From e617ad3a1714199987d88ba933f25678609bf714 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 13 Jun 2025 10:47:55 +0000 Subject: [PATCH 01/14] update did_cs_multi model section --- doc/guide/models/did/did_aggregation.rst | 4 ++ doc/guide/models/did/did_cs.rst | 74 ++++++++++++++++++++- doc/guide/models/did/did_implementation.rst | 35 ++++++++++ doc/guide/models/did/did_models.inc | 11 ++- doc/guide/models/did/did_pa.rst | 32 +-------- doc/guide/models/did/did_setup.rst | 13 +++- 6 files changed, 134 insertions(+), 35 deletions(-) create mode 100644 doc/guide/models/did/did_implementation.rst diff --git a/doc/guide/models/did/did_aggregation.rst b/doc/guide/models/did/did_aggregation.rst index d1750089..cd69a65e 100644 --- a/doc/guide/models/did/did_aggregation.rst +++ b/doc/guide/models/did/did_aggregation.rst @@ -52,6 +52,10 @@ The method ``aggregate()`` requires the ``aggregation`` argument to be set to on * ``'eventstudy'``: aggregates :math:`ATT(\mathrm{g},t)` estimates based on time difference to first treatment assignment like an event study (based on group size). * ``dictionary``: a dictionary with values containing the aggregation weights (as ``numpy.ma.MaskedArray``). +.. warning:: + Remark that ``'time'`` and ``'eventstudy'`` aggregation use internal group reweighting according to the total group size (e.g. the group decomposition should be relatively stable over time, as assumed in Assumption 2). + It can be helpful to check the aggregation weights as in the :ref:`example gallery `. + .. note:: A more detailed example on effect aggregation is available in the :ref:`example gallery `. For a detailed discussion on different aggregation schemes, we refer to of `Callaway and Sant'Anna (2021) `_. diff --git a/doc/guide/models/did/did_cs.rst b/doc/guide/models/did/did_cs.rst index dee07ecb..df87a147 100644 --- a/doc/guide/models/did/did_cs.rst +++ b/doc/guide/models/did/did_cs.rst @@ -1,2 +1,74 @@ +For the estimation of the target parameters :math:`ATT(\mathrm{g},t)` the following nuisance functions are required: + +.. math:: + \begin{align} + g^{\text{treat}}_{\mathrm{g}, t, \text{eval} + \delta}(X_i) &:= \mathbb{E}[Y_{i,t} |X_i, G_i^{\mathrm{g}}=1, T_i=t], \\ + g^{\text{control}}_{\mathrm{g}, t, \text{eval} + \delta}(X_i) &:= \mathbb{E}[Y_{i,t} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t], \\ + m_{0, \mathrm{g}, t_\text{eval} + \delta}(X_i) &:= P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t_\text{eval} + \delta}^{(\cdot)}=1). + \end{align} + +for :math:`t\in\{t_\text{pre}, t_\text{eval}\}`. +Here, :math:`g^{(\cdot)}_{\mathrm{g}, t, \text{eval} + \delta}(\cdot)` denotes the population outcome regression function (for either treatment or control group at time period :math:`t`) and :math:`m_{0, \mathrm{g}, t_\text{eval} + \delta}(\cdot)` the generalized propensity score. + .. note:: - Will be implemented soon. \ No newline at end of file + Remark that the nuisance functions depend on the control group used for the estimation of the target parameter. + By slight abuse of notation we use the same notation for both control groups :math:`C_{i,t}^{(\text{nev})}` and :math:`C_{i,t}^{(\text{nyt})}`. More specifically, the + control group only depends on :math:`\delta` for *not yet treated* units. + +For a given tuple :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` the target parameter :math:`ATT(\mathrm{g},t)` is estimated by solving the empirical version of the the following linear moment condition: + +.. math:: + ATT(\mathrm{g}, t_\text{pre}, t_\text{eval}):= -\frac{\mathbb{E}[\psi_b(W,\eta_0)]}{\mathbb{E}[\psi_a(W,\eta_0)]} + +with nuisance elements :math:`\eta_0=(g^{\text{treat}}_{\mathrm{g}, t_\text{pre}, t_\text{eval} + \delta}, g^{\text{control}}_{\mathrm{g}, t_\text{pre}, t_\text{eval} + \delta}, g^{\text{treat}}_{\mathrm{g}, t_\text{eval}, t_\text{eval} + \delta}, g^{\text{control}}_{\mathrm{g}, t_\text{eval}, t_\text{eval} + \delta}, m_{0, \mathrm{g}, t_\text{eval}})` and score function :math:`\psi(W,\theta, \eta)` defined in the :ref:`DiD Score Section`. + +Setting ``panel=False`` will estimate the target parameter for repeated cross sections. Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + import doubleml as dml + from doubleml.did.datasets import make_did_cs_CS2021 + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + np.random.seed(42) + df = make_did_cs_CS2021(n_obs=500) + dml_data = dml.data.DoubleMLPanelData( + df, + y_col="y", + d_cols="d", + id_col="id", + t_col="t", + x_cols=["Z1", "Z2", "Z3", "Z4"], + datetime_unit="M" + ) + dml_did_obj = dml.did.DoubleMLDIDMulti( + obj_dml_data=dml_data, + ml_g=RandomForestRegressor(min_samples_split=10), + ml_m=RandomForestClassifier(min_samples_split=10), + gt_combinations="standard", + control_group="never_treated", + panel=False, + ) + print(dml_did_obj.fit()) + +.. note:: + Remark that the output contains four different outcome regressions :math:`g(d,t, X)` for :math:`d,t\in\{0,1\}` . As in the :ref:`IRM model ` + the outcome regression with :math:`d=0` refers to the control group, whereas :math:`t=0` refers to the pre-treatment period, i.e. + + .. math:: + \begin{align} + g(0,0,X) &\approx g^{\text{control}}_{\mathrm{g}, t_\text{pre}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{pre}} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t_\text{pre}],\\ + g(0,1,X) &\approx g^{\text{control}}_{\mathrm{g}, t_\text{post}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{post}} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t_\text{post}],\\ + g(1,0,X) &\approx g^{\text{treat}}_{\mathrm{g}, t_\text{pre}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{pre}} |X_i, G_i^{\mathrm{g}}=1,, T_i=t_\text{pre}],\\ + g(1,1,X) &\approx g^{\text{treat}}_{\mathrm{g}, t_\text{post}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{post}} |X_i, G_i^{\mathrm{g}}=1,, T_i=t_\text{post}]. + \end{align} + +.. note:: + A more detailed example is available in the :ref:`Example Gallery `. \ No newline at end of file diff --git a/doc/guide/models/did/did_implementation.rst b/doc/guide/models/did/did_implementation.rst new file mode 100644 index 00000000..20e06e88 --- /dev/null +++ b/doc/guide/models/did/did_implementation.rst @@ -0,0 +1,35 @@ +To estimate the target parameter :math:`ATT(\mathrm{g},t_\text{eval})`, the implementation (both for panel data or repeated cross sections) is based on the following parameters: + +* :math:`\mathrm{g}` is the first post-treatment period of interest, i.e. the treatment group. +* :math:`t_\text{pre}` is the pre-treatment period, i.e. the time period from which the conditional parallel trends are assumed. +* :math:`t_\text{eval}` is the time period of interest or evaluation period, i.e. the time period where the treatment effect is evaluated. +* :math:`\delta` is number of anticipation periods, i.e. the number of time periods for which units are assumed to anticipate the treatment. + + +Under the assumptions above the target parameter :math:`ATT(\mathrm{g},t_\text{eval})` can be estimated by choosing a suitable combination +of :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval}, \delta)` if :math:`t_\text{eval} - t_\text{pre} \ge 1 + \delta`, i.e. the parallel trends are assumed to hold at least one period more than the anticipation period. + +.. note:: + The choice :math:`t_\text{pre}= \min(\mathrm{g},t_\text{eval}) -\delta-1` corresponds to the definition of :math:`ATT_{dr}(\mathrm{g},t_\text{eval};\delta)` from `Callaway and Sant'Anna (2021) `_. + + As an example, if the target parameter is the effect on the group receiving treatment in :math:`2006` but evaluated in :math:`2007` with an anticipation period of :math:`\delta=1`, then the pre-treatment period is :math:`2004`. + The parallel trend assumption is slightly stronger with anticipation as the trends have to parallel for a longer periods, i.e. :math:`ATT_{dr}(2006,2007;1)=ATT(2006,2004;2006)`. + +In the following, we will omit the subscript :math:`\delta` in the notation of the nuisance functions and the control group (implicitly assuming :math:`\delta=0`). + +For a given tuple :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` the target parameter :math:`ATT(\mathrm{g},t)` is estimated by solving the empirical version of the the following linear moment condition: + +.. math:: + ATT(\mathrm{g}, t_\text{pre}, t_\text{eval}):= -\frac{\mathbb{E}[\psi_b(W,\eta_0)]}{\mathbb{E}[\psi_a(W,\eta_0)]} + +with nuisance elements :math:`\eta_0` which depend on the parameter combination :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` and score function :math:`\psi(W,\theta, \eta)` (for details, see :ref:`Panel Data Details ` or :ref:`Repeated Cross-Section Details `). +Under the identifying assumptions above + +.. math:: + ATT(\mathrm{g}, t_\text{pre}, t_\text{eval}) = ATT(\mathrm{g},t). + +``DoubleMLDIDMulti`` implements the estimation of :math:`ATT(\mathrm{g}, t_\text{pre}, t_\text{eval})` for multiple time periods and requires :ref:`DoubleMLPanelData ` as input. + +Setting ``gt_combinations='standard'`` will estimate the target parameter for all (possible) combinations of :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` with :math:`\mathrm{g}\in\{2,\dots,\mathcal{T}\}` and :math:`(t_\text{pre}, t_\text{eval})` with :math:`t_\text{eval}\in\{2,\dots,\mathcal{T}\}` and +:math:`t_\text{pre}= \min(\mathrm{g},t_\text{eval}) -\delta-1`. +This corresponds to the setting where all trends are set as short as possible, but still respecting the anticipation period. diff --git a/doc/guide/models/did/did_models.inc b/doc/guide/models/did/did_models.inc index 88575949..f9324b18 100644 --- a/doc/guide/models/did/did_models.inc +++ b/doc/guide/models/did/did_models.inc @@ -1,10 +1,17 @@ .. include:: /guide/models/did/did_setup.rst +.. _did-implementation-model: + +Parameters & Implementation +*************************** + +.. include:: /guide/models/did/did_implementation.rst + .. _did-pa-model: Panel data -********** +****************** .. include:: /guide/models/did/did_pa.rst @@ -12,7 +19,7 @@ Panel data .. _did-cs-model: Repeated cross-sections -*********************** +******************************* .. include:: /guide/models/did/did_cs.rst diff --git a/doc/guide/models/did/did_pa.rst b/doc/guide/models/did/did_pa.rst index 3b12d6d9..08ad728a 100644 --- a/doc/guide/models/did/did_pa.rst +++ b/doc/guide/models/did/did_pa.rst @@ -6,45 +6,19 @@ For the estimation of the target parameters :math:`ATT(\mathrm{g},t)` the follow m_{0, \mathrm{g}, t_\text{eval} + \delta}(X_i) &:= P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t_\text{eval} + \delta}^{(\cdot)}=1). \end{align} -where :math:`g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval},\delta}(\cdot)` denotes the population outcome regression function and :math:`m_{0, \mathrm{g}, t_\text{eval} + \delta}(\cdot)` the generalized propensity score. -The interpretation of the parameters is as follows: - -* :math:`\mathrm{g}` is the first post-treatment period of interest, i.e. the treatment group. -* :math:`t_\text{pre}` is the pre-treatment period, i.e. the time period from which the conditional parallel trends are assumed. -* :math:`t_\text{eval}` is the time period of interest or evaluation period, i.e. the time period where the treatment effect is evaluated. -* :math:`\delta` is number of anticipation periods, i.e. the number of time periods for which units are assumed to anticipate the treatment. +where :math:`g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval},\delta}(\cdot)` denotes the population outcome change regression function and :math:`m_{0, \mathrm{g}, t_\text{eval} + \delta}(\cdot)` the generalized propensity score. .. note:: Remark that the nuisance functions depend on the control group used for the estimation of the target parameter. By slight abuse of notation we use the same notation for both control groups :math:`C_{i,t}^{(\text{nev})}` and :math:`C_{i,t}^{(\text{nyt})}`. More specifically, the control group only depends on :math:`\delta` for *not yet treated* units. -Under these assumptions the target parameter :math:`ATT(\mathrm{g},t_\text{eval})` can be estimated by choosing a suitable combination -of :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval}, \delta)` if :math:`t_\text{eval} - t_\text{pre} \ge 1 + \delta`, i.e. the parallel trends are assumed to hold at least one period more than the anticipation period. - -.. note:: - The choice :math:`t_\text{pre}= \min(\mathrm{g},t_\text{eval}) -\delta-1` corresponds to the definition of :math:`ATT_{dr}(\mathrm{g},t_\text{eval};\delta)` from `Callaway and Sant'Anna (2021) `_. - - As an example, if the target parameter is the effect on the group receiving treatment in :math:`2006` but evaluated in :math:`2007` with an anticipation period of :math:`\delta=1`, then the pre-treatment period is :math:`2004`. - The parallel trend assumption is slightly stronger with anticipation as the trends have to parallel for a longer periods, i.e. :math:`ATT_{dr}(2006,2007;1)=ATT(2006,2004;2006)`. - -In the following, we will omit the subscript :math:`\delta` in the notation of the nuisance functions and the control group (implicitly assuming :math:`\delta=0`). - For a given tuple :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` the target parameter :math:`ATT(\mathrm{g},t)` is estimated by solving the empirical version of the the following linear moment condition: .. math:: ATT(\mathrm{g}, t_\text{pre}, t_\text{eval}):= -\frac{\mathbb{E}[\psi_b(W,\eta_0)]}{\mathbb{E}[\psi_a(W,\eta_0)]} -with nuisance elements :math:`\eta_0=(g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval}}, m_{0, \mathrm{g}, t_\text{eval}})` and score function :math:`\psi(W,\theta, \eta)` being defined in section :ref:`did-pa-score`. -Under the identifying assumptions above - -.. math:: - ATT(\mathrm{g}, t_\text{pre}, t_\text{eval}) = ATT(\mathrm{g},t). - -``DoubleMLDIDMulti`` implements the estimation of :math:`ATT(\mathrm{g}, t_\text{pre}, t_\text{eval})` for multiple time periods and requires :ref:`DoubleMLPanelData ` as input. -Setting ``gt_combinations='standard'`` will estimate the target parameter for all (possible) combinations of :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` with :math:`\mathrm{g}\in\{2,\dots,\mathcal{T}\}` and :math:`(t_\text{pre}, t_\text{eval})` with :math:`t_\text{eval}\in\{2,\dots,\mathcal{T}\}` and -:math:`t_\text{pre}= \min(\mathrm{g},t_\text{eval}) -\delta-1`. -This corresponds to the setting where all trends are set as short as possible, but still respecting the anticipation period. +with nuisance elements :math:`\eta_0=(g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval}}, m_{0, \mathrm{g}, t_\text{eval}})` and score function :math:`\psi(W,\theta, \eta)` being defined in the :ref:`DiD Score Section`. Estimation is conducted via its ``fit()`` method: @@ -83,7 +57,7 @@ Estimation is conducted via its ``fit()`` method: .. note:: Remark that the output contains two different outcome regressions :math:`g(0,X)` and :math:`g(1,X)`. As in the :ref:`IRM model ` - the outcome regression :math:`g(0,X)` refers to the control group, whereas :math:`g(1,X)` refers to the outcome regression for the treatment group, i.e. + the outcome regression :math:`g(0,X)` refers to the control group, whereas :math:`g(1,X)` refers to the outcome change regression for the treatment group, i.e. .. math:: \begin{align} diff --git a/doc/guide/models/did/did_setup.rst b/doc/guide/models/did/did_setup.rst index 73fbb732..18fd0000 100644 --- a/doc/guide/models/did/did_setup.rst +++ b/doc/guide/models/did/did_setup.rst @@ -40,8 +40,15 @@ The corresponding identifying assumptions are: :math:`D_{i,1} = 0 \quad a.s.` For all :math:`t=2,\dots,\mathcal{T}`, :math:`D_{i,t-1} = 1` implies :math:`D_{i,t} = 1 \quad a.s.` -2. **Panel Data (Random Sampling):** - :math:`(Y_{i,1},\dots, Y_{i,\mathcal{T}}, X_i, D_{i,1}, \dots, D_{i,\mathcal{T}})_{i=1}^n` is independent and identically distributed. +2. **Data:** + The observed data are generated according to the following mechanisms: + + a. **Panel Data (Random Sampling):** + The sample :math:`(Y_{i,1},\dots, Y_{i,\mathcal{T}}, X_i, D_{i,1}, \dots, D_{i,\mathcal{T}})_{i=1}^n` is independent and identically distributed. + + b. **Repeated Cross Sections:** + The sample consists of :math:`(Y_{i,t},G^{2}_i,\dots,G^{\mathcal{T}}_i, C_i,T_i, X_i)_{i=1}^n`, where :math:`T_i\in \{1,\dots,\mathcal{T}\}` denotes the time period of unit :math:`i` being observed. + Conditional on :math:`T=t`, the data are independent and identically distributed from the distribution of :math:`(Y_{t},G^{2},\dots,G^{\mathcal{T}}, C, X)`, with :math:`(G^{2},\dots,G^{\mathcal{T}}, C, X)` being invariant to :math:`T`. 3. **Limited Treatment Anticipation:** There is a known :math:`\delta\ge 0` such that @@ -64,4 +71,4 @@ The corresponding identifying assumptions are: .. note:: For a detailed discussion of the assumptions see `Callaway and Sant'Anna (2021) `_. -Under the assumptions above (either Assumption 4.a or 4.b), the target parameter :math:`ATT(\mathrm{g},t)` is identified see Theorem 1. `Callaway and Sant'Anna (2021) `_. \ No newline at end of file +Under the assumptions above (either Assumption a. or b.), the target parameter :math:`ATT(\mathrm{g},t)` is identified see Theorem 1. `Callaway and Sant'Anna (2021) `_. From c9c183298fa9180ab2320d61afd2db846ebcb60c Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 13 Jun 2025 10:54:20 +0000 Subject: [PATCH 02/14] fix index --- doc/guide/models/did/did_cs.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/guide/models/did/did_cs.rst b/doc/guide/models/did/did_cs.rst index df87a147..2d3cd593 100644 --- a/doc/guide/models/did/did_cs.rst +++ b/doc/guide/models/did/did_cs.rst @@ -65,9 +65,9 @@ Setting ``panel=False`` will estimate the target parameter for repeated cross se .. math:: \begin{align} g(0,0,X) &\approx g^{\text{control}}_{\mathrm{g}, t_\text{pre}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{pre}} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t_\text{pre}],\\ - g(0,1,X) &\approx g^{\text{control}}_{\mathrm{g}, t_\text{post}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{post}} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t_\text{post}],\\ + g(0,1,X) &\approx g^{\text{control}}_{\mathrm{g}, t_\text{eval}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{eval}} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t_\text{eval}],\\ g(1,0,X) &\approx g^{\text{treat}}_{\mathrm{g}, t_\text{pre}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{pre}} |X_i, G_i^{\mathrm{g}}=1,, T_i=t_\text{pre}],\\ - g(1,1,X) &\approx g^{\text{treat}}_{\mathrm{g}, t_\text{post}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{post}} |X_i, G_i^{\mathrm{g}}=1,, T_i=t_\text{post}]. + g(1,1,X) &\approx g^{\text{treat}}_{\mathrm{g}, t_\text{eval}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{eval}} |X_i, G_i^{\mathrm{g}}=1,, T_i=t_\text{eval}]. \end{align} .. note:: From 827fc27ba8f33c2d8d4db5cd2d93151f061d01bb Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 13 Jun 2025 11:10:46 +0000 Subject: [PATCH 03/14] add did cs multi scores --- doc/guide/scores/did/did_cs_binary_score.rst | 8 +- doc/guide/scores/did/did_cs_score.rst | 125 ++++++++++++++++++- doc/guide/scores/did/did_pa_score.rst | 2 +- 3 files changed, 129 insertions(+), 6 deletions(-) diff --git a/doc/guide/scores/did/did_cs_binary_score.rst b/doc/guide/scores/did/did_cs_binary_score.rst index 72717da4..0930ec49 100644 --- a/doc/guide/scores/did/did_cs_binary_score.rst +++ b/doc/guide/scores/did/did_cs_binary_score.rst @@ -55,8 +55,8 @@ If ``in_sample_normalization='False'``, the score is set to =\; &\psi_a(W; \eta) \theta + \psi_b(W; \eta) -with :math:`\eta=(g, p, \lambda)`, where :math:`p_0 = \mathbb{E}[D]` and :math:`\lambda_0 = \mathbb{E}[T]` are estimated on the cross-fitting folds. -Remark that this will result in the same score, but just uses slightly different normalization. +with :math:`\eta=(g, p, \lambda)`, where :math:`p_0 = \mathbb{E}[D]` and :math:`\lambda_0 = \mathbb{E}[T]` are estimated on the whole sample. +Remark that this will result in a similar score, but just uses slightly different normalization. ``score='experimental'`` assumes that the treatment probability is independent of the covariates :math:`X` and implements the score function: @@ -115,5 +115,5 @@ Analogously, if ``in_sample_normalization='False'``, the score is set to =\; &\psi_a(W; \eta) \theta + \psi_b(W; \eta) -with :math:`\eta=(g, m, p, \lambda)`, where :math:`p_0 = \mathbb{E}[D]` and :math:`\lambda_0 = \mathbb{E}[T]` are estimated on the cross-fitting folds. -Remark that this will result in the same score, but just uses slightly different normalization. +with :math:`\eta=(g, m, p, \lambda)`, where :math:`p_0 = \mathbb{E}[D]` and :math:`\lambda_0 = \mathbb{E}[T]` are estimated on the whole sample. +Remark that this will result in a similar score, but just uses slightly different normalization. diff --git a/doc/guide/scores/did/did_cs_score.rst b/doc/guide/scores/did/did_cs_score.rst index dee07ecb..eefa7ad5 100644 --- a/doc/guide/scores/did/did_cs_score.rst +++ b/doc/guide/scores/did/did_cs_score.rst @@ -1,2 +1,125 @@ +As in the description of the :ref:`DiD model `, the required nuisance elements are + +.. math:: + \begin{align} + g^{\text{treat}}_{0,\mathrm{g}, t, \text{eval} + \delta}(X_i) &:= \mathbb{E}[Y_{i,t} |X_i, G_i^{\mathrm{g}}=1, T_i=t], \\ + g^{\text{control}}_{0,\mathrm{g}, t, \text{eval} + \delta}(X_i) &:= \mathbb{E}[Y_{i,t} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t], \\ + m_{0, \mathrm{g}, t_\text{eval} + \delta}(X_i) &:= P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t_\text{eval} + \delta}^{(\cdot)}=1). + \end{align} + +for :math:`t\in\{t_\text{pre}, t_\text{eval}\}` and a certain choice of :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` and :math:`\delta` and control group :math:`C_{i,t_\text{eval} + \delta}^{(\cdot)}`. + +For notational purposes, we will omit the subscripts :math:`\mathrm{g}, t_\text{pre}, t_\text{eval}, \delta` in the following and use the notation + +* :math:`g_0(1, 0, X_i) \equiv g^{\text{treat}}_{0,\mathrm{g}, t_\text{pre}, \text{eval} + \delta}(X_i)` (pop. outcome regr. function for treatment group in :math:`t_\text{pre}`) +* :math:`g_0(1, 1, X_i) \equiv g^{\text{treat}}_{0,\mathrm{g}, t_\text{eval}, \text{eval} + \delta}(X_i)` (pop. outcome regr. function for treatment group in :math:`t_\text{eval}`) +* :math:`g_0(0, 0, X_i) \equiv g^{\text{control}}_{0,\mathrm{g}, t_\text{pre}, \text{eval} + \delta}(X_i)` (pop. outcome regr. function for control group in :math:`t_\text{pre}`) +* :math:`g_0(0, 1, X_i) \equiv g^{\text{control}}_{0,\mathrm{g}, t_\text{eval}, \text{eval} + \delta}(X_i)` (pop. outcome regr. function for control group in :math:`t_\text{eval}`) +* :math:`m_0(X_i)\equiv m_{0, \mathrm{g}, t_\text{eval} + \delta}(X_i)` (generalized propensity score). + +All scores in the multi-period setting have the form + +.. math:: + + \psi(W_i,\theta, \eta) := + \begin{cases} + \tilde{\psi}(W_i,\theta, \eta) & \text{for } G_i^{\mathrm{g}} \vee C_{i,t_\text{eval} + \delta}^{(\cdot)}=1 \\ + 0 & \text{otherwise} + \end{cases} + +i.e. the score is only non-zero for units in the corresponding treatment group :math:`\mathrm{g}` and control group :math:`C_{i,t_\text{eval} + \delta}^{(\cdot)}`. + +For the difference-in-differences model implemented in ``DoubleMLDIDMulti`` one can choose between +``score='observational'`` and ``score='experimental'``. + +``score='observational'`` implements the score function (dropping the unit index :math:`i`): + +.. math:: + + \tilde{\psi}(W,\theta,\eta) :=\; & - \frac{G^{\mathrm{g}}}{\mathbb{E}_n[G^{\mathrm{g}}]}\theta + \frac{G^{\mathrm{g}}}{\mathbb{E}_n[G^{\mathrm{g}}]}\Big(g(1,1,X) - g(1,0,X) - (g(0,1,X) - g(0,0,X))\Big) + + & + \frac{G^{\mathrm{g}}T}{\mathbb{E}_n[G^{\mathrm{g}}T]} (Y - g(1,1,X)) + + & - \frac{G^{\mathrm{g}}(1-T)}{\mathbb{E}_n[G^{\mathrm{g}}(1-T)]}(Y - g(1,0,X)) + + & - \frac{m(X) (1-G^{\mathrm{g}})T}{1-m(X)} \mathbb{E}_n\left[\frac{m(X) (1-G^{\mathrm{g}})T}{1-m(X)}\right]^{-1} (Y-g(0,1,X)) + + & + \frac{m(X) (1-G^{\mathrm{g}})(1-T)}{1-m(X)} \mathbb{E}_n\left[\frac{m(X) (1-G^{\mathrm{g}})(1-T)}{1-m(X)}\right]^{-1} (Y-g(0,0,X)) + + =\; &\tilde{\psi}_a(W; \eta) \theta + \tilde{\psi}_b(W; \eta) + +where the components of the final linear score :math:`\psi` are + +.. math:: + \psi_a(W; \eta) &= \tilde{\psi}_a(W; \eta) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}), + + \psi_b(W; \eta) &= \tilde{\psi}_b(W; \eta) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}) + +and the nuisance elements :math:`\eta=(g, m)`. + .. note:: - Will be implemented soon. \ No newline at end of file + Remark that :math:`1-G^{\mathrm{g}}=C^{(\cdot)}` if :math:`G^{\mathrm{g}} \vee C_{t_\text{eval} + \delta}^{(\cdot)}=1`. + +If ``in_sample_normalization='False'``, the score is set to + +.. math:: + + \tilde{\psi}(W,\theta,\eta) :=\; & - \frac{G^{\mathrm{g}}}{p}\theta + \frac{G^{\mathrm{g}}}{p}\Big(g(1,1,X) - g(1,0,X) - (g(0,1,X) - g(0,0,X))\Big) + + & + \frac{G^{\mathrm{g}}T}{p\lambda} (Y - g(1,1,X)) + + & - \frac{G^{\mathrm{g}}(1-T)}{p(1-\lambda)}(Y - g(1,0,X)) + + & - \frac{m(X) (1-G^{\mathrm{g}})T}{p(1-m(X))\lambda} (Y-g(0,1,X)) + + & + \frac{m(X) (1-G^{\mathrm{g}})(1-T)}{p(1-m(X))(1-\lambda)} (Y-g(0,0,X)) + + =\; &\tilde{\psi}_a(W; \eta) \theta + \tilde{\psi}_b(W; \eta) + +with :math:`\eta=(g, m, p, \lambda)`, where :math:`p_0 = \mathbb{E}[G^{\mathrm{g}}]` and :math:`\lambda_0 = \mathbb{E}[T]` are estimated on the whole sample. +Remark that this will result a similar score, but just uses slightly different normalization. + +``score='experimental'`` assumes that the treatment probability is independent of the covariates :math:`X` and +implements the score function: + +.. math:: + + \tilde{\psi}(W,\theta,\eta) :=\; & - \theta + \Big(g(1,1,X) - g(1,0,X) - (g(0,1,X) - g(0,0,X))\Big) + + & + \frac{G^{\mathrm{g}}T}{\mathbb{E}_n[G^{\mathrm{g}}T]} (Y - g(1,1,X)) + + & - \frac{G^{\mathrm{g}}(1-T)}{\mathbb{E}_n[G^{\mathrm{g}}(1-T)]}(Y - g(1,0,X)) + + & - \frac{(1-G^{\mathrm{g}})T}{\mathbb{E}_n[(1-G^{\mathrm{g}})T]} (Y-g(0,1,X)) + + & + \frac{(1-G^{\mathrm{g}})(1-T)}{\mathbb{E}_n[(1-G^{\mathrm{g}})(1-T)]} (Y-g(0,0,X)) + + =\; &\tilde{\psi}_a(W; \eta) \theta + \tilde{\psi}_b(W; \eta) + +where the components of the final linear score :math:`\psi` are + +.. math:: + \psi_a(W; \eta) &= \tilde{\psi}_a(W; \eta) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}), + + \psi_b(W; \eta) &= \tilde{\psi}_b(W; \eta) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}) + +and the nuisance elements :math:`\eta=(g, m)`. + +Analogously, if ``in_sample_normalization='False'``, the score is set to + +.. math:: + + \tilde{\psi}(W,\theta,\eta) :=\; & - \theta + \Big(g(1,1,X) - g(1,0,X) - (g(0,1,X) - g(0,0,X))\Big) + + & + \frac{G^{\mathrm{g}}T}{p\lambda} (Y - g(1,1,X)) + + & - \frac{G^{\mathrm{g}}(1-T)}{p(1-\lambda)}(Y - g(1,0,X)) + + & - \frac{(1-G^{\mathrm{g}})T}{(1-p)\lambda} (Y-g(0,1,X)) + + & + \frac{(1-G^{\mathrm{g}})(1-T)}{(1-p)(1-\lambda)} (Y-g(0,0,X)) + + =\; &\tilde{\psi}_a(W; \eta) \theta + \tilde{\psi}_b(W; \eta) + +with :math:`\eta=(g, m, p, \lambda)`, where :math:`p_0 = \mathbb{E}[G^{\mathrm{g}}]` and :math:`\lambda_0 = \mathbb{E}[T]` are estimated on the whole sample. +Remark that this will result in a similar score, but just uses slightly different normalization. \ No newline at end of file diff --git a/doc/guide/scores/did/did_pa_score.rst b/doc/guide/scores/did/did_pa_score.rst index 8d6602e5..1f1ab9f5 100644 --- a/doc/guide/scores/did/did_pa_score.rst +++ b/doc/guide/scores/did/did_pa_score.rst @@ -10,7 +10,7 @@ for a certain choice of :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` and :m For notational purposes, we will omit the subscripts :math:`\mathrm{g}, t_\text{pre}, t_\text{eval}, \delta` in the following and use the notation -* :math:`g_0(0, X_i)\equiv g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval}, \delta}(X_i)` (population outcome regression function of the control group) +* :math:`g_0(0, X_i)\equiv g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval}, \delta}(X_i)` (population outcome change regression function of the control group) * :math:`m_0(X_i)\equiv m_{0, \mathrm{g}, t_\text{eval} + \delta}(X_i)` (generalized propensity score) All scores in the multi-period setting have the form From fb01b7684f1fbcd661734f254e7123e99c3f17b1 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 13 Jun 2025 11:10:51 +0000 Subject: [PATCH 04/14] fix typos --- doc/guide/models/did/did_cs.rst | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/guide/models/did/did_cs.rst b/doc/guide/models/did/did_cs.rst index 2d3cd593..980293fb 100644 --- a/doc/guide/models/did/did_cs.rst +++ b/doc/guide/models/did/did_cs.rst @@ -2,8 +2,8 @@ For the estimation of the target parameters :math:`ATT(\mathrm{g},t)` the follow .. math:: \begin{align} - g^{\text{treat}}_{\mathrm{g}, t, \text{eval} + \delta}(X_i) &:= \mathbb{E}[Y_{i,t} |X_i, G_i^{\mathrm{g}}=1, T_i=t], \\ - g^{\text{control}}_{\mathrm{g}, t, \text{eval} + \delta}(X_i) &:= \mathbb{E}[Y_{i,t} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t], \\ + g^{\text{treat}}_{0,\mathrm{g}, t, \text{eval} + \delta}(X_i) &:= \mathbb{E}[Y_{i,t} |X_i, G_i^{\mathrm{g}}=1, T_i=t], \\ + g^{\text{control}}_{0,\mathrm{g}, t, \text{eval} + \delta}(X_i) &:= \mathbb{E}[Y_{i,t} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t], \\ m_{0, \mathrm{g}, t_\text{eval} + \delta}(X_i) &:= P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t_\text{eval} + \delta}^{(\cdot)}=1). \end{align} @@ -20,7 +20,7 @@ For a given tuple :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` the target p .. math:: ATT(\mathrm{g}, t_\text{pre}, t_\text{eval}):= -\frac{\mathbb{E}[\psi_b(W,\eta_0)]}{\mathbb{E}[\psi_a(W,\eta_0)]} -with nuisance elements :math:`\eta_0=(g^{\text{treat}}_{\mathrm{g}, t_\text{pre}, t_\text{eval} + \delta}, g^{\text{control}}_{\mathrm{g}, t_\text{pre}, t_\text{eval} + \delta}, g^{\text{treat}}_{\mathrm{g}, t_\text{eval}, t_\text{eval} + \delta}, g^{\text{control}}_{\mathrm{g}, t_\text{eval}, t_\text{eval} + \delta}, m_{0, \mathrm{g}, t_\text{eval}})` and score function :math:`\psi(W,\theta, \eta)` defined in the :ref:`DiD Score Section`. +with nuisance elements :math:`\eta_0=(g^{0,\text{treat}}_{\mathrm{g}, t_\text{pre}, t_\text{eval} + \delta}, g^{0,\text{control}}_{\mathrm{g}, t_\text{pre}, t_\text{eval} + \delta}, g^{0,\text{treat}}_{\mathrm{g}, t_\text{eval}, t_\text{eval} + \delta}, g^{0,\text{control}}_{\mathrm{g}, t_\text{eval}, t_\text{eval} + \delta}, m_{0, \mathrm{g}, t_\text{eval}})` and score function :math:`\psi(W,\theta, \eta)` defined in the :ref:`DiD Score Section`. Setting ``panel=False`` will estimate the target parameter for repeated cross sections. Estimation is conducted via its ``fit()`` method: @@ -64,10 +64,10 @@ Setting ``panel=False`` will estimate the target parameter for repeated cross se .. math:: \begin{align} - g(0,0,X) &\approx g^{\text{control}}_{\mathrm{g}, t_\text{pre}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{pre}} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t_\text{pre}],\\ - g(0,1,X) &\approx g^{\text{control}}_{\mathrm{g}, t_\text{eval}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{eval}} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t_\text{eval}],\\ - g(1,0,X) &\approx g^{\text{treat}}_{\mathrm{g}, t_\text{pre}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{pre}} |X_i, G_i^{\mathrm{g}}=1,, T_i=t_\text{pre}],\\ - g(1,1,X) &\approx g^{\text{treat}}_{\mathrm{g}, t_\text{eval}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{eval}} |X_i, G_i^{\mathrm{g}}=1,, T_i=t_\text{eval}]. + g(0,0,X) &\approx g^{\text{control}}_{0,\mathrm{g}, t_\text{pre}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{pre}} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t_\text{pre}],\\ + g(0,1,X) &\approx g^{\text{control}}_{0,\mathrm{g}, t_\text{eval}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{eval}} |X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)}=1, T_i=t_\text{eval}],\\ + g(1,0,X) &\approx g^{\text{treat}}_{0,\mathrm{g}, t_\text{pre}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{pre}} |X_i, G_i^{\mathrm{g}}=1,, T_i=t_\text{pre}],\\ + g(1,1,X) &\approx g^{\text{treat}}_{0,\mathrm{g}, t_\text{eval}, \text{eval} + \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{eval}} |X_i, G_i^{\mathrm{g}}=1,, T_i=t_\text{eval}]. \end{align} .. note:: From 068a8673a50e925f16673e9bf8346c322ff6f4d6 Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Fri, 13 Jun 2025 13:33:52 +0200 Subject: [PATCH 05/14] start rep cs example --- doc/examples/did/py_rep_cs.ipynb | 1687 ++++++++++++++++++++++++++++++ 1 file changed, 1687 insertions(+) create mode 100644 doc/examples/did/py_rep_cs.ipynb diff --git a/doc/examples/did/py_rep_cs.ipynb b/doc/examples/did/py_rep_cs.ipynb new file mode 100644 index 00000000..381c8cf4 --- /dev/null +++ b/doc/examples/did/py_rep_cs.ipynb @@ -0,0 +1,1687 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python: Pepeated Cross-Sectional Data with Multiple Time Periods\n", + "\n", + "In this example, a detailed guide on Difference-in-Differences with multiple time periods using the [DoubleML-package](https://docs.doubleml.org/stable/index.html). The implementation is based on [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001).\n", + "\n", + "The notebook requires the following packages:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "from lightgbm import LGBMRegressor, LGBMClassifier\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "\n", + "from doubleml.did import DoubleMLDIDMulti\n", + "from doubleml.data import DoubleMLPanelData\n", + "\n", + "from doubleml.did.datasets import make_did_cs_CS2021" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "We will rely on the `make_did_CS2021` DGP, which is inspired by [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001) (Appendix SC) and [Sant'Anna and Zhao (2020)](https://doi.org/10.1016/j.jeconom.2020.06.003).\n", + "\n", + "We will observe `n_obs` units over `n_periods`. Remark that the dataframe includes observations of the potential outcomes `y0` and `y1`, such that we can use oracle estimates as comparisons. 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" + ], + "text/plain": [ + " id y y0 y1 d t Z1 \\\n", + "1 0 224.866386 224.866386 225.370034 2025-05-01 2025-02-01 0.467172 \n", + "3 0 240.871104 240.871104 242.163116 2025-05-01 2025-04-01 0.467172 \n", + "6 1 209.361644 209.361644 209.184807 2025-05-01 2025-01-01 -0.938355 \n", + "8 1 205.568681 205.568681 207.612815 2025-05-01 2025-03-01 -0.938355 \n", + "9 1 204.836728 204.836728 202.138470 2025-05-01 2025-04-01 -0.938355 \n", + "\n", + " Z2 Z3 Z4 ite \n", + "1 0.423923 0.589005 0.338053 0.503648 \n", + "3 0.423923 0.589005 0.338053 1.292012 \n", + "6 -0.295794 -1.301637 -0.011305 -0.176837 \n", + "8 -0.295794 -1.301637 -0.011305 2.044134 \n", + "9 -0.295794 -1.301637 -0.011305 -2.698258 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_obs = 5000\n", + "n_periods = 6\n", + "\n", + "df = make_did_cs_CS2021(n_obs, dgp_type=4, include_never_treated=True, n_periods=n_periods, n_pre_treat_periods=3, lambda_t=0.5,\n", + " time_type=\"datetime\")\n", + "df[\"ite\"] = df[\"y1\"] - df[\"y0\"]\n", + "\n", + "print(df.shape)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Details\n", + "\n", + "*Here, we slightly abuse the definition of the potential outcomes. $Y_{i,t}(1)$ corresponds to the (potential) outcome if unit $i$ would have received treatment at time period $\\mathrm{g}$ (where the group $\\mathrm{g}$ is drawn with probabilities based on $Z$).*\n", + "\n", + "The data set with repeated cross-sectional data is generated on the basis of a panel data set with the following data generating process (DGP). To obtain repeated cross-sectional data, the number of generated individuals is increased to $\\frac{n_{obs}}{\\lambda_t}$, where $\\lambda_t$ denotes the probability to observe a unit at each time period (time constant).\n", + "\n", + "More specifically\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "Y_{i,t}(0)&:= f_t(Z) + \\delta_t + \\eta_i + \\varepsilon_{i,t,0}\\\\\n", + "Y_{i,t}(1)&:= Y_{i,t}(0) + \\theta_{i,t,\\mathrm{g}} + \\epsilon_{i,t,1} - \\epsilon_{i,t,0}\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where\n", + " - $f_t(Z)$ depends on pre-treatment observable covariates $Z_1,\\dots, Z_4$ and time $t$\n", + " - $\\delta_t$ is a time fixed effect\n", + " - $\\eta_i$ is a unit fixed effect\n", + " - $\\epsilon_{i,t,\\cdot}$ are time varying unobservables (iid. $N(0,1)$)\n", + " - $\\theta_{i,t,\\mathrm{g}}$ correponds to the exposure effect of unit $i$ based on group $\\mathrm{g}$ at time $t$\n", + "\n", + "For the pre-treatment periods the exposure effect is set to\n", + "$$\n", + "\\theta_{i,t,\\mathrm{g}}:= 0 \\text{ for } t<\\mathrm{g}\n", + "$$\n", + "such that \n", + "\n", + "$$\n", + "\\mathbb{E}[Y_{i,t}(1) - Y_{i,t}(0)] = \\mathbb{E}[\\epsilon_{i,t,1} - \\epsilon_{i,t,0}]=0 \\text{ for } t<\\mathrm{g}\n", + "$$\n", + "\n", + "The [DoubleML Coverage Repository](https://docs.doubleml.org/doubleml-coverage/) includes coverage simulations based on this DGP." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Description" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data is a balanced panel where each unit is observed over `n_periods` starting Janary 2025." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "t", + "rawType": "datetime64[s]", + "type": "unknown" + }, + { + "name": "0", + "rawType": "int64", + "type": "integer" + } + ], + "ref": "4b073d89-291f-4947-893a-1ca7a31e97ad", + "rows": [ + [ + "2025-01-01 00:00:00", + "4941" + ], + [ + "2025-02-01 00:00:00", + "4969" + ], + [ + "2025-03-01 00:00:00", + "5072" + ], + [ + "2025-04-01 00:00:00", + "4973" + ], + [ + "2025-05-01 00:00:00", + "5040" + ], + [ + "2025-06-01 00:00:00", + "5003" + ] + ], + "shape": { + "columns": 1, + "rows": 6 + } + }, + "text/plain": [ + "t\n", + "2025-01-01 4941\n", + "2025-02-01 4969\n", + "2025-03-01 5072\n", + "2025-04-01 4973\n", + "2025-05-01 5040\n", + "2025-06-01 5003\n", + "dtype: int64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(\"t\").size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The treatment column `d` indicates first treatment period of the corresponding unit, whereas `NaT` units are never treated.\n", + "\n", + "*Generally, never treated units should take either on the value `np.inf` or `pd.NaT` depending on the data type (`float` or `datetime`).*\n", + "\n", + "The individual units are roughly uniformly divided between the groups, where treatment assignment depends on the pre-treatment covariates `Z1` to `Z4`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "d", + "rawType": "datetime64[s]", + "type": "unknown" + }, + { + "name": "0", + "rawType": "int64", + "type": "integer" + } + ], + "ref": "ede57e78-7aa0-4b22-9d41-8c6d64b668f3", + "rows": [ + [ + "2025-04-01 00:00:00", + "7932" + ], + [ + "2025-05-01 00:00:00", + "7150" + ], + [ + "2025-06-01 00:00:00", + "7130" + ], + [ + null, + "7786" + ] + ], + "shape": { + "columns": 1, + "rows": 4 + } + }, + "text/plain": [ + "d\n", + "2025-04-01 7932\n", + "2025-05-01 7150\n", + "2025-06-01 7130\n", + "NaT 7786\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(\"d\", dropna=False).size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, the group indicates the first treated period and `NaT` units are never treated. To simplify plotting and pands" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "d", + "rawType": "datetime64[s]", + "type": "unknown" + }, + { + "name": "0", + "rawType": "int64", + "type": "integer" + } + ], + "ref": "f7dd6712-81bd-4571-8908-ff508425d73e", + "rows": [ + [ + "2025-04-01 00:00:00", + "7932" + ], + [ + "2025-05-01 00:00:00", + "7150" + ], + [ + "2025-06-01 00:00:00", + "7130" + ], + [ + null, + "7786" + ] + ], + "shape": { + "columns": 1, + "rows": 4 + } + }, + "text/plain": [ + "d\n", + "2025-04-01 7932\n", + "2025-05-01 7150\n", + "2025-06-01 7130\n", + "NaT 7786\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(\"d\", dropna=False).size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get a better understanding of the underlying data and true effects, we will compare the unconditional averages and the true effects based on the oracle values of individual effects `ite`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "t", + "rawType": "datetime64[s]", + "type": "unknown" + }, + { + "name": "First Treated", + "rawType": "object", + "type": "string" + }, + { + "name": "y_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "y_lower_quantile", + "rawType": "float64", + "type": "float" + }, + { + "name": "y_upper_quantile", + "rawType": "float64", + "type": "float" + }, + { + "name": "ite_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "ite_lower_quantile", + "rawType": "float64", + "type": "float" + }, + { + "name": "ite_upper_quantile", + "rawType": "float64", + "type": "float" + } + ], + "ref": "ee0779d1-13a6-4566-b589-8223802eb634", + "rows": [ + [ + "0", + "2025-01-01 00:00:00", + "2025-04", + "208.3657053918318", + "198.34456813757416", + "218.09310046986172", + "-0.06488365532630465", + "-2.36260670995788", + "2.24430385417629" + ], + [ + "1", + "2025-01-01 00:00:00", + "2025-05", + "210.26636762761242", + "200.07470515160784", + "220.44672591184232", + "-0.07771433309164828", + "-2.4383396389369736", + "2.226500777605647" + ], + [ + "2", + "2025-01-01 00:00:00", + "2025-06", + "212.68365722353033", + "202.9579121351787", + "222.82228704687216", + "-0.03338876242840714", + "-2.6001308153224327", + "2.3447348204781484" + ], + [ + "3", + "2025-01-01 00:00:00", + "Never Treated", + "214.70910022987115", + "204.87669782726906", + "224.68864156717308", + "-0.07823343537672198", + "-2.4325321824175177", + "2.1783321303207828" + ], + [ + "4", + "2025-02-01 00:00:00", + "2025-04", + "208.142666371447", + "188.81256624747476", + "227.6694729267284", + "0.03228929556563467", + "-2.193444242399346", + "2.3292106874802427" + ] + ], + "shape": { + "columns": 8, + "rows": 5 + } + }, + "text/html": [ + "
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tFirst Treatedy_meany_lower_quantiley_upper_quantileite_meanite_lower_quantileite_upper_quantile
02025-01-012025-04208.365705198.344568218.093100-0.064884-2.3626072.244304
12025-01-012025-05210.266368200.074705220.446726-0.077714-2.4383402.226501
22025-01-012025-06212.683657202.957912222.822287-0.033389-2.6001312.344735
32025-01-01Never Treated214.709100204.876698224.688642-0.078233-2.4325322.178332
42025-02-012025-04208.142666188.812566227.6694730.032289-2.1934442.329211
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" + ], + "text/plain": [ + " t First Treated y_mean y_lower_quantile y_upper_quantile \\\n", + "0 2025-01-01 2025-04 208.365705 198.344568 218.093100 \n", + "1 2025-01-01 2025-05 210.266368 200.074705 220.446726 \n", + "2 2025-01-01 2025-06 212.683657 202.957912 222.822287 \n", + "3 2025-01-01 Never Treated 214.709100 204.876698 224.688642 \n", + "4 2025-02-01 2025-04 208.142666 188.812566 227.669473 \n", + "\n", + " ite_mean ite_lower_quantile ite_upper_quantile \n", + "0 -0.064884 -2.362607 2.244304 \n", + "1 -0.077714 -2.438340 2.226501 \n", + "2 -0.033389 -2.600131 2.344735 \n", + "3 -0.078233 -2.432532 2.178332 \n", + "4 0.032289 -2.193444 2.329211 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# rename for plotting\n", + "df[\"First Treated\"] = df[\"d\"].dt.strftime(\"%Y-%m\").fillna(\"Never Treated\")\n", + "\n", + "# Create aggregation dictionary for means\n", + "def agg_dict(col_name):\n", + " return {\n", + " f'{col_name}_mean': (col_name, 'mean'),\n", + " f'{col_name}_lower_quantile': (col_name, lambda x: x.quantile(0.05)),\n", + " f'{col_name}_upper_quantile': (col_name, lambda x: x.quantile(0.95))\n", + " }\n", + "\n", + "# Calculate means and confidence intervals\n", + "agg_dictionary = agg_dict(\"y\") | agg_dict(\"ite\")\n", + "\n", + "agg_df = df.groupby([\"t\", \"First Treated\"]).agg(**agg_dictionary).reset_index()\n", + "agg_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_data(df, col_name='y'):\n", + " \"\"\"\n", + " Create an improved plot with colorblind-friendly features\n", + " \n", + " Parameters:\n", + " -----------\n", + " df : DataFrame\n", + " The dataframe containing the data\n", + " col_name : str, default='y'\n", + " Column name to plot (will use '{col_name}_mean')\n", + " \"\"\"\n", + " plt.figure(figsize=(12, 7))\n", + " n_colors = df[\"First Treated\"].nunique()\n", + " color_palette = sns.color_palette(\"colorblind\", n_colors=n_colors)\n", + "\n", + " sns.lineplot(\n", + " data=df,\n", + " x='t',\n", + " y=f'{col_name}_mean',\n", + " hue='First Treated',\n", + " style='First Treated',\n", + " palette=color_palette,\n", + " markers=True,\n", + " dashes=True,\n", + " linewidth=2.5,\n", + " alpha=0.8\n", + " )\n", + " \n", + " plt.title(f'Average Values {col_name} by Group Over Time', fontsize=16)\n", + " plt.xlabel('Time', fontsize=14)\n", + " plt.ylabel(f'Average Value {col_name}', fontsize=14)\n", + " \n", + "\n", + " plt.legend(title='First Treated', title_fontsize=13, fontsize=12, \n", + " frameon=True, framealpha=0.9, loc='best')\n", + " \n", + " plt.grid(alpha=0.3, linestyle='-')\n", + " plt.tight_layout()\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So let us take a look at the average values over time" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_data(agg_df, col_name='y')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead the true average treatment treatment effects can be obtained by averaging (usually unobserved) the `ite` values.\n", + "\n", + "The true effect just equals the exposure time (in months):\n", + "\n", + "$$\n", + "ATT(\\mathrm{g}, t) = \\min(\\mathrm{t} - \\mathrm{g} + 1, 0) =: e\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_data(agg_df, col_name='ite')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DoubleMLPanelData\n", + "\n", + "Finally, we can construct our `DoubleMLPanelData`, specifying\n", + "\n", + " - `y_col` : the outcome\n", + " - `d_cols`: the group variable indicating the first treated period for each unit\n", + " - `id_col`: the unique identification column for each unit\n", + " - `t_col` : the time column\n", + " - `x_cols`: the additional pre-treatment controls\n", + " - `datetime_unit`: unit required for `datetime` columns and plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLPanelData Object ==================\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: y\n", + "Treatment variable(s): ['d']\n", + "Covariates: ['Z1', 'Z2', 'Z3', 'Z4']\n", + "Instrument variable(s): None\n", + "Time variable: t\n", + "Id variable: id\n", + "No. Observations: 29998\n", + "\n", + "------------------ DataFrame info ------------------\n", + "\n", + "RangeIndex: 29998 entries, 0 to 29997\n", + "Columns: 12 entries, id to First Treated\n", + "dtypes: datetime64[s](2), float64(8), int32(1), object(1)\n", + "memory usage: 2.6+ MB\n", + "\n" + ] + } + ], + "source": [ + "dml_data = DoubleMLPanelData(\n", + " data=df,\n", + " y_col=\"y\",\n", + " d_cols=\"d\",\n", + " id_col=\"id\",\n", + " t_col=\"t\",\n", + " x_cols=[\"Z1\", \"Z2\", \"Z3\", \"Z4\"],\n", + " datetime_unit=\"M\"\n", + ")\n", + "print(dml_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ATT Estimation\n", + "\n", + "The [DoubleML-package](https://docs.doubleml.org/stable/index.html) implements estimation of group-time average treatment effect via the `DoubleMLDIDMulti` class (see [model documentation](https://docs.doubleml.org/stable/guide/models.html#difference-in-differences-models-did))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basics\n", + "\n", + "The class basically behaves like other `DoubleML` classes and requires the specification of two learners (for more details on the regression elements, see [score documentation](https://docs.doubleml.org/stable/guide/scores.html#difference-in-differences-models)).\n", + "\n", + "The basic arguments of a `DoubleMLDIDMulti` object include\n", + "\n", + " - `ml_g` \"outcome\" regression learner\n", + " - `ml_m` propensity Score learner\n", + " - `control_group` the control group for the parallel trend assumption\n", + " - `gt_combinations` combinations of $(\\mathrm{g},t_\\text{pre}, t_\\text{eval})$\n", + " - `anticipation_periods` number of anticipation periods\n", + "\n", + "We will construct a `dict` with \"default\" arguments.\n", + "\n", + "For repeated cross-sectional data, we additionally specify the argument\n", + "\n", + " - `panel=False`" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "default_args = {\n", + " \"ml_g\": LGBMRegressor(n_estimators=500, learning_rate=0.01, verbose=-1, random_state=123),\n", + " \"ml_m\": LGBMClassifier(n_estimators=500, learning_rate=0.01, verbose=-1, random_state=123),\n", + " \"control_group\": \"never_treated\",\n", + " \"anticipation_periods\": 0,\n", + " \"n_folds\": 5,\n", + " \"n_rep\": 1,\n", + " \"panel\": False,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " The model will be estimated using the `fit()` method. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "ename": "UFuncTypeError", + "evalue": "Cannot cast ufunc 'greater' input 0 from dtype(' 1\u001b[0m dml_obj \u001b[38;5;241m=\u001b[39m \u001b[43mDoubleMLDIDMulti\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdml_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdefault_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m dml_obj\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(dml_obj)\n", + "File \u001b[1;32m~\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_multi.py:169\u001b[0m, in \u001b[0;36mDoubleMLDIDMulti.__init__\u001b[1;34m(self, obj_dml_data, ml_g, ml_m, gt_combinations, control_group, anticipation_periods, n_folds, n_rep, score, panel, in_sample_normalization, trimming_rule, trimming_threshold, draw_sample_splitting, print_periods)\u001b[0m\n\u001b[0;32m 166\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_never_treated_value \u001b[38;5;241m=\u001b[39m _get_never_treated_value(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg_values)\n\u001b[0;32m 167\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_anticipation_periods \u001b[38;5;241m=\u001b[39m _check_anticipation_periods(anticipation_periods)\n\u001b[1;32m--> 169\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gt_combinations \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_gt_combinations\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgt_combinations\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 170\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gt_index \u001b[38;5;241m=\u001b[39m _construct_gt_index(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgt_combinations, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg_values, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mt_values)\n\u001b[0;32m 171\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_post_treatment_mask \u001b[38;5;241m=\u001b[39m _construct_post_treatment_mask(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg_values, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mt_values)\n", + "File \u001b[1;32m~\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_multi.py:1254\u001b[0m, in \u001b[0;36mDoubleMLDIDMulti._validate_gt_combinations\u001b[1;34m(self, gt_combinations)\u001b[0m\n\u001b[0;32m 1251\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Validate all treatment-time combinations.\"\"\"\u001b[39;00m\n\u001b[0;32m 1253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gt_combinations, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m-> 1254\u001b[0m gt_combinations \u001b[38;5;241m=\u001b[39m \u001b[43m_construct_gt_combinations\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1255\u001b[0m \u001b[43m \u001b[49m\u001b[43mgt_combinations\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mg_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mt_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnever_treated_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manticipation_periods\u001b[49m\n\u001b[0;32m 1256\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gt_combinations, \u001b[38;5;28mlist\u001b[39m):\n\u001b[0;32m 1259\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m 1260\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgt_combinations must be a list. \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(gt_combinations)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(gt_combinations)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m was passed.