklj/jz8EA+sIL5SXUDsdqIZ/XdFcspit4VsUHDuj/6VM1OanbTE3pRMhEwj1OKovVOCSD+/z118u1KfhPEZUhxYKGCOLAe5LuI1JFqoUJF20MlYu0K0G7mEjo8UEA8AGiVJ79mJjQqBKkAaITTrBEQ/D8Id0XVlERTcPHp7JEXKRcY0mKkdeTzjt40MgBHe17esoJCCSIFBWC78oUIgSPfQtJB8QOgkMUiMgWUUBIMREZGtlOTyvBCLVSoW4nLN+HtPAc+iFSsaRJOV/YFHDuQ38ifoepWAgP1wapVRGLxoVVcQj9MfqFlNb4/ONkqBbAgEauPZ833w58SPD7QKAWWu3TxZ4VGFGmri4dNCcmdDA7WSNDPT0ip59u1Xb0biLK1tsr8uyz2tvN4VhtzM7qpBuPa/VYV5c+T6k6mhL6baF/6enR57q7TbcCCaJ6EO+dsEUEkxNp8oUFve+bmnQcwRwyLH/HY2Zy0tqKIJZFnIxeBG1I6GvEpBqPl+tDEPNiMYCgmkgIZeM0kA3FtaRq+LxczjQ5TU1GkkJtCxEIiGRYgQfBY4KmlxtGkgjQGU9FbOzElZrJHLIUCpDxTEJ4DnkkMlWZTsrlTFNFeonxmoUbr4M84X0UGh8SRRSxMY+sQphOC32cICoI3kmXInRHHxYSP6JQRObClG0iUR59xN4hbF4bCtNFjDjVKJwM1QoYGLjJQ5Ou+XnTCaEdGBqyFeHu3eWd7JuaLLwMeRoZOTnE07jfxmJmcLdli8gZZ+jz+MKwaiVlmE5r+uInP6n2ETjWA8bH9YdIQDyuixJ0KrGY/p9IEdEVoh+kj5iUmWyJcrS3W6dzxodUSj87rHDCc4x+bLiYE7khYkC6rbPTyAXVTFTGEWEOhb6QCx7H4+aJJWItSCIRax1CWo5jRZQrYhM2bU9IL5LywXKAzwzTWgjCOZciZiMAYUR/Q3VbmA6kck7EpAlEYyhfD00KKVyheW7Y9FbEUpSIo0VM6A5pQstE5GfvXt2Pzk6reOM7xnKAz4accW55P6obQ7NHCFGoTQ11ZkSbwn57fC6LSiJNpO64tuhlJ2L7CIklqoQGqoZTZrW7Z+sNhFe5OFnVQJAgObTYiESUEO3fb4I9jNwYNPft0/+Nj9enr1B7u97sAwM2WM/M6CCwZYv5b/T1aXqss9NWQ4mEVfbs2aMOzlu36vs6IXKsFRYWdCEyMmLP0XAUp2aEt/v3m1D51Vf1ug9TMmGUgAgM6YtSSaO/EI14XCNC+MYQpYnFlKxMTVm/MQwlR0b0uYEB08OQopqdtYgUhoUszEirEY3AdyeXUyIDOYG4EP2endXfobC5pcV80BobNcLW3KzH1tWl+9jRocdN+hBiA8kJrUUgH0R/Ghp0nMRAUcQiSZSNE4UiqhFGZojehASPVCFRJB7z3YWePeGCF1sGImeTkxY9wqQS/RbfIe/FPIGmB2uVMNVFBRjfM9E+BOv0lOM7puoMD62mJtObESUUMQ8jKgQhs1gPQMzQWnHewveoQdTunq1HkONHmIaXDLnq4WH9GR/Xi4zBhoGG8G86rRciF3Yd9YeRjg49ju3bbWIgRE1D2qYm3Y5VYmurEqGuLhvA+/ttAOju1rTBvn1a5ivihMhRPbAqf/FFvcdpHEvRBKXora36f7Qtvb0a4aHJKKJbJj4RHQ9Cw0KKMBgjiGIQLWH8IF3HRN3drfcflWOIoUVMQM0irbHRIkdEGNin5mZ9LXok+iS2tur+83mIxIk+4AxN+xk8dUh7haX309OmDyLNRjUu6RwidFgbUFEXirCJZtBnKzRnpGCFFCOkAnKTzRox4TuBUJFaYh+IfpHKh1h1dprWhugM311lNAqtj4iRJdKAEMVQl4X4neOCWHEcoUcdOjMRixyG2h/2BW0W1x5tZyo9lBiTSb/VqHbIyVCtIPTUwM+CVZiIDhTZrA6UOBIXixr14CLHZwOxIAMM4s1axvnna/SmrU1k82YdiHt6dDDLZk1QStPI0FqeaBCDeleXCVPx3ti0Sbd5/XVdXZ5/vsgzz1T7qB3rEWGU6KWXLGJw+um6kCEFxGROeqaxUa9pUi/d3UZAmGTREKERIlIjosSnvV3fA11K2JeK/mg0RC0U9DPGxmxVn8noe0xMWFqJqAfaRtI+RKbQkRDRJo3NPYvJ4OysuVKHUoBoVPcDE0jIFxEkIhKQh+lpawlBmkvENFOU6aOXgUixP0TT0Gyh22KCp+ReRLeFlCCAJnrU0mLnhao2PquryxZykA28qCYn9XjQP0LWiMwhpCd9GaYI6YVH2xHc1HEaR2vU22vfhYiek4EBi/CR3qKVCc7gHGdYOj83Z2J8+t5BMiur52qUCIk4GaodhOWRMzM2KFC+m81qPnlsTAcJbv5cztozlErWUJGLdGTEBrxaxRVXiJx1lsg552jKK3SM5aZ9/XUb6MJz1dCgAwYDOQNH6F6LyLG3VwehdFpXYRs2mDmew1EtEHGhwAEtyNiY6YImJnRMYHHU2WnRhGzWVvki1tuMSQjtHNFmJnUqyyAdeJC1tFhLCdI8pD1If4WGg6SouN8Yf0jPk5JJJq0hLJpG3LlJC4XiYvRLiYQRFSINRJDQyCDuJtUVRmy6u22SR2cJQQl1lKT/RGxBGv6ftBzPhSJxzi8RF84n0WneE+KKhodzODFh4x7yACJCYaUWnw8h5D1oJ8KimsVypSM3hGx8vDz6w3lEFM0CO6xmDMXQnAPIzuioXVc8191dXpXH62u0PYeToVpCNKoDSyZjIW1WM7mcVVeQ86a6DEEk7Tr6+/V/DGi1jN/6LZHLL9cbB1EgYXCaRebzqhFCQ0UFHfoIbsxwxZhI2OAW9lTatEl1CHNzuk06rekKbnKHo1rAOFVEr+XNm62UnfGgs9MiNHRkpzQbHQqRI1JSlM8TbcaQkdTY1JROjjMzuhhpadH3h4i0tmp0uVAwGwBK3NHFQJSam82Vu6FBF2OFgkZ9R0ZMx4NOhePCIJFoOBN7S4v9zVjGApFIA8eBaDiXK7cTGB8vr9ojZYf/E6JijiUkPRAZxOahFgiNFCSAlCCkjGNCj1MsKvHs6rLSd8TzmDJC8NhHIiporUidNjZaypPoD/oxNEsQVrRmoZyC10OwQgfssDEt5JEoIJ9F9AyCid4Mcka1XjptLWXCijj2pYZIkZOhWkMopK4U74WW8Kj4h4fLqwhIJU1OWtdnQqO1hgsvFHnrW0Xe8har6mB1xKqE0t5QGMlNVCzqIJzLWdVIY6OF8VnlUJWCPgGPl4MHdft0WiNEkCSRk6PyDoSCS0d9oFAQeeUV/ZvIQ0+PpcbQBYmUC5YR9Y6MaIUlAlomPyIr6GKIkDDuQJzwK6PcOqxOyuX0vuvr08gO4m7E2I2NuiAjtTQ3pyQAkS0LGBY9TNSNjSIbN1olKISNlBoVWdzTVNtCJMIqKdJz/G92VveN0nJS7A0NFj0J21YQOYNIhL9Jk42PmzhZxLQ/4XhLhBvXaJrsvvqqjlnptL4+lbLXoaUKq8jYL44/m7XvP3T/JsoYRqNo9g15a283QosnEHMNcov5eR1Dwz5kFKZMT6v+kjQj+wNxmp7W43n2WY2+9/aasSWLe8YjzCFrgBQ5GaolcDOH/XVYGTQ368XP5M1Axg3LBU11xuioltzXasTj1FNF3vUuXTGK6PFt3Kh/E87F8p5Bhjw2AyArjGTSwvn8P3TlJuwsos/39elkgTfRz3+uk8z0tGqw2tr0+VxONVqsXjOZ6pyrE0VYHuyoP6BJGRy0AgAREy0TTQ29XsbGzGuou7u8dUc2q4soLDjQsRw8qFFSGn3iOTQ6qvdjd7f+oHF67TX9/dJLVrmJ3mdkRO9LCFihoONWPG5Gg0yORFR4bVub7sv0tL4mbJcBESGSkUqVmxeSGoS8TU9bpCmdNosS0jYUaIiY6SIEKmzBEVZn0VeM17IIJa0VpvoQQjOmx+PWKxKrgZER/Ru9FVIJ9DXIA0LHZ6I7MzP6neRyRrr4fHRNsZgeG21XiN7g21QqKVnF125srFzPhEGkiH7Wyy/rdcK2dK8nKsX3snGjyLZtIjt2qBaKcZlFa1jqXwNVZtXfA4cC1k9ol/QX1SBhCDoW0+eHh60HD/2HuDlDr49axPvfL3LppbqC5GZl9Qf5o30ATq5huJgwu4iV91I+y2oDMskgQgi8u1v1SaOj+njLFqukSaf1vfr69PXnnGPh+0xG5Ne/Vu1WvUVanAidfCDtVUnSESM3N+vKvL1dfzNRh27ARF7Gx02Ai85nwwZLSc/MaIqO6AKl5Pv26fOk2drbzeYjGlVR7uioPkarl0opUWpt1ahBf7/5gaE9aWy0ViQIgklH5fP6Gjx+GDsXFvQzhodtEYUAGaE5aT+ixESoEAuHnyNiBAHyRUsVvM6oKIM0LCxoBA8dFV3l29osBTo7a6QvmTRdJ+SKiFooUqZNC2NjaLVA1A2SB/EKo+X5vC7wJid1P0JrAZrxTk3p8U1MmIAbX6piUY9r3z6RJ55QV3+yD4uZ2ba0iLzhDTpWtrZacQzXXihqZ+yucnTIyVCtgJtTxCzeuSkAgx9lpyLW32d21gSJk5O6WqtVfPjDIpddZpoIBKPhqoFBCpt6QvlhRQIDAwMHVS2cR9JqIrbyKJV0kI/FlBTt3m1agvl53aepKR2kCCn39JgYc3pa5Gc/02gSpdHj4/VDNsJwu+PkBAas+bySdxG9XimwQKyLb5eIXROY5xWL+hqq2PAZggSEuhMRq1ATsSahpH+4b4gedXToPYVH2ObN+jz7Eo3qfo6MmKgXTxveF8NEyApamOFh6+Cez5umhtQc4whEAW0M0RQEwKEQGIPEyn6QFGrggYbAPWytMjVVbm9Ayo9INyn+VMqyAUSzqETD5RtNKam5UsmMPLu6TNfEoq6hwVo1NTQoacpmbV7ZvduuBRG9VgoFswII/ZgQph88qK87GubmNM3b0qLpsnPP1e+b8yZSbivgaTJHGbiQIUMIBQlBTk/bKgvRIZUjRErGx630vhbxgQ+IXHmlpslaWvSmZbAJB1NC3ogouXFCnwoIEDfzUjcU24qUa7C2b9dzuGeP9Vvq7rZu5KmUaRnicV2RXnKJyNNP66Dw5JOmNfrFL2xAr1U4EVqf4HufmNDfEIxKUBYOqDADc3Nm5HokMG5R9AEmJvRn/3593NWl99cpp+g9l06Xp7OSSSMjjHMYBdI9PZu1CA1RIgorSLN3dJTrEYnAMN5AqBhLWlqMBFDGjs4IDQ4aIxZBRJUQDxPhJqJEVI7009iY/vT1aXQMnRNaKarIWNDNzppAmePEHmB6urxZLdV2RHbm5/WzsHSYntbvoVjU5195RT937179jffVSswhRMbwIKLfWkiCwkVrFeFkqJZAmJNoCM+JmMlaWBnCSgfyxEqPUG6t4bLLRN7zHg2fYl/f1WXHWFnmSs4eEnOkkOqxrCrCsn0crJNJfX5kxATn0aiFlanMO/VUfY/GRnWzpoSVUHdTkxIjwsb16PrtcKwVRkf1B6E4qbmuLiUItNDBL21y0rrMZzIm6p2bs6gR/dni8XLtDqX3kIOhoXKH6MpmuJgwolEaGzOtYiymEzxtKHI5S31xz4ftVTIZ0wFFozpWQNpEyqPh9JHD5LKnxzyEQo0O1XWTkzpWJpO6PwMD5fMCOh7am4yM6Hvv3av7deCAyPPPL01ujxdU1G3caP55kMTKKH8NwMlQrYEwIpGLXM4cYF96SW/igwdNT8ONjBnb3FxtVo+1tmoJ/Tnn6ABHHx9WrKwU0PpQuUKounI1cbwgXB2K9+JxNWEcHdXB5uWXrYklXidU+BGCZxBOJHSl+/TTusrt71fPpHxev5NnnjEBrMPhODLos0ga5g1v0Puxq8uax05P60KElBUpqPBeJqqCYWM2q+NIR4eRobA3GmaVpZKNo5OTFtlhPEazQ6UdabHR0fJqM4gNizmqe4n4sO/oeBBQQ5gQSXNMCwuawkIczTg/NGQi94kJJZAQNiwWMhmNeo+OKgGamNDHr7xiJHS10NtrEb/ubmsbEqYaawhOhmoNoUEW+eGJCb2hEwkdHAiLUvUwMqLPLyzUbmf6N79ZVwjd3Vb9ImI3fVhiGXpQkD4Lt1kJIOJjdbKwoKuqVErP64EDFmouFpUUheL2eFyPRcRC1qzmqOZ79VUrP67XSjSHo1r4xS/0JxZTXdHWrea1tnmzEgn6onGvJpO2WKJnGYsYIjOYACIWR2zc0qL3Lb5JeDlFIlZ1RwQYo0QRsyAgLZXNKvEijUUk/8ABfc3srL5fZ6fqDmMxjQARaQ5bYwwPa9Xenj1GlLBaePllK8+nt938vC6WsVt4/XXdt+FhjQCNjKxNR4KBAZGLL1ZCu3mzCdfDCr0ag5OhWgMOypgvsirBj4Mu0qOj5ity8GDti3hpr4FeJyQ/i1US8LiSHK00ws8R0Rt240YTOE5Pq6YhlbIwO9GsZNK0F42NenysQt/4Rh18MNIjtO5wOI4N09N6L734osiZZ+r9ODur92lPj96z+Xx55S0VXHiN5fNKREol3Q5CEdqZ0AcOg1YWmrS4aG5WskL0fWDACNL8vJIzqlb5/NFRPYZEQknI/v3m25TLWR9FytfxemIRduCAEpnRUR3nqXrNZvW96CA/Pa3v8+Mf6+vicZ0T9u7Vx2ulZWxq0vNyxhkip52m0oK+PiOfmGTWgGC6Ek6GagnctGFvHFxFBweVHFGRQIj3wAHLe9cqtm3TmyIWsyqJ0FdiOTfGat08YRUNhKu5WW/mjRtNmxU6+rJq4xgoqxWx0vxoVORtb9O04N13i/zyl/rawcHy3lTVBi61Dketo1BQUvT882rYShqmvV2JCM77GCtSSUXl2OysbkebI4pQSDnhvzQyYuaJ9AlDkkDFFxrDRELTTZTds+AhAjU5aRpFxnBarESjus/79tm4QwR5ZkbJzsyMVeMVCoePHaHIebXTXkdDS4sez6mnmice43Y4ttYgERJxMlRbIB2E7TtRIgR93KxDQ6sjeFst7NihK7iBgXJPoFq4KSr9RkjHNTVZyT/6rFLJPEqI1KVSFmKfmrLUmYhuk06L/O7vapny7t0izz2n3+H+/bXRF82JkKMe8fTT+vvss3XSTSR0Et66Ve/Fri79/8iIpcFSKX0uEtF7kPY9bW2a0iaSwoIl7LIei1mZPMSKKDHtUET0eRrizs5qeise13Fg3z5bfOGrRkuVWMx8z+bmrOKuXrB1q2koMaAkisVjxtUaRe3u2XoDlQ+waUopiUogzsO7ol7wnvdoznjbNvO+OFb79dUmTqFoG2FfpdA6LOvnf2xPPpySWxHTNmDIduGFWpL/2msaJRoeFnnqKZHHH1+943I4TnY8/3z53+m0kp4NG4yo4Gbf02Nl52gGSZNNTysBGRrS6MzgoMkOwsaviJqPBZUWAyLlEZ210PCsJCIRJXjxuB5XOq3Pt7WZLqizUx3++/utWpfMh6fJHEuCCEXY/ZiS0vl5a6bHTVkP2LpVV2vniWmkrAAAU+BJREFUnKMpJzRPyxXPIZ6u1BitNEJyw2AXWsYjsA4t5EWsIgRShA6BSjgE7qxISyX1NWpr0yhRc7OuBmvNINP1TY56RCZjaaZnnrHnk0mN2Pb06HXd0KCP9+41ofRSmpr1GD0NXbCpbE4mLWPBAr2jQxeDqZS1F9m0SX/6+vS50BpFpGbab1Si9vboKLjjjjvkc5/7nAwODsq5554rn//85+Xtb3/7ots+8sgj8s53vvOw559//nk566yzVntXjw+zsxb5wXQstMmnrLLW0dam5orbtukqobvbBI7LJTQQobXqYUP6rvLzCG1XWshDzogYQZYwViPNSe8lRIRveIMOzDSnnZ3VgblWQAqglnVoDsdyQRFDtTU1tQ6E3S0tRhxJc01Pm3cTbZHoJdfaqqQonVbyg0cUqUnSjOiFakkmEaCuyNDdd98tt9xyi9xxxx3y1re+Vf76r/9arr76annuuefkVAzxFsELL7wgSTr8ikhPT89a7O7yQQRkbs6EfZAe3ExbWmpHZ7IcvOENeoN0d5vXBg6uy7kRQnt8kdXvYXO0z1tMV0TUKNynMASMIy0NG0VMJ5BMGkkSEXnooeXZ3K8VcOF1OBwnLzo7TRtJ6xLMJukj1tioYzk90qiYw24kn1fiE4+bQD2VMuNH+tCFNio1RoRE6owM/cVf/IX8zu/8jvzu7/6uiIh8/vOfl3/+53+WL33pS3L77bcf8XW9vb2SJq95FMzNzclcEHnJ0lxvNUFaBlKUz5sBIKWTe/eqwK+Wm6+CVErDpJs3W3O+sB3GcnAkArJaN9HRPq9SVxSm+irdsEOtEVUlaBfCiNEZZ1iz3fl5/Y5r4ftNpSzdkE6b463D4Th5QH849FS4aTOe0eaJ3o30j0RELqJjOg13BwZ0XMOvbWDABNXhmFqDHkMiIrVlAbkE5ufn5cknn5Rdu3aVPb9r1y75yU9+suRrL7roIhkYGJArr7xSHn744SW3vf322yWVSh362bRp0wnv+5IgIoFeiAmXVAUdpffurV1DxRBtbSLvfrfIW99qfceIoEAUlktowjTUWtxES30e+87P0Y4BPw2iQO3tRoTwNWltVefrCy5QXdX111sEqZqgUrGxUVeAGzbUnFusw+FYJjo7rQFvLKYEqL9fCQti8/Z2/d3Xp8Sot1f9nAYGVO8J6aEBLQaXHR362r4+c/imqW4yaZFzrEhqVDwtUkeRoZGRESkUCtLX11f2fF9fnxw4ghp/YGBAvvKVr8iOHTtkbm5Ovv71r8uVV14pjzzyiFx22WWLvubTn/603HrrrYceZ7PZ1SVERB8Q6Obzxr5nZvSC2rPHPCpqGY2NIu94hzqPbthQ7tYcjx/uL3Q0rLbh4vF83rHuB9vTWDLUF4no4HTOOeVdtL/3PWt2WS2w8pua0muzvd0E/Q6Hoz7Q1aVjb2urjjWxmFXPoe1paNBFWG+vbnvwoI5b6bTZh4yPW5EJY/qWLVZOT9FFd7e5dhcK+sNCuMZRN2QIRCpOaqlUOuw5sH37dtm+ffuhxzt37pS9e/fKn/3Znx2RDLW0tEgLDfTWCpGIOaWSYsHwa2xML85aJ0IiIlddpUaDfX3WFyiR0J/OzuMXP6/1jbSaFWsh0UILxsAyOWmixfvvP7wct1qgkzlhck+ZORy1jTCtH4uZvQDmsrhwow2iWCIe12gQ/d26u3VMSqf1B1sCCA+vn5tT4tXUpAth2qSgQ6qxpqyLoW7IUHd3tzQ2Nh4WBRoaGjosWrQULr30UvnGN76x0rt3YkC4OzWlP/SwGRuzxny1zKw7O5UEvfe9qhPChDCf15uBcGmNCufWFJX6ooUF/TuZVPv60VH9e2FB5NvfrsouLgoIkRMhh6O20NFh/RRF9G8RJSlNTTp/bNyoz7W16ZiMljGV0nkGvVBbW3lftfFxS3PR86y7W8eoXE4/i15qMzNKpCBg9Hlk7KgFCcASqBsyFI1GZceOHfLggw/K9ddff+j5Bx98UK677rplv89TTz0lAwMDq7GLxwc0QzTpm5w0sS3aElT9tYZYTM0Ed+wQufZaDbOyrzMztipoadGcc40K56oGfI0oOe3vFzn3XC0BLhR08LjrrmrvpYFBLYS383A41gaV91pzs6avu7t13ti61cZetiel1dys43UkouN0W5v5MiWTSmJELGWGiSyVZJGI+TEVi/r/qSnrN9bSogu4hgZN8bPQI3pEyX4tzmP/P+qGDImI3HrrrXLjjTfKJZdcIjt37pSvfOUrsmfPHrn55ptFRPU++/fvl6997WsiotVmW7ZskXPPPVfm5+flG9/4htxzzz1yzz33VPMwyoFgl/5ijY3KqEdHlUiMj6teaGio2ntajsZGkcsv1270b36zpsaSSb1BIELFov7u6KhJk62qg/J8ERNvb9pk5aoLCyLve59qiGoJYarMiZDDsXpIJIxYULJOxSku24WCprCam/UxDWpjMSNECJd7e9UI98AB3b6rS4lNU5MSq1zOIj04TYvo59O6pKNDMxezs/o4m9V92LxZx//OTn08M6MLKD67xrMCdTVD3XDDDTI6Oiq33XabDA4OynnnnSf33XefbN68WUREBgcHZc+ePYe2n5+fl0984hOyf/9+aWtrk3PPPVe+//3vyzXXXFOtQygHFzIXyfy8su89e/QH0dr0tF5wtYLmZjVUvPJKDb+m03ojzczYDZnP602RTpdHhGrQbKtqwMl1dtZy9u3teo5mZnRgOessHZjuu6+6+xqiMlXmBo0Ox8qg8l7q6jLzWdr75PMayYFgECGantbxBCf8tjYjMaTGOjv1/t2wQYnP2Jh+Tqmk8w1VrxCo8XHzFWpo0AUvnRAwjcXDb3ZWP6Ojw/RI4T7V+LgfKZWw13Ushmw2K6lUSjKZTJlx44oA0bSIXjQvv6wkaN8+LaXnJshkRL7xjcXTFCeCzZv1wqXp4XKQTmv10/veJ3LmmXojdHXZaqS5WW8IKuF6e/VmWKvWGvUESCMtPbDAJyL4wgvaqmPfPm3w+t3vVnuPD0dzs+meloK3+HA4lkYyaSSmqclcm+NxM2fFwLapScddovCplI4h+by1PiKi1NCgzzU16RiMEe7srGUkJiaUKG3erL9Jd42N6XuNjuprIDoHD+rnJxJKqpqbLYq1caOStXxe54uenqqV1B/L/F1XkaGTCqHj8cyMtdxobtabIZnU1FihoB2UV5IItbWp2Pnqq/XCfvppkb/8y6OXc596qnaJvvBCzU/H41ZVQLoEwV5rq61iWEmsZWuNegArwMXK7js6VFBNiWqxqNfAP/9zdfe5EsshQiJ6bSUS5Q0qHQ6HLRgbGqxyi1YWdCSAUCBIZrGJLlPEdEHz8+YqPTNji9FQf5rN6nPogeJx/R+RfCI66bSOP6mUvld7u37W1q36OJPR1xPRJjOQzeq+tLXVzcJ3nc9GVQQhzny+vDlrNquRgN27y9NkK4VzzxXZtUvkN35DJ9vhYb3oP/pRkW9+U1t+VKK5WeS885Ttn3uudl9Pp+0YSiWrLsCTJhq1qoaQCImsfmuNesBi7T+IrHFOenr0PPf2ivz612qCtrAg8oMfVG23jxtNTVbeWypV30fJ4VhrUMElomNkoWBtLDo69J5obrboS2ur/vT3631/4IBVasXjZmjIONLaan3AWlttXGYRMjOjUR/SYIWCmcIOD+s4096u0SC6BUxOmkUKv5NJXaDHYhplmpvT/Uqndd9F9P0HBvR96NVY43AyVE2EJYm5nF20L7+soul83qzMVwJbtqjL8eWXKxFqb9fPLJX0RvvAB0R++Utr/UGfmZYWvYm2btWfzk7drzCaVSrp5I3JVmurNfSjMm6tWmusBU6UyFW2/wiJUVihQbi8q8uui+lpkccfX5njWCvQcqSyTQmofOxw1DvC1DBl5iJ63zPWZzI6Vs7MmPgZc9rZWSUopNMTifL0GdF4SEp3t95fRHNCslUs6hwzOWliadJaDQ2qIcL4tatLP5sIEwvceFxJEKX4vb3lzalxuW5vtwgSnRXqoJrYyVA1wYRIGWMmY1UDIvp8Pr8yqYVNm0RuuUXbZPT16U3HZHvqqVatFospo4ftl0pm1U4uuL3dcsCkyrBnb2y0SQ/9kMjSvb3qCSupfao8J9GolduTLkNkTb+3Cy5QojozI/Lzn6/ssa02wtLgyu8/ErG0QFih5uJsR7WBhkfk6Eao8biJi9EBcl+TCsNqBAkBP9GoflYspvNALKYRGxykIR54uNH0meIV/j83p2M8cwkVYfi9TU6aGW5zs+5bW5ulwdCyxuPmPVQq6fv19Oj4PjurBR5dXfoYvVJbmz6em7PjZbFX43AyVE0Q4uRGmJnRG2l+3qoDiB6dCDZv1jTYO96hhCYW088johOPaxoM8tPZqfsyPm4hzt5ea7pHtCeVshuKlQ8+FWGkQ2TtW2usFlZS+7TYOWHgCM8RxLNU0jQlqdW2tvqKEIUkZ2bGQvrRqA6sDKIhKBumkMDhWEtQVcXfkAaiKNihNDQoUSD9hTUG/QgbGy06glEh4ykLz1jMIjEtLaa7XFjQ8ZXXjI9bxCiRsNQWj2mlkUzaYrqjw8b8hQXTChWLFlFisYJOqb1dP0fEtEzNzZbOa2uzbZNJiwLR8gOChp6oxsd+J0PVBBejiN0EhYJeTKOj5jxNdOV4cNFFIv/hP4hceqlVfeFMyj4Q/TnjDFuZx+Nq/lcs6k1OtKq7W3+wBGhvL+9Mz3uuVG+vWsJiOp+V0D5Vvnaxx1SMlEr6nRaLSlp7emqzymw5wGh040YlOkeKAHE9xuPeG+1kAGPO8YCKy7UCER7IQiKhY3Njo1liUNoejquNjTreTk7aGJrPKwnJ5404QKRyOXvMYri1VQkIFV2JhN4Lp5yi98zCgp3H0FgxHldy0tlpLTEWFpTQIJTmOdpzEKnFoRoTxbY2qxRbWNDPn5zU7WnPQYSK3mWk5pBQ5PPl80ONwslQNUFKBN2QiN4EW7aIvP66XkjJpOpEjhVnn62l79dcI/KmN2l6i/5g4UXJ5M6K5cwz9eajkR/d1UmroSNCENveXr8pr2NFpc5nLbVPkYheC1wrZ52l5OjUU/U7+L//d/X3YbUwNrZ0KqxQsBRypbYIoedyq9ocawsm1TC6fbxECHHxahvQQrgiES1oiUR0TEa20Nur42M+r/dkMmkFI/G4LWCRQFAg096uZCWZ1Ht4bMzG/7Y2XWRu3KipsaYmFU7PzelnxWKWwkIAjY8Q1bqNjRaVwTwR/SapORG7h9rarEVGoaCf1d1t5I6odzKp+zA5aVEsImNEdhkLsQaB8PK917DzNHAyVE1wARFWhZFDfri4jrVx7LZtIu9/v06UZ5xhVUmLeT0goItE9MIWsVRQKqU3C5EhQqPkweskF7yiqJb2idUbK8xk0sLPc3M6sN5//9rsy0qBkD0LgSPpg/BhEiknQqxy8WBxVB9hE2IeNzebvw39s0IX86W8qkjFlEpKQlbSmgHSAlGBELAvHENnp16bMzO6fSxm+040BALBOMlxLyzo2LuwYONnQ0N5tJ8KrZ4e/axoVKNPU1PWbgM5A1EYyuVxmcYVGsISj+trk0l9zcyMCa8hSUS9IDNhFEfESFc+r+cmGrV9mJ83bRFl+9PTFkETsXQguqEah5OhaqEy5QJrLxT0JiDfHI0eu/v0zTerBuiUU/RGopvwkcA+oN2YndXPDtNhTDxNTXZDUSVW47ngFUW1tE+VUanGRhVJ5vM6+L3jHTqA/uu/rt0+nSiYdNBlHKnc/kiauUJB9RMAE7jJSSXyzc1qFeHeRquLxkaLHs/O6iQ4OqpjRiplaZVEQrejdxVYKqrX1KQkiNeFJILnjobOTrP9CPeZZqREufH5CVNx6bRuR2shGpqGC0cWspS7cz0T8UEbRJVoPq/vkU5bqo1o+759eh1v3Gg6uu5uPV+c295eJT+UyzMGsy/MG6XS4Zql1lbdd9JpVLNRrEFvTMr6OWf4G1EcgzwiJDnt7ZbKp7S/qWlxHWQNwslQNUH5I31cWlrs4kynTbW/3Iuov1/k935Py+exTYfdLwUqv+bmLPzKYMHFHkaWisXy/jXL2b86uBmOCdU4FiInDDK4veIye8UVIs8/X3+TPyvP5QABq4gNuEy6pBOYlPN5a2LpWBlQMTozY0Jarsd02shAT49pWMbHNSI0Pm7khYh0OIZQmUpqCifkZNIIy8SELvKwAzlatSELuO5uvT6I0NAPMmxpgf6lvV3HYUh46K8D0YB0zM3Z/4mY0Nl9aMgKTUQszTc+rrKFxkaN6BIpymSMMIkYqYKstLWZoHp83EgGJolkEogAlUr6fHe3VYaFcw6VYkSE8nldYGHqODmpr+fzFxb0fy0t+rp4vLzsH9LFd8p3Vic2Kk6GqgX8ePhdeeG3tuqNn8tZaeRSOOMMkQ9+UOSmm2wlcSzhSUgOLqcLCzooMTjgfQGWmybyNhwrhzAqhdCyuVknHhyqr79e5Otfr/aeHjuWm26FCImYIJQO2qGRHBNTJmMDdOVnMMFMTlrPJk+5HRltbTqJz8+bJ87kpJ5/Wh10d5uwFnf9aNSqY9GS5PNKErBToHUDZCWdNjPCQkEjTXjg5PP6Xj092hZCpDxyKmJjaKFgi7ps1nR3k5OWeiMiTkSjUNDniQD19+t78JyIHl9npxGoVErfO6y06u3Vz0omLRKD9pKUHPcwUSeqv0hHkQanQerCgkWdiNbwHdAnrKPD5hF0nqSwEH6THgPouuhUD2FC+4QGKZ22lBoCbkr9SY+Fqbg6slFxMlQNhCmyxkYjO7D62Vk1tyK0C4E40oRxzjkiH/qQyA03KLPnZqH0k0jOUiSE6BAh0JERW5XFYnZhixxbmsjbcKw8Qn8qhJvoFLJZvX6+/e2TW1TMBMfqc2HBKne6unSb+XmdBEIiFGpVEMQykZH2pbwf/xRwIlVQJwPQysTjRloiESNJRC6w5jh40OwQolHTII6PW0m3iG5LRWoqZZEVrDoyGTMTjESUGHH9b9ig+zE8rO/Z3q5/M4nHYkYmSPWH7SPQtdD/a3ZW9x9PnW3bdF/a2/X50VGNSHV36/YsRCA7LEKnp83vh/RRMmmLw4kJvVdZBNPglChvLqfHhtVK6Ck0O2s9yrDZwAGaSA/Gh319pg0ijcY+c/2HVcyQWDIEzB84SGOHAXlC58q+xGKWkhOpq4Wvz0rVQNjGgtAmaY9oVG800mOUW555pjburMQ552h7jXe+Uy9yiBYTwLGQEKIN9KwJuw1XlpAvNzXmbThWF5BY/EZ6e9VYs6FB5Ec/0qrEkw1hdQyVOpD26WlNT/T16d9ca5CYUKwLaWJCoBqot1cnqqEhfd+wW/iRADFgRX0ygYgk0UfaPIyO6jhFtIM+WTMzNlHOzlq0LRT+I/blfGETQbSICM7EhDkmNzVpBIcqqEhEXzM1ZWaAVGGx35ATEZMNhH47/LS3W9sLfHg6OnRfenr0WqGik/J6Fp6Mcdg/TE/rMfX36/sSSaGPI9dfSBhCk9X+fj0fjL+QC/SbLFr5SSSMCCJ1EDEdU7ggFtFzmkiUSzQSCYsIEcHL5fT9WChw3khpElkl+sP5F6nLRW997e3JhDDXjbsn4dlEQm/AyUnLnadSukKiGkNECdL73idy5ZVa+llZvnosJITSTMLErAzCVhHHSmAqRb+kBJ0IrSwYnBBekurs7RV55hmRRx+t9h6uLObmdBVMCoxy31TKBnGEqZXXWmjqOD6uExrh/XxeJxQWKKQEmPyWAoSssdEEsothrX1yjgVh1CwEDThTKdO8zM0ZAZmYsEg2BDMSsRRNsWivYQyKxSw1BQlKpy1yQ6UUURHE8hB/yspnZ3XxyHtFIvo5MzP6fC5nTUejUd0+l9PvnX6KFCNEIhZJ5DOIWNGsdGFBj4milrDBMotI9ENEUZqajGxwnVIgQ0QJ12qu2cZGjXBBDCFYRH7Gx/W16bQeO/olojR0pMerjuqzxkadVyCVqZSl7MIoFZooUp2plJXkI6cgitrUZJFC5pw6XPQ6GaoWwtUWJITfAwN2gS8s6I20daveaDiNtrWp18zb3qZtGtD0EGE6Fj8cIjj8YKmez9uK5XjzvkwOlESHETHHyiIeN03H5KReHxs26ED2ne9Ue+9WDohn0ayI2IoeZ+tQW8TgziAOGcnntd8bUQlM5ugnxbW7lNEj9xWlxVRVJRKaJqokF9UmQmFLFBG7H9HoHDy4+D4yoRNlJv29YYNO2tPTOj6hx2lr0/M2NWXNQItFIyZM0JGIpc+YWCEimYyS08lJS91MT+v26MS6uiwqg4s/fRWJvCBQpqKNtE9Hh94vExPmt4PWhRL67m5zckbon8mYYH9+XvdjYEAJSDZr4nHeq61NrzFEyxwLEclsVhcx2JawcMS+hBQVOiEE0rOz1tcSMj4zY+X8dJQnuoSuiH0hckN6DBLG/cIYnc1a9gJnadKI4f3E+9Vp70knQ9UE+WOIEQMI7S/Q/WzcqDf79u2WQsPoi5wwfhNcgMfih8NrKJvEKyIUPBM5Ot7oEITKdUOrA64hrPXRUYyMiLz73TqpfPObJ1cKJyQ84WMWGBxr6E0UTvRsPzamv+Nx82mhcuZopdsIuJmMuOdwJq5sL1INUPItcnhlHdECIsLptKW1qLoieoFRH4QKLzK2JVpTKJh+a25O36+z04gN5JH3QChMtKS11ZqKshgU0ddRtTQ6aj46yaSl0xi/0mlrB0F7INJklKjzWsgAn4PQuqvL0mO8DvLR3W3XElIC9jESsTYWkKFYzLx+OM/Dw7aI5Zy3tVmPrw0blLww3kMWZ2bsfPPZIcFvadH37ujQzw8rktFIYRfAuI6DNnMSBJR+aVTiYQEgYuSM17EAryPRdAifkWoFlExSPcENgCNoR4feQNy4rKC4OFk9gLDyaDkEhtUIKwAqc/jf8ZIYok3cHK4bWj2gLcBpvLdXNUP0GeruFvnCF04uQnQkVB5jpXN1JegLKGKVR8vB5KQRjHjcGmgy6VUbyaQtsML0VPh/dDSxmE7K9BwkApJImDYrHteIQC6n11g2a6LfiQnTE4XVsel0ecURqR60RQiPiSTt26fjH1ERiALROyrLGN+wVMD+o1g0wjM/r9/DyIhVHG7ZoseGDYOIVXrNzVnBCFW0RPgY+9rbrQcX0auuLjOW7Oqykvow7UWl1vS0ESNE4RDB0MqEkncRixKRMpyaMn0p54ACgKkpHQPQ1GF5wGIXuUJLiy0csFPJ501QTgqRuYXrHCKF6aKIRbvqeFx3MlRNQFhYhYrY6qW72zyHBgb0N+aJYZPLsNJssYtwuRcmKycGcEjRiZIYVkqsQOo0hFrzCKOM0agSoXzeqmRYUX/kIyJf+lK193ZtQWdtihGORFKofCK9shRC11+iR6TEQqHxYmhv14lmMedl7jM8XJigKT+vBN93IqHvmUzqpDU7a2kMqsAYJ0IyROSAKlYm8LDSKZu1tjxUKlESH6bTmWSp3pqa0scYBE5NWYS4ocE0XkRdFhaUtHR12QIKQXapZE7UVIY1Nup7i+jzmzYZWWKsaW21qAe+QvPzem7wYOPzhoet+ARdD6ksGpVCGkiL9vcbgdi40QTNaNeI3vA9cCxcJ2iNMLhNJHSbkRETaKNpQ5COr1Nrq25LqrFQsJYdYTNniBbXW1eXpcWSSasMg3AxL3EOKVbgcRj9qZwnGNc5tjqCk6FqY7FeYVysmGURak0kyhl4WI1wIuQitFQnzC9iF/+J9OEK3VGJLNVhCLXmUWlhALllspieVsG9iE769dzL7FgRip+ZpCuJSphyO5LjdQiqcNJp1ajgnYMYNfRrCdHWppNkNHr4/cZnU7VD6ojV+eSkTYJE+5i0+SyquqjUwpgyjCoQJUMsjsFeLKbb05uQtBHVUESLEd8yEeJbQ8UUXjnt7botGiyEyei7aO8wMWFRGlJwHR36fqTKSIVB+EKdFsQGzx9Sa4mE/h+DQo4nGtXvTUT3HRKczZoeCZJI5AyyiRdRS4uV3CNupi8YkSrGaaL3tL2A0GUySnqo7IKQYleAgSOVe5gyhlEnIkKI/WdmrLUIZK5UKhc+0+YDcgzZ4vsNt+W7Q2w9P29kl+MN5wUWAswbEKw6gJOhagMWDrtHnLZpkw2Mvb12Y4kYSeH1J0ouwlJ/bgiE0yfah4vJgIHfo0Irj6UsDPAvwTF42zYd1LNZke99r7r7vdaglUxfn04uCwtWjt/SohPKyMjy0ltEGEgzMAES6UEzBNB8MFmFpf6VuqL5ebtfZmZ0cUT0KZm0CRINTTRqUQ3K0JkI0ZPgKhyLqYMz+8d1ghB23z6L/jC5cj4SCVvYkEbi3A0NGcmKRm3/iHxT+k1FFyDixH4iAWDCJvrd1KTfW7goW1iwCjZSYiMjRpR6ekReecWOAUJFaqy1Ve8D0mrIESCqEIFk0r5rGq/iTxV2fycqg26K94SwQFAogScCJ2LHRXQnrCxjP8KIEIvhXM56lUHG+W6JJGGOCPGh4o5rJyQ0RPg4J+wzmlYas4YkO8wYhESK665SwlGjcDJUKyBv299vHZFF9IaDuYcVYittahUSn9Dz4kTywJWTdOgE7IRo5XC06sGWFr2uWIGj3YjHRe6+u6q7vqaAtCCQJiUhYg6/YaRgKTABEhUQsUkLhJM+k1Zbm0524+OHC8BDMIEjFuYeGhuzyBGRGVJyYeQGfRDRWPQf+Ic1NpqPDCt+Kp2Y2CEc6FMg10R36eMViZgZIJEU3MGJirCPQ0Mm9KcBak+PRT34LMhRR4f+j/NGxRbfHVEPrvfQLPDll/Wc4RzNdU96Z3zcjofITyZj1XX0H6NqjKjT5s163ogUoTEKv3faVmDASMsYtDhEirq6zLGb8081nYh+91SPkYKEiIS2A5mMGbCSUoSwZjL6mNQiiwKij+FCl3OInovKsnze3gMtUriAZvwJ/YwgRHWSMnMyVEsgHM5FFPZ2OZo26ESxFPE53s871hJ/x/HjaFE8SomnpkROO00HZhqb3nlndfZ5rVEZgQkrqyYmbJJcLtDALAdEgpisenuV2BzJjwgiVKkvoUQd/dPERHl1UTxupIhqLYTNYVokkzGfJkhNaApIxAffHa4vKp2iUX1fSueZ+GdnrZ3E8LCl9YtF68ROZdrEhI07VIKF+hR8c7q6TKR96qkavYpENMKVy+n7bNigx4PfTSymxQMYH6ZSasdAtRr739SkWh+E7xMTpoGC/FRqshCJl0r63hBIfI+mp630nXPT2ak/bW0iu3eXk6W5OYsqIXSGqIRay5CcEEEjJUb5O1XGECJSaDT/xgMpjO4wHocRolDPRMSPa6eS2IRZimLRvgN0X3VAhEScDNUWIhHLzfO4kpysNpFY6fdfiVSb4+g4WhQvEikfyObm1KqBkP/f//3a7m8tgsn5WHAsFWOU6jN59/bq5x2pcq1S10SLhYYGJRy0qYD4UB7PhIknDKmbYlE/Cz8bCAiRgERC92l+XrejvJoWGkQ2ICZUFY2MmF5oaspK6kV0X7JZ3deODk110Rk9fH8MFiEh+bxN6hs2lEdx0mkrvZ+d1eOmKerkpO5nPq+vI2VGWpRu66GJ4uioeST19Jgmhvem4ktEH5Mua27WCBeaLYTbRHLQ8YlY5J/Pzed1e6JB6Lvicf0uIKqlklazEdFpaLDviDGVVDipN/yUIMx4LPE/yFB4nYWWEKFEQsTsECDitN0ghRami3Go5pyEn1PjcDJUi1hL8rPaONFUm+PYsNQ5ZuDEG2ZqSgfRCy/UieNzn6u+KWA1sdql8KFQ+vXXj92NGjNDnIURL/Mes7Mig4NGeNH90HgZ3RBVTvQQg/jQ0gUdDqJwGoGm0zZJUs1KOm583JpKj4+bdgWXZ/Qj6HEmJ62cv6nJiB2tINCmEI3CA4rI2vS0CbCJgiDqzuU02pPJGCmMx/U52rVAWjZsMMuAsHM9Kb/eXkv1sQ8IzQcGDk97ZTIWCaM9B7qf8XF9TwwRe3qsWm5qylLY8bidD8TcQ0P6fGenkukw3YcXFOSE31gYEKkh4sY55NwQkYI0M2ZT4UzUkcU6BJsftGeQJI5fpK7GfCdDjrVBHd0UJz0Ir5dKqn+g9cQnPynyxS8u32PHcWJYLhEKfb7Gxsz/JtQrhWAiIs2EAJZJHFErEWgmehElU0Rvw87klNhz7YyMWFo/k7GqsbEx0wzlcuUNPNGdMBbk8/o+Bw6Up4nYJ0rz0fAUCmZU2NtbbggLkdq0yUrbk0nTW6ZS1oZjctL0WhggUsBCuglSRiUZXe7RVxHpQduDhgcCB1EjwgUBxMOH6BeRpelpE+GLmJiZqA7eYaFlQSym5y9M64lYtWSoZwrduDn3RIRIgRERIkJHhSB/UwUYEif2j+sNmwGOoY7GfSdDDsd6BNUmlCYzwP3+76uoevfuau+hA1SW3mPatxggK0xEtO8Rsd5rpHmYCHEZJkrBZD08rBPrxo2mqerpMa+g2Vl9/6kpSwEifEY8ToSFSBE+OURUSBORpovFLFK1YYOmkrABwAkZCwGMHru79bM6Oy1KUipZxVQup+9DaoqKPFp3sBiYn1eNUTarj+kdRqSDCBSidKrFQp0S5yUeN3sFKr2o7mI7NDjsM98z5DQU8uPL1Nxs3eZJ50GsqCZjXxBl07KEz8RzCyKNhQMRHhHzL6KLPd8lqTzIFtsTUQpTbXWUIhNxMuRwrE+gIerpscfxuEaKUimRH/1I5L77qruPjsMBWQhR2W8N80QmLECVE+aBpZKSGbR8TO6kxkjDDA4qMWECFjEBMnqS8XF9PpFQEoWBIG2FKiuYEK/zeaSXmGjRoKBnokIOQTeVUhg2YmuAlw9pqrEx0yNhVZBIWHqHNBS6pqkpFWlPTNg+pFL6PCRnasrI1fS0nWfuIY4zn9f3xb8I8TmNhUVMCyViAnbIFttCgkh/QWwosiGdlsmYLij0LSKFCPmhBD6sEhSx101PW783ji8slSfSBRFEdyZiJf98h0T26iBC5GTI4VivwNxt40ZLEeCqe8EF2hz4b//28JJxR/VQSYRIxTAxEt1paDBzxNBIj1QGqRGEwgcP6vvhRRS2z2CyDtMe2az+TcsXyuLp8xb6BuVySqZIWUEExsbK3aTn57VSLJ/XKM/oqKWqSAcVCvo/3p+JnUm6q8tcoklhQYD4DHqWEb2AZFCu/+tfWwsRSvmJ8KDRIpLKYyJG6bTq8CjHhyTMzJSboHLuIXj8jU8VBCeVMmFyNmsRG/q3QYZEjJwQ3YPw8V1CTPh+cCMnKkXEELE953R21oxE+R8GmZCiSMQ8jdCzVVYQH09vyzWEkyGHY72D1W9o979pkw7snZ0iX/2qyJ491d5Lx2KgZ5iIVTcdOGBEiHRaqAEhokEZe9ieg8kdryEmO4TZHR02CePVI2IEh3YObW369/79ltZBq8NEjT9NPm/Gi6RxsAyYn9doJSX7dLg/cEDJB+QC4bGIVY1RWo7YmmhPZ6fpfkiVEbmCXIlYA2w8mpjgs1nrTwaJgjDQH42oC6XsU1O675TOk3ZDyE16q7HRtDn8zXkK9T6jo5beQqfT06NkEIuB1lYjLWh/QvJGaguROt8fqTtSXpwPIlXDw9YQOmzaing77KkG4Tze3pZriNrdM4fDsbZgYCYicP755tFy110iTzxR7T10VCLsbTYzY+SH9BKArBw8qJHApiadUCcmyv2XqPykYSmtP+hbiEN+W5uRGzRMRFnQjDD5EVmi/1cmY/swMWEWA+xv6LGDsJdKprk5kb17dTssACAKBw8aeZmcVGIgYpM4gm0iVhwf6b7ubksfE8XI5aw1SSKhr+vpsf3KZi1isrCg0a6GBusBhrllqKeCSEFOwlJ2PJzwIZqcLDeqpZKOlB2eUjxfKinZQ1OFHQHRQyI8RIkoqY/HD2/nAVmj4gySDDni9SEha2gojwix3fH2tlxDOBlyOBwGJh1y/6efLnLVVdrp+667RP7xH6u9h47l4EiVavm8kgmap1YaUYavGxkx7Qouys3NOuFXegKh3QGk87ACOHjQqq9KJY004YI+O6uEAVKDWzdGoZCNUM+CCHlqqtzgD0E0eiAmdTQyYcuIAwf0/amwGx/XcnlK/Un30M4GUhj27KJMvalJt8cyAAJCCxbIaSaj5IuSe6IriNLDFCYEk0je3JyREaJ5iYSda9JeyaRV0hG9IhJHNAndD1E+yBpGnpAj0oMI18OIE8QX8hia64btoxBW13h1mZMhh8NxOEKTuf5+qz7r7VVSRHrEUX8oFs2D52jAt0fEzB6J4NBzishNCCZWIgb0xuM9mShx1aapa6g56uoyL6LxcauQYr9LJWvNQeXX+Hh5KiwUQufzRiLYX8gLFXjT0+axhA6oVNL3n5rSz6LwAF0QxwNBI5IESeH90eEMD5envyATOGOHguWJCStfp2Se6JeIfgZ93ML0JfofokVtbbotnkGQK5zH0fqgGeI9IXlEyhBWIwJHuA3h4TvF6TuMEtW44W59+GQHuOOOO+S0006T1tZW2bFjh/zoRz9acvtHH31UduzYIa2trbJ161b58pe/vEZ76nCcBMB/patL5IwzRN76VpEbbhB54xurvWeOE8HxmGuGREjEKpUWK/UntUKLCv6m+ggUizrh791rRIjX06qCVBLi4dDhmtQM7zMxYa1GDh5Us0JSeaTtpqbMMymf1+t7bs5aixCpQXhMd/lsVuTVVy2qlMvpPh88qJ9LA2TSSBMT+rkTEyKvvaYkaN++ckuCqSkjNpOTZupIZGV2trx6CzJDBIsoDS1ZZmb0M7JZ3Weif5yDoSHV/42NWXSL74joVyRi54jt8EdKpczxHJuHqSk9RqJEYS81OipA/GoYdRUZuvvuu+WWW26RO+64Q9761rfKX//1X8vVV18tzz33nJx66qmHbb9792655ppr5KabbpJvfOMb8uMf/1h+//d/X3p6euQ3fuM3qnAEDkcdggafsZjIO99pzr2nny7ys5+JvPhitffQUesIxdzLcfoOq9FElDAgsqYqDW3bwoISAIwJx8fLBeJEdYhCoa3i/UZHTZtDeTsNU0WsTQvl+3v3WiQrl7OoF/3JqMzs6VFCgniYNFE8rkJryEskYo7UCwv6N+aOaIEgffPz1m2eSBORmP37bb/HxvRYSV/l83qOQgPHsDs9uqauLnNHpz8ckbGurvJoEnoqhN5EuEItkki5aLqGNUORUqmyAU7t4s1vfrNcfPHF8qUvfenQc2effba8//3vl9tvv/2w7f/bf/tv8p3vfEeef/75Q8/dfPPN8vOf/1wee+yxZX1mNpuVVColmUxGksnkiR+Ew1HPmJvTyebpp3Xw3bNH5Nln9Wfv3vLKJIfjRBCNKkGgbUjYXqJYtJTVyIhVXyUSGjUh6kELCwTJ3d0m5KV6C1+kWMzKzHM5TQ/H4xr5IdJFaiiZtMavNKWlkSsiayrQ+BvCkM/b4oKKOQwniaoQocHwkSovjh3tz8SE6Y7wiUqlLLXW3q7nAaE34m0q3RoazOuoWFQSBaELG62mUnqsaJvm5syjCm1SQ4MeK6k+BN78kHJcwzL7Y5m/6yYyND8/L08++aR86lOfKnt+165d8pOf/GTR1zz22GOya9eusufe8573yJ133ikLCwvSjEV5gLm5OZkLRIVZb03gcBiiUZ1cLrpIRdU7dohcfLHIL3+pk8bgoK4sMabbt6/ae+yoV0A+9u2zrvOJhJEDJmUEu4WCXnfDwzaZz89raiga1ddkMmbsKGIRKPqRUY6PDglDxeFhfS6TsUgTneAzGSUG0aiSG7rIt7XptrheI4ZubdX/hy0wqO4aHdWoERGhlhZ97diYHmN3t95faJ2o9qM8HkE3TVIhfyKWJozFTCCOWzVl9qQwczk9XqJCEMdsVt+nvV1JVCSipBG9FSnTtjYjSKHGi0hVDZbZ19beLIGRkREpFArS19dX9nxfX58cOHBg0dccOHBg0e3z+byMjIzIwMDAYa+5/fbb5X/8j/+xcjvucJxMoNkr7QpoldDTo5PDvn2mnRgaMmK0f7/IL35R7b131BOKRSXXIha9oPy8o8NK/WkjEXoKtbXZJE8TWqIyInrdhm7WRJdoLtvRoYQBs0PalYhY+5GpKRMdZzIW3SGyRKopnVYyMzurBQgi5W7ORIiKRat4wz2aBTsVZSIaDYIoYiIJuUPUTvsP/g6NEXM5O5cdHXrcg4PWU03ExOAi1jduakqPM53Wc0I/OD4fl2w8mxC741tECo3fNZYyqxsyBCIVJ69UKh323NG2X+x58OlPf1puvfXWQ4+z2axs2rTpeHfXsZ5QYzf3qgIPEyYeWhZs3mxtHjIZq3YZG9OU2u7dOvD+6lfWksHhWA7m5/U66urSCR3NDSShs9OiLBAGEf0/VVIYJoYRIRym0c+IqFAa3y2EwmEfsLCHWz5v3ehJR7W0WBRrYkI/H33Q5KS5ZtMTjLY4RHsOHtTPpE1IQ4NGxpJJS5EtLFhPOPyLyGrgCzU+bh5GoUs2525ysjwaRFUeZKWpSc8FjWdpFgz5amnRRc/cnBKriQn9fmZnzWOqUDCht4gdc42NlXVDhrq7u6WxsfGwKNDQ0NBh0R/Q39+/6PZNTU3ShSFXBVpaWqQFgZ7DsRyEBnF1YDt/wmAgRBtApQultA0NIqecYhU9w8PqaL1pk6bXcjmRl1/W1fhzz2n0aGiousfkqB+MjipJ2LxZ7zuavTY1KSESsRQWRIXKLFp6xOP6Mz5upedUPhHxIUWUTitpgICEfj+0BEmnLc1ULJbrhKguI02HAzR6oEjEzC8HBvSziLTMzZW3YBka0mOMRnXfJyf1OCAgc3NW6YarPK1ZSAVyDOPjRmKo3MMkEnIIEduwQf8OxdGQvlxOn8/lrB9bLKbntKvLIlRhaiysKKwR1A0ZikajsmPHDnnwwQfl+uuvP/T8gw8+KNddd92ir9m5c6d897vfLXvugQcekEsuuWRRvZDDcVwInXJrNB++oqAqJjRZQ1gZ/t3bq4MhK9D5eR0wp6ZE3vxmHVxHR7XseM8ekVde0cejo/q3w3EkLCzopC9iHegp/Y/Fyltz0IsMIoQIeWrK0lVUl1EhRmk4lWLptL4unbaoEz3giJbgg9TQoAuA3l6zBYBwTUyUp6xYQMzOWhRpclKfp5UH+0qkhpQc6Su0UuwvkTJSgbGY/X901PqRUd1HOX9Liy5eSHNRDNHcrNEh5kyq5aiggwxBkBYWlNSx76QuWSSG/dRqCHU1Yt96661y4403yiWXXCI7d+6Ur3zlK7Jnzx65+eabRURTXPv375evfe1rIqKVY1/4whfk1ltvlZtuukkee+wxufPOO+Wuu+6q5mE4TiYQTubmrgPb+RUBoffFDNXC5yjDJWUwN6cDvoh2B29q0sFzelonlJdf1kaZu3crSZqfN7LkcISYnLQGp2hvhob0OotGbTLHLLK9XduATE/rpI/GhQowiFE8Xt5ni3RTMmnXLkLrSMRaXBBZwWRybs4cr6NRJW+kp/r79dp//XWNIrW3K4GandXHg4PWG21uTo9zaMhSVZAnPgt9U0ODiZu5P0lRkQLEcDGdtsgP4u6ZGSNYTU0W7YnFbDtIDtGzSMSaPIuYH1Fzc3n0TMTOTw2irsjQDTfcIKOjo3LbbbfJ4OCgnHfeeXLffffJ5s2bRURkcHBQ9gSD5mmnnSb33XeffPzjH5cvfvGLsmHDBvmrv/or9xhyrBwWi5LUuO38ioAVXiXpW+w5nhfRAZLJhrQC56+3VyeJiy7SieHgQWudsHu3yPPP6/OjozpI79ljKQjH+gTRGSq9eA6yI2LVY3SgpyQdA8OhIY1gIvalKzxC6eZmvd7oQg9RKhYtJUQ/N0wbqSybn9d0MCXuMzN6nSP8TiaVqFHdRXk7GjwIBYut2VlLpeF0TWqPNFk2K9LXp/cJeqmWFr23+NyFBY2+ounB0yhsXUKkiVQa9+0rr1jT2Xxe70lIJELz0dFyHRRl9jUYEQJ15TNUDbjPkOOoWG+aoZVCJWki3I/WY3TUeirR+iBsnpnJWHpkcFAnnaee8qq19QailIuBaJCIkoVkslwT1Nur1xikvL1dCQaO1dGouTjTjkNE/x+m3To7TZSdSNh4QHSopUWJAuXxaJGam805OxKxdiLd3dbCI5GwPmUYI4aNWcPGqvRBm5218vb+fqvCq2zWGo1ajzhK7Ull03A3k1EyBMFjv0h9ZbN6Xjdtsgq/dFo1XYmE6gc7OqoyLp6UPkMOR83iSFESx9KoPFeNjTZh4NTLREWDzulpHWTp1p7LWYPM2VmRXbvUAPL113ViGRmxUv/XXlv7Y3SsPpZytJ6a0shPMllOECirHx7W7eJxK9fP5TTaMzFhabhMxiq3SI0RScLBuq/PUr7z8/qZoXaQiMzIiL6OsvyZGf1MGp8Wi6rRIVo1P6+kbWJCX4fX0fy8brNvn3Wrh4Bls+ZvhOg6TFHjQ0TfNPRI6K7QJSGMzuXMjBFhdVubEToiVZGInsfubv0cBNxU9tWwlrJ298zhqDc4ETpxQISKRTN6ozkl+g6af5ZKZlDHSnl6WuS000z/gDB2YkInhdFRcxtuarKUAIP1q6+a9iOf13QLk8vcnE4KbglQPyCVlc1ahITms+3tOsFDfkQs7YW2JZstFxojxibthQtzqaQkh0ovSA/d39vblcjgHUT391hMPyOXM4dpWly8/ro5X9NkVUTJBqX5GBy2tel1jF4RTc/0tGmTMH4ksjMxofsPWZqfNz0S5fuRSHkVWtiCg5ThwoIe5+ioVp3hrs3xiNg5quEFo5Mhh8NRO1gsyhZqsHAbpoQfkzmEoqRFMhlrHEn6gtLq8XH9OxazUH9bmzWgDCvfGhstKtDTY53RX3tNJxEaXO7dK/LTn1qFk6N2QDPWAwd00kZbND1tYmAqqXB95lpCnwapAsPDug3NUvEYCjU5kApIBkS/vV23wXEad2z0OSMj5Q1oIepof6amlLx0dlp6j3QdpKlUsm0pYshmLUKUyegxoHdikYD7dnOzaY4oEMGJG73Q/Lw10k2nzcKA6BHHi16LKFuNwsmQw+GoPVQOmpUkqXKFCTliIkBXQSsCSqAXFqzcX8S6k9MRnFQFFTBU6KAfKRZt9Z3J2Ao8n9fV9yuv6G/ec3hYH+/dK/LjH1tFj2NtAQHit4i13+jqsianY2Mm5j9woJwAAaIdfJdM/HSfh3iEEaGpKf0cNDVcN4WC6ZB4XWOjEQ20S0RaJif1vXC2xouICCjtOCYnTaMzPKy/EwnzJcrllBixIOBaHh/Xz+Y84UnEb6rDUin9/7PP6nuQGkQ03t+vqbKuLusbV8NESMTJkMPhqCeE0aJKQIhCohSSl8ZG61SeTlsFIKQJozt6UlW+l0j5Y9IATI6bN4u88Y0WCWhosGhBKPIuFjWyNDtrhpOvvqq2Ao61x+ho+eOGhmMzASUdK2JeQjhBZ7NKPqJR0y7R/mNuTglD2FuMqrVQ5C1ivcgwOoXIlUoWDWprU8IzMqLXKFVvmDhGIqaZokoN/U8omCbiSSqQc4Sx5fy8kkd0R2NjpsNCgE1lXHe3WQcslSargfSZkyGHYzmogZvVsUwsFjFa7PvD74QS/8rUXOV7VT5GV0Kag9QEpdxomvCE2b5dJwtW+7OzVpI8OakTzNCQeb2MjOjjUklJ08svizzzjJEvQFrGsTIgmng8CImRiOnYmpr0Ow31OZCLSMS0SIXC4eQMjI2ZZ1Amo4S6u9vK1qenlXBzvUFcaMNRKFhqbeNG/f/Bg9Z0dnZWydTsrLXSoRsDPQjRzCEkR3i9d69FZElZp1IalerstMccL6ihSlwnQw7HUqihm9VxAljOd3a83yveKeHrQ50T/a3YLtQ5EZHq7lbhN1EBoklDQ1ZptLCg/5uYsEmou1vJUDqtk9XkpE5AVA2hVcH7prXVSNzYmP40NOgE29mpHjZc44WCvj8OyaGwuFQyG4RCwfpoTU9b6gn/melprXh69lmR++47vnNc70D3I6LnHKAxwrDwaIBAkxKmfQZ9yX79a43EpNOa8kK7hNEiDtO8x8yMibbzebsexsdtn0KyT1oZsXZ4XCL6PU9N6Xd/4IBphTo6LOIaulHXkHu/kyGHYynU0M3qqGOEZnOVbrxhRKqpSScPIkvbt9sqnJU4ehJ6afE+TFikUXA+phM52g1W+0STmBhbWmxbKva45jET5fMA+xOa9lGJJ6L7SIpoeFjk4x8XefFFbdT7wx+K/Pznq3O+6wVhmnW5gETjMo1rNUJw0mAQZ8gO4PsvFMxaYClUGpsejbiNj+vPL39pzthtbSJnnaVpQhFr+VFD7v1uungUuOniOgZlreFEFlZJOBxrhXCCqByyw+crTSwr26RwLVdGPEknLvXZS01SlW1pKp3Yi0WNQpCuwQ163z6Rhx4S+Zu/0bSg48TAOU8kFhd/VwMbN4p88IMiV1+thCge133EzkCk3L1/BRebbrrocKwE1murDUftoTIFd7RtRA7vAVUZnVquUehSovVwm0qtVuVn47nDvdTdraLzc88Vee97RR58UORrX9PWK47jA6S0VoiQiGrhvvpV/a43brT+Zlx7jLGVPQ7XGE6GHI6lsFRDUoej3rHWxD5MiSAgxr+pt1fkvPNEHntM5NvfVpsCx8mB0VGR735XZNs2a80R6pBEqr7IdDLkcCwFb7XhcKweWGAkk6pl2bBB5OyzRXbsEHnkEZE77zyx6i5H7eDBB0WuuEIJEaX2NRRpdzLkcCwHNXLDOhwnLahaOvVU/XvDBpE3vUnk+9/XSJGj/vHaa1qxNjBQc5F2J0MOh8PhqD5w/G5qEtm6VV2Mzz1X5JxzRN76Vo0sPPBAtffScSLA6LGhoeZsSpwMORwOh6N2QMSgvV1F1+3tqivavFkjRQ8/rK1NHPWFDRtUQN3SUnNESMTJkMPhcDhqFfgfbd6suqKzzxa57jr1KvrOd0Tuvrvae+hYLt74RpELL1Rzzxr0aqu9PXI4HA6HA0QiqiGixxWtTd7yFpF/9+/U1fq++9Tx2FGb2LFD5JprtFqwp6emtELAyZDD4XA4ah+0DcHdeuNGFeJeeKHIRz6ibss//KGSoueeE3n88WrurQOcf75G83bs0FRZW1u192hROBlyOBwOR30AkXVodbFpk6bQxsY06tDQoKaDg4MiTz2lpn9NTdp64vnn1f369derexzrBVdcIXLBBSKXXSZyxhnqjF2jcDLkcDgcjvpCKL6NRtXNOhZTIrSwoFVLZ58t8uY3KwmandXIUXOztv0YG9P2IHNz2sNrfl4JVaFgzUv7+61x7uys9tsS0febmxN55hlrgOooB/qgU07R3294Q00TIREnQw6Hw+God0QiKrQulZTwtLbqc52dSmqmp020G4lYn7S5Of3JZq3nWz6vP6mUvWZoSCNNpZK+1+SkyG/+puqX5uf1uUhEu8U3NIjs3aufMTqq75XLKamanFRi9fLL+nctobtbq/caG/V3S4udq9FRPS6sD9rbrdkqlWH874wzlAB1d6tnVF+fnssj9b6rETgZcjgcDkf9g9Y5NIxlkm5sNENHOr43NSlJSSb1+akpfV2xqH83Nqq2JRrV92hvFzn9dCVNs7NKoBIJJV28Z3u7vqahQbu1z84qCcrldL/yef3ciQmNTL38spKmoSGNNmUyui1tSjo69P3m5pTURaP6XDSq+7d7t0awnn9eyRjHL6Lb83h21s5FPq+pxNNOUyfoDRss5djcrNu3tSkZSqc1GtbSovuUy+n5isWUAHJcra36maWS/v/003U/Ewnd11isJgXTlXAy5HA4HI76x1Ktcyp7okE4EGNTpVYo6OTd1GQ/+Xz5/4gipVL6e2FB36+11SIlsZj+r1DQ50SUlJBWy+dFrrxS+69Bhqam9D3a2pRMcDzptEaR2tos6jU7q5+7b5+SksZGjeBMTCgJyWT09RA8SF1jozXI3bbNCCERrlxOPxNydc45un0+r88tLOh+xGJK6AoFfQ6S1N+v+8ZnR6NKpuoAToYcDofDcfJgOWZ+ISEKX4M7ckiompp0op+e1v93dVkUpVjUyT4ataajRKUq96elRYnEwoK+Z7GoRGdsTEkIUZhkUn+amnT7QkHfY2FBPxfCRXf6yUl9bamkrwl1Tq2t+hnz80qSGhv19fQGa2iw1FgkYqlDIkzptBKhUklfz9/FogrXiXjFYvojYu9XLNZFRAg4GXI4HA7H+sSRIkjh8+iRotHyCb5UMkIBeVqqoTPvw2c0Nenj9naNBJ1yihKLaFQJBSm38L3DfVtY0G3b2zUthh/T1JT+P6y6Q99DBKhYLI/yQOaKRSViCwv6Oo4X8sa+5/NG+iBSfFZDg6UrIYg15ja9GJwMORwOh8NxNFQ2Fg07rlf+XgpNTUYWSMnl80YaIEnh5yz23tGoEhxScpCXRMIq3NANtbZaCrFYtM8Lo1iQLUToDQ26HcL0kBy1tZWTI16HLgmSSHqxBh2nK1H7e+hwOBwOx8mCSm0TZCgUfh/Le4W/SZ+1tJiwmyovyNuRmqQuprmC0PCb1/O/yteH0bLw9UtFzGoEToYcDofD4VhrhESm0kjyRACZIpIFEVrss5faL94LbVRlZOxIryWCFBKoGidCIk6GHA6Hw+GoPlaKMCxVVbcW73WsBKpG4GTI4XA4HI6TDSsZjTmW91pJMraGqG1LSIfD4XA4HPWHOiJCIk6GHA6Hw+FwrHPUDRkaHx+XG2+8UVKplKRSKbnxxhtlYmJiydf89m//tkQikbKfSy+9dG122OFwOBwOR12gbjRD//7f/3vZt2+f3H///SIi8pGPfERuvPFG+e53v7vk66666ir5u7/7u0OPo9Hoqu6nw+FwOByO+kJdkKHnn39e7r//fnn88cflzW9+s4iI/M3f/I3s3LlTXnjhBdm+ffsRX9vS0iL9/f1rtasOh8PhcDjqDHWRJnvssccklUodIkIiIpdeeqmkUin5yU9+suRrH3nkEent7ZUzzzxTbrrpJhkaGlpy+7m5Oclms2U/DofD4XA4Tl7UBRk6cOCA9Pb2HvZ8b2+vHDhw4Iivu/rqq+Uf/uEf5Ac/+IH8+Z//ufzrv/6rXHHFFTKHVfkiuP322w/pklKplGzatGlFjsHhcDgcDkdtoqpk6E/+5E8OEzhX/jzxxBMiIhJZpEyvVCot+jy44YYb5L3vfa+cd955cu2118o//dM/yYsvvijf//73j/iaT3/605LJZA797N2798QP1OFwOBwOR82iqpqhj33sY/LBD35wyW22bNkizzzzjBw8ePCw/w0PD0tfX9+yP29gYEA2b94sL7300hG3aWlpkZawSZ7D4XA4HI6TGlUlQ93d3dLd3X3U7Xbu3CmZTEZ++tOfypve9CYREfmXf/kXyWQy8pa3vGXZnzc6Oip79+6VgYGB497ndYc6cxF1OBwOh+NYUReaobPPPluuuuoquemmm+Txxx+Xxx9/XG666SZ53/veV1ZJdtZZZ8m9994rIiKTk5PyiU98Qh577DF59dVX5ZFHHpFrr71Wuru75frrr6/WodQPSiXtpMxPqVTtPXI4HA6HY1VQF2RIROQf/uEf5A1veIPs2rVLdu3aJeeff758/etfL9vmhRdekEwmIyIijY2N8otf/EKuu+46OfPMM+VDH/qQnHnmmfLYY49Je3t7NQ6hvlAoKAFqaNDfhUK198jhcDgcjlVBpFTyJf9SyGazkkqlJJPJSDKZrPburA2ICkUi+lMq6U9Tk6fMHA6Hw1EXOJb5uy5MFx1VQKFQHg1qbFQy5HA4HA7HSYa6SZM51hCFgqbHRDRCJKKPPVXmcDgcjpMQvtR3lIOUWEODRYNC7ZBXlzkcDofjJINHhhzlCHVCkYhGg8LHToQcDofDcZLByZDjcDQ2GvFpaDAC1Ni4+PauwXc4HA5HHcPTZI7DEYlYekzEdEOFghElESu5J2oU/s/hcDgcjjqBR4YcRwZpMpHF/Ybci8jhcDgcJwGcDDmOjFBMLVIuol7qfw6Hw+Fw1BGcDDmODHRDxaI+LhbtuaX+53A4HA5HHcHJkGNpoAOC7IQi6qX+53A4HA5HncAF1I6lEYqpK6M+S/3P4XA4HI46gUeGHMvDUmTHiZDD4XA46hhOhhwOh8PhcKxrOBlyOBwOh8OxruFkyOFwOBwOx7qGkyGHw+FwOBzrGk6GHA6Hw+FwrGs4GXI4HA6Hw7Gu4WTI4XA4HA7HuoaTIYfD4XA4HOsaToYcDofD4XCsazgZcjgcDofDsa7hZMjhcDgcDse6hjdqPQpKpZKIiGSz2SrvicPhcDgcjuWCeZt5fCk4GToKcrmciIhs2rSpynvicDgcDofjWJHL5SSVSi25TaS0HMq0jlEsFuX111+X9vZ2iaxgd/ZsNiubNm2SvXv3SjKZXLH3PZng52hp+PlZGn5+jg4/R0vDz8/SqPXzUyqVJJfLyYYNG6ShYWlVkEeGjoKGhgY55ZRTVu39k8lkTV5EtQQ/R0vDz8/S8PNzdPg5Whp+fpZGLZ+fo0WEgAuoHQ6Hw+FwrGs4GXI4HA6Hw7Gu4WSoSmhpaZHPfOYz0tLSUu1dqVn4OVoafn6Whp+fo8PP0dLw87M0Tqbz4wJqh8PhcDgc6xoeGXI4HA6Hw7Gu4WTI4XA4HA7HuoaTIYfD4XA4HOsaToYcDofD4XCsazgZWkXccccdctppp0lra6vs2LFDfvSjHy25/aOPPio7duyQ1tZW2bp1q3z5y19eoz2tDo7l/DzyyCMSiUQO+/nVr361hnu8tvjhD38o1157rWzYsEEikYh8+9vfPupr1tM1dKznZ71dQ7fffru88Y1vlPb2dunt7ZX3v//98sILLxz1devlGjqe87OerqEvfelLcv755x8yVNy5c6f80z/905Kvqedrx8nQKuHuu++WW265Rf77f//v8tRTT8nb3/52ufrqq2XPnj2Lbr9792655ppr5O1vf7s89dRT8kd/9EfyB3/wB3LPPfes8Z6vDY71/IAXXnhBBgcHD/2cccYZa7THa4+pqSm54IIL5Atf+MKytl9v19Cxnh+wXq6hRx99VD760Y/K448/Lg8++KDk83nZtWuXTE1NHfE16+kaOp7zA9bDNXTKKafIn/7pn8oTTzwhTzzxhFxxxRVy3XXXybPPPrvo9nV/7ZQcq4I3velNpZtvvrnsubPOOqv0qU99atHt//AP/7B01llnlT33e7/3e6VLL7101faxmjjW8/Pwww+XRKQ0Pj6+BntXexCR0r333rvkNuvtGgqxnPOz3q+hoaGhkoiUHn300SNus56voeWcn/V+DXV0dJT+z//5P4v+r96vHY8MrQLm5+flySeflF27dpU9v2vXLvnJT36y6Gsee+yxw7Z/z3veI0888YQsLCys2r5WA8dzfsBFF10kAwMDcuWVV8rDDz+8mrtZd1hP19CJYL1eQ5lMRkREOjs7j7jNer6GlnN+wHq7hgqFgnzzm9+Uqakp2blz56Lb1Pu142RoFTAyMiKFQkH6+vrKnu/r65MDBw4s+poDBw4sun0+n5eRkZFV29dq4HjOz8DAgHzlK1+Re+65R771rW/J9u3b5corr5Qf/vCHa7HLdYH1dA0dD9bzNVQqleTWW2+Vt73tbXLeeecdcbv1eg0t9/yst2voF7/4hSQSCWlpaZGbb75Z7r33XjnnnHMW3bberx3vWr+KiEQiZY9LpdJhzx1t+8WeP1lwLOdn+/btsn379kOPd+7cKXv37pU/+7M/k8suu2xV97OesN6uoWPBer6GPvaxj8kzzzwj/+///b+jbrser6Hlnp/1dg1t375dnn76aZmYmJB77rlHPvShD8mjjz56REJUz9eOR4ZWAd3d3dLY2HhYlGNoaOgw5gz6+/sX3b6pqUm6urpWbV+rgeM5P4vh0ksvlZdeemmld69usZ6uoZXCeriG/vN//s/yne98Rx5++GE55ZRTltx2PV5Dx3J+FsPJfA1Fo1HZtm2bXHLJJXL77bfLBRdcIH/5l3+56Lb1fu04GVoFRKNR2bFjhzz44INlzz/44IPylre8ZdHX7Ny587DtH3jgAbnkkkukubl51fa1Gjie87MYnnrqKRkYGFjp3atbrKdraKVwMl9DpVJJPvaxj8m3vvUt+cEPfiCnnXbaUV+znq6h4zk/i+FkvoYqUSqVZG5ubtH/1f21UyXh9kmPb37zm6Xm5ubSnXfeWXruuedKt9xySykej5deffXVUqlUKn3qU58q3XjjjYe2f+WVV0qxWKz08Y9/vPTcc8+V7rzzzlJzc3PpH//xH6t1CKuKYz0///t//+/SvffeW3rxxRdLv/zlL0uf+tSnSiJSuueee6p1CKuOXC5Xeuqpp0pPPfVUSURKf/EXf1F66qmnSq+99lqpVPJr6FjPz3q7hv7Tf/pPpVQqVXrkkUdKg4ODh36mp6cPbbOer6HjOT/r6Rr69Kc/XfrhD39Y2r17d+mZZ54p/dEf/VGpoaGh9MADD5RKpZPv2nEytIr44he/WNq8eXMpGo2WLr744rKSzQ996EOld7zjHWXbP/LII6WLLrqoFI1GS1u2bCl96UtfWuM9Xlscy/n5X//rf5VOP/30Umtra6mjo6P0tre9rfT973+/Cnu9dqCMt/LnQx/6UKlU8mvoWM/PeruGFjs3IlL6u7/7u0PbrOdr6HjOz3q6hj784Q8fGp97enpKV1555SEiVCqdfNdOpFT6/xVODofD4XA4HOsQrhlyOBwOh8OxruFkyOFwOBwOx7qGkyGHw+FwOBzrGk6GHA6Hw+FwrGs4GXI4HA6Hw7Gu4WTI4XA4HA7HuoaTIYfD4XA4HOsaToYcDofD4XCsazgZcjgcJz0uv/xyueWWW1Zs20gkIt/+9rcPPf7Vr34ll156qbS2tsqFF1543PvpcDiqAydDDofDcYwYHByUq6+++tDjz3zmMxKPx+WFF16Qhx56SL761a9KOp2u3g46HI5jQlO1d8DhcDiWi/n5eYlGo9XeDenv7y97/Otf/1re+973yubNm6u0Rw6H40TgkSGHw1GzuPzyy+VjH/uY3HrrrdLd3S3vfve75bnnnpNrrrlGEomE9PX1yY033igjIyOHXjM1NSW/9Vu/JYlEQgYGBuTP//zPD3vfO+64Q8444wxpbW2Vvr4++bf/9t+W/b9YLMof/uEfSmdnp/T398uf/MmflP0/TJNFIhF58skn5bbbbpNIJCKXX365/Mf/+B8lk8lIJBKRSCRy2OsdDkdtwcmQw+Goafz93/+9NDU1yY9//GP50z/9U3nHO94hF154oTzxxBNy//33y8GDB+UDH/jAoe0/+clPysMPPyz33nuvPPDAA/LII4/Ik08+eej/TzzxhPzBH/yB3HbbbfLCCy/I/fffL5dddtlhnxmPx+Vf/uVf5LOf/azcdttt8uCDDy66f4ODg3LuuefKf/2v/1UGBwflO9/5jnz+85+XZDIpg4ODMjg4KJ/4xCdW5+Q4HI4VgafJHA5HTWPbtm3y2c9+VkRE/viP/1guvvhi+Z//838e+v/f/u3fyqZNm+TFF1+UDRs2yJ133ilf+9rX5N3vfreIKLE55ZRTDm2/Z88eicfj8r73vU/a29tl8+bNctFFF5V95vnnny+f+cxnRETkjDPOkC984Qvy0EMPHXrPEP39/dLU1CSJROJQ+iyVSkkkEjksneZwOGoTToYcDkdN45JLLjn095NPPikPP/ywJBKJw7b79a9/LTMzMzI/Py87d+489HxnZ6ds37790ON3v/vdsnnzZtm6datcddVVctVVV8n1118vsVjs0Dbnn39+2XsPDAzI0NDQSh6Ww+GoIXiazOFw1DTi8fihv4vFolx77bXy9NNPl/289NJLctlll0mpVDrq+7W3t8vPfvYzueuuu2RgYED++I//WC644AKZmJg4tE1zc3PZayKRiBSLxRU7JofDUVtwMuRwOOoGF198sTz77LOyZcsW2bZtW9lPPB6Xbdu2SXNzszz++OOHXjM+Pi4vvvhi2fs0NTXJu971LvnsZz8rzzzzjLz66qvygx/8YMX2MxqNSqFQWLH3czgcqwsnQw6Ho27w0Y9+VMbGxuQ3f/M35ac//am88sor8sADD8iHP/xhKRQKkkgk5Hd+53fkk5/8pDz00EPyy1/+Un77t39bGhpsqPve974nf/VXfyVPP/20vPbaa/K1r31NisViWSrtRLFlyxaZnJyUhx56SEZGRmR6enrF3tvhcKw8nAw5HI66wYYNG+THP/6xFAoFec973iPnnXee/Jf/8l8klUodIjyf+9zn5LLLLpN/82/+jbzrXe+St73tbbJjx45D75FOp+Vb3/qWXHHFFXL22WfLl7/8Zbnrrrvk3HPPXbH9fMtb3iI333yz3HDDDdLT03NIAO5wOGoTkdJykuwOh8PhcDgcJyk8MuRwOBwOh2Ndw8mQw+FwOByOdQ0nQw6Hw+FwONY1nAw5HA6Hw+FY13Ay5HA4HA6HY13DyZDD4XA4HI51DSdDDofD4XA41jWcDDkcDofD4VjXcDLkcDgcDodjXcPJkMPhcDgcjnUNJ0MOh8PhcDjWNf4/f5gHyLBv2bQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df_full['redshift'],df_full['g']-df_full['i'],'r.', alpha=0.01)\n",
+    "plt.xlabel('redshift')\n",
+    "plt.ylabel('g-i')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5bf0cb7c-611d-402e-9b56-ea4c39b77d1c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#This is a function that makes a plot of photometric redshift \n",
+    "#    as a function of spectroscopic redshift \n",
+    "#    and calculates key statistics.  It will save us a lot of work.\n",
+    "def plot_and_stats(zspec,zphot):\n",
+    "    \n",
+    "    z_spec = np.copy(zspec)\n",
+    "    z_phot=np.copy(zphot)\n",
+    "    x = np.arange(0,5.4,0.05)\n",
+    "\n",
+    "# define differences of >0.15*(1+z) as non-Gaussian 'outliers'    \n",
+    "    outlier_upper = x + 0.15*(1+x)\n",
+    "    outlier_lower = x - 0.15*(1+x)\n",
+    "\n",
+    "    mask = np.abs((z_phot - z_spec)/(1 + z_spec)) > 0.15\n",
+    "    notmask = ~mask \n",
+    "    \n",
+    "#Standard Deviation of the predicted redshifts compared to the data:\n",
+    "    std_result = np.std((z_phot - z_spec)/(1 + z_spec), ddof=1)\n",
+    "\n",
+    "#Normalized MAD (Median Absolute Deviation):\n",
+    "    nmad = 1.48 * np.median(np.abs((z_phot - z_spec)/(1 + z_spec)))\n",
+    "\n",
+    "#Percentage of delta-z > 0.15(1+z) outliers:\n",
+    "    eta = np.sum(np.abs((z_phot - z_spec)/(1 + z_spec)) > 0.15)/len(z_spec)\n",
+    "    \n",
+    "    #Median offset (normalized by (1+z); i.e., bias:\n",
+    "    bias = np.median(((z_phot - z_spec)/(1 + z_spec)))\n",
+    "    sigbias=std_result/np.sqrt(0.64*len(z_phot))\n",
+    "    \n",
+    "    # make photo-z/spec-z plot\n",
+    "    plt.figure(figsize=(8, 8))\n",
+    "    \n",
+    "    #add lines to indicate outliers\n",
+    "    plt.plot(x, outlier_upper, 'k--')\n",
+    "    plt.plot(x, outlier_lower, 'k--')\n",
+    "    plt.plot(z_spec[mask], z_phot[mask], 'r.', markersize=6,  alpha=0.05)\n",
+    "    plt.plot(z_spec[notmask], z_phot[notmask], 'b.',  markersize=6, alpha=0.05)\n",
+    "    plt.plot(x, x, linewidth=1.5, color = 'red')\n",
+    "    plt.title('$\\sigma_\\mathrm{NMAD} \\ = $%6.4f\\n'%nmad+'$(\\Delta z)>0.15(1+z) $ outliers = %6.3f'%(eta*100)+'%', fontsize=18)\n",
+    "    plt.xlim([0.0, 2])\n",
+    "    plt.ylim([0.0, 2])\n",
+    "    plt.xlabel('$z_{\\mathrm{spec}}$', fontsize = 27)\n",
+    "    plt.ylabel('$z_{\\mathrm{photo}}$', fontsize = 27)\n",
+    "    plt.grid(alpha = 0.8)\n",
+    "    plt.tick_params(labelsize=15)\n",
+    "    plt.show()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4a7eee06-0339-48fd-ba73-6d5547de2840",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#from Ellen\n",
+    "# some float64 issues going on with photerr:\n",
+    "\n",
+    "nominal_depth_LSSTY10 = {\n",
+    "    \"u\":25.6,\n",
+    "    \"g\":26.9,\n",
+    "    \"r\":26.9,\n",
+    "    \"i\":26.4,\n",
+    "    \"z\":25.6,\n",
+    "    \"y\":24.8,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30605c5a-eebf-4788-8c16-61cc8376ffd1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "61d03e79-fafa-4ef1-b5ac-7bdfd71002c8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "i_lim [24.05       24.42628749 24.64640157 24.80257499 24.92371251 25.02268906\n",
+      " 25.10637255 25.17886248 25.24280314 25.3       ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#from Ellen\n",
+    "# calculate nominal depths etc.\n",
+    "years=np.arange(1,11)\n",
+    "n_ilim=len(years)\n",
+    "ilims = 25.3+2.5/2.0*np.log10(years/10)\n",
+    "print(\"i_lim\", ilims)\n",
+    "n_m5 = 11\n",
+    "\n",
+    "\n",
+    "nominal_depth={}\n",
+    "for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
+    "    nominal_depth[band] = {}\n",
+    "    for year in years:\n",
+    "        #nominal_depth[band][year]= nominal_depth_LSSTv1[band]+2.5/2.0*np.log10(year)\n",
+    "        nominal_depth[band][year]= nominal_depth_LSSTY10[band]+2.5/2.0*np.log10(year/10)\n",
+    "#    print(band, nominal_depth[band])\n",
+    "    \n",
+    "# finally compute the M5 bins in each band:\n",
+    "M5 = {}\n",
+    "for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
+    "    M5[band]={}\n",
+    "    for year in years:\n",
+    "        M5[band][year] = np.linspace(-5,5,n_m5)*0.1 +  nominal_depth[band][year]\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b0ff8d9-2c6b-48fd-9bc2-43345fd623a0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6a20797e-b0c3-40f0-a035-193bebd50a4e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# code to produce a catalog degraded according to the depths in a particular year\n",
+    "def degrade_catalog(df_full,year):\n",
+    "\n",
+    "#code from Ellen\n",
+    "# what we want to do here is to degrade the sample and save them in a separate catalogue;\n",
+    "\n",
+    "    extendedType=\"auto\"\n",
+    "\n",
+    "    catalog_rep = df_full.copy()\n",
+    "    \n",
+    "    nominal_depth_LSSTY10 = {\n",
+    "    \"u\":25.6,\n",
+    "    \"g\":26.9,\n",
+    "    \"r\":26.9,\n",
+    "    \"i\":26.4,\n",
+    "    \"z\":25.6,\n",
+    "    \"y\":24.8,\n",
+    "}\n",
+    "\n",
+    "    nominal_depth={}\n",
+    "    for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
+    "        nominal_depth[band] = {}\n",
+    "        nominal_depth[band]= nominal_depth_LSSTY10[band]+2.5/2.0*np.log10(year/10)\n",
+    "\n",
+    "# we should save the binning in each band:\n",
+    "\n",
+    "    for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
+    "        \n",
+    "    # these are nominal values\n",
+    "        nVisYr={}\n",
+    "        m5={}\n",
+    "        for bb in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
+    "            nVisYr[bb]=1\n",
+    "            m5[bb]=nominal_depth[bb]\n",
+    "        \n",
+    "        errModel = LsstErrorModel(nYrObs=1,nVisYr=nVisYr[bb],m5={\"u\": float(m5[\"u\"]),\n",
+    "                                                                \"g\": float(m5[\"g\"]),\n",
+    "                                                                \"r\": float(m5[\"r\"]),\n",
+    "                                                                \"i\": float(m5[\"i\"]),\n",
+    "                                                                 \"z\": float(m5[\"z\"]),\n",
+    "                                                                \"y\": float(m5[\"y\"])},\n",
+    "                                     extendedType=extendedType)\n",
+    "        tmpcatalog = errModel(catalog_rep, random_state=np.random.randint(1,100_000))\n",
+    "   \n",
+    "        if 'redshift' in catalog_rep.columns:\n",
+    "             d = {\n",
+    "                \"u\": (tmpcatalog[\"u\"].to_numpy()),\n",
+    "                \"g\": (tmpcatalog[\"g\"].to_numpy()),\n",
+    "                \"r\": (tmpcatalog[\"r\"].to_numpy()),\n",
+    "                \"i\": (tmpcatalog[\"i\"].to_numpy()),\n",
+    "                \"z\": (tmpcatalog[\"z\"].to_numpy()),\n",
+    "                \"y\": (tmpcatalog[\"y\"].to_numpy()),\n",
+    "                \"redshift\": (catalog_rep['redshift'].to_numpy()),\n",
+    "        }\n",
+    "        else:\n",
+    "            d = {\n",
+    "                \"u\": (tmpcatalog[\"u\"].to_numpy()),\n",
+    "                \"g\": (tmpcatalog[\"g\"].to_numpy()),\n",
+    "                \"r\": (tmpcatalog[\"r\"].to_numpy()),\n",
+    "                \"i\": (tmpcatalog[\"i\"].to_numpy()),\n",
+    "                \"z\": (tmpcatalog[\"z\"].to_numpy()),\n",
+    "                \"y\": (tmpcatalog[\"y\"].to_numpy()),\n",
+    "        }\n",
+    "    degraded_cat = pandas.DataFrame(data=d)\n",
+    "    return(degraded_cat)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bd11106-04f7-4204-8664-5982b8313710",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "576068a9-6e9c-4b49-a31d-d97c7c1f5410",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# code to train the random forests\n",
+    "def train_rf(catalog,plot=False):\n",
+    "\n",
+    "    u_mag = catalog['u']\n",
+    "    g_mag = catalog['g']\n",
+    "    r_mag = catalog['r']\n",
+    "    i_mag = catalog['i']\n",
+    "    z_mag = catalog['z']\n",
+    "    y_mag = catalog['y']\n",
+    "\n",
+    "#Redshift array\n",
+    "    z = catalog['redshift']\n",
+    "\n",
+    "# Now, set up input data array for scikit-learn regression algorithms\n",
+    "# We will include galaxy colors (expressed as differences between magnitudes\n",
+    "# in adjoining bands) and one magnitude.\n",
+    "# np.column_stack makes a 2D array out of a set of 1d arrays :\n",
+    "# with 6 variables we get an N x 6 numpy array out\n",
+    "    data_colmag = np.column_stack((u_mag-g_mag, g_mag-r_mag, r_mag-i_mag, \n",
+    "                               i_mag-z_mag, z_mag-y_mag, i_mag))\n",
+    "\n",
+    "\n",
+    "# We will set up an implementation of the scikit-learn \n",
+    "# RandomForestRegressor in an object called 'regrf'. \n",
+    "    regrf = RandomForestRegressor(n_estimators = 100,\n",
+    "                max_depth = 30, max_features = 'sqrt')\n",
+    "\n",
+    "    # To better assess the quality of the Random Forest fitting, \n",
+    "    # we split the data into Training (90%) and Test (10%) sets. \n",
+    "    # The code below performs this task on the data_mags and data_z arrays:\n",
+    "    \n",
+    "    # 1) randomly divide the sample into 50% training and 50% testing sets  \n",
+    "    # (e.g., data_train, z_train, and scaled_train are the training \n",
+    "    # portions of data_colmag, data_z, and scaled_colmag\n",
+    "    \n",
+    "    data_train, data_test, z_train, z_test, i_train, i_test \\\n",
+    "            =train_test_split(data_colmag, z, i_mag, \\\n",
+    "                        test_size = 0.10, train_size = 0.90)\n",
+    "\n",
+    "    #Train the regressor using the training data\n",
+    "    regrf.fit(data_train,z_train)\n",
+    "\n",
+    "    #Apply the regressor to predict values for the test data\n",
+    "    z_phot = regrf.predict(data_test)\n",
+    "    z_spec = z_test\n",
+    "    isbright = i_test < 25\n",
+    "#Make a photo-z/spec-z plot and output summary statistics for the test set.\n",
+    "    if plot:\n",
+    "        plot_and_stats(z_spec,z_phot)\n",
+    "        plot_and_stats(z_spec[isbright],z_phot[isbright])\n",
+    "                        \n",
+    "    imputer = IterativeImputer()\n",
+    "    imputer.fit(data_colmag)\n",
+    "    \n",
+    "    return(regrf,imputer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bdd9452-565f-4f86-a61d-a652d097fe2f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9d34b7f3-89c9-47f3-8092-18c1527151b0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# code to apply the already-computed random forest photo-z's to a catalog,\n",
+    "#  dealing with NaNs \n",
+    "\n",
+    "def apply_rf(catalog,regrf,imputer):\n",
+    "    catalog.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "\n",
+    "    u_mag = catalog['u']\n",
+    "    g_mag = catalog['g']\n",
+    "    r_mag = catalog['r']\n",
+    "    i_mag = catalog['i']\n",
+    "    z_mag = catalog['z']\n",
+    "    y_mag = catalog['y']\n",
+    "\n",
+    "    data_colmag = np.column_stack((u_mag-g_mag, g_mag-r_mag, r_mag-i_mag, \n",
+    "                               i_mag-z_mag, z_mag-y_mag, i_mag))\n",
+    "    clean_colmag = imputer.transform(data_colmag)\n",
+    "    z_phot = regrf.predict(clean_colmag)\n",
+    "    \n",
+    "    return(z_phot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b31830ea-c3d4-493c-a856-4643a7229187",
+   "metadata": {},
+   "source": [
+    "# Below is the code box that actually does all the work!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "987b24db-7574-4c77-84f1-ce8050824b76",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set up arrays for everything\n",
+    "#CHANGE COMMENTED OUT LINES TO LOOK AT MORE YEARS OR MORE BANDS!!!!!!!\n",
+    "#years = np.arange(1,11)\n",
+    "years = np.arange(1,11)\n",
+    "ilims = 25.3+2.5/2.0*np.log10(years/10)\n",
+    "\n",
+    "n_m5 = 11\n",
+    "\n",
+    "#bands = ['u']\n",
+    "bands = ['u','g','r','i','z','y','ugrizy']\n",
+    "\n",
+    "deltas=np.linspace(-0.5,0.5,11)\n",
+    "#deltas = deltas[0:2]\n",
+    "\n",
+    "minzs=np.arange(6)*0.2\n",
+    "maxzs=minzs + 0.2\n",
+    "\n",
+    "if 1:\n",
+    "# these arrays will contain the results to turn into d<z>/dm5 and dn/dm5\n",
+    "# first index runs over redshift bins; second over bands; third over the delta depth array\n",
+    "    meanzs = np.zeros( (6,len(bands),len(deltas)) )\n",
+    "    densities = np.zeros( (6,len(bands),len(deltas)) )\n",
+    "\n",
+    "#loop over all the files, calculate mean redshift and densities in each bin...\n",
+    "\n",
+    "# outermost loop is over years\n",
+    "    for year_idx,year in enumerate(years):\n",
+    "        print(year)\n",
+    "        yearstring =np.array2string(year)\n",
+    "    # degrade the catalog to have noise appropriate to year\n",
+    "        tmp = degrade_catalog(df_full,year)\n",
+    "        tmp.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "        cleantmp = tmp.dropna()\n",
+    "    # train the random forest on the degraded catalog\n",
+    "    # skip this when rerunning while testing\n",
+    "        rf_year,imputer_year = train_rf(cleantmp)\n",
+    "\n",
+    "# next loop is over bands    \n",
+    "        for band_idx,band in enumerate(bands):\n",
+    "            for delta_idx,delta in enumerate(deltas):\n",
+    "                binstring = np.array2string(np.array(delta_idx + 1))\n",
+    "                file=glob.glob('/pscratch/sd/q/qhang/roman-rubin-sims/nonuniform-maf/Y'+yearstring+\n",
+    "                            '/roman-rubin_sample-m5-'+band+'-bin-'+binstring+'.pkl')\n",
+    "                tmp = pandas.read_pickle(file[0])\n",
+    "                tmp['redshift'] = df_full['redshift']\n",
+    "                tmp = tmp[ tmp['i'] < ilims[year_idx] ]\n",
+    "            \n",
+    "                zphot = apply_rf(tmp,rf_year,imputer_year)\n",
+    "                plt.hist(zphot,bins=100,histtype='step')\n",
+    "                tmp['zphot'] = zphot\n",
+    "# now loop over the redshift bins to assign means and counts for each bin            \n",
+    "                for z_idx,minz in enumerate(minzs):\n",
+    "                    inbin = np.logical_and(tmp['zphot'] > minz,tmp['zphot'] < maxzs[z_idx])\n",
+    "                    meanzs[z_idx, band_idx, delta_idx]=np.mean(tmp['redshift'][inbin])\n",
+    "                    densities[z_idx, band_idx, delta_idx]=np.sum(inbin)\n",
+    "        outmeanfile = 'meanzsy'+yearstring+'.pkl'\n",
+    "        outdensityfile = 'densityy'+yearstring+'.pkl'\n",
+    "        pickle.dump(meanzs, open(outmeanfile, \"wb\"))\n",
+    "        pickle.dump(densities, open(outdensityfile, \"wb\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "58c0f954-35be-408a-a6fc-754490a90a83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bands.pkl\t  densityy3.pkl  densityy9.pkl\t meanzsy2.pkl  meanzsy8.pkl\n",
+      "deltas.pkl\t  densityy4.pkl  hgb_model.pkl\t meanzsy3.pkl  meanzsy9.pkl\n",
+      "densityderiv.pkl  densityy5.pkl  maxzs.pkl\t meanzsy4.pkl  minzs.pkl\n",
+      "densityy10.pkl\t  densityy6.pkl  meanzderiv.pkl  meanzsy5.pkl  years.pkl\n",
+      "densityy1.pkl\t  densityy7.pkl  meanzsy10.pkl\t meanzsy6.pkl\n",
+      "densityy2.pkl\t  densityy8.pkl  meanzsy1.pkl\t meanzsy7.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls *.pkl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f2d826c-1e07-405c-8da7-c958f3d9b942",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e197e085-6239-4245-b651-7794a4b3591a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "7472c833-fd12-4e36-bbaf-7771be379f57",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c35eba1f-2c1a-449e-8ca2-d941d497453b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "NERSC Python",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/rubin_sim/maf/metrics/uniformity_pkl/simulation_photoz_rr.ipynb b/rubin_sim/maf/metrics/uniformity_pkl/simulation_photoz_rr.ipynb
new file mode 100644
index 000000000..81c958559
--- /dev/null
+++ b/rubin_sim/maf/metrics/uniformity_pkl/simulation_photoz_rr.ipynb
@@ -0,0 +1,1193 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "a5d40f07-0827-46ab-a814-adbf38bc4f4b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline\n",
+    "import pickle\n",
+    "import pandas\n",
+    "\n",
+    "\n",
+    "# Random forest routine from scikit-learn:\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
+    "# Cross-Validation routines:\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.model_selection import cross_val_predict\n",
+    "from photerr import LsstErrorModel\n",
+    "\n",
+    "from sklearn.experimental import enable_iterative_imputer\n",
+    "from sklearn.impute import IterativeImputer\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "0b0733c2-a9df-463f-b158-c93275a6ec79",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5458656c-1776-4823-b654-1505c4108020",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "2937e855-3b4a-41e9-ac76-ea8951664d6a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#df_full=pandas.read_pickle('/global/u2/q/qhang/desc/notebooks_for_analysis/Project285/cosmoDC2_pzflow_sample.pkl')\n",
+    "\n",
+    "df_full=pandas.read_pickle('/pscratch/sd/q/qhang/roman-rubin-sims/nonuniform-maf/roman_rubin_2023_v1.1.3_elais-subset.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "cfb5ee3e-c2b6-43c4-b0b4-4ba40fbbfc0c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'g-i')"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df_full['redshift'],df_full['g']-df_full['i'],'r.', alpha=0.01)\n",
+    "plt.xlabel('redshift')\n",
+    "plt.ylabel('g-i')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "5bf0cb7c-611d-402e-9b56-ea4c39b77d1c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#This is a function that makes a plot of photometric redshift \n",
+    "#    as a function of spectroscopic redshift \n",
+    "#    and calculates key statistics.  It will save us a lot of work.\n",
+    "def plot_and_stats(zspec,zphot):\n",
+    "    \n",
+    "    z_spec = np.copy(zspec)\n",
+    "    z_phot=np.copy(zphot)\n",
+    "    x = np.arange(0,5.4,0.05)\n",
+    "\n",
+    "# define differences of >0.15*(1+z) as non-Gaussian 'outliers'    \n",
+    "    outlier_upper = x + 0.15*(1+x)\n",
+    "    outlier_lower = x - 0.15*(1+x)\n",
+    "\n",
+    "    mask = np.abs((z_phot - z_spec)/(1 + z_spec)) > 0.15\n",
+    "    notmask = ~mask \n",
+    "    \n",
+    "#Standard Deviation of the predicted redshifts compared to the data:\n",
+    "    std_result = np.std((z_phot - z_spec)/(1 + z_spec), ddof=1)\n",
+    "\n",
+    "#Normalized MAD (Median Absolute Deviation):\n",
+    "    nmad = 1.48 * np.median(np.abs((z_phot - z_spec)/(1 + z_spec)))\n",
+    "\n",
+    "#Percentage of delta-z > 0.15(1+z) outliers:\n",
+    "    eta = np.sum(np.abs((z_phot - z_spec)/(1 + z_spec)) > 0.15)/len(z_spec)\n",
+    "    \n",
+    "    #Median offset (normalized by (1+z); i.e., bias:\n",
+    "    bias = np.median(((z_phot - z_spec)/(1 + z_spec)))\n",
+    "    sigbias=std_result/np.sqrt(0.64*len(z_phot))\n",
+    "    \n",
+    "    # make photo-z/spec-z plot\n",
+    "    plt.figure(figsize=(8, 8))\n",
+    "    \n",
+    "    #add lines to indicate outliers\n",
+    "    plt.plot(x, outlier_upper, 'k--')\n",
+    "    plt.plot(x, outlier_lower, 'k--')\n",
+    "    plt.plot(z_spec[mask], z_phot[mask], 'r.', markersize=6,  alpha=0.05)\n",
+    "    plt.plot(z_spec[notmask], z_phot[notmask], 'b.',  markersize=6, alpha=0.05)\n",
+    "    plt.plot(x, x, linewidth=1.5, color = 'red')\n",
+    "    plt.title('$\\sigma_\\mathrm{NMAD} \\ = $%6.4f\\n'%nmad+'$(\\Delta z)>0.15(1+z) $ outliers = %6.3f'%(eta*100)+'%', fontsize=18)\n",
+    "    plt.xlim([0.0, 2])\n",
+    "    plt.ylim([0.0, 2])\n",
+    "    plt.xlabel('$z_{\\mathrm{spec}}$', fontsize = 27)\n",
+    "    plt.ylabel('$z_{\\mathrm{photo}}$', fontsize = 27)\n",
+    "    plt.grid(alpha = 0.8)\n",
+    "    plt.tick_params(labelsize=15)\n",
+    "    plt.show()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "4a7eee06-0339-48fd-ba73-6d5547de2840",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#from Ellen\n",
+    "# some float64 issues going on with photerr:\n",
+    "\n",
+    "nominal_depth_LSSTY10 = {\n",
+    "    \"u\":25.6,\n",
+    "    \"g\":26.9,\n",
+    "    \"r\":26.9,\n",
+    "    \"i\":26.4,\n",
+    "    \"z\":25.6,\n",
+    "    \"y\":24.8,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30605c5a-eebf-4788-8c16-61cc8376ffd1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "61d03e79-fafa-4ef1-b5ac-7bdfd71002c8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "i_lim [24.05       24.42628749 24.64640157 24.80257499 24.92371251 25.02268906\n",
+      " 25.10637255 25.17886248 25.24280314 25.3       ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#from Ellen\n",
+    "# calculate nominal depths etc.\n",
+    "years=np.arange(1,11)\n",
+    "n_ilim=len(years)\n",
+    "ilims = 25.3+2.5/2.0*np.log10(years/10)\n",
+    "print(\"i_lim\", ilims)\n",
+    "n_m5 = 11\n",
+    "\n",
+    "\n",
+    "nominal_depth={}\n",
+    "for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
+    "    nominal_depth[band] = {}\n",
+    "    for year in years:\n",
+    "        #nominal_depth[band][year]= nominal_depth_LSSTv1[band]+2.5/2.0*np.log10(year)\n",
+    "        nominal_depth[band][year]= nominal_depth_LSSTY10[band]+2.5/2.0*np.log10(year/10)\n",
+    "#    print(band, nominal_depth[band])\n",
+    "    \n",
+    "# finally compute the M5 bins in each band:\n",
+    "M5 = {}\n",
+    "for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
+    "    M5[band]={}\n",
+    "    for year in years:\n",
+    "        M5[band][year] = np.linspace(-5,5,n_m5)*0.1 +  nominal_depth[band][year]\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b0ff8d9-2c6b-48fd-9bc2-43345fd623a0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "6a20797e-b0c3-40f0-a035-193bebd50a4e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# code to produce a catalog degraded according to the depths in a particular year\n",
+    "def degrade_catalog(df_full,year):\n",
+    "\n",
+    "#code from Ellen\n",
+    "# what we want to do here is to degrade the sample and save them in a separate catalogue;\n",
+    "\n",
+    "    extendedType=\"auto\"\n",
+    "\n",
+    "    catalog_rep = df_full.copy()\n",
+    "    \n",
+    "    nominal_depth_LSSTY10 = {\n",
+    "    \"u\":25.6,\n",
+    "    \"g\":26.9,\n",
+    "    \"r\":26.9,\n",
+    "    \"i\":26.4,\n",
+    "    \"z\":25.6,\n",
+    "    \"y\":24.8,\n",
+    "}\n",
+    "\n",
+    "    nominal_depth={}\n",
+    "    for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
+    "        nominal_depth[band] = {}\n",
+    "        nominal_depth[band]= nominal_depth_LSSTY10[band]+2.5/2.0*np.log10(year/10)\n",
+    "\n",
+    "# we should save the binning in each band:\n",
+    "\n",
+    "    for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
+    "        \n",
+    "    # these are nominal values\n",
+    "        nVisYr={}\n",
+    "        m5={}\n",
+    "        for bb in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
+    "            nVisYr[bb]=1\n",
+    "            m5[bb]=nominal_depth[bb]\n",
+    "        \n",
+    "        errModel = LsstErrorModel(nYrObs=1,nVisYr=nVisYr[bb],m5={\"u\": float(m5[\"u\"]),\n",
+    "                                                                \"g\": float(m5[\"g\"]),\n",
+    "                                                                \"r\": float(m5[\"r\"]),\n",
+    "                                                                \"i\": float(m5[\"i\"]),\n",
+    "                                                                 \"z\": float(m5[\"z\"]),\n",
+    "                                                                \"y\": float(m5[\"y\"])},\n",
+    "                                     extendedType=extendedType)\n",
+    "        tmpcatalog = errModel(catalog_rep, random_state=np.random.randint(1,100_000))\n",
+    "   \n",
+    "        if 'redshift' in catalog_rep.columns:\n",
+    "             d = {\n",
+    "                \"u\": (tmpcatalog[\"u\"].to_numpy()),\n",
+    "                \"g\": (tmpcatalog[\"g\"].to_numpy()),\n",
+    "                \"r\": (tmpcatalog[\"r\"].to_numpy()),\n",
+    "                \"i\": (tmpcatalog[\"i\"].to_numpy()),\n",
+    "                \"z\": (tmpcatalog[\"z\"].to_numpy()),\n",
+    "                \"y\": (tmpcatalog[\"y\"].to_numpy()),\n",
+    "                \"redshift\": (catalog_rep['redshift'].to_numpy()),\n",
+    "        }\n",
+    "        else:\n",
+    "            d = {\n",
+    "                \"u\": (tmpcatalog[\"u\"].to_numpy()),\n",
+    "                \"g\": (tmpcatalog[\"g\"].to_numpy()),\n",
+    "                \"r\": (tmpcatalog[\"r\"].to_numpy()),\n",
+    "                \"i\": (tmpcatalog[\"i\"].to_numpy()),\n",
+    "                \"z\": (tmpcatalog[\"z\"].to_numpy()),\n",
+    "                \"y\": (tmpcatalog[\"y\"].to_numpy()),\n",
+    "        }\n",
+    "    degraded_cat = pandas.DataFrame(data=d)\n",
+    "    return(degraded_cat)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bd11106-04f7-4204-8664-5982b8313710",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "576068a9-6e9c-4b49-a31d-d97c7c1f5410",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# code to train the random forests\n",
+    "def train_rf(catalog,plot=False):\n",
+    "\n",
+    "    u_mag = catalog['u']\n",
+    "    g_mag = catalog['g']\n",
+    "    r_mag = catalog['r']\n",
+    "    i_mag = catalog['i']\n",
+    "    z_mag = catalog['z']\n",
+    "    y_mag = catalog['y']\n",
+    "\n",
+    "#Redshift array\n",
+    "    z = catalog['redshift']\n",
+    "\n",
+    "# Now, set up input data array for scikit-learn regression algorithms\n",
+    "# We will include galaxy colors (expressed as differences between magnitudes\n",
+    "# in adjoining bands) and one magnitude.\n",
+    "# np.column_stack makes a 2D array out of a set of 1d arrays :\n",
+    "# with 6 variables we get an N x 6 numpy array out\n",
+    "    data_colmag = np.column_stack((u_mag-g_mag, g_mag-r_mag, r_mag-i_mag, \n",
+    "                               i_mag-z_mag, z_mag-y_mag, i_mag))\n",
+    "\n",
+    "\n",
+    "# We will set up an implementation of the scikit-learn \n",
+    "# RandomForestRegressor in an object called 'regrf'. \n",
+    "    regrf = RandomForestRegressor(n_estimators = 100,\n",
+    "                max_depth = 30, max_features = 'sqrt')\n",
+    "\n",
+    "    # To better assess the quality of the Random Forest fitting, \n",
+    "    # we split the data into Training (90%) and Test (10%) sets. \n",
+    "    # The code below performs this task on the data_mags and data_z arrays:\n",
+    "    \n",
+    "    # 1) randomly divide the sample into 50% training and 50% testing sets  \n",
+    "    # (e.g., data_train, z_train, and scaled_train are the training \n",
+    "    # portions of data_colmag, data_z, and scaled_colmag\n",
+    "    \n",
+    "    data_train, data_test, z_train, z_test, i_train, i_test \\\n",
+    "            =train_test_split(data_colmag, z, i_mag, \\\n",
+    "                        test_size = 0.10, train_size = 0.90)\n",
+    "\n",
+    "    #Train the regressor using the training data\n",
+    "    regrf.fit(data_train,z_train)\n",
+    "\n",
+    "    #Apply the regressor to predict values for the test data\n",
+    "    z_phot = regrf.predict(data_test)\n",
+    "    z_spec = z_test\n",
+    "    isbright = i_test < 25\n",
+    "#Make a photo-z/spec-z plot and output summary statistics for the test set.\n",
+    "    if plot:\n",
+    "        plot_and_stats(z_spec,z_phot)\n",
+    "        plot_and_stats(z_spec[isbright],z_phot[isbright])\n",
+    "                        \n",
+    "    imputer = IterativeImputer()\n",
+    "    imputer.fit(data_colmag)\n",
+    "    \n",
+    "    return(regrf,imputer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bdd9452-565f-4f86-a61d-a652d097fe2f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "9d34b7f3-89c9-47f3-8092-18c1527151b0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# code to apply the already-computed random forest photo-z's to a catalog,\n",
+    "#  dealing with NaNs \n",
+    "\n",
+    "def apply_rf(catalog,regrf,imputer):\n",
+    "    catalog.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "\n",
+    "    u_mag = catalog['u']\n",
+    "    g_mag = catalog['g']\n",
+    "    r_mag = catalog['r']\n",
+    "    i_mag = catalog['i']\n",
+    "    z_mag = catalog['z']\n",
+    "    y_mag = catalog['y']\n",
+    "\n",
+    "    data_colmag = np.column_stack((u_mag-g_mag, g_mag-r_mag, r_mag-i_mag, \n",
+    "                               i_mag-z_mag, z_mag-y_mag, i_mag))\n",
+    "    clean_colmag = imputer.transform(data_colmag)\n",
+    "    z_phot = regrf.predict(clean_colmag)\n",
+    "    \n",
+    "    return(z_phot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b31830ea-c3d4-493c-a856-4643a7229187",
+   "metadata": {},
+   "source": [
+    "# Below is the code box that actually did all the work before, reproduced here for reference!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "987b24db-7574-4c77-84f1-ce8050824b76",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# set up arrays for everything\n",
+    "#CHANGE COMMENTED OUT LINES TO LOOK AT MORE YEARS OR MORE BANDS!!!!!!!\n",
+    "#years = np.arange(1,11)\n",
+    "years = np.arange(1,11)\n",
+    "ilims = 25.3+2.5/2.0*np.log10(years/10)\n",
+    "\n",
+    "n_m5 = 11\n",
+    "\n",
+    "#bands = ['u']\n",
+    "bands = ['u','g','r','i','z','y','ugrizy']\n",
+    "\n",
+    "deltas=np.linspace(-0.5,0.5,11)\n",
+    "#deltas = deltas[0:2]\n",
+    "\n",
+    "minzs=np.arange(6)*0.2\n",
+    "maxzs=minzs + 0.2\n",
+    "\n",
+    "if 0:\n",
+    "# these arrays will contain the results to turn into d<z>/dm5 and dn/dm5\n",
+    "# first index runs over redshift bins; second over bands; third over the delta depth array\n",
+    "    meanzs = np.zeros( (6,len(bands),len(deltas)) )\n",
+    "    densities = np.zeros( (6,len(bands),len(deltas)) )\n",
+    "\n",
+    "#loop over all the files, calculate mean redshift and densities in each bin...\n",
+    "\n",
+    "# outermost loop is over years\n",
+    "    for year_idx,year in enumerate(years):\n",
+    "        print(year)\n",
+    "        yearstring =np.array2string(year)\n",
+    "    # degrade the catalog to have noise appropriate to year\n",
+    "        tmp = degrade_catalog(df_full,year)\n",
+    "        tmp.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "        cleantmp = tmp.dropna()\n",
+    "    # train the random forest on the degraded catalog\n",
+    "    # skip this when rerunning while testing\n",
+    "        rf_year,imputer_year = train_rf(cleantmp)\n",
+    "\n",
+    "# next loop is over bands    \n",
+    "        for band_idx,band in enumerate(bands):\n",
+    "            for delta_idx,delta in enumerate(deltas):\n",
+    "                binstring = np.array2string(np.array(delta_idx + 1))\n",
+    "                file=glob.glob('/pscratch/sd/q/qhang/roman-rubin-sims/nonuniform-maf/Y'+yearstring+\n",
+    "                            '/roman-rubin_sample-m5-'+band+'-bin-'+binstring+'.pkl')\n",
+    "                tmp = pandas.read_pickle(file[0])\n",
+    "                tmp['redshift'] = df_full['redshift']\n",
+    "                tmp = tmp[ tmp['i'] < ilims[year_idx] ]\n",
+    "            \n",
+    "                zphot = apply_rf(tmp,rf_year,imputer_year)\n",
+    "                plt.hist(zphot,bins=100,histtype='step')\n",
+    "                tmp['zphot'] = zphot\n",
+    "# now loop over the redshift bins to assign means and counts for each bin            \n",
+    "                for z_idx,minz in enumerate(minzs):\n",
+    "                    inbin = np.logical_and(tmp['zphot'] > minz,tmp['zphot'] < maxzs[z_idx])\n",
+    "                    meanzs[z_idx, band_idx, delta_idx]=np.mean(tmp['redshift'][inbin])\n",
+    "                    densities[z_idx, band_idx, delta_idx]=np.sum(inbin)\n",
+    "        outmeanfile = 'meanzsy'+yearstring+'.pkl'\n",
+    "        outdensityfile = 'densityy'+yearstring+'.pkl'\n",
+    "        pickle.dump(meanzs, open(outmeanfile, \"wb\"))\n",
+    "        pickle.dump(densities, open(outdensityfile, \"wb\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "58c0f954-35be-408a-a6fc-754490a90a83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bands.pkl\t  densityy3.pkl  densityy9.pkl\t meanzsy2.pkl  meanzsy8.pkl\n",
+      "deltas.pkl\t  densityy4.pkl  hgb_model.pkl\t meanzsy3.pkl  meanzsy9.pkl\n",
+      "densityderiv.pkl  densityy5.pkl  maxzs.pkl\t meanzsy4.pkl  minzs.pkl\n",
+      "densityy10.pkl\t  densityy6.pkl  meanzderiv.pkl  meanzsy5.pkl  years.pkl\n",
+      "densityy1.pkl\t  densityy7.pkl  meanzsy10.pkl\t meanzsy6.pkl\n",
+      "densityy2.pkl\t  densityy8.pkl  meanzsy1.pkl\t meanzsy7.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls *.pkl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f2d826c-1e07-405c-8da7-c958f3d9b942",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "2abe31f9-47b6-4113-bb33-b22b152f3cc4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#meanzs = np.zeros( (6,len(bands),len(deltas)) )\n",
+    "\n",
+    "tmp = pandas.read_pickle('meanzsy1.pkl')\n",
+    "for i in np.arange(1,6):\n",
+    "    plt.plot(deltas,tmp[i,0,:]-tmp[i,0,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6)\n",
+    "    fitpoly=np.polynomial.polynomial.polyfit(deltas,tmp[i,0,:]-tmp[i,0,6],2)\n",
+    "#    print(fitpoly)\n",
+    "    plt.plot(deltas,np.polynomial.polynomial.polyval(deltas,fitpoly,tensor=True),linestyle='dashed')\n",
+    "    plt.xlabel('delta m5')\n",
+    "    plt.ylabel('delta <z>')\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "2ee3c50b-88c5-41a9-a2e4-9b720ee63e4b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmp = pandas.read_pickle('meanzsy1.pkl')\n",
+    "\n",
+    "for i in np.arange(1,6):\n",
+    "    sumshift = tmp[i,0,:]+tmp[i,1,:]+tmp[i,2,:]+tmp[i,3,:]+tmp[i,4,:]+tmp[i,5,:]\n",
+    "    plt.plot(deltas,tmp[i,6,:]-tmp[i,6,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6)\n",
+    "    plt.plot(deltas,sumshift-sumshift[6],label = 'z bin '+np.array2string((i+1)),alpha=0.6,linestyle='dashed')\n",
+    "\n",
+    "#    fitpoly=np.polynomial.polynomial.polyfit(deltas,tmp[i,0,:]-tmp[i,0,6],2)\n",
+    "#    plt.plot(deltas,np.polynomial.polynomial.polyval(deltas,fitpoly,tensor=True),linestyle='dashed')\n",
+    "    plt.xlabel('delta m5')\n",
+    "    plt.ylabel('delta <z>')\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "3bdda995-d2d9-4559-af32-16ecb5928059",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#meanzs = np.zeros( (6,len(bands),len(deltas)) )\n",
+    "\n",
+    "tmpd = pandas.read_pickle('densityy1.pkl')\n",
+    "for i in np.arange(1,6):\n",
+    "    plt.plot(deltas,(tmpd[i,0,:]-tmpd[i,0,6])/tmpd[i,0,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6)\n",
+    "    fitpoly=np.polynomial.polynomial.polyfit(deltas,(tmpd[i,0,:]-tmpd[i,0,6])/tmpd[i,0,6],2)\n",
+    "    plt.plot(deltas,np.polynomial.polynomial.polyval(deltas,fitpoly,tensor=True),linestyle='dashed')\n",
+    "    plt.xlabel('delta m5')\n",
+    "    plt.ylabel('delta n/n')\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "0c7ba831-b6d4-4bdc-94d6-5caafdde4716",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmpd = pandas.read_pickle('densityy1.pkl')\n",
+    "\n",
+    "for i in np.arange(1,6):\n",
+    "    sumshift = tmpd[i,0,:]/tmpd[i,0,6]+tmpd[i,1,:]/tmpd[i,1,6]+tmpd[i,2,:]/tmpd[i,2,6] + \\\n",
+    "         tmpd[i,3,:]/tmpd[i,3,6]+tmpd[i,4,:]/tmpd[i,4,6]+tmpd[i,5,:]/tmpd[i,5,6]\n",
+    "    \n",
+    "    plt.plot(deltas,(tmpd[i,6,:]-tmpd[i,6,6])/tmpd[i,6,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6)\n",
+    "    plt.plot(deltas,(sumshift-sumshift[6]),label = 'z bin '+np.array2string((i+1)),alpha=0.6,linestyle='dashed')\n",
+    "\n",
+    "#    fitpoly=np.polynomial.polynomial.polyfit(deltas,tmp[i,0,:]-tmp[i,0,6],2)\n",
+    "#    plt.plot(deltas,np.polynomial.polynomial.polyval(deltas,fitpoly,tensor=True),linestyle='dashed')\n",
+    "    plt.xlabel('delta m5')\n",
+    "    plt.ylabel('delta n/n')\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "f0021007-651b-41ae-8b44-fe4b5329e804",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#meanzs = np.zeros( (6,len(bands),len(deltas)) )\n",
+    "\n",
+    "tmp = pandas.read_pickle('meanzsy1.pkl')\n",
+    "tmp10 = pandas.read_pickle('meanzsy10.pkl')\n",
+    "\n",
+    "for i in np.arange(1,6):\n",
+    "    plt.plot(deltas,tmp[i,0,:]-tmp[i,0,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6)\n",
+    "    plt.plot(deltas,tmp10[i,0,:]-tmp10[i,0,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6,linestyle='dashed')\n",
+    "    plt.xlabel('delta m5')\n",
+    "    plt.ylabel('delta <z>')\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c74aaf2a-0376-4aaf-990a-326ae6ccbeda",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# This is the code box that does all the new work of fitting derivatives!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "id": "7472c833-fd12-4e36-bbaf-7771be379f57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "years = [1,2,3,4,5,6,7,8,9,10]\n",
+    "nyears = len(years)\n",
+    "nbands = len(bands)\n",
+    "nbins = 5\n",
+    "meanzderiv = np.zeros((nyears,nbands,nbins))\n",
+    "densityderiv = np.zeros((nyears,nbands,nbins))\n",
+    "\n",
+    "for year_idx,year in enumerate(years):\n",
+    "    yearstring = np.array2string(np.array(year))\n",
+    "    meanzs = pandas.read_pickle('meanzsy'+yearstring+'.pkl')\n",
+    "    densitys = pandas.read_pickle('densityy'+yearstring+'.pkl')\n",
+    "    for band_idx,band in enumerate(bands):\n",
+    "        for bin_idx in np.arange(1,6):\n",
+    "            fitpoly=np.polynomial.polynomial.polyfit(deltas,\n",
+    "                meanzs[bin_idx,band_idx,:]-meanzs[bin_idx,band_idx,6],2)\n",
+    "            meanzderiv[year_idx,band_idx,bin_idx-1] = fitpoly[1]\n",
+    "            fitpolyd=np.polynomial.polynomial.polyfit(deltas,\n",
+    "                (densitys[bin_idx,band_idx,:]/densitys[bin_idx,band_idx,6]-1),2)\n",
+    "            densityderiv[year_idx,band_idx,bin_idx-1] = fitpolyd[1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "f8796c5a-334c-48c4-888d-a3fd0a8605c5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'year')"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(years,meanzderiv[:,6,0],label = '0.2-0.4')\n",
+    "plt.plot(years,meanzderiv[:,6,1],label = '0.4-0.6')\n",
+    "plt.plot(years,meanzderiv[:,6,2],label = '0.6-0.8')\n",
+    "plt.plot(years,meanzderiv[:,6,3],label = '0.8-1.0')\n",
+    "plt.plot(years,meanzderiv[:,6,4],label = '1.0-1.2')\n",
+    "plt.legend()\n",
+    "plt.ylabel(r'd<z>/d$m_5$')\n",
+    "plt.xlabel('year')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "id": "399045e5-55e1-40a4-b4c5-4bbd997f718e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'year')"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(years,densityderiv[:,6,0],label = '0.2-0.4')\n",
+    "plt.plot(years,densityderiv[:,6,1],label = '0.4-0.6')\n",
+    "plt.plot(years,densityderiv[:,6,2],label = '0.6-0.8')\n",
+    "plt.plot(years,densityderiv[:,6,3],label = '0.8-1.0')\n",
+    "plt.plot(years,densityderiv[:,6,4],label = '1.0-1.2')\n",
+    "plt.legend()\n",
+    "plt.ylabel(r'1 / n dn/d$m_5$')\n",
+    "plt.xlabel('year')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "c35eba1f-2c1a-449e-8ca2-d941d497453b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp = meanzderiv[:,0:6,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "bda29f62-2cbd-445e-bebe-de70cdc50401",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 5)"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#tmp.shape\n",
+    "sumderiv = tmp.sum(axis=1)\n",
+    "sumderiv.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "id": "f4469f8f-b890-44b0-b8d8-b8420dac2b04",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.12275823692907575\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-1.5, 1.5)"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sums = sumderiv.flatten()\n",
+    "covariant = (meanzderiv[:,6,:]).flatten()\n",
+    "plt.plot(covariant,(sums - covariant)/covariant,'b.')\n",
+    "plt.xlabel('u+g+r+i+z+y fully covariant derivative')\n",
+    "ylabel(r'(sum of derivs. - covariant deriv.) / covariant deriv.')\n",
+    "print(np.median(-(covariant - sums)/covariant))\n",
+    "plt.title('<z>')\n",
+    "plt.ylim(-1.5,1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "id": "7d458418-c1eb-46fe-8c66-88634504a232",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.058799512450450384\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmp = densityderiv[:,0:6,1:5]\n",
+    "\n",
+    "sumderiv = tmp.sum(axis=1)\n",
+    "sumderiv.shape\n",
+    "\n",
+    "sums = sumderiv.flatten()\n",
+    "covariant = (densityderiv[:,6,1:5]).flatten()\n",
+    "plt.plot(covariant,(sums - covariant)/covariant,'b.')\n",
+    "plt.xlabel('u+g+r+i+z+y fully covariant derivative')\n",
+    "ylabel(r'(sum of derivs. - covariant deriv.) / covariant deriv.')\n",
+    "plt.title('density')\n",
+    "print(np.median(-(covariant - sums)/covariant))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "b985598e-3314-4124-be1a-722e2baa933c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.06579563528202678"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.std((sums - covariant)/covariant)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "id": "047c1dab-3b0a-433e-ac8f-f0459857332d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 6, 4)"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "b58badac-ff24-4be8-b289-03bc50b22961",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#meanzderiv: axes are ((nyears,nbands,nbins))\n",
+    "#densityderiv: axes are ((nyears,nbands,nbins))\n",
+    "\n",
+    "# array of the years used (corresponding to first axis of the results arrays)\n",
+    "pickle.dump(years, open('years.pkl', \"wb\"))\n",
+    "\n",
+    "# array of the bands used (corresponding to second axis)\n",
+    "pickle.dump(bands, open('bands.pkl', \"wb\"))\n",
+    "\n",
+    "minzs=np.linspace(0.2, 1.0,5)\n",
+    "maxzs=minzs+0.2\n",
+    "\n",
+    "# array of the minz of each redshift bin  (corresponding to third axis)\n",
+    "pickle.dump(minzs, open('minzs.pkl', \"wb\"))\n",
+    "\n",
+    "# array of the maxz of each redshift bin  (corresponding to third axis)\n",
+    "pickle.dump(maxzs, open('maxzs.pkl', \"wb\"))\n",
+    "\n",
+    "# array of the derivative of <z> with respect to m5, for each combination\n",
+    "#    of year, band, and bin number\n",
+    "# i.e. axes are ((nyears,nbands,nbins))\n",
+    "pickle.dump(meanzderiv, open('meanzderiv.pkl', \"wb\"))\n",
+    "\n",
+    "# array of the logatiyhmic derivative of n with respect to m5 \n",
+    "# (i.e., 1/n * dn/dm5), for each combination\n",
+    "#    of year, band, and bin number\n",
+    "# i.e. axes are ((nyears,nbands,nbins))\n",
+    "pickle.dump(densityderiv, open('densityderiv.pkl', \"wb\"))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "992916e8-66bd-419d-8305-370efddefdc4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "203d9dd0-29cc-4ea6-90d2-06d13671575a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "1e4cc1f0-ca50-46f7-9316-dbdf6f33f152",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/global/u1/j/janewman/uniformity\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "e8cace31-3e3b-4854-997a-45fa6e0c76ae",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!chmod a+r *.*\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "2393799d-4a8d-406f-bba6-f38fc98dbe95",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!chmod g+x ../../janewman"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "8e0dc97b-e49f-4e17-8334-f591c8b5259d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 1392\n",
+      "  8 drwxrwxr--  3 janewman janewman   4096 Mar  8 00:04 ./\n",
+      "  8 drwxr-xr-x 23 janewman desi       4096 Mar  7 12:29 ../\n",
+      "  1 -rw-rw-r--  1 janewman janewman     49 Mar  7 23:04 bands.pkl\n",
+      "  1 -rw-rw-r--  1 janewman janewman    235 Mar  7 21:17 deltas.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   2954 Mar  7 23:04 densityderiv.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 23:42 densityy10.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 18:31 densityy1.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 18:54 densityy2.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 19:20 densityy3.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 19:50 densityy4.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 20:23 densityy5.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 21:00 densityy6.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 21:37 densityy7.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 22:18 densityy8.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 22:58 densityy9.pkl\n",
+      "364 -rw-rw-r--  1 janewman janewman 369355 Mar  2 23:01 hgb_model.pkl\n",
+      "  1 drwxrwx---  2 janewman janewman    512 Mar  7 13:18 .ipynb_checkpoints/\n",
+      "  1 -rw-rw-r--  1 janewman janewman    187 Mar  7 23:04 maxzs.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   2954 Mar  7 23:04 meanzderiv.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 23:42 meanzsy10.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 18:31 meanzsy1.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 18:54 meanzsy2.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 19:20 meanzsy3.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 19:50 meanzsy4.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 20:23 meanzsy5.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 21:00 meanzsy6.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 21:37 meanzsy7.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 22:18 meanzsy8.pkl\n",
+      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 22:58 meanzsy9.pkl\n",
+      "  1 -rw-rw-r--  1 janewman janewman    187 Mar  7 23:04 minzs.pkl\n",
+      "300 -rw-rw-r--  1 janewman janewman 305531 Mar  8 00:04 simulation_calcs.ipynb\n",
+      "620 -rw-rw-r--  1 janewman janewman 630864 Mar  7 23:05 simulation_photoz_rr.ipynb\n",
+      "  1 -rw-rw-r--  1 janewman janewman     36 Mar  7 23:04 years.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -pasl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2f95128-6411-4307-b0e5-b20a82c1dd9a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "NERSC Python",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/rubin_sim/maf/metrics/uniformity_pkl/years.pkl b/rubin_sim/maf/metrics/uniformity_pkl/years.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..f7228faf25a680efc97a732268dcc17a6313040d
GIT binary patch
literal 36
mcmZo*nJUQu0kKmwycxZjyqUdOyji{3yxF}uyg9wOQuP3BJq82-

literal 0
HcmV?d00001


From 9865169afda0f96985f16289c83b5c2d86d8c489 Mon Sep 17 00:00:00 2001
From: Renee Hlozek <renee.hlozek@gmail.com>
Date: Wed, 10 Apr 2024 06:59:52 -0700
Subject: [PATCH 13/31] updated summary metric

---
 .../maf/metrics/cosmology_summary_metrics.py  | 247 +++++++++++-------
 1 file changed, 148 insertions(+), 99 deletions(-)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 8076be231..3afe44a9f 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -357,7 +357,8 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
             results_sigma8_bias = (sigma8_fit - sigma8_model) / sigma8_error
             return results_sigma8_bias
 
-class UniformMeanzBiasMetricc(BaseMetric):
+class UniformMeanzBiasMetric(BaseMetric):
+    import maf
 
     """This calculates the bias in the weak lensing power given 
        the scatter in the redshift of the tomographic sample
@@ -372,8 +373,9 @@ class UniformMeanzBiasMetricc(BaseMetric):
     Returns
     -------
     result : `float` array
-        The ratio of this bias to the desired DESC y1 upper bound on the bias and the y10 
-        DESC SRD requirement. Desired values are less than 1 by Y10.
+        The ratio of this bias to the desired DESC y1 upper bound on the bias, and the ratio 
+        between the clbias and the y10 DESC SRD requirement. 
+        Desired values are less than 1 by Y10.
 
