From d648436fe4a9ab7e61dd5261488c586179d01b29 Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Wed, 10 Jul 2024 12:57:08 +1000 Subject: [PATCH 01/28] Update Python version in develop for pyproject.toml (main is already update) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 1d346cc94..fcadb60c2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,7 +13,7 @@ Scores is a package containing mathematical functions \ for the verification, evaluation and optimisation of forecasts, predictions or models. """ readme = "README.md" -requires-python = ">=3.7" +requires-python = ">=3.9" classifiers = [ "Programming Language :: Python :: 3", "License :: OSI Approved :: Apache Software License", From 33ab153c41530f47ee9d010a5123902357492eb5 Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Wed, 10 Jul 2024 13:59:41 +1000 Subject: [PATCH 02/28] Set develop branch to track to version 1.1.0 --- docs/conf.py | 2 +- src/scores/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 7ca25be96..9256776b6 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -9,7 +9,7 @@ project = "scores" copyright = "Licensed under Apache 2.0 - https://www.apache.org/licenses/LICENSE-2.0" -release = "1.0.0" +release = "1.1.0" version = __version__ diff --git a/src/scores/__init__.py b/src/scores/__init__.py index 00f7f0a51..de589e383 100644 --- a/src/scores/__init__.py +++ b/src/scores/__init__.py @@ -13,7 +13,7 @@ import scores.sample_data import scores.stats.statistical_tests # noqa: F401 -__version__ = "1.0.0" +__version__ = "1.1.0" __all__ = [ "scores.categorical", From 948643f6b6473a1a87fe0d39012a4d0af91f9bcb Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Sun, 28 Jul 2024 12:14:23 +1000 Subject: [PATCH 03/28] Specify more interesting image for thumbnail gallery for data fetching tutorial --- tutorials/First_Data_Fetching.ipynb | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/tutorials/First_Data_Fetching.ipynb b/tutorials/First_Data_Fetching.ipynb index be91f114e..4a4ff6b18 100644 --- a/tutorials/First_Data_Fetching.ipynb +++ b/tutorials/First_Data_Fetching.ipynb @@ -3,7 +3,13 @@ { "cell_type": "markdown", "id": "4f72dbcb-b44b-455d-a3f9-59325dab619c", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "# Tutorial One - Data Preparation\n", "\n", @@ -608,7 +614,13 @@ "execution_count": 8, "id": "6a4e614d-054b-4333-9f86-cc86439824ee", "metadata": { - "tags": [] + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "nbsphinx-thumbnail" + ] }, "outputs": [ { @@ -3011,7 +3023,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.4" } }, "nbformat": 4, From 0991d17be8de506f381643cc37952359f73e5551 Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Fri, 12 Jul 2024 21:52:29 +1000 Subject: [PATCH 04/28] Fixed two errors resulting from a call to a deprecated method which has now been updated --- src/scores/probability/roc_impl.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/scores/probability/roc_impl.py b/src/scores/probability/roc_impl.py index b46b7b671..693c441d3 100644 --- a/src/scores/probability/roc_impl.py +++ b/src/scores/probability/roc_impl.py @@ -120,7 +120,7 @@ def roc_curve_data( # pylint: disable=too-many-arguments pofd = pofd.transpose(*final_preserve_dims) auc = -1 * xr.apply_ufunc( - np.trapz, + np.trapezoid, pod, pofd, input_core_dims=[pod.dims, pofd.dims], # type: ignore From 5608de5925a54509a7ab625c71aa1ded8c47f37e Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Wed, 31 Jul 2024 18:01:33 +1000 Subject: [PATCH 05/28] Modify trapezoid call to work with either numpy 1 or 2 seamlessly --- src/scores/probability/roc_impl.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/scores/probability/roc_impl.py b/src/scores/probability/roc_impl.py index 693c441d3..c6f1c832b 100644 --- a/src/scores/probability/roc_impl.py +++ b/src/scores/probability/roc_impl.py @@ -12,6 +12,12 @@ from scores.processing import binary_discretise from scores.utils import gather_dimensions +# trapz was deprecated in numpy 2.0, but trapezoid was not backported to +# earlier versions. As numpy 2.0 contains some API changes, `scores` +# will try to support both interchangeably for the time being +if not hasattr(np, "trapezoid"): + np.trapezoid = np.trapz # pragma: no cover # tested manually for old numpy versions + def roc_curve_data( # pylint: disable=too-many-arguments fcst: xr.DataArray, From a4eba4db32b13278c965ffc5dc6b00f00094fe5c Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Wed, 31 Jul 2024 18:30:55 +1000 Subject: [PATCH 06/28] Remove nbviewer link from documentation Users can visualise the tutorials already, and binder is there for interactive use. It's unclear what extra value nbviewer provides, and it requires manual testing. --- docs/conf.py | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 9256776b6..54304d482 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -62,21 +62,6 @@ Interactive online version: Binder badge. Download notebook. - .. raw:: latex From 4a668c7fb2b4d50c949bade7a915af4a19f4f175 Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Wed, 31 Jul 2024 21:09:21 +1000 Subject: [PATCH 07/28] Slightly update docstring to improve rendering on readthedocs --- src/scores/continuous/quantile_loss_impl.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/scores/continuous/quantile_loss_impl.py b/src/scores/continuous/quantile_loss_impl.py index c14d7481e..6cf3bab82 100644 --- a/src/scores/continuous/quantile_loss_impl.py +++ b/src/scores/continuous/quantile_loss_impl.py @@ -61,7 +61,6 @@ def quantile_score( (1-\\alpha) x & x > 0\\end{cases} where: - - :math:`\\alpha` is the targeted quantile. - :math:`x` is the difference, fcst - obs From 7111034be6d6ef5b4ad510cb4142021b78b713e8 Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Wed, 31 Jul 2024 21:37:55 +1000 Subject: [PATCH 08/28] Update docstring for murphy_score to improve rendering and update citation style to APA7 --- src/scores/continuous/murphy_impl.py | 55 +++++++++++++++------------- 1 file changed, 30 insertions(+), 25 deletions(-) diff --git a/src/scores/continuous/murphy_impl.py b/src/scores/continuous/murphy_impl.py index 13acc365a..7bcfbc68a 100644 --- a/src/scores/continuous/murphy_impl.py +++ b/src/scores/continuous/murphy_impl.py @@ -34,12 +34,12 @@ def murphy_score( # pylint: disable=R0914 evaluated at decision thresholds specified by thetas. Optionally returns a decomposition of the score in terms of penalties for over- and under-forecasting. - Select `functional="quantile"` and `alpha=0.5` for the median functional. - Select `functional="expectile"` and `alpha=0.5` for the mean (i.e., expectation) functional. + Select ``functional="quantile"`` and ``alpha=0.5`` for the median functional. + Select ``functional="expectile"`` and ``alpha=0.5`` for the mean (i.e., expectation) functional. - Consider using `murphy_thetas` to generate thetas. If utilising dask, you may want - to store `thetas` as a netCDF on disk and pass it in as an xarray object. Otherwise, - very large objects may be created when `fcst`, `obs` and `thetas` are broadcast + Consider using ``murphy_thetas`` to generate thetas. If utilising dask, you may want + to store ``thetas`` as a netCDF on disk and pass it in as an xarray object. Otherwise, + very large objects may be created when ``fcst``, ``obs`` and ``thetas`` are broadcast together. @@ -47,17 +47,17 @@ def murphy_score( # pylint: disable=R0914 fcst: Forecast numerical values. obs: Observed numerical values. thetas: Theta thresholds. - functional: The type of elementary scoring function, one of {QUANTILE}, - {HUBER}, or {EXPECTILE}. + functional: The type of elementary scoring function, one of "quantile", + "huber", or "expectile". alpha: Risk parameter (i.e. quantile or expectile level) for the functional. Must be between 0 and 1. - huber_a: Huber transition parameter, used for {HUBER} functional only. + huber_a: Huber transition parameter, used for "huber" functional only. Must be strictly greater than 0. decomposition: True to return penalty values due to under- and over-fcst as well as the total score, False to return total score only. reduce_dims: Optionally specify which dimensions to reduce when calculating the Murphy score. All other dimensions will be preserved. As a special case, 'all' will allow all dimensions to be reduced. Only one - of `reduce_dims` and `preserve_dims` can be supplied. The default behaviour + of ``reduce_dims`` and ``preserve_dims`` can be supplied. The default behaviour if neither are supplied is to reduce all dims. preserve_dims: Optionally specify which dimensions to preserve when calculating the Murphy score. All other dimensions will be reduced. @@ -66,32 +66,37 @@ def murphy_score( # pylint: disable=R0914 shape/dimensionality as the forecast, and the errors will be the FIRM score at each point (i.e. single-value comparison against observed), and the forecast and observed dimensions - must match precisely. Only one of `reduce_dims` and `preserve_dims` can be + must match precisely. Only one of ``reduce_dims`` and ``preserve_dims`` can be supplied. The default behaviour if neither are supplied is to reduce all dims. Returns: - An xr.Dataset with dimensions based on the `preserve_dims` or `reduce_dims` arg - as well as a "theta" dimension with values matching `thetas` input. - If `decomposition` is False, the dataset's variables will contain 1 element - "total". + An xr.Dataset with dimensions based on the ``preserve_dims`` or ``reduce_dims`` arg + as well as a "theta" dimension with values matching ``thetas`` input. - If `decomposition` is True, in addition to "total", it will have - "underforecast" and "overforecast" data_vars. + If ``decomposition`` is False, the dataset's variables will contain 1 element + "total". + + If ``decomposition`` is True, in addition to "total", it will have + "underforecast" and "overforecast" data_vars. Raises: - ValueError: If `functional` is not one of the expected functions. - ValueError: If `alpha` is not strictly between 0 and 1. - ValueError: If `huber_a` is not strictly greater than 0. + ValueError: If ``functional`` is not one of the expected functions. + ValueError: If ``alpha`` is not strictly between 0 and 1. + ValueError: If ``huber_a`` is not strictly greater than 0. References: - For mean elementary score definitions, see - - Theorem 1 of Ehm et. al. (2016) "Of Quantiles and Expectiles", J. Royal Stat. Soc. B, - 78(3): 505–562. https://www.jstor.org/stable/24775351. - - Theorem 5.3 of Taggart (2022) "Point forecasting and forecast evaluation with - generalized Huber loss", Electron. J. Statist. 16(1): 201-231. - DOI: 10.1214/21-EJS1957 + For mean elementary score definitions, see: + - Theorem 1 of Ehm, W., Gneiting, T., Jordan, A., & Krüger, F. (2016). + Of quantiles and expectiles: Consistent scoring functions, Choquet + representations and forecast rankings. + *Journal of the Royal Statistical Society Series B: Statistical Methodology*, + 78(3), 505–562. https://doi.org/10.1111/rssb.12154 + - Theorem 5.3 of Taggart, R. J. (2022). Point forecasting and forecast evaluation + with generalized Huber loss. + *Electronic Journal of Statistics*, 16(1), 201-231. + https://doi.org/10.1214/21-EJS1957 """ functional_lower = functional.lower() From 56d9aa7f875dfe9b957c502e0b83eba4ebb8619a Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Thu, 1 Aug 2024 20:57:26 +1000 Subject: [PATCH 09/28] Remove nbviewer note from maintainer's guide now that nbviewer has been removed from readthedocs --- docs/maintainer.md | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/maintainer.md b/docs/maintainer.md index 084969dfd..325ef64b4 100644 --- a/docs/maintainer.md +++ b/docs/maintainer.md @@ -131,7 +131,6 @@ To check the rendering of tutorials in readthedocs: - Check the entirety of the tutorial (sometimes things will render properly in one section, while not rendering properly in a different section of the same tutorial). - If you make any changes to the code cells, re-execute the Notebook in JupyterLab before committing, otherwise some things (e.g. some plots) won't render in readthedocs. Then re-check the tutorial in readthedocs to ensure the tutorial is still rendering properly. -Ideally, also check the tutorial renders properly in nbviewer (there is a link at the top of each tutorial page in readthedocs). From 3f7f7a9e5acbf15330723050e69f4f15d02610bd Mon Sep 17 00:00:00 2001 From: Nicholas Loveday <48701367+nicholasloveday@users.noreply.github.com> Date: Fri, 2 Aug 2024 13:35:00 +1000 Subject: [PATCH 10/28] 608 add threshold weighted scores (#609) This is a major new feature introducing threshold-weighted consistent scoring to the API * add new scores to api.md and readme * update fcst test data to test test that broadcasting works * update docstrings * produced tw scoring tutorial * added to_absolute_error * added tw_quantile_score * add checks for alpha values * added tw expectile score * add comments about scaling of phi * Ignore erroneous mypy warnings where mypy is incorrect --------- Signed-off-by: Nicholas Loveday <48701367+nicholasloveday@users.noreply.github.com> Co-authored-by: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> Co-authored-by: Tennessee Leeuwenburg Co-authored-by: Tennessee Leeuwenburg <134973832+tennlee@users.noreply.github.com> --- README.md | 2 +- docs/api.md | 5 + docs/included.md | 20 + docs/maintainer.md | 6 + pyproject.toml | 1 + src/scores/continuous/__init__.py | 12 + .../continuous/threshold_weighted_impl.py | 739 + tests/continuous/test_threshold_weighted.py | 744 + tutorials/Consistent_Scores.ipynb | 6 +- tutorials/Threshold_Weighted_Scores.ipynb | 77671 ++++++++++++++++ tutorials/Tutorial_Gallery.ipynb | 3 +- 11 files changed, 79204 insertions(+), 5 deletions(-) create mode 100644 src/scores/continuous/threshold_weighted_impl.py create mode 100644 tests/continuous/test_threshold_weighted.py create mode 100644 tutorials/Threshold_Weighted_Scores.ipynb diff --git a/README.md b/README.md index 3059cf73c..88fa4b7c0 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ Here is a **curated selection** of the metrics, tools and statistical tests incl | | **Description** | **Selection of Included Functions** | |----------------------- |----------------- |-------------- | -| **[Continuous](https://scores.readthedocs.io/en/stable/included.html#continuous)** |Scores for evaluating single-valued continuous forecasts. |Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Additive Bias, Multiplicative Bias, Pearson's Correlation Coefficient, Flip-Flop Index, Quantile Loss, Murphy Score, families of consistent scoring functions for quantiles and expectiles. | +| **[Continuous](https://scores.readthedocs.io/en/stable/included.html#continuous)** |Scores for evaluating single-valued continuous forecasts. |Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Additive Bias, Multiplicative Bias, Pearson's Correlation Coefficient, Flip-Flop Index, Quantile Loss, Murphy Score, families of consistent scoring functions for quantiles and expectiles, Threshold Weighted Squared Error, Threshold Weighted Quantile Score, Threshold Weighted Absolute Error, Threshold Weighted Expectile Score, Threshold Weighted Huber Loss. | | **[Probability](https://scores.readthedocs.io/en/stable/included.html#probability)** |Scores for evaluating forecasts that are expressed as predictive distributions, ensembles, and probabilities of binary events. |Brier Score, Continuous Ranked Probability Score (CRPS) for Cumulative Density Function (CDF), Threshold weighted CRPS for CDF, CRPS for ensembles, Receiver Operating Characteristic (ROC), Isotonic Regression (reliability diagrams). | | **[Categorical](https://scores.readthedocs.io/en/stable/included.html#categorical)** |Scores (including contingency table metrics) for evaluating forecasts of categories. |Probability of Detection (POD), False Alarm Ratio (FAR), Probability of False Detection (POFD), Success Ratio, Accuracy, Peirce's Skill Score, Critical Success Index (CSI), Gilbert Skill Score, Heidke Skill Score, Odds Ratio, Odds Ratio Skill Score, F1 score, Symmetric Extremal Dependence Index, FIxed Risk Multicategorical (FIRM) Score. | | **[Spatial](https://scores.readthedocs.io/en/stable/included.html#spatial)** |Scores that take into account spatial structure. |Fractions Skill Score. | diff --git a/docs/api.md b/docs/api.md index 59e7f524c..d4551d671 100644 --- a/docs/api.md +++ b/docs/api.md @@ -26,6 +26,11 @@ .. autofunction:: scores.continuous.consistent_expectile_score .. autofunction:: scores.continuous.consistent_quantile_score .. autofunction:: scores.continuous.consistent_huber_score +.. autofunction:: scores.continuous.tw_quantile_score +.. autofunction:: scores.continuous.tw_absolute_error +.. autofunction:: scores.continuous.tw_squared_error +.. autofunction:: scores.continuous.tw_huber_loss +.. autofunction:: scores.continuous.tw_expectile_score ``` ## scores.probability diff --git a/docs/included.md b/docs/included.md index aa06ebc18..916bc7f39 100644 --- a/docs/included.md +++ b/docs/included.md @@ -117,6 +117,26 @@ - [API](api.md#scores.continuous.rmse) - [Tutorial](project:./tutorials/Root_Mean_Squared_Error.md) - [Wikipedia](https://en.wikipedia.org/wiki/Root-mean-square_deviation) +* - Threshold Weighted Absolute Error + - [API](api.md#scores.continuous.tw_absolute_error) + - [Tutorial](project:./tutorials/Threshold_Weighted_Scores.md) + - [Taggart (2022)](https://doi.org/10.1002/qj.4206) +* - Threshold Weighted Expectile Score + - [API](api.md#scores.continuous.tw_expectile_score) + - [Tutorial](project:./tutorials/Threshold_Weighted_Scores.md) + - [Taggart (2022)](https://doi.org/10.1002/qj.4206) +* - Threshold Weighted Huber Loss + - [API](api.md#scores.continuous.tw_huber_loss) + - [Tutorial](project:./tutorials/Threshold_Weighted_Scores.md) + - [Taggart (2022)](https://doi.org/10.1002/qj.4206) +* - Threshold Weighted Quantile Score + - [API](api.md#scores.continuous.tw_quantile_score) + - [Tutorial](project:./tutorials/Threshold_Weighted_Scores.md) + - [Taggart (2022)](https://doi.org/10.1002/qj.4206) +* - Threshold Weighted Squared Error + - [API](api.md#scores.continuous.tw_squared_error) + - [Tutorial](project:./tutorials/Threshold_Weighted_Scores.md) + - [Taggart (2022)](https://doi.org/10.1002/qj.4206) ``` ## Probability diff --git a/docs/maintainer.md b/docs/maintainer.md index 325ef64b4..64750a58a 100644 --- a/docs/maintainer.md +++ b/docs/maintainer.md @@ -132,6 +132,12 @@ To check the rendering of tutorials in readthedocs: - If you make any changes to the code cells, re-execute the Notebook in JupyterLab before committing, otherwise some things (e.g. some plots) won't render in readthedocs. Then re-check the tutorial in readthedocs to ensure the tutorial is still rendering properly. +### Tips for working with pull requests from forks + +It can be convenient as maintainer to have write access to people's forks to push small fixes into a PR during the process. When doing so, it's a good idea to check out the remote branch as follows (after adding the fork as a remote) + +`git checkout -b test /test` + diff --git a/pyproject.toml b/pyproject.toml index fcadb60c2..58895b629 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,6 +58,7 @@ maintainer = ["build", "sphinx-book-theme", "nbsphinx", "sphinx_gallery", + "ipywidgets", "twine", "pandoc", "pip-tools", diff --git a/src/scores/continuous/__init__.py b/src/scores/continuous/__init__.py index 4ca14f315..2e962b545 100644 --- a/src/scores/continuous/__init__.py +++ b/src/scores/continuous/__init__.py @@ -22,6 +22,13 @@ multiplicative_bias, rmse, ) +from scores.continuous.threshold_weighted_impl import ( + tw_absolute_error, + tw_expectile_score, + tw_huber_loss, + tw_quantile_score, + tw_squared_error, +) from scores.processing.isoreg_impl import isotonic_fit __all__ = [ @@ -41,4 +48,9 @@ "consistent_expectile_score", "consistent_huber_score", "consistent_quantile_score", + "tw_absolute_error", + "tw_expectile_score", + "tw_huber_loss", + "tw_quantile_score", + "tw_squared_error", ] diff --git a/src/scores/continuous/threshold_weighted_impl.py b/src/scores/continuous/threshold_weighted_impl.py new file mode 100644 index 000000000..3f8c75fa5 --- /dev/null +++ b/src/scores/continuous/threshold_weighted_impl.py @@ -0,0 +1,739 @@ +""" +Implementation of some basic threshold weighted scoring functions. +See Taggart (2022) https://doi.org/10.1002/qj.4206 +""" + +import functools +from typing import Callable, Optional, Tuple, Union + +import numpy as np +import xarray as xr + +from scores.continuous.consistent_impl import ( + check_alpha, + check_huber_param, + consistent_expectile_score, + consistent_huber_score, + consistent_quantile_score, +) +from scores.typing import FlexibleDimensionTypes + +AuxFuncType = Callable[[xr.DataArray], xr.DataArray] +EndpointType = Union[int, float, xr.DataArray] + + +def _check_tws_args( + interval_where_one: Tuple[EndpointType, EndpointType], + interval_where_positive: Optional[Tuple[EndpointType, EndpointType]], +): + """ + Some argument checks for the threshold weighted scores. + Checks for valid interval endpoints are done in :py:func`_auxiliary_funcs`. + + Args: + interval_where_one: endpoints of the interval where the threshold weights are 1. + Must be increasing. Infinite endpoints are permissible. By supplying a tuple of + arrays, endpoints can vary with dimension. + interval_where_positive: endpoints of the interval where the threshold weights are positive. + Must be increasing. Infinite endpoints are only permissible when the corresponding + ``interval_where_one`` endpoint is infinite. By supplying a tuple of + arrays, endpoints can vary with dimension. + + Raises: + ValueError: if ``interval_where_one`` is not length 2. + ValueError: if ``interval_where_one`` is not increasing. + """ + if len(interval_where_one) != 2: + raise ValueError("`interval_where_one` must have length 2") + + if interval_where_positive is not None and len(interval_where_positive) != 2: + raise ValueError("`interval_where_positive` must be length 2 when not `None`") + + +def _auxiliary_funcs( + fcst: xr.DataArray, + obs: xr.DataArray, + interval_where_one: Tuple[EndpointType, EndpointType], + interval_where_positive: Optional[Tuple[EndpointType, EndpointType]], +) -> Tuple[AuxFuncType, AuxFuncType, AuxFuncType]: + """ + Returns the three auxiliary functions g, phi and phi_prime + which are used to construct quantile, expectile or Huber mean scoring functions. + See Equations (8), (10) and (11) from Taggart (2022) for the role of g, phi and phi_prime. + + Args: + fcst: array of forecast values. + obs: array of corresponding observation values. + interval_where_one: endpoints of the interval where the threshold weights are 1. + Must be increasing. Infinite endpoints are permissible. By supplying a tuple of + arrays, endpoints can vary with dimension. + interval_where_positive: endpoints of the interval where the threshold weights are positive. + Must be increasing. Infinite endpoints are only permissible when the corresponding + ``interval_where_one`` endpoint is infinite. By supplying a tuple of + arrays, endpoints can vary with dimension. + + Raises: + ValueError: if the left endpoint of ``interval_where_one`` is not less than + the right endpoint. + ValueError: if an ``interval_where_positive`` endpoint is infinite when the + ``interval_where_one`` endpoint is infinite. + ValueError: If the right endpoint of ``interval_where_positive`` is not greater + than the right endpoint of ``interval_where_one`` and neither are infinite. + """ + + if interval_where_positive is None: # rectangular threshold weight + a, b = interval_where_one + + if isinstance(a, (float, int)): + a = xr.DataArray(a) + b = xr.DataArray(b) + + if (a >= b).any(): + raise ValueError("left endpoint of `interval_where_one` must be strictly less than right endpoint") + + # safest to work with finite a and b + a = a.where(a > -np.inf, float(min(fcst.min(), obs.min(), b.min())) - 1) # type: ignore + b = b.where(b < np.inf, float(max(fcst.max(), obs.max(), a.max())) + 1) # type: ignore + + g = functools.partial(_g_j_rect, a, b) + phi = functools.partial(_phi_j_rect, a, b) + phi_prime = functools.partial(_phi_j_prime_rect, a, b) + + else: # trapezoidal threshold weight + a, d = interval_where_positive + b, c = interval_where_one + + if isinstance(a, (float, int)): + a = xr.DataArray(a) + d = xr.DataArray(d) + + if isinstance(b, (float, int)): + b = xr.DataArray(b) + c = xr.DataArray(c) + + if (b >= c).any(): + raise ValueError("left endpoint of `interval_where_one` must be strictly less than right endpoint") + + if (np.isinf(a) & (a != b)).any() or (np.isinf(d) & (c != d)).any(): + raise ValueError( + "`interval_where_positive` endpoint can only be infinite when " + "corresponding `interval_where_one` endpoint is infinite." + ) + + if not ((a < b) | ((a == b) & np.isinf(a))).all(): + raise ValueError( + "left endpoint of `interval_where_positive` must be less than " + "left endpoint of `interval_where_one`, unless both are `-numpy.inf`." + ) + + if not ((c < d) | ((c == d) & np.isinf(c))).all(): + raise ValueError( + "right endpoint of `interval_where_positive` must be greater than " + "right endpoint of `interval_where_one`, unless both are `numpy.inf`." + ) + + # safest to work with finite intervals + b = b.where(b > -np.inf, min(fcst.min(), obs.min(), c.min()) - 1) # type: ignore + a = a.where(a > -np.inf, b.min() - 1) # type: ignore + c = c.where(c < np.inf, max(fcst.max(), obs.max(), b.max()) + 1) # type: ignore + d = d.where(d < np.inf, c.max() + 1) # type: ignore + + g = functools.partial(_g_j_trap, a, b, c, d) + phi = functools.partial(_phi_j_trap, a, b, c, d) + phi_prime = functools.partial(_phi_j_prime_trap, a, b, c, d) + + return g, phi, phi_prime + + +def _g_j_rect(a: EndpointType, b: EndpointType, x: xr.DataArray) -> xr.DataArray: + """ + Returns values of a nondecreasing function g_j, where g_j is obtained by integrating + a rectangular threshold weight function. The threshold weight is 1 on the interval [a, b) and 0 otherwise. + The formula is based on the first row of Table B1 from Taggart (2022). + + Args: + a: left endpoint of interval where the threshold weight = 1. Can be ``-np.inf``. + b: right endpoint of the interval where the threshold weight = 1. Can be ``np.inf``. + x: points where g_j is to be evaluated. + + Returns: + array of function values for the function g_j. + + Note: + Requires a < b. This is tested in :py:func:`_auxiliary_funcs`. + """ + # results correspond to each case in the first row of Table B1, Taggart (2022). + result1 = 0 + result2 = x - a + result3 = b - a + + result = result2.where(x < b, result3) + result = result.where(x >= a, result1) + result = result.where(~np.isnan(x), np.nan) + + return result + + +def _phi_j_rect(a: EndpointType, b: EndpointType, x: xr.DataArray) -> xr.DataArray: + """ + Returns values of a convex function phi_j, where phi_j is obtained by integrating + a rectangular threshold weight function. The threshold weight is 1 on the interval + [a, b) and 0 otherwise. + The formula is based on the second row of Table B1 from Taggart (2022). + + Args: + a: left endpoint of interval where the threshold weight = 1. Can be ``-np.inf``. + b: right endpoint of the interval where the threshold weight = 1. Can be ``np.inf``. + x: points where phi_j is to be evaluated. + + Returns: + array of function values for the function phi_j. + + Notes: + - Requires a < b. This is tested in :py:func:`_auxiliary_funcs`. + - phi is this case is 2x^2 to be consistent with Taggart (2022) and is used to + produce a twMSE. This means that a scaling factor is introduced for the + threshold weighted expectile and Huber Loss scores. + """ + # results correspond to each case in the second row of Table B1, Taggart (2022). + result1 = 0 + result2 = 2 * (x - a) ** 2 + result3 = 4 * (b - a) * x + 2 * (a**2 - b**2) + + result = result2.where(x < b, result3) + result = result.where(x >= a, result1) + result = result.where(~np.isnan(x), np.nan) + + return result + + +def _phi_j_prime_rect(a: EndpointType, b: EndpointType, x: xr.DataArray) -> xr.DataArray: + """ + The subderivative of `_phi_j_rect(a, b, x)` w.r.t. x. + """ + return 4 * _g_j_rect(a, b, x) + + +def _g_j_trap(a: EndpointType, b: EndpointType, c: EndpointType, d: EndpointType, x: xr.DataArray) -> xr.DataArray: + """ + Returns values of a nondecreasing function g_j, where g_j is obtained by integrating + a trapezoidal threshold weight function. The threshold weight is 1 on the interval (b, c) and 0 outside + the interval (a,d). The formula is based on the third row of Table B1 from Taggart (2022). + + Args: + a: left endpoint of interval where the threshold weight > 0. + b: left endpoint of the interval where the threshold weight = 1. + c: right endpoint of the interval where the threshold weight = 1. + d: right endpoint of interval where the threshold weight > 0. + x: points where g_j is to be evaluated. + + Returns: + array of function values for the function g_j. + + Note: + Requires a < b < c < d. This is tested in :py:func:`_auxiliary_funcs`. + """ + # results correspond to each case in the third row of Table B1, Taggart (2022). + result0 = 0 + result1 = (x - a) ** 2 / (2 * (b - a)) + result2 = x - (b + a) / 2 + result3 = -((d - x) ** 2) / (2 * (d - c)) + (d + c - a - b) / 2 + result4 = (d + c - a - b) / 2 + result = result1.where(x >= a, result0) + result = result.where(x < b, result2) + result = result.where(x < c, result3) + result = result.where(x < d, result4) + result = result.where(~np.isnan(x), np.nan) + return result + + +def _phi_j_trap(a: EndpointType, b: EndpointType, c: EndpointType, d: EndpointType, x: xr.DataArray) -> xr.DataArray: + """ + Returns values of a convex function phi_j, where phi_j is obtained by integrating + a trapezoidal threshold weight function. The threshold weight is 1 on the interval (b, c) and 0 outside + the interval (a,d). The formula is based on the fourth row of Table B1 from Taggart (2022). + + Args: + a: left endpoint of interval where the threshold weight > 0. + b: left endpoint of the interval where the threshold weight = 1. + c: right endpoint of the interval where the threshold weight = 1. + d: right endpoint of interval where the threshold weight > 0. + x: points where phi_j is to be evaluated. + + Returns: + array of function values for the function phi_j. + + Note: + - Requires a < b < c < d. This is tested in :py:func`_auxiliary_funcs`. + - phi is this case is 2x^2 to be consistent with Taggart (2022) and is used to + produce a twMSE. This means that a scaling factor is introduced for the + threshold weighted expectile and Huber Loss scores. + """ + # results correspond to each case in the fourth row of Table B1, Taggart (2022). + result0 = 0 + result1 = 2 * (x - a) ** 3 / (3 * (b - a)) + result2 = 2 * x**2 - 2 * (a + b) * x + 2 * (b - a) ** 2 / 3 + 2 * a * b + result3 = ( + 2 * (d - x) ** 3 / (3 * (d - c)) + + 2 * (d + c - a - b) * x + + 2 * ((b - a) ** 2 + 3 * a * b - (d - c) ** 2 - 3 * c * d) / 3 + ) + result4 = 2 * (d + c - a - b) * x + 2 * ((b - a) ** 2 + 3 * a * b - (d - c) ** 2 - 3 * c * d) / 3 + + result = result1.where(x >= a, result0) + result = result.where(x < b, result2) + result = result.where(x < c, result3) + result = result.where(x < d, result4) + result = result.where(~np.isnan(x), np.nan) + + return result + + +def _phi_j_prime_trap( + a: EndpointType, b: EndpointType, c: EndpointType, d: EndpointType, x: xr.DataArray +) -> xr.DataArray: + """ + Calculates the subderivative of :py:func`_phi_j_trap(a, b, c, d, x)` w.r.t. x. + + Args: + a: left endpoint of interval where the threshold weight > 0. + b: left endpoint of the interval where the threshold weight = 1. + c: right endpoint of the interval where the threshold weight = 1. + d: right endpoint of interval where the threshold weight > 0. + x: points where phi_j is to be evaluated. + + Returns: + The subderivative of :py:func`_phi_j_trap(a, b, c, d, x)` w.r.t. x. + """ + return 4 * _g_j_trap(a, b, c, d, x) + + +def tw_squared_error( + fcst: xr.DataArray, + obs: xr.DataArray, + interval_where_one: Tuple[EndpointType, EndpointType], + *, + interval_where_positive: Optional[Tuple[EndpointType, EndpointType]] = None, + reduce_dims: Optional[FlexibleDimensionTypes] = None, + preserve_dims: Optional[FlexibleDimensionTypes] = None, + weights: Optional[xr.DataArray] = None, +) -> xr.DataArray: + """ + Returns the the threshold weighted squared error. + + For more flexible threshold weighting schemes, + see :py:func:`scores.continuous.consistent_expectile_score`. + + Two types of threshold weighting are supported: rectangular and trapezoidal. + - To specify a rectangular threshold weight, set ``interval_where_positive=None`` and set + ``interval_where_one`` to be the interval where the threshold weight is 1. + For example, if ``interval_where_one=(0, 10)`` then a threshold weight of 1 + is applied to decision thresholds satisfying 0 <= threshold < 10, and a threshold weight of 0 is + applied otherwise. Interval endpoints can be ``-numpy.inf`` or ``numpy.inf``. + - To specify a trapezoidal threshold weight, specify ``interval_where_positive`` and ``interval_where_one`` + using desired endpoints. For example, if ``interval_where_positive=(-2, 10)`` and + ``interval_where_one=(2, 4)`` then a threshold weight of 1 is applied to decision thresholds + satisfying 2 <= threshold < 4. The threshold weight increases linearly from 0 to 1 on the interval + [-2, 2) and decreases linearly from 1 to 0 on the interval [4, 10], and is 0 otherwise. + Interval endpoints can only be infinite if the corresponding ``interval_where_one`` endpoint + is infinite. End points of ``interval_where_positive`` and ``interval_where_one`` must differ + except when the endpoints are infinite. + + Args: + fcst: array of forecast values. + obs: array of corresponding observation values. + interval_where_one: endpoints of the interval where the threshold weights are 1. + Must be increasing. Infinite endpoints are permissible. By supplying a tuple of + arrays, endpoints can vary with dimension. + interval_where_positive: endpoints of the interval where the threshold weights are positive. + Must be increasing. Infinite endpoints are only permissible when the corresponding + ``interval_where_one`` endpoint is infinite. By supplying a tuple of + arrays, endpoints can vary with dimension. + reduce_dims: Optionally specify which dimensions to reduce when + calculating the threshold_weighted_square_error. All other dimensions will be preserved. As a + special case, 'all' will allow all dimensions