Repository: econ-ark/HARK (language: python)

Issue:
Remake X: DiscreteDistribution should have pmv not pmf
Clarifying the scope of this ticket: It involves two changes to the DiscreteDistribution class:
 - replacing 'pmf' with 'pmv'
 - replacing 'X' with 'values'

https://github.com/econ-ark/HARK/blob/master/HARK/distribution.py#L816-L829

The class needs to be refactored with this change. Then, downstream code using DiscreteDistribution needs to be updated to reflect this change.


--------------
```
# CDC 20210621: We should not use pmf for the point masses of the realizations, because it
# is not a probability mass 'function.' Scipy.discrete_rv, sympy, and Mathematica all
# return pmf (or pdf) as a function. They have different syntaxes for retrieving the
# vector of point masses; here it is called the pmv (probability mass vector)


class DiscreteDistribution(DiscreteDistributionOld):
    __doc__ = DiscreteDistributionOld.__doc__
    __doc__ += """
        XYZ : np.array
            Restructure the dimensions of the np.array so that successive
            columns in XYZ correspond to the vectors of values of each
            discrete random variables across the broadcasted combinations.
            Thus, [instance].XYZ[0] will return the vector of discrete
            realizations of the first variable, [instance].XYZ[-1] will
            get the last RV, etc.
        pmv : The vector of point masses of the broadcasted collection
            of random variables
"""

    def __init__(self, pmf, X, seed=0):  # Get the old definition
        super().__init__(pmf, X, seed)  # execute the old def
        XYZ = np.column_stack(X)  # stack actually unstacks them
        if self.dim() == 1:  # numpy 1 dimensional arrays want to be
            XYZ = XYZ.T  # the wrong shape
        self.XYZ = XYZ  # [ N dimensional matrix ]
        # The pmf should be a function, not a vector (as now)
        # TODO: Replace invocations of pmf with pmv
        self.pmv = pmf

 
```



Discussion / hints:
Also, "X" should not be "X", but a more descriptive and lowercase term, like "values"
See discussion here:
https://github.com/econ-ark/HARK/pull/1146/files#r883016319

Because of the ambiguity about the semantics of "X" -- a dimension of X can mean either a dimension of the state values of of random variable, or the 'nature's choice' dimension -- it would be better if X/values were a different kind of data structure that makes these differences more explicit.

For example, if X were a Pandas dataframe, the index could be nature's choice, and the columns would be dimensions of the RV value. However, that might not work for matrix-valued (as opposed to vector-valued) RVs.
It might be possible to take inspiration, or an implementation, from another distribution library.

https://github.com/econ-ark/HARK/wiki/Distribution-Libraries
`xarray.DataArray` has attributes `values` and `data` by default. The `DiscreteDistributionXRA` could easily make this change without significant work. 

@llorracc how about `data` and `prob`?


Don't like ".data" because usually it will NOT be from empirical
measurement but rather from numerical approximations to theoretical objects.

A general purpose option would be "atoms" and "probs" -- encompassing the
abstract possibility that the distribution being described is, say, the
probability of a particular word occurring in a text (and the atoms are the
words).




On Wed, Aug 3, 2022 at 9:17 AM alanlujan91 ***@***.***> wrote:

> xarray.DataArray has attributes values and data by default. The
> DiscreteDistributionXRA could easily make this change without significant
> work.
>
> @llorracc <https://github.com/llorracc> how about data and prob?
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/econ-ark/HARK/issues/1051#issuecomment-1203938802>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AAKCK76WHX3ZA62LJV7CHSLVXJWPPANCNFSM5BPSM7ZA>
> .
> You are receiving this because you were mentioned.Message ID:
> ***@***.***>
>


-- 
- Chris Carroll

`scipy.stats.rv_discrete` uses `values()` to access both the atoms and the probabilities:
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_discrete.html#scipy.stats.rv_discrete
At the last meeting I think we had consensus on `.pmv` and `.atoms`. I'll work on that. 

Fix patch:
diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index ab14331ae..5ed616093 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -38,6 +38,7 @@ Release Date: TBD
 * Fixes scaling bug in Normal equiprobable approximation method. [1139](https://github.com/econ-ark/HARK/pull/1139)
 * Removes the extra-dimension that was returned by `calc_expectations` in some instances. [#1149](https://github.com/econ-ark/HARK/pull/1149)
 * Adds `HARK.distribution.expected` alias for `DiscreteDistribution.expected`. [#1156](https://github.com/econ-ark/HARK/pull/1156)
+* Renames attributes in `DiscreteDistribution`: `X` to `atoms` and `pmf` to `pmv`. [#1164](https://github.com/econ-ark/HARK/pull/1164), [#1051](https://github.com/econ-ark/HARK/pull/1151), [#1159](https://github.com/econ-ark/HARK/pull/1159). 
 
 ### 0.12.0
 
diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py
index 7eb807389..cb370787b 100644
--- a/HARK/ConsumptionSaving/ConsAggShockModel.py
+++ b/HARK/ConsumptionSaving/ConsAggShockModel.py
@@ -15,7 +15,7 @@
     BilinearInterp,
     LowerEnvelope2D,
     UpperEnvelope,
-    MargValueFuncCRRA
+    MargValueFuncCRRA,
 )
 from HARK.utilities import (
     CRRAutility,
@@ -31,7 +31,7 @@
     MeanOneLogNormal,
     Uniform,
     combine_indep_dstns,
-    calc_expectation
+    calc_expectation,
 )
 from HARK.ConsumptionSaving.ConsIndShockModel import (
     ConsumerSolution,
@@ -65,6 +65,7 @@
 utility_invP = CRRAutility_invP
 utility_inv = CRRAutility_inv
 
+
 class MargValueFunc2D(MetricObject):
     """
     A class for representing a marginal value function in models where the
@@ -164,8 +165,12 @@ def reset(self):
         None
         """
         self.initialize_sim()
-        self.state_now['aLvlNow'] = self.kInit * np.ones(self.AgentCount)  # Start simulation near SS
-        self.state_now['aNrm'] = self.state_now['aLvlNow'] / self.state_now['pLvl'] # ???
+        self.state_now["aLvlNow"] = self.kInit * np.ones(
+            self.AgentCount
+        )  # Start simulation near SS
+        self.state_now["aNrm"] = (
+            self.state_now["aLvlNow"] / self.state_now["pLvl"]
+        )  # ???
 
     def pre_solve(self):
         #        AgentType.pre_solve()
@@ -254,7 +259,7 @@ def add_AggShkDstn(self, AggShkDstn):
         -------
         None
         """
-        if len(self.IncShkDstn[0].X) > 2:
+        if len(self.IncShkDstn[0].atoms) > 2:
             self.IncShkDstn = self.IncShkDstnWithoutAggShocks
         else:
             self.IncShkDstnWithoutAggShocks = self.IncShkDstn
@@ -279,12 +284,13 @@ def sim_birth(self, which_agents):
         None
         """
         IndShockConsumerType.sim_birth(self, which_agents)
-        if 'aLvl' in self.state_now and self.state_now['aLvl'] is not None:
-            self.state_now['aLvl'][which_agents] = (
-                self.state_now['aNrm'][which_agents] * self.state_now['pLvl'][which_agents]
+        if "aLvl" in self.state_now and self.state_now["aLvl"] is not None:
+            self.state_now["aLvl"][which_agents] = (
+                self.state_now["aNrm"][which_agents]
+                * self.state_now["pLvl"][which_agents]
             )
         else:
-            self.state_now['aLvl'] = self.state_now['aNrm'] * self.state_now['pLvl']
+            self.state_now["aLvl"] = self.state_now["aNrm"] * self.state_now["pLvl"]
 
     def sim_death(self):
         """
@@ -319,11 +325,15 @@ def sim_death(self):
 
         # Divide up the wealth of those who die, giving it to those who survive
         who_lives = np.logical_not(who_dies)
-        wealth_living = np.sum(self.state_now['aLvl'][who_lives])
-        wealth_dead = np.sum(self.state_now['aLvl'][who_dies])
+        wealth_living = np.sum(self.state_now["aLvl"][who_lives])
+        wealth_dead = np.sum(self.state_now["aLvl"][who_dies])
         Ractuarial = 1.0 + wealth_dead / wealth_living
-        self.state_now['aNrm'][who_lives] = self.state_now['aNrm'][who_lives] * Ractuarial
-        self.state_now['aLvl'][who_lives] = self.state_now['aLvl'][who_lives] * Ractuarial
+        self.state_now["aNrm"][who_lives] = (
+            self.state_now["aNrm"][who_lives] * Ractuarial
+        )
+        self.state_now["aLvl"][who_lives] = (
+            self.state_now["aLvl"][who_lives] * Ractuarial
+        )
         return who_dies
 
     def get_Rfree(self):
@@ -356,10 +366,10 @@ def get_shocks(self):
         None
         """
         IndShockConsumerType.get_shocks(self)  # Update idiosyncratic shocks
-        self.shocks['TranShk'] = (
-            self.shocks['TranShk'] * self.TranShkAggNow * self.wRteNow
+        self.shocks["TranShk"] = (
+            self.shocks["TranShk"] * self.TranShkAggNow * self.wRteNow
         )
-        self.shocks['PermShk'] = self.shocks['PermShk'] * self.PermShkAggNow
+        self.shocks["PermShk"] = self.shocks["PermShk"] * self.PermShkAggNow
 
     def get_controls(self):
         """
@@ -378,12 +388,14 @@ def get_controls(self):
         MaggNow = self.get_MaggNow()
         for t in range(self.T_cycle):
             these = t == self.t_cycle
-            cNrmNow[these] = self.solution[t].cFunc(self.state_now['mNrm'][these], MaggNow[these])
+            cNrmNow[these] = self.solution[t].cFunc(
+                self.state_now["mNrm"][these], MaggNow[these]
+            )
             MPCnow[these] = self.solution[t].cFunc.derivativeX(
-                self.state_now['mNrm'][these], MaggNow[these]
+                self.state_now["mNrm"][these], MaggNow[these]
             )  # Marginal propensity to consume
 
-        self.controls['cNrm'] = cNrmNow
+        self.controls["cNrm"] = cNrmNow
         self.MPCnow = MPCnow
         return None
 
@@ -490,7 +502,7 @@ def __init__(self, **kwds):
         kwds = params
         AggShockConsumerType.__init__(self, **kwds)
 
-        self.shocks['Mrkv'] = None
+        self.shocks["Mrkv"] = None
 
         self.add_to_time_inv("MrkvArray")
         self.solve_one_period = solve_ConsAggMarkov
@@ -501,7 +513,7 @@ def add_AggShkDstn(self, AggShkDstn):
         state. AggShkDstn is a list of aggregate productivity shock distributions
         for each Markov state.
         """
-        if len(self.IncShkDstn[0][0].X) > 2:
+        if len(self.IncShkDstn[0][0].atoms) > 2:
             self.IncShkDstn = self.IncShkDstnWithoutAggShocks
         else:
             self.IncShkDstnWithoutAggShocks = self.IncShkDstn
@@ -565,7 +577,7 @@ def get_shocks(self):
             N = np.sum(these)
             if N > 0:
                 IncShkDstnNow = self.IncShkDstn[t - 1][
-                    self.shocks['Mrkv']
+                    self.shocks["Mrkv"]
                 ]  # set current income distribution
                 PermGroFacNow = self.PermGroFac[t - 1]  # and permanent growth factor
 
@@ -581,7 +593,7 @@ def get_shocks(self):
         if N > 0:
             these = newborn
             IncShkDstnNow = self.IncShkDstn[0][
-                self.shocks['Mrkv']
+                self.shocks["Mrkv"]
             ]  # set current income distribution
             PermGroFacNow = self.PermGroFac[0]  # and permanent growth factor
 
@@ -595,8 +607,8 @@ def get_shocks(self):
         # Store the shocks in self
         self.EmpNow = np.ones(self.AgentCount, dtype=bool)
         self.EmpNow[TranShkNow == self.IncUnemp] = False
-        self.shocks['TranShk'] = TranShkNow * self.TranShkAggNow * self.wRteNow
-        self.shocks['PermShk'] = PermShkNow * self.PermShkAggNow
+        self.shocks["TranShk"] = TranShkNow * self.TranShkAggNow * self.wRteNow
+        self.shocks["PermShk"] = PermShkNow * self.PermShkAggNow
 
     def get_controls(self):
         """
@@ -628,20 +640,20 @@ def get_controls(self):
             for i in range(StateCount):
                 those = np.logical_and(these, MrkvBoolArray[i, :])
                 cNrmNow[those] = self.solution[t].cFunc[i](
-                    self.state_now['mNrm'][those], MaggNow[those]
+                    self.state_now["mNrm"][those], MaggNow[those]
                 )
                 # Marginal propensity to consume
                 MPCnow[those] = (
                     self.solution[t]
                     .cFunc[i]
-                    .derivativeX(self.state_now['mNrm'][those], MaggNow[those])
+                    .derivativeX(self.state_now["mNrm"][those], MaggNow[those])
                 )
-        self.controls['cNrm'] = cNrmNow
+        self.controls["cNrm"] = cNrmNow
         self.MPCnow = MPCnow
         return None
 
     def getMrkvNow(self):  # This function exists to be overwritten in StickyE model
-        return self.shocks['Mrkv'] * np.ones(self.AgentCount, dtype=int)
+        return self.shocks["Mrkv"] * np.ones(self.AgentCount, dtype=int)
 
 
 init_KS_agents = {
@@ -687,20 +699,10 @@ def __init__(self, **kwds):
             "CRRA",
         ]
         # need better handling of this
-        self.state_now = {
-            "aNow" : None,
-            "mNow" : None,
-            "EmpNow" : None
-        }
-        self.state_prev = {
-            "aNow" : None,
-            "mNow" : None,
-            "EmpNow" : None
-        }
-
-        self.shock_vars = {
-            "Mrkv" : None
-        }
+        self.state_now = {"aNow": None, "mNow": None, "EmpNow": None}
+        self.state_prev = {"aNow": None, "mNow": None, "EmpNow": None}
+
+        self.shock_vars = {"Mrkv": None}
 
         self.solve_one_period = solve_KrusellSmith
         self.update()
@@ -772,7 +774,9 @@ def make_grid(self):
         Construct the attribute aXtraGrid from the primitive attributes aMin,
         aMax, aCount, aNestFac.
         """
-        self.aGrid = make_grid_exp_mult(self.aMin, self.aMax, self.aCount, self.aNestFac)
+        self.aGrid = make_grid_exp_mult(
+            self.aMin, self.aMax, self.aCount, self.aNestFac
+        )
         self.add_to_time_inv("aGrid")
 
     def update_solution_terminal(self):
@@ -819,10 +823,10 @@ def precompute_arrays(self):
         # Calculate (net) interest factor and wage rate next period
         KtoLnext = Knext / Lnext
         Rnext = 1.0 + Znext * CapShare * KtoLnext ** (CapShare - 1.0) - DeprFac
-        Wnext = Znext * (1.0 - CapShare) * KtoLnext ** CapShare
+        Wnext = Znext * (1.0 - CapShare) * KtoLnext**CapShare
 
         # Calculate aggregate market resources next period
-        Ynext = Znext * Knext ** CapShare * Lnext ** (1.0 - CapShare)
+        Ynext = Znext * Knext**CapShare * Lnext ** (1.0 - CapShare)
         Mnext = (1.0 - DeprFac) * Knext + Ynext
 
         # Tile the interest, wage, and aggregate market resources arrays
@@ -973,7 +977,7 @@ def market_action(self):
         self.simulate(1)
 
     def initialize_sim(self):
-        self.shocks['Mrkv'] = self.MrkvInit
+        self.shocks["Mrkv"] = self.MrkvInit
         AgentType.initialize_sim(self)
         self.state_now["EmpNow"] = self.state_now["EmpNow"].astype(bool)
         self.make_emp_idx_arrays()
@@ -990,10 +994,10 @@ def sim_birth(self, which):
         if N == 0:
             return
 
-        if self.shocks['Mrkv'] == 0:
+        if self.shocks["Mrkv"] == 0:
             unemp_N = int(np.round(self.UrateB * N))
             emp_N = self.AgentCount - unemp_N
-        elif self.shocks['Mrkv'] == 1:
+        elif self.shocks["Mrkv"] == 1:
             unemp_N = int(np.round(self.UrateG * N))
             emp_N = self.AgentCount - unemp_N
         else:
@@ -1017,8 +1021,8 @@ def get_shocks(self):
         mrkv_prev = int((unemployed.sum() / float(self.AgentCount)) != self.UrateB)
 
         # Transition some agents between unemployment and employment
-        emp_permute = self.emp_permute[mrkv_prev][self.shocks['Mrkv']]
-        unemp_permute = self.unemp_permute[mrkv_prev][self.shocks['Mrkv']]
+        emp_permute = self.emp_permute[mrkv_prev][self.shocks["Mrkv"]]
+        unemp_permute = self.unemp_permute[mrkv_prev][self.shocks["Mrkv"]]
         # TODO: replace poststate_vars functionality with shocks here
         EmpNow = self.state_now["EmpNow"]
 
@@ -1031,7 +1035,10 @@ def get_states(self):
         """
         Get each agent's idiosyncratic state, their household market resources.
         """
-        self.state_now["mNow"] = self.Rnow * self.state_prev['aNow'] + self.Wnow * self.LbrInd * self.state_now["EmpNow"]
+        self.state_now["mNow"] = (
+            self.Rnow * self.state_prev["aNow"]
+            + self.Wnow * self.LbrInd * self.state_now["EmpNow"]
+        )
 
     def get_controls(self):
         """
@@ -1041,10 +1048,10 @@ def get_controls(self):
         unemployed = np.logical_not(employed)
 
         # Get the discrete index for (un)employed agents
-        if self.shocks['Mrkv'] == 0:  # Bad macroeconomic conditions
+        if self.shocks["Mrkv"] == 0:  # Bad macroeconomic conditions
             unemp_idx = 0
             emp_idx = 1
-        elif self.shocks['Mrkv'] == 1:  # Good macroeconomic conditions
+        elif self.shocks["Mrkv"] == 1:  # Good macroeconomic conditions
             unemp_idx = 2
             emp_idx = 3
         else:
@@ -1065,7 +1072,7 @@ def get_poststates(self):
         """
         Gets each agent's retained assets after consumption.
         """
-        self.state_now['aNow'] = self.state_now["mNow"] - self.controls["cNow"]
+        self.state_now["aNow"] = self.state_now["mNow"] - self.controls["cNow"]
 
 
 ###############################################################################
@@ -1098,7 +1105,7 @@ def solveConsAggShock(
     IncShkDstn : distribution.Distribution
         A discrete
         approximation to the income process between the period being solved
-        and the one immediately following (in solution_next). Order: 
+        and the one immediately following (in solution_next). Order:
         idiosyncratic permanent shocks, idiosyncratic transitory
         shocks, aggregate permanent shocks, aggregate transitory shocks.
     LivPrb : float
@@ -1141,11 +1148,11 @@ def solveConsAggShock(
     mNrmMinNext = solution_next.mNrmMin
 
     # Unpack the income shocks
-    ShkPrbsNext = IncShkDstn.pmf
-    PermShkValsNext = IncShkDstn.X[0]
-    TranShkValsNext = IncShkDstn.X[1]
-    PermShkAggValsNext = IncShkDstn.X[2]
-    TranShkAggValsNext = IncShkDstn.X[3]
+    ShkPrbsNext = IncShkDstn.pmv
+    PermShkValsNext = IncShkDstn.atoms[0]
+    TranShkValsNext = IncShkDstn.atoms[1]
+    PermShkAggValsNext = IncShkDstn.atoms[2]
+    TranShkAggValsNext = IncShkDstn.atoms[3]
     ShkCount = ShkPrbsNext.size
 
     # Make the grid of end-of-period asset values, and a tiled version
@@ -1273,9 +1280,23 @@ def solveConsAggShock(
     return solution_now
 
 
-def solve_ConsAggShock_new(solution_next, IncShkDstn, LivPrb, DiscFac, CRRA, PermGroFac,
-                      PermGroFacAgg, aXtraGrid, BoroCnstArt, Mgrid, AFunc, Rfunc, wFunc, DeprFac):
-    '''
+def solve_ConsAggShock_new(
+    solution_next,
+    IncShkDstn,
+    LivPrb,
+    DiscFac,
+    CRRA,
+    PermGroFac,
+    PermGroFacAgg,
+    aXtraGrid,
+    BoroCnstArt,
+    Mgrid,
+    AFunc,
+    Rfunc,
+    wFunc,
+    DeprFac,
+):
+    """
     Solve one period of a consumption-saving problem with idiosyncratic and
     aggregate shocks (transitory and permanent).  This is a basic solver that
     can't handle cubic splines, nor can it calculate a value function. This
@@ -1288,7 +1309,7 @@ def solve_ConsAggShock_new(solution_next, IncShkDstn, LivPrb, DiscFac, CRRA, Per
     IncShkDstn : distribution.Distribution
         A discrete
         approximation to the income process between the period being solved
-        and the one immediately following (in solution_next). Order: 
+        and the one immediately following (in solution_next). Order:
         idiosyncratic permanent shocks, idiosyncratic transitory
         shocks, aggregate permanent shocks, aggregate transitory shocks.
     LivPrb : float
@@ -1325,69 +1346,83 @@ def solve_ConsAggShock_new(solution_next, IncShkDstn, LivPrb, DiscFac, CRRA, Per
         The solution to the single period consumption-saving problem.  Includes
         a consumption function cFunc (linear interpolation over linear interpola-
         tions) and marginal value function vPfunc.
-    '''
+    """
     # Unpack the income shocks and get grid sizes
-    PermShkValsNext = IncShkDstn.X[0]
-    TranShkValsNext = IncShkDstn.X[1]
-    PermShkAggValsNext = IncShkDstn.X[2]
-    TranShkAggValsNext = IncShkDstn.X[3]
+    PermShkValsNext = IncShkDstn.atoms[0]
+    TranShkValsNext = IncShkDstn.atoms[1]
+    PermShkAggValsNext = IncShkDstn.atoms[2]
+    TranShkAggValsNext = IncShkDstn.atoms[3]
     aCount = aXtraGrid.size
     Mcount = Mgrid.size
-    
+
     # Define a function that calculates M_{t+1} from M_t and the aggregate shocks;
     # the function also returns the wage rate and effective interest factor
-    def calcAggObjects(M,Psi,Theta):
-        A = AFunc(M) # End-of-period aggregate assets (normalized)
-        kNext = A/(PermGroFacAgg*Psi) # Next period's aggregate capital/labor ratio
-        kNextEff = kNext/Theta  # Same thing, but account for *transitory* shock
-        R = Rfunc(kNextEff)     # Interest factor on aggregate assets
-        wEff = wFunc(kNextEff)*Theta  # Effective wage rate (accounts for labor supply)
-        Reff = R/LivPrb # Account for redistribution of decedents' wealth
-        Mnext = kNext*R + wEff # Next period's aggregate market resources
+    def calcAggObjects(M, Psi, Theta):
+        A = AFunc(M)  # End-of-period aggregate assets (normalized)
+        kNext = A / (PermGroFacAgg * Psi)  # Next period's aggregate capital/labor ratio
+        kNextEff = kNext / Theta  # Same thing, but account for *transitory* shock
+        R = Rfunc(kNextEff)  # Interest factor on aggregate assets
+        wEff = (
+            wFunc(kNextEff) * Theta
+        )  # Effective wage rate (accounts for labor supply)
+        Reff = R / LivPrb  # Account for redistribution of decedents' wealth
+        Mnext = kNext * R + wEff  # Next period's aggregate market resources
         return Mnext, Reff, wEff
-    
+
     # Define a function that evaluates R*v'(m_{t+1},M_{t+1}) from a_t, M_t, and the income shocks
-    def vPnextFunc(a,M,psi,theta,Psi,Theta):
-        Mnext, Reff, wEff = calcAggObjects(M,Psi,Theta)
-        PermShkTotal = PermGroFac * PermGroFacAgg * psi * Psi  # Total / combined permanent shock
-        mNext = Reff*a/PermShkTotal + theta*wEff # Idiosyncratic market resources
-        vPnext = Reff*PermShkTotal**(-CRRA)*solution_next.vPfunc(mNext, Mnext)
+    def vPnextFunc(a, M, psi, theta, Psi, Theta):
+        Mnext, Reff, wEff = calcAggObjects(M, Psi, Theta)
+        PermShkTotal = (
+            PermGroFac * PermGroFacAgg * psi * Psi
+        )  # Total / combined permanent shock
+        mNext = Reff * a / PermShkTotal + theta * wEff  # Idiosyncratic market resources
+        vPnext = Reff * PermShkTotal ** (-CRRA) * solution_next.vPfunc(mNext, Mnext)
         return vPnext
-    
+
     # Make an array of a_t values at which to calculate end-of-period marginal value of assets
-    BoroCnstNat_vec = np.zeros(Mcount) # Natural borrowing constraint at each M_t
-    aNrmNow = np.zeros((aCount,Mcount))
+    BoroCnstNat_vec = np.zeros(Mcount)  # Natural borrowing constraint at each M_t
+    aNrmNow = np.zeros((aCount, Mcount))
     for j in range(Mcount):
-        Mnext, Reff, wEff = calcAggObjects(Mgrid[j], PermShkAggValsNext, TranShkAggValsNext)
-        aNrmMin_cand = (PermGroFac*PermGroFacAgg*PermShkValsNext*PermShkAggValsNext/Reff) * \
-                    (solution_next.mNrmMin(Mnext) - wEff*TranShkValsNext)
-        aNrmMin = np.max(aNrmMin_cand) # Lowest valid a_t value for this M_t
-        aNrmNow[:,j] = aNrmMin + aXtraGrid
+        Mnext, Reff, wEff = calcAggObjects(
+            Mgrid[j], PermShkAggValsNext, TranShkAggValsNext
+        )
+        aNrmMin_cand = (
+            PermGroFac * PermGroFacAgg * PermShkValsNext * PermShkAggValsNext / Reff
+        ) * (solution_next.mNrmMin(Mnext) - wEff * TranShkValsNext)
+        aNrmMin = np.max(aNrmMin_cand)  # Lowest valid a_t value for this M_t
+        aNrmNow[:, j] = aNrmMin + aXtraGrid
         BoroCnstNat_vec[j] = aNrmMin
-    
+
     # Compute end-of-period marginal value of assets
-    MaggNow = np.tile(np.reshape(Mgrid,(1,Mcount)),(aCount,1)) # Tiled Mgrid
-    EndOfPrdvP = DiscFac*LivPrb*calc_expectation(IncShkDstn,vPnextFunc,[aNrmNow,MaggNow])
+    MaggNow = np.tile(np.reshape(Mgrid, (1, Mcount)), (aCount, 1))  # Tiled Mgrid
+    EndOfPrdvP = (
+        DiscFac * LivPrb * calc_expectation(IncShkDstn, vPnextFunc, [aNrmNow, MaggNow])
+    )
 
     # Calculate optimal consumption from each asset gridpoint and endogenous m_t gridpoint
-    cNrmNow = EndOfPrdvP**(-1.0/CRRA)
+    cNrmNow = EndOfPrdvP ** (-1.0 / CRRA)
     mNrmNow = aNrmNow + cNrmNow
 
     # Loop through the values in Mgrid and make a linear consumption function for each
     cFuncBaseByM_list = []
     for j in range(Mcount):
-        c_temp = np.insert(cNrmNow[:,j], 0, 0.0)  # Add point at bottom
-        m_temp = np.insert(mNrmNow[:,j] - BoroCnstNat_vec[j], 0, 0.0)
+        c_temp = np.insert(cNrmNow[:, j], 0, 0.0)  # Add point at bottom
+        m_temp = np.insert(mNrmNow[:, j] - BoroCnstNat_vec[j], 0, 0.0)
         cFuncBaseByM_list.append(LinearInterp(m_temp, c_temp))
 
     # Construct the overall unconstrained consumption function by combining the M-specific functions
-    BoroCnstNat = LinearInterp(np.insert(Mgrid, 0, 0.0), np.insert(BoroCnstNat_vec, 0, 0.0))
+    BoroCnstNat = LinearInterp(
+        np.insert(Mgrid, 0, 0.0), np.insert(BoroCnstNat_vec, 0, 0.0)
+    )
     cFuncBase = LinearInterpOnInterp1D(cFuncBaseByM_list, Mgrid)
     cFuncUnc = VariableLowerBoundFunc2D(cFuncBase, BoroCnstNat)
 
     # Make the constrained consumption function and combine it with the unconstrained component
-    cFuncCnst = BilinearInterp(np.array([[0.0, 0.0], [1.0, 1.0]]),
-                               np.array([BoroCnstArt, BoroCnstArt+1.0]), np.array([0.0, 1.0]))
+    cFuncCnst = BilinearInterp(
+        np.array([[0.0, 0.0], [1.0, 1.0]]),
+        np.array([BoroCnstArt, BoroCnstArt + 1.0]),
+        np.array([0.0, 1.0]),
+    )
     cFuncNow = LowerEnvelope2D(cFuncUnc, cFuncCnst)
 
     # Make the minimum m function as the greater of the natural and artificial constraints
@@ -1397,7 +1432,9 @@ def vPnextFunc(a,M,psi,theta,Psi,Theta):
     vPfuncNow = MargValueFunc2D(cFuncNow, CRRA)
 
     # Pack up and return the solution
-    solution_now = ConsumerSolution(cFunc=cFuncNow, vPfunc=vPfuncNow, mNrmMin=mNrmMinNow)
+    solution_now = ConsumerSolution(
+        cFunc=cFuncNow, vPfunc=vPfuncNow, mNrmMin=mNrmMinNow
+    )
     return solution_now
 
 
@@ -1432,9 +1469,9 @@ def solve_ConsAggMarkov(
     solution_next : ConsumerSolution
         The solution to the succeeding one period problem.
     IncShkDstn : [distribution.Distribution]
-        A list of 
+        A list of
         discrete approximations to the income process between the period being
-        solved and the one immediately following (in solution_next). Order: 
+        solved and the one immediately following (in solution_next). Order:
         idisyncratic permanent shocks, idiosyncratic transitory
         shocks, aggregate permanent shocks, aggregate transitory shocks.
     LivPrb : float
@@ -1493,11 +1530,11 @@ def solve_ConsAggMarkov(
         mNrmMinNext = solution_next.mNrmMin[j]
 
