diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..fdce6cb
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,7 @@
+code/modeling_language
+*.pyc
+*.pdf
+*.xlsx
+*.svg
+*.png
+*ipynb_checkpoints*
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..d7aeb45
--- /dev/null
+++ b/README.md
@@ -0,0 +1,24 @@
+This code replicates the graphs from the paper *Managing Capital Outflows: The Role of Foreign. Exchange Intervention.* by Suman S. Basu, Atish R. Ghosh, Jonathan D. Ostry and Pablo E. Winant.
+
+The code requires Python 3.6 and depends on two libraries:
+
+- [dolo](https://github.com/EconForge/dolo)
+- [backtothetrees](https://github.com/albop/backtothetrees)
+
+An up-to-date version of the code can be found on [github](https://github.com/albop/managing_capital_outflows_with_limited_reserves
+
+
+To generate the results run:
+
+- `python compute_simulations.py` : creates files with various simulation results
+    - `precomputed_decision_rules.pickle`
+    - `precomputed_moving_target.pickle`
+    - `precomputed_simulations.pickle`
+
+- `python compute_welfares.py`: create welfare comparison files:
+    - `simple_rules_welfare.xlsx` (p=1.0)
+    - `simple_rules_welfare_9.xlsx` (p=0.9)
+    - `simple_rules_welfare_8.xlsx` (p=0.8)
+
+
+The graphs are then created in the notebooke `gen_graphs.ipynb` which can be opened and run using Jupyter.
diff --git a/calibrations.py b/calibrations.py
new file mode 100644
index 0000000..cac7879
--- /dev/null
+++ b/calibrations.py
@@ -0,0 +1,46 @@
+
+import copy
+from collections import OrderedDict
+
+params = dict(
+    beta=0.8/(0.8+0.15),
+    a=0.8,
+    c=0.15,
+    estar=-0.0,
+    Rbar=0.5,
+    min_f=0,
+    kappa=1.0,
+    N=40,
+    zbar=0.1,
+    p=1,
+    model='optimal'
+)
+
+
+reserve_levels = [0.01, 0.5, 1.0, 1.5, 2.0, 3.0]
+policies = ['volume','peg','optimal','time-consistent']
+cases = {
+    'baseline': {},
+    'accumulation': {'min_f': -10000},
+    'low_beta': {'beta': 0.8},
+    'high_beta': {'beta': 0.9},
+    'super_low_a': {'a': 0.01},
+    'low_a': {'a': 0.4},
+    'high_a': {'a': 1.6},
+    'low_c': {'c': 0.075},
+    'high_c': {'c': 0.30},
+    'p_8': {'p': 0.8},
+    'p_85': {'p': 0.85},
+    'p_9': {'p': 0.9},
+    'p_95': {'p': 0.95},
+}
+
+list_of_calibrations = OrderedDict()
+for case in cases.keys():
+    for pol in policies:
+        for r in reserve_levels:
+            calib = copy.copy(params)
+            calib.update(cases[case])
+            calib['Rbar'] = r
+            calib['model'] = pol
+            list_of_calibrations[(case,pol,r)] = calib.copy()
diff --git a/compute_alpha.py b/compute_alpha.py
new file mode 100644
index 0000000..264da51
--- /dev/null
+++ b/compute_alpha.py
@@ -0,0 +1,185 @@
+######## Solve
+import pandas
+import numpy
+from collections import OrderedDict
+# from solution import *
+from calibrations import *
+from time_consistent import solve as solve_time_consistent
+from time_consistent import simulate
+from bttt.trees import DeterministicTree, get_ts
+from matplotlib import pyplot as plt
+from bttt.trees import DeathTree
+from bttt.model import import_tree_model
+from matplotlib import pyplot as plt
+from numpy import *
+
+
+
+N = 25
+T = 25
+
+
+calib0 = calib.copy()
+beta = calib0['beta']
+a = calib0['a']
+p = calib0['p']
+zbar = calib0['zbar']
+max_R=5
+
+
+tree = DeterministicTree(N)
+for s in tree.nodes:
+    tree.values[s] = zbar
+    model = import_tree_model('models.yaml', key='stochastic', tree=tree)
+
+def solve_it(**cc):
+    tree = DeterministicTree(N)
+    for s in tree.nodes:
+        tree.values[s] = zbar
+    model = import_tree_model('models.yaml', key='optimal', tree=tree)
+    model.calibration.update(cc)
+    sol = model.solve(verbose=True)
+    df = numpy.concatenate( [get_ts(tree, sol, varname)[:,None] for varname in ['e','f', 'Gamma']], axis=1 )
+    df = pandas.DataFrame(df, columns=['e','f','Gamma'])
+    return df
+
+from collections import OrderedDict
+
+
+Rvec0 = linspace(0.01, 3.0, 20)
+
+
+
+pvec = [0.8, 0.85, 0.9, 0.95, 1.0]
+
+# Unconstrained solutions
+
+unconstrained_sols = OrderedDict()
+for p in pvec:
+    dfs = [solve_it(Rbar=i, min_f=0, p=p) for i in Rvec0]
+    unconstrained_sols[p] = dfs
+
+all_gammas = OrderedDict()
+for p in pvec:
+    dfs = unconstrained_sols[p]
+    max_gammas = numpy.array([dfs[i]['Gamma'].max() for i,r in enumerate(Rvec0)])
+    all_gammas[p] = max_gammas
+
+for p in pvec:
+    plt.plot(Rvec0, all_gammas[p])
+
+alpha = 0.5
+
+Rlimit = OrderedDict()
+
+for p in pvec:
+    dfs = unconstrained_sols[p]
+    max_gammas = numpy.array([dfs[i]['Gamma'].max() for i,r in enumerate(Rvec0)])
+    j = numpy.where(max_gammas<alpha)[0][0]
+    a0 = max_gammas[j-1]
+    a1 = max_gammas[j]
+    r0 = Rvec0[j-1]
+    r1 = Rvec0[j]
+    rmax = r0 + (r1-r0)*(alpha-a0)/(a1-a0)
+    Rlimit[p] = rmax
+
+# Constrained solutions
+
+R0 = 0.8
+
+constrained_solutions = OrderedDict()
+
+for p in pvec:
+    Rm = Rlimit[p]
+    R1 = min([R0, Rm])
+    if Rm>0:
+        df = solve_it(p=p, Rbar=R1)
+    elif Rm<=0:
+        df = solve_it(p=p, Rbar=0.001)
+    df['R'] = R0 - df['f'].cumsum().shift()
+    df['R'][0]  = R0
+    constrained_solutions[p] = df
+
+d = {'dfs':constrained_solutions}
+
+import pickle
+with open("precomputed_alpha_p.pickle",'wb') as f:
+    pickle.dump(d,f)
+
+###
+##
+###
+
+
+Rmax = 0.998
+nRvec0 = numpy.array([0.01, 0.5, 0.9, Rmax, 2.0, 3.0])
+ndfs = [solve_it(Rbar=i, min_f=0) for i in nRvec0]
+for i in [4,5]:
+    df = ndfs[3].copy()
+    ndfs[i] = df
+for i,df in enumerate(ndfs):
+    df['R'] = nRvec0[i] - df['f'].cumsum()
+#
+#
+# # In[43]:
+#
+#
+# def plot_dfs(dfs, labels):
+#     attributes = ['b', 'g', 'r']
+#     if not isinstance(dfs, list):
+#         dfs = [dfs]
+#     fig = plt.figure(figsize=(15,10))
+# #     plt.clear()
+#     plt.subplot(131)
+#     plt.plot(dfs[0].index,dfs[0].index*0+0.5,color='black', linestyle='--')
+#     for i,df in enumerate(dfs):
+#         plt.plot(df["Gamma"])
+#
+#     plt.grid()
+#     plt.title("Marginal Value of Intervention")
+# #     plt.figure()
+#     plt.subplot(132)
+#     for i,df in enumerate(dfs):
+#         plt.plot(df["e"], label=labels[i])
+#     plt.legend(loc='lower right')
+#     plt.grid()
+#     plt.title("Exchange Rate")
+#     plt.subplot(133)
+#     for i,df in enumerate(dfs):
+#         plt.plot(df["R"], label=labels[i])
+#     plt.grid()
+#     plt.title("Reserves")
+#     return fig
+#
+#
+# # In[44]:
+
+
+import pickle
+with open("precomputed_option_value.pickle","wb") as f:
+    pickle.dump({"commitment":ndfs},f)
+
+#
+# # In[82]:
+#
+#
+# f = plot_dfs(ndfs, labels=['$R_0={}$'.format(i) for i in Rvec0])
+# plt.savefig("optimal_choice_noconstraint.png", bbox_inches="tight")
+# plt.savefig("optimal_choice_noconstraint.pdf", bbox_inches="tight")
+# f
+#
+#
+# # In[11]:
+#
+#
+# Rvec0 = linspace(0.3, 3.0, 10)
+# dfs = [solve_it(Rbar=i) for i in Rvec0]
+#
+#
+# # In[12]:
+#
+#
+# f = plot_dfs(dfs, labels=['$R_0={}$'.format(i) for i in Rvec0])
+# plt.savefig("optimal_choice_constraint.png", bbox_inches="tight")
+# plt.savefig("optimal_choice_constraint.pdf", bbox_inches="tight")
+# f
diff --git a/compute_simulations.py b/compute_simulations.py
new file mode 100644
index 0000000..6bbb9fc
--- /dev/null
+++ b/compute_simulations.py
@@ -0,0 +1,149 @@
+######## Solve
+import pandas
+import numpy
+from collections import OrderedDict
+from calibrations import *
+from time_consistent import solve as solve_time_consistent
+from time_consistent import simulate
+import bttt
+from bttt.trees import DeterministicTree, get_ts
+from bttt.trees import DeathTree
+from bttt.model import import_tree_model
+
+
+
+N = 25
+T = 25
+
+
+
+model_types =['optimal','peg','volume','time-consistent']
+
+
+results = OrderedDict()
+
+for c,v in list_of_calibrations.items():
+    calib = v.copy()
+    beta = calib['beta']
+    a = calib['a']
+    p = calib['p']
+    zbar = calib['zbar']
+    max_R=5
+    if c[1]=='time-consistent':
+
+        if beta<0.82:
+            max_R=4
+        else:
+            max_R=4
+        r = calib['Rbar']
+        # sol = solve_time_consistent(max_R=2, **v)
+        sol = solve_time_consistent(max_R=max_R, order=100, **v)
+        sim_tc = simulate(r, sol, T)
+        results[c] = sim_tc
+    else:
+        print(p,c[1])
+        # if p == 1:
+        tree = DeterministicTree(N)
+        for s in tree.nodes:
+            tree.values[s] = zbar
+        model = import_tree_model('models.yaml', key=c[1], tree=tree)
+        model.calibration.update(calib)
+        sol = model.solve(verbose=True)
+        df = numpy.concatenate( [get_ts(tree, sol, varname)[:,None] for varname in ['e','f', 'Gamma']], axis=1 )
+        df = pandas.DataFrame(df, columns=['e','f','Gamma'])
+        results[c] = df
+
+
+
+def Gamma(t, e, parm):
+    tot = 0
+    estar = parm['estar']
+    beta = parm['beta']
+    p = parm['p']
+    alpha = parm['a']/(parm['a']+parm['c'])
+    for s in range(t+1):
+        tot+=(beta)**s*alpha**(t-s)*(e[s]-estar)
+    return tot
+
+
+list_of_calibrations
+for c,sim in results.items():
+    print(c)
+    parm = list_of_calibrations[c]
+    # add level of reserves
+    Rbar = c[2]
+    p = parm['p']
+
+    sim['R'] = Rbar - sim['f'].cumsum().shift()
+    sim['R'][0] = Rbar # - sim['f'].cumsum().shift()
+    # add Gamma
+    gg = [p**t*Gamma(t, sim['e'], parm) for t in range(T)]
+    if 'Gamma' in sim.columns:
+        diff = abs(sim['Gamma'] - gg).max()
+        # print(diff)
+        # if diff>1e-6:
+            # raise Exception("Incorrect computation of Gamma")
+    else:
+        sim['Gamma'] = gg
+
+#### save results
+from dolo import groot
+
+groot()
+
+import pickle
+with open("precomputed_simulations.pickle", 'wb') as f:
+    pickle.dump({'results': results, 'calibrations': list_of_calibrations}, f)
+
+
+
+
+#################333
+####################
+
+
+decision_rules = OrderedDict()
+
+for p in [1.0, 0.9, 0.8]:
+
+    v = list_of_calibrations[('baseline','optimal',1.0)].copy()
+    v['p'] = p
+    sol = solve_time_consistent(max_R=7, order=1000, verbose=True,**v)
+    sol = sol[:3]
+    decision_rules[p] = sol
+
+
+
+import pickle
+with open("precomputed_decision_rules.pickle",'wb') as f:
+    pickle.dump({'decision_rules': decision_rules, 'calibration': v}, f)
+
+
+
+#################
+#################
+
+from bttt.model import import_tree_model
+from collections import OrderedDict
+
+model = import_tree_model('models.yaml', key='moving_target', tree=tree)
+
+v = list_of_calibrations[('baseline', 'optimal', 1.0)].copy()
+
+od = OrderedDict()
+for R in [0.01, 1]:
+    lamvec = [0.8, 0.85, 0.9, 0.95, 1.0]
+    for lam in lamvec:
+        vv = v.copy()
+        vv['lam'] = lam
+        vv['Rbar'] = R
+        model.calibration.update(vv)
+        sol = model.solve()
+        df = numpy.concatenate( [get_ts(tree, sol, varname)[:,None] for varname in ['e','f', 'Gamma', 'target']], axis=1 )
+        df = pandas.DataFrame(df, columns=['e','f','Gamma','target'])
+        od[(R,lam)] = df
+
+
+import pickle
+with open("precomputed_moving_target.pickle",'wb') as f:
+    pickle.dump({'simulations': od, 'calibration': v}, f)
diff --git a/compute_stochastic.py b/compute_stochastic.py
new file mode 100644
index 0000000..9b1cfbc
--- /dev/null
+++ b/compute_stochastic.py
@@ -0,0 +1,132 @@
+from bttt import *
+from bttt.trees import *
+from bttt.model import import_tree_model
+from matplotlib import pyplot as plt
+
+
+
+class PersistentDeathTree(EventTree):
+
+    def __init__(self, N, p, k=5):
+        '''
+        p: [p_0, p_1]
+        '''
+
+        self.nodes = [(0,)]
+        nn = self.nodes[-1]
+        self.probas = dict()
+        for t in range(0, N-1):
+            nnb = nn
+            for j in range(k):
+                if (t==N-2) or (j>0):
+                    pb = 1
+                else:
+                    pb = p
+                nnb1 = nnb + (1,)
+                self.nodes.append(nnb1)
+                self.probas[(nnb,nnb1)] = pb
+                nnb = nnb1
+            self.probas[(nnb,nnb)] = 1.0
+
+            # create next point:
+            if t<N-2:
+                nn0 = nn + (0,)
+                self.nodes.append(nn0)
+                self.probas[(nn, nn0)] = 1-p
+                nn = nn0
+            else:
+                self.probas[(nn,nn)] = 1.0
+
+        n = self.nodes[-3]
+        self.probas[(n,n)] = (1-p)
+
+
+T = 40
+
+p = 0.2
+etree = PersistentDeathTree(p=p, N=T, k=2)
+for s in etree.nodes:
+    if sum(s)==0:
+        etree.values[s] = 0.1
+    else:
+        etree.values[s] = 0.0
+
+def get_ts(etree, sol, vname, terminal_state=None, ts_ind=0):
+    import numpy
+    if terminal_state is None:
+        terminal_states = [e for e in etree.nodes if len(e)==len(etree)]
+        terminal_state = terminal_states[ts_ind]
+    history = etree.history(terminal_state)
+    his_inds = [etree.nodes.index(e) for e in history]
+    vals = [sol["{}_{}".format(vname, h)] for h in his_inds]
+    return numpy.array(vals).astype(dtype=float)
+
+
+
+
+
+model = import_tree_model("models.yaml", tree=etree, key="stochastic")
+model.calibration["Rbar"] = 1.0
+model.calibration["beta"]=0.8/(0.8+0.15)
+model.calibration["a"]=0.8
+model.calibration["c"]=0.15
+
+sol = model.solve(verbose=True, linear=True, solver_options={"presteps": 2, 'tol': 1e-5})
+
+from numpy import *
+from matplotlib import pyplot as mplt
+from pandas import DataFrame
+import numpy
+
+# ongoing series
+z = get_ts(etree, sol, 'z')
+f = get_ts(etree, sol, 'f')
+e = get_ts(etree, sol, 'e')
+Gamma = get_ts(etree, sol, 'Gamma')
+R = get_ts(etree, sol, 'R')
+tt = linspace(0,len(f)-1,len(f))
+
+
+from collections import OrderedDict
+data = OrderedDict([
+    ('t', tt),
+    ('z',z),
+    ('f',f),
+    ('e',e),
+    ('Gamma',Gamma),
+    ('R',R)
+
+])
+df = DataFrame(data).set_index('t')
+df
+
+jsnodes = [etree.nodes.index(n) for n in etree.nodes if sum(n)==1]
+zjs, fjs, ejs, Rjs, gammajs = [array([sol['{}_{}'.format(k,ind)] for ind in jsnodes]) for k in ['z','f','e','R','Gamma'] ]
+ttjs = tt[1:]
+ttjs = ttjs[:len(zjs)]
+
+
+data_stopped = OrderedDict([
+    ('t', ttjs),
+    ('z_s',zjs),
+    ('f_s',fjs),
+    ('e_s',ejs),
+    ('Gamma_s',gammajs),
+    ('R_s',Rjs)
+])
+df_stopped = DataFrame(data_stopped).set_index('t')
+
+
+df = df.join(df_stopped)
+
+
+prob = (1-p)**tt # probability that the shock continues at t.
+E_gamma = df['Gamma']*prob + df['Gamma_s']*prob/(1-p)*p
+df['E_gamma'] = df['Gamma']*prob + df['Gamma_s']*prob/(1-p)*p
+df['E_e'] = df['e']*prob + df['e_s']*prob/(1-p)*p
+df['E_f'] = df['f']*prob + df['f_s']*prob/(1-p)*p
+
+
+import pickle
+with open("precomputed_sims_stoch","wb") as f:
+    pickle.dump(df,f)
diff --git a/compute_welfares.py b/compute_welfares.py
new file mode 100644
index 0000000..05c9560
--- /dev/null
+++ b/compute_welfares.py
@@ -0,0 +1,148 @@
+from matplotlib import pyplot as plt
+from calibrations import *
+
+import numpy
+
+calib = list_of_calibrations[('baseline', 'optimal',1.0)]
+calib['a']
+calib['c']
+calib['beta']
+calib['a']/(calib['a']+calib['c'])
+T = 20
+calib['N'] = T
+
+
+
+def perfect_foresight(calib):
+
+    from calibrations import list_of_calibrations
+    calibration = calib.copy()
+
+    from bttt.trees import DeterministicTree, get_ts
+    import numpy
+    import pandas
+    import copy
+
+    from bttt.model import import_tree_model
+
+    # R0 = calibration['Rbar']
+    N = calibration['N']
+    shock = calibration['zbar']
+    model = calibration['model']
+
+    # cc = copy.copy(calibration)
+    # cc['Rbar'] = R0
+
+    # solve deterministic case
+    etree = DeterministicTree(N)
+    for s in etree.nodes:
+        etree.values[s] = shock
+
+    model = import_tree_model('models.yaml', key=model, tree=etree)
+    model.calibration.update(**calib)
+    sol = model.solve( verbose=True, solver_options=dict(eps2=1e-6))
+
+    columns = ['Gamma', 'e', 'f']
+    tab  = [get_ts(etree, sol, c) for c in columns]
+    tab = numpy.row_stack(tab).T
+
+    df = pandas.DataFrame(tab, columns=columns)
+
+    return df
+
+
+def get_welfare(sol, params):
+    beta = params['beta']
+    estar = params['estar']
+    p = params['p']
+    res = sol['e']**2/2.0
+    N = len(res)
+    discount = (p*beta)**numpy.linspace(0,N,N)
+    return -sum(discount*res)
+
+import numpy
+
+filenames = ['simple_rules_welfares.xlsx','simple_rules_welfares_9.xlsx','simple_rules_welfares_8.xlsx']
+pvalues = [1,0.9,0.8]
+
+
+for i,p in enumerate(pvalues):
+
+    calib['p'] = p
+    filename = filenames[i]
+
+    if i==0:
+        kvec = numpy.linspace(0.5, 1.0, 50)
+    else:
+        kvec = numpy.linspace(0.6, 1.0, 50)
+
+    # from models import VolumeModel, PegModel, ConstrainedModel
+    import numpy
+    import pandas
+    import pickle
+
+    from time_consistent import solve as solve_time_consistent
+    from time_consistent import simulate
+
+    calib['Rbar'] = 1.0
+
+    sol_dr_tc = solve_time_consistent(max_R=3, order=100, **calib)
+    sol_tc = simulate(calib['Rbar'], sol_dr_tc, T)
+
+
+    calib['model'] = 'optimal'
+    commitment_solution = perfect_foresight(calib)
+
+    calibs =  [calib.copy() for k in kvec]
+    for i,cc in enumerate(calibs):
+        cc['kappa'] = kvec[i]
+        cc['model'] = 'volume'
+    volume_solutions = []
+    for cc in calibs:
+        print(cc)
+        volume_solutions.append(perfect_foresight(cc))
+
+
+    calibs =  [calib.copy() for k in kvec]
+    for i,cc in enumerate(calibs):
