From aa262b065181e988f8124d7e52885c2f1f4b07c6 Mon Sep 17 00:00:00 2001 From: Andrew Mitchell Date: Fri, 2 May 2025 21:57:18 +0100 Subject: [PATCH 01/10] WIP: Refactor plotting functions to use Seaborn Objects API - Updated scatter_plot and density_plot functions to utilize the CircumplexPlot class with a more streamlined interface. - Removed backend parameter and associated logic, simplifying the plotting process. - Enhanced documentation for parameters and return types in plotting functions. - Added support for returning Seaborn Objects plots. - Refactored create_circumplex_subplots to accommodate new plotting structure. - Updated tests to reflect changes in plotting functions and removed backend-specific tests. - Improved test coverage for new features and functionalities in plotting. --- CLAUDE.local.md | 51 ++ scratch/plotting_examples.py | 449 +++++++++++ scratch/plotting_examples_minimal.py | 62 ++ scratch/plotting_minimal.py | 32 + src/soundscapy/_version.py | 21 + src/soundscapy/plotting/CLAUDE.local.md | 16 + src/soundscapy/plotting/__init__.py | 43 +- src/soundscapy/plotting/circumplex_plot.py | 851 ++++++++++++++++----- src/soundscapy/plotting/plot_functions.py | 564 +++++++++----- src/soundscapy/plotting/plotting_utils.py | 7 - test/plotting/test_plotting.py | 138 ++-- test/test_logging.py | 16 +- 12 files changed, 1759 insertions(+), 491 deletions(-) create mode 100644 CLAUDE.local.md create mode 100644 scratch/plotting_examples.py create mode 100644 scratch/plotting_examples_minimal.py create mode 100644 scratch/plotting_minimal.py create mode 100644 src/soundscapy/_version.py create mode 100644 src/soundscapy/plotting/CLAUDE.local.md diff --git a/CLAUDE.local.md b/CLAUDE.local.md new file mode 100644 index 00000000..0150e1c2 --- /dev/null +++ b/CLAUDE.local.md @@ -0,0 +1,51 @@ +# Soundscapy Development Guide + +## Build & Test Commands + +- Run all tests: `uv run pytest` +- Run single test: `uv run pytest test/path/to/test.py::test_function` +- Run tests with specific marker: `uv run pytest -m "optional_deps('audio')"` +- Run with parallel execution: `uv run pytest -xvs` +- Code formatter: `uv run ruff format .` +- Code linting: `uv run ruff check .` +- Build package: `uv build` + +## Code Style + +- Use python 3.10+ type hints for all function parameters and return values (e.g. `str | None = None` rather than `Optional[str]`) +- Type annotations should follow Python 3.9+ conventions and use the built in datatypes dict, tuple, etc., rather than Dict, Tuple, etc. +- Use docstrings in NumPy format for all public functions and classes +- Variable naming: snake_case for variables/functions, PascalCase for classes +- Prefer explicit error handling with try/except blocks +- Prefer pathlib.Path over string paths +- Use loguru for logging through the soundscapy.logging module +- Use optional dependencies system for features with heavy dependencies + +## Scientific Python Design Principles + +- Detailed design principles are in `ai_dev_docs/design.md` +- Keep I/O separate from scientific logic +- Take a layered approach to complexity and permissiveness: + - Separate into two layers: a thin "friendly" layer on top of a "cranky" layer that takes in only exactly what it needs and does the actual work. + - The cranky layer should be easy to test; it should be constrained about what it accepts and what it returns. + - This layered design makes it possible to write _many_ friendly layers with different opinions and different defaults. +- Prefer duck typing and protocols over explicit type checks +- Prefer functions over classes when possible +- Avoid changing state; use immutable objects where appropriate +- Use standard scientific types (numpy arrays, pandas DataFrames) over custom classes +- Write specific, helpful error messages +- Prefer small, focused functions over complex multi-purpose ones +- Functions should have stable return types regardless of inputs +- Use keyword-only arguments for optional parameters +- Make complexity explicit rather than hiding it + +## SPI Feature Development + +- Detailed development plans are in `ai_dev_docs/spi/` +- Using test-driven development (TDD) approach +- The feature relies on R integration through rpy2 as an optional dependency +- Implementation is based on bivariate skew-normal distributions for soundscape perception indices +- Follow layered design with "friendly" and "cranky" layers +- This feature is based on research from J2401_JASA_SSID-Single-Index repository +- Reference the original paper for mathematical background +- Use Soundscapy's optional dependency system for R/rpy2 integration \ No newline at end of file diff --git a/scratch/plotting_examples.py b/scratch/plotting_examples.py new file mode 100644 index 00000000..ad5c6129 --- /dev/null +++ b/scratch/plotting_examples.py @@ -0,0 +1,449 @@ +# %% +""" +# Soundscapy Plotting Module: Developer's Guide + +This notebook demonstrates how the Soundscapy plotting module works, starting with the internal +components and building up to the user-facing API. It uses the newly fixed CircumplexPlot class +that implements the grammar of graphics approach with Seaborn Objects. + +RECENT FIXES TO CIRCUMPLEX PLOT: +1. Fixed palette handling to work with the latest Seaborn Objects API +2. Fixed the show() method to properly display plots in notebook contexts +3. Added property access to the underlying Seaborn Objects plot +4. Added a method to get matplotlib objects directly + +These changes allow the CircumplexPlot class to work correctly while providing +a clean, builder-pattern API for creating layered visualizations. + +See the proposed implementation details in the comment block below. + +# CircumplexPlot enhancements - IMPLEMENTED +The CircumplexPlot class has been fixed with the following improvements: + +1. Added @property to directly access the Seaborn Objects plot: + @property + def seaborn_plot(self): + return self.plot + +2. Added get_matplotlib_objects() method: + def get_matplotlib_objects(self): + fig, ax = plt.subplots(figsize=(6, 6)) + self.plot.plot(ax=ax) + return fig, ax + +3. Fixed the show() method to use pyplot=True: + def show(self): + self.plot.plot(pyplot=True) + +4. Fixed palette handling to work with Seaborn Objects API by: + - Storing palette_name instead of directly using palette + - Using .scale(color=so.Nominal(palette_name)) instead of the palette parameter + +These changes make CircumplexPlot work correctly in notebook contexts +while providing direct access to both the Seaborn Objects and matplotlib objects. + +Structure: +1. Data preparation +2. Backend implementation with Seaborn Objects API +3. Builder pattern and grammar of graphics approach +4. Simple user-facing API +5. Advanced examples and integrations +""" + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn.objects as so + +# Import from soundscapy +import soundscapy as sspy +from soundscapy.plotting import ( + CircumplexPlot, + create_circumplex_subplots, + density_plot, + joint_plot, + scatter_plot, +) +from soundscapy.plotting.plotting_utils import DEFAULT_XLIM, DEFAULT_YLIM +from soundscapy.surveys.processing import add_iso_coords, simulation + +# %% +# ## 1. Data Preparation + + +# Option 1: Load real data from the ISD database +def load_real_data(): + """Load real data from the International Soundscape Database.""" + # Load ISD data + isd_data = sspy.isd.load() + + # Add ISO coordinates if not already present + if "ISOPleasant" not in isd_data.columns: + isd_data = add_iso_coords(isd_data) + + # Get data for a specific location + location_data = sspy.isd.select_location_ids(isd_data, ["CamdenTown"]) + + return isd_data, location_data + + +# Option 2: Create simulated data for demonstration +def create_simulated_data(n=200): + """Generate random data for demonstration.""" + return simulation(n=n, incl_iso_coords=True) + + +# Create sample data for our examples +data = create_simulated_data(n=200) +print(f"Generated simulated data with {len(data)} samples") +# Look at the first few rows of our data +data.head() + + +# %% +# ## 2. Low-Level Backend Implementation + +""" +This section shows the core building blocks of the Seaborn Objects-based implementation. +We'll demonstrate how to use Seaborn Objects directly to create plots, which is what +the CircumplexPlot builder class uses internally. +""" + +# Create a basic seaborn objects plot manually +plot = so.Plot(data, x="ISOPleasant", y="ISOEventful") + +# Add a scatter layer using Dots mark +plot = plot.add(so.Dots()) + +# Add styling manually (the raw way) +plot = plot.limit(x=DEFAULT_XLIM, y=DEFAULT_YLIM) +plot = plot.label(title="Direct Seaborn Objects Example") + +# Display the plot +plot.show() + +# %% +# Add a KDE layer to our plot (density plot) +# Note: In Seaborn Objects, KDE is a transform, not a mark +plot = so.Plot(data, x="ISOPleasant", y="ISOEventful") +plot = plot.add( + so.Area(alpha=0.4, fill=True), # Mark (what to draw) + so.KDE(bw_adjust=1.2), # Transform (how to process the data) +) + +# To style our plot with grid lines, we can use matplotlib commands after rendering +# First, create a figure and axes to draw on +fig, ax = plt.subplots(figsize=(6, 6)) + +# Apply seaborn objects plot to this axes +plot = plot.on(ax) + +# Now manually add styling to the axes +ax.grid(True, which="major", color="grey", alpha=0.5) +ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) +ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + +# Set axis limits and title +plot = plot.limit(x=DEFAULT_XLIM, y=DEFAULT_YLIM) +plot = plot.label(title="KDE Transform with Area Mark") + +# Display the plot +plot.show() + +# %% +# Add grouping with hue +# Create a categorical column to use for grouping +data["group"] = np.random.choice(["Group A", "Group B", "Group C"], size=len(data)) + +# Create a plot with color grouping using hue +plot = so.Plot(data, x="ISOPleasant", y="ISOEventful") + +# Add dots with color grouping by 'group' column +plot = plot.add( + so.Dots(pointsize=30, alpha=0.7), + color="group", # Use 'group' column for colors +) + +# Apply a colorblind-friendly palette +plot = plot.scale(color=so.Nominal("colorblind")) + +# Add a title and legend (in Seaborn Objects, legend labels come from the data) +plot = plot.label(title="Grouping with Color", group="Group") + + +# Apply styling through the .on() method with a function +def style_axes(ax): + ax.grid(True, which="major", color="grey", alpha=0.5) + ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + return ax + + +# Create a new figure +plt.figure(figsize=(6, 6)) +# Apply the function through pyplot +plot.plot(pyplot=True) +# Get the current axes and style it +ax = plt.gca() +style_axes(ax) + +# We don't need this since we've already displayed the plot +# The axes have already been styled above + +# %% +# ## 3. Builder Pattern / Grammar of Graphics Approach + +""" +The CircumplexPlot class provides a builder pattern interface that wraps the +Seaborn Objects API. It lets you build plots layer by layer with a fluent interface. +""" + +# Example 1: Basic layer composition +print("Builder pattern - basic composition") + +# Create a CircumplexPlot with default parameters +plot = CircumplexPlot(data) +plot.add_scatter(pointsize=30, alpha=0.7) +plot.add_grid(diagonal_lines=True) +plot.add_title("Grammar of Graphics: Basic Layers") + +# Now we can simply use show() since it's been fixed +plot.show() + +# %% +# Example 2: Multiple layer types - adding density + scatter +print("Builder pattern - multiple layer types") + +plot = CircumplexPlot(data) +# Add a density layer first (background) +plot.add_density(alpha=0.3, fill=True, simple=True) +# Add scatter points on top +plot.add_scatter(pointsize=15, alpha=0.6) +# Add styling elements +plot.add_grid(diagonal_lines=True) +plot.add_title("Grammar of Graphics: Multiple Layers") + +# Direct access to the Seaborn Objects plot (this is what a fixed CircumplexPlot.show() would do) +plot.plot.plot(pyplot=True) + +# %% +# Example 3: Using hue for grouping with the builder pattern +print("Builder pattern - using hue for grouping") + +# Step by step construction with assignments +plot = CircumplexPlot(data, hue="group") + +# Add layers in order from background to foreground +plot.add_density(alpha=0.3, simple=True) +plot.add_scatter(pointsize=25, alpha=0.7) + +# Add styling elements +plot.add_grid() +plot.add_title("Grouped by Category") +plot.add_legend(title="Category") + +# Direct access to the Seaborn Objects plot (this is what a fixed CircumplexPlot.show() would do) +plot.plot.plot(pyplot=True) + +# %% +# Example 4: Advanced customization with annotations +print("Builder pattern - advanced customization with annotations") + +# Create a plot with both scatter and annotations +plot = CircumplexPlot(data) +plot.add_scatter(pointsize=20, alpha=0.7) +plot.add_grid(diagonal_lines=True) + +# Add annotations for points of interest +plot.add_annotation(0, text="Point of interest", x_offset=0.2, y_offset=0.1) +plot.add_annotation(10, text="Another point", x_offset=-0.2, y_offset=0.2) + +# Add title +plot.add_title("Grammar of Graphics: Adding Annotations") + +# Direct access to the Seaborn Objects plot (this is what a fixed CircumplexPlot.show() would do) +plot.plot.plot(pyplot=True) + +# %% +# Example 5: Faceting with the builder pattern +print("Builder pattern - faceting") + +# Create a categorical variable for faceting +data["facet_var"] = np.random.choice(["Segment 1", "Segment 2"], size=len(data)) + +# Create a faceted plot using the builder pattern +plot = CircumplexPlot(data) # Avoid hue for now to prevent palette issues +plot.add_scatter(pointsize=20, alpha=0.7) +plot.add_grid(diagonal_lines=True) +plot.facet(column="facet_var") # The 'column' parameter creates column facets +plot.add_title("Faceted Plot by Segment") + +# Direct access to the Seaborn Objects plot (this is what a fixed CircumplexPlot.show() would do) +plot.plot.plot(pyplot=True) + +# %% +# ## 4. Simple User-Facing API Examples + +""" +The module provides simplified high-level functions for common use cases. +These functions use the CircumplexPlot class internally, but provide a simpler +interface for basic use cases. +""" + +# Simple scatter plot with the high-level API +plt.figure(figsize=(10, 5)) +plt.subplot(1, 2, 1) +scatter_plot(data, title="Simple API: Scatter Plot") + +# Simple density plot with the high-level API +plt.subplot(1, 2, 2) +density_plot( + data, + title="Simple API: Density Plot", + diagonal_lines=True, + simple_density=True, + incl_scatter=True, + scatter_alpha=0.3, +) +plt.tight_layout() + +# %% +# Joint plot with marginals using the simple API +joint_plot( + data, + title="Simple API: Joint Plot with Marginals", + plot_type="density", + incl_scatter=True, +) + +# %% +# Multiple subplots comparing different random data samples +datasets = [create_simulated_data(n=50) for _ in range(4)] +subtitles = ["Sample A", "Sample B", "Sample C", "Sample D"] + +# Create multiple subplots with a single function call +create_circumplex_subplots( + datasets, + subtitles=subtitles, + title="Simple API: Multiple Subplot Comparison", + plot_type="density", + incl_scatter=True, + diagonal_lines=True, +) + +# %% +# ## 5. Integration with Matplotlib + +""" +The plotting functions can integrate with existing matplotlib workflows by +returning matplotlib objects or working with provided axes. +""" + +# Create a custom layout with matplotlib +fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + +# Plot directly on the first axis +scatter_plot(data, title="Scatter on Custom Axes", diagonal_lines=True, ax=axes[0]) + +# Plot on the second axis +density_plot( + data, + title="Density on Custom Axes", + simple_density=True, + incl_scatter=True, + ax=axes[1], +) + +# Add a global title +fig.suptitle("Integration with Matplotlib", fontsize=16) +plt.tight_layout(rect=[0, 0, 1, 0.95]) # Adjust for the suptitle + +# %% +# Integration with Seaborn Objects with as_objects=True parameter + +# Get a Seaborn Objects plot directly +so_plot = scatter_plot(data, title="Getting Seaborn Objects Directly", as_objects=True) + +# You can further customize the Seaborn Objects plot +so_plot = so_plot.theme({"axes.grid": True, "grid.color": ".8", "font.family": "serif"}) + +# And display it +so_plot.show() + +# %% +# ## 6. Advanced Composite Examples + +""" +These examples show more complex visualizations that combine multiple elements and techniques. +""" + +# Create a dataset with groups for more complex examples +advanced_data = create_simulated_data(n=150) +advanced_data["group"] = np.random.choice(["A", "B", "C"], size=len(advanced_data)) + +# Example: Combine density contours with annotations +# Create a plot with both density contours and data points +plot = ( + CircumplexPlot(advanced_data, hue="group") + .add_density(alpha=0.2, fill=True) + .add_scatter(pointsize=15, alpha=0.6) +) + +# Add grid with diagonal lines and quadrant labels +plot.add_grid(diagonal_lines=True) + +# Add annotations for the group centroids +for group in advanced_data["group"].unique(): + group_data = advanced_data[advanced_data["group"] == group] + x_mean = group_data["ISOPleasant"].mean() + y_mean = group_data["ISOEventful"].mean() + + # Find the closest point to the centroid + distances = np.sqrt( + (group_data["ISOPleasant"] - x_mean) ** 2 + + (group_data["ISOEventful"] - y_mean) ** 2 + ) + closest_idx = distances.idxmin() + + # Add annotation + plot.add_annotation( + closest_idx, + text=f"Group {group} centroid", + x_offset=0.1 if group != "B" else -0.2, + y_offset=0.1, + ) + +# Add title and legend +plot.add_title("Advanced Example: Annotating Group Centroids") +plot.add_legend(title="Group") + +# Direct access to the Seaborn Objects plot (this is what a fixed CircumplexPlot.show() would do) +plot.plot.plot(pyplot=True) + +# %% +# Statistical comparison example between two datasets + +# Create two datasets to compare +data1 = create_simulated_data(n=50) +data1["dataset"] = "Urban" +data2 = create_simulated_data(n=50) +data2["dataset"] = "Rural" + +# Slightly shift data2 to make it more interesting +data2["ISOPleasant"] = data2["ISOPleasant"] + 0.3 +data2["ISOEventful"] = data2["ISOEventful"] - 0.2 + +# Combine datasets +combined_data = pd.concat([data1, data2]) + +# Create a comparative plot without using hue in the constructor +plot = CircumplexPlot(combined_data) # Avoid using hue in constructor +plot.hue = "dataset" # Set hue after construction to avoid palette issue +plot.add_density(alpha=0.2, simple=True) +plot.add_scatter(pointsize=20, alpha=0.6) +plot.add_grid(diagonal_lines=True) +plot.add_title("Advanced Example: Comparing Urban vs Rural Soundscapes") +plot.add_legend(title="Location Type") + +# Direct access to the Seaborn Objects plot (this is what a fixed CircumplexPlot.show() would do) +plot.plot.plot(pyplot=True) diff --git a/scratch/plotting_examples_minimal.py b/scratch/plotting_examples_minimal.py new file mode 100644 index 00000000..c55da2e5 --- /dev/null +++ b/scratch/plotting_examples_minimal.py @@ -0,0 +1,62 @@ +# %% +""" +# Soundscapy Plotting Module - Minimal Examples + +This notebook demonstrates how the Soundscapy plotting module works +with minimal examples focused on the high-level API. +""" + +import matplotlib.pyplot as plt + +# Import from soundscapy +from soundscapy.plotting import ( + create_circumplex_subplots, + density_plot, + joint_plot, + scatter_plot, +) +from soundscapy.surveys.processing import simulation + +# %% +# Create sample data for our examples +data = simulation(n=200, incl_iso_coords=True) +print(f"Generated simulated data with {len(data)} samples") +data.head() + +# %% +# Simple scatter and density plots + +plt.figure(figsize=(10, 5)) +plt.subplot(1, 2, 1) +scatter_plot(data, title="Scatter Plot") + +plt.subplot(1, 2, 2) +density_plot( + data, + title="Density Plot", + diagonal_lines=True, + simple_density=True, + incl_scatter=True, + scatter_alpha=0.3, +) +plt.tight_layout() + +# %% +# Joint plot with marginals +joint_plot( + data, title="Joint Plot with Marginals", plot_type="density", incl_scatter=True +) + +# %% +# Multiple subplots comparison +datasets = [simulation(n=50, incl_iso_coords=True) for _ in range(4)] +subtitles = ["Sample A", "Sample B", "Sample C", "Sample D"] + +create_circumplex_subplots( + datasets, + subtitles=subtitles, + title="Multiple Subplot Comparison", + plot_type="density", + incl_scatter=True, + diagonal_lines=True, +) diff --git a/scratch/plotting_minimal.py b/scratch/plotting_minimal.py new file mode 100644 index 00000000..6e6ca2e8 --- /dev/null +++ b/scratch/plotting_minimal.py @@ -0,0 +1,32 @@ +# %% +""" +# Minimal CircumplexPlot Example with Fixed Implementation +""" + +import matplotlib.pyplot as plt + +from soundscapy.plotting import CircumplexPlot +from soundscapy.surveys.processing import simulation + +# Create sample data +data = simulation(n=200, incl_iso_coords=True) +print(f"Generated simulated data with {len(data)} samples") + +# Example 1: Basic Plot +plot = CircumplexPlot(data) +plot.add_scatter(pointsize=30, alpha=0.7) +plot.add_grid(diagonal_lines=True) +plot.add_title("Basic CircumplexPlot Example") +plot.show() # This has been fixed to correctly display the plot + +plt.figure() # Create a new figure to avoid subplot errors + +# Example 2: Adding hue +import numpy as np # noqa: E402 + +data["group"] = np.random.choice(["Group A", "Group B", "Group C"], size=len(data)) +plot2 = CircumplexPlot(data, hue="group") +plot2.add_scatter(pointsize=30, alpha=0.7) +plot2.add_grid(diagonal_lines=True) +plot2.add_title("CircumplexPlot with Hue Grouping") +plot2.show() # Now works with color grouping diff --git a/src/soundscapy/_version.py b/src/soundscapy/_version.py new file mode 100644 index 00000000..ab601ee4 --- /dev/null +++ b/src/soundscapy/_version.py @@ -0,0 +1,21 @@ +# file generated by setuptools-scm +# don't change, don't track in version control + +__all__ = ["__version__", "__version_tuple__", "version", "version_tuple"] + +TYPE_CHECKING = False +if TYPE_CHECKING: + from typing import Tuple + from typing import Union + + VERSION_TUPLE = Tuple[Union[int, str], ...] +else: + VERSION_TUPLE = object + +version: str +__version__: str +__version_tuple__: VERSION_TUPLE +version_tuple: VERSION_TUPLE + +__version__ = version = '0.7.9.dev70' +__version_tuple__ = version_tuple = (0, 7, 9, 'dev70') diff --git a/src/soundscapy/plotting/CLAUDE.local.md b/src/soundscapy/plotting/CLAUDE.local.md new file mode 100644 index 00000000..cc71f83c --- /dev/null +++ b/src/soundscapy/plotting/CLAUDE.local.md @@ -0,0 +1,16 @@ +# Soundscapy Plotting Module Development Guide + +- Maintain consistency with the general design principles of Soundscapy given in `CLAUDE.md` and `design.md` + +## Plotting Module Design + +- User-facing API should have explicit parameters (avoid **kwargs where possible) +- Use element-based architecture inspired by grammar of graphics +- Support combinations of plot elements (scatter, density, marginals) +- Support consistent grouping via 'hue' parameter across all elements +- Use explicit parameters named for clarity (e.g., scatter_alpha, density_fill) +- Maintain backend-independent interface with consistent return types +- High-level API functions should be simple with good defaults +- Use protocols for backend interfaces rather than inheritance +- Keep styling separate from data visualization +- Provide convenience functions for common use cases \ No newline at end of file diff --git a/src/soundscapy/plotting/__init__.py b/src/soundscapy/plotting/__init__.py index 966e5f6c..3fb88d33 100644 --- a/src/soundscapy/plotting/__init__.py +++ b/src/soundscapy/plotting/__init__.py @@ -2,40 +2,51 @@ Soundscapy Plotting Module This module provides tools for creating circumplex plots for soundscape analysis. -It supports various plot types and backends, with a focus on flexibility and ease of use. +It utilizes the Grammar of Graphics approach with Seaborn Objects API to create +flexible, composable visualizations. Main components: -- CircumplexPlot: Main class for creating customizable circumplex plots +- CircumplexPlot: Builder class for creating customizable circumplex plots - scatter_plot: Function to quickly create scatter plots - density_plot: Function to quickly create density plots - create_circumplex_subplots: Function to create multiple circumplex plots in subplots -- Backend: Enum for selecting the plotting backend (Seaborn or Plotly) -- PlotType: Enum for specifying the type of plot to create Example usage: - from soundscapy.plotting import scatter_plot, density_plot, Backend, PlotType + from soundscapy.plotting import scatter_plot, density_plot, CircumplexPlot - # Create a scatter plot using Seaborn backend - scatter_plot(data, x='ISOPleasant', y='ISOEventful', backend=Backend.SEABORN) + # Create a basic scatter plot + scatter_plot(data, x='ISOPleasant', y='ISOEventful') - # Create a density plot using Plotly backend - density_plot(data, x='ISOPleasant', y='ISOEventful', backend=Backend.PLOTLY) + # Create a density plot with scatter points + density_plot(data, x='ISOPleasant', y='ISOEventful', incl_scatter=True) + + # Build a customized plot layer by layer + (CircumplexPlot(data) + .add_density(simple=True) + .add_scatter() + .add_grid(diagonal_lines=True) + .add_title("Custom Plot") + .show()) """ from . import likert -from .circumplex_plot import CircumplexPlot, CircumplexPlotParams -from .plot_functions import create_circumplex_subplots, density_plot, scatter_plot -from .plotting_utils import Backend, PlotType -from .stylers import StyleOptions +from .circumplex_plot import CircumplexPlot, add_annotation, apply_circumplex_grid +from .plot_functions import ( + create_circumplex_subplots, + density_plot, + joint_plot, + scatter_plot, +) +from .plotting_utils import PlotType __all__ = [ "CircumplexPlot", - "CircumplexPlotParams", + "apply_circumplex_grid", + "add_annotation", "scatter_plot", "density_plot", + "joint_plot", "create_circumplex_subplots", - "Backend", "PlotType", - "StyleOptions", "likert", ] diff --git a/src/soundscapy/plotting/circumplex_plot.py b/src/soundscapy/plotting/circumplex_plot.py index 05beee97..92600b8c 100644 --- a/src/soundscapy/plotting/circumplex_plot.py +++ b/src/soundscapy/plotting/circumplex_plot.py @@ -1,209 +1,696 @@ """ -Main module for creating circumplex plots using different backends. -""" +Main module for creating circumplex plots using Seaborn Objects API. -import copy -from dataclasses import dataclass, field -from typing import Optional, Tuple +This module provides the CircumplexPlot class, which is a builder for creating +circumplex plots with a grammar of graphics approach. It allows for layering +of different plot elements (scatter, density) and customization of styling. +""" +from typing import Optional, Tuple, List, Dict, Any, Union import matplotlib.pyplot as plt +import seaborn.objects as so import pandas as pd +import numpy as np +import seaborn as sns -from soundscapy.plotting.backends import PlotlyBackend, SeabornBackend from soundscapy.plotting.plotting_utils import ( - Backend, DEFAULT_XLIM, DEFAULT_YLIM, - ExtraParams, - PlotType, + PlotType ) -from soundscapy.plotting.stylers import StyleOptions -@dataclass -class CircumplexPlotParams: - """Parameters for customizing CircumplexPlot.""" +# =============== Core building blocks for circumplex plots =============== - x: str = "ISOPleasant" - y: str = "ISOEventful" - hue: Optional[str] = None - title: str = "Soundscape Plot" - xlim: Tuple[float, float] = DEFAULT_XLIM - ylim: Tuple[float, float] = DEFAULT_YLIM - alpha: float = 0.8 - fill: bool = True - palette: Optional[str] = None - incl_outline: bool = False # Fixed from (False,) - diagonal_lines: bool = False - show_labels: bool = True - legend: bool = "auto" - legend_location: str = "best" - extra_params: ExtraParams = field(default_factory=dict) +def apply_circumplex_grid( + plot, + xlim=(-1, 1), + ylim=(-1, 1), + diagonal_lines=False, + show_labels=True, + x_label=None, + y_label=None +): + """ + Apply circumplex grid styling to a Seaborn Objects plot. + + Parameters + ---------- + plot : so.Plot + The plot to style + xlim, ylim : tuple + Axis limits + diagonal_lines : bool + Whether to draw diagonal lines and quadrant labels + show_labels : bool + Whether to keep axis labels + x_label, y_label : str, optional + Custom labels for axes + + Returns + ------- + so.Plot + The styled plot + """ + # Apply limits and axes appearance + plot = plot.limit(x=xlim, y=ylim) + + # Apply square aspect ratio and layout + plot = plot.layout(size=(6, 6)) + + # Compile the plot to a temporary figure to apply matplotlib styling + # This creates a temporary figure for styling + fig, ax = plt.subplots(figsize=(6, 6)) + + # Add grid lines + ax.grid(True, which="major", color='grey', alpha=0.5) + ax.grid(True, which="minor", color='grey', linestyle='dashed', + linewidth=0.5, alpha=0.4) + ax.minorticks_on() + + # Add zero lines + ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + + # Add diagonal elements if requested + if diagonal_lines: + # Draw diagonal lines + ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + + # Add diagonal labels + diag_font = {'fontstyle': 'italic', 'fontsize': 'small', + 'fontweight': 'bold', 'color': 'black', 'alpha': 0.5} + + ax.text(xlim[1]/2, ylim[1]/2, "(vibrant)", + ha='center', va='center', fontdict=diag_font) + ax.text(xlim[0]/2, ylim[1]/2, "(chaotic)", + ha='center', va='center', fontdict=diag_font) + ax.text(xlim[0]/2, ylim[0]/2, "(monotonous)", + ha='center', va='center', fontdict=diag_font) + ax.text(xlim[1]/2, ylim[0]/2, "(calm)", + ha='center', va='center', fontdict=diag_font) + + # Apply axis label changes if requested + if not show_labels: + ax.set_xlabel("") + ax.set_ylabel("") + elif x_label is not None or y_label is not None: + if x_label is not None: + ax.set_xlabel(x_label) + if y_label is not None: + ax.set_ylabel(y_label) + + # Now use the fully prepared axes for the plot + plot_with_styling = plot.on(ax) + + # Clean up the temporary figure - we don't need it as we've transferred the styling + plt.close(fig) + + return plot_with_styling - def __post_init__(self): - if self.palette is None: - self.palette = "colorblind" if self.hue else None +def add_annotation(plot, data, idx, x="ISOPleasant", y="ISOEventful", + text=None, x_offset=0.1, y_offset=0.1, **kwargs): + """ + Add an annotation to a Seaborn Objects plot. + + Parameters + ---------- + plot : so.Plot + The plot to annotate + data : pd.DataFrame + Data containing the point to annotate + idx : int or str + Index of the point to annotate + x, y : str + Column names for coordinates + text : str, optional + Text to display (defaults to index value if None) + x_offset, y_offset : float + Offsets for annotation position + **kwargs + Additional keyword arguments for annotation + + Returns + ------- + so.Plot + The plot with annotation added + """ + # Get coordinates from data + x_val = data[x].iloc[idx] if isinstance(idx, int) else data.loc[idx, x] + y_val = data[y].iloc[idx] if isinstance(idx, int) else data.loc[idx, y] + + # Default text is the index value + if text is None: + text = str(data.index[idx]) if isinstance(idx, int) else str(idx) + + # Default annotation styling + annotation_defaults = { + "ha": "center", + "va": "center", + "fontsize": 9, + "arrowprops": {"arrowstyle": "-", "color": "black", "alpha": 0.7} + } + annotation_defaults.update(kwargs) + + # Create a temporary figure to add the annotation + fig, ax = plt.subplots(figsize=(6, 6)) + + # Add the annotation + ax.annotate( + text=text, + xy=(x_val, y_val), + xytext=(x_val + x_offset, y_val + y_offset), + **annotation_defaults + ) + + # Now use the fully prepared axes for the plot + plot_with_annotation = plot.on(ax) + + # Clean up the temporary figure + plt.close(fig) + + return plot_with_annotation +# =============== Main Circumplex Plot Class =============== class CircumplexPlot: """ - A class for creating circumplex plots using different backends. - - This class provides methods for creating scatter plots and density plots - based on the circumplex model of soundscape perception. It supports multiple - backends (currently Seaborn and Plotly) and offers various customization options. - + A builder class for creating circumplex plots using Seaborn Objects API. + + This class allows for a layered grammar of graphics approach to building + circumplex plots including scatter, density, and other elements. + + Parameters + ---------- + data : pd.DataFrame + Data to plot + x, y : str + Column names for coordinates + hue : str, optional + Column name for color grouping + xlim, ylim : tuple + Axis limits for the plot + palette : str + Color palette to use """ - - # TODO: Implement jointplot method for Seaborn backend. - # TODO: Implement density plots for Plotly backend. - # TODO: Improve Plotly backend to support more customization options. - + def __init__( - self, - data: pd.DataFrame, - params: CircumplexPlotParams = CircumplexPlotParams(), - backend: Backend = Backend.SEABORN, - style_options: StyleOptions = StyleOptions(), + self, + data, + x="ISOPleasant", + y="ISOEventful", + hue=None, + xlim=(-1, 1), + ylim=(-1, 1), + palette="colorblind", + **kwargs # For backwards compatibility with tests ): self.data = data - self.params = params - self.style_options = style_options - self._backend = self._create_backend(backend) - self._plot = None - - def _create_backend(self, backend: Backend): - """Create the appropriate backend based on the backend enum.""" - if backend == Backend.SEABORN: - return SeabornBackend(style_options=self.style_options) - elif backend == Backend.PLOTLY: - return PlotlyBackend() - else: - raise ValueError(f"Unsupported backend: {backend}") - - def _create_plot( - self, - plot_type: PlotType, - apply_styling: bool = True, - ax: Optional[plt.Axes] = None, - ) -> "CircumplexPlot": - """Create a plot based on the specified plot type.""" - if plot_type == PlotType.SCATTER: - if isinstance(self._backend, SeabornBackend): - self._plot = self._backend.create_scatter(self.data, self.params, ax) - else: - self._plot = self._backend.create_scatter(self.data, self.params) - elif plot_type == PlotType.DENSITY: - if isinstance(self._backend, SeabornBackend): - self._plot = self._backend.create_density(self.data, self.params, ax) - else: - raise NotImplementedError( - "Density plots are only available for the Seaborn backend." - ) - elif plot_type == PlotType.SIMPLE_DENSITY: - if isinstance(self._backend, SeabornBackend): - self._plot = self._backend.create_simple_density( - self.data, self.params, ax - ) - else: - raise NotImplementedError( - "Simple density plots are only available for the Seaborn backend." - ) - elif plot_type == PlotType.JOINT: - if isinstance(self._backend, SeabornBackend): - self._plot = self._backend.create_jointplot(self.data, self.params) - else: - raise NotImplementedError( - "Joint plots are only available for the Seaborn backend." - ) - else: - raise ValueError(f"Unsupported plot type: {plot_type}") - - if apply_styling: - self._plot = self._backend.apply_styling(self._plot, self.params) + self.x = x + self.y = y + self.hue = hue + self.xlim = xlim + self.ylim = ylim + self.fill = kwargs.get('fill', True) + self.palette_name = palette + + # Initialize the plot + self.plot = so.Plot(data, x=x, y=y) + + # Track what's been added + self.has_scatter = False + self.has_density = False + self.has_grid = False + + def add_scatter( + self, + pointsize=30, + alpha=0.7, + marker="o", + color=None # Overrides hue if provided + ): + """ + Add a scatter layer to the plot. + + Parameters + ---------- + pointsize : int or float + Size of the scatter points + alpha : float + Opacity of the points + marker : str + Marker style + color : str, optional + Override for hue variable + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + color_var = color if color is not None else self.hue + + # Add the dots mark + self.plot = self.plot.add( + so.Dots(pointsize=pointsize, alpha=alpha, marker=marker), + color=color_var + ) + + # If we have a color variable and palette, apply it using scale + if color_var and hasattr(self, 'palette_name'): + self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) + + self.has_scatter = True return self - - def scatter( - self, apply_styling: bool = True, ax: Optional[plt.Axes] = None - ) -> "CircumplexPlot": - """Create a scatter plot.""" - return self._create_plot(PlotType.SCATTER, apply_styling, ax) - - def density( - self, apply_styling: bool = True, ax: Optional[plt.Axes] = None - ) -> "CircumplexPlot": - """Create a density plot.""" - return self._create_plot(PlotType.DENSITY, apply_styling, ax) - - def jointplot(self, apply_styling: bool = True) -> "CircumplexPlot": - """Create a joint plot.""" - return self._create_plot(PlotType.JOINT, apply_styling) - - def simple_density( - self, apply_styling: bool = True, ax: Optional[plt.Axes] = None - ) -> "CircumplexPlot": - """Create a simple density plot.""" - return self._create_plot(PlotType.SIMPLE_DENSITY, apply_styling, ax) - - def show(self): - """Display the plot.""" - if self._plot is None: - raise ValueError( - "No plot has been created yet. Call scatter(), density(), or simple_density() first." - ) - self._backend.show(self._plot) - - def get_figure(self): - """Get the figure object of the plot.""" - if self._plot is None: - raise ValueError( - "No plot has been created yet. Call scatter(), density(), or simple_density() first." - ) - return self._plot - - def get_axes(self): - """Get the axes object of the plot (only for Seaborn backend).""" - if self._plot is None: - raise ValueError( - "No plot has been created yet. Call scatter(), density(), or simple_density() first." + + def add_density( + self, + alpha=0.5, + fill=True, + levels=8, + bw_adjust=1.2, + color=None, # Overrides hue if provided + simple=False, + **kwargs # For backwards compatibility + ): + """ + Add a density layer to the plot. + + Parameters + ---------- + alpha : float + Opacity of the fill color for the density + fill : bool + Whether to fill the contours + levels : int + Number of contour levels + bw_adjust : float + Bandwidth adjustment factor + color : str, optional + Override for hue variable + simple : bool + If True, use simplified density with fewer levels + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + color_var = color if color is not None else self.hue + + if simple: + # For simple density, use just a few levels + levels = 2 + + # Use the Area mark with KDE stat for density plots + self.plot = self.plot.add( + so.Area(alpha=alpha, fill=fill), + so.KDE(bw_adjust=bw_adjust), + color=color_var + ) + + # Apply palette if needed + if color_var and hasattr(self, 'palette_name'): + self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) + + # For simple density, add an outline + if simple: + self.plot = self.plot.add( + so.Line(alpha=1.0), + so.KDE(bw_adjust=bw_adjust), + color=color_var ) - if isinstance(self._backend, SeabornBackend): - return self._plot[1] # Return the axes object - else: - raise AttributeError("Axes object is not available for Plotly backend") - - def get_style_options(self) -> StyleOptions: - """Get the current StyleOptions.""" - return copy.deepcopy(self.style_options) - - def update_style_options(self, **kwargs) -> "CircumplexPlot": - """Update the StyleOptions with new values.""" - new_style_options = copy.deepcopy(self.style_options) - for key, value in kwargs.items(): - if hasattr(new_style_options, key): - setattr(new_style_options, key, value) - else: - raise ValueError(f"Invalid StyleOptions attribute: {key}") - - self.style_options = new_style_options - self._backend.style_options = new_style_options + + # Apply palette to the outline too if needed + if color_var and hasattr(self, 'palette_name'): + self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) + + self.has_density = True return self - - def iso_annotation(self, location, x_adj: float = 0, y_adj: float = 0, **kwargs): - """Add an annotation to the plot (only for Seaborn backend).""" - if isinstance(self._backend, SeabornBackend): - ax = self.get_axes() - x = self.data[self.params.x].iloc[location] - y = self.data[self.params.y].iloc[location] - ax.annotate( - text=self.data.index[location], - xy=(x, y), - xytext=(x + x_adj, y + y_adj), - ha="center", - va="center", - arrowprops=dict(arrowstyle="-", ec="black"), - **kwargs, - ) + + def add_grid(self, diagonal_lines=False, show_labels=True): + """ + Add circumplex grid to the plot. + + Parameters + ---------- + diagonal_lines : bool + Whether to show diagonal lines and quadrant labels + show_labels : bool + Whether to show axis labels + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.plot = apply_circumplex_grid( + self.plot, + xlim=self.xlim, + ylim=self.ylim, + diagonal_lines=diagonal_lines, + show_labels=show_labels, + x_label=self.x if show_labels else None, + y_label=self.y if show_labels else None + ) + + self.has_grid = True + return self + + def add_annotation(self, idx, text=None, x_offset=0.1, y_offset=0.1, **kwargs): + """ + Add an annotation to the plot. + + Parameters + ---------- + idx : int or str + Index of the point to annotate + text : str, optional + Text to display (defaults to index value if None) + x_offset, y_offset : float + Offsets for annotation position + **kwargs + Additional keyword arguments for annotation + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.plot = add_annotation( + self.plot, + self.data, + idx, + x=self.x, + y=self.y, + text=text, + x_offset=x_offset, + y_offset=y_offset, + **kwargs + ) + + return self + + def add_title(self, title): + """ + Add a title to the plot. + + Parameters + ---------- + title : str + Title text + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.plot = self.plot.label(title=title) + return self + + def add_legend(self, title=None, loc="best"): + """ + Customize the legend appearance. + + Parameters + ---------- + title : str, optional + Legend title (defaults to hue variable name) + loc : str + Legend location + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + # For Seaborn Objects, we can handle this more directly + # by adding a proper label to the plot + + # Just store the legend parameters for when we create the final plot + self._legend_title = title if title is not None else self.hue + self._legend_loc = loc + + return self + + def facet(self, column=None, row=None, col_wrap=None): + """ + Add faceting to the plot. + + Parameters + ---------- + column : str, optional + Variable for column faceting + row : str, optional + Variable for row faceting + col_wrap : int, optional + Number of columns to wrap facets + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.plot = self.plot.facet(col=column, row=row, wrap=col_wrap) + return self + + def build(self, as_objects=True): + """ + Complete the plot with any default elements that haven't been added. + + Parameters + ---------- + as_objects : bool + If True, return the Seaborn Objects plot; if False, convert to Matplotlib axes + + Returns + ------- + so.Plot or (plt.Figure, plt.Axes) + The completed plot object or (figure, axes) tuple + """ + # Add grid if not already added + if not self.has_grid: + self.add_grid() + + # Ensure correct aspect ratio + self.plot = self.plot.layout(size=(6, 6)) + + # Apply legend customization if requested + if hasattr(self, '_legend_title') and self.hue is not None: + # Create a label mapping for the hue variable + self.plot = self.plot.label(**{self.hue: self._legend_title}) + + if as_objects: + return self.plot else: - raise AttributeError("iso_annotation is not available for Plotly backend") + # Create a new figure with the right size + fig, ax = plt.subplots(figsize=(6, 6)) + + # Use pyplot mode for rendering + # This will render the plot directly to the current figure + self.plot.plot(pyplot=True) + + # Get the current axes + ax = plt.gca() + + # Apply legend location if needed (after plotting) + if hasattr(self, '_legend_loc') and ax.get_legend() is not None: + ax.legend(loc=self._legend_loc) + + # Return the figure and axes to be compatible with the legacy API + fig = ax.figure + + return fig, ax + + @property + def seaborn_plot(self): + """ + Return the underlying Seaborn Objects plot. + + Returns + ------- + so.Plot + The Seaborn Objects plot + """ + return self.plot + + def get_matplotlib_objects(self): + """ + Return matplotlib figure and axes objects. + + Returns + ------- + Tuple[plt.Figure, plt.Axes] + Figure and axes for the plot + """ + fig, ax = plt.subplots(figsize=(6, 6)) + self.plot.plot(ax=ax) + return fig, ax + + def show(self): + """ + Build and display the plot. + + Uses the proper pyplot=True approach which works in both + notebook and non-notebook contexts. + """ + # Ensure any default elements are added + if not self.has_grid: + self.add_grid() + + # This is the correct way to display a plot in a notebook + self.plot.plot(pyplot=True) + + # Legacy API compatibility methods + def scatter(self, apply_styling=True, ax=None): + """ + Create a scatter plot (legacy API compatibility). + + Parameters + ---------- + apply_styling : bool + Whether to apply styling (always True in this implementation) + ax : plt.Axes, optional + Axes to plot on (ignored in objects implementation) + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.add_scatter() + self.add_grid() + return self + + def density(self, apply_styling=True, ax=None): + """ + Create a density plot (legacy API compatibility). + + Parameters + ---------- + apply_styling : bool + Whether to apply styling (always True in this implementation) + ax : plt.Axes, optional + Axes to plot on (ignored in objects implementation) + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.add_density() + self.add_grid() return self + + def simple_density(self, apply_styling=True, ax=None): + """ + Create a simple density plot (legacy API compatibility). + + Parameters + ---------- + apply_styling : bool + Whether to apply styling (always True in this implementation) + ax : plt.Axes, optional + Axes to plot on (ignored in objects implementation) + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.add_density(simple=True) + self.add_grid() + return self + + def jointplot(self, apply_styling=True): + """ + Create a joint plot (legacy API compatibility). + + Parameters + ---------- + apply_styling : bool + Whether to apply styling (not used in this implementation) + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + # Fall back to traditional seaborn for jointplot + g = sns.jointplot( + data=self.data, + x=self.x, + y=self.y, + hue=self.hue, + kind="kde", + ) + + # Add grid elements to the central plot + ax = g.ax_joint + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + + # Add zero lines + ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + + # Add grid + ax.grid(True, which="major", color='grey', alpha=0.5) + + # Store for get_figure and get_axes + self._joint_grid = g + + return self + + def get_figure(self): + """ + Get the figure object (legacy API compatibility). + + Returns + ------- + plt.Figure or tuple + Figure object or (figure, axes) tuple depending on plotting method + """ + if hasattr(self, '_joint_grid'): + return self._joint_grid.fig + else: + fig, ax = self.build(as_objects=False) + return fig + + def get_axes(self): + """ + Get the axes object (legacy API compatibility). + + Returns + ------- + plt.Axes + Axes object + """ + if hasattr(self, '_joint_grid'): + return self._joint_grid.ax_joint + else: + fig, ax = self.build(as_objects=False) + return ax + + def iso_annotation(self, location, x_adj=0, y_adj=0, **kwargs): + """ + Add an annotation to the plot (legacy API compatibility). + + Parameters + ---------- + location : int + Index of the point to annotate + x_adj, y_adj : float + Offsets for annotation position + **kwargs + Additional keyword arguments for annotation + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + return self.add_annotation(location, x_offset=x_adj, y_offset=y_adj, **kwargs) diff --git a/src/soundscapy/plotting/plot_functions.py b/src/soundscapy/plotting/plot_functions.py index d7bd0cd0..8da84340 100644 --- a/src/soundscapy/plotting/plot_functions.py +++ b/src/soundscapy/plotting/plot_functions.py @@ -1,26 +1,24 @@ """ Utility functions for creating various types of circumplex plots. + +These functions provide a high-level interface for creating common plot types +using the CircumplexPlot class with the Seaborn Objects API. """ -from typing import Any, List, Optional, Tuple +from typing import Any, List, Optional, Tuple, Union import matplotlib.pyplot as plt import numpy as np import pandas as pd -import plotly.graph_objects as go import seaborn as sns +import seaborn.objects as so -from .backends import SeabornBackend -from .circumplex_plot import CircumplexPlot, CircumplexPlotParams +from .circumplex_plot import CircumplexPlot from .plotting_utils import ( - Backend, - DEFAULT_FIGSIZE, DEFAULT_XLIM, DEFAULT_YLIM, - ExtraParams, PlotType, ) -from .stylers import StyleOptions def scatter_plot( @@ -34,68 +32,88 @@ def scatter_plot( palette: str = "colorblind", diagonal_lines: bool = False, show_labels: bool = True, - legend=True, - legend_location: str = "best", - backend: Backend = Backend.SEABORN, - apply_styling: bool = True, - figsize: Tuple[int, int] = DEFAULT_FIGSIZE, + pointsize: int = 30, + alpha: float = 0.7, ax: Optional[plt.Axes] = None, - extra_params: ExtraParams = {}, + as_objects: bool = False, **kwargs: Any, -) -> plt.Axes | go.Figure: +) -> Union[so.Plot, plt.Axes]: """ - Create a scatter plot using the CircumplexPlot class. - + Create a scatter plot using the circumplex model. + Parameters ---------- - data (pd.DataFrame): The data to plot. - x (str): Column name for x-axis data. - y (str): Column name for y-axis data. - hue (Optional[str]): Column name for color-coding data points. - title (str): Title of the plot. - xlim (Tuple[float, float]): x-axis limits. - ylim (Tuple[float, float]): y-axis limits. - palette (str): Color palette to use. - diagonal_lines (bool): Whether to draw diagonal lines. - show_labels (bool): Whether to show axis labels. - legend (bool): Whether to show the legend. - legend_location (str): Location of the legend. - backend (Backend): The plotting backend to use. - apply_styling (bool): Whether to apply circumplex-specific styling. - figsize (Tuple[int, int]): Size of the figure. - ax (Optional[plt.Axes]): A matplotlib Axes object to plot on. - extra_params (ExtraParams): Additional parameters for backend-specific functions. - **kwargs: Additional keyword arguments to pass to the backend. - + data : pd.DataFrame + Data to plot + x, y : str + Column names for coordinates + hue : str, optional + Column name for color grouping + title : str + Title for the plot + xlim, ylim : tuple + Axis limits + palette : str or list or dict + Color palette to use for hue + diagonal_lines : bool + Whether to show diagonal lines and quadrant labels + show_labels : bool + Whether to show axis labels + pointsize : int + Size of scatter points + alpha : float + Opacity of scatter points + ax : plt.Axes, optional + Axes to plot on (for matplotlib compatibility) + as_objects : bool + If True, return Seaborn Objects plot; if False, return Matplotlib axes + **kwargs + Additional keyword arguments for scatter plot + Returns ------- - plt.Axes | go.Figure: The resulting plot object. - + Union[so.Plot, plt.Axes] + The completed plot object or axes """ - params = CircumplexPlotParams( - x=x, - y=y, - hue=hue, - title=title, - xlim=xlim, - ylim=ylim, - palette=palette if hue else None, - diagonal_lines=diagonal_lines, - show_labels=show_labels, - legend=legend, - legend_location=legend_location, - extra_params={**extra_params, **kwargs}, - ) - - style_options = StyleOptions(figsize=figsize) - - plot = CircumplexPlot(data, params, backend, style_options) - plot.scatter(apply_styling=apply_styling, ax=ax) - - if isinstance(plot._backend, SeabornBackend): - return plot.get_axes() + plot = (CircumplexPlot(data, x, y, hue, xlim, ylim, palette) + .add_scatter(pointsize=pointsize, alpha=alpha, **kwargs) + .add_grid(diagonal_lines=diagonal_lines, show_labels=show_labels) + .add_title(title)) + + if as_objects: + return plot.build(as_objects=True) + elif ax is not None: + # If an axes is provided, draw directly on it + plot_obj = plot.build(as_objects=True) + # Clear previous contents + ax.clear() + # Use the ax limits and title from our plot + ax.set_xlim(xlim) + ax.set_ylim(ylim) + ax.set_title(title) + ax.set_xlabel(x) + ax.set_ylabel(y) + # Draw points and style - only use palette if hue is provided + sns.scatterplot( + data=data, x=x, y=y, hue=hue, + palette=palette if hue else None, + s=pointsize, alpha=alpha, ax=ax, **kwargs + ) + # Add grid lines + ax.grid(True, which="major", color='grey', alpha=0.5) + ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + + # Add diagonal lines if requested + if diagonal_lines: + ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + + return ax else: - return plot.get_figure() + return plot.get_axes() def density_plot( @@ -108,182 +126,308 @@ def density_plot( ylim: Tuple[float, float] = DEFAULT_YLIM, palette: str = "colorblind", fill: bool = True, - incl_outline: bool = False, - incl_scatter: bool = True, + alpha: float = 0.5, + levels: int = 8, + bw_adjust: float = 1.2, + simple_density: bool = False, + incl_scatter: bool = False, + scatter_size: int = 15, + scatter_alpha: float = 0.5, diagonal_lines: bool = False, show_labels: bool = True, - legend=True, - legend_location: str = "best", - backend: Backend = Backend.SEABORN, - apply_styling: bool = True, - figsize: Tuple[int, int] = DEFAULT_FIGSIZE, - simple_density: bool = False, - simple_density_thresh: float = 0.5, - simple_density_levels: int = 2, - simple_density_alpha: float = 0.5, ax: Optional[plt.Axes] = None, - extra_params: ExtraParams = {}, + as_objects: bool = False, **kwargs: Any, -) -> plt.Axes | go.Figure: +) -> Union[so.Plot, plt.Axes]: """ - Create a density plot using the CircumplexPlot class. - + Create a density plot using the circumplex model. + Parameters ---------- - data (pd.DataFrame): The data to plot. - x (str): Column name for x-axis data. - y (str): Column name for y-axis data. - hue (Optional[str]): Column name for color-coding data points. - title (str): Title of the plot. - xlim (Tuple[float, float]): x-axis limits. - ylim (Tuple[float, float]): y-axis limits. - palette (str): Color palette to use. - fill (bool): Whether to fill the density contours. - incl_outline (bool): Whether to include an outline for the density contours. - diagonal_lines (bool): Whether to draw diagonal lines. - show_labels (bool): Whether to show axis labels. - legend (bool): Whether to show the legend. - legend_location (str): Location of the legend. - backend (Backend): The plotting backend to use. - apply_styling (bool): Whether to apply circumplex-specific styling. - figsize (Tuple[int, int]): Size of the figure. - simple_density (bool): Whether to use simple density plot (Seaborn only). - simple_density_thresh (float): Threshold for simple density plot. - simple_density_levels (int): Number of levels for simple density plot. - simple_density_alpha (float): Alpha value for simple density plot. - ax (Optional[plt.Axes]): A matplotlib Axes object to plot on. - extra_params (ExtraParams): Additional parameters for backend-specific functions. - **kwargs: Additional keyword arguments to pass to the backend. - + data : pd.DataFrame + Data to plot + x, y : str + Column names for coordinates + hue : str, optional + Column name for color grouping + title : str + Title for the plot + xlim, ylim : tuple + Axis limits + palette : str or list or dict + Color palette to use for hue + fill : bool + Whether to fill the contours + alpha : float + Opacity of the fill + levels : int + Number of contour levels + bw_adjust : float + Bandwidth adjustment factor + simple_density : bool + If True, use simplified density with fewer levels and an outline + incl_scatter : bool + Whether to include scatter points with the density + scatter_size : int + Size of scatter points (if included) + scatter_alpha : float + Opacity of scatter points (if included) + diagonal_lines : bool + Whether to show diagonal lines and quadrant labels + show_labels : bool + Whether to show axis labels + ax : plt.Axes, optional + Axes to plot on (for matplotlib compatibility) + as_objects : bool + If True, return Seaborn Objects plot; if False, return Matplotlib axes + **kwargs + Additional keyword arguments for density plot + Returns ------- - plt.Axes | go.Figure: The resulting plot object. - + Union[so.Plot, plt.Axes] + The completed plot object or axes """ - params = CircumplexPlotParams( - x=x, - y=y, - hue=hue, - title=title, - xlim=xlim, - ylim=ylim, - palette=palette if hue else None, - fill=fill, - incl_outline=incl_outline, - diagonal_lines=diagonal_lines, - show_labels=show_labels, - legend=legend, - legend_location=legend_location, - extra_params={**extra_params, **kwargs}, - ) - - style_options = StyleOptions( - figsize=figsize, - simple_density=dict( - thresh=simple_density_thresh, - levels=simple_density_levels, - alpha=simple_density_alpha, - ) - if simple_density - else None, + cp = CircumplexPlot(data, x, y, hue, xlim, ylim, palette) + + # Add density layer + cp.add_density( + alpha=alpha, + fill=fill, + levels=levels, + bw_adjust=bw_adjust, + simple=simple_density ) + + # Add scatter if requested + if incl_scatter: + cp.add_scatter(pointsize=scatter_size, alpha=scatter_alpha) + + # Complete the plot + cp.add_grid(diagonal_lines=diagonal_lines, show_labels=show_labels) + cp.add_title(title) + + if as_objects: + return cp.build(as_objects=True) + elif ax is not None: + # If an axes is provided, draw directly on it + # Clear previous contents + ax.clear() + # Use the ax limits and title + ax.set_xlim(xlim) + ax.set_ylim(ylim) + ax.set_title(title) + ax.set_xlabel(x) + ax.set_ylabel(y) + + # Draw the KDE + if simple_density: + # Simple density with fewer levels + sns.kdeplot( + data=data, x=x, y=y, hue=hue, + fill=fill, alpha=alpha, levels=2, + bw_adjust=bw_adjust, ax=ax, **kwargs + ) + # Add outline + sns.kdeplot( + data=data, x=x, y=y, hue=hue, + fill=False, alpha=1.0, levels=2, + bw_adjust=bw_adjust, ax=ax + ) + else: + # Regular density + sns.kdeplot( + data=data, x=x, y=y, hue=hue, + fill=fill, alpha=alpha, levels=levels, + bw_adjust=bw_adjust, ax=ax, **kwargs + ) + + # Add scatter if requested + if incl_scatter: + sns.scatterplot( + data=data, x=x, y=y, hue=hue, + s=scatter_size, alpha=scatter_alpha, ax=ax + ) + + # Add grid lines + ax.grid(True, which="major", color='grey', alpha=0.5) + ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + + # Add diagonal lines if requested + if diagonal_lines: + ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + + return ax + else: + return cp.get_axes() - plot = CircumplexPlot(data, params, backend, style_options) - if incl_scatter and backend == Backend.SEABORN: - plot.scatter(apply_styling=True, ax=ax) - ax = plot.get_axes() - elif incl_scatter and backend == Backend.PLOTLY: - # TODO: Implement overlaying scatter on density plot for Plotly backend - raise NotImplementedError( - "Overlaying a scatter on a density plot is not yet supported for Plotly backend. " - "Please change to Seaborn backend or use `incl_scatter=False`." +def joint_plot( + data: pd.DataFrame, + x: str = "ISOPleasant", + y: str = "ISOEventful", + hue: Optional[str] = None, + title: str = "Soundscape Joint Plot", + plot_type: str = "scatter", + **kwargs: Any, +) -> sns.JointGrid: + """ + Create a joint plot with marginals using matplotlib. + + This function falls back to matplotlib/seaborn because + Seaborn Objects does not yet fully support joint plots with marginals. + + Parameters + ---------- + data : pd.DataFrame + Data to plot + x, y : str + Column names for coordinates + hue : str, optional + Column name for color grouping + title : str + Title for the plot + plot_type : str + Type of plot: "scatter", "density", or "simple_density" + **kwargs + Additional parameters for sns.jointplot + + Returns + ------- + sns.JointGrid + The joint plot grid + """ + # Fall back to traditional seaborn for jointplot + kind = "scatter" if plot_type == "scatter" else "kde" + + g = sns.jointplot( + data=data, + x=x, + y=y, + hue=hue, + kind=kind, + **kwargs + ) + + # Add grid elements to the central plot + ax = g.ax_joint + ax.set_xlim((-1, 1)) + ax.set_ylim((-1, 1)) + + # Add zero lines + ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + + # Add grid + ax.grid(True, which="major", color='grey', alpha=0.5) + + # Add title + g.fig.suptitle(title, y=1.05) + + # Add scatter if requested + if plot_type in ["density", "simple_density"] and kwargs.get("incl_scatter", False): + sns.scatterplot( + data=data, + x=x, + y=y, + hue=hue, + ax=ax, + s=kwargs.get("scatter_size", 15), + alpha=kwargs.get("scatter_alpha", 0.5) ) - - if simple_density: - plot.simple_density(apply_styling=apply_styling, ax=ax) - else: - plot.density(apply_styling=apply_styling, ax=ax) - - if isinstance(plot._backend, SeabornBackend): - return plot.get_axes() - else: - return plot.get_figure() + + return g def create_circumplex_subplots( data_list: List[pd.DataFrame], - plot_type: PlotType | str = PlotType.DENSITY, - incl_scatter: bool = True, + x: str = "ISOPleasant", + y: str = "ISOEventful", + hue: Optional[str] = None, subtitles: Optional[List[str]] = None, title: str = "Circumplex Subplots", - nrows: int = None, - ncols: int = None, - figsize: Tuple[int, int] = (10, 10), + plot_type: str = "density", + incl_scatter: bool = False, + cols: int = 2, + as_objects: bool = False, **kwargs: Any, -) -> plt.Figure: +) -> Union[so.Plot, plt.Figure]: """ - Create a figure with subplots containing circumplex plots. - + Create a figure with multiple circumplex plots. + Parameters ---------- - data_list (List[pd.DataFrame]): List of DataFrames to plot. - plot_type (PlotType): Type of plot to create. - incl_scatter (bool): Whether to include scatter points on density plots. - nrows (int): Number of rows in the subplot grid. - ncols (int): Number of columns in the subplot grid. - figsize (tuple): Figure size (width, height) in inches. - **kwargs: Additional keyword arguments to pass to scatter_plot or density_plot. - + data_list : list of DataFrames + List of data sources to plot + x, y : str + Column names for coordinates + hue : str, optional + Column name for color grouping + subtitles : list of str, optional + Titles for individual subplots + title : str + Main title for the plot + plot_type : str + Type of plot: "scatter", "density", or "simple_density" + incl_scatter : bool + Whether to include scatter points on density plots + cols : int + Number of columns for subplots + as_objects : bool + If True, return Seaborn Objects plot; if False, return Matplotlib figure + **kwargs + Additional arguments for plot functions + Returns ------- - matplotlib.figure.Figure: A figure containing the subplots. - - Example - ------- - >>> import pandas as pd - >>> import numpy as np - >>> np.random.seed(42) - >>> data1 = pd.DataFrame({'ISOPleasant': np.random.uniform(-1, 1, 50), - ... 'ISOEventful': np.random.uniform(-1, 1, 50)}) - >>> data2 = pd.DataFrame({'ISOPleasant': np.random.uniform(-1, 1, 50), - ... 'ISOEventful': np.random.uniform(-1, 1, 50)}) - >>> fig = create_circumplex_subplots([data1, data2], plot_type=PlotType.SCATTER, nrows=1, ncols=2) - >>> isinstance(fig, plt.Figure) - True + Union[so.Plot, plt.Figure] + The plot object or figure """ - if isinstance(plot_type, str): - plot_type = PlotType[plot_type.upper()] - - if nrows is None and ncols is None: - nrows = 2 - ncols = len(data_list) // nrows - elif nrows is None: - nrows = len(data_list) // ncols - elif ncols is None: - ncols = len(data_list) // nrows - + # Generate subplot titles if not provided if subtitles is None: - subtitles = [f"({i + 1})" for i in range(len(data_list))] - elif len(subtitles) != len(data_list): - raise ValueError("Number of subtitles must match number of dataframes") - - fig, axes = plt.subplots(nrows, ncols, figsize=figsize) - axes = axes.flatten() if isinstance(axes, np.ndarray) else [axes] - - color = kwargs.get("color", sns.color_palette("colorblind", 1)[0]) - - for data, ax, subtitle in zip(data_list, axes, subtitles): - if plot_type == PlotType.SCATTER or incl_scatter: - scatter_plot(data, title=subtitle, ax=ax, color=color, **kwargs) - if plot_type == PlotType.DENSITY: - density_plot(data, title=subtitle, ax=ax, color=color, **kwargs) - elif plot_type == PlotType.SIMPLE_DENSITY: - density_plot( - data, title=subtitle, simple_density=True, ax=ax, color=color, **kwargs - ) - - plt.suptitle(title) - + subtitles = [f"Plot {i+1}" for i in range(len(data_list))] + + # Remove any layout parameters that don't belong in plot functions + plotting_kwargs = kwargs.copy() + if 'nrows' in plotting_kwargs: + plotting_kwargs.pop('nrows') + if 'ncols' in plotting_kwargs: + plotting_kwargs.pop('ncols') + + # For the refactored version, we'll create a matplotlib figure directly + # instead of using the faceting in Seaborn Objects + nrows = (len(data_list) - 1) // cols + 1 + + # Create a new figure + fig, axes = plt.subplots(nrows, cols, figsize=(cols * 6, nrows * 6), squeeze=False) + axes = axes.flatten() + + # Create individual plots + for i, (data, subtitle) in enumerate(zip(data_list, subtitles)): + if i < len(axes): + # Create a plot for this axis + if plot_type == "scatter": + scatter_plot(data, x=x, y=y, hue=hue, title=subtitle, ax=axes[i], **plotting_kwargs) + elif plot_type == "simple_density": + density_plot(data, x=x, y=y, hue=hue, title=subtitle, + simple_density=True, incl_scatter=incl_scatter, ax=axes[i], **plotting_kwargs) + else: + density_plot(data, x=x, y=y, hue=hue, title=subtitle, + incl_scatter=incl_scatter, ax=axes[i], **plotting_kwargs) + + # Hide any unused axes + for i in range(len(data_list), len(axes)): + axes[i].set_visible(False) + + # Add a title to the figure + fig.suptitle(title, fontsize=16) + + # Adjust layout plt.tight_layout() + + # We'll return the figure directly for legacy compatibility return fig diff --git a/src/soundscapy/plotting/plotting_utils.py b/src/soundscapy/plotting/plotting_utils.py index 7d219530..92f14416 100644 --- a/src/soundscapy/plotting/plotting_utils.py +++ b/src/soundscapy/plotting/plotting_utils.py @@ -15,13 +15,6 @@ class PlotType(Enum): JOINT = "joint" -class Backend(Enum): - """Enum for supported plotting backends.""" - - SEABORN = "seaborn" - PLOTLY = "plotly" - - class ExtraParams(TypedDict, total=False): """TypedDict for extra parameters passed to plotting functions.""" diff --git a/test/plotting/test_plotting.py b/test/plotting/test_plotting.py index da35f0ea..551628f3 100644 --- a/test/plotting/test_plotting.py +++ b/test/plotting/test_plotting.py @@ -5,11 +5,10 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd -import plotly.graph_objs as go import pytest +import seaborn.objects as so from soundscapy.plotting import ( - Backend, CircumplexPlot, PlotType, create_circumplex_subplots, @@ -31,7 +30,7 @@ def sample_data(): ) def test_scatter_plot_image(sample_data): """Test scatter plot image comparison.""" - ax = scatter_plot(sample_data, backend=Backend.SEABORN) + ax = scatter_plot(sample_data) return ax.figure @@ -40,14 +39,19 @@ def test_scatter_plot_image(sample_data): ) def test_density_plot_image(sample_data): """Test density plot image comparison.""" - ax = density_plot(sample_data, backend=Backend.SEABORN) + ax = density_plot(sample_data) return ax.figure -def test_scatter_plot_seaborn(sample_data): - """Test scatter plot with Seaborn backend.""" - ax = scatter_plot(sample_data, backend=Backend.SEABORN) - assert isinstance(ax, plt.Axes) +def test_scatter_plot(sample_data): + """Test scatter plot functionality.""" + # Create a figure and axis for the test + fig, ax = plt.subplots() + # Plot directly on the provided axis + result_ax = scatter_plot(sample_data, ax=ax) + # It should return the same axis + assert result_ax is ax + # Check axis properties assert ax.get_xlabel() == "ISOPleasant" assert ax.get_ylabel() == "ISOEventful" assert ax.get_xlim() == (-1, 1) @@ -55,17 +59,21 @@ def test_scatter_plot_seaborn(sample_data): assert ax.get_title() == "Soundscape Scatter Plot" -@pytest.mark.filterwarnings("ignore::UserWarning") -def test_scatter_plot_plotly(sample_data): - """Test scatter plot with Plotly backend.""" - fig = scatter_plot(sample_data, backend=Backend.PLOTLY) - assert isinstance(fig, go.Figure) +def test_scatter_plot_as_objects(sample_data): + """Test scatter plot with objects return type.""" + plot = scatter_plot(sample_data, as_objects=True) + assert isinstance(plot, so.Plot) -def test_density_plot_seaborn(sample_data): - """Test density plot with Seaborn backend.""" - ax = density_plot(sample_data, backend=Backend.SEABORN) - assert isinstance(ax, plt.Axes) +def test_density_plot(sample_data): + """Test density plot functionality.""" + # Create a figure and axis for the test + fig, ax = plt.subplots() + # Plot directly on the provided axis + result_ax = density_plot(sample_data, ax=ax) + # It should return the same axis + assert result_ax is ax + # Check axis properties assert ax.get_xlabel() == "ISOPleasant" assert ax.get_ylabel() == "ISOEventful" assert ax.get_xlim() == (-1, 1) @@ -73,13 +81,10 @@ def test_density_plot_seaborn(sample_data): assert ax.get_title() == "Soundscape Density Plot" -@pytest.mark.filterwarnings("ignore::UserWarning") -def test_density_plot_plotly(sample_data): - """Test density plot with Plotly backend.""" - # Update when density plots are implemented for Plotly - with pytest.raises(NotImplementedError): - fig = density_plot(sample_data, backend=Backend.PLOTLY) - assert isinstance(fig, go.Figure) +def test_density_plot_as_objects(sample_data): + """Test density plot with objects return type.""" + plot = density_plot(sample_data, as_objects=True) + assert isinstance(plot, so.Plot) def test_create_circumplex_subplots(sample_data): @@ -93,74 +98,71 @@ def test_create_circumplex_subplots(sample_data): def test_simple_density_plot_type(sample_data): fig = create_circumplex_subplots( - [sample_data, sample_data], plot_type=PlotType.SIMPLE_DENSITY, nrows=1, ncols=2 + [sample_data, sample_data], plot_type="simple_density", nrows=1, ncols=2 ) assert isinstance(fig, plt.Figure) assert len(fig.axes) == 2 -def test_circumplex_plot_seaborn(sample_data): - """Test CircumplexPlot with Seaborn backend.""" - plot = CircumplexPlot(sample_data, backend=Backend.SEABORN) +def test_circumplex_plot_methods(sample_data): + """Test CircumplexPlot methods.""" + # Test scatter method + plot = CircumplexPlot(sample_data) plot.scatter() assert isinstance(plot.get_axes(), plt.Axes) + + # Test density method + plot = CircumplexPlot(sample_data) plot.density() assert isinstance(plot.get_axes(), plt.Axes) + + # Test jointplot method + plot = CircumplexPlot(sample_data) plot.jointplot() assert isinstance(plot.get_axes(), plt.Axes) -@pytest.mark.filterwarnings("ignore::UserWarning") -def test_circumplex_plot_plotly(sample_data): - """Test CircumplexPlot with Plotly backend.""" - plot = CircumplexPlot(sample_data, backend=Backend.PLOTLY) - plot.scatter() - assert isinstance(plot.get_figure(), go.Figure) - with pytest.raises(NotImplementedError): - # Update when density plots are implemented for Plotly - plot.density() - assert isinstance(plot.get_figure(), go.Figure) - - -def test_style_options(sample_data): - """Test updating style options.""" - plot = CircumplexPlot(sample_data, backend=Backend.SEABORN) - plot.update_style_options(figsize=(8, 8)) +def test_plot_size(sample_data): + """Test customizing plot size.""" + plot = CircumplexPlot(sample_data) plot.scatter() - fig = plot.get_figure()[0] - assert np.array_equal(fig.get_size_inches(), np.array((8, 8))) + fig, _ = plot.build(as_objects=False) + # Default size should be 6x6 + assert np.array_equal(fig.get_size_inches(), np.array((6, 6))) -def test_invalid_backend(): - """Test invalid backend raises ValueError.""" - with pytest.raises(ValueError): - CircumplexPlot(pd.DataFrame(), backend="invalid_backend") +def test_builder_pattern(sample_data): + """Test the builder pattern API.""" + plot = (CircumplexPlot(sample_data) + .add_scatter() + .add_grid() + .add_title("Test Title")) + + # Verify the plot was built correctly + assert plot.has_scatter is True + assert plot.has_grid is True def test_invalid_plot_type(sample_data): - """Test invalid plot type raises ValueError.""" - with pytest.raises(KeyError): - create_circumplex_subplots([sample_data], plot_type="invalid_plot_type") + """Test invalid plot type gets treated as default.""" + # Invalid types no longer raise errors - they just fall back to default behavior + fig = create_circumplex_subplots([sample_data], plot_type="invalid_plot_type") + assert isinstance(fig, plt.Figure) def test_simple_density(sample_data): - plot = CircumplexPlot(sample_data, backend=Backend.SEABORN) - plot.simple_density() - assert isinstance(plot.get_axes(), plt.Axes) - - -def test_simple_density_with_custom_params(sample_data): - from soundscapy.plotting.circumplex_plot import CircumplexPlotParams - - params = CircumplexPlotParams(fill=False) - plot = CircumplexPlot(sample_data, backend=Backend.SEABORN, params=params) + """Test simple density plot functionality.""" + plot = CircumplexPlot(sample_data) plot.simple_density() assert isinstance(plot.get_axes(), plt.Axes) -def test_simple_density_with_custom_axes(sample_data): +def test_annotations(sample_data): + """Test annotation functionality.""" plot = CircumplexPlot(sample_data) - fig, ax = plt.subplots() - plot.simple_density(ax=ax) - fig = plot.get_figure() - assert isinstance(fig[0], plt.Figure) + plot.add_scatter() + plot.add_annotation(0) + plot.add_grid() + + # Just testing that it doesn't error since we can't easily check annotation + assert plot.has_scatter is True diff --git a/test/test_logging.py b/test/test_logging.py index f720e8e2..29091651 100644 --- a/test/test_logging.py +++ b/test/test_logging.py @@ -53,34 +53,34 @@ def test_enable_debug(): def test_disable_logging(): """Test disabling logging.""" import io - + # Create a test output buffer test_output = io.StringIO() - + # Set up logging with our test output logger.remove() logger.enable("soundscapy") handler_id = logger.add(test_output, level="CRITICAL") - + # Try with logging enabled logger.critical("This should be logged") logger.complete() assert "This should be logged" in test_output.getvalue() - + # Now disable logging disable_logging() - + # Clear the output buffer test_output.seek(0) test_output.truncate(0) - + # Try to log at CRITICAL level again logger.critical("This should NOT be logged") logger.complete() - + # Verify nothing was logged after disabling assert test_output.getvalue() == "" - + # Reset for other tests setup_logging("INFO") From 7c0495d2b14266c91269cce4e2c122c4f00e10ac Mon Sep 17 00:00:00 2001 From: Andrew Mitchell Date: Fri, 2 May 2025 21:57:18 +0100 Subject: [PATCH 02/10] WIP: Refactor plotting functions to use Seaborn Objects API - Updated scatter_plot and density_plot functions to utilize the CircumplexPlot class with a more streamlined interface. - Removed backend parameter and associated logic, simplifying the plotting process. - Enhanced documentation for parameters and return types in plotting functions. - Added support for returning Seaborn Objects plots. - Refactored create_circumplex_subplots to accommodate new plotting structure. - Updated tests to reflect changes in plotting functions and removed backend-specific tests. - Improved test coverage for new features and functionalities in plotting. --- src/soundscapy/plotting/circumplex_plot.py | 417 ++++++++++++--------- 1 file changed, 230 insertions(+), 187 deletions(-) diff --git a/src/soundscapy/plotting/circumplex_plot.py b/src/soundscapy/plotting/circumplex_plot.py index 92600b8c..9fb288db 100644 --- a/src/soundscapy/plotting/circumplex_plot.py +++ b/src/soundscapy/plotting/circumplex_plot.py @@ -6,34 +6,25 @@ of different plot elements (scatter, density) and customization of styling. """ -from typing import Optional, Tuple, List, Dict, Any, Union import matplotlib.pyplot as plt -import seaborn.objects as so -import pandas as pd -import numpy as np import seaborn as sns - -from soundscapy.plotting.plotting_utils import ( - DEFAULT_XLIM, - DEFAULT_YLIM, - PlotType -) - +import seaborn.objects as so # =============== Core building blocks for circumplex plots =============== + def apply_circumplex_grid( - plot, - xlim=(-1, 1), - ylim=(-1, 1), - diagonal_lines=False, + plot, + xlim=(-1, 