to be reduced. Only one + of ``reduce_dims`` and ``preserve_dims`` can be supplied. The default behaviour + if neither are supplied is to reduce all dims. + preserve_dims: Optionally specify which dimensions to preserve when calculating + the threshold_weighted_squared_error. All other dimensions will be reduced. As a special case, 'all' + will allow all dimensions to be preserved. In this case, the result will be in + the same shape/dimensionality as the forecast, and the errors will be the threshold_weighted_squared_error + at each point (i.e. single-value comparison against observed), and the + forecast and observed dimensions must match precisely. Only one of ``reduce_dims`` + and ``preserve_dims`` can be supplied. The default behaviour if neither are supplied + is to reduce all dims. + weights: Optionally provide an array for weighted averaging (e.g. by area, by latitude, + by population, custom). Note that these weights are different to threshold weighting + which is done by decision threshold. + + Returns: + xarray data array of the threshold weighted squared error + + Raises: + ValueError: if ``interval_where_one`` is not length 2. + ValueError: if ``interval_where_one`` is not increasing. + ValueError: if ``interval_where_one`` is not increasing. + ValueError: if ``interval_where_one`` and ``interval_where_positive`` do not + specify a valid trapezoidal weight. + + Reference: + Taggart, R. (2022). Evaluation of point forecasts for extreme events using + consistent scoring functions. Quarterly Journal of the Royal Meteorological + Society, 148(742), 306-320. https://doi.org/10.1002/qj.4206 + """ + _check_tws_args(interval_where_one=interval_where_one, interval_where_positive=interval_where_positive) + _, phi, phi_prime = _auxiliary_funcs(fcst, obs, interval_where_one, interval_where_positive) + + return consistent_expectile_score( + fcst, obs, 0.5, phi, phi_prime, reduce_dims=reduce_dims, preserve_dims=preserve_dims, weights=weights + ) + + +def tw_absolute_error( + fcst: xr.DataArray, + obs: xr.DataArray, + interval_where_one: Tuple[EndpointType, EndpointType], + *, + interval_where_positive: Optional[Tuple[EndpointType, EndpointType]] = None, + reduce_dims: Optional[FlexibleDimensionTypes] = None, + preserve_dims: Optional[FlexibleDimensionTypes] = None, + weights: Optional[xr.DataArray] = None, +) -> xr.DataArray: + """ + Returns the threshold weighted absolute error. + + For more flexible threshold weighting schemes, + see :py:func:`scores.continuous.consistent_quantile_score`. + + Two types of threshold weighting are supported: rectangular and trapezoidal. + - To specify a rectangular threshold weight, set ``interval_where_positive=None`` and set + ``interval_where_one`` to be the interval where the threshold weight is 1. + For example, if ``interval_where_one=(0, 10)`` then a threshold weight of 1 + is applied to decision thresholds satisfying 0 <= threshold < 10, and a threshold weight of 0 is + applied otherwise. Interval endpoints can be ``-numpy.inf`` or ``numpy.inf``. + - To specify a trapezoidal threshold weight, specify ``interval_where_positive`` and ``interval_where_one`` + using desired endpoints. For example, if ``interval_where_positive=(-2, 10)`` and + ``interval_where_one=(2, 4)`` then a threshold weight of 1 is applied to decision thresholds + satisfying 2 <= threshold < 4. The threshold weight increases linearly from 0 to 1 on the interval + [-2, 2) and decreases linearly from 1 to 0 on the interval [4, 10], and is 0 otherwise. + Interval endpoints can only be infinite if the corresponding ``interval_where_one`` endpoint + is infinite. End points of ``interval_where_positive`` and ``interval_where_one`` must differ + except when the endpoints are infinite. + + Args: + fcst: array of forecast values. + obs: array of corresponding observation values. + interval_where_one: endpoints of the interval where the threshold weights are 1. + Must be increasing. Infinite endpoints are permissible. By supplying a tuple of + arrays, endpoints can vary with dimension. + interval_where_positive: endpoints of the interval where the threshold weights are positive. + Must be increasing. Infinite endpoints are only permissible when the corresponding + ``interval_where_one`` endpoint is infinite. By supplying a tuple of + arrays, endpoints can vary with dimension. + reduce_dims: Optionally specify which dimensions to reduce when + calculating the threshold_weighted_absolute_error. All other dimensions will be preserved. As a + special case, 'all' will allow all dimensions to be reduced. Only one + of ``reduce_dims`` and ``preserve_dims`` can be supplied. The default behaviour + if neither are supplied is to reduce all dims. + preserve_dims: Optionally specify which dimensions to preserve when calculating + the threshold_weighted_absolute_error. All other dimensions will be reduced. As a special case, 'all' + will allow all dimensions to be preserved. In this case, the result will be in + the same shape/dimensionality as the forecast, and the errors will be the threshold_weighted_absolute_error + at each point (i.e. single-value comparison against observed), and the + forecast and observed dimensions must match precisely. Only one of ``reduce_dims`` + and ``preserve_dims`` can be supplied. The default behaviour if neither are supplied + is to reduce all dims. + weights: Optionally provide an array for weighted averaging (e.g. by area, by latitude, + by population, custom). Note that these weights are different to threshold weighting + which is done by decision threshold. + + Returns: + xarray data array of the threshold weighted absolute error + + Raises: + ValueError: if ``interval_where_one`` is not length 2. + ValueError: if ``interval_where_one`` is not increasing. + ValueError: if ``interval_where_one`` is not increasing. + ValueError: if ``interval_where_one`` and ``interval_where_positive`` do not + specify a valid trapezoidal weight. + + References: + Taggart, R. (2022). Evaluation of point forecasts for extreme events using + consistent scoring functions. Quarterly Journal of the Royal Meteorological + Society, 148(742), 306-320. https://doi.org/10.1002/qj.4206 + """ + _check_tws_args(interval_where_one=interval_where_one, interval_where_positive=interval_where_positive) + g, _, _ = _auxiliary_funcs(fcst, obs, interval_where_one, interval_where_positive) + + # Note that the absolute error is twice the quantile score when alpha=0.5 + return 2 * consistent_quantile_score( + fcst, obs, 0.5, g, reduce_dims=reduce_dims, preserve_dims=preserve_dims, weights=weights + ) + + +def tw_quantile_score( + fcst: xr.DataArray, + obs: xr.DataArray, + alpha: float, + interval_where_one: Tuple[EndpointType, EndpointType], + *, + interval_where_positive: Optional[Tuple[EndpointType, EndpointType]] = None, + reduce_dims: Optional[FlexibleDimensionTypes] = None, + preserve_dims: Optional[FlexibleDimensionTypes] = None, + weights: Optional[xr.DataArray] = None, +) -> xr.DataArray: + """ + Returns the threshold weighted quantile score. + + For more flexible threshold weighting schemes, + see :py:func:`scores.continuous.consistent_quantile_score`. + + Two types of threshold weighting are supported: rectangular and trapezoidal. + - To specify a rectangular threshold weight, set ``interval_where_positive=None`` and set + ``interval_where_one`` to be the interval where the threshold weight is 1. + For example, if ``interval_where_one=(0, 10)`` then a threshold weight of 1 + is applied to decision thresholds satisfying 0 <= threshold < 10, and a threshold weight of 0 is + applied otherwise. Interval endpoints can be ``-numpy.inf`` or ``numpy.inf``. + - To specify a trapezoidal threshold weight, specify ``interval_where_positive`` and ``interval_where_one`` + using desired endpoints. For example, if ``interval_where_positive=(-2, 10)`` and + ``interval_where_one=(2, 4)`` then a threshold weight of 1 is applied to decision thresholds + satisfying 2 <= threshold < 4. The threshold weight increases linearly from 0 to 1 on the interval + [-2, 2) and decreases linearly from 1 to 0 on the interval [4, 10], and is 0 otherwise. + Interval endpoints can only be infinite if the corresponding ``interval_where_one`` endpoint + is infinite. End points of ``interval_where_positive`` and ``interval_where_one`` must differ + except when the endpoints are infinite. + + Args: + fcst: array of forecast values. + obs: array of corresponding observation values. + alpha: the quantile level. Must be strictly between 0 and 1. + interval_where_one: endpoints of the interval where the threshold weights are 1. + Must be increasing. Infinite endpoints are permissible. By supplying a tuple of + arrays, endpoints can vary with dimension. + interval_where_positive: endpoints of the interval where the threshold weights are positive. + Must be increasing. Infinite endpoints are only permissible when the corresponding + ``interval_where_one`` endpoint is infinite. By supplying a tuple of + arrays, endpoints can vary with dimension. + reduce_dims: Optionally specify which dimensions to reduce when + calculating the threshold_weighted_quantile_score. All other dimensions will be preserved. As a + special case, 'all' will allow all dimensions to be reduced. Only one + of ``reduce_dims`` and ``preserve_dims`` can be supplied. The default behaviour + if neither are supplied is to reduce all dims. + preserve_dims: Optionally specify which dimensions to preserve when calculating + the threshold_weighted_quantile_score. All other dimensions will be reduced. As a special case, 'all' + will allow all dimensions to be preserved. In this case, the result will be in + the same shape/dimensionality as the forecast, and the errors will be the threshold_weighted_quantile_score + at each point (i.e. single-value comparison against observed), and the + forecast and observed dimensions must match precisely. Only one of ``reduce_dims`` + and ``preserve_dims`` can be supplied. The default behaviour if neither are supplied + is to reduce all dims. + weights: Optionally provide an array for weighted averaging (e.g. by area, by latitude, + by population, custom). Note that these weights are different to threshold weighting + which is done by decision threshold. + + Returns: + xarray data array of the threshold weighted quantile error + + Raises: + ValueError: if ``interval_where_one`` is not length 2. + ValueError: if ``interval_where_one`` is not increasing. + ValueError: if ``alpha`` is not in the open interval (0,1). + ValueError: if ``interval_where_one`` is not increasing. + ValueError: if ``interval_where_one`` and ``interval_where_positive`` do not + specify a valid trapezoidal weight. + + References: + Taggart, R. (2022). Evaluation of point forecasts for extreme events using + consistent scoring functions. Quarterly Journal of the Royal Meteorological + Society, 148(742), 306-320. https://doi.org/10.1002/qj.4206 + """ + + check_alpha(alpha) + _check_tws_args(interval_where_one=interval_where_one, interval_where_positive=interval_where_positive) + g, _, _ = _auxiliary_funcs(fcst, obs, interval_where_one, interval_where_positive) + + return consistent_quantile_score( + fcst, obs, alpha, g, reduce_dims=reduce_dims, preserve_dims=preserve_dims, weights=weights + ) + + +def tw_expectile_score( + fcst: xr.DataArray, + obs: xr.DataArray, + alpha: float, + interval_where_one: Tuple[EndpointType, EndpointType], + *, + interval_where_positive: Optional[Tuple[EndpointType, EndpointType]] = None, + reduce_dims: Optional[FlexibleDimensionTypes] = None, + preserve_dims: Optional[FlexibleDimensionTypes] = None, + weights: Optional[xr.DataArray] = None, +) -> xr.DataArray: + """ + Returns the threshold weighted expectile score. + + For more flexible threshold weighting schemes, + see :py:func:`scores.continuous.consistent_expectile_score`. + + Two types of threshold weighting are supported: rectangular and trapezoidal. + - To specify a rectangular threshold weight, set ``interval_where_positive=None`` and set + ``interval_where_one`` to be the interval where the threshold weight is 1. + For example, if ``interval_where_one=(0, 10)`` then a threshold weight of 1 + is applied to decision thresholds satisfying 0 <= threshold < 10, and a threshold weight of 0 is + applied otherwise. Interval endpoints can be ``-numpy.inf`` or ``numpy.inf``. + - To specify a trapezoidal threshold weight, specify ``interval_where_positive`` and ``interval_where_one`` + using desired endpoints. For example, if ``interval_where_positive=(-2, 10)`` and + ``interval_where_one=(2, 4)`` then a threshold weight of 1 is applied to decision thresholds + satisfying 2 <= threshold < 4. The threshold weight increases linearly from 0 to 1 on the interval + [-2, 2) and decreases linearly from 1 to 0 on the interval [4, 10], and is 0 otherwise. + Interval endpoints can only be infinite if the corresponding ``interval_where_one`` endpoint + is infinite. End points of ``interval_where_positive`` and ``interval_where_one`` must differ + except when the endpoints are infinite. + + Args: + fcst: array of forecast values. + obs: array of corresponding observation values. + alpha: expectile level. Must be strictly between 0 and 1. + interval_where_one: endpoints of the interval where the threshold weights are 1. + Must be increasing. Infinite endpoints are permissible. By supplying a tuple of + arrays, endpoints can vary with dimension. + interval_where_positive: endpoints of the interval where the threshold weights are positive. + Must be increasing. Infinite endpoints are only permissible when the corresponding + ``interval_where_one`` endpoint is infinite. By supplying a tuple of + arrays, endpoints can vary with dimension. + reduce_dims: Optionally specify which dimensions to reduce when + calculating the threshold_weighted_expectile_score. All other dimensions will be preserved. As a + special case, 'all' will allow all dimensions to be reduced. Only one + of ``reduce_dims`` and ``preserve_dims`` can be supplied. The default behaviour + if neither are supplied is to reduce all dims. + preserve_dims: Optionally specify which dimensions to preserve when calculating + the threshold_weighted_expectile_score. All other dimensions will be reduced. As a special case, 'all' + will allow all dimensions to be preserved. In this case, the result will be in + the same shape/dimensionality as the forecast, and the errors will be the threshold_weighted_expectile_score + at each point (i.e. single-value comparison against observed), and the + forecast and observed dimensions must match precisely. Only one of ``reduce_dims`` + and ``preserve_dims`` can be supplied. The default behaviour if neither are supplied + is to reduce all dims. + weights: Optionally provide an array for weighted averaging (e.g. by area, by latitude, + by population, custom). Note that these weights are different to threshold weighting + which is done by decision threshold. + + Returns: + xarray data array of the threshold weighted expectile error + + Raises: + ValueError: if ``interval_where_one`` is not length 2. + ValueError: if ``interval_where_one`` is not increasing. + ValueError: if ``alpha`` is not in the open interval (0,1). + ValueError: if ``interval_where_one`` and ``interval_where_positive`` do not + specify a valid trapezoidal weight. + + References: + Taggart, R. (2022). Evaluation of point forecasts for extreme events using + consistent scoring functions. Quarterly Journal of the Royal Meteorological + Society, 148(742), 306-320. https://doi.org/10.1002/qj.4206 + """ + + check_alpha(alpha) + _check_tws_args(interval_where_one=interval_where_one, interval_where_positive=interval_where_positive) + _, phi, phi_prime = _auxiliary_funcs(fcst, obs, interval_where_one, interval_where_positive) + # We multiply the output by a factor of two here due to the scaling of phi and phi_prime + # Since phi(s)=2s^2 was used in `_auxiliary_funcs` to be consistent with Taggart (2022) + return 0.5 * consistent_expectile_score( + fcst, obs, alpha, phi, phi_prime, reduce_dims=reduce_dims, preserve_dims=preserve_dims, weights=weights + ) + + +def tw_huber_loss( + fcst: xr.DataArray, + obs: xr.DataArray, + huber_param: float, + interval_where_one: Tuple[EndpointType, EndpointType], + *, + interval_where_positive: Optional[Tuple[EndpointType, EndpointType]] = None, + reduce_dims: Optional[FlexibleDimensionTypes] = None, + preserve_dims: Optional[FlexibleDimensionTypes] = None, + weights: Optional[xr.DataArray] = None, +) -> xr.DataArray: + """ + Returns the threshold weighted huber loss. + + For more flexible threshold weighting schemes, + see :py:func:`scores.continuous.consistent_huber_score`. + + Two types of threshold weighting are supported: rectangular and trapezoidal. + - To specify a rectangular threshold weight, set ``interval_where_positive=None`` and set + ``interval_where_one`` to be the interval where the threshold weight is 1. + For example, if ``interval_where_one=(0, 10)`` then a threshold weight of 1 + is applied to decision thresholds satisfying 0 <= threshold < 10, and a threshold weight of 0 is + applied otherwise. Interval endpoints can be ``-numpy.inf`` or ``numpy.inf``. + - To specify a trapezoidal threshold weight, specify ``interval_where_positive`` and ``interval_where_one`` + using desired endpoints. For example, if ``interval_where_positive=(-2, 10)`` and + ``interval_where_one=(2, 4)`` then a threshold weight of 1 is applied to decision thresholds + satisfying 2 <= threshold < 4. The threshold weight increases linearly from 0 to 1 on the interval + [-2, 2) and decreases linearly from 1 to 0 on the interval [4, 10], and is 0 otherwise. + Interval endpoints can only be infinite if the corresponding ``interval_where_one`` endpoint + is infinite. End points of ``interval_where_positive`` and ``interval_where_one`` must differ + except when the endpoints are infinite. + + Args: + fcst: array of forecast values. + obs: array of corresponding observation values. + huber_param: the Huber transition parameter. + interval_where_one: endpoints of the interval where the threshold weights are 1. + Must be increasing. Infinite endpoints are permissible. By supplying a tuple of + arrays, endpoints can vary with dimension. + interval_where_positive: endpoints of the interval where the threshold weights are positive. + Must be increasing. Infinite endpoints are only permissible when the corresponding + ``interval_where_one`` endpoint is infinite. By supplying a tuple of + arrays, endpoints can vary with dimension. + reduce_dims: Optionally specify which dimensions to reduce when + calculating the threshold_weighted_expectile_score. All other dimensions will be preserved. As a + special case, 'all' will allow all dimensions to be reduced. Only one + of ``reduce_dims`` and ``preserve_dims`` can be supplied. The default behaviour + if neither are supplied is to reduce all dims. + preserve_dims: Optionally specify which dimensions to preserve when calculating + the threshold_weighted_expectile_score. All other dimensions will be reduced. As a special case, 'all' + will allow all dimensions to be preserved. In this case, the result will be in + the same shape/dimensionality as the forecast, and the errors will be the threshold_weighted_expectile_score + at each point (i.e. single-value comparison against observed), and the + forecast and observed dimensions must match precisely. Only one of ``reduce_dims`` + and ``preserve_dims`` can be supplied. The default behaviour if neither are supplied + is to reduce all dims. + weights: Optionally provide an array for weighted averaging (e.g. by area, by latitude, + by population, custom). Note that these weights are different to threshold weighting + which is done by decision threshold. + + Returns: + xarray data array of the threshold weighted expectile error + + Raises: + ValueError: if ``interval_where_one`` is not length 2. + ValueError: if ``interval_where_one`` is not increasing. + ValueError: if ``alpha`` is not in the open interval (0,1). + ValueError: if ``huber_param`` is not positive + ValueError: if ``interval_where_one`` is not increasing. + ValueError: if ``interval_where_one`` and ``interval_where_positive`` do not + specify a valid trapezoidal threshold weight. + + References: + Taggart, R. (2022). Evaluation of point forecasts for extreme events using + consistent scoring functions. Quarterly Journal of the Royal Meteorological + Society, 148(742), 306-320. https://doi.org/10.1002/qj.4206 + """ + + check_huber_param(huber_param) + _check_tws_args(interval_where_one=interval_where_one, interval_where_positive=interval_where_positive) + _, phi, phi_prime = _auxiliary_funcs(fcst, obs, interval_where_one, interval_where_positive) + + # We multiply the output by a factor of two here due to the scaling of phi and phi_prime + # Since phi(s)=2s^2 was used in `_auxiliary_funcs` to be consistent with Taggart (2022) + return 0.5 * consistent_huber_score( + fcst, + obs, + huber_param, + phi, + phi_prime, + reduce_dims=reduce_dims, + preserve_dims=preserve_dims, + weights=weights, + ) diff --git a/tests/continuous/test_threshold_weighted.py b/tests/continuous/test_threshold_weighted.py new file mode 100644 index 000000000..161aa6f51 --- /dev/null +++ b/tests/continuous/test_threshold_weighted.py @@ -0,0 +1,744 @@ +# pylint: disable=use-dict-literal +""" +Tests for scores.continuous.threshold_weighted_impl +""" + +import dask +import numpy as np +import pytest +import xarray as xr +from numpy import nan + +from scores.continuous import mae, mse, quantile_score +from scores.continuous.threshold_weighted_impl import ( + _auxiliary_funcs, + _g_j_rect, + _g_j_trap, + _phi_j_prime_rect, + _phi_j_prime_trap, + _phi_j_rect, + _phi_j_trap, + tw_absolute_error, + tw_expectile_score, + tw_huber_loss, + tw_quantile_score, + tw_squared_error, +) + +DA_FCST = xr.DataArray( + data=[[[3.0, 1.0, nan, 2], [3.0, 1.0, nan, 2]], [[-4.0, 0.0, 1.0, 2], [-4.0, 0.0, 1.0, 2]]], + dims=["date", "lead_day", "station"], + coords=dict( + date=["1", "2"], + lead_day=[1, 1], + station=[100, 101, 102, 0], + ), +) + +DA_OBS = xr.DataArray( + data=[[nan, 3.0, 5.0], [-4.0, 10.0, -1.0], [3.0, 2.0, -0.2]], + dims=["date", "station"], + coords=dict(date=["1", "2", "3"], station=[100, 101, 102]), +) + +DA_X1 = xr.DataArray([nan, -2.0, 1.0, 5.0]) + +EXP_G_J_RECT1 = xr.DataArray([nan, 0.0, 2.0, 3.0]) # a = -1, b = 2 + +DA_X2 = xr.DataArray( + data=[[nan, -2.0, 1.0, 11.0]], + dims=["date", "station"], + coords=dict(date=["01"], station=[100, 101, 102, 103]), +) + +DA_A = xr.DataArray( + data=[-1.0, 0.0, -1.0, 10.0], + dims=["station"], + coords=dict(station=[100, 101, 102, 103]), +) + +DA_B = xr.DataArray( + data=[2.0, 2.0, 0.0, 12.0], + dims=["station"], + coords=dict(station=[100, 101, 102, 103]), +) + +EXP_G_J_RECT2 = xr.DataArray( + data=[[nan, 0.0, 1.0, 1.0]], + dims=["date", "station"], + coords=dict(date=["01"], station=[100, 101, 102, 103]), +) + +EXP_PHI_J_RECT1 = xr.DataArray([nan, 0.0, 8.0, 54.0]) # a = -1, b = 2 + +EXP_PHI_J_RECT2 = xr.DataArray( + data=[[nan, 0.0, 6.0, 2.0]], + dims=["date", "station"], + coords=dict(date=["01"], station=[100, 101, 102, 103]), +) + +EXP_PHI_J_PRIME_RECT1 = 4 * EXP_G_J_RECT1 + +DA_X3 = xr.DataArray([nan, -3.0, 0.0, 2.0, 5.0, 10.0]) +# s = 5 +# a = -2, b = 1, c = 5, d = 8 +EXP_G_J_TRAP1 = xr.DataArray([nan, 0.0, 2 / 3, 2.5, 7 - 3 / 2, 7.0]) + +DA_A_TRAP = xr.DataArray( + data=[[0, 10]], + dims=["date", "station"], + coords=dict(date=["01"], station=[100, 101]), +) + +DA_B_TRAP = xr.DataArray( + data=[[2, 20]], + dims=["date", "station"], + coords=dict(date=["01"], station=[100, 101]), +) + +DA_C_TRAP = xr.DataArray( + data=[[5, 25]], + dims=["date", "station"], + coords=dict(date=["01"], station=[100, 101]), +) + +DA_D_TRAP = xr.DataArray( + data=[[6, 30]], + dims=["date", "station"], + coords=dict(date=["01"], station=[100, 101]), +) + +DA_X_TRAP = xr.DataArray( + data=[[3, 15]], + dims=["date", "station"], + coords=dict(date=["01"], station=[100, 101]), +) + +EXP_G_J_TRAP2 = xr.DataArray( + data=[[2.0, 1.25]], + dims=["date", "station"], + coords=dict(date=["01"], station=[100, 101]), +) + +EXP_PHI_J_TRAP1 = xr.DataArray([nan, 0.0, 16 / 9, 14, 6 + 140 - 2 * 126 / 3, 280 - 2 * 126 / 3]) + +EXP_PHI_J_TRAP2 = xr.DataArray( + data=[[6 + 8 / 3, 25 / 3]], + dims=["date", "station"], + coords=dict(date=["01"], station=[100, 101]), +) + +EXP_PHI_J_PRIME_TRAP = 4 * EXP_G_J_TRAP1 + +DA_A_INF = xr.DataArray([-np.inf, -1], dims=["station"], coords=dict(station=[100, 101])) + +DA_B_INF = xr.DataArray([-100, np.inf], dims=["station"], coords=dict(station=[100, 101])) + +DA_A_FINITE = xr.DataArray([-101, -1], dims=["station"], coords=dict(station=[100, 101])) + +DA_B_FINITE = xr.DataArray([-100, 11], dims=["station"], coords=dict(station=[100, 101])) + + +DA_FCST1 = xr.DataArray( + data=[[3.0, 1.0, nan, 1.3, 8.5], [-4.0, 0.0, 1.0, -3.6, -11.23]], + dims=["date", "station"], + coords=dict(date=["1", "2"], station=[100, 101, 102, 103, 104]), +) + +DA_OBS1 = xr.DataArray( + data=[ + [nan, 3.0, 5.0, 34.5, -28.1], + [-4.0, 10.0, -1.0, 0.001, 1.3], + [3.0, 2.0, -0.2, 1.0, nan], + ], + dims=["date", "station"], + coords=dict(date=["1", "2", "3"], station=[100, 101, 102, 103, 104]), +) + +DA_ENDPT1 = xr.DataArray( + data=[0, 1, 4, -1, 5], + dims=["station"], + coords=dict(station=[100, 101, 102, 103, 104]), +) + +DA_ENDPT2 = xr.DataArray( + data=[2, 5, 7, 0, 10], + dims=["station"], + coords=dict(station=[100, 101, 102, 103, 104]), +) + +DA_INF = xr.DataArray( + data=[np.inf, np.inf, np.inf, np.inf, np.inf], + dims=["station"], + coords=dict(station=[100, 101, 102, 103, 104]), +) +WEIGHTS = xr.DataArray( + data=[1, 2, np.nan, 4, 0], + dims=["station"], + coords=dict(station=[100, 101, 102, 103, 104]), +) +HUBER_PARAM = 0.4 +# Calculated using the Jive MSHL function +EXP_HL = HUBER_PARAM * ( + xr.DataArray( + data=[[nan, 1.8, nan, 33.0, 36.4], [0.0, 9.8, 1.8, 3.401, 12.33]], + dims=["date", "station"], + coords=dict(date=["1", "2"], station=[100, 101, 102, 103, 104]), + ) +) + + +@pytest.mark.parametrize( + ( + "scoring_func", + "interval_where_one", + "interval_where_positive", + "kwargs", + "error_msg_snippet", + ), + [ + ( + tw_squared_error, + (0, 2, 4), + None, + {}, + "`interval_where_one` must have length 2", + ), + ( + tw_squared_error, + (0, 2), + (-10, 10, 50), + {}, + "`interval_where_positive` must be length 2 when not `None`", + ), + ( + tw_absolute_error, + (0, 2, 4), + None, + {}, + "`interval_where_one` must have length 2", + ), + ( + tw_absolute_error, + (0, 2), + (-10, 10, 50), + {}, + "`interval_where_positive` must be length 2 when not `None`", + ), + ( + tw_quantile_score, + (0, 2, 4), + None, + {"alpha": 0.3}, + "`interval_where_one` must have length 2", + ), + ( + tw_quantile_score, + (0, 2), + (-10, 10, 50), + {"alpha": 0.3}, + "`interval_where_positive` must be length 2 when not `None`", + ), + ( + tw_expectile_score, + (0, 2, 4), + None, + {"alpha": 0.3}, + "`interval_where_one` must have length 2", + ), + ( + tw_expectile_score, + (0, 2), + (-10, 10, 50), + {"alpha": 0.3}, + "`interval_where_positive` must be length 2 when not `None`", + ), + ( + tw_quantile_score, + (0, 1), + None, + {"alpha": 0}, + "`alpha` must be strictly between 0 and 1", + ), + ( + tw_quantile_score, + (0, 1), + None, + {"alpha": 1}, + "`alpha` must be strictly between 0 and 1", + ), + ( + tw_expectile_score, + (0, 1), + None, + {"alpha": 0}, + "`alpha` must be strictly between 0 and 1", + ), + ( + tw_expectile_score, + (0, 1), + None, + {"alpha": 1}, + "`alpha` must be strictly between 0 and 1", + ), + ( + tw_huber_loss, + (0, 1), + None, + {"huber_param": 0}, + "`huber_param` must be positive", + ), + ( + tw_huber_loss, + (1, 0), + None, + {"huber_param": 1}, + "left endpoint of `interval_where_one` must be strictly less than right endpoint", + ), + ( + tw_huber_loss, + (1, 0), + (-2, 2), + {"huber_param": 1}, + "left endpoint of `interval_where_one` must be strictly less than right endpoint", + ), + ( + tw_huber_loss, + (DA_B, DA_A), + None, + {"huber_param": 1}, + "left endpoint of `interval_where_one` must be strictly less than right endpoint", + ), + ( + tw_huber_loss, + (DA_B, DA_A), + None, + {"huber_param": 1}, + "left endpoint of `interval_where_one` must be strictly less than right endpoint", + ), + ( + tw_huber_loss, + (-2, 0), + (-np.inf, 6), + {"huber_param": 1}, + "can only be infinite when", + ), + ( + tw_huber_loss, + (xr.DataArray([0, 0]), xr.DataArray([1, 1])), + (xr.DataArray([-np.inf, -1]), xr.DataArray([2, 2])), + {"huber_param": 1}, + "can only be infinite when", + ), + ( + tw_huber_loss, + (-2, 0), + (-4, np.inf), + {"huber_param": 1}, + "can only be infinite when", + ), + ( + tw_huber_loss, + (xr.DataArray([0, 0]), xr.DataArray([1, 1])), + (xr.DataArray([-1, -1]), xr.DataArray([np.inf, 2])), + {"huber_param": 1}, + "can only be infinite when", + ), + ( + tw_huber_loss, + (0, 1), + (0, 2), + {"huber_param": 1}, + "left endpoint of `interval_where_positive` must be less than", + ), + ( + tw_huber_loss, + (xr.DataArray([0, 1]), xr.DataArray([20, 10])), + (xr.DataArray([-1, 1]), xr.DataArray([22, 11])), + {"huber_param": 1}, + "left endpoint of `interval_where_positive` must be less than", + ), + ( + tw_huber_loss, + (0, 2), + (-1, 2), + {"huber_param": 1}, + "right endpoint of `interval_where_positive` must be greater than", + ), + ( + tw_huber_loss, + (xr.DataArray([0, 0]), xr.DataArray([2, 2])), + (xr.DataArray([-1, -1]), xr.DataArray([2, 2])), + {"huber_param": 1}, + "right endpoint of `interval_where_positive` must be greater than", + ), + ], +) +def test_threshold_weighted_score_raises( + scoring_func, + interval_where_one, + interval_where_positive, + kwargs, + error_msg_snippet, +): + """Tests that the threshold weighted scoring rules raise errors as exepected.""" + with pytest.raises(ValueError, match=error_msg_snippet): + scoring_func( + DA_FCST, + DA_OBS, + interval_where_one=interval_where_one, + interval_where_positive=interval_where_positive, + **kwargs, + ) + + +@pytest.mark.parametrize( + ("a", "b", "x", "expected"), + [ + (-1.0, 2.0, DA_X1, EXP_G_J_RECT1), # a, b float; all cases tested + (DA_A, DA_B, DA_X2, EXP_G_J_RECT2), # a, b array + ], +) +def test__g_j_rect(a, b, x, expected): + """Tests that `_g_j_rect` gives results as expected.""" + result = _g_j_rect(a, b, x) + xr.testing.assert_allclose(result, expected) + + +@pytest.mark.parametrize( + ("a", "b", "x", "expected"), + [ + (-1.0, 2.0, DA_X1, EXP_PHI_J_RECT1), # a, b float; all cases tested + (DA_A, DA_B, DA_X2, EXP_PHI_J_RECT2), # a, b array + ], +) +def test__phi_j_rect(a, b, x, expected): + """Tests that `_phi_j_rect` gives results as expected.""" + result = _phi_j_rect(a, b, x) + xr.testing.assert_allclose(result, expected) + + +def test__phi_j_prime_rect(): + """Tests that `_phi_j_prime_rect` gives results as expected.""" + result = _phi_j_prime_rect(-1.0, 2.0, DA_X1) + xr.testing.assert_allclose(result, EXP_PHI_J_PRIME_RECT1) + + +@pytest.mark.parametrize( + ("a", "b", "c", "d", "x", "expected"), + [ + (-2, 1, 5, 8, DA_X3, EXP_G_J_TRAP1), # a, b, c, d float; all cases tested + ( + DA_A_TRAP, + DA_B_TRAP, + DA_C_TRAP, + DA_D_TRAP, + DA_X_TRAP, + EXP_G_J_TRAP2, + ), # endpoints are arrays + ], +) +def test__g_j_trap(a, b, c, d, x, expected): + """Tests that `_g_j_trap` gives results as expected.""" + result = _g_j_trap(a, b, c, d, x) + xr.testing.assert_allclose(result, expected) + + +@pytest.mark.parametrize( + ("a", "b", "c", "d", "x", "expected"), + [ + (-2, 1, 5, 8, DA_X3, EXP_PHI_J_TRAP1), # endpts float; all cases tested + ( + DA_A_TRAP, + DA_B_TRAP, + DA_C_TRAP, + DA_D_TRAP, + DA_X_TRAP, + EXP_PHI_J_TRAP2, + ), # endpoints are arrays + ], +) +def test__phi_j_trap(a, b, c, d, x, expected): + """Tests that `_phi_j_trap` gives results as expected.""" + result = _phi_j_trap(a, b, c, d, x) + xr.testing.assert_allclose(result, expected) + + +def test__phi_j_prime_trap(): + """Tests that `_phi_j_prime_trap` gives results as expected.""" + result = _phi_j_prime_trap(-2, 1, 5, 8, DA_X3) + xr.testing.assert_allclose(result, EXP_PHI_J_PRIME_TRAP) + + +@pytest.mark.parametrize( + ("interval_where_one", "a", "b"), + [ + ((1, 4), 1, 4), + ((-np.inf, 5), -6, 5), + ((-6, np.inf), -6, 11), + ((DA_A_INF, DA_B_INF), DA_A_FINITE, DA_B_FINITE), + ], +) +def test__auxiliary_funcs1(interval_where_one, a, b): + """ + Tests that `_auxiliary_funcs` gives expected results for "rectangular" weights. + """ + g, phi, phi_prime = _auxiliary_funcs( + xr.DataArray([-5, 4], dims=["station"], coords=dict(station=[100, 101])), + xr.DataArray([0, 10], dims=["station"], coords=dict(station=[100, 101])), + interval_where_one, + None, + ) + + xvalues = np.linspace(-10, 12, 100) + x = xr.DataArray(data=xvalues, dims=["x_dim"], coords=dict(x_dim=xvalues)) + xr.testing.assert_allclose(g(x), _g_j_rect(a, b, x)) + xr.testing.assert_allclose(phi(x), _phi_j_rect(a, b, x)) + xr.testing.assert_allclose(phi_prime(x), _phi_j_prime_rect(a, b, x)) + + +@pytest.mark.parametrize( + ("interval_where_one", "interval_where_positive", "a", "b", "c", "d"), + [ + ((1, 4), (-1, 5), -1, 1, 4, 5), + ((-np.inf, 4), (-np.inf, 5), -7, -6, 4, 5), + ((-1, np.inf), (-3, np.inf), -3, -1, 11, 12), + ], +) +def test__auxiliary_funcs2(interval_where_one, interval_where_positive, a, b, c, d): + """ + Tests that `_auxiliary_funcs` gives expected results for "trapezoidal" weights. + """ + g, phi, phi_prime = _auxiliary_funcs( + xr.DataArray([0, 10]), + xr.DataArray([-5, 9]), + interval_where_one, + interval_where_positive, + ) + x = xr.DataArray(np.linspace(-10, 12, 50)) + xr.testing.assert_allclose(g(x), _g_j_trap(a, b, c, d, x)) + xr.testing.assert_allclose(phi(x), _phi_j_trap(a, b, c, d, x)) + xr.testing.assert_allclose(phi_prime(x), _phi_j_prime_trap(a, b, c, d, x)) + + +# Test all of the threshold weighted scores +@pytest.mark.parametrize( + ("scoring_func", "kwargs", "expected"), + [ + (tw_squared_error, {}, mse(DA_FCST1, DA_OBS1, preserve_dims=["date", "station"])), + (tw_absolute_error, {}, mae(DA_FCST1, DA_OBS1, preserve_dims=["date", "station"])), + ( + tw_quantile_score, + {"alpha": 0.3}, + quantile_score(DA_FCST1, DA_OBS1, alpha=0.3, preserve_dims=["date", "station"]), + ), + ( + tw_expectile_score, + {"alpha": 0.5}, + 0.5 * mse(DA_FCST1, DA_OBS1, preserve_dims=["date", "station"]), + ), + ( + tw_huber_loss, + {"huber_param": HUBER_PARAM}, + EXP_HL, + ), + ], +) +def test_threshold_weighted_scores1(scoring_func, kwargs, expected): + """ + Tests that each threshold weight score gives results as expected. + This