         # Unpack the income shocks
-        ShkPrbsNext = IncShkDstn[j].pmf
-        PermShkValsNext = IncShkDstn[j].X[0]
-        TranShkValsNext = IncShkDstn[j].X[1]
-        PermShkAggValsNext = IncShkDstn[j].X[2]
-        TranShkAggValsNext = IncShkDstn[j].X[3]
+        ShkPrbsNext = IncShkDstn[j].pmv
+        PermShkValsNext = IncShkDstn[j].atoms[0]
+        TranShkValsNext = IncShkDstn[j].atoms[1]
+        PermShkAggValsNext = IncShkDstn[j].atoms[2]
+        TranShkAggValsNext = IncShkDstn[j].atoms[3]
         ShkCount = ShkPrbsNext.size
         aXtra_tiled = np.tile(
             np.reshape(aXtraGrid, (1, aCount, 1)), (Mcount, 1, ShkCount)
@@ -1706,7 +1743,7 @@ def solve_KrusellSmith(
     Because this model is so specialized and only intended to be used with a very narrow
     case, many arrays can be precomputed, making the code here very short.  See the
     method KrusellSmithType.precompute_arrays() for details.
-    
+
     Parameters
     ----------
     solution_next : ConsumerSolution
@@ -1868,7 +1905,7 @@ def __init__(self, agents=None, tolerance=0.0001, act_T=1200, **kwds):
         Market.__init__(
             self,
             agents=agents,
-            reap_vars=['aLvl', 'pLvl'],
+            reap_vars=["aLvl", "pLvl"],
             track_vars=["MaggNow", "AaggNow"],
             dyn_vars=["AFunc"],
             tolerance=tolerance,
@@ -2021,10 +2058,10 @@ def make_AggShkHist(self):
         None
         """
         sim_periods = self.act_T
-        Events = np.arange(self.AggShkDstn.pmf.size)  # just a list of integers
-        EventDraws = self.AggShkDstn.draw(N=sim_periods, X=Events)
-        PermShkAggHist = self.AggShkDstn.X[0][EventDraws]
-        TranShkAggHist = self.AggShkDstn.X[1][EventDraws]
+        Events = np.arange(self.AggShkDstn.pmv.size)  # just a list of integers
+        EventDraws = self.AggShkDstn.draw(N=sim_periods, atoms=Events)
+        PermShkAggHist = self.AggShkDstn.atoms[0][EventDraws]
+        TranShkAggHist = self.AggShkDstn.atoms[1][EventDraws]
 
         # Store the histories
         self.PermShkAggHist = PermShkAggHist * self.PermGroFacAgg
@@ -2148,7 +2185,7 @@ def calc_AFunc(self, MaggNow, AaggNow):
                 + ", slope="
                 + str(slope)
                 + ", r-sq="
-                + str(r_value ** 2)
+                + str(r_value**2)
             )
 
         return AggShocksDynamicRule(AFunc)
@@ -2292,10 +2329,10 @@ def make_AggShkHist(self):
         None
         """
         sim_periods = self.act_T
-        Events = np.arange(self.AggShkDstn.pmf.size)  # just a list of integers
-        EventDraws = self.AggShkDstn.draw(N=sim_periods, X=Events)
-        PermShkAggHist = self.AggShkDstn.X[0][EventDraws]
-        TranShkAggHist = self.AggShkDstn.X[1][EventDraws]
+        Events = np.arange(self.AggShkDstn.pmv.size)  # just a list of integers
+        EventDraws = self.AggShkDstn.draw(N=sim_periods, atoms=Events)
+        PermShkAggHist = self.AggShkDstn.atoms[0][EventDraws]
+        TranShkAggHist = self.AggShkDstn.atoms[1][EventDraws]
 
         # Store the histories
         self.PermShkAggHist = PermShkAggHist
@@ -2327,7 +2364,7 @@ def get_AggShocks(self):
             Transitory shock to aggregate labor productivity this period.
         KtoLnow : float
             Capital-to-labor ratio in the economy this period.
-        
+
         """
         # Get this period's aggregate shocks
         PermShkAggNow = self.PermShkAggHist[self.Shk_idx]
@@ -2497,7 +2534,9 @@ def make_AggShkDstn(self):
                     N=self.PermShkAggCount
                 )
             )
-            AggShkDstn.append(combine_indep_dstns(PermShkAggDstn[-1], TranShkAggDstn[-1]))
+            AggShkDstn.append(
+                combine_indep_dstns(PermShkAggDstn[-1], TranShkAggDstn[-1])
+            )
 
         self.TranShkAggDstn = TranShkAggDstn
         self.PermShkAggDstn = PermShkAggDstn
@@ -2596,7 +2635,7 @@ def make_Mrkv_history(self):
         # Add histories until each state has been visited at least state_T_min times
         while go:
             draws = Uniform(seed=loops).draw(N=self.act_T_orig)
-            markov_process = MarkovProcess(self.MrkvArray,seed=loops)
+            markov_process = MarkovProcess(self.MrkvArray, seed=loops)
             for s in range(self.act_T_orig):  # Add act_T_orig more periods
                 MrkvNow_hist[t] = MrkvNow
                 MrkvNow = markov_process.draw(MrkvNow)
@@ -2728,7 +2767,7 @@ def calc_AFunc(self, MaggNow, AaggNow):
             AFunc_list.append(
                 AggregateSavingRule(intercept, slope)
             )  # Make a new next-period capital function
-            rSq_list.append(r_value ** 2)
+            rSq_list.append(r_value**2)
 
             # Save the new values as "previous" values for the next iteration
             self.intercept_prev[i] = intercept
@@ -2859,7 +2898,7 @@ def update(self):
         ]
         self.AFunc = AFunc_all
         self.KtoLSS = (
-            (1.0 ** self.CRRA / self.DiscFac - (1.0 - self.DeprFac)) / self.CapShare
+            (1.0**self.CRRA / self.DiscFac - (1.0 - self.DeprFac)) / self.CapShare
         ) ** (1.0 / (self.CapShare - 1.0))
         self.KSS = self.KtoLSS * self.LbrInd
         self.KtoYSS = self.KtoLSS ** (1.0 - self.CapShare)
@@ -2952,12 +2991,11 @@ def make_Mrkv_history(self):
         self.MrkvNow_hist = np.zeros(self.act_T, dtype=int)
         MrkvNow = self.MrkvNow_init
 
-        markov_process = MarkovProcess(self.MrkvArray, seed= 0)
+        markov_process = MarkovProcess(self.MrkvArray, seed=0)
         for s in range(self.act_T):  # Add act_T_orig more periods
             self.MrkvNow_hist[s] = MrkvNow
             MrkvNow = markov_process.draw(MrkvNow)
 
-
     def mill_rule(self, aNow, EmpNow):
         """
         Method to calculate the capital to labor ratio, interest factor, and
@@ -3096,7 +3134,7 @@ def calc_AFunc(self, Mnow, Aprev):
             AFunc_list.append(
                 AggregateSavingRule(intercept, slope)
             )  # Make a new next-period capital function
-            rSq_list.append(r_value ** 2)
+            rSq_list.append(r_value**2)
 
             # Save the new values as "previous" values for the next iteration
             self.intercept_prev[i] = intercept
diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index 343ae2c05..f0b238fbb 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -687,8 +687,8 @@ def set_and_update_values(self, solution_next, IncShkDstn, LivPrb, DiscFac):
         solution_next : ConsumerSolution
             The solution to next period's one period problem.
         IncShkDstn : distribution.DiscreteDistribution
-            A DiscreteDistribution with a pmf
-            and two point value arrays in X, order:
+            A DiscreteDistribution with a pmv
+            and two point value arrays in atoms, order:
             permanent shocks, transitory shocks.
         LivPrb : float
             Survival probability; likelihood of being alive at the beginning of
@@ -702,9 +702,9 @@ def set_and_update_values(self, solution_next, IncShkDstn, LivPrb, DiscFac):
         """
         self.DiscFacEff = DiscFac * LivPrb  # "effective" discount factor
         self.IncShkDstn = IncShkDstn
-        self.ShkPrbsNext = IncShkDstn.pmf
-        self.PermShkValsNext = IncShkDstn.X[0]
-        self.TranShkValsNext = IncShkDstn.X[1]
+        self.ShkPrbsNext = IncShkDstn.pmv
+        self.PermShkValsNext = IncShkDstn.atoms[0]
+        self.TranShkValsNext = IncShkDstn.atoms[1]
         self.PermShkMinNext = np.min(self.PermShkValsNext)
         self.TranShkMinNext = np.min(self.TranShkValsNext)
         self.vPfuncNext = solution_next.vPfunc
@@ -2283,9 +2283,9 @@ def get_shocks(self):
             # Get random draws of income shocks from the discrete distribution
             EventDraws = IncShkDstnNow.draw_events(N)
             PermShkNow[these] = (
-                IncShkDstnNow.X[0][EventDraws] * PermGroFacNow
+                IncShkDstnNow.atoms[0][EventDraws] * PermGroFacNow
             )  # permanent "shock" includes expected growth
-            TranShkNow[these] = IncShkDstnNow.X[1][EventDraws]
+            TranShkNow[these] = IncShkDstnNow.atoms[1][EventDraws]
         #        PermShkNow[newborn] = 1.0
         #  Whether Newborns have transitory shock. The default is False.
         if not NewbornTransShk:
@@ -2863,11 +2863,11 @@ def __init__(self, sigma, n_approx, neutral_measure=False, seed=0):
         logn_approx = MeanOneLogNormal(sigma).approx(
             n_approx if sigma > 0.0 else 1, tail_N=0
         )
-        # Change the pmf if necessary
+        # Change the pmv if necessary
         if neutral_measure:
-            logn_approx.pmf = (logn_approx.X * logn_approx.pmf).flatten()
+            logn_approx.pmv = (logn_approx.atoms * logn_approx.pmv).flatten()
 
-        super().__init__(pmf=logn_approx.pmf, X=logn_approx.X, seed=seed)
+        super().__init__(pmv=logn_approx.pmv, atoms=logn_approx.atoms, seed=seed)
 
 
 class MixtureTranIncShk(DiscreteDistribution):
@@ -2904,7 +2904,7 @@ def __init__(self, sigma, UnempPrb, IncUnemp, n_approx, seed=0):
                 dstn_approx, p=UnempPrb, x=IncUnemp
             )
 
-        super().__init__(pmf=dstn_approx.pmf, X=dstn_approx.X, seed=seed)
+        super().__init__(pmv=dstn_approx.pmv, atoms=dstn_approx.atoms, seed=seed)
 
 
 class BufferStockIncShkDstn(DiscreteDistribution):
@@ -2966,7 +2966,7 @@ def __init__(
 
         joint_dstn = combine_indep_dstns(perm_dstn, tran_dstn)
 
-        super().__init__(pmf=joint_dstn.pmf, X=joint_dstn.X, seed=seed)
+        super().__init__(pmv=joint_dstn.pmv, atoms=joint_dstn.atoms, seed=seed)
 
 
 # Make a dictionary to specify a "kinked R" idiosyncratic shock consumer
diff --git a/HARK/ConsumptionSaving/ConsIndShockModelFast.py b/HARK/ConsumptionSaving/ConsIndShockModelFast.py
index 8009b69b0..b86c7c316 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModelFast.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModelFast.py
@@ -587,9 +587,9 @@ def prepare_to_solve(self):
         none
         """
 
-        self.ShkPrbsNext = self.IncShkDstn.pmf
-        self.PermShkValsNext = self.IncShkDstn.X[0]
-        self.TranShkValsNext = self.IncShkDstn.X[1]
+        self.ShkPrbsNext = self.IncShkDstn.pmv
+        self.PermShkValsNext = self.IncShkDstn.atoms[0]
+        self.TranShkValsNext = self.IncShkDstn.atoms[1]
 
         (
             self.DiscFacEff,
diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py
index 4c4e5a72a..495a13575 100644
--- a/HARK/ConsumptionSaving/ConsLaborModel.py
+++ b/HARK/ConsumptionSaving/ConsLaborModel.py
@@ -20,7 +20,8 @@
     VariableLowerBoundFunc2D,
     BilinearInterp,
     ConstantFunction,
-    ValueFuncCRRA, MargValueFuncCRRA
+    ValueFuncCRRA,
+    MargValueFuncCRRA,
 )
 from HARK.ConsumptionSaving.ConsIndShockModel import (
     IndShockConsumerType,
@@ -158,10 +159,10 @@ def solve_ConsLaborIntMarg(
 
     # Unpack next period's solution and the productivity shock distribution, and define the inverse (marginal) utilty function
     vPfunc_next = solution_next.vPfunc
-    TranShkPrbs = TranShkDstn.pmf
-    TranShkVals = TranShkDstn.X.flatten()
-    PermShkPrbs = PermShkDstn.pmf
-    PermShkVals = PermShkDstn.X.flatten()
+    TranShkPrbs = TranShkDstn.pmv
+    TranShkVals = TranShkDstn.atoms.flatten()
+    PermShkPrbs = PermShkDstn.pmv
+    PermShkVals = PermShkDstn.atoms.flatten()
     TranShkCount = TranShkPrbs.size
     PermShkCount = PermShkPrbs.size
     uPinv = lambda X: CRRAutilityP_inv(X, gam=CRRA)
@@ -232,7 +233,7 @@ def solve_ConsLaborIntMarg(
     TranShkScaleFac_temp = (
         frac
         * (WageRte * TranShkGrid) ** (LbrCost * frac)
-        * (LbrCost ** (-LbrCost * frac) + LbrCost ** frac)
+        * (LbrCost ** (-LbrCost * frac) + LbrCost**frac)
     )
 
     # Flip it to be a row vector
@@ -245,7 +246,7 @@ def solve_ConsLaborIntMarg(
     TranShkGrid_rep = np.tile(
         np.reshape(TranShkGrid, (1, TranShkGrid.size)), (aXtraCount, 1)
     )
-    xNowPow = xNow ** frac  # Will use this object multiple times in math below
+    xNowPow = xNow**frac  # Will use this object multiple times in math below
 
     # Find optimal consumption from optimal composite good
     cNrmNow = (((WageRte * TranShkGrid_rep) / LbrCost) ** (LbrCost * frac)) * xNowPow
@@ -406,7 +407,7 @@ def update_LbrCost(self):
         age_vec = np.arange(self.T_cycle)
         LbrCostBase = np.zeros(self.T_cycle)
         for n in range(N):
-            LbrCostBase += Coeffs[n] * age_vec ** n
+            LbrCostBase += Coeffs[n] * age_vec**n
         LbrCost = np.exp(LbrCostBase)
         self.LbrCost = LbrCost.tolist()
         self.add_to_time_vary("LbrCost")
@@ -479,7 +480,7 @@ def get_states(self):
         None
         """
         IndShockConsumerType.get_states(self)
-        self.state_now['mNrm'][:] = np.nan  # Delete market resource calculation
+        self.state_now["mNrm"][:] = np.nan  # Delete market resource calculation
 
     def get_controls(self):
         """
@@ -500,17 +501,17 @@ def get_controls(self):
         for t in range(self.T_cycle):
             these = t == self.t_cycle
             cNrmNow[these] = self.solution[t].cFunc(
-                self.state_now['bNrm'][these], self.shocks['TranShk'][these]
+                self.state_now["bNrm"][these], self.shocks["TranShk"][these]
             )  # Assign consumption values
             MPCnow[these] = self.solution[t].cFunc.derivativeX(
-                self.state_now['bNrm'][these], self.shocks['TranShk'][these]
+                self.state_now["bNrm"][these], self.shocks["TranShk"][these]
             )  # Assign marginal propensity to consume values (derivative)
             LbrNow[these] = self.solution[t].LbrFunc(
-                self.state_now['bNrm'][these], self.shocks['TranShk'][these]
+                self.state_now["bNrm"][these], self.shocks["TranShk"][these]
             )  # Assign labor supply
-        self.controls['cNrm'] = cNrmNow
+        self.controls["cNrm"] = cNrmNow
         self.MPCnow = MPCnow
-        self.controls['Lbr'] = LbrNow
+        self.controls["Lbr"] = LbrNow
 
     def get_poststates(self):
         """
@@ -529,12 +530,14 @@ def get_poststates(self):
         for t in range(self.T_cycle):
             these = t == self.t_cycle
             mNrmNow[these] = (
-                self.state_now['bNrm'][these]
-                + self.controls['Lbr'][these] * self.shocks['TranShk'][these]
+                self.state_now["bNrm"][these]
+                + self.controls["Lbr"][these] * self.shocks["TranShk"][these]
             )  # mNrm = bNrm + yNrm
-            aNrmNow[these] = mNrmNow[these] - self.controls['cNrm'][these]  # aNrm = mNrm - cNrm
-        self.state_now['mNrm'] = mNrmNow
-        self.state_now['aNrm'] = aNrmNow
+            aNrmNow[these] = (
+                mNrmNow[these] - self.controls["cNrm"][these]
+            )  # aNrm = mNrm - cNrm
+        self.state_now["mNrm"] = mNrmNow
+        self.state_now["aNrm"] = aNrmNow
 
         # moves now to prev
         super().get_poststates()
@@ -557,7 +560,7 @@ def update_TranShkGrid(self):
         TranShkGrid = []  # Create an empty list for TranShkGrid that will be updated
         for t in range(self.T_cycle):
             TranShkGrid.append(
-                self.TranShkDstn[t].X.flatten()
+                self.TranShkDstn[t].atoms.flatten()
             )  # Update/ Extend the list of TranShkGrid with the TranShkVals for each TranShkPrbs
         self.TranShkGrid = TranShkGrid  # Save that list in self (time-varying)
         self.add_to_time_vary(
@@ -612,12 +615,12 @@ def update_solution_terminal(self):
         cFunc_terminal = BilinearInterp(cNrmTerm, bNrmGrid, TranShkGrid)
 
         # Compute the effective consumption value using consumption value and labor value at the terminal solution
-        xEffTerm = LsrTerm ** LbrCost * cNrmTerm
+        xEffTerm = LsrTerm**LbrCost * cNrmTerm
         vNvrsFunc_terminal = BilinearInterp(xEffTerm, bNrmGrid, TranShkGrid)
         vFunc_terminal = ValueFuncCRRA(vNvrsFunc_terminal, self.CRRA)
 
         # Using the envelope condition at the terminal solution to estimate the marginal value function
-        vPterm = LsrTerm ** LbrCost * CRRAutilityP(xEffTerm, gam=self.CRRA)
+        vPterm = LsrTerm**LbrCost * CRRAutilityP(xEffTerm, gam=self.CRRA)
         vPnvrsTerm = CRRAutilityP_inv(
             vPterm, gam=self.CRRA
         )  # Evaluate the inverse of the CRRA marginal utility function at a given marginal value, vP
diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py
index 8606324e6..e41f340cb 100644
--- a/HARK/ConsumptionSaving/ConsMarkovModel.py
+++ b/HARK/ConsumptionSaving/ConsMarkovModel.py
@@ -237,8 +237,8 @@ def def_boundary(self):
         self.BoroCnstNatAll = np.zeros(self.StateCount) + np.nan
         # Find the natural borrowing constraint conditional on next period's state
         for j in range(self.StateCount):
-            PermShkMinNext = np.min(self.IncShkDstn_list[j].X[0])
-            TranShkMinNext = np.min(self.IncShkDstn_list[j].X[1])
+            PermShkMinNext = np.min(self.IncShkDstn_list[j].atoms[0])
+            TranShkMinNext = np.min(self.IncShkDstn_list[j].atoms[1])
             self.BoroCnstNatAll[j] = (
                 (self.solution_next.mNrmMin[j] - TranShkMinNext)
                 * (self.PermGroFac_list[j] * PermShkMinNext)
@@ -1117,9 +1117,9 @@ def get_shocks(self):
                     # Get random draws of income shocks from the discrete distribution
                     EventDraws = IncShkDstnNow.draw_events(N)
                     PermShkNow[these] = (
-                        IncShkDstnNow.X[0][EventDraws] * PermGroFacNow
+                        IncShkDstnNow.atoms[0][EventDraws] * PermGroFacNow
                     )  # permanent "shock" includes expected growth
-                    TranShkNow[these] = IncShkDstnNow.X[1][EventDraws]
+                    TranShkNow[these] = IncShkDstnNow.atoms[1][EventDraws]
         newborn = self.t_age == 0
         PermShkNow[newborn] = 1.0
         TranShkNow[newborn] = 1.0
diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py
index f508ced92..c3cdee2b2 100644
--- a/HARK/ConsumptionSaving/ConsMedModel.py
+++ b/HARK/ConsumptionSaving/ConsMedModel.py
@@ -3,7 +3,7 @@
 """
 import numpy as np
 from scipy.optimize import brentq
-from HARK import  AgentType, MetricObject, make_one_period_oo_solver
+from HARK import AgentType, MetricObject, make_one_period_oo_solver
 from HARK.distribution import add_discrete_outcome_constant_mean, Lognormal
 from HARK.utilities import (
     CRRAutilityP_inv,
@@ -27,7 +27,7 @@
     VariableLowerBoundFunc3D,
     ValueFuncCRRA,
     MargValueFuncCRRA,
-    MargMargValueFuncCRRA
+    MargMargValueFuncCRRA,
 )
 from HARK.ConsumptionSaving.ConsGenIncProcessModel import (
     ConsGenIncProcessSolver,
@@ -569,7 +569,7 @@ class MedShockConsumerType(PersistentShockConsumerType):
     """
 
     shock_vars_ = PersistentShockConsumerType.shock_vars_ + ["MedShk"]
-    state_vars = PersistentShockConsumerType.state_vars + ['mLvl']
+    state_vars = PersistentShockConsumerType.state_vars + ["mLvl"]
 
     def __init__(self, **kwds):
         params = init_medical_shocks.copy()
@@ -622,7 +622,7 @@ def update_med_shock_process(self):
             MedShkAvgNow = self.MedShkAvg[t]  # get shock distribution parameters
             MedShkStdNow = self.MedShkStd[t]
             MedShkDstnNow = Lognormal(
-                mu=np.log(MedShkAvgNow) - 0.5 * MedShkStdNow ** 2, sigma=MedShkStdNow
+                mu=np.log(MedShkAvgNow) - 0.5 * MedShkStdNow**2, sigma=MedShkStdNow
             ).approx(
                 N=self.MedShkCount, tail_N=self.MedShkCountTail, tail_bound=[0, 0.9]
             )
@@ -649,8 +649,8 @@ def update_solution_terminal(self):
         """
         # Take last period data, whichever way time is flowing
         MedPrice = self.MedPrice[-1]
-        MedShkVals = self.MedShkDstn[-1].X.flatten()
-        MedShkPrbs = self.MedShkDstn[-1].pmf
+        MedShkVals = self.MedShkDstn[-1].atoms.flatten()
+        MedShkPrbs = self.MedShkDstn[-1].pmv
 
         # Initialize grids of medical need shocks, market resources, and optimal consumption
         MedShkGrid = MedShkVals
@@ -840,12 +840,12 @@ def get_controls(self):
         for t in range(self.T_cycle):
             these = t == self.t_cycle
             cLvlNow[these], MedNow[these] = self.solution[t].policyFunc(
-                self.state_now['mLvl'][these],
-                self.state_now['pLvl'][these],
+                self.state_now["mLvl"][these],
+                self.state_now["pLvl"][these],
                 self.shocks["MedShk"][these],
             )
-        self.controls['cLvl'] = cLvlNow
-        self.controls['Med'] = MedNow
+        self.controls["cLvl"] = cLvlNow
+        self.controls["Med"] = MedNow
         return None
 
     def get_poststates(self):
@@ -860,7 +860,11 @@ def get_poststates(self):
         -------
         None
         """
-        self.state_now['aLvl'] = self.state_now['mLvl'] - self.controls['cLvl'] - self.shocks["MedPrice"] * self.controls['Med']
+        self.state_now["aLvl"] = (
+            self.state_now["mLvl"]
+            - self.controls["cLvl"]
+            - self.shocks["MedPrice"] * self.controls["Med"]
+        )
 
         # moves now to prev
         AgentType.get_poststates(self)
@@ -887,7 +891,7 @@ class ConsMedShockSolver(ConsGenIncProcessSolver):
         and the one immediately following (in solution_next).
     MedShkDstn : distribution.Distribution
         Discrete distribution of the multiplicative utility shifter for med-
-        ical care. 
+        ical care.
     LivPrb : float
         Survival probability; likelihood of being alive at the beginning of
         the succeeding period.
@@ -992,8 +996,8 @@ def set_and_update_values(self, solution_next, IncShkDstn, LivPrb, DiscFac):
         )
 
         # Also unpack the medical shock distribution
-        self.MedShkPrbs = self.MedShkDstn.pmf
-        self.MedShkVals = self.MedShkDstn.X.flatten()
+        self.MedShkPrbs = self.MedShkDstn.pmv
+        self.MedShkVals = self.MedShkDstn.atoms.flatten()
 
     def def_utility_funcs(self):
         """
diff --git a/HARK/ConsumptionSaving/ConsPortfolioModel.py b/HARK/ConsumptionSaving/ConsPortfolioModel.py
index c5a4b5fe6..bf9a620ab 100644
--- a/HARK/ConsumptionSaving/ConsPortfolioModel.py
+++ b/HARK/ConsumptionSaving/ConsPortfolioModel.py
@@ -479,7 +479,7 @@ def set_and_update_values(self):
         self.vFuncFxd_next = self.solution_next.vFuncFxd
 
         # Unpack the shock distribution
-        TranShks_next = self.IncShkDstn.X[1]
+        TranShks_next = self.IncShkDstn.atoms[1]
 
         # Flag for whether the natural borrowing constraint is zero
         self.zero_bound = np.min(TranShks_next) == 0.0
@@ -500,7 +500,7 @@ def prepare_to_calc_EndOfPrdvP(self):
         """
 
         # Unpack the shock distribution
-        Risky_next = self.RiskyDstn.X
+        Risky_next = self.RiskyDstn.atoms
         RiskyMax = np.max(Risky_next)
         RiskyMin = np.min(Risky_next)
 
@@ -583,7 +583,7 @@ def dvds_dist(shocks, b_nrm, Share_next):
         dvdb_intermed = calc_expectation(
             self.IncShkDstn, dvdb_dist, self.bNrmNext, self.ShareNext
         )
-        
+
         dvdbNvrs_intermed = self.uPinv(dvdb_intermed)
         dvdbNvrsFunc_intermed = BilinearInterp(
             dvdbNvrs_intermed, self.bNrmGrid, self.ShareGrid
@@ -594,7 +594,7 @@ def dvds_dist(shocks, b_nrm, Share_next):
         dvds_intermed = calc_expectation(
             self.IncShkDstn, dvds_dist, self.bNrmNext, self.ShareNext
         )
-        
+
         dvdsFunc_intermed = BilinearInterp(dvds_intermed, self.bNrmGrid, self.ShareGrid)
 
         # Make tiled arrays to calculate future realizations of bNrm and Share when integrating over RiskyDstn
@@ -1019,7 +1019,7 @@ def set_and_update_values(self):
 
         # If the distributions are NOT independent...
         # Unpack the shock distribution
-        self.TranShks_next = self.ShockDstn.X[1]
+        self.TranShks_next = self.ShockDstn.atoms[1]
         # Flag for whether the natural borrowing constraint is zero
         self.zero_bound = np.min(self.TranShks_next) == 0.0
 
diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py
index 43e48cbcf..f3772bcaa 100644
--- a/HARK/ConsumptionSaving/ConsPrefShockModel.py
+++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py
@@ -24,7 +24,7 @@
     CubicInterp,
     LowerEnvelope,
     ValueFuncCRRA,
-    MargValueFuncCRRA
+    MargValueFuncCRRA,
 )
 
 
@@ -123,8 +123,11 @@ def update_pref_shock_process(self):
         for t in range(len(self.PrefShkStd)):
             PrefShkStd = self.PrefShkStd[t]
             new_dstn = MeanOneLogNormal(
-                sigma=PrefShkStd, seed=self.RNG.randint(0, 2 ** 31 - 1)
-            ).approx(N=self.PrefShkCount, tail_N=self.PrefShk_tail_N,)
+                sigma=PrefShkStd, seed=self.RNG.randint(0, 2**31 - 1)
+            ).approx(
+                N=self.PrefShkCount,
+                tail_N=self.PrefShk_tail_N,
+            )
             PrefShkDstn.append(new_dstn)
 
         # Store the preference shocks in self (time-varying) and restore time flow
@@ -193,9 +196,9 @@ def get_controls(self):
         for t in range(self.T_cycle):
             these = t == self.t_cycle
             cNrmNow[these] = self.solution[t].cFunc(
-                self.state_now['mNrm'][these], self.shocks["PrefShk"][these]
+                self.state_now["mNrm"][these], self.shocks["PrefShk"][these]
             )
-        self.controls['cNrm'] = cNrmNow
+        self.controls["cNrm"] = cNrmNow
         return None
 
     def calc_bounding_values(self):
@@ -368,8 +371,8 @@ def __init__(
             vFuncBool,
             CubicBool,
         )
-        self.PrefShkPrbs = PrefShkDstn.pmf
-        self.PrefShkVals = PrefShkDstn.X.flatten()
+        self.PrefShkPrbs = PrefShkDstn.pmv
+        self.PrefShkVals = PrefShkDstn.atoms.flatten()
 
     def get_points_for_interpolation(self, EndOfPrdvP, aNrmNow):
         """
@@ -684,5 +687,5 @@ def __init__(
             vFuncBool,
             CubicBool,
         )
-        self.PrefShkPrbs = PrefShkDstn.pmf
-        self.PrefShkVals = PrefShkDstn.X.flatten()
+        self.PrefShkPrbs = PrefShkDstn.pmv
+        self.PrefShkVals = PrefShkDstn.atoms.flatten()
diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py
index 0d23b6296..4f7693b35 100644
--- a/HARK/ConsumptionSaving/ConsRepAgentModel.py
+++ b/HARK/ConsumptionSaving/ConsRepAgentModel.py
@@ -6,7 +6,7 @@
 """
 import numpy as np
 from HARK.interpolation import LinearInterp, MargValueFuncCRRA
-from HARK.distribution import (MarkovProcess, Uniform)
+from HARK.distribution import MarkovProcess, Uniform
 from HARK.ConsumptionSaving.ConsIndShockModel import (
     IndShockConsumerType,
     ConsumerSolution,
@@ -34,7 +34,7 @@ def solve_ConsRepAgent(
     IncShkDstn : distribution.Distribution
         A discrete
         approximation to the income process between the period being solved
-        and the one immediately following (in solution_next). Order: 
+        and the one immediately following (in solution_next). Order:
         permanent shocks, transitory shocks.
     CapShare : float
         Capital's share of income in Cobb-Douglas production function.
@@ -54,9 +54,9 @@ def solve_ConsRepAgent(
     """
     # Unpack next period's solution and the income distribution
     vPfuncNext = solution_next.vPfunc
-    ShkPrbsNext = IncShkDstn.pmf
-    PermShkValsNext = IncShkDstn.X[0]
-    TranShkValsNext = IncShkDstn.X[1]
+    ShkPrbsNext = IncShkDstn.pmv
+    PermShkValsNext = IncShkDstn.atoms[0]
+    TranShkValsNext = IncShkDstn.atoms[1]
 
     # Make tiled versions of end-of-period assets, shocks, and probabilities
     aNrmNow = aXtraGrid
@@ -80,7 +80,7 @@ def solve_ConsRepAgent(
     # Calculate next period's market resources
     KtoLnext = kNrmNext / TranShkVals_tiled
     RfreeNext = 1.0 - DeprFac + CapShare * KtoLnext ** (CapShare - 1.0)
-    wRteNext = (1.0 - CapShare) * KtoLnext ** CapShare
+    wRteNext = (1.0 - CapShare) * KtoLnext**CapShare
     mNrmNext = RfreeNext * kNrmNext + wRteNext * TranShkVals_tiled
 
     # Calculate end-of-period marginal value of assets for the RA
@@ -163,9 +163,9 @@ def solve_ConsRepAgentMarkov(
     for j in range(StateCount):
         # Define next-period-state conditional objects
         vPfuncNext = solution_next.vPfunc[j]
-        ShkPrbsNext = IncShkDstn[j].pmf
-        PermShkValsNext = IncShkDstn[j].X[0]
-        TranShkValsNext = IncShkDstn[j].X[1]
+        ShkPrbsNext = IncShkDstn[j].pmv
+        PermShkValsNext = IncShkDstn[j].atoms[0]
+        TranShkValsNext = IncShkDstn[j].atoms[1]
 