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1261\u001b[0m )\n", + "File \u001b[1;32m~\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\utils\\_did_utils.py:136\u001b[0m, in \u001b[0;36m_construct_gt_combinations\u001b[1;34m(setting, g_values, t_values, never_treated_value, anticipation_periods)\u001b[0m\n\u001b[0;32m 133\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgt_combinations must be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalid_settings\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msetting\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m was passed.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 135\u001b[0m treatment_groups \u001b[38;5;241m=\u001b[39m g_values[\u001b[38;5;241m~\u001b[39m_is_never_treated(g_values, never_treated_value)]\n\u001b[1;32m--> 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdiff\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtreatment_groups\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m):\n\u001b[0;32m 137\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mg_values must be sorted in ascending order (Excluding never treated group).\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(np\u001b[38;5;241m.\u001b[39mdiff(t_values) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m):\n", + "\u001b[1;31mUFuncTypeError\u001b[0m: Cannot cast ufunc 'greater' input 0 from dtype(' t_{eval}$, e.g. $\\widehat{ATT}(g=\\text{2025-04}, t_{\\text{pre}}=\\text{2025-01}, t_{\\text{eval}}=\\text{2025-02})$ which estimates the pre-trend from January to February even if the actual treatment occured in April." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual for the DoubleML-package, you can obtain joint confidence intervals via bootstrap." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "level = 0.95\n", + "\n", + "ci = dml_obj.confint(level=level)\n", + "dml_obj.bootstrap(n_rep_boot=5000)\n", + "ci_joint = dml_obj.confint(level=level, joint=True)\n", + "ci_joint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A visualization of the effects can be obtained via the `plot_effects()` method.\n", + "\n", + "Remark that the plot used joint confidence intervals per default. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "nbsphinx-thumbnail" + ] + }, + "outputs": [], + "source": [ + "dml_obj.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity Analysis\n", + "\n", + "As descripted in the [Sensitivity Guide](https://docs.doubleml.org/stable/guide/sensitivity.html), robustness checks on omitted confounding/parallel trend violations are available, via the standard `sensitivity_analysis()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dml_obj.sensitivity_analysis()\n", + "print(dml_obj.sensitivity_summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example one can clearly, distinguish the robustness of the non-zero effects vs. the pre-treatment periods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Control Groups\n", + "\n", + "The current implementation support the following control groups\n", + "\n", + " - ``\"never_treated\"``\n", + " - ``\"not_yet_treated\"``\n", + "\n", + "Remark that the ``\"not_yet_treated\" depends on anticipation.\n", + "\n", + "For differences and recommendations, we refer to [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dml_obj_nyt = DoubleMLDIDMulti(dml_data, **(default_args | {\"control_group\": \"not_yet_treated\"}))\n", + "dml_obj_nyt.fit()\n", + "dml_obj_nyt.bootstrap(n_rep_boot=5000)\n", + "dml_obj_nyt.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear Covariate Adjustment\n", + "\n", + "Remark that we relied on boosted trees to adjust for conditional parallel trends which allow for a nonlinear adjustment. In comparison to linear adjustment, we could rely on linear learners.\n", + "\n", + "**Remark that the DGP (`dgp_type=4`) is based on nonlinear conditional expectations such that the estimates will be biased**\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "linear_learners = {\n", + " \"ml_g\": LinearRegression(),\n", + " \"ml_m\": LogisticRegression(),\n", + "}\n", + "\n", + "dml_obj_linear = DoubleMLDIDMulti(dml_data, **(default_args | linear_learners))\n", + "dml_obj_linear.fit()\n", + "dml_obj_linear.bootstrap(n_rep_boot=5000)\n", + "dml_obj_linear.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aggregated Effects\n", + "As the [did-R-package](https://bcallaway11.github.io/did/index.html), the $ATT$'s can be aggregated to summarize multiple effects.\n", + "For details on different aggregations and details on their interpretations see [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001).\n", + "\n", + "The aggregations are implemented via the `aggregate()` method." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Group Aggregation\n", + "\n", + "\n", + "To obtain group-specific effects one can would like to average $ATT(\\mathrm{g}, t_\\text{eval})$ over $t_\\text{eval}$.\n", + "As a sample oracle we will combine all `ite`'s based on group $\\mathrm{g}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_post_treatment = df[df[\"t\"] >= df[\"d\"]]\n", + "df_post_treatment.groupby(\"d\")[\"ite\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To obtain group-specific effects it is possible to aggregate several $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$ values based on the group $\\mathrm{g}$ by setting the `aggregation=\"group\"` argument." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aggregated_group = dml_obj.aggregate(aggregation=\"group\")\n", + "print(aggregated_group)\n", + "_ = aggregated_group.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output is a `DoubleMLDIDAggregation` object which includes an overall aggregation summary based on group size." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time Aggregation\n", + "\n", + "To obtain time-specific effects one can would like to average $ATT(\\mathrm{g}, t_\\text{eval})$ over $\\mathrm{g}$ (respecting group size).\n", + "As a sample oracle we will combine all `ite`'s based on group $\\mathrm{g}$. As oracle values, we obtain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_post_treatment.groupby(\"t\")[\"ite\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To aggregate $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$, based on $t_\\text{eval}$, but weighted with respect to group size. Corresponds to *Calendar Time Effects* from the [did-R-package](https://bcallaway11.github.io/did/index.html).\n", + "\n", + "For calendar time effects set `aggregation=\"time\"`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aggregated_time = dml_obj.aggregate(\"time\")\n", + "print(aggregated_time)\n", + "fig, ax = aggregated_time.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Event Study Aggregation\n", + "\n", + "To obtain event-study-type effects one can would like to aggregate $ATT(\\mathrm{g}, t_\\text{eval})$ over $e = t_\\text{eval} - \\mathrm{g}$ (respecting group size).\n", + "As a sample oracle we will combine all `ite`'s based on group $\\mathrm{g}$. As oracle values, we obtain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[\"e\"] = pd.to_datetime(df[\"t\"]).values.astype(\"datetime64[M]\") - \\\n", + " pd.to_datetime(df[\"d\"]).values.astype(\"datetime64[M]\")\n", + "df.groupby(\"e\")[\"ite\"].mean()[1:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Analogously, `aggregation=\"eventstudy\"` aggregates $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$ based on exposure time $e = t_\\text{eval} - \\mathrm{g}$ (respecting group size)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", + "print(aggregated_eventstudy)\n", + "aggregated_eventstudy.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aggregation Details\n", + "\n", + "The `DoubleMLDIDAggregation` objects include several `DoubleMLFrameworks` which support methods like `bootstrap()` or `confint()`.\n", + "Further, the weights can be accessed via the properties\n", + "\n", + " - ``overall_aggregation_weights``: weights for the overall aggregation\n", + " - ``aggregation_weights``: weights for the aggregation\n", + "\n", + "To clarify, e.g. for the eventstudy aggregation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(aggregated_eventstudy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, the overall effect aggregation aggregates each effect with positive exposure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(aggregated_eventstudy.overall_aggregation_weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If one would like to consider how the aggregated effect with $e=0$ is computed, one would have to look at the corresponding set of weights within the ``aggregation_weights`` property" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# the weights for e=0 correspond to the fifth element of the aggregation weights\n", + "aggregated_eventstudy.aggregation_weights[4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking a look at the original `dml_obj`, one can see that this combines the following estimates (only show month):\n", + "\n", + " - $\\widehat{ATT}(04,03,04)$\n", + " - $\\widehat{ATT}(05,04,05)$\n", + " - $\\widehat{ATT}(06,05,06)$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(dml_obj.summary[\"coef\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Anticipation\n", + "\n", + "As described in the [Model Guide](https://docs.doubleml.org/stable/guide/models.html#difference-in-differences-models-did), one can include anticipation periods $\\delta>0$ by setting the `anticipation_periods` parameter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data with Anticipation\n", + "\n", + "The DGP allows to include anticipation periods via the `anticipation_periods` parameter.\n", + "In this case the observations will be \"shifted\" such that units anticipate the effect earlier and the exposure effect is increased by the number of periods where the effect is anticipated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_obs = 4000\n", + "n_periods = 6\n", + "\n", + "df_anticipation = make_did_CS2021(n_obs, dgp_type=4, n_periods=n_periods, n_pre_treat_periods=3, time_type=\"datetime\", anticipation_periods=1)\n", + "\n", + "print(df_anticipation.shape)\n", + "df_anticipation.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To visualize the anticipation, we will again plot the \"oracle\" values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_anticipation[\"ite\"] = df_anticipation[\"y1\"] - df_anticipation[\"y0\"]\n", + "df_anticipation[\"First Treated\"] = df_anticipation[\"d\"].dt.strftime(\"%Y-%m\").fillna(\"Never Treated\")\n", + "agg_df_anticipation = df_anticipation.groupby([\"t\", \"First Treated\"]).agg(**agg_dictionary).reset_index()\n", + "agg_df_anticipation.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can see that the effect is already anticipated one period before the actual treatment assignment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_data(agg_df_anticipation, col_name='ite')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize a corresponding `DoubleMLPanelData` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dml_data_anticipation = DoubleMLPanelData(\n", + " data=df_anticipation,\n", + " y_col=\"y\",\n", + " d_cols=\"d\",\n", + " id_col=\"id\",\n", + " t_col=\"t\",\n", + " x_cols=[\"Z1\", \"Z2\", \"Z3\", \"Z4\"],\n", + " datetime_unit=\"M\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ATT Estimation\n", + "\n", + "Let us take a look at the estimation without anticipation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dml_obj_anticipation = DoubleMLDIDMulti(dml_data_anticipation, **default_args)\n", + "dml_obj_anticipation.fit()\n", + "dml_obj_anticipation.bootstrap(n_rep_boot=5000)\n", + "dml_obj_anticipation.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The effects are obviously biased. To include anticipation periods, one can adjust the `anticipation_periods` parameter. Correspondingly, the outcome regression (and not yet treated units) are adjusted." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dml_obj_anticipation = DoubleMLDIDMulti(dml_data_anticipation, **(default_args| {\"anticipation_periods\": 1}))\n", + "dml_obj_anticipation.fit()\n", + "dml_obj_anticipation.bootstrap(n_rep_boot=5000)\n", + "dml_obj_anticipation.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Group-Time Combinations\n", + "\n", + "The default option `gt_combinations=\"standard\"` includes all group time values with the specific choice of $t_\\text{pre} = \\min(\\mathrm{g}, t_\\text{eval}) - 1$ (without anticipation) which is the weakest possible parallel trend assumption.\n", + "\n", + "Other options are possible or only specific combinations of $(\\mathrm{g},t_\\text{pre},t_\\text{eval})$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### All combinations\n", + "\n", + "The option `gt_combinations=\"all\"` includes all relevant group time values with $t_\\text{pre} < \\min(\\mathrm{g}, t_\\text{eval})$, including longer parallel trend assumptions.\n", + "This can result in multiple estimates for the same $ATT(\\mathrm{g},t)$, which have slightly different assumptions (length of parallel trends)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dml_obj_all = DoubleMLDIDMulti(dml_data, **(default_args| {\"gt_combinations\": \"all\"}))\n", + "dml_obj_all.fit()\n", + "dml_obj_all.bootstrap(n_rep_boot=5000)\n", + "dml_obj_all.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selected Combinations\n", + "\n", + "Instead it is also possible to just submit a list of tuples containing $(\\mathrm{g}, t_\\text{pre}, t_\\text{eval})$ combinations. E.g. only two combinations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gt_dict = {\n", + " \"gt_combinations\": [\n", + " (np.datetime64('2025-04'),\n", + " np.datetime64('2025-01'),\n", + " np.datetime64('2025-02')),\n", + " (np.datetime64('2025-04'),\n", + " np.datetime64('2025-02'),\n", + " np.datetime64('2025-03')),\n", + " ]\n", + "}\n", + "\n", + "dml_obj_all = DoubleMLDIDMulti(dml_data, **(default_args| gt_dict))\n", + "dml_obj_all.fit()\n", + "dml_obj_all.bootstrap(n_rep_boot=5000)\n", + "dml_obj_all.plot_effects()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b32c44051610a894b97f9420c0637d9dc1d0b59c Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 13 Jun 2025 11:35:15 +0000 Subject: [PATCH 06/14] update sensitivity --- doc/guide/scores/did/did_cs_score.rst | 4 +- .../sensitivity/did/did_cs_sensitivity.rst | 38 ++++++++++++++++++- .../sensitivity/did/did_pa_sensitivity.rst | 6 +-- 3 files changed, 42 insertions(+), 6 deletions(-) diff --git a/doc/guide/scores/did/did_cs_score.rst b/doc/guide/scores/did/did_cs_score.rst index eefa7ad5..1f465b1f 100644 --- a/doc/guide/scores/did/did_cs_score.rst +++ b/doc/guide/scores/did/did_cs_score.rst @@ -76,7 +76,7 @@ If ``in_sample_normalization='False'``, the score is set to =\; &\tilde{\psi}_a(W; \eta) \theta + \tilde{\psi}_b(W; \eta) -with :math:`\eta=(g, m, p, \lambda)`, where :math:`p_0 = \mathbb{E}[G^{\mathrm{g}}]` and :math:`\lambda_0 = \mathbb{E}[T]` are estimated on the whole sample. +with :math:`\eta=(g, m, p, \lambda)`, where :math:`p_0 = \mathbb{E}[G^{\mathrm{g}}]` and :math:`\lambda_0 = \mathbb{E}[T]` are estimated on the subsample. Remark that this will result a similar score, but just uses slightly different normalization. ``score='experimental'`` assumes that the treatment probability is independent of the covariates :math:`X` and @@ -121,5 +121,5 @@ Analogously, if ``in_sample_normalization='False'``, the score is set to =\; &\tilde{\psi}_a(W; \eta) \theta + \tilde{\psi}_b(W; \eta) -with :math:`\eta=(g, m, p, \lambda)`, where :math:`p_0 = \mathbb{E}[G^{\mathrm{g}}]` and :math:`\lambda_0 = \mathbb{E}[T]` are estimated on the whole sample. +with :math:`\eta=(g, m, p, \lambda)`, where :math:`p_0 = \mathbb{E}[G^{\mathrm{g}}]` and :math:`\lambda_0 = \mathbb{E}[T]` are estimated on the subsample. Remark that this will result in a similar score, but just uses slightly different normalization. \ No newline at end of file diff --git a/doc/guide/sensitivity/did/did_cs_sensitivity.rst b/doc/guide/sensitivity/did/did_cs_sensitivity.rst index dee07ecb..204c4948 100644 --- a/doc/guide/sensitivity/did/did_cs_sensitivity.rst +++ b/doc/guide/sensitivity/did/did_cs_sensitivity.rst @@ -1,2 +1,38 @@ +For a detailed description of the scores and nuisance elements, see :ref:`did-cs-score`. + +In the :ref:`did-cs-model` with ``score='observational'`` and ``in_sample_normalization=True`` the score function implies the following representations + +.. math:: + + m(W,g) &= \Big(\big(g(1,1,X) - g(1,0,X)\big) - \big(g(0,1,X) - g(0,0,X)\big)\Big) \frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}) + + \alpha(W) &= \Bigg(\frac{G^{\mathrm{g}}T}{\mathbb{E}[G^{\mathrm{g}}T]} - \frac{G^{\mathrm{g}}(1-T)}{\mathbb{E}[G^{\mathrm{g}}(1-T)]} + + &\quad - \frac{m(X)(1-G^{\mathrm{g}})T}{1-m(X)}\mathbb{E}\left[\frac{m(X)(1-G^{\mathrm{g}})T}{1-m(X)}\right]^{-1} + + &\quad + \frac{m(X)(1-G^{\mathrm{g}})(1-T)}{1-m(X)}\mathbb{E}\left[\frac{m(X)(1-G^{\mathrm{g}})(1-T)}{1-m(X)}\right]^{-1} \Bigg) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}). + +If instead ``in_sample_normalization=False``, the Riesz representer changes to + +.. math:: + + \alpha(W) = \left(\frac{T}{\mathbb{E}[G^{\mathrm{g}}]\mathbb{E}[T]} + \frac{1-T}{\mathbb{E}[G^{\mathrm{g}}](1-\mathbb{E}[T])}\right)\left(G^{\mathrm{g}} - (1-G^{\mathrm{g}})\frac{m(X)}{1-m(X)}\right) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}). + +For ``score='experimental'`` the score function implies the following representations + +.. math:: + + m(W,g) &= \big(g(1,1,X) - g(1,0,X)\big) - \big(g(0,1,X) - g(0,0,X)\big) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}) + + \alpha(W) &= \left(\frac{G^{\mathrm{g}}T}{\mathbb{E}[G^{\mathrm{g}}T]} - \frac{G^{\mathrm{g}}(1-T)}{\mathbb{E}[G^{\mathrm{g}}(1-T)]} - \frac{(1-G^{\mathrm{g}})T}{\mathbb{E}[(1-G^{\mathrm{g}})T]} + \frac{(1-G^{\mathrm{g}})(1-T)}{\mathbb{E}[(1-G^{\mathrm{g}})(1-T)]}\right) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}). + +And again, if instead ``in_sample_normalization=False``, the Riesz representer changes to + +.. math:: + + \alpha(W) = \left(\frac{G^{\mathrm{g}}T}{\mathbb{E}[G^{\mathrm{g}}]\mathbb{E}[T]} - \frac{G^{\mathrm{g}}(1-T)}{\mathbb{E}[G^{\mathrm{g}}](1-\mathbb{E}[T])} - \frac{(1-G^{\mathrm{g}})T}{(1-\mathbb{E}[G^{\mathrm{g}}])\mathbb{E}[T]} + \frac{(1-G^{\mathrm{g}})(1-T)}{(1-\mathbb{E}[G^{\mathrm{g}}])(1-\mathbb{E}[T])}\right) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}). + +The ``nuisance_elements`` are then computed with plug-in versions according to the general :ref:`sensitivity_implementation`, but the scores :math:`\psi_{\sigma^2}` and :math:`\psi_{\nu^2}` are scaled according to the sample size of the subset, i.e. with scaling factor :math:`c=\frac{n_{\text{obs}}}{n_{\text{subset}}}`. + .. note:: - Will be implemented soon. \ No newline at end of file + Remark that the elements are only non-zero for units in the corresponding treatment group :math:`\mathrm{g}` and control group :math:`C^{(\cdot)}`, as :math:`1-G^{\mathrm{g}}=C^{(\cdot)}` if :math:`\max(G^{\mathrm{g}}, C^{(\cdot)})=1`. \ No newline at end of file diff --git a/doc/guide/sensitivity/did/did_pa_sensitivity.rst b/doc/guide/sensitivity/did/did_pa_sensitivity.rst index 7a9a665d..8d9b1d36 100644 --- a/doc/guide/sensitivity/did/did_pa_sensitivity.rst +++ b/doc/guide/sensitivity/did/did_pa_sensitivity.rst @@ -4,7 +4,7 @@ In the :ref:`did-pa-model` with ``score='observational'`` and ``in_sample_normal .. math:: - m(W,g) &= \big(g(1,X) - g(0,X)\big)\cdot \frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} + m(W,g) &= \big(g(1,X) - g(0,X)\big)\cdot \frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]}\cdot \max(G^{\mathrm{g}}, C^{(\cdot)}) \alpha(W) &= \left(\frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \frac{\frac{m(X)(1-G^{\mathrm{g}})}{1-m(X)}}{\mathbb{E}\left[\frac{m(X)(1-G^{\mathrm{g}})}{1-m(X)}\right]}\right) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}). @@ -22,7 +22,7 @@ For ``score='experimental'`` implies the score function implies the following re \alpha(W) &= \left(\frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \frac{1-G^{\mathrm{g}}}{1-\mathbb{E}[G^{\mathrm{g}}]}\right) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}). -The ``nuisance_elements`` are then computed with plug-in versions according to the general :ref:`sensitivity_implementation`. +The ``nuisance_elements`` are then computed with plug-in versions according to the general :ref:`sensitivity_implementation`, but the scores :math:`\psi_{\sigma^2}` and :math:`\psi_{\nu^2}` are scaled according to the sample size of the subset, i.e. with scaling factor :math:`c=\frac{n_{\text{ids}}}{n_{\text{subset}}}`. .. note:: - Remark that the elements are only non-zero for units in the corresponding treatment group :math:`\mathrm{g}` and control group :math:`C^{(\cdot)}`, as :math:`1-G^{\mathrm{g}}=C^{(\cdot)}` if :math:`G^{\mathrm{g}} \vee C_{t_\text{eval} + \delta}^{(\cdot)}=1`. + Remark that the elements are only non-zero for units in the corresponding treatment group :math:`\mathrm{g}` and control group :math:`C^{(\cdot)}`, as :math:`1-G^{\mathrm{g}}=C^{(\cdot)}` if :math:`\max(G^{\mathrm{g}}, C^{(\cdot)})=1`. 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tFirst Treatedy_meany_lower_quantiley_upper_quantileite_meanite_lower_quantileite_upper_quantile
02025-01-012025-04208.365705198.344568218.093100-0.064884-2.3626072.244304
12025-01-012025-05210.266368200.074705220.446726-0.077714-2.4383402.226501
22025-01-012025-06212.683657202.957912222.822287-0.033389-2.6001312.344735
32025-01-01Never Treated214.709100204.876698224.688642-0.078233-2.4325322.178332
42025-02-012025-04208.142666188.812566227.6694730.032289-2.1934442.329211
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" - ], - "text/plain": [ - " t First Treated y_mean y_lower_quantile y_upper_quantile \\\n", - "0 2025-01-01 2025-04 208.365705 198.344568 218.093100 \n", - "1 2025-01-01 2025-05 210.266368 200.074705 220.446726 \n", - "2 2025-01-01 2025-06 212.683657 202.957912 222.822287 \n", - "3 2025-01-01 Never Treated 214.709100 204.876698 224.688642 \n", - "4 2025-02-01 2025-04 208.142666 188.812566 227.669473 \n", - "\n", - " ite_mean ite_lower_quantile ite_upper_quantile \n", - "0 -0.064884 -2.362607 2.244304 \n", - "1 -0.077714 -2.438340 2.226501 \n", - "2 -0.033389 -2.600131 2.344735 \n", - "3 -0.078233 -2.432532 2.178332 \n", - "4 0.032289 -2.193444 2.329211 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# rename for plotting\n", "df[\"First Treated\"] = df[\"d\"].dt.strftime(\"%Y-%m\").fillna(\"Never Treated\")\n", @@ -860,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -915,20 +245,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plot_data(agg_df, col_name='y')" ] @@ -948,24 +267,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "jupyter": { "source_hidden": true } }, - "outputs": [ - { - "data": { - "image/png": 