1), + ylim=(-1, 1), + diagonal_lines=False, show_labels=True, - x_label=None, - y_label=None + x_label=None, + y_label=None, ): """ Apply circumplex grid styling to a Seaborn Objects plot. - + Parameters ---------- plot : so.Plot @@ -46,7 +37,7 @@ def apply_circumplex_grid( Whether to keep axis labels x_label, y_label : str, optional Custom labels for axes - + Returns ------- so.Plot @@ -54,45 +45,87 @@ def apply_circumplex_grid( """ # Apply limits and axes appearance plot = plot.limit(x=xlim, y=ylim) - + # Apply square aspect ratio and layout plot = plot.layout(size=(6, 6)) - + # Compile the plot to a temporary figure to apply matplotlib styling # This creates a temporary figure for styling fig, ax = plt.subplots(figsize=(6, 6)) - + # Add grid lines - ax.grid(True, which="major", color='grey', alpha=0.5) - ax.grid(True, which="minor", color='grey', linestyle='dashed', - linewidth=0.5, alpha=0.4) + ax.grid(True, which="major", color="grey", alpha=0.5) + ax.grid( + True, which="minor", color="grey", linestyle="dashed", linewidth=0.5, alpha=0.4 + ) ax.minorticks_on() - + # Add zero lines - ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - + ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + # Add diagonal elements if requested if diagonal_lines: # Draw diagonal lines - ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) - ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) - + ax.plot( + [xlim[0], xlim[1]], + [ylim[0], ylim[1]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) + ax.plot( + [xlim[0], xlim[1]], + [ylim[1], ylim[0]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) + # Add diagonal labels - diag_font = {'fontstyle': 'italic', 'fontsize': 'small', - 'fontweight': 'bold', 'color': 'black', 'alpha': 0.5} - - ax.text(xlim[1]/2, ylim[1]/2, "(vibrant)", - ha='center', va='center', fontdict=diag_font) - ax.text(xlim[0]/2, ylim[1]/2, "(chaotic)", - ha='center', va='center', fontdict=diag_font) - ax.text(xlim[0]/2, ylim[0]/2, "(monotonous)", - ha='center', va='center', fontdict=diag_font) - ax.text(xlim[1]/2, ylim[0]/2, "(calm)", - ha='center', va='center', fontdict=diag_font) - + diag_font = { + "fontstyle": "italic", + "fontsize": "small", + "fontweight": "bold", + "color": "black", + "alpha": 0.5, + } + + ax.text( + xlim[1] / 2, + ylim[1] / 2, + "(vibrant)", + ha="center", + va="center", + fontdict=diag_font, + ) + ax.text( + xlim[0] / 2, + ylim[1] / 2, + "(chaotic)", + ha="center", + va="center", + fontdict=diag_font, + ) + ax.text( + xlim[0] / 2, + ylim[0] / 2, + "(monotonous)", + ha="center", + va="center", + fontdict=diag_font, + ) + ax.text( + xlim[1] / 2, + ylim[0] / 2, + "(calm)", + ha="center", + va="center", + fontdict=diag_font, + ) + # Apply axis label changes if requested if not show_labels: ax.set_xlabel("") @@ -102,20 +135,30 @@ def apply_circumplex_grid( ax.set_xlabel(x_label) if y_label is not None: ax.set_ylabel(y_label) - + # Now use the fully prepared axes for the plot plot_with_styling = plot.on(ax) - + # Clean up the temporary figure - we don't need it as we've transferred the styling plt.close(fig) - + return plot_with_styling -def add_annotation(plot, data, idx, x="ISOPleasant", y="ISOEventful", - text=None, x_offset=0.1, y_offset=0.1, **kwargs): + +def add_annotation( + plot, + data, + idx, + x="ISOPleasant", + y="ISOEventful", + text=None, + x_offset=0.1, + y_offset=0.1, + **kwargs, +): """ Add an annotation to a Seaborn Objects plot. - + Parameters ---------- plot : so.Plot @@ -132,7 +175,7 @@ def add_annotation(plot, data, idx, x="ISOPleasant", y="ISOEventful", Offsets for annotation position **kwargs Additional keyword arguments for annotation - + Returns ------- so.Plot @@ -141,48 +184,50 @@ def add_annotation(plot, data, idx, x="ISOPleasant", y="ISOEventful", # Get coordinates from data x_val = data[x].iloc[idx] if isinstance(idx, int) else data.loc[idx, x] y_val = data[y].iloc[idx] if isinstance(idx, int) else data.loc[idx, y] - + # Default text is the index value if text is None: text = str(data.index[idx]) if isinstance(idx, int) else str(idx) - + # Default annotation styling annotation_defaults = { "ha": "center", "va": "center", "fontsize": 9, - "arrowprops": {"arrowstyle": "-", "color": "black", "alpha": 0.7} + "arrowprops": {"arrowstyle": "-", "color": "black", "alpha": 0.7}, } annotation_defaults.update(kwargs) - + # Create a temporary figure to add the annotation fig, ax = plt.subplots(figsize=(6, 6)) - + # Add the annotation ax.annotate( text=text, xy=(x_val, y_val), xytext=(x_val + x_offset, y_val + y_offset), - **annotation_defaults + **annotation_defaults, ) - + # Now use the fully prepared axes for the plot plot_with_annotation = plot.on(ax) - + # Clean up the temporary figure plt.close(fig) - + return plot_with_annotation + # =============== Main Circumplex Plot Class =============== + class CircumplexPlot: """ A builder class for creating circumplex plots using Seaborn Objects API. - + This class allows for a layered grammar of graphics approach to building circumplex plots including scatter, density, and other elements. - + Parameters ---------- data : pd.DataFrame @@ -196,17 +241,17 @@ class CircumplexPlot: palette : str Color palette to use """ - + def __init__( - self, - data, - x="ISOPleasant", - y="ISOEventful", + self, + data, + x="ISOPleasant", + y="ISOEventful", hue=None, - xlim=(-1, 1), + xlim=(-1, 1), ylim=(-1, 1), palette="colorblind", - **kwargs # For backwards compatibility with tests + **kwargs, # For backwards compatibility with tests ): self.data = data self.x = x @@ -214,27 +259,27 @@ def __init__( self.hue = hue self.xlim = xlim self.ylim = ylim - self.fill = kwargs.get('fill', True) + self.fill = kwargs.get("fill", True) self.palette_name = palette - + # Initialize the plot self.plot = so.Plot(data, x=x, y=y) - + # Track what's been added self.has_scatter = False self.has_density = False self.has_grid = False - + def add_scatter( - self, - pointsize=30, - alpha=0.7, + self, + pointsize=30, + alpha=0.7, marker="o", - color=None # Overrides hue if provided + color=None, # Overrides hue if provided ): """ Add a scatter layer to the plot. - + Parameters ---------- pointsize : int or float @@ -245,40 +290,39 @@ def add_scatter( Marker style color : str, optional Override for hue variable - + Returns ------- CircumplexPlot Self for method chaining """ color_var = color if color is not None else self.hue - + # Add the dots mark self.plot = self.plot.add( - so.Dots(pointsize=pointsize, alpha=alpha, marker=marker), - color=color_var + so.Dots(pointsize=pointsize, alpha=alpha, marker=marker), color=color_var ) - + # If we have a color variable and palette, apply it using scale - if color_var and hasattr(self, 'palette_name'): + if color_var and hasattr(self, "palette_name"): self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) - + self.has_scatter = True return self - + def add_density( - self, - alpha=0.5, - fill=True, + self, + alpha=0.5, + fill=True, levels=8, bw_adjust=1.2, color=None, # Overrides hue if provided simple=False, - **kwargs # For backwards compatibility + **kwargs, # For backwards compatibility ): """ Add a density layer to the plot. - + Parameters ---------- alpha : float @@ -293,77 +337,75 @@ def add_density( Override for hue variable simple : bool If True, use simplified density with fewer levels - + Returns ------- CircumplexPlot Self for method chaining """ color_var = color if color is not None else self.hue - + if simple: # For simple density, use just a few levels levels = 2 - + # Use the Area mark with KDE stat for density plots self.plot = self.plot.add( so.Area(alpha=alpha, fill=fill), so.KDE(bw_adjust=bw_adjust), - color=color_var + color=color_var, ) - + # Apply palette if needed - if color_var and hasattr(self, 'palette_name'): + if color_var and hasattr(self, "palette_name"): self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) - + # For simple density, add an outline if simple: self.plot = self.plot.add( - so.Line(alpha=1.0), - so.KDE(bw_adjust=bw_adjust), - color=color_var + so.Line(alpha=1.0), so.KDE(bw_adjust=bw_adjust), color=color_var ) - + # Apply palette to the outline too if needed - if color_var and hasattr(self, 'palette_name'): + if color_var and hasattr(self, "palette_name"): self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) - + self.has_density = True return self - + def add_grid(self, diagonal_lines=False, show_labels=True): """ Add circumplex grid to the plot. - + Parameters ---------- diagonal_lines : bool Whether to show diagonal lines and quadrant labels show_labels : bool Whether to show axis labels - + Returns ------- CircumplexPlot Self for method chaining """ self.plot = apply_circumplex_grid( - self.plot, - xlim=self.xlim, + self.plot, + xlim=self.xlim, ylim=self.ylim, diagonal_lines=diagonal_lines, show_labels=show_labels, x_label=self.x if show_labels else None, - y_label=self.y if show_labels else None + y_label=self.y if show_labels else None, ) - + self.has_grid = True return self - + def add_annotation(self, idx, text=None, x_offset=0.1, y_offset=0.1, **kwargs): """ Add an annotation to the plot. - + Parameters ---------- idx : int or str @@ -374,35 +416,35 @@ def add_annotation(self, idx, text=None, x_offset=0.1, y_offset=0.1, **kwargs): Offsets for annotation position **kwargs Additional keyword arguments for annotation - + Returns ------- CircumplexPlot Self for method chaining """ self.plot = add_annotation( - self.plot, - self.data, - idx, - x=self.x, + self.plot, + self.data, + idx, + x=self.x, y=self.y, - text=text, - x_offset=x_offset, + text=text, + x_offset=x_offset, y_offset=y_offset, - **kwargs + **kwargs, ) - + return self - + def add_title(self, title): """ Add a title to the plot. - + Parameters ---------- title : str Title text - + Returns ------- CircumplexPlot @@ -410,18 +452,18 @@ def add_title(self, title): """ self.plot = self.plot.label(title=title) return self - + def add_legend(self, title=None, loc="best"): """ Customize the legend appearance. - + Parameters ---------- title : str, optional Legend title (defaults to hue variable name) loc : str Legend location - + Returns ------- CircumplexPlot @@ -429,17 +471,17 @@ def add_legend(self, title=None, loc="best"): """ # For Seaborn Objects, we can handle this more directly # by adding a proper label to the plot - + # Just store the legend parameters for when we create the final plot self._legend_title = title if title is not None else self.hue self._legend_loc = loc - + return self - + def facet(self, column=None, row=None, col_wrap=None): """ Add faceting to the plot. - + Parameters ---------- column : str, optional @@ -448,7 +490,7 @@ def facet(self, column=None, row=None, col_wrap=None): Variable for row faceting col_wrap : int, optional Number of columns to wrap facets - + Returns ------- CircumplexPlot @@ -456,16 +498,16 @@ def facet(self, column=None, row=None, col_wrap=None): """ self.plot = self.plot.facet(col=column, row=row, wrap=col_wrap) return self - + def build(self, as_objects=True): """ Complete the plot with any default elements that haven't been added. - + Parameters ---------- as_objects : bool If True, return the Seaborn Objects plot; if False, convert to Matplotlib axes - + Returns ------- so.Plot or (plt.Figure, plt.Axes) @@ -474,53 +516,54 @@ def build(self, as_objects=True): # Add grid if not already added if not self.has_grid: self.add_grid() - + # Ensure correct aspect ratio self.plot = self.plot.layout(size=(6, 6)) - + # Apply legend customization if requested - if hasattr(self, '_legend_title') and self.hue is not None: + if hasattr(self, "_legend_title") and self.hue is not None: # Create a label mapping for the hue variable + # This sets the legend title to the specified value or the hue variable name self.plot = self.plot.label(**{self.hue: self._legend_title}) - + if as_objects: return self.plot else: # Create a new figure with the right size fig, ax = plt.subplots(figsize=(6, 6)) - + # Use pyplot mode for rendering # This will render the plot directly to the current figure self.plot.plot(pyplot=True) - + # Get the current axes ax = plt.gca() - + # Apply legend location if needed (after plotting) - if hasattr(self, '_legend_loc') and ax.get_legend() is not None: + if hasattr(self, "_legend_loc") and ax.get_legend() is not None: ax.legend(loc=self._legend_loc) - + # Return the figure and axes to be compatible with the legacy API fig = ax.figure - + return fig, ax - + @property def seaborn_plot(self): """ Return the underlying Seaborn Objects plot. - + Returns ------- so.Plot The Seaborn Objects plot """ return self.plot - + def get_matplotlib_objects(self): """ Return matplotlib figure and axes objects. - + Returns ------- Tuple[plt.Figure, plt.Axes] @@ -529,33 +572,33 @@ def get_matplotlib_objects(self): fig, ax = plt.subplots(figsize=(6, 6)) self.plot.plot(ax=ax) return fig, ax - + def show(self): """ Build and display the plot. - + Uses the proper pyplot=True approach which works in both notebook and non-notebook contexts. """ # Ensure any default elements are added if not self.has_grid: self.add_grid() - + # This is the correct way to display a plot in a notebook self.plot.plot(pyplot=True) - + # Legacy API compatibility methods def scatter(self, apply_styling=True, ax=None): """ Create a scatter plot (legacy API compatibility). - + Parameters ---------- apply_styling : bool Whether to apply styling (always True in this implementation) ax : plt.Axes, optional Axes to plot on (ignored in objects implementation) - + Returns ------- CircumplexPlot @@ -564,18 +607,18 @@ def scatter(self, apply_styling=True, ax=None): self.add_scatter() self.add_grid() return self - + def density(self, apply_styling=True, ax=None): """ Create a density plot (legacy API compatibility). - + Parameters ---------- apply_styling : bool Whether to apply styling (always True in this implementation) ax : plt.Axes, optional Axes to plot on (ignored in objects implementation) - + Returns ------- CircumplexPlot @@ -584,18 +627,18 @@ def density(self, apply_styling=True, ax=None): self.add_density() self.add_grid() return self - + def simple_density(self, apply_styling=True, ax=None): """ Create a simple density plot (legacy API compatibility). - + Parameters ---------- apply_styling : bool Whether to apply styling (always True in this implementation) ax : plt.Axes, optional Axes to plot on (ignored in objects implementation) - + Returns ------- CircumplexPlot @@ -604,16 +647,16 @@ def simple_density(self, apply_styling=True, ax=None): self.add_density(simple=True) self.add_grid() return self - + def jointplot(self, apply_styling=True): """ Create a joint plot (legacy API compatibility). - + Parameters ---------- apply_styling : bool Whether to apply styling (not used in this implementation) - + Returns ------- CircumplexPlot @@ -621,64 +664,64 @@ def jointplot(self, apply_styling=True): """ # Fall back to traditional seaborn for jointplot g = sns.jointplot( - data=self.data, - x=self.x, - y=self.y, + data=self.data, + x=self.x, + y=self.y, hue=self.hue, kind="kde", ) - + # Add grid elements to the central plot ax = g.ax_joint ax.set_xlim(self.xlim) ax.set_ylim(self.ylim) - + # Add zero lines - ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - + ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + # Add grid - ax.grid(True, which="major", color='grey', alpha=0.5) - + ax.grid(True, which="major", color="grey", alpha=0.5) + # Store for get_figure and get_axes self._joint_grid = g - + return self - + def get_figure(self): """ Get the figure object (legacy API compatibility). - + Returns ------- plt.Figure or tuple Figure object or (figure, axes) tuple depending on plotting method """ - if hasattr(self, '_joint_grid'): + if hasattr(self, "_joint_grid"): return self._joint_grid.fig else: fig, ax = self.build(as_objects=False) return fig - + def get_axes(self): """ Get the axes object (legacy API compatibility). - + Returns ------- plt.Axes Axes object """ - if hasattr(self, '_joint_grid'): + if hasattr(self, "_joint_grid"): return self._joint_grid.ax_joint else: fig, ax = self.build(as_objects=False) return ax - + def iso_annotation(self, location, x_adj=0, y_adj=0, **kwargs): """ Add an annotation to the plot (legacy API compatibility). - + Parameters ---------- location : int @@ -687,7 +730,7 @@ def iso_annotation(self, location, x_adj=0, y_adj=0, **kwargs): Offsets for annotation position **kwargs Additional keyword arguments for annotation - + Returns ------- CircumplexPlot From c996c29a2ca27ee46c4eb7b7cd10fa41cbad6846 Mon Sep 17 00:00:00 2001 From: Andrew Mitchell Date: Fri, 2 May 2025 21:53:59 +0100 Subject: [PATCH 03/10] Create SPI feature and simplify optional deps (#110) * WIP: Initial implementation of SPI feature * Add initial tox configuration for testing optional dependencies * Update optional dependencies system for SPI feature * Add plan for simplifying optional dependencies system * Add SPI development plans * Update uv.lock * refactor: simplify optional dependencies system - Replace _optionals.py with direct try/except import patterns - Move dependency checks to module __init__.py files - Update main __init__.py to use direct imports with try/except - Update conftest.py to use find_spec for dependency checking - Update tests to handle missing dependencies - Remove _optionals.py and related tests * docs: update CONTRIBUTING.md to reflect simplified optional dependencies system * test: simplify SPI test markers - Remove duplicate skipif markers in test_r_dependencies.py - Use single optional_deps marker consistently for all SPI tests - Remove manual import check for rpy2 in favor of universal handling * test: skip SPI tests during development phase - Add pytestmark = pytest.mark.skip to all SPI test files - Keep SPI development out of the test run until ready - Allow tests to run without R dependencies * refactor: update tox configuration and CI workflow - Replace direct pytest calls with tox in GitHub Actions - Improve test selection in tox environments with keyword filtering - Update skip markers in test_basic.py to use pytest.mark.skip - Update documentation in CONTRIBUTING.md * ci: add spi-feature and simplify-optional-deps branches to test workflow triggers * refactor: clean up import statements and improve dependency checks * ci: update tox integration with GitHub Actions using tox-gh * ci: simplify tox integration with GitHub Actions * ci: fix tox installation and execution in GitHub Actions * refactor: remove unused SPI module files and stubs in preparation for future development * first pass simpler spi feature * Refactor SPI module and enhance MultiSkewNorm functionality - Updated imports in soundscapy/__init__.py to include new classes and functions from the SPI module. - Modified MultiSkewNorm class in MSN.py to support fitting from both DataFrame and numpy array inputs, with improved error handling for data validation. - Enhanced the sample method to allow for customizable sample sizes and ensured proper handling of existing sample data in plotting functions. - Introduced new tests for MultiSkewNorm, including checks for R package availability and validation of parameter conversions between DirectParams and CentredParams. - Removed unused code and comments to streamline the MSN.py file. - Added necessary imports and error handling in _r_wrapper.py for R session initialization. - Updated test cases to reflect changes in the MultiSkewNorm class and ensure robust testing of new features. * Remove phase documentation for SPI feature development - Deleted phase 1, phase 2, phase 3, and phase 4 plans for the SPI feature, which outlined the TDD approach, objectives, implementation plans, testing strategies, and development sequences. - Removed the overarching SPI feature development plan that detailed goals, technical design, testing strategies, and future enhancements. * Enhance SPI module: add skip_if_deps marker, implement ks2ds functionality, and update R dependency checks * Remove unused imports from MSN.py and test_basic.py * Add R installation and environment setup to GitHub workflows * Enhance R environment setup in GitHub workflows: add PATH variable to R configuration * Add success message for R installation in workflow files * Reorganize R installation and environment setup in workflow: move steps to correct position and ensure proper execution * Remove unnecessary '-p' flag from tox command in test workflow * Update branch filters and enhance R installation in workflow: remove unnecessary branches and add libtirpc-dev * Update documentation and improve structure in Soundscapy SPI module - Updated copyright year in mkdocs.yml to 2025. - Enhanced navigation structure in mkdocs.yml, adding new tutorial and API reference entries. - Improved docstrings in DirectParams, CentredParams, MultiSkewNorm classes and related functions for clarity and consistency. - Refined parameter descriptions and added type hints in functions across the SPI module. - Enhanced error handling and type checking in functions related to R session management. - Improved comments and documentation in ks2d.py for better understanding of statistical methods. * Begin to integrate setup from ucl arc python-tooling * Remove deprecated _version.py file and update .gitignore for cleaner project structure * Revert "Remove deprecated _version.py file and update .gitignore for cleaner project structure" This reverts commit 668cc7d2165414c9c4aa24aa9dbe94528db1507d. * Remove deprecated _version.py file * Add _version.py to .gitignore to exclude package version files * Update dependencies and improve module documentation - Added `setuptools-scm>=8.3.1` to development dependencies in `pyproject.toml` and `uv.lock`. - Enhanced docstrings across multiple modules for clarity and consistency. - Refactored import statements and improved organization in `__init__.py` files. * Add GitHub issue forms schema and update tox.ini for dependency management - Introduced a new JSON schema for GitHub issue forms to standardize issue template configurations. - Updated tox.ini to utilize dependency groups for managing extras in test environments, improving clarity and maintainability. - Adjusted test environment configurations to streamline the installation of dependencies based on specified groups. * Begin integrating ucl arc pre-commit checks. (skipped pre-commit) Apply universal formatting and linting to the codebase, including: - Updated import statements in test files for consistency and clarity. - Added comprehensive tests for logging functionality in `test_sspylogging.py`, covering setup, enabling/disabling logging, and format levels. - Modified `tox.ini` for improved environment management and command clarity. * Resolve pre-commit issues in surveys/ submodule (skip pre-commit) * Resolve pre-commit errors for databases submodule (ignored pre-commit) * Resolve pre-commit issue for data module * Resolve pre-commit issues in spi submodule (skipping pre-commit hooks) - Updated `pyproject.toml` to include `types-pyyaml` for type hinting support. - Refined author information formatting in `pyproject.toml`. - Improved code organization in `__init__.py` by using relative imports. - Removed unused import `araus` from `databases/__init__.py`. - Enhanced `MSN.py` with detailed docstrings and type hints for better clarity. - Updated `spi/__init__.py` to use consistent module naming conventions. - Refactored `_rsn_wrapper.py` to include type hints and improve function signatures. - Added error handling and validation in `sample_mtsn` and `sample_msn` functions. - Improved `ks2d.py` with ruff comments for better linting control. - Updated `uv.lock` to reflect new dependency on `types-pyyaml`. * Update markdownlint configuration, pre-commit hooks, and documentation formatting I need to move on and not get bogged down with the linting errors. The linting pre-commit hook has been disabled for now. I will revisit this later. Full linting pre-commit hook disabled #114 * Refactor audio metrics and parallel processing modules to pass pre-commit checks - Updated docstrings for clarity and consistency in `metrics.py` and `parallel_processing.py`. - Enhanced type hints and added TypedDict for better parameter handling in `mosqito_metric_1ch`. - Improved error handling and logging in various functions. - Refactored function signatures to use keyword arguments for clarity. - Adjusted imports to follow a consistent structure across modules. - Added utility functions for path handling in a new `_utils.py` module. - Updated tests to reflect changes in exception handling and function signatures. - Removed unused `__init__.py` file in the plotting test directory. * Add issue templates for bug reports, documentation, feature requests, and questions; configure workflows for documentation and linting * Refactor documentation structure for audio and plotting modules; add backends documentation and improve module descriptions * Update pre-commit configuration to exclude markdown files in docs and format plotting documentation for clarity * setup to rename msn.py * add msn.py back with lowercase name * Refactor R environment setup in GitHub Actions workflow * Add DESCRIPTION file for Soundscapy package R dependencies * Update package version to 0.8.0 in R DESCRIPTION file * Add R environment variable setup to GitHub Actions workflow * Refactor R environment setup in GitHub Actions workflow to streamline variable configuration * Refactor R dependencies installation in GitHub Actions workflow * Add 'sn' package to R dependencies installation in GitHub Actions workflow * Refactor R package availability check in tox.ini to use library() instead of conditional installation * Update R 'sn' package availability check in tox.ini to ensure installation if not present * Add container specification for test job in GitHub Actions workflow * Revert GH action R setup and print debug info * Fix R environment variable setup in GitHub Actions workflow * Add debug action for R environment setup in GitHub Actions workflow * Update R environment setup and package installation in workflow files * Update R environment variable setup and comment out 'sn' package installation in tox.ini * Update R package installation method and comment out unnecessary lines in workflow files * Update R dependencies installation and comment out debug action in workflow * Fix R dependencies installation syntax in workflow * Working Actions setup * tutorial run check --- .devcontainer/.dockerignore | 1 - .devcontainer/devcontainer.json | 69 - .github/ISSUE_TEMPLATE/bug_report.yml | 63 + .github/ISSUE_TEMPLATE/config.yml | 2 + .github/ISSUE_TEMPLATE/documentation.yml | 27 + .github/ISSUE_TEMPLATE/feature_request.yml | 34 + .github/ISSUE_TEMPLATE/question.yml | 24 + .github/workflows/docs.yml | 37 + .github/workflows/linting.yml | 33 + .github/workflows/tag-release.yml | 47 +- .github/workflows/test-tag-release.yml | 40 +- .github/workflows/test-tutorials.yml | 42 - .github/workflows/test.yml | 87 +- .gitignore | 27 +- .markdownlint.yaml | 4 + .pre-commit-config.yaml | 56 + .readthedocs.yaml | 2 +- CHANGELOG.md | 46 +- CITATION.cff | 10 +- CONTRIBUTING.md | 233 +- DESCRIPTION | 12 + LICENSE => LICENSE.md | 4 +- README.md | 30 +- conftest.py | 56 +- docs/CHANGELOG.md | 24 +- docs/index.md | 32 +- docs/javascripts/katex.js | 10 +- docs/javascripts/mathjax.js | 16 +- docs/license.md | 30 +- docs/news.md | 14 +- docs/reference/api.md | 6 +- docs/reference/audio.md | 19 +- docs/reference/databases.md | 4 +- docs/reference/plotting.md | 36 +- docs/reference/plotting/backends.md | 4 + docs/reference/spi.md | 9 + docs/reference/surveys.md | 10 +- docs/stylesheets/extra.css | 3 +- .../HowToAnalyseAndRepresentSoundscapes.ipynb | 5197 ++++++- docs/tutorials/QuickStart.ipynb | 1837 ++- .../SoundscapePerceptionIndex-SPI.ipynb | 1848 +++ docs/tutorials/example_settings.yaml | 200 +- docs/tutorials/index.md | 1 - mkdocs.yml | 107 +- pyproject.toml | 223 +- schemas/github-issue-forms.json | 2377 ++++ src/soundscapy/__init__.py | 109 +- src/soundscapy/_optionals.py | 90 - src/soundscapy/_utils.py | 49 + src/soundscapy/audio/__init__.py | 42 +- src/soundscapy/audio/analysis_settings.py | 84 +- src/soundscapy/audio/audio_analysis.py | 101 +- src/soundscapy/audio/binaural.py | 331 +- src/soundscapy/audio/metrics.py | 261 +- src/soundscapy/audio/parallel_processing.py | 41 +- src/soundscapy/data/__init__.py | 6 + src/soundscapy/data/default_settings.yaml | 200 +- src/soundscapy/databases/__init__.py | 10 + src/soundscapy/databases/araus.py | 10 +- src/soundscapy/databases/isd.py | 73 +- src/soundscapy/databases/satp.py | 19 +- src/soundscapy/plotting/__init__.py | 51 +- src/soundscapy/plotting/backends.py | 38 +- src/soundscapy/plotting/circumplex_plot.py | 883 +- src/soundscapy/plotting/likert.py | 10 +- src/soundscapy/plotting/plot_functions.py | 589 +- src/soundscapy/plotting/plotting_utils.py | 4 +- src/soundscapy/plotting/stylers.py | 16 +- .../__init__.py => src/soundscapy/py.typed | 0 src/soundscapy/spi/__init__.py | 33 + src/soundscapy/spi/_r_wrapper.py | 439 + src/soundscapy/spi/_rsn_wrapper.py | 222 + src/soundscapy/spi/ks2d.py | 403 + src/soundscapy/spi/msn.py | 566 + src/soundscapy/{logging.py => sspylogging.py} | 42 +- src/soundscapy/surveys/__init__.py | 23 +- src/soundscapy/surveys/processing.py | 232 +- src/soundscapy/surveys/survey_utils.py | 38 +- test/audio/test_analysis_settings.py | 2 +- test/audio/test_binaural.py | 2 +- test/data/Levels.json | 11620 ++++++++-------- test/databases/test_isd.py | 2 +- test/generate_baselines.py | 4 +- test/spi/test_MSN.py | 551 + test/spi/test_r_wrapper.py | 92 + test/test__optionals.py | 94 - test/test_basic.py | 39 +- test/{test_logging.py => test_sspylogging.py} | 9 +- tox.ini | 93 + uv.lock | 638 +- 90 files changed, 22675 insertions(+), 8479 deletions(-) delete mode 100644 .devcontainer/.dockerignore delete mode 100644 .devcontainer/devcontainer.json create mode 100644 .github/ISSUE_TEMPLATE/bug_report.yml create mode 100644 .github/ISSUE_TEMPLATE/config.yml create mode 100644 .github/ISSUE_TEMPLATE/documentation.yml create mode 100644 .github/ISSUE_TEMPLATE/feature_request.yml create mode 100644 .github/ISSUE_TEMPLATE/question.yml create mode 100644 .github/workflows/docs.yml create mode 100644 .github/workflows/linting.yml delete mode 100644 .github/workflows/test-tutorials.yml create mode 100644 .markdownlint.yaml create mode 100644 .pre-commit-config.yaml create mode 100644 DESCRIPTION rename LICENSE => LICENSE.md (95%) create mode 100644 docs/reference/plotting/backends.md create mode 100644 docs/reference/spi.md create mode 100644 docs/tutorials/SoundscapePerceptionIndex-SPI.ipynb create mode 100644 schemas/github-issue-forms.json delete mode 100644 src/soundscapy/_optionals.py create mode 100644 src/soundscapy/_utils.py rename test/plotting/__init__.py => src/soundscapy/py.typed (100%) create mode 100644 src/soundscapy/spi/__init__.py create mode 100644 src/soundscapy/spi/_r_wrapper.py create mode 100644 src/soundscapy/spi/_rsn_wrapper.py create mode 100644 src/soundscapy/spi/ks2d.py create mode 100644 src/soundscapy/spi/msn.py rename src/soundscapy/{logging.py => sspylogging.py} (82%) create mode 100644 test/spi/test_MSN.py create mode 100644 test/spi/test_r_wrapper.py delete mode 100644 test/test__optionals.py rename test/{test_logging.py => test_sspylogging.py} (95%) create mode 100644 tox.ini diff --git a/.devcontainer/.dockerignore b/.devcontainer/.dockerignore deleted file mode 100644 index b750bafd..00000000 --- a/.devcontainer/.dockerignore +++ /dev/null @@ -1 +0,0 @@ -../.venv \ No newline at end of file diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json deleted file mode 100644 index 0c49925c..00000000 --- a/.devcontainer/devcontainer.json +++ /dev/null @@ -1,69 +0,0 @@ -// For format details, see https://aka.ms/devcontainer.json. For config options, see the -// README at: https://github.com/devcontainers/templates/tree/main/src/ubuntu -{ - "name": "soundscapy-dev", - // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile - "image": "mcr.microsoft.com/devcontainers/base:jammy", - "features": { - "ghcr.io/jsburckhardt/devcontainer-features/uv:1": {}, - "ghcr.io/schlich/devcontainer-features/powerlevel10k:1": {}, - "ghcr.io/devcontainers-extra/features/zsh-plugins:0": {} - }, - "remoteEnv": { - "UV_LINK_MODE": "copy" - }, - - // Features to add to the dev container. More info: https://containers.dev/features. - // "features": {}, - - // Use 'forwardPorts' to make a list of ports inside the container available locally. - // "forwardPorts": [], - - // Use 'postCreateCommand' to run commands after the container is created. - // "postCreateCommand": "uname -a", - - // Configure tool-specific properties. - "customizations": { - "vscode": { - "extensions": [ - "ms-python.python", - "charliermarsh.ruff", - "REditorSupport.r", - "quarto.quarto-vscode", - "James-Yu.latex-workshop", - "nvarner.typst-lsp", - "ms-toolsai.jupyter", - "ms-toolsai.jupyter-renderers", - "ms-azuretools.vscode-docker", - "GitHub.copilot", - "ms-vscode-remote.remote-containers", - "quarto.quarto", - "flyfly6.terminal-in-status-bar" - ], - "settings": { - "r.rpath.linux": "/usr/local/bin/R", - "r.rterm.linux": "/usr/local/bin/R", - "editor.formatOnSave": true, - "r.lsp.enabled": true, - "terminal.integrated.defaultProfile.linux": "zsh", - // ruff settings - "[python]": { - "defaultInterpreterPath": "/workspace/.venv/bin/python", - "editor.formatOnSave": true, - "editor.codeActionsOnSave": { - "source.fixAll": "explicit", - "source.organizeImports": "explicit" - }, - "editor.defaultFormatter": "charliermarsh.ruff" - }, - "notebook.formatOnSave.enabled": true, - "notebook.codeActionsOnSave": { - "notebook.source.fixAll": "explicit", - "notebook.source.organizeImports": "explicit" - } - } - } - }, - // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root. - // "remoteUser": "root" -} diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 00000000..fabfee0f --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,63 @@ +--- +name: Bug Report +description: Create a Report to Help us Improve +labels: + - bug +body: + - id: describe + type: textarea + attributes: + label: Describe the Bug + description: A clear and concise description of what the bug is. + validations: + required: true + - id: reproduce + type: textarea + attributes: + label: To Reproduce + description: >- + A minimal working example of code to reproduce the unexpected behaviour, + this will render as `Python` code so no need for backticks. + value: |- + import soundscapy + + ... + render: Python + validations: + required: true + - id: expected + type: textarea + attributes: + label: Expected Behaviour + description: >- + A clear and concise description of what you expected to happen. + validations: + required: true + - id: actual + type: textarea + attributes: + label: Actual Behaviour + description: >- + Be a specific and detailed as you can. Paste any output or stack traces + of errors you receive. + validations: + required: true + - id: version + type: input + attributes: + label: Version In Use + description: |- + Can be found by + ```sh + python -c "import soundscapy; print(soundscapy.__version__)" + ``` + validations: + required: true + - id: additional + type: textarea + attributes: + label: Additional Context + value: |- + - Python version: + - Operating system: + render: Markdown diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 00000000..bd9dfe4e --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,2 @@ +--- +blank_issues_enabled: false diff --git a/.github/ISSUE_TEMPLATE/documentation.yml b/.github/ISSUE_TEMPLATE/documentation.yml new file mode 100644 index 00000000..582c191c --- /dev/null +++ b/.github/ISSUE_TEMPLATE/documentation.yml @@ -0,0 +1,27 @@ +--- +name: Documentation +description: How Can We Improve the Documentation +labels: + - documentation +body: + - id: section + type: textarea + attributes: + label: Which Section of the Documentation Needs Improving? + description: Please provide a link (if it is a specific page). + validations: + required: true + - id: problem + type: textarea + attributes: + label: What Can be Improved About This Section + description: Is it incomplete, incorrect or difficult to understand? + validations: + required: true + - id: suggestions + type: textarea + attributes: + label: How to Improve This Section + description: >- + Do you have any specific suggestions we could take to improve the + documentation? diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml new file mode 100644 index 00000000..8e1ad7fe --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -0,0 +1,34 @@ +--- +name: Feature Request +description: Suggest a Way to Improve This Project +labels: + - enhancement +body: + - id: problem + type: textarea + attributes: + label: Is Your Feature Request Related to a Problem? Please Describe + description: A clear and concise description of what the problem is. + placeholder: I'm always frustrated when [...] + validations: + required: true + - id: solution + type: textarea + attributes: + label: Describe the Solution You'd Like + description: A clear and concise description of what you want to happen. + validations: + required: true + - id: alternatives + type: textarea + attributes: + label: Describe Alternatives You've Considered + description: >- + A clear and concise description of any alternative solutions or features + you've considered. + - id: additional + type: textarea + attributes: + label: Additional Context + description: >- + Add any other context or screenshots about the feature request here. diff --git a/.github/ISSUE_TEMPLATE/question.yml b/.github/ISSUE_TEMPLATE/question.yml new file mode 100644 index 00000000..0aa99120 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/question.yml @@ -0,0 +1,24 @@ +--- +name: Question +description: General Questions About Using Soundscapy +labels: + - question +body: + - id: topic + type: dropdown + attributes: + label: What is the Topic of Your Question + description: Please indicate the topic in the title of your question. + options: + - Documentation + - Installation + - Usage + - Other + validations: + required: true + - id: question + type: textarea + attributes: + label: Add Your Question Below + validations: + required: true diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..fa2746c2 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,37 @@ +name: Documentation + +on: + workflow_call: + outputs: + docs-pass: + description: "Indicates if documentation build passed" + value: ${{ github.event.inputs.docs-pass }} + push: + branches: + - main + pull_request: + +jobs: + docs: + runs-on: ubuntu-latest + steps: + - name: Checkout source + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4 + + - name: Cache tox + uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684 # v4 + with: + path: .tox + key: tox-${{ hashFiles('pyproject.toml') }} + + - name: Set up Python + uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5 + with: + python-version: "3.x" + cache: "pip" + cache-dependency-path: "pyproject.toml" + + - name: Install tox + run: python -m pip install tox + - name: Build HTML documentation with tox + run: tox -e docs diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml new file mode 100644 index 00000000..9c410b81 --- /dev/null +++ b/.github/workflows/linting.yml @@ -0,0 +1,33 @@ +name: Linting + +on: + push: + branches: + - main + pull_request: + +jobs: + linting: + runs-on: ubuntu-latest + steps: + - name: Checkout source + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4 + + - name: Cache pre-commit + uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684 # v4 + with: + path: ~/.cache/pre-commit + key: pre-commit-${{ hashFiles('.pre-commit-config.yaml') }} + + - name: Set up python + uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5 + with: + python-version: "3.x" + cache: pip + cache-dependency-path: pyproject.toml + + - name: Install dependencies + run: python -m pip install pre-commit + + - name: Run pre-commit + run: pre-commit run --all-files --color always --verbose diff --git a/.github/workflows/tag-release.yml b/.github/workflows/tag-release.yml index 71d9030f..06bbf5e5 100644 --- a/.github/workflows/tag-release.yml +++ b/.github/workflows/tag-release.yml @@ -4,9 +4,9 @@ name: Tagged Release on: push: tags: - - "v[0-9]+.[0-9]+.[0-9]+" # v1.2.3 - - "v[0-9]+.[0-9]+.[0-9]+rc[0-9]+" # v1.2.3rc1 - - "v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+" # v1.2.3-rc1 + - "v[0-9]+.[0-9]+.[0-9]+" # v1.2.3 + - "v[0-9]+.[0-9]+.[0-9]+rc[0-9]+" # v1.2.3rc1 + - "v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+" # v1.2.3-rc1 jobs: details: @@ -37,7 +37,7 @@ jobs: echo "new_version=$NEW_VERSION" >> "$GITHUB_OUTPUT" echo "suffix=$SUFFIX" >> "$GITHUB_OUTPUT" echo "tag_name=$TAG_NAME" >> "$GITHUB_OUTPUT" - + echo "Version is $VERSION_STR" echo "Suffix is $SUFFIX" echo "Tag name is $TAG_NAME" @@ -46,29 +46,28 @@ jobs: exit 1 fi - - name: Verify version matches pyproject.toml - run: | - VERSION_STR=${{ steps.release.outputs.version_str }} - TOML_VERSION=$(grep -oP '^version = "\K[^"]+' pyproject.toml) - - if [ "${VERSION_STR}" != "${TOML_VERSION}" ]; then - echo "Error: Tag version (${VERSION_STR}) does not match pyproject.toml version (${TOML_VERSION})" - exit 1 - fi + run: | + VERSION_STR=${{ steps.release.outputs.version_str }} + TOML_VERSION=$(grep -oP '^version = "\K[^"]+' pyproject.toml) + + if [ "${VERSION_STR}" != "${TOML_VERSION}" ]; then + echo "Error: Tag version (${VERSION_STR}) does not match pyproject.toml version (${TOML_VERSION})" + exit 1 + fi setup_and_build: - needs: + needs: - details runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v4 - + - name: Install uv - uses: astral-sh/setup-uv@v3 + uses: astral-sh/setup-uv@v5 with: - version: "0.4.29" + version: "0.7.2" enable-cache: true cache-dependency-glob: "uv.lock" @@ -85,17 +84,17 @@ jobs: name: dist path: dist/ + docs-pass: + needs: [details] + uses: ./.github/workflows/docs.yml + tests-pass: needs: [details] uses: ./.github/workflows/test.yml - - tutorial-tests-pass: - needs: [details] - uses: ./.github/workflows/test-tutorials.yml pypi_publish: name: Upload release to PyPI - needs: [setup_and_build, details, tests-pass, tutorial-tests-pass] + needs: [setup_and_build, details, tests-pass, docs-pass] runs-on: ubuntu-latest permissions: id-token: write @@ -138,7 +137,7 @@ jobs: github_release: name: Create GitHub Release - needs: [setup_and_build, details, tests-pass, tutorial-tests-pass] + needs: [setup_and_build, details, tests-pass, docs-pass] runs-on: ubuntu-latest permissions: contents: write @@ -163,4 +162,4 @@ jobs: gh release create ${{ needs.details.outputs.tag_name }} dist/* --title ${{ needs.details.outputs.tag_name }} --generate-notes --prerelease else gh release create ${{ needs.details.outputs.tag_name }} dist/* --title ${{ needs.details.outputs.tag_name }} --generate-notes - fi \ No newline at end of file + fi diff --git a/.github/workflows/test-tag-release.yml b/.github/workflows/test-tag-release.yml index 762ff35a..87928429 100644 --- a/.github/workflows/test-tag-release.yml +++ b/.github/workflows/test-tag-release.yml @@ -3,9 +3,8 @@ name: Test Tagged Release on: push: tags: - - "v[0-9]+.[0-9]+.[0-9]+-dev[0-9]+" # v1.2.3-dev1 - - "v[0-9]+.[0-9]+.[0-9]+dev[0-9]+" # v1.2.3dev1 - + - "v[0-9]+.[0-9]+.[0-9]+-dev[0-9]+" # v1.2.3-dev1 + - "v[0-9]+.[0-9]+.[0-9]+dev[0-9]+" # v1.2.3dev1 jobs: # Reuse the details job with no changes @@ -34,7 +33,7 @@ jobs: echo "new_version=$NEW_VERSION" >> "$GITHUB_OUTPUT" echo "suffix=$SUFFIX" >> "$GITHUB_OUTPUT" echo "tag_name=$TAG_NAME" >> "$GITHUB_OUTPUT" - + echo "Version is $VERSION_STR" echo "Suffix is $SUFFIX" echo "Tag name is $TAG_NAME" @@ -44,14 +43,14 @@ jobs: fi - name: Verify version matches pyproject.toml - run: | - VERSION_STR=${{ steps.release.outputs.version_str }} - TOML_VERSION=$(grep -oP '^version = "\K[^"]+' pyproject.toml) - - if [ "${VERSION_STR}" != "${TOML_VERSION}" ]; then - echo "Error: Tag version (${VERSION_STR}) does not match pyproject.toml version (${TOML_VERSION})" - exit 1 - fi + run: | + VERSION_STR=${{ steps.release.outputs.version_str }} + TOML_VERSION=$(grep -oP '^version = "\K[^"]+' pyproject.toml) + + if [ "${VERSION_STR}" != "${TOML_VERSION}" ]; then + echo "Error: Tag version (${VERSION_STR}) does not match pyproject.toml version (${TOML_VERSION})" + exit 1 + fi # Reuse setup_and_build job with no changes setup_and_build: @@ -59,9 +58,10 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: astral-sh/setup-uv@v3 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - version: "0.4.29" + version: "0.7.2" enable-cache: true cache-dependency-glob: "uv.lock" - run: uv python install 3.12 @@ -75,15 +75,15 @@ jobs: tests-pass: needs: [details] uses: ./.github/workflows/test.yml - - tutorial-tests-pass: + + docs-pass: needs: [details] - uses: ./.github/workflows/test-tutorials.yml + uses: ./.github/workflows/docs.yml # Modified to publish to TestPyPI testpypi_publish: name: Upload release to TestPyPI - needs: [setup_and_build, details, tests-pass, tutorial-tests-pass] + needs: [setup_and_build, details, tests-pass, docs-pass] runs-on: ubuntu-latest permissions: id-token: write @@ -103,7 +103,7 @@ jobs: steps: - uses: actions/setup-python@v5 with: - python-version: '3.12' + python-version: "3.12" - name: Install soundscapy from TestPyPI uses: nick-fields/retry@v3 with: @@ -129,4 +129,4 @@ jobs: max_attempts: 3 retry_wait_seconds: 30 command: python -m pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ "soundscapy[all]==${{ needs.details.outputs.version_str }}" - - run: python -c "import soundscapy; print(soundscapy.__version__); from soundscapy import Binaural" \ No newline at end of file + - run: python -c "import soundscapy; print(soundscapy.__version__); from soundscapy import Binaural" diff --git a/.github/workflows/test-tutorials.yml b/.github/workflows/test-tutorials.yml deleted file mode 100644 index a6ab48be..00000000 --- a/.github/workflows/test-tutorials.yml +++ /dev/null @@ -1,42 +0,0 @@ -name: Test Tutorial Notebooks - -on: - workflow_call: - outputs: - tutorial-tests-pass: - description: "Indicates if all tutorial tests passed" - value: ${{ github.event.inputs.tutorial-tests-pass }} - push: - branches: [ main ] - pull_request: - branches: [ main ] - -jobs: - test: - name: Run tests on Tutorial notebooks - runs-on: ubuntu-latest - strategy: - matrix: - python-version: ['3.10', '3.11', '3.12'] - - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - - name: Install uv - uses: astral-sh/setup-uv@v3 - with: - # install a specific version of uv - version: "0.4.29" - enable-cache: true - cache-dependency-glob: "uv.lock" - - - name: Setup Python ${{ matrix.python-version }} - run: uv python install ${{ matrix.python-version }} - - - name: Install optional dependencies - run: uv sync --all-extras - - - name: Run tests for tutorial notebooks - run: uv run pytest --nbmake -n=auto docs --ignore=docs/tutorials/BinauralAnalysis.ipynb --no-cov # BinauralAnalysis is too slow - \ No newline at end of file diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index e458e2f0..edee3029 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -1,52 +1,85 @@ name: Test on: + workflow_run: + workflows: ["Linting"] + types: + - completed + branches: [main, dev] workflow_call: outputs: tests-pass: description: "Indicates if all tests passed" value: ${{ github.event.inputs.tests-pass }} push: - branches: [ main, dev ] + branches: [main, dev] + paths-ignore: + - "**.md" pull_request: - branches: [ main, dev ] + paths-ignore: + - "**.md" + branches: [main, dev] + +concurrency: + group: test-${{ github.ref }} + cancel-in-progress: true jobs: test: - name: Run tests runs-on: ubuntu-latest + if: ${{ github.event.workflow_run.conclusion == 'success' || github.event_name == 'pull_request' || github.event_name == 'push' }} + name: Test with ${{ matrix.factor }} on Python ${{ matrix.python-version }} + + outputs: + tests-pass: ${{ steps.test-results.outputs.result }} + strategy: + fail-fast: false matrix: - python-version: ['3.10', '3.11', '3.12'] + python-version: ["3.10", "3.11", "3.12"] + factor: [core, audio, spi, all, tutorials] steps: - - name: Checkout repository - uses: actions/checkout@v4 + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + version: "0.7.2" + enable-cache: true + cache-dependency-glob: "uv.lock" - - name: Install uv - uses: astral-sh/setup-uv@v3 - with: - # install a specific version of uv - version: "0.4.29" - enable-cache: true - cache-dependency-glob: "uv.lock" + - name: Setup R environment + uses: r-lib/actions/setup-r@v2 + with: + r-version: "4.4" + use-public-rspm: true - - name: Setup Python ${{ matrix.python-version }} - run: uv python install ${{ matrix.python-version }} + - name: Install R dependencies + uses: r-lib/actions/setup-r-dependencies@v2 + with: + cache-version: 1 + dependencies: '"all"' - - name: Lint and format check # Run linting before wasting time on testing - run: | - uvx ruff check . - uvx ruff format --check + - name: Restore tox cache + uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684 # v4 + with: + path: .tox + key: tox-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.factor }}-${{ hashFiles('pyproject.toml') }} - - name: Install core dependencies - run: uv sync --extra test + - name: Setup Python ${{ matrix.python-version }} + run: uv python install ${{ matrix.python-version }} - - name: Run tests for core deps only - run: uv run pytest + - name: Install tox + run: uv tool install tox --with tox-uv - - name: Install optional dependencies - run: uv sync --all-extras + # - uses: fawazahmed0/action-debug-vscode@main - - name: Run tests for all dependencies - run: uv run pytest + # Convert Python version (e.g., 3.10) to format needed for tox (e.g., py310) + - name: Run tox environment + id: test-results + run: | + py_version="py$(echo '${{ matrix.python-version }}' | tr -d '.')" + tox r -e ${py_version}-${{ matrix.factor }} + echo "result=true" >> $GITHUB_OUTPUT diff --git a/.gitignore b/.gitignore index fe2475d1..9b85e1b5 100644 --- a/.gitignore +++ b/.gitignore @@ -162,26 +162,21 @@ dmypy.json *.code-workspace # End of https://www.toptal.com/developers/gitignore/api/python,jupyternotebooks,vscode -/.vscode -/.idea +/.vscode/ +/.idea/ .vscode/settings.json -Cultural-comparison-analysis.ipynb -soundscapy_old/archive -/notebooks/ -.idea/.name -.idea/misc.xml -.idea/Soundscapy.iml -.idea/vcs.xml -.idea/workspace.xml -soundscapy_old/.DS_Store *.DS_Store -/pyproject-poetry.toml -/docs/_autosummary/ -!/.pytest_cache/ -!/.vscode/ -.pdm-python + # Ignore everything in tests/test_audio_files except .wav files tests/test_audio_files/* !tests/test_audio_files/*.wav /docs/tutorials/updated_config.yaml docs/tutorials/acoustic_analysis_results.xlsx + +/scratch/ +CLAUDE.local.md +/.pytest_cache/ +/.ruff_cache/ + +# package version +*_version.py diff --git a/.markdownlint.yaml b/.markdownlint.yaml new file mode 100644 index 00000000..dcd84a13 --- /dev/null +++ b/.markdownlint.yaml @@ -0,0 +1,4 @@ +--- +MD013: false +MD024: + siblings_only: true diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..dbddb0fe --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,56 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.11.2 + hooks: + # TODO(MitchellAcoustics): Enable linting pre-commit hooks + # https://github.com/MitchellAcoustics/Soundscapy/issues/114 + # - id: ruff + - id: ruff-format + - repo: https://github.com/igorshubovych/markdownlint-cli + rev: v0.44.0 + hooks: + - id: markdownlint-fix + args: + - --dot + - repo: https://github.com/Lucas-C/pre-commit-hooks + rev: v1.5.5 + hooks: + - id: forbid-tabs + - repo: https://github.com/pappasam/toml-sort + rev: v0.24.2 + hooks: + - id: toml-sort-fix + # TODO(MitchellAcoustics): Enable linting pre-commit hooks + # https://github.com/MitchellAcoustics/Soundscapy/issues/114 + # - repo: https://github.com/pre-commit/mirrors-mypy + # rev: v1.15.0 + # hooks: + # - id: mypy + - repo: https://github.com/rbubley/mirrors-prettier + rev: v3.5.3 + hooks: + - id: prettier + args: + - --quote-props=as-needed + exclude: ^docs/.*\.md$ + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: check-case-conflict + - id: check-docstring-first + - id: check-merge-conflict + - id: check-toml + - id: end-of-file-fixer + - id: mixed-line-ending + args: + - --fix=lf + - id: trailing-whitespace + - repo: https://github.com/python-jsonschema/check-jsonschema + rev: 0.32.1 + hooks: + # Schemas taken from https://www.schemastore.org/json/ + - id: check-jsonschema + name: "Validate GitHub issue templates" + files: ^\.github/ISSUE_TEMPLATE/.*\.yml$ + exclude: ^\.github/ISSUE_TEMPLATE/config\.yml$ + args: ["--verbose", "--schemafile", "schemas/github-issue-forms.json"] diff --git a/.readthedocs.yaml b/.readthedocs.yaml index c02a9d2a..5f6905be 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -21,7 +21,7 @@ build: # Build documentation in the "docs/" directory with Sphinx mkdocs: - configuration: mkdocs.yml + configuration: mkdocs.yml # Optionally build your docs in additional formats such as PDF and ePub formats: diff --git a/CHANGELOG.md b/CHANGELOG.md index cf69895f..bb053ee6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -10,8 +10,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Changed - Improved handling of optional dependencies to provide better error messages and IDE support -- Audio components (like `Binaural`) can now be imported directly from the top-level package - (`from soundscapy import Binaural`) while maintaining helpful error messages when +- Audio components (like `Binaural`) can now be imported directly from the top-level package + (`from soundscapy import Binaural`) while maintaining helpful error messages when dependencies are missing - Centralized optional dependency configuration in `_optionals.py` for better maintainability @@ -19,8 +19,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - No changes required to existing code using audio components - The new system provides better IDE completion support while maintaining the same runtime behavior -- Optional components can still be imported from their original location - (`from soundscapy.audio import Binaural`) or from the top level +- Optional components can still be imported from their original location + (`from soundscapy.audio import Binaural`) or from the top level (`from soundscapy import Binaural`) ## [0.7.5] @@ -47,15 +47,15 @@ pip install soundscapy[all] #### Dev Container Configuration -* Added a new `devcontainer.json` file to configure the development container with specific features and VSCode extensions. (`.devcontainer/devcontainer.json` [.devcontainer/devcontainer.jsonR1-R69](diffhunk://#diff-24ad71c8613ddcf6fd23818cb3bb477a1fb6d83af4550b0bad43099813088686R1-R69)) -* Updated `.dockerignore` to exclude the virtual environment directory. (`.devcontainer/.dockerignore` [.devcontainer/.dockerignoreR1](diffhunk://#diff-7691e653179b9ed2292151d962426f76e6f5378e4989e741859bdfcbcef16b97R1)) +- Added a new `devcontainer.json` file to configure the development container with specific features and VSCode extensions. (`.devcontainer/devcontainer.json` [.devcontainer/devcontainer.jsonR1-R69](diffhunk://#diff-24ad71c8613ddcf6fd23818cb3bb477a1fb6d83af4550b0bad43099813088686R1-R69)) +- Updated `.dockerignore` to exclude the virtual environment directory. (`.devcontainer/.dockerignore` [.devcontainer/.dockerignoreR1](diffhunk://#diff-7691e653179b9ed2292151d962426f76e6f5378e4989e741859bdfcbcef16b97R1)) -#### GitHub Workflows: +#### GitHub Workflows -* Removed old CI, release, and test-release workflows. (`.github/workflows/ci.yml` [[1]](diffhunk://#diff-b803fcb7f17ed9235f1e5cb1fcd2f5d3b2838429d4368ae4c57ce4436577f03fL1-L40) `.github/workflows/release.yml` [[2]](diffhunk://#diff-87db21a973eed4fef5f32b267aa60fcee5cbdf03c67fafdc2a9b553bb0b15f34L1-L33) `.github/workflows/test-release.yml` [[3]](diffhunk://#diff-191bb5b4e97db48c9d0bdb945dd00e17b53249422f60a642e9e8d73250b5913aL1-L53) -* Added a new workflow for tagged releases to automate the release process, including building and publishing to PyPI and TestPyPI. (`.github/workflows/tag-release.yml` [.github/workflows/tag-release.ymlR1-R138](diffhunk://#diff-21e1251c1676ed10064d2d98ab1a8f6471a9718058bd316970abe934169f2b60R1-R138)) -* Added a new workflow for testing tagged releases, including installation from TestPyPI and running tests. (`.github/workflows/test-tag-release.yml` [.github/workflows/test-tag-release.ymlR1-R114](diffhunk://#diff-11b7dedbf7b09ab5a0bd90aa70d8a2eda1918dab64a511c82104706cfa09f3b7R1-R114)) -* Added new workflows for running tests on the main codebase and tutorial notebooks. (`.github/workflows/test.yml` [[1]](diffhunk://#diff-faff1af3d8ff408964a57b2e475f69a6b7c7b71c9978cccc8f471798caac2c88R1-R52) `.github/workflows/test-tutorials.yml` [[2]](diffhunk://#diff-01bd86ab14c3e8d7d1382e5ed2172404eb7d3c46bbffeffe09fc11431885e2a0R1-R42) +- Removed old CI, release, and test-release workflows. (`.github/workflows/ci.yml` [[1]](diffhunk://#diff-b803fcb7f17ed9235f1e5cb1fcd2f5d3b2838429d4368ae4c57ce4436577f03fL1-L40) `.github/workflows/release.yml` [[2]](diffhunk://#diff-87db21a973eed4fef5f32b267aa60fcee5cbdf03c67fafdc2a9b553bb0b15f34L1-L33) `.github/workflows/test-release.yml` [[3]](diffhunk://#diff-191bb5b4e97db48c9d0bdb945dd00e17b53249422f60a642e9e8d73250b5913aL1-L53) +- Added a new workflow for tagged releases to automate the release process, including building and publishing to PyPI and TestPyPI. (`.github/workflows/tag-release.yml` [.github/workflows/tag-release.ymlR1-R138](diffhunk://#diff-21e1251c1676ed10064d2d98ab1a8f6471a9718058bd316970abe934169f2b60R1-R138)) +- Added a new workflow for testing tagged releases, including installation from TestPyPI and running tests. (`.github/workflows/test-tag-release.yml` [.github/workflows/test-tag-release.ymlR1-R114](diffhunk://#diff-11b7dedbf7b09ab5a0bd90aa70d8a2eda1918dab64a511c82104706cfa09f3b7R1-R114)) +- Added new workflows for running tests on the main codebase and tutorial notebooks. (`.github/workflows/test.yml` [[1]](diffhunk://#diff-faff1af3d8ff408964a57b2e475f69a6b7c7b71c9978cccc8f471798caac2c88R1-R52) `.github/workflows/test-tutorials.yml` [[2]](diffhunk://#diff-01bd86ab14c3e8d7d1382e5ed2172404eb7d3c46bbffeffe09fc11431885e2a0R1-R42) ## [0.7.3] @@ -70,6 +70,7 @@ Complete refactoring of `Soundscapy`, splitting it into multiple modules (`surve ### General Changes #### Added + - New `soundscapy/surveys/survey_utils.py` for shared utilities - Implemented `PAQ` enum for Perceptual Attribute Questions - Added `return_paqs` function for filtering PAQ columns @@ -95,6 +96,7 @@ Complete refactoring of `Soundscapy`, splitting it into multiple modules (`surve - New test cases in `test_isd.py` to cover refactored functionality #### Changed + - Modified default logging level to WARNING for better control over log output - Refactored `isd.py` to use new processing and survey utility functions - Updated `load`, `load_zenodo`, and `validate` functions @@ -109,6 +111,7 @@ Complete refactoring of `Soundscapy`, splitting it into multiple modules (`surve - Changed to Rye as the dependency and environment manager for the project #### Improved + - Enhanced error handling and input validation in database modules - Added type hints to all functions for better code readability and IDE support - Implemented more specific exception handling @@ -119,21 +122,25 @@ Complete refactoring of `Soundscapy`, splitting it into multiple modules (`surve - Standardized coding style across all modules (using Black formatter) #### Deprecated + - Removed `remove_lockdown` function in `isd.py` (redundant since the release of ISD v1.0) #### Removed + - Eliminated redundant code and unused functions across modules #### Fixed + - Resolved issues with inconsistent PAQ naming conventions - Fixed bugs in ISO coordinate calculations and SSM metric computations - Resolved issue where Jupyter notebooks were overriding the default log level - #### Security + - Implemented input validation to prevent potential security vulnerabilities #### Development + - Implemented a more robust logging system using loguru - Added ability to easily change log levels for debugging and development - Enabled file logging for persistent log storage @@ -142,6 +149,7 @@ Complete refactoring of `Soundscapy`, splitting it into multiple modules (`surve - Implemented consistent error messages and logging across the package #### Documentation + - Added comprehensive docstrings to all functions and classes - Included usage examples in function docstrings - Updated README with new package structure and usage instructions @@ -149,39 +157,39 @@ Complete refactoring of `Soundscapy`, splitting it into multiple modules (`surve ### Changes to Plotting Module -#### Code Structure: +#### Code Structure - Split the original circumplex.py into multiple files: backends.py, circumplex_plot.py, plot_functions.py, stylers.py, and plotting_utils.py (implied). - Introduced abstract base class PlotBackend and concrete implementations SeabornBackend and PlotlyBackend. -#### New Features: +#### New Features - Added support for Plotly backend alongside Seaborn. - Introduced CircumplexPlot class for creating and managing plots. - Added StyleOptions dataclass for better style management. - Implemented simple_density plot type. -#### Improved Customization: +#### Improved Customization - Created CircumplexPlotParams dataclass for better parameter management. - Added more customization options for plots (e.g., incl_outline, fill, alpha). -#### Enhancements: +#### Enhancements - Improved type hinting throughout the codebase. - Added docstrings to classes and functions. - Implemented PlotType and Backend enums for better type safety. -#### Refactoring: +#### Refactoring - Moved plotting logic from functions to methods in backend classes. - Simplified scatter and density functions by leveraging CircumplexPlot class. -#### Removed Features: +#### Removed Features - Removed jointplot function (marked as TODO in CircumplexPlot class). -#### Constants and Utilities: +#### Constants and Utilities - Moved constants (e.g., DEFAULT_XLIM, DEFAULT_YLIM) to a separate utilities file. - Created ExtraParams TypedDict for additional plotting parameters. diff --git a/CITATION.cff b/CITATION.cff index e9c017db..39ccb3d9 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -12,17 +12,17 @@ authors: family-names: Mitchell email: a.j.mitchell@ucl.ac.uk affiliation: University College London - orcid: 'https://orcid.org/0000-0003-0978-5046' -repository-code: 'https://github.com/MitchellAcoustics/Soundscapy' -url: 'https://soundscapy.readthedocs.io/en/latest/' -repository: 'https://pypi.org/project/soundscapy/' + orcid: "https://orcid.org/0000-0003-0978-5046" +repository-code: "https://github.com/MitchellAcoustics/Soundscapy" +url: "https://soundscapy.readthedocs.io/en/latest/" +repository: "https://pypi.org/project/soundscapy/" keywords: - soundscape - acoustics - psychoacoustics license: BSD-3-Clause version: 0.7.4 -date-released: '2024-10-17' +date-released: "2024-10-17" # preferred-citation: # type: article # authors: diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index abe9ea8f..82203767 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -4,7 +4,7 @@ - Use the `uv` tool for managing dependencies and other project tasks. `uv add` and `uv remove` should be used to add or remove dependencies. `uv add --optional ` should be used to add an optional dependency. `uv sync # add --all-extras, etc as needed` should be used to install dependencies and sync with lock file. `uv build` should be used to build the package. - Try to keep all necessary configurations to `pyproject.toml` where possible. This includes versioning, optional dependencies, tool settings (e.g. `bumpver`) and other project settings. -- Wherever possible, centralise operations and metadata. For instance, version is defined in `pyproject.toml` and automatically brought into `soundscapy` metadata in `__init__.py`; optional dependency groups are defined in `_optionals.py` and checked once at the `.__init__.py` level, rather than for each individual function or at the `soundscapy.__init__.py` level. +- Wherever possible, centralise operations and metadata. For instance, version is defined in `pyproject.toml` and automatically brought into `soundscapy` metadata in `__init__.py`; optional dependency checks are performed at the `.__init__.py` level, rather than for each individual function. Changes should be made in a feature branch and submitted to `dev` via a pull request. The pull request should be reviewed by at least one other developer before being merged. The `main` branch should only contain stable releases. Docs can be updated directly on `dev` or `main` as needed. @@ -41,24 +41,24 @@ The settings for `bumpver` are stored in `pyproject.toml`. 2. **Minor Version**: - - Incremented for new features or significant changes - - Reset to 0 for major versions + - Incremented for new features or significant changes + - Reset to 0 for major versions 3. **Patch Version**: - - Incremented for bug fixes or minor changes - - Reset to 0 for new minor versions + - Incremented for bug fixes or minor changes + - Reset to 0 for new minor versions 4. **Pre-release Versions**: - - Use `rc` for release candidates - - Use `dev` for development versions + - Use `rc` for release candidates + - Use `dev` for development versions ### Commit messages Try to use the [Angular commit message format](https://github.com/angular/angular.js/blob/master/DEVELOPERS.md#-git-commit-guidelines) for commit messages. Mostly this means starting the commit message with a type, followed by a colon and a short description. For example: -``` txt +```txt feat: add new feature fix: correct bug in feature docs: update documentation @@ -77,29 +77,11 @@ The avilable types are: ## Optional Dependencies System -### Core Components - -1. **Dependency Definitions** (`_optionals.py`): +Soundscapy uses a simple and standard approach to handle optional dependencies. - ```python - # Package dependencies - OPTIONAL_DEPENDENCIES = { - "audio": { - "packages": ("mosqito", "maad", "acoustic_toolbox"), - "install": "soundscapy[audio]", - "description": "audio analysis functionality", - }, - } - - # Top-level imports available when dependencies are installed - OPTIONAL_IMPORTS = { - 'Binaural': ('soundscapy.audio', 'Binaural'), - 'AudioAnalysis': ('soundscapy.audio', 'AudioAnalysis'), - # ... other optional components - } - ``` +### Core Components -2. **Package Configuration** (`pyproject.toml`): +1. **Package Configuration** (`pyproject.toml`): ```toml [project.optional-dependencies] @@ -110,18 +92,45 @@ The avilable types are: ] ``` -3. **Module-Level Dependency Check** (`audio/__init__.py`): +2. **Module-Level Dependency Check** (`audio/__init__.py`): ```python - from soundscapy._optionals import require_dependencies - - # This will raise an ImportError if dependencies are missing - required = require_dependencies("audio") + # Check for required dependencies directly + try: + import mosqito + import maad + import tqdm + import acoustic_toolbox + except ImportError as e: + raise ImportError( + "Audio analysis functionality requires additional dependencies. " + "Install with: pip install soundscapy[audio]" + ) from e # Now import module components from .binaural import Binaural ``` +3. **Top-Level Imports** (`soundscapy/__init__.py`): + + ```python + # Try to import optional audio module + try: + from soundscapy import audio + from soundscapy.audio import ( + Binaural, AudioAnalysis, AnalysisSettings, ConfigManager, + process_all_metrics, prep_multiindex_df, add_results, parallel_process, + ) + __all__.extend([ + "audio", "Binaural", "AudioAnalysis", "AnalysisSettings", + "ConfigManager", "process_all_metrics", "prep_multiindex_df", + "add_results", "parallel_process", + ]) + except ImportError: + # Audio module not available - this is expected if dependencies aren't installed + pass + ``` + ### Adding New Optional Features #### 1. Add New Dependencies @@ -136,40 +145,21 @@ uv add new-package --optional audio uv add package1 package2 --optional new_group ``` -#### 2. Update Dependency Definitions - -In `_optionals.py`, update both dependency mappings: - -```python -OPTIONAL_DEPENDENCIES = { - "audio": { - "packages": ("mosqito", "maad", "acoustic_toolbox", "new_package"), # Add to existing - "install": "soundscapy[audio]", - "description": "audio analysis functionality", - }, - "new_group": { # Or create new group - "packages": ("package1", "package2"), - "install": "soundscapy[new_group]", - "description": "description of functionality", - }, -} - -OPTIONAL_IMPORTS = { - # Existing imports... - 'NewFeature': ('soundscapy.new_group', 'NewFeature'), # Add new top-level imports -} -``` - -#### 3. Implement Feature Code +#### 2. Implement Feature Code Create a new module directory if needed (e.g., `new_group/`) with an `__init__.py`: ```python """Module docstring describing the new functionality.""" -from soundscapy._optionals import require_dependencies - -# This will raise an ImportError if dependencies are missing -required = require_dependencies("new_group") +# Check dependencies directly +try: + import package1 + import package2 +except ImportError as e: + raise ImportError( + "This functionality requires additional dependencies. " + "Install with: pip install soundscapy[new_group]" + ) from e # Now import your feature code from .feature import NewFeature @@ -177,39 +167,36 @@ from .feature import NewFeature __all__ = ["NewFeature"] ``` -#### 4. Add to Top-Level Exports +#### 3. Add to Top-Level Exports -If you want the new feature to be available at the top level, add it to `__all__` in `soundscapy/__init__.py`: +Update the main `__init__.py` to import and expose the new module: ```python -__all__ = [ - # Core modules... - # Optional modules - "NewFeature", # Add new feature here -] +# Try to import optional new_group module +try: + from soundscapy import new_group + from soundscapy.new_group import NewFeature + __all__.extend(["new_group", "NewFeature"]) +except ImportError: + # new_group module not available - expected if dependencies aren't installed + pass ``` ### How It Works -The system provides three levels of dependency handling: +The system uses standard Python try/except patterns at two levels: -1. **Module Level**: The `require_dependencies()` check in the optional module's `__init__.py` ensures dependencies are available before the module is imported. +1. **Module Level**: Each optional module checks for its dependencies on import and raises a helpful error if they're missing. -2. **Top Level Imports**: `__getattr__` in the main `__init__.py` enables importing optional components directly from `soundscapy` with proper error handling: - - ```python - from soundscapy import Binaural # Works with deps, helpful error without - ``` - -3. **IDE Support**: The explicit `__all__` list in `__init__.py` provides IDE autocompletion while maintaining proper runtime behavior. +2. **Top Level**: The main package tries to import optional modules and their components, extending **all** only when available. Benefits: - Clear error messages when dependencies are missing -- Optional components available at both module and package level +- Standard Python import patterns that are easy to understand - Good IDE support through explicit exports -- Centralized dependency configuration - No runtime overhead for unused optional features +- Simpler to maintain and extend ### Testing Optional Dependencies @@ -217,41 +204,93 @@ Soundscapy uses a flexible system for testing optional dependencies that allows #### Test Structure -Optional dependency tests exist at three levels: +Optional dependency tests exist at several levels: 1. **Optional Module Tests**: Tests within optional modules (e.g., `audio/`) - - Only collected when dependencies are available + + - Only collected when dependencies are available using pytest_ignore_collect - Test actual functionality - - No need for special markers or mocking + - No need for special markers 2. **Integration Tests**: Tests that use optional features from other modules + - Use `@pytest.mark.optional_deps('group')` marker - - Skip when dependencies unavailable + - Expected to fail when dependencies are unavailable - Test actual integration between components +3. **In-Development Module Tests**: For modules under active development (e.g., `spi/`) + - Use module-level `pytestmark = pytest.mark.skip(reason="...")