+        cc['kappa'] = kvec[i]
+        cc['model'] = 'peg'
+    peg_solutions = []
+    for cc in calibs:
+        print(cc)
+        peg_solutions.append(perfect_foresight(cc))
+
+    do_nothing = commitment_solution.copy()
+    do_nothing['e'] = 0.1/calib['c']
+
+    tc_solutions = [sol_tc]*len(volume_solutions)
+
+    optimal_welfares = [get_welfare(commitment_solution, calib) for k in kvec]
+    donothing_welfares = [get_welfare(do_nothing, calib) for k in kvec]
+    volume_welfares = [get_welfare(sol, calib) for sol in volume_solutions]
+    peg_welfares = [get_welfare(sol, calib) for sol in peg_solutions]
+    tc_welfares = [get_welfare(sol, calib) for sol in tc_solutions]
+
+    df_welfares = pandas.DataFrame(
+        numpy.row_stack([
+                optimal_welfares,
+                tc_welfares,
+                volume_welfares,
+                peg_welfares,
+                donothing_welfares
+        ]).T,
+        columns=['commitment', 'time-consistent', 'volume', 'peg', 'do_nothing'],
+        index=kvec
+    )
+
+    df_optimal = pandas.DataFrame(numpy.row_stack([commitment_solution['e'], sol_tc['e'][:len(commitment_solution)], do_nothing['e']]).T, columns=['commitment', 'time-consistent', 'do_nothing'])
+    df_peg = pandas.DataFrame(
+        numpy.row_stack([p['e'] for p in peg_solutions]).T, columns=['{}'.format(k) for k in kvec])
+    df_volume = pandas.DataFrame(
+        numpy.row_stack([p['e'] for p in volume_solutions]).T, columns=['{}'.format(k) for k in kvec])
+    writer = pandas.ExcelWriter(filename, engine='xlsxwriter')
+    df_welfares.to_excel(writer, sheet_name="welfares")
+    df_optimal.to_excel(writer, sheet_name="optimal rules")
+    df_peg.to_excel(writer, sheet_name="peg")
+    df_volume.to_excel(writer, sheet_name="volume")
+    writer.close()
diff --git a/generate_graphs.ipynb b/generate_graphs.ipynb
new file mode 100644
index 0000000..8b4a3b8
--- /dev/null
+++ b/generate_graphs.ipynb
@@ -0,0 +1,2041 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "with open(\"precomputed_simulations.pickle\",'rb') as f:\n",
+    "    d = pickle.load(f)\n",
+    "    results = d['results']\n",
+    "    list_of_calibrations = d['calibrations']\n",
+    "import numpy\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mkdir -p graphs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Global graphs configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.500000276740003"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "469.75502/72.27"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_dir = 'graphs/'\n",
+    "\n",
+    "delta_gamma = 2.0\n",
+    "min_gamma = -delta_gamma*0.1\n",
+    "max_gamma = delta_gamma*1.1\n",
+    "min_xr = -0.66666*0.1\n",
+    "max_xr = 0.66666*1.1\n",
+    "deltaf = 0.1\n",
+    "min_f = 0 - deltaf*0.1\n",
+    "max_f = deltaf*(1.1)\n",
+    "min_r = -0.05\n",
+    "max_r = 1.05\n",
+    "\n",
+    "T = 25\n",
+    "\n",
+    "width = 6.5 # textwidth in inches\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "get_ipython().magic('matplotlib inline')\n",
+    "import matplotlib\n",
+    "font = {'family' : 'normal',\n",
+    "        'weight' : 'normal',\n",
+    "        'size'   : 6}\n",
+    "linestyles = ['solid','dashed','dotted','dashdot']\n",
+    "nointstyle = ':'\n",
+    "# matplotlib.rc('font', **font)\n",
+    "leg_fontsize = 6\n",
+    "ebarsize = 6\n",
+    "titlesize = 8\n",
+    "\n",
+    "ticksize = 6 # for ticks\n",
+    "labelsize= 6\n",
+    "# plt.rcParams.update({'figure.titlesize': 'large'})\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## small initial reserves (figure 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pablo/.local/opt/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:52: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n",
+      "  return getattr(obj, method)(*args, **kwds)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAC0CAYAAAAZ3RyeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3Xl4VOXZ+PHvnZlshJCQhLCGJJAFCEH2RVFxRX9W3BCxFESxVeuCSvu+auvb2reLrbutS11RpAKi8qIVqFU2wQW0bAbCFiCsIUAWsmfm+f0xA4Ywkwxk1uT+XNdcyZmcc587kyfnnnPOM88jxhiUUkop1bSwQCeglFJKhQItmEoppZQHtGAqpZRSHtCCqZRSSnlAC6ZSSinlAS2YSimllAe0YCqllFIe0IKplFJKeaBVFEwRGSMi9SKS7FweJiJGRNLOMt5AEZl2hvv/faPnVohIfIPl50TkAk+2PcP97haRZc7HxWcTR7VMU39DEUnzx9+l4X7OtP26iJUhIl+KyF+9l6Fqjov/53FnsN1ZHUPOVIMcl4rIpyKS2MS6fmn7/tQqCqbTOuAa5/fXAWub20BEXP7+xph1xpjXW5jPx8CPGiyfC6xqYUxXZhljxjgfn/sgvmqZNMCjg4a79nim+/FC+50MPGCMubcFMdTZafj/vDDQybgxyxhzEfAWcHMT66XhYdsPFa2pYH4OXOL8Pgf4HujmfCf0hYi8CCffIS0UkY+AsSIS6VxeLCJzRGTqiXdszq//JyIficgqEWkvIqfFdOMDnAVcRAYD64HO7rZ17vd25/e/de5bROQlEflcRP4pIh29+oopr3LVXoCfAZNF5DNXf89G7TFfRPo6Y90nIjc2sU1T+znRfq0i8q7zase7zmVX2zb8HS4G7gEeF5EBfn4JVSMicrWIPCEiYc5jVKqIvCYiy0VkUYNVc5s7Trk5nrk6/nl63Gl4Bc3VcbHJtt/o97zbuf0qEUn15mvoTa2pYNYC1SIyEtjsfK4YuMwYMxroICKZzucjjDFXG2MWAdcCq40xVwDHXAU2xlwNfIKjILuL2Xib7UB3EYnCccb7oafbNvAjYI8x5mLgb8Cdzb8MKtAatZdXcLwjvwT3f88I5zb3AOOdz13hjOG2DTSxnxOuA/KMMRfgeAN5g5ttG+b+ObDGeYazoaWvhTpjk+WHS7LDjTEfAZ1x/H0/AgYDRcaYC4GrGm7o6XGq0Xqujn/NHXcmi8h64A5glvM5V/vzpO0jIkOBAc6z1geBW8/qlfOD1lQwwdEIXsZxdgeQCMwXkWXAaKCb8/nvGmyTDpw4MKxzEXOT8+s+HO+o3MV05VPgUufj381s23AUfHF+7QtMdK7/KyChiX3hfMc4ual1lM81bi8Nuft7nmiPnwEXieNefLkxpqKJbZrazwm9G8ReC2Q0t62IRALVzu+1Pflfw0uy3zif+zswAXgNyAJWAxhj7A228/Q41Xg9V8e/5o47s3AU7rVAT+dzzR0Xm4o5Dhjq/NlTQHmwtr3WWDC/BdY4l88FFhhjxuC4f3iiEDVsaAVArvN7V5egGheyH7uJ6coHwAwc76xqmtm2FOjq/P5EPvnA285/ntHAI03sC2ASkBXMlzTagMbtpQ6wOJfd/T3tAMaYemAX8EtgQTPbNLWfE3YCQ5zfDwV2uNm2ob78cIVG21OAieO+9qPAY8BDONrDyAY/O8HT41Tj9Vwd/5o97hhjbMDjDX7man+etH2AOOA257ZjjDFPEaRtr1UVTGPMcWPMNPPDnGWfAzNEZAEQ42azBcB5IrIE6ILjj9wUT2KeyGc9kILjcmxz234GXCEiDW/0LwTSnNf9PweubCa3dsDTxpjdzayn/GcTjvY1F8/+nvOBu3B0GsPDbRrv54QPgRwRWYHjoPi+B/nmAhud32t78r+Gl2QnA/cBHzqLSC6wDejq/Jt+3EQcT49Tro5/HrU5Y0w+0ElEurjZn6dt/xXgZRFZCpxov0HZ9kTnwwQRsRpj6kXkJRzvgr4MdE5nQ0R+DhwxxsxtdmWlmqHtqW0IxuNfsLY9LZiA891Ve2C7MeaWQOejlFL+osc/z2nBVEoppTzQqu5hKqWUUr6iBVMppZTygBZMpZRSygNWX+8gPj7eZGRkNL/iWaqoqCAmpslPdwRl7NYQ/9tvvy02xnTy2Q48FMptLNTjt5U2BtrOAhXbH/E9bmfGGJ8+srKyjC8tXbo0JGO3hvjAWuPj9uPJI5TbWKjHbyttzGg7C1hsf8T3tJ3pJVmllFLKAz6/JKtUKDp06BBHjx4FIC4ujs2bNzezxdkL5fgNYyckJNC5c2ef7McbxDFr0PU4RpF51BhTISJ3AVHApcaYq5oMoNo8LZhKuXD06FGysrKwWCyUl5cTGxvrs32FcvwTsW02G1u3bg3qgolj7saHcYwxfRmOsU9fEpGxwN6AZqZCghZMpdywWBqPZa7cCaHXyjT6Co4prlxOli0iP8MxryOdOnVi2bJlPkvs+PHjIRs/lHM/E1owlfKxefPmkZeXR/v27ZkyZQrJycnNbrNr1y4WLFhAfHw8AwcOZODAgQDcf//9PPvss6esO3PmzFPWaakT+8jPz6eqqsprcYPAHOC3OC7JFjhn++gJFBrHTDGnMca8gmNwcLKzs82YMWN8ltyyZcsI1fihnPuZ0IKpVBMe++h7NhYeO6MzqH7dOvCbq3NOLhcWFpKUlMTFF19McnIyEydOZNSoUaxfv55BgwaxevVq3n33XebMmUNhYSHt2rXjqqvc307btWsXM2bM4IILLiAuLo5t27axZ88ekpKSeOmll0hISKC+vp4RI0Ywe/Zsxo0bx9KlS3n66ad58MEHeeSRR/jDH/5AUlISffv2ZcOGDSQlJbFhwwYeffRR8vLymDNnDl26dKGkpIRNmzZx8OBB9u3bxx//+EduuOEGJk6cyObNm3nkkeZmnAsexphvcUz/19Au4I/+z0aFIu0lq5SPzZgxg3HjxjFnzhwWLlxIYmIi06dPJyoqinvuuYeePXtSVlZGWVkZSUlJrF27ttmYo0aNYvr06axbt47MzEzGjRtHVFQUGzZsIDExkdLSUgAuvfRSxowZQ48ePVizZg2JiYksW7YMq9VK9+7d2bvXcetu4sSJXHnllRQUFNCvXz8mTpx4cl8rVqzgF7/4BTk5OWzatIlevXoxZcoUqqqqfPOCKRWkmj3DFJHzcdwk7wc8YIw5KiK3AElAjDHmdz7OUbVCbnosTgB+bhwTySIivwXKgSJjzKxA5Pmbq3Na3Glm4cKFbN++nZKSElJSUggPDwcgIiICESEsLAy73U5+fj6DBw/GZrM1G9Nq/eFft3fv3sydO5e77rqLnJwcSkpKGDDAMRdwZGQkAFOmTGH06NH8+9//JjIykhUrVlBRUcGgQYP47LPPsFqtiAh2u524uDjefPNN0tPTAbjwwgt56qmnKCwsZNKkSafsW/mOcTMxhkhTc9YrX/J4thIReRSYbYzZKSLPGGMeEJH/AZ43xpS42y47O9vk5+d7Kd3T6XX5wMUXkW+NMUPPctsn+KHHYoIxZoHz+WeNMfeLSEfgXmPM7060N3exfNHGNm/eTN++fYHQ7sXq6/gNYzd8zbylJW3M27r3SDHPvf4P0rJzsIYZVi9eQHbOAPr170+YsfPPD+cxdOgwBuT2p7q6mjlz5jBixAj69u1LZWUl8+bNY9SoUWRnZ3P8+HHmz5/P6NGjycjIoLS0lDfffJOxV17F9uoYPl6zHUvhWr6t68YheywdpJpzw3fxXX0PiuztiZcqRobvZm1dCsUmhgSpZHj4Hr6p68lR044kqWBoeCFf1aVSYqJJDjvOYOteVtelUWai6BxWziDrPr6oS+e4iaRrWBnnWPezsq4XFSaC7mGl5FoPsLy2N1WEkxJWQo71IEtre1NDOKlhx+hrPcRntRnUYSXdcpRsSxGf1mZiw0IvyxGyLIdZUpuFIYwMSzEZlmIW1/YBIMtymHTLUZbUZgPQx1JEiqWET2uzAOhnOURXSxmf1WYC0N96kE5hx1la6xhpKdd6gASpZHldbwDOse4nTqpZUdcLgEHWfcRILV/UOd70DbHuJVLqWV2XxvmZScyaNuKUv62n7cyjt4oi8mNgpzFmZ6Mfuay2raVnWaj3/AqWnmVNcNVj0aOf+7qNxcXFUV5eDoDNZjv5vS+EcvyGsaurq4O9vbVIrR1eXrGT7UvLEOyMjShh/u7N7PyoCAs2Los4yqzt/2Hf3EJiww3nyTE+PrCJin8V08FqI728nHXLdmBZX037sDqsRyv5eMMBEovDCaur5ECFnZ/P/o6C2vakxRrGdIjkxrQeWOOSsVWWUbWjiN49e2Dt0AlbRQnVBYfJSE3BEpuIreIY1QXFZKT3xBLTEVv5Eap3H+En6T2xxMRTX3aY0q37mTI0FUt0B+pLi6gpPMatGWmERbWnvuQgNXtLuC0zjbDIGOqP7admXym3Z6UTFhFN3dF91O4v42fZvQkLj6TuSCG1B8q5q09vxBrB/s1ribO14+6+GYjFSu1hK3WHKri3XyYSFkZtkYW6okqm93cUwNpDQl1xNdNznMsHoO5YHdP7OZZr9tuxldrp39exfHDTYTpY2zOgj/Pne+uwVRxjYLZzubAGe1UZg7Icy9V7qjA1lQzJdC7vrsDU1zKsdyapie3Oug00e4YpIjcC04DFwDFgCTAWxyXZdsaY/21qez3DbL3xW3iGOQRHd/52QAHwIjAG+A3wjjHm1QaXZA8ZY95xF0vPMAMXvy2dYaZlZJs5/1pNbb3d8bDZqam3UVNnp7rORk29neo6O1V1NqrrbFTV2qiqs1FZa6Oytp6KWhuVNfVU1Di+P15Tj83+w/G3nRWuGZzCuHO6Mzw9AUuYdy+9tuVj5Y4dO1i6dCnnn38+2dnZp/3ca2eYxpj3gPcaPf2Wx5kq5YKbHoufOx8n1vmtP3NSqimRFhjZK9Fr8YwxVNfZKa+po6LGxvYN33DZxQO8Fl/9oHfv3vTu3bvFcbSXrFI+NnPmTG644QbA8RlHd5YtW8aCBQtOe76pbc5Uw33MnTvXa3HVmRMRoiMsJMdGkZ4UQ7iXzyiVw7Zt2/j1r3/NL37xC496oDdFu7sp1Yx58+YxZMgQBg4ciM1mY9asWQwePJgBAwZQV1fH7NmzGTp0KP37Ozp7REVFnRajf//+fPzxx4DjzOKXv/wlqampAEydOvWUde+8805GjRrF8uXLeeyxx8jLy+Pjjz+mY8eOLFq0iKqqKu666y4eeughrrvuOux2O3379qV9+/YsXboUi8VCaWkpe/fu5cknn+SKK67gtttuo7i4mISEBDZu3EifPn348ssvGTduHA899BA9e/aka9eu1NbWUlBQQFlZGbfffjs5OTmNfxWlQsrzzz/PgAEDsNlsJ8eHPlt6hqmUH1x33XUsWrSI2tpaDh8+TFRUFPfeey+bNm06bd3w8HBuueUWcnNzMcbQr18/fvSjHzFnzhzS0tJIS0tj+/btdOrUiUmTJjFhwgTee+89Zs+ezaRJk/jkk0/o1KkT8fHxHDp0iJycHKZNm8aBAwfIzMzk4osvpk8fR2/FjRs3kpuby4wZM1i+fDkAV199NVOmTOHbbxtfMVcq9GRlZVFWVobVauXyyy9vUSw9w1SqGRMmTDjZscVisZxyRhgeHn7KsquzyxPuvfdexo4dywsvvEB1dTXPP/88ubm5p613YlShE5+LBHj//feZMGECixYtokePHmRlZZ38jGVkZCSRkZHU1dXRoUMHrrjiCoqKikhOTiY5OfmUz02mpKQwf/58Mp29B3Nzc5k9ezZPPfUUY8aMoaam5uT6J/atVCi7916XwwSfHU8mzWzJI5QnXQ31SVGDZdJVXz980cby8vJOfl9WVub1+A2FcvyGsRu+Zt4SLG3MhPixzNfxQzl3YzxvZ3qGqZRqE9yMLnUTjgHYdxnHJwKUckvvYSrlhidD1CmHEHmtbsYxW8kCHPNhAkx2ftUuqqpZeoaplAsJCQls3boVwG3PV28J5fgNYyckJPhkH17WePSoSGPMEyLyCjCv8cqtZdQyX8cP5dzPhBZMpVzo3LkznTt3BhyfXRw0aJDP9hXK8X2du5e5mg9zsYg8DBx0tYHR+TADHtsf8T2lBVMp1SYY16NLPRWIXFRo0nuYSimllAdCsmCuWrWKgoKCU54rKChg1apVAcpIKaVUaxeSBbNbt27Mnz//ZNEsKChg/vz5dOvWLcCZKaWUaq1C5h5mdZ2NbYeOs+VgGfkHq9jTLofX3n6X0shkunz5Nddcd/3JGeKVUkopbwuJgrlpXym3zlzD4fIaACKtYWR2jiU2PpVOJdv5tq4rb7y1laGpxVyZ24VJI1KJsIbkybNSSqkgFfQFc9X2Yn729lri20Xwtx8Pom/XDqQlxrBn9y7mz/+KxJ49iThUxMjefVi2v57HPspj3tq9PHnjAHK6xQU6faWUUq1EUBfMjzfs54G56+iV1J63bhtOlzjHB6RP3LMcP348u3fv5qIxY5g/fz4vjh/P9qp2PPLhRq752yruviiDuy/K0LPNIORmmLIHATuOD5W/DjwHbAFqjDHPByxZpZQiiDv9vLV6F/e++x8GpXRk3h2jThZLgP379zN+/PiT9yzT09MZP348+/fv57J+nfn0gQu4+pxuPPfZNq55YRWbD5QF6tdQ7rkapizFGPMskAbUAwk4xvkscLG9Ukr5VVCeYb62cie//+dmLuvXmb/ePIiocMspPz/vvPNO2yY9Pf1kAY1vF8EzNw3k/+V25ZEPN3L9i6t5efIQLszq5Jf8lccaD1PW8Gsy8L4x5h0ReQH4qOGGOmRZcMQPliHLlPKHoCuYReXVPPmvfC7t25mXJg3Gajn7k+DL+nXmnJQ4pr6xhtvfWsOTN57DNQO7ezFb1QKuhikrFJH7gV1AJTBWRHoB3zXeWIcsC474wTJkmVL+EHQF84XPt1NvM/z6qr4tKpYnJMdGMeeOkfz0rbXcP3cdxypqmXqefvwk0NwMU/ZMo+XJKKVUkGi2IolIlojMFJFrGzz3lIjcLyI3ejOZwqOV/OObPUwYlkJaUozX4naICuet24ZzWd/O/PajPJ7+Vz6OOUOVUm2FiAwWkd+LyNMiEuN87lXnsWxsoPNTwa/ZgmmM2QrMbPT0ISAKiPRmMs/+exthItx3caY3wwIQFW7hxUmDmTgshec/387vPs7ToqlU2+Kqo9lBIBawuNlGqZPO6pKsMeYvACLyjIjMNcbUNfz52XTI2HfczgffVTE2LZwt//mKLR7mcqadDsYmGI6mWnlz1S6OHtzHdZkRXot9pkI9vlIh6JQOZsaYRwGcHcs+abyydi4LfGx/xPdUswVTRLoA44FoEYkDlgCXAylAbeNiCWfXIePOWd8SE1nHHyePISHGfRFr7Gw6HYwZY3jo/Y3MXVtIbp8Mbj+/l9din4lQj69UiHHV0exuHB9f2uNqA+1cFvjY/ojvqWYLpjHmIHBPo6ff9mYS6wtLWPz9Qe6/NPOMiuXZEhH+eH0ux2vq+f0/NxMbZeWmYT19vl+lVOC46Wj210DkokJTUPSSffJf+XRsF8600f7rvWoJE565aSDHa+p56IONxERa+dEAne1EKaWUawEf6efrnUdYua2Yuy/KIDYq3K/7jrCG8fJPhjA0tSMPzF3Hym2H/bp/pZRSoSPgBfP97/YSG2nlJyNTA7L/6AgLr08dRu9O7bnrne90GD2llFIuBbRg2uyGzzYXMaZP8mnD3/lTh6hw3rx1GO0jrdz65hoOlFYFLBellFLBKaAFc13hMY5U1HJZv86BTAOArnHRvDF1GMdr6rn1zTWUV5/W+VcppVQbFtCC+a+8Q1jDJGgGRe/XrQMvThrMtqLj/Hz2d9TbdWADpZRSDgEtmJ/mHWJkr0Tiov3b2acpF2R14k/X5bJyWzFv59XqaEBKKaWAABbMHYePs/NwRVBcjm1swrAU7r04gxV763lp+Y5Ap6OUUioIBKxgfpp3CIBLg7BgAjx4WRYju1r4y+J8Ptl4INDpKKWUCrCADVzw77xD5HTrQPf46ECl0CQR4bb+kdSGR/DA3HV0i49mYEp8oNNSSikVIAE5wyw+XsO3e45xad/gPLs8IcIivDplKMkdIrn9rbXsK9GPmyilVFsVkIL5+eYijCEo7182ltg+kjduGUZNvY1pM/XjJt7iZm7CB51zE053Lv+X87mRgc1WtQZu2pyIyJMicn+g81PBLyAF8195h+geH01Otw6B2P0Zy+wcy0uThrCt6Dj3/OM/1NvsgU6pNXA1N2GKMeZZIE1EcoDBQASg71KUN7hqc3cD8wOVkAotfr+HWVVr44vth7lpaAoi4u/dn7XRmUn8/tr+PPzBRh77KI/fXZMTUvkHKdPE13Cg0BjzuIi8gnNOwhN0nsLgiB8s8xSegZNtTEQSgEwgGcgVkZeMMTUNV9Z2FvjY/ojvKb8XzC+2F1NdZ+eyfl38vesWu3l4T3YVV/D3FTtJS4rx6+wqrZCruQkLnZfGdgEbgNtE5NfAqsYb6zyFwRE/WOYp9NApbQ4oMcZMF5E04NrGxRK0nQVDbH/E95TfC+aneQeJjbQyPD3B37v2iv++og+7j1Ty+3/mkdIxmstzQq/wBwM3cxM+02j5Pj+lo9oAN20OY8wu4Fm/J6RCjl/vYTYcbD3CGvCJUs5KmHMezQHd45g+Zx0b95YGOiWllFJ+4Neqlbe/jCMVtVzaN9mfu/W66AgLr94ylISYCG57a41+3EQppdoAvxbM9XtLABjcs6M/d+sTybFRvDF1GNW1Nm598xtKq7Qjp1JKtWZ+LZib9pUSFx1Oj47BObrPmcruEsvLk4dQUFzBHbPWUlNvC3RKSimlfMS/BXN/Kbnd41rVxzHOy0jiifHn8NXOo/zivQ3YdUowpZRqlZotmCKSJSIzReTaBs/dIiIzROR/PN1RTb2N/IPl9O8ed7a5Bq1rB3Xnv67I5qP1+/nz4i2BTkcppZQPNFswjTFbgZmNnh5ojHkKQEQ8GpF868Hj1NkMua2wYALcdWFvJo9M5e8rdjJzVUGg01FKKeVlLf0cpsvrj65Gx1hW6OgUU7F3M8uO5rdwtz8IptErLoozbEq28NhHeRQV7mB4l6Zf3rYyOoZSSrUGzRZMEekCjAeiRSQOWAKsE5EZAMaYksbbuBodY8kHG+kQtZ8br7zIq/cwg230ilHn2Zj8+te8urGEkYPP4YKsTl6NfyaCZXQMpZRqDZotmMaYg8A9jZ5+60x3tGlfKf1bWYcfV6IjLLw+dRg3/f1L7pj1Le/cPoIhqaH/MRqllGrr/NJLtrbeTv7B8lZ7/7KxuOhw3p42nOQOkdw2cw35B8sDnZJSSqkW8kvB3HqonFqbvVX2kHUnOTaKd6aNICo8jMmvf82eI5WBTkmpNs3NfJjjnfOuPhro/FTw80vB3LjPMd5qWznDPCEloR2zpo2g1mbnJ69/zYFSHUJPqQByNR/mZ0ACYAlQTiqE+GW2ko37SomNspKa2M4fuwsqWZ1jmXnrcH7y2tf8+NWvmfOzkXTuEBXotJRqq06Ze9UYcwx4SEQedrWyzocZ+Nj+iO8pvxTMTftK6d+t9Xf4cWdgSjxv3TaMKa9/w82vfsWcn44kWYumUv7mag7WGTjOLl2O16nzYQY+tj/ie8rnBdMAWw6UM/W8NF/vKqgNSU1g5m3DueUNZ9H82ahAp6RUm+JmPswnApGLCk0+v4dZZ6PNdfhxZ1haAm9OHcb+kmp+/OpXlNW03XFn3XTAeFBE7heR6c7lJBFZIiIDA5utUkr5oWDWOAcjb2sdftwZ0SuRN6YOo/BYJY+vqeJQWXWgUwoUVx0wUowxzwJpIhIO3AHMC0x6Sil1Kp9fkq2th8RIK6kJba/Djzujeify5tTh3PrGV9z48pfMvn0EKW3z9TFNfD0XCAcuARKBdQ031M4YwRE/WDpjKOUPvi+YdkNO9w6EhbXNDj/ujOqdyH8Ni+K59XWMf3k1s28fQUZybKDT8idXHTAKReR+YJcxZjmwXESm0qhYgnbGCJb4wdIZQyl/8H3BtOnlWHd6xVuYe8cwfvLaN0z4+1e8fdvwNnOv100HjGdcrDfTLwkppVQzfH4P00CbKQJno0+XDrx35yiiwy3c/MpXfL3zSKBTUkop5YJfRvrRM8ympSfF8N6do+jUIZLJr3/D/63bF+iUlFJKNeLzgilAWmKMr3cT8rrFR/PBXecysGc80+es44Wl2zGm7X7sRCmlgo3PC2akBe3w46H4dhHMmjacawZ244kl+Tz8wUbqbPZAp6WUUgo/dPqJsGixPBORVgvP3jSQngnt+Ovn29lXUsULkwbTISo80KkppVSb5vMzzPhILZhnSkSYcXk2f7lhAF/uOMK1f1vFtkM6p6ZSSgWSzwumXo09exOGpfCPn46krLqOa19YxaKNBwKdklIhy81wjD8XkUdF5LFA56eCn196yaqzNzw9gY/vPZ/MzrHcNfs7/rJ4Cza7dgZS6iycNhyjMeZF4M9Al8ClpUKFX6b3Ui3TJS6KuXeM5LcL83hx2Q427ivl6QkD6RQbGejUlAo1pwzDKCJRwB+AP7paWYdgDHxsf8T3lBbMEBFptfCn63MZ0COO3yz8niufW8lTE87hwqxOgU5NqVDhajjGucD3wOXAq4030CEYAx/bH/E9pQUzxNw8vCeDe3bk3ne/45Y3vuH20en88opsIq2WQKemVFBzMxzjNYHIRYWmZu9hurlR/qpz3sKxvk9RNZbdJZaF94xmyqhUXvuigOtfXM32ouOBTksppVo1Tzr9uJq38CAQC+hpTYBEhVv43TX9eXXKUPaXVHHV8yt5efkO6nWgA6WU8glPL8mecqPcGPMogIi8AHzSeOXWcqM8FG5khwO/GW5l1mY7jy/awpxVW5mWG0lKbFjQ3ChXSqnWwJOC6epG+d1AArDH1Qat5UZ5KN3Ivmas4ZONB/mf/9vEY19W8/OLMshtty8obpQrpVRr0GzBdHOj/K++SUedLRHhqgFdGdU7kd999D3Pf7aNLu0Ea/ciLsp7oNQrAAAKnElEQVRODnR6SikV8nTgglYmISaCZycOYuatwwC49c01TJu5hl3FFQHO7FRuOpM96OxMNl1E2ovIr0TkSREZH+h8lVJKC2YrNSY7md+PjubhK/vw1c4jXP7MCv6yeAvl1XWBTu0EV53JUowxzwJpxpjjxpg/AC8A/QKTolJK/UA/h9mKWcOEOy7szXWDuvP44i28uGwH736zh5+PyWDyqFSiwgPeydk09VVE0oB7gF813rC1dCwL9fjasUy1JVow24DkDlE8PWEgU89N44kl+fzhk828/kUB912SyY1DexBuCciFBledyQpF5H5gl4jEAR8Cs4GLgEUNN24tHctCPX6wjMCilD9owWxDBvSIZ9a0EXy54whPLNnCIx9u5O8rdnDnhb25fnB3v44W5KYz2TONlgf5KR2llGqW3sNsg0b1TuT9u87ltSlD6RAVzsMfbOT8Py/l78t3BNM9TqW8yk1HswkisizAqakQoQWzjRIRLu3XmYX3nMfs20eQ1TmWPy3awnmPf87ji7aw91hloFNUyttcTe81D1gXwJxUCNFLsm2ciHBeRhLnZSSxYW8JLy/fwSsrHI+L+3RmyqhURmckEaYzgavWoXEHsyZp57LAx/ZHfE9pwVQnDegRz4uThrCvpIp/fL2bOd8U8u/Nh0hPiuGBy7IYd063QKeoVEu46mg2BhgkIj81xuj0XkEY2x/xPaUFU52me3w0vxzbh/suyWTRxoO8/eUuKmrqA52WUi3ipqPZ586HUs3SgqncirRauHZQd64d1B1jPLqCpZRSrZZ2+lEeEWmb9zCNMdj1zYJSCi2YSjXpz4vzeWRlFbX1Os+oUm2dFkyl3CiprOWt1bs4WGn458b9gU5HKRVgWjCVcmP213uoqrMRHym8trJA7+Mq1cZpwVTKhdp6O2+t3sX5mUlcmxHO9/vL+HLnkUCnpZQKIC2YSrmwcP1+ispruP38XpzbzUpiTASvrSwIdFpKqQDSgqlUI8YYXlu5k+zOsVyQmUSERZg8KpXPtxSxvag80OkppQJEC6ZSjXyxvZgtB8uZdn76yY/TTB6ZSqQ1jNe/0LNMpdoqLZhKNfLqygI6xUZyzcAfhgJMbB/J9YN78P53+yg+XhPA7JRSgaIFU6kG8g+Ws2LrYW4ZlXra/KDTRqdTW2/nna92Byg7pVQgNTs0nogMBq7HMWDxo8aYChF5ELADxhjznI9zVK1Qc+0K+CvwF+AYsNYYs8Qfeb22cidR4WFMGpF62s8ykttzSZ9kZn25mzsv7E1UuP8m3FYtp8cy1VKejCV7M/AwcC6OOeQWACnGmAdE5BlfJqdateba1TnABmPM285ltwWzrKKSP89eTERyL4zdTmXeUsI79yKiUzrGVk/l5uVEdMkgPCkVU19L5ZaVRHTNIjwxBXtdDVX5XxDRrQ/Wjt1Ysm4XEzvu5ciBTDpmZFBaWsq6devo2bMnvXr14scDEwnfuYJH3xF6pKRSX1lK1Y41RPYcgLVDJ2wVJVQXfEtU6kAssYnYKo5RXfAdUemDscR0xFZ+hOrd64hKH4IlJp76ssOUbv0Pa8vaY4nuQH1pETWFG4nOGEFYVHvqSw5Ss/d7ojNHEhYZQ/2x/dTs20x01rmERURTd3Qftfu3EJ09mrDwSOqOFFJ7YCvt+pyPWCOoK95N2a5NfFedjFis1B4uoO7QTtr1uwgJC6O2aCd1RQXE9L8EgNpD26kr3kNMzsWO5QPbqDu2j5h+YwCo2Z+PrfQQ7fpeAMDBTbtYtfF12vU53/HzvXnYKo7RLvs8x3LhJuxVZURnnQtA9Z4NmJpKojNHOpZ3r8PU1xLdezgAt41OJy463FttrDE9lqkW8XTw9cZzyDU5p1xrmUMu1Od4C5Y55JrQXLty284atrGkrj34NO8Q2zfaEOyMjahka/EhdtrqsWDjsohK8osPUmCrJZx6LomoZPPhA+y2VxNJHRdFVPL94f0U2ivpaDXEhNWxYcMG9u7dS3V1NTabjfXr17Nnzx5qKitpZxVWbCvmUH4NHaSac8Mr+e7wXorsJcRLFSPDK1lbVEixOUqCVDI8vJJvivZw1BSTJBUMDa/kq6I9lJjDJIcdZ7DVzsIvd1NmougcVs4gayVfrN7FcRNJ17AyzrFWsnLVLipMBN3DSsm1VrJ8ZQFVhJMSVkKOtZKlK3ZQQzipYcfoa63ks+U7qMNKuuUo2RY77y7bjg0LvSxHyLJUsmTpNgxhZFiKybBUsvizbQBkWQ6TbqlkiXO5j6WIFEsFnzqX+1kO09VynM+cy/2tNjqFHWfpPsdyrrWYBKlk+V7H8jnWI8RJNSsKHcuDrEeJkVq+2ONYHmI9RqTUs3qXYzmlfi9J0T69U9Qmj2W+jh/KuZ8RY0yTD2AI8L/AU8A9OO57PgDcD9zX3PZZWVnGl5YuXRqSsVtDfByXSpttQ64ezbUr5/ITOM4Irmgqlr/bmN1uN/U2x8Nmsxu73e7V+N4Wyv8jLWljjR9t+Vjm6/ihnLsxnrezZs8wjes55PTyhWoRD9vVL/2UzhkRESxtc/KWkKbHMtVS2ktWKaWU8oAWTKWUUsoDWjCVUkopD2jBVEoppTygBVMppZTygBZMpZRSygNaMJVSSikPaMFUSimlPCCOQQ58uAORciDfh7tIAopDMHZriJ9tjIn1YXyPhHgbC/X4baKNgbazAMb2R3yP2pmnY8m2RL4xZqivgovIWl/F92Xs1hLfV7HPUMi2sVCP34baGGg7C0hsf8X3ZD29JKuUUkp5QAumUkop5QF/FMxXQjh+KOfeGuJ7KtR/z1COH8q5n6lQ/121HbQwvs87/SillFKtgV6SVUoppTzgs16yIjIYuB5oBzxqjKnwcvxrgYuAAuA546VTZRHJAh4BFgC1wCAgDvhvb+yjUfw0Z+y9xpjXvRD7fOBcoB+wBEjHu7k3jL8eiMVLubcgJ21nTcdOw4ttzBlf25kX21kotjEX8dMIoXZ2tm3Ml2eYNwO/xfFiXuaD+BVAJRCDF38PY8xWYKZz8TJjzB+ATcA5Poh/DDA4fgdvxF5pjPkzsB240Qe5N4wfixdzbwFtZ03H9mobc8bXduZdIdfGXMQPqXZ2tm3M15dkTaOv3gtszKfGmIeBzcCF3o7vapdeD2jMW8aY3wFWEenljZgi8mNgJ7Cr4a68EbthfGPMY97OvQW0nbkL5oM2BtrOvBo0xNsYhGY7O5s25suBC+bgeEfWDviNt4OLyBhgBI7T9F95MW4XYDwQDawQkUdwXAaY5e34IhIHdAO6A3u9EPtGYAqwGFjng9xPxheRW/Bi7i2g7ayJ2N5uY8742s68KBTbWOP4odbOzraNaS9ZpZRSygPaS1YppZTygBZMpZRSygNaMJVSSikPaMFUSimlPKAFUymllPKAFkyllFLKA1owvURELCJyX6DzUK2btjPla9rG3NOC6T03Ab1FxCvDTinlhrYz5Wvaxtzw5Ug/bU0M8LwxZkegE1GtmrYz5WvaxtzQM0zvEXwz+LdSDWk7U76mbcwNHRpPKaWU8oCeYSqllFIe0IKplFJKeUALplJKKeUBLZhKKaWUB7RgKqWUUh7QgqmUUkp5QAumUkop5QEtmEoppZQH/j+MGbf88GZRzQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c3ff7c400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sol = results[('baseline','optimal',0.01)]\n",