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Snf1lJSkpSTM+NjZW04w4r23nFc8+ltatW+d5LNlFltyOJS82Njbo27cvAO2G59u3b0dKSgqqVq2Ktm3bvtJxvw7Z5+Ozzz7L83ysWrUKgPb52LBhA5ycnLBs2TL4+vrC3t4eXbt2xfLlyxEbG/tac34x9zt37uSZe9OmTXVyL6zKlSvnG3/06JFWvEaNGujcuTMePXqE3bt3a+Lff/89gOeNzgsj55sI88r9woULEM9m4uPEiRP5bi+v+yEiIgIAYG9vDysrq1zHVK1aVWtscSno/Kanp+fZZD+nnOfqyZMnhdp3dHQ0gGcF1pxK22djSfP09Mw1bmFhke/y7KJTzibxr3puiIio+On+70ciIqJXkP1GpIyMDJ2CB/D8S2RQUBC++OILKBQKAECbNm1QtWpVhIWF4fTp02jRogWio6Nx4MABmJiYYODAgVrbkSQJAODn54f69evnm1OzZs10YqampnmOv3jxIkaNGgWFQoGFCxeiR48e8PT0hJmZGWQyGX744QeMGjUq15kN+cnry3/2sbz11lswNzfPdxv29vYvtc93330XW7ZswdatW7F8+XKYmppqvsyOHDlSK6fXddx5yT7uvOKtWrXSFB/yknOGSOvWrREeHo4//vgDISEhOH36NA4fPoyDBw9i9uzZ+P3339GhQ4diyT0v2bm7uLjoFGVflLNwUVxy+9uMHz8ehw8fxvfff4+33noLDx8+xN69e2FhYYERI0YUetve3t6ws7NDfHw8Lly4gFatWr1Srvndg69LXtfcyyjM9V+5cmXY2tri6dOn+OeffzQz+/KSlZWF//3vfwCgM8OuNH026oNcnv//Qy9oeU6vem6IiKj4sShFRETF5vHjx5pX08fFxeHUqVN5jo2MjMShQ4fQrVs3AM8KNiNGjMBnn32GX375BS1atMCmTZuQlZWFAQMGwMbGRmt9Dw8PAEDLli2xcuXKYj2OHTt2QAiBjz/+WPN/zXPK7RE6e3t7GBsbQ6lU4v79+6hdu7bOmPDw8Fz35+Hhgdu3b+PTTz9F48aNXzn/nNq3b48qVarg7t27+O2339CsWTOcOHECCoVC59Goohx3foyMjAAAycnJuS6/f/9+rvHsv22vXr3wySefvNQ+TU1N8dZbb+Gtt94C8Gy2w6xZs/DDDz9g5MiRee6zuGTnbm9vrzOzpTjcu3cv1y/T2deWu7u7zrIuXbqgevXqCA4ORmhoKLZs2QK1Wo233347z5lIuZHL5ejWrRs2btyITZs2YcKECUU9jHy5ubkBePYZkpSUlGuO2TNessdmMzQ0hEqlQnJycq6PZxX09793716u8ezza2JiUqjCsFwuR48ePbBhwwZs3LgRn3zySb4z0vbu3YvExETNOc6pNH02lnU8N0REpQ8f3yMiomITFBQEtVqNZs2aaR7hye0nu+CRPasq24gRIyCXy7F9+3akpaXl+XgK8KyPCPDsy1zOxzOKQ3x8PADAy8tLZ1lGRgZ27dqlEzc0NIS/vz8AYMuWLblu99dff801nn0s2X2eipNMJtM8/vPzzz9rHuN74403dL7QF+W485O9/bt37yIzM1NneXbvqBdln4/sItmrcHR0xKJFiwAADx48wNOnT19pe9mFtpz9mXJq0qQJHBwccP36dYSGhr7SvnKzcePGXOPZ/Yay++HkJJPJ8PHHHwMAli1bhnXr1gEAxo4d+9L7nzZtGgwMDHDx4kXNI5TFzd3dXTNDLrfCnhBCE2/fvr3Wsuxr7saNGzrrXb16FQ8fPsx335s2bco1nn1+W7VqlWufs9xMmTIFBgYGuHbtGr799ts8xyUmJmo+EwMDA3OdHVhaPhvLOp4bIqLSh0UpIiIqNtkFj7yaMWfLbia8f/9+rb4d7u7u6NSpE5KSkjBjxgz8+++/8PT0REBAgM42GjRogH79+uHhw4fo27dvrrOQUlNTsXnz5kL3dMlWq1YtAMD69eu1ZvlkZGTgo48+ynM2xbhx4wAA3377Lc6ePau17JtvvsE///yT63pTpkyBjY0Nli1bhqVLl+ZawLl3716eX5gLMmLECCgUChw/fhw//PADgNwbnBf1uPPi5eUFHx8fJCQkYOHChVrLgoOD8fnnn+e6Xq9evdCkSROcO3cO77zzTq69XZ4+fYo1a9ZoikP379/HunXrcu13tW/fPgCAra3tS80Myk32TKTbt29DpVLpLDc0NMTs2bMhhECfPn1w8uRJnTFqtRp//fWXzjVSGL///ju2bt2qFdu5cyd27doFAwMDTfHpRSNGjIC1tTV+/vlnREdHo3379rnO5itI7dq1sXz5cgDPilozZsxAQkKCzji1Wl2k48uWPUPuyy+/xJUrVzRxIQS++uorXL58GTY2Nnj//fe11uvYsSMAYO7cuVAqlZp4eHg4hg8fXmCR8+LFi5oiZraTJ09qenBNnDix0MdQp04dzXU/adIkLFq0SKeYefPmTXTs2BF37tyBt7d3nrN3SstnY1nHc0NEVAqV2Hv+iIioXAsODhYAhLGxsYiPjy9wfMOGDQUAsWTJEq341q1bNa8RByA+//zzPLeRlJQkOnToIAAIIyMj0aRJEzFgwADRv39/0aRJE2FkZCQAiBs3bmjWyX7tedu2bfPc7tOnT4WXl5cAIOzt7UXv3r1Fv379hJOTk7C0tBTjx48XAMTw4cN11v3ggw8EAKFQKES7du3EoEGDRJ06dYRCoRATJ07M87XyISEhwsHBQQAQTk5OIiAgQAwZMkR0795dVK1aVQAQzZo1K/C85iX7NecAhKOjo8jMzCy2487vnO7atUvIZDIBQPj5+Yn+/fuLRo0aCZlMJj7//HNNTi+KiIgQfn5+AoAwNzcXLVq0EAMHDhR9+/YVfn5+QqFQCACaV8VfunRJABCGhoaa62DAgAGiQYMGAoCQyWRi3bp1hT5f2a+jnz17ts6yxo0bCwCiRo0aYsiQIeLdd98Vn376qdaYKVOmaI7N19dX9OrVSwwcOFC0a9dO2NjYCABi9erVhc4n++8yYcIEAUA0adJEDB48WDRr1kyzn2XLluW7jex1AYhdu3YVet+5CQoKEhYWFpp7z9/fX/Tv318MHjxYdOrUSdjZ2WmWzZgxQ2vdwtyDkiSJt99+WwAQBgYGokOHDmLQoEGiRo0aAoAwNTUVBw4c0Fnv7t27mvPr6ekp+vXrJ9q0aSNMTU1Fx44dRYsWLQQAcfz4ca312rZtKwCIcePGCblcLnx9fcWgQYNE27ZthVwuFwDE+PHji3SuvvnmG81nkb29vejRo4cYOHCgaNasmebeaNSokXj48GG+2ykNn40v4969e5pc7927l+e44cOHCwDil19+0Yrndw/mt1627L/pi3/ropwbIiJ6fViUIiKiYpH9BfKtt94q1PgVK1YIAKJWrVpa8YyMDM0XWplMJu7evZvvdtRqtdiyZYvo2rWrcHZ2FoaGhsLe3l7UqVNHvPPOO+L333/XKsAU9otXTEyM+Oijj0TVqlWFsbGxqFSpkhg6dKi4ffu2+OWXX/IsSkmSJH788UfRsGFDYWJiImxsbETnzp3F33//LTZs2CAAiEGDBuW6zydPnojPPvtMNGzYUFhaWgojIyPh7u4uWrRoIWbPni2uXr2ab8752bVrl+YL4qRJk4r1uAs6p3/88Ydo2bKlMDMzE+bm5qJ58+Zi27ZtQgiRZ1FKiGfXwpo1a0T79u2Fvb29MDAwEE5OTsLPz0+MGTNGHD58WDM2KSlJrFixQvTp00f4+PgICwsLYW5uLqpXry6GDRsmLly48FLnK78vxPfv3xeDBw8Wrq6uwsDAQAAQXl5eOuNOnTolhgwZIry8vISxsbGwtLQU1atXF7179xbr1q0rVPE2W3ZR6t69e2L79u3C399fc4ytW7cW+/btK3AbBw8eFACEh4eHyMrKKvS+8xIXFycWLlwoAgIChIuLizAyMhKmpqbCw8NDdO3aVSxbtkxERkbqrPcyxY8tW7ZoCnmGhobCw8NDjBgxQty8eTPPda5fvy769u0rbG1thbGxsahRo4b46quvRGZmZp6FipzxY8eOiQ4dOghra2thamoqGjduLIKCgl729GgJDw8XU6ZMEfXr1xfW1tbCyMhIVKpUSfTs2VNs3rxZqNXqArdRWj4bC6u0FqWEePlzQ0REr49MiGJ6hQ4RERHla+TIkfjll1+wdOlSTJo0Sd/pUAUzdOhQbN68GfPmzcP06dP1nU6p0q5dO4SEhOD48eO59uUiIiKi14M9pYiIiIpRaGgoUlNTtWKSJOHHH39EUFAQTExMMGjQID1lRxXVtWvXsG3bNlhYWGDUqFH6ToeIiIgIAFC414cQERFRoSxevBjbt29HgwYN4ObmhtTUVFy/fh3h4eFQKBRYtWoVXF1d9Z0mVRDvvfceUlNTcfDgQWRlZWHWrFmws7PTd1pEREREAFiUIiIiKlaBgYFISkrCxYsXcfnyZWRlZcHJyQmBgYGYMGECmjdvru8UqQL56aefIJfL4eHhgU8++QRTp07Vd0pEREREGuwpRUREREREREREJY49pYiIiIiIiIiIqMSxKEVERERERERERCWOPaXw7K1IkZGRsLS0hEwm03c6RERERERERERllhACycnJqFSpEuTyvOdDsSgFIDIyEh4eHvpOg4iIiIiIiIio3Hj48CHc3d3zXM6iFABLS0sAz06WlZWVnrN5NZIkISYmBo6OjvlWI4lIF+8fqkh4vRMVDe8dooLxPiEqmvJ07yQlJcHDw0NTb8kLi1KA5pE9KyurclGUysjIgJWVVZm/iIlKGu8fqkh4vRMVDe8dooLxPiEqmvJ47xTUIql8HCUREREREREREZUpLEoREREREREREVGJY1GKiIiIiIiIiIhKHItSRERERERERERU4liUIiIiIiIiIiKiElfq3r63evVqrF69GuHh4QAAX19ffP7553jzzTfzXGfHjh347LPPEB4eDh8fHyxcuBBdu3Z9bTmq1WpkZma+tu2/CkmSoFKpkJ6eXm669dOrMTIygkKh0HcaRERERERERFpKXVHK3d0dCxYsgI+PD4QQWL9+PXr16oVLly7B19dXZ/zp06cxaNAgzJ8/H927d8eWLVvQu3dv/O9//0OdOnWKNTchBCIiIhAfH1+s2y1OQggIIRAfH1/gqxep4rCzs4ObmxuvCSIiIiIiIio1ZEIIoe8kCmJnZ4fFixfj3Xff1VkWGBiI1NRU7N+/XxNr3rw5/Pz8sGbNmkJtPykpCdbW1khMTISVlVWe4x49eoT4+Hi4uLjA3Ny8VM5EEkJAkiTI5XIWIAiSJCE1NRVRUVGws7ODu7u7vlMq1SRJQnR0NJycnErl/U1UnHi9ExUN7x2igvE+ISqa8nTvFLbOUupmSuWkVquxY8cOpKamwt/fP9cxZ86cwaRJk7Rib7zxBnbv3p3ndpVKJZRKpeb3pKQkAM8uAEmS8swlLi4Orq6ucHR0fMkjKVnZRSkiADAzMwMAPH78GM7OznyULx+SJGkKu0TlHa93oqLhvUNUMN4nREVTnu6dwh5DqSxKXbt2Df7+/sjIyICFhQV+//131K5dO9exUVFRcHZ21oo5OzsjKioqz+3Pnz8fc+fO1YnHxMQgIyMj13VUKhWEEDA1NS31F0hpz49KnqmpKYQQiIqKgqGhob7TKbUkSUJiYiKEECzsUrnH652oaHjvEBWM9wlR0ZSneyc5OblQ40plUapGjRq4fPkyEhMTsXPnTgwfPhwhISF5FqZe1vTp07VmVyUlJcHDwwOOjo55TitLT09HfHw8FApFmbg4ykKOVHIUCgVkMhns7Oxgamqq73RKLUmSIJPJ4OjoyHuIyj1e70RFw3uHqGC8T4iKpjzdOyYmJoUaVyqLUkZGRqhWrRoAoFGjRjh//jy++eYbrF27Vmesi4sLnjx5ohV78uQJXFxc8ty+sbExjI2NdeJyuTzPP3x2j6bsn9IqZ4uw0pwnlazs6za/a5ye4XmiioTXO1HR8N4hKhjvE6KiKS/3TmHzLxNHKUmSVg+onPz9/XHs2DGt2JEjR/LsQVURBAcHQy6XIygoSN+pVFjh4eGQyWSYM2eOvlMhIiIiIiIiKpVKXVFq+vTp+PvvvxEeHo5r165h+vTpCA4OxpAhQwAAw4YNw/Tp0zXjx48fj0OHDmHp0qW4efMm5syZgwsXLmDs2LH6OoTXKjg4WGvG1os/Z8+efW37vnz5MubMmYPw8PBXzjPnj7e392vLOS9BQUFYsWJFie+XiIiIiIiIiJ4pdY/vRUdHY9iwYXj8+DGsra1Rr149HD58GJ06dQIAPHjwQGsaWIsWLbBlyxbMmjULM2bMgI+PD3bv3o06dero6xBKxKBBg9C1a1edeLVq1WBjY4O0tDQYGRkV6z4vX76MuXPnol27dgUWkmrVqoWNGzdqxX744QecOHECy5cvh4ODgyZuYWFRrHkWRlBQEMLDwzFhwoQS3zcRERERERERlcKi1E8//ZTv8uDgYJ1Y//790b9//9eUUenUsGFDDB06VCee/fpIExOTAntKCSGQmpr6WopCzs7OOvkdPXoUJ06cQO/evQssaiUnJ8PS0rLY8yIiIiIiIiKi0qHUPb5Hry63nlLZj9MFBQXh+++/R+3atWFiYoIlS5YAAEJDQ9G/f3+4ubnB2NgYLi4uaN++Pf744w8AwJw5c/DOO+8AANq3b6959G7EiBGvlGvO3kvbtm1Do0aNYGpqio8//lgz5ujRo+jcuTNsbGxgYmKCevXqYc2aNTrb+vPPPxEYGIgqVarA1NQUNjY26Ny5M0JCQrTGeXt7IyQkBPfv39d6jDBnwfP27dt4++234erqCiMjI3h7e2PKlClITU3V2e/JkyfRsmVLmJqawtnZGWPHjkVKSsornRciIiIiIiKi8q7UzZSiwklLS0NsbKxWzNjYuMBZTytWrEBcXBzef/99uLi4wMPDA3FxcQgICAAAfPjhh/Dy8kJsbCwuXLiAf/75B926dUPfvn3x+PFj/PDDD5gxYwZq1aoFAKhatWqxHM/u3bvx7bffYvTo0fjwww9hZWUF4Nkjfx9++CGaN2+OmTNnwtzcHEeOHMHo0aNx584dLF68WLONoKAgxMfHY9iwYXB3d0dERATWrVuHDh064Pjx42jdurXmHEyfPh2xsbFYvny5Zv3sY7p48SICAgJgY2ODUaNGwc3NDVeuXMG3336LU6dOISQkBIaGhgCAf/75Bx07doSlpSU+/fRT2NjYYOvWrRg2bFixnBciIiIiIiKi8opFqTJq9uzZmD17tlYsMDAQv/76a77rPXjwADdv3oSTk5MmtnfvXkRHR2Pbtm0YMGBAruvVq1cP/v7++OGHH9CpUye0a9fulY8hp9DQUFy9elVTGAKAx48fY9y4cRg4cCC2bNmiiX/00UcYP348li1bhtGjR6NKlSoAgB9//BHm5uZa2/3www/h6+uL+fPna4pSvXv3xooVK5Cenp7rI5AjR46Eq6srzp8/r/UIYYcOHdC3b19s3rxZM0Ns4sSJkCQJp06dQvXq1TX5tWrVqnhODBEREREREVE5xcf3yqgPPvgAR44c0fqZNWtWgesNGzZMqyAFANbW1gCAgwcPIikp6bXkW5Bu3bppFaQAYOfOnVAqlXj33XcRGxur9dOjRw9IkoSjR49qxucsSKWkpCAuLg4KhQLNmjXDP//8U6g8rl27hqtXr2Lw4MFQKpVa+2zVqhXMzc3x559/AnjWlP/MmTPo1auXpiAFAEZGRpg4ceKrnA4iIiIiIiKqgBQKhb5TKFGcKVVG+fj4oGPHjjpxIUS+6+UsnmRr27Ythg0bhqCgIGzevBlNmjRBx44dERgYiNq1axdbzi+b140bNwAg1+PM9uTJE80/37lzBzNnzsThw4eRkJCgNa6gpu8v7jO3mWgv7vPu3bsAgJo1a+qMKanzRkRERERERGVfijILSRlZiFMZQpWcCSsTA1gYl/+STfk/QtJiZmaWa3z9+vWYMmUKDh48iBMnTmDp0qX4+uuvsWLFCowdO1YveWUX2DZs2ABXV9dc18t+dC8lJQVt2rRBamoqJkyYgLp168LS0hJyuRzz58/HX3/9Vag8svc5efJkdOnSJdcxtra2hdoWERERERERUUFiUpT44ex9HLkVg4S0DNiYmaBzDUe839wLjhbG+k7vtWJRijTq1KmDOnXqYMqUKUhISECzZs0wbdo0jBkzRvOGupLk4+MDAHBwcMh3thQAHDt2DJGRkfj55581bwnMlttjjXkdS/Y+FQpFgfusXLkyAODmzZs6y65fv57vukREREREREQpyiz8cPY+dv8bhcwsCZFJmZDJFfj93ygAwLjWVcr1jCn2lCLEx8dDkiStmI2NDSpXroy0tDRkZGQAgObNfvHx8SWS14ABA2BsbIzZs2cjPT1dZ3liYiKUSiWA58/dvvj44p9//plrPykLCws8ffpUZ3yDBg1Qp04drFmzRvN4Xk5ZWVma43d2dkbz5s2xZ88ehIWFacZkZmZqvdWPiIiIiIiIKDfJyiwcCYtBWqYa4U/TkKaS8CgxA0IAf4bFIFmZpe8UX6vyW26jQtuwYQOWL1+OPn36oFq1ajA0NERISAgOHz6MAQMGwNTUFADQpEkTyOVyfP3113j69CnMzc1RuXJlNGvW7LXk5e7ujtWrV+O9995DrVq18Pbbb8PLywsxMTG4du0adu/ejevXr8Pb2xutWrWCi4sLJk+ejPDwcLi7u+Py5cvYuHEj6tati2vXrmltu3nz5ti/fz/Gjh2LFi1aQKFQICAgAE5OTti4cSMCAgJQr149jBw5Er6+vkhLS8N///2H3377DfPnz9e8fW/ZsmVo164dWrZsiTFjxsDGxgZbt25FVlb5/uAgIiIiIiKiV5eQrkJEYgaikpXInjORrpIQnaKETGaMxAwVXK1M9Jvka8SiFKFdu3a4dOkS9u/fj8ePH0OhUKBy5cpYsmSJVj8pT09P/Pzzz1i4cCFGjx4NlUqF4cOHv7aiFAC88847qF69OpYsWYK1a9ciISEBDg4OqFGjBr788ku4uLgAeDaz6/Dhw5g6dSq+++47ZGVloVGjRjhw4AB++uknnaLUxIkTcffuXezcuRNr1qyBJEk4fvw4nJyc4Ofnh0uXLmH+/PnYu3cv1qxZA0tLS3h7e2PEiBHo0KGDZjv+/v44cuQIpk2bhgULFsDa2hpvvfUWRo8ejbp1676280JERERERERlmyQJyGRAilKNnA/xmBrKYW9uBHMjBaxNDPWXYAmQiYJe11YBJCUlwdraGomJibCyssp1THp6Om7fvg0fHx/NzKHSSAgBSZIgl8tLvAcUlV5l5frVN0mSEB0dDScnJ8jlfLqZyjde70RFw3uHqGC8T4gKlpaZhVmHbqFBJSucvf8U6y88AgBYGMnhZmMKuUyGPnVcymxPqcLUWQDOlCIiIiIiIiIiKjGPkzIweW8obsem4k5sKqa2rwYA+PtuHBSQYGlkgM41HPFBc68yWZB6GeX76IiIiIiIiIiISomrkUmYsj8U8WkqAEBkUgZW/H0HX71ZEzM7Vkd8agbszE1gZWJQ7gtSAItSRERERERERESv3YEbT/DV0dtQqSVNzMnCGF90qYkaTpaQJAmGmcmwt7SqMI++sihFRERERERERPSaSJLAmjP38cv5B1rx2s6WWNKjNhwtjDUxtVpd0unpFYtSRERERERERESvQbpKjdmHb+H4f7Fa8Y7VHTG7U3WYGCr0lFnpwKIUEREREREREVExi05WYtLeUNyKSdGKv9/cC+8384RMJtNTZqUHi1JERERERERERMUoNCoZk/eGIi4tUxMzUsgxu3N1dK7hpMfMShcWpYiIiIiIiIiIismft6Ix988wZOZoaG5vZoSlPX3h62Kpx8xKHxaliIiIiIiIiIhekSQJrDv3AD+eva8Vr+FogWU9feFkaZzHmhUXi1JERERERERERK8gQ6XG3CNhOBoWoxVvX80Bc9+oAdMK3tA8LyxKEREREREREREVUUyKEp/su47rT5K14u808cSH/l6Qy9nQPC8sShERERERERERFcGNJ88amsekPm9obqiQY1ZHH3St5azHzMoGFqWIiIiIiIiIiF7SsdsxmH34FpRZzxua25kZYnF3X9SrZKXHzMoOub4TIP07f/48xo4dC19fX5ibm8PT0xMDBgxAWFiYztgbN26gS5cusLCwgJ2dHd5++23ExGg/M3vz5k1MnToVfn5+sLS0hKurK7p164YLFy7obG/OnDmQyWQ6PyYmJoXOX5IkLFq0CJUrV4aJiQnq1auHX3/9Nd91VCoVateuDZlMhiVLlhR6X0RERERERFSxCSHw0z8PMO2PG1oFqWoO5gga2IAFqZfAmVKEhQsX4tSpU+jfvz/q1auHqKgorFy5Eg0bNsTZs2dRp04dAMCjR4/Qpk0bWFtbY968eUhJScGSJUtw7do1nDt3DkZGRgCAdevW4aeffkK/fv3w0UcfITExEWvXrkXz5s1x6NAhdOzYUSeH1atXw8LCQvO7QlH4JnAzZ87EggUL8P7776NJkybYs2cPBg8eDJlMhoEDB+a6znfffYcHDx68zGkiIiIiIiKiCi4zS8IXR8Jw+Fa0Vrx1FXt81aUGzIxYZnkZPFuESZMmYcuWLZqiEgAEBgaibt26WLBgATZt2gQAmDdvHlJTU3Hx4kV4enoCAJo2bYpOnTohKCgIH3zwAQBg0KBBmDNnjlaRaeTIkahVqxbmzJmTa1HqrbfegoODw0vnHhERgaVLl2LMmDFYuXIlAOC9995D27ZtMWXKFPTv31+nwBUdHY0vvvgCn376KT7//POX3icRERERERFVPHGpmfhkXyj+jdJuaP52I3eMbVmZDc2LgI/v6VmKMguPkzJw40kyHidlIEWZVeI5tGjRQqsgBQA+Pj7w9fXFjRs3NLFdu3ahe/fumoIUAHTs2BHVq1fH9u3bNbFGjRppFaQAwN7eHq1bt9baXk5CCCQlJUEI8VK579mzByqVCh999JEmJpPJMHr0aDx69AhnzpzRWWfatGmoUaMGhg4d+lL7IiIiIiIiooopLCYFw3+9pFWQMpDL8Hmn6hjXugoLUkXEmVJ6FJOixA9n7+NIWAxSM9UwN1Kgc3VHvN/cC44WxnrNTQiBJ0+ewNfXF8CzGUnR0dFo3LixztimTZviwIEDBW4zKioqz9lQVapUQUpKCszNzdG7d28sXboUzs4Fv6ng0qVLMDc3R61atXRyyl7eqlUrTfzcuXNYv349Tp48CZmMHxpERERERESUv5A7sfjs0C2kq9SamI2pIRZ3rw0/N2s9Zlb2sSj1ClKUWfgvNrVI69qaGuLncw+x82qkJpaWqcbm/0UgUy3wThMPPE1XvfR2hRCoYmcKK1OjggfnY/PmzYiIiMAXX3wBAHj8+DEAwNXVVWesq6sr4uPjoVQqYWycezHtxIkTOHPmDGbNmqUVt7W1xdixY+Hv7w9jY2OcOHEC33//Pc6dO4cLFy7Ayir/BnGPHz+Gs7OzToEpO8/IyOfnVwiBjz/+GIGBgfD390d4eHj+J4GIiIiIiIgqLCEENlx4hO9P3UPOZ3qq2Jlhea86qGRd+Bd0Ue5YlHoF/8Wm4v0dV156PUtjAyzsXgs/nL2P5Fwe11t75j661XbCp/tv5Lq8IGv61UVD96IXpW7evIkxY8bA398fw4cPBwCkp6cDQK5Fp+w35aWnp+e6PDo6GoMHD0blypUxdepUrWXjx4/X+r1fv35o2rQphgwZglWrVmHatGn55prXPnPmlC0oKAjXrl3Dzp07890mERERERERVWyZWRK+PnYbB2480Yq38LbDvDdrwtyY5ZTiwJ5SemBtYoCnaao8C07JyiwkpGXB2qTkL/KoqCh069YN1tbW2Llzp6ZJuKmpKQBAqVTqrJORkaE1JqfU1FR0794dycnJ2LNnj06vqdwMHjwYLi4uOHr0qFZeOX+yi02mpqaFyikpKQnTp0/HlClT4OHhUWAOREREREREVDHFp2Vi9G9XdQpSgxq4YXlPXxakihGLUnqQmJEFWzNDWOZxIVsaG8DGzACJGSXb9DwxMRFvvvkmEhIScOjQIVSqVEmzLPtxuOzH+HJ6/Pgx7OzsdGYsZWZmom/fvrh69Sr27NmDOnXqFDoXDw8PxMfHa+0/58+2bds08aioKJ0G6dl5Zh/DkiVLkJmZicDAQISHhyM8PByPHj0CADx9+hTh4eHIzMwsdH5ERERERERU/vwXm4oRv17G1cgkTUwhk2FGBx9MaluVDc2LGct7r6Cagzl+7F+/SOvamhpilL8XdlyJ1FnWv34lOJkbY1lP35febnZPqZeVkZGBHj16ICwsDEePHkXt2rW1lru5ucHR0REXLlzQWffcuXPw8/PTikmShGHDhuHYsWPYvn072rZt+1LHEB4ejgYNGmhiR44c0RqT3YDdz88P69atw40bN7Ry/ueffzTLAeDBgwd4+vSpZr2c5s2bh3nz5uHSpUs6x0FEREREREQVw4m7cZh18CbScjQ0tzI2wKIetdHI3UZ/iZVjLEq9Agtjg1fqtD+mpTeMFDL8+cLb9z5o7gUHC2N4FWGbQghIkvRS66jVagQGBuLMmTPYs2cP/P39cx3Xr18/rF+/Hg8fPtQ8Anfs2DGEhYVh4sSJWmM//vhjbNu2DWvXrkXfvn3z3HdMTAwcHR21YqtXr0ZMTAy6dOmiiXXs2DHX9Xv16oWJEydi1apVWLlyJYBn52DNmjVwc3NDixYtAADjxo1D7969tdaNjo7GqFGjMGLECPTq1QuVK1fOM08iIiIiIiIqn4QQ2HIpAt/8fVerobm3rRmW9fKFh83LT/ygwmFRSo8cLYwxrnUVvNPUE4kZKlibPHukz6KEn0+dPHky9u7dix49eiA+Ph6bNm3SWj506FAAwIwZM7Bjxw60b98e48ePR0pKChYvXoy6devinXfe0YxfsWIFVq1aBX9/f5iZmelsr0+fPjA3NwcAeHl5ITAwEHXr1oWJiQlOnjyJrVu3ws/PD6NGjSowd3d3d0yYMAGLFy+GSqVCkyZNsHv3bpw4cQKbN2/W9MRq2LAhGjZsqLVu9tv3fH19dQpWREREREREVP6p1BIW/PUf9oZGacWbedpiftdasNRDr+eKhGdXzyz+vwjlaqW/V0levnwZALBv3z7s27dPZ3l2UcrDwwMhISGYNGkSpk2bBiMjI3Tr1g1Lly7V6ieVvb0zZ87gzJkzOtu7d++epig1ZMgQnD59Grt27UJGRga8vLwwdepUzJw5E2ZmZoXKf8GCBbC1tcXatWsRFBQEHx8fbNq0CYMHD36Z00BEREREREQVSEK6ClP3X8eliESteP/6lTC5bVUo2D/qtZOJFztEV0BJSUmwtrZGYmIirKysch2Tnp6O27dvw8fHJ9e3zJUW2Y/vyeVyyGS8geiZsnL96pskSYiOjoaTkxPkcr4Hgso3Xu9ERcN7h6hgvE+oLLgbl4pJe0MRkZihicllMnzSrir616+Uz5qvT3m6dwpTZwE4U4qIiIiIiIiIKpDT4fGYceAGUjOfNzS3NDbAgm610NTTVo+ZVTwsShERERERERFRuSeEwPYrkVgWchdSjofGPG1MsaynL7zsCtdChooPi1JEREREREREVK5lqSUsDr6D36491oo39rDBwm61YGViqKfMKjYWpYiIiIiIiIio3ErKUOHTP27gwsMErXifuq6Y2q4qDBRlu39TWcaiFBERERERERGVS/fj0zBpbygeJKRrYnKZDJPaVsGA+pX4gjA9Y1GKiIiIiIiIiMqdcw+eYtofN5CszNLEzI0UmNe1Flp42+kxM8rGohQRERERERERlSs7r0RicfAdrYbmbtYmWNbTF1XszfWYGeXEohQRERERERERlQtqSWBpyB3suBKpFW/gZo1F3WvDxpQNzUsTFqWIiIiIiIiIqMxLzsjC9AM38M+Dp1rxHrVdML1DNRiyoXmpw6IUEREREREREZVpDxPSMWlPKMKfpmliMgDj21TB4AZubGheSrEoRURERERERERl1sVHCZi67zqScjQ0NzNU4Ks3a6J1FXs9ZkYFYVGKiIiIiIiIiMqk3f8+xoJj/0Gdo6G5q6UJlvXyRTUHNjQv7fhAJeH8+fMYO3YsfH19YW5uDk9PTwwYMABhYWE6Y2/cuIEuXbrAwsICdnZ2ePvttxETE6M15ubNm5g6dSr8/PxgaWkJV1dXdOvWDRcuXNDZ3pw5cyCTyXR+TExMCp2/JElYtGgRKleuDBMTE9SrVw+//vqrzrgRI0bkuq+aNWsWel9ERERERESkf5IksCzkDr4+elurIFXX1QpBg/xYkCojOFOKsHDhQpw6dQr9+/dHvXr1EBUVhZUrV6Jhw4Y4e/Ys6tSpAwB49OgR2rRpA2tra8ybNw8pKSlYsmQJrl27hnPnzsHIyAgAsG7dOvz000/o168fPvroIyQmJmLt2rVo3rw5Dh06hI4dO+rksHr1alhYWGh+VygUhc5/5syZWLBgAd5//300adIEe/bsweDBgyGTyTBw4ECtscbGxli3bp1WzNrautD7IiIiIiIiIv1KVWZhxsGbOB0erxXvWssZMzv4wMiA82/KChalCJMmTcKWLVs0RSUACAwMRN26dbFgwQJs2rQJADBv3jykpqbi4sWL8PT0BAA0bdoUnTp1QlBQED744AMAwKBBgzBnzhytItPIkSNRq1YtzJkzJ9ei1FtvvQUHB4eXzj0iIgJLly7FmDFjsHLlSgDAe++9h7Zt22LKlCno37+/VoHLwMAAQ4cOfen9EBERERERkf5FJmZg4p5/cTdeu6H52FaV8XYjdzY0L2NYPtQzdUZCvr+XhBYtWmgVpADAx8cHvr6+uHHjhia2a9cudO/eXVOQAoCOHTuievXq2L59uybWqFEjrYIUANjb26N169Za28tJCIGkpCSIHNMuC2PPnj1QqVT46KOPNDGZTIbRo0fj0aNHOHPmjM46arUaSUlJL7UfIiIiIiIi0q9LEYkYvvWSVkHK1FCBRd1rY1hjDxakyiAWpfQoKyUSSVdWISslMtff9UkIgSdPnmhmL0VERCA6OhqNGzfWGdu0aVNcunSpwG1GRUXlORuqSpUqsLa2hqWlJYYOHYonT54UKs9Lly7B3NwctWrV0skpe3lOaWlpsLKygrW1Nezs7DBmzBikpKQUal9ERERERESkH/tCo/DRrqtISFdpYs4Wxlg3oD7aVXv5p26odODje8UgKzUK6tSol1pHbmqPlNAgpN39A6qEO7BuMBaJl1ZCFXMFQp0Jy7rvQkqPK/T2FOYuMDB3ednU87R582ZERETgiy++AAA8fvwYAODq6qoz1tXVFfHx8VAqlTA2Ns51eydOnMCZM2cwa9YsrbitrS3Gjh0Lf39/GBsb48SJE/j+++9x7tw5XLhwAVZWVvnm+fjxYzg7O+tUxLPzjIyM1IpNnToVDRs2hCRJOHToEFatWoUrV64gODgYBga8HYiIiIiIiEoTSRJYeeoeNl58pBWv42KJJT18YW9ulMeaVBbwW3gxSLu7H8nX1hU8MAdDe1/YNP0UqsRwqOJCEXt0NADAwKYqzLy74OnpOVDFhRZ6e1b1RsGyzjsvlUNebt68iTFjxsDf3x/Dhw8HAKSnpwNArkWn7Dflpaen57o8OjoagwcPRuXKlTF16lStZePHj9f6vV+/fmjatCmGDBmCVatWYdq0afnmmtc+c+aUbf78+VpjBg4ciOrVq2PmzJnYuXOnTlN0IiIiIiIi0p+0zCzMOnQLJ+5qT9h4o4YTPu9UnQ3NywH+BfVEFRcKoUqFdYOxWnHrBuOQ/vCvlypIFaeoqCh069YN1tbW2Llzp6ZJuKmpKQBAqVTqrJORkaE1JqfU1FR0794dycnJ2LNnj06vqdwMHjwYLi4uOHr0qFZeOX+yi02mpqYvnVNOEydOhFwu19oXERERERER6dfjpAy8t/2KTkFqdAtvfNmlBgtS5QT/inpiaO8LmaE5Ei+t1IonXvoWph4BMLT3LfGcEhMT8eabbyIhIQGHDh1CpUqVNMuyH4fLfowvp8ePH8POzk5nxlJmZib69u2Lq1evYs+ePahTp06hc/Hw8EB8/PPXe7q6umr9bNu2TROPiorSaZCenWfOY8iNqakp7O3ttfZFRERERERE+nM1Mgkjtl7C7dhUTczYQI4F3WphZFNPNjQvR/j4XjEwq9Idxs66DcDzk91TShUXCkN7X62eUmnhh2DbYs5L95R6FRkZGejRowfCwsJw9OhR1K5dW2u5m5sbHB0dceHCBZ11z507Bz8/P62YJEkYNmwYjh07hu3bt6Nt27aFzkUIgfDwcDRo0EATO3LkiNYYX99nRTs/Pz+sW7cON27c0Mr5n3/+0SzPT3JyMmJjY+Ho6Fjo/IiIiIiIiOj1OHDjCb46ehsqtaSJOZobYWlPX9RyttRjZvQ6sChVDAyK2GTcss67gEwBS98RMLCoBLuWXyI5NEjzOyw9XkO2utRqNQIDA3HmzBns2bMH/v7+uY7r168f1q9fj4cPH8LD41lux44dQ1hYGCZOnKg19uOPP8a2bduwdu1a9O3bN899x8TE6BSEVq9ejZiYGHTp0kUT69ixY67r9+rVCxMnTsSqVauwcuWzWWdCCKxZswZubm5o0aIFgGdFN5VKBUtL7Q+xL7/8EkIIrX0RERERERFRyZIkgTVn7uOX8w+04rWdLbGkR204WuT+Ui0q21iU0iMDi0qwqv8RFCY2uf5eUiZPnoy9e/eiR48eiI+Px6ZNm7SWDx06FAAwY8YM7NixA+3bt8f48eORkpKCxYsXo27dunjnnedN1lesWIFVq1bB398fZmZmOtvr06cPzM3NAQBeXl4IDAxE3bp1YWJigpMnT2Lr1q3w8/PDqFGjCszd3d0dEyZMwOLFi6FSqdCkSRPs3r0bJ06cwObNmzU9saKiotCgQQMMGjQINWvWBAAcPnwYBw4cQJcuXdCrV6+in0AiIiIiIiIqsnSVGrMP38Lx/2K14h18HDGnc3WYGCr0lBm9bixK6dmLBaiSLkgBwOXLlwEA+/btw759+3SWZxelPDw8EBISgkmTJmHatGkwMjJCt27dsHTpUq1+UtnbO3PmDM6cOaOzvXv37mmKUkOGDMHp06exa9cuZGRkwMvLC1OnTsXMmTNhZmZWqPwXLFgAW1tbrF27FkFBQfDx8cGmTZswePBgzRgbGxt0794dR44cwfr166FWq1GtWjXMmzcPn3zyCeRytlcjIiIiIiIqadHJSkzaG4pbMSla8febe+G9pp6Qy9k/qjyTiRc7RFdASUlJsLa2RmJiIqysrHIdk56ejtu3b8PHx6fAN7rpkxACkiRBLpez+RtplJXrV98kSUJ0dDScnJxYqKRyj9c7UdHw3iEqGO8TKqzQqGRM3huKuLRMTcxIIcfsztXRuYaTHjPTj/J07xSmzgJwphQRERERERERlbA/b0Vj7p9hyMzR0Nze7FlDc18XNjSvKFiUIiIiIiIiIqISIUkC6849wI9n72vFazhaYFlPXzhZsqF5RcKiFBERERERERG9dhkqNeYeCcPRsBitePtqDpj7Rg2YsqF5hcOiFBERERERERG9VjEpSnyy7zquP0nWir/TxBMf+nuxoXkFxaIUEREREREREb02N548a2gek/q8obmhQo5ZHX3QtZazHjMjfWNRioiIiIiIiIhei2O3YzD78C0os543NLczM8Ti7r6oVynvt7JRxcCiFBEREREREREVKyEEfj73EGvOhGvFqzmYY1lPX7hamegnMSpVWJQiIiIiIiIiomKTmSXhiyNhOHwrWiveuoo9vupSA2ZGLEXQM7wSiIiIiIiIiKhYxKVm4pN9ofg3Sruh+duN3DG2ZWU2NCctLEoRERERERER0SsLi0nBpD2heJKi1MQM5DJM7+CDnr4uesyMSisWpYiIiIiIiIjolYTcicVnh24hXaXWxKxNDLG4R200cLPWY2ZUmrEoRURERERERERFIoTAhguP8P2pexA54lXszLCsly/crE31lhuVfnJ9J0D6d/78eYwdOxa+vr4wNzeHp6cnBgwYgLCwMJ2xN27cQJcuXWBhYQE7Ozu8/fbbiImJ0Rpz8+ZNTJ06FX5+frC0tISrqyu6deuGCxcu6Gxvzpw5kMlkOj8mJoV/E4MkSVi0aBEqV64MExMT1KtXD7/++mueY1evXg0/Pz+YmprC3t4eAQEBuHLlSqH3R0RERERERM8ams/5MwwrXyhI+Xvb4edAPxakqECcKUVYuHAhTp06hf79+6NevXqIiorCypUr0bBhQ5w9exZ16tQBADx69Aht2rSBtbU15s2bh5SUFCxZsgTXrl3DuXPnYGRkBABYt24dfvrpJ/Tr1w8fffQREhMTsXbtWjRv3hyHDh1Cx44ddXJYvXo1LCwsNL8rFIpC5z9z5kwsWLAA77//Ppo0aYI9e/Zg8ODBkMlkGDhwoNbYkSNHYvPmzRg2bBjGjh2L1NRUXLp0CdHR0XlsnYiIiIiIiF4Un5aJKfuv42pkklZ8UAM3TGhdhQ3NqVBYlCJMmjQJW7Zs0RSVACAwMBB169bFggULsGnTJgDAvHnzkJqaiosXL8LT0xMA0LRpU3Tq1AlBQUH44IMPAACDBg3CnDlztIpMI0eORK1atTBnzpxci1JvvfUWHBwcXjr3iIgILF26FGPGjMHKlSsBAO+99x7atm2LKVOmoH///poC1/bt27F+/Xr89ttv6NOnz0vvi4iIiIiIiID/YlMxaU8oHidnaGIKmQyfBlRDn7quesyMyho+vkdo0aKFVkEKAHx8fODr64sbN25oYrt27UL37t01BSkA6NixI6pXr47t27drYo0aNdIqSAGAvb09WrdurbW9nIQQSEpKghAi1+V52bNnD1QqFT766CNNTCaTYfTo0Xj06BHOnDmjiS9btgxNmzZFnz59IEkSUlNTX2pfREREREREFd2Ju3F4d9tlrYKUlbEBvu9XlwUpemmlrig1f/58NGnSBJaWlnByckLv3r1x69atfNcJCgp6pZ5E+pKYmY6YjBSdn8TMdH2nBiEEnjx5opm9FBERgejoaDRu3FhnbNOmTXHp0qUCtxkVFZXnbKgqVarA2toalpaWGDp0KJ48eVKoPC9dugRzc3PUqlVLJ6fs5QCQlJSEc+fOoUmTJpgxYwasra1hYWGBKlWqaBXUiIiIiIiISJcQApv/9wiT94YiLccb9rxtzRA0qAEaudvoLzkqs0rd43shISEYM2YMmjRpgqysLMyYMQOdO3fG9evXYW5unud6VlZWWsUrmazknl+NSktCVHqyZr/17SpplkWkJiImIwUAoJDJUdfueeU4LUuFbn/+CLWQIJPJYKowhEImx+8d38G95DgkZj6rPJsaGKKGtZNmvTtJsUhWKQEAFobGqGb18o+9FWTz5s2IiIjAF198AQB4/PgxAMDVVbfy7erqivj4eCiVShgbG+e6vRMnTuDMmTOYNWuWVtzW1hZjx46Fv78/jI2NceLECXz//fc4d+4cLly4ACsrq3zzfPz4MZydnXX+3tl5RkZGAgDu3LkDIQS2bt0KAwMDLFq0CNbW1vjmm28wcOBAWFlZoUuXLoU4M0RERERERBWLSi1hwV//YW9olFa8mact5netBUuTUldaoDKi1F05hw4d0vo9KCgITk5OuHjxItq0aZPnejKZDC4uLq87vVztfXgd626dBQAYyhU41f1jzbLt9y7j17vPZuvYGJnizy6jNMtSszJxNzkeaiHBUKFANUsHABIA4MebZ3H08W0AQHVrR2xqO0Sz3orQv/FPzAMAQAN7N6xt2b9Yj+fmzZsYM2YM/P39MXz4cABAevqz2Vu5FZ2yZ6Wlp6fnujw6OhqDBw9G5cqVMXXqVK1l48eP1/q9X79+aNq0KYYMGYJVq1Zh2rRp+eaa1z5z5gQAKSnPCoNxcXE4e/YsmjVrBgDo2bMnKleujK+++opFKSIiIiIiohckpKswdf91XIpI1Ir3r18Jk9tWhYINzekVlLqi1IsSE59d+HZ2dvmOS0lJgZeXFyRJQsOGDTFv3jz4+vrmOlapVEKpVGp+T0p69rYASZIgSVKu60iSBCGE5kdLjt8FtJcL5L1Mexva//ziqPx6LeW17GX7MwHPHrHr1q0brK2tsWPHDsjlcgghNEWejIwMne1mF35MTEx0lqWmpqJ79+5ITk7GiRMnYG5uXmBegwYNwuTJk3H06FF8+umnmrxysra2hqmpKUxNTaFUKgvMKTv/ypUro2nTpprx5ubm6N69OzZv3gyVSgUDg1J/SxRJ9nWb3zVOz+9zniOqCHi9ExUN7x2igvE+KT/uxqVh8r7riEx83j9KJgc+aVsVb9VzBSAgSS//vZNyV57uncIeQ6n+Bi5JEiZMmICWLVuiTp06eY6rUaMGfv75Z9SrVw+JiYlYsmQJWrRogdDQULi7u+uMnz9/PubOnasTj4mJQUZGhk4cAFQqVZ5f6oUQz4tPQvvk57fsxdJTjpFa6714Uea3DCj8H/9FiYmJ6Nq1KxISEhAcHAwXFxfNtpydnQE8exzuxe0/fvwYdnZ2MDQ01FqWmZmJvn374urVqzhw4ABq165d6Nzc3d0RHx+vGV+pUiWt5T/99BOGDx8OFxcXHD9+HGq1WusRvoiICADPHuOTJEkzi87JyUknB0dHR6hUKiQnJ8Pa2rpQ+ZU12R9u8fHxMDQ01Hc6pZYkSUhMTIQQAnJ5qWu5R1SseL0TFQ3vHaKC8T4pH85HJGP+iQit/lEWRgrMbOOOBi4KREdH6zG78qk83TvJycmFGleqi1JjxozBv//+i5MnT+Y7zt/fH/7+/prfW7RogVq1amHt2rX48ssvdcZPnz4dkyZN0vyelJQEDw8PODo65tnDKD09HfHx8ZDL5ToXR09PXzR1fPZGOplMprU8sHIDBLj6AAAUL6xrbmCMKpZ2mp5SBjI5FLJnyz+o6Y/AKn4AnvWUyrneBN82Wj2lcrtYX/YCzsjIQO/evREWFoYjR47oFAGzz8///vc/nW2fP38efn5+WnFJkvDOO+/gr7/+wrZt29C+fftC5yKEwP3799GgQQPNNv/880+tMb6+vpDL5fDz88NPP/2EW7duoXbt2lo5AdBsw93dHS4uLoiMjNTJPyoqCiYmJrC2ti7zN35e5HI5ZDIZ7OzsYGpqqu90Si1JenYvOjo6lttrgSgbr3eiouG9Q1Qw3idlmxACO648xrKTjyGEHIYGz/6GHrYmWNqjNrxszfScYflVnu6dwr58rtQWpcaOHYv9+/fj77//znW2U34MDQ3RoEED/Pfff7kuNzY2zrUPUW4Fp5zLcr7dLydXc2u4muc+w8bdwgbuFja5LjMzMMThHD2mshnJFahiap/rOgBQzdoxz2U5H2MrbLN3tVqNgQMH4syZM9izZw9atGiR67h+/fph/fr1ePToETw8PAAAx44dQ1hYGCZOnKi1v3HjxmHbtm1Yu3Yt+vXrl+e+Y2Ji4OiofTyrV69GTEwMunTpotlmp06dcl2/d+/emDRpElavXo2VK1cCeHYO1q5dCzc3N7Rs2VKzjcDAQHzzzTc4evSoZnuxsbHYs2cPAgICoFAoCnO6yqTs6za/a5ye4XmiioTXO1HR8N4hKhjvk7IpSy1hcfBd/Hbt2Yuu8P9f8Rq522BR91qwMuFTF69bebl3Cpt/qStKCSHw8ccf4/fff0dwcDAqV6780ttQq9W4du0aunbt+hoyLD7WRqVjxsrkyZOxd+9e9OjRA/Hx8di0aZPW8qFDhwIAZsyYgR07dqB9+/YYP348UlJSsHjxYtStWxfvvPOOZvyKFSuwatUq+Pv7w8zMTGd7ffr00bxJ0cvLC4GBgahbty5MTExw8uRJbN26FX5+fhg1Srdg9yJ3d3dMmDABixcvhkqlQpMmTbB7926cOHECmzdv1io0TZ8+Hdu3b0e/fv0wadIkWFtbY82aNVCpVJg3b16Rzx8REREREVFZl5Shwqd/3MCFhwla8T51XTG1XVUYKMp2kYRKp1JXlBozZgy2bNmCPXv2wNLSUtPgOruxNQAMGzYMbm5umD9/PgDgiy++QPPmzVGtWjUkJCRg8eLFuH//Pt577z29HUdZcvnyZQDAvn37sG/fPp3l2UUpDw8PhISEYNKkSZg2bRqMjIzQrVs3LF26VGvmWfb2zpw5gzNnzuhs7969e5qi1JAhQ3D69Gns2rULGRkZ8PLywtSpUzFz5kyYmRVuWuiCBQtga2uLtWvXIigoCD4+Pti0aRMGDx6sNc7Z2RknT57EJ598guXLl0OlUsHf3x+bNm1C/fr1C7UvIiIiIiKi8uZ+fBom7Q3Fg4R0TUwuk2FimyoI9KtU6KdwiF6WTBTlFW2vUV4X+y+//IIRI0YAANq1awdvb28EBQUBACZOnIjffvsNUVFRsLW1RaNGjfDVV1+hQYMGhdpnUlISrK2tkZiYmG9Pqdu3b8PHx6dU9+TJbnye/bghEVB2rl99kyQJ0dHRcHJyKvPTZYkKwuudqGh47xAVjPdJ2XLuwVNM++MGkpVZmpi5kQLzutZCC287PWZW8ZSne6cwdRagFM6UKkyNLDg4WOv35cuXY/ny5a8pIyIiIiIiIqLyZ+eVSCwOvgMpx/dwN2sTLOvpiyr25nrMjCqKUleUIiIiIiIiIqLXRy0JLA25gx1XIrXiDdyssah7bdiYsqE5lQwWpYiIiIiIiIgqiOSMLEw/cAP/PHiqFe9R2wXTO1SDIRuaUwliUYqIiIiIiIioAniYkI5Je0IR/jRNE5MBGN+mCgY3cGNfYipxLEoRERERERERlXMXHyVg6r7rSMrR0NzMUIGv3qyJ1lXs9ZgZVWQsShERERERERGVY7v/fYwFx/6DOkdDc1dLEyzr5YtqDmxoTvrDohQRERERERFROSRJAitO3MWvlyK04nVdrbCkR23YmRnpKTOiZ1iUIiIiIiIiIipnUpVZmHHwJk6Hx2vFu9ZyxswOPjAyYENz0j8WpYiIiIiIiIjKkcjEDEzc8y/uxms3NB/TsjKGNXZnQ3MqNViUIiIiIiIiIionLkckYsr+60hIV2liJgZyfNmlJtpVc9BjZkS6WJQiIiIiIiIiKgf2hUZh3rHbyJKeNzR3sjDG8l6+qO5oocfMiHLHohQRERERERFRGSZJAitP3cPGi4+04nVcLLGkhy/szdnQnEondjYjKuPCw8Mhk8kQFBSk71SIiIiIiKiEpWVm4ZP913UKUp1rOGLNW/VYkKJSjUUpQlBQEGQyGUxMTBAREaGzvF27dqhTp44eMns1MpmsUD/BwcElks+qVatYOCIiIiIiomLzOCkD722/ghN347Tio1t446suNWFsoNBTZkSFw8f3SEOpVGLBggX47rvv9J1Ksdi4caPW7xs2bMCRI0d04rVq1SqRfFatWgUHBweMGDGiRPZHRERERETl19XIJEzZH4r4tOcNzY0N5Jj7Rg108HHUY2ZEhceiFGn4+fnhxx9/xPTp01GpUiV9p1NoqampMDc314kPHTpU6/ezZ8/iyJEjOvEXpaWlwczMrFhzJCIiIiIiKi4HbjzBV0dvQ6WWNDFHcyMs7emLWs6WesyM6OXw8b1SQJ2WiKykGKjTEvWax4wZM6BWq7FgwYJCjd+0aRMaNWoEU1NT2NnZYeDAgXj48KFm+dixY2FhYYG0tDSddQcNGgQXFxeo1WpN7ODBg2jdujXMzc1haWmJbt26ITQ0VGu9ESNGwMLCAnfu3EHXrl1haWmJIUOGFPGInz+aePHiRbRp0wZmZmaYMWMGgGczx2bPno1q1arB2NgYHh4emDp1KpRKpdY2fvnlFwQEBMDJyQnGxsaoXbs2Vq9erTXG29sboaGhCAkJ0Tw22K5dO83yhIQETJgwAR4eHjA2Nka1atWwcOFCSJKktZ2EhASMGDEC1tbWsLGxwfDhw5GQkFDk4yciIiIiorJDkgRWnQrH7MO3tApStZ0tsX5QAxakqMzhTKlSQGRl4uF3feHx8W96zaNy5coYNmwYfvzxR0ybNi3f2VJff/01PvvsMwwYMADvvfceYmJi8N1336FNmza4dOkSbGxsEBgYiO+//x5//PEH+vfvr1k3LS0N+/btw4gRI6BQPHvGeePGjRg+fDjeeOMNLFy4EGlpaVi9ejVatWqFS5cuwdvbW7N+VlYW3njjDbRq1QpLlix55VlNcXFxePPNNzFw4EAMHToUzs7OkCQJPXv2xMmTJ/HBBx+gVq1auHbtGpYvX46wsDDs3r1bs/7q1avh6+uLnj17wsDAAPv27cNHH30ESZIwZswYAMCKFSvw8ccfw8LCAjNnzgQAODs7a85H27ZtERERgVGjRsHT0xOnT5/G9OnT8fjxY6xYsQIAIIRAr169cPLkSXz44YeoVasWfv/9dwwfPvyVjp+IiIiIiEq/dJUasw/fwvH/YrXiHXwcMadzdZgYsn8UlT0sShWDrIQoZCVGFTxQroCJR12tUOaTO5AZmUDKSIZQZyLj/hUY2LrCwMpJM0ZSpiEzKqzAzRtYu0Bh7fzS+ec0c+ZMbNiwAQsXLsQ333yT65j79+9j9uzZ+OqrrzSzigCgb9++aNCgAVatWoUZM2agVatWcHNzw7Zt27SKUn/88QdSU1MRGBgIAEhJScG4cePw3nvv4YcfftCMGz58OGrUqIF58+ZpxZVKJfr374/58+e/0rFmi4qKwpo1azBq1ChNbNOmTTh69ChCQkLQqlUrTbxOnTr48MMPcfr0abRo0QIAEBISAlNTU82YsWPHokuXLli2bJmmKNW7d2/MmjULDg4OOo8PLlu2DHfu3MGlS5fg4+MDABg1ahQqVaqExYsXY/LkyfDw8MDevXvx999/Y9GiRZgyZQoAYPTo0Wjfvn2xnAciIiIiIiqdopOVmLQ3FLdiUrTi7zf3wntNPSGXy/SUGdGrYVGqGCRf2ounwT8UOE5hZgOvT48CePbInsjKhJBUyIy8h8yYe1BG3oCBlRMkZSrUaYlQmFkDAFRxDxD583sFbt+23Qewaff+Kx1LlSpV8Pbbb+OHH37AtGnT4OrqqjPmt99+gyRJGDBgAGJjn1fpXVxc4OPjg+PHj2PGjBmQyWTo378/1q5di5SUFFhYWAAAtm3bBjc3N02x58iRI0hISMCgQYO0tqdQKNCsWTMcP35cJ4fRo0e/0nHmZGxsjHfeeUcrtmPHDtSqVQs1a9bUyikgIAAAcPz4cU1RKmdBKjExESqVCm3btsXhw4eRmJgIa2vrfPe/Y8cOtG7dGra2tlr76tixIxYsWIC///4bQ4YMwYEDB2BgYKB17AqFAh9//DFOnDhR9BNARERERESlVmhUMibvDUVcWqYmZqSQY3bn6uhcwymfNYlKPxal9CT7kT1VzD2oM5IBdRYeftcPMpkCBjau8J4erLfcZs2ahY0bN2LBggW5zpa6ffs2hBCaWT0vMjQ01PxzYGAgVqxYgb1792Lw4MFISUnBgQMHMGrUKMhkMs32gOcFnxdZWVlp/W5gYAB3d/ciHVtu3NzcYGRkpBW7ffs2bty4AUfH3N9aER0drfnnU6dOYfbs2Thz5oxO/6zCFKVu376Nq1evFriv+/fvw9XVVVPcy1ajRo18t09ERERERGXTn7eiMffPMGTm6B9lb2aEJT1qo46rVT5rEpUNLErpiczACB4f/wZ1ahxU8Y/w8Lt+8Ph4FwysnCA3sYTMwKjgjbwmVapUwdChQzWzpV4kSRJkMhkOHjyo6QmVU86iSfPmzeHt7Y3t27dj8ODB2LdvH9LT0zWP7mVvD3jWV8rFxUVnewYG2pepsbEx5PLi69Gfc6ZTzpzq1q2LZcuW5bqOh4cHAODOnTvo0KEDatasiWXLlsHDwwNGRkY4cOAAli9frtOoPDeSJKFTp06YOnVqrsurV6/+EkdDRERERERlnSQJrDv3AD+eva8Vr+5ogWU9feFsaaynzIiKF4tSxcCyQU+YVmla8ED58wJO9qN5UnoSjCvVgpFjZRhXqgV1Uizkxuaa5QBgaO+JSiPXFbh5A2vdgk5RzZo1C5s2bcLChQt1llWtWhVCCFSuXLlQBZMBAwbgm2++QVJSErZt2wZvb280b95ca3sA4OTkhI4dOxbbMbyKqlWr4sqVK+jQoYNmRldu9u3bB6VSib1798LT01MTz+2Rw7y2U7VqVaSkpBR47F5eXjh27JjWo5AAcOvWrYIOh4iIiIiIyogMlRpzj4ThaFiMVrxdVQd80aUGTNnQnMqR4ptuUoEZ2LjAxMuv4J8XmpwDgJFzVcgURs9mRymMYOJVX6vJOQDIjc0KtX0Dm+IrSlWtWhVDhw7F2rVrERWl3cS9b9++UCgUmDt3LoQQWsuEEIiLi9OKBQYGQqlUYv369Th06BAGDBigtfyNN96AlZUV5s2bB5VKpZNLTEyMTux1GzBgACIiIvDjjz/qLEtPT0dqaioAaGaK5TwPiYmJ+OWXX3TWMzc3R0JCQq77OnPmDA4fPqyzLCEhAVlZWQCArl27IisrC6tXr9YsV6vV+O67717u4IiIiIiIqFSKSVFi1M6rOgWpd5p4YmG3WixIUbnDmVKlQPajfPp8ZC83M2fOxMaNG3Hr1i34+vpq4lWrVsVXX32F6dOnIzw8HL1794alpSXu3buH33//HR988AE++eQTzfiGDRuiWrVqmDlzJpRKpdaje8CznlGrV6/G22+/jYYNG2LgwIFwdHTEgwcP8Mcff6Bly5ZYuXJliR03ALz99tvYvn07PvzwQxw/fhwtW7aEWq3GzZs3sX37dhw+fBiNGzdG586dYWRkhB49emDUqFFISUnBjz/+CCcnJzx+/Fhrm40aNcLq1avx1VdfoVq1anByckJAQACmTJmCvXv3onv37hgxYgQaNWqE1NRUXLt2DTt37kR4eDgcHBzQo0cPtGzZEtOmTUN4eDhq166N3377DYmJiSV6boiIiIiIqPjdePKsoXlM6vOG5oYKOWZ19EHXWq/2lnWi0opFqVIg56N6pUm1atUwdOhQrF+/XmfZtGnTUL16dSxfvhxz584F8KzPUufOndGzZ0+d8YGBgfj6669RrVo1NGzYUGf54MGDUalSJSxYsACLFy+GUqmEm5sbWrdurfNmvJIgl8uxe/duLF++HBs2bMDvv/8OMzMzVKlSBePHj9c8tlijRg3s3LkTs2bNwieffAIXFxeMHj0ajo6OGDlypNY2P//8c9y/fx+LFi1CcnIy2rZti4CAAJiZmSEkJATz5s3Djh07sGHDBlhZWaF69eqYO3euplG6XC7H3r17MWHCBGzatAkymQw9e/bE0qVL0aBBgxI/R0REREREVDyO3Y7B7MO3oMx63pPWzswQi7v7ol4lNjSn8ksmXnz+qgJKSkqCtbU1EhMTdd70li09PR23b9+Gj49Pro2xSwshBCRJglwuz7cXElUsZeX61TdJkhAdHQ0nJ6dibaZPVBrxeicqGt47RAXjfVJ4Qgj8fO4h1pwJ14pXczDHsp6+cLUy0U9ipBfl6d4pTJ0F4EwpIiIiIiIiohKXmSXhiyNhOHwrWiveuoo9vupSA2ZG/LpO5R+vciIiIiIiIqISFJeaiU/2heLfqGSt+NuN3DG2ZWXI5XzqhSoGFqWIiIiIiIiISkhYTAom7QnFkxSlJmYgl2F6Bx/09C2+N6oTlQUsShERERERERGVgJA7sfjs0C2kq9SamLWJIRb3qI0GbqXzBVhErxOLUkRERERERESvkRACGy48wven7iHnm8aq2JlhWS9fuFnzZURUMbEoRURERERERPSaZGZJmHfsNv648UQr7u9th/lv1oS5Mb+WU8XFq/8lSZKk7xSIXhqvWyIiIiKikheflokp+6/jamSSVnxQAzdMaF2FDc2pwmNRqpCMjIwAAKmpqTA3N9dzNkQvJzU1FcDz65iIiIiIiF6v/2JTMWlPKB4nZ2hiCpkMUwOqoW9dVz1mRlR6sChVSAqFAnZ2doiKigIAmJubQy6X6zkrXUIISJIEuVwOmYxV94pOkiSkpqYiKioKdnZ2UCgU+k6JiIiIiKjcO3E3DrMO3kRajobmVsYGWNi9Nhp72OgvMaJShkWpl+Dm5gYAmsJUaSSEgBACMpmMRSnSsLOz01y/RERERET0egghsOVSBL75+65WQ3MvW1Ms6+kLT1szveVGVBqxKPUSZDIZ3N3d4erqiszMTH2nkytJkhAfHw87O7tSOZOLSp6RkRFnSBERERERvWYqtYQFf/2HvaHakxiaetpiQddasDTh12+iF/GuKAKFQgFT09L5yk5JkmBoaAhTU1MWpYiIiIiIiEpAQroKU/dfx6WIRK14//qVMLltVSjY0JwoVyxKERERERERERXR3bhUTNobiojE5w3N5TIZPmlXFf3rV9JjZkSlH4tSREREREREREVwOjweMw7cQGrm84bmlsYGWNCtFpp62uoxM6KygUUpIiIiIiIiopcghMD2K5FYFnIXknje0tzT5llDcy87NjQnKgwWpYiIiIiIiIgKKUstYXHwHfx27bFWvJG7DRZ1rwUrE0M9ZUZU9rAoRURERERERFQISRkqfPrHDVx4mKAV71PXFVPbVYWBgi+bInoZLEoRERERERERFeB+fBom7Q3Fg4R0TUwuk2FimyoI9KsEmYxv2CN6WSxKEREREREREeXj3IOnmPbHDSQrszQxcyMF5nWthRbednrMjKhsY1GKiIiIiIiIKA87r0RicfAdrYbmbtYmWNbTF1XszfWYGVHZx6IUERERERER0QvUksDSkDvYcSVSK+7nZo3F3WvDxpQNzYleFYtSRERERERERDkkZ2Rh+oEb+OfBU614j9oumN6hGgzZ0JyoWLAoRURERERERPT/HiakY9KeUIQ/TdPEZADGta6CIQ3d2NCcqBixKEVEREREREQE4OKjBEzddx1JORqamxkq8NWbNdG6ir0eMyMqn1iUIiIiIiIiogpv97+PseDYf1DnaGjuammCZb18Uc2BDc2JXgcWpYiIiIiIiKjCkiSBFSfu4tdLEVrxuq5WWNKjNuzMjPSUGVH5x6IUERERERERVUipyizMOHgTp8PjteJdazljZgcfGBmwoTnR68SiFBEREREREVU4kYkZmLjnX9yN125oPqZlZQxr7M6G5kQlgEUpIiIiIiIiqlAuRyRiyv7rSEhXaWImBnJ82aUm2lVz0GNmRBULi1JERERERERUYewLjcK8Y7eRJT1vaO5kYYzlvXxR3dFCj5kRVTwsShEREREREVG5J0kCK0/dw8aLj7TidVwssaSHL+zN2dCcqKSxKEVERERERETlWlpmFmYduoUTd+O04p1rOOLzTtVhbKDQU2ZEFRuLUkRERERERFRuPU7KwOS9obgdm6oV/9DfGyOberChOZEesShFRERERERE5dLVyCRM2R+K+LTnDc2NDeSY+0YNdPBx1GNmRASwKEVERERERETl0IEbT/DV0dtQqSVNzNHcCEt6+KK2i6UeMyOibCxKERERERERUbkhSQJrztzHL+cfaMVrOVlgSQ9fOFka6ykzInoRi1JERERERERULqSr1Jh9+BaO/xerFe/g44g5navDxJANzYlKExaliIiIiIiIqMyLTlZi0t5Q3IpJ0Yq/39wL7zX1hFzOhuZEpQ2LUkRERERERFSmhUYlY/LeUMSlZWpiRgo5Zneujs41nPSYGRHlh0UpIiIiIiIiKrP+vBWNuX+GITNHQ3N7MyMs6VEbdVyt9JgZERWERSkiIiIiIiIqcyRJYN25B/jx7H2teHVHCyzr6QtnNjQnKvVYlCIiIiIiIqIyJUOlxtwjYTgaFqMVb1fVAV90qQFTNjQnKhNYlCIiIiIiIqIyIyZFiU/2Xcf1J8la8XeaeOJDfy82NCcqQ1iUIiIiIiIiojLhxpNnDc1jUp83NDdUyDGrow+61nLWY2ZEVBQsShEREREREVGpd+x2DGYfvgVl1vOG5ramhljSwxf1KrGhOVFZxKIUERERERERlVpCCPx87iHWnAnXildzMMeynr5wtTLRT2JE9MpYlCIiIiIiIqJSKTNLwhdHwnD4VrRWvFVlO3z9Zk2YGfErLVFZxjuYiIiIiIiISp241Ex8si8U/0ZpNzR/u5E7xraszIbmROUAi1JERERERERUqoTFpGDSnlA8SVFqYgZyGaZ38EFPXxc9ZkZExYlFKSIiIiIiIio1Qu7E4rNDt5CuUmti1iaGWNS9Fhq62+gvMSIqdixKERERERERkd4JIbDhwiN8f+oeRI54FTszLOvlCzdrU73lRvS6qTMSIDOy0vpdYWKjv4RKiFzfCRAREREREVHFlpklYe6fYVj5QkHK39sOPwf6sSBF5VpWSiSSrqyClBYFU1NTSGlRSLqyClkpkfpO7bXjTCkiIiIiIiLSm/i0TEzZfx1XI5O04oMauGFC6ypsaE7lmjojAcmhQUgN24HMmKuwbPQJ4q+uhiouFABgVf+jcj1jqtTNlJo/fz6aNGkCS0tLODk5oXfv3rh161aB6+3YsQM1a9aEiYkJ6tatiwMHDpRAtkRERERERFRU/8WmYsSvl7UKUgrZs4bmk9pWZUGKyj2FiQ0sag6GwtQJ6eGHEP3HYKjiQmFo7wtL3xHluiAFlMKiVEhICMaMGYOzZ8/iyJEjUKlU6Ny5M1JTU/Nc5/Tp0xg0aBDeffddXLp0Cb1790bv3r3x77//lmDmREREREREVFgn7sbh3W2X8Tg5QxOzMjbAyr510beuqx4zIyo5yif/Q+rtnbBuOB4AINKfQFKlwrrBWBhYVNJzdq+fTAghCh6mPzExMXByckJISAjatGmT65jAwECkpqZi//79mljz5s3h5+eHNWvWFLiPpKQkWFtbIzExEVZWVgWOL80kSUJ0dDScnJwgl5e6miNRqcb7hyoSXu9ERcN7h6hgBd0nQghsuRSBb/6+q9U/ysvWFMt6+sLT1qzkkiXSo/RHJ5ByczMsa4/A01OzoHxyAUJuDENrTxg51INdyy/LbGGqsHWWUv9v0sTERACAnZ1dnmPOnDmDjh07asXeeOMNnDlz5rXmRkRERERERIWnUkv46uhtrHihINXU0xa/BDZgQYoqjLR7hxB/4lOY+7yFlJuboXxyAcZureHcez+MHOpBFReK5NAgqDMS9J3qa1WqG51LkoQJEyagZcuWqFOnTp7joqKi4OzsrBVzdnZGVFRUruOVSiWUSqXm96SkJM3+JEkqhsz1R5IkCCHK/HEQ6QPvH6pIeL0TFQ3vHaKC5XWfJKSrMO2PG7gUod3Q/K36rpjUpgoUchnvLaoQUm/vQtLFZQAEUm7+Cqu670OmMIFVwwlIVZvDtsUXSLm+Hpa+IyAzsiqT90Vhcy7VRakxY8bg33//xcmTJ4t1u/Pnz8fcuXN14jExMcjIyMhljbJDkiQkJiZCCMEp5UQvifcPVSS83omKhvcOUcFyu0/uJ2Rg9vGHiErJ1IyTy2T4sLELeta0RFxsjL7SJSoxQgio721H1u0Nmpgq6jKE3WVYNfgECRlyxMY+hoODAyxqvYtEpQFUKdF6zLjokpOTCzWu1Balxo4di/379+Pvv/+Gu7t7vmNdXFzw5MkTrdiTJ0/g4uKS6/jp06dj0qRJmt+TkpLg4eEBR0fHctFTSiaTwdHRkf+hRPSSeP9QRcLrnahoeO8QFezF++R0eDxmHotAWqaAoYEhAMDcWIH5XWuimaetnrMlKjkiS4nYS+chMzTUxMxrDoFl/fchk8lgaCYhKysLtra2kMvlMNZjrq/KxMSkUONKXVFKCIGPP/4Yv//+O4KDg1G5cuUC1/H398exY8cwYcIETezIkSPw9/fPdbyxsTGMjXX/vHK5vFz8x4VMJis3x0JU0nj/UEXC652oaHjvEBXMwMAAMpkMO64+xrKQu5CEAGTPlnnYPGto7m3H/lFUwRiZwiHgW8QeGYWs5Iewqj8aFrWHQSaTaYaUl3/HFDb/UleUGjNmDLZs2YI9e/bA0tJS0xfK2toapqamAIBhw4bBzc0N8+fPBwCMHz8ebdu2xdKlS9GtWzds3boVFy5cwA8//KC34yAiIiIiIqpoUpRZSMrIQlymAWJi06DMkuBiaYzIpGdtUhq522BR91qwMjEsYEtE5ZPCxA72Ad9CGXUB5lV76DsdvSt1RanVq1cDANq1a6cV/+WXXzBixAgAwIMHD7Sqbi1atMCWLVswa9YszJgxAz4+Pti9e3e+zdGJiIiIiIio+MSkKPHD2fs4dDMaN58kw0ChQN+6LpjavhoWHf8PzbxsMbVdVRgoyvYMEKLCkrKeFWPlBtqPshmYu8KABSkApbAoJYQocExwcLBOrH///ujfv/9ryIiIiIiIiIjyk6LMwg9n72P75Ug8TEiHMkuCLEtg/YVHkMmAuW/UQP1KVlqPKRGVZ1JmCuJCJkNuaAa71osgU3B2YG5YoiYiIiIiIqJXkqzMwp5/oxD+NA2Z6ucTDeQy4Oz9p3C2NGZBiioMdUY8Yo99hMyYK8iIPIOnZ+ZACEnfaZVKLEoRERERERHRK4lMysCN6BSoc3zvNlTI4G1nBplMhsQMlf6SIypB6tQniD0yCqqnYZqYMuo81CmResyq9GJRioiIiIiIiIrs92uPIQkBC6Pn3WFMDeXwtjODsYEc5kYKWLOxOVUAWUkPEHP02Zv1silMHeHQaQ0MLN31mFnpxaIUERERERERvTRJEvj2xF3MO3YbN5+koG9dFwCApbECbpZGMPj/x/U6V3eEpXGpa2dMVKxUT8MQc/RDqFOjNDEDi0pw6LQWhtZV9JhZ6cZPBiIiIiIiInopKrWEL46E4dDNaADAzquPMbV9NVgYG+Ds/XgkpilhbqRA5xqO+KC5FyxYlKJyTBlzBfHBkyGpUjQxQ+sqsA/4FgpTBz1mVvrxk4GIiIiIiIgKLVWZhSn7r+P8wwRNLDIpA9EpSszq6INkpRrxqRmwMzeBlYkBC1JUrmVEnkX8iU8h1EpNzMjeF/btlkNubKXHzMoGfjoQERERERFRocSkKDFhTyjCYp7PCDGQyzDnjRp4o4YTAMDcSAHDzGTYW1pBLmfHGCq/0h/8haenP4eQsjQxY+fGsGuzCHJDMz1mVnawKEVEREREREQFuheXhnG7ryEq+fmMEHMjBRZ390UTTxutsWq1uoSzIypZqXf2IeHcfEA8f+WkiXsb2LX8CjKFkR4zK1tYlCIiIiIiIqJ8XY5IxOS9oUhSPp8R4mhuhG9614GPo4UeMyMqeULKQurtnVoFKbPKb8Km2SzI5Ao9Zlb2cC4lERERERER5emv27EY89s1rYJUFTsz/Bzox4IUVUgyuQHs2y2HgaUHAMC8en/YNP+MBaki4EwpIiIiIiIiytX2y5FYEvwfRI6Yn5s1lvaoDSsTQ73lRaRvChM7OASsRPqDozCvORgymUzfKZVJLEoRERERERGRFkkSWHnqHjZefKQVD6jmgC+71ISRAR+6oYpDSGoISQW5gYlWXGHuDItaQ/SUVfnAohQRERERERFpqNQSvjgShkM3o7XiA/3cMLFNFcjlnBFCFYdQZyL+1CwItRL2bRaziXkxY3mbiIiIiIiIAACpyiyM3/2vTkFqfOsqmNSWBSmqWCRVGuKCJyHj0d9QPv4HT09/DiHxzZLFiTOliIiIiIiICDEpSkzYE4qwmBRNzEAuw5w3auCNGk56zIyo5EnKJMQFT0Bm3HVNLOPxWWQl3oWhrY8eMytfXrkolZKSgrCwMKSmpqJ169bFkRMRERERERGVoHtxaRi3+xqikpWamLmRAou7+6KJp43+EiPSA3V6LOL+GgdV4l1NTG5oAft2y1iQKmZFfnwvPDwcvXr1gq2tLZo0aYL27dtrlp06dQq1a9dGcHBwceRIREREREREr8nliES8t/2yVkHK0dwIP/avz4IUVThZKZGIPfKBdkHKxA4OHVfByLGeHjMrn4pUlHrw4AGaN2+OAwcOoFevXvD394cQz18S2qxZM8TGxuLXX38ttkSJiIiIiIioeP11OxZjfruGJGWWJlbZzgw/B/rBx9FCj5kRlTxV4l3EHvkAWSmRmpjCzBmOHdfA0La6HjMrv4pUlJo9ezaePn2KkJAQ7Ny5E506ddJabmBggNatW+PUqVPFkiQREREREREVr+2XIzHtj+vIVEuamJ+bNdYNqA8XKxM9ZkZU8jLjriP2yIdQp8dqYgZWnnDs9AMMrDz1mFn5VqSi1OHDh9GnTx+0aNEizzFeXl6IiIgocmJERERERERU/CRJ4NsTd7E4+D+IHPGAag74vk9dWJkY6i03In1QPvkfYv8aCykzSRMztK0Oh45roDB31mNm5V+RGp3Hx8fD29s73zFCCCiVynzHEBERERERUclRqSV8cSQMh25Ga8UD/dwwqU0VyOUyPWVGpB/pj07g6amZEOpMTczIsT7s2y6F3IiPsL5uRSpKOTs74/bt2/mOuXbtGjw9OcWNiIiIiIioNEhVZmHK/us4/zBBKz6udWUMbegOmYwFKapYhJCQEhqkVZAyqeQP21bzITfgI6wloUiP73Xq1An79+/H1atXc11+4sQJ/PXXX+jatesrJUdERERERESvLiZFiQ92XtUqSBnIZfiyS0283ciDBSmqkGQyOezaLoGBlRcAwNSzA+xaL2JBqgQVqSg1a9YsmJqaok2bNvj666/x33//AQAOHjyIzz77DF26dIGDgwOmTJlSrMkSERERERHRy7kXl4aR2y4jLCZFEzM3UuDb3nXRpaaTHjMj0j+FiS0cAr6Dpe8I2Lb8EjIFe6qVpCI9vuft7Y3Dhw9j4MCB+OyzzyCTySCEQPfu3SGEgKenJ3bu3AlXV9fizpeIiIiIiIgK6XJEIibvDUWSMksTczQ3wje968DHkf1yqGIRQgCSCjKFkVZcYeYEq/of6imriq1IRSkAaNasGW7fvo19+/bhn3/+QXx8PKysrNCsWTP06tULRkZGBW+EiIiIiIiIXou/bsfis0M3kamWNLHKdmb4tncduFjx8SSqWISQkHh+IbJSImHfdqlOYYr0o8hFKQAwMDBAnz590KdPn+LKh4iIiIiIiF7R9suRWBL8H0SOmJ+bNZb2qA0rEz6eRBWLUKvw9OxcpN8/CgB4evpz2Lb8GjK5Qs+ZUZF6SgUEBGDDhg35jtm0aRMCAgKKlBQRERERERG9PEkS+PbEXSx+oSAVUM0B3/epy4IUVThSVgbiT0zVFKQAIP3R38iM+1ePWVG2IhWlgoODER4enu+Y+/fvIyQkpCibJyIiIiIiopekUkuY/ectbLz4SCse6OeG+V1rwcigSF//iMosKTMFccfHIyPyjCYmUxjBrvVCGDvW12NmlO2VHt/LT2pqKgwNWYUnIiIiIiJ63VKVWZiy/zrOP0zQio9rXRlDG7pDJpPpJzEiPVFnxCPu+ASonoZpYjJDM9i3WQJj54Z6zIxyKnRR6sGDB1q/JyQk6MQAQK1W4+HDh9i1axe8vb1fOUEiIiIiIiLKW0yKEhP2hCIsJkUTM5DLMLtzDXSp6aTHzIj0Q536BLHHP0ZW0vOahdzICvbtV8DIvrYeM6MXFboo5e3tramuy2QyfPPNN/jmm2/yHC+EwOLFi189QyIiIiIiIsrVvbg0jNt9DVHJSk3M3EiBxd190cTTRn+JEelJVtIDxB4fB3VqlCamMHWEfcA3MLSuosfMKDeFLkoNGzYMMpkMQghs2LAB9evXh5+fn844hUIBOzs7BAQEoEuXLsWZKxEREREREf2/yxGJmLw3FEnKLE3M0dwI3/SuAx9HCz1mRqQfqqdhiD0+AVJGvCZmYFEJ9gErYWBRSY+ZUV4KXZQKCgrS/HNISAjeeecdjBs37nXkRERERERERPn463YsPjt0E5lqSROrbGeGb3vXgYuViR4zI9IPZcwVxAdPhqR6/hiroXUV2Ad8C4Wpgx4zo/wUqdH5vXv3ijsPIiIiIiIiKoTtlyOxJPg/iBwxPzdrLO1RG1YmfNkUVTxCCCRfWaNVkDKy94V9u+WQG1vpMTMqCN8JSkREREREVAZIksB3J+9h8QsFqYBqDvi+T10WpKjCkslksG01HwbW3gAAY5cmsA/4jgWpMqBQM6UCAgIgk8mwfv16uLu7IyAgoFAbl8lkOHbs2CslSEREREREVNGp1BK+PBKGgzejteKBfm6Y1KYK5HKZnjIjKh0UJjZwaP8tkkPXw7rheMgURvpOiQqhUEWp4OBgyGQypKWlaX4vjOy39REREREREVHRpCqzMGX/dZx/mKAVH9e6MoY2dOf3LqqQhJQFmVy7pKEwc4JNkyl6yoiKolBFKUmS8v2diIiIiIiIil9MihIT9oQiLOZ5rxwDuQyzO9dAl5pOesyMSD+EEEi+9iMyY67Cvt0yzogq49hTioiIiIiIqBS6F5eGkdsuaxWkzI0U+LZ3XRakqEISQkLixWVI/vdnKJ9cQPypWRCSWt9p0StgUYqIiIiIiKiUuRKZiPe2X0ZUslITczQ3wo/966OJp43+EiPSEyGpkXD2S6SG7dDEMh79DWXUOT1mRa+qUI/vERERERERUck4/l8sZh28iUz187Yple3M8G3vOnCxMtFjZkT6IdSZiD/1GTIehTwPyuSwaTodJpX89ZcYvTIWpYiIiIiIiEqJ7ZcjsST4P4gcMT83ayztURtWJoZ6y4tIXyRVGuL/ngrlkwuamExuANsWX8DUM0CPmVFxYFGKiIiIiIhIzyRJ4PvT4dhw4aFWPKCaA77sUhNGBuy8QhWPpExCXPBEZMaFamIyhTHsWi+ESaXmesyMiguLUkRERERERHqkUkv48kgYDt6M1ooH+rlhUpsqkMtlesqMSH/U6bGI+2scVIl3NTG5oQXs2i2FsWN9PWZGxYlFKSIiIiIiIj1JVWZhyv7rOP8wQSs+rnVlDG3oDpmMBSmqeLJSIhH311hkpURqYnITOzi0XwFD2+p6zIyKG4tSREREREREehCTosSEPaEIi0nRxAzkMszuXANdajrpMTMi/VEl3kXcX+OhTo/RxBRmznAI+A4GVp56zIxeh1d6MPn333/HgAEDUK9ePVSrVk0Tv3nzJhYtWoSIiIhXTpCIiIiIiKi8uReXhpHbLmsVpMyNFPi2d10WpKhCS7r0nVZBysDKE46dfmBBqpwq0kwpSZIwaNAg7Ny5EwBgamqK9PR0zXJbW1vMnDkTarUa06dPL55MiYiIiIiIyoErkYmYtCcUScosTczR3Ajf9K4DH0cLPWZGpH+2/nMQe/RDqBLvwdC2Ouzbr4DCxE7fadFrUqSZUsuXL8eOHTswatQoPH36FJ988onWcmdnZ7Ru3Rp//PFHsSRJRERERERUHhz/LxYf7bqmVZCqbGeGnwP9WJAiAiA3toZ9+29hVrkLHDqsYkGqnCtSUSooKAhNmjTBqlWrYGVllWvzvWrVquHevXuvnCAREREREVF5sP1yJD7dfx2ZakkT83OzxroB9eFiZaLHzIj0RwhJJ6Ywc4St/xzIjVioLe+KVJT677//0Lp163zH2NvbIy4urkhJERERERERlReSJPDdyXtYHPwfRI54QDUHfN+nLqxMDPWWG5E+pYbtRNxf4yCylPpOhfSkSEUpU1NTJCYm5jvm/v37sLGxKcrmiYiIiIiIygWVWsKcP29hw4WHWvFAPzfM71oLRgav9O4pojJJCIHk0PVIuLAEyicXEH9qFoSUVfCKVO4U6ROwQYMGOHz4MDIyMnJdHh8fj0OHDqF58+avlBwREREREVFZlarMwvjd/+LgzWit+LjWlTG5bRXI5bptUIjKOyEEki5/j6QrqzWxjIgTSH/wlx6zIn0pUlFq3LhxePToEfr164dHjx5pLbtz5w769OmDxMREjBs3rliSJCIiIiIiKktiUpT4YOdVnH+YoIkZyGX4sktNvN3II9e+vETlnRASEs8vQMqNTVpxq/ofwtSrk56yIn0yKMpKvXr1wqeffoqFCxfCy8sL5ubmAAAnJyfExcVBCIHPPvsMAQEBxZosERERERFRaXcvLg3jdl9DVPLzPjnmRgos7u6LJp42+kuMSI+EWoWnZ+ci/f5RrbhN409gXv0tPWVF+lbkB5jnz5+Pw4cPo3v37jAzM4NCoYAkSejSpQsOHjyIuXPnFmeeREREREREpd6VyES8t/2yVkHK0dwIP/avz4IUVVhSVgbiT0zVLkjJ5LD1n82CVAVXpJlS2Tp16oROnTjFjoiIiIiI6Ph/sZh18CYy1c9fcV/Zzgzf9q4DFysTPWZGpD9SZgriQiYjM+aKJiZTGMG25dcwdW+tx8yoNHilohQREREREREB2y9HYknwfxA5Yn5u1ljaozasTAz1lheRPqkzniLu+HionoZpYjJDM9i3WQxj50Z6zIxKiyIVpR48eFDosZ6enkXZBRERERERUaknSQLfnw7HhgsPteIB1RzwZZeaMDIocscUojJNnfoEscc/RlbS8/qB3MgK9u1XwMi+th4zo9KkSEUpb2/vQr0tQiaTISsrqyi7ICIiIiIiKtVUaglfHgnDwZvRWvFAPzdMalMFcjnfsEcVV+Klb7QKUgpTB9gHfAtD6yp6zIpKmyIVpYYNG5ZrUSoxMRFXrlzBvXv30LZtW3h7e79qfkRERERERKVOqjILU/Zfx/mHCVrxca0rY2hD90L9T3yi8symyTRkJYZDlXgXBhaVYB/wHQws3PSdFpUyRSpKBQUF5blMCIGlS5di0aJF+Omnn4qaFxERERERUakUk6LEhD2hCItJ0cQM5DLM7lwDXWo66TEzotJDbmwF+4BvkXB+EWyaTIXC1EHfKVEpVOwPOMtkMnzyySfw9fXFlClTinvzREREREREenMvLg0jt13WKkiZGynwbe+6LEhRhSaE0IkpTB1g32YRC1KUp9fWda9x48b466+/XtfmiYiIiIiIStSVyES8t/0yopKVmpijuRF+7F8fTTxt9JcYkZ6lP/gLccfHQWQpCx5MlMNrK0rduXOHTc6JiIiIiKhcOP5fLD7adQ1JyuffcSrbmeHnQD/4OFroMTMi/Uq9sw/xp2ZBGXUe8SdnQEisA1DhFamnVF4kSUJERASCgoKwZ88edOjQoTg3T0REREREVOK2X47EkuD/kPPhJD83ayztURtWJoZ6y4tI31JubkHi/77V/J4ReQppd/fBvFofPWZFZUmRilJyuTzft0kIIWBra4ulS5cWOTEiIiIiIiJ9kiSB70+HY8OFh1rxgGoO+LJLTRgZvLYHT4hKNSHE/7F33+FRlGsbwO+Z7clmN5VASAKhhtB7lSYgqBQ9x/7JsWEXAcXeUI+oFAHL8RwVy9EjYgO7UoxIUxBCCb0HkpC+m022z3x/bNhkCCWEJJNy/66LC/aZmc2zsMPu3vu+76B4xzso3rlYUQ/t8HeEtJ2oUlfUEFUrlBo6dOgZQylRFBEREYG+ffvi1ltvRbNmXOiPiIiIiIgaHq9fwgsr9uHHPTmK+nU9WmLG0DYQxbN/SU/UmMmyBNtfr6Fk3+eKeliX2xDWdco5B7AQna5aoVRqamoNt0FERERERFQ/lLh9mPndLmzKKFLUp16ShP/rFc8P3dRkyZIfRX+8gNLDPynq1l5TYU6+UaWuqCGr0TWliIiIiIiIGrJchxvTlqdjX64jWNOKAp4d0xFjkzkThJou2e9Bwbqn4Tr+W3lREBHe73GEth2vXmPUoDGUIiIiIiIiAnA4vxRTl+1AdnH5Ze1D9RrMubIz+iaGq9cYkcokbykK1jwC98nNwZogahEx6HmYEkeq2Bk1dFUKpUaOrN6TTBAErFq1qlrHEhERERER1ZVtmTbMWJ4Ou7v8cvYxoXosnNQF7WPMKnZGpC7JbUd+6nR48tODNUFjQOQlr8AYN0DFzqgxqFIoVd01pDjXmoiIiIiI6rtfD+ThqR/3wOOXgrWkyBAsmtQFzS1GFTsjUp9t6yJFICXqzIgcPg+GmO4qdkWNRZVCKUmSzr8TERERERFRA7M0LRNzUw9ArlDr0dKKeeNTYDHqVOuLqL6w9pwKb8EeeIsOQDRGInrEAugiOqjdFjUSotoNEBERERER1TVJkvH62sOYc1ogNbJdNN68qisDKaIyosGCqJGLYGjeFzGj3mYgRTWKC50TEREREVGT4vVLeGHFPvy4J0dRv65HS8wY2gaiyGVIiCrSGCMRPfJ1tdugRuiiQimXy4VNmzYhMzMTbrf7jPtMnjz5Yn4EERERERFRjSlx+zDzu13YlFGkqE+9JAn/1yue6+JSk+Y+uQWO3f9FxJDZELVcT41qX7VDqTfffBNPP/00bDbbGbfLsgxBEBhKERERERFRvZDrcGPa8nTsy3UEa1pRwLNjOmJscjMVOyNSn/P47yhc9yRkvweFax9H5CWvQtBwGivVrmqtKfXVV1/hgQceQEJCAubOnQtZljFx4kS89NJLGDt2LGRZxt/+9jcsXry4pvslIiIiIiK6YIfzS3HbZ2mKQCpUr8GiSV0ZSFGTV3r4RxT8/ihkvwcA4MrcAMfeJSp3RU1BtUKpBQsWoFmzZtiwYQOmT58OAOjRowceffRRfP/99/j444+xbNkytGrV6oLve82aNRg/fjzi4uIgCAKWLVt2zv1TU1MhCEKlX9nZ2dV5aERERERE1Mhsy7ThjqVpyC4uX3IkJlSPd67pjr6J4eo1RlQPlOz7AoUbZgGyFKyZEi+FueP1KnZFTUW1Qqnt27djwoQJCAkJCdb8fn/wzzfeeCNGjhyJ559//oLvu6SkBN27d8ebb755Qcft3bsXWVlZwV/NmvHbDiIiIiKipu7XA3m498sdsLt9wVpSZAgWX9cD7WPMKnZGpC5ZllGc/gGKNs9V1EPaTkDE4Bc4dY/qRLXWlPJ6vYiJiQneNplMKCoqUuzTvXt3/Oc//7ng+x43bhzGjRt3wcc1a9YM4eHhF3wcERERERE1TkvTMjE39QDkCrUeLa2YNz4FFiM/cFPTJcsy7GlvwLH7E0Xd3OkmWHrczwX/qc5UK5SKi4tDVlZW8HarVq2wdetWxT5Hjx6FVntRF/e7ID169IDb7UaXLl3w3HPPYfDgwWfd1+12K64WaLfbAQCSJEGSpLMd1iBIkgRZlhv84yBSA88fakr4fCeqHp47DYMkyXhrwxH8d/MJRX1E+yg8P6Yj9FqR/4a1iOdJ/SbLEmybXoHz0LeKeljXOxGa8g/IsgxZls9yNNWmxnTuVPUxVCs16tu3L7Zs2RK8PXbsWCxcuBCzZ8/GhAkTsHbtWnz11VcYNWpUde7+grRo0QJvv/02+vTpA7fbjXfffRfDhw/HH3/8gV69ep3xmNmzZ2PWrFmV6rm5uXC5XLXdcq2SJAk2mw2yLEMUqzU7k6jJ4vlDTQmf70TVw3On/vP6Jby2IQurDxcp6hOTI3FX70gUFeSp01gTwvOk/pIlL3w75sGf/buirut0D0pjLkdpbq5KnRHQuM6d4uLiKu0nyNWIQL/++ms88cQT+PHHH9G6dWvk5uaiT58+OH78OIDAUECr1Yo1a9aga9euF3r35c0JAr7++mtMmjTpgo4bNmwYEhMT8d///veM2880UiohIQGFhYWwWCzV7rc+kCQJubm5iImJafBPYqK6xvOHmhI+34mqh+dO/Vbi8eHR73dj0zGbov7AkNa4qVdLTkmqIzxP6ifZ50LhuifgztpYXhREhPd/CqbWY9VrjIIa07ljt9sREREBm812zpylyiOllixZgquvvhp6vR5XXXUVrrrqquC2mJgYpKWl4d1338WhQ4fQqlUr3HzzzWjZsuXFPYpq6tevH9auXXvW7QaDAQaDoVJdFMUG/w8PBMK8xvJYiOoazx9qSvh8J6oenjv1U67DjWnL07Ev1wGUZU9aUcCzYzpibDIvglTXeJ7UP7Yd/1YEUoJGj4jB/4Qp/hIVu6LTNZZzp6r9VzmUuvHGGxEZGYmbbroJt912G7p3767YHhERgZkzZ15Yl7UkLS0NLVq0ULsNIiIiIiKqA4fzSzF12Q5kF5fPhgjVazDnys7omxiuXmNE9UhY1zvgztkCb+F+CLoQRA2dA0Nsb7XboiauytHbDTfcAKfTiddffx29evVC37598fbbb8Nms53/4AvgcDiQlpaGtLQ0AMDhw4eRlpaGY8eOAQAef/xxTJ48Obj/ggULsHz5chw4cAA7d+7EtGnTsHr1atx333012hcREREREdU/2zJtuGNpmiKQignV451rujOQIqpA1IchasRC6KO7Inrk6wykqF6ocij1ySefICsrC2+++SZ69eqFv/76C/fddx/i4uJw8803IzU1tUYa2rx5M3r27ImePXsCAGbMmIGePXvimWeeAQBkZWUFAyoA8Hg8eOihh9C1a1cMGzYM27Ztw8qVK3HppZfWSD9ERERERFQ//XogD/d+uQN2ty9YS4oMweLreqB9jFnFzojqJ40xEtGj/wN9VGe1WyECUM2FzgEgPT0d7733Hj755BPk5uZCEAQkJSXhtttuwz/+8Q/V1pOqDrvdDqvVet4FuBoCSZKQk5ODZs2aNfg5qER1jecPNSV8vhNVD8+d+mNpWibmph5AxQ8zPVpaMW98CixGnWp9Ec+T+sBbuA/2He8hYtBzELUmtduhKmpM505Vc5ZqP8rOnTtj/vz5OHHiBL744guMGzcOR48exVNPPYXWrVvjiiuuwFdffVXduyciIiIiIqpEkmS8vvYw5pwWSI1sF403r+rKQIqaPHfuNuStvBeu47+h4PfHIPs9ardEdFYXHb1ptVpcffXV+O6775CRkYGXX34Zbdu2xY8//ohrr722JnokIiIiIiKC1y/huV/24qPNGYr6dT1aYvblnaDXNuyRBUQXy5W5Efmrp0LyOgAA7qw/ULzzfZW7Ijq7Kl99ryoKCwuRk5ODoqIiAEA1ZwYSEREREREplLh9mPndLmzKKFLUp16ShP/rFQ9BENRpjKiecB5bjcL1z0CWytdYMzTvC3PKzSp2RXRuFx1KORwOfPrpp1i8eDH+/PNPyLKMkJAQTJ48GbfffntN9EhERERERE1YrsONacvTsS/XEaxpRQHPjumIscnNVOyMqH4oOfgtiv6cDchSsGaMH4rIwS9C0OhV7Izo3KodSv32229YvHgxvvzySzidTsiyjL59++L222/HDTfcgLCwsJrsk4iIiIiImqDD+aWYumwHsovdwVqoXoM5V3ZG38Rw9Rojqicce/4H25ZFilpI0jiE938KgqhRqSuiqrmgUOrEiRP44IMP8MEHH+DQoUOQZRlRUVGYMmUKbr/9dnTp0qW2+iQiIiIioiZmW6YNM5anw+4un44UE6rHwkld0D7GrGJnROqTZRnF2/+D4nTlmlGhHa6Btfd0CALXWKP6r8qh1Lhx47By5Ur4/X4IgoBRo0bh9ttvx6RJk6DXczggERERERHVnF8P5OGpH/fA4y+fjpQUGYJFk7qgucWoYmdE6pNlCba/5qNk3xeKeliX2xHW9Q6usUYNRpVDqZ9//hmJiYm49dZbceuttyIxMbE2+yIiIiIioiZqaVom5qYeQMXLJvVoacW88SmwGHWq9UVUH8iSH0V/vIDSwz8p6tZeD8KcfINKXRFVzwWFUqNGjWLiSkREREREtUKSZLy5/gg+2pyhqI9sF40XxiZDr+V0JKLinYuVgZQgIrzf4whtO169poiqqcqh1OjRo2uzDyIiIiIiasK8fgkvrNiHH/fkKOrX9WiJGUPbQBT55TgRAJiTb4Arcy28BXshiFpEDH4BpoQRardFVC38qoGIiIiIiFRV4vbhwWU7KwVSUy9JwkPDGEgRVSTqzYgavgC6yGREDp3LQIoatAu6+h4REREREVFNynW4MW15OvblOoI1rSjg2TEdMTa5mYqdEdVfGmMEYi5bzCvsUYPHZzAREREREanicH4pbvssTRFIheo1WDSpKwMpIgA+RyYK1j0NyVtaaRsDKWoMOFKKiIiIiIjq3LZMG2YsT4fd7QvWYkL1WDipC9rHmFXsjKh+8NoOIX/1g/A7cyG5ixA1bB4EjV7ttohqFKNVIiIiIiKqU78eyMO9X+5QBFJJkSFYfF0PBlJEADz5u5C38h74nbkAAHf2Jti3vaVyV0Q1r0ZGShUUFKCkpAQJCQk1cXdERERERNRILU3LxNzUA5Ar1Hq0tGLe+BRYjDrV+iKqL9wntyB/zcOQK0zZ00V0gDllsopdEdWOao+UstlsePDBBxEbG4uYmBgkJSUFt/3xxx+4/PLL8ddff9VIk0RERERE1LBJkozX1x7GnNMCqZHtovHmVV0ZSBEBcB7/Hfmp0xSBlD6mO6IvfQsaY6SKnRHVjmqNlCooKMCgQYOwb98+9OrVCzExMdi9e3dwe7du3bBu3Tp88skn6N27d401S0REREREDY/XL+GFFfvw454cRf26Hi0xY2gbiKKgUmdE9Ufp4Z9QuPF5QJaCNWPcQEQMmQ1Ra1SxM6LaU62RUs899xz27duHJUuWYPPmzbjmmmsU200mE4YNG4bVq1fXSJNERERERNQwlbh9eHDZzkqB1ANDkvDQMAZSRABQsu8LFG6cpQikTImXIvKSVxlIUaNWrZFS33zzDa688kpce+21Z92ndevWWL9+fbUbIyIiIiKihi3X4ca05enYl+sI1rSigGfHdMTY5GYqdkZUP8iyDMeuj2Df9i9FPbTdRFj7PgpB4LXJqHGr1jM8KysLKSkp59zHYDCgpKSkWk0REREREVHDdji/FLd9lqYIpEJ0Giyc1IWBFBECgZQ97c1KgZS50//B2vcxBlLUJFRrpFRUVBQyMjLOuc+ePXvQokWLajVFREREREQN17ZMG2YsT4fd7QvWokP1WDipCzrEmFXsjKj+KNm7BI7dHytqlu73wJwyGYLAaa3UNFQreh06dCiWL1+O48ePn3H7rl278NNPP2HUqFEX1RwRERERETUsvx7Iw71f7lAEUkmRIVh8XQ8GUkQVhLSdAH1Up8ANQUB435kI6/wPBlLUpFQrlHryySfh9/sxePBgfPLJJ8jLywMA7N69G++99x5GjhwJg8GAmTNn1mizRERERERUfy1Ny8Sj3+2Cx1++WHP3OCvevbY7Wli4WDNRRaIuFJHDXoMuvB0iBjyL0PZ/U7slojpXrel7Xbt2xWeffYabb74ZkydPBhCYD9ulSxfIsoywsDAsXboU7du3r9FmiYiIiIio/pEkGW+uP4KPNiuX+BjRLhovjk2GXsu1cYjORGMMR8zYDyGIGrVbIVJFtUIpAJgwYQIOHz6MDz/8EH/88QcKCgpgsVjQv39/3HrrrYiOjq7JPomIiIiIqB7y+iW8sGIfftyTo6hf2z0ODw1rC1HkVCQiv6sA9m3/grXXdIi6EMU2BlLUlFU7lAKAyMhITJ8+vaZ6ISIiIiKiBqTE7cPM73ZhU0aRov7AkCTc3Duea+MQAfCXnETe6vvhK86A35GFqOHzIWj0ardFVC9wHC0REREREV2wXIcbd36xXRFIaUUBL4xNxuQ+CQykiAD47MeQu/Iu+IoDU1vdJzfD9tc8lbsiqj+qNVLqo48+Ou8+oijCYrGgY8eO6NixY3V+DBERERER1UOH80sxddkOZBe7g7UQnQZzxqegX2KEip0R1R/ewn3I+3UaJFdBsKY1x8GcMlnFrojql2qFUrfccssFffORnJyM119/HSNHjqzOjyMiIiIionpiW6YNM5anw+72BWvRoXosnNQFHWLMKnZGVH+4c7ehIPUhSF5HsKaztkHUyEXQmLj+MtEp1Qql3n//fXz11Vf49ttvMWbMGAwePBixsbE4efIk1q1bh19++QUTJkzA0KFDsWXLFnz22We4/PLL8fvvv6Nv3741/RiIiIiIiKgO/HogD0/9uAcevxSsJUWGYOGkLmhhMarYGVH94crciILfH4XsLx9JqI9KQdTwBRANFhU7I6p/qhVKWa1W/PLLL1i1ahVGjBhRaXtqaiouv/xy3HbbbZgxYwamTJmCSy+9FC+//DK+/PLLi26aiIiIiIjq1tK0TMxNPQC5Qq17nBXzJ6TAYtSp1hdRfeI8thqF65+BLJWPJDTE9kHk0FcrXXWPiKq50PlLL72Ea6+99oyBFAAMHz4c11xzDV588UUAwLBhwzB27FisXbu2+p0SEREREVGdk2UZb6w9jDmnBVIj2kXjrau7MpAiKlNy8FsUrHtKEUgZ44ciavh8BlJEZ1GtUCo9PR3x8fHn3Cc+Ph7p6enB2ykpKSgqKqrOjyMiIiIiIhV4/RKe/XkvPtycoahf2z0OL1/eCXotL+ZNBACOPZ+i6I9/AnL51NaQpLGIHDIbgkavYmdE9Vu1pu+ZzWb8/vvv59zn999/h9lcvtBhSUkJwsLCqvPjiIiIiIiojpW4fZj53S5syihS1B8YkoSbe8df0IWPiBqz0kPfw7ZloaIW2uHvsPaeAUFgcEt0LtU6QyZOnIh169bh3nvvRW5urmJbXl4e7rvvPqxbtw4TJ04M1tPS0tC2bduL65aIiIiIiGpdXokbd36xXRFIaUUBL4xNxuQ+CQykiCowJoyAPqpT8HZYl9tg7f0QAymiKqjWSKnZs2dj3bp1ePvtt/H++++jXbt2aNasGXJycnDgwAG43W4kJydj9uzZAIDs7Gw4nU7ccsstNdk7ERERERHVsCMFpZj69U5kFbuCtRCdBnPGp6BfYoSKnRHVT6IuBFHDFyBv5T0IaTse5uQb1G6JqMGoVigVFRWFP//8Ey+//DI++eQTpKenB9ePat26NW666SY8+uijwel7zZs3x5YtW2quayIiIiIiqnHbMm2YsTwddnf5Qs3RoXosnNQFHWLM5ziSqGkTDVbEjP2A60cRXaBqhVIAEBoaihdeeAEvvPACiouLYbfbYbFYuG4UEREREVED9OuBPDz14x54/OULNSdFhmDhpC5oYTGq2BlR/SF5S1G8/d8I63ZXpSvqMZAiunDVDqUqCgsLYxhFRERERNRALU3LxNzUA5Ar1LrHWTF/QgosRp1qfRHVJ5LbjvzU6fDkp8NbuB9Rw1+DoDWo3RZRg8aV14iIiIiImihZlvHG2sOYc1ogNaJdNN66uisDKaIyfmce8lbeDU9+YNkad84WFG2arXJXRA1ftUOpjIwM3HXXXWjbti1MJhM0Gk2lX1ptjQzEIiIiIiKiGub1S3j25734cHOGon5t9zi8fHkn6LX8/poIAHyOTOStuBNe26FgTTRGwpx8o4pdETUO1UqNDh06hP79+6OwsBCdO3eG2+1Gq1atYDQacejQIXi9XnTv3h3h4eE13C4REREREV2sErcPM7/bhU0ZRYr6A0OScHPveAiCoE5jRPWM13YI+asfhN+ZG6xpQpsjesQiaC2JKnZG1DhU6+uPWbNmwWazYdWqVdi2bRsA4NZbb8Xu3btx5MgRTJgwASUlJfjiiy9qtFkiIiIiIro4eSVu3PnFdkUgpRUFvDA2GZP7JDCQIirjyd+FvJX3KAIprSURMaP+zUCKqIZUK5RauXIlLr/8cgwbNixYk+XALPQWLVrgs88+AwA88cQTNdAiERERERHVhCMFpbhtyTbsy3UEayE6DRZO6oKxyc1U7IyofnGf3IK81fdDctuCNV1EB0SPehua0FgVOyNqXKo1fS8vLw/Jycnld6LVorS0NHjbYDBg9OjRWLZs2UU3SEREREREF29bpg0zlqfD7vYFa9Gheiyc1AUdYswqdkZUvziP/47CdU9C9nuCNX1Md0QNmwdRz3OFqCZVK5SKjo5GSUmJ4vaRI0eUd6zVoqio6GJ6IyIiIiKiGvDrgTw89eMeePxSsJYUGYKFk7qghcWoYmdE9Uvp4Z9QuPF5QC4/V4xxAxExZDZELc8VoppWrel77du3x8GDB4O3+/Xrh59//hmHDgWuRpCbm4svvvgCbdu2rZkuiYiIiIioWpamZeLR73YpAqnucVa8e213BlJEFTiPr0HhxlmKQMqUeCkiL3mVgRRRLalWKDVu3Dj8+uuvwZFQ06ZNQ3FxMbp164a+ffuiQ4cOyM7OxgMPPFCTvRIRERERURXJsow31h7GnNQDkCvUR7SLxltXd4XFqFOtN6L6yBDbB/rIlODt0HYTETH4BQganitEtaVaodQ999yD1NRUaDQaAMDw4cOxZMkStGrVCjt37kRsbCwWLVqEKVOm1GizRERERER0fl6/hGd/3osPN2co6td2j8PLl3eCXlutjwFEjZqoC0HU8Negs7aBudP/wdr3MQgCzxWi2lStNaUsFgv69++vqF1zzTW45ppraqQpIiIiIiKqnhK3DzO/24VNGUWK+gNDknBz73gIgqBOY0QNgGiwIHrMuxC0Jp4rRHWgWrHvyJEj8fTTT9d0L0REREREdBHySty484vtikBKKwp4YWwyJvdJ4IdsojKy3wv7tn9D8pZU2ibqQniuENWRaoVSf/zxB/x+f033QkRERERE1XSkoBS3LdmGfbmOYC1Ep8HCSV0wNrmZip0R1S+Sz4WC3x9Bcfr7KPjtYcg+t9otETVZ1QqlkpOTcfTo0ZruhYiIiIiIqmFbpg23f5aGrGJXsBYdqsc713ZHv8QIFTsjql8kjwP5vz4IV+YGAIA7ZysKNzyrcldETVe1QqkHHngAy5cvx65du2q6HyIiIiIiugC/HsjDvV/ugN3tC9aSIkOw+Loe6BBjVrEzovrF7ypE3qp74cndFqwJuhCEduDayERqqdZC523atMHw4cMxYMAA3HXXXejbty9iY2PPOO926NChF90kERERERFVtjQtE3NTD0CuUOseZ8X8CSmwGHkZe6JT/CUnkffrA/DZjwVrot6CqBELoI9KUbEzoqatWqHU8OHDIQgCZFnGvHnzzrkIHNeeIiIiIiKqWbIs4811R/Dh5gxFfUS7aLw4Nhl6LS9jT3SKz34Meb9Ohb8kO1jTmKIRNXIRdNY2KnZGRNUKpZ555hlejYCIiIiISAVev4QXVuzDj3tyFPVru8fhoWFtIYp8n050irdwH/J+nQbJVRCsac1xiBr5OrTmlip2RkRANUOp5557robbICIiIiKi8ylx+zDzu13YlFGkqD8wJAk3947nF8dEFbhzt6Eg9SFI3vIrUuqsSYga+To0pmgVOyOiU6oVShERERERUd3KK3HjwWXp2Jdb/gFbKwp4dkxHjE1upmJnRPWPK3MjCn5/FLLfHazpo1IQNfw1iAarip0RUUUXFUpt3boVn376Kfbs2YPS0lKsXLkSAHD06FH88ccfGDVqFCIjI2ukUSIiIiKipupIQSmmfr0TWcWuYC1Ep8Gc8SnolxihYmdE9Y/75BYUrHkYslR+RUpDbB9EDn0Voi5Exc6I6HTVDqUeeeQRzJs3D7IcuNZHxaHCsizjxhtvxLx58/Dggw9efJdERERERE3UtkwbZixPh91d/gE7OlSPhZO6oEOMWcXOiOonXVQn6CI7wZO3AwBgjB+KyMEvQtDoVe6MiE5XrctyvP/++5g7dy6uvPJKbN++HY8//rhie+vWrdGvXz988803NdIkEREREVFT9OuBPNz75Q5FIJUUGYLF1/VgIEV0FqLWhKhh86ALb4eQpLGIHDKbgRRRPVWtkVJvvfUWOnXqhC+//BJarRZ6feUTPDk5OTidj4iIiIiILszStEzMTT0AuUKte5wV8yekwGLUqdYXUUMgGiyIHvUvCLpQCEK1xmIQUR2o1tm5a9cujB49Glrt2TOt2NhY5OTknHU7ERERERFVJssy3lh7GHNOC6RGtIvGW1d3ZSBFVIEsSyje/TEkj6PSNlEfxkCKqJ6r1kgprVYLj8dzzn0yMzNhNnNIMRERERFRVXn9El5YsQ8/7lF+uXtt9zg8NKwtRFE4y5FETY8s+VH0x4soPfwjXMd/R9SIhRC1RrXbIqILUK3YuGvXrli9ejX8fv8Zt5+6El/v3r0vqjkiIiIioqaixO3Dg8t2VgqkHhiShIeHM5Aiqkj2e1Cw9gmUHv4RAODJ3YbCdU8FL8RFRA1DtUKp2267Dfv27cPdd98Nt9ut2Ga323HLLbcgOzsbU6ZMqZEmiYiIiIgas7wSN+78Yjs2ZRQFa1pRwAtjkzG5T4LiStdETZ3kLUV+6gy4jv8WrAmiFiFJl/NcIWpgqjV977bbbsPKlSvx3nvv4bPPPkN4eDgAoF+/fti9ezdKSkpwyy234O9//3tN9kpERERE1OgcKSjF1K93IqvYFayF6DSYMz4F/RIjVOyMqP6R3Hbkp06HJz89WBM0BkRe8gqMcQNU7IyIqqPaq77973//w7///W8kJSXhxIkTkGUZmzdvRmJiIv71r39h8eLFNdknEREREVGjsy3Thts/S1MEUtGherxzbXcGUkSn8TvzkLfybkUgJerMiBq5iIEUUQNVrZFSp0yZMgVTpkyB0+lEYWEhLBYLFzcnIiIiIqqCXw/k4akf98Djl4K1pMgQLJzUBS0sXKyZqCKfIxP5q++Hz5EZrInGSESPWABdRAcVOyOii1GtUMrhcCjCJ5PJBJPJVGNNERERERE1ZkvTMjE39QAqLsncPc6K+RNSYDHqVOuLqD7y2g4hf/WD8DtzgzVNSCyiR74OrSVRxc6I6GJVa/pebGwsbrrpJvz000+QJOn8BxAREREREWRZxhtrD2POaYHUiHbReOvqrgykiE7jyd+FvJX3KAIprSURMaP/w0CKGg2bx4lclwP57lIgzIR8dylyXQ7YPE61W6t11Rop1bZtW3z66adYsmQJYmJicMMNN+D//u//0Lt375ruj4iIiIioUfD6JbywYh9+3JOjqF/bPQ4PDWsLUeRVw4gq8hTsRt7q+yF7S4M1XUQHRI1YAI0xUsXOiGqWR/LjqpXvw+n3QPDL0Oq00AoafD3qVrVbq3XVGim1fft2pKWlYfr06dBoNFi4cCH69euHlJQUzJ49G8eOHavpPomIiIiIGqwStw8PLttZKZB6YEgSHh7OQIroTLSW1tCFtwve1sd0R/SlbzGQokbJL0vwSTKcfh/8sgy/3DRmpVX76nvdunXD3Llzcfz4cfz888+46aabcPz4cTz55JNo06YNhg8fjvfee68meyUiIiIianDySty484vt2JRRFKxpRQEvjE3G5D4JEAQGUkRnImpNiBo2D7qI9jDGDUTUiIUQ9bywFjVeeo0GMgCv5Fe7lTpT7VDqFEEQMHr0aHz00Uc4efIkPv74Y4wePRrr1q3DXXfdVRM9EhERERE1SEcKSnHbkm3Yl+sI1kJ0Giyc1AVjk5up2BlRwyDqwxA98g1EXvIqRC2vSkmNy1FHAe5d9yX8cvkqgwZRA62gUbGrulWtNaXOxufzwe12w+12cwF0IiIiImrStmXaMGN5OuxuX7AWHarHwkld0CGGoz2ITldy8BuYEkZA1Icp6qLBqlJHRLVnT1EOpm78GsVeFwrcgXXTNIIAWRCgFQVohIseQ9QgXHQo5ff78cMPP+Djjz/Gd999B5fLBVEUMWbMGNx888010SMRERERUYPy64E8PPXjHnj85V/UJkWGYOGkLmhh4WgPoopkWYY97U04dn+M0kPfBabpaU1qt0VUa2RZxis7VqPI44QAIM/lwH+H3QSrzgi/5IdG1AACoBcb/4ipaodSGzduxMcff4ylS5ciPz8fsiyjR48euPnmm3HjjTciNja2JvskIiIiImoQlqZlYm7qAcgVat3jrJg/IQUWo061vojqI1mWYNv0KkoOLAMAeHK3o/D3xxE5fD6EJjJShJoeQRDwcp8rcMfapTjpLMZHBzZjfr+JMGv1yMnJQVSzZhDFpvH8r9ajbN++PQYPHoy33noLBoMBM2fOxI4dO7BlyxZMnz79ogKpNWvWYPz48YiLi4MgCFi2bNl5j0lNTUWvXr1gMBjQrl07fPDBB9X++URERERE1SHLMt5YexhzTgukRrSLxltXd2UgRXQa2e9F4fpngoHUKcaWQxhIUaNk97iCf441heHNgVfjspYd8fqAq2HRN81RtNU607OzszF58mSsXLkSx44dw8svv4zOnTtX2s/tdl/wfZeUlKB79+548803q7T/4cOHccUVV2DEiBFIS0vDtGnTcMcdd+Dnn3++4J9NRERERFQdXr+EZ3/eiw83Zyjq13aPw8uXd4Jeyw/YRBVJPhcKfn8EzqMry4uCiIiBzyK0w9/Va4yolnx1ZAeuXvU+9tpygrVEcwRe6D0OJm3T/dKiWtP3cnJyYDKdfY7vli1b8N5772HJkiXIz8+/oPseN24cxo0bV+X93377bSQlJWHevHkAgE6dOmHt2rV47bXXcNlll13QzyYiIiIiulAlbh9mfrcLmzKKFPUHhiTh5t7xEARBncaI6inJ40D+mofhyUkL1gSNHhGD/wlT/CXqNUZUSz7cvwlv7l4HAHhg49d4Z/A1aGWOVLmr+qFaodSZAqmioiJ8/PHHeO+997B9+3bIsnzO4KqmbNiwAaNGjVLULrvsMkybNu2sx5y6QuApdrsdACBJUoO/aqAkSZBlucE/DiI18PyhpoTPd6LqOf3cySvxYNrydOzPLQnuo9EIeGZ0e4zt2AyyLEOW5bPdHVGjdK7XGL+rEAW/TYOvcH+wJmhNiLjkVRhie/N1iRodvyThj9xjkMsmdhe6S7HyxH7c2r5vpX0b0/uzqj6Gi7763sqVK/Hee+9h+fLlcLvdkGUZAwcOxK233orrrrvuYu/+vLKzsyutYRUbGwu73Q6n03nGYGz27NmYNWtWpXpubi5cLlelekMiSRJsNhtkWW4yC6MR1RSeP9SU8PlOVD2yLKOkJBBAHbd78OSqo8gp8Qa3m3QinrkkAT0jArMLiJqis73GyK5ceDY/BbnkeLAm6MKg6z4LNiEB4DlDjdTMVv3whMOOPcV5uDmxOy63JJ7xNaIxvT8rLi6u0n7VCqUyMjLw/vvv4/3338exY8cgyzJatmyJEydO4JZbbsHixYurc7d15vHHH8eMGTOCt+12OxISEhATEwOLxaJiZxdPkiQIgoCYmJgG/yQmqms8f6gp4fOd6MKVePwodvvglIzQGvQ4UFoEk8EIXdkA/KhQPRZMTEGHGLO6jRKp7EyvMb7iYyjY+BS0npOALrB+jmiMQuSIhdBZ26jZLlGN80p+bC/MQu+o+GDtX1HXYV3OYYyL73TW4xrT+zOjsWoLt1c5lPJ6vVi2bBnee+89rFq1Cn6/H6GhobjpppswefJkjBw5ElqtFlrtRQ++uiDNmzfHyZMnFbWTJ0/CYrGcdfqgwWCAwWCoVBdFscH/wwOBy0s2lsdCVNd4/lBTwuc7UdXlOtz4z8ajWLE3F8eLSlDqA67q0hyPjGyHV389AINWxMJJXdDC0jSvnkR0uoqvMV77UeSvuheSqyC4XWuOQ9TI16E1t1SxS6Ka5/J58djm77Ex9yhe7nMFhrdoBwAIN4bgisTKF4g7XWN5f1bV/qucIMXFxaGgoACCIGDEiBGYPHkyrr76aoSGhla7yZowcOBA/PDDD4raihUrMHDgQJU6IiIiIqLGxOH24T8bj2LZzmwUlHqQXeyFIAj4cHNgCtKdA1rhkjaRsBib7tWTiM5FExILrSURnrJQSmdNQtTI16ExRavcGVHNcvt9ePCPZdiafwIA8ORfP+K1/hPRLyZR5c7qrypHb/n5+RAEAdOnT8f//vc/3HzzzbUSSDkcDqSlpSEtLQ0AcPjwYaSlpeHYsWMAAlPvJk+eHNz/7rvvxqFDh/DII49gz549eOutt7B06VJMnz69xnsjIiIioqan2O3DL3tzcbLYjZPFHsW2X/blomdLKwMponMQtUZEDZ0LXUQH6KNSED3qbQZS1CjpRQ3ahEUFb+tEESKvwHpOVQ6lbrnlFphMJsyfPx/x8fGYMGECPv/8c3g8nvMffAE2b96Mnj17omfPngCAGTNmoGfPnnjmmWcAAFlZWcGACgCSkpLw/fffY8WKFejevTvmzZuHd999F5dddlmN9kVERERETVNBqQd7ch0oKPUq6hEhOoQbdbC7vWc5kqjp8buKznhb1JsRNWIhoka+AdFgrfvGiOqAIAiY2XU4xrTsCIvOgDcH/g19ohPUbqteq/L0vcWLF2PRokVYsmQJ3nvvPXz33Xf4/vvvYbFYcO211+Lmm2+ukYaGDx9+zsvmfvDBB2c8ZuvWrTXy84mIiIiITsmyueD1yxCh/Ka7mVmPqFA9QvUaWDlKiggA4HNkojj9A4R2uBZGfRik0mwUp3+AsM63QGuOg8YYoXaLRDXumKMQqdkHMbldHwCAKIh4rucYZDuLER8arm5zDcAFrZxlNptxxx13YMOGDUhPT8e0adOg1+vxzjvvYNiwYRAEAXv37sXRo0drq18iIiIiojqRdsKGyUu2YkeWHVd3bQ4AEAQgLkyHqBA9AGBMhxiEGer2Qj9E9ZHfVYTi9A/gSP8A+Svvhs6+DQVrn0LpwW9QnP5BpRFURI3BPlsupqz7HG/sWosP928K1rWihoFUFVV7OfdOnTph3rx5OHHiBJYuXYoxY8ZAEAT8/vvvaNu2LS699FL897//rcleiYiIiIjqxPe7TuLer3agyOnFF9uzcGOveNzWLwGdY80I1WsQqtfgqi7NceeAVjAzlCKCaLAgtN1EaMMS4T65GTnfXgPn0Z+gi+qMsM63QGMMV7tFohpl97hw7/ovUOguBQC8uXsdfj6xV+WuGh5BPtdcuQt0/PhxvP/++/jggw9w+PBhCIIAv99fU3dfa+x2O6xWK2w2GywWi9rtXBRJkpCTk4NmzZo1+EtIEtU1nj/UlPD5TnRmkiTjrfVH8OHmDEV9WJsoPDayHTx+GQUlLkSGGmExahlIEQGQfW4UbngWgt4CQ0wP5PxwI2RZhsYUjdgJX8DQrKfaLRLViqWH0zB3RyoAoHNEcyzsPwkWvbHa99eY3p9VNWep0UcZHx+Pp59+GgcPHsSKFStw/fXX1+TdExERERHVGqfXj0e/310pkBrdIQb/vDwZ0WYDmofpEavzonmYnoEUEQC/qxB5q++DrzQXpoSRsP01HwAgGKOhCYmBbesb8DkyVe6SqOZUHNdzbVIP3Jk8EH1jEvDmwKsvKpBqqmoterv00kvxySef1NbdExERERHVmJPFbtyxdBtSD+Yp6lMGtMI/xyXDoNUEaw1hJgBRXfAVZyBvxRR48nbCnHwDHHs+gTvnL5iSxqHZlZ9BF9UZ3vx0rilFjcayozvx2Obv4ZekYO329v2wsP8khGj1KnbWcPHrHSIiIiJq0tKzi/HQN+nIL/UEa3qNiGfHdMCYjs1U7Iyo/vLkbkf+mkcguYsAAI49n8LS7S6IxihYut2JEn8oIge/ELz6HteUoobuvwf+wuu7fgcAvLBtBZ7pMRqiIEIQBGgFzXmOprNp2JMUiYiIiIguwi97c3Dn59sUgVRUiB7/uaY7Aymis3Bm/Iq81fcHAykAkD02aC2tYO01DWJIczidToghzWHpfi+05jj1miWqAdmldvxn74bg7R8ydiM166CKHTUeDKWIiIiIqMmRZRn/2XgUT/64Bx5/+TSMDjFmfHRDT3RuHqZid0T1l2PPEhSsfQKyv8LIwuguiB79DrShsZVGRHGEFDUGzUMseKXPldCWLT5+R8cBGNGincpdNQ6cvkdERERETYrb58esX/Zhxb5cRX1Y2yi8MDYZJh2nYRCdSenRFbBtWaComRKGI2LgLAhagzpNEdUSn+RHqc8bXLx8UGxrPN9rLHJdJbihDa8oWVMYShERERFRk5Ff4sHD