     Notes
     -----
@@ -382,111 +384,158 @@ class UniformMeanzBiasMetricc(BaseMetric):
     output of Exgalm5_with_cuts.
     """
 
-    def compute_dzfromdm(zbins, band_ind, year):
-        """ This computes the dm/dz relationship calibrated from simulations
-        by Jeff Newmann.
-
-        Parameters
-        ----------
-        zbins : `int`
-            The number of tomographic bins considered. For now this is zbins < 5
-        filter : `str`
-            The assumed filter band 
-
-        Returns
-        -------
-        dzdminterp : `float` 
-            The interpolated value of the derivative dz/dm
-        meanzinterp : `float` array
-            The meanz in each tomographic bin.
+    def __init__(self, filter_list="filters",year=10, n_filters=6,**kwargs):
+        self.year = year
+        self.filter_list = filter_list
+        self.exgal_m5 = ExgalM5(m5_col=m5_col, units=units)
         
-        """
-        import pandas as pd
+        super().__init__(col="metricdata", mask_val=-666, **kwargs)
 
-        filter_list=["u","g","r","i","z","y"]
-        band_ind =filter_list.index(filter)
-        
-        deriv = pd.read_pickle('uniformity_pkl/meanzderiv.pkl')
-        # pkl file of derivatives with 10 years, 7 bands (ugrizY and combined), 5 bins
-        zvals = pd.read_pickle('uniformity_pkl/meanzsy%i.pkl'%(year+1)) 
-        # pkl file of mean z values for a given year over 5 z bins, 7 bands (ugrizY and combined),
-        # for a fixed delta density index (index 5 assumed below is for zero m_5 shift)
-        meanzinterp = zvals[0:zbins,band_ind,5]
-        dzdminterp = np.abs(deriv[year,band_ind,0:zbins])
-
-        return dzdminterp, meanzinterp
-
-    def use_zbins(meanz_vals, figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0]),  figure_9_width=0.2):
-        """ This computes which redshift bands are within the range 
-         specified in https://arxiv.org/pdf/2305.15406.pdf and can safely be used
-        to compute what Cl bias result from z fluctuations caused by rms variations in the m5.
-
-
-        Parameters
-        ----------
-        meanz_vals : `float` array
-            Array of meanz values to be used.
-        
-        Returns
-        -------
-        use_bins : `boolean` array
-            An array of boolean values of length meanz_vals 
-        
-        """
-        max_z_use = np.max(figure_9_mean_z)+2*figure_9_width
-        use_bins = meanz_vals < max_z_use
-        
-        return use_bins
 
-    def compute_Clbias(meanz_vals,scatter_mean_z_values):
-        """ This computes the Cl bias 
-        that results z fluctuations caused by rms variations in the m5.
+    def run(self, data_slice, slice_point=None):
+
+        result = np.empty(1, dtype=[("name", np.str_, 20), ("value", float)])
+        result["name"][0] = "UniformMeanzBiasMetric"
+
+        def compute_dzfromdm(zbins, band_ind, year):
+            """ This computes the dm/dz relationship calibrated from simulations
+            by Jeff Newmann.
+
+            Parameters
+            ----------
+            zbins : `int`
+                The number of tomographic bins considered. For now this is zbins < 5
+            filter : `str`
+                The assumed filter band 
+
+            Returns
+            -------
+            dzdminterp : `float` 
+                The interpolated value of the derivative dz/dm
+            meanzinterp : `float` array
+                The meanz in each tomographic bin.
+            
+            """
+            import pandas as pd
 
-        
+            filter_list=["u","g","r","i","z","y"]
+            band_ind =filter_list.index(filter)
+            
+            deriv = pd.read_pickle('uniformity_pkl/meanzderiv.pkl')
+            # pkl file of derivatives with 10 years, 7 bands (ugrizY and combined), 5 bins
+            zvals = pd.read_pickle('uniformity_pkl/meanzsy%i.pkl'%(year+1)) 
+            # pkl file of mean z values for a given year over 5 z bins, 7 bands (ugrizY and combined),
+            # for a fixed delta density index (index 5 assumed below is for zero m_5 shift)
+            meanzinterp = zvals[0:zbins,band_ind,5]
+            dzdminterp = np.abs(deriv[year,band_ind,0:zbins])
+
+            return dzdminterp, meanzinterp
+
+        def use_zbins(meanz_vals, figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0]),  figure_9_width=0.2):
+            """ This computes which redshift bands are within the range 
+            specified in https://arxiv.org/pdf/2305.15406.pdf and can safely be used
+            to compute what Cl bias result from z fluctuations caused by rms variations in the m5.
+
+
+            Parameters
+            ----------
+            meanz_vals : `float` array
+                Array of meanz values to be used.
+            
+            Returns
+            -------
+            use_bins : `boolean` array
+                An array of boolean values of length meanz_vals 
+            
+            """
+            max_z_use = np.max(figure_9_mean_z)+2*figure_9_width
+            use_bins = meanz_vals < max_z_use
+            
+            return use_bins
 
-        Parameters
-        ----------
-        meanz_vals : `float` array
-            Array of meanz values to be used.
+        def compute_Clbias(meanz_vals,scatter_mean_z_values):
+            """ This computes the Cl bias 
+            that results z fluctuations caused by rms variations in the m5.
 
-        scatter_mean_z_values : `float` array
-            Array of rms values of the z fluctuations
+            
+
+            Parameters
+            ----------
+            meanz_vals : `float` array
+                Array of meanz values to be used.
+
+            scatter_mean_z_values : `float` array
+                Array of rms values of the z fluctuations
 
-        
-        Returns
-        -------
-        clbiasvals : `float` array
-            An array of values of the clbias
             
-        mean_z_values_use :  `float` array
-            An array of the meanz values that are within the interpolation range of 2305.15406
-        
-        Notes
-        ------
-        This interpolates from the Figure 9 in https://arxiv.org/pdf/2305.15406.pdf
-
-        """
-        import numpy as np
-        figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0])
-        figure_9_Clbias =np.array([1e-3, 2e-3, 5e-3, 1.1e-2])
-        figure_9_width=0.2
-        figure_9_mean_z_scatter = 0.02
-
-        mzvals= np.array([float(mz) for mz in meanz_vals])
-        sctz = np.array([float(sz)for sz in scatter_mean_z_values])
+            Returns
+            -------
+            clbiasvals : `float` array
+                An array of values of the clbias
+
+            mean_z_values_use :  `float` array
+                An array of the meanz values that are within the interpolation range of 2305.15406
+            
+            Notes
+            ------
+            This interpolates from the Figure 9 in https://arxiv.org/pdf/2305.15406.pdf
+
+            """
+            import numpy as np
+            figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0])
+            figure_9_Clbias =np.array([1e-3, 2e-3, 5e-3, 1.1e-2])
+            figure_9_width=0.2
+            figure_9_mean_z_scatter = 0.02
+
+            mzvals= np.array([float(mz) for mz in meanz_vals])
+            sctz = np.array([float(sz)for sz in scatter_mean_z_values])
+            
+            fit_res = np.polyfit(figure_9_mean_z, figure_9_Clbias, 2)
+            poly_fit = np.poly1d(fit_res)
+            use_bins = use_zbins(meanz_vals,figure_9_mean_z, figure_9_width)
+
+            mean_z_values_use = mzvals[use_bins]
+            sctz_use = sctz[use_bins]
+
+            Clbias = poly_fit(mean_z_values_use)
+            rescale_fac =  sctz_use / figure_9_mean_z_scatter
+            Clbias *= rescale_fac
+            fit_res_bias = np.polyfit(mean_z_values_use, Clbias, 1)
+            poly_fit_bias = np.poly1d(fit_res_bias)
+
+            clbiasvals = poly_fit_bias(mean_z_values_use)
+            return clbiasvals, mean_z_values_use
+
+        totdz=0
+        avmeanz=0
+        clbiastot=0
+        for filt in self.filter_list:
+            d_s = data_slice[data_slice[self.filter_col] == filt]
+            # calculate the lsstFilter-band coadded depth
+            coadd_depth = self.exgal_m5.run(d_s, slice_point)
+
+            rmsval = np.std(coadd_depth)
+
+            dzdminterp, meanzinterp=compute_dzfromdm(self.zbins, filt,self.year)
+            stdz = [float(np.abs(dz))*float(rmsval) for dz in dzdminterp]
+    
+            clbias, meanz_use = compute_Clbias(meanzinterp,stdz)
+
+            totdz+=[float(st**2) for st in stdz]
+            totclbias+=clbias
+            avmeanz+=meanzinterp
         
-        fit_res = np.polyfit(figure_9_mean_z, figure_9_Clbias, 2)
-        poly_fit = np.poly1d(fit_res)
-        use_bins = use_zbins(meanz_vals,figure_9_mean_z, figure_9_width)
 
-        mean_z_values_use = mzvals[use_bins]
-        sctz_use = sctz[use_bins]
+        y10_req = 0.003
+        y1_goal = 0.013
+
+        clbiastot = np.max(clbias)
+        y10ratio = clbiastot/y10_req
+        y1ratio = clbiastot/y1_goal
 
-        Clbias = poly_fit(mean_z_values_use)
-        rescale_fac =  sctz_use / figure_9_mean_z_scatter
-        Clbias *= rescale_fac
-        fit_res_bias = np.polyfit(mean_z_values_use, Clbias, 1)
-        poly_fit_bias = np.poly1d(fit_res_bias)
+        result["y1ratio"]=y1ratio
+        result["y10ratio"]=y1ratio
+        
+        return result
 
-        clbiasvals = poly_fit_bias(mean_z_values_use)
-        return clbiasvals, mean_z_values_use
\ No newline at end of file
+    
\ No newline at end of file

From 85aced66c7fdf723a432b29c435182402779153e Mon Sep 17 00:00:00 2001
From: Renee Hlozek <renee.hlozek@gmail.com>
Date: Wed, 10 Apr 2024 07:00:29 -0700
Subject: [PATCH 14/31] updated summary metric

---
 rubin_sim/maf/metrics/cosmology_summary_metrics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 3afe44a9f..95811330e 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -2,7 +2,7 @@
     "TotalPowerMetric",
     "StaticProbesFoMEmulatorMetricSimple",
     "TomographicClusteringSigma8biasMetric",
-    "TomographicRedshiftFluctuationbiasMetric",
+    "UniformMeanzBiasMetric",
 )
 
 import warnings

From dd30472d01a2277ab223fc53693411ae40d89760 Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Fri, 12 Apr 2024 02:47:41 -0700
Subject: [PATCH 15/31] working version of FOM ratio metric

---
 .../maf/metrics/cosmology_summary_metrics.py  | 206 +++++++++++++++++-
 1 file changed, 205 insertions(+), 1 deletion(-)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 3be3bbe8b..327967d27 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -10,6 +10,7 @@
 import numpy as np
 from scipy import interpolate
 
+from ..maf_contrib.static_probes_fom_summary_metric import StaticProbesFoMEmulatorMetric
 from .area_summary_metrics import AreaThresholdMetric
 from .base_metric import BaseMetric
 
@@ -252,10 +253,15 @@ def run(self, data_slice, slice_point=None):
         ]
         # should be (nbins, npix)
         data_slice_arr = np.asarray(data_slice_list, dtype=float).T
+        #hp.mollview(data_slice_arr[0])
+        data_slice_arr[data_slice_arr == -666] = (
+            hp.UNSEEN
+        )
         data_slice_arr[~np.isfinite(data_slice_arr)] = (
             hp.UNSEEN
         )  # need to work with TotalPowerMetric and healpix
 
+        
         # measure valid sky fractions and total power
         # (via angular power spectra) in each bin.
         # The original metric returns an array at each slice_point (of the
@@ -282,6 +288,8 @@ def run(self, data_slice, slice_point=None):
             )
             / fskys
         )
+        print('spuriousdensitypowers:', spuriousdensitypowers)
+        print('fskys:', fskys)
 
         def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, power_multiplier):
             """
@@ -315,7 +323,7 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
                 # observations is spurious power  noiseless model
                 totalvar_obs[i, 0] = totalvar_mod[i, 0] + spurious_powers[i] * power_multiplier
 
-                # simple model variance of cell baased on Gaussian covariance
+                # simple model variance of cell based on Gaussian covariance
                 cells_var = 2 * cells_model**2 / (2 * ells + 1) / fskys[i]
                 totalvar_var[i, 0] = np.sum(cells_var * (2 * ells + 1) ** 2)
 
@@ -348,10 +356,206 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
             return results_sigma8_square_bias
 
         else:
+            
             # turn result into bias on sigma8,
             # via change of variable and simple propagation of uncertainty.
             sigma8_fit = sigma8square_fit**0.5
             sigma8_model = sigma8square_model**0.5
             sigma8_error = 0.5 * sigma8square_error * sigma8_fit / sigma8square_fit
             results_sigma8_bias = (sigma8_fit - sigma8_model) / sigma8_error
+            print(sigma8square_model, sigma8square_fit, sigma8square_error, results_sigma8_square_bias)
+            print(sigma8_model, sigma8_fit, sigma8_error, results_sigma8_bias)
             return results_sigma8_bias
+
+
+class UniformAreaFoMFractionMetric(BaseMetric):
+    """?
+    Run as summary metric on RIZDetectionCoaddExposureTime.
+
+    Parameters
+    ----------
+    ?
+
+    Returns
+    -------
+    ?
+
+    Notes
+    -----
+    ?
+    """
+
+    def __init__(
+            self,
+            min_exptime=1000,
+            verbose=True, 
+            **kwargs,
+    ):
+        super().__init__(col="metricdata", **kwargs)
+        # Set mask_val, so that we receive metric_values.filled(mask_val)
+        self.min_exptime = min_exptime
+        self.mask_val = hp.UNSEEN
+        self.verbose = verbose 
+
+    def run(self, data_slice, slice_point=None):
+
+        # cheap way to cut sky
+        data_slice[data_slice['metricdata'] < self.min_exptime] = (hp.UNSEEN, )
+        
+        # Let's make code that pulls out the northern/southern galactic regions, and gets statistics of the footprint by region.
+        from astropy import units as u
+        from astropy.coordinates import SkyCoord
+        def _is_ngp(ra, dec):
+            c = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')
+            lat = c.galactic.b.deg
+            return lat >= 0
+
+        def get_stats_by_region(use_map, nside, maskval=0, region='all'):
+            if region not in ['all','north','south']:
+                raise ValueError('Invalid region %s'%region)
+            to_use = (use_map > maskval)
+
+            if region != 'all':
+                # Find the north/south part of the map as requested
+                npix = hp.nside2npix(nside)
+                ra, dec = hp.pix2ang(hp.npix2nside(npix), range(npix), lonlat=True)
+                ngp_mask = _is_ngp(ra, dec)
+                if region=='north':
+                    reg_mask = ngp_mask
+                else: 
+                    reg_mask = ~ngp_mask
+                to_use = to_use & reg_mask
+
+            # Calculate the desired stats
+            from scipy.stats import median_abs_deviation
+            reg_mad = median_abs_deviation(use_map[to_use])
+            reg_median = np.median(use_map[to_use])
+            reg_std = np.std(use_map[to_use])
+
+            # Return the values
+            return(reg_mad, reg_median, reg_std)
+
+        def has_stripes(data_slice, nside, threshold=0.1):
+            """
+            A utility to find whether a particular routine has stripey features in the exposure time map.
+            """
+            # Analyze the exposure time map to get MAD, median, std in north/south.
+            mad = {}
+            med = {}
+            frac_scatter = {}
+            regions = ['north', 'south']
+            for region in regions:
+                mad[region], med[region], _ = get_stats_by_region(data_slice, nside, region=region)
+                frac_scatter[region] = mad[region]/med[region]
+            test_statistic = np.abs(frac_scatter['north']/frac_scatter['south']-1)
+            if test_statistic < threshold:
+                return False
+            else:
+                return True
+    
+        def apply_clustering(clustering_data):
+            # A thin wrapper around sklearn routines (can swap out which one we are using systematically).
+            # We fix parameters like `n_clusters` since realistically we know for rolling that we should expect 2 clusters.
+            #from sklearn.cluster import SpectralClustering
+            #clustering = SpectralClustering(n_clusters=2, assign_labels='discretize', random_state=0).fit(clustering_data)
+            from sklearn.cluster import KMeans
+            clustering = KMeans(n_clusters=2, random_state=0, n_init="auto").fit(clustering_data)
+            labels = clustering.labels_ + 1
+            return labels
+        
+        def expand_labels(depth_map, labels, maskval=0):
+            # A utility to apply the labels from a masked version of a depth map back to the entire depth map.
+            expanded_labels = np.zeros(hp.nside2npix(nside))
+            cutval = maskval+0.1
+            expanded_labels[depth_map>cutval] = labels
+            return expanded_labels
+        def get_area_stats(depth_map, labels, maskval=0, n_clusters=2):
+            # A routine to get some statistics of the clustering: area fractions, median map values
+            expanded_labels = expand_labels(depth_map, labels, maskval=maskval)
+            cutval = maskval+0.1
+            area_frac = []
+            med_val = []
+            for i in range(n_clusters):
+                new_map = depth_map.copy()
+                new_map[expanded_labels!=i+1] = maskval
+                this_area_frac = len(new_map[new_map>cutval])/len(depth_map[depth_map>cutval])
+                this_med_val = np.median(new_map[new_map>cutval])
+                area_frac.append(this_area_frac)
+                med_val.append(this_med_val)
+            return area_frac, med_val
+        def show_clusters(depth_map, labels, maskval=0, n_clusters=2, min=500, max=3000):
+            # A routine to show the clusters found by the unsupervised clustering algorithm (start with original map then 2 clusters).
+            expanded_labels = expand_labels(depth_map, labels, maskval=maskval)
+            hp.visufunc.mollview(depth_map, min=min, max=max)
+            for i in range(n_clusters):
+                new_map = depth_map.copy()
+                new_map[expanded_labels!=i+1] = maskval
+                hp.visufunc.mollview(new_map, min=min, max=max)
+            return get_area_stats(depth_map, labels, maskval=maskval, n_clusters=n_clusters)
+
+        def make_clustering_dataset(depth_map, maskval=0, priority_fac=0.9, nside=64):
+            # A utility routine to get a dataset for unsupervised clustering.  Note:
+            # - We want the unmasked regions of the depth map only.
+            # - We assume masked regions are set to `maskval`, and cut 0.1 magnitudes above that.
+            # - We really want it to look at depth fluctuations.  So, we have to rescale the
+            #   RA/dec dimensions to avoid them being prioritized because their values are larger and
+            #   have more variation than depth.  Currently we rescale RA/dec such that their
+            #   standard deviations are 1-priority_fac times the standard deviation of the depth map.
+            #   That's why priority_fac is a tunable parameter; it should be between 0 and 1
+            if priority_fac < 0 or priority_fac >= 1:
+                raise ValueError("priority_fac must lie between 0 and 1")
+            theta, phi = hp.pixelfunc.pix2ang(nside, ipix=np.arange(hp.nside2npix(nside)))
+            # theta is 0 at the north pole, pi/2 at equator, pi at south pole; phi maps to RA
+            ra = np.rad2deg(phi)
+            dec = np.rad2deg(0.5 * np.pi - theta)
+
+            # Make a 3D numpy array containing the unmasked regions, including a rescaling factor to prioritize the depth
+            n_unmasked = len(depth_map[depth_map > 0.1])
+            my_data = np.zeros((n_unmasked, 3))
+            cutval = 0.1 + maskval
+            my_data[:, 0] = ra[depth_map > cutval] * (1 - priority_fac) * np.std(
+                depth_map[depth_map > cutval]) / np.std(ra[depth_map > cutval])
+            my_data[:, 1] = dec[depth_map > cutval] * (1 - priority_fac) * np.std(
+                depth_map[depth_map > cutval]) / np.std(dec[depth_map > cutval])
+            my_data[:, 2] = depth_map[depth_map > cutval]
+            return my_data
+
+            # Check for stripiness
+
+        nside = hp.npix2nside(data_slice['metricdata'].size)
+        self.threebyTwoSummary = StaticProbesFoMEmulatorMetric(nside=nside, metric_name="3x2ptFoM")
+        stripes = has_stripes(data_slice['metricdata'], nside)
+
+        if not stripes:
+            return 1
+        else:
+            # Do the clustering if we got to this point
+            if self.verbose: print("Verbose mode - Carrying out the clustering exercise for this map")
+            clustering_data = make_clustering_dataset(data_slice['metricdata'], nside=nside)
+            labels = apply_clustering(clustering_data)
+            area_frac, med_val = get_area_stats(data_slice['metricdata'], labels)
+            if self.verbose:
+                print("Verbose mode - showing original map and clusters identified for this map")
+                show_clusters(data_slice['metricdata'], labels)
+                print("Area fractions", area_frac)
+                print("Median exposure time values", med_val)
+                print("Median exposure time ratio", np.max(med_val)/np.min(med_val))
+                print("Verbose mode - proceeding with area cuts")
+            # Get the FoM without/with cuts.  We want to check the FoM for each area, if we're doing cuts, and
+            # return the higher one. This will typically be for the larger area, but not necessarily, if the smaller area
+            # is deeper.
+            expanded_labels = expand_labels(data_slice['metricdata'], labels)
+            my_hpid_1 = np.where(labels == 1)[0]
+            my_hpid_2 = np.where(labels == 2)[0]
+            from copy import copy
+            # copies need to be recarrays
+            data_slice_subset_2 = copy(data_slice)
+            data_slice_subset_1 = copy(data_slice)
+            data_slice[my_hpid_2] = (hp.UNSEEN, )
+            data_slice[my_hpid_1] = (hp.UNSEEN, ) 
+            fom1 = self.threebyTwoSummary.run(data_slice_subset_1)
+            fom2 = self.threebyTwoSummary.run(data_slice_subset_2)
+            fom = np.max((fom1, fom2))
+            fom_total = self.threebyTwoSummary.run(data_slice)
+            return fom / fom_total
+

From a42ea4cd597c458597a512110c849e39fda3537f Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Fri, 12 Apr 2024 02:48:47 -0700
Subject: [PATCH 16/31] fixed imports

---
 rubin_sim/maf/maf_contrib/lss_metrics.py                    | 4 +++-
 .../lss_obs_strategy/galaxy_counts_metric_extended.py       | 4 +++-
 rubin_sim/maf/maf_contrib/lv_dwarfs/lv_dwarfs_metrics.py    | 6 +++++-
 rubin_sim/maf/maf_contrib/young_stellar_objects_metric.py   | 3 ++-
 4 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/rubin_sim/maf/maf_contrib/lss_metrics.py b/rubin_sim/maf/maf_contrib/lss_metrics.py
index 287b1e83b..ebb317863 100644
--- a/rubin_sim/maf/maf_contrib/lss_metrics.py
+++ b/rubin_sim/maf/maf_contrib/lss_metrics.py
@@ -4,7 +4,9 @@
 import numpy as np
 import scipy
 
-from rubin_sim.maf.metrics import BaseMetric, ExgalM5
+from rubin_sim.maf.metrics.base_metric import BaseMetric
+from rubin_sim.maf.metrics.simple_metrics import Coaddm5Metric
+from rubin_sim.maf.metrics.exgal_m5 import ExgalM5
 
 
 class GalaxyCountsMetric(BaseMetric):
diff --git a/rubin_sim/maf/maf_contrib/lss_obs_strategy/galaxy_counts_metric_extended.py b/rubin_sim/maf/maf_contrib/lss_obs_strategy/galaxy_counts_metric_extended.py
index 894ec1370..ba57149f8 100644
--- a/rubin_sim/maf/maf_contrib/lss_obs_strategy/galaxy_counts_metric_extended.py
+++ b/rubin_sim/maf/maf_contrib/lss_obs_strategy/galaxy_counts_metric_extended.py
@@ -29,7 +29,9 @@
     power_law_const_a,
     power_law_const_b,
 )
-from rubin_sim.maf.metrics import BaseMetric, Coaddm5Metric, ExgalM5
+from rubin_sim.maf.metrics.base_metric import BaseMetric
+from rubin_sim.maf.metrics.simple_metrics import Coaddm5Metric
+from rubin_sim.maf.metrics.exgal_m5 import ExgalM5
 
 
 class GalaxyCountsMetricExtended(BaseMetric):
diff --git a/rubin_sim/maf/maf_contrib/lv_dwarfs/lv_dwarfs_metrics.py b/rubin_sim/maf/maf_contrib/lv_dwarfs/lv_dwarfs_metrics.py
index 8cf8f3e46..b0de83835 100644
--- a/rubin_sim/maf/maf_contrib/lv_dwarfs/lv_dwarfs_metrics.py
+++ b/rubin_sim/maf/maf_contrib/lv_dwarfs/lv_dwarfs_metrics.py
@@ -14,7 +14,11 @@
 from rubin_scheduler.data import get_data_dir
 
 from rubin_sim.maf.maf_contrib.lss_obs_strategy import GalaxyCountsMetricExtended
-from rubin_sim.maf.metrics import BaseMetric, ExgalM5, StarDensityMetric
+
+from rubin_sim.maf.metrics.base_metric import BaseMetric
+from rubin_sim.maf.metrics.simple_metrics import Coaddm5Metric
+from rubin_sim.maf.metrics.exgal_m5 import ExgalM5
+from rubin_sim.maf.metrics.star_density import StarDensityMetric
 from rubin_sim.maf.slicers import UserPointsSlicer
 
 
diff --git a/rubin_sim/maf/maf_contrib/young_stellar_objects_metric.py b/rubin_sim/maf/maf_contrib/young_stellar_objects_metric.py
index de6ad6bb9..fe55df64b 100644
--- a/rubin_sim/maf/maf_contrib/young_stellar_objects_metric.py
+++ b/rubin_sim/maf/maf_contrib/young_stellar_objects_metric.py
@@ -9,7 +9,8 @@
 import scipy.integrate as integrate
 
 from rubin_sim.maf.maps import DustMap, DustMap3D, StellarDensityMap
-from rubin_sim.maf.metrics import BaseMetric, CrowdingM5Metric
+from rubin_sim.maf.metrics.base_metric import BaseMetric
+from rubin_sim.maf.metrics.crowding_metric import CrowdingM5Metric
 from rubin_sim.phot_utils import DustValues
 
 

From caf2e9f423f2f2518c37f3e1d45179528d3a3860 Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Fri, 12 Apr 2024 07:11:56 -0700
Subject: [PATCH 17/31] updated models to fix import error

---
 rubin_sim/maf/metrics/tomography_models.py | 600 ++++++++++-----------
 1 file changed, 300 insertions(+), 300 deletions(-)

diff --git a/rubin_sim/maf/metrics/tomography_models.py b/rubin_sim/maf/metrics/tomography_models.py
index 97d21f276..681cd7a8a 100644
--- a/rubin_sim/maf/metrics/tomography_models.py
+++ b/rubin_sim/maf/metrics/tomography_models.py
@@ -96,52 +96,52 @@
             {
                 "cst": 0.0,
                 "u": 0.043962974958662145,
-                "g": 0.043962974958662145,
-                "r": 0.043962974958662145,
-                "i": 0.043962974958662145,
-                "z": 0.043962974958662145,
-                "y": 0.043962974958662145,
-                "ugrizy": 0.043962974958662145,
+                "g": 0.02066177878696687,
+                "r": 0.0018693130274623476,
+                "i": 0.04276675705247136,
+                "z": 0.08638732823394031,
+                "y": 0.0657505127473751,
+                "ugrizy": 0.2472461161462007,
             },
             {
                 "cst": 0.0,
-                "u": 0.034752650841693544,
+                "u": 0.056364745687503465,
                 "g": 0.034752650841693544,
-                "r": 0.034752650841693544,
-                "i": 0.034752650841693544,
-                "z": 0.034752650841693544,
-                "y": 0.034752650841693544,
-                "ugrizy": 0.034752650841693544,
+                "r": -0.0166573314175779,
+                "i": 0.03094286532175563,
+                "z": 0.06502345706021054,
+                "y": 0.05768411667472194,
+                "ugrizy": 0.22095351609058403,
             },
             {
                 "cst": 0.0,
-                "u": 0.033787326976188484,
-                "g": 0.033787326976188484,
+                "u": 0.038530883579453466,
+                "g": 0.04538858565704199,
                 "r": 0.033787326976188484,
-                "i": 0.033787326976188484,
-                "z": 0.033787326976188484,
-                "y": 0.033787326976188484,
-                "ugrizy": 0.033787326976188484,
+                "i": -0.040733526061764155,
+                "z": 0.025969868799574303,
+                "y": 0.03702233496870881,
+                "ugrizy": 0.1250655075680508,
             },
             {
                 "cst": 0.0,
-                "u": -0.058749555322422424,
-                "g": -0.058749555322422424,
-                "r": -0.058749555322422424,
+                "u": -0.02161852795176933,
+                "g": 0.0222336809395544,
+                "r": 0.05908765697086321,
                 "i": -0.058749555322422424,
-                "z": -0.058749555322422424,
-                "y": -0.058749555322422424,
-                "ugrizy": -0.058749555322422424,
+                "z": 0.0188797224519434,
+                "y": 0.035785840616014225,
+                "ugrizy": 0.046115493661878317,
             },
             {
                 "cst": 0.0,
-                "u": -0.08739685057046752,
-                "g": -0.08739685057046752,
-                "r": -0.08739685057046752,
-                "i": -0.08739685057046752,
+                "u": -0.07624090622672856,
+                "g": -0.060659802088035716,
+                "r": -0.01676696950407968,
+                "i": -0.04141021293176227,
                 "z": -0.08739685057046752,
-                "y": -0.08739685057046752,
-                "ugrizy": -0.08739685057046752,
+                "y": -0.033876500857633024,
+                "ugrizy": -0.2762112449739721,
             },
         ],
     },
@@ -196,52 +196,52 @@
             {
                 "cst": 0.0,
                 "u": 0.055492947394854004,
-                "g": 0.055492947394854004,
-                "r": 0.055492947394854004,
-                "i": 0.055492947394854004,
-                "z": 0.055492947394854004,
-                "y": 0.055492947394854004,
-                "ugrizy": 0.055492947394854004,
+                "g": 0.012699380270242842,
+                "r": 0.007530749024132768,
+                "i": 0.03513585435458864,
+                "z": 0.07561295665384882,
+                "y": 0.0736592982969794,
+                "ugrizy": 0.24374481489942412,
             },
             {
                 "cst": 0.0,
-                "u": 0.029608214159172044,
+                "u": 0.046332184492532485,
                 "g": 0.029608214159172044,
-                "r": 0.029608214159172044,
-                "i": 0.029608214159172044,
-                "z": 0.029608214159172044,
-                "y": 0.029608214159172044,
-                "ugrizy": 0.029608214159172044,
+                "r": -0.006075560506575478,
+                "i": 0.03333244846260029,
+                "z": 0.06027754177286897,
+                "y": 0.06506093356954053,
+                "ugrizy": 0.2084760154143301,
             },
             {
                 "cst": 0.0,
-                "u": 0.01704700362928206,
-                "g": 0.01704700362928206,
+                "u": 0.03334437333732201,
+                "g": 0.032573705405151664,
                 "r": 0.01704700362928206,
-                "i": 0.01704700362928206,
-                "z": 0.01704700362928206,
-                "y": 0.01704700362928206,
-                "ugrizy": 0.01704700362928206,
+                "i": -0.02614338304111813,
+                "z": 0.03924255373249242,
+                "y": 0.041759198393945374,
+                "ugrizy": 0.13123919055353145,
             },
             {
                 "cst": 0.0,
-                "u": -0.061409368487154156,
-                "g": -0.061409368487154156,
-                "r": -0.061409368487154156,
+                "u": 0.0030051697578186262,
+                "g": 0.03783532078713637,
+                "r": 0.06049801557289073,
                 "i": -0.061409368487154156,
-                "z": -0.061409368487154156,
-                "y": -0.061409368487154156,
-                "ugrizy": -0.061409368487154156,
+                "z": 0.03808865014429153,
+                "y": 0.02731197586031816,
+                "ugrizy": 0.10545655695602359,
             },
             {
                 "cst": 0.0,
-                "u": -0.050114709867968066,
-                "g": -0.050114709867968066,
-                "r": -0.050114709867968066,
-                "i": -0.050114709867968066,
+                "u": -0.02882987071992067,
+                "g": -0.013641440195713459,
+                "r": 0.03418483342474093,
+                "i": -0.018501441200469277,
                 "z": -0.050114709867968066,
-                "y": -0.050114709867968066,
-                "ugrizy": -0.050114709867968066,
+                "y": 0.021268476475702833,
+                "ugrizy": -0.049794732397725236,
             },
         ],
     },
@@ -296,52 +296,52 @@
             {
                 "cst": 0.0,
                 "u": 0.04571336657551873,
-                "g": 0.04571336657551873,
-                "r": 0.04571336657551873,
-                "i": 0.04571336657551873,
-                "z": 0.04571336657551873,
-                "y": 0.04571336657551873,
-                "ugrizy": 0.04571336657551873,
+                "g": 0.01002695291717099,
+                "r": 0.006381748317232131,
+                "i": 0.03346386406073478,
+                "z": 0.07000026154199343,
+                "y": 0.07122103158922388,
+                "ugrizy": 0.2276375817331448,
             },
             {
                 "cst": 0.0,
-                "u": 0.03578384485292078,
+                "u": 0.05405844464453291,
                 "g": 0.03578384485292078,
-                "r": 0.03578384485292078,
-                "i": 0.03578384485292078,
-                "z": 0.03578384485292078,
-                "y": 0.03578384485292078,
-                "ugrizy": 0.03578384485292078,
+                "r": -0.006971742709660895,
+                "i": 0.03055814323956491,
+                "z": 0.0608644383751681,
+                "y": 0.06285483616682519,
+                "ugrizy": 0.2191177469764914,
             },
             {
                 "cst": 0.0,
-                "u": 0.023135003462698395,
-                "g": 0.023135003462698395,
+                "u": 0.03701180196157078,
+                "g": 0.02283319745010296,
                 "r": 0.023135003462698395,
-                "i": 0.023135003462698395,
-                "z": 0.023135003462698395,
-                "y": 0.023135003462698395,
-                "ugrizy": 0.023135003462698395,
+                "i": -0.020108849022374618,
+                "z": 0.03313299331060819,
+                "y": 0.042722067239431505,
+                "ugrizy": 0.12769514475136706,
             },
             {
                 "cst": 0.0,
-                "u": -0.05756589965508176,
-                "g": -0.05756589965508176,
-                "r": -0.05756589965508176,
+                "u": 0.005411661204687025,
+                "g": 0.03102947466687921,
+                "r": 0.04758492397282435,
                 "i": -0.05756589965508176,
-                "z": -0.05756589965508176,
-                "y": -0.05756589965508176,
-                "ugrizy": -0.05756589965508176,
+                "z": 0.038139167579015414,
+                "y": 0.028170175608809505,
+                "ugrizy": 0.09819722293690358,
             },
             {
                 "cst": 0.0,
-                "u": -0.025917249340435374,
-                "g": -0.025917249340435374,
-                "r": -0.025917249340435374,
-                "i": -0.025917249340435374,
+                "u": -0.007052479985258033,
+                "g": 0.0072989635471763055,
+                "r": 0.04502123045630644,
+                "i": -0.0073176602744123255,
                 "z": -0.025917249340435374,
-                "y": -0.025917249340435374,
-                "ugrizy": -0.025917249340435374,
+                "y": 0.035858309542520166,
+                "ugrizy": 0.042230607766813,
             },
         ],
     },
@@ -396,52 +396,52 @@
             {
                 "cst": 0.0,
                 "u": 0.054293436011708406,
-                "g": 0.054293436011708406,
-                "r": 0.054293436011708406,
-                "i": 0.054293436011708406,
-                "z": 0.054293436011708406,
-                "y": 0.054293436011708406,
-                "ugrizy": 0.054293436011708406,
+                "g": 0.009952046153107954,
+                "r": 0.0054797065106343325,
+                "i": 0.02576260292778629,
+                "z": 0.06525553880320857,
+                "y": 0.06423711415381689,
+                "ugrizy": 0.20811399377168,
             },
             {
                 "cst": 0.0,
-                "u": 0.034776420860264834,
+                "u": 0.049744205203719596,
                 "g": 0.034776420860264834,
-                "r": 0.034776420860264834,
-                "i": 0.034776420860264834,
-                "z": 0.034776420860264834,
-                "y": 0.034776420860264834,
-                "ugrizy": 0.034776420860264834,
+                "r": -0.008954957603949931,
+                "i": 0.03315665664175325,
+                "z": 0.06433009373174128,
+                "y": 0.06440730378199838,
+                "ugrizy": 0.22417139228019037,
             },
             {
                 "cst": 0.0,
-                "u": 0.023084985340137268,
-                "g": 0.023084985340137268,
+                "u": 0.04304128101754591,
+                "g": 0.017432851239669426,
                 "r": 0.023084985340137268,
-                "i": 0.023084985340137268,
-                "z": 0.023084985340137268,
-                "y": 0.023084985340137268,
-                "ugrizy": 0.023084985340137268,
+                "i": -0.01232102883069664,
+                "z": 0.03998793202306738,
+                "y": 0.044236619617342786,
+                "ugrizy": 0.1392270140374441,
             },
             {
                 "cst": 0.0,
-                "u": -0.056015513609789944,
-                "g": -0.056015513609789944,
-                "r": -0.056015513609789944,
+                "u": 0.009135822126354363,
+                "g": 0.036515151515151424,
+                "r": 0.04077785494592716,
                 "i": -0.056015513609789944,
-                "z": -0.056015513609789944,
-                "y": -0.056015513609789944,
-                "ugrizy": -0.056015513609789944,
+                "z": 0.04361845936433841,
+                "y": 0.029657965796579696,
+                "ugrizy": 0.10045949490109377,
             },
             {
                 "cst": 0.0,
-                "u": -0.02151012665531872,
-                "g": -0.02151012665531872,
-                "r": -0.02151012665531872,
-                "i": -0.02151012665531872,
+                "u": -0.001320660763385228,
+                "g": 0.009954114661184083,
+                "r": 0.05948333012553189,
+                "i": 8.127849262724018e-05,
                 "z": -0.02151012665531872,
-                "y": -0.02151012665531872,
-                "ugrizy": -0.02151012665531872,
+                "y": 0.0434040047114252,
+                "ugrizy": 0.08833836494032939,
             },
         ],
     },
@@ -496,52 +496,52 @@
             {
                 "cst": 0.0,
                 "u": 0.049239020937134025,
-                "g": 0.049239020937134025,
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-                "g": -0.057050518892029986,
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                 "i": -0.057050518892029986,
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+                "z": 0.04070290636155964,
+                "y": 0.022294059577929574,
+                "ugrizy": 0.09371570861821565,
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                 "cst": 0.0,
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-                "g": -0.022746170354716745,
-                "r": -0.022746170354716745,
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+                "u": 0.009871915026554134,
+                "g": 0.0110669656320889,
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                 "z": -0.022746170354716745,
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+                "y": 0.04106604585185824,
+                "ugrizy": 0.09054948216340614,
             },
         ],
     },
@@ -596,52 +596,52 @@
             {
                 "cst": 0.0,
                 "u": 0.04731277533039653,
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+                "y": 0.06698033256378032,
+                "ugrizy": 0.20345569174304204,
             },
             {
                 "cst": 0.0,
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                 "g": 0.03307274442076383,
-                "r": 0.03307274442076383,
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             },
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-                "g": 0.02629040528053873,
+                "u": 0.03224724233127598,
+                "g": 0.018519321739837606,
                 "r": 0.02629040528053873,
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-                "g": -0.051460395769436555,
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+                "u": 0.008432569998479788,
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                 "z": -0.0231010021253421,
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         ],
     },
@@ -696,52 +696,52 @@
             {
                 "cst": 0.0,
                 "u": 0.04691474328780031,
-                "g": 0.04691474328780031,
-                "r": 0.04691474328780031,
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                 "cst": 0.0,
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+                "u": 0.0586609387003185,
                 "g": 0.02926172329832247,
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+                "r": -0.00867144813333158,
+                "i": 0.03238973478100006,
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+                "y": 0.061258549036290445,
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+                "u": 0.037673080027243955,
+                "g": 0.024329841045213924,
                 "r": 0.024028335574766118,
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-                "z": 0.024028335574766118,
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+                "u": 0.014161985933060993,
+                "g": 0.02818398259140731,
+                "r": 0.03724357335900577,
                 "i": -0.05102903641687596,
-                "z": -0.05102903641687596,
-                "y": -0.05102903641687596,
-                "ugrizy": -0.05102903641687596,
+                "z": 0.04037709323317632,
+                "y": 0.019008759510214236,
+                "ugrizy": 0.08457753101369372,
             },
             {
                 "cst": 0.0,
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-                "g": -0.026688237135998293,
-                "r": -0.026688237135998293,
-                "i": -0.026688237135998293,
+                "u": 0.021186578579099866,
+                "g": 0.01743166214581306,
+                "r": 0.06093757433043147,
+                "i": -0.004343243303855121,
                 "z": -0.026688237135998293,
-                "y": -0.026688237135998293,
-                "ugrizy": -0.026688237135998293,
+                "y": 0.03848277935815396,
+                "ugrizy": 0.1090269688631813,
             },
         ],
     },
@@ -796,52 +796,52 @@
             {
                 "cst": 0.0,
                 "u": 0.04786137493054727,
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-                "z": 0.04786137493054727,
-                "y": 0.04786137493054727,
-                "ugrizy": 0.04786137493054727,
+                "g": 0.009655534393294174,
+                "r": -0.0004343582618342657,
+                "i": 0.022915274064171095,
+                "z": 0.059461135879046355,
+                "y": 0.0620954964843791,
+                "ugrizy": 0.19278674763853387,
             },
             {
                 "cst": 0.0,
-                "u": 0.03327254021698463,
+                "u": 0.061847229214042274,
                 "g": 0.03327254021698463,
-                "r": 0.03327254021698463,
-                "i": 0.03327254021698463,
-                "z": 0.03327254021698463,
-                "y": 0.03327254021698463,
-                "ugrizy": 0.03327254021698463,
+                "r": -0.003037483153393612,
+                "i": 0.032612371741557,
+                "z": 0.06330901593731424,
+                "y": 0.06700098294204117,
+                "ugrizy": 0.22947625091188906,
             },
             {
                 "cst": 0.0,
-                "u": 0.02176497146894746,
-                "g": 0.02176497146894746,
+                "u": 0.033831383811744026,
+                "g": 0.019055419055419107,
                 "r": 0.02176497146894746,
-                "i": 0.02176497146894746,
-                "z": 0.02176497146894746,
-                "y": 0.02176497146894746,
-                "ugrizy": 0.02176497146894746,
+                "i": -0.005173853421466327,
+                "z": 0.04318093054632704,
+                "y": 0.05310544905099789,
+                "ugrizy": 0.14598595925075922,
             },
             {
                 "cst": 0.0,
-                "u": -0.0542981307448116,
-                "g": -0.0542981307448116,
-                "r": -0.0542981307448116,
+                "u": 0.014811863932028239,
+                "g": 0.029658558690905158,
+                "r": 0.03239827338991352,
                 "i": -0.0542981307448116,
-                "z": -0.0542981307448116,
-                "y": -0.0542981307448116,
-                "ugrizy": -0.0542981307448116,
+                "z": 0.03624916138976098,
+                "y": 0.017430252211788642,
+                "ugrizy": 0.0816796281398052,
             },
             {
                 "cst": 0.0,
-                "u": -0.02285214586340533,
-                "g": -0.02285214586340533,
-                "r": -0.02285214586340533,
-                "i": -0.02285214586340533,
+                "u": 0.008756393562914043,
+                "g": 0.010614732066291235,
+                "r": 0.05656537211028906,
+                "i": 0.001649522457501062,
                 "z": -0.02285214586340533,
-                "y": -0.02285214586340533,
-                "ugrizy": -0.02285214586340533,
+                "y": 0.04088282647966966,
+                "ugrizy": 0.10694124411573667,
             },
         ],
     },
@@ -896,52 +896,52 @@
             {
                 "cst": 0.0,
                 "u": 0.044390298549263456,
-                "g": 0.044390298549263456,
-                "r": 0.044390298549263456,
-                "i": 0.044390298549263456,
-                "z": 0.044390298549263456,
-                "y": 0.044390298549263456,
-                "ugrizy": 0.044390298549263456,
+                "g": 0.011808056276693719,
+                "r": 0.006910724770876357,
+                "i": 0.018412740395619607,
+                "z": 0.05811594349550721,
+                "y": 0.06252677636632357,
+                "ugrizy": 0.18925712972055841,
             },
             {
                 "cst": 0.0,
-                "u": 0.024830399951491312,
+                "u": 0.06119330455885738,
                 "g": 0.024830399951491312,
-                "r": 0.024830399951491312,
-                "i": 0.024830399951491312,
-                "z": 0.024830399951491312,
-                "y": 0.024830399951491312,
-                "ugrizy": 0.024830399951491312,
+                "r": -0.006768194405460846,
+                "i": 0.030065619863371994,
+                "z": 0.06255158775650581,
+                "y": 0.07147566736996605,
+                "ugrizy": 0.22306568518819123,
             },
             {
                 "cst": 0.0,
-                "u": 0.022781496880732575,
-                "g": 0.022781496880732575,
+                "u": 0.03789658719578499,
+                "g": 0.027326499247561357,
                 "r": 0.022781496880732575,
-                "i": 0.022781496880732575,
-                "z": 0.022781496880732575,
-                "y": 0.022781496880732575,
-                "ugrizy": 0.022781496880732575,
+                "i": -0.007626568451506756,
+                "z": 0.04268771512656232,
+                "y": 0.04633870731223419,
+                "ugrizy": 0.1571925190810045,
             },
             {
                 "cst": 0.0,
-                "u": -0.04816414607472254,
-                "g": -0.04816414607472254,
-                "r": -0.04816414607472254,
+                "u": 0.01457970225016212,
+                "g": 0.027698293355913416,
+                "r": 0.03530620058462831,
                 "i": -0.04816414607472254,
-                "z": -0.04816414607472254,
-                "y": -0.04816414607472254,
-                "ugrizy": -0.04816414607472254,
+                "z": 0.033660844914206074,
+                "y": 0.022692062998703265,
+                "ugrizy": 0.08856228929221631,
             },
             {
                 "cst": 0.0,
-                "u": -0.024440454014018773,
-                "g": -0.024440454014018773,
-                "r": -0.024440454014018773,
-                "i": -0.024440454014018773,
+                "u": 0.01976694240068723,
+                "g": 0.01451018240142195,
+                "r": 0.04892114102580124,
+                "i": 0.004035038256796502,
                 "z": -0.024440454014018773,
-                "y": -0.024440454014018773,
-                "ugrizy": -0.024440454014018773,
+                "y": 0.03989380894491724,
+                "ugrizy": 0.10691935463909878,
             },
         ],
     },
@@ -996,52 +996,52 @@
             {
                 "cst": 0.0,
                 "u": 0.05535070282190964,
-                "g": 0.05535070282190964,
-                "r": 0.05535070282190964,
-                "i": 0.05535070282190964,
-                "z": 0.05535070282190964,
-                "y": 0.05535070282190964,
-                "ugrizy": 0.05535070282190964,
+                "g": 0.011193545843687215,
+                "r": 0.006737582346111497,
+                "i": 0.025833960371206527,
+                "z": 0.06136534095644072,
+                "y": 0.06324681820708049,
+                "ugrizy": 0.2074608064455781,
             },
             {
                 "cst": 0.0,
-                "u": 0.029973080589983655,
+                "u": 0.05615851073325299,
                 "g": 0.029973080589983655,
-                "r": 0.029973080589983655,
-                "i": 0.029973080589983655,
-                "z": 0.029973080589983655,
-                "y": 0.029973080589983655,
-                "ugrizy": 0.029973080589983655,
+                "r": -0.008700108117848502,
+                "i": 0.030847889011579494,
+                "z": 0.06273045722713866,
+                "y": 0.0650054579514396,
+                "ugrizy": 0.2228857063055601,
             },
             {
                 "cst": 0.0,
-                "u": 0.02262813226603151,
-                "g": 0.02262813226603151,
+                "u": 0.03889599549031938,
+                "g": 0.02232895038144942,
                 "r": 0.02262813226603151,
-                "i": 0.02262813226603151,
-                "z": 0.02262813226603151,
-                "y": 0.02262813226603151,
-                "ugrizy": 0.02262813226603151,
+                "i": -0.0020788337259347776,
+                "z": 0.04185161853072303,
+                "y": 0.046998102923906174,
+                "ugrizy": 0.1611649091746466,
             },
             {
                 "cst": 0.0,
-                "u": -0.046678413257774214,
-                "g": -0.046678413257774214,
-                "r": -0.046678413257774214,
+                "u": 0.01329424791323612,
+                "g": 0.025204737395854025,
+                "r": 0.031181310573597302,
                 "i": -0.046678413257774214,
-                "z": -0.046678413257774214,
-                "y": -0.046678413257774214,
-                "ugrizy": -0.046678413257774214,
+                "z": 0.04040377859096919,
+                "y": 0.020907507991371956,
+                "ugrizy": 0.08643457382953187,
             },
             {
                 "cst": 0.0,
-                "u": -0.025182273951223154,
-                "g": -0.025182273951223154,
-                "r": -0.025182273951223154,
-                "i": -0.025182273951223154,
+                "u": 0.010769393019973022,
+                "g": 0.018559094751972344,
+                "r": 0.04731613285883742,
+                "i": -0.002550702324093634,
                 "z": -0.025182273951223154,
-                "y": -0.025182273951223154,
-                "ugrizy": -0.025182273951223154,
+                "y": 0.04311795497390813,
+                "ugrizy": 0.08119502007816377,
             },
         ],
     },

From 6109b07c1bb9d97761186c99ffb9a47606d1a6d3 Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Fri, 12 Apr 2024 07:12:21 -0700
Subject: [PATCH 18/31] added nested RIZexptime and Exgal metric for the FOM
 ratio summary

---
 rubin_sim/maf/metrics/uniformity_metrics.py | 52 +++++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
index 0366ff483..09566d362 100644
--- a/rubin_sim/maf/metrics/uniformity_metrics.py
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -1,6 +1,7 @@
 __all__ = ("SingleLinearMultibandModelMetric", "NestedLinearMultibandModelMetric")
 
 import numpy as np
+import healpy as hp
 
 from .base_metric import BaseMetric
 from .exgal_m5 import ExgalM5
@@ -236,6 +237,7 @@ def __init__(
 
         self.m5_col = m5_col
         self.badval = badval
+        self.mask_val = hp.UNSEEN
         self.filter_col = filter_col
         self.post_processing_fn = post_processing_fn
 
@@ -303,3 +305,53 @@ def run(self, data_slice, slice_point=None):
         # input arr_of_model_dicts contains a scalar value resulting
         # from a linear combination of the six depths
         return result_arr
+
+
+class NestedRIZExptimeExgalM5Metric(BaseMetric):
+    """TODO"""
+
+    def __init__(
+        self,
+        m5_col="fiveSigmaDepth",
+        filter_col="filter",
+        exptime_col="visitExposureTime",
+        extinction_cut=0.2,
+        n_filters=6,
+        depth_cut=24,
+        metric_name="new_nested_FoM",
+        lsst_filter="i",
+        badval=np.nan,
+        **kwargs,
+    ):
+        maps = ["DustMap"]
+        self.m5_col = m5_col
+        self.filter_col = filter_col
+        self.exptime_col = exptime_col
+        self.lsst_filter = lsst_filter
+        self.n_filters = n_filters
+
+        cols = [self.m5_col, self.filter_col, self.exptime_col]
+        super().__init__(cols, metric_name=metric_name, maps=maps, badval=badval, **kwargs)
+        self.riz_exptime_metric = maf.RIZDetectionCoaddExposureTime(ebvlim=extinction_cut)
+        self.exgalm5_metric = maf.ExgalM5WithCuts(
+            m5_col=m5_col,
+            filter_col=filter_col,
+            extinction_cut=extinction_cut,
+            depth_cut=depth_cut,
+            lsst_filter=lsst_filter,
+            badval=badval,
+            n_filters=n_filters,
+        )
+
+        self.metric_dtype = "object"
+
+    def run(self, data_slice, slice_point):
+
+        # set up array to hold the results
+        names = ["exgal_m5", "riz_exptime"]
+        types = [float] * 2
+        result = np.zeros(1, dtype=list(zip(names, types)))
+        result["exgal_m5"] = self.exgalm5_metric.run(data_slice, slice_point)
+        result["riz_exptime"] = self.riz_exptime_metric.run(data_slice, slice_point)
+
+        return result

From f6b551a4ef16feb60ae3c01afdb6bd0121096aaf Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Fri, 12 Apr 2024 07:12:56 -0700
Subject: [PATCH 19/31] fixed bug that col wasn't set

---
 rubin_sim/maf/maf_contrib/static_probes_fom_summary_metric.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/rubin_sim/maf/maf_contrib/static_probes_fom_summary_metric.py b/rubin_sim/maf/maf_contrib/static_probes_fom_summary_metric.py
index 83793b90f..e215b641b 100644
--- a/rubin_sim/maf/maf_contrib/static_probes_fom_summary_metric.py
+++ b/rubin_sim/maf/maf_contrib/static_probes_fom_summary_metric.py
@@ -34,6 +34,8 @@ def __init__(
         super().__init__(col=col, **kwargs)
         if col is None:
             self.col = "metricdata"
+        else:
+            self.col = col
         self.shear_m = shear_m
         self.sigma_z = sigma_z
         self.sig_delta_z = sig_delta_z

From 69a2cdf904302d2ef06c85eb5429979f6ec0361b Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Fri, 12 Apr 2024 08:46:38 -0700
Subject: [PATCH 20/31] Tidied up FOM ratio metrics

---
 .../maf/metrics/cosmology_summary_metrics.py  | 173 +++++++++++-------
 rubin_sim/maf/metrics/uniformity_metrics.py   |   5 +-
 2 files changed, 106 insertions(+), 72 deletions(-)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 327967d27..699b10a85 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -9,6 +9,11 @@
 import healpy as hp
 import numpy as np
 from scipy import interpolate
+from scipy.stats import median_abs_deviation
+from astropy import units as u
+from astropy.coordinates import SkyCoord
+from sklearn.cluster import KMeans
+from copy import deepcopy
 
 from ..maf_contrib.static_probes_fom_summary_metric import StaticProbesFoMEmulatorMetric
 from .area_summary_metrics import AreaThresholdMetric
@@ -226,6 +231,7 @@ def __init__(
         super().__init__(col="metricdata", **kwargs)
         # Set mask_val, so that we receive metric_values.filled(mask_val)
         self.mask_val = hp.UNSEEN
+        self.badval = hp.UNSEEN
 
         self.convert_to_sigma8 = convert_to_sigma8
 
@@ -253,15 +259,12 @@ def run(self, data_slice, slice_point=None):
         ]
         # should be (nbins, npix)
         data_slice_arr = np.asarray(data_slice_list, dtype=float).T
-        #hp.mollview(data_slice_arr[0])
-        data_slice_arr[data_slice_arr == -666] = (
-            hp.UNSEEN
-        )
+        # hp.mollview(data_slice_arr[0])
+        ### data_slice_arr[data_slice_arr == -666] = hp.UNSEEN  ############
         data_slice_arr[~np.isfinite(data_slice_arr)] = (
             hp.UNSEEN
         )  # need to work with TotalPowerMetric and healpix
 
-        
         # measure valid sky fractions and total power
         # (via angular power spectra) in each bin.
         # The original metric returns an array at each slice_point (of the
@@ -288,8 +291,8 @@ def run(self, data_slice, slice_point=None):
             )
             / fskys
         )
-        print('spuriousdensitypowers:', spuriousdensitypowers)
-        print('fskys:', fskys)
+        print("spuriousdensitypowers:", spuriousdensitypowers)
+        print("fskys:", fskys)
 
         def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, power_multiplier):
             """
@@ -356,7 +359,7 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
             return results_sigma8_square_bias
 
         else:
-            
+
             # turn result into bias on sigma8,
             # via change of variable and simple propagation of uncertainty.
             sigma8_fit = sigma8square_fit**0.5
@@ -386,54 +389,74 @@ class UniformAreaFoMFractionMetric(BaseMetric):
     """
 
     def __init__(
-            self,
-            min_exptime=1000,
-            verbose=True, 
-            **kwargs,
+        self,
+        verbose=True,
+        nside=32,
+        **kwargs,
     ):
-        super().__init__(col="metricdata", **kwargs)
-        # Set mask_val, so that we receive metric_values.filled(mask_val)
-        self.min_exptime = min_exptime
         self.mask_val = hp.UNSEEN
-        self.verbose = verbose 
+        self.verbose = verbose
+        self.nside = nside
+        names = ["exgal_m5", "riz_exptime"]
+        types = [float] * 2
+        self.mask_val_arr = np.zeros(1, dtype=list(zip(names, types)))
+        self.mask_val_arr["exgal_m5"] = self.mask_val
+        self.mask_val_arr["riz_exptime"] = self.mask_val
+
+        self.threebyTwoSummary = StaticProbesFoMEmulatorMetric(
+            nside=nside, metric_name="3x2ptFoM", col="exgal_m5"
+        )
+        super().__init__(col="metricdata", **kwargs)
 
     def run(self, data_slice, slice_point=None):
+        data_slice_list = [
+            self.mask_val_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()
+        ]
+        data_slice_arr = np.asarray(data_slice_list, dtype=self.mask_val_arr.dtype)
+        if True:  # apply mask
+            ind = data_slice_arr["riz_exptime"] == hp.UNSEEN
+            ind |= data_slice_arr["riz_exptime"] == -666
+            # ind |= data_slice_arr['riz_exptime'] == 6066
+            ind |= ~np.isfinite(data_slice_arr["riz_exptime"])
+            # ind |= data_slice_arr['exgal_m5'] == 6066
+            ind |= data_slice_arr["exgal_m5"] == hp.UNSEEN
+            ind |= data_slice_arr["exgal_m5"] == -666
+            ind |= ~np.isfinite(data_slice_arr["exgal_m5"])
+            data_slice_arr["exgal_m5"][ind.ravel()] = hp.UNSEEN
+            data_slice_arr["riz_exptime"][ind.ravel()] = hp.UNSEEN
+        # sanity check
+        nside = hp.npix2nside(data_slice["metricdata"].size)
+        assert nside == self.nside
 
-        # cheap way to cut sky
-        data_slice[data_slice['metricdata'] < self.min_exptime] = (hp.UNSEEN, )
-        
         # Let's make code that pulls out the northern/southern galactic regions, and gets statistics of the footprint by region.
-        from astropy import units as u
-        from astropy.coordinates import SkyCoord
         def _is_ngp(ra, dec):
-            c = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')
+            c = SkyCoord(ra=ra * u.degree, dec=dec * u.degree, frame="icrs")
             lat = c.galactic.b.deg
             return lat >= 0
 
-        def get_stats_by_region(use_map, nside, maskval=0, region='all'):
-            if region not in ['all','north','south']:
-                raise ValueError('Invalid region %s'%region)
-            to_use = (use_map > maskval)
+        def get_stats_by_region(use_map, nside, maskval=0, region="all"):
+            if region not in ["all", "north", "south"]:
+                raise ValueError("Invalid region %s" % region)
+            to_use = use_map > maskval
 
-            if region != 'all':
+            if region != "all":
                 # Find the north/south part of the map as requested
                 npix = hp.nside2npix(nside)
                 ra, dec = hp.pix2ang(hp.npix2nside(npix), range(npix), lonlat=True)
                 ngp_mask = _is_ngp(ra, dec)
-                if region=='north':
+                if region == "north":
                     reg_mask = ngp_mask
-                else: 
+                else:
                     reg_mask = ~ngp_mask
                 to_use = to_use & reg_mask
 
             # Calculate the desired stats
-            from scipy.stats import median_abs_deviation
             reg_mad = median_abs_deviation(use_map[to_use])
             reg_median = np.median(use_map[to_use])
             reg_std = np.std(use_map[to_use])
 
             # Return the values
-            return(reg_mad, reg_median, reg_std)
+            return (reg_mad, reg_median, reg_std)
 
         def has_stripes(data_slice, nside, threshold=0.1):
             """
@@ -443,53 +466,55 @@ def has_stripes(data_slice, nside, threshold=0.1):
             mad = {}
             med = {}
             frac_scatter = {}
-            regions = ['north', 'south']
+            regions = ["north", "south"]
             for region in regions:
                 mad[region], med[region], _ = get_stats_by_region(data_slice, nside, region=region)
-                frac_scatter[region] = mad[region]/med[region]
-            test_statistic = np.abs(frac_scatter['north']/frac_scatter['south']-1)
+                frac_scatter[region] = mad[region] / med[region]
+            test_statistic = np.abs(frac_scatter["north"] / frac_scatter["south"] - 1)
             if test_statistic < threshold:
                 return False
             else:
                 return True
-    
+
         def apply_clustering(clustering_data):
             # A thin wrapper around sklearn routines (can swap out which one we are using systematically).
             # We fix parameters like `n_clusters` since realistically we know for rolling that we should expect 2 clusters.
-            #from sklearn.cluster import SpectralClustering
-            #clustering = SpectralClustering(n_clusters=2, assign_labels='discretize', random_state=0).fit(clustering_data)
-            from sklearn.cluster import KMeans
+            # from sklearn.cluster import SpectralClustering
+            # clustering = SpectralClustering(n_clusters=2, assign_labels='discretize', random_state=0).fit(clustering_data)
+
             clustering = KMeans(n_clusters=2, random_state=0, n_init="auto").fit(clustering_data)
             labels = clustering.labels_ + 1
             return labels
-        
+
         def expand_labels(depth_map, labels, maskval=0):
             # A utility to apply the labels from a masked version of a depth map back to the entire depth map.
             expanded_labels = np.zeros(hp.nside2npix(nside))
-            cutval = maskval+0.1
-            expanded_labels[depth_map>cutval] = labels
+            cutval = maskval + 0.1
+            expanded_labels[depth_map > cutval] = labels
             return expanded_labels
+
         def get_area_stats(depth_map, labels, maskval=0, n_clusters=2):
             # A routine to get some statistics of the clustering: area fractions, median map values
             expanded_labels = expand_labels(depth_map, labels, maskval=maskval)
-            cutval = maskval+0.1
+            cutval = maskval + 0.1
             area_frac = []
             med_val = []
             for i in range(n_clusters):
                 new_map = depth_map.copy()
-                new_map[expanded_labels!=i+1] = maskval
-                this_area_frac = len(new_map[new_map>cutval])/len(depth_map[depth_map>cutval])
-                this_med_val = np.median(new_map[new_map>cutval])
+                new_map[expanded_labels != i + 1] = maskval
+                this_area_frac = len(new_map[new_map > cutval]) / len(depth_map[depth_map > cutval])
+                this_med_val = np.median(new_map[new_map > cutval])
                 area_frac.append(this_area_frac)
                 med_val.append(this_med_val)
             return area_frac, med_val
+
         def show_clusters(depth_map, labels, maskval=0, n_clusters=2, min=500, max=3000):
             # A routine to show the clusters found by the unsupervised clustering algorithm (start with original map then 2 clusters).
             expanded_labels = expand_labels(depth_map, labels, maskval=maskval)
             hp.visufunc.mollview(depth_map, min=min, max=max)
             for i in range(n_clusters):
                 new_map = depth_map.copy()
-                new_map[expanded_labels!=i+1] = maskval
+                new_map[expanded_labels != i + 1] = maskval
                 hp.visufunc.mollview(new_map, min=min, max=max)
             return get_area_stats(depth_map, labels, maskval=maskval, n_clusters=n_clusters)
 
@@ -513,49 +538,57 @@ def make_clustering_dataset(depth_map, maskval=0, priority_fac=0.9, nside=64):
             n_unmasked = len(depth_map[depth_map > 0.1])
             my_data = np.zeros((n_unmasked, 3))
             cutval = 0.1 + maskval
-            my_data[:, 0] = ra[depth_map > cutval] * (1 - priority_fac) * np.std(
-                depth_map[depth_map > cutval]) / np.std(ra[depth_map > cutval])
-            my_data[:, 1] = dec[depth_map > cutval] * (1 - priority_fac) * np.std(
-                depth_map[depth_map > cutval]) / np.std(dec[depth_map > cutval])
+            my_data[:, 0] = (
+                ra[depth_map > cutval]
+                * (1 - priority_fac)
+                * np.std(depth_map[depth_map > cutval])
+                / np.std(ra[depth_map > cutval])
+            )
+            my_data[:, 1] = (
+                dec[depth_map > cutval]
+                * (1 - priority_fac)
+                * np.std(depth_map[depth_map > cutval])
+                / np.std(dec[depth_map > cutval])
+            )
             my_data[:, 2] = depth_map[depth_map > cutval]
             return my_data
 
-            # Check for stripiness
-
-        nside = hp.npix2nside(data_slice['metricdata'].size)
-        self.threebyTwoSummary = StaticProbesFoMEmulatorMetric(nside=nside, metric_name="3x2ptFoM")
-        stripes = has_stripes(data_slice['metricdata'], nside)
+        # Check for stripiness
+        stripes = has_stripes(data_slice_arr["riz_exptime"].ravel(), nside)
 
         if not stripes:
             return 1
         else:
             # Do the clustering if we got to this point
-            if self.verbose: print("Verbose mode - Carrying out the clustering exercise for this map")
-            clustering_data = make_clustering_dataset(data_slice['metricdata'], nside=nside)
+            if self.verbose:
+                print("Verbose mode - Carrying out the clustering exercise for this map")
+            clustering_data = make_clustering_dataset(data_slice_arr["riz_exptime"].ravel(), nside=nside)
             labels = apply_clustering(clustering_data)
-            area_frac, med_val = get_area_stats(data_slice['metricdata'], labels)
+            area_frac, med_val = get_area_stats(data_slice_arr["riz_exptime"].ravel(), labels)
             if self.verbose:
                 print("Verbose mode - showing original map and clusters identified for this map")
-                show_clusters(data_slice['metricdata'], labels)
+                show_clusters(data_slice_arr["riz_exptime"].ravel(), labels)
                 print("Area fractions", area_frac)
                 print("Median exposure time values", med_val)
-                print("Median exposure time ratio", np.max(med_val)/np.min(med_val))
+                print("Median exposure time ratio", np.max(med_val) / np.min(med_val))
                 print("Verbose mode - proceeding with area cuts")
             # Get the FoM without/with cuts.  We want to check the FoM for each area, if we're doing cuts, and
             # return the higher one. This will typically be for the larger area, but not necessarily, if the smaller area
             # is deeper.
-            expanded_labels = expand_labels(data_slice['metricdata'], labels)
-            my_hpid_1 = np.where(labels == 1)[0]
-            my_hpid_2 = np.where(labels == 2)[0]
-            from copy import copy
+            expanded_labels = expand_labels(data_slice_arr["riz_exptime"].ravel(), labels)
+            my_hpid_1 = expanded_labels == 1  # np.where(expanded_labels == 1)[0]
+            my_hpid_2 = expanded_labels == 2  # np.where(expanded_labels == 2)[0]
+            # should this be labels or expanded_labels?!
+
             # copies need to be recarrays
-            data_slice_subset_2 = copy(data_slice)
-            data_slice_subset_1 = copy(data_slice)
-            data_slice[my_hpid_2] = (hp.UNSEEN, )
-            data_slice[my_hpid_1] = (hp.UNSEEN, ) 
+            data_slice_subset_2 = deepcopy(data_slice_arr)
+            data_slice_subset_1 = deepcopy(data_slice_arr)
+            data_slice_subset_1[my_hpid_2] = self.mask_val_arr
+            data_slice_subset_2[my_hpid_1] = self.mask_val_arr
             fom1 = self.threebyTwoSummary.run(data_slice_subset_1)
             fom2 = self.threebyTwoSummary.run(data_slice_subset_2)
             fom = np.max((fom1, fom2))
-            fom_total = self.threebyTwoSummary.run(data_slice)
+            fom_total = self.threebyTwoSummary.run(data_slice_arr)
+            if self.verbose:
+                print("FOMs:", fom1, fom2, fom, fom_total)
             return fom / fom_total
-
diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
index 09566d362..bf1bcc190 100644
--- a/rubin_sim/maf/metrics/uniformity_metrics.py
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -5,6 +5,7 @@
 
 from .base_metric import BaseMetric
 from .exgal_m5 import ExgalM5
+from .weak_lensing_systematics_metric import RIZDetectionCoaddExposureTime, ExgalM5WithCuts
 
 
 class SingleLinearMultibandModelMetric(BaseMetric):
@@ -332,8 +333,8 @@ def __init__(
 
         cols = [self.m5_col, self.filter_col, self.exptime_col]
         super().__init__(cols, metric_name=metric_name, maps=maps, badval=badval, **kwargs)
-        self.riz_exptime_metric = maf.RIZDetectionCoaddExposureTime(ebvlim=extinction_cut)
-        self.exgalm5_metric = maf.ExgalM5WithCuts(
+        self.riz_exptime_metric = RIZDetectionCoaddExposureTime(ebvlim=extinction_cut)
+        self.exgalm5_metric = ExgalM5WithCuts(
             m5_col=m5_col,
             filter_col=filter_col,
             extinction_cut=extinction_cut,

From f6a2d0c3f23b42e90c10798350d2587ceb196874 Mon Sep 17 00:00:00 2001
From: Renee Hlozek <renee.hlozek@gmail.com>
Date: Tue, 16 Apr 2024 08:20:32 -0700
Subject: [PATCH 21/31] updated nested metric

---
 .../maf/metrics/cosmology_summary_metrics.py  | 216 +++++++++++++++++-
 rubin_sim/maf/metrics/uniformity_metrics.py   |  64 ++++++
 2 files changed, 273 insertions(+), 7 deletions(-)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 95811330e..e45438d32 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -364,6 +364,8 @@ class UniformMeanzBiasMetric(BaseMetric):
        the scatter in the redshift of the tomographic sample
     induced by survey non-uniformity. 
 
+    PREVIOUS UN-NESTED VERSION -- See Below for nested version inspired by Boris
+
     Parameters
     ----------
     year : `int`, optional
@@ -394,7 +396,7 @@ def __init__(self, filter_list="filters",year=10, n_filters=6,**kwargs):
 
     def run(self, data_slice, slice_point=None):
 
-        result = np.empty(1, dtype=[("name", np.str_, 20), ("value", float)])
+        result = np.empty(1, dtype=[("name", np.str_, 20), ("y1ratio", float),("y10ratio",float)])
         result["name"][0] = "UniformMeanzBiasMetric"
 
         def compute_dzfromdm(zbins, band_ind, year):
@@ -508,7 +510,7 @@ def compute_Clbias(meanz_vals,scatter_mean_z_values):
 
         totdz=0
         avmeanz=0
-        clbiastot=0
+        totclbias=0
         for filt in self.filter_list:
             d_s = data_slice[data_slice[self.filter_col] == filt]
             # calculate the lsstFilter-band coadded depth
@@ -529,13 +531,213 @@ def compute_Clbias(meanz_vals,scatter_mean_z_values):
         y10_req = 0.003
         y1_goal = 0.013
 
-        clbiastot = np.max(clbias)
-        y10ratio = clbiastot/y10_req
-        y1ratio = clbiastot/y1_goal
+        #clbiastot = np.max(clbias)
+        y10ratio = totclbias/y10_req
+        y1ratio = totclbias/y1_goal
 
         result["y1ratio"]=y1ratio
-        result["y10ratio"]=y1ratio
+        result["y10ratio"]=y10ratio
         
         return result
 
-    
\ No newline at end of file
+    
+class MultibandMeanzBiasMetric(BaseMetric):
+    """
+    Run as summary metric on MultibandExgalM5.
+
+    Parameters
+    ----------
+    will fill in asap
+
+    Returns
+    -------
+    result : `float`
+        
+
+    Notes
+    -----
+    This is a summary metric to be run on the results
+    of the MultibandExgalM5.
+
+    MultibandExgalM5 provides the m5 depth in all LSST bands given a specific slice.
+
+    This summary metric takes those depths and reads the derivatives [more to come here]...
+    """
+
+    def __init__(self, filter_list="filters",year=10, n_filters=6,**kwargs):
+        
+        super().__init__(col="metricdata", **kwargs)
+        # Set mask_val, so that we receive metric_values.filled(mask_val)
+        self.mask_val = hp.UNSEEN
+        self.rmsMetric = RmsMetric()
+        self.year = year
+        self.filter_list = filter_list
+
+    def run(self, data_slice, slice_point=None):
+
+        def compute_dzfromdm(zbins, band_ind, year):
+            """ This computes the dm/dz relationship calibrated from simulations
+            by Jeff Newmann.
+
+            Parameters
+            ----------
+            zbins : `int`
+                The number of tomographic bins considered. For now this is zbins < 5
+            filter : `str`
+                The assumed filter band 
+
+            Returns
+            -------
+            dzdminterp : `float` 
+                The interpolated value of the derivative dz/dm
+            meanzinterp : `float` array
+                The meanz in each tomographic bin.
+            
+            """
+            import pandas as pd
+
+            filter_list=["u","g","r","i","z","y"]
+            band_ind =filter_list.index(filter)
+            
+            deriv = pd.read_pickle('uniformity_pkl/meanzderiv.pkl')
+            # pkl file of derivatives with 10 years, 7 bands (ugrizY and combined), 5 bins
+            zvals = pd.read_pickle('uniformity_pkl/meanzsy%i.pkl'%(year+1)) 
+            # pkl file of mean z values for a given year over 5 z bins, 7 bands (ugrizY and combined),
+            # for a fixed delta density index (index 5 assumed below is for zero m_5 shift)
+            meanzinterp = zvals[0:zbins,band_ind,5]
+            dzdminterp = np.abs(deriv[year,band_ind,0:zbins])
+
+            return dzdminterp, meanzinterp
+
+
+        ## Not entirely sure if we should include these here or in a separate module/file
+        def use_zbins(meanz_vals, figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0]),  figure_9_width=0.2):
+            """ This computes which redshift bands are within the range 
+            specified in https://arxiv.org/pdf/2305.15406.pdf and can safely be used
+            to compute what Cl bias result from z fluctuations caused by rms variations in the m5.
+
+
+            Parameters
+            ----------
+            meanz_vals : `float` array
+                Array of meanz values to be used.
+            
+            Returns
+            -------
+            use_bins : `boolean` array
+                An array of boolean values of length meanz_vals 
+            
+            """
+            max_z_use = np.max(figure_9_mean_z)+2*figure_9_width
+            use_bins = meanz_vals < max_z_use
+            
+            return use_bins
+
+        def compute_Clbias(meanz_vals,scatter_mean_z_values):
+            """ This computes the Cl bias 
+            that results z fluctuations caused by rms variations in the m5.
+
+            
+
+            Parameters
+            ----------
+            meanz_vals : `float` array
+                Array of meanz values to be used.
+
+            scatter_mean_z_values : `float` array
+                Array of rms values of the z fluctuations
+
+            
+            Returns
+            -------
+            clbiasvals : `float` array
+                An array of values of the clbias
+
+            mean_z_values_use :  `float` array
+                An array of the meanz values that are within the interpolation range of 2305.15406
+            
+            Notes
+            ------
+            This interpolates from the Figure 9 in https://arxiv.org/pdf/2305.15406.pdf
+
+            """
+            import numpy as np
+            figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0])
+            figure_9_Clbias =np.array([1e-3, 2e-3, 5e-3, 1.1e-2])
+            figure_9_width=0.2
+            figure_9_mean_z_scatter = 0.02
+
+            mzvals= np.array([float(mz) for mz in meanz_vals])
+            sctz = np.array([float(sz)for sz in scatter_mean_z_values])
+            
+            fit_res = np.polyfit(figure_9_mean_z, figure_9_Clbias, 2)
+            poly_fit = np.poly1d(fit_res)
+            use_bins = use_zbins(meanz_vals,figure_9_mean_z, figure_9_width)
+
+            mean_z_values_use = mzvals[use_bins]
+            sctz_use = sctz[use_bins]
+
+            Clbias = poly_fit(mean_z_values_use)
+            rescale_fac =  sctz_use / figure_9_mean_z_scatter
+            Clbias *= rescale_fac
+            fit_res_bias = np.polyfit(mean_z_values_use, Clbias, 1)
+            poly_fit_bias = np.poly1d(fit_res_bias)
+
+            clbiasvals = poly_fit_bias(mean_z_values_use)
+            return clbiasvals, mean_z_values_use
+        
+
+        result = np.empty(1, dtype=[("name", np.str_, 20), ("y1ratio", float),("y10ratio",float)])
+        result["name"][0] = "MultibandMeanzBiasMetric"
+
+        # Technically don't need this for now (isn't used in previous one)
+        # need to define an array of bad values for the masked pixels
+        badval_arr = np.repeat(self.badval, len(self.filter_list))
+        # converts the input recarray to an array
+        data_slice_list = [
+            badval_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()
+        ]
+        # should be (nbins, npix)
+        data_slice_arr = np.asarray(data_slice_list, dtype=float).T
+        data_slice_arr[~np.isfinite(data_slice_arr)] = (
+            hp.UNSEEN
+        )  # need to work with TotalPowerMetric and healpix
+
+        # measure rms in each bin.
+        # The original metric returns an array at each slice_point (of the
+        # original slicer) -- so there is a bit of "rearrangement" that
+        # has to happen to be able to pass a np.array with right dtype
+        # (i.e. dtype = [("metricdata", float)]) to each call to
+        # the rmsMetric `run` methods.
+        rmsarray = np.array(
+            [
+                self.rmsMetric.run(np.core.records.fromrecords(x, dtype=[("metricdata", float)]))
+                for x in data_slice_arr
+            ]
+        )  # rms values
+        
+        totdz=0
+        avmeanz=0
+        totclbias=0
+
+        for i,filt in enumerate(self.filter_list):
+            dzdminterp, meanzinterp=compute_dzfromdm(self.zbins, filt,self.year)
+            stdz = [float(np.abs(dz))*float(rmsarray[i]) for dz in dzdminterp]
+    
+            clbias, meanz_use = compute_Clbias(meanzinterp,stdz)
+
+            totdz+=[float(st**2) for st in stdz]
+            totclbias+=clbias
+            avmeanz+=meanzinterp
+
+    # These should be included in self rather than hard coded
+        y10_req = 0.003
+        y1_goal = 0.013
+
+        #clbiastot = np.max(clbias) # if adding doesn't work over z range -- CHECK
+        y10ratio =totclbias/y10_req
+        y1ratio = totclbias/y1_goal
+
+        result["y1ratio"]=y1ratio
+        result["y10ratio"]=y10ratio
+
diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
index 0366ff483..b0dfd47b3 100644
--- a/rubin_sim/maf/metrics/uniformity_metrics.py
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -303,3 +303,67 @@ def run(self, data_slice, slice_point=None):
         # input arr_of_model_dicts contains a scalar value resulting
         # from a linear combination of the six depths
         return result_arr
+
+
+class MultibandExgalM5(BaseMetric):
+    """Calculate multiple linear combinations of depths.
+
+    """
+
+    def __init__(
+        self,
+        extinction_cut=0.2,
+        n_filters=6,
+        m5_col="fiveSigmaDepth",
+        filter_col="filter",
+        units="mag",
+        badval=np.nan,
+        **kwargs,
+    ):
+        self.n_filters = n_filters
+        self.extinction_cut = extinction_cut
+
+        self.m5_col = m5_col
+        self.badval = badval
+        self.filter_col = filter_col
+
+        self.n_bins = len(self.arr_of_model_dicts)
+        self.bad_val_arr = np.repeat(self.badval, self.n_bins)
+
+        self.exgal_m5 = ExgalM5(
+            m5_col=m5_col,
+            filter_col=filter_col,
+            badval=self.badval,
+        )
+        super().__init__(
+            col=[m5_col, filter_col],
+            badval=self.badval,
+            units=units,
+            maps=self.exgal_m5.maps,
+            metric_dtype="object",
+            **kwargs,
+        )
+
+        def run(self, data_slice, slice_point=None):
+            # apply extinction and n_filters cut
+            if slice_point["ebv"] > self.extinction_cut:
+                return self.bad_val_arr
+
+            n_filters = len(set(data_slice[self.filter_col]))
+            if n_filters < self.n_filters:
+                return self.bad_val_arr
+
+            lsst_filters = ["u", "g", "r", "i", "z", "y"]
+
+            # initialize dictionary of outputs
+            # types = [float]*n_bins
+            result_arr = np.zeros((self.n_bins,), dtype=float)
+
+            depths = np.vstack(
+                [
+                    self.exgal_m5.run(data_slice[data_slice[self.filter_col] == lsst_filter], slice_point)
+                    for lsst_filter in lsst_filters
+                ]
+            ).T
+
+            return depths
\ No newline at end of file

From 47ed7e72ab7e894f0d88dc7d78765a55f779fb2a Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Tue, 16 Apr 2024 08:38:31 -0700
Subject: [PATCH 22/31] exp time only calculated for i

---
 rubin_sim/maf/metrics/uniformity_metrics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
index bf1bcc190..01d3ed436 100644
--- a/rubin_sim/maf/metrics/uniformity_metrics.py
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -353,6 +353,6 @@ def run(self, data_slice, slice_point):
         types = [float] * 2
         result = np.zeros(1, dtype=list(zip(names, types)))
         result["exgal_m5"] = self.exgalm5_metric.run(data_slice, slice_point)
-        result["riz_exptime"] = self.riz_exptime_metric.run(data_slice, slice_point)
+        result["riz_exptime"] = self.riz_exptime_metric.run(data_slice[data_slice[self.filter_col] == 'i'], slice_point)
 
         return result

From d613522e590bba885f65182d7265c55524ef6074 Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Tue, 16 Apr 2024 09:19:48 -0700
Subject: [PATCH 23/31] Minor edits

---
 .../maf/metrics/cosmology_summary_metrics.py  | 10 ++++-
 rubin_sim/maf/metrics/uniformity_metrics.py   | 37 ++++++++-----------
 2 files changed, 24 insertions(+), 23 deletions(-)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index dfc33fb35..92d9054ae 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -19,6 +19,7 @@
 from ..maf_contrib.static_probes_fom_summary_metric import StaticProbesFoMEmulatorMetric
 from .area_summary_metrics import AreaThresholdMetric
 from .base_metric import BaseMetric
+from .simple_metrics import RmsMetric
 
 # Cosmology-related summary metrics.
 # These generally calculate a FoM for various DESC metrics.
@@ -176,6 +177,8 @@ class TomographicClusteringSigma8biasMetric(BaseMetric):
     """Compute bias on sigma8 due to spurious contamination of density maps.
     Run as summary metric on NestedLinearMultibandModelMetric.
 
+    point of contact / contributors: TODO
+    
     Parameters
     ----------
     density_tomograph_model : `dict`
@@ -373,7 +376,6 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
 
           
 class UniformMeanzBiasMetric(BaseMetric):
-    import maf
 
     """This calculates the bias in the weak lensing power given 
        the scatter in the redshift of the tomographic sample
@@ -579,11 +581,12 @@ class MultibandMeanzBiasMetric(BaseMetric):
     This summary metric takes those depths and reads the derivatives [more to come here]...
     """
 
-    def __init__(self, filter_list="filters",year=10, n_filters=6,**kwargs):
+    def __init__(self, filter_list=["u","g","r","i","z","y"] ,year=10, n_filters=6,**kwargs):
         
         super().__init__(col="metricdata", **kwargs)
         # Set mask_val, so that we receive metric_values.filled(mask_val)
         self.mask_val = hp.UNSEEN
+        self.badval = hp.UNSEEN
         self.rmsMetric = RmsMetric()
         self.year = year
         self.filter_list = filter_list
@@ -712,6 +715,7 @@ def compute_Clbias(meanz_vals,scatter_mean_z_values):
         data_slice_list = [
             badval_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()
         ]
+        lengths = np.array([len(x) for x in data_slice_list])
         # should be (nbins, npix)
         data_slice_arr = np.asarray(data_slice_list, dtype=float).T
         data_slice_arr[~np.isfinite(data_slice_arr)] = (
@@ -760,6 +764,8 @@ class UniformAreaFoMFractionMetric(BaseMetric):
     """?
     Run as summary metric on RIZDetectionCoaddExposureTime.
 
+    point of contact / contributors: TODO
+    
     Parameters
     ----------
     ?
diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
index 4e0eea114..46e9f59a6 100644
--- a/rubin_sim/maf/metrics/uniformity_metrics.py
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -330,8 +330,8 @@ def __init__(
         self.badval = badval
         self.filter_col = filter_col
 
-        self.n_bins = len(self.arr_of_model_dicts)
-        self.bad_val_arr = np.repeat(self.badval, self.n_bins)
+        self.lsst_filters = ["u", "g", "r", "i", "z", "y"]
+        self.bad_val_arr = np.repeat(self.badval, len(self.lsst_filters))
 
         self.exgal_m5 = ExgalM5(
             m5_col=m5_col,
@@ -347,29 +347,24 @@ def __init__(
             **kwargs,
         )
 
-        def run(self, data_slice, slice_point=None):
-            # apply extinction and n_filters cut
-            if slice_point["ebv"] > self.extinction_cut:
-                return self.bad_val_arr
-
-            n_filters = len(set(data_slice[self.filter_col]))
-            if n_filters < self.n_filters:
-                return self.bad_val_arr
+    def run(self, data_slice, slice_point=None):
+        # apply extinction and n_filters cut
+        if slice_point["ebv"] > self.extinction_cut:
+            return self.bad_val_arr
 
-            lsst_filters = ["u", "g", "r", "i", "z", "y"]
+        n_filters = len(set(data_slice[self.filter_col]))
+        if n_filters < self.n_filters:
+            return self.bad_val_arr
 
-            # initialize dictionary of outputs
-            # types = [float]*n_bins
-            result_arr = np.zeros((self.n_bins,), dtype=float)
 
-            depths = np.vstack(
-                [
-                    self.exgal_m5.run(data_slice[data_slice[self.filter_col] == lsst_filter], slice_point)
-                    for lsst_filter in lsst_filters
-                ]
-            ).T
+        depths = np.vstack(
+            [
+                self.exgal_m5.run(data_slice[data_slice[self.filter_col] == lsst_filter], slice_point)
+                for lsst_filter in self.lsst_filters
+            ]
+        ).T
 
-            return depths
+        return depths.ravel()
 
 class NestedRIZExptimeExgalM5Metric(BaseMetric):
     """TODO"""

From 174cb11175f4be96d99128601a96094fca2b07a6 Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Thu, 18 Apr 2024 06:51:24 -0700
Subject: [PATCH 24/31] minor edits to areaatrisk

---
 rubin_sim/maf/metrics/cosmology_summary_metrics.py | 5 ++++-
 rubin_sim/maf/metrics/uniformity_metrics.py        | 9 +++++++--
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 92d9054ae..f0e5a6657 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -781,10 +781,12 @@ class UniformAreaFoMFractionMetric(BaseMetric):
 
     def __init__(
         self,
+        year,
         verbose=True,
         nside=32,
         **kwargs,
     ):
+        self.year = year
         self.mask_val = hp.UNSEEN
         self.verbose = verbose
         self.nside = nside
@@ -945,7 +947,8 @@ def make_clustering_dataset(depth_map, maskval=0, priority_fac=0.9, nside=64):
             return my_data
 
         # Check for stripiness
-        stripes = has_stripes(data_slice_arr["riz_exptime"].ravel(), nside)
+        use_threshold = 0.7/self.year
+        stripes = has_stripes(data_slice_arr["riz_exptime"].ravel(), nside, threshold=use_threshold)
 
         if not stripes:
             return 1
diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
index 46e9f59a6..b2db98b4c 100644
--- a/rubin_sim/maf/metrics/uniformity_metrics.py
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -401,6 +401,10 @@ def __init__(
             badval=badval,
             n_filters=n_filters,
         )
+        #self.exgalm5_metric = ExgalM5(
+        #    m5_col=m5_col,
+        #    filter_col=filter_col
+        #)
 
         self.metric_dtype = "object"
 
@@ -410,7 +414,8 @@ def run(self, data_slice, slice_point):
         names = ["exgal_m5", "riz_exptime"]
         types = [float] * 2
         result = np.zeros(1, dtype=list(zip(names, types)))
-        result["exgal_m5"] = self.exgalm5_metric.run(data_slice, slice_point)
-        result["riz_exptime"] = self.riz_exptime_metric.run(data_slice[data_slice[self.filter_col] == 'i'], slice_point)
+        result["exgal_m5"] = self.exgalm5_metric.run(data_slice, slice_point)  # if using ExgalM5WithCuts
+        #result["exgal_m5"] = self.exgalm5_metric.run(data_slice[data_slice[self.filter_col] == 'i'], slice_point) # if using ExgalM5
+        result["riz_exptime"] = self.riz_exptime_metric.run(data_slice, slice_point) 
 
         return result

From 29185d13a186183e7036d49fedfe083c7978739d Mon Sep 17 00:00:00 2001
From: Boris Leistedt <boris.leistedt@gmail.com>
Date: Fri, 19 Apr 2024 16:55:41 +0100
Subject: [PATCH 25/31] added docstrings

---
 .../maf/metrics/cosmology_summary_metrics.py  | 283 +++++++++---------
 rubin_sim/maf/metrics/uniformity_metrics.py   |  47 ++-
 tests/maf/test_summarymetrics.py              |   1 +
 3 files changed, 177 insertions(+), 154 deletions(-)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index f0e5a6657..92054ddbf 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -177,8 +177,8 @@ class TomographicClusteringSigma8biasMetric(BaseMetric):
     """Compute bias on sigma8 due to spurious contamination of density maps.
     Run as summary metric on NestedLinearMultibandModelMetric.
 
-    point of contact / contributors: TODO
-    
+    Points of contact / contributors: Boris Leistedt
+
     Parameters
     ----------
     density_tomograph_model : `dict`
@@ -374,12 +374,11 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
             print(sigma8_model, sigma8_fit, sigma8_error, results_sigma8_bias)
             return results_sigma8_bias
 
-          
-class UniformMeanzBiasMetric(BaseMetric):
 
-    """This calculates the bias in the weak lensing power given 
+class UniformMeanzBiasMetric(BaseMetric):
+    """This calculates the bias in the weak lensing power given
        the scatter in the redshift of the tomographic sample
-    induced by survey non-uniformity. 
+    induced by survey non-uniformity.
 
     PREVIOUS UN-NESTED VERSION -- See Below for nested version inspired by Boris
 
@@ -392,8 +391,8 @@ class UniformMeanzBiasMetric(BaseMetric):
     Returns
     -------
     result : `float` array
-        The ratio of this bias to the desired DESC y1 upper bound on the bias, and the ratio 
-        between the clbias and the y10 DESC SRD requirement. 
+        The ratio of this bias to the desired DESC y1 upper bound on the bias, and the ratio
+        between the clbias and the y10 DESC SRD requirement.
         Desired values are less than 1 by Y10.
 
     Notes
@@ -403,21 +402,20 @@ class UniformMeanzBiasMetric(BaseMetric):
     output of Exgalm5_with_cuts.
     """
 
-    def __init__(self, filter_list="filters",year=10, n_filters=6,**kwargs):
+    def __init__(self, filter_list="filters", year=10, n_filters=6, **kwargs):
         self.year = year
         self.filter_list = filter_list
         self.exgal_m5 = ExgalM5(m5_col=m5_col, units=units)
-        
-        super().__init__(col="metricdata", mask_val=-666, **kwargs)
 
+        super().__init__(col="metricdata", mask_val=-666, **kwargs)
 
     def run(self, data_slice, slice_point=None):
 
-        result = np.empty(1, dtype=[("name", np.str_, 20), ("y1ratio", float),("y10ratio",float)])
+        result = np.empty(1, dtype=[("name", np.str_, 20), ("y1ratio", float), ("y10ratio", float)])
         result["name"][0] = "UniformMeanzBiasMetric"
 
         def compute_dzfromdm(zbins, band_ind, year):
-            """ This computes the dm/dz relationship calibrated from simulations
+            """This computes the dm/dz relationship calibrated from simulations
             by Jeff Newmann.
 
             Parameters
@@ -425,33 +423,33 @@ def compute_dzfromdm(zbins, band_ind, year):
             zbins : `int`
                 The number of tomographic bins considered. For now this is zbins < 5
             filter : `str`
-                The assumed filter band 
+                The assumed filter band
 
             Returns
             -------
-            dzdminterp : `float` 
+            dzdminterp : `float`
                 The interpolated value of the derivative dz/dm
             meanzinterp : `float` array
                 The meanz in each tomographic bin.
-            
+
             """
             import pandas as pd
 
-            filter_list=["u","g","r","i","z","y"]
-            band_ind =filter_list.index(filter)
-            
-            deriv = pd.read_pickle('uniformity_pkl/meanzderiv.pkl')
+            filter_list = ["u", "g", "r", "i", "z", "y"]
+            band_ind = filter_list.index(filter)
+
+            deriv = pd.read_pickle("uniformity_pkl/meanzderiv.pkl")
             # pkl file of derivatives with 10 years, 7 bands (ugrizY and combined), 5 bins
-            zvals = pd.read_pickle('uniformity_pkl/meanzsy%i.pkl'%(year+1)) 
+            zvals = pd.read_pickle("uniformity_pkl/meanzsy%i.pkl" % (year + 1))
             # pkl file of mean z values for a given year over 5 z bins, 7 bands (ugrizY and combined),
             # for a fixed delta density index (index 5 assumed below is for zero m_5 shift)
-            meanzinterp = zvals[0:zbins,band_ind,5]
-            dzdminterp = np.abs(deriv[year,band_ind,0:zbins])
+            meanzinterp = zvals[0:zbins, band_ind, 5]
+            dzdminterp = np.abs(deriv[year, band_ind, 0:zbins])
 
             return dzdminterp, meanzinterp
 
-        def use_zbins(meanz_vals, figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0]),  figure_9_width=0.2):
-            """ This computes which redshift bands are within the range 
+        def use_zbins(meanz_vals, figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0]), figure_9_width=0.2):
+            """This computes which redshift bands are within the range
             specified in https://arxiv.org/pdf/2305.15406.pdf and can safely be used
             to compute what Cl bias result from z fluctuations caused by rms variations in the m5.
 
@@ -460,23 +458,23 @@ def use_zbins(meanz_vals, figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0]),  figur
             ----------
             meanz_vals : `float` array
                 Array of meanz values to be used.
-            
+
             Returns
             -------
             use_bins : `boolean` array
-                An array of boolean values of length meanz_vals 
-            
+                An array of boolean values of length meanz_vals
+
             """
-            max_z_use = np.max(figure_9_mean_z)+2*figure_9_width
+            max_z_use = np.max(figure_9_mean_z) + 2 * figure_9_width
             use_bins = meanz_vals < max_z_use
-            
+
             return use_bins
 
-        def compute_Clbias(meanz_vals,scatter_mean_z_values):
-            """ This computes the Cl bias 
+        def compute_Clbias(meanz_vals, scatter_mean_z_values):
+            """This computes the Cl bias
             that results z fluctuations caused by rms variations in the m5.
 
-            
+
 
             Parameters
             ----------
@@ -486,7 +484,7 @@ def compute_Clbias(meanz_vals,scatter_mean_z_values):
             scatter_mean_z_values : `float` array
                 Array of rms values of the z fluctuations
 
-            
+
             Returns
             -------
             clbiasvals : `float` array
@@ -494,30 +492,31 @@ def compute_Clbias(meanz_vals,scatter_mean_z_values):
 
             mean_z_values_use :  `float` array
                 An array of the meanz values that are within the interpolation range of 2305.15406
-            
+
             Notes
             ------
             This interpolates from the Figure 9 in https://arxiv.org/pdf/2305.15406.pdf
 
             """
             import numpy as np
-            figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0])
-            figure_9_Clbias =np.array([1e-3, 2e-3, 5e-3, 1.1e-2])
-            figure_9_width=0.2
+
+            figure_9_mean_z = np.array([0.2, 0.4, 0.7, 1.0])
+            figure_9_Clbias = np.array([1e-3, 2e-3, 5e-3, 1.1e-2])
+            figure_9_width = 0.2
             figure_9_mean_z_scatter = 0.02
 
-            mzvals= np.array([float(mz) for mz in meanz_vals])
-            sctz = np.array([float(sz)for sz in scatter_mean_z_values])
-            
+            mzvals = np.array([float(mz) for mz in meanz_vals])
+            sctz = np.array([float(sz) for sz in scatter_mean_z_values])
+
             fit_res = np.polyfit(figure_9_mean_z, figure_9_Clbias, 2)
             poly_fit = np.poly1d(fit_res)
-            use_bins = use_zbins(meanz_vals,figure_9_mean_z, figure_9_width)
+            use_bins = use_zbins(meanz_vals, figure_9_mean_z, figure_9_width)
 
             mean_z_values_use = mzvals[use_bins]
             sctz_use = sctz[use_bins]
 
             Clbias = poly_fit(mean_z_values_use)
-            rescale_fac =  sctz_use / figure_9_mean_z_scatter
+            rescale_fac = sctz_use / figure_9_mean_z_scatter
             Clbias *= rescale_fac
             fit_res_bias = np.polyfit(mean_z_values_use, Clbias, 1)
             poly_fit_bias = np.poly1d(fit_res_bias)
@@ -525,9 +524,9 @@ def compute_Clbias(meanz_vals,scatter_mean_z_values):
             clbiasvals = poly_fit_bias(mean_z_values_use)
             return clbiasvals, mean_z_values_use
 
-        totdz=0
-        avmeanz=0
-        totclbias=0
+        totdz = 0
+        avmeanz = 0
+        totclbias = 0
         for filt in self.filter_list:
             d_s = data_slice[data_slice[self.filter_col] == filt]
             # calculate the lsstFilter-band coadded depth
@@ -535,29 +534,28 @@ def compute_Clbias(meanz_vals,scatter_mean_z_values):
 
             rmsval = np.std(coadd_depth)
 
-            dzdminterp, meanzinterp=compute_dzfromdm(self.zbins, filt,self.year)
-            stdz = [float(np.abs(dz))*float(rmsval) for dz in dzdminterp]
-    
-            clbias, meanz_use = compute_Clbias(meanzinterp,stdz)
+            dzdminterp, meanzinterp = compute_dzfromdm(self.zbins, filt, self.year)
+            stdz = [float(np.abs(dz)) * float(rmsval) for dz in dzdminterp]
+
+            clbias, meanz_use = compute_Clbias(meanzinterp, stdz)
 
-            totdz+=[float(st**2) for st in stdz]
-            totclbias+=clbias
-            avmeanz+=meanzinterp
-        
+            totdz += [float(st**2) for st in stdz]
+            totclbias += clbias
+            avmeanz += meanzinterp
 
         y10_req = 0.003
         y1_goal = 0.013
 
-        #clbiastot = np.max(clbias)
-        y10ratio = totclbias/y10_req
-        y1ratio = totclbias/y1_goal
+        # clbiastot = np.max(clbias)
+        y10ratio = totclbias / y10_req
+        y1ratio = totclbias / y1_goal
+
+        result["y1ratio"] = y1ratio
+        result["y10ratio"] = y10ratio
 
-        result["y1ratio"]=y1ratio
-        result["y10ratio"]=y10ratio
-        
         return result
 
-    
+
 class MultibandMeanzBiasMetric(BaseMetric):
     """
     Run as summary metric on MultibandExgalM5.
@@ -569,7 +567,7 @@ class MultibandMeanzBiasMetric(BaseMetric):
     Returns
     -------
     result : `float`
-        
+
 
     Notes
     -----
@@ -581,8 +579,8 @@ class MultibandMeanzBiasMetric(BaseMetric):
     This summary metric takes those depths and reads the derivatives [more to come here]...
     """
 
-    def __init__(self, filter_list=["u","g","r","i","z","y"] ,year=10, n_filters=6,**kwargs):
-        
+    def __init__(self, filter_list=["u", "g", "r", "i", "z", "y"], year=10, n_filters=6, **kwargs):
+
         super().__init__(col="metricdata", **kwargs)
         # Set mask_val, so that we receive metric_values.filled(mask_val)
         self.mask_val = hp.UNSEEN
@@ -594,7 +592,7 @@ def __init__(self, filter_list=["u","g","r","i","z","y"] ,year=10, n_filters=6,*
     def run(self, data_slice, slice_point=None):
 
         def compute_dzfromdm(zbins, band_ind, year):
-            """ This computes the dm/dz relationship calibrated from simulations
+            """This computes the dm/dz relationship calibrated from simulations
             by Jeff Newmann.
 
             Parameters
@@ -602,35 +600,34 @@ def compute_dzfromdm(zbins, band_ind, year):
             zbins : `int`
                 The number of tomographic bins considered. For now this is zbins < 5
             filter : `str`
-                The assumed filter band 
+                The assumed filter band
 
             Returns
             -------
-            dzdminterp : `float` 
+            dzdminterp : `float`
                 The interpolated value of the derivative dz/dm
             meanzinterp : `float` array
                 The meanz in each tomographic bin.
-            
+
             """
             import pandas as pd
 
-            filter_list=["u","g","r","i","z","y"]
-            band_ind =filter_list.index(filter)
-            
-            deriv = pd.read_pickle('uniformity_pkl/meanzderiv.pkl')
+            filter_list = ["u", "g", "r", "i", "z", "y"]
+            band_ind = filter_list.index(filter)
+
+            deriv = pd.read_pickle("uniformity_pkl/meanzderiv.pkl")
             # pkl file of derivatives with 10 years, 7 bands (ugrizY and combined), 5 bins
-            zvals = pd.read_pickle('uniformity_pkl/meanzsy%i.pkl'%(year+1)) 
+            zvals = pd.read_pickle("uniformity_pkl/meanzsy%i.pkl" % (year + 1))
             # pkl file of mean z values for a given year over 5 z bins, 7 bands (ugrizY and combined),
             # for a fixed delta density index (index 5 assumed below is for zero m_5 shift)
-            meanzinterp = zvals[0:zbins,band_ind,5]
-            dzdminterp = np.abs(deriv[year,band_ind,0:zbins])
+            meanzinterp = zvals[0:zbins, band_ind, 5]
+            dzdminterp = np.abs(deriv[year, band_ind, 0:zbins])
 
             return dzdminterp, meanzinterp
 
-
         ## Not entirely sure if we should include these here or in a separate module/file
-        def use_zbins(meanz_vals, figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0]),  figure_9_width=0.2):
-            """ This computes which redshift bands are within the range 
+        def use_zbins(meanz_vals, figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0]), figure_9_width=0.2):
+            """This computes which redshift bands are within the range
             specified in https://arxiv.org/pdf/2305.15406.pdf and can safely be used
             to compute what Cl bias result from z fluctuations caused by rms variations in the m5.
 
@@ -639,23 +636,23 @@ def use_zbins(meanz_vals, figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0]),  figur
             ----------
             meanz_vals : `float` array
                 Array of meanz values to be used.
-            
+
             Returns
             -------
             use_bins : `boolean` array
-                An array of boolean values of length meanz_vals 
-            
+                An array of boolean values of length meanz_vals
+
             """
-            max_z_use = np.max(figure_9_mean_z)+2*figure_9_width
+            max_z_use = np.max(figure_9_mean_z) + 2 * figure_9_width
             use_bins = meanz_vals < max_z_use
-            
+
             return use_bins
 
-        def compute_Clbias(meanz_vals,scatter_mean_z_values):
-            """ This computes the Cl bias 
+        def compute_Clbias(meanz_vals, scatter_mean_z_values):
+            """This computes the Cl bias
             that results z fluctuations caused by rms variations in the m5.
 
-            
+
 
             Parameters
             ----------
@@ -665,7 +662,7 @@ def compute_Clbias(meanz_vals,scatter_mean_z_values):
             scatter_mean_z_values : `float` array
                 Array of rms values of the z fluctuations
 
-            
+
             Returns
             -------
             clbiasvals : `float` array
@@ -673,39 +670,39 @@ def compute_Clbias(meanz_vals,scatter_mean_z_values):
 
             mean_z_values_use :  `float` array
                 An array of the meanz values that are within the interpolation range of 2305.15406
-            
+
             Notes
             ------
             This interpolates from the Figure 9 in https://arxiv.org/pdf/2305.15406.pdf
 
             """
             import numpy as np
-            figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0])
-            figure_9_Clbias =np.array([1e-3, 2e-3, 5e-3, 1.1e-2])
-            figure_9_width=0.2
+
+            figure_9_mean_z = np.array([0.2, 0.4, 0.7, 1.0])
+            figure_9_Clbias = np.array([1e-3, 2e-3, 5e-3, 1.1e-2])
+            figure_9_width = 0.2
             figure_9_mean_z_scatter = 0.02
 
-            mzvals= np.array([float(mz) for mz in meanz_vals])
-            sctz = np.array([float(sz)for sz in scatter_mean_z_values])
-            
+            mzvals = np.array([float(mz) for mz in meanz_vals])
+            sctz = np.array([float(sz) for sz in scatter_mean_z_values])
+
             fit_res = np.polyfit(figure_9_mean_z, figure_9_Clbias, 2)
             poly_fit = np.poly1d(fit_res)
-            use_bins = use_zbins(meanz_vals,figure_9_mean_z, figure_9_width)
+            use_bins = use_zbins(meanz_vals, figure_9_mean_z, figure_9_width)
 
             mean_z_values_use = mzvals[use_bins]
             sctz_use = sctz[use_bins]
 
             Clbias = poly_fit(mean_z_values_use)
-            rescale_fac =  sctz_use / figure_9_mean_z_scatter
+            rescale_fac = sctz_use / figure_9_mean_z_scatter
             Clbias *= rescale_fac
             fit_res_bias = np.polyfit(mean_z_values_use, Clbias, 1)
             poly_fit_bias = np.poly1d(fit_res_bias)
 
             clbiasvals = poly_fit_bias(mean_z_values_use)
             return clbiasvals, mean_z_values_use
-        
 
-        result = np.empty(1, dtype=[("name", np.str_, 20), ("y1ratio", float),("y10ratio",float)])
+        result = np.empty(1, dtype=[("name", np.str_, 20), ("y1ratio", float), ("y10ratio", float)])
         result["name"][0] = "MultibandMeanzBiasMetric"
 
         # Technically don't need this for now (isn't used in previous one)
@@ -734,56 +731,60 @@ def compute_Clbias(meanz_vals,scatter_mean_z_values):
                 for x in data_slice_arr
             ]
         )  # rms values
-        
-        totdz=0
-        avmeanz=0
-        totclbias=0
-
-        for i,filt in enumerate(self.filter_list):
-            dzdminterp, meanzinterp=compute_dzfromdm(self.zbins, filt,self.year)
-            stdz = [float(np.abs(dz))*float(rmsarray[i]) for dz in dzdminterp]
-    
-            clbias, meanz_use = compute_Clbias(meanzinterp,stdz)
-
-            totdz+=[float(st**2) for st in stdz]
-            totclbias+=clbias
-            avmeanz+=meanzinterp
-
-    # These should be included in self rather than hard coded
+
+        totdz = 0
+        avmeanz = 0
+        totclbias = 0
+
+        for i, filt in enumerate(self.filter_list):
+            dzdminterp, meanzinterp = compute_dzfromdm(self.zbins, filt, self.year)
+            stdz = [float(np.abs(dz)) * float(rmsarray[i]) for dz in dzdminterp]
+
+            clbias, meanz_use = compute_Clbias(meanzinterp, stdz)
+
+            totdz += [float(st**2) for st in stdz]
+            totclbias += clbias
+            avmeanz += meanzinterp
+
+        # These should be included in self rather than hard coded
         y10_req = 0.003
         y1_goal = 0.013
 
-        #clbiastot = np.max(clbias) # if adding doesn't work over z range -- CHECK
-        y10ratio =totclbias/y10_req
-        y1ratio = totclbias/y1_goal
+        # clbiastot = np.max(clbias) # if adding doesn't work over z range -- CHECK
+        y10ratio = totclbias / y10_req
+        y1ratio = totclbias / y1_goal
+
+        result["y1ratio"] = y1ratio
+        result["y10ratio"] = y10ratio
 
-        result["y1ratio"]=y1ratio
-        result["y10ratio"]=y10ratio
 
 class UniformAreaFoMFractionMetric(BaseMetric):
-    """?
-    Run as summary metric on RIZDetectionCoaddExposureTime.
+    """
+    Run as summary metric on NestedRIZExptimeExgalM5Metric.
+
+    This metric StaticProbesFoMEmulatorMetric
+
+    Points of contact / contributors: Rachel Mandelbaum, Boris Leistedt
 
-    point of contact / contributors: TODO
-    
     Parameters
     ----------
-    ?
-
+    year: `int`
+        year of observation, in order to adjust the cut on the depth fluctuations for the stipe detection
+    nside: `int`
+        must be the nside at which the base metric NestedRIZExptimeExgalM5Metric is calculated at
+    verbose: bool, optional
+        if true, will display the segmentation maps and the areas and FOMs of the two regions found
     Returns
     -------
-    ?
-
-    Notes
-    -----
-    ?
+    result: `float`
+        The ratio of the FOMs of the two areas found
     """
 
     def __init__(
         self,
         year,
+        nside,
         verbose=True,
-        nside=32,
         **kwargs,
     ):
         self.year = year
@@ -806,17 +807,13 @@ def run(self, data_slice, slice_point=None):
             self.mask_val_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()
         ]
         data_slice_arr = np.asarray(data_slice_list, dtype=self.mask_val_arr.dtype)
-        if True:  # apply mask
-            ind = data_slice_arr["riz_exptime"] == hp.UNSEEN
-            ind |= data_slice_arr["riz_exptime"] == -666
-            # ind |= data_slice_arr['riz_exptime'] == 6066
-            ind |= ~np.isfinite(data_slice_arr["riz_exptime"])
-            # ind |= data_slice_arr['exgal_m5'] == 6066
-            ind |= data_slice_arr["exgal_m5"] == hp.UNSEEN
-            ind |= data_slice_arr["exgal_m5"] == -666
-            ind |= ~np.isfinite(data_slice_arr["exgal_m5"])
-            data_slice_arr["exgal_m5"][ind.ravel()] = hp.UNSEEN
-            data_slice_arr["riz_exptime"][ind.ravel()] = hp.UNSEEN
+        # a bit of gymnastics to make sure all bad values (nan, -666) are recast as hp.UNSEEN
+        ind = data_slice_arr["riz_exptime"] == -666
+        ind |= ~np.isfinite(data_slice_arr["riz_exptime"])
+        ind = data_slice_arr["exgal_m5"] == -666
+        ind |= ~np.isfinite(data_slice_arr["exgal_m5"])
+        data_slice_arr["exgal_m5"][ind.ravel()] = hp.UNSEEN
+        data_slice_arr["riz_exptime"][ind.ravel()] = hp.UNSEEN
         # sanity check
         nside = hp.npix2nside(data_slice["metricdata"].size)
         assert nside == self.nside
@@ -947,7 +944,7 @@ def make_clustering_dataset(depth_map, maskval=0, priority_fac=0.9, nside=64):
             return my_data
 
         # Check for stripiness
-        use_threshold = 0.7/self.year
+        use_threshold = 0.7 / self.year
         stripes = has_stripes(data_slice_arr["riz_exptime"].ravel(), nside, threshold=use_threshold)
 
         if not stripes:
diff --git a/rubin_sim/maf/metrics/uniformity_metrics.py b/rubin_sim/maf/metrics/uniformity_metrics.py
index b2db98b4c..926679938 100644
--- a/rubin_sim/maf/metrics/uniformity_metrics.py
+++ b/rubin_sim/maf/metrics/uniformity_metrics.py
@@ -159,6 +159,8 @@ class NestedLinearMultibandModelMetric(BaseMetric):
     for multiple redshift bins (thus it is a nested metric).
     For a single bin, see LinearMultibandModelMetric.
 
+    Points of contact / contributors: Boris Leistedt
+
     Parameters
     ----------
     arr_of_model_dicts : `list` [ `dicts` ]
@@ -309,9 +311,7 @@ def run(self, data_slice, slice_point=None):
 
 
 class MultibandExgalM5(BaseMetric):
-    """Calculate multiple linear combinations of depths.
-
-    """
+    """Calculate multiple linear combinations of depths."""
 
     def __init__(
         self,
@@ -356,7 +356,6 @@ def run(self, data_slice, slice_point=None):
         if n_filters < self.n_filters:
             return self.bad_val_arr
 
-
         depths = np.vstack(
             [
                 self.exgal_m5.run(data_slice[data_slice[self.filter_col] == lsst_filter], slice_point)
@@ -366,8 +365,39 @@ def run(self, data_slice, slice_point=None):
 
         return depths.ravel()
 
+
 class NestedRIZExptimeExgalM5Metric(BaseMetric):
-    """TODO"""
+    """
+    This is a simple wrapper metric which returns values of both RIZDetectionCoaddExposureTime and ExgalM5WithCuts
+    in a recarray with cvolumns ["exgal_m5", "riz_exptime"]
+
+    Points of contact / contributors: Rachel Mandelbaum, Boris Leistedt
+
+    Parameters
+    ----------
+    extinction_cut : `float`, optional
+        E(B-V) cut on extinction (0.2 by default) (for ExgalM5WithCuts).
+    n_filters : `int`, optional
+        Cut on the number of filters required (6 by default) (for ExgalM5WithCuts).
+    lsst_filter : `str`, optional
+        The filter choice for ExgalM5WithCuts
+    exptime_col: `str`, optional
+        Column name for the exposure time (for RIZDetectionCoaddExposureTime).
+    m5_col : `str`, optional
+        Column name for the m5 depth (for ExgalM5WithCuts).
+    filter_col : `str`, optional
+        Column name for the filter.
+    units : `str`, optional
+        Label for "units" in the output, for use in plots.
+    badval : `float`, optional
+        Value to return for metric failure.
+
+    Returns
+    -------
+    result : `recarray`, np.array(dtype=[("exgal_m5", float), ("riz_exptime", float)])
+        The values of RIZDetectionCoaddExposureTime and ExgalM5WithCuts
+
+    """
 
     def __init__(
         self,
@@ -401,10 +431,6 @@ def __init__(
             badval=badval,
             n_filters=n_filters,
         )
-        #self.exgalm5_metric = ExgalM5(
-        #    m5_col=m5_col,
-        #    filter_col=filter_col
-        #)
 
         self.metric_dtype = "object"
 
@@ -415,7 +441,6 @@ def run(self, data_slice, slice_point):
         types = [float] * 2
         result = np.zeros(1, dtype=list(zip(names, types)))
         result["exgal_m5"] = self.exgalm5_metric.run(data_slice, slice_point)  # if using ExgalM5WithCuts
-        #result["exgal_m5"] = self.exgalm5_metric.run(data_slice[data_slice[self.filter_col] == 'i'], slice_point) # if using ExgalM5
-        result["riz_exptime"] = self.riz_exptime_metric.run(data_slice, slice_point) 
+        result["riz_exptime"] = self.riz_exptime_metric.run(data_slice, slice_point)
 
         return result
diff --git a/tests/maf/test_summarymetrics.py b/tests/maf/test_summarymetrics.py
index 9ea65be34..8ed84be99 100644
--- a/tests/maf/test_summarymetrics.py
+++ b/tests/maf/test_summarymetrics.py
@@ -77,5 +77,6 @@ def test_zeropoint_metric(self):
         result = metric.run(data)
         np.testing.assert_equal(result, np.ones(10, float) + 5.5)
 
+
 if __name__ == "__main__":
     unittest.main()

From 76313fb45169d7cc89290c391fdd7e6ad0ad6e1a Mon Sep 17 00:00:00 2001
From: Boris Leistedt <boris.leistedt@gmail.com>
Date: Fri, 3 May 2024 09:41:01 +0100
Subject: [PATCH 26/31] Added docstring for UniformAreaFoMFractionMetric,
 written by Rachel

---
 rubin_sim/maf/metrics/cosmology_summary_metrics.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 92054ddbf..98349730c 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -762,7 +762,17 @@ class UniformAreaFoMFractionMetric(BaseMetric):
     """
     Run as summary metric on NestedRIZExptimeExgalM5Metric.
 
-    This metric StaticProbesFoMEmulatorMetric
+    This metric uses maps of the combined RIZ exposure time and i-band depth maps (with a consistent set of area cuts)
+    to identify potential reductions in cosmological constraining power due to substantial large-scale power
+    in non-uniform coadds at a particular data release.
+    The RIZ exposure time map is used to identify whether there are residual rolling features.
+    If not, the metric returns 1. If there are such features, then the region is segmented into similar-depth regions
+    and the one with the largest cosmological constraining power is presumed to be used for science.
+    In that case, the metric returns the 3x2pt FoM (StaticProbesFoMEmulatorMetric,
+    quantifying weak lensing and large-scale structure constraining power) for the largest of those regions,
+    divided by that for the full region if it had been usable.
+
+
 
     Points of contact / contributors: Rachel Mandelbaum, Boris Leistedt
 

From 194a168886749b669e8937f2d86201516b166ebd Mon Sep 17 00:00:00 2001
From: Renee Hlozek <renee.hlozek@gmail.com>
Date: Wed, 8 May 2024 02:09:13 -0700
Subject: [PATCH 27/31] added meanz tomoography

---
 rubin_sim/maf/metrics/tomography_models.py | 166 +++++++++++++++++++++
 1 file changed, 166 insertions(+)

diff --git a/rubin_sim/maf/metrics/tomography_models.py b/rubin_sim/maf/metrics/tomography_models.py
index 681cd7a8a..9c042290f 100644
--- a/rubin_sim/maf/metrics/tomography_models.py
+++ b/rubin_sim/maf/metrics/tomography_models.py
@@ -1046,3 +1046,169 @@
         ],
     },
 }
+
+
+# meanz is redshift per band
+# dz_dm5 is the absolute value of derivative
+MEANZ_TOMOGRAPHY_MODEL = {
+    'year1': {
+        'dz_dm5': [
+        {'cst': 0.0, 'u': 0.0061936031125660335, 'g': 0.0017477457637154574, 'r': 0.00857817319840909, 'i': 0.002261268563912217, 'z': 0.003615973972222958, 'y': 0.001392612510731922},
+        {'cst': 0.0, 'u': 0.004345685997944008, 'g': 0.0001066449832057932, 'r': 0.02111794222593694, 'i': 0.006836212378911876, 'z': 0.006273169567217962, 'y': 0.005162933422399783},
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+        ],
+        'meanz': [
+        {'cst': 0.0, 'u': 0.1412097514129552, 'g': 0.1406497428388376, 'r': 0.1408687434840175, 'i': 0.14179709129841037, 'z': 0.1425811131555499, 'y': 0.14130581564879285},
+        {'cst': 0.0, 'u': 0.31887802365309226, 'g': 0.3190376986628495, 'r': 0.3209289780716591, 'i': 0.32072880008997834, 'z': 0.32187019107155196, 'y': 0.3201021125646},
+        {'cst': 0.0, 'u': 0.4978288897927948, 'g': 0.4979842029016838, 'r': 0.4978477649299082, 'i': 0.4976698193750615, 'z': 0.4959446673587056, 'y': 0.4966033026751485},
+        {'cst': 0.0, 'u': 0.6950850709405102, 'g': 0.6928492900731955, 'r': 0.6927398597939921, 'i': 0.6930562085764114, 'z': 0.6944189402802977, 'y': 0.6923577239768269},
+        {'cst': 0.0, 'u': 0.8682973716127108, 'g': 0.8704231456789853, 'r': 0.8727016782075796, 'i': 0.870275847683205, 'z': 0.8705515709345578, 'y': 0.8734045000404805},
+        ],
+    },
+    'year10': {
+        'dz_dm5': [
+        {'cst': 0.0, 'u': 0.003354880770259003, 'g': 0.001458765181158561, 'r': 0.0055856686517475745, 'i': 0.0009030647353411612, 'z': 0.0032068067215789953, 'y': 0.0019167284161829108},
+        {'cst': 0.0, 'u': 0.0049884576772368005, 'g': 0.005428041699247426, 'r': 0.013727547832691051, 'i': 0.005432212663811623, 'z': 0.006042559409003354, 'y': 0.00434503390924071},
+        {'cst': 0.0, 'u': 0.01032419109971061, 'g': 0.00525952527032317, 'r': 0.0005514979865284969, 'i': 0.008857060937095687, 'z': 0.013099492539662139, 'y': 0.005312102829780564},
+        {'cst': 0.0, 'u': 0.013229857206590936, 'g': 0.007145690671123007, 'r': 0.004670048309991343, 'i': 0.024555656028367286, 'z': 0.019766085030518556, 'y': 0.010660490885596226},
+        {'cst': 0.0, 'u': 0.014555683932784726, 'g': 0.01291152464999209, 'r': 0.0009053946982488664, 'i': 0.002285602665316371, 'z': 0.016921396981975284, 'y': 0.028678557472965396},
+        ],
+        'meanz': [
+        {'cst': 0.0, 'u': 0.1412097514129552, 'g': 0.1406497428388376, 'r': 0.1408687434840175, 'i': 0.14179709129841037, 'z': 0.1425811131555499, 'y': 0.14130581564879285},
+        {'cst': 0.0, 'u': 0.31887802365309226, 'g': 0.3190376986628495, 'r': 0.3209289780716591, 'i': 0.32072880008997834, 'z': 0.32187019107155196, 'y': 0.3201021125646},
+        {'cst': 0.0, 'u': 0.4978288897927948, 'g': 0.4979842029016838, 'r': 0.4978477649299082, 'i': 0.4976698193750615, 'z': 0.4959446673587056, 'y': 0.4966033026751485},
+        {'cst': 0.0, 'u': 0.6950850709405102, 'g': 0.6928492900731955, 'r': 0.6927398597939921, 'i': 0.6930562085764114, 'z': 0.6944189402802977, 'y': 0.6923577239768269},
+        {'cst': 0.0, 'u': 0.8682973716127108, 'g': 0.8704231456789853, 'r': 0.8727016782075796, 'i': 0.870275847683205, 'z': 0.8705515709345578, 'y': 0.8734045000404805},
+        ],
+    },
+}
\ No newline at end of file

From 1a57bc0727417c4183183b74d75aba80cf81f210 Mon Sep 17 00:00:00 2001
From: Renee Hlozek <renee.hlozek@gmail.com>
Date: Wed, 8 May 2024 02:20:44 -0700
Subject: [PATCH 28/31] updated comments

---
 rubin_sim/maf/metrics/tomography_models.py | 23 ++++++++++++++++++----
 1 file changed, 19 insertions(+), 4 deletions(-)

diff --git a/rubin_sim/maf/metrics/tomography_models.py b/rubin_sim/maf/metrics/tomography_models.py
index 9c042290f..db820afba 100644
--- a/rubin_sim/maf/metrics/tomography_models.py
+++ b/rubin_sim/maf/metrics/tomography_models.py
@@ -1,9 +1,9 @@
-__all__ = ("DENSITY_TOMOGRAPHY_MODEL",)
+__all__ = ("DENSITY_TOMOGRAPHY_MODEL","MEANZ_TOMOGRAPHY_MODEL")
 
 import numpy as np
 
 """A dictionary of TOMOGRAPHY models for use with the
-cosmology summary metrics (TomographicClusteringSigma8bias).
+cosmology summary metrics (TomographicClusteringSigma8bias, MultibandMeanzBiasMetric).
 
 This dictionary is derived from work shown in 
 https://github.com/ixkael/ObsStrat/blob/
@@ -43,6 +43,22 @@
 # https://github.com/ixkael/ObsStrat/blob/meanz_uniformity_maf/code/
 # meanz_uniformity/romanrubinmock_for_sigma8tomography.ipynb
 
+# The MEANZ_TOMOGRAPHY_MODEL contains a nested dictionary of:
+
+# 'meanz' whichh is the redshift per band
+# 'dz_dm5' the absolute value of the derivative of z in a bin as a function 
+# of the depth, m5 magnitude
+# this is later used to translate fluctuations in depth to fluctuations in tomographic
+# redshift 
+
+# The first set of keys are the years (year1, ..., year10),
+# since this would change typical depth and galaxy catalog cuts.
+# In what follows we have 5 tomographic bins.
+
+# Each dictionary must have keys that are the lsst bands.
+# If some are missing they are ignored in the linear model.
+# These badnpasses are the ones which will be fed to
+# NestedLinearMultibandModelMetric.
 
 DENSITY_TOMOGRAPHY_MODEL = {
     "year1": {
@@ -1048,8 +1064,7 @@
 }
 
 
-# meanz is redshift per band
-# dz_dm5 is the absolute value of derivative
+
 MEANZ_TOMOGRAPHY_MODEL = {
     'year1': {
         'dz_dm5': [

From 7171a457c0c6af065fa710151c0d6559cef3bf0b Mon Sep 17 00:00:00 2001
From: Renee Hlozek <renee.hlozek@gmail.com>
Date: Wed, 8 May 2024 02:48:49 -0700
Subject: [PATCH 29/31] cleaned up multibandmetric meanz metric

---
 .../maf/metrics/cosmology_summary_metrics.py  |  225 +---
 rubin_sim/maf/metrics/tomography_models.py    |    6 +
 .../maf/metrics/uniformity_pkl/bands.pkl      |  Bin 49 -> 0 bytes
 .../maf/metrics/uniformity_pkl/deltas.pkl     |  Bin 235 -> 0 bytes
 .../metrics/uniformity_pkl/densityderiv.pkl   |  Bin 2954 -> 0 bytes
 .../maf/metrics/uniformity_pkl/densityy1.pkl  |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/densityy10.pkl |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/densityy2.pkl  |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/densityy3.pkl  |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/densityy4.pkl  |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/densityy5.pkl  |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/densityy6.pkl  |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/densityy7.pkl  |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/densityy8.pkl  |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/densityy9.pkl  |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/hgb_model.pkl  |  Bin 369355 -> 0 bytes
 .../maf/metrics/uniformity_pkl/maxzs.pkl      |  Bin 187 -> 0 bytes
 .../maf/metrics/uniformity_pkl/meanzderiv.pkl |  Bin 2954 -> 0 bytes
 .../maf/metrics/uniformity_pkl/meanzsy1.pkl   |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/meanzsy10.pkl  |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/meanzsy2.pkl   |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/meanzsy3.pkl   |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/meanzsy4.pkl   |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/meanzsy5.pkl   |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/meanzsy6.pkl   |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/meanzsy7.pkl   |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/meanzsy8.pkl   |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/meanzsy9.pkl   |  Bin 3850 -> 0 bytes
 .../maf/metrics/uniformity_pkl/minzs.pkl      |  Bin 187 -> 0 bytes
 .../uniformity_pkl/simulation_calcs.ipynb     |  633 ---------
 .../uniformity_pkl/simulation_photoz_rr.ipynb | 1193 -----------------
 .../maf/metrics/uniformity_pkl/years.pkl      |  Bin 36 -> 0 bytes
 32 files changed, 38 insertions(+), 2019 deletions(-)
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/bands.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/deltas.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityderiv.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy1.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy10.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy2.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy3.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy4.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy5.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy6.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy7.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy8.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/densityy9.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/hgb_model.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/maxzs.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzderiv.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy1.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy10.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy2.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy3.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy4.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy5.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy6.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy7.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy8.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/meanzsy9.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/minzs.pkl
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/simulation_calcs.ipynb
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/simulation_photoz_rr.ipynb
 delete mode 100644 rubin_sim/maf/metrics/uniformity_pkl/years.pkl

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 98349730c..4dde676e5 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -2,7 +2,7 @@
     "TotalPowerMetric",
     "StaticProbesFoMEmulatorMetricSimple",
     "TomographicClusteringSigma8biasMetric",
-    "UniformMeanzBiasMetric",
+    "MultibandMeanzBiasMetric",
 )
 
 import warnings
@@ -375,12 +375,10 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
             return results_sigma8_bias
 
 
-class UniformMeanzBiasMetric(BaseMetric):
-    """This calculates the bias in the weak lensing power given
-       the scatter in the redshift of the tomographic sample
-    induced by survey non-uniformity.
 
-    PREVIOUS UN-NESTED VERSION -- See Below for nested version inspired by Boris
+class MultibandMeanzBiasMetric(BaseMetric):
+    """
+    Run as summary metric on MultibandExgalM5.
 
     Parameters
     ----------
@@ -388,6 +386,16 @@ class UniformMeanzBiasMetric(BaseMetric):
         The year of the survey to calculate the bias.
         This is used to derive the dm/dz derivative used to translate m5 rms into dz rms.
 
+    meanz_tomograph_model : `dict`
+        dictionary containing models calculated for fiducial N(z):
+
+        meanz: numpy.float 
+            the meanz within a tomographic bin at a given band
+        dz_dm5: numpy.float
+            the absolute value of the derivative of z in a bin as a function 
+            of the depth, m5 magnitude. This is later used to translate fluctuations
+            in depth to fluctuations in tomographic redshift
+
     Returns
     -------
     result : `float` array
@@ -395,179 +403,6 @@ class UniformMeanzBiasMetric(BaseMetric):
         between the clbias and the y10 DESC SRD requirement.
         Desired values are less than 1 by Y10.
 
-    Notes
-    -----
-
-    Note that this is truly a summary metric and should be run on the
-    output of Exgalm5_with_cuts.
-    """
-
-    def __init__(self, filter_list="filters", year=10, n_filters=6, **kwargs):
-        self.year = year
-        self.filter_list = filter_list
-        self.exgal_m5 = ExgalM5(m5_col=m5_col, units=units)
-
-        super().__init__(col="metricdata", mask_val=-666, **kwargs)
-
-    def run(self, data_slice, slice_point=None):
-
-        result = np.empty(1, dtype=[("name", np.str_, 20), ("y1ratio", float), ("y10ratio", float)])
-        result["name"][0] = "UniformMeanzBiasMetric"
-
-        def compute_dzfromdm(zbins, band_ind, year):
-            """This computes the dm/dz relationship calibrated from simulations
-            by Jeff Newmann.
-
-            Parameters
-            ----------
-            zbins : `int`
-                The number of tomographic bins considered. For now this is zbins < 5
-            filter : `str`
-                The assumed filter band
-
-            Returns
-            -------
-            dzdminterp : `float`
-                The interpolated value of the derivative dz/dm
-            meanzinterp : `float` array
-                The meanz in each tomographic bin.
-
-            """
-            import pandas as pd
-
-            filter_list = ["u", "g", "r", "i", "z", "y"]
-            band_ind = filter_list.index(filter)
-
-            deriv = pd.read_pickle("uniformity_pkl/meanzderiv.pkl")
-            # pkl file of derivatives with 10 years, 7 bands (ugrizY and combined), 5 bins
-            zvals = pd.read_pickle("uniformity_pkl/meanzsy%i.pkl" % (year + 1))
-            # pkl file of mean z values for a given year over 5 z bins, 7 bands (ugrizY and combined),
-            # for a fixed delta density index (index 5 assumed below is for zero m_5 shift)
-            meanzinterp = zvals[0:zbins, band_ind, 5]
-            dzdminterp = np.abs(deriv[year, band_ind, 0:zbins])
-
-            return dzdminterp, meanzinterp
-
-        def use_zbins(meanz_vals, figure_9_mean_z=np.array([0.2, 0.4, 0.7, 1.0]), figure_9_width=0.2):
-            """This computes which redshift bands are within the range
-            specified in https://arxiv.org/pdf/2305.15406.pdf and can safely be used
-            to compute what Cl bias result from z fluctuations caused by rms variations in the m5.
-
-
-            Parameters
-            ----------
-            meanz_vals : `float` array
-                Array of meanz values to be used.
-
-            Returns
-            -------
-            use_bins : `boolean` array
-                An array of boolean values of length meanz_vals
-
-            """
-            max_z_use = np.max(figure_9_mean_z) + 2 * figure_9_width
-            use_bins = meanz_vals < max_z_use
-
-            return use_bins
-
-        def compute_Clbias(meanz_vals, scatter_mean_z_values):
-            """This computes the Cl bias
-            that results z fluctuations caused by rms variations in the m5.
-
-
-
-            Parameters
-            ----------
-            meanz_vals : `float` array
-                Array of meanz values to be used.
-
-            scatter_mean_z_values : `float` array
-                Array of rms values of the z fluctuations
-
-
-            Returns
-            -------
-            clbiasvals : `float` array
-                An array of values of the clbias
-
-            mean_z_values_use :  `float` array
-                An array of the meanz values that are within the interpolation range of 2305.15406
-
-            Notes
-            ------
-            This interpolates from the Figure 9 in https://arxiv.org/pdf/2305.15406.pdf
-
-            """
-            import numpy as np
-
-            figure_9_mean_z = np.array([0.2, 0.4, 0.7, 1.0])
-            figure_9_Clbias = np.array([1e-3, 2e-3, 5e-3, 1.1e-2])
-            figure_9_width = 0.2
-            figure_9_mean_z_scatter = 0.02
-
-            mzvals = np.array([float(mz) for mz in meanz_vals])
-            sctz = np.array([float(sz) for sz in scatter_mean_z_values])
-
-            fit_res = np.polyfit(figure_9_mean_z, figure_9_Clbias, 2)
-            poly_fit = np.poly1d(fit_res)
-            use_bins = use_zbins(meanz_vals, figure_9_mean_z, figure_9_width)
-
-            mean_z_values_use = mzvals[use_bins]
-            sctz_use = sctz[use_bins]
-
-            Clbias = poly_fit(mean_z_values_use)
-            rescale_fac = sctz_use / figure_9_mean_z_scatter
-            Clbias *= rescale_fac
-            fit_res_bias = np.polyfit(mean_z_values_use, Clbias, 1)
-            poly_fit_bias = np.poly1d(fit_res_bias)
-
-            clbiasvals = poly_fit_bias(mean_z_values_use)
-            return clbiasvals, mean_z_values_use
-
-        totdz = 0
-        avmeanz = 0
-        totclbias = 0
-        for filt in self.filter_list:
-            d_s = data_slice[data_slice[self.filter_col] == filt]
-            # calculate the lsstFilter-band coadded depth
-            coadd_depth = self.exgal_m5.run(d_s, slice_point)
-
-            rmsval = np.std(coadd_depth)
-
-            dzdminterp, meanzinterp = compute_dzfromdm(self.zbins, filt, self.year)
-            stdz = [float(np.abs(dz)) * float(rmsval) for dz in dzdminterp]
-
-            clbias, meanz_use = compute_Clbias(meanzinterp, stdz)
-
-            totdz += [float(st**2) for st in stdz]
-            totclbias += clbias
-            avmeanz += meanzinterp
-
-        y10_req = 0.003
-        y1_goal = 0.013
-
-        # clbiastot = np.max(clbias)
-        y10ratio = totclbias / y10_req
-        y1ratio = totclbias / y1_goal
-
-        result["y1ratio"] = y1ratio
-        result["y10ratio"] = y10ratio
-
-        return result
-
-
-class MultibandMeanzBiasMetric(BaseMetric):
-    """
-    Run as summary metric on MultibandExgalM5.
-
-    Parameters
-    ----------
-    will fill in asap
-
-    Returns
-    -------
-    result : `float`
-
 
     Notes
     -----
@@ -576,10 +411,15 @@ class MultibandMeanzBiasMetric(BaseMetric):
 
     MultibandExgalM5 provides the m5 depth in all LSST bands given a specific slice.
 