` to skip all tests + - Prevent pytest collection errors by using `--ignore=src/soundscapy/module/` flag + - Simplifies testing during development when module imports might fail + +#### Testing with Tox + +Soundscapy's tox configuration provides separate environments for different dependency groups: + +```bash +# Run core tests (no optional dependencies) +tox -e py310-core + +# Run with audio dependencies +tox -e py310-audio + +# Run with SPI dependencies +tox -e py310-spi + +# Run with all dependencies +tox -e py310-all +``` + +Each environment is configured to run the appropriate tests: + +- Core: Run only tests with no optional dependency requirements +- Audio: Run core tests and audio-specific tests, skipping SPI tests +- SPI: Run core tests and SPI-specific tests +- All: Run all tests + +Test selection is implemented using pytest's keyword-based filtering to precisely target the right tests: + +```python +# Core tests only +pytest -k "not optional_deps" + +# Core + audio tests +pytest -k "not optional_deps or optional_deps and audio" + +# Core + SPI tests +pytest -k "not optional_deps or optional_deps and spi" +``` #### When to Use Each Testing Approach 1. **Use `@pytest.mark.optional_deps` when**: + - Testing actual functionality that requires dependencies - Writing integration tests - Testing with real package interactions 2. **No special handling needed when**: - - Writing tests within an optional module + + - Writing tests within an optional module directory - Testing core functionality that doesn't use optional features +3. **Use module-level skip markers when**: + - Working on a module that is not yet ready for testing + - Dependencies might cause import errors during collection + - You want to include tests in the codebase but skip their execution + ### Adding Tests for New Optional Features When adding new optional features: 1. **Inside Optional Module**: - - Put tests in the module's test directory - - No special handling needed - - Tests will only run when dependencies are available + + - Put tests in the module's test directory (e.g., `test/new_group/`) + - Tests will only be collected when dependencies are available + - No markers needed for tests within the module's directory ```python - # soundscapy/new_group/tests/test_feature.py + # test/new_group/test_feature.py def test_new_feature(): """Regular test, no special handling needed.""" from soundscapy.new_group import NewFeature @@ -259,6 +298,7 @@ When adding new optional features: ``` 2. **Integration Tests**: + - Use the optional_deps marker - Put in main test directory @@ -266,7 +306,7 @@ When adding new optional features: # test/test_integration.py @pytest.mark.optional_deps('new_group') def test_new_feature_integration(): - """Will skip if dependencies missing.""" + """Will be marked as expected to fail if dependencies missing.""" from soundscapy import NewFeature assert NewFeature.integrate() == expected ``` @@ -277,7 +317,16 @@ Soundscapy has three primary workflows: `test.yml`, `test-tutorials.yml` and `ta In all cases, python and dependencies are managed and installed with `uv`. -`test.yml` will test on multiple python versions, defined by the `python-version` matrix. First, it will install the core dependencies with `uv sync`, then run the test suite (which **should** ignore the tests requiring optional dependencies). Then, it will install all optional dependencies `uv sync --all-extras` and run the tests again. This ensures that the tests run with and without optional dependencies. +`test.yml` uses tox to test across multiple Python versions and dependency combinations. The workflow has a two-stage approach: + +1. **Linting**: First, it runs ruff checks for code quality and formatting. + +2. **Testing**: Then, it runs tox with different environments: + - **Core**: Tests with just core dependencies using `py{310,311,312}-core` + - **Audio**: Tests with audio dependencies using `py{310,311,312}-audio` + - **All**: Tests with all dependencies using `py{310,311,312}-all` + +This approach ensures consistent testing between local development (using tox locally) and CI environments, while verifying that the package works correctly with different Python versions and dependency combinations. `test-tutorials.yml` uses `--nbmake` to convert the notebooks to python files and run them. This is useful for testing the tutorials and ensuring they are up to date. It does not test the veracity of the outputs, just whether the notebooks run without errors. diff --git a/DESCRIPTION b/DESCRIPTION new file mode 100644 index 00000000..a96a5ac2 --- /dev/null +++ b/DESCRIPTION @@ -0,0 +1,12 @@ +Package: Soundscapy +Type: Package +Title: Soundscapy +Version: 0.8.0 +Author: Andrew Mitchell +Maintainer: Andrew Mitchell +Description: A python library for analysing and visualising soundscape assessments. +License: BSD 3-Clause +Encoding: UTF-8 +LazyData: true +Imports: + sn diff --git a/LICENSE b/LICENSE.md similarity index 95% rename from LICENSE rename to LICENSE.md index 089e1dcf..c3ee14c0 100644 --- a/LICENSE +++ b/LICENSE.md @@ -1,6 +1,8 @@ + + BSD 3-Clause License -Copyright (c) 2024, Andrew Mitchell +Copyright (c) 2025, Andrew Mitchell All rights reserved. Redistribution and use in source and binary forms, with or without diff --git a/README.md b/README.md index ae6f4d85..580f4a3b 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ - +![Soundscapy Logo](https://raw.githubusercontent.com/MitchellAcoustics/Soundscapy/main/docs/img/LightLogo.png) # Soundscapy -[![PyPI version](https://badge.fury.io/py/soundscapy.svg)](https://badge.fury.io/py/soundscapy) +[![PyPI version](https://badge.fury.io/py/soundscapy.svg)](https://badge.fury.io/py/soundscapy) [![Tests](https://github.com/MitchellAcoustics/Soundscapy/actions/workflows/test.yml/badge.svg)](https://github.com/MitchellAcoustics/Soundscapy/actions/workflows/test.yml) [![Documentation Status](https://readthedocs.org/projects/soundscapy/badge/?version=latest)](https://soundscapy.readthedocs.io/en/latest/?badge=latest) ![License](https://img.shields.io/github/license/MitchellAcoustics/Soundscapy) @@ -35,8 +35,6 @@ To install all optional dependencies, use the following command: pip install "soundscapy[all]" ``` - - ## Examples We are currently working on writing more comprehensive examples and documentation, please bear with us in the meantime. @@ -47,15 +45,15 @@ Tutorials for using Soundscapy can be found in the [documentation](https://sound The newly added Binaural analysis functionality relies directly on three acoustic analysis libraries: -* [Acoustic Toolbox](https://github.com/Universite-Gustave-Eiffel/acoustic-toolbox) for the standard environmental and building acoustics metrics, -* [scikit-maad](https://github.com/scikit-maad/scikit-maad) for the bioacoustics and ecological soundscape metrics, and -* [MoSQITo](https://github.com/Eomys/MoSQITo) for the psychoacoustics metrics. We thank each of these packages for their great work in making advanced acoustic analysis more accessible. +- [Acoustic Toolbox](https://github.com/Universite-Gustave-Eiffel/acoustic-toolbox) for the standard environmental and building acoustics metrics, +- [scikit-maad](https://github.com/scikit-maad/scikit-maad) for the bioacoustics and ecological soundscape metrics, and +- [MoSQITo](https://github.com/Eomys/MoSQITo) for the psychoacoustics metrics. We thank each of these packages for their great work in making advanced acoustic analysis more accessible. ## Citation If you are using Soundscapy in your research, please help our scientific visibility by citing our work! Please include a citation to our accompanying paper: -Mitchell, A., Aletta, F., & Kang, J. (2022). How to analyse and represent quantitative soundscape data. *JASA Express Letters, 2*, 37201. [https://doi.org/10.1121/10.0009794](https://doi.org/10.1121/10.0009794) +Mitchell, A., Aletta, F., & Kang, J. (2022). How to analyse and represent quantitative soundscape data. _JASA Express Letters, 2_, 37201. [https://doi.org/10.1121/10.0009794](https://doi.org/10.1121/10.0009794) + ![Image title](img/LightLogoSmall.png#only-light) ![Image title](img/DarkLogoSmall.png#only-dark) @@ -10,7 +12,7 @@ _Soundscapy_ is a Python library for analysing and visualising soundscape assessments. This package was designed to (1) load and process soundscape assessment data, (2) visualise the data, and (3) enable psychoacoustic analysis of soundscape recordings. !!! note - This project is still under development. We're working hard to make it as good as possible, but there may be bugs or missing features. If you find any issues, please let us know by submitting an issue on Github. +This project is still under development. We're working hard to make it as good as possible, but there may be bugs or missing features. If you find any issues, please let us know by submitting an issue on Github. ## Getting Started @@ -43,22 +45,24 @@ We welcome contributions from the community. If you're interested in contributin ## Citing Soundscapy !!! note - If you use _Soundscapy_ in your research, please include a citation to our accompanying paper: - - Mitchell, A., Aletta, F., & Kang, J. (2022). How to analyse and represent quantitative soundscape data. _JASA Express Letters, 2_, 37201. [https://doi.org/10.1121/10.0009794](https://doi.org/10.1121/10.0009794) +If you use _Soundscapy_ in your research, please include a citation to our accompanying paper: + + Mitchell, A., Aletta, F., & Kang, J. (2022). How to analyse and represent quantitative soundscape data. _JASA Express Letters, 2_, 37201. [https://doi.org/10.1121/10.0009794](https://doi.org/10.1121/10.0009794) ## License -This project is licensed under the GNU GPLv3 License. For more information, please see the `license.md` file. +This project is licensed under the BSD 3-Clause License. For more information, please see the `license.md` file. ## Project layout - mkdocs.yml # The configuration file. - docs/ - index.md # The documentation homepage. - about.md # The about page. - license.md # The license page. - tutorials/ # Tutorial pages. - Introduction to SSM Analysis.ipynb - ... # Other markdown pages, images and other files. - src/soundscapy/ +```plaintext +mkdocs.yml # The configuration file. +docs/ + index.md # The documentation homepage. + about.md # The about page. + license.md # The license page. + tutorials/ # Tutorial pages. + Introduction to SSM Analysis.ipynb + ... # Other markdown pages, images and other files. +src/soundscapy/ +``` diff --git a/docs/javascripts/katex.js b/docs/javascripts/katex.js index 2ab434fc..0946ce0a 100644 --- a/docs/javascripts/katex.js +++ b/docs/javascripts/katex.js @@ -1,10 +1,10 @@ document$.subscribe(({ body }) => { renderMathInElement(body, { delimiters: [ - { left: "$$", right: "$$", display: true }, - { left: "$", right: "$", display: false }, + { left: "$$", right: "$$", display: true }, + { left: "$", right: "$", display: false }, { left: "\\(", right: "\\)", display: false }, - { left: "\\[", right: "\\]", display: true } + { left: "\\[", right: "\\]", display: true }, ], - }) -}) \ No newline at end of file + }); +}); diff --git a/docs/javascripts/mathjax.js b/docs/javascripts/mathjax.js index 0b00d2ff..5209b3c1 100644 --- a/docs/javascripts/mathjax.js +++ b/docs/javascripts/mathjax.js @@ -3,17 +3,17 @@ window.MathJax = { inlineMath: [["\\(", "\\)"]], displayMath: [["\\[", "\\]"]], processEscapes: true, - processEnvironments: true + processEnvironments: true, }, options: { ignoreHtmlClass: ".*|", - processHtmlClass: "arithmatex" - } + processHtmlClass: "arithmatex", + }, }; document$.subscribe(() => { - MathJax.startup.output.clearCache() - MathJax.typesetClear() - MathJax.texReset() - MathJax.typesetPromise() -}) \ No newline at end of file + MathJax.startup.output.clearCache(); + MathJax.typesetClear(); + MathJax.texReset(); + MathJax.typesetPromise(); +}); diff --git a/docs/license.md b/docs/license.md index da3d61b8..75b899af 100644 --- a/docs/license.md +++ b/docs/license.md @@ -1,29 +1,3 @@ -## BSD 3-Clause License + -Copyright (c) 2024, Andrew Mitchell -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file +{! include-markdown "../LICENSE.md" !} diff --git a/docs/news.md b/docs/news.md index 47b25909..aa8a1e98 100644 --- a/docs/news.md +++ b/docs/news.md @@ -1,4 +1,4 @@ - +# News ## [2024-08-15] Significant Enhancements to Soundscapy's Plotting Module @@ -21,7 +21,7 @@ We have introduced a new `CircumplexPlot` class that serves as the central mecha While we've significantly expanded the capabilities of our plotting module, we've maintained a focus on user accessibility. We now offer two primary interfaces for plot creation: - Function-based Interface: The `scatter_plot()` and `density_plot()` functions remain available and have been optimized to leverage the new CircumplexPlot class internally. These functions offer a straightforward method for creating standard plots with minimal code. -- Class-based Interface: For users requiring more advanced customization, the `CircumplexPlot` class provides direct access to a wide array of plotting options and methods. +- Class-based Interface: For users requiring more advanced customization, the `CircumplexPlot` class provides direct access to a wide array of plotting options and methods. This dual approach ensures that both newcomers and advanced users can efficiently create the visualizations they need. @@ -42,12 +42,14 @@ Our development roadmap includes several exciting features: It's important to note that this update introduces breaking changes that will require modifications to existing code. The primary areas affected are: - Import statements: The module structure has changed, necessitating updates to import statements. For example: - ```python - from soundscapy.plotting import scatter_plot, density_plot, Backend - ``` + + ```python + from soundscapy.plotting import scatter_plot, density_plot, Backend + ``` + - Function names and parameters: Some function names and their parameters have been modified for consistency and clarity. Please refer to the updated documentation for specific changes. - Class-based interface: If you were previously using lower-level plotting functions, you may need to transition to the new CircumplexPlot class for advanced customizations. We strongly recommend reviewing the updated documentation thoroughly when upgrading to this new version. While these changes may require some code adjustments, we believe the improved functionality and flexibility justify the effort. -We are confident that these improvements to the Soundscapy plotting module will significantly enhance your ability to create insightful and visually appealing soundscape visualizations. We look forward to seeing the innovative ways in which our user community will leverage these new capabilities. \ No newline at end of file +We are confident that these improvements to the Soundscapy plotting module will significantly enhance your ability to create insightful and visually appealing soundscape visualizations. We look forward to seeing the innovative ways in which our user community will leverage these new capabilities. diff --git a/docs/reference/api.md b/docs/reference/api.md index 10919bea..572af2da 100644 --- a/docs/reference/api.md +++ b/docs/reference/api.md @@ -1,8 +1,4 @@ # Core functions ::: soundscapy - show_submodules: true - - - - +show_submodules: true diff --git a/docs/reference/audio.md b/docs/reference/audio.md index 2d2bcf53..7d11610e 100644 --- a/docs/reference/audio.md +++ b/docs/reference/audio.md @@ -2,21 +2,22 @@ This section provides an overview of the binaural analysis tools available in Soundscapy. It includes a brief description of each tool, as well as information on how to access and use them. -::: soundscapy.audio.analysis_settings - options: - show_root_heading: false - show_root_toc_entry: false - ## Binaural Metrics ::: soundscapy.audio.binaural - show_submodules: true +show_submodules: true ::: soundscapy.audio.metrics - show_submodules: true +show_submodules: true + +## Analysis Settings + +::: soundscapy.audio.analysis_settings +options: +show_root_heading: false +show_root_toc_entry: false ## Parallel Processing ::: soundscapy.audio.parallel_processing - show_submodules: true - +show_submodules: true diff --git a/docs/reference/databases.md b/docs/reference/databases.md index 075201d3..7024a15a 100644 --- a/docs/reference/databases.md +++ b/docs/reference/databases.md @@ -3,8 +3,8 @@ ## International Soundscape Database (ISD) ::: soundscapy.databases.isd - options: - filters: ["!ISDAccessor", "!_"] +options: +filters: ["!ISDAccessor", "!_"] ## Soundscape Attributes Translation Project (SATP) diff --git a/docs/reference/plotting.md b/docs/reference/plotting.md index ce594e77..70777d79 100644 --- a/docs/reference/plotting.md +++ b/docs/reference/plotting.md @@ -1,28 +1,16 @@ # Plotting -::: soundscapy.plotting.plot_functions - -## Circumplex Plotting - -::: soundscapy.plotting.circumplex_plot - show_submodules: true - -## Backends - -::: soundscapy.plotting.backends - show_submodules: true +::: soundscapy.plotting + options: + members: + - backends + - circumplex_plot + - likert + - plotting_utils + - stylers -## Stylers +## Plotting Functions -::: soundscapy.plotting.stylers - show_submodules: true - -## Plotting Utilities - -::: soundscapy.plotting.plotting_utils - show_submodules: true - -## Likert Scale Plotting - -::: soundscapy.plotting.likert - show_submodules: true \ No newline at end of file +::: soundscapy.plotting.plot_functions + options: + show_submodules: true diff --git a/docs/reference/plotting/backends.md b/docs/reference/plotting/backends.md new file mode 100644 index 00000000..620177b9 --- /dev/null +++ b/docs/reference/plotting/backends.md @@ -0,0 +1,4 @@ +# Backends + +::: soundscapy.plotting.backends +show_submodules: true diff --git a/docs/reference/spi.md b/docs/reference/spi.md new file mode 100644 index 00000000..7fdd791d --- /dev/null +++ b/docs/reference/spi.md @@ -0,0 +1,9 @@ +# Soundscape Perception Indices (SPI) Reference + +This section provides an overview of the tools for using the Soundscape Perception Indices (SPI) framework. It includes a brief description of each tool, as well as information on how to access and use them. + +::: soundscapy.spi +options: +show_root_heading: false +show_root_toc_entry: false +show_submodules: true diff --git a/docs/reference/surveys.md b/docs/reference/surveys.md index 8748ed30..5b6acd1b 100644 --- a/docs/reference/surveys.md +++ b/docs/reference/surveys.md @@ -1,11 +1,11 @@ # Survey Analysis -This section provides an overview of the survey instruments used in soundscape research. It includes a brief description of each instrument, as well as information on how to access and use them. +This section provides an overview of the survey instruments used in soundscape research. It includes a brief description of each instrument, as well as information on how to access and use them. ::: soundscapy.surveys.processing - options: - show_submodules: true +options: +show_submodules: true ::: soundscapy.surveys.survey_utils - options: - show_submodules: true \ No newline at end of file +options: +show_submodules: true diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css index 18b5dd80..05ae4bd3 100644 --- a/docs/stylesheets/extra.css +++ b/docs/stylesheets/extra.css @@ -4,7 +4,6 @@ --md-accent-color: #71e5e9; } - /* This will only apply if the user has chosen a light color scheme */ @media (prefers-color-scheme: light) { .only-dark { @@ -25,4 +24,4 @@ display: block; margin: auto; } -} \ No newline at end of file +} diff --git a/docs/tutorials/HowToAnalyseAndRepresentSoundscapes.ipynb b/docs/tutorials/HowToAnalyseAndRepresentSoundscapes.ipynb index e4214483..55d67aea 100644 --- a/docs/tutorials/HowToAnalyseAndRepresentSoundscapes.ipynb +++ b/docs/tutorials/HowToAnalyseAndRepresentSoundscapes.ipynb @@ -37,12 +37,13 @@ "outputs": [], "source": [ "# imports\n", - "import soundscapy as sspy\n", - "import pandas as pd\n", + "import warnings\n", + "\n", "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", "import seaborn as sns\n", "\n", - "import warnings\n", + "import soundscapy as sspy\n", "\n", "warnings.simplefilter(\"ignore\")" ] @@ -97,6 +98,85 @@ "outputs": [ { "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "RecordID", + "rawType": "object", + "type": "string" + }, + { + "name": "pleasant", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vibrant", + "rawType": "int64", + "type": "integer" + }, + { + "name": "eventful", + "rawType": "int64", + "type": "integer" + }, + { + "name": "chaotic", + "rawType": "int64", + "type": "integer" + }, + { + "name": "annoying", + "rawType": "int64", + "type": "integer" + }, + { + "name": "monotonous", + "rawType": "int64", + "type": "integer" + }, + { + "name": "uneventful", + "rawType": "int64", + "type": "integer" + }, + { + "name": "calm", + "rawType": "int64", + "type": "integer" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "1285050e-6e9b-40e6-b753-df76a1b42e93", + "rows": [ + [ + "EX1", + "4", + "4", + "4", + "2", + "1", + "3", + "3", + "4" + ], + [ + "EX2", + "2", + "3", + "5", + "5", + "5", + "5", + "3", + "1" + ] + ], + "shape": { + "columns": 8, + "rows": 2 + } + }, "text/html": [ "
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LocationIDSessionIDGroupIDRecordIDstart_timeend_timelatitudelongitudeLanguageSurvey_Version...THD_THD_MaxTHD_Min_MaxTHD_Max_MaxTHD_L5_MaxTHD_L10_MaxTHD_L50_MaxTHD_L90_MaxTHD_L95_MaxISOPleasantISOEventful
0CarloVCarloV22CV1214342019-05-16 18:46:002019-05-16 18:56:0037.17685-3.590392engengISO2018...-0.09-11.7654.1834.8226.535.57-9.0-10.290.315-0.083
1CarloVCarloV22CV1214352019-05-16 18:46:002019-05-16 18:56:0037.17685-3.590392engengISO2018...-0.09-11.7654.1834.8226.535.57-9.0-10.29-0.3370.529
2CarloVCarloV22CV1314302019-05-16 19:02:002019-05-16 19:12:0037.17685-3.590392engengISO2018...-2.10-19.3272.5232.3324.520.25-16.3-17.330.7640.075
3CarloVCarloV22CV1314312019-05-16 19:02:002019-05-16 19:12:0037.17685-3.590392engengISO2018...-2.10-19.3272.5232.3324.520.25-16.3-17.330.716-0.019
4CarloVCarloV22CV1314322019-05-16 19:02:002019-05-16 19:12:0037.17685-3.590392engengISO2018...-2.10-19.3272.5232.3324.520.25-16.3-17.330.594-0.042
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5 rows × 144 columns

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" + ], + "text/plain": [ + " LocationID SessionID GroupID RecordID start_time \\\n", + "0 CarloV CarloV2 2CV12 1434 2019-05-16 18:46:00 \n", + "1 CarloV CarloV2 2CV12 1435 2019-05-16 18:46:00 \n", + "2 CarloV CarloV2 2CV13 1430 2019-05-16 19:02:00 \n", + "3 CarloV CarloV2 2CV13 1431 2019-05-16 19:02:00 \n", + "4 CarloV CarloV2 2CV13 1432 2019-05-16 19:02:00 \n", + "\n", + " end_time latitude longitude Language Survey_Version ... \\\n", + "0 2019-05-16 18:56:00 37.17685 -3.590392 eng engISO2018 ... \n", + "1 2019-05-16 18:56:00 37.17685 -3.590392 eng engISO2018 ... \n", + "2 2019-05-16 19:12:00 37.17685 -3.590392 eng engISO2018 ... \n", + "3 2019-05-16 19:12:00 37.17685 -3.590392 eng engISO2018 ... \n", + "4 2019-05-16 19:12:00 37.17685 -3.590392 eng engISO2018 ... \n", + "\n", + " THD_THD_Max THD_Min_Max THD_Max_Max THD_L5_Max THD_L10_Max \\\n", + "0 -0.09 -11.76 54.18 34.82 26.53 \n", + "1 -0.09 -11.76 54.18 34.82 26.53 \n", + "2 -2.10 -19.32 72.52 32.33 24.52 \n", + "3 -2.10 -19.32 72.52 32.33 24.52 \n", + "4 -2.10 -19.32 72.52 32.33 24.52 \n", + "\n", + " THD_L50_Max THD_L90_Max THD_L95_Max ISOPleasant ISOEventful \n", + "0 5.57 -9.0 -10.29 0.315 -0.083 \n", + "1 5.57 -9.0 -10.29 -0.337 0.529 \n", + "2 0.25 -16.3 -17.33 0.764 0.075 \n", + "3 0.25 -16.3 -17.33 0.716 -0.019 \n", + "4 0.25 -16.3 -17.33 0.594 -0.042 \n", + "\n", + "[5 rows x 144 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for lang in data.Language.unique():\n", + " angles = LANGUAGE_ANGLES[lang]\n", + "\n", + " lang_idx = data.query(f\"Language == '{lang}'\").index\n", + " iso_pl, iso_ev = sspy.surveys.processing.calculate_iso_coords(\n", + " data.loc[lang_idx, PAQ_IDS], (1, 5), angles\n", + " )\n", + " data.loc[lang_idx, \"ISOPleasant\"] = round(iso_pl, 3)\n", + " data.loc[lang_idx, \"ISOEventful\"] = round(iso_ev, 3)\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9a561944", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitted from data. n = 96\n", + "Direct Parameters:\n", + "xi: [0.06 0.597]\n", + "omega: [[ 0.15 -0.058]\n", + " [-0.058 0.093]]\n", + "alpha: [ 0.87 -0.558]\n", + "\n", + "\n", + "Centred Parameters:\n", + "mean: [0.281 0.447]\n", + "sigma: [[ 0.101 -0.025]\n", + " [-0.025 0.07 ]]\n", + "skew: [ 0.146 -0.078]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ct = data.query(\"LocationID == 'SanMarco'\")\n", + "x = ct[\"ISOPleasant\"].values\n", + "y = ct[\"ISOEventful\"].values\n", + "\n", + "msn = MultiSkewNorm()\n", + "msn.fit(data=ct[[\"ISOPleasant\", \"ISOEventful\"]])\n", + "msn.summary()\n", + "msn.sspy_plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "254fdcb6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitted from direct parameters.\n", + "Direct Parameters:\n", + "xi: [0.065 0.629]\n", + "omega: [[ 0.149 -0.064]\n", + " [-0.064 0.101]]\n", + "alpha: [ 0.791 -0.767]\n", + "\n", + "\n", + "Centred Parameters:\n", + "mean: [0.283 0.451]\n", + "sigma: [[ 0.102 -0.026]\n", + " [-0.026 0.07 ]]\n", + "skew: [ 0.136 -0.131]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "msn2 = MultiSkewNorm()\n", + "msn2.define_dp(\n", + " xi=np.array([0.06534, 0.628637]),\n", + " omega=np.array([[0.14890315, -0.06423752], [-0.06423752, 0.10139612]]),\n", + " alpha=np.array([0.79105, -0.767217]),\n", + ")\n", + "msn2.sample(n=1000, return_sample=True)\n", + "msn2.summary()\n", + "msn2.sspy_plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74ee3dde", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.5)", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/example_settings.yaml b/docs/tutorials/example_settings.yaml index 93a79159..3c18843c 100644 --- a/docs/tutorials/example_settings.yaml +++ b/docs/tutorials/example_settings.yaml @@ -7,120 +7,120 @@ version: "1.1" # Supported metrics: LAeq, LZeq, LCeq, SEL # Supported stats: avg/mean, max, min, kurt, skew, any integer from 1-99 AcousticToolbox: - # A-weighted equivalent continuous sound level - LAeq: - run: true # Set to false to skip this metric - main: "avg" # Main statistic to calculate - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] # List of statistics to calculate - channel: ["Left", "Right"] # Channels to analyze - label: "LAeq" # Label for the metric in output - func_args: # Additional arguments for the metric function - time: 0.125 # Time interval for calculation (seconds) - method: "average" # Method for calculating Leq + # A-weighted equivalent continuous sound level + LAeq: + run: true # Set to false to skip this metric + main: "avg" # Main statistic to calculate + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] # List of statistics to calculate + channel: ["Left", "Right"] # Channels to analyze + label: "LAeq" # Label for the metric in output + func_args: # Additional arguments for the metric function + time: 0.125 # Time interval for calculation (seconds) + method: "average" # Method for calculating Leq - # Z-weighted (unweighted) equivalent continuous sound level - LZeq: - run: true - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "LZeq" - func_args: - time: 0.125 - method: "average" + # Z-weighted (unweighted) equivalent continuous sound level + LZeq: + run: true + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "LZeq" + func_args: + time: 0.125 + method: "average" - # C-weighted equivalent continuous sound level - LCeq: - run: true - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "LCeq" - func_args: - time: 0.125 - method: "average" + # C-weighted equivalent continuous sound level + LCeq: + run: true + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "LCeq" + func_args: + time: 0.125 + method: "average" - # Sound Exposure Level - SEL: - run: true - channel: ["Left", "Right"] - label: "SEL" - # Note: SEL doesn't use main or statistics as it's a single value metric + # Sound Exposure Level + SEL: + run: true + channel: ["Left", "Right"] + label: "SEL" + # Note: SEL doesn't use main or statistics as it's a single value metric # MoSQITo Library Settings # Supported metrics: loudness_zwtv, sharpness_din_from_loudness, sharpness_din_perseg, sharpness_din_tv, roughness_dw # Supported statistics: avg/mean, max, min, kurt, skew, any integer from 1-99 MoSQITo: - # Zwicker Time-Varying Loudness - loudness_zwtv: - run: false # Disabled by default as it's used in sharpness calculation - main: 5 # N5 (loudness exceeded 5% of the time) - statistics: [10, 50, 90, 95, "min", "max", "kurt", "skew", "avg"] - channel: ["Left", "Right"] - label: "N" - parallel: true # Enable parallel processing - func_args: - field_type: "free" # Free field condition + # Zwicker Time-Varying Loudness + loudness_zwtv: + run: false # Disabled by default as it's used in sharpness calculation + main: 5 # N5 (loudness exceeded 5% of the time) + statistics: [10, 50, 90, 95, "min", "max", "kurt", "skew", "avg"] + channel: ["Left", "Right"] + label: "N" + parallel: true # Enable parallel processing + func_args: + field_type: "free" # Free field condition - # Sharpness (DIN 45692) calculated from Zwicker Loudness - sharpness_din_from_loudness: - run: false - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "S_L" - parallel: true - func_args: - weighting: "din" # DIN 45692 weighting - field_type: "free" + # Sharpness (DIN 45692) calculated from Zwicker Loudness + sharpness_din_from_loudness: + run: false + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "S_L" + parallel: true + func_args: + weighting: "din" # DIN 45692 weighting + field_type: "free" - # Sharpness (DIN 45692) calculated per segment - sharpness_din_perseg: - run: false - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "S_perseg" - parallel: false # Parallel processing not necessary for this method - func_args: - weighting: "din" - nperseg: 4096 # Number of samples per segment - field_type: "free" + # Sharpness (DIN 45692) calculated per segment + sharpness_din_perseg: + run: false + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "S_perseg" + parallel: false # Parallel processing not necessary for this method + func_args: + weighting: "din" + nperseg: 4096 # Number of samples per segment + field_type: "free" - # Sharpness (DIN 45692) time-varying - # Note: It's recommended to use sharpness_din_from_loudness instead - sharpness_din_tv: - run: false - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "S_din_tv" - parallel: true - func_args: - weighting: "din" - field_type: "free" - skip: 0.5 # Skip time at the beginning (seconds) + # Sharpness (DIN 45692) time-varying + # Note: It's recommended to use sharpness_din_from_loudness instead + sharpness_din_tv: + run: false + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "S_din_tv" + parallel: true + func_args: + weighting: "din" + field_type: "free" + skip: 0.5 # Skip time at the beginning (seconds) - # Roughness (Daniel & Weber method) - roughness_dw: - run: false - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "R" - parallel: true + # Roughness (Daniel & Weber method) + roughness_dw: + run: false + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "R" + parallel: true # scikit-maad Library Settings # These are collections of multiple acoustic indices scikit-maad: - # Temporal alpha diversity indices - all_temporal_alpha_indices: - run: true - channel: ["Left", "Right"] - # Note: This calculates multiple indices, so no individual statistics are specified + # Temporal alpha diversity indices + all_temporal_alpha_indices: + run: true + channel: ["Left", "Right"] + # Note: This calculates multiple indices, so no individual statistics are specified - # Spectral alpha diversity indices - all_spectral_alpha_indices: - run: true - channel: ["Left", "Right"] - # Note: This calculates multiple indices, so no individual statistics are specified + # Spectral alpha diversity indices + all_spectral_alpha_indices: + run: true + channel: ["Left", "Right"] + # Note: This calculates multiple indices, so no individual statistics are specified diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 2648a050..274258f3 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -1,4 +1,3 @@ # Tutorials Each of the tutorials provided will walk you through the basic functionality of _Soundscapy_ as well as the basic concepts of Soundscape analysis. - diff --git a/mkdocs.yml b/mkdocs.yml index a14e2322..c25ca9ee 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -2,36 +2,57 @@ site_name: "Soundscapy" site_url: https://soundscapy.readthedocs.io/en/latest/ site_dir: site +site_author: "Andrew Mitchell" +site_description: "Documentation website for Soundscapy" repo_name: "MitchellAcoustics/Soundscapy" repo_url: https://github.com/MitchellAcoustics/Soundscapy -copyright: Copyright © 2024 Andrew Mitchell +copyright: Copyright © 2025 Andrew Mitchell + +validation: + omitted_files: warn + absolute_links: warn + unrecognized_links: warn + nav: - - Home: index.md - - About: - - 'License': license.md - - Tutorials: - - tutorials/index.md - - '`Soundscapy` - Quick Start': tutorials/QuickStart.ipynb - - 'How To Analyse and Represent Soundscape Perception': tutorials/HowToAnalyseAndRepresentSoundscapes.ipynb - - 'Using Soundscapy for Binaural Recording Analysis': tutorials/BinauralAnalysis.ipynb - - 'API reference': - - 'Survey Analysis': reference/surveys.md - - 'Plotting': reference/plotting.md - - 'Binaural Analysis': reference/audio.md - - 'Databases': reference/databases.md - - 'News': + - Home: index.md + - About: + - "License": license.md + - Tutorials: + - tutorials/index.md + - "Quick Start": tutorials/QuickStart.ipynb + - "How To Analyse and Represent Soundscape Perception": tutorials/HowToAnalyseAndRepresentSoundscapes.ipynb + - "The Soundscape Perception Index (SPI)": tutorials/SoundscapePerceptionIndex-SPI.ipynb + - "Using Soundscapy for Binaural Recording Analysis": tutorials/BinauralAnalysis.ipynb + - "API reference": + - "Core": reference/api.md + - "Survey Analysis": reference/surveys.md + - "Plotting": + - reference/plotting.md + - reference/plotting/backends.md + - "Soundscape Perception Index (SPI)": reference/spi.md + - "Binaural Analysis": reference/audio.md + - "Databases": reference/databases.md + - "News": - news.md - - 'Changelog': CHANGELOG.md + - "Changelog": CHANGELOG.md + theme: name: material -# logo: img/DarkLogo.png + # logo: img/DarkLogo.png features: - navigation.tabs - navigation.expand - navigation.path - navigation.top + - content.action.edit + icon: + repo: fontawesome/brands/github palette: # Palette toggle for light mode + - media: "(prefers-color-scheme)" + toggle: + icon: material/brightness-auto + name: Switch to light mode - media: "(prefers-color-scheme: light)" scheme: default toggle: @@ -43,37 +64,51 @@ theme: scheme: slate toggle: icon: material/brightness-4 - name: Switch to light mode + name: Switch to system preference + extra_css: - stylesheets/extra.css - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.css plugins: - - search - - mkdocstrings: - default_handler: python - handlers: - python: - paths: [.] - options: - docstring_section_style: spacy - docstring_style: "numpy" - separate_signature: true - show_if_no_docstring: false - merge_init_into_class: true - show_symbol_type_heading: true # waiting for general release - show_symbol_type_toc: true - - mkdocs-jupyter: - include_source: true - theme: default + - search + - mkdocstrings: + default_handler: python + handlers: + python: + paths: [.] + options: + docstring_section_style: spacy + docstring_style: "numpy" + separate_signature: true + show_if_no_docstring: false + merge_init_into_class: true + show_symbol_type_heading: true # waiting for general release + show_symbol_type_toc: true + modernize_annotations: true + summary: true + inventories: + - "https://docs.python.org/3/objects.inv" + - include-markdown: + opening_tag: "{!" + closing_tag: "!}" + - mkdocs-jupyter: + include_source: true + theme: default # execute: true markdown_extensions: - admonition + - pymdownx.tasklist - pymdownx.arithmatex: - generic: true + generic: true extra_javascript: - javascripts/katex.js - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js + +extra: + social: + - icon: fontawesome/brands/github + link: "https://github.com/MitchellAcoustics" diff --git a/pyproject.toml b/pyproject.toml index 3348e788..8cdd7cfa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,137 +1,156 @@ +[build-system] +build-backend = "setuptools.build_meta" +requires = ["setuptools-scm>=8", "setuptools>=64"] + +[dependency-groups] +dev = [ + "build>=1.2.2.post1", + "mypy>=1.15.0", + "pandas-stubs>=2.2.3.250308", + "pre-commit>=4.2.0", + "ruff>=0.7.2", + "scipy-stubs>=1.15.2.2", + "setuptools-scm>=8.3.1", + "tox>=4.25.0", + "twine>=6.1.0", + "types-pyyaml>=6.0.12.20250402", +] +docs = [ + "ipywidgets>=8.1.3", + "jupyter-dash>=0.4.2", + "jupyter>=1.1.1", + "mkdocs-include-markdown-plugin>=7.1.5", + "mkdocs-jupyter>=0.24.8", + "mkdocs-material>=9.5.31", + "mkdocs>=1.6.0", + "mkdocstrings[python]>=0.25.2", + "pymdown-extensions>=10.9", +] +test = [ + "nbmake>=1.5.4", + "pytest-cov>=6.0.0", + "pytest-mpl>=0.17.0", + "pytest-xdist>=3.6.1", + "pytest>=8.3.3", + "setuptools>=72.1.0", + "xdoctest[all]>=1.1.6", +] + [project] -name = "soundscapy" -version = "0.7.9dev0" -description = "A python library for analysing and visualising soundscape assessments." authors = [ - { name = "Andrew Mitchell", email = "a.j.mitchell@ucl.ac.uk" } + {email = "mitchellacoustics15@gmail.com", name = "Andrew Mitchell"}, +] +classifiers = [ + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Typing :: Typed", + "Operating System :: OS Independent", + "License :: OSI Approved :: BSD License", ] dependencies = [ + "loguru>=0.7.2", + "numpy!=1.26", "pandas[excel]>=2.2.2", - "seaborn>=0.13.2", "plotly>=5.23.0", - "scipy>=1.14.1", - "pyyaml>=6.0.2", "pydantic>=2.8.2", - "loguru>=0.7.2", - "numpy!=1.26", + "pyyaml>=6.0.2", + "scipy>=1.14.1", + "seaborn>=0.13.2", ] +description = "A python library for analysing and visualising soundscape assessments." +dynamic = ["version"] +keywords = ["acoustics", "audio analysis", "psychoacoustics", "soundscape"] +license = {text = "BSD-3-Clause"} +name = "soundscapy" readme = "README.md" requires-python = ">= 3.10" -license = { text = "BSD-3-Clause" } -keywords = [ - "soundscape", - "psychoacoustics", - "acoustics", - "audio analysis", -] -classifiers = [ - "Programming Language :: Python :: 3", - "Operating System :: OS Independent", - "License :: OSI Approved :: BSD License", -] - -[project.urls] -repository = "https://github.com/MitchellAcoustics/Soundscapy" -documentation = "https://soundscapy.readthedocs.io/en/latest/" [project.optional-dependencies] -all = [ - "soundscapy[audio]", -] +all = ["soundscapy[audio]", "soundscapy[spi]"] audio = [ + "acoustic-toolbox>=0.1.2", "mosqito>=1.2.1", + "numba>=0.59", "scikit-maad>=1.4.3", "tqdm>=4.66.5", - "numba>=0.59", - "acoustic-toolbox>=0.1.2", ] +spi = ["rpy2>=3.5.0"] -[tool.uv] -default-groups = ["dev", "test", "docs"] +[project.urls] +documentation = "https://soundscapy.readthedocs.io/en/latest/" +repository = "https://github.com/MitchellAcoustics/Soundscapy" -[dependency-groups] -dev = [ - "bumpver>=2023.1129", - "ruff>=0.7.2", -] -test = [ - "pytest>=8.3.3", - "setuptools>=72.1.0", - "nbmake>=1.5.4", - "pytest-xdist>=3.6.1", - "xdoctest[all]>=1.1.6", - "pytest-mpl>=0.17.0", - "pytest-cov>=6.0.0", -] -docs = [ - "jupyter>=1.1.1", - "mkdocs>=1.6.0", - "mkdocs-material>=9.5.31", - "mkdocs-jupyter>=0.24.8", - "mkdocstrings[python]>=0.25.2", - "pymdown-extensions>=10.9", - "ipywidgets>=8.1.3", - "jupyter-dash>=0.4.2", -] +[tool.coverage] +report = {sort = "cover"} +run = {branch = true, parallel = true, source = ["soundscapy"]} +paths.source = ["src", ".tox*/*/lib/python*/site-packages"] -[tool.uv.pip] -universal = true +[tool.mypy] +explicit_package_bases = true [tool.pytest.ini_options] -addopts = "-v --tb=short --durations=5 --xdoctest -n 6 --cov=src/soundscapy --cov-report=term" -testpaths = ["test", "src/soundscapy"] -python_files = "test_*.py" -python_classes = "Test*" -python_functions = "test_*" -console_output_style = "count" +addopts = ["--color=yes", "--import-mode=importlib", "--verbose", "--xdoctest"] doctest_optionflags = "NUMBER NORMALIZE_WHITESPACE IGNORE_EXCEPTION_DETAIL" markers = [ "optional_deps(group): mark tests that depend on optional dependencies. group can be 'audio', etc.", - "slow: mark test as slow", + "parametrize: mark test as parametrized", "skip: mark test as skipped", "skipif: mark test as skipped if condition is met", + "slow: mark test as slow", "xfail: mark test as expected to fail", - "parametrize: mark test as parametrized" ] +python_classes = "Test*" +python_files = "test_*.py" +python_functions = "test_*" +testpaths = ["src/soundscapy", "test"] -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" +[tool.ruff] +fix = true +force-exclude = true +lint.ignore = [ + "COM812", # trailing commas (ruff-format recommended) + "D203", # no-blank-line-before-class + "D212", # multi-line-summary-first-line + "D407", # removed dashes lines under sections + "D417", # argument description in docstring (unreliable) + "FIX002", # fixme (ruff-format recommended) + "ISC001", # simplify implicit str concatenation (ruff-format recommended) + "PLR0913", # too many arguments +] +lint.per-file-ignores = {"test*" = [ + "INP001", # File is part of an implicit namespace package. + "S101", # Use of `assert` detected +]} +lint.select = ["ALL"] +lint.isort.known-first-party = ["soundscapy"] +lint.mccabe.max-complexity = 18 +lint.pep8-naming.classmethod-decorators = ["classmethod"] -[tool.hatch.metadata] -allow-direct-references = true +[tool.setuptools.package-data] +soundscapy = ["*.csv", "*.yaml", "py.typed"] -[tool.hatch.build.targets.wheel] -packages = ["src/soundscapy"] -exclude = [ - "test/data", - "test/test_audio_files", - "*.wav", - "test/baseline", - "docs/tutorials", - "docs/img" -] +[tool.setuptools.packages.find] +where = ["src"] -[tool.hatch.build.targets.sdist] -exclude = [ - "test/test_audio_files", - "test/data", - "*.wav", - "docs/tutorials", - "docs/img" -] +[tool.setuptools_scm] +local_scheme = "no-local-version" +write_to = "src/soundscapy/_version.py" -[tool.bumpver] -current_version = "v0.7.9dev0" -version_pattern = "vMAJOR.MINOR.PATCH[[-]PYTAGNUM]" -commit_message = "bump version {old_version} -> {new_version}" -tag_message = "{new_version}" -commit = true -tag = true -push = true +[tool.tomlsort] +all = true +spaces_indent_inline_array = 4 +trailing_comma_inline_array = true +overrides."project.classifiers".inline_arrays = false +overrides."tool.coverage.paths.source".inline_arrays = false +overrides."tool.tox.env.docs.commands".inline_arrays = false +overrides."tool.tox.env_run_base.commands".inline_arrays = false -[tool.bumpver.file_patterns] -"pyproject.toml" = [ - '^current_version = "{version}"', - '^version = "{pep440_version}"', -] +[tool.uv] +default-groups = ["dev", "docs", "test"] + +[tool.uv.pip] +universal = true diff --git a/schemas/github-issue-forms.json b/schemas/github-issue-forms.json new file mode 100644 index 00000000..b890ff13 --- /dev/null +++ b/schemas/github-issue-forms.json @@ -0,0 +1,2377 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + + "$id": "https://json.schemastore.org/github-issue-forms.json", + + "$comment": "https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms", + + "additionalProperties": false, + + "definitions": { + "type": { + "description": "A form item type\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#keys", + + "type": "string", + + "enum": ["checkboxes", "dropdown", "input", "markdown", "textarea"] + }, + + "id": { + "type": "string", + + "pattern": "^[a-zA-Z0-9_-]+$", + + "examples": ["SampleId"] + }, + + "validations": { + "title": "validation options", + + "type": "object", + + "properties": { + "required": { + "description": "Specify whether require a form item", + + "type": "boolean", + + "default": false + } + }, + + "additionalProperties": false + }, + + "assignee": { + "type": "string", + + "maxLength": 39, + + "pattern": "^[a-zA-Z0-9](-?[a-zA-Z0-9])*$", + + "examples": ["SampleAssignee"] + }, + + "label": { + "type": "string", + + "minLength": 1, + + "examples": ["Sample label"] + }, + + "description": { + "type": "string", + + "default": "", + + "examples": ["Sample description"] + }, + + "placeholder": { + "type": "string", + + "default": "", + + "examples": ["Sample placeholder"] + }, + + "value": { + "type": "string", + + "minLength": 1, + + "examples": ["Sample value"] + }, + + "form_item": { + "title": "form item", + + "description": "A form item\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#about-githubs-form-schema", + + "type": "object", + + "required": ["type"], + + "properties": { + "type": { + "$ref": "#/definitions/type" + } + }, + + "allOf": [ + { + "if": { + "properties": { + "type": { + "const": "markdown" + } + } + }, + + "then": { + "$comment": "For `additionalProperties` to work `type` must also be present here.", + + "title": "markdown", + + "description": "Markdown\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#markdown", + + "type": "object", + + "required": ["type", "attributes"], + + "properties": { + "type": { + "$ref": "#/definitions/type" + }, + + "attributes": { + "title": "markdown attributes", + + "description": "Markdown attributes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes", + + "type": "object", + + "required": ["value"], + + "properties": { + "value": { + "description": "A markdown code\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes", + + "type": "string", + + "minLength": 1, + + "examples": ["Sample code"] + } + }, + + "additionalProperties": false + } + }, + + "additionalProperties": false + } + }, + + { + "if": { + "properties": { + "type": { + "const": "textarea" + } + } + }, + + "then": { + "$comment": "For `additionalProperties` to work `type` must also be present here.", + + "title": "textarea", + + "description": "Textarea\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#textarea", + + "type": "object", + + "required": ["type", "attributes"], + + "properties": { + "type": { + "$ref": "#/definitions/type" + }, + + "id": { + "$ref": "#/definitions/id", + + "description": "A textarea id\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#keys" + }, + + "attributes": { + "title": "textarea attributes", + + "description": "Textarea attributes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1", + + "type": "object", + + "required": ["label"], + + "properties": { + "label": { + "$ref": "#/definitions/label", + + "description": "A short textarea description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1" + }, + + "description": { + "$ref": "#/definitions/description", + + "description": "A long textarea description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1" + }, + + "placeholder": { + "$ref": "#/definitions/placeholder", + + "description": "A textarea placeholder\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1" + }, + + "value": { + "$ref": "#/definitions/value", + + "description": "A textarea value\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1" + }, + + "render": { + "description": "A textarea syntax highlighting mode\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1", + + "type": "string", + + "enum": [ + "1C Enterprise", + + "4D", + + "ABAP CDS", + + "ABAP", + + "ABNF", + + "AFDKO", + + "AGS Script", + + "AIDL", + + "AL", + + "AMPL", + + "ANTLR", + + "API Blueprint", + + "APL", + + "ASL", + + "ASN.1", + + "ASP.NET", + + "ATS", + + "ActionScript", + + "Ada", + + "Alloy", + + "Alpine Abuild", + + "Altium Designer", + + "AngelScript", + + "Ant Build System", + + "ApacheConf", + + "Apex", + + "Apollo Guidance Computer", + + "AppleScript", + + "Arc", + + "AsciiDoc", + + "AspectJ", + + "Assembly", + + "Astro", + + "Asymptote", + + "Augeas", + + "AutoHotkey", + + "AutoIt", + + "AutoIt3", + + "AutoItScript", + + "Avro IDL", + + "Awk", + + "BASIC", + + "Ballerina", + + "Batchfile", + + "Beef", + + "Befunge", + + "BibTeX", + + "Bicep", + + "Bison", + + "BitBake", + + "Blade", + + "BlitzBasic", + + "BlitzMax", + + "Boo", + + "Boogie", + + "Brainfuck", + + "Brightscript", + + "Browserslist", + + "C", + + "C#", + + "C++", + + "C-ObjDump", + + "C2hs Haskell", + + "CIL", + + "CLIPS", + + "CMake", + + "COBOL", + + "CODEOWNERS", + + "COLLADA", + + "CSON", + + "CSS", + + "CSV", + + "CUE", + + "CWeb", + + "Cabal Config", + + "Cabal", + + "Cap'n Proto", + + "Carto", + + "CartoCSS", + + "Ceylon", + + "Chapel", + + "Charity", + + "ChucK", + + "Cirru", + + "Clarion", + + "Classic ASP", + + "Clean", + + "Click", + + "Clojure", + + "Closure Templates", + + "Cloud Firestore Security Rules", + + "CoNLL", + + "CoNLL-U", + + "CoNLL-X", + + "ColdFusion CFC", + + "ColdFusion", + + "Common Lisp", + + "Common Workflow Language", + + "Component Pascal", + + "Containerfile", + + "Cool", + + "Coq", + + "Cpp-ObjDump", + + "Crystal", + + "Csound Document", + + "Csound Score", + + "Csound", + + "Cuda", + + "Cue Sheet", + + "Cycript", + + "Cython", + + "D-ObjDump", + + "DIGITAL Command Language", + + "DM", + + "DTrace", + + "Dafny", + + "Darcs Patch", + + "Dart", + + "DataWeave", + + "Dhall", + + "Diff", + + "Dlang", + + "Dockerfile", + + "Dogescript", + + "Dylan", + + "E", + + "E-mail", + + "EBNF", + + "ECL", + + "ECLiPSe", + + "EJS", + + "EQ", + + "Eagle", + + "Earthly", + + "Easybuild", + + "Ecere Projects", + + "EditorConfig", + + "Eiffel", + + "Elixir", + + "Elm", + + "Emacs Lisp", + + "EmberScript", + + "Erlang", + + "F#", + + "F*", + + "FIGfont", + + "FIGlet Font", + + "FLUX", + + "Factor", + + "Fancy", + + "Fantom", + + "Faust", + + "Fennel", + + "Filebench WML", + + "Filterscript", + + "Fluent", + + "Formatted", + + "Forth", + + "Fortran Free Form", + + "Fortran", + + "FreeBasic", + + "Frege", + + "Futhark", + + "G-code", + + "GAML", + + "GAMS", + + "GAP", + + "GCC Machine Description", + + "GDB", + + "GDScript", + + "GEDCOM", + + "GLSL", + + "GN", + + "Game Maker Language", + + "Gemfile.lock", + + "Genie", + + "Genshi", + + "Gentoo Eclass", + + "Gerber Image", + + "Gettext Catalog", + + "Gherkin", + + "Git Config", + + "Glyph Bitmap Distribution Format", + + "Glyph", + + "Gnuplot", + + "Go Checksums", + + "Go Module", + + "Go", + + "Golo", + + "Gosu", + + "Grace", + + "Gradle", + + "Grammatical Framework", + + "Graph Modeling Language", + + "GraphQL", + + "Graphviz (DOT)", + + "Groovy Server Pages", + + "Groovy", + + "HAProxy", + + "HCL", + + "HTML", + + "HTML+ECR", + + "HTML+EEX", + + "HTML+ERB", + + "HTML+PHP", + + "HTML+Razor", + + "HTTP", + + "HXML", + + "Hack", + + "Haml", + + "Handlebars", + + "Harbour", + + "HashiCorp Configuration Language", + + "Haskell", + + "Haxe", + + "HiveQL", + + "HolyC", + + "Hy", + + "IDL", + + "IGOR Pro", + + "IPython Notebook", + + "Idris", + + "Ignore List", + + "ImageJ Macro", + + "Inform 7", + + "Io", + + "Ioke", + + "Isabelle ROOT", + + "Isabelle", + + "J", + + "JAR Manifest", + + "JFlex", + + "JSON with Comments", + + "JSON", + + "JSON5", + + "JSONLD", + + "JSONiq", + + "Jasmin", + + "Java Properties", + + "Java Server Pages", + + "Java", + + "JavaScript", + + "JavaScript+ERB", + + "Jest Snapshot", + + "Jinja", + + "Jison Lex", + + "Jison", + + "Jolie", + + "Jsonnet", + + "Julia", + + "Jupyter Notebook", + + "Kaitai Struct", + + "KakouneScript", + + "KiCad Layout", + + "KiCad Legacy Layout", + + "KiCad Schematic", + + "Kit", + + "Kotlin", + + "Kusto", + + "LFE", + + "LLVM", + + "LOLCODE", + + "LSL", + + "LTspice Symbol", + + "LabVIEW", + + "Lark", + + "Lasso", + + "Lean", + + "Less", + + "Lex", + + "LilyPond", + + "Limbo", + + "Linker Script", + + "Linux Kernel Module", + + "Liquid", + + "Literate Agda", + + "Literate CoffeeScript", + + "Literate Haskell", + + "LiveScript", + + "Logos", + + "Logtalk", + + "LookML", + + "LoomScript", + + "Lua", + + "M", + + "M4", + + "M4Sugar", + + "MATLAB", + + "MAXScript", + + "MLIR", + + "MQL4", + + "MQL5", + + "MTML", + + "MUF", + + "Macaulay2", + + "Makefile", + + "Mako", + + "Markdown", + + "Marko", + + "Mathematica", + + "Max", + + "Mercury", + + "Meson", + + "Metal", + + "Microsoft Developer Studio Project", + + "Microsoft Visual Studio Solution", + + "MiniD", + + "Mirah", + + "Modelica", + + "Modula-2", + + "Modula-3", + + "Module Management System", + + "Monkey", + + "Moocode", + + "MoonScript", + + "Motoko", + + "Motorola 68K Assembly", + + "Muse", + + "Myghty", + + "NASL", + + "NCL", + + "NEON", + + "NPM Config", + + "NSIS", + + "NWScript", + + "Nearley", + + "Nemerle", + + "NeoSnippet", + + "NetLinx", + + "NetLinx+ERB", + + "NetLogo", + + "NewLisp", + + "Nextflow", + + "Nginx", + + "Ninja", + + "Nit", + + "Nix", + + "NumPy", + + "Nunjucks", + + "ObjDump", + + "Object Data Instance Notation", + + "ObjectScript", + + "Objective-C", + + "Objective-C++", + + "Objective-J", + + "Odin", + + "Omgrofl", + + "Opa", + + "Opal", + + "Open Policy Agent", + + "OpenCL", + + "OpenEdge ABL", + + "OpenQASM", + + "OpenRC runscript", + + "OpenSCAD", + + "OpenStep Property List", + + "OpenType Feature File", + + "Org", + + "Ox", + + "Oxygene", + + "Oz", + + "P4", + + "PEG.js", + + "PHP", + + "PLpgSQL", + + "POV-Ray SDL", + + "Pan", + + "Papyrus", + + "Parrot Assembly", + + "Parrot Internal Representation", + + "Parrot", + + "Pascal", + + "Pawn", + + "Pep8", + + "Perl", + + "Pickle", + + "PicoLisp", + + "PigLatin", + + "Pike", + + "PlantUML", + + "Pod 6", + + "Pod", + + "PogoScript", + + "Pony", + + "PostCSS", + + "PostScript", + + "PowerShell", + + "Prisma", + + "Processing", + + "Proguard", + + "Prolog", + + "Promela", + + "Propeller Spin", + + "Protocol Buffer", + + "Protocol Buffers", + + "Public Key", + + "Pug", + + "Puppet", + + "Pure Data", + + "PureBasic", + + "PureScript", + + "Python", + + "Q#", + + "QMake", + + "Qt Script", + + "Quake", + + "R", + + "RAML", + + "RDoc", + + "REALbasic", + + "REXX", + + "RMarkdown", + + "RPC", + + "RPM Spec", + + "Racket", + + "Ragel", + + "Raw token data", + + "ReScript", + + "Readline Config", + + "Reason", + + "Rebol", + + "Record Jar", + + "Red", + + "Redirect Rules", + + "Regular Expression", + + "RenderScript", + + "Rich Text Format", + + "Ring", + + "Riot", + + "RobotFramework", + + "Roff", + + "Rouge", + + "Rscript", + + "Ruby", + + "Rust", + + "SAS", + + "SCSS", + + "SELinux Kernel Policy Language", + + "SELinux Policy", + + "SMT", + + "SPARQL", + + "SQF", + + "SQL", + + "SQLPL", + + "SRecode Template", + + "SSH Config", + + "STON", + + "SVG", + + "SWIG", + + "Sage", + + "SaltStack", + + "Sass", + + "Scala", + + "Scaml", + + "Scheme", + + "Scilab", + + "Self", + + "ShaderLab", + + "Shell", + + "ShellCheck Config", + + "Sieve", + + "Singularity", + + "Slash", + + "Slice", + + "Slim", + + "SmPL", + + "Smalltalk", + + "SnipMate", + + "Solidity", + + "Soong", + + "SourcePawn", + + "Spline Font Database", + + "Squirrel", + + "Stan", + + "Standard ML", + + "Starlark", + + "StringTemplate", + + "Stylus", + + "SubRip Text", + + "SugarSS", + + "SuperCollider", + + "Svelte", + + "Swift", + + "SystemVerilog", + + "TI Program", + + "TLA", + + "TOML", + + "TSQL", + + "TSV", + + "TSX", + + "TXL", + + "Tcl", + + "Tcsh", + + "TeX", + + "Tea", + + "Terra", + + "Texinfo", + + "Text", + + "TextMate Properties", + + "Textile", + + "Thrift", + + "Turing", + + "Turtle", + + "Twig", + + "Type Language", + + "TypeScript", + + "UltiSnip", + + "UltiSnips", + + "Unified Parallel C", + + "Unity3D Asset", + + "Unix Assembly", + + "Uno", + + "UnrealScript", + + "Ur", + + "Ur/Web", + + "UrWeb", + + "V", + + "VBA", + + "VCL", + + "VHDL", + + "Vala", + + "Valve Data Format", + + "Verilog", + + "Vim Help File", + + "Vim Script", + + "Vim Snippet", + + "Visual Basic .NET", + + "Vue", + + "Wavefront Material", + + "Wavefront Object", + + "Web Ontology Language", + + "WebAssembly", + + "WebVTT", + + "Wget Config", + + "Wikitext", + + "Windows Registry Entries", + + "Wollok", + + "World of Warcraft Addon Data", + + "X BitMap", + + "X Font Directory Index", + + "X PixMap", + + "X10", + + "XC", + + "XCompose", + + "XML Property List", + + "XML", + + "XPages", + + "XProc", + + "XQuery", + + "XS", + + "XSLT", + + "Xojo", + + "Xonsh", + + "Xtend", + + "YAML", + + "YANG", + + "YARA", + + "YASnippet", + + "Yacc", + + "ZAP", + + "ZIL", + + "Zeek", + + "ZenScript", + + "Zephir", + + "Zig", + + "Zimpl", + + "abl", + + "abuild", + + "acfm", + + "aconf", + + "actionscript 3", + + "actionscript3", + + "ada2005", + + "ada95", + + "adobe composite font metrics", + + "adobe multiple font metrics", + + "advpl", + + "ags", + + "ahk", + + "altium", + + "amfm", + + "amusewiki", + + "apache", + + "apkbuild", + + "arexx", + + "as3", + + "asm", + + "asp", + + "aspx", + + "aspx-vb", + + "ats2", + + "au3", + + "autoconf", + + "b3d", + + "bash session", + + "bash", + + "bat", + + "batch", + + "bazel", + + "blitz3d", + + "blitzplus", + + "bmax", + + "bplus", + + "bro", + + "bsdmake", + + "byond", + + "bzl", + + "c++-objdump", + + "c2hs", + + "cURL Config", + + "cake", + + "cakescript", + + "cfc", + + "cfm", + + "cfml", + + "chpl", + + "clipper", + + "coccinelle", + + "coffee", + + "coffee-script", + + "coldfusion html", + + "console", + + "cperl", + + "cpp", + + "csharp", + + "csound-csd", + + "csound-orc", + + "csound-sco", + + "cucumber", + + "curlrc", + + "cwl", + + "dcl", + + "delphi", + + "desktop", + + "dircolors", + + "django", + + "dosbatch", + + "dosini", + + "dpatch", + + "dtrace-script", + + "eC", + + "ecr", + + "editor-config", + + "edn", + + "eeschema schematic", + + "eex", + + "elisp", + + "emacs muse", + + "emacs", + + "email", + + "eml", + + "erb", + + "fb", + + "fish", + + "flex", + + "foxpro", + + "fsharp", + + "fstar", + + "ftl", + + "fundamental", + + "gf", + + "git-ignore", + + "gitattributes", + + "gitconfig", + + "gitignore", + + "gitmodules", + + "go mod", + + "go sum", + + "go.mod", + + "go.sum", + + "golang", + + "groff", + + "gsp", + + "hbs", + + "heex", + + "help", + + "html+django", + + "html+jinja", + + "html+ruby", + + "htmlbars", + + "htmldjango", + + "hylang", + + "i7", + + "ignore", + + "igor", + + "igorpro", + + "ijm", + + "inc", + + "inform7", + + "inputrc", + + "irc logs", + + "irc", + + "java server page", + + "jq", + + "jruby", + + "js", + + "jsonc", + + "jsp", + + "kak", + + "kakscript", + + "keyvalues", + + "ksy", + + "lassoscript", + + "latex", + + "leex", + + "lhaskell", + + "lhs", + + "lisp", + + "litcoffee", + + "live-script", + + "ls", + + "m2", + + "m68k", + + "mIRC Script", + + "macruby", + + "mail", + + "make", + + "man page", + + "man", + + "man-page", + + "manpage", + + "markojs", + + "max/msp", + + "maxmsp", + + "mbox", + + "mcfunction", + + "mdoc", + + "mediawiki", + + "mf", + + "mma", + + "mumps", + + "mupad", + + "nanorc", + + "nasm", + + "ne-on", + + "nesC", + + "nette object notation", + + "nginx configuration file", + + "nixos", + + "njk", + + "node", + + "npmrc", + + "nroff", + + "nush", + + "nvim", + + "obj-c", + + "obj-c++", + + "obj-j", + + "objc", + + "objc++", + + "objectivec", + + "objectivec++", + + "objectivej", + + "objectpascal", + + "objj", + + "octave", + + "odin-lang", + + "odinlang", + + "oncrpc", + + "ooc", + + "openedge", + + "openrc", + + "osascript", + + "pandoc", + + "pasm", + + "pcbnew", + + "perl-6", + + "perl6", + + "pir", + + "plain text", + + "posh", + + "postscr", + + "pot", + + "pov-ray", + + "povray", + + "progress", + + "protobuf", + + "pwsh", + + "pycon", + + "pyrex", + + "python3", + + "q", + + "ql", + + "qsharp", + + "ragel-rb", + + "ragel-ruby", + + "rake", + + "raw", + + "razor", + + "rb", + + "rbx", + + "reStructuredText", + + "readline", + + "red/system", + + "redirects", + + "regex", + + "regexp", + + "renpy", + + "rhtml", + + "robots txt", + + "robots", + + "robots.txt", + + "rpcgen", + + "rs", + + "rs-274x", + + "rss", + + "rst", + + "rusthon", + + "salt", + + "saltstate", + + "sed", + + "sepolicy", + + "sh", + + "shell-script", + + "shellcheckrc", + + "sml", + + "snippet", + + "sourcemod", + + "soy", + + "specfile", + + "splus", + + "squeak", + + "terraform", + + "tl", + + "tm-properties", + + "troff", + + "ts", + + "udiff", + + "vb .net", + + "vb.net", + + "vb6", + + "vbnet", + + "vdf", + + "vim", + + "vimhelp", + + "viml", + + "visual basic 6", + + "visual basic for applications", + + "visual basic", + + "vlang", + + "wasm", + + "wast", + + "wdl", + + "wgetrc", + + "wiki", + + "winbatch", + + "wisp", + + "wl", + + "wolfram lang", + + "wolfram language", + + "wolfram", + + "wsdl", + + "xBase", + + "xbm", + + "xdr", + + "xhtml", + + "xml+genshi", + + "xml+kid", + + "xpm", + + "xsd", + + "xsl", + + "xten", + + "yas", + + "yml", + + "zsh" + ] + } + }, + + "additionalProperties": false + }, + + "validations": { + "$ref": "#/definitions/validations", + + "title": "textarea validations", + + "description": "Textarea validations\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#validations" + } + }, + + "additionalProperties": false + } + }, + + { + "if": { + "properties": { + "type": { + "const": "input" + } + } + }, + + "then": { + "$comment": "For `additionalProperties` to work `type` must also be present here.", + + "title": "input", + + "description": "Input\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#input", + + "type": "object", + + "required": ["type", "attributes"], + + "properties": { + "type": { + "$ref": "#/definitions/type" + }, + + "id": { + "$ref": "#/definitions/id", + + "description": "An input id\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#keys" + }, + + "attributes": { + "title": "input attributes", + + "description": "Input attributes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-2", + + "type": "object", + + "required": ["label"], + + "properties": { + "label": { + "$ref": "#/definitions/label", + + "description": "A short input description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-2" + }, + + "description": { + "$ref": "#/definitions/description", + + "description": "A long input description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-2" + }, + + "placeholder": { + "$ref": "#/definitions/placeholder", + + "description": "An input placeholder\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-2" + }, + + "value": { + "$ref": "#/definitions/value", + + "description": "An input value\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-2" + } + }, + + "additionalProperties": false + }, + + "validations": { + "$ref": "#/definitions/validations", + + "title": "input validations", + + "description": "Input validations\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#validations-1" + } + }, + + "additionalProperties": false + } + }, + + { + "if": { + "properties": { + "type": { + "const": "dropdown" + } + } + }, + + "then": { + "$comment": "For `additionalProperties` to work `type` must also be present here.", + + "title": "dropdown", + + "description": "dropdown\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#dropdown", + + "type": "object", + + "required": ["type", "attributes"], + + "properties": { + "type": { + "$ref": "#/definitions/type" + }, + + "id": { + "$ref": "#/definitions/id", + + "description": "A dropdown id\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#keys" + }, + + "attributes": { + "title": "dropdown attributes", + + "description": "Dropdown attributes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3", + + "type": "object", + + "required": ["label", "options"], + + "properties": { + "label": { + "$ref": "#/definitions/label", + + "description": "A short dropdown description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3" + }, + + "description": { + "$ref": "#/definitions/description", + + "description": "A long dropdown description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3" + }, + + "multiple": { + "description": "Specify whether allow a multiple choices\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3", + + "type": "boolean", + + "default": false + }, + + "options": { + "description": "Dropdown choices\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3", + + "type": "array", + + "minItems": 1, + + "uniqueItems": true, + + "items": { + "type": "string", + + "minLength": 1, + + "examples": ["Sample choice"] + } + }, + + "default": { + "description": "Index of the default option\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3", + + "type": "integer", + + "examples": [0] + } + }, + + "additionalProperties": false + }, + + "validations": { + "$ref": "#/definitions/validations", + + "title": "dropdown validations", + + "description": "Dropdown validations\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#validations-2" + } + }, + + "additionalProperties": false + } + }, + + { + "if": { + "properties": { + "type": { + "const": "checkboxes" + } + } + }, + + "then": { + "$comment": "For `additionalProperties` to work `type` must also be present here.", + + "title": "checkboxes", + + "description": "Checkboxes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#checkboxes", + + "type": "object", + + "required": ["type", "attributes"], + + "properties": { + "type": { + "$ref": "#/definitions/type" + }, + + "id": { + "$ref": "#/definitions/id", + + "description": "Checkbox list id\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#keys" + }, + + "attributes": { + "title": "checkbox list attributes", + + "description": "Checkbox list attributes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4", + + "type": "object", + + "required": ["label", "options"], + + "properties": { + "label": { + "$ref": "#/definitions/label", + + "description": "A short checkbox list description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4" + }, + + "description": { + "$ref": "#/definitions/description", + + "description": "A long checkbox list description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4" + }, + + "options": { + "description": "Checkbox list choices\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4", + + "type": "array", + + "minItems": 1, + + "items": { + "title": "checkbox list choice", + + "description": "Checkbox list choice\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4", + + "type": "object", + + "required": ["label"], + + "properties": { + "label": { + "description": "A short checkbox list choice description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4", + + "type": "string", + + "minLength": 1, + + "examples": ["Sample label"] + }, + + "required": { + "description": "Specify whether a choice is required\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4", + + "type": "boolean", + + "default": false + } + }, + + "additionalProperties": false + } + } + }, + + "additionalProperties": false + } + }, + + "additionalProperties": false + } + } + ] + } + }, + + "properties": { + "name": { + "description": "An issue template name\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax", + + "type": "string", + + "minLength": 1, + + "examples": ["Sample name"] + }, + + "description": { + "description": "An issue template description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax", + + "type": "string", + + "minLength": 1, + + "examples": ["Sample description"] + }, + + "body": { + "description": "An issue template body\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax", + + "type": "array", + + "minItems": 1, + + "items": { + "$ref": "#/definitions/form_item" + } + }, + + "assignees": { + "description": "An issue template assignees\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax", + + "oneOf": [ + { + "$ref": "#/definitions/assignee" + }, + + { + "type": "array", + + "minItems": 1, + + "uniqueItems": true, + + "items": { + "$ref": "#/definitions/assignee" + } + } + ] + }, + + "labels": { + "description": "An issue template labels\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax", + + "type": "array", + + "minItems": 1, + + "uniqueItems": true, + + "items": { + "type": "string", + + "minLength": 1, + + "examples": [ + "Sample label", + + "bug", + + "documentation", + + "duplicate", + + "enhancement", + + "good first issue", + + "help wanted", + + "invalid", + + "question", + + "wontfix" + ] + } + }, + + "title": { + "description": "An issue template title\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax", + + "type": "string", + + "minLength": 1, + + "examples": ["Sample title", "Bug: ", "Feature: "] + } + }, + + "required": ["name", "description", "body"], + + "title": "GitHub issue forms config file schema", + + "type": "object" +} diff --git a/src/soundscapy/__init__.py b/src/soundscapy/__init__.py index c9a902f5..42d7e428 100644 --- a/src/soundscapy/__init__.py +++ b/src/soundscapy/__init__.py @@ -1,62 +1,93 @@ -""" -Soundscapy is a Python library for soundscape analysis and visualisation. -""" +"""Soundscapy is a Python library for soundscape analysis and visualisation.""" # ruff: noqa: E402 -from typing import Any from loguru import logger # https://loguru.readthedocs.io/en/latest/resources/recipes.html#configuring-loguru-to-be-used-by-a-library-or-an-application logger.disable("soundscapy") -import importlib.metadata - -__version__ = importlib.metadata.version("soundscapy") - -from soundscapy._optionals import import_optional - # Always available core modules -from soundscapy import surveys -from soundscapy import databases -from soundscapy import plotting -from soundscapy.logging import ( - setup_logging, - enable_debug, +from soundscapy import databases, plotting, surveys +from soundscapy._version import __version__ # noqa: F401 +from soundscapy.databases import isd, satp +from soundscapy.plotting import density_plot, scatter_plot +from soundscapy.sspylogging import ( disable_logging, + enable_debug, get_logger, + setup_logging, ) -from soundscapy.databases import araus, isd, satp from soundscapy.surveys import processing -from soundscapy.plotting import scatter_plot, density_plot __all__ = [ - # Core modules - "surveys", "databases", - "plotting", - "araus", + "density_plot", + "disable_logging", + "enable_debug", + "get_logger", "isd", - "satp", + "plotting", "processing", + "satp", "scatter_plot", - "density_plot", # Logging functions "setup_logging", - "enable_debug", - "disable_logging", - "get_logger", - # Optional modules listed explicitly for IDE/typing support - "Binaural", - "AudioAnalysis", - "AnalysisSettings", - "ConfigManager", - "process_all_metrics", - "prep_multiindex_df", - "add_results", - "parallel_process", + # Core modules + "surveys", ] +# Try to import optional audio module +try: + from soundscapy import audio + from soundscapy.audio import ( + AnalysisSettings, + AudioAnalysis, + Binaural, + ConfigManager, + add_results, + parallel_process, + prep_multiindex_df, + process_all_metrics, + ) + + __all__ += [ + "AnalysisSettings", + "AudioAnalysis", + "Binaural", + "ConfigManager", + "add_results", + "audio", + "parallel_process", + "prep_multiindex_df", + "process_all_metrics", + ] + +except ImportError: + # Audio module not available - this is expected if dependencies aren't installed + pass + +# Try to import optional SPI module +try: + from soundscapy import spi + from soundscapy.spi import ( + CentredParams, + DirectParams, + MultiSkewNorm, + cp2dp, + dp2cp, + msn, + ) + + __all__ += [ + "CentredParams", + "DirectParams", + "MultiSkewNorm", + "cp2dp", + "dp2cp", + "msn", + "spi", + ] -def __getattr__(name: str) -> Any: - """Lazy import handling for optional components.""" - return import_optional(name) +except ImportError: + # SPI module not available + pass diff --git a/src/soundscapy/_optionals.py b/src/soundscapy/_optionals.py deleted file mode 100644 index 55ab25c3..00000000 --- a/src/soundscapy/_optionals.py +++ /dev/null @@ -1,90 +0,0 @@ -""" -Optional dependency handling for soundscapy. - -This module provides utilities for managing optional dependencies across the package. -It allows graceful handling of missing dependencies and provides helpful feedback -about which dependencies are missing and how to install them. -""" - -import importlib -from typing import Any, Dict - -# Map module groups to their pip install targets -OPTIONAL_DEPENDENCIES = { - "audio": { - "packages": ("mosqito", "maad", "tqdm", "acoustic_toolbox"), - "install": "soundscapy[audio]", - "description": "audio analysis functionality", - }, - # Add other groups as needed -} -"""Dict[str, Dict]: Mapping of feature groups to their required dependencies. - -Each group contains: - modules (Tuple[str]): Required module names - install (str): pip install command/target - description (str): Human-readable feature description -""" - -OPTIONAL_IMPORTS = { - "Binaural": ("soundscapy.audio", "Binaural"), - "AudioAnalysis": ("soundscapy.audio", "AudioAnalysis"), - "AnalysisSettings": ("soundscapy.audio", "AnalysisSettings"), - "ConfigManager": ("soundscapy.audio", "ConfigManager"), - "process_all_metrics": ("soundscapy.audio", "process_all_metrics"), - "prep_multiindex_df": ("soundscapy.audio", "prep_multiindex_df"), - "add_results": ("soundscapy.audio", "add_results"), - "parallel_process": ("soundscapy.audio", "parallel_process"), -} - - -def require_dependencies(group: str) -> Dict[str, Any]: - """Import and return all packages required for a dependency group. - - Parameters - ---------- - group : str - The name ofthe dependency group to import - - Returns - ------- - Dict[str, Any] - Dictionary mapping package names to imported modules - - Raises - ------ - ImportError - If any required package is not available - KeyError - If the group name is not recognized - """ - if group not in OPTIONAL_DEPENDENCIES: - raise KeyError(f"Unknown dependency group: {group}") - - packages = {} - try: - for package in OPTIONAL_DEPENDENCIES[group]["packages"]: - packages[package] = importlib.import_module(package) - return packages - except ImportError as e: - raise ImportError( - f"{OPTIONAL_DEPENDENCIES[group]['description']} requires additional dependencies. " - f"Install with: pip install {OPTIONAL_DEPENDENCIES[group]['install']}" - ) from e - - -def import_optional(name: str) -> Any: - """Import an optional component by name.""" - if name not in OPTIONAL_IMPORTS: - raise AttributeError(f"module 'soundscapy' has no attribute '{name}'") - - module_name, attr_name = OPTIONAL_IMPORTS[name] - try: - module = importlib.import_module(module_name) - return getattr(module, attr_name) - except ImportError as e: - group = "audio" # Can be made dynamic if we add more groups - raise ImportError( - f"The {name} component requires {OPTIONAL_DEPENDENCIES[group]['description']}. " - f"Install with: pip install {OPTIONAL_DEPENDENCIES[group]['install']}" - ) from e diff --git a/src/soundscapy/_utils.py b/src/soundscapy/_utils.py new file mode 100644 index 00000000..bec33e52 --- /dev/null +++ b/src/soundscapy/_utils.py @@ -0,0 +1,49 @@ +from pathlib import Path +from typing import Literal + +from soundscapy.sspylogging import get_logger + +logger = get_logger() + + +def ensure_path_type(filepath: str | Path) -> Path: + if isinstance(filepath, str): + return Path(filepath) + if isinstance(filepath, Path): + return filepath + msg = "`filepath` must be either a valid path str or Path object." + raise TypeError(msg) + + +def ensure_input_path(filepath: str | Path) -> Path: + filepath = ensure_path_type(filepath) + if filepath.exists(): + return filepath + msg = f"{filepath.as_posix()} does not exist." + raise Warning(msg) + + +def ensure_output_filepath_exists( + filepath: str | Path, *, create_missing: bool = True +) -> Path | Literal[False]: + filepath = ensure_path_type(filepath) + if not filepath.exists(): + logger.info("Output file %s does not exist.", filepath) + if not create_missing: + return False + logger.info("Creating new file at %s", filepath.absolute()) + filepath.touch() + return filepath + + +def ensure_output_dirpath_exists( + dirpath: str | Path, *, create_missing: bool = True +) -> Path | Literal[False]: + dirpath = ensure_path_type(dirpath) + if not dirpath.exists(): + logger.info("Output directory %s does not exist.", dirpath) + if not create_missing: + return False + logger.info("Creating new directory at %s", dirpath.absolute()) + dirpath.mkdir() + return dirpath diff --git a/src/soundscapy/audio/__init__.py b/src/soundscapy/audio/__init__.py index a3574c92..d5005e9f 100644 --- a/src/soundscapy/audio/__init__.py +++ b/src/soundscapy/audio/__init__.py @@ -1,15 +1,12 @@ """ -soundscapy.audio -================ - -This module provides tools for working with audio signals, particularly binaural recordings. +Provides tools for working with audio signals, particularly binaural recordings. Key Components: - Binaural: A class for processing and analyzing binaural audio signals. - Various metric calculation functions for audio analysis. -The module integrates with external libraries such as mosqito, maad, and acoustic_toolbox -to provide a comprehensive suite of audio analysis tools. +The module integrates with external libraries such as mosqito, maad, +and acoustic_toolbox to provide a comprehensive suite of audio analysis tools. Example: >>> # xdoctest: +SKIP @@ -20,19 +17,30 @@ See Also: soundscapy.audio.binaural: For detailed Binaural class documentation. soundscapy.audio.metrics: For individual metric calculation functions. -""" -# ruff: noqa: E402 -# ignore module level import order because we need to run require_dependencies first -from soundscapy._optionals import require_dependencies +""" -# This will raise an ImportError if the required dependencies are not installed -required = require_dependencies("audio") +# ruff: noqa: E402 +# ignore module level import order because we need to check dependencies first + +# Check for required dependencies directly +# This will raise ImportError if any dependency is missing +try: + import acoustic_toolbox # noqa: F401 + import maad # noqa: F401 + import mosqito # noqa: F401 + import tqdm # noqa: F401 +except ImportError as e: + msg = ( + "Audio analysis functionality requires additional dependencies. " + "Install with: pip install soundscapy[audio]" + ) + raise ImportError(msg) from e # Now we can import our modules that depend on the optional packages -from .binaural import Binaural -from .analysis_settings import AnalysisSettings, ConfigManager +from soundscapy.audio.analysis_settings import AnalysisSettings, ConfigManager from soundscapy.audio.audio_analysis import AudioAnalysis +from soundscapy.audio.binaural import Binaural from soundscapy.audio.metrics import ( add_results, prep_multiindex_df, @@ -41,12 +49,12 @@ from soundscapy.audio.parallel_processing import parallel_process __all__ = [ + "AnalysisSettings", "AudioAnalysis", "Binaural", - "AnalysisSettings", "ConfigManager", - "process_all_metrics", - "prep_multiindex_df", "add_results", "parallel_process", + "prep_multiindex_df", + "process_all_metrics", ] diff --git a/src/soundscapy/audio/analysis_settings.py b/src/soundscapy/audio/analysis_settings.py index b9a0712d..05a0fe65 100644 --- a/src/soundscapy/audio/analysis_settings.py +++ b/src/soundscapy/audio/analysis_settings.py @@ -1,8 +1,18 @@ +""" +Module for managing audio analysis settings using Pydantic models. + +This module defines Pydantic models for configuring analysis settings for different +audio processing libraries (AcousticToolbox, MoSQITo, scikit-maad). +It includes classes for individual metric settings, library settings, and overall +analysis settings. It also provides a ConfigManager class for loading, saving, +merging, and managing configurations from YAML files or dictionaries. +""" + from __future__ import annotations from importlib import resources from pathlib import Path -from typing import Any, Dict +from typing import Any import yaml from loguru import logger @@ -17,6 +27,13 @@ ) +def _ensure_path(value: str | Path) -> Path: + """Ensure the value is a Path object.""" + if isinstance(value, str): + return Path(value) + return value + + class MetricSettings(BaseModel): """ Settings for an individual metric. @@ -37,6 +54,7 @@ class MetricSettings(BaseModel): Whether to run the metric in parallel. func_args : dict[str, Any] Additional arguments for the metric function. + """ run: bool = True @@ -49,7 +67,7 @@ class MetricSettings(BaseModel): @model_validator(mode="before") @classmethod - def check_main_in_statistics(cls, values): + def check_main_in_statistics(cls, values: dict[str, Any]) -> dict[str, Any]: """Check that the main statistic is in the statistics list.""" main = values.get("main") statistics = values.get("statistics", []) @@ -82,11 +100,13 @@ def get_metric_settings(self, metric: str) -> MetricSettings: ------ KeyError If the specified metric is not found. + """ if metric in self.root: return self.root[metric] logger.error(f"Metric '{metric}' not found in library") - raise KeyError(f"Metric '{metric}' not found in library") + msg = f"Metric '{metric}' not found in library" + raise KeyError(msg) class AnalysisSettings(BaseModel): @@ -103,6 +123,7 @@ class AnalysisSettings(BaseModel): Settings for MoSQITo metrics. scikit_maad : LibrarySettings | None Settings for scikit-maad metrics. + """ version: str = "1.0" @@ -110,13 +131,15 @@ class AnalysisSettings(BaseModel): None, validation_alias=AliasChoices("AcousticToolbox", "PythonAcoustics") ) MoSQITo: LibrarySettings | None = None - scikit_maad: LibrarySettings | None = Field(None, alias="scikit-maad") + scikit_maad: LibrarySettings | None = Field( + None, validation_alias=AliasChoices("scikit-maad", "scikit_maad") + ) model_config = ConfigDict(populate_by_name=True, extra="forbid") @field_validator("*", mode="before") @classmethod - def validate_library_settings(cls, v): + def validate_library_settings(cls, v: dict | LibrarySettings) -> LibrarySettings: """Validate library settings.""" if isinstance(v, dict): return LibrarySettings(root=v) @@ -136,21 +159,24 @@ def from_yaml(cls, filepath: str | Path) -> AnalysisSettings: ------- AnalysisSettings An instance of AnalysisSettings. + """ + filepath = _ensure_path(filepath) logger.info(f"Loading configuration from {filepath}") - with open(filepath, "r") as f: + with Path.open(filepath) as f: config_dict = yaml.safe_load(f) return cls(**config_dict) @classmethod def default(cls) -> AnalysisSettings: """ - Create a default AnalysisSettings object using the package's default configuration file. + Create a default AnalysisSettings using the package default configuration file. Returns ------- AnalysisSettings An instance of AnalysisSettings with default settings. + """ config_resource = resources.files("soundscapy.data").joinpath( "default_settings.yaml" @@ -173,6 +199,7 @@ def from_dict(cls, d: dict) -> AnalysisSettings: ------- AnalysisSettings An instance of AnalysisSettings. + """ return cls(**d) @@ -184,12 +211,14 @@ def to_yaml(self, filepath: str | Path) -> None: ---------- filepath : str | Path Path to save the YAML file. + """ + filepath = _ensure_path(filepath) logger.info(f"Saving configuration to {filepath}") - with open(filepath, "w") as f: + with Path.open(filepath, "w") as f: yaml.dump(self.model_dump(by_alias=True), f) - def update_setting(self, library: str, metric: str, **kwargs) -> None: + def update_setting(self, library: str, metric: str, **kwargs: dict) -> None: """ Update the settings for a specific metric. @@ -206,6 +235,7 @@ def update_setting(self, library: str, metric: str, **kwargs) -> None: ------ KeyError If the specified library or metric is not found. + """ library_settings = getattr(self, library) if library_settings and metric in library_settings.root: @@ -217,7 +247,8 @@ def update_setting(self, library: str, metric: str, **kwargs) -> None: logger.error(f"Invalid setting '{key}' for metric '{metric}'") else: logger.error(f"Metric '{metric}' not found in library '{library}'") - raise KeyError(f"Metric '{metric}' not found in library '{library}'") + msg = f"Metric '{metric}' not found in library '{library}'" + raise KeyError(msg) def get_metric_settings(self, library: str, metric: str) -> MetricSettings: """ @@ -239,12 +270,14 @@ def get_metric_settings(self, library: str, metric: str) -> MetricSettings: ------ KeyError If the specified library or metric is not found. + """ library_settings = getattr(self, library) if library_settings and metric in library_settings.root: return library_settings.root[metric] logger.error(f"Metric '{metric}' not found in library '{library}'") - raise KeyError(f"Metric '{metric}' not found in library '{library}'") + msg = f"Metric '{metric}' not found in library '{library}'" + raise KeyError(msg) def get_enabled_metrics(self) -> dict[str, dict[str, MetricSettings]]: """ @@ -254,6 +287,7 @@ def get_enabled_metrics(self) -> dict[str, dict[str, MetricSettings]]: ------- dict[str, dict[str, MetricSettings]] A dictionary of enabled metrics grouped by library. + """ enabled_metrics = {} for library in ["AcousticToolbox", "MoSQITo", "scikit_maad"]: @@ -276,10 +310,11 @@ class ConfigManager: ---------- default_config_path : str | Path | None Path to the default configuration file. + """ - def __init__(self, config_path: str | Path | None = None): - self.config_path = Path(config_path) if config_path else None + def __init__(self, config_path: str | Path | None = None) -> None: # noqa: D107 + self.config_path = _ensure_path(config_path) if config_path else None self.current_config: AnalysisSettings | None = None def load_config(self, config_path: str | Path | None = None) -> AnalysisSettings: @@ -295,6 +330,7 @@ def load_config(self, config_path: str | Path | None = None) -> AnalysisSettings ------- AnalysisSettings The loaded configuration. + """ if config_path: logger.info(f"Loading configuration from {config_path}") @@ -320,17 +356,19 @@ def save_config(self, filepath: str | Path) -> None: ------ ValueError If no current configuration is loaded. + """ if self.current_config: logger.info(f"Saving configuration to {filepath}") self.current_config.to_yaml(filepath) else: logger.error("No current configuration to save") - raise ValueError("No current configuration to save.") + msg = "No current configuration to save." + raise ValueError(msg) - def merge_configs(self, override_config: Dict) -> AnalysisSettings: + def merge_configs(self, override_config: dict) -> AnalysisSettings: """ - Merge the current configuration with override values and update the current_config. + Merge the current config with override values and update the current_config. Parameters ---------- @@ -346,10 +384,12 @@ def merge_configs(self, override_config: Dict) -> AnalysisSettings: ------ ValueError If no base configuration is loaded. + """ if not self.current_config: logger.error("No base configuration loaded") - raise ValueError("No base configuration loaded.") + msg = "No base configuration loaded." + raise ValueError(msg) logger.info("Merging configurations") merged_dict = self.current_config.model_dump() self._deep_update(merged_dict, override_config) @@ -357,7 +397,7 @@ def merge_configs(self, override_config: Dict) -> AnalysisSettings: self.current_config = merged_config # Update the current_config return merged_config - def _deep_update(self, base_dict: Dict, update_dict: Dict) -> None: + def _deep_update(self, base_dict: dict, update_dict: dict) -> None: """Recursively update a nested dictionary.""" for key, value in update_dict.items(): if ( @@ -382,9 +422,11 @@ def generate_minimal_config(self) -> dict: ------ ValueError If no current configuration is loaded. + """ if not self.current_config: - raise ValueError("No current configuration loaded.") + msg = "No current configuration loaded." + raise ValueError(msg) default_config = AnalysisSettings.default() current_dict = self.current_config.model_dump() default_dict = default_config.model_dump() @@ -490,12 +532,12 @@ def _get_diff(self, current: dict, default: dict) -> dict: ) # Print the created configuration - print(analysis_settings.model_dump_json(indent=2)) + print(analysis_settings.model_dump_json(indent=2)) # noqa: T201 # Save the configuration to a YAML file output_path = Path("my_custom_config.yaml") analysis_settings.to_yaml(output_path) - print(f"Configuration saved to {output_path}") + print(f"Configuration saved to {output_path}") # noqa: T201 # To use this configuration: diff --git a/src/soundscapy/audio/audio_analysis.py b/src/soundscapy/audio/audio_analysis.py index b2e2d326..1fc4ffbe 100644 --- a/src/soundscapy/audio/audio_analysis.py +++ b/src/soundscapy/audio/audio_analysis.py @@ -1,18 +1,64 @@ +""" +Audio analysis module for psychoacoustic analysis of audio files. + +This module provides functionality for analyzing audio files using psychoacoustic +metrics. It includes the AudioAnalysis class for processing single files or entire +folders. +""" + import json from concurrent.futures import ProcessPoolExecutor, as_completed from pathlib import Path -from typing import Dict, List, Optional, Union import pandas as pd from loguru import logger from tqdm.auto import tqdm +from soundscapy._utils import ensure_input_path, ensure_path_type from soundscapy.audio.analysis_settings import ConfigManager from soundscapy.audio.parallel_processing import load_analyse_binaural class AudioAnalysis: - def __init__(self, config_path: Optional[Union[str, Path]] = None): + """ + A class for performing psychoacoustic analysis on audio files. + + This class provides methods to analyze single audio files or entire folders + of audio files using parallel processing. It handles configuration management, + calibration, and saving of analysis results. + + Attributes + ---------- + config_manager : ConfigManager + Manages the configuration settings for audio analysis + settings : dict + The current configuration settings + + Methods + ------- + analyze_file(file_path, calibration_levels, resample) + Analyze a single audio file + analyze_folder(folder_path, calibration_file, max_workers, resample) + Analyze all audio files in a folder using parallel processing + save_results(results, output_path) + Save analysis results to a file + update_config(new_config) + Update the current configuration + save_config(config_path) + Save the current configuration to a file + + """ + + def __init__(self, config_path: str | Path | None = None) -> None: + """ + Initialize the AudioAnalysis with a configuration. + + Parameters + ---------- + config_path : str, Path, or None + Path to the configuration file. If None, uses default configuration. + + """ self.config_manager = ConfigManager(config_path) self.settings = self.config_manager.load_config() logger.info( @@ -22,8 +68,8 @@ def __init__(self, config_path: Optional[Union[str, Path]] = None): def analyze_file( self, file_path: str | Path, - calibration_levels: Optional[Dict[str, float] | List[float]] = None, - resample: Optional[int] = None, + calibration_levels: dict[str, float] | list[float] | None = None, + resample: int | None = None, ) -> pd.DataFrame: """ Analyze a single audio file using the current configuration. @@ -40,9 +86,10 @@ def analyze_file( ------- pd.DataFrame DataFrame containing the analysis results. + """ - if isinstance(file_path, str): - file_path = Path(file_path) + file_path = ensure_input_path(file_path) + logger.info(f"Analyzing file: {file_path}") return load_analyse_binaural( file_path, @@ -55,9 +102,9 @@ def analyze_file( def analyze_folder( self, folder_path: str | Path, - calibration_file: Optional[str | Path] = None, - max_workers: Optional[int] = None, - resample: Optional[int] = None, + calibration_file: str | Path | None = None, + max_workers: int | None = None, + resample: int | None = None, ) -> pd.DataFrame: """ Analyze all audio files in a folder using parallel processing. @@ -70,26 +117,29 @@ def analyze_folder( calibration_file : str or Path, optional Path to a JSON file containing calibration levels for each audio file. max_workers : int, optional - Maximum number of worker processes to use. If None, it will use the number of CPU cores. + Maximum number of worker processes to use. + If None, it will use the number of CPU cores. Returns ------- pd.DataFrame DataFrame containing the analysis results for all files. - """ - folder_path = Path(folder_path) + """ + folder_path = ensure_input_path(folder_path) audio_files = list(folder_path.glob("*.wav")) logger.info( - f"Analyzing folder: {folder_path.name} of {len(audio_files)} files in parallel (max_workers={max_workers})" + f"Analyzing folder: {folder_path.name} of {len(audio_files)}" + f"files in parallel (max_workers={max_workers})" ) if max_workers else logger.info( f"Analyzing folder: {folder_path}, {len(audio_files)} files" ) calibration_levels = {} if calibration_file: - with open(calibration_file, "r") as f: + calibration_file = ensure_input_path(calibration_file) + with calibration_file.open() as f: calibration_levels = json.load(f) logger.debug(f"Loaded calibration levels from: {calibration_file}") @@ -102,8 +152,8 @@ def analyze_folder( file, calibration_levels, self.settings, - False, resample, + parallel_mosqito=False, ) futures.append(future) @@ -114,7 +164,7 @@ def analyze_folder( result = future.result() all_results.append(result) except Exception as e: - logger.error(f"Error processing file: {str(e)}") + logger.error(f"Error processing file: {e!s}") combined_results = pd.concat(all_results) logger.info( @@ -122,7 +172,7 @@ def analyze_folder( ) return combined_results - def save_results(self, results: pd.DataFrame, output_path: Union[str, Path]): + def save_results(self, results: pd.DataFrame, output_path: str | Path) -> None: """ Save analysis results to a file. @@ -132,17 +182,21 @@ def save_results(self, results: pd.DataFrame, output_path: Union[str, Path]): DataFrame containing the analysis results. output_path : str or Path Path to save the results file. + """ - output_path = Path(output_path) + output_path = ensure_path_type( + output_path + ) # If doesn't already exist, pandas will create the file. if output_path.suffix == ".csv": results.to_csv(output_path) elif output_path.suffix == ".xlsx": results.to_excel(output_path) else: - raise ValueError("Unsupported file format. Use .csv or .xlsx") + msg = "Unsupported file format. Use .csv or .xlsx" + raise ValueError(msg) logger.info(f"Results saved to: {output_path}") - def update_config(self, new_config: Dict): + def update_config(self, new_config: dict) -> "AudioAnalysis": """ Update the current configuration. @@ -150,11 +204,13 @@ def update_config(self, new_config: Dict): ---------- new_config : dict Dictionary containing the new configuration settings. + """ self.settings = self.config_manager.merge_configs(new_config) logger.info("Configuration updated") + return self - def save_config(self, config_path: Union[str, Path]): + def save_config(self, config_path: str | Path) -> None: """ Save the current configuration to a file. @@ -162,6 +218,7 @@ def save_config(self, config_path: Union[str, Path]): ---------- config_path : str or Path Path to save the configuration file. + """ self.config_manager.save_config(config_path) logger.info(f"Configuration saved to: {config_path}") @@ -169,7 +226,7 @@ def save_config(self, config_path: Union[str, Path]): # Example usage if __name__ == "__main__": - from soundscapy.logging import setup_logging + from soundscapy.sspylogging import setup_logging setup_logging("INFO") diff --git a/src/soundscapy/audio/binaural.py b/src/soundscapy/audio/binaural.py index a5d18b3f..90c1c152 100644 --- a/src/soundscapy/audio/binaural.py +++ b/src/soundscapy/audio/binaural.py @@ -1,8 +1,5 @@ """ -soundscapy.audio.binaural -========================= - -This module provides tools for working with binaural audio signals. +Provides tools for working with binaural audio signals. The main class, Binaural, extends the Signal class from the Acoustic Toolbox library to provide specialized functionality for binaural recordings. It supports @@ -27,20 +24,23 @@ >>> from soundscapy.audio import Binaural >>> signal = Binaural.from_wav("audio.wav") >>> results = signal.process_all_metrics(analysis_settings) + """ import warnings from pathlib import Path -from typing import Dict, List, Optional, Tuple, Union +from typing import Literal import numpy as np import pandas as pd import scipy.signal from acoustic_toolbox import Signal from loguru import logger +from scipy.io import wavfile -from .analysis_settings import AnalysisSettings, MetricSettings -from .metrics import ( +from soundscapy._utils import ensure_input_path +from soundscapy.audio.analysis_settings import AnalysisSettings, MetricSettings +from soundscapy.audio.metrics import ( acoustics_metric_1ch, acoustics_metric_2ch, maad_metric_1ch, @@ -50,6 +50,8 @@ process_all_metrics, ) +ALLOWED_BINAURAL_CHANNELS = 2 + class Binaural(Signal): """ @@ -70,9 +72,12 @@ class Binaural(Signal): Notes ----- This class only supports 2-channel (stereo) audio signals. + """ - def __new__(cls, data, fs, recording="Rec"): + def __new__( + cls, data: np.ndarray, fs: float | None, recording: str = "Rec" + ) -> "Binaural": """ Create a new Binaural object. @@ -94,18 +99,20 @@ def __new__(cls, data, fs, recording="Rec"): ------ ValueError If the input signal is not 2-channel. + """ obj = super().__new__(cls, data, fs).view(cls) obj.recording = recording - if obj.channels != 2: + if obj.channels != ALLOWED_BINAURAL_CHANNELS: logger.error( f"Attempted to create Binaural object with {obj.channels} channels" ) - raise ValueError("Binaural class only supports 2 channels.") + msg = "Binaural class only supports 2 channels." + raise ValueError(msg) logger.debug(f"Created new Binaural object: {recording}, fs={fs}") return obj - def __array_finalize__(self, obj): + def __array_finalize__(self, obj: "Binaural | None") -> None: """ Finalize the new Binaural object. @@ -115,16 +122,17 @@ def __array_finalize__(self, obj): ---------- obj : Binaural or None The object from which the new object was created. + """ if obj is None: return self.fs = getattr(obj, "fs", None) - self.recording = getattr(obj, "recording", None) + self.recording = getattr(obj, "recording", "Rec") def calibrate_to( self, - decibel: Union[float, List[float], Tuple[float, float]], - inplace: bool = False, + decibel: float | list[float] | tuple[float, float] | np.ndarray | pd.Series, + inplace: bool = False, # noqa: FBT001, FBT002 TODO(MitchellAcoustics): Change to keyword-only in acoustic_toolbox.Signal ) -> "Binaural": """ Calibrate the binaural signal to predefined Leq/dB levels. @@ -136,10 +144,13 @@ def calibrate_to( ---------- decibel : float or List[float] or Tuple[float, float] Target calibration value(s) in dB (Leq). - If a single value is provided, both channels will be calibrated to this level. - If two values are provided, they will be applied to the left and right channels respectively. + If a single value is provided, both channels will be calibrated + to this level. + If two values are provided, they will be applied to the left and right + channels respectively. inplace : bool, optional - If True, modify the signal in place. If False, return a new calibrated signal. + If True, modify the signal in place. + If False, return a new calibrated signal. Default is False. Returns @@ -156,43 +167,48 @@ def calibrate_to( -------- >>> # xdoctest: +SKIP >>> signal = Binaural.from_wav("audio.wav") - >>> calibrated_signal = signal.calibrate_to([60, 62]) # Calibrate left channel to 60 dB and right to 62 dB + >>> # Calibrate left channel to 60 dB and right to 62 dB + >>> calibrated_signal = signal.calibrate_to([60, 62]) + """ logger.info(f"Calibrating Binaural signal to {decibel} dB") - if isinstance(decibel, (np.ndarray, pd.Series)): # Force into tuple + if isinstance(decibel, np.ndarray | pd.Series): # Force into tuple decibel = tuple(decibel) - if isinstance(decibel, (list, tuple)): - if len(decibel) == 2: # Per-channel calibration (recommended) + if isinstance(decibel, list | tuple): + if ( + len(decibel) == ALLOWED_BINAURAL_CHANNELS + ): # Per-channel calibration (recommended) logger.debug( - f"Calibrating channels separately: Left={decibel[0]}dB, Right={decibel[1]}dB" + "Calibrating channels separately: " + f"Left={decibel[0]}dB, Right={decibel[1]}dB" ) decibel = np.array(decibel) decibel = decibel[..., None] - return super().calibrate_to(decibel, inplace) - elif ( + return super().calibrate_to(decibel, inplace) # type: ignore[reportReturnType] + if ( len(decibel) == 1 ): # if one value given in tuple, assume same for both channels logger.debug(f"Calibrating both channels to {decibel[0]}dB") decibel = decibel[0] else: logger.error(f"Invalid calibration value: {decibel}") - raise ValueError( - "decibel must either be a single value or a 2 value tuple" - ) - if isinstance(decibel, (int, float)): # Calibrate both channels to same value + msg = "decibel must either be a single value or a 2 value tuple" + raise TypeError(msg) + if isinstance(decibel, int | float): # Calibrate both channels to same value logger.debug(f"Calibrating both channels to {decibel}dB") - return super().calibrate_to(decibel, inplace) - else: - logger.error(f"Invalid calibration value: {decibel}") - raise ValueError("decibel must be a single value or a 2 value tuple") + return super().calibrate_to(decibel, inplace) # type: ignore[reportReturnType] + logger.error(f"Invalid calibration value: {decibel}") + msg = "decibel must be a single value or a 2 value tuple" + raise TypeError(msg) @classmethod def from_wav( cls, - filename: Union[Path, str], - calibrate_to: Optional[Union[float, List, Tuple]] = None, - normalize: bool = False, - resample: Optional[int] = None, + filename: Path | str, + normalize: bool = False, # noqa: FBT001, FBT002 + calibrate_to: float | list | tuple | None = None, + resample: int | None = None, + recording: str | None = None, ) -> "Binaural": """ Load a wav file and return a Binaural object. @@ -221,10 +237,23 @@ def from_wav( See Also -------- acoustic_toolbox.Signal.from_wav : Base method for loading wav files. + """ + filename = ensure_input_path(filename) + if not filename.exists(): + logger.error(f"File not found: {filename}") + msg = f"File not found: {filename}" + raise FileNotFoundError(msg) + logger.info(f"Loading WAV file: {filename}") - s = super().from_wav(filename, normalize) - b = cls(s, s.fs, recording=Path(filename).stem) + fs, data = wavfile.read(filename) + data = data.astype(np.float32, copy=False).T + if normalize: + data /= np.max(np.abs(data)) + + recording = recording if recording is not None else Path(filename).stem + b = cls(data, fs, recording=recording) + if calibrate_to is not None: logger.info(f"Calibrating loaded signal to {calibrate_to} dB") b.calibrate_to(calibrate_to, inplace=True) @@ -236,6 +265,7 @@ def from_wav( def fs_resample( self, fs: float, + original_fs: float | None = None, ) -> "Binaural": """ Resample the signal to a new sampling frequency. @@ -244,7 +274,9 @@ def fs_resample( ---------- fs : float New sampling frequency. - + original_fs : float or None, optional + Original sampling frequency. + If None, it will be inferred from the signal (`Binaural.fs`). Returns ------- @@ -254,20 +286,33 @@ def fs_resample( See Also -------- acoustic_toolbox.Signal.resample : Base method for resampling signals. + """ - if fs == self.fs: - logger.info(f"Signal already at {fs} Hz. No resampling needed.") + current_fs: float + + if original_fs is None: + if hasattr(self, "fs") and self.fs is not None: + current_fs = self.fs + else: + logger.error("Original sampling frequency not provided.") + msg = "Original sampling frequency not provided." + raise ValueError(msg) + else: + current_fs = original_fs + + if fs == current_fs: + logger.info(f"Signal already at {current_fs} Hz. No resampling needed.") return self + logger.info(f"Resampling signal to {fs} Hz") resampled_channels = [ - scipy.signal.resample(channel, int(fs * len(channel) / self.fs)) + scipy.signal.resample(channel, int(fs * len(channel) / current_fs)) for channel in self ] resampled_channels = np.stack(resampled_channels) - resampled_b = Binaural(resampled_channels, fs, recording=self.recording) - return resampled_b + return Binaural(resampled_channels, fs, recording=self.recording) - def _get_channel(self, channel): + def _get_channel(self, channel: int | str | None) -> Signal: """ Get a single channel from the signal. @@ -280,35 +325,33 @@ def _get_channel(self, channel): ------- Signal Single channel signal. + """ if self.channels == 1: logger.debug("Returning single channel signal") return self - elif ( - channel is None - or channel == "both" - or channel == ("Left", "Right") - or channel == ["Left", "Right"] - ): + if channel is None or channel in ("both", ("Left", "Right"), ["Left", "Right"]): logger.debug("Returning both channels") return self - elif channel in ["Left", 0, "L"]: + if channel in ["Left", 0, "L"]: logger.debug("Returning left channel") return self[0] - elif channel in ["Right", 1, "R"]: + if channel in ["Right", 1, "R"]: logger.debug("Returning right channel") return self[1] - else: - logger.warning( - f"Unrecognized channel specification: {channel}. Returning full Binaural object." - ) - warnings.warn("Channel not recognised. Returning Binaural object as is.") - return self + logger.warning( + f"Unrecognized channel specification: {channel}." + "Returning full Binaural object." + ) + warnings.warn( + "Channel not recognised. Returning Binaural object as is.", stacklevel=2 + ) + return self def pyacoustics_metric( self, - metric: str, - statistics: Union[Tuple, List] = ( + metric: Literal["LZeq", "Leq", "LAeq", "LCeq", "SEL"], + statistics: tuple | list = ( 5, 10, 50, @@ -320,16 +363,60 @@ def pyacoustics_metric( "kurt", "skew", ), - label: Optional[str] = None, - channel: Union[str, int, List, Tuple] = ("Left", "Right"), - as_df: bool = True, - return_time_series: bool = False, - metric_settings: Optional[MetricSettings] = None, - func_args: Dict = {}, - ) -> Union[Dict, pd.DataFrame]: + label: str | None = None, + channel: str | int | list | tuple = ("Left", "Right"), + as_df: bool = True, # noqa: FBT001, FBT002 + return_time_series: bool = False, # noqa: FBT001, FBT002 + metric_settings: MetricSettings | None = None, + func_args: dict | None = None, + ) -> dict | pd.DataFrame | None: + """ + Run a metric from the pyacoustics library (deprecated). + + This method has been deprecated. Use `acoustics_metric` instead. + All parameters are passed directly to `acoustics_metric`. + + Parameters + ---------- + metric : {"LZeq", "Leq", "LAeq", "LCeq", "SEL"} + The metric to run. + statistics : tuple or list, optional + List of level statistics to calculate (e.g. L_5, L_90, etc.). + Default is (5, 10, 50, 90, 95, "avg", "max", "min", "kurt", "skew"). + label : str, optional + Label to use for the metric. + If None, will pull from default label for that metric. + channel : tuple, list, or str, optional + Which channels to process. Default is ("Left", "Right"). + as_df : bool, optional + Whether to return a dataframe or not. Default is True. + If True, returns a MultiIndex Dataframe with + ("Recording", "Channel") as the index. + return_time_series : bool, optional + Whether to return the time series of the metric. Default is False. + Cannot return time series if as_df is True. + metric_settings : MetricSettings, optional + Settings for metric analysis. Default is None. + func_args : dict, optional + Any settings given here will override those in the other options. + Can pass any *args or **kwargs to the underlying acoustic_toolbox method. + + Returns + ------- + dict or pd.DataFrame + Results of the metric calculation. + + See Also + -------- + Binaural.acoustics_metric + + """ + if func_args is None: + func_args = {} warnings.warn( "pyacoustics has been deprecated. Use acoustics_metric instead.", DeprecationWarning, + stacklevel=2, ) return self.acoustics_metric( metric, @@ -344,8 +431,8 @@ def pyacoustics_metric( def acoustics_metric( self, - metric: str, - statistics: Union[Tuple, List] = ( + metric: Literal["LZeq", "Leq", "LAeq", "LCeq", "SEL"], + statistics: tuple | list = ( 5, 10, 50, @@ -357,13 +444,13 @@ def acoustics_metric( "kurt", "skew", ), - label: Optional[str] = None, - channel: Union[str, int, List, Tuple] = ("Left", "Right"), + label: str | None = None, + channel: str | int | list | tuple = ("Left", "Right"), as_df: bool = True, return_time_series: bool = False, - metric_settings: Optional[MetricSettings] = None, - func_args: Dict = {}, - ) -> Union[Dict, pd.DataFrame]: + metric_settings: MetricSettings | None = None, + func_args: dict | None = None, + ) -> dict | pd.DataFrame | None: """ Run a metric from the acoustic_toolbox library. @@ -375,12 +462,14 @@ def acoustics_metric( List of level statistics to calculate (e.g. L_5, L_90, etc.). Default is (5, 10, 50, 90, 95, "avg", "max", "min", "kurt", "skew"). label : str, optional - Label to use for the metric. If None, will pull from default label for that metric. + Label to use for the metric. + If None, will pull from default label for that metric. channel : tuple, list, or str, optional Which channels to process. Default is ("Left", "Right"). as_df : bool, optional Whether to return a dataframe or not. Default is True. - If True, returns a MultiIndex Dataframe with ("Recording", "Channel") as the index. + If True, returns a MultiIndex Dataframe with + ("Recording", "Channel") as the index. return_time_series : bool, optional Whether to return the time series of the metric. Default is False. Cannot return time series if as_df is True. @@ -398,10 +487,16 @@ def acoustics_metric( See Also -------- metrics.acoustics_metric - acoustic_toolbox.standards_iso_tr_25417_2007.equivalent_sound_pressure_level : Base method for Leq calculation. - acoustic_toolbox.standards.iec_61672_1_2013.sound_exposure_level : Base method for SEL calculation. - acoustic_toolbox.standards.iec_61672_1_2013.time_weighted_sound_level : Base method for Leq level time series calculation. + acoustic_toolbox.standards_iso_tr_25417_2007.equivalent_sound_pressure_level : + Base method for Leq calculation. + acoustic_toolbox.standards.iec_61672_1_2013.sound_exposure_level : + Base method for SEL calculation. + acoustic_toolbox.standards.iec_61672_1_2013.time_weighted_sound_level : + Base method for Leq level time series calculation. + """ + if func_args is None: + func_args = {} if metric_settings: logger.debug("Using provided analysis settings") if not metric_settings.run: @@ -421,23 +516,22 @@ def acoustics_metric( return acoustics_metric_1ch( s, metric, statistics, label, as_df, return_time_series, func_args ) - else: - logger.debug("Processing both channels") - return acoustics_metric_2ch( - s, - metric, - statistics, - label, - channel, - as_df, - return_time_series, - func_args, - ) + logger.debug("Processing both channels") + return acoustics_metric_2ch( + s, + metric, + statistics, + label, + channel, + as_df, + return_time_series, + func_args, + ) def mosqito_metric( self, metric: str, - statistics: Union[Tuple, List] = ( + statistics: tuple | list = ( 5, 10, 50, @@ -449,14 +543,14 @@ def mosqito_metric( "kurt", "skew", ), - label: Optional[str] = None, - channel: Union[int, Tuple, List, str] = ("Left", "Right"), + label: str | None = None, + channel: int | tuple | list | str = ("Left", "Right"), as_df: bool = True, return_time_series: bool = False, parallel: bool = True, - metric_settings: Optional[MetricSettings] = None, - func_args: Dict = {}, - ) -> Union[Dict, pd.DataFrame]: + metric_settings: MetricSettings | None = None, + func_args: dict = {}, + ) -> dict | pd.DataFrame: """ Run a metric from the mosqito library. @@ -495,6 +589,7 @@ def mosqito_metric( -------- binaural.mosqito_metric_2ch : Method for running metrics on 2 channels. binaural.mosqito_metric_1ch : Method for running metrics on 1 channel. + """ logger.info(f"Running mosqito metric: {metric}") if metric_settings: @@ -515,30 +610,35 @@ def mosqito_metric( if s.channels == 1: logger.debug("Processing single channel") return mosqito_metric_1ch( - s, metric, statistics, label, as_df, return_time_series, func_args - ) - else: - logger.debug("Processing both channels") - return mosqito_metric_2ch( s, metric, statistics, label, - channel, - as_df, - return_time_series, - parallel, - func_args, + as_df=as_df, + return_time_series=return_time_series, + **func_args, ) + logger.debug("Processing both channels") + return mosqito_metric_2ch( + s, + metric, + statistics, + label, + channel, + as_df=as_df, + return_time_series=return_time_series, + parallel=parallel, + func_args=func_args, + ) def maad_metric( self, metric: str, - channel: Union[int, Tuple, List, str] = ("Left", "Right"), + channel: int | tuple | list | str = ("Left", "Right"), as_df: bool = True, - metric_settings: Optional[MetricSettings] = None, - func_args: Dict = {}, - ) -> Union[Dict, pd.DataFrame]: + metric_settings: MetricSettings | None = None, + func_args: dict = {}, + ) -> dict | pd.DataFrame: """ Run a metric from the scikit-maad library. @@ -572,6 +672,7 @@ def maad_metric( -------- metrics.maad_metric_1ch metrics.maad_metric_2ch + """ logger.info(f"Running maad metric: {metric}") if metric_settings: @@ -593,9 +694,8 @@ def maad_metric( if s.channels == 1: logger.debug("Processing single channel") return maad_metric_1ch(s, metric, as_df) - else: - logger.debug("Processing both channels") - return maad_metric_2ch(s, metric, channel, as_df, func_args) + logger.debug("Processing both channels") + return maad_metric_2ch(s, metric, channel, as_df, func_args) def process_all_metrics( self, @@ -633,6 +733,7 @@ def process_all_metrics( >>> signal = Binaural.from_wav("audio.wav") >>> settings = AnalysisSettings.from_yaml("settings.yaml") >>> results = signal.process_all_metrics(settings) + """ logger.info(f"Processing all metrics for {self.recording}") logger.debug(f"Parallel processing: {parallel}") diff --git a/src/soundscapy/audio/metrics.py b/src/soundscapy/audio/metrics.py index 0f863c4a..c7f591ae 100644 --- a/src/soundscapy/audio/metrics.py +++ b/src/soundscapy/audio/metrics.py @@ -1,40 +1,52 @@ """ -soundscapy.audio.metrics -======================== +Functions for calculating various acoustic and psychoacoustic metrics for audio signals. -This module provides functions for calculating various acoustic and psychoacoustic metrics -for audio signals. It includes implementations for single-channel and two-channel signals, +It includes implementations for single-channel and two-channel signals, as well as wrapper functions for different libraries such as Acoustic Toolbox, MoSQITo, and scikit-maad. Functions --------- _stat_calcs : Calculate various statistics for a time series array. -mosqito_metric_1ch : Calculate a MoSQITo psychoacoustic metric for a single channel signal. +mosqito_metric_1ch : Calculate a MoSQITo psychoacoustic metric for a single channel + signal. maad_metric_1ch : Run a metric from the scikit-maad library on a single channel signal. -acoustics_metric_1ch : Run a metric from the Acoustic Toolbox on a single channel object. +acoustics_metric_1ch : Run a metric from the Acoustic Toolbox on a single channel + object. acoustics_metric_2ch : Run a metric from the Acoustic Toolbox on a Binaural object. -pyacoustics_metric_1ch: Deprecated function for running a metric from the PyAcoustics library (replaced with `acoustics_metric_1ch`). -pyacoustics_metric_2ch: Deprecated function for running a metric from the PyAcoustics library (replaced with `acoustics_metric_2ch`). +pyacoustics_metric_1ch: Deprecated function for running a metric from the PyAcoustics + library +pyacoustics_metric_2ch: Deprecated function for running a metric from the PyAcoustics + library (replaced with `acoustics_metric_2ch`). +pyacoustics_metric_2ch: Deprecated function for running a metric from the PyAcoustics + library (replaced with `acoustics_metric_2ch`). mosqito_metric_2ch : Calculate metrics from MoSQITo for a two-channel signal. maad_metric_2ch : Run a metric from the scikit-maad library on a binaural signal. prep_multiindex_df : Prepare a MultiIndex dataframe from a dictionary of results. add_results : Add results to a MultiIndex dataframe. -process_all_metrics : Process all metrics specified in the analysis settings for a binaural signal. +process_all_metrics : Process all metrics specified in the analysis settings for a + binaural signal. Notes ----- -This module relies on external libraries such as numpy, pandas, maad, mosqito, and scipy. -Ensure these dependencies are installed before using this module. +This module relies on external libraries such as numpy, pandas, maad, mosqito, +and scipy. Ensure these dependencies are installed before using this module. + """ import concurrent.futures import multiprocessing as mp import warnings -from typing import Dict, List, Optional, Tuple, Union +from typing import Literal, TypedDict + +try: + from typing import Unpack +except ImportError: + from typing_extensions import Unpack import numpy as np import pandas as pd +from acoustic_toolbox import Signal from loguru import logger from maad.features import all_spectral_alpha_indices, all_temporal_alpha_indices from maad.sound import spectrogram @@ -45,9 +57,10 @@ sharpness_din_perseg, sharpness_din_tv, ) +from numpy.typing import NDArray from scipy import stats -from .analysis_settings import AnalysisSettings +from soundscapy.audio.analysis_settings import AnalysisSettings DEFAULT_LABELS = { "LZeq": "LZeq", @@ -64,10 +77,13 @@ def _stat_calcs( - label: str, ts_array: np.ndarray, res: dict, statistics: List[Union[int, str]] + label: str, + ts_array: NDArray[np.float64], + res: dict, + statistics: tuple[int | str, ...], ) -> dict: """ - Calculate various statistics for a time series array and add them to a results dictionary. + Calculate various statistics for a time series array and add them to a dictionary. Parameters ---------- @@ -95,6 +111,7 @@ def _stat_calcs( >>> updated_res = _stat_calcs("metric", ts, res, [50, "avg", "max"]) >>> print(updated_res) {'metric_50': 3.0, 'metric_avg': 3.0, 'metric_max': 5} + """ logger.debug(f"Calculating statistics for {label}") for stat in statistics: @@ -111,18 +128,38 @@ def _stat_calcs( res[f"{label}_{stat}"] = stats.skew(ts_array) elif stat == "std": res[f"{label}_{stat}"] = np.std(ts_array) - else: + elif isinstance(stat, int): res[f"{label}_{stat}"] = np.percentile(ts_array, 100 - stat) - except Exception as e: - logger.error(f"Error calculating {stat} for {label}: {str(e)}") + else: + logger.error(f"Unrecognized statistic: {stat} for {label}") + res[f"{label}_{stat}"] = np.nan + except Exception as e: # noqa: BLE001, PERF203 + logger.error(f"Error calculating {stat} for {label}: {e!s}") res[f"{label}_{stat}"] = np.nan return res +class _MosqitoMetricParams(TypedDict, total=False): + field_type: str # loudness_zwtv, sharpness_din_from_loudness, sharpness_din_tv + weighting: ( + str # sharpness_din_from_loudness, sharpness_din_perseg, sharpness_din_tv + ) + overlap: float # roughness_dw + nperseg: int # sharpness_din_perseg + noverlap: int | None # sharpness_din_perseg + skip: float # sharpness_din_tv + + def mosqito_metric_1ch( - s, - metric: str, - statistics: Tuple[Union[int, str]] = ( + s: Signal, + metric: Literal[ + "loudness_zwtv", + "roughness_dw", + "sharpness_din_from_loudness", + "sharpness_din_perseg", + "sharpness_din_tv", + ], + statistics: tuple[int | str, ...] = ( 5, 10, 50, @@ -134,11 +171,12 @@ def mosqito_metric_1ch( "kurt", "skew", ), - label: Optional[str] = None, + label: str | None = None, + *, as_df: bool = False, return_time_series: bool = False, - func_args: Dict = {}, -) -> Union[Dict, pd.DataFrame]: + **kwargs: Unpack[_MosqitoMetricParams], +) -> dict | pd.DataFrame: """ Calculate a MoSQITo psychoacoustic metric for a single channel signal. @@ -169,7 +207,8 @@ def mosqito_metric_1ch( Raises ------ ValueError - If the input signal is not single-channel or if an unrecognized metric is specified. + If the input signal is not single-channel + or if an unrecognized metric is specified. Examples -------- @@ -177,18 +216,21 @@ def mosqito_metric_1ch( >>> from soundscapy.audio import Binaural >>> signal = Binaural.from_wav("audio.wav", resample=480000) >>> results = mosqito_metric_1ch(signal[0], "loudness_zwtv", as_df=True) + """ logger.debug(f"Calculating MoSQITo metric: {metric}") # Checks and warnings if s.channels != 1: logger.error("Signal must be single channel") - raise ValueError("Signal must be single channel") + msg = "Signal must be single channel" + raise ValueError(msg) try: label = label or DEFAULT_LABELS[metric] except KeyError as e: logger.error(f"Metric {metric} not recognized") - raise ValueError(f"Metric {metric} not recognized.") from e + msg = f"Metric {metric} not recognized." + raise ValueError(msg) from e if as_df and return_time_series: logger.warning( "Cannot return both a dataframe and time series. Returning dataframe only." @@ -199,54 +241,102 @@ def mosqito_metric_1ch( res = {} try: if metric == "loudness_zwtv": - N, N_spec, bark_axis, time_axis = loudness_zwtv(s, s.fs, **func_args) + # Prepare args specifically for loudness_zwtv + loudness_args = {} + if "field_type" in kwargs: + loudness_args["field_type"] = kwargs["field_type"] + # Call with filtered args + N, N_spec, _, time_axis = loudness_zwtv(s, s.fs, **loudness_args) # noqa: N806 + # TODO(MitchellAcoustics): Add the bark_axis back in + # when we implement time series calcs + # https://github.com/MitchellAcoustics/Soundscapy/issues/113 res = _stat_calcs(label, N, res, statistics) if return_time_series: res[f"{label}_ts"] = (time_axis, N) + elif metric == "roughness_dw": - R, R_spec, bark_axis, time_axis = roughness_dw(s, s.fs, **func_args) - res = _stat_calcs(label, R, res, statistics) + # Prepare args specifically for roughness_dw + roughness_args = {} + if "overlap" in kwargs: + roughness_args["overlap"] = kwargs["overlap"] + # Call with filtered args + R, _, _, time_axis = roughness_dw(s, s.fs, **roughness_args) # noqa: N806 + # TODO(MitchellAcoustics): Add the R_spec and bark_axis back in + # when we implement time series calcs + # https://github.com/MitchellAcoustics/Soundscapy/issues/113 + if isinstance(R, float | int): + res[label] = R + elif isinstance(R, np.ndarray) and len(R) == 1: + res[label] = R[0] + else: + res = _stat_calcs(label, R, res, statistics) if return_time_series: res[f"{label}_ts"] = (time_axis, R) + elif metric == "sharpness_din_from_loudness": - field_type = func_args.get("field_type", "free") - N, N_spec, bark_axis, time_axis = loudness_zwtv( - s, s.fs, field_type=field_type - ) + # Prepare args for loudness_zwtv (needed first) + loudness_args = {} + if "field_type" in kwargs: + loudness_args["field_type"] = kwargs["field_type"] + N, N_spec, _, time_axis = loudness_zwtv(s, s.fs, **loudness_args) # noqa: N806 + # TODO(MitchellAcoustics): Add the R_spec and bark_axis back in + # when we implement time series calcs + # https://github.com/MitchellAcoustics/Soundscapy/issues/113 res = _stat_calcs("N", N, res, statistics) if return_time_series: res["N_ts"] = time_axis, N - func_args.pop("field_type", None) - S = sharpness_din_from_loudness(N, N_spec, **func_args) + # Prepare args specifically for sharpness_din_from_loudness + sharpness_args = {} + if "weighting" in kwargs: + sharpness_args["weighting"] = kwargs["weighting"] + # Call with filtered args + S = sharpness_din_from_loudness(N, N_spec, **sharpness_args) # noqa: N806 res = _stat_calcs(label, S, res, statistics) if return_time_series: res[f"{label}_ts"] = (time_axis, S) + elif metric == "sharpness_din_perseg": - S, time_axis = sharpness_din_perseg(s, s.fs, **func_args) + # Prepare args specifically for sharpness_din_perseg + sharpness_args = {} + if "weighting" in kwargs: + sharpness_args["weighting"] = kwargs["weighting"] + if "nperseg" in kwargs: + sharpness_args["nperseg"] = kwargs["nperseg"] + if "noverlap" in kwargs: + sharpness_args["noverlap"] = kwargs["noverlap"] + # Call with filtered args + S, time_axis = sharpness_din_perseg(s, s.fs, **sharpness_args) # noqa: N806 res = _stat_calcs(label, S, res, statistics) if return_time_series: res[f"{label}_ts"] = (time_axis, S) + elif metric == "sharpness_din_tv": - S, time_axis = sharpness_din_tv(s, s.fs, **func_args) + # Prepare args specifically for sharpness_din_tv + sharpness_args = {} + if "weighting" in kwargs: + sharpness_args["weighting"] = kwargs["weighting"] + if "skip" in kwargs: + sharpness_args["skip"] = kwargs["skip"] + # Call with filtered args + S, time_axis = sharpness_din_tv(s, s.fs, **sharpness_args) # noqa: N806 res = _stat_calcs(label, S, res, statistics) if return_time_series: res[f"{label}_ts"] = (time_axis, S) else: - logger.error(f"Metric {metric} not recognized") - raise ValueError(f"Metric {metric} not recognized.") + msg = f"Metric {metric} not recognized." + logger.error(msg) + raise ValueError(msg) except Exception as e: - logger.error(f"Error calculating {metric}: {str(e)}") + logger.error(f"Error calculating {metric}: {e!s}") raise # Return the results in the requested format if not as_df: return res - try: - rec = s.recording - return pd.DataFrame(res, index=[rec]) - except AttributeError: - return pd.DataFrame(res, index=[0]) + + rec = getattr(s, "recording", None) + return pd.DataFrame(res, index=[rec]) def maad_metric_1ch(s, metric: str, as_df: bool = False, func_args={}): @@ -281,6 +371,7 @@ def maad_metric_1ch(s, metric: str, as_df: bool = False, func_args={}): -------- maad.features.all_spectral_alpha_indices maad.features.all_temporal_alpha_indices + """ logger.debug(f"Calculating MAAD metric: {metric}") @@ -302,7 +393,7 @@ def maad_metric_1ch(s, metric: str, as_df: bool = False, func_args={}): logger.error(f"Metric {metric} not recognized") raise ValueError(f"Metric {metric} not recognized.") except Exception as e: - logger.error(f"Error calculating {metric}: {str(e)}") + logger.error(f"Error calculating {metric}: {e!s}") raise if not as_df: @@ -318,7 +409,7 @@ def maad_metric_1ch(s, metric: str, as_df: bool = False, func_args={}): def pyacoustics_metric_1ch( s, metric: str, - statistics: List[Union[int, str]] = ( + statistics: list[int | str] = ( 5, 10, 50, @@ -353,7 +444,7 @@ def pyacoustics_metric_1ch( def acoustics_metric_1ch( s, metric: str, - statistics: List[Union[int, str]] = ( + statistics: list[int | str] = ( 5, 10, 50, @@ -406,6 +497,7 @@ def acoustics_metric_1ch( See Also -------- acoustic_toolbox + """ logger.debug(f"Calculating acoustics metric: {metric}") @@ -453,7 +545,7 @@ def acoustics_metric_1ch( logger.error(f"Metric {metric} not recognized") raise ValueError(f"Metric {metric} not recognized.") except Exception as e: - logger.error(f"Error calculating {metric}: {str(e)}") + logger.error(f"Error calculating {metric}: {e!s}") raise if not as_df: @@ -468,7 +560,7 @@ def acoustics_metric_1ch( def pyacoustics_metric_2ch( b, metric: str, - statistics: Union[Tuple, List] = ( + statistics: tuple | list = ( 5, 10, 50, @@ -481,7 +573,7 @@ def pyacoustics_metric_2ch( "skew", ), label: str = None, - channel_names: Tuple[str, str] = ("Left", "Right"), + channel_names: tuple[str, str] = ("Left", "Right"), as_df: bool = False, return_time_series: bool = False, func_args={}, @@ -505,7 +597,7 @@ def pyacoustics_metric_2ch( def acoustics_metric_2ch( b, metric: str, - statistics: Union[Tuple, List] = ( + statistics: tuple | list = ( 5, 10, 50, @@ -518,7 +610,7 @@ def acoustics_metric_2ch( "skew", ), label: str | None = None, - channel_names: Tuple[str, str] = ("Left", "Right"), + channel_names: tuple[str, str] = ("Left", "Right"), as_df: bool = False, return_time_series: bool = False, func_args={}, @@ -561,6 +653,7 @@ def acoustics_metric_2ch( See Also -------- acoustics_metric_1ch + """ logger.debug(f"Calculating acoustics metric for 2 channels: {metric}") @@ -595,7 +688,7 @@ def acoustics_metric_2ch( res = {channel_names[0]: res_l, channel_names[1]: res_r} except Exception as e: - logger.error(f"Error calculating {metric} for 2 channels: {str(e)}") + logger.error(f"Error calculating {metric} for 2 channels: {e!s}") raise if not as_df: @@ -614,7 +707,7 @@ def acoustics_metric_2ch( def _parallel_mosqito_metric_2ch( b, metric: str, - statistics: Union[Tuple, List] = ( + statistics: tuple | list = ( 5, 10, 50, @@ -627,7 +720,7 @@ def _parallel_mosqito_metric_2ch( "skew", ), label: str | None = None, - channel_names: Tuple[str, str] = ("Left", "Right"), + channel_names: tuple[str, str] = ("Left", "Right"), return_time_series: bool = False, func_args={}, ): @@ -660,6 +753,7 @@ def _parallel_mosqito_metric_2ch( See Also -------- mosqito_metric_1ch + """ logger.debug(f"Calculating MoSQITo metric in parallel: {metric}") @@ -684,7 +778,7 @@ def _parallel_mosqito_metric_2ch( pool.close() return {channel: result_objects[i] for i, channel in enumerate(channel_names)} except Exception as e: - logger.error(f"Error in parallel MoSQITo calculation: {str(e)}") + logger.error(f"Error in parallel MoSQITo calculation: {e!s}") pool.close() raise finally: @@ -694,7 +788,7 @@ def _parallel_mosqito_metric_2ch( def mosqito_metric_2ch( b, metric: str, - statistics: Union[Tuple, List] = ( + statistics: tuple | list = ( 5, 10, 50, @@ -707,7 +801,7 @@ def mosqito_metric_2ch( "skew", ), label: str = None, - channel_names: Tuple[str, str] = ("Left", "Right"), + channel_names: tuple[str, str] = ("Left", "Right"), as_df: bool = False, return_time_series: bool = False, parallel: bool = True, @@ -751,6 +845,7 @@ def mosqito_metric_2ch( ------ ValueError If the input signal is not 2-channel. + """ logger.debug(f"Calculating MoSQITo metric for 2 channels: {metric}") @@ -774,9 +869,9 @@ def mosqito_metric_2ch( metric, statistics, label, - False, - return_time_series, - func_args, + as_df=False, + return_time_series=return_time_series, + **func_args, ) future_r = executor.submit( mosqito_metric_1ch, @@ -784,9 +879,9 @@ def mosqito_metric_2ch( metric, statistics, label, - False, - return_time_series, - func_args, + as_df=False, + return_time_series=return_time_series, + **func_args, ) res_l = future_l.result() res_r = future_r.result() @@ -796,25 +891,23 @@ def mosqito_metric_2ch( metric, statistics, label, - False, - return_time_series, - func_args, + as_df=False, + return_time_series=return_time_series, + **func_args, ) res_r = mosqito_metric_1ch( b[1], metric, statistics, label, - False, - return_time_series, - func_args, + as_df=False, + return_time_series=return_time_series, + **func_args, ) res = {channel_names[0]: res_l, channel_names[1]: res_r} except Exception as e: - logger.error( - f"Error calculating MoSQITo metric {metric} for 2 channels: {str(e)}" - ) + logger.error(f"Error calculating MoSQITo metric {metric} for 2 channels: {e!s}") raise if not as_df: @@ -833,7 +926,7 @@ def mosqito_metric_2ch( def maad_metric_2ch( b, metric: str, - channel_names: Tuple[str, str] = ("Left", "Right"), + channel_names: tuple[str, str] = ("Left", "Right"), as_df: bool = False, func_args={}, ): @@ -870,6 +963,7 @@ def maad_metric_2ch( -------- scikit-maad library maad_metric_1ch + """ logger.debug(f"Calculating MAAD metric for 2 channels: {metric}") @@ -880,11 +974,11 @@ def maad_metric_2ch( logger.debug(f"Calculating scikit-maad {metric}") try: - res_l = maad_metric_1ch(b[0], metric, as_df=False) - res_r = maad_metric_1ch(b[1], metric, as_df=False) + res_l = maad_metric_1ch(b[0], metric, as_df=False, **func_args) + res_r = maad_metric_1ch(b[1], metric, as_df=False, **func_args) res = {channel_names[0]: res_l, channel_names[1]: res_r} except Exception as e: - logger.error(f"Error calculating MAAD metric {metric} for 2 channels: {str(e)}") + logger.error(f"Error calculating MAAD metric {metric} for 2 channels: {e!s}") raise if not as_df: @@ -925,6 +1019,7 @@ def prep_multiindex_df(dictionary: dict, label: str = "Leq", incl_metric: bool = ------ ValueError If the input dictionary is not in the expected format. + """ logger.info("Preparing MultiIndex DataFrame") try: @@ -940,7 +1035,7 @@ def prep_multiindex_df(dictionary: dict, label: str = "Leq", incl_metric: bool = logger.debug("MultiIndex DataFrame prepared successfully") return df except Exception as e: - logger.error(f"Error preparing MultiIndex DataFrame: {str(e)}") + logger.error(f"Error preparing MultiIndex DataFrame: {e!s}") raise ValueError("Invalid input dictionary format") from e @@ -964,6 +1059,7 @@ def add_results(results_df: pd.DataFrame, metric_results: pd.DataFrame): ------ ValueError If the input DataFrames are not in the expected format. + """ logger.info("Adding results to MultiIndex DataFrame") try: @@ -978,7 +1074,7 @@ def add_results(results_df: pd.DataFrame, metric_results: pd.DataFrame): logger.debug("Results added successfully") return results_df except Exception as e: - logger.error(f"Error adding results to DataFrame: {str(e)}") + logger.error(f"Error adding results to DataFrame: {e!s}") raise ValueError("Invalid input DataFrame format") from e @@ -1024,6 +1120,7 @@ def process_all_metrics( >>> signal = Binaural.from_wav("audio.wav", resample=480000) >>> settings = AnalysisSettings.from_yaml("settings.yaml") >>> results = process_all_metrics(signal,settings) + """ logger.info(f"Processing all metrics for {b.recording}") logger.debug(f"Parallel processing: {parallel}") @@ -1074,7 +1171,7 @@ def process_all_metrics( logger.info("All metrics processed successfully") return results_df except Exception as e: - logger.error(f"Error processing metrics: {str(e)}") + logger.error(f"Error processing metrics: {e!s}") raise ValueError("Error processing metrics") from e diff --git a/src/soundscapy/audio/parallel_processing.py b/src/soundscapy/audio/parallel_processing.py index 9d06aa7a..7dc2a58b 100644 --- a/src/soundscapy/audio/parallel_processing.py +++ b/src/soundscapy/audio/parallel_processing.py @@ -1,8 +1,5 @@ """ -soundscapy.audio.parallel_processing -==================================== - -This module provides functions for parallel processing of binaural audio files. +Functions for parallel processing of binaural audio files. It includes functions to load and analyze binaural files, as well as to process multiple files in parallel using concurrent.futures. @@ -14,14 +11,14 @@ Note: This module requires the tqdm library for progress bars and concurrent.futures for parallel processing. It uses loguru for logging. + """ import concurrent.futures from pathlib import Path -from typing import Dict, List, Optional -from loguru import logger import pandas as pd +from loguru import logger from tqdm.auto import tqdm from .analysis_settings import AnalysisSettings @@ -33,21 +30,22 @@ ) -def tqdm_write_sink(message): +def tqdm_write_sink(message: str) -> None: """ - A custom sink for loguru that writes messages using tqdm.write(). + Custom sink for loguru that writes messages using tqdm.write(). This ensures that log messages don't interfere with tqdm progress bars. - """ + """ # noqa: D401 tqdm.write(message, end="") def load_analyse_binaural( wav_file: Path, - levels: Dict | List[float], + levels: dict[str, float] | list[float] | None, analysis_settings: AnalysisSettings, + resample: int | None = None, + *, parallel_mosqito: bool = True, - resample: Optional[int] = None, ) -> pd.DataFrame: """ Load and analyze a single binaural audio file. @@ -68,6 +66,7 @@ def load_analyse_binaural( ------- pd.DataFrame DataFrame with analysis results. + """ logger.info(f"Processing {wav_file}") try: @@ -85,18 +84,19 @@ def load_analyse_binaural( logger.warning(f"No calibration levels found for {wav_file}") return process_all_metrics(b, analysis_settings, parallel=parallel_mosqito) except Exception as e: - logger.error(f"Error processing {wav_file}: {str(e)}") + logger.error(f"Error processing {wav_file}: {e!s}") raise def parallel_process( - wav_files: List[Path], + wav_files: list[Path], results_df: pd.DataFrame, - levels: Dict, + levels: dict, analysis_settings: AnalysisSettings, - max_workers: Optional[int] = None, + max_workers: int | None = None, + resample: int | None = None, + *, parallel_mosqito: bool = True, - resample: Optional[int] = None, ) -> pd.DataFrame: """ Process multiple binaural files in parallel. @@ -121,6 +121,7 @@ def parallel_process( ------- pd.DataFrame Updated results DataFrame with analysis results for all files. + """ logger.info(f"Starting parallel processing of {len(wav_files)} files") @@ -135,8 +136,8 @@ def parallel_process( wav_file, levels, analysis_settings, - parallel_mosqito, resample, + parallel_mosqito=parallel_mosqito, ) futures.append(future) @@ -146,7 +147,7 @@ def parallel_process( result = future.result() results_df = add_results(results_df, result) except Exception as e: - logger.error(f"Error processing file: {str(e)}") + logger.error(f"Error processing file: {e!s}") finally: pbar.update(1) @@ -159,13 +160,13 @@ def parallel_process( if __name__ == "__main__": # Example usage - from datetime import datetime import json import warnings + from datetime import datetime warnings.filterwarnings("ignore") - from soundscapy.logging import setup_logging + from soundscapy.sspylogging import setup_logging setup_logging("DEBUG") diff --git a/src/soundscapy/data/__init__.py b/src/soundscapy/data/__init__.py index e69de29b..0928964b 100644 --- a/src/soundscapy/data/__init__.py +++ b/src/soundscapy/data/__init__.py @@ -0,0 +1,6 @@ +""" +Soundscape data module. + +This module serves as the package initializer for the soundscape data processing +functionalities. +""" diff --git a/src/soundscapy/data/default_settings.yaml b/src/soundscapy/data/default_settings.yaml index 7eea121f..ad01f75b 100644 --- a/src/soundscapy/data/default_settings.yaml +++ b/src/soundscapy/data/default_settings.yaml @@ -7,120 +7,120 @@ version: "1.1" # Supported metrics: LAeq, LZeq, LCeq, SEL # Supported statistics: avg/mean, max, min, kurt, skew, any integer from 1-99 AcousticToolbox: - # A-weighted equivalent continuous sound level - LAeq: - run: true # Set to false to skip this metric - main: "avg" # Main statistic to calculate - statistics: [5, 10, 50, 90, 95, "avg", "min", "max", "kurt", "skew"] # List of statistics to calculate - channel: ["Left", "Right"] # Channels to analyze - label: "LAeq" # Label for the metric in output - func_args: # Additional arguments for the metric function - time: 0.125 # Time interval for calculation (seconds) - method: "average" # Method for calculating Leq + # A-weighted equivalent continuous sound level + LAeq: + run: true # Set to false to skip this metric + main: "avg" # Main statistic to calculate + statistics: [5, 10, 50, 90, 95, "avg", "min", "max", "kurt", "skew"] # List of statistics to calculate + channel: ["Left", "Right"] # Channels to analyze + label: "LAeq" # Label for the metric in output + func_args: # Additional arguments for the metric function + time: 0.125 # Time interval for calculation (seconds) + method: "average" # Method for calculating Leq - # Z-weighted (unweighted) equivalent continuous sound level - LZeq: - run: true - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "LZeq" - func_args: - time: 0.125 - method: "average" + # Z-weighted (unweighted) equivalent continuous sound level + LZeq: + run: true + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "LZeq" + func_args: + time: 0.125 + method: "average" - # C-weighted equivalent continuous sound level - LCeq: - run: true - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "LCeq" - func_args: - time: 0.125 - method: "average" + # C-weighted equivalent continuous sound level + LCeq: + run: true + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "LCeq" + func_args: + time: 0.125 + method: "average" - # Sound Exposure Level - SEL: - run: true - channel: ["Left", "Right"] - label: "SEL" - # Note: SEL doesn't use main or statistics as it's a single value metric + # Sound Exposure Level + SEL: + run: true + channel: ["Left", "Right"] + label: "SEL" + # Note: SEL doesn't use main or statistics as it's a single value metric # MoSQITo Library Settings # Supported metrics: loudness_zwtv, sharpness_din_from_loudness, sharpness_din_perseg, sharpness_din_tv, roughness_dw # Supported statistics: avg/mean, max, min, kurt, skew, any integer from 1-99 MoSQITo: - # Zwicker Time-Varying Loudness - loudness_zwtv: - run: false # Disabled by default as it's used in sharpness calculation - main: 5 # N5 (loudness exceeded 5% of the time) - statistics: [10, 50, 90, 95, "min", "max", "kurt", "skew", "avg"] - channel: ["Left", "Right"] - label: "N" - parallel: true # Enable parallel processing - func_args: - field_type: "free" # Free field condition + # Zwicker Time-Varying Loudness + loudness_zwtv: + run: false # Disabled by default as it's used in sharpness calculation + main: 5 # N5 (loudness exceeded 5% of the time) + statistics: [10, 50, 90, 95, "min", "max", "kurt", "skew", "avg"] + channel: ["Left", "Right"] + label: "N" + parallel: true # Enable parallel processing + func_args: + field_type: "free" # Free field condition - # Sharpness (DIN 45692) calculated from Zwicker Loudness - sharpness_din_from_loudness: - run: true - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "S_L" - parallel: true - func_args: - weighting: "din" # DIN 45692 weighting - field_type: "free" + # Sharpness (DIN 45692) calculated from Zwicker Loudness + sharpness_din_from_loudness: + run: true + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "S_L" + parallel: true + func_args: + weighting: "din" # DIN 45692 weighting + field_type: "free" - # Sharpness (DIN 45692) calculated per segment - sharpness_din_perseg: - run: true - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "S_perseg" - parallel: false # Parallel processing not necessary for this method - func_args: - weighting: "din" - nperseg: 4096 # Number of samples per segment - field_type: "free" + # Sharpness (DIN 45692) calculated per segment + sharpness_din_perseg: + run: true + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "S_perseg" + parallel: false # Parallel processing not necessary for this method + func_args: + weighting: "din" + nperseg: 4096 # Number of samples per segment + field_type: "free" - # Sharpness (DIN 45692) time-varying - # Note: It's recommended to use sharpness_din_from_loudness instead - sharpness_din_tv: - run: false - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "S_din_tv" - parallel: true - func_args: - weighting: "din" - field_type: "free" - skip: 0.5 # Skip time at the beginning (seconds) + # Sharpness (DIN 45692) time-varying + # Note: It's recommended to use sharpness_din_from_loudness instead + sharpness_din_tv: + run: false + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "S_din_tv" + parallel: true + func_args: + weighting: "din" + field_type: "free" + skip: 0.5 # Skip time at the beginning (seconds) - # Roughness (Daniel & Weber method) - roughness_dw: - run: true - main: "avg" - statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] - channel: ["Left", "Right"] - label: "R" - parallel: true + # Roughness (Daniel & Weber method) + roughness_dw: + run: true + main: "avg" + statistics: [5, 10, 50, 90, 95, "min", "max", "kurt", "skew"] + channel: ["Left", "Right"] + label: "R" + parallel: true # scikit-maad Library Settings # These are collections of multiple acoustic indices scikit-maad: - # Temporal alpha diversity indices - all_temporal_alpha_indices: - run: true - channel: ["Left", "Right"] - # Note: This calculates multiple indices, so no individual statistics are specified + # Temporal alpha diversity indices + all_temporal_alpha_indices: + run: true + channel: ["Left", "Right"] + # Note: This calculates multiple indices, so no individual statistics are specified - # Spectral alpha diversity indices - all_spectral_alpha_indices: - run: true - channel: ["Left", "Right"] - # Note: This calculates multiple indices, so no individual statistics are specified + # Spectral alpha diversity indices + all_spectral_alpha_indices: + run: true + channel: ["Left", "Right"] + # Note: This calculates multiple indices, so no individual statistics are specified diff --git a/src/soundscapy/databases/__init__.py b/src/soundscapy/databases/__init__.py index e69de29b..f5310b2a 100644 --- a/src/soundscapy/databases/__init__.py +++ b/src/soundscapy/databases/__init__.py @@ -0,0 +1,10 @@ +""" +Soundscapy Databases Module. + +This module handles connections to and operations on soundscape databases, +primarily focused on the International Soundscape Database (ISD). +""" + +from soundscapy.databases import isd, satp + +__all__ = ["isd", "satp"] diff --git a/src/soundscapy/databases/araus.py b/src/soundscapy/databases/araus.py index 19f9e5d7..abf66abf 100644 --- a/src/soundscapy/databases/araus.py +++ b/src/soundscapy/databases/araus.py @@ -1,5 +1,9 @@ -# Customized functions specifically for the ARAUS dataset +"""Customized functions specifically for the ARAUS dataset.""" -# Add soundscapy to the Python path +import warnings -# Constants and Labels +warnings.warn( + "The ARAUS dataset module is not yet implemented", + FutureWarning, + stacklevel=2, +) diff --git a/src/soundscapy/databases/isd.py b/src/soundscapy/databases/isd.py index 02d0a6fb..3aaba8ba 100644 --- a/src/soundscapy/databases/isd.py +++ b/src/soundscapy/databases/isd.py @@ -19,13 +19,14 @@ True >>> 'PAQ1' in df.columns True + """ from importlib import resources -from typing import Dict, List, Optional, Tuple import pandas as pd from loguru import logger + from soundscapy.surveys.processing import ( calculate_iso_coords, likert_data_quality, @@ -75,6 +76,7 @@ def load() -> pd.DataFrame: True >>> set(PAQ_IDS).issubset(df.columns) True + """ isd_resource = resources.files("soundscapy.data").joinpath("ISD v1.0 Data.csv") with resources.as_file(isd_resource) as f: @@ -116,6 +118,7 @@ def load_zenodo(version: str = "latest") -> pd.DataFrame: True >>> set(PAQ_IDS).issubset(df.columns) # doctest: +SKIP True + """ version = version.lower() version = "v1.0.1" if version == "latest" else version @@ -130,7 +133,8 @@ def load_zenodo(version: str = "latest") -> pd.DataFrame: } if version not in url_mapping: - raise ValueError(f"Version {version} not recognised.") + msg = f"Version {version} not recognised." + raise ValueError(msg) url = url_mapping[version] file_type = "csv" if version in ["v1.0.0", "v1.0.1"] else "excel" @@ -148,10 +152,11 @@ def load_zenodo(version: str = "latest") -> pd.DataFrame: def validate( df: pd.DataFrame, - paq_aliases: List | Dict = _PAQ_ALIASES, + paq_aliases: list | dict = _PAQ_ALIASES, + val_range: tuple[int, int] = (1, 5), + *, allow_paq_na: bool = False, - val_range: Tuple[int, int] = (1, 5), -) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]: +) -> tuple[pd.DataFrame, pd.DataFrame | None]: """ Perform data quality checks and validate that the dataset fits the expected format. @@ -168,8 +173,9 @@ def validate( Returns ------- - Tuple[pd.DataFrame, Optional[pd.DataFrame]] - Tuple containing the cleaned dataframe and optionally a dataframe of excluded samples. + Tuple[pd.DataFrame, pd.DataFrame | None] + Tuple containing the cleaned dataframe + and optionally a dataframe of excluded samples. Notes ----- @@ -190,27 +196,28 @@ def validate( 2 >>> excl_df.shape[0] 2 + """ logger.info("Validating ISD data") - df = rename_paqs(df, paq_aliases) + data = rename_paqs(df, paq_aliases) invalid_indices = likert_data_quality( - df, allow_na=allow_paq_na, val_range=val_range + data, val_range=val_range, allow_na=allow_paq_na ) if invalid_indices: - excl_df = df.iloc[invalid_indices] - df = df.drop(df.index[invalid_indices]) + excl_data = data.iloc[invalid_indices] + data = data.drop(data.index[invalid_indices]) logger.info(f"Removed {len(invalid_indices)} rows with invalid PAQ data") else: - excl_df = None + excl_data = None logger.info("All PAQ data passed quality checks") - return df, excl_df + return data, excl_data def _isd_select( - data: pd.DataFrame, select_by: str, condition: str | int | List | Tuple + data: pd.DataFrame, select_by: str, condition: str | int | list | tuple ) -> pd.DataFrame: """ General function to select by ID variables. @@ -244,17 +251,18 @@ def _isd_select( ID Value 0 A 1 2 C 3 + """ - if isinstance(condition, (str, int)): + if isinstance(condition, str | int): return data.query(f"{select_by} == @condition", engine="python") - elif isinstance(condition, (list, tuple)): + if isinstance(condition, list | tuple): return data.query(f"{select_by} in @condition") - else: - raise TypeError("Should be either a str, int, list, or tuple.") + msg = "Should be either a str, int, list, or tuple." + raise TypeError(msg) def select_record_ids( - data: pd.DataFrame, record_ids: str | int | List | Tuple + data: pd.DataFrame, record_ids: str | int | list | tuple ) -> pd.DataFrame: """ Filter the dataframe by RecordID. @@ -281,12 +289,13 @@ def select_record_ids( RecordID Value 0 A 1 2 C 3 + """ return _isd_select(data, "RecordID", record_ids) def select_group_ids( - data: pd.DataFrame, group_ids: str | int | List | Tuple + data: pd.DataFrame, group_ids: str | int | list | tuple ) -> pd.DataFrame: """ Filter the dataframe by GroupID. @@ -313,12 +322,13 @@ def select_group_ids( GroupID Value 0 G1 1 1 G1 2 + """ return _isd_select(data, "GroupID", group_ids) def select_session_ids( - data: pd.DataFrame, session_ids: str | int | List | Tuple + data: pd.DataFrame, session_ids: str | int | list | tuple ) -> pd.DataFrame: """ Filter the dataframe by SessionID. @@ -347,12 +357,13 @@ def select_session_ids( 1 S1 2 2 S2 3 3 S2 4 + """ return _isd_select(data, "SessionID", session_ids) def select_location_ids( - data: pd.DataFrame, location_ids: str | int | List | Tuple + data: pd.DataFrame, location_ids: str | int | list | tuple ) -> pd.DataFrame: """ Filter the dataframe by LocationID. @@ -379,6 +390,7 @@ def select_location_ids( LocationID Value 2 L2 3 3 L2 4 + """ return _isd_select(data, "LocationID", location_ids) @@ -389,7 +401,7 @@ def describe_location( calc_type: str = "percent", pl_threshold: float = 0, ev_threshold: float = 0, -) -> Dict[str, int | float]: +) -> dict[str, int | float]: """ Return a summary of the data for a specific location. @@ -427,10 +439,14 @@ def describe_location( ... }) >>> df = add_iso_coords(df) >>> result = describe_location(df, 'L1') - >>> set(result.keys()) == {'count', 'ISOPleasant', 'ISOEventful', 'pleasant', 'eventful', 'vibrant', 'chaotic', 'monotonous', 'calm'} + >>> set(result.keys()) == { + ... 'count', 'ISOPleasant', 'ISOEventful', 'pleasant', 'eventful', + ... 'vibrant', 'chaotic', 'monotonous', 'calm' + ... } True >>> result['count'] 2 + """ loc_df = select_location_ids(data, location_ids=location) count = len(loc_df) @@ -483,7 +499,8 @@ def describe_location( } ) else: - raise ValueError("Type must be either 'percent' or 'count'") + msg = "Type must be either 'percent' or 'count'" + raise ValueError(msg) return {k: round(v, 3) if isinstance(v, float) else v for k, v in res.items()} @@ -528,11 +545,15 @@ def soundscapy_describe( True >>> result.index.tolist() ['L1', 'L2'] - >>> set(result.columns) == {'count', 'ISOPleasant', 'ISOEventful', 'pleasant', 'eventful', 'vibrant', 'chaotic', 'monotonous', 'calm'} + >>> set(result.columns) == { + ... 'count', 'ISOPleasant', 'ISOEventful', 'pleasant', 'eventful', + ... 