+    "sol\n",
+    "i_peak = numpy.argmax(sol['Gamma'])\n",
+    "\n",
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sol['Gamma'])\n",
+    "plt.xlim(0,T)\n",
+    "# plt.vlines(i_peak, min_gamma, max_gamma,color='grey', linestyle=':')\n",
+    "\n",
+    "plt.plot(i_peak, sol['Gamma'].iloc[i_peak],'x', color='grey')\n",
+    "yl = plt.ylim()\n",
+    "plt.ylim(min_gamma, max_gamma)\n",
+    "#plt.text( i_peak+0.5, 0.03, '$t^{\\star}$', fontsize=ebarsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$', fontsize=labelsize)\n",
+    "plt.title(\"Marginal Value $\\Gamma_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sol['f'], label=\"Small Intervention\")\n",
+    "plt.plot(sol['f']*0, label=\"No Intervention\", linestyle=nointstyle, color='grey')\n",
+    "plt.legend(loc='upper center',fontsize=leg_fontsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_f, max_f)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Intervention $f_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "\n",
+    "plt.plot(sol['e'])\n",
+    "plt.plot(sol['e']*0+sol['e'].iloc[-1], linestyle=nointstyle, color='grey')\n",
+    "plt.text(23,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "yl = plt.ylim()\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_xr, max_xr)         \n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e_t$\", fontsize=titlesize)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig('graphs/small_initial_reserves.pdf')\n",
+    "fig.savefig(output_dir + 'figure_2.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Figure 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32f6b0f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# sensitivity on R-0\n",
+    "\n",
+    "cases = [\n",
+    "('baseline', 'optimal', 0.5),\n",
+    "('baseline', 'optimal', 1.0),\n",
+    "('baseline', 'optimal', 2.0)]\n",
+    "\n",
+    "sols = [results[c] for c in cases]\n",
+    "\n",
+    "fig = plt.figure(figsize=(width, 0.4*width))\n",
+    "\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "# sf.titlesize = 8\n",
+    "sf.title.fontsize = 6\n",
+    "for i in range(3):\n",
+    "    plt.plot(sols[i]['Gamma'][:T], label='$R_0={}$'.format(cases[i][2]), linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_gamma, max_gamma)\n",
+    "plt.grid()\n",
+    "# plt.legend(loc='upper right', fontsize=4)\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Marginal Value $\\Gamma_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(3):\n",
+    "    plt.plot(sols[i]['f'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim(min_f, max_f)\n",
+    "plt.xlim(0,T)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Intervention $f_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "vv = sols[i]['e'].iloc[-1]\n",
+    "plt.plot(sols[i]['e'][:T]*0+vv, color='grey', label='_'.format(cases[i][2]), linestyle=':')\n",
+    "for i in range(3):\n",
+    "    plt.plot(sols[i]['e'][:T], label='$R_0={}$'.format(cases[i][2]), linestyle=linestyles[i])\n",
+    "plt.text(3,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "plt.xlim(0,T)\n",
+    "yl = plt.ylim(min_xr, max_xr)\n",
+    "\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.title(\"Exchange Rate $e_t$\", fontsize=titlesize)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "fig.savefig(output_dir + 'figure_3.pdf')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Figure 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "with open(\"precomputed_decision_rules.pickle\",'rb') as f:\n",
+    "    decision_rules_saved= pickle.load(f)\n",
+    "decision_rules = decision_rules_saved['decision_rules']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from calibrations import calib\n",
+    "zbar = calib['zbar']\n",
+    "c = calib['c']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAC0CAYAAAAZ3RyeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3Xd8VFXe+PHPmcmkTCaNFEIPNSIthN4ERBRFRVmkiFHsrquuuuXRfR4f91ldf7vr6qprWRWVIoIsioggFiAUqQmEIiEBQkIo6Zn0NjPn90dClpoMIZNJ+b5fr3lNZube+/1OMme+ufeee47SWiOEEEKIuhncnYAQQgjREkjBFEIIIZwgBVMIIYRwghRMIYQQwglSMIUQQggnSMEUQgghnCAFUwghhHCCFEwhhBDCCVIwG4lSaoJSKk0pFVtzu/0K1nvZ1fmdEytNKbVRKfWDUiq4jmUjlFLXX0Ws3yqlBtf8vK7md7JPKTWz5rlBSqnfNXT7QjSGutrf1baBK8ihNo5SKkop9eBVbKuXUmq7UuqfdbVBaX8NIwWzcS3WWk+ouX3t7mQuY7HWeiKwEJhTx3IRQIO+LJRSBmCM1npvzVMBWusJwJ3A4wBa633A6JplhWiOInCyDVzl57g2jtY6QWv90VVsKwZ4Bvg1dbRBaX8NI78sF1JK3aaUelUpZaj5D6+bUmq+UmqTUurbcxYdoJRarZT6SSllUUp1rNkL3KqUerdmWxOUUqsuWM5LKfV1zbaXKaXmqWrvKaU2KKXWKKWCLpNe4Dl5XhQPeASIUUqtr2+bSqlf1az/k1KqGzAIOFnzmh9QWLNoXyDlnFWPAFEN/PUK0Wgu1b6opw3UrPO1Umo1kKSU6luzraeUUnfVsU5dcSYopV5WSnkopZYqpTbX3HtcZt1z38P1wBPAX4Bx1N8Gpf1dISmYjStG/eeQ7HCt9WqgPfABsBqIBrK01uOBqeeuqLW+DVgLTAJygMla67GAv1Kq92WWuwPYprWeAuTXLHIrcEJrfT3wNvDYJXLcBzwKLK557lLxPqB6b3RSXdtUSg0FBtbstT4L3A/0BlJrFukLXKuUigP+DPz+nFxSgGvq+6UK0VQuaF/OtAHPmnWeAGbUPDelZhuXbTd1xDnrTuCQ1vo64GfgF5dZ99zcNwC7a/Yk21N/G5T2d4WkYDaucw/J7qp57n1gJjAf6ANsA9BaO85Z72DN/Smq9/yCgRVKqVhgLNDxMst1B/bXPJdQc98XmF2z7n8D7S7MkerCHQd0rXnucvHOqmubtwNDa157DSgC1DmvXwv8ERhGdWH2OOc1Bcjo/6K5uLB9netybWBPzf16YKJSKgwo0lqX1LFOXXHO6nnOtuOAXvWtq5TyAsrPPjznpcu1QWl/V0gKpgvVnB94Afg/4DkgCRh5zmtnnfuhVcDdwFc1/yn+xH8+/BcudxwYUPN4YM19ErCopmiPBf5wYV5aazvVh23OvnapeFWA0YltBgAP1Kw7QWv9GpBM9XkZqG6sybp6WpyvgZvOWbd7zbaFaA4ubF/OtAEHgNbaRvUe3e+Ar+pZp644Z6UAQ2p+Hgocu8y65+oLJNb87EwblPZ3haRgNq5zD8nGAE8BK2uKyACqzxl0UEptBr6pYzsbgN8opb4CfOtY7itgjFLqOyCc6ob3NRBRc95kA3DzpVbUWicBoUqp8MvEO1iz7c/r2eYHwL+UUhuBz2ue2wd0qfm5L9WNF2AT5x9GiuQ/e8ZCNDfOtoGzVgC/5D9t26m2eEGcs1YC/Wq+KwYAXziR7wDgQM3PzrRBaX9XSMl8mC2bUspDa21TSr1H9X+z292dE1RfVgKsP6eX3oWvDwKmaK3/2rSZCdE21NUGpf01jBTMFq5m79ICHNVa3+fufIQQorWSgimEEEI4Qc5hCiGEEE6QgimEEEI4QQqmEEII4QSP+he5OoGBgbpXr171L9hISkpK8PWt60oMiSfx3BcvPj4+R2sd2mQBL0PapWviaa2x2+14eLj2q7Wt/D6bitPtUmvt0lufPn10U9q4caPEk3jNNh4Qp13c5py5Sbt0Tbx169bpP//5z7qoqKhJ4jWV1h7P2Xbp8j1MIYRoK8aMGUP79u2xWCz1LyxanHoLplIqGpgOmIEXtNYlSqlfAt7ADVrrqXVuQAghWrmqqiq01lgsFqKiZAKQ1sqZTj9zqB649ytgMoDW+j3gELDAVYkJIURLUFlZyd69e1m3bp27UxEu5uwhWX3BPVRPLfXkpRZWSj1C9RxvhIaGEhsb29D8rlhxcbHEk3jNNp5ofUwmE+Hh4fTt29fdqQgXc6ZgLqN6D9MMHK+ZZaMrkK6rR+i/iNb6A6oH5SYyMlJPmDChUZJ1RmxsLBJP4jXXeKJ1cTgcGAwGunbtSkREhLvTES5Wb8HUWscD8Rc8nQq84oqEhBCiJTh9+jRffvklM2fOdHcqoonIwAVCCNFAvr6+0iO2DZHLSoRoAS7TW30m8LiunrwbpdQfgSIgS2u92F25thUdO3bk/vvvd3caogm5vGAWFZeQkJBAVFQUdrudxYsXEx0dzcCBA6mqqmLJkiUMHTqU/v37U15ezrJlyxgxYgR9+/altLSU5cuXM2rUKCIjIykuLmbFihWMHTuWXr16UVBQwMqVK7nuuuvo0aMH+fn5JCQkEBERQUREBDk5OXzzzTdMmjSJLl26kJWVxdq1a5k8eTKdOnUiIyODdevWMWXKFMLDwzl16hQ//PADt9xyC2FhYaSnp7N+/XpuvfVWQkJCSE1NJTY2lmnTphEUFERKSgoJCQkMHjyYgIAAjh49ytatW5kxYwYWi4WkpCS2b9/OzJkzMZvNJCYmsnPnTmbPno23tzcHDx4kLi6OuXPnYjKZ2L9/P3v27CEmJgaj0UhCQgIJCQnMmzcPgPj4ePbt21d7zm337t0kJyczd+5cAHbs2MHx48eZM2cOANu2bePkyZO1h4y2bt1KRkYGM2bMAGDTpk3k5uYyffp0ADZu3EhhYSHTpk0D4McffyQlJaU23vfff09VVRVTp1ZfSXS2V+CUKVMAWLNmDSaTiRtvvBGA1atX4+Pjww033ADAqlWr8Pf3Z+LEiQB8+eWXBAcHM378eABWrFhBaWlp7Wdn+fLldO7cmdGjRwOwdOlSunfvzsiRIwFYsmQJffr0YdiwYQAsWrSIfv36MWRI9UT1CxYsICoqqs7PntlsBmiUz96qVauYMGFCnZ+9qzAHeB4YTXVv9a+01suVUqMBlFJBgENr/ZpS6h9AnQWztLRU2mUD2+WKFSs4deoUTz31FEqpFtsux02YSE5xJT98+w0GLzNhfYZQWFZFavxGHF4WdHhfyirtOFJ2kFdh4NM0XyrtmtDseIqNAZz26kqV3UGf4v3kGQNJM3bEbtcMrjpIBu1IVeE4tGa042fSdTAptMehNRMNhzhmDyFFh6K0g0keSRyxh5DiCMGInUmmIxy2hfLAd2vwwMYkz6McsrXnhCMIL6qY6HmMg1XhpDsC8aGK8Z7HOGDrwClHAL6qknGmFPbZOnLG4Y9FVTDWdJy9tk5kOvzwV+WMNqWyx9aZLIeFQFXGSFOa043Q9XuY2kFCWg5yaZIQV+1SvdWdfv3c3uvh4eEcPnwYq9WKw+HAarWSmJhIXl4edrsdq9XKoUOHyMnJwWazYbVaOXjwIJmZmVRVVWG1Wjlw4ABnzpyhsrISq9XK/v37OXnyJOXl5VitVvbt28eJEycoKyvDbreTkJBAamoqpaWlWK1W9uzZw7FjxygpKcFqtRIfH8+RI0coLi7GarUSFxeHxWKhsLAQq9XK7t278fX1paCgAKvVyq5duzCbzVitVqxWKzt27MDHx4f8/Hzsdjvbt2/H29ubvLw8rFYr27Ztw9PTk5ycHKxWKz/99BMmk4ns7GysVitbt27Fw8ODrKwsrFYrmzdvxmg0kpmZidVqZdOmTSilat9TbGwsSinOnDmD3W6v7W196tQpcnNzax+fPHkSq9Va+zg9PZ3CwsLaxydOnDivt3ZaWhqlpaW1j1NTU6moqDhv+aqqqvO253A4zoungS/XbSC7VJORdpIyu+KTxHVYKzSdytIpdhh56McKAMaaMinRnuzdVf05uc5UQIGu4OcjKXgZYbSxjGLlw8mTOZgMEGCrwmYsR6lizApMyo6fwUYX7yoMCixFmo6eDgJ8HCgF/lbo4Q0dfAClCMhTXOtjoIfZCFoRlKcYaDYSafZA6erXB3pp+vuZUA6Ff56BYb5GBvmYUHbwyzcwPMRItLcJZddY8g2Mship8jZhsDvwzTcw2s+IzcuEwWbH12pgbO1jG75WA+P8PbB5mjBWVWEucP7MpMvnwwzs1FOH3f82nz44guHd27k0FrT+XpYSr3nFO3bsGBs3bmTcuHFERkbWu7xSKl5rPfRK4yilhlB9KZcZOA68C0wAXgQ+1Vp/eM4h2Uyt9ad1bS8yMlInJSVdaRoN1tL+rnXRlxgv1p3vr6CsisNnCkk8U0jimSIOZxSSlFlEeZWjdnmDgg4BPnQI8CbUz4tQPy/C/LwIsXgR5OuJv7cJfx+PmnsTvp5GPIyGS8Zr6vfXFJxtly7fwwz19aBzkA8vLlzHH2eOYsS13V0dUogm07NnT3r27OnyOJfprb6h5nZ2mT+6PJE2yuFwsHHjRkaMGIHFYnH54Op1ySgoZ8cZG+u/Osju1DySMos4u98TZDbRt4M/dw/vRo9QX7q2M9O1nZmOgT54ekgfz6vl8r+6QcHHMYP513vb+WRFPp2feoBOgT6uDiuEyx05coSFCxdSXl7O7NmzGTr0inccRQuRlZXFjh07CA4ObvKh76rsDnYfz+OHxEw2Hs4iNbf6PL/Z8yRDugVxy4AODOgcwLUd/Anz80Ip1aT5tSVN8m9SRFgA02fdzcPLDnHfx7tY8dgoAs2eTRFaCJd56623GDhwIHa7nby8PHenI1woPDycJ554An9//yaJV1Zp58fETL4/lElsUhZF5TY8PQyM6RnMPSO7Ycw7TsytE887bCpcr8mOKwy/phvv32th3sc7eP6dZfz18Zn4+3o3VXghGl2fPn0oLCwkICCgtgeiaF0KCwvJzMykd+/eBAQEuDSW1pq4tHxWxJ1kzYEzFFfYCPb15Ob+4dzQtz1je4dg9qz+yo6NPSHF0g2a9ED8yB7B/N/17Tm0OY7nF/zAG49NxSR/dNFCPfnkJYdSFq3Ipk2bOHjwIL/+9a9rL0FqbDnFFSzdeYIVe06SlluK2dPIzf078IshnRjRPRijQQ6xNhdNfuZ61vVDWYA3C79Pw2vFfv5+1yAM8oEQQjRDU6ZMISoqyiXFMjmziI+2HGdlwikqbQ5G9Qjmqet7M6V/OL5eMqZMc9TQ+TBnUT0Ae6rW+t9XGnTe9f0p0l588mMCfy4+zgsP3nnFiQshhKtkZGQQEhKCyWSiS5cujbZdrTVbj+Ywf8txNiVn420ycNeQzjwwtjs9Q2WIvebOmX9jLhphBIgBNgEN3jV84vpe5B7eSeGJo7z3YyK/vEGmxhFCuF95eTkLFy6kb9++3H777Y223e3Hcnnt+yTi0vIJ9fPitzf24e4R3WjnKx0gW4qGzofppbV+VSn1AbD8woWdnQ9zXN+OfFThz79/TCEn4yTjOpuuKPlLae3zKUq8lh1PNH/e3t7cfvvttG/fvlG2l5Bu5e/fJbH1aA7t/b14+Y7+3DW0M14exkbZvmg6DZ0Pc51S6nkg41IrXMl8mOOuc/Dggl3EJR2kb+doZkyIvrJ3cIHWPiKFxGvZ8UTzVlJSgq+vb6NMBn08p4RX1ibyw6FM2vl68j9T+3LPyG54m6RQtlQNnQ/ztcZKwNPDwFuzBvK3N7ewcsNOOnftxsgewY21eSGEcMrPP//M119/zbx58+jQoUODt1NaaePtDUeZv+U4nh4GfjO5D/eP7Y5FOvK0eM3imo4gPzNPPvYwmQF9eWhhHAdPFbg7JSFEG9OlSxcGDRpEWFhYg9bXWrN632kmvbaJd2OPceugDmz4zXienNRbimUr0SwKJkDHkEAWPzSCEG/Nm/M/JTE9290pCSHaAIfDgdYaf39/brnlFozGKz9kmlHi4O4Pd/Lk0r0EmT1Z8dgoXp8ZRZi/DM7SmjSbggnVo+n/ZWp3wrDyu8VbOFNQ5u6UhBCt3OrVq/n6669pyMxNdodm/pYU/venMg6eLuClO/qz+smxDI1w/cxMouk1u+MEIwdG4uX3MKsW7CXmo10sf3SUdLsWQriE1rp2yLsrHbT8WHYxv1+xn/i0fAaFGnn/ofGEB8geZWvWrPYwzxrcPYz59w3Fnn+KF/65iMKySnenJIRohZRSTJgw4Yp6Sjscmg83p3DLm1s4klnE6zMH8XS0lxTLNqBZFkyoHnf23kH+GMqsPLpgJ+VVdnenJIRoJSoqKvjss8/IyLjklXGXlVVYTszHO/nz2kTG9Q7lx2fHMz26s0yp1UY0u0Oy55r3i6kEdh/AsysO8viSPbwfM0QGaxdt0mWGqHwWcFA9oMhHwJvAYaBCa/2W25JtAQoKCsjKyqKiosLpdTYmZfHb5fsoqbTxl+kDmDWsixTKNqZZVx+lFHcO6cafbruWymM7ef7jb3E4rvzEvBCtwByqBxD5iuohKgG6aK3fACIAG9CO6jGej7shvxYlLCyMJ598km7dutW7bKXNwcvfHOL+T3YT6ufF6ifGMnt4VymWbVCz3sM8a/awzpzYaWBH2hleWHWQl+/oLx9W0RZdOETlufdhwBda60+VUu8Aqy9c2dkhK12huQx5WFhYSGFhIZ06dXLqOyS71MG7CRUcL3RwfVcPZkfaOZUYz6lE5+K5isRzjxZRME0mE8899Sivfn+Ef206htnTyB9u6StFU7QllxqiMl0p9TSQCpQCNymlegB7LrWBKxmysrE1lyEP165dS05ODnfddRdeXl51bmPrkRxeWboHm8PAv+4ZzJT+4Vccz1Uknnu0iIIJYDQa+a8pkZQW5nF611reoIJnpg52d1pCNInLDFH5jwsexzRROi3WzTffzLhx4+osllprPtySwl++PUzPUAsf3DuU7iG+TZilaK7qPYeplIpWSr2slHpdKeVb89yHSqmnlVI3uT7F83Lh8fE9CfY2sGjrUT7aKqdqhBD1O3ToECUlJSil8PPzu+xypZU2nly6l1fWHmZK/3C++tUYKZailjOdfi7V2SAD8AOafNj98PD2/M9vn2Jkvx689M0hlu5Ma+oUhBAtSGlpKV999VW958TS80qZ/u421hw4w39NuYZ37o7GV8aAFedo0HyYWusXAGo6F6y9cOGm6Fzwi46aqvRs1q9ZyfGj1zK6U/VoQK395LTEa9nxRNMzm8088MADBAUFXXaZvSfyeXhRHJU2BwvuH874PqFNmKFoKRo6H+avqO7CfuJSKzRV5wKLZTf/3hjPJz9XEj1oAFP6d2j1J6clXsuOJ5qOw+HgzJkzdOrUifDwy3fYWXvgDM98nkB7f2+WPTKMXmGWJsxStCQNnQ/zn65J58qMHD6M/gOjOP7xLp5auof3Y4Yh/WaFEAC7d+9m3bp1PProo5csmFpr3tt0jL+tS2JItyA+iBlCsKXunrOibWvWAxc4w+Jt4oO5g7jdfITXP1vDzzkyhJ4QAgYPHsxtt91G+/btL3qtyu7guS8O8Ld1Sdw+qCNLHhohxVLUq8UXTIB2fmaG9OqIv8WPN/eUs+t4nrtTEkK4SWlpKQ6HA09PT6Kjoy+6Xru00sbDi+L4PC6dp67vxZuzo/A2NXn/RdECtYqCaTAYmDNrJm8+fhvBPooHP9nB3hP57k5LCNHEtNYsW7aMgwcPXnJ+S2tpJXPn72RzcjZ/mT6AZ2+MlAFQhNNaRcE8K8TixeORFdzmsY8nP97I/pNWd6ckhGhCSimGDx9Ohw4dLiqEZwrKuOtf2/n5dCHvzh3C7OFd3ZSlaKlaVcEECAsw06tbZ7y8fbhn/k4Onipwd0pCiCZgt1f3X+jfvz+hoedfFnI0q4hfvLuNjIJyFj0wvM5h7oS4nFZXML28vHjgvhgWPDIePy8P7pu/jUOnC92dlhDChfLz83n77bdJSUm56LX9J63c9a/tVNo1yx4dycgewW7IULQGra5gntWlnZmn+5YxXh3ivvnbSMoocndKQggXUUrRrl27iwYniEvNY+6HO7F4e/DlL0fTr2OAmzIUrUGrLZgAA/v2Zmx0fwxGI3Pn7+BolhRNIVqjwMBAYmJiziuY247mEPPRLkL9vFj+6Ci6BpvdmKFoDVp1wYyMjGT6rTfx2SOj8MTO7PelaArRmhw4cIDVq1djs9nOe35/to37F+ymazsznz86ig4BPm7KULQmrbpgntXRYuQXvklco08w+4OdHMmUoilEa5CXl0dOTs55PWLXHczgzT0V9AqzsPSRkYT6yYAEonG0iYLp7e1N1MD+PHHndQDM+VCKphCtwfjx47n33nsxGqsHHliz/wy/+mwPEf4GPnt4JO18Pd2coWhNGjofplJK/b1mtvdmTynFpEmTGDmgN8seGYk3lcz5cIcUTdFiXKYdPlszL+2vax7/vua5ke7N1rW01mzcuJGsrCyA2mL57YEzPLVsL4O7BPLbYd4E+JjcmaZohRo6H+avgBUuysmlbHknmcJe2lHEnA93kCxFU7QMl2qHXbTWbwARSql+QDTgCVS5JcMmUlJSQnx8PImJibXPrTuYwZNL9xLVJZAFDwzHx0NG7xGNT11q+KjzFlDqVeA5YAwQBGwBXgQKgAHAbK11xQXrnDsf5pDly5c3fuaXUVxcjMVy+el5bDYbqampeId249U9VTi05vfDfOji17Cj0/XFa2wSr2XHmzhxYrzWeuiVrndhO9Rar1JKva61flYp9TqwCJirtf6dUuoDrfUjl9hGs22XV6qyshKTyYRSij2ZNt5JqCDC38Bvh3nj46Fa/edI4jUup9ul1rrOGzAEeAl4DXgCMNQ8HwE8Xd/6ffr00U1p48aNTi+bfMaqr3t5jR70f9/pAyetLo/XGCRey44HxOl62sylbpdqh8AzwNPAUzWP3wL+B7ivvu0153Z5OeXl5TohIUE7HI7a5374OUP3+sMaffvbW3VBWWWjxrsSEq9lx3O2XTZ0Pky01qnAG/VW5GZs/08/Ms33JCvL+nH3hztY9OAIoroEujstIS5ymXb4jwseP9VE6bhFXFwc69evp2PHjoSGhhKblMXjS/bQt4M/ix4Yjr+3nLMUrtUmeslezogRI5g0cQJLHxtNgNnEPfN3Ep8mU4MJ0RyNHj2aBx54gNDQUHak5PLo4nh6hVlY/MAI6eAjmkSbLpidO3dm0KBBdA4y8+HMSMIsJmI+2sWOlFx3pyaEqJGVlUVZWRlKKTp37kxCupUHF+ymc5APix8cToBZiqVoGm26YJ5VUlLCqs8/5YnIUjoEeDPvk11sSs52d1pCtHkOh4PPP/+csx2UEs8Uct/Hu2hn8WTJQyMJtsigBKLpSMEEfH19ueGGG7j+urF8/ugoeoRYeGjhbtYdzHB3akK0aQaDgTvvvJPJkyeTkl1MzEe78DYZ+OyhkYQHeLs7PdHGSMGsMWTIEAIDAwn29eSN27rRv1MAv/psDyv3nnR3akK0SUVF1ddId+7cGW0O4p75O3FozZKHRtClnQykLpqeFMwL7N+/n6WLP+GVyeEMiwji2eX7+GznCXenJUSbcuLECd58802SkpLIK6kk5qOdFFXYWPTAcHqF+bk7PdFG1XtZSVvTr18/KioquKZXDxZERPDLT+P5w8oDlFTYePi6Hu5OT4g2oX379gwbNoywDp2Z98kuTuWXsfjBEfTvJPNZCveRPcwLeHh4MHz4cJRSaFslL93YhakDOvDntYm8+t3hsxeRCyFcwOFw4HA48PLyYuKkyTz174McPF3I23dHM7x7O3enJ9o42cOsw8qVK8nMzOT1Xz2Bv48H72w8Rn5pFS9N64/RIGNVCtHYYmNjSU9PZ/bsOfzmi4NsOZLD32YMZPK17d2dmhBSMOty0003UVxcjJeniVfuHECg2ZP3Yo9RUFbFP2ZG4ekhO+hCNKZ27dpRXl7OK+uS+Wb/GZ67+RpmDu3i7rSEAKRg1ik4OJjg4GAA0tLSeHpid4LMJl5Ze5jCsirejxni5gyFaF2ioqLYmufLws3JPDyuO49KvwHRjNRbMJVS0cB0wAy8oLUuUUrNAHoAXlrrl1yco9sVFxezZMkSBg8ezCO33EKgjyfPfbmfuz/cyYO95ZymEFfDbrezfPlyhg0bRkKBN3//Ppk7ojry/M19UUpOfYjmw5k9zDnA88Boqufh+wpYDwwFyl2XWvNhsViYNWsWXbpUHxqaOawLAWYTTy7dy59zNVFDS+W6MCEaqLS0lIKCAhJSs3luQz5jegXztxmDMEg/AdHMOHtIVp97r7XOB55TSj1/qYUvmHeP2NjYq0zTecXFxS6Ld/LkSbTWZGdnExoaym+HePKPuDJufTOWZ4d40c3f6JK453Ll+5N4wh38/PwYPXUWcz7cSa8wC+/dM0T6B4hmyZmCuYzqmd7NwHGllAH4DWAEfC61gtb6A+ADgMjISD1hwoTGyNUpsbGxuDLenj172Lx5MyNHjmRi1674mjbwzgHNq/E2PogZxOheIS6LDa5/fxJPNJW8vDzi4+PpM3gkDy6Kx9/HxIL7ZZou0Xw1dD7MV12TTvM3ePBgAgIC6Nq1KwCdLAa+eHwE8z7ezX2f7OK1mVHcPqijm7MUovlLTk4mfs8e3tjnoLwKvvjlaBkfVjRrctzjCiml6NmzJwC5ubnk5+fTIcCH5Y+NYnDXIJ5aupd3Nh6VAQ5Eo1JKRSulXlZKva6U8q157lml1NNKqV/XPA5RSn2nlIpyb7bOGTx0GPv8RnAk38EHMUPp016GvBPNmxTMq/Dtt9+SnJyM3W4nwMfE4geHMy2qI69+l8RzXxygyu5wd4qi9ZhD9amRr6jufAfQRWv9BhChlDIBjwLL3ZOe85KTk8nOzub5Lw6w/UQJf5sxkFE9g92dlhD1kuswr8Idd9zBli1bMBqrO/t4eRh5Y1YU3dqZeWvDUU4XlPHsUi0qAAAWoUlEQVTO3Gg5JyMai67jfjRgAiYBwUDChSs3h854DoeDXbt2UYwPX1p7cWcvE4EFR4iNPeKSeE1F4rXseM6SgnkVLBYLPj7V/Z4SEhLo2bMnfn5+PHtjJJ3bmfnDlwe4673tfHz/MDoFXrJ/lBDOulTnu3Sl1NNAqtZ6E7BJKTWPSxRLaD6d8XK8O/Hi14eYPrgTr80c5JJrLVt75zGJ5x5SMBtBUVERa9euZejQodx4440AzBzahU6BPjy2OJ5pb//EB/cOIbprkJszFS3VZTrf/eMSyy1okoSukNaa1NRUsrQ//7MmhcHdw/l/vxggAxOIFkXOYTYCPz8/HnzwQW644Ybznh/TK4QvHx+N2dPI7A92yGTUos3Kzc1l4cKFvLjoezoH+fB+zBC8PFx/3bIQjUkKZiNp3749BoOB8vJytmzZUttLtnd7P7761RgGdwnkmc/38dd1h3E4pAetaFu8/dtx2DOSDIL45P5hBJo93Z2SEFdMCmYjS0xMJDY2ltOnT9c+187Xk8UPjmDO8K68F3uMRz+Np6TC5sYshWgaFRUVFJWU8u6+CuKK/Xn/3mF0C/Z1d1pCNIicw2xkUVFRdOnShZCQ80f88fQw8Mqd/enT3sJL3xxi+rvbeD9mCBEh8uUhWq/Vq1dz8OgJDlt789cZUTIJtGjRZA+zkSmlaotlamoq+/btO++1+8d0Z+EDw8ksKuf2t7eyMSnLXakK4XJ55i5sK2rHTd29uEvmtRQtnBRMF9q+fTvbt2/H4Th/AINxvUNZ/cRYOgeZeWDBbt5af0TOa4pWxWazsTk5m79uzaVrn37c1UeuRRYtX0Pnw3yc6oujPbTWL7o4xxZr+vTpOBwODIaL/y/p0s7MF78czR9WHuD1H5I5cKqA12YOkkEORItXWlrKe+9/wMaCYHqHdeON2YOJ277V3WkJcdWc2cO8aEgurfW7wF+BcJdl1gp4eXnh4+ODw+Hgxx9/JCcn57zXfTyNvD5zEC/edi0bDmcx7e2fOHS60E3ZCtE4isrtpJR4UmqwMP++oVi8pKuEaB0aNB+mUsob+DPwyqUWbg5DcDWneBUVFcTHx3P69OnaWU7O1R34/VAv3ttXyrS3tzC3ryfjO3tc8qLu5vj+JJ44y2Z38LuvEtleFsFnD4+kc5BMrC5aj4bOh/k58DNwI/DhhSs0lyG4mlO8MWPGYLFYLvv6BOAXN1bwzOcJLPg5h3xTCK/cOQDfC/47b67vT+KJ48ePs2jVD+zIaM9L0wczLEJ6xIrWpaHzYU5zTTqt19limZ+fz+bNm5k6dSoeHuf/+kMsXiy8fzjvxh6tPa/5zt3R9O3g746Uhbgi6/enkZtvZc7wYcwefvGRFCFaOukl28ROnz5NUlISeXl5l3zdYFA8cX1vljw0kqJyG9Pe+YmPtx6XXrSiWdtzIp+/7Sojp9M4/mfaIHenI4RLyNn4JtavXz969uyJt3fdM8uP6hnMt78ex3+t2M+fvjlEbHI2f58xsImyFMJ569Zv4o1t2YQHBPPO3CGYjPJ/uGid5JPtBmeL5Z49e9iyZctllwuxeDH/vqG8dEd/dh3PZcqbW9iTKUPqieajuKyC2O27CbNl8eG9QwnylTFiReslBdNNtNakpaWRlpZ20cAG51JKETOyG988OY4OAd68tbeC577YT1F5VRNmK8TFtNb8ac1hVhT3Yfb024gM93N3SkK4lBySdROlFLfffjvAJQc2uFCvMAsrHx/D0x/9yPK4dDYlZ/P/pg9gQmSYq1MV4iJ2u533VnzPv/fa+NXESG4ZJMPeidZP9jDdyGg0YjQaqaysZPny5aSnp9e5vKeHgZmRnnzxy9H4enkw75Pd/Pbf+ygolb3N1k4pFa2Uelkp9bpSyrfmuWeVUk8rpX6tlLIopf5bKfV3pdQMV+fzzZZ4cg/vYkpXI89M7uPqcEI0C1Iwm4Gqqiqys7PJzc11avnBXYP45smxPD6hJyv3nmLyPzbxw6FMF2cp3OyiEbeALlrrN4AIrXWx1vrPwDvAta5M5ExBGS9tLSDOO5q/3D8Zo+HiATaEaI3kkGwz4Ovry2OPPYbRWD0Dvdb6kqP8nMvbZOT3U67h5v4d+N2KfTy8KI7J17bnxduuldFVWi9d171SKgJ4AvjvS63cGCNwFRaX8t7+CorLPJkd5cvenT85tV5rH8FJ4rXseM6SgtlMnC2W6enprF+/nlmzZuHj41PvegM6B/D1E2P5+KfjvPnjEW54fRNPTerNQ2N74OkhBxBakUuNuJWulHoaSFVKBQArgSXARODbCzfQGCNw/e/f3+HaiiKemnUvNw/s6PR6rX0EJ4nXsuM5SwpmM2Oz2SgtLaWiosKpggnV5zYfG9+T2wZ15E+rf+Zv65L4Iv4kL03rz+heIfVvQDR7lxlx6x8XPB7syhyW7jrBl7kdiIkeeEXFUojWot5dkMt0NpiplIp1eXZtUPfu3XnssccIDAwEqg/POqtToA/vxwzl43lDqbQ7uHv+Th5aGMfRrGJXpSvaiG2JJ3hx1c9E9+7Mb38x1t3pCOEWDZ3eazmQ4Lq02jaDwYDWmh9++IE1a9ZcUdEEuP6a9vzwzHh+PyWSHSm53PTGZv531UFyiytclLFozY6ln+Hb5QsZYs7hzVlR0slHtFkNmt6rPjK9V+NIT0/HbrcTGxtb2wnoSuJdC7wy2pOvjlXy6Y40/r07jandTdzQzYS3h3Nfeq3p99kc4rU0NruDF749ToW9A8/dPUlG8hFtWkOn95oADFZKPay1lum9XBRv/PjxQPUgBw6HA4PB0KB4twNHs4r5y7eJrEjMYsNpxSPX9SBmZLeLpg+7UGv6fTaHeC2J1pq/rfuZbSn5vDrjJob1lvOWom1r6PReG2puwoXO7lUWFRWxZMmSq/pi7xVmYf59w9hzIp83fzzCX749zAebU5wunKLtWfT1BrL3xDE3+nruGioj+Qgh1x20AF5eXvj6+uLl5XXV24ruGsTCB4bz5eOjGdApgL98e5gxf93A379LIquwvBGyFa3BsexiFuzJxe4TxAt3urTzrRAthuxWtACenp7cc889KKVIS0vDZrNdNPn0lTpbOPecyOf9Tcd4J/Yo728+xm2DOvLg2O706xjQSNmLlqakoopffhqP1RjIc4/dhrdJviaEACmYLcbZw7PZ2dm8++67zJs3D39//6vebnTXIN6PGUpabgmf/JTK8rh0vtxzihHd2zFneFfMdpm4ui1xOBy88s4CVJ6Jt+6bSsdA564FFqItkILZwvj4+BAaGoqnZ+P2VuwW7Msfb+/HMzf0YenuEyzZmcbTnydg9oAZxQeZObQL/TvJXmdrt3hbClnWYm7o04txvUPdnY4QzYoUzBbGYrFw6623AtV7Aw6H46oPz54rwGzisfE9eWRcD3ak5PLWmjiW7U5n0fY0ru3gz9SBHZg6oAMRIb6NFlM0DwnpVl7+NpmxPUfz7Nxh7k5HiGZHCmYLpbVmxYoVOBwOZs2aVe9g7VfKYFCM7hVC5SBvBg8fw6p9p1i59xSvfpfEq98lcW0Hf24ZEM6U/h3oGerb6PFF0zqVbeWdBcvoaOnOP2YPxmiU/oBCXEgKZgullCIiIsKpmU2uVoDZxL2jIrh3VASnrGV8e+AMaw+c4e/fJ/P375Pp0s6H63qHMr5PKKN7hWCRS1RaFIdD89LyLbS3ZxNz2/UEmmVwAiEuRb7ZWrDhw4fX/lxWVub0YO1Xo1OgDw+N68FD43pw2lrG+sRMNiXnsHLvKZbsPIGHQRHdNYihEdW3IV3bEWA2uTwv0XDvbDzKunQDf5o6i+uiZDJoIS5HCmYrkJeXx8cff8z1119PdHR0k8XtGOhDzKgIYkZFUGlzEJ+Wz+Yj2Ww7msMHm1N4N7a6h22f9haiuwbRr1MA13bw55pwPxkooZn45qd9fLY+kWlRkcSM7e3udIRo1uRbqxUIDAzkmmuuoWvXrm7LwdPDwKiewYzqGQxAaaWNfekFxKXmEZeWz7cHM1i2Ox0ApSAi2Je+HfzoEWKhe4gv3UN96SEdiZpUZkEZ3/+4gXE+dl6YNl3OQwtRDymYrYDBYKjtOQvVA4pbLBY3ZgRmT4/zCqjWmjMF5Rw6XcihM4Uknikk8UwR3/+cic3xn2s9fU0QsX8LHQN96BToQ8dAb8IDfAi1eBHq50mIxYsAH5N8uTeCJ5clkGjrzaJ7B+HnI+cthahPvQVTKRUNTKd68PUXtNYlSqlnAQegtdZvujhHcQV2797Nhg0bePjhh2nXrp2706mllKJjoA8dA3244dr2tc9X2R2k55VyPKeE4zkl/LT/CMrXm/S8Unak5FJUbrtoW55GA+18PQk0mwjwMRFoNhHo40mA2YTFy+M/N28PfL08MHsa8TEZ8TYZ8an52cvDgKeH4YqnTnOX+toh8E/gb0A+EKe1/q6u7VnLbOw+nsvrs6IY3Luzi7MXonVwZg9zDvA8MJrq+TC/ArporZ9RSl044/tFSktLSUhIICoqCrvdzuLFi4mOjmbgwIFUVVWxZMkShg4dSv/+/SkvL2fZsmWMGDGCvn37UlpayvLlyxk1ahSRkZEUFxezYsUKxo4dS69evSgoKGDlypVcd9119OjRg/z8fBISEoiIiCAiIoKcnBy++eYbJk2aRJcuXcjKymLt2rVMnjyZTp06kZGRwbp165gyZQrh4eGcOnWKH374gVtuuYWwsDDS09NZv349t956KyEhIaSmphIbG8u0adMICgoiJSWFhIQEBg8eTEBAAEePHmXr1q3MmDEDi8VCUlIS27dvZ+bMmZjNZhITE9m5cyezZ8/G29ubgwcPEhcXx9y5czGZTOzfv589e/YQExOD0WgkISGBhIQE5s2bB0B8fDz79u2rHYR99+7dJCcnM3fuXAB27NhBcnJybT7btm3j5MmTzJw5E4CtW7eSkZHBjBkzANi0aRO5ublMnz4dgI0bN1JYWMi0adMA+PHHH0lJSamN9/3331NVVcXUqVMBWLduHQBTpkwBYM2aNZhMJm688UYAVq9ejY+PDzfccAMAq1atwt/fn4kTJwLw5ZdfEhwczPjx4+kRamHFihVM9Cvl3ntvAmD58uWEtO9A52uiyCmqYOf61Si/MMqDupNbXIHH8Z/IcwRzvDQca6mV6MoDpNiCSLZXX3A/xfMwR+0hHLWHoHBwk2cyyfZQUuzBGLEz2fMISfYwHt+wDrOHg9Ekke7RkTxTGN4GG/3LD5Hp05Vi7zC8dSXdCg+Q79+dcp8wPB3lhOXupyioNzZzKCZ7Cf5Z+ykLjkRbQjFWFuGTsY/KsGvBNwRjZSGm0/ucaG6XVV87HATs11ovqnlcZ8E02sq4JzyTOwd3lnbZBO3y+PHjzJkzB6BFtUuAFStWUFpaWvvZWb58OZ07d2b06NEALF26lO7duzNy5EgAlixZQp8+fRg2rPpa3kWLFtGvXz+GDBkCwIIFC4iKiqqzJpjNZoBG+eytWrWKCRMm1PnZc1ZD58Osc37Mc+fDDA8P5/Dhw1itVhwOB1arlcTERPLy8rDb7VitVg4dOkROTg42mw2r1crBgwfJzMykqqoKq9XKgQMHOHPmDJWVlVitVvbv38/JkycpLy/HarWyb98+Tpw4QVlZGXa7nYSEBFJTUyktLcVqtbJnzx6OHTtGSUkJVquV+Ph4jhw5QnFxMVarlbi4OCwWC4WFhVitVnbv3o2vry8FBQVYrVZ27dqF2WzGarVitVrZsWMHPj4+5OfnY7fb2b59O97e3uTl5WG1Wtm2bRuenp7k5ORgtVr56aefMJlMZGdnY7Va2bp1Kx4eHmRlZWG1Wtm8eTNGo5HMzEysViubNm3CYDCQkZGB1Wqtna/xzJkztfNjApw6dYrc3NzaxydPnqSwsJCuXbuyZcsW0tLSKCwsrH39xIkT583/mJaWRmlpae3j1NRUKioqzlu+qqqq9nF6ejoOh+O8eEDt49OnT9dOQXY2Xw8Pj/Me5+Xl1T7OzMykqKiodi8vKysLT0/P2tezs7OpqKjAoO0AmKpKCHTk09knA3xg/xkb/YNtzOmkAQ/27TMwNsSTwFAzZTZNSqKRvoEmPAK8KbfZKTlhoIOfB6PMnlTZbHhlGfDy0kSaDdhsdnwLNOHeDnxMFWCrwogdo60cR1kR5Y6K6s9nYTG5RR54Oirws1eRnmUlRznwdpTTT1dy5GQuOVTho8sYRCWHjmeTq8vxo5RB6qon8K6vHTrdLsM6dKRPqA+xsbHSLpugXZ67fHp6eqtol5WVlQDk5uZit9spLy+vfZycnExJSQkA+fn5JCcnU1RUBIDVaq23JpzNtzE+e1artd7PntO01nXegCHAS8BrwBNUz3DyDPA08FR96/fp00c3pY0bN0o8rbXD4dAfffSR/vjjj7XD4XB5vMbS2uNRfbi03nZ34a2+dljz+FWq90Kn1Le93tIuJZ7Eq+Vsu2zofJj1HooV7qWUYtSoURiNRukg0wo42Q5/5+z25BMhxJWTXrKtWN++fWt/zsnJITg4WIqnEEI0kAwY2QZkZ2fzr3/9i507d7o7FSGEaLGkYLYBISEhTJw4kYEDB7o7FSGEaLGkYLYBSinGjBmD2WxGa01GRoa7UxJCiBZHCmYbs2XLFubPn09ubq67UxFCiBZFOv20McOGDcPX17dZjQIkhBAtgexhtjE+Pj4MGTIEpVTtBeBCCCHqJwWzjdJa89lnn7FixYoWM56qEEK4k3L1l6VSqghIcmmQ84UAORJP4jXTeJFaa78mjHdJ0i4lnsQ7j1PtsinOYSZprYc2QRwAlFJxEk/iNed4TRWrHtIuJZ7EOyeeM8vJIVkhhBDCCVIwhRBCCCc0RcH8oAliSDyJJ/GuTGt/3xJP4jV6PJd3+hFCCCFaAzkkK4QQQjjBZQVTKRWtlHpZKfW6UsrXVXHOiddHKbVAKXWHq2PVxBunlPovpdRCpZTLh81RSvVTSj2rlHpPKRXi6ng1Macqpb5uolh3KKXeVEo9rZpgDjKlVHel1B+VUr9VSrn8Mg+l1Pia9xarlOru6nj15CJts3HjSdts3HjNtm26cg9zDvBH4CtgsgvjAKC1TgYWuDrOOfG2aK3/ChwFApsg3s9AJhAOVLk6nlJqMOANpLg6Vo0SoBTwpWmOfDwCFAOeNMHvU2u9CfgnkKi1Pu7qePWQttm48aRtNq5m2zZd/eb1BfetilLqbiBFa90kH1yt9RJgPtC1CcLdDHQBBtc0UJfSWv+gtX4eSATGuzoe4AN8C8QDU5sgHsAdQJPsFThB2mYjkrbZqJpt23TlwAXLqP4v1gy86MI4ACilwoEZgI9Saq/WOs3F8e4C7gXWKaW6NUG8KcBAoCdN8PvUWr9SEzdCa73X1fGUUhOAEUB34L9dHY/qPZ6HABPw/5ogHsBNwKNNFKsu0jYbN560zca1gGbaNqWXrBBCCOEE6SUrhBBCOEEKphBCCOEEKZhCCCGEE6RgCiGEEE6QgimEEEI4QQqmEEII4YSmmEBauFHNNXAfAkeoHsniQzenJESbJ+2yZZI9zNZvBLCE6guAbW7ORQhRTdplCyR7mK3fCKob5D1AjJtzEUJUk3bZAskeZuvXSWv9v8BbwCx3JyOEAKRdtkgyNJ4QQgjhBNnDFEIIIZwgBVMIIYRwghRMIYQQwglSMIUQQggnSMEUQgghnCAFUwghhHCCFEwhhBDCCVIwhRBCCCf8f4F1xg1TbZAJAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32e4aa90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "sol = decision_rules[1.0]\n",
+    "\n",
+    "sf = plt.subplot(121)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sol[0], sol[1][0])\n",
+    "plt.plot(sol[0], sol[1][0][0]+0*sol[1][0], color='grey', linestyle=nointstyle)\n",
+    "slope = (sol[1][0][1]-sol[1][0][0])/(sol[0][1]-sol[0][0])\n",
+    "plt.plot(sol[0], sol[1][0]*0, linestyle=':', color='grey')\n",
+    "plt.plot(sol[0],  sol[1][0][0] + slope*sol[0], linestyle=':', color='grey')\n",
+    "plt.grid()\n",
+    "plt.text(6.5,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "plt.title(\"Exchange Rate $e(R)$\", fontsize=titlesize)\n",
+    "plt.xlim(0,7)\n",
+    "plt.ylim(min_xr, max_xr)\n",
+    "plt.xlabel('$R$',fontsize=labelsize)\n",
+    "\n",
+    "\n",
+    "sf = plt.subplot(122)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "slope = (sol[1][1][1]-sol[1][1][0])/(sol[0][1]-sol[0][0])\n",
+    "\n",
+    "plt.plot(sol[0], sol[1][1])\n",
+    "plt.plot(sol[0], sol[1][1][0] + sol[0]*slope, linestyle=':', color='grey')\n",
+    "\n",
+    "plt.plot(sol[0], sol[1][0]*0, linestyle=':', color='grey')\n",
+    "plt.plot(sol[0], sol[1][0]*0+sol[1][1][-1], linestyle=':', color='grey')\n",
+    "plt.grid()\n",
+    "plt.title(\"Intervention $f(R)$\", fontsize=titlesize)\n",
+    "plt.xlabel('$R$',fontsize=labelsize)\n",
+    "plt.ylim(min_f, max_f)\n",
+    "plt.xlim(0,7)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "fig.savefig(output_dir + 'figure_4.pdf', bbox_inches='tight')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Figure 5: time consistent : sensitivity to R1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32eb4320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "\n",
+    "sols = [\n",
+    "    results[('baseline','time-consistent', 0.5)],\n",
+    "    results[('baseline','time-consistent', 1.0)],\n",
+    "    results[('baseline','time-consistent', 2.0)]\n",
+    "]\n",
+    "labels = [\n",
+    "    \"$R_0=0.5$\",\n",
+    "    \"$R_0=1.0$\",\n",
+    "    \"$R_0=2.0$\",\n",
+    "]\n",
+    "\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i,sol in enumerate(sols):\n",
+    "    plt.plot(sol['Gamma'], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.ylim(min_gamma, max_gamma)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$', fontsize=labelsize)\n",
+    "plt.title(\"Marginal Value $\\Gamma_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i,sol in enumerate(sols):\n",
+    "    plt.plot(sol['f'], linestyle=linestyles[i])\n",
+    "plt.ylim(min_f, max_f)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Intervention $f_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i,sol in enumerate(sols):\n",
+    "    plt.plot(sol['e'], linestyle=linestyles[i], label= labels[i])\n",
+    "plt.plot(sols[i]['e'][:T]*0+vv, color='grey', label='_'.format(cases[i][2]), linestyle=':')\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "plt.text(2,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "yl = plt.ylim(min_xr, max_xr)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e_t$\", fontsize=titlesize)\n",
+    "plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "plt.tight_layout()\n",
+    "fig.savefig(output_dir + 'figure_5.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAC0CAYAAAAZ3RyeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsnXd4VEXXwH+zLZteSCMhJEASSAgQehdEpAuIghQRsIOCIO8rNl71s6CivhZUlNeCNEFEpChKC0gRpBnpNUAgAZKQ3nfn+2MDhJBkN8lusiH39zz7ZO/dMzNn907uuTNz5hwhpURBQUFBQUGhfFQ1rYCCgoKCgkJtQDGYCgoKCgoKFqAYTAUFBQUFBQtQDKaCgoKCgoIFKAZTQUFBQUHBAhSDqaCgoKCgYAGKwVRQUFBQULAAxWAqKCgoKChYwG1vMIUQPYUQb5TxWYgQolc16HC9HSFEtBDikSrUFSqE2CmE+MR6GiqYo6gfnRVCxBS9BlegXKn9z9oU03GzEGK9EKJeObLV0vcVbENFrnVNIIRYV/R/8rcQYkRN62MtbnuDaYYQwKKbhhCiKr/V9XaklAeklF9Voa6xwDQp5eQq1KFQORZIKXsWvVbVtDJlsEBKeScwHxhVjlwIFvZ9BbvF0mttMVW8zxXHXUrZE7gXmGSlOmucOmMwi57IfhZCrBZCbBdCuACPA2OFEBuFic+FEJuEEGuFEJ5FZVYJIVYDx4QQEUV1TRFCDC+nTHnt9BRCvCGE0Aghlgghthb91ZRRtvh36AU8DbwthGhZzT+hQgmEEPcIIWYLIVRFT9TBQoj/CSG2CCF+LSbaovg1FUIEFI0MtgkhPiuq65ZrL4RwKOp/64QQ3wshxpfW58pQz6OYnre0h5m+X+J7PlVUfrsQItiav6GCVfAAKON+1EUIsavo3MNWvM/dVG9xZYQQrkB60WEEcLo6fwxbUmcM5jWklPcAvwB3AV9iekq7CxgEnJNS9gLmAE8WFdEVlXkauL/oXL+iOsoqU14717gXOCylvAM4BNxXRtnium8C/ioa4cRW9bdQqDBjxY0p2Q5SytWAH6bruxpoA1yWUvYABhYvWOKaJgF3Sym7AW5CiLAy5IYCO6SU/YCrRSJl9rliOv4NPAEsKDpXWnuW9H2EEO2AlkUjmWeBCZX65RRsQclrXdp1HADMKDr3TRkyUPH7XMl6ixMBRAoh9gBvAs9Z/ZvXEHXNYB4s+nuBYk/gRUQAI4UQMcBLgFfR+X1FfzcCdwohfIEMKWVWOWXKa+caTYrVvQcINVdWCOEA5Ba9dxFCjC3nuypYn+JTsruLzn0BjAD+B4QDOwCklMZi5Upe03rA8qJ+0w0IKEOuEXDtwehA0d+y+tx1HTEZ7j1Aw6JzZbV3jfLqHAy0K/rsfSBD6Xt2Q8lrXdp1/AwYIYRYALQvQwYqfp8rWW9xIoFXi84nAZrbpc9oalqBaqZ4ahYBFADqouNjwHdSyvcBhBBaoCtgBJBSFgoh4oB/AyvNlCmvnWucBtoCa4F2wMkydCxOBHCk6P0YoIEQIlhKedbcF1ewPsK03jMTeA14Hvgb6ASsEUKoihnNktd0NLBSSvmtEGIRN65zSbkzQAtMT/ktgb8ovc/dhJTSIIR4G9NNa1QZ7Znr+9dwBx6WUv4thNAU/R88gdL37IIS1/p7br2OGinlJCFEAPAVMLcUmcrc50rW27+YWpHAaimlFEKsAvoCem6DPlPXRpglOQh0FUIsBVYBIUVz8pu4uQNcYzkwEVhTdGxJmZLtXOMnoLkQYiumm+KPFujbAvin6L0T8EFt7ny1kOJTsmOBKcBPRTeSFsAJoH7RNV1TTj2bgOlCiJWAczlyKzH1m98Af0xGzqI+J6U8BvgIIfzLaM/Svv8lMFcIsRm41n+VvmdHXLvWwG5uvY5PFOuP32K9+1zJeosTARwver8F0/LCbdFnhJIPs3YihJgEJEspl5oVVqi1FBvVfY7pKX+nHeik9D2FCnG79BnFYCoo2DFFo0sX4KSUclxN66OgUJdRDKaCgoKCgoIF1PU1TAUFBQUFBYuoa16yCnaGEKINMAyTU8BMKWWWEGIiJq+63lLKgeVWoKCgoFBNKFOyCjWKEGI28ALQBfCSUq4sOt8XcJNS/lCT+ikoKChcw+YjTA8PDxkaGmpesJJkZWXh7FyeZ37drr862ti7d2+SlNKnClXIEn/BFOWm1Hi5QojHMYV2Q6/Xt23YsGFpYlbBaDSiUtl25cLWbdT2+o8fP17V/lVpbH3/gtp/j6nt9UMF7mFSSpu+wsPDpS3ZvHmzUn8NtwHskZXsH5iCN7yOKYrM05jW1UOAFy0pX9v7V3W0Udvrr0r/qurL1v1Lytp/fWp7/VJa3seUNUyFGkVKuRfYW+J0HPBW9WujoKCgUDaKl6yCgoKCgoIFKAZTQUHhtkYIES6E+FYIMbTYuXFCiOlCiP/UpG4KtQtlSlahVpOdnc2BAweIjo7GYDCwYMEC2rRpQ8uWLSkoKGDRokW0a9eOqKgocnNz+f777+nYsSMRERFkZ2ezbNkyOnfuTNOmTcnMzGT58uV069aN0NBQ0tLSOHDgAPvV+zmUc4jZ7Wfz888/k9w4mRP5J3gj+g3WrFnDlSZXOJd/jpktZvLLL7+Q2DiRS4ZLzIiYwbp167jY5CJXjVeZGjqV9evXc77xebJFNhN9x3Nh2jQ23+3JRu1Gxl9qQ8qcT8h1yEMIgbfWm4yMdHIcclELNfW09cjISCfmoTBO+fqh2+BLn12rmHuvN6nu/nTd60f3fzaCLgUhtagM7hiMRqQ2mbU4IAxuGI1GPrvXlQzXZnTe78od/2zi3QfdEOpI7tphpO2J3Rh1SailE8LggtFoxKi7glo6g8EFaTQy60EHtKou3Lkjk1Zn9vPqWIHnsd0M3HSRZvGHi+RdodARiRGjNgm1dINCPQVqwevjdHjk302/TSepn3qO/4404Jnfj2G/7uNKUDjDX3qSiPpuVusjUsrjQohvuTn7T7SUcpoQ4j9CCA8pZWppZVMyMnn+rVkccu1ebhtN/V15694W6DTKGOR2RjGYCgpm0Kl0uGpdbxyrbz52UDngrLvhxadX63FWOd/8ufrGsaPaEaEWGC9dQp+cgke6G3laZ9BqKHTQY3AWCCFA50xhfj4GJ0CtBo3pWAoHth1Lp12hP3k6PTq1I04aR3SOWvJ1eox6B1To0BQ6UVhYSKFOi1blgLro2EHrhNQ5onPQmsqrnNFqHVE5FJCr02PU69CgRxQ4YjQYMOh1aHA0HRuN6NR6HLV6hC6fPJ0DGlQ46fRIBx15DnoKHbTocIQCJ4zGwhvH+XryVaBTOeKs0yMdtBQ46NGpwFnngFGnQ+uoR6MqmaTHptyyr664F3aEo55uf39FSPRlftX2AXGrbgYjLN+bRnpSIqMjHCqsQGZmJjExMRXXXKm/+rHEM6gqL2t5mUV9GyWjvo265Xxt99C6HbwwuU29GFefWi0n/DBBGo1Gm7UhZcWvz5Ql+2TYS7/IuKRMm9RfUey9f2HK9DIHUxqqcUXH44DpmIJllFk2zNNVHm4VKQ0vuUv50yQp83NK1fHVVQdl8Iw1cm3sxQp/v9p+fWp7/VLehl6yOpWOfGN+TauhUMs5deoUmzdvpnv37jRt2rRc2cvZl0nITzCN9myAMT8flU5XoTK7Tifz84GLTOkVSnA92+5Nu12QUiZi2rJUnPmWlFW5eUGukXTHYXgcWAiXD8GY5eDsfZPcC/0jOHA+leeWx9LM35XGPi5W0l7Bnqg1E+57x+7ln3H/mBdUUCiHJk2a8Oijj5o1lgAPRz3MjIAZNtEj//x5jnfoSMaGDRaXKTQYeWXVIQI9HJnY07ab6RVMSAcHRJMQTvxxEuOIheDiD3r3W+R0GhWfjm6DVi2YtGgfOfmGGtBWwdaYNZhCiDZCiDeEEB8IIZyLzgkhxHtCiKm2VxEMRsO1qRUFhUpz4sQJXn75Zf71r3+xZ8+eGtUla8dOZG4uusaNLS6zaNc5jiZmMHNQBI46tQ21UyhOfJ8oXOKucDZFA6O/B7UWspJg56dgNF6XC/Bw5MORrTl2KYOXVx5U7lm3IZaMMEcBr2LK/n530bmnMGXlrhZ+i/uNlt+1pMfSHqTlpVVXswq3GR9//DHBwcGEhoaSkpJiVv69v95jXeo6m+iStXMnGj8/dI0aWSSflJnH+78fo3uYN32b+9tEJ4XS6TzheYSLC7qfNt44uX8h/PYiLBkJOVevn+4R7sOUXmH8uC+eZXvO14C2CrbE0jXM67E+hRBeQBjgC7QQQnwupcwrLlzcy8zHx6fKHk5X8q7grfHGxejC1m1bcVXf8FCs7R5a1eEBZldeZjVIeHg46enpuLu706dPH7PyV3KukGHMsLoe0mgke+dOXHr1snh9dPa6Y2TnG3jlnuY2W1NVKB1nt3p43jeMlMVL8L18Ga2vL3R9BrROJqP5xR0w4jsIaA3AlLvC2H0mhbd/PcqQ6ED0WmU24HbBEoP5PaYRphNwBkiVUj4jhAgBhpY0lgBSyi+BLwGaNm0qe/bsWWVFx1F6svmYmBisUX9Z1Pb6q6uN2sDkyaXGci+Td+54xyYPGrmHj2BIS8O5S2eL5GPjU1m29zyPdW9MqK/iTFITuDwwnJT537FhzvP0/7+vTdtLOj4OgW1g2Tj4qg88sBDC+6JWCSbfFcroebtYE5vA/W0b1LT6ClbCrMGUpcf6REoZB3xoA51uIbsgG0eNo/JkrXBbkLVzBwDOnTpZJL9k93mcdRom91IcfWoK58ahxLfwwzE19+YPGrSDJ7bC+v9AYLvrpzs3rkeYrwvzd8RxX5tA5d51m1ArtpWMXDsSgcBJ48RLnV4iyjuqplVSsBJlJJB+AGgIxMkayodZaCzkyfVPElkYSU96WrXu7J07cQgPR+NjWTahLccu0zW0Hq56rVX1UKgYvZdsQGhKuWU614Ohn5reGwpg5UREp0k81DmYmT8f4sD5VFo39KxeZRVsQq3YVjK62WjuaXIP7np3VKJWqKxgOaU5lY0t+ltjj+V5hjzyDHkYpHW3Bxhzc8nesxfnzpZNxx6/lMnFtFzubOprVT0UKo7QaJBScvzYzrKF0s7D2Z3wVR9GGNbi4qDmu51nq09JBZtSO0aYzUYC8GiLR2tYEwUbUTKBtIOUcrYQ4ktgWUlhazuVlcUjTo9Y3WFKd+QInvn5nHJx4UhRveW18csZU7AOh5STxMScrlSbt4Pjmr2wcf4b1H97MUe/+5BmHfreKuDVGJ78A1ZOwmHDi6xw78LY2PFcGRCBj2vFw+Yp2Bd2bzBzCnPIN+TjpnNT1gFuT25yKhNCqIB1QogXgMTSCtjCqawsrO0wVRAZSYabG02HDkVVlEW+vDbmHt9JM/8C7u13R6XbvB0c1+yFtgPGcSDuAm0bRpYt5OQFo5bA7nmE/v4yH6pSWfpXG57uFVZ9iirYBLuf3/wj/g+6fd+NPZf2MHz1cNaeXlvTKilYESnlXinlTCnldCnlHCmlUUr5vpRylpSyxlIvxV6JZfy68STkJ1i1Xq2vL15jxlw3luWRkVvAnrir3NmsDkzHSglG+4+O4+nbkDtfnYubf1D5gkVetKrHNrGq/hQW7TpHYW4WFCrhPWszdj/CDPMM41/t/kUTjyb4O/njrFXiZyrYHqM0IhCohfX20BlSU8mIicG1Z0/UHh5m5befTKLQKOkZbt45yK4xGiE7CRDg4gO56bDtA0i/eOOVkQC9ZkLnSTWtrVmMRiM7Fr8PQtBtzL/KF/aP4s4e3ixZsJeEpc8QlHschs0DH/OhGRXsD7sfYTZyb8S45uPw0nvxyV2f0DOoZ02rpFAHiPaN5pt+3+Crtd7oLuvPXSQ8/wL5cXEWyW8+egVXvYY2wfbtYakuzIbLR+DKcdMJowFWPA7fDISPWsGbfvBeGGwv2oWm0sCOOSbnGEMB1G8JbSeY/tYCVCoVOQuXoZq7CFksNF5Z3BXhR6CHI8tSIyEt3hToYNcXN4XVU6gd2P0I80LmBdx0brjqXM0LKyjYMa597qbRTytwCDO/liWlJOb4ZbqHeaNV1+BzrZSm0G+pZ003+AZtTedXPAGXDkHaObrnpsE2oPm9MPxbUKnh8mHQuZj2JkYGglugac8igM4JXr4MKrt/Xi+TZo9PI/PF18navh2X7uUnl1arBA92CuaddTkMfWI9TbbPgF+fg+PrYMhn1aSxgjWwe4P59ManCXIN4uNeHzPu13G092/P061LZupRULAui44s4ve43xmvH2+1OoVKhT4iwiLZIwkZXErPo2d1bCfJy4CrZyEvHYK7mM79OgPObIXUc5CfaTrXoAM8ut70vjAH3AKgYUdOJefTpHUP8Gl2o84nt5XfZi02lgANBt3PiQ8+J2XhIrMGE+CB9kH8d8Nxvj6QzZtjfoA9X0PM21CYa7asgv1g9wZzSuspOGmdAAj1CMXfWQk8rWB79Go97g7uVvPMzjt9muSvv8b7yYnoGgSalY85fhnAOuuXUkJ6gmmt8NoIcftHcGQ1pJwpWl8EXANg+pGiQgI8Q6BRD/BoaHrVa3KjzhHfXX97PiaGJi16Vl3PWoTQ6cgf1IOC+T9y9vAugiM7livv5axjcKsAftp/gRn9m+HW/hGIHg1aR5BxpuvReqzJw1bBbrF7g3lnwzuvv5/ZeWYNaqJQl7gv/D7uC7/PavsLM37/nbTlP+JjYTzbmKNXaB7ghq+b3rIGpDQ5zrj4m0Zvh3+G2GWQcobuSSdhSx4INbx8yZSeqiDHFDy82UCTYfQMBs9imVP6v13xL1nH8B41mqTvVpC17Ed4tXyDCfBQ52CW743nx73xTOjayGQsAeess/DH66Z13Xs+gmYDbK26QiWxa4OZnp9OQmYCjdwboVNXLDO9goI9kbF+A46tWqH18zMrm5ZTwN5zV3myRym5MqU0bVlIiDUZxeSTkHwKUk5BQTZMOwTuDUzOJUnHwasJF3VNCGp5x80GsefzVvx2dRP/4EgK+/Yl65ctGGfkoHJ0LFe+ZQMPooM8WLDzLOM6h6BSmWYvslxC4PHN8NNE+H4UtBoF/WaBo307e9VF7Hoh4a+Ev7h/9f2cTD0JwPN/PM+0zdNqWCuFusBzW57jjT/fsEpdBRcukHvoEK5397ZIftuJJJyNmQx1PmQadayaDF/3g3ebwPndJqErx2DbfyHxH9NaYtvxMPB906gRoPNT8PRfMPp7ToU+Ah0eg7DeptFlHUII0UYI8YYQ4gMhhHPRudlCiOlCiP9WtX6vMWMwpqdz+of5FsmP6xLM6aQstp9KuvkD/xbw2Ca44znTzMCCe00PRwp2hV2PMFv6tOS9Hu8R4hYCQJhHGIXGwppVSqFO4OfsZ/LMtoJPRsZGU+Jh194lDGZhPiSfxOfydoj502QE2zzE5mNetNFfJGzDKyY5J2/wDjNN1TkUeYtHDobmQ+ucAawEo4AXgC6YYhWvBLSAP2A+i7gZHNu25XKAE4ZvvqDxg4+jMuPMNKBFfd5Yc4T5O87SPazE+rRGB71eMk2T56aZZhIMBaZ9q871qqqqghWwa4Pp4+RD35Ab8RofafFIDWqjUJeY3m46gFXWMDPWr8ehUUN0Wfvh/BUIam+aMv2wJUgDzQEOC/AMxhjeny3HDXQPbQ93/G4ylKU5gmiUuKQVoGSs4stSyreEEJ+WJlzRWMVX+t6JPiOHLZs3I9TmA1108ZOsOXKJH37ZhI+TquxYvOdiCI5bRuCF1ZwMfYzLvt1NRrSC1PZYwvYUq9iuDeahpEO46FwIdguuaVUUFMwjpWkLhoOr6f1PT1B45h+y91ylXkQm/DAe2owzGUzXAOj+LHg3Zc/ZDNr1GwVaRw5fSONKxja6RDSDhkriYStQWqziBkKIl4CM0gpUOFZxBePohkfn8Mu7mzkp6jO8Z0T5sXgv+cLPR4g88j6RhkMw4