test is based on the fact that if `interval_where_one=(-np.inf, np.inf)` + then the threshold weighted score should be the same as the original score. + """ + result = scoring_func( + DA_FCST1, DA_OBS1, interval_where_one=(-np.inf, np.inf), preserve_dims=["date", "station"], **kwargs + ) + xr.testing.assert_allclose(result, expected) + + +@pytest.mark.parametrize( + ("scoring_func", "kwargs"), + [ + (tw_squared_error, {}), + (tw_absolute_error, {}), + (tw_quantile_score, {"alpha": 0.3}), + (tw_expectile_score, {"alpha": 0.3}), + (tw_huber_loss, {"huber_param": HUBER_PARAM}), + ], +) +def test_threshold_weighted_scores2(scoring_func, kwargs): + """ + Tests that each threshold weight score gives results as expected. + This test is based on the fact that if the sum of scores whose weights + sum to 1 is the same as the score with a weight of 1 everywhere. + Tests for rectangular weights. + """ + kwargs["preserve_dims"] = ["date", "station"] + + score1 = scoring_func(DA_FCST1, DA_OBS1, interval_where_one=(-np.inf, 0), **kwargs) + score2 = scoring_func(DA_FCST1, DA_OBS1, interval_where_one=(0, np.inf), **kwargs) + score = scoring_func(DA_FCST1, DA_OBS1, interval_where_one=(-np.inf, np.inf), **kwargs) + xr.testing.assert_allclose(score1 + score2, score) + + +@pytest.mark.parametrize( + ("scoring_func", "kwargs"), + [ + (tw_squared_error, {}), + (tw_absolute_error, {}), + (tw_quantile_score, {"alpha": 0.3}), + (tw_expectile_score, {"alpha": 0.3}), + (tw_huber_loss, {"huber_param": HUBER_PARAM}), + ], +) +def test_threshold_weighted_scores3(scoring_func, kwargs): + """ + Tests that each threshold weight score gives results as expected. + This test is based on the fact that if the sum of scores whose weights + sum to 1 is the same as the score with a weight of 1 everywhere. + Tests for rectangular weights, weights vary with station dimension. + """ + + kwargs["preserve_dims"] = ["date", "station"] + + score1 = scoring_func(DA_FCST1, DA_OBS1, interval_where_one=(-DA_INF, DA_ENDPT1), **kwargs) + score2 = scoring_func(DA_FCST1, DA_OBS1, interval_where_one=(DA_ENDPT1, DA_INF), **kwargs) + score = scoring_func(DA_FCST1, DA_OBS1, interval_where_one=(-DA_INF, DA_INF), **kwargs) + xr.testing.assert_allclose(score1 + score2, score) + + +@pytest.mark.parametrize( + ("scoring_func", "kwargs"), + [ + (tw_squared_error, {}), + (tw_absolute_error, {}), + (tw_quantile_score, {"alpha": 0.3}), + (tw_expectile_score, {"alpha": 0.3}), + (tw_huber_loss, {"huber_param": HUBER_PARAM}), + ], +) +def test_threshold_weighted_scores4(scoring_func, kwargs): + """ + Tests that each threshold weight score gives results as expected. + This test is based on the fact that if the sum of scores whose weights + sum to 1 is the same as the score with a weight of 1 everywhere. + Tests for trapezoidal weights. + """ + + kwargs["preserve_dims"] = ["date", "station"] + point1 = -4 + point2 = 4 + score1 = scoring_func( + DA_FCST1, DA_OBS1, interval_where_one=(-np.inf, point1), **kwargs, interval_where_positive=(-np.inf, point2) + ) + score2 = scoring_func( + DA_FCST1, DA_OBS1, interval_where_one=(point2, np.inf), **kwargs, interval_where_positive=(point1, np.inf) + ) + score = scoring_func(DA_FCST1, DA_OBS1, interval_where_one=(-np.inf, np.inf), **kwargs) + xr.testing.assert_allclose(score1 + score2, score) + + +@pytest.mark.parametrize( + ("scoring_func", "kwargs"), + [ + (tw_squared_error, {}), + (tw_absolute_error, {}), + (tw_quantile_score, {"alpha": 0.3}), + (tw_expectile_score, {"alpha": 0.3}), + (tw_huber_loss, {"huber_param": HUBER_PARAM}), + ], +) +def test_threshold_weighted_scores5(scoring_func, kwargs): + """ + Tests that each threshold weight score gives results as expected. + This test is based on the fact that if the sum of scores whose weights + sum to 1 is the same as the score with a weight of 1 everywhere. + Tests for trapezoidal weights, with weights varying by stn dimension. + """ + + kwargs["preserve_dims"] = ["date", "station"] + score1 = scoring_func( + DA_FCST1, + DA_OBS1, + interval_where_one=(-DA_INF, DA_ENDPT1), + **kwargs, + interval_where_positive=(-DA_INF, DA_ENDPT2), + ) + score2 = scoring_func( + DA_FCST1, DA_OBS1, interval_where_one=(DA_ENDPT2, DA_INF), **kwargs, interval_where_positive=(DA_ENDPT1, DA_INF) + ) + score = scoring_func(DA_FCST1, DA_OBS1, interval_where_one=(-np.inf, np.inf), **kwargs) + xr.testing.assert_allclose(score1 + score2, score) + + +@pytest.mark.parametrize( + ("scoring_func", "kwargs"), + [ + (tw_squared_error, {}), + (tw_absolute_error, {}), + (tw_quantile_score, {"alpha": 0.3}), + (tw_expectile_score, {"alpha": 0.3}), + (tw_huber_loss, {"huber_param": HUBER_PARAM}), + ], +) +def test_threshold_weighted_scores6(scoring_func, kwargs): + """ + Tests that each threshold weight score gives results as expected when the `reduce_dims` + arg is used. + """ + score1 = scoring_func( + DA_FCST1, + DA_OBS1, + interval_where_one=(DA_ENDPT2, DA_INF), + **kwargs, + interval_where_positive=(DA_ENDPT1, DA_INF), + preserve_dims=["date", "station"], + ) + score2 = scoring_func( + DA_FCST1, + DA_OBS1, + interval_where_one=(DA_ENDPT2, DA_INF), + **kwargs, + interval_where_positive=(DA_ENDPT1, DA_INF), + reduce_dims=["date", "station"], + ) + xr.testing.assert_allclose(np.mean(score1), score2) + + +@pytest.mark.parametrize( + ("scoring_func", "kwargs", "expected"), + [ + (tw_squared_error, {}, mse(DA_FCST1, DA_OBS1, preserve_dims=["date", "station"])), + (tw_absolute_error, {}, mae(DA_FCST1, DA_OBS1, preserve_dims=["date", "station"])), + ( + tw_quantile_score, + {"alpha": 0.3}, + quantile_score(DA_FCST1, DA_OBS1, alpha=0.3, preserve_dims=["date", "station"]), + ), + (tw_expectile_score, {"alpha": 0.5}, 0.5 * mse(DA_FCST1, DA_OBS1, preserve_dims=["date", "station"])), + ( + tw_huber_loss, + {"huber_param": HUBER_PARAM}, + EXP_HL, + ), + ], +) +def test_threshold_weighted_scores_dask(scoring_func, kwargs, expected): + """ + Tests that each threshold weight score gives results as expected. + This test is based on the fact that if `interval_where_one=(-np.inf, np.inf)` + then the threshold weighted score should be the same as the original score. + """ + if dask == "Unavailable": # pragma: no cover + pytest.skip("Dask unavailable, could not run test") # pragma: no cover + result = scoring_func( + DA_FCST1.chunk(), + DA_OBS1.chunk(), + interval_where_one=(-DA_INF.chunk(), DA_INF.chunk()), + preserve_dims=["date", "station"], + **kwargs, + ) + assert isinstance(result.data, dask.array.Array) + result = result.compute() + assert isinstance(result.data, np.ndarray) + xr.testing.assert_allclose(result, expected) diff --git a/tutorials/Consistent_Scores.ipynb b/tutorials/Consistent_Scores.ipynb index 7bda794ee..387575d8d 100644 --- a/tutorials/Consistent_Scores.ipynb +++ b/tutorials/Consistent_Scores.ipynb @@ -21,9 +21,9 @@ "\n", "The consistent scoring module in `scores` provides access to these families of consistent scores that can be used to tailor emphasis of predictive performance based on decision thresholds of interest.\n", "\n", - "
\n", - "Note: Threshold weighted scoring functions will be added to scores. These threshold weighted scoring functions will use these consistent scoring functions and will allow users a more simplified approach to evaluating forecasts using scores that emphasise performance across different decision thresholds. The planned tutorial notebook for threshold weighted scoring functions is expected to complement this tutorial.\n", - "
\n", + "\n", + "Note: Threshold weighted scoring functions have been added to `scores` and provide users with a simplified approach to evaluating forecasts using scores that emphasise performance across different decision thresholds. The [tutorial notebook](./Threshold_Weighted_Scores.ipynb) for threshold weighted scoring functions complements this tutorial.\n", + "\n", "\n", "## Example 1. A score consistent with the mean\n", "Let's jump into an example that illustrates a scoring function that is consistent with predicting the mean value using `scores`. Suppose we want to evaluate the performance of expected daily precipitation forecasts, but we want to place increasing importance on correctly forecasting extreme values. MSE is consistent with predicting the mean value, but weights all decision thresholds equally (as discussed further on).\n", diff --git a/tutorials/Threshold_Weighted_Scores.ipynb b/tutorials/Threshold_Weighted_Scores.ipynb new file mode 100644 index 000000000..5d089ecaf --- /dev/null +++ b/tutorials/Threshold_Weighted_Scores.ipynb @@ -0,0 +1,77671 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Threshold weighted scores\n", + "\n", + "## Introduction\n", + "For a long time, people have wanted to be able to evaluate the performance of forecasts of extreme events using verification methods that can't be hedged. Such evaluation brings together two concepts - avoidance of hedging, particularly of probabilities, and also a focus on extreme conditions (e.g. very hot conditions).\n", + "\n", + "Hedging occurs when a forecast is optimised for a verification metric rather than for a forecast directive (see [Murphy (1978)](https://doi.org/10.1175/1520-0477(1978)059<0371:HATMOE>2.0.CO;2)). In the context of an automated system, hedging occurs when the model or system is designed or trained according to a verification metric which is misaligned to the forecast directive.\n", + "\n", + "An example of a forecast directive is to predict the 90th percentile wind speed. If the 90th percentile forecast wind speed forecasts were evaluated against observed wind speeds using mean absolute error (MAE), the forecaster or forecast system developer is faced with the dilemma of whether to produce forecasts that are consistent with the forecast directive or if they should be produced to optimise the expected MAE (since MAE optimises for the 50th percentile wind speed).\n", + "\n", + "Hedging is of greatest concern when considering the probabilities of extreme events on which significant decisions are made. These tensions can be removed by using a consistent verification score which will not penalise the forecaster or model designer for meeting the forecast directive - i.e. these scores do not allow hedging (see the [Consistent Scores](./Consistent_Scores.ipynb) tutorial).\n", + "\n", + "Threshold-weighted consistent scoring functions also allow us to measure the performance of forecasts with a focus on a range of decision thresholds while still preventing hedging. An example of someone acting on a decision threshold is a manager deciding to shut the roof of a stadium if the forecast temperature is expected to exceed 35°C. Threshold-weighted scores allow us to measure the performance of predicting such extremes or some other range of decision thresholds of interest. \n", + "\n", + "Threshold-weighted scores contrast with metrics like mean squared error (MSE) and mean absolute error (MAE), which can be interpreted as evaluating performance when equal weight is placed on all decision thresholds ([Ehm 2016](https://doi.org/10.1111/rssb.12154)).\n", + "\n", + "As another example, suppose health agencies will start taking preventative action for treating heat-related illness whenever the temperature forecast for the following day exceeds 35°C and that this preventative action is increasingly important with even higher temperature forecasts. If the temperature forecast is 20°C and the observed temperature is 10°C, then health agencies will have made the correct decision to not take preventative action, despite the large magnitude in forecast error. In this case, the MSE would be 100°C2. However, if we calculate a threshold weighted MSE (twMSE) that places an equal weight on decision thresholds above 35°C and a zero weight on decision thresholds below 35°C, the twMSE will give a value of 0 in this case. If at least one of the forecast value and the observed value is above 35°C and they differ from each other, then there will be a non-zero error.\n", + "\n", + "The arguments for threshold-weighting (`interval_where_one` and `interval_where_positive`) are separate and different from the `weights` argument that appears in most metrics in `scores`. `weights` of the normal kind can still be used in the threshold weighted scores. The `weights` argument weights the errors at each point before calculating the mean score. Examples including weighting the results based on latitude to account for grid size or by weighting results based on population density. For more information see the [weighting results tutorial](./Weighting_Results.ipynb).\n", + "\n", + "The following threshold weighted scores are available in `scores`:\n", + "\n", + " - `scores.continuous.tw_squared_error`\n", + " - `scores.continuous.tw_absolute_error`\n", + " - `scores.continuous.tw_quantile_score`\n", + " - `scores.continuous.tw_expectile_score`\n", + " - `scores.continuous.tw_huber_loss`\n", + "\n", + "\n", + "There are two types of threshold weighting that are supported: rectangular and trapezoidal. More flexible threshold weighting schemes are discussed at the end of this tutorial.\n", + "\n", + "### Rectangular Threshold Weighting\n", + "To specify a rectangular threshold weight, set `interval_where_positive=None` and set `interval_where_one` to be the interval where the threshold weight is 1. For example, if `interval_where_one=(0, 10)` then a threshold weight of 1 is applied to decision thresholds satisfying 0 ≤ threshold < 10, and a threshold weight of 0 is applied otherwise. Interval endpoints can be `-numpy.inf` or `numpy.inf`. Only a single interval can be provided. See example 3 below for a demonstration of how to calculate a threshold weighted score for multiple intervals.\n", + "\n", + "### Trapezoidal Threshold Weighting\n", + "To specify a trapezoidal threshold weight, first define `interval_where_one` which determines where there will be a threshold weight of 1 using desired endpoints. Next, determine where the threshold weights should linearly increase to the left of the rectangular threshold weight and decrease to the right of the rectangular threshold weight. This is done using the `interval_where_positive` argument. Consider an example where a threshold weight of 1 is applied to decision thresholds satisfying 2 ≤ threshold < 4. Additionally we want the threshold weight to increase linearly from 0 to 1 on the interval [-2, 2) and decrease linearly from 1 to 0 on the interval [4, 10], and is 0 otherwise. This can be done by setting `interval_where_positive=(-2, 10)` and `interval_where_one=(2, 4)`. Interval endpoints can only be infinite if the corresponding `interval_where_one` endpoint is infinite. End points of `interval_where_positive` and `interval_where_one` must differ except when the endpoints are infinite. In example 2, we show a case where we set `interval_where_positive=(30, np.inf)` and `interval_where_one=(40, np.inf)`. This corresponds to the threshold weight linearly increasing from 0 to 1 on the interval [30, 40), and a threshold weight of 1 on the interval [40, infinity). These linear increases and decrease in the threshold weighting are referred to as \"trapezoidal threshold weighting\".\n", + "\n", + "The implementation of these threshold weighted scores in `scores` is based on [Taggart, R. (2022)](https://doi.org/10.1002/qj.4206).\n", + "\n", + "## Example 1 - Rectangular Threshold Weighting\n", + "Suppose that two forecast systems are issuing forecasts for the mean (i.e. expected value) rainfall accumulation. We want to evaluate the performance for decision thresholds above 40mm. Let's generate the same synthetic data as in the [Consistent Scores tutorial](./Consistent_Scores.ipynb) and calculate the twMSE." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from scores.continuous import (\n", + " mse,\n", + " murphy_score,\n", + " murphy_thetas,\n", + " tw_absolute_error,\n", + " tw_expectile_score,\n", + " tw_huber_loss,\n", + " tw_quantile_score,\n", + " tw_squared_error,\n", + ")\n", + "from scipy.stats import skewnorm\n", + "import numpy as np\n", + "import xarray as xr\n", + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "\n", + "np.random.seed(100)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate some synthetic rainfall observations between 0 and 50mm\n", + "N = 1000\n", + "obs = xr.DataArray(data=50 * np.random.random(N), dims=[\"time\"], coords={\"time\": np.arange(0, N)})\n", + "obs = obs.clip(min=0) # don't allow negative rainfall\n", + "\n", + "# Generate synthetic forecasts by adding noise to each observation\n", + "fcst1 = 0.9 * obs + skewnorm.rvs(4, size=N) # fcst1 has a low bias\n", + "fcst1 = fcst1.clip(min=0) # don't allow negative rainfall\n", + "fcst2 = 1.1 * obs - skewnorm.rvs(4, size=N) # fcst2 has a high bias\n", + "fcst2 = fcst2.clip(min=0) # don't allow negative rainfall" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fcst1 MSE = 5.397562928167134\n", + "fcst2 MSE = 5.3763094346565685\n" + ] + } + ], + "source": [ + "# First if we calculate the MSE of fcst1 and fcst2 we will see that that have similar predictive performance.\n", + "print(f\"fcst1 MSE = {mse(fcst1, obs).item()}\")\n", + "print(f\"fcst2 MSE = {mse(fcst2, obs).item()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MSE of each forecast are similar.\n", + "\n", + "Let's apply a rectangular threshold weight that gives equal weight to decision thresholds above 40mm, and gives zero weight to thresholds below 40mm and calculate the twMSE." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fcst1 twMSE = 2.2395142870695333\n", + "fcst2 twMSE = 3.2119291100148106\n" + ] + } + ], + "source": [ + "fcst1_tw_mse_upper = tw_squared_error(fcst1, obs, interval_where_one=(40, np.inf))\n", + "fcst2_tw_mse_upper = tw_squared_error(fcst2, obs, interval_where_one=(40, np.inf))\n", + "print(f\"fcst1 twMSE = {fcst1_tw_mse_upper.item()}\")\n", + "print(f\"fcst2 twMSE = {fcst2_tw_mse_upper.item()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why is this? 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate a list of thresholds of interest\n", + "thetas = murphy_thetas([fcst1, fcst2], obs, \"expectile\")\n", + "\n", + "# Calculate the average elementary score for the mean (0.5 expectile) for each threshold theta\n", + "ms1 = 2 * murphy_score(fcst1, obs, thetas, functional=\"expectile\", alpha=0.5)\n", + "ms2 = 2 * murphy_score(fcst2, obs, thetas, functional=\"expectile\", alpha=0.5)\n", + "\n", + "fig = make_subplots(rows=2, cols=1)\n", + "fig.add_trace(\n", + " go.Scatter(x=ms1.theta, y=ms1.total, mode=\"lines\", name=\"fcst1\", line=dict(color=\"#1b9e77\")), row=1, col=1\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=ms1.theta,\n", + " y=ms1.total.where(ms2.theta >= 40),\n", + " mode=\"lines\",\n", + " line=dict(color=\"rgba(27,158,119, 1)\"),\n", + " fillcolor=\"rgba(27,158,119, 0.5)\",\n", + " fill=\"tozeroy\",\n", + " showlegend=False,\n", + " ),\n", + " row=1,\n", + " col=1,\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(x=ms2.theta, y=ms2.total, mode=\"lines\", name=\"fcst2\", line=dict(color=\"#7570b3\")), row=1, col=1\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=ms2.theta,\n", + " y=ms2.total.where(ms2.theta >= 40),\n", + " mode=\"lines\",\n", + " fill=\"tozeroy\",\n", + " line=dict(color=\"#7570b3\"),\n", + " fillcolor=\"rgba(117,112,179, 0.5)\",\n", + " showlegend=False,\n", + " ),\n", + " row=1,\n", + " col=1,\n", + ")\n", + "\n", + "fig.add_trace(\n", + " go.Scatter(x=[0, 40, 40, 55], y=[0, 0, 1, 1], mode=\"lines\", name=\"threshold weight\", line=dict(color=\"black\")),\n", + " row=2,\n", + " col=1,\n", + ")\n", + "fig.update_layout(\n", + " xaxis_title=\"Rainfall (mm)\",\n", + " yaxis_title=\"Economic Regret\",\n", + " width=800,\n", + " height=600,\n", + " legend=dict(x=0.01, y=0.99, xanchor=\"left\", yanchor=\"top\"),\n", + " margin=dict(l=50, r=20, b=20, t=20),\n", + ")\n", + "fig.update_yaxes(title_text=\"Threshold Weight\", row=2, col=1)\n", + "fig.update_xaxes(title_text=\"Rainfall (mm)\", row=2, col=1)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Our rectangular threshold weight is shown in the lower subplot. It shows a weighting of 1 for rainfall thresholds 40mm and above, and a weight of 0 otherwise.\n", + "- The area under the lines across all decision thresholds equals the MSE (due to the scaling factor that we added to the Murphy score calculation).\n", + "- The area under the curves where rainfall is 40mm and above is proportional to the twMSE.\n", + "- `fcst2` (purple) has a larger shaded area than `fcst1` (green) and so has a lower (better) twMSE.\n", + "\n", + "\n", + "Let's now apply a rectangular threshold weight with a value of 1 for all decision thresholds below 40mm, and a value of zero for all thresholds 40mm and higher." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fcst1 twMSE = 3.1580486410975848\n", + "fcst2 twMSE = 2.1643803246417592\n" + ] + } + ], + "source": [ + "fcst1_tw_mse_lower = tw_squared_error(fcst1, obs, interval_where_one=(-np.inf, 40))\n", + "fcst2_tw_mse_lower = tw_squared_error(fcst2, obs, interval_where_one=(-np.inf, 40))\n", + "print(f\"fcst1 twMSE = {fcst1_tw_mse_lower.item()}\")\n", + "print(f\"fcst2 twMSE = {fcst2_tw_mse_lower.item()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we add the twMSE values thresholds above 40mm and the twMSE values for thresholds below 40mm, we can see that it equals the MSE." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"fcst2_tw_mse_upper = tw_squared_error(fcst2, obs, interval_where_one=(40, np.inf), interval_where_positive=(30, np.inf))\n", + "print(f\"fcst1 twMSE = {fcst1_tw_mse_upper.item()}\")\n", + "print(f\"fcst2 twMSE = {fcst2_tw_mse_upper.item()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also visualise this on Murphy Diagrams" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#1b9e77" + }, + "mode": "lines", + "name": "fcst1", + "type": "scatter", + "x": [ + 0, + 0.024337973762994514, + 0.0399563244233625, + 0.047028426457913275, + 0.04747085477909729, + 0.06995115595219148, + 0.18235217726127934, + 0.23594280954862823, + 0.24251788976445504, + 0.2844253676286712, + 0.34933463655534847, + 0.35990546744086555, + 0.37193603342800396, + 0.39907146776096136, + 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RPBFiJlObRKDmBEie1JwdfSktAiRPpJUvirZmBEie1Izb418Vlqgw++xmKGRyrOn2AvtxdE4aRh9ZDTulOcY2DCj9hLt5Z3tk4P3SozIcHrwAdqYWFQay7Np+/HknEP09W+Gzjs/xE6yRtlJdeXLmeCQrDOvuaQufRmUFSVhoMlKTczBslD+atix/WsFIEYPv9TzJEwOSJ1yNk4++WI8rIWGwtrLAu3P+h+6dWuGvgyfx42/7oFarYW5uhjlTR6F/zw44czEEcz9Yjb7d2rJ3+zynLRKlUhVDqdQWGjq8bSWcHe2M9fetwnGTPKHpQATERYDkibjyQdEIR4DkiXBsqWXxECB5wk8ucooL0ffAMtbYxl7TEZ6djP9ir+NscjgczSzLHc/ZEnEWuWoVe7+ZrQfebzcCfnbuZYI5lhCKBYHbAI0GZiZKHB6yAGbyhzvc+YnceFqprjzZ+ksgEuIy4dfKHQ6OlmVA3QlPRVJ8FvoNblZuV4rxEC0/UpInPGVflwUw37B5Cp2aqWMCusydOg6RdU/HdsSQBYpBHwRInuiDMvUhBgIkT8SQBYpBaAIkT/ghnK0qQL+DWnnyZquB2BsVhLCsJPb3rhY2eKZB+zIdXUmPRnhWEu4V5bHrb+e2fBqTHqmLwr384ol1uHEvDtBwbwBLO43GgHqt+AnYCFupjjwpLCjGmhXHIZPJ0LGbN0y4X5RHnpioDMRFZaB7n0YI6NnQCGlWPGS+1/O086SSqcU3bJrFhkGA5Ilh5JFGYTgESJ4YTi5pJJUTIHlCM8QYCJA84SfLN+7F48Xj6wCZBk1t3BGWnQgN2N/C3dIOw+q3LddRTG46/o4NBmdP+rg3wxcB43E3Jw2/hp2GvZkFjsaHIiYvDe7mdkgsyEJfj2ZY3mkcPwEbYSvVkSdhN5Oxb8c12NpboEVrj3K0EuKyEBWRhvZd6qP3AO0tSvSAju3wNQl0WQCTPOGLtmG1o8vcEcOIaeeJGLJAMeiDAMkTfVCmPsRAgOSJGLJAMQhNgORJ7Qmvu30c60KPsRomTJhABo2G+yvub2TwsrTHYK/W5ToqKlFjY/gptrPEzswChwctwD9xIXjv0g5YK8xhb2oJrljssPptsC/mGtuhcnTIQlgpTGsftBG2UB158t/+m7h2OR71GzrAs759OVopSTnsCuMW/h4Y+EwLI6RZ8ZD5Xs/TzpNKphbfsGkWGwYBkieGkUcaheEQIHliOLmkkVROgOQJzRBjIEDy5MlZjs+/h6DUaDiZW6Ozi/Z2zYqegD0fc2VJYG6iYIIjv6SYiRRrhRlyiovgY+WIAZ4VH7dJKsjCv7EhKFCrsHPAHFxKvYslQXuZhjGXm6KgpAjjGnbGkfgbSC3MxqL2ozCkAhFjDHO1tmOsTJ5kZRbg+pU4xNy9h7GTO+CnVaeQnVmAVu29YG1dXlZlpOUh9HoSfJs649lxbWobmsF8z/d6nuQJyROD+eXQ10BInuiLNPVDBHQjQPJEN070lvQJkDyRfg5pBFUTIHnyZEZfhfyNLZHn0NGpIdZ2f7HCFx8WitXAw8IeFiZKROakMnnSwckHWap8eFk6orGt6xM7+jcuBHdz0vFx+5FIL8rFNyGHmITRyGRQyuSY1LgrQu8l4FxKBHq4NsVXXSZWnVh6oxyByuTJ6mXHoCpWM+7Pv9wVm74/CxOlDJ26+lRIMjurENevxsPDyw7jJ3ck2vcJkDzhaSrosgDmGzZPoVMzdUxAl7lTxyGy7unYjhiyQDHogwDJE31Qpj7EQIDkiRiyQDEITYDkyZMJzzn3G7sth7vh5sTQdyGXlS0ayn25L+YqPr7yF5QyE/Ty8EOuqhDnkiNZsZPR3h3haGZVZQqvZcTifEoEkyx5xUVIL8y5L0+ANg71EeDii9ziImyJPAtLEzMcH7qwyjbphfIEHpcnVy/GsptyoiLS8eeWy6w4LHfuqnUHT1y7HAsnZ2s0aV6x9MrPVyEoMAaOTlaY/GpXwk3yhN85oMsCmOQJv8wNpTVd5o4YxkryRAxZoBj0QYDkiT4oUx9iIEDyRAxZoBiEJkDy5MmER/+3GtE5qUxk/NJrGjKL8nEtPRpHE0ORXpCDXQNex8un1iMsKxGWCjNM9O2C5IJs7I66zBod6xsAO6VFlSlMzM/E3pirWmHCvc2dAZJxlVOA/vVawsfaibXxe+Q5cDtdtvSZiSa2blW2Sy+UJfCoPPln73XcuJqIAcNb4G54Km7fSGLMuQSw/9Fo4OvnClc36woxqlRqXDobDQsrU7zyZk9CTfKE3zmgywKY5Am/zA2lNV3mjhjGSvJEDFmgGPRBgOSJPihTH2IgQPJEDFmgGIQmQPLkyYS5q4eziwrZYtpGaY6sogL218xvAFjgPwRfBO+HjcIcw73bw9JEyX72a/gZWClNMcSrDczv/7Oq8rju1vH7wkTDistyt/CUaDRlBMx/8TcQmZ2KhjYuWNxhJJrZlb8Fpqp+jPnnD+TJsWN38O/+G9ok3pdU3F86OFmCq2XyYH9R+y7eUCrlFSLj3j9/8g579/X3nzJmrGXGzvd6nmqeVDK1+IZNs9gwCJA8MYw80igMhwDJE8PJJY2kcgIkT2iGGAMBkidPzrK2EKz21hzuSMeDC3S4IzncopmTGJzMcDK1xCif2tW92BN9he1a4fob2zAAdqbld6xcTY/BhRTtgn1OqwH4X6NuxjBFeRvjA3my+L1DKCxSw8JCCe74DXedtKmFAl7eDogMTWFXR5tbKNmRnsqei2eioFaX4JV5vWBurhVnxv7wvZ4neULyxNh/p6o9fpIn1UZGHxABQQmQPBEULzUuIgIkT0SUDApFMAIkTypGG5mdgvFH18BEJkexhiskKtNeRHx/twL3Fw/+mZelQ4VXEVcnaedTInEtI4ZddPx8o64V7ljJVOXjcupdhGelsOM8n3V6rjpdGP27nDxJic/CLz+eh6WlKatncu1SLOPi7mkLJxdrhATFs31F7vXs4NNIe1zqSc+VwBgU5qvw0uzusLOv+niWMSSA5AlPWdZlAcw3bJ5CL9PM7kOnseSbXzH+mX548+Wx1eoiOi4JqemZaO/flH135NRlrPxhO1LS7sGvUX18PP8l+Dag7XePQ9Vl7lQrEQK9TMd2BAJLzYqOAMkT0aWEAhKIAMkTgcBSs6IiQPKk4nRcz4jD5BPr4Gphg2xVAfLV3PXDD+qRPChOwlwHqz/Sx71ZrfJ6NycV/8ZfZ3Jmml/v0qMjjzeapSrAtjsX4GZhi30D3qhVn8b2MSdPjh4MxYXzMWyXiau7DS6fi2Y5bNHaA1bWZgg8c5dh8WvpBgdHy0oRBV+OQ15uEca/1Anu9WyNDWeF4+V7PU87TyqZVnzDFmIGz/3gWwS0a4ZJowZUu/mNfxxCUZEK0ycNQ1JKBkZMfhc/LH8LrZs3wre/7ETQ9TCs/+qdardr6B+QPDH0DNP4pEaA5InUMkbx1pQAyZOakqPvpESA5MnDbH0RfABhmcmY0awPclQFmB+4De7mtrBUmOJOdioslaYY6OkPK4UpwrKS2NXB3BEaPzsP9HTT/uFoTZ98tQqnksJgpzRnt+tU9mwMOwVViRr/DF4Ae9PKF/g1jccQv1OayLH6i2PIy1WhdUcvWFoqkZ6ah8JCFTw87diQ76Xno6BABRc3G5iYlL9d6VEuN4MTkXkvHyPHt4VP48p3qRgiz4rGxPd6nuSJhOXJj7/txbrN+2FjbYFRg3vhhbEDsWjFely9HgFLCzMsnDMJ3Tq2wunAECxf8zsTJUqFAvNmjoOpqRJvfvQdFAoTjBzcExNH9se1mxEY0Et7PvJmWBRmvfs1jvzxlbH8buk8TpInOqOiF4mAXgiQPNELZupEBARInoggCRSC4ARInjxE/NTBz5FVlI9xvp1xrygPh+JCmDzxtXXBmaRwuJjb4Fnv9uwDbjfK1sjz2l0LdvXQ3a2J4Ll60MGhuGDE5KTji84T0NvdT2/9Sr2j5PhMbP45EOYWpmjbqfJ6JrqMNSw0GanJORgyshWatXLX5RODf4fkCU8p1mUBXBHsuNhMFBTc3ybHUyy6NOPpZQdzc0W5V2e/9w2e7t0Jw5/uxo7vyOVyLJw9CcE3IzF9/goc3/kNxsxYhEVvvciO54SGR2PLX4exeP5L+PTrX+Hm4sB2njz+/Pz7AdwKj8byD17RJTyjekeXuSMGIHRsRwxZoBj0QYDkiT4oUx9iIEDyRAxZoBiEJkDyREs4KT8Tw//9ChqNDI1sXWClMEdEVjK6ujVCPUt7pBXmwkyugJOZVWlK/oq6hNTCHLSy90RX18ZCp6q0/ctpd3EpNQpTmvbEq83pphddwZ/+LxwXzkShXgN7NPBx0PWzJ753JzwVSfFZ6DvID2071a91e4bQAMkTnrKoywK4Itirvz6F6Kh7PEWhezOvze1e4S/Vo/JkwPh5WPXJbDRv4s0azszOhZ2NFaa+tRw+Xu54cewgNPB0Le30SfLk1IVgfPLVJvz67XtwdbbXPUgjeVOXuSMGFCRPxJAFikEfBEie6IMy9SEGAiRPxJAFikFoAsYsT66kRWF31GU0c6iH+paOeP385tKCsDZKM7a7ZKJvV3ZMp6LnVlYiEvMy2a07DawchU5VafvRuek4FBuMTi6+WNPtBb31K/WOfv72NLLu5aNVey9YW1ec0+qMMSYqA3FRGejSuxG69mpYnU8N9l2SJzylVpcFcEWw/9oRjKSkHJ6i0L2ZkaP94eZuXe6DR+VJh4EzsHvDEnh5uJR5Ly0jC99v2o3DJy/BxsoS78yeyI7zVCRP9h0+i7Ubd2PtsjfQwNNN9wCN6E1d5o4YcJA8EUMWKAZ9ECB5og/K1IcYCJA8EUMWKAahCRizPFlz4z+sDz+JxjZuGOjlj9U3DrMaJtyFOnJ2NbEG0/16C52CardfVKLGxvBTMJcrcXLYe9X+3hg/SE/JxcYfzsHMTIF2AfzsEkmMz8LdiDQ4u1jh+Ze7GCPWcmMmecLTNNBlAcw3bJ5CL9PM4ztPvvzoVfg31xZ1uhOdAE93Z1bf5MFz8vw1LPjke5ze8x2WrvqtzLEd7radVT/vxE8r58PZUVukiJ7yBHSZO2LgRvJEDFmgGPRBgOSJPihTH2IgQPJEDFmgGIQmYMzyZEnQXuyKvsxsSWfXhjifHAl7UwtW7wSQw1ZpjnG+AUKnoEbtb4u8gKzifGzrOwu+NmX/ILdGDRr4R1cvxuLo36FwdbdFwybOvIxWrdYgKDAGxSo1hj7njybNHp444KUDCTbC93qeCsZWMgn4hi3EfHtUnnBHbQqLVKyeSWh4FKa+uRwHt3yBV95eia8XvwZ3F0fEJqRg9LQPcXbvGlZE1lSpYFccc0d8Rr70PjaterfczhUh4pZymyRPpJw9it0QCZA8McSs0pgqIkDyhOaFMRAwZnnC3aZzLOEmZPcvBtZAg+6uTXA6KRyQaeBl6YDBXq1FOQ2OJNxERFYKPmg3AiMatBNljGIK6uCuEISGJKFZc1fYOz+sW1PbGONjMhF9Jw3tOzdA76drd+NSbWMRw/d8r+dJnhiQPOEEyEdfrMeVkDBYW1ng3Tn/Q/dOrfDXwZP48bd9UKvVMDc3w5ypo9C/ZwecuRiCuR+sRt9ubdG1Y0u8//nPUCrLFqU9tuNr2NuVPy4khl+GuoqB5Eldkad+iUDFBEie0MwwFgIkT4wl08Y9TmOWJ9NPrsfVjCh2TIcTKNwxHa4I6/rbJ9ktOv09WqKhDT+7FPieZcEZseyq5FHeHbCwzXC+mze49n5edRqZmQXo0q0BZAoT3sbHXVXMXVns4WmL8VM68dauVBsiecJT5nRZAPMNm6fQqZk6JqDL3KnjEFn3dGxHDFmgGPRBgOSJPihTH2IgQPJEDFmgGIQmYMzyZMyR1YjKSWWINRqwm3RG+XSESqNGZlE+nM3E+weaSflZ2BN9BU3tPbC598tCTxNJt5+bXYgfvzkFExM5evdrhDweb3IMge5qAAAgAElEQVTlju4EnrkLuVyG2W/3hdyEq5pjvA/f63naeVLJXOIbtvFOW8MaOckTw8onjUb6BEieSD+HNALdCJA80Y0TvSVtAsYkT/6ND8GJhFsYXL8Nurk2xoCDy5GpyoMJ5CjWaNDNrRFa2ntKIqElGg1+uX0SMpkMJ4YthJn8Yc1FSQxAj0HeCknCgV0hcHC0RLsOnrzKE24YQYGxKMgvwsRpneHmYaPHkYmvK77X8yRPSJ6Ib5aLPCKSJyJPEIVndARInhhdyo12wCRPjDb1RjVwY5InCy/+gcPx1zG8QTt82PYZBOz5mB3Vmdy0B7uW2FZpAYVMLpn8/xV1CSmFOVjXfQraOXlLJm59B3r071tMcHj7OqJRYyfe5UnE7RSkJubAysYUk2Z0gaWl8Yoskic8zW5dFsB8w+YpdGqmjgnoMnfqOETWPR3bEUMWKAZ9ECB5og/K1IcYCJA8EUMWKAahCRiTPHnr/O84kXSbqwXLappw/2MqM8GLTboLjVmQ9k8nheHGvXhYKU3xbZfn4e/IzxW8ggRbh41u/ukCkhOy0apdPbi6WPEuT4qLSxB8JQ6FBcV4dlwb3m7zqUNkNe6a7/U87TypJBV8w65x1ulDUREgeSKqdFAwRAAkT2gSGAsBkifGkmnjHqcxyZNZZ3/FhZQIbcI5cwIZbJRmGO/bWZKTgKt7Eph2Bwl59/Bq86cwpUlPSY5DyKDVxSVY/flRaCBD154NYW5mwrs84eIPC01BWnI2Bj3bCs393YUckqjb5ns9T/KE5ImoJ7wYgyN5IsasUEzGTIDkiTFn37jGTvLEuPJtrKM1JnnC3a4TlBEFN3NbJBVkwc7UAi5mNujr0Vyy6Q/PTsbRhJvo4tIIC1oPgaXCnBW+pUdLIPpOOv7cfBk2tuZo3c4TZqbCyJM74WlIis9E30F+aNvJeHcAkTzh6TdPlwUw37B5Cp2aqWMCusydOg6RdU/HdsSQBYpBHwRInuiDMvUhBgIkT8SQBYpBaALGJE9eOP4jbmYmIMC5IS6kRHInd9DTww9+ttLdKZClKsC2yPOATHsOqY+HH74IGC/0tJFM+2dP3MG545GoV98ODRs5CSZPYu5mIC46A916+6JzL1/J8OE7UL7X87TzpJIM8Q2b78lA7dUNAZIndcOdeiUCTyJA8oTmhrEQIHliLJk27nEakzwZd3QNIrNTMNK7PaKyUyGXy9HOsYHkJ8Cv4WdQqFZBIwOr57Kt32toaOMs+XHxMYA/N19BdGQa/Fq5w9nZSjB5khCbiajIdLTvUh+9BzTlI3RJtsH3ep7kCckTSf4i1GXQJE/qkj71TQTKEyB5QrPCWAiQPDGWTBv3OI1Jnjx7+BvE5WVgfMPOsFGaG0zib2clISIrCdmqQmSq8tkOlIVthmKUT0eDGWNNBqLRAKuXHUWxugSduvnAVCkXTJ6kJOWAu3XHzd0GbTrVR8s2HjUJWfLfGI08Wbd5HzZuP4RitRpDnuqC9+b8DyYm5a/qSr+XjXeW/IDElAzs2bCkNMHjZy5GaFiUdssYAFtrS5z4a1Xpz3VZAPMNW/KzjwbACOgyd8SAio7tiCELFIM+CJA80Qdl6kMMBEieiCELFIPQBIxFnhSWqNBz31KUQIPnG3WDhYnhXSebXVyAbREXoJFp0MWlMb7t+j+hp4+o209OyMLmnwJhYalEm45eMJHLBJMn6Wl5uH0jiYkrt3q2mDi1k6jZCBUc3+t5Ue48OXfpBt5f/jM2frMQdjZWmPnOVxjyVGdMePapMlxz8wowYeZi9O7aFsfPXS0jT4Y+/w6+WTwbjRt6VpgLXRbAfMMWYlKcvXgdDb094O7iiNc/XI0eAf54blhv3rsKuxOLGfNX4OiOr6vVduehM7F7wxIW36NPaHg0i/fvLcur1V5VL3ccNAP7fl1Wrr9Hv6uM06M8n9SXLnOnqjj18XOSJ/qgTH2IgQDJEzFkgWLQBwGSJ/qgTH3UNQFjkCcHYq9h0aWd7FgL9/9xVxObyk3qGr0g/Yfci8PZ5AhYK0xxdMhCQfqQSqNBgbE4+ncoXD1s4dvEWVB5kptbhMjbqcjNLoCTixVeeKWrVDDxGiff63lRypPFX22Ch6sjpk8axuAdPXOF7ULZ8PU7ZWDm5RcgNT2T/b9FKzeWkSe9R83Fth8+euIiWpcFMN+weZ0J9xt77d1vMP1/w9CmRSOd5ElJiQZy7v8qVfORijxJy8iCg51NpWOsTJ48ypPkSTUnCb1OBOqIAMmTOgJP3eqdAMkTvSOnDuuAgKHLk6T8THx0ZScupUbBVmEBK6UZhtVvUwek9dfljruByCjMw5a+M9HE1k1/HYuspwM7QxB6PQmNm7nAxdVaUHnCDb2wsBiXz8fAzt4cU2d3ZzSKitQwNTVMUVdRuvlez4tSnkx9aznGP9MPA3ppz8XdiU7AlDc+x7E/K971cDn4djl50u7p6ejVuTW4nzk62OLNGWPRu+vDfzEZgjz5act+rF7/F9ycHTBv5jjsP3wOjX08ceZiCKLiktDevym+/vg1dtyp0+BXMON/w/Dz7wdw5I+vkJyagY9WrGfiydLCHO/OmYR2rZpApSpmu36CQsKhLilB+1ZNsHjBS4iJT8arC7/G6CG98OeBE1Cr1Vg8/yW20yUzKxcff7kRN8OimLQY2r8rXn3xGZa7R3eecEextu46AjtbKwzsE4C/Dp4st/Ok35g3mCRr4OmGg0fO450lP+Lc/rWwMDfFhu1/Iz4xFXOnPYdPvt6Eq9cjoFSYYNKo/hj3TD/W36M7Tx70x+V/zPA++PG3vTi8bSWTTBVxWr/tYBmeD+bf47+IuswdMfy7mnaeiCELFIM+CJA80Qdl6kMMBEieiCELFIPQBAxdnvwTG4L3Lu+ADDL0dW+GRrauQiOt8/ZPJN7CrcwkvNN2GEZ7d6jzeOoqgHVfn0ROdhHaBtSHublCcHmiUpXg4tm7sLRUovfTfuyWn6dHNIdnA4e6QqD3fo1Cnkya9Slefn44enXRyg5uwfzsS+/jwoHvKwT+uDzhdld8sPxn9O/VAT0CWuPUhWtY8Mn32LPxM7ajhVk3lbrK5Jkqy1u5y5cvIysrq8pv+X6hffv2sLW1LdfsiMnv4ZMFL5XuPElKzcC6L+ZBoTDBM5Pfw0dvvYhuHVuh2/BZGDmkJ+a9Mg4ymQyjp32I8c/2w5hhfRAcegez3/sG/25dgSOnr2D73qP4acV8cEWNVn6/DU/17AAbawuMmbEIi+dPwYinu2P73mPY/fcpbP7ufXy8cgN7d9G8ycjJzce4Vz7GO69NRM/OrUvlCXfEisvrvk2fwcnBlkmRqzciysmTd5b+iG4dW7I+Pv36V4SERuLNl8choF0zzPlgFZ4d2AMXgkKRkZmNZe/OYOJmzMuL8O2nc9CscYNSecL19/xrS7Bn41J29Gv2+6twNyaR9cfJkydxepTnk3Koy9zhO/81aY+TZhqNBtzvAz1EwJAJKBRyqNUaNt+rfqq/867qNukNIqAfAkqFHKriEv10ZnC96PLvB4MbtGQHxP3hmKq46v9Wl+IAZ5/cin9ibsDP3hXd3RvDzEQhxWFUK+aQ9Hj8FxuK0b7tsaL7c9X61lBevpeRjxWfHYNCKUfP3vevDpbJwMlCIf9b/eTxSHAShSt+wgm7ydM7oXET47n5qKL1fG3mlCh3nkyb9wVGDe7F6pxwz62IGLy8YGW1dp48DuWlNz7HqCG9MGyA9rxXSmZhldy4P818/OnatSvOnTtX5bd8v3D27Fl06dKlXLOPy5O2rRpj8thB7D1ONvTt1g4jB/dEtxGzsHbZm0yyJCSlYfiLC3HhwA+lx1vGvrwI82eOZ7tU3vp4DRa9NQVdOrSAmam2eBV3bGfiq58i8KBWYHE5mbXwKxze/iW43SKrPp2DVn4N2c+++vEPFBapmEB5sPPk+JkgnDwfjNVL57J3Tl0IZnLk8Zon3G4UTuZ8+MYLTNZwO5C43TGcTOOOYnHyhRM/Kz6cidYtGrG2vli7FVYW5nh18rOl8uTE2as4FRiMVZ/MYe8c+O88Vv38Z6k8eRInXeSJLnOH7/zXpD1bSwUKi0pQSP+hXRN89I2ECDhYKZFdUIxitS6LI13ekdDgKVSjIuBsa460rAKu/h891SZA4rTayOroA24x6WBjhrSsqv9bvY5CrHG3+6Kv4oOLO6GBBiMatIOHpV2N25LSh/eK8rA14gJ8bJyw+2ntWsDYnpvBCdj3Zwgcna3g11J7dIkrGMtJ8YIi4URh1r0ChFyNZ/1x10aPnNCWHRsylqei9Xxtxq6TPOF2GYwd3qdcP9yf7m/bcwQvjR9SmxjKfbvkm19hb2uNWVNGsp9xC98/9x/Hz18uqLCfx3ee5OUX4nZkDNq2bFz6/gtzlmLSqAEY2EdbaViXoxcVbfOZNWsWbty4wet4dWnsu+++Q4sWLcq9+rg8ebRg7KO1PTh58vuaD+Ht5YaQW3dYoV23R4q45hcU4sM3XmR8Dh0LxO+7/mPHcAb1DcA7r01CbEJymYKxj9ZAadt/KivS6uWh/UX8ZesB3AqPwefvv1wqT/YcOs12fixdOJ29wwmS+YvXlpMncYmpmPP+Kqz/+h3WHydJPv16E4uBu1Vp6/cfMUFiY20JExPtziDuqBEX58LZk0rlCddfTHwK25XDPVdCwrBw6bpSefIkTrrIE13mji45FfodOrYjNGFqXywE6NiOWDJBcQhNgI7tCE2Y2hcDAUM+trPtznl8e/0wq3My2MsfliamYkCulxg2hJ2CSqPG4UFvw87UQi99iqmTo3/fQlBgDLwbOcPDU3uaQMjbdh4de1pKLmKjM5CfW4Thz7VG4+aGf1Tswfj1emyHW5SqiovRa+ScMtf8PggmIioBU17/DBf//pHXucnJEO6YzaZV78LKygIz5q3A2BF9MXpoL5y/cpMdw+COaDx4HpcnWTl5eGrMG/jq49dYTQ5ulwO3UOcW+NyREe7RZQHMN2xeId1vrDryZOvaD1ktkcSUdHak5/z+tZWGxHF8a9EadA9ohe6dWj1RnnA7T7ibjfyba7egrfx+O6uJsmDWhFJ5cuxMEE5fCMa3S7S2mfv7Zau3VHjbzqCJCzBn6mhcuxnBdq8Mf2EhJo8bzOquvD79OXA//3rxa2XmwIOBPKh5cuTUFQQG3WRzgHu4+inf/PRw5wnJEyFmI7VJBOqGAMmTuuFOveqfAMkT/TOnHvVPwJDlycSjaxGenYR2jt7o4Oyjf7h12OPfscGIyUvHioAJ6O3uV4eR1E3Xv627gOTEbLRuXw9W1trTDfqSJ1xfYaEpSEvOxqBnW6G5v3vdQKiDXvlez1e684TbfbDs2y0oVj95KxFXT2Pdinm8o9j4xyH8tHkfO+/47KAeeHvWBFargztS0qShF155YQQOn7yEeYvXgiu4wb2nVCrQsL47/vrlU3ZEhDvOkZSSznZEsIV8u+alcRqKPOGOsLw+fQx6dvYvd9vO4ztPHsgTDsJz0z/ClPGDMfSpLki/l43Pvv0NH8+bgp0HTiIzK4cdgeGeD5b/gkbe9dCjs/8T5Ql3O1KJuoTVPMnMzsW4lz9mf92lfYtSeXIvMwdTXl/GapA42tuyPHLXFVd0VfH7n/+MiKh4dvyI2wnDHT9KS8/C7JdGsaNEn3/3Owq4nTJvvohidQm+/GE7hvXvipZ+PqU7T1LSMjHz7S/ZMR9OwL3y9krEJaRWufPkUZ5PmtS6zB3efyFq0CDtPKkBNPpEkgRInkgybRR0DQiQPKkBNPpEcgQMWZ70O7gMcsjQ3tkHjW2M50//uUl4OS0Kl9Lu4oXGPTC7RX/JzcuaBFxYUIzd269CLgdi795j5RI6dX8ozfQpTyJupyAlKQcDhjVHq7b1sG/HNSTGZ6Nbn0Zo0dpwZYpe5Qk3SfILitB9xCxsWfNBuTljbmbKdjLU5OrbmkxAPr/RZQHMN2w+43/Q1pqNu7Fh20HMnTYagUG32E6b54b1Zj+uTJ5wR2gWrdyAxOR0JqUmjx3IbqzhCrG+t+wn3I6IgUwuh3+zhvj07angjtNwx2iO7tDeePTosR1OmHy88uFtO1wR2snjtHVXHr1t59tfdmLHvuOwtrLAuBF9sWnHP+z2m8efPf+cZkdsuNuVXJzs2TGgVT/vZDtluBos3HExrl4KdxSHE3t9urbF269NZDfvPHrbzhdrtmL/f+fg4eaEEU93w6Y/DuHgZm3B2CdxepQnd8yrokeXuSNErqvbJsmT6hKj96VKgOSJVDNHcVeXAMmT6hKj96VIwFDlyb6Yq/j4yi5WuHNKk55QyORSTE+NY47Ly8CB2Gto6+iNdT2m1LgdqXzIiZPrQfE4