From 0cc87046a62d7fccea6f655586d66eb8b3190653 Mon Sep 17 00:00:00 2001 From: Stephan Breimann Date: Wed, 1 Jul 2026 01:29:55 +0200 Subject: [PATCH 1/6] =?UTF-8?q?feat:=20aa.plot=5Feval=5Fheatmap=20?= =?UTF-8?q?=E2=80=94=20house-preset=20evaluation=20heatmap=20(prototype=20?= =?UTF-8?q?#310)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Consolidate the hand-built seaborn evaluation-heatmap block (duplicated in the gamma-secretase notebook cells 12/25 and the original project) into one top-level function with the house preset: viridis, fixed [vmin, vmax] color limits, integer annotations, labeled colorbar, horizontal ticks. Returns the Axes; library code never calls plt.show()/tight_layout(). The simple static sibling of the adaptive aap.plot_eval (documented in See Also). - aaanalysis/plotting/_plot_eval_heatmap.py: frontend with Validate block - wire to public API: plotting/__init__.py + __init__.py/__all__ + api.rst - numpydoc + Examples include; executed example notebook - 21 unit tests incl. seaborn-block equivalence (KPI) + bad-input ValueError - release notes Unreleased + cheat sheet entry Co-Authored-By: Claude Opus 4.8 (1M context) --- aaanalysis/__init__.py | 4 +- aaanalysis/plotting/__init__.py | 4 +- aaanalysis/plotting/_plot_eval_heatmap.py | 100 ++++++++++++ docs/_cheatsheet/content.py | 1 + docs/source/api.rst | 1 + docs/source/index/release_notes.rst | 4 + examples/plotting/plot_eval_heatmap.ipynb | 126 +++++++++++++++ .../plotting_tests/test_plot_eval_heatmap.py | 144 ++++++++++++++++++ 8 files changed, 382 insertions(+), 2 deletions(-) create mode 100644 aaanalysis/plotting/_plot_eval_heatmap.py create mode 100644 examples/plotting/plot_eval_heatmap.ipynb create mode 100644 tests/unit/plotting_tests/test_plot_eval_heatmap.py diff --git a/aaanalysis/__init__.py b/aaanalysis/__init__.py index 4fc1edee..f1ef1db6 100644 --- a/aaanalysis/__init__.py +++ b/aaanalysis/__init__.py @@ -9,7 +9,8 @@ from .explainable_ai import TreeModel from .protein_design import AAMut, AAMutPlot, SeqMut, SeqMutPlot, SeqOpt, SeqOptPlot from .plotting import (plot_get_clist, plot_get_cmap, plot_get_cdict, - plot_settings, plot_legend, plot_gcfs, plot_rank) + plot_settings, plot_legend, plot_gcfs, plot_rank, + plot_eval_heatmap) from .metrics import (comp_auc_adjusted, comp_bic_score, comp_kld, comp_per_protein_ap, comp_detection_metrics, comp_bootstrap_ci, comp_smooth_scores) @@ -65,6 +66,7 @@ "plot_legend", "plot_gcfs", "plot_rank", + "plot_eval_heatmap", "comp_auc_adjusted", "comp_bic_score", "comp_kld", diff --git a/aaanalysis/plotting/__init__.py b/aaanalysis/plotting/__init__.py index df692aad..6a3db90d 100644 --- a/aaanalysis/plotting/__init__.py +++ b/aaanalysis/plotting/__init__.py @@ -2,7 +2,7 @@ Plotting utilities: shared styling, colors, and legends for every ``*Plot`` class. Public objects: plot_get_clist, plot_get_cmap, plot_get_cdict, plot_settings, -plot_legend, plot_gcfs, plot_rank. +plot_legend, plot_gcfs, plot_rank, plot_eval_heatmap. A cross-cutting subpackage (not a pipeline stage): ``plot_settings`` sets the house rcParams, the ``plot_get_*`` helpers supply the color list / map / dict, and ``plot_legend`` / ``plot_gcfs`` / ``plot_rank`` are reused by the paired plot classes @@ -18,6 +18,7 @@ from ._plot_gcfs import plot_gcfs from ._plot_legend import plot_legend from ._plot_rank import plot_rank +from ._plot_eval_heatmap import plot_eval_heatmap __all__ = [ @@ -28,4 +29,5 @@ "plot_legend", "plot_gcfs", "plot_rank", + "plot_eval_heatmap", ] diff --git a/aaanalysis/plotting/_plot_eval_heatmap.py b/aaanalysis/plotting/_plot_eval_heatmap.py new file mode 100644 index 00000000..d32b2e83 --- /dev/null +++ b/aaanalysis/plotting/_plot_eval_heatmap.py @@ -0,0 +1,100 @@ +""" +This is a script for the frontend of the plot_eval_heatmap function — a house-preset +annotated evaluation heatmap for a static score grid. +""" +from typing import Optional, Union +import pandas as pd +import seaborn as sns +from matplotlib import pyplot as plt +from matplotlib.axes import Axes + +from aaanalysis import utils as ut + + +# II Main Functions +def plot_eval_heatmap(df_eval: pd.DataFrame, + xlabel: Optional[str] = None, + ylabel: Optional[str] = None, + vmin: Union[int, float] = 50, + vmax: Union[int, float] = 100, + cbar_label: Optional[str] = "Balanced accuracy [%]", + ax: Optional[Axes] = None, + ) -> Axes: + """ + Plot a house-preset annotated evaluation heatmap from a static score grid. + + Renders ``df_eval`` (rows × columns of evaluation scores, e.g. balanced accuracy in + percent) as a ``viridis`` heatmap with integer annotations, fixed ``[vmin, vmax]`` + color limits, and a labeled colorbar — collapsing the hand-built seaborn block that is + otherwise copied for every sweep result into a single call. Tick labels are drawn + horizontally and left/bottom ticks removed for the package look. + + .. versionadded:: 1.1.0 + + Parameters + ---------- + df_eval : pd.DataFrame, shape (n_rows, n_cols) + Numeric score grid. Each cell holds the score for one row × column configuration + (the ``index`` becomes the y-axis levels, the ``columns`` the x-axis levels). + xlabel : str, optional + Label for the x-axis. If ``None``, seaborn's default (the ``columns`` name, if any) + is kept. + ylabel : str, optional + Label for the y-axis. If ``None``, seaborn's default (the ``index`` name, if any) + is kept. + vmin : int or float, default=50 + Lower bound of the color scale (and colorbar). + vmax : int or float, default=100 + Upper bound of the color scale (and colorbar). Must be greater than ``vmin``. + cbar_label : str, optional + Label drawn next to the colorbar. If ``None``, no colorbar label is set. + ax : matplotlib.axes.Axes, optional + Axes to draw on. If ``None``, a new figure and axes are created. + + Returns + ------- + ax : matplotlib.axes.Axes + The axes with the annotated heatmap. + + See Also + -------- + * :func:`aaanalysis.pipe.plot_eval` is the adaptive sibling: it inspects a + ``find_features`` sweep table, picks the most-informative axes automatically, and + lays out one or more panels (line / heatmap / facets). ``plot_eval_heatmap`` is the + simple **static** entry point — you hand it a ready-made score grid and it applies the + house preset, with no axis selection or multi-panel logic. Use ``plot_eval_heatmap`` + for a single fixed grid; use ``aap.plot_eval`` to summarize a full sweep. + + Examples + -------- + .. include:: examples/plot_eval_heatmap.rst + """ + # Check input + ut.check_df(name="df_eval", df=df_eval, accept_none=False) + if len(df_eval) == 0 or df_eval.shape[1] == 0: + raise ValueError(f"'df_eval' (shape {df_eval.shape}) should contain at least one " + f"row and one column.") + if df_eval.select_dtypes(include="number").shape[1] != df_eval.shape[1]: + non_numeric = [c for c in df_eval.columns + if c not in df_eval.select_dtypes(include="number").columns] + raise ValueError(f"'df_eval' should be all-numeric; non-numeric columns: {non_numeric}.") + ut.check_str(name="xlabel", val=xlabel, accept_none=True) + ut.check_str(name="ylabel", val=ylabel, accept_none=True) + ut.check_vmin_vmax(vmin=vmin, vmax=vmax) + ut.check_str(name="cbar_label", val=cbar_label, accept_none=True) + ut.check_ax(ax=ax, accept_none=True) + + # Draw + if ax is None: + _, ax = plt.subplots() + cbar_kws = dict(label=cbar_label) if cbar_label is not None else None + sns.heatmap(df_eval, ax=ax, vmin=vmin, vmax=vmax, cmap="viridis", annot=True, + fmt=".0f", linewidth=0.1, cbar_kws=cbar_kws) + ax.tick_params(left=False, bottom=False) + ax.set_yticklabels(ax.get_yticklabels(), rotation=0) + ax.set_xticklabels(ax.get_xticklabels(), rotation=0) + if xlabel is not None: + ax.set_xlabel(xlabel) + if ylabel is not None: + ax.set_ylabel(ylabel) + return ax diff --git a/docs/_cheatsheet/content.py b/docs/_cheatsheet/content.py index 801adc7f..c983ae61 100644 --- a/docs/_cheatsheet/content.py +++ b/docs/_cheatsheet/content.py @@ -230,6 +230,7 @@ ("BIC score · KL divergence", "comp_bic_score(X, labels) · comp_kld", None), ("Per-protein / detection (v1.1)", "comp_per_protein_ap · comp_detection_metrics", None), ("Plot style, fonts & standalone legend", "plot_settings(font_scale) · plot_legend(ax)", None), + ("Evaluation score grid heatmap (v1.1)", "plot_eval_heatmap(df_eval)", None), ]}, {"name": "Protein Design", "tag": "mutations · design", "under_construction": True, diff --git a/docs/source/api.rst b/docs/source/api.rst index 6b8adb47..cbc04fca 100755 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -124,6 +124,7 @@ Utility Functions comp_smooth_scores display_df options + plot_eval_heatmap plot_gcfs plot_get_cdict plot_get_clist diff --git a/docs/source/index/release_notes.rst b/docs/source/index/release_notes.rst index bc2e9618..7f95f3b2 100644 --- a/docs/source/index/release_notes.rst +++ b/docs/source/index/release_notes.rst @@ -191,6 +191,10 @@ Added - **plot_rank**: Standalone per-protein max-score-vs-rank scatter with group coloring and optional threshold lines (pairs with the new ``aa.metrics`` functions). +- **plot_eval_heatmap**: House-preset annotated evaluation heatmap (``viridis``, fixed + ``[vmin, vmax]`` color limits, integer annotations, labeled colorbar) for a static score + grid. Collapses the hand-built seaborn block previously copied for every sweep result + into one call; the simple static sibling of the adaptive ``aap.plot_eval``. **Golden Pipelines** diff --git a/examples/plotting/plot_eval_heatmap.ipynb b/examples/plotting/plot_eval_heatmap.ipynb new file mode 100644 index 00000000..db1b94b4 --- /dev/null +++ b/examples/plotting/plot_eval_heatmap.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "intro-md", + "metadata": {}, + "source": [ + "The ``aa.plot_eval_heatmap()`` function renders a static evaluation score grid (e.g. balanced accuracy in percent across part sets and scale sets) as an annotated ``viridis`` heatmap with the package house preset: fixed ``[vmin, vmax]`` color limits, integer cell annotations, horizontal tick labels, and a labeled colorbar. It collapses the hand-built seaborn block that is otherwise copied for every sweep result into one call. It is the simple **static** sibling of ``aap.plot_eval`` (which adaptively summarizes a full ``find_features`` sweep): hand ``plot_eval_heatmap`` a ready-made grid and it applies the look; reach for ``aap.plot_eval`` when you want axis selection and multi-panel layout from a sweep table." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "example-code", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-30T23:27:25.399498Z", + "iopub.status.busy": "2026-06-30T23:27:25.399427Z", + "iopub.status.idle": "2026-06-30T23:27:27.242377Z", + "shell.execute_reply": "2026-06-30T23:27:27.242180Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import aaanalysis as aa\n", + "\n", + "aa.options[\"verbose\"] = False\n", + "aa.plot_settings(weight_bold=False, font_scale=0.9)\n", + "\n", + "# A small Parts x Scales balanced-accuracy grid (as in the gamma-secretase use case).\n", + "df_eval = pd.DataFrame(\n", + " [[71, 70, 72, 73, 74],\n", + " [61, 60, 64, 68, 75],\n", + " [66, 69, 78, 84, 90]],\n", + " index=[\"TMD\", \"TMD-JMD\", \"TMD+JMD_N+JMD_C\"],\n", + " columns=[f\"Set {i + 1}\" for i in range(5)],\n", + ")\n", + "ax = aa.plot_eval_heatmap(df_eval=df_eval, xlabel=\"Scales\", ylabel=\"Parts\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "further-params-md", + "metadata": {}, + "source": [ + "**Further parameters.