D3wMBOWrwS1PZawPcUqtmuD+cqOV/B18uWz3qbNvXP2z+G3uN9Yfe/qGtZM4XamwFDAPSvv4bEWj1GPcqbCLu43vRIPmjbxXz4CQR3gweWmkUD6RTITnUGm4jp2OnTqBd5FIdFUKuj1MgCZKTHXPSZ/PZiASkDPprU8HJ6dIKXcC+wtcdrq8fdSribwx1ev03H4ZPyDy99rG+DhSJ9IP5b9dZ5pvcPLr9gvEh5ZD7vmwuY34dOOMPhjaHG/FbVXsBS7NpivdX3tpuNQj1AyAzNrSBuFukKBsYDWvq3xcfLBKA2QkWhyrkn427S21Od1k+BvL8HZ7eDgBr6RpptYw2J5LsevIeOJJ9EGSPRDp5qdTpNSsiY2gS5NvPF2UaZcaxPpF+MI/99mjmnd8H/G/JachzqH8OvBRFb/fRGzj0ZqDXR5GiLuMUUI8irynr7mMa1Qbdi1wWxer/lNx/0a9aNfo341pI2CLRBCtAGGYZoymymlzBJCzAMOAUeklL9VmzJSQupZnDxDmNV9FvzxPvk7PoItaTdkvMOh92umEeKA2abwbx4Ny7xx6Vu1xKlDB4sCIPxzIY2zydlM6tnErKyCfRHSvDMXl8+nWfP2Fsl3auxFuJ8L83fGMT3KQm9Yz2AYvfTG8dpnTV7RPZ+/4QymYFPs1mCm5KZwMOkg0b7RuOncalodBdtRmhdjIuAKWC9VSGlkXIKz2+DiAUg4cGMEOf04uPqBa32S67WlfnRfU2Bwv+agd79R3q952XUX4TPJ8tm/NbEJaFSCvs2VaFa1kYCoDoApm4k5b1khBGM7hzBz5UFOBem5s1zpUjAaAQE7P4WDK0z7NiOHKCNOG2O3BjP2SiyTN01m8YDFtPBpAcDPJ3/mnd3vsOreVXg7etewhgpW5CYvRinlTIAiL8ZfSgpX1IsRKXHIS8Y14wSuGSdJ9L+LHKcA/BI3EXH0I4xCQ6ZLCJmenchwbcIfW75nfupyHvd9HM8Gj+CS6wJn8uHM/gp9KU18PIV+fqAtf+tHZmYmmzZv5sfdOUTWU3Fg944KtWOOuuTFWNP8MnM84uBx+v9k/hoOax3Iu78eZePZAh6taEMqFQz6wBTkYO00+GEchPaGQR9W2ClIwXLs1mC292/PwgELCfUMvX4u2C2YwaGD0aqUvWe3EaV5MT4FeAHnSitg1ovx2trO1ThY9yJc2AOZl0yfqTQEdxoMkT0hqwXcMQyVbyRuGh3X5jFkylHOHkmid6veHNtzrFIeesb8fE506YrboIHUf/XVcmVjYmJwbdSS5N928tLgKHq2sa53bF3yYqxpdC7uBB65Suahg7g0jypX1tlBw31tG7BwZxxXMvLwca3EunVQe3gsBv6aB9s+VEaYNsaswSxjjel+oDHgIKV83RaKOWudaeXT6qZz0b7RRPtG26I5hRqiDC/GTypUSeJBiP8L4veY/rZ6ALpPNznjJB2Dxj0hsC0EtDFFVNEWxWd1rlfqhvBmXs14vaupWx/jWIW/E5gykwS8N9uiUHgAq/9OQKdRcXekZfIK9smdT/4fJ5ZsJeP7Zbi8Xr7BBBjbOZhvd8Sx9K9zPN3LfNq3UlFroNNEaPewaX+ulLByEjTtBxGDK1enQqlYMsIsbY1pI9AOq8RBKZ3tF7aj1+hp69fWVk0o3Aa4ZJ6BuV1NB071oEH7G16ETl4wuaQtrh6ERoOrhaMuo5T88k8CPcN9cNUrsye1GbW7O+73DCJ11SqcnnkCd+/yM9M08XEhqp6ahX+e48keTdBUJffptWAW2SmQGAt/L4bGPXGqp2xBsRaWTsmWXGO6CjwvhHihNOEKrzGVwuyE2TirnJnkd8Np4nTuaeZcnsMTPk/Q1NG0n622r89Ux/rP7bzGlK/zhGGfmJITezayypTUZwc+Y+XJlfx+/++VKi/z80maOxf3YcPQNTA/vXosxcjljDzuaRVQqfYU7Iv8IXfBD8v5c95b9H2h1GBCN3FXsIaP9uWy/vAl+reoX3UFnOvB41tMOTc3v0H701tBdRDufAn0igNlVbDEYJa2xjQdkwdjqeH5KxwpoxSaZzcn15BLkOuNBezwzHCuHr1Kn7A+NHI3ZV6o7esz1bH+czuvMeXrPKDlCKvWGe4Zzl0N76p0+cytW0n67HP0LVtaZDB3JxbiqFVzV0Q1JItWsDnB7XqyJ7IhIRuOImcYEWY8Zlv5qAn0cGT+zjjrGEwwTdN2fByihnFx0VMEHl0LvZT0iFXFrMEsY41ptm3UuYGP063beQNcAq7H+FRQsBW9g3vTO9iyzCKlkfrTStQ+3rh062ZWttBgZE9iIb0i6uOks1sfPIUKEv7oM1x4djpZf/yBS48e5cqqhGBs54a8/etRjiVm0NTfinsqnb05ET6RwC7tQedsCvj/wzho94gpe41ChbDL9F5JOUksO7aMy9mXS/1cKmlvFOyUwuRkMrdswX3wYITGvAHccSqZjAK4p6UyHXs7IXt0xFjPncsLLEv79UC7IBw0Khb8GWcbhXTOpr9p500hHBfdB98NNUWwUrAYuzSYh5MP8/qfr3Mx8+JN59Py0mj9XWsWH11cQ5op1AXG/TqOZ2OerVTZtNWrobAQj6FDLZJfE3sRvVqJHXu7cTT9JMsjMsjd8ScFl0t/8C+Op7OOwa0CWLHvAum5BbZTrF4TeGoX9J1lioM8tzusfArylJCjlmCXc0BdA7qy4f4NeOpvzjjupHFifNT4W0LmKShYk97BvXHSOFW4nJSStBU/oW/RAocw81sE8guNrDuYSGs/NXqtbYMaKVQvUd5RaP79KYFqf7S+lq1Nj+sSwg974/lxbzwTujaynXIaB+g8CaJHwdb3TEnJryUeNxpApfTFsrBLg6lWqfFzvnU/mlat5Zk2z9SARgp1ibGRYytVLu/IEfKOH8f/lf9YJL/hyCXScwvpVF8JtH674ax1pm3TnhUqExXoTpuGHizYeZZxnUNQqWwchMDRE/q+WWQkVabtKPN6QftHTa9r+5UVrmOXU7Ibzm5gzek1pX5mlEYKjDacslCo00gpMUpjpcqmrvgJodXiNmCARfKLdp0l0MORFt7KE/3tyMGkg2w5vo74yZNJ/fFHi8qM6xLC6aQs/jjH28t6AAAgAElEQVSZZGPtinFtRJmXDp4h8PtLMKcd7F9kMqYK17FLg/nD8R9YcmRJqZ/d9cNdvLXrrWrWSKGukGfII/q7aOYfssxZ4xrG/HzSV6/GpfddqN3dzcqfScpi+8lkRnUIQqWEM7st+e7Qd8yK/S/GrCyMeXkWlekfVR9vFx0LdsbZVLdS8QyBh1bCQz+Dszf8PAk+7wJ5pebZrpPY5ZTsp3d9SlZBVqmfPRz1MMFuwdWskUJdQSJ5otUTtPBuUaFyxqwsXPv0wW1Af4vkl+w+h0YlGNEuiMP7LlRGVQU7Z0qbKWhUGvzvtzz7jE6jYlSHhszZfJLzKdkEeVV8Lb3KNO4Jj22Gwz+bQk1eSx126ZAp72sdfsCzyxGmRqXB3aH0p/SxkWO5o8Ed1ayRQl3BUePIU9FP0cavTYXKaTw9qf/6/+HcubNZ2dwCAz/sOc/dkX74uinrRLcrDVwb4O9sMpbSYCDn0CGLyo3u2BCVECzcddaW6pWPENB8qGmNEyD5lMmj9uu+cGqzKV5tHcTuDGaeIY+P933MoeTSO1eBsYDsguxq1krBVggh2ggh3hBCfCCEcC46J4QQ7wkhpla3PoXGwgqvkRdevUrO339bvD/4t0OJXM0uYExHZabE1pTRvx4QQvxbCDHclm1nF2Tz4/EfOZZyjKS5c4l7YCQFl8xvManv7kjf5n4s/es8uQV2sobo0RAGvg9p8bBgKHzdr04aTrszmCk5KXx18CuOpxwv9fOJ6yfy5IYnq1krBRsyClPoxZWYgvuDKb3X8ppQZufFnbRZ0IbYK7EWl0n7aaXpZnjWshHBoj/PEVLPiS5Nbs2UomB1Sutf19ygbT63+OrOV/njwh+4DxoEBgOpS5daVO6hziGkZhew6u+L5oWrA7UW2k2AKfuLDOd5WDwCMs0/ANxO2N0aZn2X+ux7cF+Znor3N70fg+K5dbtxPbi/EMILCAN8gRZCiM+llDd5TFgjuH9ZXC64zED3gcTFxpGiTrEocL2o74/uicfZERcHcXHlyl7IMLI7LocRTbVs3boFqP0B/mtBcH9Z4q+DlHK2EOJLYFlJYWv2r9cCX8MjyYMdyWfwaN6cSwsXcigyAopFgSrt95NSEugi+PT3g/hknERUYd3Q+tcnFBH9EW7px0jbe4TMzEzi5z1Iilc0KV5trb7GaU/9y+4MJpj2Yaop3dW+X0i/atZGwcbcFNwfSJVSPiOECAGGljSWYJ3g/uUxghvB3C0OXN/fMmefV1cdQqc+x4zhPajn4lCxNipJba+/ipSWPGJdUaalxNIK2Kp/ZapUnH/8CVrn5eHe+0Yc17J+v4mOZ3l55UHcGkfTNtjzls8txXbXxzRg37Z+FQ2y/qbBhdXg3xLu+Bc0u8e0t9MK2FP/srsp2Z0Xd/Lxvo/JN+SX+nmeIY+ruVerWSsFWyGl3CulnCmlnC6lnCOlaWpBShknpfywuvXJLsgmz2DZFgCAiy+9RPqvv1okm5Nv4Md98fRv4X/dWCrYltL6l5TyfSnlLCmlZREmqsDfV/7mswOfIaXEuVs3tA0bcnWRZaE9720diKuDhu92xtlUx6pSqHWDyftgyGeQnwXLHoLPOpli1t5m2J3BPJh0kAWHF6BRlT74/WDPBwz8aWA1a6VQV5j791y6LulqkWzOPwdJ+3EFBYmXLJJfHXuRjNxCxdmnDhF7JZZ5sfNIz09HqFR4jh5Fzr595B4xb0ycHTTc364Bv/yTwJUMyx/iagS1FlqPgaf/gvu+AmcfcC9KbXf5yG0Tq9buDOZjLR9j15hdqETpqt0dfDdT21S786RCHaF7g+5MazvNItmUb79F5eyMx3DLMtov2nWOUF8X2odUfnpNoXYxPHw4ux/cfX2bnMe99yL0eq4utmyUObZTMAUGyfe7z9lSTeuhUkOL+2HCWtP+TaMRlo2DD6Ng81uQeaWmNawSdmcwgTKNJUA7/3aMaGrdhMEKCtdo79+eMRFjzMoVJCSQvm4dHsOHo3ZxMSv/V1wKf59PZWyn4Co5cCjULvQaPVqV9vqx2t0d93vuIW31GgypqWbLN/ZxoXuYN4t2naPAULmQjTWKSgVD5kDDzrDlHfhvc1j9jGlfZy3E7gzmx/s+LjOOLEBuYS6JWYmKp6yCTUjJTbFon2/KgoUAeI190KJ652w6ST1nHSPaBVVJP4Xax7zYeaw9vfb6seeY0Wj8fMmPtyzC07jOISSm5/L7Icum/u2OoA4wagk8vceUIeXAEriwz/SZoaBW7eW0O4MZEx/DoaSyI2KsOrWKu5ffTVJONQYnVqgzTNowielbppcrY8jMInXZMtz69kEbGGi2zn/i09hy/AqPdG+Eo04JtF7X+DXuV/5K/Ov6sb5ZM5qsW4djlGVpCu9s5ksDT0e7d/4xi3cY3PMRTDtkiiIEsOMT+KI7HFgMhXa+TosdbitZMXhFuZ+382vHK51fwUVnfhpMQaGiTIiagF5dfri6tB+XY8zMxGv8eIvqnLP5BG56DWM7Kc4+dZEfBv2AukSOSSEExtxcDFfNe/yrVYKxnYKZ9etRjiam08zfzVaqVg8uxZKle4aAoRBWToT1r5jSirWbAC6W5RCtbuxuhGmOxh6NuT/8fpy1zjWtisJtSN+QvvQI6lHm57KwkJTvFuDYti2OLVuare/4pQx+O3SJ8V1CcNVrzcor3H6UNJZgCkwQN+IBEv7zikV1jGgXhINGxXc7azC+rC2IGgaTdsLYlRDQGmLeglVTalqrMrErg3ny6kle2vYScWlxZcrkG/KJz4gnpzCn+hRTqBNIKYnPiC93DTNz6x8UXLiA1/hxFtX52eaTOOnUTOjayFpqKtQyjqUc48U/XuRi5o0wd0IIvCdNot6jj1pUh6ezjiHRAfy07wJpObdZPmAhoMmdMGYZPL0X7iraHnv1LMzrhX/CRiiwj/u9XRnMpNwkdifuJt9YetACgEPJh+i/oj/7L++vRs0U6gI5hTn0X9GfpcfKjvfp0uMOAj/8ENdevczWdzY5i1V/X+TBTsF4OuusqapCLSLXkMvuxN1czr457qpbv744d+xgcT0PdQ4hp8DA8r3x1lbRfvAOBb9I0/vMS5CfRbNjH8P7zWDdi5B0skbVsyuD2al+J9bfv55wz/AyZYLdgnmj6xs0cW9SjZop1AXUKjVvdH2DroGlBy6QUiLUatz69UWozTvvzN1yCo1axaPdlNFlXaaVTys2DN9AtG/0LZ8VXLiAy/IfMWaVnv+3OFGB7rQN9mTBzjiMxtrjWVppgjrApD850OoN0wh09xfweWfIqblIb2YNZhnpcSYJIWYKIV6zvYo346X3YkjoEPyc/aq7aYXbHAe1A0NCh5T6wCbz84m7fzhpq1dbVFdCWg7L98bzQLsgJeelQpkUXLqM84YNpK0ueytdcR7qHExccjZbT9TuAAAWIwSpni1g+Lcw7bApipBjUeCPZeNg/X+qdU+nJSPMW9LjSCk/A94BLE8lbgHfHPyG2X/NLlfGYDRwJu2MEk9WwepkFWRxNv1sqXGMDenpqL3rofawLErPF1tOIyU80aOxtdVUqIV89c9XzNo165bzjq2jKQgK4uqiRRblU+0fVR9vF4fbz/nHElz9IHKw6X1hPhgLYccc+KQNfDsIYn+w+VqnpdtKbkqPI4TQA28Cb5UmXNn0OPtS9pFSmEJMVtny2cZsZpyfwTDPYdzpdmetT11UHalr7Ck9TkmEEG2AYZiyScyUUmYJIe4HGmNKw/R6eeVT86w3NbUncQ9Pb3qaJQOXEOUdddNnGm9vGn7xhUX1nE/JZvHuc9zbOpAGnk5W00+h9nI19ypXcm4dFQohyO7ZE+2CBeTs2YNT+/bl1qPTqBjdsSGfbDrBueRsGtaro/1Lo4ORiyAjEfYvhH3fwYpHYcB70OExMBpAqKyeaswSg1laepylwCGgDzCvZIHKpsfpiXk5g9GAjJNE1oukkXujWp+6qDpS19hTepxSGAW8AHTBNIOxEtgItANyzRVOy5OkZOXjZQWnmqZeTZnVfRZBrjdH48nYtAmHsDB0QZZF6Zn16xHUQjC9T9Mq66Rwe/Cv9v8q87Pc9u3wXLWKlEWLzRpMgDEdG/LZ5pMs3HWWFwdEWFPN2oervymdWLdnIe4PqF+01Wv/Atj1BUSPgRbDTaNTK2DWYEop9wJ7S5weYpXWK4FapWZgYyVbyW3GTTMYUsqrwPNFOQtvofgMhs4/lLeXxTCgkXW8UF1wYf+5Gx7Y2ZcucW7W2+RHRJD2xONmyx9LMfDLP7ncG6rl6P4/OWpBm7V9FsOeZzBqBTodHvfdR8r8+RRcuoTWr/ybu5+bnr7N/Vn613mm9Q5XokeBKWZt42L7p519QesIv79kWucMvQtajYTmw6o06rSbSD95hjye2vAUoyNG06th+S77p1NPo9foCXAJqCbtFGxIaTMY0wE14FhageIzGO4NwuTOKxpmjeuBWlW16ZfErESyCrJo4nHDA3vP5Mmo8vOJ/L/XcAgLK7e8wSiZPWcbAe7w1kM9Lb6R1fZZDDufwbALknKSeG7rc4yJGMNdDe+65XPPUSNJ+eYbUpcuxWeK+Y37D3UOZu0/Caz6+wIPtG9oC5VrN80GmF5XjsPfSyB2Kez8FKLuq1K1drOtJKsgizxDHoXGQrOyj/3+GF/GflkNWinYmjIS/M6WUr5tSYJfV53gfEoOW49X3WtwweEFjFo76vpxQWIiTptjcB882KyxBPhxXzyHLqYzo38z5alf4Sbcde4UGArKdOzRBQXh0qMHV5cuw5hf9j70a3Ro5EUzf1fm7zhrkbNQncUnHHq/AlMPwkjLUqqVh90YTC+9FwsGLKBPSB+zsq92eZUHmj5QDVop2DtOWoGPq4NVAlMPDR3K293fvn586e13APB++mmzZTPzCpn92zFaN/RgcCtl5kPhZrRqLQsGLKB3cO8yZTzHjMGQnEzGb7+brU8IwUOdQzickM7es8qOAbOoVKb1zqpWYwVVqp3uDboTUa+OL3YrACCAUR0aEnP8CueSzaflKo8wz7DrywEZmzeTsW4dWQP6o2tgPiPJZ5tPciUjj1fuaa7ku7QjythHLoQQ7wkh7CoTvXPXLtR78gn0zS3LYjK0dQCueg3z6+IWkxrCbgzm6lOrefi3hy3KRRiXFseJqyeqQSuF2sDoDg1RCcGi3VW7cRxLOca59HMYs7JIfP11dKFNyOpjfsbjfEo2/9t2hmGtA4kO8qiSDgpW55Z95MBTwPLqVuTzA5/z2O+Plfm5UKnwnToVh8aWRYZy0mkY0S6IX/9J4HK6WYdyBStgN04/QgiklDhqSvXzuIk3d71JdmE2iwYsqgbNFOwdf3c9fSL9WFbkNajXVm79cOb2mfg6+TJzdwMKLyYQvHgR59PTyy0jpeT/1hxGLQT/7qdsI7FTrnthCyG8gDDAF2ghhPhcSnlTIsbK7iM3x5WMK5DHLfWV9DLWnj6N5sJFcrp3M1tnmDBSaJTMWraVIaGle4rXdi9pe/LCthuDOajxIAY1HmSR7JTWUzBitLFGCrWJsZ2D+fVgImtjE7ivbYNK1fFixxfRpGdz9d+T8Rj5AE5t2oCZf9SfD1xk/eFLvNC/GfXdzT/sKVQ7N3lhA6lSymeEECHA0JLGEiq/j9wcZe0zL+llnLA5hsxNmwid8RxCZ3671K+XdrM9IZ13J9yBVn3rpGFt95K2Jy9suzGYFaGFT4uaVkHBzujcuB5NfJz57s+zlTaY0b7R4Av5P61A7Wk+BF5iWi7/+fkgbYM9ebS7EgLPHiljHzlSyjjgw2pXyAJ8npmCn4XGEmBcl2Ae/nYPvx1KZFBLxeHMltjNGubTG5/mk/2fWCR7PuM8ey/d8j+gUIcRwpSV/u/zqcTGp1a4vFEa2bV3FYmZieiCg1G7lZ/VXkrJjB9jyTcYeW94qyrvAVW4/TmcfJgBKwaYvXdpvLxQOTkhpUQazc+k9Qj3paGXE9/tUJx/bI3dGExvR288HCxzmFh8ZDFPbXzKxhop1DaGtW2Ak07Noj/PVbhs+vkz6MbPIPa/r1ok//1f59ly/Aov9I+gkbdzhdtTqHt4OHgQ5R1lkZ9G/vnznB44iMyYLWZl1SrTw+LuuBQOXyx/zV2hatiNwXy1y6uMjRxrkewDTR9gTq85NtZIobbhptcysEV9fvkngdwCQ4XKOgU0QDXhAZqPetKs7PmUbN5Yc5guTeoxtlNwZdVVqGMEuATw7h3vElkv0qys1t8fY2YmVxdbttl+eLsG6LUqFvwZV0UtFcrDbgxmRQhxD6Gdf7uaVkPBDhkSHUhGXiExxy6bFy7CkJmFTuNAq2dfJTD01iS/xTEaJf9e/jdCCN69vyUqZSpWwQYIrRaPkQ+QtW0beWfOmJX3cNIxNDqQn/ZfIC27oBo0rJvYhcGMvRLLwBUDib0Sa5H8paxLbLuwrdS8hQp1m85N6uHt4sDPBy5aJJ+xYQOn+vThYuyfbL+w3ew+4K+3n+HP0ynMHBShpO5SqDCP/PYIM7fPtEjWc/hw0Gq5umSJRfJjOweTW2Dkh73nq6KiQjnYhcF0UDvQvF5zi9cwt13YxsQNE0nJTbGxZgq1DbVKMKhlfTYevUxGbvlP2vnxF7j44kto69dnr+4iT254ksvZZY9Md5xKYtavR+kT6ceIdpal+lJQKE47/3a08LbMy1/j44Nb376krfgJY1aWWfnmAe60C/ZkwZ9nMRqV+LK2wC4MZlOvprzb410aulkWdb9HUA8W9F+Ap968679C3WNwdAD5hUZ+O3SpTBmZn8+FZ58Fo5HAD//LHY3uYn6/+QS6lh4G73xKNk8t2kcjb2feH9FKCX+nUCkmtprIiKYjLJb3HDMaY2YmaavXWCT/UJcQziZns+VE1ZMRKNyKXRjMiuLt6E20bzQOaoeaVkWhipQR63OSEGKmEOK1ytTZOsiDIC9Hfj5woUyZy+9/QG5sLPXffBNdUBDuDu608WuDVqW9RTYn38DjC/ZSaJR8ObYtrvpbZRQULKUi2UUco6NxiIzg6qJFFpXr19zflIxgR1wVNFQoC7swmP+38/8Yv268xfJpeWnEnI9RpmRvD26J9Sml/Ax4B6hUegEhBINbBbDjVDJXMm4J5ELqip9ImT8fz9GjcetrihUbcz6Gf678c4uslJLnfozlaGI6H49qTWMfl8qopKAAwLJjy2i3sB1ZBeanWMHUl73GjCHvxAmyd/9lVl6nUTG6KBnB2WTL2lCwHLuI9BNRLwIvvZfF8mfSzjB502Q+7/25DbVSqEZk8b9CCD3wJvBWacKWxPoMyDdiMEo+WrGV3sE3RoS6Q4fw+PQz8ps142iXzhwtKvtK/Cs0dmjMOJ9xN9Xz87EsVsdlc3+4FpFwmJiEw1X7pqVQ22Nx2lOsT3snzDOMMRFjMEjLtz25DRzI5Xdnk752Lc4dO5iVH92xIZ9uPsnCP8/y0kDzW1gULMcuDObw8OEVkg/3DOf7gd8T7BbMnhN7bKSVQjVxU6xPIYQKWAocAvoA80oWsDTW54JTWzmSreGNnl0AyDl4iLNffY2uaVOaLvgOtcuN0eLSrKUYpIFAlxtrmBuPXOLndXsY2KI+s0e3ttm6ZW2PxWlPsT7tnda+rWnt27pCZVR6PcGLFqILCbFI3s9NT78of5b+dZ5n71YSAlgTuzCYUsoK3YyctE4097YsZ5yCfVNGrM8h1qh7cHQA7647xvmUbIK8nMg7egSNlxdBc+feZCwB/J1vnv3dfjKJiYv2EeKmYvbwllY1lpcuXSIl5cZygru7O0eOHLFa/SWpjfV7eXnh5+dn1TrtBSklhbKw1PXysnAIDb1e1pK+OK5LCGtiE/j5wIXKrWsolEqNG8zcwly6fd+NZ9s+y+iI0RaVKTAUsCV+C6EeoTbWTqE2c09Lk8FcdeACT/UKw+P++3EbOBCV482hyc5nnGd3wm56B/fG3cGdv+JSeHT+HhrVc2ZycwNOOuv+m6SkpBAeHo5abUpDlpGRgaurq1XbKE5tq99gMHD8+PHb0mBmFWTR/fvuPNPmGcY1H2e+QDHS1q4led7/CFm2FJWZwOztgj2JqO/G/J1nea6lssXEWtS4049BGhjVbBRhnmEWl8k35jMtZhpb4s3HWVSouwR5OdHV34EGbz5H5h9/ANxiLAH2XtrLqztfJT0vndj4VCZ88xf13fUsfLQjLjrbTMNeM5YKt3I7/zZOGifGNx9PlHdUhcuqPTzQ+vlhuGo+uYAQgoc6B3MkIZ0TqUoqRGth9tFZCNEGGIZpjWmmlDJLCDECmCSl7FlVBZy1zkxvN71CZRw1jiy/Zzl+Tn4cuHKgqioo3MYMaO6LWJLB+UupRJQhM6jxIDr4d+Bqup6Hvt6Nh5OWRY91xMfV/rctffvttxw6dIjAwEAeffRRXIpNNcfFxbFy5Ur0ej2dOnUiOtoU9u/nn3/myJEjaDQaOnTowB133FHp9pOTk9m+fTv79u1j6tSpeHhYFnykJFOnTuXDD+0y25ZVEUIwpc2USpV16doVl65dLZYfEh3ArF+OsOFsgclDTqHKWDLXNAp4AeiCye1/pZRymRCii0UNnDvH4agWCCGQ0ojBaECj0kCxY6fZr9Jk4ANkbN5M/JRnOD7zv/w7Np/ep3fy5P4VZdZ9DvAC+t45hdOeDbj7zJ88uX8Fj/V/iRRHdx44sp4RR9ab1fGx/i+RonfDMzeDDJ0TheobP4vRKFGt/+Um+f5R9fl4VMUW7hWql8IrV1C5u9O3c1M695pKz2x/Pi00otPcOqmiUWlIz3Rh3Ne70GvULH60k82TQb+2+tD1zBIGg8GiUVVkgBuv3FP62r2joyMuLi7XDc/UqVOZOnVqqbKbNm3io48+un780UcfIYQgPj6ed955hzvuuINBgwZx5swZwsPDSUtLY8KECUyZMoVWrVqRm5uLt7c3er2eIUOGcPjwYU6cOMHixYvp0qUL//nPf26RGzNmDG+++Sbe3t5EREQQGxuLt7c3sbGxzJw5k8OHD/P9998zcuTISvyatQspJVkFWbjoKrdFKT8+HhDoGpQeZOMaTjoNI9oF8c32M1xOz8XXTV+p9hRuYOnijCzxt1yKu/0HeTqT0qsbepUDGYZMEgoSCNYF46DSkW7IILEgkXqpmZyPiUGdmIi+Vy8+33eZes7u+DcNIlZzZ6ltZIsrqKQOrcGV6CYehLlq8NWZ5Ls0cqbAQYOLaEysS+nli9OlkTP1L57gnl++4McHniEx8EYy4Pz8fHS6G4vzZ9IMrIm9yN31UnG1wnRddbjk1zW3//z4eM6Nn4BT2zYEvPMOzw9szutrDvPEgj18/mBb9NqbjdM72+azcNtVnFRRLHy0Iw3r1a4YsWPGjLk+eryGwVD2toWSTiOHDh3iyy+/5OWXXyYpKYmWLVsyY8YMJk6cyLPPPsszzzwDQPfu3Zk+fTqTJ09mxowZ188DhIWFMXr0aFJTU0uVi4mJQaPREBgYSHx8PAAjR44kMDCQM2fOEBkZWSeMJcCMrTM4knKE1feurnBZY24uZ4YMxbVvXwLeetOs/IOdgvlq2xkW7z7H1N7hlVFXoRiWGMzS3P57Aq2FEI9JKc26/Xf72PL9kn927MPRL//kvQEtub/tgDLl7l5+N53qd+KugrsYV8Kl/cHr7/pY1OaDQGFyMukR9ZjR5260/jf8ykq6zB+8kMagT7aR7dmEe9pbFsqvPKrDJb8uuf3nnTrFuQkPY8zLw/NBU094pFsjHLVqXlr5D+O+3s1X49vj4mDq+j/tj2fB0a9w8grjpxFPEOBh25HlNYqPFK3pNFNQUMD8+fNJTEwsU6ZHjx688847qNVqOnToQFRUFB9//DH5+fl4e3uj1ZoeEB0cTFPS1wysVqtFpVJd/7y44W3atClz586lb9++pcr16NGDrVu3kpWVRevWrdm4cSMajQYhBEajEXd3d7755hsmTJhgld/BnhnYeCDt67evVFmVXo/bPYNIW/ETvv/+FxrP8sODhng708JHzaJd55jUM7TUGRaFCiCltOkrPDxcVoRnluyTUa+sk9l5heXKnUs7J5NzkuXmzZsrVH9FKVm/0WiUXd/eKMd9vcsm9dsCW7cB7JE27kdlvYr3r4ytf8ijHTrKY127yZyjx27Rc+X+eNn4hbVy8Jxt8mpWnpyz6YQMnrFGPvDFHzI+NbnU72aL3+7w4cM3Haenp1u9jdpef/HfqCr9C2gDvAF8ADgXnZsEzAReM1e+ovevylDRPpZz7Jg83LSZTJo3zyL5j5atl8Ez1shVBy5UQjvzVPc92BZY2sfs6nEjNTufXw4mMjQ6EEdd+Ws6QW5BFYoOZAkFCQlk7d5drowQgv5R/mw/mURajpJ3zh6QUpL05TzOP/44Wn9/QpYsRt/01umnIdGBzH2wLUcS0un5XgyzfzvG0OgA5j/cmUB36/YlBbvB6qEXq4pRGknOSSbPcGvYRkvQh4fj1KEDVxcvQZYz9X6NKG81wfWc+G5nXKXaU7hBje/DLM5P+y+QX2hkZAfzqZN2XNhBniEPgfXc/pPnzSNt1WrC/9pd7ubgflH1mffHGTYfvczQ1uUvvCvYGCm5MHUaGb/9htuAAdR/43VUTmWvQd4d6cc349szecl+JvVswtAOaub98zkjm43E29G7GhVXqEasHnqxKhzLOcacy3OY4jeFMH1YpXwMHKJb4bF7N3/OmUNeq1blymZnZdHZ24Hvj2Uzf9VGgt2su22nLoVetBuDKaVkye5ztGrgTvMAd7Py3x35jrTcNJ5wecJqOjiEh2PMzKTw4kW0gWUbwtZBHvi5OfDrwQTFYNYwmsREMtavx/e55/CaMN6iKChdQ73Z+3JvhBCsPb2Wef/M496we6tBW4UawGahFytLVE4UrnGu9GrYC39n/0r5GMhu3Ti5arZTGu0AABjkSURBVDWBf8fSsJjzVWnExMTwQq+urJy1gSMFPozr2bIK2pdef10JvWg3U7L7zqVy/FImIztY5kjzRtc3+LT3p1bVwSHcFHcx9/jxcuVUKkHf5v5sOX6F7PxCq+qgUDGkWk3D/82j3sMTKhS+7prswMYD2TNmD/Wd69tKRZtSUFDAhx9+SL9+/Zg1axZLly6tcB2HDx/mlVde4YMPPmDhwoUVKltae2VtZylOXFxctey7lFLulVLOlFJOl1LOkVIapZRDpJQvylIcFqsDb0dvRkeMviUcY0UQGg2eIx8ga8cO8k6fNivv7qTl3taBrDxwgdTs/Eq3W9exmxHm97vP4aRTc0+rAIvkbTF95hBuijaUd+w4rneWvx2lX5Q/3+08S8yxKwxoUTtvtrcDBl9fnLtYtCW4TLTqGspv+c1AABwNhXBt72/zodDhMcjPhkUlkhJMWHtLFVqtlqlTp5KamsrEiRN59dVX6dix4017Jl1dXXF3d79lL+SwYcMA+N///sd7772HSmV6ft64cSPbtm0jLS2NGTNmMHnyZIYMGcL27duZPXs206ZNo3Xr1gwbNoydO3cSFRXF3LlziYyMpFu3bhw+fJiYmBhSUlI4evQoiYmJvPbaa4wZM4aRI0dy5MgRevXqxe7du9mzZw/t2rWz3W9spyTnJGOQBnydfCtdh8fw4SR9+hlXFy/B/+WXzMqP7RTCkt3n+WFPPI/d0disvMKt2MUIMz23gDWxCQxuFXDd3d8cBy4fYPnx5VbVQ+3igjYwkDwzI0yADiFeeDnr+PVg2e77CuYpI4H0CCFETHW0/2Xsl6w6tao6mqpWunfvzmuvvUZ2djbTpk3j9OnTpe6FLI2VK1fy/PPPM2DAALZu3Yqvry9jxoxBp9Ph7OxMZGQkycnJ1+V9fHwICgoiOTmZ0NBQIiMj6dmzJytWrCAgIOD6XsvGjRvz0EMPkZOTQ1hYGB06dKiTxhLgwV8e5P0971epDk29ergPHQIWzqxEBrjRIcSLBX+exWhU4stWBrsYYf584CI5BQaLp2MBNpzdwLLjy3g38F2r6uLQtCm5x4+ZldOoVfSJ9GP13xfJLTDcshlewWKqFEmqqmw6t4lwz3AGNxlcHc3dTNGIMae0fZg6p1JHlJZiyV7IazzyyCO89tpruLu74+vry9ChQ3n77bevjzC3b99+Xfbq1auo1WoyMjKuG93Lly/j7OxMfHw8SUlJZGRk8PvvvzN06FCOHj2Kl5cXDf+/vXMPj6q6FvjvzEwmk2SSISHhTSKCCaBUbEQI+ADxBfVWWhA0KCLaa/GBKbYgVlu0V6utL6xVrw0vrfiktVeRUKuEokV5yCMJkBDe5oHJTDKTSTKTeez7xwmBICQnyZmECfv3feebnDn7rLXPzMpZs/dZe63kZEymE7ebxMRE8vLy+OqrrxgzZky7rzNcmX/pfF2i/Ps88USbHkXMGpvC/au2s6GogglD2z+6PVdR1CUooSMtLU0UFrbsgH700kaCAj6Zd7nmL7+moYZAMMCOr3bo+kD4uxdfxP6XbNK2f4PBbG7xgXNu4XfMXr6FpXdcysRh7aus0B0SFyiKsk0I0a6hgqIofwQeBsYB8UKIfzS+/6IQ4rQPw06JYkx/77332tfxRkQLJZPcbnez/Kx6YLPZGDLkRKUdranx2ks4yi8uLsbpdAIwYcKEdttXR9Fy/+ooevx/evbuJTIt7bR2fLJ8XyDIuKc/VyuZzGm9GLUWukPQj9Z7WJePMPNLnBSUunjipgvb9Esp1hyackWWtDQIBGjYvx/LsDOl61YZOziRWIuJtfnl7XaYko5nkgq3f9Y9e/Y0G1GGW/mtzpBvsViajYK7G64GFyU1JQzr2fI9Rgs1n33Gt/fdT/KKFcSMGd1i2wijgczRybz4r30cqqzlvMSYDus/l+jyZ5j/t7OUCKPCTRe3bXnGvqp9rMhfgTfYvsW/ZyIyVV3w7tHwq9JsMnDtsN58uvsYvoAsodMezhDF+LkQ4qpQRzEWVBbw6BePUuYuC6UaieR7vF/4PtM/nk6dr67DsmLGjaP3Y49iuXC4pvaZlyVjMii8+dXhDus+1+hShymEYG1+GWMHJ2KLblukYn5lPs9tew530K1rn8wpKSQvX0bs1Vdran/DRX1w1vv46oC99caSs4ryunI2lW5q08yGRKIHVydfzYsTXsRo6PhUtsFiIWHmTIwaR/m94ixMGtGX97Yelcvi2kiXOsyCUhdHHfVMuqjt65Emnz+ZTbduIt7YcvLhtqKYTMRkZGCMi9PU/srUJKLNRj7Jk6OUcGNi8kQ+m/5Zh9bDdTUrVqxgwYIFzJ07l4KCgq7ujkQjg2yDmJg8kUijfjVXq1f/jap3tK3DvSMjhRqPnw+3l+qm/1ygSx1mTn45BkVNV9ZWIo2RWM1WDIr+l+DZvRv78hWa2loijNxwUR8+2llGrVf+WpNo586cO/mw+EMAfEEfd+bcyUf71ZJP9f567sy5k5yDOYAa5HYmMjMzmT17NmvWrOHZZ59l0aJF1NXVcd999/Hqq69yzz36ZcOS6IMv6KOgsoBjtcd0k1nz2WdUvPQSwYbWExOkp8QzvG8cb2w6RKgDP7sTXeswC8oZPagnPa1t/5VVXltOdl42lb5K3ftV+/VmvnvmGQLV1ZraZ16WjNvrZ80uOcoMJ576+ilWFqzs6m50mFWrVrFmzRpycnJITEzE7/eTl5dHeno6c+fOlTfEs5A6Xx23rLmFnEM5usmMn5lJwOGgJqd1mYqiMCsjhb3lNWw5VKVbH7o7IY+Sjak9CstugMg4sMSBxQaj51Ic7I2n4gBzz/NAkQsiY9XjkbEQ2xdayb5SUVfBkm+WcE+S/r+ee0ybSo+bb8Zo1RZBlp4Sz5BeVt7ecoTpo1pPHC85Oyhzl+k6JdZWlt+wHFCjTCMMEU37AFGmqGb7LUWFZ2ZmMnLkSJ5++mkcDgepqamMGDGClStX0tDQENIlJZL2EWeOY8mEJQxNGEpRReuJUrQQk5GBedAgHG+9he3Hra8rvmlkf576ZA8rNx3iskGyWo8WQu4wgwYTGExQUwaVheBxwsW3srZQMMawhyt3/S/sOuWkn38BfUbAtpWw/inViZ683fgCw3sOZ8sVL1Oy8T3Y/Bf1fbNVfU3OAJMZvDUggur7bXi4rvXh+XEUReGWUQP5nzV72FvuYmgfbc8/JV3Lnyb+qau70GFmz57d9PfDDz/c7NhVV11FaWkpEydO7OReSVpDURSuTlYDC4vQx2EqBgPxmZkce/JJ6vPyiBoxosX2UWYjM0YNZPmXhyh3euhjs+jSj+5MyB1mfVRfmP3x995fu3ojPfpeBzPuBq9L3TyNr7bGUVp8CqRepzo+b416vLYCUDAajBgPf8ngg3+Fg6ckjH74qOowc5+GTS+r70VEgzlGdZ4