-    This summary metric takes those depths and reads the derivatives [more to come here]...
+    This summary metric takes those depths and reads the derivatives from the tomogrpahic model, 
+    computes the bias in shear signal Cl and then computes the ratio of that bias to the Y1 goal and
+    Y10 science requirement. 
     """
 
-    def __init__(self, filter_list=["u", "g", "r", "i", "z", "y"], year=10, n_filters=6, **kwargs):
+    def __init__(self, 
+                 meanz_tomography_model,
+                 filter_list=["u", "g", "r", "i", "z", "y"], 
+                 year=10, n_filters=6, **kwargs):
 
         super().__init__(col="metricdata", **kwargs)
         # Set mask_val, so that we receive metric_values.filled(mask_val)
@@ -591,9 +431,9 @@ def __init__(self, filter_list=["u", "g", "r", "i", "z", "y"], year=10, n_filter
 
     def run(self, data_slice, slice_point=None):
 
-        def compute_dzfromdm(zbins, band_ind, year):
+        def compute_dzfromdm(zbins, model_z, band_ind, year):
             """This computes the dm/dz relationship calibrated from simulations
-            by Jeff Newmann.
+            by Jeff Newmann and Qianjun Hang, which forms the meanz_tomographic_model.
 
             Parameters
             ----------
@@ -601,10 +441,12 @@ def compute_dzfromdm(zbins, band_ind, year):
                 The number of tomographic bins considered. For now this is zbins < 5
             filter : `str`
                 The assumed filter band
+            model_z: dict
+                The meanz_tomographic_model assumed in this work
 
             Returns
             -------
-            dzdminterp : `float`
+            dzdminterp : `float` array
                 The interpolated value of the derivative dz/dm
             meanzinterp : `float` array
                 The meanz in each tomographic bin.
@@ -613,15 +455,12 @@ def compute_dzfromdm(zbins, band_ind, year):
             import pandas as pd
 
             filter_list = ["u", "g", "r", "i", "z", "y"]
-            band_ind = filter_list.index(filter)
-
-            deriv = pd.read_pickle("uniformity_pkl/meanzderiv.pkl")
-            # pkl file of derivatives with 10 years, 7 bands (ugrizY and combined), 5 bins
-            zvals = pd.read_pickle("uniformity_pkl/meanzsy%i.pkl" % (year + 1))
-            # pkl file of mean z values for a given year over 5 z bins, 7 bands (ugrizY and combined),
-            # for a fixed delta density index (index 5 assumed below is for zero m_5 shift)
-            meanzinterp = zvals[0:zbins, band_ind, 5]
-            dzdminterp = np.abs(deriv[year, band_ind, 0:zbins])
+            meanzinterp=np.zeros(zbins)
+            dzdminterp=np.zeros(zbins)
+
+            for z in range(zbins):
+                meanzinterp[z]=model_z["year%i"%(year+1)]["meanz"][z][filter]
+                dzdminterp[z] =model_z["year%i"%(year+1)]["dz_dm5"][z][filter]
 
             return dzdminterp, meanzinterp
 
diff --git a/rubin_sim/maf/metrics/tomography_models.py b/rubin_sim/maf/metrics/tomography_models.py
index db820afba..0064164ab 100644
--- a/rubin_sim/maf/metrics/tomography_models.py
+++ b/rubin_sim/maf/metrics/tomography_models.py
@@ -55,6 +55,12 @@
 # since this would change typical depth and galaxy catalog cuts.
 # In what follows we have 5 tomographic bins.
 