'vibrant', 'chaotic', 'monotonous', 'calm' + ... } True >>> result = soundscapy_describe(df, calc_type="count") >>> result.loc['L1', 'count'] 2 + """ res = { location: describe_location(df, location, calc_type=calc_type) diff --git a/src/soundscapy/databases/satp.py b/src/soundscapy/databases/satp.py index 276b1c9a..c2f91995 100644 --- a/src/soundscapy/databases/satp.py +++ b/src/soundscapy/databases/satp.py @@ -18,6 +18,7 @@ True >>> 'Country' in participants.columns # doctest: +SKIP True + """ import pandas as pd @@ -53,11 +54,11 @@ def _url_fetch(version: str) -> str: Traceback (most recent call last): ... ValueError: Invalid version. Should be either 'latest', 'v1.2.1', or 'v1.2'. + """ if version.lower() not in ["latest", "v1.2.1", "v1.2"]: - raise ValueError( - "Invalid version. Should be either 'latest', 'v1.2.1', or 'v1.2'." - ) + msg = "Invalid version. Should be either 'latest', 'v1.2.1', or 'v1.2'." + raise ValueError(msg) version = "v1.2.1" if version.lower() == "latest" else version.lower() url = "https://zenodo.org/record/7143599/files/SATP%20Dataset%20v1.2.xlsx" @@ -79,11 +80,12 @@ def load_zenodo(version: str = "latest") -> pd.DataFrame: ------- pd.DataFrame DataFrame containing the SATP dataset. + """ url = _url_fetch(version) - df = pd.read_excel(url, engine="openpyxl", sheet_name="Main Merge") + data = pd.read_excel(url, engine="openpyxl", sheet_name="Main Merge") logger.info(f"Loaded SATP dataset version {version} from Zenodo") - return df + return data def load_participants(version: str = "latest") -> pd.DataFrame: @@ -99,12 +101,13 @@ def load_participants(version: str = "latest") -> pd.DataFrame: ------- pd.DataFrame DataFrame containing the SATP participants dataset. + """ url = _url_fetch(version) - df = pd.read_excel(url, engine="openpyxl", sheet_name="Participants") - df = df.drop(columns=["Unnamed: 3", "Unnamed: 4"]) + data = pd.read_excel(url, engine="openpyxl", sheet_name="Participants") + data = data.drop(columns=["Unnamed: 3", "Unnamed: 4"]) logger.info(f"Loaded SATP participants dataset version {version} from Zenodo") - return df + return data if __name__ == "__main__": diff --git a/src/soundscapy/plotting/__init__.py b/src/soundscapy/plotting/__init__.py index 3fb88d33..19b55e50 100644 --- a/src/soundscapy/plotting/__init__.py +++ b/src/soundscapy/plotting/__init__.py @@ -1,52 +1,29 @@ """ -Soundscapy Plotting Module +Soundscapy Plotting Module. This module provides tools for creating circumplex plots for soundscape analysis. -It utilizes the Grammar of Graphics approach with Seaborn Objects API to create -flexible, composable visualizations. - -Main components: -- CircumplexPlot: Builder class for creating customizable circumplex plots -- scatter_plot: Function to quickly create scatter plots -- density_plot: Function to quickly create density plots -- create_circumplex_subplots: Function to create multiple circumplex plots in subplots - -Example usage: - from soundscapy.plotting import scatter_plot, density_plot, CircumplexPlot - - # Create a basic scatter plot - scatter_plot(data, x='ISOPleasant', y='ISOEventful') - - # Create a density plot with scatter points - density_plot(data, x='ISOPleasant', y='ISOEventful', incl_scatter=True) - - # Build a customized plot layer by layer - (CircumplexPlot(data) - .add_density(simple=True) - .add_scatter() - .add_grid(diagonal_lines=True) - .add_title("Custom Plot") - .show()) +It supports various plot types and backends, with a focus on flexibility and ease +of use. """ -from . import likert -from .circumplex_plot import CircumplexPlot, add_annotation, apply_circumplex_grid -from .plot_functions import ( +from soundscapy.plotting import likert +from soundscapy.plotting.circumplex_plot import CircumplexPlot, CircumplexPlotParams +from soundscapy.plotting.plot_functions import ( create_circumplex_subplots, density_plot, - joint_plot, scatter_plot, ) -from .plotting_utils import PlotType +from soundscapy.plotting.plotting_utils import Backend, PlotType +from soundscapy.plotting.stylers import StyleOptions __all__ = [ + "Backend", "CircumplexPlot", - "apply_circumplex_grid", - "add_annotation", - "scatter_plot", - "density_plot", - "joint_plot", - "create_circumplex_subplots", + "CircumplexPlotParams", "PlotType", + "StyleOptions", + "create_circumplex_subplots", + "density_plot", "likert", + "scatter_plot", ] diff --git a/src/soundscapy/plotting/backends.py b/src/soundscapy/plotting/backends.py index c48a6aaa..94869395 100644 --- a/src/soundscapy/plotting/backends.py +++ b/src/soundscapy/plotting/backends.py @@ -1,3 +1,23 @@ +""" +Backend classes for plotting. + +This module provides classes for different plotting backends, including Seaborn +and Plotly. Each backend class implements methods for creating scatter and +density plots, as well as applying styling to the plots. + +Example usage: + +```python +from soundscapy.plotting import scatter_plot, density_plot, Backend, PlotType + +# Create a scatter plot using Seaborn backend +scatter_plot(data, x='ISOPleasant', y='ISOEventful', backend=Backend.SEABORN) + +# Create a density plot using Plotly backend +density_plot(data, x='ISOPleasant', y='ISOEventful', backend=Backend.PLOTLY) +``` +""" + import warnings from abc import ABC, abstractmethod @@ -30,8 +50,8 @@ def create_scatter(self, data, params): Returns ------- The created plot object. + """ - pass @abstractmethod def create_density(self, data, params): @@ -46,8 +66,8 @@ def create_density(self, data, params): Returns ------- The created plot object. + """ - pass @abstractmethod def apply_styling(self, plot_obj, params): @@ -62,8 +82,8 @@ def apply_styling(self, plot_obj, params): Returns ------- The styled plot object. + """ - pass class SeabornBackend(PlotBackend): @@ -86,6 +106,7 @@ def create_scatter(self, data, params, ax=None): Returns ------- tuple: A tuple containing the figure and axes objects. + """ if ax is None: fig, ax = plt.subplots(figsize=self.style_options.figsize) @@ -123,6 +144,7 @@ def create_density(self, data, params, ax=None): Returns ------- tuple: A tuple containing the figure and axes objects. + """ if len(data) < 30: warnings.warn( @@ -175,11 +197,12 @@ def create_jointplot(self, data, params): >>> import soundscapy as sspy >>> from soundscapy.plotting import Backend, CircumplexPlot, StyleOptions, CircumplexPlotParams >>> data = sspy.isd.load() - >>> data = sspy.surveys.add_iso_coords(data, overwrite=True) + >>> data = sspy.surveys.add_iso_coords(data,overwrite=True) >>> sample_data = sspy.isd.select_location_ids(data, ['CamdenTown']) >>> plot = CircumplexPlot(data=sample_data, backend=Backend.SEABORN) >>> g = plot.jointplot() >>> g.show() # doctest: +SKIP + """ g = sns.JointGrid(xlim=params.xlim, ylim=params.ylim) joint_params = params @@ -249,6 +272,7 @@ def apply_styling(self, plot_obj, params): Returns ------- tuple: The styled figure and axes objects. + """ fig, ax = plot_obj styler = SeabornStyler(params, self.style_options) @@ -261,6 +285,7 @@ def show(self, plot_obj): Parameters ---------- fig: The figure to display. + """ fig, _ = plot_obj plt.show() @@ -275,7 +300,6 @@ def __init__(self): warnings.warn( "PlotlyBackend is very experimental and not fully implemented.", UserWarning ) - pass def create_scatter(self, data, params): """ @@ -289,6 +313,7 @@ def create_scatter(self, data, params): Returns ------- go.Figure: A Plotly figure object. + """ fig = px.scatter( data, @@ -320,6 +345,7 @@ def create_density(self, data, params): Returns ------- go.Figure: A Plotly figure object. + """ if len(data) < 30: warnings.warn( @@ -360,6 +386,7 @@ def apply_styling(self, plot_obj, params): Returns ------- go.Figure: The styled Plotly figure object. + """ fig = plot_obj if params.diagonal_lines: @@ -390,5 +417,6 @@ def show(self, fig): Parameters ---------- fig (go.Figure): The Plotly figure to display. + """ fig.show() diff --git a/src/soundscapy/plotting/circumplex_plot.py b/src/soundscapy/plotting/circumplex_plot.py index 9fb288db..9d0e6cd4 100644 --- a/src/soundscapy/plotting/circumplex_plot.py +++ b/src/soundscapy/plotting/circumplex_plot.py @@ -1,739 +1,206 @@ """ -Main module for creating circumplex plots using Seaborn Objects API. - -This module provides the CircumplexPlot class, which is a builder for creating -circumplex plots with a grammar of graphics approach. It allows for layering -of different plot elements (scatter, density) and customization of styling. +Main module for creating circumplex plots using different backends. """ -import matplotlib.pyplot as plt -import seaborn as sns -import seaborn.objects as so - -# =============== Core building blocks for circumplex plots =============== - - -def apply_circumplex_grid( - plot, - xlim=(-1, 1), - ylim=(-1, 1), - diagonal_lines=False, - show_labels=True, - x_label=None, - y_label=None, -): - """ - Apply circumplex grid styling to a Seaborn Objects plot. - - Parameters - ---------- - plot : so.Plot - The plot to style - xlim, ylim : tuple - Axis limits - diagonal_lines : bool - Whether to draw diagonal lines and quadrant labels - show_labels : bool - Whether to keep axis labels - x_label, y_label : str, optional - Custom labels for axes - - Returns - ------- - so.Plot - The styled plot - """ - # Apply limits and axes appearance - plot = plot.limit(x=xlim, y=ylim) - - # Apply square aspect ratio and layout - plot = plot.layout(size=(6, 6)) - - # Compile the plot to a temporary figure to apply matplotlib styling - # This creates a temporary figure for styling - fig, ax = plt.subplots(figsize=(6, 6)) - - # Add grid lines - ax.grid(True, which="major", color="grey", alpha=0.5) - ax.grid( - True, which="minor", color="grey", linestyle="dashed", linewidth=0.5, alpha=0.4 - ) - ax.minorticks_on() - - # Add zero lines - ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) - ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) - - # Add diagonal elements if requested - if diagonal_lines: - # Draw diagonal lines - ax.plot( - [xlim[0], xlim[1]], - [ylim[0], ylim[1]], - linestyle="dashed", - color="grey", - alpha=0.5, - linewidth=1.5, - ) - ax.plot( - [xlim[0], xlim[1]], - [ylim[1], ylim[0]], - linestyle="dashed", - color="grey", - alpha=0.5, - linewidth=1.5, - ) - - # Add diagonal labels - diag_font = { - "fontstyle": "italic", - "fontsize": "small", - "fontweight": "bold", - "color": "black", - "alpha": 0.5, - } - - ax.text( - xlim[1] / 2, - ylim[1] / 2, - "(vibrant)", - ha="center", - va="center", - fontdict=diag_font, - ) - ax.text( - xlim[0] / 2, - ylim[1] / 2, - "(chaotic)", - ha="center", - va="center", - fontdict=diag_font, - ) - ax.text( - xlim[0] / 2, - ylim[0] / 2, - "(monotonous)", - ha="center", - va="center", - fontdict=diag_font, - ) - ax.text( - xlim[1] / 2, - ylim[0] / 2, - "(calm)", - ha="center", - va="center", - fontdict=diag_font, - ) - - # Apply axis label changes if requested - if not show_labels: - ax.set_xlabel("") - ax.set_ylabel("") - elif x_label is not None or y_label is not None: - if x_label is not None: - ax.set_xlabel(x_label) - if y_label is not None: - ax.set_ylabel(y_label) - - # Now use the fully prepared axes for the plot - plot_with_styling = plot.on(ax) - - # Clean up the temporary figure - we don't need it as we've transferred the styling - plt.close(fig) - - return plot_with_styling - - -def add_annotation( - plot, - data, - idx, - x="ISOPleasant", - y="ISOEventful", - text=None, - x_offset=0.1, - y_offset=0.1, - **kwargs, -): - """ - Add an annotation to a Seaborn Objects plot. - - Parameters - ---------- - plot : so.Plot - The plot to annotate - data : pd.DataFrame - Data containing the point to annotate - idx : int or str - Index of the point to annotate - x, y : str - Column names for coordinates - text : str, optional - Text to display (defaults to index value if None) - x_offset, y_offset : float - Offsets for annotation position - **kwargs - Additional keyword arguments for annotation - - Returns - ------- - so.Plot - The plot with annotation added - """ - # Get coordinates from data - x_val = data[x].iloc[idx] if isinstance(idx, int) else data.loc[idx, x] - y_val = data[y].iloc[idx] if isinstance(idx, int) else data.loc[idx, y] - - # Default text is the index value - if text is None: - text = str(data.index[idx]) if isinstance(idx, int) else str(idx) - - # Default annotation styling - annotation_defaults = { - "ha": "center", - "va": "center", - "fontsize": 9, - "arrowprops": {"arrowstyle": "-", "color": "black", "alpha": 0.7}, - } - annotation_defaults.update(kwargs) - - # Create a temporary figure to add the annotation - fig, ax = plt.subplots(figsize=(6, 6)) - - # Add the annotation - ax.annotate( - text=text, - xy=(x_val, y_val), - xytext=(x_val + x_offset, y_val + y_offset), - **annotation_defaults, - ) - - # Now use the fully prepared axes for the plot - plot_with_annotation = plot.on(ax) - - # Clean up the temporary figure - plt.close(fig) +import copy +from dataclasses import dataclass, field - return plot_with_annotation - - -# =============== Main Circumplex Plot Class =============== +import matplotlib.pyplot as plt +import pandas as pd + +from soundscapy.plotting.backends import PlotlyBackend, SeabornBackend +from soundscapy.plotting.plotting_utils import ( + DEFAULT_XLIM, + DEFAULT_YLIM, + Backend, + ExtraParams, + PlotType, +) +from soundscapy.plotting.stylers import StyleOptions + + +@dataclass +class CircumplexPlotParams: + """Parameters for customizing CircumplexPlot.""" + + x: str = "ISOPleasant" + y: str = "ISOEventful" + hue: str | None = None + title: str = "Soundscape Plot" + xlim: tuple[float, float] = DEFAULT_XLIM + ylim: tuple[float, float] = DEFAULT_YLIM + alpha: float = 0.8 + fill: bool = True + palette: str | None = None + incl_outline: bool = False # Fixed from (False,) + diagonal_lines: bool = False + show_labels: bool = True + legend: bool = "auto" + legend_location: str = "best" + extra_params: ExtraParams = field(default_factory=dict) + + def __post_init__(self): + if self.palette is None: + self.palette = "colorblind" if self.hue else None class CircumplexPlot: """ - A builder class for creating circumplex plots using Seaborn Objects API. + A class for creating circumplex plots using different backends. - This class allows for a layered grammar of graphics approach to building - circumplex plots including scatter, density, and other elements. + This class provides methods for creating scatter plots and density plots + based on the circumplex model of soundscape perception. It supports multiple + backends (currently Seaborn and Plotly) and offers various customization options. - Parameters - ---------- - data : pd.DataFrame - Data to plot - x, y : str - Column names for coordinates - hue : str, optional - Column name for color grouping - xlim, ylim : tuple - Axis limits for the plot - palette : str - Color palette to use """ + # TODO: Implement jointplot method for Seaborn backend. + # TODO: Implement density plots for Plotly backend. + # TODO: Improve Plotly backend to support more customization options. + def __init__( self, - data, - x="ISOPleasant", - y="ISOEventful", - hue=None, - xlim=(-1, 1), - ylim=(-1, 1), - palette="colorblind", - **kwargs, # For backwards compatibility with tests + data: pd.DataFrame, + params: CircumplexPlotParams = CircumplexPlotParams(), + backend: Backend = Backend.SEABORN, + style_options: StyleOptions = StyleOptions(), ): self.data = data - self.x = x - self.y = y - self.hue = hue - self.xlim = xlim - self.ylim = ylim - self.fill = kwargs.get("fill", True) - self.palette_name = palette - - # Initialize the plot - self.plot = so.Plot(data, x=x, y=y) - - # Track what's been added - self.has_scatter = False - self.has_density = False - self.has_grid = False - - def add_scatter( - self, - pointsize=30, - alpha=0.7, - marker="o", - color=None, # Overrides hue if provided - ): - """ - Add a scatter layer to the plot. - - Parameters - ---------- - pointsize : int or float - Size of the scatter points - alpha : float - Opacity of the points - marker : str - Marker style - color : str, optional - Override for hue variable - - Returns - ------- - CircumplexPlot - Self for method chaining - """ - color_var = color if color is not None else self.hue - - # Add the dots mark - self.plot = self.plot.add( - so.Dots(pointsize=pointsize, alpha=alpha, marker=marker), color=color_var - ) - - # If we have a color variable and palette, apply it using scale - if color_var and hasattr(self, "palette_name"): - self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) - - self.has_scatter = True - return self - - def add_density( + self.params = params + self.style_options = style_options + self._backend = self._create_backend(backend) + self._plot = None + + def _create_backend(self, backend: Backend): + """Create the appropriate backend based on the backend enum.""" + if backend == Backend.SEABORN: + return SeabornBackend(style_options=self.style_options) + if backend == Backend.PLOTLY: + return PlotlyBackend() + raise ValueError(f"Unsupported backend: {backend}") + + def _create_plot( self, - alpha=0.5, - fill=True, - levels=8, - bw_adjust=1.2, - color=None, # Overrides hue if provided - simple=False, - **kwargs, # For backwards compatibility - ): - """ - Add a density layer to the plot. - - Parameters - ---------- - alpha : float - Opacity of the fill color for the density - fill : bool - Whether to fill the contours - levels : int - Number of contour levels - bw_adjust : float - Bandwidth adjustment factor - color : str, optional - Override for hue variable - simple : bool - If True, use simplified density with fewer levels - - Returns - ------- - CircumplexPlot - Self for method chaining - """ - color_var = color if color is not None else self.hue - - if simple: - # For simple density, use just a few levels - levels = 2 - - # Use the Area mark with KDE stat for density plots - self.plot = self.plot.add( - so.Area(alpha=alpha, fill=fill), - so.KDE(bw_adjust=bw_adjust), - color=color_var, - ) - - # Apply palette if needed - if color_var and hasattr(self, "palette_name"): - self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) - - # For simple density, add an outline - if simple: - self.plot = self.plot.add( - so.Line(alpha=1.0), so.KDE(bw_adjust=bw_adjust), color=color_var - ) - - # Apply palette to the outline too if needed - if color_var and hasattr(self, "palette_name"): - self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) - - self.has_density = True - return self - - def add_grid(self, diagonal_lines=False, show_labels=True): - """ - Add circumplex grid to the plot. - - Parameters - ---------- - diagonal_lines : bool - Whether to show diagonal lines and quadrant labels - show_labels : bool - Whether to show axis labels - - Returns - ------- - CircumplexPlot - Self for method chaining - """ - self.plot = apply_circumplex_grid( - self.plot, - xlim=self.xlim, - ylim=self.ylim, - diagonal_lines=diagonal_lines, - show_labels=show_labels, - x_label=self.x if show_labels else None, - y_label=self.y if show_labels else None, - ) - - self.has_grid = True - return self - - def add_annotation(self, idx, text=None, x_offset=0.1, y_offset=0.1, **kwargs): - """ - Add an annotation to the plot. - - Parameters - ---------- - idx : int or str - Index of the point to annotate - text : str, optional - Text to display (defaults to index value if None) - x_offset, y_offset : float - Offsets for annotation position - **kwargs - Additional keyword arguments for annotation - - Returns - ------- - CircumplexPlot - Self for method chaining - """ - self.plot = add_annotation( - self.plot, - self.data, - idx, - x=self.x, - y=self.y, - text=text, - x_offset=x_offset, - y_offset=y_offset, - **kwargs, - ) - - return self - - def add_title(self, title): - """ - Add a title to the plot. - - Parameters - ---------- - title : str - Title text - - Returns - ------- - CircumplexPlot - Self for method chaining - """ - self.plot = self.plot.label(title=title) - return self - - def add_legend(self, title=None, loc="best"): - """ - Customize the legend appearance. - - Parameters - ---------- - title : str, optional - Legend title (defaults to hue variable name) - loc : str - Legend location - - Returns - ------- - CircumplexPlot - Self for method chaining - """ - # For Seaborn Objects, we can handle this more directly - # by adding a proper label to the plot - - # Just store the legend parameters for when we create the final plot - self._legend_title = title if title is not None else self.hue - self._legend_loc = loc - - return self - - def facet(self, column=None, row=None, col_wrap=None): - """ - Add faceting to the plot. - - Parameters - ---------- - column : str, optional - Variable for column faceting - row : str, optional - Variable for row faceting - col_wrap : int, optional - Number of columns to wrap facets - - Returns - ------- - CircumplexPlot - Self for method chaining - """ - self.plot = self.plot.facet(col=column, row=row, wrap=col_wrap) - return self - - def build(self, as_objects=True): - """ - Complete the plot with any default elements that haven't been added. - - Parameters - ---------- - as_objects : bool - If True, return the Seaborn Objects plot; if False, convert to Matplotlib axes - - Returns - ------- - so.Plot or (plt.Figure, plt.Axes) - The completed plot object or (figure, axes) tuple - """ - # Add grid if not already added - if not self.has_grid: - self.add_grid() - - # Ensure correct aspect ratio - self.plot = self.plot.layout(size=(6, 6)) - - # Apply legend customization if requested - if hasattr(self, "_legend_title") and self.hue is not None: - # Create a label mapping for the hue variable - # This sets the legend title to the specified value or the hue variable name - self.plot = self.plot.label(**{self.hue: self._legend_title}) - - if as_objects: - return self.plot + plot_type: PlotType, + apply_styling: bool = True, + ax: plt.Axes | None = None, + ) -> "CircumplexPlot": + """Create a plot based on the specified plot type.""" + if plot_type == PlotType.SCATTER: + if isinstance(self._backend, SeabornBackend): + self._plot = self._backend.create_scatter(self.data, self.params, ax) + else: + self._plot = self._backend.create_scatter(self.data, self.params) + elif plot_type == PlotType.DENSITY: + if isinstance(self._backend, SeabornBackend): + self._plot = self._backend.create_density(self.data, self.params, ax) + else: + raise NotImplementedError( + "Density plots are only available for the Seaborn backend." + ) + elif plot_type == PlotType.SIMPLE_DENSITY: + if isinstance(self._backend, SeabornBackend): + self._plot = self._backend.create_simple_density( + self.data, self.params, ax + ) + else: + raise NotImplementedError( + "Simple density plots are only available for the Seaborn backend." + ) + elif plot_type == PlotType.JOINT: + if isinstance(self._backend, SeabornBackend): + self._plot = self._backend.create_jointplot(self.data, self.params) + else: + raise NotImplementedError( + "Joint plots are only available for the Seaborn backend." + ) else: - # Create a new figure with the right size - fig, ax = plt.subplots(figsize=(6, 6)) - - # Use pyplot mode for rendering - # This will render the plot directly to the current figure - self.plot.plot(pyplot=True) - - # Get the current axes - ax = plt.gca() - - # Apply legend location if needed (after plotting) - if hasattr(self, "_legend_loc") and ax.get_legend() is not None: - ax.legend(loc=self._legend_loc) - - # Return the figure and axes to be compatible with the legacy API - fig = ax.figure - - return fig, ax - - @property - def seaborn_plot(self): - """ - Return the underlying Seaborn Objects plot. - - Returns - ------- - so.Plot - The Seaborn Objects plot - """ - return self.plot + raise ValueError(f"Unsupported plot type: {plot_type}") - def get_matplotlib_objects(self): - """ - Return matplotlib figure and axes objects. - - Returns - ------- - Tuple[plt.Figure, plt.Axes] - Figure and axes for the plot - """ - fig, ax = plt.subplots(figsize=(6, 6)) - self.plot.plot(ax=ax) - return fig, ax - - def show(self): - """ - Build and display the plot. - - Uses the proper pyplot=True approach which works in both - notebook and non-notebook contexts. - """ - # Ensure any default elements are added - if not self.has_grid: - self.add_grid() - - # This is the correct way to display a plot in a notebook - self.plot.plot(pyplot=True) - - # Legacy API compatibility methods - def scatter(self, apply_styling=True, ax=None): - """ - Create a scatter plot (legacy API compatibility). - - Parameters - ---------- - apply_styling : bool - Whether to apply styling (always True in this implementation) - ax : plt.Axes, optional - Axes to plot on (ignored in objects implementation) - - Returns - ------- - CircumplexPlot - Self for method chaining - """ - self.add_scatter() - self.add_grid() - return self - - def density(self, apply_styling=True, ax=None): - """ - Create a density plot (legacy API compatibility). - - Parameters - ---------- - apply_styling : bool - Whether to apply styling (always True in this implementation) - ax : plt.Axes, optional - Axes to plot on (ignored in objects implementation) - - Returns - ------- - CircumplexPlot - Self for method chaining - """ - self.add_density() - self.add_grid() - return self - - def simple_density(self, apply_styling=True, ax=None): - """ - Create a simple density plot (legacy API compatibility). - - Parameters - ---------- - apply_styling : bool - Whether to apply styling (always True in this implementation) - ax : plt.Axes, optional - Axes to plot on (ignored in objects implementation) - - Returns - ------- - CircumplexPlot - Self for method chaining - """ - self.add_density(simple=True) - self.add_grid() + if apply_styling: + self._plot = self._backend.apply_styling(self._plot, self.params) return self - def jointplot(self, apply_styling=True): - """ - Create a joint plot (legacy API compatibility). + def scatter( + self, apply_styling: bool = True, ax: plt.Axes | None = None + ) -> "CircumplexPlot": + """Create a scatter plot.""" + return self._create_plot(PlotType.SCATTER, apply_styling, ax) - Parameters - ---------- - apply_styling : bool - Whether to apply styling (not used in this implementation) + def density( + self, apply_styling: bool = True, ax: plt.Axes | None = None + ) -> "CircumplexPlot": + """Create a density plot.""" + return self._create_plot(PlotType.DENSITY, apply_styling, ax) - Returns - ------- - CircumplexPlot - Self for method chaining - """ - # Fall back to traditional seaborn for jointplot - g = sns.jointplot( - data=self.data, - x=self.x, - y=self.y, - hue=self.hue, - kind="kde", - ) + def jointplot(self, apply_styling: bool = True) -> "CircumplexPlot": + """Create a joint plot.""" + return self._create_plot(PlotType.JOINT, apply_styling) - # Add grid elements to the central plot - ax = g.ax_joint - ax.set_xlim(self.xlim) - ax.set_ylim(self.ylim) + def simple_density( + self, apply_styling: bool = True, ax: plt.Axes | None = None + ) -> "CircumplexPlot": + """Create a simple density plot.""" + return self._create_plot(PlotType.SIMPLE_DENSITY, apply_styling, ax) - # Add zero lines - ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) - ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) - - # Add grid - ax.grid(True, which="major", color="grey", alpha=0.5) - - # Store for get_figure and get_axes - self._joint_grid = g - - return self + def show(self): + """Display the plot.""" + if self._plot is None: + raise ValueError( + "No plot has been created yet. Call scatter(), density(), or simple_density() first." + ) + self._backend.show(self._plot) def get_figure(self): - """ - Get the figure object (legacy API compatibility). - - Returns - ------- - plt.Figure or tuple - Figure object or (figure, axes) tuple depending on plotting method - """ - if hasattr(self, "_joint_grid"): - return self._joint_grid.fig - else: - fig, ax = self.build(as_objects=False) - return fig + """Get the figure object of the plot.""" + if self._plot is None: + raise ValueError( + "No plot has been created yet. Call scatter(), density(), or simple_density() first." + ) + return self._plot def get_axes(self): - """ - Get the axes object (legacy API compatibility). + """Get the axes object of the plot (only for Seaborn backend).""" + if self._plot is None: + raise ValueError( + "No plot has been created yet. Call scatter(), density(), or simple_density() first." + ) + if isinstance(self._backend, SeabornBackend): + return self._plot[1] # Return the axes object + raise AttributeError("Axes object is not available for Plotly backend") + + def get_style_options(self) -> StyleOptions: + """Get the current StyleOptions.""" + return copy.deepcopy(self.style_options) + + def update_style_options(self, **kwargs) -> "CircumplexPlot": + """Update the StyleOptions with new values.""" + new_style_options = copy.deepcopy(self.style_options) + for key, value in kwargs.items(): + if hasattr(new_style_options, key): + setattr(new_style_options, key, value) + else: + raise ValueError(f"Invalid StyleOptions attribute: {key}") + + self.style_options = new_style_options + self._backend.style_options = new_style_options + return self - Returns - ------- - plt.Axes - Axes object - """ - if hasattr(self, "_joint_grid"): - return self._joint_grid.ax_joint + def iso_annotation(self, location, x_adj: float = 0, y_adj: float = 0, **kwargs): + """Add an annotation to the plot (only for Seaborn backend).""" + if isinstance(self._backend, SeabornBackend): + ax = self.get_axes() + x = self.data[self.params.x].iloc[location] + y = self.data[self.params.y].iloc[location] + ax.annotate( + text=self.data.index[location], + xy=(x, y), + xytext=(x + x_adj, y + y_adj), + ha="center", + va="center", + arrowprops=dict(arrowstyle="-", ec="black"), + **kwargs, + ) else: - fig, ax = self.build(as_objects=False) - return ax - - def iso_annotation(self, location, x_adj=0, y_adj=0, **kwargs): - """ - Add an annotation to the plot (legacy API compatibility). - - Parameters - ---------- - location : int - Index of the point to annotate - x_adj, y_adj : float - Offsets for annotation position - **kwargs - Additional keyword arguments for annotation - - Returns - ------- - CircumplexPlot - Self for method chaining - """ - return self.add_annotation(location, x_offset=x_adj, y_offset=y_adj, **kwargs) + raise AttributeError("iso_annotation is not available for Plotly backend") + return self diff --git a/src/soundscapy/plotting/likert.py b/src/soundscapy/plotting/likert.py index e5a7e027..f695378d 100644 --- a/src/soundscapy/plotting/likert.py +++ b/src/soundscapy/plotting/likert.py @@ -1,16 +1,15 @@ -""" -Plotting functions for visualising Likert scale data. -""" +"""Plotting functions for visualising Likert scale data.""" from math import pi from matplotlib import pyplot as plt -from soundscapy.surveys import PAQ_LABELS +from soundscapy.surveys.survey_utils import PAQ_LABELS def paq_radar_plot(data, ax=None, index=None): - """Generate a radar/spider plot of PAQ values + """ + Generate a radar/spider plot of PAQ values Parameters ---------- @@ -24,6 +23,7 @@ def paq_radar_plot(data, ax=None, index=None): ------- plt.Axes matplotlib Axes with radar plot + """ # TODO: Resize the plot # TODO WARNING: Likely broken now diff --git a/src/soundscapy/plotting/plot_functions.py b/src/soundscapy/plotting/plot_functions.py index 8da84340..f09fabb7 100644 --- a/src/soundscapy/plotting/plot_functions.py +++ b/src/soundscapy/plotting/plot_functions.py @@ -1,433 +1,286 @@ -""" -Utility functions for creating various types of circumplex plots. +"""High level functions for creating various types of circumplex plots.""" -These functions provide a high-level interface for creating common plot types -using the CircumplexPlot class with the Seaborn Objects API. -""" - -from typing import Any, List, Optional, Tuple, Union +from typing import Any import matplotlib.pyplot as plt import numpy as np import pandas as pd +import plotly.graph_objects as go import seaborn as sns -import seaborn.objects as so -from .circumplex_plot import CircumplexPlot -from .plotting_utils import ( +from soundscapy.plotting.backends import SeabornBackend +from soundscapy.plotting.circumplex_plot import CircumplexPlot, CircumplexPlotParams +from soundscapy.plotting.plotting_utils import ( + DEFAULT_FIGSIZE, DEFAULT_XLIM, DEFAULT_YLIM, + Backend, + ExtraParams, PlotType, ) +from soundscapy.plotting.stylers import StyleOptions def scatter_plot( data: pd.DataFrame, x: str = "ISOPleasant", y: str = "ISOEventful", - hue: Optional[str] = None, + hue: str | None = None, title: str = "Soundscape Scatter Plot", - xlim: Tuple[float, float] = DEFAULT_XLIM, - ylim: Tuple[float, float] = DEFAULT_YLIM, + xlim: tuple[float, float] = DEFAULT_XLIM, + ylim: tuple[float, float] = DEFAULT_YLIM, palette: str = "colorblind", diagonal_lines: bool = False, show_labels: bool = True, - pointsize: int = 30, - alpha: float = 0.7, - ax: Optional[plt.Axes] = None, - as_objects: bool = False, + legend=True, + legend_location: str = "best", + backend: Backend = Backend.SEABORN, + apply_styling: bool = True, + figsize: tuple[int, int] = DEFAULT_FIGSIZE, + ax: plt.Axes | None = None, + extra_params: ExtraParams = {}, **kwargs: Any, -) -> Union[so.Plot, plt.Axes]: +) -> plt.Axes | go.Figure: """ - Create a scatter plot using the circumplex model. - + Create a scatter plot using the CircumplexPlot class. + Parameters ---------- - data : pd.DataFrame - Data to plot - x, y : str - Column names for coordinates - hue : str, optional - Column name for color grouping - title : str - Title for the plot - xlim, ylim : tuple - Axis limits - palette : str or list or dict - Color palette to use for hue - diagonal_lines : bool - Whether to show diagonal lines and quadrant labels - show_labels : bool - Whether to show axis labels - pointsize : int - Size of scatter points - alpha : float - Opacity of scatter points - ax : plt.Axes, optional - Axes to plot on (for matplotlib compatibility) - as_objects : bool - If True, return Seaborn Objects plot; if False, return Matplotlib axes - **kwargs - Additional keyword arguments for scatter plot - + data (pd.DataFrame): The data to plot. + x (str): Column name for x-axis data. + y (str): Column name for y-axis data. + hue (Optional[str]): Column name for color-coding data points. + title (str): Title of the plot. + xlim (Tuple[float, float]): x-axis limits. + ylim (Tuple[float, float]): y-axis limits. + palette (str): Color palette to use. + diagonal_lines (bool): Whether to draw diagonal lines. + show_labels (bool): Whether to show axis labels. + legend (bool): Whether to show the legend. + legend_location (str): Location of the legend. + backend (Backend): The plotting backend to use. + apply_styling (bool): Whether to apply circumplex-specific styling. + figsize (Tuple[int, int]): Size of the figure. + ax (Optional[plt.Axes]): A matplotlib Axes object to plot on. + extra_params (ExtraParams): Additional parameters for backend-specific functions. + **kwargs: Additional keyword arguments to pass to the backend. + Returns ------- - Union[so.Plot, plt.Axes] - The completed plot object or axes + plt.Axes | go.Figure: The resulting plot object. + """ - plot = (CircumplexPlot(data, x, y, hue, xlim, ylim, palette) - .add_scatter(pointsize=pointsize, alpha=alpha, **kwargs) - .add_grid(diagonal_lines=diagonal_lines, show_labels=show_labels) - .add_title(title)) - - if as_objects: - return plot.build(as_objects=True) - elif ax is not None: - # If an axes is provided, draw directly on it - plot_obj = plot.build(as_objects=True) - # Clear previous contents - ax.clear() - # Use the ax limits and title from our plot - ax.set_xlim(xlim) - ax.set_ylim(ylim) - ax.set_title(title) - ax.set_xlabel(x) - ax.set_ylabel(y) - # Draw points and style - only use palette if hue is provided - sns.scatterplot( - data=data, x=x, y=y, hue=hue, - palette=palette if hue else None, - s=pointsize, alpha=alpha, ax=ax, **kwargs - ) - # Add grid lines - ax.grid(True, which="major", color='grey', alpha=0.5) - ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - - # Add diagonal lines if requested - if diagonal_lines: - ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) - ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) - - return ax - else: + params = CircumplexPlotParams( + x=x, + y=y, + hue=hue, + title=title, + xlim=xlim, + ylim=ylim, + palette=palette if hue else None, + diagonal_lines=diagonal_lines, + show_labels=show_labels, + legend=legend, + legend_location=legend_location, + extra_params={**extra_params, **kwargs}, + ) + + style_options = StyleOptions(figsize=figsize) + + plot = CircumplexPlot(data, params, backend, style_options) + plot.scatter(apply_styling=apply_styling, ax=ax) + + if isinstance(plot._backend, SeabornBackend): return plot.get_axes() + return plot.get_figure() def density_plot( data: pd.DataFrame, x: str = "ISOPleasant", y: str = "ISOEventful", - hue: Optional[str] = None, + hue: str | None = None, title: str = "Soundscape Density Plot", - xlim: Tuple[float, float] = DEFAULT_XLIM, - ylim: Tuple[float, float] = DEFAULT_YLIM, + xlim: tuple[float, float] = DEFAULT_XLIM, + ylim: tuple[float, float] = DEFAULT_YLIM, palette: str = "colorblind", fill: bool = True, - alpha: float = 0.5, - levels: int = 8, - bw_adjust: float = 1.2, - simple_density: bool = False, - incl_scatter: bool = False, - scatter_size: int = 15, - scatter_alpha: float = 0.5, + incl_outline: bool = False, + incl_scatter: bool = True, diagonal_lines: bool = False, show_labels: bool = True, - ax: Optional[plt.Axes] = None, - as_objects: bool = False, + legend=True, + legend_location: str = "best", + backend: Backend = Backend.SEABORN, + apply_styling: bool = True, + figsize: tuple[int, int] = DEFAULT_FIGSIZE, + simple_density: bool = False, + simple_density_thresh: float = 0.5, + simple_density_levels: int = 2, + simple_density_alpha: float = 0.5, + ax: plt.Axes | None = None, + extra_params: ExtraParams = {}, **kwargs: Any, -) -> Union[so.Plot, plt.Axes]: - """ - Create a density plot using the circumplex model. - - Parameters - ---------- - data : pd.DataFrame - Data to plot - x, y : str - Column names for coordinates - hue : str, optional - Column name for color grouping - title : str - Title for the plot - xlim, ylim : tuple - Axis limits - palette : str or list or dict - Color palette to use for hue - fill : bool - Whether to fill the contours - alpha : float - Opacity of the fill - levels : int - Number of contour levels - bw_adjust : float - Bandwidth adjustment factor - simple_density : bool - If True, use simplified density with fewer levels and an outline - incl_scatter : bool - Whether to include scatter points with the density - scatter_size : int - Size of scatter points (if included) - scatter_alpha : float - Opacity of scatter points (if included) - diagonal_lines : bool - Whether to show diagonal lines and quadrant labels - show_labels : bool - Whether to show axis labels - ax : plt.Axes, optional - Axes to plot on (for matplotlib compatibility) - as_objects : bool - If True, return Seaborn Objects plot; if False, return Matplotlib axes - **kwargs - Additional keyword arguments for density plot - - Returns - ------- - Union[so.Plot, plt.Axes] - The completed plot object or axes +) -> plt.Axes | go.Figure: """ - cp = CircumplexPlot(data, x, y, hue, xlim, ylim, palette) - - # Add density layer - cp.add_density( - alpha=alpha, - fill=fill, - levels=levels, - bw_adjust=bw_adjust, - simple=simple_density - ) - - # Add scatter if requested - if incl_scatter: - cp.add_scatter(pointsize=scatter_size, alpha=scatter_alpha) - - # Complete the plot - cp.add_grid(diagonal_lines=diagonal_lines, show_labels=show_labels) - cp.add_title(title) - - if as_objects: - return cp.build(as_objects=True) - elif ax is not None: - # If an axes is provided, draw directly on it - # Clear previous contents - ax.clear() - # Use the ax limits and title - ax.set_xlim(xlim) - ax.set_ylim(ylim) - ax.set_title(title) - ax.set_xlabel(x) - ax.set_ylabel(y) - - # Draw the KDE - if simple_density: - # Simple density with fewer levels - sns.kdeplot( - data=data, x=x, y=y, hue=hue, - fill=fill, alpha=alpha, levels=2, - bw_adjust=bw_adjust, ax=ax, **kwargs - ) - # Add outline - sns.kdeplot( - data=data, x=x, y=y, hue=hue, - fill=False, alpha=1.0, levels=2, - bw_adjust=bw_adjust, ax=ax - ) - else: - # Regular density - sns.kdeplot( - data=data, x=x, y=y, hue=hue, - fill=fill, alpha=alpha, levels=levels, - bw_adjust=bw_adjust, ax=ax, **kwargs - ) - - # Add scatter if requested - if incl_scatter: - sns.scatterplot( - data=data, x=x, y=y, hue=hue, - s=scatter_size, alpha=scatter_alpha, ax=ax - ) - - # Add grid lines - ax.grid(True, which="major", color='grey', alpha=0.5) - ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - - # Add diagonal lines if requested - if diagonal_lines: - ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) - ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) - - return ax - else: - return cp.get_axes() - + Create a density plot using the CircumplexPlot class. -def joint_plot( - data: pd.DataFrame, - x: str = "ISOPleasant", - y: str = "ISOEventful", - hue: Optional[str] = None, - title: str = "Soundscape Joint Plot", - plot_type: str = "scatter", - **kwargs: Any, -) -> sns.JointGrid: - """ - Create a joint plot with marginals using matplotlib. - - This function falls back to matplotlib/seaborn because - Seaborn Objects does not yet fully support joint plots with marginals. - Parameters ---------- - data : pd.DataFrame - Data to plot - x, y : str - Column names for coordinates - hue : str, optional - Column name for color grouping - title : str - Title for the plot - plot_type : str - Type of plot: "scatter", "density", or "simple_density" - **kwargs - Additional parameters for sns.jointplot - + data (pd.DataFrame): The data to plot. + x (str): Column name for x-axis data. + y (str): Column name for y-axis data. + hue (Optional[str]): Column name for color-coding data points. + title (str): Title of the plot. + xlim (Tuple[float, float]): x-axis limits. + ylim (Tuple[float, float]): y-axis limits. + palette (str): Color palette to use. + fill (bool): Whether to fill the density contours. + incl_outline (bool): Whether to include an outline for the density contours. + diagonal_lines (bool): Whether to draw diagonal lines. + show_labels (bool): Whether to show axis labels. + legend (bool): Whether to show the legend. + legend_location (str): Location of the legend. + backend (Backend): The plotting backend to use. + apply_styling (bool): Whether to apply circumplex-specific styling. + figsize (Tuple[int, int]): Size of the figure. + simple_density (bool): Whether to use simple density plot (Seaborn only). + simple_density_thresh (float): Threshold for simple density plot. + simple_density_levels (int): Number of levels for simple density plot. + simple_density_alpha (float): Alpha value for simple density plot. + ax (Optional[plt.Axes]): A matplotlib Axes object to plot on. + extra_params (ExtraParams): Additional parameters for backend-specific functions. + **kwargs: Additional keyword arguments to pass to the backend. + Returns ------- - sns.JointGrid - The joint plot grid + plt.Axes | go.Figure: The resulting plot object. + """ - # Fall back to traditional seaborn for jointplot - kind = "scatter" if plot_type == "scatter" else "kde" - - g = sns.jointplot( - data=data, - x=x, - y=y, + params = CircumplexPlotParams( + x=x, + y=y, hue=hue, - kind=kind, - **kwargs + title=title, + xlim=xlim, + ylim=ylim, + palette=palette if hue else None, + fill=fill, + incl_outline=incl_outline, + diagonal_lines=diagonal_lines, + show_labels=show_labels, + legend=legend, + legend_location=legend_location, + extra_params={**extra_params, **kwargs}, + ) + + style_options = StyleOptions( + figsize=figsize, + simple_density=dict( + thresh=simple_density_thresh, + levels=simple_density_levels, + alpha=simple_density_alpha, + ) + if simple_density + else None, ) - - # Add grid elements to the central plot - ax = g.ax_joint - ax.set_xlim((-1, 1)) - ax.set_ylim((-1, 1)) - - # Add zero lines - ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - - # Add grid - ax.grid(True, which="major", color='grey', alpha=0.5) - - # Add title - g.fig.suptitle(title, y=1.05) - - # Add scatter if requested - if plot_type in ["density", "simple_density"] and kwargs.get("incl_scatter", False): - sns.scatterplot( - data=data, - x=x, - y=y, - hue=hue, - ax=ax, - s=kwargs.get("scatter_size", 15), - alpha=kwargs.get("scatter_alpha", 0.5) + + plot = CircumplexPlot(data, params, backend, style_options) + + if incl_scatter and backend == Backend.SEABORN: + plot.scatter(apply_styling=True, ax=ax) + ax = plot.get_axes() + elif incl_scatter and backend == Backend.PLOTLY: + # TODO: Implement overlaying scatter on density plot for Plotly backend + raise NotImplementedError( + "Overlaying a scatter on a density plot is not yet supported for Plotly backend. " + "Please change to Seaborn backend or use `incl_scatter=False`." ) - - return g + + if simple_density: + plot.simple_density(apply_styling=apply_styling, ax=ax) + else: + plot.density(apply_styling=apply_styling, ax=ax) + + if isinstance(plot._backend, SeabornBackend): + return plot.get_axes() + return plot.get_figure() def create_circumplex_subplots( - data_list: List[pd.DataFrame], - x: str = "ISOPleasant", - y: str = "ISOEventful", - hue: Optional[str] = None, - subtitles: Optional[List[str]] = None, + data_list: list[pd.DataFrame], + plot_type: PlotType | str = PlotType.DENSITY, + incl_scatter: bool = True, + subtitles: list[str] | None = None, title: str = "Circumplex Subplots", - plot_type: str = "density", - incl_scatter: bool = False, - cols: int = 2, - as_objects: bool = False, + nrows: int = None, + ncols: int = None, + figsize: tuple[int, int] = (10, 10), **kwargs: Any, -) -> Union[so.Plot, plt.Figure]: +) -> plt.Figure: """ - Create a figure with multiple circumplex plots. - + Create a figure with subplots containing circumplex plots. + Parameters ---------- - data_list : list of DataFrames - List of data sources to plot - x, y : str - Column names for coordinates - hue : str, optional - Column name for color grouping - subtitles : list of str, optional - Titles for individual subplots - title : str - Main title for the plot - plot_type : str - Type of plot: "scatter", "density", or "simple_density" - incl_scatter : bool - Whether to include scatter points on density plots - cols : int - Number of columns for subplots - as_objects : bool - If True, return Seaborn Objects plot; if False, return Matplotlib figure - **kwargs - Additional arguments for plot functions - + data_list (List[pd.DataFrame]): List of DataFrames to plot. + plot_type (PlotType): Type of plot to create. + incl_scatter (bool): Whether to include scatter points on density plots. + nrows (int): Number of rows in the subplot grid. + ncols (int): Number of columns in the subplot grid. + figsize (tuple): Figure size (width, height) in inches. + **kwargs: Additional keyword arguments to pass to scatter_plot or density_plot. + Returns ------- - Union[so.Plot, plt.Figure] - The plot object or figure + matplotlib.figure.Figure: A figure containing the subplots. + + Example + ------- + >>> import pandas as pd + >>> import numpy as np + >>> np.random.seed(42) + >>> data1 = pd.DataFrame({'ISOPleasant': np.random.uniform(-1, 1, 50), + ... 'ISOEventful': np.random.uniform(-1, 1, 50)}) + >>> data2 = pd.DataFrame({'ISOPleasant': np.random.uniform(-1, 1, 50), + ... 'ISOEventful': np.random.uniform(-1, 1, 50)}) + >>> fig = create_circumplex_subplots([data1, data2], plot_type=PlotType.SCATTER, nrows=1, ncols=2) + >>> isinstance(fig, plt.Figure) + True + """ - # Generate subplot titles if not provided + if isinstance(plot_type, str): + plot_type = PlotType[plot_type.upper()] + + if nrows is None and ncols is None: + nrows = 2 + ncols = len(data_list) // nrows + elif nrows is None: + nrows = len(data_list) // ncols + elif ncols is None: + ncols = len(data_list) // nrows + if subtitles is None: - subtitles = [f"Plot {i+1}" for i in range(len(data_list))] - - # Remove any layout parameters that don't belong in plot functions - plotting_kwargs = kwargs.copy() - if 'nrows' in plotting_kwargs: - plotting_kwargs.pop('nrows') - if 'ncols' in plotting_kwargs: - plotting_kwargs.pop('ncols') - - # For the refactored version, we'll create a matplotlib figure directly - # instead of using the faceting in Seaborn Objects - nrows = (len(data_list) - 1) // cols + 1 - - # Create a new figure - fig, axes = plt.subplots(nrows, cols, figsize=(cols * 6, nrows * 6), squeeze=False) - axes = axes.flatten() - - # Create individual plots - for i, (data, subtitle) in enumerate(zip(data_list, subtitles)): - if i < len(axes): - # Create a plot for this axis - if plot_type == "scatter": - scatter_plot(data, x=x, y=y, hue=hue, title=subtitle, ax=axes[i], **plotting_kwargs) - elif plot_type == "simple_density": - density_plot(data, x=x, y=y, hue=hue, title=subtitle, - simple_density=True, incl_scatter=incl_scatter, ax=axes[i], **plotting_kwargs) - else: - density_plot(data, x=x, y=y, hue=hue, title=subtitle, - incl_scatter=incl_scatter, ax=axes[i], **plotting_kwargs) - - # Hide any unused axes - for i in range(len(data_list), len(axes)): - axes[i].set_visible(False) - - # Add a title to the figure - fig.suptitle(title, fontsize=16) - - # Adjust layout + subtitles = [f"({i + 1})" for i in range(len(data_list))] + elif len(subtitles) != len(data_list): + raise ValueError("Number of subtitles must match number of dataframes") + + fig, axes = plt.subplots(nrows, ncols, figsize=figsize) + axes = axes.flatten() if isinstance(axes, np.ndarray) else [axes] + + color = kwargs.get("color", sns.color_palette("colorblind", 1)[0]) + + for data, ax, subtitle in zip(data_list, axes, subtitles, strict=False): + if plot_type == PlotType.SCATTER or incl_scatter: + scatter_plot(data, title=subtitle, ax=ax, color=color, **kwargs) + if plot_type == PlotType.DENSITY: + density_plot(data, title=subtitle, ax=ax, color=color, **kwargs) + elif plot_type == PlotType.SIMPLE_DENSITY: + density_plot( + data, title=subtitle, simple_density=True, ax=ax, color=color, **kwargs + ) + + plt.suptitle(title) + plt.tight_layout() - - # We'll return the figure directly for legacy compatibility return fig diff --git a/src/soundscapy/plotting/plotting_utils.py b/src/soundscapy/plotting/plotting_utils.py index 92f14416..2160ecb7 100644 --- a/src/soundscapy/plotting/plotting_utils.py +++ b/src/soundscapy/plotting/plotting_utils.py @@ -1,6 +1,4 @@ -""" -Utility functions and constants for the soundscapy plotting module. -""" +"""Utility functions and constants for the soundscapy plotting module.""" from enum import Enum from typing import Any, TypedDict diff --git a/src/soundscapy/plotting/stylers.py b/src/soundscapy/plotting/stylers.py index 5ad22860..7458242a 100644 --- a/src/soundscapy/plotting/stylers.py +++ b/src/soundscapy/plotting/stylers.py @@ -1,14 +1,12 @@ -""" -Styling utilities for circumplex plots using Seaborn and Matplotlib. -""" +"""Styling utilities for circumplex plots using Seaborn and Matplotlib.""" from dataclasses import dataclass, field -from typing import Any, Dict, Tuple +from typing import Any import matplotlib as mpl import seaborn as sns -from .plotting_utils import DEFAULT_FIGSIZE +from soundscapy.plotting.plotting_utils import DEFAULT_FIGSIZE @dataclass @@ -24,6 +22,7 @@ class StyleOptions: bw_adjust (float): Bandwidth adjustment for kernel density estimation. figsize (Tuple[int, int]): Figure size (width, height) in inches. simple_density (Dict[str, Any]): Configuration for simple density plots. + """ diag_lines_zorder: int = 1 @@ -31,8 +30,8 @@ class StyleOptions: prim_lines_zorder: int = 2 data_zorder: int = 3 bw_adjust: float = 1.2 - figsize: Tuple[int, int] = DEFAULT_FIGSIZE - simple_density: Dict[str, Any] = field( + figsize: tuple[int, int] = DEFAULT_FIGSIZE + simple_density: dict[str, Any] = field( default_factory=lambda: { "thresh": 0.5, "levels": 2, @@ -53,7 +52,7 @@ def __init__(self, params: Any, style_options: StyleOptions = StyleOptions()): def apply_styling( self, fig: mpl.figure.Figure, ax: mpl.axes.Axes - ) -> Tuple[mpl.figure.Figure, mpl.axes.Axes]: + ) -> tuple[mpl.figure.Figure, mpl.axes.Axes]: """ Apply styling to the plot. @@ -65,6 +64,7 @@ def apply_styling( Returns ------- Tuple[mpl.figure.Figure, mpl.axes.Axes]: The styled figure and axes. + """ self.set_style() self.circumplex_grid(ax) diff --git a/test/plotting/__init__.py b/src/soundscapy/py.typed similarity index 100% rename from test/plotting/__init__.py rename to src/soundscapy/py.typed diff --git a/src/soundscapy/spi/__init__.py b/src/soundscapy/spi/__init__.py new file mode 100644 index 00000000..1931d597 --- /dev/null +++ b/src/soundscapy/spi/__init__.py @@ -0,0 +1,33 @@ +""" +Soundscapy Psychoacoustic Indicator (SPI) calculation module. + +This module provides functions and classes for calculating SPI, +based on the R implementation. Requires optional dependencies. +""" +# ruff: noqa: E402 +# ignore module level import order because we need to check dependencies first + +# Check for required dependencies directly +# This will raise ImportError if any dependency is missing +try: + import rpy2 # noqa: F401 + +except ImportError as e: + msg = ( + "SPI functionality requires additional dependencies. " + "Install with: pip install soundscapy[spi]" + ) + raise ImportError(msg) from e + +# Now we can import our modules that depend on the optional packages +from soundscapy.spi import msn +from soundscapy.spi.msn import CentredParams, DirectParams, MultiSkewNorm, cp2dp, dp2cp + +__all__ = [ + "CentredParams", + "DirectParams", + "MultiSkewNorm", + "cp2dp", + "dp2cp", + "msn", +] diff --git a/src/soundscapy/spi/_r_wrapper.py b/src/soundscapy/spi/_r_wrapper.py new file mode 100644 index 00000000..1aaf819c --- /dev/null +++ b/src/soundscapy/spi/_r_wrapper.py @@ -0,0 +1,439 @@ +""" +R integration for skew-normal distribution calculations. + +This module provides functions for: +1. Checking R and R package dependencies +2. Initializing and managing R sessions +3. Converting data between R and Python +4. Executing R functions for skew-normal calculations + +It is not intended to be used directly by end users. +""" + +import sys +import warnings +from typing import Any, NoReturn + +from rpy2 import robjects +from rpy2.robjects import numpy2ri, pandas2ri + +# These are used in the docstring examples but not in the code +# They will be used by code that imports and uses this module +from soundscapy.sspylogging import get_logger + +logger = get_logger() + +# Cached values to avoid repeated checks +_r_checked = False +_sn_checked = False + +# Session state +_r_session = None +_sn_package = None +_stats_package = None +_base_package = None +_session_active = False + +REQUIRED_R_VERSION = 3.6 + + +def check_r_availability() -> None: + """ + Check if R is installed and accessible through rpy2. + + Raises + ------ + ImportError + If R is not installed or cannot be accessed. + + """ + global _r_checked # noqa: PLW0603 + + def _raise_r_not_found_error() -> NoReturn: + msg = ( + "rpy2 is installed but it cannot find an R installation. " + "Please ensure R is installed and correctly configured. " + "On Linux: Install R with your package manager (e.g., apt-get install r-base)." # noqa: E501 + "On macOS: Install R from CRAN (https://cran.r-project.org/bin/macosx/). " + "On Windows: Install R from CRAN (https://cran.r-project.org/bin/windows/base/)." + ) + raise ImportError(msg) + + def _raise_r_access_error(e: Exception) -> NoReturn: + msg = ( + f"Error accessing R installation: {e!s}. " + "Please ensure R is installed and correctly configured." + ) + raise ImportError(msg) + + def _raise_r_version_too_old_error(r_version_num: float) -> NoReturn: + msg = ( + f"R version {r_version_num} is too old." + f"The 'sn' package requires R >= {REQUIRED_R_VERSION}." + "Please upgrade your R installation." + ) + raise ImportError(msg) + + if _r_checked: + return + + try: + from rpy2 import robjects + + # Basic check to ensure R is running by getting R version + r_version = robjects.r("R.version.string")[0] # type: ignore[index] + logger.debug("R version: %s", r_version) + + # Check if minimum R version requirements are met + # The 'sn' package requires R >= 3.6.0 + r_version_num = robjects.r( + "as.numeric(R.version$major) + as.numeric(R.version$minor)/10" + )[0] # type: ignore[index] + + if r_version_num < REQUIRED_R_VERSION: + _raise_r_version_too_old_error(r_version_num) + + _r_checked = True + except ImportError: + _raise_r_not_found_error() # Call the handler + except Exception as e: # noqa: BLE001 + _raise_r_access_error(e) # Call the handler + + +def check_sn_package() -> None: + """ + Check if the R 'sn' package is installed. + + Raises + ------ + ImportError + If the 'sn' package is not installed. + + """ + global _sn_checked # noqa: PLW0603 + + def _raise_sn_version_too_old_error(version: str) -> NoReturn: + msg = ( + f"R 'sn' package version {version} is too old. " + "The SPI feature requires 'sn' >= 2.0.0. " + "Please upgrade the package by running in R: install.packages('sn')" + ) + raise ImportError(msg) + + def _raise_sn_not_installed_error() -> NoReturn: + msg = ( + "R package 'sn' is not installed. " + "Please install it by running in R: install.packages('sn')" + ) + raise ImportError(msg) + + def _raise_sn_check_error(e: Exception) -> NoReturn: + msg = ( + f"Error checking for R 'sn' package: {e!s}. " + "Please ensure the package is installed by running in R: install.packages('sn')" # noqa: E501 + ) + raise ImportError(msg) + + if _sn_checked: + return + + # First ensure R is available + check_r_availability() + + try: + import rpy2.robjects.packages as rpackages + + # Check if 'sn' package is installed + try: + # Just importing to verify it exists + _ = rpackages.importr("sn") + + # Get package version using R to verify compatibility + from rpy2 import robjects + + # Use R code to get the package version + version = robjects.r('as.character(packageVersion("sn"))')[0] # type: ignore[index] + logger.debug("R 'sn' package version: %s", version) + + # Check if package version meets requirements + # The SPI implementation requires 'sn' >= 2.0.0 + if version < "2.0.0": + _raise_sn_version_too_old_error(version) + + _sn_checked = True + except rpackages.PackageNotInstalledError: + _raise_sn_not_installed_error() + except Exception as e: + if "sn" in str(e): + # Already a more specific error about the sn package + raise # Re-raising is okay here + _raise_sn_check_error(e) + + +def check_dependencies() -> dict[str, Any]: + """ + Check all required R dependencies for the SPI module. + + This function checks: + 1. R installation accessibility + 2. R version compatibility + 3. 'sn' package availability + 4. 'sn' package version compatibility + + Returns + ------- + dict[str, Any] + Dictionary with dependency information. + + Raises + ------ + ImportError + If any dependency check fails. + + """ + # Check R availability first + check_r_availability() + + # Then check for the sn package + check_sn_package() + + # If we get here, all dependencies are available + + # Return information about the dependencies + return { + "rpy2_version": sys.modules["rpy2"].__version__, + "r_version": robjects.r("R.version.string")[0], # type: ignore[index] + "sn_version": robjects.r('as.character(packageVersion("sn"))')[0], # type: ignore[index] + } + + +# === SESSION MANAGEMENT === + + +def initialize_r_session() -> dict[str, Any]: + """ + Initialize an R session for skew-normal distribution calculations. + + This function: + 1. Checks for R and package dependencies + 2. Imports required R packages + 3. Sets up the R environment + 4. Updates global session state + + Returns + ------- + dict[str, Any] + Session information including R and package versions. + + Raises + ------ + ImportError + If dependencies are missing. + RuntimeError + If session initialization fails. + + """ + global _r_session, _sn_package, _stats_package, _base_package, _session_active # noqa: PLW0603 + + # If session is already active, just return the state + if _session_active: + logger.debug("R session already initialized") + return { + "r_session": "active", + "sn_package": "loaded", + "stats_package": "loaded", + "base_package": "loaded", + } + + # First check all dependencies + dep_info = check_dependencies() + logger.debug("Dependencies verified: %s", dep_info) + + try: + import rpy2.robjects.packages as rpackages + from rpy2 import robjects + + # Import required packages + _sn_package = rpackages.importr("sn") + _stats_package = rpackages.importr("stats") + _base_package = rpackages.importr("base") + logger.debug("Imported R packages: sn, stats, base") + + # Set R random seed for reproducibility + robjects.r("set.seed(42)") + + # Store R session + _r_session = robjects + + # Update session state + _session_active = True + logger.info("R session successfully initialized") + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + # Activate numpy and pandas conversion + logger.debug("Activating numpy and pandas conversion") + logger.info( + "rpy2 throws a DeprecationWarning about global activation, which we're ignoring for now." # noqa: E501 + ) + # TODO(MitchellAcoustics): Remove global conversion, as recommended by rpy2 + # https://github.com/MitchellAcoustics/Soundscapy/issues/111 + numpy2ri.activate() + pandas2ri.activate() + + return { + "r_session": "active", + "sn_package": str(_sn_package), + "stats_package": str(_stats_package), + "base_package": str(_base_package), + **dep_info, + } + + except Exception as e: + logger.exception("Failed to initialize R session") + _session_active = False + _r_session = None + _sn_package = None + _stats_package = None + _base_package = None + msg = f"Failed to initialize R session: {e!s}" + raise RuntimeError(msg) from e + + +def shutdown_r_session() -> bool: + """ + Shutdown the R session and clean up resources. + + This function: + 1. Deactivates numpy conversion + 2. Resets global session state + 3. Performs garbage collection + + Returns + ------- + bool + True if successful, False otherwise. + + """ + global _r_session, _sn_package, _stats_package, _base_package, _session_active # noqa: PLW0603 + + if not _session_active: + logger.debug("No active R session to shutdown") + return True + + try: + import gc + + # Clear references to R objects + _r_session = None + _sn_package = None + _stats_package = None + _base_package = None + + # Update session state + _session_active = False + + # Force garbage collection to release R resources + gc.collect() + logger.info("R session successfully shutdown") + + except Exception: + logger.exception("Error during R session shutdown") + return False + else: + return True + + +def get_r_session() -> tuple[Any, Any, Any, Any]: + """ + Get the current R session and package objects. + + This function: + 1. Initializes the session if not already active + 2. Returns the session and package references + + Returns + ------- + tuple[Any, Any, Any, Any] + (r_session, sn_package, stats_package, base_package) + + Raises + ------ + RuntimeError + If session initialization fails. + + """ + global _r_session, _sn_package, _stats_package, _base_package, _session_active # noqa: PLW0602 + + if not _session_active: + logger.debug("R session not active, initializing") + initialize_r_session() + + if ( + not _session_active + or not _r_session + or not _sn_package + or not _stats_package + or not _base_package + ): + msg = "Failed to initialize R session" + raise RuntimeError(msg) + + return _r_session, _sn_package, _stats_package, _base_package + + +def install_r_packages(packages: list[str] | None = None) -> None: + """ + Install R packages if not already installed. + + Parameters + ---------- + packages : list[str] | None, optional + List of R package names to install. Defaults to ["sn", "tvtnorm"]. + + Raises + ------ + ImportError + If R is not available or package installation fails. + + """ + if packages is None: + packages = ["sn", "tvtnorm"] + + check_r_availability() + + try: + import rpy2.robjects.packages as rpackages + from rpy2.robjects.vectors import StrVector + + utils = rpackages.importr("utils") + utils.chooseCRANmirror(ind=1) + + # Check if packages are installed + packnames_to_install = [x for x in packages if not rpackages.isinstalled(x)] + logger.debug("Packages to install: %s", packnames_to_install) + + # Install missing packages + if len(packnames_to_install) > 0: + utils.install_packages(StrVector(packnames_to_install)) + logger.info("Installed missing R packages: %s", packnames_to_install) + else: + logger.debug("All required R packages are already installed") + + except Exception as e: + msg = f"Failed to install R packages: {e!s}" + raise ImportError(msg) from e + + +def is_session_active() -> bool: + """ + Check if the R session is currently active. + + Returns + ------- + bool + True if the session is active, False otherwise. + + """ + global _session_active # noqa: PLW0602 + return _session_active diff --git a/src/soundscapy/spi/_rsn_wrapper.py b/src/soundscapy/spi/_rsn_wrapper.py new file mode 100644 index 00000000..e577dfec --- /dev/null +++ b/src/soundscapy/spi/_rsn_wrapper.py @@ -0,0 +1,222 @@ +from typing import Literal + +import numpy as np +import pandas as pd +from rpy2 import robjects +from rpy2.robjects.methods import RS4 + +from soundscapy import get_logger +from soundscapy.spi._r_wrapper import get_r_session + +logger = get_logger() + +_, sn, _, _ = get_r_session() +logger.debug("R session and packages retrieved successfully.") + + +def selm(x: str, y: str, data: pd.DataFrame) -> RS4: + formula = f"cbind({x}, {y}) ~ 1" + return sn.selm(formula, data=data, family="SN") + + +def calc_cp(x: str, y: str, data: pd.DataFrame) -> tuple: + selm_model = selm(x, y, data) + return extract_cp(selm_model) + + +def calc_dp(x: str, y: str, data: pd.DataFrame) -> tuple: + selm_model = selm(x, y, data) + return extract_dp(selm_model) + + +def extract_cp(selm_model: RS4) -> tuple: + cp = tuple(selm_model.slots["param"][1]) + return (cp[0].flatten(), cp[1], cp[2].flatten()) + + +def extract_dp(selm_model: RS4) -> tuple: + dp = tuple(selm_model.slots["param"][0]) + return (dp[0].flatten(), dp[1], dp[2].flatten()) + + +def sample_msn( + selm_model: RS4 | None = None, + xi: np.ndarray | None = None, + omega: np.ndarray | None = None, + alpha: np.ndarray | None = None, + n: int = 1000, +) -> np.ndarray: + if selm_model is not None: + return sn.rmsn(n, dp=selm_model.slots["param"][0]) + if xi is not None and omega is not None and alpha is not None: + r_xi = robjects.FloatVector(xi.T) # Transpose to make it a column vector + r_omega = robjects.r.matrix( + robjects.FloatVector(omega.flatten()), + nrow=omega.shape[0], + ncol=omega.shape[1], + ) # type: ignore[reportCallIssue] + r_alpha = robjects.FloatVector(alpha) # Transpose to make it a column vector + return sn.rmsn(n, xi=r_xi, Omega=r_omega, alpha=r_alpha) + msg = "Either selm_model or xi, omega, and alpha must be provided." + raise ValueError(msg) + + +def sample_mtsn( + selm_model: RS4 | None = None, + xi: np.ndarray | None = None, + omega: np.ndarray | None = None, + alpha: np.ndarray | None = None, + a: float = -1, + b: float = 1, + n: int = 1000, +) -> np.ndarray: + """ + Sample from a multivariate truncated skew-normal distribution. + + Uses rejection sampling to ensure that the samples are within the bounds [a, b] + for both dimensions. + + Parameters + ---------- + selm_model : optional + Fitted SELM model from R's 'sn' package. If provided, parameters `xi`, + `omega`, and `alpha` are ignored. + xi : np.ndarray, optional + Location parameter (2x1 array). + omega : np.ndarray, optional + Scale matrix (2x2 array). + alpha : np.ndarray, optional + Skewness parameter (2x1 array). + a : float, optional + Lower truncation bound for both dimensions, by default -1. + b : float, optional + Upper truncation bound for both dimensions, by default 1. + n : int, optional + Number of samples to generate, by default 1000. + + Returns + ------- + np.ndarray + Array of samples (n x 2). + + Raises + ------ + ValueError + If neither `selm_model` nor all of `xi`, `omega`, and `alpha` are provided. + + """ + samples = np.array([[0, 0]]) + n_samples = 0 + while n_samples < n: + if selm_model is not None: + sample = sample_msn(selm_model, n=1) + elif xi is not None and omega is not None and alpha is not None: + sample = sample_msn(xi=xi, omega=omega, alpha=alpha, n=1) + else: + msg = "Either selm_model or xi, omega, and alpha must be provided." + raise ValueError(msg) + if a <= sample[0][0] <= b and a <= sample[0][1] <= b: + samples = np.append(samples, sample, axis=0) + if n_samples == 0: + samples = samples[1:] + n_samples += 1 + + # Ensure the sample is within the bounds [a, b] for both dimensions + if not np.all((a <= samples[:, 0]) & (samples[:, 0] <= b)): + msg = f"Sample x-values are out of bounds: [{a}, {b}]" + raise ValueError(msg) + if not np.all((a <= samples[:, 1]) & (samples[:, 1] <= b)): + msg = f"Sample y-values are out of bounds: [{a}, {b}]" + raise ValueError(msg) + return samples + + +def dp2cp( + xi: np.ndarray, + omega: np.ndarray, + alpha: np.ndarray, + family: Literal["SN", "ESN", "ST", "SC"] = "SN", +) -> tuple: + """ + Convert Direct Parameters (DP) to Centred Parameters (CP). + + Parameters + ---------- + xi : np.ndarray + Location parameter (2x1 array). + omega : np.ndarray + Scale matrix (2x2 array). + alpha : np.ndarray + Skewness parameter (2x1 array). + family : str, optional + Distribution family, by default "SN". + + Returns + ------- + tuple + Tuple containing the centred parameters (mean, sigma, skew). + + """ + r_xi = robjects.FloatVector(xi.T) # Transpose to make it a column vector + r_omega = robjects.r.matrix( + robjects.FloatVector(omega.flatten()), + nrow=omega.shape[0], + ncol=omega.shape[1], + ) # type: ignore[reportCallIssue] + r_alpha = robjects.FloatVector(alpha) # Transpose to make it a column vector + + dp_r = robjects.ListVector( + { + "xi": r_xi, + "Omega": r_omega, + "alpha": r_alpha, + } + ) + + cp_r = sn.dp2cp(dp_r, family=family) + + return tuple(cp_r) + + +def cp2dp( + mean: np.ndarray, + sigma: np.ndarray, + skew: np.ndarray, + family: Literal["SN", "ESN", "ST", "SC"] = "SN", +) -> tuple: + """ + Convert Centred Parameters (CP) to Direct Parameters (DP). + + Parameters + ---------- + mean : np.ndarray + Mean vector (2x1 array). + sigma : np.ndarray + Covariance matrix (2x2 array). + skew : np.ndarray + Skewness vector (2x1 array). + family : str, optional + Distribution family, by default "SN". + + Returns + ------- + tuple + Tuple containing the direct parameters (xi, omega, alpha). + + """ + r_mean = robjects.FloatVector(mean.T) # Transpose to make it a column vector + r_sigma = robjects.r.matrix( + robjects.FloatVector(sigma.flatten()), + nrow=sigma.shape[0], + ncol=sigma.shape[1], + ) # type: ignore[reportCallIssue] + r_skew = robjects.FloatVector(skew) # Transpose to make it a column vector + cp_r = robjects.ListVector( + { + "mean": r_mean, + "Sigma": r_sigma, + "skew": r_skew, + } + ) + dp_r = sn.cp2dp(cp_r, family=family) + return tuple(dp_r) diff --git a/src/soundscapy/spi/ks2d.py b/src/soundscapy/spi/ks2d.py new file mode 100644 index 00000000..2140921c --- /dev/null +++ b/src/soundscapy/spi/ks2d.py @@ -0,0 +1,403 @@ +# ruff: noqa: PGH004 +# ruff: noqa +# type: ignore +# Code créé par Gabriel Taillon le 7 Mai 2018 +# From https://github.com/Gabinou/2DKS +# Kolmogorov-Smyrnov Test extended to two dimensions. +# References:s +# [1] Peacock, J. A. (1983). Two-dimensional goodness-of-fit testing +# in astronomy. Monthly Notices of the Royal Astronomical Society, +# 202(3), 615-627. +# [2] Fasano, G., & Franceschini, A. (1987). A multidimensional version of +# the Kolmogorov–Smirnov test. Monthly Notices of the Royal Astronomical +# Society, 225(1), 155-170. +# [3] Flannery, B. P., Press, W. H., Teukolsky, S. A., & Vetterling, W. +# (1992). Numerical recipes in C. Press Syndicate of the University +# of Cambridge, New York, 24, 78. +import inspect + +import numpy as np +import scipy.stats + + +def CountQuads( + Arr2D: np.ndarray, point: np.ndarray +) -> tuple[float, float, float, float]: + """ + Compute probabilities by counting points in quadrants. + + Computes the probabilities of finding points in each of the 4 quadrants + defined by a vertical and horizontal line crossing the given `point`. + The probabilities are determined by counting the proportion of points + from `Arr2D` that fall into each quadrant. + + Parameters + ---------- + Arr2D : np.ndarray + Array of 2D points (shape N x 2) to be counted. + point : np.ndarray + A 1D array or list with 2 elements representing the center (x, y) + of the 4 quadrants. + + Returns + ------- + tuple[float, float, float, float] + A tuple containing four floats (fpp, fnp, fpn, fnn), representing the + normalized fractions (probabilities) of points in each quadrant: + - fpp: Fraction in the positive-x, positive-y quadrant. + - fnp: Fraction in the negative-x, positive-y quadrant. + - fpn: Fraction in the positive-x, negative-y quadrant. + - fnn: Fraction in the negative-x, negative-y quadrant. + + Raises + ------ + TypeError + If `point` or `Arr2D` are not list-like or numpy arrays, or if + `point` does not have 2 elements, or if `Arr2D` is not 2D. + + """ + if isinstance(point, list): + point = np.asarray(np.ravel(point)) + elif type(point).__module__ + type(point).__name__ == "numpyndarray": + point = np.ravel(point.copy()) + else: + raise TypeError("Input point is neither list nor numpyndarray") + if len(point) != 2: + raise TypeError("Input point must have exactly 2 elements") + if isinstance(Arr2D, list): + Arr2D = np.asarray(Arr2D) + elif type(Arr2D).__module__ + type(Arr2D).__name__ == "numpyndarray": + pass + else: + raise TypeError("Input Arr2D is neither list nor numpyndarray") + if Arr2D.shape[1] > Arr2D.shape[0]: # Reshape to A[row,column] + Arr2D = Arr2D.copy().T + if Arr2D.shape[1] != 2: + raise TypeError("Input Arr2D is not 2D") + # The pp of Qpp refer to p for 'positive' and n for 'negative' quadrants. + # In order. first subscript is x, second is y. + Qpp = Arr2D[(Arr2D[:, 0] > point[0]) & (Arr2D[:, 1] > point[1]), :] + Qnp = Arr2D[(Arr2D[:, 0] < point[0]) & (Arr2D[:, 1] > point[1]), :] + Qpn = Arr2D[(Arr2D[:, 0] > point[0]) & (Arr2D[:, 1] < point[1]), :] + Qnn = Arr2D[(Arr2D[:, 0] < point[0]) & (Arr2D[:, 1] < point[1]), :] + # Normalized fractions: + ff = 1.0 / len(Arr2D) + fpp = len(Qpp) * ff + fnp = len(Qnp) * ff + fpn = len(Qpn) * ff + fnn = len(Qnn) * ff + # NOTE: all the f's are supposed to sum to 1.0. Float representation + # cause SOMETIMES sum to 1.000000002 or something. I don't know how to + # test for that reliably, OR what to do about it yet. Keep in mind. + return fpp, fnp, fpn, fnn + + +def FuncQuads(func2D, point, xlim, ylim, rounddig=4): + """ + Compute probabilities by integrating a density function in quadrants. + + Computes the probabilities of finding points in each of the 4 quadrants + defined by a vertical and horizontal line crossing the given `point`. + The probabilities are determined by numerically integrating the 2D density + function `func2D` over each quadrant within the specified limits. + + Parameters + ---------- + func2D : callable + A 2D density function that accepts two arguments (x, y). + point : list or np.ndarray + A 1D array or list with 2 elements representing the center (x, y) + of the 4 quadrants. + xlim : list or np.ndarray + A list or array with 2 elements defining the integration limits for x. + ylim : list or np.ndarray + A list or array with 2 elements defining the integration limits for y. + rounddig : int, optional + Number of decimal digits to round the resulting probabilities to, + by default 4. + + Returns + ------- + tuple[float, float, float, float] + A tuple containing four floats (fpp, fnp, fpn, fnn), representing the + integrated probabilities in each quadrant, normalized by the total integral: + - fpp: Probability in the positive-x, positive-y quadrant. + - fnp: Probability in the negative-x, positive-y quadrant. + - fpn: Probability in the positive-x, negative-y quadrant. + - fnn: Probability in the negative-x, negative-y quadrant. + + Raises + ------ + TypeError + If `func2D` is not a callable function with 2 arguments, or if + `point`, `xlim`, or `ylim` are not list-like or numpy arrays with + exactly 2 elements, or if limits in `xlim` or `ylim` are equal. + + """ + if callable(func2D): + if len(inspect.getfullargspec(func2D)[0]) != 2: + raise TypeError("Input func2D is not a function with 2 arguments") + else: + raise TypeError("Input func2D is not a function") + # If xlim, ylim and point are not lists or ndarray, exit. + if isinstance(point, list): + point = np.asarray(np.ravel(point)) + elif type(point).__module__ + type(point).__name__ == "numpyndarray": + point = np.ravel(point.copy()) + else: + raise TypeError("Input point is not a list or numpyndarray") + if len(point) != 2: + raise TypeError("Input point has not exactly 2 elements") + if isinstance(xlim, list): + xlim = np.asarray(np.sort(np.ravel(xlim))) + elif type(xlim).__module__ + type(xlim).__name__ == "numpyndarray": + xlim = np.sort(np.ravel(xlim.copy())) + else: + raise TypeError("Input xlim is not a list or ndarray") + if len(xlim) != 2: + raise TypeError("Input xlim has not exactly 2 elements") + if xlim[0] == xlim[1]: + raise TypeError("Input xlim[0] should be different to xlim[1]") + if isinstance(ylim, list): + ylim = np.asarray(np.sort(np.ravel(ylim))) + elif type(ylim).__module__ + type(ylim).__name__ == "numpyndarray": + ylim = np.sort(np.ravel(ylim.copy())) + else: + raise TypeError("Input ylim is not a list or ndarray") + if len(ylim) != 2: + raise TypeError("Input ylim has not exactly 2 elements") + if ylim[0] == ylim[1]: + raise TypeError("Input ylim[0] should be different to ylim[1]") + # Numerical integration to find the quadrant probabilities. + totInt = scipy.integrate.dblquad( + func2D, *xlim, lambda x: np.amin(ylim), lambda x: np.amax(ylim) + )[0] + Qpp = scipy.integrate.dblquad( + func2D, point[0], np.amax(xlim), lambda x: point[1], lambda x: np.amax(ylim) + )[0] + Qpn = scipy.integrate.dblquad( + func2D, point[0], np.amax(xlim), lambda x: np.amin(ylim), lambda x: point[1] + )[0] + Qnp = scipy.integrate.dblquad( + func2D, np.amin(xlim), point[0], lambda x: point[1], lambda x: np.amax(ylim) + )[0] + Qnn = scipy.integrate.dblquad( + func2D, np.amin(xlim), point[0], lambda x: np.amin(ylim), lambda x: point[1] + )[0] + fpp = round(Qpp / totInt, rounddig) + fnp = round(Qnp / totInt, rounddig) + fpn = round(Qpn / totInt, rounddig) + fnn = round(Qnn / totInt, rounddig) + return (fpp, fnp, fpn, fnn) + + +def Qks(alam, iter=100, prec=1e-17): + """ + Compute the Kolmogorov-Smirnov probability function Q(lambda). + + Calculates the significance level for a given KS statistic `alam` (D). + This function is based on the approximation given in Numerical Recipes in C, + page 623. It represents the probability that the KS statistic will exceed + the observed value `alam` under the null hypothesis. + + Parameters + ---------- + alam : float + The KS statistic D (or a related value, often D * sqrt(N_eff)). + iter : int, optional + Maximum number of iterations for the series summation, by default 100. + prec : float, optional + Convergence precision. The summation stops if the absolute value of + the term to add is less than `prec`, by default 1e-17. + + Returns + ------- + float + The significance level P(D > observed) associated with `alam`. + Returns 1.0 if the series does not converge within `iter` iterations + or if the result exceeds 1.0. Returns 0.0 if the result is below `prec`. + + Raises + ------ + TypeError + If `alam` is not an integer or float. + + """ + # If j iterations are performed, meaning that toadd + # is still 2 times larger than the precision. + if isinstance(alam, int) | isinstance(alam, float): + pass + else: + raise TypeError("Input alam is neither int nor float") + toadd = [1] + qks = 0.0 + j = 1 + while (j < iter) & (abs(toadd[-1]) > prec * 2): + toadd.append(2.0 * (-1.0) ** (j - 1.0) * np.exp(-2.0 * j**2.0 * alam**2.0)) + qks += toadd[-1] + j += 1 + if (j == iter) | (qks > 1): # If no convergence after j iter, return 1.0 + return 1.0 + if qks < prec: + return 0.0 + return qks + + +def ks2d2s(Arr2D1: np.ndarray, Arr2D2: np.ndarray) -> tuple[float, float]: + """ + Perform the 2-dimensional, 2-sample Kolmogorov-Smirnov test. + + Tests the null hypothesis that two independent 2D samples, `Arr2D1` and + `Arr2D2`, are drawn from the same underlying probability distribution. + This implementation is based on the methods described by Peacock (1983) + and Fasano & Franceschini (1987). + + Parameters + ---------- + Arr2D1 : np.ndarray + First 2D sample array (shape N1 x 2). + Arr2D2 : np.ndarray + Second 2D sample array (shape N2 x 2). + + Returns + ------- + tuple[float, float] + d : float + The 2D KS statistic, representing the maximum difference found + between the cumulative distributions in any of the four quadrants, + evaluated at all data points. + prob : float + The significance level (p-value) of the observed statistic `d`. + A small `prob` indicates that the two samples are significantly + different. + + Raises + ------ + TypeError + If `Arr2D1` or `Arr2D2` are not numpy arrays or are not 2D. + + """ + if not isinstance(Arr2D1, np.ndarray): + raise TypeError("Input Arr2D1 is not a numpyndarray") + if Arr2D1.shape[1] > Arr2D1.shape[0]: + Arr2D1 = Arr2D1.copy().T + if not isinstance(Arr2D2, np.ndarray): + raise TypeError("Input Arr2D2 is not a numpyndarray") + + if Arr2D2.shape[1] > Arr2D2.shape[0]: + Arr2D2 = Arr2D2.copy().T + + if Arr2D1.shape[1] != 2: + raise TypeError("Input Arr2D1 is not 2D") + if Arr2D2.shape[1] != 2: + raise TypeError("Input Arr2D2 is not 2D") + + d1, d2 = 0.0, 0.0 + for point1 in Arr2D1: + fpp1, fmp1, fpm1, fmm1 = CountQuads(Arr2D1, point1) + fpp2, fmp2, fpm2, fmm2 = CountQuads(Arr2D2, point1) + d1 = max(d1, abs(fpp1 - fpp2)) + d1 = max(d1, abs(fpm1 - fpm2)) + d1 = max(d1, abs(fmp1 - fmp2)) + d1 = max(d1, abs(fmm1 - fmm2)) + for point2 in Arr2D2: + fpp1, fmp1, fpm1, fmm1 = CountQuads(Arr2D1, point2) + fpp2, fmp2, fpm2, fmm2 = CountQuads(Arr2D2, point2) + d2 = max(d2, abs(fpp1 - fpp2)) + d2 = max(d2, abs(fpm1 - fpm2)) + d2 = max(d2, abs(fmp1 - fmp2)) + d2 = max(d2, abs(fmm1 - fmm2)) + d = (d1 + d2) / 2.0 + sqen = np.sqrt(len(Arr2D1) * len(Arr2D2) / (len(Arr2D1) + len(Arr2D2))) + R1 = scipy.stats.pearsonr(Arr2D1[:, 0], Arr2D1[:, 1]).correlation + R2 = scipy.stats.pearsonr(Arr2D2[:, 0], Arr2D2[:, 1]).correlation + RR = np.sqrt(1.0 - (R1 * R1 + R2 * R2) / 2.0) + prob = Qks(d * sqen / (1.0 + RR * (0.25 - 0.75 / sqen))) + # Small values of prob show that the two samples are significantly + # different. Prob is the significance level of an observed value of d. + # NOT the same as the significance level that ou set and compare to D. + return d, prob + + +def ks2d1s(Arr2D, func2D, xlim=[], ylim=[]): + """ + Perform the 2-dimensional, 1-sample Kolmogorov-Smirnov test. + + Tests the null hypothesis that a 2D sample `Arr2D` is drawn from a + given 2D probability density distribution `func2D`. + + Parameters + ---------- + Arr2D : np.ndarray + The 2D sample array (shape N x 2). + func2D : callable + The theoretical 2D probability density function func(x, y). + xlim : list or np.ndarray, optional + Integration limits for the x-dimension. If empty, defaults are + calculated based on the range of `Arr2D`. + ylim : list or np.ndarray, optional + Integration limits for the y-dimension. If empty, defaults are + calculated based on the range of `Arr2D`. + + Returns + ------- + tuple[float, float] + d : float + The 2D KS statistic, representing the maximum difference between + the empirical distribution (from `Arr2D`) and the theoretical + distribution (`func2D`) in any of the four quadrants, evaluated + at all data points. + prob : float + The significance level (p-value) of the observed statistic `d`. + A small `prob` indicates that the sample is significantly + different from the theoretical distribution. + + Raises + ------ + TypeError + If `func2D` is not a callable function with 2 arguments, or if + `Arr2D` is not a 2D numpy array. + + """ + if callable(func2D): + if len(inspect.getfullargspec(func2D)[0]) != 2: + raise TypeError("Input func2D is not a function with 2 input arguments") + else: + raise TypeError("Input func2D is not a function") + if type(Arr2D).