PbFMd6Oa/wKGNYI5tPBYDUT1g3IPYly5Fqf+Onn1McNCoZl6JiFEdsu37S2B++sMB/CGnkHc2H2Xxjy/U+VOUSNrOjBkzAHUEKzn7OFB9AGeDU1eZtp9MoeKFF6h6axVRT/++1fa3jUkh+4uDrNp8hPnXfr+GrKQ5XZK44Ii9jt1lLn49eRgktZAE+Pzx6nYaPH4Py+PjibrsD8y+8ieqQ21wq6/mxswsQ29Up3ePH2twg997YrRZWwkVheB1nzgeGQvjHsS98QuCR3cx4opiyD9JcdwAmN8YjfjWdDj8HzBHkxARzXqrgZ3f9MEz6SM1VV7uM+A8qjriiCjV4cafBz9oTKp9IJcE+zbVIUdEQ4QFouIhrjEBfcCnjs7lsgfd2Vq+ldd2vcZvx/yWgXFyGl3S+fx5x58pqiriofiHdJNptFqJu+nHOFf/jV4LF2CKb3kVQUrPGCak9WLV10e4f8IQzKYuX5p/VtMlDjOnQA2OuaEdy0mOExRBXtn5ClN6TAFrL3U7lZQMdTsTExap28kEfABY0lKp2rGdbSOfIf3i4Wr1iAa36sCOM3QyJJwPvlpoqMNSXU3lIYW1+WX85JIBUL4LSrap5/rqIOiDlHEnHOaaX/ID+z7IO0n/kGvhtsak8i/+ANzlqjM1WVSnO/RGmPS0evztTBCBE8dMFjjvchgxTT3+5UtgiqRv6WHYeQxMkZCUBr2GQTCg9s9kUd83WdTNHKPud3MaAg3U+eqwmOQ0lKRr+PnFP8cf9HMsT79IWYCEzEyq336H6g8+IPFnP2u1/ayMFGYv38Kqrw8ze9wgXfvS3egSh7k2v5yL+scxMCG63TKiTFHsuH0HG/+9Ucee0RRsFJmahvB4qfMmwKArT982fXaz3QQhWPZsLr02H1Ud5i1vNW8f8DU5ZABufZtt//mc9BHDwVevOtXonieOj70f6hwnjvk9qoM+jtelPhP2e9TN51Ed3ohpEPDDp48BkAY0PSYZOw+u+53q/F8f//1rGv8IjF8IrlL406Xq1LbJAsbG1zk5EB3+AQJj+49lbP9Oye8eUu666y6ys7NRFIU5c+awdOnSpkQMixcvJisrSwb9nKVcEK+WEzyGvg4z8oILiB49mqq336bnnDmttr/ygiSuTE3iiY9307sxqYHk9HS6wyxz1rP9SDW/uj6tQ3IURcGohO5GcDxFnqm0RPM5iqIwY1Qyz+Tspfg7N0N6nZK02xjRPPo38QJq4kpg0BWnF5hxX8sKT/NsuAmDER4pBZ+HTRvXkzHqEnU6OqqHetwUBbe+c8LR+j0QaID+6Y3HLXDpneo5x4/5ParjlOjC4dtnAeAPBHCcwalZx4+n511nvulNmjSJdevW0a9fP0aOHMmiReqMye23397UZuHChbzyyitkZWWRlZXVrFZmYmIiFovljLUyJaHD4XGw/bvteAP6pvcESLzvXvwVFaBhSZHBoPDabT/k9qWbmffOdpZGmrgyNUn3PnUHOt1hrmusH9mR6djjLM1biqvWxXjGd1jWqUQOGQwGA6ZvtTtMgGnpA3jun4W8u+UIv/6RttyOIUFRmgKZvJZE6Dm4+XGTGdImnfn86AS4/snQ9rELmZ87n+TYZLLST1sQJWyYMmUKWVlZWCwWYmNjufvuuwH4+9///r22gUAAUGtlPvTQQzzwwAMsXLiQBx98UHOtTIl+FDoKyVqfxYO9H9RddsxlbatEEm02seyOUcx4fRP3vLmNv959Gekp4T+TpDetOkxFUX4I/BS1msRjQohaRVHmA0FACCGWtEXh2vxyLuhlZXBSx0smrd63moEiNAEbhqgozCkpbRphAiTFRnLNsN6s/qaEX16fRqRJToe1hN72pRVbpA2rWd+yXW0l5c03gI5V+zCZTPTr1w+73c7UqVPJzs4GYNasWRwvexYVFcWyZcsoLi4G2lYrM9zpKvvSwojEEbx747sc3Xk0JPIDLhdVq97G2MOmqb0tOoI37xrNza/9h9nLt/Duf2cwvJ9cIncyrdbDbKxXeLzAb4IQ4kNFUV4QQvzi+GtL5/cYkCr+6/E3m/a/LK7k/glDmH9dx6ZkQU3evmHDBg4kHmB/9X6evFwdEb2+63VK3CU8PvZxAF7d8SoV9RX8JuM3ALz0zUvU+mpZNFqdvnph2wv4gj4WjFoAwB+3/BGjwcj0N47g3LCBiiHxFPxiMveN+yXVH3zAN++/yt5FU7l35L3Ys7PZufYtLKZIBlgHAFBQWUiVWyHG2AujQaHWWMKyqZNICl5HxuZPsDg+5/3JN5AkrmHMujfo7dqKEStmElCAOuUIJhFLBGqEW5m1hL9NnkZPMY7xG97FHdzCP6+eRoIYww3rlmJ2bSeCeCKwIQhSzxEiSCCCOAQB9iUcY/01s7CJkVy/7nWO2A7wdcZM4sQIpnz4PEHfAcwkYsJKkAY8lGImCRMxBGkgb6CLLWPuwsoFDLBZ+P20i5t9DzrUw2y3fSUOThRLPl7CzGEzAViwYQGX9rmU6WnTAXUkOa7fOKamTgVg3ufzmJg8kZuG3KSpf6Eq7zXspNJx4Vh+K9TyT/6MutK+OqMe5mP/eAxjkpHFYxcDkJ2XTXltOY+OeRSA13a+RpWnqul+9fL2l6n31/OrUb8CYMk3SwiIAPPT5wPw/NbnMRlM3Jsyk+IJV/Pv687Df9v13PylwLMrj+LqYiKMZlJikwEoqt6HxWghOXYgBlscz08ykZtnYMoXdQyKCJB9rYcYUrjlX8eIrzpGLUcwEUMkarxFTfAgkQYbZhKo7tGb16+togeXMP1fh/BYYlhx+RESuIxZH+3C4nFTy0EiiMdMDwSCOg5hJoEIbJT2TeaNy0tI4ipmfrSZkr79WXXxHvpHXM+c9z8liJ96jmImiQisBPFRz7dEkoQJK/sGnc/qSw/Tl8nc8f5H7Bp+IZ9cuJfB9ddw88frcEdH4573KHdf0Xx1ht71MEUrr6cqbyrwG9V7EKXfOZqOpcYbGOgvITdXnzRybrebvdV7KfOVkZubC0BRVREV/opm+86As2m/2FFMfbCe3PoT+wERILdW3d9v349RMXJkyFCi9u4l4HSw//B+cn25ROUX4He6KTpYRG51LjGFhQRdNTTgocqlhmSbfPXYghEoDWolFbO5AWedk4DbQb3LidXjxVnrwl/rwOD1Yvb6UIL1EKxBCDBHNDTuq19PhKmB6hon3joHDS4XRrOXKpcTT72DQE0NkV4fhmA9StAABIk0+TAE6lCEAgSJiPVhdzqp9TgIumtQojzYq6txex1Q6yZSHG8vMOA/6fwgBnwoDR4qq6txNTjw1Qhyc3VPpdVu+7IOtJJflE/usVwA9h/bj7naTK8yNWr6QPkBrC4rPUvVf+6Dxw6ys3Yntm+1/ep2u91NdqMXNput2drIQCAQ0rWS4Sjf4/Ho+bm3276SkpJ0//5Pxel1UvltZZOegqoC7H47uR51f7djNzWBmqb71R7HHjxBT9P9ao99D0GC5NY07lfuwaSY+MKVh+F3T/BF3WriDuzmcIHAfPAgwl9FQDFRValO0Sv+6sZ9P0FrDGUVFkb16UdSoBZ/jRuX10tdg5WAw47JacdqrFfvDY0VDeOMXhRRgyEo8AeN1DQ48dbb8TnseCLrcfvs+GsroaoSk7cWq8mDEnRhCAYAgdXkwRBwoQg/DeYY3D47Abfavs5swYuLMocdU7UdCGI0eTAEnCjCiyDQbN/j6IHbZ6fEpbZ32x24fXbK7ZWYqu0oXg95e4vJDRxp35clhGhxA9KB3wHPAfej5p/9BZAFzGvt/NTUVBFK1q9fL+V3sQ5gq2jFDs60nYv2tXv37mb7LpdLdx3hLv/kz6g725cQ4X+PCXf5Qmi3sVZHmEKIbcC2U95+oX3uWSJpzrlqX4FAQC73OAPHg5P04Fy1L0lo6JJ1mBLJuUxCQgJFRSfyh3o8HiyW0CVQCEf5CQkyQlNy9iEdpkTSyfTu3ZvevU/UgM3NzQ1pZGq4y5dIzhZk4kCJRCKRSDTQ6rKSDitQlBoglHHZiYD+VaS7j/zO0JEmhAjduoUW6Ab21Rk6wl1+d7YvCP/vJ9zlg0Yb64wp2ULRzjVUWlAUZauU37U6FEXZGirZGghr++oMHd1BfqhkayCk9gXd4/sJZ/nHdWhpJ6dkJRKJRCLRgHSYEolEIpFooDMc5utSfpfK7wwdnXENXaVbfj/dX35X6w73zy/c5WvWEfKgH4lEIpFIugNySlYikUgkEg2ELEr2dGV1QqBjCjABOAgsEToNlxVFSQUeAT4EGoBLABuwUA8dp8g/r1H2t0KIpR2V3Sj/CtTqDMOBdcAgdOz/aXTsBGLR8Ro09iGkNhau9nUaHecRZjYm7avDsuU9TLt8zfYVyhHmrcBi1A/02hDpqAXqgBh0vBYhRBGwonH3WiHEk0A+cPEZT2q//CrUqgkxeshulL9RCPEMUAzcrHf/T6MjFp2vQSOhtrGwtK/T6Ag7G5P21THkPaxN8jXbV6inZFsso9Nh4UJ8KoRYBOwBrgqFjlNV6i5QiJVCiCcAk6Io57d6gkYURckEDgCHTlanl/yTdQghHg/FNWgkZDbWHewLwtfGpH2FBHkPO0V+W+wrlIkL3kH9dRYN/DYUChRFGQ+MRh2u/1pHuX2AaUAU8G9FUR5BnQ54s8UT2yFfURQb0A/oD3yrk/ybgVlADrBD7/6fqkNRlDvQ+Ro0ElIbC1f7OlVHONqYtK8Oy5b3MI3y22JfMkpWIpFIJBINyChZiUQikUg0IB2mRCKRSCQakA5TIpFIJBINSIcpkUgkEokGpMOUSCQSiUQD0mFKJBKJRKIB6TB1RFEUo6Io87q6H5LuibQvSaiRNtYy0mHqywxgsKIouqU4k0hOQtqXJNRIG2uBUGb6OReJAV4SQuzv6o5IuiXSviShRtpYC8gRpr4ohC7RvEQi7UsSaqSNtYBMjSeRSCQSiQbkCFMikUgkEg1IhymRSCQSiQakw5RIJBKJRAPSYUokEolEogHpMCUSiUQi0YB0mBKJRCKRaEA6TIlEIpFINCAdpkQikUgkGvh/5BuMyWG3sfcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c35680438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sols = [\n",
+    "    results[(\"baseline\",'optimal',1.0)],\n",
+    "    results[(\"baseline\",'time-consistent',1.0)],\n",
+    "    results[(\"baseline\",'peg',1.0)],\n",
+    "    results[(\"baseline\",'volume',1.0)]\n",
+    "]\n",
+    "\n",
+    "# betas = [list_of_calibrations[c]['beta'] for c in cases]\n",
+    "labels = [\n",
+    "    'Full Commitment',\n",
+    "    'Time Consistent',\n",
+    "    'Peg',\n",
+    "    'Volume'\n",
+    "]\n",
+    "\n",
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "#\n",
+    "\n",
+    "\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['f'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.grid()\n",
+    "plt.title(\"Intervention $f_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sols[0]['e'][:T]*0+vv, color='grey', label='_', linestyle=':')\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['e'][:T], label=labels[i], linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "\n",
+    "plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e_t$\", fontsize=titlesize)\n",
+    "plt.text(2,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['R'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_r, max_r)\n",
+    "\n",
+    "plt.title(\"Reserves $R_t$\", fontsize=titlesize)\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "fig.savefig(output_dir + 'figure_6.pdf', bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# figure 7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_excel('simple_rules_welfares.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32f49eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kvec = df.index\n",
+    "plt.figure(figsize=(width*0.5,0.4*width))\n",
+    "sf = plt.subplot(111)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(kvec, df['commitment'], label='Full Commitment', linestyle=linestyles[0])\n",
+    "plt.plot(kvec, df['time-consistent'], label='Time Consistent', linestyle=linestyles[1])\n",
+    "plt.plot(kvec, df['volume'], label='Volume', linestyle=linestyles[2])\n",
+    "plt.plot(kvec, df['peg'], label='Peg', linestyle=linestyles[3])\n",
+    "plt.plot(kvec, df['do_nothing'], label='No Intervention', color='grey', linestyle='--')\n",
+    "plt.grid()\n",
+    "plt.xlim(0.5, 1.0)\n",
+    "plt.legend(fontsize=leg_fontsize)\n",
+    "plt.xlabel(\"$\\kappa$\",fontsize=labelsize)\n",
+    "plt.title(\"Welfare\", fontsize=titlesize)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(output_dir + 'figure_7.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# figure 8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pablo/.local/opt/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:52: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n",
+      "  return getattr(obj, method)(*args, **kwds)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32beaf28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sol = results[('baseline','optimal',0.01)]\n",
+    "sol_8 = results[('p_8','optimal',0.01)]\n",
+    "sol_9 = results[('p_9','optimal',0.01)]\n",
+    "sol\n",
+    "i_peak = numpy.argmax(sol['Gamma'])\n",
+    "\n",
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sol_8['Gamma'], label='$p=0.8$', linestyle=linestyles[0])\n",
+    "i_peak = numpy.argmax(sol_8['Gamma'])\n",
+    "plt.plot(i_peak, sol_8['Gamma'].iloc[i_peak],'x', color='grey')\n",
+    "plt.plot(sol_9['Gamma'], label='$p=0.9$', linestyle=linestyles[1])\n",
+    "i_peak = numpy.argmax(sol_9['Gamma'])\n",
+    "plt.plot(i_peak, sol_9['Gamma'].iloc[i_peak],'x', color='grey')\n",
+    "plt.plot(sol['Gamma'], label='$p=1.0$', linestyle=linestyles[2])\n",
+    "i_peak = numpy.argmax(sol['Gamma'])\n",
+    "plt.plot(i_peak, sol['Gamma'].iloc[i_peak],'x', color='grey')\n",
+    "# plt.vlines(i_peak, min_gamma, max_gamma,color='grey', linestyle='--')\n",
+    "yl = plt.ylim()\n",
+    "plt.ylim(min_gamma, max_gamma)\n",
+    "plt.xlim(0,T)\n",
+    "\n",
+    "# plt.text( i_peak+0.5, 0.03, '$t^{\\star}$', fontsize=ebarsize)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$', fontsize=labelsize)\n",
+    "plt.legend(loc='upper right',fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.title(\"Marginal Value $\\Gamma^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "# plt.plot(sol['f']*0, label=\"No Intervention\")\n",
+    "# plt.plot(sol['f'], label=\"Small Intervention\")\n",
+    "plt.plot(sol_8['f'], linestyle=linestyles[0])\n",
+    "plt.plot(sol_9['f'], linestyle=linestyles[1])\n",
+    "plt.plot(sol['f'], linestyle=linestyles[2])\n",
+    "\n",
+    "plt.xlim(0,T)\n",
+    "\n",
+    "plt.ylim(min_f, max_f)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Intervention $f^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "# plt.plot(sol['e']*0+sol['e'].iloc[-1], color='grey', linestyle=nointstyle)\n",
+    "\n",
+    "plt.plot(sol_8['e'], linestyle=linestyles[0])\n",
+    "plt.plot(sol_9['e'], linestyle=linestyles[1])\n",
+    "plt.plot(sol['e'], linestyle=linestyles[2])\n",
+    "\n",
+    "yl = plt.ylim(min_xr, max_xr)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig('graphs/small_initial_reserves.pdf')\n",
+    "fig.savefig(output_dir + 'figure_8.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Figure 9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pablo/.local/opt/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:194: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  self._setitem_with_indexer(indexer, value)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32d939e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(width,0.4*width*3))\n",
+    "\n",
+    "\n",
+    "\n",
+    "# betas = [list_of_calibrations[c]['beta'] for c in cases]\n",
+    "labels = [\n",
+    "    'Full Commitment',\n",
+    "    'Time Consistent',\n",
+    "    'Peg',\n",
+    "    'Volume'\n",
+    "]\n",
+    "\n",
+    "#\n",
+    "\n",
+    "sols = [\n",
+    "    results[(\"baseline\",'optimal',1.0)],\n",
+    "    results[(\"baseline\",'time-consistent',1.0)],\n",
+    "    results[(\"baseline\",'peg',1.0)],\n",
+    "    results[(\"baseline\",'volume',1.0)]\n",
+    "]\n",
+    "\n",
+    "vv = sols[0]['e'].iloc[-1]\n",
+    "\n",
+    "sf = plt.subplot(331)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['f'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.grid()\n",
+    "plt.ylabel('$p=1.0$')\n",
+    "\n",
+    "plt.title(\"Intervention $f^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(332)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sols[0]['e'][:T]*0+vv, color='grey', label='_', linestyle=nointstyle)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['e'][:T], label=labels[i], linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "plt.text(2,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "\n",
+    "#plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "plt.grid()\n",
+    "plt.ylim(min_xr, max_xr)\n",
+    "\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(333)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    sols[i]['R'].iloc[0] = 1.0\n",
+    "\n",
+    "    plt.plot(sols[i]['R'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.title(\"Reserves $R^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "plt.ylim(min_r, max_r)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "\n",
+    "####\n",
+    "#### p = 0.9\n",
+    "####\n",
+    "\n",
+    "sols = [\n",
+    "    results[(\"p_9\",'optimal',1.0)],\n",
+    "    results[(\"p_9\",'time-consistent',1.0)],\n",
+    "    results[(\"p_9\",'peg',1.0)],\n",
+    "    results[(\"p_9\",'volume',1.0)]\n",
+    "]\n",
+    "\n",
+    "vv = sols[0]['e'].iloc[-1]\n",
+    "\n",
+    "sf = plt.subplot(334)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['f'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.grid()\n",
+    "plt.ylabel('$p=0.9$')\n",
+    "plt.title(\"Intervention $f^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(335)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sols[0]['e'][:T]*0+vv, color='grey', label='_', linestyle=nointstyle)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['e'][:T], label=labels[i], linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "plt.text(2,0.46,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "plt.ylim(min_xr, max_xr)\n",
+    "\n",
+    "\n",
+    "sf = plt.subplot(336)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    sols[i]['R'].iloc[0] = 1.0\n",
+    "\n",
+    "    plt.plot(sols[i]['R'][:T], linestyle=linestyles[i], label=labels[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.legend(loc='upper right', fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_r, max_r)\n",
+    "\n",
+    "plt.title(\"Reserves $R^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "####\n",
+    "#### p = 0.8\n",
+    "####\n",
+    "\n",
+    "\n",
+    "sols = [\n",
+    "    results[(\"p_8\",'optimal',1.0)],\n",
+    "    results[(\"p_8\",'time-consistent',1.0)],\n",
+    "    results[(\"p_8\",'peg',1.0)],\n",
+    "    results[(\"p_8\",'volume',1.0)]\n",
+    "]\n",
+    "\n",
+    "vv = sols[0]['e'].iloc[-1]\n",
+    "\n",
+    "sf = plt.subplot(337)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['f'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.ylabel('$p=0.8$')\n",
+    "plt.xlim(0,T)\n",
+    "plt.grid()\n",
+    "plt.title(\"Intervention $f^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(338)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sols[0]['e'][:T]*0+vv, color='grey', label='_', linestyle=nointstyle)\n",
+    "for i in range(len(sols)):\n",
+    "\n",
+    "    plt.plot(sols[i]['e'][:T], label=labels[i], linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "plt.ylim(min_xr, max_xr)\n",
+    "plt.text(2,0.34,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "#plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(339)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    sols[i]['R'].iloc[0] = 1.0\n",
+    "    plt.plot(sols[i]['R'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_r, max_r)\n",
+    "\n",
+    "plt.title(\"Reserves $R^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "fig.savefig(output_dir + 'figure_9.pdf', bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Figure 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32d93a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "with open(\"precomputed_decision_rules.pickle\",'rb') as f:\n",
+    "    decision_rules_saved= pickle.load(f)\n",
+    "decision_rules = decision_rules_saved['decision_rules']\n",
+    "\n",
+    "\n",
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "sf = plt.subplot(121)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i,p in enumerate([1.0,0.9,0.8]):\n",
+    "    sol = decision_rules[p]\n",
+    "    plt.plot(sol[0], sol[1][0], label='$p={}$'.format(p), linestyle=linestyles[i])\n",
+    "plt.legend(loc='upper right', fontsize=leg_fontsize)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.grid()\n",
+    "plt.title(\"Exchange Rate $e^{\\\\bar{z}}(R)$\", fontsize=titlesize)\n",
+    "plt.xlabel('$R$',fontsize=labelsize)\n",
+    "sf = plt.subplot(122)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i,p in enumerate([1.0,0.9,0.8]):\n",
+    "    sol = decision_rules[p]\n",
+    "    plt.plot(sol[0], sol[1][1], label='$p={}$'.format(p), linestyle=linestyles[i])\n",
+    "plt.title(\"Intervention $f^{\\\\bar{z}}(R)$\", fontsize=titlesize)\n",
+    "plt.xlabel('$R$',fontsize=labelsize)\n",
+    "\n",
+    "plt.grid()\n",
+    "plt.tight_layout()\n",
+    "fig.savefig(output_dir + 'figure_10.pdf', bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Figure 11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c30ec1400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(width,0.4*width))\n",
+    "\n",
+    "df = pandas.read_excel('simple_rules_welfares.xlsx')\n",
+    "kvec = df.index\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(kvec, df['commitment'], label='Full Commitment', linestyle=linestyles[0])\n",
+    "plt.plot(kvec, df['time-consistent'], label='Time Consistent', linestyle=linestyles[1])\n",
+    "plt.plot(kvec, df['volume'], label='Volume', linestyle=linestyles[2])\n",
+    "plt.plot(kvec, df['peg'], label='Peg', linestyle=linestyles[3])\n",
+    "plt.plot(kvec, df['do_nothing'], label='No Intervention', color='grey', linestyle=nointstyle)\n",
