From ef64b17d16794ed41ce01cabc4ce5a97159f1a40 Mon Sep 17 00:00:00 2001 From: igerber Date: Wed, 28 Oct 2026 11:29:28 -0400 Subject: [PATCH 1/3] feat(v4): item 7a of the 4.0 cut - flip the df_convention default to "cluster" (M-004..M-006, M-128..M-131) The analytical tail-df convention for clustered fits defaults to "cluster" (the Stata/fixest t(G-1) reference) instead of "residual" (the fitted residual df t(n - K_full)) on DifferenceInDifferences, TwoWayFixedEffects (inherits DiD's __init__), LinearRegression, SunAbraham, WooldridgeDiD (OLS arms, including the second hard-coded default in _validate_constructor_args), StackedDiD and ImputationDiD (pretrends leads). LPDiD adopted "cluster" at 3.9 and has no row. The two inert-config warnings (Wooldridge GLM arms, non-pretrends ImputationDiD) now compare against the new default, so an explicit "residual" warns there and the default never does. SyntheticDiD inherits the flipped attribute without reading it. Docstrings, comments and the llms-full guide read the 4.0 default; the REGISTRY TWFE clustered-CR1 inference-df note is relabelled a plain Note (the default now matches fixest) and the SunAbraham / ImputationDiD / StackedDiD / WooldridgeDiD / LPDiD tail-df notes read the 4.0 default. What moves: p-values / CIs on clustered analytical fits (the auto-clustered TWFE / SunAbraham / Wooldridge-OLS / StackedDiD defaults, the ImputationDiD lead rows, DiD / LinearRegression with an explicit cluster, the analytical lanes inside bootstrapped Wooldridge and ImputationDiD fits, and the placebo diagnostics / power simulators that relay those p-values). What does not: point estimates, SEs, t-statistics; survey df and hc2_bm BM dof (precedence); bootstrap percentiles; unclustered and Conley fits; BJS overall inference; Wooldridge GLM arms. df_convention="residual" restores 3.x bit-for-bit. Tests: new tests/test_v4_inference_defaults.py (the rows' test_ref) pins the signature defaults, get/set_params round trips, every surface's default G-1 tail against its own cluster count with an explicit "residual" twin, the inert surfaces, the unchanged one-effective-cluster raise and the ledger. TestDfConvention, test_moderate_t_pins_cluster_df_convention (the inverted methodology pin) and the per-estimator knob suites pin the G-1 default against explicit "residual" baselines - every former default-vs-"cluster" comparison became default-vs-"residual" so none passes vacuously; test_ci_matches_r_twfe tightens to rtol 1e-9 (the old 0.003 band was the df difference). The variance-conventions matrix keeps its residual-df rows as explicit "residual" arms (the K_full accounting pins) and gains eight G-1 _default_df twins (three in the fast set); the doc table is regenerated. The committed TWFE event-study oracle lanes pooled_cluster_unit / within_cluster_unit and the ImputationDiD pretrends_all contract lanes name the "residual" convention they were captured under (metadata-only; no oracle number touched). Tutorial 24 re-executed on the Rust backend (2x2 coverage 0.796 -> 0.800, power 0.90 -> 0.89) with its drift pins updated. Ledger: M-004/M-005/M-006/M-128/M-129/M-130/M-131 -> done. Spec section 7 / item 7 SHIPPED notes; item 10 records the deferred bulk re-execution of the nine tutorials whose clustered-default outputs moved. Migration guide gains the worked before/after block and the move / no-move scope; the DEFERRED.md Version-gated (v4) row is retired. --- DEFERRED.md | 10 +- .../20261028-v4-pr7a-df-convention-flip.md | 7 + diff_diff/estimators.py | 34 +- diff_diff/guides/llms-full.txt | 14 +- diff_diff/imputation.py | 19 +- diff_diff/linalg.py | 45 +- diff_diff/lpdid.py | 4 +- diff_diff/stacked_did.py | 21 +- diff_diff/stacked_did_results.py | 4 +- diff_diff/sun_abraham.py | 39 +- diff_diff/twfe.py | 12 +- diff_diff/wooldridge.py | 37 +- diff_diff/wooldridge_results.py | 8 +- docs/methodology/REGISTRY.md | 104 ++--- docs/methodology/variance-conventions.md | 48 ++- docs/migration-4.0.md | 38 +- .../24_staggered_vs_collapsed_power.ipynb | 120 +++--- docs/v4-deprecations.yaml | 35 +- docs/v4-design.md | 24 +- tests/fixtures/README.md | 13 +- .../fixtures/v4_twfe_event_study_oracles.json | 4 +- tests/helpers/twfe_es_oracle_lanes.py | 8 +- tests/test_aggregate_contract.py | 16 +- tests/test_estimators_vcov_type.py | 63 +-- tests/test_event_study_surface.py | 4 +- tests/test_fixest_did_twfe_parity.py | 7 +- tests/test_imputation.py | 39 +- tests/test_lpdid.py | 5 +- tests/test_methodology_twfe.py | 60 +-- tests/test_stacked_did.py | 30 +- tests/test_sun_abraham.py | 38 +- ..._t24_staggered_vs_collapsed_power_drift.py | 4 +- tests/test_v4_inference_defaults.py | 394 ++++++++++++++++++ tests/test_variance_conventions.py | 213 ++++++++-- tests/test_wooldridge.py | 42 +- 35 files changed, 1153 insertions(+), 410 deletions(-) create mode 100644 changelog.d/20261028-v4-pr7a-df-convention-flip.md create mode 100644 tests/test_v4_inference_defaults.py diff --git a/DEFERRED.md b/DEFERRED.md index 2ea98aee8..89a44dd22 100644 --- a/DEFERRED.md +++ b/DEFERRED.md @@ -8,10 +8,9 @@ provenance and AI-review deviation-documentation: a row here (or in - Shippable work lives in [TODO.md](TODO.md); the public feature roadmap in [ROADMAP.md](ROADMAP.md); monitoring and current-state notes in [docs/dev-status.md](docs/dev-status.md). -- Version-gated lifecycle items (deprecated-kwarg removals, v4 default flips such as - `SyntheticDiD` `lambda_reg`/`zeta` and the clustered-df convention) are canonically - tracked in the CI-enforced ledger `docs/v4-deprecations.yaml`; rows here cross-link - their `M-xxx` ids where applicable. +- Version-gated lifecycle items (deprecated-kwarg removals and default flips) are + canonically tracked in the CI-enforced ledger `docs/v4-deprecations.yaml`; rows here + cross-link their `M-xxx` ids where applicable. - The `PR` column points to the originating PR number or review tag. --- @@ -130,7 +129,8 @@ here carry the remaining implementation work and cross-link their `M-xxx` ids. | Issue | Location | PR | Priority | |-------|----------|----|----------| -| Clustered inference df **default flip at v4** (lifecycle + roster: docs/v4-deprecations.yaml M-004..M-006 + M-128..M-131; knob semantics: v4-design §7). Implementation work the ledger rows do not carry: the migration note, and flipping the test expectations that pin the current default (`TestDfConvention`, `test_moderate_t_pins_residual_df_convention`, the per-estimator knob suites). | `diff_diff/linalg.py::LinearRegression`, `diff_diff/estimators.py`, `diff_diff/twfe.py`, `diff_diff/sun_abraham.py`, `diff_diff/wooldridge.py`, `diff_diff/stacked_did.py`, `diff_diff/imputation.py` | — | Medium | + +No open rows: the clustered-df default flip shipped with the 4.0 cut. ## Decision record — won't-fix / waived diff --git a/changelog.d/20261028-v4-pr7a-df-convention-flip.md b/changelog.d/20261028-v4-pr7a-df-convention-flip.md new file mode 100644 index 000000000..f639797b5 --- /dev/null +++ b/changelog.d/20261028-v4-pr7a-df-convention-flip.md @@ -0,0 +1,7 @@ +### Breaking Changes + +- **4.0 cut, `df_convention` default flip (`M-004`, `M-005`, `M-006`, `M-128`, `M-129`, `M-130`, `M-131`).** The analytical tail-df convention for CLUSTERED fits defaults to `"cluster"` (the Stata/fixest `t(G - 1)` reference) instead of `"residual"` (the fitted residual df `t(n - K_full)`) on `DifferenceInDifferences`, `TwoWayFixedEffects`, `LinearRegression`, `SunAbraham`, `WooldridgeDiD` (OLS arms), `StackedDiD` and `ImputationDiD` (pretrends leads); `LPDiD` already defaulted to `"cluster"` at 3.9. What moves: p-values and confidence intervals on clustered analytical fits - `TwoWayFixedEffects` (static, and event-study with `unit=`), `SunAbraham`, `WooldridgeDiD` OLS and `StackedDiD` cluster at `unit` by default, so their default fits move; `ImputationDiD` moves on the `pretrends=True` lead rows; `DifferenceInDifferences` / `LinearRegression` move only with an explicit `cluster=` / `cluster_ids=`; the analytical lanes inside bootstrapped fits (`WooldridgeDiD` per-cell inference and post-fit `aggregate()`, `ImputationDiD` bootstrapped leads) move too, as do the fake-timing / fake-group placebo diagnostics and the power simulators that relay those p-values. What does not: point estimates, standard errors and t-statistics; survey-design fits (survey df keeps precedence); `vcov_type="hc2_bm"` (Bell-McCaffrey DOF keep precedence); bootstrap percentile p-values / CIs; unclustered and Conley fits; the BJS overall/post inference of `ImputationDiD`; `WooldridgeDiD` logit/poisson arms; permutation and leave-one-out diagnostics. `df_convention="residual"` restores the 3.x inference bit-for-bit; on the surfaces where the knob is a documented no-op (non-`pretrends` `ImputationDiD`, `WooldridgeDiD` logit/poisson) the inert-config `UserWarning` now fires for an explicit `"residual"` rather than for an explicit `"cluster"`. `SyntheticDiD` inherits the flipped attribute from `DifferenceInDifferences` without reading it (no behaviour change). + +### Internal + +- Ledger rows `M-004`..`M-006` and `M-128`..`M-131` flip to `done` with `tests/test_v4_inference_defaults.py` as their `test_ref`; the spec's section 7 / item 7 SHIPPED notes and item 10 now record the deferred bulk re-execution of the nine tutorials whose clustered-default outputs moved (24 re-executed here if its outputs moved); the REGISTRY TwoWayFixedEffects clustered-CR1 inference-df note is relabelled a plain Note (the default now matches fixest) and the SunAbraham / ImputationDiD / StackedDiD / WooldridgeDiD / LPDiD tail-df notes read the 4.0 default; `TestDfConvention`, `test_moderate_t_pins_cluster_df_convention` and the per-estimator knob suites pin the G-1 default against explicit `"residual"` baselines (every default-vs-`"cluster"` comparison became default-vs-`"residual"` so none passes vacuously); `test_ci_matches_r_twfe` tightens to machine precision; the variance-conventions audit matrix keeps its residual-df rows as explicit `"residual"` arms (the K_full accounting pins) and gains eight G-1 `_default_df` twins (three in the fast set), with `docs/methodology/variance-conventions.md` regenerated; the committed TWFE event-study oracle lanes `pooled_cluster_unit` / `within_cluster_unit` and the ImputationDiD `pretrends_all` contract lanes name the `"residual"` convention they were captured under (metadata-only, no oracle number touched); the `DEFERRED.md` Version-gated (v4) row is retired. diff --git a/diff_diff/estimators.py b/diff_diff/estimators.py index 8fc23cb01..4f7ff4e0f 100644 --- a/diff_diff/estimators.py +++ b/diff_diff/estimators.py @@ -158,22 +158,24 @@ class DifferenceInDifferences(BaseEstimator): path — absent ``cluster=``, pure Conley spatial HAC applies. ``survey_design=`` + Conley and ``inference='wild_bootstrap'`` + Conley both raise ``NotImplementedError``. - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for analytical t-statistics, p-values, - and CIs. ``"residual"`` (default) uses the fitted residual df - (``n − K_full``); ``"cluster"`` uses the Stata/fixest cluster df - ``G − 1`` on clustered fits — it has no effect on unclustered fits + and CIs. ``"cluster"`` (default since 4.0) uses the Stata/fixest + cluster df ``G − 1`` on clustered fits — it has no effect on unclustered fits or on ``vcov_type="conley"`` (the combined Conley+cluster product kernel has no documented ``G − 1`` df reference and keeps the - residual df); ``"normal"`` deliberately uses normal-theory z - inference at the fallback level on every fit, clustered or not. + residual df); ``"residual"`` (the 3.x default) uses the fitted + residual df (``n − K_full``); ``"normal"`` deliberately uses + normal-theory z inference at the fallback level on every fit, + clustered or not. Applies only at the fallback level of the df resolution under every value: survey df and per-coefficient Bell-McCaffrey DOF (``vcov_type="hc2_bm"``) are more refined small-sample corrections and always take precedence. Point estimates, SEs, and t-statistics are unaffected — only the reference distribution changes. The - default flips to ``"cluster"`` at v4 (see the REGISTRY clustered-CR1 - inference-df deviation note). + default flipped from ``"residual"`` to ``"cluster"`` at 4.0 (rows + M-004/M-005; pass ``df_convention="residual"`` to reproduce 3.x + inference; see the REGISTRY clustered-CR1 inference-df note). Attributes ---------- @@ -238,7 +240,7 @@ def __init__( conley_metric: str = "haversine", conley_kernel: str = "bartlett", conley_lag_cutoff: Optional[int] = None, - df_convention: str = "residual", + df_convention: str = "cluster", ): # Resolve/validate vcov_type via the shared helper so __init__ and # set_params use identical validation logic. @@ -282,12 +284,11 @@ def __init__( # arrays are auto-derived from data[time].values + data[unit].values # at fit-time (panel estimators already take time/unit as column names). self.conley_lag_cutoff = conley_lag_cutoff - # Inference df convention for clustered analytical fits: "residual" - # (default; t/p/CI at the fitted residual df) or "cluster" (the - # Stata/fixest G-1 convention). Survey df and per-coefficient - # Bell-McCaffrey DOF always take precedence over either. The default - # flips to "cluster" at v4 (REGISTRY clustered-CR1 inference-df - # deviation note). + # Inference df convention for clustered analytical fits: "cluster" + # (default since 4.0; the Stata/fixest G-1 convention) or + # "residual" (the 3.x default; t/p/CI at the fitted residual df). + # Survey df and per-coefficient Bell-McCaffrey DOF always take + # precedence over either (REGISTRY clustered-CR1 inference-df note). self.df_convention = df_convention self.is_fitted_ = False @@ -2009,7 +2010,8 @@ def _refit_mp_absorb(w_r): n_eff_df = int(round(np.sum(survey_weights))) df = n_eff_df - k_effective - n_absorbed_effects _df_cluster_knob_invalid = False - # Opt-in Stata/fixest cluster-df convention (df_convention="cluster"): + # Stata/fixest cluster-df convention (df_convention="cluster", the 4.0 + # default): # the shared analytical df becomes G - 1 on a clustered fit. Placed # BEFORE the survey/replicate overrides below (which overwrite df, so # survey df keeps precedence) and upstream of the per-period BM-DOF diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt index 9b85c24c6..f27de7bec 100644 --- a/diff_diff/guides/llms-full.txt +++ b/diff_diff/guides/llms-full.txt @@ -60,7 +60,7 @@ DifferenceInDifferences( bootstrap_weights: str = "rademacher", # "rademacher", "webb", or "mammen" seed: int | None = None, # Random seed rank_deficient_action: str = "warn", # "warn", "error", or "silent" - df_convention: str = "residual", # Analytical t/p/CI df: "residual" (n-K, default), "cluster" (Stata/fixest G-1, clustered fits), or "normal" (deliberate z); survey df + hc2_bm BM-DOF keep precedence; default flips at v4 + df_convention: str = "cluster", # Analytical t/p/CI df: "cluster" (Stata/fixest G-1 on clustered fits; default since 4.0), "residual" (n-K; the 3.x default), or "normal" (deliberate z); survey df + hc2_bm BM-DOF keep precedence ) ``` @@ -108,7 +108,7 @@ Two-Way Fixed Effects estimator for panel data. Inherits from DifferenceInDiffer TwoWayFixedEffects( cluster: str | None = None, # Auto-clusters at unit level if None alpha: float = 0.05, - df_convention: str = "residual", # Analytical t/p/CI df: "residual" (default), "cluster" (G-1), or "normal" (z); flips at v4 + df_convention: str = "cluster", # Analytical t/p/CI df: "cluster" (G-1; default since 4.0), "residual" (n-K; 3.x default), or "normal" (z) ) ``` @@ -376,7 +376,7 @@ SunAbraham( conley_metric: str = "haversine", # "haversine" | "euclidean" conley_kernel: str = "bartlett", # "bartlett" | "uniform" conley_lag_cutoff: int | None = None, # within-unit Bartlett max lag (0 = spatial-only) - df_convention: str = "residual", # Analytical t/p/CI df for cells AND aggregates (3.9: aggregates share the cells' df — previously z): "residual" (default), "cluster" (G-1), "normal" (z); survey/BM DOF keep precedence; flips at v4 + df_convention: str = "cluster", # Analytical t/p/CI df for cells AND aggregates (3.9: aggregates share the cells' df — previously z): "cluster" (G-1; default since 4.0), "residual" (3.x default), "normal" (z); survey/BM DOF keep precedence ) ``` @@ -424,7 +424,7 @@ ImputationDiD( aux_partition: str = "cohort_horizon", # "cohort_horizon", "cohort", or "horizon" pretrends: bool = False, # Include pre-treatment horizons in event study leave_one_out: bool = False, # BJS 2024 App. A.9 leave-one-out finite-sample variance (larger, less-downward-biased SE) - df_convention: str = "residual", # Pretrends lead-regression per-lead t/p/CI only (3.9: leads moved z -> t(residual)): "residual" (default), "cluster" (G-1), "normal" (z); BJS overall/post inference + Wald F knob-independent; flips at v4 + df_convention: str = "cluster", # Pretrends lead-regression per-lead t/p/CI only (3.9: leads moved z -> t(residual); 4.0: default G-1): "cluster" (default since 4.0), "residual" (3.x default), "normal" (z); BJS overall/post inference + Wald F knob-independent ) ``` @@ -1104,7 +1104,7 @@ StackedDiD( rank_deficient_action: str = "warn", vcov_type: str = "hc1", # {"hc1","hc2_bm"}; classical/hc2 rejected (intrinsically clustered), conley deferred. survey_design=... requires hc1 balance: str = "none", # {"none","entropy"}; "entropy" = CBWSDID covariate balancing (Ustyuzhanin 2026), requires fit(covariates=[...]) + weighting="aggregate", no survey_design - df_convention: str = "residual", # Analytical t/p/CI df (3.9: hc1 lane moved z -> t(pooled residual df)): "residual" (default), "cluster" (G-1, positive-weight clusters), "normal" (z); BM/survey df keep precedence; flips at v4 + df_convention: str = "cluster", # Analytical t/p/CI df (3.9: hc1 lane moved z -> t(pooled residual df); 4.0: default G-1): "cluster" (G-1, positive-weight clusters; default since 4.0), "residual" (3.x default), "normal" (z); BM/survey df keep precedence ) ``` @@ -1613,7 +1613,7 @@ WooldridgeDiD( # column is dropped per Sec 5.4, so cohort_trend_coefs # carries G-1 entries. Rejected with survey_design= and # with control_group="never_treated". - df_convention: str = "residual", # OLS analytical t/p/CI df (3.9: default-hc1 arms moved z -> t(residual)): "residual" (default), "cluster" (G-1, hc1-clustered only), "normal" (z); survey/BM DOF keep precedence; GLM arms knob-independent (explicit non-default warns); flips at v4 + df_convention: str = "cluster", # OLS analytical t/p/CI df (3.9: hc1 arms moved z -> t(residual); 4.0: default G-1): "cluster" (G-1, hc1-clustered only; default since 4.0), "residual" (3.x default), "normal" (z); survey/BM DOF keep precedence; GLM arms knob-independent (explicit non-default warns) unsupported_period_action: str = "drop", # "drop" warns and filters; "error" refuses unsupported periods ) ``` @@ -1834,7 +1834,7 @@ Returned by `SunAbraham.fit()`. | `cohort_effects` | `dict` | Cohort-level effects | | `df_convention` | `str | None` | The configured df convention echoed onto the results ("residual" | "cluster" | "normal"; 3.9) | | `inference_df` | `float | None` | The df the overall-ATT p/CI actually used (BM contrast df / survey df / knob-resolved fallback; None under bootstrap or "normal") | -| `event_study_df` | `dict[int, float] | None` | Per-event-time df provenance (finite residual df on plain analytic fits since 3.9; None under bootstrap) | +| `event_study_df` | `dict[int, float] | None` | Per-event-time df provenance (G-1 on plain clustered analytic fits under the 4.0 default; the finite residual df under `df_convention="residual"`; None under bootstrap) | **Methods:** `summary()`, `print_summary()`, `to_dataframe(level="event_study"|"cohort")` diff --git a/diff_diff/imputation.py b/diff_diff/imputation.py index 07d4a42f3..6a0cb435b 100644 --- a/diff_diff/imputation.py +++ b/diff_diff/imputation.py @@ -148,21 +148,22 @@ class ImputationDiD(ImputationDiDBootstrapMixin, _ImputationAggregationMixin, Ba but are a library extension beyond the paper's derivation. Replicate-weight survey designs raise ``NotImplementedError`` (their variance bypasses the influence-function path where the rescale lives). - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for the PRETRENDS lead regression's per-lead t/p/CI — the one ImputationDiD surface running the shared clustered CR1 sandwich (``pretrends=True``, surfaced via - ``results.aggregate('event_study')``). ``"residual"`` (default) uses - the lead regression's residual df (``n − k_kept − absorbed - [time, unit] rank``) — the 3.9 fix: previously silent normal-theory - z on plain clustered fits; ``"cluster"`` uses ``G − 1``; - ``"normal"`` deliberately uses z. The full-design survey df keeps + ``results.aggregate('event_study')``). ``"cluster"`` (default since + 4.0) uses ``G − 1``; ``"residual"`` (the 3.x default) uses the lead + regression's residual df (``n − k_kept − absorbed [time, unit] + rank``) — the 3.9 fix: previously silent normal-theory z on plain + clustered fits; ``"normal"`` deliberately uses z. The full-design survey df keeps precedence on survey fits. Everything else — the BJS Theorem-3 overall/event-study inference and the joint pretrend Wald F (which keeps its cluster-robust ``F(q, G − 1)`` reference) — is knob-independent; an explicitly non-default value on a configuration that never surfaces the per-lead inference warns at - fit time. The default flips to ``"cluster"`` at v4. + fit time. The default flipped to ``"cluster"`` at 4.0 (row M-131); + pass ``df_convention="residual"`` for 3.x per-lead inference. Attributes ---------- @@ -219,7 +220,7 @@ def __init__( aux_partition: str = "cohort_horizon", pretrends: bool = False, leave_one_out: bool = False, - df_convention: str = "residual", + df_convention: str = "cluster", ): if rank_deficient_action not in ("warn", "error", "silent"): raise ValueError( @@ -345,7 +346,7 @@ def fit( survey_design is not None and survey_design.replicate_method is not None ) _lead_inference_reachable = self.pretrends and not _is_replicate_design - if self.df_convention != "residual" and not _lead_inference_reachable: + if self.df_convention != "cluster" and not _lead_inference_reachable: warnings.warn( f"df_convention={self.df_convention!r} affects only the " "pretrends event-study per-lead inference (pretrends=True, " diff --git a/diff_diff/linalg.py b/diff_diff/linalg.py index 107ca1d51..70c291c5e 100644 --- a/diff_diff/linalg.py +++ b/diff_diff/linalg.py @@ -4419,18 +4419,19 @@ class LinearRegression: neither is formally PSD-guaranteed in the radial pairwise form (Conley 1999's explicit PSD Bartlett formula is the 2-D separable product window, Eq 3.14, not the 1-D radial pairwise form). - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for ``get_inference`` t/p/CI. - ``"residual"`` uses the fitted residual df; ``"cluster"`` uses the - Stata/fixest cluster df ``G − 1`` (from ``n_clusters_``) — + ``"cluster"`` (default since 4.0) uses the Stata/fixest cluster df + ``G − 1`` (from ``n_clusters_``); ``"residual"`` (the 3.x default) + uses the fitted residual df — ``"cluster"`` has no effect on unclustered fits or ``vcov_type="conley"`` (no documented ``G − 1`` reference for the Conley+cluster product kernel); ``"normal"`` deliberately uses normal-theory z inference at the fallback level on every fit, clustered or not. Fallback-level only under every value: survey df and per-coefficient Bell-McCaffrey DOF always take precedence. No - effect on coefficients, SEs, or t-statistics. Default flips to - ``"cluster"`` at v4. + effect on coefficients, SEs, or t-statistics. The default flipped + to ``"cluster"`` at 4.0 (row M-006). Attributes ---------- @@ -4500,7 +4501,7 @@ def __init__( conley_time: Optional[np.ndarray] = None, conley_unit: Optional[np.ndarray] = None, conley_lag_cutoff: Optional[int] = None, - df_convention: str = "residual", + df_convention: str = "cluster", ): # Inline validation (not utils.validate_df_convention): diff_diff.utils # imports diff_diff.linalg at module level, so importing back would be @@ -4528,13 +4529,13 @@ def __init__( self.conley_unit = conley_unit self.conley_lag_cutoff = conley_lag_cutoff # Inference df convention for clustered analytical fits: - # "residual" (default) keeps the fitted residual df `n - K_full` for - # t/p/CI; "cluster" uses the Stata/fixest cluster df `G - 1` instead. + # "cluster" (default since 4.0) uses the Stata/fixest cluster df + # `G - 1` for t/p/CI; "residual" (the 3.x default) keeps the fitted + # residual df `n - K_full` instead. # Applies ONLY at the fallback level of the `get_inference` df # resolution: survey df and per-coefficient Bell-McCaffrey DOF (which - # are more refined small-sample corrections) always take precedence. - # The default flips to "cluster" at v4 (see REGISTRY clustered-CR1 - # inference-df deviation note). + # are more refined small-sample corrections) always take precedence + # (see the REGISTRY clustered-CR1 inference-df note). self.df_convention = df_convention # Resolve/validate vcov_type via the shared helper. self.vcov_type = resolve_vcov_type(vcov_type) @@ -5149,7 +5150,9 @@ def get_inference( alpha : float, optional Significance level for CI. Defaults to instance-level alpha. df : int, optional - Degrees of freedom. Defaults to fitted df (n - k - df_adjustment). + Degrees of freedom. Defaults to the ``df_convention``-resolved + df: ``G - 1`` on clustered fits under the default ``"cluster"``, + the fitted residual df (n - k - df_adjustment) otherwise. Set to None explicitly to use normal distribution instead of t. Returns @@ -5220,15 +5223,15 @@ def get_inference( and self.n_clusters_ is not None and self.vcov_type != "conley" ): - # Opt-in Stata/fixest cluster-df convention for clustered - # analytical fits: t/p/CI at df = G - 1 instead of the residual - # df. conley is excluded: the combined Conley+cluster product + # Stata/fixest cluster-df convention (the 4.0 default) for + # clustered analytical fits: t/p/CI at df = G - 1 instead of the + # residual df. conley is excluded: the combined Conley+cluster product # kernel is a diff-diff convention with no documented G-1 df # reference (REGISTRY Conley section); it keeps the residual df. # Deliberately the LAST branch before the residual fallback: # survey df and per-coefficient Bell-McCaffrey DOF are more - # refined small-sample corrections and always win. Default flips - # at v4 (REGISTRY clustered-CR1 inference-df deviation note). + # refined small-sample corrections and always win (REGISTRY + # clustered-CR1 inference-df note). if self.n_clusters_ <= 1: # Cluster df G - 1 is undefined for an effectively # one-cluster fit (e.g. weighted fit where only one cluster @@ -5310,7 +5313,9 @@ def get_inference_batch( alpha : float, optional Significance level for CIs. Defaults to instance-level alpha. df : int, optional - Degrees of freedom. Defaults to fitted df. + Degrees of freedom. Defaults to the ``df_convention``-resolved df + (``G - 1`` on clustered fits under the default ``"cluster"``, the + fitted residual df otherwise). Returns ------- @@ -5340,7 +5345,9 @@ def get_all_inference( alpha : float, optional Significance level for CIs. Defaults to instance-level alpha. df : int, optional - Degrees of freedom. Defaults to fitted df. + Degrees of freedom. Defaults to the ``df_convention``-resolved df + (``G - 1`` on clustered fits under the default ``"cluster"``, the + fitted residual df otherwise). Returns ------- diff --git a/diff_diff/lpdid.py b/diff_diff/lpdid.py index f6b232d8f..c09ea372c 100644 --- a/diff_diff/lpdid.py +++ b/diff_diff/lpdid.py @@ -95,8 +95,8 @@ def _validate_params(self) -> None: "(the estimand becomes ambiguous); use control_group='clean' (the default)" ) # LPDiD's DEFAULT is "cluster" — its t(G-1) reference is the Stata - # lpdid convention and already the library-wide 4.0 target, so the - # default moves nothing (unlike the "residual"-default estimators). + # lpdid convention; since the 4.0 flip it is the library-wide default + # too (LPDiD adopted it at 3.9, ahead of the other knob surfaces). validate_df_convention(self.df_convention) def _rhs_column_names(self, covariates=None, ylags=0, dylags=0): diff --git a/diff_diff/stacked_did.py b/diff_diff/stacked_did.py index e6f406ca6..dd82dd120 100644 --- a/diff_diff/stacked_did.py +++ b/diff_diff/stacked_did.py @@ -138,21 +138,22 @@ class StackedDiD(BaseEstimator): raise a ``ValueError``), and does not support ``survey_design=``; matching-based balancing and the repeated-treatment extension are out of scope. Default ``"none"`` reproduces plain weighted stacked DID. - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for the analytical t/p/CI on the pooled stacked regression's event-study and overall-ATT inference. - ``"residual"`` (default) uses the pooled residual df - (``n_eff − k_kept``, positive-weight rows) — the 3.9 fix: the + ``"cluster"`` (default since 4.0) uses ``G − 1`` where G counts + positive-weight clusters (``effective_cluster_count`` — may differ + from the raw ``results.n_clusters`` when a cluster's composed weight + is zero); ``"residual"`` (the 3.x default) uses the pooled residual + df (``n_eff − k_kept``, positive-weight rows) — the 3.9 fix: the non-BM non-survey lane previously used silent normal-theory z; - ``"cluster"`` uses ``G − 1`` where G counts positive-weight - clusters (``effective_cluster_count`` — may differ from the raw - ``results.n_clusters`` when a cluster's composed weight is zero); ``"normal"`` deliberately uses normal-theory z at the fallback level. hc2_bm Bell-McCaffrey contrast DOF and survey/replicate df always take precedence. Note the stacked design's residual df is typically large (control rows replicate across sub-experiments), so - the default's numeric movement vs the old z is small — the change - is a convention alignment. The default flips to ``"cluster"`` at v4. + the 3.9 z -> t(residual) step was small and the 4.0 flip to + ``G − 1`` is the materially moving step (row M-130); pass + ``df_convention="residual"`` for 3.x inference. Attributes ---------- @@ -204,7 +205,7 @@ def __init__( rank_deficient_action: str = "warn", vcov_type: str = "hc1", balance: str = "none", - df_convention: str = "residual", + df_convention: str = "cluster", ): if weighting not in ("aggregate", "population", "sample_share"): raise ValueError( @@ -945,7 +946,7 @@ def _refit_stacked(w_r): es_df_used[h] = float("nan") else: # BM DOF > survey/replicate df > the knob-resolved - # analytical fallback (residual t by default; the + # analytical fallback (t(G-1) under the default; the # replicate 0-sentinel is not None, so it keeps # precedence over the fallback). _df_eff = ( diff --git a/diff_diff/stacked_did_results.py b/diff_diff/stacked_did_results.py index 50fa31b83..90bb2721d 100644 --- a/diff_diff/stacked_did_results.py +++ b/diff_diff/stacked_did_results.py @@ -107,8 +107,8 @@ class StackedDiDResults(BaseResults, AggregationMixin): ``safe_inference`` for its stored p-value/CI (per-event Bell-McCaffrey Satterthwaite df under ``hc2_bm``; the scalar survey df under survey designs; the ``df_convention``-resolved analytical - fallback otherwise — finite residual df under the 3.9 default, - ``G − 1`` under "cluster"), or NaN when the row used normal theory + fallback otherwise — ``G − 1`` under the 4.0 default "cluster", + the finite residual df under "residual"), or NaN when the row used normal theory (``df_convention="normal"``), the df was undefined, or hc2_bm failed closed. Always populated on 3.9+ fits (row M-024); None only on pre-3.9 pickles. diff --git a/diff_diff/sun_abraham.py b/diff_diff/sun_abraham.py index 6f8e1fc95..3a982b051 100644 --- a/diff_diff/sun_abraham.py +++ b/diff_diff/sun_abraham.py @@ -194,8 +194,9 @@ class SunAbrahamResults(BaseResults): # p-value/CI's safe_inference actually received (the per-event # Bell-McCaffrey contrast df under hc2_bm; the survey design df - # post-drop under replicate refits - on survey fits; the - # df_convention-resolved fallback on plain analytic fits - FINITE - # residual df under the 3.9 default, G-1 under "cluster", NaN under + # df_convention-resolved fallback on plain analytic fits - G-1 under + # the 4.0 default "cluster", the FINITE residual df under "residual", + # NaN under # "normal" and on rows whose BM DOF was non-finite, where # safe_inference's non-finite-df guard yields all-NaN inference). None # under bootstrap: the stored percentile p/CIs never used a df (note @@ -632,18 +633,20 @@ class SunAbraham(BaseEstimator): ``conley_cutoff_km`` / ``conley_lag_cutoff``); ``survey_design=`` / ``weights`` / ``n_bootstrap>0`` are rejected. See the ``vcov_type`` parameter docs above. - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for analytical t/p/CI, applied to BOTH the per-cohort-cell inference and the aggregated event-study / - overall-ATT inference (one df source per fit). ``"residual"`` - (default) uses the saturated regression's residual df — the 3.9 fix: + overall-ATT inference (one df source per fit). ``"cluster"`` + (default since 4.0) uses the Stata/fixest cluster df ``G − 1`` + (inert under ``vcov_type="conley"``); ``"residual"`` (the 3.x + default) uses the saturated regression's residual df — the 3.9 fix: aggregates previously dropped to normal theory on plain clustered - fits; ``"cluster"`` uses the Stata/fixest cluster df ``G − 1`` - (inert under ``vcov_type="conley"``); ``"normal"`` deliberately uses + fits; ``"normal"`` deliberately uses normal-theory z at the fallback level everywhere (cells included). Survey/replicate df and hc2_bm Bell-McCaffrey contrast DOF always take precedence; bootstrap p/CI (``n_bootstrap>0``) are percentile- - based and unaffected. The default flips to ``"cluster"`` at v4. + based and unaffected. The default flipped to ``"cluster"`` at 4.0 + (row M-128); pass ``df_convention="residual"`` for 3.x inference. Attributes ---------- @@ -727,7 +730,7 @@ def __init__( conley_metric: str = "haversine", conley_kernel: str = "bartlett", conley_lag_cutoff: Optional[int] = None, - df_convention: str = "residual", + df_convention: str = "cluster", ): if control_group not in ["never_treated", "not_yet_treated"]: raise ValueError( @@ -1219,8 +1222,8 @@ def _refit_sa(w_r): # Per-row df PROVENANCE (spec section 5, row M-092): the df each # stored ES row's safe_inference actually received, recorded iff # finite and > 0 else NaN. Baseline = the knob-resolved fallback - # every _compute_iw_effects row used (the residual df on plain - # analytic fits under the default; G-1 under "cluster"; NaN under + # every _compute_iw_effects row used (G-1 under the default "cluster"; + # the residual df under "residual"; NaN under # "normal"; the TSL design df on survey fits). Overwritten below at # the hc2_bm and replicate-refit override sites, and cleared under # bootstrap, whose stored percentile p/CIs never used a df. @@ -1343,16 +1346,16 @@ def _refit_sa(w_r): _overall_att_contrast_dof = float(dof_vec[-1]) except (ValueError, np.linalg.LinAlgError) as exc: # Rank-deficient or other linalg issue: fall back to - # the knob-resolved analytical df (residual t by - # default). Emit a UserWarning so the deviation is - # visible. + # the knob-resolved analytical df (t(G-1) under the + # default "cluster"; residual t under "residual"). Emit + # a UserWarning so the deviation is visible. warnings.warn( f"SunAbraham(vcov_type='hc2_bm') aggregated inference " f"could not compute Bell-McCaffrey contrast DOF " f"({type(exc).__name__}: {exc}). Falling back to the " "df_convention-resolved analytical df; aggregated " - "p-values/CIs use t(residual df) under the default " - "instead of t(BM DOF).", + "p-values/CIs use the df_convention-resolved t df " + "(t(G-1) under the default) instead of t(BM DOF).", UserWarning, stacklevel=2, ) @@ -1874,8 +1877,8 @@ def _fit_saturated_regression( # aggregated inference layer to compute per-event-time and # overall-ATT Satterthwaite DOF on user-facing outputs. Under # other vcov_type values aggregated inference falls back to the - # knob-resolved analytical df (residual t by default; G−1 under - # df_convention="cluster"; z under "normal"). + # knob-resolved analytical df (G−1 under the default "cluster"; + # residual t under "residual"; z under "normal"). if vcov_type == "hc2_bm": bread_matrix = X.T @ X bm_artifacts: Optional[Tuple[np.ndarray, Optional[np.ndarray], np.ndarray]] = ( diff --git a/diff_diff/twfe.py b/diff_diff/twfe.py index f546da935..5f6a9382b 100644 --- a/diff_diff/twfe.py +++ b/diff_diff/twfe.py @@ -60,15 +60,17 @@ class TwoWayFixedEffects(DifferenceInDifferences): auto-cluster is also preserved (routes to CR2-BM at unit). alpha : float, default=0.05 Significance level for confidence intervals. - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Inherited from :class:`DifferenceInDifferences`: df convention for - analytical t/p/CI. ``"residual"`` (default) uses the fitted - residual df; ``"cluster"`` uses the Stata/fixest ``G − 1`` on - clustered fits (no effect on unclustered or Conley fits); + analytical t/p/CI. ``"cluster"`` (default since 4.0) uses the + Stata/fixest ``G − 1`` on clustered fits (no effect on unclustered + or Conley fits); ``"residual"`` (the 3.x default) uses the fitted + residual df; ``"normal"`` deliberately uses normal-theory z inference at the fallback level on every fit, clustered or not. Survey df and per-coefficient Bell-McCaffrey DOF keep precedence under every - value. Default flips to ``"cluster"`` at v4. + value. The default flipped to ``"cluster"`` at 4.0 (row M-005); + pass ``df_convention="residual"`` to reproduce 3.x inference. Notes ----- diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 43ea381d7..b937c7b6e 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -958,18 +958,19 @@ class WooldridgeDiD(BaseEstimator): The Section 8 trend specification is therefore unidentified on this branch. Use ``control_group="not_yet_treated"`` (the default) for the cohort_trends surface. - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for the OLS analytical t/p/CI (per-cell - and aggregated). ``"residual"`` (default) uses the fitted residual df - — the 3.9 fix: the default hc1 arms previously used silent - normal-theory z; classical/hc2 keep their historical ``n − rank(X)`` - values bit-for-bit; ``"cluster"`` uses the Stata/fixest cluster df - ``G − 1`` on hc1-clustered fits (inert on one-way and conley - families); ``"normal"`` deliberately uses normal-theory z at the + and aggregated). ``"cluster"`` (default since 4.0) uses the + Stata/fixest cluster df ``G − 1`` on hc1-clustered fits (inert on + one-way and conley families); ``"residual"`` (the 3.x default) uses + the fitted residual df — the 3.9 fix: the hc1 arms previously used + silent normal-theory z; classical/hc2 keep their historical + ``n − rank(X)`` values bit-for-bit; ``"normal"`` deliberately uses normal-theory z at the fallback level. Survey design df and hc2_bm Bell-McCaffrey DOF always take precedence; the logit/poisson arms are knob-independent (survey df or normal theory — an explicitly non-default value warns - at fit time). The default flips to ``"cluster"`` at v4. + at fit time). The default flipped to ``"cluster"`` at 4.0 (row + M-129); pass ``df_convention="residual"`` for 3.x OLS inference. unsupported_period_action : {"drop", "error"}, default "drop" How to handle periods lacking the required comparison support. ``"drop"`` removes those periods before estimation and warns; @@ -1006,7 +1007,7 @@ def __init__( conley_metric: str = "haversine", conley_kernel: str = "bartlett", conley_lag_cutoff: Optional[int] = None, - df_convention: str = "residual", + df_convention: str = "cluster", unsupported_period_action: str = "drop", ) -> None: self._validate_constructor_args( @@ -1064,7 +1065,7 @@ def _validate_constructor_args( bootstrap_weights: str, vcov_type: str, cohort_trends: bool = False, - df_convention: str = "residual", + df_convention: str = "cluster", ) -> None: """Shared validation for both ``__init__`` and ``set_params``. @@ -1964,7 +1965,7 @@ def _build(frame: pd.DataFrame, w: Optional[np.ndarray]): # paths use the survey design df when present and normal theory # otherwise, independent of the knob — an explicitly non-default # value would be silently ignored there. - if self.method != "ols" and self.df_convention != "residual": + if self.method != "ols" and self.df_convention != "cluster": warnings.warn( f"df_convention={self.df_convention!r} has no effect on the " "logit/poisson arms — their inference uses the survey design " @@ -2438,10 +2439,11 @@ def _fit_ols( # so classical/hc2 stay bit-identical under the default; the # within-transform arm (hc1/conley) additionally subtracts the # absorbed [unit, time] rank. Survey df keeps precedence (df_inf); - # otherwise resolve through the ``df_convention`` knob — residual t - # by default (the 3.9 fix: hc1 arms previously dropped to silent - # normal theory), ``G − 1`` under "cluster" (hc1-clustered only; - # conley has no documented G−1 reference and passes + # otherwise resolve through the ``df_convention`` knob — ``G − 1`` + # under the 4.0 default "cluster" (hc1-clustered only; conley has + # no documented G−1 reference and passes ``n_clusters=None``), + # residual t under "residual" (the 3.9 fix: hc1 arms previously + # dropped to silent normal theory; conley stays at # ``n_clusters=None``), z under "normal". ``resolve_tail_df``'s # guard returns None on a non-positive residual df, reproducing the # old ``df_one_way = NaN → df_inf`` degenerate lane. @@ -2584,8 +2586,9 @@ def _fit_ols( else: # Every non-BM arm consumes the ONE knob-resolved fallback: # survey df on surveyed fits, else the df_convention-resolved - # analytical df (residual t by default; classical/hc2 - # reproduce the historical df_one_way values bit-for-bit). + # analytical df (t(G-1) under the default on hc1-clustered + # fits; classical/hc2 reproduce the historical df_one_way + # values bit-for-bit). t_stat, p_value, conf_int = safe_inference( eff["att"], eff["se"], alpha=self.alpha, df=df_fallback ) diff --git a/diff_diff/wooldridge_results.py b/diff_diff/wooldridge_results.py index df10be0d2..9886b4fc3 100644 --- a/diff_diff/wooldridge_results.py +++ b/diff_diff/wooldridge_results.py @@ -210,8 +210,9 @@ class WooldridgeDiDResults(BaseResults): _df_analytic_fallback: Optional[float] = field(default=None, repr=False) """The RESOLVED analytical fallback df the fit's non-BM ``safe_inference`` calls actually used (survey design df on surveyed fits; otherwise the - ``df_convention``-resolved value — residual df by default, ``G − 1`` - under "cluster", None under "normal"; ``df_inf`` on GLM fits, whose + ``df_convention``-resolved value — ``G − 1`` under the 4.0 default + "cluster", the residual df under "residual", None under "normal"; + ``df_inf`` on GLM fits, whose inference is knob-independent). ``aggregate()`` reads it so post-fit re-aggregation reproduces fit-time inference on every OLS arm — it stays LIVE on bootstrap fits, whose per-cell inference remains @@ -243,7 +244,8 @@ def __setstate__(self, state: Dict[str, Any]) -> None: ``aggregate()`` on a legacy result reproduces its fit-time classical/hc2 inference (legacy hc1 pickles carried None there, which restores the pre-3.9 normal-theory aggregation for them). - ``df_convention`` (added 3.9) defaults to ``"residual"``. + A missing ``df_convention`` (added 3.9) is backfilled as ``"residual"`` + - the convention every pre-3.9 fit ran under (NOT the 4.0 default). Missing ``unsupported_period_action`` defaults to ``"drop"``. Pickles written before the 4.0 storage flip (``docs/v4-design.md`` section 5) carry the simple aggregate under ``overall_att`` / diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 1ba85ebbf..d691b59a9 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -592,17 +592,19 @@ This matches the behavior of R's `fixest::feols()` with absorbed FE. converged separately in 3.9 (M-127: the three-value `df_convention` knob below — the SE-scale `k` here and the inference df are independent accountings). `hc2`/`hc2_bm` use leverage / Satterthwaite DOF and are unaffected. -- **Note (deviation from R — clustered CR1 inference df):** under unit clustering, Python's - CR1 t-statistics/p-values/CIs use the **residual df** from the fitted design - (`n − K_full`, e.g. 148 on the live-R benchmark panel: n=200, 50 unit FE + time + treatment), - while `fixest::feols(cluster=...)` uses the **cluster df** `G − 1` (49 on the same panel; - Stata's `min(G−1)` convention). Both sides use t-distributions. At moderate |t| the CIs and - p-values are close; at large |t| the tails diverge by orders of magnitude (measured on the - live-R benchmark: |t|≈22 → Python `p≈1e-48` at t(148) vs fixest `p≈4e-27` at t(49) — both - numerically zero, which is what `tests/test_methodology_twfe.py::test_pvalue_matches_r_twfe` - pins; the convention itself is locked at a distinguishable moderate |t|~1.8 by - `tests/test_methodology_twfe.py::test_moderate_t_pins_residual_df_convention`, where the - t(148) and t(49) tails differ by ~5%). The SE itself matches fixest under the K_reference +- **Note (clustered CR1 inference df — the `df_convention` knob):** under unit clustering, + Python's CR1 t-statistics/p-values/CIs use the **cluster df** `G − 1` since 4.0 (49 on the + live-R benchmark panel: n=200, 50 unit FE + time + treatment) — the same reference + `fixest::feols(cluster=...)` and Stata's `min(G−1)` convention use — while the 3.x default + used the **residual df** from the fitted design (`n − K_full`, 148 on that panel), still + available as `df_convention="residual"` (the remaining deviation from R when chosen). Both + conventions use t-distributions. At moderate |t| the CIs and p-values are close; at large + |t| the tails diverge by orders of magnitude (measured on the live-R benchmark: |t|≈22 → + `p≈4e-27` at t(49), the fixest value, vs `p≈1e-48` at t(148) — both numerically zero, which + is what `tests/test_methodology_twfe.py::test_pvalue_matches_r_twfe` pins; the convention + itself is locked at a distinguishable moderate |t|~1.8 by + `tests/test_methodology_twfe.py::test_moderate_t_pins_cluster_df_convention`, where the + t(49) and t(148) tails differ by ~5%). The SE itself matches fixest under the K_reference convergence above (the historical ~0.25% non-nested-FE band is closed). **Three-value knob (2026-07, widened 3.9 / M-127):** `df_convention ∈ {"residual", "cluster", "normal"}` on the library-wide knob roster — `DifferenceInDifferences` / `TwoWayFixedEffects` / @@ -614,16 +616,19 @@ This matches the behavior of R's `fixest::feols()` with absorbed FE. unclustered, and conley alike — on lanes that were already t it is a new deliberate option, not a compatibility path). Fallback-level only under every value: survey/replicate df and per-coefficient Bell-McCaffrey DOF keep precedence, and point estimates/SEs/t-statistics - never move. The default remains `"residual"` on every surface except LPDiD and flips at v4 - (lifecycle: `docs/v4-deprecations.yaml` M-004..M-006 + M-128..M-131; work row in - `DEFERRED.md` → Version-gated (v4); flipping moves every clustered p-value/CI, so it is a - major-version change). **Out of the knob's scope** (own inference stacks): the IF-based + never move. **Since 4.0 the default is `"cluster"` on every surface** (LPDiD adopted it at + 3.9; the other seven flipped at the 4.0 cut, item 7a, 2026-10-28 — lifecycle rows + `docs/v4-deprecations.yaml` M-004..M-006 + M-128..M-131, pinned by + `tests/test_v4_inference_defaults.py`; the flip moved every clustered p-value/CI, which is + why it waited for a major version), and `"residual"` restores the 3.x residual-df inference + bit-for-bit. **Out of the knob's scope** (own inference stacks): the IF-based family — `CallawaySantAnna`, `TwoStageDiD`, `ChaisemartinDHaultfoeuille`, `EfficientDiD`, `ContinuousDiD`, `ChangesInChanges`, `SyntheticDiD`, `HeterogeneousAdoptionDiD` — and the OLS-based `TripleDifference` / `TROP` (own `safe_inference` df conventions, outside the variance-conventions audit matrix's shared-CR1 scope), plus `WooldridgeDiD`'s logit/poisson arms (survey df or normal theory, knob-independent). Locked by - `tests/test_estimators_vcov_type.py::TestDfConvention` (G−1 tail match, precedence, no-op default). The full-dummy (`fixed_effects=`) idiom carries + `tests/test_estimators_vcov_type.py::TestDfConvention` (G−1 default tail match, precedence, + the explicit `"residual"` baseline). The full-dummy (`fixed_effects=`) idiom carries `df_adjustment == 0` and is unchanged — its residual t-df already matched fixest's full-K count because every FE column sits in `k_visible`. **That claim is scoped to the residual df and the non-clustered variance families; the clustered CR1 @@ -1993,16 +1998,16 @@ where weights ŵ_{g,e} = n_{g,e} / Σ_g n_{g,e} (sample share of cohort g at eve `LinearRegression.get_inference()` inside `_fit_saturated_regression`. If the linalg helper fails (rank-deficient design, singular bread), the aggregated inference falls back to the `df_convention`-resolved - analytical df (t(residual) under the default) with an explicit + analytical df (t(G − 1) under the 4.0 default) with an explicit `UserWarning`. - **Note:** Provenance exposure (4.0 program row M-092; df values updated by the 3.9 M-127 tail-df fix): `SunAbrahamResults.event_study_df` records, per relative time, the df each stored event-study row's `safe_inference` actually received - the per-event BM contrast DOF above under `hc2_bm`, the survey design df (post-drop under a replicate refit) on survey fits, - the `df_convention`-resolved fallback on plain analytic fits (FINITE - residual df under the 3.9 default; `G − 1` under `"cluster"`; NaN under - `"normal"`), and NaN on rows whose BM DOF was non-finite, where + the `df_convention`-resolved fallback on plain analytic fits (`G − 1` + under the 4.0 default `"cluster"`; the FINITE residual df under + `"residual"`; NaN under `"normal"`), and NaN on rows whose BM DOF was non-finite, where `safe_inference`'s own non-finite-df guard already yields all-NaN inference. Note the two failure modes differ and are both faithfully recorded: a helper-level failure warns and falls back to the resolved @@ -2064,24 +2069,26 @@ where weights ŵ_{g,e} = n_{g,e} / Σ_g n_{g,e} (sample share of cohort g at eve theory inside the same fit): - Cohort-level p-values: t-distribution via `LinearRegression.get_inference()`, following the `df_convention` knob - (residual df by default; `G − 1` under `"cluster"`; z under `"normal"`). + (`G − 1` under the 4.0 default `"cluster"`; the residual df under + `"residual"`; z under `"normal"`). - Aggregated event study and overall ATT p-values: - Under `vcov_type="hc2_bm"`: t-distribution with CR2 Bell-McCaffrey contrast DOF per aggregated effect (see "Phase 1b aggregated BM contrast DOF" Note above). Matches `clubSandwich::Wald_test( test="HTZ")$df_denom`. - Under `vcov_type ∈ {"classical","hc1","hc2","conley"}` (no survey): - the SAME `df_convention`-resolved df as the cells — t(residual df of - the saturated regression) by default, t(G − 1) under `"cluster"` - (inert under conley), z under `"normal"`. + the SAME `df_convention`-resolved df as the cells — t(G − 1) under the + 4.0 default `"cluster"` (inert under conley, which keeps the residual + df), t(residual df of the saturated regression) under `"residual"`, + z under `"normal"`. - Under replicate-weight survey: t-distribution with replicate-derived DOF (`survey_metadata.df_survey`). - - **Deviation from R**: R's fixest uses t-distribution at all levels with - the CLUSTER df `G − 1` on clustered fits; the 3.9 default uses the - residual df instead (the library-wide `df_convention` deviation - documented in the TwoWayFixedEffects section — `df_convention= - "cluster"` opts into the fixest convention today, and the default flips - at v4). The `hc2_bm` aggregated path matches clubSandwich exactly. + - **Note:** R's fixest uses t-distribution at all levels with the CLUSTER + df `G − 1` on clustered fits — the 4.0 default matches it (M-128, item + 7a); the 3.9 default used the residual df instead (the library-wide + `df_convention` convention documented in the TwoWayFixedEffects + section — `df_convention="residual"` restores it). The `hc2_bm` + aggregated path matches clubSandwich exactly. **Reference implementation(s):** - R: `fixest::sunab()` (Laurent Bergé's implementation) @@ -2192,16 +2199,17 @@ where `W_it(h) = 1[K_it = h]` are lead indicators, estimated on `Omega_0` only. - **Note (pretrends lead-regression tail df, 3.9 / M-127):** the per-lead `gamma_h` t/p/CI previously used silent normal-theory z on plain clustered fits (the tail-df defect family); they now follow the `df_convention` - knob — t(residual df `n − k_kept − rank(absorbed [time, unit])` on the - Omega_0 lead design) under the default `"residual"`, t(G − 1) under - `"cluster"`, z under `"normal"` — with the full-design survey df keeping + knob — t(G − 1) under the 4.0 default `"cluster"` (M-131, item 7a), + t(residual df `n − k_kept − rank(absorbed [time, unit])` on the Omega_0 + lead design) under `"residual"` (the 3.9 default), z under `"normal"` — + with the full-design survey df keeping precedence. This is the ONE ImputationDiD surface on the shared clustered CR1 sandwich; the BJS Theorem-3 overall/post-treatment inference is knob-independent (L3), and the JOINT pretrend Wald F deliberately keeps its cluster-robust `F(q, max(G − 1, 1))` denominator (the standard joint-test convention; it consumes gamma/V_gamma, not the per-lead t's, - coheres with `df_convention="cluster"`, and converges with the t's at the - v4 flip) — the per-lead-t vs joint-F pairing is a stated convention, not + and coheres with the 4.0 default `df_convention="cluster"`, whose per-lead + t(G − 1) it now shares) — the per-lead-t vs joint-F pairing is a stated convention, not a silent split. Since the M-021 post-fit migration the inert-config warning predicate is REACHABILITY-based, no longer aggregate-keyed: an explicitly non-default value warns iff the per-lead @@ -2412,17 +2420,18 @@ Estimated via WLS with Q-weights. The delta_h coefficients identify theta_kappa^ **Note (tail df, 3.9 / M-127):** the `hc1` analytical t/p/CI previously used silent normal-theory z on the non-survey lane (the tail-df defect family); -they now follow the library-wide `df_convention` knob — t(pooled residual df -`n_eff − k_kept`, positive-weight rows) under the default `"residual"`, -t(G − 1) under `"cluster"`, z under `"normal"` — with hc2_bm BM contrast DOF +they now follow the library-wide `df_convention` knob — t(G − 1) under the +4.0 default `"cluster"` (M-130, item 7a), t(pooled residual df `n_eff − +k_kept`, positive-weight rows) under `"residual"` (the 3.9 default), z under +`"normal"` — with hc2_bm BM contrast DOF and survey/replicate df keeping precedence. Two StackedDiD-specific caveats: (a) the `"cluster"` G counts POSITIVE-WEIGHT clusters (`effective_cluster_count`), while the reported `results.n_clusters` keeps the raw unique count — the two diverge only when a cluster's total composed weight is zero; (b) the stacked design replicates control units across -sub-experiments, so the pooled residual df is typically large and the -default's z → t movement is a convention alignment with near-zero numeric -effect — the materially moving step on this surface is the v4 flip to +sub-experiments, so the pooled residual df is typically large and the 3.9 +z → t(residual) movement was a convention alignment with near-zero numeric +effect — the materially moving step on this surface was the 4.0 flip to `G − 1` (M-130). No public per-row df channel changes: `event_study_df` records the resolved df per event time as before. @@ -2594,11 +2603,12 @@ where `g(·)` is the link inverse (logistic or exp), `η_i` is the individual li aggregated t/p/CI now consume ONE `df_convention`-resolved fallback df — the default-`hc1` arms (within-transform AND `hc1`+`cohort_trends` full-dummy) previously used silent normal-theory z (the tail-df defect - family), and now use t(residual df): `n − k_kept − rank(absorbed - [unit, time])` on the within arm, the historical `n − rank(X)` on the - full-dummy arms (`classical`/`hc2` values bit-identical). `"cluster"` - gives t(G − 1) on hc1-clustered fits (inert on one-way/conley families); - `"normal"` gives z. Survey design df and hc2_bm BM DOF keep precedence; + family), and now use t(G − 1) on hc1-clustered fits under the 4.0 default + `"cluster"` (M-129, item 7a; inert on one-way/conley families, which + keep the residual df); `"residual"` (the 3.9 default) gives t(residual + df): `n − k_kept − rank(absorbed [unit, time])` on the within arm, the + historical `n − rank(X)` on the full-dummy arms (`classical`/`hc2` values + bit-identical); `"normal"` gives z. Survey design df and hc2_bm BM DOF keep precedence; the logit/poisson arms are knob-INDEPENDENT (survey df when present, normal theory otherwise — an explicitly non-default `df_convention` warns at fit time). Post-fit `aggregate()` reproduces fit-time inference @@ -2769,7 +2779,7 @@ Eq. 12 reuses the absorbing clean control and only restricts the treated set (a The paper specifies no standard-error formula (Section 1 defers to "standard, well-understood techniques"); the reference Stata `lpdid` uses `vce(cluster unit)`. The entries below document diff-diff's inference and scope choices. -1. **Note:** Standard errors are **cluster-robust at the unit level by default** - `cluster=None` auto-clusters at the unit identifier and the results record `cluster_name`/`n_clusters` - with a `t(G-1)` reference distribution (G = realized clusters in each horizon's clean-control sample). Matches Stata `lpdid` `vce(cluster unit)` - measured on the Stata-anchored surfaces (absorbing via the R/`teffects` arms; non-absorbing per Deviation 4's scoping: Eq. 12 everywhere, Eq. 13 post + pooled-post on the convention-neutral subsample; elsewhere the two implementations' clean-sample conventions differ, see Deviation 4); the paper prescribes no SE. **Since 3.9 (M-127) the reference is the `df_convention` knob's `"cluster"` value — LPDiD's DEFAULT, so nothing moved**: `"residual"` opts into t(per-horizon residual df `n_eff − k_kept`; on the RA path `n_total − k0_kept − 1`, the pooled M-estimator's parameter count — nuisance coefficients plus the ATT — a library convention with no external anchor since Stata `teffects ra` reports z), and `"normal"` into z. The G here stays the RAW unique cluster count (deliberately not `effective_cluster_count`: LPDiD's reweights are strictly positive and there is no user weight column, so the two cannot diverge). **Carve-out to the flat `t(G-1)` claim:** the degenerate lanes — the unclustered-refit fallback when the clustered solve fails, the RA `G<=1` lane, and the saturated-design early return (`n_obs <= k`) — keep the literal `df=None` (normal theory, NaN provenance) under ALL knob values, exactly as before 3.9. +1. **Note:** Standard errors are **cluster-robust at the unit level by default** - `cluster=None` auto-clusters at the unit identifier and the results record `cluster_name`/`n_clusters` - with a `t(G-1)` reference distribution (G = realized clusters in each horizon's clean-control sample). Matches Stata `lpdid` `vce(cluster unit)` - measured on the Stata-anchored surfaces (absorbing via the R/`teffects` arms; non-absorbing per Deviation 4's scoping: Eq. 12 everywhere, Eq. 13 post + pooled-post on the convention-neutral subsample; elsewhere the two implementations' clean-sample conventions differ, see Deviation 4); the paper prescribes no SE. **Since 3.9 (M-127) the reference is the `df_convention` knob's `"cluster"` value — LPDiD's DEFAULT, so nothing moved; since the 4.0 flip (item 7a) it is the library-wide default too**: `"residual"` opts into t(per-horizon residual df `n_eff − k_kept`; on the RA path `n_total − k0_kept − 1`, the pooled M-estimator's parameter count — nuisance coefficients plus the ATT — a library convention with no external anchor since Stata `teffects ra` reports z), and `"normal"` into z. The G here stays the RAW unique cluster count (deliberately not `effective_cluster_count`: LPDiD's reweights are strictly positive and there is no user weight column, so the two cannot diverge). **Carve-out to the flat `t(G-1)` claim:** the degenerate lanes — the unclustered-refit fallback when the clustered solve fails, the RA `G<=1` lane, and the saturated-design early return (`n_obs <= k`) — keep the literal `df=None` (normal theory, NaN provenance) under ALL knob values, exactly as before 3.9. 2. **Note:** The regression-adjustment (RA) covariate path (`reweight=True` with covariates/absorb) reports an **influence-function cluster variance** `sum_c (sum_{i in c} psi_i)^2 / n^2`, in the same family as `ImputationDiD`'s Theorem-3 / BJS variance (see "IF-based variance estimators vs analytical-sandwich estimators" above). Its single Gram inversion is routed through `linalg._rank_guarded_inv` (finite SE on the identified subspace under near-collinearity; NaN at rank 0). Unlike the default/weighted `solve_ols` `hc1`-cluster path - which applies the `(G/(G-1))*((n-1)/(n-K_reference))` finite-sample factor (see the nested-dummy Note below) - the RA IF variance carries **no finite-sample factor**, while both paths share the `t(G-1)` reference. **PR-B2 validated this asymmetry as faithful to the authors' own tooling**, not a defect: the no-factor RA convention matches the canonical Stata `teffects ra ... atet vce(cluster)`, while the default path matches `feols`/`reghdfe`. **Originally inferred from the authors' `lpdid_regression_adjustment.do` degrees-of-freedom comments, this is now MEASURED against a runnable `teffects`:** `benchmarks/stata/generate_lpdid_ra_golden.do` runs `teffects ra (Dy x i.time) (tdiff), atet vce(cluster unit)` on an independently reconstructed clean sample (a Stata port of the `generate_lpdid_golden.R` `prep`/`clean_h` recipe), and the library RA IF SE matches it to **~1e-16 at all 7 event-study horizons** - the SE ratio is a flat 1.0 (no `G/(G-1)`, no `(n-1)/(n-k)`), directly confirming the no-finite-sample-factor convention (golden `benchmarks/data/lpdid_ra_stata_golden.json`; test `tests/test_lpdid_ra_stata_parity.py`, which also gates the Stata clean sample's realized size and cluster count `(e(N), e(N_clust))` against the library's `(n_obs, n_clusters)`). The RA *point* estimate is triple-anchored: Python == R full-interaction (`i.dtreat##(i.time c.x)`) == Stata `teffects`, to ~1e-10..1e-13 (`tests/test_methodology_lpdid.py::test_ra_covariate_point`). Because Stata is node-locked and cannot run in CI (goldens are committed, exactly like the R arm), the CI-runnable guards remain the regression pin (`test_ra_covariate_se_regression_pin` / `RA_SE_PIN`) and the ungated Monte-Carlo coverage study `benchmarks/python/coverage_lpdid_ra.py` (~0.95 empirical coverage of the true effect at cluster counts G in {30, 100, 300}). 3. **Note:** Direct covariate inclusion (`reweight=False` with covariates/absorb) emits a `UserWarning`: per online Appendix B.2.2 it preserves the non-negative LP-DiD weighting result only under linear and homogeneous covariate effects, so the regression-adjustment path (`reweight=True`) is preferred. 4. **Deviation from R:** Scope - non-absorbing treatment (Section 4.2) implements the **entry-effect** estimands (`non_absorbing="first_entry"` / `"effect_stabilization"`, PR-C1). **PR-C2 R-parity-validated both modes against an INDEPENDENT `fixest::feols` reconstruction of the paper's Eq. 12 / Eq. 13 clean-sample restrictions** (point and SE match to ~1e-13/~1e-15 for the variance-weighted variants; the `effect_stabilization` reweighted point matches and its SE is pinned as a regression guard - a small weighted-cluster convention difference vs feols; `tests/test_methodology_lpdid.py::TestLPDiDNonAbsorbingParityR`). The recipe's independence was demonstrated when an earlier draft's Eq. 12 control off-by-one diverged from the already-correct library and was corrected against the paper, plus a hand-computed Python micro-check. **`alexCardazzi/lpdid`'s `nonabsorbing_lag` is NOT a faithful Eq. 13** (it clamps `treat_diff[<0]<-0`, so its clean-control window blocks only treatment turn-*ons* - a violation of Eq. 13's stated `Delta-D = 0` both-directions control condition; it also holds two convention differences on surfaces Eq. 13 does not specify: a forward placebo window, and NA-exclusion of pre-panel-treated rows where the library clamps pre-`min_t` to untreated): it diverges ~0.01-0.05 from Eq. 13 even on a monotone no-off-switch panel, so it is **recorded in the golden `meta` as a divergent third-party reference, not a parity gate** (the alexCardazzi-pooled precedent; the golden's `meta.alex_note`/`meta.boundary_note` strings predate this two-tier framing and are generation-time text - this entry is the living statement). `first_entry` (Eq. 12) has no R-package analogue (anchored on the independent feols recipe, and - since the Stata non-absorbing arm - on the authors' Stata `lpdid` package end-to-end via `nonabsorbing(, firsttreat notyet)`). **The Stata canonical SE is now MEASURED against the authors' `lpdid` package (SSC, v1.0.3) end-to-end** - the first external anchor for the non-absorbing reweighted SE, the non-absorbing pooled windows (points and SEs), and the Eq. 12 reweighted point: Eq. 12 agrees on the full committed panel at every surface (all event-study horizons incl. placebos + both pooled windows; att ~1e-14 vw / ~1.3e-8 rw, SE ~6e-17 vw / ~2.3e-9 rw), and Eq. 13 agrees at post horizons + pooled post on a convention-neutral subsample (att ~1.1e-14 vw / ~1.9e-8 rw, SE ~1.8e-16 vw / ~1.6e-9 rw; golden `benchmarks/data/lpdid_nonabsorbing_stata_golden.json`, test `tests/test_lpdid_nonabsorbing_stata_parity.py`). **Three measured convention differences vs the package**, adjudicated against the paper's stated Eq. 13 restriction (verified against the open-access working-paper text, whose Eq. 18 is the published JAE Eq. 13): (a) *exact-L re-entry spells* - Eq. 13's treated condition is a LEVELS condition (`D = 0` on `[t-L, t-1]`), which the library implements; the package's switch-free lag window additionally excludes an exit AT `t-L`, effectively requiring `L+1` untreated periods - **the package is stricter than the paper here**; (b) *pre-panel boundary handling* - the package's missing-lag semantics admit always-treated units as controls at early `t` where the library's clamp excludes them (a paper-silent surface; both are documented conventions); (c) *placebo windows* - the package builds `h<0` clean samples by recursive lagged intersection of its `CCS_0` indicator, the library uses the backward window `[t-max(L,-h), t-1]` (also paper-silent; both are conventions). The full-panel Eq. 13 divergence from (a)+(b) and the placebo/pooled-pre divergence from (c) are recorded in the golden and locked by divergence-documentation gates (att floors + sample-size mismatch asserts), so parity claims stay scoped: Eq. 13 full-panel and placebo/pooled-pre inference remain OUTSIDE the Stata anchor (the full-panel reweighted SEs stay pinned via `RW_SE_PIN`; the pooled-pre SE has no pin or anchor). Appendix-C exit-event dynamics and the non-absorbing RA path (the package's RA engine is `listreg`, and the committed non-absorbing panel has no covariate) remain deferred follow-ups. diff --git a/docs/methodology/variance-conventions.md b/docs/methodology/variance-conventions.md index 43a5f0bdb..a80b73c99 100644 --- a/docs/methodology/variance-conventions.md +++ b/docs/methodology/variance-conventions.md @@ -22,16 +22,16 @@ produced wrong figures three separate times while this inventory was drafted). | surface | CR1 `k` (multiset) | tail df (multiset) | status | reason | |---|---|---|---|---| -| `did_absorb_hc1_cluster_unit` | 7 | 294 | **legitimate** | K_reference (D2 fixed): k = 2 visible + rank(time given unit) = 5; matches reghdfe/fixest ssc(K.fixef='nested') | -| `did_fixed_effects_hc1_cluster_unit` | 7 | 294 | **legitimate** | K_reference (D1 fixed): 66 visible minus the 59 cluster-nested unit dummies -> identical SE to did_absorb (documented deviation from a literal explicit-dummy R comparison, which counts all 66) | -| `did_plain_hc1_cluster_unit` | 4 | 356 | **legitimate** | no absorbed FE: visible k is the whole design; nothing is omitted | -| `twfe_hc1_cluster_unit_time_post` | 3 | 298 | **legitimate** | K_reference (D2 fixed): 2 visible + rank(post given unit) = 1; matches fixest cluster arm at rel 0 (committed golden) | -| `wooldridge_hc1_within` | 15 | 286, 286, 286, 286, 286, 286, 286, 286, 286, 286 | **legitimate** | CR1 k = K_reference (D2 fixed: 9 cells + T = 15, no intercept col -> +1 term; jwdid arms at ratio 1.0); tail df converged in 3.9 (M-127): the default-hc1 arms use t(residual df = n - k_kept - absorbed rank) via the df_convention knob (was silent normal theory); G-1 under 'cluster', z under 'normal' | -| `sun_abraham_hc1` | 21 | 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280 | **legitimate** | CR1 k = K_reference (D2 fixed: 15 cells + 6, no intercept col; fixest sunab parity ~5e-15); D4 fixed in 3.9 (M-127): cells AND aggregates share the saturated fit's residual df under the df_convention knob (aggregates previously dropped to normal theory inside the same fit) | -| `stacked_did_hc1` | 6 | 309, 309, 309 | **legitimate** | L1: k_total is clubSandwich CR1S by construction (stacked_did.py pins vcovCR(type='CR1S') at atol=1e-10); tail df converged in 3.9 (M-127): t(pooled residual df n_eff - k_kept) via the df_convention knob (was silent normal theory; stacked rows make the residual df large, so z -> t is a convention alignment) | -| `lpdid_pre2_post2` | 4, 4, 5, 5, 5, 6 | 59, 59, 59, 59, 59, 59 | **legitimate** | L2: G-1 tail df (Stata/fixest convention) — since 3.9 the df_convention='cluster' DEFAULT on LPDiD (bit-identical; the one surface already at the v4 target) | -| `twfe_es_within_hc1_cluster_unit` | 11 | 290, 290, 290, 290, 290 | **legitimate** | TWFE event-study, within spec (4.0 cut, PR 4c-i): K_reference = 6 visible (intercept + 5 interactions) + rank(time given the absorbed, cluster-nested unit FE) = 5, i.e. 11; the full-dummy form would read 70 visible minus the 59 cluster-nested unit dummies = 11 | -| `twfe_es_pooled_hc1_cluster_time` | 7 | 348, 348, 348, 348, 348 | **legitimate** | TWFE event-study, pooled spec (4.0 cut, PR 4c-i): the NESTED orientation of the built-in period dummies under a time cluster - 12 visible minus their rank 5 = 7 (under-subtraction is caught here; the unit-cluster within row catches over-) | +| `did_absorb_hc1_cluster_unit` | 7 | 294 | **legitimate** | K_reference (D2 fixed): k = 2 visible + rank(time given unit) = 5; matches reghdfe/fixest ssc(K.fixef='nested') Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `did_fixed_effects_hc1_cluster_unit` | 7 | 294 | **legitimate** | K_reference (D1 fixed): 66 visible minus the 59 cluster-nested unit dummies -> identical SE to did_absorb (documented deviation from a literal explicit-dummy R comparison, which counts all 66) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `did_plain_hc1_cluster_unit` | 4 | 356 | **legitimate** | no absorbed FE: visible k is the whole design; nothing is omitted. Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `twfe_hc1_cluster_unit_time_post` | 3 | 298 | **legitimate** | K_reference (D2 fixed): 2 visible + rank(post given unit) = 1; matches fixest cluster arm at rel 0 (committed golden) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `wooldridge_hc1_within` | 15 | 286, 286, 286, 286, 286, 286, 286, 286, 286, 286 | **legitimate** | CR1 k = K_reference (D2 fixed: 9 cells + T = 15, no intercept col -> +1 term; jwdid arms at ratio 1.0); tail df converged in 3.9 (M-127): the default-hc1 arms use t(residual df = n - k_kept - absorbed rank) via the df_convention knob (was silent normal theory); G-1 under 'cluster', z under 'normal' Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `sun_abraham_hc1` | 21 | 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280 | **legitimate** | CR1 k = K_reference (D2 fixed: 15 cells + 6, no intercept col; fixest sunab parity ~5e-15); D4 fixed in 3.9 (M-127): cells AND aggregates share the saturated fit's residual df under the df_convention knob (aggregates previously dropped to normal theory inside the same fit) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `stacked_did_hc1` | 6 | 309, 309, 309 | **legitimate** | L1: k_total is clubSandwich CR1S by construction (stacked_did.py pins vcovCR(type='CR1S') at atol=1e-10); tail df converged in 3.9 (M-127): t(pooled residual df n_eff - k_kept) via the df_convention knob (was silent normal theory; stacked rows make the residual df large, so z -> t is a convention alignment) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `lpdid_pre2_post2` | 4, 4, 5, 5, 5, 6 | 59, 59, 59, 59, 59, 59 | **legitimate** | L2: G-1 tail df (Stata/fixest convention) — since 3.9 the df_convention='cluster' DEFAULT on LPDiD (bit-identical; adopted at 3.9, the library-wide default since the 4.0 flip) | +| `twfe_es_within_hc1_cluster_unit` | 11 | 290, 290, 290, 290, 290 | **legitimate** | TWFE event-study, within spec (4.0 cut, PR 4c-i): K_reference = 6 visible (intercept + 5 interactions) + rank(time given the absorbed, cluster-nested unit FE) = 5, i.e. 11; the full-dummy form would read 70 visible minus the 59 cluster-nested unit dummies = 11 Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `twfe_es_pooled_hc1_cluster_time` | 7 | 348, 348, 348, 348, 348 | **legitimate** | TWFE event-study, pooled spec (4.0 cut, PR 4c-i): the NESTED orientation of the built-in period dummies under a time cluster - 12 visible minus their rank 5 = 7 (under-subtraction is caught here; the unit-cluster within row catches over-) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | | `lpdid_absorb_nested_cluster_grp` | 4, 4, 5, 5, 5, 6 | 1, 1, 1, 1, 1, 1 | **legitimate** | LPDiD absorb dummies nested in the cluster subtract their rank (adj -1 per horizon: region == grp here); _event_time stays counted (unit-level cluster does not nest time); G-1 tail df (L2) | | `imputation_default` | — | None | **legitimate** | L3: BJS imputation variance, not the shared CR1 sandwich | | `imputation_pretrends_event_study` | unpinned | 157, 157, 157, None, None, None, None, None | **legitimate** | pretrends lead regression: CR1 k = K_reference (D2 fixed); tail df converged in 3.9 (M-127): leads use t(residual df) via the df_convention knob (was silent normal theory); the None calls are the knob-independent BJS aggregates (L3) | @@ -41,6 +41,14 @@ produced wrong figures three separate times while this inventory was drafted). | `dml_did_bad_control` | — | None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None | **legitimate** | L3: Caetano et al. (2026) Eq. 11 augmented-score plug-in variance (same psi_bar - D*theta/pi centering as Chang Thm 2; per-unit influence function; normal-theory safe_inference on no-design fits; cluster= fits use the CR1 per-cell variance with df=df_survey t-inference) and the per-cell ATT_X diagnostic tuple sharing the same branch and df (one extra safe_inference call per retained cell) | | `dml_did_rcs` | — | None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None | **legitimate** | L3: Chang (2020) Thm 2 lambda-corrected augmented-score plug-in variance (Case 2; per-observation influence function; normal-theory safe_inference on no-design fits; survey_design=/cluster= fits use the design-based CR1/weighted-IF per-cell variance — replicate designs the IF-reweighting per-cell variance — with df=df_survey t-inference outside the shared CR1 sandwich) | | `duration_did` | — | None, None | **legitimate** | L3: Deaner & Ku (2026) whole-individual pooled bootstrap (centered absolute-deviation pointwise and simultaneous bands); no CR1 sandwich, normal-theory safe_inference gate only | +| `did_absorb_hc1_cluster_unit_default_df` | 7 | 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `twfe_hc1_cluster_unit_default_df` | 3 | 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `wooldridge_hc1_within_default_df` | 15 | 59, 59, 59, 59, 59, 59, 59, 59, 59, 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `sun_abraham_hc1_default_df` | 21 | 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `stacked_did_hc1_default_df` | 6 | 59, 59, 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `twfe_es_within_hc1_cluster_unit_default_df` | 11 | 59, 59, 59, 59, 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `twfe_es_pooled_hc1_cluster_time_default_df` | 7 | 5, 5, 5, 5, 5 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `imputation_pretrends_event_study_default_df` | unpinned | 59, 59, 59, None, None, None, None, None | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | cr1_k is the sorted multiset of K_reference counts reaching the shared clustered CR1 denominator — visible columns + the signed cluster_k_adjustment @@ -127,8 +135,8 @@ output). full-dummy lm; pinned to clubSandwich::vcovCR(type="CR1S") at atol=1e-10. CR1S is a real second convention, correct by construction. - **L2 — LPDiD's G-1 tail df**: the Stata/fixest convention. Since 3.9 it is - the `df_convention="cluster"` DEFAULT on LPDiD (the one surface already at - the library-wide v4 target — bit-identical, no numbers moved), with + the `df_convention="cluster"` DEFAULT on LPDiD (adopted ahead of the + library-wide 4.0 flip — bit-identical, no numbers moved there), with "residual"/"normal" as opt-ins; the degenerate lanes (unclustered refit, RA G<=1, saturated early return) keep literal df=None under every value. - **L3 — CallawaySantAnna / DMLDiD / TwoStageDiD / ImputationDiD (default) / @@ -158,13 +166,14 @@ output). **Effective-leverage guard:** HC2, HC3, and unweighted one-way HC2-BM return warning + all-NaN covariance/DOF when any positive-weight observation has `h_ii >= 1 - 1e-8` (all rows are effective without weights); identified point estimates remain. Zero-weight rows contribute nothing and cannot trigger the guard, preserving frequency-expansion parity even with zero counts. Weighted/clustered HC2-BM retains CR2, including the all-ones-pweight boundary; its output can differ from unweighted HC2-BM at leverage one. No over-one HC1 fallback is used in the leverage-family branch. -## Tail-df landscape (converged, 3.9 / M-127) +## Tail-df landscape (converged, 3.9 / M-127; default flipped at 4.0) ONE three-value knob (`df_convention ∈ {"residual", "cluster", "normal"}`) now governs the analytical fallback df on every shared-CR1 surface: DiD/TWFE/LinearRegression + SunAbraham/WooldridgeDiD-OLS/StackedDiD/ -ImputationDiD-pretrends (default "residual") and LPDiD (default "cluster", -its Stata-anchored t(G−1)). Survey/replicate df and hc2_bm Bell-McCaffrey +ImputationDiD-pretrends and LPDiD, all at the default "cluster" (the +Stata-anchored t(G−1); LPDiD since 3.9, the rest since the 4.0 cut) with +"residual" restoring the 3.x residual df. Survey/replicate df and hc2_bm Bell-McCaffrey DOF keep precedence under every value; "cluster" fails closed warn+NaN at G<=1 and is inert on unclustered/conley fits; "normal" is deliberate z at the fallback level everywhere. Per-surface residual-df definitions: the @@ -173,9 +182,12 @@ design absorbs FE; positive-weight n_eff on weighted lanes; on the LPDiD RA path `n_total − k0_kept − 1`, the pooled M-estimator count). The pre-fix defect magnitude for the record: at |t| = 2 normal theory understated the t(G−1) p-value by 24.2% at G=20, 13.3% at G=40, 1.2% at G=500 — larger -than the D2 SE gap was before its fix. The remaining program step is the -v4 default flip "residual" → "cluster" (M-004..M-006 + M-128..M-131, the -locked #663 direction); the resolver is `diff_diff.utils.resolve_tail_df`, +than the D2 SE gap was before its fix. The 4.0 cut (item 7a, 2026-10-28) +flipped the default "residual" → "cluster" (M-004..M-006 + M-128..M-131, +the locked #663 direction; pinned by `tests/test_v4_inference_defaults.py`), +so the matrix above carries every clustered surface twice: the explicit +`"residual"` arm (the residual-df literals, which pin the K_full accounting) +and its `_default_df` twin at G−1; the resolver is `diff_diff.utils.resolve_tail_df`, and the out-of-scope roster (IF-based estimators, TripleDifference/TROP, Wooldridge GLM arms) is enumerated in the REGISTRY TwoWayFixedEffects deviation note. diff --git a/docs/migration-4.0.md b/docs/migration-4.0.md index f7aaf11e0..b66789b0f 100644 --- a/docs/migration-4.0.md +++ b/docs/migration-4.0.md @@ -301,8 +301,42 @@ and `BUSINESS_REPORT_SCHEMA_VERSION` is `"4.0"` (its `headline` block carries no Two changes alter results without changing any call: -- **`df_convention` flips to `"cluster"`** on seven estimators. Pass `df_convention="residual"` - to reproduce 3.x numbers exactly. +- **`df_convention` flips to `"cluster"`** on seven estimators (rows M-004..M-006 and + M-128..M-131): `DifferenceInDifferences`, `TwoWayFixedEffects`, `LinearRegression`, + `SunAbraham`, `WooldridgeDiD` (OLS arms), `StackedDiD` and `ImputationDiD` (pretrends + leads); `LPDiD` was already there. Clustered analytical p-values and confidence intervals + now use the Stata/fixest `t(G − 1)` reference instead of the fitted residual df + `t(n − K)`; point estimates, standard errors and t-statistics never move. + + ```python + TwoWayFixedEffects().fit(df, outcome="y", treatment="d", post="post", unit="unit") + # 4.0: p-values / CIs at t(G - 1) on the auto-clustered fit + TwoWayFixedEffects(df_convention="residual").fit( + df, outcome="y", treatment="d", post="post", unit="unit" + ) # 3.x inference, bit-identical + ``` + + **Which fits move.** Every fit whose analytical sandwich is clustered: `TwoWayFixedEffects` + (static and event-study with `unit=`), `SunAbraham`, `WooldridgeDiD` OLS arms and + `StackedDiD` cluster at `unit` by default, so their default fits move; `ImputationDiD` + moves only on the `pretrends=True` lead rows; `DifferenceInDifferences` and + `LinearRegression` move only when you pass `cluster=` / `cluster_ids=`. The analytical + lanes that survive inside a bootstrapped fit move too: `WooldridgeDiD`'s per-cell inference + and its post-fit `aggregate()`, and `ImputationDiD`'s bootstrapped pretrends leads. Code that + relays these p-values moves with them: the fake-timing / fake-group placebo diagnostics + (when `estimator_kwargs` carries `cluster=`) and the power simulators + (`simulate_power` / `simulate_mde` / `simulate_sample_size`) on `TwoWayFixedEffects`, + `SunAbraham` and `StackedDiD` profiles. + + **Which fits do not move.** Unclustered and Conley fits (the knob is inert without a + cluster count), survey-design fits (the survey df keeps precedence), `vcov_type="hc2_bm"` + (Bell-McCaffrey DOF keep precedence), bootstrap percentile p-values and CIs, the BJS + overall/post inference of `ImputationDiD`, `WooldridgeDiD`'s logit/poisson arms, the + permutation and leave-one-out diagnostics (they use only the point estimates), and every + estimator without the knob. On the surfaces where the knob is a documented no-op — + non-`pretrends` `ImputationDiD` fits and `WooldridgeDiD` logit/poisson fits — passing + `df_convention="residual"` emits the inert-config `UserWarning` (there is nothing to + restore there). - **`SpilloverDiD` auto-clusters at `unit`** when `cluster=` is omitted — it is the one estimator whose default standard errors move. Pass **`cluster=False`** for the 3.x observation-level HC numbers; `None` and omission both mean "auto-cluster", so diff --git a/docs/tutorials/24_staggered_vs_collapsed_power.ipynb b/docs/tutorials/24_staggered_vs_collapsed_power.ipynb index e1479b45c..7dcef4424 100644 --- a/docs/tutorials/24_staggered_vs_collapsed_power.ipynb +++ b/docs/tutorials/24_staggered_vs_collapsed_power.ipynb @@ -37,10 +37,10 @@ "id": "0c4f03f8", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:44.355443Z", - "iopub.status.busy": "2026-08-10T22:40:44.355143Z", - "iopub.status.idle": "2026-08-10T22:40:45.083622Z", - "shell.execute_reply": "2026-08-10T22:40:45.083227Z" + "iopub.execute_input": "2026-10-28T15:24:55.749044Z", + "iopub.status.busy": "2026-10-28T15:24:55.748981Z", + "iopub.status.idle": "2026-10-28T15:24:56.479188Z", + "shell.execute_reply": "2026-10-28T15:24:56.478728Z" } }, "outputs": [], @@ -134,10 +134,10 @@ "id": "08de7530", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:45.084992Z", - "iopub.status.busy": "2026-08-10T22:40:45.084888Z", - "iopub.status.idle": "2026-08-10T22:40:45.170232Z", - "shell.execute_reply": "2026-08-10T22:40:45.169803Z" + "iopub.execute_input": "2026-10-28T15:24:56.480611Z", + "iopub.status.busy": "2026-10-28T15:24:56.480509Z", + "iopub.status.idle": "2026-10-28T15:24:56.565409Z", + "shell.execute_reply": "2026-10-28T15:24:56.564957Z" } }, "outputs": [ @@ -228,7 +228,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -296,10 +296,10 @@ "id": "8496ad42", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:45.171194Z", - "iopub.status.busy": "2026-08-10T22:40:45.171128Z", - "iopub.status.idle": "2026-08-10T22:40:45.182869Z", - "shell.execute_reply": "2026-08-10T22:40:45.182539Z" + "iopub.execute_input": "2026-10-28T15:24:56.566499Z", + "iopub.status.busy": "2026-10-28T15:24:56.566436Z", + "iopub.status.idle": "2026-10-28T15:24:56.577928Z", + "shell.execute_reply": "2026-10-28T15:24:56.577538Z" } }, "outputs": [ @@ -310,7 +310,7 @@ "True effect-on-treated (E1): 2.61\n", "Collapsed-2x2 target (E2): 2.10 <- diluted by not-yet-live states\n", "\n", - "Collapsed 2x2 estimate : 2.17 95% CI [1.88, 2.46]\n", + "Collapsed 2x2 estimate : 2.17 95% CI [1.87, 2.46]\n", "Callaway-Sant'Anna : 2.67 95% CI [2.26, 3.09]\n", "\n", "The 2x2 reports ~81% of CS's number. That gap is not noise — both\n", @@ -337,16 +337,16 @@ "id": "77f02fec", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:45.183908Z", - "iopub.status.busy": "2026-08-10T22:40:45.183844Z", - "iopub.status.idle": "2026-08-10T22:40:45.242964Z", - "shell.execute_reply": "2026-08-10T22:40:45.242565Z" + "iopub.execute_input": "2026-10-28T15:24:56.579044Z", + "iopub.status.busy": "2026-10-28T15:24:56.578970Z", + "iopub.status.idle": "2026-10-28T15:24:56.639103Z", + "shell.execute_reply": "2026-10-28T15:24:56.638684Z" } }, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwAAAAHVCAYAAABG7Zw7AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvctoD+AAAAAlwSFlzAAAPYQAAD2EBqD+naQAAjsZJREFUeJzt3Qd4U1X/B/Bfd6G0ZRbKHi1g2XuKyHagouAAZYqAC1FwvE70/Ssi+oqigguUoQIucDAEZG+QvVcLFAqF0tI98n++p9yYpkmbpGnm9/M8geTm9ubckZszfuccH51OpxMiIiIiIvIKvs5OABEREREROQ4LAEREREREXoQFACIiIiIiL8ICABERERGRF2EBgIiIiIjIi7AAQERERETkRVgAICIiIiLyIiwAEBERERF5ERYAyKu88cYb0r59e2cnw+198MEH0rRp02KXkWtfuzxnPC6lYf78+dK4cWO5ePFikdeaK19/pvbBFbjyMSP34u/sBBCVxOHDh+Wee+6xaN2ZM2fKhQsX5OjRo6V+0DMyMmTRokXy+++/y/Hjx6VixYrStm1befbZZ6Vy5cous01bJSQkqGNf3DKyn9K4dnnOSue4vPPOO/L999/Lnj17xBtdvXpVjhw5ItnZ2aV6zyjN42xqH1wBv7NkLywAkFurV6+e/PLLLwWWDRw4UC5fvix///13geW1atVSPxaO0L17d/Xj8eKLL8rEiRNV5u3111+XWbNmydq1a22qwSmNbRJRYc8995yMGDHC5kODWmN8V8l+x5THmci+WAAgtxYUFKSaaQ0FBweLv79/oeWOhM9evHix1KxZU7+sY8eOUrduXXnppZdk6dKlLrFNIiqsSpUq6kH2w2NK5FrYB4C8Ul5enkydOlVloPH4v//7P8nNzS20HloSXn31Vbn55pulRYsWMmDAAFm+fHmx2//qq68KZNQBYTr169eXvXv36pclJiaqzx88eLDodDr98tTUVOnRo4fceeed+iZoS7dZFOwPYsnRmtCqVSv1uVu2bCmwzrFjx2TcuHHSrl07ad26tQwbNkz++ecfscW3336rCi543HTTTSqGfcyYMXLw4EGT8e3Y18mTJ0uHDh2kc+fO8u6770pWVlaBdZOSkuTNN9+Unj17qvShxQcFI1vOnaXbsnW/jK1evVr9zb59+/TL8BzL+vfvX2BdtO7g2jC8Lqy5dktyHm297rXzmJOTI1OmTFHn8Pbbb7fpWsA2cG5wLdx66636Ai6uh7feekvte5cuXeTrr782mw5LridL46y1bRZ3/J988km1r/gcbX/xOHfunF3OjSV/a2laS3q+YdeuXXL//fdLy5YtVTjm1q1bSxS7jn276667Ci3Xrh+k1dLjbOl+WboPplhzPyrq3mvrPcWavy3q+0leSEfkYdq0aaOrUaOGyffGjBmjCw8P140fP173/vvv67Zt26b78MMPdQEBAbpXXnmlwLonT55U22nZsqXup59+Uuu+9dZbat3PPvvM6nRdunRJFxwcrOvcuXOB5dg2vorYtmbw4MFq3d27d9u0TVOOHTumq169uq5Ro0a6BQsW6Hbs2KH7/vvv1d8eP35crYN9DA0N1XXr1k23bNky3Zo1a3R33323LjAwUPf777/rt/XCCy/o/Pz8Cmzf1LIrV67oDh06pB4HDhxQ27zjjjt0YWFhutOnTxc6L6NGjVLnZevWrbpPP/1UFxISoj7fUJcuXXQxMTG6X375RR2fn3/+WXf//ffrvv32W6vPnSXbMsXS/TKWmJio8/X11b377rv6ZVOnTlXHzcfHR3fx4kX98oYNG+oGDBhg07VbkvNYkuteS+Po0aN17733nm7Tpk261157zapjpm1j3Lhxuo8//lhdC88//7xK59q1a3WPPPKIbvr06Wr5Sy+9pL47S5YsMZkOS64nU0wdF0uP/9mzZ3VDhw5Vx1rbXzyysrKsOjemWPq3jrrP4fzi/nPbbbfpVq1apVuxYoWuX79+ukcffVSdl7i4uCKPqalluCaaNGlS6LP+97//qW3Gx8dbdJwt3S9r9sEUS+4hltx7Lf1+2OM+a+r7Sd6HBQDyugIAMlrIDBgaNmyYulnm5ubql/Xp00dXrVo13dWrVwusi0wHMhLGy4uDjAt+UObNm1foveeee05lDFeuXKmbMWOGWu+rr74q0TaN9ejRQ1epUiWT6c7Ozlb/t2vXTv1Qpaen69/Ly8vTtWrVSlezZk39epb+mJuSk5OjjuvEiRMLnRdkUgzNmTOnQAYPP/7mjo32w2/pubN0W5YytV+mtG3bVtezZ0/96969e6sCX5kyZXTz589Xy86cOaPS9sknn9h07ZbkPJbkutfS+MEHHxT4XFuuhVmzZhVY96abblLfa3w/DDVr1qzA8bTmerK2AGDp8UfGOygoyOS2LT03JflbR93ncC03aNBAnUcN0obzVNoFgOKOs6X7Zc0+GLP0HmLJvdfS74c97rOWfj/JszEEiLwOCr4PPfRQgWVoFk1OTpb4+Hj9CBArV66U++67T8qXL19gXTQjI0Rn/fr1Fn/mhx9+KHPnzpVBgwbJkCFDCr2vNcc+8MADalQfdJYbOXJkibZpCKFGa9asUc3OxvsD6DOB0SW2b9+u0oB+FBofHx8VZnD27FmrQ4EQgoA0olkdzetonm7SpIlqNjce/QPnxXg/kF4/Pz/57bff1GukvVy5cvLFF1+otBqGxwQEBFh17izZlj32y1jv3r1lw4YNkp6erkZ2wnOE/yBMYcWKFWodpF9b19prtyTn0R7XvfF5xOdae8ywDaTfEMIrEN7x4IMPFlpu6phbcj1Zy5LjX5SSnBtr/7a073NIz44dO9T5wDHVIG3YnjNZul8l3QdL7iGW3HtLek8pyX1W+36S92EnYPI6uGEb34gxpKYWp1mjRg2Ji4tTN0rEcmI0Ie3Gjv+1GGKMwmOJ2bNnq0x9nz591E3aFPwIvP3229KtWzcpU6aMytyXdJuGkHFC2jFqkjlapqB27dqF3qtTp476//z582KNp59+Wv04Ikb2+eefV8fd19dX/VAhA2zcodt4OFP8iFarVk2fNvwwYyjUp556SmVmKlWqJLfccovK6CD2Fiw9d5Zsyx77ZQyZegxfiJGbcN5RCOjVq5c6R4iTBmRecMyjo6OtvnZLch7tcd3jPEZERJTomGE/w8PDCyzD+jhnOE/Gy5HJMpWO4q4na1ly/ItSknNj7d+W9n1OSw9GVzNmapkjWbpfJd0HS+4hltx7S3pPsfY+a+r7Sd6HBQDyOoY1Pca0H4rQ0FD1P27k6ExlCjISxVmwYIE8+uijqkMvhivFzdcU3KTxI1K1alV9x7Xp06eXaJuGwsLC1P9aBzpTtH1G7ZkxLYOlrWMJ1LJhiFLs1wsvvFDgvUuXLhXq0JyZmamOAwpAhpAew8/t16+f6giJYRZRe/7TTz+pVhCMhIRClDXnrrht2WO/jKHjatmyZVVtPzKk6BCIjCoKBhjedf/+/bJq1SqT81tYc+3ach7tcd0b1k7beszM7ae55cYdpa25nqxhyfEvij3OjaV/W9r3ORQw4MqVK4XeM7XMUiEhISYzvKitt5Sl+6Udt5LsQ3H3EEvuvSW5p1j7t6a+n+SdGAJEZAJqa/DYvXt3gREmDB+mmnMN4YcATfMY9QEjmBhnRAw98cQTcujQIVmyZIka4eSjjz4yOZKENds0hKFCsT9//PGH2YwK3sePIjKfxv766y/1w4FwC0uhZhujTURGRhZYvm7dOrM/rsZzN2A0jrS0NBUeZaxRo0YyatQoNTEaRnn5+eefbT535rZlr/0yFBgYqFp6UABATb8W5tO8eXN1/DFSDTILxuE/lirJebTHdV8ax8xW1lxP9oRjbGq0nZKeG3t+P0t6vjH6GNJjfIwBIS+2QmsGWjKMCwFoMbPmOFuyX/bcB3P3EEvuvSX5fjjru0XujwUAIjMQhoM4UYTapKSk6JejpgcZdsNlxv78808VV4qMHuKNi8qoY3hPhPTg89CMjIm+MPwnfkxQs2TLNk2ZNm2amjETaUetESAzhGECz5w5o2JB0fKwceNGlQlFXCl+sDDE3MKFC2XChAmFwjKKguZwxKHi71ETBZjJ9rXXXpMGDRqYrFHEkI4nT55Ur5EJePzxx1VT/NChQ9Uy1I6/8sorKt5Zc+rUKZX+mJgYq86dpdsq6X6Zgsz9gQMHVMw2wrg0CAWaP3++OhcYVtAWJT2PJbnuS/OYWcuS66m0YJ+QKcM1Zq9zY+/vZ0nPN9KDexUKsphlHWlBmt577z0pCdzjkKlFmBxgm3iuxcpbcpwt3a+S7oOl95Di7r0l+X4447tFHsLZvZCJnDEMqLHvvvtOjeZgPOwmRuVp3769zt/fX1erVi01BB9GqMAwcoYjaRirXbu22l6dOnXU0G+Gj+bNm+vX++eff9QQdBgFxnhYt7p166p109LSrNpmUTD6CdKPUSQwagiOxYsvvlhgVBGMsoLRI8qVK6crX768rkKFCro333yzwP5aOqLH3r17VdowVB/OCYbLwxB4OEeGI7do5+Xo0aO6Fi1aqJFOMCoSjj2GttNcv35d9/bbb6tjgHThmJQtW1YNB4ghUa05d9Zsy5il+1XU3+Nc4vMyMjL0y7/55hu1HNsp6bVr63m05NiZYy6NtlwLxp544gk1eosxUyPBWHo92TIMqCXHH9/Zrl27qn2NiopS31EMW2nNuTHHkr911H0OMLQmzgs+r0qVKrpXX31VDd9q6yhAgGFyMSoWRs6pWrWqGrXG1ChAxR1nS/fL0n0wZs09pLh7r6XfD3vcZ4nAB/84uxBCZE+oUUGtkKnaj4sXL6pRMIw7V6I2CJ210FRrKqYef4OwDDSzWlLzfuLECf0EXsbQOathw4b69CCm19TnIj4VNTqosURcrKXbtAT2BTVQ2LapUSBwW8DxQPM6YkiN44mRLjQvo9m7qGWG7xk2U+McIc1aR7uxY8fK999/r0at0Gpr8ZnoE2EOPgv7gCZ8U7WD1pw7S7dl7X4VBaNz4Jwbdg5Ezefp06dVTa5xk74t164t57Ek1725NFpzzMxtA8uvXbtW6DpHbDiuG8PltlxPxR0XW44/vsN4oFYZ9yPD0aWKOzdFKe5vHXWfM+xvgWOM44v+LTju6GQbFRWl/z5Ze89ACBC2gbQg1Af3SewX9sl4f4s6zpbulyX7UBRL7yHF3XuL+36U5D5ryfeTvAcLAETkdMYZNiJeT0REpYd9AIiIiIiIvAgLAEREREREXoQhQETkdIxNJV5PRESOwwIAEREREZEXYQgQEREREZEXsXy8Ow+BYcIw1BemCjc1BBcRERERkbvBEMEY7rd69epqCNiieF0BAJl/S8boJiIiIiJyN3FxcWqOkKJ4XQEANf/awQkLC3N2cohcEibAmT17towYMUJNbENERESuDRO9oZJby+sWxesKAFrYDzL/LAAQmZaamqpm38RNhN8TIiIi92FJiDs7ARMREREReREWAIiIiIiIvAgLAEREREREXoQFACIiIiIiL8ICABERERGRF2EBgIiIiIjIi7AAQERERETkRVgAICIiIiLyIiwAEBERERF5ERYAiIiIiIi8CAsARERERERehAUAIiIiIiIvwgIAEREREZEXYQGAiIiIiMiL+Ds7AURERETknRKSMyQhJdPs+xGhQRIRFuzQNHkDFgCIiIiIyCnmb42V6auOmX1/fM9omdC7oUPT5A1YACAiIiIipxjSobb0jqkqGdm5MnDmZrVs8dhOEhzgp28BIPtjAYCIiIiInALhPXikZeXol8VUD5OygcyiliZ2AiYiIiIi8iIsABAREREReREWAIiIiIiIvAgLAEREREREXoQFACIiIiIiL8ICABERERGRF2EBgIiIiIjIi7AAQERERETkRVgAICIiIiLyIiwAEBERERF5ERYAiIiIiIi8CAsARERERERehAUAIiIiIiIvwgIAEREREZEXYQGAiIiIiMiLsABARERERORFWAAgIiIiIvIiLAAQEREREXkRFgCIiIiIiLwICwBERERERF6EBQAiIiIiIi/CAgARERERkRfxd3YCiIiIyL0kJGdIQkqm2fcjQoMkIizYoWkiIsuxAEBERERWmb81VqavOmb2/fE9o2VC74Y8quTxEty0MMwCABEREVllSIfa0jumqmRk58rAmZvVssVjO0lwgJ8+00PkDea7aWGYBQAiIiKyCmo08UjLytEvi6keJmUDma0g7zLETQvD/KYSEREREXlRYZijABEREREReREWAIiIiIiIvIhrt08QEZFdueuIFUREZD8sABAReRF3HbHCEAsxxOuIqGRYACAi8iLuOmKFpxViyPl4HZE3YwGAiMiLuOuIFZ5WiCHn84TriK1hZCv3ueMTERF5SCGGnM8TriO2YpCt3OcqJyIiIiKPasUg53C7AsCSJUtk+fLlcv36dWnSpImMGjVKKlWq5OxkERERETmUJ7RikHO41TwAw4cPlwULFkjLli2le/fu8ttvv0mrVq0kISHB2UkjIiIiIhvl5un0z7edulLgNdmfWxURp02bJpUrV9a/fuCBByQ8PFyWLVsmQ4cOdWraiIiIiMh6y/bHy+tLDuhfD5+9XSLDg+X1/jHSr2kkD6m3twAYZv7hxIkTkpOTI/Xr13damoiIiIjI9sz/uHm75GJywQkKL1zLUMvxPnl5CwDs3r1bJk+eLMnJyXLo0CGZO3eudO3a1ez6mZmZ6qHB3xERERGRcyHMZ/LSg2Iq2AfLfETU+71jqomfL16RV7YAQI0aNVRfgMGDB0tUVJRMnTq1yD4A77zzjgoT0h61atVyaHqJiIiIqDDE+sdfyzB7aFAIwPtYj7y8BSAiIkLuuece9fyRRx6R6Oho+fDDD+Xtt982uf5LL70kzz77bIEWABYCiIjImTiBE5FIQkqGXdcjDy4AGAoKCpI6depIXFxckevgQURE5Co4gRORiJ+PZWE9EaHBPFzeWgDIyMiQH3/8UYYMGaJftmXLFtm+fbsMGzbMqWkjIiKyBidwIm939GKK/Pf3g8WuF+TvKw2rlnNImryJ2xQAAgICZM2aNfLyyy9Lw4YNJSkpSQ4cOCBPP/20jBw50tnJIyIishgncCJvtvPMFRk5Z4dcS8+WamHBciE5Q3X4NdUZODMnT+79bJN8/khbaVQt1Amp9UxuUwDw8/OTL7/8Ui5duiR79+6VkJAQady4sZQvX97ZSSMiIiIiC/x18KI8sWCXyti3ql1evh7WTraeSlTzABgOBYp5AEZ2qSffbD4tZxLTZMCnG+WD+1tKv6bVeJy9qQCgqVKlivTs2dPZySAiIiIiKyzcHicv/bxPDf/Zo3GEfDK4tZQJ9FOTfXWJqizN3lih1pszop3cHF1FDf15X5ua8uSCXbLpRKKMnbdTxveMVg9fDgvqXcOAEhEREZH70Ol08sma4/L8j3tV5n9gm5oy65E2KvOvMRznv329ivrXFUMC5duR7WVEl7rq9fRVx2TMvJ1yPTPHCXviOVgAICIiIqJSkXdjsq/3lh9Rrx/v3kDeG9hcAvwsz4L6+/nK6/2bqL8L9PeVlQcvyoBPNsrpy6k8azZiAYCIiIiI7C4zJ1ee+n63zNl0Wr1+7c4Yeb5fY/GxcPhPY4Pa1pKFYzpJ1bAgOZZwXe6asUHWHr1k51R7BxYAiIiIiMiuUjKyZcTs7fL73ngJ8PORjx5qJSO71ivxdlvWKi9Ln+wqrWuXl+SMHBkxe5vMWntChRmR5VgAICIiIiK7wcy9D36+RXXcDQn0k9nD28tdLarbdRjd7x7rKA+0rSV5OpF3/jws47//R9Kzcu32GZ6OBQAiIiIisgvE5Q/8bLMcOJ8slcsFyvePdZKu0ZXtfnSD/P1kyn3N5K27m4i/r48s2XNeBs7cJOeS0u3+WZ6IBQAiIiIiKrH9566pTHjslTSpXbGsLB7bWZrVDC+1I4u+BI90qivzHu2gRgtCoeOujzfI1pOJpfaZnoIFACIiIiIqkQ3HLssDszbL5etZEhMZJovHdZK6lUMcclQ71q8kS57sIk2qh0liapYM+XKrzN18mv0CisACABERERHZDOE3I+Zsk9SsXOncoJL8MKajRIQGO/SI1qyQ3+LQv0V1ycnTyau/HpCXftqnRiKiwlgAICIiIiKbzN54Sp7+brdk5+rkjuaRMntEOwkNDnDK0cTEYh892FJevA1DjYp8vz1OBn+xVRKSM5ySHlfGAgARERERWQXDbr677LCa5AuGdaojHz/YSnXOdSb0Cxh7SwOZPRwFEX/Zeeaq9J+xQf6JS3JqulwNCwBEREREZLGc3Dx5fvFe+ezvE+r1pL6N5I27moivr20TfJWG7o0iZMmTXSUqopxcTM6U+2dtlsU7zzo7WS6DBQAiIiIisgjG2h8zd6cs2nlWkN9/975m8sStUTbP7lua6lUOkZ8f7yy9bqoqWTl5MnHRHnlz6UFVgPF2LAAQERERUbGuqhF2tsiqwwkS5O8rsx5pKw+0q+3SRw79ET5/pI083TNavf564ykZ+vU2tS/ezN/ZCSAi74BOWAkpmWbfjwgNUrM7EhGR6zmflK4yzscTrkt4mQD5alhbaVu3orgDhCY927uhxESGyrML96gZiu/6ZIN8/khbuSkyTLwRCwBE5BDzt8bK9FXHzL4/vme0TOjdkGeDiMjFHL2YIkO/2iYXkjMkMjxYvhnZXhpWDRV3069ppNSrXE5Gf7tDTVZ276eb5P37W8jtzSLF27AAQEQOMaRDbekdU1UysnNl4MzNatnisZ0kOMBP3wJARORouXk6/fNtp67IzdFVxM+FOrM6247TV2TknO2SnJGjOtR+O7K9VC9fRtxVo2qhatKwp77bLeuPXZbH5++SJ2+NUi0ErtSJubSxDwAROQTCe5rWCJeY6v82t+I5luHB8B8icrRl++Ol1wdr9a+Hz94uXd9drZaTyMqDF9Wsusj8t65dXlXauHPmX1O+bKAaJnT0zfXU6xlrjqtWgeSMbPEWLAAQERGR10Emf9y8XWqISEMXrmWo5d5eCPhhe6yMmbtDMnPypGfjCJn/aEeVcfYU/n6+8vIdMfK/B1pIoL+v6th8zycb5cSl6+INWAAgIiIirwv7wQRW/wb//EtbhvcNw4O8aYKvGauPyQs/7hPs/qA2NWXWI23ULLueaECrmqplA30bTl5KlXtmbJQ1hxPE07EAQERERF4DY8Av3hkn8dcyzK6DbD/eR58Ab4ICzxtLDsi0FUfV68e7N5CpA5ur2nJP1rxmeTVpWNs6FSQlM0dGfrNdPllzXBWGPBU7ARMRkVti500qTnZunhy7eF32n7sm+248DsUnq7AWS2w8fkna16voFZ2CM3Ny5dkf9sjv++IFc3q9dmeMjOiSHyPvDaqEBsmC0R3ljaUHZMHWWHlv+RE5GJ8s7w1sLmUDPS+77Hl7REREHg/x2a8vOVCg8yaa8F/vH6OG+iPvg5leMVylltnH/4cupKjlxoIDfCUju/hCwIw1J+SHHWfljmaR0r9FddUR1hVnvC2plIxsNbsvxscP8PORD+5vqfbX2wT6+8rbA5pJk+ph8vqvB+T3vfEqLAgTidWqWFY8CQsAREReyJ1rz7XOm8aN81rnzc8ebs1CgBfUVh+9cF1fq3/g/DU5HJ8iWbmFM/WhQf7SpEaYNLsx4hgetSuUlW7vrVHXjLkgj7KBfuLv6yOXUjJlzqbT6lGjfBmVMe7fIlJiIsM8ojCQkJIhw7/ermq7QwL91Oy+XaMrizcb0qGOmudg3LydqsXorhkb5JMhraVzA885LiwAEBF5GXeuPUf8NmKUzXXe9LnRebN3TDW3KdBQ0TB3yJELKfqMPv7H6+zcwldBWLC/yuAbZvbrVCxrcnx3XO8oMOIdwy1pa35wfwvp0biqrD92SZbuOS8rDl6Uc0npMnPtCfVoUCXkRmGgujSoUs4tT+Ppy6nyyNdbJe5KulQuFyhzRrRXx4xE2tWtqPoFoGUE19wjX22TV+64SYZ3rusRBT8WAIiIvEhp1p6jVQGZtXQ8snL1z9Oy8v/PuPG/qffVc/37eYXWNfy/KFrnzS/Xn1QZMxRsPOHH2lvgOkCN6/7zybL/bH5mH2E9OSZG4wkvE6DP6Of/Hya1K5a1+HzjOsf1jsKw4VCg1YwKwz1vqqoeuP7WHEmQJf+cl9VHEuTEpVT58K9j6oHWgLtaVpc7m0dKzQruESqy7+w1GT57mySmZqnjNndUe6lTKcTZyXIp1cuXkUVjO8lLP+2Tn3efU5ULB84ny3/vaaqfxNJdsQBAROQlLBn68IUf98rpxFTJzNblZ9pvZLzTjDLtBZ7fyMSbirV2lnf+PKweCOOoXyVEoqqUU7W0DSLy/69buawE+bv3D7i7h5Lh+kHYiYrZv5HZP5Zw3eTQmxXKBugz+lqmv2aFMiUu3CGT3yWqsjR7Y4V6PWdEO7P7gGEwb28WqR6ImcckWWgZwGyy2A88pvx5WPUTQOET/QZcdYJDtGqMnbtTUrNyVbw7av7RCZYKQ0YfrUE4Tm//cUgW7zwrxxOuq6FRq7ro+bUECwBERF4CGbSihj6Ea+k5MuXPIyX+LHSyLBPgl/8IvPEI8FM/pvplN14jk64tM/v+jdcIARk7b1exn189PFgSUjJVwWT/OWQykwu8j/wdaj21QoEqIESEqNeeNNmRq4SSpWXlqJr9/Ix+fqb/+CXTmf1KIYFGYTxhKva+tFpyDDP7lo74ExocIPe2rqkeV1KzZNn+C6owsOVUouyKTVKPt347KB3rV1KFgX5NqkmFENe4rpbsOS/PLfxHhVB1iaokMx9uo/aHzMO19+jN9aVRtVB5csFu+ScuSe78eIM6dm3qVBB3xAIAEZGXQGc/S7SrU0Giq4UWzMAbZcr1y4wy+HgE+fuajLm2V5M8MpnmOm/63AjhWP9CD8nT6ST2SpqcSLiuMpsnElLVLJ94jbG+TyemqQdmADXOgGotBYjzjrrxHJnQ0tovTwsle65PQzV0ojYiD467qTm1KpcLkmZGHXTdLWyrYkigDO5QWz0uJmeokWOW7j0vu2OT1Kg6eLz6y37p1rCK6jyM/inlgpyT/fp6wyl587eD6vkdzSNVzTZbwiyH1qElT3aR0d/ukKMXr8tDn2+Rt+5pIg+0q+12AyuwAEBE5CUiQi1rrn62TyPp1KCSuCL8oBbXeRPvYz0/8bmRiS8nfQzWw+Q+GNlFFQoupaoCgVYwOH8tQ8VEJ566UmgSKLRq1Ktc7kaBIL+1AM/rVQ6xOR7YnTINOG4YOjMpLUte+WV/kaFk2kRShiJCg4xi9sOlaliQW2X2i4OQkJFd66lH3JU0+W1vvKpxR+vH6sMJ6hHkv096NI6Qu1pUl1sbRzgklhznburyI/LZ3yfUa3RkxTj/LNBaD/0kfnq8i2pFWX7gopox+c99F+TQhWS3GliBBQAiIi9x4Vp6ke9rtecIg3BllnbeNAcZTsRm42E8rF9qZo4a91sVCG48EO97+nKayvwiI4dHwe2JikdXBQKDfgYoHKB22FVGY0LMfUpGjopfT77xf4rB/1iWnF5wWUrmjfduLDfVGdec1rUryC0Nq6gQHmT4XTUevrRg3Phx3Ruox/GEFFm6J16FCZ28nCp/7r+gHhh2s0+TaqploGtUFTUOfWlMhoZOrIhdh0l9G6kZfj2p4OVo5YL85bMhbWTGmuPywcqj8vfRS4XWcfVhiVkAICLyAgu3x8kLP+3Vvy6u9tzVWdN50xohQf7SrGa4ehgPP3r2arq+QJBfOEhVz6+lZ6thFPH4+8ilQp1XtVYI1XJwo5/BgXPJ8sQCy0djQgdrUxn3/NfFZ9zxMDVGvi2Mrx1zhnWuI3e3rGGXz3R3URGhMqF3qDzTK1qNIoMQod/2xKthRTG6DB7lywbIbU2rSf/m1aVD/Up2+R6isz6uM7Q8YHvvDGgm97erZZd98na+vj7yxK1RMnvjKbmalu12wxKzAEBEZKGE5AzVsdQchDi4Yi3nN5tO62uah3SoLV0aVJbJv9lWe+5KbOm8aSt/P1+pWzlEPTAkpGFoBUKG8sOI8gsEWssBMnfIGOw4c1U9LKFlrJ/6brfULH9YUjJRa58tmXYaYQmVvqi9DAsOkNBg/xuP/Of/Lvv3PSwLK2O4LED2nU2Sh77YareQM2+CWnetr8MLfRvL7rgk1SqAUKHL1zPlu21x6oEReUo6+/DV1CwZ+c121RcB/XI+GdxaesX8e+1SySFsz1Tm33hYYqznamGVLAAQEVlo/tZYmb7qmNn3x/eMlgm9G7rU8Zy19oQaDhNGda2nJrJBZuLmhvavPfdGOJboyIoHam2Na19PXi7YzwAFBDyKC6XBCC2nEtMKLUfmvbiMe9iN58YZd/xfLtC/xHHf7etVsqgjtquHkjkbzgNGkMHj1TtjZOvJRNUy8Me+C4VmH76zRaTqM2Bu9mHjviT1q5STEbO3qWsP8yV8PbyttKnD8+GsgRUSLFzPkVgAICKyEGrPe8dUVbHUA2duVssWj+2k78SHFgBXgZppFFYwSRE8eWuUGplFyzw4svbcW2FkpCbVw9XDEMI9JvzwT7F//3SPKNUio2XyywX7u8R5sqYjNll+TDtHVVaPyXc1lQ3HMftwvKw4cEG1JM1ae1I9MKcFQoTQMoCQMnN9SXDoUSZAQe3bke0lumooT0UpiLCwlcsVW8NYACAispDWcRRjmmtiqoep4Q5dCTL/U5YdVhkGrdMfYlXJNVSzMEysU4PK6vryxI7YZB46AvdoXFU9UNmA+H2ECWG4WnRQR8EeD7QGNKwaKr/8c67QNrQGgad6RDHzX4ra16votq1hrvWrRUREJZKnZvs9IN9sPqNeI7QAoT/kOtw50+CIjtj0L7QuGs4+/NchzD4cL+uOXtLPPmwOzsLHq4+rMepd+ZxofatQ2NEcPJ9coGXVFftWuXtrGAsAREQeAnHA//lpn/ywI069/r8BTWVIhzrOThZ5UKbBGEPJHAf9OAa0qqke6OD76d/H5Yv1p9yyA2pxfau0EEtX7VvlCa1hLAAQEXkAjPU9cdEe+fWf8yr+972BLeS+NjWdnSzysEwDuYYKIYFqJCF37YBqqm+VOa7Ut8qTWsNYACAicnMYI/6p73apWSn9fX1k+oOt5I7mzEC6OnfMNJDrcOcOqKb6Vrk7PzcbWMH+U84REZHDIG52zNwdKvMf6OcrMx9uw8y/G3G3TAO5Xl8Sc1cMlke6QV8Scg4WAIiI3FRqZo6MnLNd1hy5JMEBvvLV8Lac6IfIy/qSgHEhwN36kpDjsQBAROSGkjOyZdjX22TTiUQJCfSTb0a0V+EjROR9fUkiwgrGyaMvCZazLwmZwz4ARERuJiktS4Z+vU32nr2mZn39ZmR7aVW7grOTRUROwL4kZAsWAIiI3Mjl65ny8Jdb5fCFFKkYEqhm+bR0NBAi8kzsS0LWYgGAiMhNYOKowV9uUbOBVgkNkvmPdlAzgRIREVmDBQAiIjcQdyVNhny5VWKvpEn18GCZP7qj1Ksc4uxkERGRN3UCzs7Olo0bN8rcuXP1yxITE+2VLiIiuuHU5VR5YNZmlfmvXbGs/DCmEzP/RETk2AJAbGystG7dWnr27ClDhw7VLx89erQsXbrU9tQQEVEBRy+myP2zNsv5axnSoEqILBzTSWpVLMujREREji0ATJgwQTp16iTJyckFlk+cOFGmTJlie2qIiEhv/7lrqub/UkqmNK4Wqmr+MbwfERGRw/sArF27Vg4fPiyBgYEFljdr1kx27txZogQREZHIrtirapz/lIwcaV4zXI32U75swXsuERGRwwoAGRkZ4uub33jg4/PvDHPx8fFStiybpomISmLLyUQZNWe7pGblSts6FeTrEe0kLDiAB5WIiJwXAtS9e3eZOXNmgQJAamqqTJo0SXr16mWflBEReaF1Ry/J8NnbVOa/c4NK8u2o9sz8ExGR81sApk2bJt26dZM//vhDdDqdDBo0SNavX6/ew8hARETm5Obp9M+3nboiN0dXKTCJjTdbefCiPDF/l2Tl5smtjarIZw+3keAAP2cni4iIPIxNLQCNGzeW/fv3S9++faV///4qJGjMmDGyZ88eadCggf1TSUQeYdn+eOn1wVr96+Gzt0vXd1er5d7ut73nZdy8nSrz369JNZn1SFtm/omIyLUmAouIiJBXX33VvqkhIo+FTP64ebvk3/r/f2e3xfLPHm4t/ZpGijdavPOsPL94j6Bx5O6W1eX9QS3E38/maVqIiIiKxF8YInJI2M/kpQcLZf5BW4b3DcODvMW8LWdk4qL8zP+D7WrJB/e3ZOafiIhcrwXg7NmzqsPvhg0b5OrVq4Xev379uj3SRkQeArH+8dcyzL6PbD/ex3qdGlQSb/HVhlPy1m8H1fPhnevKa3fGiC/7QxA5REJyhiSkZEpGdq5+2cHzyfrQu4jQIIkI47wb5JlsKgBg9t+cnBx56623pHz58uKWslJFskx0rvPxEwkILrieOT6+IgFlbFw3zaDus9DKIoFlbVs3O11El2c+HYEhNq6bIaLLtc+6AWUxfFT+85xMkbwc+6zrX0bkxvC0kpMlkpdtp3WDRXz9rF83N1skN8v8un5BIn7+NqybI5KbWcS6gSJ+Adavm5crkpPx77Wh/a9d174BIv6Bhdc1pcC6eZJw9ZpYYtryw/L4zTXl5gYVJdDfRAOlr7+If1D+c51OJDutiDRYsa5V33u/grdOta6/1feIGWvPyLRVp9TzMV1ryYt96omPYea/tO8RWTlSRjIK74M73SOycm7stxvfI7LSTJ8HV79HaAyvI5U+f5vuEZKTbqd1Lf/ef7/5lHyw5syNVzopI5nyyMw1+vcf7x4lT/WIsu0e4eh8RKHvcwDzEY7OR2Slm/8uOyofUdT1Y8RHh2F8rISx/s+cOSNVqlQRd4PZi8PDw+Xai6ESFmRi5JHoPiJDFv37+v8izd9A6nQVGfH7v6+n1hdJSzS9bvVWIo/9/e/r/zUTuRZret0qjUWe2Prv6086iFw6bHrd8NoiE/b9+/rz7iLnd5tet2wlkedP/vt69h0iZzaY/8K8bNAxc/4gkWMrxKw3DDJ4C4eKHPzV/Lr/Of9vZuDncSJ7Fphfd9IJkZDK+c9/f05k+5fm1x2/V6RCnfznK14R2fSx+XUf3yIScVP+8zXviKwtYgbr0atFarTJf75xusjK18yvO+w3kXo35z/f9oXIHxPNrzt4oUjDvvnPd88X+fVx8+sOmiPSZED+8wM/iywabn7duz8VaTUk//nR5SIL7je/7u3TRNqPzn9+ar3IN3eqp/ESIZ/7PCyP6eZJpCTkv9/7TZEu4/Ofn9sp8kUP89u95UWRW1/Kf55wSDZ/PEIeyra8z1B5SZHb/LZLf99N0sH3kPj53LhNtXtU5I7385+nXhZ5r4hBB1oMFhnwWf5z3BTfrm5+3Zi7Re7/9t/Xb4SbXze6j6QN+k5iXluuXp4KfVR8rLhH6FITZVrO/fJJ7j1q0QT/RfK038/iU4P3CFvuEa0zZsoVCZODb/aVsitf4D3CQfcIU7J6vCGB3SbYdI+QTzuaX7fzUyJ9/pv//OoZkenNza9rxT0iPeYBOdHlPfUc3+Emcxrb7R7BfATzEc7IRyQvHCfhU1Lk2rVrEhYWZv8WgMjISElPL6IETkRkoL3vYYmURImXiv/W1hrAkorlAuWOhuXkz10n5JJUkO9ye6hHVbkid/ptkbv8Nklznam/dh+obnkr52H5Ovd29fol/wUyxv83ZyeLyCuVCfCTpjVuZOSzbB4Thcgt2dQC8Pnnn8tPP/0kM2bMUMN+Gs4G7DYtAJfOmy4dMQTIdZr3rV3XFZv33TQEKP7CRfl89lx5bMQjElmtaolDgNBkv+zgJRn7/YFCq2p3DzUKUExVyc1Kky2nkmTJvovy58HLkpzx7zmvW6mM9G9RQ+5qUV2iI8o5LQQoTeevbwE4+EpXKRtYfAhQXp5OXvlptyzYkd+y9uYd0TK0Qw2T6zoiBCgtK0fa/PcvtWjnK73+3Qc3CgHCPsT8F3PQ+OS3APjmut09Ii09zfR5cPF7hKbAdfT6bVK2TFm3CQEq1TBBB4cAFf4+MwTI0fmItIx0899lB+Ujkq9elvAq1UuvBaBNmzby4osvSnR0tMn3bShTOB5+jAx/kIpaz5ptWrxu2dJZ1/DmYNd1g0tnXXXzDSqFdfFjEejcdfGjqf1w2nVd/3+/8PZcFzcc7RrWrg38b+q6Nly32O36qnUrlzedyagWHiyv94/RDwHqF1xOutyER015MydX1h29LEv2nJe/Dl6U04np8vHq4+rRuFqo3NWyuvRvXl1qVSzmO4Kbvz2/yyr23GBdcwWAG3Jy8+T5H/fKT7viVVLevbe53N+ulpPvETmSLsFF74PL3yNwHnzc+x4RGFL8eXDFe4SewXWEwkKR6xZ9j7D7utZ87+19j3DIuobf+2K+z8xHlP49ItDXsu9yaeYjrLh+bCoAjBw5Utq1ayejR492307ARORQ7684qv6/r3V1+XHXefV8zoh2Rc4EHOTvJ71jqqpHamaO/HXooizdc17WHr0khy+kyOFlR2TqsiPSpk4F1Spwe7NIqRJq4c3dQbJz8+SZ7/+R3/fFq/384P4WcndLg5p/IiIiB7OpAHD06FFZs2aNVKyIeF4ioqJtOn5ZNp9MlEA/XxnXPUpfAGhfr6LZzL+xkCB/lXHGIyktS/7cf0GW/HNetpxKlJ1nrqrH5KUHpEtUZenforr0bVJNwstYWHNSSjC84JMLdslfhxIkwM9HPn4Ik51Vc2qaiIiIbCoA1KtXT8UXsQBARMVBSOC0FUfU84fa15Lq5a0IKzGjfNlAeah9bfW4mJwhv+2NV2FCe+KSZP2xy+rxys/7pXujKipMqGfjqlIm0MSwv6UoPStXHpu7Q6UlyN9XZj7SRm5tFOHQNBAREdmtADBu3DgVBvTRRx9JVFRUoU7AwcGcOIOI8v195JLsik1SmeAnbr0xprYdVQ0LllFd66nHmcRUFSKEwsDRi9dlxcGL6hESmB9KhMIAQo4C/Ep3EvTrmTkycs52NbFZ2UA/+XJoW+kcdWM4WyIiIncsADz99NPq/+bNm7tvJ2AiKnW4F7y/Mr/2f1jnumpWTYxWUVrqVAqRJ3tEq8fhC8kqRAiFgbNX0+WXf86rR/myAaqvAPoMtK9b0e4z715Ly5Zhs7fJP3FJEhrkL3NGtpM2dRguaU+cwZWIyAkFgPXrMewaEVHRlh+4KPvPJasa+DHd6jv0cDWuFiaN+4XJpL6NZHdckioMIFTo8vVMWbA1Vj2qhQXLnc0jVctAsxrhJR7SOPF6pjzy1TY5GJ+sChpzR3aQZjWLmDCIbDJ/a6xMX3WswLKBMzfrn4/vGS0Tejfk0SUismcBoGvXruIMa9eulWnTpsmmTZvE399fpWPKlClmhyMlIufJzdPJBzdq/0d0qSeVyjlndB5k6lvXrqAer94ZI1tOJsqv/5xTnYgvJGfIlxtOqUfdSmVVqwAKA1ERoTbVSg/5cqscS7gulcsFyrxHO6hCCNnfkA61VUiXOREuNhIUEZGrKdHUd4mJiWpEIDTzN2rUSCpVqiSlJTc3V15//XWZNGmSfPPNN5KWliZPPvmk9OrVSw4cOCDlypUrtc8mIuv9tjc/Dj802F9G3+zY2n9zMOIQRgnC4617msraI5fy5xg4hDkG0uSj1cfV46bIMFUY6N8iUmpWKGuycKNBnH/9KuVk6Fdb1TbQqjB/dAdpUIX3pNKCUDI8iIjIgQWA9PR0eeaZZ+Srr75SGXPw8/OTUaNGyYcffihlypR8lA9j2P7ff/+tf40RiNAJuU6dOrJlyxZVECAi14CJrz78Kz9E47Gb60t4WecOx2lujoE+TaqphzbHAMKEMMfAofhk9Xh32WFpizkGWubPMVC5XJAs2x8vry/5d0bj4bO3C7oRoExQs0IZWfBoR6ldyYqJucgrsR8DEbldAQC18KtXr5bFixdLx44dVRP75s2bZeLEieq9GTNmiCNcunRJ/R8ezhhbIlfy0+5zcupyqlQoGyAjutYTV1fUHAM7zlxVj8lLD0rDquXkUHxKob/XGgQe797A5TP/zHi6BvZjICK3KwD88MMPsmLFCmnVqpV+2T333KNq4/v27euQAkBOTo48++yzKg1t2rQxu15mZqZ6aJKTk0s9bUTeLCsnT6bfqP0f172BlAsqUaShwxnOMXDhGuYYOK+GFt1z9prJzL+hj1cflwfa1bZ4cjNnYMbTNbAfAxE5k02/zCkpKSqzbwzLHJHBRp+DRx99VI4cOSIbNmwQX1/zY3q/8847Mnny5FJPExHl+2FHnJxLSpcqoUHySMe6bn1YqoUHy6M311ePn3edlQkL9xS5fvy1DNUnoFOD0usPVVLMeLoG9mMgImeyaTac1q1by9SpUwuM94/nGJGnqNp4e8DnPPbYY/Lnn3+qMCRMRFaUl156Sc1arD3i4uJKNX1E3iwjO1dmrM6v/X/y1iiHz75bmiydLyAhJUNcPePZtEa42Qc71xIReT6bWgAwFGe/fv3kxx9/lHbt2qll27dvl4sXL8qyZcukNDP/Y8eOlSVLlqjMf0xMTLF/ExQUpB5EVPrmbTkjF5MzpXp4sDzYvpZHHfKI0GC7rkdERORWLQCdO3eWY8eOyZAhQyQ7O1vF4+M5luG90vLEE0/IL7/8ojL/TZo0KbXPISLrYSSdmWtPqOdP94xWo+x4kvb1KkpkeLCYawfAcryP9YiIiFyZzb3zqlatKm+88YY4yuXLl+Wzzz5Tz5s2bVrgvS+++EL1CSAi5/lm82m5fD1L6lQqK/e1qelxpwIde1/vHyPj5u1SmX2dUeYf8L4rdwAmIiKyuQUA8wD88ccfhZZjGd4rDZUrV1YhQKYezPwTOVdyRrbMWntSPR/fM1oC/Gy6tbi8fk0j5bOHW0tEWFChzsJYjveJiIhcnU2/0uhYixmAjWHZK6+8Yo90EZEb+Wr9KbmWni0NqoSosfQ9GTL5fz17i/71nBHtZMMLPZj5JyIizy4ALFiwQB5++OFCy9EP4LvvvrNHuojITVxNzZKvNpxSz5/t3cgrQmAM9xEx/96wz0RE5OUFAEyslZaWVmh5amqqXL9+3R7pIiI3MWvdSbmemSM3RYbJbU2rOTs5REREVBoFgFtuuUWF+mAEIA2ev/zyy9KtWzdbNklEbghj3s/ZlF/7/1zvhhaPlU9ERERuNgoQJgHr2rWrREdHS8eOHVVH3C1btqhWgXXr1tk/lUTkkj77+4RkZOdJi1rlpedNEc5ODhE5SEJyhiSkZKrJ/zQHzydLcED+8L8RoUGcVI7I0woAjRs3ln379snMmTNl165d4uPjI8OHD1eTdEVGchQMIm8Qfy1d5m+JVc8n9mmo7gNE5B3mb42V6avyZ/3WDJy5Wf8co4FN6N3QCSkjolIrADz44IPy/fffy+TJk82+R0Sebcbq45KVm6c6wXaNquzs5BCRAw3pUFt6x1Q1+z5aAIjIwwoAP/zwg8lMPkKBFi5cyAIAkYeLu5ImP2yP08f+s/afyLtEhAUzxIfIWwoASUlJJp9DXl6ebNy4kSFARF4ATf85eTq5ObqydKhfydnJISIicooEN+0PY1UBoEKFCiafa3x9feXdd9+1T8qIyCWduHRdftp1Vj1/ljG+RERO466ZT08y3037w1hVANi+fbv6v127dvrnmoCAAKlVq5ZUrFjRvikkIpfy4V/HJE8n0uumCGlVu3BFABEROYa7Zj49yRA37Q9jVQGgbdu26v9Tp05J3bp1SytNROSiDsUny9I959Vz/qgQETmXu2Y+PUmEm/aHsakTsL+/v5w9mx8CYErNmjVLkiYiclH/W3lU/X9Hs0hpUj3c2ckhIvJq7pr5JDctACDUpygYDYiIPMves0my4uBFwWS/E3pHOzs5RERE5MgCwKFDhwqNAHTs2DF58cUXZfz48bamhYhc2Psr8mv/72lZQ6IiQp2dHCIiInL0TMDGYmJiVMvAuHHj1IzAROQ5dpy+ImuPXhI/Xx8Z34u1/0RERO7M154ba9iwoRw8eNCemyQiF6r9v79tTalTKcTZySEiIiJXKABkZmbK+++/zw7ARB5m0/HLsvlkogT6+cqTPVj7T0RE5JUhQOXKlSu0LC0tTUJDQ2XBggX2SBcRuQB06J+24oh6/lD7WlKjfBlnJ4mIiIicUQCYN29eoWWYGbhFixZSvnz5kqaJiFzE5tPJsis2SYL8feWJW6OcnRwiIiJyVgHgnnvuscdnE5ELw2i+szbnT/o1rHPdEo81zSnriYiI3LgAoElMTJSjR4+qMIFGjRpJpUqV7JcyInKqM3nl5eildAkJ9JMx3eqXeHucsp6IiMiNCwDp6enyzDPPyFdffSW5ublqmZ+fn4waNUo+/PBDKVOGccJE7iw3Tye7s2uo5yO71pNK5Uo+nTynrCciInLjAsCkSZNk9erVsnjxYunYsaP4+PjI5s2bZeLEieq9GTNm2D+lROQwfx29Kkm6MhIa5CeP3lzy2n/glPVERERuXAD44YcfZMWKFdKqVasC/QLq1Kkjffv2ZQGAyI3l5ObJV1vj1fPBrSMkvEyAs5NEREREzp4HICUlRWX2jWFZcnKyPdJFRE7y0+5zEpeUKUGSLYNaRvA8EBEReRibCgCtW7eWqVOnqs6/GjyfMmWKtGnTxp7pIyIHysrJk+l/HVPPm/tfUB2AiYiIyLPYFAI0bdo06devn/z444/Srl07tWz79u1y8eJFWbZsmb3TSEQO8sOOODmXlC6VyvpL47xLPO5EREQeyKYWgM6dO8uxY8dkyJAhkp2dLTk5Oeo5luE9InI/Gdm5MmN1fu3/sPbVxN8nz9lJIiIiIleaB6Bq1aryxhtv2Dc1ROQ087ackYvJmVI9PFjublJZ5mzlySAiIvJENrUAEJFnSc3Mkc/+PqGeP90zWgL9eWsgIiLyVPyVJyKZs+m0JKZmSZ1KZeW+NjV5RIiIiDwYCwBEXu5aerbMWptf+/9Mr2gJ8ONtgYiIyJPxl57Iy3214ZQkZ+RIVEQ5uatFDWcnh4iIiFy1AIDRfzZu3Chz587VL0tMTLRXuojIAa6mZsnXG06p58/2bih+vj487kRERB7OpgJAbGysmgysZ8+eMnToUP3y0aNHy9KlS+2ZPiIqRbPWnZTrmTkSExkm/ZpU47EuRkJyhuw/d00Onv93xnM8xzI88D4REZFHDgM6YcIE6dSpk+zcuVOCgoL0yydOnCiTJk2S/v372zONRFQKElIyZM6m/Nr/5/o0FF/W/hdr/tZYmb4qf64EzcCZm/XPx/eMlgm9G9r/ZBERETm7ALB27Vo5fPiwBAYGFljerFkzVSggIteHYT8zsvOkZa3y0qNxhLOT4xaGdKgtvWOqmn0/IvTfChEiIiKPKgBkZGSIr29+9JCPz78xw/Hx8VK2bFn7pY6ISsX5pHSZvyVWX/tv+D0m8yLCgtWDiIjI6/oAdO/eXWbOnKmeaxmH1NRUFf7Tq1cv+6aQiOxuxprjkpWbJ+3rVZSuUZV5hImIiLyITS0A06ZNk27duskff/whOp1OBg0aJOvXr1fvYWQgInJdsYlpsnB7nHr+XG/W/hMREXkbm1oAGjduLPv375e+ffuqDr8ICRozZozs2bNHGjRoYP9UEpHdoBNrTp5Obo6uLB3qV+KRJSIi8jI2tQBARESEvPrqq/ZNDRGVquMJ1+Xn3WfV8+f6NOLRJiIi8kI2FwDy8vLk7NmzcuXKlULvtWzZsqTpIqJSqv3P04n0uqmqGv2HiIiIvI9NBYBNmzbJ4MGD5cyZMybfR78AInIth+KTZeme8/pZf4mIiMg72VQAeOKJJ1T8Pyb+qlChgv1TRUR297+VR9X/dzSPlJjqYTzCREREXsqmAsDRo0fVZGBhYcxEELmDvWeTZMXBi4LJfif0inZ2coiIiMjdRgGKioqShIQE+6eGiErF+yvya//vaVlDoiJCeZSJiIi8mMUFAAz1qT0Q+vPoo4/Kvn37JD09vcB7eBCR69h++oqsPXpJ/Hx9ZDxr/4mIiLyexSFAZcqUKbSsefPmJtdlJ2Ai14Dv4rTlR9Tz+9vWlDqVQpydJCIiInKXAoA20y8RuY9NJxJl66krEujnK0/2YOw/ERERWVEA6Nq1q/75gw8+KN9//73J9fCe4bpE5MTa/xX5tf+DO9SWGuULt+IRERGR97GpE/APP/xgNsOxcOHCkqaJiOzg7yOXZHdskgQH+Mrj3RvwmBIREZH1w4AmJSWZfK7NDLxx40aJjIy0ZpNEVMq1/8M61ZWIsGAeZyIiIrK+AGA46ZepCcB8fX3l3XfftWaTRFQKlh+4IAfOJ0tIoJ+MuYW1/0RERGRjAWD79u3q/3bt2umfawICAqRWrVpSsWJFazZJRHaWm6eTD27M+juqaz2pGBLIY0xERES2FQDatm2r/j916pTUrVvXmj8lIgf5be95OXrxuoQF+8uom+vzuBMREVHJOwEz80/kmnJy8+R/N2r/H+tWX8LLBDg7SUREROQJBQAick0/7TonpxPTVNjP8C71nJ0cIiIickEsABB5iMycXJm+6ph6Pu6WBlIuyKoIPyIiIvISLAAQeYiF2+PkXFK6RIQGycMd6zg7OUREROSibK4ixLj/Z8+elStXrhR6r2XLliVNFxFZISM7V2asOa6eP9kjSsoE+vH4ERERkf0KAJs2bZLBgwfLmTNnzE5CRESOM2/LGbmYnCk1ypeRB9rV4qEnIiIi+xYAnnjiCenbt69MnDjR5IRgROQ4qZk58tnfJ9Tzp3tGSZA/a/+JiIjIzgWAo0ePytq1ayUsLMyWPyciO5qz6bQkpmZJ3Upl5d7WNXlsiYiIyP6dgKOioiQhIcGWPyUiO7qWni2z1ubX/j/Tq6EE+LFfPxERERXNptwCQn8effRR2bdvn6Snp0tGRkaBBxE5xlcbTklyRo5ER5ST/i2q87ATERFR6YQADR06VP3fvHlzk++zEzBR6buSmiVfbzilnk/o3VD8fH142ImIiKh0CgDr16+35c+IyI5mrTsh1zNzJCYyTPo1qcZjS0RERKVXAOjatas406FDh+TIkSMqHZUrV3ZqWoicISElQ77ZdFo9f65PQ/Fl7T8RERFZqEQ9BhMTE2Xz5s1qXgA8L20Yeah79+5y2223yYABA2T//v2l/plErgjDfmZk50nLWuWlR+MIZyeHiIiIPL0AgI6/Y8aMkapVq0rnzp2lS5cu6jmW4b3ScunSJXn99ddlw4YNpfYZRK7ufFK6zN8Sq55P7NNIfHwY+09ERESlXACYNGmSrF69WhYvXizx8fFy4cIF9XzVqlXqvdIycOBAufXWW0tt+0TuYMaa45KVmycd6lWULlGVnJ0cIiIi8oY+AD/88IOsWLFCWrVqpV92zz33SJ06ddQMwTNmzBBXkZmZqR6a5ORkp6aHqCRiE9Nk4fY49fw51v4TERGRo1oAUlJSVGbfGJa5Wgb7nXfekfDwcP2jVq1azk4Skc2mrzomOXk66dawirSvV5FHkoiIiBxTAGjdurVMnTq1wHj/eD5lyhRp06aNuJKXXnpJrl27pn/ExeXXnhK5m+MJ1+Xn3WfV82d7N3R2coiIiMibQoCmTZsm/fr1kx9//FHatWunlm3fvl0uXrwoy5YtE1cSFBSkHkTu7sO/jkqeTqTXTVXV6D9EREREDmsBwMg/x44dkyFDhkh2drbk5OSo51iG94jIvg7FJ8tve+PVc9b+ExERkcNbAADDfr7xxhviSLGxsbJr1y79nAMYDjQpKUkaN26sHkSe6oOVR9X/dzSPlJjqYc5ODhEREXljAcAZjh8/LnPmzFHP7777btmxY4d6PPjggywAkMfaE5ckKw9eFEz2O6FXtLOTQ0RERG7OrQoAPXr0UA8ib6z9v6dVDYmKCHV2coiIiMgb+wAQkWNsP31F1h69JP6+PjK+J2v/iYiIyEkFAITc2PIeEVkOQ+tOW35EPR/UtpbUqRTCw0dERETOKQBgJmBzGZaFCxeWNE1EJCKbTiTK1lNXJNDPV57qEcVjQkRERI7vA4ARd0w9h7y8PNm4caNERkbaJ2VE3l77vyK/9n9wh9pSvXwZZyeJiIiIvLEAUKFCBZPPNb6+vvLuu+/aJ2VEXmzNkQTZHZskwQG+8vitDZydHCIiIvLWAgBm+wXM/qs91wQEBEitWrWkYsWK9k0hkZfJy9PJ+yvyR/4Z1qmuRIQGOztJRERE5K0FgLZt26r/T506JXXr1i2tNBF5teUHLsiB88kSEugnY25h7T8RERG5QCdgzAL8xx9/FFqOZenp6fZIF5FXys3T6cf9H9W1nlQMCXR2koiIiMjD2FQAeOmll+To0fxMiiEse+WVV+yRLiKv9Nve83Is4bqEBfvLqJvrOzs5RERE5IFsKgAsWLBAHn744ULLhwwZIt9995090kXkdXJy8+R/N2r/EfoTXibA2UkiIiIiD2RTASAzM1PS0tIKLU9NTZXr16/bI11EXuenXefkdGKaCvsZ3pl9bIiIiMiFCgC33HKLCvXJzs7WL8Pzl19+Wbp162bP9BF5hcycXJm+6ph6/nj3BhISZFX/fCIiIiKL2ZTLmDp1qnTt2lWio6OlY8eOatKiLVu2qFaBdevW2bJJIq+2cHucnEtKl4jQIHm4Yx1nJ4eIiIg8mE0tAI0bN5Z9+/bJsGHDVNgPRv4ZPny47N27V2666Sb7p5LIg2Vk58rHq4+r50/2iJLgAD9nJ4mIiIg8mM1xBpGRkTJ58mT7pobIC83bckYSUjKlRvky8kC7Ws5ODhEREXk4m1oAtJj/jRs3yty5c/XLEhMT7ZUuIq+Qmpkjn/59Qj1/umeUBPmz9p+IiIhcsAAQGxsrrVu3lp49e8rQoUP1y0ePHi1Lly61Z/qIPNqcTaflSmqW1K1UVu5tXdPZySEiIiIvYFMBYMKECdKpUydJTk4usHzixIkyZcoUe6WNyKNdS8+WWWvza/+f6dVQAvxsbpAjIiIiKt0+AGvXrpXDhw9LYGBggeXNmjWTnTt32rJJIq/z1YZTkpyRI9ER5aR/i+rOTg4RERF5CZuqHDMyMsTXN/9PfXx89Mvj4+OlbNmy9ksdkYdC2M/XG06p58/2bih+vv9+j4iIiIhcrgDQvXt3mTlzZoECAIYDnTRpkvTq1cu+KSTyQLPWnZDrmTnSpHqY9G1SzdnJISIiIi9iUwjQtGnT1Iy/f/zxh5oEbNCgQbJ+/Xr1HkYGIiLzElIy5JtNp9Xz5/o0FF/W/hMREZE7TAS2f/9+6du3r/Tv31+FBI0ZM0b27NkjDRo0sH8qiTzIp2tOSEZ2nrSqXV5ubRTh7OQQERGRl7G4BQAhP2PHjlXPL1++LBEREfLqq6+WZtqIPM75pHRZsDVWPX+ud6MCfWiIiIiIXKoFYNy4cfrnVapUKa30EHm0j1cfl6zcPOlQr6J0iark7OQQERGRF7K4AFCtWjXZsGGDivknIuvFJqbJoh1x6vlzfVj7T0RERC4eAoTJv9DxVysAFBW6wEICUWHTVx2TnDyddGtYRdrXq8hDRERERK5dAHj++eflwQcflJMnT8qtt94qK1euLN2UeYiE5AxJSMk0+35EaJBEhAU7NE3keMcTrsvPu8+q58/1bshTQERERK5fALjzzjvlt99+k9q1a6vXHO/fMvO3xqqaX3PG94yWCcwQerwP/zoqeTqR3jFVpUWt8s5ODhEREXkxiwsAy5cvl9zcXPHz8yvdFHmYIR1qq0xfRnauDJy5WS1bPLaTBAf46VsAyLMdik+W3/bG62f9JSIiInKLAkB0dLS89tpr0rFjR/UarQFFtRZQPoT34JGWlaM/JDHVw6RsoE1zsJEb+mDlUfX/nc0j5abIMGcnh4iIiLycxbnQTz/9VJ555hmZPn26ej1w4ECz62JiMCIS2ROXJCsPXhRM9vtML9b+ExERkRsVALp37y7//POPfgQgZvKJivf+jdr/Aa1qSlREOR4yIiIicp95AAzt27fP/ikh8jDbTl2RdUcvib+vj+rsTUREROS2BYCmTZtKdna2bNy4UebOnatfnpiYaM+0EbktzIXx/ooj6vmgtrWkdqWyzk4SERERke0FgNjYWGndurX07NlThg4dql8+evRoWbp0qS2bJPIom04kytZTVyTQz1ee6hHl7OQQERERlawAgFmBO3XqJMnJyQWWT5w4UaZMmWLLJok8qvZ/2o3a/8Edakv18mWcnSQiIiIiPZvGoly7dq0cPnxYAgMDCyxv1qyZ7Ny505ZNEnmMNUcSZHdskgQH+MrjtzZwdnKIiIiISt4CgBGAfH199SMCaeLj46VsWcY6k/fKy0Psf/7IP8M615WI0GBnJ4mIiIio5C0AGBJ05syZ8p///EdfAEhNTZVJkyZJr169bNkkUalKSM6QhJRMs+9jRmZM2FZSyw9ckAPnk6VckL+M7cbafyIiIvKQAsC0adOkW7du8scff6h450GDBsn69evVexgZiMjVzN8aK9NXHTP7PobpnNC7ZBN15ebp9LP+juxaTyqEFAyRIyIiInLbAkDjxo1l//79MmvWLKlUqZIKCRozZow8/vjjUrVqVfunkqiEhnSoLb1jqkpGdq4MnLlZLVs8tpMEB/jpWwBKaume83Is4bqElwmQUV3r8ZwRERGR5xQAICIiQl599VX7poaolCC8B4+0rBz9spjqYVI20OavQAHZuXny4V/5tf+PdauvCgFEREREHtMJmIgK+mnXWTmdmCYVQwJleOe6PDxERETkslgAICqhzJxc+WjVcfX88e4NJCTIPq0KRERERKWBBQCiElq4PU7OJaWrfgQPd6zD40lEREQujQUAohJAp+KPV+fX/j/VI0rfqZiIiIjIVbEAQFQC87acUfML1ChfRu5vV4vHkoiIiFweCwBENkrNzJFP/z6hn0cgyJ+1/0REROT6WAAgstGcTaflSmqW1K1UVu5tXYPHkYiIiNwCCwBENriWni2z1ubX/mMGYX8/fpWIiIjIPTDXQmSDr9aflOSMHGlYtZzc2bw6jyERERG5DQ5YTsVKSM5QHV3NwfCXmGXXWyDs56sNp9TzCb0aip+vj7OTRERERGQxFgCoWPO3xsr0VcfMvo8OsAiD8RYI/UnNypUm1cOkb5Nqzk4OERERkVVYAKBiDelQW3rHVFVj3g+cuVktWzy2k37Me7QAeIuElAz5ZvNp9fy5Pg3Fl7X/RERE5GZYAKBiIbwHj7SsHP2ymOphUjbQ+y6fT9eckIzsPGlVu7zc2ijC2ckhIiIisho7ARNZ6HxSuizYGqueT+zTSHx8GPtPRERE7ocFACILfbz6uGTl5knH+hWlc4NKPG5ERETkllgAILLAmcRUWbQjTj1/jrX/RERE5MZYACCyAEZBysnTyS0Nq0i7uhV5zIiIiMhtsQBAVIzjCSnyy+5z+pF/iIiIiNwZCwBExfjfX8ckTydqKNTmNcvzeBEREZFbYwGAqAgHzyfL73vj1fNnvWiyMyIiIvJcLAAQFeF/fx1V/9/ZPFJuigzjsSIiIiK3xwIAkRl74pJk5cGLgsl+n+nF2n8iIiLyDCwAOEgugshv2HbqSoHX5JreX5lf+z+gVU2Jiijn7OQQERER2QULAA6wbH+89Ppgrf718Nnbpeu7q9Vyck0opK07ekn8fX1kfM9oZyeHiIiIyG5YAChlyOSPm7dLLiZnFlh+4VqGWs5CgOvR6XQybcUR9fz+drWkdqWyzk4SERERkd2wAFCKEOYzeelBMRXsoy3D+wwHci0bjyeqFoBAf195qkeUs5NDREREZFf+4mZOnz4ts2fPlosXL0qzZs1k1KhREhwcLK4Imcj4axlm30chAO9jvU4NKjk0bVR87f+QDrUlMrwMDxURERF5FLdqAdi/f7+0aNFCDh48KNHR0TJz5kzp3r27ZGdniytKSMmw63pU+lYfTpB/4pKkTICfjOvegIeciIiIPI5btQC88MIL0r59e1m0aJF6/fDDD0udOnXk22+/VS0BriYi1LKWiaycvFJPCxUvL08n76/IH/lnaOc6Fp8/IiIiInfiNi0AWVlZsnLlSnnggQf0y6pWrSo9evSQpUuXiitqX6+iRIYHi08x601avFeeW7hH4q6kOShlZMryAxfkYHyylAvyl7HdWPtPREREnsltCgCxsbEq1Kdu3boFluP1iRMnzP5dZmamJCcnF3g4ip+vj7zeP0Y9Ny4EaK9b1Sqv/v9x11np+f5ambz0gFy+XnDEICp96Ij9wY1x/0d2rScVQgJ52ImIiMgjuU0IUHp6uvo/JCSkwPLQ0FD9e6a88847Mnny5ELLP//8cylTxjEdPO+qVFZWJVWU67n/Hu5yfjnSo/wVaSinpUlEoKy7VkFiM8vI7I2nZd6mk9IuNFnahl6TIF/XmTBMpxMZEHRdPf/689PiU1zThgsytw8HU0Pk2JUqEuybK7rDq+SToyvFm+Xk5Kj/Fy5cKP7+bnObICIi8lrpReSHjfnoMOyJGzhz5oyq7f/jjz/ktttu0y8fPXq07Nq1S3bu3Gm2BQAPDVoAatWqJdeuXZOwsDBxlJSMbGn2xgr1fM6IdnJzdBXVQmBow7HLMnX5Ydl79pp6XTEkUJ64NUqNRhMc4CfOlpaVIzGvLVfPD77ZV8oGul/G0NQ+ZOfmSe8P1srpxDSZ1LeROubeLj4+XhWSH3vsMYmMjHR2coiIiKgYyOOGh4dblMd1mxAgZNqxUwcOHCg0MlDTpk3N/l1QUJA6CIYPZzDM7KNvgHHmH7pGV5Zfn+ginw5pLfUrh8iV1Cx567eDKjRo0Y44zhdQSn7adVZl/iuFBMrwzgVDzIiIiIg8jdsUAHx9fVUH4K+//lquX88P4di6dat6DB48WDyFj4+P3N4sUlZM6CZT7m0m1cKC5VxSuuoo3O/Ddaqjqps02riFzJxc+WjVcfUcw36GBLlfqwYRERGRRxYAtHh+TPqFCcDuuusu6d27tzz11FPSt29f8TT+fr7yYPva8vek7vKf2xtLeJkAOZZwXcbM3Sn3frZJtpxMdHYSPcIP2+NUAatqWJA83LGOs5NDREREVOrcqrqzYsWKsm3bNlm3bp2aCfjtt98uMvzHEyD2/7FuDeSBdrXli3Un5asNp2R3bJI8+PkWuaVhFRWz3rRGuLOT6ZbSs3Ll49X5tf9P9oh2iX4WRERERKXNrQoAgBFJMPa/t0ELwMS+jdQEVTNWH5cFW2Nl7dFL6tG/RXV5rndDqVu54AhJVLTvt8fJpZRMqVG+jDzQthYPFxEREXkFtwoBovzZhd+8u6mseu4WubtldXVIlu45L70+WCsv/7xPLiZn8DAVM96/5rO/82v/x/eMlkB/fhWIiIjIOzDX46bqVAqR6Q+2kt+f7iq3NqoiOXk6mb81Vm55b428u+ywXEvLdnYSXc6y/fGqoKS5npmrRmMKCWLoDxEREXkPFgDcXJPq4TJ7RHv54bGO0qZOBcnIzpPP/j4hN09drf5HnDvlZ/7HzdslF5MzC7UIPLlgt3qfiIiIyBuwAOAhOtSvJIvHdpIvh7aVRlVDJTkjR7UEoEVg/tYzarIrb4VM/uSlB6WowVPxvmF4EBEREZGnYgHAg2AOgV4xVeWP8TfLB/e3kJoVykhCSqa8/PN+NdMt+grkeVkmF3Mm/LkvXuKvme8bgSOC97eduuLQtBERERE5g9uNAkTFQ1z7va1ryh3NI+W7rbFqqEvMdPvUd7tl1roT8nzfxnJzdGVVYPA0V1OzZM/ZJNkTd0324v+z1+Ty9YJhP+YkpLADNREREXk+FgA8WJC/nwzvUk8Gtq0lX284JZ+vOyn7zyXL0K+3Saf6leT5fo2kVe0K4q5SM3Nk/zlk9K/lZ/rPJknclfRC6/n6iFjS8IERloiIiIg8HQsAXqBckL883TNazXT76Zrj8u3mM7L5ZKIM+HST9G1SVSb2aSTRVUPFlWXl5MmRCynyz9kk2RuXpDL9xxJSTGbs61cOkeY1w6V5zfLSolZ5aVwtVI3+c+Fahsl+AGgHqRYeLO3rVXTErhARERE5FQsAXqRiSKC8cmeMjOhaTz5ceVR+3HVWlh+4KCsPXpT7WteUZ3o3VJNiORv6KZy8fF2F8eTX7F+TQ+eTJctER+ZqYcHSotaNzH7N8tKsZriaNM3Y6/1j1ChAyOwbFgJ8DN5H6BQRERGRp2MBwAshk//eoBbyWLf6Mm3FEVUIWLTzrPz6z3l5pFMdeeLWKFVYcFQn3XNJ6f+G8cQlqTCl65k5hdZFxh41+y1rlb+R4Q+XiDDLwnb6NY2Uzx5uLa8vOVBgKFDU/CPzj/eJiIiIvAELAF4MYT+zHmkru2OvqiFDt5y8Il9tOCU/bI+T0TfXl1E311PhQxrDYTIxYs7N0VWsrjVPvJ6pz+yr/+OSJDE1q9B6ZQL8pGmNMJXR1zL9tSuWLVHHZWTyu0RVlmZvrFCv54xoZ9M+EBEREbkzFgBIdQT+bnRHWX/ssioIHDifLP/766h8u/m0PNkjSgZ3qC1rDieo2nPN8NnbJbKY2vPrNzrpIpOvZfrPXi3cSdff10caR4bqa/Xxf3REOfH3s/8otYaZfcT8M/NPRERE3oYFAFJQs96tYRXpGlVZ/tgfL++vOCqnLqeqCbJmrD5uspYenWoRV4/QmlsbR8jh+BT90JvI9B+/dF10pjrpVglR8foqs1+rvMREhklwgB/PBBEREZEDsABABfj6+sidzatL3ybVZNGOs/LhX0ckIaVw5h+0vD3mF8CLbBND8lQPD84P46kVLi1rlpemNcMlLLhwJ10iIiIicgwWAMikAD9fFfpTo3ywDJu9vcijlJ2bn/EvXxaddMtLyxthPMj0c2x9IiIiItfCAgAVKSk926Ij9BqGF+1S1yNnFyYiIiLyJPbvZUkexdIa/Jsiw5j5JyIiInIDLABQkTBSDkb7MVevj+V4n7PoEhEREbkHFgCoSBgmE0N9gnEhgLPoEhEREbkfFgDI4ll0I8KCCizHLLpYzll0iYiIiNwHOwGTRTiLLhEREZFnYAGALMZZdIncX25urmRnWza6FxERuY6AgADx87PPxKksABAReQGdTicXLlyQpKQkZyeFiIhsVL58ealWrVqJR15kAYCIyAtomf+IiAgpW7Ysh+0lInKzSpy0tDRJSEhQryMjI0u0PRYAiIi8IOxHy/xXqlTJ2ckhIiIblClTRv2PQgDu5yUJB+IoQEREHk6L+UfNPxERuS/tPl7SvlwsABAReYmSxowSEZFn3MdZACAiIo91/fp1GT58uMTGxpp87cpQw4e0njhxwtlJoSJcvnxZnSctNttRduzYIW+99ZY429WrV2Xs2LGSkZHh7KSQFVgAICIij4VMyTfffCNXrlwx+drV+24grZcuXTK7Dvp2fPbZZ/Lkk0/K5MmTZd++fVZ/jj224c1QqMR5Sk5OdmiH0HHjxkmDBg1KfB737t0rzz77rIwePbrQe8eOHVOFG1MPFECgQoUK6hqdNm2aHfeQShsLAERERG4IGbCWLVuqjN5NN92kMmFt27aVOXPmOHQb5Hh//PGHnD9/Xh544IESncd77rlHHn74YTl+/LjMnTu30Pvh4eHSvXv3Ag+MRIN1q1Spol/v6aeflg8++EDS09NLYW+pNHAUICIicmkrV66UZcuWqVrPu+++W2655ZYCteTff/+9bNy4UYKCguTOO++Unj17WrX9UaNGqe1gRI06derIoEGDVCbKMMRj4sSJ8tprr8nvv/8uhw8flrp166oa2HLlyunX27NnjyxcuFDVBLdu3VoeeeQR8ff/92d2+fLlaj+gQ4cOKvNmGM+blZUlX3zxhezfv1/V7D744INFprtWrVoqwxcaGqpfhucvvPCCqqGFn3/+WVatWqUyZ4GBgWrZmjVrZP78+fL+++9btA1LFXcutOP4xhtvqAzsoUOHpGbNmuo4hoWFFdhWccfKkCXbPX36tHr/008/1XeizMzMlDFjxsirr76qjre2nRdffFH++usvtZ3atWur7WDfcG5OnTolzZs3l8cee6zQCCxoCfj444/NXh/F7Zf2+a+//rr8+OOPcvToURkxYoR06tSp0D5//vnncv/99+vTYOt5/O9//ytNmzaVL7/8UlasWFHofYw0Y/z3M2fOlD59+qjviqZbt24SHBwsixcvVtc9uT62AJSyhOQM2X/umhw8/2/TIJ5jGR54n4iITHv88cdl4MCBKvOKTA4ycchka5BJRoYNmREMkYdMJzK21kCBAjWbyGhdvHhR2rVrJ+vXry8U4tGlSxeVOY+KilI1oPgbZAxh06ZN0rFjR5WpjImJkc2bN6sMmuapp55S+4IMFTKHb7/9ttovQ/fee69KOzKj8fHxJjN+hqpWrVogwwdIG2Kyc3Jy1OsePXqoQstLL72kXqNm+KGHHlLHErW7lmwDpkyZIh999FGR6SnuXGjH8dZbb1WhJY0aNZJFixZJr169VOHOmmNlyJLtInONdVDIMuxjYRhipW0HhRYUGLAdZPr79u2rMriJiYnSuHFjeeedd+SZZ54plI7bbrvN7PVhyX5pn4/POnPmjLqecH6MYZsoxOF61Fh6Ho0h82+NAwcOyNatWwuFC6EQ07VrV5OFCHJROi9z7do13A3U/47wwYojujov/Gb2gffdRWpmtj7deO6OPGEfHOH8+fO6N954Q/1P7i89PV138OBB9X8hmdfNP7LSrVg3zbJ1rbB69Wqdj4+PbsuWLfpleXl5+uty1apVOl9fX92hQ4f073/99de6kJAQXUJCgnp96dIldc/fvXu3ydemvPjii7p+/frpX586dUr9zfjx4/XLEhMTdaGhobo5c+bo/6Z///4FtnPu3Dn1/7p163TlypXTXbhwocDfY9maNWvU67/++kvn7++vO3nypH6dd955R33u5s2bLTpeOTk5uo4dO+p69epVYPmGDRvUtlesWKG74447dJ07d1brWrONW265RXf33Xeb/WxLzoV2HD/88EP9OthfLNu7d6/Fx8qYJdvdvn27en316lX9OikpKQWOr7adjz76SL/OkiVL1LLPP/9cv2zBggXq3FtzfViyX9p2/vvf/+qKEhsbq9bbsWOH2XXMnUdzvvjiC11QUFCx602YMEFXtWpVXVZWVqH3nn/+eV2bNm0s+jwqnfu5NXlchgCVsiEdakvvmMIleE1EaFBpJ4GIyLy3q5t/L7qPyJBF/75+L0okO830unW6ioz4/d/XHzYTSUssvN4b1yw+G3/++aeqoUSohGFNozYD5tq1a6VFixaqVtawFhohPTt37pR+/fpZ9DkYvWXevHly8uRJVQuLMA9TowTdd999+ucVK1ZUNczr1q2TYcOGqXQiNGL27Nmq5hvx0dWr5x9b1MCjRvzll19WNdJarXRAQID8888/qqYY+4KwoXr16uk/Ay0IWs29JSZMmKDCVrZt21ZgOWqKsZ277rpLtaTgM81NIGRuG6jZ10KITLHmXBiGBWF/cRzOnj0rzZo1s+hYmVPUdq2BlgSN1snWeFlKSop6GNa6F3V9WLNfaHEoCj4XQkJCzK5j7jyWBFpP0LKBc4p0G0O4k5Y2cn0sAJSyiLBg9SAiIusgfAHhEuYgdMN4ZmNkshDjbemQjAj1aNOmjQpfQCYPYTGIZUaogzFk6oxfa58zZMgQFZrx7bffqlFYGjZsqMKV0GcBoSNIJz7DEEI9kOnX9sXU9i2FjCU6fCLGHJ9tKswJQ0YirMiwkGHpNoorTFlzLnB8Dfn6+upDZSw5VuYUtV1rGG4H2zC3zHjbRV0f1uxX+fLli0yfdpzx/bDlWrDVkiVL1H48+uijJt/HKEScadx9sABAROTN/nPe/Hs+RrXEk44Xsa5Rl7JnSj6UJDpyojOmOeigiQ7ChhDrnZqaWqCDYlEQK4547F9//VW/DP0ATEGrQJMmTfSvEadtmMEaOnSoeqAfAOLl0Zk4Li5O7ce1a9fUe1rm0dS+oLbYELZvCXRiRedTdC411W8AGbORI0eqzpnoFDxr1izV+dWabRTHHucCLDlWtkCnZDDsA2AuA22roq4Pe+4X4v3RCoZCqvG5Kul5LMpXX32lWirQt8AU9H9AYZrcAzsBExF5s8AQ84+AYCvWLWPZulbACCkI4UBHTA1qILXxxzGEIWrwUTOpmTp1qurgahg2VBSEFCHsR+soiUwrMsimIFOv1fru3r1bdcTUOnEiw6XNLYDMZu/evVUnU2SAEQqD7SJtxmEz586d0+8LRo8x7ESJkXuKgxFjpk+frj6/c+fOJtfBaDSVK1dWGTisizHfjxw5YtU2iusEbI9zAZYcK1ugkIewFcPO3Rj5xp6Kuj7svV+333672r4hS84jzv0PP/xg9efhe4hrE6MfmYJC75YtW+SOO+6wetvkHGwBICIil4R4cmTSMHIKYusRDoS4ZjzX3kfGFJkr1EyihhW1kBiK0DgcxBzUyKLGFGOoY3sbNmxQmUVTscyoPcYQkNHR0WpoTQzRqMWG47NR+4naUYRwIHOGWvb69eur9xE7jcwTRjBCDDky4MiUayMaYdjRV155RYUMIZb9woULKhypKBjy8s0335RWrVqpYSHx0Pzvf/9TEzThc5EpRxw+MsBoCUDfCoQsYaQi1NoXtw1AphL7hfHezZ2rkp4LwMg7xR0rWyBWH/0gcL4XLFigRlkyHjWnpIq6Puy9XwgzQ98ODDmLoU4tuRYAn4UWCG3+AEwchlF9MHqSNvM0aEOjavCdwzYGDBhgMj1oQUO/FwwPSu7BBz2BxYvgy4KbKm5OxuMOU9HSsnIk5rXl6vnBN/tK2UD3Kz96wj44An4c8QOCHyutwyW5L8x+i46tiP22JjPmKlDrj5pbjKmPTI+WmTEMtUAmBjXviKk2fB81k999953qAIuYbOPXgFp6ZNjxPzLx6CCL7Wnj8KNmG8cOmSRMgqSN896+ffsC6UBLAmpB8fuCjqfG8df4/UEBA5+DoUINw0U0GMv94MGDKvOFQgk6J6NW1XDSJQ06LRuHDRl2IEb8PUJ+8LeGsecIfUGGDQUNZPqK24ZWAEAnYAwrWpSizgWODwoE6CxrmPlGxhgZZYTJWHOsbNku5mrA+PooqOEcGR5fU9vBucQxNDwWuB6XLl0qgwcPVsfE8O/wPTN3fRS3X+b2wxxcn/h7ZNYtuRa0kDdcy5gkTGuBQJqNGV9zOP84p4adoTXIRmLo3EmTJukLFuSc+7k1eVwWAMirMs+esA+OwAKAZ3H3AoCzGRYAzMU/EzkaWonQimOuVt5R0McErUqYX4LcpwDAPgBEREREbqZatWpOz/wDQsOY+Xc/LAAQEREVAXHaiIE2NSsrEZE7YvwDERFRETDBkdY5kojIE7AFgIiIiIjIi7AAQERERETkRVgAICIiIiLyIiwAEBERERF5EXYCJiKiYiUkZ0hCSqbZ9yNCgyQijHMMEBG5A7YAEBFRseZvjZU7P95g9oH3yf3l5eXJ999/r2a7LYlLly7J77//bvXfYbI1zLy7fPly/SyzmOxq4cKFapZkR8FMvTgOmJ3XnTlqP7KystTnYFIwZ3KVdNh6LrSZpx2BBQAiIirWkA615benusrisZ30y/Acy/DA+6Xp6tWr8tdff8mvv/6qMoI5OTmF1rl48aJaZ8WKFeq5O83siYwBfvyd8ffGGShM6oSMeEk8++yzsn37dqv+5uuvv5Z27drJvHnzZM2aNWrZoEGD5JFHHpEff/xRDh06JPZgyfFCAQbHAbPtujNH7QdmoMXnYNZsZ3KVdNh6LjB77xtvvCE//fSTlDaGAJFXhS9kZOfqlx08nyzBAX7qOcMXiIqG8B48UjKy9cuuZ+ZIq9oVxM/Xp9QOHzJrEydOVJnDVq1aSUREhJw6dUrS09Nl6tSpcvfdd6v1Xn31VXn//felffv2EhISogoJ3bp1k88//1zKli3r0qcXtZXIGCDN4eHhDv97e0M6kIE5f/68VX/35ZdfygsvvCAvvfSSen3lyhWV8d+7d680a9bMbulztePlCYKCguSBBx6QChUqODspbs3Hx0d9B/7zn/+oWZ7xurSwAEBeAeEJ01cVrNEaOHOz/vn4ntEyoXdDJ6SMyH0s2x8vry85oH89fPZ2iQwPltf7x0i/ppGl8pkPP/ywbNu2TXbs2CExMTH65XFxcbJz5071/O+//5b//ve/smHDBunSpYs+lOWHH36QzMzMIgsAqDFEiAm0bNmywGy/aG1o0qSJREVFFfgbNNE3bdpUoqOji90Gmv2XLl0qd955pyQkJKha7Jo1a0qLFi30IS74HFi2bJns379fqlSpIj179iyU1uzsbNmyZYv6PPw9tmPu71u3bq3CaO677z4JCAhQ76PVZPHixdK7d2+pVKmSfruo7T948KA0aNCg0L5i2zju2r4aHgNkyo3Xh08++UTuueeeQplrc8cJ+4WM/smTJ+XEiROqdh60AsT69evlwIED0q9fPylfvnyxx1yTkpKijheOUadOnSQ0NNSq4605evSoHDlyROrWrVugIGLtsVm9erVKJ64pwDZ3794td911l/4aRZpwHiy5too7rubguK5bt05uvvlmqVGjhsnryhxz66IAYHjOi7vuiztPJdk/Ywgfw/3Az89P6tSpoyoSAm58J7TWxeK+K/bcH8C1bm4byPiPGTNGVq1aJb169ZJSo/My165d02G38T9ZJzUzW1fnhd/UA8/dycVr6bp9Z5PMPvA+/ev8+fO6N954Q/1P7i89PV138OBB9b+t/tx3Xlf3xvff8FH3xgPv29vmzZvV/XrhwoVFrvfxxx/rgoODdXl5eVZtf/HixboKFSrounXrpuvXr58uPDxcN2PGDP37AwcO1D3wwAMF/gbHEWnas2ePRds4deqUWr9///66mJgY3R133KErV66c7qmnnlLv5+bm6u6++261Dv4en/f6668XSmtcXJyufv36uqZNm+ruuusu9Xzy5Mlm/3779u1q2dWrV/XbSElJUctwXDVTpkzRBQUF6Xr27Km2fdtttxVYZ8iQIboBAwYUSAv2HevgWJhSs2ZN3ZdffmnxsU5NTVXpxnFp27ateo7H7bffrj4H+4vXp0+ftuiYw6JFi9Tyli1b6vr06aOLjo7WbdmyxeLjrZ03HI+oqCi1jbJly+rGjx+vX8faYzN27FjdoEGD9K+HDx+u1v3999/V64yMDHUdr1692uL9tPT6O3bsmHp95MgRXZ06dXRPPPGE+r6Yu65MKWrdS5cuqc/ZvXu3Rdd9cefJ0v0zZpwOGDx4sDrP9913n9o+0hMbG6t/35Lvij32x9JtQK9evQpca5bez63J47IAQF5RACDrsADgWUz9YODHH99jSx7J6Vm69v+3slDm37AQ0OH//lLrWbI9SzPqKIT6+vrqMjMzi1xv69atOh8fH5XB2rdvn0XbP3nypC4kJET3999/F8gIIAN26NAh9frnn3/WlSlTRpecnKxf5+WXX9Y1a9bM4m1oP/ojR47UpwvrI71nzpxRr+Pj49U6SHtRx6Jz587618jI/vLLL2b/3pJMzdGjR3V+fn66pUuXqtdI30MPPVRgHaQ1ICBAl5CQoN/O008/revUqZPJdF68eFH9/aZNm6w61tCgQQPdZ599pn+N97AtZDyt2RYyu4GBgboPP/xQv86FCxf0+2TJ8dbOGzJiWVlZ+usM1+O6detsOjbff/+9LiIiQv+6bt26qsAzadIk/fZQGMP31JL9tOb6wzHZuXOn+vzXXnvNouvKWFHrmisAFHXdF3eeLL1uiisAGEKaURAYPny4TQWAkuyPJdvQTJgwocCxLo0CAEOAiIi8UHp2rsS8lj/SSknhF+dCcoY0e2OFResffLOvlA0s/ucHneWqVasmgYGBRa6HuP/vvvtOJk+eLDNnzlRN7rfccos8/fTTqgnfFKyPpn10zFu0aJFqroeKFSuqkJPGjRvL7bffLmXKlFEhHUOHDlXvL1iwQMaOHWvxNjRo0tfiebt27Sq+vr4qtKR2bcs6TyMdGJnn3LlzKmwDf6/1f7AVwm4QaoKwBkD6Jk2apPZLg+OI0Je5c+eqjr3oJDx//nyZMmWKyW1evnxZ/W8YC27NcSqOJdvCOrhucP41CBuxNnQEnnnmGX1YCK6z7t27q5AShM9Ye2zwtwgfQTgTrlF0VEc/Fjy0ULYOHTpIcHCwxftp6XHFttGX5q233pKnnnrKpuvKlmuwqOu+uPNkz+sGIW4Iu8EIPAj5WrduncV/a6/9sWQbGnx/tO9SaWEBgIiIXBIySegIih/+4jrDoQMiHsicIP529uzZ0qdPH33crjGMEoIMG+J8DSFjV7lyZfUcBY+BAweqTB0KAJs2bZIzZ87I4MGDLd6GBpkWDWKR/f39VQdnS40bN07++ecflWFH3DkKNk8++aTKiNkqNjZWZWAN1atXr9B6o0aNUscTmVzEvSPdONamlCtXTv2PmGmNNcepOJZsC/uF+Ht7dKA0dXxwDdhybJARRKYVIxzh2u7cubM6j0OGDFEjEiGTjkKCpftpzXFFphT9HAwz/9ZeV7Zcg0Vd98WdJ3tcNxgsoH///qqvBUaYQh8F9B9KSEiw6O/tuT+WbEOD74+pvgP2xAIAEZEXKhPgp2riLbHt1BXV4bc4c0a0k/b1Klr02ZZAjSt+GPfs2aM6/1kCmRF05rv33ntVR13U2JsqAGC4PdSyaR1Oi+qEfOutt6rWCBQEUOurdXy0dBv2gMwA9gUZg40bN8rHH38sbdu2lePHj5tcH7WKgM6PGuNMBmpXd+3aVWAZOkQaGz58uBplCZ2xMRoTCkXmMifVq1dXhQCM1NSmTRu7HydLtoWOwiWdx8Dc8cBrw8ynNccGkMFHRh/r4LpCWtEpGMPXouD62muvWbyf1hzXb7/9VtU8Y3SZt99+26LrCqNpleQaLE5x58ke1w1aZ9DJHYU2rXA6Y8YMNdSmNd8VS9jzusP3p1GjRlKaOA8AWTSE5v5z19SwmRo8xzI88D4RuRfUUiEMx5LHzdFV1Gg/5uq1sBzvYz1LtmdpzSxGR8GIKAhdMDXuP2rytFFNUFNoCD/mWKaNGmMMI8pgFI61a9cWGsEDDw2a6JHhx9j0CP1AgcDabRRHy5gUlelAywYgU4aWjY8++kgVSgwzNoZ/r9XKGmbOtLH1DfcNBQDtOIKp8cdRc41CFEZaWrlypar1Nge1mRh+FRlEex8nS7eF44OhQ1FbbQihJJYeb80vv/yif45aeozMoo00Ze2x0QoASLthbT/+R9iQNmqMpftpzXFFARppnzVrlrz88ssWXVfGrFnXEsWdJ3tcN0gfvgvaOddC3wxZ8l2xx/5YA62NpToCEFsAyBIcQpPIu2Gcfwz1OW7eLpXZz4/Ezadl5fG+vecDQAjOkiVL5I477lC1yZgQCvG7CA3AsH0IQUDc/9atW+W5555TwxDedNNNaqhCZNbxw4sQBVPw4/rYY4+pzBvCIlDQwLCMiPfHRGJaLS4KKwj5weegEIIaXmu3URxkTlDb9+6776ohAJGpNB6W8rPPPlNDIaJfAgo1aI1o3ry5NGzYUGW6Tf09MiTIkCL8Iz4+XtWGGurbt6/KrGM/kH5klhDOYsro0aPVZyP8AyEYRcG6OO7Tpk1TIQ72Ok5gybZwXeBawTEYP368mjsCGXmEceFcWnK8NThmOO8I3fniiy+kVq1a+v4gthwbZPYR252WlqZauLRlH374oToXiP+3dD+tPa4oBKClAX+H6xqFlqKuK2NFrWvLzLvFnSd7XDf4W/R7QF8OtAjib5FB9/P7txUS57+474o99sdSKDxj6FNMglea2AJAFs8Aau5R2jOAEpHzYZz/zx5uLRFhQQWWVwsPVstLax4AxBqjFhCtAGgWR80pMi/Tp09XmXJAJg41ZhjjG7PPYmx3FAZQo2c4d4Ax1IYivhgdA/H3yAggg2McB48wDxRCEOqBsARrtoHaUsSEG2dW7r///gKx07/99pvUr19f/vjjD9XB0Rgya/h8ZE4QKoJ9xnrI/Jv7e9Tmo8UC66MjK44d0mIYwoI+EsjAYk4FZD6RduN1ABkkjFU/YsQIi1puUFAzjN225Fgjs2Y4dj6ONdJiPI+DJduaM2eOmlgMrUO4HlBANMyEFXe8tfOGzqIIa0KIDzq8Yl3DMeStPTZIKwpHuJ617SCsDJ+F82Dtflp7/WEMfLRUYL4FrFfcdWWoqHWNJwKz9Lov7jxZ+h3VGKcD82EgjSjEIWON7zG+F/fee2+Bvyvuu2KP/bF0GwitQoHFsNWiNPhgKCDxIihVoRMImvKMb+RElA83eMygitqXyMjSydiR4yDUAZln/GhqNYy2wkzA2mg/iPlH2E9pzgRMrgGTWCEkA+EeltwTkJFC5vrNN98UT2ftsSEyB31MJkyYoFpbMOqStfdza/K47ARMRETFQl+fhJRMycjO1S8rF+Qvh+Lz+wZFhAZJRFjJChfkehAWhBpjhMuMHDnS4gxux44d1cOT2XpsiMxBywVaERyBBQAiIioW+wJ5J4xV/+eff6p4ZMwRQDw25BlYACAiomKhr0/vGPMTKaEFgDxPixYt1NCPxGNDnoUFACIiKhbCexjiQ0TkGTgKEBERERGRF2EBgIjIS3jZoG9ERB5HZ6f7OAsAREQeThtvHJMPERGR+9Lu48bzUViLfQCIiDwcZr3EzJ0JCQnqNSYtwmRaRETkPjX/yPzjPo77ueFsxh5fAMjMzJRFixbJzJkz5fDhw/Lrr79Kly5dnJ0sIiKXV61aNfW/VgggIiL3g8y/dj/3mgIApqA+d+6cPPXUU/Lggw9Kdna2s5NEROQWUOOPiYoiIiJ47yQickMI+ylpzb9bFgAw2x5+xM6ePevspBARuSX8eNjrB4SIiNyTW3UCZswqEREREZEXtQDY2m8AD01ycrJT00NERERE5LUFAMTyf/fdd0Wus337dqlXr57Nn/HOO+/I5MmTCy1nQYDIvJSUFMnIyFD/h4SE8FARERG5OC1va8lcAT46J84Mc/36dZXJKErFihXF17dgpBL6ANSqVUvWrFkj3bt3t6oFAJ2IY2JiSphyIiIiIiLXExcXJzVr1nTdFoBy5cqpR2kKCgpSD8PPxIEJDQ11eJ8ClMxQcMHnh4WFOfSziefBlfC74Bp4HlwDz4Pz8Ry4Bp6HkkGdPlruq1evXuy6Ht8HwBhaE4orFZU2ZP5ZAHA+ngfn4zlwDTwProHnwfl4DlwDz4PtwsPDPW8UoO+//14qV64szZs3V6/vvvtu9Xrq1KnOThoRERERkVtwqxaAAQMGSK9evQotx7T2RERERETkYQUA43h+d4O0v/766269D56A58H5eA5cA8+Da+B5cD6eA9fA8+A4Th0FiIiIiIiIHMut+gAQEREREVHJsABARERERORFWAAgIiIiIvIiLAC4iMuXL8uGDRvUJGHkeFeuXJHdu3er/6n0YTbvHTt2SFJSEg+3k1y7dk1d8wkJCTwHTobZ6nH/P3jwoLOT4tUTUO3cuVOuXr3q7KR4rQsXLqjfhdOnTzs7KV6BBQAXkJOTo+Y0uOWWW+Sbb75xdnK8CjJAvXv3loYNG8rIkSPVTM1DhgyRjIwMZyfNI2VlZcmDDz6ojvfQoUMlMjJSpk2b5uxkeZWjR4/KPffcI/Xq1VPXfIMGDaR///7M+DjRpEmT1P3/+eefd2YyvFJubq5MnDhRqlWrJo8++qi0bNlSXnnlFWcny+sKX7fddptER0fL2LFjpW3bttKiRQs5fvy4s5Pm0VgAcAGvvfaa+jFGZogc69ixY/Liiy+qFhgUBg4dOiRr1qxR54Ts75133pG1a9fKkSNHVG3nTz/9pDI969at4+F2kBMnTsiIESMkMTFRXfOnTp1S34Px48fzHDjBkiVL1D0HGSByPNz/582bp2r/te9DjRo1eCoc6N1335V//vlHTp48qVoA0EKM+Z2eeeYZnodSxAKAk61atUrNcPzJJ584Oyle6f7775eePXvqX9euXVvuuOMO1RxP9vf111/L8OHDVUsLINPTpk0btZwcA8ccLY4+Pj7qNWZTHzRoEK95J0BGZ9y4cTJ//nwJDg52RhK8Gip+PvroIzU/z0033aSW+fr6qnNCjnPp0iWJioqSKlWqqNf4LuB3Acup9LjVRGCeBhf3sGHD5LvvvpPw8HBnJ4dEBNNioCaoadOmPB52hv4VsbGx6sZuqH379sx8Otn27dvVDzA5NvQE4YbPPfecNG/enIfeCdavX6/CEhECh8IYCgQIiQsNDeX5cCC0Pvbt21fefPNN6dKli2ohXrx4scyePZvnoRSxAGBHCGkorhNpp06dxM/PT2U0EQON2tCbb77ZnsnwaojdRxNiUapWrapiDc01RSIMiH0x7E/7blSqVKnAcrxm52vnmTNnjqxcuVJWr17txFR4H2R2AgMDZcKECc5Oitc6f/68+Pv7q/s+whFxL0I4HPoEvPXWW85Ontdo1KiRygt98MEH8ssvv8iZM2fk9ttvl86dOzs7aR6NBQA7QqZx48aNRa6zbNkyKVeunCrZ7tmzR8U/a+EmqIlADemWLVukY8eO9kya10BcM2I6i4Iby3/+859Cy3FOEPu/YMECadasWSmm0jsFBASo/407WKenp6uMEDne0qVLZcyYMTJjxgzVCZUcY9++fSrT+e233+p/M3Dvwm8Afg/QSlamTBmeDgfckzAIBzqh4rcXlXN///23CgtFR1SEypFjOsH/+uuvaoCCiIgI9Ztw1113yX333Sd//fUXT0EpYQHAjnBDt0b9+vXl1VdfLTAs3/Lly9VQoH/++ac9k+Y10HnLlvh9/BAjI4T/Bw4cWCpp83bVq1dXP7jnzp0rsByv0feCHOv3339Xsf8YhYkxz46VlpamMpiIP9ccPnxY8vLyVAUGKiH4nSh9devWVf9j9B9k/qF79+7SuHFjFR7EAoBj/Pbbb+pehMw/oPCLEcoGDx4sqampEhIS4qCUeBcWAJwEFzcehmrWrCmjR4/mEGQONnfuXHXc0YKDISqpdCDzj1pmjHqCH1ytNQCFXsRBk+OgggG1a1OnTpWnnnqKh97BOnToUKiiAhUP+D4gM0SOgXhzZC4NKyXQCoP+eVqHVCp9ONbog2EIFaE4NxgNiEoHCwDk1X7++Wc1JCJqQDEyjfajjFEIUENH9oW4WhQCEGPbrVs3mTlzpgqJe/zxx3moHQRDrg4YMEDuvfdead26tf6ax+gnjLklb4IMJvpioAIChS/0D/v888/VdwEx6eQYqIR4+OGHVVQEfhfQD+///u//5Mknn9SPVkb2xwKAC8FoKGz2dfyY6OhvgfGf8dBgToZFixY5ODWeD8caGdCPP/5Ypk+fLk2aNJEvvvhCypcv7+ykeQ1MroPCLWKeDfvLBAUFqWGJyTkwDCVqn8mxnn32WVX5g7ArxJ6j/9enn36qCgPkGA899JD6zUUrPFol0SKAyiG2yJcuHx2GoyEiIiIiIq/AicCIiIiIiLwICwBERERERF6EBQAiIiIiIi/CAgARERERkRdhAYCIiIiIyIuwAEBERERE5EVYACAiIiIi8iIsABC5kLVr18o///xT6p+zceNG2bFjR6l/DrkG4/PtzPP/559/ysWLF8Vd8Lti2sKFC+XChQviavLy8uT777+Xy5cvu+z+/PTTT3Lu3Lli18vMzFTpys3NdUi6yLuwAEDkQt555x2ZN29eqX/O//73P/nyyy/F02zYsEF27drlsL9zBZak3fh8O+v8I63jxo1z2ZmfTR1LT/2ulNTgwYMdUllhLcymjJllDx8+7LL7M3LkSNm+fXux62F2bsyUPmvWLIeki7yLv7MTQESO17VrVwkNDfW4Qz9t2jSpWbOmtG7d2iF/5wpsSbuzzv+LL74ozz33nMrYuMux9NTvCrmHl156SR588EEZNWqUy35vyD2xAEBkQmxsrOzevVvuvvtu9RpNsIsWLZKYmBhp3ry5Wnb8+HE5duyY3Hbbbeq1TqdTtYdxcXFSr149adGiRaHtWrKOsaVLl0pYWJjccsst6jU+E7Vb1atXl5YtW4qfn5/ZvzW3brt27Qr8mCD0KDw8XOrXr6/2OycnRzp06CDlypUrtE28j+PTpEkTiYqKKtH+Yd2jR4+q5xUrVlTrV61atdhjYmq/UKOGZvX09HQVAgA4NydOnCjyM8z9HY6HNftTGtdMceelqLQXxfD879+/X86ePSv9+vUrsM6+ffvUtrXltly7hvbs2SNbt26VJUuWWLx/Gks++/r167Ju3Tr1t61atZKDBw+q5dieJdeauWPpjGNlLe041q1bVx3H7Oxs6datmwQHBxdap06dOrJlyxYJDAyUnj17qvfS0tJk8+bNKuQE1yoKQdbCecV2fH191d/jHJQpU0b/Po7rr7/+KnfccUeBAtWPP/4oHTt2lBo1aqjwHYS89OrVS62P41qlShV1DozhfexHRkaGOsc4p8Zw/IvaRnGKOy7F7bPxvQHHvlmzZoXex35v27ZNLl26pO6r+D5obr31VnX94V7y8MMPW70PROawAEBkwtWrV2XAgAHqhlypUiUVL41m5f79++szMO+//75cuXJFZRISEhLknnvuUbHNTZs2lb1796offvzgaT92lqxjCBnIsWPHypo1a2TlypVq2dNPPy1z586Vm2++WW3Px8dHfvnlF5OZ5qLWRVhD5cqVpW3btvrQo+TkZLW/DRs2VJlm/LDixy8yMlKtg3Tfe++9KhPbpk0btQ72591337Vp/7RM4fLly/V/jx/B6dOnq9ouc8ztF35g4+Pj5dq1a+q1Vntb3GeY+zv86FuzP6VxzRR3XsylvbgCgOH5x+cjjefPn1cZJc2TTz6pMiPI1Npybk0VZFFYM8yoWXLdWfLZyJgj01ihQgWpVauWKmThOTJtWgHA1uvAEccK6cejKMgImisc4zgi3SiEoMCJgg4Kxbh3aJlW7Vgjzv2mm25SGWIUAFavXq2u0wYNGqhjtmnTJnnmmWfk9ddfF2vg2CYmJqr71qFDhyQ1NVV+++03dVwA7+Fz8F7jxo31fzds2DAV9ogCgBa+g0IC9qFRo0YqPUgnCgaaVatWqZAdfM+QqT5y5Ih89tln0rdvX/06U6ZMKXIbxbHkuBS3z/D111/L448/rgo5KKTiGkABTZOUlCTdu3dXBQmcO2ynd+/eMmPGDPU+7m94H9tlAYDsSkdEheTl5ekqVaqk+/HHH9XrKVOm6Nq2basrX768Ljc3Vy1r3Lix7tNPP1XP77rrLt3QoUN1OTk56nVmZqauW7duuueee06/TUvW6du3r3qdkZGhu/fee3UtWrTQxcfHq/cuXryow1d27969+vV37dqlO3XqVKH0F7fufffdpxszZkyBz8X+nj17Vr3Ozs7WNWvWTPef//xHv85tt92m69Chgy4pKUm/7Ndff7Vq/4qzfPlyXUhIiC4xMdHk+8Xt191336174oknrP4MU39n7f6UxjVjyXmxZJ+Nz7fha6StevXquhkzZujfj42N1fn4+Og2btxo07EwZcCAAWobhizZP0s+u0ePHmo9bZ1169ap62TYsGElvg4ccawWLVqke+CBB4p87Nu3z+zf4zgGBATo9uzZo17j/tGlSxfd4MGDC6xTrlw53cmTJ/XLsO+4PvH5muPHj6v11q9fryuKn5+f7s8//zT7/rhx43T9+vXTv46Li1Pn5NChQwXWwzn4+eef1fP09HS1Tv/+/dW1AAcPHlTHd9u2ber1pUuXdGFhYbqXXnpJvw3ck9asWWPxNorbH1uPi/E+JyQkqL/54osv9MvGjx+v0qft88yZM3UNGzbUpxV++umnAtt99913dfXr1zf7uUS2YAsAkQmodUENM2rQUOv9999/y5gxY+S1115TNYWoVUMICmpmUAOE2s233npLfv75Z9X8jwea4/H3YMk6mpSUFLn99ttVbZjWbA/+/v7qgRpFrRkZTc6mWLOu5q677lK1cNrfd+nSRdWsAWpoMXoLaqEMa5fxN9bunzH8LdKJz0BTOPb7wIED6vjbY7+s/Qxb98fe14wl58UeEL6Ams758+fLE088oZYtWLBA1Vp37ty5ROfWEEZlqV27dqHlRe2fJZ+NlhfU1uJ4ayFuOA+ocS3pdeCoYzVw4ED1KAnUfmuhZggZGT9+vKoxRmsZ0g1ovUBaNWjpwHEAhJgA0ovzhPSiBcQaaB1ErTtaGnCfQCuLLR599FF1LQBaK9DygWsCrRY4rkizYU08PgvfK0u3URxrjktR+/z777+rc4FOv5oXXnhBtTxpEDKEvz19+rQ+pBItiYbQAmHtqEZExWEBgMgM/KBgBAY072IowI8//ljF4eMHAGEG+EHBDwtiffHjgPhmxJsa0jIhZ86cKXYdDX6w8ZnILBpmthE6gfTgB+T5559X6XvkkUcKxSNbu67h3xjCDxfCMbQYVkCYhinW7J+hzz//XHUKRZM54vkRlwwIozCXRmv3y9rPKMn+2POaMdxnc+fFXoYMGaLCk06ePKnij5HBxbKSHAtjiM1HiISxovbPks9GnDcgk23I+LUt14GjjlVJQ4BM7S8y+ijgIOQH+wtaWJUGmU4UDhYvXlxgOQrXWugQ+kYgNEuDwq127DS43lEwQkhM+/bt9RlWhLvhvaL6KdlyL0JGvLgOsSX53lhyXCzZZ6QV33utAKadA8O0Yxu4VyA8DtcTQtkQMmTYvwrfG3ZEJ3tjAYCoiMzchAkTVM03MuK4IWMZavhwU9c65aKDLkycOFF1vDPFknU0o0ePVj8ciINFxtHwR3/48OEqZhYdHJEO1BR99dVXKh7WmDXrFkcbthE1nNHR0SXaPw3iYLV4/kGDBqllyLCgxg2ZKHOs2S9bP8OW/bH3NeNIaEVB/DFqs9GJGZlXLfNjr7Si8FhcJteYJZ+tZfQQS414cA1aBqpVq1ai68BRxwqFfa3fgTnoT1BUAQD7a/warVKGGWG8NoT0YpnW6dkUtF6hhUWD+5JxAQA13StWrFC14VrfCBwT3L+046tlgrWade05zoO19yLch0qTJcfFkn1GHwXj84LOy+hfpAkICFDDfKJVAAVHVCBgFCq0KmjX76lTp1RfBiJ74jwARGagOR0/npMnT9Y3L+P/9evXqx9EbRkyeXjMnDmz0Da0yV4sWcfwBwGd1ZBhQq2fNmkSOvmhJgg/TKjFxJCKyGRgJAxj1qxrCXSEw+Pbb78tsByhFNbunwY/jPghNPxhwwQ5hhkEa/cLtcyGtXyWfobx39myP/a+ZixlnHZboRYbtdl4IExCa+2xV1rRCRPhEYYdIItjyWcjfAgZf3Sy1aAWFmP6l/Q6cNSxQvgPMptFPVAAKAoyo+hIarh/6KxvOBKQqbAhHBvjzrE4Blom+7HHHiuQDlM10WhlwHVv2DHauPY8IiJCheQgw6xBzbc11wOggyzuidhfU/cie7DkuFiyzwhn00YAMjwvhtChHHCeUEGAliqEgWqjWGnHCS0DRPbEFgAiM7SYbtTMYfIiQAYiJCRE1c4YxpyiBho1Y4ixvfPOO1VG9Y8//lA37VdeecXidQwLAaidRMagR48eqlYJmRr87X333adG0UAaMOwhMsHGMJqJpetaCrVU2CZ+GJEmbBPx09gHa/dPyxB06tRJjcKCWHk0u+PHD/tuTnH7hZFa3nvvPVWLhowKRtux5DNM/Z21+1Ma14wlTKW9uFGAzGVq8bmffPKJ/N///V+B9+yRVi1dqDnFKDmWKu6zccwxwg1ahVA4RCgMzjEyVFqNt6XXmqlj6YxjZQsUZlDIwnFAqwQmL0NLVFEQ0vLqq6/K0KFD1chLKGQgtAlDc2JkHtRgW5opR8sXYt2R6cWoZdqISxpk/tFKh5F0UBhC3PucOXOsDg9CIRvhfwhFQj8HFP5QGMDnIg32YMlxsWSfEdaDfca1gFYhXJ8YrcjwukOBAAVJtCahhQf3DlzD2ghtCL9CvxXD4XOJ7IEtAERFQLjJAw88oG72mmeffVb9yCKWW4NaaNTYYMhBDBeHzDo6Ahr+4FuyDjKIWqdWrRDQp08flflGhhe1mog1RaYXP/j4cUIrgbHi1kUnNsPOcIafq0EztGEYAzIX+CFCSwDSj7hiwxovS/bPGDIoyAyihhxjwON/xPQjXMaU4vYLsbP/+c9/VIsAfkhRk2bJZ5j6O1v2x97XjCXnxVTajRmfb+PXgIwUMinIvGLiIUO2HgvjDCAmNTLsAGnJ/lny2YijRgd1vIeCFoamRYbfsLba1uvAGcfKFrjuUBDGZ+N7ge+HYa2xqWMNb775psq8orCE7xaO2V9//aXPgJqDa1zrU4D+BjhmKODhuOJ4LVu2TK1jGP+OghUKABgiFy0y+Fx0VNbi6lEYwN8Y1qoDMtC472hwfpFxRuEK/WlQINMy/5Zuo6j9seS4WLrPs2fPVtc9ZhlGuBPOC647bZ8xhCy+Ewhhw+fgekO/Cy2cDP2IRowYoe/HQWQvPhgKyG5bIyIiMgOdIzF6DjLZpkYEshUyT8iIaTX+yBgis4fOuih4eTp0gkctNWYyJs+BQhLmgkGrFOaiILInFgCIiMitofMkwkIQGoY6LYS/oCYYrUOmZmb1NCwAEJG1GAJERERuDeE2mPkVQ4JiJlWE8qBQ4A2Z/6LCe4iIzGELABERERGRF2ELABERERGRF2EBgIiIiIjIi7AAQERERETkRVgAICIiIiLyIiwAEBERERF5ERYAiIiIiIi8CAsARERERERehAUAIiIiIiIvwgIAEREREZF4j/8Hwm/tBRNpoewAAAAASUVORK5CYII=", + "image/png": "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", "text/plain": [ "
" ] @@ -399,10 +399,10 @@ "id": "cd8bb89e", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:45.243918Z", - "iopub.status.busy": "2026-08-10T22:40:45.243857Z", - "iopub.status.idle": "2026-08-10T22:40:51.159747Z", - "shell.execute_reply": "2026-08-10T22:40:51.159322Z" + "iopub.execute_input": "2026-10-28T15:24:56.640131Z", + "iopub.status.busy": "2026-10-28T15:24:56.640070Z", + "iopub.status.idle": "2026-10-28T15:25:02.590152Z", + "shell.execute_reply": "2026-10-28T15:25:02.589785Z" } }, "outputs": [ @@ -442,7 +442,7 @@ " 2.700\n", " 2.525\n", " 93.500\n", - " 0.796\n", + " 0.800\n", " 0.924\n", " \n", " \n", @@ -451,7 +451,7 @@ " 2.618\n", " 2.117\n", " 80.876\n", - " 0.184\n", + " 0.192\n", " 0.960\n", " \n", " \n", @@ -474,8 +474,8 @@ "2 slow 2.554 1.577 61.754 \n", "\n", " 2x2 CI covers truth CS CI covers truth \n", - "0 0.796 0.924 \n", - "1 0.184 0.960 \n", + "0 0.800 0.924 \n", + "1 0.192 0.960 \n", "2 0.004 0.928 " ] }, @@ -541,10 +541,10 @@ "id": "823fbfb7", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:51.160706Z", - "iopub.status.busy": "2026-08-10T22:40:51.160635Z", - "iopub.status.idle": "2026-08-10T22:40:53.156527Z", - "shell.execute_reply": "2026-08-10T22:40:53.156172Z" + "iopub.execute_input": "2026-10-28T15:25:02.591424Z", + "iopub.status.busy": "2026-10-28T15:25:02.591359Z", + "iopub.status.idle": "2026-10-28T15:25:04.490809Z", + "shell.execute_reply": "2026-10-28T15:25:04.490432Z" } }, "outputs": [ @@ -552,7 +552,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "True lift 0.5 (~5%), 50 states, moderate rollout: 2x2 power = 0.90 CS power = 0.75\n" + "True lift 0.5 (~5%), 50 states, moderate rollout: 2x2 power = 0.89 CS power = 0.75\n" ] } ], @@ -577,16 +577,16 @@ "id": "5f8e0c4b", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:53.157604Z", - "iopub.status.busy": "2026-08-10T22:40:53.157541Z", - "iopub.status.idle": "2026-08-10T22:41:27.624583Z", - "shell.execute_reply": "2026-08-10T22:41:27.624220Z" + "iopub.execute_input": "2026-10-28T15:25:04.492028Z", + "iopub.status.busy": "2026-10-28T15:25:04.491963Z", + "iopub.status.idle": "2026-10-28T15:25:39.729844Z", + "shell.execute_reply": "2026-10-28T15:25:39.729485Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAysAAAHqCAYAAAAEfNFkAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAsxBJREFUeJzs3Qd4E/X/B/BPdyldtLSUUSh7z7IE2chQEEQRWa4fKktUFMUt+lfEhQrKcqAMFRzgApElIiAb2XuU2UUHLd35P+9vuZCmSZOmI2n6fj3PPW0ul8vlcrn7fu47Pi46nU4nREREREREDsbV3htARERERERkCoMVIiIiIiJySAxWiIiIiIjIITFYISIiIiIih8RghYiIiIiIHBKDFSIiIiIickgMVoiIiIiIyCExWCEiIiIiIofEYIXIjvbu3SuXL192+O8gMTFRbev169cLnEflS3x8vDoG0tPT7b0pVE7ODzzmiMofBitEInLy5El1YU1JSSn2/bFv3z65ePFivvlZWVnSunVrmT17tsN/Bxs2bFDbevDgwQLnlYSEhIRiL/SUxDqdVUH76ueff1bHwOnTp0tlW3Q6nZw9e1b9nvB/aa0zOTlZ7YOMjIwiv9+1a9fUujBhvabExMTol8nOzjb5Wkz47Z0/fz7PMgW9n6npwoULZer8UBrHXHk6PxT1s+J1J06cUNdQS7+PpKQkOXr0qNnjvjh/Z+RcGKxQuYeTbdu2bdUFcNasWcW+PyIjI+WDDz4o9/vZVqtXr1bfDS5yjrxOZ+UI++q///6Thx9+WPz9/aVLly7qNxUUFCRvvvmmZGZmlvg6//nnH7UPTN10KKydO3eqdWF67733TC4zfvx4/TJXr17N99oBAwbIgw8+KMOHD5eOHTuqz9CzZ0/57rvvzL6f9hrjadGiRVISAgICpGXLluLj4yNljSMc847+WTdv3ix9+vSRwMBAue2226R///7qOMTvaN68eXkC/2XLlkmTJk0kLCxM7rzzTgkPD5cGDRrIu+++mydIKs7fGTkXBitU7i1fvlzdXcJJ9rPPPiu2O7ZEVDw+/fRT2bFjh6xZs0bOnTsnly5dUgWiV199VR5//HGHWWdhhISEyNdff53vfINmTqg9wPPmTJ06Vd2BRsCFmpVTp05Jp06dZOTIkXLfffdJTk6O2dcYT5hfEnr06KHWj0IqORdcJ7t37y7VqlVTNVyYjh07pn5Dd9xxh0ycOFHfNBSByrBhw9TyqDFEUIS/TzzxhLz22muqRobIEgYrVO4tWLBAunXrpu7y4MSJ5gvmoHoaJ2Zrmk7g7iwu1sbNOsw1X0hNTZUjR46ott4FwUUAJ3wUUKwNrAz7xuBO1uHDhyU2NjbPMij0YD6ajRSHgtZ35coVtU3GhSrsA8zX9kF0dLRqogP4zNo+RHBpCdZhqsmBpXVq24AJTfiwTEF9MtCcD8tgvdr+NfwMti4LcXFxcujQoXzflam2+zjGUHg1bBJkbh9Yq7D7H98rliuoWZKlz2UK7uD++++/csstt+jn3XvvvTJw4ED54osv9HdncTzhOzNVAMJnMdx2a9dpia37GIHFmTNnZOPGjXnmL126VNzd3eWuu+6yel1VqlSR//u//5N33nlH1a588sknUhKw73COsqa5rKk+K8bHLAq32AfG5zEcR8ePH7fq/IZ1oKBc0DFXmO+oMMd8Yc+Zxf35rf0dmVvOlvMrmvWNGzdO7rnnHlm4cKEKWDSVKlWS119/XX744Qdxdc0tXuK35OXlJR999JFUrFhRzfPw8JAJEybI2rVrxdfXV6xR1HMZlXE6onLsyJEjuBrovvvuO/W4cePGuuHDh+dbLicnRzdlyhSdj4+PrmbNmrqIiAhdaGio7sMPPzS77itXruhatmyp1h8SEqL+x/T444+r5zMzM9VzL774om7OnDm66tWr62rUqKFzc3PTPf300/nWd+3aNd3YsWN1FSpUUMsGBwerv8uXLy/wMxq+z8yZM3XVqlXTVa5cWTdr1iz1/G+//aarV6+eWm+tWrV0Hh4euhEjRuiuXr2qX8dPP/2k1rFjx44C51m7vjfeeEO9Njk5Oc9rt27dquZj3fDFF1+odWBegwYN9Ptw9erVZj/vmTNndD169NB5e3vrGjZsqAsKCtJ16NBBt23bNqvW+d9//+nnYcL+8vLy0o0ZM0aXkZGR572WLFmi9qWvr68uPDxcd9ddd+k2bNig1m38vRRm2d27d+s6deqk8/T01NWtW1e9Pz4TPpvmyy+/VK/F5xo0aJBaJ/Y19rOlfWAtS/tK24adO3fq7r77bvXbwOerUqWKbu3atfnWZ83nKowHHnhAvf+lS5f08x599FG1H7Zs2aKfl5CQoI7JJk2aqN9RYde5atUqNe/06dP6ebbuY+07x77Dvrz//vvzPN+mTRs1D+cALBcTE5Pvtdpv11h6errajkaNGln9Gmvs2rVL16VLF3Vuql27ts7f3183ePBgXVRUVKHOD9rx8u+//+oGDBigji3sv/bt26vPie/p9ttvV/Nx/sAxd+LEiTzboq0D54r+/furYw7n5apVq+p++eWXPMva8h1Zc86x5hxnSnF8/sL8jiwtZ8v5Fb8vLH/o0CGdNW677Tb1/eA6ZElx/s7IuTBYoXINBYKwsDB9IfTjjz9WJ/TY2Ng8y3399dfqJLpp0yb9PCzz/PPPW3wPc8GHFkR07NhR9+qrr6qACD766CM1/88//9Qvi+d69uypthUXac17772nc3V1VQUSc7T3adeune6FF17QZWdnq0INCpgo0GH7Ro4cqUtLS1PLo3CB4Kpr1676dVhbGLF2fdYGK/DNN9+oeXv27NFZo0+fPuqCa1hw2L59u27BggU2r/Off/7RVapUSffyyy/r5/311186FxcX3cSJE3VZWVlqHr4HXFiNA5DCLIsA2s/PTzdw4EBdfHy8mofP0r17d1WguH79ep6CDwoDf//9tz7Qwj61Zh9Yq6B9pW3DHXfcoQ8OUlNT1XeN4AnHWWE/l7WwDhRcUFjVfjuA4w4FfgT+WkEfwRyCqMOHD9u0Tnz/2J8XLlzQz7N1HxsGK7jZUbFiRf3vYN++feq59evX2xSsQL9+/dQy2nZpr5k6dar6Do0nFJALcvDgQbXv+vbtq27AAPbNihUr9AFpYYMVBBk4/8Dly5fVsYLCPgJFFOQhOjpaFaRxvBjS1tG7d2/d5s2b1TwcZ6NGjVJBg+Fxaut3VNAxb+05zpTi+PzW/o6sXa6w50K8FjfqrIVjFesfPXq07uTJkwUuW5y/M3IuDFao3MIFDhcYwwJoYmKiKjygBsIQLvS406VdnArDUrDSrFmzPAUjFGZRYMKdfMM7eVgWQZMxBCEoSJijvQ/uBCJQMYS7eyiEGwcNCNq0QlNhCiPWrq8kgxUUUnFhLIg160QAi7uae/fuVcvhs+G70qCAjru5hgVyePbZZ/MFIIVZFjV7uHNtfJdWqwVctGhRnoLPK6+8YtM+KM5gZfr06Xnm4w435m/cuLHQn8taQ4cONfubOHXqlC4wMFAVaN966y213LffflukdRbXPjYMVhCIoICNO9zw5JNPqlpbnA9sDVZQK4Nljh07luc1qIU1rDHUJsObIub2Cb434xs4hgobrMyYMSPP61Hri2D+zTffzDMfxzbmIwA2Xsdrr72WZ9mkpCT1naPQX9TvqKBj3tpznCnF8fmt/R1Zu1xhz69YZ4sWLXTWwrE8bdo0dU3D+yAwQwCFYxjfmSXFeS6jsot9VqjcWrlypWrn3759e31bXfQDQUdAdCA0hPbtaHuNTqxoD16cw3F27txZXFxc9I/d3NwkIiJCdfrVaP1oatSoIQcOHJD9+/er/gmYsCw6CluCEY+0dsSabdu2Sbt27fK1G+7Vq5f6u3Xr1kJ9luJeny369u0rS5Yskfvvv1+1ndb6h1gL3/Njjz2mRrlBf4ZRo0apUZPw2bT23YDHOHY8PT3zvP7WW2/Nt87CLIvvulGjRqo9vOF3nZaWpkZWMv6u0d+quPdBYRl/jrp166q/xsdwYT5XQdApHANj4HsaPXp0vudr166tOq+vW7dOXnjhBdVhHp18i7LOktjHlStXVh2S0fYffdywPhxrhueDwtKGfUU/AWs62Pfu3bvA9f3111/qNx0cHCzFBedRQziH4eap8Xx8j5iPY8aY8XHv5+cnbdq0yXOOKYnfQXGc44ry+a39HRXn780QzmGFGYEPx/Irr7yi+uFs375ddarHqGGTJ09W24c+UAUp7XMZOSZ3e28AkT071qNz4EsvvZTvOZxAcdHROt9iZBtctOfMmSNvvPGGGu2katWq8txzz6lRTYoCBRZjFSpUyNOJVeuA/dRTT5lcBy5qlmDYSGPoGIpOkca0eYXtbG/t+swVxtABvajwHbVq1UoVPBFooDMrvkd08MQQ1ZY8++yzsnjxYlm1apV07dpVPx+FyO+//17/GN8PAhpjpj5/YZbFd43Otdh2Y/Xr18+3HlPfa1H3QVGPYRy/YHwMF+ZzmYPf34wZM+SBBx5QI3qZU6dOHVVgR+FMK0gWdZ0lsY9xXKEz/ccff6w6QGMbigKDC3h7e5s8LmyBDs3FGaiYOl6wvQXNN9Wh39TxgnmG56yS+B0UxzmzKJ/f2t9Rcf3eTL0WQS7O1RgIwlpYFkEeJgwZjg72uMnxzDPPyK+//mr2daV9LiPHxGCFyiWMwIKRSDDGPGpNjKGQimDGcKQgzNMKrxiVBKOePPnkk9K4cWOT6yhOqFEBDGlas2ZNm9ZhXKsC1atXz1NboNHm4fnCsHZ92kUZOSQM71Aa3om3FUaaQTCJCXeZEWSOHTtW5ZnA6DuW7lr//vvv6m6zYaACxnkIEOiauuMbFRWVb15hlsV3jYLmpk2bxNbvtaj7oCQU9nOZghH7cJcWhRaMMmTqs2sF7Lvvvlvlc8C+R+Foz549Jn871q6zpPYxalYwTPHzzz+vanVxl91WOJ7wOXE+Mq7FsxX2mSMOL4vPikKsIZw/DM9ZJfE7KO5zZkn9jorj92YKAmvcyMN5EjlTTMHIbPgdYf+iFgbfgzFcW/H7RG1PQRzxXEalj83AqFz6/PPP1V0r4wKpBgmuMD48Mu6C8TCmDRs2lBdffFH9bymZFu5gYTjcosCQqmgeNnfuXJPP25p9GMO0omoew28a7x+83+23314i60OApzWpMITqfmPaHUBr96HhvkCBDQnL0AQIzRC0ITkLWieS2RkPJYzEesbNJnCxREHAOOD45ptv8q2zMMuOGDFC7Rdt2GtjqCkojn0AaB5izfELRT2Gi/q5Zs+erWq9MOQvmk0VFFSMGTNGFVzRbARD+aKGZejQofkyYxdmnbbuY2vuOE+aNEnlI8HdZlth/z3yyCPqM6CpTXHBZ9q1a5ds2bIl33MFDeld0jDEsyE0zcV24rdW1O+ooGO+uM+ZJfU7sna5wv6+ESigmefTTz9tskkW1otmlNqxgWMSw4kbw1D6SP5oKTgvrt8ZlW2sWaFyB3d9vvzyS3UXU6tmNxWsoK07CpNow44TM/q3DBo0SJ2oUZidOXOmqvo3d3dJg3bIuAuF5HOhoaGqMGxNsy1DaNuLZiKoycFJfsiQISrbNvrYoLYFNRXWNl8x9PLLL6ttQ7vgt956S92N++mnn1SfnenTpxd6O61dH4JENAdAu2UUrrBPvvrqK3WnFK83hHloVoTPh8IACp64wJlrxoB9hcInMnsj/wSa9OGOOQoZWlONgtaJu/Do44DP0q9fP5WfAE0RcIFEPycNmg+uWLFCXTzRjAhNZRBs1atXTz1veMevMMuibwEyOWNZFKTR1wV3J1EYQ4Fo1qxZJvupFHYfAH4DuPuKoMWcwu5/c4ryudAsDwX6Dh06qKaQaH9vfPNAa3qG3wluNOB4at68uZqHgAUZ3vE7xvsUdp1F2cfWwI0P7eaHNZDnSctThAIbCumoCcb/CNDw2zL3GmPo66H1MTL3vSEXDM6JqIHC94aCIoIF/CYs9QUqKWiGi4Iz3h/nRGwnvjMcW0X9jgo65ov7nFlY1v6OrF2usL9vHC9okYBrIV6LZtCoJUHQjVo9/P5wUwbBv5bjBc21/ve//6laGfRXQaCHGk0EH9iHBSnO3xmVXQxWqNzBhR2F+4IusjgJ40SOpHEIVnAHFgVVXJROnDihTthaxntUZRcEBd1p06apzNi464SO7jiho4DasmVLddE11S7Y2Pjx49VFAe+J9vXoeInCLjJWDx482Oz7F/Q+CHhwlxAXLlwA0N4abf1xMcLFWINgAutAx8yC5lm7PkA7ZTSlw0ULzWBQcETB+bffflPr1mAeCmCoVcKdZ7SVxuc3Xp8GF0zsI1yQcecPnxsXRMO22wWtE80N8P7ffvut6qCNTru//PKLLFq0KE9CT2wzjg8UUD744AN14dQ65iOQNQyEC7Ms/sf+wvvjmEM/GSzftGlTVejWaqWwr7H/TQXc1uwDHIv4fpo1a2ZyP1qzr8xtAwohmG/YBt/az2UK7sy2aNFCbTMKPcbwehRU0RQHQQjawaNDrga/uffff1/dpMD3gADF2nWaY80+NgXNHrFvsO8KguZEWM6wX4D2WvSnwoQmMij8IdhAIRo3MbTEe+ZeYwyFQHM1toCCLJrMYt/hd4BAEIVZ9LXRbtQU5vxg6njR5hsHhzg+jOdry+J8gdoe/KZwHON8jgI6zs1F/Y4sHfPWnuOMFcfnt/Z3ZO1yhT2/Aq47CHxxLCBwww0z3HRCk0GcP1GzojVDxHvj+MEy+A2i6S9u1iCpJGpAa9WqVeA+s/U7JOfigiHB7L0RRETOAnf0UZDDnXpLgUBhli1uKECg5gi1Krh7SURE5IgYrBAR2Qh9IIw7MqOJGwZwwGTYB6Iwy5YG3ElFM8J33nmnVN+XiIioMNgMjIjIRmg3jeYSaJON/gJoroCmKRhm0zj4KMyypQHt/YmIiBwda1aIiGyETsvof7R7927Vdh3NqRAEoE9EUZYlIiKiXAxWiIiIiIjIITHPChEREREROSQGK0RERERE5JDYwb6UIHkXsrViDHjDBHBERERERGWVTqeT5ORkqVatWokMGMNgpZQgULGUPJCIiIiIqCyKioqSGjVqFPt6GayUEi2rLr5IZBwu7VodZI1F9lp7DJFKRFSaeM4jovIkx87lvKSkJHVDXivrFjcGK6VEa/qFQMUewUpWVpZ6XwYrROTseM4jovIkx0HKeSXVzYG32YmIiIiIyCExWCEiIiIiIofEYIWIiIiIiBwS+6w4kOzsbMnMzCyRtoxYb1paGvusODlPT09+x0REROQ0GKw4yPjUly9floSEhBJbvzZSBHO8ODd0rKtdu7YKWoiIiIjKOgYrDkALVEJDQ8XHx6fYAwoEKxglwt3dncFKOUg8eunSJalZsya/ayIiIirzGKw4QNMvLVAJDg4ukfdgsFJ+hISEqIAFwamHh4e9N4eIiIioSNjB3s60PiqoUSEqKq35F4JgIiIiorKONSsOgn1JiMcRERERFcaFhOuy+XiMxFxNkpBKqXJr/RCpHljBqXYigxUnO2D/ORErKelZUtHLXTrXq+zwB+zatWulWrVq0qRJE5OPHdnGjRtV073mzZvbe1OIiIioHNkblSCz1h2X9UejRacTcXURydHh5rdIz0ahMqlnfWkZHijOgMFKOThgH+9RT5pW9RVH9NJLL8mAAQP0wYnxY0f2f//3f9KxY8cCgxWMwLZr1y41SlerVq0kKCio0O9THOsgIiIi57D6wCWZuHSP6FS/5Nx5KPfJjccbj8bIX0djZPaI1tKvWVUp69hnxQkO2HvmbJGNx2LMHrD3zN0qfxy8YtftLI/Gjx8vzZo1kxkzZsirr74qtWrVkgULFpT6OoiIiMh5blBPXLpHsnN0ajJFew7L7YsqmbQYpYk1K05ywJo+XHMPWAyE/OSy/6RGUEVpVbNSsQ+Xu2PHDrly5Yq0bt1awsPD8zx//fp1+eeff+TatWvSpk0bNaRuYWzevFnOnz+v+vRgxDTULFSqlPczrF69WurUqaPm79mzR42C1aVLFzVUs6EDBw7IqVOnJCIiQlq0aJHnOXRI3759u0RHR0uDBg2kcePGJms4sD2VK1dW22EJalxmzpwpXl5e6jGCjHHjxkmvXr3U9l64cEH+/vtvuf3228Xf31+/HT/++KMKULANltZBRERE5ces9cdza1QsLKctM2v9CfnsgbZSljFYKS8HrE5k9gYcsO2K7f1PnDghgwcPlsTERFX4P3z4sEyaNEmefPJJ9TyaLqFJF/p1hIWFyZYtW+T555+Xl19+2er3+Pfff1UwBFFRUXLw4EFZtGiRDBw4UL/MM888o4bsPX78uNqOffv2SdWqVVX/l8DAQBVQDRs2TDZt2qSabWE92KaVK1eqUdgQwGjrq1u3ruzcuVM6d+4s33zzjT7g2bp1qwoqEOggKMLwwFgv1mcOggpDd999tzz66KMqaEKggW1Gjckvv/wiS5YsUcu8+eab8umnn8r+/futWgcRERGVn77J64/kNvm3Bm5YrztyRb3O0fswF4TBSnk5YHU4YKOL7YBF7pZ77rlHZUtfvny5eHt7q9wef/zxh3oeBfmHH35YbrvtNvnqq69UzQie69+/v5ratrUuyn/66afzPP7iiy9UYR21LW5ubvr5CFAwoWYHwVOHDh1k+vTpKhhAbctPP/2kkiUiQNA6x6enp6tg5d5775U777xTLQ+oBWrXrp3Mnj1bBV74LHhPBDxz585VyyxevFhGjx5dqH2G4AmaNm2qH2Z46dKlEhkZqdaHQOmNN96Qn3/+Wb+dltZBRERE5cM/J2KtLvdpsPyWE7EytG3eli9lCYMVBzVw1maJSU43+3xqRpZNB2z/DzeJj6f5rz3Ez0t+efxWi+vau3evCg4QiCBQAdRC3HHHHep/1LL8999/8u233+qHZe7bt68qmC9btszqYAUuX76sahLi4uJUMyk8PnfunAqUNAgktCZoAQEB8thjj8nHH3+sgpUKFSqogAPr6NGjh1qme/fu6i+2ETVAY8aMke+//14FYZjq1asnGzZsUMHKoUOH1GtXrFihf7+RI0fK1KlTrf4M2N7HH39cBT0ISjRo6vX+++/LhAkTVI0NalIQzBVmHUREROT8UtKz9IMoWQvLX0vPkrKMwYqDQqByOSmt2NeblJalpqJCwRnQv8OUs2fPqr/GTZVQyNaes8brr7+uAg4EOeizokHfEsNgBc2zDOE5NPdC4IGRxbAO1KD4+vpKz549Va0PmnqdOXNGLY/AxDDXTcWKFaV+/fp5Pis6t2uwrPF7moMaHdQw4TPMmjUr3/MIrN566y2JiYlRf21ZBxERETm3il7uhQpUAMv7epXt4n7Z3nonhhqOgqBmxZagw9/b3WLNijXQFwRQ21GjRo18z6MTOsTHx6v+Ixo81oIAS9ABHSNgoa/LLbfcouYhuPjhhx9UEGLc+d34MYb41QKQKVOmyOTJk1VtEF7frVs3Wbdunb5jO5qAmesDgv4tkJCQoP9cpt7TFNQCIThC8ISO81qGeUPoQI+BCPz8/OS9996T1157rdDrICIiIufWoXbhUxegGNSp3s2yS1nEYMVBWWqKhb4nt85YX6imYDhgVz3ZtVj6rKBPBwKWr7/+Wl544QX9fNQOoL8F+lSgWRMK12jiBBgxDKNfPfTQQ1a9B5aHhg0b6uehqZYp6KSOWgnkIgG8L2pOtG1C0zAU8jEiGSY06ULn/YkTJ6rtRF+Ud955R78+BEMIEhBo4bPgs+I1aC6mNXPDdNdddxW4/QgyUCOD12rN5QwheHrxxRdV3xU0V0PfGTSX04Iza9ZBREREzi06KU2eWb6vUK9xc3WRHg1Dy3TneqcMVtBP4bPPPlN3vVGgfvfddy2278eoVribjSF2cTce/RSMO0/bst6ShAMPCR+RR8XcONuG3FxcpEejkGI7YNExHaNWPfDAA6pZF/YJOrKnpKTIwoULVTOqt99+W40OhiZMKPR/8sknqq8K+pdYA0EC+o6g+daIESNUvxGMBGYKmoWhrwdGy8KoX6g12bZtm3oOQQWaWg0dOlTVnqCPCrYZo3vhc2A4YKwfNTno04IAASOFof8IAis0HUMNDz4LlkFwg+MBAZA5GRkZanhhBErPPvtsnv4u2Fc4dtLS0tT7jho1SoYMGaKeGzt2rHqMPkEYrtjSOoiIiMi57TgTL+OX7C6wL7MxlxvT4z3rSVnnVEkhUYDEXX4EHmvWrFF32XFXGqM7mXP06FFp3769KhiuWrVKDSGL/gvHjh0r0npLw6Se9fUHY0HUMi4iE3sU7wE7fPhwNawwCu0Y2hfDBiOg06AjOPYpmk9hOdSw/Pnnn/raD0A/DMORrQwf4ztBTQyGB8boXQga8D4IdgybYwG+HwSYKOQj4EStiZZLpWvXrvL777+r/Ct//fWXPh8LcpkAAhx0tEcgg/dDEDF//vw8NUAIYDFiF4IcjESGoAlDJhvna9FgZDSsH8EG8sAg0NCm06dP60f2QtDx4Ycf6l+HIBgBE0ZOs2YdRERE5JzQyuPLf07L8Pnb9IFK1QBvea5fI3F3dVE1J6ZgPqbZI9pIy/DcZvtlmYvOuPF/GYXRnqpXr64KyNOmTVPzkpOTVafsDz74IF++Cg3urmOoW9SqGMJuQX8HW9drLCkpSRXq8V5aPwlAwRgFT/RHsKWJDzLYIzEkvkRTNSw4WHEofzSshdzeonqeTuTOAgV6NM/S8ruUZ0U9noicAc7b6B+HGxeGN0eIiMoK9E1+/sf9snLvRf28TnWDZdbw1hLs66Uy03+8/rg+jQWKd9rfXo2qqBqV0gpUzJVxi4vTNANDQkD0Mejdu7d+Hjos46487pabCipQM4KaEjRPMqYV6m1Zb2nq16yq/DCuQp4DVhvWDh8BbRUn9qgrTav62nU7iYiIiMiyM7EpMnbxLjlyOVk/77FudWRKn4bi7pZ7AwaByOcPtFN9mP85HiPRV5MktJK/dK5ffE3+HYXTBCvIKA5VqlTJMx+P0WfClJMnT6q8HejY3KdPH9W3oWbNmjJ+/HiVR8PW9QISDmIyjDq1O36YNPhfy+1hayVXixoB8tn9bdUBu/VknCSnZYmft7vcUjdYf8CiSRE4SUVaHv369VNDKDvjZyss7TgyPs6IyhPtvMrfABGVNesOX5HJy/9TZTmo6Okm79zTQvo3C1OPjc9rVf29ZEjranL1agXVzB21yaV97ivp93OaYEUrqBpmNdcSFZrbiQhUtGFt0cm6VatWqh8BOo1jfejobMt6taFwtWZjhtBBXwscIDMzU60H8wzn26KKr4cMbpl7MGu0dWqf1RlpmeeLuv+cAfYBjidUxaamptp7c4jsAr8BNNfF+ZvNwIioLEBT/vlbzsvn2242+6od5C3vDmogEcGeqmmro57z8N4lyWmCFQyXCxg5yXCUJG0oXVO0JIPoj4IhYwHJAtEZGx2oEazYsl54/vnnVV4Pw5oVZFhH1GvcZwUBDIIfTCWppNdP9ofvGCcqtB1lnxUqr3DhRlNe7S4jEZEju5qaIVO+2yebjsfq56EmZcbdza1K6Gjvc16Jl1/FSTRq1EgV0DZv3qz6k2jDx2JUKOSxMAXJDBFAoBmYIQxnq0WwtqxXG8kKkzEcRIYHEv7HAaZNJcGweZQzdrCnm7TjyPg4Iypv+DsgorLgwIVE1T/l/NXr6jH6HU/t30ge6VKnUGU2Fzte+0v6PZ2mNINhaZGjAiN0HTp0SPUXQTCBgvr999+vXw59UTBUreapp55Sw9RqQxUjWzoSCmp5L6xdLxERERGRtZbvjJK752zRByrBFT1l8ZgO8mjXury57Iw1K/DGG2+o5laRkZGqjwYSCv722295OsejLT+aXmkw3K2W6BH9R5DLA024tKzr1q6XiIiIiMiS9KxsmfbLIVn67zn9vFbhgTJnVBupGuBcI3kVB6fJs2IIAcX169dV4GFMG5XLeBxotPfDa5B53Zb12ivPijXwFaPjNdoUshmYc2OeFSLmWSEix3Up8bqMW7xb9kYl6OeN6lhTXh7QRLzc8w7mVFZySyUxz0rhYeQucwGFuWQ1+HILClQsrdcuEqJEUuOsWFAnkpUt4h8qElizFDaMiIiIiAxtORErj3+zR+JSMtRjL3dXefOu5nJPZA3uqPLSDKxcQaAyO1Ik62YuF3PQPcsDIYu7l8jEXSKB4SWySah1Mh6swBgGJ/D09CyR9yciIiJyNGjhMn/TKZmx+ohK2g01KlWQuaMipVn1AHtvnsNzmg725Q5qVKwIVAy5YHmramKsh4EJ7rvvPgkMDFTVj/h72223ybp16/TLnDt3Tu69915Vq4WmcGjy9uqrr0pCws0qUCIiIiJncy09S8Yv2S3TV90MVLo1CJFfH7+VgYqVGKyQzXbv3i1t27ZVQzTv3btX1axERUXJCy+8IJ9//rl+uQEDBqj2jBhNDcv8/fffqnZlzZo13PtERETklE5EX5NBszfLqgOX9fMm9awnXzzYTgJ92MrEWmwGRjZ75JFHpEmTJvLVV1/p5/n5+UmPHj3UBAhe9u/fL++//77KawP4W1COGiIiIqKybNX+S/LM8n2SkpGtHvt5u8uHw1pJr8YcSbawWLNCNjlx4oSqWZk4cWKBy6FpGGpRfvrpJ9VfhYiIiMhZZWXnyPTfD8u4Jbv1gUqjMD/5ZeKtDFRsxJoVRzWvm8i1aPPPZ9tY8F98t4hbAVWPvqEij/1lVbAC9evXL3A5jLCGpJsIahYtWiQdO3aUzp07qz4sqJUhIiIicgax19Ll8aV7ZOupm/2DB7WqJtOHNBcfTxa5bcU956gQqCRfLP71psYWy2owjDMgf4slDzzwgNx1112q0/22bdvk+++/lzfffFMWLFggDz74YLFsDxEREZG97Dl3VXWkv5SYm3jc3dVFXrqjsTzQKYI57oqIwYqjQg1HQVCzYkvg4VPZcs2KFRo1aqT+Hj58WNWUWIKRwBCwYHr77bflnnvukZdeeonBChEREZXpYYmXbj8n034+JBnZOWpeqJ+XfDqyjbSNCLL35jkFBiuOylJTrIt7ReZ3K/x6R/0gUq2VFFV4eLh0795dPvjgAxVwuLu75/vxuri46P8awuNWrVrJ+vXri7wdRERERPaQlpktL604IN/vOq+f1z4iSGaPaC2h/t78UooJO9iTzT777DNJTEyUvn37ysaNGyU6OloOHjwoCxcuVIGMNhpYp06dZOnSpXLkyBG5ePGi/PjjjzJr1izVb4WIiIiorImKT5V75m7JE6g83Lm2LHmkAwOVYsaaFbJZ3bp1Zc+ePfLee+/JpEmT5PLly1K1alVp37696lQPNWvWlJkzZ8onn3wib7zxhsq3gqGLp0yZIk888QT3PhEREZUpfx2LkSe+3SMJqZnqcQUPN3n77uYyqFV1e2+aU2KwQkUSGhoq77zzjprM6dChg5qIiIiIyqqcHJ18suGEfLD2mOhuZKOPCPaRuaMjpVGYv703z2kxWCmrfIJF3L1EstKtfonO3Utc8DoiIiIislri9Ux5etleWXv4ZlqJ3o2ryAfDWoq/twf3ZAlisFJWBYaLTNwlknpzLG9zdKKTrKxscfcPzX0dEREREVnlyOUkGbtol5yJS1WPMW7Q07c1kPHd64mra95BhKj4MVgpyxB4WBN8oK4S+VCMRuwiIiIiIvNW7r0gU3/YL9czc7PRB/p4yMf3tZauDUK420oJS69ERERERAYys3Pkzd8Oy8ItZ/TzmlX3lzkjIyU8yIf7qhQxWCEiIiIiuiE6KU0mLN0tO85c1e+ToZE15I3BzcTbw437qZQxWHEQSJ5IxOOIiIjIfnaciZfxS3ZLTHLuAEaebq4ybVBTua9deL4k11Q6GKzYmYdH7ggSqampUqFCBXtvDpVxGRkZ6q+bG+/8EBERFeamMZp8oelXVk7uDeSqAd4yZ1SktAoP5I60IwYrdoZCZWBgoMr+Dj4+PsUeueMHmJWVJe7u7rwr4MRycnIkJiZGHUP4romIiMiy1Iwsef7H/bJy70X9vE51g2XW8NYS7OvlWLswISr/SLA6nbglJoqkB+QOVWYIKSvK+EiwLNE4gLCwMPVXC1iKG4IVFGRdXV0ZrDg5fMc1a9bk90xERGSFM7EpMnbxLjlyOVk/77FudWRKn4bi7ubqeIHK7Mh8OfawlZXMvQY5+ZDqogwHLAxWHABqUqpWraqywWdmZhb7+hGoJCYmSkBAgCrMkvPy9PTkd0xERGSFtYeuyFPL9kpyWpZ6XNHTTd4b2lL6N6/qmPsvNa5QycAVLI/XMVih4moSVhJ9DRCsoE+Mt7c3C7JERERUrmXn6OTDtcdk1voT+nn1Qn1l7qhI9ZccC2tWiIiIiKhcSEjNkEnf7pVNx2L0825vHibv3NNSfL1YLHZE/FaIiIiIyOkduJCo+qecv3pdPXZ1EZnav5E80qUO+3o6MAYrREREROTUlu+MkpdWHJD0rBz1OLiip8wa0Vo61a0sZUJOjkjyJSmPGKwQERERkVNKz8qWab8ckqX/ntPPQ96UOaPaSNWACo4ZlCScFYk5cmM6KhJ9WCT2mEhmqpRHDFaIiIiIyOlcSrwu4xbvlr1RCfp5ozrWlJcHNBEvdzsnT87JFrl6xkRQclwkK7eZGuVisEJERERETmXLiVh5/Js9EpeSoR57ubvKm3c1l3sia5TuhmRniVw9bRSUHMmtKcm2chhiF1eRSrVF/KqKnN0s5Q2DFSIiIiJyCkiEPX/TKZmx+ojk6HLn1ahUQQ1L3Kx6QMm9cXamSPypvLUk+Bt3XCQ7N2CyyMVNJKiOSEhDkZBGIqGNc/8PrifiUUHk4l6R+d2kvGGwQkRERERl3rX0LJmyfJ+sOnBZP69bgxD56L5WEujjWTxvkpUhEn/SRFByQiQn0/qgBAGIPihplPsX85BxnvJgsEJEREREZdqJ6Gvy2KKdcjImRT9vUs968kTvBuKGMYoLC5nfEYAYByUIVHJyM95b5OphOigJqiviXkzBUznAYIWIiIiIyqxV+y/JM8v3SUpGtnrs5+0uHw5rJb0aV7H84sy03KZaCEQQmOiDklMiutz1WeTmKRJc30RQUkfEzUOKjU9wbs0LAilrYXm8rgxjsEJEREREZU5Wdo68+8dRmbfplH5eozA/1T8lonLFvAtnXs/t1K4PSm50eEfnd11u7hWL3LxEKjfIH5Sg87tbKRSpA8NFJu4SSY3LMztHp5PExEQJCAgQVxejWiQEKnhdGcZghYiIiIjKlNhr6fL40j2y9dTNgvugVtVk+sC64pN4TGSfcVByBt3vrVu5u/eNoATBSMMbHd0biQTWKp2gpCCB4fmDj5wcyfaKFwkKEnF1FWfDYIWIiIiIyow9567K04v/Ed/kU3K36wVp4HZBbq+SIDUunxOXd89ZH5R4+JgIShrmBiWuds7DQnoMVoiIiIjIMaUl3Wi+dUR00Yfl4om9EhJ9RNa7xIoYDpwVW8A6PCrmb7qFxwE1nbImwtkwWCEiIiIi+0pLzN+fBI+TzusXQW+M6to/pnj6mQ5K/GswKCnDGKwQERERUem4ftV0UJJ80epVJOkqSKJvXalev7W4VrnRdAuBiX91EeMO5lTmMVghIiIiouKVGn8jEDEKSq7dTNhokXeAJPrVk7UxgXIgs5oc19WQKLeaMnlIVxnUuga/sXKCwQoRERER2SYlNm9+Ei1ASYmxfh0VKomENM7TyT0nuKF8siNZPlh3XHQ3+stHBPvIvNGR0ijMn99WOcJghYiIiIjMQ7SA4MO4liTmcL6cHwXyqZx/5C08rhiSp/lW4vVMeXrZXll7OFo/r3fjKvLBsJbi712MSRapTGCw4mwSovKfOHQ6cUtMFEkPyN+W0wmSBREREVExBSXXrpgOStDXxFoVQ010dEdQUtniS49cTpKxi3bJmbhU9RjFlqdvayDju9cTV1f2RymPGKw4W6AyO1IkKz3PbAzKV8nca9y9crOhMmAhIiIqP0FJ8iUTQckRkbQE69fjG2Y6KPEJsmmzVu69IFN/2C/XM7PV40AfD/n4vtbStUGITesj58BgxZmgRsUoULEIy+N1DFaIiIicLyhJumAiKDkqkp5o/Xr8quVvuoVkijYGJcYys3Pkzd8Oy8ItyDKfq1l1f5kzMlLCg3yK5T2o7GKwQkRERFSW5eTk5iNBEJKno/tRkYxk69eDfCTGQQn+egeU2KZHJ6XJhKW7ZceZm83MhkbWkDcGNxNvD2aRJwYrRERERGUnKEk8l7/pFv5mpli/HmRuV0GJQdMt1JR4l+4oWzvOxMv4JbslJjm3VYinm6u8dmdTGd4+XFyYL4VuYM0KERERkSPJyRZJOJu/k3vMMZGs69avJ7BW/mzulRuKePmKPel0OtXkC02/snJyxyWuGuAtc0ZFSqvwQLtuGzkeBiskEvVv7nAbGL0Dwwe68bAgIiIqlaDk6pkbTbcMgpLY4yJZaVauxEWkUoSJoKSBiGdFcTSpGVny/I/7ZeXemxnrO9UNllnDW0uwr5ddt40cE0ulJLLqWYO94JLbYc63Sm7ggr++obkTghntf8zHsMeubE9KRERUoOwskaun8ydORFCSbeXAOC6uIpVq5+9PgqDEo0KZ+ALOxKbI2MW75Mjlm/1oHutWR6b0aSjubhi7lCg/BitkRJc7Opg1SZ5w4kSCJxXQhBQQ4FTJzU7ryhMRERE5sexMkfhT+YOSuBMi2RnWrcPFTSSoTt7+JJiC64l4eEtZtfbQFXlq2V5JTstSjyt6usl7Q1tK/+ZV7b1p5OAYrJBI6/tzA4lr0TenlGjLVdC6nNzlMF2xsCNd3XMDGUu1NXgegQ071hERkaPKyhCJP2kQlNz4i6AkJ7cwbhGui0F18zbdCmksElw3Nweak8jO0cmHa4/JrPUn9PPqhfrK3FGR6i+RJQxWSKTd/0Sqtco/Nnt6ksi1mNxstghI9MEMHt+Yrz2fk1nwnsTJGwmoMFni5nkjqAktuLYGtTle/gxsiIioZCAXGZpq5enkjqDkpIguN3GhRa4eubUixkEJak/cPZ36m0tIzZBJ3+6VTcdi9PNubx4m79zTUny9WAQl6/BIIdNQs4Fx1TFVrlfwXkJgg4y3WiBjWDuTL8CJtnyCR1U5klhhsngEexvVzpiprcFfO49+QkREDiozTST2WN6mW5jQpAutCKyBG23B9U0EJbVF3DykvDlwIVH1Tzl/NXf0MlcXkan9G8kjXepwWGIqFAYrVDyBDZpuYcLJ2dIY8devWldbkxpr+SKBpmoYcx6TJR4+1tXW4H9PZswlInI6GammgxKMyGV1UOKV26ldBSVaR/fGuSNycTRNZfnOKHlpxQFJz8rdp8EVPWXWiNbSqW7lEvxyyVkxWHEmGJ0L7VxRbW0tLI/XlRb0jakYnDtJE8tDOqKjv2GNjbkAx5oBATJTcy9ImCzx9DNRW2M4kIDBc07UtpiIyClkpNwISIyDkrO5A8lYAzX3KigxHH2rUW5QwpEwTUrPypZpvxySpf/evIGIvClzRrWRqgFlY8QycjwMVpxJYLjIxF35Cu45Op0kJiZKQECAuBp3XEeggtc5IlwMtIBAmlkegSUltoDaGoOmaajZsSQjWSQe00nLy6KpnHEAYzLACSmXTQGIiEpMenJuokTDgARTghW17Ya17qaCksCaDEoK4VLidRm3eLfsjUrQzxvVsaa8PKCJeLkzzQHZjsGKs0HgYRx85ORItle8SFCQ8w4fjCDAv2ruZM0oLloQow9mDJqfGTZHS0+0vL60xNwJTQssqRBkXW0NhoRmcwIiopvn2XxByVGRxCjr95Cnb978JFpQEhDuvNfGUrLlZKw8vnSPxKXkDs/s5e4qb97VXO6JrGHvTSMnwGCFyh+MvhJQPXeyptOlNbU1+JtxzfL6rsfnTrjQFgjJOYPzBjPmAhyVnJMXWiJyAtcT8jfdwmNrBlwxbMZr3J8E/wfU4OiRxUyn08n8TadkxuojknOjdV2NShXUsMTNqgcU99tROcVghaggSMCFpgCYrGkjrYIXo2DGVICTlTs6SsHJOWNzp2grEohVrGy5tkZLzskcNkRkb6nxpoMSa4a313gFGAUlNyb/ajzPlYJr6VkyZfk+WXXgsn5etwYh8tF9rSTQx7mHZKbSxWCFqLh4VswdohKTpaGeUQtjTW0NpmwLAyZgKGitKZtYk5wz1HJtDeajLw4DGyIqipS4/P1JEJRYc77SeAfm70+CyS+M5yg7ORF9TR5btFNOxqTo503qWU+e6N1A3DBGMVExYrBCVNoQAHj55U7IVGwxh02imWDGsPbmxjxLmZNVcs6LuZM1OQPyDPNsprYGz+OzMLAhKr/Qx884IMFfnLushT59poISnGt4fnEYq/ZfkmeW75OUjNycaX7e7vLhsFbSq3EVe28aOSkGK0QOn8MmMHeqXN9yDhvD5JxmA5yY3OesSc6JzqvWdGB1r2AimDET4KAGiojKHtw8wbnEOJs7HlszfLwGA4iYCkrQnJVBicPKys6Rd/84KvM2ndLPaxTmp/qnRFTmeZ1KjtMFK1988YV89tlncvXqVWnXrp289dZbUqOG+dEoHn30UVmzZk2eebfeeqssXry4UMsQ2R062fsE5U5ox20xOWe8+WDGMK8NhoS2lJcAfXAwVKg1w4ViRB5ramvwvwfH5SeyS1CSfNl0UGLN0O8a/K6Ns7njL4ISKlNir6Wr0b62nroZlA5qVU2mD2kuPp5OV5QkB+NUR9j8+fPliSeekHnz5knjxo3l1Vdfle7du8v+/fulQgXThZ7o6Gjp1q2bTJs2TT/PeFlrliEqU1Ryzsq5U5WmBS+bnXUjOecVo5HRTAQ4CIAsQX8dTFdPW17Wy9+gX00BtTX4H6O8EVHhgpKki6abb6H5qbV8w0wHJbhxQmXennNXZfyS3XIpMU09dnd1kZfuaCwPdIoQF9aEUSlwd6bh89544w156qmn5P7771fzli5dKlWqVJElS5bImDFjzL7Wz89PIiIiCly/NcsQOSXke/GrkjtZopJzxliurcH/1hSG0pNyp7gT1nXC1QYNyBPgGNbW4C9y2DA5J5WzoCTxvEEtiRaUHM39fVnLv3r+plshDXJHGSSng3LV0u3nZNrPhyQjO0fNC/Xzkk9HtpG2EQxEqRwEK8nJySqYWLVqlar5QLOtSpUqSYsWLaR///4yfPhwFSBY68SJE3L+/Hnp27evfl5gYKB07NhRNm7cWGCwsmLFClm7dq2EhIRI79695dlnn81Xc2LNMkTlnkrOWS13siQrPf/IZ6YCHAQ/1hSo0F8HU+xRy8siN425YMZwnsphw8zLVEageSf6mBk33cJfa/JAaZAk0VRQghECqVxIy8yWl1cckOW7zuvntY8IktkjWkuov7ddt43Kn1IPVtLS0uSdd96R9957T9V6dO3aVfUJ8ff3l6SkJDl27Jh6/plnnpEpU6aoydvb8g8DgQqEhYXlmY/HFy6YTyaF/iwDBgxQQc3x48flueeek3Xr1qkAx/VGoj1rljGWnp6uJg0+G+Tk5KipNOH9cIektN+XqECuCGyq506WZF6/OWCAfuCAGHFJMQpqrkWLS+bNoTTNQrM2TCjQFUDngn5AyGETIlLxRhO0iqGiM669Uck5g0SwPNmd05/zdDm5/cNuNN1yQTCCID3mmHXHv7Ya5I+qjKCkoei0pluVG+SO7meKs+5PyuP81VQZv2SPHLh48ybRg51qyfP9G4mHm6vz/q7KsBw7n/NK+n1LPVhBkHLgwAFVS9G+fXuzy/3777/ywQcfqOVfeuklq3eUu3vej+Th4SHZ2eZHPfr444/1AUezZs2kXr16qnbnzz//1NfSWLOMsenTp+fp46JBDVJWloXhZYsZ9g1qsnAgmwuuiByfr4gPproiIQUslpEirtdjxTU1RlxT4278jRWXG39vTjHiYiGHjQsKhWqQAWTmPHhzvolldS5uklMhWHQVQ9TfHJ8QyfGprP+rM/wfyezY1rvEOM05LydbXJOixP3qCXGLOy5uV0+Iezz+nhSXrNz+A5boxEVy/MMlO6ieZAXVk+yg+pJdqZ5kVapremS+FDTltKLvGTmlracT5KXfTkpiWm45xdvdVV7qW1v6Na4syYkJ9t48ctBzHt7bqYIV1JhYU1PSoUMH+e6771RNjDUqV84dXSQuLk7q1r2ZuyI2NlY13TLH+Ett3ry5+Pr6yqFDh/SBiDXLGHv++edl8uTJeWpWwsPDVVM31CKV9kGMTnB47zJ94SayCtpSh1tcCid1XUZyvkScLup/o1qcFAQ2GQWuz0WXLW6p0SKYLL03apX0AwXc/KszHAlNq7XBIAMMbJz7nIf8R1fP5K0pwf+xxy0G1IZBiUpIq68pudF8q3J9cfHwURd7p+mkSsUuJ0cnn248KTPXHVddnKBWsI/MGdlGDU9Mji3Hzuc844qCYl+/lDJrAhVblsfoXwggtmzZoq+xyczMlO3bt6v+JdaKiYmRa9euSVBQUJGW8fLyUpMxHET2OJBwENvrvYkclpbDBu3xLSbn1HLYGOex0QIaLeiJsZic0yUnM3cUJkyG800t7OZlNAqaib412jwvXxt2gnNyyHMeBqCIP523P0n0EZG447l5jayBpoZBdfKNvOWCPEwGQ30zhzhZK/F6pjy9bK+sPXzzRkvvxlXkg2Etxd+bg5GUFS52POeV9Hs6xI2W1atXq2ZfqE3p16+fREVFyfXr16VBAwsFCAOenp6qE/3MmTPlrrvuUrUYb775pmRkZOhHB4P//e9/qi8JcqRcvHhRDXP85JNPqmg0Pj5ePY9amoEDB6rlrVmGiMpDcs5KuRMKiBZz2Fw1MVCAiYEEVHJOC219cWc98VzuZImHT8EjoRn2tfH0Kdw+IOtlZYjEnzIRlJwQQaBqDRc3keC6eYcCRoASXE/Egx2cqXgcuZwkYxftkjNxqbmHnYvI07c1kPHd64mrK0Necgx2D1ZGjx4tv//+uxr5C80yEKzAoEGDZN++fSoIsRb6iaAZWP369VWNDIKLn376SapVuzky0ZUrV/RNy9A8DCN6oVYGVWhoqoUO/+g4r9WaWLMMEVHeHDYYbSw4N0t3QXKyRVLjjYIZ40SdNyaVIdxCcs7M1NzmRJgs8fSzUFtjEPC4568ldhgJUfmzp+t04paYKJJuom8QRngLtNxM0CoY0S7uZP6gJP6kxdo1PVf33ADEVFDC3EFUglbuvSBTf9gv1zNz+/UG+njIx/e1lq4NCuoUSFT6XHSIEGyA5lYoyCMwKMxzhlDgf+ihh2TXrl0ya9YsFay89tpr6rkhQ4ao4YuHDh1a6G1LSUlRnX0w2phxwiIkeMT74Dnjpl0IPtzczA9Tas0y5iDICQgIkMTERLv0WUGNELbdoZpEEJH1VHLO2AJqawyaphUmy7g1MGStCl4KaI6G5zGvNAvYCFRmR+YGDdZC4DVxV+EClsy03FoRw+SJKig5JaIzP4BLHuinhKZa+YKSusz7Q6UqMztH3vztsCzccvOmRrPq/jJnZKSEB7HGtSzKsXM5r6TLuDbXrHzxxRdqKF9TAQmeu+WWWywGKwhSEJRg5yKoMIyb0EkewwTbomLFimoyJTQ01OT8gjrhF2YZIqKSS84ZljtZ0wwJgYul2hpM6VYk50QCT0zoW2EJmsqZC2YMgx0MCY3PVBSoUSlMoAJYHq8zFaxgiOzY43mzuUcfFrl62nJzPY2bZ+7wv8ZBCTq/Mxkp2Vl0UppMWLpbdpy5eUNjaGQNeWNwM/H2YE4pKkfNwNDPA02wLEETL0Rhphw8eFAGDx5cAltHROTkULsRUD13sqbWQAUwFmpr8DxGT7MEtTqYUNgvkEtukyxLtTUqsAkq3uScGPb34p68SRNVUII7zVY2NnD3vlFTYpg4sZFIpYiiB2FEJWDHmXgZv2S3xCTnBviebq7y2p1NZXj78HytUIgcSaHPqOgXsnLlSjl16pRs2LBBPvvsszzPoxoKSRg///xzi+tCpvpXXnlFJkyYoP+hICcKmoQh6eL8+fMLu3lERFQY6KyN5ICYLMlIzR/M5KutuTEv67qFlelym7VhirZiBCwEMnkGDgjNHV3LFl+YHnLeJPcKuaPF5QlKGuYGJcUZQBGVELRaQZMvNP3KyskNxqsGeMucUZHSKjyQ+52cL1hBMkQEFCtWrFAjbkVGRuqfQ8CBJl3oJF+1alWL60JyxRkzZqjmZOjEjgmjeaWmpqpABZnjiYjIQWAEMc+I3IJ6QdCkN+OamaZnJoZ9tpRLBE2w1PJXRK5IycBIalqTLcOgJLBW7qAJRGVQakaWPP/jflm59+Yw6Z3qBsus4a0l2NeBB84gsjVYmTt3ruqHgozy6PuBfiW9evWSonj00UelT58+8vPPP8ulS5fUeu+88848iR2JiKgMQU25l1/uhA7klgKb9CQzwYzxvGjrh/4tCPqSVI80CE4aigSEMyghp3ImNkXGLt4lRy7fbL75WLc6MqVPQ3F3YwBOThqs7N27V9WeIEBBssWijjiwefNmVZuC2plJkyYVaV1ERFRGAxuMNoYJfUAsBTboD6PV1kTtEFn/euHf8665ItVa2bzJRI5u7aEr8tSyvZKcljuEdkVPN3lvaEvp39xyqxeiMh2soNnW0qVLVfMvdKI/fPiwrF271uSyTZo0yZPfxNxoYEi22KhRIxk1apSMHDlSIiIsNC8gIqLyG9igsz0maSTi5W9bsELkpLJzdPLh2mMya/0J/by6IRVl3ui2Ui/U167bRlQqeVYwcheCCnSsRy6TgixYsEBllLfk5MmTsmTJEjVhqOLOnTur97j33nutGlGsrGCeFSKiYnZxr8j8boV/3aN/sWaFnE5CaoZM+navbDoWo593e/MweeeeluLrxRHqnFmOk+dZKdQnwob88ssvcu3aNfnf//6nAhLEOqYmawIVQN8UjAh29OhR1bSsbdu2KjFkWFiYVSOKEREREZVnBy4kyoBZm/WBiquLyAu3N5JPRrRhoEJlns2h9ssvv2w28aKtEKggOszIyFCjgUVFRRXr+omIiIicyfKdUfLSigOSnpWbuDS4oqfMGtFaOtWtbO9NIyr9YCU9PV0yMzPF29tb1Xzgf9SymIJl3N2tW/2xY8dUXxg0BUP+lq5du8qcOXNk6NChhdk8IiIionIhPStbpv1ySJb+e04/D3lT5oxqI1UDKth124js1gwMyRv9/Pxk4cKF+v/NTVjGEiR+bN++vTRs2FB+/PFHeeSRR+Ts2bOqTwyakaHZGRERkUk+wSLuhcwVgeXxOqIy7FLidRk2b1ueQGVUx5ry3WMdGahQ+e5gj1qP6Oho1c8kOTlZ/W8OlgkJCSlwfWjqhQ72o0ePlmbNmokzYwd7IqISkBAlkhqXZ1aOTqc6euKGlytGEDOEQCUwnF8FlVlbTsbK40v3SFxKhnrs5e4qb97VXO6JZCLt8irHyTvYFypYIdsxWCEiKh8XbqKSgOLa/E2nZMbqI5Jzo+RWo1IFmTsqUppVZ0uU8izHyYMVm/qsWMNcnxXUzmCqU6eO/rE5WEZbjoiIiKg8upaeJVOW75NVBy7r53VrECIf3ddKAn087bptRCWtUMEK+qlYO5ywuTwrX3/9tUybNk1effVV9Rj/m4NlMIwxERERUXl0IvqaPLZop5yMuZnfblLPevJE7wbihjGKiZycTX1WrGFNn5XyhM3AiIjKR5MIouKyav8leWb5PknJyFaP/bzd5cNhraRX4yrcyeQw5zyHagZW3M2yZs+erf5OnDixUM8REREROaus7Bx594+jMm/TzabyjcL8VP+UiMrFm+OOyGmTQhaH2NhYs89dvnxZKlTgOOFERERUfsReS5dJ3+yRLSdvjnI3qFU1mT6kufh42rXYRmQXdjnqz5w5o59g48aNeZ7HsMi//PKLPPvss/bYPCIiIqJSt+fcVRm/ZLdcSkxTj91dXeSlOxrLA50ixMV4GG6icsIuwQoSRhp2rP/qq6/yPO/r6ys9e/aUu+++2w5bR0RERFR60H146fZzMu3nQ5KRnaPmhfh5yacj20i7iCB+FVSu2SXPCjoCYXrnnXfUY8MaFNw5cHNzE2fDDvZEROWjsylRYaRlZsvLKw7I8l3n9fPaRwTJ7BGtJdTfmzuTHP6c51Ad7E05d+6c7N+/Xxo3bqw63yMXC4INUzlWNNiRmF544YWivj0RERFRmRQVnyrjluySAxeS9PMe6hwhL9zeWDzcGGgTFTlYQbAxY8YMFXjMmTNHBSurV69WuVR++OEHk69hUkgiIiIq7/46FiNPfLtHElJzk21X8HCTt+9uLoNaVbf3phE5R7Dy559/yqJFi+S///6TmTNn6ucPGjRIpk6dKkePHpWGDRvmex2TQhIREVF5lZOjk082nJAP1h4TrSF+RLCPzB0dKY3Cir8JDVG5DVbWr18v48aNk6ZNm+YboaJFixayY8cOk8EKMtIbZqVnhnoiIiIqDxKvZ8rTy/bK2sM3E2z3blxFPhjWUvy9Pey6bUROF6ykpqaqjjxgHKxcuXJFfHx8bFrv2bNn5cKFC9K2bVvx9PS0dfOIiIiIHMaRy0kydtEuOROXqh6j6PT0bQ1kfPd64urKYYmJzLG591aXLl3km2++kevXr+cJVn766SfZtm2bdOrUyeI6MBAZmo3t27dPPd6wYYPUr19fOnfuLL1791ajGxARERGVZSv3XpC7PtmiD1QCfTxk4UPtZWLP+gxUiEoqWBkyZIhUr15dNQNDkzD0X7n11lvVfORQCQsLs7iOP/74QwUkLVu2VI/fffdd1d8Fme3j4uJUZ30iIiKisigzO0de+/mgPPHtXrmema3mNavuL79MvFW6NQix9+YROXewghHAVqxYIa+88oq0atVKPY6IiJBVq1bJc889Z9U6MOQxgh3IysqSTZs2yfjx4yU4OFgGDBgghw4dsnXziIiIiOwmOilNRizYJgu3nNHPGxpZQ74f20nCg2xrKk9UHhVp6GLkU3nwwQfVZAv0edm4caP6f82aNVKzZk19jQz6vSB3CxEREVFZsuNMvIxfsltiktPVY083V3ntzqYyvH14vn6+RFSMwQoSPmZm5o4Hbom3t3eBiSFh8ODBKns9OtOfPHlS1dIA+sH8888/Mn369MJsHhEREZHdoC8ualLe/O2wZOXkjktcNcBb5oyKlFbhgfxmiEo6WJkwYYJ8/vnnVi27YMECGTNmTIHLoLnXrl27VHOyGjVqyN13363mHzlyRPV7qVq1amE2j4iIiMguUjOy5Pkf98vKvRf18zrVDZZZw1tLsK8XvxUiG7nocBvASsg+Hx19c2zwgtStW1dCQth5TJOUlCQBAQGSmJgo/v6lm/QJgxjEx8erZnfoW0RE5Mx4zqPSdiY2RcYu3iVHLifr5z3WrY5M6dNQ3N143SXnPucllXAZt1A1K3Xq1FFTce/gLVu2qPwqxk3M0HEfExEREZEjWnvoijy1bK8kp2WpxxU93eS9oS2lf3O2DiGyewd7QKDx119/SUxMjISHh8udd96palWskZaWJt26dZOdO3eqWhjjPi6TJ09msEJEREQOJztHJx+uPSaz1p/Qz6sbUlHmjW4r9UJ97bptRM7E5mAFQw0PHz5cfvjhBzX8cOXKlVXfE3SYf/311+X555+3uI7ly5erqqPz58+zfwoRERGVCQmpGTLp272y6ViMft7tzcPknXtaiq9Xke8DE5EBm39RX375pWzdujVPrhRYuXKlDBs2TOVJad68eYHrQP+XgQMHMlAhIiKiMuHAhUTVP+X81evqsauLyNT+jeSRLnU4LDFRCbC5F85///2nRgczDFRg0KBB0qdPH/W8Jchcf+DAAVs3gYiIiKjULN8ZJXfP2aIPVIIresriMR3k0a51GagQOVrNSu3ateXChQsmn4uLi1PPW9KoUSPVBOzxxx9XQY6vb942nhjOGBMRERGRvaRnZcvrvxySJf+e089D3pQ5o9pI1YAK/GKIHDFYGTVqlLRv315lnR86dKjKmRIVFSUffviheHh4SIcOHSyu47PPPlPNyDDNnj073/OvvvqqvPbaa7ZuIhEREVGRXEq8LuMW75a9UQn6eSM71JRXBjYRL3c37l0iR8qzgiSPxZkUMiMjQ03meHp6qskZMM8KEVH5yDlAzmPLyVh5fOkeiUvJLat4ubvK/w1uJkPbhtt704gc5pznUHlWnn76abnvvvusWrZJkyYWl3GmYISIiIicA+7jzt90SmasPiI5N27p1qhUQeaOipRm1QPsvXlE5UqhgpXGjRurqbitXr1a/v33X9V0rF+/fqo52fXr16VBgwbF/l5ERERE5lxLz5Ipy/fJqgOX9fO6NQiRj+5rJYE+vMFKVNrsPhj46NGj5ffffxc/Pz91JwPBCqDD/b59+1jzQkRERKXiRPQ1eWzRTjkZk6KfN6lnPXmidwNxwxjFRFS2gpUzZ87IBx98IIcPH5bk5OQ8z73wwgsqm31BNm7cKJs3b5bjx4/LrFmzVLAC4eHhqgYHOVvQeZ+IiIioJK3af0meWb5PUjKy1WM/b3f5cFgr6dW4Cnc8kR3Z3Avn8uXL0rp1a7l48aLq1FOlShVp1qyZHDp0SHWaxyhhluzatUuGDBmiOgS5uOS9Y1G3bl0VxBARERGVlKzsHJn++2EZt2S3PlBpFOYnv0y8lYEKUVkOVhYtWiS33367fP/99ypoQSZ6DEW8d+9elTslNDTU4jrQuR4jB5hy8OBBqVy5sq2bR0RERFSg2Gvpcv8X22XeplP6eYNaVZMfx3eSiMoVufeIynKwcurUKencubP639vbW65du6b+r1OnjnTt2lU177Kkf//+8sMPP8iePXv0NSvZ2dkqV8u6detUMERERERU3PacuyoDZ22WLSfj1GN3Vxd5bWAT1fTLx9PuXXqJ6Aabf42ZmZn6zu/oY4KAw7CJmDXjPNerV09mzJghHTt2lAoVKqhp5syZkpqaKvPnz2f2eiIiIipW6B+7dPs5mfbzIcnIzlHzQvy85NORbaRdRBD3NpGDKZZbB/fee6/qEI/hhpEY5sCBA6p2xRqPPvqo9OnTR37++We5dOmSaj6Gjvnos0JERERUXNIys+XlFQdk+a7z+nntI4Jk9ojWEurvzR1NVNYz2BuKi4sTLy8v8fX1VY+RJ+XLL79UdywmTJggLVq0sGmDzp49KxcuXJC2bds61bDFzGBPRFQ+sjmTY4qKT5VxS3bJgQtJ+nkPdY6QF25vLB5uPE6o7Mpx8gz2Nn8i1ITs3r1b/xgJHefOnSvz5s1To3xt2rTJ4joQ2Gj5VGDDhg1Sv3591Remd+/eaucTERERFcVfx2Jk4OzN+kClgoebSvL46sCmDFSIHJzNwco///wjx44dM/kcOtefOHHC4jr++OMPFZC0bNlSPX733Xdl6tSpEhsbq2pukNmeiIiIyBY5OTqZte64PPjldklIzVTzIoJ95KcJnWRQq+rcqUTOGKxkZWVJWlqaGrVL+99wQt4VDF+MvCuW7N+/X5o2bapfL2pjxo8fL8HBwTJgwACVs4WIiIiosBKvZ8qji3bK+38eE63Be+/GVeTnx2+VRmHF31SFiBwkWBk7dqwatWvhwoUybtw4/She2lS9enWVFLJnz54W14W2dQhYYM2aNSqRZFhYmHp85coV5lkhIiKiQjtyOUkGzd4saw9Hq8fIjvBMnwYyf3Sk+Ht7cI8SOXMHezTvwtDEGHIYtSKoAdGvzMVFBSANGjQQNzc3i+tCUy8sW7t2bTl58qS88sor8tRTT6lRxdBBHzUtVatWFWfADvZEROWjsynZ18q9F2TqD/vlemZuNvpAHw/56L7W0q1BCL8acko5Tt7BvtBDFyM3ijahJgUbZys090Jn/BUrVqicKnfffbeaf+TIEZk2bZrTBCpERERUsjKzc+TN3w7Lwi1n9POaVfeXOSMjJTzIh7ufqLwNXQzbt29XtSIhITfvVqDTPWpYMKoX3cSaFSKi8nGXkUpfdFKaTFi6W3acuaqfNzSyhrwxuJl4e1hu6UFUluU4ec2KzZ8IfU3QGd64ZsXb21vuuusuSUlJKY7tIyIiIjJr55l4uWPWZn2g4unmKm/d1VzeuacFAxUiJ2BzsPLdd9/JkCFD8iVuRCf5OnXqWJVnhYiIiMgWaBjy5T+n5b752yQmOV3NqxrgLcvG3iIjOtRUrTyIqOwrdJ8VDWpOzHWixxDGqBIiIiIiKm6pGVny/I/7ZeXei/p5neoGy6zhrSXY14s7nMiJ2Fyz0rVrV1mwYIGcP38+z/z169erWpVbbrmlOLaPiIiISO9MbIoM+XRLnkDlsW515OuH2zNQIXJCNtesDBo0SD777DNp2LCh9O3bV0JDQ9Xww+vWrVNDEKM5mLWQqf7ff/+VDh06SL9+/SQqKkoNX4xhjYmIiIhg7aEr8tSyvZKclqUeV/R0k/eGtpT+zTl6KJGzsrlmBaMNrFy5Uj766CP1+OjRoyqh42+//Savvfaa1esZPXq0jBw5Ur788kvZtm1bnmAIySULa9myZdKnTx9p166dGgAgOjo3IZQ5TzzxhDRr1izP9MgjjxR5vURERFQ8snN08v6aozLm6536QKVuSEVZOfFWBipETs69SC92d5cxY8aoyRYbN26UzZs3y/Hjx2XWrFmqsxyEh4dL48aNVTA0dOhQq9e3aNEitS0zZ85Ur3/jjTeke/fusmfPHvHyMt2G9ezZsyoB5QsvvKCf5+vrW+T1EhERUdElpGbIpG/3yqZjMfp5tzcPk3fuaSm+XkUqxhBRGVDkX/m5c+fUMMYoxGMUsPT0dNXxHoGMJUgIiRHFMC40Ru0wTPlSt25dFcQUxssvvyyTJk1SNR+AIASJJb/55ht58MEHzb4O748aleJeLxEREdnuwIVEGbt4l5y/el09dnURmdq/kTzSpQ5H+yIqJ4qUOQa1EUgKOXjwYNWxXut/MmzYMKtej2GPkUDGlIMHD0rlypWt3pZTp06pWpLbb79dPy84OFj1g9mwYUOBr0XTtbZt20r//v3l/fffl8zMzGJZLxEREdlm+c4ouXvOFn2gElzRUxaP6SCPdq3LQIWoHLG5ZuXPP/9UzaP+++8/1TzKsK/J1KlTVR8WdL4vCIIDdMafMGGC/sSTnZ2tmoSho/78+fOt3h50ygfUeBjCY+05UzAwAPqtdOzYUdXkoBZlzZo1KujCNtm6XtQwYdJoQzkjyyim0oT3Q61Vab8vEZE98JxXtqVnZcsbvx6WpdtvXmNbhQfIJyNaS9WACryWETnYOa+k39fmYAU1KePGjZOmTZvmu8OBZlI7duywGKzUq1dPZsyYoQKFChUqqAmBT2pqqgpUatSoYfX2ZGXldrgzTlKJPiWGNSXGPv30U32TNWxHkyZNVC0LgqXevXvbvN7p06fLtGnT8s2/evWqfp2leRAlJyerAxkDIxAROTOe88quK8np8tzPx+XApRT9vLtbhsrTPWqJZ/Z1iY/PrWUhIsc55+G9HTJYQUCBvh5gHKxcuXJFfHx8rFrPo48+qkbZ+vnnn+XSpUuqpuPOO+9UfVYKA02zIC4uTvWd0eBxQc3JjPvWREZGqg72Bw4cUMGKret9/vnnZfLkyXlqVjBwQKVKlcTf319K+yDGd4T3ZrBCRM6O57yyaevJOJn07SGJS8kdCdTL3VXeGNRU7om0/sYlUXmUY+dynjX91Iu0fltf2KVLF3nrrbdk4sSJeYKVn376SQ1B/O2331pcx1dffaWiQHRSRwf2omjUqJEKkJCvBcMLa03KUMODZl7WQs1HSkqKPqCwdb2oeTE1UhgOInscSPiO7PXeRESljee8sgPlgPmbTsmM1Uck58Y4OzUqVZC5oyKlWfUAe28eUZngYsdyXkm/p81rxyhe1atXV83A0CQM/VduvfVWNR/Nn5BzxRLUTqAGozh4e3vLAw88IB9++KGq2QF0lr927Zrcf//9+uXQP0bLo4KanHfffVfS0tLUYyz72GOPqUBl4MCBhVovERERFc619CyZsHS3TF91M1Dp1iBEfn38VgYqRFS0mhVEUStWrFBBCkbTio2NVc2cXnrpJZWF3hq33Xab3HfffSq4qVixYpG/EgQely9flpo1a0pgYKC6W/Pdd9+pxxqM7KUFJ2jihdHIqlWrppZH8NK6dWvVXyUkJKRQ6yUiIiLrnYi+Jo8t2iknY272T5nUs5480buBuGGMYiIi1BrpDJObFAIyzqNfSdeuXQv1nKG1a9eqZmTomIORwYz7gKDPCKbCQuCUkJAgERER+drRIS8M2vbhOQ0eI4hBf5mCgqaC1msJ+qwEBASo4MgefVbi4+NVHyM2AyMiZ8dznuNbtf+SPLN8n6RkZKvHft7uMvPeVtK7SRV7bxpRmZNj53JeSZdxba5Z+eeff1TfDVMBCbLSIwayFKxcvHhRP+LXmTNn1GQII3PZAkGPuc7vpmpD8MUiX0xR1ktEREQFy8rOkXf/OCrzNp3Sz2sU5qf6p0RULnoLCyJyPoUOVjDsLiYEKvirNanSILLbu3ev6rtiCfp8sN8HERGR84u9li6TvtkjW07G6ecNalVNpg9pLj6eJTuaEBGVXYWuKxo7dqzKh7Jw4UKVZ0XLj6JN6HSfkZEhPXv2LJktJiIiojJlz7mrMnDWZn2g4u7qIq8NbCIfDmvFQIWIClToWxnITo+hhpHMESOBDRgwIM+waWgv16BBA3Fzc7O6nR066a9atUouXLiggh30Xxk9ejT7VxAREZVhaBK+dPs5mfbzIcnIzs1yHeLnJZ+ObCPtInJztRERFWuwgqzz2oSaFHSoKcpJDIEJ+rjccccdKiEjRuQaP368LF26VFavXp0v4SQRERE5vrTMbHl5xQFZvuu8fl77iCCZPaK1hPp723XbiKjssLmRqJZHBaNr7d+/Xxo3bqwyvKenp6taFWtGy/r999/l4MGDcvToUX1Hezh//rx07NhRPY8ghoiIiMqOqPhUGbdklxy4kKSf91DnCHnh9sbi4cbkxERkvSKdMV544QU1itbgwYNVYkhAbciwYcOsev1///0n99xzT55ABfD47rvvVs8TERFR2fHXsRgZOHuzPlCp4OEmH93XSl4d2JSBChGVXrDy559/qr4mCCiQ4V0zaNAgOXTokKotsaRSpUpy7Ngxk8/h9ej/QkRERI4vJ0cns9Ydlwe/3C4JqZlqXkSwj/w0oZMMalXd3ptHROUtWEFNCkYDQyd7434lLVq0kB07dlhcBwKbv//+W8aMGSPbtm1TiRnx9+GHH1Z5XPA8ERERObbE65ny6KKd8v6fx0RLNd27caisnHirNAor3UTIRORcbO6zkpqaqq/5MA5Wrly5Ij4+PhbXUbVqVVmzZo3qUP/555/r57dq1UrN1/rFEBERkWM6cjlJxi7aJWfiUtVjFAmevq2BjO9eT1xdOUgOEdmpZqVLly7yzTffyPXr1/MEKz/99JOqHenUqZNV67nllltkz549Kpv9zp071V88xnwiIiJyXCv3XpC7PtmiD1QCfTxk4UPtZWLP+gxUiMi+NSvIUP/VV1+pZmAY/evIkSMqUSSab7399tuFrhVBLQsmIiIicmyZ2Tny5m+HZeGWM/p5zar7y5yRkRIeZLllBRFRiQcrrq6usmLFCtXJ/rfffpPY2FgJDw+Xl156Sfr162f1ejDUMRJCor9KZmZuhzwNamesraEhIiKikhedlCYTlu6WHWeu6ucNjawhbwxuJt4e1iWEJiIq8WAFUKOCbPaYbJGQkCAdOnRQTb9q1aqVLzeLr68vgxUiIiIHsfNMvIxbsltiktPVY083V3ntzqYyvH04kzgTkf2DlWvXrklaWppVy/r5+YmXl1eByyxbtkwCAwNl9+7dUrFixcJsChEREZUSnU6nmnyh6VdWTu5wX1UDvGXOqEhpFR7I74GIHCNYefLJJ/OM2lWQBQsWqCGJC5KUlCRdu3ZloEJEROSgUjOy5Pkf98vKvRf18zrVDZZZw1tLsG/BNyWJiEo1WHn99ddVwAIYBQyZ6/v27asy1leuXFn1O5k9e7bk5OSoDPSW9OzZUwU0WVlZ+ZqAERERkX2diU2RsYt3yZHLyfp5j3WrI1P6NBR3N5sHFCUislqhIoRq1aqpCebOnSu9e/eWL774Qv98ZGSk3HnnnSpPyrlz51SG+oK0adNGevXqpZZHp3z0UTHUvXt3NREREVHpWnvoijy1bK8kp2WpxxU93eS9oS2lf3OO3ElEpcfm6ozDhw9L8+bN86/Q3V0aNWokhw4dkpYtWxa4DgQ0aC6G12zevDlf7UrNmjUZrBAREZWi7BydfLj2mMxaf0I/r25IRZk3uq3UC817U5GIyGGDlRo1asjixYvlscceU53pNceOHZN169bpm4sV5Mcff5RmzZrJ+vXrxdPT09ZNISIiomKQkJohT3y7V/46FqOfd3vzMHnnnpbi68Xm2kRU+mw+8yBIQY6ViIgI1W8lODhYoqKiVM6UoUOHyq233mr5zd3dpWPHjgxUiIiI7OzAhUTVP+X81evqsauLyNT+jeSRLnU4LDER2Y3NveP8/f1l586dMn36dPUYGeyDgoJUbQlqXKzRuXNn+eOPP1RiSCIiIrKP5Tuj5O45W/SBSnBFT1k8poM82rUuAxUisqsi1emi6dajjz6qJlvExcVJSkqKagqG2hnjDvbowI+JiIiIil96Vra8/sshWfLvOf085E2ZM6qNVA2owF1ORHZn1waoyFxfp04dfc2MsSZNmthhq4iIiJzfpcTrMm7xbtkblaCfN7JDTXllYBPxcnez67YRETlEsHL//feriYiIiErPlpOx8vjSPRKXkqEee7m7yv8NbiZD24bzayAih8KhPYiIiMoJnU4n8zedkhmrj0iOLndejUoVZO6oSGlWPcDem0dElA+DFSIionLgWnqWPPv9Pvl9/2X9vG4NQuSj+1pJoA/TBxCRY2KwQkRE5ORORF+TxxbtlJMxKfp5k3rWkyd6NxA3jFFMRORsQxfD7NmzJTIyUqpUqSKVK1fOM1k7fDERERGVnFX7L8mg2Zv1gYqft7t8dn9bmdynIQMVInLempVly5bJiy++KE8//bTUq1dPXF3zxj3t27cvju0jIiIiG2Rl58i7fxyVeZtO6ec1CvNT/VMiKlfkPiUi5w5WNm/eLM8++6wKWIrizJkz6m9ERESe/4mIiMg2sdfSZdI3e2TLyTj9vEGtqsn0Ic3Fx5MtwImoHDQDQ9OvzMzMIm/AwoUL1WT8PxERERUe8qYMnLVZH6i4u7rIawObyIfDWjFQIaIyx+bbKw8++KD07NlTunbtqiZ3d96pISIisuewxN9sj5LXfj4oGdk5al6In5d8OrKNtIsI4hdDROWrZiU0NFTq168vvXr1Em9v73wTa0iIiIhKR1pmtjz7/X/ywk/79YFKu4hK8tvjtzJQIaIyzebqkAULFsjWrVvl/fffN9nBvmXLlsWxfURERFSAqPhUGbdklxy4kKSf91DnCHnh9sbi4VakQT+JiMpusHL48GGZMmWKTJ48uXi3iIiIiKzy17EYeeLbPZKQmtuHtIKHm7x9d3MZ1Ko69yARle9gBSN2JSQkFO/WEBERkUU5OTr5ZMMJ+WDtMdHpblyXg31k7uhIaRTmzz1IRE7D5mBl2LBh0qNHD+nQoYPqt1KhQoXi3TIiIiLKJ/F6pjy9bK+sPRytn9e7cai8f28rCajgwT1GRE7F5sasr732mpw4cUIGDhwoPj4+4uLikmf67LPPindLiYiIyrkjl5NUNnotUHFxEXmmTwOZP7otAxUicko216wgc/19991n9vkmTZpYtR4Me2zqfyIiIrpp5d4LMvWH/XI9M1s9DvTxkI/uay3dGoRwNxGR07I5WGncuLGaigq5Wkz9T0RERCKZ2Tny1u+H5ct/zuh3R7Pq/jJnZKSEB/lwFxGRU7M5WLl27ZqkpaWZfd7Pz0+8vLxsXT0REVG5F52UJhOW7pYdZ67q98XQyBryxuBm4u3hVu73DxE5P5v7rDz55JMSEhJidlq0aFHxbikREVE5svNMvNwxa7M+UPF0c5W37mou79zTgoEKEZUbNtesvPrqqzJx4sQ8865evSpffPGFXLp0SYYMGVIc20dERFSu6HQ6WbjljLz522HJyskdl7hqgLfMGRUprcID7b15RERlI1gJDw9Xk7Hu3btL69atVcASFBRU1O0jIiIqN1IzsuT5H/fLyr0X9fM61Q2WWcNbS7Avm1YTUfljczMwczBscaNGjeTAgQMWl/3qq69k4cKFxb0JREREZc6Z2BQZ8umWPIHKY93qyNcPt2egQkTlVrEHKwhS1q1bJ9WqVbO4bFxcnFVBDRERkTNbe+iKDJy9WY5cTlaPK3q6yZyRbeT5/o3F3a3YL9VERGWGzWfAp556SipXrpxn8vf3l+bNm6shiLt06WJxHbfddpusWrVKUlJSbN0MIiKiMis7RyfvrzkqY77eKclpWWpe3ZCKsnLirdK/eVV7bx4RUdntszJixAjp0KFDnnkYqrhevXoqYLHGlStXJDs7Wxo0aCD9+/dXAY+h3r17q4mIiMjZJKRmyBPf7pW/jsXo593ePEzeuael+HrZfHkmInIqNp8N27Vrp6aiuHjxotSoUUP9f+bMGTUZatKkSZHWT0RE5IgOXEiUsYt3yfmr19VjVxeRqf0bySNd6qi+n0RElMuut27uv/9+NREREZUXy3dGyUsrDkh6Vo56HFzRU2aNaC2d6uZtXUBEREXsYD979myJjIyUKlWq5Ou/snjxYu5fIiKiG9KzsuXFn/bLlO//0wcqyJvy66RbGagQERV3zcqyZcvkxRdflKefflr1U3F1zRv3tG/f3qr1XL58WV577TX5999/5YEHHpAnn3xSdu/eLf/99588+OCDtm4eERGRw7iUeF3GLd4te6MS9PNGdqgprwxsIl7ubnbdNiIipwxWNm/eLM8++6wKWGx1/fp1NWpY06ZNVe1MQkLuSRyPhw8fLn379pWqVTkaChERlV1bTsbK40v3SFxKhnrs5e4q/ze4mQxtmz+xMhERFVMzMAQXmZmZUhQ///yz6mC/YsUKueWWW/KMKobHeJ6IiKgs0ul0Mu+vkzLqs3/1gUqNShXkh3GdGKgQEZV0zQqaaCGfSteuXdXk7l74VZ0+fVrfXAyjn+DErqlUqZLEx8fbunlERER2cy09S579fp/8vv+yfl7XBiHy0bBWUqmiJ78ZIqKSrlkJDQ2V+vXrS69evcTb2zvftHDhQovrqF69uhw8eFD9bzhUI2ps/vzzT9UXhoiIqCw5EX1NBs3enCdQmdSznnz5YDsGKkREpVWzsmDBAtm6dau8//77JjvYt2zZ0uI6Bg8eLFOnTlUd7GNiYsTT01PWrl0rM2bMkOTkZBkwYICtm0dERFTqVu2/JM8s3ycpGdnqsZ+3u8y8t5X0blKF3wYRUWkGK4cPH5YpU6bI5MmTbV2F+Pn5yZo1a1STsp07d6p5H374obRu3VpWrVolFSpUsHndREREpSUrO0fe/eOozNt0Sj+vUZifzB0VKRGVK/KLICIq7WAlIiJCP3pXUWDkrx07dsi5c+fk0qVLqnlZ7dq1i7xeIiKi0hB7LV0mfbNHtpyM088b1KqaTB/SXHw87Zp7mYiozLP5LDps2DDp0aOHdOjQQfVbKWotSM2aNdVERERUViBvyrjFu+RSYpp67O7qIi/d0Vge6BSRpy8mERGVcrCCfiYnTpyQgQMHmu3TMmbMmHzzf//9dzVZ4/bbb1cTERGRI8Hold9sj5LXfj4oGdm52ehD/Lzk05FtpF1EkL03j4jIadgcrCBz/X333Wf2+SZNmpicn5SUJOfPn7fqPbAsERGRI0nLzJaXVxyQ5btuXsvaRVSST0a0kVB/b7tuGxGRs3HRGSY3oRKDwCsgIEASExPF39+/VPd0Tk6OylkTFBSUb9Q2IiJnU5LnvKj4VBm3ZJccuHDzZtpDnSPkhdsbi4cbz69EVPpy7FzOK+kybqFqVtLT01UOFORRyc7OLjCDPZaxJVFkUf3xxx/y+eefy9WrV6Vdu3by3HPPqR1ojU8++US++eYbGTdunIwcOVI//4UXXpBNmzblWRbrnjlzZrFvPxEROaa/jsXIE9/ukYTU3GtfBQ83efvu5jKoVXV7bxoRkdMqVDQxYcIEFQigP8q2bdvU/+bYo8/KDz/8oJqmTZs2TRo3bixvv/22GhoZ22opcNq+fbvKGRMXFydRUVF5njt06JBUqVJFnnrqKf28wMBAq7eLiIjKrpwcnXyy4YR8sPaYaG0RIoJ9ZO7oSGkUVro15URE5U2hghXUMCAAqVu3rvTs2dNkMKLBMqXdZ+X5559XtSLYTujYsaOEh4fLsmXLZMSIEWZfh2orPP/ZZ5+ZXa5q1apy6623Fmp7iIiobEu8nilPL9sraw9H6+f1bhwq79/bSgIqeNh124iIyoNCBSt16tRRE4SEhOj/LwzUfBTUMd9WZ86ckePHj8unn36aJ8Bo3769/PnnnwUGK48++qjcddddKgAzZ+3atdK7d2/1ufH3oYceYv8PIiInduRykoxdtEvOxKWqxxiJ+OnbGsj47vXE1ZXDEhMRlYYidypJTU2VCxcuqD4shhAoWNtXpDicPXtW/a1ePW/bYTxGwsmCmqsdO3ZMFi1aZHYZfA4EWKipQUD04osvqqZsaHZWUP8eTMa1ROgEhak04f0wjkJpvy8RkT0Uxznv530X5fkfD8j1zNxrW2AFD/lwWEvp2iAEAxerpmFERI4gx87lvJJ+X5uDFXSuv//+++W7775TO8jaPiumREdHy+HDh1WneEONGjVSk7Xbo3XsN4RklRkZGSZfg74oaDqGzvOenp5m1z1v3jz9evv16ydt2rRRTcL++usv6datm8nXTJ8+XfWdMYbPmJWVJaV9ECUnJ6vviaOBEZGzK8o5Lys7Rz76K0q+2X1ZP69RFR95584GUi3ATY24Q0TkSHLsXM7DeztksIL+Hf/++6+sXr1aGjZsKG5ubnmex/Bp1iaXfPPNN/U7WAs6PDw81HNa/xNLtPdDB/natWvr5+OxuW3ByF94XzQD0+BCNHfuXNm8ebP8+uuvJgOgzp07S8WKFWXv3r1mgxUEQZMnT85Ts4L+M5UqVbLL0MXIpIz3ZrBCRM7O1nNedFKaTFy+V3aevXnj7J7I6vL6nU3F2yPvNY6IyFHk2LmcV9Kj/9q89iNHjqjRwfr06WPzm2MErtmzZ6u/K1euVPNQwJ8zZ47qe/L4449bvS6M/oWgYteuXdK2bVv9l4fHY8eONfmaRx55RPr27Ztn3qBBg6R///7y4IMPFhhBovmbj4+P2WW8vLzUZAwHkT0OJBzE9npvIiJHP+ftPBMv45bslpjk3Oa7nm6u8tqdTWV4+3C1LiIiR+Zix3JeSb+nzWtH53rUWhQFambuvfdead26tdrJqFVBrQNyo0RGRsqKFSusXheae6Ffyccff6xG94L58+ermpLRo0frl8O6tSGIa9asqZpzGU6o0alVq5Z06NBBLXPlyhXVpE1rj4d+KE888YQKVAYMGFCkz09ERPaF2vUv/zkt983fpg9UqgZ4y7Kxt8iIDjUZqBAR2ZnNNSsIALp06SKdOnWSXr16qWChsBDshIaGqv8rV64su3fvzjP0MTruFwaSNA4ZMkQ1t0IH/0uXLsmXX36ZZxjlgwcPSlpamtXrROf6ffv2qe1EcIOO/GFhYaqDPd6DiIjKptSMLHn+x/2ycu9F/bxOdYNl1vDWEuybv2aciIjKULDy7LPPqg7qAwcONPl8YTrYA4YYRh+Vv//+WzXnQsf9Dz74oFDbhESN69evl5MnT6qO7Ggahr4lht55550CRy34+eefpVq1avrH2BY0VZsxY4YaNQxBC55nswAiorLrTGyKjF28S45cvtkx9LFudWRKn4bi7sbmskREZT5YefrppwvMl9KkSROL69D6lkC7du1k5MiRqsM6quUHDx6scp/YwlxCSmu2C0GTKQh60FyNiIjKtrWHrshTy/ZKclruyIwVPd3kvaEtpX9z1pYTETkaF52pcYftCJ3X0UwLyRedCUYDQ5My9Kexx2hg6LuDUdHYwZ6InNmFhOuy+XiMxFxNkpBK/nJr/RCpHpjbTDk7Rycfrj0ms9af0C9fN6SizBvdVuqF+tpxq4mIym45L6mEy7iFqllB53J0gkfTKCSB1IYZNgXL2DKUmZ+fn5qIiIistTcqQWatOy7rj0YLbsEhwTzyNmIgr56NQuWhThGy4O/T8texGP1rbm8eJu/c01J8vUp22E0iIrJdoc7QGKr4888/V/1Rtm3bpv43x9o+KwiAVq1apTquGwc/6LyPiYiIyJzVBy7JxKV7BM0EtLYCWoJ5PN5wJFrWHY7WL49AZmr/RvJIlzrsf0hE5EzBChI0IgBBn5CePXsWGIwU1G9Ek5CQoIYIvnjxohou2LgmxtfXl8EKEREVWKOCQAVNvMy1adYCFwjwdpc5oyOlU93K3KtERM4WrCC3CiZAnxLtf1stW7ZMjeCFIYuNR+0iIiKyZNb647k1KlbuqhbhgQxUiIjKELuOz4gOOV27dmWgQkRENnWmX38kWtWqWGvziVj1OiIiKgfBCvKPINN8lSpVVFJHw2nx4sUWX4+mZOvWrZOsrNzhI4mIiKz1z4lYfR8Va2H5LSdiuZOJiMoI96I04XrxxRdVvpV69erlGyrNXL4SQ23atJFevXpJq1atpF+/fqqPiqHu3buriYiIyNi5uBRxKUQTMK1z/bV03iAjInL6YGXz5s0qiz0CFludO3dOjRqGjvVYn3EH+5o1azJYISIiBWnBDl1Kkj8OXJbVBy/LsSvXCr1n0GKMQxUTEZWDYAVNvwrKs2KNH3/8UZo1aybr168XT0/PIq2LiIicT06OTvZEXZXVNwKUqPii9TdB3pVO9TgSGBGR0wcrDz74oOpzgg7ymGxJAInXdOzYkYEKERHpZWbnyL+n4mX1wUuy5uAViU5ON7l3ImtVkqTrmXIy5lqe4YnNcXN1kR4NQ/UZ7YmIyImDldDQUKlfv77qc+Lm5pYvWJk7d64KaArSuXNnmTdvnrz55pvi5eVl66YQEVEZl5aZLX8fj1U1KGsPX5HE65kmg41b6gRL32Zh0qdJFani7y37ohLk7jlbVBOxguIVlxvT4z3rlejnICIiBwlW0Ndk69at8v7775vsYN+yZUuL64iLi5OUlBTVFKxv3775Otj37t1bTURE5HyS0zJlw9EY1Qdlw9FoSc3IzreMp7urdK0fIv2ahUnvxqES6JO3yXDL8ECZPaK1PoO9qWGMEeQgUJk9oo1anoiIykGwcvjwYZkyZYpMnjzZ5jdH5notseSRI0fyPd+kSROb101ERI4nPiVD1h66ovqfbD4eKxnZOfmWqejpJj0bV5F+TcOke8MQqehV8KWqX7Oq8sO4CvLx+uMq7wqGJ0bfFO0vmn6hRoWBChFROQpWIiIiJCEhoUhvfv/996uJiIic16XE66rvyaoDl2T76XiT/Usq+XjIbU2qqBqUTnUri7eHW6HeA4HI5w+0Uwkf/zkeI9FXkyS0kr90rh/CPipEROUxWBk2bJj06NFDOnTooPqtVKhgW4fFqKgo1V8FfWCys7Nl/vz5cvr0adXfhTUrRERl0+nYFP0IXuhXYkqYv7cKTvo0rSLtI4LE3a1IeYoVdJ6/J7KGxMfHS1BQUL4mykREVE6Clddee01OnDghAwcONNunZcyYMQWuAzUzt99+u/z555/q8ezZs9V60Yfls88+U0FLQECArZtIRESlBB3cD19KVsEJ+qAcvZJscrmIYB/VbAtBSovqAeKKLI1ERETFHawgc/19991n9nlrakV+/fVXadu2rYSFhanHixYtki+++ELuuusuufPOO2XlypVsJkZE5NA5UBLkj4OXVS3KufhUk8s1ruqv+p8gQGlQxVdc0JGEiIioJIOVxo0bq6ko0ME+JCRE/X/16lU5ePCgGhVMW//ly5eLtH4iIir+HCjod4LgBEGKuRwobWoGquCkb9MwqRVckV8DERGVbrBSHBo2bCgLFy6USZMmyZdffim33HKL+Pj4qOeOHTtmMU8LERGVTg4UjNyFJl7IgZKQajoHSsc6QaoG5bYmYRIW4M2vhoiIynawMmDAAPn4448lPDxcKlasKL/99puaf+bMGTWUcf/+/e25eURE5da19CzZcCRaBSgbj0RLitkcKJVV7UnvxlWkUsW8OVCIiIjKdLCCzPfr1q1TwUmlSpX0nek9PT3l999/V3+JiKgUc6AcvqI6yP9dQA6UHo1CVROv7g1DxddCDhQiIqKiKPWrDPqpVK1aNU8HS+RsMVStWjX96DJYvnr16qW9mURE5cLlxDRZcyi3g/y/p+NNZoAPRA4UJGlsFiad6xU+BwoREVGZCVa+/fZbWbJkiUyZMkWN+oUcK8ZSU1Plxx9/lPfff19Gjx4tkydPLu3NJCJyWmeQA+XGCF57zeRAqeLvpZp3oQ9K+9rFkwOFiIjI4YMVBB6oKXnllVdUHpb27dtLvXr1xN/fX5KSkuTo0aOyY8cOqVmzpkybNk0lnyQiItuhlvrI5WT9CF7435RaKgdK7gherWoEMgcKERHZnYsOVzE7wNuuX79eVq9erYYsxtDF6LeChJD9+vWTHj16ONVY/AjE0CcnMTFRBWalKScnh9mcicphDpS95xNU/xPUopyNM50DpVGYnwpQMDWs4ucU512e84ioPMmxczmvpMu4dusZiQtir1691EREREWXpeVAQRb5g5flSpLpHCitkQOlaW4NSkRl5kAhIiLHxWFciIjKeA6Uf07EqiZeGMnrqpkcKB1qB6nakz7MgUJERGUIgxUiojKYA2Xj0WgVoGwwlwPFzVW6IAdKs9wcKEHMgUJERGUQgxUiojLgqpYD5eBl2YQcKFn5c6D4aDlQmoapv8yBQkREZR2DFSIiB3UlKU3WYIjhg5dl2ynzOVBQc4IA5db6zIFCRETOhcEKEZEDORuXopp3IUDZc850DpRQP6/cEbyYA4WIiJwcgxUiIjvCMO5Hr+TmQMFkLgdKzSAf6Y8O8k3DpHU4c6AQEVH5wGCFiMgOOVD2nU/IHWL4wGU5U0AOFJVFvlmY+t8ZcqAQEREVBoMVIqLSyoFyJl4FJ38cvCKXk9JMLtcqPFCfRb42c6AQEVE5x2CFiKiEpGfdzIHy5yHTOVBcXUQ61A7OzYHStIpUDajA74OIiOgGBitERMUoReVAiVFNvJADBTlRTOVAwchd6CDfuwlzoBAREZnDYIWIqIgSUpEDJTdJ46bjMeZzoDQMVUkaezQMET9vD+53IiIiCxisEBHZIDopTf44dEX1Qdl6Ks5kDpSACjdyoDQLU9nkvT3cuK+JiIgKgcEKEZGVzsWlyuqDl1QNyu4CcqBoI3i1rx0kHm6u3L9EREQ2YrBCRFRADpRjV67pkzQevpRkcrnwoArSv1lVFaQwBwoREVHxYbBCRGQUoOw7n6gClD8OXpbTsSkm90/DKn6q/wk6yTeuyhwoREREJYHBChGVe8iBsuPMVRWcYLqUaDoHSkvkQGmKHChVpE6Ib7nfb0RERCWNwQoRldscKFtOxOXmQDl8ReJTMkzmQEG/EwQofZqGSbVA5kAhIiIqTQxWiKhc5UD561iMClDWF5ADpXO93CSNGMkr2NfLLttKREREDFaIqBzkQFmHHCgHL8umYzGSbiIHSgUPN+nRKER1kO/RKFT8mQOFiIjIIbBmhYicNgfKmoOXZevJOMkykwOlV+NQ1cSra4MQ5kAhIiJyQAxWiMgpRMWn6ocY3n3uqujyxycSonKgVJF+TatKhzrMgUJEROToGKwQUZkdYvh49I0cKAcuyyEzOVBqVEIOlNwkja3DK4kres0TERFRmcBghYjKVIDyH3KgYIjhA5fllJkcKA2q+OYOMdwsTJpU9RcXFwYoREREZRGDFSJyaNk5OtlxJl7VnqAPykVzOVBqBKjgBJ3k6zIHChERkVNgsEJEjpkD5WScqj3589AViTOTA6VdRJBq3oUcKNWZA4WIiMjpMFghIoeQmpElfx2NUU281h+OlmQTOVA83Fykc73KqolX7yZVpDJzoBARETk1BitEZDeJqZmy7sgV1cTrrwJyoHRvGKJqUJgDhYiIqHxhsEJEpSo6OU3WHLwifxSQA8Xf213VnKD/Sdf6IVLB043fEhERUTnEYIWISiUHCoIT1KDsMpMDBU26VA6UZmHSsU6weLi58pshIiIq5xisEFGJDDF8QsuBcvCyHLxoPgcK+p+oHCg1K4kbc6AQERGRAQYrRFRsAcr+C4n6AOVUjOkcKPVDfVVwgiZeTasxBwoRERGZx2CFiIqUA2UncqDcSNJoLgdKC+RAQZLGpmFSL9SXe5yIiIiswmCFiAolIytHtpyMVX1Q0FHeVA4UFy0HSlPkQKkiNSr5cC8TERFRoTFYISKrcqBsOhajmnitKyAHSqe6lVUTr96Nq0iInxf3LBERERUJgxUiMinxeqasN8iBkpaZPweKt4erdG8Qqs+BElDBg3uTiIiIig2DFSLKkwPlz0O5AYq5HCh+3u5yW+Mq0qdpmHRrwBwoREREVHKcLljZvHmzfPHFF3L16lVp166dPPHEE1KxYkWrXvvll1/Kd999Jw8//LDce++9xbZeorKQAwXTzrPmcqB4quAEfVCQA8XTnTlQiIiIqOQ5VbDy22+/yeDBg2XKlCnSo0cPef/999W8TZs2iZtbwRmw9+7dK9OmTZP4+Hjp3r17sa2XyBGdiE7WDzF84ILpHCjVAyuo5l2Y2jAHChEREdmBUwUrCCbGjBkjb731lnqMoCMiIkJ+/PFHGTp0qNnXpaSkyPDhw2XOnDny0EMPFdt6iRwpBwqCktUHL6kg5aSZHCgYVlhL0sgcKERERGRvThOsnDt3Tg4fPiwzZ87UzwsPD1dNtlavXl1gUDFhwgTp3bu39O/fv1jXS2TvHCi7zl5VwQmaeF1IuG5yuebVA24kaawi9UL9Sn07iYiIiJw+WDlz5ow+kDBUo0YN/XOmLFmyRHbs2CE7d+4s1vWmp6erSZOUlNvUJicnR02lCe+HO+ul/b5knxwo207FyR8Hr8iaQ+ZzoLStVUkFJ32bhEn1ShX0z/EYIWfAcx4RlSc5di7nlfT7Ok2wkpGRWyirUOFmwQt8fHzkypUrJl9z4sQJ1VF+7dq1+V5XlPXC9OnTVR8YY+ign5WVP0dFSR9EycnJ6kB2dWXHaGeTlpktW88kyvrj8fL3yQS5lp6dbxk3VxdpX9NfetQPkm71KklwxRtDDOuuS3y86RoXorKK5zwiKk9y7FzOw3uXJKcJVgIDA9VfdJCvXbu2fn5cXJxUqlTJ5Gu++uor9aVOnTo1TzCBUcH27NmjRgazZb3w/PPPy+TJk/PUrKB2Bq/x9/eX0j6IXVxc1HszWHEOSciBcjRa1aAUlAOla/0Q6de0ivRsFCr+zIFC5QTPeURUnuTYuZzn7l6y4YTTBCuNGzcWT09PFWRERkaqeYgw9+3bZ7LTPNx///3SuXPnPPO2b9+u5o0aNcrm9YKXl5eajOEgsseBhIPYXu9NxSMmOT03B8pB5ECJlcxs0zlQkD2+L3OgUDnHcx4RlScudiznlfR7Ok2wgpwn99xzj8yePVuN7IXHX3/9tVy+fFlGjhypX+7VV19VTbvQTKt+/fpqMoTApEGDBtKzZ89CrZeoJJy/ihwoV+SPA5dlx9l4szlQbmuSO4LXLcyBQkRERE7EaYIV+Oijj+SOO+5QzbXQ5OrIkSNqOOJGjRrpl9m1a5ekpaUV+3qJisuJ6Gtq9C6M4rX/QqLZHCioPUGAElmrkuqTQkRERORsnCpYqVy5smzbtk3+++8/1fekRYsWEhQUlGeZ119/vcBRC7755huVQ6Ww6yWyFZoVHryYpE/SiGDFlDohFaU/kjQ2rSrNqvurKl8iIiIiZ+ZUwQqgANeyZUuzz7dp06bA1yNDvS3rJSpsDpTd53JzoGAylwMFQYmWpJE5UIiIiKi8cbpghcjRc6Cg9mTNwSsSe+1mHp78OVCQpDFMwoN87LKtRERERI6AwQpRCbqekS2bjseoDvJrD1+RpLT8OXbcXV3klrrBqvbktiZVJNTPm98JEREREYMVouKXlJYpG45Eq+ZdG4/GyPXM/EkavdxdpVuDEBWg9GpURQJ8biRpJCIiIiI91qwQFQM06Vp7IwfKPyfM5EDxcpdejUNVgNK1QYj4ePLnR0RERFQQlpaIbIRO8WjehQBl55l4yTGRAyW4oqf0aZqbpLFT3cri6c6knERERETWYrBCVAgnY66p5l3Ig/LfedM5UKoFeEtfNcRwmLSNCGIOFCIiIiIbMVghsiIHipak8bi5HCiVK6rmXZiaVw9gDhQiIiKiYsBghchIjmEOlIOX5fxV0zlQmlYzzIHiywCFiIiIqJgxWCESkczsGzlQDlyWNYeuSEyy6RwokTUrqeCEOVCIiIiISh6DFSq30jKzZdOxGFV7gpG8CsqBguCkD3Kg+DMHChEREVFpYbBC5TIHCvqgbDhiPgcKhhZGEy8MNRzo42mXbSUiIiIq7xiskNOLu5Yuf1qRA6UncqA0DZNuDZkDhYiIiMgRMFghp3QROVBujOC1w0wOlCDkQGlSRQ0z3KlusHi5u9ljU4mIiIjIDAYr5DROIQfKwcsqUeM+MzlQqiIHyo0RvNrWqiTubkzSSEREROSoGKxQmc6BcuhSkj6L/LErpnOg1NZyoDQNkxY1mAOFiIiIqKxgsEJlLgfKnqibOVCi4k3nQGlS1V+fpLE+c6AQERERlUkMVqhM5ED591S8rD54Sf44aDoHCkTWqqRqT9DMq2awT6lvJxEREREVLwYr5LA5UP4+HqtqUNYeviKJ1zPzLeOGHCh1glUH+b7MgUJERETkdBiskMNITsuU9UeiZc3BK7LhaLSkZpjOgdKlfohq3tWbOVCIiIiInBqDFbJ7DhTUnKAG5Z8TcZKRnZNvGV/kQGkUqgKUbg1CpKIXD1siIiKi8oClPip1lxKv60fw2n7adA6USj4e0qdJbgf5TvWYA4WIiIioPGKwQqXidGyKfgSvfVEJJpcJ8/dWwQk6yLeLYA4UIiIiovKOwQqVWA6Uw5eS9Ukaj15JNrlcRLCP9GtWVQUpLaoHiKurC78RIiIiIlIYrFAx50BJkD8OXla1KOfiU00u1xg5UG5kkW9QxVdcXBigEBEREVF+DFaoyDlQ0O8EwQmClGgzOVDa1AzUN/GqFVyRe52IiIiILGKwQjblQNmMHCgHc3OgJKSazoHSsU6QqkHp0zRMqvh7c08TERERUaEwWCGrc6BsOBqjak82HDGdA8XT3VW61g+Rvk2rSO/GVaRSRU/uXSIiIiKyGYMVJ3ch4bpsPh4jMVeTJKRSqtxaP0SqB1aw6rXxKRmy9tAVVYOCmhRTOVAqerpJz8ZVVA1Kt4YhKicKEREREVFxYMnSSe2NSpBZ647L+qPRotOJYJAt5DNBX3YkWJzUs760DA80mQMFGeTRB+Xf03Fmc6Dc1qRKbg6UupXF28OtdD4UEREREZUrDFac0OoDl2Ti0j2COAOBCmhBBx5vPBojfx2NkdkjWqthg88gB8qNEbwQ5JjLgYLmXX2bhUn7iCBxd3MtxU9EREREROURgxUng2ADgUp2jk4FK6bgORi/ZLeEB/nI2TjTQwzXUjlQwlQTr5Y1ApkDhYiIiIhKFYMVJzNr/fHcGhUrlkXMYhyoNArzyw1QmoVJwyp+zIFCRERERHbDYMXJOtOvP5LbR6UwmlbzlztbVlM5UCIqMwcKERERETkGBitO5J8TsYUOVODBThEytG14SWwSEREREZHN2EvaiaSkZ6lRvwoDy19LzyqpTSIiIiIishmDFSdS0cvd5FDDBcHyzI1CRERERI6IwYoT6VyvssqjUhhYvlO9yiW1SURERERENmOw4kSQmR4JH92sbAuG5Xo1qmJ1RnsiIiIiotLEYMXJIDM9QhVL4Yq2zOM965XSlhERERERFQ6DFSfTMjxQZaZHrYm5Ghbtudkj2qjliYiIiIgcEYMVJ9SvWVX5YVwn6d4wRN+HxfBvj4ah6nkkfiQiIiIiclTMs+KkUGPy+QPtVKLIf47HSPTVJAmt5C+d64ewjwoRERERlQkMVpwcOs/fE1lD4uPjJSgoSFxdWZlGRERERGUDS65EREREROSQGKwQEREREZFDYrBCREREREQOicEKERERERE5JAYrRERERETkkBisEBERERGRQ2KwQkREREREDol5VkqJTqdTf5OSkqS05eTkSHJysri7uzPPChE5PZ7ziKg8ybFzOU8r22pl3eLGYKWU4CCC8PDw0npLIiIiIqJSK+sGBAQU+3pddCUVBlG+qPfixYvi5+cnLi4upR7xIkiKiooSf39/fjNE5NR4ziOi8iTJzuU8hBIIVKpVq1YiNTusWSkl+PJq1Kgh9oQDmMEKEZUXPOcRUXnib8dyXknUqGjYwZ6IiIiIiBwSgxUiIiIiInJIDFbKAS8vL3n11VfVXyIiZ8dzHhGVJ15OXs5jB3siIiIiInJIrFkhIiIiIiKHxGCFiIiIiIgcEoMVIiIiIiJySAxWiIiIiIjIITFYKcO2bt0q999/v9x2222ycuVKe28OEVGpW7NmjYwaNYp7noic3qhRo2TVqlVS3jBYKaOSkpKkX79+UqdOHXnuueckMjKyyOtcsmSJTJo0qVi2j4ioNFy8eFE2b95cajv7xx9/lEceeaTU3o+ISINz3YULF6S8cbf3BpBtDh8+rAKWqVOnire3d7HsxtOnT8v27dv5lRARmXHu3DlVq01ERKWDwUoZtHz5cnn77bfV/3fccYe4uLiou33btm2Td955R8339fWVpk2bylNPPSWVK1fWvzYlJUU++eQT+ffff9UygwYNkiFDhsivv/4qCxculJiYGOndu7da9tlnn5U+ffrY6VMSUVn07bffyt9//y0DBgyQFStWyKVLl6Rnz56q1nb16tWqBjc9PV2dd0aMGJHntUePHpWPP/5Y3TipUaOGjB07Vtq0aZNnmV27dqllcC5r3769BAYG5tuGy5cvy6xZs+S///6TkJAQufvuu9W50ngb7733Xvnss8/kypUratv++ecfmTZtmlqmYsWK0rhxY3nyySclLCxMzVu3bp3MmTNH3dnUzpOPP/64Oo9aek8iImvgHDJ37lw5f/681K9fX507a9WqZXJZnU6nym6///67Oq926dJFJk6cKBUqVFDPf/3112p97733nnqMst+LL76Yp3z3+uuvS1BQkHqdo2IzsDKoQ4cO+os8moChdgUHZvPmzdX/mNCX5ciRI9KuXTu5fv26/rUPPvig/PDDD+oijWZkuGjjYG7durU6yKtXr65fB9ZHRFQYZ86ckS+++EIV+hGk4DyDi2OPHj1k+vTpMnjwYOnatas8/PDD6iaJ5vjx49K2bVu5du2ajBkzRjw9PaVTp07q4qo5dOiQOk+hNhnnQNQwP/3003nePyoqSgU4cXFx8r///U+dA7E+BBKG24gLPAKRvn37qgs3bvo0bNhQf/576KGHVC0K1oVabMANoF69eqlgRFsOTXCteU8iIktwzuncubO6mfzoo49KzZo15c4775ScnByTyz/zzDPq/IXzEsp1OPcOHDhQ/zxuVs+bN0+ysrLU419++UU2bdokP/30k36ZBQsWmLzp41B0VCZt2LBBh68vMzPT7DLZ2dm6OnXq6JYsWaKfFxwcrPv555/zLJeUlKT+vvHGG7oOHTqU4FYTkbObPn26rkKFCrrY2Fj9vIcffljn5+enS0hI0M+79957dQ899JD+8fDhw3U9evTIs66hQ4fqevbsmWeZ/v3751mma9euulq1aukf33///bqRI0fmWebHH3/UBQYG6nJycvTb6OHhoTt//rzFz9O8eXPdvHnz9I9nzpypa9q0aZ5lrHlPIiJLli9frqtWrVqeecnJyfrzCM51CxYsUP+fPXtW5+7urvvtt9/0y548eVLn5uam+/333/XlOzzeunWrety5c2fdI488omvYsKF6fPz4cVWWjIqKcugvh83AnAgibzQRw0gRaJKASPrq1aty4sQJ/TKI2F9++WV19xJ3OtG8wc/Pz67bTUTOpV69ehIcHKx/jCZdjRo1koCAgDzzUDOiQQ3KhAkT8qwHdxTHjRunf4ymrmjaaghNrT799FP9YzTV8vf3VzU6aCKBCU3GEhISVGd81B4DBifR/tdgWTRdw91HNF/LzMxU51LDc6gp1r4nEVFBUEMbHx8v48ePl+HDh6uWNL6+viaXRZNYQO2wBue1Zs2aqfNp//79VfkO69y4caO0bNlSduzYIYsWLVItanCOw/y6deuq87EjY7DiRBCEfPXVV6paEAesj4+PaiKRmpqqXwbNvubPny9ffvmlGtEGBy/abKNtNhFRcUATLkNoYmVqnmHTBhTsUeA3hOAGhf6MjAz1+sTExHw3V4wfo8kW+osYNoXQoF22xlQBAH0BP/zwQ9WsAs3VcA596aWX8pxDTbH2PYmICoKy286dO1U5DTdqzp49q5qUvv/+++LqmrfnBs6Z6Fvn5uaW77yJ5zTdu3eXDRs2qGa2CExq166t+vshUMGE5x0dgxUngkAEHaXQFhxwdw81K4bQt+WJJ55QEy7AQ4cOlcmTJ6vaGOMfAhFRaV6kjx07lq/DPWoltEAnIiJC9W0xZPwYF2Kc27QO8IU9h6J/jeEQ7uhAb8jUebIo70lEZAh94z766CP1/759+1RggYACA3kYnzNxAyc6OlpCQ0PVPNwAQk0wBjDR4LWofW7VqpU+MNECmL/++kv1JXR0LJ06EUTYuLhrcIcQHT81OIgRnWPECMBdQ3S+QlAD6DSKJg9ERKXtgQceUDW+p06dUo9xLsLIhRgURDN69GjVgVQ7r2FZ1CYbwghiaOKwfv16/TzcZZw9e3ahz6HoeIqBSgzhPInCgWGtUFHek4hIg3MIRiXU4GaNh4eHvpxmCAOQYLSwV155Rf88ghKce3AjWoNBSdLS0tT5DM3/tWDl+++/VyOOsWaFStVbb72l2jj+8ccfKiBBMwvD5l14jDaKaJuIqkDUuqB5hTYqBJow4KBHVF+1alUOXUxEpQYFfrSzbtGihToHIUjAqGEYcUuDZhHoH9KkSRN1bsPIObgQ7927N88yOM9h6GTUeKBWBsEFakwseeONN1RzLoyWg2AE50c0lTWEfimoocY24FyKmpeivCcRkQZlL4wChnMbRgI7cOCAqlHBucUYghj0P8E5a82aNappK5qNff7551KtWjX9clq/FfRX6datmz7QQW1wWeivAi7oZW/vjaDCQ6CBzlUYrg5BiOF8DO+JgxOdrFCFiP/R4VWDzvUHDx7U5xEwbO+IYY7xHKoWcTHGD4eIyFq4WCJvCZouaFADgk6jaDNt2HwL/VHQNMEQLtIYWhh3FHEhNQXnOFxocf5CfxGsH4OHGEpOTlYXepzn0LnfsM+MqW3U4NyHcyBqnjF8O/738vJSwxprcDMI68YdTMzXLvYFvScRkbVwHkStB5q+VjMIPFDrghsihvMwEAjKetrNFZx/TJ0zY2Nj1Q0gDZLb4jxnfEPGETFYISIiIiIih8Q+K0RERERE5JAYrBARERERkUNisEJERERERA6JwQoRERERETkkBislCBniMUKDucdFXb40YRhP5DswzjlARKTBML4Yftjc46IuX9oc6RxMRI7nmWeeUUOdm3tc1OVL2+MOdg7WMFgpQRj/2jApo/Hjoi5f2sEKkq8VNmkkMqm+8MILKtDBkMmm7NmzR+V0GT9+vHzzzTcmkx8RkePD7/f06dNmHxd1+dJmyzkYwy6//PLL6pyHoUILgqRsWA45Eoio7MFv+NixY2YfF3X50vZNIc/BGC556dKl8uSTT6qy3tq1a00uhxvdyDuFnFTIA5OVlVWo7WKw4sCQbdQ4B0FZMmHCBOnfv7868BHoIIOqsV9++UXlOkDOhPDwcHn66afzZKwmovIDGd87dOggZRVuuvTs2VN/zjN3gwYOHz4skydPlu+++07lQCCi8ue9997TJ2osaxJv5OP7/fffVT4Yd3d3GTp0qEyaNCnPclu2bJHWrVvLhQsXpE6dOjJ9+nSV5LIwN6YZrNgIVXcoVD/88MMq6zsygxY3VA3GxcWp/2fMmCGLFy/Ot8xzzz0nv/76a56EQbhgoioP0SuSBZnz5Zdfyvz58/WPN2/erD6T4YXz7bfflpUrV5p8/fbt2+Whhx5SSdBMQU0J7hg88MADJp/HgYqDeuLEiaqQ8vzzz8vy5cvl66+/dshqSKLy7KWXXlLnB/zm8T8uQMUN5y9kfocPP/xQPvvss3zL4HyLu5EanHuR5R7nkXnz5qmEjebgDuDHH3+sf7xz5071mXbv3q2fh/dFAGHKf//9pz4/EvKagnWhNnns2LEFfk7cuBk2bJh6LyScJCLH88Ybb+jPeag12LhxY7G/x7Zt2+TSpUvq/7lz58onn3ySb5m33npLlixZkqc1CrYHN4Q//fRTlczbnB9//FHeffdd/eP9+/erz4SEkJo5c+aocpe5GhGUc02d73Huwj5B2RQ1K6+//ro6Z8+aNStPjfRTTz0lgwcPloULF8qUKVNk1apVqjb5559/FmsxWLFRx44dpXv37nLrrbeqAwWZ5FHQLqkmCK6urvLqq6/mef7o0aPyzjvv6LMnI6C55557VPbSBg0aqGAEEbu56jZcMD/44IM81X94T+0Ays7OVhEwomVjqOq77bbb5JZbbpFmzZqZXH/Tpk3FxcXF7OdDZmg0mcBFW4Ms1DVr1lQ1LkTkONq1a6fOeV26dFHnhttvv11dfEqqCQIuhAhM8F4aZHR+8803JSwsTD3GTY477rhDPDw8VMb4ZcuWqXOSuYAF68I5U4NzNs55uKBrcB41BTdz8PlbtGghkZGRJpfBXUacqy3BxRs1ykOGDLG4LBHZR5s2bdRvHlnf3dzc1O8V55ziZNgMDGU33AgyPH+hKelrr70mwcHB6jHKdSh7aecblJVwPkpJSTG5fpTBUI5DU35YsWKFOucZ3pBB7Y6pG9u4EYTzfe3ataVTp075nvf29taXPzX16tVTf2NiYvR/cWPbsJxXv359adu2beHKeToqFrNnz9Y1a9YszzwvLy/dL7/8YvaxsYKWj4qK0rm6uuq2bNmif/7ll1/WNW3aVP1//Phxnbu7u+7w4cP659PS0nQ1a9bULVq0yOT7HTp0CHVwuosXL6rHTZo00Q0bNkzXp08f9Xj79u06Nzc3XWJioi4zM1Mtu2HDBt2yZct0fn5+6q81Vq1apV4bExOTZ/5PP/2k5l+6dCnP/C5duujuu+8+q9ZNRPaxcOFCXURERJ55wcHBum+++cbsY2MFLR8bG6vz8PDQrVmzRv/8jBkz1Hvm5OTozp8/r57fuXOn/nmcpxo2bKibM2eOyffDeRTnnGPHjqnH7du3V+e8Tp065Tknauck7RyMCee8r776yqp98/fff6v1nD59Ot9zP/zwg65evXq65ORk9TggIEA3c+ZMq9ZLRPbz/fff6ypXrpxnXq1atXQLFiww+9hYQcvjnODj46P78ccf9c9/8sknuipVquiysrLUObFChQq6jRs36p/Pzs7WtWrVSvfuu++afL+4uDidi4uLbvfu3epxz5491TmvRYsWec6JJ06cyHMOXrt2rTo3mTuXmjNu3DhdaGioLj09XT3eunWrWv+ePXvyLDd06FBd9+7drV5v/lvmZHVbPVTLoXYD/1+5ckVFx4herbmzVliIXlFLgvfEnUOtScOYMWPU/6tXrxZPT08VIWvtAPEX27N3714ZNWpUvnU2btxYqlSpoqrxevfurZovINpGrRGibMxHO0N/f3997QyalqHmBXci8Zqi0KoucTfBkJ+fX4HVmkRU+tD/Aucf9LXAOQ9NVM+ePatqaHGHrbjhTmK/fv3Ue2p3EvH/yJEj1d3CP//8U9+EQTvfYcJdSZzzzJ1H0WYa57aqVauq5hSoHULfQNyZxHzU0Gg1N/Dtt9+qcx6aOtx5551F+kznzp1TTcRwR9HX17dI6yKikoW+tKjtRVP3hIQEdd5DTQfOfVpNR3HCOWHQoEHqPHfXXXepefj/vvvuUzU7GzZsUOdb1Ixg0s55OHeZO+cFBQVJ8+bN1bkNrV3Q/As1Jijb4XNgnTgv1q1bV/8aNP1H9wI06TKsEbEETcnQtQC1NyiPWirn4XxoLQYrNrh69aqqHkTVGJpCtGzZUo4fP64CBoyMUBIXbsBFGv060M4Zba1PnTolI0aMUM/hoMOXj2ZphlB92bBhQ7PrRACEgxVNvVCViCZdlStXVgczDm5UgRrCgY4LOZpCFBWCIMBJANuuiY+PV83YiMhxAhVU2+PcgIspfv9ooopCN54ryXMehg9GQILzHfqMaM0XcM7z8fExec4zvPAawzlNu0AjMMFNGzRdQH8ZU+c8tClHwcRc06/CQHt0XMTxebQgCwUi3HjChduwWS4R2Q+CAtwYxrkNzb9QNkKTpp9++kmSk5NLJFjRznl33323CoxQFkJfkY8++kh/zkPzWFPnPDSft3TOwzkc5TcELQhW/vrrL5PnPJT/UD5DU1VroUktbp5jYCh0njdVzjOEzxYQEGD1+hms2ABBCe7eod+GVotiqvN7cUN/FHQiRcckdFAyPEBx4cWXj5EYjCPYguAgnTlzpmrz3aNHD30As27dOtVGG53kDaEzFzpPYcQbHPwhISE2fx5E+1rfFYwEBqjBQW0V23ITOQ781jFsOTpn4lwBuHtW0lCTgWAFQRFqQRAwIMDQznlJSUkycODAQhUccM7DwCQ452jnPO1ijkFNcDPI0Pvvv6+CCSyDC3v16tVt/jw4PyM4MoR+NgiWtBpzIrI/3JhFWQQ3pytUqKDmmRuWtzj17dtXFfB/+OEH1fEeN5vbtm2rP+chiEIfaa3MZA2cu1DrgfOn4TkP5zNM6KxvCB3lUXOtLYMb85b63aD1DgYIwGAEhrD9uGagnId+jxo8xrnbWuxgbwM0QUCwgjtigCo44wtcSUAUis6kGBITFzjDpl2424kfFGpeDIeDwwUezTbMwcGIWiE06zI8iBGU4HOhc5Uh3BXEjwgHLwIWrROVLRBoodMWOqxpnb/wg8KdWgRmROQ45zw0DdU6ceKCiUJ8ScM5DTcucDMIzTEMz3kYFh1BCkY/NOyEjyYbqIExB+c5FAJwrjE856E5GAIy42FE0fwCTTFwJxLLX7x40ebPg0IHRuIxnHBOxR1MBDJE5DjnPJxXUIsCaDVjOKpWSUErl3vvvVedczAZnvN69eqlbpbgnGfYIR5dEMyNUAg4p6GmBsGE4TkPQQZqrI1rVnATHk3+sSwmDIRkDsqOqA1CTTFGDTPVtA1BCd4b+xAw1DHKncOHD7d+v1i9JOW524fRFdD8C83BMMyu8YgIJQUHLtoyoirQsECP5hk4aNAsDNE/qvkwqg4OOrS5Nkfrt4JaGW20BxycuGjjwqpV4ZkKWFCIQMCyfv16kzUsaK6BGiCMrQ0YThnbjdoh7U4BfhDo+4KCANqQ484mqjwtRfJEVHr69OmjmkGgbwcK1mgmgN9raZ3z0GcFQYPhxQ3nJtTu4MKO8xjOx2hKpSUpM0frt4LzI2qnARdr9Ds07q9iWIDAOvH+OD+iFqZatWr5lsP2YNKGX8YQ97hYo3mEcdMNInJcuFGL0UlRxkPZCDd+i9KSpLDnPNS0ImBCIKBBkzScX1D2w7kK24YREnETydzQw4b9VnATRwtM8Plws9m4v4oGZccvvvhC1ZRoNSzIpWII743+NDhnouyGSYO0FNg+wHDxKCtiGzASGM6f06ZN05cDreGCXvZWL016uCBih6OQjy8gNDRURYujR49WF1VAByhc2LRAxvixMWuW1y7E+NGglsUYOjOh+Ra2CwcFgoCChg8GNCvDwa516AJE9Dgw8WMFHCao0UGHV+1ijtqlgpowoF+NqRws+Ey1atXSP0YNFfYlalTQud/wOSJyDGiiiQsWLnAIDlAzio7nKMBruUJwYwRJHbWbDcaPjVmzPGpdcS4MDAxUNcjGcB7S8rPgXIQLpKVBTnCDBe2/DWszcHMFN24M7zIan4OxD7AcPrtxrTOgk6upjq4IVLQhPY3hfI4gEMOQEpHjwLkH5zzcvEVzJvSlxY1aBAvaABmoncCNEpS3TD02Zu3yqE1GcGKqlUlmZqY656GGGDdeUOjXyp3m/P3336qfodbPGXCDGzd9DAdLMj4HYx+gnIcyLgIOQ6itQR8eU7CsYT8alF2xL1E2xTm6sP2SGawQEREREZFDYp8VIiIiIiJySAxWiIiIiIjIITFYISIiIiIih8RghYiIiIiIHBKDFSIiIiIickgMVoiIiIiIyCExWCEiIiIiIofEYIWIiIiIiBwSgxWiMmDNmjVy+PBhe29GuYfMwcjwm5CQUOC+QMbjVatW5Zl39OhRlTF47dq1Tr0fjx8/Lr///rvZxyVp//79smvXrlJ5L3Ju+J1HR0cXuAyycf/8888W15WVlaXWFxcXJ47m2rVrKis7kSNjsEJUBrzwwguycuVKcTR//PGHKoSXFykpKTJ8+HA5c+ZMgcs9/vjjsm/fPv3jTz/9VDp27ChLliyRv/76q1i2A4UfFDQc8ZiYPHmy2cfF5dixY7J69ep88++44w5JTEws9vcrz9avX68CwfIEv/NDhw4VuMxzzz0nW7ZssbiutLQ0tT4E7o7G19dXZsyYIUuXLrX3phCZxWCFiGyGi/Uvv/zCPWhg586dqiYMAYtmwYIF8sorr6g7mG+88UaR91dMTIwq/KAGp7xCbc0zzzyTZ17z5s2lffv28sEHH9htu5zR66+/Lt999529N8OhIFhevHhxvmOwLJo6daq8+OKLkpOTY+9NITLJ3fRsInJ02dnZ8u+//0psbKw0aNBAGjVqlG8Z3A09ffq01K5dWxXkrH3OmvVs3LhRNYfau3evussP99xzj2zdulUuXLggLi4uEhoaKq1atZJKlSrlWyeaRPzzzz8SEhIirVu3VncosXyzZs0KtYyl/YBCbf369cXPz0927Nih1oVaDsAdeGyvq6ur2k6s2xhqjjDVq1dPqlWrJpbMnj1b7YeKFSuqO6orVqyQc+fOqcIN9lObNm3U9miFP3d3d6lVq5b6fPjfUEZGhvpsSUlJ6nm8P5qUaAHib7/9JlWqVJGwsDDp3r27xc+UnJysXjNo0CB1lxdThw4dpEaNGnne19Jye/bsUccDHrdr105914VV0DpwTOFzdOvWTT/v1KlTcvDgQRk4cKCcPXtWvR7LaMdeZGSk2q+jR4+WSZMmycsvv5xvf9oDmm9GRUWp7we1bZcuXVL7Et9benq6OqbxF8dkYGBgofaT4brxnV+5ckWGDh2qlkHBE8c7Atq6devm+c0UtK34rmvWrCktWrRQxxB+f2gOhVoGbV/juMBnQQ0j3qty5crSsmVL9dcYzhGbN29W5wAcw7t371a/DfxfmGUsfR40r8TvA8c7fjP+/v7SpUsX9RxqILGfca7Ab6Jq1ar5tvPkyZNy4MABqVOnjlX7CrWlt99+e57PjN8m3vvq1atq/2E/FgTfF5b39PSUTp06qW3WoAYW+wPrgRMnTqgbIag5xLkM/vzzTwkPD9ef8wr6nNq5qH///ur8fOTIEbUMPu+AAQNkzJgx6lyJ/4kcjf3P5EROChdXFKpQwC9uuHCh0Obm5qYuNriIo2CHZkaYh4sVCi24cKFgdP78eXVRRVMyDw8Ps895e3vneZ+C1oNCDAqLKEDiIg133XWXKjShsAEopKMAhO3ChV2zYcMGVeBBABAQEKAu2ugPMnLkSH1BwZplLO0HQBOk6tWrq8IIClR9+vRRBcNvvvlGxo8fry7YXl5esm3bNnn33XflkUceyXNHefr06XLrrbeqC3xERESB34tOp1MX/JkzZ6rHKISigHD9+nW1T1CIwWfBZ8J8wGdC4RzNMdCsSStgoCCIz4ICLIIZFKQee+wxefLJJ1XNjVZYwesQQKLAaukz4TOgRgYFEuwP7EcUgo2DFXPL4bvHd4z9jBoMFKSx3xHYmCpom4JCk6V14I41Pq9hsIKmSP/3f/+nvm8U0BFAI6jS9mNQUJAKVnr27KkKtCjYaUGpPeG3goKtj4+PKliinwMKip988om8/fbb6rvFsY3aMvx2tAKuNfsJ654zZ4767DiuELQiUMbv9M4771THHQJ4vBbHPmr2cFyY8sADD6h1d+7cWS5evKi2F/0x8P64EYCAXdvX+A2hb9Dff/+tHmN5HMOfffaZ3Hvvvfp1bt++XRWO8ftDEIHzId6/d+/e+kDEmmXwfVv6PC+99JL6H6/H7wGBCiYE9g8++KA0adJE/Vawj1HLadg08cMPP1S1C/js+KzGvwdTfv31V3nqqaf0j3HM4XjFeQfHIc6L2BdvvfWWydfPnz9f/Zbx3aJZJ85luIGBfQvY1/jtaX1i3n//fZk7d676zEOGDFHn5rvvvlu9BsGKpc+JgBC/aQQ7uHGC/YdgCMcUgiV8dnymogYrOL4BxyRRsdERUbFLSkrSDRw4UNe9e3ddTk5OkdcXGRmpmz59uvof62vZsqXuxRdfzPN+9evX182aNUs93rZtm87Dw0MXGxurX2bdunW6hISEAp8zZmlZbMe7775b4LbPmzdPV61aNV12drZ6nJWVpWvQoIFuwoQJ+mUWLVqkw+no1VdftXoZa/YDNGzYUFezZs08n+HIkSO6ihUr6rZs2aKf988//+i8vb11J0+eVI8PHDigc3V11a1Zs0Y9xvYPGTJEbcOePXtMftazZ8+q53fv3p1nfvXq1XVffvml2X2EzztgwADd2LFj9fOmTp2q69mzZ55lVq5cqf4/ffq0ep/jx48X6jMdPnxYvW706NEFHpfmlnv77bd1VatW1V28eFE9jo+PV9/TU089pV8G+x773Nxja9bx9NNP6/r27ZtnmxYsWKCrVauW/vHMmTN1TZs2Nbn9WP/HH3+scwT43WJfascR4Lzg4uKi27Rpk/7YateunW7KlCmF2k/aupcvX57nPbt27aqbOHGi/rtLTU3VtW7dWvfGG2+Y3EYcO1gPjl8NjqNLly6p/7t165bnd2bKd999pwsKClLvZXjeGjVqlH47fvnlF/U+TzzxRKGWsebzdOjQQVe5cmX9/oKoqCidr6+v7o8//tDP27dvn/pN4C+cOXNG5+npmWcfPvjgg2obNmzYYPKzJiYmqufXr1+f5/vCZ9G2EX9/+ukn9X9ycrJafuvWrfr39PLy0i1cuFD/+meeeUadJ1JSUtTjFStW6AIDA/XnzUaNGunatm2re/zxx/XnZjc3N3XOs+Zz4rvENgwaNEidS4w9//zzujZt2uiKatq0abp69eqpcwhRcWHNClExQ20C7k7hDhPuBJtr6427o/369Sv0+nFXEU0wJkyYIN9//726m48Jd+tRGzFx4kSpUKGCuvNmeHcad5yhoOeMFWZZQ2jqgtdgH2DbcOcVd+txZ/m///5Td/a02gFAbcmzzz6rf2zNMtbsB8PXBgcH6x+j5gV3obFNy5cvV68DNK9AjRHuNmI+mmDcdttt6jk0iUH7dIzoZQ7uyoKpZm+mYB+hiQ/urKJZEO4yG+57NL/BvkRtC+7Y4u6yOdZ8Jg32jTVNt4yXQzOghx56SF/7g8+Jmhx00LW2n0hxrMMSrFP7LhwB9r12HAFqfPDdas2UcGyh5hLHfGH3E55HbYphc7lNmzapWhnchdd+F2g6hd8FaiCMoUYV3zN+d1rNzi233GLxc+EzoIYL+xpNFvF7Rw0BajZwXKP25fPPP9cfQzgvGjbTtGaZwnwe1AIbNn1atmyZOv7RjBK/CcBr8VtDMyv8vn/66SfVfMxwH+I8s3DhwkL9zvF7Rc0paoGwD/F5Bg8ebPL1qDVBLeX999+vn4c+I++9955qDofaFZxvsd04z6GmB/sBxwRqS7RmuGj6iM+HPnGWPqcGx5BW62zrbwbbZG5gFdR8oZYMxzi2xfC4J7IVgxWiYoaLCgodKBybGq1Ig4K7LcGKNhLVunXr8sxHe2ftIo+L05tvvqmaCWA+Aoz//e9/qgBS0HPGCrOs5tVXX1UXXVxI0axDKzSjYIPPjIs5CmeG7blxYUdzGI01y1izHzTGbdTxWjSzQZBjCJ9PK4Ag6DRu9mWpSR+aXwCCj4LgeTSLQz+Atm3bqm3GNhkOlYp+FwhmtPb5KMAgeEBAYoo1n0ljqs2+KcbLoYmNYdAD2D4EVCisojmJJcWxDkuwf7V2/Y7AeP+juZKpefj+CrufTB3bgCZaaPKpQQEV/aVMwe9q1qxZ6reN7ejRo4dqUoS/5qBZEn7raE6EY1J7L+0Yxm8YjH9Dho+tWaYwn8fUvsDNFuPfBArSKMgX5+8cfT7Q9BDnHkwopONGiql+K1rzYMMbAbh5hWsGntMe4/yLgAznTfRXwjngvvvuU00GEaxo/dSs+Zzm9pEtvxkEqAUNR47mmWgijP44DFaoODBYISpmuIDgQoI7gWi7jH4PtnRANkfrhPnOO+8U2IETbbCnTJmi2pLjIoZ+F7jLhr8FPVeY9RjDhR+fFzUEuLgC7rSiNkILWtCWGf15cBcQ7ew1uCupsWYZa/cDGO9/vBaBlNZh2BQUHBB4GjJ8f3OFPhT2cMe4adOmZpfDnWTUgGB/4Y4sIMBD23kNCiu4M4xOs7jbiuewT7E/TbHmM2msPR6Nl8PdYK1NugaP8d7WBhnWrAOBqvHIRIYF+YKgnxAK9A0bNpSyzNp9berYBgQShnfULUHBety4caogijv/KBjjL/o4GEP/B5wTMCy1VhjFXXnUIhv+zrVlC/qdW1qmMJ/H1L5AR31Lv3Pj37Wl3zkG6UCwid85+noA+vh8/fXXql8J+ougTxJu2OD3alyTYeq7RbCBAr5hh33tWoJgBYEjPgsCNNygQW0pbmhY+znN7SMNPou1vxnUCBnWChnS+s6gL9XYsWOtWh+RJRy6mKgE4I4WCuyovi/OQEVbNy7suCgaQiEBhTTAnTd03MZFEhdMdBJH8yhsU0HPGbO0LO4wGhYitaF0DS96xnf70EQEd/AM88agw7FhExhrlrFmP5iDGi0EX+iAbgiFBe1uKYIxPG84PHBBTcAAgQoKLyhIFATrRIClBSrYZuN1I5jR9jG2F5320XEa87U7u4b73prPVFTYJ2g2oxVIQQtgi3Md2oAIhsvgDrMh42NPgw7h0LVrVynLbN3XWk2H8e8C0BzTFIy6h32JIBGvR2CAQjHujJva16g9wXYV9DvHjRoU6g1/wzh+tcE3rF3Gls9j+JtAIRxBlSH8HrRcPNifCNBwvFn7O8c5HcGD4e9c+70iaOnVq5cKVhDAGa5Xg+Z/GHjEMNEvAkPsf+0mjxasoEYJg0totSj4i1qt1NRU/bFgzee0BJ8FgxoUFT4/mn8xUKHixJoVohKCu2EYvam4oeAwb948dWcLzSjQthmFX1zsnnjiCTV0Ky6+aDKENtxoSoLgAhd2jLpT0HPGLC2LJkwYgQpBGdq9o705mjeg/TeaK6ANPPpSGEKAgSSXaDuNJg94jCYouDuoBXbWLGPNfjAH24nX9e3bV+VDwTaj4IBCE+5k4i4l+oeg4ICCB7YD7/HVV19Z/H4w8ha2HaMAmQtUMaIVaoTQBwYFPhT08P54Xw1qUrD/sI343Lhri/2tNR9B0xU00cO60Obems9UVK+99poqxGKUNrwvAghM1iTGK8w60OwQ+xAjVOF7xfaj0IaCkAb7AgU0rWZNG7oYd/dx/BneqS+LbN3XaCqFmjvsQ9xsQA0JCs244z1ixIg8fbk02I/o14XfOfYhjjv89jEMt7avcfxhpCl8BzjW0DQRr0G/GtxIMP5tIHBHLStGvML7ozkSRkXD71b7XVizjC2fR4PCPGofMHoWlsNvDTc8EIxglC0cIzi+cD7DerXt+OKLL6z6neP39vHHH6tt/PLLL9XofNg3qK3Bcdi4cWNVw4obPsbBCo5RBBkYrQtBBUaGQy224Uhk2DbUrKKZF4Y21oIVLItRxLRmW9Z8zoKg39D/t3dHuYnDUABFZyT2yA+7YAUsgdWwveogeWRFtGQ0I+qKe35QWxNISMp79rOjg8dqYf/K/8vkf2tkJfkBBKACheF4PN570ZUdCeLUsPtCHwG6eQqWoTQiItBTWqC9L8+v/rb1rK0bHAoo/U2S4EtbD50g3+98SfrZ+52XsvSl7P0KkvSkWqpWcjfXTO9p8+w4oIzFpM8tE2jdtVnPowBQb76eZI8Ilsw5EpCoRfe6yju85lcT6AUhErex5CgCzrkO3pwfx0dvtW0KMpR8aTdYclipjX2XJHpdweoI4rw3gY3lZkcP77N9kvTYzhz0P/JZO0mBRQ0sKet4O+4m28736XGs59Kh7c97tmG/xj1xBFLOf0mxoH2wPLORB+eHhMyjciKlMM6dVbhuvf+ZQH/bi+14jN7zvcfp0bYxH8p8J4GyEkIBr+T3s8BeMqK0yOetxNP567iPXn43f5UA25br3HXmXBSYau+6N6LnnJnnVOldd14bdfD5KA3ynuZreE+bPfujHO1R6eX1er2fH8oDPVci4XjO89p0FtiWfTa6Ma7zR/ddGhx31/S487uJ/hZUMerk2pMAeZSQOT62N5d4ed7lcrl/xjpk/H/z80wpqI6X8/n8ZxRWYqIjaDty8Ww/Pd97eLTEuI4g+793+fHk1X5bEuzlr5rkranXnpOXUS+tZ3KsOranzaoECZIRNybM69xut3sv/3z/i3wfcz/mxN79ZCwSYLR1JOZ72qxKciPRUR77U5kMLxE1ijbKS5PVlKwkeTk1zcpK9JjqJfVFabRGsDlGDva0SbIuS49bOELSocffqInRCqMxh8Nhd5sk761kJcm3UMYk8bAKjvrr0+l0L8H42zZJ1qVczPwSJY/m4FgpapuE7GmT5H2VrCRJkiRZUl2USZIkSZZUspIkSZJkSSUrSZIkSZZUspIkSZJkSSUrSZIkSZZUspIkSZJkSSUrSZIkSZZUspIkSZJkSSUrSZIkSX6t6ANcufjrnHWlJwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -629,27 +629,27 @@ " fast\n", " 10\n", " 93.500\n", - " 0.370\n", + " 0.373\n", " 0.562\n", - " 1.521\n", + " 1.508\n", " \n", " \n", " 1\n", " moderate\n", " 14\n", " 80.876\n", - " 0.437\n", + " 0.441\n", " 0.543\n", - " 1.243\n", + " 1.232\n", " \n", " \n", " 2\n", " slow\n", " 20\n", " 61.754\n", - " 0.601\n", + " 0.606\n", " 0.578\n", - " 0.961\n", + " 0.953\n", " \n", " \n", "\n", @@ -657,14 +657,14 @@ ], "text/plain": [ " rollout all live by wk 2x2 reports (% of truth) MDE 2x2 MDE CS \\\n", - "0 fast 10 93.500 0.370 0.562 \n", - "1 moderate 14 80.876 0.437 0.543 \n", - "2 slow 20 61.754 0.601 0.578 \n", + "0 fast 10 93.500 0.373 0.562 \n", + "1 moderate 14 80.876 0.441 0.543 \n", + "2 slow 20 61.754 0.606 0.578 \n", "\n", " CS / 2x2 MDE \n", - "0 1.521 \n", - "1 1.243 \n", - "2 0.961 " + "0 1.508 \n", + "1 1.232 \n", + "2 0.953 " ] }, "execution_count": 7, @@ -760,10 +760,10 @@ "id": "9a902d30", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:41:27.625613Z", - "iopub.status.busy": "2026-08-10T22:41:27.625554Z", - "iopub.status.idle": "2026-08-10T22:41:28.227131Z", - "shell.execute_reply": "2026-08-10T22:41:28.226735Z" + "iopub.execute_input": "2026-10-28T15:25:39.731346Z", + "iopub.status.busy": "2026-10-28T15:25:39.731284Z", + "iopub.status.idle": "2026-10-28T15:25:40.315036Z", + "shell.execute_reply": "2026-10-28T15:25:40.314661Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "id": "34cfb285", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:41:28.228106Z", - "iopub.status.busy": "2026-08-10T22:41:28.228036Z", - "iopub.status.idle": "2026-08-10T22:41:40.367042Z", - "shell.execute_reply": "2026-08-10T22:41:40.366647Z" + "iopub.execute_input": "2026-10-28T15:25:40.316293Z", + "iopub.status.busy": "2026-10-28T15:25:40.316231Z", + "iopub.status.idle": "2026-10-28T15:25:52.443766Z", + "shell.execute_reply": "2026-10-28T15:25:52.443183Z" } }, "outputs": [ @@ -819,9 +819,9 @@ "output_type": "stream", "text": [ "Flat effects, clean all-treated tail, moderate rollout, 50 states:\n", - " lift 0.20 (~2%): clean-tail 2x2 power 0.25 CS power 0.15\n", - " lift 0.30 (~3%): clean-tail 2x2 power 0.41 CS power 0.21\n", - " lift 0.40 (~4%): clean-tail 2x2 power 0.61 CS power 0.37\n", + " lift 0.20 (~2%): clean-tail 2x2 power 0.24 CS power 0.15\n", + " lift 0.30 (~3%): clean-tail 2x2 power 0.40 CS power 0.21\n", + " lift 0.40 (~4%): clean-tail 2x2 power 0.60 CS power 0.37\n", " lift 0.50 (~5%): clean-tail 2x2 power 0.78 CS power 0.54\n", " lift 0.65 (~6%): clean-tail 2x2 power 0.95 CS power 0.71\n", " lift 0.85 (~8%): clean-tail 2x2 power 1.00 CS power 0.94\n", diff --git a/docs/v4-deprecations.yaml b/docs/v4-deprecations.yaml index 490a49647..69c77c8ba 100644 --- a/docs/v4-deprecations.yaml +++ b/docs/v4-deprecations.yaml @@ -79,12 +79,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/estimators.py, diff_diff/linalg.py] - notes: "Locked #663 direction; moves every clustered p-value/CI. Migration note + flip TestDfConvention expectations." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: Locked #663 direction; moves every clustered p-value/CI. Migration note + flip TestDfConvention expectations." - id: M-005 kind: default-flip group: df-convention-flip @@ -92,12 +93,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/twfe.py, diff_diff/linalg.py] - notes: "MultiPeriodDiD's knob is subsumed by its class removal [M-010]; merged event-study mode inherits this row." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: MultiPeriodDiD's knob is subsumed by its class removal [M-010]; merged event-study mode inherits this row." - id: M-006 kind: default-flip group: df-convention-flip @@ -105,12 +107,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/linalg.py] - notes: "The 'evaluate extending the knob to standalone CR1-t estimators' item was resolved EARLY by PR C ([M-127], 3.9): the knob now lives on SunAbraham/WooldridgeDiD/StackedDiD/ImputationDiD (their flips are [M-128]..[M-131]) and LPDiD (already at 'cluster', no flip row)." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: The 'evaluate extending the knob to standalone CR1-t estimators' item was resolved EARLY by PR C ([M-127], 3.9): the knob now lives on SunAbraham/WooldridgeDiD/StackedDiD/ImputationDiD (their flips are [M-128]..[M-131]) and LPDiD (already at 'cluster', no flip row)." - id: M-007 kind: warning-retirement group: obligation-warning-retirements @@ -1555,12 +1558,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/sun_abraham.py] - notes: "PR C ([M-127]) extended the knob here with default 'residual'; flips with [M-004]..[M-006] at 4.0 (G-1 = the fixest/Stata clustered convention). Moves every clustered p-value/CI on this surface; covered by the same migration note." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: PR C ([M-127]) extended the knob here with default 'residual'; flips with [M-004]..[M-006] at 4.0 (G-1 = the fixest/Stata clustered convention). Moves every clustered p-value/CI on this surface; covered by the same migration note." - id: M-129 kind: default-flip group: df-convention-flip @@ -1568,12 +1572,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/wooldridge.py] - notes: "PR C ([M-127]) extended the knob here with default 'residual' (OLS arms only; GLM arms are knob-independent); flips with [M-004]..[M-006] at 4.0. Moves every clustered OLS p-value/CI on this surface; covered by the same migration note." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: PR C ([M-127]) extended the knob here with default 'residual' (OLS arms only; GLM arms are knob-independent); flips with [M-004]..[M-006] at 4.0. Moves every clustered OLS p-value/CI on this surface; covered by the same migration note." - id: M-130 kind: default-flip group: df-convention-flip @@ -1581,12 +1586,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/stacked_did.py] - notes: "PR C ([M-127]) extended the knob here with default 'residual'; flips with [M-004]..[M-006] at 4.0. The stacked residual df is typically large (t ~= z), so the flip to G-1 is the materially moving step on this surface; covered by the same migration note." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: PR C ([M-127]) extended the knob here with default 'residual'; flips with [M-004]..[M-006] at 4.0. The stacked residual df is typically large (t ~= z), so the flip to G-1 is the materially moving step on this surface; covered by the same migration note." - id: M-131 kind: default-flip group: df-convention-flip @@ -1594,12 +1600,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/imputation.py] - notes: "PR C ([M-127]) extended the knob here with default 'residual' (pretrends lead regression only; BJS overall inference and the joint pretrend Wald F are knob-independent); flips with [M-004]..[M-006] at 4.0; covered by the same migration note." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: PR C ([M-127]) extended the knob here with default 'residual' (pretrends lead regression only; BJS overall inference and the joint pretrend Wald F are knob-independent); flips with [M-004]..[M-006] at 4.0; covered by the same migration note." # ---- Alias diet (2026-07-31 consolidation-scope decision) ---------------- - id: M-132 diff --git a/docs/v4-design.md b/docs/v4-design.md index 36ac4aaa0..9c9965f6d 100644 --- a/docs/v4-design.md +++ b/docs/v4-design.md @@ -862,8 +862,15 @@ domain vocabulary, not drift. ImputationDiD-pretrends (their 4.0 flips are [M-128]..[M-131]) and LPDiD (default already `"cluster"`, no flip row). The flip moves every clustered p-value/CI; the flip PR updates `TestDfConvention` / - `test_moderate_t_pins_residual_df_convention` / the per-estimator knob - suites' expectations and adds the migration-guide entry. + `test_moderate_t_pins_cluster_df_convention` / the per-estimator knob + suites' expectations and adds the migration-guide entry. (SHIPPED, item + 7a, 2026-10-28: the six signature defaults - TWFE inherits DiD's - read + `"cluster"`; `TestDfConvention`, `test_moderate_t_pins_cluster_df_convention` + and the per-estimator knob suites pin the G-1 default with explicit + `"residual"` baselines; the variance-conventions matrix keeps its + residual-df rows as explicit `"residual"` arms and gains G-1 `_default_df` + twins; the committed oracle lanes that embed residual-df inference name + that convention explicitly. Pinned by `tests/test_v4_inference_defaults.py`.) - **Fast-path default** [M-008]: decided 2026-10-18 (4.0-cut framing, user decision) - evaluated, KEPT OFF. No opt-in telemetry was ever collected in the 3.9 -> 3.13 gap and the golden recapture never ran, so there is no @@ -1199,12 +1206,21 @@ tests-first PR that flips no rows). report schemas gain an additive `design` key under the unshipped DR 3.0 / BR 4.0 numbers. Pinned by `tests/test_v4_unify_ddd.py`.) 7. Inference defaults: the df_convention flips + [M-080] as re-scoped + - the uniform `cluster=False` off-switch. + the uniform `cluster=False` off-switch. (7a SHIPPED 2026-10-28: the + df_convention flips [M-004]..[M-006] + [M-128]..[M-131], pinned by + `tests/test_v4_inference_defaults.py`; 7b = [M-080] pending.) 8. [M-093] sentinel retirement + event-study schema enforcement. 9. [M-090] docs reorganization: Bacon to the diagnostics family, catalogue counts, JOSS prose, REGISTRY archive framing for the removed classes. 10. Release: bump to 4.0.0 (closes the cut window), changelog compile, - migration-guide retitle, due gate green. + migration-guide retitle, due gate green; re-execute (Rust backend) the + tutorials whose clustered-default outputs moved at item 7a and were + deliberately left stale - 01_basic_did, 02_staggered_did, + 04_parallel_trends, 05_honest_did, 07_pretrends_power, + 09_real_world_examples, 11_imputation_did, 13_stacked_did, + 16_wooldridge_etwfe (NOT 16_survey_did: the survey df wins) - plus any + 7b/8/9 movers; 24 is handled in 7a and 23 in 7b (each re-executed there + if its outputs moved). Ordering constraints: 4a before 4b before 4c-i before 4c-ii; 4 before 5 and 6; 5 before 6 (the unified DDD container is born canonical); 7 and 8 independent; 9 after diff --git a/tests/fixtures/README.md b/tests/fixtures/README.md index 54bdf1f2a..ca3d1d55e 100644 --- a/tests/fixtures/README.md +++ b/tests/fixtures/README.md @@ -64,7 +64,11 @@ route was closed at capture): the contract test pins reference MEMBERSHIP against it, so a surface that mis-marks an estimated horizon as reference fails instead of having that row skipped. Re-capturing on a post-removal tree is NOT a substitute: the fit-time route is gone, so -nothing independent remains to disagree with. +nothing independent remains to disagree with. The ImputationDiD +`pretrends_all` lanes (analytic and boot) were captured under the 3.x +`df_convention="residual"` default (lead rows at t(residual df)); since the +4.0 flip to `"cluster"` (item 7a) the contract tests fit those lanes with +`df_convention="residual"` explicitly, so the oracle numbers are untouched. ## `v4_twfe_event_study_oracles.json` - TWFE event-study decoupling oracles (4.0 PR 4c-i) @@ -76,7 +80,12 @@ built its surface through an internal `MultiPeriodDiDResults` and `results_base._from_mpd`, and while `MultiPeriodDiD` still existed as the independent twin of every lane. Per-lane capture SHAs: 11 lanes at `68e383bd` (top-level `provenance`), the two cluster + hc2_bm lanes at `d45d5316` -(`provenance.lane_additions`). Layout: +(`provenance.lane_additions`). The two unit-clustered analytical lanes +(`pooled_cluster_unit`, `within_cluster_unit`) were captured under the 3.x +`df_convention="residual"` default; since the 4.0 flip to `"cluster"` (item +7a) their lane definitions and `echo.twfe_ctor` carry +`"df_convention": "residual"` explicitly - a metadata-only change, no +numeric field was touched. Layout: - `provenance` - capture SHA, `diff_diff.__version__`, weight backend, numpy/scipy/pandas/python versions, plus `lane_additions.