cTistE4Vd2Vwc3+P0iHoU57cjUhDUnwWeg1oAndPO2xff5HF1am7N3r005a6+HfvDZgo5Kwui4eXPdw8bKSC+4lx8r2e16nmSVGRCqamSrZQTUrJgKe79Cv06rIA5hu25GefxAbAVa5+IPYuXAllNwdt++GjWo9Cl7lT6054aIDkCQ8QqQlJECB5Iok0UZA8ECB5wgNEakL0BAxVniy6sgsHYoLYDZVTmhqfPCnWlICre2Iik+PksPcMXh5duRCD4//cZvmW3a9X7dnAHl7eD68J1qc8iYnKQHz0PXTu2RB3wlKRlJjN/l3QpoMn+g1uhuDLcTh8IJRze2DhyoAJL3WCW73yt72K/l8ijwTI93peJ3mSnZOHpas2Y/9/Z6FWl+D6sQ3sqMf8T9Zi+fuvlNYRkRJIXRbAfMOWEh+px8rNz4ET5uP3NR+gcUNPcEeBLC3M8e6cSbUemi5zp9ad8NAAyRMeIFITkiBA8kQSaaIgeSBA8oQHiNSE6AkYqjyZcWoDbtyLg7e1I3q7NxN9HoQIcOfdi0grzMMvvabB38FTiC5E0+bOzZcRfSeDXRDMjIRGg+at64HbffLg0ac8SYjLRHRkOmxszZGVVVAaQ7NWbugz0A/rvzuNwnw1Ez3cjhQu6iGjWqFpC+3NQFJ9+F7P6yRPuIVnSto9Vgdj4qufMHnC3Wiz+KuNKCgoYsU7pfbosgDmG7bUGEk9Xu6WqHWb97F6LC2aeuOTBVNhb2dd62HpMndq3QkPDZA84QEiNSEJAiRPJJEmCpIHAiRPeIBITYiegKHKkyGHViKlIAvjfLvAVvlwAS36hPAY4Kmk27iZmYA3Wg7ExEZdeWxZXE0VFamx5vNj0NzfcWJjZ87KJDRrWbbchT7lCVesNjIsBTLI2G4Y7mhOemouGjV1hoWlEiFBCbCxNYOJ0oStnbIzC9C1TyN07iHtwsZ8r+d1kie9R83FrvWfwsHOBi37TGbyhHu421gGjp+Hs/vWiGvG6hCNLgtgvmHrEBa9IgECuswdMQyD5IkYskAx6IMAyRN9UKY+xECA5IkYskAxCE3AEOVJYUkxeuz7lC1cpzXtJTRC0bZ/OysJxxNC0d+zBT7rOFa0cdY2sNs3krF/ZzBs7SwqLcaqT3nCXVccFpoMmUYDMwslGjZxwc3geLi62SA5KYeVOmjT0QtmZgqkJOcgIjQZzVp5YPDIlrXFUaff872e10medBg4A6d2r4aFuWkZecLdoNJ/3Ju8X1WsD8K6LID5hq2PcVEfwhPQZe4IH0XVPZA8qZoRvWEYBEieGEYeaRRVEyB5UjUjekP6BAxRnoRlJWHCsbVwUFpiTMNO0k9SDUeQWZSP7XcuwM7MEocHLahhK+L/7NDuG7gRnABvXyd4eD65Zog+5cm99HyEXk9iR4j8WrhBaWqCkKD4B4eKUN/HAZ717RncnOxC9jNHJ0uYmilga2+O3gOawtpGe8WylB6+1/M6yZOXF6xk19W+MWMM2g6YxnaeJCSlYemq39hVsWuXvSElhixWXRbAfMOWHCQKuEICuswdMaAjeSKGLFAM+iBA8kQflKkPMRAgeSKGLFAMQhMwRHlyNiUcc85sRn0rBwzy8hcaoajb3xR+BkUlKuzu/zo8LLWLdUN6uCMxP3x5Avn5KrTtVB/m5oonDk+f8qS4uAQpSdkoyFehYWNnFt+1i7GsvgkXY5uO9UsL26rVGgSeieIO+NyvfgL0GdiEjUdqD9/reZ3kSWxCCt5c9B27vlZVrGZXzebk5sO/uS++/OhV1JPg7Tu6LID5hi21yUbxVkxAl7kjBnYkT8SQBYpBHwRInuiDMvUhBgIkT8SQBYpBaAKGJk/SCnOx4toB/JdwAz7WTuhfT9rHIGqb/0NxIYjJTcMn7UdjoAGKpPiYTGzfeBHmFkp2DKayR5/y5PE4uLosl89HMznSso0Hq3fy6HPpXBRURSVQKOXgxEuHzg3YNcdSe/hez+skTx5ACg69g+i4JMhlMjTwdENLP+kWkNFlAcw3bKlNNoqX5AnNASIgBQIkT6SQJYqRDwIkT/igSG2InYChyZPbmYmYdOwHdn1JExtX9PEwzpt2Hsy7oPRoXEy9gzENAzDff4jYp2O14zt1NByBp+7Co749vBs6ilaecIGpikugKiyGpZVpuTivByXAysYUXLHbsBtJaNzcFcOfa11tHnX9Ad/r+SrlSbFajTc++g6fvj0VdjZWdT1+3vonecIbSqNrSJe5IwYotPNEDFmgGPRBgOSJPihTH2IgQPJEDFmgGIQmYIjy5H/Hv2cHIJrbeaCHW1OhEYq6/fi8ezgQexV+9vXwa68Zoo61JsH9+sN5VnCV281ha1f5rUp1ufOkqrHl56nYLTy5OYUIvhIPFzcb/G96QFWfie7nepcnHIHhLyzEB2+8iIB2hmNKdVkA8w1bdLOJAqoRAV3mTo0a5vkjkic8A6XmREuA5IloU0OB8UyA5AnPQKk5URIwFHmSUZSHyOwUpBdm472LO+BsZoPeHs3gYGopSu76CqpYU4L1t0+yq3unN+uDri6N0crBU1/dC9pPbnYh1n1zCnITOTp282bFWCt7xCxPHsTNHdm5eDYKChM5Zi/sKyg/IRrnez1f5c4TbhDrtx7Ejv3H0a5VE9Sv5wpTZdnCN1PGDxZirIK2KZUFsKAQqHGDJkDyxKDTS4N7hADJE5oOxkKA5ImxZNq4x2ko8uRAzDUsurwTGpmMu+AETmaWGOXT0biTe3/0f0VdQmpBLitI+oxPB7zXZrhBcAkKjMXRv2/B2dUajZu5VDkmKcgTbhCBZ+5CXazBzHm9WC0XKT11Ik9GT/sQSoUJSkvwPkZs69oPpcSQxUryRHIpo4CrSYDkSTWB0euSJUDyRLKpo8CrSYDkSTWB0euiI3Ai8RbuZKfixSbdnxibociTbZEXsCLkAGQa7Z0lTuZWGOXdQXQ5qYuAziSH4XpGPOu6ka0btvadWRdh8N7nX1uCcDciDU2au8LJpepyF1KRJ9cuxyE/pxATpwXA1ePJVy/zDpSHButEnvAQt+iaIHkiupRQQDwTIHnCM1BqTrQESJ6INjUUGM8ESJ7wDJSa0xuBuzlpWHJlD65mRKO5fT1s7DXd4OXJ+rCTWHvzP5jKleyyDS9LB6MvFvsg6eFZyTiaeJPtyOGO7xwdshBWivJFS/U2QavZ0cWz0YgMS0GbDp7wa+nOvi4uVmP1smPQQIZO3bxhYlLVoR1AKvLk1o0k3EvLw9DR/kwMSempE3my55/TUJhUfEe1XC6Dq7MDmjfxhoW5dCY9yRMpTXuKtSYESJ7UhBp9I0UCJE+kmDWKuSYESJ7UhBp9IwYCh+Ou491LO1goMshwdOg7sDSpeN1gKDtPVl3/F7+Gn0aAiy/aONYXQxpEE0NOcSGOJ4YirSAXRepirO72AgJcGoomvqoC2bj2HNJTc1hNE1NzBV6d3wcxd9Kx47crsLIxg3+7elU1wX4uFXkSFZmOhLh76NW/CTp08dZpbGJ5qU7kyYjJ7yEhKQ15+QVwsLMBJ0zSMrJgZWkOW2tLpN3LZjfxrF46F638pDHxSZ6IZUpTHEIRIHkiFFlqV2wESJ6ILSMUj1AESJ4IRZbaFZrAL7dP4vvQI6XdfBEwHr3d/Srs1lDkybKr+7Dj7kX0dGuK5vYeQiOWZPvnksMRnBGHac364GW/PpIYQ0F+MdauPMaOYz2oCNvIz4XVOTl/8g7c6tnCp5GTTmORijxJjM9ix5Hs7MwxcmI7ODhJp+hxncgTbufJ0dNXsODVCfBw006GhOR0fPnDNgx5qgt6dm6NtRt34/zlm/ht9Xs6TZa6fonkSV1ngPoXmgDJE6EJU/tiIUDyRCyZoDiEJkDyRGjC1L5QBD69sht7YoLgYm6DlIJsjPUJwLzWFV84UR15kltchLPJYchXq9DWsT7qW+m2aBVqnI+2+/6lP3EoLhhPebSAr03VxUP1EZPY+ojMTsWR+Ovo6OKLNd1eEFt4FcYTFpqM/X8Eg7kTrp6NTAPP+vaQy4GYuxlo2sIdjs66yQWpyJNitQa3ghOQnV0AW1sLTJ3z5JpFYktinciTvs+9jt0blrJdJo8+2Tl5GPvyxzi4+XPk5hWgz+i5CDz4g9iYVRgPyRNJpImCrAUBkie1gEefSooAyRNJpYuCrQUBkie1gEef1hmBXVGXsSnsNGLz0jGzeT+svXkEPtZO2N7vtQpjqo48CcmIw0sn17HDQM3tPLCx94w6G+fjHc+/sBXHEkMx0LMVGohI6ogGEIACdTF+jTgNM7kSJ4a+y2rDiP3hbtMJCoyBh5c924HB1QNRF5ewTShcYeAOXb2hVMh1GoZU5MmDwZw7cYfdH/P6+0/pND4xvFQn8qTbiFnY9M27aNyw7B3c0XFJGDNjEc7vX4uwO7GY+uZynPhrlRg4VRkDyZMqEdELEidA8kTiCaTwdSZA8kRnVPSixAmQPJF4Ao0w/J77lqKwRFU68p39Z2Pi0e9RoFbh70Hz4Gha/kaS6siTy6lReOXMBkCjYQvXI0PegY3SXBSk3zi/BacTb+NpLzJsf+QAACAASURBVJInlSVk253zyFYVYEOvGWhhr1utkLpM8K8/nENqci5ad/CEpZUpkycZqbksJHNLU7Tt6KVzeFKTJxfPRqFYpcbMeb0lc2VxnciTxV9twtHTl/HMwB7w9HBmhZ7ik1Kx59BptG3VBJ8smIrBkxZgSL/OWDBrgs4Tpi5fJHlSl/Spb30QIHmiD8rUhxgIkDwRQxYoBn0QIHmiD8rUR1ZRPmxNLXgBEbDn49J2uF0F54Z/iHkXtoK7snh4/bboV68Furs1KdNXdeTJhZRIzDq7SftH/jIZlnYYjQGerXiJvbaNzDn3G84mhWOQlz/qWznWtjmD/Z7bnROelYTXWw7CxEZdRDnO7RsvITenCN37+eLAn8EwUZigY1dt4VRWDyQ8jdU/cXW3gW8TZ53HIDV5wu24KcxX4cVZ3eDgqNvRJJ1hCPRincgTlaoYG7b/jWNngpCYnA7O7To72qFrh5Z4+fnhsLQwx9bdRzBmWB+YmOi2TUkgPjo3S/JEZ1T0okQJkDyRaOIo7GoTIHlSbWT0gUQJkDyRaOIkEnZqYQ5+vnUce6OD8M/g+U+8DUfX4eSpi9Bn/2fsdVulOZZ0HIPOLr7YGnkeX4b8zf55E1t3bO7zco3lyamk23jj/O9s5wknT0Y0aIcP2o7QNURB35t19ldwcmeIlz88LR0E7UvKjYdmJuBk0m1WG2ZZp7GiGkpRoRqXL0Tj3PFIFhcnR5ISs9lxHb8WbuyfZWUW4Oa1BHZFcSM/Z7i4Wus8BqnJk5CgeORkF6LfID+0qcYOG52BCPBincgTAcZR502SPKnzFFAAAhMgeSIwYGpeNARInogmFRSIwARInggM2Mibf/3cZpxJDoeFiSnWdn+x1kco0gpzMPjQSjiaWWFo/TaY3LgnLqffhYeFA/53/PtS2u2dfPBllwmlsqY6O0+OJ97C/PO/w1ppzo5+OJvb4ODAt0SRyRmn1uNKWjSGNmiDehZ2oohJjEFkFOVhx51A2JlZ4vCgBaIKMeJWCvb8ca00pgd1Tbx9neDhacv+eUmJBhdO32W7n9p1rg8zM4XOY5CaPElPzcPtm8mQaTQYOakdvH3Fv6OqTuQJNyl2HzqFXX+fQlxiKg5vW4mCwiJs3H4IUycOgcLEROdJIpYXSZ6IJRMUh1AESJ4IRZbaFRsBkidiywjFIxQBkidCkaV2OQIvHP8R3C4A7hnTsBPm+w+pFZiI7GRMOLoWjWxc8XvfmYjISsZ/8dfZFcVzz29BeqG2TgT3jPbpiAGeLcGJlOrIk8Px17Ew8A92m01C/j12686OfrPhbV23t+5E56Zh9H/fsrFN9O0KK4VprVga+sebIs6gSF2MP5+aLaojTsf/uY0r52Og4aqkss1NGrbJyb99PVhZm9U6LVKTJ9yAuSuLE+Oz0alrffR4quyRu1oDEaCBOpEnP23Zj627/sO4Z/rh63U7cP3YBqSmZ2LG/BXo3skfb70iri1WunAneaILJXpHygRInkg5exR7dQiQPKkOLXpXygRInkg5e+KNPSY3vXSh/yBKJzPrWu/guJIWhZdPb0BHZx+s6fYigtKjcCE5Ep5WjlDI5dgffRXnUiJKwXR1aYxvuk5i8uSv+EAkZ+dhfMPOlRaAPRgbjA8v70QTGxdW9iQiKwVvtR4ML0sHHI2/gbSiXLzecqBgMmVj2Glsv3Mew+q3ZTcJPXhWBB/EtsgLaGLnij7uzcSbfJFE9k9cCKJy0rCo/bMYWr+tSKICfvvxPFKScrTx3N92YmIiQ8fuPuxva/tIUZ5wNV6iwtPQppMX+g7yqy0Cwb+vE3kyaOICfLd0Lhr5eKJln8lMnnBPTHwy/vfaEhzf+Y3gA+e7A5InfBOl9sRGgOSJ2DJC8QhFgOSJUGSpXbERIHkitoxIJx7u5peQ9FhMa9YH5iYK+Nl5oJmdBxvA7axE/O/YD6WD4W7ASS/KxeY+r6CJrbauQ3Uf7vjPdzcOIywrCf3rtcTSjs+xIrGh9+JZU5Mad0NqQQ5+jzyHnXcvsn9mbqLEwYHzEJGViNfO/oqC4mIsDxiHPh5Plg97YoLw6ZVdaGrrDg9LBxxLvIkebk3hbGaDXdGX2eWx9kpL/NRzqiAC5a3zv+NE0i00sHKGi4UVSkpkWNF5PL4MOYgDMddY7I1tXKuLz+jev5oegwupdzDSuwPebTOszsd/7uQdaNQlOHfyLuQmMjRr5Y6oyDTk5RTB3tESfi1r9nvx+MCkKE+SE7Nx53YqWrarhwHDmtd5rqoKoE7kSbunpyPw4PfseM6j8oQ7utN1+Cxc+Ye7X11aD8kTaeWLoq0+AZIn1WdGX0iTAMkTaeaNoq4+AZIn1WdGXwBqTQm67v2kDIpnH1mkXk67i1dObyz9+diGAdh+5wJmNOuLaU171QjhSyd/QkhGHJManweMZQVT98dcRVxuOmuvi2tj2Cgt0NDGGZ32LGJ/rM/9ST73/u3MBHZMQqYBuFjmtR78xBj+vHsRn1/bh2Z29dDB2Qe/RZxhNVu6uzbF4YQQmMmVKFSr0M7JB6MbdsB3N46gub0HPuepMCl31OkmJ4RkpRsT8GXniTgcFwJuVwy366SxLcmTqiZRUn4W9kQHsfmwvd+sql7n/eecFNnw3RmYWSpRkFcEVZEaGra1RAZbe3O08PdAsVqDm1fj4eRqjXpe/NSwkaI8SU3OQcStVPi1dMXgkeK42aqyCVEn8mT0tA8xfdIwDOobUCpPNBoN1m3eh3+OX8SOdQ+vIeN9NgvUIMkTgcBSs6IhQPJENKmgQAQmQPJEYMDUvGgIkDwRTSokFUhUTirGHPmuTMyuFrbYN+AN9s+4HSHc9cHcw4mHLwLGsZ0fTe088FvvGTUa64h/v0Zifib+HjQP3E6WArUKu6IugbsGmXtczG1Rz9Ie7pb2+ODSDtzMTCw9BsHd6vng4ZTKyWHvMglS0fM7u7nnIFrZe6Kra2PsvHsJXKFarvhoZlEenqrXAsfiQ1GiKUFbJ292dMhKYYajQxaWay5LVQAzEwXM5OULfuYWFyE+PwNNbB7uOFh3+zi2hJ9FjqqALbLtTc2RWZSPsb6d2TiZPPHwq/HunRqBl+hHJdBg/e2TKNFocGzouzrXiLl2KQ7xsRlo2doT9RvW/EajG1fj8c/em+xGWWZNHjmT4+XtAK8G9oysukSDYpW6WkVhK0uJFOWJtmhsEhr7uWD4mNain3F1Ik/OXryOOR98i5Z+PggMCkW/7u1wOzIW6feysHrp6+jcTvxbdh7PLMkT0c91CrCWBEie1BIgfS4ZAiRPJJMqCrSWBEie1BKgkX5+OjkMb5zbwmqPXM+IR766iJHo5toYs1sMwO2sJHx0eScG1GuFhW2HwcJEif4HPwcnDA4MfAvOZrpfvfoAMbfThdvxcmbY+1DITXAp9S4upd4pkwE3SzvI7hfiTM7Pxg+3jmrLSmiARnbOyCjIQ3pRHub7D2Y7UCp6NoWfweob/6KNoxc6Ofuyq4GvZsQyAcIVae3l7oeQjFhwxWu5FTHXNrel5ZceU+HvWL9Mkwdir2HZ1X3o6ebHdqk0tnGDrakFe6fHviUoLCnGmm4voJNzQ6hK1Oi+71NWRJRbcE9t2ovVV9l19xLbZVPf2pHVc+nr0ZyO7ej4e8ddkc0JN+766p96vaTTVdmb151HSmIOOnX3hoOzFRRKOXwaOcPUtHqXmezfGYyw68n3d5twlw5r7Qk3X1q2rQcb29oXh60IgxTlyb30fIReT4SPrxNGThRPfZonTbM6kSdcMFyB2D3/nEZ0bDJkchm8Pd0wYmB3ONrb6PgrIa7XSJ6IKx8UDf8ESJ7wz5RaFCcBkifizAtFxT8Bkif8MzWGFrdEnMPX1w+xG21GeLfHjzePghMq3MPJE6WJCb4M/hvP+XTCgtbaG3bev/QnuCKe3I073M07VT05qkKcSQ6DWqNBd9fG6P/3ciYeuKtnI7NScCr5NgqKtdKmosfVwg5Lg/ZCaaJAIxtndKvni9jMTByKD2H1S/Y+/QYUMnnpp0uC9mJX9CW2yOV2wwe4+KKNY31kqfKxK+oyCtXFbPfApEbdkFGYA06MsHfBLYyBKU17YWazhwVeuYa/vfEvOBnDPZYmpuzdE0PfRVqB9spl7nnJrxdeadYXOcWF6HtgGftn5nIFnm/cjf31b+FnkF+iYlcnK2CC/p4t4GBqWRU++jmAwJQ7CMqIhmO+Fd7v/gx6ejWtlEt+ngrfrzzB8ty2oxeuXoxl7w8Z1QpNW+hek4QTJGtXHENhgRr3XZ62X+52HbkMAVxxWD6qw1YwGinKk6zMAty4lsB244x5oYPo526dyZMnkSksUsHMtOKtdGKmSfJEzNmh2PggQPKED4rUhhQIkDyRQpYoRj4IkDzhg6LxtbH82gHsuBvIbp2Z2KgLK2S66MpfDMSDXSWphTl4v90zGHH/ppN/40Lw3qU/mZRY3fX5SqFdSovCl8EHWXFY7tnQazomn1jHrg/e2vdV7I6+jKS8zCrB56qLoNGUwFSugKOVBXLyVdgZdYldafxhu2cxrH4b1scX17i+EpFTrD0uw20P6Onuh2Z27qyP6Nx0HIoLhkJmgilNerBDQFsiziKvWKVdEXMa5f4Wl78GzGU7RbiHK6p7KilMu93g/ip6VddJsFVaYPJJbX1HO6UF1veahh13LuF3rr6KwhReVg7off9GnWMJoQjP1nLgtjFw4snu/u6VqgBouPAKABnnWqq3caKqpiXx8+icNASG3cXgCG43gwyvv/9UpXFfv5qAf/beYO/cvwiH5a5DF2/0GqD7FbrxMZnYtjEQFhamaNPRi7XHHc/JzS6E3EQOa2vhrpmWojzJyS5ESFA83D1sMGFqxTvCxDTh9CpPOJO7dfcRHD5xCariYvTr0R7Pj34aJiZa8xt8MxILP1uHfZs+ExMjnWIheaITJnpJwgRInkg4eRR6tQiQPKkWLnpZwgRInkg4eXUY+uyzv+F8SgS+DBiPHu5+SCnIxtB/viwTka3SHDv7zwX3v9zD7azgju5wz+HBb8Na8eRjC9obb3aXtjfWJwDb715AgEtDrAiYgE1hp3QevaXCDPnqQliZK5k8Cc9OxtH4UHbEiLvGmNud8OrZjVoHcn/VbG9qie6uTVgNFe4p1pQgU5XPxqKUaS3E5bQoXEq7CxczGzZ+7U4CGV5u1pfdwpOnVuHnW8cRn5uBBtZOrF4Kt4tlgm9XtHTwZCKJW5iz4xycr2GtajC8QVu4mz8sHhqRlYwjiTdLl/PjGnYuZVoVBFWEBqrLJZA7y2HeV7vVoSQdKMnQQO4mgzoGUIWroXCTQdFIDrlTVS1K6+fcsajb55LhG8ftGpHh1QW9S2uLFBaq8cu3J2FlbYZxUwJgZmaCfTuCER6arNVh7PiUVqI0bu6KYc/56zz4M8cjceHkHbh52sHH11Hn7/h4UYrypLi4BBfPREGpNMFr7/ThA4OgbehVnmzY9jdW/fwnRg7uCRMTE+w+dApjh/fFnGmjsWbDLvz8+34MH9ANS96ZJuighWic5IkQVKlNMREgeSKmbFAsQhIgeSIkXWpbTARInogpG9KJZeR/qxCXm8FuMfGxdmaBb444g+13ApGQd4/9PScfvuoyscyguKKxXA2RT9uPxtNeT75VY+3N/7D+EUHCHVPJ4Iq1erTEwjZDsTXyXLVgcWLjgTzhFtSbwk/DVKbA8WHvsnjmnt3MVsvcrpkGVk7wtXWt8mgM105ecRF7j7v1Jz6fG7cMdkquyCu33UMboglkeKlpT1Z7Y29MELytnDC8QXt8d/NfVrS2gDsOBMBKoWQiqoFV2cV2UYka/8ZfR1pBNitMO6R+G1ZDRpen8GwJimM1kNvJYPG09g+q8/aVQFMAyG0ATTGgyX9Yj0PZWgYFd9u0QgaZtjSL5J+8f0qgydLKkOdnd4GTvRUbU2hIIv7edZ0Jklfe6AkLK1Os+eIYigrVMFHIwS3oH+w/qe9tj+ee1/04ye8/X0BiQja7jtjeQb8gpShPuHxcPh/NbiR6cWZXODiJ+1iaXuXJkP+9zW7Z4eQJ91y8eguvLvwKXh4uyM7Nx8fzJqNbR/FfUVTRv0lInkj+3680gCoIkDyhKWIsBEieGEumaZwkT2gOVJcAV7SVK2zK3WJydvgHMHmkbgjX1mdX9+KvqMvo7tYEX3UuK0/+uBOIL4IPYIBnKyzpMPqJXb94Yh0yCnPR3L4ejiZwuy60z9OerVhNld1Rl6oV9qPyhPuQq2GSWpCNhrYuuJOVwtpys7BDb49mOu/qeDQArkBueGYy4vK01yZrj+ho9y24mFuBu8aZezaGnUaRRo1uLo1xJiUcnZx9cCUtiu1ssVGYY7xv52qNq7KXVaEaqG5qALX2LbMeciZECv7l7qF54HY00Gi0R464f6ZwA0ruASUFgHkPOUw4kSLhR5MH5O8v0V52owGaDXRD39bNcCUwBpG3U5CUkMVIvPJWL6Sn5uKPDZegNDOBhaUSmfe4W5y0BszZxQrPv9ylQhLpKbnsOmIrK+1RHE6+rPniKFfchBWdlQtV3OQJeZGqPAm9noTM9Dx4+zrimfFtIZcLVBSGh/msV3nS5qmp2LvpMzTw1N5PXlKiQfunp2HU0N6Y98o4WFoIU3mYB05VNkHypEpE9ILECZA8kXgCKXydCZA80RkVvShxAiRPJJ7AOgifqyPx3JHV8LC0x+7+c8tFcDYlAvujg9g1v0Prtynzc64OypBDK9mtNdzRncfFy4OXxx39DneyU7Gt7yy8cW7z/V0dwGAvf0xu0hNc/ZTqPI/Lk8DUSFxNi31Y2EIGNLR2ZtcQ1+Y5FBuMmNx0Jk+01yPL4PtIu0fibyIyRytruJ8P9PRHTE4abmbGs+uXR/l0rE33pd9qsoH8QyUP/A3rjTsepGwlQ1EwV3/lkZoepfsrtFfq3j99BNPOcijKXh7ES2z6bKQ4HCi6UgK1SQnkJXLIzABnWyukpuSW5ocb74DhLXAvPRcXTkfBzcMGRUVqZKTlwdrGDFw9Du5/p8/tUWHoa5YfZe9PeCkAbvVsS3e02DlaollL3YvM8sVFqvIkL7cI16/Go0StQe+nm6JdgHgnn17lScs+k/HfH1/C3eXhlrSOg2Zg58+foIGn/icYXxOVa4fkCZ80qS0xEiB5IsasUExCECB5IgRVavP/7F0FWFxX2n7vHcHdCQRIAsTd3b1p06RN09TdXba7/bfbbrvbbm1T123qTTXSSONO3ElIIBDcHcbn3v855zLABJsZhhF6vufp02TmyHfecyHMy/e9rysiwMgTV7wV185pX9FFPHH4e4wK64X3OxB+be0kN+/5BBeqCqloLBGPvTIIwXLttncoOUPGELeZ7zMPIszTj5ILfgpPVGnrrQLpSvIkX1UpueVQXQtSeSEiwS8MM6I6R56cqyrAgeL0BntaKcU+fuHUXpgEqVDZXZTWYFsr0vMQEVpCuIR4+phpnVh1wCsG6zNE6E6I4AOlthxRRyVtwfkAYj2otomxTBL2kPXgAZ0IYyURdzGRKhxk0SI8xze5EXUmH2fN1ewWYCwVoY3QQ1msAN8kLtN0R6KIQcN7oCC3ChVlKiQNiISqTovqag0iIv2oBgov4/DIc01OSrU1GhBxWZ3GgGOHsim2N941GuGRfrQV6PzZIsT3DkFktL/Dj+6u5AkBqqSoFpnpZUjuH0Edjlw1GHlip5th5ImdgGTLuCwCjDxx2athidkZAUae2BlQtpzLIsDIE5e9GpdN7PvMQ3j77GZcGzcCfxmy0Oo8P7uwG59c2EVdY4ht8ZVRoqnFwi1vIdzLH7/PepxqkhCtFBKkraU9odm2krmSPCGtR6vS94E2sDQY4fRuRnJYfaiGCfUGHb7LTJH+RgtPOCT6hWNqVF/6ksaox9eXDjTaId+eNMnMLtnWfZvPMxaK0J0UIdQDsmiOqN1SYoQjzjskLRFQ9OUgVJCWHUAWwgENHIk+XQRHdFAIJl6A90LHkyckf1HPQRYOcJLWcLuh3ixAqAOUgzkokppaPYjTUP0aARwvgk8EDBdF8OSgDe1UyQMjoKrXI+9yBTUuMRoFKD3lGDbKvOLh0L4sepcPPjMVCqUkFnxg1yUc3ne5EU/yfBHyJCzCT7Io1hoxfHRPKD0cb3HkzuRJfb0OZ4/nIzDYG7c9MK6jq3fa+w4nTz589XEEBzUxcbc8/C+8/n/3IyJcsvUiMTA5wWmA2LoxI09sRY7NcxcEGHniLjfF8uwsAow86SyCbL67IMDIE3e5KdfJ02RT/Gj/WVjRZ7zViV2sLsRNuz9BiIcvNs15ssX8+/Z/iYyaIszsMRB/GbwAhOiYuuFVaAU9lsaPQqCFNr3NF76SPCHvmURee3gHopdfOMK8/BGilMREOxO/ZR8DqZ4hH7h9FJ7U7nh4SFzjksRmuURTB3+5J5b1sr8tq3qTQKtLSA2JPEmyKDZcFiESCQ/yqhcgj29DT0IARL0IQ6ZUgeK1kLeIwOgMXmZzRUBNBG21gHwQB2VyyzyNxaCCt7IwwJAN6FKJsCvA+wCe03ho9gkQCVkUTFyFpNdlPTlo04zgRR6e/nJEhPojqkcAamo0OHeqUKpA4oCBQ6Npi07zMAmZ3vnIBPj5e6K8VIWvP04BcZBt7HECsOi6wfD198T3nx2Gh5cCQ0dJFsWODncmTwjfeJiSVSIeenYa5ArHk0+W3JfDyRNLkkrdtcqSYS41hpEnLnUdLJkuQICRJ10AKlvSJRFg5IlLXgtLqgsQYORJF4DazZd85OA3OFhyCW+MvgGTI5NtOu28P95EubYOX0++B8mBTaqk5dp6zPvjDepC88P0+9HDW/rFamZtKQKUXiDEg8HYoIBqxc6tkSf5qirUGTSI8gq0SSS2re3PVxeiVF1DxWhjvVva1KZVFyKzphQxvkEYHGRfXQcqkLqxQRDWA5D3ksgHQ7YI1EvFMHw4IAttX4zTkCtCrAM8RvCQO/D32cZyEdqdUjUQydVzOidVxjQLzTaBitrK4gBjtqQdAxkHziBCOVoG3RFjg8KL9J4siqftS/VZOsg0Mnj0kmFkj3i6Yl2NFmdPFVAOJCTcF32Sw1pc6+lj+VCpdLiJVJZE+uHHVUdRkFdNW3mIPodJfHfitD5URHn/rkuIiPJHQh/n+D67M3lCwD9zogCqWi2W3zmKasi4YjiUPCmrqLYIg9DgJn9ziya4wCBGnrjAJbAUuhQBRp50KbxscRdCgJEnLnQZLJUuRYCRJ10Kb7dc3GRT/MO0+2nFhi3xxplN+DHrMO7uOxV3J01pXKJAVYVrtq2kzjfrZz1m/qHZoMdXGfts2Y66yZisim1awE0m6TNF6I9LH+j5YEAWIREPxhIRAtU4AeQJXIfVJGI1YCgA5DEiPMY6rnVHd0qE/iLJXsrVczJp32nWilPfQA5xRGVEajviCb9mBIhILheCxnNKejaAglSvyIAqnYqScNHegY1CxkQM9uyJArpXYt9whIS1rDw6d7oQNdUaLF0xDKGRfvj4zT3UyjipfwTyc6qh0eigVesxamI88i9XUWKl7yDHWxSbHlF3J08unitGRbkKC5YMQlI/276/dPWXq0PJk64+jDPXZ+SJM9FnezsCAUaeOAJltocrIMDIE1e4BZaDIxBg5IkjUHbNPXLqy3GyIhc9fYIxNLinRUk2tynet/BvUPJyi+ZdOSilNAOPpnyLvgFR+GrKPY1vX64rw/U73kesTwh+mfGQ2TQiEksIF1uiO5MnxlwRxhqA9yOVGCIMxZIgrCyYMCVNaJG2HWORSMmTjkJUibSqgw8FPKc6jjxRbTQCKg4iD3BGyV65uV2y4SKgPSXQShGRsCOcpHNCRGGFchEcdTkiWi9Saw85s6lFiTy7pypyqcPTbUkTqXhsXZ0OZ0/kUzhGjY+HTNYSm4vnSlBTrcbi5cOo887mNWcRFOJNyRMSxYW1uJxRjgFDo5B6khAxnFMsirsLeXL5UjmKCmow1YUddxh50tF3EAvfZ+SJhUCxYW6LACNP3PbqWOJWIsDIEysBY8PdFgFGnrjt1XU68dfPbMRPWUdolcfwkJ54YuA82hrTXpRqarFgy1vU+WbD7CdszoEIq07b+G86n+ieEP0TEiY9lET/CHw79T6z9QvqK/F77kmb9uy25IkAqNYKEI0N8hsNOrXy3gCn7JgkaQtM4tBjvCxVr3hOdwx5QlpxNFsFiHKA95YqSYi1MufHQU54Cjmg2SFAKOcocUK5E39AFsNBrBVhyG04Dd9QbUKOT+RQmqV/vqoAaqMeC2OHIso7AEajiKoKFRWLJU45rUVWRjmKC2owbW4SSopqcO5kEeL6NDnplJfWIz2tBEqFDDq9EQGBXug3KNKm59Qek9y98oRU7uRkVmDkuDhMmtnHHpDYfQ1GntgJUkae2AlItozLIsDIE5e9GpaYnRFg5ImdAWXLuSwCjDxx2avp8sReOP4bNuWdbtzn7uQpuDt5aot9D5dmYWPeSfqb+juSpuDa7e+gh08QfpvxSKdyfGD/lzhafhl/G3IVro4bTtc6U5mHO/d+jv6B0Vg1+W4QsoYQNSSyakuxNf+sTXt2V/LEQJx19kttLlR7g/apAPJE24kTupZahOEyIAvi4Dmjc2u1dWHaFAGcB3HJ4SkxQoRfDecBLlAiPcSapplcEKhtsmqDJA7b0LQDWQ+A9+fo4fUXBDqP9+WoQGxrkVtfjlJNHUaExpuJ+Lb3UNEP81kVCAj0hEalp046Q0bFwMtLQadVVaqRdrZI0o4F0DMhGFExzpOfcHfypKZKg3NnCuHlJcfN946Dj6/Spq/5rpzEyBM7ocvIEzsByZZxWQQYeeKyV8MSszMCjDyxM6BsOZdFgJEnLa/mWHk2ClVVKFRXYURIHIaHSOKS3S2eOvwD9hRdaDyWv8ITkyKTw+KsawAAIABJREFUkRQQgeW9mmxC/3liLdY3VHxMCE/E/pJ0JPiFYfW0BzoFyXeXUvDf1C3Uxvc/o5bhlZPrQSoDLtYUYVhIHF4ftYySJ0YISPKPpCKr2woYedIcdO0xEcYsonFiohMAPqDBorgTtyNqRBiypLW8Ztm/8sRYLEK7R9Jm8ZrDUQJE/YcAoYaDPE6EUAOIVRIhRIISE34iUEuqTgCOkCtcQ4VJQ3oC0XVRk2oZjrYwtRaVOhUl4ZrrnnQEU1lJHTIulJiygELBY8TYJvck0spD23UabKCHjIyBl7dErDgj3J08IZiRSh5S0dMrMRRXLxviDBjb3dNp5ElldS0tkzKJw2bnFcPXxwshzWyMHYFWTn4J/vrvT3E+PRs9IkPx0jN3YOiA1suEft+WghffXIWXn70Lc6aOMkuPkSeOuC22hzMRYOSJM9FnezsSAUaeOBJttpczEWDkiTn6KoMOUxvaScg748L7YOXYFc68oi7b+74Dq3C8LLvF+sEePrSVhnwcJ3HPvlU4WWE+blBQLD6fdEencstXVWLxtneos87CnkPwy+WjjeuNCeuFB/rNQImmFlW6elzdczguVhdhV+F5m/bsrpUnqvWSrS8RRIVR8n2R9yDtLJ2sFtEBhksSdUFagBR9eHBtEBK2XIjuuAB9pjTTczIPzhvQEItlOSQNk2IRYjk5DtE/EcAZqTwsOA+OOueIesKgiI2CuJbm0JruSUdzVSo9MtJKqCgssSLu2SsYPj5N1RBqtR6nj+ZR8VqlhwzDx1imH9TRvra+3x3IE4NRxKmjudDrDEjqG4G51wygIr2uEk4hT1KOpuLh51fi70/cikWzJ1AsVq3ejPdXrcG7rzyCscP7Owyfmx9+BRNGDcKdNy7A7pST+Nc73+CP79+AQm7uLb3qx804duoCSsurcPsN8xl54rAbYhu5CgKMPHGVm2B5dDUCjDzpaoTZ+q6CgKuTJ5+k7cSNfcbDV+7hEMiq9WrM2vSfxr0UnAw7F/zFZmFUhyRt4yY37/4YF6qL0C8wGjKOw9lKSTiTxOcT78CgYMlGd/4fb6JMW4d/j1qKN05vRrR3EN4YcwOClN4W7WwQjCCklH8reirLdn5AKwGujIkRSbglcQIOl1yiby1JGIW8+gocavi7RRs3G9QdyRNi66shtr6EcEjkKInCEYFY848v1kIljW8gTyh9QpyAQ4n+CA/Oi7TF2LZk81mq9UaIWome8xjDQagH9GdF2rIjiyIaJoCxjJAlkmaJUEn+L0KR3PkP0OeqCqAx6nFVz6GI9Oq4vUarMeDE4VxK1iT0CUNElDmLZDAIUltPkBfVO5E7+UN+dyBPyLNCSKvzpwug1wmITQjE0ptGdP7Bs9MKTiFPFt/xPJZdPR03XD3d7Bg/rtuJH9buwK+f/9NOx2t/mfLKGsy98Wmk/P4B5DLpu83Su1/Asw8ux6ihfc0mp2XkILl3LO568nVcv2gaI08cckNsE1dCgJEnrnQbLJeuRICRJ12JLlvblRBwdfJk9LoX4afwpJa2NySM6RR0n13cQ397fXPv8fCUmZfVp1bm4/a9n8GDl0MrGMz2eW/cLRgdltCpvV1xssly+OfpDyHWNxiLt76DAnUVTXV577F4fMAcisWk318Bz3E4eNXfUaNTt0qCtHc+Ui2i4OWYEJHYYti757bi64wD9HVCrsg5HhXaesyI6o9xEX1QpJLy6RfUAwajEek1RTZB2R3JE91pAfoLDZbEkZ2sNLkSVQNgyBEh6oj+SJOLDbEF5gmBEsxB0de2PYkrjpqQPg2hHMLDkC1AqBQhj+PBXeEWTBxzyHuckrj/2LZn8+OZdE9GhsbT9rCOQqsl5EkeSGkJqSoh1SWuHN2FPCEYq+p1OH0sHz4+CtzzxGSXgd0p5MnQWXdh35p3aZtO8yCtPNOWPo6TWz9zCEDHz6Tjpbe+xJovXm7c76mXPsSY4f1w3cKWollk0J1P/IeRJw65HbaJqyHAyBNXuxGWT1chwMiTrkKWretqCLgyeVKtU2HW5tcbIftj7tO02oG0chwqvYQjpZkoVtdgTHhvXBc/ipIsbQWpbiBVDiTeGn0DJkYmmw1tq4WFDLqx9zg8NmC2q11dp/Mh2BKMN895EsENbjfptcVYsfMjkNad32Y+goL6Kizf9SHifUPw43Rz62BLE/j58hHU6jWUtJLz5h88T1bk4J59X9ClyIdZ4vyzIfcU5vQYSCtfiD0xCYVMjmClD4rV1ZZuazauO5InVCOkFpD3BDifzpMKrQFLyBNDBiFPTIq0DaO8AO8FtlWB6E6L0F8k6zS0BcVwMOSS8hb7VJZ09IBU6uqRVVtGRY/nxwzuaDh9v7ysHnqdEZHR/haNd+ag7kSeGPRGHE3Jhpe3Evc9+ScnTxbe8hzuu2URFs5sEqQiD9q3v27F6rU7se7LfznkuTtw9CxWfvoLVn/8QuN+f3v1MyT1jsWt181pNYe2yBOtXlKAZsEQ6K4IKGQcBFGEkT3q3fWK2bkaEFDKOZCeW6Hpl2MMG4ZAt0TAQ8FDpxcahRld6ZC5tRWYuvaNxpReHbcE1/Uegc/P78W/jm0yS/XpYXOwuNcwRHi1/HBTo9PgH0fWYW2WZHN7c9JY/GP0IvrnvPpK/Jp5HF+mpaBKq0K4px8lZ25PHo/egeF4/tAa9A2MwIaFj7oSNHbJpc83f6OVOKnLX4KnjPR7SDFtzRvIqauAr8IDr467Fg/t+R7TeiTjs2m3WrWvxqCHp1yBj1N3Q23QY3bPAegfFGW2Btl/6OqXUKfX4oGBUzE7dgBSKwqQGBiOw8WZ0BqNVu3Z1mDy2Z+XcdSatjsEaXOpXKODyANe/WVNPsV2PpxoEKE9b/5DH0GQ9+YQvNh6UdT6YwZo0qT1iHaJsUqSuiUhC+SgiLWNkLHm2EbBiGOlOZDzPO4bOJW6SHWnIEQXz6Fb/KxuMBixb1cWdd7564uzXOaayL+b9gxOFIlkTvuxY/8JPPGP95HcKxY9osIgigIuZRciJ78YK196GFPGOUZZ98TZdDz/2ufY8PWrjQk/8n/vYNKYwVZXnpTXkNo2FgyB7ouAn7ec/pDNiMLue8fsZBICgT4K1GkMlEBhwRDozgiE+CtRUaNzSfLkfGUhbtjxYSP8s2L6U62NT9N2473U7S2uhWh3PDlkLqK8AhDjE9T4/pXr9PQJxvq5j9H3dxam4bED3zWOPXTN/+HlY+twR9/J6OkXgglrX6H6CDsXPkurMS5UF+K79IMI8fTDIwNnuu2joTEaMGbNS/SD68klL5qd452z2/D5hT30tcEhsThdnosbe4/Fs0PnW3XeCk09avRqbMyV7JCjvAOxOF6yJG4eG3JOIbuuHLNiBiDRP4K+VafX4Kt0qZ3HHkE+Hnt6yKDW2oeMaS8nYzkgVItUBJXjCUnAQawXIRoB3o+TdDw6Gbo0AdpTIvgAEbIY+36QM0vNCOjTCLkqidESK2TickNad3wXNRFulhxHqBNRv8EI8jGRkwHyGB76bKmqhTos9+TA+TmGyEityKdf19fED0ekd8e6J5acz1XGEOJEKeeh6Qa/1DcaBBzcmwUPTzke/6u51Icz8Sb/btozLCJPyIbFpZVYv/UA8gokoajYHuG4atZ4hIcSg2/HBGkTmnn9k9i/7j14ekhALLj5L/jnM3dg+KCkVpNgbTuOuRu2i+shwNp2XO9OWEZdgwBr2+kaXNmqroeAK7ftHC+/jPv2f4ke3kEgzizeMgV2LfgrPjy/HV+k76OvkzYPMq55jAvrg5Xjmhxy9hen4/FDTQQJGfv7rMcR7uWPtdnH8cqp9dLPoT4h+GWGeWvK04d/wO6iC/j70KuxsOdQmOx1ZRyPLXOfbrdVyPVuuymjCl095m5+A4FKb3qO5kHsgm/d86nZa7cnTcL9fa378ELsng+XZaJY1dRqkxwQhZFhCfDpQACYuPscLmmwY7EDkI5s29HsFCCUSUmLnAhlXx76tAYiXgQ8Z3LggzpHEpA9CEkj6wFq89uVYcgUIeolIgh6ieggeHovNSdtjIWAUCVCFg7wIS1z0h4WYMiGJDobDHCeHHX0oR1BpGUnie+yCpor8cmpL0eZpo4+i8OCneuOY++7605tOwQbQp6Qh+6Bp6bAw8s6ws7e2JrWc4rmSVcdxpZ173zyPxgxOBl3r1iIP3YdxsrPfsGmb1+jArLEmpg4/5jslMn6jDyxBWU2pzsgwMiT7nCL7AyWIMDIE0tQYmO6AwKuTJ4Q0oKQF1Mik5FbX4HM2lI8N2QhDpdkYXthKp4YNJeKyL5w/Ffk1VfhTGVu45UcXtTUjv177im8dGINwjz9qOYG+VD/zOD5WBo/Cl9l7Md757bReUTQ9O0xN5pd66+Xj+LV0xswu8dAvDxiCVZnHcabZ6SWoScHzcOyhNFu+Rh8mb4f75/f1qaWiel90+Ee7DcTtyZK7piWBqkm+SNPqjppHuQD6/CQ+DaX0QsGfHFxr6XbWDTOUeSJWAMQLRLRJBIiEpJABLSEaBBp1YXHeBm1E7Y5tEDdeiMlMKj7TNdyJ41pkmoaoQ4gZyRbXkmeNNdg8RhtTqyQOaotUtWPvDdPxV9BRGnTpZoW3p8QQQ46CACT7gmpUJtnoe6Jzffl4IndjTw5czwf9fVaXHfzSMTEOa7Aor1rcxh5Mn7Rg3jvlccwfFAiyJ/biwPr3nfYo1ZYXI5nX/kYqRcuIzY6HK/85S4MSJa+qU9e/Aj++9JDtAqFuPBkXM4H6b+S8Tw4nsNrf7sHc6ZK/3AWlKsdljPbiCHgDAQYeeIM1NmezkCAkSfOQJ3t6QwEXJk8IcKhL55Yg/mxg6lY6DeXUswgIkTK4rgm+0pSgfKvk+uRU18B4pAT5xuMvcUXKVmyOvMwVvQei76B0Xj2yI+0hWRej0HYlH+Gvv9Q/5m4pU9LcqBEXYOFW9+m+h//HL7ErIKFtP/8PONhZ1xbp/ecvOFftG2BEEKEGGotfsw6DFKFQuLquOEY2s5v6DMvlkGnNSI2IQg+vlIl94WqQuwuSmuxtLfcAyv6jGvUurhyQK1eje8vHez0GZsv4AjyRJtCBFwFCNV8o50v0SahlRWmDlCZCI9hPOQJHAw5gO6kEfIeHJQjLG+9MWSJ0B4VJcIhxnGEgwlPQngQ4sNzbtM5xXpAvckIUkciCwY8p5ufR7NfgLFApC1MsuimnPXnSR8QaT3iuryCpvnzYBQFnKrIBakguy1pYrfSPelu5ElmehlKi2oxbW4yhoyMsev3BVsXcxh5sn3vcQwfnIigAD+QP7cXMya17Im09YCOmsfIE0chzfZxFgKMPHEW8mxfRyPAyBNHI872cxYCrkyerM46hDfPbMayhDGYGt0P9+9fZQbTP4YvbuGWsTJ1C769lELJEfIBiZAfpnhzzHJMikjC/D/eRJm2zmytvw65CtfEtf6z55Lt79LKF1Ll8kPWIarLQVxfavQafDP1XiT5Rzrr+trc92jZZfyeewKDg2JxbfzIFuPGrn+JCsAfuOr/qD1wZ+OT/+6Fqk6HybOSMHxMLF2uvdab2T0GId4vtNVtiXDvj1mHOpuS2fyuJk+M5SI0jfa7IhSJPIjYqiGLkCcirUThfSG54xDCgxdhJKoFakk41XOm5Xeg3SfAUERICKJ54gTy5LIIYh/sMYyDvLe0v/6CCP0ZUlkjteR4zWh2HgOgWiOJUisSOaBZ54VIKll0EgawHAK7PBvnqgoogbgobhgiPF3fRcfSQ3c38qSkqBaEQPH188CKO0fDu4GctRSPrhjnMPKkK5J3pTUZeeJKt8Fy6QoEGHnSFaiyNV0RAUaeuOKtsJy6AgFXJk8+vbALn17YjTuSJuO+vtPwwIEvUW/QNVZDfDD+Vmpv2zyOlWe3IFnI+4RM+WHa/fCSKfFT1hG8fmZj47SV427CuLDebcJLxpI5prguYRT9I3nt+oTReGrQvK64mk6t+XbqFnx/KQWJ/pH4duq9Zmutzz2Jf55YS19r3t5k64bFhbX4/vPDdHrigHAsWDyICr5uzU9FqaaJvGq+fqxvSJvtEuXaOvzSDG9b82o+r6vJE91pAYaLUoWJqSKEkALGSw1Sq76ALJyDIRPglKSNp8Gol/AoPoDXPIk5ECpF8MR2uC09SkJErDNCFDgokslke6Bj3RrGfBFig4yN1wIOnDcHzS4BRqLzQhyHQ8wrTwzZInSHRcAbkMc7nuxp63Qm3ZNRYQntVlVZh47zR3c38oQgmnqqELXVakT2CMDyO6Tvv84Mp5An+UVl+PrnLcjOK4ZOp29x/s/fesaZmNi0NyNPbIKNTXIjBBh54kaXxVLtFAKMPOkUfGyyGyHgyuTJ22f/wPeZB/H4wNlY3mtcI6ozN/8HoR6+eHfczVTH5MqYuuFfUBn1GBESh2jvIBghYF7MEIwJ60WH6gUjFmx5C1U6Ff17RwTCvuKLeOLQ943bfDThVpDWk1t2f0Lbef436W6qHeJK8fLJdViXc4KmdFufiVS4dG/RRZRqaqlrENEjseTsHZ1p58k0nN9XDF2VgQ4NiPTC7XeNR159BTbmnmp3+rJeYxGg9GoxhlQLrck+1tHWVr3f1eSJSe+DDwLIf5yHZCFjEoqVRUmuO6RNpbGNhxAkOkkTxfsqGYQqQLNVan3xXsyDa0Ub05AnQntQcvKRxzmHiBDrGqpmNAAXIJ3XSKpRGsRXiJ6J9yIe+nSA95WqbwwFImQRpCrFOTm39rBUauuRVVeGWJ9gzI0ZZNXz5MqDuyN5YjAIOH0sD3qtEQuWDkJiv3CnXoFTyJPr7vkH/Hy8MGRAH3goW/qE33fLIqeCYsvmjDyxBTU2x50QYOSJO90Wy7UzCDDypDPosbnuhIArkyfLd36IS7UleGHYNVgQO8RiWJ878hOtTnlwwAzMim5dz2NV+j58cH47dcvZPu/ZdtdWG3WYvvE12gZEYv2sxxHh5Y+l29+l+irNqzu2FaTi56yjIL/NvjNpssU523vg34//is15Z9pddnpUP7w66nqbt67RqbHqswMQKsyXCBnjg2EjY3CgOL3dtQcH98TY8JYVPwWqSvyec9LmvFqb2FXkiagBtEcFCIWAKG9wjGmWgFAhWRTLCGkgA4hzDak6IUG0PwipYBJfJW0vpP2FhNdCHpynNE6fIcKQIUCo4yTHG4EDHyl22rGnMwAbi0R671QXVyo4ofmKGg68pwDlcBm0B6SvF9P7ij5E4bYzu9p3rl404kxFHhS8DLcmTnSU7q59D9HKat2RPCHHLCupR0ZaCULCvHHLfU1kepcD2soGTiFPpi19DNt/fBs8MaPuJsHIk25ykewYbSLAyBP2cPxZEGDkyZ/lptk5XYU8MVVKLIkfiduTJuOnrEMgji8kSIWJqWrEXjemMuqwreAcbddprXrlyn2I605K6SVEePjj4YEz4cErkFtfjht3fgytoG+0Pn7t9Ab8cvloqxbAncn9bGUeqnVq6ghkSTx35GfqSNRe/DbzEWr3bGtcKCrCps9SwXkAXlfx0G4XYayUPvwrQmVQTBTN9C2u3MdTpsCNvcdDzjf1nlyqKYFSJsOm3JYuPbbmSeZ1FXlCrHe1RwSqa8IRMdSo9j/XkJYX4lpD2m2IUw6pTCEzCFmi3mGEqOKkv8/mwfkDxhIRmt0mtVkRHEd3goI8Bk50bSXtRYRAIYoutJKGkEGRgLFIEsiV9QSM2Q2tSSRjTw7yXq73mS+1Kh9ao4EKIoe3UsXWmWfOWXO7K3lC8Dx5NA8atR4r7hqN8MiWVYeOwtwp5MldT72Of/3lboSHuoblkD3AZuSJPVBka7gyAow8ceXbYbnZEwFGntgTTbaWKyPgKuTJDTs/oFbEIR6+uKnPOKxM3UphI/oiTw+a77IQPn1kNXYXpuEvgxdgXHgf3L3/i0aR2m+n3kfFZTsbm/LOUDtm0qq0cc6TrS5HBGwNotBIhhBHoZ2F52nlTZDSG5vzTyPeNwynm9k5b5rzJMXb1vh9xylkHCiDLBbwGMNDd0qAoaHYRJQBXpM48KHtf2AmQsAmwd2cunKklKRjdFhvbM0/a2tarc7rKvJEs0+ghAHvB/BhHCWS2guhGhBKCZkgOeUYMkSIeoBTAKJeIiNIeE7kKRmhPSBK1SlK8r7EUpC5xK3HmUH1XMok7RNTG5IiiYM+SwRH8iRdSyIhe3gilwtZGDp8FpxxHpPuCalAmx0zkLp6uXt0Z/Ik9VQB6qq1uP62EYiOdR6H4BTyJCe/GA/97R2MHd4PYSEtD3/3ioVu9+wy8sTtrowlbCUCjDyxEjA23G0RYOSJ214dS9xKBFyBPCEisNM2/rsx80ClN9UjIa06dyVP6VR1hJVwWD18bc5xvHJyPSVJiJ6ISUeFLHRj73FYmjASMd7BVq/bfMKGvFN48fga+tKXk+9Gv8Bos/W+zjiAd89txYyo/vj3qOvw2MFvcaAkg2o5vDD8Guq4Y4rHD32H/Q3tNHvm/xWecuv7KIrya0A0CDZuPgNVqQ7KsRx1kCF6HLqDpioJyXXlSsvaK4EI9/LHNQ120zsKziGjppiSKRdrijqF2ZWTu4I8MVwUoTstOcwQ4sCmShA9oL8sEQ4UOU4iI5QjOEqeqDY0VPIkctATi2BS1eEqRATJPUPKyUToEBtlqCV3IdKiwwdINyELdK2WHdPzUaGrx+VaonQLKj49LCTOrs+dMxbrzuTJudOFqK3SYOktwxETZ3vVXGfvxSnkyX3PvonjZ9LRKy66Vc2TL1c+19lzOXw+I08cDjnb0MEIMPLEwYCz7ZyGACNPnAY929jBCLgCeXK4NBMPpXxtdnKe47Br/nMgrR2uHCWaWizc8labKRLi5NeZD9P312QfBzkrsQ6+0iWorQVu3vMJLlQVNr59Z/IU3Js81Wx48xadZwcvwDupW0F0WkhsmP2EWVvSlryzeP74L7TZ4tCiv1sNrUZtwMdv7aaEAQ1ehPfVMqrnQaoR1OuadC44H8BjNAc+pP0qiSXxo+Cn9AQhgYyC0eqcLJnQFeSJaqMRYr0kBEv0S2wNQ64IsZaUlBDnGtA1lQN5omwM3UWJgCDrG8k4QfozqVRxhSACuCRMhI4xT6R2zBw5TqjkMOTKYdI9ITlGegXgqp5DXTldi3Lr3uRJEWqqVFh603DEJnSOlLYIzDYGOYU8GX/Vg9j07X8Q4O/+5VEmXBl50pnHkM11BwQYeeIOt8RytAcCjDyxB4psDXdAwJnkSY1eg9VZh5BakUcrJUhFBRF6JUEEV98fd4s7QIgVOz9Cem1xY64JfqHIavhtNnnx+6n3o7d/OO7a9z+crsil5Alp87EkiKNPWnUTeZLgF4Zb+0xEoIc3xof3oUtcs+0dEJFVEtFegajWqzAhIgkzovtjWlQ/s220ggG/Xj6Cnj6hreqnnDiciyMHLiN5QCSmzGqpr3L2RD62bUhrXJOPBjzHm/vlilpAvb6BRFECXtN4cO3IE/QNjEa8Xyg221nnpPnB7U2eEGcc9TaBVpsoEjtHEBhLRQik+EFOhGUBoUSEvDcPYvFLWnXkvUhlR+f2sORZs2WMgbjsaInzj5QjFZKVHkWqcdJRG5Mte9p7Dml5y1dV0WWJcKySl9l7C4eu153Jk7SzRaiuVGP+koFI6tf5lkhbL8Yp5Mmye1/EN+8/D4XcvR/Q5qAz8sTWR5DNcxcEGHniLjfF8uwsAow86SyCbL67INCV5EmeqqJB94CjAqtXCrMSu9zrdrzXCNWLw6/FW2c2oVqvxhOD5uKGhDFuAePe4os4X5mPYnUN/RA2Nbovzf3Ns5uwOvMwbk+chPv7TYepioTgQCpCLIkVuz9GenXLFpYIrwCsn/UY6vRaTN/0aoullvUajScHzrNkC7MxP646ioK8anj7KrHg2oHo0dO8NH7tD6eQlSG1OZBQjuJaWuaKgOqXpgoUkyZKW8nIeRn6BkbhbEWe1flaOsHe5Ik+VYD+PMAFEaHUThIbBkCoF8HJOIgGEcYCSQMFRDuEWBLHd3J9S0GywzihXBK5dQVdFmuOk15TjFq9hhKOvfzCrJnqcmO7M3lSXFiLyxlliOsVgsU3Oq9KyCnkyR+7DuOPXUdw9ZyJVDSWqEc3j759errcw9hRQow86Qgh9r67I8DIE3e/QZa/pQgw8sRSpNg4d0egq8iTJw99D0IqmIK0qTw3ZCFifUJAPqiQqhNSr3Dv/lWNY0h7i9qgp+8l+0fCV9GB+qaLg3+qMgd37/0CUd6BWNhzKH7OPIxKnYpmbapGae8IpHpl2c736ZBFsUMpbuebVaEMCeoJGc/jePllJPpFmFW/rOg9Do8OmG0VQqo6HT75797GOSPGxmHSTKm6hYROZ8QH/9lF/xwa44sKbR08J/Ctan2QFhPtMREwgLrGyGM5KnJqst+1KjE7DLY3eaJNEWDIk4gN0mpjryBOPEJ+o24sSGUPH+A+5Am5b9K+xclFQOk+eZuqT5IDIjE5Mtle1+mUdbozeWIURBxLyYYoCLjz4YnwC2jw83Yw0k4hTwZMva3dY6buavrH1MF42LwdI09sho5NdBMEGHniJhfF0uw0Aow86TSEbAE3QaCryJMl299Fbn2FGQqEOPllxkONbSZTIpOxu+gCHROg8MLWec+4CWqWpzl38xsgopRXxoP9ZuLWxAltLkSch4gDkSn2LvwbdfV5/tgvrc65PmE0SjQ12FUotdQ8PnA2lvcaZ3mixAb0SB52/SHdhymuun4weidJv4knJfOb16QioU8o4mcF4VDppXbXJ/oXxMYXRulDtMcUnmpjOCPsSZ6IdYB6swDiKESFYu0YRPuEkDJEM0QkLUF2Xt+OqXarpYhG0PmqQqqxdHOf8W59tu5MnpCLyUwvQ0lRLXy8FZg6NxlJ/Vtv31Gr9fDy6hpxIKeQJyq1FjKZeY9k8yfVQ9m0BzRYAAAgAElEQVQ1h+3KrwZGnnQlumxtV0CAkSeucAssB0cgwMgTR6DM9nAFBLqKPBm97sVWj/fxhNvMqk3IoP6B0Vg1+W5XgMPuObx2egN+uXy0xboDg2Lwv0l30tc/PL8DX6TvxeiwBAQofPDk4Ln4KfMwfjx7BAbOCLVCh8OLXkCdQYunDv0AoyCAVLU0jxeGXUN1UN48vRleMgW+nXaf1Wf56atjyM+pQq+kUGRelFpzRk2IQ3WlBoV51XRfUp0y66r+KAuvxoVmVTCtbWYoFqHb2+S+oxzD0QoUZ4Q9yRMikqo7K4InLTtR9j2PWC/CkEN8fgUqtCuLsO/6zsDeXfY8U5kHnWDEkviRCPFwX03O7k6e6LRGXDxfjPpaLbWoSh4UgXnXDGx8zIiY9Znj+di3Ix1jJ/XC8LH272ZxCnlCTqjR6nDo+HnkFZbSA/fsEUGtixUKubt8nZnlycgTt7w2lrQVCDDyxAqw2FC3RoCRJ259fSx5KxDoCvLkUk0Jlu/60CwL8htdjVEPQhqcrTTXtmhOJFiRulsMPVaejfsbWpP8lV5I8o/A0bLLNHeie/LyiXU4X11gZnFssmpefG4UvAweyBlYireuucHsvGRdEkFKb7x3bhvu7z+dtu6YYtuG87QlfszEBPj6d9z+RFx0PnpzNziew/1PTcGhvZk4lmJO0JjWvu/JKdhcfIpWurQXQq0IzR9N5IliGAdFb+eQAfYkT9RbBYjVgIyIpHrb9zyiSgQRYSV+xfIE3mltTm7xxWXnJHPqy1GmqcPosF4YEtxk723nbbp8ue5OnpgAzM+tRm5WBXx8FLjnicn05YoyFbasT0VRXg3Ehi/N628dgR6xgXbF3SnkyaXL+bj98ddQU1uP4CB/eqDyihqEhQaC2BT3iAy16yEdsRgjTxyBMtvDmQgw8sSZ6LO9HYkAI08ciTbby5kIdAV5klJ6CY+mfIM43xAQUVgSpBSeWNG2FoODYvHZpDucCYND934w5SscKc2iWiiFDS4fVyYQqPLB/Ixh9OX5iwciaYDlzhJqlR4fv7WHzl1683DExJmLvpr2qqpUoapcjfg+ITh1LB87N6WhV2IoFi0bQodsWnMWF842uQiR12LiA7H0phH4Mn0ftEaiaNp+kNYdQgYYiwDFQB6Kvh3N6Jr37UWeSC07RogyrmtaagyAsUwEjICsh32Jma5BtvusWqVTgbTLBXl4Y0xYb8T6OM8KtzOo/lnIE4LRwb1ZhGfEw3+ZhpS9mTi6TyKVeQUHP38vVJer4OEtx013jbGrPopTyJM7Hn8N/RLj8ODt18DbSxJ7qa1T4e1Pf0ZRSTk++PfjnXlunDKXkSdOgZ1t6kAEGHniQLDZVk5FgJEnToWfbe5ABLqCPNlWkIq/Hv0Z06P6YUx4b/gpPOmHkfl/vAlilUuCvDc/dgitXkgKiAQhUP4s8e2lA1iZurXFccO9/FGilqo5rtOOgeKC1MI+Z9EA9BscaRE8xMbzi/ebSCrSYjNgSFSrc4kt8e4tFzFxWh9kZ1Ug93IF5l4zAH0HNu3123cnkJ1ZgYAgL/j6e2LIiB6ITQ7C1+n7LcqHDNJfFKE/LUKeCCiHtN2yb/GCNgy0F3miPydAmwrIuqBlx4ZjsSl2RECAgJMVubQVhLTtEEtxd4w/E3lyNCUbBr1Aq+vqarWUSAmL9EPP+GDIFTzOnyHWxirqGkYqUOwVTiFPxi58ADt+ehveXualhPUqDWbd8CQOrJPUxd0pGHniTrfFcrUFAUae2IIam+OOCDDyxB1vjeVsCwJdQZ6syzmBl0+uo+TIP4Zd05jWc0d+xvbCVBCb3duTJuHaOPv9MGvL2Z01p3lb07KEMShUVyHGJxj39p2KR37/DvICGQaoY2BQSXa/U+ckYegoy8gl8mFi3/aMxqONmhiPCVN7t3rUn785hrzLVWbvPfDMVCiVssbXaqs15LMk/Ju5WtToNPghM8Vi+AzZInRHRMh6Ah6j3Zs8UW8RINQA8i5o2bEYUDawyxAgrTuV2noYRRHLe4+Fr7zjlrcuS8bGhf9M5AkRudZo9JTw8vJRUHFrX7+mOzMaRRw9kA2lkgf53mavcAp5Mv26x/Hte88jKiLE7ByFJRW49o7nkfJ7k8K4vQ7a1esw8qSrEWbrOxsBRp44+wbY/o5CgJEnjkKa7eNsBCwhT0i1yL7ii/RDxdL4UR2m/N2lFPw3dQuIA8xTg+Y1jt+cfwZ/P/Yrwjz98Pvsx8HR3xN2j7iUVor9uzLQb1A0FVntKK7ZuhJKmQwvDFuMAUE9GoevWXsSl89IrU6mIOtNmNZkGdze2qtXHaXirk1z4zFhWkvyRKs14qM3doGIK5qCCMUuul5q2WkvyjS1+LUVEdy25hgLRWj3i5BFcvCY6Jw7t0flSVe67HSEOXvfcQiYtE/GhPdyy4q4PxN5knqqAKp6HWLjgxER7d/qvyiH9l2GKIp49Lnp4GX2+f7jFPLklZVf42TqJdx781VIiI2k37yzcgvx8dfraTvPP59xv95XRp447hsb28k5CDDyxDm4s10djwAjTxyPOdvROQhYQp5crivD9TukiuCvJt+NvoHRrSZbrVPj5ZNrcb6qACWaWvx1yFW4Jm64cw7m4F3X/nAKWRllCA33xU33jLF5d6JVQjRLSPRODsOlC6UYOCwaMxf063BNjUqPj97aA5mcpy4T+3dmYPiYnpg8K9FsLnGryMooxabfUs1en7d4AJIHtN4eRLRZ/BReUBu11F66QlPXYT6mAcZyQLtTAB8MeE5338oT3TkR+lRyDo4SQSy6JwLVehUu1ZSCtNFd3VPSHXKn+DORJ8SyOCjYG4pm1XJX3tWJI7nQqvW48c7RlGCxRziFPFFrdHjzo9X4deMeaHXSPxJenkosXTgVj961lP7Z3YKRJ+52YyxfaxFg5Im1iLHx7ooAI0/c9eZY3tYiYAl5cqGqEDfv+YQuTVpt/jJkYavbFKurcdXW/za+t2Xu0yDOMd096ut0+GzlXvqLwOBQH9xy31ibjpyfW4WfvjwGbx8lrRbx9FJg/U+nqXWwJRUhqScLsPX380jsF45+g6OwbvUp9EwIxrUrzD8AHtmfTYmVK+PKlp3m72/NT0VWbYlN5xLqAM1mAZwP4DXPfckT9SYBQj1r2bHpIXCjSSJEnK7Ipa07K/qMg7fMvT6T/pnIE0seq7MnC6itMak+65UUhmGjeyImrnPuO04hT0yHJWU0ZRVSeWFocAC1VXPXYOSJu94cy9tSBBh5YilSbJy7I8DIE3e/QZa/pQhYQp6cqcjFnfv+R5f0kimxZd7T8ODlLbYoUFeBtKOQ92b1GIC/N9M7sTQfdxxHysJTdl2iqZPe+jsfngQPr5b4dHS2o/uzqWPEoKHRmDo3GQW5Vfjxy2OIjAnADbd1LF5pqn4hoq/EYeezlfug9JDhgafNe/1Xf3EUhfnSz95ECJaIzJpcdNrK8aesI6jUWl5t0nwdUSdCvU4ElID3IvckT4RqEZqtIkQZusZlp6OHg73vUASy6kpRoVVhYkQi+rdRaefQhKzYjJEn5mBVVaqpGLaqTkf1mwjLTTSkps1NtgJV86FOI0/SMnKQlVMIjVbXIvnF8ybZfCBnTWTkibOQZ/s6CgFGnjgKabaPsxFg5Imzb4Dt7ygELCFPjpRl4cEDXzWm9M/h12JOzKDGv28vPIdyTR38ld74+7FfEOsTgl9mPOSoIzh9n8/f2Y/aGg0Cg7xB7H+tEXg1JS8IIj59ey/Uaj2uv3UkomMDUFmuwpcfpiAw2Au3PTC+3XMaDEZ88PpuiIKI+56aCg8PGbUrJi1AN987Fvt3ZMA/0AsDhkbj208P0bWICCzRUslML0WffuFI7Bve6h7kN/GfXdhNdQPaC1EPcJJBUIuo/1mgegTeS3g4Q+qms5onulQR+vMieOKyw1p2nP4119UJVOrqkVVbhh4+QZgfM7irt7Pr+ow8aR3O2hot8rIrUV2lRnK/CMxfMtBm3J1Cnrzx0WqsWr0ZEaFBUChasvObv/uPzQdy1kRGnjgLebavoxBg5ImjkGb7OBsBRp44+wbY/o5CwBLyhIjFPnHo+8aUxob3xjtjb6J/L1JXY1GzVh3yWrxvCH6c/ucgT4iNL7Hz9QvwRN8BEThyIJsSEpaIxja/46yLpVj742lKaNzx8AT6llZjwIdv7IZcLsPICXEYMjIGXl6tsxMXz5Vg469nzNp01nx/EpcvlSOpfwQuniuma8YlBFNb4uFjY5HYNwJRMQFmj5raoIOX3LxNoUqnwo+ZEuHSVohGEUI5IAtvvYJctd4IaDl4XcWDc4KBSWvkibEMMKQbwYfzUPRuv/Kdtew46juSa+wjQMSp8lxSpoBbEidCyTc5ULlGhm1nwciTtrGpKFMh/Vwx+vQLw8KltpNiTiFPplz7KD5782kkJsS4+jNocX6MPLEYKjbQTRFg5ImbXhxL22oEGHliNWRsQgcIEDFVGcfDV+HYT45Eh0QnGBHrE9xqhpaQJzsLz+PZIz9iVFgCjpRm0XVWjluBcWF9kF9ficXb3zFbO8EvDKunPfCneCY2/Hwa6WmlGDe1F209P7DzEsZMjMe4NuyB2wJlwy9nkH6+BGMnJ2Ds5F6Nw/778vbGPy+/Y1SbgodE/PVCahGmz+uLwSMk9x7SSkRaiq4MmYy0Fk2At68SpZoahHn642hZFkaGJuBQySX0C4yGv9KrcVpmTQm2FZiLy165plApwlgLKHq2TkKo/xAg1gKes3nw9tFstOr5ao080aYIMOQB4ETIQnh4Tms9d6GqoWVHzlp2rALdzQdfqi0B+b49Jaovkvwj3OY0jDxp+6oqK1S4cLYYvYmO1LKOncXaWskp5MmCm/+CDV+/6jYPoiWJMvLEEpTYGHdGgJEn7nx7LHdrEGDkiTVosbEdIfCP479hY95pTIvqh8VxIzE2vOnDsWmuyqjD4ZJMRHoHoG9AVEdLWvT+xtzT+MeJ3+jY8eF98N+xK1rMs4Q82Zx3Bn8//ivmxQyCyqjH7sI0us7ro29AH/9wLN7WRJ4EKLwwJrw3Xh6xxKIc3XkQabH55O29tIf+rkcnISu9FNs2pGHgsB6YuaCvxUfT6Yy0woS03Nz+0HgEBDYRF5/+dy+IIC2J624dgR6xLYUOScvPR2/sBlnn7scmwcdXqhzJSCvF7z+fbpEH6fcnrUUkdhacR4J/GLbkncF1vUbjeNll6AUBc5u1ZZ2qyKGkSlsh1gOGEhGiFlAmcRDVgLFahDy6iYzQ7BIglAEeUznIQjunb0jce4QS0kJDXG8sg7k5eWLIBvSZRojlnKSBIPEn8JjAg5MD/BXdS7qzAvRpYC07lkHdbUaVa+uQXVeOON8QzO5he4uHowFh5EnbiBPy5GJqMeITQ3GNu5Enr73/PYYNTMTsKR0LYDn6obN1P0ae2Iocm+cuCDDyxF1uiuXZWQQYedJZBNl8gkC5th7E5vfN0xuR0eBUkuAXitXTHmwBkIlgIb/x/3Ly3ejhHdRpENfmnMArJ9c1rrNpzlMI8fAxW9cS8mRN9jH869TvuDpuOO5MmozvMlLwQ9YhTI5MxkP9Z+H6He/RSoXXRy/DsOC4TuftLgscO5iNvdsykJAYiquXDUFWehnWrj6FhD6huPoGy3+reeZ4PrZvTEN0bCCuv3WE2fGPHczBmWP5VEulLfIkJ6sSv357nFalkOoUUxC9k4N7MkFai6oqVPRlYmNM2oJ8fCSCZVPuaZRp66A2aM32nRiZBE9eCa2gR4W2HqmVpESj9dBnSMQJCXkMINRzEDUi5LFcowaK5oAAoQDwGMdB1oODUAFqXWxLaPYIEEoALghQJvPQHjdS+2DPiW2L0TYnT0gVjFDbIL3iCcAIEL0WyERwBg6KgRwU/ZoIHtqyUydCHsdTxyAWfw4EjKKAUxW54DketyZOgJxzjtixtWgz8qRtxIh4bNrZYsT3CsbiG4daC23jeKdUnvzt1c+wZfdRREeGICo8pIXLzoevPm7zgZw1kZEnzkKe7esoBBh54iik2T7ORoCRJ86+ge6xPyEZ/ntuS4vDfDv1PiReUQb+3NGfsb2hNWJZr9F4cuC8ToPwy+WjeO30hsZ1Hh84F8t7jTFb1xLyZHXmYbx5dhOuTxiNpwbNQ6VOhTmbX6fr/G3oIkrQkBYe0srzZ4pVH6RQUoKUf/dKDEVpUS2+/ewwwiL9sOKu0RZDQRx1iLPOjPl9MWi41HLTPH76+jjysysxeVYiho/p2eL9HZvScPpYPrU3HjUhvtV98y5XIfV0PsIi/MzW+C37GErVNa3OUfIK+Ck9Ua6pbfMsghowZDYJyXJKDkT/hIjCykI4cF4A7wNojwowNnQQ8aGgVSiKITwUiRbDRAcSkka9Xmia5CFSLRUSXlfzACFBSCXJFQ7ZJvKkOk8PzU4BkAG8J8CHcCAtR41kCiGABnBQNpAnxkoR2m0iRNayY91FdZPR6TVFqNVrMSN6AHr5hbrFqRh50vY1aTR6nDqSBw9POe5/aorN9+kU8uT1D3+AjG+bwXvi3uttPpCzJjLyxFnIs30dhQAjTxyFNNvH2Qgw8sTZN9A99ifEBSEwSAQpveEpV6JQVUXLwGdE9cd9/aY3HvTpwz9gd9GFxrEb5zxJNVI6Ez9mHcYbZzaBtNJU69UYFBSLzyfdYbakJeTJVxn78d65bbi5zwQ83H8mnf/koe+xt/giIr0CqGjsDQlj8MSguZ1J163m5udU4aevjlHdkLsfnQTy4VxVr6NtPF7eStz7hGWukXW1WmopzPEc/WFeqWwpTLllfSrOnSpq0074k//upTact9w3FsGh1pVGfJuRgnqDxmbsDUWSUGxrwXkCnBcHWbAIYwEHQy6p4ACgbhjtLcJrqqwF0dFeMoaLInSnW3f94XwBsQ60OsRrnvnXjok8qdirheEywIVL5A4JY6EIsapp1+bkif6MCN0F5rJj8wPi5hNLNbXIqa9Aon84bbt0h2DkSfu3dPZkAeprtJh/7UAkDbBNy8Yp5Ik7PHzW5sjIE2sRY+PdDQFGnrjbjbF8bUWAkSe2IsfmNUfgkZSvcbA0k1pdLk0YjWqdCo8f+o4OUXAyEIIkoEGY89GUb5FSmtE4feW4mzAurHenAP0+8yDePvsHru45HJvyTsNTJseqKXcjxrupX8IS8oTY1H5yYRfuTJ6Ce5On0px2Fqbh2SOrG/N7ZvB8LI1vahnpVOJuMPmPtak4f6YIoybGY0KDOCxx8n333ztANEge+et08HzH2h6H9mQiZU8WEvuFY8GSJvvn5hAQh4ivPkqBt48S9zw+CQV51dTGOKpHAPR6I77//DD8Az1xx0OSS4818fmF3SDtCbaG/oII0dD2bM4PINUotMojENCmiDDmN5Ef8n4clAM6xsmQJYIP46A7KECoklp2xMq291WO5SCPaVqXkCfecgVKV2up5bIikafVJySMpSLEMuKrIrXyKAY0te2oNwhUw0XWk2MtO7Y+JG48Ty8acaYiDwpehlsSJ4B3hs+2lfgx8qR9wEqKapGZXkadx65dMcxKdKXhTiFPjEYBX/+yBRu3H0ReYSlNpGePCFw7fzKuv0r6h9ndgpEn7nZjLF9rEWDkibWIsfHuigAjT9z15lwr72u2rUSBqoo6zxAHGhKfpO3EvuJ0pFUX4tEBs7Ci93h8cG47VmXso++bHG2IOOuLw6/t1IG+zjiAd89txc19xlPdig25p+ifH+4/q3FdS8iT989tx5cZ+/BAvxm4LXFi49yZm15DjV6qWnh/3C009z9D6LRGfPTmHgiCQPVDiL2wKT57Zx/qarTUzYbYF3cU/3t3P2qqNbj6+sFISJKekdaCVKeQKpWBw6KRk1WBmioNfPw8oFTwqKxQUyvjidP6dLSd2fuENPn84h4qeGttUMJEI0Kf3cFMYi4lAjIi7hoKGPNEEAFWsU4iNhTJgGJQ+xVWxFJYu6sZwaMAlCMIkSLlLUIE1/ChlvMXIdZwkCcCyiFN6xLyhM/lUXPQAN4XkMU2EStChQhjMfm7tJ4iHlCO5GGsEKHdzlp2rH02utv4C9WFqDfoqIhyW65lrnRmRp60fxtGQcSR/Zfpd4zHnp9h09U5hTz56Kt1+H7NdiyeNwmx0ZKsdVZuIX7btBcP3HoNVlwrlYW6UzDyxJ1ui+VqCwKMPLEFNTbHHRFg5Ik73prr5Tx63Ys0qcOLXjBLbn9xemMFiukND16