         # Make tiled versions of end-of-period assets, shocks, and probabilities
         ShkCount = ShkPrbsNext.size
@@ -187,7 +187,7 @@ def solve_ConsRepAgentMarkov(
         # Calculate next period's market resources
         KtoLnext = kNrmNext / TranShkVals_tiled
         RfreeNext = 1.0 - DeprFac + CapShare * KtoLnext ** (CapShare - 1.0)
-        wRteNext = (1.0 - CapShare) * KtoLnext ** CapShare
+        wRteNext = (1.0 - CapShare) * KtoLnext**CapShare
         mNrmNext = RfreeNext * kNrmNext + wRteNext * TranShkVals_tiled
 
         # Calculate end-of-period marginal value of assets for the RA
@@ -260,30 +260,28 @@ def get_states(self):
         -------
         None
         """
-        pLvlPrev = self.state_prev['pLvl']
-        aNrmPrev = self.state_prev['aNrm']
+        pLvlPrev = self.state_prev["pLvl"]
+        aNrmPrev = self.state_prev["aNrm"]
 
         # Calculate new states: normalized market resources and permanent income level
-        self.pLvlNow = (
-            pLvlPrev * self.shocks['PermShk']
-        )  # Same as in IndShockConsType
-        self.kNrmNow = aNrmPrev / self.shocks['PermShk']
-        self.yNrmNow = self.kNrmNow ** self.CapShare * self.shocks['TranShk'] ** (
+        self.pLvlNow = pLvlPrev * self.shocks["PermShk"]  # Same as in IndShockConsType
+        self.kNrmNow = aNrmPrev / self.shocks["PermShk"]
+        self.yNrmNow = self.kNrmNow**self.CapShare * self.shocks["TranShk"] ** (
             1.0 - self.CapShare
         )
         self.Rfree = (
             1.0
             + self.CapShare
             * self.kNrmNow ** (self.CapShare - 1.0)
-            * self.shocks['TranShk'] ** (1.0 - self.CapShare)
+            * self.shocks["TranShk"] ** (1.0 - self.CapShare)
             - self.DeprFac
         )
         self.wRte = (
             (1.0 - self.CapShare)
-            * self.kNrmNow ** self.CapShare
-            * self.shocks['TranShk'] ** (-self.CapShare)
+            * self.kNrmNow**self.CapShare
+            * self.shocks["TranShk"] ** (-self.CapShare)
         )
-        self.mNrmNow = self.Rfree * self.kNrmNow + self.wRte * self.shocks['TranShk']
+        self.mNrmNow = self.Rfree * self.kNrmNow + self.wRte * self.shocks["TranShk"]
 
 
 class RepAgentMarkovConsumerType(RepAgentConsumerType):
@@ -308,7 +306,7 @@ def pre_solve(self):
         self.update_solution_terminal()
 
     def initialize_sim(self):
-        #self.shocks["Mrkv"] = np.zeros(self.AgentCount, dtype=int)
+        # self.shocks["Mrkv"] = np.zeros(self.AgentCount, dtype=int)
         RepAgentConsumerType.initialize_sim(self)
         self.shocks["Mrkv"] = self.Mrkv
 
@@ -349,9 +347,8 @@ def get_shocks(self):
         None
         """
         self.shocks["Mrkv"] = MarkovProcess(
-            self.MrkvArray,
-            seed=self.RNG.randint(0, 2 ** 31 - 1)
-            ).draw(self.shocks["Mrkv"])
+            self.MrkvArray, seed=self.RNG.randint(0, 2**31 - 1)
+        ).draw(self.shocks["Mrkv"])
 
         t = self.t_cycle[0]
         i = self.shocks["Mrkv"]
@@ -360,11 +357,11 @@ def get_shocks(self):
         # Get random draws of income shocks from the discrete distribution
         EventDraw = IncShkDstnNow.draw_events(1)
         PermShkNow = (
-            IncShkDstnNow.X[0][EventDraw] * PermGroFacNow
+            IncShkDstnNow.atoms[0][EventDraw] * PermGroFacNow
         )  # permanent "shock" includes expected growth
-        TranShkNow = IncShkDstnNow.X[1][EventDraw]
-        self.shocks['PermShk'] = np.array(PermShkNow)
-        self.shocks['TranShk'] = np.array(TranShkNow)
+        TranShkNow = IncShkDstnNow.atoms[1][EventDraw]
+        self.shocks["PermShk"] = np.array(PermShkNow)
+        self.shocks["TranShk"] = np.array(TranShkNow)
 
     def get_controls(self):
         """
@@ -380,7 +377,7 @@ def get_controls(self):
         """
         t = self.t_cycle[0]
         i = self.shocks["Mrkv"]
-        self.controls['cNrm'] = self.solution[t].cFunc[i](self.mNrmNow)
+        self.controls["cNrm"] = self.solution[t].cFunc[i](self.mNrmNow)
 
 
 # Define the default dictionary for a representative agent type
diff --git a/HARK/ConsumptionSaving/ConsRiskyAssetModel.py b/HARK/ConsumptionSaving/ConsRiskyAssetModel.py
index c2f9168d7..9004da113 100644
--- a/HARK/ConsumptionSaving/ConsRiskyAssetModel.py
+++ b/HARK/ConsumptionSaving/ConsRiskyAssetModel.py
@@ -216,9 +216,9 @@ def update_ShareLimit(self):
 
                 def temp_f(s):
                     return -((1.0 - self.CRRA) ** -1) * np.dot(
-                        (self.Rfree[t] + s * (RiskyDstn.X - self.Rfree[t]))
+                        (self.Rfree[t] + s * (RiskyDstn.atoms - self.Rfree[t]))
                         ** (1.0 - self.CRRA),
-                        RiskyDstn.pmf,
+                        RiskyDstn.pmv,
                     )
 
                 SharePF = minimize_scalar(temp_f, bounds=(0.0, 1.0), method="bounded").x
@@ -230,9 +230,9 @@ def temp_f(s):
 
             def temp_f(s):
                 return -((1.0 - self.CRRA) ** -1) * np.dot(
-                    (self.Rfree[0] + s * (RiskyDstn.X - self.Rfree[0]))
+                    (self.Rfree[0] + s * (RiskyDstn.atoms - self.Rfree[0]))
                     ** (1.0 - self.CRRA),
-                    RiskyDstn.pmf,
+                    RiskyDstn.pmv,
                 )
 
             SharePF = minimize_scalar(temp_f, bounds=(0.0, 1.0), method="bounded").x
@@ -438,7 +438,7 @@ def set_and_update_values(self, solution_next, IncShkDstn, LivPrb, DiscFac):
         solution_next : ConsumerSolution
             The solution to next period's one period problem.
         IncShkDstn : distribution.DiscreteDistribution
-            A DiscreteDistribution with a pmf
+            A DiscreteDistribution with a pmv
             and two point value arrays in X, order:
             permanent shocks, transitory shocks.
         LivPrb : float
@@ -515,9 +515,9 @@ def def_BoroCnst(self, BoroCnstArt):
 
         # Calculate the minimum allowable value of money resources in this period
         self.BoroCnstNat = (
-            (self.solution_next.mNrmMin - self.TranShkDstn.X.min())
-            * (self.PermGroFac * self.PermShkDstn.X.min())
-            / self.RiskyDstn.X.max()
+            (self.solution_next.mNrmMin - self.TranShkDstn.atoms.min())
+            * (self.PermGroFac * self.PermShkDstn.atoms.min())
+            / self.RiskyDstn.atoms.max()
         )
 
         # Flag for whether the natural borrowing constraint is zero
@@ -560,20 +560,20 @@ def prepare_to_calc_EndOfPrdvP(self):
 
             if self.IndepDstnBool:
                 bNrmNext = np.append(
-                    aNrmNow[0] * self.RiskyDstn.X.min(),
-                    aNrmNow * self.RiskyDstn.X.max(),
+                    aNrmNow[0] * self.RiskyDstn.atoms.min(),
+                    aNrmNow * self.RiskyDstn.atoms.max(),
                 )
                 wNrmNext = np.append(
-                    bNrmNext[0] / (self.PermGroFac * self.PermShkDstn.X.max()),
-                    bNrmNext / (self.PermGroFac * self.PermShkDstn.X.min()),
+                    bNrmNext[0] / (self.PermGroFac * self.PermShkDstn.atoms.max()),
+                    bNrmNext / (self.PermGroFac * self.PermShkDstn.atoms.min()),
                 )
         else:
             # add zero to aNrmNow
             aNrmNow = np.append(self.BoroCnstArt, self.aXtraGrid)
 
             if self.IndepDstnBool:
-                bNrmNext = aNrmNow * self.RiskyDstn.X.max()
-                wNrmNext = bNrmNext / (self.PermGroFac * self.PermShkDstn.X.min())
+                bNrmNext = aNrmNow * self.RiskyDstn.atoms.max()
+                wNrmNext = bNrmNext / (self.PermGroFac * self.PermShkDstn.atoms.min())
 
         self.aNrmNow = aNrmNow
 
@@ -808,7 +808,7 @@ def set_and_update_values(self, solution_next, IncShkDstn, LivPrb, DiscFac):
         solution_next : ConsumerSolution
             The solution to next period's one period problem.
         IncShkDstn : distribution.DiscreteDistribution
-            A DiscreteDistribution with a pmf
+            A DiscreteDistribution with a pmv
             and two point value arrays in X, order:
             permanent shocks, transitory shocks.
         LivPrb : float
@@ -1127,7 +1127,7 @@ def set_and_update_values(self, solution_next, IncShkDstn, LivPrb, DiscFac):
         solution_next : ConsumerSolution
             The solution to next period's one period problem.
         IncShkDstn : distribution.DiscreteDistribution
-            A DiscreteDistribution with a pmf
+            A DiscreteDistribution with a pmv
             and two point value arrays in X, order:
             permanent shocks, transitory shocks.
         LivPrb : float
@@ -1196,13 +1196,14 @@ def def_BoroCnst(self, BoroCnstArt):
 
         # in worst case scenario, debt gets highest return possible
         self.RPortMax = (
-            self.Rfree + (self.RiskyDstn.X.max() - self.Rfree) * self.RiskyShareFixed
+            self.Rfree
+            + (self.RiskyDstn.atoms.max() - self.Rfree) * self.RiskyShareFixed
         )
 
         # Calculate the minimum allowable value of money resources in this period
         self.BoroCnstNat = (
-            (self.solution_next.mNrmMin - self.TranShkDstn.X.min())
-            * (self.PermGroFac * self.PermShkDstn.X.min())
+            (self.solution_next.mNrmMin - self.TranShkDstn.atoms.min())
+            * (self.PermGroFac * self.PermShkDstn.atoms.min())
             / self.RPortMax
         )
 
diff --git a/HARK/ConsumptionSaving/ConsRiskyContribModel.py b/HARK/ConsumptionSaving/ConsRiskyContribModel.py
index 1445d572a..a10f0837b 100644
--- a/HARK/ConsumptionSaving/ConsRiskyContribModel.py
+++ b/HARK/ConsumptionSaving/ConsRiskyContribModel.py
@@ -60,12 +60,12 @@ class RiskyContribConsumerType(RiskyAssetConsumerType):
     who can save in both a risk-free and a risky asset but faces frictions to
     moving funds between them. The agent can only consume out of his risk-free
     asset.
-    
+
     The frictions are:
         - A proportional tax on funds moved from the risky to the risk-free
          asset.
         - A stochastic inability to move funds between his accounts.
-    
+
     To partially avoid the second friction, the agent can commit to have a
     fraction of his labor income, which is usually deposited in his risk-free
     account, diverted to his risky account. He can change this fraction
@@ -121,12 +121,12 @@ def __init__(self, verbose=False, quiet=False, joint_dist_solver=False, **kwds):
             "Sha": self.get_controls_Sha,
             "Cns": self.get_controls_Cns,
         }
-        
+
         # The model can be solved more quickly if income and risky returns are
         # independent. However, people might want to use the general solver
         # even when they are independent for debugging and testing.
-        self.joint_dist_solver=joint_dist_solver
-        
+        self.joint_dist_solver = joint_dist_solver
+
         # Set the solver for the portfolio model, and update various constructed attributes
         self.solve_one_period = solveRiskyContrib
         self.update()
@@ -149,7 +149,7 @@ def update_solution_terminal(self):
         Cns: agent will consume all of his liquid resources.
         Sha: irrelevant as there is no "next" period.
         Reb: agent will shift all of his resources to the risk-free asset.
-        
+
         Parameters
         ----------
         None
@@ -259,7 +259,7 @@ def update_tau(self):
     def update_share_grid(self):
         """
         Creates grid for the income contribution share.
-        
+
         Parameters
         ----------
         None
@@ -290,11 +290,11 @@ def update_nNrm_grid(self):
         """
         Updates the agent's iliquid assets grid by constructing a
         multi-exponentially spaced grid of nNrm values.
-        
+
         Parameters
         ----------
         None
-        
+
         Returns
         -------
         None.
@@ -316,11 +316,11 @@ def update_mNrm_grid(self):
         """
         Updates the agent's liquid assets exogenous grid by constructing a
         multi-exponentially spaced grid of mNrm values.
-        
+
         Parameters
         ----------
         None
-        
+
         Returns
         -------
         None.
@@ -375,10 +375,10 @@ def sim_birth(self, which_agents):
     def sim_one_period(self):
         """
         Simulates one period for this type.
-        
+
         Has to be re-defined instead of using AgentType.sim_one_period() because
         of the "stages" structure.
-        
+
         Parameters
         ----------
         None
@@ -501,7 +501,7 @@ def get_controls_Reb(self):
 
         # Limit dfrac to [-1,1] to prevent negative balances. Values outside
         # the range can come from extrapolation.
-        self.controls["dfrac"] = np.minimum(np.maximum(dfrac,-1),1.0)
+        self.controls["dfrac"] = np.minimum(np.maximum(dfrac, -1), 1.0)
 
     def get_states_Sha(self):
         """
@@ -636,7 +636,7 @@ class RiskyContribRebSolution(MetricObject):
     """
     A class for representing the solution to the asset-rebalancing stage of
     the 'RiskyContrib' model.
-    
+
     Parameters
     ----------
     vFunc_Adj : ValueFunc2D
@@ -728,7 +728,7 @@ class RiskyContribShaSolution(MetricObject):
     """
     A class for representing the solution to the contribution-share stage of
     the 'RiskyContrib' model.
-    
+
     Parameters
     ----------
     vFunc_Adj : ValueFunc2D
@@ -821,7 +821,7 @@ class RiskyContribCnsSolution(MetricObject):
     """
     A class for representing the solution to the consumption stage of the
     'RiskyContrib' model.
-    
+
     Parameters
     ----------
     vFunc : ValueFunc3D
@@ -873,7 +873,7 @@ class RiskyContribSolution(MetricObject):
     """
     A class for representing the solution to a full time-period of the
     'RiskyContrib' agent type's problem.
-    
+
     Parameters
     ----------
     Reb : RiskyContribRebSolution
@@ -1089,7 +1089,7 @@ def solve_RiskyContrib_Cns(
     joint_dist_solver: bool
         Should the general solver be used even if income and returns are
         independent?
-    
+
     Returns
     -------
     solution : RiskyContribCnsSolution
@@ -1154,7 +1154,6 @@ def solve_RiskyContrib_Cns(
         if vFuncBool:
             v_next = lambda m, n, s: vFunc_Reb_Adj_next(m, n)
 
-    
     if IndepDstnBool and not joint_dist_solver:
 
         # If income and returns are independent we can use the law of iterated
@@ -1206,7 +1205,9 @@ def post_return_derivs(inc_shocks, b_aux, g_aux, s):
 
         # Define grids
         b_aux_grid = np.concatenate([np.array([0.0]), Rfree * aXtraGrid])
-        g_aux_grid = np.concatenate([np.array([0.0]), max(RiskyDstn.X.flatten()) * nNrmGrid])
+        g_aux_grid = np.concatenate(
+            [np.array([0.0]), max(RiskyDstn.atoms.flatten()) * nNrmGrid]
+        )
 
         # Create tiled arrays with conforming dimensions.
         b_aux_tiled, g_aux_tiled, Share_tiled = np.meshgrid(
@@ -1221,17 +1222,20 @@ def post_return_derivs(inc_shocks, b_aux, g_aux, s):
 
         # Unpack results and create interpolators
         pr_dvdb_func = MargValueFuncCRRA(
-            TrilinearInterp(uPinv(pr_derivs[0]), b_aux_grid, g_aux_grid, ShareGrid), CRRA
+            TrilinearInterp(uPinv(pr_derivs[0]), b_aux_grid, g_aux_grid, ShareGrid),
+            CRRA,
         )
         pr_dvdg_func = MargValueFuncCRRA(
-            TrilinearInterp(uPinv(pr_derivs[1]), b_aux_grid, g_aux_grid, ShareGrid), CRRA
+            TrilinearInterp(uPinv(pr_derivs[1]), b_aux_grid, g_aux_grid, ShareGrid),
+            CRRA,
         )
         pr_dvds_func = TrilinearInterp(pr_derivs[2], b_aux_grid, g_aux_grid, ShareGrid)
 
         if vFuncBool:
 
             pr_vFunc = ValueFuncCRRA(
-                TrilinearInterp(uInv(pr_derivs[3]), b_aux_grid, g_aux_grid, ShareGrid), CRRA
+                TrilinearInterp(uInv(pr_derivs[3]), b_aux_grid, g_aux_grid, ShareGrid),
+                CRRA,
             )
 
         # Now construct a function that produces end-of-period derivatives
@@ -1241,7 +1245,7 @@ def end_of_period_derivs(risky_ret, a, nTil, s):
             Computes the end-of-period derivatives (and optionally the value) of the
             continuation value function, conditional on risky returns. This is so that the
             expectations can be calculated by integrating over risky returns.
-        
+
             Parameters
             ----------
             risky_ret : float
@@ -1293,7 +1297,7 @@ def end_of_period_derivs(shocks, a, nTil, s):
             Computes the end-of-period derivatives (and optionally the value) of the
             continuation value function, conditional on shocks. This is so that the
             expectations can be calculated by integrating over shocks.
-        
+
             Parameters
             ----------
             shocks : np.array
@@ -1462,7 +1466,10 @@ def end_of_period_derivs(shocks, a, nTil, s):
                 dvds_vals[:, nInd, sInd] = LinearInterp(
                     m_ns,
                     np.concatenate(
-                        [np.array([eop_dvds[0, nInd, sInd]]), eop_dvds[:, nInd, sInd],]
+                        [
+                            np.array([eop_dvds[0, nInd, sInd]]),
+                            eop_dvds[:, nInd, sInd],
+                        ]
                     ),
                 )(mNrmGrid)
 
@@ -1706,7 +1713,7 @@ def solve_RiskyContrib_Reb(
         [-nNrm*dfracGrid, dfracGrid*mNrm].
     vFuncBool : bool
         Determines whether the level of th value function must be computed.
-    
+
     Returns
     -------
     solution : RiskyContribShaSolution
@@ -1775,7 +1782,14 @@ def solve_RiskyContrib_Reb(
     constrained_bot = dvdDNvrs[0, :, :] >= 0.0
     # If it is positive (inverse negative) at the highest d, then d[-1] = 1.0
     # is optimal
-    constrained_top = dvdDNvrs[-1, :, :,] <= 0.0
+    constrained_top = (
+        dvdDNvrs[
+            -1,
+            :,
+            :,
+        ]
+        <= 0.0
+    )
 
     # Find indices at which the derivative crosses 0 for the 1st time
     # will be 0 if it never does, but "constrained_top/bot" deals with that
diff --git a/HARK/core.py b/HARK/core.py
index 733845346..d4295871a 100644
--- a/HARK/core.py
+++ b/HARK/core.py
@@ -52,9 +52,9 @@ def distance_metric(thing_a, thing_b):
             distance = max(distance_temp)
         else:
             warn(
-                'Objects of different lengths are being compared. ' +
-                'Returning difference in lengths.'
-                )
+                "Objects of different lengths are being compared. "
+                + "Returning difference in lengths."
+            )
             distance = float(abs(len_a - len_b))
     # If both inputs are dictionaries, call distance on the list of its elements
     elif type_a is dict and type_b is dict:
@@ -71,20 +71,18 @@ def distance_metric(thing_a, thing_b):
             # If keys don't match, print a warning.
             if list(sorted_a.keys()) != list(sorted_b.keys()):
                 warn(
-                    'Dictionaries with keys that do not match are being ' + 
-                    'compared.'
+                    "Dictionaries with keys that do not match are being " + "compared."
                 )
 
-            distance = distance_metric(list(sorted_a.values()),
-                                      list(sorted_b.values()))
+            distance = distance_metric(list(sorted_a.values()), list(sorted_b.values()))
 
         else:
             # If they have different lengths, log a warning and return the
             # difference in lengths.
             warn(
-                'Objects of different lengths are being compared. ' + 
-                'Returning difference in lengths.'
-                )
+                "Objects of different lengths are being compared. "
+                + "Returning difference in lengths."
+            )
             distance = float(abs(len_a - len_b))
 
     # If both inputs are numbers, return their difference
@@ -150,10 +148,12 @@ def distance(self, other):
                 )  # if either object lacks attribute, they are not the same
         return max(distance_list)
 
+
 class Model(object):
     """
     A class with special handling of parameters assignment.
     """
+
     def assign_parameters(self, **kwds):
         """
         Assign an arbitrary number of attributes to this agent.
@@ -195,7 +195,7 @@ def __eq__(self, other):
         return notImplemented
 
     def __init__(self):
-        if not hasattr(self, 'parameters'):
+        if not hasattr(self, "parameters"):
             self.parameters = {}
 
     def __str__(self):
@@ -270,7 +270,7 @@ def __init__(
         pseudo_terminal=True,
         tolerance=0.000001,
         seed=0,
-        **kwds
+        **kwds,
     ):
         super().__init__()
 
@@ -283,7 +283,7 @@ def __init__(
         self.tolerance = tolerance  # NOQA
         self.seed = seed  # NOQA
         self.track_vars = []  # NOQA
-        self.state_now = {sv : None for sv in self.state_vars}
+        self.state_now = {sv: None for sv in self.state_vars}
         self.state_prev = self.state_now.copy()
         self.controls = {}
         self.shocks = {}
@@ -513,7 +513,7 @@ def initialize_sim(self):
             if self.state_now[var] is None:
                 self.state_now[var] = copy(blank_array)
 
-            #elif self.state_prev[var] is None:
+            # elif self.state_prev[var] is None:
             #    self.state_prev[var] = copy(blank_array)
         self.t_age = np.zeros(
             self.AgentCount, dtype=int
@@ -854,7 +854,7 @@ def transition(self):
         Parameters
         ----------
         None
- 
+
         [Eventually, to match dolo spec:
         exogenous_prev, endogenous_prev, controls, exogenous, parameters]
 
@@ -884,9 +884,9 @@ def get_controls(self):
 
     def get_poststates(self):
         """
-        Gets values of post-decision state variables for the current period, 
+        Gets values of post-decision state variables for the current period,
         probably by current
-        states and controls and maybe market-level events or shock variables.  
+        states and controls and maybe market-level events or shock variables.
         Does nothing by
         default, but can be overwritten by subclasses of AgentType.
 
@@ -1123,9 +1123,9 @@ def solve_one_cycle(agent, solution_last):
     # Initialize the solution for this cycle, then iterate on periods
     solution_cycle = []
     solution_next = solution_last
-    
+
     cycles_range = [0] + list(range(T - 1, 0, -1))
-    for k in (range(T-1, -1, -1) if agent.cycles == 1 else cycles_range):
+    for k in range(T - 1, -1, -1) if agent.cycles == 1 else cycles_range:
         # Update which single period solver to use (if it depends on time)
         if hasattr(agent.solve_one_period, "__getitem__"):
             solve_one_period = agent.solve_one_period[k]
@@ -1244,7 +1244,7 @@ def __init__(
         calc_dynamics=None,
         act_T=1000,
         tolerance=0.000001,
-        **kwds
+        **kwds,
     ):
         super().__init__()
         self.agents = agents if agents is not None else list()  # NOQA
@@ -1446,11 +1446,7 @@ def reset(self):
         none
         """
         # Reset the history of tracked variables
-        self.history = {
-            var_name: []
-            for var_name
-            in self.track_vars
-        }
+        self.history = {var_name: [] for var_name in self.track_vars}
 
         # Set the sow variables to their initial levels
         for var_name in self.sow_state:
@@ -1564,11 +1560,11 @@ def distribute_params(agent, param_name, param_count, distribution):
     agent_set = [deepcopy(agent) for i in range(param_count)]
 
     for j in range(param_count):
-        agent_set[j].assign_parameters(**{'AgentCount': int(agent.AgentCount * param_dist.pmf[j])})
-        # agent_set[j].__dict__[param_name] = param_dist.X[j]
-
-        agent_set[j].assign_parameters(**{param_name: param_dist.X[0,j]})
-
+        agent_set[j].assign_parameters(
+            **{"AgentCount": int(agent.AgentCount * param_dist.pmv[j])}
+        )
+        # agent_set[j].__dict__[param_name] = param_dist.atoms[j]
 
+        agent_set[j].assign_parameters(**{param_name: param_dist.atoms[0, j]})
 
     return agent_set
diff --git a/HARK/distribution.py b/HARK/distribution.py
index 44378c82e..3d033a8b0 100644
--- a/HARK/distribution.py
+++ b/HARK/distribution.py
@@ -390,8 +390,8 @@ def approx(self, N, tail_N=0, tail_bound=None, tail_order=np.e):
             CDF_vals = np.array(CDF_vals)
 
             K = CDF_vals.size - 1  # number of points in approximation
-            pmf = CDF_vals[1 : (K + 1)] - CDF_vals[0:K]
-            X = np.zeros(K)
+            pmv = CDF_vals[1 : (K + 1)] - CDF_vals[0:K]
+            atoms = np.zeros(K)
             for i in range(K):
                 zBot = cutoffs[i]
                 zTop = cutoffs[i + 1]
@@ -407,25 +407,25 @@ def approx(self, N, tail_N=0, tail_bound=None, tail_order=np.e):
                     np.sqrt(2) * self.sigma
                 )
                 if tempBot <= 4:
-                    X[i] = (
+                    atoms[i] = (
                         -0.5
                         * np.exp(self.mu + (self.sigma**2) * 0.5)
                         * (erf(tempTop) - erf(tempBot))
-                        / pmf[i]
+                        / pmv[i]
                     )
                 else:
-                    X[i] = (
+                    atoms[i] = (
                         -0.5
                         * np.exp(self.mu + (self.sigma**2) * 0.5)
                         * (erfc(tempBot) - erfc(tempTop))
-                        / pmf[i]
+                        / pmv[i]
                     )
 
         else:
-            pmf = np.ones(N) / N
-            X = np.exp(self.mu) * np.ones(N)
+            pmv = np.ones(N) / N
+            atoms = np.exp(self.mu) * np.ones(N)
         return DiscreteDistribution(
-            pmf, X, seed=self.RNG.randint(0, 2**31 - 1, dtype="int32")
+            pmv, atoms, seed=self.RNG.randint(0, 2**31 - 1, dtype="int32")
         )
 
     @classmethod
@@ -523,11 +523,11 @@ def approx(self, N):
         """
         x, w = np.polynomial.hermite.hermgauss(N)
         # normalize w
-        pmf = w * np.pi**-0.5
+        pmv = w * np.pi**-0.5
         # correct x
-        X = math.sqrt(2.0) * self.sigma * x + self.mu
+        atoms = math.sqrt(2.0) * self.sigma * x + self.mu
         return DiscreteDistribution(
-            pmf, X, seed=self.RNG.randint(0, 2**31 - 1, dtype="int32")
+            pmv, atoms, seed=self.RNG.randint(0, 2**31 - 1, dtype="int32")
         )
 
     def approx_equiprobable(self, N):
@@ -537,11 +537,11 @@ def approx_equiprobable(self, N):
         pdf = stats.norm.pdf(lims)
 
         # Find conditional means using Mills's ratio
-        pmf = np.diff(CDF)
-        X = self.mu - np.diff(pdf) / pmf * self.sigma
+        pmv = np.diff(CDF)
+        atoms = self.mu - np.diff(pdf) / pmv * self.sigma
 
         return DiscreteDistribution(
-            pmf, X, seed=self.RNG.randint(0, 2**31 - 1, dtype="int32")
+            pmv, atoms, seed=self.RNG.randint(0, 2**31 - 1, dtype="int32")
         )
 
 
@@ -617,16 +617,16 @@ def approx(self, N, equiprobable=False):
         else:
             z_approx = Normal().approx(N)
 
-        # Now create the multivariate grid and pmf
-        Z = np.array(list(product(*[z_approx.X.flatten()] * self.M)))
-        pmf = np.prod(np.array(list(product(*[z_approx.pmf] * self.M))), axis=1)
+        # Now create the multivariate grid and pmv
+        Z = np.array(list(product(*[z_approx.atoms.flatten()] * self.M)))
+        pmv = np.prod(np.array(list(product(*[z_approx.pmv] * self.M))), axis=1)
 
         # Apply mean and standard deviation to the Z grid
-        X = self.mu[None, ...] + np.matmul(Z, A.T)
+        atoms = self.mu[None, ...] + np.matmul(Z, A.T)
 
         # Construct and return discrete distribution
         return DiscreteDistribution(
-            pmf, X.T, seed=self.RNG.randint(0, 2**31 - 1, dtype="int32")
+            pmv, atoms.T, seed=self.RNG.randint(0, 2**31 - 1, dtype="int32")
         )
 
 
@@ -757,14 +757,14 @@ def approx(self, N):
             Probability associated with each point in array of discrete
             points for discrete probability mass function.
         """
-        pmf = np.ones(N) / float(N)
+        pmv = np.ones(N) / float(N)
         center = (self.top + self.bot) / 2.0
         width = (self.top - self.bot) / 2.0
-        X = center + width * np.linspace(-(N - 1.0) / 2.0, (N - 1.0) / 2.0, N) / (
+        atoms = center + width * np.linspace(-(N - 1.0) / 2.0, (N - 1.0) / 2.0, N) / (
             N / 2.0
         )
         return DiscreteDistribution(
-            pmf, X, seed=self.RNG.randint(0, 2**31 - 1, dtype="int32")
+            pmv, atoms, seed=self.RNG.randint(0, 2**31 - 1, dtype="int32")
         )
 
 
@@ -821,71 +821,71 @@ class DiscreteDistribution(Distribution):
 
     Parameters
     ----------
-    pmf : np.array
+    pmv : np.array
         An array of floats representing a probability mass function.
-    X : np.array
+    atoms : np.array
         Discrete point values for each probability mass.
-        For multivariate distributions, the last dimension of X must index
-        "nature" or the random realization. For instance, if X.shape == (2,6,4),
+        For multivariate distributions, the last dimension of atoms must index
+        "nature" or the random realization. For instance, if atoms.shape == (2,6,4),
         the random variable has 4 possible realizations and each of them has shape (2,6).
     seed : int
         Seed for random number generator.
     """
 
-    pmf = None
-    X = None
+    pmv = None
+    atoms = None
 
-    def __init__(self, pmf, X, seed=0):
+    def __init__(self, pmv, atoms, seed=0):
 
-        self.pmf = pmf
+        self.pmv = pmv
 
-        if len(X.shape) < 2:
-            self.X = X[None, ...]
+        if len(atoms.shape) < 2:
+            self.atoms = atoms[None, ...]
         else:
-            self.X = X
+            self.atoms = atoms
 
         # Set up the RNG
         super().__init__(seed)
 
-        # Check that pmf and X have compatible dimensions.
-        same_dims = len(pmf) == X.shape[-1]
+        # Check that pmv and atoms have compatible dimensions.
+        same_dims = len(pmv) == atoms.shape[-1]
         if not same_dims:
             raise ValueError(
-                "Provided pmf and X arrays have incompatible dimensions. "
-                + "The length of the pmf must be equal to that of X's last dimension."
+                "Provided pmv and atoms arrays have incompatible dimensions. "
+                + "The length of the pmv must be equal to that of atoms's last dimension."
             )
 
     def dim(self):
         """
-        Last dimension of self.X indexes "nature."
+        Last dimension of self.atoms indexes "nature."
         """
-        return self.X.shape[:-1]
+        return self.atoms.shape[:-1]
 
     def draw_events(self, n):
         """
         Draws N 'events' from the distribution PMF.
-        These events are indices into X.
+        These events are indices into atoms.
         """
         # Generate a cumulative distribution
         base_draws = self.RNG.uniform(size=n)
-        cum_dist = np.cumsum(self.pmf)
+        cum_dist = np.cumsum(self.pmv)
 
         # Convert the basic uniform draws into discrete draws
         indices = cum_dist.searchsorted(base_draws)
 
         return indices
 
-    def draw(self, N, X=None, exact_match=False):
+    def draw(self, N, atoms=None, exact_match=False):
         """
-        Simulates N draws from a discrete distribution with probabilities P and outcomes X.
+        Simulates N draws from a discrete distribution with probabilities P and outcomes atoms.
 