` with the diff --git a/tests/fixtures/v4_twfe_event_study_oracles.json b/tests/fixtures/v4_twfe_event_study_oracles.json index fa7223688..74977ffa7 100644 --- a/tests/fixtures/v4_twfe_event_study_oracles.json +++ b/tests/fixtures/v4_twfe_event_study_oracles.json @@ -93,7 +93,7 @@ "echo": { "panel": "main", "survey_label": null, - "twfe_ctor": {"cluster": "unit"}, + "twfe_ctor": {"cluster": "unit", "df_convention": "residual"}, "twfe_fit": { "outcome": "outcome", "post_periods": [4, 5, 6, 7], @@ -715,7 +715,7 @@ "echo": { "panel": "main", "survey_label": null, - "twfe_ctor": {"cluster": "unit"}, + "twfe_ctor": {"cluster": "unit", "df_convention": "residual"}, "twfe_fit": { "outcome": "outcome", "post_periods": [4, 5, 6, 7], diff --git a/tests/helpers/twfe_es_oracle_lanes.py b/tests/helpers/twfe_es_oracle_lanes.py index 8871dd698..0b7b20127 100644 --- a/tests/helpers/twfe_es_oracle_lanes.py +++ b/tests/helpers/twfe_es_oracle_lanes.py @@ -163,9 +163,13 @@ def _within(ctor: Dict[str, Any], survey_label: Optional[str] = None) -> Lane: LANES: Dict[str, Lane] = { "pooled_default": _pooled({}), - "pooled_cluster_unit": _pooled({"cluster": "unit"}), + # The two unit-clustered analytical lanes were captured under the 3.x + # df_convention="residual" default; since the 4.0 flip (G-1 default) + # they pin the captured convention explicitly (metadata-only change - + # the oracle numbers are untouched). + "pooled_cluster_unit": _pooled({"cluster": "unit", "df_convention": "residual"}), "pooled_hc2_bm": _pooled({"vcov_type": "hc2_bm"}), - "within_cluster_unit": _within({"cluster": "unit"}), + "within_cluster_unit": _within({"cluster": "unit", "df_convention": "residual"}), "within_hc2": _within({"vcov_type": "hc2"}), # Cluster + hc2_bm: the CR2 Bell-McCaffrey bypass that no other lane # reaches; captured at d45d5316 (PR 4c-ii, before the core's post-average diff --git a/tests/test_aggregate_contract.py b/tests/test_aggregate_contract.py index 929637959..d2e113ae7 100644 --- a/tests/test_aggregate_contract.py +++ b/tests/test_aggregate_contract.py @@ -3724,7 +3724,12 @@ def test_replicate_overall_row_migration_delta(self): ) def test_pretrends_arm_matches_oracle(self, imputation_panel): - plain = _fit_imputation(imputation_panel, est_kw={"pretrends": True}) + # The oracle was captured under the 3.x df_convention="residual" + # default (lead rows at t(residual df)); the 4.0 default is G-1, so + # the lane names the captured convention explicitly. + plain = _fit_imputation( + imputation_panel, est_kw={"pretrends": True, "df_convention": "residual"} + ) es = plain.aggregate("event_study") _assert_es_matches_oracle(es, _im_oracle("pretrends_all"), **ANALYTIC_TOL, name="imp/pre") assert (np.asarray(es.event_time) < -1).any() # leads present @@ -3741,7 +3746,7 @@ def test_isolation_under_public_field_mutation(self, imputation_panel): object.__setattr__(res, "anticipation", 3) # Imputation-only public config fields the kit snapshots: object.__setattr__(res, "leave_one_out", True) - object.__setattr__(res, "df_convention", "cluster") + object.__setattr__(res, "df_convention", "residual") pd.testing.assert_frame_equal(res.aggregate("event_study").to_dataframe(), base_es) pd.testing.assert_frame_equal(res.aggregate("group").to_dataframe(), base_gr) pd.testing.assert_frame_equal(res.aggregate("simple").to_dataframe(), base_sm) @@ -3786,7 +3791,12 @@ def test_bootstrap_relays_fit_time_surfaces(self, imputation_panel): _assert_bootstrap_simple_relay(res) def test_bootstrap_pretrends_relay_matches_oracle(self, imputation_panel): - res = _fit_imputation(imputation_panel, est_kw={**_IM_BOOT_KW, "pretrends": True}) + # Lead rows stay analytical under bootstrap and were captured under + # the 3.x df_convention="residual" default (see the analytic arm). + res = _fit_imputation( + imputation_panel, + est_kw={**_IM_BOOT_KW, "pretrends": True, "df_convention": "residual"}, + ) es = res.aggregate("event_study") _assert_es_matches_oracle( es, _im_boot_oracle("pretrends_all"), **BOOT_TOL, p_atol=_IM_P_ATOL, name="imp/boot/pre" diff --git a/tests/test_estimators_vcov_type.py b/tests/test_estimators_vcov_type.py index ff752369d..f37a49c34 100644 --- a/tests/test_estimators_vcov_type.py +++ b/tests/test_estimators_vcov_type.py @@ -2265,12 +2265,14 @@ def test_within_event_study_saturated_full_k_df_le_zero_nan_inference(self): class TestDfConvention: - """`df_convention="cluster"` (opt-in Stata/fixest G-1 inference df on - clustered analytical fits; REGISTRY clustered-CR1 inference-df deviation - note). The knob changes ONLY the reference t-distribution for t/p/CI — - never the point estimate, SE, or t-statistic — and sits at the fallback - level of the df resolution (survey df and per-coefficient Bell-McCaffrey - DOF always win). Default stays "residual" until the v4 flip.""" + """`df_convention` (Stata/fixest G-1 inference df on clustered analytical + fits under the 4.0 default "cluster"; "residual" restores the 3.x + residual-df tail; REGISTRY clustered-CR1 inference-df note). The knob + changes ONLY the reference t-distribution for t/p/CI — never the point + estimate, SE, or t-statistic — and sits at the fallback level of the df + resolution (survey df and per-coefficient Bell-McCaffrey DOF always + win). The default flipped from "residual" to "cluster" at 4.0 (rows + M-004/M-005), so every "off" baseline below is an EXPLICIT "residual".""" @staticmethod def _clustered_panel(n_units=50, seed=42): @@ -2295,15 +2297,17 @@ def _clustered_panel(n_units=50, seed=42): ) return pd.DataFrame(rows) - def test_default_residual_is_noop(self): - """Explicit df_convention="residual" is byte-identical to the default.""" + def test_default_cluster_is_g_minus_1(self): + """The 4.0 default: explicit df_convention="cluster" is byte-identical + to the default, the default p-value IS the t(G-1) tail, and the 3.x + residual-df tail is only reachable through explicit "residual".""" from scipy import stats data = self._clustered_panel() r0 = DifferenceInDifferences(cluster="unit").fit( data, outcome="y", treatment="group", post="post", unit="unit" ) - r1 = DifferenceInDifferences(cluster="unit", df_convention="residual").fit( + r1 = DifferenceInDifferences(cluster="unit", df_convention="cluster").fit( data, outcome="y", treatment="group", post="post", unit="unit" ) assert (r0.att, r0.se, r0.t_stat, r0.p_value, r0.conf_int) == ( @@ -2313,17 +2317,25 @@ def test_default_residual_is_noop(self): r1.p_value, r1.conf_int, ) - # and the default really is the residual-df tail, not G-1 - assert abs(r0.p_value - 2 * stats.t.sf(abs(r0.t_stat), 49)) > 1e-4 + G = data["unit"].nunique() + assert r0.inference_df == G - 1 + np.testing.assert_allclose(r0.p_value, 2 * stats.t.sf(abs(r0.t_stat), G - 1), rtol=1e-12) + # and the residual-df tail (the 3.x default) differs measurably + rr = DifferenceInDifferences(cluster="unit", df_convention="residual").fit( + data, outcome="y", treatment="group", post="post", unit="unit" + ) + assert rr.inference_df > G - 1 + assert abs(r0.p_value - rr.p_value) > 1e-4 @pytest.mark.parametrize("estimator_cls", [DifferenceInDifferences]) def test_cluster_convention_matches_g_minus_1(self, estimator_cls): - """Knob on: p/CI follow t(G-1) exactly; att/SE/t unchanged.""" + """Knob on: p/CI follow t(G-1) exactly; att/SE/t unchanged vs the + explicit "residual" baseline (the 3.x default).""" from scipy import stats data = self._clustered_panel() kw = dict(outcome="y", treatment="group", post="post", unit="unit") - r0 = estimator_cls(cluster="unit").fit(data, **kw) + r0 = estimator_cls(cluster="unit", df_convention="residual").fit(data, **kw) r1 = estimator_cls(cluster="unit", df_convention="cluster").fit(data, **kw) assert r0.att == r1.att and r0.se == r1.se and r0.t_stat == r1.t_stat G = data["unit"].nunique() @@ -2342,9 +2354,10 @@ def test_twfe_inherits_knob(self): data = self._clustered_panel() data["treated"] = data["group"] * data["post"] kw = dict(outcome="y", treatment="treated", post="post", unit="unit") - r0 = TwoWayFixedEffects().fit(data, **kw) + r0 = TwoWayFixedEffects(df_convention="residual").fit(data, **kw) r1 = TwoWayFixedEffects(df_convention="cluster").fit(data, **kw) assert r0.se == r1.se and r0.t_stat == r1.t_stat + assert r0.p_value != r1.p_value G = data["unit"].nunique() np.testing.assert_allclose(r1.p_value, 2 * stats.t.sf(abs(r1.t_stat), G - 1), rtol=1e-12) @@ -2363,7 +2376,7 @@ def test_event_study_period_effects_follow_convention(self): spec="pooled", post_periods=[2, 3], ) - m0 = TwoWayFixedEffects(cluster="unit").fit(data, **kw) + m0 = TwoWayFixedEffects(cluster="unit", df_convention="residual").fit(data, **kw) m1 = TwoWayFixedEffects(cluster="unit", df_convention="cluster").fit(data, **kw) G = data["unit"].nunique() for i in np.where(~m1.is_reference)[0]: @@ -2409,7 +2422,7 @@ def test_conley_fits_excluded_from_knob(self): conley_cutoff_km=100.0, conley_lag_cutoff=0, ) - r0 = DifferenceInDifferences(**common).fit(data, **kw) + r0 = DifferenceInDifferences(**common, df_convention="residual").fit(data, **kw) r1 = DifferenceInDifferences(**common, df_convention="cluster").fit(data, **kw) assert (r0.p_value, r0.conf_int) == (r1.p_value, r1.conf_int) @@ -2421,7 +2434,9 @@ def test_bm_dof_precedence_over_knob(self): kw = dict(outcome="y", treatment="group", post="post", unit="unit") with warnings.catch_warnings(): warnings.simplefilter("ignore") - r0 = DifferenceInDifferences(cluster="unit", vcov_type="hc2_bm").fit(data, **kw) + r0 = DifferenceInDifferences( + cluster="unit", vcov_type="hc2_bm", df_convention="residual" + ).fit(data, **kw) r1 = DifferenceInDifferences( cluster="unit", vcov_type="hc2_bm", df_convention="cluster" ).fit(data, **kw) @@ -2578,22 +2593,22 @@ def test_validation_and_transactional_set_params(self): est = DifferenceInDifferences() with pytest.raises(ValueError, match="df_convention"): est.set_params(df_convention="bogus") - assert est.df_convention == "residual" # unchanged after failed set - est.set_params(df_convention="cluster") - assert est.df_convention == "cluster" + assert est.df_convention == "cluster" # the 4.0 default, unchanged after failed set + est.set_params(df_convention="residual") + assert est.df_convention == "residual" def test_get_params_roundtrip(self): - est = DifferenceInDifferences(cluster="unit", df_convention="cluster") + est = DifferenceInDifferences(cluster="unit", df_convention="residual") params = est.get_params() - assert params["df_convention"] == "cluster" + assert params["df_convention"] == "residual" clone = DifferenceInDifferences(**params) - assert clone.df_convention == "cluster" + assert clone.df_convention == "residual" def test_unclustered_fit_knob_is_inert(self): """No cluster -> n_clusters_ is None -> knob has zero effect.""" data = self._clustered_panel() kw = dict(outcome="y", treatment="group", post="post") - r0 = DifferenceInDifferences().fit(data, **kw) + r0 = DifferenceInDifferences(df_convention="residual").fit(data, **kw) r1 = DifferenceInDifferences(df_convention="cluster").fit(data, **kw) assert (r0.p_value, r0.conf_int) == (r1.p_value, r1.conf_int) diff --git a/tests/test_event_study_surface.py b/tests/test_event_study_surface.py index ccfd3cd9c..9867f1ed6 100644 --- a/tests/test_event_study_surface.py +++ b/tests/test_event_study_surface.py @@ -1470,8 +1470,8 @@ def test_stacked_hc2_bm_per_row_df(): if et in bm.event_study_df and np.isfinite(bm.event_study_df[et]): assert df_val == bm.event_study_df[et] - # hc1 non-survey (3.9 / M-127): the df_convention default resolves to - # t(pooled residual df), so every finite-p row now carries that df. + # hc1 non-survey (3.9 / M-127; 4.0 default G-1): the df_convention knob + # resolves one df per fit, so every finite-p row carries that df. s_hc1 = build_event_study_surface(hc1) finite_hc1 = np.isfinite(s_hc1.p_value) assert np.isfinite(s_hc1.df[finite_hc1]).all() diff --git a/tests/test_fixest_did_twfe_parity.py b/tests/test_fixest_did_twfe_parity.py index 311ea43fc..d92cdb8cb 100644 --- a/tests/test_fixest_did_twfe_parity.py +++ b/tests/test_fixest_did_twfe_parity.py @@ -183,8 +183,9 @@ class TestFixestTailDfParity: since it was generated, but nothing asserted them until the tail-df consolidation. Two lanes: - - iid arms under the DEFAULT `df_convention="residual"`: fixest's iid - t-reference is the residual df, so Python matches directly (measured + - iid arms (unclustered, so the `df_convention` knob is inert and the + residual df applies under every value): fixest's iid t-reference is + the residual df, so Python matches directly (measured during plan review: Python residual df == fixest implied df on all four arms — did 396, twfe 148, did_hetero 209, twfe_hetero 125; p/CI agree to ~1e-13 relative). @@ -206,7 +207,7 @@ def _fit(self, key, golden, **est_kw): df, outcome="outcome", treatment="treated", post="post", unit="unit" ) - def test_iid_p_and_ci_match_fixest_under_residual_default(self): + def test_iid_p_and_ci_match_fixest_on_the_knob_inert_iid_lane(self): golden = _load_golden() for key, tol in self._P_TOL.items(): res = self._fit(key, golden, vcov_type="classical") diff --git a/tests/test_imputation.py b/tests/test_imputation.py index 4ccbbcbca..be291688b 100644 --- a/tests/test_imputation.py +++ b/tests/test_imputation.py @@ -3214,16 +3214,26 @@ def _lead(self, res): h = min(k for k in effects if k < -1) return h, effects[h] - def test_leads_are_t_residual_not_z(self): - res = ImputationDiD(pretrends=True).fit(self._panel(), **self._kw) + def test_leads_are_t_g_minus_1_not_z(self): + """The 4.0 default (M-131): the lead rows use t(G-1); explicit + "residual" keeps the 3.9 t(lead-regression residual df).""" + data = self._panel() + res = ImputationDiD(pretrends=True).fit(data, **self._kw) h, e = self._lead(res) assert e["p_value"] != self._z_p(e["t_stat"]) + G = data["unit"].nunique() match = [d for d in range(2, 600) if abs(e["p_value"] - self._t_p(e["t_stat"], d)) < 1e-13] - assert len(match) == 1 + assert match == [G - 1], match + rr = ImputationDiD(pretrends=True, df_convention="residual").fit(data, **self._kw) + _, er = self._lead(rr) + match_r = [ + d for d in range(2, 600) if abs(er["p_value"] - self._t_p(er["t_stat"], d)) < 1e-13 + ] + assert len(match_r) == 1 and match_r[0] > G - 1, match_r def test_cluster_matches_g_minus_1_on_leads_only(self): data = self._panel() - r0 = ImputationDiD(pretrends=True).fit(data, **self._kw) + r0 = ImputationDiD(pretrends=True, df_convention="residual").fit(data, **self._kw) rc = ImputationDiD(pretrends=True, df_convention="cluster").fit(data, **self._kw) h, e0 = self._lead(r0) es0, esc = es_dict(r0), es_dict(rc) @@ -3246,7 +3256,11 @@ def test_normal_reproduces_pre39_z_on_leads(self): def test_pretrend_wald_f_is_knob_independent(self): data = self._panel() - pt0 = ImputationDiD(pretrends=True).fit(data, **self._kw).pretrend_test() + pt0 = ( + ImputationDiD(pretrends=True, df_convention="residual") + .fit(data, **self._kw) + .pretrend_test() + ) ptc = ( ImputationDiD(pretrends=True, df_convention="cluster") .fit(data, **self._kw) @@ -3286,17 +3300,22 @@ def test_inert_config_warns_on_explicit_nondefault(self): data = self._panel() base = dict(outcome="outcome", unit="unit", time="time", first_treat="first_treat") with pytest.warns(UserWarning, match="affects only the pretrends"): - ImputationDiD(df_convention="cluster").fit(data, **base) + ImputationDiD(df_convention="residual").fit(data, **base) + # the default itself never warns (it is not an explicitly non-default value) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + ImputationDiD().fit(data, **base) + assert not any("affects only the pretrends" in str(w.message) for w in caught) # pretrends=True, analytical: reachable post-fit -> no warning. with warnings.catch_warnings(record=True) as caught: warnings.simplefilter("always") - ImputationDiD(pretrends=True, df_convention="cluster").fit(data, **base) + ImputationDiD(pretrends=True, df_convention="residual").fit(data, **base) assert not any("affects only the pretrends" in str(w.message) for w in caught) # pretrends=True + bootstrap: the lead surface is materialized at # fit (analytical lead inference) and relayed -> no warning. with warnings.catch_warnings(record=True) as caught: warnings.simplefilter("always") - ImputationDiD(pretrends=True, df_convention="cluster", n_bootstrap=9, seed=1).fit( + ImputationDiD(pretrends=True, df_convention="residual", n_bootstrap=9, seed=1).fit( data, **base ) assert not any("affects only the pretrends" in str(w.message) for w in caught) @@ -3310,7 +3329,7 @@ def test_inert_config_warns_on_explicit_nondefault(self): weight_type="pweight", ) with pytest.warns(UserWarning, match="affects only the pretrends"): - ImputationDiD(pretrends=True, df_convention="cluster").fit( + ImputationDiD(pretrends=True, df_convention="residual").fit( rep_data, survey_design=design, **base ) @@ -3320,7 +3339,7 @@ def test_validation_and_transactional_set_params(self): est = ImputationDiD() with pytest.raises(ValueError, match="df_convention"): est.set_params(df_convention="bogus", alpha=0.10) - assert est.df_convention == "residual" and est.alpha == 0.05 + assert est.df_convention == "cluster" and est.alpha == 0.05 # Valid value + unknown key: the unknown-key rejection must also # leave the estimator fully unchanged (no partial application). before = est.get_params() diff --git a/tests/test_lpdid.py b/tests/test_lpdid.py index 47f1a1aed..460d434fa 100644 --- a/tests/test_lpdid.py +++ b/tests/test_lpdid.py @@ -1979,8 +1979,9 @@ class TestLPDiDDfConvention: """The three-value df_convention knob on LPDiD (3.9 / M-127). LPDiD's DEFAULT is "cluster" — the pre-existing Stata lpdid t(G-1) - reference — so the default moves NOTHING (unlike the "residual"-default - estimators). "residual"/"normal" are new opt-ins; the degenerate lanes + reference, adopted at 3.9 ahead of the library-wide 4.0 flip — so the + 3.9 knob introduction moved NOTHING here. "residual"/"normal" are + opt-ins; the degenerate lanes (unclustered refit, RA G<=1, saturated early return) keep literal df=None under every value. """ diff --git a/tests/test_methodology_twfe.py b/tests/test_methodology_twfe.py index 4b8d93b51..02aecf4de 100644 --- a/tests/test_methodology_twfe.py +++ b/tests/test_methodology_twfe.py @@ -490,13 +490,12 @@ def test_se_matches_r_twfe(self, r_twfe_results, r_benchmark_panel_data): def test_pvalue_matches_r_twfe(self, r_twfe_results, r_benchmark_panel_data): """Both p-values are numerically zero at this effect size. - Python p ~1e-48 (t-dist at residual df=148) vs R ~4e-27 (fixest's - cluster df G-1=49): both sides use t-distributions, but the df - conventions differ — the documented clustered-CR1 inference-df - deviation (REGISTRY §TwoWayFixedEffects) — so at |t|~22 the tails - diverge by ~21 orders and a relative comparison is meaningless. - The assert pins that both are numerically zero (atol=1e-12, - ~15 orders of headroom above the larger tail).""" + Since the 4.0 df_convention flip both sides sit on fixest's cluster + df G-1=49 (Python ~4e-27 like R; under the 3.x residual-df default + it was ~1e-48 at t(148) - the convention itself is pinned by + test_moderate_t_pins_cluster_df_convention below, where the tails + are distinguishable). The assert pins that both are numerically + zero (atol=1e-12).""" data, _ = r_benchmark_panel_data py_results = self._run_python_twfe(data) @@ -508,7 +507,7 @@ def test_pvalue_matches_r_twfe(self, r_twfe_results, r_benchmark_panel_data): err_msg=f"P-value mismatch: Python={py_results.p_value:.6f}, R={r_twfe_results['p_value']:.6f}", ) - def test_moderate_t_pins_residual_df_convention(self): + def test_moderate_t_pins_cluster_df_convention(self): """Locks WHICH df convention clustered-CR1 inference follows, in a regime where the conventions are distinguishable. @@ -516,12 +515,12 @@ def test_moderate_t_pins_residual_df_convention(self): and cluster-df (G-1) p-values are numerically zero, so they cannot tell the conventions apart. This fixture is tuned to a moderate |t|~1.8, where t(residual df=148) and t(G-1=49) p-values differ by - ~5% relative: the estimator's p-value must match the residual-df - tail exactly AND differ measurably from the cluster-df tail — the - documented clustered-CR1 inference-df deviation (REGISTRY - §TwoWayFixedEffects). If the default convention ever flips to - cluster df (the planned opt-in knob's v4 default), this test fails - loudly and must be updated alongside the REGISTRY note.""" + ~5% relative: since the 4.0 flip (rows M-004/M-005) the estimator's + default p-value must match the cluster-df (G-1) tail exactly AND + differ measurably from the residual-df tail, which explicit + df_convention="residual" restores (REGISTRY §TwoWayFixedEffects + clustered-CR1 inference-df note). If the default ever moves again, + this test fails loudly and must be updated alongside that note.""" from scipy import stats as _stats rng = np.random.default_rng(42) @@ -561,27 +560,32 @@ def test_moderate_t_pins_residual_df_convention(self): np.testing.assert_allclose( res.p_value, - p_residual, + p_cluster, rtol=1e-10, - err_msg="clustered-CR1 p-value no longer follows the residual-df convention", + err_msg="clustered-CR1 p-value no longer follows the 4.0 cluster-df (G-1) convention", ) rel_gap = abs(p_cluster - p_residual) / p_residual assert ( rel_gap > 0.03 ), f"fixture no longer distinguishes the df conventions (rel gap {rel_gap:.4f})" assert ( - abs(res.p_value - p_cluster) / p_cluster > 0.03 - ), "p-value matches the cluster-df convention — the documented deviation flipped" + abs(res.p_value - p_residual) / p_residual > 0.03 + ), "p-value matches the residual-df convention — the 4.0 default flipped back" + # explicit "residual" restores the 3.x residual-df tail exactly + res_r = TwoWayFixedEffects(df_convention="residual").fit( + data, outcome="outcome", treatment="treated", post="post", unit="unit" + ) + np.testing.assert_allclose(res_r.p_value, p_residual, rtol=1e-10) def test_ci_matches_r_twfe(self, r_twfe_results, r_benchmark_panel_data): - """CI bounds within the remaining df-convention band (measured ~1.7e-3). - - Post-K_reference the SE side matches fixest at machine precision, so - the bounds' residual gap through att ± crit*se is SOLELY the clustered - inference-df convention (Python t(residual df=148) critical value vs - fixest t(G−1=49); REGISTRY §TwoWayFixedEffects — PR C scope). - Tightened 0.005 → 0.003 accordingly (measured 1.7e-3/1.4e-3). The - ATT itself matches at machine precision.""" + """CI bounds match fixest at machine precision since the 4.0 flip. + + Post-K_reference the SE side matches fixest at machine precision, and + since the df_convention default flipped to "cluster" (M-005, item 7a) + the critical value is the same t(G−1=49) quantile fixest uses, so the + bounds through att ± crit*se agree to rounding (the 3.x residual-df + default left a ~1.7e-3 / 1.4e-3 band here, which is what the old + rtol=0.003 absorbed). The ATT itself matches at machine precision.""" data, _ = r_benchmark_panel_data py_results = self._run_python_twfe(data) @@ -589,13 +593,13 @@ def test_ci_matches_r_twfe(self, r_twfe_results, r_benchmark_panel_data): np.testing.assert_allclose( py_results.conf_int[0], r_twfe_results["ci_lower"], - rtol=0.003, + rtol=1e-9, err_msg=f"CI lower mismatch: Python={py_results.conf_int[0]:.6f}, R={r_twfe_results['ci_lower']:.6f}", ) np.testing.assert_allclose( py_results.conf_int[1], r_twfe_results["ci_upper"], - rtol=0.003, + rtol=1e-9, err_msg=f"CI upper mismatch: Python={py_results.conf_int[1]:.6f}, R={r_twfe_results['ci_upper']:.6f}", ) diff --git a/tests/test_stacked_did.py b/tests/test_stacked_did.py index 687379c55..8b9c25770 100644 --- a/tests/test_stacked_did.py +++ b/tests/test_stacked_did.py @@ -1964,18 +1964,30 @@ def _z_p(t_stat): return 2 * stats.norm.sf(abs(t_stat)) - def test_residual_default_is_t_with_pinned_df(self): - """The defect fix: hc1 lane moved z -> t(pooled residual df). Assert - the RESOLVED df value directly (a p-movement assertion is vacuous on - stacked designs: the residual df is large, t ~= z).""" + def test_default_is_t_g_minus_1_with_pinned_df(self): + """The 4.0 default (M-130): the hc1 lane uses t(G-1). Assert the + RESOLVED df value directly (a p-movement assertion is vacuous on + stacked designs for the residual arm: that df is large, t ~= z).""" res = StackedDiD(kappa_pre=2, kappa_post=2).fit(self._panel(), **self._kw) + assert res.inference_df == res.n_clusters - 1 + assert res.p_value == pytest.approx(self._t_p(res.t_stat, res.n_clusters - 1), rel=1e-12) + finite = {v for v in res.event_study_df.values() if np.isfinite(v)} + assert finite == {float(res.n_clusters - 1)} + + def test_residual_is_t_with_pinned_df(self): + """The 3.9 defect fix, now behind explicit "residual": the hc1 lane + uses t(pooled residual df), asserted through the RESOLVED df value + (that df is large, so t ~= z and a p-movement assertion is vacuous).""" + res = StackedDiD(kappa_pre=2, kappa_post=2, df_convention="residual").fit( + self._panel(), **self._kw + ) dfres = res.inference_df assert dfres is not None and dfres > res.n_clusters assert res.p_value == pytest.approx(self._t_p(res.t_stat, dfres), rel=1e-12) finite = {v for v in res.event_study_df.values() if np.isfinite(v)} assert finite == {dfres} - def test_small_n_residual_default_moves_off_z(self): + def test_small_n_default_moves_off_z(self): """On a SMALL stacked design the z -> t movement is measurable.""" res = StackedDiD(kappa_pre=1, kappa_post=1).fit(self._panel(n_units=12), **self._kw) assert np.isfinite(res.p_value) @@ -1983,7 +1995,7 @@ def test_small_n_residual_default_moves_off_z(self): def test_cluster_matches_g_minus_1_effective_count(self): data = self._panel() - r0 = StackedDiD(kappa_pre=2, kappa_post=2).fit(data, **self._kw) + r0 = StackedDiD(kappa_pre=2, kappa_post=2, df_convention="residual").fit(data, **self._kw) r1 = StackedDiD(kappa_pre=2, kappa_post=2, df_convention="cluster").fit(data, **self._kw) G = r1.n_clusters assert r1.att == r0.att and r1.se == r0.se @@ -2020,7 +2032,9 @@ def test_normal_reproduces_pre39_z(self): def test_bm_dof_precedence_over_knob(self): data = self._panel() - r_res = StackedDiD(kappa_pre=2, kappa_post=2, vcov_type="hc2_bm").fit(data, **self._kw) + r_res = StackedDiD( + kappa_pre=2, kappa_post=2, vcov_type="hc2_bm", df_convention="residual" + ).fit(data, **self._kw) r_clu = StackedDiD( kappa_pre=2, kappa_post=2, vcov_type="hc2_bm", df_convention="cluster" ).fit(data, **self._kw) @@ -2056,7 +2070,7 @@ def test_validation_and_transactional_set_params(self): est = StackedDiD() with pytest.raises(ValueError, match="df_convention"): est.set_params(df_convention="bogus", alpha=0.10) - assert est.df_convention == "residual" and est.alpha == 0.05 + assert est.df_convention == "cluster" and est.alpha == 0.05 # Valid value + unknown key: the unknown-key rejection must also # leave the estimator fully unchanged (no partial application). before = est.get_params() diff --git a/tests/test_sun_abraham.py b/tests/test_sun_abraham.py index 30c3ef82a..2e0539c7a 100644 --- a/tests/test_sun_abraham.py +++ b/tests/test_sun_abraham.py @@ -1565,7 +1565,7 @@ def test_hc2_bm_contrast_dof_helper_failure_falls_back(self): Distinct from the per-row NaN-DOF case above: when the helper RAISES, SA leaves the ``_compute_iw_effects`` output in place after warning. Since 3.9 (M-127) that output carries the df_convention-resolved - analytical df - FINITE t(residual df) under the default, recorded as + analytical df - FINITE t(G-1) under the 4.0 default, recorded as genuine provenance (previously normal theory with NaN provenance). """ import diff_diff.linalg as _linalg @@ -2094,20 +2094,28 @@ def _panel(seed=3): _kw = dict(outcome="outcome", unit="unit", time="time", first_treat="first_treat") - def test_residual_default_is_t_not_z(self): - """The D4/defect fix: aggregates use t(saturated residual df), not z.""" - res = SunAbraham().fit(self._panel(), **self._kw) + def test_default_is_t_g_minus_1_not_z(self): + """The 4.0 default: aggregates use t(G-1) (M-128), not z and not the + residual df; explicit "residual" keeps the D4-fix t(saturated + residual df).""" + data = self._panel() + res = SunAbraham().fit(data, **self._kw) p_z = 2 * stats.norm.sf(abs(res.t_stat)) assert res.p_value != p_z - dfres = res.inference_df - assert dfres is not None and dfres > 0 - assert res.p_value == pytest.approx(2 * stats.t.sf(abs(res.t_stat), dfres), rel=1e-12) - # every ES row shares the same finite residual df (one df source) - assert set(res.event_study_df.values()) == {dfres} + G = data["unit"].nunique() + assert res.inference_df == G - 1 + assert res.p_value == pytest.approx(2 * stats.t.sf(abs(res.t_stat), G - 1), rel=1e-12) + # every ES row shares the same G-1 df (one df source) + assert set(res.event_study_df.values()) == {float(G - 1)} + rr = SunAbraham(df_convention="residual").fit(data, **self._kw) + dfres = rr.inference_df + assert dfres is not None and dfres > G - 1 + assert rr.p_value == pytest.approx(2 * stats.t.sf(abs(rr.t_stat), dfres), rel=1e-12) + assert set(rr.event_study_df.values()) == {dfres} def test_cluster_matches_hand_reconstructed_g_minus_1(self): data = self._panel() - r0 = SunAbraham().fit(data, **self._kw) + r0 = SunAbraham(df_convention="residual").fit(data, **self._kw) r1 = SunAbraham(df_convention="cluster").fit(data, **self._kw) G = data["unit"].nunique() assert r1.att == r0.att and r1.se == r0.se @@ -2134,7 +2142,7 @@ def test_normal_reproduces_z_on_aggregates(self): def test_bm_dof_precedence_over_knob(self): """hc2_bm BM contrast DOF wins under every knob value.""" data = self._panel() - r_res = SunAbraham(vcov_type="hc2_bm").fit(data, **self._kw) + r_res = SunAbraham(vcov_type="hc2_bm", df_convention="residual").fit(data, **self._kw) r_clu = SunAbraham(vcov_type="hc2_bm", df_convention="cluster").fit(data, **self._kw) assert r_res.p_value == r_clu.p_value assert r_res.conf_int == r_clu.conf_int @@ -2174,7 +2182,7 @@ def test_validation_and_transactional_set_params(self): est = SunAbraham() with pytest.raises(ValueError, match="df_convention"): est.set_params(df_convention="bogus", alpha=0.10) - assert est.df_convention == "residual" and est.alpha == 0.05 + assert est.df_convention == "cluster" and est.alpha == 0.05 # Valid value + unknown key: the unknown-key rejection must also # leave the estimator fully unchanged (no partial application). before = est.get_params() @@ -2183,15 +2191,15 @@ def test_validation_and_transactional_set_params(self): assert est.get_params() == before def test_get_params_roundtrip_and_repeat_fit(self): - est = SunAbraham(df_convention="cluster") - assert est.get_params()["df_convention"] == "cluster" + est = SunAbraham(df_convention="residual") + assert est.get_params()["df_convention"] == "residual" clone = SunAbraham(**est.get_params()) data = self._panel() r1 = clone.fit(data, **self._kw) p1, d1 = r1.p_value, r1.inference_df r2 = clone.fit(data, **self._kw) assert r2.p_value == p1 and r2.inference_df == d1 - assert clone.df_convention == "cluster" + assert clone.df_convention == "residual" @pytest.fixture(scope="module") diff --git a/tests/test_t24_staggered_vs_collapsed_power_drift.py b/tests/test_t24_staggered_vs_collapsed_power_drift.py index 9744ceda8..286e82e3a 100644 --- a/tests/test_t24_staggered_vs_collapsed_power_drift.py +++ b/tests/test_t24_staggered_vs_collapsed_power_drift.py @@ -231,12 +231,12 @@ def test_rendered_surface_quotes(): "61.754", "94% / 80% / 61%", "~60% of the true lift", - "0.796", # 2x2 coverage, fast + "0.800", # 2x2 coverage, fast (4.0 df_convention flip: t(G-1) CIs; was 0.796) "0.004", # 2x2 coverage, slow ("almost never") "0.924", # CS coverage, fast (~95% regardless of speed) "~81%", # "2x2 reports ~81% of CS's number" # Section 2 headline: paired power + the MDE crossover prose - "2x2 power = 0.90", + "2x2 power = 0.89", # 4.0 df_convention flip (was 0.90 under residual df) "CS power = 0.75", "~0.37", # 2x2 MDE, fast "~0.60", # 2x2 MDE, slow (climbs) diff --git a/tests/test_v4_inference_defaults.py b/tests/test_v4_inference_defaults.py new file mode 100644 index 000000000..779b2873d --- /dev/null +++ b/tests/test_v4_inference_defaults.py @@ -0,0 +1,394 @@ +"""Item 7a of the 4.0 cut: the ``df_convention`` default flip. + +Ledger rows M-004 (DifferenceInDifferences), M-005 (TwoWayFixedEffects), +M-006 (LinearRegression), M-128 (SunAbraham), M-129 (WooldridgeDiD), M-130 +(StackedDiD), M-131 (ImputationDiD): the analytical tail-df convention for +CLUSTERED fits flipped from ``"residual"`` (the fit's ``n - K_full``) to +``"cluster"`` (the Stata/fixest ``G - 1``). LPDiD adopted ``"cluster"`` at 3.9 +and has no row. + +What moves: p-values and confidence intervals on clustered analytical fits +(TWFE / SunAbraham / WooldridgeDiD-OLS / StackedDiD / ImputationDiD-pretrends +cluster at unit BY DEFAULT, so their default fits move; plain +DifferenceInDifferences moves only with an explicit ``cluster=``). What does +not: point estimates, SEs and t-statistics; survey/replicate df and hc2_bm +Bell-McCaffrey DOF (they keep precedence); bootstrap percentile p/CIs; +unclustered and Conley fits (the knob is inert without a cluster count). +``df_convention="residual"`` restores the 3.x inference bit-for-bit. + +Every G-1 pin below takes G from the surface's own clustered sample: +``data[unit].nunique()`` for DiD / TWFE / SunAbraham (their results carry no +analytical ``n_clusters``), ``res.n_clusters`` for WooldridgeDiD and +StackedDiD, and the lead regression's untreated sample for the ImputationDiD +leads (every unit of the shared panel has untreated rows, so that count is +the panel's unit count too). +""" + +from __future__ import annotations + +import inspect +import pathlib +import warnings + +import numpy as np +import pandas as pd +import pytest +import yaml +from scipy import stats + +from diff_diff import ( + DifferenceInDifferences, + ImputationDiD, + LPDiD, + StackedDiD, + SunAbraham, + SurveyDesign, + SyntheticDiD, + TwoWayFixedEffects, + WooldridgeDiD, + generate_staggered_data, +) +from diff_diff.linalg import LinearRegression + +REPO = pathlib.Path(__file__).resolve().parent.parent +THIS_TEST_REF = "tests/test_v4_inference_defaults.py" + +FLIP_ROWS = { + "M-004": DifferenceInDifferences, + "M-005": TwoWayFixedEffects, + "M-006": LinearRegression, + "M-128": SunAbraham, + "M-129": WooldridgeDiD, + "M-130": StackedDiD, + "M-131": ImputationDiD, +} +# LinearRegression is a plain class (no BaseEstimator mixin): no get/set_params. +BASE_ESTIMATOR_KNOB_CLASSES = [ + DifferenceInDifferences, + TwoWayFixedEffects, + SunAbraham, + WooldridgeDiD, + StackedDiD, + ImputationDiD, + LPDiD, +] + + +def t_tail(t_stat, df): + return 2 * stats.t.sf(abs(t_stat), df) + + +def z_tail(t_stat): + return 2 * stats.norm.sf(abs(t_stat)) + + +@pytest.fixture(scope="module") +def panel(): + """60 units x 8 periods, cohorts {never, 4, 6}; every unit has untreated + rows (treatment starts at period 4 at the earliest).""" + rng = np.random.default_rng(7) + rows = [] + for u in range(60): + ft = [0, 4, 6][u % 3] + lat, lon = 40.0 + 0.05 * (u // 6), -100.0 + 0.05 * (u % 6) + for t in range(1, 9): + eff = 0.6 if (ft and t >= ft) else 0.0 + rows.append( + dict( + unit=u, + time=t, + first_treat=ft, + grp=int(ft > 0), + post=int(t >= 4), + outcome=0.3 * u / 60 + 0.1 * t + eff + rng.standard_normal() * 0.6, + lat=lat, + lon=lon, + ) + ) + df = pd.DataFrame(rows) + df["d"] = df["grp"] * df["post"] + return df + + +SA_KW = dict(outcome="outcome", unit="unit", time="time", first_treat="first_treat") + + +# --------------------------------------------------------------------------- # +# (1) Signature / params pins +# --------------------------------------------------------------------------- # + + +class TestSignatureDefaults: + @pytest.mark.parametrize("cls", list(FLIP_ROWS.values()) + [LPDiD], ids=lambda c: c.__name__) + def test_signature_default_is_cluster(self, cls): + default = inspect.signature(cls.__init__).parameters["df_convention"].default + assert default == "cluster" + + @pytest.mark.parametrize("cls", BASE_ESTIMATOR_KNOB_CLASSES, ids=lambda c: c.