BaJ8APDlwPh5K+QpKXo7t85+FBy+3+mAVunpk1ZZhU+4prMs5gdsTJ2JCRBLu2vc/Kuy6ec5TjeKHlpAnb57ZjNVZh/DkoLlYltCkmfL22c34PvMQze+Hafejl98VNiVWZ+4eE04dzcPOzRcQGx+MJTeZ/+byhy+OoCi/hgq3EgHX9qIgtxo/fnkUSk8Z7ntiSruVKscP5WDP1vQ2l7vhtpGIjAmwCsAanRo/ZB60ag4ZLKhF2h4j6jgQG19Lgg8B5JFNhIXhkgjdCRHy3oByWPvkifaYAKPkkE1DngDIojlo90t7y2I46AsEKKI5cEEcSKuNLAzwnGJOnmh3iNCXiZDFArxfs2oXAxG5FSGqQKtaZOGA52SersNadiy53e49hti956uq0DcwCpMiJJcqVw5GnnR8O0cOZEMwGnH/01Ph4WH9v7FOIU/mLH8aK//5MPr2MRe+On3uEv766mf4/at/d3xyFxvByBMXuxCWjt0RYOSJ3SFlC7ooAow8cdGLcdG0TpRnY33OCfp76+W9xyLJPxI59eVYuv09qgmybtZjZpkLooirt60E+aG8eYR7+uH32U/gjr2f4WxlPl4acS3m9mi9laM9KEiLzgvHJYtiEs8OXoAl8SNxw84PkFlbin+NWIqZPQbQ9ywhTx448CWOll3Gm2OWm314IDae1+14j66zde7TCFBeodLpovdlbVrffnoIep2AuN7B8PJRIv1cMcpL6zF/ccue+V++OYHcyxVYsmIYYhPat5MhDjvEaae5dXBbuWm1RhDbYoPeSId4eSlArJJJmNp5rD3X9oJzuFRTbO00GAoBsYYUrIjUqcaS4ANIG03TSKIzQsgPYgtMiApDLmmZESEL5wBSrWIKAVARgdgGIVjysscUDsYqQB7F0SoS0jrERwG8nIP+kkj/zskA78VN5IlYI0L9hwjIAXlS621CRIyWkDq8nwivuTKQlh1BJUIez1uly2IJHmyM+yCgEwz0+7GHTEEr9zpuMnPu2Rh50jH+xMWMfC8lbZDk+6e14RTyZNS8e7F/7XtQKhVm+ep0eoxd+ACOb/nU2nM4fTwjT5x+BSyBLkaAkSddDDBb3mUQYOSJy1yFWyTy+MHvsL9Eqgq4sfc4jAnvBaJhEqz0AbF8fX7oohbn+CJ9Hz48v52+Hu7ljxJ1DfoFRlObYpNLzoSIRLw95karMbh1z6c4X1VA5w0N7klJDz+FJ0wCsiNDE/DB+Fvo+5aQJwu3vg0vmQIfT7ydnql5XKwpQqRnAK1o6Y5BXHCIG86VofRoqBaRmX+UIlbFxLJ4wNBozFrYtsCkYBTx0du7odMYLapSIfuT9p4Th3JQUlxHXXXWfHdCuuNRMZg6J9lq+D9N20VbXqwNUo2BdnROWluPiLkq4pqwEusBYgFMRGU95/CSg05DZ45iIA9FX2kVY4EI7YFmOXqI8JzKw5ADyKI4cEoRhmzSasPBUAqI9SL0mSI4raRdQoJUqRizBOjSibsOIIto4+OvCOjTSAsQ4Dmdh2a7wFx2rH04uul48v1UbdRjYc+hiPKyrsLL0ZAw8qRjxI8fzoFea8Sdj0yAn3/H7ZVXrugU8mTZvS9i6VVTcN1Cc32Tn3/fjW9+2Yo1X7zc8cldbAQjT1zsQlg6dkeAkSd2h5Qt6KIIMPLERS/GRdNqLvZK2m96+gQjvbYYkyOT8cboG1rNulqnxtw/3qBinW+NWY6JzcrBa/UazN78Ov1gu2XuM/BXWP7DHdl3xc6PKCEzJKgn5sUOaly7Tq+lFsNEBJFUw5CqmNfOrseOvDQ8PGA2FsYOaTXXmZv+gxq9GlvnPUOde/5Msem3VFxILWpx5GFjYjFlVssS/rQzxdi3I53qk9x0zxiEhvu2Clf6+RJs+OUMgkO9cct942yC9L1Xd8JgELD05hGIiQu0ag290YAv0vdaNYcMFuss0DlpZVXOg4PiCkkW1c8SW6IYzEHfzEFH1gNQ9OfB+XDQEYvjPBHyXoT44MEpJGUSYi/MB0k6JUIlQCpbhGqJZCGCtKQyxhSynoBQCAh6QEE0mJVt1w7oL5JqGhGcSOpgAD4YkDVrN7IaMDahWyBQqK5Coaoag4JjMLaTQt5dDQgjTzpG+OSRPGg1etz6wDgEBVtfMekU8uTwiTTc88wbSIiNRELPKFr6l5VThJz8Yqz85yOYNMb6MtWOoeraEYw86Vp82erOR4CRJ86/A5aBYxBg5IljcO4uu5hcdZqfJ8TDF88NWUgJlLZic94ZEI/b8eF9WhAkzx75ETsLz1MR1tsTJ2NkaHy7cBFLzTU5x7E++wS1Dp4SmYzXWyFuXjq5Fr/nnMRNvcfjkQGzsGDrmyhV19H1Pxh/a6t7tKXd0l3ur61zkLaYj9/c0/i2QiGj1sS+vh6YsaAfJT5ai42/nMXF88WYs2gA+g2OpEOIY86xg9nonRyGXomhWP/TaVy6UIqJ0/tg5Pg4m6AkrTzVlSqER/pZPX9P0QWkNVQnWTPZkE90QayvVgEPKPuZkxaabZJzDkjVvI442gBChWQ3TENJKlw4WpHitYAH18Db6dKkdiFStSKSdp4rWoeEEhFEZJaIxDYPMl8W337TBXH1EYkGcoP1jjyOYy071jwg3XSs2qjD+apC+Co8sLzXWJc+JSNPOr6e08fyoVLpcMs9YxESbp29O1ndKeQJ2bi4tJQvqP4AACAASURBVBLrtx5AXkGD205MOBbNnoDQYNcuh2rrShh50vHDyka4NwKMPHHv+2PZW44AI08sx+rPOJI4zKQ3aEUk+0fi7n3/w6XaEgwPicfx8stU5DXaJ5C2yzS3BbYGq91FF/D04R/oFGIBTKyA2wvyYfiphvFDg+Pw/vibqb3mlUF694mmCmmz2TD7CUz6/ZXGIcTdh7j8NA9CyizY8hb8ld7YNvdps/fyssknX1hd9WANDs4ce3j/ZRzYeYkSHlddN9jiVLb+fh6pJwswc0FfDBzWg87bvTWdttz4B3phxV2j8eEbu+nrdz06Eb5+zUU+LN7G6oEao54KBct5GRUArtaqWl3DWCJCFsxRfZDGIKKqGhHGPEC0UOfkysUpedJMG1Z7kFSVSKNEolGykId6bUvbZNJqQ8RfyS9aRRUHQ0775I2xTIRIWngarIfp+iKgjOEh+rc/lxAnoloEdBxEgwhZD1dXuLD6MWATbETgbGUedIIRi+OGI9TTesLSxm2tnsbIk44hI1pTqnodbrxzFMKj2hf2bm01p5EnldW1IJbFJrIkO68Yvj5eCAmy/hAdw9T1Ixh50vUYsx2ciwAjT5yLP9vdcQgw8sRxWLvjTpvyzuCF47/S1Jf3GodfLx9BD58gPDtkAb64sAe9/CPw2IDZnTqaQTBi9h+vg7TaBHv4YNOcJxvtWFtb2KRnMj9mMM3DS9a2CJ5JOPa+ftPx0fkdjcsRPZNHBs5GgNILE8IT6eupVQW4fc+nSPSPxLdT720cm59ThZ++krRAbntwPAKDulc7D/mwTWyEa2s0uHbFMPTsQPy1+Z0cP5iDPdvSMXR0LKbOllp7Pnh9F4jFMYmY+EDkXa5CbEIQlqwY3qnnxJrJROCXCMROj+6PbzIOQGXQtpwuALqLInh/gPfnwHmAtsoYi4m7jgihWTuMNXuTsYokjq5lCkO2CN0RicwgbTnK4TxUvzZpn5jGKYZxkAWDVp4YK0jLTvs7C+UkX1ESntWK4DgOIgd495dBL7QkZ6w9Bxv/50QgT1WBEnUthoXEdVgJ6EyEGHnSMfpnTxSgvk6LZbePQlQP63kHp5AnKUdT8fDzK/H3J26l1SYkVq3ejPdXrcG7rzyCscP7d3xyFxvByBMXuxCWjt0RYOSJ3SFlC7ooAow8cdGLcUJaRH+EiK2agjjrkLaX9bknG1/zkXtgaEhPm8Rd2zvS62c24qesI3TI5xPvwKDg2DaHm2yDH+0/Cyv6jG8XKZMgLcm73qClrUFGQaRVMyQIWUPsjEnsLEzDs0dWU6vjt8csb1x36/pzSD1VSP8+YVofjJrQsvWEtK7w4NCnn/tZGGddLMXaH08jIMgLtz/YPp5Xgn3ySC52/XGxUcg17WwRNq9JbXEnsxf1R//BUV32VJP77BsQhdz6CuiMRpypykWdToPJUX2xpzCt1X2NFSKMhaBuNaY2GSLMqk+3oVXnih0UCQDnbV7JoU0RIOoA5WCOapiQqhLSOiPUAiDtM8RNaBEPQ4EIeDUQJx1VvpBWHl6kVspkPU4AOH/AK14GnZ6RJ132wHXzhesMGlysLkaQhzetBnTVYORJxzdD/u0ixPiyW0cgOtY6vSiyulPIk8V3PI9lV0/HDVdPNzvhj+t24oe1O/Dr5//s+OQuNoKRJy52ISwduyPAyBO7Q8oWdFEEGHniohfjwLQMooDvLh3E/y7swk8zHkaYpx8VcB2z7qVWsyAVH/8YvtiuGaqMOnx+YTe+zjhA23+WJIzAit6tf5AnLT6k1effI6/HjOi2XV5IgqR/f8qGfzfmelXsUEq4kIoUU/x71FLMiBpAyRtC4lwbNwJ/GbKQvm0iB0xj/QI8cefD0i/CmsdXH6VQrY+kfhGYv2Sg2XvEJrK6UoOAYE/I5S3bi+wKpA2LrfnhJC5nlGPyrCQMH9M2adXa0qeO5mHn5guNb0XHBKIgrwoJfUIoHtVVanrme56YBKWy686+s+A8dKIBefWVMAodMQ5SusSpRlRfcSqSomXT20VaFgPw3lL1CbEA5mRSZUtroTslwpAuWRB7juZpNQwVetVZRuJQYVk9YMgVATVA9vYIYOSJDV8KbEozBE5V5FKR72vjRyLEw3qtDEeAyciTjlE+d7qQupctvXk4YuOCOp5wxQinkCdDZ92FfWvepW06zYO08kxb+jhObv3M6oM4ewIjT5x9A2z/rkaAkSddjTBb31UQYOSJq9yE4/IgbTgpJZfwQL8ZuCZuOLblp+Kvx36mCdzfbzpuT5yEQlUVrt62kr5GLICJyOpnF/cg1MMX9/efAUJC2DvOVRXgtj2fNi770YRbkVtfiat7DjPbasXuj5FeXUStjonlcUfx8sl1WJcjWd3ekzwVdyVPAdlrb9EFfH5xD9VvIXt9lbEf753bRgVmbwwbB7VKj1++OU7nhYT5oLy0vnGr5s4xxQU1+P5/UtUMiQVLBsGgF6i4KRHoMxEwPr5K3P3YpI7Sdej7tdUafP7ufvA8j3ufnAwPD+sIjtYqTeQKGe59YhK2rDsH4rTTd2Ak5l4zoEvPtbXgLLJqJF1BS0LUiNBfsmSkbWNoJYuRg7ynCH0OaQsC/p+96wBv6si6R5Ll3ns37gYbY8DYmN5LgECAQAhJIL33TbKbZP9stqbuptdNgbRNIYUQIPTejY2xce+9yUVWL+//ZmTLFm6SLdsynvt9+20szdy5c+Yh6Z1377lWvbUNJh11JACPryNaNN0bHpkUBBGPFVrxWeaJSaixwVcjUCERoU4uRpiTFxb6W2aVBCNP+r9usy/XUBJ7nYklmR2eR4Q8WXnbn3Dfbddj5SLD9mxf/bgf3/5yGDu3/bP/nVvYCEaeWNiBsHDMjgAjT8wOKXNooQgw8sRCD2YIw1q29zWIlBIs9o/DPxLX4bP8E3g/+yBd0d/eDT8vegT7KjPxfOoOmgXy6Zw76RPI637/N81I+WfieizyH5qb4VX730CtrMVg929Mvxkz2nVJyBuzdv0DSq0a+5c9BRfr/lsvdgjHkrmPxy7FpnBdBwkiKrr899cgUSvxv/kP4GhNDt7PPoRb+LOg7axUomNXbYjH7p8yoWkvhSA3qA//aQH4fB66lvWQsYQ0qashtRjAhq1TsffnK2ht1qU4EPKEkCiWYscPFNDOOLEJ/li8su8snp5irihtwg9f6AimDoufGoAFy2OGdYuEHKuR6kR9jTF1NUe73Qy1kdIdTspB4AkIriZP1EQAxTCCHrNhTAySkScmAsaG94iAQqtCVlMVBDw+bomYAeseRLlHGjpGnvR/AoTgbm6SYc1NkxAa4dn/hKtGjAh5cuhkGp74y7uIDgtCgJ8XOE6LwtJqXavivz6MuSmTTN7ISE9g5MlInwBbf6gRYOTJUCPM/FsKAow8sZSTGL44OtrxkhWJvsjuigwQbZAOeyZ+BT7KOYwmpdSg+w0ZJ+DxkOId2a3dsDmjJ2KfGw69S4kaYqQ0h5ToEGtVybBozyuw5lvhxMrnjF62Qzj2DxOXY0Nokn7e65f30m4s68YloqS6ASKlFDNzo2m3lQ67+a4kSoicOJgPUaMU1RUtNCtl1fp4BIe7492Xj9Ch0XE+yM2sNYgpcrw3zb7osLWbpyA4tOfUaVLes+fnLDg62WL63FDs33mFdqyZt1QnxGpuI40MPn7jOOQyNTr2OJA1JBIlCnPqcWiPTlvk1nun00yd4bKDVVm0XEehIQIgxpkym6NtgYfLiA6JMEingcJpOHAtPGiVgJUvKbnhwBPywCkAVYFxpTp9xc3Ik+E61Wt/HdJpjWhhTfcOx0S3QIvbMCNP+j+S3KxaNImkWL1hEsKiRgl5QrZ1davioABvrFo8A96epgu39A/T0I9g5MnQY8xWGFkEGHkysviz1YcPAUaeDB/WI7HS779kgaTtjp/oh6WrJ6Bc0oh1B9/Rh5JEBVS1SG0sRaiTJ4rFDfr3fOxcaNvg2T5Dc/PeFx4vpv2C39qFasmTz/3Lnoaj0Aa5zdW49dhHCHfyxjfz7zca0ipZM3xcbGGltG2nZHRTicDouoNv0/9em5UMW42uRYqNrRXITahfoCtWbzR8yNWhEULGzVsWhSN78xAQ4oa1mxLw/mvHoFZ3F82wtuZDqdRi8aoJiJ3Us3BqRmqlnoCYMS8cp47o6koee36h0fs0ZSCphSelNb7+zrjpjsGLQubn1EEmUYFkngylyTVK2LZ3WMptqcbRXgRhe4qBtB7mWjmoq4Yywu6+iS4J6cBDjGS8qGmFEQfraB409YDAC9DUAZp6Rp4M78mw1fpCoFkpRZG4npLlG8KSYWnNrBl50v/1m3ellpL+K9fHIzLGq/8JV40YkcwTk6M0w4SPv9qFbd/9DrVGg+sWTsdzj9wCgaBLw/n2NXobd9P9f0VOfinoLwcAzo72OPbTW/rIGHlihkNiLiwaAUaeWPTxsODMiAAjT8wD5qf5x5HTXIUHxy9CiKOHeZya6EWhVdPuOOmNpbTFZBh8cOTLPOqF/AZ4+E/z8UPuefx8Mh0uHrYotKml5Tski8PHzhkvJW3A5sMf0PFu1vZI9AzDswmr4GA1/GUmpHTnclMF3c+pugI8OXEZNoYmd+mIE2lyxx8/dzvUiGQG5AnZ6wOntqGmSIw5JZ1lK0S3hGSN9GSiegkO/JZDhVFt7KygkKmx/IY4RMf64MBv2SjKb4BfgAsKc3UaHEQDJCExABdOl2H63DBMnx3ao99t751Ck0hX3mNtK4BSriNhHnh63pCIrf7vs/OoqWzFUHfCMfEy7nf4weosxLoEwtfeBRmicpypK+h3DhmgaQQEzhzUFURbxKgpZh0kjOaBZwWoCjlw7d11rMeTbBPAKhBQVQJQDn5JlnkyeAyZh04EMprKodZqsSxwIoIc3C0KGkae9H8cZcUimi050NLMYSVP7v/jf/DXp26Hl4cuu+T42ctInBQNO1vdj5CqmgYsvfkpXD70Wf87N2HEmdQreP6VT7DtzT/BxckBJI7rFiZj0xrDJxd9jVtx6x9pSVFEaM9PDxh5YsKBsKGjEgFGnozKY2NBDwABRp70DVqltAnfFJzBjWFJvZIiuS01uPXoh9TR7ZGzcP/4ockU6O94s5ursKWL4OoN1dNgV9/Z4oN/vQaXT1chtj4IakcN/FY54s2s/Xq3x1c+i2fOfY9TdfkYio46/cXf0/vn6ovx0OntCHZwx0ez78CJ2ny8cuk3KnRLSnBMsd7Ik9P1hTj0Yw4c6nVtmoVCAe7/w1zwBb0/ZyUlKiRThJi1jQD3PWE4nojIZqRWoLVVjpBQD9g5WFNtFAcnG2jUGrh7OlJRz9JiEVzd7DEuwoMKy/Zkdz0yC47OvbRqMQWALmMJAbT9wzOUlCFCsT09YBug6yGfti3/BIQCAS21OlmTj4JWw1KpngLg1KAtiK0CdS19R8KsQkhbZB7t8tNhRFiWdPzhu7W3JjZDYIw8MQOIzIUegWpZM6qlLfT7b0mAYSexkYaJkSf9n4BKpcXFs6Xg83i0FT3pGGeKDSt5EjtvK/Z89TKCA3xojInL7qFtiTv+rqxpwJKb/oCsI5+bsod+x/71P9vh5+2Ouzfr2uwdPpVGs1A+f+OPBnP7Gjd37aP49sMX4OvVM8PIyJN+j4ENGOUIMPJklB8gC99oBK4F8qRNrUClpAnRLr5G79vYgTccfIv6Jna1XkaHjx9LLuCljN/on6TN729LnjDWvVnHbT9wCicL8jGu2QtNdhL4i91hJeTDyopPdS2yJ5cjKj0AAk6Xibr5/iQUaevpTb2jlw0eWrYAeZJakFKIOPdARDrpfr+MtK058BaqpLozcLW2B0klfzxuKTaF6YRfjbXeyBOiX/Lhv49RN74BzvD0dsSiFX2Lp3Zt0Ts5OQhzF/dd2lRaJMJPX+s6/vRlETFeKMgx7Bpzw6bJCAk37xNfkjmTmVaJKdODMWdRZH9hWcz7NbIW7Cxt74Bk64hGeRvVECEZGzy73skuTQ1HM094ttBnfQz3pvjOPPAEHDS6S3nIjJEnQwbtmHSs4jTIFFUA4OGm8GQ4WpmXyB0MqIw8MQ69wrwG1NeKET/ZHwv7+W672uOYIE/ufPIV3LR6ARbPSaT7Ly6rxu2Pv4wjO94wwKOvcZOX3I05yfG4eDkP7m7OeOKeDQbCtow8Me5iZaNGLwKMPBm9Z8ciNw2Ba4E8mbP7n5CrVfh30ibM8jWvPsf83S9BolZQUBPcQ/DRrK3dAH7vykF8XnBC//pHs26n7X2H05RKDd5+/SAEGsNWs/JoJQJb3NFQ04Yah2b4Sjq11lbdGE9+D+PX7zJoqEQ0lGRBiFsVOHeyCGGRnoiONT8hZSoun+Ydxwc5hwym/Tt5E2aZqMXSG3ly8UwZjh3Ip50IVt9knIh/aVEjfvpa15Jn6wMpcHXvv+sPKds5faQQRKiVGMlYUSo69VGILspdj83B6aNFuHKpirb4JSRNyrxwJM8aZypsvY5XKDT48PVj0Gq1uPPhmSY/iTRbIL04KmtrhLuNI9W46TC1VgMrvgAXGopxsaHEYKaGyPTwOQjceyZP1BUctIYNnIZ6CyPqn5EnIwr/Nbl4sbieCoiT77VpXj2XHY7Exhl5Yhzq5AEB+S4hrehJGagpNibIk80P/h333roKc6brfgCQ8qA1dzyPc7t1dcwd1tu4M7vex59f+QSL5kzFrKR4nDiXgaf/9gF2bvsXzWghplB1F0Mz5SDYWIaApSNgJeBDq+Wg5UYmvdfS8WHxXTsICAV8qLUcuFF8rUd/9WeoOS3m+kfh0wVbzHo44V/qOro4W9uhVSnD6fV/hLetk8Ea6/a+j/SGCsS6+yNLVIVNkdPw9+Q1Zo2jL2f1dRK8teMIUGyobdZo24bfo9LxhHgZaorb9C7iJvkh81I1Fi6JREF+A0qLdY/C77gvGccOFtLXOuxvr5hWGjMUmxbJJZj2wz/1rpcHT8TLM9aarMVibSWAsgcx1zdfOYqGBik2bp6MuEnGkUWErMrLroNcrkFisnFdKJqbZSguEMHe0Rqeng7w8LRHXa0En3xwBlKJEtNnhGDFmgl0n1kZNbQN8tfbL8LJ2Rabt0xBQJCLWeA9faIEu3dmIyLKE1vuGrxQrFmC6uLkUkMFroiqsSlqGi7WlyHA0RXp9WWolbdBJOu8jskU8rElz9bSshcbv+7afhoxB3nxMLbVMTcYA/RHUvTZ75cBgsemdUNArJQju6kGtlZC3DVhFvgWIh3L4/HA5wEaLfut3t9le+JoEVRKDR5+cjZ8fA1/w/Q110Zo+ECmv3X6e5/H9fFrc6TKdu76w6tYu3wO1TkhlltYjnuffr1b5omx4+gPqsdfxtrr5mDl4hTqs6FF9xSOGUPgWkXAyd4KSpUWCtXY+9F1rZ4p21fPCLg6CtEmU0NNUt9HoRW01mH9gXf1ke9d/gR87cxzk1korsO6/e/Cw9YBc31j8GNJKh6NW4zbo2YZIHXL4Q+R2VSFF6asxosXf4GdwBqnVxvfRncwsKtUGrz9ylGQNrf0+zm6BVvjZsHRyQZfVpzCd3XnkFIWhdBmnfjpxAQ/hEZ6Yef3GfDwdEBjg0S/vLOrLVqb25Us2191dLTGA3+YO5gQzTb372m/4kRNPu6MmY0bQ02/6fdwtoGoVWEgGFtT1YrtH52l3XUeemoeBH3onJhtI1c5ImJ+NdWtCIvwhIubncG7H711As0iGRYuj8bUZPNkM3X4XHtTAkiZkCXZmdoCNCvlKBbXYbybP0rFjbRcQMmpaBOEq03TxEFVqcs6Efp3tAIm2iK6kcpSDlrx6PxsG+i5kMwT8gBIpWa/XwaKIZvXHYHMpkooNGos8B+PKBfLKOkkBDPRjlIo2UP9/q7Z7EzSdacNy6+PRfwU4zuiebqYt0zLIsmTf7z5BVydHfHg7TdQHHcfPIsdvx3FJ/9+2gDX3sa9/Y9HkVdUjoTYCP342x75JzavXYyl83Q/VljZTn+XKHt/tCPAynZG+wmy+I1FYLSX7VxprsLWLiKpN4en4LHYJcZuv9dxFxtL8PyFHWhQtGG6Vxi2Rs3GfSe3YZyjB75b8BDuPv4ZCsS1eG/mFrya8RvEKjleT96EP5z9BiVtjXhl2kbM84sZdBx9OSA/Zrf9fBIBlbruPnX2rWhLacNbKbfSv6ulzSB6IRw4OCnsMDswCn9JWQNRgxTbPzitd93RRrfjhYTEQCpk2tSoa0nywFPzaInJaLery3YKsuuQdr4clWXNmJQYhPnLzFvyZQ68OtoXT5kehDmLBh9fWYkIP36ZRrNZ7nh4ZkdTRXOEahYf3xWfQ7Oik9Dryyl5fqkuBDgFwHMAhON05IlGpAXfhTyOJiKxZglrVDlhZTuj6rhGTbD18laUS5rgbeeE1cFTLCJuVrZj/DFUVbSgrEiEuMn+WLyyb02vrl6HvWwnaXIMbG10jA0pfyHddjr+ViiUOJuWbXbBWKJTQspstr/1LBwc7HDPH17DhuvnY92KOXQ90oEnJiKY6pn0NG7x3EQsvPFx/OfFhzAraSJOnLuMp/76PnZ98RI83JzpXhh5YvzFykaOTgQYeTI6z41FbToCo508+Uf6r/il7CItpamTi2l5zYFlhg8LjEGFEA1VshZaCkLa9K7ar9MJc7dxwPcLHoKT0BYr9/2brvHhrK2494RO7J28L2q/2ftp0SPYX5mF97IPYq5vNF5NusmYpQc85rn9O+B1VqdhUufQjILgOny37n4Df7+Wp+NcXSHq5W24MSwRC/1i6ft7fspEUV4DSObK7EWROH5Ad5cZEOKGG2+dQl///L3T4LQcttyXQlvykhv56opmxMT5mV3AdMAgmDCxK3kik6nw8RvHoW3PuLr5zmnw9tP9xrEky82qwZ6fsmipz4YtU6kmzUDttx2XkZ9dR6fPnB+BaTNDBupqSObJNSp8XXS6xwyTnhYkGSXqMt07PGsehJE6IkVdCQi8AW0DB61xPMyQ7GeknDLyZKSQv7bX1YJDRmM5yP+vD51GvydH2hh5YvwJEC2zrEtVNOP0tvuMF1sfVvLkxdeN66LzwpPdxeeMh6Lnkdu+/x3//WoXVGoN1iybhWce3ARSF/bki+8hMjQQ9912PZ3Y2zjSVvnV9/+H2noRAv288PSDm5A8uZOlYuTJYE+Izbd0BBh5YuknxOIzFwKjlTxRaFX4tSwdr2Tshr1AiE/n3I1/XPoVl0XleG7SKqwOMe3JGGnxS1r9EvOxc0GtrIUSI/P9xuOZ+BX09bev7McXBaeQ6DkOF7qIVtoKhJjjG42/T11HyRVCsgh4fOxb9hQlXQZrbWIF6mvECI30RLmkEb+WpkOqUaJ5nwKeMidke1Yizb8YcW6B+HT2nUYtp5CrUVLYAI7jISbOB3SN2jbY2lrBL1BX9rT9gzMQNUhwyz3JtAPNB68fg1ymwrhwD6zZlGDUOpY0qCt5cmx/Pi6e1d15u7nbYcsDMywpVH0sUqkKO75IRWO9BAFBrrhxy9QBxdnWqsAnb5+gGiEk1f2ex2bD1l44IF9DMamwtQ5polKI5IaaJn2tpSrmwOmSo6gJAnjQikjrXw6w4gHqsVWu04EDI0+G4gplPgkCZZJGNMjbaGc78p030sbIE+NPgHz2nztZDE4LKhpLxGONsWElT4wJaLSOYeTJaD05FrexCDDyxFik2LjRjsBoJU9ey9iD70rOUfhXBk3C/01eg32VmXg+dQd9jWRY/Gva+n6Pp+ucroOdhbbYPu9e+Nt1dqcpENfh5sPvd/M53sUPm8JTsCxwIn3v/pPbkNpYgmcmrcS6kJ5vdlMbS3GyJhdlEhGeTVgFd2uHXmM9e7yYdmCxthbAejEfH5UfRlCzB2aXjYear0XxzBpwQi3FwFx6LySYH75MRUVJM9bfMhXNTVIc+C2bxujsaoc7HrJMsqGvA+8gT6QyFf775glo1FokJAUhLNILwaFu/V4rIzWgtqoVh3/PRZNISlsiT4j3MzmU4wfzkXq6DAHBrohN8B+QD5MXNXICuSErbKpDbnUN+EbqGHIygJAnBgI2Rq53rQ9j5Mm1fsIjtz+SHUZKZcnDgVsiZsCab9wN+FBFzMgT05DNulQNcYsMN9w8mT4EMcYYeWIMSkaMYeSJESCxIaMaAUaejOrjY8GbgMBoJU+eOvc/HK3JpTtdEzIVz05aSTvuLPv9NdoVh9jX8+9HhJNOKLU368gmIe+TVsR5LVU0UzPQwR1fzL2327SNh99FsVjXjSbUyRPfzn+w25hfSi/SLJh49yD8d9Yd+veJfkppWyM0Wi0eOr1d/zop8dkQmtxrC8hP3joJcatOyNV2rgDnsosRX6cTD22KasOLG1abcOLGDyWlPblZtbhubRxOHCxAa0unmOxjzy803pGFjOwgT04cKsT5UyXwDXTBTVsTLSS6vsPIzqjB7zuzKOmx5HpdRx5jjbRGJq2JSYeg2+5LgbvnyKfbk9iVWjWkaiV+Kk2FvEZNRV75uqaO/Zq6nIO2td9hY3IAI0/G5LEP26ZzW6ohUSsx3SscE92N6zQ2VMEx8sQ0ZMuKRagqb8H0uWFImWNcy2lGnpiGca+jGXliJiCZG4tFgJEnFns0LDAzIzBayRMiEkuegJGuK4Q8iXTuVP9/9fJufF98HtO9w/HW9Ft6RIx2DtCq8d+cwyBZII/GLsbSwHgoNCoE2PeehXC6vhAHKzIpcZPsHU5Lda62NrUCC3a/RF/eGjELD0zQEQ2f5h/HB9mHaDcemUZpMI1kuPy8+FGD16oqmnHqSCHN/ugwvj+g1VUXoc1aDucVQjxiBoHcnkA6si8P6efKMWGSL3Iz66jIqEymBCn5ue+JORZV9tHfPwui9WEr4KG5TYmDu3Po8LWbp1h0xknXPRXlN2Dnt5co8TF3cbRJmjOXL1bSPZOskxtvG1jZT3/49vR+AUkVKQAAIABJREFUq1IOZ+vuZWtHq3Og4bSoljXDw8YJpY0NUJVwELjwIDCiUzTRNVEVjM2SHGPOgZEnxqDExgwUAZGijYqik+zMDWHJI9q0mJEnpp0i6a6Xf6UOUeO9sWK9LlO2P2PkSX8IGfk+I0+MBIoNG7UIMPJk1B4dC9xEBEYbeUKeeKm0amw+8gHq5WLsWvw4vO0MhT4bFRKs2f8miC7KhtAkbAhLwhuZ+1Da1kBJlkPVuvKTrrZryRNUdNZc9qfzP+BgdRZ1l+QVii0Rs/FlwSmcri+gr7la2yPJKwzBDu60BbJIKaFlRh2CrqSkZNsHZ9DarMuiudpcp9li8ZRYBHh1lhWZK/YOPxdOluLEYV28xBavmoDUMyUQ1Utx670p8PCyjAyG/vatUGho5oVW29m61cvHEZvvTu5vqsW8X1HajB++SKXxEE2ajSZkzJDOSqTD0op1ExE5vu9MLHNumAg5E10Et/aStGaFFGK1DHvKM/TLcFIOqmLdn3wHHqzG9R+BpgogLYqZ9YwAI0/YlTGUCJDubRmiCkqAklLVIAcj08WGIChGnpgGKin9zMuqxbgID6y5yTjdMkaemIZxr6MZeWImIJkbi0WAkScWezQsMDMjMJrIk3ezD2Bb/kn6Y61cIqJInLv+hR4Ree/KQXxecIK+F+Pih5yW6l6RI0/QDix/xqzIHq/Nw4fZh1HS1kDLE3oykjFyS3gKPs07jg9yDiHcyRufJd8FvhUfp48UIu1cOZxdbeHkYgcXVxtcuVRD3VQ5NuHhuxcM+Y/WmooW7PrxMojYaIfOyQ9fXkRFSRPW3jIZweNG7kezKYd18UwZjrV3E+qYt3rjJCrAO1qMEEDZGVU4fawIapUWD/9xvlGhlxeLsOOrNDg62eDOR2aZvTVxWmMpolx06SIOVrrukh3X+/8Kz8DGSoi1wVOhAUe7Yik1GsjUCjqO0wDqQg6cSrcVnpAHYTjo37xedJY5NQdVHpls1PbH5CBGnozJYx/WTVdKm1Ara0WwoweWBsQN69pdF2PkiWnQtzTLkZ1RjeBxblh3q3Gi+ow8MQ3jXkcz8sRMQDI3FosAI08s9mhYYGZGYDSRJ0Qn5Fx9+2NqAJ42jti99MkeEWlTKfDE2a+RLmrvZdplFOmiY8XjY/W4qZjiMQ72VtYgoq9DYaQk6P6Tht33NoxLgkSjwPUhkzHZPYTWjy/b+yotI3pEuhQNBZ39Vbc8kAI3d3tIxAocOJyNK1eqkB1Uie2b7x6KcLv5zL5cg+L8BkRN8EFEjBdtm0va5y5fE4voOCNqLIYlyr4X+eydU2hplmHS5ABwfECr5bBoRWcHQQsI0egQOvRv7nxkJi2j6mriFjnUai2OHyqAl5cDUuaF01IfUvIza2EkElN0OjmmWJWsCY4CW0qIePaQmUXITKIRJFcrEOLoiYX+E2gZXINcjMp2gjPC2Ye2EL/YpUMViUFdzdHuOAY3Q5488Jw48O15PYapqeOgqTdlB2NvLCNPxt6ZD/eOyecBKX0FeNgUngzHduJ0uONg5IlpiHe0K/Y3IXuRkSemYdzraEaemAlI5sZiEWDkicUeDQvMzAiMJvJk9q5/UIKBGGkn/N2CB6h+SF92y5EPqSgl0T+5KWw63sz6HS8nbaTkyXDZxsPvoVhcj0luwZBrlT0K0b6RtQ+7stKxIm8KeO1V5DPnh2PazM46hg4hWnKD+q/EG4crfIN1jh7IR9qZMsxZFIkp002/GTd30HKpCjKZClcyqnAlo4aSTDa2VgiL8sTS62NRWy3GN5+cg5OLLZ5/YRFqRLJRnbTQUYLTk37JJ2+fBCFQOmzazBCcP1lK/7z/ybmwsbPqFX5CdlxNjpAbpKM1ObR9sEKjwUT3AMS6BcKab0WfOsvUSuyrvGzgk/ggvq42K4EAao1G/3JfuiVE90Tg0UmeaBoAng3AswdU+RzQ6cbcl9M14Y+RJ9fEMVr8JgrFtWhRyjHJPYiWoI6EMfLENNTbxApkplXBx98ZN985zajJjDwxCqb+BzHypH+M2IjRjQAjT0b3+bHojUdgtJAnZ+qK8MiZL+BgZQ13GyfM94vBQxMWGb/RERy5tzITUpUcy4LiYd8L2VPb1oqXP9uD4BZPiIUyKIQqxN8QQDVbOuxv6Tvxa1kanoxbjo1hna8P59ZSz5Th+IF8RMf5YPmaoU3XrixrAo/PB3lKRjr91FW1IvNSFcZP9EV0rC7rZf+ubGSltyvoXgXEvKVRtMUzEbglmRerVkSPevJk7y+ZKCkQQS5TITTCA/OWRcPF1Q65mbXY83Nmj5dC3GT/XjNtpGoF6uRikA4apA0pScG3bb9GrzRX4kQNqZHpNHdbR0z1CMX+ysu4mhAx5TpUlwFacS+1NwJAGMYDzxpQ1wDaZlLSw4HvwoOmltXr9IczI0/6Q4i9bw4EWpRSFIrrYSMQYnP4dNq+eLiNkSemIS6Xq3HpfDkCQtxwIyvbMQ28wY5m5MlgEWTzLR0BRp5Y+gmx+MyFwGghT/6TtQ/fFJ7G1shZeGD86GuT29d5NTdJ8fM3l9AsktJhP40/B5lQSYVwf1r4CIR8AX39xkPvUtHbL+feg6ghKjPq77pSqzR4//VjID9aH3h6Xn/DB/w+aZFMWiUTm5QYiEsXKvS+EmeGYNb8CJpl8vGbOl2bDrO2EUCpMExNsLIS4K5HZyE0wGnUkydkn0d+z8OVS1WwtrGCUqEmmfMQ8Pk0A4eYq7s9+HyA1LcT4eG+xH0LW+twsCpLT4Q4WdtiWUA8+Dw+fq+4jGZlZwnZgA/zqonaNg5qXUJMr0baFsMW4LouTxJnepYPMldo14QfRp5cE8c4KjZBSndIhtqSgDiEOHoMe8yMPDENco4Dzp3QlT4//Kf5EAj6J7xY5olpGPc6mpEnZgKSubFYBBh5YrFHwwIzMwKjgTwpENfhuQvfo1jcgPdmbEGipxEtOcyM01C5I11USEcbIs5KLMezEhf9O3Vd/jhpJfJbaujeLzaWwF4gxJEVzw5VOEb57dAQ2fpgClzdzN9xR6nU4NO3T0Au6/lOmWRcrL4pAUf25iL9QgUVs5VLFUicEYrwaG98+9lZKJW6zjp29kLccPNkePs6wc/d7pogTyRtCnz8hiFpRPZKxIVvvXc6lHI1HJx04q29GdEEutxUjjJJA1oUhh2dSFmOUCCARKUTdjWncRwHdSFAynaYDQ0CjDwZGlyZ1+4I1MlbUSFpgq+dC1YFG9e9xZw4MvLEdDTTz1dALlPitvtIxzyHfh0w8qRfiIwbwMgT43Bio0YvAow8Gb1nxyI3DQFLJ08IabDx8Lv6TR1f+Rxs+L1rN5i2+5EdTZ4CffpOp04FeQoUvsEdEV4+yGupxV/TfgYRtyVifGXt4ptTPULw/sytIxr4j1+loaxYhNUb4hEa5WXWWET1ElxKraCZJuSHXWO9LvXA3dMeKXPD8duOy7C25sPLz4USThqNFrfdOx2OzrYgWSfEPnj9GC1rIcTJtBnj9Nos1wp5QvZItF4O/56LitImSNqUdN9EBJeU6BhjO0rOo1HeZsxQs47RiDhoem98Zda1xqozRp6M1ZMf/n1rwSFTVAE1p8V8//GIcBq+Vuhkt4w8Mf3MSVZnU6ME12+chHAjvr8ZeWI6xj3OYOSJmYBkbiwWAUaeWOzRsMDMjIAlkSc/lJxHan0pbgxLpF1wiH2Ucxj/zTtG/5vonRy+7k9mRmDk3OVdqcXuHzNhZydEQIgrvHydkTxLt28Np8X6g++AtITsagv9Y/GvxPUjFzSAg7tzcPlipdlFYzPTq3BsXx5I5gkx8mQs/XwZGhukSJo5DiFh7njn5SMgpUMdRl4jmSVd7bvPL6CqogVLV8dSfZQOu5bIk449dZwFEcm99/HZ4PeTht2qlKNVJcXu8kuDuoa0LToNEpNMy0GVD3Cs9MYk2EwdzMgTUxFj4weDQL1cjHKJiH4/E1F2PrkAh8kYeWI60OUlTSB6Yl4+TtiwJVH/0KE3T4w8MR3jHmcw8sRMQDI3FosAI08s9mhYYGZGwFLIkzpZK80wIW175/hG47Wkm9CmVmDZ3tdoTfWWyJmY6hGG6d4jo+pvZtipu+0fngHJtFi8cjxiE7pnDOypuIwXLv6oX/rT2Xcizi1wKEIxyefFM2U4diAffgEuSJ4bhsAgF1gJdVkfRM2fkEECq/5rqbsuWlcjxv8+vQCtVlduM3V6CGYviugWF1m7IKceVRXN9L0bt0xFQJBrt3FtrQo4OhuWrlyL5ElDXRtyLlfD2dUe8VMD+j3HDFEFztTl9zuurwFaGaAu42AVxAPfhKotTR2gqWeCr4MC34jJjDwxAiQ2xGwIcOCQ1VRFv6dJV7uJw/gdxcgT049Rq+VwJaOaaoaR7/D1t07p8/uakSemY8zIEzNhxtyMLgQYeTK6zotFO3AELIU8+Sz/BN7PPqjfyL+mrcdvZRk4UZuHIAcP7Fj40MA3aYEzi/IbsPPbS5RouPuxWb1mDBBCSa3VItrFDy9MWWMRJUv5OfX47YcMPaqE+CEEELGvPj6L+to2LF41gYqYFhfUIyLGG5HjDdO5pVIVGmrbEBzqRrVNyDxxq67Nrr2DNe54eAaI0GtPRtoPnzyUDz6fjzWbjK+zvxbJE1Mu7QqJCKkNJaiV6fR1BmqqQg6cHOB7AlY+xj1l5lQcVIaNewa6PJvXDwKMPGGXyHAj0KSUoph23rGi2SfW7SLnQx0HI08GhrBGwyEzvRIyiQrjwt2phhif3/NnOSNPBoZxt1ks88RMQDI3FosAI08s9mhYYGZGwFLIk/9k7sM3RafhIrRDi0oGL1snkHRgYhNc/fH5nLvNvPPhd0fayYoaJVTL4+LZMtRUttIWuokpwcMfzCBXJATJvp1ZlCiJmeiDaTNCkXauDJlpurbBPn5OaGqU0hKcwHGuWH/LVIMVO8ReiZYJIUlI5ol/oCtc3e0QFuWFiBjzaqmQxccSeUKeAh+svIIYV3/42TvT9sOkBG6wpq7loG3QeeE5AMJxxpEn6nIO2tbBrs7mG4MAI0+MQYmNMTcCOS1VkKpVSPAIxjTPUHO779EfI08GDrNarUXWpWrIpUpMnBKAhdfF9OiMkScDx9hgJiNPzAQkc2OxCDDyxGKPhgVmZgQGS54otCr8UpqG3OZq/HnyaqOiu9JcBaK9wPG0EECAJK9QvJj2M34rv4TnElbh/exDECl0QqHjXfzwcOySUd9hh7Qj3vbeaRCR2A4jpS33PjEH1tY9Z1gYBeYIDiouaMQv/0unZJCNjVBfStNTSLc/OAMubnb0rdYWOT5756QBFta2Atx2Twps7KwgbC8BMvfWxgp50qyQIr2xFHmtNXoIXWzs0aLQtcIeqGnlHNRFADquYT5gHcOjrZL7Mq2Mg5o0kGIVOwOF3qR5jDwxCS422EwISNQK5LbUQMDjY1P4dNgJhGby3LsbRp4MDmLS3p6Is9vZWeG+J+f26IyRJ4PDWD+bkSdmApK5sVgEGHlisUfDAjMzAoMlT/JaqnHL0Y9oVI/HLcOmsGR9hO9dOYg9lZfx1MTlVMeEWLNSiiV7X9WPcRLa4pfFj+H/Un+kJTpvpmxGpaQJr2TspmOSvMLwTsqtZt718Lvb+3MWcjI7b2ZJBJOnB2PuosjhD8ZMK5KsE1Ju02GE+EhIDEJFWTMqS5uQkBSE0sJGmoGy5YEUuLnrBDJ2/ZBBdUsmTPJFYV4DFDI1VqyfiMiYoe3UMFbIk6ymSpysNX+NTEe5TtfLRxgK8Oy7syfaJoDvyAFCHlRFHDjDbshmugKZm54QYOQJuy5GCoFCcR1alDJEu/jqv/OHMhZGngwe3QunS6FRaXXf0R7dRawYeTJ4jKkHRp6YCUjmxmIRYOSJxR4NC8zMCAyUPPl72i8Id/GBQqPGe+1aJYQI+WnRo3AW2uLB01/gfD15TA10ff2LglN4+8p+g11sCpsOcsOX0VROy3PID691B95GlawZs32i8HryJjPvGiBPXPb/egUurnaYuyTK7P67OuzIOiGv3fHwTBz4NRtqLYfr1sTCwclQ1HRIAzGzc5lUiQ//fVzvdeb8cEybOQ7iFjl++iYNN92RhG8/O0/bDW+6M4mW8pAuOKQbDukOc+cjs4Y162YskCelbQ04Up0LhUbXvthc1pvYK9+dB74LB34XAoWU55AyHZ49IVAAMpfZ8CHAyJPhw5qtZIgAyUQl4rEkHW1D2DRahjuUxsiTwaObl10HUUMblqyc0KNwPSNPBo8x9cDIEzMBydxYLAKMPLHYo2GBmRkBU8mTozW52FN+CecaitCmUuij8bBxRKOiDTeGTsNTE6/Doj0vo1Ulh6eNIxoUbZjrG41Xk27C6gNvolraDCII62XrjLuOf2qwox0LH0aQgzuO1eTiQn0xlgRORJxb/11ETIXl6P48pJ0t10/bsHUq1dwYCvv9lyvIvlyNuMkBWLSi57rioVh3OHy+9c9DIOr99vZCSoZ0dNkh7YRJB549P2eCaL24eznA29cR9TVtlExZtGI84iZ37zA0lDFfy+RJk1KCjMZyFIjroNF2tnI2B560u05R7zU3Al9A4KHLPiFCsiRDhdnIIcDIk5HDnq0MlEka0SBvQ7CjB5YGxA0pJIw8GTy8NVWtNEO0q/B7V6+MPBk8xow8MROGzI1lI8DIE8s+Hxad+RDg26jw0oX9ECtl+NvUdZTwIDXLDlbWBosUietxuakc/8091q1bB9EseSZ+JdYdfJvO+XfyJjxx9hu4Wtvjo1l34JYjH9A2hndGzcEnecdoZsqB5c/QsX+5+BN2V3R2bjmw/Gk4m+FpVWmhCCHh7vo9tDTJwHEcXN3tIZEo8elbJ6HR6NriEguL8sT1GyYNGljSMYaQCSSjhRjJwvj0nZPg8Xkguh9OzraDXsOSHPz6fQYqypowb0k0xk/07RZaRWkzzhwrBPn/DiMaKbfeO33Yt3GtkSdn64vgam0HpUaD04NsP9zXYSgLOKCTJ+02lO8O8O144DnqSBZONexHyxbsggAjT9jlMJIIqDkNLosqQVoYXx8yGT62zkMWDiNPBg8t6Xx3ObUCdvQByMxuXe4YeTJ4jKkHlnliJiCZG4tFgJEnFns0LDAzI3Df6c9wsb6Meu3IHlk7LhF/jF9BXyP1y0TMleiRXG3O1nZIcA/GcwnXw83aHm9m7cNXhadpmY5YJdeX3GwvOIl3rhzQTw9z8sb/5t+v//tUXQEeO/MVfO1csHPxY4PeIVGQJyU53r5OtBWum6cj/ZuYg6M1/INckZ9dR5+0TJ8dik/ePglbOyHue3KOUWuTuRWlTZgQ7wcff8MfhtveP001PoJC3SihkHq6FFcyqjEpMRDzl+l0X8aayaUqnDlWhCuXq+Hl60wxDxrnNuwwjHbyhPxblGjkSG8oR4Wkcejw03LQKgAejwdNS2d3nd4W5NnxACUHzgp9kixDFzDz3BUBRp6w62GkEaiSNqFG1gpPW0fcEGLYac2csTHyxDxoZqZXoU2swKz20tuuXhl5Yh6MGXliJhyZG8tFgJEnlns2LLKBI1AhFSHQvjMbY+Ph91Asru/R4TPxKzDVMxSXRGX4R/pOHfFgZQ2JWkk73zwau5Rqk3Q18t4NB96korDE7o2ehzuj59InUFuOfoyclmr6eqJnKN6bcZvBXJLqG+zgMfDNASClIvt/y0Z5kQjkaUpfRtrj3vHQDNg7WuO9V49AqdDgjodmwtm178wQkkHxwxep1HV4tBdW3RiP/buy4eXjCIlYgfOnSntc9p7HZtO1xrIV5tUjPMr8LYiNxXQ0kyfZzVUgXaoa29t3G7tnU8dpxYC2iQOn4NHuOJyKleCYiuFIj2fkyUifAFtfCy3NPtFwWiwKiEWoo+eQgMLIE/PA2tIsQ87lGljbCHDXo7MNtMgYeWIejBl5YiYcmRvLRYCRJ5Z7Niwy0xGok4tx85H30aqUYfvcexDj4kedzN71TzhYW+PxuKUoaxNBqVGhtK0RR6pzui0ywzsCL05ZCxWnoTomHaZQaFCQU4fQSE+qe0EIkq8LT+NwVQ5eSd6IFK9wOrRS2oQPsg9hvt8E2kHHUWg+oVRSKtMikuOHL3WkBjFCZpDMkiO/6zJmiBZHYkoIjZVobiTODMGs+RH0vZ+/SUdJYSOuWxuHqAk+vQJcW9WK77dfhFqt05SwtbOi2S1lxU19HsrkpKAhF6U1/aoYezNGK3mS2lBCBZXlZhaBvfoK4BQcVETjubOabexdJNfAjhl5cg0c4jWwhTp5KyokTTQTdUNYEvj99TQfwJ4ZeTIA0HqZQrNPWuWYOT8CSbPG6Ucx8sRMGLOyHTMBydxYLAKMPLHYo2GBDQCB/NZabD7yAZ0Z6uSJr+bdDyseH0k7X6Svpa19ESq17o5JoVVj48F3aaebDvPkHPGv0I2IjvSlhEFX2/NTFnKzauDqZoetD86gb6VfKIdIJMWEyb7wdHWkwqFDZRdOluLE4QID9wEhbpizMAJEW+Pz907TdNQp04MwZ1EUWlvk2PHlRWy+O1n/dOXUkUKcO1FCS3CaGiTw9HYCEZDtMJJRIpdr8O22c1DKNfAPdKFdY3qy+KkByL1SS9vvkta9xNeqdRNhay8cKgiYXyMRGI3kSUFrLS43VaBe1mrkLgc2jNNwUBcDXB/aJgPzzGYNNwKMPBluxNl6PSFAMk5J5x2idzbTJxITXM0vEM7IE/Nde81NMuRk1sDdwx5b7k/RO2bkiZkwZuSJmYBkbiwWAUaeWOzRjMnASHcaIuLqbWe68BrJNvmm6AwVaiVmpRXgjglzUCNpws7ydJoBcnzVs0hPrURZcSNE9RI4htniJfFOvD5jE/JbalG4pwHurY7wDXTBTVsT9WdQlNeAnd9don+TzI67HplFtUQO7dFlrvgGOENoLcC6zVOG5NxIqun2D85A0078BAS5Yu7SKJoN0tUa66VwcrHRkyUqlQbCLoQOKcU5sCsbpKUwMUIQ3XxXMpxdbHHyUAEtxbG25kOp1CIs2ou2GH7n5SN0rIubHeRyFSVLHJ1tcNt9KbQVb0NdG5auju1RRHVIwGBO+0VgNJAnVbImlIubIFHLQTroNMrb+t3XYAcQIWVNGaAd+qUGGyqbbwQCjDwxAiQ2ZFgQIJ9hxeIG2AmE2BieDCHPvA9SGHlivmMkWcRpZ0upqP1dj87SO2bkiZkwZuSJmYBkbiwWAUaeWOzRXJOB/d/FH/HXKWv1eyPZHxcbS1HW1oCdJWnIF9fCz94V78/cAn8749vpEg2S+09+rtcaWeWcAO0FHn6PyICarys92RozA1t85+DTd08ZdJ/xiLVHiKMnbbEra9cPIfWwROdj6fWxkLYpaZmMqEFHOBBz97KHqL7z747XV2+cRMt6zGEnDubD1d2Btrkl61eUNNOMEdLdZu6SKCoIOxD7fnsqKss6s21IVx4+Hwb7I917btg0mbqvLG9GXbWYlvkQ0iUvqxZhUV4ICdNpyjSJpHBztx9IKGzOECFg6eSJSN6GH0rOD9Hue3erru1fFHbYg2ILDhgBRp4MGDo2cQgQyGmpglStwlTPcZjiEWLWFRh5Yj44lUoNUs+UwtHRBvc8PlvvmJEnZsKYkSdmApK5sVgEGHlisUdzzQV2uq4Aj575Cgv8xuMfietphsmbWfvxVeGpbnuNcPLG53PvhjXfsHSm68A2lQKk1phoj/w35yiq25qh4Wtpm2C7Y3YQ1UlQ6FaLs0H5dNpzU5dDvp+H6opW2sZXJlVCIVfTLA3yZdqTkXIYohtCLDrWB8mzw7D9g9P6oSnzwnH6SKH+b0I6zJgXAblMBRdXW/AFfJrVYaqVFzdhx1cX6bTAEFfa/pZkvNx233R9a2BTfXaMr69tQ3VFC2xsrLDn50wDN6RLjkDAp1kkZD1moxMBSydPMkTlOFNnWII21EhrWzmoy4d6FeZ/OBFg5Mlwos3W6g8BiVqB3JYaWip8c3gKbAS9/37pz9fV7zPyxFTEeh9PSrdTT5XA1t4a93fpPMjIEzNhzMgTMwHJ3FgsAow8sdijuWYCIyRHRlM5nrvwA8iPC2JLA+Lwt6nr8M9Lv+LnUh1JQGyhXyzI0xsiuroqKAF/nrxa/55MoyRNMWh6/66ydLSqZNhRcoFmqBDdkhVNCYgQ+kFar6TdYIhJbOVITSjCQ06LIS6UorioiWZs3HZvCtXp+Ojfx/TdahKSghAT60vLUPKza1FaJKI+iI6Jt48T1mxKoArtb798GBqVlpIpy2+IQ0ZqJUqLGlGYq+vmQ/xL2pT6uOcti0JCYpDR5ymTqbDtvdOUgOlqM+aHI2lmp7iZ0Q77GEjSV88eL4RapUXsJP9u7YjNsQbzMfwIWDp5Qv7dDnU3na6oc0pAVcgxgdjhvxSHdEVGngwpvMz5ABAg2k2tKjli3fwxwztyAB56nsLIE7NBSTOPz50sha2NAA88PU/vmJEnZsKYkSdmApK5sVgEGHlisUczagMjXWxSG0ug5bTIaarC75WZVJy1wzraAF8XFI86WSsuNJTgtaSbMMk9GC7WdigQ12HLkY9ot5tnJ63EmpCptDMOyVqpknZ2ewlu8YSnxAklbvWw1lhhQVGcAWbOrnZobZbB1tMK8obO9ddunoLgUDc6lrTezUqvopolN90+TT+f6Hp88u4JKpp6w82T9SUqZEBbqwLFBQ2YOCXAYL2P/nMcUkknadLxZkycL5atie3zPIkoq6urHXh8Hvb8dJl2tSG6JqRkhpintyNuuSd51F4TLPDhRcBSyROxSo5jNbmolOiIyeEwTg2oSjiACcQOB9zDugYjT4YVbraYEQiQhzzZzdW03w7RPnGyMj3ztKdlGHliBPhGDtFqOSqcLxTy8dAf5+tnMfLESAD7G8bIk/4QYu+PdgQYeTLaT3Dk4icZJUpODXdrB315ENdIAAAgAElEQVQQP5Wm4l+XdvUa1Dy/GNwSMQMPnvwCCq0us8JWIMS7Kbdiontndsbeykz8X+oO+v6X8+7FS5d2IbOpkv4d5OABO7kQk9JDIeS6i7IRzZFJiYFoaZLh8N5cOoeIpiYmB0FoK8SU6cEG8REdD6G1FRwcDDVECKlCSI3FK8cbBfLxg/lIPV1GszfGhXvQsiCSlRI13gfXrTMkdro6JF1xvvzoDJSKztIhG1sr3HbvdBw/lA9SwkOEaN29OnE2KiA2aMwiYInkCSmvO1WXD7Wm5xK5oTgsTsVBXQKQzBNm1x4CjDy59s70WthRaVsDGhUSxLoGYIZPhFm2xMgTs8BInXAccOZ4MQR84NHnFuodM/LETBgz8sRMQDI3FovAWCdPXsvYgyM12fjLlLVI9DRvSYTFHvogAksXlYF0tSEtgT/MOYwQRw98v+Ah6vHPqTtolklXI11zAu3dUS9vxRMTl2FmexrrqboCbM8/SYdujZyN6d5h3aJ6JWM3FZUkXXIIUUMyTeyCrfBq0k3Y98UViOqkaHWTwk/lSstkrGz4sLEW4rZ7ptOSHJI9QnRDmpul2LhlGmIi3NEsUelbFRsDA9FCIZooxhpZkxhZnzzdeOelw/TvB5+e101DRK3S0JKgLz48o9dV6Vjn+o2TEGYm4VljY2fjrh0ELIU8kWtUIISqWCkbdnBJK2J1KQfOsPpt2ONgCw4dAow8GTpsmeeBI6DSamjbdWJLAyci2EEnrj4YY+TJYNDrPpeQJyQ76LHnGXliXmQBMPLE7JAyhxaGwFgmTwpb67DpyPv6EwlycMe7M26Dr52LhZ3SwMPJTKui3VrMYb+Wp+Nvab9QVy5CO7SodDdENnwhPpp9O7Yc/Yj+fXvkbPqD4d4Tn+G5hOtBsk3ITRTJMDHFSHveRzO+xJXmKgS2umNOyQTEJPqgKr8FJFvD1s0Kq7ckoC63DacOF2DTHUm0pCZwXM9derxcbEwmT0yJt6ex321LRVV5M9beMhnB4zp/QNXViLH/1yvw9nOmZUPOrraYszASajUHckMQHecz2KXZ/DGMgCWQJ1qOw56KjGEt0ek4ck4GqEo5YPiSXMbw1TZyW2fkychhz1buGwFSYlwja4WNQIh14xJBypUHY4w8GQx63eeeO1EM0rr+4T8toCL5xFjmiZkwZuSJmYBkbiwWgbFMnnyWfxzvZx+Cp40jGhRt9IxIScgEVz88PnGZQTmKxR5gL4GRtMR9O7OQk1mDe5+YC1u7wau+v31lP74o6OyM423rhDq52CACHzsX/Lr4MfpasbgBoU7Gt+0lWRtEEJbEWlvVim8+PQ8bWwFkGhWswEd7lQ/1bW0rwOa7kvWdZwgZ4e3r1OcxjQR5cvJwIc6fLEHynDCkzAml8ZF2yF/99ywleoiRL+6b7pwGL2/H0XaZsXgtFIGRJk8IcUIyToZTFLbjKLRtgLqcicNa6KVp1rAYeWJWOJkzMyLAgaOdd6RqJXzsnLEqeDLNdBioMfJkoMj1PO/cqRJo1Vo89Mx8CNuzixl5YiaMh5o8MTUlnGxrIHPMBAdzcw0iMFbJE9IelxABIoUEf5u6lnZw+a38Ei1HIUYIlden34zxLn6j8tRJC9rcTN1eiPDopGmBSD9XDjdPB6M1PK7e+ONnv8bJ2nwQodfz9cX4bM7dIATKe1cO4vOCE3T49UEJeL5LhxxjwSMlLN9vT0VttRiTEoNQnF9Ps0t6s6tFXI1ZZyTIk9JCEX76Jo1mwyy6bjwO7s5BS7OcCtl22KKV4xGXYJ7sIGNwYGOufQRGijxpVcohUrQhXVRKxaCH2zQtHDS6bHlmYwABRp6MgUMexVsk5Tskc1bDaTHZIxiJnroHKAMxRp4MBLXe56SeKaVdBjfdmQQfP92DN0aemAnjoSJPigsakXO5Gr4BLvT/l1wfC48+xABVKg0a6yRwcrHFVx+fxfI1cSAp7ZETfGBjY1iPz8gVMx3+GHEzVsgTorGxp/wS5vhFI8bFH+sOvg0bvhX9Mntq0nW03S3JPrn1yIdobM9CseZb4fmE67EscKLZrgapRomzdYWY72coQkrEU38ru4TMpgosDIjVa4McqsrGZM8QuFnb9xhDdUULrG2swOfzcOJwAZoaJBC3KEA+M3qzuYsj4RnnCFKmZIqtO/gOyiWN+G7BQ/C0dYSjlY1++oe5R/BJ7lG8PG0j5vvFmOKWjv3pqzSUFht24CAtf23thPD1d0ZAiBsVfQ2N8ECbWAFX957x6GvhkSBPSCvg9189AisrAc2Wkba3MHZwsoGjozVcPezp5zkzhoA5ERgJ8oS0F/+tLN30bWg5qGsBgRcPPBMT5EgnHTJH2wpwag6aatOXZzNGLwKMPBm9ZzdWIicdxjoeyq0ImgR/+57LivvDg5En/SFk2vvkPpw8rIue4I3r1up+gzHyxDQMex1tLvKE/IC+fLESEyb6IiOtEmnnyqiYYVdLmjkOybNDDUQFaypaoFRpcWDXFcjlKmjIjwONVj+NpKmTdpkBwW7IulQJd09HNNa1QSFXYdHKCbR2fiSM3LiRGx1mI4cA6TPvLOy/RdpQkyeEFCCaGCNppPUt0d8gX2JdjeBzYPkzBq+RpwQ/FJ+DTK3Cweor9L1bI2bg4QmLB70FQsq8lrEXB6uz4GxtBx4HzPaNQqSLDz7PO4EmpVS/xjPxK2id7DPnv8UlUTn+mbgeUzx0grZEtDXBPRiiBim++eRcj0QJuVmPme8DpViDvLQa1NuLEWzrAVm1CpOSA5ETWIGC1jqotGrcFTMP4U4+3Wpy29QKSpCQ1nt2Amss2vsqWpVS7F/2FFyuInNqKlvpHlZ6TkLcRMMWvr0BR0iQX7+7BLVaSwVTiTArIUVICQ7Jlll4XYxZO8yMBHlC9k4I7/paXVkYIclX3Rg/IPJn0BcgczBmEBgO8qRJKUFZWyOaFBKawddwVQlff2BzbRw0hPQQE+KD1K8BfBfd/3gCHnid3GyPrjTNgKaWo6nwdD6zMYcAI0/G3JGPyg131T9ZHzoN9ibqv5FNM/LEvEevUKiRfq4MPPBw56Oz4Ohkw8gTc0E8WPKkorSZdlxoqBPj2P58+oO5WdR5g3R1nI7ONpD6y6GwVSEq3gf2V+yQdaFqQNsJi/bC9TfGD2juQCaROn47e91N8qkjhbTt5pzFkfSJ+FgxQliQp29DVeqR1VSJZpVUn5XQE66kPdr2/BO03vzYimdRLhFRwSpSXtFhXYkVc5AnJJviTG0htNDCzcYRUz1C6FI7Si6gWSnFnVFz9Gt/V3wOG0KThu2SIOTN7F3/pOsRwVJ7gTVESgn9e7yrP7bNubvXWL4vPo9XL++m70e7+CLc2Rurgqfo99ffJvZWXAYpD3IS2iLFO5IKJ/5YmtrjNKFWgKTyCKr5UeXShBLXOprxkt1chdK2Rjrn71PXYYrnONyw/y3MVcXAM8tQ2Ja057VzsIa3jxPt9vJx/WEcqc6GVKNrNxHU4oHZpePR5iLDyahcfYYNec9b6IQnE5bDxdoOGi2HaV6huPHQO1joNwFpojKapbKzLI36IdcVwZL8G1epNdj7UyZqq1qgVOqI3dhJfpgxLxwku6I3I4z/L9+kQSrVxcbj87D+lslwcLRBxsUKzFkU1R+8Jr8/UuQJaZd86UIF3DzssXHrNLPoz5i8eTZhTCEw1ORJlawJJ2sK0NSepWcUuFpAKwZ4toCmjqPZIn0Zzw4QeAM8Gx54QoAjH9vtmSmaag5a3cc4szGMACNPxvDhj7Kt57XW0K6BpEPhkgDTs00ZeWL+A8/PrkNjgwRTkoIwd0kUI0/MAXFRYSNOn6lA0syQPm8CelurMLcev36fQd+2tubrbyzI384utgib6gVHoS2a6iU401wA71oXyCSdPfWqnETwUbtAINNlcLh72sMv0JV2ZiDZJqJ6CRqa29CskcBO2vNNin+gK1ZvSuhW2mMOfK728eNXFyGVqBAT64uiggbaYcLH35kSOH3dRBE/aWfLYOdggxgTO0yQUowZ3ubpoT4YTAhB8UH2YeyvyoSrtT32LXtqMO66zSUExIGqLKSLKrCvIgN8Hg/TvcKxJmQqzVwQ8PggH8z7KzOxrb39K3Hyr2nr8ZfUn6HQqhHl7Euvt7XjpuL51B2Y5ROFDaHJWB4eA7lCA5nS9LYEpFsNaSNbLxfjjuOf6OOe6BaEJK9QfJJ3DMleYXg75VYcrcnFufpCEEIi1i0AbyZvptkXQ21EsOvWox/SZaZ7heGFKWvx6Okv4Wxti2fiV9Ivsr6MzH/i7Nd0jx32bsptlFwgRspYfi69SFv03hezAGLagYbDt4XnUCVr7tH1+nHTMNM3EqJWKfLO1eCAIAvzyibARqEjH62dBDjjVIDAFjfke9TCQWmDK94V9MydtHZQpGpp9xli2Z6VcPS3wfzgGKxMmKRfT6JWYtne10DIow4TaPnYmDmD/rkj9iz8W91R4dIAF5kDZpfG4PfISwjwcKUppnFuAchsquwW/6qgBPx58mrkZtXg4J4cQMsZfLZ1nbD1gRSDDIumRinOnSxGeJQ39vyUSbPoQsLc4enjCC9vZ8RMHNoOMyNFnpAv6LSz5Vi1IV5PMA/1dc/8j20Ehoo8KZM04nRdAVoUvT8EosiTTJB2okPbxkHbwoNWPIjuN1Y8gHSicgQ4XRIXM4YAza4WWvFphjYzhoAlI6DiNMhuroZaq0Gydzji3QJNCpeRJybBZdRgSZsSl9MqYWXFxz2Pz0Gov3lF+3kc6eczxuzpx3fpbmSsBVSTJCLGq1cEKkqa4e3vRMcSkUNxiwwHdmWjSdQpCmhnJ4R/iCslPewnCfFqw2/UH7n5JGxksksYiqsa4alxRHxFCPhqXeskYqT2f2JKEM465mGp7SRcbi1DLqqwszydphw9w18Fea0KN9+ZRJ8Gp50vR/rZMshkKvq00z/IFfFTAiiZMVgjqfxvZ+2nT+F3laXTm8/1bsk4/HVuj67JvqfPDYOHlyMCQ3S1fiTF3zdAF4tcqsLhfblU3HJCvB8WLI+GlVBAn9C/dEl3BsTOXf+C/r9JVsUX+adQIRUZvD7YvZkyn5RgkHP7KOcIJU26Ginx2BA2zaRyFZKlQDINWpQymhFgayWkOhxXdzi5Osabw1Mw3tUPf0790ajwbwiZSrNSOszOSoglgbF4Ln61UfM7BmW3VOtb0/Y1keiGLPAfD7K/nuyrefch0tl8N81lxU0024mUtF3KKKekxjFlDmZEhOPmiBRKIpHsrzaxEq7udjRVrz9rVcrwp9TvqUhqhz0etxTn6ouogGpX89I6wqvBlZIdHUa0VYQCAb1eiBDtthn3IvtsFS5dqIRG3fePPg1PCwHHR7VjE3K8qpFSHgFbtTUU1iocDs6CyL7zToJcLxPcAlAkrsMcn2gq4hrm5IUXp6ylmTOkM87RHXmoLG6GXKCCrUaoe6Lbzq+k+ZUg20sXN8mE4XE8Stw02UngpLBDUm0E/njLchz9PQ9F+Q3dYCOfk0qFGhkXyyGql2LesigkJAbRcRmplTi6L8+g7DBp1jiaoTJcNlLkCStjHK4TZut0IDAU5ElqQwlSG0uAPn4OakSAtoEDuavlOQDaFtb1hl2VQ4cAI0+GDlvm2fwISNUK5LXWgnQjS/AIwjTPMKMXYeSJ0VCZNDAzvQptYjnW3jwFKdOMKzk3doExTZ50gESeis5eGIUrl6oxIcEPDg66nt2N9VJ8/clZuLjaIi4hAMcOdN5MEd0BtVoDZ1c7RE3wBi+Ow5uZv6OkPQ3/6gMgugLb5t4DTZsGX3x5Bq5yB0iEcijmKZBWXYZafkuPZ0bagYbb+uC6sHiaUUCMkDg/fXVRT+DwBTxcd8PEPkmg/i4I8iSdPLXvKCMg470lLlhUqBPUrHAWQcVXw6fNBfZqG0hdFLBv6bw5JToo5IaV1P9vvicZu77LoDdbhOTpMF4Ah9yAalTWN6HNWg4VXwO5UIkE9xC42tihVtoCcuPeYb8teQJeXUpS+tuDOd5/4NQ2XGgo6dMVqWn8esEDlADpz74sPI23svbhyYnL8PrlvXR4gL0bVoVMxndFZ2k9eaSTDy3ZIVkm38x/gJI23xSdNnBNMjlemLwGZ2sL8V3JOVhrhJipjkI054s6YQt+FFygfklpESm7ILoVtTLdNfXg+EWIdw/E5PaSm75iJgriGw+9R8mrDiOlOu/MuA1V0mZ8lncMu8ov9erCwcqG4pIvrqUlMO/P3NofRPr3iSZJoL1bj1krRDz1hy8uGtygk4lKgQqOrjaYnRyFguw6vTDprIWRSEwJ7nFtQmoIrDoJTDLom6yzSJeW4nB1drc5TrBDXFkgQpt1RNBvURcRLvLB7TNm4xCXhT9NWgm5TI3zp4qRfr6iG2kSGeMNLz8nKJVqlBeJaDvb3rrNtLpK8YetS/FO0QH8UHIehEAjmUk9dbcgWimL/GP18ZIsiH2/XoGqh0wjl3G2qAlphuKsBlpwsFfZwNvdCZyHFuIcJfVBMOlK+Iyf6IsZ88NRWyXWf7ZUVTTju89Tqe7R5nuScPJQIci6XY2IpEabmGlm9EXSy8CRIk8GGzebzxAwFQFzkicKjYp+556rKzQIg1PpiBGtmAdOAZAME5pxwowhMEwIMPJkmIBmy5gNga4ESqybP2Z4Rxrlm5EnRsFk8qCSwkbUVrVi9qIIrFpherODvhYck+TJpfQqeAW54cKpUpw4VEDxITcD5CkiuYGYPjsM02aG4NThQpw7aXgjTUQBSScdkvHh5eMIsb0Mr6fvxYVm3ZPrRM9x9GYqyMEDpJvGHy98Rzt/vJmyWS8KScQjn/rhW2TZV0DDN3w67WHjSDNW5vhG03arHUbKEpYFTUJRax0KWmtR2izC5KoQTHQORmmOTjeh6w0juXn9vfwyfTp/Y+g0bAxLBrkxvtRYhouNJZjg5g8PGydaDjLeNQD/ydyrF90kGS+xNUGYWBdEs1/UfA12xlzAjKAIpDWWQiXm0GYjQ2xdECbV6DQwuhrJ0iGdgbqaQqCCjUZXuiCzUtL/JmSMRKjAFZ8KlLnonnYTYsJea4sGojRHCBw7ZzwyYTGtIyRpxXUZEsokSsQKqrvi5KwTTt39YybVG4iO9YF/kBtN+ZRIlHoirK9/BKT84VhNLvaWX8bx2jw6lAihJnqNw6OxS1Eirqckzu3H/6t3Q5S1t0TOxrg+SkN+LLmAlzJ0WUik5IeU6FxthOggOhPECNnhY+eCInE97jv5OcY5etIsoDi3QCz0n0BJEUIiHLqUDZsWISqKOktHdkelodlWVyge6eKLr+beiwV7XkZbu5AqwfXIimepJkiHPkhPmHRkBfnbu1EdDtLD/moCi5S5bMs7gZN1+VBzWjwTfx0tbyFdZkhZyP3jF2DFvn+DZHW8Mf3mfr9AiK4KYes/zjmKHylhMB1bo2aDrxboy9K++PAMFR7VCDRQ2WjB8TjYSXQkZ0/m6maPlHmhiI711b9NbvKPH8jHmk0JcPd00GFeLaZZE+QDdu0tk/FG9V5ahpTkEYYHvRZBIBBg/84rUEm6lz6RzjYxE/1w7kQxMi52ZpoQXRAroRUuXSjHguUxiJ9qyHgX5tXj1+90ZX8dxhfy0RQpRoNfCy2FIkZ0cEgZFLEj1TnYX5lFS3XItUrsl0WPwq8Hdfeqmmb6mUAUx+MmB+AQKcEx0oie0rwlURC3yqmwa0/WtVVyx/vkc1HSpsCajQnwDTTUazFy6UENY+TJoOBjk0cRAuYgT8jnyOGqHJS1dckyI3xJG6AVcdCSrypWLTGKroprL1RGnlx7ZzoWdtSVQIlx8aPl9/0ZI0/6Q2hg75MGBUV59bTy4fY7EgfmpJdZY5I8IVh0CMaSrhYHfsumOh69GRGDFbfI6VNvcsM0OTmY6iS8e+UAdlfoboIIWfLExKUGgp/kxvKR01/g+cmru2UpkOyGT3KPoFLajAcmLIKW02KeXwwVoSVlAFK1khIdZ+oLQW7Ce7MPyZP9Qj6O7c+jGbeCYOCgXxaI6FtXIzfAXbUdevK3SBQHe4ENnNpswTXqxGDzgqtQxW9GlbMIX8+/n9b0Ec0F4uv/UnfAQ+oEIonfai3DMkk8nEp0bUatHPkoDKqBppGDT5MLzsTmYl5rLKxKrcD1IMFh42wFxxhrxCb44fj2ArQI5ah0ESHNvZgSKE9PvA7Pn/oR8Y1BiKj1o2vYOlgherwfJsT7Uo2Gumod4WLjYAXXcXZoKJRAYM9DytQwRMX5GhAppITmkqiMPtUnN6byduFNMn9L5EzcFT23W2nOCxd/hJ+dK0g2iVKrewz3dPx1tHwitbEUTXIJ/ZvY3qIM/LwvHfZKG9iphbBVWcNaa4U949MR6uGJia6BiPcIQrxbEN2fsen/R/bmIv1CZ9kIKdciN/4ddimmFGuSErA6eArSW4tQLRbjk5xjKJOIKKm3r+IyjbVDuyTJOwyT3XUE2Md5R/FLSSotAXl20iqsCZky4A+bb4vP6jNtVvlNxjKbiQiJ8jQQtyUEHyED/5+9+4CuomjDAPymh5CQ0HsHUbp0RaoUqdIVEaQKiuCPVEVEkV6lWBAQBJHemxSlKKBIk957INQQEkJ6/jMbEtKAe5O5u7P3vnuORw27M98+3yYkb3ZnV1zcj4cXI3HLIwhBnqHwDcuIF+7k0u70yFnaG1FXYrXXd7tmcMb84ru0mvIEZ0FV/2LI9UImPDoTCY8MbihXKb8WmomAJH773+ev48KZOzh22F/7t9jEG1Gy5/LR9kt810T+wpnx5jvlMXL1OuQ47xv3BqzHj96IIEEs+nzx3B3t8TmxideLi9fTxr8lS9ypUa1mEfhmzqC9VSYmKuaZQYI4dtmCA/D0dMMbLUvB1y8DLHmT0ryzf2HBud34PdnbhBI3K/FrzXduPautPSQ2ES6eP30HBYtlwflTt1G4eDbtsb8zJ2+iVLm8CY/fPavx/+6+hN3b435TLcJLEUaFPAhHluxeCWFmmi+cNB7I8CSNcDzMdALpCU/EHWwinL8YcgvBEWFxfxfHxK01IhZ65ZttTHc52G3BDE/strV2f2LJA5TXcr2gvTnsaRvDE9tcEuKtj8cOXde+3x80pJbUSRw+PInXFOty7Nh8Gr5Z44IScWeD2Iq+mB016xWHq4uz9qaZYi/lxIaIQ1h84R/tB2jxmEL3EjXRoWjcYo3WbCKEyOj69N+ei7GWXNyHSUc3wdctA4pkyqE94iEWxNwVcEZbSLSYTw5kcvdC1kAfZPnPR1tDISDjfRzPdxVVixdBuawFMOvUDgQ8foRDvC0mKDIM4vVa4r/FGzjEo0Y1nV+E696kr51t/W4F/BVzClOObdF+kBY/UCfexJ0Fv/kfxdF7cetPuEe7IsYpFvmCssI/011EusT9kOkW7YpPKzVF0/zltLtBxB0/R/Zf037oFD/MXRGPMjx+vMczgxvCEj3q8zBTGDbnO4Isod6ofakkHriHIlNEXEBj7eaaywkBWYIQkiEc+6PPJwQgYpzqOYujbp6SqJWzBH5ffkp7tCJbzozIlsNH+ydrjowJ4Yu4O+O7f37XHt0Qj8+4x7hod32czxOAtQ0+wYWA25i74U8UvpNyvQ9XN2fkzeeHwi9kR9ES2RJ+2PS/eh8bVhzVXnmdOWtGZM2WMdUfvJcvOADxpicRFOQv6IeCxbLi11n/JDzCJV4D2axNWeQrmBk5s2bQFoydcHgz5p/b/VQucSdK4vBI7ChjvZIvtq5G0OlHKBCUTbtT5J7nQ8TkjkblVwoho5snRh1ag1z3MqNMQH6tp2ey3kCOiEzwC467KyTx5p7RFdGlorAg7C/twx8Vr49OL8V9zonP1fiFi8Vizru3n9Ne9Ss28Vjdg/tP1idKPq54c02V6oWTBC7J96lep5h2J1r8dvjfq9ixOe4OJbG98FJOvFK7iLYGkbWbCFnEGi7WbiJ4El8LLN12bDmDl0rn0tZGEq9SF+d9+cI9bVFXazdhu2vbGcREx6BJ67Lw8Hy8eqS1A0ncn+GJREwOpbSAteGJuKPxYvAdLTB5EByGmIexiA0Vj+PEIjbpG96VPm8W51gCDE8cq9/2draJAxSx9l+t3C8+NUBheGK77