36ZjZ3axov6PPgm4d1BriKKgUmdE9Z8pfjhKm/eFO3sTAMCcfD0sPadCEDgBhxoXt9+Hxzd/j5MuB94e9DeE6QLB1Ki4Dip31vjwfw8iIiIiahL25Trwj0+3KgIprSjg2TEdcf+QJAZSROchaHSIHDIbuoj2sPaaBmuvaQykqNEp8boxdePXWHvyMPbbcjH9j2/g8nnVbqvR4kgpIiIiImr01hzMx1M/7YHT6w/Wwk06zLkyBT1aWlXsjKj+kmUZgqAMa0W9GTFjFkPQ6FTqiqh22b1uZJQUBW/vt+fiYHE+Okc0V6+pRoyxNhERERE1WrIs46PNGXj423RFINUmMgQfXN+DgRTRWXhytyPvl9vhdxVU2sZAihqzFiEWvDHwalj1RoTpDHhjwNUMpGoRR0oRERERUaPk8UmYvXo/vtt1UlEf2DoSL41LhtnAt8JEZ+LM+BWF65+F7Peg4LeHEHXpWxC1JrXbIqo1GY4ieCQf2lqiAQBtwqKwcMAk6EUt2pXVqHbwlZiIiIiIGp0ipxczv9uFtBM2Rf2Gni3x4CVtoOH6UURn5NizBLatCwFZBgB48nfDkf4BLN3vUbkzotqx35aL+zd+DQHAu0OuRXxoOAAgJZyjo+oCp+8RERERUaNyKL8E//h0qyKQ0ggCHr+0PWYMa8tAiugMZFmCbcsC2LYsCAZSAGBKGI6wzrep1xhRLdpny8Xd679AobsUBe5S3LfhK+S6HGq31aRwpBQRERERNRrrjxTgiR92o8RTvn5UmEGLV65IQd/EcPUaI6rHZJ8bhRuehTMjVVE3J18PS8+pvMIeNVotQyxoGWrFnqIcAEC43gS9qFG5q6aFoRQRERERNXiyLGPptkzM/+0QpAqjPBLDTXhtYmckRoSo2B1R/eV3FaJgzSPw5O0oLwoCrD0fhDn5evUaI6oDoToDFvW/ClPWLUW0MRTz+k1AiFavdltNCkMpIiIiImrQfH4Jc387iC+3ZynqfRLC8coVnWAx8kphRGfiK85Afup0+IqPB2uCRo+IQbNgShihYmdEtefbY+mIMIRgSGwSACDcYMLbg/6OMJ0Beg0jkrrGv3EiIiIiarDsLi8e+343NmUUKepXdW2BR4a3hVbDaUdEZ+LJ3Y78NY9AchcFa6IhHFFDX4U+ppt6jRHVov8d3IIF6WugFzVYNOAq9IqOBwBEGUNV7qzp4qs0ERERETVIxwpLcdtnaYpAShQETB/aBo+PbMdAiugsnBm/Im/1/YpAShsWj5gx7zCQokbrt+yDWJC+BgDgkfyY8edyZJXaVe6K+EpNRERERA3OX8eLcOuSNBwtdAZrIToN5k/ojBt7xUMQeIU9orNxnVgL2e8J3tZHd0H06HegDUtQsSui2nVJbBJGtGgXvH1dm55obgpTsSMCOH2PiIiIiBqYZTuz8PKqA/BXWNA8zmLE/Amd0TaaUzCIzie872Pwl5yE++RmmBKGI2LgLAhag9ptEdU4SZYgll09UhREvNBrLB7e9C36x7TCTW17qdwdAQyliIiIiKiBkCQZC34/hE+3nlDUu8VZMOfKFESG8IpJRFUhaHSIvORllBz4GuZON0EQOIGGGh+P34fH//oBfaITcEObngAAvUaLBf0nBoMqUh9DKSIiIiKq90rcPjz50x6sO1ygqF/eKRZPXtoeei0/YBCdid9VBNlbXGlqnqg3IyzlZpW6IqpdpT4PHvrzG/yVdxy/Zx+CWavH+MTOAMBAqp7hvwYRERER1WuZNhduX7qtUiB13+AkPDemAwMporPwFWcgb8UdyP91KvyugvMfQNRIpGYdxF95x4O35+1Mhc3jPMcRpBa+ghMRERFRvbU9045blmzFwfySYM2oFfHqlSm4pW8CFzQnOgtP7nbk/jIFvuLj8DmyUPDbw5B8LrXbIqoTlyd0wq3t+wIAzDo9Fg24Cla9SeWu6Ew4fY+IiIiI6qUfdp/Eiyv3w+uXgrVmZgPmT+iMjs3MKnZGVL85M1JRuP4ZxRX2JI8dkrsQoraFeo0R1aJclwNRhpDg9Ly7kwcBAC6N64AO1hg1W6NzYChFRERERPWKJMl4e8NRvL/pmKKeEhuGueNTEGPmVcKIzsaxZwlsWxcCFa5OqY/ugsihc6AxRqjYGVHtOWjPw/0bv8YlsUl4vNulEAQBgiDgnk6D1W6NzoOhFBERERHVG06vH8/+vBe/HshT1Ed1iMGzozvAqNOo1BlR/SbLEuxbF8GxZ4mibkoYjoiBsyBoGeZS47SzMAvTNi6D3evGsqM7EaYz4P5OQzi9u4FgKEVERERE9UJOsRsPfZuOPTkORf2O/omY0r8VRJEfMIjORPa5UbjhWTgzUhV1c/L1sPScCoFXG6NGrMBdCoevfKrqprwMuP0+GLU6FbuiquL/TkRERESkut0ni3HLkq2KQEqvEfHiuGTcNbA1Aymis/C7CpG3+n5lICUIsPaaBmuvaQykqNEb2rwtnukxBgDQM6ol/jXwbwykGhCOlCIiIiIiVa3an4tnf94Lt698QfPIEB3mju+Mri0sKnZGVL/5ijOQnzodvuLjwZqg0SNi0CyYEkao2BlR7VqTfRB9ohMQotUDCFxtL0xnQL+YRBg0jDkaEv5rEREREZEqZFnG4j8z8PaGI4p6hxgz5o1PQXOLUZ3GiBoAX0kWcn+ZAsldFKyJhnBEDX0V+phu6jVGVMuWHNqK+Tt/Q+/oeCzsPwn6shDqkuZtVO6MqoNjOYmIiIioznl8Ep7+aW+lQOqSNlF455puDKSIzkMT0hzGuIHB29qweMSMeYeBFDVqnxzcgvk7fwMA/JV3HI//9QP8knSeo6g+40gpIiIiIqpTBaUePPzNLuzItivqN/eOx/2Dk7h+FFEVCIKA8H5PwO/Mg+xzInLoHGiMEWq3RVSr+kUnIExnQLHXDQBoGxYFkVfZa9AYShERERFRndmf68CMb9KRXewO1rSigMcvbY8JnZur2BlRwyNodIi85GUIghaC1qB2O0S1rr01Bq/1n4gHNn6N2zv0x+R2fdRuiS4Sp+8RERERUZ1YczAfdyzdpgikrEYd3ry6KwMponOQfW4UbnwRXvvRSttEXSgDKWq0PH4fXtm+Glml5SNru0XG4YuR/2Ag1UhwpBQRERER1SpZlvHJlhNY9PshyBXqSZEhmD+hM+LDTar1RlTf+V1FKFgzE568HfDk/IXoMe9BY4xUuy2iWlfq82Dmpm+xKTcDf+YewztDrkWkIQQAEGM0q9wd1RSOlCIiIiKiWuP1S3hx5X4sPC2Q6p8YgcXX9WAgRXQOvuIM5K24A568HYHbjiwUrn0Ssiyf50iihm/ezlRsys0AAGSUFGHmpm/53G+EOFKKiIiIiGqFzenFzO92YesJm6J+Tfc4PDSsLTRc0JzorDx5O5D/20xI7qJgTTSEw9L9Hghc2JmagHuTB2NrfiaOlxQhVKvHA52G8LnfCHGkFBERERHVuCMFpbhlyVZFICUKAh4Z0Q6PjGjHQIroHJwZqchbdZ8ikNKGxSNmzDvQx3RTrS+i2uaXpOCfo4yheGPgVehobYa3B/8dPaJaqtgZ1RaGUkRERERUozYeLcStS9Jw3OYK1sx6LRZN6oJrusep2BlR/efY+xkK1j4O2e8J1vTRXRE9+l1owxJU7Iyodh0qzse1v36E7QWZwVpciBUfDb0BHa3NVOyMahNDKSIiIiKqMUvTMvHgsp1weHzBWrzViPev74H+rSJU7IyofpNlCbYtC2D76zWgwro5poThiB75BjTGcPWaI6plu4qycefapcgoKcL0P5Zjvy03uI1T9ho3hlJEREREdNH8koxXfz2AOakHIFX4QN0r3ooPru+J1pEhKnZHVL/JPjcK1z4Bx54liro5+XpEDHkJgtagUmdEdeN/B7fC7nUDAIq9bryxe53KHVFd4ULnRERERHRRil0+PP7DbvxxrFBRn9ilOR4d0Q46Db8HJTobv6sIBWtmBq+wBwAQBFh7Pghz8vXqNUZUh57uMRq5Lge25p9A98g4vNh7rNotUR1hKEVERERE1Xa8yInpy9NxpLA0WBMAPDi0DW7s2ZLTLojOw3nkR0UgJWj0iBg0C6aEESp2RVT7MhxFSDCHAwAMGi3m95uAd/f9gbs6DoRRq1O3Oaoz/NqKiIiIiKply/Ei3LIkTRFIheg0mDehM27qFc9AiqgKQjtej5CkwKgQ0RCO6JFvMJCiRm/p4TRc8+uH+PnE3mAtVGfAg52HMpBqYjhSioiIiIgu2Dfp2Zi9aj98Uvn6Uc3DDJg/oTPax5hV7IyoYREEAeH9ngREHcJSJvMKe9SoybKM9/dvwtt71gMAntvyE0K1egyJTVK5M1ILQykiIiIiqjJJkvH6usP4+K/jinrXFhbMHZ+CyBC9Sp0RNQx+Zx40pmhFTdDoENH/SZU6IqpbxV5X8M9+WcaOwiyGUk0Yp+8RERERUZWUenx4+LtdlQKpscnN8PbfujGQIjoHWZZg27IAOd/fCK/9qNrtEKlCEARMTbkEExI7AwDu7TQY9yQPUrkrUhNHShERERHReWXZXXjom3TszytR1O8Z1Bq39k3g+lFE5yD73Cjc8CycGakAgILU6Yge8y40xkhV+yKqCx6/Dz+d2IvxCSkQBAGCIOCJ7pfi0rj2GNistdrtkcoYShERERHROe3IsuPhb9NRUOoN1gxaEbMu64hL28eo2BlR/ed3FaFgzUzFFfZ8JVlwn/wLIa1Gq9gZUe1z+rx4ZNO3+CP3GDJLbbi7bFSUKIgMpAgAQykiIiIiOoef9uTghRX74PFLwVpMqB7zJnRGp9gwFTsjqv98xRnIT50OX3H5lFdBo0fEoFm8wh41epIs4cE/liEt/wQAYPG+P2HVm3BDm54qd0b1CUMpIiIiIqpEkmT8Z+NRvPfnMUW9UzMz5o7vjGZhBpU6I2oYPHk7kP/bTEjuomBNNFgRNXQO9DHd1GuMqI6IgogrE1KCoVSIVoeOFo6uJSWGUkRERESk4PL68dwv+7Bqf66iPqJdNJ6/rCOMOo1KnRE1DM6MVBSufway3xOsacPiETVsPrSWRPUaI6pjExI7o9jrxof7N2HhgEnoFB6rdktUzzCUIiIiIqKgXIcbD3+7C7tOFivqt/ZNxN0DW0EUuaA50bk49n4G25YFgCwHa/roLogcOgcaY4R6jRHVgcPF+fj44F94rNul0ImBLzBuatsLV8R3QrjBpHJ3VB8xlCIiIiIiAMCenGI89M0u5DjcwZpOI+KpUe1xeSd+u010LrIswb51ERx7lijqpoThiBg4C4KWU16pcdtddBIPblyGIo8TpT4v/tl7HERBBAAGUnRWDKWIiIiICKv35+HZn/fA5Stf0DwyRIc5V3ZGtziLip0R1X+yLKFw7RNwZqQq6ubk62HpORVC2QdzosbK4/fh4T+/RZHHCQBYlbkf7SzRuL1Df5U7o/qO/zsSERERNWGyLOP9P4/h0e93KQKptlGh+OD6ngykiKpAEERorW0qFmDt9SCsvaYxkKImQa/R4oVeY6Evm7LXNbIFrk3qrnJX1BBwpBQRERFRE+XxSXhx5T78uCdHUR+cFImXxiUjRM+3ikRVFdZ1CvwlmXAeW42IQbNgShihdktEtc4r+YNrR/WKjsfLfa/El0e246Xel8Ok1ancHTUEfKdBRERE1AQVlHrw8Le7sCPLrqjf2KslHhzShguaE10gQRAQ3u9JmJNvhC6ig9rtENW6L49sx+eHt+Hfg/8Oqz6wZtSQ2CQMbtYagsDXEKoajiUlIiIiamIO5pXg1iVpikBKIwh4clR7TB/aloEU0Xk4M1Lhzt1WqS5odAykqNGTZRnv7/8Tr2xfjUPF+Zi6cRlKvOUXyGAgRReCoRQRERFRE7LucAFu+ywNmXZXsGYxaPHm37piUpcWKnZG1DA49ixBwdrHUfDbTPjsx9Ruh6jOlfg8WHZ0Z/D27qKT+PnEPhU7ooaMoRQRERFREyDLMv635ThmfJOOUq8/WG8VYcIHN/RE7/hw9ZojagBkWYJtywLYtiwAZBmSx4781GmQ3Da1WyOqU2adAW8MuBoRhhAAwN3Jg3BVqy4qd0UNFdeUIiIiImrkfH4Jr/x6AMt2ZivqfRPC8fIVnWAxcjFaonORfW4UbngWzoxURd0YPxSCPkyVnojqklfyI6vUjkRzBAAgwRyONwZchR2F2bi6dVeVu6OGjKEUERERUSNmd3nxyHe78dfxIkX9793i8NCwNtBqOHCe6Fz8riIUrJkJT96O8qIgwNpzKszJN6jXGFEdcfm8eHTz99hVlI3/DL4GSWFRAID21hi0t8ao3B01dHwXQkRERNRIHSssxa1L0hSBlCgImDm8HR4d2Y6BFNF5+IozkLfiDkUgJWj0iBzyEgMpahIcXjce2Pg1NuQcgc3jwv0bvkZmKaesUs3hSCkiIiKiRujPY4V47PvdKHb7grVQvQazL++Ega0jVeyMqGHw5O1A/m8zIbmLgjXRYEXU0DnQx3RTrzGiOqQRREiQg7dLfG6cdDoQF2JVsStqTPj1GBEREVEj89WOLDzw9U5FINXSasTi63owkCKqAmdGKvJW3acIpLRh8YgZ/Q4DKWpSTFodXus3Ee0s0bDoDHhr0N/QM6ql2m1RI8KRUkRERESNhF+S8dqaQ/gs7YSi3qOlFXOuTEG4iQuaE52PY88S2LYuBOTy0SH66C6IHDoHGmOEip0R1Y2jjgIcdRRiaPO2AACL3og3Bl6NIo8TbcrWkyKqKQyliIiIiBoBh9uHJ37cgw1HChT18SnN8djIdtBrOUCe6Hw8eTth27JAUTMlDEfEwFkQtAZ1miKqQ3uKcjD1j6/h8Loxv99EDGjWCgAQaQhBpCFE5e6oMeK7EyIiIqIG7oTNids/S1MEUgKAB4Yk4enR7RlIEVWRProLwrrcFrxtTr4eEUNeYiBFTcIxRyHuWf8FitxO+CQJj2z6FumF2Wq3RY0cR0oRERERNWBbT9jwyHe7UOT0BmsmnQYvjk3G0LacZkF0ocK6ToG/9CR04e1hTr5e7XaI6kx8qBVDm7fBj8f3AADaWaKREBqublPU6DGUIiIiImqgvtt1Ev9cuQ8+qXztm1izAfMndkaHGLOKnRE1DLLfA0GjV9QEQUB4/6cgCIJKXRGpQxREPN1jNBw+D1x+L+b0HY8Qrf78BxJdBIZSRERERA2MJMl4c/0RfLQ5Q1Hv0jwMc8d3RlQoP0QQnY8ndzsK1j2FiEHPw9Csh2IbAylqKr4+ugOFbidu69APAKAVNZjd+3IAgF7DuIBqH59lRERERA1IqceHZ37ei98O5ivqYzrG4JnRHWDQalTqjKjhcGakonD9M5D9HhSseQTRY96BztJK7baI6tRHBzbjjV1rAQBmnR7XJvUAwDCK6hZXvSQiIiJqIE4WuzHl8+2VAqk7B7TCi2OTGUgRVYFjzxIUrH0cst8DAJA8dhTveEflrojq1s7CrGAgBQBzd6RiVxEXNae6x1CKiIiIqAFIzy7GPz7din25jmBNrxHx0uWdMGVAK043IjoPWZZg27IAti0LALl8HTZTwnBE9H9avcaIVNAlogXuTh4UvD2l4wB0ssaq2BE1VfU2lHrzzTfRunVrGI1G9O/fH3/++edZ9/3ggw8gCILil9ForMNuiYiIiGrPL3tzcOfn25Bf6gnWokL0+M813TG6Q4yKnRE1DLLPjcK1T8CxZ4mibu54HSKGvARBa1CpM6K645P88En+4O1b2/fFze16Y0aXYZjScQC/3CBV1MvJop999hlmzJiBt99+G/3798eCBQtw2WWXYe/evWjWrNkZj7FYLNi7d2/wNk8oIiIiauhkWcY7fxzDOxuPKuodYsx4bUJnNAvjB2mi8/G7ilCwZiY8eTvKi4IAa8+pMCffoF5jRHXI7ffhsc3fw6Iz4NmeYyAKIgRBwAMpl6jdGjVx9TKUmj9/PqZMmYJbb70VAPD222/j+++/x+LFi/HYY4+d8RhBENC8efO6bJOIiIio1rh9fsz6ZR9W7MtV1Ie3jcbzYzvCpOP6UUTn4yvOQH7qdPiKjwdrgkaPiEGzYEoYoWJnRHWnxOvGjD+/wdb8EwAAs86Ah7sM50AOqhfq3fQ9j8eDv/76C6NGjQrWRFHEqFGjsGHDhrMe53A40KpVKyQkJGDixIlIT0+vi3aJiIiIalxeiRt3fb69UiB1S98EvHJFJwZSRFXgyduB3F+mKAIp0WBF9Mg3GEhRk7LfnoedheWLmH+XsQsnSm0qdkRUrt6NlMrLy4Pf70dsrHKRtdjYWOzZs+eMx3Ts2BGLFy9Gt27dYLPZMHfuXAwaNAjp6emIj4+vtL/b7Ybb7Q7ettvtAABJkiBJUg0+mronSRJkWW7wj4NIDTx/qCnh873+2pfrwIxvdiHXUb5+lFYj4IlL2+GKTrEAZEiSfPY7oFrFc6dhcGWkomjDc5Cl8vNIY45H5LC50IYl8t+vlvE8qV+6RbTA8z0vwxN//QizTo8F/SYizmThv0891JjOnao+hnoXSlXHwIEDMXDgwODtQYMGoVOnTvj3v/+NF154odL+s2fPxqxZsyrVc3Nz4XK5arXX2iZJEmw2G2RZhijWu4FwRPUazx9qSvh8r582ZNjxytoTcPnK38hZDFo8MzweXaIE5OTkqNgdATx3Ggp/bga87pLgbTE8GWKPp1HgNAJOnke1jeeJ+k447bBoDQjTBdYe7KyxYFpSPySFRCDGK/L1pJ5qTOdOcXFxlfard6FUdHQ0NBoNTp48qaifPHmyymtG6XQ69OzZEwcOHDjj9scffxwzZswI3rbb7UhISEBMTAwsFkv1m68HJEmCIAiIiYlp8E9iorrG84eaEj7f6xdZlvHxlhN4c102ZGig0wam5yVFhWD++BTEWXlV4fqC504D0exmFGudcOz6AMb4YQgf8ByvsFeHeJ6oa58tF0+k/YqWoVa83v8qmLQ6AMB1Z7loGNUfjencMRqr9t6l3oVSer0evXv3xqpVqzBp0iQAgX+YVatW4f7776/Sffj9fuzYsQOXX375GbcbDAYYDJVflERRbPD/8EBg0ffG8liI6hrPH2pK+HyvHzw+CbNXH8B3u8q+kCtbd3Zg60jMHpeMUEO9e7vW5PHcaRgs3e+CLjwJplajIQj8t6prPE/Usb0gE9P+WAaH14NCjxOP/fU95vWbAL2GryUNRWM5d6raf718Zs6YMQP/+Mc/0KdPH/Tr1w8LFixASUlJ8Gp8kydPRsuWLTF79mwAwPPPP48BAwagXbt2KCoqwpw5c3D06FHccccdaj4MIiIionMqcnox87tdSDuhXHD2hp4t8eAlbaAReWUkovPxu4rgLdwHY4t+irogCAhpfZlKXRGpw6IzQiuUXwyj2OuGy+9jKEX1Vr18Zl533XXIzc3FM888g+zsbPTo0QM//fRTcPHzY8eOKVK3wsJCTJkyBdnZ2YiIiEDv3r2xfv16pKSkqPUQiIiIiM7pUH4Jpi9PR6a9fD1LjSDgkZHtcHXXFip2RtRw+IozkJ86Hf7SHESNfB2GmO5qt0SkqtZhkVg4YBLuWf8FUsKbY26/8QjR6tVui+isBFmWm/zlW+x2O6xWK2w2W6NYUyonJwfNmjVr8MP9iOoazx9qSvh8V9f6IwV44ofdKPH4g7UwgxavXJGCvonh6jVG58Vzp/7w5O1A/m8zIbmLAACi3oKYMe9Ca0lUtzHieVLHfsjYjS4RzZFojgjW9tly0docwRFSDUxjOneqmrPwGUpERERUR2RZxmdpmXhtzSFIFb4XTAw34bWJnZEYEaJid0QNhzMjFYXrn4Hs9wRroqFhf7lMVB0fH/wLi9J/R/OQMLw7+Fo0M4UBADpYY1TujKhqGnb0RkRERNRA+PwSXl59APN+O6gIpPokhOP963swkCKqIsfez1Cw9nFFIKWP7oLo0e9wlBQ1KV8d2YFF6b8DALJLi3Hfhq9Q6vOc5yii+oWhFBEREVEts7u8mLpsJ77akaWoX9W1BV6f1AUWo06lzogaDlmWYNuyALa/XgMqBLumhOGIHvkmNMaIcxxN1PhcGtcOSWGRwdujW3aEScPXE2pYOH2PiIiIqBYdKyzF9OXpOFbkDNZEQcC0oW1wfY84CAKvsEd0PrLPjcINz8KZkaqomzteB0uvByEI/K6dmh6r3oTXB1yFO9d9gWuTuuPGtr3UbonogjGUIiIiIqolmzOK8Oh3u2B3+4K1EJ0Gs6/ohEGtI89xJBGd4ncVoWDNTHjydpQXBQHWnlNhTr5BvcaI6pjb78NzW3/G31t3R+/oeABAM1MYPh3+fzBpOUKKGiaGUkRERES14OsdWXhl9QH4K0wzirMY8drEzmgTFapiZ0QNh684A/mp0+ErPh6sCRo9IgbNgilhhIqdEdWtUp8HM/74Blvyj2NDzhG8NehvSAlvDgAMpKhB4zhXIiIiohokSTLm/3YQL63arwikusVZ8MH1PRhIEVWR5HMib+U9ikBKNFgRPfINBlLU5Hx0YDO25AfOhVKfF49t/h5eya9yV0QXj6EUERERUQ0pcfsw/Zt0fLr1hKJ+eadY/OvqbogI0avUGVHDI2pNCOt2Z/C2NiweMaPfgT6mm4pdEanj9g79MbBZawBAmM6AF3uNg07UqNsUUQ3g9D0iIiKiGpBpc2HGN+k4mF+iqN83OAn/6BPPBc2JqiG07QT4S7Lhzv4TkUPn8Ap71KSUeN0I1RkAADpRg1f6XIFnt/6MOzr0R3trjMrdEdUMhlJEREREF2lbpg0zv92FQqc3WDNqRTw/Nhkj2kWr2BlRwyHL8hnD27CuUxDW+RYIGo40pKZjvy0XD2z8GvckD8LEVl0AAEatDq/0vVLlzohqFqfvEREREV2EH3afxD1f7lAEUs3MBrx7bQ8GUkRVJPvcKFz3JJzHVlXaJggCAylqUrYXZOLu9V+gwF2K2dtXYVXmfrVbIqo1HClFREREVA2SJONfG47gg00ZinpKbBjmTUhBdKhBpc6IGha/qwgFa2bCk7cDrhNrIZqiYYjprnZbRKrZlJeBYq8bACDJMv53aAtGtGgLUeCYEmp8+KwmIiIiukBOrx+P/bC7UiA1qkMM/nNNNwZSRFXkK85A3oo74MnbAQCQ/R4Urnsast+jcmdE6rmtfT9cmxQIZntGtcSi/pMYSFGjxZFSRERERBcgp9iNh75Nx54ch6I+ZUArTOmfyAXNiarIk7cD+b/NhOQuCtZEgxWRg1/kdD1qctLyT6B7ZFxguqogYEaXYUgIjcCkVl1g0PBjOzVejFuJiIiIqmhXdjH+sWSrIpDSa0T8c1wy7hzQioEUURU5M1KRt+o+RSClDYtHzOh3oI/pplpfRGr438EtuHPd51i463fIsgwAEAUR17XpwUCKGj0+w4mIiIiqYNX+XDz78164fVKwFhmiw7zxndGlhUXFzogaFsfez2DbsgAo+/ANAProLogcOgcaY4R6jRGp4N19f+A/ezYACIRTYToDbu/QX+WuiOoOQykiIiKic5BlGYv/zMDbG44o6h1izJg3PgXNLUZ1GiNqYGRZgn3rIjj2LFHUTQnDETFwFgQt12KjpicxNByCUJ7Revw+dRsiqmMMpYiIiIjOwuOT8PyKffh5b46ifkmbKLw4tiNC9HwrRVQVss+Nwg3PwpmRqqibO14HS68HIXARZ2qixrTsCIfXg5e3r8LUzpfg/9r2VrslojrFd1JEREREZ5Bf4sHD36ZjZ3axon5z73jcPzgJosj1o4iqwu8qQsGamcEr7AEABAHWnlNhTr5BvcaIVODx+/DBgc34R7s+wfWirm7dFV0jmqO9NUbl7ojqHkMpIiIiotPsz3Vg+vJ0nHS4gzWtKODxS9tjQufmKnZG1PD4ivbDk58evC1o9IgYNAumhBEqdkVU90p9Hjz857fYnJeBPUUn8WrfK6EVNQDAQIqaLI6TJSIiIqpgzcF83L50myKQshp1eOtv3RhIEVWDoXlfhPd9FAAgGqyIHvkGAylqkh7f/AM252UAANaePIxXtv+qckdE6uNIKSIiIiIEFjT/eMtxvP77YcgV6kmRIXhtYme0tJpU642ooQttNxGyzwlj3CBoLYlqt0Okijs69EdawQk4fV6YdXpcmZiidktEquNIKSIiImryvH4JL6zYj0WnBVIDWkVg8XU9GEgRXQBv4b4z1s3J1zOQoiZHlstfVbpGtsCcvuMRawrD24OuQffIOBU7I6ofGEoRERFRk2ZzenHfVzvw7a5sRf3a7nFYMLELzAYOLCeqClmWYNuyEDk//QPOY6vUbodIdYeK83H3+i+Q7yoJ1vrFJOLLkf9AB64hRQSAoRQRERE1YUcKSnHLkq3YesIWrImCgEdHtMPMEe2g4RX2iKpE9rlRuPYJOPZ8CsgyCjfMgjt3m9ptEalmZ2EW7ly7FFvzT+D+jV/D7nEFt+k1/LKD6BSGUkRERNQkbTxaiFuXpOG4rfyDglmvxaJJXfD37pxSQVRVflcR8lbfD2dGarAmS174ig6q1hORmmRZxqs7foXdG7hgxkF7Ht7YvVblrojqJ4ZSRERE1OQsTcvEg8t2wuHxBWvxViPev74H+reKULEzoobFV3wceSumwJO3I1gTNHpEDnkJoe2vVrEzIvUIgoBX+1yJZiYzAKBnVEtMTblE5a6I6ieOGyQiIqImwy/JmJt6EF9sz1TUe8Vb8eoVKbCadCp1RtTwePJ2Iv+3hyG5i4I10WBF1NA50Md0U68xIpXYPE5Y9YELYzQPseDNgVfj/f2b8Hi3S2HglD2iM+JIKSIiImoSil0+TF22s1IgNbFLc7xxVVcGUkQXwJnxG/JW3acIpLTmlogZ/Q4DKWqSPjuUhr+t+gD7bLnBWitzJJ7reRkDKaJzYChFREREjV5GkRO3fZaGP48VBmsCgOlD2+DJS9tDp+FbIqKqcuz9DAVrH4Psdwdr+uguiB7zLrSWRBU7I6p7sizjnb0bMW9nKuxeNx7Y+DWOOQrPfyARAWAoRURERI3cluNFuOXTrThSWBqsheg0mD+xM27sFQ9B4BX2iKpCliXYtiyA7a/XAFkO1k0JwxE98k1ojFyPjZoevyxha/6J4O1CdylSs7nIP1FVMZQiIiKiRuub9Gzc99UO2N3lC5q3CDPivet6YEhSlIqdETU8RX/8E449SxQ1c8frEDHkJQhag0pdEalLK2owt994pITHAgDu6zQYk9v1UbkrooaDk1uJiIio0ZEkGa+vO4yP/zquqHdtYcHc8SmIDNGr1BlRw2VKHIXSwz8CsgQIAqw9p8KcfIPabRHVOY/fhx2F2egdHQ8ACNHqsXDAJGzMPYbLWnZUuTuihoUjpYiIiKhRKfX48PB3uyoFUmOTm+Htv3VjIEVUTca4gQjv+ygEjR6RQ15iIEVNktPnxYw/v8F9G77EmgrT9Kx6EwMpomrgSCkiIiJqNLLsLjz0TTr255Uo6vcMao1b+yZw/SiiixTabiKMcQOhCWmmditEdc7p8+L+jV9hR0EWAOCJzT9gwYBJ6BOdoHJnRA0XR0oRERFRo7Ajy45blmxVBFIGrYhXrkjBbf0SGUgRXQBnRioce5eecRsDKWqqjBot2oVFB2/rRA00Aj9SE10MjpQiIiKiBu+nPTl4YcU+ePxSsBYTqse8CZ3RKTZMxc6IGh7H3s9g27IAAKAxRcOUOFLdhojqCUEQ8Gi3EXD43NiUl4HXB1yFjlaGtEQXg6EUERERNViSJOPfG49i8Z/HFPVOzcyYO74zmoXximBEVSXLEuxbFymusFe44TloLa2gC2+rYmdE6jlUnI+1Jw8Hr6gnCiKe63kZcl0OxIVYVe6OqOFjKEVEREQNksvrx7M/78XqA3mK+sh20Zh1WUcYdRqVOiNqeGSfG4UbnoUzI1VRD213FbTWJFV6IlLbrqJsPLhxGWweFwQIuLldbwCBaXsMpIhqBkMpIiIianByHW48/O0u7DpZrKjf1i8Rdw1oBVHk+lFEVeV3FaFgzUx48naUFwUB1p5TeYU9arIK3aW4b/1XKPF5AACv7/odLULCMCqug8qdETUuXJWNiIiIGpQ9OcX4x6dbFYGUXiPi+cs64p5BrRlIEV0AX/Fx5K2YogikBI0ekUNeYiBFTVqEIQR3Jg8M3u4WGYf+MYkqdkTUOHGkFBERETUYq/fn4Zmf98DtK1/QPDJEhzlXdka3OIuKnRE1PJ68ncj/7WFI7qJgTTRYETV0DvQx3dRrjEhFsiwHr9Z6Q5ueKPa6kV6YjVf6XAGjVqdyd0SND0MpIiIiqvdkWcb7mzLwr/VHFPV20aGYP6EzWliM6jRG1EA5M35D4fpnIPvdwZo2LB5Rw+ZDa+FoEGqaPj+8DX/lZeCfvS+HRgxMKprSoT8kWQ7eJqKaxVCKiIiI6jWPT8KLK/fhxz05ivqQpEj8c1wyQvR8O0N0IRx7l8K25TVAloM1fXQXRA6dA40xQsXOiNQhyzLe378Jb+9ZDwD457aVeKrHKIiCCEEQoBE4LZyotjDuJSIionqroNSDu7/cXimQuqlXPOaN78xAiugC+UtzYd/+tiKQMiUMR/TINxlIUZOVWWrH4n1/BG9/l7ELa08eVrEjoqaDoRQRERHVSwfySnDLp2nYkWUP1jSCgKdGdcC0oW24oDlRNWhCYhA5+J+AEPgYYO54HSKGvARBa1C5MyL1tAy14pW+VwZHRN3baTCGNm+rcldETQO/XiQiIqJ6Z+3hfDz5wx6Uev3BmsWgxavjU9A7Ply9xogaAWPcQIT3ewyyt4RX2KMmyyv54fR5YdEH1iQcHJuEWb3Gwu514e+tu6vcHVHTwVCKiIiI6g1ZlvHp1hNY+PthSBWmF7WKMOG1iV2QEG5SsTuihsfvKoRoCA9eTeyU0LYTVOqISH1OnxePbPoWRR4X/jXobzDrAiMFx7TsqHJnRE0Pp+8RERFRveD1S3hp1X68tuaQIpDqlxiB96/ryUCK6AJ58nYi5/sbULJ3idqtENUbxV4X7t/4Ff7IPYa9thw89Oc3cPt9ardF1GQxlCIiIiLV2V1e3P/1Dizbma2o/71bHBZN6oIwIwd3E10IZ8ZvyFt1HyR3EWxbF8F5bLXaLRHVC8VeNzJLy9cq3GvLwRFHgYodETVtDKWIiIhIVUcLSnHLkjRsOW4L1kRBwMzh7fDoyHbQcEFzogvi2PsZCtY+BtnvDhRkGSX7v4RcYQQiUVMVF2LFGwOugkVngFVvxFuD/oaO1mZqt0XUZPFrRyIiIlLNn8cK8dj3u1HsLp86EarX4OUrUjCgFS9PT3QhZFmCfesiOPYop+uZEoYjYuCsSutKETUVR4oLIEFGm7AoAEBbSzQWDrgKIVodkspqRKQOhlJERESkii+3Z+LVXw8q1o9qaTXitQldkBQVomJnRA2P7HOjcMOzcGakKurm5Oth6TkVgsAJEtQ07SnKwdSNX0Mrinh3yLWIC7ECADpHNFe5MyICOH2PiIiI6phfkjE39SBeXn1AEUj1bGnFB9f3ZCBFdIH8riLkrb5fGUgJAqy9HoS11zQGUtRk7SrKxj3rv0CRx4k8Vwnu2/AV8lwlardFRBVwpBQRERHVGYfbhyd+3IMNR5SLyo5PaY7HL20HnYYfnokuhK/4OPJTp8NXnBGsCRo9IgbNgilhhIqdEakvMTQCLUOt2GfLBQBEGkKgFzUqd0VEFTGUIiIiojpxwubE9OXpOFxQGqwJAO4fkoSbe8dzvRuiC+TJ24H832ZCchcFa6LBiqihc6CP6aZeY0T1hFlnwKIBV2HK2qWIC7Hg1b7jYdLq1G6LiCpgKEVERES1busJG2Z+uws2lzdYM+k0eHFsMoa25SKzRBfKdWItCtY+AdnvCda05paIGv4atJZEFTsjUtdXR3aguSkMg2JbAwiMjvr34Gtg0Rmg1/DjL1F9w7OSiIiIatW36dl4adV++KTy9aNizQbMn9gZHWLMKnZG1HCJphhA1AJloZQ+ugsih86BxsirVlLT9cH+TXhr9zoYNFq8PuAq9IhqCQCINoaq3BkRnQ0XbiAiIqJaIUkyFv1+CM+v2KcIpLo2t+DDG3oykCK6CPrIjogc/E9AEGFKGI6okW8wkKImbVXmfry1ex0AwO33Ycafy5HjLFa5KyI6H46UIiIiohpX6vHh6Z/2Ys2hfEV9TMcYPDu6I/Rafi9GdLGMcQMRM+pt6KK78Ap71OQNb94Ww1u0RWrWQQDA/7Xtgxgjv/wgqu/46kVEREQ16mSxG1M+314pkLprYCu8ODaZgRTRBfK7iuDYuxSyLFfapo/pxkCKmixJloJ/1ogiXuw1Dv1iEvFw1+G4rUM/XkCDqAHgSCkiIiKqMenZxXjom3Tkl5YvvqzXiHjuso4Y3SFGxc6IGiZf8XHkp06HrzgDkP0wJ9+gdktE9YLL58Wjm7/HoGatcV2bHgAAvUaLRQMmQWRQS9RgMJQiIiKiGvHL3hzM+mUfPP7yb66jQ/WYN74zUpqHqdgZUcPkyduB/N9mQnIXAQBsWxdBE9ocpoQR6jZGpDKH143pfyzHtoJMbMg5gjCdAZcndAIABlJEDQzPWCIiIroosizjPxuP4skf9ygCqY4xZnx4fU8GUkTV4MxIRd6q+4KBFABoQ+Ogs7ZVrSei+uLXrAPYVpAZvD1vZyqKvS4VOyKi6uJIKSIiIqo2t8+PWb/sw4p9uYr68LbReH5sR5h0GpU6I2q4HHs/g23LAqDCGlL66C6IHDqHV9gjAjA+sTOOOQrx4YHNsOgMWDjgKoTpjGq3RUTVwFCKiIiIqiWvxI2HvtmFXSeVl9y+pW8C7hnYGqLIBWaJLoQsS7BvXQTHniWKuilhOCIGzoKgNajUGZH6cpzFiDaGBqfn3dtpMERBwGXxyWgTFqVyd0RUXQyliIiI6ILtzXFgxjfpyHG4gzWdRsSTl7bHFSmxKnZG1DDJPjcKNzwLZ0aqom7ueB0svR7kFfaoSdtry8EDG7/GyBbt8GjXkRAEAYIg4J5Og9VujYguEkMpIqIyssuOCGPgd4SEq90OUb2VeiAPT/+0By5f+fpRESYd5oxPQfc4q4qdEdUem8cJj+QHZABhJuS7SwEB0IsaWPWmi7pvv6sIBWtmwpO3o7woCLD2nMqr7VGTl5Z/AtP/WI4SnwdfHdkBi86IexlGETUaDKWIiMrIPg+OLpiENjO+UbsVonpJlmV8tPk43lx3GHKFepvIELw2sQvirFzPgxoXWZbh9Hvh8vvglyVctfJ9+GQ/St1uyKIAg0aL70ZPgUUnQxCqN13VV5yB/NTp8BUfD9YEjR4Rg2bxKntEAGxeF5x+b/D2prwMePw+6DX8KEvUGPBMJiIqI3lckEsLIfs8cGcfgNYcCTEkHILIKRNEHp+El1btx/e7Tyrqg1pH4qVxyQg18C0F1V8evw92rxsOrxutwyKD9U25x/BX/nEUewPTUGd2LQ+B5uz4FV8d2Q6/LCPRHIF/Dfob/LIEvyyj0OtCkdcFjSAiz+VA85DyK0wuPZyGv/KOI8oQghYhVtzcrndwW6G7FDpRg1CtHoIgQJYlFKx9QhFIiQYroobOgT6mW23+lRA1GMOat8XTPUZj1tZf0DcmAXP6jmcgRdSI8GwmokZNliT4HXnwFWbCW3gCvsIT8BaeACCj2dXPw19qg+zzAAC8eYfgt2XBnbUHWkszeAtLgcITMCZ0Lb8/vw9Fa96DxhwFjTk68HtYNLTmKAhavUqPkqh2FZZ6MPO73diWaVPUb+jZEtMuacMFzalOSLIEu8cNu9cFu9eFDpaY4AfTnYVZWHFiH4q9bti8Lvyz1zgYtToAwAf7N+Gt3euC97Nx/NTgQsmb8zLw/v5NAALT8CqGUlpBhL/s6ncVLzUvBH8P/On0EVLbC7Lwa9YBAEArc4QilHp5+2r8mnUAelGDXtHxWDTgKoT3fwp5q+7Bzy4jnMYYJHS5HUnaGHS56L8xooZrdeZ+DGjWCiFl762uSEiBRWdE/5hEBlJEjQzPaCJqVJxHtqAkfSV8hccDIVRRVjB0qkjQ6iFfNQuyz4OM168GJD88eRmA34eM1/8GQdAAGi3aPLNBcZy/pACFqe+c8WeLJgu0FYIq68CbYIhLDm6XPE7Ifh9Eo7na0zyI6tqh/BJMX56OTHv5h3KNIOCRke1wddcWKnZGDZVP8iPH5YDN40Kx140OlhiEGwJrMh0pLsAnB7fA7g1se6TriODIptVZB/DE5h+C9/P5yMloZQ5sO+IoxKeHtga32b2uYCgVetoXBg6vBxZ9YKppmL58yqlH8sPt98FQ9oG34jaH1w3IgUDq7V5jEanR4oTbiSt/XwrNaf+fF3pKg3+ONIQothW4S4M/Sy4LvPSRHRE5+J/46fclOKRPgLB3Cy6xFWFevwnB4+5a9zkKPU5E6E0Y1rwtbmzbK7htY85RhGh1iNCHINoYClPZ4yZqqD46sBlv7FqLvjEJeK3fxGAIdUnzNip3RkS1gaEUETUYzoN/wlt4PDjayVeYiejxTyiCH2/uIdj/XHre+5J9Hvgd+RC0eiQ88BUAwHPyAI7MvQwJD3wJraVZYD9ZVhznL847631KTjs8TjuQewgAENZjvGJ7yc4VyF3+PAStHhpzNLRh0dC3SEb0FY8o9vPkHoFoCIXGHAlB1Jz3sRDVlvVHCvDED7tR4vEHaxaDFi9fkYK+ieHqNUb1giRLOGjPR7E3MHqpTVgUEs0RAALT1ObuTA2MXPK4cGfHARgcmwQAOFRcgP/77ZPg/czrNyH4YdPudWH5sZ3BbbkuRzCUsuiUa5bZPeVXfrToDMptXjeamQJT6qz6047zuoKhlEVnhFYUEaYzIkxngNPnDYZSvSJb4vYO/RGmMyC8bH+dqEEznR475oxDl5k/ok1YBMyn9RUfEo5CixP57lJEG0MV2wKhlAxAUARWxriBKI3cBcFZDACI1CvDrCOOQhS6S3EEQDtLtGLbE399D4c38OXLjW17YVrnoQACr1+PbPoOFr0BkfoQDGneBt0j4wAE/u2KPC6E643BUWNE9UHFkY2bcjPw1JYf8XKfK/g8JWrEGEoRkepkWYZUUlg+va4oE6LOCOvAGxX75Xz5FPwlBYqaN/+YIpTSRrQ89w8TRGitzaGLiIPscUIbVfHNvQxtTFsY4jrBk3MQfnsejInKNT38TjsgagDJj/PRhCk/OPgcgUBL9nngK8qErygzcF+nyf7oXvjsOYAgQBMSEZgeGBYNTWhgBJYmLDAay9C8I3RRCeftg+hCybKMz9Iy8dqaQ5AqBLOJ4Sa8NrEzEiNCznE0NUQ7C7NQ6Hai2OtGfKgV3crCC1mW8fCmb1HkccLuceHq1t1wQ5ueAACP34+bKoRLD3YeipvKQikBAlac2Bfcll0WtgCVA6RT6zkFthmrvM1eYUqdVW+CRWdAmN4Ii86o+EIh2doMD6RcAqs+EDxVDIOuTOiEK+M7QaywdqDfUYDS/evQ1mlHa6cNktMOyeuG/opH8MWlt8DiLkHbsAjECMDSY+vg/eBPnNCZIGj1EHQG3Ko1QNQZIGgNQFYBitbmIXzIZMg+N27x7UJuiAUGfwQS7Zko2bMmeJym1AaUffCONJggS35A8kMSNbB5nMH+Iir07/H7goFU4LjybaU+D37LPli+zRgaDKWKPC6M/fk/EITA393DXYZjTMuOgb9XjwtfHNmOCIMJkYYQdI1oUWnEF1FtGdSsNT7cvwklZaPc21liglNliZqCpng1cIZSRFSn3Jm74TqaBl+Rco0nucKHCwDQRSVWCqW0ES0rh1KFxxW3dREtIYZYoQtvCW1ES+giWkIbEQddRDy0EXHQWptDOMdaBILOCEGjQ2jHoWfcHtJuAJKe3gDJaQ+sVVWcB78jH35HHvzFefA58uEvzoPfkVcplDrTKCuNWbmPLEnlj1GW4S8pgL+kAJ7sfZWOjRhxNyKG31H+d1FwArnLZpWHWGVTCc3dxnLEFVWZzy/h1dSD+HpHlqLeJyEcr1zRCRYjpwbVJ7KsvOrb9oJMZDuLYfO4EGUIwci49sFtz275CfvsebB7XBgSm4THu18a3Pbopu+R63IAAK5u3TUYSgmCgLT8E8Fw6GSFcMmg0UIvauApC+krhkRhlUYulW+znDZyyXbatlhTGCx6I8K0BoTqyqfexYVY8Fi3SxGmM8CqN6KjNSa4rXtkHFaOu0dxvyW7VsNfaoPFZcflpWXhksuOolIbCpx2SE4b/E47Ii+9V/F64y3KQu6yWeV3JAiIvX4eDK5ihEKAz54D2euDvygbLa96DgDgLynCySUPAaeNrgUAfWx7hPWZgII1M9ErfwcAwHNEguyWUPGyAf+96jlou1yGQk8pjBodnAc2IPuTaZBlGfO8brhFDdyCiGa7v8Px0EgIOiNcgoj/yzqIrzuPRYk+NBgele5bh+wDGzD08Gb4RA28Gi2aN2sJlAWKhWXTCFvYsuEXNDDYT8IXFgFBo8fxkkL8e/dayGUB2YL+kzAotjWAQHj55F8/ItIQgkhDCB7oNCQ4ki3fVYJDxfmINIQgwhDCUVhULR2sMXit/0RM3fg17uk0OBiCEzUVTfFq4AyliKhGyJIEn/0kfIWZZUFTYJpd1LiHoQmNCO5Xkr4SRWs/PO/9+YqyIEuS4sp3uog4uI/vgKDVQxseB11ES+jC4xTH6aIS0frRVRfcv8Ptg0bQIX7qMngFHVxuH8xnuZqYIIrQhIZDExoOfWy7Kv8Mc9ex0EW3CoRWxXnwl+TDENdJsY/ktEP2+6p0f5VGYtmy4Tq6RbmTqIG52zhFKe/7V+E+vjM46kprLh99dWpaoSY0EgLXJWly7C4vHvt+NzZlFCnqV3VtgUeGt4VWww+YtUGWZTj93uCCvkDgw//+sgBJJ2oUawjN25GKNScPodjrQlJYFN4bcl1w21u712NLfiCs7xOdoAiljjoKcdAeCMdPBVCnWPTGYK3itDgACNMZg6GUYsFvQYBZZwiuk1TsqbDumCiic0RzaAURFr0RLUOswW0mjQ7P9xoLS9mUufjQ8m0ROiOWDbkmECA57fDbMiFZoiEaQmHRG3F1664o2f0rird+ixKnDcXOYvidNhhadkbzG+cr+s5d/gIkVzHOx++0K25rTBblDkLg//xjCydBAMpGMPlw/K3rIbtLAI0WrR9dGRjlJJ9pFK0PeSumwFecEax4Cw9CY0yAIJa/zghaA0xaHUzawN/HqZEigiCgRcUgr7QIntKi4M3Rfh8G9LkChToTukY0BwA4D/0Bz/qPcbnTDp8kwS9LiAgNAwZcAyAwjVCQJUzZFBjpFrXrOxwr+z/f53XjmZIi+EUNfKIW4TuXofjyhxHW40rkukqQVWqHP+cguh1cC/felcgJDYegM2KfswTTjeVB4Zcjb0HUyb3wO/Kxu6QI6/MzEWYKQ6jRjMtb94DRaIaoNcAJQGMIRYip/AqG1HS4/T7M3/kbbuvQD7Flz4EeUS3x1aW3Vpr+SqQ2WZIAyQdZliGe9uWLJ+8oZJ8b8Psg+32QJR9kvxfwewO3y37Xx7aFoXmH4HH+kkLYN30Z2F/yI6zrWKDC2oRNAUMpIqoSWZYhOe3BkU2i0YyQdgPLt/u9yHhtfKXjLP2uU4RS551eBwSmrYVGQHLaFMdGjnoAkWOmQWOOUoRVFyvH4cbb64/gh90n4XB6EGbSY1ynWNzRvxV0GgEaUYA2+EuEVhSqdbUxY2K3StMBTyfoTYi98bXA6Kuy4MpfrByRdWrhdo05SnGs31F5JJb2DH9XnpMH4M7cdd5+QzuPQuy1Lytqtj+WQhNiDYzCOnXVQUMoF25vBI4VlmL68nQcKyqfJiQKAqYPbYPresTx3/g8ZFmG2++D3etCtDE0OEJkT1EO/so/DrvHiVKfFw91HR48ZvG+P/HpoS0o9rph1uoVI32+PbYLXx8NjKqJMZoVoZTd60JWaSBIsXmUo0wrrp9k956+zVRhmzJ4qjil7vTjeke3RCtXBKx6I7pEKBe3f6HXWGhFDax6I6IN5R8gZcmPd3tdBr/TDqnUBk1IeeAgCAIuDQlD4ep/QXLZUVpqg8Nph99pC4RIp402ipvyIYzxnYO3fUXZKN27RrGP35GP04nGsCqFUtJpoZRYIUATtHqIpnDIHhcSpn4FQdDAZ8vG0X/fgoQHvoDGHAnIMiR3KUxJfSF7XZB9bsheNySfG1JpPty5myCENy+/f4MVOmsHyF6v4ucKWuUHHOm0f4czERAYsdYhto0iTJO8Lhg0WrQuWwgeAMwV1qJqZY7A452HImb9O/DLEvQVRtOeuuKgRvIHfjntwSnrpwLIMHcJOuQdgugthqPsua6TZaDPzcH7iTSYYFv/MZwHNkBwl6C9szwIzbU2C/6fkucqwfrQGCzrcx2iDKH4YuQ/IAgCMt64BoUuBxwyoNObYDSEosOkpxUf5lzHtqFkz2+B6ZKawDRIQWeEoDUEpkVqDRB1Rgg6AwwJ3RT/j0led+C2Rsf/31RS4nVjxp/fYGv+CfyVfxz/GXxNcLQfA6mmRXKXQHIWlwU3XsiSrzzcCd72AoKIkPaDFMc60lfBb8sOhDq+wPEh7QfBmNg9uI83PwOFqf8J3h/KAiPZd+rP5SFS7LWvQN+sfFF92/pPULDqzcCXxrIEADAl9UGLW95W9JH13u3wV/jC4Gwihk9RhlKlNuibd4AmNBwA4Mk7DMnjgs92Eqeu9ypo9dBUeG1qbBhKEZGCtzAT3rwj8BYcD6x7FFxU/AQkd0lwP1Pb/opQStQZoAmLrjRFzVt4QhHEnAqlRENoYHpdZHzg9/A4aCPKptyFt4Bw2tWSAEBb4U39ufj8EopcXhSUelHkDPxeUOpBodOLwrLbRaVeFDo9uLZ7HNYeLsCHmwMjC2RZhmDzYEe2AyeL3ejfKgKvrz1c6WeIQiCk0mkCQVXgdwE6jaj4/VSgVV4/8/5axX6J0GlaQxsmQBd+2n4CoPM5oXcX4KQQA11GUXC7xqmBENcDKCmAUJIPuB0QTJEodvmCP08jCmcMr85EPG2hXdnnQf4Pr1ba7/QPjM4jW+A6llY+AuvUKKyQiBoNE6nmbDpWhMe+3wW7u3yUXohOg9lXdMKg1pHnOLLx8Ur+wMLdHhdahFiCi14fLs7Hisz9cHhdsHvdeDDlkuDaPt8eS8cr21cHp7GtGHtXMADanJ+BRem/B+//gZQhwStJ+WUpGCo5fB5IshQMsyqGRA7f6QHS2YOnitPmik8LnnpExSFUq0eYzoBW5gjFtpldR0CS5eDUuFNknwePt+kZGLXktEEqzUfxlm8Cf3ba0cbnQdTY6Yr7yv7fjEqhUcSIuxAxfEr5/fo9cOz4CVUhOW2K2+LpI5lQOVgCAE2INbB2HwL/n4khVogmCzSmwO+n/mxIUH5ZIBrMSHzoB4gma6Vvwsu6hyCK0MckBS+KAQCmyW8o9nJm/IbC9c9A9pe/dmnNLRE1/DVgtCkYXsm+QIClj1FeWcwQ1wlRl02HVGE/xTHe8tviaettCaIGgs4Y+Na+LGSquE8zUxjGt+yAo2f44G/VG2HRG+EvG2GlFcVgYJYQGo7xiSnQ+0pg0uqgqTA9zyOUB1t6UYMQrR62suegTyoPGjWCqAiBfLIEn0aLUp8XBo0HgiBAlmV4846iuKQIRWXraelEDdpXCGHn7UiFa9PnGLzrZxg1WjQzmoPbXH4fABkaQYRWECGIIpKe/VPxOHO/egYlu1YBghAIsXQGWPvfoJgW73cUIOfrZ8vCLUNwv0DoFfizWPZ7SPshivcp/pJC+IqyKhyjh6AzQcMRYUGv7vgVW/NPAACOOQrx2Obv8e9Bf2dIeIFkSQoE4qfCG583GLAERuqUhy76mDbB8AMAvAXH4TqypWx/HyD5IBpCEdZrouJnFK15H15bVuC+ysIfVPgZsj8QGoV0HIrwS24JHud3FuP4G38/rS8fEh74ArqoxPL7X/shitYsPu9j1Zij0Grmz4qa/Y8lcB3dqqiJRrMilJJcxXBs/7Eqf52QKqzhd8rpV/KW/d5K++Acy4Moj1XOiBA0WmhCw3HklVGBv1/ZDyEkEpmL7wgsvyFqghdlaqwYShE1IbIkBdZBKixbz6koE+FDb1cEBQUr30DJzl/Oe1++wsxKNV1Ey/JQStRAF94CgasMlTO16olWj62GaAyr8psOSZJhc3kVoVJhWahUUFpeLyz1oLDUq/hgfS5hBi3axYTioW93QVM28kmWEezr653ZuKVfAsIMWhSfdp+SLMPjlxG4KNn5Fz2vHY7TbusBTAZEAGGAJtQLY6kTJW+vV+x1eW4LRHu1CPPbEeazIdRnhwgJghBYnDjwO5C+z4X0L7YHAy2LJx+jbC6c+mcTy/7wU3oJpCOHg4Fb3M7vEbPn88DCpGX3JQiAIGggh4QDIVEQQiMhhMdDGPZgIGzTCNCJIjSuQuiMZuiMRuhEZcBXndFpdG4ajQbf7crGS6sOBkdHAECcxYjXJnZGm6iG+U21JEtweD2we12we11oYbIEA6QcZzGWHEqDzRtY1Pv2Dv3R0RoIFjblHsN9G8rf+L035Dp0jQyMDMooKcK7ezcGt93UplfwPg0abTCQAgLT306FUpUX53YjuuyNa8XwR5JllPq8MJeFIGEVtgkAfJIf2rLRLF0jW6DU74FVZ0K4oXz0EwDc2XEAbmnfFxadEWadMty/tX0/yLIM16FN8LsKYN/8FaRSG/wuO1r1vQa6iPLp0PY/P0f+zwsCoca5CCIixzyoeB05fcQPUDk00piq/o3v6cdqrc1haNkZmhArRGMgXNJaYysdF3v9XAgaHUST5ZxrCZ5OEEVF2HRG51mnz7F3KWxbXlOM+tJHd0Hk0DnQGCPOcWQ5fUwS9DFJVdr3dNFXPIroKx4NLPju9wY+UJ22vpOoM6HF5LcgnRrd5fOUhVyuQPDl9wSCL68LuujWAIB+MYnoF5MIpyUCBfkHFEFZnDEM7wy5FgXuUjh93kC4VBaaagQBeo0WfkmC5rQvJ3ySBK8YmDoYXFBd8gOyBF/ZqAQgMCVUqBASHnYUQO8oRInXXelKuSedxSgt+xAZqtOjVXiL4Gv7G7vWotjrRp+cI4j1umHWGcoeswtuj1OxTpvkdsB5YEOV/s6b3/y6IpQq3fs7cpc/r9hHH9MG8fcrrxCc9cHd8BZlBcOtswVfxjZ9Ye48Knic7PPAsf3HSvsbE7oqvtiTvG5AlgLb6/CLoaos1vxAyiXYVpCFzFIbLDoDpqYMUS2QCk7LUky7UoYuot4EXWS84riSPWsC50BZKCNLPoR0uARaS/lUVlfGDpTuST1t1E/5dK7ykTs+tDgt3M7/aT5K9/4ePE72e2Hpew0iR94d3MdnP3nG2QpnEnvDfIQml6+b6srYXul5qotKrBRKOdJXnHF909Od+r/iFEHUnHEka6VgRqxqoFM5DDrTsZX2u4DXAFTh2DMttSFoqrbsxem9CRod/KVFgeVHBMBnz8Hxxfcg7rZ3obUG/k8505f1jQlDKaJGRnKXBKbX6YyKbyC8hZmBbypOS/rDek1UvPk+/cX2bHy2yms+RVx6HyDLgQXFLc3OuLi2oNVD1OjgcPsrhUoFZaFSefDkQZHLiyKnT3EFsJpiNWpRWOrF+zd2QbS58gtJnsOLolIfrMbKoVRD4Bd1KBErP64fYq5T3BZkCSF+B8w+G8J8Npj9gd+P+drg6PGi4H4tnYfRz1X57+GjXQ74hfJvrydlHUKPYk+l/QAARaUAAoFmnj4Wb5wcpth815EX0cJ1DC6NCQ6tFcUaK9ZFXoYD5i7B0WkhsgsR/kK4DBGQdGZotWIgwNII0J2aYlkWpGlPjVCrEHwFf68wck2rOW3k21n211YaEXfm/bWnjZCrb9/6Otw+2F0+HC4W0MJiwr2DW+PL7VnItLvQPc6KOVd2QkSI+m+AZFlGtrM4MHLJ60IzoxmJZSN8Tq1DUux1w+Z14m+tugXXT8pxOjBhZfk3rs/2HIMrElIAAKU+Lz4++Fdw29iWycFQKuwCrvxW1avCnRrxpBc1MOsMcPrK34wmW5vh+jY9YNYZYdEZFKNOrm7VFVcmpCBMZ4DutP9Lx7TsiOF6PXxFWZBK82D7YymkspFLcNqgddrhcNphc9pgaNkFza4uX7RbEARkfzqj0sUlQtoOVIRSglZ//kAKAGQJsqcUQoVRKpXWZELldZtEYxh00a2hMVkCI5JMFkXIpKkwqkkbqbzKqCmpN1reef61Cc8UVNUEQatHq2nLzvghQZYl2Le+DseeTxV1U8JwhA98DqLWWOmY2iQIAqDVn7FXQauDqW2/at2vqU1ftGzz/nn3a37z65A9TsSfCq88Lvh9Lgg+b3C0V0F+Bjpqjbg