+# the mean z values for a given year over 5 z bins were taken from Jeff Newman and Qianjun Hang's 
+# code which originally produced results for 7 bands (ugrizY and combined)
+# 
+# We are assuming a fixed delta density index (we pulled from index 5 
+# in the is for zero m_5 shift) in Qianjun's simulation
+
 # Each dictionary must have keys that are the lsst bands.
 # If some are missing they are ignored in the linear model.
 # These badnpasses are the ones which will be fed to
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diff --git a/rubin_sim/maf/metrics/uniformity_pkl/minzs.pkl b/rubin_sim/maf/metrics/uniformity_pkl/minzs.pkl
deleted file mode 100644
index 07a0d1aef320a84f28e90ce4eea3d08f2fb8cbef..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/rubin_sim/maf/metrics/uniformity_pkl/simulation_calcs.ipynb b/rubin_sim/maf/metrics/uniformity_pkl/simulation_calcs.ipynb
deleted file mode 100644
index db13f3bec..000000000
--- a/rubin_sim/maf/metrics/uniformity_pkl/simulation_calcs.ipynb
+++ /dev/null
@@ -1,633 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "a5d40f07-0827-46ab-a814-adbf38bc4f4b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pylab inline\n",
-    "import pickle\n",
-    "import pandas\n",
-    "\n",
-    "\n",
-    "# Random forest routine from scikit-learn:\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
-    "# Cross-Validation routines:\n",
-    "from sklearn.model_selection import KFold\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.model_selection import cross_val_predict\n",
-    "from photerr import LsstErrorModel\n",
-    "\n",
-    "from sklearn.experimental import enable_iterative_imputer\n",
-    "from sklearn.impute import IterativeImputer\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "0b0733c2-a9df-463f-b158-c93275a6ec79",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5458656c-1776-4823-b654-1505c4108020",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "2937e855-3b4a-41e9-ac76-ea8951664d6a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#df_full=pandas.read_pickle('/global/u2/q/qhang/desc/notebooks_for_analysis/Project285/cosmoDC2_pzflow_sample.pkl')\n",
-    "\n",
-    "df_full=pandas.read_pickle('/pscratch/sd/q/qhang/roman-rubin-sims/nonuniform-maf/roman_rubin_2023_v1.1.3_elais-subset.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "cfb5ee3e-c2b6-43c4-b0b4-4ba40fbbfc0c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'g-i')"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(df_full['redshift'],df_full['g']-df_full['i'],'r.', alpha=0.01)\n",
-    "plt.xlabel('redshift')\n",
-    "plt.ylabel('g-i')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "5bf0cb7c-611d-402e-9b56-ea4c39b77d1c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#This is a function that makes a plot of photometric redshift \n",
-    "#    as a function of spectroscopic redshift \n",
-    "#    and calculates key statistics.  It will save us a lot of work.\n",
-    "def plot_and_stats(zspec,zphot):\n",
-    "    \n",
-    "    z_spec = np.copy(zspec)\n",
-    "    z_phot=np.copy(zphot)\n",
-    "    x = np.arange(0,5.4,0.05)\n",
-    "\n",
-    "# define differences of >0.15*(1+z) as non-Gaussian 'outliers'    \n",
-    "    outlier_upper = x + 0.15*(1+x)\n",
-    "    outlier_lower = x - 0.15*(1+x)\n",
-    "\n",
-    "    mask = np.abs((z_phot - z_spec)/(1 + z_spec)) > 0.15\n",
-    "    notmask = ~mask \n",
-    "    \n",
-    "#Standard Deviation of the predicted redshifts compared to the data:\n",
-    "    std_result = np.std((z_phot - z_spec)/(1 + z_spec), ddof=1)\n",
-    "\n",
-    "#Normalized MAD (Median Absolute Deviation):\n",
-    "    nmad = 1.48 * np.median(np.abs((z_phot - z_spec)/(1 + z_spec)))\n",
-    "\n",
-    "#Percentage of delta-z > 0.15(1+z) outliers:\n",
-    "    eta = np.sum(np.abs((z_phot - z_spec)/(1 + z_spec)) > 0.15)/len(z_spec)\n",
-    "    \n",
-    "    #Median offset (normalized by (1+z); i.e., bias:\n",
-    "    bias = np.median(((z_phot - z_spec)/(1 + z_spec)))\n",
-    "    sigbias=std_result/np.sqrt(0.64*len(z_phot))\n",
-    "    \n",
-    "    # make photo-z/spec-z plot\n",
-    "    plt.figure(figsize=(8, 8))\n",
-    "    \n",
-    "    #add lines to indicate outliers\n",
-    "    plt.plot(x, outlier_upper, 'k--')\n",
-    "    plt.plot(x, outlier_lower, 'k--')\n",
-    "    plt.plot(z_spec[mask], z_phot[mask], 'r.', markersize=6,  alpha=0.05)\n",
-    "    plt.plot(z_spec[notmask], z_phot[notmask], 'b.',  markersize=6, alpha=0.05)\n",
-    "    plt.plot(x, x, linewidth=1.5, color = 'red')\n",
-    "    plt.title('$\\sigma_\\mathrm{NMAD} \\ = $%6.4f\\n'%nmad+'$(\\Delta z)>0.15(1+z) $ outliers = %6.3f'%(eta*100)+'%', fontsize=18)\n",
-    "    plt.xlim([0.0, 2])\n",
-    "    plt.ylim([0.0, 2])\n",
-    "    plt.xlabel('$z_{\\mathrm{spec}}$', fontsize = 27)\n",
-    "    plt.ylabel('$z_{\\mathrm{photo}}$', fontsize = 27)\n",
-    "    plt.grid(alpha = 0.8)\n",
-    "    plt.tick_params(labelsize=15)\n",
-    "    plt.show()\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "4a7eee06-0339-48fd-ba73-6d5547de2840",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#from Ellen\n",
-    "# some float64 issues going on with photerr:\n",
-    "\n",
-    "nominal_depth_LSSTY10 = {\n",
-    "    \"u\":25.6,\n",
-    "    \"g\":26.9,\n",
-    "    \"r\":26.9,\n",
-    "    \"i\":26.4,\n",
-    "    \"z\":25.6,\n",
-    "    \"y\":24.8,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "30605c5a-eebf-4788-8c16-61cc8376ffd1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "61d03e79-fafa-4ef1-b5ac-7bdfd71002c8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "i_lim [24.05       24.42628749 24.64640157 24.80257499 24.92371251 25.02268906\n",
-      " 25.10637255 25.17886248 25.24280314 25.3       ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#from Ellen\n",
-    "# calculate nominal depths etc.\n",
-    "years=np.arange(1,11)\n",
-    "n_ilim=len(years)\n",
-    "ilims = 25.3+2.5/2.0*np.log10(years/10)\n",
-    "print(\"i_lim\", ilims)\n",
-    "n_m5 = 11\n",
-    "\n",
-    "\n",
-    "nominal_depth={}\n",
-    "for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
-    "    nominal_depth[band] = {}\n",
-    "    for year in years:\n",
-    "        #nominal_depth[band][year]= nominal_depth_LSSTv1[band]+2.5/2.0*np.log10(year)\n",
-    "        nominal_depth[band][year]= nominal_depth_LSSTY10[band]+2.5/2.0*np.log10(year/10)\n",
-    "#    print(band, nominal_depth[band])\n",
-    "    \n",
-    "# finally compute the M5 bins in each band:\n",
-    "M5 = {}\n",
-    "for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
-    "    M5[band]={}\n",
-    "    for year in years:\n",
-    "        M5[band][year] = np.linspace(-5,5,n_m5)*0.1 +  nominal_depth[band][year]\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4b0ff8d9-2c6b-48fd-9bc2-43345fd623a0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "6a20797e-b0c3-40f0-a035-193bebd50a4e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# code to produce a catalog degraded according to the depths in a particular year\n",
-    "def degrade_catalog(df_full,year):\n",
-    "\n",
-    "#code from Ellen\n",
-    "# what we want to do here is to degrade the sample and save them in a separate catalogue;\n",
-    "\n",
-    "    extendedType=\"auto\"\n",
-    "\n",
-    "    catalog_rep = df_full.copy()\n",
-    "    \n",
-    "    nominal_depth_LSSTY10 = {\n",
-    "    \"u\":25.6,\n",
-    "    \"g\":26.9,\n",
-    "    \"r\":26.9,\n",
-    "    \"i\":26.4,\n",
-    "    \"z\":25.6,\n",
-    "    \"y\":24.8,\n",
-    "}\n",
-    "\n",
-    "    nominal_depth={}\n",
-    "    for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
-    "        nominal_depth[band] = {}\n",
-    "        nominal_depth[band]= nominal_depth_LSSTY10[band]+2.5/2.0*np.log10(year/10)\n",
-    "\n",
-    "# we should save the binning in each band:\n",
-    "\n",
-    "    for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
-    "        \n",
-    "    # these are nominal values\n",
-    "        nVisYr={}\n",
-    "        m5={}\n",
-    "        for bb in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
-    "            nVisYr[bb]=1\n",
-    "            m5[bb]=nominal_depth[bb]\n",
-    "        \n",
-    "        errModel = LsstErrorModel(nYrObs=1,nVisYr=nVisYr[bb],m5={\"u\": float(m5[\"u\"]),\n",
-    "                                                                \"g\": float(m5[\"g\"]),\n",
-    "                                                                \"r\": float(m5[\"r\"]),\n",
-    "                                                                \"i\": float(m5[\"i\"]),\n",
-    "                                                                 \"z\": float(m5[\"z\"]),\n",
-    "                                                                \"y\": float(m5[\"y\"])},\n",
-    "                                     extendedType=extendedType)\n",
-    "        tmpcatalog = errModel(catalog_rep, random_state=np.random.randint(1,100_000))\n",
-    "   \n",
-    "        if 'redshift' in catalog_rep.columns:\n",
-    "             d = {\n",
-    "                \"u\": (tmpcatalog[\"u\"].to_numpy()),\n",
-    "                \"g\": (tmpcatalog[\"g\"].to_numpy()),\n",
-    "                \"r\": (tmpcatalog[\"r\"].to_numpy()),\n",
-    "                \"i\": (tmpcatalog[\"i\"].to_numpy()),\n",
-    "                \"z\": (tmpcatalog[\"z\"].to_numpy()),\n",
-    "                \"y\": (tmpcatalog[\"y\"].to_numpy()),\n",
-    "                \"redshift\": (catalog_rep['redshift'].to_numpy()),\n",
-    "        }\n",
-    "        else:\n",
-    "            d = {\n",
-    "                \"u\": (tmpcatalog[\"u\"].to_numpy()),\n",
-    "                \"g\": (tmpcatalog[\"g\"].to_numpy()),\n",
-    "                \"r\": (tmpcatalog[\"r\"].to_numpy()),\n",
-    "                \"i\": (tmpcatalog[\"i\"].to_numpy()),\n",
-    "                \"z\": (tmpcatalog[\"z\"].to_numpy()),\n",
-    "                \"y\": (tmpcatalog[\"y\"].to_numpy()),\n",
-    "        }\n",
-    "    degraded_cat = pandas.DataFrame(data=d)\n",
-    "    return(degraded_cat)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7bd11106-04f7-4204-8664-5982b8313710",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "576068a9-6e9c-4b49-a31d-d97c7c1f5410",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# code to train the random forests\n",
-    "def train_rf(catalog,plot=False):\n",
-    "\n",
-    "    u_mag = catalog['u']\n",
-    "    g_mag = catalog['g']\n",
-    "    r_mag = catalog['r']\n",
-    "    i_mag = catalog['i']\n",
-    "    z_mag = catalog['z']\n",
-    "    y_mag = catalog['y']\n",
-    "\n",
-    "#Redshift array\n",
-    "    z = catalog['redshift']\n",
-    "\n",
-    "# Now, set up input data array for scikit-learn regression algorithms\n",
-    "# We will include galaxy colors (expressed as differences between magnitudes\n",
-    "# in adjoining bands) and one magnitude.\n",
-    "# np.column_stack makes a 2D array out of a set of 1d arrays :\n",
-    "# with 6 variables we get an N x 6 numpy array out\n",
-    "    data_colmag = np.column_stack((u_mag-g_mag, g_mag-r_mag, r_mag-i_mag, \n",
-    "                               i_mag-z_mag, z_mag-y_mag, i_mag))\n",
-    "\n",
-    "\n",
-    "# We will set up an implementation of the scikit-learn \n",
-    "# RandomForestRegressor in an object called 'regrf'. \n",
-    "    regrf = RandomForestRegressor(n_estimators = 100,\n",
-    "                max_depth = 30, max_features = 'sqrt')\n",
-    "\n",
-    "    # To better assess the quality of the Random Forest fitting, \n",
-    "    # we split the data into Training (90%) and Test (10%) sets. \n",
-    "    # The code below performs this task on the data_mags and data_z arrays:\n",
-    "    \n",
-    "    # 1) randomly divide the sample into 50% training and 50% testing sets  \n",
-    "    # (e.g., data_train, z_train, and scaled_train are the training \n",
-    "    # portions of data_colmag, data_z, and scaled_colmag\n",
-    "    \n",
-    "    data_train, data_test, z_train, z_test, i_train, i_test \\\n",
-    "            =train_test_split(data_colmag, z, i_mag, \\\n",
-    "                        test_size = 0.10, train_size = 0.90)\n",
-    "\n",
-    "    #Train the regressor using the training data\n",
-    "    regrf.fit(data_train,z_train)\n",
-    "\n",
-    "    #Apply the regressor to predict values for the test data\n",
-    "    z_phot = regrf.predict(data_test)\n",
-    "    z_spec = z_test\n",
-    "    isbright = i_test < 25\n",
-    "#Make a photo-z/spec-z plot and output summary statistics for the test set.\n",
-    "    if plot:\n",
-    "        plot_and_stats(z_spec,z_phot)\n",
-    "        plot_and_stats(z_spec[isbright],z_phot[isbright])\n",
-    "                        \n",
-    "    imputer = IterativeImputer()\n",
-    "    imputer.fit(data_colmag)\n",
-    "    \n",
-    "    return(regrf,imputer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7bdd9452-565f-4f86-a61d-a652d097fe2f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "9d34b7f3-89c9-47f3-8092-18c1527151b0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# code to apply the already-computed random forest photo-z's to a catalog,\n",
-    "#  dealing with NaNs \n",
-    "\n",
-    "def apply_rf(catalog,regrf,imputer):\n",
-    "    catalog.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
-    "\n",
-    "    u_mag = catalog['u']\n",
-    "    g_mag = catalog['g']\n",
-    "    r_mag = catalog['r']\n",
-    "    i_mag = catalog['i']\n",
-    "    z_mag = catalog['z']\n",
-    "    y_mag = catalog['y']\n",
-    "\n",
-    "    data_colmag = np.column_stack((u_mag-g_mag, g_mag-r_mag, r_mag-i_mag, \n",
-    "                               i_mag-z_mag, z_mag-y_mag, i_mag))\n",
-    "    clean_colmag = imputer.transform(data_colmag)\n",
-    "    z_phot = regrf.predict(clean_colmag)\n",
-    "    \n",
-    "    return(z_phot)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b31830ea-c3d4-493c-a856-4643a7229187",
-   "metadata": {},
-   "source": [
-    "# Below is the code box that actually does all the work!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "987b24db-7574-4c77-84f1-ce8050824b76",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n",
-      "6\n",
-      "7\n",
-      "8\n",
-      "9\n",
-      "10\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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nE4vFWL9+faHMunXriMViw8rs3LmT7u7uQpnly5fjcrmYM2fO6XxZwt850zSGHXvIUGsPDjtn25AIH2d2liAIgnBWnfKsq2QyycGDBwvHLS0tbN26lbKyMhoaGigvLx9W3uFwUFNTw4QJEwAIhUJ84hOf4O6776a8vJyysjLuuecepk2bVpiFNWnSJK655hpuv/12fvnLXwLwqU99isWLFxfqWbhwIZMnT2bp0qV8//vfJxwOc88993D77beLnhrhtDLeFOiMNw/xfruVB5hROGcaJg/961PcdPeF1I4VuV+CIAjnilPu0dm4cSOzZs1i1qxZAHzxi19k1qxZfOMb3zjpOn70ox9x4403cvPNN7NgwQK8Xi/PPPMMiqIUyjzyyCNMmzaNhQsXsnDhQqZPn85DDz1UuK4oCs899xxut5sFCxZw8803c+ONN3Lfffed6ksShOM6EE7SQhNhu5RDJSXs8+eHsFqVkTwgDV82wZlNo3b9hCe+9k/EB/rORnMFQRCE45Bs2/67nSkbj8cJhULEYjHRCyQMs7tvgEXbW9AVx7Dzqmnw8Q2/ptOcxqhMNT+/ahzftu9hYJmX9z19ABsw7v0Xpt+09PgVC4IgCH+1U/n+FntdCcJxdCdT6IqDO+wf095Vx6iWMiYo02npWoZSEeaQ3wuZYnmtvIFXFozmis0byHa1nb2GC4IgCMOIQEcQ3kYdHSRMlTFRmfkOD4lYjEgFOLEwjlo28FZjLu7KRuwrb6Ars+0stlgQBEE42mlfMFAQ3msyUhL7TWshK5KBbhW3htiYNtiUMpBUJ9ms991uoiAIgvAWRKAjCCdwSN1BVtWGn5RDlCnFrUa2lO7kkLMDAMtUEARBEM4NItARhOOI5qKFx3Wpy3EZw0d5VSuDfNSKyW7JZk7WA4Bsil8rQRCEc4X4F1kQjiOcCBceT4xJZNXh24popIcdx+Q4LcrQat622AlLEAThXCECHUE4DiNXXCQwll2H23j7VRjknII3mu/h0XNihWRBEIRzhQh0BOEEKtVJSPbwoSuXrsFRCcpOw0XGVQZAKpVBEARBODeIQEcQTkBTg+hG4KgzEsFsDnUo0OliJI5MlDbfXvYGZHrMNtpWv352GisIgiAMI9bREYTjiCUMyOcWkzV8GM6j97uSUGyVEjONauo8oNwF+W3a+Bng0D/CiN3raLjw4ne51YIgCMKbiUBHEI5D601BY/7xzvoq4pqHRUP5xynLQVJzoGoaH9/1GKOmbWTXvnGM752EWno+X5/uoStjvGXdgiAIwrtHBDqCcBxmMll4fOnq53DHG2HEVEzJZoNZzw7GYekRGvUwTbTgGd1CrS+G1T5n6C6RkCwIgnAuEIGOIBxHLlWcXm46XFh2PtG4ud7iYlczk6MBGh0dJMmvgjy46zqkvvNwBFOAD81KH69aQRAE4V0mAh1BOA5NLwYq3Y1ljDFCABgOKJU1FDtNqRImY7gAyFmw391CvzMMVGHox6tVEARBeLeJWVeCcBwWxUX/3m9NYaJjdPGibSHbNk4pyoI33gDgoOqiLlLDefoYAHKW+BtCEAThXCACHUF4s2g7oaMOSzIjGeWqx7Bt0qQxNBMN8FngzuZzceKlB5Ac3fhtNwCGLJKRBUEQzgXiz05BeLP0IB65uDHnZqWNbELDsJqI+uJk037SshM55yuUuSwSod7uIzF0bCMCHUEQhHOB6NERhOOQj9quql89QKe6i8zQQsg5E2wU0koIhuKhec/1Ur05VbhHtUSSjiAIwrlABDqCcBzWUY93OfbQLnUVr9kWmimR9gTg0hgAL8+bQU9DTaGM2NZTEATh3CCGrgTheOxiqPPhlRaRfpv2yfnj/kCQDmcpI40+8OTL5UaCrmUwfT1A1VlosCAIgnA8ItARhDfpTBjDunRenbQYn9+Da+h4y4xZ7C29kFhPiEs7/0DOgvPHbYJx0Mce4D6cilgwUBAE4VwgAh1BeJNIxhp2LCk9uFQVOA+AGTu38elNW9kyZxKKanNvj5uLBy5mXrcD75jRMA5Uh0hGFgRBOBeIHB1BeBPjQDOqWQxUKgbC1PX2Fo5lyyJRqhJI5ROOo6ZMm1ZGJlaNnCl/19srCIIgvDXRoyMIb2L1Dw5LJt42Ygwu08u4oWNJsqn2x+iziqXKjRRZZ0lheMsSf0MIgiCcE8S/xoJwAnLAJlPvLxy7JBO3qQwrY/a3YpuRwrH9rrVOEARBeDsi0BGEE6hxdjPZtatw7FBVSjUPkqRSalpIlsprMw+xq7K7UCaHdbyqBEEQhHeZGLoShDdJGOlhC+F4S5NUJGyiQ8cyBilZAslJrWlS3f1hDmkllPaGoTRfRpMtupPd1Ppr3+3mC4IgCEcRgY7wrunIaoT1YpJvmUNlpNt5Flt0fFkzNyxHJ9VTyt7EKI4sB6goBobDUbgujaihPttJQ1exF8dGIpKLiEBHEAThLBOBjvCu6MhqXLxuLxmrGAx4ZJnX5048J4Md66hfjZypMqBWUHP0dber8Hic1UnSmyPg0MkMnbMlsTayIAjCuUAEOsK7IqwbZCyL+5pkxrglDmVt7mmx6El2M9I96mw3b5j+iEnaHFc4buq9gLroKGQzhyQl8Fo1OM0QSTVA2nbxs9b/AKBduZw3mACAqar0ajaTz8orEARBEI4QgY7wrtBy/QBkm79ImhayNIF0H9t3fIap83+F2113lltYFE5L2K5inv6UPQ+idLqwrQxLyvfxlP98bKuBqBXiqtz3uVKN4XYHmGoeLNyjYBMzxNwrQRCEs00EOsK7QjfiAIxpups5FfV4UzbsMbHsLJoePqcCHf1Nk8MfvXIyW9TLqd93iEfc29kaPMQyx4vM23wpzzddx2ul28mUq4xv956lFguCIAhv5ZSnl69cuZIbbriBuro6JEniySefLFzTdZ2vfOUrTJs2DZ/PR11dHR/96Efp6uoaVkcul+Nzn/scFRUV+Hw+lixZQkdHx7AykUiEpUuXEgqFCIVCLF26lGg0OqxMW1sbN9xwAz6fj4qKCu688040TTvVlyS8i9yekQQDU/H5xpztprwlyx6+fYOlZpga2UXUGwBgQirEtYPTUM18Rk6FXEetWKlBEAThnHTK/zqnUilmzJjBT3/602OupdNpNm/ezNe//nU2b97M448/zv79+1myZMmwcnfddRdPPPEEjz76KKtWrSKZTLJ48WJM0yyUufXWW9m6dSvLli1j2bJlbN26laVLlxaum6bJ9ddfTyqVYtWqVTz66KM89thj3H333af6koS/IR1Zje2J9LCfjuzpDW5Ne3iPjtPUaUzECsdB06BeL14/JDdygDmkcCEIgiCcW0556Oraa6/l2muvPe61UCjEihUrhp27//77ueCCC2hra6OhoYFYLMZvf/tbHnroIa666ioAHn74Yerr63nxxRdZtGgRe/bsYdmyZaxdu5a5c+cC8Otf/5r58+ezb98+JkyYwPLly9m9ezft7e3U1eWHPX7wgx9w22238Z3vfIdgMHiqL014F3QmOtjtUGhOn/qCesebuQXgkeHFmeWMCdWfrmYO41BM3P4EpPLHKQtykkZIs7EViVi3G7phi9rAfMRmnoIgCOeSM97fHovFkCSJkpISADZt2oSu6yxcuLBQpq6ujqlTp7J69WoA1qxZQygUKgQ5APPmzSMUCg0rM3Xq1EKQA7Bo0SJyuRybNm06bltyuRzxeHzYj/DuiGajANy/5ad86NkP8dXXv1q4NpAeOKk6jszc+gwP8G37Hr5t38Md9o/JWPDa5n8mm+06cSUnwbD0Yce+UAL/1GKicQKVHDY1mRi5BVUs0dZypXMlE4zu4fVEchjR7GlpkyAIgvDOnNFk5Gw2y1e/+lVuvfXWQg9LT08PTqeT0tLSYWWrq6vp6ekplKmqqjqmvqqqqmFlqqurh10vLS3F6XQWyrzZvffey7e+9a2/+nUJpy6lJ4EAH5r4IS4bcR770xb/52C+9yOhnVrAWWs3s3jy5/D6xrArZfPAaUpqzma70PQwrsAguq8fhrbxdEZlRh66grFSJwC2bZFz6zhUwKNy5Y6NAERGXzqsvsTyw/TGslTfPQe1xP2O2yUIgiC8c2cs0NF1nVtuuQXLsnjggQdOWN62baSjFlmTjrPg2jspc7Svfe1rfPGLXywcx+Nx6uvPzHCHcHw+w0dJroSArgORE5Z/K17fmHxSM2lg/1/drmy2izVrF2JZGWZfDi0cBi7EMmWuy6xlmrWShU5Iy27SlgdDVvjBTfn2G/M8rHGPYGrb8Dp9c2uxnzOxUgaU/NVNFARBEN6BMxLo6LrOzTffTEtLCy+//PKwfJmamho0TSMSiQzr1enr6+PCCy8slOnt7T2m3v7+/kIvTk1NDevWrRt2PRKJoOv6MT09R7hcLlwukTB6NuSGEoZ3rNlBS/8O+v0hmHM5XYzkQNaBlEif1S0hND2MZWWYMvmHPPLTZ8j5Z8Nc2LpzDDHdxWv9M3k+WE3v7EYu3PUC6ZxGdGhD85dDF5P1xeBNgY4ccgHpd/21CIIgCEWnPUfnSJBz4MABXnzxRcrLy4ddnzNnDg6HY1jScnd3Nzt37iwEOvPnzycWi7F+/fpCmXXr1hGLxYaV2blzJ93dxbyI5cuX43K5mDNnzul+WcJfyRja42rs9LF86lOf4qYrr0A1dR6Q7uKzHbUs3Lifi9ftPe0zqE6V1zeGbNiPnqgEQNMcRCUvCq3sspvodFbhztocvdROXGrDYeWOqat5cIABKU7z4OC71XxBEAThTU65RyeZTHLwYDExs6Wlha1bt1JWVkZdXR0f/OAH2bx5M88++yymaRbyZcrKynA6nYRCIT7xiU9w9913U15eTllZGffccw/Tpk0rzMKaNGkS11xzDbfffju//OUvAfjUpz7F4sWLmTAhv8T+woULmTx5MkuXLuX73/8+4XCYe+65h9tvv13MuDoX5RIAeEhRRx8oUT6+axmjpm2kpP5upMBcPrOnjbBunFSvTn9/P8lEF/1Z/YRlT5X8pgUDbSQOx2UIDD13yE3lQF/h+seyH+Rl9zpgeJDWsfIllhs2iSc384/V/4eJ1ZWnva2CIAjC2zvlQGfjxo1cfvnlheMjOS8f+9jH+OY3v8nTTz8NwMyZM4fd98orr3DZZZcB8KMf/QhVVbn55pvJZDJceeWVPPjggyiKUij/yCOPcOeddxZmZy1ZsmTY2j2KovDcc89xxx13sGDBAjweD7feeiv33Xffqb4k4UyLtmPv+DOMmwm7noCXvgxUEfRfQxMtVLh1JN+Jk3UTiUTh8eOPP04qWV4YAjut3rRzgwU4GhUYiqninhKSHCpcf9h3iJxbp87l5uhXceWqHYzr6uTZ6xbRm0iIQEcQBOEsOOVA57LLLsO233oPn7e7doTb7eb+++/n/vvvf8syZWVlPPzww29bT0NDA88+++wJn084u5JdB5HMfC9Na2I0T7SXInsk8J9aPdlscar2vIXzkB2NbB9M89jp7tR502dYAmS3VAh0TI+XaCCIbLWimDrLZkwHplPe38pFFBcW7Lrucir+9CQO/fT3OgmCIAgnR+x1JZxxmf4UTm0xAOOd8zi/sokBKc5+lr3jOn+/6bf059wknHXQ9NnT0s4BKnhs+x7aayuxvMXeRSTy3TpDNjRNpLU2hBL5Md/d9QlCePlNaCENA3VQX9zvaqfpoHfRQrBt9P5WGDv6tLRTEARBOHki0BHOvJyMNPRRGxiXIjJPJbajC6KnVo2VLvaMfKXr47gTjbxRkuLzTX99E7e19/FlfkJOc8Ml+fVzVNPAqSdABeOAD4aW6Gnq2I8nrtMXUrivxARS6I7NLNLLgHygIxkuygYmEcjESJTuoWvfDph/mofYBEEQhBMSgY7w7rJfoC91HznvFIiOOrVbc8VulbX9z+CONyIHryqcG0gPEAy8s2YdDsfISWVc1/Iq/j4n5YlKLnOU8aw5iOkIsaNkfKGsnkhSngwxt+0KNrmrqXbE8NZ0YknF7R9csSaqo06wSxmwLVJJMc1cEAThbBCBjvCuimRnY6j/TCa6ETj+CtZvJd3bA578jLqy8gpKfA10HnU90d0Gx19C6cTtGohCCBqjCuNaSnCrDsYEnOAA1dZYLK9jD40AGGoJDltmXNdBJtj5xQo7HSqWVJxinvPvYu2IrczrvJXtga1UpSe8s4YJgiAIf5UzvteVIOQyxZ4OvQ12P95B5PCpb4mQ7TtceGxNfgXr/MeHF0im3lH7jGiWirZ8Ts54JcTFpfWcHyjHsDRSUgrQKJOK21QEPW7Kkglk2+KFyivZO2EB5UaYo9fjdtk5ZhzKJ2BfsquOVDr5jtomCIIg/HVEj45wxumaWXhsxNqxousxjJFQ5TilepLpTOFx/54FxJPTcBy1ZFJCSxznrhOzUgby0ESrg7EtpDtfA0Az0vRPHqDSCmId9TfBh194ibLBCK3lKhFHKSOTB/B5FUyluP6P0xmgtKkOVx9YDaMZtJP07+si5A8i+1Sx95UgCMK7RPToCO+qYKiCSSOvp6Si9pTvTaaLgYwZjpHt3D7seuYd5sGYieKQk7Oxi9Clh0m5rmJyyyA5p0lWcrBLKs6YOljhp7VcxUIio7ixPS5sZNIOE9vKB3VjtTksylzIZQEHS7Q5lDqh88Et9N2/hd4fbBK7mguCILxLRI+OcMaZZjEAiTtzrA/0UmUXg4vDO5txVTafsB5dKwYH2WAlDud5w65LxonXcDqedH+xp8ibHkNT9030qFW4h0bcdIdKwllc9CdSUc/ETAkb3AZJNYAaj2OGZLKOJJmWXTBzFs2sJWMsw6P6meyfS8jSaZ0WoKyigeyLrWKjT0EQhHeJCHSEMy5i5ih81HQXHb0hKoNJCOTzVna//jyR2C744GdIRcMQ8B5Th97VhWkVOyD3j2xiW+UMPruruI9UVD61obBC3bni0Fo8lWLz4Y3gua5wzjT8HPJOKuzPKck+nIoNvpkAKO6L0NmKiYlnIN/rZExLY07rI0kfe9jGCCNG87LZPJM2kLG5tmuQxhGnuGKiIAiCcMrE0JVwxiVyxQBENVVm6DV4jOIu8mPGXYDqXgBA7i0Sio1IBPmoj2t1OsGXtrUMK5OS3lncnkkVnzMtO+l0D98rbXe2ln1pP6oNtkPmom0HUdAY35xf8DDq7MFWTOIuN+VtEVxajgeku/g36T7+TbqPe+yfElZDpKs2Eg/txULif595jGg0+o7aKwiCIJw8EegIZ16suNmlaofx5V7EYbYXzjndOZBDJ65HKgZMI3pcRHvLTkvzjOxROTpWgArpomHX59HCR5PL+fK+x8Cj8tr4maiKTklmAFuRWGvWkzac9Pu9/Gb++Uzc/WUufPkpRi/fwa2rX0WXHSQIoJp+/EYIjwSmaRGNtp2W9guCIAhvTQQ6whkX7S1+oZc761g44jbqfcV1ZfRoH75M/9vWkUyY2Ed9XLPOATRz+9vccfK6B4s5OkYuHzxJaDj0/NCaYZlUptqpTnUD4K5IEy7ppUxPkltQxfvtNbgki4wOEZ+PrrIwkYk+WuZNIGgUp6VfSyM3ORq4IqiiSjYtAwOnpf2CIAjCWxOBjnDG5ZLFL/ukMcieQ48wmO0tnLP3RRkV3QpAoqf7uHVkMxaWUdx/SkmNxKlMPy3ti8aLa9xI3i52BbcTiP4X7lwEANMRHVY+rCQYmWnGKLPBo3Lp5k24SINWrGfC4F9Y2PpTHHpxScM3Ii+wduA1VEkCE3oG3j64EwRBEP56ItARzjiXXUy6daYSjNzxGu5wB6qd//j1jw+ydX4VAK/uXPOWuSu2XQx0ooFWWuWW45Y7VdlYMRAzVJ2UuZUBpRi0DHgrh5W/csZK3O/rInx7flrWpotqMTwKppwjYVTgtizeCPWzObSFTaHijLODdSW0jNDJBg4TViQ6InEEQRCEM0vMuhLOOEspBjppr4uOS8Zj2hae5m6smQoTJ76Biy7gRiZMfJVY7BpKSmYdU48tFWdH9Yb2MxCIMz5z61/dvqSdwju0rnFairJ5+iYGQiGu2ZS/XjmYwFSKQdZv9l3NlLKHmD8U//zvZAc3dPghBz3GaG5t/0cmS2H2BLqxsk5WA7Yhc9nM5wFoBa42VF7u/NRf3fYT6chqhHVj2Lkyh8pIt/Mt7hAEQXhvEYGOcMZlcRU+aIYssc5Vx9RcllQ4ze41VxCwmvFJo+ASkBUTw4weU8fuVAcctWmmW57IdG8OMscUPfl2ZbvQ9DByqBnLUwJAqyc/PVxx5dvgsiwqPUESIYB8L08mNZKukE2ABC4rS6Lin1lhbqC2bS8AA7mZxAbdZMauxi/nh64OvjoGPWYSdJQzu3EO3dN/xX5ZpSOrnbGgoyOrcfG6vWQsa9h5jyzz+tyJItgRBOHvggh0hDPu6K/ZchJ8rOR1ft/9TeCPSC3TSCmX0lPx9mvK9HXtR5KLNXkSYfydFRB8m5uOsrtvgO6jpq4rSpLm5q9g2VmYC22MBC6nKlXN1av68VgZIMIP+wbYlW6hJ+wp3PvPtotHLKgwY3xP+TwJO0C3bfCyPBkAR+kmpFEHySXG4Rma8p5qCJEOVpJLNHI4UYkLGGF0ENaNMxZwhHWDjGXxs0kNjPPlt5w4kMrymT1tw543Go2STheH2LxeLyUlJWekTYIgCO82EegIZ55dnBbep1TymZpvcEFvAEkBPZ0fzjH9U4FbAIj09cLo4VWoqQSyUQw2Llu9GW9qNgevPvHT7+4bYOH2wxjKmz/u3+bonThV08CbdJCdPQ4jp8OO1VSYFhO2tzMq4mRnUyMAYWeYqCmzb8cUPqhHeTXbQCx0CIt8j1PMKKevywS/D8NWUE2DP0/IvzZqQTV1fkAFVXoSvS+NYcpndO+rUWqCRroA6MxGgABbDjxPTM2Sy+bYvmo7uZyDXM4HgM+X47rrL8HtLrYp4K+hqmrSGWujIAjCmSICHeGMyyoyhT6L5CjmttVjKzpjl/TT2jqGoD4ZU2oolI/1R4+pI2eDdNQQzIoFE/FEx705Hjqu9mg7hqJyh/1j6ugonJckFzOmPcALP3uB8Y5G1kpbqB7op2ZaC76W4to6kUnlrNVr+P3F+ef/feXTBHJwU+pFSvRGjNQMVLcXU82vF9Tg2sHYYCf24Bzi5Qo/+97XiAfy09YPj57Dz65bSMIOgKkTeXQfvVmovnvOGQt2tu/4DDF7DwCdNIF0H3r052i0IAEzZoJtO6gLfgNNM+hLf4eenj8Nq8M0VWbOeFIEO4Ig/M0RgY5wxplScQ8qw9XOQ+UBPpjbh688zuSqdcA6DvcuAfLTxQ3dPKaOXj0KVBeOM4FqHNrJbaFgmgkgyIyKa7m8aQr9/f08/vjjXHXljVRrDqqjg1R6Sgk4M8zY1ELDc/lp35qioros1O1xYpNHE/Xnc3S+0vVxzo/t4i/OFi7KtR/zfHKkhhh+yqwkc6PrmTF7D1tSTTTLNXjN4n5dJgbBhaOwH2s5o3tfWXaWKZN/SG8swn2bnoVaMPaFcGZV9jinoUteJk58g42Jn6PhIkglTdtuouTqacheB4MDO0kkf0wi2SMCHUEQ/uaIQEc44xTrqM02XRYf2fESI/xOSlb/Cy/71uPq6yRXWgo1b11HihS+N53z+hJQXHSZaNde1rz0ExonXURt3Zxj6nA4agkGpmKZUbTsap566nXgdQhBB7uQLIvtsxtY5ZyA35TZrjXyiO8/iExtGJZoVJ+rQU33k9YswtogyGCbSuG3qSdehZVyQnmUOl1mhFtnhHs/sJ+fJ+cPa1PEn6LupN7Fv04qHSQ5mCKXrgcgU/1hOqw4P1frufXg0wD8PPVZDltNuIB/yWnM8UzEUelFN6QjediCIAh/c0SgI5xRHVkNyS5GCY6cRnRSKfX9EbyDTlJWGSlvObKzolCmN3ycb1XbOOZUNhmEo/J4HYdV9i07xPfmr+aBj375mGAnHGnmULMJSbgpcz6apReumbbNmq4/0jfGxaiLW5DjOtF10wDoGzGWvoQD01HcpmJ13wY0rZe9rkpcleDNedBDOWxF4kV3A7jzQ3HbsvX8g2srq5rqcJakyb0x/FcuPZBfS8dMaJxuiUSi8Pjxxx8nlSxnkr+B3cD/mrXIdj1lkRhxRz6EvG7bRuKJw6TR+H+yn2/sc0ObilsK8Z9U0HjaWygIgnDmiUBHOGOy2S5aO1rxBjqxPPlApa6li0te3JkvoGzjQ1f+Czmfn53eYk5M+ngbe9rDp0gHrRheO4plF3uLFgYvxT2pkYsiFht3dHLDUKCTGuwFgiR6XqW7rw1nqo4689Os8uym3zRxJFKoqYnkchGgmr17LiKdKa6b86spl7EjMD7fZNOgRLcZUTWbjcln0dMytuEgZDi5bp+f/766gtxRTTUGBqEdDh++lIPVbqqk4QFNYGX+ePDh3dTcc95pzdPJZovDZFddtIAte6Kc197Ic4ZNW8VQ/1hlFfvMDzCVV/iwNB231IhhaSzr/DXv66+idtG1fGZPGwkCp61dgiAI7yYR6AhnRDbbxZq1C7GsDDVXQgtNwOV0jBzFTz4ygTmtHVz0+jriO1/AaRvo502lMNUqe+yKwdbR06OAy9IrGW/s4r+MyRzJ3WlufoqmVh+e8z5JpGUQACOaxdicgWlQ2n4Fjd1NAJiyRtrdi90XRtJ8ZKxiXpC3ox9ftJbWkvzxxM1bmKq9wfNTD3FZx2XUZOfSWbWSZKWMaxfIhheA+ojGY5s0Hip9CTtnUJrM4CqNkLZdfIT/hR5Y4SwucOjVFJZL2+mUVa40JhLriVBeUvvXv/lDMkf16Kx56Ne4443MHDGFf3n2SdQFrahukz2JJn5X93722ZNortmMEjxMgzWKrCOIp20VDT0zTlt7BEEQzgYR6AhnhKaHsawMctm36H10G/7aaTAdUmoQX9JHT30+sNhbewnJQAMp/1F7X2nZYXVls134fEkUpRgAbXLP5kVjOlqq2H3Sen4fUl0Fk3SQk/lAx0oZHMmFPqz2YBgOyFSiWTKZ8NUogAHIVn42lt9Ic4l2iL2WTVTK9yzNTa+nPVmJQ2+lzMgHD5o/yYhkFNvpwusIkkAHnNRkbSalM0hxID2AFoxyVe77jPJ3EUo4mG7FCu2tGhhPbXQCYBP2ZfH291E+8fQFOnomzZGxPWftNJz+RgDUcAe+6B4aanvIxftRq2/gAeWuYVP6XfZ47jW/wL777oJP3AeA+RZbcwiCIJzLRKAjnFGSoxE92oU7WMw0Hq23kA3lg5mt3k5WVdSyWC7m5VhHDVMd6Rm6YGqGFiLAzdiGTNgqJSt5hz1Xd+Z8nF0eJlWC6ggTT+wkORDD8uSDnnTMSyQKMv00eQZpcx2isieMJ5HG393DhqYQF9tvMC8QY15gC7cBORx4EnphvR1bBoU+LhxYjWJbUJ8fhns9O4tes5aclGNx7Hqihs0b0oMAdFFBXa6ToBGmk+KaQlN8IWrM/LFhm/Qc7mDsxaevByXbl4FAPtC5TJqDW2pExyBnphnX6qfp0EiUlMHHt/yM1PhGvJaBU3YiBcfys3GTSSs+XpkzEVU7TFwySezrgNmnrXmCIAjvChHoCGecrAxPJHYt2svcvQcBD2VqBzdFOlCrxxWuS1Yxj+VIz9DOAxPRYzPgfOh+fQZZX4qMAm6KQ051vVNR3KPQ3YOUT36MDRv+CEB6bH7YLJfchZbITwffH7OYfbgHp5G/P+51AfCG8yJkeRHdpsmvs2mmuHuY7jTx22EAVFRkKY5iW7zkmEiyeQTumvPQlHWkzQRf17cyqjxJuabiHAzgpRdbkdioz2CjA0KO4p4Vsf3PoLSupm3qx5njU0n1DJy+Nx2I9w9AIJ9A/YpjF1lnH7LUTtZMENbmst8cT8yOUhJ7EXf3KC6VL6Tc9rNTy8LQ/w4ruItxBw/xhMPE7koyPpwlUHbmFjcUBEE43USgI5xxkmIPO9568ApSRi0L2cg9jv+Hp0znt647Ctedx5lhlc54kZP5/R6MrAt8KTTVxuNcC0zJlylZRlWmCdMxBVkxeGjQyZjWKYxzLIHpYHpCtNS2cPlBP+3uNBtGD5/YrSsWnaXV9PVto7wzxK6q8yh3alxXWo3l7AFWE1TdOKRmAA46a3FopQTUcajGRsBkIKuwLz6GsXqC8zSD1vRN5BZUMbXjRYIWqN0yvYwFYHCMQknJBST0/PsjxXROp1RkABgDwFizhlJ9HGG3xTZ1PwfbtgPbh0qqhBKz2Ca7CSkGrqPqeF/XNPRYObriIhnsIJvURaAjCMLfFBHoCO+qtJzFY27FH87nunzWuo3xo8LIPcXp5fJRu5R35WxaaKLPU4MUGL6xlW2NQ5KbC8c7qvvplEbilW0O08SMETeT6/Fw0M5vM+42TWq7VcJSjEv29bB29gS21yuYionL08vGkgTjXVO4YMVOej0TAdgV7ORz/t8NPYODsdarlDlXkbUdPNN9Ce9nFw6HH9mWsBVY1P8S9IOkVCG7GkErA49Ka2kLit7J5OSF+dfFSLobDNrHbye1awz1so0d7z6t77WVKeYDHR5cT0vvDtQym6ZFWXq3XIyRbsIyXZiKBN719IZkHPFKXOQXBbQNhcoFzxXrMxR6+i/DVToRp6MMt/vdWAFIEAThryMCHeGMah3ceFRWCuzztDPgbeeQx+ZCwOFejn/XaAx3cbhKHlqdryOrce0uk4x0H+Rnd+f3o8pm0QCnoqNa+WngXYzkOu0Cepwj+fi0WnTpPsgBU4v31cTinLe/Hdmy8OgGaxr2sHWsDEBT1iTlVrCR+PPkGVw8tAVEnTmOw66J3PDSn3j+io/Sl84vrvd/tLvoGNMI/bsA8Kk22ev9PLXxGkKNAcpcLuate4OFr/+W/3fpN0hU/DMAq/0aiqnnk3+P7HpRBb8yDb7zis6hWDtjQvV/9fveGc2Q04sz1XKOqeiuMci5MhKrh6bOS6AoBo7sXl4Y9TymopPQR3GJnV8/aPeeS4nq+Tc+4EkwbtLr9MW+Qt8GkGU38+etEMGOIAjnPBHoCGdEW38bAM+1/C/XKDeQk/LJx425Gs7v/wojzEfRHc18/lkd2MfOWWPhyvy9Dn+GeGIn7SmbjAV32D8mciBATecI+jwSJekkUWRUPYE7l0E9EjgUgiGdr9j/jjc3h7YdfmYaE2npWobL7OLBqwyqYjYfXgm9yRuJKtOwPQrzss/RwiZ0U6XT2UCfM78Jpl9T+ciOVahxF+9vfQ7LDoME/ZRwsbwaZAgYNjIWtk+m31XJiFwL9clBHGorDb3NfL358/jrPKzXLudgSwl+uY0rZ26leWUTuZibZNUYnpp3GX1OF4cHO09LoHNgbxtHdYyhZtcTivQRrlzC5N0P4kv35N9rPYk7F+HSdUdKNrN9yh/gws/ijdr4wjW4lVoG67YAsGnX5fgUjYkT3yAcbqWuTgQ6giCc20SgI5x20WiU5U8tZ8YMOL//AtpCBv2OQ0ADLkthodJEJjuPFV+J81pnmLmeeoID+Z3JLVOhZs4uNmx4X37tHek+Rkj9ELOpTPrIyMX8EDmXJZSM8PFdjzFq2ka6tl2BnC0n521h+rStZNK7GSXNYWp8FolYjMk727gok+8tyikqlfi5umcdTinGslQdjNrEYJuDAa2eP42ox1YkgnKaqngzYYKU0F94brelErRc+KtKGJF10OFw4XVksBWJreEmttLEl6TdACTXquhemd5p4MvqOBw2TbSwoq6frLuO2fHz8+Vkk3g4fszO7aeiI6vR3N3KM6++jl1TXPSwe2QaSQpTmobk5R08RTcJLd+blXVJJDwydakLmJpwk4v05duT2UWgPUO29v2FejyZKs6bNookb5DJDl8GQBAE4Vwkn+oNK1eu5IYbbqCurg5JknjyySeHXbdtm29+85vU1dXh8Xi47LLL2LVr17AyuVyOz33uc1RUVODz+ViyZAkdHR3DykQiEZYuXUooFCIUCrF06VKib1rHo62tjRtuuAGfz0dFRQV33nknmnb6l9IXTk06ncY0890JvlEqDTGVy7R8wjCSye+qNpKS3OyOXkBLjYSmVWIq+UBndfMNPL9nKc/vWcrqw4sB2Ll9AVa6ibagG9WWkYd2MddUCdXKEswlaKIFj9mNkeumTMvPbIq+Po3knnyPg4TEpvMW8acbL2P9DRcw7rp+Hgn9B19K/jdXxjZhWPkBNk+TzHz5ALd3biC3oIoSNcNAeT5/SFXyrylhVnBJbCx1O65ES1+PHze67SbkSZBbUMWS0S9xz9hf4pbz7aycEqH+kh4U2cSWNRQzPy3+fGcD441GHMGhqfXONEbunSckd2Q1Ll63m5sPp3hk7nn8YeoVuOwsPiNDVeftlKZvQzZz/Cao8dJolfUTZdZPlNnepNBapTLDHscF7jkkx+UDr7/MCfCz68voKnEXtonoT6bY+9RyBqgQ6+oIgvA34ZR7dFKpFDNmzODjH/84H/jAB465/r3vfY8f/vCHPPjgg4wfP55vf/vbXH311ezbt49AIL+M/F133cUzzzzDo48+Snl5OXfffTeLFy9m06ZNKEr+r9Bbb72Vjo4Oli1bBsCnPvUpli5dyjPPPAOAaZpcf/31VFZWsmrVKgYHB/nYxz6Gbdvcf//97/gNEU6PuMNHC00MuNy4/SFMX3ELgRL3frKMRjfy83v6MhdTlW3FbdgsG3/RsHpU00BO5z8To2MZLnBdxWrjUUAlZdo4yOJvOwjngbuzBVc0wcVj5hHmWeZV3oDb3YhhW0zzXYJHfpop7hUAGDi5Zdr3ubPndwwmK2Bo1nc8UMJ0eyaZkYBHBSVHb6CMWnpwSDpZzUVWDqIi0z5hKyW9GeAScvZQgOJRebL/GuSEzjftBwGQ2yYwOtmPowZSPpNM1INs2sxq2A0Nu2nhAHAhI2dtIZka8Y7f87BuFIb6onuaqEhUkvG0YXZPZWvpLrK1brzKAZrLY9zbN8BovRhUlZoWtcp/gAJ1chW/My8lUfHPJCrg56NBNRuZyitsmDadX1V8HJed5feJ1iOjhYIgCOesUw50rr32Wq699trjXrNtmx//+Mf867/+K+9/f767+3/+53+orq7mD3/4A5/+9KeJxWL89re/5aGHHuKqq64C4OGHH6a+vp4XX3yRRYsWsWfPHpYtW8batWuZO3cuAL/+9a+ZP38++/btY8KECSxfvpzdu3fT3t5eyBP4wQ9+wG233cZ3vvMdgsHgcdsonHndusnvpnwAQ7oFAsAV+fMOXcetZ9kZyXJBSkKpyHcopgKHcQ8M8NnnWsiwHk8gyCVLPkp8eSubpFX0ObtJSRq+iAM/xaErzfTjkkymHeogATjcAbLOcnYkMowAtsZW4ejax2DgCs7DxcqS89k/q5MRdHHI0cCrnguYXLoaKa7B1mL7TfNPTN+XhoWfBzXL+Xu200EVuX4nlYmRUJof2sl5k0hqGgzIKcW9ui6z9lNnm+TUfI/OIUeI/UzHRCXu8uHKOpi/McK+XA3NdgN9nvfBXJBUEyMTfsfve0dPPu+mjg58nZcQjDnorHSzQ42xqmF1oZzHspiTzVE71OtmSvDGmFrWy/lgNJTNsmrDUl6fOpKIOZLde2/mz3PrSdgBzm/r5drcbn5aMoFYQDm2EYIgCOeY05qj09LSQk9PDwsXLiycc7lcXHrppaxevZpPf/rTbNq0CV3Xh5Wpq6tj6tSprF69mkWLFrFmzRpCoVAhyAGYN28eoVCI1atXM2HCBNasWcPUqVOHJUMuWrSIXC7Hpk2buPzyy49pXy6XI5crfiHF48fuqST89aKmhaE4uMP+MfE9jZT1VHOBezrb2/+EUddNUyeUJFdxiXYpGwGnI40EuJI5pMROgkY1M+tK6Eu00NazFzWTz42JOathBJiSTVp24cpkGfT4kcgvtHd+xya0eBcHR5UDMDN0Eem4kw4bIlKCLWVZZq0fhcP0s2XkeBxje3jAdReSpDGV/DRq1TYYNWkT3i2l+RejuQklUnQAliHjMn2EjZEA2HJxfSBTNQmQwGGYLL8o3yu19I0dXAKUD2jYKYk+Vy+Zqkm4ZBt3zuJfcp+hXx/N9UZxE1M5aaJ1JpF96ilv8BntbwfyCwRKUhtH1tBBsjm/71I+mpqPxGHGKz8gkbyesD4LPTBA8oI/ULH/49Sl8r9LGXsfI4370XafjyMWpFJKF56job+W3IEa7pCj6FccZ/NVQRCEc8xpDXR6hv6irK6uHna+urqa1tbWQhmn00lpaekxZY7c39PTQ1VV1TH1V1VVDSvz5ucpLS3F6XQWyrzZvffey7e+9a138MqEd6KODip8CqGYg4m6RVsqySRtPfNLY1AKO821QDnBwTHAjsJ9Kg70vqEvV1PGMdGHHZdxpvPr4TTXW+xJBzkgj6LVO4+XlrTzWdMi+nET6KCO32LpCi9n2pBsC6RK/rvqMTZXdrCm8siz9FDRtYpFh6cTV0az2xUF4MZ9r5FrKU7LVuNufDEFQqCrEu3mfDpjN2JgkYqD6s5CEpxmhlIGuL3tl+wOzeXSQ/1odpysAy5ftxnYzMj6RjbfPg1tph92AZODmLoBh4rvme+wRN+WLUgOmeq755x8sBNtJ/vKE3DhbQBUKJUYruLQVI0eY0r2l9ju86gyTYzAJGSpEsmnk3Mr7O2vIh3ND5s5HWGmlYA/YzLgtHBIu4AJAEzZ8yDZyCh2T76Ngd0dCIIgnOvOyKwrSRq+07Rt28ece7M3lzle+XdS5mhf+9rX+OIXv1g4jsfj1Nf/9VN5hbcmS8awfce3OqbwnHk5F4efpb4uPyup2q8Rj2Y5MhBS6lXpeeYVXOR7GHJyiBLtYiQ9n2huOGBMuc6Ew3txBJp50avwp9YruWT0VrzbLaI9n2Tqxt9zccfDJPz1bDhvAYZiMyJ7OfUJN14thyFrrKtez3NjtnJk3MpjWTjTBvGks9DerNNN54yJcDiN5lKoVnbz+2k+ulpVvjDmZUKpStgBmmUi5WBB00ss4CUog56uGl6cMoIZpR0E1AzrffneFlz5X7v7D7XygtRM39DKzgC95VnKFk4g/Md9WCkDSk7ufY7uP0Qp1xfrCe4lJZej2jIpKcEdrKfSrQPbMGVonf0rcu78Oy4ZTpbXvIArYHFrtoV7qxTe36fyAfL5cb9x/UOh3v+9NIXS0cvYNNhpk3Uvr2DuFVefXCMFQRDOgtMa6NTU5Ddu7Onpoba2uAtzX19fofelpqYGTdOIRCLDenX6+vq48MILC2V6e3t5s/7+/mH1rFu3btj1SCSCruvH9PQc4XK5cLlcx70mnCHW8MOk5OM3ly+k/pkXmW5aSDh4uPx3TE+OYN7QSGJs9B5avWHG7/5ngo5yDN3FvEAFKbk47Ci5Tbx+QM9/hFOGl9c7zmNGXw9SdBSPTnfSfLXC6F6JcUOdQ9OjA9wa/UcAXLZEID2ZbqeO29BoJsPvlftZGQkSPyoPyIFOwhkA8pX8hRD72hUcss6W/VNxq1lsO0dFdgSV/7eDluum4suOx6nGiNQ4UJT97Ajnt3ywJuXrDTtCaKiM5j4+qI3mgaMCHUO1UauGb1Z6Mrq6UxhScWnGcpyU9I8hl1jO7qYeXq6aRLKyDSyVQGcpz3dPITXg4xNrd+Gf8QUOTFVwZ/vxtv4Lh3yVNDtgvA4HI2OJVxZ/T8eVT6FcM8m2wlrfGpYdWMvPL5hKrf/07bouCIJwOp3WQKepqYmamhpWrFjBrFmzANA0jddee43//M//BGDOnDk4HA5WrFjBzTffDEB3dzc7d+7ke9/7HgDz588nFouxfv16LrjgAgDWrVtHLBYrBEPz58/nO9/5Dt3d3YWgavny5bhcLubMmXM6X5bwV+jUNAJvinbuXP9fnK/spdY0qfV+kl3BOlyHNiJb+aGW+MZPcthRzeigxbyqG2Bo66tSnx/DtkmTJuxUIOjDmfUU6m2OHWC07SAILCbOI44ReK1iUnqplmWTsgxHZAmGpTBJqmKWHCes2Lh9LdQ6TcLlFzHgrsCpHgDA2Rch3taLrFrYKowvbeXl+Zfwya2Pssfhxc5GIP2f9LoD1Eck9IPdlOecZPzV6FX1rFg0j6/v68UJPDXUc9TpruYbVTfTNOAjTTH/BUAmTTK7h2zgMFm9BifjOBkpbfh+YjVj13Jo73louQgA0dgEyno/Ra/m5pnal5jz1H4cvTKHQ/XUZkv514MP0KyUsDF+M5a+md/0jEdVKkjUTKFU7yrUe23HVbgTjRhBm8FUNe10EMlFRKAjCMI565QDnWQyycGDBwvHLS0tbN26lbKyMhoaGrjrrrv47ne/y7hx4xg3bhzf/e538Xq93HrrrQCEQiE+8YlPcPfdd1NeXk5ZWRn33HMP06ZNK8zCmjRpEtdccw233347v/zlL4H89PLFixczYUI+V2DhwoVMnjyZpUuX8v3vf59wOMw999zD7bffLmZcnWWRdDE3JB09jGE0Dru+WNmO5c4HP/+4408kKypIDPiYtvM1NjSF6KzejT/ay4uROuTMC2TqmrhCHsl2LUIqPYOoL05voJrXGuYxqSsFxusErCSXHJSozEn0Aq+a49CSfVjke0dUbCx3Fy9U78JTs4aRKZWvZ/bhPioIS0sufjP/Kjrd1cAiVEPH4Q0QmFVL/aSVbN3TyISMgh104iMFBFGQwY6iK9VkHTB5XRgI4wk14Jxdy9bQaL5gBvnc7mWUTMmgWPmZTstrFtA1aSrvW7ecukTxvVGlA2xpvQXmQ3uzmzk8idtRe8Lk5L7ePmBk4dj1UuWw6xlb52PjJtC3tQv98A0wmfwP4CLJZ3J1uO0wq1yTSB26nD/5hobvYjAtUAzG9uq9BBMyowMNXBa9gt2pJ9+yTYIgCOeCUw50Nm7cOGxG05Gcl4997GM8+OCDfPnLXyaTyXDHHXcQiUSYO3cuy5cvL6yhA/CjH/0IVVW5+eabyWQyXHnllTz44IOFNXQAHnnkEe68887C7KwlS5bw05/+tHBdURSee+457rjjDhYsWIDH4+HWW2/lvvvuO/V3QTitIuEIDO1wNabNi5nog2B+thSqxCvaUmrsF5nJSny2m7qeAHuNNE49DoSQtD6c2SRpy4Gu9ZKijLDvEbrNuTjsGQAEUzG+tbedXfoeXmmEhcl1fLD0AJFENY/Gc9TmbqN2qCNCNnN8LXWYiVYPpADyyeq6JfPnjql0SpUsr7qK3tmNTDzYwoLwS1y5agdrLhiHV6mjN5hgTMAA28Zs2wtAbmh/hSMTr1LeUr5wu8KHu+up7PYQkCU8toXtkBnA4N8mL+Lr2R9w+YFNHKqcTVdpfhOudeOu4KbNLYX3zjNQxozQZxnc2U7HhU/R98ha3InGEyYnpzLJYblQ+2wJ2yxOVc8qDppd4LIcfAGYSn4BwBaHzbfNDD/MXFUoa8swS3qei4PtrC67kZGRCBuGrrX1P4873sjowG3sSK1kzKBYXF0QhHPbKf8rddlll2Hb9ltelySJb37zm3zzm998yzJut5v777//bRf2Kysr4+GHH37btjQ0NPDss8+esM3CuyvZ0Qm1jQBMqbwKj5HPmWqutwi6bCKxLkqcabBgnL2D6c417HQswWlYmLJFRVsnADoHkCQVW1X5alUFdbqHS4byeFKqByO7AndYg0ZwS53sdjqgPMxIz5d51DmCstwCcq5aagKHmRjr4bclV9KTCHNrtIXl4clsGjWH9gsaCfVl2ZVuIuesZEnXG1SG32B8exjfpEtYWxqkTMkPy8iWSedIPwC2Mx9W2JKE6rSQLZXBkES6L4Ev4ERXc8S0XvCozG1q4/y+cZg5k+p4N2XdX+GD4UuZllzAq/Z2YFThvZuw+iD9v96KpNhoTQHKbpmIOz7qhMnJtjk86Rs7jp5+Htmy0VWTNmcdytC6ObnYZlqThwHoLq8ku/CDXLa+g5IU9IUMVs9uZFdKZ69uUmqsYLQvH5R1MZK1F4QZ1VFGAzJRnw/LTtCX0490DgmCIJxzxJ9jwmlnJ+IwlLKx13GYUY4gdeRnS2VlgxqXB9mVhCS8KE/j5zRxvrcXj26wYXIno3LzKc1UE4xNJF52CEvVKRm4japUcQhlZO8KXvB1Ma3lfLBUvlZVMawNHqud9+1vJ5eVGOHLB06Bli6iPRX8jzKfh0fegpF1wOF8eQWwHTIzNm5lfHuYrAOcrhAdFRXsrOlkIuAwdNKufK+jOdT7mHB4UPwZJPJBRFjN0uHVyRoOAulmXHaWl8dfxMvj4Wt/DKK4kijmIKXZTYS1PlxGOUcHOrsuGkflWJPxK7fxROwfaQq5Ge0+ieTk3CBHbxN/YNYljF47nhk7f8cr55m4YmnG9fXSggM9F0UfpVBheTmozwWPyiUzH0NbF6LPuBTVIRd2W48C0WSsuHFqA2xugCcAh/4RLtm3gvqMyWUnbqEgCMJZIQId4bSTssUcnSu5CLe/sZBE7JZkxvimoJX+L3pc5ovyGmANmZyDw1RSETeotbKEcqXk1Gqm7d3MikYvszwyrqO2ZpuYVZEGp1M9mCRx+B5kqbh43Xi5nd9JP+N/SkLIGQP30LT0gbqx7A1NIYkDQ3Mw1orgsQ1q0jr+wFj+16PSc00pmtbCz3wq3wyHqLd3My/cDAFwajmGdqMgJem49CyDrhKum/Y9rj/4JzyWxZNNOaAdgE8Ouvgen2fz7oUkM2Wk0woBO9+WRcZaJvM6/+b4NBy1PdvEEeuQqmDgPBjT2cHe3gFGlzSd8D3PZTQ8RwU6IzoCyGo17qFE7skxE1dW4lce6AuVsMCzHkkpJ2x/EABnsJsJV6/G2KFz3vOVvObqZSAg45nu5NKO15E1lVHTNrJrz3TcvQ1Ulk/ne9PLyDncJK3jNEgQBOEcIQId4fTTi998G3MrsGONZLiMqC9OSA3RxmZGulV+d2gOlZe34ndnOdg8n4vU7dz5jAWsI+vaz9oLJtBedy0TNQrBgGzm0JUUklKJ4ZyC315Fdu4EJN3iX3/7U2JlPlbXTqHWZRKygpQYZZTpzQCEQzU8c8G1+HuzsHWQC1M1VJv54KnXykcEGx0zmZYZyWDoeQhDaXmY+tr1aBY4UjIM5bnnFAvT7AVpDJ3OKlJ2GU91dPOkPQq3XspWbQSREYeYSi+z+17C6m2iVclhJvJJvk9VT+eAJ4HW7cUxlErTZdbDUMdUgASzS/5C18A0KDn/bd/ujbEUUkYqtA1gwZan2Fw7E0OFuAfKXUEMZwD0DH+6bBF/DN5QKOu0clQ8Z5HO1bNgcwubZ1j8+8rf0l2q8NOpH2eWvoUDxjSaaCGSmURKjnJI3wZcjsvOHdsgQRCEc4gIdITTTs+ZhccD1aBoA1jR4nVj1H7SfWUkDDf72mqZ7gyQDo5g+z/s4I9uhcs662iw+xjb9XVCrWX8ZfJcnmu4iEnZg3xhxSM8PzaOYtXw/AXXUOccCR4V2wMHLwA9Fik8T83kvxAM9BFIGLAFxgxEuXNwJYMxi8fkcaj+HQQ9B3FmdKp2xuDar7Ev28TmdDEHLdY2B0e3k3Hm7xiI1BOpzm8v0e/S2Vq2h5GpBgAyupta22TOrDByZ4TBzaOJDY1IBVQV1e/hkKRS3pnvefp1+y1Y2RG8L9hKZc5ENQ0eUD9feF6XnuN76p3YJ7FNSWsmh8sYvuv5lvMXQAf89qILGQw9xjNhOJDNYCsSV6x/nlHadGS1DNnUWLDuxzT0dgMQDeYX0Fx34Yc5UJ1fc2qCtp1EU/6fimwA3JFSztObeAxwWRqCIAjnMhHoCKedQbbweNKkNzAYxcHo0DXLyd598whH+vBJu6jbF2QAcJ6fhXKZloDEnauaGXHAAeSAFK7MKDxlYfyDfbhzEUAhmMxwy+o/UxEs7pmWqBhDtx3jyNZMbYd1HBE/k7LTwf004e7xZJlMVrbAp1Mz8xXqqnfhaJOIbM6vVzPHZTPGmeaXQ3VWZtxMiE+h2pXv8VkxZgkuO0tNUmN2YhJ487OXkpofHHD45Vr8rkqcikx6KAhwLegEOjGen4zPTAAqX97wCCMGHOy+7mJCWR8f2vASVel6TD1AX9DBo/NrSNgBHOHut11XJ5vtQskcxizVMH09QH7rFGdyHEnFpt+T70EyxgXJ+StRJRj71H7KDD/uxGNccKiZ5QtkXrkiyMSBClJdHkxJQ9FGEUwqQC+KZvNKqoOPlUJV517MWCN+X37215HcJEEQhHOVCHSE008q9i507vwIelsxeSSmBdjl7eDmyAzaR7u4PFdN3LueSM4kXekCdCqmJWlsMjAyMp2ry7hxxypu3LEKgJyiEPeAHFaRov04A/m5Rg7T4rkJ70Oq1Whas4e05OLzkfyif7gPk7adPBKopov8UIvDtkhs+wAt3IQ7cZgA6wE4qO4h4X+j0N7G/Rugqbhy8c07f8+4y1o4qKnkmEBOG77Sdi7qxiipxpOMEbXzEdfOfXMoT5QjeWP84KZ8ks/L107FZZfSlJTor2smkJNYZISoIMheW+bRofpc/c288spXkUek8W7/PpfWLMPtzm+Nkc12sWbtQgJWBq6AFvYB92GZCjvKe8hU5ohr+RXGZ+5Zx/mRvXiyaYKpOIb9CrJl4s5FcKtX47bL6CkbwNF3mBfHfp8Z4SVIxmgUMx/g9Q/9S7F+xhQu3NyD6asEqnCpOaRcJ/GEE6ejrNA2QRCEc4UIdITTTraLScPpvirMvhKG1u2jOVlJx8AF/BBQKaHf0cJkpx8lIZG2y0Dq5Ydj/pHuyvzO9dJ5/di7wswMHEbx5NiX8TIYWkO2PYcW7WGV8yngY3xkVQ8O6znWuVzs40ru7bme8en8+jRhtYQ/Vt7EP2x5kbHR/BCNX0/TPHoySZ+FK10cHjpQuoHD/l4cpkLQ8PP62DqmHdVpkQyWUsEGDloVjLcbODy0kI5tFffHysgKJeG+fHCQg6kTNgEwuGMECW8+MNsTWAlACYvZ4a7mss4or6t/YZrWx57cBcDQ/lEDGoc3eAAPklLGtOnN1DXmg4nOtoNYVoaezO3Ia3vwV8yG6fDU4XLaqv5QaI/Hsrh05yGCfRXsmnwbJf5nGbdpJ/FgGQDeRCO2NQbTs5Kk28C24oQrnkRNjWPmoXx7kxbolsKtpc/AldDCFuA+ZozeirNvHRv62pFlN/PnrRDBjiAI5xQR6Ain3ZHVfwHGND9NtG8UfWNvAWCe8wA/sp5kv93Al/XbeZWxvJrK7wU1sS0Ao57G2d5O7QGb/rEKXe7lWHOsoe0l8zyWhWxDsqyUA4F89m5PdjtTcvtxy/n91rxpF91WBdWmTL/7A3ThJ1VXwxtjYoSyfq7ZsIsLdxZ7bnY2NQLwla6P05TMEjT8BK1SfHoXqiMJgCVJ9HvzC186vRcwkASc+d4r28wPYZU50+iqBsEsLV6F3X+uozKbRRoxAW/AhxKtxWp8jMnhyZSmMqiuJH0lEzGkAQaVSl71VNLvL/5aOtxj8dTPx59OE0++RHdvhLpG6Gzt5Zm/PMS06TDYvBe3NgJHugSAi2JZ3qd1F+rwmTLbLq1mIOVH7qgmNkLigYnXMW6wh/onehh3YAVt816jRS7jyUu6MFSbTrJQNsCVXfkesKgps2znVOIeLwvaRuKvmgzTQVFMdu0/n5bsDKaOe4NwuJW6OhHoCIJw7hCBjnDaSXZx1tXW6VXIhx2FbTK9DpvYhjHoo+bzQW8zs9hC2ArQ7Khgu+7HY1msa9oD7AHAthxonUuZ39CLL53lQE8Zv1fuZ602kite2ch3bhzKWZFNDtXUkLIrIQt1MZP6vgQOv4NDpeVAjnUz5rFhRn5Tz79cPchHV7xGSWKQtlofD58XBWAdOTJaDnf7yzyy6IP8qOdVypMH0C0nu8ZegleJ5Z8vu4NGtZbNWn5Lkh4pgG45uX7EPmAf3VMV3FYdv7okX++YTJibw5Wk0qNwWA52l+2GMoAW6lMBlo98EcP4IJNbIpTJmcL7N8MxG7daixGwebI0wuHDEebMhY62nZhmfkgwlwsQ9jjodxwCGkiadazuuxYLk6c8F7IwtJpbB/bQnzJ4BfAkNK7skCnX8rlUMW8v5RsybJk1FkO1qdIuxGG4qUpI9JaEIPIzAKrre6nyqZjbp6P4agptXD84hrZEPc6+S3mwPo2IcwRBOJeIQEc47SSKs5ayATeS14N7KEFYTXrYWn07ZKE+CwNMB6DKH0Z19OSnaI9uIurOh0ZuJEZUPUq5J8Lq+FxaWydQ6zKxZSePXj6JukgYh67x/NXXA9cjxTVca/p5afxoOmbGmR6tIDb0Kd/YVF9o14Gacr6+9P3D2i2bGqtHGuwLyCw+1EtU1YjvqKW37jO8kfRyYKrG5/q/hz0O5tl9XGxuoVlpzNcnlfOx7Ne4pvcZQmop1I/m6Y7/5u6SCewIJjmvp4HSEj//7HwRVfPRtb8OHAlMeSx7GipIO0wk9SG2TYL66BTgOgA2pV/EnZrKKP9knHaWgeb89hXZbISoGqSFJg5WZajLuRnjyG+PETaDdI0oQ9Jr2BUfxeisjz8NfnLoNeaY/UIH7tyOfD0O+NFijcGQChxGMRUaEyOpjGTxGyYr68fD0ES28h0mOTe4bGPY+6aPDRBMaMQPuohlEQRBOKeIQEc47aSjNiPIxWbi14q7Vjo1HaNqC/bgeJIl+3g/fyGZHMcKeTzgotY0mbHcwDGgsWfm++kv68CO1NFn+Il5irk/UonNgdp9TNoaYumffobhcqLXW2zyjaRVmctKpkN2Os1ugBy2InFh605KjF7WVq/FlIbPFlJshUVrA4zoi+ZPqDbvO/AEo4IxdHssaUtnRORJHLaJBryam0xXxQxqzDhOVcdWJFYzgWyVyU0lrcjZMdRiUtc/n/qtnYRKvBh+D/IIi0nVXdipJsweDW/USWVJOVsa78Uemi5WkukvtKutNEjKkWILG5AsC2+sn9ZDB+mO9vO7KR/AkG6BevI/gEPXac852Bm/AABbkUiWbqfN/BMfeN7k6cuSNF9rMSd3HTvdXdxXu4PzjDl4uqoI5tL4+hT6qkwChoY3m0UpvuU8k1lAjTnIxdbwLWDU7gzReAUyBj2Dw6e5C4IgnG0i0BFOK72rC47+HlQ3kFG8BIz8F68smch9s8iqcQxHklfky/FqgFPhyEzlaE0d2TqJHgZIJyrZabbS7G3D0udwZG+JEbN28ImAyurqHE+vWsItk5/Abo8TctjkFtzAt9/4FfOUdfRER/GY9y6eWBDkgt2vME97ivWGizu6PkSruxkrp6O0RxlZ2sGfx0GbciHV1a+zeHKGHfsDOLwmR/ovVG8ztpkflouaIbqdDqoTCb7n+SKvVb0PqmJ4OmVsPUCJZYMC2fRY1pdexT8oazCO2indHfCT7clgKhbjEi6u2neQ56dfyL9vy7Alu4cD5PeWQhvEkS2nLjSbneyhROun5aF1xCuzGBUO7rB/zKvpq6k96OLjqVq2t/+JaEmG6ZnHiPoCPL5gKTSnycoDBJJJlhoZtkzxcmjUk3T2jWAwJ3FRywLOy04CYCAU50k2cCWX4cgcxLG2G4Y2Qp+Z3Y3bdOIwh/fojBqbwJMYILnNYP1gGR8/jZ8nQRCEv5YIdIS31Xm4j8HeVgwz3yvj9EJVXS1VVZOOW75j1x4kq/hFuL96D0p8NFVDC+gqksHvKnQk3eZayabfMsEHhmWCmf84ZjxBBigjRJoyO8vBkr3opb2UDVRRNbTfwMCWSdw/vp3RikxSDdA8WEfAHupN8Kg8NWYhD1ZdT0NrnGDcCR6VQ5VJMt4gpHNMyTWxonQ1EaWXJQO1/HkCxAMmzQtmMFV/GWfAoMQcvljf1SNS6KU6pqmQyzlJYjDWyOBhgCa9m//xfYCPmo/gz4RoMG1wwMVZB5MMN0Z5GZoUIylZSKaT0WNeYN+BOSTlGKbuwavn36B2eRsuKYZLy/KA8y6YX3x+1azj068/SuZiD6n2/P+POjpoGOxizkGbif5q2lJJvK4Y25xhSvXPgUfFo5QxOjIfWMGL1nVYA4O0D1aRtoGqV9mR3cv+SH6Gmq8sn1QdldLIDKCka9Ct/JT4D1dvYLKm89TA9KN3reCBvf9OMGmSdrl4n/YDOrIaI91OBEEQzgUi0BHeUufhPp74z23EPY7CZpaSrDFu7he5Zt4PjxvstPcMYknFnotQ/wW4oqFiAclgslNDyngpGZxDRLLwSpD27CBiV6BZKh+Qj5pjJQGZoR+eAifkbAfWgJeOJpnS/rHYisTLg1cx1XwVl6cNh2GzoSk/k+vg0LbasqmxStmDks7hslSChp8PRctp15yAwX/09fPc4PtZX7riyJp71Bk5bEYUmvJCj4N/2O1ilXo9gVwMj8OgUusnOXT9DvtHpKiiMhXF6cp3a/ntx1k7KstobTyWMRrTneIn7VVUSQqzcgEGFZtMKo47pKEaBr+aNo+G3lou3/YoF523k20dYxhzwIdeP58fjBuH5pd4Yv9rhByeQruu3vQCjsgs8B95zyw+ZHh40Z2fPu7XM+T8+dyesCODO9tELFtO3DsIwBp3I7tLxwBwkb2dJtviVecumtQ4yexk/tQ9C+o7+bz2GSZrGpe+6Z+NVxOT0WIe3ufYRI3RT1g3RKAjCMI5QwQ6wluKDSaJexz84jovmlL84nLa32RMtPe4gU5SG77DYwUKLm8IhjpHJHS+ky4BB6S1DP+m9TGIzVwDuvwV/KD9Sm4pW0fQkWVA97N+oJ4XKq4k4ixlnNzJAWsEs9Q26kvzX9KVehRrXimGpWAeCKHkuvjMs30kve5CG2RTI+F6lquVj+HFSdDwU2V4mZ5Zw2A2zcPMpk6XyRlulrRW8NxQoFPvm4iZvAXNttAs6PTeiHz4AXI+H1bJAC3BMcxne+F5RtDFPqkSzXZg2AE0VK6vXM31OdijdrMhN51rW/v4+eh/Z+6eF5DNGCl1JAHThSeb40MbXyLrcGK6U/SbKnVWC00jW2AktLAduI+GGdt4yv4w5zUXd3J/emo/45v7uXDoWFIc7Ep9FsWd73dRBk3UdH5hww1VG2ipKeZQKZaCkra5JRbFjUSpnGZe6HmeNheAkc8strX8mOJowlRKOvZQAvYRlVsV3IdTHDSq8N82+DafKEEQhHefCHSEt2QNDpJ2SWiKk08//nsqYlm2TVrIs/MaiZnH38wxGk1i2cWgKGuFyMm+I2keDPg9fFrL0aDF+dCW+/nWQL6nYVvtbP4wdzYRw8/jHdOKFUoyEyw3Vq6fPp+DXWYTo+VBSrz5dWL+KfcoX9r5HGFHiOcyo3khFOHqx76DKRcDHctOsvnKqRxyD+2eKYOpxpG6Z2JrOmDyz9pdjHBl8cmTgFcBaE/tpdr+Kt+xbmeq3cCCnfshLYMPepwjcOFGlYoBB4Bkg2rL6FTwMz5GfU8HLppoq9nOIecOrkhHULylrDn/o1zS9yCyWUqZHiDcPxGkNKWBAbLl7bSkG/lRlwtVlbiwaxLjHEtgOkiqxZe3bWQwXcpfhp7zht0TcMeroAokySagwOXeL7Fq5xhY+C/MW32Q8e2HyTrgg7EbiWVcSIaHSGI7ajqHM76i0H4DWBWuoYxDuHBDoPjaVjCNu+214BneW7Nr0m1kamu4aM2/44v3vOXn6WR1ZDXCenH4s8yhih4iQRDeMRHoCG/JTiZxyh3ANAZG1SMlY7gcLUAj0YHoce+JR0wydlPheEq4kVx0VGHX8ZfGX8ve0go2UcHzF93Lfc9/DzkzkkguPxTj8i/BqefXkVEkjemzfoxzxE8BeHrfYgiDjc0BV349G4dksLW9GjnrxjnCYDAkkbryFlrDb9BuNVNiVPKn87u4Pu6htzuO38gyqvQSvmsZ6GUXUJnr55bMn+mTS2lSOxmlbmfdUNuzZgaJw0SH0pEvem03usMPi2CicydGJolLipGgOIzktBRC9iCGbREjyOWlH6PCDtJpdZMmzchcH3f2/5h98XF4zByDfhdPj/wuNYM3UJVKMCIdJ12VQLWC/Ka1h3+1bsEebESpKa5bs8T8X57Vi1Pj51XegNvdiGFbXFW2jz2eGl6ursS/I5/Hs3bmdDouGMn/q17DN8OTiGYHKLc11veE2V83H19ZP5MiA6Qbyrm4fx3rBmpwq5DLL/RTMN69C9sI4nDoHB3mVvnjtEdHoTv8OE2dwd69xHlnW0J0ZDUuXreXjFXsGfTIMq/PnSiCHUEQ3hER6AhvyaH1My/0Bx7gXi7xrOQ8drDRM43HuJxc5PBx7zENC/uoOclT9jyI0unCspM8NTXK/Jb9NO+ZxCQ5wbZ5Y9hZeQkj8KC358e2SuR1NFRHSOf8hJPXEeoIIfXkv/Q0K5+EoltO6uMqlOafI5JS8aqlWHJ+iMXyhdB73Li0cqIVHgZDEgf7R/JCIJ+w4zcSVOgZFsWSBDMd2IA2sxRX5362z2yH8JHXctTcaiA6J4BZkg/C5LI0CePYYRqn6cOy23nOtQ2A16PLCITcWNgwNO2+ejDK+NK/kPPWENTGs60iyoGchuWsZ1HsAI3hanrOayS0TeW/eYR2LmctMwvPsSI+i06rmDv0ChuI2W10ZIP8uHQnHWop/+77NPfwKwBiTh9TSgYZDEkQhi45wCEpHwSZriZ2MpudZTDasZF/CvbznPQ1Mg6LsYObhr022/ESOde1lCTT9B51PpkOFh47LJVDbV/GamtBlj3Mn7f8bYOdN/ferO+OkY1m+VpTDQ1uF23ZHPd29Im8H0EQ3jER6AhvKZzowHTlh4ASkxIcxiZB/gvS8D5HNvvBY77EipOx81ac76C6zuYl/4X8Lv7fPJNL84v1/87BuilsmzeGWmUMTVYZO0tbAehMuPmLOYuJqRQj/RY3mXdhWMVNQW1FQlIkXqjpx22plA5N91adOrJSLNc1fjJ7guMZlU0Du7GBL+x7jUYrSbOnC2to0bsjM+EzoSAdvUHWGZcymV3515JTOWpJINZXzMdwJDEkAzNoks4kOVo6HUInn7SdljQ8lsJgdTmDgJsUcYeLdM7FR/vfgH7YnE7j3BbmjQkynhF/zL/+HpspB66js2QEV6k/oDQVYwHZYXuWZ+LlGO5iL1K9dzlBvYZ2JT9Fq1+t5KK9m2mtyQdDU9UWZuvPcGRq/uZaN4ecMosPwrYZIX6+L0BN1uYlFGJemV9eOgJDUbl+0wAz9xaf986nTQ5eblMuDw4LdKKsxDIWklVBsiXKfF9gyiiDXbu/iKaH3zLQOab3JmPgeqMPl2nzozXFtYRcisSmRhW12kmpq5Raf+1x6xMEQTgeEegIb2kgrpFRbwDAe/B6RvVX0O+387kisvm2X2JHZANlqNUjSOUaqTVNRu4/TFPbAN7ETly5HP/fvPxKd1J8LK41/YzL9nL7+kdJ+Ot5du50DMmBu+5RSrQIM3su5MlLFuLfqPNPLY3MUc+jVvlG/n5AVoqTnqf1zaaxxybh74AaMGSLqROuozLXz8Gu/6G/1sWAJ4Ulg+6eSCJQQkbaR3BwA4o6FN1YJhF7JEEp/2uyvnI9EW83umRwhVmDka0C8tOy/Z4IHZ2TUP1J6AeH6aSkrwEt+yw9k2yqUz6aQxEuHvs7bn31eUKJGF5nilHRZt7/Wi0Ng/14NZ2esSWEhmbJ13lTvDHtAsatXcmYo9Ymmlh1BbpUfN9rpTDX0M2HnFtIyy6+NeMWOt3VQH4BwbSlsnlUKUdi0AvXv8QkrRdLUon6fHxmjodJcYsrOyTCjhCGovKzPf9OIGqzj38oPM+28yfhU0xc8vA8nGA6hZZ7kd1N5Wg27GjOUDtqAgNUHPN5OLoH50AqSyal8cVKGOmS6Awb/My0+doN9SxoHMVAeoA7Xvol6cOX8p0Nf0B17MatuvnvK3/M7PKRb/u5EwRBOEIEOsJbSuJGHvqIrHHsZmOwC1uaBUPbNgykBwgGht+jH90FAvitUip8jRxJ6jgweRpN23tIupzUd7UyI74KNe3BsGfzHH4GG2fjLbuQLqmjUMc9fRaVBqyVVfCo2LYTya6l2ggy1IGCSzJRlKENNm2NOb58uw+68wWiksamlEGllv+SHZH8IPXZfDDQXZovM9P0cXv/P5BRm6H2dWTTZEXiC0xXq0HSqZFG8x8DL/OFzN38QZ/OFKkF2f3vkIPxk9YAEE9UQUspbsuNYjpQsmnGxutxyTpxb5JEsJzv35jfjmFm33quG3waf1Yi7Ksl7AOHHceZyic4X6iMZ1XQydaR5QS68pFOFyPZG+qhS1KBBgB+GJpCzBlCcgZoG/ERrjm4DXXXQZIjGhmTqkPSK5D23wqjHwegM1iHd6CXWG2QRKCEmQdeZFzKpqfMwjGUX7WxaxSjYjFs2yj0avW65+HwHCZh7MOyiytLF3KELI2fOZbzO28Dv1iXwan+EH/Mop786xnUDP5p5+E39eD08sBRi1Q75Rzl8TuodX8X04pS5mwhzaWkSj6IHcwPXb1/xwCr5lWJoSxBEE6KCHSEt5QwDUJDn5AJVf1MGrWX/QPFv6QTWvyYe9KawVG5uaRdgwzY+4H8VPSMJ8DPP/IhGndu5KYXfl8o11ntBu8FtPozrJzxInrnDAbb81+IbZGPkzNllKGAxDYlZugj4KjvOUl1cCRFttX5E8oc+W/nqDMfIMS1HFrNOrp623EDLaENVFgSFakB9tfZwBcZdd4efPwFHyCbNlZO5urQj9g2ox6pvQZVrmKyplNn6Fwy7X9w7pyN4jD5yWEXd6TAMTtH8+4rqJN2DHtPznP0sc8eh19fA91fxZLzuUZTutzoMhCYT9TRT8YyqXfsxtJTABysaQHK6Cmp44lIH7JVygPyXVBYG8ggICc4WLeUQ86LAHDZWUbqfeg5jQxR6twVlLin0ubM98LkbBttpM6oahd1cw/isrO8Pu4qXi+09npUS8eTqyDqCeLkJVxxBdVUeXncQwC8UAkLB1YDHwBgrfIGvkSA8wPljIilmb12/VAD4RtaE50lwxOL/3f6aMqdKjtb9vAvJnzzGpPyyhF0pRNIUpZYIsiL275EBQP8U9lI/i/wk7FBnEGDL629n0TFP4ucHUEQTpoIdIS3pKUGwZVfVCbePpuWw+8jXqkXtgQ4HutN2wO4Qhmkup3Qnw90ZN1i7djpjNqxkWev+CD/54k/Uh4dIFyZ349qRXo0K3aMzt/s01Ftk2ygh7QUJ6bEgGvBTtMZO0RdZXHZYBsZh2aiGhI/GDl8HyvVkLi4cyOebP7jrksqkUQNIzrbaBupYLnzQUDD7iznZ/M7WNqaxB9joykNNOMMKWQHAoT0fE6OE4Nq9wBGLgsBaPEqPG/OYwkv050YwZOllfi04vuwNxagIlTGUx3dfNT8IC7LTV22hylSlojcBYnVlAAlQKA0TFnUhcOweLxqDgAxfynu7AAT+7/CrVUKDw246DUkRshxKqoGsGQvt258kjo6qZ+znbRWQdJTzfKRy3lefr74Ppgy3zZy3GXs4GrvGjY4SvncG/9BLBvEp4ZwjfERGLmR9s5xWNXz8onl8QP4+1TqBioo0aZR7nezUP8d/+MdKNRbNnEHAy1XAXCRspO7XMUAVt/p4uA/rUIP5gNknzVIpdwGQJgOQKW8cgSfG1DIWkfehe8i21km9X+ZSs0LQLp/P2oOylNZEr7hnzFBEIS3IwId4S3JueLHw2/6mGhWs92MvO09JsaR0SQADHX4UFZOkfm3gw9zABgsrcI8/1J2JA8S0HLkFlRx9cb1VEc7saV5lPe8wK/+4cP8PtRQuN+VyyJbaSYE9zNsfwSgJAc3rqzj6RGX0xiK0OiJkOqcQG3Pct4ILKL3stHYPgcZt5eEJ8iLW8OMkrbjH1r1eWtuDDMGP8GmtMHr3heoMLJkZRdZh0pOTnJpuBXk/JBe17qpBNO7C8/dq+anYst2ikF3JT7tqEBgSzcDsyVqQyaBXCWb7Sb0XANXe7YRvyyJc8NCTJzYdgrD/TANvc386NUv8udxH6DNX05GLiFuBDAPX0dT1S+oG1iMlC6nyjsIVZ3YSgmT5/yBJlqwDRlLGoEV0jFlk/ldI+jJjeM6bQw7bRd/vngGLx5aRUlJLZCl3KFQG0rhOVBDtkShtrZ5aJHC5QC07JhB7BDMHwwSoZIKt4vRuk6Xp7h+UEvLjMJyO/uUUaxUZtJYX8tr+yP8xPkAjoE+TKrJ5fpZueMObDvf89adqgE+SksqQdYqITDwc+6a+n5MRx3fbXfzuQUPMMLI8I/7OvjmsiO9NzfjOtBH58QQ0wNHp2gLgiAcnwh0hLdk2cUEnKjRyxZ9gLhRCUwBIJM4dujKMIcHOtmIyr7Dkwp5Hn2BIP9W9Uk+cOC/cOgdOD0NOB0eUn1bcMsGKy6+8Ki7P4lD1/nw888Q1Pqw0Hh5wl5K1amUOuNIUvuw55Z08GdVpFwNmyJT2RSBSq2fW7IqsbIQ5+/bzh2+Z/lPPsLqefPQz6vgIFcAV+Cys1TpKXR7LGFDJ4KHCrJ8Ysr/5Rb3/yBh8nTlZVwzuJkoQfpdKq6q4ivtSeb3iPLkcrT5AhAvBjqHR1XjsvK9QQ1lL5F0BpjQEQI8mCPT/KXF4gp1E5as44sGgF4m5Pby6OF/49WSOXxn9P/hvuwDdG0IoM1x8MmJzxbqNk2FhBJA+dNoWkr+EcP5ODX+AwQD+bn3luqiITaBxcxg0GoFj4rT72ZnbBpTR2zgA+orBMMm+8wk6zILmb8xwhPVs/D78wFtLlkOdOPyllOWduAx80OB2lGJ36SO6kHLKLSWSLR1HSKSGQlO+PyjW9llx4YKfH74B0bW+a8DT0LdbfjsQd4fquBQWxvTe6Kks5DweLn3MpOUbKEFZB7aupzW5oVEksd+9gRBEI5HBDrCW5KVYm9MNhJB6+4m55hZOKdlMyeso0zLUhEZTba8EwBPOsk9u7r50SVdGMb9fGtoOZgml82D37qH/3ft9ewZOZaq8AAffuFpts2eSZu7i3LlEEpsJHFvmEYlRoX7EPADdFslY6hguJB1HfAQaDJZeGALfjPL0B6gnOccZN2cWYzd8DO+9dxP6fnLH4n584GcXm1j/lMEh1ldaLc+9EXe5ywHQEFm0C6uF4MENe19HJn3fZn+GgABu595h5KkPZVIsgdbUsjlwpTEHVAK0zo8VElZqhM+qISQJ8HGS6bh2mtSPtDN7Ex+xWclqpDRHPjMNIyGCm8PNQd6sb4Blj//a5tzOLnnk18nHgzRU54mODiK1XU626sBIiiWgh2dTYWzhp1Ok45cCzAVz8EKTEc+D+hnxs3IlocmZ5TmlIqlOvhwW3H9nD9Y0+gGyOr4BmO4nO5hOVgArlwx6Jm4q4fZr6zOfz5U0K9VYE4Zv71gLj6jlR1b/4UnrY+iD80is1WJsP9aDAn+e+7XkH5zK3VE+bFaDJ5qALeh8pHpv0VziWErQRBOjQh0hLdk5IrJnqpjHM7AFSiO4pq4zmz02Jvs4V9E11ibmVGbn5GUlt3ETAXZ7MNQbXxphZTXJJhQmdkxglDyEJ/9QzG/w1AUds+ewft2qbhL/ez15jcH7aOS3tyPMW2b12I5EsZzSIkG8LWCarP49ccLdbgATXVQqfUwZ4fBvwY/x6eCD9F/lRd8DtJmip1VnVyDTsI7jaFlgjDU4Xk+PkOjMdYMCjjQcUkGdtLAbah4LIuX6nu4wAJmdFL6KrzkG83Vigd38OPYVgbV0Q68jN+8nKwxhoySJMgqAiRwOeGNWecBIL2YZa5jB+5VQQ4DXfWlcD5QFab/GwHad82AXBlldieDczKMSb6O1f84h2U304GJuXIqOicA4DU87M408JdLG/hF0AncgEvLcuXelyjzDhC9BNamRrNNn8Kl2f1Ue0NcPPN3XLlzL6NyQaY4unCoh5BVC5r7MelH8x0b6GDb5NQMSbKsnldDs7OUgB7jw08dpMdQ0bwq3XIXo3O7+djeDdxurR12u6l62H7pA3i39rEpF+BbY1Uy8vDFGj2WRSDdSlZ+85MLgiC8PRHoCG9JsbKFxyGzi6n7d7Ky+obCOSnefMw91puOlzGDnk0u/mvqNfTObmTS/j1klPwwzoRIHfP6q3BqjfSZe/nC7Qp37ZnAeKkHGx+G4x+5SarDPy6/aKHD3QasJmlaxLSnWZX+HPtLV1IZBtvbgqIYBG7O8dPcF/nwypcI6jmSbpX/vvJG7v3Tf3DTzhYsP/R9w6DMdQiAMmAkYNmQSufbZRlhHPrQQoSWToAEsznMPygrSdsuPqmsZKM0g/DoGj6S/k8+3f9rniwdYMPBS+lNjmN3U5qrZv6C/+31///svXd4HfWd7/+acnpX781dcu8NMNX0FkoCoaQACWwICaSQspvsJrApm5CQQBJCKKEmQADTq22Me2+yZMnq9Uin92m/P0aWLMze3Xsv93l+z3Jef+nM+c58p2nmfT6VFQNLaQhb0UTTlbXQ8Re2jZTR4HMwkCskoEX4uXQb8TE3YTDg5Bs3SZxxaAkSVcS9YWKCk6xoRSuAxrItTItnOeRzYS+w0njoXBYPXAaAqGXxqG7K45XkrGH0jl7eKZzJjI4d+CxvcvGUFG5rHPG2ESKAkIWaoSB25z5OL3+f+LEZDJ1xI4+vNs1sc+KtfL21hZlXtXNw72nkwvMoMYaB9SiiMn6Nw34PSbmd54wOjrmi7ChtoX7Q4HPAN0uKELUgd3VMZ07cxtu6xh2zfsC1s6bwi6138/2qzzNt2/0sePcLAGguC2mxnC+9KzOtK4MgGXR//lR+L23BZaSxZ08oKJQnT548/w3yQifPxxIPZRDFCetM1qXz4WL7pDFqeOBj1pwsdUKShy2Vszhk1JO1FrNAn+j2LcVs1AoB0AsZBkZ9AgPeS0gkqvCIAoscMj+YY+dbPT8lkQ2zZagc6iGlq+j0kTYg68iii5ANpbE6dEQPDHsrOM26jQXiEd7zNBL33EjkiyojYxXzcoaF36g3EjfcVCSbSWTWc6VdRB+0gQvU1OvMasuBbHB18+MUlYywTZ3ObVP+idihAHfhA3kvtcI59AMJ7SI6rU8wK7OKZM6gB53VcpKETaF04ct49iwhHvKh6DamuQ4zzWUGMR+OO9j6+rmcW7yVDwemkBVVppSEiAZE/r5yJ7ATgIq2Axxom055YxeRaU52YGYiZXXIFnVzUaKavvQxCra8zlsXzEbOhCgyWukbcynVjFZgKWmjzppleEchfy+dTyTXzF2PC9w6MhHvc7TWwtU73kVIBvAYAVY6QhS6uwh7VHDZ6HKVM6XdFIMZaUIEO2I5nNEqgoFeGlO13NC3n2w0CXjJiiLXpVq4o6AA31i21d3L1jBKgmabla5dL3KL9kNu5lmsR3PEZ+QAjTU3fInpwxGGfvcXEu/ugHPMuVY1G/x9ogB2njx58vyX5IVOno8lk1AwhIk3iiQanK918tIJY6KZ4EnrqYY06bOBSIc00ZBSkRL0GuZ6h0okLhz0YhdA0cwX5yyHxNSxeRUM9gQkXgpdjV3eRlwxWzPkTvhR75FhcGUBtUeteFLHADOOZmdwJsFBD8eazArC7w/KPI8p1BI6RLQnAYgBDh1i3XMoEM3WArLzPCLVu1i57AWGWs24nZhm46CjDhnLeNuIVtubkF3LkOZCS1cwpFrByBLJ+Dl26Gzkkg94ImTjfEWlQi3kqZH7cWQfIatmcJWUYaRseOJpKrwDPC/NYsHATpqq3FzXa+BwhUgMFWHPxtHjOcqORbh3hR0Cn0GXS/DGu0hkXuPMcDdapg05G8eejZAV7Hh1gwrhNPowu5JHfAPjFQG0bichI0BzucRtK27AVl/DGaNbWKW9S8FrI3jSCZDTGPRzTB1imdrMjrGmYrWhCSvOiUiKgazaAHApHjrTN5PLNVNsawZiLOx6isajjwGQMmwci0cYzbVSGDWIVJ5HQniEX4vKeLyTXTco3fzPODWN+rMhaU1xvKaBrAF5oZMnT57/DfJCJ8/HEx9EEyZES0qHDdqySUOSmY86qgAmCx0BnRWWfjZg1sbZWrefXUoXkiqQis7nLbGci40UGd0Mjt1qf503i5ro62tgbq6UkEXgt7NnURL0cWGLKXRsuSAxzazvU6aEOGJxY3dksCRTeIhj0bLc88V/Gt8Hm5HBItZQIhaydHgf8ZSXsrnDDGy/nDm6g0XS/bysncpc5ynoag5VLKB2xjGsHpXqRX1omkRUPPntKru7sGRUnkrUQ+J2NgCni2YmmJwqojZ9NY3CPkozThSrlUSqmOpmgz3VbirjMlmpn9mjCkyFU4u6GfBNB8cgDjnKlKYkkhSj/a0a3B1RvGlIKCLqyD8AU6DZcgJnvtyOGIMSIGOBjMVNQpDZmJkIpdFUwwxWAmxON3VGkGZgNJAh4Pw77ZkI83fZqIrFcYUipNxOfLhZIzZNOl5DHP64OwVZTSKMWfLkyBQSWiWwkK1Ls9jVe3jWvYh9BcfIZR2Mhs8iuuEPeNUQ171XyK7SXQirFS5eX8Dc4SS7ltj5hnSYveHreU8poEAeorbY7ANWnRukQhqhSejAmRI+dl/y5MmT56PkhU6ejyUx3DXpc0XHIAu2tvD62rnjyzL6f12ZVkLHJzQDZuXetf3TmB1fxDOVL7C8ZiezotMw4o7xjuGJ3Cij6SD7iyS2Tg1QeCCKoeksFLaMb9OXGWZb6hZEa46UxYeWKMft2ENOkClihO9tuh25x8uQV+TtFTK3Fo2gOEXK3cOY7ZfSaJrEgMWgLzvMhZoGusHIgedJ5KykG64i3L6M+uqdtK1vIOSYzwgRDMEJKMQFM9vM5Y7znf3vkWis5k+Bt6nXLkPu00E0X8Ld3VOpd6UROUbG4cCRAmvjJRB/jZTTiixrCPEEim7lVuVvAHTpMzlg9yL9ysOR6aX0WRwsSg9SFIP5R3X6CwVKIgaf3ajz0kqBB6/5PD2BRiSlH1IPsjwhcNBWwOGsj88ChijgNQaYoppCRNdKkGKnQ8WvcFQ+SwbYWwjRuQY/2wX2qVvIFAdwDc/HPzy5n1ThQB9YAydd42TAoCrVQi9WVH87JQe3IQs6/WVXUDviYI+8CzKQcoRJ1f8VC5AG/vQZgBCGbkERCul1e6jvFSiv0njcLdCLnSqs1I7N892BR2j0KHyNl1DftsHMXeCv/i/vwTx58ny6+cSFjqqq/OhHP+LJJ59kcHCQ8vJybrzxRn7wgx8gjmVSGIbBj3/8Y/70pz8RDodZtmwZv//972lqmvgFmc1mueuuu3j66adJp9OceeaZPPDAA1RVTTx8w+Ewt99+Oy+//DIAF198Mffffz9+v/+TPqxPHeHgyKTPiRI/zXMnl0ROfsR683FogsBwkWU8m6lYLmaq3cedDSls4gHgAKmgHduxaiwYvFTbC5h9rqzRZxmd+XUMyUuqv5+Ie8J1cqr/dxxYHmBgQECyJMhlA/ROvZh6XuDl6Ql6682Cdg2KShE50t2fpTY6k4iQ5H3rIRYn53JepgKH0AFWsPe20Vz2JUS5FBUdS8KcKxP1kBVdqJ5SBMENhNllOUYAHaMow+AUNzPlNJKjH6uuYpENcnMK4BAsLTxGlSVILD1RZPFQdqzIoGyQ9dr45lUZzu+roSRVS41SirVYQCnp4t/O1SiO9ZExZlI5o4aGoW6+/cKEBU2VBJbVednktFCsjCLoWVp9AoFgjEF/AR67Bv3APDeXVr1NEQpCFmIuHZvUztrhVaxLzUQxrCyPH+SSwzuBMIf70vRlM1SrMhcf7zVxnOIqiCZPusYHi+upCaUBK4acpbdiEc6xYVd+AJ6EmcGmSRKHpwewfuv7qAPtDPz1FeTFc4nt38eymYsZyom8m84CGwmppYxSjkeYiBO7rfZuLt+9jzZbJb+xPgCp0bzQyZMnz3/JJy50fvazn/GHP/yBxx57jKamJnbu3MkXvvAFfD4fX/+6WSzs5z//Ob/61a949NFHmT59Oj/5yU84++yzaWlpweMxs0/uuOMO1q1bxzPPPENhYSF33nknF154Ibt27UKSzBfsNddcQ29vL2+88QYAN998M9dddx3r1q37pA/rU4eSyU36bLFqnF++kwc4fXyZVftv1DQxBCL9pRwvnTuslIFlFJsIR46soVYVkMN23JlOvmEzEBJZsBv0GH6e0nX8wV8AcFgE5oNVBYcicufSr/DZgsco6bGTsKcJZ6pRA2a37HOkQnrS80la7Hxn6BE6yr2kYwdp7e8HipGLLbSEuinLjbAiYMYPiZpKhfwapxYe5CvqLVxo6URXJUT5HGCQaZZmLot3so7zWJNr4pnqh2jwDBN2ixwP2slIGnIuhVU2Bf2m6csZClVydbqXtjGhVzQUIjFmFLFlVEarBTY4EnRbj3Jhbzmn6lOoEg/RFfwWBf42LGqYRCDDN26SuCxSiz1lHmMmEMdXu59FLSn0hJ22zDZap0IuFmZOawdzAGSDa8NPMdxbxlueODNDjXSqPu6SnqVr1MaiFdtIG07s7RZmbBZBMrh9nSmmBEcM/ZwJYelxxEm4BwlEP9LFFdBkFyl57F7IaSiJV0moDqzq+QQbHczxB8nFZPq3Brig6jDtR55C0UWEOh8znlqHpOuwv5NSoFESObzGSZV9O258+IkSGovZOmqvoV/ro82o/K/vuzx58uQZ4xMXOlu2bOGSSy7hggsuAKCuro6nn36anTvNDBLDMLjvvvv4/ve/z+WXXw7AY489RmlpKU899RS33HIL0WiUhx9+mL/+9a+cdZbZQ+eJJ56gurqad955h7Vr19Lc3Mwbb7zB1q1bWbbMjB156KGHWLFiBS0tLcyYMeOTPrRPFepYJd/jZLDxxsCZUD6xzGJo/HfQMtZxoWMoOqI1hg6szq1jXiTLFtUHzMUxYsU2ZjTykeZuVNrkahyuJMlBgdyHLpaNDjLiso4X8msgzDGK0chhGOZLOkwta0JTCOl23GOieK31A7zF5v6ehcxLI+dTOOqAwESgdGXxAYotHWR0lYjaQO6NMzAyAngG8doHMIJmtKwfF7Kqoo7YCAjyuNAxjCjutMov9qX5ALik+3VKSlqx2/VxixYNNgiDlBMoTik4dJ1uq+kCbC7s4Nz4AnNbqp9STUOKxFAzCqPTBHzTW6i2HiGsCtw7aCc35AD/82Z7KEDSJSjK4VvSQakFZLvKwSMrUEfsbK88Rl2rFatP4K7MTdyqPkr6VRdNyWESbj+ekgw1q4fZlpmDV4/RaVXp057nC4qBoslMm/UBmXANwz1XoOoTNW76qeIS/T2OVqxCMzJY24aQchkgQy7+JEeAQ8VzsVrSrCWCGpeJDe4iYbPily1Ius7B6ZeRdBahEWTRwReR3/VzFs3jc2RlP4U3G4Td/63bLU+ePHkm8YkLndWrV/OHP/yB1tZWpk+fzr59+9i0aRP33XcfAB0dHQwODnLOOeeMr2Oz2TjttNPYvHkzt9xyC7t27UJRlEljKioqmD17Nps3b2bt2rVs2bIFn883LnIAli9fjs/nY/PmzR8rdLLZLNnsRMG7WCxfRv4/IxSJcmJ78AxW7i84gwuYcF1oiu3kFf+LMidCbBihyIxx2RsqJ5Ragm6rQpG20/XB5F/qoqyz5Kp2rFaVlMNOa6yAohGRwRMCgzstpVgNiZSUJKMfAWB92yoej5sujXWeO7iVh/ibdA4aAYoI8RnewGotIF5QMGm+enWYlM1O2PAwZB8lY/0Tl+wU2XDGCkRRRVMn/l1O311MNAMBazee2TORBcjkNpFiAQWpHDhhTbiW9oKDHMrMQhcVDAwG9EuAR8lkBAJxnZd6B9jU6CPpkhjZWYNPdeCRcljFLK+Hl1GcbeDspJkhNrLvfOSslSMcIVfbxW3Dl1CWnOgD5lVdvFn8OKP7amiMZ0lf1cV85mLxlvI4H9I0vZi5sxbz1t+fwzFtJp93vo1V1IE+KAdFt9GevJWEXozh/TU9ln7sWZ1HN99AneMYs3qtJLQCXu6dhViXwKZkeMByB2Nx5tiMDP/e+A2Sb1yMENRB8qOkXudDiw2hxMpylwBboXCrRiFpII0hQca9l4zFTk7UCPtmYNHAqk700rIoCbzpGLI+QNQRI2cLMiBJJ2ruPHny5PlP+cSFzne+8x2i0SgzZ85EkiQ0TeOnP/0pn/vc5wAYHDQ7RZeWlk5ar7S0lK6urvExVquVQCBw0pjj6w8ODlJSUnLS/CUlJeNjPsq9997Lj3/84/+7A/yUkMoonCh0bGKOm+c8Rl/rFePL9BNe/AOJAcLZMJqQ5SSEidYJNreB4YwAENfsDCh+KtzTePHUF/l1b4judDVxFuAbOEy36uTo23PJFpfiV9PAEFIcM8Xo+H4KMm4kkpKN4rG09dWFO/B5diLYF7Kt0jwGIzaNmsE6rOH3YBZY5c3kpGpkwQwcNoA/ei9nXeVZBI9YUGJ78NKDVTHFUECLTDokWRdp+vwFxEIihX0G15UV8drwWvqtA2S23I902ko+ZBhal5AsAFtWJVS8HVG3IPgXM+qz0+Wexj3hfaTaVhCUChHTMobmY/Gu7/AdaZRWTwe6kcQStiCrAo+VvT8xvyqQaekklJ2oZVRWdZhOwYEnIZAJ+4EuRhWNkbR5XmbGn2TqpkeZWg4kQBdgl7SMsJDjef9CiHxIfcnfcPbYsWRGydrjoEI842XAVUe1B0hBSrMiaYNc/t4bZEo99M3wsnxwiN/WX0hScqGXdxGIVxHXzErWZ7VdjyiXsm1plg+r7iEjmzFLJREPK3u/RtplWtUk4PCck28fUcviyN6LJ/UQH8w0l12il/Pn2CBzK04enydPnjwn8okLnWeffZYnnniCp556iqamJvbu3csdd9xBRUUFN9xww/g4QZicHmoYxknLPspHx3zc+P/Vdu6++26++c1vjn+OxWJUV+eDGT8ONZMBXBMLDJC3+KDw5LEDiQEueekS0mqa5fKK454UABKah4RjBWDGezimd6IWvU9WBzUrkgmFIQBJh4bPmSIiz2WAIhKZmUhDnSjBNGKwkxigCzpWVYcTYoOiVnDK0GcPUJiykTW2ceb09WPfvs8iQNVksmErw3GJCmkqmiZyXlUr0Aq8i6JbebXkIrbFZ8IRsKByxqGD+GMFjAZMkaY7DbTw5IKJ1Q0LaNE7oC+KV5bIirVE7Tl+fMUArtQbFNstzC8N8EHvKvbMWUrBjjB2DaqEIDNSh9ndsBLCIPfPRc6VkvTt4Hnbq3wpczVrKWRVtIaEt53mBQf5Y7vGBmEmMd2NKxciO6JQ7lc5OiVILpfGd9jLodHrGKl8lRF3I8d8jdSzj6B9MweTZoHBSPwiOrVFKFoGSRjCI/8Vd7oBoWQPL9WupcC1nk1iK/UOg2uHirHqE8HPsmQljIjvhONPe/eh6iXEMo2UR0fHl8+zFOCtKyIUH+RAAgT7h2TkSuyZZVwZdlPu6gRgMDSFIVcZpblXSDsz5Cgio64hl3wTQzPFmVP1ohVczLSu2WyZdQq6KDO1dROjFe9x55FWHqk7gyr7f539lydPnk8vn7jQ+da3vsV3v/tdPvvZzwIwZ84curq6uPfee7nhhhsoKzN/vR3PyDrO8PDwuJWnrKyMXC5HOByeZNUZHh5m5cqV42OGhoZOmj8YDJ5kLTqOzWbDZvsYd0uek8hlkpyoarKCykiq8GOFTjgbJq2mufeUe3l327ZJ3x1Sp9DpUrDoChmLyAejMjtUO6pqcFuPglY1+Sd54eAoUU8Biq2AM1o+QJEn4kGsqo5F11BPqL6clAsJ2DKMukT8O3v40PkZcKonrKNiS+jMP9ZBaeoQhfO/zLAyF1E13ZY6ChtjPj7jeZ3zig5x97SvcEHn2+ywrz5hrzTEwgytllmwd2JpkaOIYcsw9pyIU0tiMXLIqoc/xwYJSyIPFkzF6kyQilr4geeHDKwuI6U4sKVEijYnuHbHdvCCfckmijqmEElm6BFHeM6yDafhQNBlioR2xFKNJaFelgpmNtqQ7uKJzEL0bCOBrtXo6hA5niRjuJijlHKaayMlfZvIAK9VtNJbLOLQdTKizPu9e07wLl6AJGRo8BcgOQoIFd2LmItR6OjHl3mduxLh8fo7U9vaWNopcLi8EltOwmLAh5VtQBvEN/Oq95Txrcr1G8joNvR4Do75aepuw9Xfyo7FywjsLKbVdwqKJGIVSqEYhqwXMla0Gqeg4xBSqIUzCHuKKe7rICqAVZFY3rYBn1BAZ9YPgKIbhBQ1L3Ty5Mnzv+QTFzqpVGo8jfw4kiShj/06rK+vp6ysjLfffpsFC8zAy1wux4YNG/jZz34GwKJFi7BYLLz99ttcddVVAAwMDHDw4EF+/vOfA7BixQqi0Sjbt29n6dKlAGzbto1oNDouhvL8n6N/JNC4wxWnc1kva/oXjS9LShNj/JJOtUUn4Isje4IcL3M7x9LKPCXM/OZe7j7v+0ztns0N1DJF+jnR3mJa6g3AzPA6ZrFQVraQIqrJYbBtmQ0pHccuyXhLRnikeDNnByvJ9ZgxOromIRIaz3JyphIUt60il3kFu+hkVcllSKKM3bDibjStMaqe473B9WSNFO5pNtakjjCi/YDSIxq+ygzGIisOWSHjTHPMtZEBi8xXRnXs9hRnvLOTv1jPnnReZvmLUTMq4iuLOdd/hA2UUK5plGsai+NFaL44QaWYrp2rcQ83U8kQ8hQXvrQL4hHwwjT2oZYcYKB3DqqgYhudgy3Xi2pzYQ3kMEqSXGL8mqqMKeyt0QTVnfuYYfsHOwoOo+tJckfgfP/Peae0Du+8ETKApsEFzTOYIy5kuvwrRH0l0yunnnSt1ajC17a9ybBfZeasTVgOOPGnE5SUmOpjqtBHZ4kfydmLI6LhzshcueM2snYHbWUyvVNGWNWzhU1j26s48BXs8TpUFDqkh4gWq1iHsgi6QsvMGyfmxbTAitJBDKeOAyunS03IvqvGbkKgfBqqYfBi2otD6aRgqkFn60QA+bHIMYqFQsrd+YidPHnyfDyfuNC56KKL+OlPf0pNTQ1NTU3s2bOHX/3qV3zxi18ETHfTHXfcwT333MO0adOYNm0a99xzD06nk2uuuQYAn8/Hl770Je68804KCwspKCjgrrvuYs6cOeNZWLNmzeLcc8/lpptu4o9//CNgppdfeOGF+Yyr/wfMyWT4SnyEN05YJohm2wYtN8zdZRlGjn6DVWdDB/XASnRNRNYVGkJdFChmALI742Sa4KTUpvGd1RdyjhhnoXgPsu7k7pIi4MmJCeon74NDN8iNeDgujJoPn4bsWY/g8NDnLwMNJM2ClEnRMBgl2fkEireMvrKlqKn1rCruxGcZ5qzSURyyilfN0o15rzxy+hUU+ibcL1NzxzjozRCxqaQEP12xKlJZ6cSwJQCKCm0MGglWO66iPdvJBiE6/p0hxACBlOQkm3Vhz15OWksj597CbtgQkxo5ZOa2mdYlu2MrO41ZWBSBrDwVNLDFS/AwwrzhVpTgaciaE5u1DUPax/uDlTBoZsfJgobTEmWws5DGpxxYNYUPVp/GVcp1GBylXNB4JRYirEcQRNMlqaujOJUdLC+5iOqkH03sokHrRK4Dud1MY88imzVrDMAF66QQrTGZ6Qv+TuRQI3GtnCF5KkX6xHHv4X28wVk0FS/HJjnpLbYQbEgzpesZvPWnk1YjqGjEDZ2B9CFWFJ9lXjdBRpYlto2+TDRjVpD0WgpZXnIRpRaoTbpJdtYBE3227tryG9yE+cuZ97GwcHKBwzx58uSB/wdC5/777+eHP/wht956K8PDw1RUVHDLLbfwz//8z+Njvv3tb5NOp7n11lvHCwa+9dZb4zV0AH79618jyzJXXXXVeMHARx99dLyGDsCTTz7J7bffPp6ddfHFF/O73/3ukz6kPEDaCPCLKdcx58iE20iUzFioULIbmwj/6LFQ2FGKzX45LIO9B6dALoV0QgZN1hmDtBn3Ena4CJKkkBiv9Y4SlkRe7ZuJt7iQGVxFesefeXW+mzKphjViNVOkn/Nybi6JMaGTUxzM2BNDP9/K/vKZHDhtPfJRc56OaTciyqYLM6f3oueGOOou5lnlTE6p6KHU4+HwUIpk0ks50Fiwh15jwtqxW3CSHTof6l7mb5kzOLp1KXjMlHrfCTFgcmkxongTb/TOJ+ObCR6z8UIWWJRuZqs2e3ys9bR3yQky/T3l9NrLWNx5hN/rN+IUU3iiMXS9n0xFkOXb/40Hzp5PzOGhwWq6q7xKkuaCHjSbTIF9kCmz4cXYFazq2EeAMPa6IOcX/pFzdr9IJCrwiystnDLcS2vobwTSPVRPAVewDdfgXlKOEjrrL0DXVJKZHlRDZZZRT0t2BN/mf8EbHSQ9Vqn5zxVn8Jr/QhTNfFSsaN+Ilz6snkFEqRSfAjDZSlRSt4Hk/sLj7amwKDmiFgtHSx3kIs9jfMRa+N7gUwAErKWcU3kjMWWE6VN0NGxEg2ZsV4Ee5rPqSxwIz6CHJRxPN0gU30YCuPzACJuWl+TdWHny5DmJT1zoeDwe7rvvvvF08o9DEAR+9KMf8aMf/eg/HWO327n//vu5//77/9MxBQUFPPHEE/8Xe5vnP0MzJvexUgWZskNtIDVMjBHM2yehJLEDX3pGRAnaeH2t+YbL5SzYRYkm7zCRQTN7aYlUgSGZNVKmSSliLjeX+f7CnKNb+aX9t+xKyNjkYqY6akhkDO587zCCO4xnSSmFVg1FnlyNOSZ7KbCZv/DTZRY8Y0KnsfC3RGwBfqFdzfLUHqqjsNOygA22BbhCYQjaeOXUqyiKwk1vx3jbeSGDBTI2I4NDzbDZG8c1ah7fxY6juOavx33gehqSUykzcuzQxsSbv5rBNbcR/ssL6HoDxztMPSb76fecQmzvxHk8Gq5l9vTtGD1TSfj8uBIRhKHziWZeoTYukLVK2Mp07NkwsrCBtFugRaxjDuB0Rrkt9Uf86SiqIvCqfzEHG+ZzfuBNptkOMeAooU+oxJEoQvdkGfXZ+FxiO40VHwJm6vib00c4vKgZRQ6wtu1sJCClxVmnrEd3mEH5R4eHcMTS1KdNd+Cisu0UurtJjBVCGhyanOmo6ycH/u8NLmVJ+oT08GwOryaiGSkMQ0N2rEGUK9G1EFZ9B6GyUjR07GOhzpYzswxFkki5DG3amfikHJYhmcS+udQDd+rb+E61QSlDXF6T5P6Ox4kXfTUfr5MnT56PJd/rKs/HYugGnPAOUwwZMaEwKe1mDCFtvswTThd9Uyc3/lQtcLSwhOKxaFO/5a+UKRtQDBvzBpaQ0Iu5GPDL1Yw1FychgMQwDafsx4oGjAAHUA0bA/ZCCoiMb39j2Sou5YOT9mmP0kjWZcEWhcNaLdW08cLUJnJlJbzItQDImsrXm+OkgJ/sTyMEBknM/SXdylLOL8mxYcyTVVy2iwKXgda6nhLvY8QcOinNPz6X3V00/nfY8KCqIjcTgYF1bNWms37sO9dgBKZDYS7F3sIAm1evwp7wYmRSRHExr3+U++qGCUll3L7OtHokGkSi8y3MnPkhERg7cgMvZlZSwm9jgBJ69VqQoLhoGKkgBdjYNFzH/uyVCFIBGd3L0YoduPUBzslcyF+bfs9le82SD0a4BD0tQSn0ygVUyk7EuPloOHxgIW5nkKFAjFTERUOilAgT9af0j2kDsi+wgPniIUaEGJpVIBacuF6SIFOU7cAeaSZrpNFr/VgCVmpyHvyU0K7n+G75N1GrJh5NsqbyzfhqbFWXAma6eUn8HmwehZof/ZzCizXiReTJkyfPx5IXOnk+FsOYLHRGNAe77QsnFQw8jjISwSbD1hXLyYTdKNJE6wAN2Dkyh/PYA8Df5DIKO5bS4foyyanvckTWaBNv4MYjE5NtqVvI+UOjWNEYzd2JalSTQ2FTRKO9PEsB7YiGgl3JsaZjG9pCCWSwOSOooh2QEfqGsPfBxfzd3A9RIu1ys6z1IPMFC65MlmWhSp5PqMzAgrXhQXriU6ljhG6geciJYosA4B9cxa7+QiSxi8MjpttNtthIxSXioQyFnolUtH6KeHLPGrasGCZRdiZrQ9vheKsru2ntKdKsLPa/zbBzDva0C4vvy0iJbmzKTirtChvObUBypRnWNI6ls9S8eRdvB5JIwoR1SJKtWBZqPCDdMbYALKrBc1NWYNc9oLxLVLGTVWoQjVJUIUd/yXaqRkP4P3yJ4avjNBfupiEuoqReR89ZobiJRKCbYSOEmpNRdBtfVN6DKAwkJP7FsYQpinPyxdfM6yafUD9pVrCH9+fXAzsQa8qpRMWStBLI5CjwLufAwF76rHG8bhvnuy9HjExkQm72x1ElmbNbt+CNZ1AcDl5uXE6kuI95qRZGUhlUx5lYNBdKVqTPXokcT0H6v9GOJE+ePJ9K8kInz8fzkQrHPinLisxesE07aWhoYBD3WDkizZIm7B4CGjHQUEWdEu9EGYCkPow0YpBwFCPbBgkrKa7d+jAUTKRzx7rDFHn+BMCmoT2M5rrIailSWpwlIdBFkXOO7MVpqDR0DvHuwdNhPiQLJRxTXkWIliCoLpzKMtS4wVBRN23l1cQ9fr52wEJNVCOnQxaDIUNlBqC4BxhIFFI3tg/dvgEitmYcuo47OY2smGVaRCZSdi26BoLo4LUHO5CtXZzzRdOd49GyOIws/zLny5AA2swWCccFow0VD3EOF9dTXfoWOzsW0VD6BDWJBnIWFfWIiGHANZ4NABy2W9hqW8zU/gbOSb+F4bJjiBLVzKUUB8LGFCOygIqCuv95nl7WTUdJBwAOXcelSFgzcSKeMM8veAYpcBpOxYYl+SQgs7thlIq0F81RTuHQKPFUN86RJIVpG7mMjadG7udgYBt12XZ6a/u4emgGXWMGnEzEiprT8GkRZM1MCT/OuzMXY41W8M1DEdZbD1E+PYTXaQZNZ6UjhA+eQjZbRoFdIxUx2Od/H7tSwDZHClUygAqKMmGmcgxDCfAyyxEdMQoDs0DbPx6fMzJo5xdTT4Ug2ELDBOc2gOcjQixPnjyfevJCJ8/Ho+uc6JWwoOMylEn6x6cUEN+4DzGoQDU0ZPy4ovVsyx2vNqwSsfrx+G10lZnLipKzga0AxNExtBj1Uegd68ZQYE1RXZdADkUAiKlhwkUuLhDbeTv6DYL+PQRL9/LD5BPsUqchZlMISR1ZU0zrxtKJ/SsLqdz0doxXlkxjsEDGrhpsEbtojpcgGmYMyqlYQMohSFkqBgthlrmuN+THnjmDx6X7iakesGaRRTsIpZx5RQP+IgfReI73nj1KsNtsZNUQ3Mwr9mc4vLWG31wsEi27nLOCH/Jc9CIAsoqHOB6ssXbcVUn2u5y01e2mrKOOf0qEUFMyb7Z9jp0FAosz3UTLilHs7YhaFgvncNxjFBdAMRTmHPoNJbk4Ri6BpowyMq+WBR3X0ptp5TTXi2xXV1N+5DkOnBInYUvw5YPbKQ46sbitiIZIwr6T4ZLpBFQXkhSno6SF7oZRrjg6A9ohoRfTQTk2PYSeduAlhSgXIAoG3e9VAkkgySVvPjF+3gDqR7vYXDwHz5j4qThwC0XGWOFFMYvf2YWYLSMlDvCcbQtqRgdGsCYg6jZ9o42zNtAgt41l8F1LZeNmelqPoiXt6I4hLDkoqspw2fp3eHQBDIfPIpaasCTmyZMnz3HyQifPx2J8JBgZQNMU9BOkzsrsZURfizHTPYcEb9M/uodc/x7E8tnAXDRJZ2f9GrZa62AZWNQcFx78kGyJKTJ2yAppNQV4yehesshcUNnCBaEWADKGhbQqYxOjFNoVxGQpgl1l2J6iIjnMnjH7iy8d5eJtbzJ/6SZEST1hfytQuZEf7I/ht9qwawa7p+5G7Dof25IX6FTL2F00h7mhVvYmL6au+BC1Y+vKqgzZYspt2nhESuy0BXAE9He6UXMhBC2DLJew/ZUBQGbIo3KKESIXlXDKEs6hLjyuOIxlXm+ctoyXEmfRWLmXpY4NnOXbx2agyteHPKCBZKV214bxfdg7bYTM6iTLt/8brWtmkAh4ccYTTNnQwyuz5pM8ZxRRGuUlSeffoyG+pg0AW8EOimIjV/IhrVVWni5aCWzgA3EOjxc/jrMky5weibAkMhqt5N2xAtgx3U6DMhuPZGd8pwGn6OKIMhuszUhWG0unpwk1DTH44WzsiTiyMR94DzAtWGvUXfzDcTHvL3gdDpaxb84LFKbcpAarWJlYTlZ1oBgCg0YcVdCpcCaJjpUqSDjM+0uQNY4cWUUuVwXzQJA04gyQVh0Qf5LGhES3P06ZN0qhS2I4TJ48efJ8LHmhk+dj+WjBQICKYJRjgQkBtMX2Amdbr0RSJEYoIr3AhWWBjdHeNQAoss65G97noh2t5GQ4sLgSyxXDpOUSeBvqMvPZVnkKmryehF7Mtv1NdHlEWufO547g09ybuopytYOyCgXMsipkDcf4/C9rq7iYZ7GoWYSIxOMfXslZ/kMsmvI+rYMLmVlwjE4grW9HrGnmWekMzsoOMwKU+w9RH3id0wGOF98uB00RUZXJrR7iwljdFr8fANdSN6mffh0jm2GpLYBicZNxWLE1LEUzJrpu/zsPsWd0LVlDxjBEzjyyy1zfPQplUG4bNs+rHEN2wJ/P+yyNIwX8za6jIiJ7+7HxPPZsmBHNTZ9QSUIWmJ/YzJbAKYx4V+GxHKHD1cKGwFRaDhdzpGgJQ24PycgBts7aBrMUYAMOXed+8S/IusAXlW8ylC1kqtDH17Qdk451p/8AC4ZmTVo21beQSwwLIdEUoMXuLJniDLJVIuivoiLtw6fEEPUMD4h3jDf5/E3BF7nK9j6/cF9HQy7Lhc7XiKWiOLI+HIKBnq1iT2EEYd5eAqIpcNKYtXAMTUQdLCfr8o/vR1PxWUhZH7s1A1Kv40tqhD47xMpYCc29oChh4OOroufJk+fTS17o5DmJgcQAQUeSgo8YddqqfJxY8zoihGh99WUSF5zDt/kN2eoxgVANdtXApiTJaS5i5TIPL+7g9HARr/Zcjip1swCoUyM8Vvk5Dp22B1phoMjHUEzAv72NR1lCOR3ogkgoUwKYcT5xy0StpZxq+tYMEdChN1NGPNuL0xVDyVaiy4cRpCyx4FJiwaXMB0a4EUNQ2LujAV0uxcUC5jW/hj1rplglRA/Zha5J/xm7LO0IejEu2UIOECQFI5vBvuhLVH15DXpigP5vfZuCz5zH/j2nEeAAALfO/CFn7e0niY233IPMzQZY0LCXgaMNaKpIXf1eGHJgKTuG3FGOVhTirYLNLGutJi3pDDrUcbuKEQkzohVQlOwHwFH5LB/4TXegQ9fJjNbREtF5wjeLdNhGYfELnD5UzFfU/fxSuYqiTIL1oRhzqpZx7tLn8ez6JoVpB2AKHZfso1otZU53KZLcBUx0iH9Li5H1FOIXJHRlcvBWbbYUBSjNDTMr+G0+V2KlvXs+j9R+HkWwojs1bog/ypBWTUHtNl43FHI5F9msi7jNwd/mXYgiXTZpmzYjQ8OxNbitJbwhjYwvX+9rpXywGM0yCz1nxWoIDH+wgP1lZtSOqp0cKJ8nT548eaGTZxIDiQGuX3ch8rwMlxyrR3dMSJuYx45/zNDTTxUxl0zb8rOo8kpkBTvXNL9O4KiB372aFZLII5YoOUcpyelNjPo6UWMCTUcaEeXT0QUFmTRF8QjdzlqqACHtY2j6Io6VF/GjtgewoGGTdNypHgaVOgC82Y7xYFRrnY66WyBuS433SnKM7a8mSfx67xc4v+p9aonjnH6E4X3nkho5BloQXziGI+0n41xCt6+Zf8wYxeI8l6yylcUxqDbSSEIfAGtyTWwY2I+rfjZhIBnNIgKipwzb9JloEdPKJNpEVGUig6jNVcuZWhd2sgyMXsAA8OZ+sz3Jjg+nssR3EKlgB8+nssS9dvwZF6WpIqRMFz5DQBrS6WkEQzI4c+cuzsS0CGUsMCM5m/q+UooI8WXtVV5WT6Wj6ig/eP/PPH1mkkHXKHeHB/AZMkfSy1jlGMASiBMscBJz+OmU0tSJwxSOZdFVFy5mzljzz+7Eh8BO8yAEgXcy09lNiB/XivQlu2Ei7hi3UkhYMjOurPoo9WT4IDkfEgq4Lcxq3MAUqW18fCDwJpom0t01mw5LFYpk4VbjPl7ZfwaDyTIsosI1c56jb1obSoUDsefU8XWdmpv+MifQBcVNwCgjqp+EcnLPuzx58uQ5Tl7o5JnESLyFbxRHsIlA2XNjwaCnoykSupHBmVMnAn9nwFgHBWxGhrNSpcy1TQMF9EyWREGCxUYlM9VK4FWyQoZtM7sZsT7LyuHF6PEGrti9HrdNRpCyBF1fpG4Y6oZhIz89ad90QeOovxdZMK0KToeD/V/yM9oZwhHPgQSBnOk38SqVRDJeEoaTI7YQc+UEacVHiZhBDYR4xuvl1m6BFiPDqMeF6PVyW/hNfukz08e+p7VTYd1CWrDh1cuR1T1074giUsSBDT3M+5hzJ7pcqMbkPm+V9kFeUb5Hr+gg5JKJTHOyaeda9uamIsTCOMtPZU3/ei7INfO2tYnzpCtI1UYIqn0knEcZ9fXw+GeaqEyax6zJYV6sDHLdSIpALIMbKLdrNChBTs/tRxsuJJa9iMGRMI8GbLzgPovgEQveeW+iedu5kd+QFU6DlVCZGeKlDX9B1DTWWw+N77OQUDlv7O+znFuYP9iFl2X0DNWgudsnHZ9Hc4wLneNkFDc17c10z5uLKGlUHBHQNZFjdS7K+rJEp6jUN+wHzCDuYn0QT2UWKTHMQEsAezSFqooIZLGXdY5v9/RcE3K2mlEhwQfWZjxCDLcgk1MjiEIHztTJxQvz5MmTJy908kxCV2PYRNhxdB72yGzK7FNhLqg5C33+PhqP1fGFQ89TO2cnwQOXoiSKsHraqZ/9MnpqGkbqZTZotzNr74OELw7TIbWTspvulrSQ5qbYQ/ygqhxdVrmc1ykixB57AOnc9+l+s4L3Z6yiueIMfrI/TbZnPQkhTbzcxZ8XrGVt99tc1p9k61hA9BU9OhuWWahprmHAMNtCdEXamKuIRGreI9t/BYg6WraKPXuqwDbIsGUKBSNXc86onRYxQ6hoJ4jFzB86h43A0iCImspj0mlsys7n9Egrn/WZ6eP93qNURabQXrj3Y4WOFAigZs1/qcoRg1hPH705ieuVu7lYOEKgbJCp3o0cEiZcLAu7eyhRS6m3bgRgZ0pFEbqoSOdocpzHC/pOXp3SOmkeu25wfnQT5aJpXlN0G6G0m9WaTifwj9AUjugTfZ8MEZ7vOYcGBLKz7fyzEmb1imWEmot4rugWjjlEZm3fOl42SZUrUUXz08IX4ngS7wDvkGxo4PUzJo7cIbtR9AmRk9BB1QRunvtXOqjnB/wSTZf4Tek12MUMF7mex5O18Ozhc+m0VNFS0Qg+eK7zsxTHdQw9Qkhw8dCB6yfuR48Fxnr0vu08yuLBUYpd8wGYrRxirXzMrKJo24D6th1m7gR/9X9yd+fJk+fTSF7o5JnE6FiG7ovTPk91l42rBsxYjawgU5qSSdmacaZEqrUu6uf8enw9TZNox0aX/VyIgZY1c5VEOU2XdSzSxDDIai48ihlns9E5j6PVdVzf/ygRl8belTr98gYGC86hqyTBDOsZ+IDWohCDBTLaYJbaTvjAZ76Ee6OdoMnk7BEqKAQFBmU7PwteRWFvlCQ2+l1OLtn9Np5gihF7OfsXzqXH8y5HyjuZFy1FNkqIcZSRonZuiUT5bkkRZ6W8tCdWcShTz80Dx8arQc+vmc1IBKaWfaTb6BiSx4NuONBlyaxsvO53tPkq+cvpRexXFayqzlTALuTIiBYUQ6QonSPjUGDsvIfEGFaliaAdAqqMpfUuLsypWOUetGSERns7feo5bFQn/nUVw47FNmGRUYDFviBe21HebjgfxWpj0FHJIGbsVGN3jGmVHYwMZBhIVvDGokZcbds5WH+UU9odzBBsvLp0ORUqGKtj1OlBcjGZ4f2taPoMBMFs0DrVuxBrJMAxfQRVF4loIhX7otSiITnLYRa0pKeyOLAZD3EGtVK+3PQfpKWJgHJUnXjITlZU6Zg/Fy0HgmIGh4mazsLe/ezAFJpZI0w85aB4LEtsl30h+9TT6ff2YIxM5TfWB3i/YztGhcF0X1m+HUSePHmAvNDJ8xESoSgu4IsvP4uqzoYKs6XDiNPPe4uuZeXWTcxND7F756XIloku0mrWgkXVSSW6KKRifLkixslYzSI5gqGzVT2HpUEXIgLzUsuZYv8tRTmdUU3iM2URTkPm20aGf2ssP2GvXOM9qDxqjJqxeNhwPIKn9RK6HC9QmzNfjhmLihbdTVJbDkBK8fL6zBRFpREuXJ9j34JGXA4PiyJzAJBUlS5XKxkZwrYIEZvMY/3XoWcqsQGVMy/BMLJktZQZdwI0D7cyd2zPRlLB8aQtubiYoTPnI88u5Pee59EMcI+Yrqxh/34sgQEAAp4IoaZqgs0tlKTDhNIliIKOxVBI+dpJYRopjhhWtPQcfEknVkwrxWACbEKGFf6f4RBNMSkLaQ4Y1xz3BAHQVLmHU6te4SztfRz7vo0na/Yf86WzOD68l9Znw+SKmvAuM9tA9AYWE/G08NLCKOe35VBVO4Th6FwHQqGLbLufoq05MoaEODbv6/ooq1WdBAW83DsLqkO8H1pAlTXG1LEO409xIwgWRF1lRryFdMBB09ARDhVPh5SGbdcoPQsaMLxWHFqaPx39F96rnsJwuoRXKy7DNjxRLiDgCWMUOlE9PbisoySR6NDm4I8u5LARAeCeHo0DkRB2McSmZY15sZMnT5680MkzmVQshAs4Up1gav9EmnVROsKVG+/Bm6zD29OKarVOKh5oMRQiMwL0ldko7J9Y/l65GYgqqwKebI5623v8qcLJnZun4SlfzNHDC5inbGLXzks44/A7bF6a5moeZHpTH7ktTnxZCU1KYDslRreylOJ5cb6nDZMybIRUncHBvbTVwEzNFCFlqQWs7VnBQT8cxLQ8fE8boVga5WiuhIvWvU7WNhEwjJbl3c9nGXUI3O0owqrDNVUvEh5ZQOPeLEJ/M48vW4xdiyMe2AOuehp6JlLvY0qcwAmVFYsDJQRGbayeU8qvxa/xxT1PsTkCU41yDqRVNK0H/6IOEGSWLniHWrWLXS9cxqUX/obC6CjWnPliP7W3j0tT8ygOv0O3pZim3i5Uq4Pw9GKmuZO8X3IKcksPCZcTz+hMWjzvMy92gpVnuJRQ+zeZKxRxMNeDsvQ52qJXsbe5j5u/H0UQVWAfA8SAXzJcVYIl9iXmKts5JxnmTa957nYONHEwVktxJMb5NKNqjBeStNsH2eXVscUCpDRTUEz5sIeZao7kZab8+8Lgi5RIEX5WfxNDDlO8Bn2FIIqUpQaJZAyWN28jorspzwV5MnsqwUE/hktmjqcVt2iqN12TmN2wHxr2E+V1FgKqJvLSfieaUEnOlmBAkvhy4Sg/6H8w3+QzT5484+SFTp5JJNMJih1QbjSAzTW+3KKrFA7FkVwKaw628/gZMqtKs1jEcnravszRolcZ1fpxKxM56aIhcEpkOVWKBeXAMQJFcf5cCyk5SlFPF5RDVAmgGRLZrIvqKQl+ODrMTvsIURwUrZex9ojkqnVGTlHpBvo3+/nXU0vYn7iZC+bGyfZvZtCm8pZh2lh6PCGCTo2MIUHCfNkem/8NIjYFLD/lX30yn6s5l1LBQXDn0zxYIyAa8J0BnYX6ECNKOQOzwnRHYgzbhlhfLmDvNrOdHFoRcUA6oZhi75aDOBY2jX+2y26kjERv65moc+uwJWuwqTneSFwFbfCXvn1cYN8LS6Gzcz71dR3Mc02h7M0XUC/rhuYbCGfj6LlhigwvxZkYIWucsPsAU0clBq3n8Ig3wL7qaXz3QJwRVwBRL+CCt1L0JwJkLRZiVhdnjsxgBhKGmiMjb6Lau4U3O+Yy6PEiiDlqCr5FUclSLM/twTY7Q0e9ef4C8RICPY9iHRMu+/ztjLh7qc8anA8I+oQVz4OHWrmJNibidHqnLaDx0DYKlCiipvHI1CvHvwvZTcvesL0YUdeY1t7CDqZTRZBa1xD3qA/hHOtEjwrshp3uWbzBWnZ0nc+gNkDTsQS1vtls829m5swP6Sh/m94i02p2iV7O/S47ktL3f3Dn58mT538qeaGTZxKZTAYc0OnoI2AJUMxUAEQgbnHhkuyIusEtb5ovpIwtzcBSPzMiXzzeWhtRyyIbcRalF+GV65me7OdwxrzV/n04SOjDKRRlbWSAKF42JC4Ajylinj5N5Sy7Gax773VX40gUcEfw54AZ15GNW2gTXPRTRFO1h+5+EC1RnNoQUM3eWdMxvFaEWA7bliA2VUVechWdqSAzd/87gyUG9+ReNnd0johNMSgJw/upc/i8+BduyF3FBY5XURU70/0LGEzXousCguggLnqRmPyyL/3Lk9yVvpJ/BXLHjsFoEs1wI6YkbJpOe6CMP73yc+67+gJa/B9gD1XgyZgmr36q6KCebbU2fMNFnMcGjubWYTdAsjnH43bW7umhPBQjFDCFmye1gRsOhBCY6Ov02PJl/FLczZeFOwk7PQxX30fL0ApO3fYP7Kebrb3DNTbKe8zkfC3mJjB/ISuvb+TlzjYOj3TSFWzDFTNFnV03rXnf73SQTjYy5K4n7t7N5JwylUH75O7lbSU2pgwJVBhhZmzdyMKRIwQKBljuy/ALruB2XqJaGaJAiZLMWPmm8FWK0s3MyERwkuFx22xGU9UMydUkphZy88BzAKwrX4VkGeZbbYfRaWBQ6WQm0BhczazwVHbbehmteI+YmiJPnjx5TiQvdPJMQjVM14nfMAhknXCCl8chGyQtHu45/1wyRW9wd995VOTmMcPSysPFr7MwNoOc6GfB0E7mnNHCG+I8RKOPUXkiy6hBUYnXlMJ1F2PfZAUMIh4zYyobt9AjThSqWzTFyxxXOb2bYHKtYhOLTcaqyhiClYhzD6QWs+iD3aTtNhyZLAflalrcJVzaar7cpyx+nD/XasSUBK/ufpPr+v5Bdoebx5Za0UolCMIs5wDtB08hl3EjLJgLWx0ssqRwa0kgiKxlaU70ju+DVVXJ6QaG3U7/t76N213NtmXX4rGnOW/nXyiOj1KSjnButJu28n587vn4hnZiy2Z5rX4Nr7EGoTbHaYMjaJrEtHmm0FAHFsAB8FoKicypxCIvJasoSKrGzNRiAIIBkA0RSZdomr8Px4BCcEYNnz/0IfN/24IaOMLwPysU24IA3Bb4LQRA1WSCkTTeflNwFZcUcFpJARt3R5G7hgAZXVQAnZb4P5kHGoMdi8+hPLcNhz0LWImTZkQ7io9KbDkJSZd4Y8ZO3pjhAByI+mMoyhKqoynq1F7Ozq3jD7YCLs3MxCHB5dbH+LPznwlLpnw6hIXFRgs4zArMg/0FjAoyoqaiO2U0Kvj38834L1n7DLN5H3/CijWUo8RmYXQiNCxPnjx5xskLnTyTUHJmoGmZJ4UoTq5LogkCajZJ2OVisEzApbxGee5dRhSd/bUa3+/bSWNOgXpIGTYUXeQ521YadInZlGDTABn6C2vI2tzIGCzKrEQUWxixhoh5vUi6ZsaBoPFe1yM8rohUFQS4i8xJ+2p12CmMG8SKf0JV6CCk4Ly9G6mJD9PtKeHg4mu5c9kcpsw2s6QKLGZw6v54iq4Dg8zM/g1xXpgFWSD4ECnDRlCq57pYI8/bNlDtK2WAGMKO32GJdY3P2whoogXB6gYg5POj/v05puUybN+6n9x2BXSNskwRZTHTwpDUzXYPWatGaSbIYz++k0PnlmA7Jcar/TfQrpSya/uFyDbTjON0Wmg0ciwvuWjSMZcpMf6q/I2ko5CZowZnuc5nux6lFLPZk+GykHbJHPuGh2C4lmrbbmytFxGu3MBAx2d5MymxkCGUbB/v7v/TSee03lEERNClLBZxmP7qGBl/hrrOQ8zYkqXVMw+3NwlYKZjRQn9SQgtW4M7IXL3zK2TtDlwMM7Xs1/xzSSFfsf2DRsE8plttIf6R/Xf+ah+gRhjhdEnn3KoadPHkdiMnUtD/bS6JT6HfVsLctgBG+UJ+PreAuOHh4vVvYe0RWStJ3FENUdX4X24rT548nz7yQifPJKKaTAf1dBlTsbh8ZF0Tt4ghiwhqkuP51o/GF5KINVBh3Q+08ZDncqqiCeI2nbdGz2OR4zAXf2gGoIqyjksTcMgqf4/WcOhdieO+Ga9expXGMNtWLMejwHu2URbyGl+tvw6noRM4cD+RMtNN0+suJquZ6elOj5uVdQWMfLAfQ4jQZWT5xeJrx/fXKhosndVApcdBXyRNOJYmQpodkQSdShlnZX+JQ5iILwkJbkoqCyiKerEJGYoFU+hJus7emhL2lC5kZmwbmu7CU3A1q52F4+uqkoyjqYnadIxDW1pxtR9C9l6EI2tayIp1s3t31rCATceXGGX1piAjp6gsbT3MHttq2tVyFkXCFIkqqSov35ye4IKdAqJuWrw8ksESbxlLhkuJKaN4LYW4XXYMMc78/iEU0ULI4sNSmuNwaBlOr5nWvyWhkdx1CZBmDpDFhWi0csln7qS0aCK7bePu9ynY8ybHm38JgkbC2su+yrmU9G3ElUqg51aPj68alvGo5by8eIQF74JuH2BBe5LesvN5WZ0CRHh9+Fx2yFmWuPfQKATxCynaC49QKHQQjovookZ5chVnhvq5SNzMoX0BwoqMxaVSPidOWpD55xK4NrofgEOx28A7kYn1l6vORGyzcvNLb+JNS/xxQEOX/P+tez1PnjyfDvJCJ884vZkcj1R8HlWwQC0cb6MtayqOXBqrbiUjTLxkzj+8hcqjO+mbpvD8Qgft/hqamtaTipXQP1rEPMOKPmcmpwkpRme/i/NADV41y7LgbmbHdiKJOsU1cX6jfJk10fWI23ReuFDjEkUjqEm40g8BEJnmRNFkVMXOzxZfSyjlQkJF7h3Eb9hYsauFlrkBXsl9l8Nba/jZBaX0pq7hCzOthJM52oYTfOWvu0grJ1oOHDgMke9vfoxH16ZRiguYFyxmrWUQuB2f28KJJGxWKm2L6K2pxTX8Dja7f9L32mAnNNRSV+HgDPk/eFWtwT1yALxmewKfkRw7lxq4db5xk8RnUwVMY5BgrQsGIeawMVQ5hDXbTWmHxIrVO/gPzy0sqAxSF+/jK6ENqNr3Jll5dC1Hxr2OF7MrqSpcSJ+9lLq6vdTXdZj7pcrUqgKWeW9wID2LFw+cx6ULXmG5fzfBN99nzk3/hOw3HYPOo05sxuQqx2kjTElkCEnMjS+zKgaGJiHXt1BIC1cH7bRSj+QcBV+Zec0E8/zFpbMpStmx+DaCBjOFLhhYRpVQBe4jAHwv/A5r1CB9yPz0XDuaCOajyU59xgdEedizknLFTYMic+JVHBabwVJGxhYAYqgpFU0pg7RKnjx58kBe6OQ5gZCioopm7yHnUQvCUA018jK2CnvwpuNoFgHVNnHLvLuojIJqDbUkCRjEXB5e6vYjR0xLh46LhSufIg7YNYOp0lFSio3iOau4sqmB1373H/grUtAFDiONK5xCEzVKswZv7LyUUxfWMXBkD+coL/BD660EsjLf2fkkD61Jc2ZzJbtHr0KXzoV6KM+1UWE8Ri4q4bZkkFD542H44+FNADgsEo99cSmFLitPHd7HB8FhHt32PXLDEn+TJex6jPJYDJvHDK4tsDnRMx95WWoJigfWk5ocfwuAkhrLqXcWUmCNI2k6MakVR8y0Ck01PgSKuSr0BhnJyqhPoFrvQNDsVE7bhRFcyPbQFLaHplCcCvOj5ANUWXIkZI2NA2WMCmkqvDv598hbWMUQBZY4n4kf47vCZ8lVBHBoXkL2OgCGtyyhoqOSTls/akZESRzDctRHtKKCkOGipX8OVdYujpV3kz24kwWLl1Jlt+IUQciK6Cd4LK9PbGdu/Fn+yMLxZfasTmb3HHJSMV2BKRy0x2lkN5KaZMASRVeHsIwFUqvaOwxZQrw5OBOp/CD/wYS7bLdgBoB12DMMKh7Sh4uocags6T6fkpyFyoon+NdaM/PvH6MXMiubY5EywkSbTzjz6PVkI7VsXZrFrt5D+mgMLXM6tqPDBOc2gMdJnjx5Pt3khU6ek6igly1Dy7HHAyyyRPFYzZoq9Y4++qlE1Ow4dJ1X6qMwViTYoeuIopO6pEJzxvw1Lxgy21pu4pqwTpnwCzYnruAH2mquK55KQWUpAGn75FswhoiKQTbron7LfazCrJkTUBJg9VOVCOKUY2RLr0VIW5i353f0nR7gaK4ExowOVjHC1JLtHBtezh9vXIYoxUFOUuwNMpIa4b3BP+DxXUqFEaITs5DerG4P7k6Rwf4S5lZCSaCEcCgIJ2Q2JXI7GS6QqYmebC3IxULmH/5qPNc8zKk3fZ140wq2OQHaOCCeD+zggYorGej0Y9H/wncDhfiHdBaEK8muKuGClvdY072L7mXwXMuZfN7xFF/2HaY/oVAuhUgJNr7r/8P4nKkCG3vShXTMWI3hNdPBZU0loEfpcKdZkbqEp8rfI+JWcau1jFpmYcQEPhxs5MPBRgCePByhZOR9Hl++kik+NyNJgYFYNfjNOd60z2VZphXDEOCEyklCIIS3vxK3NUfMV4go63AsSJIg0MysdgutlTIvLdg7vs6fjHLO6yxFFrIEUipvlpuutV8VBSi0GPy6Ocvy/QDrANAkF9avJkh7BOYUfEClZMc+5J503gccfVj0bjyxU7BoLtRqF+fs2MFb/iXEUspJ1ylPnjyfPvJCJ8848fhEad2ZzihJXWY9Iwi6jojKsNuPmrEjZz281DvAo56bseV82K1HuSz+ItcX+tiTPg07ZlaSjEE8XY4jrmO36RyWZPq1Iso8nvF5BMG8BY8KVbh9Sbp1kWYxDDocOFDPHq2e3866gs8deZ/OeX50GWIOKK16kYsX3U70vWbc828j3raJ8bbmQJUeogUR0RLm21uvI62mx79T5Ers6gAdFnm8v1NHWYpTG/spj9qhBwq9DQy1hzhR6HQWTqfbEacmumd8WcpZSiAhMpQLT0zuKcWhqHhnVuNqPgZAErOGTNBVQFQtI9N+F4KcJAnUWmLgkLFrFmYVn47iepqtdrOyc2HchX+kkQJpP6/0z+eJGRegCaZJKWx4CIo+bt7/KNNaQGlay0GOMPX9Aar6jwAHuNYC37hJYtTXwdyQj+yqq7mg5T1W1b/Kb5LfIHrAwu/3/oBXd32Xm788j6ihYqQniuw5Rp2ksxb6faXAICndQ8qwcUH/bmA3hOASxcmuq7wc3HsaUzoq6RWK8KZe57zds7GpESKWNsIBG7unaASlxdREGkkAU/paGJyyjqs2+pnab2BXRtk15wpmtH9AZqpK0f5RvGlIOgSO+fZyDLhQOn3SPXt+RiMcDtJy3MpmlyjJxsmTJ0+e4+SFTp5x+nsnXtbTYk7ciakU2QKE9z/H3kaFUb0IbSyrpVzTeK9hIQc805kTn8Ktu5/Hquf4uvImf9DNLoxWYGpuFFlIANCgmp3FiwqLADMbqdZ7CRYhx932m+F0IA53CSmusu5jyxxzOyvop3PeDCRDYfOVBqM+ActwMYHCEqJAua8USZxsZbHq5ud4LkpaTXN31Z2kwm7+2rKf0cp1jKp/5rslRfwMDQlQrTqtR2uxBW8GLxx+w0FUq0RCx6IkABd3Wp5jRB5hG40YmEUDu2d/gasOG2RYypF9O5g5bwlyIIDgcKA88xRVRTMBkFWzDpBsU3nH9i0+mF5B3G7lL92XE02Y2wo7Pbyl5tCoHD8Ot6+F7UVbqY/VMte3gvm9O3HUppHtGhYBKub1kN0xlzJ/PXI8R5c1zdOnCYzYJSpHDG5fp/P1gQgPiwtpytbwrkMmYE1R6+1l6mAPu2ggKHmw5aKkhVI6xTlYjAnf1Wnvt9CZKMZ2qZn1llOLWZu7F+9Y1empQh//ZvwRq0cFl400CoJkirqzdw/jzhlANUFXjD0NCXbUvMaOmtfGty9rMn6uxGJPAY9hyDVIhkbR/lHzex0u766hLJulpaweHJNFjLzzKbzhWlj8v7ix8+TJ86kmL3TyjJNJTgSi5pwtxHAyM3cGRkpDw0BMaGT0CUFx7YFNDBj7qdFMgXTZhkG60t+h2m+2X1AtMl/K/AisoGBBNszCdVaHjMPrRbbZaH52HddKbmShlHlth3jotDL2i59j1vCHlO/N8vPzy+hJfRZ7xTOozl7SchpZF/BXzRzfD7WnH0t8olrxiWipHH5J58D+TWxKLKZfq8ApqBT2n8G/jzwLBLjSY+FdzcKCXQG0WvNfYu65Bex9fZil+ihGNgy4eFRbwyXCMwA0295ktXEdi8az0ixoz8ZIlnbhqqhlyquvEO04xvu//CPTRyCumpYhIa1SKYzSMHQa8fkbWZgSORrPIis53pu1mPcAIbaYKbkDaJpM+aLXuASALWiahGP7hciHOxFVBblUpXB5mN1uF+1owCEEXQBvEYbvMKPiucCb2DiNguAAcsq8dmGbjw7q8adNsfK8cCFLvR3klAFsDj+6NPFYOLq6jAsj+6mKDgJ2vIZIbOoM2oqsWIwcJcEURCfO99TSlewcjQBwqPFGRNl0UYpalh89+WMUh4ddNVZWHu7Aohl40ypFsQfNayXIzDr8GI7s6KRrWHnIjjajkcp0DsnbPem7PVOq8Ycd5MmTJ89/Rl7o5PlY+pHRNSdLT1gWSIPVniMkekkZNm5UHhv/LqdL2GZ8gOB/jaOd8yFyJqKm8iP/T1nQbmOw5CBljAmdrIY96+Hz3/4PRkP72Xf4XxC2C9Qlh7hpA3ztdBg6Wg7iMP0WDyHDxZKR6fzYtpGtDWWMtE5l+ufPQLablpPYL/9A8bQp4/FCJxKN9HN3WYZ4xQGcsRSvfLiQ0kEDv8VJ5Sj0A+X2JKtaP0Og1E52rHbQUGQAUbDhQON4rd0O0YdXNy1azWWH+I/Mv/Gl9XaeO7WM5dHPs8hlJRcL4yqrxVJRQVFFBcY7m6B5Jy+OZavJR806RUqyBgB7u0gUG1OP3UNT4jTmWt9juMbDw5lr+dfQPbhdpjVsbe8e5k5fh2xTkL0X4dft1Bd2o3EUf6IQf3AK6dS7qLZT+Sf5KRYmBomln6FbKqT+H7u5G2hrbMOuruHVKafzKqcjNOSwDQV5o3EFM5Vf0NJ9GWLZ+WjtccBMh6dgKVb9NRraRoAqAFb3/Z2wEaXVdwVlwomhwWATDTyuHjJxWOSUOBx9jQppNh3WGkriZZQVr8Q/YFAyGmao8hRsyT0cm91Izl9FSjCIpiV6fLuQ1Qhn74lROxgl7l+Cv7MUsekQinVyJPj26RkWHXBj6LH/3o2dJ0+eTx15oZNnnFR0FHym20EVk1ilYSZyeQ0Ui4yfJB2rGvnxm5dTkRgkLnvYUrCMWeGDDFXUo/TGiAmmNBCyNja6ayhW3KSFLsrGthR6toXhsc6QoieKfWqOId1H/xVf4hdlbsQubaIeTgQcZLiPv1Gag5A3QVaXKXD7sZRUmJaTQ/vZtf7Pk7p352QV0d7H5p0fcEFpgAeNO/EE4/zhtfuwaxrwIv0EMCSJfe1nskZYQFGx+XI3JIG+RDcy0yadn7jhooIckqBx6j5TtK0vh0DbILFAAvCjfSQA1llsBjt/ofl9nqk08FiHODzqoMMxiF0VsClhhoUa6sRRihNR3q0f5lzMl/YtxzT6ikaJGTm83cB0c5uBmgPYgrMwrGbWkjfWT3VWYktqhAMVj6FPWUu8ZA5LtvyAGecPoGbNysNJsZ1nNiUYDQwyMPePKK1f4Wc40CSJ+PB8jOJ9BJbX0BvaCJThFHMkI90EtcYT5B78KLuR4t5jpHpe41sFX510vBEhRUQ2HyuSpCDqYQbUNFiht/I0rIefxSNZ2Lr0h+iSjfaateY512Nko48CKkUjAAI76n009Ho55cgTOBSVjdPOxGmkSZ/w1JrdbiOSUoFHsZRM2hV2hnoReoJ4rV6KnEUUWOR8k888eT6F5IVOnnES/YfAdwoAO/z9OCylXG6WgUHWrWRlAcEwwCEzt72fKX1djNh9/HX15Rzy10PnxLYMScDyn1SpLbh6BiUlZkByctCHpVtENLIUWu3cNv0hOpVLUXWRCm07sRIosQbps2l0WNykJQtiRqCqpA4AS0UFXsOLtP7tSXMovijz5af46kPD2BSDB/kXALKyyE+vFrk9lWJONk6XeCcRS5ZN6jucNv8S9r2vs/BSN4ff2MyymA1dn7BYBPGz0bmS4moLwWCWhKWI5Qf2s6+2FEM33X7RVzrw180Zr02TK5mFJlpZ0DPAgkcANgABmiwb+U6pDWfxTozBuUw7ugBcUDdYh+Y2T3p3zfOUuwYp1ATUY6arzuOIE8QDFfsJOaPMBHxWN0rSdOnk5DgLapexavZnOLy1mLTP9Cvt9nYwHO+hulehQHiSejr4m9wHx3uZaRpdsSpKi2ew3vUOBUlocpdRZEwH60U4p3wIw++Z29rbwNC8aq63bsCuT7g7DQTWWw8haklcwHvGNvyihqLuQ9TLKQ3uAdlL1lmGLtkwIi+QFaIIgoA3UE4WFZt9EaJYiCI4EIwcSup1HriwEk/WYIGSoVAKc6Jjy+I8H0HKkNK2II+JcmfWdMn9ZUjj4bQKhIAQDlHkg2Uz82InT55PGXmhk2eC1PD4n/PDPvwjfsYML1h0DwlZID0Wa7JtdSmMdmD06dyz4yHu+NoPUOWJ28lmKJRuHOW4P0lTbQygAyIJ3SCqGdjdFlxlDcxY92+EpryCoLgIaGGKmv48vh1Nk0gcXE2H0MSi5Eo+7HkDyTb5thVdMmnNS8aYKPL33aGNlA9bGVR8/OZiK/2F5lsw5oCUx6C6N4pFEwjnAsAg4SqNsqWL2PjODuL/8mO+EDoGbCcDKJJAThYxRIFbFn4fu0XlwueeICcWclZ25+RzqBvE+4YJ+E3XlLuykq1Lf8j8fb/jxeU2uqcs5LtHHse+yYs9JWC44lhR+VBaw6XGQZzpWXQcmYaMykMHrh/f7FSjg7tz7zBt1geTptNUkdbgIRTdgqTrhN0aKUcBR5JZ/NevYebYNWlIxPjN078gbUAyE8QD6FoXx4WOlLbzr1u/DVsVKuyXcg5dbE0EcebSeGSZ4hPidgJDcVy74yhzJDSHMHGPeEdxYYBFBVHAOnBswg4UfZQD0y5GEMx4GiF3jJzRA4aGAURHzPT8bGYXoiAzXFVHRcSMMJ47eBGCVEDOsx+bvH/S8Vsq2gjE3QRjHhhrg2HVTEvX96olRMcQ9++5n1uX3ss9PRohRc0LnTx5PmXkhU6ecYyJ4rdYlSrcyqzxl5gBJBxurGHTDfJWzQDvl0N9UYQvBT0oBU6u23yAglQb++07ueW1KEcap3A8HUZRHPyJLBZDYtOfD7PfEJGtIld+ZQ6WTCHlS09F2lHArp2X4I22krBovL/sSk47cBBrZDYpS4hVah1a0o58Qno6gOy3E/L7+GrkDv6FRwH40DqLz+vNAPQXaqx0LOLixKv8MnwVFWGDlw1I4SBqHUTQdXwnprznFF5fsZBa9WwWu2QecT2Jp1fC7s5x3cEe/rqknqzVjvAxxXezmkg2OdHEtN7vZI+9AF2y4gt4GKycTX13dtz4lRb9XGVdz+FcI0nBThCFXNLP77SnecO+CKsrwsyZm9h19FTuff8Ozi7eQUN5C++PFtAXXIPb9g5Gldm0M+zW6CsSuf0fIQx1Ew6LxB+uW0ShywpuF/7KpTAIG6rLuZpBjPREc9Jhwcdn6t+kcHgJz6VcYIPA6iw9Rz+gKpwhKS7FOOFpUdgZob2nGD4jYHUYKJrM3Klbx7/PzZbJpawMbphLmdDE0dAWlMQLHzlbMhb3pbgyB5FVjZi/AV2RUZNvUhFZhiA6ABkl9ToAYtiLURJDP6E28urUSuz+OlRvjjbjjwB0N62AFCy1V+MpcfAHpYsam0CePHk+neSFTp5xtBMsIrnEEEq4DcrNwnK6oBG3uynOmmpoSXApBckpkB0hJ5u/xlevf4rpPZ1cCmQsMlHNwKJOqIF/wcFIQYSLb7uE0ECSdx45zGB3HF01KLHPR6GHbNbFbnsRGSGBFPSSzboIjGyh1vBCCQi6QUX5R4IxALfTS9gw42MqRww2ZxdwVezo+Pe+kX4aRYWhbCMzImHsc9ppsOUobjuT7YMvUb1q5qTtGYZA0ulG8peiGQ4gR1Oih0DObJGtIjG5SYRJr5Emqxvj8UgnUqcOIYqTs8Pahs+iNVcOGJx9+mwas6M8+t4oqx3HqMt9CYv2r3S5+mHaRl4d/Tanl39AyGHjiDKfY0oTsjOJtXgjlQOn8hvl9/QV/5rycy5kNJnjK3/dxQ1/2T4+l0f38WV0lvZcALP2MEWfwztj34UtGgeHljIyfwol2zuQJJnm5gGgmo0eiFk6OXaZyNkd8NRnP8+irpdY/PZhbKqCPavz6OYbWGgL0yR20FvuwRqVqWl8D6vboF6bxiH/fgxrGfbhNrIlszCsTpAsJC2DGKGVyEYRqhQj6dmGIwkx3xFEuRQPN9J48EE65jRg0zwY4luslzYDnwFgs2crBeFO5strcOLEpuYonvsibL2WZHwQT2n+EZcnz6ed/FMgzziTImpcXvBMvK5Vi0pDqpOUYaYLFyWL8IRzpNwe7vmcWaZfXWzhvdl+3iyLE/PdyGl7W/je4T9ykO8AUIuIVOJFDQYhGEaS4P0XzYJ6vldHmD2W6DN7aDUezcX8lMgrDlhechFFhhfV0JneG6Pm+nI+SoW1gCu0HJokc/s6FXibfgLkJJGYA7ozvvEDXOw7hRndawDIkUPLxqkcgeTOCTdUVTSHFNgOXER9f4BheRTFoRBxrweuZaC4kpqBUT7K32rLeCeVZVMmR5Xdilc2TWJJZxlefYBzD26cNF6Z6kU6KFKlDXB/8x/5/srb6Be83JL7Bj/DzsPxW3Bubmat9Qh2jEnuLFHI0uR7A1vOQXkuRyMK06qnYqk0m66+c+dphJMTZrrheJZvPbaTAqvKnZpE6bx3YKvZ2uG02S/xebWbp9q+TE/WxxnnXYUsdzDy+r9QnCzjyUAtUbdpFQmKQQYDplVFF80YnXjGy3uVPqrajjJUBSUp//i8MWWUWaPwjxl7mDfsYV/JbhIuA0mHRd3T0aUk81v72V9fyKp5q9k+3Et1bz+D00C1ViKJFkRLMYJhuqS67BMCNp5xYMllSchmnZ/6bD8FexSahA4siRww46RrlCdPnk8XeaGT52PRLTnSlhMaPAoCcauEb/kQds3g6RUrJ423KlkK14fI1CXomC+AzYsiS6hiYNK43t3DdG3rHP/sQWGh24E420OuxQwiLRt5F7/qIFF0DgBDR54lUarQKtzE7GSMIl/tSfsb8LgZsYiMnHsTvyr5E1dG5zI/u4EBt8Gor5DrEutJZW3EdAf7hp6jSzPnymopMkqMyP1vMGrdC403gmhhRfU1iM5CdCPHKws6aa79Aks7djBtQQsAq+ZupWdg2kn78aMDv2NPxW2ElBlU2a3UFjqRZDjceCMkoDzTDuI/xscbDlMI3Wf/A9/R3HQf/QXfXv5Vmt/bR9pRwZeohXQtpM/lSdJIlnuQBTMWJSDEqRweE1vWPSjYEALF49uu9Duo9E+uMfPyd04nnMzx3roRAl0TmVTalgbkFR3ctu4xbN0ioYVzKVniI0uYAs2NTbMh6abbst/2DnFXDxcC8cCB8Wy3CsL0lvhI2F0ExsJgJLuXg+G94POzctCP4TJoyJXQEajhzIP7WOE7D1UEFsEKINOsEKmfxogwZvkq2sPGNQsoGcnhEExBrTGR2ZYSswQL3LQbWyAr8bWRp2kUFG6zvYX2oY32Kc+cdI3y5Mnz6eL/idDp6+vjO9/5Dq+//jrpdJrp06fz8MMPs2jRIgAMw+DHP/4xf/rTnwiHwyxbtozf//73NDU1jW8jm81y11138fTTT5NOpznzzDN54IEHqKqqGh8TDoe5/fbbefnllwG4+OKLuf/++/H7/f8vDutThVdIUiBPxJpYNJHieJwXEgncHoPvvP4UZdEgbWUGDzeOMq+lgX+bdx3Ti98A2gDIWmQOxy9BsGgIRg67YcWRilJ/6Be8tdDGgu4C1NLz8ctOVIuKaFgRdIOWpqljsx5C0HVi8xWS2RjpsXZSGgFyfWZ9GdElI/vt1FUUsd8IEWl5j54miZ+XHYCxtguyJvBIbAVLDlpoaHqDqYX1uFUvdgLszVRiMZwcKDTNSRI6G0+/jIL+dcjZ82gs+yNHC4eJu91U+1SEscCctu7VqNYoxkcafCa3Z5myoAsws9c8BXY+8/kiOv7pLsS5ZxCL74HCk8+3XVCYnvXx0IELcGRk1jod3KDHcI+1yCgmgkyUBpZQalSwTreCfoINThT46VWrmV8z9eSNn8Bx8dNeP4tUuz4egxUbq3F0dEYj02IpultaaVzRSD8qugxOzcm5h0333tde1ohN9QOjSEZifNuvF5/Da1MuBOBUaQ+NvMCQ30JSnjhg2RC5IbsCd6+dhL+O52xbUIUJd57NlsQl56gyrITTMka6iAF7HHt2GOymaI46TAHeTxXWsIQkuwjbdZw5iW+UX8/y9nZ6LJX8VHiGgb4gWrqCnmAW0h+fBZgnT57/2XziQiccDrNq1SpOP/10Xn/9dUpKSmhvb58kPn7+85/zq1/9ikcffZTp06fzk5/8hLPPPpuWlhY8Y0Ghd9xxB+vWreOZZ56hsLCQO++8kwsvvJBdu3YhSebT+ZprrqG3t5c33ngDgJtvvpnrrruOdevWfdKH9aljqZGhVh7meIZ4WnJTbAzSZ7cQsYt0VNkZLCgmKfdiUboQCn34fENET7ijBE1jse/PbPVdxGdSS7DrIvXHtlIc6eRaM1MZ7dhO9LP+lQTluA0rV+dWkxFMd4umq2wZeI7ubBxBKsU25toKPdGM6DZfsIYFEl/0o2YjAHhDaS7dUE5pYRy7VSUpWRjpdTNl7jHmtOlUXXcvgenL8DgsSBrsuX8/Tc2PMvXH30SLauQ2J7nPKyF0DmG1KWhCZPx46qa04NUy2AWdt2oXs3BgO5wWhd7qiYPWwJVM0dx6kLmLzHKLbo+EJ9FDwalFDD3RNy50KkcM/NJuchE/ySKBGsXJTXMeoyzwU3751hx+ckkDfqeNjoFRfrpBRcPLBsWcS0Jnkbcfu2RwztpzmFlWyPwy33/7+torK/nFjDC0mdaRarcpYpYVX4y8uoZdXd0Qc2GVFN6VzB8grpwF3QLzK2IER0yxYVEmyiIv6dnIzGgzsYXF7PWYxz6zaAhLeAnN0iDT1XJ2WNvJCDnchp2MkEMVdNbkmvBqVrYlnmbKpXuQJFNMFgBoVkI7L0SxytjG5ok5E8h6jgfEO+Asc5msqVz14RD94tu8MA3gGJfo5QTeOEZKuZ3/6BzEJgnsqpORS60EbAHK3Se7QPPkyfM/j09c6PzsZz+jurqaRx55ZHxZXV3d+N+GYXDffffx/e9/n8svvxyAxx57jNLSUp566iluueUWotEoDz/8MH/961856yzzSfbEE09QXV3NO++8w9q1a2lubuaNN95g69atLFu2DICHHnqIFStW0NLSwowZed/8/y4yE2m3hdptqEYdqmGg51LExXKKAVFPYNUyPD7fDAYtHjqEnDzEId9+wEz9lVUBrFZcySgNRc38Xa5gdPce9hflGJiWpG+Zg8VHAvhTKvO7hzFyCUJqkm3RBHr6RSTdjAXJainSWpSly2Zg16axs93cN0PV+WXdG+Q0he/1XMR33rkHRzTFqXwdxVqENxUhmQlw3B7ls1oodWqAwLTaBig0O2CHBswRrtQgRSUWEhkVWRSwndCgs/eElPlw80pW1k/nue0p2iWD+w0HOftkk07KWUppXOWXe37L9PJ/Y0HFRLlm75IVvNs/yLRQG4Zs9qGCFwHQLbDsB7vpSNXjtzeTpIZptVXMrvQxo8TJ4a3vsur0c7BareRyOYxMnP1bB7jmmmuYPr3hf/taW+0S7cV+bG1mxtY77l18DjgQ3sgS+Xokq86DzwSpbvwKycPhSeumnGVI/THibhmbNgqYdX/qA528YTuD6Yf6oEkFG4xGLcjuYaLZFOtLR3GFob39eXbbBArTErZFPoJqB1WucgrsCSRJZXjzpRDxIRaEKFq+DtmSIZDKoIw1Lpe0US7pfIAF9b182HceBf0unly6HA/VFGW+y6ruTbinHORJKcJd83Qe6P0DfZapKD1n8dMdTyFbDmOX7fzlzPtYWFhFnjx5/mfziQudl19+mbVr13LllVeyYcMGKisrufXWW7npppsA6OjoYHBwkHPOOWd8HZvNxmmnncbmzZu55ZZb2LVrF4qiTBpTUVHB7Nmz2bx5M2vXrmXLli34fL5xkQOwfPlyfD4fmzdv/lihk81myWYn4k5isXzZ+BM5sS/m6P5HsQ5XsmfmjUxTUhguEbcmYs9FYfC76KL51qmPGyzaXIHsy1EYKyI0uwjlwDGMqevwJDJQBNWjOuFckJQmUzbixRcVSdkMxLEKynp8kHR3kgzTWBY4hHL6haC64WdPYEvncOzrIO4+BIu/O7F/LBt3u9jsX6RDuJ81kk7bzC9h0WMYuhmDI4oGC5ZHSfVEGQ342fjECKo6UQRQlhlr2gmjsTBu7MjaRMDw7wJ+JCOFLrrpQcVfWI43Y2ZV7Qts4UPfLGxtoDIKlHJg+sVUtctc2zWHzjlDk4RO7tgxCl31vNz3G3znb+eBwjeZoa/kBy0v0781wFC6lHDXNWR27qTJ4iMTzUElOJ1OAlaDfetfmXS9LBYLJSUnZ6D9dyj3W7AaEzE6SYcpZpJqFGTQhCwJenFLNdhCGm1lGpADI8cG7XazPFI9zBs6AoV3I6Px0oHzANiJn+p9vbAU/pI4hUSigItth+l2WZkVhjaPgqGmiVRKLFq8HknS6AKKAE0RCHYcRElYsOv+MYcaKPrkR9X0wsPUM0pzLkJp3PyfFnNVXLGnELgSwhfjnn8vBc89woM9brbe901+3JMi6b8Cw2sK+ssPjLBpeUm+rk6ePP/D+cSFzrFjx3jwwQf55je/yfe+9z22b9/O7bffjs1m4/rrr2dwcBCA0tLSSeuVlpbS1dUFwODgIFarlUAgcNKY4+sPDg5+7EO+pKRkfMxHuffee/nxj3/8f32M/1MRtAmls3OGStg/hYABP1l6AwsK91MWCfHVw59lVcEveLVvJtiupqSgjZFMO93T3AwXNLEk28FQRkY3RgEzeFSMJGkplQGZQw0Jtiy4lO89/jxDbheaaCGz62ECLdWw+Lvom70stvwai0sjtcRC9/oiDCZqoBgWC4LVzTeffc5csOwmfvjH33DrtQrLLsmifu83FN1zN639D+L6i5ODM25mz/Yy4AcwD2QDzrumCk/dmNuiv4ORd8yXfCKUxo0du+oHTDH078ERns3O4KnaIhqzOprsIylHcKs21mi1dJZ3EpLdkNoOgUbU1OtklCB27xfp6R2BZcvHu5n3f+vbBNzVtC3+LuHBDjqaBOLiVKwD5nl/QPgGnStr+Orbfs6Pudn++31kv1FHbVUNt912G6nUhDABUwD9n8ajVdgEvs4v+C03AzBTMh1DiiwzIsTw2PtptPZj5KbgzpoZVgIpRl3P8IXcIYL7PUSpYniuGYukMmHZ8hXGEXKmz1OZG0AZ9kIPpLGCrpOungKA5B5FkjQOHvsMFwzN4TcVYQ45vXyWPQie5UjWVmALAFnDNu66AijtS4IXnGoaUTAT/S20s7ksRVOsH19qGXbFRWehj8qjKaYlzUDqB2bVIjiCfGvr/cSLvpovIJgnz6eAT1zo6LrO4sWLueeeewBYsGABhw4d4sEHH+T66ydSYwVhcgEvwzBOWvZRPjrm48b/r7Zz9913881vfnP8cywWo7q6+mPHfjqZOG+as4ASz1SUBITtHnKagdfeQdOSJ2hsVujI5Rip7KU2KzICDBS00+xqYSAps4xKnKpEZmxbAXstVc1HODz7y0S8f6A0exjBM0JfeR0H3bezwmGn1T8AEUAXUC94GMv0eoTWDox3v0fFL35OzF0Nz4Zob/oSlc5CDjXeCMAaQBDsVI5ksc8zEBI9lFeWUHHqI6RWtVC1v4/gL58CxRQTFiVBbnMGx29/i1QQIBc0xXUqlqP1oJMyD0wLX0iQRwGdRi1EQDczzA7opQyuewWLTeYz6jI+9O2j2gMPX30HF215m3MMmDYwyvZCHUNP48w8xoct9ayasZIpr76CGg6zf1sfbIfs2L9efe9EnZsrjae4R/4+aaWb6W0HGZp6FT/8oJOOhgQfLGuk6hMMsrdaCggI6fHPXckmKos7OFI/nYOxg/gtOjdH3iJ0xMKh6mvG743KpECRrYNc2o96QgXBy+V3OGD5KuoUD2J/ZHz51/S/MiTOQgyEKRI8lCgzmWEISKJERukEYP5wJYlEAbXtI2yYW0JarkfUZMA5aZ/TuhtFNwXVLCXICB5mJjvoE80U+ZK+Pi7b+hhxdzU7Fi/DlzBwL14FW98mlzFF4jSXHdEuIil9n9i5zJMnz/+/+cSFTnl5OY2NjZOWzZo1i+effx6AsjKzNsvg4CDl5RPBgMPDw+NWnrKyMnK5HOFweJJVZ3h4mJUrV46PGRoaOmn+YDB4krXoODabDZvN9rHf5YETK+lYPUEqp/2Zzrd/aH6TVvGIBnFPHAWJCypbINfCMdHLLuZROeqBlI3PjdZxjAG8uQmhI2ki7pyBIJrRxDf+7UMaOxMI8SiJOj+SvxRbZyv4zXozw8kqnOoUMgbE3dXE3NWknGVACMXqB6CWfdgtdmA2quxkTiZLLNzN8XBcu70C+7QKHLlDZMI/peIXP8fa0IAWCtN7++30jLlSAQSHAyWbRsf81X808Ar+CKQDR/BII8QtZoD8ysZFzKny88ILLzAoRljmOoV+1hH3+InZ50EanDmF46LA0iYzpLwDM1ZiqajAUlFBgeqDbUcIli4HmnGk03RYZATAqQ0A8EJxOU8tquT2gV4u73+bn9Td+b9teejN5AgpExa6jza0tNsrmDvnAdjSghzL8HrhSl6Pr4QqsEoCVxx4j7a3KygIh5FXpqDGtM4VuaeActJ0lOcG/j/2zjs8jvLc2/e07VVadckqlmzLvTfAGDC9JSQBAiGhhDQSAqEkkAYpcCghEEpoIST0Enq1wTYYG3dc5CJbzep9pe1lyvfHyJKFDUnOBzkQ9r4uLrRT3nlndrzzm6dyXHopW6VFRDQrQlpHM0Ty3LvIc5sVqidoq1gfOo4VaRsiBnnObiYA6y11RK1BBAN+8OYrWFJJkvoOMEan70e0LB7tmgslTbwlTmA6bTQ4C7DETPHTXpHH78b6yA0KVMbAmYSkewCbP4Ur3sgkQaetKZekX0aPBSB+iNLWGTJk+K/jExc6hx12GLW1taOW7dmzh9JSs/ZJeXk5+fn5LFu2jBkzZgCQSqV45513uOmmmwCYNWsWiqKwbNkyzjzzTAA6Ojqoqanh5ptvBmDBggUMDg6yfv165s41MzzWrVvH4ODgsBjK8O9hHHA76OE8+ptPHFmZVihEpV5ReKcyh45lBeQscFPWVYIoN1Fc46EYaKADUdaxGCMpw4G2vWgHGNlqJ3ybhrLdeH2NWPp1VD1FxZglNIVS7Jx4PjtfDsPLG8yNZ/8MnjKbMoqSgWCYA02VnqTDlQ/qZABOS25k2fsxTgK6UyofrrRjqajAPlS+YL91ZT+y38/OuvcBU1QnFDOPfV1FLnTBltxqrJrOeF8OY8YUoMgKK9mBs7ePMWPCKHqausoxsD2N4MnHbwFZUpi01IF94GkaJ86hfK55LaumFdO/eA/tDy/llYkS745tpqUzwE1ovNo3gDiml6gljiB4cfc+x5zNe3mgqIOeqjHgHm3h+ChaEymOWLebuK4jqr2IegSrIPCXyWWM9+QOZxvlZxVhV+qI7wgjHdD63ZAEgh4HfzpNJKtf5KydLTDGzMqq0xqZkXYQEiyjKkyeZN/NSewmveMvnCLfRG2imIfbLkIu1lgSWk6RpwkDWDjttVFz1XSZEpeNnLSX9bQzP/fk4eKQb8dHep7t76MW1sy6QIOtEkwGSe5DdJrfm+7V+EF4gD5RpGVovzkDr1F+fJryumt41QqxpVaWJG8hxdewNnbTM7XiX76uGTJk+HzyiQudyy+/nIULF3LDDTdw5plnsn79eu6//37uv/9+wHQ3XXbZZdxwww1UVVVRVVXFDTfcgMPh4JxzzgHA6/Vy0UUXccUVV5CdnU1WVhZXXnklU6ZMGc7Cqq6u5oQTTuDiiy/mvvvMHjff+c53OOWUUzIZV/8Lwv0JIkwe/qz0LSQUrsDQ08RFiBsuLEmFcRsNnk4diZhoJ91dzlhrPhPOfJvsmmMQY+NR7bsITlnB4MpsBFHBLqvsmzAfj9wyPHbSCJGlH0F/YhZeXeP11gcpiTnJLY5TvLYH2+yLCHx7MVq4g46rrqbglpsRij0se+UWxLqvARCS4Va/n+t6IG7L5m7PNDQhwknAdcve4zog1yKTamg46Fz3W1f20xHp4Fcb/8pX1POBPGwpGUgxuaMbRDisbSNX7smm+rSZOH0+LvnhJYS7B4i37aY5/Sa3yj+kTZ5GknPInXw++0PotcN+SHzZz2nZ+RMKpk7DZjOPOXVqHp59+/j5gJ26rgpOT68B/GiGmdV2mK8WdzTFpnl2lGQuJbtf4aft77Fk4WQOK5lEaW7lQRYagLaBOMFoir3RBImBBJf4o7zQ+jNSQx3Gf9QBNsnKMyc+SFn2dIp89uHqyas2v4hV+wvvbTye5eo0UobEyckTyRf/jDW1v7qyBB2tPMNUPHlJpnVAQvegGgKyYKoeRTJ4wfgFx3ArK6yLMAQHNiXJWTSxe+cRpNOmULEocaonvoskqeSMM3tgzdIkHt03EXcqm4ua01jFkcSBlGKeqyehYugKK6Z2M1+HeeXraaQH+ApFE9ewfc0kXNERVX1J6TX8+pkXmPjNryMXesh64xJuPNXGt7e8RbplCTX9EXK8tuHtD3VdM2TI8PnmExc6c+bM4fnnn+eaa67hN7/5DeXl5dx+++2ce+65w9tcffXVxONxfvCDHwwXDFy6dOlwDR2AP/7xj8iyzJlnnjlcMPDhhx8erqED8Nhjj3HppZcOZ2eddtpp3HXXXZ/0KX0hSETScEBAacT3PttEK7P27CCcdQpp3cYTwVuwOl4ibTixAg1hhX3lOosdacIL3gTeBMCmGcTtfqoO68bTm8SX9pFWOobH9qnP403J+HNd9EfOJaaFCezbiXe3+UB1kEbVQ+TkKkQiLQRyFdL5MQbiVvbX/Y0KIjGL6RxrqDiVhe2nIqU6UZUbufqhu4g9dNdw40zBbkf+UGD7ftLt7TSveonD1huoyuvgP5/ydj89Ug+lmil0fKkIuYmRRpI+n88MAh5XBiv+xAebXqWYEq61rsHmXseRWwP4Pacyx52NYHGxN50if3AP42yFo449o+Y0dFcL5YY6PNey0CZm57yFv7gHuyHyeF42++xmFtir+17i1X0KiezfkHIV8kRlmJmBKhIJB5tauvn5U9tIM4jDMkC1qLNSGiQry02BVMxOyxzKLK9Tmxzk3Y3fIP/IpdhshcMFBAf2uUgmWtkhjBSJ7EukGZ8sJqkMWTycC7AIhwMg67uB94noOTwrf4vJtjd4OHoEt/IMdlL4hTCR7m66c8voEPLpJYBqBDEiWRQHiujIXYUkaezYcw5a1AOeQaaOfYJJkX5mdwYBFx6b6eZ2OAaxeEANQ140hXvQzZz+JTzR24fd7WTigAdmgiBplO9qxdLsZ3CqOeW9tjEMRl1EWhIU5prjlYS78aXb6AH+p7GTG/v6h8/ZLoqsmjchI3YyZPgv4lOpjHzKKadwyimnfOR6QRC47rrruO666z5yG5vNxp133smdd975kdtkZWXx6KOP/v9MNcMQak/PqM82ew9HSm8Q0czqwroqcp09myuiJ3M2N/I84/E31+JuTzA7OIhhHfFjCEmFV4ws7DYz+80pmHHG+zm5aDcTU2neN8ax/xGzZ/E0bK2DTNm+Bz3cSWz7Fry5I2/anbFeBnUI6FHAR0S1oSTNYNpJOx8mODXKLi7h9fPP5OXAZO6eOIYqh7m/7PePsuDsJ93eTsNXzkNWbARK8/G7jgZg87geLtA2MosoMcNKVDCtEKJdOmgMW1YV0Ug2PtlPSGqk09LK1JSGfEAz04flK7lr+Q28dup4ClwFw1lY6dVPYS+rJlFlZg1ZdZ3B2As8FAOwIwtQKXj5WXsUdzqHZdIcWrK3Ub59NevGTOfNztt5N5Kgq2cRugHH6HHEdJStnmZUyUbIbjBo7aNT6AWjntokCDoMNCWJVdRiqxq5JrLdQTIBkiMJg2C1RlBSPjbEfoajQgO201y8mXaln5xBOKl+pIfW+7KdE8Ve3IV10D5ybXrGmONvsC9kmzGTP3qvQOpMU6H6cHhM9/azfUU0h0uYktzD1LFQ4+qjuWAL3+xcwBTr0TRoy5gwYTWJvDE0vG66pC1ihN1ZTQQ0D405hzOlo2n4mHsKAlhSvuHPzkSEfXn5jHnoWYQnuyg/HoTrb+enYjlXHmFmYY0tMF+w9kYTXLKrOZOJlSHDfxmZXlcZAEgOpfbvR+nTmPePel6fZKbwG0mFtCAQZTP9ogcnEmlXJUl7Gc82f5my2Y/gzO0gGc6nYc1PMFxvkWwrAuqQPpQE98vU6VzYtp2CkkFU0YxsHQyfSMqpoIm/JbHpL4ibzGemYLfTb01z7rIrmRGfwpjIy+A9j239FRzZDxSBxSLjE8ErwRuRcvbOLIcJ47D/k9iLZGsvjsOuwSlbh91NKUNjZ8EAd7d9ia7ERCKClSMdexBkESXf+ZFj5YgaX6t8ib91HyLYXbQQNywEk0EKXAUohYXDcUJN79bw/a3z+DUP86WWL7OsROXq5r/wq9KvUau+R7d/Jm+mFzDP8LMwnU1O36kALNmX4nfF5QQdIcSSreT29/LHJzRsaThn6LAJRebyiw2aSy/mKxuWkhYHeHtsCOfSYhofvhb3P/4xLADt/jIGeyXc1e2wFsrKtzB+0gY6np9LWjgNgDODxxAwzIDydGU9PR88DkCDlqYtKVCnuOkRRXJ0Mz5rcc8yVojH8Z3gk9xfch79pXbKS3fQzQ7cQI8R4ISpK9EMib5YLr0EaLEXsDN3Cm8X6vjTdubt/h5Kso6CzpHv0hfLZU7nl3AKUTqqQBJGRLZS4seRFgkN6UxrKkHb9Gq2FB/BCTPjsPNajIvPwXqfmbZe5bQxOROjkyHDfzUZoZMBAD0Wg6GsI4Co28oDS37KmwEfJAA1hZUUdzpP5E5OhHI4PvYOk9LFxAQvu+svRqkXSaOj6zmUpYKc7HiHmGFFxz3qWPuMAP0pBzOFOt6qeoijN7p4q/LvWMR83NIvma8kiMyxUjF/IrLfz17rAAktSY7qAcziflOzGrgrx8rRoRTeGRcAkGuFBfESzojr/DM6Ih007NhNqVzA3zzNFOxajuw4kXez2+hRghiVY5neJrBg0n3s27WArPOqkX22g8YRDrDyuJUocOisPrc0us/S/jihgrSVnq0D5ja7GpiVLqPYpvDHnn/wk9wASfElBoBn7NAbsDIY+DGGNNQLgzPRRReK4OJ7zddiS3dw96kSzQGzvcSlL6tMapEoTQeZnhdHCHTxdsrO9tn5tEcrcezYRPWQ0An4Svnjtktoyi8CoL19HOKEdUwsOYLBMLQA7/EOkbhOgZ5msTRn+Fw8+g5+puQTopGf5AZ4pLMbgOa6QorrannUeTQEVFaGT2FragBFS4Mg8Ezx6aRdytAFgueNs/hu9BHmdOwZub4JPxE1myppLhsx200sqfsmopxHh18CupAP6Pk1sbIBQyijfg/kDhjMkOqZXrSeokA7rTGDAiDhzQibDBm+SGSEzheID6ccw0jwZTKmwgEtINKiyHOunOGoHUMPc0+8Gc3Wz0ZtAHdTD5tLA1TWP09jxfnsVjWs1i5mxZvopZztlmJ2aRfxXHoa15A/6pgJ0RQMBZrG1+oS9OFiYTrBG1mdpG1ZSG4FSmzDWVL0DZj/10fiZGK2NI2eEK/vfJC5rSGEWTZa1B8yyylT0Tu6ZcGH6Yh0cOoLpzGvPcA1XEurRcOWMntbDQz12TLsMg6jj2x7kH2A5Dq0K2P/8nBaQjMOXb8pe1AjKbhp62tnYvbo0gs5ZUXE7S4SkkJVxxaqOrbQhg+A32AA5jmrssCtX0kS7DCzDkN26PMKyIJAMP9G7i76EfdzLc0BaC89i+sH78WQ3fzwZQ14amgMC9nfMWj312NRSxDfuJaOqZMoyKugyGfnKwuO4zs9Yaz0kEqa7roVlh1ErWZzrgnOt/DHemnrPhohz4Ummuf745d03BGNpCxw94XmMhsp2hiqCh0F63vdvHX44WAf+cmRtTQn1L6J1xZFy4rxrPsbqJVRcmkFwE0YZRC2bT2RD3pfIt8m0xSGtyr/zoXpkwmmB4EqRH2kwnnh9u9hC5dR6jGY9p4bR9+rQ2sUdMUgfYLE5tdewjtUzLJhsAHRZsVv9cNQcYK90cTweJng5AwZPv9khM4XhANTjg/ELsKqeRNRU9qo5Qoad674Ix+UVfJQ+amkZIM9A+uQylxYBy2UdXazuTSAMzYAQL6komGhQZmOB1BDEpN8x9GOihoOIeojb9Exo4BmezGqsYUTfc08OhBgUbKeNxjpXh/rS4yajy9ksDOrkbGdpmh6yuNGIE1MC5MOtyIYdsJDb/aBwdEVhD9MMBkkqSUYE5wCMlR1dGIcYruEI3yIpaNxOBzIksQ7Whs0HgbWneYKPQTkkZQlvvx+FwP+C9gs3skRYyYOZ1+B2U38t1ecyPkTvBzRvIsp3RGenlCFuDeMjkgOA9yt/4me1S5+9tTILJOSzKPHTGJPUQ0n1j9JSffInM4deJsZlgivnFfKoOhkb2wKlzc+TvtaP544+KxuRCWNRVeJhLshz+yVNbkwH6XTFA1bSsZxDhDo7GOCdQ6blAYeEfIwPEm+vRfE8mz2jDVTzltmKyxJBGlf68c5lCj16+0P0Sv7yJ0a5rupy2jTc3ENhInY/WAYIAioKYE3C45CMdKcHn4aQUxzj/Oy4fOQDJXfi9cAEJKShEK9pvDWg7Sym6ghAVXo1pFA97a+HejpTqqc83n2WCc7hixupoVLp1OVCRDD4YFJQiN3rLiFblcfdtnOvSc8h10UuWRX8/B4meDkDBk+/2SEzheE/rRKXNe5hHsoMMyU63aKuUe/jM5Ix0EOl0HJw2tHFfNOUS60gSqKxLQQ1IcQ5DEkJZmY4sYW70XUUngGJwzvK2op5JRKj08BVD7AT17gREQtSdARRQzaeMD3dV5NHktJupW5vM/u1jI8LtDVfiCP+L5GwGwv0NvcwYXLNF5YOCLSftQ/wM8Kvgq8Q8RqQRhU0C3mvp7+CKH2CIz/eBfFVM8eiEFxr5eWQ6yfpw/80+vq8/n4/re+Q/P9G9kXLeQN93Y0SUJNrAaq2FSWT9DYjEUtxtovMdDbSX7x6MDo8QEXgxNKWFVSyKWv7GT8s/dy/5IicvTp6JLEHZxP0enNlGbvwFLnZK5eS8N7uVy0dOvQCBsBMGSDkB2O768hZlhgu4IvorP0a4fz494nho93Wd9WytQaVrmm4k72sb8sX4nPzo/rUtwJLO7vhCxQVBW3xbTAnf2OTlmzTnuF+T2kLabbaYvTRrXzwLrasH1GHiXxDiaLbViHKgweXrMcpT3Na3knUaj2kVvXDkPidJMxEwvdVGW14xXDSPbxrCrOoUfzAwaxwlKEdBJnQw2LtgZI0IQWOLj7+F6HBV8wRRUwN09jt3IGuwLjsbnXAcsIyRZOKGmCEljEz0n32Xhy8k3cuOsJWrt6eGZ8BRaneV6Z4OQMGf47yAidLxgFRgOHFXwDi7WUmkg39EFaDaFEo4B9eLu2eIA3ORHawBBhcrgPRD95okRdyRhuChxGriVI8bF7uD9nA31yIXmxbi7sepG7hZN413ocEEcxNPJi3UwSnsKeU88zfo1vNK1nivUktqab2SDLpAWZ/riHRR+AajFTvPs7Gofn0tIfoSUvZ9R5FKkamj0bJIOtpXkQA79q7nvO248THOeD8SdyKGL1ZiCqETNdMhF57yG3kxypQy7/MNlj8vBeeQxdr9xLVNN44djFfGe9GetiiAKKdQmILqY/IxKf1QUfaphdbLPw3rxqdkfiNEXaKVneh0yQM5c1gmT+Ew0WWRG/G8FS3Y99Y5Lqk9rZq1v4WW6AH/YPUJAyWOc4gT7vem6Svk1N4RFcWPVnAoP9zO1ax/fTl/FrHgbgJdvJ/Cz0Amsdxby0+Ve8UjrTzAbz2Sg9rhye3Eqse4DGynJ6vXk4BNMq8vgRbkr3LOHErt5R82/wRvlTOJsfMyJEz5I3cvnkK1nUdCsypplnpT4feqG0t5G3rFfhUEbq5CQFiaPit1HXPyICrS093C+ew9fdaxCTDiYrjaSKKtFnLmVXwwyIm/erJI60skgFVxMKlYF7EcG1Fkqn2bHErWg2s7xAW8UZ1K7YSGl3jCcPP4lb5Lt58o0IMeNSLm1qx6p0cN+FxeR6ZPyC61/6/jNkyPDZJiN0viCEwyNumFde3UQ00kQkB5g4nlgsSqy9DryB4W2K7L1cYXuAO30+vtSVR07f8RheC4NAZRguzurmRRWKbGnmF60j5LHg7k8xL7mL1v4y5vZYqM6Zz5r4i2A9lec5nHyhgk7rB1jLc/Am/KQHrXRIxTxaXM7h0kt0+IN8pf4wAKytbaTb2+nKCnB7ohDfMeczvu5dPEr28BwTTheJCR4WeD/A2JNNW+qMoTU6gz2bSSSmjXITAfTU1rN7/UZOCH6JdPRIcIIszwd2o6MStfUNbxsc24Zbk8Bw4XB8vHVI9tkYVzUJS41AWH6IpRMnsaTtEgC2Fw8wrTMPQ7BD6NDusGKbhWKbhfikcpoAR1zivuOjOIY8eLIqUfFsJYpDZJ9kMNU7jaCcpD33ba7I318jaD1gYdm4JWRHHIx52Yot5UEc76dnKO4HoE803VPlGqT01HA2GIDfY1pvXphyDM8LJ8JiKBzs57QtEHeDXqmj9Yr0CiG0oZpW334rF2dMJ+wCq9YHdCEbcLpgFgKc6HqI77yTz3szJUIOKDHAQZI/i6cQtVlpzKni7n3/w+LKJ1mnjcWDiFWp4F3rPNgeJC37OSs0lXq9DVHuITc/QXu7n0jKtENalMHhc2uePoM5W4ay43SB/FiQPMcaOgzzQtZu2Ywl4aS6M8ilR06H1XD9l8q4ePt9xGNeEu1n84M3r0KytyPZx0POLz72e8+QIcNnn4zQ+YKQSIzEvMw/bj6iUsb23kZQoTfchzTQyXCjKMCnh7C227HmbCKHkzAECwy+jKOgg7h2FNnpYhDMN/v62GyObvKxPW1my5Q4j2WeYxyqnmJzMggCtJJDyBahv+Am7ija7yg7Drk/jryhn/y8NmotDhjqh5Uu12hsfINU+AieWB3HplcBVZALupZCwHxYl0iH4SpbgxFMQYdneP7LBlZirHqMIyc9SU6hWT2ufVsPqcfaWCycw2IAJ+hamnplKR7gjQl/oTWnD7uuI4oOcnZVMf6oq5k7Z+6/1CW8Yt6x/Hbn+bzZto/DdVN8eJRsSuMRvBIoip2uluEQ3UMi+/1oFis/fiX5oTU6caWFAXchOyaeT4P6LrkczY3BmaQsBmEFIoKGsPtdnLFn6cv/Es1+U2i5wjpQPzxSXDXdMB2Ci9KOSXywt3U4SDrParptfiL14zduZefmI3hp0rEYwOQBs3L28nk5wAakAg/ZvTo7hxqsAkzr2g3ZZlzN1j3TWaI08Nt4E875jXxVMobbRsQMC4/EjqM9FmBSsBGsUO1RqQjU4iZMw4DAO5Ipems85VzS5+FPucewsOsV9svxtHjwz9eUMc/Dlq8Nfx4Xr2WCuJrWQSvgIWn1IsluEtYo5UOB0bNLinlt0n1saO7k0r+38z+L/gfR1saVa+/9mG8qQ4YMnxcyQucLyA01N9CaFilWxkPBZB7d9ShnGAenRSvCFKAZGdOFM6N+B7FAiHZNw3JA9kx5cxdrAIdqBRm297/L3vAmQgQpHlToL4GIbKc/vwVEK5cNfsD0xnxaRB+/KpOQAcdAHuQeYO2QYMX22yl2JJmmT6a28xGer0wxs1aisjVEzpReLKpKvuI75DkeUZwiriV57uaf8vVf/A1B9LDyge0scipsiqqEdQNRS1O17S52zRnPPNqxDE5gTlzk9zzJt/1uJk77EaUfalD7z8gPjMW/L4RLkNGNJPNzT2U+gBv0mWfRXPvax+6vFBaS+ssTXP2XdzAYncUlCyrVrhWMH/DRmviamZuU+NAAnnNJeCAtJnlz/AOcJZ2ItG4dssMUKUW9BtFkHnEUdgj5TPDk0vj3Ptryuykqyx0exi85EENphMEUEcNCv8PN+PpefMEBCmJRpKmnstKyg7h/JdVGF8F2C4X7wjRPPpkkElY0rlBMi45bGR0AnzJg36YAiyfdT7ffTdJ3LHTCI9oFbBMmIekqM6kbOe8u0zWV3Zlm4IDK6Anh4AKOZXtl6nSJXiGEKkvsFHK40zuHHCPC9fSyTS6ibOIMPqiUyG/cZaabNzQQqMilwlsBtFPhrUC0jfx7ODALCzKZWBkyfN7ICJ0vID+s/Arlnpls6OniAxVUPY0uiR/aykAKmHExUZJYjTT2ZAQ1pSHai3EcEL5cFVJYbAuwI7IVsiCiDjKg9QEyiv8oDFTaHW6OyvewCwh4HsM6o5PJBc/CbtMyEx4Iw8hzFkt6DN39afq6W5jGZPKzd5CIugmmZNRkGhWZX24TkS0hDlU1RxQNEEBLp4iHQoiynf0JZ7udzezR34M+Py/MGc8p9jfpoZSfGa+xQBskhcxPt6gUn533b19bu1MBA5qs9WSn/kFN80z2uicyX5rJLKcFeV0D/TXbyZo85SPHmDmnmpty89nX3Yyuh6kf6OG9XZs5r+p13q6ZyczlNyIIIzV9EorCby6+lK857+Ht9FHUKxXELQYJz8XMsCr0rXqa73UNoMkyl76sAstoIocrpCd58oQvo2rdPH3PX7jg2u/jd1qxKxK/eycMXA2AdXU3UY+FWKKepBAj5S9kpmG68mIIJIVuErobZ6ybiJ7Dr12n8Z3Ey9yinkmps4eiZAhVgJjkZNLkMsq3PEb17HZuoB0SkG5bCxJcUPsuRteL/LXiqyjpOEVaN70IzNfb0KngXDmLt5QR8bf/x0sWRqxfaxLHEc1TaGQDQkk+3q5KvtpbjE6ahPV6DCGFMnsVpZ51tARVCoCOq64imfCi/vXp0feQHsEmMCoLCzKZWBkyfN7ICJ0vCrF+9ufFFK+5l4kDSZpy5sLEeeZ6YXSCtQHcUvQwsirQJyTIj7+JLRkkjAuHagZpBg03SUPiaM/fABifBTHDynjfifREtjLoPY6NgadpHjuf8N6xPCOcgzWVYKr/LMKR/yFhaQTMmJtcfx4JpYGkkUTVU0y2HM3kkNmSQdfSJNYW8GWrhd05IVRRZK3tEt4X9qIIIRbrIh+WaSIaydSIGEj27xz+uyUgssO5iS+15qNERHooJS3prBHnIc1Yz+bao5kf9CO5D1387+MoKPYhouI3QBP6CKa6aKmoZFKjeX3bfTmIu2qZ+zFCB2BWqZ9ZpSP9uY6aWE2o7jnOnPM28bEQiviJYoqNtCvFxVm/IiBGmTv5NiSrGW+Tpcgo7eZD2rm4gs5EJX9y/oPFyXwuaqihfa0fn9iLYBWZsXonkcZGimfM4K0rjmTnzs20dF/L8r1n8V5fKSlDIjUvn66WMvytO4bn5RqsoN04ChfNgFkpuSXWyWXWAlBWscsKfbkHWKb66rCOKeYfdR3khVP0WgSK7WkShsLXhSdhAM7ZbPZMi4k2lnAzOPsIezawzluD3DkyVmAghqymsQtmOYF2itk+tpo5G1rJcc+nhl34vbVkN7TQF1hAWjHv255tU2iXJzN+ktkM2PbN80jc8RRqaKQeD4Ck9fHX8QrZrhFnYyYTK0OGzx8ZofMFIRnsBMwH4GPhM/lzqAS7OzK83hBGSwUBOPW9fKwpCWF8PwmLmeEih3UUey+4cmknwG3NS5goqszPPZVXg2/zoO9YfquUIhrbEEQPYVs/ZT0b2clYLl/3ErNXr6b677exMWanvvEPwA0ANIo6mtLHsuL7UXcXMrXXgmLYqRv7ZRKCQnzKD9DVLgg/xt2TT+Fctw1da+Hkk8+j49VafHSMmr8cj9PYOBOHHKU5uJvutY8A3wWgPLKN7RYBRRPZWljG950v8dvqK5g4ECTs3kxc8GCXrYjOf/+fhy+3hEuVp9iWzsYYuqQnBV9FxxSUO2YWcbm/jNWJ1L/1oJxbMoFEzjJS6f6P3MaiZB0UfB3yeJCtVupr6iifuIDGfIHjXSEsvWbhyLKyLWRV9bNX+BUF7QMww6ztI42xIyZa8Uvp4bFaDY2Q18tQPT9zWcnbHBsM0xTPQmd0AUGAhALfP/lsum3mvSdbOqHoGf5gOYNSrw2fpZ6LUiv4Xuoypun1ON1Jnh17MjOiu7ip9U78Qpicqs10el6iEuhUGwRjPQAA7o1JREFUioaPndevcOwb/8AxLoqoJ7hHvAx8sOpYc72sFXLp2qcZHOOHodJKopgiqQi0JAVsqkiJJPH+iqVUA4nwwYHi0XgjeRbzvPxWPzi9B22TIUOGzzYZofMFoae9E3LNh02OXcChxYnYzBgHT9wNpD+0h0B2yLRoqKrK1kofR64R8G+OYS94hf6cCwEonTKHuWOmkd5oZ4L/BdqNAB/GpZtWG0OQsF/5Y9ylM1iQt5SCSB/WDQkMSaA2PpcXW1/hh9GTiGm7WOePsnJmDxe83YI05mQmFf+VG2wBFn0g0W53sr9icCAQQJOLiCdHpzwv2d3Clvz5xHPsfHUwgD//Kq7cab6x+1xtw9slhQIWyIPozhxmpM0mpGcsmE3R5MMP2fLhn+IrwffD5RT0trH00RsBjafcbr42YK7ulteRNg6jM9JBsa303xraZis8SMj8MzyBXC647c/saNnCb9/9HQA1wSxgqJZS+3gC1e8i2JOE9o2IRYfXFGGKzexmbrVGKIsX0adacStZw9v1UUQiu5bAPo2EzfyeNx2Ww5HyNp5OT+SspR3YCn3ES6cjpHSsOwWswCvqNPREEfMFKxexgkHNyR3iV80OH1tB8pQMH2Nl2/l8uSvOB/mbOdy/dni5xUhiDKZ4JDKG6p6r+Xquha6GqSyub2Zd/g+4eWoWYkUzxdUbaFr2S/O82EqbuI0OOyxtc3JLsYP7ciZia1mBJbiDSYJOW1Mu1tws5HQ5P13+P4hDWV122c7/LHn237r+GTJk+L8nI3S+IHT1JYdjYKaknVhTAbZoAwAUJwLwIYvIgaiijaxYknfHlzAlt5++1Ek4DTMGJq97Dlq3hEAChzV+cHAsIAimYMq3NDJ2/EmA+dCeYCvkjTnbeVV/APfOMgqiGkJ+MTTtIjzBQ2N+L2/O8jJBC+J0tjJg1dlvlToQe/ZM1iWmcGACeEgUmNw2g0GjjJO2DVLWvwW3OBeAmpZJMNbsnl0umuLmJ8JNSGMiiJpB3rhD97X6l/GVEJeysaQCJOgi6vsa+ysS6oKBp2cXe3sGmexS/m3h8r/BE8ilzDaFzu1hMMAeGcv+IoOplGmpM7R+gvsidDXUYfd4sLiyEEU7/sIaaJ5GWfkW5jg/oGL1jSi5p9E7lPU2OQh6tg+r5CPtNL/8yY4IE61xDovuBVyM66rla/E6asdLPDdxIcTgp0vKWfvWXnrSbpDhF+v/xk+PzaU9W0BVKxB7R1x7b/nGs8wlA7NpSVXyDecDALjkXEjWMYANi95EOQni0hiqHHW00ApkEWsuobMzD13tIm4zqyPvR1UMfnSxjDXyAuXHpymvu4ZXrRBbamVJ8haCfBerInDfhcVEjGauWXUNodRo91aGDBk++2SEzheEVHLkB75Sn4RNL6NDM1OOA3EV5UMtIA5EEnUG9F5yLQppn0wonMvOiAZu+CCnjqwZk0mqEnuSN8LqTsKqgSr5DhrHK2rkukbHvRRaBabbt+HXTZeAYRvy9wh2FF1g9YTtrGY7ECB78NC3q5E9GbG1nf25ywlbgF95pzMpVM9XlXGc1CkCC4fSyVNc6HiCzUPB1JdL/0BDoaF2JounHkHZzKnYcmf/C1f048lSZJJWP9CFLo+2ch2zbiXvCz7yO3/FggXL/iNip8BVwO1H3cElyy8hEGw7aL09vY76bont11yGbLVywW1/ZsH8pXTs6YC1nbS3j4MJ63gw0MS8LR9QEjBF4xJ5EYEBD5RCX2GKPVGo7Z/FhEAzWsJL2GVBCLl5TJtI+9oAiqMdV74fPRLmmAXVvPDuRnZqldh1kTOas+gOSaxQc8GigxVEUceyfaR32aBPAPPQ9CZNkeqOG7TooKoiE0vfYXspRPg7cCv5M7cSfUogHaln0xgL9okzqfOWMyu0nRnv7METh5rCr2O/7zU4/TTId1DedBd3z9SoD5Ry5dJ9BCyl5NpG33uZflgZMnx+yAidLwhiaiQzZZ++jTzBQBRNUWEIOrL60R2/nUTpTVcwjhH3kKiDVUtzV08Bdy0dKbKnGLArquHxHoOoJUlLUaJDzS6zFZUB1Uqsvf2A0c24CGNIeIwZengV9cSZaskirBYgRov5ifIU94aXAHtRxBRuYSQo1VWUjWvLAAnJjDmqLz+V2Z1Z+EQV2SIS3/ggb4/PwsWpbJFqudDfCZhuo+Xh8xkgjy6COAqOxJY79t+8soem2GahsmIuO3buRtJGX9tXjzubaZteJyQk6duzg6Kpn77QAQg4TMElGCONXQcUF42UM6HoDMYoOdRvupHNORAPhfAEKrHbJKCTVNK0lzV6FMYmWxkjmGrjr57dzGnZQk5iLOWBo8FIkxQO5+m+wwGonw3TU0muM67k6eJJvNC5hNM7r0HfbGUQg6OYxQpmIVQnKe/6NePa+ylVGvjHxNMAOCpvNzZPFj/a18yTOSFqgmXDc99TXEqgqYGpTbB9osiKV0vxFBdzirSNHumbMBVEWSc6vgznrhmkY6+T8Lh4/PDTWD8wi/vfuRaAq/OO4C/iFgoffgObPwXHg++Pv8eqjIeFl1DXHUG0JtHihezracYmFB7UD+uhyWVkW0Z+TjPiJ0OGzw4ZofMFwTjg4dYf6iYcaYL8MgAEBPhQzZYDsejqQcs8hsgf1zzC9eedR9qZzylrI5Q3v0Ze/z4CyaGgTi3GU1MGCPZbsIhJrPPP4u5HniOdHokH8noHmDoNJIuTRELi+p4H+Kswi8FemNLrwhRCu1jDVKayF0SJH859EFvTuTA0rZLZ4wh1B1nVoHFcK5Q2vYIe12mbcj4AarSLMfapNIRCaPY97Ex58EWG+hm50+j2ILIh4vR/siX/s1xm1tRJm/eCPAsARzqP7IiV07eIjH1WpqXs9+TeOwml8D8jdgAiYoRGRUYA1hRW8VfhHCwzNJ57L45H/fcyzbYXFOFybmDsm004cgWcDJLofwe70UnCnqa81cXeceeze9ssFqztZKJrDXsmfBNr0eMklBCr+yah5zayqP6b3HxsNr2uOBAhP/U4qWaRn/Q/Bv2PATCvR+Yc+arhY4fdXgJA2D8DQ2/i1ZldQBfr4yWc258/vF2uVoRiySOadBNx6Zy19mHmaJ3D61XRQv0lTkpd38KppmDbNdTmZ+FpCmKXBC57asvQlpfyx31JbFW/xGYzrYeKJUAo8GO+vq1h1HXJpKBnyPDZISN0vijoI+njXnsTYzy72MIPAdAEAc34aKETN2z4rT0HLbdLTo5pe5/1FWeQp4lM6qhh46RpTLJPY233y+z1eujzDjJDb+XiqlfxOn5DOp3mtKNOIsvqZd9r21mTWAeahb5pL/J8YhJ/HfwerlQ7Z2ibOVpZhTRUJSeNwvlTfseCwY1Mz6lhY0sdipI/3JqhamIlb3WYlqXXFlXRqq3ixC5znrZFg9yfs5QjWuwUD6rsYyKLMIOjc6PTmXHsFCrH5ZE95t+vm/NxWB2mmHJaBxA0DVCY1vNNyjohlvt1Ii1t9NFLTetm8j3CcBuGTwu/1Y9NtPJSeR/bOwPchIYvcifnpyfzsHI1jZYYWS4HuqyRbGggHk+iRg8uyncgp66Lo6eqsabNfmEiOjMbduGOtKCKAr8/K4sjOxKjqieLWpJZz+7ElgxyuLKb5xYdD8BXembQuifAYt/bXFWRzfnS+QSGSgRMk/dykbgMjxAdHkceuqfz1UqueaKYzQtdVLQOUlm5jz5hRJwfqVdi8+eher/Nq8bLfG3lerKCI+4wANWRg3P6HHJlA7ZBTFEojQd55fQi4kVlAGxsbuW6F5v4UdUvKclWCCVD/GHTH7CHfoV9SPjYZBs/mnczP28IZVLQM2T4jJAROl8A0u3tKMnY8Oddqp9lYjmzhj4nLBZ67Yfu5aSJOugeZGEkhkcAJMzS/0U98OWeKKJkoKQjJIpkJL2UaNRFym0qjWJLH9n2IJEVZl6y+EYPspHEpzhJJp2McdyLsKqFhM3K9vJSrJIPH93cOf1OfLEwcl2Irpll9GQHuLjwLtAsnHH0OWRXVA+3Zpjgc1Bd1wJMZI6jky7ycfeY4q3Wp2CoIqBSnzeOitw0raqVU0KzGXP2IsbN/XSsKVaLaR3ptKeYkF4GnMrWnL/TlXMU87aXoOfkU9TQxGVrf0Zsr4MXT3/xUxU7Ba4C/rHwQd7484843NgL+DEEHXdPD8WWVl6XGhkzdxLoGvW/+hXR/kG6Jy+EyjOGx5ANg5TFRmyoWnY69jpSIkEK5/A2ttkXYQu3kNj0F2K2AYzUb5mWVBhfFuau8KnkKT3sPrGcU4N11K3J55R1W9kw+0TmvrKRYyItGLKH7O8Y/MLyCBOHsgH77VYY6d2JwzHI/J5u2gCHNcyEqIVBPcjC5iCWik5uzn8OhoT8M/YmxnRLzLLnYMXGTbPOYWJbPd/a9YY5WEKlfd23efHtPs67xHXAmYBtoANXwFyyIM+DXZG45dUDRf/BAcvZYowMGTJ8dsgInS8AajCIoSvDnx3pI5g2OAP8Q8IjnaYlS8P/of0qixfRksrFrb5Fp2I+cGIpGbR+FgtpQhv/ygez83l1ylf4qbUL29tBuhgx4df527HrOvahgjLGUKxK1tkTyA3kYUR74fF1OMur8GdPpukfuzEUEUVMs5zFsBW6yEYRPVze00hH+3uMjV5AQC+g+IczRmVGFfnsnLuoAHVFivldZ5ptF5yQJM2DjgLcAzagj7njGhjTdxIR2slxeqkYN5Iq/UnjyTYDrKeEC0nSCxaoCEt8ffAldkqXMGZcKwM9Bt+Vl3CD+vao5pqfFmPGTifv8G9RuPKXw3l2X973AZcnL+SE5C3MU1N0uMKIF19OYcCB8cAzAITtHhopJzfSy/bqSpoStdhUGOtZSFPiLeIHFJxcUdmBZ2+t+R0Aj8yzYFVlrh2IsUYRuDKyjqNs+7AUqNxw5Q9Z+F4bCtB84kW8Ub+HKzY/TnWLQc8WH7+bJpLO9XBt2mwJEU/b0TSZCRNWM2BpgyY7zsqVCO6vY0m/gS3RiQ70KQPD85H1bvb6NRqNOgLAeX3LKIq0jFwU1WBioBudCL3NCZxAliuFzZ9i+59/Sb9gJ5q2ojvsvHDj3aStIz3V6rojXPbUln8asPyv8FmL60m3t6N+yPIl+/3/UTdrhgyfBBmh8wWgJ5xEOKB2sMeyHsW7jxRmdlG/1U+5TaPRrYzab5aygHAiTSTtRLKnQYixp92L3/I6Dv+pqLE2Fq1sYdHKDYBZHC5mE4eLs03rLucP8goeck4bNa6cY8dS6EJqH0nVHa5CbJf53mF/o2vdLB6ZdRznbn6LuXOfo2Lrt3klZUM+bhHFEysOmf5dOL6UJx+9EVnc/04uYThP4aiB76KrXaR4jM72sTRZ2pElieILZ/7/pZH/E1yFAWTFQkPf2zgkN6orxanK+QDkWkHXfkbDtHtZ3bQHJn1q0ziIvNlf5aJu+N0bfwBgRf54LmmuwS2E6bfIWK1RPtgYZeJ51bjipqvoPfdUVoVnQjEYikheapAvb1lFI3Z0m4O68sMpGKotIBaVU7jBrHcjazIRWz8R4AqXBXiOq3SJ33QE+FKqkxy9jzWzJnDkFggGdzE+IKPKEpe+bLr6vtICKTnO4KkSuVaVcMLDC9uPpVwXyOmxAbvo3nYYPVIKOJqt1WVM4Y8kJHMu7RSzK2sKY0MuwvYWsiM6FHkIW0qxd3fBUJp8bMZ9lNNIS8ygWITFY/bCGCinF10VqH8th0RCwd3dQuH8hR97fQfjLdiEgoNaR/wz/i/jeloTKfrTI+4+taeH8EUXkdsxOkNPsNsZ++orGbGT4XNFRuh8AQjF06NaPPR6YyREmeq0itVI8Gz1qVBtrpO1NG4xTAwnqmGQEtJE0Um5YlSUuMmSa2gMfgPRkc2WyTNQxZnoShbZkxt5PPEsdrd1WOg4NCsFmkaAj6890tvbi2rEh9whTqJ2L2VF0zA8Fo7yV2Gzj7xV5pb5PlKceCqmcfZvr2HZP97hg+59+JtrefVI6Pd7CPSFOPFtsPaVY9g2c95lP//EY3IOmk8glwtuv5fXn3ma/roob7tvRdhTyJ7icSyIzGWW08K+grG8uOgM3F1XE+5shOx/r4no/4bigIMB64glS/OOBIfnzX2OYmcHe3qKaN+xl/LedmySAAekeIuCQXSuFwOIZHdA9kRi9PKs0U+hkM97TQqiXISSFeTCd1wY6S6SFi8NFceztuQZ9hYO8FeHSBkWLun4A8RLWcEtLHJuIktupfsEO7/zWghbBinqNWvf3Oryc3PadBklw+OIaj3EZBWnKJCOvT48t5TNzLwT9QgWLcE90mVQDivKQdaq+MnKD+iOLKLbDg1zk9jUGwiE6lD5GvH2eoLjn6Pty1fT2XA/4/N/ib0vhGX99bguPhf1jqcYrNuLlGsWpLJ7PHBAzze/1Y9dtnPr+1fjkLKxiS5sso3bjrxtOOPto/hPt5Y4UNj0pVQurGkiro/ODrT99HfcpYfJKzStjK7WZowrr0ANBjNCJ8PniozQ+QKgq6OrBr/ra0aLOzg8UsXN/Bhj2yUEImMJClG2OZcTmNJLF04unmbjTPft6OtsTCraR246C3+ynRfsz7Mk9kNsrkKc/c30ZU+n8OVX+JJb583jRo5jtZkPJtdQp/OwMNqU73A4UBSF554zu1zHLU5sLOEe4TIYB3YtSSn30PVvnKunYhqVp3vY8Zd7ADhuSwNpaztJcShY1vBy0sWXUlz+yaSR/9P5BHKxF5cjtrRhG9tMZ43ELm87k0NzANhj3YbGEnTRxbb6Rub9Byw7xTYLD00du1+PsjKscOnQ33v3zKV09nNIlm761sg4iPHY+EFixU72hv7AHdHLGdyuEBetxHO9SAOd+GsbcfoW0+PvJpXezNwtrSSAD0oUQGNhHfiiDUxofpBZ+cX89OtBGvwy5/nNzChP3MXZvSpvDV4+PMd5MY35vh/zrjEWqCMljlgkNzuyGejO5kIjQn1ZN+XT17J792FYuhVk1QvUIGl9nFp/L1PHdiKJKu0Uc490Ge4jn0N95zA83RJJeT62tAtNfJy7I4AH5E47P1P8uNQKSgrm4C0xYP31uHIkEv4UH7x0H72vP0JYtSFbrcz76a3D8ypwFfDi6S8STJqisGGwgWtWXUO+HGGie8yn9XX+27QmUhyxbvcoYWMXRZ6YWjGcIt++t57vdcO37TkQNAWR3VnIX/3ZlP1fTDpDhv8PMkLnC8BAcmDU57NDYQojDdSkZ1BGL93a8zRzMnIiSb5vpMbN9gILZxkhHIKAT5RQKmtgB/Qr7ehqkvm5pw5XW9YXfJ9l0s9HHeeEdC0x0YZY2oemSaynDUX2DmdK+Xw+LrnkEmKxGJ17W3lpxWs84uqhNnwzYwsuwpOSkBYeD5EHDhJJH0eOy4ohywiChNLzFgrgAERBZuY5hUybPP1/cRX/9+QX5HJf+c1cPNRPLGE7bXidLhiIWhi/Vkjv+jRbK7b8R+aXa5FpGvo7oR+QcRc3v1Cb2kRTqotk9UQSLz/K3GQvpZf1YldTDGK6OJvc00k7kpy6qxdJ99BDN+WJbJSoQsRuR/KVsDv0JuEZ52FPqqR2vUhB5de59aW7qfMM8E6FxBE7De48rR8peR35ISdB0cM/xpzDyRE/5YKVTf7R7lQAa66DE1o9FDkS1NtrcOQkoMWKLyggeEd6Ue3pKCUcnoxRajCzrQumgSCpHGsvw5ZVhmoYiK3l7Ms+ifpxJUjpNjx99/LTWA4Xr/oJLSv7+MaVY3ApDtw1t+I+3nRlGbKNunl38tKDf6en0YxJ21RTT1+bBavTSUlROUU++6fyvX0S9KdV4rrO3dVjqHKa1tEsRSavvxe10xRppW1NPHzDTTj/fC/WsRXDFqdBl/v/cuoZMvyvyAidLwBqeHSsQDRRzAvC0RTFbWiaRO70bcC24fVaWiSljaQVS2KU7vqFBLwaBdrf6PL00fLq76gtH8v4LpXe8eczy2mhbNBD8ACv0jLLQu6ZcwEXd+5hVukUckNRis6fMZwpBabY8fl8qD1mSk2h5EYVO9E7fsEAMACHFEkfh8PhQHa4CI+diKCab6OiJHHK6acyfd5h/+bV+/+numIKi5+4lGZxB7CeeY0NwHgAcgcMKlvb+cXOw+kteIiVD9dQ/rMb8QRy/+PzBJghmi6tKVmLyJcnoRo6r7fcy+6xR1PGPXx3xd+53v19AL7S3cthPflQdD5dDNII5I1ZSMAwg3U1NJqja6jFw15nHsyuRkJH9s9hn1yLJvZS3pHGpun8+cgwZs2kTgLhv0LNTwCQDmgquh9XLEzOUCXthGiMWhfXXaR18969w3I3EyNp2AH3KOcOb9M+5V4su2dT3P9VSqtDHNXRx6yQDYt7kFe9oKoCuS6dVC90d4cwzn8EdddOBn55B3nf/RqOpj8TUGO4ENn0+APIxWfz6xUjLVRs8m7evnLx8OeGwdE1dvxW/0cGnf+zissfjqX5/wlgrnLamOo2/z3tq23ijYsvQfAKCB6ZpCKTnDEO+956xD5o1zRQVVIWzz8ZNUOGzx4ZofMFQI52ACMF1BLYaBchP57DpvWnIFsPeJjoOlJDI4nyoY+ahEVTKUlUUNY/kZfdVaRzdB6YtZMpnU20TQiR0syHzZy9BUxpyYLJ5r6trtm02fLI7hBw1Eo4FR/+vI/PchIGPcwqf56uSCMvvPEyRx99ND7yyQ31HySSPooDLUX7cTgc/9K+nwbuLBvf+/nJbF/m4v3m9ehRYzi047SNuZy29DFsySCFCGw9InuoKvH/jdD5hzPEsZQTsW1mTmAXpQ1fxSo5SMYcCFgxjg3DUF/N0JQH2bz2K6Sa6qjMOxKA56V2kkKYaWEHM2x2rJKDyi+X0/f+VvLeXc3OiefjVfIoT8UobyihvjTG6XtiZNubiEUlxud08AdHMQD9ajE7mhYx3ZXEqvXBkBNTcaTRvftIOhKIQnzU/CNaFi+3ToCSft6Xc3hDqWZLYCyL2ka6vtfs8VPYpFDsAfrBFQmzql9HtGhkVxpE9gXpDPSQ1Tufup230hhehzQAOVELXfc9Q/nxMPCb33Jk3IPn3rv5ssPPQEIj1N3Fy8+9wtLcJQSjKbK9ZszONauuGTVHu2znj4v/iN9m5jn6rX6ylGzsovixFZcPFUtzqADmD4uhD/PhbLC2gTgn/H0nZUcfz48PuxtFUtn/vpLU3ucXq66lP5GFVRJYPeWbLA5rfHbtVRkyHExG6HwBUA+R5upz9SHZNzJrGbQeKWLJg64+PzNfr+WeE+NMsZrBhrtrjuCU8ON0ZO9jXHwSOUYFO50+QqKDKTSRbU+zLwSqoWOf/W3smH8ntRjPVVYhayrKZAe5s6YjOuWPDCQW7OZbeGhpExbDQ0zRiErZFOTPI2B46DY+wOv6198m91uKPiu4s2xk5ZrzN3xO9LgpDpsnX8CArtEU281ZW57AmvLS3ZkkOyvxqWaE7aeo18DQksT7FXxCmOVjj+Ed4SikaUl+1X0XTiGEKkuoMR+V7ocYvOd6qDT3FWSNuGWAaLoPm2HBQEeQ27ABtT6oN0RyZYl7nn+HLE3guzGzGvEkpQqvUgaAJlu5Q3yEp8p0FE3n9m6dhBLFwIzZGQdsmD2LaV27IdsUDLnyPnbMrmMHkNV3sMVHi9uRNYnb8wD2AnsJZs8b2aAxTn84CB5oarJw9LalnJx+DYDECvjeCYOohdlY3B9gEY4lx34xYkWEpstuY3rgy7D5lwQu+zGtv/4zzp4+/BVecAr0W9JsiJlxaX1tLeQkbTy66C+ojhHraDAR5PKVl/O9t743vMwu23nx9BdZNW/CQQHCh6q4vD+WZr87ad1AhP4hF9RHBRZ/GLsokqWYP//BaAq/1sNjvhv4QHIyaXcYS5/BLk8xjVOyyRubQAmlSDWn6LBbScQ/fuwMGT5rZITOFwA1cfDb3WlaGw2aSEnnXuyxwykILuL9SJD8/vUkLdJwQ4jqtTXYSiKkbAZ9F1Sz+al/ADNGjRU34Gnn81hjYT7I0zh810nEtDDfbG6mvXM32Sd9GUvRx7dXkFzmG6l+Qg4p3UlwWSdIoPbEUfX/jtvU5TCvwRxjCxFfGF07iVlOKyCDexrGMRNoM1Yx8aV2Ol8fIP+KWZ+a2JH9fgyblUtfTgIRmsjhaulxtOwYEw9v4R7xMra1zaTDugGhJB9Pew8DsUK8F18LK0ay6FKkSFkUEkKK8kQpA5EAojqA4rDQ5alFF0UuqXkWi6oTtOVh6CESm/+Csr+OjWThyOop+IIVdLnqCGEhUhBkYeGPKEk6iA5aaNw2nu6pR5jbi/DO9nm8gylcql2tTCjfPPo6J2SO2LUAQVYIBhQaxjjoxKzD004xlsKFRFwRdrtFgm43zYtKKGQn/XGJ0jUWSvS9hP3dKMdsp1EVWfNIGR71BwjiL4na6vmqD2zV1Qh2O+1XXT3q2DP9xfy9DN655Qb2JHrQHXa+fveDoyx0hwpYDiaDTHQVjLLMHCh89nOgqypLkQ+yAsHBgcWH4sMuL78QptFSSCNZrE1OxlsX5pZzv0NSsIJpYMM6Nkl6Xe1Hjpkhw2eV/44nSIaPRY2GR32OYeHt3mlkiVsB6Gs36O7UCBsjrh5JA6ua4OHFHdzUaWAxJGZWBlhXoSBpIz++sZSMrnbR16EgOURCmkZcN6so5yVThI3RroWPYn8G1ksrzDdrLCAbIrEnGujHhqCIiM7/jtvVbUjUO9bQGVvBX9zTOKLlmzSL/XzTmcem0uns9nZyeSPEBnLx+Ko+lTkohYXw2B389JVL+J/J36fomd/SvtbPhGA3hZiFJLPzWljcv4CVlh2k0++x7J6ldFsCUPS14XE65S6iJfm8wAYEw8CitWGJjcWhmbEfmmiwtfSANP7Bh7nl69/CpfQybXUXx9e8SnXNpv3VDdAEF9klBq8lnJzhaoaEjDM2EmB8ofE62wMTIJlFwmuwy+8bXifoCUTdtPBUBRchynl0INFcvgVV24qspc108wPK4Cjpb/DDpU/Q0J6PpPZRyh5SuatZFhFYFjEdNPLsTn67o4dsw09zamg/goz9201ooaF/W1YvasJK3c9vMK9dEPLDBqoYZ9PG3Yyb7x4OUC5wFfxLhSGLbZaPjb8ptln+qRj6WAZaINaHrTdCoT3IlybdR1pQYDowHRQtzU+N39JRk0VLtIIV80/EMAaIhgf++dgZMnyG+O94cmT4WNRUgvQBb3c7yOdNZjNXDnOMsovxm1YDq5kIJCWJkB30iJ2Ht17IpTkSVknDNmQKP/3IE9j75H2skKvQBIk97V7gMTP5ql9m8cBpqIaZzu4QzH089oMzZz7Mh+NqtEgKm2EZdld9nNvr84Ijx4ckKGxpS0FbgFVAhdjDgDuEamsA8jCMODPy/8a+/EZaGqwsyH8Lm+1TqlmSn0NjvgBji7B4zIflGeK71Id9FFm6mBJtxOUah1WIYrEdi6S4kIXIqCHK1QI629uYHFjMSssOpO4a1PQHkH02ABsmBDlVHUfRk2/zevVReLQ67FqS/iw3R5a9hzPvejZ3rySqp7EmE4xpa6EwGOep8QM8RQHlksEvgITuIWnIfMXyHl+JvGcevBe6kw62F5iiSjSiTKl5gA3lXgblZhpyXsblHQ/40I1OvrXjWcqnbGL3rsOJx70EHW6WV88mKC+gxJONoKdIWH9DeWMh+doEpsUaiMzbyKP9VuTjINH0HImtC9ElO+JzF6MAw3e24oBL1jP1gXuw/30nt8weCX5mZRj76ne497xZZDtNAeJ3WkZlZv07AcsH8s/E0Ecy0AJ3z4V0jErgKPuJvCydyg+M2xHf8xOLe5HyE0ydsoVmazVayrRIWewDpBKZFhcZPl9khM4XAFXT0BiJE5hEJ3lhK6ncEJdfLDG7swpVzCcoOglpIfq87yMN6ESVIBBAsggkrL7h/Qv0fq5ZfDuB90/BzkP8xlfBzGAu/oYY6YhZE0eQdXKHYgByXP9aR+zPWlzNJ01WRQnfvOFPtLc0cu/a3/C17hY2tlZh6HHCWTshsQBX8HFe7P8qN7UO0jH1fgZ6O8kv/nSLszWkQ2hWOzIgE2T+ti5WGd/kxdnjGBx3C7M0CalmPsniIrKmLoTtI/smgvVoZWPwhU2xkSiajLN+PYZumj4qIn4m9axFtOdxXG+QbQE3Z73xFjmhXlJeD53TPHT6TmN/J7WuAji8LcUJddczu3ovg51WoJCInsNOoYQZNAIQFyw87l/C11JvYhYPALvsxZIOAV5ywzsIhAQa5kQAH3E5Tns6ytE0EjcOw9bViZw1luWAxdeMnDJQYwHSiovKLoG2ggQ+dxh/xBTrkl8k3dlDRM9h38QHyS6KIUsuLN4AcqIJnrsYYn2Ujp/OW1flEYym6F67hujNfyB92RX8fCd866H1w9fNrkjce94sDMGKnC7n6qV3jvpOrLKVh068kxlFpZ/odw1m8HF83z4q0zFajrqDve0yQkM2AIW0slg6BtXI5/XUK+aycDkpzIKH2ZKTlng9Uz/xWWXI8OmRETpfALQPBSY6SCFpNZT29bJ8jMGb3joYimEAsOs67ngP15QGkHSRb0/+A5eLo8VKtj1Ilc2CbERoywnRmtOHs0TCmxBYGJ/A3Imbse04DVKD/4lT/NyQVVFCVkUJF07+C0vvvB0Fs5BhcbAMM5VFw96ewBI1xU3duq3kF8/8VOayv5LvNZtvoTzg5SY0fpYboCVP5LWWVnLqDWz6N0hMeRTvpIXkhyaxfXscGAkArlowg8TukZKOgmha4NJDXecXJZeQcJ8GR4ITOEKHta4oTaL54DQiIQQ1RFb4fQZ8MmPq+2isPJPKbhcTx6bZs8vLwNDY/5M8mwguKoU27rDcQ4OYM+p8Kj3TiLMagGrLOJyik8GO7UglhUNzMnm8egrn92zBZZgWQs+YDRRXvULTsl+iKwZLdtXArhoANItM9rcNdu7bixIP0W3dyobVLsr1AuyyC4slScX5ZaN+SIt8dop8dtrcAqHBNpyxNl45dQ7BhCnnQoqDS15rOkD4fPeg7yYGnH3vDpZfkfuJ1uRpG4iz5A/vUKHW8aoVvvdGBEWr5FzfSEHGjTEVcSDKYlc1/bzCUalJNFLBPwABkY5gzSc2nwwZ/hN86kLnxhtv5Nprr+XHP/4xt99+OwCGYXD99ddz//33EwwGmTdvHnfffTeTJo2UhU0mk1x55ZU88cQTxONxjjnmGO655x6Ki4uHtwkGg1x66aW89NJLAJx22mnceeed/9VWgX+XjkgH/WLyoOVnrryPxglFvJjTwStdU8npGWBNyVQ+cJXzd+lOXk2N4am2Wp6QT+GvJZVMLJQOGiNt66EkojHbciq2cDM5+iDfC6/kuewpWNzqv1Xk74vGzOxSmHMKK3b9EYCIOvLQtg4a7BUSWICanXXkZr9DRfkY5NLyT3QOB1bybX2/Bvg183qPoTF/OUFJpEkPsBoPh1FO1JCoSm3DapkEpBEiKtuMycSUUhpkAafFfFDGHB04gKSqIxgiKy07Rh1TNkQ0p4WUsJWj1Q1sDXowUgnaRSAEu3OAwYfZ7fExpc9CQVmYgaHuEydtX4u9WKV7KLBXV8VRY4ctEjZA0nTWx4Z6bUVgsfYmT0+FvCHJNDe5CTk3iqZ2ArkMtOTT1u1DV7vo/obG1u3j2eKcRnGonTNWvYc3JvJg7FVTiM58iJdUgS+9W0K29WJk0YO3pZU8INb6NlKqB6slBxzZCB4PqigQvcXsKba/A5vLbueNJ58j7PpwG12ThsEGrn7rNhLtZ7OpfTeDmvVfdmX9M4LRFPG0xpXHj4d34LrjStjz5GbwzR7eprTmYSwtBnr+XADq9Y20J3X2VweNJfoPNXSGDJ9ZPlWhs2HDBu6//36mTh1t6Lz55pu57bbbePjhhxk3bhy/+93vOPbYY6mtrcXtNitvXnbZZbz88ss8+eSTZGdnc8UVV3DKKaewadMmJMl86J5zzjm0trbyxhtvAPCd73yH8847j5dffvnTPK3PDR2RDk5/8XTyAzFmR0eWRyw2Xj76GNJZFk7SNtCfNYO2gJd2Wy6WQZ0Cq4aExMRUmsqUwlkb3qbgW+cN76+mbWBYaJ/4PGM2CjxUe/vwuphoZUxZzUiRP+lfK/L3RSS/xHxwGVo/zqHChsX9ubyfSnF7Os332r3EbRH0N1J0Uk/+9/lUxE6BqwBXIEEUWJkc6cn0fPexbDpsHq8Lx6DMU7lp2TbGISAbGmwP8hLHDm1ZxvOiwVcCBtM9raiWPMSZK0nvkFhVMcjcYAnuvqPo87chi70Y3gQKCqtYiODT+Ur8WTyRCE21cwkZcWpzVcra4rRuD6AJDCsEfyJG1coW+sr6SE+RSLlGYr8cjkHeL9nK2C8XU7ovh4gRoEosYE9qJc1RMyD+fN6kV3Nzmu1pqIZGdgK3MmbGThJPCaQjrdSttjO2pI8/228nnlZoIgdr25cJj6/mS83LKa4I8Lf+l0laUuSPXUpX41epbXqagAiO134zcmEVB+5zXuflKZWIsZGAfI9mMLWhjVw1SmlR2SG/k2xvPnbHIAng6rduQ7R0Y5WtXDHrCop9fnI95s+23+pHV30Eo6lR+384/qdtID68TV23GWNVkmWu/8euOzkyPZ8DI692zyngg8kJLpEnMcDzjBVns88YyZpUVVAH/jPlDzJk+CT41IROJBLh3HPP5YEHHuB3v/vd8HLDMLj99tv5+c9/zhlnnAHA3/72N/Ly8nj88cf57ne/y+DgIH/5y1945JFHWLJkCQCPPvooJSUlvPXWWxx//PHs2rWLN954g7Vr1zJvnplq+sADD7BgwQJqa2sZP378p3Vqnxt6w7VkC1GO1V0MeEaUTk1+Ba/MOZ+pnTv4ee1f2JCVpM7WQ1K3UzFoPlUiVvOHTUDDnRz5oXY4HOi6j/XrT0FWEtTYIugiWCNpit3VbBfb8a2JMm/h1zgxxL9c5O+LiKswgCQppGOv41fzwD+T4thXOVGVmb/+tyir4/R9o5O/Tmrhe/VZDNZ045ZNy8/HBWerAwn06Egmzr8SyJ3jthIFfrKogqv33yq6wYT2Bzi6Isk90mX0+cPkD4gs9L5OzuB8fPPfBkGlLlrJO9vn4a/YS5HzAyrG3ojQ5Ga7fRVr4xtpj7cwOZ6iXJvAXK+KKphOpAEhxkrLDmwn3I61Zyvu3dsRx50EA8+xpXo6vT3ZlLd2st9wkz9g/j+7aYD6lhyk81QshkFak5kwYfXIyRy+G6cm8e6GL1HW5CSmmZbFH6XP4ah39hGsDnBh12Z6pG/CVBBlHdeYWcit44lGl7FXHs+Ng7MoTHUx1boPnwpJsYLJUx5ETi0H7MgOlcrxZ9DVCAV51xCe+T3SoT20tj3GuMKv4lz5W9zhPZz768tJRk0Z0T8QZ+WfHjD/bmtBth86ds3p8fDQiXdy9r07SLSbQd0x4Bd1ERD6cY79A6IyiEXPI9pwBYn0aNe0XZF464ojTffZkKsqntZGrd+fIOBIRdCE0VlbT1dspyNpQUk3cxLwmn0rhCcPrxcQzPvL97G3VIYMnxk+NaFzySWXcPLJJ7NkyZJRQqexsZHOzk6OO26k+6PVauXII49kzZo1fPe732XTpk2k0+lR2xQWFjJ58mTWrFnD8ccfz/vvv4/X6x0WOQDz58/H6/WyZs2aQwqdZDJJMjnixgmFPr6r9ueZRKKdntofcGV+EvITNPIucBqGKjKhpRmx5na8VkCCom4vcaMIgEqpbWiEoSBMWwoO8EDtz47qf38HoXcHSMtP86pUzlcSa9GS03nfmubUEy6ixFlMv1H7bxX5+6LhCeRy+k9v46W73kVxbQKg2fU2vvBpPDj1q/xwwwPk5tVTXvR7GvMtlK++kfgq881cUETyhursHChs9Giavkd2Yhzw8Dtw239GaaGXobAhAPJ6symsMGNJ+sbso3ewkgrRS3mkEGX1qahSKx1SEwDt7eNgwjo2xd5gvnguPe0tHNYEkCTBYwzYXVw3Ic2xzlzE/kpc7eVggQ3L9zIu30Ii24PhsMEAKMJ0egqPp6cQ4kIddgOeXVzEd2lgV6iKse82YdHS2NI6D6/5FllOjW9IAqtoYnxUw73wXQRLnJTswRodQNQF+gqX8yyAWs82j59v94xYrxbJk7Fll6H6S3mrp4+kGCACrJ2bxC+sA6CxcTp23QGWRgSnjOLyASGSKxNEZAcwnRymE9rZhcNqQ3juYlzAfltIlmynIascmz/Fugd/Q6/iIKwe/J3IVisX3PZnll9x1LAlpjfWy47OPm55tYcfjL8N0drNHWufIpHWuf2s6VTmmkep645w2VNb2NDYz15Xgh2dfcTTGlednENJtiluPHaJxqbl9FoUPBGJkJLkwLzI8v4ZqD1TaJxkuqoaHWFKjAPEkHBAb7QMGT4HfCpC58knn2Tz5s1s2LDhoHWdnWZ11Ly8vFHL8/Ly2Ldv3/A2FosFv99/0Db79+/s7CQ39+Ay+bm5ucPbfJgbb7yR66+//t8/oc8hqXQ/Pbqbf/RXMrnFT5dtDkyAjlXTyE7toSDioahtJ+kKiTss94ze15DQFOMjRjbFjpJfhcWoJV59LuxZg332FUQ2mT/K8Vc76Dei/1W1bz4tXP4AslSCN7AHwtDhHcAXhrjXTOG9LXwFJXWNnFr0HD2JRiYflk95cQX9T9WiR1VUEnT9YdNBwiZw4WREp4LaHRve9l96A29qpbzTgD4ZnxpGc4y4LHraS3jBugE57mCeG1zkkySLddF+sEMqabooX5m9gPmbYcKYGahxF5FUP7t6lhOIpEkldvC0MQ7B5uPMtIxsiNQqPdT2AZMDyEYdvj4/U/Wl9A4UU1y3jnULJ0KqEp+/n702mbUOP2NpQkibpp5wwsNx054h7mllrCqwY00l84fmrGoWXAmZqQ25DHq9FKe97CmewGXBP/LHvOeAHwJwn2czR3W1MtVxOFKglqDQjGdAB2ZTLBqURWJYy3vwxtuhF4RiN7sGN2D12XCfMZPcorGkkj3s2HQ13o3fJZS8G0kwX6QURaD4a/lIr17I8eU7odxsDqqLVrpn3IaeU4E09FvW39bCa3f9gXgoRFHFgYHIXqpyCrlr6Tvc8moPIABnY1UE5pRnDW/nd1qwKxI3P/UWfsGs8TNJTPP8juvpspuWWV/IIOaAVFEBEOIENcjcA26BHn0qzdEqvh5cCQHQ5AKMA8RNWsz8m87w+eITv2NbWlr48Y9/zNKlS7HZPvoNUvjQW4FhGAct+zAf3uZQ23/cONdccw0/+clPhj+HQiFKSko+9pifV9qTBldzB8lsG++amaPImoplwACHQWF0C+G0m782zMQum29rBgJvzz2Z+T3vUlzcDK0g6uIhxxft5q0T3Z4GK8MiByDr7AnkBvL+K2rffNrYXAqyRaS3OwvsUKRn45UgO1CHYM9C3t3GKvc01jVN4KK8N0hvfA+f5zTSNi9qtymGjLRO1lnjkXOH6sn8L6677Pcj2O3wmzu4CQA/V0uP86dTRzqtrygp5oymFMTjrImEGOd5gjL9AmRxdB0XQdDoFUJsc3XRqfQixqM4eyAnovCLZwqBdSSUjWyddim5vbMRZdOVZVFiNLhqkRUnon06FWKKhNaF2ziWKLDMl+IJl0K5uo1zgYTK8C9Yw+6TOVzLx+VqQC8ZidFz9IfpE8GqyszsOI2caAmz+pdS4U8TkkbcuV1ON11CBxESOJMxKpN2hMF+el1gi4nM29tMS8vhNNuLwfcS9pIaIso25MWwvFdhvH4rVYWLyK78PW+924qWzgLMvm4ScKZlGlmXrCfdvJvWSy/DYo1QtGCA+A2/JpnwUvynPyFl+VHjSWypg9tagJnN9dYVRxKMpmgYbOBn7/6Mny38EYNaI4N9I9s9dKbE7BeuQtFGrNd6r42Wk25Es/vZUruaXw++yY3dvdw69mcUDwbhgAS2RU01fO/dF9gXMKtR7ylfQH5T98i18uSypz/K5H9S7TxDhs8Kn7jQ2bRpE93d3cyaNWt4maZpvPvuu9x1113U1polxDs7OykoGMki6O7uHrby5Ofnk0qlCAaDo6w63d3dLFy4cHibrq6RtNb99PT0HGQt2o/VasVq/ddqunzeaexpJCmM4aQ9y+gQ8xjXHUYWI/hiEUS7zhHremgp8nDthT8ipZiG67jNQcpl47DkO1i65gO7kbVD3yJKvgNkgf0FUAaEGPtL9cg5diyFmR/BfwV3lo1zrpvPznU66ooU58W+BG5YnLgI45gk1ba1+OO19EedSOP6Uaa1slv7DcJhFnjxRpRENigilnIPcR0SkTSEzP9sLmV/79BhUQSHFkJKYSFjX32FvU2b+dmqn/GThhB57zgJaA2EVA0UqOytoUUXmWCNEjKSWMMhcJqd4QEwTKvSxH0f8II1GxIielylShpHJ7vYVzqfjrKJ5hy0JHM2/QnRMG+gAU8+zVO+AoAuwroes4YL40qQ+najeGZxRO0EBn0ydi2LsKsDSRgR4Xsp4xVFp8EtMs0bIHsow0qylUDKVAENOSsIu/2cFDLTo9PiiKCYl2iit8TL68Yr5A22M5BYj8XqQNSOJGU5gqohI7GoFdM9fhWPEhx1/eTOazjuufOwa7lIDpHDvlKJLnZSt/ElOvcuZnufhXFjxlIw9ViKH1+G1rAW3rqAgp+cT8fdT9Jz9beGxzpOEIitfYm+vip0qw/9gGwrGVOTWNwyLneQWz64Bj4YfU9VJ1M8rSX5ZV4+lx53NzkGiE+dR+lLlwOQsChQVEC+JFIR20CxxUXaaWagATQX9pE8eQpTk6bbeereV7Co04fHNwTYFY4zmQwZPh984kLnmGOOYfv27aOWXXDBBUyYMIGf/vSnVFRUkJ+fz7Jly5gxw+yZlEqleOedd7jpJvNdctasWSiKwrJlyzjzzDMB6OjooKamhptvvhmABQsWMDg4yPr165k71zS8rlu3jsHBwWEx9EWmdvdu8I0hS40ypsesuiobIoauocg6y398JCf0F3NXRz0DivlGnrbvI1b5LpLsIT8ydbjD9qGQfTbyr5yNtasf5ZkPWImZRqwoSibL6t/EnWWjbPwYnnvkl1iVPCyu05jqfJos+Vx+uGwV+qDZyyi+2sLFv7yJcc4avm3/M7+aGqZZKiFhlfh7Is3KGzahpkZcWLJF5OyfzEBQRPqfGulR9FExO0phIX6PQGe9ldvwcNM7GrsC21naKSAW9HJa9WrKaUTTJHa01JNQzcD1nEULYU2UaZPNR9+eMTM4d4ublZYdbJpxFLvjSU5ozcMmdZAU64ikQTdms2FcDrqnhvx0inEbIuzbr5pd8xFlA2u0gfyu92nISSMbBnmpU8kbMixsmA1j43VmAIwosG7AyTqA/lns8fr46bw7AOixJiEF+aFskvE42aKENW0KnIRkBp9p+uhgZkMT8QwoGLqA3vMbiDupbV5MKqzgt82humYR0TnZHN25gyyxB7lqJ/foDl6b9PfhMZ7eNfSHHZi6kX80gK3JxhOz7ibgsSGVe7DKNmw7bqV88SFuij3Xwx5I6yJ/rZ91UCyPbLXy6O9HGoZGB/p56bYb0VIpCpUo5HYyfmMuwlQbcZ8f4bhHITlIsK+TR3e/jU/aTe9UJ+eK70MhNFIH3IquSTgjxyCkYjQ72igHqsJOQowOeG4NdQBlh5h4hgyfPT5xoeN2u5k8ebTWdzqdZGdnDy+/7LLLuOGGG6iqqqKqqoobbrgBh8PBOeecA4DX6+Wiiy7iiiuuIDs7m6ysLK688kqmTJkynIVVXV3NCSecwMUXX8x9990HmOnlp5xySibjCmjpaAEfiKkEejxEfrKAo23z+VthAxNlgQnjl9E0tK1ywP9dmkF/MoBDMJsupvhod6Lss5HjK+SSH/5wuHWDw+HIZFn9L4lpYfqyi/GnYVCCLAOiC12UqDpGKErfOjeOeJyinghUw9SGDYwNKNxbOoHtmxtQUzpLLphIVoGT7rZ63n/xAwZSDjzftyOl3NiUgn8as1PgKuDFhTcRvOcbgJ85qQCNRh9iKMjS+OFcH2qifYKGbE0RTh4GQFIxH4KB/ALohYuKdZStpnj+/bYEAcMDReej6ileb32QlBYG6piSewb1fWlC4V5syVZsvW8h+/Lp9jaBF0Ciu+Jw0HXkrk706BqcsS7wphlX5zUbfbqg2reLzePmcP7epdT4nAx2jaRW5+o2GoG85CmImmnp7RB3k8VI0bva+Fge5gLO73oTNaUyYcJqmlcWEO81xxFlnSPGr8UnGixPzuGEhma+UjNS5VgiwHlfLmGfMGR50XXsrfUookHhTJ2uXeeQ7+/h7pK/0fr0XiypPBoPuwZpph1L2oYgWpk65W6slhwSDQ10XHUVzquuwOpR8b73S8664DRU70hZgf6BOC89+He8aTt52ZUw0EJffz3jIlEOP/MbZFlj8N4vmberg56LLqbngO+3d9p4hPEyM7pyUApb0Nqykdom4rIfDVOhefMiLlUfQHN00Wy3E8FGgbqThMsM3LbbB7HIMcLh3n/39s6Q4f+M/5Oosquvvpp4PM4PfvCD4YKBS5cuHa6hA/DHP/4RWZY588wzhwsGPvzww8M1dAAee+wxLr300uHsrNNOO4277rrrP34+n0WkoSQJ2ZLiNO9UGkICLmyoCqiGznvN3+Bb7VZW6TtQ+hqJeIvwqVORx69gulDLWMt7xAwr+2QniiB/rJXmv711w3+SSUcsom25wb7QmZS7YWfqEt7XVM70/AgAIZome2c7fWV+wCDH8zTwKzavaKDIiKN2doNdoSv+S8qPS1Jn9udEFO0smL8UmX+eBVdgD+BLqzQBDT1TYcwKqrfX0JoqZ1PeVBKEaMutIBQxKNRD1NXXA2PpjnaRB8iOZjpnPoyzcQZvu7bwUtUJfLNmDceHp6NUlaKnuhAbemjRNqG4JiMQpNvdh4sSTk9OQxNGAuH3p6BbZJV+CyTlFJN69+CMjRneJktYz2RhETN5D6tYxHLmDK9zunIhCnvdjxGzqeSHssnWR9f1Wl87FldkJ++nZzPJb5pi4gXlpNQ0fjFGtD9Oo16Bbp0ASY2dE88ftb+oJYlLL1DlK8Rrg87eVmYeeypbnn4VXwjUQQupQtO/1Fq4ksCe8yj2/BkxIJGIt9LQeBv9Yi4FhdMwggqJoIUspQSr14Eh2fC+98tRx8uSbbjlKfS3tSBGOgi89FWy1QTnlQMbzOPoohUSAs6rriBn/gIiYY2muM6mze9RvHssSqoT+B3NO76PLVyKtcj8wQim3GjWLk4oHoNHSXOtnqBgbhMJBOB0JlS/R4nezNKBw2hNpP53fbYyZPgP8x8ROitXrhz1WRAErrvuOq677rqP3Mdms3HnnXdy5513fuQ2WVlZPProo5/QLP+78OtmIKItq5masS+R2nQahM34iC7JyqR348i5S0i31dOdysUaPZdu0UrFvJdYs/dwatKHcZNh4QL7Hs745sUZIfMfYuHsat4Q9tC7fROkFtMlr8CmHUWD5MfDIMq2IPf5TkdZfRKnlG6gwBhgenQrDvsydHWA59d50feKFM4sJLL1GI46bDoWdxftHb8lle7H9i8IHRzZIJuukquUpzmLHO6w3E2ZIbKo8CFahWKYD7KWJrHjH1gS+QA8vWkvwcJ5yKE6cmx2Zs58Dd0QeSE2Abl8C5FtE5gwYz1hoY3dzWMZ6NsHmJmWGyoKkdT3cRHg7tLnOSY+Dn/daeQ4N4EFFEScioKBFW/cfCgndA+qLnFtpJZrN18MwGlRme3ySMd3i+REQKS0Xcf8uRskJC/DqYLNMAVVV2ktUroNV2QMobh5fSaLPYTSefQ5bOhyD+7qt3HkvEogmoUW8/PB9kVsdOdQ0TPA2MgcNsiz+GDcVG7mx8wc04uuvklezgLiH8g4eYvWPVEIwJ4dFQSjbfA3iFgHSVh0BPF8Nq/fwtk/8lI2FBTeftXVAMgOD7LVBVYbeVddhSz0Y1l/PeNsA+y68+f47HGOLk+wvLGCID6O/+mvcPn8dG+qQX3892iCjc4undeW9vM/x3iZJds5VrISUZYBYBe3YBMH0DGr0iccGpt0K7qokdcymT87p/D9bc/TLR0Gh0FT03TKyxpZneXiuXW7WTVvQkbsZPjMk8kT/C/FGAqw2RfJYvPgOA5Lmi4oRdcZFMuYn3sqqqHjmw5ZOSEU5x1IlgiibZC/SgtpSBViQSXLMUD2mEMHd2f45Olva+GIsTksX94NXthe1sKcWvhTlo9fMIhRLZAK+GF7kKdcJ6NIOueof8c+L4pqt/IAPyKZkBHCOlKpxral6ylWWjlyDvT0bMNvxEm4m0ik87FQdehJ+ErgzL/Da5fA0b+E3nu53VPF/b07uWLf7UR8Ih1SIfdIl1E6ZSMFUYnl/eNob63kb62VABiSwDeKV3D0+Oc4rXYljZESWqzvM3bXIibpJdTPXEFp8Qfs3n0YyQEr9vZGVEkk0tNGviGwkzaq9RRqrBwc9QAc6zoDd6SZ+vRWACJ6DoNaEdmiGcOUNGSsqHiEkWwqvy2Pk0u+S1KLYeg6bZ1rqLOM481aOBuNu/JShAPfB+B9YKOWZjIraLaMITpmKF0xK4+du/IQW2QcjkEmTFiNwz2OX722DtVwsWHiHMr29LN2oky9rQJFT+GVQ8ysnDjcs6xBHOQZ6ukV30QVFBIWnRUze1DlEevViyvu4uUvv8TYV19BDY4EO2v9QVovvZR9V9yE7FAZe5LA4pKRYke6KlCyPUKhDvQpxA0FwgKCPQvb7gLkPQPk+sIY+gBTOvdgI4dK9xIa6caVNwvdlc8upxm31Gpr4BpXAHSZyvEam6RxOAZkkMx4rETcTDKYE+7jGU8F/Wk1I3QyfObJCJ3/QtSBBGWYWW/Hxg7n8A7Tv68aBueEOnnH5ebHE62c/H6Efnc+3s4u5FSaqWKIpcLpnCnupC9cQa7ncXJtn1xDwQwfjd3jQbZaee0usy+S35IHXpi12wMCnBUya6Ic0fYax/Tu4g2m0TIU8/8Ep2NIAsnDchENHfvqTgzdFLbLqUQQx1IdfQeafmnGZS2AlgYbs3gBm1Jw6HR095C49ZdCL3SK5njPWhN0dwWxWSTIAX+jE195DRcKy3AOluMvmMbdWZvobx2Pd+LhoD3H2xNmM2ddD0gRRN8CUl1OZOpw5LyPr14nJuqoAKJMQ2QjVREBiJPm7yhFbsCMfVk5sAK7FCNXzB0uwvfBxgqkSh9vjFvETcGD3darBt5ASaVxyl7m+JcQ1NMIUhZh1caRqW18Y8O57HaUc13pGXSlPyAc+D5hw82eMVM5o3039u75rLTsoLK3hMJuH2F7F0ZgL5okIC78HragWfX4GzEnTXvqed1/JhuMLi41bqPX98BwHFTcomCJK6yaPpIHbkXhqPV+JlQVsKN3KsurHiGYDFJQOBGlcHTH+gPFTyrWCcmRZrmdok5ocYqum2+h/hcX44mDK+mHvGpcooX3y4P8znId/s40DfYi/KlZXDdzCmnhVg5sQy5rKqmcar6k1/FozTdo8ncjFB461d2tHnp5hgyfRTJC57+Qpk3bEYdSb2vknex2tpLdY8WZnsGxUhCXJlHvFIkbkIj7OU7tIUA/bUYuScFJwLMFTRPxC1Fsrpx/crQMnwSeQC4X3PZn4kPVugd2tsB74EqIxOwQGCoIeJa2lonSCq4SrTwYOoper0aH7OFNdT6Bmt3EU1loeg6FWb00jq9mQss2GlsL2LD1ZBYVScydv5CBtzfRMfFeuh9biy1c9i9VTj42sBi6d2DpOwIpEqOl1LSy2BNmEHpWbC+T98Wp8s5glTXGSmDl3reZVQGDdiubJ85gTu0q3rZI/GWKjQv65tIRr8WSEkh6ZdLtAmL6w01gQ1iSZqi8gIYgH0EC2DWhCN9mM/3c0xfHva+Xr7/7Et2nyORaRyr4Oh2DRMd6SaAQTOvsS7xPyQExfs8IZdhTOvO6YvxP8ma+XWy6dtspZl9A4m3VyUwhikaCMmEsefEyYArqxkXUul9jQ6gfe8IAG4SbN3DtbjMHXUlHsCUlhustANkWg7O/NJUFS75E1iTzOGrXIMte+z1549O0x01huW7nCrpdbdhkL3ZZwmfxUmDLBzwknE5UUQS3GZ9kcylELEHOfPF04moczgWQsQlWztryE/KTWUywCVxVrOLuTRN2XExWtJOwTSAtKfzAuB11Qw70WkkZKXpL3Lwy9VQmbRvEonpZ2+NjUm/jIe+HUFQDu3rIdRkyfNbICJ3/Qnp2twz/HROjbMhbTaE6mUVdZjp/OpUiENrf+8bgOU40/xQAXWdg58LhO8PizqSK/6fwBHLxDHXmNnrTqPRSlW2nKQkfaEczx7WBNf0zWRdNs8S+kudLa+n0m9+jkw0IukhpyzwaOJ2k/R0MzzQag4WAQU/Sz+s13by6fRXzp7gpwyzsaAuV/kuVk8eVTIadMINm/BIcqvZ4WtfRtRRHJI9gJRFODS4CXmVCw98oq5mI4Xfzg7okASMOBIjvu5xrJ0JMTOEuGuSU9AsEVAO7aDBY76S3WyWWMicVVnahBZeTq3nwxTq5/UsKRzfBHaeJeMIil76scqvLz83pHuJpO5ohMv6AlHFdVdi46VQQR7IInb2FtOfmU+PazRwjhqhHELU090iXAbCtYCaPFoDVmMBE7SdsfG8RWXE7C8SjcaeSBHmKhEUG9SvsmHAGgmjG9whikoqp1/Nn+XQG8TC1czfff/Qt8twDhPe+gTCwDZ+9FMMoRrM5SAkKVgNkTeG2hntHXVOrpnDr3l/hSflZH9X2J+ADZvmAWZf5iKtxbjziRiq8FTQMNnDNqmtocTQw+Wgf6+q24UiY1h9xhw292wJDxqJCWmkyBpi8tYlXJh2JTRkHQGW7yuTwJmptMjvmlMF6ED+UXr6tRcFa203j2Fymjh9DhgyfZTJC57+QLb3d+H2mQHFF4fjeIyhpixMZ6gI9mE7y5XVRdFGjLF1JIj1IVHLzNzS+Wv8iDm0yyRxQrSKQ8b//n2ATUfUUZZaTKLOAahzH27PnoeAioSXpXd7KzXqQN2ePR2cCW7KCdJeuRtILmEYj56wN8mDpHvJs+2iM5bAqPRaUsQCsqFW5IddPMNRKSW41cHBBwWGGWkIolg7i/QoOq4jP1suB1goAmy2LfWOPoDMVYVfKtAxtkxUUynHYskj6Q0h+hb7yLgZ1O43vbWUWE5i2ZwOqYD5E66igyRD5anIBYxUbFEGL2kYju7GHB4lq/fTQDw6YsTsXbHBJLMheNQB0kxJHWkKs75/L1P5BnGIhatFaZFsUWUkg6ElE3XS75KZPJ78rj7R4PA8V/AJXugc6r8KpOHEM6SFJqGBn4CKikoPdtjBTevrAA2pTHDUFoAJPIUgG4xwe9JiLFs/X0XLsCLFn6NPTNA21U/FEW3Dm7Ebrh14D9uxZiNWbT/tecLCJM/YeTvW297FHTHdYe4GbP50YZ2usnWzVg00Ax+QXiLhaSMez6dh7BHt7zTTvCm8FE7MnIvZIWLGwvOoRlvc9An6QImDoCqnKQiYmVtI7FHgMkJbhT1+24+zyUVM0FVsyQfXa5Ww7bCFH2xZQ4xwAQDEOlFiQ5++nOVxC32CMDBk+62SEzn8hnWknfoaCHFPj8Q9MJOI002AtzjDhtIenj7GSt30P21K5QA4YYNVUprclsEu7CObMIxmQsFkyrqv/CxyFfl7quRFZlfAo2czPPRU9/gKGXUaXzqZ9kUjhSjj9/Vqgli8DCQW6zniAMZ4U/gk6Z21eDkDMbmNR9V/olLIY29jEvh4vtelxSB270BUnYVcPkacSuDAFiqCIZJ1b+KGWEA/SRA6LlS08f8Ex6LEBiKtYGepEnu0lajXdWYNSNqgVvFBRwPPCrVAMFIPVSFCdvpzepz3oSSfvelyouToFWaW8lDOGL8Wfoa05n9ctH6CoGnldA1TlLgJAtvtJOJ2UJh142uqpkwSwQbaRS5foI+yyYtX6gC4qhTZcDXlkOQaoc/dSYhsJThaNKFNqHmBDuRdNa8AZ3QbeY7mgTaDS0UVCHKm2XqvYuCHXtNK0U8z0gnYa5EV0RgVcZWPRRfPfRjIZIdHYxObsYsS8PPx9kAwVcHjbYZRF85HVfkLONxn/ZD8c0D4zl9G9AFVJorGkCHvFSRiObApyHMAd+PPiFKc6GJDC/M6xmiQpsNfB1HWwC6xY6XsryLbYDra/V8fP+2bgnrYYWRBYnfM2XnUHezfPJZD9Op5kisHiNhgKRFeFOGFHBCdw5r7lVOg1GIedSDBnIvaYE4YqTH8YuzUjcDJ8fsgInf8SWrub6I8MABC2WGmxm6+kNnaQN+ZdYobCiq6ZdLtORO9N84+681kpns+PMQOVNV2lJqLROOUS8zMa6awE7pzyQx4vw6eLJ5DL2bfcTDwUMt1Yz/XilwKsLO5mfgc8VzyNyLl7+UXHAPn/j733DpCrrtf/X6dNr9t7SzbJpmx6D0noPVQBESkCFlCaWNF7LShSVFDpKkWkF0PoBEhI773uJtt7mV5P+/0xm91Evd57vz+VwJ3XXzszZ858zpnZOc+8y/M2QkSiMVjnZmncT4dDQEbi1P6LyXMZfE75FXe8+Qdu/MIdHBw9GmtfHw8Jt2CxGdzTezP+2QF2bLuQq8+7CWdcZvCFA0jufEa9+QbdnQ3ctuI2UnqK0n6Tm5ZpbEytQG3+FtamXuIFCg7AMxDGFjBRLB4GfJnhkWeu62eOzyRYEabDpvOCfyKNyli2z08yo+gC0mskoIPWDgtbJuUyK2xAK6Q8QcqEcgaVFLaUBdkUac+zAlYaAbloHL7mTiDN8lBmrEHnDBjb1UEi/9bMkNoEkICZgzKv+WvJd2ds81RFQdajgJeJ+/6CKOawZ3w91hV+vnWhiNZ/BZogMlro4Cuux5H0GPKRdFYlUAlP6gaPr5nGmGQmyqGbGuusLzNTnYCVArYKBl0brsMOw31t22bOZx7dbHPtoNjXiq1sMwCuXVeSG6shHmlF3vInmsaMJZAjAIOkxE4UXeLe0ieHPxc2wcZ9477F4K73yG89A1kQ8GguCjQDGGR+no+PfRPpM1uIySKlB3VqVytQf4Axpx9GUkxSBIHFmLpEmmqgE4D8dBdjcyooaYVz+nqJO48aqvxXM35LfJlIkq6FyJLleCcrdD4DtPc2c8LObhLSUDHpCZlcu9VMIkkBulSFcLKAjxzjODdtUMpGcpIhusRuBswP8EdANWZSnDxEV3oXoutU1uZ1cIY9jM2bjeh8Uhyp2dFyknTLA8wpOJc5UcANCwYv5cOwxsdmplB3nHMLRf6PuOVjSFvcpA1IqmvYXz8JyqCsV+cbK17mz8qJRIGZTa1snDwaW+5tSP3fBzGG6gbZNVKTpZSUUF5Swm/GvUGgfQM8/lXAjyBqjC18lXDHEiKGAwfQM9pDIpiDEhawoqGYGh8KMh8GgaAHi6GhLEzykPMWqIa1gHyixuc2HaTFuxxFq+Ul4XIuZgVvls0lZrfw7cga2D2ffmcfE1rs6MkwbvsoGvJiyDKo6lucb1/Hq8kTqD/Yz97xV3MdtxFUvWCYjBY6eMDyEO/uO5UrZj2bOSZXCQ2j0ghmmB2VQ51lkT+ztdSCmEhxq+t35KXt9IkZ81LD7OaaPS9ROWkrzc1T6KSMt6oX87ZvK97AyCBboaqanc0fYtHB4jXJndtBY+s8tHg5jpQGsTpEVzkNuUH2GWnmk0ubL5/EmEEqOm3UWDKCcZrFjWb1EhPS7DJjfHH9RLpsMabNPQ3bAYmCEyqpLS2hoes5Khoq2VfwHrsH/ej6i8iigsdVQv2Lb4yMubD6WTv9q4j2p5AUk/a1F9OrVMAsYO0s2stH5rx4EhEOvHeQPaaEQ3JTPWHUUZ/GYx3SQ+HM+dGNbGQny/FPVuh8Bmju3ENCKuXLxgNUCCOFyA49Rk+4iCZlFj3aTCBF0OJgNElOSd3LQt9uvn7KQhqfeIY98olEtS5MvZd3xzyKGT0BlxL7r180y78N2WdDviSft+69h7O+cTvxoA3Lh+3ULM5n10c9OAWJGuc85EWZkQxOMlYCH4Y1UoZORH+VwzXn4hkYxYWSwdNuDf9QSsIWj/7d1zy6Ziff6ae4aDoJUx4eG/KryEqqrKv4jeVM4lSztnIMweoiztu7EndS4KuBNuLqycz1eGkVJH4iJvjK9mbMia9j1RbQLeTwrH0048+o5tD21/jii78GvxPTXsLJ+7cAsJ/RNFrXISpxuowmnFFwKCPePxP2HSA6GCWvZg/OeCbkkFIVYrKdprxKFL8OjXB0HW1ZzlR8/vkktQhpPUlKE9gXiaLG30bRJH4w8VLm9e3nusCHxEyFhJzgnPSLxHSJ6qommqjmLRbjm3iQnEQQDpVSHDqF3ewgLiRJaXEYgPC7NszxA9DXSlp1YbfVESfFF//yemZ2mWSy6JxWTp1+LdN9q2jo6OECYH/tVjy1g+TRz6w+icbgXEoT5fSsyzg2N7x1kH3Pxzn783fSb2vEnPQipZJGP3lEcBNvPYxVVXlkxiI6nR4mpEOUyTZsSi7QjFcRiZmZdNygtYCAz4CMcwEWNUXMlHCV5xK15rHR8TEwFo2R4alHEIyhFFzq739+smQ5nsgKnc8A7b19oJRSIbSR2l9CPO4FoD9loS0Y5013PZDClARExyBOZ5pO8rBoBmXvX4xpG8MeDQLIuIG4JcJUdwe59sA/fN0s/z4El0Qg3UMg1Y3fV4QGzBpTSFmBgz1/aUMWBLbENCKGiUMymeWwMK55Gdsqz2VvJJ/CxscIF1SzqvhCwE3ElUIIp3ku2MIkSz099lz29vQx1plmUIkSfWHHMTU7hd+cPmIiCPzGM5ZH+nfzYc5p7BQmgJSJINaM30Qe/RiTRQ6uLECIddNorcOqV/H7qBvWf2H4mKxSLw3zPua8qgJsk8/EqUI63MKK3W18POpybu5/no7WIq7svRBDPchHfIBoKebIlfnZRQKKJvL5tYPASJqlRF3H4XAZscqRqd8ADnuITTkfD9/WVBtEinDFM21Iki4Qiv+Ft53wtrOEnIQDiPOD2FVMXxvg+85n+L3wJeQpKg+Lt2QUZT1YdYOLNx2kzGPHl+uk0J1Lw+BBwmKUWOkoZNWFfQBWiI2ET5mEFivk7I1xxOQe5mzfSKNZgxZ0A1tZs3oebYziNvWXVD0WoyS98Zhj0BSJ/VdeyrqGDUSre0lJ5RxqXMAz1WejygpTo6uYwkNMDB2iQrXitFRzSkEhmudcWtjCRG08qpCJZPXmBNDjIy39kjNzXtPBM7DJhcQcmQmqhiiBeWzuap9vHAD90eyPoSzHP1mh8xlgoDkwXAwwOzgPOVKObpqs63wBrcJPamYm/WRKGpaWFj5SpyFHNaIJnVtchcwMFoILIqaDI9PGRIv5918syyfC0YaCDsnNmWXXM/jCAWzAdLuEKQnM/Wo9fRgc2r8FNlmQ1Iw5nbDaQ2G0jdy2nXSc1Y8uXcXWxiqsjX2sYDYfSXNIzS/g+YE013z8CA6HjqF7+fLnvjRcs2PEtBETQWB23Rdg1fe4suUpwuEET+WPo8V/I769dg4Kcxkzbh2UNLE17sXPIS5K9GPvszHTPxFTirNTtPBw1M6ftGswsUAoI5TukR7k3EkDrBQ/hyWaSb80hLdR+3dGVyzWLdTrAZQBg4hrROgEXTNBMxFimecnVDuqLjOubs0xzzc0ha1rR4TXooYzmdYXxmYdpMyzg+/XZqJahyhFoQhPVCec8nLRwfVMGruUrZ0n02MWsKZsOkpuiOoZG0FKkyZTzmPoInu2COgJF6ZRhTM8FieZgcM763WsYhMXSy9gkcIILgNTgu9sepYjNcqGBXZen8djtq+iCgoVvZ3c8fyjLE16aCkoBoqhbTJYYXR7E6f0rcZ9KFNvU5jjodTvQFLzkAWRjaFV5AIb4ltoFIqB0/nYuo+kvhu7YWDRLXQNTsPJZmoOLyM/3M8uaTRMWoxPNBDNY6M6tV0HaWIu0ayVTpZPAVmh8xkgEUkN/72/90PSA25SehxVDZOblriobRnxQB/rq/djNUUu1hsZYwVPf5TJK2pJ2fII1oIoRzLdslmOO442FEyEQ7z3wG+RtZEWb03WOU39BnkeL7nllWib+mm7YDHsgs7Fcd4VRG5aZnBr316+6f4Oyw/MZPvCy9hT3crmXfVckn6GF+1XUzlpM9V0snHD2cfU7Gi9cfRgYvj1Shw+AN6PnERnMkHInUk3RYVclidmIuu95I07QB5DRau6xJ7XJtLWWYFVdGBKAnjgzs4QpQ0qHTNk7qAACm/A0v0T3EfyKUDaliI8pLt1cyQPVZO8An/6VKI8NHyfZOjsDZcioqHsDIA1027+0bZ5nOCIUBteQoQ4272bqK5bgWSNDEcrEu4JWIRCjDS096QoyPsJvf4opzof54zYyLn+oHQUp9HJRSW/p4lq1jAdx+hOGo1KmlrmURiRqZMsKHUPY2nrINFvJy09Qc0ZCRSnSSpcTNeG6/BN/Jj9u64knSwHQJzdyy7rDvaMqqdM7KTKtZ/nmpZgDImMRDyPkCOfm5a+ObyWjDnhSOQ1afUT9lTTmDOVuMNGvpZDJUla8yzkAp1+nd4hy4i6xMm0uWfw7OGfcbvmJTzQixP4uC7IvopOCo2MsHUZYf46ezVr217ey5+LGMnW6GQ5/skKnc8AWnzky9/f3Qu9ModrljBp3xO8P15i0LaTXRWHsaYE3KkSdpsTwFQQtIXsm1QGZFrPa0ONDDoMBFklV039Vy+X5RPiaEPBIx1ZAIlwiKW/+jmv3PWfQGZ8xGmlVzPBU0vc7MEs/zrJ+KsEPEGS2/zYUgFqxUb+cvAQo2pb2Uw9g4dmwDQYlXc5Rv/PkJVMSkN0ygiKyOALB9CDLcNrMYYGa164oJ4W3cpDXfsQYmlurMtM2l7DIixmiq80PM2cpBtp0kvIVpXwwIsABCx54PkcgY8+pqrmZFKPLIUrv0Bnm0hEqcZDkEO2Akx/kjaXh12hPvIHIG2MRBpfzGmivruBuUedowsOr0Kq+JDWohI030kwVLLmGpNClw5xsL2H11w+Jg9mimtNJYHF2kA6CpZ4C7XWMIPJQXq8s6nqs9Hrj/LV/jYmkoKhaSjh7RLfz/sx9nESs5p2YKlO8pBwS8ZaqCqzjaKr3MdL+AtmYRUE0oNNpDZUoVssyLoND32ENl1CeniYBeiWfKyyHbkxTDcueqSx1Cg70MudSO0xHKbChlk/RD7Kw0giyUXKrbiEAQbDhayw/CeGqGBNgDUBTgmSngQhq4smqglEk0TETKdUpa2MLreHYl2n2mzBQ+Y9H/AY7NUvY6xlMHOO6Ec71jaJSKGd/GgAISt0snwKyAqdzwCmNvJzq7P8XNK+qYDKLy9M4HL6yOl3cHqsG3ern8JymTn6SWzsXkpEiNFte4eAJ8xX3x7k53MWMKa4necG1rPcM+mTO6As/y1Hix7gmPERia4A2tII7j0KJ/kLoQumml/nw+kaCVNlrvYt7KtVxhrNKNE4JfSTEodmF638PcN+ctEe5JISCr85HSOmkTpoJ74CSvtN4of2sDdi588ffsgm61RM7SpsTd38R97v+INxBofGTSXt9NMuVCDGLRjAtNyTcJsFbKp8k/CADzmt8ruaOUAMqk7BtrqHuxfUgeU+AHb6p2H1JbnLuJ2DHV9lTHMn0lHOxgIdBDm2vTnhj/Ol/W109UZ458Qxw/f3UUCfsxfGvss8wFMUHH5MSWVSfAY72ZfUMDQFtCoW7oKNY+DgtgJETWfywnZGCx2c27oFocfkFf+p7AhV8cXEo0iiTCJcTkRQ8JgqTxcsIWK6Ea0qqi2PE8qvwCcclX5zgGakecu6kkHDjaQ58ITGUWjL4Zt738WWTCFrUSRXGrEpE4GJy0Xsz59Jl7GJiChSgoaL2az0zqJRLkGWndjjCqW+Hhpde6huaMNvuOktOIcnJlyEJlwGi4bOnaHzcW6c+nAzAGO7NtGbdAEyiBpmvJydkZHCb008VunM2dPO3LbH+JB61M7Ov5nNlSXL8URW6HwGMMQRoWN3fkSO9yV22HJozwtSpXlwhlooVTSMpIwgqeRpHmQNBj3t7K3YQ0WviC2l4hftzKjooLhVx1V1GEG3YHHnfoJHluV/yjHCpwaCxR0k+8P0trRz8O33mVNwLtsr32Rs89n8sqKEH9DCF/PfoLwtxY3WD7knfiGrmMGhOhfVDJnmDY0OkH028IEglIDVyk3LUsDrgJ/vKW/yna98SCfjiLZ/gXsdApL9Cbzhjwk6f0RSshAT0tiBfdp+XJ4cDvfXEbIKfL5jKUWTF1HUnU9kajE/2tbFD6MDbD74NEW6jcSYEp53XURctPH9dhNb6dX0EOLI9CV72olAHjH7SO3Q05Ns9M+1A1EmJ54cvv8l4XJeFEZcvmulffyIHwCZTqxQrJW4nhGKiEDkzxwozHw9rpyQQ0PCBLmPr1ge50gQ5oSmN7BpMl9Yew9N88cj5QpMTu/kPPU9YAkAA3KKmCvJX8wtRC25dDnSlA5u4ZzINCpsMykVQlQKSRKiSA+jcUfG01Q7fmiVKqXzH0JxZNaVCkuwAU7oPoC/s52Yo5C942dzKD0PzYxhKg7sgD7uD4xzdkKdiZEsZLB/FppUxA3m/YxpOMSP3F4e7jzMidGOzH5NgcKcVkqCpexTvsllGx6EE19iupLHUuN0sBqklGMvFXvqrkYtKMItPIsWCGSFTpbjmqzQ+ZQTPrzjGIeLZSVt7LUaQAy7YVDTE6O9fBtGQqYcMMWMb4biOpfzNvyWKz7QAR1BMnjQ9VuUVp24aEMfmM+suTfgKqj5BI4qy/9ffDWlUFOKkKew+fVMxOJk56lEJFjcOwfBHuG9yAl4hSIu9jyA/7CGMDHNruRYPK5BHI4QXYN7SRw2kSUfXm81vpISql54jQMPv0PCPIw3/iy21R5ssTRfm7CEe9vhF339/Eo+ie6hoZ8pTPbaG5kOpAoSBOKb8QEu1UaqspK+YDN9tmaCu53AeOxbFS7hKjYFX8JZ1josKu6sNTjnvWWMzl80fIxFXWUIUj27x9YyvvEdAH7REGC/x85up5sWfzsAo4UO1MB+pP1JakoHabPl0ZuTA4Bsiqx1tiCMqkPQNCRE/N0hNGUhbksYWX9seOL4C4wIqiPYDQP/4Q4Wvhdh1YkzKd43SLA+YyTYSRkFtQMoyRQD3TkUmu14E14ClhI6pAAVgLN6P0JlAzbAHVnJwX0LSWkygm7F1j2LjjU3H/N6gpQiWDeTnuKzMBABA1d47PDjpjFI5xqRsRenkRQTaCNa/DRwH4VGNxMi+1FsRXxgm0xkexUhKU5NG/jCFmKOIoTxHtaMLWfu/EL63t7Cqfv2g8tATB7ro9Pgf5OK4A1s8fdw2uA6xh41ViJLluONrND5lBPcd2wnyWXhCGPTmV+ANk3m6biFih2lAAiygaaWoBsqKk4aRtXhdKU53bmbVkcF5wvf4ER289zMC/hJjiMrcj4jpPQ4yALTO5xk2upOxjx5AVWhu3DrmS6d9wqmYF3Xx1LO4G3xJO6c/zOCxhqCzZlC4h3bL+L667+Db1w1E398DWbLNrQ/PDHsq1OwuYkSSqhRNWoMdXjo596AncNaNVNNgXFHDdlUNYV9fxmPGpORbT6iRVUopsZPhKGCZ9/5SNs0WKiBDbod0F7tpyB6pCBaQI2/DUAsrXIkeyus9jAr2sZUOcqd1wokTCXjlHxkoY2QECxcOuWn4IbJVjeC5kWxyETSBhsI4LaBLNjwKTrn7rseW3w/KbkKNf4WKyf3c2mDjb7RMDvZzPcK8pgot9Jjn8C09RvZZ+bx0eYrkearPCTeAh7AA9b8JPdwMznGADsa5rEjNINJJLHvPxVaT8G0xEiPfYmq6mXD50irfAsDJ+LeJRSnK0k7uojP/QOjd96EbahFvMOMsEJeS0lPH47IAEmPTsAuIikmbdsWYqpuRHs91MOb4Us4IZ1xZN7pmo7DV4q1788Ea76AKGf2p6Pz3KJLeTrPpCN2gE67DhYQzRFjQYCyWKYuUBVFAvHW/5ePZZYs/zayQudTTqKvjaM9RAaS1ayWi9mZrGRrcgzz6WJMZQFjlMnsi3lIx7ewtv0dEjlnUONsZnH5NuKClV9YLmdPqJpce4oOWyG2fP9//aJZPlXE9QjRhSqi5mP90kOEvRs4X17COssYSm39zAIu7NrA1gtmUdvcSGuXlze2n0VdopkzL5jNQOhXIESJx+P4fL5MKosyNNk2/BoTLL9kuSmy0pzKleY6NsbrEcJposUHSAqNfL9/LF4zzo05rXibT0MZ/R5yXimjHRG2WSy4hDSX9f6F8bMvpqDZydaiAA92WxBUA2xw8v7NBNVctiiHAXC5ZqJrAjmCl5zAIbZVZtYiWjRwaygRmWRa5iquJZ4qwRwq4D3iluzYEUCbJyNPfA4TSAGSLmPdvIRJOYvJMz1APgtNk/f1MMlUEemIlco+gbpNQfIHSyip1qEAbldeYryZcV6OC1ZOid/L4VgBXlcAD0EWJz7iT44vsavpBE6seg0z5kV2DfKyuQ5/PI9kJMy0xlaad9WPpM+GMEUBzbGBBm01diHOeOcgqx3vk2eMQkEmbPSgHGykd0jpGXELQu1ooIE+00UsmUOfnOlgiwje4f2qjQruJKSB5aOfpqZ/IYLiYn1FE8H8K/mPHXfwDdOOSUbgmJJ6zLoEo3L474Fsj3mW45ys0PmU0xuWUI/qwthENe8GLwDAlAQEvYeknkuROI39qopoWcVgzoWIeoq/WCdwb+pUAqabzkQepiTw3qRFyLqGT/pbN9Qsnz6O+O+8+YdfDt/nTRRCCXjUfvqjXez1KgTcvQgOiYaq0Vi7+nh/4jl8ZIcTrSOty/1Dk7IdDgc+X/kxBoLhed/FseKn/C59LnvMamgEa2MfqnQa0flXELTLtBkpNgz8gmJCTATslR00R3JZFB/NWqEDyYihbksw1u9hyyE3OEc6/z4YN4NF+3sYO1jCRksjEx2F5Nk/D4CRn+ZQ12uYwL5xU6je68HiiuNOiUQML4XmITSfnffLl+AProI+iCTcPNhwK3ONJvo9aUoSEaaM/ghZSbK+dxmjJyTobRrHQucCchNuOoaMgC9fWYCESeEh2Ds4CkZF2dTlpd9UEcqs5BoB3FonxQdkTI+FtjG1NA+0ggMshZkU2Lixq4l0V3Owo575cmVGVI2bT7WR5oX47+lToshimAUpKwf6LGieHAyLDcOSIBE6TPnkNUAmOubuKMbXOJb8PCd2TSGl2NgrZsTSiekJyKlyDtoEXgHUo+psEs6R/++go5eYmKRw7lg+XzaPn3VrHLKU0W+RyU0lwQKSfKwDcgqVIzI3EM92aGY5vskKnU858bSBfpTJxQS6OXHDs7QXFHDfVV/AtS5Gp3c+R5pTIpIFa+8f2TEqwHLnuVzc24fP6yXl7cIx6U3cDadxIB6mePwXP6EjyvLP5Gj/nUB3nPceX4WobQWgrcTJ6hKRp63FKEWdXP7mQ5iFxbzCXE5obODjaRNoawuTYwVZknn11VcBUBSFG2+8EetRBoKeotEA/PK0cXS8K+M6oZTlrTt5vMXGwzt/TFuOnZ/Xfp/8gJ2l0UU4zF6E6jAFdKDxIdN1idbOuVT1bQH/WXhK+yHoHt5/0OFGlPpxDHnAHDLaWd+/jRJLPWO8tVhEgaSpE7acw44pmefM6wQNnd97VCJBE0swSFCcB5ZMPc+mkmls9MwBYJR+kCl8BEBYG6THahJ0ZC7gUec4BDUEyBw4qkwnastB1KPcN2NkPhgUIxgvct7h2QgJC3/IL2OVfx4Ar9kvYj4f85fW07ktsoyD1KNZHiRCmoPSPKbHT6W6aBqilqZVXkPVhzq9ZQX4++NApo27/fBkCoQBHIkIiuyjrvJ2JpaOFFn3C2HWK6toohqvZyP7XDHEMU3Adcd8LuoHB4G84dt7FB/vrbUC3dgkgdiUa4iYLzA6nXE+Tut/bSB6lGhKDpAly/FMVuh8ygkGgmAZGbxpKCI7p3lZURcDu0y+o5hzQ7lopokqpEnEE6hKkD3l3SxueZ8JnEOTpQmb3McU+062qmXkGEU4HI7/+kWzfKo40pHl8CWRbftg6Mf5pK5zqGhPUOp6gN/UJPlSwVLGxA3WUcuCtR/w8bQJHNrdSc508ATrOPfzpxFPh3n11VeJx+McXbVxONYBFgVD2E6B3UrB6iZmayKPSzbk4ELilgQYKe4d820AfshsIOOG/IvDr1BQ8yqjSqbicdXST5hkazMMWRx0UkYKCz12NxFL5lV32bugvIhuetlp9lMgpajd/yj+WJi0BBYd/nBmCfV9V3LfnkdIKWF+ce5XMBtHUljjAm9z9gGZ11xRNo2becw529h4JilrMecBVU0vE5JtpEvOodEIoSpgT8ZwxTby7dfGYtEyF3q7MwCze/heQR7rpUIsiXymbNpCt6uAjnljkDpUmkqrqXPn8kr+bPSuJC94JhESXEzQO6nq3c00o4iZuFGiC2EWnPZX76VmpHm7/ffEZQt+SyFjRQvr+peRnDOI0zA5EBwz0kp+VH2wZGjUDh4evl2trAdG2u/PGdjEzIEtxGSFX1UsoGWLSVqTiAiZuE1QOmpgGCAa+si+0xGyZDmeyQqdTznRSBQtdyR1tbu4hjdmXQ2ARddJhbfyPvvJo5vRB3vorRgxKCsur2JsPEWnpY/Z48pQgfNOPJuCUXPw+Xz/3gPJ8i/HnWPjgm/O492fZSIXDnUjmjpAKFHPPRsaeW/+GMj5GKd0mJA985ka8AzQRDVBBLyOXByeTPSgv78ffTBjKFc1KPPY8odBKiK880niFSZL27s4EQslsbvR9VrGhKLkdt2BLo7U9WiWCqK5X2adbuc8YLNyiJg1CEAw1wkqWMMqD3MDAM8VF/BKmcDndu7FYoTwNRwm138ibb4eDFEkUqszEBKZuitjbOiIZTqFxle3kic2cdL+r/JF6Q7ShnRMgfKCpMTFkUpwg8MRQi/UqHKuol4YS9Co5nCexkD6IAQPIgMWQWZ8zvnsjm1ksPCi4UJeDx1MTd8GwM3Ox6ixZOpaglIRF+tP0l5WzQ+4D8oBTsWal+QW7mXsULh1f/VW8uhH0xXWrb2CG5JvgZkRFClsbFNOYZq6GHf5bAyiWIZa0ryVuUzotiIrrXTUbUCTTucG835Wpxdz557HsZkpctQQZaletipOAD5wSZyeGWWFbJqcVfE+SkUmavNb3mGneCnJhA09mYmqDcojM7EAJNWJofWgpCGZsP8vP4lZsvx7yQqdTzkmJro48jZO7DrMavkHyIbI+KiIGu6nbm0EbyJNUoHBMaMoiGTe+EnjlxKSTaYBKiDoFopyR+HJipzPLMWjyzn/hz8k+ofDzCk4d/h+oyRNg/NRXsgbT9T6Kq+USyj9HTxTdA5mcgnKAoP5B/dSN2MsTmeKd997HFs0yWJZ5GtLR2o0dIvM16+DwKn/SfE7/8FMcT91S06jxlnImIHPI67/Dvepl6CWR9lEplsn7st8fveMKkRKF3PF4QgfiR0og2NQ9wSPiRyZkkAkVybinMkpB/qw6CMX2RLH5ymV08T5Gbok8fmPTTbNgK71fqLRKKZsYjm9n9e3zOaZGaejIh9TnJyer2Q6w8Yd2ePHdOoyZt9cJsR8HFRTGDLoyQSd0UyNzEejniXg0RjTXUh935X8xqxDMAf5XsFIWshmGLyx9Qpk0UVaEYnKFn4sXs3+sdO4W/jh8HayrvJo983YSnq4tug37KlxYUgjbd3exOsYa+ay2Jh+1BlJMl37MykhQUwRSaTmgBtKaKdAC1Ae38cdxmU0lVXjNzvo0TcAUc4OJ4anvXw5EOSqqhJsSQFrWhpa80qupQbTmAZAxH7s8E45volEZDfjoxIDExzsHHJIzlFkymwWsmQ5nsgKnc8YLjXFpYetiA1p9szuAZysWVDOmZbNvGk/k2l74zTbkki51zJl2lyEcJBXX32V8048G2VZEtv44v/2NbJ8uvHVlOL6Vi7RzhhvPLgSW2I9cwrOZW7fV5nYk+SN4o9prXwTZU8HUhJkUSO6oJT+hsOEijuZMfN1DGNoXMBEMMMy+/Yuwt6fZs76DXgSElH3KAzZzgM8BO9lZlGNl2x06zn8kT+zt1vhwqpMfYyHJlRD4oveJwAw8jMGvnWRFSTXzIPZezAllR2xSSzddSallTs4e3cRhUMGgo1DoZmk9gqb+yTGA62LzuTPRbuY1wkPLBHxRDKzvpJ5b5Awcviy9BbXzryTyw7fC6FMcfJ7g2cwKt2HmjaQBiRUi5MpE5cztngqO2IW/E27sahdDMpuuvSDWHEzq3kBYU8XeQEDZFjcUMrJDYMki6rotvaju7t4rsDNuz0i1f0mqt/G9IK9WBJ9XLblZeJ5LpaefhaF7e30lJWxyTGJE+hhZ1kR6biFt7pPpUvLI1eP8SvzN+yY+wMs+khaOaGnWbPrfKzWBLKUpLc3d7j0xogHOa+smIS4CliVmTqmgGBI7DFcjNEzETkJsCUFvrhuIpI8EvFtlmHRvtfRKmWinmOFjsdbhys9jVjsfdYUl/Pg5oMA2EWRVbPHZcVOluOKrND5FNMV7aLfnvyb+z27DARZJCFpOFUo6kszfnKCtyU3L887ke21ZXyu6zDWQT99XSqxaC5yuBwlOfgJHEWWTwLZZ8PnszH/+nF89MvMkEhPcjchZSILQz6eAQpjAzhLYjT2jEEMpNle+RcSzQfJs+mMKfoh3ooZxGOH2LP3Nk5f/HksrQLR9RsA2NLXRveJ9/PIhwdQdYMaI8SjPMGN6n8Q1UUuG2VB5HUAlokWVg3UYhclzncnkPdVI1mTjBu3hnalhIm7Z9CiJ7AqmQu8IBmst22lsGUXowtHDAStUjexeKaF2iMX0pm7GzrhPDGESwoCHkxRJ2YzWO0WwCny9KjP8cWt7wGQo/RSl5tZP5UQC2TmwCU9PcwUK6B+EoI6B2s8h5WtT5EkjhJcRW4QFMOH4e2h7MAA+UEdaASgz2/jua+YPFsvkGnmTnOkYHlc+3hmJ4K8PbCIiDOz7lBbJbpbwmaPYSPGZe4X0XSJvbuvJBrKZ8KuTuyWTJGVqoisqajCak0wY9ZriIJJE9XA+ai6QupQjESJSFHnQu4w3qU7YWdbbxGOlIQrqaPyNoZgEpTczOwcT6BqDJowUouTNiW0zRt4VziFqHTsTKs5ztnYjCo0fyXptpe4ekYKo6iWG/e1MqhqWaGT5bgiK3Q+pXRFuzhv6Xkkxic4q/XEYx6bNWk3VkHlc30pAgUab3jHAxtw6Rr5g1vIjXqx9XTwh2efBDLusOl3O7EqDkRn9iPxf4kjNTcAluge8E/E27+J3JAJYh8+KQ5iLZZdAf7AF5DROFt/l9zAO1x5TTHkZJ6vRRsQopk0S3m/wNsbHgQg7oUBr0BAsLCmy4Zf2EPCzOfFgzZmSRM5aD3M4byvDc8q/6WZ5lLjLYpimQLfXn87y6IJZEGk0pwPQ4Mno2ICu3qs58xqYz5F4l4AnLoDi57pRnzY76PeHOSrQ9t1O3OI9McYs/Ueyhnxh9nJFN5UL+bElk24CTO5bAW6LiFNfGF4opauS2zfdD7VpRNoaduIMORLnhaDEPkzmypteEffSm3Th3iireQHApT3wYmtLvaPnsR4dQ1jh1JbeytH05jKZ9KWbXS7CojPc/OeMJnNgz+lqK+Z+fmbsAzaGDt2HRbrQV50nULMYqPaaiNt+AkZdprao+Q4BhAFk4cDX6W1rRAmwwOp2ygv2wIGzNO6aQjMIx08hYLYiyiOMxHcOYiGyqrKZ1kY+DYz7O30+tupihZhGD5EMcDWPIN9s8voN0QCcvCYc70htQZHxM1Mdy65msEja27lOxc////yEcyS5V9O9qr2KSWQCpDQEpx1qAqUYx/r7L4DU9SJL3qEMU0DfD/6AnHTSkx3UJWIsmRXjPU2k8XpCVScOh53iR+vy4PolIfM4LL8X6SwN8EhP0zbE2H2Vp3nl6zhgCxgG91DxP9tzLgJuwK8uOBqZLuA5cnHcRgtjLtEoL3/bqQk5FsUvr5sxEDOsCg8ed40VozZxVeLCoBVQGZ0wpPtXXh3SnxbuYHtNi89lQeI5H2NVcmxLLFsHN6H3VBIiCrBjnXgnwowLGKO4JS9OLRp2PIqSMY9CHoO3277GvuBm1uvQ0yuI+LaiU1LsGZ8PZZUPq4AGATB+iEAq4XFmBYLT9TWYNE17hU3I2xaiCEm6fOkEPVMlEmwJKBKRwgX0GV14o8GsMVVZMcCtPh7xBWVnXXngakzY/sjyEYQb2eIKqGPHWMsnBHOiKsHLA8yfkhobbHUcTaPEDbtRBoVWvCzPjqNvLokPzO3MG7cGvrJ49s8QEo49n9UMVRyaKC1rZAuKTPawt3SwDWp33NXrp08o5V6Q2KjeGbmXDleZpfVgl8RmYJC7Rl3Isppyv7q8zAlVY60R6Ej/beGgL3pELla5n4DiIhOwunw32yXJcvxQFbofIrxxHOwpYpJWI+dQ3PXiVO50fJz/LY4t8W+RiTlIWI6uTDZTFVuHbZERhn5JRcVU0dlxU0WAOSLzoXdYE6JYlsNVyVbqO02uNZtsj2/gT5XpkpXNAxUxcb0miuZOM+Pau1HN8NQI6L9pp2u5vsBFblbwP8k1HenOLf+GyhimKQMhz0a97c+wZoxX+HifQ9xS/oF0mI/F6qZmp0HLb9lv5gxvRwtjGGHmInupI2j3XmPFTqTchYOuRkXQvEENNNkcygJpkZHyA+cRduMs5jfnuKE4luYZs0MqUiZI/u54I23eO2cs1i4ax0fT5pLu1zCpLG78R6YR5ejAXFwJPrV3Dmd5lwLk8O7kTEJ0EI6vgrhqNEUAOtrvDgSUX67ZCSddaM9Ywdxa0E+FtOkTNO4PhLEpidJ1uccc1y9msnTh+7nwGiVqjaTVIWNhe27ObHkzyiCSgQv9wvfyhQ1Tx56kpEi5n2Ru3Q7dsPgw+Iw64Vq5gzZCuTtHuQrPWkAgmMLiJ+QJv3eBHbnVeJOSQRlKA4X0+tvJ8dowhE71iwQwHLUr6uYaUFL19LWl4LEX/vtZMnyyZMVOp9SulsPINZ8lXfHWlmwp+moRwwGvCYn7NnL7uCXuD4xB91Q2RUO4ifOZuUwDl1BkWTKvjQtK3KyDGOtGY25s4VtahGLCNDeXs34ur3c8r7Mx73P0DWjghe4AEPMiINmp4iwOgjI+FQ/xbpA+TdPxlYyiWUP/JCTLjoT/dnfM2PVdli1HQBNknA+cQ8A7opJsA/2qVfTO8qHEV8PCY02ZzGvFLk5l2oery9l4cYoTjVFv3ek/0qzGqjSyNfXquA7KOmMEHLKXmb6T8EqpBi78yms6TCmKBG35bN3/NXEDA/tmsCfHGP4XnolAIqp8lbuTKzr+tjAaKzr+nhEvIqvT3kc99jleAF3TibB5rZHiOSsowoY8NjQk4XkNMdJJPtGTqaYg2KbjRp/m5MPjOGa5XsQjUz9S8AFv7jEpHPIqbjZorDZZvDq1quRxUwxsG6IXCN8l/6pFayozbSvd1Vkdv1x2UQO6Tdwz85f8OP4xTzm/A65QoJ2RL6bn8f3+lqZGcvMMLMZJl/31nNCYN7w0vwJD+2zz0Cy+gl62/HyAjF5HLGog/pYBf1ijFjMQ0xJkJfqJy/UDxw768qQLaTkBFGS7NeL6O2ayi9f7MYqCfTV14A768OV5fghK3Q+hSSTneiD3+NHSuaLvWlCNXAypiYipAUeWfsO44wuPg4cIphsQ40MYnEsoVMvokTt5KzzT6OganLWKycLdo8HyZL5db76ud+RSg+QthQRcUXZ5B7LXL0XhzOBqOcQ2VlACf0EAhJhw8d/VMqYigh2Gbuu8tLqFPkxDYtcQKLfjq14As3/oTIq72dAEcE9e5B/+zuiqzdSrZqIHCYxqPC8YLJmXzlQjvVwDzcu+B7YZTYwB1HSMfO34AyHGdTyONITrQsq7y9cwqSGFAIwUJh7zHG1mOsoYTR7SwspT1qpbtiKMGRy91pyCkJaQNd12iw5lAuDjPa/gyh/gCm4WJyw8EzVxQzulvj11huG91lo7+an8+6mtm7V8H2GLrNn7yKmKFMIFm2gtd2KMzCByEAz+lAaaVRLCFc6M1hXUaOUDQZ44DGd8FBnfHOByCPniPxSq8Vrzefb8Vep0jUWpTexYP1TOCwp+kUfg4IXjxAjJx3Co0XZM3gKbqtGX/8peMVOxuS+hOKLoSdV+oxc+mWBRwoF5u2byZwBF1uGAlK7Jl6PZrWRir+GXYvgBQJ6N8g1CNM8vNn+CrONjDCym3+VFx8i6bMzIB/iZbOJUiHGJNteJs84gWeWDxCOq3/3OVmyfFJkhc6nkMa2jYiCyr7ds0lGXATtZTAVulZNpiixj7Nd9xE3rQyEQ1jicUDGZkokZAmfYlAxrhabzfdJH0aW4wBPXgFLbvs+sT+1MPuUy9mw/FlkxynsnpmDz4Rn+s7kpIpv8jVpDYryMXHZwin77yNBRjSYkoA2N5+EUyGopNF645iqit9SSLitBz0H4jlhPL5S/N5KAg9LFD/2AncD8Geayef7yp9p/JbAU/1r6O69mM/vfZwdjgrKxkZ4RLyZcaM3UE0TicAc2DQDALclxezB91iTCxZTZmPeWOau+BDZcRpWC/T529FFnabRtVgaD1MNmGYUUU8xKpiZkUUc3pcWc3nerdyXWH/Mebn+0LvcVHsDb+aeyVnaX7CHk7xx4GRW7zqLPCWI6dQwIw7GTfiYSZM+IMEHWIHaGhD09eSt+CkruzP+QrvHLkEYEj2YJrZ0AsWQKVObiKmNVPVuAUT6rY1EtEa+7crnqXB3xtRQAzSIm1a+qt7CgOkBIFcI84j7fm4SUsPBlh1mRjn9uigjTnJDJv4egfnr16MZBTD+IgA02SSYvwdTHIvgGgD2Ecz1IkdEpk6bR3clxF/YAU6JXKUMU+gZPi/pIaXpD0RwRCfT52/HKRlMHbDiTWW7NrMcn2SFzqeQ9Vs34M7Po1kaQ8LqJWDLuJdqcRmHpHN26mdEDTs364MUuJ0oog2XxcLPLQ7KUjZstpJP+AiyHE+4SwuIK21491k4rfRqAAw9Tcu2l9k5+nOE1pSzPyHw1lkTOdf/Ib+U7uKDvOn0ef0s3XUmxtAcpG05g8Tf2oMDC/WjrsG+uhNjvkR7/91kTFxA/okVl+1b/Hz3b1FNldJ+k5uW6TzT8TQPat14rRs41z6eXuch9veWQxEU7pPZL8xHKDkSzpEyxn5HMUOX6bTPIO7oxBcTOZJEGhMfxG0LEXGVI4yeSbm5gpgpIidT5DT1s3f81dzJ+RS5O/AlevjYlos9OosHjN9hcTiYKDTTqkwh4MqkYjSrj8mJPNpK3qOtZzZ/WnsdUxwt1KlluE0bkrOf1KRn0e39nOAtY0VMOaZmByCNjNV7NQfEhYj6HAqjjUCMm17XqekBXRD47iWFnLnNzoCtAN2l8bmKbdxtv4/AUcN2G0yJ13tPIuqQcRgxBtUAEOXSDyTmHUzhictokgiEiNvtmEO+OTKDmKLA4vQEXOkBQrzFiekJ2JMVRNatw28Ps8aVSzFBlMKpBCwDQEfm1IuZ98CVCmNqI+ksAzt6KAyMzCfLkuV44Z8udO666y5effVV9u/fj91uZ968edx9992MHTt2eBvTNPnxj3/MY489RiAQYPbs2Tz44INMmDAynCWVSnH77bfz3HPPkUgkOPnkk3nooYcoKxvpDQgEAtx00028/nrGj2PJkiX89re//cynZGKHO/lp/gOkxo/U19g0k4A3imoKzDi8j4mt3bjdE5GNtezx52BwOoe8Vk4zrf9gz1n+LyL7bBR+czr9B5t467f3ceYVt8BHQQbPnAsN8OICkWvfMVmTt5dnioqxGSa3f9iO44yRLhvFTPPLceXDt20YPLfJweh196ArmdqWlLuT7omPkVtczF2TnqYrNsjmxx8GdpA3KPBh9RKmHnqH8nQ1dbV1LDfbAMhJJGgwvUCmKHlb50JC3aPwSCbT42U021Yxbtwa5pefgS1SxWFnMwc4BEaIWKyOmLWOrikgmhLzNt2PLRUAUSbiyJhjnivsYHYkY3h3YayJLyTPQlVEHtp/5/DxxEUrp3Av0dIgDZ7dgIXKGaux7p5COFTAZhJAAqclyDTgA8tuPIGtuGQvdsnNpJyFrO9dRkSLA0lS4RcRpDwU+1z2jLkIeJpDM06go/0wRb0dnLZCRCeFL9SG2QMPiwU8PV9BF49tPKCs8Zib1rTJov0pZD2XVWO9GEdvH38bEPBYDxJ3yXT3L2eUJeMuaN3yEtLhJKmlg4wG5o0aS9P0elZY9pB2p4d30e3Jp4lqAp5C4lYbEasdrxbD+KtlZclyPPFPFzorV67kxhtvZObMmWiaxh133MFpp53G3r17cTozc1buuecefvWrX/Hkk08yZswY7rzzTk499VQOHDiA2535RXDLLbewbNkynn/+eXJzc/nmN7/JOeecw5YtW5CkjE355ZdfTnt7O++8k5lG/OUvf5kvfvGLLFu27J99WMcVacVOSrAxpfNJmoVG5nXWcUP0PF41QvQ4S/AkOyhMxGksKKTXNQ4luguLNZNqEL2eT3j1WY5HZJ8NIU8hkO4hrPbjRKaubhJtB1oo7/IDLcxvz+XL4kG+V5DHO6MnMLhzDCVCP81RPxN79zMqZxcOWxLTLvMn4VqWz1zKKSXfRE9Y2P/UsyymEoAPn/k5aszFgmu+hmTLw7RauWlZCtgA+LlZWc+t11kxjPGQ0HATY6ykcoCMO+/6nAmsc2VajBQJrjp0iHHA+r5luMvLKOrJ1JfYjDak1DYAUnqcuJ7g7Rk1lMSCzN5+aPjYDwsukkIZpiyzON1MWlB4dvtiqpPdBJ0ubF6N06q34hcivCRczotCptjFKiW5Z/LNjE3G0dTMjw6TTMGxKQr48uvx6XYcgg2L7GN+wflsGVhOb6oVwQxjamHSkcOYmomsKzxevw7q4b/6WramTb7zqo4nbmIIcPfFEov2eNCdFZywaQerp1azo7SVjllX0Z8ox4g+l/HMGUqbicCcPIPgyXdSImXESxgQTAXz3Ev5YEcrAclFQgqTSmyhqHE3hiKTPyqBqMfASLGm5GTWcDKcMPS50au5fusykskk2WhOluOVf7rQOSI6jvDEE09QUFDAli1bWLhwIaZpcv/993PHHXdw4YUXAvDUU09RWFjIs88+y1e+8hVCoRB/+MMf+NOf/sQpp5wCwDPPPEN5eTnLly/n9NNPZ9++fbzzzjusX7+e2bMzk5Aff/xx5s6dy4EDB46JIH3WkIWMYCyNx1nUM5GSwWqKLCZXJCNs0YoIea/Gpj2EJDtwCvWk2YUmqqhSiuKK7IiHLH8fu8eDbLWy5oVnOK30ana+/BwONYhkZDqBxjSoVMhQbZh8K/Uy4+0p4qaVU3bdyz4K2XeoEFMSMOb4URwqD4o38mBnEkhiX3I2E9a3I2Cl8uRMR1D3wK2MXyyRWvwzREOmt3sPpasexrbagyepoQRqiTbtZUtuDsHSvZQpcWRRhV2B4TWbksBAQUa8Dxgh0tvdVJZlalUm5Swkz595TNfTvNXxe3L7BumucB5z3Im9CqmIjjBlkC67hIDBf064bvjxCUITp7GV0UIHtrb3iORWk5c6yEf+s3m/4xzGaW2MDqXoyDEYV7cWyAwHbSk4QAugqZnhmBczl9mVZ0BCoi/RxobBNSi2OTjjb3PR+rNIWUeirSlFR1UE/BYBu6aBOkg6GSOR70SMRajfs5EpTRK7qsKU9u+ipgceLW6lK9dKS3M5DiGT4lKT6/FaCpmTv4Qd8cO8VJLiVCnN/v3zUTqnI6oSakJiT9wNTMAn2vGLHuAUTFeYnOL1aLTxpb4+fte+j8WlARaUvUn/2hra5XN5eXY5cdnKhqqdnGzOA9y0JlPZ2VdZjiv+5TU6oVDGUzQnJ/Oroqmpie7ubk477bThbaxWK4sWLWLt2rV85StfYcuWLaiqesw2JSUlTJw4kbVr13L66aezbt06vF7vsMgBmDNnDl6vl7Vr1/5doZNKpUilRgYQhsOfPoOrzp19FEqTADg1voAF0WqwgGaauPUoupBEEDNf7utLniZitzH1gMj7Y55iyWg3k2ru+CSXn+U4xpNXwDW/eph4ZwDtxb7hoZ+GYBBvPsi45l5o9nE3OppUwIcLzuCk4seZ7XmJfZWzORCbD4cTmCpo4ki3jqQbJCSR3Y486lzPIWkdHHrxSSZePJ+B9G9YvvVjYtFcvISZ49BpH3reA5YHqTJFTvdehCi08kW7hZK6PzK6p56FA1P4izPI9mgxKSPzNbb0tMu5vm85ZLJQvKe+jyeQxO1ayHR7PlbJQUlZB4dtQy3cQ+UuEw+BOyqQbPNxzXUC1wmPs9c1nVCylFdCc/DGI8Qt1kxxcD/QP1QcfNBKFz56PQLOio+JHcphsM6PVw8dU0Ok6zJ79y4iFD9IruEBG+Ta3DhDLlS5EJCxh7Zz7AzwTB2PIHowTRNBEHAACQdoUoigp58rV450NwU8Agsb56M15yLTClQBoJbUE7WVIesp3nQ7UcNdnArE414ajc2MagkgHWV9IwkK84uuJZnuQdu7kp7Ro3DrFt5Mnw6hfPK8SappwhV0kbKOjJ/RRQPBzIibXzR1c9dAph4oO/sqy/HAv1TomKbJbbfdxoIFC5g4cSIA3d0Zo67CwsJjti0sLKSlpWV4G4vFgt/v/5ttjjy/u7ubgoKCv3nNgoKC4W3+mrvuuosf//jH//8O6hOmu6UbcejXWrRlFbE1fybuKGTbuKs5xR5it+im2kijqFGm7XcSdoiIok64/HLGHfLgn1n3CR9BluMZT14BnrwCtIpKDq/dx86Xn2NOwbl47nqUAa2b37//C9JiGzct04n3bmFvpcI56weINMWJWpppdxcyZ/NmCjz9FI9roHFwKu9Wn4pipPlhvR1iJjatmJd9N+FcfZiBWXDKgvm4PJNIaDov/j7EPDJRkd0Tv8b4rb/B7ljAzvwJ/ACQSpOMUl/En27DbwGiIxFKC5C2WtBNE8kUibt8xF3QzUGazEYKZIntzpPJHZCJuIL0l58EwM7xi7DE9jKupY/ckEinvZu5qXcQBZPFvtdpEyq5IX0TF8RX0ue3s6ViHr9su5enLZneMZJAE8RlGycnH+cL254lz7DQVxFGDxYwbnymMyvKBxyx3hN0C6dJP2dfJEpCPgOLkQTTJG3EaY7uRjAjpCKvI4p2FNcSjrY/V61etky5HtNIHPPeWU2wqgA5CEOFx6fJfmRHnLctbzBVMHA4hoZZGAajW2IIpkAsv5rx3rkMRg4iB1ppSLfhS6TJi3cDo8iVQ5QKYQ4LKgfiKTL+yiNFOVYjzeREjIJkCxMEk29UlFAyegwNsWR29lWW44J/qdD5+te/zs6dO1m9evXfPCYIx1avHfnV8o/4623+3vb/aD/f+973uO2224Zvh8NhysvL/+62xyvNB1aDkgm97yzSCZ/hQ9ZzkPrhV94ldJohvrDhJ9i0QQoX6+THTqLFUkVjwXTE5r7sLKss/yNknw2lxEpYzRQAf/haJyEdxsrf4uOCHwN9PDanj6aiYqznRZm8O80PDjzPoxPPJCEotITy2b6+hj7RC0UaN9vuwaWH6Lfcxu8oZlViLecqGZGy7k+Po0Z8nPXdnxBlKC3bb2IEdPZG7Fyw50lcbfMoHh3hFdvlzK/aiatqKdWBuXzUPwZx6KJ78v7NNEXLabNupdDwElfD2Po6ENwz6PK1Y4gitv48BCmHDVNqEE0FUU+Tcswl5ZjLpnyY1wmqmOI/ptxF1JpJkdkLmljavpbioTq3y9vW87lxv0I1ZJSdAbRRTqaH3+Ln4fdwxaK8WzyLqwqfojrqYld8NNs2n4usJHEYViQEFEeQqvEfErUMMtkyJqPQAN00CAkxLJ0baXBDcV8D3TkeUqEnQbAARwZuGphGiP9J/W/XrF9i86czpT9Hnq1JVHUH6B4zkVXFY7AaGqP645RYnTh9dRSqVnwehe5+AYkYsqgyLXcvZ+YGaI5minNUYaT7q0Jr5yf9mc6yG62gvWFl4Nq3wZ39UZXl+OBfdtX7xje+weuvv87HH398TKdUUVERkInIFBeP/Brr7e0djvIUFRWRTqcJBALHRHV6e3uZN2/e8DY9PSP+Dkfo6+v7m2jREaxWK1brp7vrKNAewVmduRj4tGlYe8YDkMakVc/BZZNp+orCH7zfY0vJ5OHnybpGwZmjs07IWf7HHD3w89QvTSComSx/Yi8nHKgE+rhovZPC2g4eyvXxOc9zVCxI8zO24tcNinUd1ZBYlP41sUABW+LzGZ+/ib3dDTC2mA9r4hQnTaJUU3vJNex86DlSsSgpqxXDauGmZWlY9hrgZ47SzgvXLWXb1m9hNXvRSqy8oZ1DTmWmiXyUnvl//2DcDBYP7iB52MMsbTR5eCAfesxBltEOfzWeAWQmeCoJ9dop6mnGnhpkzbw5WNKLmdlto8AWZVWOjU5F5lr9qwipEkYLHTxgeQhla4BT92/nwbHnozYqyMIisL7H+B27qak/hKQ5iLhFqqavIb6lmvboaGJDYyucYpIqYK1vFQG/wGBAZ5wkMj9YwYfOdqJTZ2FvXEdbbT2IFkwtgRLsAEZyTEdEjs1jI1Wah37U1HHJFBnf1I3NPoDuT9O35SzaBCgs3kXUZkU3CjnHdjWdxgGWt/QRMxTeFcI8otyPQxzx5SmZDnHRR6sg47b14hUkctOZyJJlyHwRoMFSw/PVs7CoEVbuPZEHLA9x+9vXcM05b/z//xBmyfJP4J8udEzT5Bvf+AavvfYaK1asoLq6+pjHq6urKSoq4v3332fq1MyAvnQ6zcqVK7n77kwoePr06SiKwvvvv88ll1wCQFdXF7t37+aeezL28XPnziUUCrFx40ZmzZoFwIYNGwiFQsNi6LNISrVzpJSyy7mVjWVp4kYFgaiDCcogdsnNDXUPIOsq39l9gNypEzDtMtU+DwtHZf1zsvy/YfT3IugaHiHGpLO/QaTpx9QfDmDb5+ZudJKKlxuulxjwCoiGyLz2qTzMa8wW9+LfHSGMwtuHTqdDyodylU1jzmcTAPdhNXRurvQTC+6AfJE3TzsNUwvhEPs5ObRhuDj5rMLX2dZxFmpCIZZ2kTPkltMgdTMT8NgGWZEzmZN7N/OLuhgJzc4VH71Lbf7CoaMwAQnFdS6N1iYqB3bQYM5BLCgkaW9l1pa7Keo8i8E8mLC9gKiSIjV/ALzwbeVF3vBdwJcCGaGkijLpcSluNh7iHWcdfi1AypB5QH4QMgPUiYs2Tpr4CLfO/gUzkruGO7MM00TXpSGH5SGXZd3C65vOJZ52IJDCFETkwZbh828KIomy0YiShclaLYpmYLdHSZ/0GIL8d5yIF5KxdNQVnMnRxPQAyxK3EtxjpcoQuJgAM4T/YKky0joeF218sehHDDYpzMo9xGihkwmJBJoxCptzB2nVwv6SD6gAREMafl5CsPOyPoV5roM0mhkH6JSeGh7y2RBLcjTZAuUs/27+6ULnxhtv5Nlnn2Xp0qW43e7hehmv14vdbkcQBG655RZ+/vOfU1tbS21tLT//+c9xOBxcfvnlw9tee+21fPOb3yQ3N5ecnBxuv/12Jk2aNNyFVVdXxxlnnMH111/Po48+CmTay88555zPdMeVdpQvhibHOai5MJJ2TNnEZelh7mCckzY/wkatmosvupTyMVWf3GKzfKqxezxoso5mpOGjIA7gRK8PbUeSDyd/k5RgIHU+jF0qZ8aed7lg5xdpLtBYPvZ52r3riQcEfm15ZHh/KUHmxMSvaNLy+Jr5a4r1HjqEEh6RbsJzRhdR7mbKZJE9SydgpJ2cctW1lL20drg4+cbIB1RZP+IFYxrqUUM9c+QamvRaTpPeBAe4p0WYbL7LT+N3EZvbT7gxU8ti5hTjBCSbFa89AAOwqzBMwWA75+17G1OUGdX8FoN59Uxt7cUd7eWkvXDr9RJ5Rpj7go8Nh1LqhBZWivNBhEDMzR48PLcrwmv1i9GQhiM/uVs6+fm8H+K2xPjq2ufRjDglJzajqgq7dp6CYUo4HJniZVlJMT86FaupkCofiy71Y5F1BECRLaiCi5VmB660F5/FgeZuQ5VV9u+fjztcTjzYS1qQSOXIMJS+11QbKT2Tgpves5Y6ZxUpw8IOs40zjDQ3p2/AlSczzxNlp6+flfJMxMMhttbOYqLQzEPb70Uwx5JSBJSkTCBQAdUgmCMRHVVIsdsxhS36VMbII+LMKwvYRZEb97Ue+7nKFihn+TfzTxc6Dz/8MACLFy8+5v4nnniCq6++GoBvf/vbJBIJbrjhhmHDwPfee2/YQwfg17/+NbIsc8kllwwbBj755JPDHjoAf/7zn7npppuGu7OWLFnC7373u3/2IR1X/PVs4LGew4wSB9jkX0OpbhBlKs2xInIUDXeB75NYYpbPCJ68Ai679x7inQFIGoR6u9n16tvMKTiXE86v4b3XDpNweGlx72QGIMZWUNk9m2qvyTWH3KzxulnuNbAkbITNhTxgeQi/EKE5qkH3WKTCZgY6K6AOind+hdKZfXTwS0667HyWP/gyOaVjSZ73OLz3bQCW5pzJ97pfJynqqJYAziFfnT/nlGDGfwKAqYgoVpP7hK+z5MAKmqPl9FgOIpsiPU4DnEXAIaxmEVZrhImdGra0naQQIuXK5+DYqwCIOYowBBHRNKhvifNmahGbUjIhxyEuytvAfZbHhs9THJFTU/fyo0nX/e0/qGFSsa6DzTOnsr+jhoJAHy2MpcQSxkYIb0kEb3HGGFEXDNZYDgBg9cSYPmMppqRjAinAMCRsm89lBXsAcFoGmAYk4h4OWRTeF04nYtpxDqQoECMssjSxVS1FNK3kW8KcVP8nvrXuO6QNKxOEBGdYoVEo49Lxkzl10hQmtW3h8PL3eZ9ZfHlvO17Xx+hCJkKnIvDEgIUCIZO6koyRVFm/1IUS+jMK0FUj09We+Y4utAismj2OQVUb3jZboJzlk+Bfkrr67xAEgR/96Ef86Ec/+i+3sdls/Pa3v+W3v/3tf7lNTk4OzzzzzP/LMj8zOIROVla+iawJiAMVOFVY8oUL8VRUfOYdorP86znShQUgHFYIv5ApTjb1AKYRRrIUUDKoEfDILNgbwpZ6gdPXgioleHv+pfhjRZy6/gleXRwHC8hoWHYF+COToWkyomBAlcYBIRct5aKPamRRw18gkkg14M8Z+fEj5k+A7td5x1NGp7eJSbLlb3x1RMEgfkIxEZubD8bN4Mz9JpMHnWxQGqhSCzD6G5BcHhrcKl57CWp8K4jQMH4KKSOJbnchorF3/NXD+6wLp5i0+3EsapSUDLedP40bxDacej+6xaRK1Zlf/Gt6Ej7sKhiGG7tUByqMljpo1EuZtn4j24qqCRTW00EeZ0/5iCnOXXTuHYvN2YEDqPU1oRVvJjJYQMJiQ5J0DvTX0dlXRbEQZty4NbgqWnCkZNJhLx5H5r2w2qIUJwU+597IG3kLSLjdaEmBSLcdh02DVAyPvZloJI9as5OZeQepNnohDukpfkq2CPSu2IaIyBXaCayQUjwVsjIhPI4zncsRAJcISU0+uvlrmJpwMY74ZNpt1ejCawQkET2Vz67GPhaMrabel51knuWTJduC8ynHpUl8KawQ3lyArXgcaGkq6uv/+ydmyfK/5OhJ50fMBBXXuah2J9tyQZJM9NCLNOW1cMlqgw73u7Tlhpjs8vGlwIvggQeSD3J11Y84UFSDoJlYdgUQ0jr31A8NveQ+rOkk91zwOIe6vo6l3UYemS4sj6eLvRE7klZFl+88HhBdGAt8iEmVyS0bWVCwlsd3XYmgGmCDiM3OH8eP5rE1XSSENBONYvL8Y+kxB2lgG+W5M/DljFyEZQPWBiOoRzn8WpKDqIqLHZO/Pnzf4mbYIaZ4Yagzy2YYvN7eRbHePrxN2thCwrTwgPzQMd+yccHKKYl7eU24hFflK6Aeis3TuIdbcdfsA8Bf3AuAgYA9J4osx+huK6RWl6gr2vY378v48Zk6H12X8ewNooathBUnT8y8CE1SKE328MdNd+E4mOR662qO9LjHRRtRlx/HySq67MKRymXUsnYeqXVDQ4LB8t2YwX76CDFRNrmmdS4rqjMpw7Q8kjqsbejg6vVbgC0kFeBckWTkMr7zWhS7spLl31xEqc9OliyfFFmh86nj2IiZZAgYq0twCTJNokKeEfmE1pXls44nr4Dz7vgh0T82DZsJApgSBOfaWP12F7gupTqZGTJeF05xydpMJGRg10Sclnb0rSq/efcuvjfvy0w8cScvcAFKLIkqCJiKiMsWJyp40Pd+i5yaBkKO32Nandy0TAXeA/z8UHmeW69/iX6PQso+mUjBLQy0+il2jvhndVLG/I5tkOhl3YxNWPcXsGLMCg4kzuHmhoy4WWHZc8zxyaZIiVBESjPRpSjjkGi05FCttGCP9+PatRKAv8wvpTT1RWq6HXT4Bhnwiqxw2EgX5XNwsJ6ceJzL0x/xaOJyBodEU9h0kiuE+KXlseH03Xkr3sOWTvLyWedwi/0h3Bz7vxvBzYCUD36w+JIUsh8PQQAseorIoUKcWppkroQigOLqw1OZSX0lKUWPmVxiPsmkeAMOI8nTBfN5oe0szrGuQvIIxL12vtz9NN1dIbrdMQYbz+AMFjB5UiWxhgPY2mZjOF4kMRSl9wgeFD1T+GzmpVHEPqCW3nE+dpfbEONRxr8pY6ZEbt39Zz647FqeO1DMls79hHQrfqsf8ALHFihni5Oz/KvJCp1PCV3RLnrCB4dn6RyN2/MFkoKFwvB+4jb97zw7S5Z/Dr6aUly35xLp6OX1X/0cp+lhTsG5HHj1JRyEWfjFW3EW5BEdGOCd51uPiYRs0VPMSf8UmxHgrnWPsmOwgNfGpEjvAitRFFFlYl0X68rmss/pon1rKzlzq9m+wIEymKTkhJmM3fokttUebotWc4e3maJkE38t7RUzzUPCLVCRuW01Z3DXjFtxbV/CG6OsuBBZO+FkkvFuzvnwJTRPOWl3Es2Sj2IewpnKjFpv0uOk9ASlpSIHumSKo20gKpy/JiPkrltegC0Fd18Iv/H7iWoB8GwHDzxvFPN6+7MU65n/xzgSN6QyHl61Qgvshjabj4DVjbR2kEC9F0kLMTrRTX3OBtzWKF57BEyI4OXXfIs7hZ8OH6MFjd+ufhvB0kbhJR8yoPv4wZrvkzZG7DOs9PE6p3JIGM311tW80HYWB8wK6szJuMJpCDMkmwBTRHBmvlsO2ILErM/B2HZOO6gTHyr8lm1OklqatCmTP+cAUdLAPPKnbaT8Hh9dQj5JaxQrAUaFe/lF54vAzXz34+8i2Tuxy3YeOePVvylQzhYnZ/lXkxU6nwK6ol1c9+qFOE2DhbknYthHxI6AwFml9/KY/RTSh5NYi7OzrLL8a5F9Nvy+Ci64+yd/My6Cd3tJy304LsmnfG4fT7T9CV00yYvlMa3vGn5zgYsbOttwfexiUs0+rtX/wLvOMqR0DQ3GRPLEJoTwdH7vz8EsuBYEGetFGje/8wDOymqK9xl0A6MHNkFpPg907ud6TwdWu0kHmdbm2QMrKWgUSUoSMc1gxYRTiNuciHKIG5o30y9Uc07jcpolF1IyjpCTxtAENAvHpLNkU2ZV65NsiJxMgXSIiCtIV2E5dmsEwUgP1/Gc2gQ0pahyfwdFHCDgELizys8Wm5UaVSVpESlOZApykyjcf1Qhc9qQuV67hf7tGb+wbtPNVi5DEE3y5yUZE2lhUeAgX+Z1uuK5mHIppmRwKM9k66wK6tKlqHvHEdQU0oaVcz1hkuY+imt6WM9CPmc+S22yFVrhfGsbg6qMV5WpaFnOgTInpuQg5JFwybkErQG6Pbl8/NFqSiMhXPZMDVBEyKzdJoioyUIeDn+Ri7c20OucDbNBkHV2z7iSVLCS7qoUs43bsZNk9sFCGoth8q5aZEcuW0YN0N7Tx0tja7A4MynQz1JxcnsyfUzhdTZSdfyQFTqfArY2LOPm4gEkSYfyl2iiGjiRlCbgxCTHbEdLCIwu2YfLO/GTXm6W/yMcPS7iryM8q+//A2F1gHMoJqXHiekBTG+Y9lwLP7V7uftjnf/Iz6O9oI3T944iiECzpPL+rkVYh/xxFFHlgnnLedp5HZ4zuggmHuEb+te5g2e4V70E+AibqVO0uYltU2bwUHoMFnGQTQ1TjlmndU0voXluAnqMWLSfndZ+pLSbqZWtxO2lKP1x4kKa/qqcY9JZsilSYHWT1FK0e+tpn1KPaEpM3vs8k7p/jypbcSYDxG157B1/NfkfFeGOquQrkH+dyPcK8ob3ZTcMZjZ189XULXiEGGHTiSGIPKL8mqcs9w1vp+oSz3acxIuFi6HTQa+7iMe8tfTulRF0kxL6WW79Fo6B1NGHSFyw8iT38vKEOkosOazadCUO4/nhxzUR6ma8QMom0U8e+xIlxNVM3YyXGD46qFRihOzrmAWohkDP+rFgh5g0YtpqVW2ETTeBJMi2kTqdAet6Rrd+RHfF1aimG7XIxYXeSeyTmmlxz6STPGiGm5o7sSpdPPqlMsbm54Pzb1NZ8OkTCe3JNCds2E/iqG40uyjyx4lV5FpGLrOftuP6rJAVOsc5yWQnu9a8S974Cpqbp5BMuBi0+6Aa4mkThyBzSuperm3cQWlJHflzZn7SS87yf4x/GOEhMxT0zZZH0IwEp+1dgpIaJOL6kBsPD/AgKRY5XqZEgeLQLbxj8dBhljPF08KOYCXhw4UwKdN+PmpmH7bJj8OHcOrMEjYfuaaYKpYtA8OvZ4pw2eTXGC0fZvXe01gZm8TO4CRkyYUiu5iqF9Jl7qL84Nn4ioeiNwJsqPoTajxMeJOLAnspzUVeUlYrycRKMEDQNETNYPukq4cH51qTg8h6RnQE55xHT2IHo9es4palTgTBhSHCSyfPocO9iWvl95kmZuqIdATeZAI3pG7mcuMNJJLst9n4irifqyre5yreh8yQd+KmlVP1u0h5D1Jt9OFQU9xsfJ1GNRO9PeLZM1vci2/QpEroxmEkuWHcD2iwleHp+w3e1ADCIZmEksvKsl8grwsh6MfW+1kxuN/VTb6oEzX2EaYZAFU62k0+85wuX4iCo9Loh1NVjDa3AqAhMmbyHuBnvAroFiv3HZ7D25N1uuUckp2X8dVl92J3hPj+vPuwpYxPvdfOoKqRMAwerKugUo7Qkwzx9UMGn995+Jjtjj4utbMTLTDSMSj7/SglWVPXfwVZoXOcs//wVh4c9x1UwQJHmUwrqoqo9mOYJQyaeeSFBhCLS5g8OztfJssnw9ERHiOWCeFrvXEGXzhAvq2cuCTiF0eTtsKmGbMQ9RTf/svjmGaU/7hApKf2DVJKkNObTsIp2dhBJf1SLgAHhFwi/SLYcoBO5OTvwZKJSPzI+QhdhosOm8Lv3XMYaD6V3QNVnDbuI7omr2fF+kms3DV7eJ1viwIX5RmsS20klcr4jMumSJN4AxFnhDOMV1FsBcimykBRLpBZA4aJ89AuuqMbmNndgDsZoat4FoHcmQhAsxagyjqJrvx+OkZdjzEkEOY2AyxmAynKlVtwCQPIVoMlzt0sse4eXtdc08q31K+iI6CaEmEc5AkRHrA8xAyxkcZwOXmCCBaYu3cnSy84HVUTGTzgJm5aM+aMQ359cdNCX1Mbu8cvYlT0BBas6kXRDLry8lnhFhB0k883L2fThFHsqZ/FCc3L2dg1mTciCaoMjfGFY8grWg8jjWQA2NKZmqN+X4LS0Mj9M3QLbcXTUYDXzZnsb52IyzGTGW0PM2rqIS45uIdztzlpvOtKvtUDyc7LSALfamzApoj84SuzyfdmnKM/zemsSjlCZPfZ2IwEvyCPCG5EwUb9pAdp0dzcuK+VNxv3UJLoJPDUn8gZ7KG8rxMxKqAkHYx6842s2PkXkBU6xzmv/WUN6txLucG8H3G1H2e4gEn+hexqeYF1pQPUxC1MCiYpH2igId+dnWWV5RNH9tnAl/lbdMogC3/TpdVQ1MW+XZ7hYuVTm0BoTvKnqb/gFtszjIlpbKCO/fmjwDSH2s9zqA1+ncf4Po7wSVRra2FAptM+mqjfhiG7icmZsQMDjgYADqUOcdm0Z3GKIIgmjbHRrNw1G39NIzPtG+mKlRONVtNzuIJvHIiQZ3qg9Go0I83kZApNzEQwgkKcFZY9WC1+/PFdNOZDxlRmG4SbsLuvQJXm0WAAEyagCSm2FzyERY1wxYcmmqWIveOvpmldJe6oiCYLmOcNEnbYiJk2Iqab2WYTvxYeHj5PGiIrqEEzBR6wPDR8v24KLBi9nUf3f4uncty05+fxOWExiiVNgRakJJqiI3Qa8XQ+09ZvBCpYPjZTme2LRzhh7TsEJYEpfR8zZc1avjclnzUVU1G6DF6dNwfBKXJB88tcld8N7VAu9A2/dkEohWjoHLYmmHrU8GQ1/hZ6Kg/FMgtNV/jPsy/l+vWDTD//Rmi5jYbSAsp3B+l55Pd83p1Lt0fk4ykDXFf9HX7zUYJCQWKi+9Pnt5NMdpJWB4nFMp+Tjs71uIwEha5bKE36SZud9OmPMrDh1wRjbpTcJfxnnwKUwkXfxWomuYV78ZpxfA+ZRHoHKc7J+9QJvOOdrNA5Djm6qK3fnRmCWkI7U+Q5FMkT0MIa9gOddBTOYnwyxijpd1Sf2kR7ycWf5LKzZPkbZJ8N+ZJ83rr3HuZfegUeJQ/9oyDV4yvZtuZ+xo6bRsEb7zOQX83hykup6XbwqlbGD8Qm/EKEiaFtLLNezue050m1FFOpe0hYrIx6fBWZyXh+RiuHaLglhdcncKN2K78EigZqSJeu48rcOLB+eD2mDiuZTUP7yRSP72Wg+zR01UT3B1gXe4/nJ1zJl/veYFHjSbSKAZJGjEDnGix2PxTb8VvyUDWQHYvp1ppJWTQcg1sIGmup7O7GF0/Sl+Mj5Ikyul8nP13E/lMLWaHsZF4nPLBExBMRuWmZwXc8eTQVHRELSWSzlK9Fyxid20GstZQzEus4hUb+eky5JJiU2cKUEebUEDQqMpogIMdMHIZJWBLBmxF6REXcCbBpfuJpLz3bvGAc2WEuKRlOXfdHYhYRX2Quda8OsHVyPeMPx+maW0GaKLcrL7IJP7ptAG/cxpxN61k2PsCAOZJ2cZQU4ggbRI0wgiUEdNBRs5pX+ru4UpKYceYCQh2vsuTz5yPXVrC95xBrBv5AvjXNf8Xx3ILenkzTHe1i564bMcwknZSBcAt9/Y9j1yWWfdBKKjWA1Rpj+gwJ2b6MKjvcx3tE0h7kt1302Qt44OTruFv6YeY9/joQ0LBv2P+pStt9GsgKneOM9mSa+et2kzoyy2fiIqyGhluIsEfdxEFbN8mehUyMhXFECogX97FKLeR1aS5nl1X/451nyfIJ4CjxExGCvPGnX+K3FHJa6dVse2EpuQ4vwf27qTJj0NfE4Uq4+XUDe0Jj73lWim17aWktZ9rhjWw2J9Mr+Pmyz86Xf3Avzmg73r4HKO1XuWmZwR/7nXSLJi933slz3MOJUgeJg7n8Ij+HawPtVMVj7LTOw1WSybcs99fxfuTnUJoZG2G1adyl30pIOIPHOIdFjXFenFBJk0vEGaskNzzAqO5dx3Rl1ZODaMqsCR2G6Fa6HdDtAAgzb3s/vtgAuiiizbuaFRVD4kIsIuyGiMvg9AMTSbTmYOgpeqytdOf2sMEoZHmskua8Yl5PjOdzSZN+2pGNNEXqALXGLj5QJqHro9hhqFwnvc3T2qn0mH68ci9ry/aREDPfHbkhk18/p5MS/ayf9cNMKm3a374/9YNH3bDDtIMA1TSvWsIPSz+gJebmOuNJkrVvQuOXkZKZhv6AOGKTHO/sIZG2Q/pJ+guS+KM/YZ0D1jngZaOY19v/QPXpOhz8PhyExaKFOncuTbaDwCje3LyWPe0OCnPK0CXv39TtHE+FvccWHt85LERlXUXqWcK2hg85adMBdo32YmKlc/lcrLKO29Swikl0XWGn040qC1y66UM6y9o4uXQ/Oc8Xkrjsa3zTcH0q03bHM1mhc5zx9rvPk/LUc4N5PyVDCXK3EMGvBzgYmoQQr0fUUxhGkrRpRZDTJORSiFmomXPiJ7z6LFn+Fk9eAdf86mES4TBmVEd9oYdZOWcOP27WmCS87Xhb4e0FPi55p43veAtoKlqF3TBY2t5Fnm6yKHk/TxcqhCQv3xgs4gPvPGZUj4Zlj3OFMZlnencQHRSo1jq5O/9qTFfm6+3bpRo5we8zxtzN5YqIKKhYjhobIQkmsROKiNuc/KzhIwZyG+nwncztDXn4kiWARFC08YrlvzAZ9J6MmjYwBCtBWzPuvnVsmT6V8uYOnPEYzm1LuX2TwfpZKea1XwlkfHgA7EO2VzUpqOkEUc+MmzDNRp45UeB5Wxmm5EETHVQLSR6lDtJ2oJOYFW50zSTjhhPEGzUZ31LKFeE4bkODqIhqdbF0fhElKSs785+mx9VN3CVxzcAMegfzqR7nx+VSearpJTRTpyBo8vmPTZ46vYjxA1ezrHg+8UaBKTt/wNzwsR5dg4ykyY3qfKyGk9RgD4vtKoccVzIzmsKR9xpLB9Os8k1h/DIdORnGbR+kdG6Qe95P8JMTn2KCeAVL1+VkOrPYD4BFFvjKmfkUuT0gu/lxa/c/LOz9d3Kk8PjO4jR0fh/llVwGhTLaSwuYGp5InjkVJjFkdjDEkVM3VL+9UId+I8xfhE18uX00RunbuCYs4PDORpg8hXSqD9yV/9bj+iyTFTrHEQcP7MXbJMJkSDfnERjMuKoGgMMpC32JdlrwkZJ0njjhS5ztfweAGcnNTL3mj9nZVlmOW46emRV0uVj6s5+ip1WskoP5BedTHCin2A0LjK+StP+Qi9Zr9OTD6lExHjL9/JR+/OIgnfvzkAjwEKD0nU7Y7CZhsTLpqa3DqaxvS89y0Cynz5FpizYlgctLqzlk+gmX7qR+0mPorTMZb+r0pb18mBiPGEizw15PV2E7YCM+6S/UWFrp2HoVg2aautBMFlr8dBlWBlt3Ies6WJ10F7hRjH240gFE95nk6vWolhZIxBko9NOpW4nrdnzeiXgC71HUO4BomshqDKuamcVgihIt1ZMYtFvRrAuG65bqAxnhM2fjT7GlAiStR0Vm/gEHjvwhAzOgJAWCkORM+yZkcYDykE6tvolTbPfSd9jFV8ZpFI/7Iu8ZIb6z+acoUTfOeGYXlT1vso9zeKdwDAu6BoEQFiOTbkqLIwOWjYRI7WAezVKcnEiCsZ0qm3wTSEXzgd+xp/FEOsfUoxgSS07owTj4HapmBfljLAiWH6Gi8KOeubw5FVKqQqL9izy6rBfIjMOwKSI/umgiXkdG1Axg8IOOHjYEoww6M4Lr3xHhSSY7icUyHX6ta19iUVUTW6vriEXd+JMp9vZ/TDLZP7S1hOxYBAy9X4JOXtFyZDkBQESTEYwK1tPBdF0iOulPxKgGprB919coqP8ZFblT/6XH838FwfyfTOH8jBIOh/F6vYRCITwezyeyhshgkgN9zQyoETZsXolV8PHrihl8bd0zFB7KfBGmjSRxNcTuWXU4W1Sc6SiHxrQyN2Cl3AGlRXksvuKx/+aVsmQ5fgj395IIZwqHA41trP3TM/g95zLTnUvv5j8i9B3AlspEXdKSgHJePw8X5xMOzqdfL6ekeID3m07lilwHr9fofHvrGl70vMRth8MUrnSy+TobaqFCQ8tJrIzW0TypdjjCY5FV7rHfRh799Mdy+eGaH5BGOmZ9pgjfm38/kX3jiEUzXVeyKXJxai6uoUhGhxjmbcsmFqcn4DMz6SybaRl+HEAzDd5uewTVugBByhm+XxBkLIH3UEgzobUfJRUYFjOq1Y8y5kxabCod8mS84hbS1jCbHG1MHLiSnflPg97FFR+aWEf86dBEeOoUAX+kECPtwpA0Dle0E3CbJJUYUWvmfMqGyFvtbaxSb+eO1ARUYeT37szEXv7jgydJ2ErZNOO75B74A9+dfTk3SnbmppNsC4RIWh/k6VkhRvdez7oZC7nTvJ3F667CFqlCM9K0bnqE/K5MN1lKkbnlepOa1CzcqhsMkalOiVHSQXxvDKAk48MRnrb1fgauuAazvIBg0uRAMsVvut7hezN+xs+X9pNQRyJKNkUkOb+QpHXEy+fvRXj+GSZ+kcEkrTsbiQwcJOq7g2ahlB8I93GneTvVeju+tXcgJXOQDVgnvEtIkpEkjer8vdjFTAhHD7toOfy1Y0SqaYRJxP6MKQsozjQ+hwOz/CS+Xz+DG8z7aWhMcF7tFYyfMJfxR3kyZcnwv7l+ZyM6nyCRwST3P/g4j8+fSVqwQeUpAFjMJFK0CFXIBD8lQWV73WPUpXLwyz4SlipKyjqZ1OxgsCCIZ1zWJDDLp4ujIzwAgXQPij4I5LKn9kKiYxyk+x+kofwgN76p8xs1n/6oya8G/0KRrKF2m5zCVP5YUospy6wsmECL91V+hYe7V+q8pKt0a2mWmo/xQ6vMKbvuHUqNZCI8a2qmMF/aQ6TCxvdrHsGIO+nTTmE/cXqSNeyNyvSp+Uze04TL00LaV8F6IY/9edswfPux2r2M2nUWAsIx6SxBMKjKb8VvDaKkyqluXUKuo5JoaisMXW9Tepy4nsDwLiEl2NngA0FQKIi8hxTvZ2zjAaTND1Jp9dM1axwhpkMCJiZAROfS4FQswkScE3R8yaELveJg0Gdlc81vSCudw+uxqAJfes+PKylhinm8O7mQ3TV72GKzYsn7ExfFZuEOOxG0XB6pO41Ne8bz3Rt+yikHt2NJwpqazHdLUN+JJI7GZERsRIURAbExtYHy9k6qy+aRPNlKIG7g641iXe3BG7OwqWTT8LYf6wqXbv8+7pocJAwuPCeNseUqyucEKG/8FTRmtptviLxSWYQeWcddZ9USSUlY7Hb603bufbOPL1tV3F4Zl+wijJ272nuPifAMpDW+tLv5H5r4/XfCJzKY5Nc//QsJWwgpL0HV1FL2tp4DlbB/3wLY34QYXAFA0oyTsOZhCcXwqDFGN3YiHeVVlO/8KX88zUfYblAQNLlonZOtky5DMHRIg6QrVAb8yLrKQ9ItUAvvA/LOJr6rvML8otEUujP2IQ6HIxvB/1+QFTqfIG+/8Arj3H7Sgo3zO97CFcqEhC1xnVB8O/W+QQo1HcUa4kyCvBSr5C+1i+gpKODSvV3UanY2FB3EV56dVp7l04vd40G2WlFja8BXi0NbB/JcxPzrsIUPoom/56ZlmQtslFz2SwLSef34LO1YdmXEy/vYYfDbKL5G4HluMOp5qHcn7aVn4tv+Lr+fsZf32+K0SS7WBsfxUt61vOgZusDVgtVMcg83M1UP8PL+q9gbnUJjbDS9sw3oG0d18EOseTFsg7Xs1juordqNIZ7Mpcl5JIXM/23MTPC+ZSdNvVU0DR3bFus6Ls47+ZgoT9pM83b770lGXz3qLMi0eycxpbWDXl8lAiaqXaMzdw1nfLQFRddJWdyAgDWdiYShRkkORb0MUSJ39tf51oqpDAoj9UdWVcalKSCDKivM7oADlcKIa7NjK/Zcg5fa++hslXmDGXgPNdKrOSkDprc08J5/HH+Wa+kQWhjja0VJ/O17uMO+jrx05ld10urjHXk8RjLOVdYIvp5phMcv4HLrMwipbpYFU5hjtiFqMbrbprIqaKW06vu4nnoZ9+c/j7OqFDnajG/Hj5gZT/NWw+8ACEgi3bKMoXpB+CaPvdt/zBpsksA3YhqmJSP+TEXE7rTwXH0NuRZ5WPgcXetjF0UeK3Hji8WG72tv7SfeeQAz1U5cSKIW5/PMtBNRJQVYDJUg6xoEHUTTJyNZFURDZdqOx/DFtg/vJ6nALy4WCTsy1cphe5iYO8TZ3RNxiQa+0G5O2HA/quICIOUbRbnvMp7a1ErTjN+DpNJJGQ9Jt2DTX6KvtZV3Np9HKuVEURRuvPHGrNj5H5JNXX2Cqatnv3Y74rh6bqmv59srnmbUPpOO/CmM2vcid53fgjJ4EZZUPrJp0D57FOeveJXN7gn0ywVc07mWPeO3MHdskCUnfoTNljWZyvLpJdzfS7wzgPpCD4I+0k9tCCYtnma2bnuFFVOaKQpomdbsayQ6CgQWHZhLi20clBlsaazDO07kT/d9G0XTjtm/KZt4z+mn1KZmnIblH9Gh+HFZe7EVHaK96EssbHiRq0e9SmuynLvW3IJw1PxcU4TvzP0Nod31wyaDogllRhJ7VZBA5zjmxWtZbm1ld56HWTtWkxR1wsUl5PYMoGgasm0hTslJnV1ibzxOXIvR5NuMI1ZI8UAzsm0+DDkuq4KFqOCg5uDvkGQRzVJIe/EFmOLIb1PBUClpfRubGsEXbkf8777KRQX3hAtIOtIkpG4EEnQqQZ4qWMfXB4PsjJ7EK/ZJdNtKyE/6uSpqY9z+J0mpMZafm8dU7wHmNnfwWrSQp2eFGN93MSunn8ed5u2s2x/l3LWLqCpfyNZgiISZscUQ9RR95ioePvd8iuVO7GonseCj6EefXEDWFS7afTOelIcZZgybpRVr3m8RRHV4G48h8uaBUdScdAFJRx4RTSQZa2JlYDknTbiM19aLqEdFUBRJ4NIT/OTl5JBwFgIwoJpEdROX7MoUOTe0kwJK073kaCFy4wLfaC2hxx1kYPKjI2JDuIUbzPsp1rrpXn8JSshBXfxFiqyZ6FlKivHbPJ2UOJJKS9sMvpMM8bZwOnHBjsNMcEPqDaqGnLQ7kgq3eAuGn5MX8bGg5xZIvofkjCHZVLr9Fdy38Apubd1MbvnzuHZdSTTmZp9wkAX04LYlUS1OLFV1zFw4C4uS83/mWvC/uX5nhc4nKXRu/CbimMncUl/Pl9etprB1PJqQ4p3RP6cjN8aA92cYYm6m/dWicePL9yGpuWhpONe7jqaKU6meeRrjTzz93772LFn+FQQPd7D0zp+iqyoeJZc5BeeyvvdN4pY5RLX3INnOiZsbaFkS4VsT/LzQ0UVdSmWXUM2S5M9IT/KTr4bwRdvxxB5CQKW03+SmZQYPnStwZvVEqg59zKPSWey31iJrbgLYOXzCZHzBH5FvNOEWZUpDp1Ns5KOUbmNL12y2t4xmyZz3yQupaP1++uUBcnDjjJvDwgeOreMJCmFesW3ANMV/cMSZ55yi1mMzR9q1dUNjdzJFwNXNoOjEroq4B/2knFsoc2zDpXkIBJdg/IOgfNo0SBjHdkodmdVlGSqEBlDU6HA9VFKBb15nYUHyZPKbz0IcWrvLMUBN/Q8Ysy/C46FaXpg7yPcD+dw6+Vfcad5OYGvdcC2TiMEMqwtXVy670tXMSm7hlXKZet+b5DBILwoRWSTfkTEi7FRk/iAozOydiVt1kxJUNhasQZWPFUN2w+A371rwdYJVDSNZDcoWBBDlf3wJi5tWvqrewoD5t9/xNkPGJwa5yvI6VkGlk0ncOv8bJOVjzYtkXeUn63/K/9feeYfZVVb7/7Pb6WXO9MkkU9ImvXcCgQSCIEizCxdBvYAFERtwr5ffVa8oIqIiqFfkWq6AEEEQuBBJoyWk90baTKaX09tu7++PM5nJkAAJQgLJ/jzPfp7Mnnef8+51VmZ/z3rXu9bodCtyp4ZrNQw/Zzf3DruIQEcWWY2Q8IaIK2mKE10slUZiW16SxqC+JVOAQXQRkZJ9Pwe1Vr7lfhBPb1RwT6aKRVQzOlZMQPcRdy3g/ksaMNSBPqTZBnfZN1GqtA04b1sKbasvIq+FqZ0xjcraakpLK6gKnHrNnp0cnQ8IadVLsPffvsAqplX+Hk1OcGYuyQ/iVzIpfC+SR8crFMpaTNKhAFJXnFCpwuLSeZwVn8/IyfNO6j04OLybFA2t5rI7v0v3wS70Lh17SYJZ5R/u/e2nsITFwaZ7ad2ToL6khYMbRzCiZic524uqmrA5ShyIMwhVu5ncNC+X92zHfv4vfPEpE9gMRPis9iq3fn4lv020ETZVPnfwO8xN+5CtQTxS5OOK1HP8InMNN45ci78qzYYDw3ks/XFEkQpF/bV3vmL/hK07qtBECfOjozioreGXI7YhlCCfPqAzP1eHpReWJizb4vXYajRboEqFZbO8ZNBSHuD/XBuONEahwwUyPViyjEvegppIk0oPZ2zkPCZGBv5xT5HrW0YrvJ/JhuhSDKtQeE+2bVQT9k64gkM7gXRhkxMyM3z/SSDdg+elEIGcxaX2H7l9dBuz99dw7op1PHDBIAavngOB58hYhbn/0ryo770u51m2pOcR1j7EMtdWklV/I+f1YG/5PNkdi7l6ZVPf2FoOrX0VhN0YFzz9OVhd3p/Ho1oaH95yLcXJLJaskQnu54kxywhNaGLUqEKUp1VR2K56aV3jh97bDuUgkobkpy8iFYjQmWxjZHwRt7jv7jWSDPmCaMjZJazK/hhDrmGdPBrNGyVeqpNTJS5tfoZpg5awd0U9+biHQCrBpJX78PYmNpuqzI+qPk57NoDs01C1gsjz6DpZUcz8g3G6PTZZVyPQiGnr6LaORxmNaheEcbdfYvHUSyhd30BNLkpW02mRm9lWv5i1cjMAg7t2MnPdEHzh8ZwxbDmKYhYiTPJNbBcN5F6fh2m4UJAZq/nRhz9LwD8JV6qSV1dvJrt2LUhQ5aqm2l/CsLHV1I0YcdoteTlC5ySR2LsRg/5vcPv8lSwp+xAAUVeAyzc+wQFpGi2DRzGraR0H0zYl8fE8FNKZXnGAsO6h5rppTssHh1OOAVvRhx4Z4dlZU0zW9XH+88/34MlH2b21nF3zR3KjvZ2Ru5by6KjZ2A02S/fOxE5FeCmynvTXhxLOTMfrFwzp7iD4q39Qf1Dij9pEzsvt5jv7vwcBm4hlc3VSZf6EX1PWsp27t12NYqtIkv2G2js2+qQIP/F8E4YUzv2j3uZH7nuYoRS2RGcrwLIU1vbmVQCoFUUDojemZBF37WR7qJLJW1ZiG2lydgaP7MMlF/5vy5qPjooiWiqD0PvVqFWsxasrkM8RzA/Hlm3aI40I+Q3RDW9R3z/fLHK0IbqcNvPzuI0mfNXN1HU04aGLKxs72SyKCKaaSGkysdxUvEUmZu+28rbDogwpNcFiZQyf7919dmBpNUouA/wva+pd1HrmE+gAIYFvUAevSkOIlgUpT8W54P+e41Md1Ri+wrZtYcvEYrVkIhKp0sISmC4K+92fEWex3A5RGtR4xF6NjgkzCnNIeCER1LhidQOBta0o+Y38bn43RqiwbFUSF9zxqITUW4w1460kP76L5kHNmEHB6DHLMZUqYAbTBi1hiNXIxqKZZNxBXq+fyEtzLqS+5c/IbeMod0G4s4HarIRXlRnnK2JdehlxNYXhz6GNbmGYlEExdB7ymqhGECQvOc8mSpMwNVZCjTyKpd4xPDavN8cya1L/0lYebnqYnFoQVFGlg6+V95CVN7Ov3WZkVHDxShX1Gp1fKTfCiDf+71kAEw7792G4LYvrXn6C8GKJCdPOw+NxE1KgpuiwiOQp2ljUEToniWxn84Cf3ZZFKrWMHn8rnnSeTrOeadn9TN75PJFcnKcPTGJJMEKzbwzfO6OMcePPOe1UucPpx+ERHqMlDytShLQiVDnFs+fOwDCbueDlOK9pe2gtXc1ldHG2tJp1zcNZKs/EtTnKP5jDYuUM8meUo6o2V7/2Bz7t1rjxKQNoA4LkNPjaFxTiQZlnDh4klEuwetyFffNwp9MM2bGeoeV7qfbEeWzDxSjr4wM2pQtFYt3sKehtMnlJ4UN6OaGGBzhPDEPNDyEh6SzRNh8RvVENmW+2VBIo/iSmrPPqkEV0xMDamSM7XCaY91Kdl8l3tYIQWIpCZ2UJSbcNboXkodRnIbByNsWpyUgoeCRQZZAtkyFNT7Fy/JA3iRyFaCIKZQHUutGMa7+Y/Kv3MiF/kAlyIyBTHhOY3jpCWp4tM85Dtv+AWv4P4GwAbikvw+9eAo2jCvaqHAtCIRyrIJtaQosyF3lIQXAIycQK72BQ9wg8uRxp73pmLzowYEqmtpXffXIYSVcSlzDQ7RzD2wQr1F2EdMHX/2DzPWPAJRiqzF2XmayqLWxtT3jBUBWKElOAEKP2NbNyeA5bltACBqrHRC9axkOzbsBUNKDwebsti9xrDTQb5XQVtdNTtJcD4SSynaJjUBwj+zH+ZA6hEvmQ7sQWOueXPYvm7eK28htp3TkN7ILoTKYEHWohgiZl0nQN/gM7qg4CGxm5/QWufiGAZglixbX88KNX8nH9txSbcSTDRt2ZIJJKEFYKUblkOk3dhv/jT9+5mXgg2Hfvcb+fbKmC/LEeJO3IpbxDSc3mhCbcZhutrw6iKBvBJflZvPl/CfQUymOrismIW2+mqLau3z9PAfHj5OicpByd9lXPsOjPqykeWs9NEyZw5c7fke/IURPqwu3K094zkZoDbeRcMnapyeNDz+e88FOMVPdyybl/PW0SzhwcDmHGcrTdtQbM/j9Zpm2wMtGBacPw1x/Hk2zFle1gzfzpbK/qpNNfRZNrCjv3D2XaoN2sGjwTU1EoS/UQybeiWTE+0f44c//UybJP+XnWk+PLPTEOaoOJR1woQuGJYA3bqq8Df3/TSXcmzVeNOykihhCCHdHhPLrrCvTxkb56PZpl8hn5f0i2VFKaVSgXYzGEiWWpZDDJqd247Q6sjMTYQfvxy1C07yKWqNswD0vWlWxBVVMrCAl1iguPLDAsGckwyey0+npXybaNKkykqRZtB6Zimx7cph+vWcUe9tFY66Uy1cklry5j9ywX/up9KNlSUjtrwRao3ggHS91UNnXS48oyNNnBxDV7MFTIeYawb9bH+ETpN1jYcB+ZAy1cMPl5fuH6MV8U91DWvI9n1HI+3/whdrGB+ekZVGTrej8jnbVlv4RIHFP30bnpKqTD8lYEBkXWbtJ6DBMD2TKZuGstqj0wR2eALyiwZPwEWkQNtuyh1I5x+fon8Gb6o26GCo9fVIypangNiYbdAfZrAnfFLBou+hOyarCPev5duqs30bgV88WR6JkU7WqWOaXLubW8lEzrJ9Hrh6NIcOHGZoaXbGPITh/plIvBg3byrHsu7WYRUckgKoK0UIomG3x00lP4XIVlOmHJZHeNxDIUmlWLV/VKLqxdS7inBD2qE0jsQlGLSYYCGJLF1LU9bJpyAY9qZfgTGUYl96AGt2BJEqGcSkOLhi3L6JrMS/5xWEqGYqWDMm8Uj0unXOQYTzONcgWWGSTrG8k9s87rFXS9PqzrXLvqcco6bbId/SUSZBumNul4evP5XYrJ8Lu+i1JcKMD5fhE+To7OB4BoY+cR54x0gE0+P8M6Kxi6bzNdIsbyhQtYVzcf1TIwt9Zy1kXfdkSOw2mJWuSh8hvTSDZ3EGuPkemI418Hc4t6i+3P+DKmrdO86n7+XrGf/ZU9/LRjPf7k03xRvYU1LQ0oLT0oQFY2OK9U5y8TzuQXxeOZ6voGZz+U7o1PBCnT4nztCwqZoODhpu18dt+Z5EN5LNUgX9FCZ9nHecQup1gYfCSsU+mKor1uwGHLWwAPcclb3FEQIdczq2wfTemCiHKX78Kv2hTLMratUWGWcIC9jPCOISg80JtD7NEjhfYUhdSQAfk51kETKboEW09hyioe34WMo55hBwAq2TVyPHJnnmFL/xuXkSKn7sGQszx/bgNeBuNXA+wr11hbYTNin4dfnfc6GVnmIx39M5etIMO1BNqhmi+DC+c3VeS4k9/QKf7BM3s+yvld3QxKfIr4pghRXQcEkvJ7hn4oi+YXmPkgzS/fQMwejOjNc7E1WDdpOpL81g+vSO8BgAIrZ0xmUO19ICWQcuBfonDmiv6k4mjAh7vSReewJB51MPv3Tyo046wHc1sdf/Nv4sDw/hpET1GKisTVo15le8ZPzAqzfWIl4d3b2TpsCGm/hyrLS6R6M0VKQZT5SVNEjICiU7fzCgzDw2vqbnKmCztvAzZVeRdyvpK/7/wIAJIaIzD5Bbx2G0O6S5nacgUPzv8FldFWrmuqRfL6iRUFSZaWIslJdiWqEK4oV0hP86viAJ/pWImtlWF7C7lTr/vH8JdhZ/HVjVfjYTNJpZSHmufxmdeWYYXTjBr9Emta5vNE9YU0Di7DW9KKu3o0uqQgZzVy7V2sDY9H7bWubBt03/oTgrnCFnyXYjL28T+9L8TOseJEdE5CRKe1ZS3P/fxHZLJjB0R0Mnt8DE7OImK5Geu/jfJwO+umN1BUcQelSgnDSkOU1FScsHk6OLzfie1tJrq/m1cW7cZn55germF1vJGkrbAr8gTt3jZ2R7pIBBSufLWIUFYnV+fhJ3ye83y7MVQvsiZRnMzgS1SybIybK7e/wtynXuLFS3z8vTjPl3tiPCfNZ5OnHtnykDQG0SyXkZ9bDt7+74pyOs2Mrp/zkaI2lncFGJXysdPTgNdSONT5UVZMKsr2Iyk23Xopj284f+A2dkUif8bA13WLHDfxY4LE+87ZlsKzPZ8nJpfhNfLMaVqCW8n0v47u5SOJ8/rq95iYvGCvQrfSaGkTXZuGaXlR7N48ICtPd/UyTLwDKj2rQuX5TDN/b3iU67Zf3hfRye1NcvvsH7N6yafQtAi1lYv4cdlYDkS+xOf3/Ylz6h7nsYOfYKbcyawtH2d3aDFJuwdaZeLRJHJYR9YK92jLKumOGOKwsgKSIpDq6rCko7e7UBST6uptKErh8WXqPpIbL0XOrAKsI8f7c3h8Cv7KM7lh6iSMwxqSuk2bszb8mQ3lzzGmZRI9xjDsEX4WtO9FElkSUpB8yk8g24PtDqJp1UjZLlzJFCHdzfYSqA+34RIwVgzBo3kw8kGkZAXbE2vIyBprhk0maGYI5pPU7tlJCje6zwaPTTIcRPckKBu8EVmxkZorqWtu429hH0PMERRHu5i7YhcZfyl7xlxBxrWFvAskV5S/jNiKKQ+MfFlKCT1VP6Jaj1FsxCFvUrqqndmdG/CFoPbiV+hRi/kWPyMvDczxdNk6t616DH832Ak/WS2H6YrSaWTwSPMJmTXIVp6Pfq6aslknt36bE9F5H9PasparH/0snojgQ5kxA37nNfNkpQ1UVTSyLziaXUzhw6NvpGrYxJM0WweH9zdFQ6spGlpN6aSRdO1ownyiienhGgDm8yXMtE7zsvv488wka4fIgJcef5pA8V0s3vMNhFkEgEvO8xFtF3vDZ3Hv2EqmPvcaZ/4tw5kABLlSW81PLl9L3if4j2iK71ufRWsuxbT9bAo2UGU3URRSKEkNJRhu5kODEsieLmawrm+uXZSSJIhs2RzYO4lITuGK0teIS4Vk0KTh4bWeYczb/AqjkgkUGywNHpy7kB+p3+m/6ayJZNgcqkGoqi7GjYgTlBN9QzxWlt/v0lAbbSJCMLNoGq1eHVNSIKgAm5GERHFyMKG0wFKH0izVUybaB1R6VoVMeXsrxYmSvnMjco1kpIKo8gudsjaTMdou4sFCi4tcpnA/41IH6cyEeabk7yxMzylEoIJg+gsFE1NWYZu1TwkSUQdRXTMcVVXJm3Fa25qgKQfk3vSzT+3rb3rpVnycW16HGhmJ4enG0gqvvd8l6PRlSY98AkMx2MnLGNJ0rrd/weZmk4SlkZM62VC+F49tM6E+iubbQvv6BXS2lJMIHGBCTqM4pjN206tsnfB1plfX8Ir3KXaFXdhGOUMzfsrjk5nqLWN7Mk2yt4aTLmzy9nQUYMxOQUnnatxGjrSk8cTgqZiywuBsOzObX0NWFIJ7qvF4Zbr9Ye4+8xoC6Sai2RRadR0ra4cjsk/htv9MWczHxatAwosU/QKpoiRWby8QSZiEO5ro9DzGo3Pn0KZ4KBYS8fFhXDH4btvPkF82aLTL+U1wO8vDr1M3dA2yYvXt5uqY1cEgq53gpmspT3hJxjawoWIxB8L3cJ39BVq3FtPd4aXsTT+Z9x9OROcER3QW/c+dBH73JDl3kP1j51JRP6QvotPUWcc8fxELG4Yh+/2Ehw5xIjgODsdBbG8zXRv3smpxOz6RPyzC40aS3Rj4yZt5nhz1Ay7bFKY6Bn+cFmFN9qOYdv/3vqpsD94GDcuTpa57D1/7w99wH9YzKafBTy6XSXpUqjsm8QXpBdwenYjXpMqyyMgerqu9jXnRF5DJstxXTJv/AkRvJKFHDdPhjTCr5QVEbw+nVL6IHR3DEfTvZlKxmOlrxOdXQYa0nWNV11AsMbA311uhSBbnV27Bp+rE/GXUNzcx3D2YvcYBLkicTakIYQpB09qHsHNNpHwa+4sCGKWVxMr9VDa1sbU4y7T0hXy6/AY0LBIBhdVTIuzYtJDSnTWMH/ITLq+bRazq+4zbsZ6PDn8EVSnYK0iSIjvOE41fx87VcEOTye8H5enWdOZZq5nUfBZ+2zegevTReOP2+TdiCZNGZTHBs59HUiy6KD1q1EKzDG7f8RV+FkxT1zKTWi1BfaYJQ7eIuX1o6VLK2s/ByDzLGbuaCGd7lwRVmdZrG3CNaCew7nr82coBc7aETmf+xyTkBH8MBdAPKx5oKGmE3M3Xe2KEbJsdcgU/Lx2FqXrx5i1cpsCf9zDv9TSW16bT7cUKlKHKhYhWyBSM9zeQ8reTcMFSO0HcehK3FUKzCsJSsmWqklV4s0lc2TQzdyfIeAMkvWH2NhQTUXW8So50iUVpWReJvTWYyVIMr41PzpFTarlr9rwBeTyqZfC5rY9xwZ7XCXS56C6roCeb5kC6i2t/9usBbVxONE7BwGPkZAidW2+9jlXj5hPWU0zalaWhMtQndEZ2TOPaGz/ubBl3cPgnSfbk6NrRhPREE6o8cDv1ymSUVnU5DWorZVoM29fK2vRlNPsMsHLkbY1tooY5yn6kQARTrSDftZ9XRk4koh/kP7b+FNdSH5I5sKhcToO7L9UIpiYXhI80sDozbrtvC3vYVFlg/JgDkxsGtCxwizyfNn6P286yo2MMqw+MPULUaLLBxyY8SbS9gaKojkvO0+hSMCw3k+yJZLwGDwwvRZdcSCkT1+bogCRpKDQ2vcP1DeI7xlO2vohZ5RezJNpO3DKQZC+KcFPb/hivTRlCZVMbQ/Zn2D/is9QkHuT+C88ltKeZa874PapsYjeHqThwkE/WjSFV+UN05Q3Cwta5S/oKjetm9BUVBHC706haIWIjhMyOzCiiuTKELGGMCIFbxqPruCwTNZ+nrmMbPley75qE5ierDnwvjzdFXd2GvvybZ+rPZt6+Zzk79yIPeiMI2cYSUQypGcWSWfjSMCrSby6ehk5pIZGtx8yGGNmcxmOZSCmb++YZ9AT6H52qkDi/uZ60z+S5mjnocgpbFBKRJWEzPL6HYN5iaFcWa0SWx3ZfSJPsQVL7W0+UEaM23UFRJkOVkcbrG0vOb5EPRrE0heKAytCKfSReH0Rij05HQKCrOrJbR7gFwmWyUd2GywiwcPuncPU8icss3JstQcadYWybxcqzz6NYTVIs2rDVHO2uMCFfmOfkaXzm4N/oDPmQprTRqlZzn3TTEc1LXZLg9ehiZn7zK1QMHf6mtnuvcYTOMXIyhM7Pv/NRMl43uiyoTBUTqBnGzSPO4bPb/pvvX/QN1Nr6EzIPB4fTgdjeZnJdCXKpFCsfeohZkQ+jyv1NHE0hWJnOsa3or5ybacStdqEF4njUQk2XnHDxA/PTZM0iZrtaEH4Nr8jjSep0aRF6VImoy4Wcg5v+/BQew3yzqfRtYU+EZH7R3s6invOJZosASOOh0VNCy+RqLE8JVabFiu2fI2r5iYrCNuKc7OLz9bfT7i0Bt4LQZFSX4Jqti/BlrAEVmnWfTVU8Sx43v7ZnYYiBokwoEjfO+W8G7RhF984Ec0suZFvXCnK5LixZxeO7iKbiF9E1H8NasyQkGcQ5IKk0++ARV5bp3o3c7b+f7xlX4VJbealsNVm1FFsuFEjULDhr70gWnfUvfFHcQ82BVpbqg4iZEyn17GV67Q7Uw/JLbEth776pmMbRm2zKqs7QoetQFZMkYe7hm0dEa96IZOeJtH4bxepGsVXGNX6KkCFQkyupzM8kaNYiMEkoq7EViZKyLvxyBi2YoHjYQRSfzcuvjqNu7wj8ehTDnUO4c1R4d6JqUWQNFF0QjPspsgPIHpk2bzFtXhnLGwefTdhIUKF3kJPd/Mz7KQJtcWxFxYPBGYHN7BsxiHuVr6NL/fc9Ib6T+/Z8j296RxDMKyiolCOzYPYWZNVAT6qYOZWEBWYcUt1F2LuH0jD4XJKBDrpVmWdcfipiHbjNPJqhUpwIs2Tk/1CUdVNsl1ET9TNnG2wZnuHcV+LsnD+I9nI/KbeKEAbbc1U8O/0TXHbwaaZWL2XH9rlks2ECusmsWA9lo6czdUYDcHJ2YjlC5xg50ULnvu/dyPAZz6AcVufg0PbGa7f8gR985e73fA4ODqcrh/ppdW/eR/O+1+l8fSdzyy5D6Q3Vm8LmH9F20naOreV/I+0pfNsesOygmX3LU1BoL3CecSdlxTYHissIZdqQ5Cy4BkZh6tta+Naf/sornw7wtDuLLyP4+l9tPIfVgjl8OWxExyiulZfzB/M82kWECinKv6iLB7SuSEheds+dyBc991JmN/N8dwjTlBiXGkIkaSD1JkAnLQ+G7WZmdiJBvYyXvTa/y2aZN34VvriMnvfhlk2KDZ0ru87oW45pJ8pTnnV9ycl5W8KwIZPaR2L3ZjrHTuGGyDf5k2sBvxELuXH6XazdtAB3UwMJ82mWTu0h7woTrfohQn73o9SynaM+eg+qnURCItQ9nmE5GXQNXffgAopySXyxF8lbElbWSyBXiGpJSNQEByECEQ5U2BRZBpa/jeETX2Pn+nG0GwJDssmnvIQ6w1wqb2NieX/u0qG2EjECTAg3oSlHF7gyEiNEMRFZIi/7icthVtFG1lYotuN8P/lrhNtiv6+CqBomj8ZDmXMoFVH+I/MQPYogqvQvgWXypej40S0ZS0hgGth7NTZPnY5l2ZTrMtXBKJJisUcfTNKMkJNlNBnSfljjLiWgdwIyZT0p/vXhJ9Esk5wrjBmUyYckUsMN9mjjuSdyDunZI7CVgQnhqmXw7U1/IJX1MPPZdVR1tSB5vQx7+u8nVOw4ycjvU3z5FqJaCZv3n0kuW/jW0+MtKmxvPKyuhIODw7vPoYrLlRMaGNLVwYM338Azzf+NW/H1VV0uMtcgG93MaipB9V0OuJGFSd2BpWipbrqtBN++Qmf+bi8VpJhUupNz0s/TnCol0ghpt4u4r4TqUIaAEsJWKikSIZ4dOYys62nm/DnFnN75mBps+KwX4RZM2NuOe2mIf3vEptDPYBMQ4WrWHHYHhdYVv7h0Jf9qxnBbMj/f9jGWVo1HlmYQ89bSXF7Npjfs0qohSSld2NZjtCz5OvV2FcgyyzfP7HtloUiIiQHa/WvwdsQpkiWmqMOQsmJAcjJAaS7GjNheLGsqAN12BiSIqILBvix76qDqtQCXLtfIuyxSvp+gFSvMqDiIctgWs4yQ+UkwTF4qCLKgIvANDDwdQUZATpe4eFMIb17FcleTyr2OnvVTnNlH0EodeZEUxj8qwtCG9ci9ESTVY+IKbgOg+LChti1xufYqRbqBJ2eDAlQWhM2/6N8mLvk4d+QKbK9gkrKWMZsupyxazxZrK0nAZeloIo2FIC8Ee4SfNcE1SL33rVo+IrafYhQGZYM8Kn2MdJFJj2KhZ91IVFE9YjcBJcET+kgi2SwuyyKDnyatkh67mHQ2hIULrzCJo5EK6xTHbFTZw+ZIFeVSDJ9IEZTzhLxdjIhspD3tIZuoYKKVJuUrIuiy8VRlefUj5zOtZyxpSUHOxXnJs4eW0o1Ma8/xnaYX+PexxfiVKDcG/ogmmX2FB72TttNgHmB761jKJ9yO8cufY0aj79st547QOYHEwyFu52fk6wd+u1EtCzVz7MmFDg4O/xyh0nKuuft+sonCTiWRsjD/0sms8ouPfkHxlZjC5vVENx99XWdPyTJiZBn2Wj1XWAAxbD1Dxkpw76XwjVg3AUvhu9bH2SBHWRA9yLMfnksgn8V0SWwvGUlTURWbyguh/5rKZj7ueZJyM4V7WBNZ28BUZXKahoRKsetsSuM9VPxmCd98VABhAG7U/o+fXC6T9whu70myMTSSJ+sW8ndlAXGthmZXObJlsLBjBUMrN9NTt59Abg9XWG4kEUISAo9dyZ+jIax1SRZTmI9QJP5nTpjPbNuDoVcyfO8GLG0Mbix6gmlMuV+RLFzVyOopHRiWyqhRLzOKl9GHqugZF3v3TiViupBMN7GmEN7eXVRqTkfqauNf1Azdh3Vk9yk6bqV/e3jeUvp6ax3CrSsEcod2mPUQlhTG1o0CTWGL0oQl2aiqztCha5Flu1fUmAhTwbOynhI5hXLYS3qlGGGpFQCXIbBzGv9qfJNuKcwYfxq/nCeu+im3BGdZe4m0RNBNCTsXJp3bQtrOYZsSdnocedlLHrAUAyFblHrTxJhKTkkRMPejyuC1svhVL5rXQIp3kO1wkwtoGN4EeUnDtasYFC9d+UpylgxGDQo+woQIA6aks664lb90TqFCRPmH+5v4PHnydgPrGcYKRhPVhhAY+QKurEndlhT+A2FGDptCVsnTKbqJhTZgdrlp1SawuvJ1/LJF3pLw78uyugKGts/l7Jfv4w+bd7Ojpo7//cQ1fHLZC6iDJsFMSG+vRRm9jwa9lZW7/oE4+0sMTlqH2rO973CWrk7g0tV37/4K903+HF9c8Rcmb2jFiAwjNOwcdmX3oGpt/Ovt33rP5+Dg4HB0zFgOO11YgkjHesinC0tXscb9rH/2OWaXXDwgv+eI64XNqkQ3+d6KvoXtxVnOKv8R412Fli+titK3FJETLu7MX0PSqOqrqCsUCWNS8YAEZbxqXy2dkp5u9LhJ3rZxdUrMeiiKavT/CT+0/JX3CL7Sk+L30sU0+kr76/8oZTAnzE3eu/FbKfbum4qWtZFyFlElRIQqDkqCJZ2DyM8u43Pdf2FB+2CCwlNoStpzkN0hN7UHbWTXWD5d+iU0ubD+lnPL9GgB0paHPyrnMGfi8jdd0gHQkyq5rJuNTTPwZ2Rc3SlUCcpGt6Mc9hXcsiSam8dgWYWTh9fQkdUMnkgPruCR7yNZMGybTsToLXTXK2LerJO5LNkMDXWiu2SiLj9FdhcitwtLllDJ4xEWHivAkPh8hBHEzpSi2B5kK8/4Lf+NECn+NF8i7VIp6poFFRE8ROmyXiKjZcip/cUkPWaEMw7ehir6l4UOf52Hz4ZL9DRt9hA0jxfb6yMnTPKmQjbnI6HmWF2WIpwOU68LarKVdBd34NWyfCmzCDcGKVcpB1xlLHeV0mpWMapDxrIitMVLODu2naLxc1lHila9gzYzxrCuCI+f1cCVOx6m2TuFUPY10sliNgwOEom1U2zPQFJGcP+5I7hm/WKGTXoO4/mxeOq6GTx0NVX5HzDu4ive9PN+t3FydI6REyl0vvjAnVR3d/OL6Z/ink2bmNtaSDo2heC5+DKu+9V/vKfv7+Dg8M45lN+T3H2Ajq172LVnHeKwFgVuxccZ5ZcOTHS2dV7ueIKsnUG4XSBJHIxkCjtgyBCXElhqiu+mdhE2Jf4gXcUzoRIMtb92jIxguLedHYOGY8sysuRFkcL0qGGaXeWUpXooM2Jcav0OT3v6bYRPgt9Ll7Bv5GAkXxhFCtPtKaIrXMKdfJWIFWXbtrNpyZbxl9gc9PERPur/C2UUSiKXaZ3U5EzWbToH29Yo7ppGUOrBc1j9Hq8U5YLIj9Bkk5xbRtfefC3K0GQ2jQlhKwPHWJbCzq1zmZNbS5GaRjVAyw8cY7gFpsYRyMLmXvMj4O4mbyvkcyFUISFUGUtRicouWlUPipkjnM0yvu1cVAqfmdeGkJCPfNE3vscbBMnlRjvL3MOJuiOkAz7cksLw4Y0UBQpLh2Y6i6UbpPM+2htHINkeDHee8nA3iuXFnbSQkwGk9o+hHMciS9LVg8u/iRLPDvLCTdrQOCgbDDZU6uxivLNeoNMsJWe6SUe9RHMRtieqWaNPpiSdpM7IU4KJZhuUBPZSndlHZH+OjfNmUIzJCNFBV6OFlVnH38aYTE98BDU3lV9eUIt5mBJVLZMfHLgb12aZC//97hO25fy0Ejr33XcfP/7xj2ltbWXs2LHcc889nHnmmcd07YkSOmYsx3e+/12CpdX8fOYZfGP1KioPFNrOmqaBX3qEj/3qd+/Z+zs4OLy7JLo6+pa9DpFu7iHf2Ea0u518zmRoz2gUjr4kfUgE5a08tT3gMfIIWfDiGImMq/BQNxXIubPoSoKUnOKAP0omKPhpRxdeU+HO/DVkjAraxw6mMVjowVAe7aY03cM59uNU9bQy87EelKMkPOc9gq/3FPJ87qn9JHqghXnuOHmXixZRwmMbL8ayBz50hQyXTXoOM9qAP64xNCkoMjzslbuwJIFka4RjY44QP332kQT5N+gI1ZsgoLUyN/Aoeu96kmpAIKOzeWsDecONqXhoGnwO4rD6LpJlMOTgUlQr12urwpi0VIxulh2TYIF+0SLbKV6ePYSMV0WgYNkSEhaS5UI2gggkVMsmaCWR3RlsTxbDK4FXozyfx20IknYZMVFLQKkj6Oumkc3YxNEVA0nNklEUFH0obkw8hklANvCoeTRTwRYKnUYZdj6E0AU5O41GknDOYurqXajWwMe0qUi8OruOnFulLVuEKVkkI5uosIoYZUjkvDaKojNcP4iywwNWwZ+W1w0j5gtwrrISSbNpCwR5yVOOnJyGFUsz7WAjXo+JJtmETTexzBmMaryT718mc9M/Ivzw4zdQ3fEiRT4PUxq2smf3OTw48sN8b+OvsfMwe+rnmHDutGOy/T/LaSN0HnnkEa666iruu+8+zjjjDH7961/z29/+lm3btlFTU/O217/XQifR1cFjv7wKWdGxjSK6iidx94RL+PSmBwl1Zpi+LU1G1jn/V/ed1MJLDg4O7z5mLEdsSxN7t24nmY6BmcU2Ddw5mdr8aBTp2L+925ZO0/bHWD4yS9ZViAgllRifzKbQbIm/WWeQREKXsn3XZLQgqh3EU6VgKmkqEq18ZtHLuMyBLRJyGvziUol/zRWEz73qJezxDkKXI6SUcrpqq1AwkbenjmhXUT46wANbM9i9bRcStsnLyutY0ls/VhQhMTNViazbbKcKn9w9QBzl7BApu7/2rhAGwngWXUmyrTLBhK5rByz7FLCoVHegYOARboKWH0tYNKa2Y4neon+STDISwEcOFwaylEMlyyhlMpJ3JDnJ4B/apgFNVd8pqpA515iAR2hHvO7hNYQOYRoeYqKcEakqktkEKTVNt50iFE9RrAsUWUWVc/hkkNxedI8XTU/TJaXoCgaJemzcSg8hy015rhhNN3GJPJFMBhddpNQ8GaEzQuzDqxiAn45ADV3hmSSJ4zU2UzamhWwqRPVaA31PJa+NGE9rwI/u+TvzUzN40aUTjQSJlfv5YulDNCm1/Lt0F98X36DGbqRp7Sf43Le/90/b7lg4bYTOzJkzmTJlCvfff3/fudGjR3PppZdyxx13vO3176XQSXR18Njdt+I9extptVDfooXB3CfdxKWbfk1pqoeaxhCf/K//ckSOg8NpxqF8oNd376Snq7fBbyqDHkuRT+kY6RhdzTtAiLdYFnsSyz0DKDzwZWEypGkJqpUHQJBDGtGFW7WxRZKsZSFybiQTlmoNpBUIqTk+snwLLrP/wT5wuSvJo/qHSAkvMW8xBDQ0zcKwNTYmx2ILhQokwvQvLbnIo72xWOJhaJJBg7a7bweWbLmR7aOsQx2GLRvYSuG+MkoGS8godr89XJYLF0rfmLfirQTIG39/CEuYbM6uRjfzmKrAVlU4rII1ZhroAimLIpvkcSOLAAE7jEsKIiTIySY5ycS2TIRl47K9aLILSzLJKxZJTwhT00GS3nIuh5PPdvKavhpL9LaAsDVcpoBcFLdcSA1OeAZTVjKGHqmNDa5XOKPzIlCyGK4uJDWJbZu4lYMkrQpemjGMqtejTPFsJp9Oo+KnOV+H6QtQnFpGuz2TNRPGs6BpGUYsQalVyf/70DX8x7bf0zD6Sbqe/BCGUcT4YfVMvPnzb/tZ/DOcFkJH13V8Ph+PPvool112Wd/5r371q2zYsIHly5cfcU0+nyef7/+PEI/Hqampoamp6V0XOnfecg9Tqsr56qQR5A9rTKdaJvNXLmFmLsnV//mf7+p7Ojg4nDokujvpae5Gz5pIGZue/R3kOrpRDcEQo/pNl8WOhmnrrOx8Gt3KAAqq7wwOCSQtF8cQbfhSG/AFexi8vgfZevvHQqc3zL6aSpJh31uOM1wapqpiKiq25gHVhfCopPwKOVVjVclELFk5NFG03QmwoRgJP2+93zyNIGtlcFv9f9fzipusUnjIH+01DKGimhZuu3BNDp0eVSKreAm5k5RKFu5e22QtD0k9RAJBBwK/mcR72HtlFTdptVDQUVJjSOphjVVNX18vtTcjIFIMzusEXC34NYHwu/Ep3Xi1KH4jRTDnQhblGLoHybKRLIEiQCVNwN8BEpgoWJYPbBf5vI+sy83Q9F6EbWFKCu3uYopcNopLJm4UkZDdyNlSPPkEpq0jq3ncZpTy7QfZNzjEwbIIQzosvKqJUSoTLfcSTRVRIlkUxXazaN7l3L79++xNn4/ZkuKHn76SL+zZQeXQP+Hdez5yrpiynMUBYxOeM89lzLi5lLs0yt1vLWaPl0QiwZAhQ4jFYoTD4bceLD6gNDc3C0C8/PLLA87/13/9lxg5cuRRr7n99tsF4BzO4RzO4RzO4RynwNHU1PS2euEDX0dHkt5Q2lyII84d4tZbb+Xmm2/u+9m2bXp6eigpKXnTa46VQ+ryvYgOnYo49jo+HHsdH469jg/HXseHY6/j472wlxCCZDLJoGMoUviBFTqlpaUoikJbW9uA8x0dHVRUHL3jt9vtxu0emMBWVFT0rs4rFAo5jn8cOPY6Phx7HR+OvY4Px17Hh2Ov4+PdttfbLln1cmx78N6HuFwupk6dyuLFiwecX7x4MXPmzHmTqxwcHBwcHBxOJz6wER2Am2++mauuuopp06Yxe/ZsfvOb39DY2Mj1119/sqfm4ODg4ODg8D7gAy10PvGJT9Dd3c13v/tdWltbGTduHM888wy1tbUnfC5ut5vbb7/9iKUxh6Pj2Ov4cOx1fDj2Oj4cex0fjr2Oj5Ntrw/s9nIHBwcHBwcHh7fjA5uj4+Dg4ODg4ODwdjhCx8HBwcHBweGUxRE6Dg4ODg4ODqcsjtBxcHBwcHBwOGVxhM5xcN9991FfX4/H42Hq1Km8+OKLbzl++fLlTJ06FY/Hw9ChQ/nVr351gmb6/uB47LVs2TIkSTri2LFjxwmc8clhxYoVXHzxxQwaNAhJknjiiSfe9prT2beO116ns28B3HHHHUyfPp1gMEh5eTmXXnopO3fufNvrTlcfeyf2Op197P7772fChAl9xQBnz57Ns88++5bXnGjfcoTOMfLII49w00038W//9m+sX7+eM888kwsuuIDGxsajjt+3bx8XXnghZ555JuvXr+e2227jxhtvZNGiRSd45ieH47XXIXbu3Elra2vfMWLEiBM045NHOp1m4sSJ3Hvvvcc0/nT3reO11yFOR9+CwkPlS1/6EitXrmTx4sWYpsnChQtJp9Nves3p7GPvxF6HOB19bPDgwfzwhz9kzZo1rFmzhvnz53PJJZewdevWo44/Kb71T3fXPE2YMWOGuP766wecGzVqlLjllluOOv5b3/qWGDVq1IBz1113nZg1a9Z7Nsf3E8drr6VLlwpARKPREzC79y+AePzxx99yzOnuW4dzLPZyfGsgHR0dAhDLly9/0zGOj/VzLPZyfGwgkUhE/Pa3vz3q706GbzkRnWNA13XWrl3LwoULB5xfuHAhr7zyylGvefXVV48Yf/7557NmzRoMw3jP5vp+4J3Y6xCTJ0+mqqqKBQsWsHTp0vdymh9YTmff+mdwfKtAPB4HoLi4+E3HOD7Wz7HY6xCnu49ZlsXDDz9MOp1m9uzZRx1zMnzLETrHQFdXF5ZlHdEstKKi4oimoodoa2s76njTNOnq6nrP5vp+4J3Yq6qqit/85jcsWrSIv/71rzQ0NLBgwQJWrFhxIqb8geJ09q13guNb/QghuPnmm5k7dy7jxo1703GOjxU4Vnud7j62efNmAoEAbreb66+/nscff5wxY8YcdezJ8K0PdAuIE40kSQN+FkIcce7txh/t/KnK8diroaGBhoaGvp9nz55NU1MTd911F2edddZ7Os8PIqe7bx0Pjm/18+Uvf5lNmzbx0ksvve1Yx8eO3V6nu481NDSwYcMGYrEYixYt4uqrr2b58uVvKnZOtG85EZ1joLS0FEVRjohGdHR0HKFMD1FZWXnU8aqqUlJS8p7N9f3AO7HX0Zg1axa7d+9+t6f3ged09q13i9PRt77yla/w5JNPsnTpUgYPHvyWYx0fOz57HY3TycdcLhfDhw9n2rRp3HHHHUycOJGf/exnRx17MnzLETrHgMvlYurUqSxevHjA+cWLFzNnzpyjXjN79uwjxj///PNMmzYNTdPes7m+H3gn9joa69evp6qq6t2e3gee09m33i1OJ98SQvDlL3+Zv/71ryxZsoT6+vq3veZ09rF3Yq+jcTr52BsRQpDP54/6u5PiW+9ZmvMpxsMPPyw0TRMPPPCA2LZtm7jpppuE3+8X+/fvF0IIccstt4irrrqqb/zevXuFz+cTX/va18S2bdvEAw88IDRNE4899tjJuoUTyvHa66c//al4/PHHxa5du8SWLVvELbfcIgCxaNGik3ULJ4xkMinWr18v1q9fLwBx9913i/Xr14sDBw4IIRzfeiPHa6/T2beEEOKGG24Q4XBYLFu2TLS2tvYdmUymb4zjY/28E3udzj526623ihUrVoh9+/aJTZs2idtuu03Isiyef/55IcT7w7ccoXMc/PKXvxS1tbXC5XKJKVOmDNhuePXVV4t58+YNGL9s2TIxefJk4XK5RF1dnbj//vtP8IxPLsdjrx/96Edi2LBhwuPxiEgkIubOnSuefvrpkzDrE8+hralvPK6++mohhONbb+R47XU6+5YQ4qi2AsSDDz7YN8bxsX7eib1OZx+79tpr+/7Ol5WViQULFvSJHCHeH74lCdGbBeTg4ODg4ODgcIrh5Og4ODg4ODg4nLI4QsfBwcHBwcHhlMUROg4ODg4ODg6nLI7QcXBwcHBwcDhlcYSOg4ODg4ODwymLI3QcHBwcHBwcTlkcoePg4ODg4OBwyuIIHQcHBwcHB4dTFkfoODg4ODg4OJyyOELHwcHBwcHB4ZTFEToODg4ODg4OpyyO0HFwcHBwcHA4Zfn/KPMJP2Ky/osAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# set up arrays for everything\n",
-    "#CHANGE COMMENTED OUT LINES TO LOOK AT MORE YEARS OR MORE BANDS!!!!!!!\n",
-    "#years = np.arange(1,11)\n",
-    "years = np.arange(1,11)\n",
-    "ilims = 25.3+2.5/2.0*np.log10(years/10)\n",
-    "\n",
-    "n_m5 = 11\n",
-    "\n",
-    "#bands = ['u']\n",
-    "bands = ['u','g','r','i','z','y','ugrizy']\n",
-    "\n",
-    "deltas=np.linspace(-0.5,0.5,11)\n",
-    "#deltas = deltas[0:2]\n",
-    "\n",
-    "minzs=np.arange(6)*0.2\n",
-    "maxzs=minzs + 0.2\n",
-    "\n",
-    "if 1:\n",
-    "# these arrays will contain the results to turn into d<z>/dm5 and dn/dm5\n",
-    "# first index runs over redshift bins; second over bands; third over the delta depth array\n",
-    "    meanzs = np.zeros( (6,len(bands),len(deltas)) )\n",
-    "    densities = np.zeros( (6,len(bands),len(deltas)) )\n",
-    "\n",
-    "#loop over all the files, calculate mean redshift and densities in each bin...\n",
-    "\n",
-    "# outermost loop is over years\n",
-    "    for year_idx,year in enumerate(years):\n",
-    "        print(year)\n",
-    "        yearstring =np.array2string(year)\n",
-    "    # degrade the catalog to have noise appropriate to year\n",
-    "        tmp = degrade_catalog(df_full,year)\n",
-    "        tmp.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
-    "        cleantmp = tmp.dropna()\n",
-    "    # train the random forest on the degraded catalog\n",
-    "    # skip this when rerunning while testing\n",
-    "        rf_year,imputer_year = train_rf(cleantmp)\n",
-    "\n",
-    "# next loop is over bands    \n",
-    "        for band_idx,band in enumerate(bands):\n",
-    "            for delta_idx,delta in enumerate(deltas):\n",
-    "                binstring = np.array2string(np.array(delta_idx + 1))\n",
-    "                file=glob.glob('/pscratch/sd/q/qhang/roman-rubin-sims/nonuniform-maf/Y'+yearstring+\n",
-    "                            '/roman-rubin_sample-m5-'+band+'-bin-'+binstring+'.pkl')\n",
-    "                tmp = pandas.read_pickle(file[0])\n",
-    "                tmp['redshift'] = df_full['redshift']\n",
-    "                tmp = tmp[ tmp['i'] < ilims[year_idx] ]\n",
-    "            \n",
-    "                zphot = apply_rf(tmp,rf_year,imputer_year)\n",
-    "                plt.hist(zphot,bins=100,histtype='step')\n",
-    "                tmp['zphot'] = zphot\n",
-    "# now loop over the redshift bins to assign means and counts for each bin            \n",
-    "                for z_idx,minz in enumerate(minzs):\n",
-    "                    inbin = np.logical_and(tmp['zphot'] > minz,tmp['zphot'] < maxzs[z_idx])\n",
-    "                    meanzs[z_idx, band_idx, delta_idx]=np.mean(tmp['redshift'][inbin])\n",
-    "                    densities[z_idx, band_idx, delta_idx]=np.sum(inbin)\n",
-    "        outmeanfile = 'meanzsy'+yearstring+'.pkl'\n",
-    "        outdensityfile = 'densityy'+yearstring+'.pkl'\n",
-    "        pickle.dump(meanzs, open(outmeanfile, \"wb\"))\n",
-    "        pickle.dump(densities, open(outdensityfile, \"wb\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "58c0f954-35be-408a-a6fc-754490a90a83",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bands.pkl\t  densityy3.pkl  densityy9.pkl\t meanzsy2.pkl  meanzsy8.pkl\n",
-      "deltas.pkl\t  densityy4.pkl  hgb_model.pkl\t meanzsy3.pkl  meanzsy9.pkl\n",
-      "densityderiv.pkl  densityy5.pkl  maxzs.pkl\t meanzsy4.pkl  minzs.pkl\n",
-      "densityy10.pkl\t  densityy6.pkl  meanzderiv.pkl  meanzsy5.pkl  years.pkl\n",
-      "densityy1.pkl\t  densityy7.pkl  meanzsy10.pkl\t meanzsy6.pkl\n",
-      "densityy2.pkl\t  densityy8.pkl  meanzsy1.pkl\t meanzsy7.pkl\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls *.pkl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6f2d826c-1e07-405c-8da7-c958f3d9b942",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e197e085-6239-4245-b651-7794a4b3591a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "7472c833-fd12-4e36-bbaf-7771be379f57",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c35eba1f-2c1a-449e-8ca2-d941d497453b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "NERSC Python",
-   "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": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/rubin_sim/maf/metrics/uniformity_pkl/simulation_photoz_rr.ipynb b/rubin_sim/maf/metrics/uniformity_pkl/simulation_photoz_rr.ipynb
deleted file mode 100644
index 81c958559..000000000
--- a/rubin_sim/maf/metrics/uniformity_pkl/simulation_photoz_rr.ipynb
+++ /dev/null
@@ -1,1193 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "id": "a5d40f07-0827-46ab-a814-adbf38bc4f4b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pylab inline\n",
-    "import pickle\n",
-    "import pandas\n",
-    "\n",
-    "\n",
-    "# Random forest routine from scikit-learn:\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
-    "# Cross-Validation routines:\n",
-    "from sklearn.model_selection import KFold\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.model_selection import cross_val_predict\n",
-    "from photerr import LsstErrorModel\n",
-    "\n",
-    "from sklearn.experimental import enable_iterative_imputer\n",
-    "from sklearn.impute import IterativeImputer\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "id": "0b0733c2-a9df-463f-b158-c93275a6ec79",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5458656c-1776-4823-b654-1505c4108020",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "id": "2937e855-3b4a-41e9-ac76-ea8951664d6a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#df_full=pandas.read_pickle('/global/u2/q/qhang/desc/notebooks_for_analysis/Project285/cosmoDC2_pzflow_sample.pkl')\n",
-    "\n",
-    "df_full=pandas.read_pickle('/pscratch/sd/q/qhang/roman-rubin-sims/nonuniform-maf/roman_rubin_2023_v1.1.3_elais-subset.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "id": "cfb5ee3e-c2b6-43c4-b0b4-4ba40fbbfc0c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'g-i')"
-      ]
-     },
-     "execution_count": 166,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(df_full['redshift'],df_full['g']-df_full['i'],'r.', alpha=0.01)\n",
-    "plt.xlabel('redshift')\n",
-    "plt.ylabel('g-i')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "id": "5bf0cb7c-611d-402e-9b56-ea4c39b77d1c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#This is a function that makes a plot of photometric redshift \n",
-    "#    as a function of spectroscopic redshift \n",
-    "#    and calculates key statistics.  It will save us a lot of work.\n",
-    "def plot_and_stats(zspec,zphot):\n",
-    "    \n",
-    "    z_spec = np.copy(zspec)\n",
-    "    z_phot=np.copy(zphot)\n",
-    "    x = np.arange(0,5.4,0.05)\n",
-    "\n",
-    "# define differences of >0.15*(1+z) as non-Gaussian 'outliers'    \n",
-    "    outlier_upper = x + 0.15*(1+x)\n",
-    "    outlier_lower = x - 0.15*(1+x)\n",
-    "\n",
-    "    mask = np.abs((z_phot - z_spec)/(1 + z_spec)) > 0.15\n",
-    "    notmask = ~mask \n",
-    "    \n",
-    "#Standard Deviation of the predicted redshifts compared to the data:\n",
-    "    std_result = np.std((z_phot - z_spec)/(1 + z_spec), ddof=1)\n",
-    "\n",
-    "#Normalized MAD (Median Absolute Deviation):\n",
-    "    nmad = 1.48 * np.median(np.abs((z_phot - z_spec)/(1 + z_spec)))\n",
-    "\n",
-    "#Percentage of delta-z > 0.15(1+z) outliers:\n",
-    "    eta = np.sum(np.abs((z_phot - z_spec)/(1 + z_spec)) > 0.15)/len(z_spec)\n",
-    "    \n",
-    "    #Median offset (normalized by (1+z); i.e., bias:\n",
-    "    bias = np.median(((z_phot - z_spec)/(1 + z_spec)))\n",
-    "    sigbias=std_result/np.sqrt(0.64*len(z_phot))\n",
-    "    \n",
-    "    # make photo-z/spec-z plot\n",
-    "    plt.figure(figsize=(8, 8))\n",
-    "    \n",
-    "    #add lines to indicate outliers\n",
-    "    plt.plot(x, outlier_upper, 'k--')\n",
-    "    plt.plot(x, outlier_lower, 'k--')\n",
-    "    plt.plot(z_spec[mask], z_phot[mask], 'r.', markersize=6,  alpha=0.05)\n",
-    "    plt.plot(z_spec[notmask], z_phot[notmask], 'b.',  markersize=6, alpha=0.05)\n",
-    "    plt.plot(x, x, linewidth=1.5, color = 'red')\n",
-    "    plt.title('$\\sigma_\\mathrm{NMAD} \\ = $%6.4f\\n'%nmad+'$(\\Delta z)>0.15(1+z) $ outliers = %6.3f'%(eta*100)+'%', fontsize=18)\n",
-    "    plt.xlim([0.0, 2])\n",
-    "    plt.ylim([0.0, 2])\n",
-    "    plt.xlabel('$z_{\\mathrm{spec}}$', fontsize = 27)\n",
-    "    plt.ylabel('$z_{\\mathrm{photo}}$', fontsize = 27)\n",
-    "    plt.grid(alpha = 0.8)\n",
-    "    plt.tick_params(labelsize=15)\n",
-    "    plt.show()\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "id": "4a7eee06-0339-48fd-ba73-6d5547de2840",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#from Ellen\n",
-    "# some float64 issues going on with photerr:\n",
-    "\n",
-    "nominal_depth_LSSTY10 = {\n",
-    "    \"u\":25.6,\n",
-    "    \"g\":26.9,\n",
-    "    \"r\":26.9,\n",
-    "    \"i\":26.4,\n",
-    "    \"z\":25.6,\n",
-    "    \"y\":24.8,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "30605c5a-eebf-4788-8c16-61cc8376ffd1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "id": "61d03e79-fafa-4ef1-b5ac-7bdfd71002c8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "i_lim [24.05       24.42628749 24.64640157 24.80257499 24.92371251 25.02268906\n",
-      " 25.10637255 25.17886248 25.24280314 25.3       ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#from Ellen\n",
-    "# calculate nominal depths etc.\n",
-    "years=np.arange(1,11)\n",
-    "n_ilim=len(years)\n",
-    "ilims = 25.3+2.5/2.0*np.log10(years/10)\n",
-    "print(\"i_lim\", ilims)\n",
-    "n_m5 = 11\n",
-    "\n",
-    "\n",
-    "nominal_depth={}\n",
-    "for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
-    "    nominal_depth[band] = {}\n",
-    "    for year in years:\n",
-    "        #nominal_depth[band][year]= nominal_depth_LSSTv1[band]+2.5/2.0*np.log10(year)\n",
-    "        nominal_depth[band][year]= nominal_depth_LSSTY10[band]+2.5/2.0*np.log10(year/10)\n",
-    "#    print(band, nominal_depth[band])\n",
-    "    \n",
-    "# finally compute the M5 bins in each band:\n",
-    "M5 = {}\n",
-    "for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
-    "    M5[band]={}\n",
-    "    for year in years:\n",
-    "        M5[band][year] = np.linspace(-5,5,n_m5)*0.1 +  nominal_depth[band][year]\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4b0ff8d9-2c6b-48fd-9bc2-43345fd623a0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "id": "6a20797e-b0c3-40f0-a035-193bebd50a4e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# code to produce a catalog degraded according to the depths in a particular year\n",
-    "def degrade_catalog(df_full,year):\n",
-    "\n",
-    "#code from Ellen\n",
-    "# what we want to do here is to degrade the sample and save them in a separate catalogue;\n",
-    "\n",
-    "    extendedType=\"auto\"\n",
-    "\n",
-    "    catalog_rep = df_full.copy()\n",
-    "    \n",
-    "    nominal_depth_LSSTY10 = {\n",
-    "    \"u\":25.6,\n",
-    "    \"g\":26.9,\n",
-    "    \"r\":26.9,\n",
-    "    \"i\":26.4,\n",
-    "    \"z\":25.6,\n",
-    "    \"y\":24.8,\n",
-    "}\n",
-    "\n",
-    "    nominal_depth={}\n",
-    "    for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
-    "        nominal_depth[band] = {}\n",
-    "        nominal_depth[band]= nominal_depth_LSSTY10[band]+2.5/2.0*np.log10(year/10)\n",
-    "\n",
-    "# we should save the binning in each band:\n",
-    "\n",
-    "    for band in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
-    "        \n",
-    "    # these are nominal values\n",
-    "        nVisYr={}\n",
-    "        m5={}\n",
-    "        for bb in [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]:\n",
-    "            nVisYr[bb]=1\n",
-    "            m5[bb]=nominal_depth[bb]\n",
-    "        \n",
-    "        errModel = LsstErrorModel(nYrObs=1,nVisYr=nVisYr[bb],m5={\"u\": float(m5[\"u\"]),\n",
-    "                                                                \"g\": float(m5[\"g\"]),\n",
-    "                                                                \"r\": float(m5[\"r\"]),\n",
-    "                                                                \"i\": float(m5[\"i\"]),\n",
-    "                                                                 \"z\": float(m5[\"z\"]),\n",
-    "                                                                \"y\": float(m5[\"y\"])},\n",
-    "                                     extendedType=extendedType)\n",
-    "        tmpcatalog = errModel(catalog_rep, random_state=np.random.randint(1,100_000))\n",
-    "   \n",
-    "        if 'redshift' in catalog_rep.columns:\n",
-    "             d = {\n",
-    "                \"u\": (tmpcatalog[\"u\"].to_numpy()),\n",
-    "                \"g\": (tmpcatalog[\"g\"].to_numpy()),\n",
-    "                \"r\": (tmpcatalog[\"r\"].to_numpy()),\n",
-    "                \"i\": (tmpcatalog[\"i\"].to_numpy()),\n",
-    "                \"z\": (tmpcatalog[\"z\"].to_numpy()),\n",
-    "                \"y\": (tmpcatalog[\"y\"].to_numpy()),\n",
-    "                \"redshift\": (catalog_rep['redshift'].to_numpy()),\n",
-    "        }\n",
-    "        else:\n",
-    "            d = {\n",
-    "                \"u\": (tmpcatalog[\"u\"].to_numpy()),\n",
-    "                \"g\": (tmpcatalog[\"g\"].to_numpy()),\n",
-    "                \"r\": (tmpcatalog[\"r\"].to_numpy()),\n",
-    "                \"i\": (tmpcatalog[\"i\"].to_numpy()),\n",
-    "                \"z\": (tmpcatalog[\"z\"].to_numpy()),\n",
-    "                \"y\": (tmpcatalog[\"y\"].to_numpy()),\n",
-    "        }\n",
-    "    degraded_cat = pandas.DataFrame(data=d)\n",
-    "    return(degraded_cat)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7bd11106-04f7-4204-8664-5982b8313710",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "id": "576068a9-6e9c-4b49-a31d-d97c7c1f5410",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# code to train the random forests\n",
-    "def train_rf(catalog,plot=False):\n",
-    "\n",
-    "    u_mag = catalog['u']\n",
-    "    g_mag = catalog['g']\n",
-    "    r_mag = catalog['r']\n",
-    "    i_mag = catalog['i']\n",
-    "    z_mag = catalog['z']\n",
-    "    y_mag = catalog['y']\n",
-    "\n",
-    "#Redshift array\n",
-    "    z = catalog['redshift']\n",
-    "\n",
-    "# Now, set up input data array for scikit-learn regression algorithms\n",
-    "# We will include galaxy colors (expressed as differences between magnitudes\n",
-    "# in adjoining bands) and one magnitude.\n",
-    "# np.column_stack makes a 2D array out of a set of 1d arrays :\n",
-    "# with 6 variables we get an N x 6 numpy array out\n",
-    "    data_colmag = np.column_stack((u_mag-g_mag, g_mag-r_mag, r_mag-i_mag, \n",
-    "                               i_mag-z_mag, z_mag-y_mag, i_mag))\n",
-    "\n",
-    "\n",
-    "# We will set up an implementation of the scikit-learn \n",
-    "# RandomForestRegressor in an object called 'regrf'. \n",
-    "    regrf = RandomForestRegressor(n_estimators = 100,\n",
-    "                max_depth = 30, max_features = 'sqrt')\n",
-    "\n",
-    "    # To better assess the quality of the Random Forest fitting, \n",
-    "    # we split the data into Training (90%) and Test (10%) sets. \n",
-    "    # The code below performs this task on the data_mags and data_z arrays:\n",
-    "    \n",
-    "    # 1) randomly divide the sample into 50% training and 50% testing sets  \n",
-    "    # (e.g., data_train, z_train, and scaled_train are the training \n",
-    "    # portions of data_colmag, data_z, and scaled_colmag\n",
-    "    \n",
-    "    data_train, data_test, z_train, z_test, i_train, i_test \\\n",
-    "            =train_test_split(data_colmag, z, i_mag, \\\n",
-    "                        test_size = 0.10, train_size = 0.90)\n",
-    "\n",
-    "    #Train the regressor using the training data\n",
-    "    regrf.fit(data_train,z_train)\n",
-    "\n",
-    "    #Apply the regressor to predict values for the test data\n",
-    "    z_phot = regrf.predict(data_test)\n",
-    "    z_spec = z_test\n",
-    "    isbright = i_test < 25\n",
-    "#Make a photo-z/spec-z plot and output summary statistics for the test set.\n",
-    "    if plot:\n",
-    "        plot_and_stats(z_spec,z_phot)\n",
-    "        plot_and_stats(z_spec[isbright],z_phot[isbright])\n",
-    "                        \n",
-    "    imputer = IterativeImputer()\n",
-    "    imputer.fit(data_colmag)\n",
-    "    \n",
-    "    return(regrf,imputer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7bdd9452-565f-4f86-a61d-a652d097fe2f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "id": "9d34b7f3-89c9-47f3-8092-18c1527151b0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# code to apply the already-computed random forest photo-z's to a catalog,\n",
-    "#  dealing with NaNs \n",
-    "\n",
-    "def apply_rf(catalog,regrf,imputer):\n",
-    "    catalog.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
-    "\n",
-    "    u_mag = catalog['u']\n",
-    "    g_mag = catalog['g']\n",
-    "    r_mag = catalog['r']\n",
-    "    i_mag = catalog['i']\n",
-    "    z_mag = catalog['z']\n",
-    "    y_mag = catalog['y']\n",
-    "\n",
-    "    data_colmag = np.column_stack((u_mag-g_mag, g_mag-r_mag, r_mag-i_mag, \n",
-    "                               i_mag-z_mag, z_mag-y_mag, i_mag))\n",
-    "    clean_colmag = imputer.transform(data_colmag)\n",
-    "    z_phot = regrf.predict(clean_colmag)\n",
-    "    \n",
-    "    return(z_phot)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b31830ea-c3d4-493c-a856-4643a7229187",
-   "metadata": {},
-   "source": [
-    "# Below is the code box that actually did all the work before, reproduced here for reference!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "id": "987b24db-7574-4c77-84f1-ce8050824b76",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# set up arrays for everything\n",
-    "#CHANGE COMMENTED OUT LINES TO LOOK AT MORE YEARS OR MORE BANDS!!!!!!!\n",
-    "#years = np.arange(1,11)\n",
-    "years = np.arange(1,11)\n",
-    "ilims = 25.3+2.5/2.0*np.log10(years/10)\n",
-    "\n",
-    "n_m5 = 11\n",
-    "\n",
-    "#bands = ['u']\n",
-    "bands = ['u','g','r','i','z','y','ugrizy']\n",
-    "\n",
-    "deltas=np.linspace(-0.5,0.5,11)\n",
-    "#deltas = deltas[0:2]\n",
-    "\n",
-    "minzs=np.arange(6)*0.2\n",
-    "maxzs=minzs + 0.2\n",
-    "\n",
-    "if 0:\n",
-    "# these arrays will contain the results to turn into d<z>/dm5 and dn/dm5\n",
-    "# first index runs over redshift bins; second over bands; third over the delta depth array\n",
-    "    meanzs = np.zeros( (6,len(bands),len(deltas)) )\n",
-    "    densities = np.zeros( (6,len(bands),len(deltas)) )\n",
-    "\n",
-    "#loop over all the files, calculate mean redshift and densities in each bin...\n",
-    "\n",
-    "# outermost loop is over years\n",
-    "    for year_idx,year in enumerate(years):\n",
-    "        print(year)\n",
-    "        yearstring =np.array2string(year)\n",
-    "    # degrade the catalog to have noise appropriate to year\n",
-    "        tmp = degrade_catalog(df_full,year)\n",
-    "        tmp.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
-    "        cleantmp = tmp.dropna()\n",
-    "    # train the random forest on the degraded catalog\n",
-    "    # skip this when rerunning while testing\n",
-    "        rf_year,imputer_year = train_rf(cleantmp)\n",
-    "\n",
-    "# next loop is over bands    \n",
-    "        for band_idx,band in enumerate(bands):\n",
-    "            for delta_idx,delta in enumerate(deltas):\n",
-    "                binstring = np.array2string(np.array(delta_idx + 1))\n",
-    "                file=glob.glob('/pscratch/sd/q/qhang/roman-rubin-sims/nonuniform-maf/Y'+yearstring+\n",
-    "                            '/roman-rubin_sample-m5-'+band+'-bin-'+binstring+'.pkl')\n",
-    "                tmp = pandas.read_pickle(file[0])\n",
-    "                tmp['redshift'] = df_full['redshift']\n",
-    "                tmp = tmp[ tmp['i'] < ilims[year_idx] ]\n",
-    "            \n",
-    "                zphot = apply_rf(tmp,rf_year,imputer_year)\n",
-    "                plt.hist(zphot,bins=100,histtype='step')\n",
-    "                tmp['zphot'] = zphot\n",
-    "# now loop over the redshift bins to assign means and counts for each bin            \n",
-    "                for z_idx,minz in enumerate(minzs):\n",
-    "                    inbin = np.logical_and(tmp['zphot'] > minz,tmp['zphot'] < maxzs[z_idx])\n",
-    "                    meanzs[z_idx, band_idx, delta_idx]=np.mean(tmp['redshift'][inbin])\n",
-    "                    densities[z_idx, band_idx, delta_idx]=np.sum(inbin)\n",
-    "        outmeanfile = 'meanzsy'+yearstring+'.pkl'\n",
-    "        outdensityfile = 'densityy'+yearstring+'.pkl'\n",
-    "        pickle.dump(meanzs, open(outmeanfile, \"wb\"))\n",
-    "        pickle.dump(densities, open(outdensityfile, \"wb\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "id": "58c0f954-35be-408a-a6fc-754490a90a83",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bands.pkl\t  densityy3.pkl  densityy9.pkl\t meanzsy2.pkl  meanzsy8.pkl\n",
-      "deltas.pkl\t  densityy4.pkl  hgb_model.pkl\t meanzsy3.pkl  meanzsy9.pkl\n",
-      "densityderiv.pkl  densityy5.pkl  maxzs.pkl\t meanzsy4.pkl  minzs.pkl\n",
-      "densityy10.pkl\t  densityy6.pkl  meanzderiv.pkl  meanzsy5.pkl  years.pkl\n",
-      "densityy1.pkl\t  densityy7.pkl  meanzsy10.pkl\t meanzsy6.pkl\n",
-      "densityy2.pkl\t  densityy8.pkl  meanzsy1.pkl\t meanzsy7.pkl\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls *.pkl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6f2d826c-1e07-405c-8da7-c958f3d9b942",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "2abe31f9-47b6-4113-bb33-b22b152f3cc4",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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dO3dyxx13cOutt7Jp06YhfU2ePJnu7u78snv37nN6rgKBQCAQvBPS6V46On+PomZx2MdRXvZ+gsEgjz76KNuDYZ6etYw3xs0gbTAy1WHl77PG8uMJNXhMJ3ZeqTmF2PpO5P4UkkmPY1ElepcQUwCSqqrqhR7EqTJv3jxmzZrFQw89lF83ceJE3ve+9/HAAw8Ma//lL3+ZZ599lv379+fX3X333ezcuZMNGzYAcNtttxGJRHjxxRfzba677joKCwv505/+BGgWqqeffpodO3ac8ljT6TTpdDr/ORKJUF1dTTgcxuVynXI/AoFAIBCcLtlsmKNHf0U2F0bCy7hxn0an04LKn3zySbpsLr5nKcZp0PHl+nLurPQOSYMwEpqY6iLnTyIZdTgWV2IosJyP07mgRCIR3G732z6/R42FKpPJsG3bNq655poh66+55hrWr18/4j4bNmwY1v7aa69l69atZLPZk7Z5a5+HDx+moqKC+vp6PvShD3HkyJGTjveBBx7A7Xbnl+rq6lM6T4FAIBAI3gmynKC9/WHCkW4OHezhiSeC9Ifi7IomALjlllv4t+uv5icTqlk3byL/VFX89mJKVoht7NbElEGH4/KLT0ypisqFtBGNGkHl9/uRZZnS0tIh60tLS+np6Rlxn56enhHb53I5/H7/Sdsc3+e8efN49NFHefnll/nNb35DT08PCxcuJBAInHC8999/P+FwOL+0t7ef1vkKBAKBQHC6KEqG/QceYt/+Deze3czu3SX0WDx8YM9RbnrzMC2JdH7m3ofKPRSb3j6QXJVV4hu7yfUmBsRUBYaiCy+mMqkc/o4YR3b0sX1FG2/89TDJaPaCjWfUzfJ76xROVVVPOq1zpPZvXf92fV5//fX591OnTmXBggU0NjbyyCOPcN999414XLPZjNl84rpIAoFAIBCcTXp7fWzc+AMi0X0osp6WnhkcmH0Nb1jcKDLY9RIH4knqbaf+bFIVlfjmbrK+BOgl7AvKMXis5/AsTjKOcJqIP0m4L0nEnyIRzQxrF/EnsV2gmK5RI6i8Xi96vX6YNaq3t3eYhekYZWVlI7Y3GAx4PJ6TtjlRnwB2u52pU6dy+PDhMzkVgUAgEAjOKul0mqefuR+brQtFldhdcCd/rxtPUFYAeF9JAd8cU0mZ+dRTG6iKSnxLD9nuOOgkHAsqMBbbztUpDCGblo8TT0kigRRyThnWzmwDNdtDLuNn2pVXUlB24WKUR42gMplMzJ49mxUrVvD+978/v37FihXcfPPNI+6zYMECnnvuuSHrXnnlFebMmYPRaMy3WbFiBffee++QNgsXLjzhWNLpNPv372fx4sXv5JQEAoFAIDhjIpEITqcTSZKIRNbQ2KiSSBbwW88X2JQ0gKww1mbme2OrWFw0cvHjE6EqKoltPrKdMU1MzS/HWHJuxJSqqiTCmbx4Cvclh1ifVFUhkwiSSfShykG8NVVMWHg5Lq+VZCTAb/71CwCMmTuZwvLCczLGU2HUCCqA++67jzvuuIM5c+awYMECfv3rX9PW1sbdd98NaHFLnZ2dPProo4A2o+8Xv/gF9913H3fddRcbNmzgd7/7XX72HsDnPvc5lixZwg9+8ANuvvlmnnnmGV599VXWrl2bb/OFL3yBm266iZqaGnp7e/nOd75DJBLh4x//+Pm9AAKBQCC45AmHw6xZs4bt27dz++23U1Dowx9YRUVFBWVl76M5VMWuo73cV1fKJ6uLMelOL1xaVVUS23vJtEdBAvtlZRjL7Gdt/NmMZn2K+FNEBkRU7jjrk5LL0NO0klzKTzreSzzci5IbjI3S6RbhqbwWAIPHi7e6FldxCTr9hQ0LH1WC6rbbbiMQCPDtb3+b7u5upkyZwgsvvEBtrZaQrLu7e0hOqvr6el544QXuvfdefvnLX1JRUcHPf/5zPvCBD+TbLFy4kD//+c98/etf5xvf+AaNjY08/vjjzJs3L9+mo6ODD3/4w/j9foqLi5k/fz4bN27MH1cgEAgEgnNNNBrljTfeYNu2bciyDMChplc5UqxQoi9iWcVMCgvm8imXwgfLiqiynHoskaqqKPEsud4kma4Yud7EgJgqx1ThOOMxq6pKIpIh0pck7E8OvIZIRX2kYr3aEvVhc5cxedmtuLxWHIUmdr2yEiWXy/ejNxopKq+kqLKa6slT8+slnY6P/+iXZzy+s8moykM1mjnVPBZnRNtGSEWgYgY4Ss5u3wKBQCC4oMRiMdauXcvWrVvJDYiMuro6qi4by0/642zLlDDWnOH1eXMwnoaVRsnI5HoTZPuS5HwJlMRxM+QksM8pw1R9eq7CXFYm4k8R7k0Q6AqSiurIZmVUVaVpw69JRrrJZWLD9iupb+SO7/8s/3nd47/HZLPjqaymqKIKV0kJOp3+tMZytjjV5/eoslAJTsCGX8L+Z7X3rkoonwEVMweWGWD3XsjRCQQCgeAMUVWVxx57LD95qrq6mvlLl/GELPP1rhg57JglhfeVV6OePJUUqqyQC6Q0EdWbQA6nYcCkogIKCjk3ZAvBXOHEVHZyMaWqKtFgnK5DbfQ0t+Bvbyfq7yIV1SxPFlcZExZ/Fr1eh9NjQVWieTHl9BRTVFlFUWUVnspqvNV1Q/q+/LY7zuRyXVCEoHo3UFgL3vHgPwSRTm05+Hdtm94EX+0C/cDMjt79YC8Bu+fCjVcgEAgEJySZTGI0GjEYDEiSxMKFC9m4cSPLly/nkLuYjzW105mWAR2X20L8aMpc6u3DY5xyco5YIESiJ0zGFyMXSJHNZckpOXJKlqySJW5O4bdH8NmC9NtiKHoV0kALjOkfw/Lq5VQ4KsgkE/QebaO/O4DTO16LgepLseOl75KO+0c8j1w6yOzranEUWtDpJLwVn8VosVJUWYXJcv5TL5xrhMvvPHFOXX7HSEehZzd0bR9YdoDFBXe9PtjmoUXg2w3uGs16VTFDs2SVzwBb0bkZl0AgEAjellQqxaZNm1i/fj1XXnkll112GQCKoiBJEquDUT60S6vSUaKLcpdzD/NKp5NUssSzcWLZGNlYCl1QxtQvYQ8bMeSGuskyxhwhZ5yQI0HIGSdrlIdsN+lMWPtyJDt8SH0pdP40xkgGJREHwGCyM+26b+XbN2/6LbFgC05POUWV1ZTU1VBSV0tRZTUFpeXoDaPfbiNcfpcI7cEEWVnB4zDjsjiQahdC7XEpH+TjfOKKDMrA53CbthxzFQLUL4WPH/c5EwfT2ZvZIRAIBIKh5JQc4USYzZs3s33zdjIpLV3A+h3r6SrqIp6NDy7pMGOYRi2tLGU9vanxvHLET0HMRkHUjjtmoyg9NBBd0akk3TkyhaB49BhdNopMXmqMDsw5PdnufnKBCFOXX4saMxAPZFnx5+8gd7UO9jHwKhmtWNyleCstFJa5cHmtLLj5m5jt9pMm2L5UEIJqlLPqYC/7uqMAGPUSHrsZj8OE12HC4zDjsZvwOMBlMSDp9PCZTZAKQ/dOzYLVvUOzZgWPDI21UhT4rwlg8wxasSpmQvl0sLgvxKkKBALBqKMn3kN7tH2IMEpkE8SyMWKpGKnWFLQAx9Iu2YFGCJWFeKanh+25sdxg2oiZDBXyAT7JZiyKFV3sSsbGy3EmrBh0Bgw6A0aTEYPViKHIgrnEgb2iELvXjU6vIxWL4Wtpwre/id6WnRxsaSLU050fpxycCAPFk63uRuScDqurDFtxMSF3mi5XAMUrEzMpNHm3s6RqCS7ru89t904QgmqU47QY8dhN9CcyZGWVnkiKnkhqWDuTXtIElsOEx27C65iCZ/xsimaZNLGVCsPxMy9CrZCOaEt/C+x9anBbUSPM+hgsuuecn59AIBCMRlK5FK+1vcY23zZURo6sUXepMKBpJJuEa6ILb72XtL6AJyPFbE9r9fKylvfy0dwqMukSdKkKStO3YFbKwASYQOc0YSyxYiixY/RaSSaj9B5pwltUnc/NtPqx/2XPyleGjcFkLcDqriKdimN3WXGX2Gic+SHcxTacReb8/j3xHla3r+ZA/wF2+Xexx7+H6SXTWVK5hAJLwVm/fqMREUN1njjXMVSyotKfyBCIZQjE0vjj2msglqE/kUE5yV/ZbNBRZDcNiC3zoHVLH8cZ3IvUtX3QkhUayPO15Itwxde197Fe+L/rj5tdOAPKpmnxWwKBQHAJoaoq+4P7eanlJaJZzXswpmAMbrMbm86GRWehwF6A3Wgn5o/xyjOvsGTJEmbMmEFOkniorZefHvWRUlQMwMeyRj6S3oxs2AboKE7fhM3UiLHYhqHERsacoa+rRbM+HWmmt6WZaKAPgJu/8H0MljJCvgRNm1+j+/AqbO5KbAVV2NyVuLw1eKuLKSi1UVBqw1FgRtKd3HXXFetiZftKmkJNAOglPbNKZrGoahEu07vznn+qz28hqM4T5yUo/QTkZIX+RJZAXBNY/gGhFYin6U9kOdk3wGzQ4bGbKHKY8DrMlBrilMUP4iitw14xUfObH3oZ/njrW/aUwDNGE1iz7oD6Jef0HAUCgeBCE06HebHlRQ72HwSgyFLEjQ03UuOoYdeuXaxevZpx48Zxww035PdRFAVJVnmtNcDXu3y0KlqQ+Nysjm8kzJTqthMyrUOy6CmyXoenbj6WEjeSJPHmi8+y8uFfjzgWs91L9bQP4CoeC2hCz2QxUFBio6DESkGpDXuB+Yxjn9oj7axsX0lLpAXQhNXcsrksqlyE3fjuir0VQemCPAa9jmKnmWLn8Arjbye20jmFrnCKrvDxbsRqaJUxG/bhsZsoM1fRsPg3lMUPUBDai9W/G12kEwKHtaVx+eCu3Tthw4ODVqyyKectJktVVWRFJSurZHIKGVkhe9ySzin5bVl5YPuxdbKMokB1kY3xpU7ctlMvMCoQCN7dKKrC5p7NrGxbSUbJoJf0XF5xOQsrFnJw30F+ueqXBINBAA4ePMjVV10NkdxgPqj+FI9bU7SaZbyKxJcSJq6VVPy6jbSqLxDvD9O7T0/gUBM33mNnfOli5KyCyVoMkoTNXYrFUYnVXYmtoBKbqxK90TIgoKyaiCq1YXObzlrweLWrmo9N/hit4VZWtq+kLdrGxu6NbPNt47Kyy1hYsRCb8fwUUr5YEBaq88SFtFCdKcfE1vEiyz/gUgwlT27ZKlJDjFeaqcseJjzmfdhKx+B1mCjb/zDmV786tHFBLZRNRSmdSnbKrWSc1WRlVRM1xwmfvNDJqXkRNLh9+Lpj7Y8XSSdzfZ4OpS4z40udjCtzUltkw3CBa0gJBIILQ0+8h+ePPE9nrBOAamc1N9bfSKAtwOuvv05fn+Z+s1mtzJs4h2mFY5H6s2RyMnEJ3KqEqij0u4w84pD5aC7Oqkf+C9nYjXdyP0gQ7bATbnEh6XRMXvYPFNctIRJIIctZVEVGb9B+LJuthrx4Kii1YXUaz8vsO1VVaQ41s6pjVf46mPVm5pfPZ175PKyGsx+8rqTTkMuhG8i/lTp0CIPHg8Fz9nMsCpffRcZoFFQnIycrBPMxW6cutsqTh5gaW09N+hDe+GHc6e4h2x9qfIg2u1anaVx0I2OjW+iyjqHbMpZeSx2KdHaMqjoJTAYdJr0Ok0GHUX9skfLrjXodRoMOk17CqNchKyrNfXHa+xNDzs9s0NFY4mB8qVNYrwSCS4SMnGF1x2o2dm1EQcGit3BlzZXMLp3N+vXrWbFiBQBmo5nZpZOY7h6LUWcgk0ywNhfjhyUmSqJRPvzas4y9ahFzb/0HAMK9PTz61Y9RMj2E2WFDl22A+DL0xjIsrnJ0+sH7i8VmpKBUs0C5S86fgDoRqqpyqP8QK9tX4kv4tDHqLSysWMi88nmY9KdeWxBAicdJ7t5NtqODTGcn2Y5Osh0dZDs6yPX1UfLFL+L5p38EILVvH0o8jm3u3LN+XsLlJzinGPQ6SpwWSpyWYdtyskIwnsEfywy8pvHH0gTjGXqkcXRbx+XbWnMRylJNVCSbqEgdosfSiCSBSa9jSnwTc/1/zbeVJSMhRyMh1wQiBRPoqnkvkq0IY14USYNCaGCdSa/DaJDy68wD4kn/NoGXJyORyXHYF+OgL8phX5RYWmZfV4R9XRFg0Ho1ttRJnUdYrwSCdxtN/U38veXvhNIhACZ7JnNF5RUU2bXkyNOnTWf9G+uYXNDALM9EdLJK6/ZtHE728IfJ9bzZOAaAPswczvhwdjaTTcuEfAn83QEmvbcRkJDkOki8B8muJee02o1561NBiQ2L4+L68SZJEuOLxjOucBz7g/tZ1b6KvmQfr7e/zsbujVxecTlzy+Zi1BtRFYVcn59spyaQMh0dZDs6cSxZjOu66wBIt7bSducnTni8rK8n/95UW0u2p+eEbc8HwkJ1nni3WajOlKys0B/PEEll0euGCqFjliKDTtJ+ZR1+FZpe1bK/9+yGdHhoZ184PFgMetdfINgCZVO1xV0F58nU3RlKcsgX5WBPbGTrVbGdcaVOxpc5KbCd3i80gUBw8RDLxHi59WX2BPYA4Da5mW+fT9PWJhRF4fqli+nZdoD0vhAWk5PKMePRu83oxrm4+7FfsWbmUjJmC5KqckW4hztVmQJzKbJcSDKaQ5ViqI4nQIoiyaXYdLdSWFqAeyAOymK/uATUyVBVlVx/Pwf2rmZTdA+dhVp60JKAzOKfrcXgC6BmMsP2K/zoRyn7hjaDXA6Hab3tQxirqjBWVWKqqtLeV2qf9QUF58UiJ1x+FxlCUL1DVBVCRwfFVf9RuOVXg9sf+yA0rRj8bCkYEFfTtNepHxysZ3gOGcl6dTwlTjPjy5yME9YrgWDUoKoqO/p28ErrK6TkFBISUy1TCW3t42hn17FGTPQplOm0H3kGu4U5n7qV7lIz/7Svlb0xbWLPBMnAv8TNlIeG3htUUhg9z2C0hbE5i2kc+ynsjoLzeZpnjByNEn7qabKdHWSOc8spca1cTcHtH8X3LzeyumM1iYCP9/2bltdQ1UkYy8oHhVJVJbZZs7HPn3chT2cYQlBdZAhBdY558/dwdL0mtvr2g5Ib3GZ2wZePgm5AvGz5Lci5AcF17mYZHrNeHRNYbcHh1quGY9arUieFdmG9EgguNvxJP883P8/RvmZ0/SmKSutwd7pp2qflYUJVccWzTEh7cWLB6nZjqLZhnVSFs2wsvf4EH0v0kUDlQxEDyxN6dGhWFUeBeSD+yUgs91fS2TYMBid1tZ/EaCy8gGetoWQy5Lq6BkVS56Brzr7ocko+9zkAcv39HF6wcMQ+DMXFuG66idIvfZGckmOHbzt7VvwZv1tHoshGgd3DkqolTCuehk66OH9gCkF1kSEE1Xkkl4a+g9CzSxNYqgI3/HBw+89nQbB58PPALEPKpml5s8Zdc06GlcjkaOqNcbAnyuHeGNFUbsj2YqcWezW+zEGdxy6sVwLBBUJVVfp7u1m18RkO79mC1BlFH0iiuAtJVo1FUTT3VanFwcS0l3JXBSaLGwpdRJxm1qXTjE+SF05HjArFskSF24K7ZDAGymjWo6oKnV1/Jhrdi05nprbmLiyW8vNynkoySbarS1s6uzCUluBcrqW5kUMhDi1YyIlmGDmWL6f6oQcB7Xp1ffFLGEpKhrrmKirQWYbH2WaVLNt821jXuY5YVqvQ4bF4WFa9jMmeyRddXUAhqC4yhKC6SFBVWP2fWub3nt0Qbh+6vWwa3P3G4Od1Pwd7sWbJ8o4Hw9mxIqmqSlc4xaGe6IjWK5NeorHEwdgSLfaqSFivBILzwqan/8q2l54h2R8CQAWOPd4LKqoIV4+l0FHETPMEXHEbmVSOtAoxtxmfTcfDBTk2WxX+OWrkgzYnLq8Vl9eKu8SK0aQfcixVVenxPUMotAVJMlBTfSc2W/1ZOxc5EkFJpjCWam5IJZ2m64tfyosoeSA31jEcV1xB9YO/zI/t0Ow5WkLQqkotbqmyMu+aMzc2Ym5oeEfjy8gZtvZsZW3XWpK5JAAl1hKWVS9jQtGEi0ZYCUF1kSEE1UVKIgi+PYOxWQW1sPx+bZuche9VgDwQOKkzgncclE6CkklQfRnULTorw0hmZA73Rjnki3HIFx1uvXKYGFemuQbrvHaMwnolEJwx2UwaX9NhOg/uo/PAXq779L3Y3AUkc0ke/98fEHhtK4rBQLaqGp3DyxUzFmIvqCcT1pM+0oczpT3oFUki4TQRcxhY79HxsCVNHK1kzOfryri3vuyk4+jzv4bf/zoSEhWVH8LlnHLa56IqCtFXXiHbeczS1JkXTEosNkwkHZw9BzWRyO+vs9sxVlRgrKjAOns23n+5K79NDoXQud3nXNik5TSbujexoWsDKVmLNSuzlbGsehnjCsddcGElBNVFhhBUo5BUGFY+cOJZhpPeB7c+or1XFHjhC1A8HkomaoLL7j2jw6qqSnc4lQ9sPxpIDElIatJLNBQ78jMHhfVKIDg5qXiMzgN76di/l86D+/A1N6HIgz9abrrvfjKNbl5ufoVESwTaVdSkAWWgDMzE0gXUUYY9lkFSQaeTkMrsWCYVESgy8e2An00RLQB7htPGf02oZrLj5Mks+/s30+N7BoCy0vdSWDg0EFuVZXI+36BLbsAtl+3qwlRXl58JN5JIOh7b3LnU/v7R/OfQ00+jdzrzIkrncl1wwXKMZC7Jhq4NbOreREbRfshWOiq5ovoK6t31F2ycQlBdZAhBNcpRVa0wdO9+6N2rvdYthtkf17YHW+DnM4buYy8ZFFfjroHGK87o0MmMTHOfFnt1yBclcgLr1bhSJ/XCeiW4xFFVlUhfL0aLBZtLm3Cyd/VrvPTgT4a0sxcWUdYwAWtJNe0GhUA0hxxKks2EUFXtf8xucjPWOYnxUgkWow6TxYClzI5rbinGIit/7g7w5UMdpBUVq07H/Q1l/FNVMfq3efBHo3vpaP8DSjyGOzUOh68MyWzBfdON+XM4NG8+SiQy4v7mCRNoePqp/OfOL34JFEUTSZWVGCs1sWQsL0dnG33lXxLZBOu71rOpexO5gb9FrbOW5TXLqXXVnvfxCEF1kSEE1bucSLc2e/CY4OpvHbp90X1w1b9r76M+eP6eQbFVMgm8Y08prYOqqvREUnlx9VbrlVEv0eC1592DHsfw+o0CwbsJRZHxtx2l88BeOg/so/PgPmLBAEtv/0fm3HQLoGUff/KBb1JcMx5XcQNmZzWZlJ2ucA9dsS5kJUMi3j4opKwu5oyfy0RTBaaEjKSTkKwGbFO8GKsceUvJzmiC67ceYmmRkx+Mq6LGqv2/qaqKEomgxGIYKyvzY+366teI+vcQajiCkk5hapawbtYhIWGeOJGGp/6Wb9v8nhvJtLVhLCvLW5OOCSZTbQ222bPP1yW+YMQyMdZ2rWVrz1ZkVbMWNrgbWFa9jGpn9XkbhxBUFxlCUF1ipGPaTMPefdoy/nqoX6Jta3oNHrtlaHudURNVJZNg5u1DC0qfhFRWzs8cPNQbJZIcar3yOkxUF9mocFspc1uoKLBgM4kCCYLRTzTo55Vf/TddB/eTSQ51d+n0eqZecSOTln6QiD9F2J8kERlMIpnIJjgaOUpCjqM40nhKnGS7Q8TjUZYuWsJ4YxW5ozEtIl0vYRlbiGVcIXFUNoXjXOkZvIdv+PvLNLY0kevpJtfdQ7anh2x3N2oiMUwkHbztWvrHHUE1gLFDwr7Njqm8EmNFBeaxYyn90hfzbeVQCJ3TiaQfGsh+KRJOh3mj8w129O7IC6uxBWNZXr2ccse5nxEpBNVFhhBUgjyhdjj4gia0fPs0q1YmOrj9vb+AWXdo79u3wEtf0axZpZMHLVqO4mHdHrNeHfLFONQTpTUQH7EYtNtqpKLAQrnbSrnbQkWBlULbha0BJhCcCEWW6Wk+ROvO7dhcbmZc+x4AcpkMv/jErci5HEaLleLasbhLGrE4a5EMZYxUWc1s19POEQ6HdpELJLDMMXPtxGuZUTyDSDiC3pcldyiMHI6hJBKgiyLJHeR6u1iJke9Pn0/A5uDVBZOYYNdipJrfcyOZ5uZhxwIw1dXR+NKLAGSz/RxafT8ySWyusdSM+STGQq/4vzsNQqkQazrXsLN3Jwpa6oqJRRNZVr2MElvJOTuuEFQXGUJQCU6IqkK4Y9CaNfG94GnUtm3+jRbs/lbsxZrIWvZVqF0w2M9xN+dUVqbFH6c7nKQrlKI7nCQYz444BLNBR7nbQnmBlQq3hTK3hVKXRcRjCS4IIV8PR3e9ydFdO2jbs5N0Qgv49tbUccf3f06sP03Yn+TQhrWouNEZi5HekhTSYNDh8lpxeiy4i610S228sON5kntjqHGt7QyXkwU5BRQrltk3oUQ1K1b4ub+S2vUyaryXfqeLX/zDx3l9rpa4sjzQx2+uXsgctx2A3p/8FDkYwFBejrGsHGN5GcbycgxlZfkcTLlcnKNtvyaT8WM2l1Jbcxd6/cmD1gUnJpAMsKZjDbv9u1FRkZCY7JnM0uqleK1nNhnoZAhBdZEhBJXgjIh0QfumAUvWwBJsQfNFAHe+AHWXa++3/wFWfx9KJg/GZ5VOAs8YMGixHamsTHdYE1fdoRQ9kRQ94RS5EUxZOklLNlrhtlJ+nEXLbhYuQ8G548///mU6D+wdss5sd1DaMBlbwRjsRTOH5ZqUAJvbjNMpYVNjWNNBzGoK93XXEMlE+Nvmv9H/zA7C9gIADNks4w4dYsKRTmwl09B7Gyl4//uQTHqskzz0/vB+UocP88riK/n5/OWETWZ0qsonpDSfL3VTNGniKZ+PoqRpa/tfkqkOjMYCams+idEongFng95EL6vaV7E/uB8ACYn3Nr6XGSUzzupxTvX5Le6MAsHFjKsCJr9fW46RSUDfAc1VWDZ1cH3vPm0mYqgNDr04uF7SQWEd3PoolrKp1Hvt1NvSUOkESxWyouKPpekKJekOp/KviYyML5LGF0mz/bj8p8dchmUuzV1Y5rbgsZuE60JwymhuvMMc3bWdnuZDvO+L30AaKA1VUFpG9+EDlI+dQPnYqVhcDaSTRchpGSWVRlXBZNbj8lph0+vojuzC1H0EpbsDJRolASQAndvNwekFvPT0Syg+BewF6GSZMYebmNIXwFU9G8OyG9DZ7Ug2G6ZGN9aJHnQmPVUP/pI797Twsl+bZTfVYeVHE6qZ7jy9GXOynKaz648kUx3o9Taqq+4UYuosUmIr4dbxt9IT72Fl+0qaQ83Uueou2HiEoBIIRhsmG1TO0pbjWfIFGH/DoCXrWHxWOgzBI2A7zhS+/r9h7Y/BUYa+eByl3vGUFo9npncc1I1HtdcRScl0hZP0hFP5V38sQziZJZzMsr97MO7LbNBR5rbkY7LKhctQ8BbCvT6O7tpO6643NTfeQOFcgN7WI5Q2jAFgSlkD5VMdBP06om8kCSe3oiQSGOJBCnM9zHzyt1idWsxf22NvEF+7luOnYuhcLqQSL34XvNr8dxSTBBJMqK/hyrkLcBpLSB0Ko6a04GZDiQ3btGL0rsF8bpIkMc/tYHUwyhfry/lkVTEG3en9YMhkgnR0PkY67UMnGamuugOzeXjso+CdU2Yv48MTPkw4HcZtPje1WU8FIagEgncL1kLN/XfMBQhaXFXMB/5D4Dwua3PMN/Daoy0ta4Z0JX1uF+7CWtw2IxOz+0DqhzHjSDnq8cWy+Zis7rDmMkznFI4GEhwNHJeB+TiX4bEZhmVuKw7hMrzk2PB/v2b9S88OWWdEojirUJzM4PR4CfkSdDeHOPJSN5meXgAkVcYZ68AdbsaW8KGzWLCYlLw1tPDDH8J5zdUYyyswlpfRbzbwt1Uv4C/xo7pUTDoTi5YvYqJ7IgXYSezqI9mvlVvROYxYpxZjLLMhSRI7owlkVWWWS4uN+peqYt5T7KbWevqpRxKJFjo6/4gsJzAYnFRV3o7VWvVOLqHgFLiQYgpEDNV5Q8RQCS46kiEINGnpHfwHoe+Q9hrrha+0w4ALhr98HPY9rb3Xm7WYrOJxWm3D4nEo42+kL6lld+8OJekaeI1n5BEP67IaKHcdC4DX4rOEy3D0oWaz5Pr6yPp85Hw+0l1d+FqP0NnVTk8iwpIvfo36mXMA2PqJj7Mm7qcgnqI4msAbTeJOppH1FsKuegxfeIBkSnsUpQ8dwhjtpbRYR2m9E2tFKYbSMoxlpSfM6h0Oh3n+1ec5vOewFl7ohQlXTeD6+utxKnaSe/xk2jWLqmTQYZlQhLmxAEkvEZdlftjSw6/b+2iwmXl1zngs78Cy2h/ags/3LKqqYLVUUll5u3DzjXJEDJVAIDg51gKomqMtxyNnB8UUaPFXpVPAfxjk9ECm+IGgYb0J3Ve7KXUZKHVZmNH+GCR6UCvGEXeNodtYQ0fKRPeARSsQzxBJ5ogkYxz0xfKHON5lWO62Mr7Midv69olOBecGJZnUyp70+Mj5esj6epGDQUq/8uV8m/bPfIa+DRvwO634nTb8Dis5w2DOpCNbN+UFVeWESdy4ZQvWunoMJaXEC2rol4oJyw4kq5VsQkFv0lNa56L82qtxeiynJLBjsRir1qxi29ZtqAMTK/TFeq644goWjl1A6nCI8MFWkFWQwFTjwjrZg86iPfpWBSN88WAH7Sltdt9Uh5W0opyRoFJVhd7eFwj2bwDA5ZxKefkt6HSiNNSlgrBQnSeEhUow6lFkLeDdf2jQqpXLwAd+M9jmV0uge+fQ/RylA0WlJ5O+6rv4Ihm6wsl8OgdfJEVWHnob0kkwvaqAy8d6qSy4eKeXRzIRDgQOcDh0GIveQp27jnp3PYXmwovS4qYqCnIoRK63V1v6/BR8YDDJbPe3vkXkhRdRwuER9x+3dQt6hwOAQ1/+Es+17huy3aQ3UO4tpbpxLOM/eBsFlYPZrJOxDN1NYXqOhEkfl4DW7bVS3uimpNaF3njqQmb9+vW89vpryLkBS2ghTLxsIu+d8170vTLJ3X6UhJYmxOCxYJ1WjKFQS2MQyOT496ZOnvD1A1BpNvKD8dVc5Tmze7MsJ+ns+jPxeBMAxd6r8HiWXZTfAcHpI9ImXGQIQSW4JHjz99Cza0B0HYJo1+A2zxj4t22Dn39/C6RCqN5xxJ2N9FnqaNdXsS9ZxNHQYFbrBq+dRWO9TChzXhQPqFgmxv7gfvb699IWbUNl+C3UZXJR766nzqUJrHMd26Gq6oBQ6iPn78Nx+WAcnf9/fkV05evatr4+yA3Npj9+21Z0di1uqPsb/4/QX/8KgGSzYSgtJVJchN9qwidnKJw4iRs//1UA5ESC//vKZ3EUFlE7bSZ102ZR2jgGnW7QSiXLCv72GN1NIfp9g/F1RrOesno35WPc2N2nH6MUSAb448t/JLAjAC4omFLALfNvoUItJbmrj1xfUjuHEcrFtCXTXLftEMGsjAT8c5WXr9SXYzecWUbydMZPR8fvyWT86HRGKsr/Aadz8hn1Jbg4ES4/gUBw/jmW4f0YqYjmKvQfRMsWNICqQsdWSIeROrfhABxAPbBEbyJZtYhnp/yMXR1hjvjjpNreZHVBGdMnTGBWXSHmM3z4nSnxbJx9gX3sC+zjaOToEBFV7axmYtFE0nKa1nArHbEOIpkIO/t2srNPs9YVmgupd9fnRZbD5Dil46qqihKNkuvtxdTYmBcF/X/5C/G168j19eWtTWp2MGnruK1b0Ts0kZTt6iK1c9eQfvUeD4biYgylJSipVF5Qef7lLgw3vYfOvm7aDu6nfc9OUvEQpLX9wnt3oigyOp0evc3GJ378P+gNwx8jsf403c0hfC0RsgOxdBJQWG6nvNGNt8qB7jTcarlcjm3btuFyufA7/azpWEO2OIt+lp5ls5Yx3zOP7MEQ0Za2YeViJMPQ41RbTEx32uhOZ/nx+GpmDSToPBPi8SY6u/6MLCcxGtxUVd2OxVJxxv0JRjfCQnWeEBYqgeA4VFVL6eA/NNSF6G+CXBLGXA23P0E4kWXDET+Ln56PPRcirbMSNFcheRopqJ6IpXSclsC0YsZZH2I8G+dA8AB7/XtpjbQOEVFVjiomeSYxyTNpmPUpI2foiHbQEmmhJdxCd6w7XybjGMUWL/XGcmrLxlPnrsdmtBFZsYLk1q1ke3s1a9IxoZTW1Mzx7rbu//fvhP7yl2Fj1hcUYCgpofrXv8JYps3qTO7eTc7n0wRUSQkGrxfJOBifls2kMZoGrUR//Prn6T58MP/ZbLNTM3U6ddNmUTttBu6S42aLHkcuK9PbGqW7OUQkkMqvt9gMlDUWUN7gxuI49bg4VVUJp8Ns3LaRHRt2kIql0Nl1KAsVJJ1Eg7uBG+puwN6lJ7U/gJrVrrGx0oF1ihe9XTuWrKo80unnltJCCoya+AtkcjgNOky6Mws+V1WV/tBGentf0ILPrTVUVX4Eg8F5Rv0JLm6EhUogEFy8SJKWxb100tD1igLhNi0wHnDbjFw3zo3qLEQNRTArScqTh6HjMHS8BECqZimWfzxuSv7z92pxW0WN4GnQXq0FpzSsRDahiajAXlrDrUOEUKWjkklFmogqsJy4P5PeRENBAw0FDSS2bCG6T6W/o5lYVzvZvl70gTCWUBJjOsffHvwAOauRUlsps17cjPOFDSP2qXO7kUOhvKByXX8d5rFjNYFUUoyhuARDsRedebj7zDp1KkwdTACrqir+tlaatm6ideeb+JoPc/evf4/Zpllq6mfMQdLpqZs2k9ppMylrHIvuBAV6VVUl4k/S3Rym92gUOaddL50k4alyUD7GTVGZHeltcjipqkowFaQn3kNPvIfueDcdzR2kDqbg2NwFMyi1CjaDjWsbrmWiMobkOj/JgXIxercZ6zQvxuLB5Jt7Y0nuO9DGzmiSfbEUP5qgxXR53kGBcFWV8fmepz+0GQC3ayZlZe9DpxOP00sd8Q0QCE5CJBOhPdJOW7SNQDJArauWacXTLni+k3ctuoGs7sdjsiF9bgfkMijBVtqbd9PVvAcpeARvpp3WVAMta45ocVaFKtLW/x3er82r1Uec8B64/HOD6zMJkjqJg8GD7A3s5UjoyBARVWGvyFuiCi2FAKT27yd0cBWy30+uz0/O79dcbwPutzErX0c/8Cs2/PzfCT3+OAAjOZaqklZarTl8CR87G/V4r59AqsCKpbQCT/VYymsnU10/DYt96K9i+4IF2BcsOOXLqigyXQf307RlI81bNxHydQ/Z3nVwf35G3vwPfIgFH/zwSfvLpHL4WiJ0N4WIRwbj3WwuExWNBZQ2uDBZRn68yIpMX7IvL5564j30JHpIy5olTg2pcAAYiIuXjBJlk8uYPGsyVe4qyigmty9MvFuLzztWLsZU58oLt6Ss8OPWHh5s70VWwWXQMdN1elnORxy7nKCj848kEi1ISBQXX0tR0aKLIrZPcOERLr/zhHD5Xfyoqoo/6act2kZbpI32aDv96f5h7SQk6t31zCiewQTPBIw6Mb3/QtDRn2Bdk59dHWGOlSKssGS4OfcSlUoX+uARCDYPJjEFmP0JuOmnJHNJDvp2sPfxf6DF5sIWc+AOm7GkjHizNkoyTlxpM7r+KDm/n4bnnkXv1kR097e+RehPfz7huBr+/jzmRq24dejpp4m/sXbAkqRZk4wlJZr7rbgYnd1OPBunNdxKS6SF1nArgVRgSH86dFQ6K6lz1VHnrqPaWX3a37mdK17g1d8+mP+sNxqpnTqDhllzqZ02i4LSkd14x6MqKsGeON1NYQIdMZSBR4der6Ok1kn5GDcur3WIuMjIGXwJ3xDx5Ev4kNXhOcr0kp4yexnWkJXDrx3GYDBw2Zy5LJx6GSbFgJLIIseyWj4pRUuDYG4swDKhCJ1p0IK2tj/KFw+205LUhN57it18b2wVpeZ39n+aTvfS0fEomWw/Op2JiorbcDomvKM+BaMDMcvvIkMIqouPnJKjO96dt0C1RdtI5pJD2khIlNpKqXHVUGgp5GDwIK2R1vx2s97MZM9kZpTMoMpRJX6pXgCOxVltagmSGoijsRr1XG5NMkXuxxToRm4/TKqrlXA4RiIcg0A/r35hLpmjLwBw+So9lXtPnC+o4ZknMfevhqJG+t84RHTdmxiKi9F7vRi8xRi83nyMkqmqEsl05rmHIpkIreFWWiOttIRbCKVDQ7brJT3Vzuq8wKp0VGIYcDcloxGOvLmFpi0bGTN3PpOXXqn16e/l91/6LA2z5tI4dz5102dhspxaOopUPEtPc5ju5jCpxGDgu8tjobyxgJI6JwajnkQ2QU+ih56YZnHqiffgT/pHnAVp0Vsot5RRoS/DHreihlWml0+CpIycyLLtyG7G2quwG0Ye40jlYgD+2B3gvgNa4ckyk5Hvj6viuuJ3bk2OxQ7S2fU4ipLGZCykquoOzObSd9yvYHQgBNVFhhBUF560nM6Lp/ZoOx3RDnLq0CnkBslAlbOKamc1Nc4aql3VmPVD41L6U/35GVzHP+w8Fg/Ti6cLl+A5JNvVRebo0QE324C7zd+H7PeT6e2j7zs/Y50vSyCe4bJnf8eEjS+fsK/NP/oo9RNnMgkLuidfILJ6CwaLjMGYwEAYw/Rr0C/4EAZvMWZXFt3/Lh3cWWeAglotFYSnEcZdBw1LT3isd0J/qp/WSGveihXNRIdsN0ZlPB0KUlOA2JEOjt3S62fO4ZavfDPfTpHlE8ZCvRVFVgh0xulqCtHfHc9LIqNRT0mdE2etgYgxMMRlN0T4qWDM6TFnjRQqLsp0JXgpolB145LtmDIGovEYm3t3szfYjFGn584JN2M1WIYORAKd1YjOZtAWqxGD14KhxDbij5dAJsfSzQd4T7GbrzVW4HqHs0FVVSXYv46+3pdQUbHZ6qis+AgGw5nPDBSMPt61gurBBx/khz/8Id3d3UyePJmf/vSnLF68+ITtV69ezX333cfevXupqKjgS1/6EnffffeQNk8++STf+MY3aG5uprGxke9+97u8//3vf0fHfStCUJ1/jo9/aou04Uv4hv1athqs1Dhr8uKp3F6e/7X/dqiqytHIUbb3bmd/cD9ZRfv1LqHNQJpRMoPxReOFS/BtyPb0kGlrQw4EyAWC5AJ+ZH9gQCz5qf71rzAUavFLPf/xHfr/8IcT9tXw3LOodTW80rSdnkf+l7pNO4k6TEQdZjJuK/byEuobJ1FbP53ymQvR2U4QV6OqoORAP/C3690PK78LgSNaoem3WDK58v/B4s8PtD0Af/gHKKwdWOqgsH7gtQ5sHi0o/wxQVZVAKqCJq/4jHH3ob0jHZZwHUIvtOCfV0zh3PlMmzafUVnrKltN4OE13UxhfS5hMWgYV0nIKCjJkSyKEHD34Uj0kMgnMWQPmjHHwNaOJp0LcuBQHdp0Nm9E27PufzKXZ2ruXnYGDyKpmUWzw1nD1rKUUeovQWQfEk82IZNafNKC9M5XhSV8//1ZTkj/HcDaH2/jOw4MVJYfP9wyh8JsAFLjnUFp6kwg+vwR5V87ye/zxx7nnnnt48MEHufzyy/nVr37F9ddfz759+6ipqRnWvqWlhRtuuIG77rqLxx57jHXr1vHpT3+a4uJiPvCBDwCwYcMGbrvtNv7jP/6D97///Tz11FPceuutrF27lnnz5p3RcQXnn2MPmqORoyeNfyo0F2rWJ5cmorxW7xm76SRJos6tuV1ukG9gf2A/O3p3cDR6lOZwM83hZix6C5M8ky45l2CmvZ3MkSPkAkHk4HFCKRAkFwxS87vfYigqAiDwu/+l//e/P2Ffub6+vKAy1VRjGtM46GYbcLXhKaDHnOa52EYObvuLZnm8uZKma0uJJixk4pW4dA2YpQJ8TjO6ci/FJgsndMxJ0qCYAiiZCLc9pr1XFC1haaBZi9EKNEPtcQWp+1u0mYrhNmh9Y3jfV30TFt2rvY/2wL5nB4VXQQ0Yh7u55FyOjv176Gs9wpybbsFr9TKnbA5/8e6go28PrsYaaPQSqJJIOSAO9OR2sm7XTix6Sz7/VZ27jmJr8ZDvoZxV6G2L0nk4SG9PP4lsglQmiSwlyNqDqPYEppyEudWII2PEky3DnDViMViwGqzYjDZsBhtWgxXD8UJGAsliQD9gWcqZFLa17mFT01bSGS2+qaamhquuuuq076OyqvJwp5/vHekmLivUWc28t6QA4KyIqVwuRmfnH0gk25CQKCm5gcLCBZfM/6/gzBhVFqp58+Yxa9YsHnroofy6iRMn8r73vY8HHnhgWPsvf/nLPPvss+zfvz+/7u6772bnzp1s2KBNT77tttuIRCK8+OKL+TbXXXcdhYWF/OlPfzqj446EsFCdXXJKjp54z5AA8kQuMaTN8fFPxyxQLtO5v/bBVJBdfbtGdAnOKJnBtOJp52UcZ5v0kSOkDx0mFwxoFqRgQBNIgQByIEDtH/+AweMBoOd736P/0ROLpPpnnsEyfhwAgYcfJvTnx9F7PRiKPOg9RQNiqRhDsRfb3LnoncPz+2TlLIdDh9kX2Meh/kN5CyFAkaWIyZ7JTPJMotRWSiSZGxZnZTPpuay+iPkNnrNbNzAd1Sxa/a3Dl0gXfOC3MPWDWtuDL8Gfbhu6v7McCmrJOGposcyj6WiIlu1bSCfiIEn8838/jNXpBhWCXe1YHC4sdhcqqpaZPOHnaOQo7eF2OmNdZOSMluxS1cSATW+j1FpBQdqGwacj59OhpLJIuRxGRYfBJCNZsqhmzR0uSTpsBqsmngya1clqsKIz6NHZjJpFyW4ctCxZDfn1x1uXgsEgv/jFL1AUhdLSUq666irGjBlz2iLlQDzJ5w+0sy2i/b/Pcdn4rwk1jLdb3mbPUyOV6qaj8zGy2RB6nYWKyg/hsI89K30LRifvOpdfJpPBZrPx17/+dYg77nOf+xw7duxg9erVw/ZZsmQJM2fO5Gc/+1l+3TELVCKRwGg0UlNTw7333su9996bb/OTn/yEn/70pxw9evSMjguQTqdJDyTkA+0PUl1dLQTVGZKW03REO2iLaMHjpxL/VOWswvLWmIzziKqqtEZa2dG7g32BffnxSkg0FjQyvXj6BXcJpg4cILX/QN6KpLndBsVS/RN/1SxAgO+BBwg+8ugJ+6p/5mks48cDEPzDHwg9+SQGjxdDUZGWmdtz7NWDdebMfE6l0yGrZGnqb8qLqIwyOGW/0FzIZM9kJnsnn9DNlc7JbDvaz7omP8G4JsD0OphWVcCiMV4qTqFuoKqqZFIyiXCaRDhDIqItck5BVVVUFRh41RYVBl7VXE57r9Nr28Kd0LUTNRVFTUVByRFKxvHF+4mm40Nc1AajFbfRQpmnAbPDC2YnmJxgdmjvzS7QD7XOKIpKfzJKMBkmlI4Qy8TIKQrF+jiTq7cAKkRqUcPjUPU2DA4Fh0WzOlltdhxuFw6XG73NmI9h0tm1V8mkO6kYUhSFtrY26urq8uvWrFlDYWEhkydPRneaSTUzisJPj/r476O9ZFUVh17H1xor+HiFB91ZshxFo/vo6v4ripLBZPJQVfUxzCbvWelbMHp517n8/H4/sixTWjp0ZkVpaSk9PT0j7tPT0zNi+1wuh9/vp7y8/IRtjvV5JscFeOCBB/jWt751yucnGEo0E9WCxwdioHriPWc1/ul8IElSvtzIDQ03sC+wj529OzkaPUpTqImmUBMWvYXJ3slML55+xi5BNZMBozG/b3LHDpK79yD395PrDw642QJ5d1vDc89iLCkBIPzUUycVSblgMC+oTA2NWGfNwuAZsCIdsyZ5vBg8RZiqqvL7FX30oxR99KOnfS4jkVWyHAkdYV9gHweCB4aIqAJzQd4SVW4vf9vrZzboWdjoZX69h33dEdY3+2nxJ9jeFmJ7W4jGYjuXj9HqBqJCMpbVBFM4nRdO8XCaXFY56XHenoEfA+ZSUt4Z6E02jCYH5NJkjq4nsu/v2mZbEe7yqRSUTcGeSyC1bwY5BeEOACRUJElFQiVXt5S4q4F4Jocc7cMSPUIMGwm9g5Tegawvx2IEoxJnkvdlJJ1KWieTLm1GrjxKUq6nPzsPbLVUljtpKHXg9Tpw2k9vxqKqqhw8eJDXXnuNvr4+7r77bsoGsrYvWbLkjK/YP+9p5ZVABIDrvC6+N7aKCsuZz6Z865gDwdX09a0AwG4fQ2XFh9DrL97C3IKLj4vn6XOKvPWGqarqSW+iI7V/6/pT6fN0j3v//fdz33335T8fs1AJhnMs/umY9akt0nbO45/ON2a9mZklM5lZMpNAMpB3CYYzYbb5trHNt01zCRZPZ4q1EVs8h7GyEmlgVlZs9WrimzcjB/sHhVJ/CDkYRInFGLN6FcYB0R958SWCjzxywrHIwWBeUJnHjce+cGHe3WbwetAXHbMmeTHV1ub3K7ztVgpvu/UcXqVBckqO5lAz+wL7ONh/MJ/0EbTCw8csURX2ijP6Duh0ElMq3UypdHO0N8baPT4Ot4Xp7A3y5HY/TlWi3GLCYzOhGyEoWgIsDiN2txmby4TNZcJg0iPpBu4VkvYqSYP3jmPbVFWh72gTR3dtoXXHZkK+Lhb+w53MuO69SEik4lXsXV1A4+z5FFZUodPrtH7kHERnkg20EOtpJuM/gtrfiinShj3ZySOFs2mzK4CO+azj5uiPtcHK2qJIBlK2egK25fhLQxicJVROv5P2rjUEQnsIZQJImedIyKUc7p7Jmx11gI5Cm5GGYgcNxXYavQ7cthNbVFtaWnjttdfo6NAEn8ViIRgM5gXVO+GuqmK2RRI8MK6Km4rdZ+1/X1GydPf8jUhEq3dYWDif0pL3IElnVpZGcOkyagSV1+tFr9cPswr19vYOsx4do6ysbMT2BoMBz0Csx4naHOvzTI4LYDabMY9QBkIAiqrQHe/WBNSAiLpY4p/ONmouhxwKkQsGkYP92GbPwmP1sLxmOTO3R/CteIFobwfZ/iDmaApTNEO3rFk/lL/9inETFmDUGYlv2Ejw4YdPeBy5vz8vqCxTpuC89lr0hQXoCwvzFiT9gGAyHifsCz5wCwUfuOWcXoO3I6fk6E/1E0gFCCQD+BI+DvcfJiUP1oNzmpx5S9SZWvKyaZn4EDddmng4QzqepQLwYsGXSdEby5JSVFqiWdr1SSrK7IytK8DrsWJzm7C5zFhdRvSnUdxXzuU4unt7PlN5IhzKb9PpDWRSEawOzdpicXi4/NYPoygq/niankCcnnAKXyRFTyRDMF4ClIBuAXjQFkCHSonTTJnbQo1nFsGCO3AkOjBG25H6jyLJMtnIFMKefej05ZROuI6ioikUNW0gs/qPBIushDxuskYbGd0KkrgJJsawN3sT2xJlbDuq/cjx2E00FNtpKHZQ77Xjthrp6uritddeo7m5GQCj0cj8+fNZuHAhVuuZWXn2xZK0JNO8p7gAgMVFTjYvmIj9FFM/nArZbITOzj+QTHUgSTpKS26isPCys9a/4NJi1Agqk8nE7NmzWbFixZBYphUrVnDzzTePuM+CBQt47rnnhqx75ZVXmDNnDsaB4qALFixgxYoVQ2KoXnnlFRYuXHjGxxUM5VgG8pZwC0fCR2iNtA6xOMDFF/80EqqqoiYS5Po1K5EcDJIL9uN6zw3oBhI5Bh/9PZEXX9S2hUIo4fCQPsasWpkvWps+cBDl1TUjliTJmfS8uvcpno9tYLJ3MlOmNVJ458cxFBahLyrU4pIKCzXBVFiI7ji/vvumG3HfdOM5uw5ngqqqRDKRvGjKvyYDhNKhEZM/Oo1OJnkmMdkzmSrnqYkoVVVJx3PEIwMuurC2xCNpsunh2bmPYTTpcXut1E0oxGA30BpP82ZvGH9Wxi/B3nCIaU5Y5LbjKDi1H0rH531Scjme+68HyGU1d6XJaqN+5hzGzJlH3YzZZPVmDvui9ERS9IS1pTeaJqeMHOLqshgodVkod1sodVsoc1kodpox5kVeDXDT4HWRcyTWH6a/61Vk82as1VV4Swfcb6kQppxEmS+B15+kv8BIv9uITQ8eaQeV1Vn85qtpDo+juOUFFhx+gpCxlLCplLXGUnL2CvYc7SebzaHT6Zg9ezZLlizBOcJEglMhp6j8os3Hf7X6MOokpjis1Fq1a342xVQy2Uln52NkcxH0ehtVlR/BZqs/a/0LLj1GjaACuO+++7jjjjuYM2cOCxYs4Ne//jVtbW35vFL3338/nZ2dPPqoFhNy991384tf/IL77ruPu+66iw0bNvC73/0uP3sPtODyJUuW8IMf/ICbb76ZZ555hldffZW1a9ee8nEFw4lkIpqACh0ZMRmhRW+h1lV7weKf1GwWORJBDoWOW8L5997PfBqdRRN0fT//OaEnnkQOh1HT6WF92efPQ1deDmg5lZLbtw9tIEnoCwrQFxWhpo6zulyxHGNZKfqiIvQFhZpQKiwkbFHZFTsIfTtJZSKaS7AQiq8rZnrxZKYWT8V5kVrrUrnUELF0vIA6fhbeWzHrzXgsHjxWDx6Lhzp3HTXOmhOKKEVWSEaz+ZimwTinLLJ84vgmi804YGUyYR+wNtncJoxm/ZBjNQLLlUr2dUdY1+SnNTA0zmrRWC/jS53Dxhfx99G8dSNNWzeRjse4/YGfAmC0WJi4ZDmqpKNo4izUskZ6EznWhlM8sbKNeGZksWfSS3nBVOYaFE928+n9r6SbIiT8SSKuZgxlFZTV3YJONxB/tOSLcPm9EO3GEO6gONyOJ9RKKLaLYO4INqeVGuM26py7ceWO4O0+hDvZgYM4x86+jOl0Uk5X1fUcMo0l1xxhWnYtleHtmDw14K4CdzW4KsF44h9KB+JJPrdfK2YMcGWhE9tpWAJPlUhkF93df0NRs5hNJVRV3Y7J5DnrxxFcWoyaWX7HePDBB/nP//xPuru7mTJlCj/5yU/ygY533nknra2trFq1Kt9+9erV3HvvvfnEnl/+8peHCaEnnniCr3/96xw5ciSf2POWW4a6QU523FPh3Z42IZVLcTRylCPhI7SEW+hL9g3ZbpAM1LhqaHA3UO+up8xehu4sxyhkjh4l09Y+KJDC4SGCqernP8sncuz62tcIP/m3E/Y15vXXMFZUAOD7zx8S/N/BgruS2ayJoMICDIVFlH3rW5iqKrXrsH8/mfb2QQtSURF6lysfC3U6qKpKS6SFnb07R5wlOKN4BuOKxp33WYI5JUcoHcqLJn/KTzAZxJ/0E8/FT7ifDh2FlsK8aDr+1WF0jCiechl5SDD4MXddMpblRLcunSRhdRqxHYtvcpu0WCenCb3xzL5z7UGtbuDuzsG6gcUOE5eP8VJvSnJk8zqatmzAd6RpyH5LvvEzwjoHPRHNZReIZxhp2JIEXrtpUDwNvBbZTe84VijTGSO+qRu/6UXSJe04S8dSU3PXKVr8ZCKR3QSDb5BK95BLhOnp6uZIh5UFhWVMkgJkAkeRwh1YUr18f8KTRExafN613Q+xrG+EJKz2Yk1cvf9XUKylzsgFW3ioM8wPA5BRocCg57tjK7mltPCsxkmqqorf/xr+wEoAHPZxVFR8CL1ehGcITsy7Lm3CaOfdJqhySo6OaEfejdcV60Jh0DIgIVHhqMjPcjuVgq5KOo0cCmMoGUw8GFu3jtTefUMtSccJpTGvvIzOrjnNzkQk6dxu9G63ZkEqcKN3F6AvKMD7yX/B4NWmS2c6OpHDIfTuAgxFhUhW63kPhk/LafYF9rGjdwdt0bb8eovewhTvFGaUzDjjAO2RUFWVaDY6xNLkT/oJpoKEUqEhf+u34jQ68Vg9FFmKhoimQnMhet3JhWU2LdO2N0A0mCIRyZBO5k7Y1mDQ5WOaNNGkvbc4jCMGkp8NQokMG5oDbG7V8lnZdr2Ebf/xqVMkpLJ6EuUTSJZPRHEOn3LvHHDXacLJTKnLQqnLcpy77uyRC6WJrm4npXbiL34Go8dGXd2nsVgqTqufVCrFxo1/40jL85hMQQA8Hg9TJl+Dx7MEq7UOScmRyMERf4Ij/jjSgecp8b1BYaYHd7aXwkwPJnXQQnv4o5uoqh2L0aDj/ateZ4ukWYiuDm7kh71/pczuhIJqTXzN+UewD1xLVT2jTPOKkqar+0mi0b3a+IsWU1x8jQg+F7wtQlBdZIx2QaWqKr6EL2+BOho5OsyFU2QposFZR4OxnEq1AFMio4mfcATntdfkH/b9j/+F+MYNw9xsalIz84/buhW9Y0Akff3rhJ948oTjGvPaqxgrNeuQ/1e/JvLCCwPi6LhlQDA5r74qnyBSSaeRDIYzshxdaALJQL6WYCQTya8vthYzvXg6U4unnnIAf1pOa1amAbF0/Pvj0xO8FZPONKKlyWP1DKt9eKpE/En2vtE1pAAvgNli0ITTgGCyD7jsTFbDeRW2yViUwxvXUTNlOhZPCW8e7Wfd66uQXn+UbGkj6eqpZComolq0/FpGvXSccNJEU5nbguM03XVnipLMEVnVjpLM0ud5EqUkRkHBHMrL3//2Ow+Qy+XYunUra9asIZHQJo5UVlqYM8eGxdoLA7FvVksVRZ7FOB2ThgiUWDpHS1+cI/4YR3pjxEJ9uLM+CjI+DroWoOoMVBZYaYlv5llzLf/R9DNu9b3MsL/qvXs1tyHAK9+AHX/QEqA6y8FVftz7CqhbBKahkYnZbJiOjt+TSncjSQbKyt5LgXv26V5SwSWKEFQXGaNRUAXam2hv3UVPTxP+3qOo0SjGeAZTPIMxmeXgJ5bQUNhIg7uBwl8+QfqlV5GjUa00x1sYt3VLPpHjSUWSXs+YFa/kLUmhvz1FYvPm4SJpwKJkbmhAMp2dXDSjDUVVaA23srPvBC7BkhmMKxyHDh396f4R45pi2dgJ+9eho8BSMKJochqHxw+dKaqq0t0U5vBWH4qiYnOaqJnswe42YXWZMJounOjNpJI0b93EgXWrad25HUXOMf8DH+LyW28HIJvJsOtID3v7FXSSNOiuc1soOkHKhfOBmlOIvtGB3J8m7jxEtHIdOoOFxoZ7MRhOPVj8T3/6EwcPHgSgsLCQK664Ip+UM5MJEAyuIxx+E0XVRLDJWERR0WLc7pnoRrBIR1NZjvTF2eAL0xFMIMe076yMSkqCAiXLOFuEceYQNfogXrkXQ7QTbvzpYNLSJ/8Zdv/1xIO+dx+4tR9ZrP0JyeZn6fDI5MxmDCY3la6rsBVOyWekf2syVIHgrQhBdZFxvgWVqigosRhKNJq34ICWzyh9+DByOKIFZYfDKBHNipSLRcn+4ccciWjB5BP/6zmqtnWc8Bhjt2zGMGDxeatIksxm9C4XOrcLvctN1S/+O1+7LbZuHZnm5hFFks7hQDrNDMoCLYZtX2AfO/p20B5tz6836UzklNxJXXQOo2NE0VRgLjjnEwXknMKhzT56WrTZkMVVDiYsKMdwAUWUIsu07NjK/rWrad62idxxExGKa+uZef1NTF1+zQUb39uhqirxzT1kO2OoJpm+hidQdElKSq7HU7TobfdVFAX9gOW2qamJp59+mmXLljFz5sz8+uPJ5WL0hzbR378RWdasWAa9ncLCBRQWzkOvHyxAragqv+3o43tHuplgt/KHSXW0+eMcGbBiHctefwydBNVFNhq8dhqK7dQU2TFlwxDuhGi3tkS6tdqKkYHPd63Mi6TwUx+kO74eVQJLWqGqK4Uxd9wj7779mlULYNvD0LZp0OLlqgBnGTgrwFECb+OuHq1kZYVwMkskmSWSyg28ZpGQqPXYqPfaT3sSxLsNIaguMs6loOp78EGSW7cOiqRIBCUS0WIN9Hom7NmdtyZ0/Ntnia5YccK+/vbgB8gN1DWb9ftt1GzvQudyYSooxFpYjMHtRufS4o48n7gzH7+U6ehETSXRuVzo3W50IgfXBcOf9OcThx5zCRp1xhFFk8fiuWDpKRKRDHvf6CQWSiMBDTOKqZ5UdMGTteayWf7nk7eTjmsB9gVl5Uy4fCkTFi7FU3XxJ+dN7guQOhAEnURy6j7C2U2YTB7q6z6L7iQC+VhSznHjxuUn3KiqSi6Xy6eZORmKkiYUfpNgcB3ZrJazSqczUeCeQ1HRQjqyNu490MbGsHZdlxQ6+NXkOgqPK2YcSmQ4ckxg9cXof4v716CT8DrMOCwGnGYDdrMBh8WAw6zHYTZq700GbCaJ/uBrBDqfhVQIZ85JRcqDLtI7KMRivfCllkEL1RP/BHueGPnkJL0mvpwDuQf3Pwf+Q5rYcpYNiK9ysFw83gdVVYln5LxAiiQ1sRQ+/nMqS+IEM0yPp9Rlpt5rzy9Oy4Url3UheNeVnhGcmPT+A8TXbxhxm2Q0osQT6B12FFUhO2M8KWKETFmCxjRpm5GM3UjGZiJjN+FxlVHnHUO9q566//7KKcfDHJvlJrjweK1erqi5gmXVy+hN9GI1WHGZXBdcqByPvyPK/vXd5LIKJrOeyYsrKSi1vf2OZxFVVelpPsSBdWvwHWnitm9+H0mSMBiNTL/6BuRshgkLl1LaOPaiunYnI9Me1cQUYJiqJ5LbCkBJyQ0nFFNvTcoZDoe5/PLL0eu1VBKnIqYAdDozRYULKCyYRzS6h0BgDal0N/7gen7X2cevI9NIqTrseh3fHFPB7eWeYde1wGZiVo2JWTWFAATjGVr8MZr7NJEVTmbpiaQgMtIIBsZBhgrTqxQYj2LU68gYrkNnWYLDZcRRPSC8zAZtSSnYzTJmgx5mfBhKJg4Irh6tkHW0G2I+QAXbcWkV9j4Fe0YIWzA5NIH1TyvAplnkObpe68Neolm6HCVa3cV38J3KygrRVO44y9KgQDq2LprKnTCP2bBh6yVcViMuixG31YjLaiCdU2jxx/FF0vll4xHtu1XsMFFfbKfOY6fhbbLnX0oIC9V54lxaqOIbNpDr7c1bh/RutzZV3+UipMTygeQt4ZYh2adBy0Dd4G7IpzNwms4sGZ9AcCqoikrLTj9H9wUAcHutTF5cgfk83pADHW0cWLeaA+vWEPJ159d/5Lv/RfmY8edtHGebXCBJ9I1OUFTMYwsJFr5ENLYfh30sVVUfHyZe/H4/r7/+Ovv27QM4K0k5j0dVVTrDTXzyQA/bklp/M029fLPcz5TSedhsDaclVFVVJRjPEIxniKZzxFI54ukc0bT2GkvlSGUCFKjPYZKCqBjoziwnIo99275NemnA0mXUrF0WA3bTgPXLCG4ljNVTidNsxGLUIb35KLRvGhRdkW5IDyTxlfTwjb5BF+FfPwF73zL7WG/WhJW9GD72TN6ypbauJR3qIW4sJKIrIqgrpD9nIZLO5V1y4eSpWZWO4TDrNaFk0wSTy2oYeB0QT5aBczrB3yKeztHij9MaiNPSF6c7khqW+qPIbqTe66Dea6Pe66DQZhw1P0JOBeHyu8g4XzFUsUxME08DcVDhzNBM3Wa9mXqXlsqgoaABj2X4r0SB4FyQSeXYt7aLfp8WZ1M1vpDGWSXnLXC7Zcc23vjD/9HX1ppfZzCbaZw9jwmXL6Vu+iwMp2iNudhQElkiq9pRUzLGcjtMidHe+QiSpKO+7t8wm0uGtH/zzTd57rnn8rm8pk2bxrJlyygaiHM8W+QUlZvePMyBeILPen1cq3sDSTo2M7CSoqJFOJ1TzkrqgkSihY7OP2pxXJIdZ9FtZCglls5pSyo3+P64z1n59B6Beh3Yzce5HM0GnBYDLn2WgpwflxJCX7cAh1kTZbpV34OWNajxXoj1ImUGJ4Ko6Hj8+jcJpxQiqSzX7b+fqaHXh15DyUjMUEjMUMRvG35GWq+FWYxN7KBYCoOjGJ2zFJO7DJvLg8tqyosmp8WA4Syn40hmZE1c+bWlK5TkrYYwt9VIg9dO3YCL0Ot45/nULiTC5XeJkJEz+YSaR8JH6E30Dtmul/RUO6s1AeVuoMJRcdYTagoEb0e4L8HeN7pIJ3PoDTomzC+jpPbcxpskwiEURcFRqIkEnV5PX1srOr2BuukzmbBoGY2zL8NkObNacxcLalYhtqEbNSWjd5uxzSmmtV2bBVdYMH+YmAKora1FkiTGjh3LlVdeedK6pKdLRyqD12jAotdh0En8clItOgnqrDPIZOYS7F9HOLSNZKqTzq7HMRlfoahoEW73rMHs7adJKLSVHt8zqKqC1VJJZeXtGI1v//1SVZWMrOTFVXTA6nW88Dpm/Yqmc6SyCrLCQDzSSDnSjEAxNGsJXiUJ7Kb3Q9n7iQ2UPjIqKRy5II5sEKsc5VDnoMDymWpx2KfjkvtxZAOY5TgGNUtBthd3LsBHFk/EZTXjshqwPv1DpH3PDD283qRZvezFcOfz2meAljc0t6OjBByl2nZr4Rm5Ha0mPRPLXUws165vKivTFkxwpE8TWB39CcLJLNvbQ2xvDwFa7rV674CLsNhOidM8qgXWiRAWqvPEubJQPbTjIXqTQ0VUma2MhoIG6l311LhqMOkvzbQCgguPqqp0Huyn6c0+VFXF5jIxZUkldve5mbSQTsQ5vHkDB9atpm3PTmZeeyPL7/wXABRFZt/q12mcOx+r493h2lYVlfimbrLdcSSLHteyakLprfh8z6PX22hsuA9VNbJlyxZCoRDXX399ft/+/n4KCwvP3lhUlT92B/n3pk7urPTy9cYTJw/N5eKEQpsI9m94y8zA+RQWzh8yM/Dkx1To7X2RYP96AFzOqZSX33LGwuztyMkK8bRMNJ0lnpaJpbVYpWPC6/j38Yw8zDVm0ElDXG7Hxywdc78NsSplUxDvhVgfpPphzFWDna18AFrf0ILrY72DLkcYcDv64diM6cfvgP3PDh2MzqgJK0cx3Pl3MA/8T7Ss0fqzF2txYDYPWItOWjLoeNI5mfZgcsCCFaM9mBwWy2U36anz2vNWrHK35aIWWMJCdYlQ66olo2TycVB17jrsxpHK7QoE5xc5q3BwUw++o1oEcUmNk/HzyzAYz+7082wmTcubWziwbg1Htm9Bzg7ODAv19uTf63R6piy/+qwe+0KT3Bsg2x0HnYRjXjmKKYO/8zUAvJ4r2bPnEK+//jrhgSLds2bNylujzqaY6kpl+PzBdlYGtZqdW8NxcoqK4QTuXIPBjtd7BUVFiwiH3yQYXEsm20+f/zUCwTW43bPxFC3CaDzxGGU5SWfXn4nHNWtQsfcqPJ5l5/TBbNDrcNt0pxSErSgq8YwmsABcFiM2k/70xme0QEGNtryV5fcD9w9+zqYg3qcJsGRoUEwBlEyCRFCzUsV7IRUGJaulm4j3wvHPjM2/GS6+QGtj88CnN4BZyynInifB3zQgvDTxZbYWMcbmYcxYD0wqJSsrtAcTtAa0iQVtwQTxjMzergh7u7R7g9Wop85ry88irHBbL1gOt3eCsFCdJ86VhSqn5M5rUWGB4FSIh9PsXdNJPJJBkiQaZxVTNf7s1mUDzSry8H2fItg1mC+tqKKKCYuWMmHhEgrL372zT9OtYRJvatZp+9wyTNVOenqepT+0iXjMyJYtJfh82nan08ny5cuZPn36iLmkzhRVVflzj2aViuQULDqJr9SXc1d1MfrTCjhXtJmBwTdIpboAkCQdTucUPEWLh5XKSWf8dHT8nkzGj05npKL8H3A6J5+183rXk0tr4ivWC6kQNF4xuG3V96F1rbY9EYREANSBIHidQbN8HfvbPn67lkLiRHylDSxu7f2GB6FzG4q1kIjkold20JWx0pa0EpKc+CwNKJL2LDMbdPkcWA1eB5WFVvQXUGAJC9UlghBTgouN3qMRDmzsQc4pmK0GJi+uwF38zlMiqIpC16EDNG3dyOKPfBydTvu1XzdjNtlMmgkLlzDh8qUU19Zf1O6Ds0G2L0FiuyaWLBOKMFU7SaV78PneoLW1lQMHqkgmwWw2s3jxYi677DJMZ7migC+d5fMH23k1oFkZZrls/GxCDWPtp5/XTJJ0uFzTcDqnkkgcIRBcQzzeRCSyi0hkF3b7GDxFi7HZGkkkmuns+jOynMRocFNVdftp1ya85DGYtVI+x8r5HM+yrwz9rKqaRSsZ1F6P/98ac7VmtUoEBsTXgABLaukVMB8nPo6ugwPPowMKBpZxxx1m3T9spzmio8UfZ2nbg4zbt4m4wU2/3k230Y3RWYy9sIRCbxmFs2/BaB64p8i5iybbvbBQnSdGY+kZgeB0UBSVI9v7aB/Ig1RQYmPyogpM1jO/2amqSt/RFg6sX8OBdauJ+vsA+ODXv0Pt1BkAZFMpDCbTJZNhX45liK7qQM3IGKsc2OeWAdDe/n9EoofYvKmbzs4xzJs3j0WLFmGznZv8Xu2pDMs2HyCrqHypvoxP1ZScllXq7UiluggG1xKJ7kZVtUz/ZnMpmUyfFnxuraGq8iOnVUpHcJ5QVUhHhyY6bXoV+g4eJ74GXpNBSEXg3j0gSSiKSvoPH8Ha/MIJu/+Pqa9QVlxMvdfO3N3fxHX4b0g2D3zgd1C74KyfjrBQCQSC80Y6kWXvG12E/VqB65pJRTRML0Y6QzN9rD/IntdfYf+61QQ7jyulY7UyZu4CbC53fp3RcmEyvV8IlIyszejLyOgLLSjjbKxdu5bp04uIJ5oxGMwsWvQ5SkoaKCgoOOvHj+dk7AbNZVhtMfGLiTU02CyMPwOr1NthsVRQUXErxdmrCQbXEQpvJZ32AeB2zaSs7H0nzfwuuIBI0vCs8WOuGhpUfwJ0Ognrdd+E0D9CIoCaCBAP9RLr7yUT6UNO9JPAqmXU98cp72xnci4FkU42tkVpKExR4row9wRhoTpPCAuV4N1KyJdg79ouMqkcBoOOCQvLKa5+Z1aDnqZD/OFr9wGgNxppmDmXCZcvoX7WXIymS7OskaqoxNZ3ketNkDUq7DK3sWnLZnK5NNdel8Ht1uP1LKW4+OzXGVRVlad7Q3ztcAf/M6mOJUXn3yokywlCoW3oDTbcrlnvereuYGRUVcUfywzOIvT1ocT82HNhfJY6Pnz5hHxKh7OFsFAJBIJziqqqtO8PcmSHH1VVcRSYmby4Epvr1GN1VFWlY99udq9cgdXpYvnH7wKgtHEsk5deRfXkqYyZOx+zTcxcTe7qI9UTZXf/YbYE9pFMadbAsWOzGAwpDIYyPJ6lZ/24fZksXznUwd/7tJmC/9fpvyCCSq+34fEsPu/HFVxcSJJEsdNMsdPMZfVFqGo1/YksLf4YR/ri1Hku3L1CCCqBQHDa5DIyBzb20NeuTZMvq3cx7rIy9IZTi2OKBQPsXf0ae1auyJd/MVmtLPrQHRjNWk6a6z59z7ka/qgj2dTPzm07WN+zk2hWKy7s9Xq54or56PV/R1F1lBRfi053dq13z/aG+MqhdoJZGaMkcV9dKf9ac/aSgAoE7xRJkiiymyiyFzG79uxm+j9dhKASCASnRaw/zd43OklEM+h0EmPnlFA+puCUXDCtu7az/cVnadm+LR9obLRYmXD5EqYuvwbDJerOOxlZX5zkrj629e0nmo0PSYHg632KcDiD1VKNyzXjrB0zkMlx/+EOnu0NATDZYeHnE2uZ7BjdWeUFgnOJEFQCgeCU6WkJc2hjD7KiYrEZmby4Apf31B+ynQf2cuTNLQBUjJ/E1CuuYfz8RZdUYPmp0tHRgddaSGpTDxISy6dfTtCRZN68eZhMJpLJDsLh7QCUlr7nrMYUrQvFeLY3hF6Cz9WWck9tKaZLZBalQHCmCEElEAjeFkVWaNrWS+fhEABFZXYmXl6OyTLyLSSTSnJow1p2r1zB3Pd+gDFz5gEwZdlV5DIZpiy/Gk9l9fka/qjC7/fz+uuvs2/fPi6vmcWcgokYPFYmLRqDpNdEk6qq+Hr/Dmgz3qzWd34tVVXNi7Kbit38W00JN5UUMM15btIuCATvNoSgEggEJyUVz7L3jU4igRQAdVO91E3xDEuJoKoq3YcPsmflKxxY/wbZgaDpPStdeUHlLilj6e3/eH5PYJQQjUZZtWoVb775JscmX8fjcXSVRuzzy/JiCiAS2Uky2YZOZzors/pe9of5z5ZuHp8+Bq/JgCRJfO0ktfgEAsFwhKASCAQnJNgVZ9+6LrIZGaNRz8TLy/FUOoa0URSZN194lj0rVxDoaMuvLygrZ8qyq5m89MrzPexRRSqVYt26dWzcuJHsQB3CxtJa5rsmU+z04FhQjs48eKtWlDR9fS8D4PEsxWg88ynioWyOrx/u5AlfPwD/3ebjW2PeveV6BIJziRBUAoFgGKqqcnRPgNZdflTAWWhh8pIKrA5Tfvsx95BOp2f/G6sIdLRhMJkZN28hU664hqqJU0SuoFPgpZdeYseOHQBUVVWxZMJ8invNIIF9Xhl619BA/UBgDdlcBKOxkKLCy8/4uK8GInzhQDs9mSw64FM1JXyxruwdnIlAcGkjBJVAIBhCNi2zf30XgS5ten7FmALGzClBr9cR7u1hz8oVHNywlo9+78f5/FDzb7mNRCTEhMuXipxRb4OiKGSzWcxmTSgtWrSIrq4uli9fToOrisTmHgCsU4sxlg69ltlsP4HgWgBKS65HpzOe9vHjssy3mrp4tCsAQKPVzM8m1jDHLf5uAsE7QQgqgUCQJxpMsWdNJ6l4Fr1OYuxlZRRXWzm0YQ17Vr5C255d+bYHN7zBtCuvA2DsvIUXasijBlVVaWpq4tVXX6W8vJz3ve99gJZP6lOf+hRyJENsdQeoYK53Y250D+vD1/siqprDbmvA4Zh0RuP4SasvL6Y+WVXMVxrKserFDD6B4J0iBJVAIACguynEoS0+FEXFajdSN83C3lV/ZP/aVaTjmrUKSaJ26gymXnENjXPmX9gBjyI6Ojp49dVXaW1tBbRSFslkEqtVSzmhpmXi67tQcwqGYhvW6cXD3KWJRAvR6F4kJEpKbjhjd+rnakvZEo7zhboyFl+AjOcCwbsVIagEgkscWVY4vMVHd3MYVVUprnIyYWE5qVg/O195EVVVcHqLmbLsKqYsuxpXccmFHvKo4fgUCAB6vZ7LLruMxYsXD4opWSG2sRslmUPnMGKfVzbCDEoFn+95AAoKLsNiKT/lMbQl0/ypO8iX6suQJAmnQc/TM8eI+DaB4CwjBJVAcAmTjGXYs7qd7sP7CLRtxmKXWP7RbyFJEsYiL4s+/DFK6hqomTodnU5/oYc7qjhw4ACPP/54PgXCjBkzWLZsGQUFBfk2qqqS2NaLHEwhGXU4FlagMw2/zqHwNlLpHvR6K17vqc2aVFWVJ3z93H+og5isUGExckeFF0CIKYHgHCAElUBwiXJ0bxsbn3ye3pZNZBJaTA2SRCwYwOnRHryX3fzBCzjC0U1dXR0Wi4Xq6mquvPJKSkuH18BLHQiS6YhqM/rml6N3DC8sLctJ/H0rAPB6rsBgePvg8VA2x5cODZaOmeuys6RQuPcEgnOJEFQCwSVG255drPnjn/E17wY064nJasvX03MUeS7sAEchuVyOLVu20NLSwoc//GEkScJisfCZz3wGh8Mx4j6Zjiip/UEAbDNKMBaPnJHcH1hJTo5jNpVQWDjvbceytj/KZ/e30ZXOopfgC3Vl/FtNKQadsEoJBOcSIagEgkuAY3mjMqkce1bvw9eszdbzVI1jzo03MH7hIoxmUU/vdFEUhT179vD6668TCoUAOHz4MOPGjQM4oZjKBVPEt/kAMI8pwFw/fEYfQDrdR3//BgBKSq5Hkk7udn2orZdvN3ehAg1WM7+YVMMsl0iHIBCcD4SgEgjepWRTKQ6sX8Pula8wYeESxsy9mr1vdGJyTKR83FXMee/1jJs7/kIPc9Ry5MgRVqxYQXd3NwBOp5Nly5bR2Nh40v2URJbYxi6QVYxldqxTvCds29v7Aqqq4HBMwOEY97Zjmuu2o5Pgw2UevjWmArtBxL0JBOcLIagEgncZIV8PO15+nj2rVuTTHaSiKaL941FUFWehkwWf/xT2AvPb9CQYiUQiwVNPPcXhw4cBMJlMLFq0iPnz52MyDY+BOh41pxDb0I2aktG7TNjnDp/Rd4xY7CCx+CEkSU9JyfUj96eqHIinmOjQZgzOcdtZc9kEGm3C2igQnG+EoBII3iUc3bWDN198hiPbt8LAzDJ3SRmlYxZidkxBUVWKq51MWFCGwSgsF2eKxWIhHA6j0+mYM2cOS5cuxW5/e7eaqqrEt/Ygh9NIZj32BRVIxpETaipKDl/vCwAUFS7EbBpuxfKls9xzoI0NoRgvzxnPeLsmooSYEgguDEJQCQTvEra//DxH3twCQO30WYyZexXpZCWJaBZJkmicUUzVxEIxZf40SaVSbN68mQULFmA0GtHpdNx8881YLBY8nlMP4E/tDZDtioNOwjG/HL39xGVjQqFNZDJ+DHo7Hs+yYdtf7Avx+YPtBLMyFp3EwXgqL6gEAsGFQQgqgWAUEuzqYMfLf2f2e27GXaIVtJ39nptxFHmomLCUaMBCf28GyGKyGJi8qIKC0pFnkQlGRpZltm7dyurVq0kkEkiSxOLFiwGorKw8rb7SRyOkDvUDYJtVgsFjPWHbXC6G3/86AMXF16DXDwqleE7m/zV18odubXbgFIeVX06qFWJKILgIEIJKIBglKIpMy/ZtbH/pOY7u2g6A3mhk6e3/SDKaIZUowey4El+rAmQwGHSUNbqpnezBZBX/6qeKqqrs37+fV199lWBQEy4ej2fEPFKnQs6fJLG9FwDL+CLMNa6Ttu/zv4qspLBYKnC7Z+XXvxmO85n9R2lJZpCAT9eU8OX6Mkw6UYdPILgYEHdZgeAiJxWLsWflK+xY8QJhX4+2UpJonHUZ3pop7F7VQaAzNpBRCmxOE1XjCyltcIlYqdOkra2NV155hY6ODgDsdjvLli1j1qxZ6PWnfy3leJbYxm5QVIyVDiyTik7aPpXqIhzaCkBpyQ1I0qBYWtUfpSWZocJs5L8n1nC5SNQpEFxUCEElEFzEKLLMw1/4NPF+zVJisTuYvPRqKiYsItRrpLc9A8QA8FTYqRxfSFG5XcRJnSHr16+no6MDo9HIwoULWbhwIWbzmc2GVLMysfVdqBkZfaEZ++zSk/5dVFXF1/t3VFRczqnYbPX5/GEAn60pRVHhn6u8FBjFrVsguNg44//KTZs2cf311/Pkk0+yfPnyszkmgeCSRZFlWne9Sf2MOUiShE6vZ/z8RbTv3cWU5Tdg90yl72iKziYZyKA36ChvcFM5vhCb6+RT9gXDicU0MXosAedVV12Vt0o5nWduAVIVldjmHpRoBslqwDG/AslwctdcNLaXRKIVnWSkuPha/tQd4I9dQf46oxGLXodBJ/GF+rIzHpNAIDi3nLHz/eGHHyYej/N///d/Z3M8J6S/v5877rgDt9uN2+3mjjvuyGcmPhGqqvLNb36TiooKrFYry5YtY+/evUPapNNp/u3f/g2v14vdbue9731v3tx/jLq6OiRJGrJ85StfOdunKLiESUTCbHrqL/zm3/6Jp77/LTr27wG07/DUq25l5o1fIdLfQFdTnGxWxmo3MmZ2CQvf38jYuaVCTJ0mmUyG1atX8/Of/5xXX301v97r9XLTTTe9IzEFkNztJ+dLgF6b0ad7mxg2RcnS2/sSADr3Ej51KMS9B9rZEonzWHfgHY1FIBCcH87IQpVOp/nLX/7Cd77zHb71rW/x4IMPnrDEwtniIx/5CB0dHbz0knbT+Zd/+RfuuOMOnnvuuRPu85//+Z/8+Mc/5uGHH2bcuHF85zvf4eqrr+bgwYP5G+Y999zDc889x5///Gc8Hg+f//znufHGG9m2bduQmIlvf/vb3HXXXfnP5/p8BZcGPc2H2fHy8xxYvwY5mwXA6nITDQTobgrRcbCfWCidb19Uprn1PBX2EyaEFJwYWZbZsWMHK1euzFun+vr6yOVyGAxnx42WPhIm3RwCwD6nDEPh28/ACwbXks32syNXzw+aS+jJhDFI8OX6cj5ReeJM6gKB4OLhjO4gzzzzDA6Hgy984Qs88sgj/PWvf+UTn/jE2R5bnv379/PSSy+xceNG5s3TioP+5je/YcGCBRw8eJDx44eXz1BVlZ/+9Kd87Wtf45ZbbgHgkUceobS0lD/+8Y988pOfJBwO87vf/Y7f//73XHXVVQA89thjVFdX8+qrr3Lttdfm+3M6nZSVnbq5PZ1Ok04PPggjkcgZnbvg3Uk81M8zP/oO3YcP5teVNoxlyvLrsRdNwdcSx9emBaDrdRKlDW6qxheK7OZniKqqHDp0iFdffZW+vj4ACgoKuOqqq5g8efJZiznL9iZI7ByY0TfJg6ny7X94ZbNhugJv8OvIdJ6IjwNyjLGZeXBSLdOcItWFQDBaOCOX3yOPPMJHP/pRJEni9ttv5+GHHz7LwxrKhg0bcLvdeTEFMH/+fNxuN+vXrx9xn5aWFnp6erjmmmvy68xmM0uXLs3vs23bNrLZ7JA2FRUVTJkyZVi/P/jBD/B4PMyYMYPvfve7ZDKZk475gQceyLsn3W431dXVp33egncXueO+MzaXm1Qsik5vYMKiZbz3C99l5g1fIOSvpeNghGxGxmIz0jizmAW3jGH8vDIhpt4BW7du5U9/+hN9fX1YrVauu+46/vVf/5UpU6acNTElRzPEN3WDCqZqJ5bxhae0X1/fK/ysf8qAmII7K728Mme8EFMCwSjjtC1UPT09rFixgh//+McAfPSjH+XrX/86LS0t1NfXn/UBHjtmSUnJsPUlJSX09PSccB9gWO6Y0tJSjh49mm9jMpkoLCwc1ub4fj/3uc8xa9YsCgsL2bx5M/fffz8tLS389re/PeGY77//fu67777850gkIkTVJYiqqnQdOsD2l56jc/8e/unnv8VgMiHpdFz7qXvJpGwEulQ6DqWAKAAFJTaqxhfirXIIt947QFEUdAM5mqZMmcKaNWuYNm0aixYtwmo9cWLNMzpWRia2oQs1q2DwWLDNKjkloZZIHCUc2cEdDjv71LF8e2wNV3vdZ3VsAoHg/HDaguqxxx5jxowZeTdbdXU1y5Yt49FHH+Xf//3fT6uvb37zm3zrW986aZstW7RSGiPdnI6fUnwi3rr9VPZ5a5t77703/37atGkUFhbywQ9+MG+1Ggmz2XzG060Fo59sJs3BdWvY/vLz9LY059cf3b2d6kmz6TwcouuQgUw6CYBOJ1Fa76JqfCGOU4i5EZyYRCLBmjVr8Pl8fOxjH0OSJKxWK5/73OdOGCelKiooKqqsgKyiyqq2Th667lgbVR7YpqggK2R7kyixLDqbEfv8ciT9yY3/3ekMr/gjLM3+HYDxngmsnTwFvUh3IRCMWk5bUD3yyCN88pOfHLLu9ttv5z/+4z9OW1D967/+Kx/60IdO2qauro5du3bh8/mGbevr6zth9uJj8U49PT2Ul5fn1/f29ub3KSsrI5PJ0N/fP8RK1dvby8KFC084pvnz5wPQ1NR0WrW8BO9+4qF+3nzhGXa9/gqpqBY3ZzCamLBoKWPnXU0qUcCGp5pRBooXm60GKscVUj7GjckicguBJm7UnDJEsBwvYNScMiBsjq3TtmfTWbYd2snGg1tJZzX36oEX36TaXabtJx8nkN76WX2bQf3/9u47vqr6fvz469yZfbMXmewtSyCIDBmCWOqoVqngQJTytahtRfhZKygOrFqto2pr0VpcFLVOBAeoQAhBwiYIJCSQHZKbeff5/XHJ1ZBB9uL9fDzuw9xzz+ec9z1ect/5fD7n/WkCRafBLykKjbHx/4+fFJRyX3o2JQ4na4IdjPc2EhY2Q5IpIbq5Zv0Gz87OJiwsjBtvvLHW9l/96lf85z//4ccff6Rfv35NPl5oaCihoee/gyUpKQmz2UxKSgpjx44F3HWwzGZzg4lPYmIikZGRbN68mZEjRwI/3Sq9Zs0aAEaPHo1er2fz5s1cf/31AOTm5nLgwAGefPLJBuPZs8e97MfPEzUhwF3VPOV//wXAPzSMi2ZcQVS/CRSdcnDyUDXgTrJMod7EDAwiNNYfjQzrAe6eYevxUiyHzriTpma0O1Kawfa8vVTYqwAI9QpiYtRIIi0B2C1VzQtEo6BoFdAqKBrN2f8qKDoFNJqfXtMqoNW4X9Mq6GP90Zoa7pUudzj504+neTfPXaR1gN5MpLaS0JCp6HRS9VyI7k5RVbUN/jZrf7NnzyYnJ4dXXnkFcJdNiI+Pr1U2YeDAgTz++ONcffXVgHsi+eOPP87atWvp168fjz32GFu2bKlVNuG3v/0tn3zyCa+//jrBwcH88Y9/pLi42FM2YceOHSQnJzN16lRMJhO7du3i3nvvZcyYMfzvf/9rcvxlZWWYTCbMZjMBAY2v5SW6B7vFwqHvvqG8uIiJN8z3bP/2rdcJi++Ll19/co+XYa12AKBRFMIT/Ok1IIiARhbHvRC5LA4qd+e7azfVUHAPnf08wdH+lOCgVaiwV/P+7s8pLHfXavL39mPS0PEMThyIVq+tlfTUlwTVOaZGaZd5a7vMlfzfoZNkWdzr8C0Mq+B67UZ8jcEkJixFo5HeSSG6qqZ+fzf7X3FWVhYRERF15gepqkp2djZxcXHNj7YJ1q1bx9KlSz135M2dO5cXXnih1j7p6emYzWbP82XLllFdXc2SJUsoKSlh3LhxbNq0qVbRvr/+9a/odDquv/56qqurmTZtGq+//rqnBpXRaOTdd99l1apVWK1W4uPjWbRoEcuWLWuX9ym6vtK8XNI2fcKBb77EWlWJRqtlxOVz8AsKpvyMhfDEy8nPLMPlcvdEGLx09OoXSHS/QFmkuB623Eqqduej2pygVfAeGoox0dSkxMbX5UI5pMVoM3LppZcybtw49Hp9B0TddC9mFfDo8RxcQIyXnr/2DSLszAeoqkp4+BWSTAnRQzS7h0qj0TBo0CA++ugj+vTp49men59PdHQ0TqezzYPsCaSHqntTXS5O7tvDni8+4cSeVDj7zyYwIooRM+cQ1X8C+ZkWSgt/6mEJCPGi14AgwuP80ZxnkvKFSHW4qD5QhPWE+48grcmI78WRaBup+m42m9m+fTvTp0/3JE75+fn4+fnh6+vbIXE314f5JSw+dJJfRQTxWP8YyvLeobziEL6+fYmNuUXWXRSii2u3HiqAQYMGMXbsWN577z2mTZvm2d5NRg+FaLa9mz/nq3/93fM8YcRohk+7AoNPb3J+NJO+y90bpSgK4XHuYT1TmAzrNcRRaqVyl3utOwBj30C8h4S6h+HqUV1dzffff8/OnTtxOBz4+/szceJEoG5plM6mqio5Vju9vNyJ4VURQcR4GRhj8qWy8hjlFYdQFA0R4XMkmRKiB2l2QqUoCi+99BLr1q1jzpw5PPnkkyxdutTzmhA9ga26isrSEoKiegEw8JLJ7PzgXfqPn0j/pBlUlHqTe9yM0+Weu2MwaonqG0iv/oEYfbrWkFNXoqoq1mOlVB8sBpeK4qXDd0wE+vD6i1g6HA5SU1PZunUr1dXuEhPx8fHtVvOutYptDv6Yns0ucyXfjB1AmMH9WRhj8kVVXeQXfAZAUOA4jMa6tfWEEN1XsxOqml6oe++9l4EDB3LjjTeyb98+/vznP7d5cEJ0tMrSEvZs/IS0TZ8QHB3DjY88haIoGH19uXrFc+T8WM7R1CrAvayQf5CRXgOCCU/wRyvDeo1yVZ+deF7gHhbVR/niMyoCjVFb7/4HDhzgq6++oqSkBHDfFTxjxgz69+/fJf94+7q4jHuOZFFgc6BXFHaZK7kiLNDzemlpClZrPlqtD6Gh0xo+kBCiW2rVbMjZs2ezfft25s6dS0pKSlvFJESHK8nLYfcnH3Bgy5eeRYotFRVUl5ehM/hyeHsuZ3IrAXdPbGiMHzED3cN6XfHLvauxna6gak+BZ+K5z/AwDAkBjV67AwcOUFJSgp+fH1OnTmXEiBG1FizvKixOF48cz+G100UA9Pfx4qXBcQz92dIxTmcVhUVfARAWOg2tVoaDhehpmp1QTZ48GYPhp0mjgwcPJiUlhauvvlrmUIlupzArk+QN7/Djzu2oqrv2UVTfAVz8y2vpM2YcZYVW0jZnYrU40GoUeg0Molf/ILx8ZVivKVSHi6r9Rdgyzk48Dzw78dy/7sTzoqIijEaj5y7cadOmERkZyYQJE2r9zulKjlVZuPNgJgcrLAAs7BXKn/pE431Ob2Vh0Vc4nVUYjREEBo7tjFCFEO2s2QnVN998U2dbcHAwW7dubZOAhOhIZ06f4mjy9wAkjhzD2Lm/otegIaDCyYPFZO4rQgV8AwwMubSXLFDcDI4Si3vieYUdFPDqF4TXoJA6E88rKyvZsmULqampjBgxgl/+8pcAhIWFMWXKlE6IvOn+nlXAwQoLIXodfxsUx7SQuncAWa35lJa6e/DdE9FlaFiInkgKoIgLhsvpJH3Hd6iqyuBLpwLQb1wSo+dcxZAp0wmLSwDAVu1wD/HluYf4IhNN9L84Aq1evgibQnXVTDwvAhUUbx2+o+tOPLfb7ezcuZPvvvsOq9U9J62qqqrWosZd3aq+vXAByxOjiDDW7bVUVZX8gs9QVRf+/kPw9e1T9yBCiB6h2UvPjBw5ktTUVBISEjzb8/PzGTp0KNu2baN///5tHaMQrWK3WNj/zWZ2f/oBZYUF+AYF03/8RHR6PRqNlikLbvfsW5JXyaFtudgsDrRaDf3HRhDZ29SJ0Xcvrio7lan5OIrcd+Tpe/nhMzIcjeGnuU+qqnLgwAG+/PJLTyHeyMhIZs6cSe/evTsl7qY6WFHN27nFPNK3F4qi4KfT8teBDRczrqg4QmXlMRRFR3jYrA6MVAjR0ZqVUMXGxjJkyBDefPNNHnzwQc/2t99+m9jYWEmmRJdSVWYm7YtP2PPFp56Fir0DTIyYcQWq0wk/q6itulQyDxRzcv/ZIT6TkSGXRuPbyNpsojbbqXL3xHO7C0WnwXt4KIb4uhPPk5OT+eKLLwDw9/dn2rRpDB8+vEv3Sqmqyus5xaw8dhqrS6Wvjxe39Gp8HVKXy0HB2TIJIcGXYDAEd0SoQohO0uwhvwULFvDkk0/WSqjefPNNbrnllraMS4hWObj1K77850s4bO6hJFNEJGOuvIYhU6ahN9ROkqzVDg5vy6Hk7DpyUX1M9BsTgVbXdb/guxLV7qJqXyG2k+6kVRvkhe/FEWj9fppI/vNhvBEjRpCcnMyoUaNISkrqshPOa5jtDn6fns2nhe7etBkhAfziZ+UQGlJSsgOb/Qw6nT/BwZPbOUohRGdr9tIz5eXlREZG8tVXXzF+/HgOHTrEqFGjOH36NCEhIe0VZ7cnS8+0P5fTiebsbfUFmSd48/6lRPTuy8Vzf0W/cUloNHVvuT+TW8nhbTnYrE60urNDfIkyxNdUjjNnJ55Xnp143v/sxPOz6/BVVVXx7bffkp+fz4IFCzy9VU6ns0uWQDjXbnMldx7K5JTFjl5R+FOfKO6ICTtvqQyHo5zjJ/6Ky2UlOupXmEwjOyhiIURba7elZ/z9/bnmmmv497//zfjx43nzzTeZPXu2JFOiU6iqStaBvez6aAMBoWHMvNNdtT88oTc3Pf4s4Yl96v3yU10qGfuKyDpYjAr4BRoZPFGG+JpKdalYjpZgOVwMKmi8dfhcHIk+1F1fyeFwkJKSwrfffovF4i4pkJWVRXx8PEC3SKbW5RRz/9FsHCrEexl4eUgCIwPqr+h+rsLCzbhcVry9YggIGNG+gQohuoQW3eW3YMECbrzxRv7617+ybt06/va3v7V1XEI0yuVy8uPO7ez6aAP5J44BoDMamTz/dow+7i+9iN59621rrbJz6Ptcz0LG0X0D6TsmXCqdN5Gz0k7V7p9NPI/xw2eEe+K5qqocOnSIL7/80lPhPDw8nJkzZ3qSqe5ikK8XAL8MD+QvA2IJ0DUtCayuPo3Z/AMAERGyXp8QF4oWJVTTp0/Hx8eH3//+91gsFq688sq2jkuIetltVg5u+YrUT97HnJ8HgM5gZOjUGYy58ipPMtWQ4pwKDm/PxX52iG/AuEgiEmQItqls2eVUpf1s4vmIMAyx/iiKQnl5Oe+++y6nTp0CwM/Pj8suu4wRI0Z06QnnP1dos3vW3xtl8mXzmAEM9PVqclKkqioFBZ+gomIKGIG3d8N3AAohepYWJVSKonDTTTexZs0a7rrrLnQ6KWclOsYPn/6P79/5NwBe/gGMvHwOIy6/Ep+Axuc9qS6VjL1FnDzkXszYL9DIkEt74RPQtSdEdxWq3UXV3gJsWeUAaIO98B1Te+K5j48PVqsVvV7PhAkTmDBhAkZj9xhCdbhUnjmZx8vZhXwyqh+D/dxDl4P8mrdETHn5fqqqs9Bo9ISFXd4eoQohuqgWZ0K33norO3bsYOHChW0ZjxC1lBUVYKuqIvRs0c1h0y7n8PdbGD59NsOmzkDv5XXeY1gq7RzaloO50D1E1atfIH1GyxBfUzmKq6lMzf9p4vmAYLwGBmOxWti5ZQuXXHIJer0erVbLNddcg6+vb7e68SLXauO3B0+SbHYXcv280OxJqJrD5bJSULARgJDgyej13ecaCCFar9l3+YmWkbv8mqcwK5PUjzZwZPu3RPcfxK9XPuF5TVXVJg/BFJ8+O8Rnc6LTaRgwPpLweLn+TaG6VCxHzmBJP+OeeO6jx/fiCDDpSU1NZevWrVRXVzN9+nQmTpzY2eG2yOYiM3cfyeKM3YmvVsNfBsRyTURQi45VWPQVRUVfo9cH0TvxbjQaWe9RiJ6g3e7yE6K9qKrKqcMH2PXRBjL2pHq2KxoNNks1Bi93r0FTkimXSyUjrZCsw2cA8A/yYvDEaBniayJnpZ3KXXk4z7jv0DPE+uN9USjpx39k87rNnDnjvq5hYWFERkZ2ZqgtYnO5eOxELi9nFwIw3M+bl4ck0NunZUOUdnspZ4q/AyA8fJYkU0JcgCShEl1C1oG9fP/2v8k9lg6AomjoN24CF//iGiL7Nq8Cv6XSzqHvczCfvQstpn8QfUaFoZEhvvNSVRVbdjnVaYWoDvfEc5+R4RRqynj3P/8mKysLAF9fX6ZOncrIkSO7RQmEc72bd8aTTN0eE8qDfaIxtmLifEHBRlyqHR+fRPz9hrRVmEKIbkQSKtElVJaWkHssHa1ez9Ap0xl95dUERUY3+zhFpyo4sj0Xu92JTq9h4PgowuL82yHinsdlc1KdVojtlHviuS7EC58xkWh99Xz79idkZWWh0+lISkpi4sSJ3WbCeX3mRYWw5Uw510UEMyusZYVcXS4rZWX7KS3dRbXlFAoKEeFSJkGIC5UkVKLDWasq2bv5c3wCTAydOgOAAUmXUlZUyNAp0/ENbP4cFpfTxYm0IrKPnB3iC/ZiyKXRePvJEF9T2IuqqUrNw1XlAAWUvn5oEgPQ+rqHrmbMmIGXlxeXXXYZJlP3qyRf7XTxcnYBv40Nx0urQasovDY0sdnHUVUViyWHUvMuysr24nLZAFAULWGh0/Dyimrr0IUQ3YQkVKLDWKuq2PP5R+z+9EMslRX4hYQy6NKpaHU6NFot4666rkXHtVTYOfj9acqK3fN9YgYE0WekDPE1hepSsRw+g+Woe+K56q3lqFcu337+AQMHDmTu3LkAhIaGcvXVV3dytC1ztNLCnQczOVxpId/m4In+Mc0+htNpoaxsL6Wlu7BYcz3bDYZQAk1jMJlGotP5tWXYQohuplUJVXV1NXa7vdY2uYNNnOvcRAogODqGsVdd1+rhkcLsctJ35GG3O9HrtQxIiiQsVob4msJZYaNyVz7OEguqqpLtVcJ3x3ZRVFwEQHZ2Nna7Hb2+e06wVlWVd/LO8P+Onqba5SJUr2N2aNN719y9UdmUlu6irHw/Lpf7d52i6AjwH0pg4Bi8vRNkiE8IAbQgoaqqqmLZsmW89957FBcX13nd6XS2SWCiZ0jf8T1f/uOFWonU+F/dyICkifUuVtxULqeL43sKOZXuXt4kIMSLIRN74eXXPb/8O5Kqqtiyyqne6554XmgvYVvpfk7mZgPuAp1Tpkxh9OjR3XLCOUCFw8n9R0+xId/9+ZgU5McLg+IJN57/8+F0VmE2p1FqTsVqzfdsNxojCDSNxmQaiVbbtDX9hBAXjmYnVPfddx/ffPMNL730EgsWLODFF1/k9OnTvPLKKzzxxBPnP4C4oJjCwrFUVrRZIgVQXWHj4Hc5lJ+9pT92YDC9R4TKEF8TuGxOqvYUYD/tTnCPOXP49PA3gHvB4poJ515NKJjaVR2prOa2/ZmcqLaiVWBZQhS/iw9H00hPkqqqVFdnUlqaSln5AVTVAYBG0RMQMAyTaQze3nHSGyWEaFCzC3vGxcXx73//mylTphAQEMAPP/xA3759efPNN3n77bf57LPP2ivWbu1CKOxpraoi7YtPcNhtXHL9TZ7tWQf2EjN4aKsTKYDCrHKOJOfisLvQ67UMnBBFaIzMXWkKe2EVVan5uKrdE8+9B4fgivHi+Reep2/fvkybNo3AwMDODrPVTllsTN+Vjq9Ww98HxzM2sOHPh8NRgdm8h1JzKjZbkWe7lzGKwMCLCQi4CK22+yaXQojWa7fCnmfOnCEx0X13TEBAgKfA38SJE/ntb3/bwnBFd1aTSKV+8gGWinK0ej3Dp8/CPzgUgLihF7X6HC6ni+M/FHLqqHsIxxTqzeCJ0Xj5yhDf+ahOFcvhYqqOFnOw+DinLAVcN+/X6EPchVJ/97vf4XOeRaW7OovThdfZHsoYLwNvDu9NXx8jQfq6v+JUVaWq6jilpamUVxxCVd3TFDQaAwEBFxFouhgvr2jpjRJCNEuzE6revXuTmZlJfHw8gwcP5r333mPs2LF8/PHHPeKvW9F05yZSAEHRMSRde0OLSh80pKrMxqHvcygvcQ/xxQ0OJvGiMDQa+cI7H2e5jYpduZzIyuC73D2csZoByCjOpn+Iu2Bqd0+mdpkr+e2hTB7vF8OMs5POLzb51tnP4Sin1Lwbc2kqNnuJZ7u3VwyBgRfj7z8Mrbb71tYSQnSuZidUt956K3v37mXy5MmsWLGCOXPm8Pzzz+NwOHjmmWfaI0bRBWUd2MvHf32iTiI1YMKlbTK0V6PgZBnpyXk4HC70Bi2DJkQR0kuG+M5HdapYj5eStfsY353eTXZFHgDe3t5MmTKF3r17d3KEredSVV7IKmBNRi5OFZ49mc/0kIBaPUuq6qKy8kdKzbupqDiMqroA0GiMmAJGEBh4sdSOEkK0iWYnVPfee6/n56lTp3LkyBFSU1Pp06cPF13U+qEd0T2ExsbjsNkIiurlTqQumdSmiZTT6eL47gJO/1gKyBBfc9jzKyn9IYctR5M5WHIccE84HzduHJdeeine3t6dHGHrFdrs3HUoi60l7oT+moggnuwf40mm7HYzZvNuSs27sdtLPe18vOPO9kYNRaORoq9CiLbT7ITq3//+N7/+9a89y07ExcURFxeHzWbj3//+NwsWLGjzIEXnslVXseeLTyk8mcGVdy8DwMcUyA0PP0lYfEKbJlJQM8R3mvISKwDxQ0JIHB6KIkN8jXJW2qneV4g9txKN6iK32j3JesiQIUyfPp2goLYbhu1M354p5/8On6TQ5sBbo/Bo/xhujAwGVMrLD1NqTqWyIh0V9/02Wq03poCRBAaOwWiM6NzghRA9VrPv8tNqteTm5hIeHl5re3FxMeHh4VKHqgHd8S6/mkQq9ZMPsJSXAXDjI38huv+gdjtnfmYZ6TvzcDpc6I1nh/iiZYivMarDRfWRMxxM3UeCXzQ6rRZjn0AK/SpRdBri4uI6O8Q2c7iimst2paMCA329eGVIAr0N1ZSWpmI2/4DdUebZ18cn0d0b5TcYjUZ6NoUQLdNud/mpqlrv3S+nTp3qlmt8dXel+VU47E70Rh0GLy16Ly1anaZVdyjVl0gFRfVi/LU3ENmnf1uFXovT6eJYagE5x0oBCAzzYfDEKIw+8kXYEFVVsZ+u4MSOw2zNSCG3qohL+45lytzpaAMMxBPW2SG2uUF+3vwmKgRQWRZlxnLmbY5XHvf0Rum0vphMozAFjsFoCO3cYIUQF5QmJ1QjR45EURQURWHatGnodD81dTqdZGRkMGvWrHYJUjQs61AxxTmVtbZpNIo7uTLq0HtpPT+7/6tF7/VT8mUw6tDoFE8CVngyg/ceeeBniVQ046+9kYETJqFpp6rZlWYrh77PoaLUigLEyRDfeTnLrOTtzODbg8mkl2YCoNfp8O4diDagZ80N2lRkZoS/D+FGPTZbEX8IOkRF2W6Kc3/63Pv69iXQNAY/v0FoNLJEqRCi4zX5N89VV10FQFpaGpdffjl+fj8NwxgMBhISErj22mvbPEDROG9/A/7BTuxWJ/ZqB06XisulYqlyYKlyNOkYGgUM3nr0Xlp0elAUPb5BEQyZ/Et6j56Il6+BihIbBi8deqMWrb7tKpLnZZg5mpKP0+HCYNQy6JJogqPq3vIu3Fw2J2X789i+cwe7Cw/jPFtDacTwi7hs+rRuM5zcFFaXi9XHc/jHqSIu8Yenw3ZTXX3C87pO50+gaRQm0xgMhuBOjFQIIVowh+qNN97g17/+dbdemqIzdNQcKqfdhd3qxGZxnP2vE7vn559vc2KpqCTv+PeU5u5nwMS7UM5OLrdUFGH0CfI8P5dWo6D3didXtXq/zukJM3jpPEOQdeJ0uPgxNZ/c4+66SEERPgy6JBqjt/Qu1EdVVWwny6g+WMwXx77jcEkGAHExccy6YhbR0dGdHGHbyqiysujAMQ5Uuhckvs43nTv896NXwNe3H4GBY/HzG4CiyHJDQoj21W5zqG6++eZWBSbal1avQavXNLpIsM1STdoXn3J46/tUnx3aCww5TdzwCWeTrzB30mV1eJKvmp9dLhWnS8VZacdy9svuvDHpNHWSr7KiairLbChA/LBQEoaGyBBfAxxnLFSk5aOW2gAYmzCCAtXMtJnTGTRoUI+q6K2qKm9l7uXBk06qVC0BipXlgSlM8qvGFDiVQNNo9PrAzg5TCCHqaFJCFRQU1ORf2jVL0YiupyaRSv34Z4lUZBTjr7mBQROnnHeOlKqqOB1ne8Cqzw4zWhzYrO6kq6YHzP6znjCXy93G6XDVScAMXjoGXxJFUKQM8dXHZXGQk3KCb1K/w1tnZHpCEl6DggnsHcj/KcPQaHpW74zF6eCP+7bx31IToGW4oZDHo4sZEDYTX99+0hslhOjSmpRQPfvss+0chmhvlaUlvHHfXVSXuYfYmpNI1VAUBZ1ei06vxbsJlQw8CZjFWafHCyCqjwmDDPHVobpUzEfy+XbLt6QVHsGlutBqNMyYMAevUHctKYWe0ysFoKpOMnM+YEd5JAoqi8KqWdF/LN4GuXNYCNE9NOnbrCsM85WUlLB06VI++ugjAObOncvzzz/f6PqBqqqyatUqXn31VUpKShg3bhwvvvgiQ4YM8ezz6quv8tZbb/HDDz9QXl5OSUlJnWO25NxdgepyoZztxfANDCIsLoGywgLGX9u8RKqlaiVg/u16qh7DmlfBzk3fs+PkHqqd7sKmveMSmXXlbEyhPaMw57lcLgc5ue+hVh7k4eBA1MDLuTJmQmeHJYQQzdLsSekAx48fZ+3atRw/fpznnnuO8PBwNm7cSGxsbK1kpS3Nnj2bU6dO8eqrrwJwxx13kJCQwMcff9xgmzVr1vDoo4/y+uuv079/f1avXs23335Leno6/v7ub/hnn30Wi8W96O6KFSvqTahacu5zdWRhT7vFQtrmz0j74lN+8+jT+JgCAXcvlbd/QLsnUqL5nJV2snYc5eNdmzwLGIeYgrn8iln0H9A+tb86m1NVeTojB21VGlfotqMoOnr1uhF/v4GdHZoQQng09fu72QnV1q1bmT17Npdccgnffvsthw8fpnfv3jz55JOkpKTw3//+t9XBn+vw4cMMHjyY5ORkxo0bB0BycjJJSUkcOXKEAQMG1GmjqirR0dHcc8893H///QBYrVYiIiJYs2YNd955Z639t2zZwtSpU+skVC05d306IqGqSaR2fbTBM7R3yfU3Mf7aG9rlfKL1VKcLy9ES98Nq4fX0j9BoNUyZMoUx4y5G20OT32KbgyWHMthaUokOF2+Gf8nYhGvw9e3b2aEJIUQt7XaX3/Lly1m9ejW///3vPb084F4o+bnnnmtZtOexY8cOTCaTJ6EBGD9+PCaTie3bt9eb1GRkZJCXl8fMmTM924xGI5MnT2b79u11Eqq2PDe4kzer1ep5XlZWVu9+baG+RMoUEemZIyW6HlVVMWcUsmfrLi4K6IeiKPhFBvLrIdcT1btXj1jAuCE/lFVy+4EMcqwOjDj4Q9BexiVeh49PYmeHJoQQLdbshGr//v289dZbdbaHhYVRXFzcJkGdKy8vr87agQDh4eHk5eU12AYgIqL2YqgRERGcPHmyXc8N8Pjjj7Nq1aomn6elHHY7a3//W8qLC4HaiZRWJxO+uyJbaTXbPt3CjuM/YHPZCejnx7BJo9DH+OHfg0ognEtVVV7PKebPP57CrkKMtpxHQnYzpfc1eHv3nPUGhRAXpmbfhxwYGEhubm6d7Xv27KFXr17NOtbKlSs9y9k09EhNTQWot2xDQ+sK/ty5rzelzfmO0ZTjrFixArPZ7HlkZ2c365xNpdPr6T16LKaISC7/7T3c+szLDJ0yXZKpLshlc5K2cScvvvQiW3/cic1lJ9wUSuiEeAyx/j2qntS5VFXl7iNZrDjqTqYu9TrFqxHbmdrnekmmhBA9QrO/defNm8f999/P+vXrURQFl8vFtm3b+OMf/8iCBQuaday77rqLG25ofH5PQkIC+/btIz8/v85rhYWFdXqgakRGRgLuHqaoqCjP9oKCggbbNHSc5p4b3MOLRqOxyedpjUtvvJmpNy+SJKqLUlWVrLTjbP56M6fK3Z8lH4M3l02dyqhxY3pcPan6KIpCf28NWlTu8N/HjQGniYu/DS9jZGeHJoQQbaLZ38CPPvoot9xyC7169UJVVQYPHozT6WTevHn86U9/ataxQkNDCQ09/4rwSUlJmM1mUlJSGDt2LAA7d+7EbDYzYUL9t1cnJiYSGRnJ5s2bGTlyJAA2m42tW7eyZs2aJsfYknN3NKOPT2eHIBrgKLVQuaeAj3Z+TLHFjFbRMG7EWCbPmtphCXdnqnQ68dVqsdvNzHL+l8RQGwO8ITZ2EUZjWGeHJ4QQbaZFZRPAXTphz549uFwuRo4cSb9+/do6tlpmz55NTk4Or7zyCuAuXRAfH1+rdMHAgQN5/PHHufrqqwF32YTHH3+ctWvX0q9fPx577DG2bNlSq2xCXl4eeXl5pKamsmjRIr799lv8/f2Ji4sjODi4yec+n44smyA6n7XCgjW9BGdWBaiQWZnLUfspZv5yNkEhPbOe1M/ZXSqrj+fwzZlyProojOLTa7HZS9DrA4mLvQ2DIaSzQxRCiCZpt7v8avTp04c+ffq0tHmzrVu3jqVLl3ru2ps7dy4vvPBCrX3S09Mxm82e58uWLaO6upolS5Z4Cntu2rSp1t2JL7/8cq3J45MmTQJg7dq13HLLLU0+txAALqeLPd+ksGXnt4wIHciosEEYYvy5aFgiIy+QqvC5Vht3HjxJirkSgHeOfspkQwkGfTBxcQtlLT4hRI/UpB6q3//+900+4DPPPNOqgHoq6aHq+TL2H+OLTV+QV+6+4zLUJ4g7bl2EIezCGZL9vqScxQdPUmR34K9VWB6YygTDCYyGMGJjb0Ovl8++EKJ7adMeqj179tR6vnv3bpxOp6cG09GjR9FqtYwePboVIQvRPZ3JLWLTRxs5knsMAL1GR9LwsUycPQWD0dDJ0XUMl6ryYlYBj5/IxQUM9NHxUMCXRCqFeBkjiY29FZ2uCQtACiFEN9WkhOqbb77x/PzMM8/g7+/PG2+8QVCQey5ISUkJt956K5deemn7RClEF6Q6VXZv3sHnO7/CqboXfB4WN5Dpv5yFKSSwc4PrYE9l5vFMpvsOxmtDjdxp+AC9Wom3Vy9iY29Bq71weumEEBemZk9K79WrF5s2baqzZt+BAweYOXMmOTk5bRpgTyFDfj2LPa+Sqn2FFBUV8Z+jnxITGMnlV1xOTP+Ezg6tU+Rb7cz54ShLovRMsL6Dqlrx8Y4jJuZmtFqvzg5PCCFarN0mpZeVlZGfn18noSooKKC8vLz5kQrRjRw/9CMn9xxjpLd7zbmQwGBuu+omeg1PvCDqSf3cD+ZKRpl8AYgw6tk01Ehh7n9wqXZ8fBKJjZmPRtPzS0MIIQS0IKG6+uqrufXWW3n66acZP3484F4s+L777uOaa65p8wCF6AqK8gr54qPP+THnBAoKsf1D6DWsN94DgwnUX1iJlMXp4oEfT7Eu9wwvD47nqoggKiqOUpjzFi7Vjp9vP3r1modGc2HMHxNCCGhBQvXyyy/zxz/+kZtuugm73e4+iE7HwoUL+ctf/tLmAQrRWVSXSuWpErZ+s5XdmftxqS4UFC6KGUTktP74RAR2dogd7mS1ldsPZLK/ohoFyLHaKS8/zOmct1FVJ/5+g4iOvgGN5sIoESGEEDVaXNizsrKS48ePo6oqffv2xdfXt61j61FkDlX34Sy3YcksZc8PaWzL/oFqhwWA+KBezJw+g+jB8T163b2GbCoy87vDWZgdToL1Wv4+OIGRugxyctejqi4C/IcSHX09iqLt7FCFEKLNtHthT19fX4YPH97S5kJ0Kardhe10ObaTZTiKLVgcVr7LSsXqtBHkF8jMaTMYOGLwBZlIOVWVJzPyeO6k+y6+0QE+vDokAT/rAXJy3kdFxRQwkqioa1CUC2v4Uwghaki/vLhgqaqKo6ga28kybKcrqLRU4aPzQtEo+McEMy1kCg4vGDtuLLoLeOHp5NIKTzK1sFcoD/WNprJsNzl5HwIQGHgxkRG/vCCTTSGEqHHhfkuIC5aryo41y90b5aq0Y3c5SC04yO6iw1w18QqGJF2ExlvHWKI7O9Qu4ZIgf+6Nj6C/rxdXRwRx5sw28gs+AyAoaDwR4VdKMiWEuOBJQiUuCKrThT2nEuvJMhyFVaC6e6iOlp/k+7w0KizudeeOl2UzzPvCrvivqipv5BRzeWgAUWcrvd/fOwqA4uKtFBRuAiAkeBJhYTMlmRJCCCShEj2Yqqo4S6zuIb1T5ah2l+e1IkMFW7JSOF2QC0BgYCAzZ85k0KBBnRVul1DhcPL79Gw+KihlQ54v74/si16joKoqRcVfU1T0NQChoZcRGnKZJFNCCHGWJFSix3FZHNiy3UN6zjKbZ7vGR4chLoDt2Xv4bsf3AOj1eiZNmsT48ePR6/WdFXKXkF5p4fYDGfxYZUWnwC8jAtEp7sS0sHATxWe+BSA87HJCQiZ1crRCCNG1SEIlegTVpWLPq8R2sgx7XiXUFAPRKhii/TDEB6AL80ZRFGJ1cbADLrroIqZNmyZlLIAP8kv4Q3o2VU4XkQY9/xiawMUmX1RVJb/gE0pKkgGICL+C4OBLOjlaIYToelpch0o0j9Shah/OMivWzDJs2eWoVqdnuzbYC2NcALpevqQfP4rNZmPEiBGAu8elqKiIsLCwToq667C5XKw8lsO/ThcBMDHQj78PiSfMoEdVVfLy/0dp6S4UFCIif0lQ4MWdHLEQQnSsdq9DJURncdmc2E+VYz1ZjrPE4tmueGkxxAZgjA9AG2AgNzeXjW+9z8mTJzEajfTr1w9fX18URZFk6iyHCttLKwC4Oz6CZYmRaBUFVXWRm/s+5rI9KChERV2LyTSyk6MVQoiuSxIq0S2oqoqjsBpbZhm2nApwne1YVUAf5Ysh3oQ+wgdFo1BRUcE3H3/B7t27AffSSOPGjbvg50jVx0er4bWhCRyvsjIz1ASAqjrJyXmPsvIDKIqG6KjrCAiQIr5CCNEYSahEl+astLvv0jtZhqva4dmuDTBgiA/AEOePxuj+GDscDlKSU9i6dStWqxWAIUOGMGPGDAIDAzsj/C7Hpar87WQ+WkXhd/ERAPTx8aKPj5f7dZeDnJx3KK84jKLo6BV9A/7+F/adj0II0RSSUIkuR3W4sJ2ucC8DU1Tt2a7oNRhi/THEB6ANNNa5Zf/MmTNs3rwZVVWJjIxk9uzZxMfHd3T4XVap3cFdh7P4srgMDTAz1MQAXy/P6y6XjVOn11FZeQyNoqdXr3n4+fXvvICFEKIbkYRKdAmqquI8Y8GaWYb9dAWq42zNKAV0YT4YEwLQR/miaGuvFVdZWelZmDs8PJxJkyZhMpkYMWIEGo2sK1djb3kVtx/IJNtiw0uj8Fj/mHOSKSvZp96kqioDjUZPTK/5+Pr26cSIhRCie5GESnQqV7UDW1YZ1pNluCrsnu0aXz2G+ACMcf5ofOrOfaqurmbLli3s3r2bO+64g/DwcACmTp3aYbF3B6qqsi73DA/8eAqrSyXey8A/hyYwzN/Hs4/TaSH71BtUV2eh0RiJjbkZHx/p2RNCiOaQhEp0ONWlYs+txJZpxl5Q5akZpeg06HudrRkV4lVvFW6n08nu3bv55ptvqK52DwcePnzYk1CJ2u4/eop/5xQDcHloAH8bGIdJ/9M/e6eziuzs16m2nEar9SY25ha8vWM6K1whhOi2JKESHUp1uKhIzsVRUOXZpgvxxpAQgCHaD0Xf8DDdiRMn2LhxIwUFBQCEhYUxa9Ys+vSRoamGDPHzRgOs6B3F/8WFo/lZkupwVJCVvRarNQ+d1pfY2Fvx8orqvGCFEKIbk4RKdBjV7qJiRw6OomoUnQZjH5N7grmf4bxtP/zwQ9LS0gDw9vZm6tSpjB49Gq1W285Rdz82lwvD2fljC6JDGBfoy0Bf71r72O1lZGf/C6utEJ3Oj7jYhRiN0ssnhBAtJQmV6BAum5OKbadxllhR9Br8JkSjC/E+f8OzIiIiUBSFsWPHMnnyZHx8fM7f6AKjqiprTxex9nQRH4/qR6Beh6Io9SRTpWRlvYbNfga9zkRc3G0YDKGdFLUQQvQMklCJdueyOKj4/jTOMhuKQYvfxGh0gV4N7+9ykZaWhslk8gznXXzxxfTt21cqnDfA6nKx/Ogp3s49A8DbuWf4bVzdHiebrZis7LXY7SUY9EHExS1Erw/q6HCFEKLHkYRKtCtXlZ3y70/jqrCjeOnwn9gLbUDDQ3wnT57k888/Jy8vj5CQEJYsWYJWq0Wn00ky1YA8q53bDmTwQ1kVGuDBPtEsjq17razWQrKz/4XdUYbBEEpc7G3o9aaOD1gIIXogSahEu3FW2Kj4PgdXlR2Njw6/ib0anC9VWlrK5s2bOXjwIABGo5HRo0d3ZLjdUqq5ktsOZFBgcxCo0/LKkAQmB/vX2c9izSM76184nJUYjRHExd6KTld3PyGEEC0jCZVoF84yG+XbTqNWO9D46fGf2KveelI2m43vv/+e7du343C4l5YZPXo0U6dOxc/Pr6PD7lY2F5m57UAmdlVloK8XbwxLJN7bWGc/iyWHrOy1OJ1VeBmjiI29FZ3OtxMiFkKInksSKtHmHKVWKr4/jWpzog0w4DexFxqv+j9qmZmZfPvttwAkJCQwa9YsIiMjOzLcbmtUgC8RRh0X+fvwt4Fx+Orq3vFYXZ1FdvYbOF0WvL1iiY29Ga226TcDCCGEaBpJqESbchRXU7E9B9XuQhtkxO+SXmgMtb/oq6ur8fZ2f6n369ePkSNH0q9fPwYNGlRvMc+uoLr6FAWFn6O67HVeU2sqk6pqA63r366e5/X6jlfp0uKrcXjO+2KIgRCtjbys+o/ssJfhUu34+CQQ02sBWm3dHiwhhBCtJwmVaDP2wioqd+SiOlzoQrzwmxCNov8pmaqoqODLL78kPT2d3/3ud/j4+KAoCr/85S87MerzU1UXuXnvY7Xmd2ocR2xB/LnkYm72P8gcn0wAAgC7q/F2vr59iek1D41GkikhhGgvklCJNmHPq6RiZy44VXThPviNj0LRuYtLOp1Odu7cydatW7FarQCkp6czcuTIzgy5yUpLU7Ba89FqfYiKvAZFqa+aewM9az/rcVPq3aehHjnlbHP3f98vtPH/8qqxqfCJfRx3xE5DV29vnnJOex1eXtFdtudPCCF6CkmoRKvZTldQuSsPXCr6KF98x0aiaN1Jx7Fjx9i4cSNFRUUAREVFccUVVxAbG9uZITeZ01lNYdFXAISFTsPff1CHnt/uUnn4+Gn+ccoMwIyQAF4cHE9APfOlhBBCdB5JqESrWLPKqNqdDyroY/zwHROJolFwuVysX7+ew4cPA+Dj48P06dMZMWIEGk3D6/V1NUVFX+N0VmE0hBMYOLZDz11sc3DHwUy2lVYAcG98BPclRtZaj08IIUTXIAmVaDHrCTNVewtABUN8AD4jw1E07i97jUaDr68vGo3Gs1xMzUT07sJqK6KkNBmA8IgrGhjqax+VTiezdx8ly2LDV6vh+UFxXBEW2GHnF0II0TySUIkWsfxYQvV+9zCesY8Jr2GhHDh4gMjISE9F88suu4yxY8cSHt49F90tKPgMVXXh5zcQP99+HXpuX62WG6KCWZ93hrXDEuusxyeEEKJrkYRKNIuqqliOnMFy2L1mnFf/IEqCbWx8/XWysrLo06cPN910E4qi4OPj020XMa6oOEpFRTqKoiE8fHaHnNOpqpTanYQY3P8s74mPYFFMGP4yX0oIIbo8SahEk6mqSvWBYqw/lgDg6uPDlxk72P3f3QDo9Xri4uJQVbVb31Wmqi4KCj4HICgoCaMhtN3PWWp38NtDJym0OfhoVD98tBo0iiLJlBAXGKfTid1et96daD96vR6ttvW/ayWhEk2iqirVewuxnjDjUl0cNubw/ec7sVgsAAwdOpQZM2ZgMnX/xXZLSnditRWg1foQGjK13c93pLKaW/ZnkFltw1ujcKC8irGBsuyOEBcSVVXJy8ujtLS0s0O5IAUGBhIZGdmqzoBuk1CVlJSwdOlSPvroIwDmzp3L888/T2BgYINtVFVl1apVvPrqq5SUlDBu3DhefPFFhgwZ4tnn1Vdf5a233uKHH36gvLyckpKSOsdMSEjg5MmTtbbdf//9PPHEE232/roy1aVS9UMBtqwyUOBH3wK+3LEVgIiICGbPnk1CQkLnBtlGnM4qioq+BiAsdHq7L9PyWWEpvzucRaXTRYyXnteHJjLUv3sOkwohWq4mmQoPD/cUPRbtT1VVqqqqKCgoANylfVqq2yRU8+bN49SpU2zcuBGAO+64g/nz5/Pxxx832ObJJ5/kmWee4fXXX6d///6sXr2aGTNmkJ6ejr+/PwBVVVXMmjWLWbNmsWLFigaP9fDDD7No0SLP8wtl4V7VqVKZmof1VBkajQaf0RGMiU7kQHY6F110EaNHj+5WZRDOx1MmwRhBYODF7XYel6ryVGYez2S6q69fEujHq0MSPPOnhBAXDqfT6UmmQkJCOjucC07NHegFBQWEh4e3ePivW/z2Pnz4MBs3biQ5OZlx48YB8I9//IOkpCTS09MZMGBAnTaqqvLss8/ywAMPcM011wDwxhtvEBERwVtvvcWdd94JwD333APAli1bGo3B39//glu0V3W6KN1+iuQDu8goO82t827GGBsAwMKFC3vcX1BWawElpTsBiAhv3zIJj53I5YUs919Ed8SE8ec+0eg0Pet6CiGapmbOVHe9iacnqLn2dru9xQlVt+ha2LFjByaTyZNMAYwfPx6TycT27dvrbZORkUFeXh4zZ870bDMajUyePLnBNo1Zs2YNISEhjBgxgkcffRSbzdbo/larlbKyslqP7sRld/LDB9v417fvsjN/PwXVZzhW+tMKvD0tmQIoKPgcVXXh7zcIX9++7XquW3qF0suo52+D4ni4Xy9JpoQQPfL3anfRFte+W/RQ5eXl1VvLKDw8nLy8vAbbgHuOz89FRETUmQ91PnfffTejRo0iKCiIlJQUVqxYQUZGBv/85z8bbPP444+zatWqZp2nq8g7lcun//2I7NJcAAL8/Jk56/Jac896moqKdCoqj6IoWsLDZ7XLOTKqrCT6uBcojvEysG3cILy03eJvGiGEEOfRqb/NV65ciaIojT5SU1OB+rPHptyef+7rLbml/95772Xy5MkMHz6c22+/nZdffpnXXnuN4uLiBtusWLECs9nseWRnZzfrnJ3B4XDw2cef8so/XyW7NBetouXSsZdw19LfMXTo0B7715OqOmuVSTC0cZkEVVV5NjOPS3YeZmOh2bNdkikhxIVGURQ+/PDDBl/PzMxEURTS0tI6LKa20qk9VHfddRc33HBDo/skJCSwb98+8vPz67xWWFhYpweqRs18p7y8vFqz9gsKChps01Tjx48H3Av/NjSB0Gg0YjQaW3WejqbYVXKPnUJFpW9QHLOunkNoXOuuVXdQUrITq60Qnda3zcskVDqcLD2SxadnE6lkcwWzwrp/aQkhhGgPsbGx5ObmEhra8j9sMzMzeeSRR/j666/Jy8sjOjqam266iQceeACDwdCG0dbWqQlVaGhoky5aUlISZrOZlJQUxo51L1C7c+dOzGYzEyZMqLdNYmIikZGRbN68mZEjRwJgs9nYunUra9asaVXce/bsAVp3e2VXcfLkScLDwzG4dFR8f5opEaOpjLIxZM4YtP7t98HrKpzOKoqK3WUSQkOno9V6tdmxM6ut3LI/gyOVFvSKwmP9ezE/uv2LhAohRHel1WpbfQPYkSNHcLlcvPLKK/Tt25cDBw6waNEiKisreeqpp9oo0rq6xZjDoEGDmDVrFosWLSI5OZnk5GQWLVrElVdeWesOv4EDB/LBBx8A7m7Fe+65h8cee4wPPviAAwcOcMstt+Dj48O8efM8bfLy8khLS+PYsWMA7N+/n7S0NM6ccS+tsmPHDv7617+SlpZGRkYG7733HnfeeSdz584lLi6uA69C2yorK2PDhg2sXbuWrzd9RcW3p3BV2gkNCWXILy6+IJIpgMKir3A6q/EyRhIYOKbNjrv1TDmzUo9ypNJCuEHH+yP7SjIlhOhRaobnzn1MmTKl0Xa5ubnMnj0bb29vEhMTWb9+fZ1j1gz5bdmyBUVR+OqrrxgzZgw+Pj5MmDCB9PT0Bo8/a9Ys1q5dy8yZM+nduzdz587lj3/8I++//35bvO0GdYtJ6QDr1q1j6dKlnrv25s6dywsvvFBrn/T0dMzmn+aoLFu2jOrqapYsWeIp7Llp0yZPDSqAl19+udbk8UmTJgGwdu1abrnlFoxGI++++y6rVq3CarUSHx/PokWLWLZsWXu+3XZjt9vZsWMH3333nedWXevpcpwRdrT+Bvwn9kLjo+/kKDuG1ZpPaWkKAOHhc9qsTEJ6pYUb9x7HBYz09+FfwxKIMl4YCaoQom2oqorN6eqUcxu0mibNma0ZnquRl5fH9OnTPd+jDXnwwQd54okneO6553jzzTe58cYbGTp0KIMGDWqwzQMPPMDTTz9NWFgYixcv5rbbbmPbtm1Nfk9ms5ng4OAm798SiqqqarueQQDuHiGTyYTZbCYgIKDDz6+qKunp6XzxxReUlLjX4ouJimFS4EWE6QPRmoz4TYxGY+w2OXarqKpK9qnXqaw8hr/fYGJiftOmx3/g6CkqnS6e6B8jk8+FEI2yWCxkZGSQmJiIl5d72oHV4WTlR4c6JZ6VcwdjbOY6ohaLhSlTphAWFsb//ve/Bgs+K4rC4sWL+fvf/+7ZNn78eEaNGsVLL71EZmYmiYmJ7NmzhxEjRrBlyxamTp3Kl19+ybRp0wD47LPPmDNnDtXV1Z7r1Zjjx48zatQonn76aW6//fYG4z/3/0GNpn5/XxjfnoLk5GS++OILwF2kdFrSFOILTeBU0QZ54XdJNBrDhbMQb2XlUSorj7VZmYRTFhteGg2hZyudP9yvFxqkrowQ4sKwcOFCysvL2bx583lXz0hKSqrz/Hx39Q0fPtzzc8385YKCgvNOvcnJyWHWrFlcd911DSZTbUUSqgvEsGHD+P777xk5ciRJg8ZgSy0Cp4ou1Bu/pGgU/YXTi+JyOcgv+AyA4KAJGAytW+phW0k5iw5mMsDXi/cu6oteo6CVREoI0QoGrYaVcwd32rmbY/Xq1WzcuJGUlJRaU2qa43x/fOr1P01FqdnX5Wp8SDQnJ4epU6eSlJTEq6++2qK4mkMSqh7I5XKRlpZGVlYWV111FeBee/Duu++GYjuVO3PBpaKL8MFvXBSK7sJJpgBKS3disxWh0/oS0ooyCaqq8q/TRfz52GmcKlQ4XJQ6HIQZLow5aEKI9qMoSrOH3TrDhg0bePjhh/n888/p06dPk9okJyezYMGCWs9r7sZvK6dPn2bq1KmMHj2atWvXdsias5JQ9TDZ2dl8/vnn5OTkAO6eKc+HvMBK5a48UEEf7YfvxZEo2gurJ8XhqKSo+BsAwsJmotW2rFaYxeli+dFTvJPnvhv0moggnhoQi4/MlxJCXCAOHDjAggULuP/++xkyZIhnhRKDwdDoBPD169czZswYJk6cyLp160hJSeG1115rs7hycnKYMmUKcXFxPPXUUxQWFnpea881eSWh6iHKy8v58ssv2bt3L+D+QE+ZMoX4+HgArCfLqPohH1QwxPrjMzoC5QJcP67oZ2USTKZRLTpGrtXGbfsz2VNehQZ4sE80i2PDZL6UEOKCkpqaSlVVFatXr2b16tWe7ZMnT2bLli0Ntlu1ahXvvPMOS5YsITIyknXr1jF4cNsNb27atIljx45x7NgxYmJiar3WnvfhyV1+HaS97vJzOBzs3LmTrVu3ehZsHjFiBNOmTfOMZVuOl1K9152hGxJN+FwUdkEmU1ZrPhkZz6OiEh93Oz4+iS06zlU//EiyuZJAnZZXhiQwObhlcwaEEAIav8NMdAy5y0+gqiqpqanYbDZ69erF7Nmza2XklqMlVB8oAsDYNxDvYaEXZE+KqqrkF3yGioq//5AWJ1MAawbE8scj2Tw/OI4E7+61vJAQQoj2IQlVN6fX65kzZw7l5eVcdNFFnol3qqpiOXQGS7p7jo/XgGC8BgdfkMkUQEVl+tkyCTrCw5pXJsHhUtldVsm4QD8ABvh68dGovhfstRRCCFGXJFQ9QN++fWs9V1WV6v1FWI+VAuA9NBSv/kGdEFnX4HI5KKgpkxA8AYOh6dVyzXYHdxw8ybbSct65qA8Tg9zDe5JMCSGE+DlJqHoY1aVStbcQW4Z7CR7vi8Lw6hPYuUF1MneZhGJ0Oj9Cgqc0uV1GlZUF+0/wY5UVb42Gqk5aBkIIIUTXJwlVD6K6VKp252PLLgcFfEZFYIzv+GVuuhKHo4Kioq8BCAud0eQyCdtLKlh4IIMSh5Noo55/D0tkqL9Pe4YqhBCiG5OEqodQnSqVu/Kw51SAAr4XR2KIkbvPioq+wumy4GWManKZhLdyi7k//RR2VWWkvw+vD0skwijFOoUQQjRMEqoeQHW4qNiZiyO/CjQKvuOiMET5dnZYnc5iyaW0dBcAERFzUJTzF938vqSc3x/JBuCX4YE8OzAObynWKYQQ4jwkoermVLuLih05OIqqQavglxSNPlyGplRVpaDgc1RUAvyHNrlMwiWBfvwqIogEbyN/SIiQyedCCCGaRBKqbkx1qpRvO43zjAVFp8FvQjS6UO/ODqtLqKg4QmXVcXeZhPDGyyScstgI0mnx1WlRFIXnB8VJIiWEEKJZZCyjG1O0CvooXxSDFr9Le0kyddbPyySEBF+CXt9wyYhUcyWzUo9y1+EsXGcXDZBkSggh2oeiKHz44YcNvp6ZmYmiKKSlpXVYTG1FEqpuzntAMAHT49AFyXIFNUpKdmCzn0Gn8yM4eHKD+23IO8O1accosjvIslgpdTg7MEohhBDnio2NJTc3l6FDh7bqOHPnziUuLg4vLy+ioqKYP38+OTk5bRRl/SSh6gE0XjJyW8PhqKCo+BsAwsJm1lsmwaWqrDmRy/8dzsLqUpkVGsBHI/sRrJfrKIQQnUmr1RIZGYlO17rfx1OnTuW9994jPT2dDRs2cPz4cX71q1+1UZT1k4RK9CiFRV/iclnx9uqFKaBumYQqp4s7Dmby15P5APxfXDj/GpqIr07b0aEKIUS3VjM8d+5jypQpjbbLzc1l9uzZeHt7k5iYyPr16+scs2bIb8uWLSiKwldffcWYMWPw8fFhwoQJpKenN3qOe++9l/HjxxMfH8+ECRNYvnw5ycnJ2O321r7tBklCJXoMiyUHc2kqAOHhV9Q7F+rOg5l8UmhGryg8OzCWB/tEo5E5U0KILkZVVWxOW6c81LPzSc+nZniu5rFnzx5CQkKYNGlSo+0efPBBrr32Wvbu3ctNN93EjTfeyOHDhxtt88ADD/D000+TmpqKTqfjtttua/K1PHPmDOvWrWPChAno9e1XU1DGOESPoKoq+QWfnS2TMAwfn4R697s3PoKDFdW8NDie8WcXOxZCiK7G7rLzeMrjnXLuFWNXYNAazrtfzfAcgMVi4aqrriIpKYmVK1c22u66667j9ttvB+CRRx5h8+bNPP/887z00ksNtnn00UeZPNk9J3b58uXMmTMHi8WCl1fD84fvv/9+XnjhBaqqqhg/fjyffPLJed9Ta0gPlegRKioOUVWVgUbREx5+ea3XCm0/dfGOMvmyY/wgSaaEEKINLVy4kPLyct566y00msZTi6SkpDrPz9dDNXz4cM/PUVFRABQUFDTa5r777mPPnj1s2rQJrVbLggULmtz71hLSQyW6PXeZhM8BCP5ZmQRVVXnuZD5/yyrgw5F9GX52LT7jef6xCyFEZ9Nr9KwYu6LTzt0cq1evZuPGjaSkpODv37Ilz85XrubnQ3U1+7pcjS9YHxoaSmhoKP3792fQoEHExsaSnJxcJ6FrK5JQiW6vpGQ7NnsJel0AISHusXuL08Uf0rPZkF8CwJfFZZ6ESgghujpFUZo07NbZNmzYwMMPP8znn39Onz59mtQmOTmZBQsW1Ho+cuTI9goRwNMzZbVa2+0cklCJbs3hKKeoeAvgLpOg0RgptNm5dX8GqWVVaBV4vF8MC3qFdm6gQgjRwxw4cIAFCxZw//33M2TIEPLy8gAwGAwEBwc32G79+vWMGTOGiRMnsm7dOlJSUnjttdfaLK6UlBRSUlKYOHEiQUFBnDhxgj//+c/06dOn3XqnQOZQiW6usHDz2TIJMQQEjOBwRTWzUo+SWlaFSaflneF9JJkSQoh2kJqaSlVVFatXryYqKsrzuOaaaxptt2rVKt555x2GDx/OG2+8wbp16xg8eHCbxeXt7c3777/PtGnTGDBgALfddhtDhw5l69atGI11axO2FUVtzxlawqOsrAyTyYTZbCYgIKCzw+kRLJYcMjNfQkUlPu4OTrrCufKHH6l0uujtbeTfwxPp6yMV5IUQXZvFYiEjI4PExMRG71oT7aex/wdN/f6WIT/RLbnLJHzqLpMQMBwfn3j6qyrjTL5YXSr/HJpAkFQ+F0II0UHkG0d0S+UVB6mqysSJgcCQmQBoFYV/DEnAoNGg10ixTiGEEB1H5lCJbsflslNQsBGzy8CKsiv4U0a55w4OX51WkikhhBAdThIq0e2cKdnG8WoHdxVPZ1elno8LSsmy2Do7LCGEEBcwSahEt+JwlLPx9CGWFF3GKYcvsV4GPh7Vj3jv9rtzQwghhDgfmUMlupUXjyazpng8LjRcHODDv4YlEmZov8UuhRBCiKaQhEp0Gw+np/NSfhgAV4caeHZIX1lGRgghRJcg30aiW1BVlQGuNLS4uCu0iJeGDpJkSgghRJchPVSiS3OqKlpFobx8P8OVfawLP8kl/e8870KaQgghREeSP/FFl7WjtIJLdx7hWEUFBYWbABgaMQa93tTJkQkhhGgJRVH48MMPG3w9MzMTRVFIS0vrsJjaiiRUokt6O7eY69OOc6LaymNHD2G3l6DXmQgOntjZoQkhhGgnsbGx5ObmMnTo0DY5ntVqZcSIER2SpElCJboUp6qy6thp7j2SjV1VuTLUl7t93L1TYeGXo9EYOjlCIYQQ7UWr1RIZGYlO1zYzkpYtW0Z0dHSbHOt8JKESXUaFw8ltBzL4e3YhAL9PiGBlyF4MVOPtHUeA//BOjlAIITqGqqo47a5OedSsPHE+NcNz5z6mTJnSaLvc3Fxmz56Nt7c3iYmJrF+/vs4xa3qTtmzZgqIofPXVV4wZMwYfHx8mTJhAenr6eeP7/PPP2bRpE0899VST3k9rdZtJ6SUlJSxdupSPPvoIgLlz5/L8888TGBjYYBtVVVm1ahWvvvoqJSUljBs3jhdffJEhQ4YAcObMGR566CE2bdpEdnY2oaGhXHXVVTzyyCOYTD/N02nJuUXzFFjt3LD3OIcqLRg1Cs8OjGNWQCWZJ/cAEBE+RyaiCyEuGC6HyrfvHe2Uc0+6vj9a/fl/39YMz9XIy8tj+vTpTJo0qdF2Dz74IE888QTPPfccb775JjfeeCNDhw5l0KBBDbZ54IEHePrppwkLC2Px4sXcdtttbNu2rcH98/PzWbRoER9++CE+Pj7nfS9todv0UM2bN4+0tDQ2btzIxo0bSUtLY/78+Y22efLJJ3nmmWd44YUX2LVrF5GRkcyYMYPy8nIAcnJyyMnJ4amnnmL//v28/vrrbNy4kYULF7b63KJ5AnRafLVawgw6PhjRl6vCAyko+BQAU8BIvL1jOjlCIYQQP1czPBcZGUlgYCCLFy8mKSmJlStXNtruuuuu4/bbb6d///488sgjjBkzhueff77RNo8++iiTJ09m8ODBLF++nO3bt2OxWOrdV1VVbrnlFhYvXsyYMWNa+vaarVv0UB0+fJiNGzeSnJzMuHHjAPjHP/5BUlIS6enpDBgwoE4bVVV59tlneeCBB7jmmmsAeOONN4iIiOCtt97izjvvZOjQoWzYsMHTpk+fPjz66KPcdNNNOBwOdDpdi84N7olwVqvV87ysrKzNrkdPoqoqiqLgpdXwr2EJWF0qMV4Gysr2UVWdhUajJyxsZmeHKYQQHUqjU5h0ff9OO3dzLVy4kPLycjZv3ozmPDUCk5KS6jw/34Tx4cN/mvIRFRUFQEFBAXFxcXX2ff755ykrK2PFihVNjL5tdIseqh07dmAymTwJDcD48eMxmUxs37693jYZGRnk5eUxc+ZPX8ZGo5HJkyc32AbAbDYTEBDgmRDXknMDPP7445hMJs8jNja2ye/3QuBSVZ7MyOWxEz91F4cZ9MR4GXC5bBQUbAQgJHgyen1AZ4UphBCdQlEUtHpNpzyaO71i9erVbNy4kY8++gh/f/8Wv9/G6PU/LTFWs6/L5ap336+//prk5GSMRiM6nY6+ffsCMGbMGG6++eYWxdcU3SKhysvLIzw8vM728PBw8vLyGmwDEBERUWt7REREg22Ki4t55JFHuPPOO1t1boAVK1ZgNps9j+zs7Ab3vdBUOV0sPnSSZzLzeT6rgL3lVbVeP3NmG3aHGb0+UMokCCFEF7ZhwwYefvhh3nvvPfr06dOkNsnJyXWeDxw4sM1i+tvf/sbevXtJS0sjLS2Nzz77DIB3332XRx99tM3Oc65OHfJbuXIlq1atanSfXbt2AfVnrzXDRY059/WG2pSVlTFnzhwGDx7MQw891OgxmnJuo9GI0WhsNLYLUaHNzs37M/ihrAq9ovCXATFc5P/ThEG7vYziM1sBCA+7HI1GFj4WQoiu6MCBAyxYsID777+fIUOGeDoZDAYDwcHBDbZbv349Y8aMYeLEiaxbt46UlBRee+21Novr3GFAPz8/wD2tJyam/ebjdmpCddddd3HDDTc0uk9CQgL79u0jPz+/zmuFhYV1eqBqREZGAu4epprxVnCPuZ7bpry8nFmzZuHn58cHH3xQq2sxMjKy2ecW9TtWZeE3e09w0mIjSKflX8MSSQr0q7VPYeEmXC47Pt5x+PsP66RIhRBCnE9qaipVVVWsXr2a1atXe7ZPnjyZLVu2NNhu1apVvPPOOyxZsoTIyEjWrVvH4MGDOyDi9tWpCVVoaCihoaHn3S8pKQmz2UxKSgpjx44FYOfOnZjNZiZMmFBvm8TERCIjI9m8eTMjR44EwGazsXXrVtasWePZr6ysjMsvvxyj0chHH32El5dXq88t6tpZWsEt+zMocTiJ9zLw1kW96eNT+1pXV2djLnOXSQgPv1LKJAghRBd2yy23cMsttzSrTU2NqyVLltT7ekJCQq06WFOmTKlTF2vEiBFNrpVV3zHbS7eYQzVo0CBmzZrFokWLSE5OJjk5mUWLFnHllVfWustu4MCBfPDBB4B7mO6ee+7hscce44MPPuDAgQPccsst+Pj4MG/ePMDdMzVz5kwqKyt57bXXKCsrIy8vj7y8PJxOZ7POLRp32mqnxOFkVIAPn4zuVyeZUlWV/AL3OLfJNBJv716dEaYQQgjRIt2ibALAunXrWLp0qeeuvblz5/LCCy/U2ic9PR2z2ex5vmzZMqqrq1myZImnsOemTZs8dyHs3r2bnTt3AnjuAqiRkZFBQkJCk88tGndNRBAGReGykAB8tHXz+LLyfVRXZ6HRGAgLlTIJQgghuhdF7Yh+MEFZWRkmk8lTlqGnc7hUns7M45ZeoUQYG59Y7nJZOXHiOewOM2FhMwgNmdIxQQohRBdgsVjIyMggMTGxzrQT0TEa+3/Q1O/vbjHkJ7qXSqeTWw9k8NeT+SzYfwLneXL2n8okBBEcdEkHRSmEEEK0nW4z5Ce6hwKrnZv2n2BfeTVeGoWl8RFoG5lcbrebKS7+FpAyCUIIIbovSahEm0mvtPCbfcc5ZbETrNfy5rDejDb5NtqmsHATLtWOj08C/v5DOyhSIYQQom1JQiXaxLaScm47kInZ4aS3t5F1w3uT6NN4YdPq6izMZWkoKESEz5EyCUIIIbotSahEq7lUlZXHcjA7nFwc4MvrwxIJMTT+0VJVlfz8TwEwmUbh5RXdEaEKIYQQ7UImpYtW0ygK/xqWyILoEN4b0ee8yRRAWdleqi2n3GUSwmZ0QJRCCCFE+5GESrSI3aXy3Zlyz/NYLwNPDojFu54aU+dyuawUFn4BQGjIFHS6lq1OLoQQontRFIUPP/ywwdczMzNRFIW0tLQOi6mtSEIlmq3C4WTB/hNcv/c4nxeWNqut3V7G6Zz3sDvKMOiDCAqS5XuEEEK4xcbGkpuby9ChrbtJKSEhAUVRaj2WL1/eRlHWT+ZQiWbJtdq4ad8JDlZY8NZoGi2J8HOq6qKkdCeFhZtxuawoioaIiF9ImQQhhBAeWq2WyMjINjnWww8/zKJFizzP/fz82uS4DZEeKtFkhyuqmbP7Rw5WWAgz6PhgZF9mhprO2666+jQnT75Mfv4nuFxWvL1iSYhfgp+frIUohBD1UVUV1eHqnEcTF1CpGZ479zFlypRG2+Xm5jJ79my8vb1JTExk/fr1dY5ZM+S3ZcsWFEXhq6++YsyYMfj4+DBhwgTS09PPG5+/vz+RkZGeR3snVNJDJZrk2zPlLDyQQbnTRT8fd1mEOO/GyyI4nVYKizZRWrITFRWtxouwsMsJDLxYSiQIIURjnCqlHx3vlFMHzu0DuvP/jq4ZnquRl5fH9OnTmTRpUqPtHnzwQZ544gmee+453nzzTW688UaGDh3KoEGDGmzzwAMP8PTTTxMWFsbixYu57bbb2LZtW6PnWbNmDY888gixsbFcd9113HfffRgMhvO+r5aShEqcV3qlhXn7juNQISnQl7VDEwnUN/zRUVWV8vID5Bd8isPhnrhuCriI8PDZMgFdCCF6iJ8Pz1ksFq666iqSkpJYuXJlo+2uu+46br/9dgAeeeQRNm/ezPPPP89LL73UYJtHH32UyZMnA7B8+XLmzJmDxWJpcO3Du+++m1GjRhEUFERKSgorVqwgIyODf/7zny14p00jCZU4r/4+Rm6ODqXE4eSvA2MxahoeKbbZzpCf/zEVlUcBMBhCiIyYi69v344KVwghuj+t4u4p6qRzN9fChQspLy9n8+bNaBr5jgBISkqq8/x8d/UNHz7c83NUVBQABQUFxMXF1bv/vffeW6ttUFAQv/rVr1izZg0hISGNnqulJKES9bK5XFhdKv46LYqi8HC/Xii4a07Vx+VycKbke4qLtuBS7SiKjpCQSYQET5KJ50II0UyKojRp2K0rWL16NRs3biQlJQV//5aNQpxvGohe/9P3SM2+LperyccfP348AMeOHWu3hEompYs6yhxOfrPvBLfuz8B29gOrVZQGk6mqqgwyM19038Gn2vH16UNi4u8IC50myZQQQvRgGzZs4OGHH+a9996jT5+m9aglJyfXeT5w4MD2CM9jz549wE+9W+1BeqhELactNn6z7wRHKi34ajWkV1oY5u9T774ORyWFhRspNf8AgE7rS3j4FQQEXCSTzoUQooc7cOAACxYs4P7772fIkCHk5eUBYDAYCA4ObrDd+vXrGTNmDBMnTmTdunWkpKTw2muvtVlcO3bsIDk5malTp2Iymdi1axf33nsvc+fObXCIsC1IQiU89pdXcdO+E+TbHEQYdKwb3puh9SRTqqpiLvuBgoKNOJ1VAAQGXkx42OVotd4dHbYQQohOkJqaSlVVFatXr2b16tWe7ZMnT2bLli0Ntlu1ahXvvPMOS5YsITIyknXr1jF48OA2i8toNPLuu++yatUqrFYr8fHxLFq0iGXLlrXZOeqjqE0tOCFapaysDJPJhNlsJiAgoLPDqeOr4jLuOJhJpdPFQF8v/jO8NzFedW8vtVoLyMv/H1VVmQAYjZFERszFxye+gyMWQoiewWKxkJGRQWJiYoN3rYn21dj/g6Z+f0sPlWBD3hmWHsnCqcKlQX68NjSRAJ221j4ul53i4m8oPvM9qupEo9ETGjKN4OAJKIq2gSMLIYQQFwZJqASD/bzx0WiYHWbiqQGxGM655bWi4ij5+R9hs5cA4Oc3kMiIK9HrgzojXCGEEKLLkYTqAqWqqmfi+CA/bzZfPIB4L0OtyeR2exkFBZ9RVr4fAL3ORETElfj5DZJJ50IIIcTPSEJ1ASq1O1h88CR3J0SQFOhe2yjhZ8vI1LeQcVBQEqEh09BqG19uRgghhLgQSUJ1gTlZbeWmfSf4scrK8Wor28cNQq/5qbfJYskhL+9Dqi2nAfD2iiEy8pd4eUV3VshCCCFElycJ1QUkrcxdFqHI7iDaqOffwxI9yZTTaaWo6EtKSnb8bCHjmWcXMpb6r0IIIURjJKG6QGwqMnPnwZNUu1wM8XOXRYgyGtwLGVccpCD/U+yOMgACAoYTEX6FLGQshBBCNJEkVBeAf50q5E8/nsYFTA3259UhCfjrtNjtJeTlffTTQsb6YCIi5+Ln269zAxZCCCG6GUmoejhVVdlprsQF/CYqmCf6x6JTXBQXf0tR0ddnFzLWEhI8iZCQybL2nhBCCNECMjmmh1MUhecGxvHswFieGhCL3XKSjMwXKSj8Apdqx8cnkcSE3xEWNl2SKSGEEO1KURQ+/PDDBl/PzMxEURTS0tI6LKa2IglVD3TG7uCZzDxcZ1cV8tJquC7cm7y8DziZ9Q+s1nx0Wl+io35FXOxCjMawTo5YCCGEgNjYWHJzcxk6dGirj/Xpp58ybtw4vL29CQ0N5ZprrmmDCBsmQ349TGa1lXl7T3Ci2opDVbkvIZKysj0UFGzE4awEahYynolWW3fhYyGEEKKzaLVaIiMjW32cDRs2sGjRIh577DEuu+wyVFVl//79bRBhw6SHqgfZba7kit1HOVFtJcZLz+xAlazs18jJ3YDDWYnRGEF83B1ERV4lyZQQQnRhqqriclk75aGeHd04n5rhuXMfU6ZMabRdbm4us2fPxtvbm8TERNavX1/nmDVDflu2bEFRFL766ivGjBmDj48PEyZMID09vcHjOxwO7r77bv7yl7+wePFi+vfvz4ABA/jVr37VpPfVUtJD1UN8VljKkkMnsbhUhvt58VyvUygF71KlOtEoekJDLyM4+BJZyFgIIboBVbWRfvThTjn3gP5/RlHOvypGzfBcjby8PKZPn86kSZMabffggw/yxBNP8Nxzz/Hmm29y4403MnToUAYNGtRgmwceeICnn36asLAwFi9ezG233ca2bdvq3feHH37g9OnTaDQaRo4cSV5eHiNGjOCpp55iyJAh531fLSU9VD3Aq9kFLDyQicWlMjVQy9OBG6HsG1TViZ/fQHr3vpuQkEmSTAkhhGgzNcNzkZGRBAYGsnjxYpKSkli5cmWj7a677jpuv/12+vfvzyOPPMKYMWN4/vnnG23z6KOPMnnyZAYPHszy5cvZvn07Foul3n1PnDgBwMqVK/nTn/7EJ598QlBQEJMnT+bMmTMteq9NIT1U3VxmtZXVx3NRgV+ZSljs9RVap4peF3B2IePBspCxEEJ0M4piYED/P3fauZtr4cKFlJeXs3nzZjSaxvtqkpKS6jw/3119w4cP9/wcFRUFQEFBAXFxcXX2dblcgLtX69prrwVg7dq1xMTEsH79eu68887zvp+WkISqm4v30vNYnJUfS47xa++DaBSFoOBLZCFjIYToxtzzkbrH7/DVq1ezceNGUlJS8Pdv2Qob5/vDX6//qaxPzb41idO5ahKuwYMHe7YZjUZ69+5NVlZWi+JrChny68ZU1UVW1j8YbfsfN/gexMc7hoSEJUSEXyHJlBBCiHa3YcMGHn74Yd577z369OnTpDbJycl1ng8cOLDNYho9ejRGo7HWxHW73U5mZibx8fFtdp5zSQ9VN6YoGry847BY8wkPm0lg4FhZyFgIIUSHOHDgAAsWLOD+++9nyJAh5OXlAWAwGAgODm6w3fr16xkzZgwTJ05k3bp1pKSk8Nprr7VZXAEBASxevJiHHnqI2NhY4uPj+ctf/gK452+1F0mourmw0MsICZ4oCxkLIYToUKmpqVRVVbF69WpWr17t2T558mS2bNnSYLtVq1bxzjvvsGTJEiIjI1m3bl2t4bm28Je//AWdTsf8+fOprq5m3LhxfP311wQFBbXpeX5OUZtacEK0SllZGSaTCbPZTEBAQGeHI4QQoouwWCxkZGSQmJiIl5dXZ4dzQWrs/0FTv7+7zfhQSUkJ8+fPx2QyYTKZmD9/PqWlpY22UVWVlStXEh0djbe3N1OmTOHgwYOe18+cOcPvfvc7BgwYgI+PD3FxcSxduhSz2VzrOAkJCXUKly1fvrw93qYQQgghuqFuk1DNmzePtLQ0Nm7cyMaNG0lLS2P+/PmNtnnyySd55plneOGFF9i1axeRkZHMmDGD8vJyAHJycsjJyeGpp55i//79vP7662zcuJGFCxfWOdbDDz9Mbm6u5/GnP/2pXd6nEEIIIbqfbjGH6vDhw2zcuJHk5GTGjRsHwD/+8Q+SkpJIT09nwIABddqoqsqzzz7LAw884FkQ8Y033iAiIoK33nqLO++8k6FDh7JhwwZPmz59+vDoo49y00034XA40Ol+ujz+/v5tsr6QEEIIIXqebtFDtWPHDkwmkyeZAhg/fjwmk4nt27fX2yYjI4O8vDxmzpzp2WY0Gpk8eXKDbQDPGOnPkymANWvWEBISwogRI3j00Uex2WyNxmy1WikrK6v1EEIIIUTP1C16qPLy8ggPD6+zPTw83HObZn1tACIiImptj4iI4OTJk/W2KS4u5pFHHqlTRfXuu+9m1KhRBAUFkZKSwooVK8jIyOCf//xngzE//vjjrFq1qtH3JYQQQtSQe8Q6T1tc+07toVq5cmW9K1X//JGamgrUX0VVVdXzVlc99/WG2pSVlTFnzhwGDx7MQw89VOu1e++9l8mTJzN8+HBuv/12Xn75ZV577TWKi4sbPO+KFSswm82eR3Z2dqNxCiGEuDDVVAGvqqrq5EguXDXX/ucV2ZurU3uo7rrrLm644YZG90lISGDfvn3k5+fXea2wsLBOD1SNmvlOeXl5njL04F7759w25eXlzJo1Cz8/Pz744IPzXtDx48cDcOzYMUJCQurdx2g0YjRKtXIhhBCN02q1BAYGUlBQAICPj4+swdpBVFWlqqqKgoICAgMD0Wq1LT5WpyZUoaGhhIaGnne/pKQkzGYzKSkpjB07FoCdO3diNpuZMGFCvW0SExOJjIxk8+bNjBw5EgCbzcbWrVtZs2aNZ7+ysjIuv/xyjEYjH330UZNqgOzZswegVqImhBBCtFRNJ0BNUiU6VmBgYKtvPOsWc6gGDRrErFmzWLRoEa+88goAd9xxB1deeWWtO/wGDhzI448/ztVXX42iKNxzzz089thj9OvXj379+vHYY4/h4+PDvHnzAHfP1MyZM6mqquI///lPrcnjYWFhaLVaduzYQXJyMlOnTsVkMrFr1y7uvfde5s6dW+8q10IIIURzKYpCVFQU4eHh2O32zg7ngqLX61vVM1WjWyRUAOvWrWPp0qWeu/bmzp3LCy+8UGuf9PT0WkU5ly1bRnV1NUuWLKGkpIRx48axadMmz2rYu3fvZufOnQD07du31rEyMjJISEjAaDTy7rvvsmrVKqxWK/Hx8SxatIhly5a159sVQghxAdJqtW3y5S46niw900Fk6RkhhBCi++lxS88IIYQQQnRVklAJIYQQQrRSt5lD1d3VjKxKxXQhhBCi+6j53j7fDClJqDpIzYLMsbGxnRyJEEIIIZqrvLwck8nU4OsyKb2DuFwucnJy8Pf3v+ALtpWVlREbG0t2drZM0G9ncq07hlznjiHXuWPIda5NVVXKy8uJjo5Go2l4ppT0UHUQjUZDTExMZ4fRpQQEBMg/1g4i17pjyHXuGHKdO4Zc55801jNVQyalCyGEEEK0kiRUQgghhBCtJAmV6HBGo5GHHnpIFo/uAHKtO4Zc544h17ljyHVuGZmULoQQQgjRStJDJYQQQgjRSpJQCSGEEEK0kiRUQgghhBCtJAmVEEIIIUQrSUIlOkRJSQnz58/HZDJhMpmYP38+paWlTW5/5513oigKzz77bLvF2BM09zrb7Xbuv/9+hg0bhq+vL9HR0SxYsICcnJyOC7qbeOmll0hMTMTLy4vRo0fz3XffNbr/1q1bGT16NF5eXvTu3ZuXX365gyLt3ppznd9//31mzJhBWFgYAQEBJCUl8cUXX3RgtN1Xcz/PNbZt24ZOp2PEiBHtG2A3JAmV6BDz5s0jLS2NjRs3snHjRtLS0pg/f36T2n744Yfs3LmT6Ojodo6y+2vuda6qquKHH37gwQcf5IcffuD999/n6NGjzJ07twOj7vreffdd7rnnHh544AH27NnDpZdeyuzZs8nKyqp3/4yMDK644gouvfRS9uzZw//7f/+PpUuXsmHDhg6OvHtp7nX+9ttvmTFjBp999hm7d+9m6tSp/OIXv2DPnj0dHHn30tzrXMNsNrNgwQKmTZvWQZF2M6oQ7ezQoUMqoCYnJ3u27dixQwXUI0eONNr21KlTaq9evdQDBw6o8fHx6l//+td2jrb7as11/rmUlBQVUE+ePNkeYXZLY8eOVRcvXlxr28CBA9Xly5fXu/+yZcvUgQMH1tp25513quPHj2+3GHuC5l7n+gwePFhdtWpVW4fWo7T0Ov/6179W//SnP6kPPfSQetFFF7VjhN2T9FCJdrdjxw5MJhPjxo3zbBs/fjwmk4nt27c32M7lcjF//nzuu+8+hgwZ0hGhdmstvc7nMpvNKIpCYGBgO0TZ/dhsNnbv3s3MmTNrbZ85c2aD13XHjh119r/88stJTU3Fbre3W6zdWUuu87lcLhfl5eUEBwe3R4g9Qkuv89q1azl+/DgPPfRQe4fYbcniyKLd5eXlER4eXmd7eHg4eXl5DbZbs2YNOp2OpUuXtmd4PUZLr/PPWSwWli9fzrx582RR1LOKiopwOp1ERETU2h4REdHgdc3Ly6t3f4fDQVFREVFRUe0Wb3fVkut8rqeffprKykquv/769gixR2jJdf7xxx9Zvnw53333HTqdpA0NkR4q0WIrV65EUZRGH6mpqQAoilKnvaqq9W4H2L17N8899xyvv/56g/tcKNrzOv+c3W7nhhtuwOVy8dJLL7X5++juzr2G57uu9e1f33ZRW3Ovc423336blStX8u6779b7h4WoranX2el0Mm/ePFatWkX//v07KrxuSVJN0WJ33XUXN9xwQ6P7JCQksG/fPvLz8+u8VlhYWOevpBrfffcdBQUFxMXFebY5nU7+8Ic/8Oyzz5KZmdmq2LuT9rzONex2O9dffz0ZGRl8/fXX0jv1M6GhoWi12jp/vRcUFDR4XSMjI+vdX6fTERIS0m6xdmctuc413n33XRYuXMj69euZPn16e4bZ7TX3OpeXl5OamsqePXu46667APfQqqqq6HQ6Nm3axGWXXdYhsXd1klCJFgsNDSU0NPS8+yUlJWE2m0lJSWHs2LEA7Ny5E7PZzIQJE+ptM3/+/Dq/GC+//HLmz5/Prbfe2vrgu5H2vM7wUzL1448/8s0338gX/jkMBgOjR49m8+bNXH311Z7tmzdv5pe//GW9bZKSkvj4449rbdu0aRNjxoxBr9e3a7zdVUuuM7h7pm677Tbefvtt5syZ0xGhdmvNvc4BAQHs37+/1raXXnqJr7/+mv/+978kJia2e8zdRidOiBcXkFmzZqnDhw9Xd+zYoe7YsUMdNmyYeuWVV9baZ8CAAer777/f4DHkLr/za+51ttvt6ty5c9WYmBg1LS1Nzc3N9TysVmtnvIUu6Z133lH1er362muvqYcOHVLvuece1dfXV83MzFRVVVWXL1+uzp8/37P/iRMnVB8fH/Xee+9VDx06pL722muqXq9X//vf/3bWW+gWmnud33rrLVWn06kvvvhirc9uaWlpZ72FbqG51/lccpdf/SShEh2iuLhY/c1vfqP6+/ur/v7+6m9+8xu1pKSk1j6Aunbt2gaPIQnV+TX3OmdkZKhAvY9vvvmmw+Pvyl588UU1Pj5eNRgM6qhRo9StW7d6Xrv55pvVyZMn19p/y5Yt6siRI1WDwaAmJCSof//73zs44u6pOdd58uTJ9X52b7755o4PvJtp7uf55yShqp+iqmdnSgohhBBCiBaRu/yEEEIIIVpJEiohhBBCiFaShEoIIYQQopUkoRJCCCGEaCVJqIQQQgghWkkSKiGEEEKIVpKESgghhBCilSShEkIIIYRoJUmohBA93pQpU7jnnnuavP/rr79OYGBgu8UjhOh5JKESQojzWLlyJSNGjOi082/ZsgVFUeo8jhw50mkxCSFq03V2AEIIIZomPT2dgIAAz/OwsLBOjEYI8XPSQyWE6FEqKytZsGABfn5+REVF8fTTT9fZx2azsWzZMnr16oWvry/jxo1jy5Yt9R7v9ddfZ9WqVezdu9fTM/T6668D8MwzzzBs2DB8fX2JjY1lyZIlVFRUNBqfoii88sorXHnllfj4+DBo0CB27NjBsWPHmDJlCr6+viQlJXH8+PE6bcPDw4mMjPQ8tFpts6+PEKJ9SEIlhOhR7rvvPr755hs++OADNm3axJYtW9i9e3etfW699Va2bdvGO++8w759+7juuuuYNWsWP/74Y53j/frXv+YPf/gDQ4YMITc3l9zcXH79618DoNFo+Nvf/saBAwd44403+Prrr1m2bNl5Y3zkkUdYsGABaWlpDBw4kHnz5nHnnXeyYsUKUlNTAbjrrrvqtBs5ciRRUVFMmzaNb775piWXRwjRXlQhhOghysvLVYPBoL7zzjuebcXFxaq3t7d69913q6qqqseOHVMVRVFPnz5dq+20adPUFStWqKqqqmvXrlVNJpPntYceeki96KKLznv+9957Tw0JCWl0H0D905/+5Hm+Y8cOFVBfe+01z7a3335b9fLy8jw/cuSI+uqrr6q7d+9Wt2/frv72t79VFUVRt27det6YhBAdQ+ZQCSF6jOPHj2Oz2UhKSvJsCw4OZsCAAZ7nP/zwA6qq0r9//1ptrVYrISEhzTrfN998w2OPPcahQ4coKyvD4XBgsViorKzE19e3wXbDhw/3/BwREQHAsGHDam2zWCyUlZUREBDAgAEDar2HpKQksrOzeeqpp5g0aVKzYhZCtA9JqIQQPYaqqufdx+VyodVq2b17d505SH5+fk0+18mTJ7niiitYvHgxjzzyCMHBwXz//fcsXLgQu93eaFu9Xu/5WVGUBre5XK4GjzF+/Hj+85//NDleIUT7koRKCNFj9O3bF71eT3JyMnFxcQCUlJRw9OhRJk+eDLjnITmdTgoKCrj00kubdFyDwYDT6ay1LTU1FYfDwdNPP41G456O+t5777Xhu2ncnj17iIqK6rDzCSEaJwmVEKLH8PPzY+HChdx3332EhIQQERHBAw884El4APr3789vfvMbFixYwNNPP83IkSMpKiri66+/ZtiwYVxxxRV1jpuQkEBGRgZpaWnExMTg7+9Pnz59cDgcPP/88/ziF79g27ZtvPzyy+3yvp599lkSEhIYMmQINpuN//znP2zYsIENGza0y/mEEM0nd/kJIXqUv/zlL0yaNIm5c+cyffp0Jk6cyOjRo2vts3btWhYsWMAf/vAHBgwYwNy5c9m5cyexsbH1HvPaa69l1qxZTJ06lbCwMN5++21GjBjBM888w5o1axg6dCjr1q3j8ccfb5f3ZLPZ+OMf/8jw4cO59NJL+f777/n000+55ppr2uV8QojmU9SmTDoQQgghhBANkh4qIYQQQohWkoRKCCGEEKKVJKESQgghhGglSaiEEEIIIVpJEiohhBBCiFaShEoIIYQQopUkoRJCCCGEaCVJqIQQQgghWkkSKiGEEEKIVpKESgghhBCilSShEkIIIYRopf8PqVNPxRKyyUYAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#meanzs = np.zeros( (6,len(bands),len(deltas)) )\n",
-    "\n",
-    "tmp = pandas.read_pickle('meanzsy1.pkl')\n",
-    "for i in np.arange(1,6):\n",
-    "    plt.plot(deltas,tmp[i,0,:]-tmp[i,0,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6)\n",
-    "    fitpoly=np.polynomial.polynomial.polyfit(deltas,tmp[i,0,:]-tmp[i,0,6],2)\n",
-    "#    print(fitpoly)\n",
-    "    plt.plot(deltas,np.polynomial.polynomial.polyval(deltas,fitpoly,tensor=True),linestyle='dashed')\n",
-    "    plt.xlabel('delta m5')\n",
-    "    plt.ylabel('delta <z>')\n",
-    "    plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "id": "2ee3c50b-88c5-41a9-a2e4-9b720ee63e4b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tmp = pandas.read_pickle('meanzsy1.pkl')\n",
-    "\n",
-    "for i in np.arange(1,6):\n",
-    "    sumshift = tmp[i,0,:]+tmp[i,1,:]+tmp[i,2,:]+tmp[i,3,:]+tmp[i,4,:]+tmp[i,5,:]\n",
-    "    plt.plot(deltas,tmp[i,6,:]-tmp[i,6,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6)\n",
-    "    plt.plot(deltas,sumshift-sumshift[6],label = 'z bin '+np.array2string((i+1)),alpha=0.6,linestyle='dashed')\n",
-    "\n",
-    "#    fitpoly=np.polynomial.polynomial.polyfit(deltas,tmp[i,0,:]-tmp[i,0,6],2)\n",
-    "#    plt.plot(deltas,np.polynomial.polynomial.polyval(deltas,fitpoly,tensor=True),linestyle='dashed')\n",
-    "    plt.xlabel('delta m5')\n",
-    "    plt.ylabel('delta <z>')\n",
-    "    plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "id": "3bdda995-d2d9-4559-af32-16ecb5928059",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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l1Wv4/XV93V1BEARBGBBEsHSGCskKP95XzvyvD3HA7ev0uiSpSE+7BpMxm3DYS0XFEoLBrlfTCYIgCMJQJoKlM5RXljnk9tEUDHHt9iKKPZ0zTCqVlszMm9DpEgmGHFRWLiEc7r2NCgVBEITBS5Ik3nnnnWO+XlpaiiRJbN++vc/61FNEsHSGsmjULJ2Yz5gYA3WBEFdvL6KsiyE5tdpEVuYtaNQx+Py1VFW/jqKE+6HHgiAIwmCWlZVFTU0NhYWFPXI+v9/PxIkT+yQAE8HSGSxeq2HZxHxGmPRU+4Ncs/0w1b5Ap3Y6XTyZmbegUmlxu4uoqX1b1GASBEEQToparSY1NRWNpmfWlv3iF78gPT29R851PCJYOsMl6bQsnzicYUYd5b4AV28/TJ2/83YnRmMGGenXI0kqHI5tNDau7YfeCoIgCH2hfcjs27c5c+Z0e1xNTQ3z58/HaDSSm5vL8uXLO52zPQu0YcMGJEli7dq1TJkyBZPJxPTp0zlw4MBx+/fhhx+yevVqnn766dO5zBMmgiWBVL2Wf08cTqZBS6UvwP4uJnwDmM2jSEm5DIDGpvXYW77qy24KgiAMCYqiEA7K/XI70VGB9iGz9tu2bdtISEhg1qxZ3R734IMPsmjRInbs2MGNN97I9ddfz759+7o95oEHHuCZZ55h69ataDQabrvttm7b19XVceedd/LPf/4Tk8l0QtdzukSdJQGATIOOFROHU+YNMCvecsx2triphIIOGpvWU1f3LlqNFbN5VB/2VBAEYXCTQwqfLDvYL+8969qRqLXScdu1D5kB+Hw+rrjiCqZNm8bDDz/c7XHXXHMNd9xxBwCPPfYYa9as4bnnnuOFF1445jFPPPEEs2fPBuC+++5j4cKF+Hw+DAZDp7aKonDrrbeyePFipkyZQmlp6XGvpSeIzJIQlWPUdwiUyr1+nKHOk7kTEy8gNnYSiiJTVf0GXm9VX3ZTEARB6EO33347LpeL119/HZWq+7Bh2rRpnR4fL7M0fvz46P20tDQA6uvru2z73HPP4XQ6uf/++0+k6z1GZJaELh32+Lh6+2Ey9FqWTsjHrFFHX5MkidSUKwiFXLjdRVRWLiEnZzE6XXw/9lgQBGFwUGkkZl07st/e+2Q8/vjjrFq1ii1btmCxHHvUoTuS1P17arXaTm1lWe6y7bp169i8eTN6vb7D81OmTOGGG25gyZIlp9TH4xl0maUXXniB3NxcDAYDkydP5tNPP+22/caNG5k8eTIGg4G8vDxefPHFY7ZdunQpkiRxxRVX9HCvBx+frOANy2x1erhpVzGecMcfXJVKQ0b69zDoUwmF3VRULiEc9vRTbwVBEAYPSZJQa1X9cjte4HK0FStW8Oijj7Js2TLy8/NP6JjNmzd3ejx69OiT+vp0509/+hM7duxg+/btbN++nZUrVwLw5ptv8sQTT/TY+3zboAqW3nzzTX7yk5/wwAMPsG3bNmbOnMn8+fMpLy/vsn1JSQkLFixg5syZbNu2jV/+8pf8+Mc/ZsWKFZ3alpWV8bOf/YyZM2f29mUMCgVmI29MyMeiVrGpxc33d5Xg+1bApFbrycy8Ba0mlkCgkcrKfyLLnVfSCYIgCIPL7t27ufnmm7n33nspKCigtraW2tpampubuz1u+fLl/O1vf+PgwYM89NBDbNmyhf/+7//usX5lZ2dTWFgYvY0cGcnQ5efnk5mZ2WPv822DKlj6/e9/z+23384dd9zBmDFjePbZZ8nKyuIvf/lLl+1ffPFFsrOzefbZZxkzZgx33HEHt912W6elhuFwmBtuuIFHHnmEvLy8vriUQWGS1cTrE/IxqVVstLu4Y08pgW+lRrVaK1lZt6BWG/F4y6muWY6idJ0+FQRBEAaHrVu34vF4ePzxx0lLS4verrrqqm6Pe+SRR1i6dCnjx49nyZIlvPbaa4wdO7aPet17JGWQVBcMBAKYTCaWL1/OlVdeGX3+rrvuYvv27WzcuLHTMbNmzWLSpEn88Y9/jD739ttvc+211+LxeKLjpA899BA7d+7k7bff5tZbb6WlpaXbku1+vx+//0i1a6fTSVZWFg6HA6vV2gNXO7B8YW/lhp2H8coKCxJjealgGFpVx1Sux1NCecU/UJQQ8bZpJCcvPKl0ryAIwlDk8/koKSmJTh8R+l533wOn00lsbOxxP78HTWapsbGRcDhMSkpKh+dTUlKora3t8pja2tou24dCIRobGwH4/PPPeeWVV3j55ZdPuC9PPvkksbGx0VtWVtZJXs3gMt1m5h/j8tCrJCp8AdzhzivkTKZc0tMWAdBs30SLY2tfd1MQBEEQesWgCZbafTtboShKtxmMrtq3P+9yubjxxht5+eWXSUxMPOE+3H///TgcjuitoqLiJK5gcJodb+H18Xksn5hPnLbrRZRW63iSki4CoKFhNeFw18UtBUEQBGEwGTSlAxITE1Gr1Z2ySPX19Z2yR+1SU1O7bK/RaEhISGDPnj2Ulpbyne98J/p6+3JFjUbDgQMHulwBoNfrOy1bPBOcZ+u4bPQLeyvT4mI6BKQJ8bNwOLYRCDTS1LyR5KRL+rqbgiAIgtCjBk1mSafTMXnyZNasWdPh+TVr1jB9+vQuj5k2bVqn9qtXr2bKlClotVpGjx7Nrl27oksQt2/fzmWXXcbcuXPZvn37kB9eOx0vVzRw1fYiflVU1aF8viSpSE6aB4C9eRPBoKO/uigIgiAIPWLQZJYA7rnnHm666SamTJnCtGnT+Otf/0p5eTmLFy8GIsNjVVVVvPrqqwAsXryYP//5z9xzzz3ceeedbNq0iVdeeYU33ngDAIPBQGFhYYf3iIuLA+j0vNCRRROJs1+ubESnUvG/eWnRDJPZPBqTaRgeTykNjWtIT7u6P7sqCIIgCKdlUAVL1113HU1NTTz66KPU1NRQWFjIypUrycnJASK7HR9dcyk3N5eVK1dy99138/zzz5Oens6f/vQnFi1a1F+XMGR8Ny2BgKzwi4OVPF9ej0El8fPcSJl6SZJITppHadmLOB3bibedh8GQ1s89FgRBEIRTM2hKBwxkJ7r0cCh6uaKBB4sie8P9Mi+NH+ccmT9WVbUUp2sXMTHDyc76fn91URAEod+I0gH974wqHSAMTHdmJfG/eZGs0a+La3ip4sjmh0lJFyFJatzuIlrdh/qri4IgCIJwWkSwJJy2/85J4WfDUgFoDR2p3q3TJWCLOweAhvpVorK3IAiCMCiJYEnoET8dlsJbE4fz09zUDs8nJs5FrTLg89fidO7op94JgiAIvU2SpG53vygtLUWSJLZv395nfeopIlgSeoQkSUy3maOPXaEwy2qbUatNJCTMBqChYY3YaFcQBOEMlZWVFV2cdTqGDRuGJEkdbvfdd18P9bJrg2o1nDA4BGWFm3cVs6nFTYnHz89yzsVu30ww5MBu30RCwqz+7qIgCILQx9RqNampqcdveAIeffRR7rzzzuhjs9ncTevTJzJLQo/TqiTmxkdWFfyhrI5fHa4nITGyDUpj0wZCIXd/dk8QBEE4jvYhs2/f5syZ0+1xNTU1zJ8/H6PRSG5uLsuXL+90zvZhuA0bNiBJEmvXrmXKlCmYTCamT5/OgQMHjts/i8VCampq9CaCJWFQ+nFOCr8ekQHAK1WNPFwTj0aXiiz7aWra0L+dEwRB6EeKoqCE5P65nWC1oPYhs/bbtm3bSEhIYNas7kcGHnzwQRYtWsSOHTu48cYbuf7669m3b1+3xzzwwAM888wzbN26FY1Gw2233Xbc/v32t78lISGBiRMn8sQTTxAIBE7ouk6VGIYTes1tmUlYNGp+sr+cN2vtOGxzuFu/DHvLl9hs56LTJfR3FwVBEPpeWKHl3cP98tZxl+WD5tibz7c7esjM5/NxxRVXMG3aNB5++OFuj7vmmmu44447AHjsscdYs2YNzz33HC+88MIxj3niiSeYPTsyt/W+++5j4cKF+Hy+Y9aluuuuuzjrrLOw2Wxs2bKF+++/n5KSEv7v//7vuNd1qkSwJPSqa1LjiVGrWLynjFX2MGrLXH5mXktDwxoyMr7b390TBEEQjuP222/H5XKxZs0aVKruB6SmTZvW6fHxVr+NHz8+ej8tLVK3r76+nuzs7C7b33333R2OtdlsXH311dFsU28QwZLQ6xYkxfHP8Wru2V/Oj/NGINWvw+naRbz3PIxGsVmxIAhnGLUUyfD003ufjMcff5xVq1axZcsWLBbLKb1l+76hx6LVaju1leUTr8t37rnnAlBUVNRrwZKYsyT0idnxFr44dwwT4rOIjZ0EQG3dhyc8fi4IgjBUSJKEpFH1z+04gcvRVqxYwaOPPsqyZcvIzz+x4G7z5s2dHo8ePfqkvj4na9u2bcCRrFRvEJkloc/o29K3iYkX8klDNc83DucVyz5GJIzt554JgiAIR9u9ezc333wz9957LwUFBdTW1gKg0+mIj48/5nHLly9nypQpzJgxg9dee40tW7bwyiuv9Fi/Nm3axObNm5k7dy6xsbF89dVX3H333Vx22WXHHLbrCSKzJPQ5lcbKn1qncTAYz3V7HZR6vP3dJUEQBOEoW7duxePx8Pjjj5OWlha9XXXVVd0e98gjj7B06VLGjx/PkiVLeO211xg7tuf+INbr9bz55pvMmTOHsWPH8qtf/Yo777yTN954o8feoyuSIsZBTtuJ7losHFHmdrHom51UhmJI0igsP2s0o2OM/d0tQRCEHtXdjvdC3+jue3Cin98isyT0i5wYC6+NUpGrcdAQkrjymyK2Oz393S1BEARB6EQES0K/GZE4lRdSdzBa24Q9FObq7UVsamnt724JgiAIQgciWBL6jUqlIT/1fJ6J/4SJugZawzKvVTf1d7cEQRAEoQMRLAn9ymIuICEmnd/Ef8J/Jdn5/WhRd0kQBEEYWESwJPQrSZJITpqHXpK5RrMWJdgAgKwofGEXQ3KCIAhC/xPBktDvTKYcLJYCFBTqG1ajKAr/e6iKq7YX8deK+v7uniAIgnCGE8GSMCAkJV2MJKlobd2Px1OCXhWpMvuromqeLqkVlb4FQRCEfiOCJWFA0OsSiYudCkBDwyoezEvj3tzIjtdPl9by8OFqETAJgiAI/UIES8KAkZg4F5VKh9dXRWvrbu4elspjwzMAeKmigZ8dqCAsAiZBEAShj4lgSRgwNBoLCfGzAKhvWI0sh7gzK4k/jM5CBbxW08xd+8r7t5OCIAhClyRJ4p133jnm66WlpUiSxPbt2/usTz1FBEvCgBIfPx2NxkIwaKel5UsArk9L4MWCYRhUEvMSY/u5h4IgCMKpyMrKoqamhsLCwtM+1wcffMA555yD0WgkMTHxuHvWnS5Nr55dEE6SSqUnKfFCamrfprFpPbGxZ6FWG7ksOY5zY2NI1mv7u4uCIAjCKVCr1aSmpp72eVasWMGdd97Jr3/9a84//3wURWHXrl090MNjE5klYcCJjT0LvT6FcNhLU9PG6PNHB0qVvgC37SqhORjqjy4KgiAMae1DZt++zZkzp9vjampqmD9/PkajkdzcXJYvX97pnO3DcBs2bECSJNauXcuUKVMwmUxMnz6dAwcOHPP8oVCIu+66i6eeeorFixczcuRIRo0axdVXX90Tl31MIlgSBhxJUpGcdAkAzfZNBIP2Dq8risLiPaWsbHRw5bYi6vzB/uimIAjCKVEUBVn298vtRFcVtw+Ztd+2bdtGQkICs2bN6va4Bx98kEWLFrFjxw5uvPFGrr/+evbt29ftMQ888ADPPPMMW7duRaPRcNtttx2z7TfffENVVRUqlYpJkyaRlpbG/Pnz2bNnzwld16kSw3DCgBQTM5IYUx5uTzENDR+Tnn5N9DVJknhmdDbXbi/igNvH5dsOsWxCPtlGfT/2WBAE4cQoSoADBx/tl/ceNfJXSNLxf1cePWTm8/m44oormDZtGg8//HC3x11zzTXccccdADz22GOsWbOG5557jhdeeOGYxzzxxBPMnj0bgPvuu4+FCxfi8/kwGAyd2hYXFwPw8MMP8/vf/55hw4bxzDPPMHv2bA4ePEh8fPxxr+1UiMySMCBJkkRS0jwAHM7t+HzVHV4fFWPg3bNGkG3QUeoNcPm2Ig65ff3RVUEQhCHt9ttvx+Vy8frrr6NSdR82TJs2rdPj42WWxo8fH72flpYGQH1917s3yLIMRLJRixYtYvLkyfz9739HkqQOQ349TWSWhAHLaMwg1joBh3MH9fWryMr6PpIkRV/PMer5z1nDuW57MQc9Pq7YVsTSCXmMs5j6sdeCIAjdkyQdo0b+qt/e+2Q8/vjjrFq1ii1btmCxWE7xPaVuX9dqj8xHbW/bHhR9W3swNXbs2Ohzer2evLw8yst7r7SMyCwJA1pS0kVIkga35zBu98FOr6fpdbw9aTjjLUaagiEePFQlKn0LgjCgSZKESqXvl9vxApejrVixgkcffZRly5aRn59/Qsds3ry50+PRo0ef1NenO5MnT0av13eYBB4MBiktLSUnJ6fH3ufbRGZJGNC0Whs227k0N39GfcNHxMSMQJI6xvgJOg0rJg7nfw9V8b/5aSf1y0AQBEHobPfu3dx8883ce++9FBQUUFtbC4BOp+t2XtDy5cuZMmUKM2bM4LXXXmPLli288sorPdYvq9XK4sWLeeihh8jKyiInJ4ennnoKiMyX6i0isyQMeIkJc1Crjfj9dTgc33TZxqJR88cx2STpjqRzy7z+vuqiIAjCkLJ161Y8Hg+PP/44aWlp0dvxij8+8sgjLF26lPHjx7NkyRJee+21DkNmPeGpp57iu9/9LjfddBNTp06lrKyMdevWYbPZevR9jiYpYszitDmdTmJjY3E4HFit1v7uzpDU3Pw5dfUr0Wqs5OX9BJWq+9Ucb9Y089MD5TwzKpvr0npndYQgCMLx+Hw+SkpKyM3N7XJ1l9D7uvsenOjnt8gsCYNCXNw5aLU2giEnzc1fHLf9Vw43IQXu2l/OK5UNfdBDQRAEYagSwZIwKKhUGpKTLgagqflTQqHWbtv/blQmd2YmAvDAoSr+WFonJn4LgiAIp0QES8KgYbGMw2jIQJb9NDau67atSpJ4dHgG9wxLAeDJkhoeL64RAZMgCIJw0kSwJAwaRxeqbHF8hT/QeNz2v8hN4+H8dACeL6/n3oOVImASBEEQTooIloRBJSYmD7N5FIoi09Cw+oSOWZydzDOjspCAeK1GlBYQBKHPiT/S+k9PfO1FnSVh0ElOugR360Fcrj14PGWYTMcvRHZDegKFFiPjzcY+6KEgCEJEe3Vqj8eD0Sh+//QHj8cDdKwUfrJEsCQMOnp9CrGxk2lxbKW+YRU52T84oWzRhKO2QfGEZZ4uqeWnw1KI0ah7s7uCIJzB1Go1cXFx0b3OTCaTyG73EUVR8Hg81NfXExcXh1p96r/rRbAkDEqJiRfgdO3A6y3H1boHq6XwpI7/yf5y3q1vYbOjldfG52HTiv8KgiD0jtTUVODYm8MKvSsuLi76PThV4hNCGJS0Wivxthk0Nq2noWE15pjRqFQn/uO8OCuJT5pdfOP0sGhbEW9OzO9Q/VsQBKGnSJJEWloaycnJBIPB/u7OGUWr1Z5WRqmdCJaEQSs+fiYtLVsIBJpocXxFvG3aCR97ljWGtycN57odh9nr9nH5N5GAKctwcjtyC4IgnCi1Wt0jH9xC3xOr4YRBS63Wk5h4AQBNjesJh30ndfwYs5H/TBpBpkFLsdfPwq8Pss3p6Y2uCoIgCIOYCJaEQS0ubgo6XSKhsJvm5k9O+vhck57/TBrBmBgD9YEQP9xTSlAWS3wFQRCEI0SwJAxqkqQmOekSAJqbvyAYdJz0OTIMOt49awQLk2J5sSAHrUqsVBEEQRCOEMGSMOiZzWMwGXOQlSCNjR+f0jksGjWvFOZyljUm+twOl0dkmQRBEAQRLAmDnyRJJCdHtkFxOLbh89Wc9jl3ujxc8U0R1+84TEswdNrnEwRBEAYvESwJQ4LRmI3VUoiCQkPDR6d9vqZACJUEn7W0cuk3hyj1+nugl4IgCMLJKneWs2TPEjzB/luAI4IlYchISroYSVLT6j6E2110Wueam2Dl3bNGkK7XUuTxs+Drg2xpae2hngqCIAjH4wl6ePfwu/x9z98pdZbySdXJL+LpKSJYEoYMnS4BW9zZANTXrzrtzRMLzEY+nDyS8RYjzcEwV28/zIra5p7oqiAIgnAMiqKwo2EHz29/nm312wA4K/ksZmXM6rc+iaKUwpCSkDCXFsc3+Pw1OJ3biY2ddFrnS9FreXvScP5nbzkrGx38175y4rQaLkiw9lCPBUEQhHaN3kY+KP6AUmcpAMnGZBbmLSTbmt2v/RLBkjCkaDQxJCbMpr5hNQ0Na7BYClGpTm8bkxi1mv8rHMYTxTUccPuYE2/pod4KgiAIAEE5yGeVn/F59eeElTAaScOcrDmck3YOmpPYyqq39H8PBKGH2WzTsdu/JBhyYLdvIiHh9FO3Kkniwfx0QrKCum3H8IAs4wrJJOjEfyNBEIRTVdxSzAclH9Dsi0xzGB43nAW5C7AZbP3csyPEb3lhyFGptCQlXUh1zQqamjYSFzcFtdrUI+fWtBWsVBSF+w5W8pm9lX+Nz2NkjKFHzi8IgnCmaA20srpsNbsadwFg0VmYN2weY+LHIEkDqziwCJaEIclqnUhz8+f4/LU0Nq4nJWVhj56/ORjmc3sr5b4Al35zkFcKcpkphucEQRCOS1EUvq77mrXla/GFfUhITE2dyvnZ56NX6/u7e10Sq+GEIUmSVCS1Faq0t3xJINCzq9gSdBo+mDySs2NjcIZkrt95mNeqm3r0PQRBEIaaWnctf9v9Nz4o+QBf2EdaTBp3jLuD+bnzB2ygBCJYEoYwc8wIYmKGoyhhGhpW9/j5E3Ualk3I56oUGyEFfnqggkeLqpFPs2SBIAjCUBMIB1hTtoaXd75MZWslOpWOS4Zdwh3j7iDdnN7f3TsuMQwnDGnJSfModT+P07WLeO8MjMbMHj2/Qa3i+THZ5Bn1PF1aywsV9TQGg/xpTE6Pvo8gCMJgdaD5AB+WfIgjENnofEz8GOblzsOqGzwlWESwJAxpBkMa1tiJOBzbqG/4kOysO3p84qAkSfwsN5Vco46fHajkyuTeWcGhyAphZ4BQk5dws4+ww4/KokOfY0WTbEJSDawJkYIgnNkcfgcflX7EvuZ9AMTp45ifO5+RtpH93LOTJ4IlYchLSrwIl3M3Hk8pre4DWMyje+V9FqXGMzveSuJRpQQCsoxOdWqj3XIgTLjZR6jJS6jZR9juRwnJHdqEnQGCVa1IRg36LAu6HCtqi+60rkMQBOF0yIrMltotrC9fT0AOoELFtPRpzMqchU59cr+fwq2teL76CtOkSajj4nqnwydg0M1ZeuGFF8jNzcVgMDB58mQ+/fTTbttv3LiRyZMnYzAYyMvL48UXX+zw+ssvv8zMmTOx2WzYbDYuvPBCtmzZ0puXIPQxrTYWW/w0ABrqV6Eo8nGOOHVHB0rFHj/Tv9zHmkbHcY9TZIWww4+/xIH76zocq0txvF9M6xfV+A7YCTV4UUIykkaFJtmEYUw8Meemoc+PQ9KpUbwhfAftONeU4dxQgb/EgRLsvesUBEHoSlVrFS/vfJmPSj8iIAfINGfyg/E/4MKcC086UGr99FMOnnMulf/vR7R+/nkv9fjEDKrM0ptvvslPfvITXnjhBc477zxeeukl5s+fz969e8nO7lwKvaSkhAULFnDnnXfyr3/9i88//5wf/ehHJCUlsWjRIgA2bNjA9ddfz/Tp0zEYDPzud7/j4osvZs+ePWRkZPT1JQq9JCF+Ni0tW/EHGmhxfI0tbmqvv+fz5XVU+oLcsquER0dkcEdmUvQ1ORAmbPcRavJFskbNvk5ZIwCVWYsmwYgm3oAmwYDKouswjKhLN2MsTCRY6yZQ5iRY5ybc7MPT7MOzswFduhldjhVNknHA1S0RBGHo8IV8rCtfx9a6rSgoGNQGLsy5kLOSz+r2d48iy/j378e9aRPuTZsxz55N/E03AqAfNQrCYbQ52RAO99WldElSTne30T50zjnncNZZZ/GXv/wl+tyYMWO44oorePLJJzu1v/fee3n33XfZt29f9LnFixezY8cONm3a1OV7hMNhbDYbf/7zn7n55ptPqF9Op5PY2FgcDgdW6+CZsHamaW7+grr6D9BozOTn3YNK1bvLVIOywr0HK3i9JlK24GarlQcwIdn9hF0B+Nb/PEmjQm0zRAMjdbwBlU59Uu8p+0IEyl34y5zIrkD0eZVJiy67bZgu5vS2fxEEQWinKAp7m/byUelHuIIuAMYnjufiYRcTo43psn2wvBz3ps24N23C8+WXhFtaoq/HTJ9O9t9eiT4O1tWhTUnptf6f6Of3oMksBQIBvv76a+67774Oz1988cV88cUXXR6zadMmLr744g7PXXLJJbzyyisEg0G02s4fGh6Ph2AwSHx8/DH74vf78fv90cdOp/NkLkXoJ3FxZ2O3byIQbKa5+XMSE8/vlfdRQjKh5kjG6JFGFRkBPU/p/LzqdFISdPP7Vj1mJFQxWjTxkaBIk2BEbdWd9iRtlUGDYaQN/Yg4wnY/gTIngQoXsieIb38zvv3NaJKM6HKs6NLNSJpBNxIvCMIAYffZWVmykqKWIgDiDfEszFtIXmxeh3ayz4fK0LbLQTBI8eVXoPh80ddVJhOms88mZtq5xEyf3uHY3gyUTsagCZYaGxsJh8OkfOsLl5KSQm1tbZfH1NbWdtk+FArR2NhIWlpap2Puu+8+MjIyuPDCC4/ZlyeffJJHHnnkFK5C6E8qlYakpIupql5KU/OnxMVNRaM5varbiqIgu4PRobRQk4+w098ha3QbGjJDEveafHyqDXNjSoil43JJsxpP84qOTZKkSIYq3oBxfCLBajf+MiehBg+hBi+hBi9eTQPaTDP6HCvqeIMYphME4YSE5BCbqjfxSeUnhJQQaknNjIwZnJdxHlqVlnCrG89XW/Bs3oz7i00oikz+++8DIOl0xEybhuxyYZp2LjHTpmMcV4jURfJiIBk0wVK7b/9CVxSl21/yXbXv6nmA3/3ud7zxxhts2LABg+HYe33df//93HPPPdHHTqeTrKysE+q/0L8slkKMhiy8vgoaG9eRmnr5SR2vhGRCLf4Oq9QUf+exdJVJE8kYxUfmG10bq2ek28stu4qxGrTYYvquUq2kVqHLsqDLsiB7gvjLXQTKnMjuIIFSJ4FSJyqzFl2OFX22FZVx0P1aEAShj5Q5y/ig+AMavA0A5FpzWZC3gJiiGlrefhH3pk14d+2CUOjIQZJEqKkJTUICAJkvPD/o/jgbNL8VExMTUavVnbJI9fX1nbJH7VJTU7tsr9FoSGj7prV7+umn+fWvf83HH3/M+PHju+2LXq9Hr++bD7v169fz1VdfYTQaMRqNmEym6H2j0cjkyZMxm80AtLa2EgwGMZlM6HS6QffD2BckSSI5eR5l5S/T4tiKzTYNvT65y7aKoqB4Q9FJ2KFmL+EWf6e5Rqgk1HH6trlGkeCoq4BjktXEyskj0askDOr+Gf5SmbQYR8djGGUj1OglUO4iWNWK3BrEt6cJ394mNMkm9DlWtGkxSP3UT0EQBhZP0MPH5R+zrX4byApp1T7OnfldxqVMQJIkqpf+AceKt6LttdnZxJx7LjHTp2E65xw0tiP15wbjZ9OgCZZ0Oh2TJ09mzZo1XHnlldHn16xZw+WXd50dmDZtGu+9916H51avXs2UKVM6zFd66qmnePzxx/noo4+YMmVK71zAKXK73Xg8HjweT5evFxYWRoOlL7/8MlpKQaVSdQiqTCYTl1xySXQuVm1tLU1NTZ3aaLXaQfmDfDJMpmFYzGNxte6lvuEjsjJvAkAJK4RbfNH5RqFmH4o31Ol4yaBBk2CIDnOp4/QnHFRkGjounX2yuAazWsV/Zyf36dddkiS0SSa0SSaU8UkEqlsJlDkJNXoJ1XkI1XmQtG0ZqRxr5BqH+M+FIAidKYrCjvrtfP7VW1h3lTJtbx3pB5pQOz0MW34DUmrk94LlggtQvL5IcHTuNHSZQ2s1+aAJlgDuuecebrrpJqZMmcK0adP461//Snl5OYsXLwYiw2NVVVW8+uqrQGTl25///Gfuuece7rzzTjZt2sQrr7zCG2+8ET3n7373Ox588EFef/11hg0bFs1Emc3maBDSn84//3ymTp2K1+vF6/Xi8Xii971eLzExR1YbyLKMWq0mHA4jyzJutxu32x19/ejJ7nv27OmyRpVarcZoNHLjjTeSmpoKQFFREcXFxV1mtoxGI2azGbX65FZt9TZFUSCsoATCyEEZJSijBMLRfy2+qbQ4t9HSsA1D6Qj0wQzCDj/I316ixpGsUbwxskLNpOmRwOFrh5s/ltUBUOz189uRmadcwPJ0SFoV+hwr+hwr4dYAgTIXgXInsjeEv9iBv9iB2qqLTArPtqDSD6pfG4IgnKLanV9y4KXfY9pxmPMb3R1eU5lMBCsqMI4bB4Dl/POxnN87i2YGgkH1W++6666jqamJRx99lJqaGgoLC1m5ciU5OZF9uGpqaigvL4+2z83NZeXKldx99908//zzpKen86c//SlaYwkiRS4DgQBXX311h/d66KGHePjhh/vkurpjMpkwmUwn1Paiiy7ioosuIhAIdAio2gMsi+XIZGar1Up2dnaHNrIsEw6HaW1t7ZB5KysrO+aKQ4A77riDzMzInmvbt29n27Zt0WDTbDZjsVii9xMSErpchdgVRVEgJCMH2oKdYBgl8K1/gzJKQEYOhjsFRJ2GyzqQMGpH0qrZSaO8hhT/dUhISDp1NGukTjCiidP32oqxybExPDEigwcPVfFGTTPl3gCvFA4jTtt//y3VZh3GggQMY+IJNXgigVN1K2FnAO+uRry7G9GmxqDLsaJNjRFbrAjCEBFudePZ+hXa1FTUI/P5tPJTDmx7hzlrdwKgqNWYJk4kZto0YqZPwzhu3ICflN2TBlWdpYFqKNRZUhSlQ5CVlJSERhP50D506BDFxcXHzG7993//d3R47+OPP+azzz475vvcetWNpMenoARl9hzax+7ifcToTMRoDZg0RkxqAzEqAyZJT4xkQH26ReYlkHRqJK0q+q9Kq0bSqQirPVR4/4oiBUmNv5q4lEmoYvp+GPLjJic/3FOKOyyTb9Tzr/F55Jr6bgL48ciBMMHKVvxlTsL2I8t9Jb0aXZYF/TArauvA6a8gCMenBAJ4d+yI1DvavBnvzp0QCqFatJCPFmVj99tRhcLMfL+C0RddQ/K02ahiOtdNGuxO9PNbBEs9YCgES6dKDoYJtwZRWoOEXQHqauqob6zD7fHg9nlwBzy4gz48IS/uoJfvjpiHVRcZ3vysZhtfN+w95rmvG34JqaZEUEkUtVZQ1FJOjMFEjDEGsykGc0wMZrMFi8WMyWJGpVMj6dSotKpocIRa6jb4aWxcT0Pjx+i0NnJzf4JK1T9Znb2tXm7aWUyVP0i8Vs3fCnM5N67/h4G/Lez04y9zEahwoviOrAJU2/Tosq3osiwnXUhTEIS+E3a5qLrnp3i2bkXxeju8Fki1cfDcDPZeXohFZ2HesHmMiR8zpOcrDrmilEL/URQFxR8m7Aogu4KEWwPR+7In2KFtLFpitZkQS+TWRtK0BTBHZXgKYgtIzEzBE/RGgiq/h1afB7fXTavbTepFI4lLtCGpVbSsKeFAUckx+3jnnXeSkR6ZULh//34OHjzYYSjw6OHAo4cB4+OnY2/5kkDQTn39ByQnz+v1yt5dGWs2snLySG7ZVcJ2l4caf/D4B/UDtVWPaZweY0ECwTpPZIuVmlbCdj9eewPeXY1o02PQ51jRJJnEMJ0g9CNFUfDt3UuwohLrvEsAUJnNBEpKULxe1PHxmM49l8axaXya1kJzvBYJiXNSz2Zu9lz0apExbieCJSFKkSMFFiOBUIBwW2AkuwLdbsoq6dSoLVpUZh1qiw6VWYtKH8nySDoVkkaNpO78oWkmnbwuzged62GNHj0as9lMa2srLpeL1tbW6M3j8XSYjF9eXs4333xzzP7+4Ac/ID09HYCiojJKSqzo9Idobl5FdfUWUlLmkZo6vc8nrafotbw1aTjrmpxcmhzXp+99siSVhC4tBl1aDLI/RKDCRaDMRdjhJ1jZSrCyFcmoQZ9tQZdtRW05uQ00BUE4NUoggHvLV7SuW4tr3XpCtbWoLBYsF5yP1LbaOe3xx1Hb4rCnW/igdCVVrVWAlvSYdBbmLSTdnN7flzHgiGDpDCQHwpFgqDXYFhS1ZYrcwWNPipYiNXrUFh0qS+RftVmHyqJDpe/5oOLbad+srKxjFv4Mh8Md2o8YMQK9Xt9lYBUKhTqsICwtLWXTpnpiYmJITCpDqy1h776v8fvN+LyFXHPNj0hOjtRhqq+vj6ZsY2Nj0el6PgAwqVUdAqVaf5A/l9fxv3np/Vab6XhUeg2G4Tb0+XGEHe1brLSieEP4DtjxHbCjSTBEVtNlWJC0A/M6BGEwa/30M1reWoH7k0+Rj1oFLRmNxJx7DuGWFjRJkc28tVMnsaFiA1/u/hIZGb1az9ysuUxNnYpKEv8/uyKCpSFKURRkTwj5qCGzsCtAuDXQYa7Jt0kaVSQYaguE1BZdJGsUox2wBQq/nQHKzc0lNze3UztFUfD7/R2CnPZ2ra2tOBx2fN7d6A2H0Otb0es343INw2a7Aq02jm3btnXYgNloNEYDp7i4OGbMmBFdcRgMBtFoTq/EgKIo3L67hK+dHrY7Pfx9XC5JuoG7+kSSJDRxBjRxBoyFSQRr3QRKHQTrPZHCnk0+PDsaIvvgxUR+plRmLWqzFpVJK/apE4STEKisQpMQj8oY2TbJu2MHrg9XAaBOSsQyZy7mC84nZto0VG1FlO0+O4fsh/i8+nOcgciepmPixzAvdx5W3Zk13/ZkiQnePaA/J3grIbljhqj1yLwiwsf+1kpGDWpzWyDUHhSZtUjGnqkhNJgFAg4qK9+nqfkrzDEmVCod8fEz2L9fza5d+3A4HB02Um53zz33RL//H330EVu2bIkGU+0BVfv9rKysEyqh8Jndxe27S3GEwmQbdPxzfB6jYo69Fc9AJHsjw3T+Ugdy67HnYklGTccgKqZtaDdGK7JRwhlPURR8e/ZGhtfWrsN/4AAZzz4bnYvkLyrC8e57WC44H8O4cUgqFSE5RLmrnEP2QxS1FNHobYyeL04fx/zc+Yy0jeyvSxoQxGq4PtTbwdLJTLDuQCVF/mqPDpkdGT4THz7H5/NVU1e/Eo8nMrFcq7GSlHQxVutE/H4/DoeDlpYWHA4HDoeDCy64AFVbUclly5axd++xV/odHVh9+eWXlJWVdRlYGY1GDnv93LizmFJvAKtGxf8V5DIr/vQ2AO4PiqIQbvETdgaQW4OR+XHuyP3u5sRBpEyBKqYtCxXTlvlsC6okreqMD/CFoUkJBHB/uYXW9eui84+iVCqSfvw/JLYVZW7n8DsoainikP0QJY4SAnLgyCGoyLZmM8I2gqkpU9GqB26muq+IYKkP9Vaw5DtoJ1DdevITrNuHzkxasRrpNCmKQmvrXurrPyQQtANgNGSSnLwAkynnmMeFQiFcLlc0kDo6qHK5XCxevPiEAiutVstdd92FX2fg+7tL2OJwowJ+kWDk1uwULBbLgKuefirkQPhIANUaaPs38rirjYqPJmlVbZko3beyUlokvVoEUsKgosgyUtvvhkBlFYcvvDD6mmQyYT7vPMwXnI959mw0NhthOUyFqyISILUcot5T3+F8Zq2Z4XHDGWEbQW5sLkaNsU+vZ6ATwVIf6q1gybOjHv9hR+RBH0+wFjqS5SB2+yYamzYgy5EhOKt1PMlJl6DVxp3WuUtLS6mtre0UVLndblQqFf/7v/+LSqXCL8ss+ugzthqs2NxOrv56A1oJbDYb8fHxJCQkcP755/fKxPP+pARlwu5gZP6dO3hUVirY5d59R5M0qo4B1FH3xZCzMFAEKqtoXbcO17p1qC0WMp/7U/S18jvuRJuejvn8udH5R66AK5o9KnYU4w8fmRYgIZFpyWR43HBG2kaSYkoRP+fd6LVgKRwO849//IO1a9dSX1+PLHfMeKxbt+7UejyI9VawFGr2IXuC0eX4A3WC9ZkkFHLR0LAGh+MbFBRUkpb4+BkkJMzs8fpMwWCQ1tZWbEft1r1x4ycsaWolp7YcVVMD4fBRhSHVah544IFoxurtt9+moqKChIQE4uPjO9zi4uKGREZKCcnIniNZKLk1eCSg8nazuhNAJXUY2oveN+tQGTUiKyv0GkVR8O3eExlea5t/1E7S6xn55WZUhiNzE2VFpspVxaGWQxyyH6LWU9vhfCaNKZo9yovNw6Q9sS2yhF4sSnnXXXfxj3/8g4ULF1JYWCgi1l6kiTdA/OCazDvUaTQW0tKuwmY7l7r6D/B4SmlsWo/D8XV0PlNP/Z/QarUdAiWA2bNnMbvtvizLOJ1O/lpaS6a/layANxooATQ0NNDc3Exzc3MX16Hhl7/8ZbR9UVERiqIMukBK0qhQW/VdbreihBVkT/CoIb3AkWDKHQRZQW6rKdb5xHQIoHQZFjSJYvhC6BlVd/0E1+rVR55QqTBNnoz5gshmtCqDAXfQTVFLEUX2IopaivCFj9pqCIl0c3o0QEqPSRefxb3spIOlpUuXsmzZMhYsWNAb/RGEQcFgSCc76w5crXtoqF9FIGinuubf2O2bjzufqaeoVCp2K2r+YPehkbQ8PDyHGYoS/aV53XXX0dzcTFNTUzRoar9ZrdYOgdX69eupqqoCIiUA4uLiolmopKQkzj777F6/np4mqaW20hc6vj2NVZEVZG+oLRvVeXiPsBJ53BokVAf+YgfmmZloRcAknISww0Hrxo241q0n9VcPomnbQ9M4cSKtn32GecYMLBecT8ysWajj4qh2V7PLfoiiXaupbq1GOSo1alAbGB43nOG24QyPG06Mdujt0zaQnfQwXHp6Ohs2bGDkyDN7ueHRzuS94YSj5zOtR25bedJT85mOxxkK85N95axsjMxtuyw5jt+PysKsOXZmSJZlfD4fJtORVP0777xDdXU1zc3NhEId5wHZbDbuuuuu6OPly5fj8/k6DOslJCQQFxcX3Xx5MFMUBcUbimagApWthOo9SHo11rlZqExiBZFwbJH5R5Hl/Z6tW6FtqDzt178m7qorASJFIzUafKowxY5iDtkPcbjlMO6Qu8O5Uk2pjLCNYETcCDIsGaJgZC/otTlLzzzzDMXFxfz5z38Wab82IlgSoG/nMx1NURT+WtnAY4erCSmQb9TzcuEwxppPPguiKAoul6tDFkqr1TJ79uxom9/97nd4PJ5Ox0qSRFZWFrfddlv0ubKyMkwmEzabbdAGUkpIxrWxkrDDjzpWj2V2piigKXTi3bOHml8+0GH+EYB+xAjMF5xP7GWXocvNpc5Tx0H7QYpaiqh0VXbIHunVevJi8xhhG0F+XL4oFNkHei1YuvLKK1m/fj3x8fEUFBR0Kqz31ltvnVqPBzERLAlHi9Rnisxngo71mXrzD4ytDjc/2FNKtT+IQSXxu1FZXJsa36PvoSgKZWVlnYb1mpqaCAaD5OXlcfPNN0fbP/3007S2tgIQGxtLfHw8iYmJJCUlkZaWdswtbAYa2RPEub4CxR9Gl2nBNFWsMDqTKYqCd/t2UBRMZ50FQKixkUMzZ0XnH1kuOB/z+ecjpyVFs0dFLUW0Bls7nCvZmMwI2wiGxw0ny5KFWjU45gsOFb02wTsuLo4rr7zytDonCEPZ0fOZ6utXETxqPlNKykKMxuxeed8psTGsmTKK/95XxvpmFyG556uCSJLEsGHDGDZsWIfnI/WoWgkGjxRJDYVCWCwWAoEAgUAgWhKhpCRS5DMnJ4fvf//70fZr167FYrGQlJREUlISMTExAyYgUZm0xJyTRuunlQQqXajj9BhG2o5/oDCk+IuKcLz3Ps4PPiBYWYnp7LPJeXUJAJrERLJe/AuG8eNp0gXY11LEIftaKiorkDmyalyn0pEbmxsNkGL1sf11OcJJOOHMUmtra4ed3YUjRGZJOJbIfKYv2uozHT2faR5abe/8kpQVhdWNTi5JtEaDjbCioO6nwENRFNxud3SieWNjIw0NDaSnpzNnzhwA/H4/Tz75ZIfjjEYjSUlJJCcnk5eXx9ixY/uh9x35ix14tteDBOZp6WhTxSTboS5YXY1z5Uoc73+Af//+6POSyYT1ootI+/UTBAhR4iiJZo/a911rl2hMjKxcixtBtjUbjWpwDkkPRT0+DGcwGJg7dy6XXXYZl19+Oenp6T3W2cFOBEvC8XQ5nylhJgnxM3p1PhNAczDEVduKuHtYCpcnD8xsiMfj4fPPP6ehoYGGhgbsdnuH1ydNmsTll18OROpP/fOf/4xmoNoDKrPZ3CeZKM+2evwlDiSNCsvcLNSWoVUEVOio/I47cX/2WeSBVot55kxiL11IzJw57HEXsb1+O+WucsLKkZpnGknDsNhhjLSNZHjccGyGgfn/TuiFYKmsrIx3332X//znP3z66aeMHz8+GjiNHz++xzo+GIlgSThRXc9nugSrdUKvfdD/priGZ8vqALg9I5GHhqejUw3sCcqBQICmpibq6+tpaGggMzOT0aNHA1BbW8uLL77Y6RiDwUBSUhKTJk3irLZ5JO2/3nrya6uEFVo/qyLU5EVl1mKZk4VKJ+aZDHay241r3Xqc779P6sMPoU1LA8Dx7ru0/HsF1ksXYr34YtRxcdS6a1lZspIKV0X0eJveFl25lhObg1YlVk0OBr263YnD4WDlypX85z//YdWqVdhstmjgNHv27EFT0K6niGBJOBmKonSYzwRgNGSRkrKgV+YzhWSF35TU8OfyyJ5Rkywm/lo4jCzD4MyIeDweioqKolmo9uKb7b/KLrjgAmbOnAlAY2MjL7/8cocMVPt9q9V6ykGU7AvhWl+B7A2hTTERMy1dVPwehJRAgNbPP8f5/ge41q1D8XoBSP75z0m4/bZO7T1BD+sr1vN13dcoKOhUOqanT6cgsYAEQ8KAmWMnnLg+2xsuFAqxbt063nvvPd59911cLhfPPfccN9xww+mcdlARwZJwKrqazxRrnUBS0iW9Mp9pdaODH+8rpyUUJk6j5rkx2VyUODQmlwaDQZqammhoaCA1NZWkpCQA9u7dy7Jly7o8RqfTcckllzB58mQgks3yeDzExsae0IdeqMWHa2MlhBUMI20YCxN77oKEXhVqaKDhz8/jWrWKsMMRfV6bnU3spZcSe/ll6HKOFJaVFZlt9dtYW74WbygSUBUmFHJhzoVigvYg128b6W7bto1QKMTUqVN78rQDmgiWhNPRl/OZyr1+frCnjO2uSJ2k58dks6iHywsMJKFQKBpENTQ0RIf1mpubkWWZa665hoKCAgAOHjzI66+/jk6nIzExsUMWKjk5ucsgKlDhwv1VZJ+umKmp6LIsfX6NwvEpioLscKCOiwMg3NrKoennoQQCqJMSiV2wAOull2LoYguvqtYqVhavpNpdDUSW+s/Pnc+w2GF9fBVCb+jVYKmlpYUtW7Z02khXkiRuuummU+vxICaCJaEn9NV8Jr8s80hRNWuanKyZMpI47Zm3MicUCtHc3IzFYsFojBTv/Oabb3j//fc7bQ7e7sorr2TChAlAZHWwx+MhISGBwD47voN2UEtYZmWisYn9HAeKQHk5zg8+wPH+B6j0enLfWhF9rfn119EPG4bpnHOQupg64g66WVu+lm3124BIwcg5WXOYmjJV1EIaQnotWHrvvfe44YYbcLvdWCyWDr/EJUnqctPOoU4ES0JP6cv5TI5giNi2QElRFPa7fYw5harfQ0k4HKa5ublDFqqhoYHGxkZ+8IMfkJqaCsCXX37Jhx9+iFqtJiUlhUTJSrxiISUukdx54zBYxa7v/SXU2Ijzw1U43n8P346d0eclnY78j9egTU7u9nhZkdlau5X1Feujm9dOSJrAhdkXYtaJ8jlDTa8FSyNHjmTBggX8+te/7rC31JlMBEtCT5PlIM32z2lq2tgn85lerWrkvoOV/Dw3lbtyUlCJiaodhEIhVCpVdPPhTz/9lE8//ZRAINBl+ztuv4PMrEwg8vtBpVKJOnV9oPHFF2n403PQnh1UqYg591ysl16K5aILUVu6HyYtc5bxYcmH1Hkiq0dTTaksyF1AlnVwVJoXTl6vVfCuqqrixz/+sQiUBKEXqVRaEhPmEBc7OTqfyeHcgcu1t20+00xUqp5bzban1YsM/Lakli0ON38ek0OC7swbnjuWb+9rN3PmTM477zzsdju1tbXU1tZSU1lNTWU17qCXmBqg7fP1s88+Y8uWLZjNZlJTU6O3tLQ0bDZbNAATTo7s99P6yScYxoxFl5kBgH74cJBlDBPGE7vwUqzz56Fpm+zfHVfAxcdlH7OzMZKJMqgNXJB9AWelnDXkNq9tdgeosntJsepJNOtRDfRVnKEA1O+F+Fww9N9k+pPOLF111VV897vf5dprr+2tPg06IrMk9Davt4r6hpVHzWeKbdtvrufmM71R08T9ByvxyQoZei0vFQxjSqyoUH0ygrVuGj4twajWY5qYjD4vlhUrVrBr164u22u1Wu65557ovCmXy4XBYOi056YQoYTDeLZswfH++7hWr0F2uUj80Y9I+vH/ACAHAoRqa9Fln9iQdVgO82Xtl2ys2EhADiAhMSl5EhdkX4BJO7QSAp5AiHX769l0uIn2nZD0GhUZcUay4o1k2kxk2ozEGrX9VwIhHIS63VC9HWq2R/6t3wvhAFz3LxjznR5/y17LLC1cuJCf//zn7N27l3HjxnX6T33ZZZedfG8FQeiW0ZjRxX5zy6mtexeDIQ2DPh2DIQ29Pg29PhlJOvkJqNenJTDBYuKO3aUUe/1cse0Qv8pP587MJFE/5gRpU2OIn5CBd3cjnh31qKw6Fi1axHe+8x3q6uqiWaja2lrq6urQ6/XRQAng3XffpaioiKSkpA5ZqNTU1DM2m68oCr7du3G+/z6OlSsJNzRGX9OkpqI6amhNpdOdcKBU7ChmVckqGrwNAGSYM1iQu4B089DanSIUlvmypJm1++rxBiNVxpMtelo8AfwhmeJGN8WN7mh7s15NVrypLYiKBFCm3sgyB31QvwfUOkgdF3nOXgp/ndO5rSEOvC0934eTcNKZpe5SxpIkEQ6Hj/n6UCUyS0JfOjKf6RNk2d/pdUnSoNcnY9CnYTCko9enYTCknnAZAlcozD37K3ivoQUVsGbqKArO8InfJ0NRFDxf1RGodCHp1VjmZKGO6ZwpCofDuFwu4tqWswO88MIL1NfXd3ne5ORk/t//+3/RwNXj8WA0God8ICsHAhw6bwayywWAOjYWy7x5xF66EOPkyUgnOYzp8DtYXbaavU17AYjRxHBBzgVMTJo4pL6WiqKwr8bFh7traGyNzK1LsepZOC6NESkWZFmh3uWn0u6h0u6lotlDrdNHV/tvx8doybKZotmntDgDes1J/EEW9ELdHqje1pYx2gEN+0AOQeEiuPpvkXayDH+aAPH5kD4R0iZC2gSwDYNe+t70W52lM5EIloT+oChh/P56/P4afL4afP5qfL6argMoJLS6hCMBlCENgz4NjabrSceKovC3qkY8YZn/yUnp7UsZcpSQjGtjJWGHH3WsHsvsTCTN8T/UFUXB6XR2yEDV1tZit9vJzs7mttuOVJX+05/+hMfj6TQPKjExcdDuohCsq8O58kO833xDxp/+GA1eah97nLDdjvXSSzHPOA9Jd/Lz9UJyiE3Vm/i06lOCchAJiampU5mTNQejZmj9MVDd4uWDnTXRjJFZr+aisalMybF1O0cpGJapafFRYfdQZfdSYfdEA62jSRKkWg0dsk8pVgNqlQQBD7gbwNZW1DMchCezoK2YZwemBBh9KVz2px657lMhgqU+JIIlYaBQFIVg0I7PX43fV4PPV43PX0Mo5OqyvVZjjQZO7VkordbW5V/Yhz0+vmxxc31a/JD6C7y3yJ4gzvUVKP4w2kwzMVNTT/nr5vV68Xq9xMdHCogGg0F+85vfdJnJV6vVjB07lkWLFkWfUxRlwH7P5ECA1rVraVm+HPemzdD2kTRs+XKM4wp75D0O2Q+xqnQVzb5IaZscSw7zcueRGpPaI+cfKBzeIGv21vFNuR1FAY1KYsaIRGaPTMKgPbUA2hsIU9XiocLupbI5koVy+kIAaGUfad5DZHgPkOU7QLb/EDZPCb6EAjy3fkxCjC7yc/eXGeCqOZItav83NrPXMkYnSgRLfUgES8JAFwq58PlqOmShAoGmLtuqVYZvBVCpyJpEFn5zmH1uH1en2PjtqExiBmn2oi8FG720floJChgKEjCO6rlq6aFQiIaGhiOr8WpqqK2tJRAIMHHiRK644gogMtz3zDPPkJSUREZGBhkZGWRmZp7W3ng9IVBZhf2113C88w5huz36vHHyZGIvXYh1wQLUsae3+snus7OqdBUH7QcBsGgtXJRzEYWJnSt1D2b+UJjPDjXyycEGAuHIR/qEzFguKUjFFtNDq2ZDAdBEzuXwBlG9fg3mio1IdC7iatem8NTo5Rh0WjJsRnLNIVKTkslMMGE1DKzFC702wVsQhMFHo7FgNlswm0dGnwuH/fj9tR2yUP5APWHZh8dTgsdTEm2roGGu7iwOuLP5d52dnU4HLxcMY5RF/HHQHW2iEdOEZDzb6/HtbUJt1aNL65kVhhqNhrS0NNLS0qLPybJMS0tLh3b19fV4PB7KysooKyuLPm82m8nIyGDChAmMHTu2R/p0MgIlJTT//e8AaJKTibt6EbFXLYqWATgdwXCQz6s/57OqzwgrYVSoODf9XGZlzkKv7tkthPqToihsq2jhoz21OL2RbE92vImF49LITjiNBQH+Vqjd2XFVmrsBflEMkkSsUQtmCyCDOQUlbSKehEJqzaM5rBnBYa8FtcOHNximqL6VonqguBwAq1FDls1Ehs3YNg/KeMpZr74kMks9QGSWhKFClkMEAg1HAqi2TFT7PKjt/kQebzmXJtmIQQpxb8IBLkvQYNCnHXce1JnMs70ef7EDSaOKTPi29lyNrOORZZnGxkaqqqqorKykqqqKuro62n/1n3/++cyaNQsAh8PBunXrohmolJSUTjWmToXv4EFalv8bTUI8iYsXA6DIMjX/+yCWCy/EPGsmUg+8j6Io7G/ez+qy1bT4WwDIteYyP3c+Sabj11saTIobWlm5q4aqlkiVcZtJy7zCVMZlnNhG0F367FnY/ho0HgK6CA3u2nlkLlJzMWhNYOl6KDMUlqlz+aNDdxV2D/UuP11FHElmHZltc5+ybCZSYw1o1X1T30oMw/UhESwJQ1lkHlQzPn8Nfl81Va0N/LImlW/8CQBcZjrMf1m3o5Mi6fjIPKh0DPrU486DOlMoYYXWz6sINXpRmbVY5mSh0vXfX9OBQIDa2lqqqqrIzc2NbuOyZ88eli9fHm2nVqtJTU0lMzOTjIwMcnNzsRynCnY72ePB+eEqWpYtw7tjR+R88fGM2LD+lCZoH0+jt5FVJas47DgMgFVn5ZJhlzAmfsyQ+tlrbPWzancte6qdQKRW0tzRyUzPTzh+gBH0RVakVX4VyRjV7IDb14CpbXh4zUPw+bOR+9aMjvOL0iaA5fQWe/hDYapbfNEVeJV2D83uYKd2ahWkxRo71IBK6qUCmn0SLHm9XoLBjhd6JgYLIlgSzjRhReF3h8v5Y4WdCaYQL2UcIhyoOfY8KLURvT4Vgz4Nq3UiRuPpD7UMNrI/hGt9BbInhCbFhHlaOtIAq55cX1/Pnj17qKqqoqqqCq+34wqmq6++msLCyKTr5uZmGhsbycjIICbmyNCib/9+7EuX4nz/A+TW1siTGg2WuXOJu/YaYs47r8vl/nVOHy2eILmJMehOYOVgu0A4wCeVn7C5ZjNhJYxaUjM9fTozMmagU/ddBq+3eQPhSFHJ4kbCcmRe9Dm58VwwJgWzvpusXM0O2PM2lG+Gqq8jBR6PdtPbkH9+5H79fnBURAIjc/d76PWUVn+IqrbAqaLZQ1WLl1Z/54ULeo2KSwpSmZaf0KPv32tzljweD7/4xS9YtmwZTU2dfzGeiXWWBOFMo5Yk7h+ew7k2GyNjDGQYpgAQCvkIBGqjWSifryYyDyrsjc6DsrdsJjlpHjbb9CH1F//xqPQaYs5Nx7WxglCdB9/eJoyFif3drQ6Sk5NJbttoVlEUmpubo4FTVVUVGRlHgtw9e/awdu1aAGw2W3TozvjxWtTL/40mHEabnU3c1VcTd+UVx9x2JBCSWb23li8ON6EokQ/F0akWxmfGMTLFjOYY2RJFUdjTtIfVZatxBSKrPYfHDWfesHkkGHv2A7U/hWWFL0uaWLuvHk8g8vk6MsXMgnFppFgNHRu3lEeCouxzIa6tOGfVN/DZH460iUmCrHMgfVIka5Qx5chryaMjtz5k1msYlWphVGokY6koCi2eYDTzVGn3UtXixR+Suw8Ke9lJZ5b+67/+i/Xr1/Poo49y88038/zzz1NVVcVLL73Eb37zG2644Ybe6uuAJTJLghDxaFE1MgoP5KWjbcuaROZB1ePz19Dq2oerdR8Q2Rg4NfXyEy6WOVQEKl24t9QCEDM1FV3WiQ1rDTSbN29my+ef0+zqXJZCUhRunTGD7AsuQFKp8Pl86HS6TkWNy5s8/PvrChraavlYDBpcbcvSAQxaFWPTrEzIiiM/yRyp4wPUe+r5sORDSp2lANj0Ni4Zdgmj4kf10tX2PUVR2F/r4sNdNdGvT4pVz4JxaYxMsYAchvp9UL4pEiCVbwZnZeTgBU/D2XdG7jcdjgRL2dMiQVR8Xr8v1z9ZsqzQ0Oon1qjt8cngvTYMl52dzauvvsqcOXOwWq188803DB8+nH/+85+88cYbrFy58rQ7P9iIYEkQYL/by5wtBwA4OzaGlwpySNN3HAZRFAW7/QvqG1ahKDJ6fQqZGd9DpxtYGZbe5t3TiO+AHdQSllmZaGyG4x80QISdThzvvkfL8uX4DxwgoNXiX7AA38IFHYbv7r///ujk8HfffZfdu3eTnp5ORkYGqekZlHh0bK70oiiRFVJXTcpkZIqZSruXHZUt7KpyRFd4AZh0akal6fFqdlHm2YmCgkbSMDNzJtPSp6FVDawl6aejusXLyl01HG44UlTywjEpTB0WH5m3U7MD/vEd8Ds6HqjSRIbQzv4hTLiuH3o++PRasGQ2m9mzZw85OTlkZmby1ltvcfbZZ1NSUsK4ceNobR+jPoOIYEkQIlY2tHDXvnJcYZl4rZq/jB3G7PjOmROPp4Sq6qWEQq2oVQbS0q/BYu7b9H9/UmQF9+YagrVuVEYNlrlZqAwDt5KLoih4t22j5c1lOD/6CMUXWYEl6fVY511C3HXXYTrrrGhbt9uN2XxkVeTLL79MVVVV5/NqjVgTUlh86w3EfKv+jqIolDZ52FnZwq7KFiq9+6kJfkUIL1q1ioLEMVw35lLGpaUNmeFcpy/Imj11fF1uxxh0kOfdxTTtIYa5d6LKmwUX/CrS0O+C32RHVqNlnX0ka5QxGXRi8+uT0WtzlvLy8igtLSUnJ4exY8eybNkyzj77bN57770OexwJgnDmWZAUx1izkTt3l7Kr1ct3dxzmnmEp3DMsFfVRH2gmUy7Dcv6L6uo38HjLqaz8J4kJc0hMvABJ6pslw/1JUknETE3Bub4CuTWI+8sazDMykdQD9ENfUai+/36CZZFaOfoRI4i79lpiL/tOp8KRkiR1CJQAbrvtNmrr6ln39X4OlZSBx47K50QKetEGnB0CpZUrVyJJEtnZ2WRnZ3POCDWN6s3YG0qwuWU8HhvJqnOQHZm8sbmJlUYn4zJiGZ8ZS6ZtcO6VFwjJfH6wBvuXb5Dp2sEM905S/KUdGx19WXoL/GhzZA819cANsoeSk84s/eEPf0CtVvPjH/+Y9evXs3DhQsLhMKFQiN///vfcddddvdXXAUtklgShI19Y5sGiKv5ZHVkEsiAxlr+Ny+3UTpZD1Dd8iN2+GQBzzAjS069FrT6NgnqDSNgVwLWhAiUoo8uNxTQxqd8/7BVFwfPlFhzvvUvqr36FSh+ZU9b8z3/h27cP27XXYJgw4aT6Wd3iZfnWSmqdkYzUhMxY5o1NxNHUgN/vZ+TISLHUcDjMb3/7WwKBo1ZsmYA40MRrOGfsOcwcMZuyJj87KlvYW+3EHzpSQTo+Rsu4jDgmZMWSajX0+9fymOQw1O1GcdWy3XA2H+2pw+EJ8Mt9l2MJNR9plzgqkjGKzjfq/H9IOD19VmepvLycrVu3kp+fz4QJE07nVIOWCJYEoWv/rm3m5wcq+cPoLK5IsR2zncOxndrad5CVIDqtjYyM72EwpPdhT/tPsNZN66ZqUMA4IQlDfly/9CPU1ITj7bdpWf5vAm2VvtOfeorY71x6yucMywobDtSzbn89sgIxOjVXTMqgMKPrbUxCoRD79u3j6wNfU1ZehuLs+PGUl5fHzTffHH1cWVWNXTGyp9rFvhpndKsPiBQ6HJ8Zx/jMWJK/vWqsrwU8kWX75ZsjE7IrtkDAhUebwGOj3wFJIs6k5VbvP0k2SUg50yMr1mKGzqq+garXgqVXX32V6667Dr2+4wqWQCDA0qVLO/wgnylEsCQIx1bvD5KsPzLMcsjtI9eoR/OtGkM+Xw1VVa8RCNpRSVpSUy8nNnZSX3e3X/gO2vHubgQJzDMy0Cb1TWZNkWXcX2yiZflyXOvWQVvdPJXJhPU73yH+phvRDx9+SueudfhYvrWCakckm1SYYeXyiRndLv+udFXyYcmHVLurAUjUJDJBOwF/k5/y8nJGjBjBjBkzAGhtbeXpp59Gq9VGimZmZoE5keqgiUONPkLykY+2VKuB8VmxjM+IJcHcx6sv3/0f2P46yKEOT/tUMZTFFPJW3uNMGzuM84Yn9lnVauGIXguW1Go1NTU10Voc7ZqamkhOTj4j6yyJYEkQTkxjIMTsLfvJMer44+hsRsR0/Is/HPZSXb2MVndk41Nb3DkkJy9ApRra8zIURcGztY5AhQtJp8YyNwt1TO+v7vKXlFA8f0H0sWH8eGzXXoN1/nxUMac2UViWFTYeamDtvjrCcmQV22UT0hmfeextOBpqa9i/dRv19lpcJh8+a4hJo6YyJfNs1Kqul4qXl5fz+uuv42ubbN5OkiRSU9PIGD2RJm0yh+pdhI/a6zXTZozOcYoz9UDRSkWJbP1Rvqkta/QV3Lk2Mq8IYPX/whfPoVjSqImdxNfKKIpN46k35jE1N4kLxiRjGWCby55Jei1YUqlU1NXVkfStAmM7duxg7ty5NDc3H+PIoUsES4JwYj6zu/j+rhJcYRm9SuK+3DR+kJXUYfK3oig0Na2nsXEdCgpGYzYZ6dej1Q7t/1tKWMa1sZJwix91rB7L7Eykk6hkffzzh2n99FMCxSUk3Pb96PMVP/ovtKmpxF17DYbRp7cisd7pY/nXlVTaI5W/x6ZZuGJSxjGDgZrKCoq27sRfc6RWU4IxgQxzBlqVFrVVhybBiDrBgCbBiMqk6RBwybJMQ0MD5eXl0ZvDEVlOf+WVVzJhwgS8gTCbdhfx9Vdf4dDEIpsSUHRmkCRyEkyMz4ilMDMW68kELM4aKFoDh9dB6efgru/4+k3vQP5cAML2CraXN7GyXIsnGInaRiSbWTi+i6KSQp/r8WBp0qRJSJLEjh07KCgo6LC5YjgcpqSkhHnz5rFs2bLT7/0gI4IlQThx1b4APz1QwfrmyAfkVGsMz47JIt/U8YOjtfUA1TXLCYe9aNQxZGRcj8k0tCe4yp4gzvUVKP4w2gwzMWennvYk5WB1NS0r3qLlrbcI1dSAVsuIjRvQxMf3UK8j2aTPihpZs7eOkKxg0Kr4zoR0JmXFdeq/oihUlZVy+OvdBOsjdYQUCTTpJkYML8DmMxNq8iG7Ap3eRzJq0LQFTppEI2qLrtOWMS0tLZSXl3fYx+6LL75g9erV0TYqnYGQMQE5JgHZlAimWHITLYzPjKUgI7bzUKHPCZIK9G2r/Da/CKvuPfK6WgfpZx2ZjJ0zHUVv4UCdi5W7amlwRTaiTrboWTi+raikMCD0eLD0yCOPRP/96U9/2mFpqE6nY9iwYSxatAhdL2yQONCJYEkQTo6iKLxe08xDRVW0hmUMKolf5qVxZ2bH1WCBQBNVVa/j89ciSaozYpuUUKMX12dVICsYChIwjjr5oEYJBmnduBH78uW4P/mU9q3e1bGxxF5xBQl33oEmsWcKgTa4/Pz760rKmz0AjEoxc+VZmcQaO2ZqZFmmvLiIsm/2EWyKZJ4UCbRZZkZNnUhacmbH9v4QoSZf281L2O6Db31aSRpVNOukSTCgsRm6zMZVVVWxd+9eysvLqa6u7jRdJJA/GyUm8vVQhf3kJcZwnrmKfOdXaMs2RjaeXfh7mHxL20UfhP/8VyR7lDs7Ut9IeyTYr3F4WbmrlqL6SN3BTkUlhQGj14bhlixZwnXXXYfBINKH7USwJAinptIX4J795Xxib+WGtHieGZ3dqY0sB6it/Q8O53YArJZxpKVdOaS3SfGXOPBsqwcJYs5NR5d2cvOHmpcsoe7J30Qfm845h7hrrsFy0YXRUgCnS1EUvjjcxEd7agmGFfQaFZeOT2Nyjq3TUFnxwf1UbDtAuCWSYVEk0OfGMmbKJJISUk/s/UIyIftRwVOTD+WosgEAqCTUcfojwVOCEZW+45ynYDBIdXU15eXllJWVUVNTw60/+C8OVtYS3PYmFWUVlAQTSaOObKrIpppsqtFPuRHNpU9320eXL8iavXVsLbOjKKBRSZw3PIE5o5J7fJsOoWf0WekAQQRLgnA6FEXhzdpmFibFYdFEPlCcoTBmtQpV24euoijYWzZTX78yuk1KRsb30A/hbVI82+vxFzuQNCosc7JQW4+dtfcXFyO7PRjHFQIQbmmh5OprItW1r74a3bBhPdq3ZneAf39dQUljJJs0PNnMorMyOkyYlsMyh/btpmpHEbIzMqQmqxVMefEUTJmCLe70lsUrskLYGSDU5G27+VC8oU7tVBbdkaG7BAOqGG0kmPM0g7sRJXHEkce/y+OfXMFhhnV+P72F2KQ0LrhoHmMy4tAdlcEKhmU+O9TIxoMN0bpP4zNjuaQglfiYM2+0ZTDp0WDJZrOdcNpbTPAWwZIgnA5FUfjezmJ8ssyzo7PJMR7JhHg8ZVRVv0Eo5EKl0pOedg0Wy5he64usyBy0H0RRFIbbhvfp/mOKrND6WRWhRi8qsxbLnCxUuiPZCUVRcH/xBc1LluD+5FOMEycybOkbHV7v6eFKRVHYXNzMR3tq8Ydk9BoV8wtTOTs3Pvpe4VCYA7t3UruzGNkdKUWgaCBmeCIFUyYTazl2va3T7ZvsCUWzTqEmL2HnUfOeZBnc9UiecjTuHWicX6LJSEF955tH5j29fzeKJQ1H8jTK/GYOFJdRWlqGx2mPvIfWRGDMfPQaFaNTLWgbD4JKwwGPEZdiBEki02bk0vFp5CSIbUdOlqIotNqbaCovo6GijJHnnEdsckqvvmePbnfy7LPP9lS/BEEQunXI42dzixuvLDP3qwM8mJ/OLekJqCQJkymH3GH/RVX1UjyeUiqr/kViwmwSEy/s0W1SFEXhoP0gH5d/TKO3EQCdSsfo+NGMTRhLflw+ml4uZyCpJGLOScXVviXKV7WYp6WjBPw43nsP+6uv4j9U1NZYQp2QgOz1ojIa257q2UDJ7g6w4pvK6OaueYkxLJqcGc2cBIMBDuzcQd2usmiGR9GCZVQK4yZPJcZkPua5e4IkSahjtJGSC9mRDz05ECb02TLC+3cSqrUTCseioCaIhiDnQUM80ntFqBNMkezT2U+giTcQp1ERB0yYGKnz5Xa72XmgmMN1LVRLWuyeIDsqWtDt/RIpHAkI9VoDmdk5nDVsFFZVAiCCpeNxNtZT/M1WGstLaawoo6miDJ/7yP6yMXG2aLCkKAoodJrQ31fEMFwPEJklQehZZV4/P9lfzqaWyAfzjDgzvx+dRXZblklRwtTXr6LZ/gUAMTHDyUi/rke2Sal0VfJx2ceUuSJVrI0aI3q1nhZ/S7SNQW1gTMIYChIKyI3NRdWL+9mFWvy4NlZAWCHcdIDmv/2asD2S6VCZTMQuWkT8TTeiy+4836snKIrC1jI7H+yswR+S0aol5hWkMi0/AUmSCAT87PtmG417I6v4AGSdhG1sGgWTJmMy9GHQ0NoQqXU09rIjzy29Afa/H7kWUyqhjAWEbDMI68cS8uhQgt+a9yRx1LyntqG7ozY5VhSFSruX7WVNbP/6S8KOBlSeRuRvTRovKCjgmmuu6bVLHSyCAT/NlRU0VpTRWFFG/pRzyBxdAEDxtq94+zePdGgvqVTEJqdhTcokq3AWloTheF0BPM4gY6ankZTdsysJe20jXYDDhw/z97//ncOHD/PHP/6R5ORkVq1aRVZWFgUFBafcaUEQBIAco54VE4fzt6pGnjhczWctrcz96gAP5adzU3oCkqQmJWUhRmMWNbVv4XYXUVL6Ahnp12M0ZpzSezZ5m1hXsY69TXsB0Egazk0/l/PSz0Ov1lPZWsmepj3sbdyLK+hiW/02ttVvI0YTw+iE0RQmFJJtze7xwElt1RIzOQX3llrCHhMKcWjTjdhuuom4qxehtvTeMnSHJ8hb2yo5WBf5az8nwcTVkzNJNOvxeT3s/WYb9v3VKIFIoKAYVCQUZFAwcTJ6XR8sAgr6IsHR4XVQvB5qd0We//E2iM+L3J90E2SdDXlzkVIK0apUtA+mKoqCHJ33FBm6kz0hwnY/Ybsff1ELACqztsOk8Uybkaz4LC6dmIkkSYRCISoqKigpKaGkpISqqioSj1pt6PF4+Mc//sGwYcPIzc1l2LBhGNsygEONq7mRnR9/RFNFGY0VpbTU1qIoRwJSrV5P+ogxeF1BVOokUodPICYuDZ0pBY0+CbUuAZU68h3yusHrPlKDy9NFOYm+ctKZpY0bNzJ//nzOO+88PvnkE/bt20deXh6/+93v2LJlC//+9797q68DlsgsCULvKfFEskxfOtzkG/V8PHUUxqO2hfD5a6mqfI1AsBlJ0pCa8h3i4qac8PndQTefVH7C1tqtyMhISExImsCcrDnE6jvvYSYrMuXOcnY37WZf0z48IU/0NYvOwtiEsRQkFJBpzjzloTAlHKZ1wwaa/7GEmJkzSfzBnXj3NOLd10SotgbbdVPRJvVexkZRFL4pb+H9ndX4gjIalcTFBSmcl5+Iz+tmz9avcRyshVDk40MxqUgal8PYcZPQaftgQvPh9fD5HyOBUqhjBW9SxsGlf4Csqad0atkTjAZOoSYfYae/c8kCvfrIpPF4A+o4PdJRP5N+v59wOIzJFMl07tu3jzfffPPI8ZJEWloaubm55Obmkp2dPWjK7iiKgqupIZIpKo9ki3LGTaRg9gUANFdX8fe7f9jhGJ3JjCUhA1NsGtbEMRis+d/+knagN2gwWnWYrDpMFl30vsGs7fHSC722Gm7atGlcc8013HPPPVgsFnbs2EFeXh5fffUVV1xxBVVVVafd+cFGBEuC0LtkReH/Khs4yxrDlNiY6HMSkQ+ecNhLdc2/aW3dD0Bc3FRSki/tdpuUYDjI5prNfF79Of5wZEn78LjhXJh9ISkxJzapVFZkShwl7G7czf7m/fjCRz644/RxFCQUUJhYSIop5YQCp3CrG8dbb9H8r38RLC8HQJuRQf6a1SBJuDfXEKxxIxk1WOdmdRge6ilOX5B3tlWxr62qdqbNyDVTMjERYM9XW3EVNUDbhrWKWU3ahDxGj53QoVBxz3aoJpI1ypgMSaMiz+17H968IXLfkh6pd5Q3F/Jmgzn52Oc6BXIgTLj5SPAUavaB/K2PTZWEOlaHxmZAbTOgselRmY8UzPR6vdGsU3FxMU1NTR0O/853vsPkyZOByD6rKpWq976ep8Db6uKz15dEh9ICXk+H1/OnzuScKxfjdQVwt/jYueZVtIYk9JZUjJZUNHpLp59/jUbVZUBktGjR9GGZhV4LlsxmM7t27YpWR20PlkpLSxk9enSnfXrOBCJYEoS+95fyej6xu3h6VBYZBl3bNikbaGxcG9kmxZBJRsb30Go7ZodkRWZHww7WV6zHFYgEBGkxaVyYcyF5sXmn3J+QHOJwy2H2NO3hQPMBAvKRIYMEQ0I0cEoyJXU6NlBZhf1f/6Ll3/9Gbo0MealiY7Fdey22G76HNjVSi0gJhnFuqER2BdAkGDDPyERS98xf2oqisKPSwbvbq/EGw2hUEheMSWZCopr933yDu7g5GiQosRoyJ45g5OjCY+7ddsoC7sgWIsXrI8NrDZEAmFm/gPMfiNz3OSKb0+bNjQRQfVikVAkrhFuO1HsKNfuic7WOJmlUkblP8UcCKMkY2a7F6XR2CJ5uvfVW4tsqqn/55Zd8/PHHZGdnk5ubS15eHqmpqahUvTcvLujz0VRZTkNFKU0VZTSUl5GSm8+sGyLb4vh9fp7//jUocmQ4TVKpMVmTMVhS0cWkYI4fhiWx84bLKknCYNFisrQFQm2BkcmqQ2tQD4jisr02ZykuLo6amhpycztuO7Bt2zYyMk5troAgCMLJaA2FebasDkcozJwt+3l0RAbfTY0nMXEuBkMG1TXL8PoqKS19nvT07xITk4eiKBxqOcTasrXUeyN7ecXp47gg+wIKEgpO+xe3RqVhVPwoRsWPIhgOcqjlELsbd3PIfogmXxOfVH3CJ1WfkGxKpiChgIKEAhKMkVpDDX/6I8533wNAN2wY8bfcTOzll6MydZywLmnVmKel4VpfQajJh2d7Paazkk+7763+EO9sq2JPtROAjDgDF+cbadj7DV+vd0QrgEs2HVlnjWL48DE9/+HtrIYVd0LFlyAHj3pBgvRJEHvU54shFs79fz37/idIUkvRyd9gi5YsCNt9hOz+tn8jBTNDjV5Cjd4jxxrUaGwGtDYDY1OHM25MIZJW1eH7V1VVRTAY5PDhwxw+fBgAg8EQne80ceJE9D1QWFSWw7z3+9/QWF5KS31t9HvcztnoJDb1QjzOAD5PkPQxC9HqrRitqejNSR2ytgaTBmMXAZEhRttvq9d62klnln7xi1+wadMmli9fzsiRI/nmm2+oq6vj5ptv5uabb+ahhx7qrb4OWCKzJAh975Dbx137y/nGGRkSuCDeytOjM0nT6wgEmtu2SalBQkJlmcImewulbSvcDGoDszJnMTV1aq+XAPCH/RxoPsCepj0cbjmMHAySubWClhwbluGjKUgoYES9Gu9zLxF/yy3EzJyJdJxAJFjnpvWLalDAOCEJQ37cKfdvd5WDd7ZV4Q6EUUkwLU3C2lBOoMoV/QBVJekZdtZYhg0b0TNBUjgUCYq8zTDmO0eee3o4eO0Qmx0ZWss/H3Jngann9rHrC4qsILsChOw+wnY/oeau5z4BqGK0keG7eD0amwGVVUdDcyPFxcWUlJRQVlaG3x8ZJlapVNx3333R+U1VVVWYTCZsts61qxRFwdXYQO3hg9QWF1F3+CA6o5mLF/8MvyeE1xngnd/9BI8jUhpDozdjtKRhsEaGzkyxGZjijmxBo9WqMVq1bUNlbcNnVh1Gsw61tveyXr2t14bhgsEgt956K0uXLkVRFDQaDeFwmO9973v84x//QK0+80q6i2BJEPpHWFH4S3k9T5XW4pcVrBoVjw3P5NpUG4oSorjyDQ7WrKLZ14xblUCjZgTnpM/gvIzzMGr6djVSuKWF+qWvY3/tNaSGZopn57P11iOTkLMsWRQmFDI2YSxm3fFrEvkO2vHubgQJzDMy0CadXNkETyDEf7ZXs7PSAUCq2sXoQAPqozIh6lQj+WcVkp2Tf1Ln7pK/NTKsdmAlHPwoEihZM+Hu3UeG0Q6vg7icyEq2ATBE05OUsEy4xX8kgLL7kFuDnRtKoLbqUce3Dd3F6qhzNVJaVorb7WbevHnRpi+99BI1NTXEWmPJSM8mLSkTZ9E+7KUHsNeUEvC6OpxarTUyft6j0UxWS+0e1Bo9BksKWr0FlUo6EghF5xFpMVp0aPUDY9isp/X6dieHDx9m27ZtyLLMpEmTGDFixCl3drATwZIg9K8Dbh937Stnu8uDVpJYMzmH0qbNbK35iphwNUnhMhIMNnLix5OffRt6fed5Q73FX1xC8z9fxfHOf1C8kUBEnZiI5abrqbr8bPY07aHMWYbSlnaQkMix5lCYWMiY+DGYtF0HQYqi4NlaR6DChaRTY5mbFSnIeAL2Vjt5Z3sVLl8IvbeOMUE78Z4wkhTZt02XHsPwyRPIyOiB2k173o7MLyreCG0T6QEw2mDEJbDwadD3XvmDgUwOhDsO3zX7CLmDyGGl7SYTDivIikJIryGoURFQS7SGvDTWlbLPvgsvIfhWDKPyedG4mtE31WG0phETl4UpLgtTXCbmuAx0pq7nEelNmiEzbHaixN5wfUgES4LQ/0Kywp/La6h0VZDk3RBdmTY8bjgzk0cTsH9MMORs2yZlERZL79eEq77/lzjefjv6WD96NPG33IJ14QJURy0Vdwac7G3ay57GPVS2VkafV6EiLy6PgoQCRsePxqDpWLtICcu4PqkkbPejjtVjmZ2JpDn2kIg3EOa9ndV8U9qMwVNLVmsj6bKEVq2KbG6bZWHUlEmkpKSf2gUrCtTvg8QR0FYrhw/vhS9fjNy35cLohTBqPmSdC+qBs+KrNyiKQiggE/CFCPrCBP1hAt4QAX+YoC9EwBcm6AsT8Le/HkIlK2gDYTQBOfJvUEb2tRLwOQgF3IT8rcghPyElgDvkwBVyYJlyCS1SK03uOhzuyEq7jIQ05l94JQaLEY1OzVfbviB7WDbDhuX0yJynoaJHg6V77rnnhN/497///Qm3HSpEsCQI/UtWZHY27GR9xXqcgcgk5VRTKllJ57OiWcOTIzKJV/uoqn4Tj6cEgIT4WSQlXdSj26TIfj+SVhudc9Tw3J9pfOEFzHPnEn/LLZjOnnrcoYwWXwt7mvawu3E3tZ7a6PNqSc3wuOEUJhYy0jYSnToSbMmeIM4NFSi+MNoMMzFnp3b5HgdqXaz4upxAYzmpLQ2kKipidBpQgXFYHKOnTiIx4RT24QoHI/WODnwI+z+AljK45b3IXCOAqq+heAOMWtjnK9d6m8cZoKXOEwmG/G2BT9v9QFtwdDL5CDkUwOusxtdaReaYGehjjOj0Koo++Tf2g3uJ0cQSo4nFpLGgN5oxWmMxxVqJT8tErdWiMmkJmBWq/A1Yk23kjslH0qioq6vjL3/5CxCZ95SVlcXw4cMZPnw4qald/7z0JEVWUEIyhGSUsIISllFCCrT9q4TbXgt181pYwTDShja1Z+uL9WiwNHfu3A6Pv/76a8LhMKNGRWpeHDx4ELVazeTJk1m3bt1pdr17L7zwAk899RQ1NTUUFBTw7LPPMnPmzGO237hxI/fccw979uwhPT2dX/ziFyxevLhDmxUrVvDggw9y+PBh8vPzeeKJJ7jyyitPuE8iWBKE/qEoCodbDrOmfA31nsgKt1hdLOdnn09hQiHzvj7EzlYvNo2aX4/M5PIkKw2NH9Hc/DkAMaZ80tOvRaM5vX3LQo2N2F9/A/vSpaQ9/jiW8yO/M0N2O7LDgW7YsFM6b6O3kb1Ne9nduJsGb0P0eY0UWXlXkFDAcNtwJHsI16dVICsYxiZgHH1kQrQvGOb97VXs3bOL1JZGYkMSVoMGjVZFTF4CY6eeRVxcwsl1zN8Kh1ZHAqRDq8HXcuQ1tR7m/xamfP+UrnkwcDZ5Kd/TTGOFq9viiu20WjVagxqtXo3OqEGrV6PRKrgdNTjrSrHXlNBUWUxzdUV0ef73Hn+GtBFtn7GbP2P/F5+QkjeC1PwRJOfkow1p2+Y/RYbx5K6qW0ugtuho0Xj5qmInZfWVOF3ODk3MZjMXX3wx4wrGdQhMlLZ/OzwOyRB9TYajgpvo47bXjj62U12qU2SamIw+r3Oh2NPRa8Nwv//979mwYQNLliyJzsC32+18//vfZ+bMmfz0pz89vZ5348033+Smm27ihRde4LzzzuOll17i//7v/9i7dy/ZXeyLVFJSQmFhIXfeeSc//OEP+fzzz/nRj37EG2+8waJFiwDYtGkTM2fO5LHHHuPKK6/k7bff5le/+hWfffYZ55xzzgn1SwRLgtD3alprWFO2hhJnJFNkUBuYmTGTqWlT0aoiQ0B7W738eF85u1sjc4UWJsXym5GZ6H37qKl9G1kOoNXEkpHxPYzGzGO+17H49u+n+R9LcH7wAUowMlnXumABGb9/poeu8oh6Tz27G3ezp2kPzb7m6PN6tZ5RtlGM847AdkiLSlIRc24aunQzB2tbeHft58TWNxITBJNOTYxJg2VEEgWTp2CxnsQHTzh0ZNisejv8dfaR10wJMHIejFoQWcWmG3qbyCqKgqPeS9nuJppr3dHnbSkmDGYtOr0mEhAZ1NH7urYACRSaqiqwxCdiMEcC863vvcXGf/2t0/uYYuNIzR/BtEXXkzp85In3Lxg+qnRBZAJ5+4bGR19DS6iVqkADJc2VVDhqCMohrsg7nxxzGgC1nibKXNXkWNJIMSb0bNZJitSfQqNCUktIahWSRgK1Cqn9OY0K2v7tcF8tobYZTnhe3onqtWApIyOD1atXd9oDbvfu3Vx88cVUV1efWo9PwDnnnMNZZ50VTScCjBkzhiuuuIInn3yyU/t7772Xd999l3379kWfW7x4MTt27GDTpk0AXHfddTidTj788MNom3nz5mGz2XjjjTdOqF8iWBKEvtPia2F9xXp2Nu4EIsNTZ6eezYyMGV1Ohg7KCn8qq+MPZbWEFIjXqvnNyCwuifVTWfUagUDTSW2ToigKrevX07zkVTxffhl93jhxIvG33IzloouQerH6sqIo1LhrokN17cOOAKOqMxnlysFqjGOLthlPaTXGkIRaJWE2a0kam0bB5MnExJzAhGpFiey1dmBlZHgtpRCu/MuR15Z8J1L/aNSCyN5rPV2ccoBQFIXmajdlu5twtK0UlCSJlGEWsgsSiIntOP9HkWXstTXUFR+i9nDkVl96mJDfz4L/+RljZswBoGLvLt59+glS8iPZopT8EaTmjcAc33MBiuwNdcg+he2+DhsHh+QwNZ4G0kxJaNq+f5/WfMM3DZHPTKNGT44tg9zELIYlZxFjijkqoOk6uJHUUiQY0rQFQ+2P2+8PML1WlNLpdFJXV9cpWKqvr8flch3jqNMXCAT4+uuvue+++zo8f/HFF/PFF190ecymTZu4+OKLOzx3ySWX8MorrxAMBtFqtWzatIm77767U5tnn332mH3x+/3RuhcQ+ZoIgtC7PEEPn1Z9yle1XxFWIhWTxyeOZ27WXOIMccc8TquS+GluKpckWvnxvnL2un38YE8p/xqfx9ycH1FT829crZFMk9dbQUrKpahU3f/12vDnP+Pfuw/UaqyXXEL8LTdjnDChJy+3GwpJ+hhmJI/iHFsqNc7DlLTspcZ5GHfSbqpUBhpaId4Vi8F9HrJVIXNyJhOmTMVgOE65hFAAyj6LDK8d+BAcFUdec9WALINKFZl3dOv7vXuZ/UyRFRoqXJTvacJlb69zJJGaF0t2QTxGc6RqfDgURK2J/LxU7t/DO799FL/H3el8WoMR71GfkZmjC/jRK2/06nwhlVGDzmiG9Eg2S1EU5NYgsicY2ctOoyJendch4BlxIIR3JxQXF+P1+9nfUMz+hmLYB2lpadx4442YYoZe5vB4TjpYuvLKK/n+97/PM888w7nnngvA5s2b+fnPf85VV13V4x1s19jYSDgcJiWl4wTElJQUamtruzymtra2y/ahUIjGxkbS0tKO2eZY5wR48skneeSRR07xSgRBOBlBOchXNV/xadWn0RVuudZcLsq5iLS2oYMTUWgxsWrKSP5QWsc3Tg/nx1tQSRIZGTfQ1LyRxoaPaXFsxe+vadsmJS7y/lVV2JcuJeGHP0RtNiNJEok/+CG+3buw3XAD2rQT78PxKEqYUMhJMOgkFHIQDDkJBR0EQw5Cbc+FQq5omYF2ORrIiU+hxWeipK6ROFSo9A60KQ2YDBdgDMbS2hhGn650vzT8HwuhcsuRxxpjpDDk6AWRZf69uOXGQCHLCnUlTsr3NEV3uVdrVKQPjyNjlBVnfSX7Pt1C1b49VB3Yy4SLFjDt6usBiE1Kwe9xo9HqSMrNIzVvBCl5w0nNH4ktPR3VUdm34xUe7Q2SJKG26FBbjr1p75gxYxgzZgzhcJjKykqKioo4dOgQtbW1uFyu6ObAENmaRavVMnz48CE/qnLSwdKLL77Iz372M2688UaCbWP0Go2G22+/naeeeqrHO/ht347CFUXpNjLvqv23nz/Zc95///0dVgg6nU6ysrKO33lBEE6YoijsbNzJ+vL1OAKRwokpphQuzL6Q/Lj8U/qLXKdScW9eGrKioGo73hOW+VPzCO5MTcfbsByvr4qS0udJks/D+4/VON59F8JhNImJxN9yCwDWeZdgnXfJSb23LIcIhboIgkIuQsEWgiEH4ZC7UyDUFUlSodHEotVY0Ghi0WhjUautbF3bSGOZgtPYzPhJu1F85fjrfDRVq2mqbsVg0pCWH0daohN9+WooWgPX/hN0bR+AuTPBXgqj2uYf5c0Bbd8W7+wv4bBMTZGDir3N+DyRzzatVk1KnpGGkk/Z8dE+PnxuHwGvt8NxdSVF0fvm+ARu/t1zxGdkoR5AG+GeCrVaTU5ODjk5OVxwwQW4XC7sdnv0/50sy2zcuBGPJ1JBPyUlJbrCLisra0BtBNwTTvpqTCZTdEXa4cOHURSF4cOHE9PLabnExETUanWnjE99fX2nzFC71NTULttrNBoSEhK6bXOscwLo9XpRp0IQelFxSzFrytZEl85bdVbmZs1lfNJ4VD2w1F91VKD1RHENf6tq5O16DU/m38qoyldw7lpHU8USDAck9GGJ/9/efcdXWd6P/3/dZ4+cnJOc7B1ICHuvMEVAcOFeWGxra7V+/Do6rLa2am3rz9bVVq2jVmsVF+6FoAiyIUBYgUD2XifJSXJOzr5/f5xwIAKBQCZez8cjD8l97nHdt8nJ+1zX+3pfxmnT0WYNP+n5AgHPd3qEjvQEtXT8247Pf/zQzIlIkioYBKnNqFXmjmAoHLUqPBggqc0olWGdgkVZlnnnywKqDknBJUsumIHZ4qW9vQx9eiG+xjnU7C3EVVlK8fZyStptRKlLiFfnE1m0Dmn4hcETzf4lzHvge9GDdITP66fqUDPlBxpxtrbS1liMUikz/oJ5JGRakCSZz/+xAl9H6oVGbyAhawRJw0eROHwksUOPFmSWJIno1PSTXWpQM5lMmExHc918Ph9TpkyhoKCAyspKamtrqa2tZePGjWg0GiZNmsSiRd37QDGQnXHoZzQaGTt2bE+2pUtHHv7q1as7TetfvXo1l1122QmPyc7O5pNPPum0bdWqVUyePBm1Wh3aZ/Xq1Z3yllatWsWMGTN64S4EQehKjaOGr0q/otDesYCoUsesxFlMjZ8amuHW066Ji2RDUxuHnC5+eqCeBdsi+GlBI9okGf+F8eh/czGx42/C72uj2b7juCDI62vB73ee1rUUkroj8DF3BD7hqI4JglSqcJRKY7d7zVZtr6RkRz0SMCU7kXHjYmhvv4iS0udpb/iCtC2/J91WT713CNXekTRLCdTrsqk3X4dudwIJPhvxQ81o9N+fXBSPy8ehbQUUbMvFXldIW2MxrtZgcB4Rn0jKyKtD+0697Gq0hjCSRowiKiW103Da95VGo2HevHnMmzcPh8NBUVERBQUFFBQU4HA4Oi195vF4WLNmDRkZGaSmpob+/g4mg6qC95HSAc8//zzZ2dm8+OKLvPTSS+zfv5/U1FTuv/9+Kisree2114CjpQNuvfVWbrnlFjZv3sxtt93WqXTApk2bmDNnDn/+85+57LLL+Oijj3jggQdE6QBB6EN2t501ZWvY27AXGRmlpGRK3BRmJ84+6XIfPcnlD/B4SQ3PldYQkBREuZz8IaqZseFbkWXfqU8AKBTqjqGxo4HPsUGQWm1GodD3eELvtsMNrHnvMJJPZkRmOJeOLkIyxULKNKqq3sVe9y2GnHdJqVUgZcyH4RfjiJ1HVQXUFrXg9QaT5SVJIjo5jIRMC5ZYwzm3DtiR9Aq300v5gSa+eeVRWuoPH7dfREISScNHsuCn/4fie7jW6dkKBALU1NSg1+tD5YUOHTrE8uXLgWDaTlpaGpmZmWRkZBAZGdmvP2vn7HInzz33HH/961+prq5m9OjRPPXUU8yZE6wU+6Mf/YiSkhLWrl0b2n/dunXcc889oaKUv/nNb44rSrlixQoeeOABioqKQkUpu5OsLoIlQTgz7b52NlRuYGv11tAMt9HW0Zyfcj4RuuNXUu8psizj3LoV24svEvfQQ2g66rRtyS/kl3VtFPqCb4sPpRm5QP4cr88e7A060isUGiILDwVDCoWuz9/0D1W38P7yfUTYaxmqKuI85TOovI0w/BK4/g28XjtFxU8RsJeROPR2wiM7l0bw+wLUlbZSXdAcmhYPYDBpiM8wEzfEjEY3OHNP/D4vtUWFVB4MJmLXFRcxe9mfqCtpIxCQKc19F1v5dqyJaaSOHRMaVjOYLf3d9HNOdXU127Zto6Cg4LhZ8xERESxZsoT09P4Zvjxng6WBSARLgtA9voCP7TXb+bbi29AMt7TwNBamLiQh7AzXJTsNsizTtm4dtudfoD03FwDLNdcQ/8gfQ/u0+wP8tbiat2sa+WbKcGK1A3PIwLZ3Fds/3IiyyY8OB5OM72FQ2iE8CcbfAOc/AEBDwxrqG75GrbYwJP3uk5ZFaGtyUXW4mdriFny+YC0ehUIiOsVEQoYFc0zP94r1tNqiAgpytlB5MI/qw/n4PO5Or2fNuQujJRlLtIGoFAUxqZFo9b3fcykEybJMXV1daIZdWVkZgUCAO+64g6ioKAAKCgqoq6sjIyOD6OjoXv+ZE8FSHxLBkiCcHlmW2dewjzXla2h2NwMQo49hQeoCMiwZvfbGKAcCtH71FQ3PPx+sjwRIWi2Wa67B+pObTzj93+71YVYf7VV5sqSGJTEWMgy64/btE7IcWlfN7vRS+vhN2JpSkIDRUVuJmjgNRl0JSVM6JWgHAh6Kip7G67MTHbWAqKh5J7lAkN8boLa0harDzbQ2ukLbjeEa4jMtxA0xo9b0//CUo7mJyvw8kkeOQW8Kvu9uee8tNr7zemgftc6IwZJGWGQ6YdZ0koZnkTY2BkuMCJAGArfbTVlZGRkZR3/333nnHfLy8gAwm82hGXbp6enodD3/uyeCpT4kgiVBODW72867h96lsq0SAJPGxPnJ5/fYDLeTkWWZ0qU30r5rFwCSwUDEDddj/dGPUEVHn9Y5vra1cOOeIlQS/CQpml+mxRGu6oOAQZahcifsfx8Ofgo/W0u7MpwXPs8nPGc35oCdcXOzyFg4p8sK2i0te6isehuFQsOQ9HtQq0/vfarF1k51gZ3akhb8Hb1NSoVEdGo4CZkWwqP6ZuhRlmWaa6tDtY0qD+6nqTq4WsQld99HVvYsAKoO57Ptww9R65KR1AnowmJQSAqiU4LVtk2R/RToCqdtx44d5OXlUVJSgt/vD22fO3fucevU9oReq+AtCILQXc2uZl7Le40mdxNapZaZCTOZHj8dtbJ3hrhkrxdUKiRJQpIkDNOn4S4oIHLZD4hYtgxVRPfyoYbotSy0hrPa1sIL5fWsqGnit0PiuT4+EmVPBwuyDLX7YN/7sO89aC4NveTP+5TXm6bjOmAnYExnzNQEhp6fHOpxOhmTaQx6/Wba28uob1hFQvzVXe5/RLhVT7hVz9CJ0dQWB3ub2prd1BTbqSm2E2bRkpBhIXZIOCp17wSPFXn7+PTvj+Fobur8giQRlZwKBIMpW6WDmiId5oTLOl6WiEsPJ3lk5HFLkggD16RJk5g0aRIej4fS0tLQDLuMjIx+bZfoWeoBomdJEE7O1m7jtbzXaPG0EKGN4KaRN3W5PMnZCLjdNL/3HrZ//5v4Pz5C2KyZAPjb2kCWUZpOY020LqyxtfBgQSWHncFcmLFheh7JTGSaJeys2w5ARQ58+HNoOHR0m9oAWRcij7qSFU1ZHN5kQ+OWmTg8irlLhqJUn16vXHt7BSWlwbXd0lJ/fkYLB8uyTEtDMLepvrQFf8dq8kqVgthUE/GZFsKt3S9i6ff5qC7IpyJvH5UH9zN00jTGL7oYgJb6Ol6642aUKhWxQ4eROHwkScNHkTBsBFqDkbqy4JIkbc1HlySJzzCTMsKKLmxg5psJ3XeqYtFnSgzD9SERLAnCidU76/lf3v9o9bYSpY9i2chlhGt6/nck4HTS9PY72P7zMv76BgBMCxeQ9M9/9vi1vAGZVyrrebykhhZfgFSdho3TRqDqahmRk2kqAXcrxI0Jft9aA08MB6UGMhfC6Ktg2CLQGPlibzU5a8vR2n2MSDaz8Kph3Q4GqqpWYG/ZhUGfQkrKz87qj4/X46e2yE7V4WYcLZ7QdlOkLtjblBZ+0kBOlmUaKyso3buL0j27KM/bh9d1dDbekElTueLeP4S+r8w/QGz6UFSa4DIdAX+AmuIWyvMaQ0uSqFQKEoZZSB4eiUYvBk2E0yOCpT4kgiVBOF6No4bX817H4XMQY4jhppE3YVT3bNFDf2srTW+8QeOr/8Xf3AyAKj4e6y0/xXLVVSh6sdJ+vcfLY0U1zLeauDDaAoAvIOOVZfTKLnp7Wqpg/wfBYbbKHEifCz/8+Ojrh7+C5KmgO/pesqXIxsqvS9DXuhkSbWTBZRlExHX/WXq9LRQVP0kg4CUx4TrCw8++sLAsy9jr2qkqaKa+rJVAR2+TSqUgNj2Y2xQWocPn8YSCHa/HzbM3X4+/Y8ksAL0pnORRY0kcPorkUWOITkk77lp+b4CqwmbK8xpxtwfrX6k1SpKGR5CYFTEgEs+FwUXkLAmC0G+q2qp4/cDrtPvaiTfG84MRP+iV4pLlP7s1lLitTkkh6tafYb70UiTNyRcK7SnRGjWPD++8JuRrVQ08V17Hg0MTuSTafLTnpq0e8j4MBkmlm+DI+m+SIpiY7feBsuPtOHNBp3PmVbXw+cYyjLVukix6sucmn1GgBKBWh2ONnEt9w1fU1a0kLGzESUsJnC5JkrDEGrDEGvBM8lFb1EJVQTOtTa3kbdjP1g8O42gqQK1T8aPHn0GpUqDWaEkdMx6f10vqmPGkjp1ATGr6SReX9Xr8VOY3UZHfhNcdTPrV6lUkj4gkIcNy2kORgnCmRLAkCEKPKm8t540Db+D2u0kKS2LpiKXoVT2zGKuvvh6FyYSiYwpxxNIb8Le2EHXrbYRfuBipHxfvlGWZ5dWNVLi83LK/hBmWMP6UmcjIMD28fwsUfXN05+TpwSG2kZeB6eTrUJY3Onl7YwmGchfRYVomTowlMevsinVGRs6iuXk7Xp+dxsYNpywl0B3NNWVU5edQsmcXVfkHCPiPrX4usf6tPSQOjych08Ll9/7hlMOAHpePioNNVOY3hWo/6Y1qUkZZiRsSjqKrHjxB6EFiGK4HiGE4QQgqsZfw5sE38QQ8pJpSuWHEDWiVZz8U5q2uxvbyf2h+911ifvUrIpf9AADZ7wdJOmmPRF9zOu08uyeHZ53huCQ1CuAHCVZ+07IO665/BwOkUVeA+dTJ1bY2N8+vKUDKb8WqUDJxRDQTF6Wg7IEA4WgpATVD0n9x2qUEvqu5tgZzdEzo+X/xzBPkrT8aFJqiokkeOQ5T9DDkQCI+/9GfBUu0gfhMM9EppuPuyeXwUp7XSHVBcyiJ3GjWkjrKSkyqCelM8sME4QREzlIfEsGSIEBRcxFvHnwTn+xjiHkI12ddf9alATxlZdhe+jfNH34IHfktpkWLSPr702ff4J7iccLhVcFp/odXgc9FuTaWR2Y+x8f+YFVis0rJU8OTuagjt+lU2tw+XlhbQPvBFixumbHpEUy7OB2toWdmd8myTFnZizjbyzCHTyAh4fRKCbS3tVK+bzele3Ip3bsLe10tyx77BzFpQwA4tGUDBzasJXXMBFLHjscSlxDqPZJlmcZqB9UFdhoq2jjyp0etURI3xExCpgWAsjwbNUUtodfDrTpSR1uxJoYN+AriwuAjcpYEQegz+Y35vHvoXfyynwxLBtdmXYv6LHJh3IWFNLzwAi2ffgaB4PCLYdo0on5+G4bTXOC619kr4KuH4ODn4HUc3W7NIHn0Vbw4Kp0fKWL5fUEFeW0uUnSnl0fl8QX43+ZS2kraMLcHyEo0M+68pB4LlCCYZxQTczElpf/C3rKLiIjpJy0l0FRdyb61X1G6J5fa4oJgHagOCqUSW3lpKFgaNn0Ww6bPOuk1rQlhWBPCcDu9VBfaqS5oxuX0UX6wkfKDjUiEsrmIiDWQOtp6Ti7qKww+IlgSBOGsHLAd4L3D7+GX/YyIHMGVmVeiUpzdW0vdU0/R9tXXABjnzCbqttswTJzYE809c34ftNUcHULThEHeR+D3gDkFRl8ZHGaLGxMqEjkDWDU5i212B6NNRxPc365uZJrFSJq+8xBlICDzdk451aUtmG1esuLDGZUdjzm655Pj9fokzOYJ2O27qK37jNSUnwHQUFaCWqvDEhdcAqaloZ5tH74bOs6alELqmPGkjBlP8sjRaM5gbTWtQU3amChSR1mxVTmoOtxMY1UbMhCVGEbKKCvm6J7JcxOEniCCJUEQztje+r18WPAhAQKMto7m8ozLUXax7MbJtO/Zgyo2FnVsMNk56tbbgv+97efoR4/q0TZ3iyxD2WbYuyIYGFlS4GcdOTl6C1z0OMSMhKTJJ62irZQkso8pWnnY4eKX+WUokLg1OZq7UmMJUymRZZlP91ZzsLgJc5WLzBgTQ0dbSciw9NrtRUddQGP9DqqqNnNgVSEl26tw2puZeNFlzPvhLQAkZo1k1Nz5JI8aS8qYcZgio3rs+pJCIiopjKikMNztPuSAjM4oCkkKA48IlgRBOCO5dbl8XPgxMjLjo8dz6dBLu73Gm3P7dhr+9TyOTZuIWLqUuD/8HgD9mNEkP/NMbzT79DSXw+43IfeNYOHIY7U3gb5jRtqkH3b71CpJYpbFxNqmVv5ZVsc7NY38bmgCMXYfWw41YCp3MTTSSHJKOBmTTj5T7mz4PB6+feMVSvfm4lUcwJzWit+tpL01GpVW12kWm0qjYfHt9/RKO46lFYUkhQFM/HQKgtBtOTU5fFb8GQCTYidxcfrFp51XIssyjo2baHj+X7Tn7AhuVKmQ5UCvLWnQLWv+DN/+jVD2jMYUnOI/+spgAUnl2b1tphu0vDluCKttLfyhoJKSdg93Higj2gdzm7yMCdOREGNg1JwEFD0w6yvg91NTeIhWmy204KxSrebw9s202RqQFCZiR+oITw9nwrxFZI65CZVa9O4IwrFEsCQIQrdsqd7ClyVfAjAtfhqLUheddoDTtm4d9c88i2vvXgAktRrz1Vdh/clP0SQl9lqbT0qWg+uxWZLBFBfcFjcakCFtNkz4AYy4FDQ9W3lckiQuiDIzN9LEYwcreanGRr0KPhmi4uIGHaPnJqHRnfnbc1NNFSW7d1K6J5fy/XvwtDvRhZnInJaNQqFEkiRmXbcMjU5P8qixeAJFHaUEypFxAuaeu1lBOAeIYEkQhNO2vmI9a8rXADArYRbnp5zfrZ4gx5atuPbuRdLpiLjuOiJvvhl1bExvNffkWmtg91uQuxwa8uG838J5vwm+Nmwx3LUbItJ6vRn2Ng8UtnJdU4D9Bhih0jBxWgKmyGDRTW9ARt2N3qWdX3xM7qrPaaqq6LRdZwwjZdRY3E4n+rDgYsKj5s4Pva6Vx2DQb8HZXkp9/SoSEq7pgbsThHOHCJYEQTglWZZZV7GOdRXrADgv6TzmJM05ZaDUvncfkkaDLmsYANaf3YKk1RC5bBkqq7XX292JzwOHvoBdb0DBVyAHl81ApQff0UVcUWn7JlBq9/LKphI8rR6G1Pu5MCaM1JGRxKYHa72sa2zl3vxyHs5IZFFU+HHP2uVooyR3B0MnTUPdUdHcabfTVFWBQqkkcfioo0uJpA9B0UXi/ZFSAqWl/8LekktExDT0+pTeu3lBGGREUcoeIIpSCucyWZb5uuxrNlZtBGBBygJmJs7s8hh3URH1f/8HrV9+iWHaNFJefaV/c5ECfvjHeGguO7oteRqMvzFYUVvXt7+3Lq+fl74totrWTmyFi5FRYcQkmhg7LylUnfra3AK+bWoDYG6EiT9mJhLTYqNwxzaKdm6n8uB+An4/l/3qATKmTAegsaqS+tJi0sZNQGvo/tBhdfV7NNt3otclk5p6a//njwlCLxNFKQVBOGuyLPNlyZdsrdkKwKK0RUyPn37S/b1VVdQ/+yz2Dz4MFpOUJNRxscgeD5L27Jc9OW0OGxxaCeOXBqf0K5SQNgcKv4Zx1weDpKjMvmvPMfwBmeVby6hqbsda4yYrMgyTWcvI2QmdlvF4ZXQ6/yir47myWtY1tTJvSx7j929h5vY16DwuIFjzKBDwh46JTEgkMuHMc7+ioy+gpXUf7a5yWlp2YzaPP+NzCcK5RPQs9QDRsySci2RZ5tOiT9lZtxOAi9MvZnLc5BPu62tsxPbCizQtX47csSxJ2Pz5RN91J7phw/qmwX5fcHgt93XIXwkBL/xsLSRMCL7e3hSc2XaWs9nOhizLvLezkh2lTZjq3IzV6Ag3api0KBWjJRhMuhxtuB1tmGOCCed7Ssu4/esNFKSPBMDodXOH3MKPx40OFY7sSQ22tdTXr0atCmfIkLtRKPowyBWEPiZ6lgRBOGMBOcBHBR+xp2EPEhJLhi5hfMz4k+7f+tVXNP73vwAYpk4l5hf3oB9/8v17VH0+7Hod9rwNbbVHt8ePA88xy5AcqY3Uj74+UMeO0ia0zV5GKDUYtSpGzIjH47Jx8LNtFO7YRuXB/QyZOJXLfvU7AMampvAndTuVaicvqCwcAjJHTcESY+mVNkZGzKS5OQevtwlb4waio+af+iBBOMeJYEkQhE78AT8fFHzAftt+FCi4IvMKRkeN7rRPwO3GW16ONiMDAMuVV+LYtBnL1VdjnDmj73JdSjfBKxce/d5ghbHXBYfZ4kaf/Lh+kFPSyNcH61C2+xnlVaFyV9LaVsinT+6j8Tuz19qabJ1qTp237CcAXBeQ+bzBzsXRR6f2r2tsJV2vIUXfMz1ACoWamOhFVFa9RaNtPRbzZNRqUUpA+H4TwZIgCCG+gI8Vh1aQ35SPUlJyVeZVjLCOCL0u+3zYP/qI+meeRVIoGPrF50gaDZJKRdLTT3V57laXlzUH6wjXqZmSHkmYtptvP4EAFK8LDqeNvjK4LWkqhCcF12ObcCNkLgLV6S1Y25fya1r5YHsxUkDFSIdEVJiGwv1fYK8tAoIL0iaNGM3QSVMZMnHqSYfXVAqJJcf0KDV7ffw8rwSnP8DPk2O4IzUGo7L7y818l8k0GoMhDaezhPr6L0lIuPaszykIg5nIWeoBImdJOBd4A17eyX+HguYClJKS67KuIzMimAQtyzKtX66i/h//wFMU/AOviosj5d8vhXqXunKguoX3d1bQ5g4mI6uVEuOTLczMiCI2XNf1wY1FkPtmcPkRezmY4uGe/cGkbQCvC9SnOEc/aaqpYteGjWz/dgPK+lIi5/6G9LAIjGYNSuU+aosOM3TSVNLGTTyj2WvlLg93HShjU3Nw1lyCVs3vhyZweYzlrHv3XK4qSkqeQ0YmLfVWUUpAOCed7t9vESz1ABEsCYOdx+/hrYNvUdxSjEpSccPwGxhiGRJcmmTTJuqfehrXvn0AKC0WrLfeSsTSG1CcYoab2+fni701bC1uBCAuXIdKKVHRdLSuUUZMGLMyohgWG3b0D7y7Lbhwbe4bULrx6Al1Zhh9NSx4MPjvASbg91OVf4DCndso2rHtuOG19Mk/JCZ1PBMXp2II75keMFmW+azezkOFlVS4gsn1U81G/piRyPhww1mdu7r6fZrtO9DrkkhNvU2UEhDOOSJY6kMiWBIGM7ffzfIDyylrLUOj0LB0xFJSw1MBcOXlUXzlVQAoDAYif/QjIm/+McqwsFOet7zRyTs55TS0eQCYnRnFBSNjUSokSm1ONhY2sL+qhSPvQNEmLTOHWhmfYkG7+rew9fmOM0kw9PxgGYDhlwzYXiSAvWtWseqFfxzdICnwRKejixnOsOjRGE3RjJmXhDXh1M+vu9r9AZ4vr+MfpXW0BwIoJdiRPYo47Zmv8+bztVJY9CSBgIeE+GtEKQHhnCNmwwmCcErtvnbeOPAGlW2V6JQ6lo5YSkLg6BuGbuRIwhbMR5OYiPXWW1FFRp7ynIGAzLpD9Xx1oJaADOF6FddMSiYj5miAkBZlJC3KSKPDw669+2D3mxx0TuHD1hF8ub+WBeYFTIv4EuWEH8C4G8DcD+vGdaG5prqjOORWhk2fzbiFwSTztPET0ZnCSR03kRJ9KlVhqZiVBka1K1ArFQwZH90rgRKAXqngnrQ4rouL5C9F1UgSnQKlQqeLoYbuBZoqlYko63nU1a+ivv5LTKYRopSA8L0kepZ6gOhZEgYjp9fJ//L+R42zBr1Kz1LzQhT/eYfW1asZuvILVNHRAJ1mZZ1Ko8PDOznllNqcAIxJNHP5hAQMmu98LvO2w8HPgsNshd8AMrVDr+a1mF/T6PCCLKOQYExSMK8pOfLshpPOVsDvp+rQgWCA9J3htdSxE7j6d4+Evvf7fbydU8XeSjt6Gaa71ahliE0NZ8TM+D4bygrIMoqOaxU4XczeepAZljB+kRbLDEvYabcjEPBSVPx3vN4moqLOF6UEhHOK6FkSBOGk2jxt/C/vf9S11xHhUHDJejut7/8QOgpKtn7zDRHXBmdAnc4fVVmW2VXezMe5Vbh9AbQqBUvGJzAh+ZhEY1mGqp3Bmkj73gOX/egJUmcRO/5CfjkqiwM1LWwqsFHU4GB3hZ3dFXZSrQZmZUQxMj4cRTcWlu0Jfp+Pl+64GUdTY2hbcPbaKIZMnMbQSVM77b9yfz17K+0okZke0KGWA5gitGRNj+vTnB/FMdfKsTtQSRIbm9vYmNvGNLORe9JimRthOmWbFAo1MTGLqax8s6OUwCTUaksvt14QBhbRs9QDRM+SMJi0eFp4bf9rtDRWM2ZVMRmrDkJ7MOHaOCOb6HvuQT9mzGmfz+nx8eGuYE8KQKrVwLWTk4k0fieBOeCHv48LzmgDMCcHh9jG3wCRQ447b2VzOxsLGthT0Yw/ENwWYVCTPdTKlLRIdOqznyL/XT6Ph5I9u6grLmTGNUtD21f8+ffUFh4mfcJkhnTMXtMZjx9O21jQwKd7qkGWOU9jQNPiQ61VMnlxGrqwM88d6gkVLg/PlNWxvMqGp+Ntf2K4gXtSY1lgPX6h3mPJskxZ+b9xOksIDx9LYsJ1fdVsQehVIsG7D4lgSRgsml3NvJb3Gva2Bi659zN0zcHhMt2YMcT84h6M2dndOl9BXRvv7iinpd2HQoIFI2OZmxkd7P2xFULucjjvPlB2BAqb/gnVu4NFI9PngkJxymu0uLxsKbSxrbgRhydYekCrUjAxNYKZQ61Yw84uh8bn8VCyeyf5m9dTtHMbno7A8ZZnXyE8KjgU6WhuQm8KR9FFDaN9lXaWbytDlmFmmAFjvRdJkhg/PxlLbP8OIx6rxu3lubI6XqtqwBWQsaiU5GSPJEzVdfB5bCmB1JSfYTCk9lGLBaH3iGCpD4lgSRgMGhz1vH7wDeweOxHaCC7/rBHvlhyi774L04IF3Roi8voDrNpfy4aCBgCiwzRcMzmZZLMGDn8J2/8NhWuCO1/zKoy64qzb7/UHyC1vZmNBA7UtbiC4Ru6IOBMzM6JIjzJ26x6qDh1k18pPOgVIAGHWKIZNm8nkS67AZI06rXOV2hy8vKEYr19mksWItTo4AzBzcixJWf2/zMqJ1Hu8/KusnmiNip+nxADBHqSvG1uZF2lCeYJnKUoJCOcakbMkCAIAciBA5cfvUv30UyhumYp15DBuGnUTYcNVSFotUjcrPtfYXby9vZyaFhcA09IjuTBdgXbPs5DzKrQcSX6WIPMCCO+ZmWxqpYIpaZFMTo2gsL6NDYcbyK9tI6+6lbzqVhLMOmZmRjE20YxKeXyPldfjJuDzozUEe3nstdUc3LgOAJM1mmHTZzBs+iziM7KQTqPH64j6VjevbS7F65cZbjEQ2+DDB8QPNZM4zNITt94rojVq/pCR0GnbV7YWlu0tJtOg5e7UWC6LiUB1TI5YdPRCWlr30e6qoKUlF7N5Ql83WxD6hehZ6gGiZ0kYiGRZxrFhA1WP/w1//mEA6qZnMOnFNwnTdH/6uizLbCq0sXJfDb6ATJhWyZUTkxhhaIG/j4dAMDkcfSRMXAaTb4aItJ67oROoa3GxqdDGzrImvP7gW5lJp2L6kEimplvR4qM4dweHNm+gaOd2pl15HdMuvwYAt9PJ5hXLOwKkYd0KkI5odXl5fl0hjQ4vSeE6JjoUuNq8mKP0jF+QjOIEQdtA9la1jQcLqrD7gsOd6XoNd6XGclVsJOqOoMlm+5a6+i9Rq8IZMuRuUUpAGNTEMFwfEsGSMNA4d+6i/skncebkAODVqahcMoXZv3iMMEt0t89nb/eyYkcFBXVtaPxOZuhKmLHwSky6jlykly8AOQBTfgojL+/zwpFOj49txY1sLrLR0upEU52PrmIf2uqDyF5PaL8hk6Zyxb1/6JFrun1+/r2+mIqmdqwGNeepDbTWONHqVEy6MBWtoX8Tus9Ui8/PKxUNvFBRR6M3GDQl6zTcmRrDDXFWFPgoLv47Hm8TUdZ5REcv6OcWC8KZE8FSHxLBkjCQVP/+DzS/+y4AfrWCgvMzab72fK6bdgt6lb7b59tXaeeDXZWEtRxmRuOHTLKvQin7kH5xAAwdRSrdraA19eRtnBGv18e/br0Jr6MltM1vjMCQNZEp581jypSxKM6gB+m7AgGZ1zaXkF/bhlGjZElMBLbDdhQKiQkLUwiP6v5zHmgcPj+vVtn4V1kdDV4fI4w6vp6ShUKSaG3dT0XlchSSmiFD7kKtHph5WYJwKiJnSRC+p3SjRsH771Eyawh7Lx1BdNoIlo5YilbZveESl9fPZ7mluPd+zI22DxjiyD36ojUDmkuPBkv9ECh5XO0U78qh6tBB5v3wFgDUahXpY8ZSW3SY2HHTsEUP55DfQhMSlZWw0X6YGRlRTEixoD3F7K+TkWWZj3ZXkl/bhlopcVlaNA27bQAMmxp3TgRKAEaVkv9LieHHiVG8UWUjTa8J1W5S6LP41DuNBaoc6upXiVICwjlP9Cz1ANGzJPQXb10dtuefxzB5MuEXXQRAYcMhPt3wMs2xBtLD07l++PVolN1btLXU5mDTmo+5OP93hPuCgYAsKZGGXxQcakufG5yK1sc8rnaKdmzj0JaNFOfuwOcJzor70RP/wpqUDARzkTR6fWimVpPDw6ZCG9tLGnH7ggWb9GolU9MjyB4Shbmbw2XfHKxjVV4tkgRXj4ynLbcRvy9A0rAIMqfE9uDdDlzPl9XxUGEVEQoX1xgP8f+y5hBtSuvvZglCt4meJUE4h/nb2rC99G8aX3sNub2dtvUbMF1wAYdbi3in4B38sQYyLBlcm3UtasVpBgOyjN9hY02Zn2/y6zAGEjD67fgMMaim/Bhp4g/7bY22qkMH2P7x+5Tk7sB3TA6SJTaeYdNnotYdzZE6MtvtiAijhovHxjN/RAw7S5vYWNhAo8PLukMNrD/cwJhE82kvqbKzrIlVebUAXDwiDm9+C35fAEuMgaGTYnrobge+OK2aFJ2GMhe82DqWt3fWc1uqjp8kRRN+hj12gjCQiZ6lHiB6loS+Ivv9NL//PvVP/x2/Ldjjox83juhf/IKyDBPvHX4Pv+wnKyKLq4ddjUpxGp+H2pth95v4tv2bmoCFZ1OeBmBCsoXLoirQJk8CVfd6ps6Wp92J3+dDbwr+PhXu2MqHfw2uv2aJi2fY9FkMmz6LmLQh3a71EwjIHKxpZWNBA0UNjtD2VKuBmUOjGJVw4iVVCupaeWVjCQEZZmdYSWzw01jtQGdUM2lxKhrd9+uzpzcgs6K6iicKi6nwB2dXmlVKbkmK5pdpsaIGkzAoiJ4lQTjHtO/eTfVDD+M+cAAATVoaMb/+FWHnn89+234+OLSCAAFGWUdxRcYVKBWn+IRfvQe2/xt577tIXicqIEphwEoLC6eMYlyyBUju7dsKcTudFO3YSv6WjZTs3sHkS65g1vU3AZA6diLTr7qezKkziE5NP6s/xAqFxMiEcEYmhFPV3M6GjiVVSm1OSm1lWAxqsocEl1TRa4LPsNrezutbygjIMC7JzDCfivLqFpQKidFzEr93gRKAWiFxQ2Ii87WFvF2+hdfbRlPqC+OAo10ESsI55/v3Gy4Ig1SgvR33gQMowsOJ/r/bibjhBiSNhty6XD4u/BgZmbFRY7ks4zIUUhczvgq+hrX/H1RsA0ACarTpbIm6EnvGFdwybXi383jOlNvppHDHVg5t2UDJ7p34OxbyBagrLgz9W6VWM/PaH/T49RMseq6dnMyFo+PYUtTI1iIbzU4vX+yrYc3BOiamRjAm0cxb28tw+wIMiTIyJyqc/M01AAyfEY8psm/LJAw0UZEzuMi+jfN1X7BLfQGT4rJCr5W2u3m1soGfJ8cQox2cpRQEAcQwXI8Qw3BCb/C3OXDt24dx+rTQtqa33sK0aBGqiOBU7ZyaHD4r/gyAiTETuWTIJSf+VC/LRxOyd78NH/wMWaEizzKPDRGXUx42lgtGxTM7M6rPegXkQIAXb/8RbU2NoW0RCUlkTZ/JsOmziEpJ6/MeCq8/wO7yZjYcs6TKETEmLT8Ym8iBNRX4AzIpIyMZOuH7k6fUlSOlBCRJxdAhd4dKCfzqYDmvV9vQKSSWJVi5PSWGeG3fDukKQldEnaU+JIIloSfJfj/2Dz+k7qmnCTidDF35BeqYzn+UfQEf22q2sbp0NQBT46ayOG1x5+AiEIDCr4PrtKXPhezbAfC6nRz+6G98IM+lTW0lxqTl+qnJxJt7b8q71+OmMGcrJbk7WfTzu0Lt/PL5f1CVn8ew7GAOUlRy6oAYwpFludOSKuF6FT+dlkrBumpcTi/WBCNj5iYhnSC36ftIlmXKy/+Dw1lEuGkMiYnXA7DG1sLjJTXsbAku2KyRJG6Ij+T/pcaSpBNBk9D/RLDUh0SwJPQUZ04ONX/5C+68YF6SOjWFhMcfx5kRT2VbJZWtlVQ5qqhx1OCXg9WVZybMZH7K/KNBhsMGua/D9peDtZAAIofA/9tJVce6bnWtwV6TGUOtLB4dh7oXluWQAwEqDu4n79s1HNqyEU978A/m0j89QXxmcKjG5/GgVKsHRIB0MnanF5VC4tD6KprrnBhMGiYuTkWtEbO+juVyVVNS8iwyMqkpt2AwpAHBQOrbpjaeKqlhiz2YUK+WJG5OiuLhjP6ZXSkIR4gEb0EYRDwVldQ9/jitK1cGNxgNtNy4iMPzh1HZ/jmuXNdxx+hVemYmzGRGwoxgsFGxA7a/BPveB3/HEJLODON/QGDSj1l/uIHVeTX4A8H1066elMSw2J4vJtnSUMeer1aSt/4bWhvqQ9tNUdGMnD2PsEhraJtKM/B7F8wGNYe319Jc50SlUjB6bqIIlE5Ap4vHbJlMc/N26uo+JzX150iShCRJzI00MTfSxKamNp4sqWFDcxv6HqikLgh9RQRLgtCPPH4PVbUFOJfciOR0IUsSRXOHsO+KMbjDteAM9gypJBXxYfEkhiWGvixaS+cemc3PwP73g/+OHwdTboHRV9HsU/FuTgVFDcGk5FEJ4VwxIRGjtud+/WVZDrWluaaGrR+8A4BGb2DY9FmMnDOPpOGjzmix2r4gyzKBgEzAf+QrEPp3c62TikNNAIyYGY/RLBaOPZnoqAW0tOyh3VWJvWUnFvOkTq/PiAhjRkQG25rbyDAeTYxf39jK8mobd6bGMiLs3KiALpxbRLAkCH0kIAeoc9ZR2VZJVWslFY5K6p31yMiMm52KpbSJ3KUTaUmOIFofTUJYAolhiSSZkojWR3cuBdBUCmv/BpN+BNHDgtum3QoqbTBISpwIkkRueTMf5Rbj8gbQqhRcMjaeSakRPTLsdSQP6cD6b4hISOK8ZT8BIHnkaEbNnU/6hMkMmTQVtebUwUVXwcpx3wdOti3QxfEygcBJzhkIfp1K+pgoopL6f/27gUylCiPKOo+6+pXU16/CFDYa5QmW2ZlqCev0/ZOlNWxudvBBXTPTzUZ+kGDl4mgL+l4YHhaEMyFylnqAyFkSvkuWZexuO5WOjjyjtiqqHFV4A16sh+sZ/9YudiybTHNaJCaNiSRdPInmZBJMiSSEJZx8HbeKHNj0TzjwMcgBmHYbXPjYcbu1e/x8vLuS3HI7ACmRBq6dnIQ17Ox6ReRAgIoD+8hb/02nPCR9uJnbnn8NhTIY0Pm8flxtPlwOb/CrLfhft9OLzxM4PuA5jWClr0iAQqVAoZBQKINf0ckmhkyIHtC5VQNFIOCjuPjveLyNRFnnEh19wSmP2dvq5OnSWr6otxPo2GZRKbkmLoIbE6wMNw7M3ia3pwG/rxWDIb2/myKcIZHg3YdEsCS0+9qpaqsKJmG3BYOjNm9bp30MNgfjV+wlaUtJcEP2RBJf+BfhmlP8zAT8kP9FcJitbPPR7UPPhxl3wtB5nXYvqm/jnZwK7O1eFBKcPzyGeVkxJ6xK3R05n37Azi8+7pSHZIyIImV0NvHDpqPWReFq8+By+PB6/Gd8HQlQKBWhQOVo0HLMthN9rzjB64qT769UKpAUnb9XKCUxw60HtLYeoKLydSRJxZD0u9BoIk/ruGq3hzerG3mjykalO1hza7zJwMrJw3qzud3W3l6OzfYtrW15AERYphITczGK06mYLwwoIsFbEHqJL+Cj1llLZWtlKDiyuWzH7adAQawxliRVFMkf7UD11pfg9oAkYbn6KqLvugvVqQIlWYaXF0Lljo6TqmHMNZD9fxA3unO7/AG+OlDLt4cbkGWwGjVcOzmZFOup1zzrfEkZnydAU3UDMjo87X5cDi8le0ppbahHqdJhSRhLZNIkwqzpSJKC5nqA1k7nUWuU6IwqdGEadEY1WqMKnVGNWqM8ecCjVCBJiB6cQS4sbDhGw1AczkLq678kMfGG0zouXqvhF2lx3JUay9rGVl6vsrEo6ujviN3r44mSWpYmRPZ5b5Msyzgch7E1fovTWRzaLiHR1LwNl7uGxIQbUKvFB+ZzkehZ6gGiZ+ncJcsyNpct1FtU2VbZadr+sSK0EcHka1MwATvOGEf76m+o/fOf8dXVAWCYPJnY396PbuTIk1/U0QAG69Eikl89BDn/gck3w9RbITz+uEPqWoIlAarswVlzk1MjuHhsPDr18bO2ZFnG6/aHhsaODpX5cNgd1BTm0lCynZb6Q2RM+wnhMcFp/q62etrtlZjjRqFQqtFolWiNanRGNfow9Xf+rUJ1gmsL3x+dSwn8tEeGql6uqOd3hysBmNqR23RpL+c2yXKA1tZ92Gzf4nJXAyBJCszh44mMnI3X20xV9Tv4/e2olEYSE28Qw3KDiBiG60MiWDp3tHnaQr1FRwIkl//E0/YTwxJJCksKJWIb1Mf34DS99RY1Dz2MOjGRmHvvxXTBwpP3mtQdDA617XkHlr4VHGaD4EK3ChVow447RJZlthQ18sW+arx+GYNGyeXjE8iyhtF+JE/I4e30b1ebF/8xOUKyHKDNVkRjxU6aqvYQ8B2939Rxixkx+wp0YcFASGdUB/9tUKNUi+RboWs1NR/R1LwNnTaetLTbkbpahuc0bGtu44WKelY22PF3/AibVUqujo3gBwnWHp1JFwh4sdt30di4Ho83WGVeodBgsUwhMmImarU5tK/H00hl5Ru43DVIkoKY6AuJiMgWPaSDgAiW+pAIlgY3u9vOmrI1lLaUYvfYj3v9yLT9hLAEksKSTjxtv4O3uhpfbS368eMBkH0+mleswHzFFSi0J0iulmUo/jYYJB1edXT79Nth8aNdtrumpo3PtlZQUdOGwisTq1MzPNKI7A6cMmFaAjR6FZLkZOdnj+NqPbrkiMkazYjZ8xg5Zx7WxL5bSFc49/h8bRQWPUkg4CY+7goslsk9ct5at5e3qht5vdpGucsDgFKCPTNGY9WcXXaJ3++iuXkrjU2b8PmCeYdKpYHIiGwiIqajVJ54WDsQcFNT8xH2lt0AmMPHExd3GQrFwK8l9n0mgqU+JIKlwauhvYH/5f2PFk8LEMw/iNJHdapnFGOI6Txt/wQC7e3Y/v0ytpdfRmW1MuTzz04cHIUO8MO+92DTP6Bmb8dGCYZfHEzaTpmGLMu0e/00Ob00Oz00O700O700tbqwH7TTXtOOX5ZRAMmRBmLDdcEoKHgmtIYjPUEqtIbg8JgsO2mzVZIxZRIKpQJZlnnlFz/H0dQ4KOohCYOPrXEDdXVfoFKFMST9FycsJXCmArLMt02t/K/KhlKSeHFUWui1f1fUM8MSxsjT7G3y+VppbNxEU/NWAoFgUVe1ykxk5CwslkkoFKdXAqOpaRN19SuR5QA6bTyJiUtPO8Fd6HsiWOpDIlganGocNbye9zoOnwOrzspF6ReRaEo8+bT9E5BlmZZPP6XuiSfx1QSLPuonTSLxicdRx8V1dSDyc9OR6g8SUOmwZVxDYcYPqVEm0NQRGNnbvbh9gU6HqVp9GKtdSL7gr60hUsvU4dHERBk65QtpDerQ7Lcj9ZDyvl1Dye6dqLU6bnvxf6H6R7bKcsKjY06rHpIgdFcg4KO45B94PDas1jnERC/qnevIMoqO3t5ip5vsrcElgyaFG/hBgpUlMRaMyuM/9Hg8DTQ2bqTZvhNZ9gGg1cRgtc4hPHwsktT93Duns5jKyjfx+R0olXoS4q8lLGxgzegTgkSw1IdEsDT4lLeUs/zgclx+F3GGOH4w8gcY1cZunaN9zx5q//wX2ncHu93VCQnE3PtrTIsWIUkSHl8Ae3uwV8hRV4x532vsTL8Fm0dFs9NLcvWXRLrK2Wa9HKfKfNLrhGmVWLRqDHVulA0eNCoFYWYtWdlxDE2znLAkgBwIUJ63jwMbOtdDAogbmslFd/6aiLiEbt2vIJypMy0lcKYOO1w8VlzNygY7HZ8rMCkVXB0XybIEKyPD9LhcVcHp/637kAnupNenYLXOJcyYddb5Rl6vncrK5bS7KoI91tELsEbOFXlMA4wIlvqQCJYGl8LmQt7OfxtvwEuKKYUbht+ATqU79YHHcB06RPGSywCQdXrar/0BtRdcSaNPCgVIbW4/ic6DzK5/k9H2tSjx81HCL9gSdWXoPAoJLAY1Fr0Gs0FNhEFDhEGNxaDGrNdgMahxNLg4sLkal8OLBCRmRTBkfDRK1cmHyrZ9tIL1y18NfR8eHcOIWcE8pMiEpG7dqyCcLVmWKa94FYejAJNpFEmJS/vkuvWejtymKhulHblNAH+LK2KytCP0fZhxGFbrHPT6tB4NZgIBH7V1n9LcvB0Ak2kU8XFX9ehQpHB2RJ0lQTiBPFse7x9+H7/sJ8OSwbXDrkWtVB+3XyAg0+ry0eT0BIfF2r3YHR6a2r2hIbIZIybj1hvZdcENtIdHQlmwzpAkB8hq3cTs+rcY4sgNnbM+ejopIyaRlpYcCpBMOtVJi0X6fQGKdtVTkR9cl0xvVDM8Ox5LbOcEU6e9mYMb1xGVkk7K6LEADJs2k20fvcuwaTMZOft8EoePFHlIQr+RJImYmAspKX6G1tb9OJ3FfTK9Plqj5v+lxnJ7cjSrq/fzv8padrSHMZpcJCTCw8dyUDmNGEM0yYbu1SM7HQqFivi4y9HpEqmt/ZTW1v143HUkJt6IVhvd49cTeo/oWeoBomdpcMity+Xjwo+RkRllHcXlGZejUqhodXnJKWmivs0dSqS2t3sJTSiTZdL2bGLMN++z+ie/x2WyACD5/YQZtZj1wZ6gCIOGSI2fCV9cjrb5cPBQhQpp9FWQfQfEjz3tttrr2zm4uRpna/DTcEKGhaETo0O1i3weDwU5W0J5SHIgQMaU6Vz2qwdC5/D7vChVxweCgtBfamo+pql5a4+VEjiVQMBHS+tubLZv8XgaAPCiJcYynsjIWajVEczZdpDDTjfjTQZuSrByWYwFo6rna4S1t5dTWbkcr68FhUJLQvzVmExd1FsT+oToWRKEY2yp3sKXJV8CMDFmIhcPuRh/ANbm17E2v/64RGoIDpGl1pcw7qNXCC8MJotecegbDHfdg8WgwaxXo1YqwN3WuQbSzkxor4PJP0KaeiuYE0+7nQF/gOI9DZTnNSIDWr2KrOlxWBOC57dVlLHn6y/J+3YNrrajFbPjhmaSNq7zCu8iUBIGmqio82lp2Y3LXY3dvrPHSgl8VyDgprk5h8bGDXh9wZmuSoWOiIjpRERko1IFf59afX5GhOkpafeQ2+okN9/JHwoquTI2gmUJVsaYeq63Sa9PJi3tdiqr3sLpLKGi8g2irHOJilrQ60GjcPZEz1IPED1LA5csy6yrWMe6inUAzEiYwfzk+eRVt/L53mqanMH1p5Ii9IxONGPp6CUytTXT/q9naPnwQwAkvR7rLT/FevPNKHQd+U0Nh4P1kfaugJ9vgojU4PbmctBbQNu9FepbG10c2FSNwx6cthyXHk7G5FjUmqOfcv/3m7uoKykEIMwaxag580UekjCoNDZupLbuc1RKI0OG/LJH83d8PgdNzVtoatqM398OgEplIjJiJhbL1JNeq97j5Z2aJl6vaqC4/Whu0x0pMTwwtGcnQsiyn7q6lTQ2bQIgzJhJQsK1J63fJPSucy7Bu6mpiTvvvJOPP/4YgCVLlvDPf/4Ti8Vy0mNkWebhhx/mxRdfpKmpiWnTpvHss88yatQoABobG3nwwQdZtWoV5eXlREVFcfnll/PII49gNp98dtJ3iWBpYJJlmS9LvmRrzVYAzk8+nzTDRL7YV0NxQ3B2WLhexeJRcYxPPlpk0vbyy9Q/+xyyM7iP+bIlRP/iF6hjY4NFJEs3wqZ/wqGVRy+28BGYeecZtTMQkCnbZ6Nknw1ZltFolWRNjyPgq2ff2tXMvHYZ2o58ij1fraRoVw5j5y8ibfxEFKeo/yQIA02wlMA/8XgasEbOISbm7EsJeL1N2Bo3YG/eQUAOfgDSaKxYI+cQHj7+tBe4Dcgym5rb+F+Vjc/r7SwfO4TZkcEPPZUuDzavj7E91Ntkb9lNTfUHBGQvGnUEiYk3otMdv5SR0LvOuWG4pUuXUlFRwcqVwT9QP/vZz1i2bBmffPLJSY/561//ypNPPsmrr77KsGHD+NOf/sTChQvJz8/HZDJRVVVFVVUVjz/+OCNHjqS0tJTbbruNqqoqVqxY0Ve3JvSCgBzg48KP2V0fnNY/J+ECamuT+aKsCFkGtVJiTmY0s4dFof1OfoKvrg7Z6UQ/bhyxv70f/bhx4PcFe5A2/ROqczv2lCDrIphxB6Rkn1E7Hc1uDmyuprUxuMRIRKyagC+fL597jprCYN5TVFIqYxcsBmDsgsWhfwvCYKRQqIiJuZCKiv/R2LQJi2UyGo31jM7ldtdis62npXU3shwcStfrEom0zsEUNrLbw1sKSWJWhIlZESYaPD4ij1nf8KWKep4vr2esSc+yBCtXxEQQdha5TebwcWg1MVRWvoHH20Rp6QvExV+BOXzcGZ9T6D2DomfpwIEDjBw5ki1btjBt2jQAtmzZQnZ2NgcPHiQrK+u4Y2RZJiEhgbvvvpvf/OY3ALjdbmJjY3nssce49dZbT3itd999lx/84Ac4HA5UqtOLJUXP0sDiC/h4//D7HGg8ALJEsnoOZdXRobykCckWFo2Kw2wI5vS48g8hKRVoMzIA8NvttK3fQPjFFx2dRuyyw5OjwNMKKh2MXwrT/w+iMs6ojXJApvxgI8W7G/D7A3jaKnG37aZ0z2a87mDgpFCqyJgynUkXX07CsOFn+VQEYeA421ICTmcptsZvaWs7GNpmNGZgjZyDwTCkV2oZ/fZQBa9X2fB0/Mk0KBVcGRPB0vhIJoQbzviafr+Tqqp3aHMEPxxFRswgJmbxGRXDFLrvnOpZ2rx5M2azORQoAUyfPh2z2cymTZtOGCwVFxdTU1PDBRdcENqm1WqZO3cumzZtOmmwdOSBdRUoud1u3G536PuWlpYzuS2hF3j8Ht7Of5ui5iKanX40zhkc9lmBACmRBi4ZG09yZLAbPeBwUP/sczT+978YJk4k5bX/IkkSSrMZ8+xxwXyk7DtAkkBnhll3B5cpmfITMEadcRudLR4Obq7G3hDMqQiz+Nnw6T9Cn4wj4hMZM38Ro+acj8FsOcsnIggDjyRJxMZcRHHxP0+7lIAsyzgch7A1fovTWRI8DxJhppFYI+ei15/+RIoz8ZdhSfwyLY53aoJ1mwrb3bxebeP1ahtTwo18MinzjM6rVBpISrqJhoavabCtpbFpEy53NYkJ16FSdS/vUeg9gyJYqqmpISYm5rjtMTEx1HQsMXGiYwBiY2M7bY+NjaW0tPSEx9hsNh555JGTBlJHPProozz88MOn03ShD7X72ll+YDn5DSVUNHmx+s9Dp0zErFdz4eg4xiaZQ5/+Wr/+mpo//RlfdTUAyogIZJcLqSk/GCTt/wACPogfD+mzgxeY86uzap8sy1TkN7Hnq6202kpJHH4+GZNjiRsSTs2h6ag0Wsaev4jEEaNElV/hnKfVxmKxTKWpeSu1tZ+SlvZ/Jxw2k+UALS17sDWux+0Ovq9LkhJz+AQirbPRas78g0t3WTUqfp4Sw23J0WxudvB6tY2VDXbGmI6uP+cLyPwyv5y5kSbmR5owq0/9Z1aSFERHL0SnS6SqegVOZzElJc+RmLgUvV4sZj0Q9Guw9NBDD50y6Ni+PVj59ER/PGRZPuUfle++frJjWlpauPjiixk5ciQPPvhgl+e8//77+cUvftHp2ORk8QPdn9o8bby85zV2VpbQ5IB0zSIitHHMHRbNrMyo4BR/wFtVRc2f/0Lb118DoE5MJPaBBzAluuHta6Bk/dGTps0G9ektwnkqTdU21r/5MeX71+NqqwMk5v9oCdGpwYkEl95zvwiQhO+dqKj5tLTuweWuodm+gwjLlNBrgYAHu30ntsYNeL3BwqwKhYYIyzQiImagVvdfyoMkScyICGNGRBgufwCH/2jpka32Nt6uaeTtmkZUEmRbwlgUZWZxlJkknabL85pMI0nT/JzKyjdwe+opLfs3sbGXdHouQv/o12Dpjjvu4Prrr+9yn7S0NPbs2UNtbe1xr9XX1x/Xc3REXMcipjU1NcTHH51hUFdXd9wxra2tLF68mLCwMD744APU6q7r02i1WrRdrSgv9Klah43HNr7EYVsNClnHUM1FzEwfwsKRsZj1R/9ftu/dR+lNNyG3t4NKhfXmm4m6cQmK92+CdfuCO0lKGH1lcPgtYfxZtUsOBCjbv4ftH39K2b7tyAE/AEq1huEz56DRH81JEIGS8H2kUhmJss6jtu5zGupXE24aA8g0NQWn//v8juB+SiMRkTOIsExDqeyZDzA9RadUoFMe7RFL0mn4fykxrGywc9jpZn1TG+ub2njgcCWjw/Q8lJHArIiTD69ptdGkpv6c6pr3aG3dT03Nh7jaK4iNvfS0Z/UJPa9fn3xUVBRRUafuQs3OzsZut7Nt2zamTp0KwNatW7Hb7cyYMeOEx6SnpxMXF8fq1auZMGECAB6Ph3Xr1vHYY4+F9mtpaWHRokVotVo+/vhjdLrurREm9B9ZlvmmoJAXcl+lzduKRgrjvPiruGr8cJIijp/eqxuehSY5GUW4ifgHH0SbmRnMQfK5QBMGk34E038O5p6pWbR3zdesfunvoe/DrMlMXHwRYxfMD5UCEITvu4iI6TQ1b8PjaaC84r+43TUEAsFaR2p1BNbIWZjNk1AoBkeR1VS9lt8NTeB3QxMocrpZ2WDnywY72+0O9rW1E6Y8+iFpf1s7jR4f0y1hqI9Z9kip1JKYcAO2xnU01H9Fsz0Ht7uGxMSlqNWnX9ZG6DmDYjYcwIUXXkhVVRUvvPACECwdkJqa2ql0wPDhw3n00Ue54oorAHjsscd49NFHeeWVV8jMzOQvf/kLa9euDZUOaG1tZeHChTidTj744AOMxqOrzkdHR6NUnt5sBDEbru+V2hy8uXMPm+s/xIcLi8bKnZNvZmpqYqiXxtfURON//0vU7bej0GjA58G3/mWUxR8jLfsA1B2BcfVuMCeD4cxXQg8E/JTuyQUgbdxE6kpaObiplN2rHsUSN4pxFyxm7LyJSCdZB04Qvs/a2vIpr3gt9L1WG4fVOodw05hzprp1g8fH2sYWroqNCL1H3XWgjLdrGjGrlMy3hrMoKpzzI8MxHVOSoM1xmKqqt/H721EpjSQm3tAn6+p9X5xTs+EA3njjDe68887Q7LYlS5bwzDPPdNonPz8fu90e+v7ee++lvb2d22+/PVSUctWqVZhMwS7QHTt2sHVrsGBhRkbnKeDFxcWkpaX14h0JZ6LJ4WHl/ho2lR2kxLMKSfIxLjad38z4KWZdcAkDWZaxf/AhdX/7G/6mJhQqJVHT9LDhaVT28uCJdr8Jk38c/Hf8mdc1abU1sG/tavZ9s5qW+jqsyWm02WNpqGgD1GRf+wgjZyVhNIthW0E4GaNxGNbI2bg9dURYpmE0DjvnhqajNCqujuv8gcyqVhGlVtHg9fF+bRPv1zahliRmRQTznG5KsBJmzCQt9XYqK5fjcldTVv4fYqIXExEx45x7RgPZoOlZGshEz1Lvc/v8rMuvZ/3hBpq85ZR6V2M1qchOHs6PxtyItmMZA3dBATUPPYwzJwcAbWIkcRNtGIzBWW8YY4KVtiffDBrjyS7XpYDfT9GuHPZ+vZLiXTtCU/41eiORiROJy7oIpUpD2hgrKaOsKERvkiAIJ+GXZXa2OEPDdQXOYFmaYQYd3047Wl+tzOlA2fQ5La25QLCoZVzc5SgUXSeNC10753qWhO8nWZbZWdbEl/traXX5aPYXYVduZGR0GONih3NN1jWoFWoC7e00/Ot5bP/5D/h8SDot0WOcRKbvQ1IA4Ykw826YuOysZ7iteuGf7F/3Vej7hKxRRKVOQ6HORKFUE2bRMjw7HlOkyH8TBKFrSkliitnIFLOR3w9NoMDpYmW9HcsxJQfa/QHmbi8kWjOKuWHpTJQ3Mca+B7e7lsTEG9FozjyFQDg9IlgSBqziBgef7amisjlY0dqnLkQXnkOcwcjoqNFcnnE5yo610Woe+RP2998HIOy884h94AE06+6B+oMw+xcwbimouv8JzO/zUpizlbihwwiPDtb6GjZ9JkU7tzHqvAUkj55FTZECd7sPCUgZZSVtjBWF8tzIsxAEoW9lGHTckdr5g9YBRzsyMmUuD/9zqfgfczBJHqbrqpnd8gFL0mYRZz6+OLPQc8QwXA8Qw3A9q9Hh4Yt91eyrDFZG16oUJMSXUeXZiqSQmBQ7iYvSL0JxJPHTYcPz8f9H+VOfEPPAHwm79JrgWH5bPegtoOz+LJrGqkr2rvmSvG/X4LQ3M/Xya5h9ww+BYDK31+2lZE8zVQXNABhMGoZnx2OOHljTmgVBODc4/H7WNbayssHOV7YWGr3+0Gs/M+3lzrQkrNbzRB5TN4lhOGHQcXn9rM2vY2OBDV9ARpJgaloEmvADbK/bhqSQmJkwk/MT5tL82v/wlhURO9UHOf9B43Uy5EKQLKXB5UkAwqK7dX2fx8PhbZvY+/WXlOftDW03WiLQhx2ti9JSH1z81uUIrm6elBXBkPHRKFWiN0kQhN5hVCq5KNrCRdEWfAGZ7S0OVtY38XltDTN1FdQ3HMTlqmKLciH/qWpmkdXMouhwsgw6EUD1ANGz1ANEz9LZCQRkckqbWJ1XQ5s7+GkpIyaMi8fEsatxLdtqtgEwP2U+k2zhVP/+d7jzCwBIX1SHLsIXnNE259eQdTEouh+0yIEAL9/9M+y1R5ZTUJA2fiJj5i9iyIQpKFUq/L4ARbn1VOQHqwnrjGpGZMdjiRU1kwRB6B+yLGO376Cm9hNk2ccf7XP5xnl0ebA0vSZUQXxKuBGVmHDSyen+/RbBUg8QwdKZK6hr4/O91VTbg3lJ0WEaLhwTz7BYIx8Xfsyehj1ISFwYNYfkN7+l6c23QJZRaALEjGvBMmck0tzfQObCoz1Kp0EOBKg4sI+kkWNCn7q+/s+/KMzZxuh5Cxk9byHhUUd7puz17RzcXI2zNVgsLyHDwtCJ0ajUYmVwQRD6X3t7BZWVy6lxu9nsSSZHnsCWVnAHjv6Jt6pVbJ0+gjCVeN86QgRLfUgES93X0Obmi73V5FW3AqBXK5k/IoZp6ZHIkp/3Dr1HflM+ClniinwNmpc/w9/QAIB5Sgox0yVUF94H6XO7FSS5nQ72ffMVuas+pbmmmusefoyk4aM6XnOi1mlRKI6+kQT8AUr22CjLsyEDWr2KrOlxWBPCeu5hCIIg9ACfr43KqrdwOosBMFjOY49iMl82tPCVrYV0vZYvJg8L7f/jvcVoFBITww1MCjcyOkzfaemW7wORsyQMSO0eP2sO1rG5qAF/ABQSTBtiZcGIGAwaFW6/m7cPvE1xSzFKVwtXl5WheGoPfo8CTXo6cQ8+iHHKxG4nbdsqytn15afkrfsarzvYi6U1GGmpq4WOYOm7S5C0Nro4sKkahz1Y9yQuPZyMybGoNeJTmSAIA49KFUZK8s3U1X9JY+MGnM1rGWes4KKs65ClFOo83tC+7f4Aq212fDJ8VNcMgFqSGB2mZ5LZwJwIExdEiaVVjhA9Sz1A9CydWiAgs62kka/yanF4gnlJWbFhXDQmnpjw4DRZp9fJ8oPLqa7MRVW/lxtK95Hm82Ev0eONOZ/IP70aXLakG5wtdj7/5+OU7tkV2mZNSmHC4ksYMXseGt3xs9cCAZmyfTZK9tmQZRmNVsmwqXFEp5x88UtBEISBpKVlD9U17xMIeFGrI0hKXIpOlxB63RuQ2WpvY2eLkx0tDnLsTmxeX+j1S6MtvDQ6DQjmRT1bVsc4k4Hx4YZOy7EMdqJnSRgwDte28tneampbgj00MSYtF4+NZ1js0eCj1dPK61seQ/pmNYs/byZ6XAtpCX4YeTnm234FcWNO+3oBvx9Fx7p+urAwmmurQZIYOmkaExZfQsrocSedHeKwuzmwqZrWxo4cqmQTw6bGotGJXxVBEAaP8PCxaDTRVFYux+NtpLT0ReLiLsdsHg+AWiExK8LErIjg+7AsB+s4HQmeJocfXeGguN3Dn4qCqyBIQJZRx6SOobuJZgPDDDoU5/iMO9Gz1ANEz9KJ1bW6+GJvDQdrgnlJBo2SBSNimZoeifKYGRnNrmbe2vgv0v7xP1Lzgz+OuqQw0t54DSl2xGlfr6G8lF0rP6F0zy5+9OTzqNTBobqKvH2YoqIwx8Sd9Fg5IFN+sJHi3Q0EAjJqtZLMqbHEpJrEtFtBEAYtv7+dqqp3aHMcAiAyIpuYmAuRpNPvHTrscPF4SQ07WhxUuLzHvX53aiz3DYkHwOUP0OYPEKUZHB8wRc+S0G+cHh9fH6hjS5GNgBzMS5oxNIrzh8egP5LvI8tQtJY661C+ff6PZL+9DU27DBJEXH0p0b/5A1LYqZOoAwE/hTu2kbvyE8r27QltL965ncxpMwBIGjm645IyPk8AT7sPj8uHu92Ht92Px+Wjuc5Jiy3Ym2RNCCNrWixaQ/eLWQqCIAwkSqWepKSbaLCtoaFhDY1Nm2l3VRJuGoNen4JWG4dC0XUokGnU8cKoNABq3V52tjg6eqCc5LY6GWc6ms6wubmNG/YUkabXMCncyISOHqhRYTo0Z1DWZaAQPUs9QPQsBcmyzK7yZj7dXU17R3XZEfEmLhwdT7RJe2QnOLQSvv0b5Qf3Ub4nhYhyBwDqUSNIfPgR9KNHnfJaLkcbe7/+ktxVn9NSXwuAJEkkj5rC0CkLsMRl4HUHAyN3uw+vy4en3U+gix93lUpBxqQY4oaaRW+SIAjnnNbWA1RVv0sg4A5tkyQVel0COn0yel0yen0KavXpJ3b7AjIywWE9gH9X1PPA4crj9tMqJMaGGXgoI4FJ5jNbxLw3iNIBfUgES+Bw+/hgVyX7q4JLlMSGa7lkbDwZMR15SYEAHPgIvn0CavdSolLxdUsEMz5W49NriLnnHmJuXIbUkWskB2Q8bn+wF+jIl8sf6hVqrCxn2wd/AkCpMRCVMp3otGw0hohTtlWtUaLRq9DojvxXhdagIjrFhM4oepMEQTh3eTyNtLTspr29jHZXBX6/87h91KrwTsGTTpeAQnH67412r49drU522IP5T7tanDT5gh+gv5mSxYiwYE/UippGPq+3MzHcwMRwI+PC9RiVfZs8LoKlPvR9D5YO1rTw/s5KWl0+FBIsGBnL3MxoFAop2JO091349nECdfm4HHoKrdF8mjCRgHk4w/f4GDH7aiSdGU/HkJin3YfX7efID6Yc8NNcsx+3o4G4zPND1y3b8z4GSxKRiRNQqzXBwCf0pUSjCwZCnQMjpVjkVhAEgeBogNdro729vOOrDLenFlkOdNpPkhTotPHf6X2KOO0eeFmWKW73sKPFwZWxESg7jrsjr5QVtU2h/ZQSjDDqO4InA5fGWHo9eBLBUh/6vgZLbp+fz/dWs604+MMeY9Jy9fhEVHYvLucxPUKbXsFVXUtzfSQet5rSyUn49BosGgvplvSjC+J+h9/toKlqO7VFG3E7mpAUShbf8QThUdZg8KM7GhgpVQoxdCYIgnCWAgE3LlfV0QDKVYbP13bcfiqlMRg86ZPR61LQ6xNRKLTdutbuVicbm9pCvU9V7qPJ4xJwePaYULXxZq8Pi7rn06xFgrfQq0ptDt7JKafR4UWSYObQKKZGmShYX4mrNA8iUkGtB38AV1MarnIgICNLMjqHE31iLGMSRqIzaI4bEmtpKOPA+pXkb16P3xtcXkQfbmbs/MWkj41Bb/r+BKSCIAh9SaHQYjCkYzCkAx0TY3zNx/Q+leNyV+HzO2hrO0hb20EAJCS02tjgsJ0+Cb0uBY0mqssPseNMBsaZjhYDrjqmdIHN6+u0LIvd5++VYOl0iZ6lHvB96lny+QN8fbCOdYfqkWWwGNRcNT4Byh1UbMpBrspF56shcsQIZMtEWpf/F7m0EJXfReOQMPYsHc3wiedzYfqFJ/wl2r/ua1Y+91To+9ghGUxYfClZ2bNRdbMgpSAIgtDzAgEvbnf1Mb1P5Xi9zcftp1Tq0euSOvKektDrk1Eqjy8EfDqavD4iRM+SMBjUtrh4Z3s5VR2L3k5IsTAvyULxZ+txFOSCy0685iBDYwupz2nAvvZZwgC/xcT2G0dRNj2VWUmzOT/5/FCg5Gyx47Q3E5WcCkD6hMlo9HqGTJzKhMWXEJ85XAyvCYIgDCAKhRq9PgW9PiW0zettweU6Gjy52ivx+9tpcxymzXE4tJ9WE41en4xOn4Jel4RWG4t0klSMY/VGoNQdomepB5zrPUuyLLOxwMaX+2vwBWQMGiWXj0sgbH8OpetzkJ1NaKR2siJ2ETX/WpjyU2qffobG/7yC/cJprLkoAa9Rw8KUhcxIDNY+qi0qYNfKTzi4cR2xQ4dxwx//Grqep92JRm84WXMEQRCEAU6W/bhcNZ0CKI/Hdtx+CoWmI2k8KRRAqVR9t1C56FkSekSTw8OKHRUUNQRrIWXFhnFhRgzlO+tp2FUOziZi9JWkTxiKavwLkDkSgMif38aOERq2W5uRkLhkyMWMt47j4MZ17Fz5CdWHDoauEfD7OgVIIlASBEEY3CRJiV6fiF6fSETEdAB8vjbaXRW4jhm+CwQ8OJyFOJyFoWM16shOuU86XVy3Ko73BtGz1APOxZ4lWZbZWdbMJ7urcPsCaFUKro6rIzwQSXGxLrgkCC4ylKvQBWKpffIZtEOHkrr8DXxSgBWHVnCo6RAKFFyZeSVSXi3fLn8VR1MjAAqliqzsWUxYfCnxmVn9fLeCIAhCX5PlAG5PXTBpvCOAcnvqjttPIanR6RKIjJyJyXTqosXdIXqWhDPW5vbx4TEFJsfrqllct5yi9QEKjVMhcwGR8UaGphlp/PMhmja+AoCMTLutjnfrV1HSUoJKVnLtiOvIjMgkX92Io6kRoyWCsQsuZNzCCzFaTl1AUhAEQTg3Bes3xaHTxoFlChBcy+67vU9+fzvO9lIsgcn91lYRLAmdHKhu4YNdwQKTUd5KljrfJJBziN2ubPxoUGrUDJ1kRb9rDZW/+isBpxNJqyX6zjtxXb2Qlw69TdveQgy76xk7Yz6ZMzIByJiSzSV3/4aMKdNRqkSVbEEQBOF4SqWeMGMmYcbg3w5ZlvF4bbjayzAYMvqtXSJYEgBweYMFJreXNBHureeGptcZVrOaQ45Z2HxzICINy+gpZExNpfGBe6ndsgUA/cSJxP/pT2yXytjwv0dR7qpG1xYsLFa5eTvytT9FkiSUKhVZ2bP78xYFQRCEQUaSJLSaKLSaqH5thwiWhOMKTC7RbCe6PJec9qvwmoagSJ5I+owRJA+PhICfhtZWJJ2OmHvuxnfBebz67rO0bs9H7Qmu/WOwWJhwwSWMXbBYTPsXBEEQBj0RLH2P+fwBvjpQx9aDJZg9tVgis7hiTDzeguvZrw2H5BGYUtIYmiZjzjAhKSRQqEj421+RlEqqI2DFk3+EA7VIgCE2ilmXL2XE7Hmo1GKoTRAEQTg3iGDpe6q2xcV7Ww6TVrScX9e/jk9npS17DcVb6vG4fEgZ55M60oIpdyW1v30a749+SPRdd1F5YD9GayR7vQWs3b8WaYIVY6uHOVfeyKTsC5AUYpFaQRAE4dwigqXvmUBAZmN+FU0b/8MPal4h3GfDJ6spd0+l6psi0IZhCNcwNMlH22P3UL9rFzJQsCuH1b/7JTWFh9BNHELDHCsAY0Zmc/ElF6NRiqVIBEEQhHOTCJa+R5pa28n9/EXGFf6LSE81AI26iRyKuId2QwaSQkHiMAuW3M9peOAf+DweKuOjKEtPosXRDIXNyEqJloADjSKei4ZcxLjocf17U4IgCILQy0Sw9D0QLDDZxN4Nn/Kjw38AwKWNpTz9QSq945EVSnRGNUNT/Dgeu4uG3XsojjJTlJiMGxkcrSgNOtrHROIdF0NMVBJXD7uaKH3/zk4QBEEQhL4ggqVzmSzjqCvmg2JlsMCkbjzFUfMwpMyjNLAYh0MGBcQPNZMxKYZATRWNhw6jCAtDt3A+7rzdhEVF458UT32GCtRKJsdO5oK0C1ArRAK3IAiC8P0ggqVzVdkWHF88iLJuL0VZ76LUhDM/KxZ56EscyGtElmU0OhUZwzRgdLDy2ccZdd4CEp98At2IESQb9LD5c7abS3HJbnRKHZcOvZSR1pH9fWeCIAiC0KdEsHSuqd6N/6tHUBauxgh4JQ3jOMiYiVfTuL+J0kYXANEJejzr/8sXr3yLzagDwGlv5toHH8Ub8LKu9Cu2hR8CGRLDErkq8yoidGJ5EkEQBOH7RwRL54qGw/DNn2H/BygBP0p2RF5E69R7GGtKpWxjDYGAjFIJkn0rm/+1ghZJBqMOCRg+ex6TL7mChvYG3jv0HjXOGgBmJMzg/OTzUSr6d8VnQRAEQegvIlg6FzhsyP+aieR3E0Bij2UB21N/xuwJU1AfaqWksAGAyBgd+1c8THVzPUigDAQYMXI80++4C3N0LHvq9/DZnrfxBDwYVAYuz7iczIjMfr45QRAEQehfIlgarNytoDUBUOMzUh99EUpnPatjf0pC1mQWhhkp21qPq60ZtU5Peowf/zP3EVNbSWOClSxLNNkPPkJYahoev4ePCj4itz4XgLTwNK7IvIJwTXg/3qAgCIIgDAwiWBpsnI2w6R+w7SUCN69iQ0sMq/NqCUTfhV6r5ZIRcSjKnOTl7KWucB2NFTuZce1NRLjUVOXnkxQRwaQf/x+WSy5BkiRqHbW8d/g96tvrkZCYmzSX2UmzUUiiErcgCIIggAiWBg93K2x5PhgouVsA2PP5i3xh+SkAIxIimBVpIn/VdioPfI29Ni90aENZAeF3/hpfXT3myy9DZbUiyzI5NTl8WfIlPtmHSWPiyowrSTOn9cfdCYIgCMKAJYKlgc7rgpz/wPonwBnMPXJGZPFhxE/Ya8xGq1KweHgMzs3bWLX5cxxNpaFD41w+5vzhjyRPmgqA9Sc3A+Dyufik6BPybMGAKsOSweUZl2NUG/v45gRBEARh4BPB0kAmy/DyQqjZA0AgYggbk3/GF/IMZElBqtXAwoQIavY0UrxrA46mUhQKJckuP6lFZYS5vYQdKoSOYAmgsq2S9w69R5O7CQUK5qfOJzs+G0mS+usuBUEQBGFAE8HSQCZJMPY6cNqoGn8X/3Vk0+KVUPmcjGjcy3DDVMq2eQFIGT2fWF8rSevWofX6UEZHEf/QQ5jmzweCS55srt7M16VfEyBAhDaCq4ZdRWJYYn/eoSAIgiAMeCJYGuBcE27mC9WFbKtwonA0EV28GcWhrVR7PfhK6kgZeyXRmmY0rz2Jr7QYAPNllxF7/30oLRYAnF4nHxR8QEFzAQAjrSO5ZMgl6FX6/rotQRAEQRg0RLA0gJU0OHh3Rzn28hJM+d+iLd+HLAfwA/rwBCISsxi/IAXn069hLy1GFRND3B8fxnTeeUfPYS/h/YL3afW0opSULE5bzKTYSWLYTRAEQRBOkwiWBrCvD9bhXfUqERX7QttM0cOIzTiPzMkTGTYtHpVaiem+36AMNxH1f/+HMjxYGykgB1hfsZ51FeuQkYnSR3FV5lXEGeP663YEQRAEYVASwdIAdtXERN7bnoi9Mo+IxPHEDJ2LOTKB6P2fY3j9C5QznwNAaTYTe//9oeNaPC18cPgDSlpKABgfPZ4L0y9Eo9T0x20IgiAIwqAmgqUBTO2VGZk2D3vYJLSGSCy+evT//gWBihLagPbcXAwTJnQ6pqCpgA8KPsDpc6JRaLh4yMWMjR7bPzcgCIIgCOcAESwNYPlba3A5VRgMZqz7P0Xx/svIgDohgfg//6lToOQP+FlTvoZNVZsAiDPEcdWwq4jSR/VT6wVBEATh3CCCpQEsa1ocB1ZswbD8/0OqLAEgYukNRP/ilyjDjhaQbHI18d7h96hsqwRgatxUFqQuQK1Q90ezBUEQBOGcIoKlAcxgUBDxzqN4KktRJycT/6c/YZw2tdM+ebY8Pin8BJffhU6pY8nQJYywjuinFguCIAjCuUcESwOYpFYT/+c/0fLlKmLuuRuFwRB6zRvwsqpkFTm1OQAkhSVxVeZVWHSWfmqtIAiCIJybRLA0wBkmT8YweXKnbQ3tDaw4tIJaZy0AMxNmMi95HkqFsj+aKAiCIAjnNBEsDTK5dbl8UfwFnoAHo8rI5RmXkxGR0d/NEgRBEIRzlgiWBgmP38NnRZ+xpyG4qG56eDpXZF6BSWPq55YJgiAIwrlNBEuDQI2jhhWHVmBz2ZCQOC/5PGYlzkIhKfq7aYIgCIJwzhPB0gAmyzI5tTl8WfIlftmPSWPiqsyrSA1P7e+mCYIgCML3hgiWBihZlvmg4AP2NuwFYFjEMC4behkGteEURwqCIAiC0JNEsDRASZJEUlgSebY8FqQuYFrcNCRJ6u9mCYIgCML3jgiWBrApcVMYYhkiliwRBEEQhH4kMoQHMEmSRKAkCIIgCP1MBEuCIAiCIAhdEMGSIAiCIAhCF0SwJAiCIAiC0IVBEyw1NTWxbNkyzGYzZrOZZcuW0dzc3OUxsizz0EMPkZCQgF6v57zzzmP//v0n3ffCCy9EkiQ+/PDDnr8BQRAEQRAGpUETLC1dupTc3FxWrlzJypUryc3NZdmyZV0e89e//pUnn3ySZ555hu3btxMXF8fChQtpbW09bt+nn35aTM0XBEEQBOE4g6J0wIEDB1i5ciVbtmxh2rRpALz00ktkZ2eTn59PVlbWccfIsszTTz/N7373O6688koA/vvf/xIbG8vy5cu59dZbQ/vu3r2bJ598ku3btxMfH983NyUIgiAIwqAwKHqWNm/ejNlsDgVKANOnT8dsNrNp06YTHlNcXExNTQ0XXHBBaJtWq2Xu3LmdjnE6ndxwww0888wzxMXFnVZ73G43LS0tnb4EQRAEQTg3DYpgqaamhpiYmOO2x8TEUFNTc9JjAGJjYzttj42N7XTMPffcw4wZM7jssstOuz2PPvpoKHfKbDaTnJx82scKgiAIgjC49Guw9NBDDyFJUpdfOTk5ACfMJ5Jl+ZR5Rt99/dhjPv74Y9asWcPTTz/drXbff//92O320Fd5eXm3jhcEQRAEYfDo15ylO+64g+uvv77LfdLS0tizZw+1tbXHvVZfX39cz9ERR4bUampqOuUh1dXVhY5Zs2YNhYWFWCyWTsdeddVVzJ49m7Vr157w3FqtFq1W22W7BUEQBEE4N/RrsBQVFUVU1KmX88jOzsZut7Nt2zamTp0KwNatW7Hb7cyYMeOEx6SnpxMXF8fq1auZMGECAB6Ph3Xr1vHYY48BcN999/HTn/6003Fjxozhqaee4tJLLz2bWxMEQRAE4RwxKGbDjRgxgsWLF3PLLbfwwgsvAPCzn/2MSy65pNNMuOHDh/Poo49yxRVXIEkSd999N3/5y1/IzMwkMzOTv/zlLxgMBpYuXQoEe59OlNSdkpJCenp639ycIAiCIAgD2qAIlgDeeOMN7rzzztDstiVLlvDMM8902ic/Px+73R76/t5776W9vZ3bb7+dpqYmpk2bxqpVqzCZTH3adkEQBEEQBi9JlmW5vxsx2NntdiwWC+Xl5YSHh/d3cwRBEARBOA0tLS0kJyfT3NyM2Ww+6X6DpmdpIDtSEVyUEBAEQRCEwae1tbXLYEn0LPWAQCBAVVUVJpNJLJnC0Uhd9LT1LvGc+4Z4zn1DPOe+IZ5zZ7Is09raSkJCAgrFyaspiZ6lHqBQKEhKSurvZgw44eHh4pexD4jn3DfEc+4b4jn3DfGcj+qqR+mIQVHBWxAEQRAEob+IYEkQBEEQBKELIlgSepxWq+XBBx8UVc57mXjOfUM8574hnnPfEM/5zIgEb0EQBEEQhC6IniVBEARBEIQuiGBJEARBEAShCyJYEgRBEARB6IIIlgRBEARBELoggiXhrDU1NbFs2TLMZjNms5lly5bR3Nx82sffeuutSJLE008/3WttPBd09zl7vV5+85vfMGbMGIxGIwkJCdx0001UVVX1XaMHieeee4709HR0Oh2TJk1i/fr1Xe6/bt06Jk2ahE6nY8iQITz//PN91NLBrTvP+f3332fhwoVER0cTHh5OdnY2X375ZR+2dvDq7s/zERs3bkSlUjF+/PjebeAgJIIl4awtXbqU3NxcVq5cycqVK8nNzWXZsmWndeyHH37I1q1bSUhI6OVWDn7dfc5Op5OdO3fy+9//np07d/L+++9z6NAhlixZ0oetHvjefvtt7r77bn73u9+xa9cuZs+ezYUXXkhZWdkJ9y8uLuaiiy5i9uzZ7Nq1i9/+9rfceeedvPfee33c8sGlu8/522+/ZeHChXz++efs2LGDefPmcemll7Jr164+bvng0t3nfITdbuemm25i/vz5fdTSQUYWhLOQl5cnA/KWLVtC2zZv3iwD8sGDB7s8tqKiQk5MTJT37dsnp6amyk899VQvt3bwOpvnfKxt27bJgFxaWtobzRyUpk6dKt92222dtg0fPly+7777Trj/vffeKw8fPrzTtltvvVWePn16r7XxXNDd53wiI0eOlB9++OGebto55Uyf83XXXSc/8MAD8oMPPiiPGzeuF1s4OImeJeGsbN68GbPZzLRp00Lbpk+fjtlsZtOmTSc9LhAIsGzZMn79618zatSovmjqoHamz/m77HY7kiRhsVh6oZWDj8fjYceOHVxwwQWdtl9wwQUnfa6bN28+bv9FixaRk5OD1+vttbYOZmfynL8rEAjQ2tpKZGRkbzTxnHCmz/mVV16hsLCQBx98sLebOGiJhXSFs1JTU0NMTMxx22NiYqipqTnpcY899hgqlYo777yzN5t3zjjT53wsl8vFfffdx9KlS8UCmh0aGhrw+/3ExsZ22h4bG3vS51pTU3PC/X0+Hw0NDcTHx/daewerM3nO3/XEE0/gcDi49tpre6OJ54Qzec6HDx/mvvvuY/369ahUIiQ4GdGzJJzQQw89hCRJXX7l5OQAIEnSccfLsnzC7QA7duzg73//O6+++upJ9/m+6M3nfCyv18v1119PIBDgueee6/H7GOy++wxP9VxPtP+Jtguddfc5H/Hmm2/y0EMP8fbbb5/wQ4PQ2ek+Z7/fz9KlS3n44YcZNmxYXzVvUBJhpHBCd9xxB9dff32X+6SlpbFnzx5qa2uPe62+vv64TzdHrF+/nrq6OlJSUkLb/H4/v/zlL3n66acpKSk5q7YPJr35nI/wer1ce+21FBcXs2bNGtGrdIyoqCiUSuVxn7rr6upO+lzj4uJOuL9KpcJqtfZaWwezM3nOR7z99tv85Cc/4d1332XBggW92cxBr7vPubW1lZycHHbt2sUdd9wBBIc7ZVlGpVKxatUqzj///D5p+0AngiXhhKKiooiKijrlftnZ2djtdrZt28bUqVMB2Lp1K3a7nRkzZpzwmGXLlh33prdo0SKWLVvGj3/847Nv/CDSm88ZjgZKhw8f5ptvvhF/zL9Do9EwadIkVq9ezRVXXBHavnr1ai677LITHpOdnc0nn3zSaduqVauYPHkyarW6V9s7WJ3Jc4Zgj9LNN9/Mm2++ycUXX9wXTR3Uuvucw8PD2bt3b6dtzz33HGvWrGHFihWkp6f3epsHjX5MLhfOEYsXL5bHjh0rb968Wd68ebM8ZswY+ZJLLum0T1ZWlvz++++f9BxiNtypdfc5e71eecmSJXJSUpKcm5srV1dXh77cbnd/3MKA9NZbb8lqtVp++eWX5by8PPnuu++WjUajXFJSIsuyLN93333ysmXLQvsXFRXJBoNBvueee+S8vDz55ZdfltVqtbxixYr+uoVBobvPefny5bJKpZKfffbZTj+7zc3N/XULg0J3n/N3idlwJyaCJeGs2Ww2+cYbb5RNJpNsMpnkG2+8UW5qauq0DyC/8sorJz2HCJZOrbvPubi4WAZO+PXNN9/0efsHsmeffVZOTU2VNRqNPHHiRHndunWh1374wx/Kc+fO7bT/2rVr5QkTJsgajUZOS0uT//Wvf/Vxiwen7jznuXPnnvBn94c//GHfN3yQ6e7P87FEsHRikix3ZCYKgiAIgiAIxxGz4QRBEARBELoggiVBEARBEIQuiGBJEARBEAShCyJYEgRBEARB6IIIlgRBEARBELoggiVBEARBEIQuiGBJEARBEAShCyJYEgRBEARB6IIIlgRBGNTOO+887r777tPe/9VXX8VisfRaewRBOPeIYEkQhO+1hx56iPHjx/fb9deuXYskScd9HTx4sN/aJAhCZ6r+boAgCIIA+fn5hIeHh76Pjo7ux9YIgnAs0bMkCMKg4XA4uOmmmwgLCyM+Pp4nnnjiuH08Hg/33nsviYmJGI1Gpk2bxtq1a094vldffZWHH36Y3bt3h3p0Xn31VQCefPJJxowZg9FoJDk5mdtvv522trYu2ydJEi+88AKXXHIJBoOBESNGsHnzZgoKCjjvvPMwGo1kZ2dTWFh43LExMTHExcWFvpRKZbefjyAIvUMES4IgDBq//vWv+eabb/jggw9YtWoVa9euZceOHZ32+fGPf8zGjRt566232LNnD9dccw2LFy/m8OHDx53vuuuu45e//CWjRo2iurqa6upqrrvuOgAUCgX/+Mc/2LdvH//9739Zs2YN99577ynb+Mgjj3DTTTeRm5vL8OHDWbp0Kbfeeiv3338/OTk5ANxxxx3HHTdhwgTi4+OZP38+33zzzZk8HkEQeossCIIwCLS2tsoajUZ+6623QttsNpus1+vlu+66S5ZlWS4oKJAlSZIrKys7HTt//nz5/vvvl2VZll955RXZbDaHXnvwwQflcePGnfL677zzjmy1WrvcB5AfeOCB0PebN2+WAfnll18ObXvzzTdlnU4X+v7gwYPyiy++KO/YsUPetGmT/POf/1yWJElet27dKdskCELfEDlLgiAMCoWFhXg8HrKzs0PbIiMjycrKCn2/c+dOZFlm2LBhnY51u91YrdZuXe+bb77hL3/5C3l5ebS0tODz+XC5XDgcDoxG40mPGzt2bOjfsbGxAIwZM6bTNpfLRUtLC+Hh4WRlZXW6h+zsbMrLy3n88ceZM2dOt9osCELvEMGSIAiDgizLp9wnEAigVCrZsWPHcTk/YWFhp32t0tJSLrroIm677TYeeeQRIiMj2bBhAz/5yU/wer1dHqtWq0P/liTppNsCgcBJzzF9+nRef/31026vIAi9SwRLgiAMChkZGajVarZs2UJKSgoATU1NHDp0iLlz5wLBvB+/309dXR2zZ88+rfNqNBr8fn+nbTk5Ofh8Pp544gkUimBq5zvvvNODd9O1Xbt2ER8f32fXEwShayJYEgRhUAgLC+MnP/kJv/71r7FarcTGxvK73/0uFMwADBs2jBtvvJGbbrqJJ554ggkTJtDQ0MCaNWsYM2YMF1100XHnTUtLo7i4mNzcXJKSkjCZTAwdOhSfz8c///lPLr30UjZu3Mjzzz/fK/f19NNPk5aWxqhRo/B4PLz++uu89957vPfee71yPUEQuk/MhhMEYdD429/+xpw5c1iyZAkLFixg1qxZTJo0qdM+r7zyCjfddBO//OUvycrKYsmSJWzdupXk5OQTnvOqq65i8eLFzJs3j+joaN58803Gjx/Pk08+yWOPPcbo0aN54403ePTRR3vlnjweD7/61a8YO3Yss2fPZsOGDXz22WdceeWVvXI9QRC6T5JPJxFAEARBEAThe0r0LAmCIAiCIHRBBEuCIAiCIAhdEMGSIAiCIAhCF0SwJAiCIAiC0AURLAmCIAiCIHRBBEuCIAiCIAhdEMGSIAiCIAhCF0SwJAiCIAiC0AURLAmCIAiCIHRBBEuCIAiCIAhdEMGSIAiCIAhCF/5/luCyg9BWh3UAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#meanzs = np.zeros( (6,len(bands),len(deltas)) )\n",
-    "\n",
-    "tmpd = pandas.read_pickle('densityy1.pkl')\n",
-    "for i in np.arange(1,6):\n",
-    "    plt.plot(deltas,(tmpd[i,0,:]-tmpd[i,0,6])/tmpd[i,0,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6)\n",
-    "    fitpoly=np.polynomial.polynomial.polyfit(deltas,(tmpd[i,0,:]-tmpd[i,0,6])/tmpd[i,0,6],2)\n",
-    "    plt.plot(deltas,np.polynomial.polynomial.polyval(deltas,fitpoly,tensor=True),linestyle='dashed')\n",
-    "    plt.xlabel('delta m5')\n",
-    "    plt.ylabel('delta n/n')\n",
-    "    plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "id": "0c7ba831-b6d4-4bdc-94d6-5caafdde4716",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tmpd = pandas.read_pickle('densityy1.pkl')\n",
-    "\n",
-    "for i in np.arange(1,6):\n",
-    "    sumshift = tmpd[i,0,:]/tmpd[i,0,6]+tmpd[i,1,:]/tmpd[i,1,6]+tmpd[i,2,:]/tmpd[i,2,6] + \\\n",
-    "         tmpd[i,3,:]/tmpd[i,3,6]+tmpd[i,4,:]/tmpd[i,4,6]+tmpd[i,5,:]/tmpd[i,5,6]\n",
-    "    \n",
-    "    plt.plot(deltas,(tmpd[i,6,:]-tmpd[i,6,6])/tmpd[i,6,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6)\n",
-    "    plt.plot(deltas,(sumshift-sumshift[6]),label = 'z bin '+np.array2string((i+1)),alpha=0.6,linestyle='dashed')\n",
-    "\n",
-    "#    fitpoly=np.polynomial.polynomial.polyfit(deltas,tmp[i,0,:]-tmp[i,0,6],2)\n",
-    "#    plt.plot(deltas,np.polynomial.polynomial.polyval(deltas,fitpoly,tensor=True),linestyle='dashed')\n",
-    "    plt.xlabel('delta m5')\n",
-    "    plt.ylabel('delta n/n')\n",
-    "    plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "id": "f0021007-651b-41ae-8b44-fe4b5329e804",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#meanzs = np.zeros( (6,len(bands),len(deltas)) )\n",
-    "\n",
-    "tmp = pandas.read_pickle('meanzsy1.pkl')\n",
-    "tmp10 = pandas.read_pickle('meanzsy10.pkl')\n",
-    "\n",
-    "for i in np.arange(1,6):\n",
-    "    plt.plot(deltas,tmp[i,0,:]-tmp[i,0,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6)\n",
-    "    plt.plot(deltas,tmp10[i,0,:]-tmp10[i,0,6],label = 'z bin '+np.array2string((i+1)),alpha=0.6,linestyle='dashed')\n",
-    "    plt.xlabel('delta m5')\n",
-    "    plt.ylabel('delta <z>')\n",
-    "    plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c74aaf2a-0376-4aaf-990a-326ae6ccbeda",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# This is the code box that does all the new work of fitting derivatives!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "id": "7472c833-fd12-4e36-bbaf-7771be379f57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "years = [1,2,3,4,5,6,7,8,9,10]\n",
-    "nyears = len(years)\n",
-    "nbands = len(bands)\n",
-    "nbins = 5\n",
-    "meanzderiv = np.zeros((nyears,nbands,nbins))\n",
-    "densityderiv = np.zeros((nyears,nbands,nbins))\n",
-    "\n",
-    "for year_idx,year in enumerate(years):\n",
-    "    yearstring = np.array2string(np.array(year))\n",
-    "    meanzs = pandas.read_pickle('meanzsy'+yearstring+'.pkl')\n",
-    "    densitys = pandas.read_pickle('densityy'+yearstring+'.pkl')\n",
-    "    for band_idx,band in enumerate(bands):\n",
-    "        for bin_idx in np.arange(1,6):\n",
-    "            fitpoly=np.polynomial.polynomial.polyfit(deltas,\n",
-    "                meanzs[bin_idx,band_idx,:]-meanzs[bin_idx,band_idx,6],2)\n",
-    "            meanzderiv[year_idx,band_idx,bin_idx-1] = fitpoly[1]\n",
-    "            fitpolyd=np.polynomial.polynomial.polyfit(deltas,\n",
-    "                (densitys[bin_idx,band_idx,:]/densitys[bin_idx,band_idx,6]-1),2)\n",
-    "            densityderiv[year_idx,band_idx,bin_idx-1] = fitpolyd[1]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "id": "f8796c5a-334c-48c4-888d-a3fd0a8605c5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'year')"
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(years,meanzderiv[:,6,0],label = '0.2-0.4')\n",
-    "plt.plot(years,meanzderiv[:,6,1],label = '0.4-0.6')\n",
-    "plt.plot(years,meanzderiv[:,6,2],label = '0.6-0.8')\n",
-    "plt.plot(years,meanzderiv[:,6,3],label = '0.8-1.0')\n",
-    "plt.plot(years,meanzderiv[:,6,4],label = '1.0-1.2')\n",
-    "plt.legend()\n",
-    "plt.ylabel(r'd<z>/d$m_5$')\n",
-    "plt.xlabel('year')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "id": "399045e5-55e1-40a4-b4c5-4bbd997f718e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'year')"
-      ]
-     },
-     "execution_count": 194,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(years,densityderiv[:,6,0],label = '0.2-0.4')\n",
-    "plt.plot(years,densityderiv[:,6,1],label = '0.4-0.6')\n",
-    "plt.plot(years,densityderiv[:,6,2],label = '0.6-0.8')\n",
-    "plt.plot(years,densityderiv[:,6,3],label = '0.8-1.0')\n",
-    "plt.plot(years,densityderiv[:,6,4],label = '1.0-1.2')\n",
-    "plt.legend()\n",
-    "plt.ylabel(r'1 / n dn/d$m_5$')\n",
-    "plt.xlabel('year')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "c35eba1f-2c1a-449e-8ca2-d941d497453b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tmp = meanzderiv[:,0:6,:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "id": "bda29f62-2cbd-445e-bebe-de70cdc50401",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10, 5)"
-      ]
-     },
-     "execution_count": 186,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#tmp.shape\n",
-    "sumderiv = tmp.sum(axis=1)\n",
-    "sumderiv.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "id": "f4469f8f-b890-44b0-b8d8-b8420dac2b04",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.12275823692907575\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(-1.5, 1.5)"
-      ]
-     },
-     "execution_count": 187,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sums = sumderiv.flatten()\n",
-    "covariant = (meanzderiv[:,6,:]).flatten()\n",
-    "plt.plot(covariant,(sums - covariant)/covariant,'b.')\n",
-    "plt.xlabel('u+g+r+i+z+y fully covariant derivative')\n",
-    "ylabel(r'(sum of derivs. - covariant deriv.) / covariant deriv.')\n",
-    "print(np.median(-(covariant - sums)/covariant))\n",
-    "plt.title('<z>')\n",
-    "plt.ylim(-1.5,1.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "id": "7d458418-c1eb-46fe-8c66-88634504a232",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.058799512450450384\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tmp = densityderiv[:,0:6,1:5]\n",
-    "\n",
-    "sumderiv = tmp.sum(axis=1)\n",
-    "sumderiv.shape\n",
-    "\n",
-    "sums = sumderiv.flatten()\n",
-    "covariant = (densityderiv[:,6,1:5]).flatten()\n",
-    "plt.plot(covariant,(sums - covariant)/covariant,'b.')\n",
-    "plt.xlabel('u+g+r+i+z+y fully covariant derivative')\n",
-    "ylabel(r'(sum of derivs. - covariant deriv.) / covariant deriv.')\n",
-    "plt.title('density')\n",
-    "print(np.median(-(covariant - sums)/covariant))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "id": "b985598e-3314-4124-be1a-722e2baa933c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.06579563528202678"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std((sums - covariant)/covariant)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "id": "047c1dab-3b0a-433e-ac8f-f0459857332d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10, 6, 4)"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "id": "b58badac-ff24-4be8-b289-03bc50b22961",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#meanzderiv: axes are ((nyears,nbands,nbins))\n",
-    "#densityderiv: axes are ((nyears,nbands,nbins))\n",
-    "\n",
-    "# array of the years used (corresponding to first axis of the results arrays)\n",
-    "pickle.dump(years, open('years.pkl', \"wb\"))\n",
-    "\n",
-    "# array of the bands used (corresponding to second axis)\n",
-    "pickle.dump(bands, open('bands.pkl', \"wb\"))\n",
-    "\n",
-    "minzs=np.linspace(0.2, 1.0,5)\n",
-    "maxzs=minzs+0.2\n",
-    "\n",
-    "# array of the minz of each redshift bin  (corresponding to third axis)\n",
-    "pickle.dump(minzs, open('minzs.pkl', \"wb\"))\n",
-    "\n",
-    "# array of the maxz of each redshift bin  (corresponding to third axis)\n",
-    "pickle.dump(maxzs, open('maxzs.pkl', \"wb\"))\n",
-    "\n",
-    "# array of the derivative of <z> with respect to m5, for each combination\n",
-    "#    of year, band, and bin number\n",
-    "# i.e. axes are ((nyears,nbands,nbins))\n",
-    "pickle.dump(meanzderiv, open('meanzderiv.pkl', \"wb\"))\n",
-    "\n",
-    "# array of the logatiyhmic derivative of n with respect to m5 \n",
-    "# (i.e., 1/n * dn/dm5), for each combination\n",
-    "#    of year, band, and bin number\n",
-    "# i.e. axes are ((nyears,nbands,nbins))\n",
-    "pickle.dump(densityderiv, open('densityderiv.pkl', \"wb\"))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "992916e8-66bd-419d-8305-370efddefdc4",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "203d9dd0-29cc-4ea6-90d2-06d13671575a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "1e4cc1f0-ca50-46f7-9316-dbdf6f33f152",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/global/u1/j/janewman/uniformity\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pwd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "id": "e8cace31-3e3b-4854-997a-45fa6e0c76ae",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!chmod a+r *.*\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "id": "2393799d-4a8d-406f-bba6-f38fc98dbe95",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!chmod g+x ../../janewman"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "8e0dc97b-e49f-4e17-8334-f591c8b5259d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "total 1392\n",
-      "  8 drwxrwxr--  3 janewman janewman   4096 Mar  8 00:04 ./\n",
-      "  8 drwxr-xr-x 23 janewman desi       4096 Mar  7 12:29 ../\n",
-      "  1 -rw-rw-r--  1 janewman janewman     49 Mar  7 23:04 bands.pkl\n",
-      "  1 -rw-rw-r--  1 janewman janewman    235 Mar  7 21:17 deltas.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   2954 Mar  7 23:04 densityderiv.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 23:42 densityy10.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 18:31 densityy1.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 18:54 densityy2.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 19:20 densityy3.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 19:50 densityy4.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 20:23 densityy5.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 21:00 densityy6.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 21:37 densityy7.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 22:18 densityy8.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 22:58 densityy9.pkl\n",
-      "364 -rw-rw-r--  1 janewman janewman 369355 Mar  2 23:01 hgb_model.pkl\n",
-      "  1 drwxrwx---  2 janewman janewman    512 Mar  7 13:18 .ipynb_checkpoints/\n",
-      "  1 -rw-rw-r--  1 janewman janewman    187 Mar  7 23:04 maxzs.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   2954 Mar  7 23:04 meanzderiv.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 23:42 meanzsy10.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 18:31 meanzsy1.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 18:54 meanzsy2.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 19:20 meanzsy3.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 19:50 meanzsy4.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 20:23 meanzsy5.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 21:00 meanzsy6.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 21:37 meanzsy7.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 22:18 meanzsy8.pkl\n",
-      "  4 -rw-rw-r--  1 janewman janewman   3850 Mar  7 22:58 meanzsy9.pkl\n",
-      "  1 -rw-rw-r--  1 janewman janewman    187 Mar  7 23:04 minzs.pkl\n",
-      "300 -rw-rw-r--  1 janewman janewman 305531 Mar  8 00:04 simulation_calcs.ipynb\n",
-      "620 -rw-rw-r--  1 janewman janewman 630864 Mar  7 23:05 simulation_photoz_rr.ipynb\n",
-      "  1 -rw-rw-r--  1 janewman janewman     36 Mar  7 23:04 years.pkl\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls -pasl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d2f95128-6411-4307-b0e5-b20a82c1dd9a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "NERSC Python",
-   "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": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/rubin_sim/maf/metrics/uniformity_pkl/years.pkl b/rubin_sim/maf/metrics/uniformity_pkl/years.pkl
deleted file mode 100644
index f7228faf25a680efc97a732268dcc17a6313040d..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 36
mcmZo*nJUQu0kKmwycxZjyqUdOyji{3yxF}uyg9wOQuP3BJq82-