** ``plot_eval_heatmap`` also accepts: ``vmin`` / ``vmax`` — lower / upper bounds of the color scale and colorbar (defaults ``50`` / ``100``); ``cbar_label`` — label drawn next to the colorbar (default ``\"Balanced accuracy [%]\"``, set ``None`` to omit); ``xlabel`` / ``ylabel`` — axis labels (kept as seaborn's default when ``None``); ``ax`` — an existing ``Axes`` to draw on (a new figure is created when ``None``)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "custom-code", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-30T23:27:27.243631Z", + "iopub.status.busy": "2026-06-30T23:27:27.243475Z", + "iopub.status.idle": "2026-06-30T23:27:27.293993Z", + "shell.execute_reply": "2026-06-30T23:27:27.293745Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# A different metric range with a custom colorbar label, drawn on a provided Axes.\n", + "rng = np.random.default_rng(42)\n", + "df_eval2 = pd.DataFrame(\n", + " rng.uniform(40, 95, size=(4, 4)).round(1),\n", + " index=[f\"{n} negatives\" for n in (10, 20, 30, 40)],\n", + " columns=[f\"{n} features\" for n in (25, 50, 100, 150)],\n", + ")\n", + "fig, ax = plt.subplots(figsize=(5, 4))\n", + "aa.plot_eval_heatmap(df_eval=df_eval2, vmin=40, vmax=100,\n", + " cbar_label=\"Balanced accuracy [%]\", ax=ax)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tests/unit/plotting_tests/test_plot_eval_heatmap.py b/tests/unit/plotting_tests/test_plot_eval_heatmap.py new file mode 100644 index 00000000..c166e538 --- /dev/null +++ b/tests/unit/plotting_tests/test_plot_eval_heatmap.py @@ -0,0 +1,144 @@ +"""This is a script to test the plot_eval_heatmap() house-preset evaluation heatmap (#310).""" +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +import pytest + +import aaanalysis as aa +from aaanalysis.plotting import plot_eval_heatmap + +aa.options["verbose"] = False + + +# Helper functions +def _df(n_rows=2, n_cols=3, seed=0): + rng = np.random.default_rng(seed) + vals = rng.uniform(55, 95, size=(n_rows, n_cols)) + return pd.DataFrame(vals, + index=[f"row{i}" for i in range(n_rows)], + columns=[f"col{j}" for j in range(n_cols)]) + + +@pytest.fixture(autouse=True) +def _close_figs(): + yield + plt.close("all") + + +class TestPlotEvalHeatmap: + """Normal cases for plot_eval_heatmap.""" + + def test_returns_ax(self): + ax = plot_eval_heatmap(df_eval=_df()) + assert isinstance(ax, plt.Axes) + + def test_is_public_export(self): + assert "plot_eval_heatmap" in aa.__all__ + assert aa.plot_eval_heatmap is plot_eval_heatmap + + def test_draws_on_passed_ax(self): + fig0, ax0 = plt.subplots() + ax = plot_eval_heatmap(df_eval=_df(), ax=ax0) + assert ax is ax0 + + def test_vmin_vmax_respected(self): + ax = plot_eval_heatmap(df_eval=_df(), vmin=40, vmax=90) + assert ax.collections[0].get_clim() == (40.0, 90.0) + + def test_default_vmin_vmax(self): + ax = plot_eval_heatmap(df_eval=_df()) + assert ax.collections[0].get_clim() == (50.0, 100.0) + + def test_xlabel_ylabel_set(self): + ax = plot_eval_heatmap(df_eval=_df(), xlabel="Scales", ylabel="Parts") + assert ax.get_xlabel() == "Scales" and ax.get_ylabel() == "Parts" + + def test_labels_default_none_keeps_seaborn(self): + # With unnamed axes and xlabel/ylabel=None, no custom label is forced. + ax = plot_eval_heatmap(df_eval=_df()) + assert ax.get_xlabel() == "" and ax.get_ylabel() == "" + + def test_cbar_label_default(self): + ax = plot_eval_heatmap(df_eval=_df()) + # The colorbar lives on a sibling axes of the same figure. + cbar_axes = [a for a in ax.figure.axes if a is not ax] + assert any(a.get_ylabel() == "Balanced accuracy [%]" for a in cbar_axes) + + def test_cbar_label_custom(self): + ax = plot_eval_heatmap(df_eval=_df(), cbar_label="F1 [%]") + cbar_axes = [a for a in ax.figure.axes if a is not ax] + assert any(a.get_ylabel() == "F1 [%]" for a in cbar_axes) + + def test_cbar_label_none_no_colorbar_label(self): + ax = plot_eval_heatmap(df_eval=_df(), cbar_label=None) + cbar_axes = [a for a in ax.figure.axes if a is not ax] + assert all(a.get_ylabel() == "" for a in cbar_axes) + + def test_annotation_count_matches_cells(self): + df = _df(n_rows=2, n_cols=3) + ax = plot_eval_heatmap(df_eval=df) + assert len(ax.texts) == df.size + + def test_ticklabels_horizontal(self): + ax = plot_eval_heatmap(df_eval=_df()) + assert all(t.get_rotation() == 0 for t in ax.get_xticklabels()) + assert all(t.get_rotation() == 0 for t in ax.get_yticklabels()) + + def test_single_cell(self): + ax = plot_eval_heatmap(df_eval=pd.DataFrame([[88.0]])) + assert isinstance(ax, plt.Axes) and len(ax.texts) == 1 + + +class TestPlotEvalHeatmapEquivalence: + """KPI #310: equivalent to the hand-built seaborn block it consolidates.""" + + def test_matches_raw_seaborn_block(self): + # The exact block duplicated in gamma-secretase notebook cells 12/25. + df = _df() + fig_raw, ax_raw = plt.subplots() + sns.heatmap(df, ax=ax_raw, vmin=50, vmax=100, cmap="viridis", annot=True, + fmt=".0f", linewidth=0.1, cbar_kws=dict(label="Balanced accuracy [%]")) + ax_raw.tick_params(left=False, bottom=False) + ax_new = plot_eval_heatmap(df_eval=df) + # Same heatmap data, color limits, colormap, and annotations. + raw_mesh, new_mesh = ax_raw.collections[0], ax_new.collections[0] + assert np.allclose(raw_mesh.get_array(), new_mesh.get_array()) + assert raw_mesh.get_clim() == new_mesh.get_clim() + assert raw_mesh.get_cmap().name == new_mesh.get_cmap().name == "viridis" + assert ([t.get_text() for t in ax_raw.texts] + == [t.get_text() for t in ax_new.texts]) + + +class TestPlotEvalHeatmapErrors: + """Negative cases — bad input raises ValueError.""" + + def test_not_a_dataframe(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval="not a frame") + + def test_none_df(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=None) + + def test_empty_df(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=pd.DataFrame()) + + def test_non_numeric_df(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=pd.DataFrame({"a": ["x", "y"], "b": ["u", "v"]})) + + def test_vmin_not_below_vmax(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=_df(), vmin=100, vmax=50) + + def test_bad_xlabel_type(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=_df(), xlabel=123) + + def test_bad_ax_type(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=_df(), ax="not an ax") From 2de39092c0eb4cf7c80d13f1702fd0d82e800a00 Mon Sep 17 00:00:00 2001 From: Stephan Breimann Date: Wed, 1 Jul 2026 04:54:26 +0200 Subject: [PATCH 2/6] fix(plot_eval_heatmap): fix x-tick overlap, add figsize/tick rotation, correct seaborn linewidths Critical-review pass on #310: - Long/normal-length column labels overlapped into unreadable mush because the x-tick labels were forced horizontal (rotation=0) with no sizing control. Add xtick_rotation / ytick_rotation (right-aligned when rotated) and a figsize arg; the committed example's long-label panel now renders clean. - Use seaborn's documented linewidths= (was the ambiguous kwargs-passthrough linewidth=, which collides with seaborn's own linewidths=0 default). - Compute the all-numeric column check once; add the # I Helper Functions skeleton header; validate the new params in the Validate block. - Tests + example notebook updated and re-executed (inline backend). Co-Authored-By: Claude Opus 4.8 (1M context) --- aaanalysis/plotting/_plot_eval_heatmap.py | 48 +++++++++++++------ examples/plotting/plot_eval_heatmap.ipynb | 35 +++++++------- .../plotting_tests/test_plot_eval_heatmap.py | 21 ++++++++ 3 files changed, 72 insertions(+), 32 deletions(-) diff --git a/aaanalysis/plotting/_plot_eval_heatmap.py b/aaanalysis/plotting/_plot_eval_heatmap.py index d32b2e83..fb768abc 100644 --- a/aaanalysis/plotting/_plot_eval_heatmap.py +++ b/aaanalysis/plotting/_plot_eval_heatmap.py @@ -2,7 +2,7 @@ This is a script for the frontend of the plot_eval_heatmap function — a house-preset annotated evaluation heatmap for a static score grid. """ -from typing import Optional, Union +from typing import Optional, Union, Tuple import pandas as pd import seaborn as sns from matplotlib import pyplot as plt @@ -11,6 +11,9 @@ from aaanalysis import utils as ut +# I Helper Functions + + # II Main Functions def plot_eval_heatmap(df_eval: pd.DataFrame, xlabel: Optional[str] = None, @@ -18,6 +21,9 @@ def plot_eval_heatmap(df_eval: pd.DataFrame, vmin: Union[int, float] = 50, vmax: Union[int, float] = 100, cbar_label: Optional[str] = "Balanced accuracy [%]", + xtick_rotation: Union[int, float] = 0, + ytick_rotation: Union[int, float] = 0, + figsize: Optional[Tuple[Union[int, float], Union[int, float]]] = None, ax: Optional[Axes] = None, ) -> Axes: """ @@ -26,8 +32,9 @@ def plot_eval_heatmap(df_eval: pd.DataFrame, Renders ``df_eval`` (rows × columns of evaluation scores, e.g. balanced accuracy in percent) as a ``viridis`` heatmap with integer annotations, fixed ``[vmin, vmax]`` color limits, and a labeled colorbar — collapsing the hand-built seaborn block that is - otherwise copied for every sweep result into a single call. Tick labels are drawn - horizontally and left/bottom ticks removed for the package look. + otherwise copied for every sweep result into a single call. Left/bottom ticks are + removed for the package look; tick labels are horizontal by default and can be rotated + (e.g. for long column labels) via ``xtick_rotation`` / ``ytick_rotation``. .. versionadded:: 1.1.0 @@ -48,6 +55,14 @@ def plot_eval_heatmap(df_eval: pd.DataFrame, Upper bound of the color scale (and colorbar). Must be greater than ``vmin``. cbar_label : str, optional Label drawn next to the colorbar. If ``None``, no colorbar label is set. + xtick_rotation : int or float, default=0 + Rotation (degrees) of the x-axis tick labels. Non-zero values are right-aligned to + keep long column labels legible without overlap. + ytick_rotation : int or float, default=0 + Rotation (degrees) of the y-axis tick labels. + figsize : tuple, optional + Figure ``(width, height)`` used when ``ax`` is ``None``. If ``None``, matplotlib's + default figure size is used. ax : matplotlib.axes.Axes, optional Axes to draw on. If ``None``, a new figure and axes are created. @@ -59,11 +74,10 @@ def plot_eval_heatmap(df_eval: pd.DataFrame, See Also -------- * :func:`aaanalysis.pipe.plot_eval` is the adaptive sibling: it inspects a - ``find_features`` sweep table, picks the most-informative axes automatically, and - lays out one or more panels (line / heatmap / facets). ``plot_eval_heatmap`` is the - simple **static** entry point — you hand it a ready-made score grid and it applies the - house preset, with no axis selection or multi-panel logic. Use ``plot_eval_heatmap`` - for a single fixed grid; use ``aap.plot_eval`` to summarize a full sweep. + ``find_features`` sweep table and lays out one or more panels automatically. + ``plot_eval_heatmap`` is the simple **static** entry point — hand it a ready-made + score grid and it applies the house preset, with no axis selection or multi-panel + logic. Examples -------- @@ -74,25 +88,29 @@ def plot_eval_heatmap(df_eval: pd.DataFrame, if len(df_eval) == 0 or df_eval.shape[1] == 0: raise ValueError(f"'df_eval' (shape {df_eval.shape}) should contain at least one " f"row and one column.") - if df_eval.select_dtypes(include="number").shape[1] != df_eval.shape[1]: - non_numeric = [c for c in df_eval.columns - if c not in df_eval.select_dtypes(include="number").columns] + num_cols = df_eval.select_dtypes(include="number").columns + if len(num_cols) != df_eval.shape[1]: + non_numeric = [c for c in df_eval.columns if c not in num_cols] raise ValueError(f"'df_eval' should be all-numeric; non-numeric columns: {non_numeric}.") ut.check_str(name="xlabel", val=xlabel, accept_none=True) ut.check_str(name="ylabel", val=ylabel, accept_none=True) ut.check_vmin_vmax(vmin=vmin, vmax=vmax) ut.check_str(name="cbar_label", val=cbar_label, accept_none=True) + ut.check_number_val(name="xtick_rotation", val=xtick_rotation, just_int=False) + ut.check_number_val(name="ytick_rotation", val=ytick_rotation, just_int=False) + ut.check_figsize(figsize=figsize, accept_none=True) ut.check_ax(ax=ax, accept_none=True) # Draw if ax is None: - _, ax = plt.subplots() + _, ax = plt.subplots(figsize=figsize) cbar_kws = dict(label=cbar_label) if cbar_label is not None else None sns.heatmap(df_eval, ax=ax, vmin=vmin, vmax=vmax, cmap="viridis", annot=True, - fmt=".0f", linewidth=0.1, cbar_kws=cbar_kws) + fmt=".0f", linewidths=0.1, cbar_kws=cbar_kws) ax.tick_params(left=False, bottom=False) - ax.set_yticklabels(ax.get_yticklabels(), rotation=0) - ax.set_xticklabels(ax.get_xticklabels(), rotation=0) + x_ha = "right" if xtick_rotation % 360 != 0 else "center" + ax.set_yticklabels(ax.get_yticklabels(), rotation=ytick_rotation) + ax.set_xticklabels(ax.get_xticklabels(), rotation=xtick_rotation, ha=x_ha) if xlabel is not None: ax.set_xlabel(xlabel) if ylabel is not None: diff --git a/examples/plotting/plot_eval_heatmap.ipynb b/examples/plotting/plot_eval_heatmap.ipynb index db1b94b4..c51391e9 100644 --- a/examples/plotting/plot_eval_heatmap.ipynb +++ b/examples/plotting/plot_eval_heatmap.ipynb @@ -14,16 +14,16 @@ "id": "example-code", "metadata": { "execution": { - "iopub.execute_input": "2026-06-30T23:27:25.399498Z", - "iopub.status.busy": "2026-06-30T23:27:25.399427Z", - "iopub.status.idle": "2026-06-30T23:27:27.242377Z", - "shell.execute_reply": "2026-06-30T23:27:27.242180Z" + "iopub.execute_input": "2026-07-01T02:52:45.686886Z", + "iopub.status.busy": "2026-07-01T02:52:45.686789Z", + "iopub.status.idle": "2026-07-01T02:52:47.344958Z", + "shell.execute_reply": "2026-07-01T02:52:47.344675Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -59,7 +59,7 @@ "id": "further-params-md", "metadata": {}, "source": [ - "**Further parameters.** ``plot_eval_heatmap`` also accepts: ``vmin`` / ``vmax`` — lower / upper bounds of the color scale and colorbar (defaults ``50`` / ``100``); ``cbar_label`` — label drawn next to the colorbar (default ``\"Balanced accuracy [%]\"``, set ``None`` to omit); ``xlabel`` / ``ylabel`` — axis labels (kept as seaborn's default when ``None``); ``ax`` — an existing ``Axes`` to draw on (a new figure is created when ``None``)." + "**Further parameters.** ``plot_eval_heatmap`` also accepts: ``vmin`` / ``vmax`` — lower / upper bounds of the color scale and colorbar (defaults ``50`` / ``100``); ``cbar_label`` — label drawn next to the colorbar (default ``\"Balanced accuracy [%]\"``, set ``None`` to omit); ``xlabel`` / ``ylabel`` — axis labels (kept as seaborn's default when ``None``); ``xtick_rotation`` / ``ytick_rotation`` — tick-label rotation in degrees (rotate the x labels to keep long column names from overlapping); ``figsize`` — figure size when a new figure is created; ``ax`` — an existing ``Axes`` to draw on (a new figure is created when ``None``)." ] }, { @@ -68,18 +68,18 @@ "id": "custom-code", "metadata": { "execution": { - "iopub.execute_input": "2026-06-30T23:27:27.243631Z", - "iopub.status.busy": "2026-06-30T23:27:27.243475Z", - "iopub.status.idle": "2026-06-30T23:27:27.293993Z", - "shell.execute_reply": "2026-06-30T23:27:27.293745Z" + "iopub.execute_input": "2026-07-01T02:52:47.346320Z", + "iopub.status.busy": "2026-07-01T02:52:47.346176Z", + "iopub.status.idle": "2026-07-01T02:52:47.406052Z", + "shell.execute_reply": "2026-07-01T02:52:47.405835Z" } }, "outputs": [ { "data": { - "image/png": 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", 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0EMf9sd3Ozk4oVfn4iP5AwQIYqCHcnntObeLVBb/MatWq0aZNm0TkSQmiShA/MKOb+j4RoXN2dhYpS5jXobR/+uknIa6w7f79+2RJXIoLpcmnv6WkjBQaWKEjTan1Ar1S9Rlhop4XslxUlBmiT7m2NLnWcOpRtiU52DnQ4psbKSTe8r9Y80KAj5e4jk5MFpGenVNH0o8vD6BfRg6kvdNH02sdm2e7T/WAkjSpZ1s6euM2/bTnKNkyTSdXoUpdS1J0SBKd++UOnVl4Wwgh3+ru1PbTIHLxMWzmfHQunqIuJZKbvxNV7GD9xSM58WK1VuTt7EZb7p6nW4mR2W6PTE2k5//7H12IuUdPV2hAHzXqTzPqP031/CrQ8ptHaMJxdYTdGvoMGXuxVh4+fEhPP/20WAt1M09NmjQR6xZO3I8ePUr37t0T+1++fJkWLVpEH3zwgVaBUrHpMyQjKSil14fcnpSUlOfHRN8A+Iwkbdq0oRMnTog0E8JnMtoEI5Wrq6uIekjRBRAlgc8JaSKICERhINgQRXFwcBCCRpa447Gxf506dbK9RggcmJ/r1auntR0tA/BaIJAgLAICAigwMJCqVq0qbofoyK2E/qWXXhKptTVr1mjEHvjjjz+EtwhiUGLs+8RrgjjFcSxTRm3wRLqxZ8+eQpR6eakXSUsh2LsKzawzknycPEUaLCwpXFNN9nq1gUI8bnugv0T8ZuI9+u3mRirtWoLal2pM42oMpvq+1emzS79Zfc8hDxf1Yv3hgK7UpEp5uvEwiu5Ex1JgSV+q7O9Hb3VrTW7OTjRv6wGxn7OjA335XC9KTc+gaau2UA41E1ZPVoaKTi8Io7v7o0UVmU8VN7J3sqfYm0miouzYlzepxbtVyMlD/1frjY0PxXW1PqXJ3tF2IwJuDk40uFITylJl0S/X/tO7T4CrNy18ariIAkWnJtLluHDydHShWj5laEjlZqL687Pzm8nSsdW0lz5gU9m+fbuw48Cugp8/+eQTUbEOjy1EDwqv9AUpsHbC9lHsxJAsdTOUSktLS8tRLOlDX0dKCBIIkJSUFE06CekniIRevXpl21/uJz1ASFPBlQ4Pja5QqVy5skgLIo2kRLYSwGJ79+5dkQYLCQkR/ZRwyel95wZ+oRBaiARJMQTBhzwpftGenp4mvU+EEZ966imhttHds1atWhoh1KNHD7HNknBzcKFZdUaRt5MH/XpjPf11Z5dGxDT2q0kzgl+l8UHPUWjSfb0Rny3hT846VoRtp3mNJorS+4MRZ62+ogzRS1A/sAxNWLaRtp1XVzWBQc3q0uwBXWlE2ya06sg5uhcTR+/0akdBZfzp7T82UHisbbeBuPr3AyGEvAJdqdmUKuQRoPYQpSdm0OmFtyn8aCyd+iGMmr+jPgFSknA/lR6ejicnDwdhnLZlEOlBpRi8Qtfjs1fags+bDBRC6N87Z2n2mQ2ankIwTX/bfCg9X/UpupUQSctDLTtSydVk2qBA6/jx48JbO2PGDKpfv75YF2fPnk2vvPKKsMusXr1arLmwuOAEH9VkzZo1o/xQaGLIx0ddISHL33SR2+V+eaF06dLZtiHtA2DGBjItBDEAoWMIhNeATA1VqVJF7376KubwC4BaRSpNX8l8fkAkqVOnTsJ8DqGFX7QUY8oUmbHvE+BxkDqDYEPvI1x+/fVXcnd3F3NgkC6UC2Vxp7V/fSGEjkVdpNV3dmrddjL6Cv0euoleq9ZfpMPmXsm5fcPD1ChafXsHja7Wn1r517d6MZT02BO06shZLSEE/jp2ntrVrEKd61Sn1kGVKCwyhoa2bEiX7z0Un41udZ+0gahQQv23iy7UmVkqCgmPoNAI/X/v1kLYLnU6p+EbgRohBBAJajQukHaOu0QPjsdRUkQauftrG6nvH4oRTRpRPebgbNvRgO7l1H4zCB19VPYoSU1KVqaHKXE08/Q60XlacjMhgt4/9Tf90XYUDa3awuLFUCYPas0GvmtgJ0EwAuvsRx99JDyzqB6bNm1anoevFgsxhIgFog2IYGDBxs9K0I0awCOTV+DhyQ30HACI8qA6zBAQAEoxZci7FBmZPZcNQbFx40bxXM2bNxdpMPh24KX67bffNFV0pgLRs3PnTpEShEjBNR4flWKmvk95rBFlglcIFWR4nfAwIQ0IcYfnKIwPWWFQ1lVtUr0Yqzan63Iu9prGKJ0X7iarRS0M2NbO/Zh40TDx0j39Qv7y/UdCDJX0dBedpkGtcqXpm+ef1rv/i20ai8s3W/aL7tXWSmZ6FqVEpgvDNNJjuji6OpBvNTd6eCqeEu+lZhdDR9U+wHKtfcmW8XV2pyYlK1FyRhrtDlevA/p6D4GLMfe1hJDkbPRdSs1Mp4rufqI8XzZqtESs2QNUEBkmCCCsiSjwweXnn38WaTT0LjQms2TW2WQo9UY4a/PmzSK0peTvv//W7FOQIJKDAwixVbZsWeHbyQnp+zly5AglJiZq9fxBtElX2CAlBiEEzw1CeUqDOEzLn3/+uea+pgI1jHJ8VKbh9SEKBB+RMmpj7PtUgnQZLjJdideMx0cVmqWIITRSBPAI6UOKmvTHX6QQRZU9ygpfUVjSE1O5pJK72kMVk/7EiG+tXLz3UIgd+IP0UeaxwRpdqO/HxtG2c9rRI0mtcqWEz0jMJotNoBuPLL/MOSfsHezIzsGOstJVlBafKXoL6esqLfZ10o6wpsVnUOyNZNFtulQ9y/LmFTQt/KuQo70D/ffwqsFxGimZ6uMY4Oat93YfZzdRWQZBZMlCCGSxZyhXYA+BfQQtb+DZxXqFYidsQ+oMNpD8UqiSFL2EAAxPsuQNXhrkAeEGRy4QDvGCBGIGjZmQyoIAg7FagggIZqkhmoLqKQAhgbQUBAc8OrGxsWI7olmIypw7p92FNzX1SUM1ZYn69evXRXk+vD0Azy+BOdtQlEkfULuIPkGg4RcOEYScaX7eJ14XfsbjyPcI8NjS7I5jYSmcjVG/346lm1J5neiPk50DDQ3sphUhquRRlt4LfoWm1X6ZXO21z9jRmHFgxU7i30ejsg+JtDakuBncoh5VfJzqklQp5Uc9G9SkrCwVHboWRvtDbomeQ/ouuB0sPXBS/Lzr4nWyZuzs7ahksIdIdV1Zlb3y8t7hGIq7lUIOLvbkU/VJRBZEX1MXYaDiDI9jy6AaDJyJum1wn/MxdykpI02M6ehSNnv18pu11H+vJyL191OzJMxVTQbPq3I9ywvG7m8KCCQg+gMfL4INONFHcRTWNGREsCaiggyVYygSqlu3rig4KtaRITjBUdmE6iosxBg7AUGAC9JTaFpYGB4VhNJQZg6zMPoSwIUOgXHz5k0RCcEBhtiQoDQfznUYttCbB+kilM5LE7IyRYfHwwURGaTHkHrCYyIdqBxNAr+PRM5fgykaZm/kPzGaJCcQFsQHAlEolBnKijRT3ydSeFDX6N6J3CvM4fgd4H1CDMG7BdVtKVyOv0VHIy9Q85J1aF6jCbT5/kG6mXifSjp7U/cyLcXMsei0eFp3d5/Y/0jkebqd9EBEh+Y3nkzbw4+IifUV3QOod9k25OXkLqbd73xg2f6DvIAIzt/HL1D/pnVo5dhhtOLIGbr5KFoIIfQSwpiOdScvigozRpugQWUo8sI1urUtUnSaLtfSV1SFYUTH7b3qyFiNAQHk6KK9YKHaDPjqiCRbBAIHoGTeEMmZ6fTrtf00rlYn+rLJYNpw5wwdjwwlT0dXIY6a+VcR/YkWXNlDlk5Re4bgFYXvBusy1gnYWV5++WWRelLaKiSwkCASgywP/u3r6yv8PLBW+Pnpjy7nB4zwgvUD/lmsU1h78bx4Poge2DywHmIdQ4NhvG4UV8GygiozUyfeF3qyEi8ebw4LL9zfEEKoZEIDQKUHpiDBAUTkCQ0GkUpC1RXEC8QKfonoVaAsRW/QoIEoq2/UqJHoRH3t2jXxIUHEBeXyQOYm8YtB90sIFESG8J5CQ0NFWToaNOKXA5BykkAIImqE50eTSUMdppWgAaOcWaY0TufnfcInhLJ6RKrwHi9evCj6M+G9IB2oT3AVZ764vFSYpT0d3cUcsndqvUCvVu0rhNDDlGh6/9xCistQd1HOUGXSrPM/0q3E+0IAoR8R+hJhLhmE0OW4UJp54SeLD7nnlQ/X7RTjNHzcXem1ji3os2d7iGsvVxfad+Umfbxul7lfYrGkZG1Pajiukoj+YE7ZuZ/viMn0t/eohVCVXqWoer/sPrXkCHXaB00ZbR1MsAf3krV7qenyU8g+WnbjsEip9Q9sTHMaDaBp9XoJIYSo0bSTa+hMtOHokiVVkxl7MRWsF7BIYC1AlAfpJczO/PDDD8V6rGwuDLA+oLoL7WcgSLCOowceStyxP6wlBQnWR9hDsH4iwIA1DCf8yGzgxB0n9OivJ8GaDVGH9RsRJfTbMxU7VU6TVvMAIh0QCrkNSEPqBos1RnHoqwrLCfyyIKIgDnTneOV0m3xe7IPwGiIluU23xaw0pJEgNHAfpKIQnUEaSreyDB8cNH7C1FxcAKJJCOHhl6a7P/ZFt2koXhjKc3vtOF7oxomojjRMG8KY9ykjWUhZQiwZU9GXEz33jSdzgFL6Jn61xQiO5MwU0Yxx36NTYhyHLmjlj4qxej7VydPRjWLSE+h09JVs88qKis3tvhXXdaZl75tRFDSpXJ461q5Kpbw9RBPG/SGhIjWWFwY3q0etalSi3w+cpFO3in6UyYVPJ4jryWeGFPlzp8SkixL7+LAU0XvIPcCZyj3lS96V9Bs6r294SNEhiVStb2nyq27eWYRfNVA3K6y3fpZZnv/jRv1Fn6HpJ9dSqh5ztC4YvdGtbB0q7+FLGVlZFBL3gDbdPSuaMhY1556ZXeCP+WtIG6Pv80rQfqPvA7GAlBKEBSYZLFiwQPTRw4nx66+/Lop2YGlBxkaCRoYoaUfAANXI8LBCsKCBMCwkuA1Ro4ICXmIIL0NjutDAGE2OMfZKF0gZrL15aQpdKGLIGkBuEmICv+ygoCDNBwdRLfzSIeAgkiyl7NxcmEsMWTLmFkOWjDnFkCVjbjFkyRSGGPo5pK3R9xkZpL9RZW5RIfSWQ1oMqTJvb2+tE/tatWqJk2+0i5ENeOEjxc8IBijbz8AjK0djIDNSUGsjhBqiPZiMAIuHrEKHTIEIgg8ZPqFJkyZRQcM1fUTiF4oPCn658AtBLaNbNH4ZiJ58+eWXLIQYhmEYix3UiqgJwLgLpRAC3t7ewi6B1BkKd6TgQRAAXhzdPnzwyqLJIbIX8KgWFIjqIDqFQh9kS5BFQvYC/4aQQwamsLythWqgthSQ/0SpPExb+OVKUO2GMGBuZmeGYRiGKc59hmQSSLfnn251dGhoqJZ4QmpNHwgawMwsi4kKii+++EIEIrAew5OLrA0CFijGgkjKzepiKiyGHvcwwJwv/BJQYg+DGMr5kB5jGIZhmMIiy4RqMkRwcipzh3dU12cqfb3wwKIyS9mwMDo6WlP0I03UsgULsiT6kFXSukPFC4KmTZuKS1HCaTLlwbC3FzlShOpYCDEMwzDFsc/Qp59+KgpfDF1wuy5Id0EQwTDdvXt3IX5Q1LNr1y7xsxwtJRsGy0oxQ8U7cntB9h7av3+/yMSgbUxRPw5HhhiGYRjGgjpQo0/QxIkTDd6uT8DgZB/jojDUFOXqytY2np6ewpgMy4isbJaRIzlUXRfZh0+arQsClM+jdY2hVF5hPg6LIYZhGIYxE5lkfJpMXxosL8CEjKos9BWCIEIVGMrsZ86cSf/884/YR/amk2kwORRcF7kdTRgLGnh3MZPTVNCI2FhYDDEMwzCMlc8mg4EaPiBMKsA0BH2pJVk4BGS/Ht2RVBI5YgtG6oIGYsYUQZMfWAwxDMMwjAVFhkwBbWLQYwhiCNMHlL2BLl26RFu2bBGVY7KMHo2HUSWGSBL6EilFz8mTJ+nMmTOiYTAqsQsK9PnDkPKCwpjZpyyGGIZhGMbKI0MoScdoDXhpRo0aJUZHob/Qnj17RG8fjKJCx2kl2A/+JPQmQsdqiAuII3SsRqRp/PiCbbSLgay4mAMWQwzDMAxjJkxtomgK8+bNE+Lnl19+ERclEydOpIEDB2bbBvGEfkK9evXSuq1Hjx40evRoshZYDDEMwzCMmcgqojSZTH1B2KCZMDxCqDALDg4WXZ0xh1MXDGbFEFRMYcB4KpTfI82GpohvvvlmoTVANAcshhiGYRjGBiJDADPIli9fnuf9PTw8RPpMN4VmbbAYYhiGYRgL6kDNFDwshhiGYRjGymeTMTnDYohhGIZhzARHhooHLEkZhmEYxkxkkb3RF1vhwIEDNH/+fINdsAsS2zmqDMMwDFPMyFTZGX2xFW7fvi16GZUvX16U8i9dupQSEhIK5blYDDEMwzCMGdNkxl5shY4dO4o5aqiA27p1K7344osUEBBAQ4cOpY0bN4pGkQUFiyGGYRiGMWMHamMvtkJAQAC9//77dP78eTESBANlAwMDxRBX9EUqW7as6JGEdBo6YucH2zmqDMMwDFMMZ5MZe7FFgoODRbNIzFHDXLTp06eTl5cXLVy4kNq0aSPmqL333nt069Ytkx6fxRDDMAzDmAlOkxlP/fr1ac6cOWK0SL9+/cS20NBQ+uSTT6h69er0yiuvUEREhFGPyaX1DMMwDGMmbCntVRDcu3eP/vzzT5EqO3HihNhWsmRJGj58OKWkpIjtixcvFkIJQ2VxW15gMcQwDMMwNjCbzFKJi4ujtWvX0h9//EG7d++mrKwsMVcNFWaIAvXt21fMUQOfffYZde7cmU6ePEkLFiwQnqO8wGKIYRiGYcyELZXKG8u1a9eED2jDhg2UnJwstiENNmLECHFByb0uvr6+NHbsWHr11VdF6iyvsBhiCozN7b7lo2kiFz6dwMfORL5qsJKPnQmce2Y2H7diAKfJDHP8+HFatWoVubu70wsvvCAETvv27Sk3MjMzha+oS5culFfsVPmtR2MYhmEYxiReODLS6PssbfGzTRztQ4cO0blz5+i5554jb2/vQn0ujgwxBUbPKhP5aBrJ5ptfi+vtN2vzsTOSrlUuievOu/lzZww7O6o/c333j+PPnJGsa/N9gR8z9gwZpmXLluICUFIfFhZG3bt319x+8OBBYZJGE8bSpUtTfmAbO8MwDMOYCS6tz5mkpCSRIkOfIVSJKYE4evvtt0VKDFGk/MBiiGEYhmHMBHegzhkYpVFFVrFiRRoyZIjWbZ06dRIdqDHIFR2po6KiyFRYDDEMwzCMmeDIkGEQ+fnrr7/Izc2N9u/fT/3799e6HamxH374gZ5++mmKjIykZcuWkamwGGIYhmEYMwHPkLEXW+H06dNi5li7du3ETDJD9OrVS7O/qbCBmmEYhmHMhC1NoTcWJycncS17DBkiIyOD8gtHhhiGYRjGTHCazDC1a6urbDF2486dOwb3+/fff8U1TNamwmKIYRiGYcwEiyHDVK5cmTp27EiJiYk0ePBgunr1qtbtCQkJ9O6779LWrVuFrwj9iEyF02QMwzAMYyY4TZYzv/zyC7Vo0YIOHz5MQUFBVLVqVWGchkAKCQmh1NRUMads4cKFesdz5BWODDEMwzCMmWADdc5UqVKFzp8/T6NHjyZ/f3+6ceOGEEboTA2vECJHGN760ksvUX7gyBDDMAzDmImMLI5J5AYiQT/++KOI/ty9e5cePXpELi4uIo3m4