5Oql8iqksIazP1Th4cAvySwqhlXxIMIQoRskVepyIEQS4tQaEnvahURFmCcowa0XmPmSV2hGWfxwhZaHUKf86uBm/uBchwhCCv7fujpss4cFtBe5SaAQRGkGEQaOF7rSApyrTL4UzjLzzFmXBV3jiHH+Lp+5frwil/E47cpe/UGm/xOnfKcKxwl/fhm3dfwP3odEFpzUGpjnqg6GXNjwOzf7+ouK+bBv+B78jXxF8mZL6Qh/btvxxuhzw5BxSBGqi1gDJ78XR1yYgacY3kIsCU6u04S0gQQgGk9aSfCxqtKfyFQAAR5VJREFUkYh/7VqD21r2QovMdLiKcxUj7GVJgm39f8vDmwrBTvB22cgdS++rFM9p94ldyPvuZUUIJGj1iL/vM8XjzP54Kkr3K7/AO5OQDkPQ/KYFilrO549X+r+yxS1vK0IpT/beKq2neurxVnxv7Xfkw1uQodynwswFoOphCABAOn8YdMbgp6qBy+n3f5Yw6PT9qhwanSkMqrgGqiBC0GgrXbFR1Jmgi0mCIGoD54FGC4haCFpd4O+grCZodIop3ABgaNkZ4cPuCB4naHSVLlQEANETngr0V7ZP4Gcp71sQtRArvF4Cgdeqiq9XPnsOxBBroF7hedSY1dtQ6s0338ScOXOQnZ2N7t274/XXX0e/fmd/4fz888/x9NNP48iRI2jfvj1eeeUVXH755XXYMVHdkP0++GzZwSl1gel1ZdPsik5AKg1McwjrNQkxE58KHqcNiz7ji4yvMFMZSlVY80nQGQPT6iLjgwuLV7yi3akXTVmW4fT6URSRgoJSDwryvSg8nlseNDm9wbCpsOx2xaH81SVBggw/JEGCAAFaufxNt0NTAL/ghQQ/9HIIQv3h0GtERIbokKXbD1EnIV8HHPSIGN6iDW5IXYwshwc2fwk0GsCs02LlZfchBCG4c0ArOH0S3j6yIhCOyQK6hLZCx5BW8EkynD4vfi7YBFkWIMtAkj4esdoYeP0SnH4Ptjn3QpYBSQJiheYIhQU+v4RSvwcnpOPwyzIkGTD7IqHxm+D1y/DIHpT8f3v3HSZJVS5+/HuqquP0dE8OG2ZzYoFd4sKCssgKKCgYUDCAXESiolzlXkxcrgEVvGD8EZSgKCpwhQuigGRJCixhgSVsTpNTz3SqcH5/VE9P98zs7uyyzO7svp/nmWemu6pOn6qp09311jnvsToh/7EadSsK++dikzH9XlJKK8JeDCP/du7i4BhZ0P6WAR1GYZQcr4EyFSYKhVYG/VacfitOC6UXf8U2Rqbz/dk/LQSvyp0ewm4/rir9KAl4WwhIDdFnDe8pEXP88zfspgm7aWpo5vkK/47eQO+0aX2vc9qGX/j7q0w/eGUl6LMS9Jnxwt+9VoK3o/vgjhCYG2umGiGgVRxIKxruucUhoVsIeI1uSKj/OxowqYxa3PTP9fzu+Q1sTmaJhwN8ZN96Ljl6Jv9a3835i6cStHb+3fR1fV10ZlP02hkqg9FCDySAK156iOZ0kl47w5KGGZwx6xDAv6A+5eFbCr2Qzpx9KOfN9XNJWMoo5FwCP6n3gKGzuxXnT9r6zG+hIcvSRcvCGEpRHggRD4RL+oA2xSo4c/ahxPPBpcbo4IXzEfXTeOKECwvDAAG0Y+Nlksx1s8yfur+flyjP7tpEz1O3FmaGS6d68HIpJl14e0lws+uhX9L/+iNsixkd3ivHjMRxhlwwu6MYIjeUCoQxI3G8XKrkS3Z07lFYlRMGA0yROFZFaS4qZQWY/MU7tvkauyMVjtPVm6EuXnqMtJOl6+nLSK9/tOT52JxPEj/wItReOBOdGU3ANvKgHJz/KaasILUf+jpnak2fk6Urmx/CVzTs9Mi6qaw57DT+duDJHFIzmaNmHVrotXXxA9eTyfYTcB0+PGEmh8w4qLDdwMyD/5q0kIZ4JfvUT/MDZLk0b7ZuxNWa9kw/jvZQZpDwlANw7QzL176C5blYrs1UK0i9FUQ7OTytWd3XyXWvPAptm/jY1P05tLbJ73mmNWnXwTL8YBYjBTJHE/xleNBL2yNvNzTwVbxeYTjXkKAGQGCEXDjJF+8d1jum5kNfLwlKZTe9zuZb8vnwlPKDAoZJ4+euxW15C7e3lc03n4v2XOzP38J/rnia7xx4PPtWNtL7z9txnrmNswGW/4VWILbfccPyb3Y++LMR93WoyLSDSoJSXi49LIfmSD05UaOboXjE3jFWYNj/cPjwrO0MGhlF58kI2w7raTPKXkYj1m2ESW1G2k+zvCY/g3Vgq0GXYP3s0g0Ni/ihnxgMBOW3M8tKUwOUzTkKK9E4GLwpWnfwsYUyh7ehuk/8yL8mMawt9gYMVE9m8oW3b+vwjCg8aV/Ck/bd5nrRGYt2qPwR7WWzZu+WQak//vGPXHzxxVx77bUsWrSIa665huOOO4433niDurrh3amfeuopTjvtNK644gpOPPFEfv/733PyySfzwgsvsO++2z6BhNidaK3x+ruwuzbi9neVdLEF2HzzOWTWvbTNcgbyaAxQVhCrvBant7XoSQNnSH6hyOwjmXDWjej4BHqMWNHQOJvOdI6uNpuudTadqZV0Fz2fdbyScjS65C6FrbI4KlcIikQZ/JKaNNvIGik85WLpIFX2YG+tluBK0mYvGpewV05jdk5h2broMhwrhWkopgQmcFzVYVRFA1RGAly38VV6nH5ChuL4ifO4/MDFRAImSimOu/8lurIpMkCPm6QyYkC2nYZYJb19LihIOzaRADRGQsyo8Ycw/XBNp38hqmD/prn82+ypAKScHHfcd1+hXsftuw+fnO7PqteW6eOEBx4rLPv8AftzYr7HxppkJ5945J+FN+LvH3IQRzf6s/m93LGZs/7xBzT+AMgfHngA+1VMwvE8nmtfz7deurcwXOHS+R+mKVqD42n+2b6aa1c/gtYaDVww48NUWuX+sq63ubvleb9MrTl7womECWN7Hi/2vc2TvS/7/zOtODl+LGgTx/VYkV3F2/Yqf8e14lB1BI6naXU1G/Ra2tSL/rBHz2JSel9un3gOd2oHR71JwNtI3OmnKpdhUqbM74nl9BD0mllVFqI5+BYBHaLabkJpj5jbi6tsNB75wYQkreJzpR3lrcZVNgqFqSFhd5KwO/GUUwgUKBSGNvnBrKtxCZAx+tBolrT/nZmpVfSbVfRZcfrMBGsiE1gfnZHfxigE+Pzz2K/HQJk7ytUa19H4X1t31XBP+NKR05g/McoJ+1Vxwn6lXwgDCs46dPI2A1Krkh00p5L02GnCZqBwzgL89NUneKVrM712hvkVDXz7gGMLyy75172sSvpd+JdOmF0SlHq2bR2b8gH1abHSepUHw3Rk/AuoobO7lVnBwgxlfUUXXhEzgKlUYUhib66051IsEKQ8P/NbWdFFYlUwyoX7HFkIPO1bOdjbYFqsiqdP/FIhMKS1xstl8DK91HpuIVg2oPPha3H7O/FSPYP5mPIzynlFs+pUvf+LVBx5RuGxl+2n95+lw3vAv7BURQG10QSNYORcS0YkAUWfBUY4NuwiJ1g3g4r3/htG2A8qDc3HZEbiWxxOEJ21eFgi2j2dm+mm8/GvkWsfDJSiFIkDvkRs7mm7rmLjnFIq31aH9zA7b94Rw9cPRiAY4Yfv+xwdmRSduRRTyioJ599vcq5DU6ySzmyKN+tnk9vnPVTmJxHoyaVZ+bfrCmVVBiMEa6cy4d9uoC3Txy8f+FVh2TcXvp/DmuajtWZjbzvf+fuvybkab/PbhffE2PyldJTX8oN/3UvAswm4Dp+d+z4Gbv2t7+vm1289y4K6OZRXTWVGpJwIOt+DzB9CiZsr5BAb2ntjS8GsYcGrHQx6AcN6VG61fKWoP/XHhXxFTm8rWrs4va00fu5atNas8lw29ffw5Wfu4rojTqFiVAEXIz+zpTds3WHbOsOHRW2rfH+fdmzY1WhfQ1khP0+nNTTIEhjWc0d7Xsm3jciUA/zvH9ZgECjctLCkfCMUpfqDXysEiigK7Cgz4Adr8o8D1VNKto3MXEzT1+4fVo+hGk778aiO0bDjYxjUnHDJNtcL1s8oCXZuDyM4tj1Q321b6427p9otg1L/8z//w9lnn82ZZ54JwLXXXstf/vIXbrzxRv7zP/9z2Po/+clPOP744/na174GwHe+8x0efPBBfv7zn3PttdcOW1+IXc3LZUC7GEUzFWWb36Ttzm9hd20sfAlQpsXUbz5VEvW3KibAKIJSdueGkseO62EsOh0755IM19NhVrPZKKetw6b1by/gZoN0Zxy6Ujk2p3vo8t70e9Yolwp7QuGCPGl2kLTa0MpDo5mUGUxw3R5YQ1dgE57yMLTJrNRgIvT24Bp6rGYAAjrCjNTgnayeQCt9ph8cK1dxppfPpiIaoCoa5InUKjbaWSxDMac8zHcWLKAqEqAyGuAr/9rMK12bAVhcV8F3D5tbKPOBR8p4u7ffT9iYa0G1Pk0614OX68Xq34CTzYF2se1pKKcfL9uN66SwVATPswENbhbV+hQtL1yDh0mup5GBJEmpV1fQtvZXKGXSh4WTDGKEqzACZYX8VAD9zc/j9G8ubJdZeTc97Q+hlElPzsVN54OCSpFb9xB9Pc+hDAsnOBGlBsMgVZEQNWYPuZ43SGS6Cep0Ybu5oWZml+VAWXRneym33PzrKY5rMpmciGMEY0TWdvFk3+AH3HmHTaUqn+T2dyuTrHp18IvmVSfMLyRjvm5FP79+c43/vzNM7jjxkMJ6Vy/PcNsqPwBaETS5/7j34Gk/R8h3X8zxlw1p2qjEDMf50aJP4Xgax9P86NUHeKZjNXHdzeRoFd+cNx/HtjHmfJuHVz9GX+9Gyu0UEzyPTxy5kHSkHsfT3LjhTcpSa/CsLCEVIG5FCsG5LieFm//SGlQBwmYZsyc14Hrw9+xzZHSaitbnmJHeQJAwWvvB03vqpvNo7eGgocqezJR0HWevvYI+K8GaSA+9VpTuQBSPaSg1kz4zQa9Vxsvx1/ATeClqc9OocPwAhqNyrAu/XGgztbmpxFx/pkRbZdgcerOwrCY3hYjnBxdyKk17cK3/b0VRlZtMSPv5VbIqRU+gmYHQWIU9gYAO5bdL0VfUqy5u12PmP95zKk3GTIJWRIMmM2r3QSnNe/7vOhztgvIATcgyeepDF9KXcykPB7jprX/y901v0ZvLUBmK8Jv3fqrwP79uxdM8svltAGYlakuCUiuTHbzU6Z8PQy8itz4rXLgQlBq6LB4IFYJSQ2eMmx6vJuXYxAOhwlTi4F/IXrpgKWHTGpbU2zJMHv7A+YXHWmu8bD9uqgeV7uUU5eEmm/1A0ttP4Cz8EFa8thCM6n3uf+l69Hq8dG9hOHR46kFMOHPwYhYg+dz/4vZ3si1uqrSH0pZm1/EyvSVfvI2hOZmUkR8KN9g7yYgk/M+MIeo+/j2UaeV7MpWPONQ6UN1E1THnD3tejEy7Gdz+zYXHygxSufhyIpOP3oW12nvNSdTBCE0paFrcetSnAb/tu0WBjoBh8tX9ltCVTdOZTTEnMThsZqCn1oDK/DBDpRRdrkOm6P1uIC+WVdFAj+PwZu3rhWXnzR0csr4h1c1961/nvgo/b9jN7z2Vaflhd481r+Rr/7yHeCBEZSjK1YtOoiIf7FmV7GBZx0aqAiGqPn8zs6MJTM/JB69yqCHvvbH9jidYN7OQN8wrmiFSu7lCDjGrcvh7hRGMYgSjeE62MAPj0Atlb+B9WRnDkjXjOqz/2cf8WSuVourbz2AoRa+d5ZcrnuIbo87VY/lD77YRdBkauDPLKojOXVK6rmmV5A0bOEahifNLh3ENGdKlTAtjhJ6nE876NaBKehAVf78HiO1/PLH9jx+27WiUH3jSsPxOw46PFSSx6JNbXWdLjEBoCxM6iF1lS71x92S7XVAql8vx/PPPc+mllxaeMwyDpUuX8vTTIycafPrpp7n44otLnjvuuOO46667Rlw/m82SzQ5+se3t9e8iep6H5207Cr878zzP/4I9zvdjvNOeh5tsLZm5zunahNO9CbtrA15/FxVHn0PFe88a3MgMkWtdOViGBlwHu6cZN1bD2r4uMq6Da4Yo9zxMZeBqTUYrNltRusOVtIcqSCTmkg5NpFnVsOy2h3kx8zpp1yHnOkxK70tQ+1+kuq23aA6/UXi9mf2HYeUvcjsD7bSG3i4sS+TqUfm3i6zRT6/VUlTPwR5RHhpX+XeHtAI0xEImldEAhhXF9SwsQ5EIBPn3g6f7gadIgFvX9/CP9jSmUjTFEtx29ODU5//xzzd4tLkN7bkkzB7muC8Qih2GskxCpoXO943paX2JlvvuQOeSeLledHoSdv6Cviv5Ip0bBi8Y6+3pmAQIoamzTFQgRjAUxc71E0OjtYdpBglaIZKv/RanbxOehgO8AB4KF6ju7yCX7vaPiTZpcmZihRrQkQrKzVChDdrdqynPtue3UzgbltG3KX/x7YVxncGeX+m3nqHH8Kcv7591VmHfBmRaltH99GV0eXEcZzD5bc9TD9CWz+nU5VVgO4N3wdof+DcScz9G/IAv4nhuoUxtp2m981jShkYpiw63lpxT7/8nlaLt7o9gmhYok+5MAjtbBijMUHnJ+4vjuWjPxu1vwTWh8x/fKHTdt9sdSDv+MEK3g/jK6/wvjsqkrL+TkOdihOLUloVYPMX/oqenfZRb/76R5/saUCgOjCe4eko3SiVBmTzS1c+qiTO4M1HBoaEgn6isxuvvwcv0sX79Gxj9XZTZKRKBAJMbJ3HthyajlMmJj0boyMGk1Vmqy4I0RAa/MD5aU8XcOn/o0Wenz+D0RA1t16fQup83k235Ex2qQq9TEYig8YNua9d38ev5J/NmZROfmNjI4VWzoe1tspte5+b2N+gNRumxyjhq6n7MLm/C8Tyas93c0rzM/z9oOLi8jImBWmzPY3O2gwf7OwpBtmmJ6cSJ4biaZrefDXqjH0jTmgajgaBr4ngeSdVHa3DwvaPMrsLUfntNWd00h/yhFwHToLnvQGZHy5hWGWRVb5IsWUxDoUy/bXanbepjQdrTfbzZ4/ekybpOyf+83AoVzqNkLjNkWbCwrDeXLlkWK1rWM2S7WeU1mEoRD4SZl6gvWfbFeUcWZoWrC8dKlt2w+JTiJoLneTg9LbjJNt6X7i3kWQpOmIdXNMNarn0NrX/4aqHn0tbuwIcm7Y8Rqy56DXfYLKNuumfY564RiY86KFWybSiGGav2g0vhwRniPE3p8Tzoo0T3OQYjkvB7LQWjWxy2MLRuVlEiWk0+wa8YtZG+axmROirfexUdD52LMoJUvvdKgjX7yfex3ZyBKvyPwobFx6eUDhsbWDazvJrHP3A+XTk/YDWpLFFYFjEtjps4h85siu5cmqpgpLCsPdNX8lleGQgXlnVm+kuWJazBZR1pf1mPnaHHzhBSZmHZv9rWcdXyRwvbPXjsOYVhyXetXc5dT/yBylCEqmCUbyw4htDUgwhNPYhNqR76nRxVwSgVwciwhPPF+zug8Qu/KfytPdfvsWkGStYLNS2k/vRf+r25Igmm/9dzgMbpaWbdLz9N0/m/LyRrXmNncbXHgdWTuHzhsQT7u4jMfu9gfp98QGdoPZoufXzUSdqLtzUrJ1H3yeGzBmutS5LjR+YuITJsrW2XP/Aaw8pH3lfFjtuTrudHuw+7XVCqvb0d13Wpry9NTllfX8+KFStG3Ka5uXnE9Zubm0dc/4orruDyyy8f9nxbWxuZzPBuquOJ53n09PSgtcaQqdffNVprsqlusqkusvF6woZVSGbbd/vFdG98GTwHT0PYNAnlu8F6aNqyKbTWPLrsAWrrD+SQfA6n5r5+NvW0oT0PT2sqA2UEVYCH73+CZaEG7uz9B64HE/rbOCj8Abq8JjqsalqDsKF8cLz8xO45RFz/TlG/1UlrWX7GCwU5N4vy8nVRXskHcs7Nor187iGj9MM662ax8o+tIJgGfv4bpThqSpTqcJCKiMWL6U4e6w1iGhAwDO78wAyClonWml+vaePOTX4PiqjpsWSChdv6NLprPbGOVujPYOGi+1ay4a570HYf2L3UO1Us1DFCeDT0ZmhtvongkTdglE3g/YkmDorWEjJMEuv+TLpt8G7kxdoPqoXxCHkutj24P99mMBgXCCg0imC0Ds9eQyM2aIdwrJGgtkk1vwTaHbYdLtj5UVghbK5hOYFJJ2I2vAeA1lb/gj6S7uMW9eKI203SNv/Lczj4dbAcDzt/426i63HTgg/7w760pjoL3V0d2LbNLN3NlbyGh8IDqu00tvLf9Ofobr5ODheFhyJmp+nrT5FpbWWyDnFe00F4WuMkV2OsymIrDWSZpeGTZP0ytSLXu6kwy95UXcGxxHFRBMzphX0DqPECHBhN4HQ9RwyX5KrVhWUJ3ch0ncBDUZvJ0f3q4PTvAT2FysBkCFcTdnWhTG33kWt+DijHRpHte5225t8Wtuv35tFClJaQRRmdpHv+BYBR0cRvG85gcyaJ0h4nJho4Z9J8Nt35IbSXI+UtIIfFBlMxUWWo7mtB2y54mo7yGnKdfuCmr/dZutLNeG5+KGBROzCyXRi5NgaG8xmuie31EMpsZFLLHcxPKuw1b9K/4gXOsgurUdn6PDPO8BMCr+zPcG+3wXtWP4Gr4CS9ijmJalQ0zuvhEK/k+v2cHCgumLyO+bFuUCbP9iX57/UOyooAiqsXNjGtzA/kPbQhyZWrjXx9Fb84rJyqQBmONnigI8lNGwP5yx7FPrVREhETK9dBbTRKe9ZBKdDaJh5SmJZFW1sbKmPj2P7d6g4nSUtLS+GussoOLmv3kiXnQ4MKsX9ZLTErRGM4VrLsI9UzOb5iKjErSCIQKln2+cb9KNayYQ1esg2dTTIlk0RnkuhsEjuTZG3+b51JgpOj7OOlQwr677kMZ/WzJc+FDjmNcGhwKJ7X20u6eSWj0bF5HYHo4EWHnfGw7dKhGW53W8n+AOQCcbxwBSocR4ViqHC5/xMqL/nbrpw0bNvQZ0sT4npAZ9qDdPF6FljVYAN2Ckgh3l2BQIBowKEva9Dd3Y1lWcRCHinbyp8TFTD/P9HhWrq9ehjyfxXjnwKqUaRzvaTxb2rHgAsmHjC4UtqlNd9Wp+oI35tzNN12hi47g06maE357x+9Pb3UmhG67Qxp18bp6aO1z78OWdfeUnifVQpyPUlae/0eo2vaBpdZhkG6q4eM8uvyessGXmn3k6ZHzADnTFhQqNYNq/7J/232v8ckAmH+cOhgUP+vzW+xOtVFZSBCYzjGkqJZH23PxVLG1ifsKMtPrFPUyamyagq5YBlmVROd+b4Apgsn1c3hs00L6Ovs9o9ooChhtAukHEhJ2xF7rz3pej6ZTI5qvd0uKDUWLr300pKeVb29vUyePJna2lri47ybnOd5KKWora0d9yfxOzG0W+6G/m56chkynkPUDDCvYjCI+X/rXqUlkyTjOswsr+YDk+YVll36/H1sTvWSdR1ODBi8f+NLhcTiK1pX0RGO85PFZ3H6jIO5IJ/bwImEaSlKGFsVLMM0gv6wJVfTlcuggfa2dfzppQ1UOdCdtmnJ9LE4PouMEaAtGMdV+2Ibc+jeVEPaSNNf5gcdVoVqyMT3Ie743cotI1myr4ZlEFD++PagGRyyTBHw8suMkD88TPm5WWbWx5hSVk1FJEArBg90bSgkXv7p+/djTmUVlZEAj2xazq/eTBJWmjAel0xvI+ql0dkeZne30+QlCXkZgm6awLKvou0evFySU4/5Le+bvg9hM0DUDFCXqKXz9UfJbn6G8zScD4UgCAOxYQNOC3YAxVPJBqiKBwlW13FSUY65nuzTpHoHx/VPLMouNPStThlBVCiOEYyTqGsiozT/u+Q0fyrffBYnZQQIWWGiB12Ak1yfn57a9WcL0V7+d/457YLnEmuYTmhI3rv+3kb6K5ry2xav7+Sf8wjpgW9xg0No4pVVlE0unQo8lY7REwhQCVRSfFFsFrZtBBpLLk5NYvFKyuvqqKOOQ/ATUGY3B+jcMHhc9ifH/hT3/hg8lkfSz5H4X4YjEw6gomgfz6ir41PdDbS33zhsu9Np5/QtlPkVNhFqbKLqqNLhQV42yPdCawYfazDU4HZX6lXY+YCbiS6c61akjP/3nk+Qc11c7VGeTza9+UUDdIBveGuxUbgz4lSrMDGVn0Lc9ThGu7zXWIOLYjppAgGH6EEfxulto6JlJVa6GyPTRyjdgsoncTeACgK8x2qnX/cxrb8LI52CzmYMp49ygoV4VnnNpEI+xExfgMMapnHCE9dgOhlqV9tk8gHFBm1whQrTFwzRb4Wpei2LUxfDqopjeOXEnCkEKucScnLUVlVRlx9aYi57FPp7CkM9y1/+DxL5Hosxt4aAOzh5QXXf87iBeah0G39a+kVqQ2Vsyqb44CO/RTspErn1dD3xVaa6MRY7ccqVS0xpnKcfxVL+LIInegGOUiYxpSlXwL+eJHHwJYTqD+L8/H5qrelb8UdSz16EEQyiMJidD7ZlVq9E2zYZO4e2HbSdI7LPAqxEJSiDsjmnkdu4ns6/DQabPLsPL9Ndcq4MvGFYb1yTzznil2+lVuBm/R5uRrAcI1hOmelRna+b3f02fa13058beF9RhQBi4XGRYMvfCZe1EKjeh/DE95DTB5DsOQ0jUg6mgdOzAiMSI9Rxf35bP9Fv2VFLGRhGS36yAZRfT6tsAqHG0oSo2smQ2fBoURmqZJuBHGvFj1EKIxgnWFMa1NNak2t9wV8X8gm2VdF2/mNV8tjPjWLFpzKU07fJ701WXN7AtkPqVPxaRrB8WFmFXCsl2+xes1NuiZdqJvnqzVTscwZWLEMsnKPvtVtIzP8cxkD+sbpjt16I2KvUATO3MIHIJ+vq+OR8/30g49qEi3ITHR3Yh3g8TlcuTca1aawfDKq7zRZWwP/8rgvHSm7M25uMwrL6aKIkF6+9wRxcFitdtnztszzevgrwhz5+Yv7g+9OXn72bFzo2UB2KckhNE19fcExh2cOb38L2PCqDESZGE0wsS9BrZ7E9F53rJxCOoA2FmSgjYJjUBUJcOmF0szwLsbfak67nw+HR5fva7YJSNTU1mKZJS0tLyfMtLS00NDSMuE1DQ8N2rR8KhQiFho+dNQxj3P/jwR/fvrvvi+t5pF2btGuTc10mlg2OAX+zp43VfZ1kHBtDKT7UNJiz6K61y1nWsYGM61AVivIf+7+vsOy/lt3Pky2rSTs2cyvq+dWRnygs+85Lfy/kOTm8bio/Oexkf4hdXwePv3APvW1rqEx3U2eZNEdjOF0bqVx6IW/2trGx3w8w9QQN+l97BNfTOFqjNZSneunuz/LQylY6N75FV9pm/iaLakcXumS32lnatUF3oJquQA1vVtbRFoyzIVLF5o4k1bZ/sa8xuHnyYK6B+txMKm3/HFaqNDGyVm7husnIByMUBgYmhqGoiQSpjAQwQxZl3kQ/qW8wyAcbZzGvopaKsIntdbK8O0ZMuYR0loOjaWLuCnQuSTLdzRejKeoP+XciVpCIFcDIX4wc+uZPWdgx2CPJaYeBNLrz8j8DijtsTg1bzCwv/SJihvz/u7G91yN2ctj5Haqeh84swQglUEE/4GQE4xihOEYwkf/t/12cpNPNdJN+6Re4q/5CoHofYvudQ98r12F3vAbTT6BswfmY4YrtrOCg8tkfo3z2x7a5ntYeeE4hwKXM4LCu6tGmownV7lu0npMPdHlFAbPhwa9AxYxhxysQbyK+4LzS9YsDbSVlOYV1QjX7DCvLtEL+RbH2p2XGy792oSy35LmBspUZHFbW0CGLQ8+NiPJG7GKvDIsp5aUJsrXWhV5u+xrDZxryp0o3eS+ld3HMRC31H/Gn2R4I52gnx+bbT8BJtqBzDjrnMMG2+WykveSCWuccLKBBDc5AWFU7tbCfU+PV/OLgD/L2HeeiVWl+pLDymE4KcinIASnwKvxz9RAjyR+Dy6mcfwbNv/0KvPJn1kcrMctrWNSzgv+dFEQrAxeI4OGl/bKPCbRxSKAXD3BRuG/dgXHEDwiH4zQGI7xy5bHs99W/MTdgEAjGsDc+hJfr5XB6OVxt8o+h46G0WWjPNUBFVy9efwbtuPQ7Lu7mH6PMWElCb6dvMyqYIbLP1JL9TL/5Ojil72lmlcLL+jeFolOOxYxWlP6zPMfvPTmC9Oq/YwQHv864/ZvROf9dSZtBoBwv21f4H3ipZtJr78MoBxUwUaYJ+d/KKv3BMnEyL9D32guUzfoY0clHEW6cTfhDfn7LXMertN1/C6Qg1/HQiPUbSWTyEiITDy95zrWTdD/z36MuY0Cwej61x/265DntuXQ+8sXtLsssa6DhpLuGPd/x4Ofxst3bVZYyAkw49Ylhz7f+7d+wu0fopVYSeBsMlA0E0BpOuhsjVHrjsPMf3yDbuiy/+dDAW3GwqzSQV/3eq7DiTSVl9b58A5lN/2B4sM7fpnzff6P/jTvoW/F7Mhv/QXzhBXQ8/N+FPFLxd/hZIfZuUaP0umRB9UQWVE8ccd3/XPA+zpu3uBCwKv4cnVtRT9LJ0ZlNUR+OlSzryqUL6RaqwtGSZd12prCsOlQ2bLuc57I5naTHzpQsu+HNZ1md9Icpv3/ibL530AextcuJD/6Kqxe8j8Yv/IYXulu44IUHeOAD5+7W1yZC7E7Gw/X8aIy2/rtdUCoYDHLQQQfx0EMPcfLJJwN+tPChhx7iwgsvHHGbww8/nIceeogvf/nLhecefPBBDj/88BHX3xP15D8w0EB5hI5sChQEDZNEcLSjpH0Dw7YGvsz12Vk29PeQcW0yrsMB1RMLU1u/2dPGEy2ryLoOacfmgnlHEM7PYPHAxjf44+oXybgOGcfm9vedXghsXP/G09z0lj/sJmxaPH7C4P/2/9Yt50+r/UTelaFoSVDqla5N/HWDP4xzUj7h44CMY9OTn2Epnb8Tqx0bZQWIDMyqoTWHPHED6/91K073JjwnxwnJTlL52TrCZoBNVjmup3niyedZkaiiw0n5CZb7AkxrHbwoyhoeWmmcjg6e665mY9YPjHaqfbDqW2gOxmgLlmMbcwjqhej8dLOrov8C7YeQKuzBZJEGJhX2BD+wpA3Crn+HWQHV4SjVwYOpCIeoDIdpiiVoLItRGTapCmSpCEyi0swQt2wqJx+GUXIlv5iuZ7+P07MKb9PteGt68XJJ0B7FYVsXKE63WwFUHfbvw5IfGsGtT+28JV5u+AxQKuhfYKhAtCiIlBjyd7kfZMr/bYQSmGXDk3FGp59AdPoJ210vM1xB+fzPAVA+/3P0u2VULb6c5Ks3Uz7/c2N2kaGUAWZwq/O7DfT62Bms8kmUzz9j2yuOpqx4E7XH3rBTyjLClTR89G8jBMdKA26FwJz20J6LYY18J6Zi0TdAO0OCYu4WyvJfxwiUDStHWUEiU9/jn8cD9SkKIg5sbx00FS+XQWfSeNk0XjZH+YLS89JN94Bplgxz2OLxCJR+TLv93f4fWuP2d/qzu2XbiTRNoDgMnF69GbfHf7+KmQYqYKGCFv3BlQT6a/lj42zqAkFmRmLUR+Pc+5FLCVlBMpuWk3plJdpx0Y5bCB6VHTa/JPjmtPXgdAy+Y6QzL41w3DTaHT7ToLJMv+ziNYsfK2OEZN/5YKWhSgJGyjQHl+UF6ioxK8pRlkl8/88RP+ALqGC0qCi/J2141nberR+pN88oZoMaraEB2VFTI33h27GyttxjaQfK21JZegtlab+Xqt6Ol/NyvXiZbeftGv5Swxuf278Ru/ONEdb29b58A/H9zibX9iLZTU/RuvFJlFJEprx/TD8rhDCUQWUoSmUoOmzZqdMP4NTpB4ywFVw8/yg2p3vpzKZLJp4AiFkhKkIRenJpqkKl1w2d2aLZQoe8ZnEC+Mqi6w1DKT74+B8LMwHXR2Kj3j8hxN5ntwtKAVx88cWcccYZHHzwwRx66KFcc8019Pf3F2bjO/3005k4cSJXXHEFABdddBFHHXUUP/7xjznhhBP4wx/+wHPPPcf111+/K3djTOU8l4/8/SZynkNPJo1hGhhK8dDx5xWCUhv7e7jxrWdJOw4Z1+bcuYuZnR/+8UL7Bi59/j6y+cDTze85jbkVfrfeZ9rW8vXnBqe7v+N9Z9CUn8loRU8r160YTEB/xqxDCkGpzmyKVzoHZ6LJuA7R/IwdoaIuyhnXwdNeIWBV3H05O2RK1eJl6SHL6no2cczbT5BIdzMhl+aVR68nk5jKmqOv4M2WNJv7Mriexm1ey4qsh+t5uB7kDAdP+dO+O45LLt/DYO3alfRPmYhpKCxtklQxWkIT6Q5U0x2oYWM4QHuwgjJ3FniDs3G8Xn4gOTWvMCW9hYkuumiYnjqE8pBFZSRARWWAqkiQymiAykiAquhMKiMBKiNBqrqfoIx1RHQ/2k7iZXvySbx78Pp78brywaUiGWWgTvXv9BZzut8iV9S7abS8XO+wIIjaSlBkIGgy2Fup3O+lFIxjhCqGrR9feAGJAy/yZzjZhazYBOILzkcF46RbWymva5C73ruIUsZOO+5KKcpmfGinlAVQefi3d0o5gYpGZl/Vgpvpw0224SbbcJKt+d/t/nP9nbh97VQdeyHBhumFwFf6reEzbwbr9qHqvV8vCZbZG64Ax+8dWrjYdxUNn7gBs6warV3s5jfwOlfjtLxOonIiRqiCyAHn0v3gH/0BkhaFbwnRqSdiBEP5IIzG63kMr++VQh1GasPKDKECISKTl+Sr4dcjuzKJq3pQwSBGIIgKBAnWzyFY54fJzUgNgfgc6k+9spDEO9f5Mqk196KM/AwK5PPe5eszsI9oDxL5nodoAtVThs2CpMwgZjQ/bEV7+XUZsSxd9HjE96ktBVh2xE4McO14WdsZSNqRsnZq8G1HbX9ZdserpNc/TOKgi2m9b3A2ysSBF2HFht8oEWJ3MytRy6yiGQWL/eSwkwF/JEPOK71pcMasg2nP9NOR7Wdh9eC57noePXZRUKooYOXlZzbUWlMfjVMTHn6zRwghBuyWQalPfvKTtLW18e1vf5vm5mYWLlzI3/72t8KY6XXr1pV0BVu8eDG///3v+eY3v8nXv/51Zs2axV133cW+++67q3Zhl3C1/0GyMd2LUgpTGdhFGe/7nRz3rBtMiP3xqQsKQSmlFF1Fd0KKAz6RokAQ+EGkAeEhU7lmirYLj7DdQFBq6HY51yVs5YNSVlHgybHp7mihq3ktfW0bqFz5NCe3vkFFpodHpx7H1+97na60TXfaJrZhNSese97vfqwV63SQ7naba3ibjJGgXM1DaYNePZkaZzAZc9DzEwdrpeixKtkYrqU7UM3q6FzqczNL6vn/pl3GUBENEcukMhagJgJ1oRy1QYuaYJYqK0ncTBE3MsSMFGHdR1j3g+3PEOdle1DaoP6Y/x1WbsuLN+L0bWbkAStboD20nUIFS+9IbVfvJqUwAuUYoTjazQ5bXDbzI4QnLB7suRQcDECNNK341mypd8uuYIYrSmdskYCUeJeZ4RhmOAZFCWW3aUaEmg9/E7evA7evHbevAyteT2TSe0tWU/zP8ECwYWLGalhzxVH+VOB4mOEEG6//NHg2RqSKKZc8jlnWUJj6e0D53M8RqB7Mi+IlK7BbOjDC5ZiRBEa4HCPqB5CMSCI/Y1wcM1ZDbL/SHDtVR/5gVLMolc07uvB3sH4msXkfHeVB2rrwhMNpOPn/dkpZwdr9mfipZwoBN4YEyvRAoAxKg14jBLjMaD0NJ91dus3A34WyNIUecfnn1Ujvo4ZF7fv9G3MDAbr8g0I9dHEgL1+eMkeeFrxi0TfQ+XxqA687EBxEe0PKytdzC0Gk2LxP42a7i4J/lOzTiMcSjTKDw8qKTDqKQGJ6fn9Kj8tg8LLocf74GcHhvTYCVfOIOJn8YRr+f7PiTYQnLaHzyW+hAlG0VpiRKnqX34SVmC6BKbFHMA2DyJD3509MWzjiuoZSPPKB8+nMpujMpkoCTxpNIhgmalgkQts3YkMIsffZLYNSABdeeOEWh+s9+uijw5475ZRTOOWUU4avvJdRQ+5MFg8HGBoISpcEkIYsc7YclBoasFLKD0CFTAu76EJmQjTO4rqpRKwAYdPCzHflT9suTaE6Tp18GDkbcg787oWN9GZcent7qX/1D5zdt4lIqo1Ypp0n7/t/DNxxbcz/ACQMxYM9bYXXm+pNJ+CVfjlPOF0Y2iHsDfbuWVO2P2mziq5gDV2BWroCNXQFaugJVOEp/zhEDYfGcI7F4S5qQzmqAxkqrQwVZopyI00kFCS8z9mFHk7hgB+M2Xz7MXiZ/sFE3SMYFuZRxrDE7AAqmAA2D117m/zeTaVfuAM1+4JhFXoxGcE4qqQX02AOJhWM5XNzjCxUf+B210kIsXMEa6YQrJmyzfXqTrkCN9nu97zKB6/cZAdeLs3USx8Hz8FJtrPhhjOZctG9GNEYKhDDCISJzjwCI1KOGU1ghOMYEf+nWOVRn6fy6HNGPUV3sR3ZZnc3mIMIiics2J50ecowMcvqt73iqOpjEKzdf9srjlKkKN/hOxWdfuJOK6ts9sd3WlmxOZ+EOZ/c4nI3003vS7/ES7cRmfoBYvufR9/L/w+741WSr94svWvFXkcpRdQKErWCw1JqBA2LSWUJHNvBzN8oF0KILdltg1JixxiqdBah4qBU1AoysSxBxPSDRGXW4F3H2nCMT0xbQNgMELYCTCpKPD4zXsM1i04mbFqELYupscFkwkfUT+Xx479IT8ahM52jucPm9Q0tdKZsutIuVal96Ezn2JSyueip+ynrXEEs00qF3UGl3UZnZBr3130SWOfXXzt8a83fUCVDBUb+Wl9pt5c87grU4BgW3VZ1PuBUQ18gQaVuJ6wcKsIGudgs0pM/TU80QE0kyKyIyfyNVxIlRUj3EfT6MZ0khi4aGujmf4qPc7iKxoaLGcoIxfHs4QmVt0p7aCeFGpKPxRjIt2RYhUTdamjepVC8JP+SCpZjRmqGvUR8v89vX52EEONaZNrBW13u9LViVjRixOuxKiaggsHCBXXDp/9nm+UrK7DNdYTYk4yYf/CI74x5/kEhdndBw+TPS88EDa7nYhpmIc+tEEKMRIJSexR/2NqUaDWWYRCyTKJF3d1rwmX8+ZgzR9yyJlzGV/fzh0q4nqYnY/N2ez+dqRxdaZuuVCAfaErRle6gM+UPmetK2fTlHMJuP5V2G5V2B5W5Nirtdh6tOZE+q6LwGkva/8GS9ntLXtdT5pDHFj1WFRV2B1uStBKoIEwJN3NK2cNUWmkqzAzlqp9Y435MVmnCuoeAtwlLuZjGY5hKYSWmUn/CH4aVt+n219F2aoRX2jKd7dnu3k3KDGKEKvJD3gaDSFsaWld1xHf8xNdmeNxMly2EGB+sWB1Obyvke8LKBbUQ2yb5B4XYtoFctp7n0draSnVd3bifQUwI8e6SoNQeQmmD3x55OhnbxdMaQynCAROFgedpklknH1Tyg0z+335QaSDw1J1/vjdjbzENadjtZ9/ef9FgtzPP9oNPlXY7YTc9bN3l8YNLglLdgWoMXAw8FB4GHlPstzmcR4nST4WZocpKMynWRcROYYQs3Fg9ndPPIFI9iVhtExX1k6iMhcn83/uAlfmfkQ4I+Tymgx+CXnb47G/g90hytyMopawIRjCOdjMoq3ScfPm8z+A5qZKZ4/yeTAmUNXKuji0xQjs2y50QQoyGsoJM+fJdKGt4rh4hxMgk/6AQQgixc0lQag/Ql3W455V2ZiUcfvxUO8+v72bBhDhfPaKWu1s0dbEQP/3H6m2WU+b0Up1rYYrdQZXtD7FbG53NssQRhXVCXoYTW36HkQ8qDf4MBps8TPopp8HrJBcPUxkJ0BBKccrqmwkZG1GGQgUDqFAQI+Iyv/peLMMYnD16vzjgD10LJKay6IRzh9V1UyC6/b2bcr0j9m4K1S7ALW8aMhRuIL9SPrAUiufzL5WPmGx1QGTK0u2qkxBC7CoqHKerN0NdPL7tlYUQQgghhHgXSFBqD5BxXJZMsEkuv4XfnHw+6exEIiGTVc/+khPnnY4bTlAessimkoWeTZV2O89VvpegyhKlHw+T97Tdx8KepwrlGrjsxwscmHieCCnK6CceTNFgrAf8zkgK8sEkVfhbWVGCjZN5z/tmEj/kUAC0a7PxtgS6sgwVCox6ONo2ezcNzBRXFDhSRcm8S/IvBctHLKty8eWjPNJCCCGEEEIIIYTYWSQotQcIuUlaX/8NG179MzMWnErnTz5G0xf/SO7xXxB6+X4iwVq+u/IFTDtd0rvp5NifMMJBTEOxOXIwfdY0qux/YRoKy1CYyiWkNrFfdQjLUH4SdUz6N4TRORsjFESFg/nfgcLjUO0s6k+6vaSOygxghGJoYwu9m5RRCByVJPEOVY24es3SazGs6DZnihNCCCGEEEIIIcTuSYJSe4CsWU5k3ulUtq9CO1l0x2qUnaM8bWCqPrKpVSS8FMqkMEROAVWxAFaFP+PbvLoQodqDaGu7O7+CgVVeg53sJGiWBn2i+80A0xixt5NSJiow8vC2+H5fQBkWqqjnkh+ASqAC0e0KLlllDaNeVwghhBBCCCGEELsfCUrtAeKkCUXKaDzuCrLNr6E9F6e3lalfeRCtNW6yhY3XnwK6NH25ztmFv71sD5Hph9Lw2Z8TqJyIlWhAWQHa7j8TzNDwoXBF+ZdU4XECZUW2ODQvNvfUd/U4CCGEEEIIIYQQYvyQoNQewHBt2n5xMtnudeQySQKew/qfnOQPmQvHmXLJA4SmzsGMJbASdVgVjVgVE7HKawuBJjNcjVVeg1VeU1J27XE37aK9EkIIIYQQQgghxJ5MglJ7AsNg0vm34tkp3L4uNv3qbCacdT1meRVGIIoRrmTqxc/t6loKIYQQQgghhBBCFEiG6D2AFavGCEdJr/8rwbo5qEgVwfq5pNf/FSMcxYpV7+oqCiGEEEIIIYQQQpSQnlJ7CCs2gfj+56BzOTxAKYP4/udghit2ddWEEEIIIYQQQgghhpGeUnsQM1yBsoJM+fJdKCsoASkhhBBCCCGEEELstiQotYdR4ThdGf+3EEIIIYQQQgghxO5KglJCCCGEEEIIIYQQYsxJUEoIIYQQQgghhBBCjDkJSgkhhBBCCCGEEEKIMSdBKSGEEEIIIYQQQggx5iQoJYQQQgghhBBCCCHGnASlhBBCCCGEEEIIIcSYk6CUEEIIIYQQQgghhBhzEpQSQgghhBBCCCGEEGNOglJCCCGEEEIIIYQQYsxJUEoIIYQQQgghhBBCjDkJSgkhhBBCCCGEEEKIMSdBKSGEEEIIIYQQQggx5iQoJYQQQgghhBBCCCHGnASlhBBCCCGEEEIIIcSYk6CUEEIIIYQQQgghhBhzEpQSQgghhBBCCCGEEGNOglJCCCGEEEIIIYQQYsxJUEoIIYQQQgghhBBCjDkJSgkhhBBCCCGEEEKIMSdBKSGEEEIIIYQQQggx5qxdXYHdgdYagN7e3l1ck3fO8zySySThcBjDkJijENtD2o/Ym8j5LsSOkbYjxLZJOxFix+xJbWcgvjIQb9kSCUoByWQSgMmTJ+/imgghhBBCCCGEEELsGZLJJIlEYovLld5W2Gov4HkemzZtory8HKXUrq7OO9Lb28vkyZNZv3498Xh8V1dHiHFF2o/Ym8j5LsSOkbYjxLZJOxFix+xJbUdrTTKZZMKECVvt9SU9pQDDMJg0adKursZOFY/Hx/1JLMSuIu1H7E3kfBdix0jbEWLbpJ0IsWP2lLaztR5SA8b3IEUhhBBCCCGEEEIIMS5JUEoIIYQQQgghhBBCjDkJSu1hQqEQl112GaFQaFdXRYhxR9qP2JvI+S7EjpG2I8S2STsRYsfsjW1HEp0LIYQQQgghhBBCiDEnPaWEEEIIIYQQQgghxJiToJQQQgghhBBCCCGEGHMSlBJCCCGEEEIIIYQQY06CUmPgiiuu4JBDDqG8vJy6ujpOPvlk3njjjZJ1MpkMF1xwAdXV1cRiMT72sY/R0tJSWP7SSy9x2mmnMXnyZCKRCPPmzeMnP/lJSRmPPvooSqlhP83NzVutn9aab3/72zQ2NhKJRFi6dClvvfVWyTrf+973WLx4MdFolIqKind2QITYDuO9/axZs4azzjqLadOmEYlEmDFjBpdddhm5XG4nHB2xpxnv5zvAhz/8YZqamgiHwzQ2NvLZz36WTZs2vcMjI8S27QntZ0A2m2XhwoUopXjxxRd37IAIMcSe0EamTp06rNwf/OAH7/DICLFte0L7AfjLX/7CokWLiEQiVFZWcvLJJ+/4QdlJJCg1Bh577DEuuOACnnnmGR588EFs2+bYY4+lv7+/sM5XvvIV7rnnHm6//XYee+wxNm3axEc/+tHC8ueff566ujpuvfVWXn31Vb7xjW9w6aWX8vOf/3zY673xxhts3ry58FNXV7fV+v3oRz/ipz/9Kddeey3PPvssZWVlHHfccWQymcI6uVyOU045hfPOO28nHBEhRm+8t58VK1bgeR7XXXcdr776KldffTXXXnstX//613fSERJ7kvF+vgMcffTR/OlPf+KNN97gzjvvZOXKlXz84x/fCUdHiK3bE9rPgEsuuYQJEya8g6MhxHB7Shv57//+75Jyv/jFL77DIyPEtu0J7efOO+/ks5/9LGeeeSYvvfQSTz75JJ/61Kd2wtF5h7QYc62trRrQjz32mNZa6+7ubh0IBPTtt99eWOf111/XgH766ae3WM7555+vjz766MLjRx55RAO6q6tr1HXxPE83NDToK6+8svBcd3e3DoVC+rbbbhu2/k033aQTicSoyxdiZxvP7WfAj370Iz1t2rRRv47Ye+0J5/vdd9+tlVI6l8uN+rWE2BnGa/u577779Ny5c/Wrr76qAb1s2bJRv44Q22M8tpEpU6boq6++etTlCvFuGW/tx7ZtPXHiRP2rX/1q1OWOFekptQv09PQAUFVVBfgRU9u2Wbp0aWGduXPn0tTUxNNPP73VcgbKKLZw4UIaGxt5//vfz5NPPrnVuqxevZrm5uaS104kEixatGirry3ErrIntJ8tvbYQQ433872zs5Pf/e53LF68mEAgsNXyhdjZxmP7aWlp4eyzz+a3v/0t0Wh0dDsqxA4aj20E4Ac/+AHV1dUccMABXHnllTiOs+2dFWInG2/t54UXXmDjxo0YhsEBBxxAY2MjH/jAB1i+fPnod/pdIkGpMeZ5Hl/+8pc54ogj2HfffQFobm4mGAwOy9VUX1+/xbGjTz31FH/84x/5whe+UHiusbGRa6+9ljvvvJM777yTyZMns2TJEl544YUt1meg/Pr6+lG/thC7yp7Qft5++21+9rOfcc4552xzf8XebTyf7//xH/9BWVkZ1dXVrFu3jrvvvnvU+y3EzjAe24/Wms997nOce+65HHzwwdu9z0Jsj/HYRgC+9KUv8Yc//IFHHnmEc845h+9///tccskl27XvQrxT47H9rFq1CoD/+q//4pvf/Cb33nsvlZWVLFmyhM7Ozu07ADuZtUtffS90wQUXsHz5cv7xj3/scBnLly/npJNO4rLLLuPYY48tPD9nzhzmzJlTeLx48WJWrlzJ1VdfzW9/+1t+97vflVwI//Wvf8U0zR2uhxBjbby3n40bN3L88cdzyimncPbZZ+/wPoi9w3g+37/2ta9x1llnsXbtWi6//HJOP/107r33XpRSO7wvQmyP8dh+fvazn5FMJrn00kt3uM5CjNZ4bCMAF198ceHv/fffn2AwyDnnnMMVV1xBKBTa4X0RYnuMx/bjeR4A3/jGN/jYxz4GwE033cSkSZO4/fbbd+kNcwlKjaELL7yQe++9l8cff5xJkyYVnm9oaCCXy9Hd3V0SWW1paaGhoaGkjNdee41jjjmGL3zhC3zzm9/c5mseeuihhcby4Q9/mEWLFhWWTZw4kc2bNxdeq7GxseS1Fy5cuCO7KcS7Yry3n02bNnH00UezePFirr/++lHvt9g7jffzvaamhpqaGmbPns28efOYPHkyzzzzDIcffvioj4EQO2q8tp+HH36Yp59+etiF9cEHH8ynP/1pbrnlltEdACG2Yby2kZEsWrQIx3FYs2ZNyYW8EO+W8dp+Bp7fZ599CstDoRDTp09n3bp1o9z7d8muTmq1N/A8T19wwQV6woQJ+s033xy2fCAp2h133FF4bsWKFcOSoi1fvlzX1dXpr33ta6N+7aVLl+qPfOQjW61bQ0ODvuqqqwrP9fT0SKJzsdvYE9rPhg0b9KxZs/Spp56qHccZ9euLvc+ecL4PtXbtWg3oRx55ZNR1EWJHjPf2s3btWv3KK68Ufu6//34N6DvuuEOvX79+1HURYkvGexsZya233qoNw9CdnZ2jrosQO2K8t5+Bx8WJznO5nK6rq9PXXXfdqOvybpCg1Bg477zzdCKR0I8++qjevHlz4SeVShXWOffcc3VTU5N++OGH9XPPPacPP/xwffjhhxeWv/LKK7q2tlZ/5jOfKSmjtbW1sM7VV1+t77rrLv3WW2/pV155RV900UXaMAz997//fav1+8EPfqArKir03XffrV9++WV90kkn6WnTpul0Ol1YZ+3atXrZsmX68ssv17FYTC9btkwvW7ZMJ5PJnXikhBhuvLefDRs26JkzZ+pjjjlGb9iwoeT1hRhqvJ/vzzzzjP7Zz36mly1bptesWaMfeughvXjxYj1jxgydyWR28tESotR4bz9DrV69WmbfEzvVeG8jTz31lL766qv1iy++qFeuXKlvvfVWXVtbq08//fSdfKSEGG68tx+ttb7ooov0xIkT9f33369XrFihzzrrLF1XV7fLg7oSlBoDwIg/N910U2GddDqtzz//fF1ZWamj0aj+yEc+UnLRetlll41YxpQpUwrr/PCHP9QzZszQ4XBYV1VV6SVLluiHH354m/XzPE9/61vf0vX19ToUCuljjjlGv/HGGyXrnHHGGSO+vtz5Fu+28d5+brrppi3ugxBDjffz/eWXX9ZHH320rqqq0qFQSE+dOlWfe+65esOGDTvl+AixNeO9/QwlQSmxs433NvL888/rRYsW6UQiocPhsJ43b57+/ve/Lzc9xJgY7+1Ha79n1L//+7/ruro6XV5erpcuXaqXL1/+jo/NO6W01no7RvsJIYQQQgghhBBCCPGOGbu6AkIIIYQQQgghhBBi7yNBKSGEEEIIIYQQQggx5iQoJYQQQgghhBBCCCHGnASlhBBCCCGEEEIIIcSYk6CUEEIIIYQQQgghhBhzEpQSQgghhBBCCCGEEGNOglJCCCGEEEIIIYQQYsxJUEoIIYQQQgghhBBCjDkJSgkhhBBC7GJLlixBKbWrqyGEEEIIMaasXV0BIYQQQog9yfYGl7TW71JNhBBCCCF2bxKUEkIIIYTYiS677LJhz11zzTX09PSMuAzgN7/5DalU6t2umhBCCCHEbkVpuT0nhBBCCPGumjp1KmvXrpVeUUIIIYQQRSSnlBBCCCHELjZSTqmbb74ZpRQ333wz99xzD4sWLSIajTJx4kS+9a1v4XkeALfccgsLFiwgEonQ1NTElVdeOeJraK258cYbOeKII4jH40SjUQ4++GBuvPHGd33/hBBCCCFGIsP3hBBCCCF2Y3/+85954IEHOPnkkzniiCP4y1/+wne/+1201iQSCb773e9y0kknsWTJEu68804uueQS6uvrOf300wtlaK359Kc/zW233casWbP41Kc+RTAY5MEHH+Sss87itdde46qrrtqFeymEEEKIvZEM3xNCCCGEeJdta/jekiVLeOyxx0qW33zzzZx55pkEAgGefPJJDjnkEACSySQzZ86kr6+PeDzOk08+yfTp0wFYv349M2fOZM6cObz88suFsm644Qa+8IUvcOaZZ3LdddcRCAQAyOVyfPzjH+eee+7hueee46CDDnq3DoEQQgghxDAyfE8IIYQQYjf2mc98phCQAigvL+fEE08klUpx3nnnFQJSAJMnT+bII4/ktddew3GcwvM///nPKSsr4xe/+EUhIAUQDAb53ve+B8Btt902BnsjhBBCCDFIhu8JIYQQQuzGFi5cOOy5xsbGrS5zXZeWlhYmTpxIKpXilVdeYcKECfzwhz8ctr5t2wCsWLFip9ZbCCGEEGJbJCglhBBCCLEbi8fjw56zLGubywaCTV1dXWit2bhxI5dffvkWX6e/v39nVFcIIYQQYtQkKCWEEEIIsQcbCFwddNBBPPfcc7u4NkIIIYQQgySnlBBCCCHEHqy8vJx58+bx+uuv093dvaurI4QQQghRIEEpIYQQQog93Je+9CVSqRRnn332iMP0Vq9ezZo1a8a+YkIIIYTYq8nwPSGEEEKIPdw555zDM888wy233MKTTz7J0qVLmTBhAi0tLaxYsYJnn32W3//+90ydOnVXV1UIIYQQexEJSgkhhBBC7OGUUtx888188IMf5IYbbuDee++lr6+Puro6Zs2axVVXXcXSpUt3dTWFEEIIsZdRWmu9qyshhBBCCCGEEEIIIfYuklNKCCGEEEIIIYQQQow5CUoJIYQQQgghhBBCiDEnQSkhhBBCCCGEEEIIMeYkKCWEEEIIIYQQQgghxpwEpYQQQgghhBBCCCHEmJOglBBCCCGEEEIIIYQYcxKUEkIIIYQQQgghhBBjToJSQgghhBBCCCGEEGLMSVBKCCGEEEIIIYQQQow5CUoJIYQQQgghhBBCiDEnQSkhhBBCCCGEEEIIMeYkKCWEEEIIIYQQQgghxpwEpYQQQgghhBBCCCHEmPv/VeCXSWwDoeMAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_data(agg_df, col_name='ite')" ] @@ -988,34 +296,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLPanelData Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: y\n", - "Treatment variable(s): ['d']\n", - "Covariates: ['Z1', 'Z2', 'Z3', 'Z4']\n", - "Instrument variable(s): None\n", - "Time variable: t\n", - "Id variable: id\n", - "No. Observations: 29998\n", - "\n", - "------------------ DataFrame info ------------------\n", - "\n", - "RangeIndex: 29998 entries, 0 to 29997\n", - "Columns: 12 entries, id to First Treated\n", - "dtypes: datetime64[s](2), float64(8), int32(1), object(1)\n", - "memory usage: 2.6+ MB\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dml_data = DoubleMLPanelData(\n", " data=df,\n", @@ -1063,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1087,24 +370,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "ename": "UFuncTypeError", - "evalue": "Cannot cast ufunc 'greater' input 0 from dtype(' 1\u001b[0m dml_obj \u001b[38;5;241m=\u001b[39m \u001b[43mDoubleMLDIDMulti\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdml_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdefault_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m dml_obj\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(dml_obj)\n", - "File \u001b[1;32m~\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_multi.py:169\u001b[0m, in \u001b[0;36mDoubleMLDIDMulti.