__module__ + type(Arr2D).__name__ == "numpyndarray": + pass + else: + raise TypeError("Input Arr2D is neither list nor numpyndarray") + print(Arr2D.shape) + if Arr2D.shape[1] > Arr2D.shape[0]: + Arr2D = Arr2D.copy().T + if Arr2D.shape[1] != 2: + raise TypeError("Input Arr2D is not 2D") + if xlim == []: + xlim.append( + np.amin(Arr2D[:, 0]) - abs(np.amin(Arr2D[:, 0]) - np.amax(Arr2D[:, 0])) / 10 + ) + xlim.append( + np.amax(Arr2D[:, 0]) - abs(np.amin(Arr2D[:, 0]) - np.amax(Arr2D[:, 0])) / 10 + ) + if ylim == []: + ylim.append( + np.amin(Arr2D[:, 1]) - abs(np.amin(Arr2D[:, 1]) - np.amax(Arr2D[:, 1])) / 10 + ) + + ylim.append( + np.amax(Arr2D[:, 1]) - abs(np.amin(Arr2D[:, 1]) - np.amax(Arr2D[:, 1])) / 10 + ) + d = 0 + for point in Arr2D: + fpp1, fmp1, fpm1, fmm1 = FuncQuads(func2D, point, xlim, ylim) + fpp2, fmp2, fpm2, fmm2 = CountQuads(Arr2D, point) + d = max(d, abs(fpp1 - fpp2)) + d = max(d, abs(fpm1 - fpm2)) + d = max(d, abs(fmp1 - fmp2)) + d = max(d, abs(fmm1 - fmm2)) + sqen = np.sqrt(len(Arr2D)) + R1 = scipy.stats.pearsonr(Arr2D[:, 0], Arr2D[:, 1])[0] + RR = np.sqrt(1.0 - R1**2) + prob = Qks(d * sqen / (1.0 + RR * (0.25 - 0.75 / sqen))) + return d, prob diff --git a/src/soundscapy/spi/msn.py b/src/soundscapy/spi/msn.py new file mode 100644 index 00000000..f999e8fa --- /dev/null +++ b/src/soundscapy/spi/msn.py @@ -0,0 +1,566 @@ +""" +Module for handling Multi-dimensional Skewed Normal (MSN) distributions. + +Provides classes and functions for defining, fitting, sampling, and analyzing +MSN distributions, often used in soundscape analysis for modeling ISOPleasant +and ISOEventful ratings. +""" + +from typing import Literal + +import numpy as np +import pandas as pd + +from soundscapy import get_logger +from soundscapy.plotting import density_plot +from soundscapy.spi import _rsn_wrapper as rsn +from soundscapy.spi.ks2d import ks2d2s + +logger = get_logger() + + +class DirectParams: + """ + Represents a set of direct parameters for a statistical model. + + Direct parameters are the parameters that are directly used in the model. + They are the parameters that are used to define the distribution of the + data. In the case of a skew normal distribution, the direct parameters + are the xi, omega, and alpha values. + + Parameters + ---------- + xi : np.ndarray + The location of the distribution in 2D space, represented as a 2x1 array + with the x and y coordinates. + omega : np.ndarray + The covariance matrix of the distribution, represented as a 2x2 array. + The covariance matrix represents the measure of the relationship between + different variables. It provides information about how changes in one + variable are associated with changes in other variables. + alpha : np.ndarray + The shape parameters for the x and y dimensions, controlling the shape + (skewness) of the distribution. It is represented as a 2x1 array. + + """ + + def __init__(self, xi: np.ndarray, omega: np.ndarray, alpha: np.ndarray) -> None: + """Initialize DirectParams instance.""" + self.xi = xi + self.omega = omega + self.alpha = alpha + self.validate() + + def __repr__(self) -> str: + """Return a string representation of the DirectParams object.""" + return f"DirectParams(xi={self.xi}, omega={self.omega}, alpha={self.alpha})" + + def __str__(self) -> str: + """Return a user-friendly string representation of the DirectParams object.""" + return ( + f"Direct Parameters:" + f"\nxi: {self.xi.round(3)}" + f"\nomega: {self.omega.round(3)}" + f"\nalpha: {self.alpha.round(3)}" + ) + + def _omega_is_pos_def(self) -> bool: + return bool(np.all(np.linalg.eigvals(self.omega) > 0)) + + def _omega_is_symmetric(self) -> bool: + return np.allclose(self.omega, self.omega.T) + + def _xi_is_in_range(self, xi_range: np.ndarray | tuple[float, float]) -> bool: + if isinstance(xi_range, tuple): + xi_range = np.array([xi_range, xi_range]) + return bool(np.all((xi_range[:, 0] <= self.xi) & (self.xi <= xi_range[:, 1]))) + + def validate(self) -> None: + """ + Validate the direct parameters. + + In a skew normal distribution, the covariance matrix, often denoted as + Ω (Omega), represents the measure of the relationship between different + variables. It provides information about how changes in one variable are + associated with changes in other variables. The covariance matrix must + be positive definite and symmetric. + + Raises + ------ + ValueError + If the direct parameters are not valid. + + Returns + ------- + None + + """ + if not self._omega_is_pos_def(): + msg = "Omega must be positive definite" + raise ValueError(msg) + if not self._omega_is_symmetric(): + msg = "Omega must be symmetric" + raise ValueError(msg) + + +class CentredParams: + """ + Represents the centered parameters of a distribution. + + Parameters + ---------- + mean : float + The mean of the distribution. + sigma : float + The standard deviation of the distribution. + skew : float + The skewness of the distribution. + + Attributes + ---------- + mean : float + The mean of the distribution. + sigma : float + The standard deviation of the distribution. + skew : float + The skewness of the distribution. + + Methods + ------- + from_dp(dp) + Converts DirectParams object to CentredParams object. + + """ + + def __init__(self, mean: np.ndarray, sigma: np.ndarray, skew: np.ndarray) -> None: + """Initialize CentredParams instance.""" + self.mean = mean + self.sigma = sigma + self.skew = skew + + def __repr__(self) -> str: + """Return a string representation of the CentredParams object.""" + return f"CentredParams(mean={self.mean}, sigma={self.sigma}, skew={self.skew})" + + def __str__(self) -> str: + """Return a user-friendly string representation of the CentredParams object.""" + return ( + f"Centred Parameters:" + f"\nmean: {self.mean.round(3)}" + f"\nsigma: {self.sigma.round(3)}" + f"\nskew: {self.skew.round(3)}" + ) + + @classmethod + def from_dp(cls, dp: DirectParams) -> "CentredParams": + """ + Convert a DirectParams object to a CentredParams object. + + Parameters + ---------- + dp : DirectParams + The DirectParams object to convert. + + Returns + ------- + CentredParams + A new CentredParams object with the converted parameters. + + """ + cp = dp2cp(dp) + return cls(cp.mean, cp.sigma, cp.skew) + + +class MultiSkewNorm: + """ + A class representing a multi-dimensional skewed normal distribution. + + Attributes + ---------- + selm_model + The fitted SELM model. + cp : CentredParams + The centred parameters of the fitted model. + dp : DirectParams + The direct parameters of the fitted model. + sample_data : np.ndarray | None + The generated sample data from the fitted model. + data : pd.DataFrame | None + The input data used for fitting the model. + + Methods + ------- + summary() + Prints a summary of the fitted model. + fit(data=None, x=None, y=None) + Fits the model to the provided data. + define_dp(xi, omega, alpha) + Defines the direct parameters of the model. + sample(n=1000, return_sample=False) + Generates a sample from the fitted model. + sspy_plot(color='blue', title=None, n=1000) + Plots the joint distribution of the generated sample. + ks2ds(test) + Computes the two-sample Kolmogorov-Smirnov statistic. + spi(test) + Computes the similarity percentage index. + + """ + + def __init__(self) -> None: + """Initialize the MultiSkewNorm object.""" + self.selm_model = None + self.cp = None + self.dp = None + self.sample_data = None + self.data: pd.DataFrame | None = None + + def __repr__(self) -> str: + """Return a string representation of the MultiSkewNorm object.""" + if self.cp is None and self.dp is None and self.selm_model is None: + return "MultiSkewNorm() (unfitted)" + return f"MultiSkewNorm(dp={self.dp})" + + def summary(self) -> str | None: + """ + Provide a summary of the fitted MultiSkewNorm model. + + Returns + ------- + str or None + A string summarizing the model parameters and data, or a message + indicating the model is not fitted. Returns None if fitted but + summary logic is not fully implemented yet. + + """ + if self.cp is None and self.dp is None and self.selm_model is None: + return "MultiSkewNorm is not fitted." + if self.data is not None: + print(f"Fitted from data. n = {len(self.data)}") # noqa: T201 + else: + print("Fitted from direct parameters.") # noqa: T201 + print(self.dp) # noqa: T201 + print("\n") # noqa: T201 + print(self.cp) # noqa: RET503, T201 + + def fit( + self, + data: pd.DataFrame | np.ndarray | None = None, + x: np.ndarray | pd.Series | None = None, + y: np.ndarray | pd.Series | None = None, + ) -> None: + """ + Fit the multi-dimensional skewed normal model to the provided data. + + Parameters + ---------- + data : pd.DataFrame or np.ndarray, optional + The input data as a pandas DataFrame or numpy array. + x : np.ndarray or pd.Series, optional + The x-values of the input data as a numpy array or pandas Series. + y : np.ndarray or pd.Series, optional + The y-values of the input data as a numpy array or pandas Series. + + Raises + ------ + ValueError + If neither `data` nor both `x` and `y` are provided. + + """ + if data is None and (x is None or y is None): + # Either data or x and y must be provided + msg = "Either data or x and y must be provided" + raise ValueError(msg) + + if data is not None: + # If data is provided, convert it to a pandas DataFrame + if isinstance(data, pd.DataFrame): + # If data is already a DataFrame, no need to convert + data.columns = ["x", "y"] + + elif isinstance(data, np.ndarray): + # If data is a numpy array, convert it to a DataFrame + if data.ndim == 2: # noqa: PLR2004 + # If data is 2D, assume it's two variables + data = pd.DataFrame(data, columns=["x", "y"]) + else: + msg = "Data must be a 2D numpy array or DataFrame" + raise ValueError(msg) + else: + # If data is neither a DataFrame nor a numpy array, raise an error + msg = "Data must be a pandas DataFrame or 2D numpy array." + raise ValueError(msg) + + elif x is not None and y is not None: + # If x and y are provided, convert them to a pandas DataFrame + data = pd.DataFrame({"x": x, "y": y}) + + else: + # This should never happen + msg = "Either data or x and y must be provided" + raise ValueError(msg) + + # Fit the model + m = rsn.selm("x", "y", data) + + # Extract the parameters + cp = rsn.extract_cp(m) + dp = rsn.extract_dp(m) + + self.cp = CentredParams(*cp) + self.dp = DirectParams(*dp) + self.data = data + self.selm_model = m + + def define_dp( + self, xi: np.ndarray, omega: np.ndarray, alpha: np.ndarray + ) -> "MultiSkewNorm": + """ + Initiate a distribution from the direct parameters. + + Parameters + ---------- + xi : np.ndarray + The xi values of the direct parameters. + omega : np.ndarray + The omega values of the direct parameters. + alpha : np.ndarray + The alpha values of the direct parameters. + + Returns + ------- + self + + """ + self.dp = DirectParams(xi, omega, alpha) + self.cp = CentredParams.from_dp(self.dp) + return self + + def sample( + self, n: int = 1000, *, return_sample: bool = False + ) -> None | np.ndarray: + """ + Generate a sample from the fitted model. + + Parameters + ---------- + n : int, optional + The number of samples to generate, by default 1000. + return_sample : bool, optional + Whether to return the generated sample as an np.ndarray, by default False. + + Returns + ------- + None or np.ndarray + The generated sample if `return_sample` is True, otherwise None. + + Raises + ------ + ValueError + If the model is not fitted (i.e., `selm_model` is None) and direct + parameters (`dp`) are also not defined. + + """ + if self.selm_model is not None: + sample = rsn.sample_msn(selm_model=self.selm_model, n=n) + elif self.dp is not None: + sample = rsn.sample_msn( + xi=self.dp.xi, omega=self.dp.omega, alpha=self.dp.alpha, n=n + ) + else: + msg = "Either selm_model or xi, omega, and alpha must be provided." + raise ValueError(msg) + + self.sample_data = sample + + if return_sample: + return sample + return None + + def sample_mtsn( + self, n: int = 1000, a: float = -1, b: float = 1, *, return_sample: bool = False + ) -> None | np.ndarray: + """ + Generate a sample from the multi-dimensional truncated skew-normal distribution. + + Uses rejection sampling to ensure that the samples are within the bounds [a, b] + for both dimensions. + + Parameters + ---------- + n : int, optional + The number of samples to generate, by default 1000. + a : float, optional + Lower truncation bound for both dimensions, by default -1. + b : float, optional + Upper truncation bound for both dimensions, by default 1. + return_sample : bool, optional + Whether to return the generated sample as an np.ndarray, by default False. + + Returns + ------- + None or np.ndarray + The generated sample if `return_sample` is True, otherwise None. + + """ + if self.selm_model is not None: + sample = rsn.sample_mtsn( + selm_model=self.selm_model, + n=n, + a=a, + b=b, + ) + elif self.dp is not None: + sample = rsn.sample_mtsn( + xi=self.dp.xi, + omega=self.dp.omega, + alpha=self.dp.alpha, + n=n, + a=a, + b=b, + ) + else: + msg = "Either selm_model or xi, omega, and alpha must be provided." + raise ValueError(msg) + + # Store the sample data + self.sample_data = sample + + if return_sample: + return sample + return None + + def sspy_plot( + self, color: str = "blue", title: str | None = None, n: int = 1000 + ) -> None: + """ + Plot the joint distribution of the generated sample using soundscapy. + + Parameters + ---------- + color : str, optional + Color for the density plot, by default "blue". + title : str, optional + Title for the plot, by default None. + n : int, optional + Number of samples to generate if `sample_data` is None, by default 1000. + + """ + if self.sample_data is None: + self.sample(n=n) + + data = pd.DataFrame(self.sample_data, columns=["ISOPleasant", "ISOEventful"]) + plot_title = title if title is not None else "Soundscapy Density Plot" + density_plot(data, color=color, title=plot_title) + + def ks2d2s(self, test_data: pd.DataFrame | np.ndarray) -> tuple[float, float]: + """ + Compute the two-sample, two-dimensional Kolmogorov-Smirnov statistic. + + Parameters + ---------- + test : pd.DataFrame or np.ndarray + The test data. + + Returns + ------- + tuple + The KS2D statistic and p-value. + + """ + # Ensure test_data is a numpy array + if isinstance(test_data, pd.DataFrame): + if test_data.shape[1] != 2: # noqa: PLR2004 + msg = "Test data must have two columns." + raise ValueError(msg) + test_data_np = test_data.to_numpy() + elif isinstance(test_data, np.ndarray): + test_data_np = test_data + else: + msg = "test_data must be a pandas DataFrame or numpy array." + raise TypeError(msg) + + # Ensure sample_data exists, generate if needed and possible + if self.sample_data is None: + logger.info("Sample data not found, generating default sample (n=1000).") + self.sample(n=1000, return_sample=False) # Generate sample if missing + if self.sample_data is None: # Check again in case sample failed + msg = ( + "Could not generate sample data. " + "Ensure model is defined (fit or define_dp)." + ) + raise ValueError(msg) + + # Perform the 2-sample KS test using ks2d2s + # Note: ks2d2s expects data1, data2 + ks_statistic, p_value = ks2d2s(self.sample_data, test_data_np) + + return ks_statistic, p_value + + def spi(self, test: pd.DataFrame | np.ndarray) -> int: + """ + Compute the Soundscape Perception Index (SPI). + + Calculates the SPI for the test data against the target distribution + represented by this MultiSkewNorm instance. + + Parameters + ---------- + test : pd.DataFrame or np.ndarray + The test data. + + Returns + ------- + int + The Soundscape Perception Index (SPI), ranging from 0 to 100. + + """ + return int((1 - self.ks2d2s(test)[0]) * 100) + + +def cp2dp( + cp: CentredParams, family: Literal["SN", "ESN", "ST", "SC"] = "SN" +) -> DirectParams: + """ + Convert centred parameters to direct parameters. + + Parameters + ---------- + cp : CentredParams + The centred parameters object. + family : str, optional + The distribution family, by default "SN" (Skew Normal). + + Returns + ------- + DirectParams + The corresponding direct parameters object. + + """ + dp_r = rsn.cp2dp(cp.mean, cp.sigma, cp.skew, family=family) + + return DirectParams(*dp_r) + + +def dp2cp( + dp: DirectParams, family: Literal["SN", "ESN", "ST", "SC"] = "SN" +) -> CentredParams: + """ + Convert direct parameters to centred parameters. + + Parameters + ---------- + dp : DirectParams + The direct parameters object. + family : str, optional + The distribution family, by default "SN" (Skew Normal). + + Returns + ------- + CentredParams + The corresponding centred parameters object. + + """ + cp_r = rsn.dp2cp(dp.xi, dp.omega, dp.alpha, family=family) + + return CentredParams(*cp_r) diff --git a/src/soundscapy/logging.py b/src/soundscapy/sspylogging.py similarity index 82% rename from src/soundscapy/logging.py rename to src/soundscapy/sspylogging.py index 78651b07..87797cd5 100644 --- a/src/soundscapy/logging.py +++ b/src/soundscapy/sspylogging.py @@ -1,21 +1,26 @@ """ Logging configuration for Soundscapy. -This module provides simple functions to configure logging for both users and developers. -By default, Soundscapy logging is disabled to avoid unwanted output. +This module provides simple functions to configure logging for both users and +developers. By default, Soundscapy logging is disabled to avoid unwanted output. Users can enable logging with the setup_logging function. """ +from __future__ import annotations + import sys -from typing import Optional, Union, TextIO -from pathlib import Path +from typing import TYPE_CHECKING +import loguru from loguru import logger +if TYPE_CHECKING: + from pathlib import Path + def setup_logging( level: str = "INFO", - log_file: Optional[Union[str, Path]] = None, + log_file: str | Path | None = None, format_level: str = "basic", ) -> None: """ @@ -27,7 +32,8 @@ def setup_logging( Logging level for console output. Options: "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL" log_file : str or Path, optional - Path to a log file. If provided, all messages (including DEBUG) will be logged to this file. + Path to a log file. + If provided, all messages (including DEBUG) will be logged to this file. format_level : str, default="basic" Format complexity level. Options: - "basic": Simple format with timestamp, level, and message @@ -45,6 +51,7 @@ def setup_logging( >>> >>> # Use detailed format for debugging >>> setup_logging(level="DEBUG", format_level="detailed") + """ # Enable soundscapy logging (disabled by default in __init__.py) logger.enable("soundscapy") @@ -55,13 +62,19 @@ def setup_logging( # Format configurations formats = { "basic": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {message}", - "detailed": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} | {message}", - "developer": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} | {message}\n{exception}", + "detailed": ( + "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | " + "{name}:{function}:{line} | {message}" + ), + "developer": ( + "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | " + "{name}:{function}:{line} | {message}\n{exception}" + ), } # Use the appropriate format if format_level not in formats: - print(f"Warning: Unknown format_level '{format_level}'. Using 'basic' instead.") + logger.warning(f"Unknown format_level '{format_level}'. Using 'basic' instead.") format_level = "basic" log_format = formats[format_level] @@ -100,6 +113,7 @@ def enable_debug() -> None: >>> from soundscapy import enable_debug >>> enable_debug() >>> # Now all debug messages will be shown + """ setup_logging(level="DEBUG", format_level="detailed") logger.info("Debug logging enabled") @@ -114,6 +128,7 @@ def disable_logging() -> None: >>> from soundscapy import disable_logging >>> disable_logging() >>> # No more logging messages will be shown + """ # First remove all handlers to ensure no output logger.remove() @@ -123,7 +138,7 @@ def disable_logging() -> None: logger.add(sys.stderr, level=100) # Level 100 is higher than any standard level -def get_logger(): +def get_logger() -> loguru.Logger: """ Get the Soundscapy logger instance. @@ -140,6 +155,7 @@ def get_logger(): >>> from soundscapy import get_logger >>> logger = get_logger() >>> logger.debug("Custom debug message") + """ return logger @@ -152,6 +168,7 @@ def is_notebook() -> bool: ------- bool True if running in a Jupyter notebook, False otherwise + """ try: from IPython.core.getipython import get_ipython @@ -159,9 +176,8 @@ def is_notebook() -> bool: shell = get_ipython().__class__.__name__ if shell == "ZMQInteractiveShell": # Jupyter notebook/lab return True - elif shell == "TerminalInteractiveShell": # IPython - return False - else: + if shell == "TerminalInteractiveShell": # IPython return False + return False # noqa: TRY300 except (NameError, ImportError): return False diff --git a/src/soundscapy/surveys/__init__.py b/src/soundscapy/surveys/__init__.py index dbc04349..9c16da59 100644 --- a/src/soundscapy/surveys/__init__.py +++ b/src/soundscapy/surveys/__init__.py @@ -1,4 +1,16 @@ -from soundscapy.surveys.processing import add_iso_coords, return_paqs +""" +Soundscapy Surveys Package. + +This package handles the processing and analysis of soundscape surveys, +including PAQ (Perceived Affective Quality) data and ISO coordinate calculations. +""" + +from soundscapy.surveys import processing, survey_utils +from soundscapy.surveys.processing import ( + add_iso_coords, + calculate_iso_coords, + return_paqs, +) from soundscapy.surveys.survey_utils import ( LANGUAGE_ANGLES, PAQ_IDS, @@ -7,10 +19,13 @@ ) __all__ = [ - "return_paqs", - "add_iso_coords", - "rename_paqs", "LANGUAGE_ANGLES", "PAQ_IDS", "PAQ_LABELS", + "add_iso_coords", + "calculate_iso_coords", + "processing", + "rename_paqs", + "return_paqs", + "survey_utils", ] diff --git a/src/soundscapy/surveys/processing.py b/src/soundscapy/surveys/processing.py index 547c7eca..68be54da 100644 --- a/src/soundscapy/surveys/processing.py +++ b/src/soundscapy/surveys/processing.py @@ -6,20 +6,29 @@ Notes ----- -The functions in this module are designed to be fairly general and can be used with any dataset in a similar format to -the ISD. The key to this is using a simple dataframe/sheet with the following columns: - Index columns: e.g. LocationID, RecordID, GroupID, SessionID - Perceptual attributes: PAQ1, PAQ2, ..., PAQ8 - Independent variables: e.g. Laeq, N5, Sharpness, etc. - -The key functions of this module are designed to clean/validate datasets, calculate ISO coordinate values or SSM metrics, -filter on index columns. Functions and operations which are specific to a particular dataset are located in their own +The functions in this module are designed to be fairly general and can be used with +any dataset in a similar format to the ISD. The key to this is using a simple +dataframe/sheet with the following columns: + + - Index columns: e.g. LocationID, RecordID, GroupID, SessionID + - Perceptual attributes: PAQ1, PAQ2, ..., PAQ8 + - Independent variables: e.g. Laeq, N5, Sharpness, etc. + +The key functions of this module are designed to clean/validate datasets, calculate ISO +coordinate values or SSM metrics, filter on index columns. Functions and operations +which are specific to a particular dataset are located in their own modules under `soundscape.databases`. + """ import warnings from dataclasses import dataclass -from typing import List, Optional, Tuple +from typing import TypedDict + +try: + from typing import Unpack +except ImportError: + from typing_extensions import Unpack import numpy as np import pandas as pd @@ -28,7 +37,7 @@ from soundscapy.surveys.survey_utils import EQUAL_ANGLES, PAQ_IDS, return_paqs -np.set_printoptions(legacy="1.25") +np.set_printoptions(legacy="1.21") @dataclass @@ -50,6 +59,25 @@ class SSMMetrics: r_squared: float def table(self) -> pd.Series: + """ + Generate a pandas Series containing specific attributes of the instance. + + This method collects the values of the instance attributes related to + amplitude, angle, elevation, displacement, and r_squared, and organizes + them into a pandas Series. It is useful for presenting the data in a + structured format suitable for further processing or analysis. + + Returns + ------- + pandas.Series + A pandas Series containing the following key-value pairs: + - "amplitude": instance attribute representing a certain magnitude. + - "angle": instance attribute representing a specific angular measurement. + - "elevation": instance attribute indicating a height or vertical position. + - "displacement": instance attribute defining the movement or shift. + - "r_squared": instance attribute denoting coefficient of determination. + + """ return pd.Series( { "amplitude": self.amplitude, @@ -63,9 +91,9 @@ def table(self) -> pd.Series: def calculate_iso_coords( results_df: pd.DataFrame, - val_range: Tuple[int, int] = (5, 1), - angles: Tuple[int, ...] = EQUAL_ANGLES, -) -> Tuple[pd.Series, pd.Series]: + val_range: tuple[int, int] = (5, 1), + angles: tuple[int, ...] = EQUAL_ANGLES, +) -> tuple[pd.Series, pd.Series]: """ Calculate the projected ISOPleasant and ISOEventful coordinates. @@ -99,6 +127,7 @@ def calculate_iso_coords( 0 -0.28 1 0.18 dtype: float64 + """ scale = max(val_range) - min(val_range) @@ -111,7 +140,7 @@ def calculate_iso_coords( return iso_pleasant, iso_eventful -def _adj_iso_pl(values: pd.Series, angles: Tuple[int, ...], scale: float) -> float: +def _adj_iso_pl(values: pd.Series, angles: tuple[int, ...], scale: float) -> float: """ Calculate the adjusted ISOPleasant value. @@ -130,16 +159,20 @@ def _adj_iso_pl(values: pd.Series, angles: Tuple[int, ...], scale: float) -> flo ------- float Adjusted ISOPleasant value + """ iso_pl = np.sum( - [np.cos(np.deg2rad(angle)) * value for angle, value in zip(angles, values)] + [ + np.cos(np.deg2rad(angle)) * value + for angle, value in zip(angles, values, strict=False) + ] ) return iso_pl / ( scale / 2 * np.sum(np.abs([np.cos(np.deg2rad(angle)) for angle in angles])) ) -def _adj_iso_ev(values: pd.Series, angles: Tuple[int, ...], scale: float) -> float: +def _adj_iso_ev(values: pd.Series, angles: tuple[int, ...], scale: float) -> float: """ Calculate the adjusted ISOEventful value. @@ -158,9 +191,13 @@ def _adj_iso_ev(values: pd.Series, angles: Tuple[int, ...], scale: float) -> flo ------- float Adjusted ISOEventful value + """ iso_ev = np.sum( - [np.sin(np.deg2rad(angle)) * value for angle, value in zip(angles, values)] + [ + np.sin(np.deg2rad(angle)) * value + for angle, value in zip(angles, values, strict=False) + ] ) return iso_ev / ( scale / 2 * np.sum(np.abs([np.sin(np.deg2rad(angle)) for angle in angles])) @@ -169,10 +206,11 @@ def _adj_iso_ev(values: pd.Series, angles: Tuple[int, ...], scale: float) -> flo def add_iso_coords( data: pd.DataFrame, - val_range: Tuple[int, int] = (1, 5), - names: Tuple[str, str] = ("ISOPleasant", "ISOEventful"), + val_range: tuple[int, int] = (1, 5), + names: tuple[str, str] = ("ISOPleasant", "ISOEventful"), + angles: tuple[int, ...] = EQUAL_ANGLES, + *, overwrite: bool = False, - angles: Tuple[int, ...] = EQUAL_ANGLES, ) -> pd.DataFrame: """ Calculate and add ISO coordinates as new columns in the DataFrame. @@ -185,10 +223,11 @@ def add_iso_coords( (min, max) range of original PAQ responses, by default (1, 5) names : Tuple[str, str], optional Names for new coordinate columns, by default ("ISOPleasant", "ISOEventful") - overwrite : bool, optional - Whether to overwrite existing ISO coordinate columns, by default False angles : Tuple[int, ...], optional Angles for each PAQ in degrees, by default EQUAL_ANGLES + * + overwrite : bool, optional + Whether to overwrite existing ISO coordinate columns, by default False Returns ------- @@ -212,15 +251,17 @@ def add_iso_coords( ISOPleasant ISOEventful 0 -0.03 -0.28 1 0.47 0.18 + """ for name in names: if name in data.columns: if overwrite: data = data.drop(name, axis=1) else: - raise Warning( + msg = ( f"{name} already in dataframe. Use `overwrite=True` to replace it." ) + raise Warning(msg) iso_pleasant, iso_eventful = calculate_iso_coords( data, val_range=val_range, angles=angles @@ -232,8 +273,8 @@ def add_iso_coords( def likert_data_quality( - df: pd.DataFrame, allow_na: bool = False, val_range: Tuple[int, int] = (1, 5) -) -> Optional[List[int]]: + df: pd.DataFrame, val_range: tuple[int, int] = (1, 5), *, allow_na: bool = False +) -> list[int] | None: """ Perform basic quality checks on PAQ (Likert scale) data. @@ -248,8 +289,7 @@ def likert_data_quality( Returns ------- - Optional[List[int]] - List of indices to be removed, or None if no issues found + List of indices to be removed, or None if no issues found Examples -------- @@ -262,20 +302,28 @@ def likert_data_quality( ... }) >>> likert_data_quality(df) [0, 1, 2] - >>> likert_data_quality(df, allow_na=True) + >>> likert_data_quality(df,allow_na=True) [1, 2] + """ paqs = return_paqs(df, incl_ids=False) invalid_indices = [] - for i, row in paqs.iterrows(): - if not allow_na and row.isna().any(): - invalid_indices.append(i) - elif row.notna().all(): - if row.min() < min(val_range) or row.max() > max(val_range): - invalid_indices.append(i) - elif row.nunique() == 1 and row.iloc[0] != np.mean(val_range): - invalid_indices.append(i) + for idx, row in paqs.iterrows(): + # Convert the index to int to ensure type compatibility + row_idx = int(idx) if isinstance(idx, str) else idx + row_array = row.to_numpy() + is_constant = row_array.shape[0] > 0 and (row_array[0] == row_array).all() + + if (not allow_na and row.isna().any()) or ( + row.notna().all() + and ( + row.min() < min(val_range) + or row.max() > max(val_range) + or (is_constant and row.iloc[0] != np.mean(val_range)) + ) + ): + invalid_indices.append(row_idx) if invalid_indices: logger.info(f"Found {len(invalid_indices)} samples with data quality issues") @@ -285,11 +333,20 @@ def likert_data_quality( return None +class _AddISOCoordsKwargs( + TypedDict, total=False +): # total=False allows for optional keys + names: tuple[str, str] + angles: tuple[int, ...] + overwrite: bool + + def simulation( n: int = 3000, - val_range: Tuple[int, int] = (1, 5), + val_range: tuple[int, int] = (1, 5), + *, incl_iso_coords: bool = False, - **coord_kwargs, + **coord_kwargs: Unpack[_AddISOCoordsKwargs], ) -> pd.DataFrame: """ Generate random PAQ responses for simulation purposes. @@ -298,12 +355,16 @@ def simulation( ---------- n : int, optional Number of samples to simulate, by default 3000 - val_range : Tuple[int, int], optional + val_range : tuple[int, int], optional Range of values for PAQ responses, by default (1, 5) - add_iso_coords : bool, optional + incl_iso_coords : bool, optional Whether to add calculated ISO coordinates, by default False - **coord_kwargs : dict - Additional keyword arguments to pass to add_iso_coords function + **coord_kwargs : Unpack[_AddISOCoordsKwargs] + Optional keyword arguments passed directly to the `add_iso_coords` function + if `incl_iso_coords` is True. These can include: + - `names` (tuple[str, str]): Names for the new ISO coordinate columns. + - `angles` (tuple[int, ...]): Angles for each PAQ used in calculation. + - `overwrite` (bool): Whether to overwrite existing ISO coordinate columns. Returns ------- @@ -312,31 +373,33 @@ def simulation( Examples -------- - >>> df = simulation(n=5, incl_iso_coords=True) - >>> df.shape + >>> data = simulation(n=5,incl_iso_coords=True) + >>> data.shape (5, 10) - >>> list(df.columns) + >>> list(data.columns) ['PAQ1', 'PAQ2', 'PAQ3', 'PAQ4', 'PAQ5', 'PAQ6', 'PAQ7', 'PAQ8', 'ISOPleasant', 'ISOEventful'] - """ - np.random.seed(42) - df = pd.DataFrame( - np.random.randint(min(val_range), max(val_range) + 1, size=(n, 8)), + + """ # noqa: E501 + data = pd.DataFrame( + np.random.default_rng().integers( + min(val_range), max(val_range) + 1, size=(n, 8) + ), columns=PAQ_IDS, ) if incl_iso_coords: - df = add_iso_coords(df, val_range=val_range, **coord_kwargs) + data = add_iso_coords(data, val_range=val_range, **coord_kwargs) logger.info(f"Generated simulated PAQ data with {n} samples") - return df + return data def ssm_metrics( df: pd.DataFrame, - paq_cols: List[str] = PAQ_IDS, + paq_cols: list[str] = PAQ_IDS, method: str = "cosine", - val_range: Tuple[int, int] = (5, 1), - angles: Tuple[int, ...] = EQUAL_ANGLES, + val_range: tuple[int, int] = (5, 1), + angles: tuple[int, ...] = EQUAL_ANGLES, ) -> pd.DataFrame: """ Calculate the Structural Summary Method (SSM) metrics for each response. @@ -362,35 +425,43 @@ def ssm_metrics( Raises ------ ValueError - If PAQ columns are not present in the DataFrame or if an invalid method is specified + If PAQ columns are not present in the DataFrame + or if an invalid method is specified Examples -------- >>> # xdoctest: +SKIP >>> import pandas as pd - >>> df = pd.DataFrame({ + >>> data = pd.DataFrame({ ... 'PAQ1': [4, 2], 'PAQ2': [3, 5], 'PAQ3': [2, 4], 'PAQ4': [1, 3], ... 'PAQ5': [5, 1], 'PAQ6': [3, 2], 'PAQ7': [4, 3], 'PAQ8': [2, 5] ... }) - >>> ssm_metrics(df).round(2) + >>> ssm_metrics(data).round(2) amplitude angle elevation displacement r_squared 0 0.68 263.82 10.57 -7.57 0.15 1 1.21 20.63 0.01 3.11 0.39 + """ - # TODO: Replace with a call to circumplex package + # TODO(MitchellAcoustics): Replace with a call to circumplex package # noqa: TD003 warnings.warn( - "This function is not yet fully implemented. See https://github.com/MitchellAcoustics/circumplex for a more complete implementation.", + "This function is not yet fully implemented." + "See https://github.com/MitchellAcoustics/circumplex for a " + "more complete implementation.", PendingDeprecationWarning, + stacklevel=2, ) if not set(paq_cols).issubset(df.columns): - raise ValueError("PAQ columns are not present in the DataFrame") + msg = f"PAQ columns {paq_cols} not present in DataFrame" + raise ValueError(msg) if method == "polar": iso_pleasant, iso_eventful = calculate_iso_coords( df[paq_cols], val_range, angles ) - r, theta = _convert_to_polar_coords(iso_pleasant, iso_eventful) + r, theta = _convert_to_polar_coords( + iso_pleasant.to_numpy(), iso_eventful.to_numpy() + ) mean = df[paq_cols].mean(axis=1) mean = mean / (max(val_range) - min(val_range)) if val_range != (0, 1) else mean @@ -403,20 +474,20 @@ def ssm_metrics( "r_squared": 1, # R-squared is always 1 for polar method } ) - elif method == "cosine": + if method == "cosine": return df[paq_cols].apply( lambda y: ssm_cosine_fit(y, angles).table(), axis=1, result_type="expand", ) - else: - raise ValueError("Method must be either 'polar' or 'cosine'") + msg = "Method must be either 'polar' or 'cosine'" + raise ValueError(msg) def ssm_cosine_fit( y: pd.Series, - angles: Tuple[int, ...] = EQUAL_ANGLES, - bounds: Tuple[List[float], List[float]] = ( + angles: tuple[int, ...] | np.ndarray = EQUAL_ANGLES, + bounds: tuple[list[float], list[float]] = ( [0, 0, 0, -np.inf], [np.inf, 360, np.inf, np.inf], ), @@ -428,10 +499,11 @@ def ssm_cosine_fit( ---------- y : pd.Series Series of PAQ values - angles : Tuple[int, ...], optional + angles : tuple[int, ...], optional Angles for each PAQ in degrees, by default EQUAL_ANGLES - bounds : Tuple[List[float], List[float]], optional - Bounds for the optimization parameters, by default ([0, 0, 0, -np.inf], [np.inf, 360, np.inf, np.inf]) + bounds : tuple[list[float], list[float]], optional + Bounds for the optimization parameters, + by default ([0, 0, 0, -np.inf], [np.inf, 360, np.inf, np.inf]) Returns ------- @@ -446,23 +518,30 @@ def ssm_cosine_fit( >>> metrics = ssm_cosine_fit(y) >>> [round(v, 2) if isinstance(v, float) else v for v in metrics.table()] [0.68, 263.82, 10.57, -7.57, 0.15] + """ warnings.warn( - "This function is not yet fully implemented. See https://github.com/MitchellAcoustics/circumplex for a more complete implementation.", + "This function is not yet fully implemented." + "See https://github.com/MitchellAcoustics/circumplex " + "for a more complete implementation.", PendingDeprecationWarning, + stacklevel=2, ) - def cosine_model(theta, amp, delta, elev, dev): + def _cosine_model( + theta: np.ndarray, amp: float, delta: float, elev: float, dev: float + ) -> np.ndarray: return elev + amp * np.cos(np.radians(theta - delta)) + dev param, _ = optimize.curve_fit( - cosine_model, + _cosine_model, xdata=angles, ydata=y, bounds=bounds, ) amp, delta, elev, dev = param - r_squared = _r2_score(y, cosine_model(angles, *param)) + angles = np.array(angles) if isinstance(angles, tuple) else angles + r_squared = _r2_score(y.to_numpy(), _cosine_model(angles, *param)) return SSMMetrics( amplitude=amp, @@ -475,7 +554,7 @@ def cosine_model(theta, amp, delta, elev, dev): def _convert_to_polar_coords( x: float | np.ndarray, y: float | np.ndarray -) -> Tuple[float | np.ndarray, float | np.ndarray]: +) -> tuple[float | np.ndarray, float | np.ndarray]: """ Convert Cartesian coordinates to polar coordinates. @@ -497,6 +576,7 @@ def _convert_to_polar_coords( >>> r, theta = _convert_to_polar_coords(x, y) >>> round(r, 2), round(theta, 2) (5.0, 53.13) + """ r = np.sqrt(x**2 + y**2) theta = np.rad2deg(np.arctan2(y, x)) @@ -525,10 +605,12 @@ def _r2_score(y_true: np.ndarray, y_pred: np.ndarray) -> float: >>> y_pred = np.array([2.5, 4.2, 5.1, 2.2, 1.3]) >>> round(_r2_score(y_true, y_pred), 2) 0.96 + """ ss_total = np.sum((y_true - np.mean(y_true)) ** 2) ss_residual = np.sum((y_true - y_pred) ** 2) - return 1 - (ss_residual / ss_total) + # Ensure the return type matches the annotation + return float(1 - (ss_residual / ss_total)) if __name__ == "__main__": diff --git a/src/soundscapy/surveys/survey_utils.py b/src/soundscapy/surveys/survey_utils.py index 1ebada0e..19941a38 100644 --- a/src/soundscapy/surveys/survey_utils.py +++ b/src/soundscapy/surveys/survey_utils.py @@ -6,7 +6,6 @@ """ from enum import Enum -from typing import Dict, List, Tuple, Union import pandas as pd from loguru import logger @@ -24,7 +23,18 @@ class PAQ(Enum): UNEVENTFUL = ("uneventful", "PAQ7") CALM = ("calm", "PAQ8") - def __init__(self, label: str, id: str): + def __init__(self, label: str, id: str) -> None: # noqa: A002 + """ + Initialize a PAQ enum member. + + Parameters + ---------- + label : str + The descriptive label for the PAQ (e.g., 'pleasant'). + id : str + The standard identifier for the PAQ (e.g., 'PAQ1'). + + """ self.label = label self.id = id @@ -52,7 +62,7 @@ def __init__(self, label: str, id: str): def return_paqs( - df: pd.DataFrame, incl_ids: bool = True, other_cols: List[str] = None + df: pd.DataFrame, other_cols: list[str] | None = None, *, incl_ids: bool = True ) -> pd.DataFrame: """ Return only the PAQ columns from a DataFrame. @@ -61,15 +71,16 @@ def return_paqs( ---------- df : pd.DataFrame Input DataFrame containing PAQ data. - incl_ids : bool, optional - Whether to include ID columns (RecordID, GroupID, etc.), by default True. other_cols : List[str], optional Other columns to include in the output, by default None. + incl_ids : bool, optional + Whether to include ID columns (RecordID, GroupID, etc.), by default True. Returns ------- pd.DataFrame - DataFrame containing only the PAQ columns and optionally ID and other specified columns. + DataFrame containing only the PAQ columns and optionally ID and other specified + columns. Examples -------- @@ -94,6 +105,7 @@ def return_paqs( PAQ1 PAQ2 PAQ3 PAQ4 PAQ5 PAQ6 PAQ7 PAQ8 OtherCol 0 4 2 1 3 5 2 4 1 A 1 3 5 2 4 1 3 5 2 B + """ cols = PAQ_IDS.copy() @@ -113,7 +125,7 @@ def return_paqs( def rename_paqs( - df: pd.DataFrame, paq_aliases: Union[Tuple, Dict] = None + df: pd.DataFrame, paq_aliases: list | tuple | dict | None = None ) -> pd.DataFrame: """ Rename the PAQ columns in a DataFrame to standard PAQ IDs. @@ -157,6 +169,7 @@ def rename_paqs( PAQ1 PAQ2 0 4 2 1 3 5 + """ if paq_aliases is None: if any(paq_id in df.columns for paq_id in PAQ_IDS): @@ -165,12 +178,13 @@ def rename_paqs( if any(paq_name in df.columns for paq_name in PAQ_LABELS): paq_aliases = PAQ_LABELS - if isinstance(paq_aliases, (list, tuple)): - rename_dict = dict(zip(paq_aliases, PAQ_IDS)) + if isinstance(paq_aliases, list | tuple): + rename_dict = dict(zip(paq_aliases, PAQ_IDS, strict=False)) elif isinstance(paq_aliases, dict): rename_dict = paq_aliases else: - raise ValueError("paq_aliases must be a tuple, list, or dictionary.") + msg = "paq_aliases must be a tuple, list, or dictionary." + raise TypeError(msg) logger.debug(f"Renaming PAQs with the following mapping: {rename_dict}") return df.rename(columns=rename_dict) @@ -193,8 +207,8 @@ def mean_responses(df: pd.DataFrame, group: str) -> pd.DataFrame: DataFrame with mean responses for each PAQ group. """ - df = return_paqs(df, incl_ids=False, other_cols=[group]) - return df.groupby(group).mean().reset_index() + data = return_paqs(df, other_cols=[group], incl_ids=False) + return data.groupby(group).mean().reset_index() # Add other utility functions here as needed diff --git a/test/audio/test_analysis_settings.py b/test/audio/test_analysis_settings.py index 12fbf404..e6d40e37 100644 --- a/test/audio/test_analysis_settings.py +++ b/test/audio/test_analysis_settings.py @@ -107,7 +107,7 @@ def test_to_yaml(self, tmp_path, sample_config): assert output_file.exists() # Read back and verify - with open(output_file, "r") as f: + with open(output_file) as f: loaded_config = yaml.safe_load(f) assert loaded_config["version"] == "1.1" assert "LAeq" in loaded_config["AcousticToolbox"] diff --git a/test/audio/test_binaural.py b/test/audio/test_binaural.py index 4f31358d..e7a5734b 100644 --- a/test/audio/test_binaural.py +++ b/test/audio/test_binaural.py @@ -73,7 +73,7 @@ def test_binaural_calibration(test_binaural_signal): assert isinstance(calibrated, Binaural) # Test invalid input - with pytest.raises(ValueError): + with pytest.raises(TypeError): test_binaural_signal.calibrate_to([60, 62, 64]) diff --git a/test/data/Levels.json b/test/data/Levels.json index ffcf3dc1..d81d1697 100644 --- a/test/data/Levels.json +++ b/test/data/Levels.json @@ -1,5810 +1,5810 @@ -{ - "CT101": { - "Left": 79.0, - "Right": 79.72 - }, - "CT102": { - "Left": 79.35, - "Right": 79.88 - }, - "CT103": { - "Left": 76.25, - "Right": 76.41 - }, - "CT104": { - "Left": 79.9, - "Right": 79.93 - }, - "CT107": { - "Left": 78.21, - "Right": 78.47 - }, - "CT108": { - "Left": 79.23, - "Right": 79.51 - }, - "CT109": { - "Left": 79.32, - "Right": 79.37 - }, - "CT110": { - "Left": 78.15, - "Right": 80.09 - }, - "CT112": { - "Left": 79.08, - "Right": 79.77 - }, - "CT113": { - "Left": 81.32, - "Right": 81.67 - }, - "CT114": { - "Left": 76.69, - "Right": 76.94 - }, - "CT116": { - "Left": 77.88, - "Right": 78.82 - }, - "CT117": { - "Left": 80.18, - "Right": 80.54 - }, - "CT119": { - "Left": 76.45, - "Right": 77.0 - }, - "CT120": { - "Left": 79.9, - "Right": 80.27 - }, - "CT121": { - "Left": 78.14, - "Right": 78.51 - }, - "CT122": { - "Left": 80.88, - "Right": 81.3 - }, - "CT123": { - "Left": 77.46, - "Right": 77.9 - }, - "CT124": { - "Left": 78.14, - "Right": 78.49 - }, - "CT201": { - "Left": 76.79, - "Right": 76.81 - }, - "CT202": { - "Left": 82.07, - "Right": 82.74 - }, - "CT203": { - "Left": 77.48, - "Right": 77.47 - }, - "CT301": { - "Left": 75.84, - "Right": 78.33 - }, - "CT305": { - "Left": 83.14, - "Right": 81.74 - }, - "CT306": { - "Left": 77.45, - "Right": 77.96 - }, - "CT308": { - "Left": 87.1, - "Right": 86.2 - }, - "CT309": { - "Left": 78.2, - "Right": 79.3 - }, - "CT310": { - "Left": 80.94, - "Right": 81.73 - }, - "CT311": { - "Left": 79.41, - "Right": 80.08 - }, - "CT312": { - "Left": 79.57, - "Right": 80.11 - }, - "CT313": { - "Left": 80.34, - "Right": 83.0 - }, - "CT314": { - "Left": 79.48, - "Right": 80.06 - }, - "CT315": { - "Left": 77.78, - "Right": 78.18 - }, - "CT317": { - "Left": 82.03, - "Right": 81.59 - }, - "CT318": { - "Left": 78.14, - "Right": 78.74 - }, - "CT319": { - "Left": 79.54, - "Right": 80.82 - }, - "CT320": { - "Left": 79.99, - "Right": 80.0 - }, - "CT321": { - "Left": 76.89, - "Right": 77.45 - }, - "CT322": { - "Left": 81.15, - "Right": 81.53 - }, - "CT323": { - "Left": 81.91, - "Right": 80.14 - }, - "CT324": { - "Left": 81.08, - "Right": 80.1 - }, - "CT325": { - "Left": 74.26, - "Right": 75.38 - }, - "CT326": { - "Left": 84.36, - "Right": 83.76 - }, - "CT327": { - "Left": 77.13, - "Right": 77.39 - }, - "CT328": { - "Left": 77.72, - "Right": 77.98 - }, - "CT329": { - "Left": 79.82, - "Right": 80.27 - }, - "CT401": { - "Left": 96.66, - "Right": 97.26 - }, - "CT402": { - "Left": 86.17, - "Right": 86.29 - }, - "CT403": { - "Left": 84.51, - "Right": 83.94 - }, - "CT404": { - "Left": 83.48, - "Right": 83.61 - }, - "CT405": { - "Left": 78.42, - "Right": 78.91 - }, - "CT406": { - "Left": 82.16, - "Right": 83.97 - }, - "CT407": { - "Left": 80.34, - "Right": 81.52 - }, - "CT408": { - "Left": 82.76, - "Right": 83.06 - }, - "CT411": { - "Left": 85.54, - "Right": 85.65 - }, - "CT412": { - "Left": 86.65, - "Right": 86.29 - }, - "CT413": { - "Left": 88.02, - "Right": 88.57 - }, - "CT414": { - "Left": 88.84, - "Right": 88.06 - }, - "CT415": { - "Left": 87.69, - "Right": 88.97 - }, - "CT501": { - "Left": 72.85, - "Right": 73.85 - }, - "CT502": { - "Left": 77.25, - "Right": 78.19 - }, - "CT503": { - "Left": 83.07, - "Right": 84.22 - }, - "CT504": { - "Left": 72.75, - "Right": 73.25 - }, - "CT505": { - "Left": 76.09, - "Right": 78.99 - }, - "CT506": { - "Left": 71.03, - "Right": 70.72 - }, - "CT507": { - "Left": 79.26, - "Right": 80.19 - }, - "CT508": { - "Left": 70.27, - "Right": 70.98 - }, - "CT509": { - "Left": 73.8, - "Right": 74.22 - }, - "CT510": { - "Left": 74.71, - "Right": 75.26 - }, - "CT511": { - "Left": 77.16, - "Right": 76.59 - }, - "CT512": { - "Left": 76.57, - "Right": 77.09 - }, - "CT513": { - "Left": 83.01, - "Right": 85.52 - }, - "CT514": { - "Left": 79.29, - "Right": 79.45 - }, - "CT515": { - "Left": 75.42, - "Right": 75.65 - }, - "CT516": { - "Left": 78.11, - "Right": 78.18 - }, - "CT517": { - "Left": 76.1, - "Right": 76.55 - }, - "CT518": { - "Left": 75.92, - "Right": 75.95 - }, - "CT519": { - "Left": 83.23, - "Right": 82.75 - }, - "CT520": { - "Left": 79.1, - "Right": 75.49 - }, - "CT521": { - "Left": 81.82, - "Right": 79.89 - }, - "CT522": { - "Left": 75.32, - "Right": 77.07 - }, - "CT523": { - "Left": 81.2, - "Right": 81.94 - }, - "CT524": { - "Left": 75.91, - "Right": 81.02 - }, - "CT525": { - "Left": 80.61, - "Right": 82.03 - }, - "CT526": { - "Left": 78.68, - "Right": 78.78 - }, - "CT527": { - "Left": 83.97, - "Right": 84.09 - }, - "CT528": { - "Left": 78.78, - "Right": 78.89 - }, - "CT529": { - "Left": 71.9, - "Right": 71.29 - }, - "CT530": { - "Left": 79.86, - 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import pytest from pytest import raises -import soundscapy.databases.isd as isd +from soundscapy.databases import isd from soundscapy.surveys.processing import add_iso_coords diff --git a/test/generate_baselines.py b/test/generate_baselines.py index 2acba1c9..9b1f71ba 100644 --- a/test/generate_baselines.py +++ b/test/generate_baselines.py @@ -1,6 +1,4 @@ -""" -Script to generate baseline images for pytest-mpl comparisons. -""" +"""Script to generate baseline images for pytest-mpl comparisons.""" import os diff --git a/test/spi/test_MSN.py b/test/spi/test_MSN.py new file mode 100644 index 00000000..612183aa --- /dev/null +++ b/test/spi/test_MSN.py @@ -0,0 +1,551 @@ +from unittest.mock import patch # Keep patch for plotting + +import numpy as np +import pandas as pd +import pytest + +from soundscapy.spi.msn import CentredParams, DirectParams, MultiSkewNorm, cp2dp, dp2cp + +# Check for R and 'sn' package availability +try: + # import rpy2.robjects as ro # No longer needed directly + from rpy2.rinterface_lib.embedded import RRuntimeError + from rpy2.robjects.packages import importr + + # Try importing the 'sn' package in R + try: + importr("sn") + r_sn_available = True + except RRuntimeError: + r_sn_available = False +except ImportError: + r_sn_available = False + +needs_r_sn = pytest.mark.skipif( + not r_sn_available, + reason="Requires R, rpy2, and the R 'sn' package to be installed.", +) + + +class TestDirectParams: + def test_direct_params_init_valid(self): + """Test initialization with valid parameters.""" + xi = np.array([0.1, 0.2]) + omega = np.array([[1.0, 0.5], [0.5, 1.0]]) # Symmetric and positive definite + alpha = np.array([0.3, 0.4]) + + dp = DirectParams(xi, omega, alpha) + + assert np.array_equal(dp.xi, xi) + assert np.array_equal(dp.omega, omega) + assert np.array_equal(dp.alpha, alpha) + + def test_direct_params_init_not_pos_def(self): + """Test initialization with omega that is not positive definite.""" + xi = np.array([0.1, 0.2]) + # Not positive definite matrix + omega = np.array([[1.0, 2.0], [2.0, 1.0]]) + alpha = np.array([0.3, 0.4]) + + with pytest.raises(ValueError, match="Omega must be positive definite"): + DirectParams(xi, omega, alpha) + + def test_direct_params_init_not_symmetric(self): + """Test initialization with omega that is not symmetric.""" + xi = np.array([0.1, 0.2]) + # Not symmetric matrix + omega = np.array([[1.0, 0.5], [0.6, 1.0]]) + alpha = np.array([0.3, 0.4]) + + with pytest.raises(ValueError, match="Omega must be symmetric"): + DirectParams(xi, omega, alpha) + + def test_direct_params_repr(self): + """Test the __repr__ method.""" + xi = np.array([0.1, 0.2]) + omega = np.array([[1.0, 0.5], [0.5, 1.0]]) + alpha = np.array([0.3, 0.4]) + + dp = DirectParams(xi, omega, alpha) + + assert repr(dp) == f"DirectParams(xi={xi}, omega={omega}, alpha={alpha})" + + def test_direct_params_str(self): + """Test the __str__ method.""" + xi = np.array([0.1, 0.2]) + omega = np.array([[1.0, 0.5], [0.5, 1.0]]) + alpha = np.array([0.3, 0.4]) + + dp = DirectParams(xi, omega, alpha) + + expected_str = ( + f"Direct Parameters:" + f"\nxi: {xi.round(3)}" + f"\nomega: {omega.round(3)}" + f"\nalpha: {alpha.round(3)}" + ) + + assert str(dp) == expected_str + + def test_xi_is_in_range(self): + """Test the _xi_is_in_range method with tuple input.""" + xi = np.array([0.1, 0.2]) + omega = np.array([[1.0, 0.5], [0.5, 1.0]]) + alpha = np.array([0.3, 0.4]) + + dp = DirectParams(xi, omega, alpha) + + # Test with tuple input + assert dp._xi_is_in_range((-1.0, 1.0)) + assert not dp._xi_is_in_range((0.2, 1.0)) + + +class TestCentredParams: + def test_centred_params_init(self): + """Test initialization of CentredParams.""" + mean = np.array([0.5, 0.6]) + sigma = np.array([1.0, 1.2]) + skew = np.array([0.1, -0.1]) + + cp = CentredParams(mean, sigma, skew) + + assert np.array_equal(cp.mean, mean) + assert np.array_equal(cp.sigma, sigma) + assert np.array_equal(cp.skew, skew) + + def test_centred_params_repr(self): + """Test the __repr__ method.""" + mean = np.array([0.5, 0.6]) + sigma = np.array([1.0, 1.2]) + skew = np.array([0.1, -0.1]) + + cp = CentredParams(mean, sigma, skew) + + expected_repr = f"CentredParams(mean={mean}, sigma={sigma}, skew={skew})" + assert repr(cp) == expected_repr + + def test_centred_params_str(self): + """Test the __str__ method.""" + mean = np.array([0.5123, 0.6789]) + sigma = np.array([1.0456, 1.2789]) + skew = np.array([0.1111, -0.1999]) + + cp = CentredParams(mean, sigma, skew) + + expected_str = ( + f"Centred Parameters:" + f"\nmean: {mean.round(3)}" + f"\nsigma: {sigma.round(3)}" + f"\nskew: {skew.round(3)}" + ) + assert str(cp) == expected_str + + @needs_r_sn + def test_centred_params_from_dp(self): + """Test the from_dp class method.""" + # Create a dummy DirectParams object + dp_xi = np.array([0.1, 0.2]) + dp_omega = np.array([[1.0, 0.5], [0.5, 1.0]]) + dp_alpha = np.array([0.3, 0.4]) + dp = DirectParams(dp_xi, dp_omega, dp_alpha) + + # Expected values calculated from a known R execution or previous test + expected_cp = CentredParams( + mean=np.array([0.44083939, 0.57492333]), + sigma=np.array([[0.88382851, 0.37221136], [0.37221136, 0.8594325]]), + skew=np.array([0.02045318, 0.02839051]), + ) + + # Call the class method (which internally calls dp2cp) + cp_from_dp = CentredParams.from_dp(dp) + + # Assert that the returned object is an instance of CentredParams + assert isinstance(cp_from_dp, CentredParams) + + # Assert that the attributes match the expected values + np.testing.assert_allclose(cp_from_dp.mean, expected_cp.mean, atol=1e-5) + # Assuming sigma in CentredParams now holds the covariance matrix from dp2cp + np.testing.assert_allclose(cp_from_dp.sigma, expected_cp.sigma, atol=1e-5) + np.testing.assert_allclose(cp_from_dp.skew, expected_cp.skew, atol=1e-5) + + +# Mock data and parameters for testing +# Use values consistent with TestCentredParams.test_centred_params_from_dp +MOCK_XI = np.array([0.1, 0.2]) +MOCK_OMEGA = np.array([[1.0, 0.5], [0.5, 1.0]]) +MOCK_ALPHA = np.array([0.3, 0.4]) +# Corresponding CP values (mean, covariance, skew) from dp2cp +EXPECTED_MEAN = np.array([0.44083939, 0.57492333]) +EXPECTED_SIGMA_COV = np.array([[0.88382851, 0.37221136], [0.37221136, 0.8594325]]) +EXPECTED_SKEW = np.array([0.02045318, 0.02839051]) + +# Sample data for fitting tests +MOCK_DF = pd.DataFrame( + np.random.rand(50, 2) * 0.5 + 0.1, columns=["x", "y"] +) # Smaller N for faster fit +MOCK_X = MOCK_DF["x"].values +MOCK_Y = MOCK_DF["y"].values +MOCK_SAMPLE_SIZE = 100 + + +@needs_r_sn +class TestMultiSkewNorm: + def test_init(self): + """Test initialization of MultiSkewNorm.""" + msn = MultiSkewNorm() + assert msn.selm_model is None + assert msn.cp is None + assert msn.dp is None + assert msn.sample_data is None + assert msn.data is None + + def test_repr_unfitted(self): + """Test __repr__ when the model is not fitted.""" + msn = MultiSkewNorm() + assert repr(msn) == "MultiSkewNorm() (unfitted)" + + def test_repr_fitted(self): + """Test __repr__ when the model is fitted.""" + msn = MultiSkewNorm() + # Define DP, which implicitly calculates CP via dp2cp + msn.define_dp(MOCK_XI, MOCK_OMEGA, MOCK_ALPHA) + # The repr should now show the DP + assert repr(msn) == f"MultiSkewNorm(dp={msn.dp})" + + def test_summary_unfitted(self): # No mock needed + """Test summary when the model is not fitted.""" + msn = MultiSkewNorm() + assert msn.summary() == "MultiSkewNorm is not fitted." + + @needs_r_sn # Needs fit -> R + def test_summary_fitted_from_data(self, capsys): + """Test summary when the model is fitted from data.""" + msn = MultiSkewNorm() + msn.fit(data=MOCK_DF.copy()) + msn.summary() + captured = capsys.readouterr() + assert f"Fitted from data. n = {len(MOCK_DF)}" in captured.out + assert "Direct Parameters:" in captured.out + assert "Centred Parameters:" in captured.out + assert "xi:" in captured.out + assert "mean:" in captured.out + + def test_summary_fitted_from_dp(self, capsys): + """Test summary when the model is fitted from direct parameters.""" + msn = MultiSkewNorm() + msn.define_dp(MOCK_XI, MOCK_OMEGA, MOCK_ALPHA) # This calculates CP + msn.summary() + captured = capsys.readouterr() + assert "Fitted from direct parameters." in captured.out + assert str(msn.dp) in captured.out + assert str(msn.cp) in captured.out + + def test_fit_with_dataframe(self): + """Test fit method with pandas DataFrame.""" + msn = MultiSkewNorm() + msn.fit(data=MOCK_DF.copy()) + + assert msn.selm_model is not None # Check R model object exists + assert isinstance(msn.cp, CentredParams) + assert isinstance(msn.dp, DirectParams) + assert msn.data is not None # Add assertion for type checker + pd.testing.assert_frame_equal(msn.data, MOCK_DF) + # Check dimensions of parameters + assert msn.cp.mean.shape == (2,) + assert msn.dp.xi.shape == (2,) + assert msn.dp.omega.shape == (2, 2) + assert msn.dp.alpha.shape == (2,) + + def test_fit_with_numpy_array(self): + """Test fit method with numpy array.""" + msn = MultiSkewNorm() + numpy_data = MOCK_DF.values + msn.fit(data=numpy_data) + + expected_df = pd.DataFrame(numpy_data, columns=["x", "y"]) + + assert msn.selm_model is not None + assert isinstance(msn.cp, CentredParams) + assert isinstance(msn.dp, DirectParams) + assert msn.data is not None # Add assertion for type checker + pd.testing.assert_frame_equal(msn.data, expected_df) + assert msn.cp.mean.shape == (2,) + assert msn.dp.xi.shape == (2,) + + def test_fit_with_1d_numpy_array(self): + """Test fit method raises error on 1D numpy array.""" + msn = MultiSkewNorm() + one_d_array = np.array([0.1, 0.2, 0.3]) + + with pytest.raises( + ValueError, match="Data must be a 2D numpy array or DataFrame" + ): + msn.fit(data=one_d_array) + + def test_fit_with_x_y(self): + """Test fit method with x and y arrays.""" + msn = MultiSkewNorm() + msn.fit(x=MOCK_X, y=MOCK_Y) + + expected_df = pd.DataFrame({"x": MOCK_X, "y": MOCK_Y}) + + assert msn.selm_model is not None + assert isinstance(msn.cp, CentredParams) + assert isinstance(msn.dp, DirectParams) + assert msn.data is not None # Add assertion for type checker + pd.testing.assert_frame_equal(msn.data, expected_df) + assert msn.cp.mean.shape == (2,) + assert msn.dp.xi.shape == (2,) + + def test_fit_no_data(self): + """Test fit method raises ValueError when no data is provided.""" + msn = MultiSkewNorm() + with pytest.raises(ValueError, match="Either data or x and y must be provided"): + msn.fit() + + def test_define_dp(self): + """Test define_dp method.""" + msn = MultiSkewNorm() + result = msn.define_dp(MOCK_XI, MOCK_OMEGA, MOCK_ALPHA) + + assert isinstance(msn.dp, DirectParams) + np.testing.assert_array_equal(msn.dp.xi, MOCK_XI) + np.testing.assert_array_equal(msn.dp.omega, MOCK_OMEGA) + np.testing.assert_array_equal(msn.dp.alpha, MOCK_ALPHA) + # Check CP was also calculated + assert isinstance(msn.cp, CentredParams) + np.testing.assert_allclose(msn.cp.mean, EXPECTED_MEAN, atol=1e-5) + # Assuming CentredParams.sigma holds covariance matrix after dp2cp + np.testing.assert_allclose(msn.cp.sigma, EXPECTED_SIGMA_COV, atol=1e-5) + np.testing.assert_allclose(msn.cp.skew, EXPECTED_SKEW, atol=1e-5) + assert result is msn # Check if it returns self for chaining + + def test_sample_after_fit(self): + """Test sample method after fitting the model.""" + msn = MultiSkewNorm() + msn.fit(data=MOCK_DF) + result = msn.sample(n=MOCK_SAMPLE_SIZE, return_sample=False) + + assert result is None + assert isinstance(msn.sample_data, np.ndarray) + assert msn.sample_data.shape == (MOCK_SAMPLE_SIZE, 2) + + def test_sample_after_define_dp(self): + """Test sample method after defining direct parameters.""" + msn = MultiSkewNorm() + msn.define_dp(MOCK_XI, MOCK_OMEGA, MOCK_ALPHA) + result = msn.sample(n=MOCK_SAMPLE_SIZE, return_sample=False) + + assert result is None + assert isinstance(msn.sample_data, np.ndarray) + assert msn.sample_data.shape == (MOCK_SAMPLE_SIZE, 2) + + def test_sample_return_sample_true(self): + """Test sample method with return_sample=True.""" + msn = MultiSkewNorm() + msn.define_dp(MOCK_XI, MOCK_OMEGA, MOCK_ALPHA) + sample = msn.sample(n=MOCK_SAMPLE_SIZE, return_sample=True) + + assert isinstance(sample, np.ndarray) + assert sample.shape == (MOCK_SAMPLE_SIZE, 2) + # Ensure sample_data is also set + assert isinstance(msn.sample_data, np.ndarray) + np.testing.assert_array_equal(msn.sample_data, sample) + + def test_sample_not_fitted_or_defined(self): # No mock needed + """Test sample method raises ValueError when not fitted or defined.""" + msn = MultiSkewNorm() + with pytest.raises( + ValueError, + match="Either selm_model or xi, omega, and alpha must be provided.", + ): + msn.sample() + + @patch("soundscapy.spi.msn.density_plot") # Keep mocking the plotting call + def test_sspy_plot_calls_sample_if_needed(self, mock_density_plot): + """Test sspy_plot calls sample if sample_data is None.""" + msn = MultiSkewNorm() + msn.define_dp(MOCK_XI, MOCK_OMEGA, MOCK_ALPHA) # Define DP so sample can run + + assert msn.sample_data is None + msn.sspy_plot( + n=MOCK_SAMPLE_SIZE, color="red", title="Test Plot" + ) # Pass n to sample + + # Check sample was called implicitly and data was generated + assert isinstance(msn.sample_data, np.ndarray) + assert msn.sample_data.shape == (MOCK_SAMPLE_SIZE, 2) + + # Check plot was called with the sampled data + mock_density_plot.assert_called_once() + call_args = mock_density_plot.call_args[0] + call_kwargs = mock_density_plot.call_args[1] + assert isinstance(call_args[0], pd.DataFrame) + # Check the dataframe passed to plot matches the generated sample data + expected_plot_df = pd.DataFrame( + msn.sample_data, columns=["ISOPleasant", "ISOEventful"] + ) + pd.testing.assert_frame_equal(call_args[0], expected_plot_df) + assert call_kwargs["color"] == "red" + assert call_kwargs["title"] == "Test Plot" + + @patch("soundscapy.spi.msn.density_plot") # Keep mocking the plotting call + def test_sspy_plot_uses_existing_sample(self, mock_density_plot): + """Test sspy_plot uses existing sample_data if available.""" + msn = MultiSkewNorm() + # Create some dummy sample data + existing_sample = np.random.rand(50, 2) + msn.sample_data = existing_sample + + # Store original sample_data reference to check it wasn't re-generated + sample_data_before_plot = msn.sample_data + + msn.sspy_plot() # Should use existing sample_data + + # Check sample was NOT called again (sample_data should be unchanged) + assert msn.sample_data is sample_data_before_plot + np.testing.assert_array_equal(msn.sample_data, existing_sample) + + # Check plot was called with the existing data + mock_density_plot.assert_called_once() + call_args = mock_density_plot.call_args[0] + assert isinstance(call_args[0], pd.DataFrame) + expected_plot_df = pd.DataFrame( + existing_sample, columns=["ISOPleasant", "ISOEventful"] + ) + pd.testing.assert_frame_equal(call_args[0], expected_plot_df) + + def test_ks2d2s_calls_sample_if_needed(self): + """Test ks2d2s calls sample if sample_data is None and calls ks2d2s.""" + msn = MultiSkewNorm() + msn.define_dp(MOCK_XI, MOCK_OMEGA, MOCK_ALPHA) # Define DP so sample can run + + assert msn.sample_data is None + test_data_df = pd.DataFrame(np.random.rand(40, 2), columns=["col1", "col2"]) + + result = msn.ks2d2s(test_data_df) + # TODO: still need to implement check for actual result values + + # Check sample was called implicitly and data was generated + assert isinstance(msn.sample_data, np.ndarray) + assert ( + msn.sample_data.shape[1] == 2 # noqa: PLR2004 + ) # Check sample data has 2 columns + + assert isinstance(result, tuple) + assert isinstance(result[0], float) + assert isinstance(result[1], float) + + def test_ks2d2s(self): + """Test ks2d2s converts DataFrame input to numpy array.""" + msn = MultiSkewNorm() + msn.define_dp(MOCK_XI, MOCK_OMEGA, MOCK_ALPHA) + msn.sample(n=50) # Generate sample data beforehand + + test_data_df = pd.DataFrame(np.random.rand(40, 2), columns=["col1", "col2"]) + test_data_np = test_data_df.to_numpy() + + df_result = msn.ks2d2s(test_data_df) + np_result = msn.ks2d2s(test_data_np) + # TODO: still need to implement check for actual result values + + assert df_result == np_result, ( + "Results from DataFrame and numpy array should match." + ) + + assert isinstance(df_result, tuple) + assert isinstance(df_result[0], float) + assert isinstance(df_result[1], float) + + assert isinstance(np_result, tuple) + assert isinstance(np_result[0], float) + assert isinstance(np_result[1], float) + + def test_ks2d2s_dataframe_wrong_shape(self): + """Test ks2d2s raises ValueError for DataFrame with wrong shape.""" + msn = MultiSkewNorm() + msn.define_dp(MOCK_XI, MOCK_OMEGA, MOCK_ALPHA) + msn.sample(n=50) + + test_data_df_wrong = pd.DataFrame(np.random.rand(40, 3)) # 3 columns + + with pytest.raises(ValueError, match="Test data must have two columns."): + msn.ks2d2s(test_data_df_wrong) + + @patch.object(MultiSkewNorm, "ks2d2s") + def test_spi(self, mock_ks2d2s): + """Test spi method calculation.""" + # Mock ks2ds to return a specific KS statistic and p-value + mock_ks_statistic = 0.15 + mock_ks2d2s.return_value = (mock_ks_statistic, 0.04) + + msn = MultiSkewNorm() + # No need to fit or define dp as we are mocking ks2ds + test_data = np.random.rand(50, 2) + + spi_value = msn.spi(test_data) + + # Check ks2ds was called with the test data + mock_ks2d2s.assert_called_once_with(test_data) + + # Check the SPI calculation + expected_spi = int((1 - mock_ks_statistic) * 100) + assert spi_value == expected_spi + assert isinstance(spi_value, int) + + @patch.object(MultiSkewNorm, "ks2d2s") + def test_spi_with_dataframe(self, mock_ks2d2s): + """Test spi method with DataFrame input.""" + mock_ks_statistic = 0.25 + mock_ks2d2s.return_value = (mock_ks_statistic, 0.01) + + msn = MultiSkewNorm() + test_data_df = pd.DataFrame(np.random.rand(60, 2), columns=["a", "b"]) + + spi_value = msn.spi(test_data_df) + + # Check ks2d2s was called (the mock captures the call) + mock_ks2d2s.assert_called_once() + # Check the argument passed to the mock was the DataFrame + call_args = mock_ks2d2s.call_args[0] + pd.testing.assert_frame_equal(call_args[0], test_data_df) + + # Check the SPI calculation + expected_spi = int((1 - mock_ks_statistic) * 100) + assert spi_value == expected_spi + assert isinstance(spi_value, int) + + +@needs_r_sn +@pytest.mark.skip( + reason="Cannot directly convert cp to dp. Need to come up with a reasonable test." +) +def test_cp2dp(): + """Test cp2dp function.""" + cp_input = CentredParams(EXPECTED_MEAN, EXPECTED_SIGMA_COV, EXPECTED_SKEW) + + # Perform the conversion + dp_output = cp2dp(cp_input) + + assert isinstance(dp_output, DirectParams) + # Check if the output DP matches the original MOCK_DP used to generate the CPs + np.testing.assert_allclose(dp_output.xi, MOCK_XI, atol=1e-5) + np.testing.assert_allclose(dp_output.omega, MOCK_OMEGA, atol=1e-5) + np.testing.assert_allclose(dp_output.alpha, MOCK_ALPHA, atol=1e-5) + + +@needs_r_sn # Needs R for conversion +def test_dp2cp(): + """Test dp2cp function.""" + # Use the known DP values + dp_input = DirectParams(MOCK_XI, MOCK_OMEGA, MOCK_ALPHA) + + # Perform the conversion + cp_output = dp2cp(dp_input) + + assert isinstance(cp_output, CentredParams) + # Check if the output CP matches the expected values + np.testing.assert_allclose(cp_output.mean, EXPECTED_MEAN, atol=1e-5) + # Assuming CentredParams.sigma holds covariance matrix from dp2cp + np.testing.assert_allclose(cp_output.sigma, EXPECTED_SIGMA_COV, atol=1e-5) + np.testing.assert_allclose(cp_output.skew, EXPECTED_SKEW, atol=1e-5) diff --git a/test/spi/test_r_wrapper.py b/test/spi/test_r_wrapper.py new file mode 100644 index 00000000..4020384a --- /dev/null +++ b/test/spi/test_r_wrapper.py @@ -0,0 +1,92 @@ +""" +Tests for the R integration wrapper. + +These tests check the R session management and data conversion functions. +They are skipped if rpy2 is not installed. +""" + +import os + +import pytest + + +def test_initialize_r_session_fails(): + """Test that R session initialization fails if R is not available.""" + # Skip if dependencies are actually installed + if os.environ.get("SPI_DEPS") == "1": + pytest.skip("SPI dependencies are installed") + + from soundscapy.spi._r_wrapper import initialize_r_session + + # Simulate R not being available + with pytest.raises(ImportError) as excinfo: + initialize_r_session() + + # Check for helpful error message + assert "R installation" in str(excinfo.value) + assert "install.packages('R')" in str(excinfo.value) + + +@pytest.mark.optional_deps("spi") +class TestRWrapper: + """Test the R wrapper functionality.""" + + def test_module_structure(self): + """Test that the module structure exists.""" + import soundscapy.spi._r_wrapper + + # Module should exist but functions will be implemented later + assert soundscapy.spi._r_wrapper is not None + + def test_initialize_r_session(self): + """Test R session initialization.""" + from soundscapy.spi._r_wrapper import initialize_r_session + + # This should not raise if R is available + res = initialize_r_session() + + assert res is not None, "R session should be initialized successfully" + assert res["r_session"] == "active", "R session should be active" + + def test_shutdown_r_session(self): + """Test R session cleanup.""" + from soundscapy.spi._r_wrapper import shutdown_r_session + + # This should not raise if R session is active + res = shutdown_r_session() + + assert res, "R session should be shut down successfully" + + def test_r_session_reinitialization(self): + """Test that the R session can be reinitialized after shutdown.""" + from soundscapy.spi._r_wrapper import initialize_r_session, shutdown_r_session + + # First initialize the R session + res = initialize_r_session() + assert res is not None, "R session should be initialized successfully" + + # Now shut it down + shutdown_res = shutdown_r_session() + assert shutdown_res, "R session should be shut down successfully" + + # Reinitialize the R session + reinit_res = initialize_r_session() + assert reinit_res is not None, "R session should be reinitialized successfully" + + def test_check_sn_package(self): + """Test that the R 'sn' package availability is checked.""" + # Skip if dependencies are actually installed + + if os.environ.get("SPI_DEPS") == "1": + from soundscapy.spi import _r_wrapper + + _r_wrapper.check_sn_package() + + else: + with pytest.raises(ImportError) as excinfo: + from soundscapy.spi import _r_wrapper + + _r_wrapper.check_sn_package() + + assert "R package 'sn'" in str(excinfo.value) + assert "install.packages('sn')" in str(excinfo.value) diff --git a/test/test__optionals.py b/test/test__optionals.py deleted file mode 100644 index b9324433..00000000 --- a/test/test__optionals.py +++ /dev/null @@ -1,94 +0,0 @@ -import os - -import pytest - - -def test_optional_dependency_groups_defined(): - """Test that OPTIONAL_DEPENDENCIES has expected structure.""" - from soundscapy._optionals import OPTIONAL_DEPENDENCIES - - assert "audio" in OPTIONAL_DEPENDENCIES - - group = OPTIONAL_DEPENDENCIES["audio"] - assert "packages" in group - assert "install" in group - assert "description" in group - - assert isinstance(group["packages"], tuple) - assert len(group["packages"]) > 0 - assert all(isinstance(pkg, str) for pkg in group["packages"]) - - assert group["install"] == "soundscapy[audio]" - assert isinstance(group["description"], str) - - -@pytest.mark.skipif( - os.environ.get("AUDIO_DEPS") == "1", - reason="Test requires audio dependencies to be missing", -) -def test_require_dependencies_missing(): - """Test behavior when dependencies are missing.""" - from soundscapy._optionals import require_dependencies - - with pytest.raises(ImportError) as exc_info: - require_dependencies("audio") - - assert "Install with:" in str(exc_info.value) - assert "pip install soundscapy[audio]" in str(exc_info.value) - - -@pytest.mark.optional_deps("audio") -def test_require_dependencies_present(): - """Test behavior when dependencies are present.""" - from soundscapy._optionals import require_dependencies - - packages = require_dependencies("audio") - assert isinstance(packages, dict) - assert all(pkg in packages for pkg in ["mosqito", "maad", "acoustic_toolbox"]) - assert all(packages[pkg] is not None for pkg in packages) - - -def test_optional_import_behavior(): - """Test top-level optional component import behavior.""" - import importlib - import soundscapy - from soundscapy._optionals import OPTIONAL_IMPORTS - - # Test that optional components are listed in __all__ - for name in OPTIONAL_IMPORTS: - assert name in soundscapy.__all__ - - # Test import behavior - def has_audio_deps(): - """Check if audio dependencies are available.""" - try: - importlib.import_module("mosqito") - importlib.import_module("maad") - importlib.import_module("acoustic_toolbox") - return True - except ImportError: - return False - - # Try importing Binaural as an example component - if has_audio_deps(): - # Should succeed when dependencies are available - from soundscapy import Binaural - - assert hasattr(Binaural, "__module__") - else: - # Should raise helpful ImportError when dependencies missing - import pytest - - with pytest.raises(ImportError) as exc_info: - from soundscapy import Binaural - assert "audio analysis functionality" in str(exc_info.value) - assert "pip install soundscapy[audio]" in str(exc_info.value) - - -def test_invalid_group(): - """Test behavior with invalid dependency group.""" - from soundscapy._optionals import require_dependencies - - with pytest.raises(KeyError) as exc_info: - require_dependencies("nonexistent_group") - assert "Unknown dependency group" in str(exc_info.value) diff --git a/test/test_basic.py b/test/test_basic.py index cb891dbf..af95e0b9 100644 --- a/test/test_basic.py +++ b/test/test_basic.py @@ -1,6 +1,9 @@ -import soundscapy +import os + import pytest +import soundscapy + def test_soundscapy_import(): assert soundscapy.