         Parameters
         ----------
         N : int
             Number of draws to simulate.
-        X : None, int, or np.array
-            If None, then use this distribution's X for point values.
-            If an int, then the index of X for the point values.
+        atoms : None, int, or np.array
+            If None, then use this distribution's atoms for point values.
+            If an int, then the index of atoms for the point values.
             If an np.array, use the array for the point values.
         exact_match : boolean
             Whether the draws should "exactly" match the discrete distribution (as
@@ -897,16 +897,16 @@ def draw(self, N, X=None, exact_match=False):
         Returns
         -------
         draws : np.array
-            An array of draws from the discrete distribution; each element is a value in X.
+            An array of draws from the discrete distribution; each element is a value in atoms.
         """
-        if X is None:
-            X = self.X
-        elif isinstance(X, int):
-            X = self.X[X]
+        if atoms is None:
+            atoms = self.atoms
+        elif isinstance(atoms, int):
+            atoms = self.atoms[atoms]
 
         if exact_match:
-            events = np.arange(self.pmf.size)  # just a list of integers
-            cutoffs = np.round(np.cumsum(self.pmf) * N).astype(
+            events = np.arange(self.pmv.size)  # just a list of integers
+            cutoffs = np.round(np.cumsum(self.pmv) * N).astype(
                 int
             )  # cutoff points between discrete outcomes
             top = 0
@@ -926,7 +926,7 @@ def draw(self, N, X=None, exact_match=False):
             indices = self.draw_events(N)
 
         # Create and fill in the output array of draws based on the output of event indices
-        draws = X[..., indices]
+        draws = atoms[..., indices]
 
         # TODO: some models expect univariate draws to just be a 1d vector. Fix those models.
         if len(draws.shape) == 2 and draws.shape[0] == 1:
@@ -966,7 +966,7 @@ def expected(self, func=None, *args):
         if func is None:
             # if no function is provided, it's much faster to go straight
             # to dot product instead of calling the dummy function.
-            f_query = self.X
+            f_query = self.atoms
         else:
             # if a function is provided, we need to add one more dimension,
             # the nature dimension, to any inputs that are n-dim arrays.
@@ -978,16 +978,16 @@ def expected(self, func=None, *args):
                 for arg in args
             ]
 
-            f_query = func(self.X, *args)
+            f_query = func(self.atoms, *args)
 
-        f_exp = np.dot(f_query, self.pmf)
+        f_exp = np.dot(f_query, self.pmv)
 
         return f_exp
 
     def dist_of_func(self, func=lambda x: x, xarray=False, *args, **kwargs):
         """
         Finds the distribution of a random variable Y that is a function
-        of discrete random variable X, Y=f(X).
+        of discrete random variable atoms, Y=f(atoms).
 
         Parameters
         ----------
@@ -1010,14 +1010,14 @@ def dist_of_func(self, func=lambda x: x, xarray=False, *args, **kwargs):
         args = [
             arg[..., np.newaxis] if isinstance(arg, np.ndarray) else arg for arg in args
         ]
-        f_query = func(self.X, *args)
+        f_query = func(self.atoms, *args)
 
         if xarray:
             f_dstn = DiscreteDistributionXRA(
-                list(self.pmf), f_query, seed=self.seed, **kwargs
+                list(self.pmv), f_query, seed=self.seed, **kwargs
             )
         else:
-            f_dstn = DiscreteDistribution(list(self.pmf), f_query, seed=self.seed)
+            f_dstn = DiscreteDistribution(list(self.pmv), f_query, seed=self.seed)
 
         return f_dstn
 
@@ -1029,12 +1029,12 @@ class DiscreteDistributionXRA(DiscreteDistribution):
 
     Parameters
     ----------
-    pmf : np.array
+    pmv : np.array
         An array of floats representing a probability mass function.
     data : np.array
         Discrete point values for each probability mass.
-        For multivariate distributions, the last dimension of X must index
-        "nature" or the random realization. For instance, if X.shape == (2,6,4),
+        For multivariate distributions, the last dimension of atoms must index
+        "nature" or the random realization. For instance, if atoms.shape == (2,6,4),
         the random variable has 4 possible realizations and each of them has shape (2,6).
     seed : int
         Seed for random number generator.
@@ -1048,8 +1048,8 @@ class DiscreteDistributionXRA(DiscreteDistribution):
 
     def __init__(
         self,
-        pmf,
-        data,
+        pmv,
+        atoms,
         seed=0,
         name="DiscreteDistributionXRA",
         var_names=None,
@@ -1058,9 +1058,9 @@ def __init__(
     ):
 
         # vector-value distributions
-        if data.ndim < 2:
-            data = data[np.newaxis, ...]
-        if data.ndim > 2:
+        if atoms.ndim < 2:
+            atoms = atoms[np.newaxis, ...]
+        if atoms.ndim > 2:
             raise NotImplementedError(
                 "Only vector-valued distributions are supported for now."
             )
@@ -1069,11 +1069,11 @@ def __init__(
             attrs = {}
 
         attrs["name"] = name
-        attrs["pmf"] = np.asarray(pmf)
+        attrs["pmv"] = np.asarray(pmv)
         attrs["seed"] = seed
         attrs["RNG"] = np.random.RandomState(seed)
 
-        n_var = data.shape[0]
+        n_var = atoms.shape[0]
 
         if var_names is None:
             var_names = ["var_" + str(i) for i in range(n_var)]
@@ -1087,7 +1087,7 @@ def __init__(
 
         self.dataset = Dataset(
             {
-                var_names[i]: DataArray(data[i], attrs=var_attrs[i])
+                var_names[i]: DataArray(atoms[i], attrs=var_attrs[i])
                 for i in range(n_var)
             },
             attrs=attrs,
@@ -1098,7 +1098,7 @@ def variables(self):
         return self.dataset.data_vars
 
     @property
-    def X(self):
+    def atoms(self):
         """
         Returns the distribution's data as a numpy.ndarray.
         """
@@ -1106,11 +1106,11 @@ def X(self):
         return np.vstack([data_vars[key].values for key in data_vars.keys()])
 
     @property
-    def pmf(self):
+    def pmv(self):
         """
         Returns the distribution's probability mass function.
         """
-        return self.dataset.pmf
+        return self.dataset.pmv
 
     @property
     def RNG(self):
@@ -1187,7 +1187,7 @@ def func_wrapper(x, *args):
 
 def approx_lognormal_gauss_hermite(N, mu=0.0, sigma=1.0, seed=0):
     d = Normal(mu, sigma).approx(N)
-    return DiscreteDistribution(d.pmf, np.exp(d.X), seed=seed)
+    return DiscreteDistribution(d.pmv, np.exp(d.atoms), seed=seed)
 
 
 def calc_normal_style_pars_from_lognormal_pars(avg_lognormal, std_lognormal):
@@ -1229,9 +1229,9 @@ def approx_beta(N, a=1.0, b=1.0):
     """
     P = 1000
     vals = np.reshape(stats.beta.ppf(np.linspace(0.0, 1.0, N * P), a, b), (N, P))
-    X = np.mean(vals, axis=1)
-    pmf = np.ones(N) / float(N)
-    return DiscreteDistribution(pmf, X)
+    atoms = np.mean(vals, axis=1)
+    pmv = np.ones(N) / float(N)
+    return DiscreteDistribution(pmv, atoms)
 
 
 def make_markov_approx_to_normal(x_grid, mu, sigma, K=351, bound=3.5):
@@ -1414,7 +1414,7 @@ def add_discrete_outcome_constant_mean(distribution, x, p, sort=False):
     p : float
         The probability of the discrete outcome x occuring.
     sort: bool
-        Whether or not to sort X before returning it
+        Whether or not to sort atoms before returning it
 
     Returns
     -------
@@ -1424,15 +1424,15 @@ def add_discrete_outcome_constant_mean(distribution, x, p, sort=False):
     """
 
     if type(distribution) != TimeVaryingDiscreteDistribution:
-        X = np.append(x, distribution.X * (1 - p * x) / (1 - p))
-        pmf = np.append(p, distribution.pmf * (1 - p))
+        atoms = np.append(x, distribution.atoms * (1 - p * x) / (1 - p))
+        pmv = np.append(p, distribution.pmv * (1 - p))
 
         if sort:
-            indices = np.argsort(X)
-            X = X[indices]
-            pmf = pmf[indices]
+            indices = np.argsort(atoms)
+            atoms = atoms[indices]
+            pmv = pmv[indices]
 
-        return DiscreteDistribution(pmf, X)
+        return DiscreteDistribution(pmv, atoms)
     elif type(distribution) == TimeVaryingDiscreteDistribution:
         # apply recursively on all the internal distributions
         return TimeVaryingDiscreteDistribution(
@@ -1465,15 +1465,15 @@ def add_discrete_outcome(distribution, x, p, sort=False):
         points for discrete probability mass function.
     """
 
-    X = np.append(x, distribution.X)
-    pmf = np.append(p, distribution.pmf * (1 - p))
+    atoms = np.append(x, distribution.atoms)
+    pmv = np.append(p, distribution.pmv * (1 - p))
 
     if sort:
-        indices = np.argsort(X)
-        X = X[indices]
-        pmf = pmf[indices]
+        indices = np.argsort(atoms)
+        atoms = atoms[indices]
+        pmv = pmv[indices]
 
-    return DiscreteDistribution(pmf, X)
+    return DiscreteDistribution(pmv, atoms)
 
 
 def combine_indep_dstns(*distributions, seed=0):
@@ -1503,7 +1503,7 @@ def combine_indep_dstns(*distributions, seed=0):
             )
 
         dist_dims += (dist.dim(),)
-        dist_lengths += (len(dist.pmf),)
+        dist_lengths += (len(dist.pmv),)
 
     number_of_distributions = len(distributions)
 
@@ -1514,19 +1514,19 @@ def combine_indep_dstns(*distributions, seed=0):
     )
     inds = [x.flatten() for x in inds]
 
-    X_out = []
+    atoms_out = []
     P_temp = []
     for i, ind_vec in enumerate(inds):
-        X_out += [distributions[i].X[..., ind_vec]]
-        P_temp += [distributions[i].pmf[ind_vec]]
+        atoms_out += [distributions[i].atoms[..., ind_vec]]
+        P_temp += [distributions[i].pmv[ind_vec]]
 
-    X_out = np.concatenate(X_out, axis=0)
+    atoms_out = np.concatenate(atoms_out, axis=0)
     P_temp = np.stack(P_temp, axis=0)
     P_out = np.prod(P_temp, axis=0)
 
     assert np.isclose(np.sum(P_out), 1), "Probabilities do not sum to 1!"
 
-    return DiscreteDistribution(P_out, X_out, seed=seed)
+    return DiscreteDistribution(P_out, atoms_out, seed=seed)
 
 
 def calc_expectation(dstn, func=lambda x: x, *args):
@@ -1555,7 +1555,7 @@ def calc_expectation(dstn, func=lambda x: x, *args):
         Scalar if only one value.
     """
 
-    f_query = [func(dstn.X[..., i], *args) for i in range(len(dstn.pmf))]
+    f_query = [func(dstn.atoms[..., i], *args) for i in range(len(dstn.pmv))]
 
     f_query = np.stack(f_query, axis=-1)
 
@@ -1564,7 +1564,7 @@ def calc_expectation(dstn, func=lambda x: x, *args):
     # If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.
     # Thus, if func returns scalars, f_exp will be a scalar and if it returns arrays f_exp
     # will be an array of the same shape.
-    f_exp = np.dot(f_query, dstn.pmf)
+    f_exp = np.dot(f_query, dstn.pmv)
 
     return f_exp
 
@@ -1572,7 +1572,7 @@ def calc_expectation(dstn, func=lambda x: x, *args):
 def distr_of_function(dstn, func=lambda x: x, *args):
     """
     Finds the distribution of a random variable Y that is a function
-    of discrete random variable X, Y=f(X).
+    of discrete random variable atoms, Y=f(atoms).
 
     Parameters
     ----------
@@ -1592,12 +1592,12 @@ def distr_of_function(dstn, func=lambda x: x, *args):
         The distribution of func(dstn).
     """
     # Apply function to every event realization
-    f_query = [func(dstn.X[..., i], *args) for i in range(len(dstn.pmf))]
+    f_query = [func(dstn.atoms[..., i], *args) for i in range(len(dstn.pmv))]
 
     # Stack results along their last (new) axis
     f_query = np.stack(f_query, axis=-1)
 
-    f_dstn = DiscreteDistribution(dstn.pmf, f_query)
+    f_dstn = DiscreteDistribution(dstn.pmv, f_query)
 
     return f_dstn
 
@@ -1656,7 +1656,7 @@ def sample(s):
 def expected(func=None, dist=None, args=(), labels=False):
     """
     Expectation of a function, given an array of configurations of its inputs
-    along with a DiscreteDistribution(XRA) object that specifies the probability
+    along with a DiscreteDistribution(atomsRA) object that specifies the probability
     of each configuration.
 