v/950XNffyUplInsdvw5Ir/LXw2ZhZOnr2MvLmyYcSgrihf6snbW1J7VXF4eLT2g1bmLBkQHByOzFme/DAkfrj86+YZ7db5+HURmucvj96l6iOzu/U/NFnaxTvhIVrjxeM8ibfUFhvN8sgb9S6VhWtk3HoO4vWmxV/MiewFvLXfNiV+za54k4i4WyZ+W7P4sHabv1g8NyoqBpGRMWjbsQIeRUcgMjr6uW9PEY+7nAz0x+2wEKy5clAbVgRL7QpXRi4vPzTOXzbJnRwinAi89xDirUHit/tiYR/xG22RFIpNvIFInLhYnFJsMW4xcH58Xk7uwBG/K8gV4ofsoZlw3+Mh/MLjfuDeXOw/5HbNjHzBmZHxlieiEI0Mmd3hfCvpGhdi3wifSGTL7o1ar76IooWyaY8aicUv//o96UKh8UaNWpZCRm9PuLm74PzpW9j315NHusTin7cyBsEZzsj60AeusXHzvVQ2F14snVtb72LD8pQLq4rHHSq9Ukj7YXbtkidriYjFeHv2f/Ia4PgaZk7apQVNPT5+LSEwEI/yHDvkr9mJH4jjtwIFM6NQsazImMsDf0edx9+3zmp3nYhHwkQYJp51Pxl4XXtlrljIVmzitbWzanRFFveUAUbya1Z4Xb14D2JVa/HIhvhH3E0R/viRrVvXH2h31CTebnjfx978Z+ATkQGVrxWFX3jKzx03TxeExITDI8IVAX73cdrvOvwzPTkvMd6K1z9Kcv0mnkPU9fvGk9pCxfHXUsVXCuG/f69qd4TEbyKAeqVWEe1xry3rjuPEfwHaH7l7xr0Fq0TJXJfC3OUAACAASURBVHi5SoG4azHZJt4uI8Zy93BF4WLPfqtPioP5AekCDE+kk3JARQUsDU/EL0i2XT+JsIgw7TXC0fedECvWLuFGARMIMDwxQZNY4jMFxM9PZ4ICEB0bi6I+OVA7z4twTiVCYXhiuwvpvwPXtF+GT2B4Yhlyxz6jUL1yGXR7pwl27j2M0dN+weZFE+HmGveDUWrhSfKRxWtg99w8i03XjmDPzXNJ7lQQrySNf0TDsork7iVeC7vx2n9aICHWMBHblxVaoobPC1ix8JD21pH4Tfz2vXT5PChZLneqbyARv4n+YfJObRX9Xv1qaj/Ip3UTocyIg2vRslBF1MtbEuKNLNZsIkS5cfW+tqZM8ZdyYt2y/3D1UtwjSOK35oWKZkVUnigsu/MPMsR6wMPJDW6ezvCIcYfHVTd4F/BAtwo1tP3PBd9Cjy1ztYV5xQKZRe7mROkH+eAcljRIEYv/5sybCffvPtSCALGJuwHEHQe3xTNzZ+9ALJSZ2ibCD7EWR4otfwzKvpwf1YoVhZdX3B09qxcf1sIF8XiIeCuR/5W4McUP714Z3bW7JcSjIOGPIp76ilixvwj33vsw5Z1OYgFjEQBcOncHVy4FIipRcCFeqZ2vcFY4OQN5ivmhYrkni6mKkCEwMBSPHkZo4YsTYrU31rh7usLFxVm7w6V+s5JJTlE8KnT21C3tLqJnbXny+eL6tSDtDUzinJNvIpgQC6qKO29CHkSgQrUC2hohIhA6GXkD1QsWxU+n/8Sv5/dqr2UWm3idrnitriWbWMBYrE2UeBN3p2TL4a09XhO/iTBEBCHi7VdiCw+LUuKOCkvOkfsADE94FTiKwPPCE/Fojlh8+9z1m4gIjEZMiBMQzdDEUa4PezlPhif20knHPo/EAUohn2za+oXJAxSGJ7a7Rs6duo07t4MxYUrSJyfSO6Nd3nlyN/AB3nhnIPau/w6uLnFhSZsewzG4d3tULh+34u7TwhPxA5q4w2TbtRPav8XCavGbWNuift7SaJivNPJ5WX+7e3qb9bTjjwRehberp7b2Rvwm7qI5fthfe/tH4scWRABRvEQOFCqeFVER0dpaEWdO3sLeHedRpHg2NH+rnK3KTPO4ImQ4dSwAlV4tqK0LYc320d752Pf4NbTxbycR6zOI9Ss2rzmOk0fj7jaI37y83VH51UJaSBP/GIYIR8SdHY8eRSAsNO4NQuI1zOL/q7xWBC4uTsjo445YD/HqaQ/ky5H6Qpvi7U2+fh7a65rFJoKaWd/8mWT+pm3Kau8l/+fPi7jhn/obmESQIRYlfd4WeDMYJ4/fxNnTtyHCgfRsIkwSzw2KR4rEdZV4vIKFs2hWGX08NQcvbw94e8f9WwRHIjASrv/uvqw9UiM2v2wZkLNcJjR6pbRFZd0Nf4hvT2zFhqtHsKD2+9priblRIF6A4QmvBUcRSC08EWuRid9wHrvujzs3HyImmG/HcZTrwV7Pk+GJvXbW8c5LvBTh7OM7UMRSCWI5BXEniqv4baZ406GzEzzcXRAaxleayb46xC9vr1y4iwnfMDx5ru3Bo2cxYvLPWD13ZMK+A0Z8j6oVXkLbprWx7epJnLsXqL3+87b4JywYYVFxIcnZoJsIinyyRoJ4VXD9fKXRKF8ZFMn0JJx4bhEK7SDuEDj4zxVcSvRIR2rltWpfXrsLwJ42sdbL1Ydxd65UzlYoyalduRio/dDvk8lDe0xLrJ2RLXvGhHBDDwf/q0Hw8HDV7j4Ra0eItz7J2ny8XBERGYPwyBg8uB+Gc2du41bAA/x3wD/FFCVeyoGbN4NRqVpBZM/5ZA2Oyxfv4ejBawh+EPc4Vfzm7eOB3Hl9tddT16pbHC5uKR+LSu08rl25rwVgwjwtm3ibT24vXy0s5EaBeAG/jG4ICYtCFH/DzovCzgWyZnLHvQcREPeSBIY/xIGrV3DmagDC78dAe8UwNwrYgYB4XN3VxQmR/JpuB93kKTyKCsfp+zcRHRv3GjM3ZxftracFfbJpLzoQ62pGPn5BAbXkCYQGReDm6Qe888QS0j37j2HqrBVYMnN4wu5Dx87GC0Xz4722DZFv7pBnDlM4U1Y0K1wWTQuXRcnMz/8Nf1TMk3f6xWrf0qTc4t/aof2JExC/n3hDTvIt9vEHtX8lWqI5tX3FSHH/xG9PDojfXxvPCdrdEmfP3MXZE7dx8fxdbRFUcSeFWMQ1T+5MeKtLortOxGfz4y2+nuSnlvRcnfCk7sf1OIkngeLGSXJOiapNTUuUmzCnIEi8TPXjA5I7pzqOUE70B0mHifuD5MclMRYHxIoxku/1+Jzij07WqDjvpGtri48l90rVN0mRKfsaN8oz+h1faypm8fOJ1wKncrkksUp8zmINnIArD3DT/yEunwpEroI+qFI3P5xdkq0frv1vUquEa/Dxx8Uu4vXXic89ha+TeHDoyZa43Cf7iosr0U6JPgcT/0Fiq6RjPsUgJmWvU35Op37tJJxT3GUT/6me8O8k55HoDBPOKcU1k/hdoXEnG3dJPqnxqeeUCCeh38+4JhIu5YS6kl2/ia+7p3xNckpCl/rXQfE2sRRbsn4nvRBTfmnTPpdS+ZR86tejhGsv5Xr3Sa6n5F/vhHUqn49POhF3QMqvDyk/nnicuNqT15L863j8GEm1kn8+pRgmsW/CFOJrc+LPpyRXTUrgVL8yPq4nyXWb/JM06eek9nU82fUkPvdTfD4l/H2X+ufkUy6ZJDBJPicShklWT5KvEY8P1z6hUl6Sqf9dG38+8QfEAz/5u+/JJ33CX1QpBxdreKX+6ZHs771EXyef9n1FeCyiwvlO4VSR+UEKUIACCgnEIBZXg+/hzP1buBMWt56l9qU95bcmClVt7lJcY1xROiAfpk9sJfVE7PKxnUPHzuLzcXOwYcHYBKy+w6ahRtWy2p0nPbf/or2SV7wiNqdXJuQQ/3j6IKeXD3J5+UoF5mAUMErAzcUJ4gfnaH5vbVQLOK9OAu6uTtpdJ0/7oVSnMjgNBWwu4OHmrN1R+JT8xebzcwIK6CEgfp50e3yt6zEf56CAngInA29g/aUjuBx8D4HihQ3il0hPS+31LMxO51rRuKfUM7PL8CQwKBj12vXH7rUz4OkRtwBkk45D8PWgrqhQ5gXt/y1ZMFaqNAejgM4CmX3ctVcVP3r89hudp+d0FNBNgGue6EbNiQwWeN6CsQaXx+kpIEXA2QnIkTkDAu49eYxeysAchAKKCbi7OiNTRjfcCUr6eLxiZZq6nDxZrVsv83kna5fhiTjpbv3Ho2LZEujRoSk279iHqbNXYNPCcQkLyDI8ed6lwT83uwDDE7N3kPVbKsDwxFIp7md2AYYnZu8g67dEgOGJJUrcxx4EGJ7YvosMTyw0vnHzLgaPmonjpy8hf54cGDWkO0qVeLJgKMMTCyG5m2kFGJ6YtnUs3EoBhidWgnF30wowPDFt61i4FQIMT6zA4q6mFmB4Yvv2MTyRZMzwRBIkh1FWgOGJsq1hYZIFGJ5IBuVwygowPFG2NSxMogDDE4mYHEppAYYntm8PwxNJxgxPJEFyGGUFGJ4o2xoWJlmA4YlkUA6nrADDE2Vbw8IkCjA8kYjJoZQWYHhi+/YwPJFkzPBEEiSHUVaA4YmyrWFhkgUYnkgG5XDKCjA8UbY1LEyiAMMTiZgcSmkBhie2bw/DE0nGDE8kQXIYZQUYnijbGhYmWYDhiWRQDqesAMMTZVvDwiQKMDyRiMmhlBZgeGL79jA8kWTM8EQSJIdRVoDhibKtYWGSBRieSAblcMoKMDxRtjUsTKIAwxOJmBxKaQGGJ7ZvD8MTScYMTyRBchhlBRieKNsaFiZZgOGJZFAOp6wAwxNlW8PCJAowPJGIyaGUFmB4Yvv2MDyRZMzwRBIkh1FWwM/bHWER0do/3ChgzwLZfD0QFBKByOhYez5NnhsFkCtLBty89wi80nkx2LOACE+y+3niZmCYPZ8mz40CEOGJj5cb7j4Ip4aNBBie2AiWw1KAAhSgAAUoQAEKUIACFKAABShAgdQEnGJjY/kLDF4bFKAABShAAQpQgAIUoAAFKEABClDgKQIMT3hpUIACFKAABShAAQpQgAIUoAAFKECBZwgwPOHlQQEKUIACFKAABShAAQpQgAIUoAAFGJ7ECcxauB4/L92MqOhoNH69Gob2fRcuLs68QChgFwLrt+3FV5PmYeTg7mhYu3LCOfG6t4v28iQeC/zx10FMmrkUt+/eR4mi+fHVwK4oUiA3v8bzCrE7gbVbduPbuasRGBSMF4sVwIiBXVEofy6EhUdg+IS52L7nEDJ4euCjri3Rtmltuzt/npDjCXw7dxWWrN2OXaumaSfPa93xrgF7PuPJM5di3tLf4Oz85GfPJT8M176XueJ/C5+NmYWTZy8jb65sGDGoK8qXKmbPHKY9N4e58+TvAyfw+fg5+Hnqp/D1yYgPhkxB49eron2L103bPBZOgXgB8cX4wH+ntR8ou7zdOCE84XXPa8SeBG7eDkTzzp9h5vj+KPtSUUz/aSUOHz+LuVOGgNe6PXWa53Lhyg28+9FIzPvmUxQtmAdTZi3DidOX8NOUwZg2ZwVOnr2CScM/gPiceO/jMZgzeRCKF85HOAqYVuDS1QD0/uwbBIeEJoQnvNZN204WnoqA+AVn8SL58U7LlD97duwzCtUrl0G3d5pg597DGD3tF2xeNBFuri60VEzAYcKTEVPmI3eOLOjRoanWAvEbG3EXyrxvhijWEpZDAesFTp27oiXX3ftPQLvmdRLCE1731lvyCHUFxA+KR06eR/2albQixW9oxDfbfyybAl7r6vaNlVkv4B9wBxcu30CNqmW0g4+cOI9PvvwW25ZORrNOn2LkkO4oV7Ko9mfjv10E74wZ8GHnFtZPxCMooIhAl35j8VbzutoPjfF3nvBaV6Q5LEOKwIAR36NWtXJo1uDVJOPdDXyAN94ZiL3rv4OrS1xY0qbHcAzu3R6Vy78oZW4OIk/AYcKTbv3H4+036yZ8033xyg106TcOO1Z8I0+TI1HAYIFun4xPEp7wuje4IZzepgJzFm3E6XNXMH5YL/Batyk1BzdQQPwmfuyMX+Hp4Y5h/Tqh3OvdtB8ufTNl1KpaunY79v93Wvs84EYBMwqs/u0v/HPwJAb1fhtvdh6aEJ7wWjdjN1nz0wR6DpoE8ZLb85euw8nZCe2a1cb77zbDwaNnMWLyz1g9d2TCoSJoqVrhJT6SqeDl5DDhSYfeI9GzYzPUrFZOa8P1gDto0fVz7Nv4g4JtYUkUSJtA8vCE133aHHmU+gJ/7TuKr6fMx4LpQ5Ejmx94ravfM1ZovcCE7xdj3pLf8HLp4pgx6mNkzJgB5et1w/7ffkQGT3dtQPGD57ZdBzBj9MfWT8AjKGCwwP2gELzbZxQWTP9MqyQ+PImMiua1bnBvOL1cgR9/WaetU9WmaW1cv3kH7w+YiMEfvQPvjJ6YOmsFlswcnjDh0LGz8ULR/HivbUO5RXC0dAs4THjSfcAEtGpUU1vnRGynz1+FSAB550m6ryEOoJBA8vCE171CzWEp0gTE4sjf/7wG34/thwJ5c2rj8lqXxsuBFBN4FBaBJWv+wJrNf2HlnK9Rvl53/L5sMrJl8dUq/WXFVu2xHt55oljjWI5FAuKHxCovv4Q3G1bXFkdOfucJr3WLGLmTCQW+n78GN27eRctGNfD5uDnYsGBswln0HTYNNaqW5Z0nCvbVYcKTUVMXwC+TN3p3aam1YePv/2DFhp3aImvcKGAvAsnDE1739tJZnke8gHjbzrQ5KzF70sCEHx7Fn/Fa5zViTwJiHav7D0JQrUJJ7bRiYmJRrl5XbF/+jfaI2tC+HVHl5bhn4cUihDmzZ0GvTs3tiYDn4iACrzbvnbDOg3ikITAoBFn8fLDu5zHo9PFoXusOch04wmkePHoGpUoUhoe7m3a6M35apX2d792lBeq164/da2doj2eKrUnHIfh6UFdUKPOCI9CY6hwdJjwRF+ygr3/A/Gmfabe9ilulxMKarZvUNFXDWCwFniWQPDzhdc/rxZ4EgoIfomXXz7Wv4/lyZ09yarzW7anTPBfxWNqw8XO0az1/nhzaozniNZc7VkyFeP38oWNnMPnLj3Dtxm2IhTZ/mT4UhR+/spt6FDCrQPI7T2YuWMdr3azNZN0pBMTjxdUqlkTvzi3jvnb/byy+HNBFWxhchOIVy5bQXmyyecc+TJ29ApsWjksIFsmpjoDDhCeC/OdlmzF74XqI5yhbvPGatoqxk5OTOt1gJRRIo4BYlfvcJX9ERUXDxdlZW4hq3ND30bB2FV73aTTlYeoJrNr0p3Zrq5uba5Lidiz/Bn6+3rzW1WsZK0qHwE+LN+LXldsQEhqGAnlz4NM+HbS1TyIjo/DlpHnYums/vDJ4ot/7bbVHHrhRwOwCycMTXutm7yjrTyxwxf8Wvpw4FyfOXoaPt5e2nsm7retru4jHdwaPmonjpy9pgfmoId1RqkQhAioo4FDhiYL+LIkCFKAABShAAQpQgAIUoAAFKEABxQUYnijeIJZHAQpQgAIUoAAFKEABClCAAhSggLECDE+M9efsFKAABShAAQpQgAIUoAAFKEABCiguwPBE8QaxPApQgAIUoAAFKEABClCAAhSgAAWMFWB4Yqw/Z6cABShAAQpQgAIUoAAFKEABClBAcQGGJ4o3iOVRgAIUoAAFKEABClCAAhSgAAUoYKwAwxNj/Tk7BShAAQpQgAIUoAAFKEABClCAAooLMDxRvEEsjwIUoAAFKEABClCAAhSgAAUoQAFjBRieGOvP2SlAAQpQgAIUoAAFKEABClCAAhRQXIDhieINYnkUoAAFKEABClCAAhSgAAUoQAEKGCvA8MRYf85OAQpQgAIUoAAFKEABClCAAhSggOICDE8UbxDLowAFKEABClCAAhSgAAUoQAEKUMBYAYYnxvpzdgpQgAIUoAAFKEABClCAAhSgAAUUF2B4oniDWB4FKEABClCAAhSgAAUoQAEKUIACxgowPDHWn7NTgAIUoAAFKEABClCAAhSgAAUooLgAwxPFG8TyKEABClCAAhSgAAUoQAEKUIACFDBWgOGJsf6cnQIUoAAFKECBxwKtug1D6ya10KFVveea3LwdiA+GTMbFqwFYM3ckCuTN+dRjfv/zIIZNmIM9a7/Fg5BQvNL0Q6yeOxLFC+dL9Zj7QSFo3f0LTPiiFyqUeeG5taR1h1FTF+BhaBhGf9ojrUPwOApQgAIUoAAFdBJgeKITNKehAAUoQAEK2KvAxt//wcCvv09yehk83VGsUF707tISNaqWtejUj52+iOxZ/JAze+bn7v/zss1YvPp3LPx2GHx9MsLFxVlaeNJv+AzkyZUNAz94+7l1pGeH8IhItOgyFP/r0RYNa1dOz1A8lgIUoAAFKEABGwswPLExMIenAAUoQAEK2LuACE+GjZ+DDb+MTTjVhw8fYfVvuzF/2WYsmTkcLxYrIJVh2pwVOHbqIn6cMOC541pz58mpc1fQofdIbF0yCVn8fJ47dnp3WL5+J35e+hvW/jwaTk5O6R2Ox1OAAhSgAAUoYCMBhic2guWwFKAABShAAUcREOHJFxPmYP9vP6Y45ebvfYZGr1fFB53eRExMLKb8uAzrtu5BUPBDFM6fC4N6t0e1CiW14xI/tjPh+8UIevAQvpkyYufe/xAcEopmDV7FgF5v4ZtZyzF38SbExMbAw90Ny2eNQPDDRxg341ecOncZnh4eeL1GBQzt+y7c3d1gTXjy1eSfIYKf8cN6aTWJOsRjPB4e7vjrnyOIiIzC5//riJu372HJmu0IDArGe+3eQPd3mmj7t+v5JRrXrYY9+4/h9Pmr8PP1xqQvPsT85Zvxz8GTiI6JwdcDu+KVSqW0/SMiIvFKs974YdwnqFz+RUe5ZHieFKAABShAAdMJMDwxXctYMAUoQAEKUEAtgWeFJy27fo7XX6uIj7q2hLjLYurs5Zg39VPkzZUNv67ahtkLN2Dnyqlwc3NNEp5MnrkUi1b/gZGDu6Jh7SpaECHWIVk+6yvtLpaps1fg+Om4O09iY2NRr11/LaTp3bkl7gYGoceACWjXrA66vN3IqvCkUYfB6Na+Mdo0raUhizoWr/kD343ph0rlSmjBjfj/Tm0a4MPOLfDPoZPaXLtWTtOCkrc/GIGQh48wd8pgZPHLhM7/G4vzl/0x5auPUPXllzDjp1XYvucQVswekdDEbv3Ho1zJoujbrbVajWU1FKAABShAAQokCDA84cVAAQpQgAIUoEC6BFILTyKjorFyw06MmDJfe2yndInCEGt8hD4KQ2bfuMdhxB0d1d/8COvmj0GRArlThCc7//4Pa+aOSqjt9bafYMAHb6FR3apJwhOxw737wfD28tTuNBGbWIxVfGzS8A8tDk+ioqNR7vVu+PW7YVqYER+e/H3wBJbO/FL7/z//OYpegydpi8+Ku2LEeZav103781IlCmnhScWyLySslyLCl11/H9EWqBXb3v3H0XfYNPy7aWbCeY37dhH8A25j2td909UHHkwBClCAAhSggO0EGJ7YzpYjU4ACFKAABRxCIH7BWLFIbPwmgpIcWTOjb/fWeLNhde3D4jGcb2Yvx7+HTyEsLFz72I1b97S7MMTdJIkf2xGhw5kL17THWeK3N94ZhJ4dm6FloxopwpM/dh/C3MUbtfHE9iD4oXanx/RRH1scntwNfICaLfti4y/jUDBf3Nt7RB0XLt/AjNEfa/+/79ApvD9wAg5vm5NQV5m6XfDz1E+1N/OI8ESEO++1baj9+bdzV+Hw8fOYNTFubZaDR8/gvY/H4OgfcxOOn7lgHf7adxQLpn/mENcLT5ICFKAABShgRgGGJ2bsGmumAAUoQAEKKCQQv2Dsqp++Tqiq/1ffo+xLRTCsX6eEjw0Z/SMuX7uJaV/3QfasftrjLVWbfPDU8OTsRX98P7bfc8OTi1duoEWXzzFiUFc0q/8qnJ2dIO7muHb9llXhyZ17QajV6mNsWjgu4dXHIjwR44sQJiE8GTQRh7fOfmp40rhuVXRKFJ78d+J8wsK2qYUnsxau1+5OYXii0EXNUihAAQpQgALJBBie8JKgAAUoQAEKUCBdAqk9tiPeWvNWz6/w/bh+eLVSaW38hu0HokeHpgnriYjHYbp9Mj7d4cnaLbu1hWi3L/8m4Tw69R2tvcLYmjtP4h/bWfTdMJRN9NiOrcOT8SLo4WM76boGeTAFKEABClDA1gIMT2wtzPEpQAEKUIACdi7wtAVjxeKq67bs0db78PH20h5XyZ0zK0YP6YGLV65jwvdLtDVApo/qi5rVyqV4bMfSO0/EozRi0daVc0Ygf54c+O7nNdpjMK4uzlj8w3CLH9sRbRILxvbo0AStGtfUuqbHnSc9BkxE6RcL4+PuXDDWzj9VeHoUoAAFKGBiAYYnJm4eS6cABShAAQqoIPC08ES8hrdV9y+0x3dGf9oDR09dxNAxs3Dj1l28VLwgRg7ujpkL1mLbnwfw/dhPMPKb+WjdpBY6tKqnhRaWhifCQCxMu2HbXnhl8ECHVvXxWpUy6PrJOO0NOW82eA3DJszRFnl9EBKKV5p+qAU6xQvnS8EnXlX8KCwcYz97X5fwJDIyCq82741vR/dDlZf5qmIVrmfWQAEKUIACFEhNgOEJrwsKUIACFKAABSjwWODk2cvo2GcUti2ZrL162Nbbqk1/Ys6ijVg7b7S2Vgs3ClCAAhSgAAXUFGB4omZfWBUFKEABClCAAgYJfDxsurZgbP9e7WxagXgjUcuun6Nvt9Z4o04Vm87FwSlAAQpQgAIUSJ8Aw5P0+fFoClCAAhSgAAXsTCAwKBhtug/HhC96aa8fttU2etovCA55hDGf9bDVFByXAhSgAAUoQAFJAgxPJEFyGApQgAIUoAAFKEABClCAAhSgAAXsU4DhiX32lWdFAQpQgAIUoAAFKEABClCAAhSggCQBhieSIDkMBShAAQpQgAIUoAAFKEABClCAAvYpwPDEPvvKs6IABShAAQpQgAIUoAAFKEABClBAkgDDE0mQHIYCFKAABShAAQpQgAIUoAAFKEAB+xRgeGKffeVZUYACFKAABShAAQpQgAIUoAAFKCBJgOGJJEgOQwEKUIACFKAABShAAQpQgAIUoIB9CjA8sc++8qwoQAEKUIACFKAABShAAQpQgAIUkCTA8EQSJIehAAUoQAEKUIACFKAABShAAQpQwD4FGJ7YZ195VhSgAAUoQAEKUIACFKAABShAAQpIEmB4IgmSw1CAAhSgAAUoQAEKUIACFKAABShgnwIMT+yzrzwrClCAAhSgAAUoQAEKUIACFKAABSQJMDyRBMlhKEABClCAAhSgAAUoQAEKUIACFLBPAYYn9tlXnhUFKEABClCAAhSgAAUoQAEKUIACkgQYnkiC5DAUoAAFKEABClCAAhSgAAUoQAEK2KcAwxP77CvPigIUoAAFKEABClCAAhSgAAUoQAFJAgxPJEFyGApQgAIUoAAFKEABClCAAhSgAAXsU4DhiX32lWdFAQpQgAIUoAAFKEABClCAAhSggCQBhieSIDkMBShAAQpQgAIUoAAFKEABClCAAvYpwPDEPvvKs6IABShAAQpQgAIUoAAFKEABClBAkgDDE0mQHIYCFKAABShAAQpQgAIUoAAFKEAB+xRgeGKffeVZUYACFKAABShAAQpQgAIUoAAFKCBJgOGJJEgOQwEKUIACFKAABShAAQpQgAIUoIB9CjA8sc++8qwoQAEKUIACFKAABShAAQpQgAIUkCTA8EQSJIehAAUoQAEKUIACFKAABShAAQpQwD4FGJ7YZ195VhSgAAUoQAEKUIACFKAABShAAQpIEmB4IgmSw1CAAhSgAAUoQAEKUIACFKAABShgnwIMT+yzrzwrClCAAhSgAAUoQAEKUIACFKAABSQJMDyRHb/ItwAAIABJREFUBMlhKEABClCAAhSgAAUoQAEKUIACFLBPAYYn9tlXnhUFKEABClCAAhSgAAUoQAEKUIACkgQYnkiC5DAUoAAFKEABClCAAhSgAAUoQAEK2KcAwxP77CvPigIUoAAFKEABClCAAhSgAAUoQAFJAgxPJEFyGApQgAIUoAAFKEABClCAAhSgAAXsU4DhiX32lWdFAQpQgAIUoAAFKEABClCAAhSggCQBhieSIDkMBShAAQpQgAIUoAAFKEABClCAAvYpwPDEPvvKs6IABShAAQpQgAIUoAAFKEABClBAkoDDhifX7z6SRMhhKKCeQBYfd4SGRyMsIlq94lgRBSQJ5MzsiTtB4YiOiZU0IoehgFoCTk5AzswZEHCP37Oo1RlWI1PA3dUZmTK6aV/PuVHAXgUyerrC1cUJQQ8j7fUUlTyvPFkzSK2L4YlUTg5GATUEGJ6o0QdWYVsBhie29eXoxguI8CRX5gy4wfDE+GawApsJMDyxGS0HVkiA4YkxzWB4Ismdd55IguQwSgowPFGyLSxKsgDDE8mgHE45AYYnyrWEBdlAgOGJDVA5pHICDE+MaQnDE0nuDE8kQXIYJQUYnijZFhYlWYDhiWRQDqecAMMT5VrCgmwgwPDEBqgcUjkBhifGtIThiSR3hieSIDmMkgIMT5RsC4uSLMDwRDIoh1NOgOGJci1hQTYQYHhiA1QOqZwAwxNjWsLwRJI7wxNJkBxGSQGGJ0q2hUVJFmB4IhmUwyknwPBEuZawIBsIMDyxASqHVE6A4YkxLWF4YoX7+m178dWkeRg5uDsa1q6c5EiGJ1ZAclfTCTA8MV3LWHAaBBiepAGNh5hKgOGJqdrFYtMowPAkjXA8zFQCDE+MaRfDEwvd5y39DQf+O43bd++jy9uNGZ5Y6Mbd7EOA4Yl99JFn8WwBhie8QuxdgOGJvXeY5ycEGJ7wOnAEAYYnxnSZ4YmF7qfOXUGJovnRvf8EtGteh+GJhW7czT4EGJ7YRx95FgxPeA04tgDDE8fuv6OcPcMTR+m0Y58nwxNj+s/wxEr3bp+MZ3hipRl3N78AwxPz95Bn8HwB3nnyfCPuYW4Bhifm7h+rt0yA4YllTtzL3AIMT4zpH8MTK92fFp7cDY6wciTuTgHzCPhkcEV4ZAwiomLMUzQrpYCVApm93fHgYQSiY608kLtTwCQCTgBEGM7vWUzSMJaZJoHFv/yMpUsWIppfzNPkx4PMIeAs0nAnICaG37To2bG//twpdTqn2NhYu+7g08KT8IhoqZAcjAIqCbi5OiM6JpZfoFVqCmuRLuDu5oyIqFjAvv8ak+7GAU0k4CTWg3BBRCS/ZzFR11iqFQKrVq1E+7fbWXEEd6UABShguYDsqMNhwxO+bcfyi457mk+Aj+2Yr2es2HoBPrZjvRmPMJcAH9sxV79YrXUCf+78A2+3bqod1PujPqhdv4l1A3BvCphIIIO7C1ycnRASFmWiqs1fapvmDaWeBMMTqZwcjAJqCDA8UaMPrMK2AgxPbOvL0Y0XYHhifA9YgW0EDh86gDZvNsSj0FC0avMWFi36FXeCwm0zGUelgAICXPPEmCZwzRML3dv0GI5zl/wRFRUNF2dnODk7YdzQ99GwdhVtBN55YiEkdzOlAMMTU7aNRVspwPDESjDubjoBhiemaxkLtkDgwrmzaNqwFoKC7qNeg0b4ZdFyZM7kyfDEAjvuYl4BhifG9I7hiSR3hieSIDmMkgIMT5RsC4uSLMDwRDIoh1NOgOGJci1hQekUuO5/Dc0a1kJAwA1UqlINy1b/Bm8vT2TK6MbwJJ22PFxtAYYnxvSH4Ykkd4YnkiA5jJICDE+UbAuLkizA8EQyKIdTToDhiXItYUHpEAi8dxdNGtTE5UsXUbJ0WaxavxXe3j7gq4rTgcpDTSPA8MSYVjE8keTO8EQSJIdRUoDhiZJtYVGSBRieSAblcMoJMDxRriUsKI0CISHBaNm0Pk4cO4IiRYtj7aY/kDlLVm00hidpROVhphJgeGJMuxieSHJneCIJksMoKcDwRMm2sCjJAgxPJINyOOUEGJ4o1xIWlAaBiIgItG3xBvbv+xu5cuXGhm1/af+O3xiepAGVh5hOgOGJMS1jeCLJneGJJEgOo6QAwxMl28KiJAswPJEMyuGUE2B4olxLWJCVAjExMejyblts27JJu9Nk7cY/UKRY8SSjMDyxEpW7m1KA4YkxbWN4Ismd4YkkSA6jpADDEyXbwqIkCzA8kQzK4ZQTYHiiXEtYkJUC/fr0xNJFC5DBywtrNv6BUqXLphiB4YmVqNzdlAIMT4xpG8MTSe4MTyRBchglBRieKNkWFiVZgOGJZFAOp5wAwxPlWsKCrBAY9dXn+G76ZLi7u2PhsrV4tXrNVI9meGIFKnc1rQDDE2Nax/BEkjvDE0mQHEZJAYYnSraFRUkWYHgiGZTDKSfA8ES5lrAgCwVmz/wWw4cOhLOzM+YuWIp6DRs/9UiGJxaicjdTCzA8MaZ9DE8kuTM8kQTJYZQUYHiiZFtYlGQBhieSQTmccgIMT5RrCQuyQGD1iqXo3bOztueU6TPRrn3HZx7F8MQCVO5iegGGJ8a0kOGJJHeGJ5IgOYySAgxPlGwLi5IswPBEMiiHU06A4YlyLWFBzxHYtnkjunZ6C9HR0fh02Ah89PGA55oxPHkuEXewAwGGJ8Y0keGJJHeGJ5IgOYySAgxPlGwLi5IswPBEMiiHU06A4YlyLWFBzxAQryIWryQWrybu3rM3vho1wSIvhicWMXEnkwswPDGmgQxPJLkzPJEEyWGUFGB4omRbWJRkAYYnkkE5nHICDE+UawkLeorA8WNH0KppfYSEBKNFq3b49sd5FlsxPLGYijuaWIDhiTHNY3giyZ3hiSRIDqOkAMMTJdvCoiQLMDyRDMrhlBNgeKJcS1hQKgKXL11EkwY1EXjvLuo1aISfFiyFi4uLxVYMTyym4o4mFmB4YkzzGJ5Icmd4IgmSwygpwPBEybawKMkCDE8kg3I45QQYnijXEhaUTCAg4AaaNayF6/7XUKlKNSxb/Zv2amJrNoYn1mhxX7MKMDwxpnMMTyS5MzyRBMlhlBRgeKJkW1iUZAGGJ5JBOZxyAgxPlGsJC0okEBR0H00b1MKF82dRsnRZrFq/Fd7ePlYbMTyxmowHmFCA4YkxTWN4Ismd4YkkSA6jpADDEyXbwqIkCzA8kQzK4ZQTYHiiXEtY0GOBR6GhaPNmQxw+dAAFCxXGhi27kDlL1jT5MDxJExsPMpkAwxNjGsbwRJI7wxNJkBxGSQGGJ0q2hUVJFmB4IhmUwyknwPBEuZawIABRUVFo37op9uzehVy5cmPd5p3Ikzdfmm0YnqSZjgeaSIDhiTHNYngiyZ3hiSRIDqOkAMMTJdvCoiQLMDyRDMrhlBNgeKJcSxy+oJiYGPTq9i42rFsNX18/rN+8E0WKFU+XC8OTdPHxYJMIMDwxplEMTyS5MzyRBMlhlBRgeKJkW1iUZAGGJ5JBOZxyAgxPlGuJwxfUr09PLF20ABm8vLB8zWaUf7liuk0YnqSbkAOYQIDhiTFNYngiyZ3hiSRIDqOkAMMTJdvCoiQLMDyRDMrhlBNgeKJcSxy6oHGjv8K0yePg6uqKRSvW49XqNaV4MDyRwshBFBdgeGJMgxieSHJneCIJksMoKcDwRMm2sCjJAgxPJINyOOUEGJ4o1xKHLWj+vNn4dEBfODs744c5v6BJsxbSLBieSKPkQAoLMDwxpjkMTyS5MzyRBMlhlBRgeKJkW1iUZAGGJ5JBOZxyAgxPlGuJQxa0esVSfNSrC2JjYzFl+ky0a99RqgPDE6mcHExRAYYnxjSG4Ykkd4YnkiA5jJICDE+UbAuLkizA8EQyKIdTToDhiXItcbiCdm7fho5vt0R0dDQGD/0SffsNkm7A8EQ6KQdUUIDhiTFNYXgiyZ3hiSRIDqOkAMMTJdvCoiQLMDyRDMrhlBNgeKJcSxyqoP37/sZbrRojLCwMHTt3x9iJ02xy/gxPbMLKQRUTYHhiTEMYnkhyZ3giCZLDKCnA8ETJtrAoyQIMTySDcjjlBBieKNcShyno9KkTaP5GHYSEBKNFq3aYMXMunMQFaYON4YkNUDmkcgIMT4xpCcMTSe4MTyRBchglBRieKNkWFiVZgOGJZFAOp5wAwxPlWuIQBV2+dBHNG9XBndu3UKtOPSxYvAouLi42O3eGJzaj5cAKCTA8MaYZDE8kuTM8kQTJYZQUYHiiZFtYlGQBhieSQTmccgIMT5Rrid0XJAKTRvVew3X/a6hUpRqWrNwIT09Pm543wxOb8nJwRQQYnhjTCIYnktwZnkiC5DBKCjA8UbItLEqyAMMTyaAcTjkBhifKtcSuCwoKuo+WTepBPLJTsnRZrFq/Fd7ePjY/Z4YnNifmBAoIMDwxpgkMTyS5MzyRBMlhlBRgeKJkW1iUZAGGJ5JBOZxyAgxPlGuJ3RYkFoVt3aw+Dh86gIKFCmPDll3InCWrLufL8EQXZk5isADDE2MawPBEkjvDE0mQHEZJAYYnSraFRUkWYHgiGZTDKSfA8ES5lthlQVFRUejUvhXEa4lz5cqNdZt3Ik/efLqdK8MT3ag5kYECDE+MwWd4Ismd4YkkSA6jpADDEyXbwqIkCzA8kQzK4ZQTYHiiXEvsrqDY2Fj07NoBG9athq+vH9Zv3okixYrrep4MT3Tl5mQGCTA8MQae4Ykkd4YnkiA5jJICDE+UbAuLkizA8EQyKIdTToDhiXItsbuChgzoiwXzZiODlxeWr9mM8i9X1P0cGZ7oTs4JDRBgeGIAOgCHD0+u+N/CZ2Nm4eTZy8ibKxtGDOqK8qWKpejGqXNXMGLyz7h3PxieHu7o36sdalQtm7AfwxNjLmDOqo8AwxN9nDmLsQIMT4z15+y2F2B4YntjR55hyoTRmDhuJFxdXbFoxXq8Wr2mIRwMTwxh56Q6CzA80Rn88XQOH5507DMK1SuXQbd3mmDn3sMYPe0XbF40EW6uSd8/37zzUPTq2ByNX68KEaR06jsaO1Z8A68Mca9bY3hizAXMWfURYHiijzNnMVaA4Ymx/pzd9gIMT2xv7KgzzJ83G58O6AtnZ2f8MOcXNGnWwjAKhieG0XNiHQUYnuiInWgqhw5P7gY+wBvvDMTe9d/B1SUuLGnTYzgG926PyuVfTGASz2+Wfb0rdq2ahsy+ca9Ye7V5byyYPhRFC+ZheGLMtctZdRRgeKIjNqcyTIDhiWH0nFgnAYYnOkE72DRifROxzon4fnnK9Jlo176joQIMTwzl5+Q6CTA80Qk62TQOHZ4cPHpWexRn9dyRCSwDRnyPqhVeQtumtZNQdftkPOrXqoS336yLg0fPYMioH7Hhl3EJd6jwzhNjLmDOqo8AwxN9nDmLsQIMT4z15+y2F2B4YntjR5tBvFFHvFlHvGFn0Kdf4OP+QwwnYHhieAtYgA4CDE90QE5lCocOT/bsP4aps1ZgyczhCTRDx87GC0Xz4722DZNwnT5/FV36jYWTkxNCH4Vj4rAP8HqNCgn7xMYa00DOSgE9BMQ33NolzutcD27OYZCAdp3zGjdIn9PqJcDrXC9p+5/n33//Ra1aNREWFoZevXrhu+++V+OknQAn8S0Lv56r0Q9WYRMB8bVc+9ac17lNfJ82aLy7rEmdYsU9eybZDh07i8/HzcGGBWMTKu47bJq2EGziO0/CIyLRtNOnGP7Je3itShlcuHIDXf43Fgumf4YCeXNqx96498gkZ80yKWC9QGZvdzwKj0ZYZLT1B/MICphEIIefJ+4+CEd0jGn+GjOJLMtURUB805fTLwMCAvk9iyo9MWsd58+dRdMGtRAUdB8tWrfDtzPnar9gVGETd574eLlpX8+5UcBeBTJ6uMLFxQkPQiPt9RSVPK/cWTJIrctU4UlgUDDqteuP3WtnaG/QEVuTjkPw9aCuqFDmhQQY8SaeXoMnY+fKqQkf6z5gApo3eBXNG1TXPsbHdqReRxxMMQE+tqNYQ1iOTQT42I5NWDmoQgJ8bEehZpi4lOv+19CsYS0EBNxArTr1sGDxKrg8XjtQhdPiYzsqdIE12FqAj+3YWjj18R36sR1B0q3/eFQsWwI9OjTF5h37MHX2CmxaOE5bQHb9tr2oVqEk3N3d8HrbfpgzaRDKliyK23fvo2XXYZg1cQBeKl6Q4Ykx1y5n1VGA4YmO2JzKMAGGJ4bRc2KdBBie6ARtx9ME3ruLJg1q4vKli6hUpRqWrNwIT8+4N0+qsjE8UaUTrMOWAgxPbKn79LEdPjy5cfMuBo+aieOnLyF/nhwYNaQ7SpUopInVbNkX34z4SLsLZefe/zB19nJtvRMXF2d0bNNAWzw2fuOdJ8ZcwJxVHwGGJ/o4cxZjBRieGOvP2W0vwPDE9sb2PENISDBaNq2PE8eOoMSLJbH2t+3w9o57C6VKG8MTlbrBWmwlwPDEVrLPHtfhwxNZ7AxPZElyHBUFGJ6o2BXWJFuA4YlsUY6nmgDDE9U6Yp56IiIi0LbFG9i/728ULFQYazdtR7bsOZQ8AYYnSraFRUkWYHgiGdTC4ZQLT5au24F2zZK+Jlicy8PQMCxZ+we6vt3YwlPTdzeGJ/p6czZ9BRie6OvN2YwRYHhijDtn1U+A4Yl+1vY0U3R0NLp2bIdtWzZpgcmmbX8hT958yp4iwxNlW8PCJAowPJGIacVQyoQnkZFRiIyK0h6V2bVqWopTOH9ZvOFmDPb/9qMVp6ffrgxP9LPmTPoLMDzR35wz6i/A8ER/c86orwDDE3297WW23u93xuqVS+Hr64dVG7Zpj+yovDE8Ubk7rE2WAMMTWZLWjaNMeLJo9e8YO/1XREU//VWor1YqrS3SquLG8ETFrrAmWQIMT2RJchyVBRieqNwd1iZDgOGJDEXHGmP40IGYPfNbbVHYFeu2ovzLFZUHYHiifItYoAQBhicSENMwhDLhiaj9UVgEqjfvjV+/G5biVMSrhAvkzQlnZzXeIZ+8QIYnabj6eIhpBBiemKZVLDQdAgxP0oHHQ00hwPDEFG1SpsgZUydizNdfwNXVFfMXrdReS2yGjeGJGbrEGtMrwPAkvYJpO16p8EScQkREpPZqYLNtDE/M1jHWa40AwxNrtLivWQUYnpi1c6zbUgGGJ5ZKcb+lixagX5+ecHJywsyfFqJJsxamQWF4YppWsdB0CDA8SQdeOg5VLjw5e/Eaps1ZiYtXbiAsPCLFqW1bMikdp2u7Qxme2M6WIxsvwPDE+B6wAtsLMDyxvTFnMFaA4Ymx/maZfdvmjejSsR1iYmIwZuI0dOrc3Syla3UyPDFVu1hsGgUYnqQRLp2HKReetO7+BTL7+eCN2lXgkcodKM0avJrOU7bN4QxPbOPKUdUQYHiiRh9YhW0FGJ7Y1pejGy/A8MT4HqhewZ7du9ChbXOIVxMPGPw5+g38TPWSU9TH8MR0LWPBaRBgeJIGNAmHKBeelK/fHbvXzEBGL08Jp6ffEAxP9LPmTPoLMDzR35wz6i/A8ER/c86orwDDE329zTbb8WNH8GbjungUGoqOnbtj7MSUb780wzkxPDFDl1hjegUYnqRXMG3HKxeetOv5JcYN7YnCBXKn7YwMOorhiUHwnFYXAYYnujBzEoMFGJ4Y3ABOb3MBhic2JzbtBBfOnUXzxnUReO8uWrRqhxkz52rrnZhxY3hixq6xZmsFGJ5YKyZnfyXCk2OnLyaczVX/W1i85g+81bwu8ufJDqdkb9cpXaKwnDOXPArDE8mgHE4pAYYnSrWDxdhIgOGJjWA5rDICDE+UaYVShQQE3ECTeq9B/Fu8UWfB4lVwcXFRqkZrimF4Yo0W9zWrAMMTYzqnRHhSqnZni8/++I55Fu+r544MT/TU5lx6CzA80Vuc8xkhwPDECHXOqacAwxM9tc0xl7jTpHmjurhw/iwqVamGJSs3wtPTXI/OJ5dmeGKOa49Vpk+A4Un6/NJ6tBLhSXhEpMX1p7aIrMUH23BHhic2xOXQhgswPDG8BSxABwGGJzogcwpDBRieGMqv3ORibRPxqM6JY0dQ4sWSWPvbdnh7+yhXp7UFMTyxVoz7m1GA4YkxXVMiPEl86jdu3YOba+q3CopnLzP7+sA52aM8xtAlnZXhiQpdYA22EmB4YitZjquSAMMTlbrBWmwhwPDEFqrmHFO8TUe8VUe8XadgocJYu2k7smXPYc6TSVY1wxO7aCNP4jkCDE+MuUSUC0+e9wiPCFZqVCuH4Z+8h2xZfI1RS2VWhifKtIKF2ECA4YkNUDmkcgIMT5RrCQuSLMDwRDKoSYeLiYlBl3fbYtuWTVpgsmnbX8iTN59JzyZl2QxP7KaVPJFnCDA8MebyUC48+WvfUcz4aSXat6yH0iUKwcnZGcdPXcSStdvRq1NzeGXwxMwFa5HRKwMmf/mhMWoMT5RxZyH6CDA80ceZsxgrwPDEWH/ObnsBhie2NzbDDP369MTSRQvg6+uHVRu2aY/s2NPG8MSeuslzeZoAwxNjrg3lwpMWXT7HtJF9UCBvziQiV/xv4dPRP2Lht5/jzr0gNO/8Gfas/dYYNYYnyrizEH0EGJ7o48xZjBVgeGKsP2e3vQDDE9sbqz7DmK+/wIypE7VFYVes24ryL1dUvWSr62N4YjUZDzChAMMTY5qmXHhSuVFPbFk8UVvbJPEWHBKKOm3+h/2//aiFJ006DsE/G743Ro3hiTLuLEQfAYYn+jhzFmMFGJ4Y68/ZbS/A8MT2xirPMHvmtxg+dCBcXV0xf9FK7bXE9rgxPLHHrvKckgswPDHmmlAuPOkxYCLEs5hd2zdGvtzZIf6ivx5wF/OW/oYHwQ8x95sh+HDIFGTw9MCM0R8bo8bwRBl3FqKPAMMTfZw5i7ECDE+M9efsthdgeGJ7Y1VnWL1iKXr37Azx8oWZPy1Ek2YtVC013XUxPEk3IQcwgQDDE2OapFx4cuvOfQwdOxt/HzyOmJjYBJWyJYti7GfvI3+eHBgw4nt81rcDF4w15prhrA4owPDEAZvugKfM8MQBm+5gp8zwxMEa/vh0t23eiK6d3kJ0dDTGTJyGTp272zUEwxO7bi9P7rEAwxNjLgXlwpN4hvCISNy6E4jYWCBblkzaQrEqb3zbjsrdYW3pFWB4kl5BHm8GAYYnZugSa0yPAMOT9OiZ89j9+/5G2xZvQLyauP+gofhk0FBznogVVTM8sQKLu5pWgOGJMa1TIjxZuHIb6tWoiJzZM0P897O2Dq3UfD6T4YkxFzBn1UeA4Yk+zpzFWAGGJ8b6c3bbCzA8sb2xSjMcP3YErZrWR0hIMDp27o6xE6epVJ7NamF4YjNaDqyQAMMTY5qhRHjSqtswjBjUFaVLFIb472dtK+d8bYzUc2ZleKJkW1iUJAGGJ5IgOYzSAgxPlG4Pi5MgwPBEAqJJhrh86SKaNKiJwHt3tfVNxDonYr0TR9gYnjhCl3mODE+MuQaUCE+MOXW5szI8kevJ0dQSYHiiVj9YjW0EGJ7YxpWjqiPA8ESdXtiykoCAG2jWsBau+1/T3qgj3qwj3rDjKBvDE0fptGOfJ8MTY/qvZHgSFR2Ng0fOwj/gNlo2qqHJPAwNQ0Yvddc9YXhizAXMWfURYHiijzNnMVaA4Ymx/pzd9gIMT2xvbPQMQUH30bRBLVw4fxblX66IFeu2wtNT3e+fbeHF8MQWqhxTNQGGJ8Z0RLnw5OKVG/hgyBTcuXcfj8IicHzHPPgH3EGb7l9g5vj+EG/dUXFjeKJiV1iTLAGGJ7IkOY7KAgxPVO4Oa5MhwPBEhqK6YzwKDUWbNxvi8KEDKPFiSazasA2+vn7qFmyjyhie2AiWwyolwPDEmHYoF550HzABZV8qgt6dW6Ls61218ERsC1duxcbf/8HCbz83Ruo5szI8UbItLEqSAMMTSZAcRmkBhidKt4fFSRBgeCIBUdEhoqKi0L51U+zZvQt58ubDpm1/IVv2HIpWa9uyGJ7Y1pejqyHA8MSYPigXnrzS9EPsWDkVHu5uKFW7c0J4EhkVjVeafoD9v/1ojBTDEyXdWZQ+AgxP9HHmLMYKMDwx1p+z216A4YntjY2YISYmBr26vYsN61ZrgcnaTdtRsFBhI0pRYk6GJ0q0gUXYWIDhiY2BnzK8cuHJq816Y828Ucie1S9JeHLhyg107DMKu9fMMEaK4YmS7ixKHwGGJ/o4cxZjBRieGOvP2W0vwPDE9sZGzNCvT08sXbQA3t4+WPvbdu2RHUfeGJ44cvcd59wZnhjTa+XCk68mzcPFqwHo3bkFOv9vLFbMHoHT56/ih/lr8WqlUhjWr5MxUgxPlHRnUfoIMDzRx5mzGCvA8MRYf85uewGGJ7Y31nuGieNGYsqE0dqisEtWbkSlKtX0LkG5+RieKNcSFmQDAYYnNkC1YEjlwpOw8AhM/2kllq7djtBH4dopeGXwxNtv1sVHXVtqj/OouHHNExW7wppkCTA8kSXJcVQWYHiicndYmwwBhicyFNUZY/682fh0QF+4uLhgweJV2muJuQEMT3gVOIIAwxNjuqxMeDLlx2WoUbUsypcuBlcXF8TGxuLOvSA4OTkhWxZfm+lc8b+Fz8bMwsmzl5E3VzaMGNQV5UsVSzFfZGQUvpr8M7bs/BfeGTPg4+5t8GbD6gn7MTyxWYs4sAICDE8UaAJLsLkAwxObE3MCgwUYnhjcAInTi/VNenbtoI0444e5aNG6ncTRzT0UwxNz94/VWybA8MQyJ9l7KROevNllKM5d9NeCiVcqltKClNeqlEHO7Jlln3OS8cQ6KtUrl0G3d5pg597DGD3tF2xeNBFuri5J9pvx0yqcu+SPMZ+9r/17+ISf8Ot3w+Dp4a7tx/DEpm3i4AYLMDwxuAGcXhcBhie6MHMSAwUYnhiIL3Hqndu3oVP7VhBv2BkzcRo6de4ucXTzD8XwxPx7EODsAAAgAElEQVQ95Bk8X4DhyfONbLGHMuGJOLnbd+9j74Hj2LP/OPbuP67deVK8cD4tSBH/vFymeIpQIz0odwMf4I13BmLv+u+0u13E1qbHcAzu3R6Vy7+YZOjX236COZMHoVD+XKlOyfAkPZ3gsaoLMDxRvUOsT4YAwxMZihxDZQGGJyp3x7LaDh86gNbN6iMsLAz9Bn6GAYM/t+xAB9qL4YkDNduBT5XhiTHNVyo8SU4g7kQRYcq//53C4WPn8CgsAv9u+kGa1MGjZzFi8s9YPXdkwpgDRnyPqhVeQtumtRM+9iAkFDVb9sWAXm9h4cqt8HB3R99urVD3tQoJ+zA8kdYWDqSgAMMTBZvCkqQLMDyRTsoBFRNgeKJYQ6ws5/SpE2jZpB6Cgu6jY+fuGDtxmpUjOMbuDE8co8+OfpYMT4y5ApQNT6KjY3D01AX8c/AkDhw5jf9OnIevT0ZsWTxRmtSe/ccwddYKLJk5PGHMoWNn44Wi+fFe24YJH/MPuKPdodKnayt0f6epVtf7Aydi3c9jkCObn7bfo/BoaXVxIAqoJuDu5oyo6FjExMSqVhrroYA0AU93Z4RHxiCWl7k0Uw6knoCnuwvCIvg9i3qdeXZF165exWvVq+LWrVto0bIVFv66RFsXkFtKAWdnwM0l7us5NwrYq4CrixPEl4DIKH7TomePM3gkXdojvXM7xYqVXtO4XboaEPfIzoHj2HfopPZmHXEXSLUKpVCtYkltQVeZ26FjZ/H5uDnYsGBswrB9h03THhFKfufJK00/xD8bvtfWZBFbt0/Go13zOmhYu7L2/4EhETJL41gUUErA29MVEVEx2j/cKGCvAr4Z3REcGomYtP81Zq80PC87ERDfaPtldOf3LCbr5+3bt9Dw9Vq4dPEC6r5eH4uXr4arq6vJzkK/cl1dnCF+wBFfz7lRwF4FPN1c4OzshNDwKHs9RSXPK7N33HqnsrY0hydiTZEHIQ+14KJCmRe00ESsd2LLLTAoGPXa9cfutTMSFn5t0nEIvh7UVash8SbCk2WzvkK+3Nm1D3ftNw7vtq6f8OgOH9uxZac4ttECfGzH6A5wfj0E+NiOHsqcw0gBPrZjpH7a5g4JCUbzN+pAPLJT/uWKWLFuKzw9PdM2mIMcxcd2HKTRDn6afGzHmAtAmcd2Bo+aqS0SK25cqVi2hHanSbUKJZ+6QKssrm79x2vz9ejQFJt37MPU2SuwaeE4bQHZ9dv2ajWIVyWLt/CEPgrHlwM648TpS3h/0CSsnz8m4TXKDE9kdYTjqCjA8ETFrrAm2QIMT2SLcjzVBBieqNaRZ9cjFoV9q1Vj7N/3N0q8WBKrNmyDr2/c4+Lcni7A8IRXhyMIMDwxpsvKhCfi9EVwcvr8VS1EEY/u7P/vNDL7+uCVSqW0f0SQkTVzJqlSN27ehQhujp++hPx5cmDUkO4oVaKQNodYJPabER9pd6EEh4Tis7GztceJsvhlwsAP3uKCsVI7wcFUFmB4onJ3WJssAYYnsiQ5jqoCDE9U7UzKuqKjo9Hx7ZYQryXOkzcfNm37C9my5zDPCRhYKcMTA/E5tW4CDE90o04ykVLhSXKCiIhIHDp+DgeOnMG6LXtwxf8mju+YZ4zUc2blnSdKtoVFSRJgeCIJksMoLcDwROn2sDgJAgxPJCDqMIT4ZeJHPbtg9cqlWmCydtN2FCxUWIeZ7WMKhif20UeexbMFGJ4Yc4UoG54kXjz20NGzCHkYqt0B8tOUwcZIMTxR0p1F6SPA8EQfZ85irADDE2P9ObvtBRie2N5YxgxDBvTFgnmz4e3tg7W/bdce2eFmuQDDE8utuKd5BRieGNM7ZcKToOCH+PvACYjXB+/+9xjE4zR5cmXDa1XKaIvIikd2vDJ4GKNkway888QCJO5iWgGGJ6ZtHQu3QoDhiRVY3NWUAgxP1G/b1EljMX7MCG1R2CUrN6JSlWrqF61YhQxPFGsIy7GJAMMTm7A+d1BlwpPSdbrA1dUFlcqV0MISEZoULZjnuSegyg4MT1TpBOuwhQDDE1uockzVBBieqNYR1iNbgOGJbFG54y1dtAD9+vSEi4sLFixehVp16smdwEFGY3jiII128NNkeGLMBaBMeLJjz2FUrVASGTzlvjtZL1aGJ3pJcx4jBBieGKHOOfUWYHiitzjn01uA4Yne4pbPt2HdavTq9q728oQZP8xFi9btLD+YeyYRYHjCC8IRBBieGNNlZcITY05f3qwMT+RZciT1BBieqNcTViRfgOGJfFOOqJYAwxO1+hFfzZ7du9C+dVNERUVhzMRp6NS5u5qFmqQqhicmaRTLTJcAw5N08aX5YIYnaaZLeiDDE0mQHEZJAYYnSraFRUkWYHgiGZTDKSfA8ES5luDwoQNo82ZDPAoNxcf9h2DQp1+oV6TJKmJ4YrKGsdw0CTA8SRNbug9ieJJuwrgBGJ5IguQwSgowPFGyLSxKsgDDE8mgHE45AYYnarXkwrmzaNqwFoKC7qNd+46YMn2mWgWatBqGJyZtHMu2SoDhiVVc0nZmeCKJkuGJJEgOo6QAwxMl28KiJAswPJEMyuGUE2B4ok5LrvtfQ7OGtRAQcANNmrXAD3N+gbOzszoFmrgShicmbh5Lt1iA4YnFVFJ3VCI8+WrSPItOKjIqGiMHd7NoX713Yniitzjn01OA4Yme2pzLKAGGJ0bJc169BBie6CX97HkC791FkwY1cfnSRbxavSYWrVgPV1dXNYqzgyoYnthBE3kKzxVgePJcIpvsoER4Mnjkk9sUY2JjIN68ky93dhTImxPR0TG4cOU67gY+QOPXq2H4J+/ZBCK9gzI8Sa8gj1dZgOGJyt1hbbIEGJ7IkuQ4qgowPDG+MyEhwWjZtD5OHDuC8i9XxPI1m5HBy8v4wuyoAoYndtRMnspTBRieGHNxKBGeJD71EVPmo1zJonizYfUkIvOXbcblazcxrF8nY6SeMyvDEyXbwqIkCTA8kQTJYZQWYHiidHtYnAQBhicSENMxREREBNq2eAP79/2NIkWLY/2WnfD19UvHiDw0NQGGJ7wuHEGA4YkxXVYuPKnSuBd2r5kBN7ekty+GPHyEOm364d9NPxgjxfBESXcWpY8AwxN9nDmLsQIMT4z15+y2F2B4Ynvjp80QHR2Nrh3bYduWTciTNx/Wbd6JXLlyG1eQHc/M8MSOm8tTSxBgeGLMxaBcePJ620/w5YDOqFG1bBKRbX8ewJhpC/H7ssnGSDE8UdKdRekjwPBEH2fOYqwAwxNj/Tm77QUYntje+Gkz9H6/M1avXIrMWbJiw5ZdKFiosHHF2PnMDE/svME8PU2A4YkxF4Jy4cniNX9g5DcLUOalIsiTMytiY4HrN+/g6MkLGNS7Pd5r29AYKYYnSrqzKH0EGJ7o48xZjBVgeGKsP2e3vQDDE9sbpzbD8KEDMXvmt/D29sHK9VtRqnTSXxAaU5X9zsrwxH57yzN7IsDwxJirQbnwRDCcuXANW3f+i5t3AhERGYUcWf1Qs1o5VCpXwhglC2blmicWIHEX0wowPDFt61i4FQIMT6zA4q6mFGB4on/bvps+GaO++hyenp5YsnIjKlWppn8RDjYjwxMHa7iDni7DE2Mar2R4YgxF+mZleJI+Px6ttgDDE7X7w+rkCDA8kePIUdQVYHiib2+WLlqAfn16wsXFBQsWr0KtOvX0LcBBZ2N44qCNd7DTZnhiTMOVCE/+98UMi8/+mxEfWbyvnjsyPNFTm3PpLcDwRG9xzmeEAMMTI9Q5p54CDE/00962eSO6dGyH2NhYzPhhLlq0bqff5A4+E8MTB78AHOT0GZ4Y02glwpMx0xdafPaf9ulg8b567sjwRE9tzqW3AMMTvcU5nxECDE+MUOecegowPNFHe8/uXejQtjnEq4nHTJyGTp276zMxZ9EEGJ7wQnAEAYYnxnRZifDEmFOXOyvDE7meHE0tAYYnavWD1dhGgOGJbVw5qjoCDE9s34vjx47gzcZ18Sg0FH37DcLgoV/aflLOkESA4QkvCEcQYHhiTJeVC0+io2OwYMUWbPz9b1y7cVtTKZA3J1o1rol2zWobo2TBrAxPLEDiLqYVYHhi2taxcCsEGJ5YgcVdTSnA8MS2bbtw7iyaN66LwHt30a59R0yZPtO2E3L0VAUYnvDCcAQBhifGdFm58OSH+WuxaPXvaNmoBvLnyaGpXLx6A6s2/YkP32uBDq3UXGyL4YkxFzBn1UeA4Yk+zpzFWAGGJ8b6c3bbCzA8sZ1xQMANNKn3GrR/N2uBH+b8AmdnZ9tNyJGfKsDwhBeHIwgwPDGmy8qFJw3bD8TUr/vgxWIFkogcOXEen42djfXzxxgj9ZxZGZ4o2RYWJUmA4YkkSA6jtADDE6Xbw+IkCDA8kYCYyhBBQffRtEEtXDh/Fq9Wr4lFK9bD1dXVNpNx1OcKMDx5LhF3sAMBhifGNFG58KRyo57YvWYG3N3dkohERESiWtMPcXDLLGOkGJ4o6c6i9BFgeKKPM2cxVoDhibH+nN32AgxP5BuLtU3avNkQhw8dQPmXK2L5ms3I4OUlfyKOaLEAwxOLqbijiQUYnhjTPOXCk7d6foU2zWqhbdOk65ssX78Tv6zYitVzRxojxfBESXcWpY8AwxN9nDmLsQIMT4z15+y2F2B4Itc4KioK7Vs3hXi7TpGixbF+y074+vrJnYSjWS3A8MRqMh5gQgGGJ8Y0TbnwZN+hU3h/0EQUzp8LhQvkRmxsLC5eCcAV/5uY+nVf1KhaxhgphidKurMofQQYnujjzFmMFWB4Yqw/Z7e9AMMTecYxMTHo1e1dbFi3Gnny5sO6zTuRK1dueRNwpDQLMDxJMx0PNJEAwxNjmqVceCIYbt4OxLqte3Dt+uO37eTLgeYNqiNbFl9jlCyYlWueWIDEXUwrwPDEtK1j4VYIMDyxAou7mlKA4Ym8tvXr0xNLFy1A5ixZsWHLLhQsVFje4BwpXQIMT9LFx4NNIsDwxJhGKRmeGEORvlkZnqTPj0erLcDwRO3+sDo5AgxP5DhyFHUFGJ7I6c24UV9i2pTx8Pb2wcr1W1GqdFk5A3MUKQIMT6QwchDFBRieGNMg5cKTsxevYdqclbh45QbCwiNSqGxbMskYqefMyvBEybawKEkCDE8kQXIYpQUYnijdHhYnQYDhSfoR58+bjU8H9IW7uzuWrf4NlapUS/+gHEGqAMMTqZwcTFEBhifGNEa58KR19y+Q2c8Hb9SuAo9kb9wRRM0avGqMFMMTJd1ZlD4CDE/0ceYsxgowPDHWn7PbXoDhSfqMV69Yio96dYGzszN+mr8E9Ro2Tt+APNomAgxPbMLKQRUTYHhiTEOUC0/K1++uvao4o5enMSJpnJV3nqQRjoeZQoDhiSnaxCLTKcDwJJ2APFx5AYYnaW/Rzu3b0PHtloiOjsa3M+ehRet2aR+MR9pUgOGJTXk5uCICDE+MaYRy4Um7nl9i3NCe2pt29Niu+N/CZ2Nm4eTZy8ibKxtGDOqK8qWKPXXq+0EhaNxxMD7u1hpvvVk3YT+GJ3p0i3MYJcDwxCh5zqunAMMTPbU5lxECDE/Spr5/3994q1VjhIWF4atRE9C9Z++0DcSjdBFgeKILMycxWIDhiTENUCI8OXb6YsLZX/W/hcVr/sBbzesif57scHJ2SiJTuoTc1cw79hmF6pXLoNs7TbBz72GMnvYLNi+aCDdXl1Q7IoKWfYdPocc7TRieGHPNclYDBBieGIDOKXUXYHiiOzkn1FmA4Yn14KdPnUDzN+ogJCQYff43EEM+/8r6QXiErgIMT3Tl5mQGCTA8MQZeifCkVO3OFp/98R3zLN73eTveDXyAN94ZiL3rv4OrS1xY0qbHcAzu3R6Vy7+Y4vB9h07hu59Xo1ihvCheOC/Dk+cB88/tRoDhid20kifyDAGGJ7w87F2A4Yl1Hb586SKaN6qDO7dvoV37jpgyfaZ1A3BvQwQYnhjCzkl1FmB4ojP44+mUCE/CIyItPvvUFpG1+OBkOx48ehYjJv+M1XNHJvzJgBHfo2qFl9C2ae0ke0dGRkE8UjTpy974deU2hidpRedxphRgeGLKtrFoKwUYnlgJxt1NJ8DwxPKWicCkUb3XcN3/Guo1aIS5vyzTForlpr4AwxP1e8QK0y/A8CT9hmkZQYnwJHnhp89fRYmi+bUPXw+4g61/HkD+3NlR97UKaTnHpx6zZ/8xTJ21AktmDk/YZ+jY2XihaH6817ZhkuO+m7casbGx6N2lJUZ+syBFeHIvOFxqbRyMAioJeHu6IjwqBpFRMSqVpUgtSR8tVKQolpEGAb+MbngQGomY2DQczEMoYAIBEZ74ZXRHYEiECao1rsSg+/fRuGEdnDp5Aq/VqIVlq9ZrrybmZg4BVxcneHm44EFolA0L5l8UNsTl0BYIeLi5wMXZCaHhtrzOLSjEwXbJ4uMh9YydYkXCkI5twfItEEHFX2tmIDgkFM3e+xQ5smXGnXtBeLd1ffTo0DQdoyc99NCxs/h83BxsWDA24Q/6DpuGGlXLJrnz5NLVAPT/6jss+m4Y3N3dUg1PHoVHS6uLA1FANQF3N2dER8cimj9VptKadH3JU63VDl2Ph7sLIiJjtKCcGwXsVcDT3RVhEfxm+2n9FYvC1q9XBwf270fZsuXwx45d8PLystfLwS7Py9nJCa6uzoiItOX35vzFiV1ePCY6KRESikA8Morfs+jZtgweqa+LmtYa0h2e1H97AL756iOUKlEIPy/bjPVb92LpzOG4eDUAvQZNwpbFE9NaW4rjAoOCUa9df+xeOwOeHnG/UWjScQi+HtQVFcq8kLD/vKW/Yeb8tXBzc9U+9jA0DC4uzninZT38r0cb7WN82460tnAgBQX42I6CTWFJ0gX42I50Ug6omAAf23l2Q6KiotCpfSuI1xIXKVocazf9gcxZsirWRZbzPAE+tvM8If65PQjwsR1juqjcYzvl63fHwc2z4OzshG79x6N65dLo+nZjxMTEokLDHji8dbZUKTFHxbIltDtaNu/Yh6mzV2DTwnHaArLrt+1FtQolkS2Lb5I5U3tsh+GJ1LZwMMUEGJ4o1hCWYxMBhic2YeWgCgkwPHl6M8QdZz27dsCGdauRJ28+rNu8E7ly5VaoeyzFUgGGJ5ZKcT8zCzA8MaZ7yoUn4s6TaV/3QSafjGjy7mCsnjsKhfLnwoUrN9C9/3j8sWyKVKkbN+9i8KiZOH76EvLnyYFRQ7prd72IrWbLvvhmxEdJ7kIRH2d4IrUFHMwEAgxPTNAklphuAYYn6SbkAIoLMDx5eoOGDOiLBfNma3eabNiyCwULFVa8myzvaQIMT3htOIIAwxNjuqxcePLLiq2Y+MMSODk5oVGdKhj9aQ/cDwrBu31GoW71l/FJz3bGSD1nVt55omRbWJQkAYYnkiA5jNICDE+Ubg+LkyDA8CR1xMnjR2HS+FHw9vbByvVbUap0WQnaHMIoAYYnRslzXj0FGJ7oqf1kLuXCE1HauYv+ePgoDKVLFNbWFomMisby9TvQrlkd7f9V3BieqNgV1iRLgOGJLEmOo7IAwxOVu8PaZAgwPEmpOH/ebHw6oK/2Np1lq39DpSrVZFBzDAMFGJ4YiM+pdRNgeKIbdZKJlAxPoqKjcfDIWfgH3EbLRjW0gsUirRm9PI1RsmBWhicWIHEX0wowPDFt61i4FQIMT6zA4q6mFGB4krRtYn0Tsc6Js7Mzfpq/BPUaNjZlX1l0UgGGJ7wiHEGA4YkxXVYuPLl45QY+GDIFd+7dx6OwCBzfMQ/+AXfQpvsXmDm+P8qWLGqM1HNmZXiiZFtYlCQBhieSIDmM0gIMT5RuD4uTIMDw5AmieKOOeLOOeMPO/9u78zidyv+P4++ZscdXSVJalYSSJLRJUvY9Co2sSbbssiT7vhNjyTJ8haxZS0KhpChSkkSJFkmWZJvf4xxf84uWuWfmuu9znft+nX96lHOu87men6viPedcZ2zcVFWtYedr4QbaHnFDEJ5EXMsjcsKEJ9603brwpHH7wSqYL7ea16+mgo82dMMT55g5/y0te/sDzRzbzRspwhMr3SkqNAKEJ6Fx5i7eChCeeOvP3YMvQHhy3njrlo9Uo9JjOnnypHr2HazGTZsHH587hEyA8CRk1NzIQwHCE2/wrQtP7qv4vNbMH6n06dKqQMn6ieGJs+/JfRWbafOKCd5IEZ5Y6U5RoREgPAmNM3fxVoDwxFt/7h58AcITaecXO1StQmkdOfKrmrdqpy4v9Q4+PHcIqQDhSUi5uZlHAoQn3sBbF57cX6m5Fk3tq6uuvPyi8MT5VHFsy75av2iMN1KEJ1a6U1RoBAhPQuPMXbwVIDzx1p+7B18g0sOT7/d/p3KlH9TPP/2oWrVjNXx0XPDRuUPIBQhPQk7ODT0QIDzxAF2SdeFJz6FTtefbg2pev6rqvzBA8yb10s7d32r89MW6v0gBdW9TzxspwhMr3SkqNAKEJ6Fx5i7eChCeeOvP3YMvEMnhiROYVC73iPZ+s0elHy+nKTPmuhvFcoSfAOFJ+PWUGf1VgPDEm1VhXXhy8o9TGv3qfM1Z/I5O/P6Hq5IpYwY9VaWUWjSs5r7OY+PBhrE2doWaTAkQnpiSZBybBQhPbO4OtZkQiNTw5Nixo6pc9hH3lZ37HyihmXMXu58m5ghPAcKT8Owrs7pYgPDEmxVhXXhygSEhIUE//3JEUVFRyp4tqzc6ybgr4UkysDjVdwKEJ75rGQWnQIDwJAVoXOIrgUgMT5xNYZ+sXl6bN72v/HcU1OJlq5UxUyZf9Y1ikydAeJI8L872pwDhiTd9syo8OXP2rEpUa6XFU/v5IjD5c8sIT7xZwNw1NAKEJ6Fx5i7eChCeeOvP3YMvEGnhydmzZxX7VDU5nyXOfUseLV6+WldkuzL40NzBUwHCE0/5uXmIBAhPQgR9yW2sCk+c2lp2G6XihfOrbvXS3oik8K6EJymE4zJfCBCe+KJNFJlKAcKTVAJyufUCkRSeOE8wt2jaQAvnz1HOnNdo6ar33L9yhL8A4Un495gZSoQn3qwC68KTbgMn671N25QubRpdnyuH0qW9eI+TcQPaeCOVxF0JT6xsC0UZEiA8MQTJMFYLEJ5Y3R6KMyAQSeFJ5/atFD91kvukifOqTu5b8xgQZAg/CBCe+KFL1JhaAcKT1Aqm7HrrwpOBY2cpTUyMnP/B/93RtmmtlM00yFcRngQZmOE9FSA88ZSfm4dIgPAkRNDcxjOBSAlPRg0fpIF9X3b3Nlm0bLUK3FHQM3NuHHoBwpPQm3PH0AsQnoTe3LmjdeGJNwypvyvhSeoNGcFeAcITe3tDZeYECE/MWTKSnQKREJ7MmRWvNi2bul/Tcb6q43xdhyOyBAhPIqvfkTpbwhNvOm9deHL8xEnNX7ZOX+87oD/+OPUXlX4vNvFGKom7Ep5Y2RaKMiRAeGIIkmGsFiA8sbo9FGdAINzDk1Url6lB7PknlKfEz1HpMuUNqDGE3wQIT/zWMepNiQDhSUrUUn+NdeFJs87DtePLb9xNY9Olu3i/E2e6vTs2TP2sgzAC4UkQUBnSGgHCE2taQSFBFCA8CSIuQ1shEM7hyYb161S3ZmWdOnVKw0fHqVbtWCvMKSL0AoQnoTfnjqEXIDwJvblzR+vCk3vKPKvF0/opV87s3oik8K6EJymE4zJfCBCe+KJNFJlKAcKTVAJyufUC4RqefLb9U1UpX0q/nzihrj366PmWba3vBQUGT4DwJHi2jGyPAOGJN72wLjx57Kn2mhPXQ1dkzeKNSArvSniSQjgu84UA4Ykv2kSRqRQgPEklIJdbLxCO4cnXX+1S5fKldPiXQ2rctLl69h1sfR8oMLgChCfB9WV0OwQIT7zpg3XhydvvfqzV6z9Wm2drKnu2rN6opOCuhCcpQOMS3wgQnvimVRSaCgHCk1TgcakvBMItPDl48IAqlH5Qzl+rVq+lsROm+qIPFBlcAcKT4Poyuh0ChCfe9MGK8KRI2WcTZ58mTRqdPn1GJ/84pfTp0io6+uJvFm9eMcEbqSTuSnhiZVsoypAA4YkhSIaxWoDwxOr2UJwBgXAKT5wnTSqXK6Wvd+9S6cfL6dX4OYqJiTGgxBB+FyA88XsHqT8QAcKTQJTMn2NFePLuB9sCntlDxe4M+NxQnkh4Ekpt7hVqAcKTUItzPy8ECE+8UOeeoRQIl/DE2dvEeVVnx/ZP3U8RO58kdj5NzIGAI0B4wjqIBAHCE2+6bEV44kx9/PTFql3tUWXNcpk3Eqm8K+FJKgG53GoBwhOr20NxhgQITwxBMoy1AuEQnjhf03G+quN8XSf/HQW1eNlqZcyUyVpzCgu9AOFJ6M25Y+gFCE9Cb+7c0ZrwpEDJ+lo+c6BuyHW1NxKpvCvhSSoBudxqAcITq9tDcYYECE8MQTKMtQJ+D0/OnTunBk/X1Ko3lyv3LXm0ePlqXZHtSmu9KcwbAcITb9y5a2gFCE9C633hboQnhtwJTwxBMoyVAoQnVraFogwLEJ4YBmU46wT8Hp60adlUc2bFK2fOa7R01XvuXzkQuFSA8IQ1EQkChCfedNmq8GT8wLbKmSPbv0rkufk6b6SSuCvhiZVtoShDAoQnhiAZxmoBwhOr20NxBgT8HJ7069VdY0cNdZ80cV7VyX1rHgMiDBGOAoQn4dhV5nSpAOGJN2vCqvAkEILP1tj5GTrCk0C6xzl+FSA88WvnqDs5AoQnydHiXD8K+DU8mRQ3Vj26dnD3Nlm0bLUK3FHQj/zUHCIBwpMQQXMbTwUIT7zhtyo8mTqis67Nmf1fJXIl8eveMEqEJ17Jc99QCBCehEKZe3gtQHjidQe4f7AF/BieLJw3R82b1ne/puN8Vcf5ug4HAv8mQHjC+ogEAcITb7psVXjChrHeLALuikBSAoQnSQnx6+EgQHgSDl1kDv8m4LfwZNXKZWpY71Go5+AAACAASURBVEklJCRoSvwclS5TngYjkKQA4UmSRJwQBgKEJ940kfDEkDtPnhiCZBgrBQhPrGwLRRkWIDwxDMpw1gn4KTzZvOl91axaVs6niYePjlOt2rHWeVKQnQKEJ3b2harMChCemPUMdDRrwpPpc1eqWrmHlCVzpkBrt+o8whOr2kExhgUITwyDMpyVAoQnVraFogwK+CU8+Wz7p6pe8TEdO3ZUL3bvpRat2xtUYKhwFyA8CfcOMz9HgPDEm3VgTXjizfTN3ZXwxJwlI9knQHhiX0+oyLwA4Yl5U0a0S8AP4cneb/aowuMldPiXQ2rctLl69h1sFyLVWC9AeGJ9iyjQgADhiQHEFAwR8eHJvv0/qkv/ifp81145m9H26thQhQrc+hfK3d/s18tDp2nn7n3Kni2r2jd7SqUeuDvxPMKTFKw+LvGNAOGJb1pFoakQIDxJBR6X+kLA9vDk4MEDqlTmYX2//ztVrV5LYyfY+YVFXzQ7goskPIng5kfQ1AlPvGl2xIcnsS376oF771SjOhW0duNW9Rs1QytnDVHaNDEXdaRKg656osLDqlv9Ma3/cLvavjxG6xaMVsYM6dzzCE+8WcDcNTQChCehceYu3goQnnjrz92DL2BzeHLkyK+q+PjD+nr3LpV+vJxejZ+jmJiLfy8WfCHuEA4ChCfh0EXmkJQA4UlSQsH59YgOTw4d/k1l63TQxiWvKM3//gf9RJMe6tS8tu4tdHui+JmzZ7Vg+bvuniwXzitWoZnmTuipG3LlIDwJztpkVIsECE8sagalBE2A8CRotAxsiYCt4cnvJ07oiSpltHXLRypStLjmLlzhfpqYA4GUCBCepESNa/wmQHjiTcciOjz5eNsu9Ro2TQun9EnUb99rnIoVzqeaFUv+Y0e2ff61Wr80WqtmD1N0dBThiTdrl7uGUIDwJITY3MozAcITz+i5cYgEbAxPzpw5o9o1KmrD+nXKf0dBLVjyljJnzhIiEW4TjgKEJ+HYVeZ0qQDhiTdrIqLDkw2bt2vkxHmaHdcjUb/rgEm67Zbr9UzNMn/bke