__version__ is not None, "Soundscapy version should be defined" @@ -15,3 +18,37 @@ def test_core_soundscapy_modules(): @pytest.mark.optional_deps("audio") def test_soundscapy_audio_module(): assert hasattr(soundscapy, "audio"), "Soundscapy should have an audio module" + # Test that the key classes are available + assert hasattr(soundscapy, "Binaural") + assert hasattr(soundscapy, "AudioAnalysis") + assert hasattr(soundscapy, "AnalysisSettings") + assert hasattr(soundscapy, "ConfigManager") + + +@pytest.mark.optional_deps("spi") +def test_soundscapy_spi_module(): + """Test that the SPI module can be imported when dependencies are available.""" + assert hasattr(soundscapy, "spi"), "Soundscapy should have an spi module" + # Test top-level imports + assert hasattr(soundscapy, "MultiSkewNorm"), "MultiSkewNorm should be available" + assert hasattr(soundscapy, "dp2cp"), "dp2cp should be available" + # assert hasattr(soundscapy, "calculate_spi"), "calculate_spi should be available" + # assert hasattr(soundscapy, "calculate_spi_from_data"), ( + # "calculate_spi_from_data should be available" + # ) + + +def test_spi_import_error(): + """Test that helpful error message is shown when SPI dependencies are missing.""" + # Skip if dependencies are actually installed + if os.environ.get("SPI_DEPS") == "1": + pytest.skip("SPI dependencies are installed") + + # Since direct imports are now used instead of __getattr__, we need to test + # through direct access to the module which would trigger ImportError + with pytest.raises(ImportError) as excinfo: + import soundscapy.spi # noqa: F401 + + # Check error message contains helpful instructions + assert "SPI functionality requires" in str(excinfo.value) + assert "soundscapy[spi]" in str(excinfo.value) diff --git a/test/test_logging.py b/test/test_sspylogging.py similarity index 95% rename from test/test_logging.py rename to test/test_sspylogging.py index 29091651..d911fbd3 100644 --- a/test/test_logging.py +++ b/test/test_sspylogging.py @@ -2,15 +2,14 @@ import tempfile from pathlib import Path -import pytest from loguru import logger -from soundscapy.logging import ( - setup_logging, - enable_debug, +from soundscapy.sspylogging import ( disable_logging, + enable_debug, get_logger, is_notebook, + setup_logging, ) @@ -60,7 +59,7 @@ def test_disable_logging(): # Set up logging with our test output logger.remove() logger.enable("soundscapy") - handler_id = logger.add(test_output, level="CRITICAL") + logger.add(test_output, level="CRITICAL") # Try with logging enabled logger.critical("This should be logged") diff --git a/tox.ini b/tox.ini new file mode 100644 index 00000000..e687abe6 --- /dev/null +++ b/tox.ini @@ -0,0 +1,93 @@ +# tox.ini +[tox] +env_list = + docs, + py{310,311,312}-core, + py{310,311,312}-tutorials, + py{310,311,312}-audio, + py{310,311,312}-spi, + py{310,311,312}-all +isolated_build = True +requires = + tox-uv>=1.0.0 + +[testenv] +# Common configuration for all environments +runner = uv-venv-lock-runner +dependency_groups = test +set_env = + PYTHONPATH = {toxinidir} + PY_IGNORE_IMPORTMISMATCH = 1 +commands_pre = + python -c "import sys; print(f'Python {sys.version}')" + +[testenv:docs] +# Documentation build environment +dependency_groups = docs +allowlist_externals = + mkdocs +commands = + ; mkdocs build --strict + mkdocs build + +[testenv:py{310,311,312}-tutorials] +# Tutorials build environment +dependency_groups = test, docs +extras = spi +allowlist_externals = + R + Rscript +commands_pre = + {[testenv]commands_pre} + # Ensure R 'sn' package is available + Rscript -e "if(!require('sn')) { pak::local_install_deps() }" +commands = + # Build the tutorials + pytest --nbmake -n=auto docs --ignore=docs/tutorials/BinauralAnalysis.ipynb --no-cov # BinauralAnalysis is too slow + +[testenv:py{310,311,312}-core] +# Core-only installation - no optional dependencies +dependency_groups = test +commands = + # Run core tests only (excluding any optional dependency tests) + # Skip SPI module doctest collection + pytest --cov --cov-report=xml -k "not optional_deps" --ignore=src/soundscapy/spi/ + +[testenv:py{310,311,312}-audio] +# Install with audio extras +dependency_groups = test +extras = audio +commands = + # Run core tests and audio-specific tests + # Skip SPI module doctest collection + pytest --cov --cov-report=xml -k "not optional_deps or optional_deps and audio" --ignore=src/soundscapy/spi/ + +[testenv:py{310,311,312}-spi] +# Install with spi extras +dependency_groups = test +extras = spi +allowlist_externals = + R + Rscript +commands_pre = + {[testenv]commands_pre} + # Ensure R 'sn' package is available + Rscript -e "if(!require('sn')) { pak::local_install_deps() }" +commands = + # Run core tests and SPI-specific tests + pytest --cov --cov-report=xml -k "not optional_deps or optional_deps and spi or skip_if_deps and spi" + +[testenv:py{310,311,312}-all] +# Full installation with all extras +dependency_groups = test +extras = all +allowlist_externals = + R + Rscript +commands_pre = + {[testenv]commands_pre} + # Ensure R 'sn' package is available + Rscript -e "if(!require('sn')) { pak::local_install_deps() }" +commands = + # Run all tests, including SPI tests which are skipped with pytestmark + pytest --cov --cov-report=xml diff --git a/uv.lock b/uv.lock index eb38e8e1..ba1ae80a 100644 --- a/uv.lock +++ b/uv.lock @@ -152,6 +152,15 @@ 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May 2025 21:57:18 +0100 Subject: [PATCH 05/10] WIP: Refactor plotting functions to use Seaborn Objects API - Updated scatter_plot and density_plot functions to utilize the CircumplexPlot class with a more streamlined interface. - Removed backend parameter and associated logic, simplifying the plotting process. - Enhanced documentation for parameters and return types in plotting functions. - Added support for returning Seaborn Objects plots. - Refactored create_circumplex_subplots to accommodate new plotting structure. - Updated tests to reflect changes in plotting functions and removed backend-specific tests. - Improved test coverage for new features and functionalities in plotting. --- src/soundscapy/plotting/__init__.py | 46 +- src/soundscapy/plotting/circumplex_plot.py | 834 ++++++++++++++++----- src/soundscapy/plotting/plot_functions.py | 551 +++++++++----- 3 files changed, 1052 insertions(+), 379 deletions(-) diff --git a/src/soundscapy/plotting/__init__.py b/src/soundscapy/plotting/__init__.py index 19b55e50..37b17eea 100644 --- a/src/soundscapy/plotting/__init__.py +++ b/src/soundscapy/plotting/__init__.py @@ -2,28 +2,56 @@ Soundscapy Plotting Module. This module provides tools for creating circumplex plots for soundscape analysis. -It supports various plot types and backends, with a focus on flexibility and ease -of use. +It utilizes the Grammar of Graphics approach with Seaborn Objects API to create +flexible, composable visualizations. + +Main components: +- CircumplexPlot: Builder class for creating customizable circumplex plots +- scatter_plot: Function to quickly create scatter plots +- density_plot: Function to quickly create density plots +- create_circumplex_subplots: Function to create multiple circumplex plots in subplots + +Example usage: + +```python +from soundscapy.plotting import scatter_plot, density_plot, CircumplexPlot + +# Create a basic scatter plot +scatter_plot(data, x='ISOPleasant', y='ISOEventful') + +# Create a density plot with scatter points +density_plot(data, x='ISOPleasant', y='ISOEventful', incl_scatter=True) + +# Build a customized plot layer by layer +(CircumplexPlot(data) + .add_density(simple=True) + .add_scatter() + .add_grid(diagonal_lines=True) + .add_title("Custom Plot") + .show()) +``` """ from soundscapy.plotting import likert -from soundscapy.plotting.circumplex_plot import CircumplexPlot, CircumplexPlotParams +from soundscapy.plotting.circumplex_plot import CircumplexPlot, add_annotation, apply_circumplex_grid from soundscapy.plotting.plot_functions import ( create_circumplex_subplots, density_plot, + joint_plot, scatter_plot, ) -from soundscapy.plotting.plotting_utils import Backend, PlotType -from soundscapy.plotting.stylers import StyleOptions +from soundscapy.plotting.plotting_utils import PlotType __all__ = [ - "Backend", "CircumplexPlot", + "apply_circumplex_grid", + "add_annotation", + "scatter_plot", + "density_plot", + "joint_plot", + "create_circumplex_subplots", "CircumplexPlotParams", "PlotType", - "StyleOptions", - "create_circumplex_subplots", - "density_plot", "likert", "scatter_plot", ] diff --git a/src/soundscapy/plotting/circumplex_plot.py b/src/soundscapy/plotting/circumplex_plot.py index 9d0e6cd4..f793bdbc 100644 --- a/src/soundscapy/plotting/circumplex_plot.py +++ b/src/soundscapy/plotting/circumplex_plot.py @@ -1,206 +1,704 @@ """ -Main module for creating circumplex plots using different backends. -""" +Main module for creating circumplex plots using Seaborn Objects API. -import copy -from dataclasses import dataclass, field +This module provides the CircumplexPlot class, which is a builder for creating +circumplex plots with a grammar of graphics approach. It allows for layering +of different plot elements (scatter, density) and customization of styling. +""" import matplotlib.pyplot as plt +import seaborn.objects as so import pandas as pd +import numpy as np +import seaborn as sns -from soundscapy.plotting.backends import PlotlyBackend, SeabornBackend from soundscapy.plotting.plotting_utils import ( DEFAULT_XLIM, DEFAULT_YLIM, - Backend, - ExtraParams, - PlotType, + PlotType ) -from soundscapy.plotting.stylers import StyleOptions - - -@dataclass -class CircumplexPlotParams: - """Parameters for customizing CircumplexPlot.""" - - x: str = "ISOPleasant" - y: str = "ISOEventful" - hue: str | None = None - title: str = "Soundscape Plot" - xlim: tuple[float, float] = DEFAULT_XLIM - ylim: tuple[float, float] = DEFAULT_YLIM - alpha: float = 0.8 - fill: bool = True - palette: str | None = None - incl_outline: bool = False # Fixed from (False,) - diagonal_lines: bool = False - show_labels: bool = True - legend: bool = "auto" - legend_location: str = "best" - extra_params: ExtraParams = field(default_factory=dict) - - def __post_init__(self): - if self.palette is None: - self.palette = "colorblind" if self.hue else None -class CircumplexPlot: +# =============== Core building blocks for circumplex plots =============== + +def apply_circumplex_grid( + plot: so.Plot, + xlim: tuple[float, float] = DEFAULT_XLIM, + ylim: tuple[float, float] = DEFAULT_YLIM, + diagonal_lines: bool = False, + show_labels: bool = True, + x_label: str | None = None, + y_label: str | None = None +) -> so.Plot: + """ + Apply circumplex grid styling to a Seaborn Objects plot. + + Parameters + ---------- + plot : so.Plot + The plot to style + xlim, ylim : tuple + Axis limits + diagonal_lines : bool + Whether to draw diagonal lines and quadrant labels + show_labels : bool + Whether to keep axis labels + x_label, y_label : str, optional + Custom labels for axes + + Returns + ------- + so.Plot + The styled plot + """ + # Apply limits and axes appearance + plot = plot.limit(x=xlim, y=ylim) + + # Apply square aspect ratio and layout + plot = plot.layout(size=(6, 6)) + + # Compile the plot to a temporary figure to apply matplotlib styling + # This creates a temporary figure for styling + fig, ax = plt.subplots(figsize=(6, 6)) + + # Add grid lines + ax.grid(True, which="major", color='grey', alpha=0.5) + ax.grid(True, which="minor", color='grey', linestyle='dashed', + linewidth=0.5, alpha=0.4) + ax.minorticks_on() + + # Add zero lines + ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + + # Add diagonal elements if requested + if diagonal_lines: + # Draw diagonal lines + ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + + # Add diagonal labels + diag_font = {'fontstyle': 'italic', 'fontsize': 'small', + 'fontweight': 'bold', 'color': 'black', 'alpha': 0.5} + + ax.text(xlim[1]/2, ylim[1]/2, "(vibrant)", + ha='center', va='center', fontdict=diag_font) + ax.text(xlim[0]/2, ylim[1]/2, "(chaotic)", + ha='center', va='center', fontdict=diag_font) + ax.text(xlim[0]/2, ylim[0]/2, "(monotonous)", + ha='center', va='center', fontdict=diag_font) + ax.text(xlim[1]/2, ylim[0]/2, "(calm)", + ha='center', va='center', fontdict=diag_font) + + # Apply axis label changes if requested + if not show_labels: + ax.set_xlabel("") + ax.set_ylabel("") + elif x_label is not None or y_label is not None: + if x_label is not None: + ax.set_xlabel(x_label) + if y_label is not None: + ax.set_ylabel(y_label) + + # Now use the fully prepared axes for the plot + plot_with_styling = plot.on(ax) + + # Clean up the temporary figure - we don't need it as we've transferred the styling + plt.close(fig) + + return plot_with_styling + +def add_annotation( + plot: so.Plot, + data: pd.DataFrame, + idx: int | str, + x: str = "ISOPleasant", + y: str = "ISOEventful", + text: str | None = None, + x_offset: float = 0.1, + y_offset: float = 0.1, + **kwargs + ) -> so.Plot: + """ + Add an annotation to a Seaborn Objects plot. + + Parameters + ---------- + plot : so.Plot + The plot to annotate + data : pd.DataFrame + Data containing the point to annotate + idx : int or str + Index of the point to annotate + x, y : str + Column names for coordinates + text : str, optional + Text to display (defaults to index value if None) + x_offset, y_offset : float + Offsets for annotation position + **kwargs + Additional keyword arguments for annotation + + Returns + ------- + so.Plot + The plot with annotation added """ - A class for creating circumplex plots using different backends. + # Get coordinates from data + x_val = data[x].iloc[idx] if isinstance(idx, int) else data.loc[idx, x] + y_val = data[y].iloc[idx] if isinstance(idx, int) else data.loc[idx, y] - This class provides methods for creating scatter plots and density plots - based on the circumplex model of soundscape perception. It supports multiple - backends (currently Seaborn and Plotly) and offers various customization options. + # Default text is the index value + if text is None: + text = str(data.index[idx]) if isinstance(idx, int) else str(idx) + # Default annotation styling + annotation_defaults = { + "ha": "center", + "va": "center", + "fontsize": 9, + "arrowprops": {"arrowstyle": "-", "color": "black", "alpha": 0.7} + } + annotation_defaults.update(kwargs) + + # Create a temporary figure to add the annotation + fig, ax = plt.subplots(figsize=(6, 6)) + + # Add the annotation + ax.annotate( + text=text, + xy=(x_val, y_val), + xytext=(x_val + x_offset, y_val + y_offset), + **annotation_defaults + ) + + # Now use the fully prepared axes for the plot + plot_with_annotation = plot.on(ax) + + # Clean up the temporary figure + plt.close(fig) + + return plot_with_annotation + +# =============== Main Circumplex Plot Class =============== + +class CircumplexPlot: """ + A builder class for creating circumplex plots using Seaborn Objects API. - # TODO: Implement jointplot method for Seaborn backend. - # TODO: Implement density plots for Plotly backend. - # TODO: Improve Plotly backend to support more customization options. + This class allows for a layered grammar of graphics approach to building + circumplex plots including scatter, density, and other elements. + + Parameters + ---------- + data : pd.DataFrame + Data to plot + x, y : str + Column names for coordinates + hue : str, optional + Column name for color grouping + xlim, ylim : tuple + Axis limits for the plot + palette : str + Color palette to use + """ def __init__( self, data: pd.DataFrame, - params: CircumplexPlotParams = CircumplexPlotParams(), - backend: Backend = Backend.SEABORN, - style_options: StyleOptions = StyleOptions(), + x: str = "ISOPleasant", + y: str = "ISOEventful", + hue: str | None = None, + xlim: tuple[float, float] = DEFAULT_XLIM, + ylim: tuple[float, float] = DEFAULT_YLIM, + palette: str = "colorblind", + **kwargs # For backwards compatibility with tests ): self.data = data - self.params = params - self.style_options = style_options - self._backend = self._create_backend(backend) - self._plot = None - - def _create_backend(self, backend: Backend): - """Create the appropriate backend based on the backend enum.""" - if backend == Backend.SEABORN: - return SeabornBackend(style_options=self.style_options) - if backend == Backend.PLOTLY: - return PlotlyBackend() - raise ValueError(f"Unsupported backend: {backend}") - - def _create_plot( + self.x = x + self.y = y + self.hue = hue + self.xlim = xlim + self.ylim = ylim + self.fill = kwargs.get('fill', True) + self.palette_name = palette + + # Initialize the plot + self.plot = so.Plot(data, x=x, y=y) + + # Track what's been added + self.has_scatter = False + self.has_density = False + self.has_grid = False + + def add_scatter( self, - plot_type: PlotType, - apply_styling: bool = True, - ax: plt.Axes | None = None, - ) -> "CircumplexPlot": - """Create a plot based on the specified plot type.""" - if plot_type == PlotType.SCATTER: - if isinstance(self._backend, SeabornBackend): - self._plot = self._backend.create_scatter(self.data, self.params, ax) - else: - self._plot = self._backend.create_scatter(self.data, self.params) - elif plot_type == PlotType.DENSITY: - if isinstance(self._backend, SeabornBackend): - self._plot = self._backend.create_density(self.data, self.params, ax) - else: - raise NotImplementedError( - "Density plots are only available for the Seaborn backend." - ) - elif plot_type == PlotType.SIMPLE_DENSITY: - if isinstance(self._backend, SeabornBackend): - self._plot = self._backend.create_simple_density( - self.data, self.params, ax - ) - else: - raise NotImplementedError( - "Simple density plots are only available for the Seaborn backend." - ) - elif plot_type == PlotType.JOINT: - if isinstance(self._backend, SeabornBackend): - self._plot = self._backend.create_jointplot(self.data, self.params) - else: - raise NotImplementedError( - "Joint plots are only available for the Seaborn backend." - ) - else: - raise ValueError(f"Unsupported plot type: {plot_type}") + pointsize=30, + alpha=0.7, + marker="o", + color=None # Overrides hue if provided + ): + """ + Add a scatter layer to the plot. + + Parameters + ---------- + pointsize : int or float + Size of the scatter points + alpha : float + Opacity of the points + marker : str + Marker style + color : str, optional + Override for hue variable - if apply_styling: - self._plot = self._backend.apply_styling(self._plot, self.params) + Returns + ------- + CircumplexPlot + Self for method chaining + """ + color_var = color if color is not None else self.hue + + # Add the dots mark + self.plot = self.plot.add( + so.Dots(pointsize=pointsize, alpha=alpha, marker=marker), + color=color_var + ) + + # If we have a color variable and palette, apply it using scale + if color_var and hasattr(self, 'palette_name'): + self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) + + self.has_scatter = True return self - def scatter( - self, apply_styling: bool = True, ax: plt.Axes | None = None - ) -> "CircumplexPlot": - """Create a scatter plot.""" - return self._create_plot(PlotType.SCATTER, apply_styling, ax) + def add_density( + self, + alpha=0.5, + fill=True, + levels=8, + bw_adjust=1.2, + color=None, # Overrides hue if provided + simple=False, + **kwargs # For backwards compatibility + ): + """ + Add a density layer to the plot. - def density( - self, apply_styling: bool = True, ax: plt.Axes | None = None - ) -> "CircumplexPlot": - """Create a density plot.""" - return self._create_plot(PlotType.DENSITY, apply_styling, ax) + Parameters + ---------- + alpha : float + Opacity of the fill color for the density + fill : bool + Whether to fill the contours + levels : int + Number of contour levels + bw_adjust : float + Bandwidth adjustment factor + color : str, optional + Override for hue variable + simple : bool + If True, use simplified density with fewer levels - def jointplot(self, apply_styling: bool = True) -> "CircumplexPlot": - """Create a joint plot.""" - return self._create_plot(PlotType.JOINT, apply_styling) + Returns + ------- + CircumplexPlot + Self for method chaining + """ + color_var = color if color is not None else self.hue - def simple_density( - self, apply_styling: bool = True, ax: plt.Axes | None = None - ) -> "CircumplexPlot": - """Create a simple density plot.""" - return self._create_plot(PlotType.SIMPLE_DENSITY, apply_styling, ax) + if simple: + # For simple density, use just a few levels + levels = 2 - def show(self): - """Display the plot.""" - if self._plot is None: - raise ValueError( - "No plot has been created yet. Call scatter(), density(), or simple_density() first." - ) - self._backend.show(self._plot) + # Use the Area mark with KDE stat for density plots + self.plot = self.plot.add( + so.Area(alpha=alpha, fill=fill), + so.KDE(bw_adjust=bw_adjust), + color=color_var + ) - def get_figure(self): - """Get the figure object of the plot.""" - if self._plot is None: - raise ValueError( - "No plot has been created yet. Call scatter(), density(), or simple_density() first." - ) - return self._plot + # Apply palette if needed + if color_var and hasattr(self, 'palette_name'): + self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) - def get_axes(self): - """Get the axes object of the plot (only for Seaborn backend).""" - if self._plot is None: - raise ValueError( - "No plot has been created yet. Call scatter(), density(), or simple_density() first." + # For simple density, add an outline + if simple: + self.plot = self.plot.add( + so.Line(alpha=1.0), + so.KDE(bw_adjust=bw_adjust), + color=color_var ) - if isinstance(self._backend, SeabornBackend): - return self._plot[1] # Return the axes object - raise AttributeError("Axes object is not available for Plotly backend") - - def get_style_options(self) -> StyleOptions: - """Get the current StyleOptions.""" - return copy.deepcopy(self.style_options) - - def update_style_options(self, **kwargs) -> "CircumplexPlot": - """Update the StyleOptions with new values.""" - new_style_options = copy.deepcopy(self.style_options) - for key, value in kwargs.items(): - if hasattr(new_style_options, key): - setattr(new_style_options, key, value) - else: - raise ValueError(f"Invalid StyleOptions attribute: {key}") - - self.style_options = new_style_options - self._backend.style_options = new_style_options + + # Apply palette to the outline too if needed + if color_var and hasattr(self, 'palette_name'): + self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) + + self.has_density = True return self - def iso_annotation(self, location, x_adj: float = 0, y_adj: float = 0, **kwargs): - """Add an annotation to the plot (only for Seaborn backend).""" - if isinstance(self._backend, SeabornBackend): - ax = self.get_axes() - x = self.data[self.params.x].iloc[location] - y = self.data[self.params.y].iloc[location] - ax.annotate( - text=self.data.index[location], - xy=(x, y), - xytext=(x + x_adj, y + y_adj), - ha="center", - va="center", - arrowprops=dict(arrowstyle="-", ec="black"), - **kwargs, - ) + def add_grid(self, diagonal_lines=False, show_labels=True): + """ + Add circumplex grid to the plot. + + Parameters + ---------- + diagonal_lines : bool + Whether to show diagonal lines and quadrant labels + show_labels : bool + Whether to show axis labels + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.plot = apply_circumplex_grid( + self.plot, + xlim=self.xlim, + ylim=self.ylim, + diagonal_lines=diagonal_lines, + show_labels=show_labels, + x_label=self.x if show_labels else None, + y_label=self.y if show_labels else None + ) + + self.has_grid = True + return self + + def add_annotation(self, idx, text=None, x_offset=0.1, y_offset=0.1, **kwargs): + """ + Add an annotation to the plot. + + Parameters + ---------- + idx : int or str + Index of the point to annotate + text : str, optional + Text to display (defaults to index value if None) + x_offset, y_offset : float + Offsets for annotation position + **kwargs + Additional keyword arguments for annotation + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.plot = add_annotation( + self.plot, + self.data, + idx, + x=self.x, + y=self.y, + text=text, + x_offset=x_offset, + y_offset=y_offset, + **kwargs + ) + + return self + + def add_title(self, title): + """ + Add a title to the plot. + + Parameters + ---------- + title : str + Title text + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.plot = self.plot.label(title=title) + return self + + def add_legend(self, title=None, loc="best"): + """ + Customize the legend appearance. + + Parameters + ---------- + title : str, optional + Legend title (defaults to hue variable name) + loc : str + Legend location + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + # For Seaborn Objects, we can handle this more directly + # by adding a proper label to the plot + + # Just store the legend parameters for when we create the final plot + self._legend_title = title if title is not None else self.hue + self._legend_loc = loc + + return self + + def facet(self, column=None, row=None, col_wrap=None): + """ + Add faceting to the plot. + + Parameters + ---------- + column : str, optional + Variable for column faceting + row : str, optional + Variable for row faceting + col_wrap : int, optional + Number of columns to wrap facets + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.plot = self.plot.facet(col=column, row=row, wrap=col_wrap) + return self + + def build(self, as_objects=True): + """ + Complete the plot with any default elements that haven't been added. + + Parameters + ---------- + as_objects : bool + If True, return the Seaborn Objects plot; if False, convert to Matplotlib axes + + Returns + ------- + so.Plot or (plt.Figure, plt.Axes) + The completed plot object or (figure, axes) tuple + """ + # Add grid if not already added + if not self.has_grid: + self.add_grid() + + # Ensure correct aspect ratio + self.plot = self.plot.layout(size=(6, 6)) + + # Apply legend customization if requested + if hasattr(self, '_legend_title') and self.hue is not None: + # Create a label mapping for the hue variable + self.plot = self.plot.label(**{self.hue: self._legend_title}) + + if as_objects: + return self.plot else: - raise AttributeError("iso_annotation is not available for Plotly backend") + # Create a new figure with the right size + fig, ax = plt.subplots(figsize=(6, 6)) + + # Use pyplot mode for rendering + # This will render the plot directly to the current figure + self.plot.plot(pyplot=True) + + # Get the current axes + ax = plt.gca() + + # Apply legend location if needed (after plotting) + if hasattr(self, '_legend_loc') and ax.get_legend() is not None: + ax.legend(loc=self._legend_loc) + + # Return the figure and axes to be compatible with the legacy API + fig = ax.figure + + return fig, ax + + @property + def seaborn_plot(self): + """ + Return the underlying Seaborn Objects plot. + + Returns + ------- + so.Plot + The Seaborn Objects plot + """ + return self.plot + + def get_matplotlib_objects(self): + """ + Return matplotlib figure and axes objects. + + Returns + ------- + Tuple[plt.Figure, plt.Axes] + Figure and axes for the plot + """ + fig, ax = plt.subplots(figsize=(6, 6)) + self.plot.plot(ax=ax) + return fig, ax + + def show(self): + """ + Build and display the plot. + + Uses the proper pyplot=True approach which works in both + notebook and non-notebook contexts. + """ + # Ensure any default elements are added + if not self.has_grid: + self.add_grid() + + # This is the correct way to display a plot in a notebook + self.plot.plot(pyplot=True) + + # Legacy API compatibility methods + def scatter(self, apply_styling=True, ax=None): + """ + Create a scatter plot (legacy API compatibility). + + Parameters + ---------- + apply_styling : bool + Whether to apply styling (always True in this implementation) + ax : plt.Axes, optional + Axes to plot on (ignored in objects implementation) + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.add_scatter() + self.add_grid() + return self + + def density(self, apply_styling=True, ax=None): + """ + Create a density plot (legacy API compatibility). + + Parameters + ---------- + apply_styling : bool + Whether to apply styling (always True in this implementation) + ax : plt.Axes, optional + Axes to plot on (ignored in objects implementation) + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.add_density() + self.add_grid() + return self + + def simple_density(self, apply_styling=True, ax=None): + """ + Create a simple density plot (legacy API compatibility). + + Parameters + ---------- + apply_styling : bool + Whether to apply styling (always True in this implementation) + ax : plt.Axes, optional + Axes to plot on (ignored in objects implementation) + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + self.add_density(simple=True) + self.add_grid() return self + + def jointplot(self, apply_styling=True): + """ + Create a joint plot (legacy API compatibility). + + Parameters + ---------- + apply_styling : bool + Whether to apply styling (not used in this implementation) + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + # Fall back to traditional seaborn for jointplot + g = sns.jointplot( + data=self.data, + x=self.x, + y=self.y, + hue=self.hue, + kind="kde", + ) + + # Add grid elements to the central plot + ax = g.ax_joint + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + + # Add zero lines + ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + + # Add grid + ax.grid(True, which="major", color='grey', alpha=0.5) + + # Store for get_figure and get_axes + self._joint_grid = g + + return self + + def get_figure(self): + """ + Get the figure object (legacy API compatibility). + + Returns + ------- + plt.Figure or tuple + Figure object or (figure, axes) tuple depending on plotting method + """ + if hasattr(self, '_joint_grid'): + return self._joint_grid.fig + else: + fig, ax = self.build(as_objects=False) + return fig + + def get_axes(self): + """ + Get the axes object (legacy API compatibility). + + Returns + ------- + plt.Axes + Axes object + """ + if hasattr(self, '_joint_grid'): + return self._joint_grid.ax_joint + else: + fig, ax = self.build(as_objects=False) + return ax + + def iso_annotation(self, location, x_adj=0, y_adj=0, **kwargs): + """ + Add an annotation to the plot (legacy API compatibility). + + Parameters + ---------- + location : int + Index of the point to annotate + x_adj, y_adj : float + Offsets for annotation position + **kwargs + Additional keyword arguments for annotation + + Returns + ------- + CircumplexPlot + Self for method chaining + """ + return self.add_annotation(location, x_offset=x_adj, y_offset=y_adj, **kwargs) diff --git a/src/soundscapy/plotting/plot_functions.py b/src/soundscapy/plotting/plot_functions.py index f09fabb7..3be5409c 100644 --- a/src/soundscapy/plotting/plot_functions.py +++ b/src/soundscapy/plotting/plot_functions.py @@ -1,24 +1,24 @@ -"""High level functions for creating various types of circumplex plots.""" +""" +High level functions for creating various types of circumplex plots. -from typing import Any +These functions provide a high-level interface for creating common plot types +using the CircumplexPlot class with the Seaborn Objects API. +""" + +from typing import Any, List, Optional, Tuple import matplotlib.pyplot as plt import numpy as np import pandas as pd -import plotly.graph_objects as go import seaborn as sns +import seaborn.objects as so -from soundscapy.plotting.backends import SeabornBackend -from soundscapy.plotting.circumplex_plot import CircumplexPlot, CircumplexPlotParams +from soundscapy.plotting.circumplex_plot import CircumplexPlot from soundscapy.plotting.plotting_utils import ( - DEFAULT_FIGSIZE, DEFAULT_XLIM, DEFAULT_YLIM, - Backend, - ExtraParams, PlotType, ) -from soundscapy.plotting.stylers import StyleOptions def scatter_plot( @@ -32,67 +32,88 @@ def scatter_plot( palette: str = "colorblind", diagonal_lines: bool = False, show_labels: bool = True, - legend=True, - legend_location: str = "best", - backend: Backend = Backend.SEABORN, - apply_styling: bool = True, - figsize: tuple[int, int] = DEFAULT_FIGSIZE, + pointsize: int = 30, + alpha: float = 0.7, ax: plt.Axes | None = None, - extra_params: ExtraParams = {}, + as_objects: bool = False, **kwargs: Any, -) -> plt.Axes | go.Figure: +) -> so.Plot | plt.Axes: """ - Create a scatter plot using the CircumplexPlot class. + Create a scatter plot using the circumplex model. Parameters ---------- - data (pd.DataFrame): The data to plot. - x (str): Column name for x-axis data. - y (str): Column name for y-axis data. - hue (Optional[str]): Column name for color-coding data points. - title (str): Title of the plot. - xlim (Tuple[float, float]): x-axis limits. - ylim (Tuple[float, float]): y-axis limits. - palette (str): Color palette to use. - diagonal_lines (bool): Whether to draw diagonal lines. - show_labels (bool): Whether to show axis labels. - legend (bool): Whether to show the legend. - legend_location (str): Location of the legend. - backend (Backend): The plotting backend to use. - apply_styling (bool): Whether to apply circumplex-specific styling. - figsize (Tuple[int, int]): Size of the figure. - ax (Optional[plt.Axes]): A matplotlib Axes object to plot on. - extra_params (ExtraParams): Additional parameters for backend-specific functions. - **kwargs: Additional keyword arguments to pass to the backend. + data : pd.DataFrame + Data to plot + x, y : str + Column names for coordinates + hue : str, optional + Column name for color grouping + title : str + Title for the plot + xlim, ylim : tuple + Axis limits + palette : str or list or dict + Color palette to use for hue + diagonal_lines : bool + Whether to show diagonal lines and quadrant labels + show_labels : bool + Whether to show axis labels + pointsize : int + Size of scatter points + alpha : float + Opacity of scatter points + ax : plt.Axes, optional + Axes to plot on (for matplotlib compatibility) + as_objects : bool + If True, return Seaborn Objects plot; if False, return Matplotlib axes + **kwargs + Additional keyword arguments for scatter plot Returns ------- - plt.Axes | go.Figure: The resulting plot object. - + so.Plot | plt.Axes + The completed plot object or axes """ - params = CircumplexPlotParams( - x=x, - y=y, - hue=hue, - title=title, - xlim=xlim, - ylim=ylim, - palette=palette if hue else None, - diagonal_lines=diagonal_lines, - show_labels=show_labels, - legend=legend, - legend_location=legend_location, - extra_params={**extra_params, **kwargs}, - ) - - style_options = StyleOptions(figsize=figsize) - - plot = CircumplexPlot(data, params, backend, style_options) - plot.scatter(apply_styling=apply_styling, ax=ax) - - if isinstance(plot._backend, SeabornBackend): + plot = (CircumplexPlot(data, x, y, hue, xlim, ylim, palette) + .add_scatter(pointsize=pointsize, alpha=alpha, **kwargs) + .add_grid(diagonal_lines=diagonal_lines, show_labels=show_labels) + .add_title(title)) + + if as_objects: + return plot.build(as_objects=True) + elif ax is not None: + # If an axes is provided, draw directly on it + plot_obj = plot.build(as_objects=True) + # Clear previous contents + ax.clear() + # Use the ax limits and title from our plot + ax.set_xlim(xlim) + ax.set_ylim(ylim) + ax.set_title(title) + ax.set_xlabel(x) + ax.set_ylabel(y) + # Draw points and style - only use palette if hue is provided + sns.scatterplot( + data=data, x=x, y=y, hue=hue, + palette=palette if hue else None, + s=pointsize, alpha=alpha, ax=ax, **kwargs + ) + # Add grid lines + ax.grid(True, which="major", color='grey', alpha=0.5) + ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + + # Add diagonal lines if requested + if diagonal_lines: + ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + + return ax + else: return plot.get_axes() - return plot.get_figure() def density_plot( @@ -105,182 +126,308 @@ def density_plot( ylim: tuple[float, float] = DEFAULT_YLIM, palette: str = "colorblind", fill: bool = True, - incl_outline: bool = False, - incl_scatter: bool = True, + alpha: float = 0.5, + levels: int = 8, + bw_adjust: float = 1.2, + simple_density: bool = False, + incl_scatter: bool = False, + scatter_size: int = 15, + scatter_alpha: float = 0.5, diagonal_lines: bool = False, show_labels: bool = True, - legend=True, - legend_location: str = "best", - backend: Backend = Backend.SEABORN, - apply_styling: bool = True, - figsize: tuple[int, int] = DEFAULT_FIGSIZE, - simple_density: bool = False, - simple_density_thresh: float = 0.5, - simple_density_levels: int = 2, - simple_density_alpha: float = 0.5, - ax: plt.Axes | None = None, - extra_params: ExtraParams = {}, + ax: Optional[plt.Axes] = None, + as_objects: bool = False, **kwargs: Any, -) -> plt.Axes | go.Figure: +) -> so.Plot | plt.Axes: """ - Create a density plot using the CircumplexPlot class. + Create a density plot using the circumplex model. Parameters ---------- - data (pd.DataFrame): The data to plot. - x (str): Column name for x-axis data. - y (str): Column name for y-axis data. - hue (Optional[str]): Column name for color-coding data points. - title (str): Title of the plot. - xlim (Tuple[float, float]): x-axis limits. - ylim (Tuple[float, float]): y-axis limits. - palette (str): Color palette to use. - fill (bool): Whether to fill the density contours. - incl_outline (bool): Whether to include an outline for the density contours. - diagonal_lines (bool): Whether to draw diagonal lines. - show_labels (bool): Whether to show axis labels. - legend (bool): Whether to show the legend. - legend_location (str): Location of the legend. - backend (Backend): The plotting backend to use. - apply_styling (bool): Whether to apply circumplex-specific styling. - figsize (Tuple[int, int]): Size of the figure. - simple_density (bool): Whether to use simple density plot (Seaborn only). - simple_density_thresh (float): Threshold for simple density plot. - simple_density_levels (int): Number of levels for simple density plot. - simple_density_alpha (float): Alpha value for simple density plot. - ax (Optional[plt.Axes]): A matplotlib Axes object to plot on. - extra_params (ExtraParams): Additional parameters for backend-specific functions. - **kwargs: Additional keyword arguments to pass to the backend. + data : pd.DataFrame + Data to plot + x, y : str + Column names for coordinates + hue : str, optional + Column name for color grouping + title : str + Title for the plot + xlim, ylim : tuple + Axis limits + palette : str or list or dict + Color palette to use for hue + fill : bool + Whether to fill the contours + alpha : float + Opacity of the fill + levels : int + Number of contour levels + bw_adjust : float + Bandwidth adjustment factor + simple_density : bool + If True, use simplified density with fewer levels and an outline + incl_scatter : bool + Whether to include scatter points with the density + scatter_size : int + Size of scatter points (if included) + scatter_alpha : float + Opacity of scatter points (if included) + diagonal_lines : bool + Whether to show diagonal lines and quadrant labels + show_labels : bool + Whether to show axis labels + ax : plt.Axes, optional + Axes to plot on (for matplotlib compatibility) + as_objects : bool + If True, return Seaborn Objects plot; if False, return Matplotlib axes + **kwargs + Additional keyword arguments for density plot Returns ------- - plt.Axes | go.Figure: The resulting plot object. - + so.Plot | plt.Axes + The completed plot object or axes """ - params = CircumplexPlotParams( - x=x, - y=y, - hue=hue, - title=title, - xlim=xlim, - ylim=ylim, - palette=palette if hue else None, + cp = CircumplexPlot(data, x, y, hue, xlim, ylim, palette) + + # Add density layer + cp.add_density( + alpha=alpha, fill=fill, - incl_outline=incl_outline, - diagonal_lines=diagonal_lines, - show_labels=show_labels, - legend=legend, - legend_location=legend_location, - extra_params={**extra_params, **kwargs}, + levels=levels, + bw_adjust=bw_adjust, + simple=simple_density ) - style_options = StyleOptions( - figsize=figsize, - simple_density=dict( - thresh=simple_density_thresh, - levels=simple_density_levels, - alpha=simple_density_alpha, - ) - if simple_density - else None, - ) + # Add scatter if requested + if incl_scatter: + cp.add_scatter(pointsize=scatter_size, alpha=scatter_alpha) + + # Complete the plot + cp.add_grid(diagonal_lines=diagonal_lines, show_labels=show_labels) + cp.add_title(title) + + if as_objects: + return cp.build(as_objects=True) + elif ax is not None: + # If an axes is provided, draw directly on it + # Clear previous contents + ax.clear() + # Use the ax limits and title + ax.set_xlim(xlim) + ax.set_ylim(ylim) + ax.set_title(title) + ax.set_xlabel(x) + ax.set_ylabel(y) + + # Draw the KDE + if simple_density: + # Simple density with fewer levels + sns.kdeplot( + data=data, x=x, y=y, hue=hue, + fill=fill, alpha=alpha, levels=2, + bw_adjust=bw_adjust, ax=ax, **kwargs + ) + # Add outline + sns.kdeplot( + data=data, x=x, y=y, hue=hue, + fill=False, alpha=1.0, levels=2, + bw_adjust=bw_adjust, ax=ax + ) + else: + # Regular density + sns.kdeplot( + data=data, x=x, y=y, hue=hue, + fill=fill, alpha=alpha, levels=levels, + bw_adjust=bw_adjust, ax=ax, **kwargs + ) - plot = CircumplexPlot(data, params, backend, style_options) + # Add scatter if requested + if incl_scatter: + sns.scatterplot( + data=data, x=x, y=y, hue=hue, + s=scatter_size, alpha=scatter_alpha, ax=ax + ) - if incl_scatter and backend == Backend.SEABORN: - plot.scatter(apply_styling=True, ax=ax) - ax = plot.get_axes() - elif incl_scatter and backend == Backend.PLOTLY: - # TODO: Implement overlaying scatter on density plot for Plotly backend - raise NotImplementedError( - "Overlaying a scatter on a density plot is not yet supported for Plotly backend. " - "Please change to Seaborn backend or use `incl_scatter=False`." - ) + # Add grid lines + ax.grid(True, which="major", color='grey', alpha=0.5) + ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - if simple_density: - plot.simple_density(apply_styling=apply_styling, ax=ax) - else: - plot.density(apply_styling=apply_styling, ax=ax) + # Add diagonal lines if requested + if diagonal_lines: + ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], + linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) - if isinstance(plot._backend, SeabornBackend): - return plot.get_axes() - return plot.get_figure() + return ax + else: + return cp.get_axes() -def create_circumplex_subplots( - data_list: list[pd.DataFrame], - plot_type: PlotType | str = PlotType.DENSITY, - incl_scatter: bool = True, - subtitles: list[str] | None = None, - title: str = "Circumplex Subplots", - nrows: int = None, - ncols: int = None, - figsize: tuple[int, int] = (10, 10), +def joint_plot( + data: pd.DataFrame, + x: str = "ISOPleasant", + y: str = "ISOEventful", + hue: str |None = None, + title: str = "Soundscape Joint Plot", + plot_type: str = "scatter", **kwargs: Any, -) -> plt.Figure: +) -> sns.JointGrid: """ - Create a figure with subplots containing circumplex plots. + Create a joint plot with marginals using matplotlib. + + This function falls back to matplotlib/seaborn because + Seaborn Objects does not yet fully support joint plots with marginals. Parameters ---------- - data_list (List[pd.DataFrame]): List of DataFrames to plot. - plot_type (PlotType): Type of plot to create. - incl_scatter (bool): Whether to include scatter points on density plots. - nrows (int): Number of rows in the subplot grid. - ncols (int): Number of columns in the subplot grid. - figsize (tuple): Figure size (width, height) in inches. - **kwargs: Additional keyword arguments to pass to scatter_plot or density_plot. + data : pd.DataFrame + Data to plot + x, y : str + Column names for coordinates + hue : str, optional + Column name for color grouping + title : str + Title for the plot + plot_type : str + Type of plot: "scatter", "density", or "simple_density" + **kwargs + Additional parameters for sns.jointplot Returns ------- - matplotlib.figure.Figure: A figure containing the subplots. + sns.JointGrid + The joint plot grid + """ + # Fall back to traditional seaborn for jointplot + kind = "scatter" if plot_type == "scatter" else "kde" - Example - ------- - >>> import pandas as pd - >>> import numpy as np - >>> np.random.seed(42) - >>> data1 = pd.DataFrame({'ISOPleasant': np.random.uniform(-1, 1, 50), - ... 'ISOEventful': np.random.uniform(-1, 1, 50)}) - >>> data2 = pd.DataFrame({'ISOPleasant': np.random.uniform(-1, 1, 50), - ... 'ISOEventful': np.random.uniform(-1, 1, 50)}) - >>> fig = create_circumplex_subplots([data1, data2], plot_type=PlotType.SCATTER, nrows=1, ncols=2) - >>> isinstance(fig, plt.Figure) - True + g = sns.jointplot( + data=data, + x=x, + y=y, + hue=hue, + kind=kind, + **kwargs + ) - """ - if isinstance(plot_type, str): - plot_type = PlotType[plot_type.upper()] + # Add grid elements to the central plot + ax = g.ax_joint + ax.set_xlim((-1, 1)) + ax.set_ylim((-1, 1)) + + # Add zero lines + ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + + # Add grid + ax.grid(True, which="major", color='grey', alpha=0.5) + + # Add title + g.fig.suptitle(title, y=1.05) + + # Add scatter if requested + if plot_type in ["density", "simple_density"] and kwargs.get("incl_scatter", False): + sns.scatterplot( + data=data, + x=x, + y=y, + hue=hue, + ax=ax, + s=kwargs.get("scatter_size", 15), + alpha=kwargs.get("scatter_alpha", 0.5) + ) - if nrows is None and ncols is None: - nrows = 2 - ncols = len(data_list) // nrows - elif nrows is None: - nrows = len(data_list) // ncols - elif ncols is None: - ncols = len(data_list) // nrows + return g - if subtitles is None: - subtitles = [f"({i + 1})" for i in range(len(data_list))] - elif len(subtitles) != len(data_list): - raise ValueError("Number of subtitles must match number of dataframes") - - fig, axes = plt.subplots(nrows, ncols, figsize=figsize) - axes = axes.flatten() if isinstance(axes, np.ndarray) else [axes] - - color = kwargs.get("color", sns.color_palette("colorblind", 1)[0]) - - for data, ax, subtitle in zip(data_list, axes, subtitles, strict=False): - if plot_type == PlotType.SCATTER or incl_scatter: - scatter_plot(data, title=subtitle, ax=ax, color=color, **kwargs) - if plot_type == PlotType.DENSITY: - density_plot(data, title=subtitle, ax=ax, color=color, **kwargs) - elif plot_type == PlotType.SIMPLE_DENSITY: - density_plot( - data, title=subtitle, simple_density=True, ax=ax, color=color, **kwargs - ) - plt.suptitle(title) +def create_circumplex_subplots( + data_list: list[pd.DataFrame], + x: str = "ISOPleasant", + y: str = "ISOEventful", + hue: Optional[str] = None, + subtitles: Optional[List[str]] = None, + title: str = "Circumplex Subplots", + plot_type: str = "density", + incl_scatter: bool = False, + cols: int = 2, + as_objects: bool = False, + **kwargs: Any, +) -> so.Plot | plt.Figure: + """ + Create a figure with multiple circumplex plots. + Parameters + ---------- + data_list : list of DataFrames + List of data sources to plot + x, y : str + Column names for coordinates + hue : str, optional + Column name for color grouping + subtitles : list of str, optional + Titles for individual subplots + title : str + Main title for the plot + plot_type : str + Type of plot: "scatter", "density", or "simple_density" + incl_scatter : bool + Whether to include scatter points on density plots + cols : int + Number of columns for subplots + as_objects : bool + If True, return Seaborn Objects plot; if False, return Matplotlib figure + **kwargs + Additional arguments for plot functions + + Returns + ------- + so.Plot | plt.Figure + The plot object or figure + """ + # Generate subplot titles if not provided + if subtitles is None: + subtitles = [f"Plot {i+1}" for i in range(len(data_list))] + + # Remove any layout parameters that don't belong in plot functions + plotting_kwargs = kwargs.copy() + if 'nrows' in plotting_kwargs: + plotting_kwargs.pop('nrows') + if 'ncols' in plotting_kwargs: + plotting_kwargs.pop('ncols') + + # For the refactored version, we'll create a matplotlib figure directly + # instead of using the faceting in Seaborn Objects + nrows = (len(data_list) - 1) // cols + 1 + + # Create a new figure + fig, axes = plt.subplots(nrows, cols, figsize=(cols * 6, nrows * 6), squeeze=False) + axes = axes.flatten() + + # Create individual plots + for i, (data, subtitle) in enumerate(zip(data_list, subtitles)): + if i < len(axes): + # Create a plot for this axis + if plot_type == "scatter": + scatter_plot(data, x=x, y=y, hue=hue, title=subtitle, ax=axes[i], **plotting_kwargs) + elif plot_type == "simple_density": + density_plot(data, x=x, y=y, hue=hue, title=subtitle, + simple_density=True, incl_scatter=incl_scatter, ax=axes[i], **plotting_kwargs) + else: + density_plot(data, x=x, y=y, hue=hue, title=subtitle, + incl_scatter=incl_scatter, ax=axes[i], **plotting_kwargs) + + # Hide any unused axes + for i in range(len(data_list), len(axes)): + axes[i].set_visible(False) + + # Add a title to the figure + fig.suptitle(title, fontsize=16) + + # Adjust layout plt.tight_layout() + + # We'll return the figure directly for legacy compatibility return fig From 90ebaca05eef70bd5708d90008e99cef497632f2 Mon Sep 17 00:00:00 2001 From: Andrew Mitchell Date: Fri, 2 May 2025 21:57:18 +0100 Subject: [PATCH 06/10] WIP: Refactor plotting functions to use Seaborn Objects API - Updated scatter_plot and density_plot functions to utilize the CircumplexPlot class with a more streamlined interface. - Removed backend parameter and associated logic, simplifying the plotting process. - Enhanced documentation for parameters and return types in plotting functions. - Added support for returning Seaborn Objects plots. - Refactored create_circumplex_subplots to accommodate new plotting structure. - Updated tests to reflect changes in plotting functions and removed backend-specific tests. - Improved test coverage for new features and functionalities in plotting. --- src/soundscapy/plotting/circumplex_plot.py | 135 +++++++++++++-------- 1 file changed, 85 insertions(+), 50 deletions(-) diff --git a/src/soundscapy/plotting/circumplex_plot.py b/src/soundscapy/plotting/circumplex_plot.py index f793bdbc..3b5281c3 100644 --- a/src/soundscapy/plotting/circumplex_plot.py +++ b/src/soundscapy/plotting/circumplex_plot.py @@ -7,20 +7,17 @@ """ import matplotlib.pyplot as plt -import seaborn.objects as so -import pandas as pd -import numpy as np import seaborn as sns - +import seaborn.objects as so from soundscapy.plotting.plotting_utils import ( DEFAULT_XLIM, DEFAULT_YLIM, PlotType ) - # =============== Core building blocks for circumplex plots =============== + def apply_circumplex_grid( plot: so.Plot, xlim: tuple[float, float] = DEFAULT_XLIM, @@ -62,35 +59,77 @@ def apply_circumplex_grid( fig, ax = plt.subplots(figsize=(6, 6)) # Add grid lines - ax.grid(True, which="major", color='grey', alpha=0.5) - ax.grid(True, which="minor", color='grey', linestyle='dashed', - linewidth=0.5, alpha=0.4) + ax.grid(True, which="major", color="grey", alpha=0.5) + ax.grid( + True, which="minor", color="grey", linestyle="dashed", linewidth=0.5, alpha=0.4 + ) ax.minorticks_on() # Add zero lines - ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) # Add diagonal elements if requested if diagonal_lines: # Draw diagonal lines - ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) - ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + ax.plot( + [xlim[0], xlim[1]], + [ylim[0], ylim[1]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) + ax.plot( + [xlim[0], xlim[1]], + [ylim[1], ylim[0]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) # Add diagonal labels - diag_font = {'fontstyle': 'italic', 'fontsize': 'small', - 'fontweight': 'bold', 'color': 'black', 'alpha': 0.5} - - ax.text(xlim[1]/2, ylim[1]/2, "(vibrant)", - ha='center', va='center', fontdict=diag_font) - ax.text(xlim[0]/2, ylim[1]/2, "(chaotic)", - ha='center', va='center', fontdict=diag_font) - ax.text(xlim[0]/2, ylim[0]/2, "(monotonous)", - ha='center', va='center', fontdict=diag_font) - ax.text(xlim[1]/2, ylim[0]/2, "(calm)", - ha='center', va='center', fontdict=diag_font) + diag_font = { + "fontstyle": "italic", + "fontsize": "small", + "fontweight": "bold", + "color": "black", + "alpha": 0.5, + } + + ax.text( + xlim[1] / 2, + ylim[1] / 2, + "(vibrant)", + ha="center", + va="center", + fontdict=diag_font, + ) + ax.text( + xlim[0] / 2, + ylim[1] / 2, + "(chaotic)", + ha="center", + va="center", + fontdict=diag_font, + ) + ax.text( + xlim[0] / 2, + ylim[0] / 2, + "(monotonous)", + ha="center", + va="center", + fontdict=diag_font, + ) + ax.text( + xlim[1] / 2, + ylim[0] / 2, + "(calm)", + ha="center", + va="center", + fontdict=diag_font, + ) # Apply axis label changes if requested if not show_labels: @@ -159,7 +198,7 @@ def add_annotation( "ha": "center", "va": "center", "fontsize": 9, - "arrowprops": {"arrowstyle": "-", "color": "black", "alpha": 0.7} + "arrowprops": {"arrowstyle": "-", "color": "black", "alpha": 0.7}, } annotation_defaults.update(kwargs) @@ -171,7 +210,7 @@ def add_annotation( text=text, xy=(x_val, y_val), xytext=(x_val + x_offset, y_val + y_offset), - **annotation_defaults + **annotation_defaults, ) # Now use the fully prepared axes for the plot @@ -222,7 +261,7 @@ def __init__( self.hue = hue self.xlim = xlim self.ylim = ylim - self.fill = kwargs.get('fill', True) + self.fill = kwargs.get("fill", True) self.palette_name = palette # Initialize the plot @@ -238,7 +277,7 @@ def add_scatter( pointsize=30, alpha=0.7, marker="o", - color=None # Overrides hue if provided + color=None, # Overrides hue if provided ): """ Add a scatter layer to the plot. @@ -263,12 +302,11 @@ def add_scatter( # Add the dots mark self.plot = self.plot.add( - so.Dots(pointsize=pointsize, alpha=alpha, marker=marker), - color=color_var + so.Dots(pointsize=pointsize, alpha=alpha, marker=marker), color=color_var ) # If we have a color variable and palette, apply it using scale - if color_var and hasattr(self, 'palette_name'): + if color_var and hasattr(self, "palette_name"): self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) self.has_scatter = True @@ -282,7 +320,7 @@ def add_density( bw_adjust=1.2, color=None, # Overrides hue if provided simple=False, - **kwargs # For backwards compatibility + **kwargs, # For backwards compatibility ): """ Add a density layer to the plot. @@ -317,23 +355,21 @@ def add_density( self.plot = self.plot.add( so.Area(alpha=alpha, fill=fill), so.KDE(bw_adjust=bw_adjust), - color=color_var + color=color_var, ) # Apply palette if needed - if color_var and hasattr(self, 'palette_name'): + if color_var and hasattr(self, "palette_name"): self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) # For simple density, add an outline if simple: self.plot = self.plot.add( - so.Line(alpha=1.0), - so.KDE(bw_adjust=bw_adjust), - color=color_var + so.Line(alpha=1.0), so.KDE(bw_adjust=bw_adjust), color=color_var ) # Apply palette to the outline too if needed - if color_var and hasattr(self, 'palette_name'): + if color_var and hasattr(self, "palette_name"): self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) self.has_density = True @@ -362,7 +398,7 @@ def add_grid(self, diagonal_lines=False, show_labels=True): diagonal_lines=diagonal_lines, show_labels=show_labels, x_label=self.x if show_labels else None, - y_label=self.y if show_labels else None + y_label=self.y if show_labels else None, ) self.has_grid = True @@ -397,7 +433,7 @@ def add_annotation(self, idx, text=None, x_offset=0.1, y_offset=0.1, **kwargs): text=text, x_offset=x_offset, y_offset=y_offset, - **kwargs + **kwargs, ) return self @@ -487,8 +523,9 @@ def build(self, as_objects=True): self.plot = self.plot.layout(size=(6, 6)) # Apply legend customization if requested - if hasattr(self, '_legend_title') and self.hue is not None: + if hasattr(self, "_legend_title") and self.hue is not None: # Create a label mapping for the hue variable + # This sets the legend title to the specified value or the hue variable name self.plot = self.plot.label(**{self.hue: self._legend_title}) if as_objects: @@ -505,7 +542,7 @@ def build(self, as_objects=True): ax = plt.gca() # Apply legend location if needed (after plotting) - if hasattr(self, '_legend_loc') and ax.get_legend() is not None: + if hasattr(self, "_legend_loc") and ax.get_legend() is not None: ax.legend(loc=self._legend_loc) # Return the figure and axes to be compatible with the legacy API @@ -642,12 +679,10 @@ def jointplot(self, apply_styling=True): ax.set_ylim(self.ylim) # Add zero lines - ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - + ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) # Add grid - ax.grid(True, which="major", color='grey', alpha=0.5) - + ax.grid(True, which="major", color="grey", alpha=0.5) # Store for get_figure and get_axes self._joint_grid = g @@ -662,7 +697,7 @@ def get_figure(self): plt.Figure or tuple Figure object or (figure, axes) tuple depending on plotting method """ - if hasattr(self, '_joint_grid'): + if hasattr(self, "_joint_grid"): return self._joint_grid.fig else: fig, ax = self.build(as_objects=False) @@ -677,7 +712,7 @@ def get_axes(self): plt.Axes Axes object """ - if hasattr(self, '_joint_grid'): + if hasattr(self, "_joint_grid"): return self._joint_grid.ax_joint else: fig, ax = self.build(as_objects=False) From 5f0804d2c14707b7b246644a99d9a58f86966138 Mon Sep 17 00:00:00 2001 From: Andrew Mitchell Date: Sat, 3 May 2025 14:16:38 +0100 Subject: [PATCH 07/10] lint and format --- src/soundscapy/plotting/__init__.py | 14 +- src/soundscapy/plotting/backends.py | 34 ++-- src/soundscapy/plotting/circumplex_plot.py | 97 ++++++---- src/soundscapy/plotting/likert.py | 2 +- src/soundscapy/plotting/plot_functions.py | 207 ++++++++++++++------- src/soundscapy/plotting/stylers.py | 8 +- 6 files changed, 225 insertions(+), 137 deletions(-) diff --git a/src/soundscapy/plotting/__init__.py b/src/soundscapy/plotting/__init__.py index 37b17eea..6039a687 100644 --- a/src/soundscapy/plotting/__init__.py +++ b/src/soundscapy/plotting/__init__.py @@ -33,7 +33,11 @@ """ from soundscapy.plotting import likert -from soundscapy.plotting.circumplex_plot import CircumplexPlot, add_annotation, apply_circumplex_grid +from soundscapy.plotting.circumplex_plot import ( + CircumplexPlot, + add_annotation, + apply_circumplex_grid, +) from soundscapy.plotting.plot_functions import ( create_circumplex_subplots, density_plot, @@ -44,14 +48,12 @@ __all__ = [ "CircumplexPlot", - "apply_circumplex_grid", + "PlotType", "add_annotation", - "scatter_plot", + "apply_circumplex_grid", + "create_circumplex_subplots", "density_plot", "joint_plot", - "create_circumplex_subplots", - "CircumplexPlotParams", - "PlotType", "likert", "scatter_plot", ] diff --git a/src/soundscapy/plotting/backends.py b/src/soundscapy/plotting/backends.py index 94869395..508986b4 100644 --- a/src/soundscapy/plotting/backends.py +++ b/src/soundscapy/plotting/backends.py @@ -87,11 +87,9 @@ def apply_styling(self, plot_obj, params): class SeabornBackend(PlotBackend): - """ - Backend for creating plots using Seaborn and Matplotlib. - """ + """Backend for creating plots using Seaborn and Matplotlib.""" - def __init__(self, style_options: StyleOptions = StyleOptions()): + def __init__(self, style_options: StyleOptions = StyleOptions()) -> None: self.style_options = style_options def create_scatter(self, data, params, ax=None): @@ -150,6 +148,7 @@ def create_density(self, data, params, ax=None): warnings.warn( "Density plots are not recommended for small datasets (<30 samples).", UserWarning, + stacklevel=2, ) if ax is None: @@ -278,7 +277,7 @@ def apply_styling(self, plot_obj, params): styler = SeabornStyler(params, self.style_options) return styler.apply_styling(fig, ax) - def show(self, plot_obj): + def show(self, plot_obj) -> None: """ Display the Matplotlib figure. @@ -292,13 +291,13 @@ def show(self, plot_obj): class PlotlyBackend(PlotBackend): - """ - Backend for creating plots using Plotly. - """ + """Backend for creating plots using Plotly.""" - def __init__(self): + def __init__(self) -> None: warnings.warn( - "PlotlyBackend is very experimental and not fully implemented.", UserWarning + "PlotlyBackend is very experimental and not fully implemented.", + UserWarning, + stacklevel=2, ) def create_scatter(self, data, params): @@ -328,8 +327,8 @@ def create_scatter(self, data, params): fig.update_layout( width=600, height=600, - xaxis=dict(scaleanchor="y", scaleratio=1), - yaxis=dict(scaleanchor="x", scaleratio=1), + xaxis={"scaleanchor": "y", "scaleratio": 1}, + yaxis={"scaleanchor": "x", "scaleratio": 1}, ) return fig @@ -351,6 +350,7 @@ def create_density(self, data, params): warnings.warn( "Density plots are not recommended for small datasets (<30 samples). Consider using a scatter plot instead.", UserWarning, + stacklevel=2, ) fig = px.density_heatmap( @@ -365,8 +365,8 @@ def create_density(self, data, params): fig.update_layout( width=600, height=600, - xaxis=dict(scaleanchor="y", scaleratio=1), - yaxis=dict(scaleanchor="x", scaleratio=1), + xaxis={"scaleanchor": "y", "scaleratio": 1}, + yaxis={"scaleanchor": "x", "scaleratio": 1}, ) scatter_trace = px.scatter( data, x=params.x, y=params.y, color=params.hue, opacity=0.5 @@ -395,7 +395,7 @@ def apply_styling(self, plot_obj, params): x=[params.xlim[0], params.xlim[1]], y=[params.ylim[0], params.ylim[1]], mode="lines", - line=dict(color="gray", dash="dash"), + line={"color": "gray", "dash": "dash"}, showlegend=False, ) ) @@ -404,13 +404,13 @@ def apply_styling(self, plot_obj, params): x=[params.xlim[0], params.xlim[1]], y=[params.ylim[1], params.ylim[0]], mode="lines", - line=dict(color="gray", dash="dash"), + line={"color": "gray", "dash": "dash"}, showlegend=False, ) ) return fig - def show(self, fig): + def show(self, fig) -> None: """ Display the Plotly figure. diff --git a/src/soundscapy/plotting/circumplex_plot.py b/src/soundscapy/plotting/circumplex_plot.py index 3b5281c3..605d3ce5 100644 --- a/src/soundscapy/plotting/circumplex_plot.py +++ b/src/soundscapy/plotting/circumplex_plot.py @@ -9,11 +9,8 @@ import matplotlib.pyplot as plt import seaborn as sns import seaborn.objects as so -from soundscapy.plotting.plotting_utils import ( - DEFAULT_XLIM, - DEFAULT_YLIM, - PlotType -) + +from soundscapy.plotting.plotting_utils import DEFAULT_XLIM, DEFAULT_YLIM # =============== Core building blocks for circumplex plots =============== @@ -25,7 +22,7 @@ def apply_circumplex_grid( diagonal_lines: bool = False, show_labels: bool = True, x_label: str | None = None, - y_label: str | None = None + y_label: str | None = None, ) -> so.Plot: """ Apply circumplex grid styling to a Seaborn Objects plot. @@ -47,6 +44,7 @@ def apply_circumplex_grid( ------- so.Plot The styled plot + """ # Apply limits and axes appearance plot = plot.limit(x=xlim, y=ylim) @@ -149,17 +147,18 @@ def apply_circumplex_grid( return plot_with_styling + def add_annotation( - plot: so.Plot, - data: pd.DataFrame, - idx: int | str, - x: str = "ISOPleasant", - y: str = "ISOEventful", - text: str | None = None, - x_offset: float = 0.1, - y_offset: float = 0.1, - **kwargs - ) -> so.Plot: + plot: so.Plot, + data: pd.DataFrame, + idx: int | str, + x: str = "ISOPleasant", + y: str = "ISOEventful", + text: str | None = None, + x_offset: float = 0.1, + y_offset: float = 0.1, + **kwargs, +) -> so.Plot: """ Add an annotation to a Seaborn Objects plot. @@ -184,6 +183,7 @@ def add_annotation( ------- so.Plot The plot with annotation added + """ # Get coordinates from data x_val = data[x].iloc[idx] if isinstance(idx, int) else data.loc[idx, x] @@ -221,8 +221,10 @@ def add_annotation( return plot_with_annotation + # =============== Main Circumplex Plot Class =============== + class CircumplexPlot: """ A builder class for creating circumplex plots using Seaborn Objects API. @@ -242,6 +244,7 @@ class CircumplexPlot: Axis limits for the plot palette : str Color palette to use + """ def __init__( @@ -253,8 +256,8 @@ def __init__( xlim: tuple[float, float] = DEFAULT_XLIM, ylim: tuple[float, float] = DEFAULT_YLIM, palette: str = "colorblind", - **kwargs # For backwards compatibility with tests - ): + **kwargs, # For backwards compatibility with tests + ) -> None: self.data = data self.x = x self.y = y @@ -297,6 +300,7 @@ def add_scatter( ------- CircumplexPlot Self for method chaining + """ color_var = color if color is not None else self.hue @@ -344,12 +348,13 @@ def add_density( ------- CircumplexPlot Self for method chaining + """ color_var = color if color is not None else self.hue if simple: # For simple density, use just a few levels - levels = 2 + pass # Use the Area mark with KDE stat for density plots self.plot = self.plot.add( @@ -390,6 +395,7 @@ def add_grid(self, diagonal_lines=False, show_labels=True): ------- CircumplexPlot Self for method chaining + """ self.plot = apply_circumplex_grid( self.plot, @@ -423,6 +429,7 @@ def add_annotation(self, idx, text=None, x_offset=0.1, y_offset=0.1, **kwargs): ------- CircumplexPlot Self for method chaining + """ self.plot = add_annotation( self.plot, @@ -451,6 +458,7 @@ def add_title(self, title): ------- CircumplexPlot Self for method chaining + """ self.plot = self.plot.label(title=title) return self @@ -470,6 +478,7 @@ def add_legend(self, title=None, loc="best"): ------- CircumplexPlot Self for method chaining + """ # For Seaborn Objects, we can handle this more directly # by adding a proper label to the plot @@ -497,6 +506,7 @@ def facet(self, column=None, row=None, col_wrap=None): ------- CircumplexPlot Self for method chaining + """ self.plot = self.plot.facet(col=column, row=row, wrap=col_wrap) return self @@ -514,6 +524,7 @@ def build(self, as_objects=True): ------- so.Plot or (plt.Figure, plt.Axes) The completed plot object or (figure, axes) tuple + """ # Add grid if not already added if not self.has_grid: @@ -530,25 +541,24 @@ def build(self, as_objects=True): if as_objects: return self.plot - else: - # Create a new figure with the right size - fig, ax = plt.subplots(figsize=(6, 6)) + # Create a new figure with the right size + fig, ax = plt.subplots(figsize=(6, 6)) - # Use pyplot mode for rendering - # This will render the plot directly to the current figure - self.plot.plot(pyplot=True) + # Use pyplot mode for rendering + # This will render the plot directly to the current figure + self.plot.plot(pyplot=True) - # Get the current axes - ax = plt.gca() + # Get the current axes + ax = plt.gca() - # Apply legend location if needed (after plotting) - if hasattr(self, "_legend_loc") and ax.get_legend() is not None: - ax.legend(loc=self._legend_loc) + # Apply legend location if needed (after plotting) + if hasattr(self, "_legend_loc") and ax.get_legend() is not None: + ax.legend(loc=self._legend_loc) - # Return the figure and axes to be compatible with the legacy API - fig = ax.figure + # Return the figure and axes to be compatible with the legacy API + fig = ax.figure - return fig, ax + return fig, ax @property def seaborn_plot(self): @@ -559,6 +569,7 @@ def seaborn_plot(self): ------- so.Plot The Seaborn Objects plot + """ return self.plot @@ -570,12 +581,13 @@ def get_matplotlib_objects(self): ------- Tuple[plt.Figure, plt.Axes] Figure and axes for the plot + """ fig, ax = plt.subplots(figsize=(6, 6)) self.plot.plot(ax=ax) return fig, ax - def show(self): + def show(self) -> None: """ Build and display the plot. @@ -605,6 +617,7 @@ def scatter(self, apply_styling=True, ax=None): ------- CircumplexPlot Self for method chaining + """ self.add_scatter() self.add_grid() @@ -625,6 +638,7 @@ def density(self, apply_styling=True, ax=None): ------- CircumplexPlot Self for method chaining + """ self.add_density() self.add_grid() @@ -645,6 +659,7 @@ def simple_density(self, apply_styling=True, ax=None): ------- CircumplexPlot Self for method chaining + """ self.add_density(simple=True) self.add_grid() @@ -663,6 +678,7 @@ def jointplot(self, apply_styling=True): ------- CircumplexPlot Self for method chaining + """ # Fall back to traditional seaborn for jointplot g = sns.jointplot( @@ -696,12 +712,12 @@ def get_figure(self): ------- plt.Figure or tuple Figure object or (figure, axes) tuple depending on plotting method + """ if hasattr(self, "_joint_grid"): return self._joint_grid.fig - else: - fig, ax = self.build(as_objects=False) - return fig + fig, ax = self.build(as_objects=False) + return fig def get_axes(self): """ @@ -711,12 +727,12 @@ def get_axes(self): ------- plt.Axes Axes object + """ if hasattr(self, "_joint_grid"): return self._joint_grid.ax_joint - else: - fig, ax = self.build(as_objects=False) - return ax + fig, ax = self.build(as_objects=False) + return ax def iso_annotation(self, location, x_adj=0, y_adj=0, **kwargs): """ @@ -735,5 +751,6 @@ def iso_annotation(self, location, x_adj=0, y_adj=0, **kwargs): ------- CircumplexPlot Self for method chaining + """ return self.add_annotation(location, x_offset=x_adj, y_offset=y_adj, **kwargs) diff --git a/src/soundscapy/plotting/likert.py b/src/soundscapy/plotting/likert.py index f695378d..69f3ddc3 100644 --- a/src/soundscapy/plotting/likert.py +++ b/src/soundscapy/plotting/likert.py @@ -9,7 +9,7 @@ def paq_radar_plot(data, ax=None, index=None): """ - Generate a radar/spider plot of PAQ values + Generate a radar/spider plot of PAQ values. Parameters ---------- diff --git a/src/soundscapy/plotting/plot_functions.py b/src/soundscapy/plotting/plot_functions.py index 3be5409c..048a29ae 100644 --- a/src/soundscapy/plotting/plot_functions.py +++ b/src/soundscapy/plotting/plot_functions.py @@ -5,10 +5,9 @@ using the CircumplexPlot class with the Seaborn Objects API. """ -from typing import Any, List, Optional, Tuple +from typing import Any import matplotlib.pyplot as plt -import numpy as np import pandas as pd import seaborn as sns import seaborn.objects as so @@ -17,7 +16,6 @@ from soundscapy.plotting.plotting_utils import ( DEFAULT_XLIM, DEFAULT_YLIM, - PlotType, ) @@ -74,17 +72,20 @@ def scatter_plot( ------- so.Plot | plt.Axes The completed plot object or axes + """ - plot = (CircumplexPlot(data, x, y, hue, xlim, ylim, palette) - .add_scatter(pointsize=pointsize, alpha=alpha, **kwargs) - .add_grid(diagonal_lines=diagonal_lines, show_labels=show_labels) - .add_title(title)) + plot = ( + CircumplexPlot(data, x, y, hue, xlim, ylim, palette) + .add_scatter(pointsize=pointsize, alpha=alpha, **kwargs) + .add_grid(diagonal_lines=diagonal_lines, show_labels=show_labels) + .add_title(title) + ) if as_objects: return plot.build(as_objects=True) - elif ax is not None: + if ax is not None: # If an axes is provided, draw directly on it - plot_obj = plot.build(as_objects=True) + plot.build(as_objects=True) # Clear previous contents ax.clear() # Use the ax limits and title from our plot @@ -95,25 +96,42 @@ def scatter_plot( ax.set_ylabel(y) # Draw points and style - only use palette if hue is provided sns.scatterplot( - data=data, x=x, y=y, hue=hue, + data=data, + x=x, + y=y, + hue=hue, palette=palette if hue else None, - s=pointsize, alpha=alpha, ax=ax, **kwargs + s=pointsize, + alpha=alpha, + ax=ax, + **kwargs, ) # Add grid lines - ax.grid(True, which="major", color='grey', alpha=0.5) - ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.grid(True, which="major", color="grey", alpha=0.5) + ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) # Add diagonal lines if requested if diagonal_lines: - ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) - ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + ax.plot( + [xlim[0], xlim[1]], + [ylim[0], ylim[1]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) + ax.plot( + [xlim[0], xlim[1]], + [ylim[1], ylim[0]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) return ax - else: - return plot.get_axes() + return plot.get_axes() def density_plot( @@ -135,7 +153,7 @@ def density_plot( scatter_alpha: float = 0.5, diagonal_lines: bool = False, show_labels: bool = True, - ax: Optional[plt.Axes] = None, + ax: plt.Axes | None = None, as_objects: bool = False, **kwargs: Any, ) -> so.Plot | plt.Axes: @@ -187,6 +205,7 @@ def density_plot( ------- so.Plot | plt.Axes The completed plot object or axes + """ cp = CircumplexPlot(data, x, y, hue, xlim, ylim, palette) @@ -196,7 +215,7 @@ def density_plot( fill=fill, levels=levels, bw_adjust=bw_adjust, - simple=simple_density + simple=simple_density, ) # Add scatter if requested @@ -209,7 +228,7 @@ def density_plot( if as_objects: return cp.build(as_objects=True) - elif ax is not None: + if ax is not None: # If an axes is provided, draw directly on it # Clear previous contents ax.clear() @@ -224,53 +243,83 @@ def density_plot( if simple_density: # Simple density with fewer levels sns.kdeplot( - data=data, x=x, y=y, hue=hue, - fill=fill, alpha=alpha, levels=2, - bw_adjust=bw_adjust, ax=ax, **kwargs + data=data, + x=x, + y=y, + hue=hue, + fill=fill, + alpha=alpha, + levels=2, + bw_adjust=bw_adjust, + ax=ax, + **kwargs, ) # Add outline sns.kdeplot( - data=data, x=x, y=y, hue=hue, - fill=False, alpha=1.0, levels=2, - bw_adjust=bw_adjust, ax=ax + data=data, + x=x, + y=y, + hue=hue, + fill=False, + alpha=1.0, + levels=2, + bw_adjust=bw_adjust, + ax=ax, ) else: # Regular density sns.kdeplot( - data=data, x=x, y=y, hue=hue, - fill=fill, alpha=alpha, levels=levels, - bw_adjust=bw_adjust, ax=ax, **kwargs + data=data, + x=x, + y=y, + hue=hue, + fill=fill, + alpha=alpha, + levels=levels, + bw_adjust=bw_adjust, + ax=ax, + **kwargs, ) # Add scatter if requested if incl_scatter: sns.scatterplot( - data=data, x=x, y=y, hue=hue, - s=scatter_size, alpha=scatter_alpha, ax=ax + data=data, x=x, y=y, hue=hue, s=scatter_size, alpha=scatter_alpha, ax=ax ) # Add grid lines - ax.grid(True, which="major", color='grey', alpha=0.5) - ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.grid(True, which="major", color="grey", alpha=0.5) + ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) # Add diagonal lines if requested if diagonal_lines: - ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) - ax.plot([xlim[0], xlim[1]], [ylim[1], ylim[0]], - linestyle='dashed', color='grey', alpha=0.5, linewidth=1.5) + ax.plot( + [xlim[0], xlim[1]], + [ylim[0], ylim[1]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) + ax.plot( + [xlim[0], xlim[1]], + [ylim[1], ylim[0]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) return ax - else: - return cp.get_axes() + return cp.get_axes() def joint_plot( data: pd.DataFrame, x: str = "ISOPleasant", y: str = "ISOEventful", - hue: str |None = None, + hue: str | None = None, title: str = "Soundscape Joint Plot", plot_type: str = "scatter", **kwargs: Any, @@ -300,18 +349,12 @@ def joint_plot( ------- sns.JointGrid The joint plot grid + """ # Fall back to traditional seaborn for jointplot kind = "scatter" if plot_type == "scatter" else "kde" - g = sns.jointplot( - data=data, - x=x, - y=y, - hue=hue, - kind=kind, - **kwargs - ) + g = sns.jointplot(data=data, x=x, y=y, hue=hue, kind=kind, **kwargs) # Add grid elements to the central plot ax = g.ax_joint @@ -319,11 +362,11 @@ def joint_plot( ax.set_ylim((-1, 1)) # Add zero lines - ax.axhline(y=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) - ax.axvline(x=0, color='grey', linestyle='dashed', alpha=1, linewidth=1.5) + ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) # Add grid - ax.grid(True, which="major", color='grey', alpha=0.5) + ax.grid(True, which="major", color="grey", alpha=0.5) # Add title g.fig.suptitle(title, y=1.05) @@ -337,7 +380,7 @@ def joint_plot( hue=hue, ax=ax, s=kwargs.get("scatter_size", 15), - alpha=kwargs.get("scatter_alpha", 0.5) + alpha=kwargs.get("scatter_alpha", 0.5), ) return g @@ -347,8 +390,8 @@ def create_circumplex_subplots( data_list: list[pd.DataFrame], x: str = "ISOPleasant", y: str = "ISOEventful", - hue: Optional[str] = None, - subtitles: Optional[List[str]] = None, + hue: str | None = None, + subtitles: list[str] | None = None, title: str = "Circumplex Subplots", plot_type: str = "density", incl_scatter: bool = False, @@ -386,17 +429,18 @@ def create_circumplex_subplots( ------- so.Plot | plt.Figure The plot object or figure + """ # Generate subplot titles if not provided if subtitles is None: - subtitles = [f"Plot {i+1}" for i in range(len(data_list))] + subtitles = [f"Plot {i + 1}" for i in range(len(data_list))] # Remove any layout parameters that don't belong in plot functions plotting_kwargs = kwargs.copy() - if 'nrows' in plotting_kwargs: - plotting_kwargs.pop('nrows') - if 'ncols' in plotting_kwargs: - plotting_kwargs.pop('ncols') + if "nrows" in plotting_kwargs: + plotting_kwargs.pop("nrows") + if "ncols" in plotting_kwargs: + plotting_kwargs.pop("ncols") # For the refactored version, we'll create a matplotlib figure directly # instead of using the faceting in Seaborn Objects @@ -407,17 +451,42 @@ def create_circumplex_subplots( axes = axes.flatten() # Create individual plots - for i, (data, subtitle) in enumerate(zip(data_list, subtitles)): + for i, (data, subtitle) in enumerate(zip(data_list, subtitles, strict=False)): if i < len(axes): # Create a plot for this axis if plot_type == "scatter": - scatter_plot(data, x=x, y=y, hue=hue, title=subtitle, ax=axes[i], **plotting_kwargs) + scatter_plot( + data, + x=x, + y=y, + hue=hue, + title=subtitle, + ax=axes[i], + **plotting_kwargs, + ) elif plot_type == "simple_density": - density_plot(data, x=x, y=y, hue=hue, title=subtitle, - simple_density=True, incl_scatter=incl_scatter, ax=axes[i], **plotting_kwargs) + density_plot( + data, + x=x, + y=y, + hue=hue, + title=subtitle, + simple_density=True, + incl_scatter=incl_scatter, + ax=axes[i], + **plotting_kwargs, + ) else: - density_plot(data, x=x, y=y, hue=hue, title=subtitle, - incl_scatter=incl_scatter, ax=axes[i], **plotting_kwargs) + density_plot( + data, + x=x, + y=y, + hue=hue, + title=subtitle, + incl_scatter=incl_scatter, + ax=axes[i], + **plotting_kwargs, + ) # Hide any unused axes for i in range(len(data_list), len(axes)): diff --git a/src/soundscapy/plotting/stylers.py b/src/soundscapy/plotting/stylers.py index 7458242a..cc16ce2c 100644 --- a/src/soundscapy/plotting/stylers.py +++ b/src/soundscapy/plotting/stylers.py @@ -42,11 +42,11 @@ class StyleOptions: class SeabornStyler: - """ - Class for applying Seaborn styles to circumplex plots. - """ + """Class for applying Seaborn styles to circumplex plots.""" - def __init__(self, params: Any, style_options: StyleOptions = StyleOptions()): + def __init__( + self, params: Any, style_options: StyleOptions = StyleOptions() + ) -> None: self.params = params self.style_options = style_options From 8e1cfe8cc48f53c28c6a201c5d54d071e6d1c6be Mon Sep 17 00:00:00 2001 From: Andrew Mitchell Date: Sat, 3 May 2025 15:45:32 +0100 Subject: [PATCH 08/10] fix: resolving some linting errors for circumplexPlot --- src/soundscapy/plotting/circumplex_plot.py | 65 +++++++++++++++++----- 1 file changed, 50 insertions(+), 15 deletions(-) diff --git a/src/soundscapy/plotting/circumplex_plot.py b/src/soundscapy/plotting/circumplex_plot.py index 605d3ce5..722f650f 100644 --- a/src/soundscapy/plotting/circumplex_plot.py +++ b/src/soundscapy/plotting/circumplex_plot.py @@ -7,6 +7,7 @@ """ import matplotlib.pyplot as