     Parameters
diff --git a/examples/ConsumptionSaving/example_ConsMedModel.ipynb b/examples/ConsumptionSaving/example_ConsMedModel.ipynb
index 8ba5b93ff..6d6a6088d 100644
--- a/examples/ConsumptionSaving/example_ConsMedModel.ipynb
+++ b/examples/ConsumptionSaving/example_ConsMedModel.ipynb
@@ -74,20 +74,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solving a medical shocks consumer took 0.1120 seconds.\n"
+      "Solving a medical shocks consumer took 0.0725 seconds.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\GitHub\\HARK\\HARK\\ConsumptionSaving\\ConsMedModel.py:699: RuntimeWarning: divide by zero encountered in power\n",
+      "c:\\users\\alujan\\github\\alanlujan91\\hark\\HARK\\ConsumptionSaving\\ConsMedModel.py:699: RuntimeWarning: divide by zero encountered in power\n",
       "  vPgrid = cLvlGrid ** (-self.CRRA)\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:141: RuntimeWarning: divide by zero encountered in reciprocal\n",
+      "c:\\users\\alujan\\github\\alanlujan91\\hark\\HARK\\utilities.py:141: RuntimeWarning: divide by zero encountered in reciprocal\n",
       "  return c ** (1.0 - gam) / (1.0 - gam)\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:141: RuntimeWarning: divide by zero encountered in power\n",
+      "c:\\users\\alujan\\github\\alanlujan91\\hark\\HARK\\utilities.py:141: RuntimeWarning: divide by zero encountered in power\n",
       "  return c ** (1.0 - gam) / (1.0 - gam)\n",
-      "C:\\GitHub\\HARK\\HARK\\ConsumptionSaving\\ConsMedModel.py:718: RuntimeWarning: invalid value encountered in multiply\n",
+      "c:\\users\\alujan\\github\\alanlujan91\\hark\\HARK\\ConsumptionSaving\\ConsMedModel.py:718: RuntimeWarning: invalid value encountered in multiply\n",
       "  vGrid = utility(cLvlGrid, gam=self.CRRA) + MedShkGrid_tiled * utility(\n"
      ]
     }
@@ -115,7 +115,7 @@
     },
     {
      "data": {
-      "image/png": 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brHwCanMhZAxMfx18Eo793GNgsVn4ofgH3s16lxZDC5eGX8oDqQ/gpfU6+Rf3EVHARUREzio2q5Ulzz6KsUpJkksqCgdvVqvyaUPPoCQ5nl5rMRqr8fG5gtDwp/iiXuCNXYWYBYHHQ3y4L8gLdU+s2d6aNp75yW6XpAW78uJlw4jzO45d0lYNa5+Dvd+DUwD8czHEXX5KZYFba7by1p63KGktYbD3YBZOXkic+5mrCRcFXERE5KxhD1nYgK/Ok2jfNDKUlRTJ03F3lzM1rYLu7s3IZGGkpCxhvzSJS3Oq2dvZzQQ3R16NDCBUa2+Bb9ObeWtNIUt2VuCuU/L2PwdxZepx7BKzAbbNgy2zQLDBuKdg1IOg1P75ucehrLWMmXtmsqVmCwEOAbwz/h0mBU06Y0k8hxAFXERE5IzT1dXOl489ibzLieFeI2nVyvhJlYkRI6NGmlEof8RoNBEW+hBOfrfzWnkTSw4U461U8GF8CBd7OiORSLDZBL7LqOb1lQW06k3cPMJulzhrjmGXCAIU/AKr/wOtFRB3mb0s0DW41+tuMbSwIGsB3xZ9i0au4dHBj3J97PUoZcp+fHd6jyjgIiIiZ5RfFs2mZl014U6R+PjFslVZyEFpK6EhMiIi0zEY8nF2GkVU1Av80u7Ci7vLaLVYuCvAk8dDfXDoiTXbW9PGc8v2klHZC7ukvgBWPQllG8ErDm7+2d4G30vMVjNfFnzJ+9nvo7fouSrqKv6V/C/c1L0/5BwIRAEXERE5I9hDFt7GQerGGN+xlKpb+VG5G6VKYOKoRszm1dhsbsTHvUOrwxRuKKxmR1slaU5a3ogOJ97BXs3Rpjfz9tpCluyowE2n5K1/DuLKFH+k0mPYF92tsPF12PUBqBzhgpn2CpNelgUKgsCGqg3M2jOLyo5KRvmP4vG0xwl3Ce/Hd+b0EQVcRERkQBEEgaUvPklnMSS4pKB09GG1qoA2ukgeBG7uazGb6/H3vx7f4EeYW6Pn/YJCHGUy3o4O5DpfN6Q9dsn3PXZJi97ETSeyS2xWyPwc1r9or+1OuxUmPAM6916vO78pn5l7ZrK7djfhzuEsnLyQ0f6n1swz0IgCLiIiMmBkbVvHjvnL8NF5kuKbRoaqkmJ5Bh4eEqakFmEw7EKliiUp8V22m0O5IaOGGqOZa33ceCbcDw+lXaLyDrTx3LI80itaGBzsymeXDSXez/nYN63cASseh9ocCBoJF7wBvkm9XnODvoG5mXNZVrIMZ5Uz/xn2H66KumpAZpn0lXNvRSIiIuc9JpORzx95CEm7E8N6DimXqTIxSUyMGtGFXLEKs1lCRMS/weN67i+pZW1TOTE6Ncvighnm4gBAW7eZWWsK+XxHBa5aJTOvSuIfqQHHtkvaD9jLAnO/BSd/uGoRxF/Z67JAg8XAp3mf8vHejzHbzNwcfzN3Jt2Jk/I4vvopIAjCgFSoiAIuIiLSr6xZ+gH7fy4k1CkcX784timKOChrJSxUICx8O0ZjKW5ukwmNeI5P6uXM3l2MRCLhuXA/7gzwRCG12yU/ZNbw+sp8mrtM3Dg8mEemROOsPYZdYjbA9vnw+yywWWDs4zD6YVDqerVeQRBYsX8FszNmU9tVy+SgyTwy+BECnXo/tOpE1+5Yu5bG+e8SMHcOypCQPl/zSEQBFxER6RcaGmr48cmXcBDcGe07hjJ1Oz8p9qDSWJk48iBm83rAl6TE9yiUD+ei3GqK9UYu9HDmpUh//NX2Urx9B9p5btle9lS0kBrkwuJbh5Lgfwy7RBCgcCWs/je0lEPMxTDtFXAN6fWas+qzmLl7JjmNOcS6xfLq6FcZ4jOkX96Prh07qH97FobcXJRhYVhbW/vlukciCriIiEif+fqN52jLNRDvkoLS0Zc1ynzaJHpSUsy4uK7BYmknKPB2HPzv47/lrXxXV0qQWsnniaFM8bCLc1u3mXfWFvHZ9nJctErevCqJq45nlzQU2lNxSjeAZwzc+BOET+j1eg90HuCd9HdYVb4KT40nL416iUvDL+2XgVPduXtpeOcdurZtQ+7ri+8rr+B82aVI5GdhHrhEIlkEXAzUC4KQ0PPYTOASwASUArcKgtDa76sTERE5pynI3cHGNz7HW+3FaN9xZKqqKJZn4OUpMDl5L0ZjFlptMpFRL/FjhxevpVfQbRV4KNibB4K90cqkCILADxk1vNZjl9wwLJjHph7HLjG0wcY3YNf7oNDZ48yG3A6y48w5OYoucxcf5X7EZ3mfIZVIuTvpbm5LuA2tovddmMfDWLafhrlz6Vi1CpmLC15PPYnrddchVan6fO3j0ZtfCYuB+cBnRzy2Fvi3IAgWiUTyBvBv4Mn+X56IiMi5iMVs4fMnH0JodGCIx0jatQqWKbMwS42MHtmKVLYGq1VNdPRLNDpeyrVFB8jqqGa0iwOvRwcQoVUDkH/QbpfsLm8h5UR2ic0GWUtg3X9B3wSDb4aJz4LOo1frtdqs/FTyE/My59FkaOKisIt4KPUhfHQ+fX4vzLW1NL77Lq0//IhEpcLjX//C7bZbkTk49PnaJ6M3kWqbJRJJyFGPrTniwx3AVf28LhERkXOUzcu+JP+r3YQ4BePrF882RSG1sjYiIiwEh2zBZKrCy+tSvEOe4p0aM4uLSnBXylkQF8wVXi5IJBLaDYfskgqcNQre/EcSVw0+jl1Stcs+LfBAJgQOgxnfg19yr9e78+BOZu6eSWFLIcmeycybOI9Ez8Q+vw+WlhaaPvyIliVLQBBwveF6PO6+G7l772vN+0p/mDK3AV8f75MSieQu4C6AoKCgfridiIjI2aCtpZGvH38GrdWdUX5j2a/uYJliD2qdmYnDKjFbtiCTBTNo0KdstsRzXVYNjSYLt/p78GSoD84KeY9dUs2rKwpo6jJyw7AgHpsajYv2GLNE2g/Cuhcg5ytw9IUrP4LEq3pdFljWVsasPbPYVL0JP50fM8fNZFrwtD6X89n0epo/+4ymjz7G1tWF82WX4fF//4cywL9P1z0d+iTgEonkP4AF+OJ4zxEE4QPgA4C0tDShL/cTERE5O/ww7zUadzQT6zIItZM/a5T5tEv0pKZ24eS8DovVSGjI/Zg9b+Pe0np+b6lgkKOGJUlhDHK0+8sFte0891Meu8qbSQ504ZNbhpAYcAy7xGKEHQtg00ywmWHMozD6EVD1zpJoNjSzIGsB3xV9h0au4aHUh5gRNwOVrG9etGAy0fLddzQuWIi1sRGHSZPwfPAB1FH9m3N5Kpy2gEskkpuxH25OEgRBFGYRkb8g+4v2sualhXiqvBjlN44sVTXF8gx8vC0MSczCZMrH0XEYIZEv8lGDjnfT96OWSXgtKoCb/NyR9dgls9cW8+n2cpzUct74RyL/HBz4Z7tEEKBotb0ssLkMoi+CaS+DW1iv1mq0Glmybwkf5n6IwWLot4FTgs1G+68raJg7F3NVFZq0wXjNnYs2NaVP1+0PTkvAJRLJdOyHluMEQdD375JERETONjarlc+feRRrtZrBbsNp16lYrszCKjcyekQjEsl6BMGZuNi3yFNM5N69NVQaOrnK25Xnwv3wUikQBIGfMmt4ZUU+jZ1Grh8axOPTjmOXNBbDqn9DyVrwiIIZP0DEpF6tVRAEVu5fyZyMORzoOsD4gPE8PPhhwlx6J/wnum7X779TP+sdjAUFqGJiCPzgfXRjxpzxud/HozdlhEuB8YCHRCKpBp7HXnWiAtb2vJAdgiDcM4DrFBEROUPsWreczEUbCHL0w98/nm2KImplbURFGQgI3IzZXIef79VoAx/lmfIOfm3YT6RWxXfJ4Yx2tceaFdZ28Oyyveza38ygAGc+vjmNpACXP9/M0A6b3oCd74FCC9NehaF39bosMLM+k5m7Z5LbmEuMWwwvjnqRYb7D+vwe6DMzaXh7Fvo9e1AEBuI3cyZOF12IRNr/wcR9oTdVKNcd4+GPB2AtIiIiZ5Gurna+fPQJNAZ3RvqMpUzbwU/yPTg4mpkwpASLZTdKZRSxCXP5tiOItzIOYBME/h3qy71BniilUjoMZmavK2bxNrtd8tqViVyTdgy7xGaD7C/tZYFdDZAyAyY9Dw6evVprVXsV72TYk9+9NF68NOolLgm7BJlU1qf3wFhcTP3sOXSuX4/MwwPv557F9aqrkCjPTmDDyRA7MUVERPjl49kc2FBNjEsiar8A1ijy6ZDoSUtrQ+ewAZvNRnj4E9Q6Xcs/iw9S0HWAye5OvBLpT7BG9Se75LqhQTw+NRpX3TGEr3qPvSywJh0ChsL1X4N/aq/W2WZs4/2c91lasBSFVMG/kv/FzXE397kRx1xTQ8O8+bQtW4ZUp8PzoQdxu+kmpNq+N/gMJKKAi4j8jTlQVcLPz7yNu8KHkX7jyFLVUCLPxM/XyJC4PZjNZbi6TsAz9Dlm1sBXZfvxVyn4JCGE6R72WLMj7ZKkAGc+uimNQYEuf75ZR529LDD7S3DwgSveh8SroRe2hNlq5qvCr3gv+z06zZ1cEXEF9yXfh6e2dzv242Fpbqbp/fdp+XIpSCS43Xor7nfegdzVtU/XPVOIAi4i8jfli/8+gbFUSqrrCDocVCxXZCMoDIwZfgAkm5FKvYmPX8A6y2BeyT5Ih9XKvwK9eDTEG51cRofBzJx1xXyyrRxHtZxXr0jkmiGByI62Sywm2LnQXhZoNcKoh2DsY/aEnJMgCALrK9fzTvo7VHZUMsJ3BI+mPUq0W3SfXru1s4vmTxfTvOgTbN3dOF95BZ733YfC17dP1z3TiAIuIvI3I2v7enbMX06Qgx9+vvFsVxZRJ2sjJqYTX7/NWCytBAbegsHzXu4obWJPezXDnXW8FhVArIMGQRBYllXDK7/m09Bp5NohQTwx7Th2SdEae1lgUwlETbcfUrr3Lo5sb+NeZu6eSUZ9BuHO4SyYtIDR/qP7VAFiM5lo/eprGt97D2tzM45Tp+L50IOowvpWsXK2EAVcRORvgslk5LNHH0bV4cpI7zHs13ayXJ6Oo5OBCWkFWCw5aDVJBER+wnsNrnyUVYWzXMbsmECu8XFDIpFQVNfBsz/tZWePXfLBTWkkH8suaSq1lwUWrwb3CLjhO4ic0qt1Hug8wJyMOazYvwI3tRvPDn+WKyOv7FMijmC10v7LLzTMnYe5pgbt8OF4PfIwmqTeJ/WcLl1tRrLXVzH4ghBUmv6VXFHARUT+Bqz98kPKfy0kyjkOtV8ga+X5dMq6SEtrRKPdiCAoiYr8L1nKC7lr30EOGhuY4evO0+G+uCnkdBotzFlXxCdby9Gp5LxyRQLXDgn6s11i7IDNM2H7ApCrYerLMPRukJ+8iqPT1MlHuR/x+b7PkUgk3Jl4J7cn3o5O0btghmMhCAKdv22k4Z13MBYXo46Lw+fF/6IbOXLAa7k7mg1krK4gf+tBbDYBnzBnwpL75tkfjSjgIiJ/YRobavjhiZdwlfow3G8sWaoaSuWZBAZ0kRa1C4ulGi/Pi1EGPskT5d381lxJvIOaj+JDGOysQxAElmcf4JVf91HXbuTaIYE8MT0Gt6PtEpsNcr6Gdc9DZx0kz4BJz4Gj90nXaLFZ+L7oexZkL6DZ0MzFYRfzYOqDfZ4UqE9Pp/7tWXRnZKAMDsZ/9js4Tp064LXcbQ160ldVULi9FiQQM8KX1GnBOHtq+v1eooCLiPxF+fr1Z+nMM5PsOpx2RzU/K3KQqLoYM6wS2IFCEURk7Ccs7YxiblYdComElyL8udXfA7lUQnFdB88ty2N7WROJ/s68N2MwKUHHqM6oSYcVT0DNHvBPg2uXQsDgk65PEAR+r/mdt/e8TVlbGYO9B7Ng0gLiPeL79LoNhYU0zHqHzk2bkHt54fPf/+Jy5RVIFL1rDjpdmg92kb6qnOJddUhlUuLH+pMyNQhHN/WA3VMUcBGRvxgF2TvYNPMLArT+RPnFs11ZTL2sjbi4Zry8fscmGAgOvo8Kx5u5qrSesu5aLvF04cVIP3xVSjqNFuauL2bRlv3oVHJevjyB64Yewy7prIf1/4XMJaDzgssXQtK1vSoLLGwuZOaemew8uJNgp2BmT5jNxMCJfbI1TFVVNMybR/vPvyB1dMTrsUdxveEGpJr+3/keSWN1J3tWlFOaWY9cIWXQpECSpwShcx64IIdDiAIuIvIXwR6y8DCyJkeGe46lTNfFcnkGrq6dTEjei8VahKPTMFxC/8sbBxQsr6giVKPkq0FhjHdzQhAEfs4+wMs9dsk1aYE8MT0ad4ejhMhisifibHoTzN0w8gF7kLD65Ont9fp65mXOY1nJMpxUTjw19CmujroaRS9b54/5uhsbaVz4Hi3ffINEJsP9jjtwv+N2ZM7HmHTYj9RXtLNnRTn7sxtRqGUMnhbMoEmBaBzPXNemKOAiIn8BNi//koKv9xDpHIXaL4C1ikL08g6GDDmISrUFidSZqMiZ/GoZzczcWiyCnsdDfLgvyAu1TEpJvd0u2VbaRLyfEwtnDCb1WHZJ8Tp7FmVTMUROhWmvgUfESdenN+tZnLeYxXmLsdgs3BR3E3cm3Ymz6vRF1trRQfMnn9C0+FMEoxGXf16Fx73/QuHtddrX7A0HS1rZs7KcyrxmVFo5Qy8JJXF8AGrdwFo0x0IUcBGR85hDIQsugg/D/MaSrTpImTyb4OBWhoTtxGKtx8/3alq9HuTm0lb2dR1ggpsjr0UFEKJR0WW08NrqQj7esh+tUsZLlydw/bHskqZSWP0fKFoJbuFw/TcQNe2k67ParCwvXc68zHk0dDcwLWQaD6Y+SKBj4Gm/ZpvRSMuXS2l6/32sra04XXgBng88gDIk5LSveTIEQaCmsIU9K8qpKWpF7aBg+OVhJI4LQNnPpYGngijgIiLnKT/Me42Wna0kuQ2lw1HDr/JcZJouxgwtATJRqaMICp/HvEYvvsypxU+l4OOEEC7sSYH/OfsAr/yaT227gavTAnhyesyf7RJjJ/z+Fmx/F2RKmPxfGH4vyE/u724/sJ2397xNYUshSZ5JzBo/i2Sv5NN+vYLFQtuy5TTMn4/l4EF0o0bh+fDDaBL6duh5wnsKApV5zexZUU5tWRtaZyWjroogfow/ClXfBmf1B6KAi4icZ1QU72XViwvx1wSQ4j+K7coSGmTlJCTW4+6+BRAICXmCLYoruDW/jg5r8x9a4IvqOni+p7ok3s+Jd29IZXDwUXaJIEDON/aywI6DMOg6mPwCOJ68tK+0tZS397zN7zW/4+/gz8yxM5kWcvpRZoIg0Ll+PfXvzMZUWoo6KQm/115FN3z4aV2vV/e0CezPaWTPinIaKjtwcFMx9tooYkf5IlecfeE+hCjgIiLnCYdCFjigZYjHWMp1en6WZ+Lu3sb4pGys1nJcXSdg9X+G+ytM7Gk/wDBnHa/3tMB3GMy8vKqQxdvKT1xdciATVj4JVTvBLwWu/hwCh5x0fU3dTSzIWsD3xd+jlWt5dPCjXBd7XZ+izLp27KD+nXcwZOegDAvDf+4cHKdMGbAmHJtNoDSjnvSV5TTVdOHkqWHCjTFED/NBJj+3ZoGDKOAiIucFO9f/TPaiDUQ4haLyC2S9ohCjsp2haVUolTuQy70Jil7IJx1xfJzbiItCxpyYIK72se+sf8y0Bwk3dtqbcR6fdoxmnM4G2PAiZHwOOg+47F0YdP1JywINFgOf7/ucj/d+jNFi5Jroa7hn0D24qk9/ol93Vhb1s+eg37EDua8vvi+/hPPllyORD4xk2aw2inbXkb6ygtY6Pa4+WibfGkdkmhdS2bkn3IfoTSLPIuzZl/WCICT0POaGPYk+BCgHrhYEoWXgliki8vdEr+/ki0cex8nkzTC/sWSpDrJfnk1YWCNBwTuwWtsJCLiVPMfbuK+0iXpTIzf6ufPvMF9cFXLyD7bz/DJ7kPCg4416tZph14ew8XUwd8GI+2DcE6A+cYWITbDxa9mvzMmYQ52+jomBE3lo8EOEOoee9us1FBTQMHsOnRs3InN3x/vpp3G55mqkqoGpqbZabBRsP0jG6graGw24+zsw7c4EwlI8/xxCcQ7Sm19ni4H5wGdHPPYUsF4QhNclEslTPR8/2f/LExH5+/LLx7Op++0ACa5pdLhr+UW+F6WunbFpBQjsQ6dNQhXyEs/VaNhUU0eig4ZPEkJJddbR1m3mhZV5fL6jAie1nNevTOTqYyXjlG6AlU9BYyGET4Lpr4PnyVPWdx3cxVt73iK/OZ9493heH/M6aT5pp/1ajfv30zhvHu0rViJ1csLz4Ydxm3EDUt3pz0E5ERazlfytduHubDHiFezI6H9GEpLogeQ8EO5D9CZSbbNEIgk56uHLsOdkAnwKbEQUcBGRfsEesjALX2UAyf4j2a4qpUlWTmJSDS4u25HJVPiHvMT3lvG8m9eAStrFK5H+3OLvgUSAb/dU8caqApq6TNwwLIjHph4jSLh5P6x5Bgp+AddQuO4r+7jXk3jLZa1lzEqfxabqTfjqfHl9zOtcEHoBUsnp2QzmmhoaFiyg7adlSFQq3O+5G/fbbkPmdPKmoNO6n9FK3u81ZK6pRN9uwjfcmQkzYgiMcztngopPhdM1lLwFQTgIIAjCQYlEctzKeYlEchdwF0BQUNBp3k5E5O/Bkv8+gbVUTpr7GMp13fwiz8LLq5lx8ZlYrQfw8ryYKo/HuH5/B5WGeq70duWFnhT4vTVtPLdsLxmVraQGubD41qEk+B9lg5i64PdZsG0eSOX2HMoR9520LPDIA0qNXMPDgx/mhtgbTvuA0tLQQOP7H9D69df2JJwZN+B+113I3d1P63onw9RtIXdTNVnrqjB0mvGPdmXq7fH4Rbmcl8J9iAE/xBQE4QPgA4C0tDRhoO8nInI+krPDHrIQ5hiK2j+QDYoiTKpWhqWVoVBkoFQG4RTyKbMa/VmR30TEESnwrXoTz/60ly92VuCqVTLzqiT+kRrwR7tEEGDv97DmWeg4AEnX2MsCnfxOuK5uSzdL9i05fEB5dfTV3DPoHtzUbqf1Oq2trTR9vIjmJUsQTCZcrrwSj3/dO2BJOIYuMzkbqsj5rRqj3kJQvDtpF4bgGz6wbfZnitMV8DqJROLbs/v2Ber7c1EiIn8XTGYTnz3yEA5dngzxHUe2+iDlshwiIw7iF7ATQTDiH3Q/a2X/ZFZxE4LQztNhvtwT6IkcCV/tquTN1YW06k3cNCKEh6dE4aw5qqX7QJa9/b1yO/gOgn9+AkEnrqG2CTZ+KfuFuRlz++WA0trZRcvnn9H08SJsXV04XXQRnvf/H8rg4NO63snQt5vIXl9F7qZqzAYroYM8SLswBK/ggbFmzhanK+DLgZuB13v+XdZvKxIR+Zuw9ssPqFhRTKxLCp2+On6V56F1amZsSh4CpTg5DaPd93nuqhIo7GpgqrsTL0f6E6RRkVPdyrPL8siuamVIiCsvXjaMWN+jxOnIskCtO1wyB1JuBOmJG1H684DSZjTSsnQpTe9/gLWlBYfJk/C8/wHU0Sc/KD0dutqMZK6pJG9zDRaLjYjBXqRdEIK7v8OA3O9s05sywqXYDyw9JBJJNfA8duH+RiKR3A5UAv8cyEWKiPyVaGo4yHdPvIiPPJBB/qPYqSqlWV7OoEEVODjuQq5wxj1kFu93JPNNQQsBagWfJoYyzcOZ5i4T//4hh692V+HhoOKdawZxebL/H31ciwl2fQCb3gCz3u5xj30cNC4nXFd/HlAKZjOtP/xI44IFWOrq0I0ciedDDw5YhFlHs4HM1RXs60m/iRrizeALgnH1GZgqllPBZrVSumcnoalDkPfzTPLeVKFcd5xPTerXlYiI/A34+o3nMeSZGew2hnIHAyvkWfj61jMmOh2brRFv32vZpbubO8vb0FtbeSDIiwdDvFFLpCzZUcFbawrpMFi4fVQoD06OxFF9lCAcGSIcMQWmvwYekSdcU38eUApWK+0rVtAwbz7myko0KSn4vfkmumFDT/lavaG9sZv01RUUbDsIAkSP8GHw9GCcPbUDcr9TQd/eRu761WStXUFnUyMXP/Qk0SPG9Os9xE5MEZEzQH72Ln5/63NCHcLQ9BxSWjUtDB9chFy+F40mCkvguzx2wIms2mZGuTjwWlQAUTo16RUtPL98L3tr2hke5saLlyUQ5e34xxs0FsPqp6F4jT1E+PpvIWrqCdd09AFlXzooBUGgY906GufOxVhcgio2loD3FuIwbtyAVHm01ulJX1VO4c46JFKIG+VHyrQgnNwHNryhN9TtLyVz1c8UbN2E1WwmKGEQk269h7DBJx9HcKqIAi4iMoBYLRY+e+Ih1C3upHmPJ1tTS6U8h6ioKrx9diGRSPAMepovzFP4tLAZd6WJd2ODuNLblaYuE49/m8236dV4O6mYe10KlyT5/lEQDW32YIWd74FC26sQ4WMdUD48+GFCnENO+fUJgkDX1m00zJ6NYe9elKGh+L8zC8dp0wYke7L5QBd7VpZTsqcOqVxK4nh/UqYE4+A68Ok3J8JqsVCyezuZq36mpmAfcpWKhPGTSZ52MR6BA3NQC6KAi4gMGJuWL6Xkm3SiXRLp9HNghTwPB5dGxiTnIAhVuLlOJN/9KR6o7KbJ1Mwt/h48GeqDTirl023lvL22iG6TlbvHhfHAxEh0qiN+XG1We5TZ+hdB3wSpN8LEZ8HhxGEGOw/u5O09b5PfnE+Ce0KfDij16ek0vDMb/Z49KPz88H31VZwvvWRA5pX8IbZMKWPQ5CCSJweekdiyE6FvbyNn3Sqy166gs7kJZy9vxt14Ownjp6B2GPiDU1HARUT6mY62ZpY++jRekkCS/EexQ1VKm7KcpEGl6HQZKJXeyIPe58XGYLaVdJDsqGVJUhiDHLXsLm/m2Z/2UlDbwegID164NJ4Ir6OEoGI7rHoSDmZD4HCY8Z19auAJOPKA0k/nxxtj3mB66PTTOqDszsujYc4cujb/jszTA+9nn8Hln/9Equz/KLFjxpZNDkTjcOZiy45FXVmJ3SbZttlukyQmM/mOfxGakob0JFU+/Yko4CIi/cgPc1+lY08nyW6jqXAwskKehX/AARLC0xHoxNPvDn6SXssHpW1oZQbeiApghp87TR1GHv46ix8za/BzVrPwhlSmJ/j80S5pq4a1z9kbcpz84R8fQ8I/Ttj+3tjdyMKshf1yQGksLaVh7jw6Vq9G6uw8oKHBtWVt7P61nMq8JlRaOUMuDiVpwtmJLTuE1WKheNc2Mlf+zIGifBQqNQnjp5Ay/WLcA85Ol7ko4CIi/UB5US5rXvqAYF0oHv6RbFQUI+gaGZGaj0xWhIPjIGq8X+SJagnVhlau9nHl2XA/XGQyPtmyn9nrijFZbNw3IZz7JkSgVR7xo2nS21vft7wDCDDuSRj1ICiPXyLXbem2j3jN/RiT1dSnA0pTdTWN89+lbflypGo1Hv/6F2633oLM0fHkX3yK1BTZY8uqC1pQ686N2DJ9W+v/bJKWZpy9fRh/0x3Ej5+MWnd268tFARcR6QM2q5XPn34MRb0Tqd5jydHUUSXPISZmP55ee5DJ1GgCX2Nex2DWlHQQrVPzY0owI1wc2F7axPPL91JU18n4aE+evySeUI8jRFkQIO9H+667rQrir4ApL4LL8Xd7/XlAaa6rp+n992j59jskUiluN9+M+513IHc7vTb64yEIAtUFduE+UNyKxknJyCsjiB/rh1J99iSqtrSYzFU/U7htM1aLheCkFKbcdT+hyYMH5ID2dBAFXETkNNm17mdyFm8kyjmaLj9HVsnzcXavZUxiFoJQh6vnZfym+T/mVXYAXTwb7sddAZ40dRi5f2kmP2cfIMBVwwc3DmZKnPcf7ZKDOfb294qt4J0IV7wHIaNPuJ6dB3fy1p63KGgu6NMBpaWlhaaPPqJlyRcIVisuV/0Dj3vvReHtfcrXOhGCIFCxt4k9K8qp29+OzkXF6KsjiR/th1x5dmLLrBYLxTu3krHqZw4WFaBQa0icNI3kaRfj7n/6QcwDhSjgIiKniF7fyZKHH8fTGkCi/0h2qvbTodpP8qAiNNoc1OpgWvyW8ECtC8UN7Vzk6cyLEf54yuUs+r2MueuLMdsEHpwUyb3jw1EfmbHY1QgbXoL0T0HjChe/A6k3n7D9vbS1lFnps9hcvblPB5TWzk6aF39K8yefYNPrcb70Ejz+7/9QBvavcB2dN+nopmbc9dHEjvBFpjg7O9uu1hZy1q8ie+1KulqacfHxZcLNdxI/fjIq7dnv5jweooCLiJwCv3w0h8ZNtQxyG0GFo4mVshyCQypICMkATDgGPMoi43R+LGsnWC2wJCmMye5ObClu5Kbleylt6GJyrBfPXRxPkPsR3YJHp+IMv9eeiqM5vmfd2N14uINSJ9fxyOBHuD72+lM+oLR1d9Py5Zc0ffgR1tZWHKdOxfOB+1FFRJzmu3Sc+xydN+mhtudNDvdBdpZiy2pLiuw2yfbfsVoshCQPZurd9xM66NyxSU6EKOAiIr3gYHUZv/znLQI14YQGpLBRUYLUsZZRKXuRSitwdBrBHtdnmFVtwmjr4JEQb+4P8qalw8i/vkhnRW4twe5aFt2SxsSYo6yIknWw6t/QWAThE3tScaKPu5ajDyivjb72tA4oBZOJlu++o2nhe1gaGtCNGYPngw+iSYg/nbfouNisNor32IW7pVaPi7eWybfEEjnE+6zkTVotZop2bCVz1c8cLC5EodaQNPkCkqddhJtfwBlfT18QBVxE5CQsef5JpBUqkj3GkaOtp0aRTWxsCW7umSgUzhgD5vHvxnD2VnQz1tXeAh+gVPDR5jLmbyhBQODRKVHcOTbsj3ZJU6m9/b1oFbiFwXVfQ9S045YFWm1WlpcuZ37WfOr19UwKmsRDqQ+d8gGlYLXStvxnGufPx1xTg2bwYPzfmYU27fQj0Y65XquNop21pK+soK2hGzc/HVPviCc81eus5E12tbaQvXYlOetW0tXagquvHxNuuZv4cZNQac/+7JTTQRRwEZHjkLN9A7sW/EyEUyR6fydWyQtx96pidGwWAs04+NzIt5IZfFHeiZfSzPvxwVzq6cKmogbu+Hkf+xu7mBbvzbMXxxHgeoRAGNph80zYsRDkantlybB7TpiKs61mG2+nv01RSxGJHom8OfZNBnsPPqXXI9hsdKxZS8PcuZjKylDHxeHzwvPoRo/u13klVrON/J6g4I4mAx6BDlxwdyKhg85O3uTBkkIyV/5M4fYt2KwWQpMHk3LBpYQkpZwXNsmJEAVcROQojCYjnz/yMK4Gf+L9RrFLtR+9pozUQfmo1QVotdHke73PWweUtJg7uTPAk8dDfWhtN3L35+ms2VdHmIeOT28byrgoz/9d2GaDrC9g/X+hqwGSZ8Ck58Dx+NUdhc2FzEqfxbYD2/B38Gfm2JlMC5l2SoIrCAJdv/9O/ezZGPflowwPx3/OHBynTulX4baYrOzbepDMNT1BwSFOjL02iuAE9zMeW2a1mCnavoWMVT9TW1KEUqNh0NQLSJ56MW5+/md0LQOJKOAiIkewdumH1KwoJd5tGJU+JtbIcwgNKyMxMAOJRIrg/wKvdQxjZ4WeNCclXw8KI0Kt4v2NZSzYWIJUIuGJ6dHcPjoUlfwIu6RyJ6x8Ag5mQcBQuP5r8D/+Drq2q5b5mfNZXrocR6Ujj6c9zrUx16KUnVoLedeuXTTMnkN3RgaKgAB8X38N50suQSLrvzI9Y7eFvZuqyV5fRXeHGd8IZybcGENg7JkPCu5saSZn3Uqy165E39aKq18AE2+12yRKzflpk5wIUcBFRLCHLHz/xEsEKEMJCUhhs7IEhfMBRiblIJUeQOs2jV9VD7KophsnmZFZ0YFc6+vGbwX1/OvnnVQ267koyZf/XBiLn8sRreVtNbDuecj9Fhx94coPIfGfx/W5O02dLNq7iM/3fY5VsHJz/M3ckXgHzqpTy3Dszs2lYfYcurZuRe7lhc8Lz+Ny5ZVI+nFeib7dRM6GKnI31WDqthAY58bgacFnJSj4YHEhGSuXU7RjKzablbCUNFKmXUzwX8AmORF9EnCJRPIwcAcgALnArYIgGPpjYSIiZ4qvX38eS4FAsvtYcrQN1KqyiIstwNk1B6XSh2qfRcys9+BAczfX+7rxnzA/OjuM3PnpHtYX1BPh5cAXdwxjVITH/y5q7oZt82HLLPvkwLGPw6iHQHXs1muzzcz3Rd+zMHshzYZmLgy9kAdSH8Df4dT+3DcWF9Mwdy4da9chc3HB64kncL3+OqRqdR/eoT/S3tRN1toq8rcewGKxEZ7iSeq04DOeN2kxmyna/juZq36mtrQYpUZL8rSLSJ52Ea4+Jw5r/qtw2gIukUj8gQeAOEEQuiUSyTfAtcDiflqbiMiAUpCziy1vfkG4cxR6fydWywvx8i1nRFQW0IXM934WGC/ktyo9cToZ78eHkKBRs3BjCe9tLkMhlfD0hTHcMjIUpbxnlycIkL8c1jwDrZUQeylMfQlcQ465BkEQ2FC1gdnpsylvLyfNO40FkxYQ73FqpXymykoa5s+n/edfkOp0eNz/f7jdfDOyfhxp2nywi8zVFRTtqgMgargPqVODznhsmb2aZMVhm8TNL4BJt91L3LiJKNVnP9DhTNJXC0UOaCQSiRnQAgf6viQRkYHFarHw6eMP49zuQ7z/aHapyjHoSkhLykWp2o/acTCbnJ7l/VobcomBFyP8uNXPgw0F9Uz+eSc1rd1cluzH0xfG4u10xM62dq+9/b38d/CKh5uWQ9i4464jpyGHt/e8TUZ9BqHOocybOI9xAaeWYGOuraVx4Xu0fv89Erkc99tvw+3225G7nvrQquNRX9FO+qoKyrIakMulJIz3J3lyEI5u/ber7w21pcVkrFxO4bbf/2eTTL/EbpOcYcvmXOG0BVwQhBqJRPIW9lDjbmCNIAhrjn6eRCK5C7gLICjo7IxcFBE5xO/Ll1LybSYxbqlU+lpYo8ghIrwIH78s5HINDT6zmNUcSdkBE5d5ufBChB/d7Sbu+HQPm4oaiPZ25Ku7hjM8zP1/F+1qgt9ehvTFoHaGi96G1FtAduwfr6qOKuZmzGVV+Src1e48O/xZroy8Erm09z+OluZmmt7/gJalSxEEAderr8b9nrtReJ040KG3CIJATVEr6SvtkwFVWjlpF4SQNCEAjeOZm8V9aIRrxsrlHCwqOFxNkjL9kvPCJhFsAsbSVrp21+JyURiyfg6g6IuF4gpcBoQCrcC3EolkhiAIS458niAIHwAfAKSlpQmnv1QRkdOno62ZpY/8B395KMEBqWxWlqJxrWBUYg4SSQMyj2v5nJv4paabUA18NSiMoQ5a5m8o4aPf96OSS3nu4jhuHBGM4lD3oNUMuz+Cja+BsROG3AnjnwLtsaf1tRnbeD/nfZYWLEUhVXB30t3cmnArOkXvLQhreztNn3xC86efIRgMOF92GR733YcyoH9K4w7NKclYXUHd/nY0TkpGXBlOwhj/MzrS9XAg8Jpf6Wxuss8mueUu4sdNPi+abqztJrrS6+jaXYu12YBUK8dcrz93BByYDOwXBKEBQCKR/ACMBJac8KtERM4wP8x9DX16N0keY8jVNlKvziQ+Pg9Hp3yU6lD2uM9hfp0Gi2DgiVAf7g3wZMO+eib9upuDbQauTPXnqQti8HI8wjIoXmvvomwsgrAJ9vR3r9hj3t9oNfJVwVe8n/M+XeYuLo+4nPuS78NL2/vdsk2vp3nJFzR9/DG2tjYcp0+3zysJC+vr22O/fk+7e8bqCpoP2OeUjLs+mpgRPsgVZ24yYEPFfjJW/kzBlo1YzCaCk1KYfMd9hKWknfPVJIJNwFDcQteuWgz5TWADVZgzzlOD0cR7IBmAQV19EfBKYLhEItFit1AmAXv6ZVUiIv1AeVEe615+nzCHKBQB/qyVF+EbUMLw8GwkEjMd3s8wu3M4+QeMTHTT8WpUAJYOE3cs3s3WkiZifZ2Yd10KaSFH7KgbiuzCXbK2p/39K4iafsyyQJtgY9X+VczNnEtNZw2j/Ufz8OCHiXKN6vVrsJlMtH79DY3vv4+1sRGHcePwfPAB1HFx/fEWYTFZyd92kMy1lXQ0GXDz0zH51jgi07zO2JwSm81KafouMlcsp2pfLnKlirhxE0m94NKzlnRzKpgOdKLPrEef1YCtw4RUp8BhdAC6Id4oPAf2r4W+eOA7JRLJd0AGYAEy6bFKRETOJodCFnSNbsT6jGK3ugKzYxFDEnNQKKuQOk/kR9VDfFNnxk9l4+OEEMY66pi3oYRFW/ajVcp48bJ4rh8ahPyQiHW32NPfd33Qq/T33bW7eXvP2+Q15RHjFsOHUz9kuO/wXr8GwWKh7aefaFiwAMuBg2iHDMFz7ly0qSfOvuwtpm4LezfXkLW+iu52E96hToy5OpKQxDPX7m7o6mTvhjVkrv6V9oY6HD08GXvDrSRMnIrGof/TfvoTa5sRfVYD+sw6zLV6kEpQR7uiTfVCE+uORH5mfvlJBOHM2dJpaWnCnj3iJl1k4Ni5djn7PttMpFssVY5WSlQVREbk4+mdi0zuSp7nq8xv9KbDauWuAC8eCfZifV4dr67Ip67dyDVpgTwxPRp3hx6v0mqBjE9hw8t2EU+9qSf93fOY9y9rK+Od9HfYWLURb603D6Q+wMVhF/d6Nrdgs9G+ciWN8+ZjKi9HnZiI50MPohs5sl8qLbo7TGRvqCJ349lrvmmqqSJz1S/s27Qes9FAQGwCqRdcSnjaMKT92CHa39iMVrr3NqLPrMdY2goCKIMc0aZ4oUnyRDaAeZ0SiSRdEIQ/TRsTOzFF/hJ06zv54uEn8BFCCAoYzBZlKQ4eZYyIy0YiaaXb8x4WGi8mvdbIcGc1r0UFIO20cPui3ezc30yivzPvzRhMStAR5Xdlm+xjXuvzIHiU3ef2HXTM+x8dHvxg6oPMiJ2BWt67UjtBEOj8bSMNc+ZgLCxEFRlJwPx5OEya1C/C2tFsIHNtJflbeppvkj1JnX7mmm8Em43y7AwyVi6nPDsDmVxOzKjxpFxwCd6h4WdkDaeDYLVXkegz6ujOa0Iw25C5qXGcGIQ2xQuFx9mtOxcFXOS855cP59C6pZE4j1Hs1TbSpNtDfFwuOodSpLpBrHL4iM/rJbgqrMyJCWK6iwNz1pXw6fZyHNVyXrkigWuHBCE7ZB00l8GaZ6HgF3v+5D8/hbjLjulzd1u6+SzvMxbtXXQ4PPjuQXfjpu59bmTXjh00vDOb7uxsFEFB+M2cidOFF/TLvJKW2i4yVldQtPPsNN+YuvXkbVpP5qpfaDlYg87VjVFXzyBp8nS0zi5nZA2niiAImA902X3t7HpsHWYkajnaVC+0KV4og53OmbpzUcBFzlsOVpXx87OzCNNG4xQQwXpFIQHBBQwNzkUqkVPmPZO5LVHU11u40c+dp0J92Li3jkkfZ9DUZeS6oUE8PjUaV12Pj23sgM1vwY4FIFXYrZIR/weKP++iD8/mzpxPfXc9k4Mm89Dghwh2Cu71+ruzs6mfPRv99h3Ivb3xefG/uFxxBRJF3/8Ur69oJ2NVBaWHmm/G+ZM85cw137TW1ZK1+mdyN6zF1K3HJyKKC+9/jKjho5DJB85q6AuWNqNdtDPrsdTpQSZBHe2GLtULdYzbGfO1TwVRwEXOS5Y89ySqagfivEazW12J4LqPofFZyOX1GN2u5hPbDH6vM5PkoOCTxFDUnVbu+ng3eypaSA50YdEtaSQFuNgvZrNB9pew/kXorINB18Gk58HJ90/3FQSBrQe2Mit9FsUtxSR5JvHW+LdI8er94aKhoICGufPo3LABmZsb3v9+Cpdrr0Wq6luN8KHmm4xV5VTlt6DUyBk8PZhBEwPPSPONIAhU5eWQsXI5pem7kEqlRA0fTeoFl+IbefyEobOJzWihO7cJfWYdxrK2w762y+XhaBIH1tfuD0QBFzmvyN72G+kLVxDhGk21n5WN6iyio3Jx8yhAogphs8sSFjVoUUmtvBrpzxVuzsxeW8TnOypw0Sp58x9JXDU44H+JMJU7YOWTPWNeh8C1SyHg2GNeC5oLeHvP2+w4uINAx0DeHvc2U4J7P1PbWFJCw7z5dKxejdTREc+HHsTtxhuR6vpmZwg2gfLcRtJXHdF8c0U4CWPPTPON2Wggf8tGMlf+TGNVBRpHJ4ZfcTVJUy7A0c3j5Bc4wwhWAUNJC/qMegz7/uhr61K8kJ9lX/tUEAVc5LzAHrLwCF6mYIICktmq3I+rTwHDo3KQSo3UeD7DvI6hVNab+Ye3C8+G+rJ5bx2TP8miRW9ixvBgHp0SjbO2Z0fVWmUf87r3e3D0O+GY19quWuZlzuPn0p9xUjnx5JAnuSb6GhSy3u3OjPv30/juAtp//RWpRoP7vffgfsstyJxPbUTs0RzdfOPormbcdVHEjPBFrhz4ao72xgay1/xKzvrVGDo78AwOZdo9DxIzahzyfhxb2x8c9rUz6tBnN2DrNCPR9Pjaqd4ogxzPGV/7VBAFXOScZ82XH1K3qpxYz+HsdW2mzXEXiXFZaLTVmBwnsFT+IGsarERqpXyXHI5zl417P9lNZmUrg4Nd+eyyocT79YilqQu2zoGtcwEBxj0Jox4E5Z93wZ2mTj7e+zGf7/scQRC4JeEW7ki8Aydl7yo3TNXVNL67gLZly5CoVP02aOpsNt8IgsCBwnwyVi6neNc2ECBiyHBSL7gU/9j4c04ELa1G9Fn16DPqsdT3+NoxbuhSzl1f+1QQBVzknKWp/iA/PPkKIeoonAIj+U1ZSHDoXiIC9oHMhV3u7/FhkzcCNp4O8+Vad2fmrC3my12VuOtUvP3PQVyZ6m8XFUGA3O/su+72Goi/Eqb8115lchRmm5nvir5jYdZCWowtXBx2Mfen3I+fQ++GJ5kPHrRPCPzhByRSKW433oj7nXcg9+ibnXB08o1PmBNjrokiJMF9wJtvLGYzhds2k7nqZ+rKSlDpdAy+6HKSp16Es9fxI+HOBjaDxV6vnVGPcX+Prx3shMvlEWiTPJBqz21f+1QQBVzknOTr155DUqwg2msk6apqZB45DI3NRCZrpd79Xyw0TKeowcw0DwdeCPNj+956pn26mXaDhVtGhvDwlCic1D0/qDXpsPIpqN5lr+P+x8cQPOJP9xQEgQ2VG5idYZ/NPdRnKI+kPUK8e+9mc5vr6ml6/31av/0WAewTAu++G4V33yYE6ttNZK+vYu+makwGK0HxbqROC8YvcuCbb/40e9s/kMl3/Iu4MRNR9GNIRF8RrPY5JPrMerrzmsBiQ+auxmmSvV5b7n7++NqngijgIucUBdm72PbWV4S7xlDtL7BFm0lMdBbOLmWYtSl8p36a5c1SAtTwaWIo3t0CD36yh5zqNoaGuvHiZfHE+PRYHO0H7ZUl2V+CzgsunQ/JN8AxhiJl1Wfx9p63yWrIItw5nHcnvcsY/zG9EkhLUxNNH3xIy1dfIVituFxxBR733oPCr2/jTtsbu8lcU0n+toNYrTYiUr1InRaMZ9DAt5n/Yfa21UJY6hBSLriU4MTkc8YmEQQBc03PHJIeX1uqlaNL87bXa5+nvvapIAq4yDmB1WLh08cewb3Ln4CAwWxTluIRkMfQsFwkUikZ7m/zYWsY3QaBB4O9uNHDhflri/l6TxWeDirmXJvMpYP87D+wZgNsnw+/zwKb2e5xj3kM1H/2rsvaypiTPocNVRvw1HjywogXuCzisl7N5ra0tNC8aBHNS75AMBpxvvRSPO77F8rAwD69F001nWSsrqB4Tz0SKcQM9yVlShAu3gM7GOno2dsKdc/s7WkX4+p77iS5W1oN6DPtc0gs9d0gk6CJdUOb4oU6+vz3tU8FUcBFzjqbly2l4vtcojzS2OvbTJfzNgbFZaBS1VPvfC0fWa4lr8nKWFctL0X4syu3jos+/50uo4U7RofywKRIHNUKu8+9b9n/4sxiLrbHmbn9eeRqY3cjC7IW8EPxD6jlau5PuZ8ZsTPQKk4uktb2dpoXL6b508+w6fU4XXghHvfdhyostE/vw8HSNjJWlVOe24RcJWPQxAAGTQrCwbV/Z0gfzZ9mb3v7MuHmO4kfP+Wcmb1tM1jozu2ZQ1LWBvT42lf4o038a/nap4Io4CJnjY62