+    "plt.grid()\n",
+    "plt.xlim(0.6, 1.0)\n",
+    "# plt.legend(fontsize=leg_fontsize)\n",
+    "plt.xlabel(\"$\\kappa$\",fontsize=labelsize)\n",
+    "plt.title(\"$p=1.0$\", fontsize=titlesize)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "\n",
+    "df = pandas.read_excel('simple_rules_welfares_9.xlsx')\n",
+    "kvec = df.index\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(kvec, df['commitment'], label='Full Commitment', linestyle=linestyles[0])\n",
+    "plt.plot(kvec, df['time-consistent'], label='Time Consistent', linestyle=linestyles[1])\n",
+    "plt.plot(kvec, df['volume'], label='Volume', linestyle=linestyles[2])\n",
+    "plt.plot(kvec, df['peg'], label='Peg', linestyle=linestyles[3])\n",
+    "plt.plot(kvec, df['do_nothing'], label='No Intervention', color='grey', linestyle=nointstyle)\n",
+    "plt.grid()\n",
+    "plt.xlim(0.6, 1.0)\n",
+    "plt.xlabel(\"$\\kappa$\",fontsize=labelsize)\n",
+    "plt.title(\"$p=0.9$\", fontsize=titlesize)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "\n",
+    "df = pandas.read_excel('simple_rules_welfares_8.xlsx')\n",
+    "kvec = df.index\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(kvec, df['commitment'], label='Full Commitment', linestyle=linestyles[0])\n",
+    "plt.plot(kvec, df['time-consistent'], label='Time Consistent', linestyle=linestyles[1])\n",
+    "plt.plot(kvec, df['volume'], label='Volume', linestyle=linestyles[2])\n",
+    "plt.plot(kvec, df['peg'], label='Peg', linestyle=linestyles[3])\n",
+    "plt.plot(kvec, df['do_nothing'], label='No Intervention', color='grey', linestyle=nointstyle)\n",
+    "plt.grid()\n",
+    "plt.xlim(0.6, 1.0)\n",
+    "plt.legend(fontsize=leg_fontsize, loc='lower right')\n",
+    "plt.xlabel(\"$\\kappa$\",fontsize=labelsize)\n",
+    "plt.title(\"$p=0.8$\", fontsize=titlesize)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.savefig(output_dir + 'figure_11.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Moving target : A1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "with open(\"precomputed_moving_target.pickle\",'rb') as f:\n",
+    "    od_saved = pickle.load(f)\n",
+    "od = od_saved['simulations']\n",
+    "lamvec = [0.8, 0.9,  1.0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32ccb748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i,lam in enumerate(lamvec):\n",
+    "    df = od[(1.0,lam)]\n",
+    "    plt.plot(df['Gamma'], linestyle=linestyles[i])\n",
+    "plt.grid()\n",
+    "plt.xlim(0,T)\n",
+    "\n",
+    "plt.title(\"$\\Gamma^{\\lambda}_t$\", fontsize=titlesize)\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i,lam in enumerate(lamvec):\n",
+    "    df = od[(1.0,lam)]\n",
+    "    plt.plot(df['e'], linestyle=linestyles[i])\n",
+    "for lam in lamvec:\n",
+    "    df = od[(0.01,lam)]\n",
+    "    plt.plot(df['target'],linestyle=':', color='grey')\n",
+    "plt.grid()\n",
+    "plt.xlim(0,T)\n",
+    "\n",
+    "# plt.xlim(0,15)\n",
+    "plt.title(\"Exchange Rate $e^{\\lambda}_{t}$\", fontsize=titlesize)\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i,lam in enumerate(lamvec):\n",
+    "    df = od[(1.0,lam)]\n",
+    "    plt.plot(df['f'], label='$\\lambda={}$'.format(lam), linestyle=linestyles[i])\n",
+    "plt.grid()\n",
+    "plt.xlim(0,T)\n",
+    "plt.legend(loc='upper right',fontsize=leg_fontsize)\n",
+    "plt.title(\"Intervention $f^{\\lambda}_{t}$\", fontsize=titlesize)\n",
+    "plt.xlim(0,25)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "fig.savefig(output_dir + 'figure_A1_1.pdf', bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pablo/.local/opt/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:52: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n",
+      "  return getattr(obj, method)(*args, **kwds)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32bbcac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "\n",
+    "for i,lam in enumerate(lamvec):\n",
+    "    df = od[(0.01,lam)]\n",
+    "    plt.plot(df['Gamma'], linestyle=linestyles[i])\n",
+    "for lam in lamvec:\n",
+    "    df = od[(0.01,lam)]\n",
+    "    i_peak = numpy.argmax(df['Gamma'])\n",
+    "    plt.plot(i_peak, df['Gamma'].iloc[i_peak],'x', color='grey')\n",
+    "\n",
+    "plt.xlim(0,T)\n",
+    "\n",
+    "plt.grid()\n",
+    "plt.title(\"Marginal Value $\\Gamma^{\\lambda}_t$\", fontsize=titlesize)\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i,lam in enumerate(lamvec):\n",
+    "    df = od[(0.01,lam)]\n",
+    "    plt.plot(df['e'], linestyle=linestyles[i])\n",
+    "# for lam in lamvec:\n",
+    "#     df = od[(0.01,lam)]\n",
+    "#     plt.plot(df['target'],linestyle=':', color='grey')\n",
+    "# plt.text(20,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "plt.text(22,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.grid()\n",
+    "plt.xlim(0,T)\n",
+    "\n",
+    "plt.xlim(0,25)\n",
+    "plt.title(\"Exchange Rate $e^{\\lambda}_{t}$\", fontsize=titlesize)\n",
+    "plt.ylim(min_xr,max_xr)\n",
+    "\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i,lam in enumerate(lamvec):\n",
+    "    df = od[(0.01,lam)]\n",
+    "    plt.plot(df['f'], label='$\\lambda={}$'.format(lam), linestyle=linestyles[i])\n",
+    "plt.grid()\n",
+    "plt.xlim(0,T)\n",
+    "\n",
+    "plt.legend(loc='upper right',fontsize=leg_fontsize)\n",
+    "plt.title(\"Intervention $f^{\\lambda}_{t}$\", fontsize=titlesize)\n",
+    "plt.xlim(0,25)\n",
+    "plt.ylim(min_f,max_f)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "\n",
+    "fig.savefig(output_dir + 'figure_A1_2.pdf', bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## sensitivity_on a, $\\beta$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c3dbd8ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cases = [\n",
+    "('super_low_a', 'optimal', 1.0),\n",
+    "('low_a', 'optimal', 1.0),\n",
+    "('baseline', 'optimal', 1.0),\n",
+    "('high_a', 'optimal', 1.0)]\n",
+    "\n",
+    "sols = [results[c] for c in cases]\n",
+    "\n",
+    "betas = [list_of_calibrations[c]['a'] for c in cases]\n",
+    "vv = sols[0]['e'][T-1]\n",
+    "\n",
+    "fig = plt.figure(figsize=(width, 0.4*width))\n",
+    "\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(4):\n",
+    "    plt.plot(sols[i]['Gamma'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_gamma, max_gamma)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Marginal Value $\\Gamma_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(4):\n",
+    "    plt.plot(sols[i]['f'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.title(\"Intervention $f_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sols[3]['e'][:T]*0+vv, color='grey', label='_'.format(cases[i][2]), linestyle=':')\n",
+    "for i in range(4):\n",
+    "    plt.plot(sols[i]['e'][:T], label='$a={:.2f}$'.format(betas[i]), linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.text(3,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "\n",
+    "plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.title(\"Exchange Rate $e_t$\", fontsize=titlesize)\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.savefig(\"sensitivity_to_a.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c328e1470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "len(results.keys())\n",
+    "\n",
+    "cases = [\n",
+    "# ('super_low_a', 'optimal', 1.0),\n",
+    "('low_a', 'optimal', 1.0),\n",
+    "('baseline', 'optimal', 1.0),\n",
+    "('high_a', 'optimal', 1.0)]\n",
+    "\n",
+    "sols = [results[c] for c in cases]\n",
+    "\n",
+    "betas = [list_of_calibrations[c]['a'] for c in cases]\n",
+    "vv = sols[0]['e'][T-1]\n",
+    "\n",
+    "fig = plt.figure(figsize=(width, 0.4*width*2))\n",
+    "\n",
+    "sf = plt.subplot(231)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['Gamma'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_gamma, max_gamma)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Marginal Value $\\Gamma_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(232)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['f'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.title(\"Intervention $f_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(233)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sols[i]['e'][:T]*0+vv, color='grey', label='_'.format(cases[i][2]), linestyle=':')\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['e'][:T], label='$a={:.2f}$'.format(betas[i]), linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "plt.text(3,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.title(\"Exchange Rate $e_t$\", fontsize=titlesize)\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "### Second row\n",
+    "\n",
+    "cases = [\n",
+    "('low_c', 'optimal', 1.0),\n",
+    "('baseline', 'optimal', 1.0),\n",
+    "('high_c', 'optimal', 1.0)]\n",
+    "\n",
+    "sols = [results[c] for c in cases]\n",
+    "\n",
+    "# betas = [list_of_calibrations[c]['beta'] for c in cases]\n",
+    "betas = [list_of_calibrations[c]['c'] for c in cases]\n",
+    "\n",
+    "# fig = plt.figure(figsize=(width,0.4*width))\n",
+    "\n",
+    "sf = plt.subplot(234)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['Gamma'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_gamma, max_gamma)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Marginal Value $\\Gamma_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(235)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['f'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.title(\"Intervention $f_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(236)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "# plt.plot(sols[i]['e'][:T]*0+vv, color='grey', label='_'.format(cases[i][2]), linestyle=':')\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['e'][:T], label='$c={:.2f}$'.format(betas[i]), linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "# plt.text(3,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e_t$\", fontsize=titlesize)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "\n",
+    "fig.savefig(output_dir + 'figure_A2.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## sensitivity to a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32c71438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cases = [\n",
+    "#     ('super_low_a', 'time-consistent', 1.0),\n",
+    "    ('low_a', 'time-consistent', 1.0),\n",
+    "    ('baseline', 'time-consistent', 1.0),\n",
+    "    ('high_a', 'time-consistent', 1.0)\n",
+    "]\n",
+    "\n",
+    "sols = [results[c] for c in cases]\n",
+    "\n",
+    "\n",
+    "betas = [list_of_calibrations[c]['a'] for c in cases]\n",
+    "c = cases[0]\n",
+    "vv = list_of_calibrations[c]['zbar']/list_of_calibrations[c]['c']\n",
+    "\n",
+    "fig = plt.figure(figsize=(width,0.4*width*2))\n",
+    "\n",
+    "sf = plt.subplot(231)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['Gamma'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim(min_gamma, max_gamma)\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(yl[0],yl[1])\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Marginal Value $\\Gamma_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(232)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['f'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.ylim(min_f, max_f)\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "\n",
+    "plt.grid()\n",
+    "plt.title(\"Intervention $f_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(233)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sols[i]['e'][:T]*0+vv, color='grey', label='_'.format(cases[i][2]), linestyle=':')\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['e'][:T], label='$a={:.2f}$'.format(betas[i]), linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_xr,max_xr)\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "plt.text(3,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "# plt.ylim(min_xr, max_xr)\n",
+    "plt.title(\"Exchange Rate $e_t$\", fontsize=titlesize)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(output_dir + 'figure_8.pdf', bbox_inches='tight')\n",
+    "\n",
+    "#######\n",
+    "#######\n",
+    "\n",
+    "cases = [\n",
+    "('low_c', 'time-consistent', 1.0),\n",
+    "('baseline', 'time-consistent', 1.0),\n",
+    "('high_c', 'time-consistent', 1.0)]\n",
+    "\n",
+    "sols = [results[c] for c in cases]\n",
+    "\n",
+    "betas = [list_of_calibrations[c]['c'] for c in cases]\n",
+    "c = cases[0]\n",
+    "# vv = list_of_calibrations[c]['zbar']/list_of_calibrations[c]['c']\n",
+    "\n",
+    "# fig = plt.figure(figsize=(width,0.4*width))\n",
+    "\n",
+    "sf = plt.subplot(234)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(3):\n",
+    "    plt.plot(sols[i]['Gamma'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlim(0,T)\n",
+    "# plt.ylim(yl[0],yl[1])\n",
+    "plt.ylim(min_gamma, 4)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Marginal Value $\\Gamma_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(235)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(3):\n",
+    "    plt.plot(sols[i]['f'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_f, max_f)\n",
+    "plt.title(\"Intervention $f_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(236)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "# plt.plot(sols[i]['e'][:T]*0+vv, color='grey', label='_'.format(cases[i][2]), linestyle=':')\n",
+    "for i in range(3):\n",
+    "    plt.plot(sols[i]['e'][:T], label='$c={:.2f}$'.format(betas[i]), linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "\n",
+    "plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e_t$\", fontsize=titlesize)\n",
+    "plt.ylim(min_xr, 1.5)\n",
+    "plt.tight_layout()\n",
+    "fig.savefig(output_dir + 'figure_A3.pdf', bbox_inches='tight')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## stochastic shocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "with open(\"precomputed_sims_stoch\",\"rb\") as f:\n",
+    "    dfs = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df0 = results[('p_8','optimal',1.0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p = 0.8\n",
+    "tvec = p**dfs.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32c7f160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "\n",
+    "plt.xlim(0,T)\n",
+    "\n",
+    "# plt.plot(dfs['E_gamma'], linestyle=linestyles[0])\n",
+    "plt.plot(df0['Gamma'], linestyle=linestyles[0])\n",
+    "plt.plot(tvec*dfs['Gamma'], linestyle=linestyles[1])\n",
+    "plt.plot(tvec*dfs['Gamma_s'], linestyle=linestyles[1])\n",
+    "# plt.plot(dfs['Gamma_s'], linestyle=linestyles[2])\n",
+    "\n",
+    "plt.grid()\n",
+    "plt.title(\"Marginal Value $\\Gamma_t$\", fontsize=titlesize)\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "# plt.plot(dfs['E_e'], linestyle=linestyles[1])\n",
+    "vv = df0['e'][T-1]\n",
+    "plt.plot(df0['e']*0+vv, color='grey', linestyle=':')\n",
+    "\n",
+    "plt.plot(df0['e'], linestyle=linestyles[0])\n",
+    "plt.plot(dfs['e'], linestyle=linestyles[1])\n",
+    "plt.plot(dfs['e_s'], linestyle=linestyles[2])\n",
+    "plt.text(3,0.33,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "# plt.plot(dfs['e_s'], linestyle=linestyles[2])\n",
+    "# for lam in lamvec:\n",
+    "#     df = od[(0.01,lam)]\n",
+    "#     plt.plot(df['target'],linestyle=':', color='grey')\n",
+    "plt.grid()\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(-0.3,0.4)\n",
+    "\n",
+    "plt.xlim(0,25)\n",
+    "plt.title(\"Exchange Rate $e_{t}$\", fontsize=titlesize)\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "# plt.plot(dfs['E_f'], label='$E[]$', linestyle=linestyles[1])\n",
+    "plt.plot(df0['f'], label='No Post Shock Intervention', linestyle=linestyles[0])\n",
+    "plt.plot(dfs['f'], label='With Post Shock Intervention', linestyle=linestyles[1])\n",
+    "plt.plot(dfs['f_s'], label='After Shock', linestyle=linestyles[2])\n",
+    "\n",
+    "# plt.plot(dfs['f_s'], label='$Stopped$', linestyle=linestyles[2])\n",
+    "plt.grid()\n",
+    "plt.xlim(0,T)\n",
+    "\n",
+    "plt.legend(loc='upper center',fontsize=5)\n",
+    "plt.title(\"Intervention $f_{t}$\", fontsize=titlesize)\n",
+    "plt.xlim(0,25)\n",
+    "plt.ylim(-0.05,0.4)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.savefig(output_dir+ \"post-shock-intervention.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3225815014151255"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df0['e'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fig = plt.figure(figsize=(width,0.4*width))\n",
+    "# sf = plt.subplot(131)\n",
+    "# sf.tick_params(labelsize=ticksize)\n",
+    "\n",
+    "# plt.xlim(0,T)\n",
+    "# plt.plot(dfs['E_gamma'], linestyle=linestyles[0])\n",
+    "# plt.plot(dfs['Gamma'], linestyle=linestyles[1])\n",
+    "# plt.plot(dfs['Gamma_s'], linestyle=linestyles[2])\n",
+    "\n",
+    "# plt.grid()\n",
+    "# plt.title(\"Marginal Value $\\Gamma^{\\lambda}_t$\", fontsize=titlesize)\n",
+    "# sf = plt.subplot(132)\n",
+    "# sf.tick_params(labelsize=ticksize)\n",
+    "# plt.plot(dfs['E_e'], linestyle=linestyles[1])\n",
+    "# plt.plot(dfs['e'], linestyle=linestyles[1])\n",
+    "# plt.plot(dfs['e_s'], linestyle=linestyles[2])\n",
+    "# # for lam in lamvec:\n",
+    "# #     df = od[(0.01,lam)]\n",
+    "# #     plt.plot(df['target'],linestyle=':', color='grey')\n",
+    "# plt.grid()\n",