eFBBQGLIYZhGIYxE5wmyztIh6H5Ii76gEgqVaoUmQKLIYZhGIaxETGUlZUlGhmiAgviISAgQDQtHDBgANnZ6X8tO3fuFPdBRVeZMmWEUblz585F9pqvXLlCa9asEc+fnp4ueg8BXMMzFBMTIyrOMNF+xYoVJj0HiyGGYRiGMROqIhRDEBIDBw6kDRs2aG1fsmSJ6O68atUqcnTUlgWY/fXtt99qbfv555/F9m+++abQX/PKlSvp+eefp8zMzFz3zc9ke05WMgzDMIwNGKi///57IYR8fX1pzpw5tG7dOvrkk0/Ez3///TfNnz9fa//Vq1cLIQR/zsyZM8X+s2bNEmXs8+bNo7Vr11Jhi7dx48YJITRy5EjxGkCtWrXo999/F68Fpmq5Db4iU+HIEMMwDMPYQJrsn3/+EdeY4SVHWDzzzDPUsGFD8TNSYRMnTtTs//nnn4trRIywn9wfU+IRYfrqq69Eeq2wOH78OEVERAiP0E8//SQm2EO8YWbZ0KFDRRRr2LBh1LhxY7p8+TKdOnVK/NsUODLEMAzDMGZMkxl7MZW0tDStzs6SRo0aZRt7ER0dTSdPnhR9faQQkkAAYTtK3DEgtbAIDw8X1/Xq1RN+JlSO1ahRQ4gi9BgC6D00fPhw8e+9e/ea/FwshhiGYRjGBjpQS9Pzxx9/rBFG6NXz0UcfiX+jZ48EURYYlJ966im9j9W8eXNxO3oAFRZIzwGlj6lmzZriWooh0LRpU3GNiJGpcJqMYRiGYWzAQD19+nS6cOEC/frrr7Rx40aqXr26aGIYHh5O3bp1o9mzZ2v2lREfQ2Xsstvzw4cPC+31IuoD8Jol8AYBiLB+/fqJf0thFxsba/JzcWSIYRiGYSwoMoRy8ri4OIMX3K4PGJ979OghmhhCxBw8eFAIIV9fXzH7y9PTU7MvHge4u7sbfCyAsRiFBcRagwYN6Pr16zRlyhSKj4+nVq1aiduWLl0qnhtC6M8//9R0qy7SyBD6FKAVNkJSzs7OwkxVtmzZHO9z8+ZN0SsAB7BZs2YGD7A1A0e8g4OD5mf0RcBxQWiyZMmSZn1t1kLtxpXp7c+H0P7NZ2jp11uy3T7hi+eoduNKBu//wchf6F5oBNkCj+6paO86FYVeIVJlEQUEErXpZUdVg/N2prrjLxUd2qKiAaPtqE7zou2VYm5qeFagvuVbU1XPcmRvZ0d3kyNoW/gxOhJ5Se/+rf3rUvvSDamCWylytHeg8OQoOhBxjraHH6csUvdMsRXijj2guCP3Kf1RMtk52ZNrJW/y61SRXMo/WYglqiwVxR64R/EnHlBGdCrZeziRe00/8utQgRx91CkUS+dxyxyj+PTTT7WiOLqgyuqDDz7Ith0VWYgKwe/z3nvvCfGANei7776jUaNGiTUJXZ6BXKuQRtOH3O7q6kqFyaJFi6hLly7CrI3u03/88YeIDsEwDbGE14ntTk5ONGjQoKITQ3Cjjx8/Xis3B2MTnOUoaytRooTW/nCCjxgxQjR4knh5edGXX35Jr732GtkCKSkp9MUXX4hrOOEl+NBhCN1///1Hbdq0MetrtAbcvVzpnXnPU5mKJalEaf39Jpp3rE2+/l4GH8PJ2TYyx2cPqWjxZypKV5xA3gohOrpDRV2fVdEzL9sZbMAGQi+raP1iFWVlEiUnkU3RvUwzmlTzWXKwf3JiU8OrAnUo3ZDW3vmPfrj6t2a7vZ09zazzIrUtVV/rMap5lqPWpepSlzJNaPrZnyk9S/+CY03AX3L/1wsUd/C+1vaUm3EUu/8ulR1Vj7ybBjzZP0tFd74/TYlntE9Okq9EU/SOMKo4oTG5VfUhS8eUUvlp06ZpVX0Z8tooOXPmjBBC1apVE6LHx+fJsXvllVeEBwjCA+syqstkzx5DqSe5Xfk4hQE8S3i9WEMRuUIXalS9QfhAEElN8cMPP2QzhhuDUd/827dvF6IHkSHMBMETo/Pj0aNHxYuDOtu/f794sQD7obPlkSNHxIvFwY6KihLGrDFjxoiDiE6W1s6ePXuEUoeIVALTF46f7JPA5I83Px4khJAhSgb4CCF0KyScPn59id59wm8XXmVEcSEy/IkQqlCNqNsQOypZhujOdaJ/l6po+yqiUmWJWvXUf/+UJBUt+VwthGyNks7eND5okBBCO8JP0L/3D1NaVjo9VTKYhgZ2pgEV2tKxyMt0NEodIepXvrUQQjFpCbQibCddiA0VkaDGfjVoWGAXauwXRK9W6UWLrq8nayf++AMhhOwc7ajk01XJo25JykrNpJhdtyn+xEMKX3yBPGr5kYOns9g/anOoEEJOpd3Iv09VETnKiEujqC2hlHQ5mu79dI6qftKa7OztbM4zBLGjT/DkBAQFgIjQFTCenp6iseGMGTNEhRjEEKIu4OrVq3ofTxqYK1UyHGkviGwK+gmheg2BA0mdOnWEj+j06dOiF1HdunXzPaPMKDEEBzoEDrpOYmGXZ45QnJ06daJDhw6Jhk59+/bV9CaAEMIL37ZtG5UrV05shzp99dVX6Z133hF5SmXqyJaAIMSFyT9dBjajDs80ptAr96lyTf0p22p11Ia/K6dv0Z0bhWf6K+7s3/RECE2eZ0eOTuq/40pBRNXrEX0+VkXrl6ioRVciB8fsX9Qrv1NRZDhRmUCicNOLNyySRn41yMXBiU5EhdCnl5Zptl+OCyN7sqPnK3cVKTEphjoHNBHXn1z8g05Eh2jtfzvpEX1QdwR1L9vcJsRQwll1hMe/bzUq2euJtwNpr1ufHKOUG7GUeCmavJupo0Mx/91FaI0qTmxCzqXcnuxfqwTdmPqfSLOl3k0g14qGI72WQFH1GZLrLIIWOZWx2z1e1xHsQBADAQ74h5TdnRHUgN8Ioio/0ZjcOHbsmIhaLViwQPxbCYIupvYUyreBGq5yqEe04VaG0GFwmjRpkpb6BMuXLxfXSIlJIQTw5rp27Uq3b98WBzo38JiIPEEl4oJIFEQXtssZJYaAekWKbseOHeIXmBPIgeKAb9q0Sbw2AOMWnvvBgwd690eUC4+PC0QhxKJu0yj5Hq9duyYeC4+pfF/StQ9Fbui5AD58uF1f2NKY94n3hlkzmzdv1ipPtFTKVvKn1z/oTyFnwmjFDzsM7ifFUMg59e/WVgl7/CvvPPCJEJIEVLCjGg2IEmKJblzMft9ju1R0bDdRx/5E1eqSzaF67O+JTMv+NxieEi2uld+Ncy7+QSOOfEanorOfXe9/dI6SM1LJ28mdPB2fLPbWijwujr4u2bY7lXzsO5Ezp7JUVGF8I6r6SSstIQTsnezJzkW9sNs5WHZUSL5lYy+mAG8qStSxLv/2229at61du1Y0NYTAQGADwIODTBBMy/ATyR5E6PGDYAZ+Rn+fnNLp+UWuhUr9UFgYJYZgXMJFH9KFrgxVYXGHwRrmJ11k90tEjnID3hpEkLDYw/DVokUL0QQKaSao0rNnz2a7D4QJjNroSYBUHcQXHPSvv/668O7oAmGAXCpSeb179xbPM3XqVLEdzw3RI4EAQ2dOPB6UKR4fF4QWcb9du3ZptT9H23OA14/HwmMq39elS+qzSAgx/Izcpy4IBSLi9sYbb2iZz415n/hQd+/enQIDA8XvBL8D3A9iFo9jiTg42tPUb9UNtz5/+w/KzDCcu6kW/LgU9G40jZjSm775ezx9u24CvTlnMAVWf+JVsHbS1VWo5GMgo+j+2Md6L1R7e0S4ilZ+rxIRJXiKbBFEhJIyUqhDqYbUyr+OZntljzI0tJK6h8vBiCd9V+4lR9DtpId6TdLO9o7ikpaZLh7T2vFsXFpcR2y8San3EjTb444/oIRTD8nexYHca6s9p0h9uZT1IOdS2oU2mQnp9GDlFUp/mExuNXzJpVx207WlUVRNF/G9j9J6BBTg461QoQK1bdtWpLkGDhwoqrImT54smhpKYNJGRAhZHty/Xbt24hreYQxsxeMVJi1bthSVboXd3BEUiFsU0RCUuQEstgClfSjdQ58AKExdIDwAnOx5BUIBz4UDhF/Qvn37RIXakCFDNIICYBt+aQjtQVGieyXULAQNDGKYfLt+/XqNoj1w4IAQV4j04AOCtB6MWTBsIRepCwTOu+++K0QJnsfPz09EayBmYCyHD0q62yFUEAlCdEiWCcpcrC5oK44PI8oEP/zwQ63btmzZIszoSE/K42ns+8QfgExX4nUgbIq8K8Qk+jUgN6w7pK+48+LEnlSzQSB9PWW5qAKrWrtcrpGhd+YNJ0/vJ2ebQfUrUteBzWjulOW0d8MT0Wut+GFNukgUclpFQQ20v1gz0lV08/GfUlwUFnD17ZmZKlrymdon9PK7MqJkW1VQICY9gd479wtNqTWEPqr3KsWlJ1JaVgaVcPai5Mw0+j7kbzocqSekpodeZZ8S3qODjy7YREWZV8NSVHpoTYr45zrdnHlIVINlpWVSVlIGOZVyo7Iv1yFHL7VfSJfES1EU/vtFSo9MIcpUkVeT0lTmxWCyBoqyzxDEDU6W0WQRa5RMmfn7+4vqMl1fK4QPsg0vv/yyWCtQ7AMQkMCw1sKO2EghJk3dyExh7YJA0heRQlU7bjeFAln5YA5GOujZZ58VLzgvTnO5HdGKvIJf4tatWyk4WP1HAJEhZ5IgBSVFBvoRQCB89tlnQlzIXCmUJQ4mmk0hFYYIEMA+EEIYCAc/FAQBRBc+OLqiRKaZ8F4hlpTmMbQvx+sJDQ0VrwdRq7Fjxwrh17NnT/F8GG5nCJTX9+nTh9asWSMiZvjASaTYhKCRGPM+8f4wZA/H6OLFixpBhfeJEkxsg3iCMd5SqP9UdRo4uqMQMNv/0s4n6+Lh5SrM1ZmZWWL/A1vOUmxkApWr7C8eo1bDSjTpy6F089I9CrumP01pLTRsY0cn9qho+2oi31IqatWdyN7BjhLiVLTqBxVF3NeOIIFNS1UUeplo2Hg7Cqhom1EhSUpmKt1LjqRybv7k7fQkEh6RGkP3U/J29hroXppGVOkhokJLbmZvAWGtOHg6kaOPM6UlZ1BGzJNSRucyHuTglf2kWZJ2P5EyolJhsBE/J12LofjTD8m3jfoEx5IpytlkAGsSMgf4zsdagQrw4OBgg95dnNCjwSFOvh89eiTsMoVpmlaCCBRmkOnOSjMEAiMrVqwwjxh6//33hbEaERTkHCWyI6ShSIM88Loem5xAKaEUQgAiA3lQLPKImmChRxMmpKEQrcHtmMSrBA2bIKiQI4VIgDJGCA4i4Ouvv9a8XuROIRLwWLrGLYggfSCHig8KxBAiNKYABQ4xhEF6UgxB8MAjBcUrxaax7xPvC2Y4dBuF6MNxw+MjradP8BV3PH3cacrXwyjifgx9997qXPdPTUmnUZ0/o5TkNHEfyY1L9+jg1nM096+3qFajStTv1fY0f9oqsmYatiaq9xTRucNEK+araM0iHE8VxTxexxu1ITq1HwJJ/fO1cyratgoiChVmti2EankH0lcNXydneydad/cA7Xt4RkSGGvlVF9VkH9V7hT679CftfHDS4GOg19AXDcaQp5MbfXFpOYUmqo2r1k7U9lv0cEUI2bs5UqnBNcg9yE9EheKOhlPswXt060YsVZrWTKTHdPFpVZZ8O1agrJRMijt0nx6uDqHwxRfJwcOJvBqp02+WiqkeoPyA9U1f1iMnYKmQozCKCoguCJy8IhsyFqkYgh8Fpip4iKAcEYFQRoGkd8iQKJDbjelRgDSQLrJZIfKgMlqEKAiiNEirGUKWC8o+BXgPuuk8hOGQU9UVQwAmZURrkJKCwIARWWl8zs3YbQh0B0UuduXKlSJKBdG4evVqcbyVUSFj3ydAfhcVfBCtUrjiw42yRQikojCpFRQTPh9CfqW8aOrQBZQYn7vfIiM902AFWVaWilYv2kXv//gy1WliOZExU8HneuQMok1/qGjfRqLkBKLoR+rqsv6j7Oh+KMSQilzd7CgpXkW/faEi35LqqJCt82rVXuTm4EL/u76RVoQ98QZejAsVZfNfNBxDY6o9Q/sendXbO6imV0WaU3+kiCjNu/IXbQ3POaJpLWQmZ4j0mKgOm9CI3Kr5am5Dib1jCReK3HCTHq29RhXGZk9z2LuqlyoHN0fRoFGVmSWEFcrvLV8M8d+VIWCJwaUoMEkMoQQPZl5UdWEhXrJkSbaO0sjpYRtSL/qQ243pvKyv06XMG0rxIU3DEBStW7c2+FjowKlsJS5bi+uibE+uDN0hDaUUelDaMjJ1/752UzFjgPiBQx/dNpGrhQcLghNCDc8pMfZ9yrQa/FAoU0Q1GR4DoU90M4XHCK0Rilr5mwJSW62616O01HQa/9mzWre5eag/I216NqA6TavQpROh9M3Ulbk+5r0wddmvh8JLZM2gZL7PCDvq9YKKYiOInN2IPL3Vf0sn96mjtb7+RCf2qoWSuxfR3An4G3si8uMeB9j+/p+KNv+hok4D7ai1lUeO6nhXoSxVFv1zN3sV7OmYa3Q1/o6IHlX1KEtX4rWrFtv416Npwc+LirTZ538THahthdTb8SKq41bNR0sISUp0q0SRG29S0qWcK2ElXk0ChBhKvfPEiG2psBgqHhgthuCFweRbiBksojAS6wMiBQsvoiqIYkjDtAQLr3LabEEREBCgETcoQ88NGQ3JrbGUBGIHERqkxNBiABVZEBxIj6FyDimp/IghmSqDGELuE8dw7969wuBdqlQpk9+nBK8XF7x+/A7gTUKZJUQRqtjmz59PxR3Z1NPZxYkqVtNfBQaDNC4R9/M2uC+gvJ+4jom0/C9XY3BwsKMSOofwymMPOSJF1x/PR0yKV1/0ERtJhKOcqB5lZLU42TuKHkOpmemUkqkwVCmIz1CfICm9RGBAhXY0pvozFJEaSzPP/UrXEvT3erFWspLVUTJ7d/2+IFSSoUwe+6kyssjOMedCZ+wjrh97iCwZy38HhcfJkydFX8K80qRJE7F+FroYQnUYehDgGtERmH1zAsIAYggjKJTdI1FBhoopLORIQxV0jhH+HzwHnhP9EJRAxMEjBPECzwx8OHDSQxigU3SHDh00+yKFBs+NEogGmMORskMaSwnuj4iL0jOljF7l1R8FXxTSdvAJwYSNqJcyRWbK+0Q0T1a0wXeEY4/fJS6YLYeyfKTcLAF0iYb/Rx+N29Wk12f1p/82nabf524RHiHQdVAzGjymE538L4QWzdb2V4Fuz6r9WecOq3tAWTNIgW1coqI2T9tRx3522cZ0wEBdojRR+aokhFLNRvqjPUizndyrTq3VaU7klf2E36pA2uthSjSVdvWjpiVq0vGoK1q3ezu6Uy2vQPFvzCqTDK7YQQihkPjbYvRGdFrei0asBacAdeYg+Wo0ZSamC6+PkqRL0aTKUJFjSVchhFLuxNO9hWdFlVnFt7M31oPPCLhYeMNFwJEhwyAYoa/VjCHgLyoSMYRuyaikQuk4Suf1RSTkYgtGjx4tTMlQdjA4ow04fDVz584VERZEVgpjQCm8MXhueJqQasKCj8UfpYEoB8RrwWuHSED6CQ0jMesFFV/o4wNxgAgYhtdJUSMFDQzHiExgUC2aR8KIjH3QWwiRHCl45MRfZXoP5fHwAiGyltsIDogfuP5RKYaIkKx8M/V9onX7Cy+8INJwuA9EICJZEFNIm4Fu3bqRJZCT/6dyzTLiGj4i5T5XzoRR+SqlqFwlfwoPi6R/lx2g9LRMcnV3ppcm9aJW3eoJk/X633NvAmrpBFZHryWizctUVLkmUZXa6s/2pRMqWvaN+jy1x1D1bDL0HJJ9h3Rx81Bp+hWVsZEKM3h8XqjcjabUeo4+vbhMpMYAKstQbu/l5E7nYm6I/kKgeYlaNLra03Qn6RFNPr2QEm2gn5A+XMp4kGtVH9Fl+s53p6nsK3XIubRaICVejKT7i9UhSJ/W5TT7Y1RH4rlIevDnZfLvV40c3J2EVyhm312K3HBD7OfXuSJZPBwaMgj8QrKSWpeEhASxTmNcB0rw4YPFAFdTybMYQkREVixh0TdUvoYFHH14ZM0/XijEE/rd4CJB0z8s9IUBxAEWeTy+7mvFFzyqxNBkSoKydITjYFSGeJNA9ECIoJxQVpmhegutBHBZvHixuCj3R8Tm1q1bwlgNMzRAdAciBak4HAsIL+XAVn2gnBDVc4jWKHsLmfo+8bohkF566aVsES0ANQ2vkrUSdvUBrVq4i54b24Vem9mPXprSi2KjEsg/wIccHB2EiP3+vdV0/5b1T6wvWcaOOg5Q0a41ah+QdwmVmFof/9gD1Kgt0VOWoYuLnD9v7aQmfjUp2KcSzW30BsWmJVCGKotKuqhHFWAG2VeX1R41jOcYV2OAGNbq4ehK3zfW7uGiZMrpRRShp6u1NVH25WC69dlxSr4aQzemHxCdqOEjkik0t+o+VLKXuoAB0aEyLwUL4RS98zZF77kj+hJlxqVpUmQQTj5P6R+9Y0lwZMgwWE9zK+PHOtmoUSNRBKSv2KnAxRD6ESgFhCHwopTAaI2GiPClQAygp0H79u1FBVNe23jnNNDU0G0QG1jcIXCgHhEZQVQHYTTdpocQOmjshIo4XJAGgw8I5nD0NYCBGYZwycyZM0VECGmse/fuiefG/vD1ICKEUnXpa5H9kZBCg9EcahaRp9zeF8QL2hZAVEH0GMKY9wkDNp4T0SKEH1GNVr58eZHuLOh0ZXHkt6820d0bD2nQa52oUlAZci1fQhM1wm2n9lv+aJK80vcVO3JyUtG+DWiuqN7mXYKoU38IJXXfISY7GMo6+fQCeqlKd+pRpjn5OKvDZvARofP0/25spAePx3IEeVek8u7qv20/Zy9xMYSD4vvCWkG36Mrvt6BHq0PEnLKM6FRN7yGftuXJ/5mqZO/0pNeNZz1/qvRuM3q05iolhcRQRpQ6qobUWYnulcivoxVEhcxUWm9NlCtXTmStsO4i+IIeSqZgpzK1BtyKQJ8iiBddDxRSgTAwI/qCUnpj2gDYIj2rTDT3SyA3DxcxnT4xLpmiI+Jz7FPk5eNGsdGJlJSH0vzCYvNNdSRy+83CG3aYE1mZKlEt5oJqMh/jBFBctIqSEkiU3bu6F7146lrl8TDU3eb53NmRnZhiDyETlRpH6SrtUTCu9s5UyjVvRiqk1TIRnisCdnZUf+b67h9H5iIrPYsyYlOFaRoRotxOjJEyy4hLJXsXR3L01t+luihY10ad9ShIqq1Uj2syhutD3ivw12HJIEMD2wq8s8iAmIJlzV4oJJC2QvQKXaWRvkM5PSI+UJmohMOoChZClkFyYmqeJtInxCaJi62DCFBJtc3KaLz97MhbXYRnk6BEPqfUVkpWmphLxmQHw1ad/fPexgLVZrpzyqwG7jOUbzCaC+i2+DEGFkNEoowdKTGkynBRAvPxjz/+mN/fFcMwDMNkg3MzhkHBFYqV9IFGyxjnhUkLsKAAFEGZCouhx713MGB1+fLlIkIEHw/6+MAXhINracNLGYZhGAvB5o0qhtm9e7fWbLKcgA+ZxVABgBJ/uNEZhmEYpqjgajLDoE9eToVbCFSg4httZWBnyQ8c8mAYhmEYc8GRIYNg1FRO46YKEuuv52QYhmGYYhwZMvZii1y6dEn4g5QcPHiQvv32WzEVI7+wGGIYhmEYc6Ey4WJDJCUliekJGFOlbHIM4PXFJAv07pPzTk2FxRDDMAzDmA07Ey62w4gRI0TjY/iH0ExYCbxCGKGFqjP0CUQ/QFNhMcQwDMMw5oIjQwZB5AcTEzDOCoPG+/fvr3U7pjtgkCsGjWNKxrJly8hUWAwxDMMwjLlgMWSQ06dPE4ZktGvXjgIDA3NsjyP3NxWuJmMYhmEYc2Gjhui8IAeUJycn57gf5mzmF44MMQzDMIwZO1Abe7EVatdWz2w8ceIE3blzx+B+//77r7iGydpUWAwxDMMwjLngNJlBKleuLCZBJCYmipFZV69e1bo9ISGB3n33XVFyD1/Rc889R6bCaTKGYRiGMRecJsuRX375RcwIPXz4MAUFBVHVqlWFcRoCKSQkhFJTU8ne3p4WLlxI5cuXJ1PhyBDDMAzDmAk7lfEXW6JKlSp0/vx5Gj16NPn7+9ONGzeEMMIAV3iFEDnCDLOXXnopX8/DkSGGYRiGMRc2Jm5MAZGgH3/8UUR/7t69S48ePSIXFxeRRvPw8KCCgMUQwzAMw5gLTpPleRwH+g51795dNGCU4ziOHTsmJttDMOUHFkMMwzAMY+WRIQiJ9evX57pf27ZtqUGDBtlGYvz333+ioqtMmTLUuXNncnV1LcRXq/3cr732muhCjQ7UEEO64zg+/fRT+vvvv6lly5YmPw+LIYZhGIaxcjF08eJFevPNN3Pd75tvvtESQ5s2baJXX32VwsPDNdtKlSpFv/32G/Xs2ZOKYhzH6tWrcxzHsWDBAjGOA4bqEiVKmPQ8LIYYhmEYxsrFUKVKlWjs2LEGoy9LliwhR0dHatWqlWY7TMqDBg0STQ8bNmxIjRo1olOnTolOzwMHDhT9f2QvoKIYx6HbhVqO48B+GzduFOM48iL49MFiiGEYhmGs3DME0fL999/rve2ll14SYy/mzZtHzZs312z/8MMPhRCaMGECzZ07l+zs7MR+SE3Nnz+fZs+eTStWrCgW4zgghvIzjoNL6xmGYRjGRkvrly5dSr///jv16NFDpJwkaWlptGHDBvL19RWeHAgh8Xrt7Ojzzz8X2+FBSklJocKCx3EwDMMwjC1gxg7UMTExNHnyZJGGQrpJCaIsaGgIQzXK2JXAPN26dWsRNYIXqbDgcRwMwzAMwxQqH3zwAT18+JAmTZokOjsruXfvnriuVq2a3vvK/W/dulVor4/HcTAWyeabX5v7JVgsXatcMvdLsFh2duTPnSmsa6PfP8IULaakvRCxwcUQiOToRnN0QePCn376iby8vIQnSJfY2Fhx7e3trff+uJ+cD1aY8DgOhmEYhrEFA7WRF3h4fHx8DF5we27AAI00FyrMSugpR8eoC4C5X/qQHqLChsdxMBZHz+Dp5n4JFsfmi5+I6+AZ35j7pVgcFz9Wn802GsvHzhhO/aA+bj+HtC2U34s1MzLov4J/UBMiQ9OmTaOJEycavD23qBAqtFBKD0GDmV/6kGMuUHavDwxKBRBfhQ2P42AYhmEYa8YEMZSXNFhOYIwFuknDBI3Iiz7kyAv08NGH3J7fMRjGgCgVXpd8bfpSf2gIaQrcZ4hhGIZhzIQ5ptDLsRy6HZ2V1K1bV4gPCCdEkpRpsaysLLEdpe+6ozsKgytXrtCaNWuEgEtPTxevB+Aa3ilUxaEBZPv27U3ue8RiiGEYhmHMhRnE0KFDh8Q1mhkawsfHR9y+Z88eMTF+zJgxmtu+++47MZ4Dc8JQll+YrFy5kp5//nnKzMzMdV9DZu+8wGKIYRiGYWxEDCGagigKREy9evVy3Hf69OlCDKEZIwa1NmnSREyJh0BB1Oj9998v1NeKKNC4ceOEEBo5ciSVK1dOdMWuVauWeG3Xr18X/ZEiIiLENog2U+EO1AzDMAxjIx2o4auBKRrjLQxVikm6du1Kn332mdjvzz//FP2IkIZCemzhwoXCc1SYHD9+XAgdeITQBuCdd94R89PgVxo6dKjok3TgwAFh9r58+bKYm2YqHBliGIZhGCufTaZ5OpVKlNPXqFEjT/tPnTqV+vbtS+vWrRNCCsKkX79+YvBrYYNUHEAEC54liB687kuXLokJ9cHBwRQUFETDhw8XUaG9e/dS48aNTXouFkMMwzAMYyNpsoCAAIMDWw2BFBQuRY2smEM0SFKzZk0tMQSaNm0qxJChyre8wGkyhmEYhrHRQa3FmaCgIHF94cIFzTYpys6fP681VFbZNdsUWAwxDMMwjA0Oai3uVK9eXZTuwyg9ZcoUio+Pp1atWonbli5dKho/QgjBzwQM9UzKC5wmYxiGYRgzYUuRHlNYtGgRdenShb766iu6e/cu/fHHHyI6BMM0xJKDg4PYDlP3oEGDyFQ4MsQwDMMw5oIjQzny1FNPiVYAr7zyiugujcq2v/76SwgiGKwhhDA0FgNda9euTabCkSGGYRiGMRccGcoVmKYhdiR16tQRPqLTp0+LXkToli1nqZkKiyGGYRiGMROcJjMNRIhMLaPX+3gF9kgMwzAMwzAWCEeGGIZhGMZccJqsWMBiiGEYhmHMBKfJigcshhiGYRjGXHBkqFjAYohhGIZhzAWLoWIBiyGGYRiGMROcJisesBhirApnF0dq3bUO3Q+Lostnb2u2l61Ygmo1DMz1/vExSXT8vxCyJWqXLU0V/LwpNSOTzt65TzFJKXr361kviBzt9RegpqRn0PaL18iWcHJ0oPqVy1AJbw+Kjk+ic6H3KTU90+D+Jb3dqXo5f3JzdqJr9yLoToTpc5QsmaxMFYVfzaD4yEzy8LWngGpO5OSat8nt0fcz6P6VDAru4EpWA0eGNJw6dYp+++03MhWU2r/44otFI4ZUKpWYCbJixQq6c+cOlS5dmnr16kVjxowhd3d3vfc5dOiQmJJ75coVcnNzo86dO9PEiRPJ29ubbIWIiAjRIAptxSXvv/8+rV69WsxVKch+CbbM6Km9qfdzLWjLX8e0xFD95lXp7Y8G5Hr/kPN3bEYMNalcnj7s24WqlCqh2ZaWkUGL95+gb3cc1Nq3hIcbzR3S2+BjRcQn2pQYGtSmPr3ZtzV5uz9ZlKMTkmnO8h2087T2cbC3s6OJA9rRs+0aCAEl2Xv2On3wxzaKSdQvPq2RG8dTafuCeIp7lKXZ5uZlRy2f86Amz+hfPyQZ6Spa/1kcPQq1LjHEkaEnQCN8++23ZCpDhgwpOjGElthLlizR2rZz50768ccf6b///hPiSMnPP/9Mo0aN0tq2f/9+MV/kwIEDFBAQQNbOuXPnqH379uKXpBRD9+/fF7/8pKQks74+a6FFx1pCCOnjXlgk7dpwyuB9m7WrRV4+bnTxVBjZAvUrlKH/vTSAXJ0c6UFcAp29fZ8CfLzE9tc6tKBH8Yn055EzWtEjEBoRTefuhGd7vLiUVLIVhnVsRFMGdRD/vno3gsIeRVON8qUosJQvzRnRk+7NXUmXbj/U7D9lUHt6rkMjSs/IpEOXblFaRiY1D6pI7etXo3lj+tLLX68klQ1EB26eTKW1H8WSKovI1dOOygY5UUaaiu5dTqdd/0ugxOgsaveSZw5CKJYe3sggO2vrjmcDv/u80rJlSxFsUYJBrHPmzKFbt27Rq6++KoIv5cqVo8jISDp79izNnTuXMjMzxeyyNm3akKkYJYYQDYIQqlSpEn344YdUr149CgsLow8++EBEPSZPnky///67Zv+bN2/S66+/TnZ2dvTOO+9Qv379KCoqimbMmCHCYW+99RatXLmSrB3MTomOjs62/eOPPxbHDMeTyR8lSnnRhI8HUmZGJjkozr4l547dFBd9tOlWlzr1aUSXTofRz19usolfxYf9ugohtPV8CL2zegulZ6rTO51rV6Nvh/ahNzo9RcuPntEs0rXLqcXQqmNnacmBk2SrBPh60rhnWot/f712Ly3dqT4WdnZEb/drSy92aUqvdm9Ok3/eKLZXDvCjwe0aUHJqOo2ct5ouhj0Q28uW8KKfxg+mBlXLUc+mtWjTsctkzSCjsGNRghBCVZo409OTvcnVU61qIsIyaPXMGDq6NolqtnERaTPd1NjGL+Io/FoGWSUshjRgLdRdD59//nm6ceOGCKDg30p69OhBI0aMEJPtoTGgQ0zFKI29ePFicb1hwwYR5WjUqBH17duXtm7dKlpjb968WWv/7777jjIyMui9996jzz77TAxcg6rbvXu3iAghRXTv3j2yVcqUKSOGzSF1yOSPyZ8NJncPF1r9yz6j7leqrA+N/7A/pSSn0ZdTV1FmxpPwvbXSuFI5CirjT/dj4+ndv54IIbDz0nX6ce8ROnD1FpVUzPqpXbaUuD5/V72Y2yp9ngoWnp8dp65qhBCAaFy48RBtPHKRHsUlarYPbF2PHOztafV/ZzRCCNyPiqd5f6s/q31aBJO1E3Erk2LuZ5KLh52WEAL+gY7U4WVPIZROb07Wut+hlYm0eGyUEEJ1u7iSozNZHUiTGXuxFS5duiRsJJg9piuEJMhGIejy6NEj+umnn4pGDP3www8UEhIiIkJKMDHW0dGRnJ21P6kQPWD06NFa2318fMQbw9nC9u3bc31eeGsgGuLi4mjhwoXUrVs3oQR79uwpIlF4HH2sW7eOnn32WSHaIMTefPNNunZNv68hNTVVPHb37t2FfwfpQES2li1bJp4bqT1dD9Dnn39OvXv3Fo+PS58+fWjRokVicJxk2rRpmjQhXiseC4+pfF8nT57URIrwM163PgYPHkzBwcHil27q+9yzZ49Q0ghHNmnSRNwPotTQMbQEBr/ajhq1rE6Lv9lK1y/dN+q+o9/pTZ7ebvTnwl10/3YU2QJtalQW18sOnRamaV2+23mIpv61hSISErXSZJlZWXTx3kMKLOFD3erUoNbVK5GHixWuTjnQKlh97JZsP6bXRP7+71vp81Xq7z3QqHp5cb333I1s+x+4ECqEKKJDiCxZM/ER6s9Z6SqOWkJIUqaGOklx9+KT705weV8KefrZU9/pPtRzvDfZ2VvhgeKp9QaB3gAVK1bM+aS2lPpkDZmqIkmTVa9ePds2+F5ghkZeD/k8CRZXeGWg2vS9ESzc4Pz587k+r/TWQGzs2/fkzB/5wi1bttCZM2dE3lCSlZUlIldSdEiOHDkiJt+uWrWKnn76ac32xMREEbFSPjbSeIiAvfDCC+K5ExIStH5B8ACFh2t7JxCi27hxI+3YsYP++usvzWuH0RwgVSYv+jxDHTt2FALp119/FRE3JRBmeEzsI3/xxr7PNWvWCPGD+0kgxCCGxo0bJyJ5lkaNOuXphbe60In9IfT3bwdEyiuvwFTdpntdCr8TRf/8doBshaAAf3F94tZdcV3O10uInYysLDoddp9ik7UNve7OThRYwpciE5Po04HdqWudGprbktPSRSTpp73ZxYE1gmowvOdLYWpPUFAFtVcoJiGZzt68L/xASiqW8hXXN+5H6hVPD6ITqIK/D5Xx8xLRImvFyUUtYpIT9Ede4RcCsQ+0j1/7lz0psIEzOTpZoQiSWO55aKFTvrz6ZOLEiRNinTY0mX7TJrW9oVq1aiY/l8lWtF27doncXoUKFYSXCN4XeIckiOLA1ASjkz7Kli0rrmGCyisQKxAnMGwfO3aMxo4dK7ZjEcfzSebNmycEAg4MwmbYd+/evcKrBP/SsGHDhBCRzJ49Wzw2Unf/+9//xP4o70N1nD5n+9SpU4UQgucJ94PoQ+Ro5syZIl0I0SGF0qeffioeE0C4IOw3fPhwve+vdevWFBQUJAQevFVKkC8FiOqY+j4heJCSQ/QKHy6IN0SrkK5D1A/eJkvCxc2J3vlyCCXGpdDc6WrxaQzDx3UW16t+3kfpOZREWxulvdUm1fiUVPry2Z60beKr9N3zz9DCF/rR3qmj6K0urbQiFbXKliJ7ezsq5eVBzatUpH1XbtKuS9fpXkycSBm93bUNTexmunHRUnBzcSIvNxd6GJ1AlUr70bJ3htHKacPpy5FP0//eHkybPx5JXRrV0KoiQ7VZVpbKYMUYRBTwdHUha6Z0NUdycCSKCM2ky/+lZCu1P7xKfUKYkUaUmf5EHVRt6mLdQojTZDmCLA3WxIcPH9JLL72ktc4DZGGgOxCEQGbK1EqyfPUZunz5Mj148EATZUDUol27diJ6A+Lj1Wc5hvwwcrsxlVQQEUqDdtOmTenw4cNiYUdaSJanf/3110LIHDx4UKu6Da+vcuXKNHLkSBE5gWjAwYSQQJoPER3kJuVjIwqDtJQyKgTgWv/iiy+oRo0nX3xSzBw9elSIGYghiAyIPghG4OfnJ9JgOYFfODxWOJ7K9CLEkKenJw0cOFCzzZj3Ce8Wfl94PUgvBgaqe+4g3Yi0I6JVeH2WxBsznqEKlf1p5pjfKDpC+3eUG0F1y1O9plUo8mEc7fj7BNkSHs5qg+qcAd2oQcWydPn+Q7odFUuVSvoJL9GYDi3IxdGBvtzyn9gPnpcNpy/RzYhoWrz/uCa1hsUe+47r3JJebtOE/jl1kW48st5Uo0wJOjs50KK3BpKfpxudvHaH4pJSqVbF0iK68/krvWniT+tFWsz5sZE/NcOw8RetDICrs3W3fHNxt6eGvdzoxPpk2vhVHF07kkrlaztRWrKKLv+XSlF3MsjJhSg9FRFvouwlEFYMR4YMguACMiVdu3YVQQb4kps3b07+/v5iXT5+/LiwrODkHyf5uaXTcsLkv0AoMJiWELpCFGXChAnUv39/EW2AoJD+ISzC+kBaDRjqTaQPPL4uECsQQykp6rON0NBQEeFwcXERosDQ86L3EcDrjY2NFb2PpBCS4MBCfOg2gZKhOES+kL6C0x2pM6ScIIZyet95Oa5IlSHiI8UQHhOP//LLL2vChMa+T4g9lB2i/QEiengPLVq0EMIIvqfcRFpxo22PetStfxP6Z+lBOrbvitH3f2Z4K3G94c9DNhUVAvjiAMFlS9MbS9fRnitP/Cx9GtamTwd0pxdaNqblR87SnehYOnbzjrjokqVS0YLdh6l51QoiYtS1TnX6cY/682+NyGhZ2RLedDcilgbPWUq3HqpT3o4O9qLcHr2EJg/qIMSQFEG4zRCODuplPzPL+lfEdiM8KT4ii0IOptKlveoLcHazo6en+NC2H+IoPU1llSbpnLAlQ7QpyCADMjLwGMP3qgTiCP7dDh3U7S6KXAwhSiGvka5CGAvl9ugrhPSNbKior6Rcud2YxovSK6NEii4ZocLrkIZo+HEMIfeT6SxEUvRRtWrVbNugSJFaQ/oLQqogQRQJvYjwS799+7YQZLLvgjJFZuz7BHicoUOHCoF0/fp1cYFTH4IKv8Mvv/xSKPHijqePG705qx+lJKXRzSv3qWOfhprbguqqo3BlK5YU26MfxdPpw9e17u/o5EBPdawt/r1745NeOrZCYqpaKKN0XimEACJAKK+XBumVx87m+nhHbtwWYgi+ImsmMUV93MBXa/ZqhBDIyMyiL1fvoU4NqwsPEErqQx9EU1JqGrm7OJOLk4Pe7tSerurvr4Rk6+/ThHRX32k+dOtMmmi+mJqoohIVHEQDRZiqk+NUogGjFOs2A4uhXEGg4t9//xVpMviMEQ2CdkB2RvqK8kuBxWZRxSQjFsDV1VWkYtAoKTk5OVu6DN4ZQ6ZsQ+TljwTPKw3aWOgNIV+P3F8pGpTgoOsyaNAg0U4AUS1EZSCYEG2pXbu2EEjwNOUHiJ5t27bR8uXLhTkdvZjwHG3btjX5fQK8RqTUEA2DORzVfoiq4QOGlBuiRW+88QYVd3xLeIoGiQC9hfTRoEVVcTl16Fo2MYTKMw8vV9FX6OG9GLI1UFJfp3wAhYRn/2yDqw8ihBjy88hbywdEiAAM2NZMUmo6xSamkI+Hq2i2qAvef2h4FPl7e5Cfp7sQQ3cexQqTdfmSPnQjXDuFiK8zRJlQpfcgxnrN07pUauAsLkruXEgTpfV+5a07XagXFkN5BoVZWPvgI0I2oyAbFuf5k4cnRSl2yZIlRZgKaRclsvRcGqMByrexkMPchLJwJWvXrhXXrVqp0xUFBcSVk5OTEFtQjoYM3BK0CYDIgvcIPie0CZAgDQajuBJ4kyCE8LioOFN6deA/mjVrlvi3smLLWJAORPsB5Ejr168vSulhflaKQWPfp5KGDRuKC9JxsnPnu+++K36HliCGEuNTDHaTLlXWV3iB0HH68pkwCr2avS9O41ZqAX5o10WyRS7cfUBdgqtrjeFQUs5XHa2VpfWtqgdSSQ93OnrzjuhWrUvDiuq/+esP814MYalcCntAT9WuRFXK+NHdyOxRYYgbEPm41xA6UUMMNalRIZsYqle5rDBlh9x5lONMM2vh0r4Usncgqtk6+ygN+IZAhWDthou2gI3FwUzi4sWLIjCAjAnWVozdQOHW+vXrxZqL4iVDfYjySp5zIoiCIE2FERqvvfYaxcSoz6ixmMK4BEMxQBpGgsovMH78eFHpJL0sqDxDRAKG54KeyYXXiU7X8BChPF1Zuo8IyJQpU4SilKX4MDnDnAXBAfOyrG5DKgzC4MKFC1qPL71AuFY62/HLQnm+TFkhGiZxeOwLMBR90gXK97nnnhPHDClHiCBdl7yx7xOvDz/jQ6SMdkHAycaX+TGfFSXREfH05dTVei/r/1B7pM4evSF+Xv1z9iaMNeur3+clGxm9ocuW8+reHc82q0dV/LVN8zUCSlLPejVFBdTBa7fEtu51g+jzwT1pas/22R6ra3B1al+zquiXs/X8VbJ2tp1UH7vXn24lhIyS/q3qUnl/Hwp7FCMuYOdp9TFBZ2pv9ycVYw72dvTG0+oTwS3Hjfe8WSIn1ieJ2WL3rmj3Erofkk5ntyULoVS/m/XMHLOEPkMIAMD3m1dgCynqnnRonQN/K4IQurYarJXw06K4Cn368oNRMcn58+eLVA3c3fCfIBqBCiVpXp40aZKWmRfGXPS5QWQIJiekzeAVwi8APpX8DGTLCaR8EOWA0xyRH0RvkC5Cvx+IN6SLlCoS+yPN9/fff4sGhnhf8BIpTdDSS4PwXJ06dYRIqlmzpvD4QODp9hySvYUAnO8AaS8IHJTbI9WWW6oM897wAUBVmz5PkzHvE0ZzRO3Qewh9hZBnhd8KpfcQbqgks4SoUEFQtZY6knHtkm12P78VGSPGajzbrD6tGDOU1hw/TzcioqiqfwkhkDCmY82J83Tvcermj0OnqF/DYOpRL4jK+nqJER7J6RnUpFI56lVPbbz/ae9RvVEja2PDkYs0tEMjCg4MEGX16w9foJiEFGpUvRz1aPL4WGw6rNl//4WbdD40nOpWLkPLpj5Pq/adoZS0dHq6RTDVr1KWIuISRXdqW6BeVze6fyWe1s6OoQY93cgnwIEibmXQmS3JlJlO1OQZN/IrZ3tpsqI2UMNnijUI/l7ZTqVRo0bChIzAgC5Y26ZPny7WDQQJcCKOfnUIgOjz8RY06F+I50UGA+uWMuCCgABsIgi8IDqErArWZ1Mwyi2L1Ao8JzhgCFXBDwQhBIGAUR14sbpg8UWKB1VQ6A4JIYTHQYlcfoaq5QQECtzn6LODXxwiMnitEGAQGYhuISIkwcGDfwZpPbwviAmIHxx0lJ0rvTfYDsGE7VDIeE/4sOBDgU7TKGMHSsc7ytfx4VGm2XID4kxWeCmN0/l5n3jdiH5hX7xHVMEhMoT3gn5JtjAjzcfPg1xcnSg2OlEYsG2VORt307pTF8nL1YVGtGkiZpXhGmbfHRev0Scbn3RRvvogkias2CiaMaIU/52e7WnWM53p6QZqEzrK7X/Y9UQAWDMwSo/9Ya0YrYGGimP7tKb3hnamXs3Ux2LBhoP071G1HxLgJPqdX/6lWw+ihbEa0+unP9dZCCGk0t5c8A8lKIzZ1kyD7m5Up5MrJcer+wpt/S5elNpnpKsjQh1f1T+k1eopwsgQTpIRoEABEIQQbCFY006dOiUyG7IaWhkJQkEP1ncIEuwPywxmlKLxsGyhU1ighQ+ySFjr3n77bb37YJ1G5gTrMQSbqdipTIx5IbQGEYCwVV7UIaInSMfAc6T05eQFPA/SclisdY3YOd0mnxfRDwgENFXMzYSNyBXSTDj4SG/hgwMXO0SMbndLCEE8PyI/sroOzweRAcEhe/lIkILDcYO6xWPn9trxuvFhhHlad9RJft4n/iDwu0DkC/sa094gJ3oGTydzg/5BfV9oRWeO3KBta7P3D/Lz96KRU3pQRHicGN9hbjZf/ERcB8/4xizPX698AHWsXU00VIxKTKb9V0P1ltEDb1cXkTJDE0YnB3sROYJwumYmr9DFjyeI60Zji/7Y4c+rfb2q1KR6BfJwdaZ7UXEihRb2UL8hHz2HujetSXUCA8Raho7UEE0wZRc1p35QH7efQ54UZBQloafT6OYJdTWZd2kHqt7CmUpXyZtXaMv8OMrKJOo1Ie9VyAXJyCB1762CpMFbxn9+z8xX/w6NBbYLtMFBpgA9++ADxpo0c+ZMTYZEtmMBOLnHxHj03UMEBtVbV69eFTYOtJKB7xRV5IUFTuAR/YFQwzoMnxDEj/QMSdBPD331IIp0W+HkFZNjkoj0GNP6Gou5ofL13EB0QxnhyOtt8nlzi3jAxI0PBBobIt2ElBGEAlKBaKCIx9dXYo98pe57wvMZ6tkDIYhLXl870lpKQ3pO5OV9SiDELMUfZCwh5+8Kr1BufiNGzbm7D8QlL8SlpNLq4+f40D2O+Ow5e0Nc8gLGdGw4fFFcbJ3KDZ3FxRR6vGUeEWQtabLvv/9eFOcgOyGbAWMtnzt3rogK4UQewQDpzUF1NNYLFPPIk3sIIvyMa0SMEGUqrHYI0m+LE/ickI2RZZW1KdheglYPSPNBTaJyC74bRHmQcsIBxi/5m2++sb3eFwzDMEzhU0RiCMU96C2HZr5SCCG7AaHh4eEhGvIqQQQI6yCaHupmORAEgA8YthkIqPzMBMsJrMkAaTzlAHQlCFzIaJDuEHljKP4d9ooATKvHCAxEafDLxy8XQgi/bITmEBJkGIZhmMKIDBl7MQXM0JSCASXqzZo1E/YMnPw3btxYbNM3Md6QIRmpNpBT09/8AhGGKQlYlxGB0nX1QLChUAtiCe9DenNNgSNDj83RKMvDBZ4h+HgQITI0IZdhGIZhCgSVaRVhuBgC3lFclMiKZxT3YMg4hAUEBE78T506JUQHCp4GDBgg9pPtcwx5gqXlo6CnMOiC2aEotoJ3Sfpb0f8P0SkUDAGYwLGfsu+fsXBkSAf4hapUqcJCiGEYhimWkSGUxsP7Y+iC2w35auD3wSxORH5QDRYVFSXaqiBdhnmjskeevDZUvCO35yTKCgKk9FBRNmbMGE2LG/QFhBCCfQVV4BBHypJ7U+DIEMMwDMNYUGRo2rRpoiOzIXSjQgATCwDsIJgAIau6EQD4/vvvxZgmeID27t1LPXr00FQ4GxI7sr+gsdXhpoAoFOws3333nSi3R2U2zNKYxKAsSsoPLIYYhmEYxoLEkL40WG5A9ABEUnQFjJ2dnWjuCzF08+ZNsU2KDDRW1odMu/n6Fs2AZoyeQl+/7t27a7bh9aKRMaJC+UmRAU6TMQzDMIyVG6gxSFzpBTKURvPx8dEyTp89e9bgmIz8VnDlBTR5RIdpGLZRyq8E4gjNGFF1puyPZAoshhiGYRjGyjtQo8EiokOYNqCcZQkSExPpn3/+0ewH0IsOaSj4daTwkaAnEUQSeurlNyKTG5imgB6AeD1otqikU6dOwu+E6FWfPn2E/8lUWAwxDMMwjJmwU6mMvpgCDM/SKI0RTMuWLRMpMZiPu3btKgzJMFZj5qYEQ9kxogqTGDC7E5EYGLDRFRoYGpFRUOD5/vrrL+FfwhxOzB5TAiH2ww8/iNcHHxHek6mwZ4hhGIZhzEURdqDG+Ax4bLZt2yYmvev29Pn555+1to0fP57Wr18vGjLKknsJIjEYgVGYwNSNFgAYAK/b+FEJxnVgIDz2NxUWQwzDMAxjA+M4YLretGkT/frrr2LWGCJDMEB3796dJk+enK2nECrQIJww0wwRIZS0I12F5oaIGslxGYWFrICTZf6GQBfq/MJiiGEYhmHMRRGKIQABM2rUKHHJC66urvTuu++KS1EjTd/wLd25c0czRkQXTIpQdsU2BfYMMQzDMIyVV5NZIpUrVxYl/zB4Dx48WIzf0K2Ag0jD4Fn4ivIzOosjQwzDMAxjLmxI3JjCL7/8Qi1atKDDhw+LoepVq1YVxmkIJHTRRlNIdKZGU8by5cuTqXBkiGEYhmHMBEeGcgbjsdAKYPTo0eTv7y8GqUMYYfAsvEKIHO3evZteeumlfP0eODLEMAzDMOaCI0O5gkjQjz/+KKI/d+/eFUZumMGRRiuogeoshhiGYRjGTNiSByi/IB2GajZcChoWQwzDMAxjLkxsomhLnDt3TjRfREVZWlqa6D2kD8xdGzt2rEnPwWKIYRiGYcwER4ZyZsmSJfTqq6+KTti5AQ8RiyGGYRiGsTQ4MJTjkNa33npLCKGmTZsKUVS2bFmDzR7zU03GkSGGYRiGMRN2uQc8bJZDhw5RfHw8BQQE0J49ewrMLK0PFkMMwzAMYy44MmQQCCGAPkOFKYQAiyGGYRiGMRPsGTIMSufB7du3qbDhposMwzAMYy5QGWXsxUZo0KAB1alTh06dOiUGxhYmdipDNWoMwzAMwxQqbQZ+ZfR99q+ZTLZAcnIy7dq1S8wls7OzoxEjRlDz5s3J29tb/KwLBrnCaG0KLIYYhmEYxky0GWCCGFprG2JoxYoVNHTo0DzvP2TIEHEfU2DPEFNgVF3+CR9NI7kxdLq4fvnYy3zsjGRxs8XiOvi9b/jYGcHFORPEdcfun/NxM5LdW6cW+DFjz1DOc8mMmTkGo7WpsBhiGIZhGHPBTpUcxU1+BI4xsIGaYRiGYcwET60vODDA1VQ4MsQwDMMw5oJLmHLlypUrtGbNGjGbLD09XTObDNepqakUExNDJ06coPbt27NniGEYhmEsDfYM5czKlSvp+eefp8zMzFz2JFFlZiqcJmMYhmEYc5GlMv5iI6Snp9O4ceOEEBo5ciTNnDlTbK9Vqxb9/vvvNGvWLPL399ds+/HHH01+Lk6TMQzDMIyZ4Nlkhjl+/DhFRERQxYoV6aeffhKDWz/55BMKCwsTJfeOjo40bNgwaty4MV2+fFk0Z8S/TYEjQwzDMAxjLrgDtUHCw8PFdb169USTRcwnq1GjhhBFISEh4ragoCAaPny4+PfevXvJVFgMMQzDMIyZ4Goyw7i4uIhrRIAkNWvWFNdSDAHZdRoRI1NhMcQwDMMw5kJlwsVGCAoKEtcXLlzQbIM3CJw/f16zLS0tTVzHxsaa/FwshhiGYRjGTNipVEZfbIXq1auLYa3Xr1+nKVOmUHx8PLVq1UrctnTpUkpMTBRC6M8//9R0rDYVNlAzDMMwjLnI4kOfE4sWLaIuXbrQV199RXfv3qU//vhDRIdgmIZYcnBwENudnJxo0KBBZCoshhiGYRjGTBR1pOfdd9+l/fv3672tUqVKtGzZsmzbN23aRKtXrxZND8uUKUPPPfcc9e7duwheLdFTTz0lGip+8cUX5OnpSfb29vTXX38J4QNBBLy8vOiHH36g2rVrm/w8LIYYhmEYxlwUcdYLnZyvXbum97aIiIhs28aMGZOtfw+iM9i+cOFCKgpgmv7ll180P9epU0f4iE6fPi16EdWtW1dUmuUHFkMMwzAMYy6KMDIEjw38NyhVX7BgQbbb3d3dtX6GFwdCCNtnzJhBTZo0oZMnT9LHH38s0lcdO3akZ599lswBIkSm9hTSB4shhmEYhrGBcRznzp0T87xatmxJbdq0yXX/L7/8UhNN6tGjh/h3t27dRGTmmWeeoblz5xaoGHrw4IF4jaaCFB6iRKbAYohhGIZhbCAydObMGXHdqFGjXPeNjIwUaSg0OZRCSNKnTx+x/ejRoyK1Jkdi5Jfdu3eLztKmMmTIEB7UyjAMwzCWRlGO4zh79qy4rlq1Kn366aeiYzMaGrZu3Vp4gPz8/DT7QgiB5s2b630spMyuXr0q+v106NChQF4fxm4MHDgwX2ZrU+HIEMMwDMPYUGTohRdeoIcPH2q2//vvv/T999/T1q1bNWkmRIZAhQoV9D5W+fLlxfWjR48K7PVBlOFiDlgMMQzDMIy5MEELpaamiktOYyzkKAsl0o/Tvn17UZqOSNCVK1fo66+/pps3b1Lfvn3p0qVL5OzsLBoc6jNVS+R2mLKLCxBmpUqVMo8YyszMpFWrVon+BLIzpD5wgHHQ3dzchHnL29ubbA2UAKIxlOTw4cOixLF79+4m/wJtld6BtWlqg4657nc55iGN/u8vzc/ftHyGmvjrP9MBL+1ZQTfjo8gWSIlIofub71PclTjKTMkkZz9nKtGoBJXpUobsnbWb02dlZNHpqadJlan/m9vRy5EafNSAbAV3Zyca3rIhtQ2qQiU93SkqMZn2Xr5Bvx88SakZmTne186OaP6wPlSrbCl6dsFyik5KJlvh2UHNqX8fwxVAP/26h3bvVfeOAW5uzvRM74bUrGkVKuXvRZmZWXQrLJI2bTlLx07cJFvtM4QU1+zZsw3ePmvWLPrggw+yrdWIAKEnD7o6S7p27SoGnTZs2JBu3LhB69ato8GDB2vmgWVkZBhcz4CrqysVNtAOMHGjzxGeFyZwgGuIwpiYGNGLCCJvxYoV5hFD7733Hn3++ef0/PPP6xVDcIfjtp07d2opyk8++YTGjx9PtgAm7KIUER9GHCvJzz//LHon/PfffyyGjMTD0ZkqePrmul94svrsRtKmTFUq6ar/TAc42TuQLRB/PZ4ufn6RMpOeLNwp4SkUdymOHh16RHWm1SFH9ydfD8l3kynlQYrBx8tKt502uuX9vGnxK4OoQgkfzbbK/n7UuFI56lCrKr3082rKyDJ8PEa1a0adg6uLfzvY25Et0aBeRSpT5slx0wXiRxIQ4E1ffz6UypXV/juvUrkUdWhXi1asPkI//ryHLB4TxNC0adNo4sSJBm/XFxVCp2ZDFWS+vr40bNgwIbKQSoMYkgELQ/O+IECAj4/h32dBsHLlSqEhsH7mRn6CLPkSQxs3bhRdIQ2BF9+rVy/RlwDhOIilqKgoOnToEL399ttUokQJkbu0dvbt2yc+ZLriDxGylJQUKl26tNlem6WyMewiHXgQqvc2P2c3WtppKLk6ONFHJ7drtpdx8xJC6ErMQxq5b7Xe+z5I0hZP1sq1n64JIeQV5EUV+1ck5xLOlHAjgW6tvEWJNxPp9trbVGX4kzk/ibfUofCAzgFU/mm1V0CJnY0s6ojqzB3SSwihG4+i6Jut+yk0IppqlytNb3dtTY0qlaOR7ZvRot1H9N6/XoUAGtu5JdkqNaoFiOjOy6N/obT07BGH2NgnUbKJb3UXQujSlXu0YtVRuhUWQZ6ertS1UzD16d2Inhvcgs6eu02Hjlwni8aE8whDabD84OnpKa5l1AWjLnSnwyuR2/MzDyw3EAUaN26c0BIjR46kcuXK0YcffijGcUyfPl30TELnaVS0YZtuc8giEUO3bt2iF198kcqWLUv37t3Tu8/y5cuFEEJIbtu2bZpFH42coPTQFhxqFIrVFnn11VfFhTGepIx0SsrQf8byYZPu5OPsRh8c30bnosI124P9AsT16ch7dDfR9OnGlk5qRKqI9Dh6OFLw1GBycFH//bmXdyeXki504ZMLFH0qWlsMharFkE8tH3ItVfhh8eJKp1rVqH7FsvQwLoFe/N8qkR4D1x9F0fWHkbRm3HDqXreGXjGE1NqXz/ai2KQUYRMp5ZW/jrmWho+PG5Uq5UU3Qx/R7Ts5p6K9vFypaeMqlJCYShOnrKCUVHVKBly4eFcIqgH9mlKPbvUsXgwV1TgOlK2///771K5dO5GZ0WX7dvWJI3oIAYgLRH2QuUAUCNEjCcTHwYMHRZAD3aELi+PHj4vnQpXZTz/9JLIseO1hYWGiBB+pPGgINF/EaI5Tp06Z3IjRpKn1mBKLMBpMVnCg5xTeko2blNEPvPiePXsKEYWoSW7AW4P238hd4rlxH8xJOXDgQI6hMyhclBIi14goVnh4eK7vC7/4tWvXatqVQ/3iue/fv59tf0R1EOXC4+Ny5MgRTR5Vgtt37dol/o1fFh5LKmr5vqQbH89t6Lnkhxm3y/Ckqe8T7w2547///lvcT54JWAPPVm1AHctXp2MPb9PvV49r3VbHr4y4Phel//jaCnYO6igOfEFSCEngG1LvpH0fGRnyqGJbC7guPeoFieuFu49ohJDk0v1H1PmLn2ns0nV67/v+M52ogp8PTV+zleJTDJtfrTkqBK6E5Pz9BJKS0mjYiEU0YuTPWkJIsuuxr6hMQOGmaIoEfP8aezEBiBasURh4ioCE/N6H52bKlCm0Z88eKlmypDBRAzn4FAbpl19+mRISEsR2GKtHjBgh1j8EROwQLi0k5FqGjtl4HozcQH8jiCK5jgYFBQnPE0CrgCKNDCFXiYjPjh07cjRPYbFHKA8tu3WBaXjz5s2iaZO+25VIbw2e680339Ra7BGigzBCzwNdRfnKK69odbPEwXzppZdEWE3XIQ/T2Ouvv64RItgXz4VQ4VtvvSVeK6JgICsriz766CPRfVM67iXYZ/HixeL9AYTtfvvtN/FvlC3i8t1334lfoK5nCK917NixIvw3Z86cbEINH0x8QDEkz5T3CRGFrqF4PiU4A1i6dCk1bdqULBkvJxea3KADZWZl0awTW7LdLiND4UnxNKNRF2oZUIns7ezpfPR9+vXyUboU86TU1JqB4PGq4UXxV+MpdEUoVRxQkRycHSgtOo1u/HZD7FOyWUnN/vjSTAxLJAd3B8pIzKCQ70OEOLJ3tSefOj5Urmc5cvZ54vWwZupVUAvqLef0pw7ux+pPs/aqX5P6Ngqm3w6cpP1Xb5EtUr26+u8P6a7nnm1BbVsFkaeni4gS/bv5jFaEB5GfBw/icowcgajo4lPJZDJFdDKKFBM8vjBeIzMDAYSOzegVFB8fL6IsWI+UM76wL4ID//zzjyixx7oFM3NcXJz4Gd6lwkSmAqWZW4o6FGRBDAUHB4ttWLuw1iJiVGRiCNEeLLJYrNFoCYJHH1CNCG/h4CnfiKRatWriGuV8eQVhMcwjgXiCUQoRF9wf25V5zYsXL4rXBkULsVS/fn2hJI8dO0ZLliwRggfiRipaqMkBAwYIkYNmVFChONjz588XQkGXefPmCac+XPloTY5QIUxmiALhsRH5wjUiZ/BJ3b59W7xWPBZEm6GwIt4HhCZUu64YwtRg+K1wuzyexr5PqHgIocqVK4sOpEhPomEWIlZ4/8i/KqvdLI3Xg1uSv6sHrb15ji7HZO99UeexGJrXqi95Oj3Jt9f0LUXPVKpD045uEve1BWqOr0nXfrxG9zbeExVljp6OlB6XLrw/ZbqWoYoDK2r2hXE6MzlTRJLOfXBOqxQY/qJH/z0S6TaPQOuOGjna24vIDlJksckp1Dm4Gg1sUpcCS/pSTFIK7bp0nZYdOpWtmqycrzfNfKYTXb7/kL7eqn9auC1Fhl4Y1orc3Z/8/QVWLEmtW9agtetO0HcLduTpsfo+re6gfODgVbJ4irD2AOsWukWjoAeZGWlxqVevniiv79KlS7ZeQshIwM6Bai2sLQCpNoiPgAD177SwgH4AGMoqkWsy1q5+/fppggU5mb0LXAxBEY4aNYp69+6dqyKEcszJaS5d3zL0lldli6o0aeyCCoQfCcoWF4TPABQvBAJEC6I6UgzgQCGchlTShg0bRJRE7g8hNGnSJFHtBZGAs2GYnqGkdYEogbDA7XhNEqhrWZ6IVBRU6+jRoykwMFCIIUSL8JoMAVGFECVaFSAfq6zOQ+QGIDwpMeZ9IsWI1BiOHYSeFFTyfUJY3b17VwglS60uG1a9MWWpVLTo4qFst3s7uYrqs/SsTFp94wxtuX2FIlOSqLKXH42u/RQ1Lx1InzTrRReiwulKbME1ESuuoETeycdJpMzw7/RYdSrC3sWenH2dtQzRMkWGSFDFQRXJp7YP2TnaCSEUtiaM0iLT6PK8y9Toi0Zk72hS5t0i8HBxJnt7O1EKP713BxreSnukAarJetUPopd/WaNJg9nb2dEXz/YkRwcHmrxyM6XnoSLGWqleTW2VOH4ylP7dfJbuh8eQf0lP6talrvD+DOjbhEKuhtPW7edzfJxePerTU82rCe/Rlu2Wf/JSVJ4hCQzJb7zxhlgzsVYgOhQYGGhwf6yxyEDAJwxLB/w7hS2CJFiv8PyocMN6N3PmTM26iDVxwoQJ4gQeAYT8mrnzLIYQcUCaBhVgv//+e655QumdMWSOltshQvIKoiJSCAH8AhElgvcF3TIhhiCukIqCsED+c9myZVqPAbECkYD7QCSgbwHULiJCn332meZ14f0hXQUxoRv9gqrWBz4o+GBBDCUnm9Y7BLlZiCG8bvlLR3oLQgbGMCh4YOz7hPiBAQ7RH6h8HLcWLVpQ7dq1xfu0dIZUa0Dezq609fYVuhYXke32pIw0arv+B0rNzKCIlCeh9RvxkbT73jVa0fkFala6Ir1SqzlNPfIvWTPpCel0/qPzwkjt28CXyvUoR06+TpQUliSqyMJWh1FyeDLVeE19cuFbz5caf9NYGK6V5fYeFT3EbaemnqLUh6kUeTSSSrWy3n5Zbs7q917VvwTVLFOK1p44T3+fvEhxySnUoGJZerNLKwouF0DvPd2R3v1LnaZ9o9NTQiR98M8OUX1my0yZpvaQPnz0JJWIFNmpM2EUF59Mzw5sLirEchJDnTvWFlVmsXHJNOujfygjwwpaOpjBs4kMi7HG50qVKolLUbNo0SIRsYLXCSfs8M0iOoSMBvQA1mxslx6nQhdD77zzjghLoTwcKRsJFleAdBVeJA5W27ZtNXlHQ6JAdq00pkeBvmm0cpaKNFLj9eDf0dHROZbtS4M0ol2gWbNmetN5aA2uLxWI/klIRcG9DvGDNJ0yRGeqKRkNsBBtgiD69ttvxWvCv2FyU0aFjH2fshEXfn8Qs7gA/L6QIsN2c3zQC4qBVeqL65XX1fN0dMlQZRmsIMNv6n+XDwsx1Khk9rJxa+P+1vtCCPk18qPak2pri5v6vnRm+hmR+kLzRa9qXkIAKUWQrv+oVOtSFL49XHiQrFkMpTwuBXdydKDfD5ykzzY9MWtefRBJ5+6E06o3hgl/0JyNu6lGQEka3b45bb9wlVYds/wIRn5RiiBdVqw6IsRQ5Ur+5OHhQomJ2Q3miBy98VonIYTemb4q14o0i8GKClgKA8wbQ4oObXxQ/g8h99dffwnhA0EEYFmBfQcn94UuhuSTYoHWB9I6uGDIGsQQRA4EEfwy+pDbjem8rK+vgoxQKZ3xAIIiJ2O2TAdJUWaoZ4PSTCaBOEGaTNkOHf4giDWIE6hUU4HKhbhBug7tCNCnCSITjw8vksTY9wmQSsNrXLBggYgqIbqE0Oc333wjjHPwPElDmiUR6OlLtf0CKDo1ifaHm9aV9k5ijMaEbe1AtACIHV2cvJzIv7W/8BKhASPEUG7IUnv4iqyZhJQ0ysjMIkcHe/r75BMPg+RKeARdfRAhokNVS5Wg4S0biX0bBZaj7ZNf0dq3tLe6p8vK14dRliqL3lq2QVSj2SrRMUmUkpJOrq5O5OHurCWG8BU/bkxnUUofeiuCps/8i+6HW1FrDBZDuYIoFtYoCcr/4SPCMFlkobCu6VurC0UMIUyFFJAuMEljYUWuDmkdOeEWIgUvEKV8Sj+PBAsvKOgKJvkaETKDiMgNOYRORogMiUAJBASaP+EXgD5JECtIsaGKDIoVLQPyI4ZkqgxiCG3FcQz3799P/fv3F+kwU9+npFOnTuKC14/0IH4/v/76q4j6QSTl1CqhuNK1vNpkhxQZIkCmUM5dHaGMSLWC6pRckKLFwU1/CtvR7XEb/gT9bfh1QZQJOHlbrvk+L6Cr9K3IaKpWuiRlZuk/m09/nLbBvvJEzT+HfkJlfb000SZbBj2IIIRQRYbIj8TZ2ZFmTn9GGKyPHLtBH32yjhKT1GZZq8EKMn3mAOutqT2F8iWGsPDrAykkiCEIId1F+emnnxaLLTpGSgMwQEoJDRlR9o0oUkECHxEq1ZBGwuIOo5iS0NBQIS4QMUFrclRgoQcSXieaTiFNJUG5Ojw3SvDaYZSGdwfGYyWI5MieQtLdDuSXYl79UVDBCA3C84OwH6JeyhSZKe8TVQN4bXDnQ7BCROF3hguEap8+fTSmd0uj8eNZY4cf3sqx/9C4Oq1pX/hNmnFsc7bbn6+u/qM69MD6y57dyrpRwvUEijoeRd5B2u3r8VmLPhMt/u1aVh3xufrjVRElQoVZ6bba3dJRah9xRO3RQpm9tXPoWpgQQ70b1KRvtx/Uui3A25OCyvhTWkYG3YqIpg/X7aQvN+vvo/brKwNFFdqzC/4U/YoexVu3CA+qHkCz3+8vSuHHTViaLRgiq8MuXb5HqalqEe7oaE8fzxog5pKt23CS5i/YQVkGRKglU9QGamsgJSVFFFPBroI1EtMcivXUelRSoRePbCyIFBpePFJtMGTDHS49PwXJjBkzRHQFPXsgcBAJwYBYhNXg84EhGX0TADw58ENNnjxZiDdEfSCQ4LWBcUsawaWggQBBKgtCCWkrpKggfCA0IJxkuk4pLGQvJpigUVqPqjJ9UTYlED9jxowRESI49xFxys/7RAjxtddeE4IM94FDH6k3eL3Q2RPoew5LoEFJdf+nkxGGI3LHH92hsu7eNKRqA7qTEENLQo5RSmYG+Ti70pT6HUSjxuSMdPo9RLtRozVSun1perT/Ed3bck+M4QjoFCD6DKG0PnR5KMWHxIueQiWbqiORnlU9hYcodFmoMFHDa4S/h6S7SXT95+uiEs090F2Yqa0deH+GtKhPr7RtShHxSbT6+DlKy8gUabFPB3UnN2cn2nj6EiWkqk+GDA1hlbPLwmPjKSIhiaydG6GPyMnJgYJrl6Opk3qJmWJIjWHbM083ouefUy9mmDcmeX1URyGEtm4/R/O+fzJWx+pgMZQNVD+jvxHa3sAbiwGscngs1l5UtCstODjBh48IlW7FUgwh4oKSN7wJ2XBQgjej20unoICQQNk9IlIQA1IQAAgZVI3JLpsAc9JQuofoFaIsEhx4iBY0eZT9d+CFwutGpAzRLVyU+yMSAyEFw1ePHj3EdvhwIEZgtMZrmzp1qngNOYHGiigbhCkb1V/6zN3GvE+8bjR/hIBbuHBhtsdCw0n0ObI0XBwcqZyHj6gSu5PDiA1UjX13YT+9Xa8dvdOwI02o345i01JEXyKARo3vHv03x8ewFlAaX653Obr37z0K/SOUbq24JQzSEEMA5faoJIPwAWU6lRGVYogOXf76sii/t3ey16TRHL0dKWhckE3MJ7v2MJK+3PwfTX+6A73XpyNN7tGWktLSyc/DTdx+PyZOy1jNqEHV11ffbqGPZvan7l3rUdfOdSkmNom8vVzJ8XGKcPWaY3TgkLrgo1JgSer7eLp9k8aVaflvY/QeyvAHsTThnSffwRaJFUa78gMCJghMoJxfgp5GMEgjW4LeQhBCqCSDdQT7oXkz1jr8G+kzs4ghGKDRzRJVV/pAtAF+HESH4B1CaT5UHsRQXslpoKmh29CPAAs/RlRAnMAgDV8ThJluTwUIB1RXQVygUg4CBD4gRLIgNFC2rqx6g5hBRAi/GKSf0MQK++MXiGgMZqcgQqM8RkhZ4TngOULlWm7vC8+H7p8QaejtZAhj3ifc94hMQXEj3Qf1jaZaSJHJ12RplHb11HSVzo355/fT9bhI0VcIozkghNCX6Pij2zT37F469ki/2d8aqTy0MrlXdBdG6aQ7SeqGiw525F3LmwIHB5JX9SfGaWwPfieY7m68Sw/2PBB9hbJSs0QTxhJNSlClIZXIxd/6jeeSPw6dottRMaJsHh2pXZwcKTktnbacD6G5W/7LNqaDUXP4yHV6a+IyevnFttSwQSCV8FOfiMAUjYiQsqS+besgcnBQL2r+JQ2b+HESY/FwZChbRgmiBsVBWPuQPULWBVEiWD4QVIBFRVp34D9G82NUdqNBZOfOnckU7FTWNJjKRFAdhrA/BIQStAVAVAfRF1SJyUaRjH6qLs8+/K+wcbSzpwB3L0rLzKBHiv5BueHm4ETezi4UnZpMaVnmq4K6MVTd4+nlYy+b1VCdmZIpulAj4pOXPkWqDJUwTJszGrS42WJxHfzeN2Z7DW5OjqIZY2RiklFrWmlvD3K0dxBpMgjyouTinAniumP3z8lcwA/k4+1OSclplJyc3RCNMR0osc+NzIwsiojMe+Pe/LJ769QCf8yeNd4x+j6br35B1sjDhw9FMRIsHCheku1ekCpD12tUrEMkITKkjADBUoLoETpso4VMsUuTWQqI/sBng+aEqJpDLwNEfBDNghEZIomFUPEkp/5BOZGcmU7JydkHQNoiqCozVFmmDydP664aM4bk9AxxMZaHcdZtmM5L2iwyyrCISUhIFRebgOMRGuAHgq8VHiBl3ztkb5DZgBhC8ZJuKgxZFoghQ6188gKLISLRZwcpsfXr14uLElS76fPYMAzDMEy+Yc+QBmRggD7riOxJqC8wIfsEojDLVFgMPe76jLEcSJdhbhd8P6jggi8I+UdTDVkMwzAMkyMm9kazRjIyMgyO8ZJFRDmtx8aM98r2+Cbf08qA2kRZPcMwDMMUGZwmKxawGGIYhmEYc8FpsmIBiyGGYRiGMRccGcoGJkLoTl1Ag+DcbssPLIYYhmEYxlywGMoGqrhxMfa2/MBiiGEYhmHMBYshDRgTpTvz0xjQF9BUWAwxDMMwjLmwhi7aBQSGruJiDlgMMQzDMIy54MhQsYDFEMMwDMOYCxZDxQIWQwzDMAxjLri0vljAYohhGIZhzISKO1AXC1gMMQzDMIy54MhQsYDFEMMwDMOYC/YMFQtYDDEMwzCMueDS+mIBiyGGYRiGMRccGSoWsBhiGIZhGDOhMnNkKCQkhNavX0+9e/c22PAwLi6Odu/eTXfu3KEyZcpQ9+7dydPTk6wJFkMMwzAMY4ORocTEROrXrx9dunRJiBx9Yuiff/6hkSNHUmRkpGabr68vLVmyhPr27UvWgr25XwDDMAzD2HQ1mbGXAuK1114TQsgQp0+fpueee04IoRYtWtDrr78urmNiYmjIkCF07tw5shZYDDEMwzCMuUCfIWMvBcCiRYto2bJlVL16dYP7zJ49m1JTU2nq1Kl0+PBhWrBggbieMmWK2P7hhx+StcBiiGEYhmHMhCpLZfQlv5w8eZLefvttGjp0KD3//PN694HY2bRpE/n5+WUTPR999JHYvnHjRkpOTiZrgMUQwzAMw9hIZAgprsGDB1OVKlXop59+yjFFlpaWRm3btiVnZ2et21xcXKhNmzaUkpJCFy9eJGuAxRDDMAzD2EhkaMSIEfTgwQNas2ZNjhVh9+7dE9fVqlXTezvEFLh16xZZA1xNxhQYN4ZO56NpIoubLeZjZyIX50zgY2cCu7dO5eNWHDAh0oMUFi6GQOQGF12+/PJLWrduHS1dupSCg4NzfI7Y2Fhx7eXlpfd2KaRQkWYNsBhiGIZhGDOxPWu10ff54IMPhLnZELNmzRL7KNm/fz9Nnz5dVJANHz481+fIetz/yN5efwLJzs5O69rSYTHEMAzDMBbEtGnTaOLEiQZv140KqVQqUQoPYVOxYkX66quvNLcdPHhQXMMsHR4eLhoq1qtXL9fIj9zu4+ND1gCLIYZhGIaxIAylwQyRmZmp8QDNmDFD7z7Lly8XFzRUhBiqUKFCjp4gub106dJkDbAYYhiGYRgrBhGhSZMm6b0NkaFDhw5Rr169RAfq+vXri+1169YlBwcHcTsiS8p0GMTVgQMHRJVZgwYNyBpgMcQwDMMwVi6GlKkxJfAWHTp0SPQcUnqJvL29qUOHDrRz50767rvv6K233tLcNnfuXHr48KEQUK6urmQNsBhiGIZhGCYbSKlBDI0fP5727t1LTZo0oWPHjol5ZYgazZw5k6wFFkMMwzAMw2QDkaF58+aJ8Rtr164VF+Dm5iZGc2BOmbVgp0IykGEYhmEYm2Pbtm3iMmzYMGrcuLHefW7evEnr16+nR48eiWq0Pn36ULly5ciaYDHEMAzDMIxNw+M4GIZhGIaxaVgMMQzDMAxj07AYYhiGYRjGpmExxDAMwzCMTcNiiGEYhmEYm4bFEMMwDMMwNg2LIYZhGIZhbBoWQwzDMAzD2DQshhiGYRiGsWlYDDEWSXJyMoWFhVFmZqa5X4rFERMTQ/fu3TP3y7BI7t+/T1FRUeZ+GRYH/k7x95qSkmLul8IwemExxFgU586dowEDBpCfnx9VqlRJzMnZtWuXuV+WRbBhwwZ66qmnqESJElS+fHlq1qwZ3b5929wvq9iTkZFBc+fOpcqVK4t5TP7+/jRixAhKT08390sr9jx48IDGjRtHJUuWFH+v+Oz98MMP5n5ZDJMNnlrPWAx//PEHjRo1Spxduri4UJ06dSgiIoLu3Llj7pdWrMEs5rfffpvmz58vfoaQxIKempoqIkQQlIx+8Pnq378/7d+/X/wcGBgojltcXJyIEAUEBPChM8DRo0fFscNnzN7enmrVqkUPHz6ku3fvUlZWltjGMMUFHtTKWATHjh2jli1bii9RnGnOmjVLnG3yl2rufP311zRp0iTy8vKiTz75hEaPHk3Ozs587PJAr169aPPmzVSjRg36/vvvqVu3bnzc8piKDQoKElPOe/bsSfPmzRM/898rU1zhyBBjMVEh+A6wsE+YMEFsS0tLEyLpxIkTIpXRt29fqlatmrlfarHjt99+E2fhu3fvpiZNmohtsbGxdPDgQTp//jx5e3vTkCFDyNfX19wvtViBhRxCqEqVKuJz5uPjI7YjsnHgwAEKDQ2l6tWri7QtRzm0wXHD8Xv22Wdp5cqVmgglPm9HjhyhyMhI6tChg0jbMkxxgMUQY1HEx8eLxf2ff/6hbdu2UVJSkua2GTNm0M6dO0UEidHG0dGRrl69KtI9q1evpsOHD2uZz7/44gux4MPTwZBm8QZubm60Z88ekfb566+/KCQkROsQDRo