Zr5+5FmClFE4BkaySV1AWHgWkT5FmBSh/OD4Nt+3aPFRSnk/PoAAvcAji/aQd6CdkeHu/PfSeCK9e+yE2lz73JLy38ErDm5aBmHj/3TPLnMXn+Z9yuK8xZit5lNqfbd2dtHy+Wc0fbIYW3s7jlOn4vF/96GO6v142KMRBIHKvGbSV5VzsKQNtU7B0EtCSRwfgHqAm0iOnr0dlJjM5DvuIzRlMFLp2R8qJVhtGIp75pDsawaLDbm7GqfJf21f+1QQBVzkrPDDnFcxZ5mJ8BxJhroKlU86Q6OyQGok2+05PuxIpr3Vxt2Bntzq4cq8NcV8n1GNj5Oa+dencFGir90u6WqEDS9BxmegdjlunJnZZuaHoh9YkL2AZkMz00Km8UDKAwQ5nXzetE2vp+XLL2n66GOsra04TJyI5/3/hzr22AEOvcFmEyjNsNdwN1Z14uCqYvQ/I4kb7YdCNXDieczZ22MnknLBJXgE9n4MwEAhCALm6iN87a4eX3tIj68d+Nf3tU8FUcBFzigVhftY/+oHhDrFUhMgsNNhDzEx6Tg41tCom8YnkrvJahEY7qzh5Qg/dufWc8mS3zGYrdw7Ppz/mxCBTiUHi8k+m3vTm2Duss/mHv8kaP5YYy0IAusr1zMnY87h1Pf5E+eT6Jl40rXaDAZavvqKpg8/wtrUhG7MGDwfuB9N4sm/9nhYzTYKdhwkY00l7Q3duPpomXhTLFFDvZENoHerb29j729ryV670j57292TMdffQuKkaefE7G1Li+F/c0gaenztOHe7rx3let772h0dHWi1WmT9PC5XFHCRM4LNauXz/zyGc5M3/n5p7FCX4BuURVrQPkxyD5Y7fcC3re64KWTMjfUjSC/w2KJ0Cmo7GBPpwQuXxhPu6WD3uQtXwZr/QFMJREyBaa+C559tjIy6DGalzyK7IZsIl4heV5bYTCZav/2Wpvc/wFJfj3b4cDznzUWbmnrar99ksJC3+QBZ6yvRt5nwCnZk5N0JhA3yHLAabkEQOFhcQNaaFRRt/x2rxUJgXCLjZtxKxJARZ332ts1goTunka7MOkz72wFQhjjhMsYfbaIn0jOYwTkQtLa2kp+fT35+PpWVldx8882EhvbtnORo+vQOSSQSF+AjIAEQgNsEQdjeD+sS+Quxc+1yCpdsI8R9EPt8WzC6byYlOh25spVc54f5uHssja02bvH34DZ3F95dW8wTWQfwd9Hw3ozBTIv3totufYE9zqx0PbhHwvXfQtTUP92vrK2M2emz+a3qN7w0Xrw48kUuDb8U2Ul8XcFspvXHH2l87z0sBw6iGTwYv5kz0Q0betqvvbvDRM5v1eRurMaotxAQ48rkW+MIiHYdMCvAbDCQv3UjWWtW0FBehlKjIXHSdJKnXnjWI8oEqw1DYU+9dn4TWATkHhqcpgTbfe0zNC1xoGhubmbfvn3s27ePAwcOAODt7c348eNx7WMH7rHo66+4OcAqQRCukkgkSuDcOLIWOSewhyw8hb8kAseAKLZr8wmLTMfdYz8NquF8oXiE3e0yUhzVLE7wIyOnnsuXbMFsFbh/YgT/Gh+BRikDfTNsfN2eAK90gGmvwdA74ahZJA36BhZkL+DH4h9Ry9U8kPIAM+JmoJGf+LBLsFho+/kXGhcswFxVhTopCd8XX0I36vRTcDqaDWStrWTflgNYzDbCegIUvEMGLkChqaaK7LUr2LdpA0Z9F55BIUy+4z5ix4xHqT57B36HfO2ujDq6cxqwdVmQ6uTohvigS/VGEeBwXvvaDQ0Nh0W7rs4+d93Pz49JkyYRFxeHu7v7gN37tAVcIpE4AWOBWwAEQTABpv5Zlsj5zq8fzqFjWwvhnsPIVFejC9hBWlguJpmCFU6z+bojEAebjJnRvoTp4alF6RTXdzIxxovnLo4jxENnjzPb9SH89goY2mDwLTDhP6D7Y0t6l7mLxXmL+TTvU8w2M9fGXMtdSXedtLJEsFppX7mKxvn2+DJVXCwBCxfgMH78aQtK88EuMldXULSrJ0BhmDcpU4Nx8x2YAAWrxULpnh1kr11B5d4cpDI5UcNHkTz1IvyiY8+qMP7J15ZL0MT2+NrRrkjOUGhyfyMIArW1teTn57Nv3z4aGxsBCAwMZNq0acTGxuLi4nJG1tKXHXgY0AB8IpFIBgHpwIOCIHQd+SSJRHIXcBdAUNC5nzAt0jcOVpWx4tk5hDjG0hzgTbrzLqKjd6PRNbBPdyufWC7hYLvA9b5u3OHuxrtri3g25yCBbho+uimNyXE9czVKN8Cqp6EhH0LH2nfdPgl/uJfZZub7ou9ZmL2QZkMz00Om80DKAwQ6nbiRRrDZ6Fi7jsb58zAWl6CKjMR/3lwcJ08+bcGr299OxuoKyrLPTIBCe2MDuRtWk7thDV0tzTh6eDL62ptInDj1rCbdHKrX7sqox7S/p1471AnXMQFoEj3OW19bEARqamoOi3ZLSwsSiYTg4GCGDh1KTEwMTk5nJp7uSE471FgikaQBO4BRPQn1c4B2QRCePd7XiKHGf22WPPckugOuqD39yNGU4Be6Bz+/IpoUcSxVP832LjXxDmpeCvcnO6eeeRuKsdoE/jU+grvHhaFWyKCx2N6IU7QKXENg6isQc9EfxrwKgsDairXMzZxLRXsFQ3yG8MjgR0jwSDj+4jiUO/kbDfPmY8zPRxkWhuf/3Yfj9OlIjtFefzIEQaC6oIX0VRXUFLag0spJHB9A0sQANA79P05VsNkoz8kke+0KytJ3IyAQlpJG0uQLzmrt9pHztQ/NIZF7aNCmeJ3XvrbNZqOqquqwaLe3tyOVSgkNDSUuLo7o6GgcHBzOyFoGItS4GqgWBGFnz8ffAU/14Xoi5ylZWzeQ8/5qgtxj2OfXgs17A8mRGdjkFlY7vcxXnTEoDVJejvQlvEvgmU/SKWvoYkqcN89dHEegm9ae+L5+Jux6H+QamPIiDLsH5H/sQkyvS2dW+ixyGnJ6XVkiCAJdW7bQMHcehtxce+7kG6/jdPHFp5U7KdgEyrIbyFhVQX1FB1pnJSOvjCB+rB9Kdf/vMA+VAOasW0lbfR1aZxeGXPYPkiZNP2uTAAVBwHywC31GPfqs+j/OIUk9f+u1rVYrFRUV7Nu3j4KCAjo7O5HJZISHhzNx4kSio6PRaM6dBqLT/m4TBKFWIpFUSSSSaEEQCoFJwL7+W5rIuY7RZGTJw4/iaw1HFxjFToc8IqJ24eJaQ4H6Cj4RZlDdAVd6u3KnmwsL1xTz8r46Qty1fHLrECZEex3hc79qF/HBN9t9boc/TvAray3jnYx32Fi1ES9t7ytLunbsoGHuPLozMlD4+eH78ks4X3bZaeVOWi02inbVkrG6ktY6Pc6eGsbfEE3McF9kiv71cwVBoKYgj+y1KyneuRWrxUJAXAKjr7uZyKEjzlqupLXdeHgOibn2rzFf22KxsH///sOi3d3djUKhICIigri4OKKiolD1Me5uoOjrduF+4IueCpQy4Na+L0nkfGDtFx/StLaGEM+hZGurcA7+ncHBebRI/fhAt4hNXc5EalV8EelLdnY91365DalEwhPTo7l9dCgquQxK1tvLAhsKIGQMTH8NfP7YJFOvr2dB1gJ+LPkRrVzLg6kPckPsDSetLNGnp9MwZy76XbvsgcEvPI/LlVciOY2kGLPRyr4tB8haV0lnixGPQAem3hFPeKrX/6LZ+gmjXs++3zeQs3YljVUVqLQ6kqZcwKDJF5y1EkCbyYohr4mujDqMJa3nXW7ksTCbzZSWlrJv3z4KCwsxGo0olUqio6OJjY0lIiIC5TmWKnQs+iTggiBkAX/yZUT+ujQ31vLj468S7BCLKdCLHPetxETtQaruZL3ucZYa0pB0S/lPqBch7TaeW5RBTWs3lwzy4+kLY/B11th97tX/geLV4BoK13zxJ5+709TJJ3mf8Pm+zzHbzFwfcz13Jd2Fq/rEtbTdOTk0zJlL19atyDw88H76aVyuufq0AoO7O03k/lZN7sYaDF1m/CJdGD8jhqA4t363B+r2l5K9dgUFWzZhNhrwDotg6t0PEDNy7FmZuy3YBIxlrXZfe28TgsmKzFWF44RAtCleKDzPv4rh1tZWiouLKSoqYv/+/VgsFtRqNbGxscTGxhIWFobiNP4yO5ucn0fCImeFr199DkWZBl+fwWToCgkM302SdxmFykl8Jrmbcr2MizydudXZmfdWF/N2SSMxPo58dddwhoe52+u5V/4Xdn8ICi1MeQmG3f0Hn9tsNfNt0be8n/M+zYZmLgi5gPtT7yfQ8cSVJYZ9++xJ7xs3InN1xevxx3G9/jqkp+FXtjd2k7Wuivyt9hrukCQPUqcF4xvetwzLozGbjBRt30L2mhUcLClErlQRM2osg6ZciE94ZL/eq9drqus6XPpnbTMhUcnQDvK0zyEJcRrw5J/+xGq1UlVVdVi0GxoaAHB1dSU1NZWoqChCQ0P7vb39TCIKuMhJKczaxY53viPIPYZ8vxak/qtJCcumTe7IIu0C1uu9CdEo+Tjah8yMWm7Zlo9WKeO/l8Zzw7Ag5Nj+WM+desjn9jx8D0EQWFOxhrkZc6nsqLSn4Qx+hHiPE6fhGAoLaZz/Lh1r1yJ1dsbzoYdwnTEDmcOp1103VneQsbqSkvR6JBKIGuZDyuQg3Pz6t4a7+UANOetWkLdxPYauTtz8Aphw853EjZ2E+gxVNRyJtdOEPrsBfUY95ppOkII60hXnC8PQxLkhUZw/AtfZ2UlJSQnFxcWUlJRgNBqRSqUEBweTkpJCVFQU7u7u5+UB67EQBVzkuFgtFj577FG8jCHoAqJId84iMmoXaqdmNmnuYalpIhaDhMeCvfBvMvP8ogyauoxckxbI49OicXdQQck6u11y2Od+/U/13Htq9/BO+jvkNNorSxZMWsBo/9En/CEzFBbR+O67dKxZg9TBAY//+z/cbr4JmeOpDWYSBIGaolYyV1dQua8ZxeE53IE4uPafdfHnhhsZEUNHkjzlAgLiEs+4oAhmG935Tegz6jEUtYBNQOGnw/niMLSDPJE5nvv+L9hL/Q4ePEhxcTHFxcXU1NQA4ODgQFxcHJGRkYSFhaE+h+Lf+hNRwEWOyaafllLzUz6BXqnkuFbiHrqeZP9CSuRD+EIxl2KDkkluTtzk4Mj7q4rIqGwlOdCFRbekkRTgAg1FsOw/ULzG7nNf+yVEX/gHn7u0tZTZ6bPZWN37yhJDURGN7y6gY/VqpDodHv+6F7ebb0bmfGr2hs1qozSjgcy1lTRUdqBxVDDssjASxvr36xzu4zXcJEyYgs6l/2djnAhBEDBVtNtL/3IaEAxWpE5KHMb4o0vxQuEzMN2i/Y3BYKC0tPTwLruzsxMAf39/JkyYQGRkJD4+PkhPo7b/fEMUcJE/0NHWwjePPk+QJgZzoDf5XpuIi0xHr5TyuXYmq7tD8EfBvDAvMtIPcs/ufNx1SmZelcQ/UgOQGlpg5ZP2uSUKLUx9GYbe9Qef+1iVJTNiZ6CWH3+XZCwupuHdBXSsWoVUp8P93ntwv/lmZKfYsmwyWMjfepDs9VV0NBtw8dYy/oZooof5IFf2j1VwrIab0OTBDLrz/85Kw42lqZuujB5fu9mARCFFk+CBNtULVbjLOe9rC4JAY2MjRUVFFBcXU1lZic1mQ61WExERQWRkJBEREeh058cvoP5EFHCRw/zwzmsIewV8vFLJdtxHcORO/NwP8LtyBl/ZLqXbIOW+AA986o28vDiTTqOF20aF8uDkSJwUwO4P7PXcxvZj+tydpk4W7V3E5/s+xyJYelVZYiwupmHBAjpWrUaq0eB+z92433LLKQt3V6uRnN+qyfu9BqPegm+EM2OuiSQk0aPfBOxcarix6c3ocxvRZ9RjqmgHCajCXXCaHIQm3gPpAIZG9Adms5ny8vLDot3a2gqAl5cXI0eOJDIykoCAgPP6ALI/EAVchIqifWx8dRH+rrEUBjSjCvyFQSG5VMiieVf1KgVGHaNdHLhOpePDX4spqO1gVIQ7L1zSE2lWvM5ez91YCKHj7PO5j/C5D1WWvJf9Hi3GFi4IvYD7U05cWWIsKaFxwQLaV66yC/fdd+F2883IT3EkZ1NNJ1nrKinaVYdgEwhL8SR5ShA+of1TUSIIAjWF+8hes+KsN9wIFhuGohZ7BFl+M1gF5F5anKaH2Fvanc/NZpRDHKvMT6FQEBoayujRo4mIiDhjQ6LOF0QB/xsj2Gx8/u/HcWvzR+sfQ65bOhFRO7HpLCxVP8dKYzxegpzXgzzI2HWAR7ML8XfRsOCGVC5I8EHSWARL/gMla+3BwdcuhegLDvvcNsHGmvI1zM2cS1VHFcN8hvFw2sPEux+/ssRYWkrjuwtoX7nSLtx33YXbLacm3HZRbSFzbSWVec3IlVLix/gzaFIAzv1Uv3yuNNz8YVRrdgM2vQWpToHDcF+0qd4o/HTnbMVFb8r8goODz7va7DOJKOB/U3avXU7xkt34eCey138/3uGrSPApY5v8cr7ietpNUm73dce9xsCbn2ZhsQk8MDGCe8dHoLG0/c/nVjrYB04NvQvk/6tc2HlwJ7PSZ7GvaR+RrpEnrSwxlpbSuGAh7StWINFocL/zTtxuveWUhNtqtVGaXk/m2koaqzrtB5OXhpIwNgC1Q/+IQN3+UnLWriR/y8az2nBjae0Z1ZpxxKjWOHe0qd6oI13O2VGtXV1dhytG/g5lfgONKOB/Mwzder586Cn8FNEYg7wp9VtHfHgmNXI/3lR9SK7JlaHOOv4hVbP4l1L2N9qHTj17URxBLgp7E87G1+w+9zHmcxc2F/JO+jtsPbAVX50vr45+lQtDLzxuZYmxrMwu3L/+ahfuO+7A7bZbT0m4TQYL+7YcIHtDFZ3NRly8tUyYEUPUMG/k/VDDfNyGm8kX4BNx+on0p4q1w0R3biP67Aa7r409guxcHtX6dy/zA+jurqaldQeeHpNQKPq38ujc+x8XGTB++WAuhp0deHqlsM8ll5Co7Xg4d/C98kF+MQ/FVZDzrI8rGbsO8Hx+PWEeOhbfOoTx0V5QvBaWPg2NRRA23u5ze//PCqnprGF+5nx+LfsVR6Ujj6U9xrUx16KSHdt3NZbtp3Fhj3Cr1bjfcTtut96K3O3EIQxH0tliJOe3KvJ+P4Cp24JfpAtjr40mJMG9Xw4mz4WGG2uXme68RrpzGjGWtoIAcm/tOR1BZjAYKCsro6io6G9Z5ndIsFtbdtLSuhODwf5LS57wLl5e0/v1XqKA/w2oqypn5XNz8XeJpSJQjy7kRxID89klm8xS6e00meVc7+mKa5WeOatyUEgl/PuCGG4dFYqyuQiW3GtvyHELh+u+gqjph33uFkMLH+Z+yFcFXyGVSLk14VZuT7wdJ+Wxh9sb9/cI9y+/IlGpcL/tVtxuu+2UhLupppOstZUU7bYfTIanepE8OQjv0L4P1LdaLJSm7yR7zQoq92aflYYbm8FCd14T3TkNGIpbwWbPjXScEIh2kCcK73OrXO5Qmd8hL/vIMr/w8HCioqL+0mV+xxNshcINV5dhBAXdiavLMHS6/h+PIAr4X5wvnn8apzp3tH4x5HvtJCJyN41qZ95WziPL7EOSRs09VjVf/lzCgTYDlyf78e8LY/GW62HNk7D7454cyldhyJ2Hfe5uSzdf5H/Bx7kfo7fouTzicu4ddC8+Op9jrsO4fz9N771H28+/IFGpcLv1Ftxvuw15L/MCD4UnZK2tpHJfz8HkWH8GTQzE2bPv85nPdsONzWTFUNCMPrsBQ2EzWARkLiocxvijTfI85w4jD5X5HbJGWlpaAHuZ34gRI4iKivrLlvmdimAP9P+ZKOB/Ufbu3ET2wjW4e0ZQEFiGb8QvhHvW8bP8Dn62TkAnyHnE3ZmMHQd4q6yJOF8n5lyXwpAAB7vPvekNMHZA2m0w/mnQ2YXWYrPwU8lPLMxaSH13PeMDx/NgyoNEuEYccx2m8nIaF75H288/I1EqcbvlFtxv771wW602SvbUk7Wu52DSScmwS8NIGNf3jkmbzcr+zHRyN6w+Kw03gqUnoT2nAcO+JgSzDamjEodhvmiSPFEGnVuhCG1tbX8o8zObzcjlcsLCwg7XZv8Vy/zOJcE+mtOOVDsdxEi1gcdiNvP5w4/hQyQH3WxYArcQHJJDpnwoS2X3UWdVcaWbM077O/l2VxUOKjmPTYvm+iGByIp+hbXPQXMZhE+0V5d4xwH2HfCGqg3MyZjD/rb9DPIcxMODH2aw9+BjrsNUUfE/4VYocL3uOrtwe3gc8/l/+vpuC3lbDpCzoYrOFiOuPlqSpwQRNbTvB5PtDfXk/raWvRvX0tnUiNbZhYQJU85Iw41gtWEsaUWf00h3XqO9nV0rR5PogSbJE1Wo8znTGWm1Wqmurj4s2vX19QC4uLgQFRVFZGQkISEhf7kyv5MJtovrsB7BjkAiOTM+/kBEqomcY2z4ejHNq6tw9x5EoXsWoVHb6HJQMk/xOnssIcSqVFxnkPPt8hKa9SauGxrEY1OjcWvLg8/uhYqt4BkDN3wPkZMPXzezPpNZe2aR1ZBFiFMIs8fPZmLQxGPuNkwVFTS+9z5ty5cjkctxu/FGu3B7ev7puceio9lAzm/V7Pu9BpPBil+kC+Ouiya4jweTVouFsvRd5GxYTXl2BgAhg1KZePNdhA0eikw+cD8Kgk3AuL+N7pwGunMbsektSNQyNPEeaAd5ogp3PmfK/rq6uv4wzc9gMCCVSgkKCmLKlClERUXh4eFxTv1l0FdOJNguLkMJCrrjiB32ufH/dIg+f9dKJBIZsAeoEQTh4r4vSeRUaWtq5McnXsHPKZa6ECnOYd8S5bufFbIZLOcilIKMexwdyNxxgPeq2xgc7Mqnlw4lwaET1jwA2UtB6wEXzbK3wMvs3xZFLUXMy5jHxuqNeGg8eG7Ec1wRcQVy6Z+/bUyVlfYd9yHhnjED9ztu77Vw11e0k7WuipJ0+y4vvKdj0jukbweTLbUHyN2whryN69C3teLg7sHwK68lccIUnDy9Tn6B00QQBEyVHXRnN6DPbcDWYUailKKOdUc7yBN1lOs5ET8mCAK1tbWHd9nV1dUA6HQ6YmJiiIyMJDw8/C9V5nc+C/bR9Me240EgH+h7CYDIKfPdzJdQlmhQ+0RT4reF8PB08pTxzJJ/zAGrjgscHXAo7WRxZj6ejipmXT2IK+KdkWybB1vngmCFUQ/BmEdAbW8vr+6oZkHWAn4p+wWdQscDKQ9wQ+wNaBV/7mI0VVXZhXvZsh7hvgG3229H4XVycbTZBMpzGslaV8nBkjYUahlJEwNImhCAk/vpH0xazGZKdm0jd8NqKvfmIJFKCUsdQtKk6YQkpw6Yty0IAuaaTrs9ktOAtdVob7CJdkMzyBN1jBvSfhqY1ReMRiNlZWWHDyA7OjoA8PPzY9y4cURFReHr6/uXKfM7vmC74uIy7LwS7KPpk4BLJJIA4CLgFeCRflmRSK8oy89h25tLcfeIoCi0iIDIH3F3tfCe/Bl2WGMJVyi5wyhl2S+ldJus3DU2jPvHh+JY8C3Mexk6ayH+Spj8PLiGANDU3cSHuR/ydeHXyCQybom/hdsSbsNF7fKn+5uqq2lcuJC2n5YhkclwveF63O+4o1fCbTZaKdhunwjY1tCNg5uKUVdFEDfKD2UfmlGaqqvI3bCKvM2/Yehox8nTm1HX3EjC+Mk4uPXu0PRUEQQBc62e7pwG9DkNWJsMIJWgjnTBaWowmjh3pAOQVH+qNDU1HR4MVVFRgdVqRaVSER4eTmRkJJGRkTichTCJgaC7u4bW1h20HBZs+18VfwXBPpq+fmfNBp4AjjtFXyKR3AXcBRAUdHZCWf9K2KxWljz1JO7dwZiCvDgY8gtRgQWsll3BT5KrkAgyblJpydpxgCV1nYyJ9OD5S+KJ6EyHT++EulwIGALXfA6BQwH7lMDP9n3Gp3mfYrAauCLiCu4ZdM8xSwJN1dU0vveeXbilUlyv7xFu75MLd1erkZyN1eRttk8E9ApxYuplYYSneCI9TQ/YbDRQtGMrOetXc6BwH1KZnIghw0mcNI3ghEFIBmgXaa7Xo89uoDunwd7KLu2Z9jc+EE28O1Lt2T3Ys1gsVFRUHBbt5uZmADw8PBg2bBiRkZEEBQX9Jcr8Ti7Yt/9lBPtoTlvAJRLJxUC9IAjpEolk/PGeJwjCB8AHYK9COd37icDO1T9R8XU2Tt4xlEXsITRiB2XaYJ6TL6TK6sIEtRZNcTvf5BUR4Krh/RsHM9WrHcnaO6BoJTgHwVWL7DtviQST1cTXhV/zYc6HtBhbmBI8hf9L+T/CnMP+dG9TVZVduJcttwv3tdfifuedvRLuxuoOstZVUby7DptNICzZk+RJgfiEO5/2YVh9eRk561dTsGUjRn0Xrr7+jJ1xG/FjJ6J1djmta54MS2M3+hy7aJtr9fYRraHOOIzyR5Pgjszh7KbYtLe3H7ZFysrKMJlMyGQyQkNDGTZsGFFRUbie4jTHcw1BEDAYamht3XkcwR5KUOBtuLoO/0sK9tH0ZQc+CrhUIpFcCKgBJ4lEskQQhBn9szSRQxi69Xz10H/w1kbTFAYuEUvx9Wplsex+ttgGEySVc207rN5Qhk0QeGhyJPcMcUW9dSZ8/zHINTD5BRh2LyjUWG1Wfin9hXez3uVg10GG+Q7jodSHSPBI+NO9/1BVIpP1CPcdKLxPXG4n2AQq8prIXl9FdUELcpWsp/Hm9CcCmrr1FGzbTO761dSWFiNTKIgaNorESdMIiE0YkMoIS7OB7twG9DmN9rxIQBnshMslYWgSPZE5nT3Rttls1NTUHN5l19bWAuDk5ERSUhKRkZGEhoaiVJ4f8WjHwmrtpr1jL+1tGbS1ZdLWnoXJZJ9a+HcU7KPplzrwnh34YyerQhHrwE+dX96bgyXDRLenA+1BmwkMzWaDfBo/SG/AioKLUZG76yCVzXqmx/vwn2lhBJZ8AZvftDfiDL7F3ojj4IkgCGys2sjczLmUtJYQ5x7HQ6kPMcJvxJ/u+4fOSYUC12uv6dXhpMVkpXBnLdnrq2ip1aNzVpI0MZC40X6n1XgjCAK1pUXkrl9NwdbNmI0GPAKDSZw0jdgxE9A4nFoGZm+wthnR5zbSnd2Aqcp+wKcIcECb5IkmyRO5y9mbq63X6/8QJ6bX65FIJAQGBh6uzfby8jovy/zsu+uqHqHOpK0tk87OAgTBAoBGE4SzUypOzsm4ugz9Wwm2WAd+nlFXuZ81LyzEzT2S6shKAiJ/p8XJgxdlsym3eTFcrkJV0MavJdWEe+pYcttQRlu2w9LboWU/REy2x5l5xQL24ODZGbPJbsgm2CmYt8a9xdTgqX/6QTeWldG48D37kCmlErebbupVHbe+3cTeTdXs3VxDd4cZj0AHJt8aR8RgL2SnUS5n6Ook//ffyF2/mobKcuQqFdEjxpA0aTq+kdH9LlDWDhPde4+Y9CeAwldnD0NI9EDeh6qYviAIAnV1dYetkaqqKgRBQKPRHD58jIiIQKM5O+vrC1arnvb23D8IttncBIBMpsXJMYmgoDtxdk7B2SkZpXJgDqLPZ8ROzHOQpc8+jWOzN/WeJuThv+HgV8s30tvYyGh8ZTKGNFrZmH4QpVzKQ5MjuSWkBfnaZ6ByG3jGwrSX7QKOfbzr7IzZbKnZgpfGi3uT7+WyiMtQSP+4G7Yn4CykfeVKJGo1rtdf16tZJc0HushaX0nRzjqsFhshie4kTw7CL8rllEX2ULpN7vrVFO3YisVkxCs0nKRJ04gZNR6Vtn/CGA5h7TLTvdde8mcsazs86c++0/ZA0U/hD6eKyWRi//79h62R9nb76FgfH5/Du2x/f//zqsxPEAS6uysO2yDtbZl0dhUgCFYANJoQu1A7p+LslIxOF4X0GP0Gf1fEHfh5QO72jez74DfU3iFUxuwgOGI3W1Wj+Vb6At2CggtMcval17Km3ciVqf48PcoRj51vwPqvQOcJF8+GlBtBJqeqo4r5mfNZuX8lDkoHHh78MNfFXIdG/sedmqGoiMaFC/+XOXnHHfYghRNMBzw8WGqdPfFGppASM8KHQZMCcT2NZHN9exv7Nm8gd8MammuqUGo0xI+bSOLEaXiHHXvGyuli67bQndeIPqcRY0kL2PjfpL8kz7OWzN7c3Hy4maa8vByr1YpSqSQsLIxx48YRGRmJk9P502phsXTS3pFLe49g23fX9koYmUyHk9MggoPutgu2c3K/z8n+uyAK+DmAxWzmi4efxEMeQXOkDY+oz1G4aXhN+iolQiDJghxFfhu/VbaS4O/Ee1dHk1L5GXwyDwQbjH4ERj8Maicauxt5P/t9viv6DrlUzu2Jt3NL/C04q/6YAdmdl0fTe+/RsXadPeW9F9FlVrON4j11ZK2roqnm0GCpUOLH+qM5xQoMwWajMi+H3PWrKdm9HavFgm9UDNPueZDoEWP6Nd3GZrDQnd9Md3YDhuIWsArIXFU4jglAc5Ym/ZnNZiorKw+3rTc2NgLg5ubGkCFDiIyMJDg4GPkAtvj3F4JgQ68vo60ti7b2TNrbs+jsLAJsAGi1YXi4T8DZOQUn5xQcdJHYG7hF+sq5/93xF2fDV4tpXV+LwjuM2tD1+AaV8Z38BtYzCQ9ByuQ6C9uya3DWKHjt8jiuUfyO9KfbobMOEq6yN+K4BNFh6mBx5jw+3/c5JquJf0T+g7sH3Y2X9o+HjvrMTBrfe4+uTZuROjnh8a9/4XbTjSdMeTd0mtm7uYbcjdXo2024+emYeFMMkUNOfbBUZ0szeZvWs3fDGlrrDqLWOZA05QKSJk7DIyjkNN7BY2MzWTHkN9sn/R0az+qsxGGEn32mdoDDGRXtQ152aWkppaWlVFZWYrFYkMlkBAcHk5aWRmRkJO69nNJ4NjGb22hvzzpCsLOxWOw2j1zuiJNTMqEhU3F2TsbJaRAKhcvZXfBZRhAEGkwWnOQy1P0880YU8LNEa3Mjy594A1eXSGpjawiI3EKhLpU3pQvosKkZ1yWlMLOObd1mZgwP5omoWhw23tjTiDMUrvkCAodgtBr5Ku9TPsz9kDZjGxeEXMB9KfcR7BR8+F6CIKDftZvGhQvR79iBzNUVz4cfxvX665A5Hr+Ko7VOT/b6Kgq2H8RithEU58agyYEExrqdkvhZLWbKMnaz97e17M9KR7DZCIhLYOQ/rydy2Cjk/VTmZjNZMRb1jGfNb+4Zz6rAYagvmiQPlEFOZ3TSX0dHB6WlpZSVlVFaWkpXVxcAnp6epKWlERYWRnBwMCrVuZsWb7NZ6Ooqtgt1j2Dr9WU9n5Xi4BCFl9eFODul4OycjFYb9repDDkWXVYrhZ0G8rsM5Hd1k99p/7fZbOWbQeGMdevfqilRwM8C37/5EvIKB8xB7nREfovG28IsyX8oIII4owRJfis7ajsZGuLGq2NVRGS9BF+vApcguOoTiL8Ci2Dl5+IfeTfrXer0dYzyG8UDqQ8Q5x53+D6CINC1ZQuNC9+jOyMDmacHXk8+ies1VyM9zoGgIAgcKG4la10V5bmNSGUSoofa/W13/1NrtW6oLCdv41r2bf6N7o52HFzdGHLJlcSPn4Kbn3+f3sND2AwWDAXNdOc1YSjoEW2dHG2q1xkfz2oymaisrDy8yz40flWr1RIWFkZ4eDhhYWE4Ozuf5EpnD6Op8Q++dUdHLlarHrAPe3J2TsHX5wqcnFNwckxELv9rtN+fKhabwP5uo12oO7sp6BHsim4Th8pCNFIp0To10zycidWpCdX2/y9qsQrlDFK2L5udM79F5R1AY8gWPEPzWK74B2skF+JokRBTYyKroBEfJzUvTPZhWsMnSPYsAqUOxjwKw+5BkKtYX7meeZnzKGsrI9EjkYdSH2Ko79DD9xFsNjp/+43Ghe9h2LsXua8v7nfegcs//oH0OLu9Q4nuWeuqaKjsQK1TkDDOn8TxAWhPoVnF0NlJwbbN7P1tLXVlxfbW9rRhxE+YTEhSKtJ+aN22dpkx5DfRvbfpsKctdVCgiXdHk+CBKswFiewMRJ/ZbNTW1h7eYVdWVmK1WpHJZAQFBREeHk54eDje3t7nZMWIzWaiozP/CMHOwmCoAkAikePoEIeTc/Lh3bVaHXhe1pf3BUEQqDdZ/rCbzu80UKQ3YLTZtVMKhGlVxOjUxOo0xDrY/w3WKJH20/t1vCoUUcDPADarlS+efAoXczA1fjV4Rf3GPucovpTeSYvVgSEtNkpyGrBYBe4dHcC/dBtQbX0bTB0w+FYY/28EnQfbD2xnTuYc9jXtI9Q5lAdTHvzDXG7BaqVjzRoa33sfY2EhiqAgPO66E+dLL0VyHJvC0GVm35YD5G6sprPFnuiePDmQ6GE+yHs5OU+w2ajYm03exnUU79qG1WzGMyiEhAlTiBk9Hq1T33ec1g6TPSdybyPGslawgcxFZRftxDNnjzQ3N1NWVkZZWRn79++nu7sbsEeJHRLsoKCgc677URAEjMaDPSV8WbS1ZdDRmYfNZgJApfLB2SmlR7CTcXRMQCb764yQ7Q1dFmvPTtq+q87vMlDQY38cwkspJ1anIcZBTaxOTayDhiitGs0Az3MXywjPEttX/EjNj/nIfQNoDl+NNqCNd6X3k0c8Ye02nAtayGnuZnKMF6/F7sdzxwxoKYeIKTD1JfCKJbM+k7lbnmBP3R78dH68POplLg67GFnPWFTBYqHtl19oev8DTPv3owwPx2/mmzhdcAGS41QxNNV0kvNbNUU7a7GYbfhHuzDu+miC43sfnNBaV0vepnXkbVpPR2MDap0DiROnkTB+Ml6h4X3erVlaDIdF+1BzjdxDg+PYADQJHij8B/4gsquri/379x8W7dbWVgAcHR2Jjo4mLCyM0NBQHE9wlnA2sFq76ejIo60t47BoG011AEilKhwdEwjwvxGnniYZtdr3LK/4zGGxCZR1G8nv6qbgiF11hcF0+DkaqZQYnZrpHs7EOWgO767dleeWZJ5bq/kLYdDr+frh53BxDKcpsQLPiD38pr6QFZLL0RgEEiu6KC5vJcxDx/eXqhhc8Cys3A5ecTDjB4iYREFzAfPW38fm6s24q915etjT/CPyHyhl9t2dzWSi7aefaPrgQ8zV1ahiYvCfPRvHqVOOOYXPZrVRntNEzm9V1BS1IlNIiR7qTeKEQDwCeudlmo0GinduY+9va6nalwsSCSFJKYybcRvhg4f1+UDS3Nhtb67Z24i52j57ROGjxWlSEJoED+Te2gEV7UPlfYcE++DBgwCoVCpCQkIYOXIkYWFhuLu7nzN2wuEmmUO76/Y/t6C7ug4/vLt2cIhBKj23/kIYCARBoM5kObybPiTUxUfZH+FaFUmOWq7xdTu8qw5S95/9MZCIFsoA8Mv7c7BkW2jx06ONXke5uy9LJHfRYHEhoc5MeX4TarmMf49y4Nr2xcj2fmNvxJn4DCTPoLyzmnez3mVV+SqclE7clnAb18VcdzhQwWYw0PrtdzR9/DGW2lrUiYl43HsvDhPGH1NUDJ1m9m09QO6majqbjTi4qUgcF0DcKD/UDiefTyIIAgeLC9m7cS2F2zZj6u7GxduX+PGTiRs7ESeP3qXuHO/aljq9ffbI3kYsdT0HZgEOaBI87Dttj4FrE7fZbBw8ePCwYB/ysaVSKYGBgYSFhREWFoafn985M3rVYumgvT3nCMHOOqpJJglnp+Se3fUglMre5ZCez3Qetj/+51UXdBposfzP/vD+g/2hIc5BTaRW3e+lfQOBaKGcAeoq97P+vx+i9PKnLXUrquCDfCK7lSxSCGgy41vQREmniesHufEfl9Xodi0EQbAfUI56iIOWLt7b+RLLSpahlCm5K+kubo6/GSelvQPP1tVFy1df0/TJJ1gbG9GkDcb3lZfRjRx5TOFurO4k97cqCnfVYe2xScb8M4qQJPdezd/uam1h3+YN7P1tLc0Hqu3zSIaPJmH8FPxj4097ByoIAubqzsM7bUuTAST2KX/OF4ehSXBH7jIw/qsgCH/ysQ0GAwDe3t4MHTr0cHnfueBjC4KNLn2p/aCx57Cxq6sYemodtNoIPDwm4uyUjLNzak/Q7rnxi2YgsNgESruNf6j8yO80UHmE/aGT2e2Pizxd/uBVuyn+enL313tFZ4mlzz6Dts2brjgDqqjPyNBN4GfJE8g7BCLK2qmu7STZ34GfhpUQnPUQFNZD4j9h0nM0qXR8lLOQrwu/RoKE62Ov5/aE23HX2Js6rB0dtCxZQvPiT7G2taEbORKPd2ahHTLkT+uwWW3sz24k57dqDhS3IldIiR7uQ9L4gF6VAR6u2d64jv2ZexBsNvyiYpl69wNEjxiNUnN680EEm4Cpor1HtJuwthlBKkEV7ozD2AA0ce7IHAdGMI/0sUtLS2lrawPsY1djYmIIDw8nNDT0nEikMZtbehpk/re7tlrtVpJc7oyzc3JP3fWhJpnzp73+VBAEgVqTuWc3fehQsZviLiOmHtdAJoEwjYpkJy3X+bodrgAJPE/sj/5AFPA+snf7RvZ9/Ds2fxc6h/1Ch68D70pepNbkQUS1kZqSFrq1ShaP62Jc+StItuZB4DC4bintXlEs3ruYJflLMFlNXB5x+R+ScCwtLTR/9hktS77A1tGBw/jxeNxzN5rk5D+to7vTxL4tB9i7qYbOFiOObmpGXhlB7CjfXo1xbawsZ+/Gdez7/Te629vQubqRdsmVJIyfjJtfwGm9N4LFhrGsje68RrrzmrB1mkEuQR3pao8bi3UbkOSaQ/XYh3bZh+Zkq1QqQkNDGTVq1DnhY9ubZAoPdzS2tWXS3V3e81kpDg4x+Phc2iPWKWi1oeeM796fHLI/9h3yqnt2161H2B8+SgWxDmrGujoS66AhTqcm4jyxPwYSUcBPE4vZzNJHnsZBFUJ7aima8BK+UsxgtzAM71oDboWNHDRbeSxVwl2GD1DsXAMuwfDPxegjp/Jl4VIW/f4AHaaOP3VPmuvqaV68mJavv0bQ63GcOhWPe+5GHRf3p3U0VHWQ+1s1RbsP2SSujLkmipAkD6QnqSYxdHX+f3tnHl9Hed7773vWOZtWS9YuWbK1WRgDjg228QZkgdAkhKTQ3pCmbF1ImtzbpC1pm6S56e1tG0IJueQ6IaQJCYkvSyCUsCRgvLAEDMabZO27Lcvazr7Mmff+MXOOjrwvsq1j5vv5zGfOzJkjzauxfn70e5/3eWjbvoW9m1/iYJees123bDkt666j5tIzy9lOL6zZN0Z0/wQyltS7sTcU4GqZh9KYj8U5u//sNE1jeHg4LdgDAwNpH7uqqooNGzZQW1tLaWnpBfWxY7FR/P5pK8Tv342m6WmIdnshubmXUVb6KXJzL8Pna8FmuzCFtc4FqXzqjnCUjnCMzpA+mdgZjjEcS6Sv81gtNHkUbizOm5FXnX8R2h+zgflTOQO2PvkYoy8NoVa5UBt+zt68FTwlvg0TUN45wdh4hA8tsPG/C58nb++j+kKc6/6J+BWf4/GeZ9n41A2MRcdYW7GWz1/2eRoKGgC9+83YDx9m6le/QmoaOddfz7y77sS5aNGM768lNbp3HmbXKwMc6JzC5rDQeGUJl5yCTSI1jf49u9iz+SU6f/86aiLOvKoa1n/2zjPO2VYnY/rCmn1jxLqmQNMX1riXFKE0F6AszEOcZs2UE44hw8fu6uqit7c37WOXlJSwYsUKamtrL2g+tqoGCAT26t1k/Lvw+3emu6ELYcfna6a87A/TC2UUpfyiiK4TmqQ/GqMzHKM9pAu0LtRR/KqWvs5jtbDQ7WRlnpdFboVGr0Kj5/1lf8wGpoCfBqHAFL/66/+Fo7CM8IptBCuSfM/ydwxGS6jsDTPaHyAnz8bPPrCThvbvIw6GYNmfoq75Ms+OvM5Dz36K4dAwy+Yv4/7197O0eCkA0bY2xjb+AP/zzyNsNnI/eROFt9+Oo7Jyxvc/0ibJmaew8pMLaVp5cptkfHiIfVteZt/WlwkcHsXp8dCy4Tpa1l132jnbqU7s0b2HibSOp1uN2YpceK8ux9VciKPSN6sLa6ampujt7aW3t5fu7u60j52bm0tTU1M6H/tC+NiqGsAf2EMgsIeAfw/+wJ4MKwQUZxk5uUuprPgTcnOX4vUuxmqdu/VPToaUksMJlc5wjO5wjM5wlK5wjO5IjN5IDDUjsW2+w8ZCt8JN8wtY6HZS71ZY6HZS6rRfFP9hXWjOOI1QCFEJ/AQoQa8buVFK+R8n+kw2pxE+++D9JNoF47UduOp38qRyM9u0tRQMhEh0BbAKyX2L+/jwwYewTPbBog+hXfcNfhse4MGdD9Iz1cPiwsV84fIvcFXpVQghCO/YweGNG/XKgB4P+bfeQv5ttx3Vtmy0P8CuzYN0/F5vmlDRmM+S9RVUX3JimyQaDLL/9S3s3fIyB9rbEMJC9ZKlLF53LQuXXXlaOdsyKYn1ThHdp0fayYmYnjlS6UNpLsTVXIi9ePYaIExNTdHX10dPTw+9vb1MTEwAoCgKCxYsSKf3FRScXmGts+Vosd5NJNKXft/pLCUn5xJ8vhZyfC34fIuzNo0vnNToiegC3R2O0RXWI+vuyMxo2iEEC9xOFrqd1Lqc1BlCXed2kmtaH7PCrC+lF0KUAqVSyneEED5gB/BxKeW+430mGwV8ZLCPV77xI7QKF6LxRd4rauSX/DHqqCCvM0QgEOcvFk3yV+ojOA+8BcWLkR/8JttdTh545wFax1upy63j85d9ng1VGwAIbd3K4Y0biby9A2t+PgWfvY38W2/FmlHkKJnU6H53lN2bBzNsklIuWVdBQdnxvdGkqtL73jvse/V3dO14k6SqUlhRxeK119C0eh3eglMvV6rFkkTbJ3TRbhtHRlR9EnJhPq7mQpSmglnLHPH7/ekIu7e3l/FxPa9ZURSqq6upqamhpqbmvNYVSST8ulAH9qRFO1OsFWcZvpyWDLFuybq2X0kpGYrG6QrH6IroIt1lRNRDGd40QLnTTq3bSZ0RRafEukJxYDWj6XPKrOeBSykPAAeM1wEhRCtQDhxXwLONX379a9gDhUQ/0EFkwSQ/sf4lvaEq5nUGCYyEubQowncqnqGo52nwFMOND7CjrJkHdj7IO4feodxbzj+v/meuX3A9FgmB3/yGwz/4IbHWVmylpcy/917yPnUzlox+hpFAnL1bh9mzZYjQpG6TrLpZt0mcx8nYkFIy2tfD3ld/R9v2VwlPTeLy5bDkuo+weM01p2WRJANxIq1jRPeOEe2aBFVicdtwNRXgai7EuSgfi/Ps/eyTCfYHPvCB8yrYR4v1biKR/vT7ilKOz9dCWenN+NKRdfaI9URCNewOQ6ANse6JxNKrEgF8Vgt1boUr87zUuXWBrnM5WeB24pkjC5lMppmtrvQ1wBagRUrpP+K9u4C7AKqqqq7o6+s7+gvMMfbveINdG18mUhvE1rSN//LewMvqtfh6gqh9IYodcTYu2MYlAz9FAFx1D/uaP8J39z7MtqFtFLmKuHvJ3dy06CasSakvd3/4YRJ9/TgWLKDwzjvJ/egNMwpMHerzs/uVQTrePkRS1ahsymfJ+kqqWgqPa5P4Rw/Rum0zrds2MzbYj9Vmo/aK5TSvuYYFS6/AegrdXKSUqKMRPWtk35jehV2CtUDB1VSA0lyIsyb3rKv7zaUIO5GYOkZkfbRYp6JqPbI+fou5uUJM0+iNxOk2sjtSvnRnODqjIJNNQLWii3Ot28lCw+6oczkpcthMb3oOcs6qEQohvMCrwLeklE+e6Nq5bqEkVZVffPnvcSjFRJtepqO8hMf4DJFhG96uIIlYnH+r3cXHJx7BEh6FSz5N9/LP8b2uJ3ix70Vynbnc3nI7tzTegjOmMbHp/zH+yCOohw6hLF5M4V134bv2GoQRyaRskl0vD3Kwewqb05rOJikoPbZNEg0GaX9jG63bNjPYugeAsoZmmlavo2Hl1bi8Jy+qJDVJvN9PZN840X1jqIeNVLZyL67mQlyLC8+65sjxBDtVU+R8CfZRYu3fQySaKdYVR4j14jkt1qkFLl3hWHrrDEfpjsToj8TRMq4tctioc+kCXWt41HVuJ1WKE/t5bGxhcvacEwEXQtiBZ4EXpJT3nez6uSzgrz/3FMPPdhJo6CXe0M2jjs/QMbWQvHY/kYkYnyvp4W8sP0UZb4PKKxle89956NA2nul6BsWqcNvi27it+TZcIZWJR3/GxKOPkpyawr1iBYV33TljuXvYH2fftiH2vDpEaCpOTpGLJesqaFxZitN1dNSsxuN0v/sWrVtfoefdt0mqKgVlFTRdvZ6m1WvJLS456fhkIkm0Y1KPtFvH0UIJsAqctbm6n91ciC33zDMjTiTYmRF2SUnJORPslFhnTjKeSKxzclrmbDPdoJo8ypNO+dTh5LRMuywi7UvXGZ50nRFR59hMy+Ni4VxMYgrgP4FxKeUXT+Uzc1HAo5EwT3zpm1jn5xBv2cwrhat4IfZhXJ1BkkMRlntHeaDwCUpGtkB+DYfXfZkfRAfY1L4JCxZuabyF2y+5Hd9UgvFHfszEpk3IcBjvhg3Mu+vOGasmR3r97N48SMfbI2iqpKq5gEvWVxyzhKvUNAb27aF122Y63txOLBzCk5dP46o1NK1ef0q+tt74QF9UE+uYQCY0hNOK0liAq7kApaEAi3Jm0yB+v39Glsj5FuxEYjKdZx3w78Yf2JNuRgCgKJXTUXVOCzm+xXNOrFVNMhCNz0jDS3nUI3E1fZ0AKhXHtCftVljo0u2PUqfdzJt+H3AuilmtAj4D7BZC7DTO3SulfO4svuZ55cVHNuLfGSRx2SDdtRZ+Jr5CoM+Bu3uMAm2Kh6pf4vJDTyEmvUxd8/c8olj4+Z4HiCfjfGLRJ7h7yd0UjEYZ+9Z3GPnV06Bp5NxwPYV33IFSXw9AIp6k460R9m4Z4lBfALvTyuLV5Vyyrpz8kqNtktG+HvZtfYW217YQHDuMXXGxaPlVNF29nqqWJVgsJ46q1DHdz47sHUvX0LbmOnAvm69PQi7IRdhOX1BTaX19fX309vYyNjYGTAv2smXLzplgJxKTafsjFWEfS6zLy29Np+7NlUa6qZzpI9PwusIxeiNxEhkBVL7NSp3bybqCnAyxdlKjON/3S8ZNjs37spxs0D/Jr79yH1q1RrBlJ497bmbveCOetilEMMI3S7dzc+gXWBMhwlfcxqMlNfy4fRPBRJCPLPgIf7H0L5g/FObwxo0EXnhx5uKbCr1uyMTBEHu3DNP2xgFiYZWCMg8ta8ppWFGC4wibxH94lLbtr9K69RUOD/RhsVqpufRymlavo27ZCuzO41fmk5okPhBIL19Pl2Mt8aA065kjp9v4QNM0Dh8+TH9/P319ffT396cXzpzLCFtKjUikn2CwjUBwH8FgG8FAK9HYcPoal1J1ROre3BDrcFKjN8Py6DQi6q5wjKmMmh4OIahxTfvRmZOIF2O1PJPZwWypZvDcQw8S6o4RanmNNysb+HX0D7C3h5AHI3wm5z3utT+GKzRAbOG1bKpfyQ97nmE8Os76yvXcc9k9VHT59cU3W7bqi2/+6FYKbrsNW1ERyaRG73uH2bNliMG2CSxWQd1lRbSsLad0Yd4MEY2GgrS/sZ3Wba8wuM+YjKxvomn1OuqvWn3CJe3JYFzPz94/QaxjAi2sggWcNbnpRTW2glMvx5pMJjlw4EBarPv7+9OtwjweD9XV1VRVVVFdXT1rk47JZIRgcD/BYCuBYCvBYCvB4H6SSb1zuxBW3O5avN5GfN6mdDaI3X5hGgJLKZlQk/RGYvRF4vRG9Ai611h9mGl5AJQ57ek86cxJRDNn2uRMeN8L+NTYOM999QGSDROMNB3kJ7bbONyXg9IT4FJ6+G7B45T73yFR3MTTSz7K9w9sZiQ8worSFXxh6edZsG+CsY0/0Lu75+dT8NnPkv9Ht2LNySE4EWPftiH2bhsmPBXHW+Bk8dXlNK8qm9EQOBGP0b3jLdq2v0rPu2+RVFXyyypoWr2WplXryCs5dlsrqUkSQ0EibeNE9xtL1yVYvHaU+nyUhgKURXmnXNkvFosxODiYjrAHBwdRVV2ACgoK0mJdVVV11isdpZTE44cIBPYZkbUu1uFwLxg5E1arF5+3Ca+vEZ+3Ga+3EY+n/rz3ZNSk5GAsQU+mSEfjadHOjKRBr5BX43JQ7XKywOWgxhDsWpcTjzmBaDKLvK8F/Jn7v0PsYIzA0td5vngt28ZX4mqdYl7oEN+Z9zQrgy+huefx/BWf5HtTe+gPDLCkaAlfWPKXNL6nC3ds/369u/uf/il5N38SoSgMtk2wZ8sQPe8dRkpJVXMhLWvLqc7I3U6qKv27d9K2/VU63nqDRDSCJy+fhquupunq9cyvXXhMgUyGEsQ69Cg72j6OFlKnl643FKA05GMv855SvZFgMJiOrPv7+zlw4ABSSoQQlJSUUFVVld7OprejpiUIh7t1kc4Q7FS3GDAyQbxNeL1N+Hz6XlEqzlvucVzTGIjGZ0TPqdf90fiMRS02oU8e1ricVLuc1CgOFridVLscVClO3KYvbXISpJREExqTkTj5bgfKGRZ1e18KuH9slP/66vdQmw7R3pTgMfWPibcn8Ryc5CveF7hNPo0Fjc1LP853k6N0THVRn1/PFxb/OUveHmPs4R+R6O/HUVtL4R13kPvRG4glBPvfOMieLUNMjoRRPHaaVpayeE05uUX6ikqpaQzt30fb9i20v7GNSMCP0+OhfsUqGletpaK55ajJSKlJEsNBXbD3j6cX1Fg8NpR6XbCdi/KxnqRolZSSiYmJtFj39fWlJxxtNhvl5eXpCLuiogJFObMoN5GYmrY/Aq26Xx3qQEq9M4rF4sDjqdeF2hBsr7fxvDQgCBpWR0qY+4wouicSYziamJEr7bJY0tFztbHXNwflTgc2M1/aBNA0SSCmMhVOMBmJMxlOMBlJMBVJMBWePp4MJ5iKxJkyXk9GEsSNujE/vX05Vy86s/aD7zsBf/H/PsRE3wSHL3+XJ/NuoK2/DkeXn09YXuOfXJvwxg/xVuO13O8W7JrYT3VONfc03sGyN8aZ+NGPUUdHUVpaKLzrTnzXXsvoQJA9W4bo+P0IakKjpDaHljXl1F1RjM1u1esd93bTtv1V9r+2lcDYKDaHk7plK2hctZaaSy/HZp8pvlo4QbRjkuj+caLtE3rDA6EvqElF2Y6KE1f1U1WV4eFhBgYG0lsopPvIiqLMiK7LysqwncLqzEz0icWBmV71EROLdnshPl+z4Vfre7e7Fovl3EzKpTI7MqPoTF/6cGKmH11gt7LAEOa0SBuRtbny8P1FXNV00TVEOFNop8LxDBGeec4fSaCdQCrdDit5Lju5boe+d9nJc9vJddvJcznIc9tZW19EWd6Z9Xd93wh4LBbliS99i2TTKO80unkieBOWvSEaQ938R+7PaYjuor2shftLq9k6vpv57vncs/BzrHx9ksmfPEpychL38uXM+7O7cVyxnK53Rtn96hCHev3YHBbql5fQsqacoirdahgfHqJt+6u0vbaFieHBdAZJ46q11C1bgUOZfmBSShIHQrpg759Ip/kJl83wsvNR6vOxeo9fICoYDM4Q6+HhYZJJ3ZvNz8+nqqqKyspKKisrKSoqOq0JR1UNEQq1G0K935hYbEtPLIIFt7s2bX34vI14vc04nWfe1Ph4RJK61dFnWBv9kTh90ZixjxPKWMwi0CcNU5FzZhRd43LiM/3oiwopJeF40hBYPSKemhEBTwt0pjhPRRKE4snjfl0h0IXXEOCUGOe59XM5Ljt5mefc+rlclx3nOf439r4Q8Hd++xs6X3iXwyt28ljOx+npKqOg/xD3up7i0/IFRtx5PFi/nF9P7sPr8PLnVX/ENa+FCfxiE1oohHftWgr/7G7i5Q3s3TJE6+sHiIVU8kvctKwtp+FKfaVkYOww+1/bQttrWxjp7gQhqGxqoXHVWhatWInLN20TaFF1OsreP4EW0C0GPcrWJyCPVzs7M50vJdipBTNWq5XS0tIZgn2qtbCl1IhGBw2Pelqo9XogRr9BqzedAeL16TbIbE4sJo0Jw35DpPsicUOw4/RHj87qcFksVLkcVCsOqo4Q6UrFgfM8VSg0mT2SmiQQTWTYD3FDfBMZ4quLcyoiTlkUieTxdcthtRiRrxEFGxFwSpz1yHhmpJzncuBTbCftYnWhuOgF/Mlv/jMR5wTvLFV5fPITWPYFuDmxma+5NiG0AN9vXMnPYwcA+JOKT/KxbXHCTzyNjMXwfehDzLv7LsZtJex4vo/+veNYLIIFS4u4ZG05ZfV5qIk4HW++xp6XX2SgdQ9IyfzaRTSuWkPDyqvxFeg1n1PFoaJt40Tbxon1+kGTCMWKsig/bY0cqwxrIpFgeHg47V8PDAykO8243W4qKyvTgl1aWordfvKsk3h8TI+mQ/sJBdsJhtoJhToyomqBy1Vt+NQNeL2NxsTi2XeImUqo9KVFWRfqlEgPRuPp5rQAFqBMsVOl6DaHLtTOtGDPs5tWx1xDSklM1fBHE/gjKv5ogkBUxR8x9lHdevBHE0xF1LRA6+IcJxBTOZH8eJ22aSviCDHOyzifPmcIsWK3XHT/Vi5qAX/sS19FLjzEM42L2Np9Jd6uMb7v/QHr1e3sq7ycr+TY6A+P8AcLbuSO/gXEv/tDksEguTfeSMGddzAaz+ft53oZ7pjElePgkrV6CqAnz8mh3m52v/wCrVs3EwuHyJtfSvOaDTSuWkN+aTkAMqkR6/Gn62Ynx3XRtc1342rUl6w7qnOOqugXDocZGBhIC3amHVJUVDRDsE+WzpdMhgmGOggF9xMMtRsWyH4SibH0NXZ7AV5PPR5vA15PvSHW9VitZ9aIIaZpDKbtjWmbY8A4PjLtLt9mpcrI4Kh2OdLZHNUuB2VOOw4zij6vaJokGM8Q3EgCf1QlYAjvtAirBGLGPqpfk3o/ntRO+D1sFoFPsZHndmREu/b0cW6GHZEZKee67NjNLJ8052Ip/ZzgmW9/G2tFlOcaK9nWdSWl3YM8kf9dKiOt/PQDn+Y74zvIJ59Hav+RwgcfJ/Lek7iWXcH8f/gHRuKFPPt4LyM9fXjynFz9h4toXlVGUo3Stv137H75RUa6O7Ha7dSvWMUlGz5IRVMLwmJBi6mEd40aoj2BjKpgs6DU5eJbU47SUIAtf9puODI7pL+/n9HRUQAsFgvl5eVceeWVacF2u48tqpqmEon0pqPqYHA/oVA7kcgAKfvDYnHh9Sxi3rwNhlA34PE24LCfXhf2VCPalA99ZCQ9HEuQ+d+/0yKoVBxUKQ4uz/VQraREWo+mzeJKs0tc1Y4SVD0KPjoi9mdExKnrgieJgAFcdis5Lhs+xU6OYiPf46Cq0EOOYpxz2chR7PgUGzku/ZocRfeGfYoNl9160UXDc4msjsDffeW3dL/2Ki8tT/Js94do6m3jl977kMLPPzavYsvUfj44bzVfeLuY8C8fx5qXR9GXv8x4zUp2/KaP0f4AvgKFyz9cTeOK+Qx37GPv5pdof/M11HiMouoFXLLhgzStXo/i9ZL0G80O9o0R7ZyEpN7sQC8OVYizPh+LQxcpTdMYGRmZIdiBQADQl6MfmR1ypB0ipSQWO2hYH6moup1wuBNNixtXWXC7F+jWR0qoPfW4XFUIcfLoJSklI7EEQ7EEg9F4xpagP6qLdPSIqfdSp90QZF2YqzNsjvkOs7DSqZKaiJspsscS3OmoN5C2JPTX0cSJo18hwOdMCeu0yPoyRDYluNMCPPM6MwqeG1x0EfjE6CF6fvcaW9ZEeLbjo6wbfJ0fuh5gp9fHvcULmfJ38W/B66nduJ3w+Di5t9zC1Nr/xvNbRhl/YQ85RS7Wf6aR0oVW2ra9zI//+rdMHRrB6faweO0GWtZ/kOKaOhJDQaKvjzHV3kliQBdga4GC96oyvXmvYY2Ew2G6+roZGhpKTzjG47rQ5uTkpFc2VlVVUVxcnM4OkTJJNDrE1FQXoXAX4ZC+D4U6UdWp9HidzhK8nnoKClbh9TTg9TbgdtedsDluNKkxFNMFeSiqTxIOxuIMRXXBHo7FZzSgBd3mKFccLHIrXFOYMy3SLgcVTodZVInpxRmBWIJQLEkwenyL4ZgRsbFPnigvDXDYLLrQKjZ8htiW5bpmRMSZgnxkROxxzN1JOZPZIWsj8E3/42v8/tpJftH5cT5z8Nd83f5j/k/FAh6xJ7gqWMIXN3tgdxuOSy8jcNOX2L1HxT8aoaDMw6XXlIDWzb4tL9O3eydISVXLpbSsv47a5mWovaGZdUZSKyCNSFsW2BkZGWFoaCi9pbJDAIqLi2dE2Hl5eSSTMcKRHsKhTkLhbkKhTsLhLsLhHjQtlv6s3V6Ix7MQj6cOr6ch7VcfWQMkktQ4GEtwIJbgQEy3M1LHw7EEQ7E4o0dkcliAEqedCsVBubFPbeWKnQqnA+9FanOkJtwCUZVQTCWY2qIqobg64/xR18SM4+j08Um0F9Cj32NFvb6TRL2p4zNdtWdy8XFRTWI+9sWv8u41IX7ZdQNfPfwIG1wv8rcVNQyHo/zdrhoWbu5GKypn6mNfpH00j9BkjKJKF+X1ASaGd9K94/ckYlFyioppWX0d9bUrsI0Jol2TR9UZsdX5mMpNMDI5ysGDBxkaGmJkZARN0/98zcnJoby8nPLycsrKSiksFCS1ESKRfsLhbkIhPaqORKc9ahC4lErcnlo87jo8noW4PXV43HXYbLn41eQMQT6QFuZ4+tyEenQ+q89qocRpp8xpCHJKoI3jMqcjqzqxaJokqiYJxZKE4ycT1iTBWMI4nyQYNaLjjGtPFvGCbjt4nbb05jFEOPU6/Z5ivJdxPhX95ih2vIoNaxb9rE3mNheNgD/+jf/JW0vG+a/uNdw/eR/D+d1831fAH7xj58Y3NcLWQkbX3s5ArJREPEFByQROpZcDHTuIhUK4vXksWXINVYWLcQYcxAeCoEmwCmS5gn9+kglPhNHwBAcOHmB0dJTUz8jpdFJWNp/ycifz5kk83jAyJdaRPqLRgQx/Wl9O7nbX6ptrIcK1kJC9hoC1lMOqhUPxBIeMiDkzeo5oR3ub8+w2ypx2Spx2StObI+O1/YJFz6noNhxPEoqp+j6uEjnmsS7GobhKOJZMv5f6bCQxfU34BIsuMhECvA5DSBXbMQXY47TiddrxOq26+DqOvtZrTrqZzFEuCgHf8ovHeFbsYlfPAu5N/DsPF8K8Nju3vKEQcjYy0nIjo0kfyD5c3iFC4x1YE4IS3wJqq66g0FGGZUqSUBNMWEIECpP4vTHGCTEWnMAf8COEhsMRJi9Po7jYRl5eErc7itU2TiJxgGh0iFQVPQlERCGqq5Goo5aIvYqQpQS/ZR4TMofxpIPReFIX6rh6TGG2CZjv0KNjPXo+UqTtzHfaz3qhipSSeFIjGteIJJJEE8kZ+0g8SSieJBxTCcWTROLqEcfJtOimxTjj+FQshRSK3YLbYcPtsOJx2HA79b3LYcXjsOJ22vA4rLgctvSx264LbzrizRBfl91qer0mFzXnqifmh4H/AKzAD6WU/3Ki689GwPvbWrnvzV9i6/NTqTzFoW4X69oWEMhZxsGiahLqCA7LCD6ZJM9eRIG3HLe7mIhU8YsIfkuEgDtMyHGIOOM4HBEcjgguV4ScHBWnO0rCFieMSgg3ITwEyMFPDiHLfCK2EoKWIgLk4cfNVNLBRFIcNQmYosBupchhp9hho9hhZ56xL87YF9hseAQkVD2CjasaUVUXy2mB1YhmCu2R5+LHvy6WmCnWp/uo7VaBO1NEHdYM0U2J7EwR1q858tiKxzn9nmktmJicHrOehSKEsALfA64DBoG3hBDPSCn3nfltHptYNMq/bv45CztfxT5SRkHsVubl5DO2RMXqCZPv6SfusRBRchhxxul2TBG1T6A5JKrVQsJmJW6xExYKQS2fkKwgrHkIay4ieIhqLqIhJ5omEFLqAXZqr0mElLiFBbcQuC0WXAgqSVInwQ7YJFg1sEiJSOqVBdWkRkzViKhJ2hMae5IasYRGTE0SU/X3TsWTPR6K3YJit+IyNqfdiss4l+uyozisKDYrLofF2FtR7Nb0ZxS7xdhbcdoteBy6zaALth4NO86g9ZqJicn542zSCJcDnVLKbgAhxC+AjwGzLuAf2biJ3qkrkTlXgk+kI0kphe5jxIBo6iS6+Er99cljvSQQxE7whFclgCljS2G3Cpw2K06bBafNgsNm0Y/t+rHLENPU+06b1bjGYlxjnfk543xKWJUMoXWlBVm/zvRpTUxMzkbAy4GBjONBYMWRFwkh7gLuAqiqqjqjb5SjRfG6olgAiwCrBawWCzarBbvNhmKzotgdKDY7TpsDxerAabOhWC24bXpk6rFZcdms2G0WHFaBzWLBbrNgtwjsVgs2q8BhNb6mVT9nP+K1zSqwW3SRdVgtpu9qYmJyQTkbAT+Weh3lCUgpNwIbQffAz+Qb/eqLd53Jx0xMTEwuas7G5BwEKjOOK4Dh41xrYmJiYjLLnI2AvwUsEkIsEEI4gFuAZ2bntkxMTExMTsYZWyhSSlUIcQ/wAnoa4Y+klHtn7c5MTExMTE7IWRWzklI+Bzw3S/diYmJiYnIamIm+JiYmJlmKKeAmJiYmWYop4CYmJiZZiingJiYmJlnKea1GKIQYBfrO8OPzgMOzeDsXEnMsc4+LZRxgjmWucjZjqZZSFh158rwK+NkghHj7WNW4shFzLHOPi2UcYI5lrnIuxmJaKCYmJiZZiingJiYmJllKNgn4xgt9A7OIOZa5x8UyDjDHMleZ9bFkjQduYmJiYjKTbIrATUxMTEwyMAXcxMTEJEvJCgEXQnxYCLFfCNEphPjbC30/Z4MQolcIsVsIsVMIcWYdni8AQogfCSEOCSH2ZJwrEEK8JIToMPb5F/IeT5XjjOXrQogh47nsFEJcfyHv8VQQQlQKIV4RQrQKIfYKIf7KOJ91z+UEY8nG56IIIX4vhHjPGMs3jPOz/lzmvAduNE9uJ6N5MnDruWiefD4QQvQCy6SUWbU4QQixBggCP5FSthjn/hUYl1L+i/Efa76U8m8u5H2eCscZy9eBoJTy3y/kvZ0OQohSoFRK+Y4QwgfsAD4O/AlZ9lxOMJZPk33PRQAeKWVQCGEHtgF/BdzELD+XbIjA082TpZRxINU82eQ8IqXcAowfcfpjwH8ar/8T/RduznOcsWQdUsoDUsp3jNcBoBW9V23WPZcTjCXrkDqpLul2Y5Ocg+eSDQJ+rObJWflgDSTwohBih9HwOZuZL6U8APovIFB8ge/nbLlHCLHLsFjmvO2QiRCiBrgMeJMsfy5HjAWy8LkIIaxCiJ3AIeAlKeU5eS7ZIOCn1Dw5i1glpbwc+Ajwl8af8yYXnoeAOmApcAD49gW9m9NACOEFngC+KKX0X+j7ORuOMZasfC5SyqSUcil6r+DlQoiWc/F9skHAL6rmyVLKYWN/CHgK3SLKVkYM7zLlYR66wPdzxkgpR4xfOg34AVnyXAyP9QngZ1LKJ43TWflcjjWWbH0uKaSUk8Bm4MOcg+eSDQJ+0TRPFkJ4jAkahBAe4IPAnhN/ak7zDPBZ4/Vngacv4L2cFalfLINPkAXPxZgsexholVLel/FW1j2X440lS59LkRAiz3jtAq4F2jgHz2XOZ6EAGKlD9zPdPPlbF/aOzgwhRC161A16P9KfZ8tYhBCPAevQS2KOAF8DfgVsAqqAfuBTUso5Pzl4nLGsQ/8zXQK9wN0pv3KuIoRYDWwFdgOacfpedO84q57LCcZyK9n3XJagT1Ja0YPkTVLKfxJCFDLLzyUrBNzExMTE5GiywUIxMTExMTkGpoCbmJiYZCmmgJuYmJhkKaaAm5iYmGQppoCbmJiYZCmmgJuYmJhkKaaAm5iYmGQp/x/MuDaTyao9hgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -131,8 +131,8 @@
     "M = np.linspace(0, 30, 300)\n",
     "pLvl = 1.0\n",
     "P = pLvl * np.ones_like(M)\n",
-    "for j in range(MedicalExample.MedShkDstn[0].pmf.size):\n",
-    "    MedShk = MedicalExample.MedShkDstn[0].X.flatten()[j] * np.ones_like(M)\n",
+    "for j in range(MedicalExample.MedShkDstn[0].pmv.size):\n",
+    "    MedShk = MedicalExample.MedShkDstn[0].atoms.flatten()[j] * np.ones_like(M)\n",
     "    M_temp = M + MedicalExample.solution[0].mLvlMin(pLvl)\n",
     "    C = MedicalExample.solution[0].cFunc(M_temp, P, MedShk)\n",
     "    plt.plot(M_temp, C)\n",
@@ -154,7 +154,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -167,8 +167,8 @@
    ],
    "source": [
     "# Plot the medical care function\n",
-    "for j in range(MedicalExample.MedShkDstn[0].pmf.size):\n",
-    "    MedShk = MedicalExample.MedShkDstn[0].X.flatten()[j] * np.ones_like(M)\n",
+    "for j in range(MedicalExample.MedShkDstn[0].pmv.size):\n",
+    "    MedShk = MedicalExample.MedShkDstn[0].atoms.flatten()[j] * np.ones_like(M)\n",
     "    Med = MedicalExample.solution[0].MedFunc(M_temp, P, MedShk)\n",
     "    plt.plot(M_temp, Med)\n",
     "print(\"Medical care function by medical need shock (constant permanent income)\")\n",
@@ -190,7 +190,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -203,8 +203,8 @@
    ],
    "source": [
     "# Plot the savings function\n",
-    "for j in range(MedicalExample.MedShkDstn[0].pmf.size):\n",
-    "    MedShk = MedicalExample.MedShkDstn[0].X.flatten()[j] * np.ones_like(M)\n",
+    "for j in range(MedicalExample.MedShkDstn[0].pmv.size):\n",
+    "    MedShk = MedicalExample.MedShkDstn[0].atoms.flatten()[j] * np.ones_like(M)\n",
     "    Sav = (\n",
     "        M_temp\n",
     "        - MedicalExample.solution[0].cFunc(M_temp, P, MedShk)\n",
@@ -232,67 +232,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
-      "  return c ** -gam\n",
-      "C:\\GitHub\\HARK\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
+      "c:\\users\\alujan\\github\\alanlujan91\\hark\\HARK\\utilities.py:242: RuntimeWarning: divide by zero encountered in power\n",
       "  return c ** -gam\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -346,9 +292,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\GitHub\\HARK\\HARK\\core.py:548: UserWarning: The option for reading shocks was activated but the model requires state PlvlAgg, not contained in newborn_init_history.\n",
+      "c:\\users\\alujan\\github\\alanlujan91\\hark\\HARK\\core.py:548: UserWarning: The option for reading shocks was activated but the model requires state PlvlAgg, not contained in newborn_init_history.\n",
       "  warn(\n",
-      "C:\\GitHub\\HARK\\HARK\\core.py:740: UserWarning: The option for reading shocks was activated but the model requires state PlvlAgg, not contained in newborn_init_history.\n",
+      "c:\\users\\alujan\\github\\alanlujan91\\hark\\HARK\\core.py:740: UserWarning: The option for reading shocks was activated but the model requires state PlvlAgg, not contained in newborn_init_history.\n",
       "  warn(\n"
      ]
     },
@@ -356,7 +302,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Simulating 10000 agents for 100 periods took 3.2067 seconds.\n"
+      "Simulating 10000 agents for 100 periods took 1.8607 seconds.\n"
      ]
     }
    ],
@@ -409,7 +355,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.13"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/examples/ConsumptionSaving/example_ConsPrefShockModel.ipynb b/examples/ConsumptionSaving/example_ConsPrefShockModel.ipynb
index 03ca02b79..08b08b76d 100644
--- a/examples/ConsumptionSaving/example_ConsPrefShockModel.ipynb
+++ b/examples/ConsumptionSaving/example_ConsPrefShockModel.ipynb
@@ -116,7 +116,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solving a preference shock consumer took 0.734375 seconds.\n"
+      "Solving a preference shock consumer took 0.3125 seconds.\n"
      ]
     }
    ],
@@ -141,7 +141,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -156,8 +156,8 @@
     "# Plot the consumption function at each discrete shock\n",
     "m = np.linspace(PrefShockExample.solution[0].mNrmMin, 5, 200)\n",
     "print(\"Consumption functions at each discrete shock:\")\n",
-    "for j in range(PrefShockExample.PrefShkDstn[0].pmf.size):\n",
-    "    PrefShk = PrefShockExample.PrefShkDstn[0].X.flatten()[j]\n",
+    "for j in range(PrefShockExample.PrefShkDstn[0].pmv.size):\n",
+    "    PrefShk = PrefShockExample.PrefShkDstn[0].atoms.flatten()[j]\n",
     "    c = PrefShockExample.solution[0].cFunc(m, PrefShk * np.ones_like(m))\n",
     "    plt.plot(m, c)\n",
     "plt.xlim([0.0, None])\n",
@@ -179,7 +179,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -281,7 +281,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solving a kinky preference consumer took 0.609375 seconds.\n"
+      "Solving a kinky preference consumer took 0.265625 seconds.\n"
      ]
     }
    ],
@@ -306,7 +306,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -321,8 +321,8 @@
     "# Plot the consumption function at each discrete shock\n",
     "m = np.linspace(KinkyPrefExample.solution[0].mNrmMin, 5, 200)\n",
     "print(\"Consumption functions at each discrete shock:\")\n",
-    "for j in range(KinkyPrefExample.PrefShkDstn[0].X.size):\n",
-    "    PrefShk = KinkyPrefExample.PrefShkDstn[0].X.flatten()[j]\n",
+    "for j in range(KinkyPrefExample.PrefShkDstn[0].atoms.size):\n",
+    "    PrefShk = KinkyPrefExample.PrefShkDstn[0].atoms.flatten()[j]\n",
     "    c = KinkyPrefExample.solution[0].cFunc(m, PrefShk * np.ones_like(m))\n",
     "    plt.plot(m, c)\n",
     "plt.ylim([0.0, None])\n",
@@ -343,7 +343,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -429,7 +429,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.13"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/examples/Distributions/DiscreteDistributionXRA.ipynb b/examples/Distributions/DiscreteDistributionXRA.ipynb
index c200ffd35..a386a646b 100644
--- a/examples/Distributions/DiscreteDistributionXRA.ipynb
+++ b/examples/Distributions/DiscreteDistributionXRA.ipynb
@@ -61,8 +61,8 @@
    "outputs": [],
    "source": [
     "x_dist = DiscreteDistributionXRA(\n",
-    "    IncShkDstn.pmf,\n",
-    "    IncShkDstn.X,\n",
+    "    IncShkDstn.pmv,\n",
+    "    IncShkDstn.atoms,\n",
     "    name=\"Distribution of Shocks to Income\",\n",
     "    var_names=[\"perm_shk\", \"tran_shk\"],\n",
     "    var_attrs=[\n",
@@ -453,11 +453,11 @@
        "    tran_shk  (dim_0) float64 0.03491 0.05308 0.0644 ... 5.724 6.966 11.51\n",
        "Attributes:\n",
        "    name:     Distribution of Shocks to Income\n",
-       "    pmf:      [2.5e-05 2.5e-05 2.5e-05 ... 2.5e-05 2.5e-05 2.5e-05]\n",
+       "    pmv:      [2.5e-05 2.5e-05 2.5e-05 ... 2.5e-05 2.5e-05 2.5e-05]\n",
        "    seed:     0\n",
-       "    RNG:      RandomState(MT19937)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dd8fb8e5-6b7f-4082-a150-5b58500dbb05' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dd8fb8e5-6b7f-4082-a150-5b58500dbb05' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 40000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ab999ceb-7d19-4cfe-b5bb-5e9fe20deba0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ab999ceb-7d19-4cfe-b5bb-5e9fe20deba0' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b12dd1a9-bb80-4998-8c41-b5386dc32fd3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b12dd1a9-bb80-4998-8c41-b5386dc32fd3' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>perm_shk</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03491 0.03491 ... 11.51 11.51</div><input id='attrs-9f297c05-cb30-4aab-83cd-feafac933e41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f297c05-cb30-4aab-83cd-feafac933e41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08f7314d-2bba-49e7-bcd1-0df756191572' class='xr-var-data-in' type='checkbox'><label for='data-08f7314d-2bba-49e7-bcd1-0df756191572' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>Permanent Shocks to Income</dd><dt><span>limit :</span></dt><dd>{&#x27;type&#x27;: &#x27;Lognormal&#x27;, &#x27;mean&#x27;: -0.5, &#x27;variance&#x27;: 1.0}</dd></dl></div><div class='xr-var-data'><pre>array([ 0.03491194,  0.03491194,  0.03491194, ..., 11.50651472,\n",
-       "       11.50651472, 11.50651472])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tran_shk</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03491 0.05308 ... 6.966 11.51</div><input id='attrs-c2e9a330-facc-4012-9b4d-873b542aabd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2e9a330-facc-4012-9b4d-873b542aabd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ea3c00e-78b6-4330-8703-9038cc6b43c5' class='xr-var-data-in' type='checkbox'><label for='data-9ea3c00e-78b6-4330-8703-9038cc6b43c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>Transitory Shocks to Income</dd><dt><span>limit :</span></dt><dd>{&#x27;type&#x27;: &#x27;Lognormal&#x27;, &#x27;mean&#x27;: -0.5, &#x27;variance&#x27;: 1.0}</dd></dl></div><div class='xr-var-data'><pre>array([ 0.03491194,  0.0530801 ,  0.06439949, ...,  5.72401042,\n",
-       "        6.9659349 , 11.50651472])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7120bdfd-1ed8-4ba9-a48d-52bb42416074' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7120bdfd-1ed8-4ba9-a48d-52bb42416074' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>Distribution of Shocks to Income</dd><dt><span>pmf :</span></dt><dd>[2.5e-05 2.5e-05 2.5e-05 ... 2.5e-05 2.5e-05 2.5e-05]</dd><dt><span>seed :</span></dt><dd>0</dd><dt><span>RNG :</span></dt><dd>RandomState(MT19937)</dd></dl></div></li></ul></div></div>"
+       "    RNG:      RandomState(MT19937)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9003f1da-6cc9-41d2-a282-fdda70d50d63' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9003f1da-6cc9-41d2-a282-fdda70d50d63' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 40000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-614e26a7-bd98-4c5f-92b3-1ebbc19baae7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-614e26a7-bd98-4c5f-92b3-1ebbc19baae7' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e7ceaf3c-9ac9-485e-a6ec-170311b1e47b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7ceaf3c-9ac9-485e-a6ec-170311b1e47b' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>perm_shk</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03491 0.03491 ... 11.51 11.51</div><input id='attrs-48745889-650b-48f6-90ea-330968366875' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48745889-650b-48f6-90ea-330968366875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-793add3e-a47b-40d0-a016-eaae2562c383' class='xr-var-data-in' type='checkbox'><label for='data-793add3e-a47b-40d0-a016-eaae2562c383' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>Permanent Shocks to Income</dd><dt><span>limit :</span></dt><dd>{&#x27;type&#x27;: &#x27;Lognormal&#x27;, &#x27;mean&#x27;: -0.5, &#x27;variance&#x27;: 1.0}</dd></dl></div><div class='xr-var-data'><pre>array([ 0.03491194,  0.03491194,  0.03491194, ..., 11.50651472,\n",
+       "       11.50651472, 11.50651472])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tran_shk</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03491 0.05308 ... 6.966 11.51</div><input id='attrs-1e4a9344-302e-4432-9ba3-b9056b85a6f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e4a9344-302e-4432-9ba3-b9056b85a6f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-439459e4-49e9-416a-ab0a-e2a279b9d3d1' class='xr-var-data-in' type='checkbox'><label for='data-439459e4-49e9-416a-ab0a-e2a279b9d3d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>Transitory Shocks to Income</dd><dt><span>limit :</span></dt><dd>{&#x27;type&#x27;: &#x27;Lognormal&#x27;, &#x27;mean&#x27;: -0.5, &#x27;variance&#x27;: 1.0}</dd></dl></div><div class='xr-var-data'><pre>array([ 0.03491194,  0.0530801 ,  0.06439949, ...,  5.72401042,\n",
+       "        6.9659349 , 11.50651472])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb794355-7ad2-44ca-bcae-e5750647c9a9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb794355-7ad2-44ca-bcae-e5750647c9a9' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>Distribution of Shocks to Income</dd><dt><span>pmv :</span></dt><dd>[2.5e-05 2.5e-05 2.5e-05 ... 2.5e-05 2.5e-05 2.5e-05]</dd><dt><span>seed :</span></dt><dd>0</dd><dt><span>RNG :</span></dt><dd>RandomState(MT19937)</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -468,7 +468,7 @@
        "    tran_shk  (dim_0) float64 0.03491 0.05308 0.0644 ... 5.724 6.966 11.51\n",
        "Attributes:\n",
        "    name:     Distribution of Shocks to Income\n",
-       "    pmf:      [2.5e-05 2.5e-05 2.5e-05 ... 2.5e-05 2.5e-05 2.5e-05]\n",
+       "    pmv:      [2.5e-05 2.5e-05 2.5e-05 ... 2.5e-05 2.5e-05 2.5e-05]\n",
        "    seed:     0\n",
        "    RNG:      RandomState(MT19937)"
       ]
@@ -542,7 +542,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15.8 ms ± 319 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "15.7 ms ± 200 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -569,7 +569,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "241 ms ± 17.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "222 ms ± 4.64 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -601,7 +601,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "16.7 ms ± 2.55 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "16.1 ms ± 117 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -678,7 +678,7 @@
  ],
  "metadata": {
   "jupytext": {
-   "formats": "ipynb,auto:percent"
+   "formats": "ipynb,py:percent"
   },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
diff --git a/examples/Distributions/DiscreteDistributionXRA.py b/examples/Distributions/DiscreteDistributionXRA.py
index 76a5d214e..fe177cc1a 100644
--- a/examples/Distributions/DiscreteDistributionXRA.py
+++ b/examples/Distributions/DiscreteDistributionXRA.py
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.0
+#       jupytext_version: 1.14.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -45,8 +45,8 @@
 