__name__) + def test_get_params_reads_cluster_and_set_params_round_trips(self, cls): + est = cls() + assert est.get_params()["df_convention"] == "cluster" + for value in ("residual", "normal", "cluster"): + est.set_params(df_convention=value) + assert est.get_params()["df_convention"] == value + assert cls(**est.get_params()).df_convention == value + + def test_synthetic_did_inherits_the_flip_inertly(self): + """SyntheticDiD subclasses DifferenceInDifferences and never reads the + knob (placebo / bootstrap / jackknife variance); the inherited + attribute flips with the base class and is NOT a SyntheticDiD + parameter.""" + sdid = SyntheticDiD() + assert sdid.df_convention == "cluster" + assert "df_convention" not in sdid.get_params() + + +# --------------------------------------------------------------------------- # +# (2) Default fits sit on t(G-1); "residual" restores the 3.x tail +# --------------------------------------------------------------------------- # + + +class TestDefaultFitsUseClusterDf: + def test_did_explicit_cluster(self, panel): + G = panel["unit"].nunique() + kw = dict(outcome="outcome", treatment="grp", post="post", unit="unit") + res = DifferenceInDifferences(cluster="unit").fit(panel, **kw) + assert res.inference_df == G - 1 + np.testing.assert_allclose(res.p_value, t_tail(res.t_stat, G - 1), rtol=1e-12) + rr = DifferenceInDifferences(cluster="unit", df_convention="residual").fit(panel, **kw) + assert rr.inference_df > G - 1 # n - K_full + assert (rr.att, rr.se, rr.t_stat) == (res.att, res.se, res.t_stat) + assert rr.p_value != res.p_value + np.testing.assert_allclose(rr.p_value, t_tail(rr.t_stat, rr.inference_df), rtol=1e-12) + + def test_twfe_static_auto_cluster(self, panel): + G = panel["unit"].nunique() + kw = dict(outcome="outcome", treatment="d", post="post", unit="unit") + res = TwoWayFixedEffects().fit(panel, **kw) + assert res.inference_df == G - 1 + np.testing.assert_allclose(res.p_value, t_tail(res.t_stat, G - 1), rtol=1e-12) + rr = TwoWayFixedEffects(df_convention="residual").fit(panel, **kw) + assert rr.se == res.se and rr.t_stat == res.t_stat and rr.p_value != res.p_value + + @pytest.mark.parametrize("spec", ["within", "pooled"]) + def test_twfe_event_study_auto_cluster(self, panel, spec): + """Both specs auto-cluster at unit when ``unit=`` is passed (a pooled + fit WITHOUT ``unit=`` is unclustered and the knob is inert there - + that is the committed ``pooled_default`` oracle lane).""" + G = panel["unit"].nunique() + kw = dict( + outcome="outcome", + treatment="grp", + unit="unit", + time="time", + event_study=True, + spec=spec, + post_periods=[4, 5, 6, 7, 8], + reference_period=3, + ) + res = TwoWayFixedEffects().fit(panel, **kw) + est = ~res.is_reference + assert est.sum() == 7 # periods 1, 2 (pre) + 4..8 (post) + np.testing.assert_array_equal(res.df[est], float(G - 1)) + np.testing.assert_allclose( + res.p_value[est], t_tail(res.att[est] / res.se[est], G - 1), rtol=1e-12 + ) + rr = TwoWayFixedEffects(df_convention="residual").fit(panel, **kw) + np.testing.assert_array_equal(rr.se, res.se) + assert (rr.df[est] > G - 1).all() + assert (rr.p_value[est] != res.p_value[est]).all() + + def test_sun_abraham(self, panel): + G = panel["unit"].nunique() + res = SunAbraham().fit(panel, **SA_KW) + assert res.inference_df == G - 1 + np.testing.assert_allclose(res.p_value, t_tail(res.t_stat, G - 1), rtol=1e-12) + assert set(res.event_study_df.values()) == {float(G - 1)} + # every event-study row (the cohort x event-time cells keep only + # coefficient/SE on the public surface) + for eff in res.event_study_effects.values(): + np.testing.assert_allclose(eff["p_value"], t_tail(eff["t_stat"], G - 1), rtol=1e-12) + rr = SunAbraham(df_convention="residual").fit(panel, **SA_KW) + assert rr.inference_df > G - 1 and rr.se == res.se and rr.p_value != res.p_value + + def test_wooldridge_ols(self, panel): + res = WooldridgeDiD(method="ols").fit(panel, **SA_KW) + G = res.n_clusters + assert G == panel["unit"].nunique() + np.testing.assert_allclose(res.p_value, t_tail(res.t_stat, G - 1), rtol=1e-12) + for eff in res.group_time_effects.values(): + np.testing.assert_allclose(eff["p_value"], t_tail(eff["t_stat"], G - 1), rtol=1e-12) + rr = WooldridgeDiD(method="ols", df_convention="residual").fit(panel, **SA_KW) + assert rr.se == res.se and rr.p_value != res.p_value + assert rr.p_value != t_tail(rr.t_stat, G - 1) + + def test_wooldridge_bootstrap_fit_keeps_analytical_lanes_on_g_minus_1(self, panel): + """Only the overall p/CI are percentile-overridden on a bootstrap fit; + post-fit aggregate('group') re-derives ANALYTICAL inference from the + stored fallback, which now carries G-1.""" + res = WooldridgeDiD(method="ols", n_bootstrap=19, seed=1).fit(panel, **SA_KW) + G = res.n_clusters + for ge in res.aggregate("group").group_effects.values(): + np.testing.assert_allclose(ge["p_value"], t_tail(ge["t_stat"], G - 1), rtol=1e-12) + + def test_wooldridge_glm_inert_config_warning_tracks_the_new_default(self, panel): + data = panel.copy() + data["bin"] = (data["outcome"] > data["outcome"].median()).astype(int) + kw = dict(outcome="bin", unit="unit", time="time", first_treat="first_treat") + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + WooldridgeDiD(method="logit").fit(data, **kw) + assert not any("no effect on the logit/poisson" in str(w.message) for w in caught) + with pytest.warns(UserWarning, match="no effect on the logit/poisson"): + WooldridgeDiD(method="logit", df_convention="residual").fit(data, **kw) + + def test_stacked_did(self): + data = generate_staggered_data(n_units=60, n_periods=10, seed=9) + kw = dict(outcome="outcome", unit="unit", time="period", first_treat="first_treat") + res = StackedDiD(kappa_pre=2, kappa_post=2).fit(data, **kw) + G = res.n_clusters + assert res.inference_df == G - 1 + np.testing.assert_allclose(res.p_value, t_tail(res.t_stat, G - 1), rtol=1e-12) + assert set(res.event_study_df.values()) == {float(G - 1)} + surface = res.aggregate("event_study") + rows = ~surface.is_reference & np.isfinite(surface.p_value) + assert rows.any() + np.testing.assert_array_equal(surface.df[rows], float(G - 1)) + rr = StackedDiD(kappa_pre=2, kappa_post=2, df_convention="residual").fit(data, **kw) + assert rr.inference_df > G - 1 and rr.se == res.se + + def test_imputation_pretrends_leads(self, panel): + """The lead regression clusters on the untreated sample; every unit of + the panel has untreated rows, so G_lead is the unit count. The + surface's ``df`` column is NaN on plain fits by design (it carries the + survey df only), so the pin goes through the p-value.""" + G_lead = panel.loc[ + panel["time"] < panel["first_treat"].replace(0, np.inf), "unit" + ].nunique() + assert G_lead == panel["unit"].nunique() + res = ImputationDiD(pretrends=True).fit(panel, **SA_KW) + es = res.aggregate("event_study") + leads = (es.event_time < -1) & np.isfinite(es.p_value) + assert leads.sum() >= 2 + np.testing.assert_allclose( + es.p_value[leads], t_tail(es.att[leads] / es.se[leads], G_lead - 1), rtol=1e-12 + ) + assert np.isnan(np.asarray(es.df, dtype=float)[leads]).all() + rr = ImputationDiD(pretrends=True, df_convention="residual").fit(panel, **SA_KW) + es_r = rr.aggregate("event_study") + np.testing.assert_array_equal(es_r.se[leads], es.se[leads]) + assert (es_r.p_value[leads] != es.p_value[leads]).all() + # post rows are BJS (knob-independent) + post = es.event_time >= 0 + np.testing.assert_array_equal(es_r.p_value[post], es.p_value[post]) + + def test_imputation_bootstrap_fit_keeps_analytical_leads_on_g_minus_1(self, panel): + G_lead = panel["unit"].nunique() + res = ImputationDiD(pretrends=True, n_bootstrap=19, seed=1).fit(panel, **SA_KW) + es = res.aggregate("event_study") + leads = (es.event_time < -1) & np.isfinite(es.p_value) + np.testing.assert_allclose( + es.p_value[leads], t_tail(es.att[leads] / es.se[leads], G_lead - 1), rtol=1e-12 + ) + + def test_imputation_inert_config_warning_tracks_the_new_default(self, panel): + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + ImputationDiD().fit(panel, **SA_KW) + assert not any("affects only the pretrends" in str(w.message) for w in caught) + with pytest.warns(UserWarning, match="affects only the pretrends"): + ImputationDiD(df_convention="residual").fit(panel, **SA_KW) + + def test_linear_regression_direct(self): + rng = np.random.default_rng(3) + n, G = 120, 12 + X = np.column_stack([np.ones(n), rng.normal(size=(n, 2))]) + y = X @ np.array([1.0, 0.4, 2.0]) + rng.normal(size=n) + cl = np.repeat(np.arange(G), n // G) + reg = LinearRegression(cluster_ids=cl, include_intercept=False).fit(X, y) + inf = reg.get_inference(1) + assert inf.df == G - 1 + np.testing.assert_allclose(inf.p_value, t_tail(inf.t_stat, G - 1), rtol=1e-12) + reg_r = LinearRegression( + cluster_ids=cl, include_intercept=False, df_convention="residual" + ).fit(X, y) + inf_r = reg_r.get_inference(1) + assert inf_r.df == n - 3 and inf_r.se == inf.se and inf_r.p_value != inf.p_value + + +# --------------------------------------------------------------------------- # +# (3) Where the knob is inert, nothing moved +# --------------------------------------------------------------------------- # + + +class TestInertSurfaces: + def test_unclustered_did_is_bit_identical(self, panel): + kw = dict(outcome="outcome", treatment="grp", post="post") + r0 = DifferenceInDifferences().fit(panel, **kw) + rr = DifferenceInDifferences(df_convention="residual").fit(panel, **kw) + assert (r0.p_value, r0.conf_int, r0.inference_df) == ( + rr.p_value, + rr.conf_int, + rr.inference_df, + ) + + def test_conley_twfe_is_bit_identical(self, panel): + kw = dict(outcome="outcome", treatment="d", post="post", unit="unit") + common = dict( + vcov_type="conley", + conley_coords=("lat", "lon"), + conley_cutoff_km=50.0, + conley_lag_cutoff=0, + ) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + r0 = TwoWayFixedEffects(**common).fit(panel, **kw) + rr = TwoWayFixedEffects(**common, df_convention="residual").fit(panel, **kw) + assert (r0.p_value, r0.conf_int) == (rr.p_value, rr.conf_int) + + def test_survey_df_keeps_precedence(self, panel): + data = panel.copy() + data["w"] = 1.0 + (data["unit"] % 5) * 0.2 + data["stratum"] = data["unit"] % 4 + design = SurveyDesign(weights="w", strata="stratum", psu="unit") + kw = dict(outcome="outcome", treatment="grp", post="post", unit="unit") + r0 = DifferenceInDifferences().fit(data, survey_design=design, **kw) + rr = DifferenceInDifferences(df_convention="residual").fit(data, survey_design=design, **kw) + assert (r0.p_value, r0.conf_int) == (rr.p_value, rr.conf_int) + assert r0.inference_df is not None and r0.inference_df != panel["unit"].nunique() - 1 + + def test_normal_is_deliberate_z(self, panel): + kw = dict(outcome="outcome", treatment="grp", post="post", unit="unit") + r0 = DifferenceInDifferences(cluster="unit").fit(panel, **kw) + rn = DifferenceInDifferences(cluster="unit", df_convention="normal").fit(panel, **kw) + assert rn.se == r0.se and rn.p_value != r0.p_value + np.testing.assert_allclose(rn.p_value, z_tail(rn.t_stat), rtol=1e-14) + + def test_one_effective_cluster_still_raises_at_the_vcov_layer(self): + """Unchanged from 3.x: a weighted CR1 fit with ONE positive-weight + cluster raises before any df is resolved (under every convention).""" + rng = np.random.default_rng(5) + n = 60 + X = np.column_stack([np.ones(n), rng.normal(size=(n, 2))]) + y = X @ np.array([1.0, 0.5, 2.0]) + rng.normal(size=n) + cl = np.repeat(np.arange(3), 20) + w = np.where(cl == 0, 2.0, 0.0) + with pytest.raises(ValueError, match="at least 2 clusters"): + LinearRegression(cluster_ids=cl, include_intercept=False, weights=w).fit(X, y) + + +# --------------------------------------------------------------------------- # +# (4) Ledger +# --------------------------------------------------------------------------- # + + +def test_ledger_rows_are_done_and_point_here(): + ledger = yaml.safe_load((REPO / "docs" / "v4-deprecations.yaml").read_text()) + rows = {r["id"]: r for r in ledger["rows"]} + for rid, cls in FLIP_ROWS.items(): + row = rows[rid] + assert row["kind"] == "default-flip" + assert row["status"] == "done", rid + assert row["test_ref"] == THIS_TEST_REF, rid + assert row["new_default"] == "'cluster'" + assert row["old"].endswith(f":{cls.__name__}[df_convention]"), (rid, row["old"]) diff --git a/tests/test_variance_conventions.py b/tests/test_variance_conventions.py index fa2703b3a..3612769b3 100644 --- a/tests/test_variance_conventions.py +++ b/tests/test_variance_conventions.py @@ -188,57 +188,60 @@ def snapshot(self): ROWS = [ dict( key="did_absorb_hc1_cluster_unit", - fit=lambda df: diff_diff.DifferenceInDifferences(cluster="unit").fit( - df, outcome="y", treatment="grp", post="post", absorb=["unit", "time"] - ), + fit=lambda df: diff_diff.DifferenceInDifferences( + cluster="unit", df_convention="residual" + ).fit(df, outcome="y", treatment="grp", post="post", absorb=["unit", "time"]), cr1_k=(7,), tail_df=(294.0,), status="legitimate", reason=( "K_reference (D2 fixed): k = 2 visible + rank(time given unit) = 5; " - "matches reghdfe/fixest ssc(K.fixef='nested')" + "matches reghdfe/fixest ssc(K.fixef='nested') Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="did_fixed_effects_hc1_cluster_unit", - fit=lambda df: diff_diff.DifferenceInDifferences(cluster="unit").fit( - df, outcome="y", treatment="grp", post="post", fixed_effects=["unit", "time"] - ), + fit=lambda df: diff_diff.DifferenceInDifferences( + cluster="unit", df_convention="residual" + ).fit(df, outcome="y", treatment="grp", post="post", fixed_effects=["unit", "time"]), cr1_k=(7,), tail_df=(294.0,), status="legitimate", reason=( "K_reference (D1 fixed): 66 visible minus the 59 cluster-nested " "unit dummies -> identical SE to did_absorb (documented deviation " - "from a literal explicit-dummy R comparison, which counts all 66)" + "from a literal explicit-dummy R comparison, which counts all 66) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="did_plain_hc1_cluster_unit", - fit=lambda df: diff_diff.DifferenceInDifferences(cluster="unit").fit( - df, outcome="y", treatment="grp", post="post" - ), + fit=lambda df: diff_diff.DifferenceInDifferences( + cluster="unit", df_convention="residual" + ).fit(df, outcome="y", treatment="grp", post="post"), cr1_k=(4,), tail_df=(356.0,), status="legitimate", - reason="no absorbed FE: visible k is the whole design; nothing is omitted", + reason=( + "no absorbed FE: visible k is the whole design; nothing is omitted." + " Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." + ), ), dict( key="twfe_hc1_cluster_unit_time_post", - fit=lambda df: diff_diff.TwoWayFixedEffects(vcov_type="hc1", cluster="unit").fit( - df, outcome="y", treatment="grp", post="post", unit="unit" - ), + fit=lambda df: diff_diff.TwoWayFixedEffects( + vcov_type="hc1", cluster="unit", df_convention="residual" + ).fit(df, outcome="y", treatment="grp", post="post", unit="unit"), cr1_k=(3,), tail_df=(298.0,), status="legitimate", reason=( "K_reference (D2 fixed): 2 visible + rank(post given unit) = 1; " - "matches fixest cluster arm at rel 0 (committed golden)" + "matches fixest cluster arm at rel 0 (committed golden) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="wooldridge_hc1_within", - fit=lambda df: diff_diff.WooldridgeDiD(method="ols").fit( + fit=lambda df: diff_diff.WooldridgeDiD(method="ols", df_convention="residual").fit( df, outcome="y", unit="unit", time="time", first_treat="first_treat" ), cr1_k=(15,), @@ -249,12 +252,12 @@ def snapshot(self): "col -> +1 term; jwdid arms at ratio 1.0); tail df converged in " "3.9 (M-127): the default-hc1 arms use t(residual df = n - " "k_kept - absorbed rank) via the df_convention knob (was silent " - "normal theory); G-1 under 'cluster', z under 'normal'" + "normal theory); G-1 under 'cluster', z under 'normal' Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="sun_abraham_hc1", - fit=lambda df: diff_diff.SunAbraham().fit( + fit=lambda df: diff_diff.SunAbraham(df_convention="residual").fit( df, outcome="y", unit="unit", time="time", first_treat="first_treat" ), cr1_k=(21,), @@ -265,12 +268,12 @@ def snapshot(self): "fixest sunab parity ~5e-15); D4 fixed in 3.9 (M-127): cells AND " "aggregates share the saturated fit's residual df under the " "df_convention knob (aggregates previously dropped to normal " - "theory inside the same fit)" + "theory inside the same fit) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="stacked_did_hc1", - fit=lambda df: diff_diff.StackedDiD().fit( + fit=lambda df: diff_diff.StackedDiD(df_convention="residual").fit( df, outcome="y", unit="unit", time="time", first_treat="first_treat" ), cr1_k=(6,), @@ -285,7 +288,7 @@ def snapshot(self): "pins vcovCR(type='CR1S') at atol=1e-10); tail df converged in " "3.9 (M-127): t(pooled residual df n_eff - k_kept) via the " "df_convention knob (was silent normal theory; stacked rows make " - "the residual df large, so z -> t is a convention alignment)" + "the residual df large, so z -> t is a convention alignment) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( @@ -298,13 +301,13 @@ def snapshot(self): status="legitimate", reason=( "L2: G-1 tail df (Stata/fixest convention) — since 3.9 the " - "df_convention='cluster' DEFAULT on LPDiD (bit-identical; the " - "one surface already at the v4 target)" + "df_convention='cluster' DEFAULT on LPDiD (bit-identical; adopted " + "at 3.9, the library-wide default since the 4.0 flip)" ), ), dict( key="twfe_es_within_hc1_cluster_unit", - fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="unit").fit( + fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="unit", df_convention="residual").fit( df, outcome="y", treatment="grp", @@ -324,12 +327,12 @@ def snapshot(self): "TWFE event-study, within spec (4.0 cut, PR 4c-i): K_reference = 6 " "visible (intercept + 5 interactions) + rank(time given the " "absorbed, cluster-nested unit FE) = 5, i.e. 11; the full-dummy form " - "would read 70 visible minus the 59 cluster-nested unit dummies = 11" + "would read 70 visible minus the 59 cluster-nested unit dummies = 11 Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="twfe_es_pooled_hc1_cluster_time", - fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="time").fit( + fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="time", df_convention="residual").fit( df, outcome="y", treatment="grp", @@ -346,7 +349,7 @@ def snapshot(self): "TWFE event-study, pooled spec (4.0 cut, PR 4c-i): the NESTED " "orientation of the built-in period dummies under a time cluster - 12 " "visible minus their rank 5 = 7 (under-subtraction is caught here; the " - "unit-cluster within row catches over-)" + "unit-cluster within row catches over-) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( @@ -383,14 +386,16 @@ def snapshot(self): key="imputation_pretrends_event_study", # The pretrends lead regression runs on the post-fit event-study # route (4.0: no fit-time aggregate=), so the row spies fit + aggregate. - fit=lambda df: diff_diff.ImputationDiD(pretrends=True) + fit=lambda df: diff_diff.ImputationDiD(pretrends=True, df_convention="residual") .fit(df, outcome="y", unit="unit", time="time", first_treat="first_treat") .aggregate("event_study"), cr1_k=None, # contract row: shared CR1 REACHED; exact k is config-detail # Pinned since 3.9 (M-127): the three lead calls carry the knob- # resolved residual df (157 = n_untreated - k_kept - absorbed rank - # on the audit panel); the None entries are the knob-INDEPENDENT - # BJS overall/event-study calls (L3) captured by the same spy. + # on the audit panel; explicit 'residual' since the 4.0 default is + # G-1 - see the _default_df twin); the None entries are the + # knob-INDEPENDENT BJS overall/event-study calls (L3) captured by + # the same spy. tail_df=(157.0,) * 3 + (None,) * 5, # The increment IS pinned even though k is not: every clustered-hc1 # call on this surface must carry the [time, unit] no-intercept @@ -540,6 +545,133 @@ def snapshot(self): "no CR1 sandwich, normal-theory safe_inference gate only" ), ), + # ---- 4.0 default twins: identical fits WITHOUT the explicit knob, so the + # tail df is G-1 (G = clusters with positive weight on each surface's + # clustered sample). The cr1_k literals are copied from the residual twins + # (the knob never touches the sandwich). + dict( + key="did_absorb_hc1_cluster_unit_default_df", + fit=lambda df: diff_diff.DifferenceInDifferences(cluster="unit").fit( + df, outcome="y", treatment="grp", post="post", absorb=["unit", "time"] + ), + cr1_k=(7,), + tail_df=(59.0,), + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="twfe_hc1_cluster_unit_default_df", + fit=lambda df: diff_diff.TwoWayFixedEffects(vcov_type="hc1", cluster="unit").fit( + df, outcome="y", treatment="grp", post="post", unit="unit" + ), + cr1_k=(3,), + tail_df=(59.0,), + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="wooldridge_hc1_within_default_df", + fit=lambda df: diff_diff.WooldridgeDiD(method="ols").fit( + df, outcome="y", unit="unit", time="time", first_treat="first_treat" + ), + cr1_k=(15,), + tail_df=(59.0,) * 10, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="sun_abraham_hc1_default_df", + fit=lambda df: diff_diff.SunAbraham().fit( + df, outcome="y", unit="unit", time="time", first_treat="first_treat" + ), + cr1_k=(21,), + tail_df=(59.0,) * 23, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="stacked_did_hc1_default_df", + fit=lambda df: diff_diff.StackedDiD().fit( + df, outcome="y", unit="unit", time="time", first_treat="first_treat" + ), + cr1_k=(6,), + tail_df=(59.0,) * 3, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="twfe_es_within_hc1_cluster_unit_default_df", + fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="unit").fit( + df, + outcome="y", + treatment="grp", + unit="unit", + time="time", + event_study=True, + spec="within", + post_periods=[4, 5, 6], + reference_period=1, + ), + cr1_k=(11,), + tail_df=(59.0,) * 5, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="twfe_es_pooled_hc1_cluster_time_default_df", + fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="time").fit( + df, + outcome="y", + treatment="grp", + time="time", + event_study=True, + spec="pooled", + post_periods=[4, 5, 6], + reference_period=1, + ), + cr1_k=(7,), + tail_df=(5.0,) * 5, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="imputation_pretrends_event_study_default_df", + fit=lambda df: diff_diff.ImputationDiD(pretrends=True) + .fit(df, outcome="y", unit="unit", time="time", first_treat="first_treat") + .aggregate("event_study"), + cr1_k=None, + # G-1 of the lead regression's untreated sample (59 on the audit + # panel: every unit has untreated rows); the None entries are the + # knob-independent BJS calls, exactly as on the residual twin. + tail_df=(59.0,) * 3 + (None,) * 5, + expected_adjustment=6, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), ] _FAST_KEYS = { @@ -548,6 +680,9 @@ def snapshot(self): "did_fixed_effects_hc1_cluster_unit", "twfe_hc1_cluster_unit_time_post", "wooldridge_hc1_within", + "did_absorb_hc1_cluster_unit_default_df", + "twfe_hc1_cluster_unit_default_df", + "wooldridge_hc1_within_default_df", "lpdid_pre2_post2", "callaway_santanna_default", } @@ -970,15 +1105,21 @@ def test_disconnected_end_to_end_twfe(self): df = self._disconnected(effect=0.5) with warnings.catch_warnings(): warnings.simplefilter("ignore") - tw = diff_diff.TwoWayFixedEffects(vcov_type="hc1", cluster="unit").fit( + tw = diff_diff.TwoWayFixedEffects( + vcov_type="hc1", cluster="unit", df_convention="residual" + ).fit(df, outcome="y", treatment="grp", post="time", unit="unit") + tw_default = diff_diff.TwoWayFixedEffects(vcov_type="hc1", cluster="unit").fit( df, outcome="y", treatment="grp", post="time", unit="unit" ) assert np.isfinite(tw.se) - # Discriminating df pin: the C=2 rank lowers the absorbed adjustment - # by 1, so the reported residual df moves off the legacy value - - # reverting the caller to sum(levels-1) reads 114.0 on this fixture - # and fails here. + # Discriminating df pin (explicit 'residual' since the 4.0 default is + # G-1): the C=2 rank lowers the absorbed adjustment by 1, so the + # reported residual df moves off the legacy value - reverting the + # caller to sum(levels-1) reads 114.0 on this fixture and fails here. assert tw.inference_df == 115.0 + # The 4.0 default on the same fit: t(G-1), G = unique units. + assert tw_default.inference_df == df["unit"].nunique() - 1 + assert tw_default.se == tw.se def test_fail_closed_boundary_moves_both_directions(self): """Lowering the absorbed df by C-1 moves the n - k - adj boundary. diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 9814f8372..045611bec 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -4662,21 +4662,29 @@ def _z_p(t_stat): return 2 * stats.norm.sf(abs(t_stat)) - def test_default_hc1_is_t_residual_not_z(self): - """The defect fix: the auto-clustered hc1 arm moved z -> t(residual).""" - res = WooldridgeDiD().fit(self._panel(), **self._kw) + def test_default_hc1_is_t_g_minus_1_not_z(self): + """The 4.0 default (M-129): the auto-clustered hc1 arm uses t(G-1) - + not z (the pre-3.9 defect) and not the residual df (the 3.x default).""" + data = self._panel() + res = WooldridgeDiD().fit(data, **self._kw) assert res.p_value != self._z_p(res.t_stat) - # residual df = n - k_kept - absorbed [unit, time] rank; recover it + G = res.n_clusters + assert G == data["unit"].nunique() + # recover the df the p-value was computed at: it is exactly G-1 n = 300 match = [d for d in range(2, n) if abs(res.p_value - self._t_p(res.t_stat, d)) < 1e-13] - assert len(match) == 1, match + assert match == [G - 1], match # per-cell inference shares the same df _, eff = next(iter(res.group_time_effects.items())) - assert eff["p_value"] == pytest.approx(self._t_p(eff["t_stat"], match[0]), rel=1e-12) + assert eff["p_value"] == pytest.approx(self._t_p(eff["t_stat"], G - 1), rel=1e-12) + # explicit "residual" is the 3.x residual df (n - k_kept - absorbed rank) + rr = WooldridgeDiD(df_convention="residual").fit(data, **self._kw) + match_r = [d for d in range(2, n) if abs(rr.p_value - self._t_p(rr.t_stat, d)) < 1e-13] + assert len(match_r) == 1 and match_r[0] > G - 1, match_r def test_cluster_matches_g_minus_1(self): data = self._panel() - r0 = WooldridgeDiD().fit(data, **self._kw) + r0 = WooldridgeDiD(df_convention="residual").fit(data, **self._kw) r1 = WooldridgeDiD(df_convention="cluster").fit(data, **self._kw) G = data["unit"].nunique() assert r1.att == r0.att and r1.se == r0.se @@ -4707,10 +4715,12 @@ def test_classical_and_hc2_bit_identical_under_default(self): def test_hc1_cohort_trends_uses_full_dummy_residual_df(self): """The easy-to-miss lane: hc1 + cohort_trends routes FULL-DUMMY, so - its residual df is n - k_kept (no absorbed-rank subtraction).""" + its residual df is n - k_kept (no absorbed-rank subtraction). Both + arms pin the explicit "residual" branch (the 3.x default); the + default twin at the end pins G-1 on the same full-dummy lane.""" data = self._panel() - res = WooldridgeDiD(cohort_trends=True).fit(data, **self._kw) - res_within = WooldridgeDiD().fit(data, **self._kw) + res = WooldridgeDiD(cohort_trends=True, df_convention="residual").fit(data, **self._kw) + res_within = WooldridgeDiD(df_convention="residual").fit(data, **self._kw) _, eff = next(iter(res.group_time_effects.items())) n = len(data) # full-dummy residual df: recover as n - k_kept @@ -4727,6 +4737,12 @@ def test_hc1_cohort_trends_uses_full_dummy_residual_df(self): d for d in range(2, n) if abs(effw["p_value"] - self._t_p(effw["t_stat"], d)) < 1e-13 ] assert match_w and (n - match_fd[0]) != match_w[0] + # default twin: the full-dummy lane also honours the 4.0 G-1 default + res_def = WooldridgeDiD(cohort_trends=True).fit(data, **self._kw) + _, eff_def = next(iter(res_def.group_time_effects.items())) + assert eff_def["p_value"] == pytest.approx( + self._t_p(eff_def["t_stat"], res_def.n_clusters - 1), rel=1e-12 + ) def test_postfit_aggregate_reproduces_fit_time_convention(self): """aggregate() consumes the stored resolved fallback on every OLS arm.""" @@ -4754,7 +4770,7 @@ def test_glm_warns_on_explicit_nondefault_only(self): data["bin"] = (data["outcome"] > data["outcome"].median()).astype(int) kw = dict(outcome="bin", unit="unit", time="time", first_treat="cohort") with pytest.warns(UserWarning, match="no effect on the logit/poisson"): - WooldridgeDiD(method="logit", df_convention="cluster").fit(data, **kw) + WooldridgeDiD(method="logit", df_convention="residual").fit(data, **kw) with warnings.catch_warnings(record=True) as caught: warnings.simplefilter("always") WooldridgeDiD(method="logit").fit(data, **kw) @@ -4808,11 +4824,11 @@ def test_validation_and_transactional_set_params(self): est = WooldridgeDiD() with pytest.raises(ValueError, match="df_convention"): est.set_params(df_convention="bogus", alpha=0.10) - assert est.df_convention == "residual" and est.alpha == 0.05 + assert est.df_convention == "cluster" and est.alpha == 0.05 # Valid value + unknown key: the unknown-key rejection must also # leave the estimator fully unchanged (no partial application). before = est.get_params() with pytest.raises(ValueError, match="Unknown parameter"): est.set_params(df_convention="normal", nonexistent_param=1) assert est.get_params() == before - assert WooldridgeDiD(df_convention="cluster").get_params()["df_convention"] == "cluster" + assert WooldridgeDiD(df_convention="residual").get_params()["df_convention"] == "residual" From 9c0645bec9c761695c9064a707ced896e26aa217 Mon Sep 17 00:00:00 2001 From: igerber Date: Wed, 28 Oct 2026 12:17:01 -0400 Subject: [PATCH 2/3] docs(todo): track the deferred tutorial refresh after the df_convention flip CI review on PR #879 (P2): the nine tutorials whose committed outputs still show the 3.x residual-df p-values/CIs were scheduled only in the spec's item 10. Add the TODO.md Actionable Backlog row (Testing / docs) listing the notebooks, the Rust-backend re-execution recipe and the release-PR timing, so the obligation lives in the canonical backlog. --- TODO.md | 1 + 1 file changed, 1 insertion(+) diff --git a/TODO.md b/TODO.md index edd9e74da..39ffc6ab8 100644 --- a/TODO.md +++ b/TODO.md @@ -85,6 +85,7 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m | Issue | Location | Origin | Effort | Priority | |-------|----------|--------|--------|----------| +| Re-execute the nine tutorials whose committed outputs still show the 3.x residual-df p-values/CIs after the `df_convention` default moved to `"cluster"` (t(G-1)): `01_basic_did`, `02_staggered_did`, `04_parallel_trends`, `05_honest_did`, `07_pretrends_power`, `09_real_world_examples`, `11_imputation_did`, `13_stacked_did`, `16_wooldridge_etwfe` (not `16_survey_did`: the survey df wins). Run under `DIFF_DIFF_BACKEND=rust` with the per-worktree kernel, reconcile any prose that restates a moved number, and `--nbmake` each; scheduled for the 4.0 release PR (`docs/v4-design.md` section 9 item 10) together with any item-7b/8/9 movers (24 was refreshed in #879; 23 belongs to 7b). | `docs/tutorials/*.ipynb` | #879 | Mid | Medium | | TWFE event-study one-way HC2-BM absorbed-FE R parity against the `mpd_absorbed_fe_did` golden (`clubSandwich::vcovCR(cluster=1:n, type="CR2")`): the within spec with hc2_bm auto-clusters at unit, so the one-way lane needs an explicit singleton-cluster column (`cluster=` + hc2_bm = CR2 with G=n, PT2018 §3.3) once the G=n CR2 path is verified; the hc2 lane is pinned by `TestTWFEEventStudyAbsorbedFERParity` and the clustered hc2_bm per-period lane by `test_twfe_event_study_cluster_hc2_bm_per_period_df_matches_clubsandwich`; the MultiPeriodDiD lanes died at the 4.0 cut (PR 4c-ii) | `tests/test_estimators_vcov_type.py` | #v4-4c-i | Mid | Medium | | TWFE event-study power profile: `simulate_power`/`simulate_mde`/`simulate_sample_size` have no `_EstimatorProfile` for `TwoWayFixedEffects.fit(event_study=True)` since the MultiPeriodDiD registry entry went with the class (PR 4c-ii); `EventStudyResults` carries no scalar headline, so the profile needs a design (post-period average via the `tests/helpers/event_study_average.py` arithmetic, or a named period) plus its `_SURVEY_FIT_BUILDERS` twin | `diff_diff/power.py` | #v4-4c-ii | Mid | Low | | Clustered hc2_bm Bell-McCaffrey DOF guard is batch-relative (`linalg._cr2_bm_dof_inner`: a contrast is NaN'd when its scaled max\|B\| falls below 1e-10 of the batch maximum), so the event-study core keeps the otherwise-unused post-period-average contrast column as the anchor to hold the 3.x per-period NaN masks bit-identical (PR 4c-ii, user decision 2026-10-25). Reproduction: `x_nc = treated * 1[period >= 4] + 3e-6 * default_rng(seed).normal(size=n)` as a covariate on the oracle panel flips the mask between `[eye(k), avg]` and `eye(k)` on 20/20 seeds (cond(X) ~ 9e5; 1e-7 never flips, <= 3e-8 rank-drops) - too BLAS-sensitive for a committed oracle, hence the structural pin `test_hc2_bm_contrast_batch_keeps_post_average_anchor`. Decide whether the guard should use a batch-independent anchor (the treatment contrast, or the absolute floor only) and then drop the column | `diff_diff/estimators.py`, `diff_diff/linalg.py` | #v4-4c-ii | Mid | Medium | From aedf4d3118276e255b23455033041690e24c4ddd Mon Sep 17 00:00:00 2001 From: igerber Date: Wed, 28 Oct 2026 19:50:34 -0400 Subject: [PATCH 3/3] test(v4): read the ledger through the matrix gate's parser, not pyyaml CI on PR #879 collected tests/test_v4_inference_defaults.py and failed at import on every leg: the module imported pyyaml, which is not a test dependency (the library depends on numpy, pandas and scipy only, and the matrix gate parses docs/v4-deprecations.yaml with its own restricted-YAML parser for that reason). The ledger pin now reads the rows the gate already parsed (tests.test_v4_matrix.ROWS, the same sibling import tests/test_naming_guard.py uses); the local run had passed only because the dev venv happens to carry pyyaml. --- tests/test_v4_inference_defaults.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/test_v4_inference_defaults.py b/tests/test_v4_inference_defaults.py index 779b2873d..665650c61 100644 --- a/tests/test_v4_inference_defaults.py +++ b/tests/test_v4_inference_defaults.py @@ -27,13 +27,11 @@ from __future__ import annotations import inspect -import pathlib import warnings import numpy as np import pandas as pd import pytest -import yaml from scipy import stats from diff_diff import ( @@ -50,7 +48,10 @@ ) from diff_diff.linalg import LinearRegression -REPO = pathlib.Path(__file__).resolve().parent.parent +# The ledger is restricted YAML; the matrix gate parses it without pyyaml (not a +# test dependency) and exposes the parsed rows. +from tests.test_v4_matrix import ROWS as LEDGER_ROWS + THIS_TEST_REF = "tests/test_v4_inference_defaults.py" FLIP_ROWS = { @@ -383,8 +384,7 @@ def test_one_effective_cluster_still_raises_at_the_vcov_layer(self): def test_ledger_rows_are_done_and_point_here(): - ledger = yaml.safe_load((REPO / "docs" / "v4-deprecations.yaml").read_text()) - rows = {r["id"]: r for r in ledger["rows"]} + rows = {r["id"]: r for r in LEDGER_ROWS} for rid, cls in FLIP_ROWS.items(): row = rows[rid] assert row["kind"] == "default-flip"