+    "# plt.xlim(0,T)\n",
+    "\n",
+    "# plt.xlim(0,25)\n",
+    "# plt.title(\"Exchange Rate $e^{\\lambda}_{t}$\", fontsize=titlesize)\n",
+    "# sf = plt.subplot(133)\n",
+    "# sf.tick_params(labelsize=ticksize)\n",
+    "# plt.plot(dfs['E_f'], label='$E[]$', linestyle=linestyles[1])\n",
+    "# plt.plot(dfs['f'], label='$Continuing$', linestyle=linestyles[1])\n",
+    "# plt.plot(dfs['f_s'], label='$Stopped$', linestyle=linestyles[2])\n",
+    "# plt.grid()\n",
+    "# plt.xlim(0,T)\n",
+    "\n",
+    "# plt.legend(loc='upper right',fontsize=leg_fontsize)\n",
+    "# plt.title(\"Intervention $f^{\\lambda}_{t}$\", fontsize=titlesize)\n",
+    "# plt.xlim(0,25)\n",
+    "# plt.tight_layout()\n",
+    "\n",
+    "# fig.savefig(output_dir + 'figure_stoch.pdf', bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Accumulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32f642e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sols = [\n",
+    "    results[(\"baseline\",'optimal',1.0)],\n",
+    "#     results[(\"accumulation\",'optimal',0.5)],\n",
+    "    results[(\"accumulation\",'optimal',1.0)],\n",
+    "#     results[(\"accumulation\",'optimal',2.0)]\n",
+    "]\n",
+    "\n",
+    "# sols = [\n",
+    "#     results[(\"baseline\",'optimal',2.0)],\n",
+    "#     results[(\"accumulation\",'optimal',2.0)],\n",
+    "# ]\n",
+    "\n",
+    "# betas = [list_of_calibrations[c]['beta'] for c in cases]\n",
+    "labels = [\n",
+    "    'No Accumulation', 'Accumulation'\n",
+    "]\n",
+    "\n",
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "#a\n",
+    "\n",
+    "vv = sols[0]['e'][T-1]\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['f'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.grid()\n",
+    "plt.title(\"Intervention $f_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sols[0]['e'][:T]*0+vv, color='grey', label='_', linestyle=':')\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['e'][:T], label=labels[i], linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "plt.text(3,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['R'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_r, max_r)\n",
+    "plt.ylim(min_r, 1.4)\n",
+    "\n",
+    "plt.title(\"Reserves $R_t$\", fontsize=titlesize)\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "fig.savefig(output_dir + 'figure_acc.pdf', bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Option cost (non stochastic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "with open(\"precomputed_option_value.pickle\",\"rb\") as f:\n",
+    "    d = pickle.load(f)\n",
+    "dfs = d['commitment']\n",
+    "RVec = [df['R'][0] for df in dfs]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c32e15320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sols = dfs[:3] + [dfs[4]]\n",
+    "\n",
+    "# betas = [list_of_calibrations[c]['beta'] for c in cases]\n",
+    "labels = [\n",
+    "    'R=$0.01$',\n",
+    "    'R=$0.5$',\n",
+    "    'R=$0.9$',\n",
+    "    'R=$2.0$',\n",
+    "#     'R=$3.0$'\n",
+    "]\n",
+    "\n",
+    "\n",
+    "\n",
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "#a\n",
+    "\n",
+    "\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sols[i]['Gamma'][:T]*0+0.5, color='grey')\n",
+    "plt.text(20,0.53,\"$\\\\theta=0.5$\", fontsize=leg_fontsize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['Gamma'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.grid()\n",
+    "plt.title(\"Marginal Value $\\Gamma^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sols[0]['e'][:T]*0+vv, color='grey', label='_', linestyle=':')\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['e'][:T], label=labels[i], linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "\n",
+    "plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "plt.grid()\n",
+    "plt.text(22,0.62,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['R'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_r, max_r)\n",
+    "plt.ylim(min_r, 2.5)\n",
+    "\n",
+    "plt.title(\"Reserves $R^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "fig.savefig(output_dir + 'figure_alpha_commitment_R.pdf', bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## option value (stochastic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/pablo/Mobilhome/published/managing_capital_outflows_with_limited_reserves'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "with open(\"precomputed_alpha_p.pickle\",\"rb\") as f:\n",
+    "    d = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvec = [*d['dfs'].keys()]\n",
+    "dfs = [*d['dfs'].values()]\n",
+    "pvec.reverse()\n",
+    "dfs.reverse()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linestyles = ['solid', 'dashed', 'dotted', 'dashdot', 'dashed']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2c3293b278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sols = dfs\n",
+    "\n",
+    "# sols = [\n",
+    "#     results[(\"baseline\",'optimal',2.0)],\n",
+    "#     results[(\"accumulation\",'optimal',2.0)],\n",
+    "# ]\n",
+    "\n",
+    "# betas = [list_of_calibrations[c]['beta'] for c in cases]\n",
+    "labels = ['p=${}$'.format(x) for x in pvec]\n",
+    "\n",
+    "\n",
+    "\n",
+    "fig = plt.figure(figsize=(width,0.4*width))\n",
+    "#a\n",
+    "\n",
+    "\n",
+    "sf = plt.subplot(131)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "plt.plot(sols[0]['Gamma'][:T]*0+0.5, color='grey', label='_', linestyle='--')\n",
+    "plt.text(20,0.53,\"$\\\\theta=0.5$\", fontsize=leg_fontsize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['Gamma'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.grid()\n",
+    "plt.title(\"Marginal Value $\\Gamma^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(132)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "# plt.plot(sols[0]['e'][:T]*0+vv, color='grey', label='_', linestyle='--')\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['e'][:T], label=labels[i], linestyle=linestyles[i])\n",
+    "plt.xlim(0,T)\n",
+    "#plt.text( 1, vv*0.92, '$\\overline{e}$', fontsize=ebarsize)\n",
+    "\n",
+    "plt.legend(loc='lower right', fontsize=leg_fontsize)\n",
+    "plt.grid()\n",
+    "# plt.text(2,0.63,\"$\\\\bar{e}$\",fontsize=leg_fontsize)\n",
+    "\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.title(\"Exchange Rate $e^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "sf = plt.subplot(133)\n",
+    "sf.tick_params(labelsize=ticksize)\n",
+    "for i in range(len(sols)):\n",
+    "    plt.plot(sols[i]['R'][:T], linestyle=linestyles[i])\n",
+    "yl = plt.ylim()\n",
+    "plt.grid()\n",
+    "plt.xlabel('$t$',fontsize=labelsize)\n",
+    "plt.xlim(0,T)\n",
+    "plt.ylim(min_r, max_r)\n",
+    "plt.ylim(min_r, 1.0)\n",
+    "\n",
+    "plt.title(\"Reserves $R^{\\\\bar{z}}_t$\", fontsize=titlesize)\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "fig.savefig(output_dir + 'figure_alpha_commitment_p.pdf', bbox_inches='tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  },
+  "nav_menu": {},
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/models.yaml b/models.yaml
new file mode 100644
index 0000000..3221fb0
--- /dev/null
+++ b/models.yaml
@@ -0,0 +1,149 @@
+calibration:
+    beta: 0.842105263  #0.8/(0.8+0.15),
+    a: 0.8
+    c: 0.15
+    estar: -0.0
+    lam: 0.8
+    Rbar: 1.0
+    min_f: 0
+    kappa: 1.0
+    N: 40
+    zbar: 0.1
+    p: 1.0
+    b: 0.0
+    Delta: 0.0
+    L: 0.0
+    theta: 1000
+    T: 5
+
+
+optimal:
+  - -z[s] + etree.values[s]   ⟂  z[s]
+  - -e[s] + a/(a+c)*p*E[ e[x] | x in S(s)] + 1.0/(a+c)*(z[s]-f[s]*(1-theta/2*f[s]))  ⟂  e[s]
+  - len(s)==1:
+    - (-Gamma[s]) + (1-theta*f[s])*(e[s]-estar)       ⟂  Gamma[s]
+  - len(s)>1:
+    - (-Gamma[s]) + a/(a+c)*p*Gamma[P(s)] + (1-theta*f[s])*(p*beta)**t*(e[s]-estar)         ⟂  Gamma[s]
+  - len(s)<len(etree):
+      - (-Gamma[s]) + E[ Gamma[x] | x in S(s) ] + psi[s] - phi[s]     ⟂  f[s]
+  - len(s)==len(etree): # two periods outflows
+    - -f[s]                                                           ⟂  f[s]
+  - Rbar - Sum[f[x] | x in H(s)] - R[s]                        ⟂  R[s]
+  - Sum[ f[x] | x in H(s) ] - Rbar                             ⟂  0 <= psi[s]
+  - (-(f[s]-min_f))                                            ⟂  0 <= phi[s]
+
+
+# peg
+peg:
+  - -z[s] + etree.values[s]                                         ⟂ z[s]
+  - -e[s] + a/(a+c)*p*E[ e[x] | x in S(s)] + 1.0/(a+c)*(z[s]-f[s])  ⟂ e[s]
+  - len(s)==1:
+    - -Gamma[s] + (e[s]-estar)                                   ⟂ Gamma[s]
+  - len(s)>1:
+    - -Gamma[s] + a/(a+c)*p*Gamma[P(s)] + (p*beta)**t*(e[s]-estar)     ⟂ Gamma[s]
+  - len(s)<len(etree):
+    - -e[s] + z[s]/(a*(1-p)+c)*(1-kappa) + psi[s]                      ⟂ f[s]
+  - len(s)==len(etree):
+    - f[s]                                                     ⟂ f[s]
+  - Sum[ f[x] | x in H(s) ] - Rbar                             ⟂ 0 <= psi[s]
+  - (-(f[s]-min_f))                                            ⟂ 0 <= phi[s]
+
+
+# volume
+volume:
+  - -z[s] + etree.values[s]                                     ⟂ z[s]
+  - -e[s] + a/(a+c)*p*E[ e[x] | x in S(s)] + 1.0/(a+c)*(z[s]-f[s])  ⟂ e[s]
+  - len(s)==1:
+        - -Gamma[s] + (e[s]-estar)                              ⟂ Gamma[s]
+  - len(s)>1:
+    - -Gamma[s] + a/(a+c)*p*Gamma[P(s)] + (p*beta)**t*(e[s]-estar)  ⟂ Gamma[s]
+  - len(s)<len(etree):
+    - -f[s] + kappa*z[s] - psi[s]                                 ⟂ f[s]
+  - len(s)==len(etree):
+    - f[s]                                                       ⟂ f[s]
+  - Sum[ f[x] | x in H(s) ] - Rbar                                  ⟂ 0 <= psi[s]
+  - (-(f[s]-min_f))                                                 ⟂  0 <= phi[s]
+
+moving_target:
+  - -z[s] + etree.values[s]                                         ⟂ z[s]
+  - -e[s] + a/(a+c)*p*E[ e[x]   | x in S(s)] + 1.0/(a+c)*(z[s]-f[s])  ⟂ e[s]
+  - (-target[s]) + zbar/c*(1-lam**t)                                  ⟂ target[s]
+  - len(s)==1:
+    - (-Gamma[s]) + (e[s]-target[s])                                  ⟂ Gamma[s]
+  - len(s)>1:
+    - (-Gamma[s]) + a/(a+c)*Gamma[P(s)] + (p*beta)**t*(e[s]-target[s])  ⟂ Gamma[s]
+  - sum(s)<1:
+    - (-Gamma[s]) + E[ Gamma[x] | x in S(s) ] + psi[s] - phi[s]     ⟂ f[s]
+  - sum(s)==1: # two periods outflows
+    - -f[s]                                                     ⟂ f[s]
+  # - sum(s)<=1:
+  #   - (-Gamma[s]) + E[ Gamma[x] | x in S(s) ] + psi[s] - phi[s]     | f[s]
+  # - sum(s)==2: # two periods outflows
+  #   - -f[s]                                                         | f[s]
+  - Rbar - Sum[f[x] | x in H(s)] - R[s]                             ⟂ R[s]
+  - Sum[ f[x] | x in H(s) ] - Rbar                                  ⟂ 0 <= psi[s]
+  - (-(f[s]-min_f))                                                 ⟂ 0 <= phi[s]
+
+
+
+stochastic:
+  - -z[s] + etree.values[s]                                ⟂ z[s]
+  - -e[s] + a/(a+c)*E[ e[x] | x in S(s)] + 1.0/(a+c)*(z[s]-f[s])   ⟂ e[s]
+  - t==0:
+    - (-Gamma[s]) + (e[s]-estar)                           ⟂ Gamma[s]
+  - t>=1:
+    - (-Gamma[s]) + a/(a+c)*Gamma[P(s)] + (beta)**t*(e[s]-estar)         ⟂ Gamma[s]
+  - pp[s] - prob(s)  ⟂  pp[s]
+  - sum(s)<1:
+    - (-Gamma[s]/prob(s)) + E[ Gamma[x]/prob(s) | x in S(s) ] + psi[s] - phi[s]   ⟂ f[s]
+  - sum(s)==1:
+#      - -f[s]                                                           ⟂ f[s]
+    -  (Gamma[s] - psi[s])   ⟂ f[s]
+  - sum(s)==2: # two periods outflows
+    - -f[s]                                                           ⟂ f[s]
+  - Rbar - Sum[f[x] | x in H(s)] - R[s]                             ⟂ R[s]
+  - Sum[ f[x] | x in H(s) ] - Rbar                                  ⟂ 0 <= psi[s]
+  - (-(f[s]-min_f))                                                 ⟂ 0 <= phi[s]
+
+
+peg_T:
+  - -z[s] + etree.values[s]                                       ⟂ z[s]
+  - -e[s] + a/(a+c)*p*E[ e[x] | x in S(s)] + 1.0/(a+c)*(z[s]-f[s])  ⟂ e[s]
+  - len(s)==1:
+    - -Gamma[s] + (e[s]-estar)                                    ⟂ Gamma[s]
+  - len(s)>1:
+    - -Gamma[s] + a/(a+c)*p*Gamma[P(s)] + (p*beta)**t*(e[s]-estar)      ⟂ Gamma[s]
+  - len(s)<=T:
+    - -e[s] + z[s]/(a*(1-p)+c)*(1-kappa) + psi[s]                           ⟂ f[s]
+  - len(s)>T:
+    - f[s]                                                      ⟂ f[s]
+  - Sum[ f[x] | x in H(s) ] - Rbar                                  ⟂ 0 <= psi[s]
+  - (-(f[s]-min_f))                                                 ⟂ 0 <= phi[s]
+
+
+optimal_stop:
+  - -z[s] + etree.values[s]                                               ⟂ z[s]
+  - -e[s] + a/(a+c)*p*E[ e[x]   | x in S(s)] + 1.0/(a+c)*(z[s]-f[s])  ⟂ e[s]
+  - len(s)==1:
+    - (-Gamma[s]) + (e[s]-estar)                                    ⟂ Gamma[s]
+  - len(s)>1:
+    - (-Gamma[s]) + a/(a+c)*p*Gamma[P(s)] + (beta*p)**t*(e[s]-estar)      ⟂ Gamma[s]
+  - ((sum(s)<1) and len(s)>T)
+    - (-Gamma[s]) + E[ Gamma[x] | x in S(s) ] + psi[s] - phi[s]     ⟂ f[s]
+  - ((sum(s)<1) and len(s)<=T):
+    - e[s]     ⟂ f[s]
+  #- sum(s)==1: # two periods outflows
+  #  - -f[s]                                                         | f[s]
+  # - sum(s)<=1:
+  #   - (-Gamma[s]) + E[ Gamma[x] | x in S(s) ] + psi[s] - phi[s]     | f[s]
+  # - sum(s)==2: # two periods outflows
+  #   - -f[s]                                                         | f[s]
+  - len(s)<=T:
+      - Rbar - Sum[f[x] | x in H(s)] - R[s]                             ⟂ R[s]
+  - len(s)>T:
+      - Rbar - Sum[f[x] | x in H(s)] - L - R[s]                        ⟂ R[s]
+  - len(s)<=T:
+      - Sum[ f[x] | x in H(s) ] - Rbar                                  ⟂ 0 <= psi[s]
+  - len(s)>T:
+      - Sum[ f[x] | x in H(s) ] + L - Rbar                              ⟂ 0 <= psi[s]
+  - (-(f[s]-min_f))                                                 ⟂ 0 <= phi[s]
diff --git a/time_consistent.py b/time_consistent.py
new file mode 100644
index 0000000..5a6e6b4
--- /dev/null
+++ b/time_consistent.py
@@ -0,0 +1,151 @@
+import numpy
+from numpy import exp
+import scipy.optimize
+from scipy.interpolate import Rbf, InterpolatedUnivariateSpline, splev, splrep
+
+
+calibration = dict(
+    beta=0.8/(0.8+0.15),
+    a=0.8,
+    c=0.15,
+    estar=-0.0,
+    Rbar=0.5,
+    min_f=0,
+    kappa=1.0,
+    N=40,
+    zbar=0.1,
+    lam=0.9, # probability that crisis continues
+    model='optimal',
+    theta=0.0,
+    p=1.0,
+    b=0.0
+)
+
+
+def residuals(rvec, e, f, calib):
+
+    a = calib['a']
+    c = calib['c']
+    estar = calib['estar']
+    beta = calib['beta']
+    zbar = calib['zbar']
+    theta = calib['theta']
+    p = calib['p']
+    b = calib['b']
+
+    fun_e = splrep(rvec, e, k=5)
+    fun_f = splrep(rvec, f, k=5)
+
+    R_f = rvec - f
+
+    f_f = splev(R_f, fun_f, der=0)
+    e_f = splev(R_f, fun_e, der=0)
+
+    d_f_f = splev( R_f, fun_f, der=1)
+    d_e_f = splev( R_f, fun_e, der=1)
+
+    psi = (1-b)*(f-f**2*theta/2)
+    psi_f = (1-b)*(f-f_f**2*theta/2)
+
+    d_psi = (1-b)*(1-f*theta)
+    d_psi_f = (1-b)*(1-f_f*theta)
+
+    cond_1 = (e - estar)*(d_psi+a*p*d_e_f) - beta*(e_f - estar)*d_psi_f*p
+    cond_2 = e - p*a/(a+c)*e_f + 1/(a+c)*(psi_f-zbar)
+
+    return [cond_1, cond_2]
+
+def make_init(rvec, calib):
+
+    init = numpy.concatenate( [rvec[None,:], rvec[None,:]], axis=0)
+    beta = calib['beta']
+    zbar = calib['zbar']
+    a = calib['a']
+    c = calib['c']
+    p = calib['p']
+
+    xstar = 1.0/(a+c-a*p)*zbar
+    r0 = xstar/(a+c)
+    r2vec = numpy.concatenate([rvec-rvec.max(), rvec])
+
+    e =  numpy.maximum(xstar+(beta*p-1)/(p*a)*r2vec, 0)
+    f = numpy.minimum( (1-beta)/beta*c/a*r2vec, zbar )
+
+    N = len(rvec)
+
+    evec = e[N:]
+    fvec = f[N:]
+    init = numpy.row_stack([evec,fvec*0.999])
+    return init
+
+
+
+
+def solve(initial_guess=None, max_R=8, N=20, **cc):
+
+    calib = calibration.copy()
+    calib.update(cc)
+
+    rvec = numpy.linspace(0.0001,max_R,N)
+    # max_f = numpy.minimum(rvec*1.1)
+    max_f = rvec*1.1
+
+    def from_xi(u):
+        uu = u.copy().reshape((2,-1))
+        e = uu[0,:]
+        xx = uu[1,:]
+        f = max_f*(1+numpy.tanh(xx))/2
+        uu[1,:] = f
+        return uu
+
+    def to_xi(u):
+        uu = u.copy()
+        f = uu[1,:]
+        uu[1,:] = numpy.arctanh( 2*f/max_f-1 )
+        return uu
+    def fobj(u):
+        uu = u.reshape((2,-1))
+        e = uu[0,:]
+        xx = uu[1,:]
+
+        f = max_f*(1+numpy.tanh(xx))/2
+        res = residuals(rvec, e, f, calib)
+        return numpy.concatenate(res)
+
+    if initial_guess is None:
+        init = make_init(rvec, calib)
+    elif isinstance(initial_guess,tuple):
+        fun_e, fun_f = initial_guess[3]
+        init = numpy.row_stack([
+            fun_e(rvec),
+            fun_f(rvec)
+        ])
+    else:
+        init = initial_guess
+
+    res = scipy.optimize.root(
+        fobj,
+        to_xi(init),
+        method='lm',
+        options={'ftol':1e-10, 'xtol':1e-10}
+    )
+    x = from_xi(res.x)
+
+    spl_e = splrep(rvec, x[0,:], k=5)
+    spl_f = splrep(rvec, x[1,:], k=5)
+    fun_e = lambda x: splev(x, spl_e )
+    fun_f = lambda x: splev(x, spl_f )
+
+    return rvec, x, res.x, [fun_e, fun_f]
+
+def simulate(r0, drs, N):
+    dr_e, dr_f = drs[3]
+    import pandas
+    vals = []
+    R = r0
+    for i in range(N):
+        e = dr_e(R)
+        f = dr_f(R)
+        vals.append([R,e,f])
+        R = R - f
+    return pandas.DataFrame(vals, columns=['R','e','f'])