 # %%
 x_dist = DiscreteDistributionXRA(
-    IncShkDstn.pmf,
-    IncShkDstn.X,
+    IncShkDstn.pmv,
+    IncShkDstn.atoms,
     name="Distribution of Shocks to Income",
     var_names=["perm_shk", "tran_shk"],
     var_attrs=[


Test patch:
diff --git a/HARK/ConsumptionSaving/tests/test_ConsPrefShockModel.py b/HARK/ConsumptionSaving/tests/test_ConsPrefShockModel.py
index 3c9ebbc1e..eeb44ca6e 100644
--- a/HARK/ConsumptionSaving/tests/test_ConsPrefShockModel.py
+++ b/HARK/ConsumptionSaving/tests/test_ConsPrefShockModel.py
@@ -18,7 +18,7 @@ def test_solution(self):
         self.assertEqual(self.agent.solution[0].mNrmMin, 0)
         m = np.linspace(self.agent.solution[0].mNrmMin, 5, 200)
 
-        self.assertAlmostEqual(self.agent.PrefShkDstn[0].X[0,5], 0.69046812)
+        self.assertAlmostEqual(self.agent.PrefShkDstn[0].atoms[0, 5], 0.69046812)
 
         self.assertAlmostEqual(
             self.agent.solution[0].cFunc(m, np.ones_like(m))[35], 0.8123891603954809
@@ -31,12 +31,12 @@ def test_solution(self):
 
     def test_simulation(self):
         self.agent.T_sim = 10
-        self.agent.track_vars = ['cNrm', "PrefShk"]
+        self.agent.track_vars = ["cNrm", "PrefShk"]
         self.agent.make_shock_history()  # This is optional
         self.agent.initialize_sim()
         self.agent.simulate()
 
-        self.assertAlmostEqual(self.agent.history['cNrm'][0][5], 0.7366020536567589)
+        self.assertAlmostEqual(self.agent.history["cNrm"][0][5], 0.7366020536567589)
 
         self.assertEqual(
             self.agent.shock_history["PrefShk"][0][5],
@@ -58,7 +58,9 @@ def test_solution(self):
 
         m = np.linspace(self.agent.solution[0].mNrmMin, 5, 200)
 
-        self.assertAlmostEqual(self.agent.PrefShkDstn[0].X[0,5], 0.6904681186891202)
+        self.assertAlmostEqual(
+            self.agent.PrefShkDstn[0].atoms[0, 5], 0.6904681186891202
+        )
 
         c = self.agent.solution[0].cFunc(m, np.ones_like(m))
         self.assertAlmostEqual(c[5], 0.13237946)
@@ -71,8 +73,8 @@ def test_solution(self):
 
     def test_simulation(self):
         self.agent.T_sim = 10
-        self.agent.track_vars = ['cNrm', "PrefShk"]
+        self.agent.track_vars = ["cNrm", "PrefShk"]
         self.agent.initialize_sim()
         self.agent.simulate()
 
-        self.assertAlmostEqual(self.agent.history['cNrm'][0][5], 0.7717096928111515)
+        self.assertAlmostEqual(self.agent.history["cNrm"][0][5], 0.7717096928111515)
diff --git a/HARK/tests/test_approxDstns.py b/HARK/tests/test_approxDstns.py
index 826b45796..165ea0f0f 100644
--- a/HARK/tests/test_approxDstns.py
+++ b/HARK/tests/test_approxDstns.py
@@ -18,15 +18,17 @@ def test_mu_normal(self):
         for mu_normal in self.mu_normals:
             for std_normal in self.std_normals:
                 d = distribution.Normal(mu_normal).approx(40)
-                self.assertTrue(sum(d.pmf * d.X[0,:]) - mu_normal < 1e-12)
+                self.assertTrue(sum(d.pmv * d.atoms[0, :]) - mu_normal < 1e-12)
 
     def test_mu_lognormal_from_normal(self):
         for mu_normal in self.mu_normals:
             for std_normal in self.std_normals:
-                d = distribution.approx_lognormal_gauss_hermite(40, mu_normal, std_normal)
+                d = distribution.approx_lognormal_gauss_hermite(
+                    40, mu_normal, std_normal
+                )
                 self.assertTrue(
                     abs(
-                        sum(d.pmf * d.X[0,:])
+                        sum(d.pmv * d.atoms[0, :])
                         - distribution.calc_lognormal_style_pars_from_normal_pars(
                             mu_normal, std_normal
                         )[0]
@@ -64,12 +66,8 @@ def test_VCOV(self):
 
         vcov_fun = lambda X, mu: np.outer(X - mu, X - mu)
 
-        Sig_2D = distribution.calc_expectation(
-            self.dist2D_approx, vcov_fun, self.mu2
-        )
+        Sig_2D = distribution.calc_expectation(self.dist2D_approx, vcov_fun, self.mu2)
         self.assertTrue(np.allclose(Sig_2D, self.Sigma2, rtol=1e-5))
 
-        Sig_3D = distribution.calc_expectation(
-            self.dist3D_approx, vcov_fun, self.mu3
-        )
+        Sig_3D = distribution.calc_expectation(self.dist3D_approx, vcov_fun, self.mu3)
         self.assertTrue(np.allclose(Sig_3D, self.Sigma3, rtol=1e-5))
diff --git a/HARK/tests/test_core.py b/HARK/tests/test_core.py
index 935ef856d..ff719839a 100644
--- a/HARK/tests/test_core.py
+++ b/HARK/tests/test_core.py
@@ -79,7 +79,7 @@ def test_distance(self):
 
 class test_AgentType(unittest.TestCase):
     def setUp(self):
-        self.agent = AgentType(cycles = 1)
+        self.agent = AgentType(cycles=1)
 
     def test_solve(self):
         self.agent.time_vary = ["vary_1"]
@@ -94,27 +94,32 @@ def test_solve(self):
         self.assertTrue(isinstance(self.agent.solution[0], MetricObject))
 
     def test___repr__(self):
-        self.assertTrue('Parameters' in self.agent.__repr__())
+        self.assertTrue("Parameters" in self.agent.__repr__())
 
     def test___eq__(self):
-        agent2 = AgentType(cycles = 1)
-        agent3 = AgentType(cycels = 2)
+        agent2 = AgentType(cycles=1)
+        agent3 = AgentType(cycels=2)
 
         self.assertEqual(self.agent, agent2)
         self.assertNotEqual(self.agent, agent3)
 
+
 class test_distribute_params(unittest.TestCase):
     def setUp(self):
         self.agent = AgentType(cycles=1, AgentCount=3)
 
-
     def test_distribute_params(self):
         dist = Uniform(bot=0.9, top=0.94)
 
-        self.agents = distribute_params(self.agent, 'DiscFac', 3, dist)
-
-        self.assertTrue(all(['DiscFac' in agent.parameters for agent in self.agents]))
-        self.assertTrue(all([self.agents[i].parameters['DiscFac'] == dist.approx(3).X[0,i] for i in range(3)]))
-        self.assertEqual(self.agents[0].parameters['AgentCount'], 1)
-
+        self.agents = distribute_params(self.agent, "DiscFac", 3, dist)
 
+        self.assertTrue(all(["DiscFac" in agent.parameters for agent in self.agents]))
+        self.assertTrue(
+            all(
+                [
+                    self.agents[i].parameters["DiscFac"] == dist.approx(3).atoms[0, i]
+                    for i in range(3)
+                ]
+            )
+        )
+        self.assertEqual(self.agents[0].parameters["AgentCount"], 1)
diff --git a/HARK/tests/test_distribution.py b/HARK/tests/test_distribution.py
index d1356e1a7..7d56ea1eb 100644
--- a/HARK/tests/test_distribution.py
+++ b/HARK/tests/test_distribution.py
@@ -110,13 +110,13 @@ def test_calc_expectation(self):
         TranShkDstn = MeanOneLogNormal().approx(200)
         IncShkDstn = combine_indep_dstns(PermShkDstn, TranShkDstn)
 
-        ce8 = calc_expectation(IncShkDstn, lambda X: X[0] + X[1])
+        ce8 = calc_expectation(IncShkDstn, lambda atoms: atoms[0] + atoms[1])
 
         self.assertAlmostEqual(ce8, 2.0)
 
         ce9 = calc_expectation(
             IncShkDstn,
-            lambda X, a, r: r / X[0] * a + X[1],
+            lambda atoms, a, r: r / atoms[0] * a + atoms[1],
             np.array([0, 1, 2, 3, 4, 5]),  # an aNrmNow grid?
             1.05,  # an interest rate?
         )
@@ -160,12 +160,12 @@ def test_self_expected_value(self):
         TranShkDstn = MeanOneLogNormal().approx(200)
         IncShkDstn = combine_indep_dstns(PermShkDstn, TranShkDstn)
 
-        ce8 = expected(lambda X: X[0] + X[1], dist=IncShkDstn)
+        ce8 = expected(lambda atoms: atoms[0] + atoms[1], dist=IncShkDstn)
 
         self.assertAlmostEqual(ce8, 2.0)
 
         ce9 = expected(
-            func=lambda X, a, r: r / X[0] * a + X[1],
+            func=lambda atoms, a, r: r / atoms[0] * a + atoms[1],
             dist=IncShkDstn,
             args=(
                 np.array([0, 1, 2, 3, 4, 5]),  # an aNrmNow grid?
@@ -234,10 +234,10 @@ def setUp(self):
 
         self.draw_2 = -1 * self.draw_1
 
-        X = np.stack([self.draw_1, self.draw_2], axis=-1)
-        pmf = np.array([0.5, 0.5])
+        atoms = np.stack([self.draw_1, self.draw_2], axis=-1)
+        pmv = np.array([0.5, 0.5])
 
-        self.mat_distr = DiscreteDistribution(pmf, X, seed=0)
+        self.mat_distr = DiscreteDistribution(pmv, atoms, seed=0)
 
     def test_draw(self):
         """
@@ -418,7 +418,7 @@ def test_approx_equiprobable(self):
 
         mu, sigma = 5.0, 27.0
 
-        points = Normal(mu, sigma).approx_equiprobable(701).X
+        points = Normal(mu, sigma).approx_equiprobable(701).atoms
 
         self.assertAlmostEqual(np.mean(points), mu, places=7)
         self.assertAlmostEqual(np.std(points), sigma, places=2)
@@ -505,8 +505,8 @@ def test_self_expected_value(self):
         )
 
         IncShkDstn = DiscreteDistributionXRA(
-            IncShkDstn.pmf,
-            IncShkDstn.X,
+            IncShkDstn.pmv,
+            IncShkDstn.atoms,
             name="Distribution of shocks to Income",
             var_names=["perm_shk", "tran_shk"],
         )