From 184a2538a8bb41418735f5b9864594abff21d6b7 Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Tue, 13 Aug 2024 17:07:46 +0100
Subject: [PATCH 30/31] draft stripiness metric

---
 .../maf/metrics/cosmology_summary_metrics.py  | 393 +++++++++++++++---
 1 file changed, 346 insertions(+), 47 deletions(-)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index 98349730c..edc0f684e 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -757,6 +757,102 @@ def compute_Clbias(meanz_vals, scatter_mean_z_values):
         result["y1ratio"] = y1ratio
         result["y10ratio"] = y10ratio
 
+def get_stats_by_region(use_map, nside, maskval=0, region="all"):
+    if region not in ["all", "north", "south"]:
+        raise ValueError("Invalid region %s" % region)
+    to_use = use_map > maskval
+
+    if region != "all":
+        # Find the north/south part of the map as requested
+        npix = hp.nside2npix(nside)
+        ra, dec = hp.pix2ang(hp.npix2nside(npix), range(npix), lonlat=True)
+        ngp_mask = _is_ngp(ra, dec)
+        if region == "north":
+            reg_mask = ngp_mask
+        else:
+            reg_mask = ~ngp_mask
+        to_use = to_use & reg_mask
+
+    # Calculate the desired stats
+    reg_mad = median_abs_deviation(use_map[to_use])
+    reg_median = np.median(use_map[to_use])
+    reg_std = np.std(use_map[to_use])
+
+    # Return the values
+    return (reg_mad, reg_median, reg_std)
+
+def stripiness_test_statistic(data_slice, nside):
+    """
+    A utility to find whether a particular routine has stripey features in the exposure time map.
+    """
+    # Analyze the exposure time map to get MAD, median, std in north/south.
+    mad = {}
+    med = {}
+    frac_scatter = {}
+    regions = ["north", "south"]
+    for region in regions:
+        mad[region], med[region], _ = get_stats_by_region(data_slice, nside, region=region)
+        frac_scatter[region] = mad[region] / med[region]
+    test_statistic = frac_scatter["north"] / frac_scatter["south"] - 1
+    return test_statistic
+
+class StripinessMetric(BaseMetric):
+    """
+    Run as summary metric on NestedRIZExptimeExgalM5Metric or ExgalM5Metric
+
+    TODO
+
+
+    Points of contact / contributors: Rachel Mandelbaum, Boris Leistedt
+
+    Parameters
+    ----------
+    year: `int`
+        year of observation, in order to adjust the cut on the depth fluctuations for the stipe detection
+    nside: `int`
+        must be the nside at which the base metric NestedRIZExptimeExgalM5Metric is calculated at
+    verbose: bool, optional
+        if true, will display the segmentation maps and the areas and FOMs of the two regions found
+    Returns
+    -------
+    result: `float`
+        The ratio of the FOMs of the two areas found
+    """
+
+    def __init__(
+        self,
+        year,
+        nside,
+        verbose=True,
+        **kwargs,
+    ):
+        self.year = year
+        self.mask_val = hp.UNSEEN
+        self.verbose = verbose
+        self.nside = nside
+        names = ["riz_exptime"]
+        types = [float]
+        self.mask_val_arr = np.zeros(1, dtype=list(zip(names, types)))
+        self.mask_val_arr["riz_exptime"] = self.mask_val
+
+        super().__init__(col="metricdata", **kwargs)
+
+    def run(self, data_slice, slice_point=None):
+        data_slice_list = [
+            self.mask_val_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()
+        ]
+        data_slice_arr = np.asarray(data_slice_list, dtype=self.mask_val_arr.dtype)
+        # a bit of gymnastics to make sure all bad values (nan, -666) are recast as hp.UNSEEN
+        ind = data_slice_arr["riz_exptime"] == -666
+        ind |= ~np.isfinite(data_slice_arr["riz_exptime"])
+        data_slice_arr["riz_exptime"][ind.ravel()] = hp.UNSEEN
+        # sanity check
+        nside = hp.npix2nside(data_slice["metricdata"].size)
+        assert nside == self.nside
+
+        test_statistic = stripiness_test_statistic(data_slice_arr["riz_exptime"].ravel(), nside)
+
+        return test_statistic
 