8O/KRnOwxR9xfq6b4iBRLPSUhI8KaD3BUBBBBAAAEEEAhYwPmhjx2/Zzl37pyefPJJzZs3T7lz59YHH3ygK6+8MuCZcCICCCCAAAKhFIhyfgph8IhK8FGKsGX7LnUbOFlL4wckErTqPkoPFSv4t0+e7Nz9rVp3H63OLeqo5P2FLmJjzxODq4ihrBPgyRPrWkJBQRDgyZMgoDKkVQK2PXnSpmVTzZkVr5w5r9EbK9fq2lzXWeVFMf4U4MkTf/aNqpMnwJMnyfMydXZEP3ly+MhRla7VTusXj1GG9Offra0Q21m9OzZU4Ttvu8j42+9/VJP2Q9TvxSYqfGeev/gTnphakoxjowDhiY1doSbTAoQnpkUZzzYBm8KTQf17aeTQAcqa9XItWblWuW/96++tbPOjHn8IEJ74o09UmToBwpPU+aX06ogOTxy0Ru0G6Z6CedWkbkWtXLNJIyfN0/KZA92NYZes2qjihfO7nyau/8IAPVn5EZUrVexvrQlPUroEuc4PAoQnfugSNaZWgPAktYJcb7uALeHJ9KmT9GL7VsqYKZNeX7RShe6+x3Y66vORAOGJj5pFqSkWIDxJMV2qLoz48OTAD4fUqW+cPtv5ja6/Nof6dm6sAnlvclFLVGulEb1aKEf2K1SmdgelTZvmIuwhLzVT6YfO/w+f8CRV65CLLRcgPLG8QZRnRIDwxAgjg1gsYEN4snDeHLV4roH7GeJZ85bo/gdKWCxGaX4UIDzxY9eoObkChCfJFTNzfsSHJ2YYCU9MOTKOnQKEJ3b2harMChCemPVkNPsEvA5P1r6zSrFPVZOzPd74yTNUoVJV+5CoyPcChCe+byETCECA8CQApCCcQnhiCJUnTwxBMoyVAoQnVraFogwLEJ4YBmU46wS8DE82b3pfT1Yvr5MnT2r46DjVqh1rnQ8FhYcA4Ul49JFZ/LsA4Yk3K4TwxJA74YkhSIaxUoDwxMq2UJRhAcITw6AMZ52AV+HJzi92qHLZR3Ts2FF16vqyWrXpaJ0NBYWPAOFJ+PSSmfyzAOGJN6uD8MSQO+GJIUiGsVKA8MTKtlCUYQHCE8OgDGedgBfhyd5v9qhyuUf0808/qnHT5urZd7B1LhQUXgKEJ+HVT2bz9wKEJ96sDMITQ+6EJ4YgGcZKAcITK9tCUYYFCE8MgzKcdQKhDk+cwKRc6Qf1/f7vVLV6LY2dMNU6EwoKPwHCk/DrKTP6qwDhiTergvDEkDvhiSFIhrFSgPDEyrZQlGEBwhPDoAxnnUAow5MjR35VtQql5byyU/rxcno1fo77hR0OBIItQHgSbGHGt0GA8MSbLhCeGHInPDEEyTBWChCeWNkWijIsQHhiGJThrBMIVXjibApbo9Jj2rrlIxUpWlxzF65QunTprPOgoPAUIDwJz74yq4sFCE+8WRGEJ4bcCU8MQTKMlQKEJ1a2haIMCxCeGAZlOOsEQhGenD171v0csfNZ4vx3FNSCJW8pc+Ys1llQUPgKEJ6Eb2+Z2f8LEJ54sxoITwy5E54YgmQYKwUIT6xsC0UZFiA8MQzKcNYJBDs8SUhIUIumDbRw/hzdeNPNWvrmOl2R7UrrHCgovAUIT8K7v8zuvADhiTcrgfDEkDvhiSFIhrFSgPDEyrZQlGEBwhPDoAxnnUCww5PO7Vspfuok5cx5jd5YuVbX5rrOOgMKCn8BwpPw7zEzJDzxag0QnhiSJzwxBMkwVgoQnljZFooyLEB4YhiU4awTCGZ4MmJIfw0e0FtZs16uJSvXKveteaybPwVFhgDhSWT0OdJnyZMn3qwAwhND7oQnhiAZxkoBwhMr20JRhgUITwyDMpx1AsEKT+bMileblk2VMVMmvb5opQrdfY91c6egyBEgPImcXkfyTAlPvOk+4Ykhd8ITQ5AMY6UA4YmVbaEowwKEJ4ZBGc46gWCEJ0vfWKjnGj2t6OhozZq3RPc/UMK6eVNQZAkQnkRWvyN1toQn3nSe8MSQO+GJIUiGsVKA8MTKtlCUYQHCE8OgDGedgOnwZMP6dapdo6LOnTun8ZNnqEKlqtbNmYIiT4DwJPJ6HokzJjzxpuuEJ4bcCU8MQTKMlQKEJ1a2haIMCxCeGAZlOOsETIYnW7d8pCeqlNHvJ05o+Og41aoda918KSgyBQhPIrPvkTZrwhNvOk54Ysid8MQQJMNYKUB4YmVbKMqwAOGJYVCGs07AVHjy9Ve7VLHMwzpy5Fd17NJDrdt2sm6uFBS5AoQnkdv7SJo54Yk33SY8MeROeGIIkmGsFCA8sbItFGVYgPDEMCjDWSdgIjz5fv93qlTmYR08eECx9RtrwJBR1s2TgiJbgPAksvsfKbMnPPGm04QnhtwJTwxBMoyVAoQnVraFogwLEJ4YBmU46wRSG54c/uWQKjxeQnu/2aOq1WtpTNwURTmDciBgkQDhiUXNoJSgCRCeBI32XwcmPDHkTnhiCJJhrBQgPLGyLRRlWIDwxDAow1knkJrw5Nixo6pW8THt2P6pHn6ktOJfW6CYmBjr5khBCBCesAYiQYDwxJsuE54Ycic8MQTJMFYKEJ5Y2RaKMixAeGIYlOGsE0hpeHLq1CnVrFpWmze9ryJFi2v2/GXKkCGDdfOjIAQcAcIT1kEkCBCeeNNlwhND7oQnhiAZxkoBwhMr20JRhgUITwyDMpx1AikJT86ePauGsbW06s3lynt7fi1e8Y4yZ85i3dwoCIELAoQnrIVIECA88abLhCeG3AlPDEEyjJUChCdWtoWiDAsQnhgGZTjrBFISnjR/tr4Wzp+jG2+6WYuXv6PsV+Wwbl4UhMCfBQhPWA+RIEB44k2XCU8MuROeGIJkGCsFCE+sbAtFGRYgPDEMynDWCSQ3POnRtYMmxY11A5Plq97Ttbmus25OFITApQKEJ6yJSBAgPPGmy4QnhtwJTwxBMoyVAoQnVraFogwLEJ4YBmU46wSSE56MHTVU/Xp1V9asl2vB0lXuKzscCPhBgPDED12ixtQKEJ6kVjBl1xOepMztL1cRnhiCZBgrBQhPrGwLRRkWIDwxDMpw1gkEGp7MmRWvNi2bupvCznvjLRW6+x7r5kJBCPyTAOEJayMSBAhPvOky4Ykhd8ITQ5AMY6UA4YmVbaEowwKEJ4ZBGc46gUDCk1Url6lBbC1FR0dr+qz57meJORDwkwDhiZ+6Ra0pFSA8Salc6q4jPEmdX+LVhCeGIBnGSgHCEyvbQlGGBQhPDIMynHUCSYUnG9avU92alXXmzBmNnzxDFSpVtW4OFIRAUgKEJ0kJ8evhIEB44k0XCU8MuROeGIJkGCsFCE+sbAtFGRYgPDEMynDWCfxbePLZ9k9VpXwp/X7ihPoPGaV69RtbVz8FIRCIAOFJIEqc43cBwhNvOkh4Ysid8MQQJMNYKUB4YmVbKMqwAOGJYVCGs07gn8KTr7/apcrlS+nwL4fUoXN3vdD+RetqpyAEAhUgPAlUivP8LEB44k33CE8MuROeGIJkGCsFCE+sbAtFGRYgPDEMynDWCfxdeHLw4AFVKP2gnL/G1m+sAUNGWVc3BSGQHAHCk+Roca5fBQhPvOkc4Ykhd8ITQ5AMY6UA4YmVbaEowwKEJ4ZBGc46gUvDE+dJk8rlSunr3btUtXotjYmboijnJA4EfCxAeOLj5lF6wAKEJwFTGT2R8CRAzn37f1SX/hP1+a69ypUzu3p1bKhCBW5NvJrwJEBITvOlAOGJL9tG0ckUIDxJJhin+07gz+GJs7eJ86rOju2ful/UiX9tgWJiYnw3JwpG4FIBwhPWRCQIEJ5402XCkwDdY1v21QP33qlGdSpo7cat6jdqhlbOGqK0ac7/RoPwJEBITvOlAOGJL9tG0ckUIDxJJhin+07gQniy9+AR96s6ztd1ihQtrtnzlylDhgy+mw8FI/B3AoQnrItIECA88abLhCcBuB86/JvK1umgjUteUZr//VTmiSY91Kl5bd1b6HbCkwAMOcXfAoQn/u4f1QcmQHgSmBNn+VfACU9yZE2vsuUratWby5X39vxavOIdZc6cxb+TonIELhEgPGFJRIIA4Yk3XSY8CcD942271GvYNC2c0ifx7Pa9xqlY4XyqWbGk1qxZo9+Onw5gJE5BwJ8CGTPE6PSZBJ05c86fE6BqBAIQyJwprY6fPK0ElnkAWpziS4EoadG81/Tqq6/qhhtv0sq339VVV+Xw5VQoGoF/EkgTEyXnD5ZH+L05iySMBTKki1FMtHT85NkwnqV9U7vyP+mMFhWVkJCQYHRECwbbsHm7Rk6cp9lxPRKr6Tpgkm675Xo9U7MMm6tZ0CNKQAABBBBAAIHABHLmzKmNGzfqpptuCuwCzkIAAQQQQAAB4wJhGZ5s2b5L3QZO1tL4AYlgrbqP0kPFCrpPnpQsWVLhFxkZXxsM6GMB51FvNxUNu2jUx02hdOMC7jpnjRt3ZUC7BKKjozR8xGjly5/frsKoBgFDAtFRUkxMlPvELAcC4SoQEx0l5/ctZ86yzkPZ4/Rpo43eLizDk8NHjqp0rXZav3iMMqQ//6hOhdjO6t2xoQrfeZv792wYa3QdMZhlAux5YllDKCcoAux5EhRWBrVI4NJPFVtUGqUgYEyAPU+MUTKQxQLseeJNc9jzJED3Ru0G6Z6CedWkbkWtXLNJIyfN0/KZAxM3kCU8CRCS03wpQHjiy7ZRdDIFCE+SCcbpvhMgPPFdyyg4BQKEJylA4xLfCRCeeNMywpMA3Q/8cEid+sbps53f6Pprc6hv58YqkPf/3xUmPAkQktN8KUB44su2UXQyBQhPkgnG6b4TIDzxXcsoOAUChCcpQOMS3wkQnnjTMsITQ+6EJ4YgGcZKAcITK9tCUYYFCE8MgzKcdQKEJ9a1hIKCIEB4EgRUhrROgPDEm5YQnhhyJzwxBMkwVgoQnljZFooyLEB4YhiU4awTIDyxriUUFAQBwpMgoDKkdQKEJ960hPDEkDvhiSFIhrFSgPDEyrZQlGEBwhPDoAxnnQDhiXUtoaAgCBCeBAGVIa0TIDzxpiWEJ4bcCU8MQTKMlQKEJ1a2haIMCxCeGAZlOOsECE+sawkFBUGA8CQIqAxpnQDhiTctITwx5E54YgiSYawUIDyxsi0UZViA8MQwKMNZJ0B4Yl1LKCgIAoQnQUBlSOsECE+8aQnhiSF3whNDkAxjpQDhiZVtoSjDAoQnhkEZzjoBwhPrWkJBQRAgPAkCKkNaJ0B44k1LCE8MuROeGIJkGCsFCE+sbAtFGRYgPDEMynDWCRCeWNcSCgqCAOFJEFAZ0joBwhNvWkJ44o07d0UAAQQQQAABBBBAAAEEEEAAgQgViEpISEiI0LkzbQQQQAABBBBAAAEEEEAAAQQQQCBJAcKTJIk4AQEEEEAAAQQQQAABBBBAAAEEIlmA8CSSu8/cEUAAAQQQQAABBBBAAAEEEEAgSYGICk8mzlyiaXNW6szZsyr/aHF1bfW0YmKik0TiBARsF1iyaqN6Dp2qPp0aq0zJexPLZc3b3jnqC1Rg9Xsfa2jcHP106FflveV69ezQULlvuMa9nHUeqCLn2Sxw7lyCho6frcVvrtfZc+f0UNGC6tGuvjJlTK+Tf5xSj8FT9M6GLcqYIb1aNKymmhVL2jwdakMgSYH6LwzQlVf8R0N7PO+eu2//j+rSf6I+37VXuXJmV6+ODVWowK1JjsMJCNgmMCxujqbOWaHo6P//c+bs8T3c37+8+8Gn6jdqhvv7mbsK3KqBXZsqe7astk2Bev5BIGLCk/c/2qFugyZr2sgXlTXLZWrWebjKP1pMtas+yuJAwNcCzn+cP/pkp/sf4QZPlU8MT1jzvm4rxf9J4IefDqty/S6KG9ROBfPdotGvztfWz3ZpyvDOYp2zVMJFYO6SNZq3ZK3GD2yntGnT6PkXh6v4PfnVrF4VjZo8T5/v2qehPZrJ+ffhmdb9NXlYR+W5+bpwmT7ziDCBBcvf1dipC3VX/lsSw5PYln31wL13qlGdClq7cav7B8yVs4YobZqYCNNhun4XcH6gmSf39apT7eI/Z/527ITK1u6gIT2a6d5C+TRiwlwd+PGQhr3c3O9Tjpj6IyY86TV8uq7JkU1N6lZ0m+v89MZ5CmXqiM4R02wmGp4CX3y1z02yG7cbrFqVH0kMT1jz4dnvSJyV84fFTz/frcdKFHGn7/xUsnmXEVo9d7hY55G4IsJzzp/s2K306dLq9ltvcCc46b9Ltfub79W/SxNVqvei+nRu7P5B0zkGjZ2lzJdl1PP1q4YnBrMKa4FfjxxT3RZ9VO+Jx7Vp6xdueHLo8G8qW6eDNi55RWlizoclTzTpoU7Na+veQreHtQeTCz+B9r3G6eHid6nS4/dfNLkV72zS/GXrNGFwe/efHz12Qg9Xb633l7yidOnShh9EGM4oYsKTRu0G6akqpRJ/871n3wE1aDNQa+aNCMO2MqVIFGjUdtBF4QlrPhJXQWTMefKsZdr51T4N6v6cWOeR0fNIm+X+gz+rdffRalyngso+UlR3PdpI6xaMUtb/XOZSzFn8jjZ/stP9d4ADAb8JdB0wSUXuyqtMGTPozbUfuuHJx9t2qdewaVo4pU/idJw/gBYrnI9X1PzWYOpV045D5XzQ1gnAo6KjVKtSST37dCXFxb+hQ4ePqEurpxOVnPBk+qguuvG6q5HzgUDEhCd1m/dR09hKKlH8Lrct3x/8WVUbdtOmZeN90CZKRCBpgUvDE9Z80mac4T+B9zZtU+/h0xU/uqtyZL9crHP/9ZCK/13gyaY9tX3nHvcHPl1bx7r7nxQq3UibV0xQxgzp3IsXrnhPq9Z9pDH9WsOJgK8EPtz6hV6ZttB97XLlmg8Tw5MNm7dr5MR5mh3XI3E+Tshy2y3X65maZXw1R4pFYMKMN9z9qZ6oWFLf//Cznm0/RJ1a1NGOL79x995s/9yTiUiPPdVeo3q3VL48NwLnA4GICU8atx+s6uVKuPucOMfO3d+6qSBPnvhglVJiQAKXhies+YDYOMlHAs7GyOOmLdK4AW10Q67zP6FhnfuogZQasIDzCsOAMTOVNUtmdXsh1n3y5O25wxI3FZwx7y19umM3T54ELMqJNgicPn1GTzXrpSEvNdPNN1xzUXiyZfsudRs4WUvjBySW2qr7KD1UrCBPntjQPGpIlcC46Yt04IdDuu6aq9y/OpuBXzjuq/i8XhvfgydPUiUcuosjJjzpOzJel/8ns5o3qObqLnv7A81butbdcI0DgXAQuDQ8Yc2HQ1eZwwUB52s7oybP16ShHS7alZ51zhoJFwHnCwy5rrkq8StSzk/oXx461f3DZJUGXdW1VayK3n1+7wdnM8Krr8qm5+pVDpfpM48IENj2xR41ajtQGdKff4Lq1Okz+uPUaRXMl1sjerVQ6VrttH7xmMRfrxDbWb07NlThO2+LAB2mGE4CH2/7UgXy3uzuY+UcY15doF9/O6aid+fTzPlvuR8wcQ7nYw9l63R09zxxNgrnsF8gYsITZxF37D3efafssssyuo9POZtr1qhQwv4uUSECAQhcGp6w5gNA4xRfCBw5elzVGnZz//vt/NTmzwfr3BctpMgABIZPmKsdX+7V8J7N3d9w9xkZ724m6HyFwXlPfsv2LzXs5Rb67sBPatBmgGaM7ur+9J4DAb8K/Pm1HWcOzh5W9xTM637cYeWaTRo5aZ6WzxyYuIGsX+dJ3ZEn4LxS7HwtrXn9auf/m/3CAL3cvoEK35nH3Rh5YLemuveu2zVgzH917MTv7ueKOfwhEDHhidOOaXNXatLMJTp95qyqln3Q3cE7KirKH52iSgT+QcDZjf6rb/brzJmziomOdjemGtj1WZUpWZQ1z6oJCwHnk5bO49yX/lRmzesjdHnWzKzzsOgykzj5xyn1GRGvNRu26lzCOd19Rx71aFvf3dvHed3BeQrlrXWb3U022zxbU1XKPAAaAr4WuDQ8cV5n6NQ3Tp/t/EbXX5tDfTs3VoG8N/l6jhQfmQL79v+ol4dM0Y5de5UlcyZ3356nazzmYrz/8Q71HDpNPx06rCJOgNLlWff3Mhz+EIio8MQfLaFKBBBAAAEEEEAAAQQQQAABBBCwSYDwxKZuUAsCCCCAAAIIIIAAAggggAACCFgnQHhiXUsoCAEEEEAAAQQQQAABBBBAAAEEbBIgPLGpG9SCAAIIIIAAAggggAACCCCAAALWCRCeWNcSCkIAAQQQQAABBBBAAAEEEEAAAZsECE9s6ga1IIAAAggggAACCCCAAAIIIICAdQKEJ9a1hIIQQAABBBBAAAEEEEAAAQQQQMAmAcITm7pBLQgggAACCCCAAAIIIIAAAgggYJ0A4Yl1LaEgBBBAAAEEEEAAAQQQQAABBBCwSYDwxKZuUAsCCCCAAAIIIIAAAggggAACCFgnQHhiXUsoCAEEEEAAAQQQQAABBBBAAAEEbBIgPLGpG9SCAAIIIIAAAggggAACCCCAAALWCRCeWNcSCkIAAQQQQAABBBBAAAEEEEAAAZsECE9s6ga1IIAAAggggAACCCCAAAIIIICAdQKEJ9a1hIIQQAABBBBAAAEEEEAAAQQQQMA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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def weight_function(x):\n", + " if x < 30:\n", + " return 0\n", + " elif 30 <= x <= 40:\n", + " return (x - 30) / 10\n", + " else:\n", + " return 1\n", + "\n", + "\n", + "y_values1 = np.array([weight_function(x) for x in ms1.theta])\n", + "y_values2 = np.array([weight_function(x) for x in ms2.theta])\n", + "\n", + "fig = make_subplots(rows=2, cols=1)\n", + "fig.add_trace(\n", + " go.Scatter(x=ms1.theta, y=ms1.total, mode=\"lines\", name=\"fcst1\", line=dict(color=\"#1b9e77\")), row=1, col=1\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=ms1.theta,\n", + " y=ms1.total * y_values1,\n", + " mode=\"lines\",\n", + " line=dict(width=0),\n", + " fillcolor=\"rgba(27,158,119, 0.5)\",\n", + " fill=\"tozeroy\",\n", + " showlegend=False,\n", + " ),\n", + " row=1,\n", + " col=1,\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(x=ms2.theta, y=ms2.total, mode=\"lines\", name=\"fcst2\", line=dict(color=\"#7570b3\")), row=1, col=1\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=ms2.theta,\n", + " y=ms2.total * y_values2,\n", + " mode=\"lines\",\n", + " fill=\"tozeroy\",\n", + " line=dict(width=0),\n", + " fillcolor=\"rgba(117,112,179, 0.5)\",\n", + " showlegend=False,\n", + " ),\n", + " row=1,\n", + " col=1,\n", + ")\n", + "\n", + "fig.add_trace(\n", + " go.Scatter(x=[0, 30, 40, 55], y=[0, 0, 1, 1], mode=\"lines\", name=\"threshold weight\", line=dict(color=\"black\")),\n", + " row=2,\n", + " col=1,\n", + ")\n", + "fig.update_layout(\n", + " xaxis_title=\"Rainfall (mm)\",\n", + " yaxis_title=\"Economic Regret\",\n", + " width=800,\n", + " height=600,\n", + " margin=dict(l=50, r=20, b=20, t=20),\n", + " legend=dict(x=0.01, y=0.99, xanchor=\"left\", yanchor=\"top\"),\n", + ")\n", + "fig.update_yaxes(title_text=\"Threshold Weight\", row=2, col=1)\n", + "fig.update_xaxes(title_text=\"Rainfall (mm)\", row=2, col=1)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see how the threshold weighting function linearly increases the area under the curve between 30 and 40mm. \n", + "\n", + "## Example 3 - Using rectangular threshold weights to weight both high and low extremes.\n", + "Suppose our synthetic data is now temperature data and we want to weight decision thresholds below 4°C and above 40°C. We can calculate the threshold weighted scores twice; one for cold decision thresholds and one for hot decision thresholds and then sum those twMSE results together." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fcst1 twMSE = 2.2835391217454557\n", + "fcst2 twMSE = 3.269698259598884\n" + ] + } + ], + "source": [ + "fcst1_tw_mse_hot = tw_squared_error(fcst1, obs, interval_where_one=(40, np.inf))\n", + "fcst2_tw_mse_hot = tw_squared_error(fcst2, obs, interval_where_one=(40, np.inf))\n", + "\n", + "fcst1_tw_mse_cold = tw_squared_error(fcst1, obs, interval_where_one=(-np.inf, 4))\n", + "fcst2_tw_mse_cold = tw_squared_error(fcst2, obs, interval_where_one=(-np.inf, 4))\n", + "\n", + "fcst1_tw_mse = fcst1_tw_mse_hot + fcst1_tw_mse_cold\n", + "fcst2_tw_mse = fcst2_tw_mse_hot + fcst2_tw_mse_cold\n", + "\n", + "print(f\"fcst1 twMSE = {fcst1_tw_mse.item()}\")\n", + "print(f\"fcst2 twMSE = {fcst2_tw_mse.item()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can again visualise what's happening with a Murphy Diagram" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#1b9e77" + }, + "mode": "lines", + "name": "fcst1", + "type": "scatter", + "x": [ + 0, + 0.024337973762994514, + 0.0399563244233625, + 0.047028426457913275, + 0.04747085477909729, + 0.06995115595219148, + 0.18235217726127934, + 0.23594280954862823, + 0.24251788976445504, + 0.2844253676286712, + 0.34933463655534847, + 0.35990546744086555, + 0.37193603342800396, + 0.39907146776096136, + 0.4188223000903546, + 0.4437588490781075, + 0.45082045809306515, + 0.4894550906141251, + 0.49626502014477325, + 0.49668468354914785, + 0.5699320605473506, + 0.6102068468610562, + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate a list of thresholds of interest\n", + "thetas = murphy_thetas([fcst1, fcst2], obs, \"expectile\")\n", + "\n", + "# Calculate the average elementary score for the mean (0.5 expectile) for each threshold theta\n", + "ms1 = 2 * murphy_score(fcst1, obs, thetas, functional=\"expectile\", alpha=0.5)\n", + "ms2 = 2 * murphy_score(fcst2, obs, thetas, functional=\"expectile\", alpha=0.5)\n", + "\n", + "fig = make_subplots(rows=2, cols=1)\n", + "fig.add_trace(\n", + " go.Scatter(x=ms1.theta, y=ms1.total, mode=\"lines\", name=\"fcst1\", line=dict(color=\"#1b9e77\")), row=1, col=1\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=ms1.theta,\n", + " y=xr.where((ms1.theta >= 40) | (ms1.theta < 4), ms1.total, 0),\n", + " mode=\"lines\",\n", + " line=dict(color=\"rgba(27,158,119, 1)\"),\n", + " fillcolor=\"rgba(27,158,119, 0.5)\",\n", + " fill=\"tozeroy\",\n", + " showlegend=False,\n", + " ),\n", + " row=1,\n", + " col=1,\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(x=ms2.theta, y=ms2.total, mode=\"lines\", name=\"fcst2\", line=dict(color=\"#7570b3\")), row=1, col=1\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=ms2.theta,\n", + " y=xr.where((ms2.theta >= 40) | (ms2.theta < 4), ms2.total, 0),\n", + " mode=\"lines\",\n", + " fill=\"tozeroy\",\n", + " line=dict(color=\"#7570b3\"),\n", + " fillcolor=\"rgba(117,112,179, 0.5)\",\n", + " showlegend=False,\n", + " ),\n", + " row=1,\n", + " col=1,\n", + ")\n", + "\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=[0, 4, 4, 40, 40, 55], y=[1, 1, 0, 0, 1, 1], mode=\"lines\", name=\"threshold weight\", line=dict(color=\"black\")\n", + " ),\n", + " row=2,\n", + " col=1,\n", + ")\n", + "fig.update_layout(\n", + " xaxis_title=\"Temperature (°C)\",\n", + " yaxis_title=\"Economic Regret\",\n", + " width=800,\n", + " height=600,\n", + " legend=dict(x=0.01, y=0.99, xanchor=\"left\", yanchor=\"top\"),\n", + " margin=dict(l=50, r=20, b=20, t=20),\n", + ")\n", + "fig.update_yaxes(title_text=\"Threshold Weight\", row=2, col=1)\n", + "fig.update_xaxes(title_text=\"Temperature (°C)\", row=2, col=1)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the area under the Murphy diagram curves are shaded in for decision threshold less than 4°C and above 40°C which aligns with the threshold weight function.\n", + "\n", + "## Other Threshold Weighted Scores\n", + "\n", + "In this tutorial, we demonstrated how the `tw_squared_error` function could be used to calculate twMSE. There are other threshold weighted scores that work the same way. We show how they can be called.\n", + "\n", + "### Threshold Weighted Absolute Error" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "\n", + "array(1.27710106)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tw_huber_loss(fcst1, obs, huber_param=2, interval_where_one=(40, np.inf), interval_where_positive=(30, np.inf))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Things To Try Next\n", + "- Play around with different `interval_where_one` and `interval_where_positive` values.\n", + "- Rather than providing a `float` for `interval_where_one` and `interval_where_positive`, provide `xr.DataArray` objects where these values vary across a dimension(s).\n", + "- If you require more complicated threshold weightings than rectangular and trapezoidal threshold weightings, you can use `scores`' consistent scores. See the [Consistent Scoring Rules tutorial](./Consistent_Scores.ipynb). \n", + "- Try to replicate example three using `scores.continuous.consistent_expectile_score`, so that the function only has to be called once to evaluate both hot and cold extremes at the same time.\n", + "\n", + "### Reference\n", + "[Taggart, R. (2022). Evaluation of point forecasts for extreme events using consistent scoring functions. Quarterly Journal of the Royal Meteorological Society, 148(742), 306-320.](https://doi.org/10.1002/qj.4206) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/Tutorial_Gallery.ipynb b/tutorials/Tutorial_Gallery.ipynb index 38d1a8851..feb1b16cf 100644 --- a/tutorials/Tutorial_Gallery.ipynb +++ b/tutorials/Tutorial_Gallery.ipynb @@ -57,7 +57,8 @@ "- [Quantile Loss](./Quantile_Loss.ipynb)\n", "- [Murphy Diagrams](./Murphy_Diagrams.ipynb)\n", "- [Flip-Flop Index](./Flip_Flop_Index.ipynb)\n", - "- [Consistent Scores](./Consistent_Scores.ipynb)" + "- [Consistent Scores](./Consistent_Scores.ipynb)\n", + "- [Threshold Weighted Scores](./Threshold_Weighted_Scores.ipynb)" ] }, { From 2b75fb7179c1fad92121043a4f22fdd5747d248f Mon Sep 17 00:00:00 2001 From: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> Date: Fri, 2 Aug 2024 17:23:47 +1000 Subject: [PATCH 11/28] Minor updates to maintainers notes (#617) * Remove quotation mark * Update numbering in maintainers notes --- docs/maintainer.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/maintainer.md b/docs/maintainer.md index 64750a58a..bc61e2c55 100644 --- a/docs/maintainer.md +++ b/docs/maintainer.md @@ -11,9 +11,9 @@ Information relevant for package maintenance 5. Perform the merge to main 6. Build a new environment and perform basic testing on main 7. Update github with a tagged release -7. Prepare the package files (see below) -8. Prepare the PyPI update (should move to a Github Action) -9. Perform the PyPI update (should move to a Github Action) +8. Prepare the package files (see below) +9. Prepare the PyPI update (should move to a Github Action) +10. Perform the PyPI update (should move to a Github Action) ### Test the new main branch @@ -46,7 +46,7 @@ Information relevant for package maintenance ## Version X.Y.Z (Month Day, Year) e.g. "Version 0.9.3 (July 7, 2024)" -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/(X-1).(Y-1).(Z-1)...X.Y.Z). Below are the changes we think users may wish to be aware of." +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/(X-1).(Y-1).(Z-1)...X.Y.Z). Below are the changes we think users may wish to be aware of. ### Features ### Breaking Changes From c946d7dba75aaaa293d9f759880cea54f7e5cf4b Mon Sep 17 00:00:00 2001 From: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> Date: Fri, 2 Aug 2024 17:41:52 +1000 Subject: [PATCH 12/28] Update release notes (#616) * Update release notes --------- Signed-off-by: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> --- docs/release_notes.md | 95 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) diff --git a/docs/release_notes.md b/docs/release_notes.md index 4415f54cd..23ad03fb8 100644 --- a/docs/release_notes.md +++ b/docs/release_notes.md @@ -1,5 +1,34 @@ # Release Notes (What's New) +## Version 1.1.0 (Upcoming Release) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/1.0.0...develop). Below are the changes we think users may wish to be aware of. + +### Features + +- Added five new threshold weighted scores + - threshold weighted squared error: ` scores.continuous.tw_squared_error` + - threshold weighted absolute error: `scores.continuous.tw_absolute_error` + - threshold weighted quantile score: `scores.continuous.tw_quantile_score` + - threshold weighted expectile score: `scores.continuous.tw_expectile_score` + - threshold weighted Huber loss: `scores.continuous.tw_huber_loss`. +See [PR #609](https://github.com/nci/scores/pull/609) by [@nicholasloveday](https://github.com/nicholasloveday). + +### Breaking Changes + +### Deprecations + +### Bug Fixes + +### Documentation + +- Added "Threshold Weighted Scores" tutorial. See [PR #609](https://github.com/nci/scores/pull/609) by [@nicholasloveday](https://github.com/nicholasloveday). +- Removed nbviewer link from documentation. See [PR #615](https://github.com/nci/scores/pull/615) by [@tennlee](https://github.com/tennlee). + +### Internal Changes + +- Modified `numpy.trapezoid` call to work with either NumPy 1 or 2. See [PR #610](https://github.com/nci/scores/pull/610) by [@tennlee](https://github.com/tennlee). + ## Version 1.0.0 (July 10, 2024) We are happy to have reached the point of releasing “Version 1.0.0” of `scores`. While we look forward to many version increments to come, version 1.0.0 represents a milestone. It signifies a stabilisation of the API, and marks a turning point from the initial construction period. We have also published a [paper](https://doi.org/10.21105/joss.06889) in the Journal of Open Source Software (see citation further below). @@ -47,3 +76,69 @@ For the full details of all changes in this release, see the [GitHub commit hist - Introduced pinned versions for dependencies on main. See [PR #580](https://github.com/nci/scores/pull/580) by [@tennlee](https://github.com/tennlee). +## Version 0.9.2 (June 26, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.9.1...0.9.2). + +## Version 0.9.1 (June 14, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.9.0...0.9.1). + +## Version 0.9.0 (June 12, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.6...0.9.0). + +## Version 0.8.6 (June 11, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.5...0.8.6). + +## Version 0.8.5 (June 9, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.4...0.8.5). + +## Version 0.8.4 (June 3, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.3...0.8.4). + +## Version 0.8.3 (June 2, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.2...0.8.3). + +## Version 0.8.2 (May 21, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.1...0.8.2). + +## Version 0.8.1 (May 16, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8...0.8.1). + +## Version 0.8 (May 14, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.7...0.8). + +## Version 0.7 (May 8, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/v0.6...0.7). + +## Version 0.6 (April 6, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/v0.5...v0.6). + +Note: version 0.6 was initially tagged as "v0.6" and released on 6th April 2024. On 7th April 2024, an identical version was released with the tag "0.6" (i.e. with the "v" ommitted from the tag). + +## Version 0.5 (April 6, 2024) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/v0.4...v0.5). + +## Version 0.4 (September 15, 2023) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/v0.0.2...v0.4). + +## Version 0.0.2 (June 9, 2023) + +For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/commits/v0.0.2/). + +## Version 0.0.1 (January 16, 2023) + +Version 0.0.1 was released on PyPI as a placeholder, while very early development and package design was being undertaken. + From 42351670e6c5a85d5bf9172ad34f2e1d80584b62 Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Fri, 2 Aug 2024 17:50:46 +1000 Subject: [PATCH 13/28] Update the capitalisation of Huber in two places --- src/scores/continuous/threshold_weighted_impl.py | 2 +- tutorials/Consistent_Scores.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/scores/continuous/threshold_weighted_impl.py b/src/scores/continuous/threshold_weighted_impl.py index 3f8c75fa5..991c13b05 100644 --- a/src/scores/continuous/threshold_weighted_impl.py +++ b/src/scores/continuous/threshold_weighted_impl.py @@ -655,7 +655,7 @@ def tw_huber_loss( weights: Optional[xr.DataArray] = None, ) -> xr.DataArray: """ - Returns the threshold weighted huber loss. + Returns the threshold weighted Huber loss. For more flexible threshold weighting schemes, see :py:func:`scores.continuous.consistent_huber_score`. diff --git a/tutorials/Consistent_Scores.ipynb b/tutorials/Consistent_Scores.ipynb index 387575d8d..6cb266eff 100644 --- a/tutorials/Consistent_Scores.ipynb +++ b/tutorials/Consistent_Scores.ipynb @@ -436,7 +436,7 @@ "## Things to try next\n", "\n", "- Test out different weighting functions. \n", - "- Try out `consistent_huber_score`. This uses $\\phi$ and $\\phi'$ like `consistent_expectile_score`, but also takes a huber parameter.\n", + "- Try out `consistent_huber_score`. This uses $\\phi$ and $\\phi'$ like `consistent_expectile_score`, but also takes a Huber parameter.\n", "\n", "## Further reading\n", "- [Gneiting, T. (2011a). Making and evaluating point forecasts. Journal of the American Statistical Association, 106(494), 746-762.](https://doi.org/10.1198/jasa.2011.r10138)\n", From d42bc00fca0824bb4994428b47a2ebf88e104efb Mon Sep 17 00:00:00 2001 From: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> Date: Fri, 2 Aug 2024 20:11:11 +1000 Subject: [PATCH 14/28] Relocates one paragraph in maintainers notes (#618) Signed-off-by: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> --- docs/maintainer.md | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/docs/maintainer.md b/docs/maintainer.md index bc61e2c55..a4b376a3a 100644 --- a/docs/maintainer.md +++ b/docs/maintainer.md @@ -98,6 +98,12 @@ For each entry: "Brief description. See [PR #ABC](add link) by [@username](add l ## This section covers checking the documentation renders properly in readthedocs +### Tips for working with pull requests from forks + +It can be convenient as maintainer to have write access to people's forks to push small fixes into a PR during the process. When doing so, it's a good idea to check out the remote branch as follows (after adding the fork as a remote) + +`git checkout -b test /test` + ### What documentation needs checking in readthedocs Each time an existing function is modified or a new function is added to `scores`, the rendering in readthedocs for any modified or newly created documentation must be checked. @@ -132,12 +138,3 @@ To check the rendering of tutorials in readthedocs: - If you make any changes to the code cells, re-execute the Notebook in JupyterLab before committing, otherwise some things (e.g. some plots) won't render in readthedocs. Then re-check the tutorial in readthedocs to ensure the tutorial is still rendering properly. -### Tips for working with pull requests from forks - -It can be convenient as maintainer to have write access to people's forks to push small fixes into a PR during the process. When doing so, it's a good idea to check out the remote branch as follows (after adding the fork as a remote) - -`git checkout -b test /test` - - - - From b0f99b15f8e1238bec167c58d7d348fe799b07f5 Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Fri, 2 Aug 2024 20:28:37 +1000 Subject: [PATCH 15/28] Improved docstring rendering for murphy_thetas and murphy_score --- src/scores/continuous/murphy_impl.py | 35 +++++++++++++++------------- 1 file changed, 19 insertions(+), 16 deletions(-) diff --git a/src/scores/continuous/murphy_impl.py b/src/scores/continuous/murphy_impl.py index 7bcfbc68a..12e2e0879 100644 --- a/src/scores/continuous/murphy_impl.py +++ b/src/scores/continuous/murphy_impl.py @@ -37,7 +37,7 @@ def murphy_score( # pylint: disable=R0914 Select ``functional="quantile"`` and ``alpha=0.5`` for the median functional. Select ``functional="expectile"`` and ``alpha=0.5`` for the mean (i.e., expectation) functional. - Consider using ``murphy_thetas`` to generate thetas. If utilising dask, you may want + Consider using :py:func:`murphy_thetas` to generate thetas. If utilising dask, you may want to store ``thetas`` as a netCDF on disk and pass it in as an xarray object. Otherwise, very large objects may be created when ``fcst``, ``obs`` and ``thetas`` are broadcast together. @@ -192,12 +192,12 @@ def murphy_thetas( Args: forecasts: Forecast values, one array per source. obs: Observed values. - functional: The type of elementary scoring function, one of {QUANTILE}, - {HUBER}, or {EXPECTILE}. - huber_a: Huber transition parameter, used for {HUBER} functional only. + functional: The type of elementary scoring function, one of "quantile", + "huber", or "expectile". + huber_a: Huber transition parameter, used for "huber" functional only. Must be strictly greater than 0. - left_limit_delta: Approximation of the left hand limit, used for {HUBER} - and {EXPECTILE} functionals. Must be greater than or equal to 0. + left_limit_delta: Approximation of the left hand limit, used for "huber" + and "expectile" functionals. Must be greater than or equal to 0. None will be treated as 0. Ideally, left_limit_delta should be small relative to the fcst and obs precision, and not greater than that precision. @@ -206,18 +206,21 @@ def murphy_thetas( List[float]: theta thresholds to be used to compute murphy scores. Raises: - ValueError: If `functional` is not one of the expected functions. - ValueError: If `huber_a` is not strictly greater than 0. - ValueError: If `left_limit_delta` is not greater than or equal to 0. + ValueError: If ``functional`` is not one of the expected functions. + ValueError: If ``huber_a`` is not strictly greater than 0. + ValueError: If ``left_limit_delta`` is not greater than or equal to 0. Notes: - For theta values at which evaluate elementary scores, see - - - Corollary 2 (p.521) of Ehm et. al. (2016) "Of Quantiles and Expectiles", J. Royal Stat. Soc. B, - 78(3): 505–562. https://www.jstor.org/stable/24775351. - - Corollary 5.6 of Taggart (2022) "Point forecasting and forecast evaluation with - generalized Huber loss", Electron. J. Statist. 