0SBxTJjsODg7imG3ZsoXWrVsn0o5KFixYQK+//jofOsbscJqMKTbAC7Rjxw5xxg0fS9euXcnd3V3cdvr0aWrevLle02qDBg2EDwYLVqdOnYQgsjXgxcD7jo6OFl6qdu3akZ2dnbgNhlWkFnVxcnKiVq1aiTP4ixcv0ocffkjvv/8+2RqXL1/WeIJwPIKDgzW3wfMC4a0LokStW7cWAhLHD1E2WxPhEIs4bihqwPHA32vp0qU1Jy3wCOmrWoSRul69eiJ6hNQtTNZI2zKMWVExTDHgzz//VJUuXRqn4ppLyZIlVX/99Zdmn23btqk6duyoatCggWro0KGqBQsWqG7cuCFuy8jIUDk7O6vKly+vsiXS0tJU06dPV7m4uGgdu7p166rOnTun2W/+/PmqZs2aictrr72mWr58uSoqKkrctm/fPnGf559/XmVLhIeHq/r166d13HAZOHCgKiYmRuwTFxenevPNN1X16tVTdejQQTV16lTV1q1bVampqeL2mTNnivssXrxYZUscOXJEVadOHa3jhr+/Dz/8ULPP5cuXVf379xefxT59+qg+++wz1YkTJzS3t2rVStzv9u3bZnoXDPMEFkOM2fnhhx80X6hYdGbMmKF69913VX5+fip7e3vV33//netjzJkzR9y/e/fuKlti8ODB4n3b2dmJ9/7RRx+pRo0aJY6bv7+/6vr16znePyUlRfX000+Lx/j0009VtkJERISqevXq4n27u7urRowYIT5DnTt3Fttat24thGZOhIWFqQIDA8X+x44dU9kK+/fvV7m5uYn3Xa5cOdWkSZNUs2fPVtWoUUNsgzjPjS1btqgcHR1VJUqUUGVmZhbJ62aYnGAxxJiVu3fvqlxdXcXiDVEkwSLetm1b8eWKL8wHDx5o3S89PV1cnzp1StW7d2/Nmenhw4dVtsKaNWvE+y5VqpSI7kh2794tImS4DcdQFxw7LEDr169XBQcHi/2wqMtoiC3w+uuvi/fdokUL1Z07d8Q2HBNEG/FZxG0ffPCB1n1we1ZWliohIUE1b948la+vr9hvwIABKlsB779mzZrifY8ePVqIaYDPziuvvKIR5srPo/LvFVGgsWPHao4xjjfDFAdYDDFFSlJSkuqPP/5Q7d27V/z8/fffiy/FiRMnip/j4+NV06ZN06R9PD09VZ988onmSxdfxu+//77K29tbVbZsWU1ECVEkLO7WzP3791Xfffed6tatW+LnQYMGifcu3/fNmzc123CpXLmyavXq1VrRkGeffVbl4+MjjpfcD4LoypUrKmvm9OnTqq+++krzM96/l5eXRmTv2bNHpF/lMUFK7MyZM5r9T548qWrUqJEqICBAiHe5H4RQYmKiylqBANy0aZNIq4Ljx4+L992mTRvxt4jbf/zxRyHIsR0iByLp0aNHmsdYtmyZOG4VK1YUQgn7ISpkS5FIpvjDYogpMiIjI1VVq1YVX4bwrQAIH/wMH8Zvv/2mETj40nzppZdU9+7dE/vJM3KABV4+DsL0EFLwf1gzBw4cEOkcvGe5MLVs2VIcJ3h/kFqUi7SHh4fq448/ViUnJ4v9sFhj0cJl1qxZmogGPB+IcEihaa18+eWXGvGCRRrHA/9u3769KjQ0VJNq1CcgIc6lkBwyZIjKyclJXOBds3bxjb+5nj17iuMCoQjWrVsnfkY6Fic0DRs21Bw7HE+ITt1jB59Q8+bNxWcVJzHwpin9bAxTHGAxxBTJQj5lyhTV//73P/GliS/QzZs3a/mFYJaWX6pY5I8ePaq5/6VLl1RPPfVUNu+QXOyteTH6/fffxXHDAoJjA8MvjKlALuLy2GGxGT58uEg9SrBgV6hQQRUdHa31uNIAbK1gIX7vvfdEJENGcyZMmCAM0TIyBHGpFJBY4JWfKZiBEQHRTffArG/NoCgBJyswSePYIOUqU9gQNrp/rxCQq1at0twfnzUIR/j+lOAzh88ewxRHWAwxhY5cyHHmCIO00piK1I70D+BLd+nSpdm+MHv16iVul6k1WwERG0TAcDYNATlmzBit2xFJkwsSzrx1/VKxsbEi0gazq7ULR10QeXBwcBBiBp8vZVUieOGFFzQCEp9P6RuSHDx4UNzetWtXla2BVCzee7t27YRgVIprRBelHw0CEoJR97OFbbj9888/N8OrZxjTYDHEFDj4crx27ZrmZyw0+OJUpseUDBs2TNyGMlzlWTf8RSNHjtScfVr7GTnAwoN0omTt2rUawSPTY8ozbRwX3DZ37txslU6IpuE2RItsAd3UyxtvvKGVHtMnlrDYQ/gogX9ItnlYsmSJytrBycf58+e1ol+ybF6mx5SgTQNuq127tkgfKh/n22+/FccVnj/ldwDDFHdYDDEFChadSpUqid4iSvEizxYRGdIFJtYyZcqI22vVqiV6uaDaRy5IMFHbQpXYrl27xHvVjQB16tRJHAf0u9Fl586dYvHB7SgLR1kzejDJ0uegoCBNPyFrBmXxiPIoI0BYqKVRHF4XXWDEx21YuBEpgn8NXiBp8sVxtAXwucHJijIChJ5eOAbwRylTrAB/14i44Xb8jb711luivF5WmeH4/fTTT2Z4JwxjOiyGmAJHprVQKaaM8kAkYTsWfV1wZir7vigvaBIoPTLWDvwsEIUQN8pKprNnz4ptqALTXZgADL+ojFIeN5n+QarMVnxpeN9VqlTRStsgUoHtEJS6IJIxefJkjfiRF0SLkOKxFX+LPFHRjSCiUSK2o5pTFwhsCEfdv1dUjKEYgmEsDR7HweRKVFSUUTOrrly5ItrtYwAo5mFhVAZYtWqVGAjatGlTMedJjotQjuNYu3YtnTx5Uozh6Nixo7jY0rH79ddf6dVXXxXve9euXZrtb7zxBi1cuJAmTpxIc+fO1TuOAwMxb9y4QWXLlqU+ffpQ7dq1yVJJSEgQIxqMGdMwbNgwWr58Oc2ZM4emT58utmGAL8a1YNzIpk2bxAR1XTA8FCM38LuqWbOmGLxaqlQpspXPXHJysvishIWF0aFDh6hFixZi+7Vr18RoF/wtYh6b7jHByTTmA+7du1dMo8ff9TPPPMOjNRjLxNxqjCneoJkawt9KHwuACfrrr78WZcv6QOWOvtQOTJnY/uuvv6qsHTQ/7NKli95UIvws+iJkiEY0bdpUHCM0VdRN+SBtYe09gQAqwTC+QRdEyTAuQ1/qDw39ENVBqlGZ8kGkQqZgc+sqbQ1gzIWyGlPpP0MPIH2sXLlS04RSGRFDFahssMgw1gyLISZHFi1aJL4M0TVWCRYb+AzgTZFNAHVFFBqxobnahQsXNNvRMRrVPUgHyTJna0U2QNRtCfDPP/+I7fXr19drCse4A5nyUfYAkikfpCGtGVQsoXQbokb2mZKMGzdOHIPx48frvS/GQuB2eID0pXwg4K0Z2RQR5nmlqMHnDD4+3LZhwwa995UnKqhSlOBvFK0J8Der7CHEMNYGiyEmR+A5gUkSnhVEKrAQS5Mq+rLgyxM9RfQhu0t369ZNa7usEHvnnXes+uj/999/4n2iPB4GXkR8ZMQCESPctnDhwhyFFIzByiofVPBgO7oCWzNoDon3Cd/TN998Iz5DWNwRocR4FojsixcvZrsfvGloJwAfkNJ0HxISIsa1oOHkw4cPVdaMNDejoSbM+Iiyge3bt4vtmCGmL0KG+WqykalsmAh+/vlnTWsMhrFWWAwxRg1SlU0TAYyqsrQbC78uiGpIY6+yWy+qx2AGhsiy5lEGAO0ClMcOhl1laTeGqeozRUtDsG50BAMusR3DVa0ZiBpEJJTHDu9d2QenR48eOUaHdFM+OPZSJFgzslmivOAzJD9jffv2FduUo0mUyHmAymGriNQ1adJECFBEdhnGGmExxOQIBqbKuUOyCZ3yzBqdZ7EdX5b6pk/Ldv04G1V2PcZZqm7vF2sDxwYeH3nsULqtPCOXfXDefvttvWlGeb8XX3xR6zZE5qzd+4K0rHz/WISVYhoRMjlgduPGjdnui+Mj74sO3soo57///quyZvC5QPsK+f5RoamMoF29elVEyHAyojv8GCCShPuhMzc6UUuQItMXiWMYa4HFEJMnQybEDr4kq1Wrlm2WlfQaYGCjEniJIAbk2egXX3xhU0cbg1Wx6GDAJ94/Zq0pkaZoLPbKpncADRbR/wbRD6R8cLZvS8BXhc7bENE4duhMrkSaorHY634eX331VfGZhYhHykfOtLMV4KvCccPxg6hBl3cl0hT98ssvZxNSuJ/8e8UQWoaxFVgMMVoguoOFGWeQElm5A28QviR1q3ykKRpfvpjuDVDxhKgQFnr4ZOCRwQJnzWDRxftXRs7gcUHKR07s1q3ykaZoCCYZKUM6CL4YHD/0WEKPHGsfbIkoBY6dMm2KY4dZWPhsYQSErqiRpuhRo0YJgzAWcwhumY7ETDcII2tvOolOz/h8yJQg/D6IwsoBtfCfKZGmaNz2xx9/aI619Knh8wdfn6FKUYaxRlgMMRr/DyadK1NiMK8qU1+I9KB6DD4gRD2UoAutbPaHhUs2stM9+7RGUNKNRReRHNm1F1V4Sv7880/NEFolWMQbN26s6YQsu27rVvVYK/BGQezJzws+f6iIUvLKK6+I22bMmKG1HWkc+GFwG4zREOOyaSIM09YMhA9GhaDDuHI+ndJ/BkEkI2sYMaLv84gLKjuROsO/kWJDGpJhbA0WQ4yYCi9b6eOCL0a5OOmaTWfOnKlX5CBV8cwzz2geA+ZgnLEjKmLNwJ8iF2EpaKTPRbcUuXXr1uK2ZcuWaW1HGkO5qOHxrN3ki8Uc1YTK7s9yQUb1nfJzEx4erkn5hIaGaj0OPEDK7tu4r+7Cb20g0iWrEXFB5AyfN31dpOG1kjPGdD19GEeiPP4YywFhzzC2CIshGwdffjIahLQMepAgWoHqMEQ44HlRpn2QxqhQoYJWykdZsYO0BuYaYQGzdrDQYCGSqQiIHxwLlMNjGypzlCDigf1x/GTKRx47nMXv3btXXGzB4wLTuBSPMJYj0ghB3b17d7EdFWFKZPpLmfKRxw4pHniIcHytfZgvPifSvwc/FFKBEI74O0ZLAWxHnyolaEuA7TJaqfx7RYpt8+bNWmlxhrFFWAzZOEjvyA6zyvA4vARyACgiPPpC7PDB9OzZU3wpo+rMlsCCgrYCEIXSdwFw9o1jKc+20dlXX8oHTfGQ1kC0SFllZwsghYVjAN8KOkorDeVyoUeqSxmlUKZ8EIHEscdAX1sDfalwDFC0oEyJ4cREDqXFMVQKHjQ9ReQIKUWYotHQUrYpYBhGDYshGweDUNFJWi7I+BLFWTkWeUQx8AWKa0R89J1t4oL76ytxtmZQpq3b6wbl8Oj/I1NduA4MDNQyBSPKpuyfg2oo3WofawfNO3V73SgH9SIaievnnntO6347duzQSuvgWNuav+X1118X712ZgkULB/wNKo8dGiUqQd8gZVoNg2gZhnkCiyEbB12iZYgcXgQ5cR5frli0MLFamjOVngPsi8628BDpG8dhC+C9y6nwWJzQdkAKHFkFhp/fffddrfuh8geGczQPtIWUmC6IVGCxlp8neKjkYg6BgyaT0hiNflRKMNoE8+4wZ8tWpsrretRkg1MIQZluhMBBR3ikqPEzmnki0iZB+hDic9KkSdlObBiGYTFkFSkH9BUxZBqFOXrEiBEiQoGqEZQj79u3T281GUyW+CKtU6eOuB/AQFV5Rvnhhx+qrImdO3eqnn32Wa3RA0rgpUAaEJEczAlD+kuf8EMzOrl4w9MiBdKwYcM0Zmp4gawFlLDjc6E7hFd5O4Qeoo5IyaBCCQu1PjO9/HwhJYuWDVLgyOgZ0mH6mgNaKhAoqNpcsGCB3tvxXtEHCAUN8PIhHYa0tD7k5wvCB54pgLSj/HtF93N9jVAZhskOR4YsGHhVZPUSyuB1QWm2vF2WfMuzSPS3UYKyZdyG9JeMVuCLFN4Wef+cZmlZaroBl8WLF2vdhvf92muvab1veeyQhlDOvALyGGGelgSGYBntwAUVT7IHkyWDiCBEjmyjoCsO0StJ3i73kZVOGEyrjFZg4ccxwmdUGQFC1Ed57DFg1JBgtSRg8JYiD1FEXQ4ePKjV2gICURr0Ub2pjITJYb+IQip/B7KrubzAE8gwTO6wGLJQYJ6UQgedjXXPnvHFKr9IcTvSNnIYI3oFYZFSLkByATtz5ozm7F4KApzdz58/X6R2ULlj6chyYyzSGAKq28EYZ+64HaXcSBPiPWMhkg0S0QtIVthJ7xC2ycUKQghpRWzv2LGjKCHH8bMGf4vsJwXTPCJnuuD9Sq/UihUrxOcIEaHBgwdrxrnoeoeGDh2q9bnF5002ToTIR+rHGoAYxPtCd2zd0Rb4+0WjTdzeoUMHTUUYUoaISuo2O5Vi/pdfftGaISi9fvi8vfDCC1wlxjB5hMWQhYLqLVl1I0PhSqOuNPJ++umnmm1YVORMJ90qMbl/7969hdlS+l/w+Lt371ZZExgbIv0pACJFzvqCMEL0B5EgNASUfgukfeRiBRElK8hwX0R9sAhBKODMXJpYUf2k25zS0pGiRvZBUn7msIDLXj9SNOMac8ZkZSKOoWyIKPeH+IH/auDAgZr9kG60tjJ5GfWR5mflsfvggw/EbTi+8u8ZpnJlP6GmTZtqbpP7o3M5IpIYSisjcfABMgxjHCyGLBh8OeILEAs1vEBomiaBj0BGK2CQVjZERGh93bp1Wo+F0lxEjJQhdng9YHa1NnC2LTtpI3oBgYgIEMBCpYxWIHoGD5U8Johs6B4TCAPlcZOLGqrLrI3Vq1eL94eWAPjcQcjIuWkw6MpoBYQMFmWlgIS3TTeyKEdqyAv2g4C3RnO0HD4L0YwL+k1hNAZAVSJuQ8oLxwjHSqYXcQxxrJXiEJGkSpUqaR07/L1zyTzDmAaLIQsGDRKVX4ZY3GW/H5yFwjCNXiyysy9uR0mtLKOHD0PpHYL5EpENdLGF38iaJ6OPHz9e69jhDBsLMNKE0jslB1ZKAamcrYbjjGGqyt8FjhumfsOYba3gGMHDozx2qFACc+fOFT/jM6TcBwJSOYh2165dqkOHDmmM+xBNMLKj6s6ax2hgRp/0nklPEKrjAIz62AajuUwT4nYIKCkgIYbg2ZMGfTweorhoQYBjr+w7xDCMcbAYslDgrVDOsYJHRdn1WX65SsM0DJjK2xFuRxoHXWttDfgplIsS/BdSIOJaVoZJAQmvhrIxIvxX8GvBk2FryDSZvCgrnVAxp7xNV0ACdEy2lblrShDJUUYYEblVNpzEaAzlsUN6TCkglYOSIYIYhilY7IkpVhw8eJDefffdXPerVasWZWVlUevWrcnV1ZWuXLlCdnZ2mttHjhwprh0dHWnPnj3066+/UkBAgOb2WbNm0dWrV6lVq1Zka9StW5cyMzOpU6dO4ufz58+Ts7Oz+DeuX3jhBfHvqlWrimM0depUze3Jycn0+uuvU2pqqk0euxo1apC3tzc1b95c/BwSEqK5rU2bNuJzCQYPHkwXLlygvn37am6/fv06zZgxQ/zb1o5d6dKlyc/Pj6pVq0ZVqlShiIgIioqK0tw+YsQI8bcK5s+fT9u3b6c6depobv/nn39o1apVVKlSJSpXrpxZ3gPDWDUFLK4YE8HoAfhU5JmhbrM5fciUmCyLHzlypFY6A7OxsB1eIvhckPaCNwj9R2S1lO4wUUsElV1ITaF0Pa9eE3nsUNmDYwHvkNJTJEcbTJgwQZzVoyIKUQ6Z/qldu7ZVNEyEnwyfNUyAzwt4z4gwwhiOqBkM9mFhYdlSt/BkoYcQTMIop4fnRR5T3SG/lghSVmheiJRfXiss79y5Iwz66Naub3iqTN0iYouiBfy94veDxorSWK475JdhmIKBxZCZwSKL8RdYVGT1FipFjJn2jgWqfPnyIh126tQpzXYsUihx1jX3ygoefaXRlgQWWjRCVKa8MP0dC1ReQasBVODgOCmPOY6N9FrpXuAvsvRZbHj97du313pfMPHqToXPCaQPdUvjAQav6jtuuMBXZcxnuziCz4Ysd8cFnxMIGfif8oo0TKOyUYLjgiaL+o4bnkNW8DEMU/CwGDIjmCmkFCswQiqHUxoDSr3xGPgyVYKoBjpQS7EFrwI8MpbuO0BJuywnhhhq2bKlZraVsvQ9L+D46JuUDqGEvjnyrByPDwO6bl8iSwNRIBmlwXWbNm00Zd8w3ee1ghA+KtmCQXdS+u+//66qVauWxrOGCByaKVo6GLwr+3ehmgvvCxFW/IzjmNdoITq843OK466sOsRnC2Nu5O8Df7doOWANEVyGKc6wGDIjaK4mIw26i4mxID0EQaBvUjpAOB5futZSsix7BUFMKhvYYRGGOMKZtJzhlBvKlI8+MYoFyhpSYromaFTMydJuvEeUc2M7oh7KTtE5gWoofZPSJThuSvO5pQtwKVLQF0m+X6RVZboVKei8ItNiSMXqA3+v1tZriWGKKyyGzAiGVeLLEOXvSvAli0nmxkaJ4AdCygdnrJaeisgNWVmjO0pDOe8Ki7qysV1OoLeNvpSPNYIUKaIb+uasyXlXuM4rsjGgshuyNYIUNN4n5obpgnJ3eH1wOz5/eQGl8IjUQrzLDvEMw5gHFkNmBGd+CLFjGKUy4oFyd5k6w4K+dOnSPD8mRm9Y01BVpPnQa0W3k7NsIikb/umC1AJuh38lLyAyIlM+svO0tYIIGCJh+kCkCM0AcRx27NiRp8dDCThSiZi7JSNN1jAdXtejg3YWOUV/8FmEyETqMa89f9A3CI/Zq1evAnndDMOYBouhQgYpnEWLFonGcvry/hg7gC9DNKFDF2QpghDhwUX+nFfzJEL26JODyIklg0oadDSWoy10B07KlI6+yBCAJwqLPgaBYnhoXlM+SK9hxpMlA2GHzxP6/uhL70kvj7IKTF+XaaR+8goEK/pe6Q6xtTTQdFOmr9FLChFaZTWYociQRM7zQxotLyANhpllqCzjpokMYz5YDBUSWGjkvC/lBZEb5cBOOX0aoghRIqS44PmBzwIpHpm+wSKd1womQ4ucpYDSY1SFyWOGkQ0oMVaCsQO6gz91mTJlitgHrQfyiqkG9uICmhnKyei4QAxi8KyyhBsDUHEbzOCGaNy4sdhnz549eY5yys7IlggEM7qHS7M8hN1PP/2kddxAw4YNxe0oqzckwiGiIOLzmqLFfXSfh2GYooXFUCGAShG5IGExGjBggBjtIKfMo3ReGQGR7fchhJRdonXPNjGuwJqBb6J79+6ahRwzwwxNLMdxg3ka0TNDCxNEIdIWOZ3JWxNffvml5tgh+oOu5HK+lXLw6blz58RxQzsGQ+XgEFW4HyI+1gyOyTfffKPy9fXVVCZOnDjR4Fw5OYcO40MMgc7k2EeO2mAYpvjDYqgQQGWNrNZBQ0BlqTYEEb54lVUiKHXHGSl8CvqQ0707deqksmaQTsT7xFk1ZqYpI2g57Z/TcZERDqQPrRl4qxBZhPhDRENy5coVIQZ1q5ZkenbOnDl6Hy8qKkojSK0Z/B2inYDss5SbkRniEZ4+iElDKUG0zMDjYRgrwzCWAYuhAgY9WmSUR1+YHEZJ3K5MeSGShLN1Q6AbrS2IISzASAcqDeXKNAZSZcoybUSHpP/F0KwrdN/G7dbep0WWuCOypi9CJqOPsiElPnM41ugUrZuCBBCiEOiodrJ20OEZx0a3UAHHAMcGQlMJxCb2h89HX9sAdPSWfcMYhrEMeDZZARMTEyOumzZtSu7u7tlu9/X1FdfKOWKY54R5WYb47rvvxHWTJk3ImsHspl69elFoaCgdPnxYbDtz5oyYv1aqVCkxF6tEiRL01ltvUWJiIjk5OdH//vc/cSzHjh0rZowpweynAwcOiH9XqFCBrBnMTAPBwcHZbqtYsSItWrRI/Pu1116jjIwM8Zl77733xP0wRywhIUHrPps3bxbz26z9uIHhw4eL6xUrVohrzPx7//33qWTJkuIzh5l+jRs3FjP+5Ny/du3aic/m+PHjsz3ehg0bxLUtHDuGsRrMrcasDXgNcDaN6jF9oEst0hn6UkAoS8aIA9kdWnaPlpUtlj4CIi8gVYj3i4Z0iOZ4e3trfDDS3IoL5q7JY/jRRx+JbWiIt3z5chFhwpwymSJDF2lrANVhhjxUMpWqLzIkkX4s2QcHx0/2CGratKl4DFQ0rVu3TtOh2lAazZJA3y60rMjJHI9Zc/AL4bMjey3J7tmyqhO3yxE2eCyZXnvxxRdVISEhIiWOilBZCYqKPoZhLAMWQ4WAoW7F8fHxIjWBBUgfPXv21IyTQH8hOXMLQgg+BFsAZeHwVJUrV06kBXEM3n//fU2fIaR5pDkdhmHJpEmT9M50QspNXxrIEsGCjfSrPtMzvC+ygZ8+Ez6A0RzHBM0BJRA/huZhwfNmDd2jcXKRWyNJVNzJ2Wm4btasmUh3QUihG/eoUaM040pkQ1OIdXxO9R07a+nzxTC2AouhIgQTp/FFiUZr+sD0a3RAlhVAiIT07t1bdfbsWZUtIRceXPTNs5IeKl1vEdoUYF4ZBCcWLfQiyutYieIO/FGoTMxpoZXel3feecfg4yCihn2UA30heL744gvRdBKfPfiwvv76a6sp90ZETX6e0DhRHxhQKyNAiN7qCk4cC3nslHPvINJRkg8hipMW7LNp06ZCf08MwxQsLIaKEJxp4wsToicnkC5DVYu1dPM1dfHKqXGk7JKsrNazZnbu3KnVOwgNAHXBwgxDNC6Gps9D9OAxIHZsBXQhl8cO6UBD8/kgZHBsDZntZfUiV4kxjPXBBuoi4u7du7Rjxw7q37+/MAFLYGCdPXs2nTp1SrPNy8uLatasKa5tkbZt29KaNWto+fLlBveBCdiW+PHHH8X1yy+/LMzjU6dOzbZPmTJlaMKECeIz9cYbbxg0qYOHDx+SLZCenk6LFy+mypUrU8eOHen48eP022+/6d33s88+oytXrlCDBg0MPhbDMFaKudWYrSAb4innPa1YsUI0DsR2+DaYvCH7uFSsWNEmDtnx48eFkReduJG+gZ8MKR19KR/cLgeGwoyvi5zpZsjgb23ISBj6VmHUBlLPSKEaG3WF2bxOnTo5tnFgGMZyYTFURMCHAOMvvAeodJL+A1waNWqk2rdvX1G9FIsTPrKCBwvSzz//LGaO4bhhdpktgPEZ8EFhzp2y4g4mX30pH6R5pL8II0kwnR4mYqR3sC2nNJq1gfEZ8EBJIzj8PTgGU6dONXgfHCuMwZGFEDDgoyIR90PX7ryO2WAYxnJgMVQEoNIHixnMvSNHjhRn+TnNP2K0R0LgAo+Qssz++eeft5njBi/QggULtLbJYaKGBtUiAqk8XvICg7QsrbcFMOcO0+SVzTtRrQjv3rVr17LtD9FUvXp1jWhE9Z40VpcoUUJ19OjRIn4HDMMUBSyGigD0C1IuSHL+kSUPtiwqMAtKdk/GBaMlOE2h0qR8MBoCLRv0gcUe1YkYb4JIEcZNcO8blZhFhs8S5gUaOm7oXC5FEEQRBizL/l8Mw1gfdvifuX1L1g4Mr56enuLf6LD8zTffUFBQkLlflsWATsjoSu3m5kblypUz98spNowZM0YYq2GmhvmXyRsw39evX58uXbpE27dvpy5duujdLzIyUlwCAwPJ1dWVDy/DWDEshooIVAENGTKEevToUVRPyVg5ERERYlwEqscuXLhA1apVM/dLshi2bNlCPXv2FGNwTp8+TQ4ODuZ+SQzDmBEurS8iUN7LQogpSPz9/WnWrFmUmppKkydP5oNrBPhb7N27t5hnJ+e2MQxju3BkiGEsGPS+Qcrn8uXLoo9V586dzf2SLIaQkBARGUI/r6tXr2r1/2IYxrbgyBDDWDBOTk7Cg+bj40Ph4eHmfjkWBXx7b775poiw3blzx9wvh2EYM8KRIYaxAmJiYsjX19fcL8PiSEpKEoISF4ZhbBcWQwzDMAzD2DScJmMYhmEYxqZhMcQwDMMwjE3DYohhGIZhGJuGxRDDMAzDMDYNiyGGYRiGYWwaFkMMwzAMw9g0LIYYhmEYhrFpWAwxDMMwDGPTsBhiGIZhGMamYTHEMAzDMIxNw2KIYRiGYRibhsUQwzAMwzA2DYshhmEYhmFsGhZDDMMwDMPYNCyGGIZhGIYhW+b/0OOQF8/0TlAAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, @@ -87,16 +87,17 @@ } ], "source": [ - "# A different metric range with a custom colorbar label, drawn on a provided Axes.