 class UniformAreaFoMFractionMetric(BaseMetric):
     """
@@ -828,54 +924,20 @@ def run(self, data_slice, slice_point=None):
         nside = hp.npix2nside(data_slice["metricdata"].size)
         assert nside == self.nside
 
+        # Check for stripiness
+        threshold = 0.7 / np.sqrt(self.year)
+        test_statistic = stripiness_test_statistic(data_slice_arr["riz_exptime"].ravel(), nside)
+
+        if np.abs(test_statistic) < np.abs(threshold):
+            stripes = False
+        else:
+            stripes = True
+
         # Let's make code that pulls out the northern/southern galactic regions, and gets statistics of the footprint by region.
         def _is_ngp(ra, dec):
             c = SkyCoord(ra=ra * u.degree, dec=dec * u.degree, frame="icrs")
             lat = c.galactic.b.deg
             return lat >= 0
-
-        def get_stats_by_region(use_map, nside, maskval=0, region="all"):
-            if region not in ["all", "north", "south"]:
-                raise ValueError("Invalid region %s" % region)
-            to_use = use_map > maskval
-
-            if region != "all":
-                # Find the north/south part of the map as requested
-                npix = hp.nside2npix(nside)
-                ra, dec = hp.pix2ang(hp.npix2nside(npix), range(npix), lonlat=True)
-                ngp_mask = _is_ngp(ra, dec)
-                if region == "north":
-                    reg_mask = ngp_mask
-                else:
-                    reg_mask = ~ngp_mask
-                to_use = to_use & reg_mask
-
-            # Calculate the desired stats
-            reg_mad = median_abs_deviation(use_map[to_use])
-            reg_median = np.median(use_map[to_use])
-            reg_std = np.std(use_map[to_use])
-
-            # Return the values
-            return (reg_mad, reg_median, reg_std)
-
-        def has_stripes(data_slice, nside, threshold=0.1):
-            """
-            A utility to find whether a particular routine has stripey features in the exposure time map.
-            """
-            # Analyze the exposure time map to get MAD, median, std in north/south.
-            mad = {}
-            med = {}
-            frac_scatter = {}
-            regions = ["north", "south"]
-            for region in regions:
-                mad[region], med[region], _ = get_stats_by_region(data_slice, nside, region=region)
-                frac_scatter[region] = mad[region] / med[region]
-            test_statistic = np.abs(frac_scatter["north"] / frac_scatter["south"] - 1)
-            if test_statistic < threshold:
-                return False
-            else:
-                return True
-
         def apply_clustering(clustering_data):
             # A thin wrapper around sklearn routines (can swap out which one we are using systematically).
             # We fix parameters like `n_clusters` since realistically we know for rolling that we should expect 2 clusters.
@@ -953,10 +1015,6 @@ def make_clustering_dataset(depth_map, maskval=0, priority_fac=0.9, nside=64):
             my_data[:, 2] = depth_map[depth_map > cutval]
             return my_data
 
-        # Check for stripiness
-        use_threshold = 0.7 / self.year
-        stripes = has_stripes(data_slice_arr["riz_exptime"].ravel(), nside, threshold=use_threshold)
-
         if not stripes:
             return 1
         else:
@@ -993,3 +1051,244 @@ def make_clustering_dataset(depth_map, maskval=0, priority_fac=0.9, nside=64):
             if self.verbose:
                 print("FOMs:", fom1, fom2, fom, fom_total)
             return fom / fom_total
+
+
+
+
+class uDropoutsNumbersShallowestDepth(BaseMetric):
+    """
+    Run as summary metric on MultibandExgalM5.
+
+
+
+    Parameters
+    ----------
+    year : `int`, optional
+        The year of the survey to calculate the bias.
+        This is used to derive the dm/dz derivative used to translate m5 rms into dz rms.
+
+    Returns
+    -------
+    result : `float` array
+        The ratio of this bias to the desired DESC y1 upper bound on the bias, and the ratio
+        between the clbias and the y10 DESC SRD requirement.
+        Desired values are less than 1 by Y10.
+
+
+    Notes
+    -----
+    This is a summary metric to be run on the results
+    of the MultibandExgalM5.
+
+    MultibandExgalM5 provides the m5 depth in all LSST bands given a specific slice.
+
+    """
+
+    def __init__(self,
+                 filter_list=["u", "g", "r", "i", "z", "y"],
+                 **kwargs):
+
+        super().__init__(col="metricdata", percentile=5, **kwargs)
+        # Set mask_val, so that we receive metric_values.filled(mask_val)
+        self.mask_val = hp.UNSEEN
+        self.badval = hp.UNSEEN
+        self.percentileMetric = PercentileMetric(percentile=5, name='percentile')
+        self.filter_list = filter_list
+
+    def run(self, data_slice, slice_point=None):
+
+        # Technically don't need this for now (isn't used in previous one)
+        # need to define an array of bad values for the masked pixels
+        badval_arr = np.repeat(self.badval, len(self.filter_list))
+        # converts the input recarray to an array
+        data_slice_list = [
+            badval_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()
+        ]
+        lengths = np.array([len(x) for x in data_slice_list])
+        # should be (nbins, npix)
+        data_slice_arr = np.asarray(data_slice_list, dtype=float).T
+        data_slice_arr[~np.isfinite(data_slice_arr)] = (
+            hp.UNSEEN
+        )  # need to work with TotalPowerMetric and healpix
+
+        # measure rms in each bin.
+        # The original metric returns an array at each slice_point (of the
+        # original slicer) -- so there is a bit of "rearrangement" that
+        # has to happen to be able to pass a np.array with right dtype
+        # (i.e. dtype = [("metricdata", float)]) to each call to
+        # the rmsMetric `run` methods.
+        percentiles = np.array(
+            [
+                self.percentileMetric.run(np.core.records.fromrecords(x, dtype=[("metricdata", float)]))
+                for x in data_slice_arr
+            ]
+        ) # 6 numbers
+
+        from astropy.modeling.models import Schechter1D
+        from astropy.cosmology import Planck18 as cosmo
+        import astropy.units as u
+
+        def schecter_lf(
+            m_grid: np.ndarray,
+            log_phi_star: float = -2.84,
+            M_star: float = -20.91,
+            alpha: float = -1.68,
+            redshift: float = 3,
+        ) -> np.ndarray:
+            """Schecter Luminosity Function on grid of apparent magnitudes.
+
+            Defaults are for z~3 u-dropout Luminosity Function from Table 6
+            of Harikane et al. 2022.
+
+            Parameters
+            ----------
+            m_grid: np.ndarray
+                Array of apparent AB magnitudes on which to calculate the
+                luminosity function.
+            log_phi_star: float, default=-2.84
+                Natural log of phi_star, the normalization of the luminosity
+                function in units of mag^-1 Mpc^-3
+            M_star: float, default=-20.91
+                The characteristic absolute magnitude where the power-law form
+                of the luminosity function cuts off.
+            alpha: float, default=-1.68
+                The power law index, also known as the faint-end slope.
+            redshift: float, default=3
+                Redshift used for converting apparent magnitudes into absolute
+                magnitudes.
+
+            Returns
+            -------
+            np.ndarray
+                Number density in units mag^-1 Mpc^-3
+            """
+            # Convert observed magnitudes to absolute
+            DL = cosmo.luminosity_distance(redshift).to(u.pc).value  # Lum. Dist. in pc
+            M_grid = m_grid - 5 * np.log10(DL / 10) + 2.5 * np.log10(1 + redshift)
+
+            # Calculate luminosity function in absolute magnitudes
+            schechter = Schechter1D(10**log_phi_star, M_star, alpha)
+
+            return schechter(M_grid)
+
+        def number_density(
+            m_grid: np.ndarray,
+            LF: np.ndarray,
+            redshift: float = 3,
+            dz: float = 1,
+        ) -> float:
+            """Calculate number density per deg^2.
+
+            Parameters
+            ----------
+            m_grid: np.ndarray
+                Array of apparent AB magnitudes corresponding to the
+                luminosity function values.
+            LF: np.ndarray
+                The luminosity function evaluated on m_grid, in units
+                mag^-1 Mpc^-3.
+            redshift: float, default=3
+                The central redshift used for evaluating comoving quantities.
+            dz: float, default=1
+                The full width of the redshift bin
+
+            Returns
+            -------
+            float
+                The total number density of galaxies in units deg^-2.
+            """
+            # Calculate comoving depth of redshift bin (Mpc)
+            chi_far = cosmo.comoving_distance(redshift + dz / 2)
+            chi_near = cosmo.comoving_distance(redshift - dz / 2)
+            dchi = chi_far - chi_near
+
+            # Calculate number density (mag^-1 Mpc^-2)
+            n_dm = (LF / u.Mpc**3) * dchi
+
+            # Convert to mag^-1 deg^-2
+            deg_per_Mpc = cosmo.arcsec_per_kpc_comoving(redshift).to(u.deg / u.Mpc)
+            n_dm /= deg_per_Mpc**2
+
+            # Integrate the luminosity function
+            n = np.trapz(n_dm, m_grid)
+
+            return n.value
+
+
+        u5 = percentiles[self.filter_list == 'u'] # uband m5
+        u3 = u5 + 2.5*np.log10(5 / 3) # convert to uband 3-sigma
+        g_cut = u3 - 1.5 # cut on g band
+        # Calculate LF up to g_cut
+        m_grid = np.linspace(20, g_cut)
+        LF = schecter_lf(m_grid)
+        n_deg2 = number_density(m_grid, LF) # number of objects per sq deg
+
+        return n_deg2
+
+
+class uDropoutsArea(BaseMetric):
+    """
+    Run as summary metric on MultibandExgalM5.
+
+    Parameters
+    ----------
+    year : `int`, optional
+        The year of the survey to calculate the bias.
+        This is used to derive the dm/dz derivative used to translate m5 rms into dz rms.
+
+    Returns
+    -------
+    result : `float` array
+        The ratio of this bias to the desired DESC y1 upper bound on the bias, and the ratio
+        between the clbias and the y10 DESC SRD requirement.
+        Desired values are less than 1 by Y10.
+
+
+    Notes
+    -----
+    This is a summary metric to be run on the results
+    of the MultibandExgalM5.
+
+    MultibandExgalM5 provides the m5 depth in all LSST bands given a specific slice.
+
+    """
+
+    def __init__(self,
+                 filter_list=["u", "g", "r", "i", "z", "y"],
+                 **kwargs):
+
+        super().__init__(col="metricdata", percentile=5, **kwargs)
+        # Set mask_val, so that we receive metric_values.filled(mask_val)
+        self.mask_val = hp.UNSEEN
+        self.badval = hp.UNSEEN
+        self.percentileMetric = PercentileMetric(percentile=5, name='percentile')
+        self.filter_list = filter_list
+
+    def run(self, data_slice, slice_point=None):
+
+        # Technically don't need this for now (isn't used in previous one)
+        # need to define an array of bad values for the masked pixels
+        badval_arr = np.repeat(self.badval, len(self.filter_list))
+        # converts the input recarray to an array
+        data_slice_list = [
+            badval_arr if isinstance(x, float) else x for x in data_slice["metricdata"].tolist()
+        ]
+        lengths = np.array([len(x) for x in data_slice_list])
+        # should be (nbins, npix)
+        data_slice_arr = np.asarray(data_slice_list, dtype=float).T
+        data_slice_arr[~np.isfinite(data_slice_arr)] = (
+            hp.UNSEEN
+        )  # need to work with TotalPowerMetric and healpix
+
+        # measure rms in each bin.
+        # The original metric returns an array at each slice_point (of the
+        # original slicer) -- so there is a bit of "rearrangement" that
+        # has to happen to be able to pass a np.array with right dtype
+        # (i.e. dtype = [("metricdata", float)]) to each call to
+        # the rmsMetric `run` methods.
+        percentiles = np.array(
+            [
+                self.percentileMetric.run(np.core.records.fromrecords(x, dtype=[("metricdata", float)]))
+                for x in data_slice_arr
+            ]
+        ) # 6 numbers
\ No newline at end of file

From dad71050557fce458b82a068c1ca0d765c87f273 Mon Sep 17 00:00:00 2001
From: ixkael <boris.leistedt@gmail.com>
Date: Wed, 14 Aug 2024 12:01:31 +0100
Subject: [PATCH 31/31] working stripiness metric

---
 .../maf/metrics/cosmology_summary_metrics.py  | 176 +++++++++---------
 1 file changed, 90 insertions(+), 86 deletions(-)

diff --git a/rubin_sim/maf/metrics/cosmology_summary_metrics.py b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
index edc0f684e..1046e6167 100644
--- a/rubin_sim/maf/metrics/cosmology_summary_metrics.py
+++ b/rubin_sim/maf/metrics/cosmology_summary_metrics.py
@@ -21,6 +21,7 @@
 from .base_metric import BaseMetric
 from .simple_metrics import RmsMetric
 
+
 # Cosmology-related summary metrics.
 # These generally calculate a FoM for various DESC metrics.
 
@@ -51,14 +52,14 @@ class TotalPowerMetric(BaseMetric):
     """
 
     def __init__(
-        self,
-        lmin=100.0,
-        lmax=300.0,
-        remove_monopole=True,
-        remove_dipole=True,
-        col="metricdata",
-        mask_val=np.nan,
-        **kwargs,
+            self,
+            lmin=100.0,
+            lmax=300.0,
+            remove_monopole=True,
+            remove_dipole=True,
+            col="metricdata",
+            mask_val=np.nan,
+            **kwargs,
     ):
         self.lmin = lmin
         self.lmax = lmax
@@ -225,12 +226,12 @@ class TomographicClusteringSigma8biasMetric(BaseMetric):
     """
 
     def __init__(
-        self,
-        density_tomography_model,
-        power_multiplier=0.1,
-        lmin=10,
-        convert_to_sigma8=True,
-        **kwargs,
+            self,
+            density_tomography_model,
+            power_multiplier=0.1,
+            lmin=10,
+            convert_to_sigma8=True,
+            **kwargs,
     ):
         super().__init__(col="metricdata", **kwargs)
         # Set mask_val, so that we receive metric_values.filled(mask_val)
@@ -285,15 +286,15 @@ def run(self, data_slice, slice_point=None):
             ]
         )  # sky fraction
         spuriousdensitypowers = (
-            np.array(
-                [
-                    self.totalPowerMetrics[i].run(
-                        np.core.records.fromrecords(x, dtype=[("metricdata", float)])
-                    )
-                    for i, x in enumerate(data_slice_arr)
-                ]
-            )
-            / fskys
+                np.array(
+                    [
+                        self.totalPowerMetrics[i].run(
+                            np.core.records.fromrecords(x, dtype=[("metricdata", float)])
+                        )
+                        for i, x in enumerate(data_slice_arr)
+                    ]
+                )
+                / fskys
         )
         print("spuriousdensitypowers:", spuriousdensitypowers)
         print("fskys:", fskys)
@@ -331,7 +332,7 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
                 totalvar_obs[i, 0] = totalvar_mod[i, 0] + spurious_powers[i] * power_multiplier
 
                 # simple model variance of cell based on Gaussian covariance
-                cells_var = 2 * cells_model**2 / (2 * ells + 1) / fskys[i]
+                cells_var = 2 * cells_model ** 2 / (2 * ells + 1) / fskys[i]
                 totalvar_var[i, 0] = np.sum(cells_var * (2 * ells + 1) ** 2)
 
             # model assumed sigma8 = 0.8
@@ -349,7 +350,7 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
             FTT = np.sum(transfers[:, 0] * transfers[:, 0] / totalvar_var[:, 0])
             # mean and stddev of multiplicative factor
             sigma8square_fit = FOT / FTT
-            sigma8square_error = FTT**-0.5
+            sigma8square_error = FTT ** -0.5
 
             return sigma8square_fit, sigma8square_error, sigma8square_model
 
@@ -366,8 +367,8 @@ def solve_for_multiplicative_factor(spurious_powers, model_cells, fskys, lmin, p
 
             # turn result into bias on sigma8,
             # via change of variable and simple propagation of uncertainty.
-            sigma8_fit = sigma8square_fit**0.5
-            sigma8_model = sigma8square_model**0.5
+            sigma8_fit = sigma8square_fit ** 0.5
+            sigma8_model = sigma8square_model ** 0.5
             sigma8_error = 0.5 * sigma8square_error * sigma8_fit / sigma8square_fit
             results_sigma8_bias = (sigma8_fit - sigma8_model) / sigma8_error
             print(sigma8square_model, sigma8square_fit, sigma8square_error, results_sigma8_square_bias)
@@ -410,7 +411,6 @@ def __init__(self, filter_list="filters", year=10, n_filters=6, **kwargs):
         super().__init__(col="metricdata", mask_val=-666, **kwargs)
 
     def run(self, data_slice, slice_point=None):
-
         result = np.empty(1, dtype=[("name", np.str_, 20), ("y1ratio", float), ("y10ratio", float)])
         result["name"][0] = "UniformMeanzBiasMetric"
 
@@ -539,7 +539,7 @@ def compute_Clbias(meanz_vals, scatter_mean_z_values):
 
             clbias, meanz_use = compute_Clbias(meanzinterp, stdz)
 
-            totdz += [float(st**2) for st in stdz]
+            totdz += [float(st ** 2) for st in stdz]
             totclbias += clbias
             avmeanz += meanzinterp
 
@@ -580,7 +580,6 @@ class MultibandMeanzBiasMetric(BaseMetric):
     """
 
     def __init__(self, filter_list=["u", "g", "r", "i", "z", "y"], year=10, n_filters=6, **kwargs):
-
         super().__init__(col="metricdata", **kwargs)
         # Set mask_val, so that we receive metric_values.filled(mask_val)
         self.mask_val = hp.UNSEEN
@@ -590,7 +589,6 @@ def __init__(self, filter_list=["u", "g", "r", "i", "z", "y"], year=10, n_filter
         self.filter_list = filter_list
 
     def run(self, data_slice, slice_point=None):
-
         def compute_dzfromdm(zbins, band_ind, year):
             """This computes the dm/dz relationship calibrated from simulations
             by Jeff Newmann.
@@ -742,7 +740,7 @@ def compute_Clbias(meanz_vals, scatter_mean_z_values):
 
             clbias, meanz_use = compute_Clbias(meanzinterp, stdz)
 
-            totdz += [float(st**2) for st in stdz]
+            totdz += [float(st ** 2) for st in stdz]
             totclbias += clbias
             avmeanz += meanzinterp
 
@@ -757,6 +755,14 @@ def compute_Clbias(meanz_vals, scatter_mean_z_values):
         result["y1ratio"] = y1ratio
         result["y10ratio"] = y10ratio
 
+
+# Let's make code that pulls out the northern/southern galactic regions, and gets statistics of the footprint by region.
+def _is_ngp(ra, dec):
+    c = SkyCoord(ra=ra * u.degree, dec=dec * u.degree, frame="icrs")
+    lat = c.galactic.b.deg
+    return lat >= 0
+
+
 def get_stats_by_region(use_map, nside, maskval=0, region="all"):
     if region not in ["all", "north", "south"]:
         raise ValueError("Invalid region %s" % region)
@@ -781,6 +787,7 @@ def get_stats_by_region(use_map, nside, maskval=0, region="all"):
     # Return the values
     return (reg_mad, reg_median, reg_std)
 
+
 def stripiness_test_statistic(data_slice, nside):
     """
     A utility to find whether a particular routine has stripey features in the exposure time map.
@@ -796,12 +803,16 @@ def stripiness_test_statistic(data_slice, nside):
     test_statistic = frac_scatter["north"] / frac_scatter["south"] - 1
     return test_statistic
 
+
 class StripinessMetric(BaseMetric):
     """
-    Run as summary metric on NestedRIZExptimeExgalM5Metric or ExgalM5Metric
-
-    TODO
+    Run as summary metric on NestedRIZExptimeExgalM5Metric.
 
+    This metric uses maps of the combined RIZ exposure time to identify stripes or residual rolling features,
+    for the UniformAreaFoMFractionMetric.
+    The number returned quantifies the stripiness of the field.
+    In UniformAreaFoMFractionMetric it is a test statistic: if it is outside of +-0.7/np.sqrt(year) then
+    the RIZ exposure time map is considered to have stripes.
 
     Points of contact / contributors: Rachel Mandelbaum, Boris Leistedt
 
@@ -816,23 +827,24 @@ class StripinessMetric(BaseMetric):
     Returns
     -------
     result: `float`
-        The ratio of the FOMs of the two areas found
+        A number quantifying the stripiness of the RIZ exposure time.
     """
 
     def __init__(
-        self,
-        year,
-        nside,
-        verbose=True,
-        **kwargs,
+            self,
+            year,
+            nside,
+            verbose=True,
+            **kwargs,
     ):
         self.year = year
         self.mask_val = hp.UNSEEN
         self.verbose = verbose
         self.nside = nside
-        names = ["riz_exptime"]
-        types = [float]
+        names = ["exgal_m5", "riz_exptime"]
+        types = [float] * 2
         self.mask_val_arr = np.zeros(1, dtype=list(zip(names, types)))
+        self.mask_val_arr["exgal_m5"] = self.mask_val
         self.mask_val_arr["riz_exptime"] = self.mask_val
 
         super().__init__(col="metricdata", **kwargs)
@@ -854,6 +866,7 @@ def run(self, data_slice, slice_point=None):
 
         return test_statistic
 
+
 class UniformAreaFoMFractionMetric(BaseMetric):
     """
     Run as summary metric on NestedRIZExptimeExgalM5Metric.
@@ -862,6 +875,7 @@ class UniformAreaFoMFractionMetric(BaseMetric):
     to identify potential reductions in cosmological constraining power due to substantial large-scale power
     in non-uniform coadds at a particular data release.
     The RIZ exposure time map is used to identify whether there are residual rolling features.
+    Under the hood, this runs the StripinessMetric.
     If not, the metric returns 1. If there are such features, then the region is segmented into similar-depth regions
     and the one with the largest cosmological constraining power is presumed to be used for science.
     In that case, the metric returns the 3x2pt FoM (StaticProbesFoMEmulatorMetric,
@@ -887,11 +901,11 @@ class UniformAreaFoMFractionMetric(BaseMetric):
     """
 
     def __init__(
-        self,
-        year,
-        nside,
-        verbose=True,
-        **kwargs,
+            self,
+            year,
+            nside,
+            verbose=True,
+            **kwargs,
     ):
         self.year = year
         self.mask_val = hp.UNSEEN
@@ -933,11 +947,6 @@ def run(self, data_slice, slice_point=None):
         else:
             stripes = True
 
-        # Let's make code that pulls out the northern/southern galactic regions, and gets statistics of the footprint by region.
-        def _is_ngp(ra, dec):
-            c = SkyCoord(ra=ra * u.degree, dec=dec * u.degree, frame="icrs")
-            lat = c.galactic.b.deg
-            return lat >= 0
         def apply_clustering(clustering_data):
             # A thin wrapper around sklearn routines (can swap out which one we are using systematically).
             # We fix parameters like `n_clusters` since realistically we know for rolling that we should expect 2 clusters.
@@ -1001,16 +1010,16 @@ def make_clustering_dataset(depth_map, maskval=0, priority_fac=0.9, nside=64):
             my_data = np.zeros((n_unmasked, 3))
             cutval = 0.1 + maskval
             my_data[:, 0] = (
-                ra[depth_map > cutval]
-                * (1 - priority_fac)
-                * np.std(depth_map[depth_map > cutval])
-                / np.std(ra[depth_map > cutval])
+                    ra[depth_map > cutval]
+                    * (1 - priority_fac)
+                    * np.std(depth_map[depth_map > cutval])
+                    / np.std(ra[depth_map > cutval])
             )
             my_data[:, 1] = (
-                dec[depth_map > cutval]
-                * (1 - priority_fac)
-                * np.std(depth_map[depth_map > cutval])
-                / np.std(dec[depth_map > cutval])
+                    dec[depth_map > cutval]
+                    * (1 - priority_fac)
+                    * np.std(depth_map[depth_map > cutval])
+                    / np.std(dec[depth_map > cutval])
             )
             my_data[:, 2] = depth_map[depth_map > cutval]
             return my_data
@@ -1053,8 +1062,6 @@ def make_clustering_dataset(depth_map, maskval=0, priority_fac=0.9, nside=64):
             return fom / fom_total
 
 
-
-
 class uDropoutsNumbersShallowestDepth(BaseMetric):
     """
     Run as summary metric on MultibandExgalM5.
@@ -1087,7 +1094,6 @@ class uDropoutsNumbersShallowestDepth(BaseMetric):
     def __init__(self,
                  filter_list=["u", "g", "r", "i", "z", "y"],
                  **kwargs):
-
         super().__init__(col="metricdata", percentile=5, **kwargs)
         # Set mask_val, so that we receive metric_values.filled(mask_val)
         self.mask_val = hp.UNSEEN
@@ -1096,7 +1102,6 @@ def __init__(self,
         self.filter_list = filter_list
 
     def run(self, data_slice, slice_point=None):
-
         # Technically don't need this for now (isn't used in previous one)
         # need to define an array of bad values for the masked pixels
         badval_arr = np.repeat(self.badval, len(self.filter_list))
@@ -1122,18 +1127,18 @@ def run(self, data_slice, slice_point=None):
                 self.percentileMetric.run(np.core.records.fromrecords(x, dtype=[("metricdata", float)]))
                 for x in data_slice_arr
             ]
-        ) # 6 numbers
+        )  # 6 numbers
 
         from astropy.modeling.models import Schechter1D
         from astropy.cosmology import Planck18 as cosmo
         import astropy.units as u
 
         def schecter_lf(
-            m_grid: np.ndarray,
-            log_phi_star: float = -2.84,
-            M_star: float = -20.91,
-            alpha: float = -1.68,
-            redshift: float = 3,
+                m_grid: np.ndarray,
+                log_phi_star: float = -2.84,
+                M_star: float = -20.91,
+                alpha: float = -1.68,
+                redshift: float = 3,
         ) -> np.ndarray:
             """Schecter Luminosity Function on grid of apparent magnitudes.
 
@@ -1167,15 +1172,15 @@ def schecter_lf(
             M_grid = m_grid - 5 * np.log10(DL / 10) + 2.5 * np.log10(1 + redshift)
 
             # Calculate luminosity function in absolute magnitudes
-            schechter = Schechter1D(10**log_phi_star, M_star, alpha)
+            schechter = Schechter1D(10 ** log_phi_star, M_star, alpha)
 
             return schechter(M_grid)
 
         def number_density(
-            m_grid: np.ndarray,
-            LF: np.ndarray,
-            redshift: float = 3,
-            dz: float = 1,
+                m_grid: np.ndarray,
+                LF: np.ndarray,
+                redshift: float = 3,
+                dz: float = 1,
         ) -> float:
             """Calculate number density per deg^2.
 
@@ -1203,25 +1208,24 @@ def number_density(
             dchi = chi_far - chi_near
 
             # Calculate number density (mag^-1 Mpc^-2)
-            n_dm = (LF / u.Mpc**3) * dchi
+            n_dm = (LF / u.Mpc ** 3) * dchi
 
             # Convert to mag^-1 deg^-2
             deg_per_Mpc = cosmo.arcsec_per_kpc_comoving(redshift).to(u.deg / u.Mpc)
-            n_dm /= deg_per_Mpc**2
+            n_dm /= deg_per_Mpc ** 2
 
             # Integrate the luminosity function
             n = np.trapz(n_dm, m_grid)
 
             return n.value
 
-
-        u5 = percentiles[self.filter_list == 'u'] # uband m5
-        u3 = u5 + 2.5*np.log10(5 / 3) # convert to uband 3-sigma
-        g_cut = u3 - 1.5 # cut on g band
+        u5 = percentiles[self.filter_list == 'u']  # uband m5
+        u3 = u5 + 2.5 * np.log10(5 / 3)  # convert to uband 3-sigma
+        g_cut = u3 - 1.5  # cut on g band
         # Calculate LF up to g_cut
         m_grid = np.linspace(20, g_cut)
         LF = schecter_lf(m_grid)
-        n_deg2 = number_density(m_grid, LF) # number of objects per sq deg
+        n_deg2 = number_density(m_grid, LF)  # number of objects per sq deg
 
         return n_deg2
 
@@ -1256,7 +1260,6 @@ class uDropoutsArea(BaseMetric):
     def __init__(self,
                  filter_list=["u", "g", "r", "i", "z", "y"],
                  **kwargs):
-
         super().__init__(col="metricdata", percentile=5, **kwargs)
         # Set mask_val, so that we receive metric_values.filled(mask_val)
         self.mask_val = hp.UNSEEN
@@ -1265,7 +1268,6 @@ def __init__(self,
         self.filter_list = filter_list
 
     def run(self, data_slice, slice_point=None):
-
         # Technically don't need this for now (isn't used in previous one)
         # need to define an array of bad values for the masked pixels
         badval_arr = np.repeat(self.badval, len(self.filter_list))
@@ -1291,4 +1293,6 @@ def run(self, data_slice, slice_point=None):
                 self.percentileMetric.run(np.core.records.fromrecords(x, dtype=[("metricdata", float)]))
                 for x in data_slice_arr
             ]
-        ) # 6 numbers
\ No newline at end of file
+        )  # 6 numbers
+
+