16(1): 201-231. - DOI: 10.1214/21-EJS1957 + For theta values at which to evaluate elementary scores, see + + - Corollary 2 (p.521) of Ehm, W., Gneiting, T., Jordan, A., & Krüger, F. (2016). + Of quantiles and expectiles: Consistent scoring functions, Choquet + representations and forecast rankings. + *Journal of the Royal Statistical Society Series B: Statistical Methodology*, + 78(3), 505–562. https://doi.org/10.1111/rssb.12154 + - Corollary 5.6 of Taggart, R. J. (2022). Point forecasting and forecast evaluation + with generalized Huber loss. *Electronic Journal of Statistics*, 16(1), 201-231. + https://doi.org/10.1214/21-EJS1957 """ _check_murphy_inputs(functional=functional, huber_a=huber_a, left_limit_delta=left_limit_delta) From 11f8823f4ced35fa460d22c746b9c9d87f15b98b Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Fri, 2 Aug 2024 20:32:48 +1000 Subject: [PATCH 16/28] Docstring improvements for brier score --- src/scores/probability/brier_impl.py | 30 ++++++++++++++-------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/src/scores/probability/brier_impl.py b/src/scores/probability/brier_impl.py index 622134b7e..f3248be41 100644 --- a/src/scores/probability/brier_impl.py +++ b/src/scores/probability/brier_impl.py @@ -19,28 +19,28 @@ def brier_score( preserve_dims: Optional[FlexibleDimensionTypes] = None, weights: Optional[xr.DataArray] = None, check_args: bool = True, -): +) -> XarrayLike: """ Calculates the Brier score for forecast and observed data. For an explanation of the - Brier score, see [Wikipedia](https://en.wikipedia.org/wiki/Brier_score). + Brier score, see https://en.wikipedia.org/wiki/Brier_score. If you want to speed up performance with Dask and are confident of your input data, or if you want observations to take values between 0 and 1, set `check_inputs` to `False`. Args: - fcst: Forecast or predicted variables in xarray. - obs: Observed variables in xarray. - reduce_dims: Optionally specify which dimensions to reduce when - calculating the Brier score. All other dimensions will be preserved. - preserve_dims: Optionally specify which dimensions to preserve when - calculating the Brier score. All other dimensions will be reduced. As a - special case, 'all' will allow all dimensions to be preserved. In - this case, the result will be in the same shape/dimensionality - as the forecast, and the errors will be the absolute error at each - point (i.e. single-value comparison against observed), and the - forecast and observed dimensions must match precisely. - weights: Optionally provide an array for weighted averaging (e.g. by area, by latitude, - by population, custom) + fcst: Forecast or predicted variables in xarray. + obs: Observed variables in xarray. + reduce_dims: Optionally specify which dimensions to reduce when + calculating the Brier score. All other dimensions will be preserved. + preserve_dims: Optionally specify which dimensions to preserve when + calculating the Brier score. All other dimensions will be reduced. As a + special case, 'all' will allow all dimensions to be preserved. In + this case, the result will be in the same shape/dimensionality + as the forecast, and the errors will be the absolute error at each + point (i.e. single-value comparison against observed), and the + forecast and observed dimensions must match precisely. + weights: Optionally provide an array for weighted averaging (e.g. by area, by latitude, + by population, custom) Raises: ValueError: if `fcst` contains non-nan values outside of the range [0, 1] ValueError: if `obs` contains non-nan values not in the set {0, 1} From 0f55eb9be7071038687452e5f28575ee8d6e642a Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Fri, 2 Aug 2024 20:49:39 +1000 Subject: [PATCH 17/28] Resolve all curent pylint errors --- py.typed | 1 + src/scores/fast/fss/fss_numpy.py | 2 ++ tests/categorical/test_contingency.py | 2 +- tests/continuous/test_consistent.py | 16 ++++++++-------- tests/continuous/test_correlation.py | 4 ++++ tests/processing/cdf/functions_test_data.py | 8 ++------ 6 files changed, 18 insertions(+), 15 deletions(-) diff --git a/py.typed b/py.typed index e69de29bb..8d1c8b69c 100644 --- a/py.typed +++ b/py.typed @@ -0,0 +1 @@ + diff --git a/src/scores/fast/fss/fss_numpy.py b/src/scores/fast/fss/fss_numpy.py index 57cda822b..5979f0a3b 100644 --- a/src/scores/fast/fss/fss_numpy.py +++ b/src/scores/fast/fss/fss_numpy.py @@ -2,6 +2,8 @@ Backend for computing Fractions Skill Score (FSS) using numpy """ +# pylint: disable=R0801 + from dataclasses import dataclass import numpy as np diff --git a/tests/categorical/test_contingency.py b/tests/categorical/test_contingency.py index b175073be..9d86e385f 100644 --- a/tests/categorical/test_contingency.py +++ b/tests/categorical/test_contingency.py @@ -142,7 +142,7 @@ def test_str_view(): _stringrep = str(table) -def test_categorical_table(): +def test_categorical_table(): # pylint: disable=too-many-statements """ Test the basic calculations of the contingency table """ diff --git a/tests/continuous/test_consistent.py b/tests/continuous/test_consistent.py index e27236681..9dd176bb5 100644 --- a/tests/continuous/test_consistent.py +++ b/tests/continuous/test_consistent.py @@ -25,13 +25,13 @@ DA_FCST = xr.DataArray( data=[[3.0, 1.0, nan, 3.0], [-4.0, 0.0, 1.0, 3.0]], dims=["date", "station"], - coords=dict(date=["1", "2"], station=[100, 101, 102, 104]), + coords={"date": ["1", "2"], "station": [100, 101, 102, 104]}, ) DA_OBS = xr.DataArray( data=[[nan, 3.0, 5.0], [-4.0, 10.0, -1.0], [3.0, 2.0, -0.2]], dims=["date", "station"], - coords=dict(date=["1", "2", "3"], station=[100, 101, 102]), + coords={"date": ["1", "2", "3"], "station": [100, 101, 102]}, ) WEIGHTS = DA_FCST * 0 + 2 @@ -41,13 +41,13 @@ EXP_EXPECTILE_SCORE1 = xr.DataArray( data=[[nan, ALPHA * 4.0, nan], [0.0, ALPHA * 100, (1 - ALPHA) * 4]], dims=["date", "station"], - coords=dict(date=["1", "2"], station=[100, 101, 102]), + coords={"date": ["1", "2"], "station": [100, 101, 102]}, ) EXP_EXPECTILE_SCORE2 = xr.DataArray( data=[0.0, ALPHA * (4 + 100) / 2, (1 - ALPHA) * 4], dims=["station"], - coords=dict(station=[100, 101, 102]), + coords={"station": [100, 101, 102]}, ) EXP_EXPECTILE_SCORE3 = xr.DataArray((ALPHA * 4.0 + ALPHA * 100 + (1 - ALPHA) * 4) / 4) @@ -56,13 +56,13 @@ EXP_HUBER_SCORE1 = xr.DataArray( data=[[nan, 2.0, nan], [0.0, 18.0, 2.0]], dims=["date", "station"], - coords=dict(date=["1", "2"], station=[100, 101, 102]), + coords={"date": ["1", "2"], "station": [100, 101, 102]}, ) EXP_HUBER_SCORE2 = xr.DataArray( data=[2.0, 20 / 3], dims=["date"], - coords=dict(date=["1", "2"]), + coords={"date": ["1", "2"]}, ) EXP_HUBER_SCORE3 = xr.DataArray( @@ -72,13 +72,13 @@ EXP_QUANTILE_SCORE1 = xr.DataArray( data=[[nan, ALPHA * 2.0, nan], [0.0, ALPHA * 10, (1 - ALPHA) * 2]], dims=["date", "station"], - coords=dict(date=["1", "2"], station=[100, 101, 102]), + coords={"date": ["1", "2"], "station": [100, 101, 102]}, ) EXP_QUANTILE_SCORE2 = xr.DataArray( data=[ALPHA * 2.0, (ALPHA * 10 + (1 - ALPHA) * 2) / 3], dims=["date"], - coords=dict(date=["1", "2"]), + coords={"date": ["1", "2"]}, ) EXP_QUANTILE_SCORE3 = xr.DataArray((ALPHA * 2.0 + ALPHA * 10 + (1 - ALPHA) * 2) / 4) diff --git a/tests/continuous/test_correlation.py b/tests/continuous/test_correlation.py index c100b95ed..faa40c073 100644 --- a/tests/continuous/test_correlation.py +++ b/tests/continuous/test_correlation.py @@ -1,3 +1,7 @@ +""" +Tests for correlation calculations +""" + import numpy as np import pytest import xarray as xr diff --git a/tests/processing/cdf/functions_test_data.py b/tests/processing/cdf/functions_test_data.py index ad42c13e7..7905634ee 100644 --- a/tests/processing/cdf/functions_test_data.py +++ b/tests/processing/cdf/functions_test_data.py @@ -34,17 +34,13 @@ EXP_ADD_THRESHOLDS1 = xr.DataArray( data=[[[0.2, 0.4, 1, 1, 1], [0, 0, 0.6, 0.8, 1]]], dims=["date", "station", "x"], - coords=dict( - station=[1001, 1002], date=["2020-01-01"], x=[0, 0.2, 0.5, 0.75, 1] - ), # pylint: disable=use-dict-literal + coords={"station": [1001, 1002], "date": ["2020-01-01"], "x": [0, 0.2, 0.5, 0.75, 1]}, ) EXP_ADD_THRESHOLDS2 = xr.DataArray( data=[[[0.2, 0.4, 1, np.nan, 1], [0, 0, 0.6, np.nan, 1]]], dims=["date", "station", "x"], - coords=dict( - station=[1001, 1002], date=["2020-01-01"], x=[0, 0.2, 0.5, 0.75, 1] - ), # pylint: disable=use-dict-literal + coords={"station": [1001, 1002], "date": ["2020-01-01"], "x": [0, 0.2, 0.5, 0.75, 1]}, ) DA_DECREASING_CDFS1 = xr.DataArray( From 91a1285795e28326c4bb0a215671fe1d19106f66 Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Fri, 2 Aug 2024 20:50:56 +1000 Subject: [PATCH 18/28] Address all current pylint errors --- py.typed | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/py.typed b/py.typed index 8d1c8b69c..8b1378917 100644 --- a/py.typed +++ b/py.typed @@ -1 +1 @@ - + From 64304bf7b18d2421ab8130b3f106c1a448dee418 Mon Sep 17 00:00:00 2001 From: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> Date: Mon, 5 Aug 2024 22:53:18 +1000 Subject: [PATCH 19/28] Minor update to readme (#625) * Add journal article link early in README Signed-off-by: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> --- README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 88fa4b7c0..1bf5662c6 100644 --- a/README.md +++ b/README.md @@ -7,9 +7,10 @@ `scores` is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It supports labelled n-dimensional (multidimensional) data, which is used in many scientific fields and in machine learning. At present, `scores` primarily supports the geoscience communities; in particular, the meteorological, climatological and oceanographic communities. -Documentation is hosted at [scores.readthedocs.io](https://scores.readthedocs.io). -Source code is hosted at [github.com/nci/scores](https://github.com/nci/scores). -The tutorial gallery is hosted at [as part of the documentation, here](https://scores.readthedocs.io/en/stable/tutorials/Tutorial_Gallery.html). +Documentation: [scores.readthedocs.io](https://scores.readthedocs.io) +Source code: [github.com/nci/scores](https://github.com/nci/scores) +Tutorial gallery: [available here](https://scores.readthedocs.io/en/stable/tutorials/Tutorial_Gallery.html) +Journal article: [*scores: A Python package for verifying and evaluating models and predictions with xarray*](https://doi.org/10.21105/joss.06889) ## Overview Here is a **curated selection** of the metrics, tools and statistical tests included in `scores`: From 1c24a055c8ab03693a0970e41f9740fe87dafd1b Mon Sep 17 00:00:00 2001 From: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> Date: Mon, 5 Aug 2024 23:00:34 +1000 Subject: [PATCH 20/28] Update to release notes (#622) * Update Release Notes --- docs/release_notes.md | 62 ++++++++++++++++++++++++------------------- 1 file changed, 35 insertions(+), 27 deletions(-) diff --git a/docs/release_notes.md b/docs/release_notes.md index 23ad03fb8..adfaa1e90 100644 --- a/docs/release_notes.md +++ b/docs/release_notes.md @@ -2,17 +2,17 @@ ## Version 1.1.0 (Upcoming Release) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/1.0.0...develop). Below are the changes we think users may wish to be aware of. +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/1.0.0...develop). Below are the changes we think users may wish to be aware of. ### Features -- Added five new threshold weighted scores +- Added five new scores - threshold weighted squared error: ` scores.continuous.tw_squared_error` - threshold weighted absolute error: `scores.continuous.tw_absolute_error` - threshold weighted quantile score: `scores.continuous.tw_quantile_score` - threshold weighted expectile score: `scores.continuous.tw_expectile_score` - threshold weighted Huber loss: `scores.continuous.tw_huber_loss`. -See [PR #609](https://github.com/nci/scores/pull/609) by [@nicholasloveday](https://github.com/nicholasloveday). +See [PR #609](https://github.com/nci/scores/pull/609). ### Breaking Changes @@ -22,12 +22,16 @@ See [PR #609](https://github.com/nci/scores/pull/609) by [@nicholasloveday](http ### Documentation -- Added "Threshold Weighted Scores" tutorial. See [PR #609](https://github.com/nci/scores/pull/609) by [@nicholasloveday](https://github.com/nicholasloveday). -- Removed nbviewer link from documentation. See [PR #615](https://github.com/nci/scores/pull/615) by [@tennlee](https://github.com/tennlee). +- Added "Threshold Weighted Scores" tutorial. See [PR #609](https://github.com/nci/scores/pull/609). +- Removed nbviewer link from documentation. See [PR #615](https://github.com/nci/scores/pull/615). ### Internal Changes -- Modified `numpy.trapezoid` call to work with either NumPy 1 or 2. See [PR #610](https://github.com/nci/scores/pull/610) by [@tennlee](https://github.com/tennlee). +- Modified `numpy.trapezoid` call to work with either NumPy 1 or 2. See [PR #610](https://github.com/nci/scores/pull/610). + +### Contributors to this Release + +Nicholas Loveday ([@nicholasloveday](https://github.com/nicholasloveday)), Tennessee Leeuwenburg ([@tennlee](https://github.com/tennlee)), Stephanie Chong ([@Steph-Chong](https://github.com/Steph-Chong)) and Robert J. Taggart ([@rob-taggart](https://github.com/rob-taggart)). ## Version 1.0.0 (July 10, 2024) @@ -57,86 +61,90 @@ year = {2024} } ``` -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.9.3...1.0.0). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.9.3...1.0.0). ## Version 0.9.3 (July 9, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.9.2...0.9.3). Below are the changes we think users may wish to be aware of. +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.9.2...0.9.3). Below are the changes we think users may wish to be aware of. ### Breaking Changes -- Renamed and relocated function `scores.continuous.correlation` to `scores.continuous.correlation.pearsonr`. See [PR #583](https://github.com/nci/scores/pull/583) by [@nicholasloveday](https://github.com/nicholasloveday). +- Renamed and relocated function `scores.continuous.correlation` to `scores.continuous.correlation.pearsonr`. See [PR #583](https://github.com/nci/scores/pull/583). ### Documentation -- Added "Dimension Handling" tutorial, which describes reducing and preserving dimensions. See [PR #589](https://github.com/nci/scores/pull/589) by [@nicholasloveday](https://github.com/nicholasloveday). -- Updated "Detailed Installation Guide" with information on installing kernels in a Jupyter environment. See [PR #586](https://github.com/nci/scores/pull/586) by [@tennlee](https://github.com/tennlee) and [PR #587](https://github.com/nci/scores/pull/587) by [@Steph-Chong](https://github.com/Steph-Chong). +- Added "Dimension Handling" tutorial, which describes reducing and preserving dimensions. See [PR #589](https://github.com/nci/scores/pull/589). +- Updated "Detailed Installation Guide" with information on installing kernels in a Jupyter environment. See [PR #586](https://github.com/nci/scores/pull/586) and [PR #587](https://github.com/nci/scores/pull/587). ### Internal Changes -- Introduced pinned versions for dependencies on main. See [PR #580](https://github.com/nci/scores/pull/580) by [@tennlee](https://github.com/tennlee). +- Introduced pinned versions for dependencies on main. See [PR #580](https://github.com/nci/scores/pull/580). + +### Contributors to this Release + +Tennessee Leeuwenburg ([@tennlee](https://github.com/tennlee)), Stephanie Chong ([@Steph-Chong](https://github.com/Steph-Chong)) and Nicholas Loveday ([@nicholasloveday](https://github.com/nicholasloveday)). ## Version 0.9.2 (June 26, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.9.1...0.9.2). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.9.1...0.9.2). ## Version 0.9.1 (June 14, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.9.0...0.9.1). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.9.0...0.9.1). ## Version 0.9.0 (June 12, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.6...0.9.0). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.8.6...0.9.0). ## Version 0.8.6 (June 11, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.5...0.8.6). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.8.5...0.8.6). ## Version 0.8.5 (June 9, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.4...0.8.5). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.8.4...0.8.5). ## Version 0.8.4 (June 3, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.3...0.8.4). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.8.3...0.8.4). ## Version 0.8.3 (June 2, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.2...0.8.3). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.8.2...0.8.3). ## Version 0.8.2 (May 21, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8.1...0.8.2). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.8.1...0.8.2). ## Version 0.8.1 (May 16, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.8...0.8.1). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.8...0.8.1). ## Version 0.8 (May 14, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/0.7...0.8). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/0.7...0.8). ## Version 0.7 (May 8, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/v0.6...0.7). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/v0.6...0.7). ## Version 0.6 (April 6, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/v0.5...v0.6). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/v0.5...v0.6). Note: version 0.6 was initially tagged as "v0.6" and released on 6th April 2024. On 7th April 2024, an identical version was released with the tag "0.6" (i.e. with the "v" ommitted from the tag). ## Version 0.5 (April 6, 2024) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/v0.4...v0.5). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/v0.4...v0.5). ## Version 0.4 (September 15, 2023) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/compare/v0.0.2...v0.4). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/v0.0.2...v0.4). ## Version 0.0.2 (June 9, 2023) -For the full details of all changes in this release, see the [GitHub commit history](https://github.com/nci/scores/commits/v0.0.2/). +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/commits/v0.0.2/). ## Version 0.0.1 (January 16, 2023) From ba813886787a4e3b26a8556c16d9015a33f7da13 Mon Sep 17 00:00:00 2001 From: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> Date: Mon, 5 Aug 2024 23:26:22 +1000 Subject: [PATCH 21/28] (ready for review) Add .zenodo.json file (#621) * Create .zenodo.json --------- Signed-off-by: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> --- .zenodo.json | 71 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 .zenodo.json diff --git a/.zenodo.json b/.zenodo.json new file mode 100644 index 000000000..b59063498 --- /dev/null +++ b/.zenodo.json @@ -0,0 +1,71 @@ +{ + "creators": [ + { + "orcid": "https://orcid.org/0009-0008-2024-1967", + "affiliation": "Bureau of Meteorology", + "name": "Leeuwenburg, Tennessee" + }, + { + "orcid": "https://orcid.org/0009-0000-5796-7069", + "affiliation": "Bureau of Meteorology", + "name": "Loveday, Nicholas" + }, + { + "affiliation": "Bureau of Meteorology", + "name": "Ebert, Elizabeth E." + }, + { + "orcid": "https://orcid.org/0009-0009-3207-4876", + "affiliation": "Bureau of Meteorology", + "name": "Cook, Harrison" + }, + { + "orcid": "https://orcid.org/0000-0002-5017-9622", + "affiliation": "Bureau of Meteorology", + "name": "Khanarmuei, Mohammadreza" + }, + { + "orcid": "https://orcid.org/0000-0002-0067-5687", + "affiliation": "Bureau of Meteorology", + "name": "Taggart, Robert J." + }, + { + "orcid": "https://orcid.org/0009-0002-7406-7438", + "affiliation": "Bureau of Meteorology", + "name": "Ramanathan, Nikeeth" + }, + { + "orcid": "https://orcid.org/0009-0008-6830-8251", + "affiliation": "Bureau of Meteorology", + "name": "Carroll, Maree" + }, + { + "orcid": "https://orcid.org/0009-0007-0796-4127", + "affiliation": "Independent Contributor", + "name": "Chong, Stephanie" + }, + { + "affiliation": "Work undertaken while at the Bureau of Meteorology", + "name": "Griffiths, Aidan" + }, + { + "affiliation": "Bureau of Meteorology", + "name": "Sharples, John" + }, + ], + + "license": "Apache-2.0", + + "title": "scores: A Python package for verifying and evaluating models and predictions with xarray", + + "related_identifiers": [ + { + "scheme": "doi", + "identifier": "10.21105/joss.06889", + "relation": "isDocumentedBy", + "resource_type": "publication-article" + } + ], + + "keywords": ["verification", "statistics", "modelling", "geoscience", "earth system science"], +} From c8d0ff6156b7bb905bd79cd3530acd2a7d71c059 Mon Sep 17 00:00:00 2001 From: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> Date: Wed, 7 Aug 2024 15:06:47 +1000 Subject: [PATCH 22/28] Adds PyPI and conda-forge badges to README.md (#626) * Add PyPI badge to README.md * Add Conda-Forge badge Signed-off-by: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1bf5662c6..3eca25076 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # scores: Verification and Evaluation for Forecasts and Models -[![CodeQL](https://github.com/nci/scores/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/nci/scores/actions/workflows/github-code-scanning/codeql) [![Coverage Status](https://coveralls.io/repos/github/nci/scores/badge.svg)](https://coveralls.io/github/nci/scores) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/nci/scores/main?labpath=tutorials%2FTutorial_Gallery.ipynb) [![DOI](https://joss.theoj.org/papers/10.21105/joss.06889/status.svg)](https://doi.org/10.21105/joss.06889) +[![CodeQL](https://github.com/nci/scores/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/nci/scores/actions/workflows/github-code-scanning/codeql) [![Coverage Status](https://coveralls.io/repos/github/nci/scores/badge.svg)](https://coveralls.io/github/nci/scores) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/nci/scores/main?labpath=tutorials%2FTutorial_Gallery.ipynb) [![PyPI Version](https://img.shields.io/pypi/v/scores.svg)](https://pypi.org/project/scores/) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/scores.svg)](https://anaconda.org/conda-forge/scores) > > **A list of over 50 metrics, statistical techniques and data processing tools contained in `scores` is [available here](https://scores.readthedocs.io/en/stable/included.html).** From d4a53e92c095612b8fa84fb271d4e218f53cee23 Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Wed, 7 Aug 2024 17:15:27 +1000 Subject: [PATCH 23/28] Nudge tutorial notebook to see if it resolves docs build error --- tutorials/Additive_and_multiplicative_bias.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/Additive_and_multiplicative_bias.ipynb b/tutorials/Additive_and_multiplicative_bias.ipynb index 09e19d1c7..77c4e9995 100644 --- a/tutorials/Additive_and_multiplicative_bias.ipynb +++ b/tutorials/Additive_and_multiplicative_bias.ipynb @@ -963,7 +963,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.12.4" } }, "nbformat": 4, From e72e1e8693b9e30a75573e24d3bf7b9f7785539d Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Wed, 7 Aug 2024 18:01:42 +1000 Subject: [PATCH 24/28] Updates to the documentation tech stack have resulted in errors and the docs could not be built. This change introduces a workaround, however there are still warnings being raised. It is likely that they will persist until the underlying libraries adapt to the new versions. This commit should restore the ability for readthedocs to build the documentation (hopefully). --- docs/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 54304d482..8213efedf 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -54,7 +54,7 @@ # -- nbsphinx --------------------------------------------------------------- # This is processed by Jinja2 and inserted after each notebook nbsphinx_prolog = r""" -{% set docname = '' + env.doc2path(env.docname, base=False) %} +{% set docname = '' + env.doc2path(env.docname, base=False)|string() %} .. raw:: html @@ -73,7 +73,7 @@ # This is processed by Jinja2 and inserted after each notebook nbsphinx_epilog = r""" -{% set docname = 'doc/' + env.doc2path(env.docname, base=None) %} +{% set docname = 'doc/' + env.doc2path(env.docname, base=None)|string() %} .. raw:: latex \nbsphinxstopnotebook{\scriptsize\noindent\strut From 18eaf90e793b26b1cf6e3183d5dabb7eda1dc533 Mon Sep 17 00:00:00 2001 From: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> Date: Fri, 9 Aug 2024 18:16:07 +1000 Subject: [PATCH 25/28] Add conda install instructions to installation.md and README.md, plus various updates to README.md (#627) * Add conda installation instructions to installation.md among other updates * Change path to environment to named environment * Updated installation instructions in README.md among other updates --------- Signed-off-by: Stephanie Chong <168800785+Steph-Chong@users.noreply.github.com> Co-authored-by: Nicholas Loveday <48701367+nicholasloveday@users.noreply.github.com> --- README.md | 35 ++++++++++++++++----------- docs/installation.md | 57 ++++++++++++++++++++++++++------------------ 2 files changed, 55 insertions(+), 37 deletions(-) diff --git a/README.md b/README.md index 3eca25076..20016065c 100644 --- a/README.md +++ b/README.md @@ -25,28 +25,40 @@ Here is a **curated selection** of the metrics, tools and statistical tests incl | **[Processing Tools](https://scores.readthedocs.io/en/stable/included.html#processing-tools-for-preparing-data)** |Tools to pre-process data. |Data matching, Discretisation, Cumulative Density Function Manipulation. | -`scores` not only includes common scores (e.g. MAE, RMSE), it includes novel scores not commonly found elsewhere (e.g. FIRM, Flip-Flop Index), complex scores (e.g. threshold weighted CRPS), and statistical tests (such as the Diebold Mariano test). Additionally, it provides pre-processing tools for preparing data for scores in a variety of formats including cumulative distribution functions (CDF). `scores` provides its own implementations where relevant to avoid extensive dependencies. +`scores` not only includes common scores (e.g., MAE, RMSE), it includes novel scores not commonly found elsewhere (e.g., FIRM, Flip-Flop Index), complex scores (e.g., threshold weighted CRPS), and statistical tests (such as the Diebold Mariano test). Additionally, it provides pre-processing tools for preparing data for scores in a variety of formats including cumulative distribution functions (CDF). `scores` provides its own implementations where relevant to avoid extensive dependencies. `scores` primarily supports xarray datatypes for Earth system data allowing it to work with NetCDF4, HDF5, Zarr and GRIB data sources among others. `scores` uses Dask for scaling and performance. Some metrics work with pandas and we aim to expand this capability. All of the scores and metrics in this package have undergone a thorough scientific review. Every score has a companion Jupyter Notebook tutorial that demonstrates its use in practice. ## Contributing -To find out more about contributing, see our [Contributing Guide](https://scores.readthedocs.io/en/stable/contributing.html). +To find out more about contributing, see our [contributing guide](https://scores.readthedocs.io/en/stable/contributing.html). -All interactions in discussions, issues, emails and code (e.g. pull requests, code comments) will be managed according to the expectations outlined in the [ code of conduct ](https://github.com/nci/scores/blob/main/CODE_OF_CONDUCT.md) and in accordance with all relevant laws and obligations. This project is an inclusive, respectful and open project with high standards for respectful behaviour and language. The code of conduct is the Contributor Covenant, adopted by over 40,000 open source projects. Any concerns will be dealt with fairly and respectfully, with the processes described in the code of conduct. +All interactions in discussions, issues, emails and code (e.g., pull requests, code comments) will be managed according to the expectations outlined in the [ code of conduct ](https://github.com/nci/scores/blob/main/CODE_OF_CONDUCT.md) and in accordance with all relevant laws and obligations. This project is an inclusive, respectful and open project with high standards for respectful behaviour and language. The code of conduct is the Contributor Covenant, adopted by over 40,000 open source projects. Any concerns will be dealt with fairly and respectfully, with the processes described in the code of conduct. -## Using This Package +## Installation The [installation guide](https://scores.readthedocs.io/en/stable/installation.html) describes four different use cases for installing, using and working with this package. -Most users currently want the *all* installation option. This includes the mathematical functions (scores, metrics, statistical tests etc.), the tutorial notebooks and development libraries. +**Most users currently want the *all* installation option.** This includes the mathematical functions (scores, metrics, statistical tests etc.), the tutorial dependencies and development libraries. -From a Local Checkout of the Git Repository +```bash +# From a local checkout of the Git repository +pip install -e .[all] +``` +**To install the mathematical functions ONLY** (no tutorial dependencies, no developer libraries), use the default *minimal* installation option. *minimal* is a stable version with limited dependencies. This can be installed from the [Python Package Index (PyPI)](https://pypi.org/project/scores/) or with [conda](https://anaconda.org/conda-forge/scores). ```bash -> pip install -e .[all] +# From PyPI +pip install scores ``` +```bash +# From conda-forge +conda install conda-forge::scores +``` +(Note: at present, only the *minimal* installation option is available from conda. In time, we intend to add more installation options to conda.) + +## Using `scores` Here is a short example of the use of scores: @@ -59,16 +71,11 @@ Here is a short example of the use of scores: array(2.) ``` - -To install the mathematical functions ONLY (no tutorial notebooks, no developer libraries), use the *minimal* installation option. *minimal* is a stable version with limited dependencies and can be installed from the Python Package Index. - -```bash -> pip install scores -``` +[Jupyter Notebook tutorials](https://scores.readthedocs.io/en/stable/tutorials/Tutorial_Gallery.html) are provided for each metric and statistical test in `scores`, as well as for some of the key features of `scores` (e.g., [dimension handling](https://scores.readthedocs.io/en/stable/tutorials/Dimension_Handling.html) and [weighting results](https://scores.readthedocs.io/en/stable/tutorials/Weighting_Results.html)). ## Finding, Downloading and Working With Data -All metrics, statistical techniques and data processing tools in `scores` work with [xarray](https://xarray.dev). [Some metrics](https://scores.readthedocs.io/en/stable/included.html#pandas) work with [pandas](https://pandas.pydata.org/). As such, `scores` works with any data source for which xarray or pandas can be used. See the [Data Sources](https://scores.readthedocs.io/en/stable/data.html) page and [this tutorial](https://scores.readthedocs.io/en/stable/tutorials/First_Data_Fetching.html) for more information on finding, downloading and working with different sources of data. +All metrics, statistical techniques and data processing tools in `scores` work with [xarray](https://xarray.dev). [Some metrics](https://scores.readthedocs.io/en/stable/included.html#pandas) work with [pandas](https://pandas.pydata.org/). As such, `scores` works with any data source for which xarray or pandas can be used. See the [data sources](https://scores.readthedocs.io/en/stable/data.html) page and [this tutorial](https://scores.readthedocs.io/en/stable/tutorials/First_Data_Fetching.html) for more information on finding, downloading and working with different sources of data. ## Acknowledging This Work diff --git a/docs/installation.md b/docs/installation.md index 05b9f4dbe..b6ff8f7bb 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -5,7 +5,7 @@ This page describes: - Setting up a virtual environment. -- The most common installation options for `scores`. (Expert users of pip and conda will note that more variations are possible). +- The most common installation options for `scores`. (Expert users of pip and conda will note that more variations are possible.) - An advanced installation option for Jupyter Notebook, for users who wish to separate the Jupyter environment and the `scores` execution environment. ## Setting up a Virtual Environment @@ -25,35 +25,37 @@ source /bin/activate Here is a command to create and activate a new virtual environment with *conda* ```py -conda create -p python=3 -conda activate - +conda create --name +conda activate ``` ## Installation Options -Most users will want the "all" installation option. There are also more specialised options for those who need them. +There are multiple installation options. Most users currently want the "all" installation option. The 4 supported installation options are: -- all: contains mathematical functions, tutorials and development libraries. Excludes maintainer requirements. -- minimal: ONLY contains mathematical functions (so has limited dependencies). -- tutorial: ONLY contains mathematical functions and tutorials. -- maintainer: ONLY contains tools for building the documentation and building for PyPI. +- all: contains mathematical functions, tutorial dependencies and development libraries. +- minimal: ONLY contains mathematical functions (so has limited dependencies). +- tutorial: ONLY contains mathematical functions and tutorial dependencies. +- maintainer: contains tools for building the documentation and building releases. + +Each of the above installation options are available on PyPI. "Minimal" is also available on conda-forge. (In time, we intend to add more installation options to conda-forge.) ### 1. "All" Dependencies (excludes some maintainer-only packages) Use this for `scores` development and general use. Installs: -* Mathematical API code and libraries -* Everything needed to run the tutorial notebooks -* Testing, static analysis and other developer libraries -* Does **not** install tools for making packages and releasing new versions +* Mathematical API code and libraries. +* Everything needed to run the tutorial notebooks. +* Testing, static analysis and other developer libraries. +* Does **not** install tools for making packages and releasing new versions. -#### From a Local Checkout of the Git Repository +#### With pip ```bash +# From a local checkout of the Git repository pip install -e .[all] ``` @@ -61,26 +63,34 @@ pip install -e .[all] Use this to install the `scores` code into another package or system. Installs: -* Mathematical API functions and libraries +* Mathematical API functions and libraries. * Only the required core dependencies. Nothing extra - no tutorials, no developer requirements. -* (Note for high-performance users - dask is not included by default in the minimal install, but will be used if installed into the environment) +* (Note for high-performance users - dask is not included by default in the minimal install, but will be used if installed into the environment.) #### From PyPI ```bash pip install scores ``` +#### With conda + +```bash +conda install conda-forge::scores +``` + +(Note: at present, only the "minimal" installation option is available from conda. In time, we intend to add more installation options to conda.) ### 3. "Tutorial" Dependencies Use this for running tutorials using `scores`, but when you don't need or want developer tools. Installs: -* Mathematical API functions and libraries -* JupyterLab, Plotly, and libraries for reading data, so that the tutorial notebooks can be run +* Mathematical API functions and libraries. +* JupyterLab, Plotly, and libraries for reading data, so that the tutorial notebooks can be run. -#### From a Local Git Repository +#### With pip ```bash +# From a local checkout of the Git repository pip install .[tutorial] ``` @@ -88,13 +98,14 @@ pip install .[tutorial] Use this to build the docs, create packages and prepare new versions for release. Installs: -* Mathematical API functions and libraries -* Dependencies for building new versions of the `scores` package -* Dependencies for building the documentation as HTML +* Mathematical API functions and libraries. +* Dependencies for building new versions of the `scores` package. +* Dependencies for building the documentation as HTML. -#### From a Local Git Repository +#### With pip ```bash +# From a local checkout of the Git repository pip install -e .[maintainer] ``` From 6b0caa953e37a033c7d507960fcf25777ba682b5 Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Fri, 9 Aug 2024 18:25:39 +1000 Subject: [PATCH 26/28] Add a missing ":" in the installation doc --- docs/installation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/installation.md b/docs/installation.md index b6ff8f7bb..602251dbb 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -23,7 +23,7 @@ python -m venv source /bin/activate ``` -Here is a command to create and activate a new virtual environment with *conda* +Here is a command to create and activate a new virtual environment with *conda*: ```py conda create --name conda activate From ecb6af019ca1da2fd0319735046031a6780e6a94 Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Fri, 9 Aug 2024 18:37:38 +1000 Subject: [PATCH 27/28] Update release notes for upcoming release --- docs/release_notes.md | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/docs/release_notes.md b/docs/release_notes.md index adfaa1e90..bcd6dbf01 100644 --- a/docs/release_notes.md +++ b/docs/release_notes.md @@ -2,10 +2,11 @@ ## Version 1.1.0 (Upcoming Release) -For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/1.0.0...develop). Below are the changes we think users may wish to be aware of. +For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/1.0.0...1.1.0). Below are the changes we think users may wish to be aware of. ### Features +- `scores` is now available on [conda-forge](https://anaconda.org/conda-forge/scores). - Added five new scores - threshold weighted squared error: ` scores.continuous.tw_squared_error` - threshold weighted absolute error: `scores.continuous.tw_absolute_error` @@ -14,12 +15,6 @@ For a list of all changes in this release, see the [full changelog](https://gith - threshold weighted Huber loss: `scores.continuous.tw_huber_loss`. See [PR #609](https://github.com/nci/scores/pull/609). -### Breaking Changes - -### Deprecations - -### Bug Fixes - ### Documentation - Added "Threshold Weighted Scores" tutorial. See [PR #609](https://github.com/nci/scores/pull/609). From e6c0db26ab9d4dad2acf8cd28b65e9198987064f Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Fri, 9 Aug 2024 18:50:19 +1000 Subject: [PATCH 28/28] Finalise release notes for release 1.1.0 --- docs/release_notes.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/release_notes.md b/docs/release_notes.md index bcd6dbf01..e03d0fa6e 100644 --- a/docs/release_notes.md +++ b/docs/release_notes.md @@ -1,13 +1,13 @@ # Release Notes (What's New) -## Version 1.1.0 (Upcoming Release) +## Version 1.1.0 (August 9, 2024) For a list of all changes in this release, see the [full changelog](https://github.com/nci/scores/compare/1.0.0...1.1.0). Below are the changes we think users may wish to be aware of. ### Features - `scores` is now available on [conda-forge](https://anaconda.org/conda-forge/scores). -- Added five new scores +- Added five new metrics - threshold weighted squared error: ` scores.continuous.tw_squared_error` - threshold weighted absolute error: `scores.continuous.tw_absolute_error` - threshold weighted quantile score: `scores.continuous.tw_quantile_score`