__init__\u001b[1;34m(self, obj_dml_data, ml_g, ml_m, gt_combinations, control_group, anticipation_periods, n_folds, n_rep, score, panel, in_sample_normalization, trimming_rule, trimming_threshold, draw_sample_splitting, print_periods)\u001b[0m\n\u001b[0;32m 166\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_never_treated_value \u001b[38;5;241m=\u001b[39m _get_never_treated_value(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg_values)\n\u001b[0;32m 167\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_anticipation_periods \u001b[38;5;241m=\u001b[39m _check_anticipation_periods(anticipation_periods)\n\u001b[1;32m--> 169\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gt_combinations \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_gt_combinations\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgt_combinations\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 170\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gt_index \u001b[38;5;241m=\u001b[39m _construct_gt_index(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgt_combinations, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg_values, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mt_values)\n\u001b[0;32m 171\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_post_treatment_mask \u001b[38;5;241m=\u001b[39m _construct_post_treatment_mask(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg_values, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mt_values)\n", - "File \u001b[1;32m~\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_multi.py:1254\u001b[0m, in \u001b[0;36mDoubleMLDIDMulti._validate_gt_combinations\u001b[1;34m(self, gt_combinations)\u001b[0m\n\u001b[0;32m 1251\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Validate all treatment-time combinations.\"\"\"\u001b[39;00m\n\u001b[0;32m 1253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gt_combinations, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m-> 1254\u001b[0m gt_combinations \u001b[38;5;241m=\u001b[39m \u001b[43m_construct_gt_combinations\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1255\u001b[0m \u001b[43m \u001b[49m\u001b[43mgt_combinations\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mg_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mt_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnever_treated_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manticipation_periods\u001b[49m\n\u001b[0;32m 1256\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gt_combinations, \u001b[38;5;28mlist\u001b[39m):\n\u001b[0;32m 1259\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m 1260\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgt_combinations must be a list. \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(gt_combinations)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(gt_combinations)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m was passed.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1261\u001b[0m )\n", - "File \u001b[1;32m~\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\utils\\_did_utils.py:136\u001b[0m, in \u001b[0;36m_construct_gt_combinations\u001b[1;34m(setting, g_values, t_values, never_treated_value, anticipation_periods)\u001b[0m\n\u001b[0;32m 133\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgt_combinations must be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalid_settings\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msetting\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m was passed.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 135\u001b[0m treatment_groups \u001b[38;5;241m=\u001b[39m g_values[\u001b[38;5;241m~\u001b[39m_is_never_treated(g_values, never_treated_value)]\n\u001b[1;32m--> 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdiff\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtreatment_groups\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m):\n\u001b[0;32m 137\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mg_values must be sorted in ascending order (Excluding never treated group).\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(np\u001b[38;5;241m.\u001b[39mdiff(t_values) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m):\n", - "\u001b[1;31mUFuncTypeError\u001b[0m: Cannot cast ufunc 'greater' input 0 from dtype(' Date: Mon, 16 Jun 2025 11:15:55 +0000 Subject: [PATCH 08/14] update dgp for anticipation --- doc/examples/did/py_rep_cs.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/examples/did/py_rep_cs.ipynb b/doc/examples/did/py_rep_cs.ipynb index 97e77bcd..2d50f619 100644 --- a/doc/examples/did/py_rep_cs.ipynb +++ b/doc/examples/did/py_rep_cs.ipynb @@ -760,7 +760,7 @@ "n_obs = 4000\n", "n_periods = 6\n", "\n", - "df_anticipation = make_did_CS2021(n_obs, dgp_type=4, n_periods=n_periods, n_pre_treat_periods=3, time_type=\"datetime\", anticipation_periods=1)\n", + "df_anticipation = make_did_cs_CS2021(n_obs, dgp_type=4, n_periods=n_periods, n_pre_treat_periods=3, time_type=\"datetime\", anticipation_periods=1)\n", "\n", "print(df_anticipation.shape)\n", "df_anticipation.head()\n" @@ -933,7 +933,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -947,7 +947,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.3" } }, "nbformat": 4, From 08f199122cf2b45e410b62e8e0f56199401f7baf Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 20 Jun 2025 08:09:25 +0000 Subject: [PATCH 09/14] add universal gt combinations to example notebooks --- doc/examples/did/py_panel.ipynb | 25 ++++++++++++++++++++++++- doc/examples/did/py_rep_cs.ipynb | 25 ++++++++++++++++++++++++- 2 files changed, 48 insertions(+), 2 deletions(-) diff --git a/doc/examples/did/py_panel.ipynb b/doc/examples/did/py_panel.ipynb index 64d84be8..69ceb24e 100644 --- a/doc/examples/did/py_panel.ipynb +++ b/doc/examples/did/py_panel.ipynb @@ -873,7 +873,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### All combinations\n", + "### All Combinations\n", "\n", "The option `gt_combinations=\"all\"` includes all relevant group time values with $t_\\text{pre} < \\min(\\mathrm{g}, t_\\text{eval})$, including longer parallel trend assumptions.\n", "This can result in multiple estimates for the same $ATT(\\mathrm{g},t)$, which have slightly different assumptions (length of parallel trends)." @@ -891,6 +891,29 @@ "dml_obj_all.plot_effects()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Universal Base Period\n", + "\n", + "The option `gt_combinations=\"universal\"` set $t_\\text{pre} = \\mathrm{g} - \\delta - 1$, corresponding to a universal/constant comparison or base period.\n", + "\n", + "Remark that this implies $t_\\text{pre} > t_\\text{eval}$ for all pre-treatment periods (accounting for anticipation). Therefore these effects do not have the same straightforward interpretation as ATT's." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dml_obj_universal = DoubleMLDIDMulti(dml_data, **(default_args| {\"gt_combinations\": \"universal\"}))\n", + "dml_obj_universal.fit()\n", + "dml_obj_universal.bootstrap(n_rep_boot=5000)\n", + "dml_obj_universal.plot_effects()" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/doc/examples/did/py_rep_cs.ipynb b/doc/examples/did/py_rep_cs.ipynb index 2d50f619..fdc291ff 100644 --- a/doc/examples/did/py_rep_cs.ipynb +++ b/doc/examples/did/py_rep_cs.ipynb @@ -880,7 +880,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### All combinations\n", + "### All Combinations\n", "\n", "The option `gt_combinations=\"all\"` includes all relevant group time values with $t_\\text{pre} < \\min(\\mathrm{g}, t_\\text{eval})$, including longer parallel trend assumptions.\n", "This can result in multiple estimates for the same $ATT(\\mathrm{g},t)$, which have slightly different assumptions (length of parallel trends)." @@ -898,6 +898,29 @@ "dml_obj_all.plot_effects()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Universal Base Period\n", + "\n", + "The option `gt_combinations=\"universal\"` set $t_\\text{pre} = \\mathrm{g} - \\delta - 1$, corresponding to a universal/constant comparison or base period.\n", + "\n", + "Remark that this implies $t_\\text{pre} > t_\\text{eval}$ for all pre-treatment periods (accounting for anticipation). Therefore these effects do not have the same straightforward interpretation as ATT's." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dml_obj_universal = DoubleMLDIDMulti(dml_data, **(default_args| {\"gt_combinations\": \"universal\"}))\n", + "dml_obj_universal.fit()\n", + "dml_obj_universal.bootstrap(n_rep_boot=5000)\n", + "dml_obj_universal.plot_effects()" + ] + }, { "cell_type": "markdown", "metadata": {}, From 067cbb642a53b1a7a6a997dc0e2dfeabb14fe8e4 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 23 Jun 2025 08:16:59 +0200 Subject: [PATCH 10/14] fix panel notebook output --- doc/examples/did/py_panel.ipynb | 2 +- doc/examples/did/py_rep_cs.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/examples/did/py_panel.ipynb b/doc/examples/did/py_panel.ipynb index 69ceb24e..9defe82b 100644 --- a/doc/examples/did/py_panel.ipynb +++ b/doc/examples/did/py_panel.ipynb @@ -64,7 +64,7 @@ "source": [ "### Data Details\n", "\n", - "*Here, we slightly abuse the definition of the potential outcomes. $Y_{i,t}(1)$ corresponds to the (potential) outcome if unit $i$ would have received treatment at time period $\\mathrm{g}$ (where the group $\\mathrm{g}$ is drawn with probabilities based on $Z$).*\n", + "Here, we slightly abuse the definition of the potential outcomes. $Y_{i,t}(1)$ corresponds to the (potential) outcome if unit $i$ would have received treatment at time period $\\mathrm{g}$ (where the group $\\mathrm{g}$ is drawn with probabilities based on $Z$).\n", "\n", "More specifically\n", "\n", diff --git a/doc/examples/did/py_rep_cs.ipynb b/doc/examples/did/py_rep_cs.ipynb index fdc291ff..ae47c838 100644 --- a/doc/examples/did/py_rep_cs.ipynb +++ b/doc/examples/did/py_rep_cs.ipynb @@ -65,7 +65,7 @@ "source": [ "### Data Details\n", "\n", - "*Here, we slightly abuse the definition of the potential outcomes. $Y_{i,t}(1)$ corresponds to the (potential) outcome if unit $i$ would have received treatment at time period $\\mathrm{g}$ (where the group $\\mathrm{g}$ is drawn with probabilities based on $Z$).*\n", + "Here, we slightly abuse the definition of the potential outcomes. $Y_{i,t}(1)$ corresponds to the (potential) outcome if unit $i$ would have received treatment at time period $\\mathrm{g}$ (where the group $\\mathrm{g}$ is drawn with probabilities based on $Z$).\n", "\n", "The data set with repeated cross-sectional data is generated on the basis of a panel data set with the following data generating process (DGP). To obtain repeated cross-sectional data, the number of generated individuals is increased to $\\frac{n_{obs}}{\\lambda_t}$, where $\\lambda_t$ denotes the probability to observe a unit at each time period (time constant).\n", "\n", From 3a0aafd24824fbb4cb2fa9e1087168cd08dfb275 Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Tue, 1 Jul 2025 14:54:40 +0200 Subject: [PATCH 11/14] upd notebook (float periods) --- doc/examples/did/py_rep_cs.ipynb | 73 +++++++++++++++----------------- 1 file changed, 33 insertions(+), 40 deletions(-) diff --git a/doc/examples/did/py_rep_cs.ipynb b/doc/examples/did/py_rep_cs.ipynb index ae47c838..d666dee0 100644 --- a/doc/examples/did/py_rep_cs.ipynb +++ b/doc/examples/did/py_rep_cs.ipynb @@ -51,8 +51,8 @@ "n_obs = 5000\n", "n_periods = 6\n", "\n", - "df = make_did_cs_CS2021(n_obs, dgp_type=4, include_never_treated=True, n_periods=n_periods, n_pre_treat_periods=3, lambda_t=0.5,\n", - " time_type=\"datetime\")\n", + "df = make_did_cs_CS2021(n_obs, dgp_type=4, include_never_treated=True, n_periods=n_periods, n_pre_treat_periods=3,\n", + " lambda_t=0.5, time_type=\"float\")\n", "df[\"ite\"] = df[\"y1\"] - df[\"y0\"]\n", "\n", "print(df.shape)\n", @@ -65,7 +65,7 @@ "source": [ "### Data Details\n", "\n", - "Here, we slightly abuse the definition of the potential outcomes. $Y_{i,t}(1)$ corresponds to the (potential) outcome if unit $i$ would have received treatment at time period $\\mathrm{g}$ (where the group $\\mathrm{g}$ is drawn with probabilities based on $Z$).\n", + "*Here, we slightly abuse the definition of the potential outcomes. $Y_{i,t}(1)$ corresponds to the (potential) outcome if unit $i$ would have received treatment at time period $\\mathrm{g}$ (where the group $\\mathrm{g}$ is drawn with probabilities based on $Z$).*\n", "\n", "The data set with repeated cross-sectional data is generated on the basis of a panel data set with the following data generating process (DGP). To obtain repeated cross-sectional data, the number of generated individuals is increased to $\\frac{n_{obs}}{\\lambda_t}$, where $\\lambda_t$ denotes the probability to observe a unit at each time period (time constant).\n", "\n", @@ -171,7 +171,6 @@ "outputs": [], "source": [ "# rename for plotting\n", - "df[\"First Treated\"] = df[\"d\"].dt.strftime(\"%Y-%m\").fillna(\"Never Treated\")\n", "\n", "# Create aggregation dictionary for means\n", "def agg_dict(col_name):\n", @@ -184,7 +183,7 @@ "# Calculate means and confidence intervals\n", "agg_dictionary = agg_dict(\"y\") | agg_dict(\"ite\")\n", "\n", - "agg_df = df.groupby([\"t\", \"First Treated\"]).agg(**agg_dictionary).reset_index()\n", + "agg_df = df.groupby([\"t\", \"d\"]).agg(**agg_dictionary).reset_index()\n", "agg_df.head()" ] }, @@ -206,15 +205,15 @@ " Column name to plot (will use '{col_name}_mean')\n", " \"\"\"\n", " plt.figure(figsize=(12, 7))\n", - " n_colors = df[\"First Treated\"].nunique()\n", + " n_colors = df[\"d\"].nunique()\n", " color_palette = sns.color_palette(\"colorblind\", n_colors=n_colors)\n", "\n", " sns.lineplot(\n", " data=df,\n", " x='t',\n", " y=f'{col_name}_mean',\n", - " hue='First Treated',\n", - " style='First Treated',\n", + " hue='d',\n", + " style='d',\n", " palette=color_palette,\n", " markers=True,\n", " dashes=True,\n", @@ -227,7 +226,7 @@ " plt.ylabel(f'Average Value {col_name}', fontsize=14)\n", " \n", "\n", - " plt.legend(title='First Treated', title_fontsize=13, fontsize=12, \n", + " plt.legend(title='d', title_fontsize=13, fontsize=12, \n", " frameon=True, framealpha=0.9, loc='best')\n", " \n", " plt.grid(alpha=0.3, linestyle='-')\n", @@ -307,7 +306,6 @@ " id_col=\"id\",\n", " t_col=\"t\",\n", " x_cols=[\"Z1\", \"Z2\", \"Z3\", \"Z4\"],\n", - " datetime_unit=\"M\"\n", ")\n", "print(dml_data)" ] @@ -780,8 +778,7 @@ "outputs": [], "source": [ "df_anticipation[\"ite\"] = df_anticipation[\"y1\"] - df_anticipation[\"y0\"]\n", - "df_anticipation[\"First Treated\"] = df_anticipation[\"d\"].dt.strftime(\"%Y-%m\").fillna(\"Never Treated\")\n", - "agg_df_anticipation = df_anticipation.groupby([\"t\", \"First Treated\"]).agg(**agg_dictionary).reset_index()\n", + "agg_df_anticipation = df_anticipation.groupby([\"t\", \"d\"]).agg(**agg_dictionary).reset_index()\n", "agg_df_anticipation.head()" ] }, @@ -880,7 +877,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### All Combinations\n", + "### All combinations\n", "\n", "The option `gt_combinations=\"all\"` includes all relevant group time values with $t_\\text{pre} < \\min(\\mathrm{g}, t_\\text{eval})$, including longer parallel trend assumptions.\n", "This can result in multiple estimates for the same $ATT(\\mathrm{g},t)$, which have slightly different assumptions (length of parallel trends)." @@ -902,11 +899,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Universal Base Period\n", - "\n", - "The option `gt_combinations=\"universal\"` set $t_\\text{pre} = \\mathrm{g} - \\delta - 1$, corresponding to a universal/constant comparison or base period.\n", + "### Selected Combinations\n", "\n", - "Remark that this implies $t_\\text{pre} > t_\\text{eval}$ for all pre-treatment periods (accounting for anticipation). Therefore these effects do not have the same straightforward interpretation as ATT's." + "Instead it is also possible to just submit a list of tuples containing $(\\mathrm{g}, t_\\text{pre}, t_\\text{eval})$ combinations. E.g. only two combinations" ] }, { @@ -915,19 +910,28 @@ "metadata": {}, "outputs": [], "source": [ - "dml_obj_universal = DoubleMLDIDMulti(dml_data, **(default_args| {\"gt_combinations\": \"universal\"}))\n", - "dml_obj_universal.fit()\n", - "dml_obj_universal.bootstrap(n_rep_boot=5000)\n", - "dml_obj_universal.plot_effects()" + "gt_dict = {\n", + " \"gt_combinations\": [\n", + " (4.0, 1, 2),\n", + " (4.0, 1, 3),\n", + " ]\n", + "}\n", + "\n", + "dml_obj_all = DoubleMLDIDMulti(dml_data, **(default_args| gt_dict))\n", + "dml_obj_all.fit()\n", + "dml_obj_all.bootstrap(n_rep_boot=5000)\n", + "dml_obj_all.plot_effects()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Selected Combinations\n", + "### Universal Base Period\n", "\n", - "Instead it is also possible to just submit a list of tuples containing $(\\mathrm{g}, t_\\text{pre}, t_\\text{eval})$ combinations. E.g. only two combinations" + "The option `gt_combinations=\"universal\"` set $t_\\text{pre} = \\mathrm{g} - \\delta - 1$, corresponding to a universal/constant comparison or base period.\n", + "\n", + "Remark that this implies $t_\\text{pre} > t_\\text{eval}$ for all pre-treatment periods (accounting for anticipation). Therefore these effects do not have the same straightforward interpretation as ATT's." ] }, { @@ -936,27 +940,16 @@ "metadata": {}, "outputs": [], "source": [ - "gt_dict = {\n", - " \"gt_combinations\": [\n", - " (np.datetime64('2025-04'),\n", - " np.datetime64('2025-01'),\n", - " np.datetime64('2025-02')),\n", - " (np.datetime64('2025-04'),\n", - " np.datetime64('2025-02'),\n", - " np.datetime64('2025-03')),\n", - " ]\n", - "}\n", - "\n", - "dml_obj_all = DoubleMLDIDMulti(dml_data, **(default_args| gt_dict))\n", - "dml_obj_all.fit()\n", - "dml_obj_all.bootstrap(n_rep_boot=5000)\n", - "dml_obj_all.plot_effects()" + "dml_obj_universal = DoubleMLDIDMulti(dml_data, **(default_args| {\"gt_combinations\": \"universal\"}))\n", + "dml_obj_universal.fit()\n", + "dml_obj_universal.bootstrap(n_rep_boot=5000)\n", + "dml_obj_universal.plot_effects()" ] } ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -970,7 +963,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.10" } }, "nbformat": 4, From 84246d43290f8a9c7096c01d79c10dc8309603ec Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 7 Jul 2025 13:28:58 +0000 Subject: [PATCH 12/14] add release notes for 0.10.1 --- doc/release/release.rst | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/doc/release/release.rst b/doc/release/release.rst index a5febb76..ab4a4cf7 100644 --- a/doc/release/release.rst +++ b/doc/release/release.rst @@ -7,6 +7,33 @@ Release Notes .. tab-item:: Python + .. dropdown:: DoubleML 0.10.1 + :class-title: sd-bg-primary sd-font-weight-bold + :open: + + - **Release highlight:** Multi-Period Difference-in-Differences for Repeated Cross Sections + + - Implementation via ``DoubleMLDIDMulti`` class + `Py #330 `_ + `Py #345 `_ + - Extended User Guide and Example Gallery + `Docs #243 `_ + + - Allow user defined bandwidth for ``RDFlex`` + `Py #343 `_ + + - Maintenance package + `Py #327 `_ + `Py #336 `_ + + - Maintenance documentation + `Docs #241 `_ + `Docs #242 `_ + `Docs #244 `_ + `Docs #245 `_ + `Docs #246 `_ + + .. dropdown:: DoubleML 0.10.0 :class-title: sd-bg-primary sd-font-weight-bold :open: From bb363c70fdac0cfdc9235e6d4017b39635544290 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 7 Jul 2025 17:19:34 +0200 Subject: [PATCH 13/14] fix release note formatting --- doc/release/release.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/release/release.rst b/doc/release/release.rst index ab4a4cf7..9c0c3892 100644 --- a/doc/release/release.rst +++ b/doc/release/release.rst @@ -19,12 +19,12 @@ Release Notes - Extended User Guide and Example Gallery `Docs #243 `_ - - Allow user defined bandwidth for ``RDFlex`` - `Py #343 `_ + - Allow user defined bandwidth for ``RDFlex`` + `Py #343 `_ - - Maintenance package - `Py #327 `_ - `Py #336 `_ + - Maintenance package + `Py #327 `_ + `Py #336 `_ - Maintenance documentation `Docs #241 `_ From 23fa23c63ea393b7b68026afdeba6278db7fe0a4 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 7 Jul 2025 17:36:20 +0200 Subject: [PATCH 14/14] update cs example and add to gallery --- doc/examples/did/py_rep_cs.ipynb | 62 ++++++++++++++++---------------- doc/examples/index.rst | 1 + 2 files changed, 33 insertions(+), 30 deletions(-) diff --git a/doc/examples/did/py_rep_cs.ipynb b/doc/examples/did/py_rep_cs.ipynb index d666dee0..c27f08ca 100644 --- a/doc/examples/did/py_rep_cs.ipynb +++ b/doc/examples/did/py_rep_cs.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Python: Pepeated Cross-Sectional Data with Multiple Time Periods\n", + "# Python: Repeated Cross-Sectional Data with Multiple Time Periods\n", "\n", "In this example, a detailed guide on Difference-in-Differences with multiple time periods using the [DoubleML-package](https://docs.doubleml.org/stable/index.html). The implementation is based on [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001).\n", "\n", @@ -37,9 +37,11 @@ "source": [ "## Data\n", "\n", - "We will rely on the `make_did_CS2021` DGP, which is inspired by [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001) (Appendix SC) and [Sant'Anna and Zhao (2020)](https://doi.org/10.1016/j.jeconom.2020.06.003).\n", + "We will rely on the `make_did_cs_CS2021` DGP, which is inspired by [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001) (Appendix SC) and [Sant'Anna and Zhao (2020)](https://doi.org/10.1016/j.jeconom.2020.06.003).\n", "\n", - "We will observe `n_obs` units over `n_periods`. Remark that the dataframe includes observations of the potential outcomes `y0` and `y1`, such that we can use oracle estimates as comparisons. " + "We will observe approximately `n_obs` units over `n_periods`. The parameter `lambda_t` determines the probability of observing a unit ``i`` in time period ``t``. The parameter `lambda_t` is set to 0.5 for all time periods, which means that each unit has a 50% chance of being observed in each time period.\n", + "\n", + "Remark that the dataframe includes observations of the potential outcomes `y0` and `y1`, such that we can use oracle estimates as comparisons." ] }, { @@ -389,7 +391,7 @@ "The choice `gt_combinations=\"standard\"`, used estimates all possible combinations of $ATT(g,t_\\text{eval})$ via $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$,\n", "where the standard choice is $t_\\text{pre} = \\min(\\mathrm{g}, t_\\text{eval}) - 1$ (without anticipation).\n", "\n", - "Remark that this includes pre-tests effects if $\\mathrm{g} > t_{eval}$, e.g. $\\widehat{ATT}(g=\\text{2025-04}, t_{\\text{pre}}=\\text{2025-01}, t_{\\text{eval}}=\\text{2025-02})$ which estimates the pre-trend from January to February even if the actual treatment occured in April." + "Remark that this includes pre-tests effects if $\\mathrm{g} > t_{eval}$, e.g. $\\widehat{ATT}(g=3, t_{\\text{pre}}=0, t_{\\text{eval}}=1)$ which estimates the pre-trend from time period $0$ to $1$ even if the actual treatment occured in time period $3$." ] }, { @@ -630,9 +632,9 @@ "metadata": {}, "outputs": [], "source": [ - "df[\"e\"] = pd.to_datetime(df[\"t\"]).values.astype(\"datetime64[M]\") - \\\n", - " pd.to_datetime(df[\"d\"]).values.astype(\"datetime64[M]\")\n", - "df.groupby(\"e\")[\"ite\"].mean()[1:]" + "df_treated = df[df[\"d\"] != np.inf].copy()\n", + "df_treated[\"e\"] = df_treated[\"t\"] - df_treated[\"d\"]\n", + "df_treated.groupby(\"e\")[\"ite\"].mean().iloc[1:]" ] }, { @@ -899,9 +901,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Selected Combinations\n", + "### Universal Base Period\n", "\n", - "Instead it is also possible to just submit a list of tuples containing $(\\mathrm{g}, t_\\text{pre}, t_\\text{eval})$ combinations. E.g. only two combinations" + "The option `gt_combinations=\"universal\"` set $t_\\text{pre} = \\mathrm{g} - \\delta - 1$, corresponding to a universal/constant comparison or base period.\n", + "\n", + "Remark that this implies $t_\\text{pre} > t_\\text{eval}$ for all pre-treatment periods (accounting for anticipation). Therefore these effects do not have the same straightforward interpretation as ATT's." ] }, { @@ -910,28 +914,19 @@ "metadata": {}, "outputs": [], "source": [ - "gt_dict = {\n", - " \"gt_combinations\": [\n", - " (4.0, 1, 2),\n", - " (4.0, 1, 3),\n", - " ]\n", - "}\n", - "\n", - "dml_obj_all = DoubleMLDIDMulti(dml_data, **(default_args| gt_dict))\n", - "dml_obj_all.fit()\n", - "dml_obj_all.bootstrap(n_rep_boot=5000)\n", - "dml_obj_all.plot_effects()" + "dml_obj_universal = DoubleMLDIDMulti(dml_data, **(default_args| {\"gt_combinations\": \"universal\"}))\n", + "dml_obj_universal.fit()\n", + "dml_obj_universal.bootstrap(n_rep_boot=5000)\n", + "dml_obj_universal.plot_effects()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Universal Base Period\n", - "\n", - "The option `gt_combinations=\"universal\"` set $t_\\text{pre} = \\mathrm{g} - \\delta - 1$, corresponding to a universal/constant comparison or base period.\n", + "### Selected Combinations\n", "\n", - "Remark that this implies $t_\\text{pre} > t_\\text{eval}$ for all pre-treatment periods (accounting for anticipation). Therefore these effects do not have the same straightforward interpretation as ATT's." + "Instead it is also possible to just submit a list of tuples containing $(\\mathrm{g}, t_\\text{pre}, t_\\text{eval})$ combinations. E.g. only two combinations" ] }, { @@ -940,16 +935,23 @@ "metadata": {}, "outputs": [], "source": [ - "dml_obj_universal = DoubleMLDIDMulti(dml_data, **(default_args| {\"gt_combinations\": \"universal\"}))\n", - "dml_obj_universal.fit()\n", - "dml_obj_universal.bootstrap(n_rep_boot=5000)\n", - "dml_obj_universal.plot_effects()" + "gt_dict = {\n", + " \"gt_combinations\": [\n", + " (4.0, 1, 2),\n", + " (4.0, 1, 3),\n", + " ]\n", + "}\n", + "\n", + "dml_obj_all = DoubleMLDIDMulti(dml_data, **(default_args| gt_dict))\n", + "dml_obj_all.fit()\n", + "dml_obj_all.bootstrap(n_rep_boot=5000)\n", + "dml_obj_all.plot_effects()" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "dml_dev", "language": "python", "name": "python3" }, @@ -963,7 +965,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/doc/examples/index.rst b/doc/examples/index.rst index 97fb0848..b3e60803 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -62,6 +62,7 @@ Difference-in-Differences did/py_panel_simple.ipynb did/py_panel.ipynb did/py_panel_data_example.ipynb + did/py_rep_cs.ipynb R: Case studies