\n", + "# A different metric range with long column labels: rotate the x ticks and size the\n", + "# figure through the function itself (no pre-built Axes needed).\n", "rng = np.random.default_rng(42)\n", "df_eval2 = pd.DataFrame(\n", " rng.uniform(40, 95, size=(4, 4)).round(1),\n", " index=[f\"{n} negatives\" for n in (10, 20, 30, 40)],\n", " columns=[f\"{n} features\" for n in (25, 50, 100, 150)],\n", ")\n", - "fig, ax = plt.subplots(figsize=(5, 4))\n", - "aa.plot_eval_heatmap(df_eval=df_eval2, vmin=40, vmax=100,\n", - " cbar_label=\"Balanced accuracy [%]\", ax=ax)\n", + "ax = aa.plot_eval_heatmap(df_eval=df_eval2, vmin=40, vmax=100,\n", + " cbar_label=\"Balanced accuracy [%]\",\n", + " xtick_rotation=30, figsize=(6, 4))\n", "plt.tight_layout()\n", "plt.show()" ] @@ -118,7 +119,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.0" + "version": "3.13.11" } }, "nbformat": 4, diff --git a/tests/unit/plotting_tests/test_plot_eval_heatmap.py b/tests/unit/plotting_tests/test_plot_eval_heatmap.py index c166e538..7faa7ef1 100644 --- a/tests/unit/plotting_tests/test_plot_eval_heatmap.py +++ b/tests/unit/plotting_tests/test_plot_eval_heatmap.py @@ -91,6 +91,19 @@ def test_single_cell(self): ax = plot_eval_heatmap(df_eval=pd.DataFrame([[88.0]])) assert isinstance(ax, plt.Axes) and len(ax.texts) == 1 + def test_xtick_rotation(self): + ax = plot_eval_heatmap(df_eval=_df(), xtick_rotation=45) + assert all(t.get_rotation() == 45 for t in ax.get_xticklabels()) + assert all(t.get_ha() == "right" for t in ax.get_xticklabels()) + + def test_ytick_rotation(self): + ax = plot_eval_heatmap(df_eval=_df(), ytick_rotation=90) + assert all(t.get_rotation() == 90 for t in ax.get_yticklabels()) + + def test_figsize_respected(self): + ax = plot_eval_heatmap(df_eval=_df(), figsize=(8, 3)) + assert tuple(ax.figure.get_size_inches()) == (8.0, 3.0) + class TestPlotEvalHeatmapEquivalence: """KPI #310: equivalent to the hand-built seaborn block it consolidates.""" @@ -142,3 +155,11 @@ def test_bad_xlabel_type(self): def test_bad_ax_type(self): with pytest.raises(ValueError): plot_eval_heatmap(df_eval=_df(), ax="not an ax") + + def test_bad_xtick_rotation_type(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=_df(), xtick_rotation="sideways") + + def test_bad_figsize_type(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=_df(), figsize="wide") From f8d1828753f407badf9e1b09298eddb5bb5ecc59 Mon Sep 17 00:00:00 2001 From: Stephan Breimann Date: Wed, 1 Jul 2026 05:19:22 +0200 Subject: [PATCH 3/6] round2(plot_eval_heatmap): add NaN-graceful + integer-dtype edge tests Adversarial correctness pass found no defect; NaN cells render blank and never raise. Guard NaN-graceful annotation and integer-dtype acceptance. Co-Authored-By: Claude Opus 4.8 (1M context) --- tests/unit/plotting_tests/test_plot_eval_heatmap.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/tests/unit/plotting_tests/test_plot_eval_heatmap.py b/tests/unit/plotting_tests/test_plot_eval_heatmap.py index 7faa7ef1..827bd5d5 100644 --- a/tests/unit/plotting_tests/test_plot_eval_heatmap.py +++ b/tests/unit/plotting_tests/test_plot_eval_heatmap.py @@ -104,6 +104,19 @@ def test_figsize_respected(self): ax = plot_eval_heatmap(df_eval=_df(), figsize=(8, 3)) assert tuple(ax.figure.get_size_inches()) == (8.0, 3.0) + def test_nan_cells_render_gracefully(self): + # A sweep table can carry NaN for a config that failed; NaN cells stay blank + # (not annotated) and the call must not raise. + df = pd.DataFrame([[88.0, np.nan], [70.0, 62.0]], + index=["a", "b"], columns=["x", "y"]) + ax = plot_eval_heatmap(df_eval=df) + assert isinstance(ax, plt.Axes) + assert len(ax.texts) == 3 # only the three non-NaN cells are annotated + + def test_integer_grid_accepted(self): + ax = plot_eval_heatmap(df_eval=pd.DataFrame([[88, 70], [60, 90]])) + assert isinstance(ax, plt.Axes) and len(ax.texts) == 4 + class TestPlotEvalHeatmapEquivalence: """KPI #310: equivalent to the hand-built seaborn block it consolidates.""" From 0bccdbd49ec60454613626b7979b8767ab36d87c Mon Sep 17 00:00:00 2001 From: Stephan Breimann Date: Wed, 1 Jul 2026 05:23:38 +0200 Subject: [PATCH 4/6] round4(plot_eval_heatmap): simplify empty + non-numeric guards Use idiomatic df.empty for the min-shape guard and a single select_dtypes(exclude='number') for the all-numeric guard (drops the length compare + O(n^2) membership comprehension). Behavior identical. Co-Authored-By: Claude Opus 4.8 (1M context) --- aaanalysis/plotting/_plot_eval_heatmap.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/aaanalysis/plotting/_plot_eval_heatmap.py b/aaanalysis/plotting/_plot_eval_heatmap.py index fb768abc..b51c402d 100644 --- a/aaanalysis/plotting/_plot_eval_heatmap.py +++ b/aaanalysis/plotting/_plot_eval_heatmap.py @@ -85,12 +85,11 @@ def plot_eval_heatmap(df_eval: pd.DataFrame, """ # Check input ut.check_df(name="df_eval", df=df_eval, accept_none=False) - if len(df_eval) == 0 or df_eval.shape[1] == 0: + if df_eval.empty: raise ValueError(f"'df_eval' (shape {df_eval.shape}) should contain at least one " f"row and one column.") - num_cols = df_eval.select_dtypes(include="number").columns - if len(num_cols) != df_eval.shape[1]: - non_numeric = [c for c in df_eval.columns if c not in num_cols] + non_numeric = df_eval.select_dtypes(exclude="number").columns.tolist() + if non_numeric: raise ValueError(f"'df_eval' should be all-numeric; non-numeric columns: {non_numeric}.") ut.check_str(name="xlabel", val=xlabel, accept_none=True) ut.check_str(name="ylabel", val=ylabel, accept_none=True) From d3b48add6f4a1c1ef556ab097591c73903d4e9f2 Mon Sep 17 00:00:00 2001 From: Stephan Breimann Date: Wed, 1 Jul 2026 05:26:27 +0200 Subject: [PATCH 5/6] round5(plot_eval_heatmap): add negative-type tests for ylabel/cbar_label/ytick_rotation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Completes positive+negative coverage for every public param. Docstring checker clean (0 defects; RAISES-UNDOCUMENTED advisory is house-consistent — no plotting sibling documents Raises). Co-Authored-By: Claude Opus 4.8 (1M context) --- tests/unit/plotting_tests/test_plot_eval_heatmap.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/tests/unit/plotting_tests/test_plot_eval_heatmap.py b/tests/unit/plotting_tests/test_plot_eval_heatmap.py index 827bd5d5..d8c09d6f 100644 --- a/tests/unit/plotting_tests/test_plot_eval_heatmap.py +++ b/tests/unit/plotting_tests/test_plot_eval_heatmap.py @@ -165,6 +165,18 @@ def test_bad_xlabel_type(self): with pytest.raises(ValueError): plot_eval_heatmap(df_eval=_df(), xlabel=123) + def test_bad_ylabel_type(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=_df(), ylabel=123) + + def test_bad_cbar_label_type(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=_df(), cbar_label=123) + + def test_bad_ytick_rotation_type(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=_df(), ytick_rotation="sideways") + def test_bad_ax_type(self): with pytest.raises(ValueError): plot_eval_heatmap(df_eval=_df(), ax="not an ax") From 871c047fa02272347a275a403e722f4dec63b2eb Mon Sep 17 00:00:00 2001 From: Stephan Breimann Date: Wed, 1 Jul 2026 11:33:13 +0200 Subject: [PATCH 6/6] feat(plot_eval_heatmap): square cells by default (equal width/height for evaluation maps) Evaluation maps should read as a square grid of equal-sized cells; sns.heatmap defaulted square=False (cells stretched to the figure). Add a square: bool=True param (opt out with square=False). Additive; equivalence test unaffected (checks data/clim/cmap/annotations, not aspect). --- aaanalysis/plotting/_plot_eval_heatmap.py | 8 +++++++- .../plotting_tests/test_plot_eval_heatmap.py | 17 +++++++++++++++++ 2 files changed, 24 insertions(+), 1 deletion(-) diff --git a/aaanalysis/plotting/_plot_eval_heatmap.py b/aaanalysis/plotting/_plot_eval_heatmap.py index b51c402d..e0987a21 100644 --- a/aaanalysis/plotting/_plot_eval_heatmap.py +++ b/aaanalysis/plotting/_plot_eval_heatmap.py @@ -23,6 +23,7 @@ def plot_eval_heatmap(df_eval: pd.DataFrame, cbar_label: Optional[str] = "Balanced accuracy [%]", xtick_rotation: Union[int, float] = 0, ytick_rotation: Union[int, float] = 0, + square: bool = True, figsize: Optional[Tuple[Union[int, float], Union[int, float]]] = None, ax: Optional[Axes] = None, ) -> Axes: @@ -60,6 +61,10 @@ def plot_eval_heatmap(df_eval: pd.DataFrame, keep long column labels legible without overlap. ytick_rotation : int or float, default=0 Rotation (degrees) of the y-axis tick labels. + square : bool, default=True + If ``True``, force each heatmap cell to be equal in width and height (a square grid), + the convention for an evaluation map. Set ``False`` to let the cells stretch to fill + the axes/figure. figsize : tuple, optional Figure ``(width, height)`` used when ``ax`` is ``None``. If ``None``, matplotlib's default figure size is used. @@ -97,6 +102,7 @@ def plot_eval_heatmap(df_eval: pd.DataFrame, ut.check_str(name="cbar_label", val=cbar_label, accept_none=True) ut.check_number_val(name="xtick_rotation", val=xtick_rotation, just_int=False) ut.check_number_val(name="ytick_rotation", val=ytick_rotation, just_int=False) + ut.check_bool(name="square", val=square) ut.check_figsize(figsize=figsize, accept_none=True) ut.check_ax(ax=ax, accept_none=True) @@ -105,7 +111,7 @@ def plot_eval_heatmap(df_eval: pd.DataFrame, _, ax = plt.subplots(figsize=figsize) cbar_kws = dict(label=cbar_label) if cbar_label is not None else None sns.heatmap(df_eval, ax=ax, vmin=vmin, vmax=vmax, cmap="viridis", annot=True, - fmt=".0f", linewidths=0.1, cbar_kws=cbar_kws) + fmt=".0f", linewidths=0.1, square=square, cbar_kws=cbar_kws) ax.tick_params(left=False, bottom=False) x_ha = "right" if xtick_rotation % 360 != 0 else "center" ax.set_yticklabels(ax.get_yticklabels(), rotation=ytick_rotation) diff --git a/tests/unit/plotting_tests/test_plot_eval_heatmap.py b/tests/unit/plotting_tests/test_plot_eval_heatmap.py index d8c09d6f..fc2ada79 100644 --- a/tests/unit/plotting_tests/test_plot_eval_heatmap.py +++ b/tests/unit/plotting_tests/test_plot_eval_heatmap.py @@ -188,3 +188,20 @@ def test_bad_xtick_rotation_type(self): def test_bad_figsize_type(self): with pytest.raises(ValueError): plot_eval_heatmap(df_eval=_df(), figsize="wide") + + +class TestPlotEvalHeatmapSquare: + """`square` — evaluation-map cells are equal width and height by default.""" + + def test_square_default_equal_aspect(self): + ax = plot_eval_heatmap(df_eval=_df()) + # square=True makes seaborn force a box (data) aspect of 1.0 -> equal cells. + assert ax.get_aspect() in (1.0, "equal") + + def test_square_false_not_forced(self): + ax = plot_eval_heatmap(df_eval=_df(), square=False) + assert ax.get_aspect() not in (1.0, "equal") + + def test_square_bad_type(self): + with pytest.raises(ValueError): + plot_eval_heatmap(df_eval=_df(), square="yes")