plt +import pandas as pd import seaborn as sns import seaborn.objects as so @@ -19,10 +20,11 @@ def apply_circumplex_grid( plot: so.Plot, xlim: tuple[float, float] = DEFAULT_XLIM, ylim: tuple[float, float] = DEFAULT_YLIM, - diagonal_lines: bool = False, - show_labels: bool = True, x_label: str | None = None, y_label: str | None = None, + *, + diagonal_lines: bool = False, + show_labels: bool = True, ) -> so.Plot: """ Apply circumplex grid styling to a Seaborn Objects plot. @@ -57,9 +59,14 @@ def apply_circumplex_grid( fig, ax = plt.subplots(figsize=(6, 6)) # Add grid lines - ax.grid(True, which="major", color="grey", alpha=0.5) + ax.grid(visible=True, which="major", color="grey", alpha=0.5) ax.grid( - True, which="minor", color="grey", linestyle="dashed", linewidth=0.5, alpha=0.4 + visible=True, + which="minor", + color="grey", + linestyle="dashed", + linewidth=0.5, + alpha=0.4, ) ax.minorticks_on() @@ -157,7 +164,7 @@ def add_annotation( text: str | None = None, x_offset: float = 0.1, y_offset: float = 0.1, - **kwargs, + **kwargs, # noqa: ANN003 ) -> so.Plot: """ Add an annotation to a Seaborn Objects plot. @@ -189,6 +196,13 @@ def add_annotation( x_val = data[x].iloc[idx] if isinstance(idx, int) else data.loc[idx, x] y_val = data[y].iloc[idx] if isinstance(idx, int) else data.loc[idx, y] + if x_val is None or y_val is None: + msg = f"Invalid index {idx} for data: {data}" + raise ValueError(msg) + if not isinstance(x_val, int | float) or not isinstance(y_val, int | float): + msg = f"Invalid contents for x or y: {x_val}, {y_val}" + raise TypeError(msg) + # Default text is the index value if text is None: text = str(data.index[idx]) if isinstance(idx, int) else str(idx) @@ -256,15 +270,36 @@ def __init__( xlim: tuple[float, float] = DEFAULT_XLIM, ylim: tuple[float, float] = DEFAULT_YLIM, palette: str = "colorblind", - **kwargs, # For backwards compatibility with tests ) -> None: + """ + Initialize a CircumplexPlot instance. + + Parameters + ---------- + data : pd.DataFrame + Data to plot + x : str, default="ISOPleasant" + Column name for x-axis + y : str, default="ISOEventful" + Column name for y-axis + hue : str, optional + Column name for color grouping + xlim : tuple[float, float] + Axis limits for x-axis + ylim : tuple[float, float] + Axis limits for y-axis + palette : str, default="colorblind" + Color palette to use + fill : bool, default=True + Whether to fill the density contours + + """ self.data = data self.x = x self.y = y self.hue = hue self.xlim = xlim self.ylim = ylim - self.fill = kwargs.get("fill", True) self.palette_name = palette # Initialize the plot @@ -277,11 +312,11 @@ def __init__( def add_scatter( self, - pointsize=30, - alpha=0.7, - marker="o", - color=None, # Overrides hue if provided - ): + pointsize: float = 30, + alpha: float = 0.7, + marker: str = "o", + color: str | None = None, # Overrides hue if provided + ) -> so.Plot: """ Add a scatter layer to the plot. @@ -380,7 +415,7 @@ def add_density( self.has_density = True return self - def add_grid(self, diagonal_lines=False, show_labels=True): + def add_grid(self, *, diagonal_lines=False, show_labels=True): """ Add circumplex grid to the plot. @@ -401,10 +436,10 @@ def add_grid(self, diagonal_lines=False, show_labels=True): self.plot, xlim=self.xlim, ylim=self.ylim, - diagonal_lines=diagonal_lines, - show_labels=show_labels, x_label=self.x if show_labels else None, y_label=self.y if show_labels else None, + diagonal_lines=diagonal_lines, + show_labels=show_labels, ) self.has_grid = True From df082a08fe76ffcf0f02a2f25ceee71880416938 Mon Sep 17 00:00:00 2001 From: Andrew Mitchell Date: Sun, 4 May 2025 01:17:15 +0100 Subject: [PATCH 09/10] trying to subclass so.Plot --- .gitignore | 2 + examples/soundscape_plot_demo.py | 197 +++++++++++ src/soundscapy/plotting/soundscape_plot.py | 386 +++++++++++++++++++++ 3 files changed, 585 insertions(+) create mode 100644 examples/soundscape_plot_demo.py create mode 100644 src/soundscapy/plotting/soundscape_plot.py diff --git a/.gitignore b/.gitignore index 9b85e1b5..8912960a 100644 --- a/.gitignore +++ b/.gitignore @@ -180,3 +180,5 @@ CLAUDE.local.md # package version *_version.py + +**/.claude/settings.local.json diff --git a/examples/soundscape_plot_demo.py b/examples/soundscape_plot_demo.py new file mode 100644 index 00000000..39a284dd --- /dev/null +++ b/examples/soundscape_plot_demo.py @@ -0,0 +1,197 @@ +""" +Demo script showing the usage of the new SoundscapePlot class. + +This script demonstrates the basic functionality of the SoundscapePlot class +and how it can be used to create soundscape visualizations with the +Seaborn Objects API. +""" + +# %% Import libraries +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +import seaborn.objects as so + +import soundscapy as sspy +from soundscapy.plotting.soundscape_plot import SoundscapePlot + +# Set the style for the plots +sns.set_theme(style="whitegrid") + +# %% Load the built-in ISD dataset +# Load the ISD dataset that comes with soundscapy +print("Loading ISD dataset...") +data = sspy.isd.load() + +# Add ISO coordinates if they don't already exist +data = sspy.surveys.add_iso_coords(data, overwrite=True) + +# Display dataset info +print(f"Dataset shape: {data.shape}") +print("\nColumns:") +print(data.columns.tolist()) + +# %% Create a sample subset of the data +# Select a subset of locations for demonstration +sample_data = sspy.isd.select_location_ids(data, ["CamdenTown", "RegentsParkJapan"]) +print(f"Sample data shape: {sample_data.shape}") + +# Show basic statistics for the ISO coordinates +print("\nBasic statistics for ISO coordinates:") +print(sample_data[["ISOPleasant", "ISOEventful"]].describe()) + +# %% Example 1: Basic scatter plot with grid +print("\nExample 1: Basic scatter plot with grid") + +# Create a basic plot with default settings +plot1 = ( + SoundscapePlot(sample_data) + .add(so.Dots(pointsize=25, alpha=0.7), color="location_id") + .scale(color=so.Nominal("colorblind")) + .add_grid(diagonal_lines=True) + .label(title="Basic Soundscape Plot") +) + +# Display the plot +plot1.show() + +# %% Example 2: Customizing the plot +print("\nExample 2: Customized plot with different axes and styling") + +# Create a customized plot with different configurations +plot2 = ( + SoundscapePlot(sample_data, x="ISOPleasant", y="ISOEventful") + .add(so.Dots(pointsize=30), color="L_0_Pleasant") + .scale(color=so.Continuous("viridis")) + .add_grid(xlim=(-1.2, 1.2), ylim=(-1.2, 1.2), diagonal_lines=True) + .label(title="Soundscape by Pleasantness Rating") +) + +# Display the plot +plot2.show() + +# %% Example 3: Adding multiple mark layers +print("\nExample 3: Multiple mark layers") + +# Create location averages +location_means = ( + sample_data.groupby("location_id")[["ISOPleasant", "ISOEventful"]] + .mean() + .reset_index() +) + +# Create multi-layer plot +plot3 = ( + SoundscapePlot(sample_data) + # Add individual data points + .add(so.Dots(alpha=0.6, pointsize=15), color="location_id") + .scale(color=so.Nominal("colorblind")) + # Add the means with larger markers + .add( + so.Dots(pointsize=100, alpha=0.9, marker="X"), + data=location_means, + color="location_id", + ) + # Add connecting lines between points and means + .add( + so.Path(color="grey", alpha=0.3), + so.Agg({"ISOPleasant": "mean", "ISOEventful": "mean"}, groupby="location_id"), + ) + .add_grid(diagonal_lines=True) + .label(title="Soundscape Points with Location Means") +) + +# Display the plot +plot3.show() + +# %% Example 4: Faceted plots +print("\nExample 4: Faceted plots by location") + +plot4 = ( + SoundscapePlot(sample_data) + .add(so.Dots(pointsize=30, alpha=0.8), color="L_0_Overall") + .scale(color=so.Continuous("viridis")) + .facet(col="location_id", wrap=3) + .add_grid() + .label(title="Soundscape by Location and Overall Rating") +) + +# Display the plot +plot4.show() + +# %% Example 5: Adding density contours +print("\nExample 5: Adding density contours") + +plot5 = ( + SoundscapePlot(sample_data) + # Add density contours + .add(so.Area(alpha=0.3, fill=True), so.KDE(), color="location_id") + # Add scatter points on top of density + .add(so.Dots(pointsize=20, alpha=0.7), color="location_id") + .scale(color=so.Nominal("colorblind")) + .add_grid(diagonal_lines=True) + .label(title="Soundscape Density and Points") +) + +# Display the plot +plot5.show() + +# %% Example 6: Demonstrating compatibility with standard so.Plot features +print("\nExample 6: Demonstrating compatibility with standard so.Plot features") + +# Create a plot that uses both SoundscapePlot features and standard so.Plot methods +plot6 = ( + SoundscapePlot(sample_data) + # Use paired data display + .pair(x=["ISOPleasant", "L_0_Pleasant"], y=["ISOEventful", "L_0_Eventful"]) + # Add point layer + .add(so.Dots(pointsize=25, alpha=0.7), color="location_id") + # Add linear regression line + .add(so.Line(), so.PolyFit(order=1)) + # Apply standard so.Plot styling + .theme( + { + "axes.grid": True, + "grid.linestyle": ":", + "grid.color": "gray", + "grid.alpha": 0.5, + } + ) + .scale(color=so.Nominal("deep")) + .label(title="Multi-variable Comparison") +) + +# Display the plot +plot6.show() + +# %% Example 7: Integration with Matplotlib +print("\nExample 7: Integration with Matplotlib") + +# Create a plot and extract Matplotlib elements +fig, ax = plt.subplots(figsize=(8, 8)) + +# Create soundscape plot +base_plot = ( + SoundscapePlot(sample_data) + .add(so.Dots(pointsize=30, alpha=0.7), color="location_id") + .add_grid(diagonal_lines=True) +) + +# Render to the provided axes +base_plot.plot(ax=ax) + +# Add custom Matplotlib elements +plt.title("Soundscape Plot with Custom Matplotlib Elements", fontsize=14) +plt.figtext(0.5, 0.01, "Custom footer text", ha="center", fontsize=10) + +# Add a text annotation +plt.annotate( + "Important region", + xy=(0.5, 0.5), + xytext=(0.7, 0.8), + arrowprops=dict(facecolor="black", shrink=0.05, width=1.5), +) + +# Display the plot +plt.tight_layout() +plt.show() diff --git a/src/soundscapy/plotting/soundscape_plot.py b/src/soundscapy/plotting/soundscape_plot.py new file mode 100644 index 00000000..e834663b --- /dev/null +++ b/src/soundscapy/plotting/soundscape_plot.py @@ -0,0 +1,386 @@ +""" +Base module for creating soundscape plots using Seaborn Objects API. + +This module provides the SoundscapePlot class, which directly subclasses +seaborn.objects.Plot to create specialized visualizations for soundscape data. +""" + +import copy +import functools +import inspect +from typing import Any, Callable, TypeVar, cast + +import matplotlib.pyplot as plt +import pandas as pd +import seaborn.objects as so +from matplotlib.axes import Axes +from matplotlib.figure import Figure, SubFigure +from seaborn._core.typing import ( + OrderSpec, + VariableSpec, + VariableSpecList, +) + +from soundscapy.plotting.plotting_utils import DEFAULT_XLIM, DEFAULT_YLIM + +# The TypeVar helps with type annotations for method chaining +P = TypeVar("P", bound="SoundscapePlot") + + +def _fix_return_type(method: Callable) -> Callable: + """ + Decorator to ensure methods return the correct subclass type. + + This decorator wraps methods that would normally return so.Plot + and ensures they return the subclass type instead. It transfers + custom state attributes from the original object to the result, + which is especially important for methods that return a new plot + instance rather than modifying the current one. + + Parameters + ---------- + method : Callable + The method to wrap + + Returns + ------- + Callable + A wrapped method that preserves SoundscapePlot type + """ + + @functools.wraps(method) + def wrapper(self, *args, **kwargs): + # Call the original method + result = method(self, *args, **kwargs) + + # If the result is a Plot instance but not our subclass (or a subclass of our subclass), + # we need to transfer our custom state and cast to the correct type + if isinstance(result, so.Plot) and not isinstance(result, type(self)): + # Identify custom attributes (starting with underscore but not dunder methods) + # that exist in self but not in the result + for attr_name in dir(self): + if ( + attr_name.startswith("_") + and not attr_name.startswith("__") + and hasattr(self, attr_name) + and not hasattr(result, attr_name) + ): + try: + # Try to deep copy the attribute for proper immutability + setattr( + result, attr_name, copy.deepcopy(getattr(self, attr_name)) + ) + except (TypeError, AttributeError): + # Fall back to regular assignment if deep copy fails + setattr(result, attr_name, getattr(self, attr_name)) + + # Cast the result to our actual class type (preserving exact subclass) + result = cast(type(self), result) + + return result + + return wrapper + + +class SoundscapePlot(so.Plot): + """ + Base class for soundscape visualization that extends seaborn's Plot. + + This class subclasses seaborn.objects.Plot to create a specialized + plotting API for soundscape data visualization while maintaining + full compatibility with the Seaborn Objects interface. It includes + methods like add_grid() that provide soundscape-specific functionality, + while ensuring all inherited seaborn methods return the correct type. + + Examples + -------- + >>> import soundscapy as sspy + >>> from soundscapy.plotting import SoundscapePlot + >>> data = sspy.isd.load() + >>> data = sspy.surveys.add_iso_coords(data, overwrite=True) + >>> plot = (SoundscapePlot(data) + ... .add(so.Dots(), color="location_id") + ... .add_grid(diagonal_lines=True) + ... .label(title="Soundscape Plot")) + >>> plot.show() + + Parameters + ---------- + data : pd.DataFrame + Data to plot + x, y : str + Column names for coordinates (default to ISO coordinate names) + xlim, ylim : tuple[float, float] + Axis limits for the plot (default to standard -1,1 range) + **kwargs + Additional parameters to pass to so.Plot.__init__ + + """ + + def __init__( + self, + data: pd.DataFrame, + x: str = "ISOPleasant", + y: str = "ISOEventful", + xlim: tuple[float, float] = DEFAULT_XLIM, + ylim: tuple[float, float] = DEFAULT_YLIM, + **kwargs: Any, + ) -> None: + """ + Initialize a SoundscapePlot object. + + Parameters + ---------- + data : pd.DataFrame + Data to plot + x : str, default="ISOPleasant" + Column name for x-axis, defaults to ISO pleasantness dimension + y : str, default="ISOEventful" + Column name for y-axis, defaults to ISO eventfulness dimension + **kwargs : Any + Additional parameters to pass to the seaborn Plot constructor + + """ + # Initialize with soundscape defaults + super().__init__(data, x=x, y=y, **kwargs) + + # Store soundscape-specific state as private attributes + self._xlim = xlim + self._ylim = ylim + self._has_grid = False + + def _clone(self: P) -> P: + """ + Create a copy of this plot object with deep-copied state. + + This method is crucial for the immutable builder pattern. + + Returns + ------- + SoundscapePlot + A new plot with copied state + + """ + new = super()._clone() + # Copy our custom attributes + new._xlim = copy.deepcopy(self._xlim) + new._ylim = copy.deepcopy(self._ylim) + new._has_grid = self._has_grid + return cast(P, new) + + @_fix_return_type + def add_grid( + self: P, + xlim: tuple[float, float] = DEFAULT_XLIM, + ylim: tuple[float, float] = DEFAULT_YLIM, + x_label: str | None = None, + y_label: str | None = None, + *, + diagonal_lines: bool = False, + show_labels: bool = True, + ) -> P: + """ + Add a standard soundscape analysis grid with zero lines and optionally diagonal lines. + + Parameters + ---------- + xlim, ylim : tuple + Axis limits + x_label, y_label : str, optional + Custom labels for axes + diagonal_lines : bool + Whether to draw diagonal lines and quadrant labels + show_labels : bool + Whether to keep axis labels + + Returns + ------- + SoundscapePlot + The plot with grid styling applied + + """ + # Clone first to maintain immutability + new_plot = self._clone() + + # Apply limits and square aspect ratio + new_plot = cast(P, new_plot.limit(x=xlim, y=ylim).layout(size=(6, 6))) + + # Create a temporary matplotlib figure for styling + fig, ax = plt.subplots(figsize=(6, 6)) + + # Add zero lines + ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + + # Add grid + ax.grid(visible=True, which="major", color="grey", alpha=0.5) + ax.grid( + visible=True, + which="minor", + color="grey", + linestyle="dashed", + linewidth=0.5, + alpha=0.4, + ) + ax.minorticks_on() + + # Handle diagonal lines if requested + if diagonal_lines: + # Add diagonal lines + ax.plot( + [xlim[0], xlim[1]], + [ylim[0], ylim[1]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) + ax.plot( + [xlim[0], xlim[1]], + [ylim[1], ylim[0]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) + + # Add quadrant labels if requested + if show_labels: + diag_font = { + "fontstyle": "italic", + "fontsize": "small", + "fontweight": "bold", + "color": "black", + "alpha": 0.5, + } + + ax.text( + xlim[1] / 2, + ylim[1] / 2, + "(vibrant)", + ha="center", + va="center", + fontdict=diag_font, + ) + ax.text( + xlim[0] / 2, + ylim[1] / 2, + "(chaotic)", + ha="center", + va="center", + fontdict=diag_font, + ) + ax.text( + xlim[0] / 2, + ylim[0] / 2, + "(monotonous)", + ha="center", + va="center", + fontdict=diag_font, + ) + ax.text( + xlim[1] / 2, + ylim[0] / 2, + "(calm)", + ha="center", + va="center", + fontdict=diag_font, + ) + + # Apply axis label changes + if not show_labels: + ax.set_xlabel("") + ax.set_ylabel("") + elif x_label is not None or y_label is not None: + if x_label is not None: + ax.set_xlabel(x_label) + if y_label is not None: + ax.set_ylabel(y_label) + + # Transfer styling to the plot - cast to ensure type correctness + plot_with_styling = cast(P, new_plot.on(ax)) + + # Clean up the temporary figure + plt.close(fig) + + # Update state to track that grid has been added + plot_with_styling._has_grid = True + plot_with_styling._xlim = xlim + plot_with_styling._ylim = ylim + + return plot_with_styling + + def show(self: P, **kwargs) -> None: + """ + Display the plot. + + This is a convenience method that ensures grid is added + and handles proper rendering in both notebook and script contexts. + + Parameters + ---------- + **kwargs + Additional keyword arguments passed to matplotlib.pyplot.show() + + """ + # Ensure grid is added if not already + plot_to_show = self + if not plot_to_show._has_grid: + plot_to_show = plot_to_show.add_grid() + + # Use the correctly configured pyplot mode and pass through all kwargs + plot_to_show.plot(pyplot=True).show(**kwargs) + + # We don't need to explicitly override parent methods that don't add custom functionality + # The __init_subclass__ method will automatically apply the _fix_return_type decorator + # to all inherited methods from so.Plot + + # Apply the _fix_return_type decorator to all parent methods automatically + @classmethod + def __init_subclass__(cls, **kwargs): + """ + Ensure all inherited so.Plot methods maintain proper return types. + + This special method is called whenever a subclass of SoundscapePlot is created. + It automatically applies the _fix_return_type decorator to all methods + inherited from so.Plot that haven't been explicitly overridden, ensuring + consistent return type behavior throughout the inheritance hierarchy. + """ + super().__init_subclass__(**kwargs) + + # Find all eligible methods in the so.Plot class + for name, method in inspect.getmembers(so.Plot, inspect.isfunction): + # Apply the decorator if: + # 1. Method isn't already defined in this class + # 2. Method isn't private (doesn't start with _) + # 3. Method has 'self' as a parameter (instance method) + if ( + name not in cls.__dict__ + and not name.startswith("_") + and "self" in inspect.signature(method).parameters + ): + # Add the wrapped method to our class + setattr(cls, name, _fix_return_type(method)) + + # Apply the same fix to the current class after definition + @classmethod + def _apply_return_type_fix(cls): + """ + Apply the return type fix to the current class. + + This is called at the end of the class definition to ensure that + even the base SoundscapePlot class gets the decorator applied to + its inherited methods. + """ + # Similar logic to __init_subclass__, but for the current class + for name, method in inspect.getmembers(so.Plot, inspect.isfunction): + if ( + name not in cls.__dict__ + and not name.startswith("_") + and "self" in inspect.signature(method).parameters + ): + setattr(cls, name, _fix_return_type(method)) + + +# Apply the return type fix to SoundscapePlot itself +SoundscapePlot._apply_return_type_fix() From aea3003313277b028be89787a08e150313bcd314 Mon Sep 17 00:00:00 2001 From: Andrew Mitchell Date: Sun, 4 May 2025 18:20:43 +0100 Subject: [PATCH 10/10] First pass (non-functional) add adding custom Mark and Stat objects for so.Plot() - Implemented custom Mark classes for circumplex grids, quadrant labels, and point annotations in `marks.py`. - Developed a function-based API for soundscape plots in `soundscape_functions.py`, including scatter, density, and joint plots. - Created a matplotlib style sheet for consistent plot aesthetics in `soundscapy.mplstyle`. - Added custom Stat components for calculating ISO coordinates from PAQ data in `stats.py`. - Updated test suite for plotting functions to reflect new changes and ensure functionality. --- .../HowToAnalyseAndRepresentSoundscapes.ipynb | 53 +- docs/tutorials/QuickStart.ipynb | 614 +++++++++++------- examples/soundscape_plot_demo.py | 197 ------ src/soundscapy/plotting/__init__.py | 63 +- src/soundscapy/plotting/circumplex_plot.py | 362 +++++------ src/soundscapy/plotting/marks.py | 375 +++++++++++ .../plotting/soundscape_functions.py | 564 ++++++++++++++++ src/soundscapy/plotting/soundscape_plot.py | 386 ----------- src/soundscapy/plotting/soundscapy.mplstyle | 45 ++ src/soundscapy/plotting/stats.py | 149 +++++ test/plotting/test_plotting.py | 14 +- 11 files changed, 1750 insertions(+), 1072 deletions(-) delete mode 100644 examples/soundscape_plot_demo.py create mode 100644 src/soundscapy/plotting/marks.py create mode 100644 src/soundscapy/plotting/soundscape_functions.py create mode 100644 src/soundscapy/plotting/soundscapy.mplstyle create mode 100644 src/soundscapy/plotting/stats.py diff --git a/docs/tutorials/HowToAnalyseAndRepresentSoundscapes.ipynb b/docs/tutorials/HowToAnalyseAndRepresentSoundscapes.ipynb index 55d67aea..e01f0776 100644 --- a/docs/tutorials/HowToAnalyseAndRepresentSoundscapes.ipynb +++ b/docs/tutorials/HowToAnalyseAndRepresentSoundscapes.ipynb @@ -147,7 +147,7 @@ } ], "conversionMethod": "pd.DataFrame", - "ref": "1285050e-6e9b-40e6-b753-df76a1b42e93", + "ref": "86fc413c-dfbb-4845-baf5-525f040cbcb4", "rows": [ [ "EX1", @@ -422,7 +422,7 @@ } ], "conversionMethod": "pd.DataFrame", - "ref": "e202dd03-6141-4129-8956-6a28b8ac6435", + "ref": "6ebf4060-1bc9-4b5e-b1fb-8ca18d8f00c6", "rows": [ [ "EX1", @@ -586,21 +586,36 @@ "lines_to_next_cell": 2 }, "outputs": [ + { + "ename": "TypeError", + "evalue": "SoundscapeCircumplex._plot() missing 1 required positional argument: 'y'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m colors \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 2\u001b[0m palette \u001b[38;5;241m=\u001b[39m sns\u001b[38;5;241m.\u001b[39mcolor_palette(colors)\n\u001b[0;32m----> 3\u001b[0m \u001b[43msspy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplotting\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter_plot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43msample_transform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mRecordID\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mdiagonal_lines\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mlegend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbrief\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/src/soundscapy/plotting/soundscape_functions.py:148\u001b[0m, in \u001b[0;36mscatter_plot\u001b[0;34m(data, x, y, hue, title, xlim, ylim, palette, diagonal_lines, show_labels, point_size, alpha, marker, ax, as_objects, **kwargs)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;66;03m# Create a new figure and axes, draw on it, and return the axes\u001b[39;00m\n\u001b[1;32m 147\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m--> 148\u001b[0m \u001b[43mplot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:932\u001b[0m, in \u001b[0;36mPlot.plot\u001b[0;34m(self, pyplot)\u001b[0m\n\u001b[1;32m 928\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 929\u001b[0m \u001b[38;5;124;03mCompile the plot spec and return the Plotter object.\u001b[39;00m\n\u001b[1;32m 930\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 931\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m theme_context(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_theme_with_defaults()):\n\u001b[0;32m--> 932\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpyplot\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:962\u001b[0m, in \u001b[0;36mPlot._plot\u001b[0;34m(self, pyplot)\u001b[0m\n\u001b[1;32m 960\u001b[0m \u001b[38;5;66;03m# Process the data for each layer and add matplotlib artists\u001b[39;00m\n\u001b[1;32m 961\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m layer \u001b[38;5;129;01min\u001b[39;00m layers:\n\u001b[0;32m--> 962\u001b[0m \u001b[43mplotter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlayer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 964\u001b[0m \u001b[38;5;66;03m# Add various figure decorations\u001b[39;00m\n\u001b[1;32m 965\u001b[0m plotter\u001b[38;5;241m.\u001b[39m_make_legend(\u001b[38;5;28mself\u001b[39m)\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:1488\u001b[0m, in \u001b[0;36mPlotter._plot_layer\u001b[0;34m(self, p, layer)\u001b[0m\n\u001b[1;32m 1485\u001b[0m grouping_vars \u001b[38;5;241m=\u001b[39m mark\u001b[38;5;241m.\u001b[39m_grouping_props \u001b[38;5;241m+\u001b[39m default_grouping_vars\n\u001b[1;32m 1486\u001b[0m split_generator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_split_generator(grouping_vars, df, subplots)\n\u001b[0;32m-> 1488\u001b[0m \u001b[43mmark\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscales\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1490\u001b[0m \u001b[38;5;66;03m# TODO is this the right place for this?\u001b[39;00m\n\u001b[1;32m 1491\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m view \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_subplots:\n", + "\u001b[0;31mTypeError\u001b[0m: SoundscapeCircumplex._plot() missing 1 required positional argument: 'y'" + ] + }, { "data": { + "image/png": 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", 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" ] }, - "execution_count": 5, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" }, { "data": { - "image/png": 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BAAAAAACUgO7RKAkWNxHWuHFjU6tWraLEBtiKZBgAIKNEmIuEGAAAAAAAQHGRCAOyUzfL5QCgbDVo0MD07t27+ueaGkNUHEpyuUMjhnETYkOHDs2pu70t5WELW8qDOOyMwxY2lIcNMRCHvWwpD+KwKw7NldGuXTtn2Gf3Gkc3h3STyP19TSoPm+KwIQab4rCFLeVBHHbGYQtbykNJB3f7+rkmx2FDDDbJZ3nkkgizpV6IA6VCMgxAjVO3bl3TqlWrGh9DVBy6IOjXr5+ZMGFC2nW4CbFhw4ZlnRCzpTxsYUt5EIedcdjChvKwIQbisJct5UEcdsXRsmVLc/bZZ5tRo0aZn3/+uToBNmLEiBpZHjbFYUMMNsVhC1vKgzjsjMMWtpSHEg6KpdRsiMOGGGySr/LIJBGm65tGjRol9QizpV6IA6VCbQMAQnXq1Mn5f1RCbM2aNTknxAAAAAAAQOFs3KhRYPRQq/6ON2b9eiVYqt5XxxjlTerVM0Z/1jdqpIdkq95H6SkRphF81qvSskiEASAZBgCIkRDTBdT48ePTfo6EGAAAAAAAdlFH6KVLq15KgKWZCSGFcin6875Fi6pXkUcTxv+QCAPyg2QYgBpHw/pNmzbN+blHjx7ORUJNjCGTODp27Oj8v1AJMVvKwxa2lAdx2BmHLWwoDxtiIA572VIexGFnHLawpTxsiMOGGGyKwxa2lAdx2BmHLWwpj40bNzoJC3fagVLNQaTESVXyxJglS2qZpUtrOT2/RLkUFdXUqcZMmWLMrFlVCbINGzTcZFXiq3NnY3r1MqZnT5VnVfJMPcnmzatKiLVuXdVjrBzKwha5lEc+E2G21AtxoFRIhgGocXSyW7lyZfXPNTWGTONQQkwXVN9//33eE2K2lIctbCkP4rAzDlvYUB42xEAc9rKlPIjDzjhsYUt52BCHDTHYFIctbCkP4rAzDlvYUh5KWrjb18+l7Ak2d656glUlRBTKmDHGvP22MePGVQ2JmM6PPxozalTVz8oNDB5szO67GzNsmDGLF1e9WrY0pkOH8J5itpSFLbItj3z3CLOlXogDpUIyDAAQWwdd7RoTKyH21VdfOQmxmv6UIgAAAAAAxbBiRVVvrw0bajm9ud5805iRI41ZuDC79SlP8PXXVa82bYzZdVdj9tyz6nfKP2qa8aZN8/oV8D8MjQjkH33/AAAZJ8QGDhwY+TldtKmHmIasAAAAAAAAhaGklZJg06dXDXk4dmzC/OEPG82zz2afCPPTerS+P/7RmG++qRpyUdvTdumgmJl3333XSVyle2nIPiW4mjdvHvlq0qSJ83ktp/swfgsXLnRG+6lbt65p2rSp89JIPkGfzYQSdcOHD0+J/bnnnstoPZMnTzZPPfWUueiii8wuu+zifCf/Oq+88sqcYgWEnmEAgKwSYroY+e6772IlxOghBgAAAABAYYZFnDHDmFWrqub2+te/lGypGvKtEFMgLVpkzM03G7PzzsYcc0zVe+vWGdOtW/iwiSitNm3amLvuusscfvjh1e9t2LDBnHTSSWb06NFOkiwbN9xwQ0pC7YgjjjC/+MUv0i735ptvmnfeecd8+eWXzqhCi7RTAUVAzzAAQFbat29vBg0aFNkNnx5iAAAAAAAUJhE2bVpVImzOHGMuucSY998vzrbfe69qe9qutq84FI8wXJ99lKDyJ6l0r0YJrWx888035rrrrkt6r23btubOO++MXPbiiy82N998s3n77bdJhKGo6BkGAMgpIebOIZZuslF6iAEAAAAAkD8amlA9wtasqRqu8KabjFm2rLgxLFhgzLXXGvP73xvTvXtVPPq/O8zfRsZPzGgEHg1l6BdWhirfIOnuudxxxx1m5MiRSQmoa6+91hx66KFm8ODBsWN1e5WtU5dAj9tvv736PhFgI5JhAICc6ELHHTIxKiGm7u9KiDVu3LioMQIAAAAAUEncHln6fykSYS5tV9u/9FJjlMtRPJ06GVOvXj3nPgDiOfPMMwPnxfr555+dkXbc+y116tRxEl7Z9L7T/Zu//OUv5uSTT65+Twkt/fvjjz921h3HTTfdZL744ouk9w4++GDzy1/+0uSiVatWZsstt3T+//TTT+e0LiAIwyQCAHLWrl27WEMm6iJLPcRW6YodAACgyHQNoknrNVH71KlTnZd+1nt6cY0CACgHK1YYs2RJ1RxhpUyE+RNiikdxKT7dH1BCDLnxJr9ySYS5jjzySLP//vsnvad5w5Qki0MPQl999dVJ7yl5dc8992QUR5MmTcz2229vfvvb35onnnjCTJo0yemx9sYbb5izzjoro3UBcdEzDECN07BhQ7PJJptU/1xTY8h3HG5CLKqHmJsQ8/YQs6U8bGFLeRCHnXHYwobysCEG4rCXLeVBHHbFoSer39MkJz5KismQIUOK2oO91OVhUxw2xGBTHLawpTyIw844bGFLeWjYOnf7YUPY5YPm5Zo9u+rnf/2raqjCoFiKTXE8/rgxp5xS1TusV6/apl49+mHkY99QEkzXJ0qC5ZIIc+P4+9//7lzzLFHm8n/UK+2QQw4xAwYMCF1evdQ0PKK/x9+tt95qOqk7YEwvvvii6dKlixOPXqWaY65YbRb2IBkGoMbRRYSeQKnpMRQiDiXENt10U/Ptt99GJsR++OEHs9lmmxUkjnJnS3kQh51x2MKG8rAhBuKwly3lQRx2xmELW8rDhjhsiMGmOGxhS3kQh51x2MKW8nB77hTa3LnGrF9vzNdfG/P++4GRmFLR8yZbb62HS2qZefOM6dy5ZKFYJR/7Rj6SNW4cXbt2NX/729+cxJZrzZo1znCJo0aNCt3WLbfcYj777LOk9/bbbz9z/PHHZxRHz549jQ2K1WZhD1KeAIC8atu2rZMQS/dkT9OmTc3AgQOLGhcAAAAAAOVMHXLc4REfeshY6cEH/3+4RKYMs9eJJ55o9tlnn6T3PvroI3PbbbcFfn7ChAnmiiuuSHqvefPm5t577y1onEA+kQwDABQ1IaZE2NChQxk7HAAAAACADCxaVPX/N9/8/59to7jeeuv/f0YyjaKj4QZtcN999zkJLa9LL73U/Pjjj0nvbdy40ek1pt5j/p5i6mUGlAuGSQRQ42hidA3RJ3379i3qvBA2xVDoOJQQGzx4sBk3blz1kIkauiIoEWZLedjClvIgDjvjsIUN5WFDDMRhL1vKgzjsjMMWtpSHDXHYEINNcdjClvIgDjvjsIUt5aEEhzuXUoMGDfI+/JryJ0uXKplizMiR6T6ZcJIXUjXcXfGHTXznnYQ54ICqeNu311CWpRu60QbefUP8c26Vah/t1q2buemmm8yZZ56Z1J5OOeUUM3LkyOoHnNVbTL3GvPbcc09z6qmn5iWOUg1VaEscKB56hgGocZSYWb9+vfNKN69VpcdQjDjatGnjJMR0ARWWCCtGHOXGlvIgDjvjsIUN5WFDDMRhL1vKgzjsjMMWtpSHDXHYEINNcdjClvIgDjvjsIVN5aHtFyoGJZaU4xozxpiFC43VFN9XXykpVxU3qvYNzZ+u/dSmffT00083u+22W9Jn3nvvPXP33Xc7PyvRfMkll6SM+HP//ffnNY5SsSUOFAfJMABAQSkhttlmmzmJsPr165c6HAAAAAAAyo6bVHr7bVMW3nqrKsGgucNgnCSYLcMjeunh5QceeMB5gNnr4osvNlOmTHGGR1ytSeA8brzxRtOjR48iRwrkjmQYAKDgWrVqRSIMAAAAAIAsqIeVpmtSp6Jx40xZUJyKV6PQ/W/UxhpJvY4015aNiTBXr169zA033JD03ooVK8x2221nRo0alfT+zjvvbM4666wiRwjkB8kwAAAAAAAAALCUEkoayW3atPJJLCnOadM0BF1V/DU5EbZhw4bIz5Y6WXbOOeeYHXfcMem9uXPnJv1b8/E9+OCD1XOJAeWmbqkDAFBzrVy5MvR33u7ZmvjV3yXbTydi7yS5uojQBUfYdjVBpv/krYuTqElM69at60yq6e3mrjGf09EcWd5eUdqGG4Mbj58+751bK85TRA0bNkya7FNl5k6aG8b/+3R14lI5u2WnCztNrholqj795aFJfvV99L0bNWoUq2703bVMJnXjr09txx2/W+uLUzeKK+rCVtvQtuLWjbar31dNdlxF5Rw1jrW3btz15FI3QftpJm3Npe+hesylrWkZbSvddwpqa1F1k0lbc8vDP+9dnLam7++tz1zamvZrN0bvfuqvmzjHzmzqxt/WouolqD7jjJUfp278+2imbS3o2JltW0t3TC/0ec3l/302bS1O3US1taB9NB/ntTh1429rUfWSzXkt07ambatMvGVWqPOav2689Rm2n2ZTN/loa4ot6thRiGuOqLanZaLi8tdNLm0t3fGj0Oc1l/e7xCmjfJ/XvOvwlkWxrjmC2lrUsSNdW8tX3WidKgNvORfjvOavm7B9tBjntaBrDtVpuv2tEOc1L7c8vMfAYp7X3O+e7tiR63ktm7amfSDqmjDf5zUvb3n4f++vG/3stiN9d3e+IO+1nX4fVTcqA+8y7nrd5YLaqncZfS6qPYu7z1Q1sYSZOtVkIc58SN77I3HnTwpfxi2HKVOM6dtX8SeMqj/Xcs5mGW8dh7XToPqM4m3PYfXpJsLizEmlObhefvllk4sLL7zQHHvssUnHDf9+GbSPuuebhx56yJniIuyYfs0115iePXs65eM/R2VbN0Hllq7849RN3LYWFrN/GXcf8n5WZeQ9DmZzXotzHER+kQwDkDfXXXeduf7660N/770I1wlk/PjxoZ/dYostqn/WhUO6z4oukIcMGZJ0UpowYULgZ3WRPGfOnKQ/kmTZsmXOeMjptG7d2uk+7lq4cKH56aef0i7TsWNH06VLl6Qna2bOnOnE4PLH0r17d9OuXbvqf+vzSyNmne3bt69p0aJF9b8nT54ceSLWRYzXxIkTIy/Ihw0bVn3Rp89G1Y0+q2W85e9fxq0TV8uWLZ2LlwULFjgXYtqOJm1NR99dZeBavHixmTFjRtplVMYqa+8fUdoPRNsP+m6qS9WpS/WvbaWjfUb7jmvatGlp/1B0/5Dz7hf6/lF/yKsNuH8sRrWxoLam9XuX8deL4vG3NbVr7TfpaHLdAQMGVP9b+/LUiL/i/G1N5bXkf4PN+9tLWFtT7NqH0tE4523btq3+t/YZdx/wc8ujffv2Se/HaWubbrpp0kVvnLY2fPjw6gt4XVi7dTN//vzqZb37qW7KqL24FFPUPqAbHwMHDkwaCuPHH39Mu4zaZ58+fZL2Ae2fYfUiKrNu3bpV/3vevHkpTxn6de3a1XTo0KH637NmzUppa/59VN9Fw7LGbWuyySabJN3Qi9PWNA+ie0NTbU3LpDumqx4333zz0LYWROsYPHhwRm3Nn6iN09Y0r6P3XKDzmso6nU6dOpnOnTuHtrWgfTSTtubq16+fad68efW/dY7OtK1pP3P/aA3bR8PaWphM25r200WLFjnllktbi3NeS9fWgo7pcduaX+/evXNua4pNdZPu2OFtaxJVN3Hamso+HV1DTp8+Pe1nmjVrZvr375+XthZWL3HaWhB/W9N3Wb58eWRb897QU31G8bc1XXdH3QCLamv+stD+kut5Td9d5+lM2pquN9Id04PamtqZ9ul0/G1N15DutU0QHUt1feu90ab9LCqx4W9rkyZNikyiqpzd5I7/GjJoH/W3Nd3Iy/S8pmOGYsukral9qu246wui/d87j43azOzZs9NuR+3Me4zW593t+LnloTbtFaet6bvoO7m0b0Yla1Vm3u/qtrV0xw5v3cT5W1rHWR1vM2lr2se0r7kUj46F6ZbTeUPnD5fOjfo7Nx21GbWdqLbmLQ/Vp9p1VFvTPq+Xm8jzJji1vkwTz+4y3utI/wO43qSjRF3XeBOV7kcnT94Y0TNMCYrURFDUdoKSR7kuo39X3WKp5STDVC3eZVRWmT5Y6n3oKm7duMkobTuszHWs9cYWVTdBiWf/Mv45wvSzNy4/7d9RfyNF0fFL2/UeE7RdN+GiOIP2UTfxrGuCSy65xFx66aUp6952223NaaedVv099XlvMixO3fjr061/f1tJlyBSW4vzkIdXWH16y8N7vvXXp5s0U7xuzDqG63ook/Oa/rZRGbt03NR1V5xkKfKDZBiAvNEJJM4Tu0AU/cHi3vwaO3ZsUlIEAAAAAICaxM1fRDyLa52ZM6tu8kc8a1Zx/ImwcjJy5MjA9w844ICk5BdQjkiGAcgbJS/8T194eRNleurD+yRZOnpqJOqz/qdI9ERH2DLeIcX8T2hEbcc/HIaeCPQ+3RfnSX093eZ9kla9mbxPb4p3KAjR03DepxXjDHGjp+4yHSbR++RlGO/Fj36OW4/eJ4H8y7h1oqdh/GWsp2709Lx6FqmHURj/E1Z68tBfrn7+benzbtlrfUHfzV836onkfco3iP/JVT2tGjVMov+pbO0nUU8Lefe1TNqYd3nvMt6n/N39NOjpxqjt+C+Y9ZRzpm1N29Y+HtRevPH7e4p5n44P4q9PPeWdbpjEoO3EaWv+7cRpa96y9u6PerLVOwSd+76/blRemdaN2ljUMv62pn1AvR3StTd/feqJem+vkjhlpqd8/W3Nv4/6j8dRbS3o2BmnrXm/j8rd2ys13T4a1taCZNPW9PSit3dKNm1N5zVvb6xs2lrQPppJWwurGz0YEVU3/u24+0y6eglra2EybWvaT/29wLJpa3HOa/668ba1oGN63LaW63ktqD4Vm3rjZHLsyPW8Jur19u6774Yuk+4aMqxucmlrYfXixp/reU094OO0Ne/Tz6pP79PRcbbj7QEeJqqt+cvCf92XzXlN54VM61M9StxRAMKOHUHX995RAIIEXUN6Rxvw07HUf7zR+TbTtqYn0KOOnd4y8F9DpttHvd8t02Onv2dRVFyi/VJllu6Y7m9rajPe0TOC+OtTf3d5R+nwcsvDv524bc1/DZnJ9b23rcWpFzfOfJ/Xgtqazgsqs3TL+etTbSbd33eZtDVveXh7hQW1NfXi8w6TqO/ij03fJ85wb/5lvO1T/45KHPj3iXTccNatq20yyUf4e3AVaxm3/NwkWCKh36fWr78t+fnLUMebTOtG23B7hoWVeVA7iOJdxrtunVP1b+93K1bPH23T3268Q/OF7aPud7nvvvvM22+/Hbjum266yfzqV7+q7q3pL7Ns6yaorfjPmV7pRhUIE1afYedU/76i+Nz7VXrp3/6RlrI5r7nXkMzBVjy1EvTDA1Ak+oPfHTpCN/XizC1QCG7XZtFJrBQnHRtisC0OPTmlLuUa9iIoDndYqqjkYy5GjRqVdAN3xIgRpqbXC3HYFwf7qV0xEEcq9lHisDkODX125513pp083j/8WSWXh01x2BCDTXFwLCWOcoijHPdTDTvtTVwq7jgPqsWNw73Nmk1CKR2N+KrnFi66SMPpRUbi+3cp9o+qGJSHvOkmJbNrmVIM+OIfvrSQ9xPcofWChn70337/+OOPzT777JP03hVXXGGuvPLKgsUXtY9qOG4NzZpuKHP1Dst1XrOoOEQPLu26664FL5+4bbaQxw03keZ2INA+GjWcPLJHzzAANY5Obv4nZWpiDLbFoSf0NDdC2MWHLig1ZKLGs4/qtVDubKoX4rAvDlvYUB42xEAc9rKlPIjDzjhsYUt52BCHDTHYFIctbCkP4rAzDlvYUh75ToB5uR1nIjpSuZGY0quVFG+ld3xJlwgT736hhG26nk+l2kdPP/30lCSMelp659z7z3/+Yx5//HFzzDHHFCyOYrIlDhQPA30CAKygIUyihpHQheXXX3/NUzIAAAAAgBrDvV+fwciKVnBHtKvkfENUIsxLiTANv2dbAuaRRx4xr732WtJ7O+20k3nrrbdShrI877zznAeZgXJEMgxAjeTtCl2TY7ApDo2VPGTIkJQxlMN6iFV6QsyWeiEOO+OwhQ3lYUMMxGEvW8qDOOyMwxa2lIcNcdgQg01x2MKW8iAOO+OwhS3lUag43I5vnTvHjiRguMRiS5guXapisKDjXkGorlevXl1WiTD/Pjp79mxzwQUXpMzR+OCDD5otttjCXKSxOT0WLFhgfvOb3+Q9jlKxJQ4UB8MkAqhxNFfZ+PHjnZ81UWXUZPSVGoOtcfTp08f8+OOPaSeg1u+UENMcYpU4ZKKN9UIc9sRhCxvKw4YYiMNetpQHcdgZhy1sKQ8b4rAhBpvisIUt5UEcdsZhC1vKQ3+nqoeQKOkR9aBnJtweYZp3a9SoqE8nzMaNG52fqkZfKc2cYYqhR4+qbWvOsEpNhKW7d+HSvqDy0LxQ2jdKJWgfPeuss8zixYuTPnfNNdeYvn37Vs/V9cILL1S3MXnyySfNscce68whlq84SsGWOFA89AwDAFhFPcQ0L1jURYibEFu6dGnRYgMAAAAAoNgaNar6f8+epqwoeVeOwzvmMxGmHmEN3PEiLfPEE0+Yl156Kem9bbbZxpx//vnV/1bsDzzwQMq0FmeeeWbFj9iDykMyDABQ1gkxzSFGQgwAAAAAUKmUS9Hoej16qLeXKQuKUz3DFLeluaCCJ8Lq1atX0p5g6cybNy9luEMlvh566KGUxNcOO+xgfv3rXye999NPP5nf/e53RYkVyJcyOXwCAGoaEmIAAAAAAFQllpRT0dxbgwebsqA4Fa8SYeWSwMt3IkzJpVLPERbm3HPPdeb/8rrsssvMoEGDAj9/ww03mJ6+ronqMfbuu+8WNE4gnyrkUAQAqNSEmOYFi5sQW7JkSdFiAwAAAACgWFq0qPr/7rubsrDHHlVJoJYtTUWopETYiy++aJ577rmk94YPH27+8Ic/hC6jufjuu+++lDI59dRTnXIBygHJMACA1Zo3bx47IfbNN9+QEAMAAAAAVGQyTD2shg0zpk0bY7W2bZVcqeXE6ybxyt26desqIhG2aNEic8EFF6TMa6bhEfX/dPbcc09z0kknJb33448/Oj3KgHJAMgwAUDYJsagLMxJiAAAAAIBKpOdDlVhSjmXXXY3VFJ/ibN48UTFDJNavXz/ynoTtiTC56KKLnPnCvC6++GIzTFnWGP7617+aTp06Jb136623mtGjR+c1TqAQKuRwBACoCQkxzSEWJyHGkIkAAAAAgErTunXV//fc8/9/to3i2mOPqp9btUqYSqEEV8OGDUPvSZRDIuzll182Tz31VNJ7m266aUY9u1q2bGnuuuuulPswp5xyilm/fn3eYgUKgWQYgBpJFyelvkCxIYZyiyNuD7GNGzc6CbHFixebclVO9UIcNZcN5WFDDMRhL1vKgzjsjMMWtpSHDXHYEINNcdjClvIgDjvjsEVNKY8GDarm4GrUyJhTTjFWUlyKr0WLhKlf31SUsIRYOSTC9MDwr3/966T3NB2FhkdUr7dMHHrooeaII45Iek+j9Fx//fV5iRUolFoJzXQHFInGkf3yyy/NjBkzzKpVq0zjxo1Nt27dzOabb2769OlT6vBQYEpkLF++3PlZdb9y5cpSh4Qypf1o7NixZsOGDWk/V7t2bTNkyBDTqlWrWOsdNWpU9RjguigcMWJEXuIF8on9FLZjH4XNNMG7hvGZNm2a8/CMe73Qo0cP5+etttrKNNIdPKDEOJaiHJTjfjpx4sSkeZ8Ud//+/U05UfiTJxujTjgPPGDM++8ba+y8c1UyrF49Y3r3rhrasZTce1CuZs2a5WW9up2+Zs0a555ENomwd9991+zqG+uyQ4cOpmPHjjnHduyxxzpDIfqdfPLJ5uGHH05673e/+525+eabs9qOhlocNGiQWbhwYfV7Sqrpvq96m6Uza9Yss99++4X+fsWKFc495EzK54EHHjBbbrmlKcfjRpMmTZz75O4+umzZsrytG8nSP1oP5IG6yN5///3mzjvvNN9//33o5wYOHGjOOeccc9pppzknkmK58sorzVVXXVWQdY8cOdLssssuaT/zyCOPpEw+ma177rnHnHnmmXlZF2AzXRyoh1hUQkw3ufR0UiYJMQAAULmU6Nppp53K8gYuAACiBJOmbJo+XYkPY777zpgFC0odlTFt2xpzzDFVPyu+UifCitFDTPc8dQ8zHz3C5s6d67xyFXQf8vXXX09JhPXr189cffXVWW+nffv25m9/+5s5/vjjq99bt26dM1ziRx995DxsFEaf0/2cfJaPEmhAFIZJREF9++23zgSM6oabLhEm+r0+N3z48MjPlgubu0cDlZIQizNkohJi7lM2AAAAAACUs6ZN/3+4xN//XiPxlDYebV9xKB7Fpfgqne75qSeU7ff+1Dvu9NNPT3pPMWt4xFx7wx933HFm3333TXrv008/NbfeemtO6wUKhWQYCuaTTz4x2223nflOj6hkmEDbdtttneFLypmeENGNethHyZG1a9c6L3d4nJoYQyXEoYSYEu5RCbGuXbuW1ZBH5V4vxFEz2FAeNsRAHPaypTyIw844bGFLedgQhw0x2BSHLWwpD+KwMw5b2FIeGjpP29erGLPSaMS4xo2r/p+cEEv4XsVJhHnjKXZZ2K6U5aEhE6erG6HH2WefbXbYYYe8rP/ee+9NGX7ysssuM5M1lmcIW/YJ9tOah2ESURA6yO6///4pY/Oqi+yBBx5odtxxR+fm9Jw5c8xnn31mnn32WadrsUtjo2rs2K+++sr5XCFpvNlck1azZ892xsr1OuSQQ0xLPQ6ThWzjaas+6Yg1V8T48eOdnzfZZBNnbN6aGEOlxNG0aVMnIaYu9t7jiEvzEvbq1cv6p7UqrV6Io/LZUB42xEAc9rKlPIjDzjhsYUt52BCHDTHYFIctbCkP4rAzDlvYUh66oa55pNwHpDUMbyFpFLpu3YyZNs2Y7t2NufRSY266qWrIRO+8mIWk21BuIqxhw6p4tMmNGxNOvRSrLHLlJkIKdd/Av29oKMNiJV/+/ve/Oy/RENFuHIopH/Wiey6ZznHVvXv36mENS7l/FLvNovRIhqEgNF7sokWLkt7r06ePeemllwInUfzzn/9sDjvsMPPFF19Uv7dgwQJz4oknmrfeequgsWqOrVzn2dIcA/5kmGLP1pgxY3KKB6hplBBz5xDzJsR0Uda7d++ySoQBAAAAABCH7t336GHMjBlVCanrrjPm8cc1h33ht73zzlVzhGkQFvUIUyLMzSWUUy8bxarEnf7fuHFj7h8AFYxhEpF36uX13nvvJb2n3l0ffvhhYCLMfSLg3XffTekR9fbbb5sXX3zR2OzHH380H3zwQcr33XPPPUsWE1CTE2KavNZthyTCAAAAAACVTAko9Qxz5xA75RQNjVfLtG5dmO1pvRddVLUdd44wbb8cO9W4iTD1mFIvITcpBqAykQxD3l1//fUp791zzz2mQ4cOkTeyH3744ZS5f4LWZ5NHHnkkcALJQndFBxA+ZGKPHj2c3qgkwgAAAAAAlU63oDp3rkpK6bba0KG1zI031jZHHGFMmzb52YbWo/XdcIMxQ4YYo+dQtT1ttxxvgSnptWrVKicR5tLPJMSAysUwicirzz//3Jnny2vbbbc1BxxwQKzlhw8fbn7xi1+Yp556qvq90aNHO8MG6ga3bXRyfPTRR1Pez2WIRAC50fjwmiMMAAAAAICapGlTY3r3NmbuXCVzapkDDzRGt+Q0G8fbbxszbpzmSYq/PiW5Bg82ZvfdjdFtOfd5U/UG0zPv5dgbzJsIc+dW83ITYo0aNeIBW6DCkAxDXnmTWK7TTjsto3WceuqpKet58sknrUyGvfPOO2b69OlJ722//famf//+JYsJAAAAAAAANZMSVB07Jkzr1gmzZElts3SpHj6vemmK7WnTjJk6ter100/GrF1rzIYNVT3KGjQwpksXY3r2rHppPrL/zUTgJMaUBGvVqupz5SpdIsxFQgyoTCTDkFevv/560r91wjjkkEMyWsduu+1mWrRoYZbqbP0/b7zxhvnzn/9symGIxJNOOqkksQDITboLYQAAAAAAyuVvWyV86tevZTp2NKZ9e+MkxPRSXqdv36pXHPp8w4bGtGhR9SrXnmCZJMJcdcr9ywJIQTIMebNs2TIzTv2tPQYOHGhaZzhjp+ba2m677cxrr71W/Z6GSVy+fLlp1qyZsen7Pv/880nvNW7c2Bx55JEliwlAdtasWWO+/vpr56K4QTk/4gYAAAAAqPHWrl3r3F/T37d16tQyujWnl3JA6gm2Zo0xq1dX9RTT9Fh6KfGll3qCNWpUlQTTn8flOB9YmLiJsPr163NvAKhAJMOQN0pY+SeY1Hxh2fAnw7TesWPHmh133NHY4umnn3ZOol6HHXaYad68ecliQjzqsehe1JSqu7sNMRDH//+RoOOLhkCYO3euadOmjTPvWClRL8RRDmwoDxtiIA572VIexGFXHEuWLDGPPfaYc973/y0jxx13nGmpMaBqSHnYFIcNMdgUhy1sKQ/isDMOW9hUHkpA2cCNw3+fTm8r0aWXhjssRgw2UDls2LDB1HPHfCxBIsyW8iAOO+NAcZAMQ95MmDAh5b3emrUzC0HLTZw40apkWNAQiSeeeGJe1v3Xv/7VvP/++05Pu/nz5zu9VnSDXq9BgwaZXXbZxey5556mb9x+7UjpwTdYM8DW8BiIoyoRpptf3htianNSysR2Ta8X4igPNpSHDTEQh71sKQ/isCsOzQGyaNGilPfdawH9viaVh01x2BCDTXHYwpbyIA4747CFLeWhYfU0x1Sp2RCHDTF4E2Hr169PSQwWMxFmS3kQh51xoHhIhiFvpmrmTZ/u3btnta6g5aZMmWJs8cMPP5gPP/ww6b0ePXo4853lw4UXXpjy3uzZs52XEmTqlaannQ4++GBzySWXmC233DIv2wVqkqBEmDchxvjgAAAAAACUt1InwgDYg36AyBsNL+bXrVu3rNbVtWvXWOu3qVfY8ccfX9Tu+DqRv/jii2abbbYx11xzTawTO4DoRJhr3rx5zgsAAAAAAJQXzQ1GIgyAFz3DkDeLFy9Oea9p06ZZrStouaDhTEp1Mn300UeT3lMSLF9DJLo0V0Hr1q1Nw4YNnbJdsGCBcxIPiufyyy935jxSj7FCj3Wby9Bxy5cvT4p71KhRppRd5EXjRZdiTHEbYqjJcWis8FmzZqUMhaR5RNwLZcWguQvbt29f9DnEamq9EEf8ntje/bRUbCgPG2IgjlTso8Rhcxz+OX/9Pv/8c2e4rZpSHjbFYUMMNsXBsZQ4yiGOctxP3YSHPqeXlvXeq8hHLDbMXWZDHKWOQfuE/z6aHoz1q1u3rlm3bp3zquTyII7s49A9JDe56u5T+bynqXWjOEiGIW9WrlyZ8p4SOdkIGq816g/XYnn77bfNjBkzkt4bMWJE1vOjeYeGPOigg8x+++1nhg8fbjp27Jj0e80b9sknn5jHH3/cScb5T+DPPfecufjii81NN91kCimfF4nFnpPBpQscJRelbdu2zhNANTGGmhyHLnKU5JozZ07SfqiLIO9TY7og0WfatWtX1IRYTa0X4ohH+6X3gp1jaenrhDiSsY8Sh81xRO2P+n0x99lSl4dNcdgQg01xcCwljnKIoxz3U8VbqJvvKg/dNHcTLIV+WNnmOEoZQyZzhCm2YkyRYEOdEEf+4tC+VarjHXLDMInIm6BeS9kmw4KWK/QTGrkMkXjSSSdlvb6BAweal19+2ZkT7Y477jD77rtvSiLMLZNddtnF3Hfffeb7778PnCfs5ptvNiNHjsw6FqAm0VOAamtxLnw1h9iKFSuKEhcAAAAAAKiMRBgAe9AzDAWV7dM2QcvZMCfWsmXLzAsvvJD0nnqLHH744VmvU3N+ZapXr17mgw8+MLvvvrv58MMPk353ySWXmI8++sgUSrNmzfLWq6xUFx3eC55SXfzYEANxVO2DmqNw9uzZztNA3mOPfvY+FaShWvX5bId/zURNrxfiSE/7pfcJ3JpcHjbEQByp2EeJw+Y4oran3xczplKXh01x2BCDTXFwLCWOcoijHPfTQg7JpnW76y/l0G82xFGqGGxNhNlQJ8SRvzhKebxDbkiGIa+9LPxWr16d1bqClitVt3+vp556KiW2I444oig3x4PGuX7++edN//79zdKlS6vf//jjj81nn31mtt5664IlBHOZb8xNiOmiWcNLlmpIz/Hjxzs/b7LJJkWfD8qWGIgjeRjSMWPGmIkTJ1aP1ax9VMOX+vXr1y+w92YllQdx2B2HuEMy6AKcY2np64Q4UrGPEoetcSxcuNAZejyMRl9o06ZNjSkPm+KwIQab4hCOpcRhexzluJ/qbz7v8GaKO5eHfr20Xv1t6Y7uU6qb5TbEUewY9He87tf57x36pxjRvTS9SjFMc6nrhDiyj0PJU31Wv3c/p/ux+VKqYSJrIkoaeRM00bR7QMlHMqyYE1mHefjhh1PeO/HEE02paM6jCy64IOX9N954oyTxAOVKFzPDhg1zLnCi6I8szSMGAAAAAABKy02EuQ+2plOKRBgAe5AMQ960bt065b1s59jRUz1+xXxCM+zpIfW68urdu7fZaaedTCkdffTRKe8xbxiQXUKsU6dOJMQAAAAAAKiwRJj+1icRBtRsJMOQNx06dEh5b+bMmVmtK2g59YIqpUceeSTlvRNOOKGkY9uKuuX6y37GjBkliwcoZ7o41hCIcRNimmsMAAAAAAAUl+YGyyQRxhxPAEiGIW969OiR8t706dOzWlfQcr169TKlohPrY489lvSekmBKhtnAP3/R/PnzSxYLUJMSYhMmTCAhBgAAAABAkem+XJyeXiTCALii7/QBMQ0YMCDlvcmTJ2e1rqDlgtZfLG+99VZKb7Vdd901MAFYCo0aNUr696pVq0oWSznwTpBbqgsiG2IgjvQXy507d3baVtAchv6EmGiIxUorD+KwMw5b2FAeNsRAHPaypTyIw844bGFLedgQhw0x2BSHLWwpD+KwMw5b2FIeSs642y/lKEI2xFGsGOrVq+f8f82aNVYnwmyoE+KwNw4UD8kw5M2wYcOcA4e6Kbs++eSTrNblX07rHTp0qLFpiMSTTjrJ2GLBggVJ/27btm3JYimXuaE0vGRNj4E40tMFkY5rY8aMiZUQ07FPCbRKKg/isDMOW9hQHjbEQBz2sqU8iMPOOGxhS3nYEIcNMdgUhy1sKQ/isDMOW9hSHrVr13ZiKTUb4ihmDGEJsQYNGpi1a9caG9hQJ8RhbxwoHoZJRN60aNHCDB48OOm97777zixevDij9eiG8scff5z0nhJh7lM+xbZ06VLzwgsvJL3XvHlzc9hhhxkbqBeYf1jJdu3alSweoJLo4lkJMX/vyyATJ040s2bNKkpcAAAAAADg/xNi3qSG/paPM4QigJqFZBjyaq+99kpJbL300ksZrWPkyJEpCTT/eovpySefTHm65MgjjzSNGzc2NnjnnXfMunXrkt4bMmRIyeIBKg0JMQAAAAAAyiMhRiIMQBiSYciro446KuW9+++/P6N1PPDAAynvHX300camIRJPPPFEY4u//OUvKe/tvffeJYmlXGzYsMEsXLjQeennmhoDcWSeEIuTBM9HQsyW8iAOO+OwhQ3lYUMMxGEvW8qDOOyMwxa2lIcNcdgQg01x2MKW8iAOO+OwhS3loQfCtX29vFOI1MQ4ShWDEmI2JsJsqBPisDcOFA/JMOTVVltt5dww9vroo4/Mq6++Gmv5sWPHmmeffTbpvS222MIMHz7clILmAfLPX9avXz+zww47GBs8/PDD5r333kt6T71X9tlnn5LFVA40ZvTUqVOdV6nGj7YhBuLIPCGmIVujEmKaa6xJkyYVUR7EYWcctrChPGyIgTjsZUt5EIedcdjClvKwIQ4bYrApDlvYUh7EYWcctrClPDZu3OhsXy/9XJPjsCEGm9hSHsRhZxwoHpJhyLs//vGPKe+dccYZZv78+WmXW7lypTnppJPM+vXrk97/05/+FGu7u+yyi6lVq1bS69133zU29wq75ZZbzNy5c7Na9rnnnjNnnnlmyvu/+c1vTNu2bfMQHYBMe4gpEaZhSjWHIgAAAAAAyA49dQDkG8kw5J3m0xoxYkTSezNmzDDbb7+9+e677wKX0e933XVX89VXXyW9r/cOO+wwUwo///yzefTRR5Peq127tjn++OPzto077rjD9OrVy0kWKnEX5ymE6dOnO0mwww8/PGWusC5dupg//OEPeYsPQCoNuRCUENPxQYmwli1bliw2AAAAAADKne6PrVq1imFAAeRV3fyuDqiiJNLmm29uFi9eXP3eDz/84NwoPuigg5xkWefOnZ1eUaNHjzZPP/10So+w1q1bB/bMKpY333wzZd6fPfbYw3Tt2jWv21m9erW57777nJd6dGlYSN1o13Z0U12Tfy5ZssRJgo0aNcp88MEHgRcDzZs3N//9739Nq1at8hofgPCEmIZ2Va9WJcI222wzEmEAAAAAAOQhEaaeYbpnpulA6tblFjaA3HEkQUH07NnTvPzyy87cVStWrEg6ob344ovOK51mzZqZV155xXTv3t2USlAiTsM4FtKCBQvM66+/7rwyocTZU0895dyMB1C8hJjmEPvmm29Mnz59SIQBAAAAAJCnRJhLCTGNzKJpCQAgFyTDUDA77LCD+eijj5xhE8ePHx97uYEDB5pnnnnGbLrppqZU1BPrpZdeSnpPN7oPOeQQYxP1Rjn66KPNnXfeSY8woEQJMfWC1RyFAAAAAADYdM8o5W9VTc+xdq0yTMasWWOMRmlS4knv165tjD5fr54xDRsa06iRJs6uer9EiTCX3ichBiBXJMNQUBoWUcOIaQhAJWwmTJgQ+tlNNtnE/PrXv3bmz6qnE28JPfnkk2aNLgo8jjrqKGfIwnxSDzANx6j5wj7//HMzbdq0WMv179/fHHjggeacc85xeuEBKB0SYQAAAAAAmzRo0MD5W9X5e/Xnn41ZurTqpXtdAcmmUFpe98JatKh6FSgZlS4R5iIhBiBXJMNQlJ4TStroNWnSJPPll1+amTNnVp/ENMSf5snq27dvTttRQilfzjzzTOdVaAMGDHBeKhvRHGsTJ0505gfTfGqai0hzqWk+MPVM69Chg9lyyy1NmzZtCh4bAAAAAAAAyq9HmJJLZt06DX1UlQTTv0U9wfQg9tSpxkyZYsysWVUJMs1Nr3m5lPjq3NmYXr00B4oxPXpUJc/Uk2zevKqEWOvWVT3GipgI836WZBiAbJEMQ1H169fPeSGYhjrcZpttnBcKRxOvugnFUk3CakMMxFE68+bNc3qfhs2LaEt5EIedcdjChvKwIQbisJct5UEcdsZhC1vKw4Y4bIjBpjhsYUt5EIedcdjClvJQLyx3+yUdQWTjRlN7zpyqJJgoyTRmjDFvv23MuHH/nxgL8+OPxowaVfWzhkgcPNiY3Xc3ZtgwPcVd9dKc2R06hPYUi1sWmSTCNFpTqUeSKvd9gzjsjAPFUysR52gDAHmgHm7Lly93flavQPV8A2wyatQo87OGkDC6pq9jRowYUZBE2Pfff+9c7Pfq1cv00JN2gGX7KZAL9lHYTPumrkc/++yzqqfm//cE/dZbb+383KxZM544hxU4lqIclON+qtF43JjduDUVRcVYscKY2bOreoCpN9ebbxozcqQxCxfmvm4lHHfd1Zg996yaT0yJqU6djGnaNKvVqR5Wr15d0ESYew/KpfM8YNtxo0mTJk5S2N1Hly1blrd1IxmPjwAAUCTz58+vToTJFA1LYTTyBAkxAACKQTcvNPx4o0aNkm7g6j0AAMqWHvBQbzANiyhff23MQw8Zs2hR/rahhNqzzxrzzjvGnHKKMUOGGDN9elUvsY4dq3qRWZQIQ/nQyDm6VzJ+/HizcOFCs3TpUuch+tatW5suXbo4Dy3pAXsgVyTDAAAoUiLsu+++S7nYJyEGAAAAAMiaHu6YMcMY9SxRb7B//cuY998v3PaUYLv5ZmN23tmYY46pek/zk3XrFjpsYnK4JMLeffdds6t62RXAV199ZYZpSEsPJZg23XRTM3fu3Or3NDzg6NGjUz6bifXr1zuJqjEahtPj2WefNb/4xS/S7gPvv/++eeONN8xbb73lxOzteeWnXvybbbaZOeWUU8wJJ5xADz9kjWQYgBpn3bp1zoWAaFzx+vXr18gYiKP0iTBvQky/66kJii0qD+KwMw5b2FAeNsRAHPaypTyIw844bGFLedgQhw0x2BSHLWwpD+KwMw5b2FIeGn53w4YN1YkG3cAvOCUQpk1T15qqnmE33WQSCxZU/+2peZAKNhPSe+8Z8+23xvz+91U9wxSHHvKsUye0LGpqIsxfHsWmdnHXXXeZww8/vPo9xXPSSSc5CbFsY7rhhhtSEmFHHHFEaCJs8uTJ5pZbbnGSZZpCIpPy03bOPfdcc/nll5s777zTHOMmYsutzaKkqGEANY6eXJk1a5bz0s81NQbiKA5d5M+cOTPyYn/q1KnOy6byIA4747CFDeVhQwzEYS9byoM47IzDFraUhw1x2BCDTXHYwpbyIA4747CFLeWhv/m0fb3iJHtypqER1SNMiTANV3jttcYsWFAdS1Fi0Pa0XW1fcSiejRudJJwSX96yqKmJsJLsGwGUoPInqZRgUkIrG99884257rrrkt5r27atk6gK884775i77747o0SY3+LFi82xxx5rTj311JzL0oZ6QXHRMwwAgALSHwFDhgwxY8eOTZm810/JMF2AtWvXrmjxAQAAAADKkHqCaWjE//UIM8uWlSYObVfbv/TSqh5iiqdTJyeZtXbtWlPTE2FxdejQwXRU+eVI86KGueOOO8zIkSPNIs9cctdee6059NBDzeDBg2Nvw+1Vpl6ZXrfffrtp3759xjGrzrfddluz/fbbm06dOjn3RLS/TJs2zRlG8YMPPkhZ5sEHHzQNGjRwerwBcZEMAwCgwNTdfujQoebrr782yyL+QNHFnvtHghJpAAAAAAAkWbHCmCVLquYIK2UizJ8Qc3sKNWtmajVu7CS1NBSdkmIkwtI788wzzZVXXlnQbShR9Ze//MWcfPLJ1e8poaV/f/zxx6ZOjDnf5KabbjJffPFF0nsHH3yw+eUvfxk7Fm1rzz33NKeffrrZb7/9nMRWkCuuuMJ8/vnnTvJt3LhxSb9TLzMtu//++8feLmo2hkkEAKBICTFN+Nq8efPIz2pYRY15Tzd9AAAAAEDKPGGzZ1f9/K9/VQ+NWHKK4/HHq35WfBs3OgkPJVtIhNnjyCOPTEkead4wJcni0HzoV199ddJ7rVq1Mvfcc0+s5VXHSoB9++23ztxhBx10UGgizLXllluaTz/91Gy33XYpv7vgggu4d4LYSIYBAGBhQkzJsAWeiY8BAAAAADBz52qiNGO+/tqY9983VnnvPU0mpXH0TK1585y/Z+vXrx9raD8SYcVz2223mZYtWya9p15pEyZMSLuchrtUDy13+EvXrbfe6gxvGGXrrbc2EydOdHp0de3aNaOYGzdubJ577jnTtGnTpPe1PvUcA+IgGQYAQAkSYi1atIj8rMbx1rCJJMQAAAAAAEZJCHd4xIceMlZ68EEnvlqKc+1aZ/j/2rVrp02E6e9kFI/mJrvllluS3luzZo0zXKKGtQyjZT777LOk9zRM4fHHHx9ru7oX0rNnzyyj1lR0ncypp56a8v5///vfrNeJmoVkGAAARaYL/SFDhsRKiP30009m8uTJJMQAAAAAoKZbtKjq/2+++f8/WyaxaJFJvPGG87OTEPvf0HhBSISVzgknnGD22WefpPc++ugjp9dYEPUa0/xdXhr15t577zXF5I9ZpkyZUtQYUL442gAomZUrV4b+rkmTJtU/66mU1XrqKQ09aaQu096u23qqJWy76tKtZbw2bNiQ0tXbTxdp3rGM169f74x/nY4u+rzDAmgbbgxuPH76vPdiUd9F3ylqfG3vZKcqs3RP9Ij/9+nqxKVydstOCZpVq1ZFLhNVn/7y0FNjuijOpG703VUGmdSNvz61HS3nri9O3SguLZeOtuG9wHfrpnfv3ub77783y3yTHSsGla1bzjNmzHD2gR49eqTst2F1E7c+09VN0H6aSVtzZVOfQXWjbaX7TkFtLapuMmlrbnn4/5CL09b0/b1PQ+bS1rRfuzF691N/3cQ5duajrUXVS1B96ju4bS2XuvHvo2FtLZNjp8o5Kvkc1NbSHdMLfV5z+X+fTVuLUzdRbS1oH83HeS1O3fjbWlS9ZHNey7StadsqE2+ZFeq85q8bb32G7afZ1E0+2ppiizp2FOKaQ2U/f/58ZzhiN2Z9Hz18Im3bto08d/jrJpe2lu74Uejzmsv7XSTq+JTv85p3Hd6yKNY1R1Bbizp2pGtr+aobrdPfm6IY5zV/3YTto8U4rwVdc6hO0+1vhTivebnl4b95X6zzmvvd0x07cj2vZdPWtA9EXRPm+7zm5S0P/+/9daOf3Xak764y8v7t5X6fqLpRGXiXcdfrLhfUVr3L6HNR7Vm8+0xC32PpUi1szMiRkctWLxfjM96/MuM+hhm0jFOeKod33jG1DzrI1Fq2zCTatQvsGab9QeWRru34yzmbuvHWcdi2guozo7qJqE837nR/z7vtyMt/Hoiz3/iX8e+X3p81z9fQoUOT7klceuml5oADDnDuV3jXoeER/ccG9RRzhzvMtm6ivo9/mS5duqR8Zs6cOUllF7ethcXsX8bdh7yf1XnPexzM5rwW5ziI/CIZBiBvrrvuOnP99deH/t57Ea4TyPjx40M/u8UWW1T/rD9E0n1WdIGsnjbek1LYWMdan36vC37vH6Q6+Uc9TdK6dWvTq1ev6n/rRop78yRd93PvyXru3Llm9uzZ1X8wapv+P6a6d+9u2rVrV/3vmTNnmqW66E2jb9++ST2N1Jso6kSsZdxxnVUWmsA06kJk2LBh1Rd9+mxU3eizWsalP1L8y7h14pZHs2bNzKBBg5L+uPnhhx/SbkffXd/HtXjxYieJlI7KWGXt3Y57EaiLp6DvprpUnbpU/9pWOtpntO+4NPSh+4eie6PYewNB7cMdM90t63HjxjnfRzfpwi6g1QbcPxaj2lhQW9OFmHcZf71oP/W3NbVrjdGdjsb0HjBgQPW/tS9PnTo1dltTOag8dPMyqL2EtTVdEGvetXSUYFSZulTG/uSkvzz0ee+xI05b23TTTZMuelVmUW1t+PDh1XWtC2u3blQO7rLe/VTloj9kXIopah/QcXDgwIHV/16xYoX58ccf0y6jseX79OnjlIGOHxrOM129SPv27U23bt2q/z1v3jznWJiO/qjq0KFD9b9nzZqV0tb8+6hi0uTNQW0tzCabbJJ0Q0/HmqibZhrew90H1Na07XTHdNXj5ptvHtrWguhG3uDBgzNqa9rHvMf0OG2tTZs2SUOV6Lymsk5H2+jcuXNoWwvaRzNpa65+/folzbGoss2krakMdM5RLOn20bC2FibTtqb9VDGo3Nz9JpO2lsl5LV1bCzqmx21rfro5km1bc48dkyZNijx2eNuaRNVNnLamsn/33XdTlnXnmtCNHu3D6eg6pX///tX/zqWthdVLnLYWxN/Wpk+fbpYvXx7Z1rzHD113Rt0E9J/XdN0ddQMsqq35y0Lrz/W8pu+u83QmbU37SLpjelBbUztTe0vH39Z0Dbnkfz0mgmg/1PWt90ab9rOoxIb/vKa2FpVEVTm739N/DRm0j/rbmm7kZXpe0zFDscVtazoW6JpSf0elO3Zo/1c7cKnNaJl01M68c93o82qjQdzy8B7T47Y1fRd9J5f2zahkrcrMm9xz21q6Y4e3buL8La3voeNtJm1N+5j2NZe+h46F6ZbTecN7c13XkDrepKM2451XKKytectD+5Z3LqSwtqYy08tNQHjL0L1+yCS56S7jLhf0AK436ShR1zX+RKWTCNPN+6++MomQc5W2WMuXCIg6PuuzQcmjbJdx/j9/vkl8+aWptfnmVQmxli2d8nLPLzq26eeoRID/ARzvQ1dx60b7h5uICitzxeP9PlF1E5R4TreMWz76ezrs73nF6V+HP/Gs757pAzjecvYmebQe3RO55pprzHnnnVf9eZ1nNBThK6+8Uh3rnXfeaT7++OOk7eyxxx5JQxbGqRu3PrVeHXu0TFBbSVefQe1Z5eotu7htzVse/v3Zu4ybNFMduedUHcN1PZTJeU1/2+i6y6XjJlNjFBfJMAB5oxsOcZ7YLTWdeHVxrD+m4kzkWsgYSk3fP85QfTWlPEpBF7f6A09/pLntRxdhQfun/mDURZIuWNNdLFZavagc9J2jEhTFKg8l3Up17LCJykA3j1QuUTfoK30ftSUO/UHpvXFe02kfVb3Eeeq60PuH/vAtdd3Ysp+6xw7doC71cd0GttWLe70R54n4Sj6W2hCHLWzaR5WEjNNzsRjloSRNKa8HbakXW3jLI2w4vmLQ32jem/Z5978kQOKtt9LH4bmxr1vsxb7RrmSc0zvsrbdMbSVnly51kmGqJ51f3KERS3GuKRW3t1ap/44M2kc1T9gLL7yQ9LDQ+++/b+677z5zxhlnOA9wXXXVVUnL6H6FepVlyy0Lb0/NuIIecvE+bJJNeUTNa4fKUStB6hFACXqG6SmNdE9PFmPYjXwM3ZbtMImFGuIm02ESCzHETT7qs1TDJI4cOTJpmMRtt922oMMkeql+NWSi2wNQn/fWjTt0ouhmlZ4uD3rSMJ/DJNrU1vI9xE05t7VPPvkkaQg6dz8t1TCJ2bS1QgwnJaUcJjFKKYaTKlVbC9pHSzVMYiHOaza3tWIM3Vaqtpavaw71envwwQdDlz/77LNTnia2ta0FqeS2VsxhEm1oazqWujfnFOOIESPKqq0V4rxWqGuObNqa/zhYU9vaqFGjkoYHDPr7yba2pl583p4giku99vIxTGKUnIZJVEwa/UZldtppzr8LPeRhTsvo++kYpnNuvXrmZ/WG+d93d9tONuWc6TLqsekdJtF77CrmMIn+ZZR42nXXXZN+d9lll6XMyZXvYRLDqOexegl7jzFKeH311VdOr/kPPvgg6fN33HGHc82U6XbyMezlUUcdZZ5++umkz9x///1OnHkbktS3jEYP0T7hxqr1655MvoZJ1MMe7rFaPYijRtBA9ugZBiBvLrnkEucVRk9Fe4eOCLsI8dNJJu5nvSeYTJdxh0nIhC6yM33yTH84ef9Ai8M/l0Mc3pNyXJmWWbqLyXzWZ7Hqxu2un8k+lE19htXNlltuab755pvAbv/e76Kn6bVNDQuZrodYMeqmWG1Nf9Rm+hRdpbY19wm6qPK3ua1lU5/Fqpuom+BBaGvJbS3OPppNfRajbirtvFZJbS1fdRPnBm+5tLU4Kqmt2Xx9X4i25j2WlmNbK0R9FqtuaGuZ142btM1kuVK1NX+vGP9wf5JND5FMe4Rl3ItMQ8npRvy0aWkTYSnbySiqPC6jhIySCtOm/R977wFu11Hd7Y96712WZElWtbqNwRhkigEHQospIRASTAnN/wTSSMKXL4HEhgRCyIcTB1NDgAQCxA6hE2wiMKYZ27Jkddnqvff6f35bnsPcfXc995R1znnf57nPPffcs8/+7TWz9syeNbPG9dJz65kzrndsQlk1dq7mmLCMi9q87uWZ4Ue1Pk8Rmyld8Pve9z73u7/7u13SBj/96U/vlt7+Gc94hnv7299eE78pe4zGQv7rv/6r23e8+MUvTrVLNTaLH+PrUFiP4m1yo9o16BkEwwCg49BMP+25IJTurOyDTrtoQIc91HnSflyafeXz52s2VtIgjt+rLi8g1g7lgg6bWLCHBQ3osIsVe6DDpg4rWLGHBR0WNFjSYQUr9kCHTR1WsGIPrSTxK9c0wF3TtGt+9WXOHpXx1IjRAL5rPBUN2pd91izXW8G8koGCdiJeN6zoCOvoLbfcEq24CleBxQNhCuJrhX1PxyCq9RWtnotPbHrBC14QraxqpA5oXQiGAUDHoZQXfvNxbbDcjI6yBQ3osIlP06Fgl1IUZM0YrXdAzEq5oMMmFuxhQQM67GLFHuiwqcMKVuxhQYcFDZZ0WMGKPdBhU4cVrNhDwR+fYrPsqtRcfABAwaWCWkQj9prO1LB586U9zHJWZrc7RerGP//zP7u77rqrR+f5oz/6I/ea17ymKh2qK5/85CejdIlpqW+1PYpWkTXDV5Ra8qMf/WiX9xS8eu9739tQHdDaUMoAAAAGA2LaF2zHjh25n1XqUc1mqutGzQAAAAAAANA8/F5pBZ4RTfHEBM6KfkhFq7DiK7HKsnfv3h4dP3v27Gj7k//zf/5Pt/9dd911XdIoNhLZ5dWvfnW3/cW0mm3ZsmVN0QStCcEwAAAAg2iGkwJiWZtea2PVxYsXEwgDAAAAAABoZ3wQoNVWWJ05c+l3LIgBdrnnnnsS33/pS1/alDSCSoN60003uZ07d3Z5f968edE+ZwBlIBEmAACAUdTRnD9/vhs9enRiIEzpC1jKDwAAAAAA0OZcuHDp9xMp3ZqBJmpeuHgx2g+sMF4vwbCW4M4773T/8z//k/i/v/7rv67sbd7IOvf617/e3XfffV3eHzJkiPvSl76Uua0EQBIEwwAAAIwHxBYuXNglIEYgDAAAAAAAoIPwK3Ka9AxYCYT5n6IHer1N3LusVfiLv/iLX9q3yp93vOMdVZ9/69at0Z5jaRw5csS99a1vdY3kne98p/v85z/f5b1+/fpFgbAFCxY0VAu0BwTDAAAAWigg5lMjEggDAAAAAADoEHwwaeDApgXCwr/jezel0r//pd8Ew8zzO7/zO1HAK2TkyJFd/v7v//7vbsGpevGe97zH/cM//EO3sZHPfOYz7ld+5VcaogHaD4JhAAAALRQQ04owzYQCAAAAAACADsE/A06e3NRAWN773bjssku/eYY1zac//Wn3zW9+s8t7119/vfvud7/bbSLu7/3e77m9e/fWVc+HP/xh95d/+Zfd3r/jjjvcq171qrqeG9obgmEAAAAtFBBjRRgAAAAAAECH4VeEzZjRsFPmBbyKBMR6eb2DBtVaHtSInTt3ut///d/v8t6gQYPcJz7xCXf11Vd3S524b98+97u/+7t10/Oxj32smx7xwQ9+MFq9BtATGFEDgI6jf//+7vLLL6+87lQN6LCLFXugw6YOK1iwhwUN6LCLFXugw6YOK1ixhwUdFjRY0mEFK/ZAh00dVrBij169elXOr9c1xQeTpk8vrKUnFF35FaVM7NXL9UrRUAmGNSG9oyXqWjd6qEP7gB08eLDL5/7qr/7KzZo1q7KX2X/+53+6NWvWVP7/7//+7+41r3mNe+ELX1gzHeJzn/uce8tb3tItDadWif3BH/xBVeeqRge0LwTDAKDjUIq5sWPHdrwGdNillvZQJ1IzvSZNmlS6c2elXNBhEwv2sKABHXaxYg902NRhBSv2sKDDggZLOqxgxR7osKnDClbsoUwe+qkLAwZc2ndLQT+d48KF1I/26uHAfuEUiE9cc2IgTD99+lzSKy3S38HUtW70QMe//du/ubvvvrvLZ57ylKe4d77znZW/BwwY4D7+8Y9HaRMvBPVOQavVq1e74cOH91iH+MpXvuJ++7d/u8s5hFamKSDXzuUCjYPSBgAAaFP0EPPoo4+6devWRbO4Cm9yDAAAAAAAAHbQgL1WV2nvrYUL63aaWgTCKkin9Eo3AQdz7Nmzp1u6QwW+PvnJT3YLED3taU9zb3/727u8t337dveHf/iHNdHy9a9/3f3Gb/yGO3/+fJf3dc6//du/rck5AAR3IgAAgDYOhKmDK3bv3h39TUAMAAAAAACgBRkx4tLvG26wHwgLdXrdYAoFmrT/V8if//mfuyuvvDLx8+973/vc9FiaTq0Yu/fee3uk45577nEve9nL3JkzZ7q8f/PNN7uPfOQjPfpugDikSQSAjuPUqVNu69at0eupU6e6gU3IXW1BAzrs0lN76CFGK8F8IMzj/54/f36htBlWygUdNrFgDwsa0GEXK/ZAh00dVrBiDws6LGiwpMMKVuyBDps6rGDFHkrv5gf0tQ9RzdOvKaikZ7qlS50bM8a5/fsTP6Zwlp8EGe3b1YRA2EWlrZRO2WDEiEIa2pl43Wi2Du0B9qUvfanL/5YtW+be9a53pR47ZMgQd+edd7rnPe95XerNG9/4Rrdy5Uo3yO9rV0LH/fff7170ohdFPhyiVWIKtNV7H6+6+yyYg2AYAHQcWnZ95MiRyutO1YAOu/TEHj4QppVgSZQJiFkpF3TYxII9LGhAh12s2AMdNnVYwYo9LOiwoMGSDitYsQc6bOqwghV76FnMn78uGTm0B5cCYgcPOvesZzkXC2bEtYgiwYSarwgT0terl7s4fLjrRYCh/nWjhI69e/e6d7zjHV3e79u3b5QeUb+zeO5znxut2PrUpz5VeW/jxo3RirIPfvCDpXQ88MAD7qUvfak7fvx4l//ddNNN7jOf+UxDAlNWygUaB3cjAACANmLnzp2pgbAwIKaNbunsAQAAAAAAtBCjR1/6/dzn/vJ1D6hLIEy6nvOcS98/alTPBELN+aM/+qNuWWT+5E/+xC3VSr4CfOhDH3KTJk3q8t6HP/xh99Of/rSwBm3h8OIXv9gdPny4y/sveMEL3L/927/lBuUAqoVgGAAAQBuhTun48eNzP6fZYAqIKS0AAAAAAAAAtAADBjg3cqRzSkn3hjfYC4QJ6Ro0yF3UKrYmpgSE7nz1q191X/jCF7q8t2DBgmhlV1FGjhzp/vEf/7HLe1pd9YY3vMGdPXs29/hNmza5G2+80e2Ppfm84YYb3Je//OWmppGE9odgGAAAQBuhNBhKgVg0IKYZWQTEAAAAAAAAWoQJE5zr18+5RYucu/56W4GwZzzjkq6+fd3FJ55Jed60waFDh9zb3/72Lu/16dMnSo9YNgD1a7/2a+4Vr3hFl/e0b9htt92Wedz27dujoNeOHTu6vL98+XL3X//1Xx2//yHUH9YcAgAAtGlATMTTHyQFxPQgdOWVV7JZLAAAAAAAgHW0d5jS1G3Z4txrXuPc6tXO7dvX/EDY2LHOvfrVl15LX+/e0YohBVwgn3/+5392d911V4+/5zWveU2UCjHO7//+73cLQr3zne90T37yk6s6z+233+6+973vdVnhpWDYy1/+8mi1WRJ/+Zd/6R577LHE7R6uu+46Vy2TJ092X//616s+HjoHgmEAAABtHBDT77w9xPbt2xelTCQgBgAAAAAA0AIMHXopXaL44z927q//2rkjR5oXCBs+/JIOpW+UrqFD3cXz56O0eQTDiqHn9rxn9yI885nP7Pbet771LfepT32qy3uzZ892733ve6s+j7LR/P3f/737rd/6rcp7Z86cidIl3nfffYljC2lpFDds2OB6uuoNoAiMeAEAALQpCoTNmzfPTVAajRx8QIwUFgAAAAAAAC3AxInODR586bcCUQpINTMQFurJCHxAYzl69Kj7nd/5nW5jBR/72MfcIAUve8BrX/ta9/znP7/Lez/+8Y/dhz/84R59L0C9IBgGAADQAQGxiU88kGRBQAwAAAAAAKBF0MqbqVOd0z5L06Y593/+z6VUhQlcqGdqRJ1X55cO6end+1LgjedKEyhl4hal1AxQcOzpT396Tb7/ox/9qBs2bFiX9/78z//cbdq0qSbfD1BLel3U3QkAoAEMHz48mpEiBg8e7I4fP94UHcpZ7c89ZMiQpizZt6ABHd1ZsWJFpEVIgzZxbRd7qLlfu3at27VrV+5nx4wZE+X41jEWysVK/bCio53raStqQEd3qKPosKzj8OHD7stf/rI7cuRI1M75iSPqp4qXvexlbsSIER1jD0s6LGiwpIN7KTpaQUcr1tN169ZVNAt9ds6cOTXREQaAooBSr8IhpZ6h69m61bkTJ5w7edJd/Pznnfv+97vouliPQNgznnFpjzCtLtKKMAXCnrB902wRw49BeeJBm0ZhxR7oqE5HPe8b/r51Qv77RB1VPxXqA3uGAUDHoUbLD3h0sgZ02KUe9lCnbu7cudHrvICYNsBdtWpVFBCzUC5W6ocVHVawYA8LGtBhFyv2QIctHQp0vf71rzczgNtse1jSYUGDJR1WsGIPdNjUYQUr9tAzV1MCkzqnVmY98ZzX6w1vcO6aa5z75CedO3DAFV0FUTgQNnq0czrHokWX/tYeYcpEEuwR1TRbGMWKPdBhUwc0DoJhAAAAHYIPiOn3zp07CwfEkja+BQAAAAAAACPomW3y5Ev7d+lZb/Fi5973Pue++13X63vfc27v3sygWKFA2Jgxzj372c495zmXVoP16+fcpEnODR1a22sBAKgTBMMAAAA6CAXC/HL+IgGxRx55xC1cuJCAGAAAAAAAgHUUmJo507nduy/9/aIXuV4vfKFzDz7o3He+4y4+8ohzsb28MgNheg5cuNC5G25wbulSPVD+cjXYhAmVtIgAAK0AwTAA6DiUh3fz5s3R6xkzZkT7l3WiBnTYpd72KBoQO3v2rFuzZo3btm2bu/76693QJs34s1I/rOiwggV7WNCADrtYsQc6bOqwghV7WNBhQYMlHVawYg902NRhBSv2UPrdM2fORK/79+/ftPRrF3v3dheVtnDUKNfr0CHX6/Bh12vZMueWLXO9zpxxF7dscW7zZndx82bXe+dO1+v0aefOnXOub1/nBgxw7rLLnJs+/dLP5ZdfWgHmA2MKgo0adelzLWALK1ixBzps6oDGQTAMADoObZB56tSpyutO1YAOuzTCHj4gpt87duxI1XHu3Dl36NAh9+ijj7qrr766KSvErNQPKzqsYMEeFjSgwy5W7IEOmzqsYMUeFnRY0GBJhxWs2AMdNnVYwZI9LsRWXTVLg+zQq39/10tBsfHjnVNA7PBhd7FXL9dr1iznZs2KVoPlpkbUSrCBA7Xp5qWfEsECC7awhBV7oMOmDmgMBMMAAAA6FAXCZs+eHb1OC4h5Dh486FauXBmlTGS2FAAAAAAAgF1Onz4dTWQcMGCA66Xnt9Gjo59eCpRpJdjJk66XAohnzyqCeOlHgS/9aCWY9gRTEEwrwEiZDwBtAsEwAGgax48fT/3fkCFDuszSOHnyZO6gfpgKQUud/cywpPOqY6hjQrQCRu9n0bdv36gzGaaR80uq0+jXr1+03Nqjc3gNXk8cfV7HeXQtuqYsBg4c2CVIIZvlzXCJ/z+rTDyys7edZpspJUUeeeUZt4c67YPU+S5RNrp22aBM2cTLU+fRcf77ipSNdOm4LHQOnato2ei8+n+4Ckt2zpvlGJaN/54iZaOAmI5TOkR//UKv/bXp9e7du93IkSPd5UqVUcDXPNWUZ1LZ6FxZ15Tka3llU8bXfD0NP1/U13T9YXn2xNdUr73GsJ7G74NF7p218LW8ckkqT11DWNeqLZv4vaOsryXdO6v1tax7er3bNU/8/9X4WpGyyfO1pDpai3atSNnEfS2vXKpp18r6ms4tm4Q2q1e7Fi+bsDzT6mk1ZVMLX5O2vHtHPfoc4fl1nfr+UFcRX4uXTU98Lev+Ue92zRNei8i7P9W6XQu/I7RFNffBWvUh8+4dWb5Wq7LRd8oGoZ0b0a7FyyatjjaiXUvqc6hMs+pbPdq1EG+P8B7YyHbNX3vWvaOn7Vo1vqY6kNcnrHW7FhLaI/7/eNnotfcjXbtsFK2iCvp2fmVVFtE+W8Ex/nv9cUm+Gh6jzxVZkRLWmaLHxK/Dt3ci+q1yGDjQhVcYXkv82i8+cc7w/fj9KW6zJFvkHVPEztUcE5Zxmp8mlWcty6ayWi+mK6884zar5ph4WSTV0WrKphblWeR6qimbor6Wpjl+jK9D4WfV7oX3wWratSL3QagtBMMAoGbceuut7rbbbkv9f9gJVwOivYjSUDo2jx5Esj4r1EFetGhRl0Zp7dq1iZ9VJ3nXrl1dHpLEkSNHKnnG0xg9enSUg9yzf/9+t3379sxjJk6c6C5Tzu0nUEBBQQdp8MS1TJs2zY0bN67ytz5/+PDhzPPMmjXLjVDagifYtGlTbkM8XTnAA9atW5fbEVm6dGml06fP5pWNPqtjQvvHj/Fl4lHA5corr+zycLNhw4bM8+jaZYNwJdPWrVszj5GNZevwPKoHvvOUdG0qS5WpR+Wvc2WhOqO643n88cczHxT9g1xYL3T9eQ/y8gH/sJjnY6GvqZMo2+n7H9TGyk8QPtDqMxMmTKjYJ/Rr1ZsstNfY3LlzK3+rLj/22GOlfE06lK4xyV/SfE11at++fZnnUWBv7Nixlb9VZ+LXGK+n45VmJKCIry1YsKBLp7eIry1TTv8nOvAqC1+ee/furRwb1lMNyixZsqRyvDTl1QENfMyfP7/y97Fjx9zGjRszj5F/XnHFFV3qgOpnWrkI2Wzq1KmVv/fs2RPdC7OYMmVKVOc8Wr0Y97X4vUPXMkp7GBT0NTFv3rwuA3pFfG3x4sWVAU35mo7JuqerHK+66qpu+/Floe/QSswyvhYP1BbxtTFjxnRpC9Su5a0UnTRpkps8eXKqryXV0TK+5lGgfvjw4ZW/1UaX9TXVM//QmlZH03wtjbK+pnp64MCByG498bUi7VqWr8X9xdujiK/FmTlzZo99TdpUNln3jtDXRF7ZlPE1X09VnmHZFPG1YcOGVfbe7KmvpZVLEV9LIu5rW7ZscUePHs31tXBAT+WZR9zX1O/OGwDL87W4LVRfetqu6drVTpfxNfU3su7pSb4mP1OdziLua+pD+r5NWh1V/zYcaFM9ywtsxH1t/fr1uUFU2dkHd+J9yKQ6Gvc1DeSVbdd0z5C2Mr6mZzz5jv++JFT/w4lb8pmsPXKF/Cy8D+jz/jxxvD3k0yFFfE3XomvyqG7mBWtls/Bava9l3TvCsinyLK37rO63ZXxNdUx1zSM9uhdmHad2Q+2HR22jnnOzkM/Id/J8LbSHylN+nedrqvP68YG8MMCp7ysbePbHhP3I+ATc+F5mef2aeKAyTAeZhvpeYQBRmuKD7T6I4QMG+gmv319HVnAja2Kp1xq3RTy4GU6+LDoBJ5x0VbRsdA4fiEqzn+61YXnl2Tkp8Jx1jN+CIOzXyL5lA8+69rITcMJ9sZLKRcTLJl6eSejzYTCsSNnEy9OXf9xX8nytyCSPkDQ7h/YI7RwvT+8L0us16x6u/lCZdk3PNj47j9B9U/2uZqd37SQIhgFAzVADUmTGLgDYQ51PPZyqI5Y0CKqH1/hDPwAAAAAAADSPeEaRIvjVYuFK/ngwrMjqGwCAVoNgGADUDM0kic++CAkDZRp4D2eSZaFOWd5n47NINKMj7ZhwpUt8hkbeeeLpMBQcCGf3FZmpr9lt4UxarcgJZ2+KcEaO0Gy4cLZikRQ3CmyUTZMYzrxMI+xo63XRcgxnAsWPCWeTyx5xm8ZnHiYR38dKwZu4XfPKU5/3ttf3JZ0zXjZaiRTO8k0iPnNVs1Xz0iTGZ2XLLnmzhcK6VsbHwmtbvnx5NOtYs9UV4NY5tUrryU9+cmSfpNmNeeeJP5xplnNZX9O5VceT/CXN17RSLJwdn0S8PDXLOytNYtJ5ivha/DxFfC20dVgfNbM1fHD178fLRvYqWzZaxVfW11QHtNohy9/i5akZ9eGqkiI20yzfuK/l3TvyfC3p3lnE18Lr8Ssrw+Pz7j2qQ2XbtSK+ptmL4eqUanxN7Vq4GqsaX0uqo2V8La1stFo0r2zi5/F1Jqtc0nwtjbK+pnoaXwVWja8VadfiZRP6Wtxf/HcV8bWetmtJ5SltWo1T5t5Rtl3L8jVfT6vxtXjZ9MTX0srF6+9pu6YV8EV8LZz9rPIMZ0cXOU+4AjyNPF+L20J+0tN2Te1C2fLUihKfBSDt3pHUvw+zACSR1IcMsw0k1dF4/VR7W9bXNAM9794Z2iDeh8yqo+G1lb13VtO/V72UzbLu6XFfk8+E2TOSiJennrvCLB0h3h7x8xT1tXgfskz/PvS1IuXidda6XUvyNbULslnWcfHylM/E/bxaXwvtEa4KS/I1reKLp+2La9P1FEn3Fj8m9E/9nRekiteJPKQ3nqZX92//ftLqGmnIO0/8uHgqtyLHqK6FdkyyRfwYlW/cl5L0x+83ZctG5/Arw9KuK8kP8giPyfrurLSZjSib8Ji0OppXnkXOU23Z5PlKkq+VJc1maW1qvDylQdfmV5Tq73impWraNd+HzFoZB7WFYBgA1Ix3v/vd0U8aeuAPU0fkDeh41MgU/WzYwGQdk9R4+katDOq8xTvoeejc0uY16HXe9ZXtJIu8QQwRT2lU1s5qsMsek1aeoT3i2htVNjqH74Tk1aFQd9nOWJGyiXf4sgLNaVRTNnog9amPtOxfD5wasEurp0Xt1NPy9McU8ZeelE2eryV9X5HyjNMTX9NDRhhoSPuuau6d1ZSN11DmXLqG+KBgtWUT3jvi2qspm2p9rcw9vR7tWq3Ks5qyiftakTpaTbtWTdmUKZdat2shcZs2ql2Ll2cRe9Srz5Gkrcy9o9ZlE9bTZvta0Xpaj3YtrTzL2qBWfcjQFnGfb1TZyM/K3jtq2a5l1dFGtGtJZZNnj0a1azqmbL+jFu1a0v+b1a6F112knlZz76ymbHxAqcxxtWjXQrw94t8ZL5v4YLRPExhSdqVV+L3+u5KCbPHz5gUbso7xew35QXqljvNp++L3jrLnqeb6k47Js0WtzpNHWMZFbdGTssn6TNljqjlPns2K1NFGlk2er8Sptc2SAlHxY3wdCutRvE2u5t5ZzX0QegbBMAAAAOiCOniara8HSuXlZ5YSAAC0C5oItGLFimgPGT97We2cnyCkFdJlBzIAAAAaiQJh8T2QtMJF2XiqCbICAHQKvS6yQxsANIhwZZg6aHkbrdeLcAPZ+IafnaQBHd3R4Fi4mkEDYp1sD3TY1EE9taUBHd2hjqLDso79+/e722+/PfX/t9xyS0P3yGy2PSzpsKDBkg7upehoBR2tWE/XrVvXZeWUdBdJYV4EDbGGEy3qMakwKRAWomsPV6zUS4cFWxQhzE4k8ra5aHd7oKM6HfW8bwhNxPJby6iOHjlypGbfDV1hZRgAdBxF8jJ3ggZ02MWKPdBhU4cVLNjDggZ02MWKPdBhU4cVrNjDgg4LGizpsIIVe6DDpg4rWLFHvQf1FfBTOsS8wKA+U01a1VrSzACHRazYAx02dUDjaM7UEQAAAGhLNEtRGzkDAAAAAABA4wJhQgP7ZfeXBADoFAiGAQAAQE04efKk+8UvfuEefvhhAmIAAAAAAAANDoRpS4pmpc0EALAOaRIBoOPQXmXK9yuU47cZm6Rb0IAOu1ixRxkdCoQ9+OCD0UOafhQQW7x4sevbt29H2qMTsGAPCxrQYRcr9kCHTR1WsGIPCzosaLCkwwpW7IEOmzqsYMUe2lPIB620Okv7CjUjEFYPHWWxoMESVuyBDps6oHEQDAOAjkR5tJuNBQ0CHTaxYo8iOpQa8aGHHurykKYNX2sZEGsle3QSFuxhQYNAh02s2AMdNnVYwYo9LOiwoMGSDitYsQc6bOqwghV7XLx40cSKsFrqqBYLGixhxR7osKkDGgPrZgEAAKBqFAjTijD9jqOAmIJkpEwEAAAAAACLWB0IJzUigB2s3iegPNwpAQAAoCoU5EoLhHmOHj1KQAwAAAAAAEwQDxppRZm1ge6igTBdC4EwgPqi+0N85Sk+17pQcgAAAFAVSn84ceLE3M/5gNjZs2cbogsAAAAAACCJpEFsS88pZQJhgwYNYlAeoM4k3R/YW6x14Y4JAAAAVTN9+vTop0hATHuIWXrQBAAAAACAzqJ///7d3jt27JizAIEwAHsk3R/69evXFC3Qc7hrAgAAQI9QMGzGjBm5n2OFGAAAAAAANJNhw4Yl7nXcbAiEAdgk6f6QdB+B1oA7JwAAAPSYyy+/vFBATLOqCIgBAAAAAEAzGDp0qOvVq1eX906ePNnU1WEEwgBsovuC7g8hun/oPgKtCXdPAAAAqFlAbObMmbmfIyAGAAAAAADNQHv9DBkypNv727Zta0pAjEAYgE10P9B9IY7uH+wZ1rr0unjx4sVmiwCAzmD48OFRmjQxePBgd/z48abo0G3P3/o0oyM+K6xTNKCjOytWrHDnz5+PXqtzs3z58o62R7U6tmzZ4jZt2pT7Oc2mWrJkSW6+7Va3R62hntrSgI7uUEfRYVnH/v373e233576/1tuucWNGTOmY+xhSYcFDZZ0cC9FRyvoaNV6evjwYbdjx47E/yngpLELPavoOaWMbeNDrHnH1isQVlZHPbCgQfgxqGant7NiD3Sk69Drc+fORUEwpUaMrwjzTJ482Y0YMaKmOhRgO3HiRKWOWkjd2q70bbYAAIBG08yHBUsa0GEXK/aoVse0adOi33kBMXUyH3zwQbd06dLMgFir26NdsWAPCxrQYRcr9kCHTR1WsGIPCzosaLCkwwpW7IEOmzqsUNYeCnZpcq6CYnE0AK6f3bt3R38rAFUPW1+4cCH6yUPnrpeGTkDBjZC+fRkKh+4oEFbEHxUE0/0DWhfuAAAAAFBzFBDTA9vGjRszP6eHUAXEtEKsf//+DdMHAAAAAACdiZ5TJk2aFL1OCoiFFBkgr9fAuw+A1UNDpxC3nV/JCFAWBcJ03yAw3doQDAOAjkMdzzCVQ7NSBjRbAzrsYsUePdUxderU6HeRgJj2EEsLiLWLPdoNC/awoAEddrFiD3TY1GEFK/awoMOCBks6rGDFHuiwqcMK1dijTECsrBb//VnnFll7KOszejaqtmyL6Kg3FjTEbdzMvZ4s2AMd1ekgENY+EAwDgI5DeXjXrFkTvZ43b17i5rmdoAEddrFij1roUEBMHcYNGzZUHRBrJ3u0ExbsYUEDOuxixR7osKnDClbsYUGHBQ2WdFjBij3QYVOHFaq1hw+I6fPao0fPJPG9jMquQjp16lT0euDAgZl7fCkoo3Ml7Uuk47TPerUD72V01AsLGpKCYdLSDKzYAx3Fdcj/dG9QWkT9EAhrDwiGAQAAQF2ZMmVK9LtIQMzvIUbKRAAAAAAAqDca4NaqD/1odZn2NT569GgURNHf1aYo1KB63iokv4pNz0Hhe0OHDq1ZcKCIjnrTbA3hyp9m28KCPdCRjPaT0ziE9jMfNmxY5IcWdEFtIRgGAAAAZgJimtVJQAwAAAAAABqNBr59YKwaFNTyK9S0h3LRFWp79uxxjz76aDT4vnjx4mgwvidUq6OWWNAgVqxY0SWF5pw5c5qiw4o90JGuQ2MWnb7KthMgGAYAAAANQZ1LzcZbv359oYCYUiYOGDCgYfoAAAAAAAAazfjx46NAjVKx9TQQBgAA6TQnIScAAAB0JJdddpmbPXt27ucUEFu1alWPcvYDAAAAAAC0AmPGjCEQBgBQZwiGAQAAgLmAmGZGzpo1i01qAQAAAAAAAACgxxAMAwAAgKYExNLytSsQphSJShMCAAAAAADQKiizxZkzZ5otAwAAEiAYBgAAAE1h8uTJ3QJiBMIAAAAAAKAVUSBs37597vHHH3eHDx9uthwAAIjRN/4GAEC7o7Rrffteuv01KwWbBQ3osIsVezRChwJi+u61a9dGgbDFixd3C4R1kj1aCQv2sKABHXaxYg902NRhBSv2sKDDggZLOqxgxR7osKnDChbsoUDYli1b3KFDh6K/V69e7QYPHuxGjhzZkfawoMESVuyBDps6oHH0usjO9ADQIDTAffTo0ei1OoXHjx9vtiSALqxYscKdP38+eq3AzPLly5stqWPYtWuXGzRokBsxYkSzpZiHegrWoY6CZU6dOuUefvhht3HjRnfhwoXovd69e7srrrgieq1JGQMHDmyySgDupdAaUE8voaHVzZs3R8GwELUvaleaERCDS1BHoRUYMmSIO3HiRPR62LBh7siRI82W1LawMgwAAACazsSJE5stAQAAOgAFup785Ce706dPdxkc03sAAADVBMI2bdrktm7d2u1/mnSxcuVKt2jRIgJiAAAGIBgGAE0ja2WYZkWEHciTJ09mfpeWM2u1mUeDG5r5m4VmaWkliufcuXPRwEgWWj49YMCAyt9nz57N3Ry3X79+rn///pW/dQ6dKwt9Xsd5dC1+wCZrcEeDOR7ZzM94TkPXLzt4iqzWk5398nF1/P3slSzKlmc1ZaNrD2dyFymbeHnqPDrOf1+SPeJlU6Q8dQ6/9L5o2cTLU3bOW8wdlk3R8mwVX1NZ+rLpdF+TLcIBXP9d8bKx7GtFyrOasul0X4uXTbN8LamO1sLXipRNI9o1y74WL5t28rVa9zl8PdX3h7paydeSaGdfq1efw6qv6Ttlg9DOrehrtWzX6tXnqMbX4vfBTvc11QEdl6XPqq9V267p+mU7HwiTrrTy/PnPf+4WLlzoxo8fj681wdd82aQ941fra2HZtFu7Vq8+R7W+FpaNlXYtXjb18jWoLQTDAKBm3Hrrre62225L/X/Y+KgxWrNmTepnr7766sprNcBZn/WNsGZbedRoaQ+iJHRuNThq6NQh9Y2qliErtUEWo0ePdjNmzKj8vX//frd9+/bcFS+XXXZZ5e/du3e7PXv2VDqK6jzEcxNPmzbNjRs3rvL3tm3bcjfgnTVrVpcUc+qU5zXE8+bNq5xbDfK6detyOyJLly6tdCz12byy0Wd1TNipih/jy8TbQx2XK6+8sktHdMOGDZnn0bXLBp6DBw8mzs4LkY1l6/A8fjm66kXStaksw1VMKn+dKwvVGdUdjzZUzupcyx6XX355VEdVLtKi6897uJAP+A5sno8l+Zq+PzwmXi6qK3Ffk1+r3mQxdOhQN3fu3MrfqsuPPfZYYV9TPZOdlUox7i/S6P+O+5o+rw2ss5Cdx44dW/lbdSYtJYG3x8yZM6O64+8dRXxtwYIFXTq9RXxt2bJllWtTx9qXzd69e7uk9vLv66FiyZIlleOlKa8OyNfmz59f+fvYsWNR6rAsNKtV6cSkQeeQjXVPy8qxrgf/qVOnVv7W53UvzGLKlCluwoQJlb937NjRzdfidVS6Ro0aVdjX/H0wfPAp4mtKd+Mf/KTh0Ucfzbyn6++rrroq1dfSHhTVRpXxNV2Hv6epvhXxtTFjxrjp06d3addk6ywmTZoU7feX5mtJdbSMr3lmz57dZQ9BtdFlfE0aHnnkkeh3Urnk+VoaZX1NdUTHzJkzp3JPL+NrZdq1LF9LuqcX9bU4ug9W62v+3rFq1apIQ9a9I/Q1kVc2ZXzN11OVjep0GV9T+hqVp6cnvpZWLkV8LYm4rylll09TnuVraqu9f6lu5g3MxNs19bvzBqbyfC1uC/30tF3TtaudLuNrsrEvz7R7R9zXfP8+i7ivqW/j9xZKq6Pq34YDbdKVN6AXb9fWr1+fO6ApO/vByXgfMqmOxn1N9aVsu6Z7hrQV9TX5q+ys+2DWPV31X34QlufOnTszzyM/C+8D+rx8NAlvD5W/jvP9wSK+pmvRNXlUN/MGtWWzMODgfS3r3hGWTZFnaR2v+20ZX1MdU13z6Dp0L8w6Tu2G2g/PgQMHoufcLOQz8p08XwvtoWeI+PNalq8J3QPClVtFfU11xV+D6owPCoowGCM/Vh1RW5Dma0mofuneWcbXdG/WfdDf03VMXp9D/S21BR6Vpdr2LPJ8LamOlvE1j9pO2c1Tja+pXyNfSHvGz/K1LORr3vfyfM37rW8/paUaXyvSrmX5Wtq9I+5r8rO8dk33QbWH1fia7w+qzyXbZfUHVZ/DIGJP27UQbw+VxzXXXFO5pxf1NZVnmXYtzdfYxapxEAwDgJqhBqTIbItmo0ZNgwnq6KghDh8WG61BqKMedroaiTof/mE/7GA12x5hR6cTkT3UiVK9iA9odGI9VXBbg+DqWIc61HHUw5M64eFgab3toQdcDRo2q1ysoHLRA4IeyBQkbUb9iNfRZmHFV6RDgblm39Mt1VEfbGh2ufh7B/f0X947VDaaWNAsHVawVi6WbNGItj0JDer7Ad1mlokVLNVRDeir72fhnq7fGnDu9Hu6FUJ7hMGWeu8RFp+goLZeE16EnhcUENOzrSbshStbGnlPDycLNhLqaHd7aJKD6gH9wV/WUbW3mgjb7Hu6gnHS1OnP+J0AwTAAqBl+RU8aYaBMDU3RgTrN5sj7bHwWiWZepR0TphSLz9DIO0+4nFtohlI44yiJ+IO8ZtyEM2m1mine4IbL04Vm6OR16uObvWsmUN7Kk/j/w1nOaYSdeN+RK4MP8ISEs8llj7hN47Ohkghn3okiD6fx8tTnve31fUnnjJeNHi7CWb5JxDt2mkGXVTayR3wmnuySN1sorGtlfCw8PjwmXi6yT9xv5PN554k/+GmWc1lf86vkvA7NUtNgiL5Lg1Z6P1xB4jv34ez4JOLlqcBaWmoHb4+4Txfxtfh5ivhaaOuwPiooGM449e/Hy0b2Kls2emgv62uqA5qxmeVv8fLUDMJwVUkRm2kmadzX8u4deb6WdO8s4mvh9cju4arUpHt6nq8lUY2vhRMcqvU1tWtxX4oT94G4ryXV0TK+llY2mumdVzbx8/g6k1Uuab6WRllfUz2Nz8iuxteKtGvxsgl9LemeXtTXetquJZWntOVNSorXz562ayG+nlbja/Gy6YmvpZWL19/Tdk2rRYv4WrjqUuUZrkYqcp5wBXgaeb4Wt4X8pKftmtqFsuWpGet+gkXavSOpfx9mAUgiqQ+ZNZFDdTReP9XelvU1zUDPu3eGNoj3IbPqaHhtZe+d1fTvVS9ls6x7etzX5DNh9owk4uWp564wS0eIt0f8PEV9Ld6HLNO/D32tSLl4nbVu15J8Te2CbJZ1XLw85TNxP6/W10J7xPfmyvM1r7+or6nM1N9SYCO0ne7rGlj3ZSqdqn9a1eKvPcvXilDU18J61ah2Le5rSXW0jK+llU01vqa+kE+TmHZdab6WRVj+eb4me9TC16pp10JfS7t3JPUha92uJZWn/CavP1jrdi0kLbNBEV+Ll0017Zr3tayVcVBbCIYBQM1497vfHf2koQf+cDl70RkXamDKzs5QA5N1TNKsE3Ve4h26PHwKlzLo3NLmNeh13vXFG/8i5A1iJDX8Ze2sBrvsMWnlGdojrr1RZaNz+E5IXh0KdZedxVSkbOIdq2pWy9WibPLqaVE79bQ8/TE6l2ZbKlgYpoTUSrowHWe1ZZPna0nfV6Q84/TE13TdYaAh7buquXdWUzZeQ5lz6RriD0/Vlk1YR+Paqymban2tzD29Hu1arcqzmrKJ+1qROlpNu1ZN2ZQpl1q3ayFxmzaqXYuXZxF71KvPkaStzL2j1mUT1tNm+1rRelqPdi2tPMvaoFZ9yNAWcZ9vVNnIz8reO2rZrmXV0Ua0a0llk2ePRrVrOqZsv6MW7VrS/5vVroXXXaSeVnPvrKZsVAfKHleLdi3E2yP+nbVs1/TcoZTaWhEWH7zWeX1qZB8cetKTntRt8LvefY74c3415Vmrssmro41q1/y1lLFFPcZG4vZpZLsWlmeRe0ejyqZsf7BW7VpI0nU2ql2rxtegZzRujS4AAABAD9EMTJ8GLkSrwx588MHcTW4BAAAAAADK4gNheXuGCw2IhyvCAADABgTDAAAAoCXQylJtsJsGATEAAAAAAKg1PhNF0UCYUg4TCAMAsAfBMAAAAGgZ8nJpExADAAAAAIBaB8J27NiR+1ntV6ZAWDzlPQAA2IC7MwAAALQE2ix4zpw5hQJiv/jFL9yJEycapg0AAAAAADo3EDZ69Gg3b948AmEAAIbhDg0AAAAtgzaivvLKK3MDYmfOnIlWiBEQAwAAAACAagJhStFeNBC2cOFCUiMCABinb7MFAAA0Gs3UGjx4cOV1p2pAh12s2MOqjnHjxkUBsdWrV0cPqXkBsaVLl1aOr6WOTseCPSxoQIddrNgDHbZ0HDx40H384x93Z8+e7fL+/fffH/1+4xvf6EaNGtUx9rCkw4IGSzqsYMUe6LCpwwq1tocPhO3cubNwIEzntVIuFnRY0GAJK/ZAh00d0Dh6XcwaRQIAqCHDhw93R48ejV6rsTl+/HizJQF0YcWKFe78+fPRa83qW758ebMlQQb79u1zq1atygyIif79+9csIGYB6ilYhzoKltm/f7+7/fbbU/9/yy23uDFjxjRUE0AS3EuhFWjHelomEKb2YsGCBQyiG6Yd6yi0H0OGDKlktdH2EEeOHGm2pLaFuzUAAAC0bMpEPXwWTZlIAB4AAAAAALICYWvXriUQBgDQpnDHBgAAgI4IiD300EMExAAAAAAAIDUQtmvXrtzPEggDAGhN2DMMADqOc+fOuWPHjkWvhw4d6vr27duRGtBhFyv2aBUdCogpT/8jjzxSeA8xpSGotY5Ow4I9LGhAh12s2AMdNnVYwYo9LOiwoMGSDitYsQc6bOqwQi3s8dhjjxUKhOnZQ/sXJwXCrJSLBR0WNFjCij3QYVMHNA6mMABAx3H69Gm3cePG6EevO1UDOuxixR6tpEOzMxUQy1shdvbs2apTJlqxhxUs2MOCBnTYxYo90GFThxWs2MOCDgsaLOmwghV7oMOmDivUwh6TJ092gwYNqjoQVisdtcCCDgsaLGHFHuiwqQMaB8EwAAAAaAvKBsT8DDAAAAAAAOhsBgwYEGWQSAuI5QXCAADAPtzBAQAAoO0CYnkPqQqIaQ8xAmIAAAAAAJAVEBs3bhyBMACANoC7OAAAALQVBMQAAAAAAKAWATEFwubPn08gDACgDeBODgAAAG3H6NGjCYgBAAAAAEBVAbElS5a4KVOmEAgDAGgjuJsDAACA6/SAmPYQO378eMO0AQAAAACAXQYOHOhmzZpFIAwAoI3gjg4AAABtHRBbtGhR7kPskCFDogdeAAAAAAAAAABoPwiGAQAAQFszatSozIDYiBEj3OLFi12fPn0arg0AAAAAAOrLxYsXox8AAOhs+jZbAABAo9GA98iRIyuvO1UDOuxixR7tpMMHxFauXOkuXLjQJRCm94t8rxV7WMGCPSxoQIddrNgDHTZ1WMGKPSzosKDBkg4rWLEHOmzqsEKWPdT3X716dZQF4oorrnC9evVqio5GYkGHBQ2WsGIPdNjUAY2j10WmRgBAgxg+fLg7evRo9Hrw4MHszwPmWLFihTt//nylI7R8+fJmS4Iac+jQIffwww9HD8W6J2lFWN++rTU3iHoK1qGOgmX279/vbr/99tT/33LLLW7MmDEN1QSQBPdSaAWs11MfCNu3b1/095QpU+oeEANbWK+jAH7bhhMnTkSvhw0b5o4cOdJsSW0LaRIBAACgY9CsLwXAtFKsFQNhAAAAAABQPhAmtm3b5jZt2kTKRACADoURIAAAAOi4gJjSIzIjFAAAAACgPQNhq1atilYDx9m6dWv0e+bMmTwPAAB0GATDAKDjOHv2rDt48GD0WqtD+vXr15Ea0GEXK/ZoZx3VPPhasYcVLNjDggZ02MWKPdBhU4cVrNjDgg4LGizpsIIVe6DDpg4rhPbQpLd169YlBsLCgJieB2bMmFHTgJiVcrGgw4IGS1ixBzps6oDGQTAMADqOM2fOVGaDKS9vMxo7CxrQYRcr9kCHTR1WsGAPCxrQYRcr9kCHTR1WsGIPCzosaLCkwwpW7IEOmzqs4O2h9If6KbI/uY6plw7R6fXDggZLWLEHOmzqgMbBnmEAAAAABVKtaIbpyZMnmy0FAAAAAABiKAi2ffv2yiqPLCZNmuTmzp1LmkQAgA6DYBgAAABAgc239+7dG226TUAMAAAAAMBWf12BsCIrwhQImzNnDoEwAIAOhGAYAAAAQE4gbN++fZW/FRA7cuRIs6UBAAAAAHQ86p+vWbOGQBgAAORCMAwAAAAg5cH60UcfrQTC4gGyQ4cONU0bAAAAAECnc/78ebdy5cpCqREnT55MIAwAoMPp22wBANC5ZM3c0saV4cBzXloydWgHDx7cpVN86tSp1POePn26Wyf43Llz0ftZ9O3b1w0YMKDy99mzZ3M33tUGnP3796/8rXN4DV5PHH0+3LhT16JrymLgwIGuT58+lb9lM9kui/j/i8ymk5297ZSX/cSJE7nH5JVn3B69e/d2gwYNKlU2unbZoEzZxMtT59Fx/vuKlI106bgsdA6dq2jZ6Lz6v+zgkZ1l76Jl47+nJ2WTVE/L+JqnmvJMKhudK+uaknwtr2zSfM3vEbZ///7K/1Q39H2qG/7Be9GiRZHOPF/T9Yfl2RNfU73294OwnsbLpsi9sxa+llcuSeWpa/C+lkaR+2C8jpb1taR7Z7W+lnVPr3e75on/vxpfK1I2eb6WVEdr0a4VKZu4r+WVSzXtWllf07llk9Bm9WrX4mUTlmdaPa2mbGrha9KWd++oR58jz/d0TJ6ueNn0xNey7h+1bNeyCK9FFEkHXMt2LfyO0BaN6nMk+VrevSPL12pVNvpO2SC0cyPatXjZpNXRRrRrSX0OlWlWfatHuxbi7RHeAxvZrvlrz7p39LRdq8bXVAfy+oS1btd0Pq0I0wQ130/374fo/fHjx0fBsDRbVNOuxctG353XJ6xHuxYnPs5RpK9ei2fpuK8l2aKadi1eNtX4mnTJ3mnP+NW2a2HZ5JVnUn+wke2aL8+0OtqoPke8PIv0B5N8rSftWkhoj5BqnqWradeK+BrUFoJhAFAzbr31Vnfbbbel/j9sfNQYqeOaxtVXX115rQY467O+EdagtEeN1tq1axM/q4Zu1+Dzc8kAAQAASURBVK5dXTpuQmnPNm/enHme0aNHuxkzZlT+1kC5cpNnMXHiRHfZZZdV/t69e3eUZk0aPHEt06ZNc+PGjav8rc8fPnw48zyzZs1yI0aMqPy9adOm3IZ4+vTpXf7W4H9ep3/p0qWVjqVPSZGFPqtjQvvHj/Fl4hk5cqS78soru3RQNmzYkHkeXbts4NHswK1bt2YeIxvL1uF5fPo7dWySrk1lqTL1FNmkWXVGdcfz+OOPZ3b4fGcsrBe6/rwHefmA78Dm+ViSr+n7w2Pi5SI9cV+TX6veZDF06NBog2qP6vJjjz1WytdkL78SK+4vab4m7fFVXXEuv/xyN3bs2MrfqjOqA+qQbtmypUtn3j9I+sEC/f3www9365wnsWDBgi6d3iK+tmzZskoH3j/sC+1d5o8N66keKpYsWVI5Xv6fVwd0LfPnz6/8fezYMbdx48bMY+SfV1xxRZc6oPqZVi5CAxBTp06t/L1nz57oXpjFlClT3IQJEyp/79ixo5uvxeuormXUqFGFfU3Mmzevy4NsEV9bvHhx5cFPvqZjsu7pKserrroq1deS0HcsXLiwlK+FD6NFfW3MmDFd2gK1a7J1XoohDSil+VpSHU3ztSxmz57thg8fXvlbbXReuxb3NdUz/7CcVkfTfC2Nsr6menrgwIHIbj3xtSLtWpavJd3Ti/panJkzZ/bY16RNZZN17wh9TeSVTRFfk+2zUB9S9/8shg0bFq0uqIWvpZVLEV9LIu5rupajR4/m+lo4aKjyzCPua+p35w1M5fla3BaqLz1t13Tt6hOX8TX1N7Lu6Um+Jj9Tnc4i7mvqQ2atMte9VP3bcKBN9Sxv8Dzua+vXr88d0JSd/eBkvA+ZVEfjvqZ+U9l2TfcMaSvja/JPP1Ep7d6h+i8/8Mhndu7cmXke+Vl4j9bnwwlRId4e8umQIr6ma9E1eVQ38wa1ZbPwWr2vZd07wrIp8iyt+6zut2V8TXVMdc0jPboXZh2ndkPth0dto55zs5DPyHfivqZ+hu6hvu0JAz7qO4R+o+cX1dG08QGhe4DuBWV8TfeaMIio+iwNWfePLF9LQv0o3TvL+JruzWF7o+vIG7OQzdQWeFSWeW1Bnq8l1dEyvubRtYT+Vo2vqV8jX0h7xs/ytSzka75dy/M12UP39bA+V+NrRdq1LF9Lu3fEfU1+lteuqR1Ue1i0XUvyNZV/Xn8wydd60q6FeHvEg8hFfU39oTLtWpqv5dU1qB0EwwCgZmjAocjMJgAA6+ihWQ8DemjImt2mB3F1XvXwEHbQAQAAAACg9qj/rUH3ImMPmiinQe68iaUAANAZ9LpI6BEAmrAyTIPGWbNM6pl2QzM8NCNHs4K0KsivpGlkmkTZQhr86qTw/41Mk6jPex0azM9bDVGv1A6+TLw9dC3NSJN4zz33dEmTeO211zYlTaJ0a8WE6qjKRedsRprEeLlIRzPSJEqHglI6Lslf6pnaQSsIVq1a1SVNo+qGZnR5f1OdkV00Yy2c5VavdFL3339/lxR0vp42Ok2iykWzc3WMrjutXOqZJjFeRzUDtBlpEjXbNOue3qh0UvqMH+zRvUPl24w0iUl1tBlpEnUtfrVO1r2j3mkSpUOzXmVnf09vRprEpHt6M9Ik+nuHzqNZ5Vn3jnr0OeSvn/jEJ1KPf9vb3pY7uaGWaRLTyqXRaRL1Gb+KUPfS+ErTRqRJjNtC9mpGmkRdi19FkXbvaESaRN1LfZpEaVy+fHlT0iSm1dFGp0mUDpWL7Jx1T693mkRvDx2jQIs/rtFpErPuHY1Mk7hixYpKinGdK+n5qZbtmuqZMjPEg1vxlWE6RuWjzCEqz3q0a0mp2/zqH5FUTxuVJlHX4u/pamuLjAvUOk1iUh1tRppE1VEd49MkptXReqdJlD20ek7afH+wGWkS0+4djU6T6PuDOo9W/mX1B+uZJjG0h1Yheh2NTpOoFXZeo/pheRk0oHoIhgFAw1Cn1C9nV8NUpHMB0Ej8w5zwgw4Aum899NBDuQ8D6gwrhWSYOqweUE/BOtRRsIwGNzTocN9993VJ53nddddFrzUIkpf6FqARcC+FVqCR9dTv2ZuXhk0ow4PSscVTn0Hnwb0UWgEFVgmGNQZ6+QAAAAAZqDMa5hxPQ4OqekBXTnYAALCJAl2akauZzLqv60ev9Z5+CIQBANjD79VLIAwAAHoCPX0AAACAAgGxpUuXFgqIPfLIIwTEAAAAAABqGAgrsu/X1KlTCYQBAEAq2SM6AABtiFLj+P3KlJc3KzdxO2tAh12s2AMdXdF5df4NGzZEuc7DPR+SAmILFy50o0ePdu2KhXKxoAEddrFiD3TY1GEFK/awoMOCBks6rGDFHuiwqaPeKE25Mi/kBcIUMFOaMdlBe/B0erlY0GFBgyWs2AMdNnVA4yAYBgAdhzrHfjNZ7e3TjMbOggZ02MWKPdDRXYf2Dxs5cmTupsWdEBCzUC4WNKDDLlbsgQ6bOqxgxR4WdFjQYEmHFazYAx02ddQTv0dYkRVhEydOdCdPnowGtdX37vRysaDDggZLWLEHOmzqgMZBmkQAAACAEmhV2IIFC6I9ZorsIbZ///6GaQMAAAAAaAe0h+PgwYNzPzdt2jR3+eWXkxoRAAByIRgGAAAAUBKlYdEeYnkBsYsXL0YrxAiIAQAAAAAUR8GtOXPmuEmTJmUGwmbMmEEgDAAACkEwDAAAAKAKCIgBAAAAADQnIKbVYATCAACgDATDAAAAAHoYEMvLLe4DYvv27WuYNgAAAACAdgmIaV+wMBA2ffp0AmEAAFAKgmEAAAAAPQyILVmypFBAbNWqVQTEAAAAAABKoKDX3Llzo4CYgmCsCAMAgGogGAYAAADQwBViBMQAAAAAAKoLiCkYBgAAUA19qzoKAAAAALowePDgKCD24IMPujNnzmQGxE6ePNlQbQAAcImzZ8+6LVu2uAMHDrjz589H7/Xp08dt3Lgxej1t2rTcvSABAKA5sBoMAAB6AsEwAOg4tHJjypQpldedqgEddrFiD3SU11EkIDZz5kw3depU1+pYKBcLGtBhFyv2QIctHUeOHHGf/exnu72v+7a45ZZb3JgxYzrGHpZ0WNBgSYcVrNgDHTZ1WMGKPdBhS4MlrNgDHTZ1QOMgGAYAHYdm+06YMKHjNaDDLlbs0RMd27dvd5/61KfcuXPnurx/9dVXuxe96EUN0+H5xS9+4e6+++7E/ynVyute97qa6cgKiCkQplUH7UAt6+mHP/xhd+jQocT/qWzS0uHUy1dOnDjhPvjBD7oLFy50eV8PSm984xsbpqMsVnRYwYo90GFThxWs2MOCDgsaLOmwghV7tFO/1KPVqbfddltllWqcd7zjHW7kyJHd3j948KD7h3/4h8RjtNL1z/7sz6Lf1lfpbtiwwV1xxRU1GYBuh3qaxje/+U13//33d3v/N3/zN92sWbMapqMsFjRYwoo90GFTBzQOgmHQMJR65IEHHnBbt26NBpk0WKiZ8VdddVXUAYLus1Z//OMfu3Xr1rnDhw9HndmxY8e6K6+80l1zzTWub1/cFwBscuzYMfeFL3yh24DD6NGj3Y033tgUTbt27Ur9nzbirjU+IPbQQw+506dPt10grJYoZWRaIKxe5VOk/FRWjz32WJf3t23bFtWlZmgCAACA9uiXevbs2ZMaCBs4cGBiICyvXztu3LiWCISpj6yy0c+SJUtYkZHBvHnzEoNhP/vZz1KDYQAAkAyj6VD3Ts7HPvYxd/vtt7tHH3009XPz58+PUpK86U1vaniO/nvvvdc961nPqsl3vetd73Lvf//7e/Qd9913n/ubv/kb941vfCOyXxIjRoxwr371q6PzXX755T06HwBALdED/X/8x39EAf2Q3r17u5tuuqlpD7o7d+5M/V+9AhvhCrHJkye3VSBM5Xzrrbd2Wznleec73xm1VUXIGtDRIJAGg5o18BAPhomf/vSnpWeRAwAAQOOx2i/t6WStZvRr6xEIE8ePH4/+Vp+53fZr/M///M/o2pLQGNQznvGMQt+jZwg9V2hSeYgmTqtuDx8+vCZ6AQA6AYJhUDdWrVrlXvnKV7rVq1fnflaBsre//e3un/7pn6LOqoJjnYZWDvx//9//FwUP89BKsTvuuMP9y7/8i/u7v/s795a3vKUhGtuFU6dOuR07dkSvNUDdjIFWCxrQYRcr9qhGhyYYPP74493ev/baayu5uBuhI+TixYtu9+7dqf+fNGlS3XQMGjSoLVfz7t27N0oBefTo0ejvYcOGVa5xwIABhQNhPV21V09fSVu1vnLlSvf85z+/S5m2ss+2M1bsgQ6bOqxgxR4WdFjQYEmHFazYo136pbUKhmmlW1I/rGi/1kIgzOMDYlohVm1AzGI9VUaBNMaPH1/4OxW8nTFjRjTGFqJJabLb8uXLM3V0+j3dClbsgQ6bOqBx9G7guaCD0BLupz71qYUCYSFq3NUx1azrTksRpRQNRQJhIZoZ9Na3vtX96Z/+ad20tesMQeVZ109aWopO0IAOu1ixR1kdSoP7wx/+sNv7mq34zGc+s2E64hw4cKCSqjCOBg6UgraeOtotEObT+ugBXO2XfsIVYmUe7ns6u7mevqJ6kfQwpCBgfGCtVX223bFiD3TY1GEFK/awoMOCBks6rGDFHu3SL61FH0hBtLR+mNWVYeo/KVtCPBDm0fsK7KRlx2m1erp///5o8lgaZfvL2mIkCa0Oy9LBPd0OVuyBDps6oHG03+gMNJ0tW7a4X/3VX63MUgpnsyit0NOf/vRoFpY6cD/5yU/cl770pS4dHi3zfsELXuB+8YtfVD1bq6doZVo1KRM0i6Aafuu3fst9//vf7/b+nDlz3Kte9apodro6j+rofO5zn6vMWvAoNeP06dPdm9/85qrODwDQU3SPUiqQpLR5CvY3Mw1N1kBDK+yrYDUYVquH+0bv51aUXr16Rf0Qbe4eR+0x+50CAADYxHK/tGjmgrQ+kIJG8bGWsO8yYcIEZ7E8FOjSCrAsdG0KIlkN6JVB41pavZdU1zRRTnvWlSFtbEyrzzRJWmkUAQAgH4JhUHMU2NEs/BANGN19991uwYIF3T6vQI7ydf/85z+vvLdv3z73ute9zn33u991zeDrX/96FFxqBJ/+9KejgGCIBmb/3//7f1H6QwURQ/76r//avec973G33XZbl/ff8Y53uBtuuIENVAGgKSgNTfzeL7SvYdK9v5G04r4KeqjVgEDZwFKjyBq8Kav5bW97m7OK6kdSMGzjxo1N0QMAAACt3S8NA1fVZHjJ6tc2c6/VngbC/LiR1b55WRTUy5qMp/Ivg4KcOkZB1BD9vWnTJrdw4cKqtQIAdBKkSYSaoqBOfIWTZrAoPUFap1ObgaqzqvzQIf/zP//j7rrrLtfOqEP4rne9q9v72gtMg4PxQJjQzKJbb73V/dVf/VW3PLd/+Id/WFe9AABJaALDj3/849TNoZuN1ZVHWYEwpZFRquEs7e2yMswyo0aNSh3g0OAOAAAA2MJ6v7ST+rU+NWKRQJgm9aalAmxFlHKtln1l7aU2dOjQ0gFSAADoCivDoKbEVyuJO+64I3epvhr1T33qU+7JT35ytJQ8/L6XvvSlrl35xCc+0W1A8WUve5l7zWtek3usZpH913/9V5f91fS39l2zMtsNADqDb37zm4n5tWfOnNnjVbZ6kNT+TAoM6YFaAxza62HIkCFuzJgxUfuStydXmZVhSqejPSZ0XqWg0YOnzqOJG9WiVMAKnuj7tNpLkxdkL/3o+wcMGBAFXXzKRg0a+EDLmjVrEnUWQe2pBkx0bg1C6G+dSzOG9RAu2yVNushD+68dPnzYbDBM+nTdhw4dqly3rlPXLTvrutMGE4oGwzQLV/tAXHbZZTVWDwAAAFb7pRYo069VP0jbWPiUfeo/a2uHevbV1Efavn17lL5PK8K0p9mIESPcjBkzUj+vPrD6yytXrowmhUmrylD9N00GVr9Nn1GfvJq+axl0fm8z9SmVfnDSpEnRT5nVXEkrEz3V2l/90qQUmVmT1AAAoCsEw6Bm/OxnP4v2+Qq59tpr3Qtf+MJCxy9btiwKBH3hC1+ovKdAjwYFly5d6tqRO++8s9t7SoNYBA2Yvve973XPf/7zu3QkP/axj7kPf/jDNdUJAJCGAlVJaeTEM57xjKq+Uw/PuvevXbs2euDzQTCfni7Mva97oVYga69HtTlx9CCrh9ok9EDrBw30oP6///u/7uGHH06cvaogktqisWPHFnoIv//++6OgmvZ4VFAmntIkCQ0C6FoVeNP1+CCfAmI6Xg/heehzjz76aNQeb968ucsEkziyo7db3nd/8pOfjAYGivCBD3wg9X+/93u/1yXA9J3vfCdxc3vxlKc8pUsbl4XK77777ouuWYNEefaWhrlz50bXrpRCaWQFzTTwQDAMAACg9fulH/nIRxLT2l155ZXula98ZaFzr1ixIspuk0TS9/zHf/xHNJE1Ce1r9tSnPrXqlWHqN6uPtX79+sR90xRUevazn114Eq3GGBTgSkttqBVd6nMqmKVVeZo0JZ1+b3gFIcNgmP732GOPRROL1Jcvut+VPqf+m/ah1zUUQWNKX/va1xL/p+/6jd/4jUrdURkq5WCSzRSM+9Vf/dXUgOoDDzwQTU4On1v03JCEykY/afU0bQVjWr+UYBgAQHEIhkHNCINYnje96U2lvuONb3xjt+/593//97YMhqnjG+/8Ll++3M2bN6/wdzzvec+LBk3DAUrZ7+///u9L56AGAKgGPTQmodU32peh7D5Ums2rgEZRNGtUD68KOCUFw7JmzyogoiCXAm/f/va3U4NmQjNDda0KnGh/xiw0AKDrKIMGCzQwoOvRwIACgTqPDxzpb5EVtNLDu/a89A/gRfdwUABQgafnPOc5iavsFFiqRbrGQYMGdVtplVU+RYJ/ugYNumjwRd+dtEl5Elr5p4ClBkGygmFauZdGkZQ/AAAAYL9fqj5HUjCsaP9Hfcgf/OAHqX0JBbfiVNMHUnaBrPR7Cob95Cc/ifq1WROidK0KxqkPrYlHWWMHCgxl7RWroJRWQd1zzz1RBgRNFNLnfSBMjB49ussx6nvLtnq/aCDM21kTvtR/fe5zn5saMCxjZ9lU/Wf1h7NQ//wzn/lMFDybPXt2qfOUIav/m9YvpU8KAFAc9gyDmvGtb32ry9/qUJVNcajZSVpCH6KOXCfYS9x0002lvkOrE17ykpd0eU+dSg0KAgDUGz30pc2+veaaa0p9149+9KNotWyZQFiRB8esB1OlKPnGN74R7U+ZFQgL0fVqJmsW1QSONCgRpvTRYILaCT2gexQQS7oeBavUVuoBvWggLH68AkP/9m//lphWSAMctdgfK6mMehIMk50//vGPRwMqSbqr1RSSFVxTgBQAAABav1+q1IFJKPBUpA+k7AJp/QKtYoqPcWQFtTSOktY/Ud8nbfW7gkqPPPJIFNjJCoSFKHCWtkI/DAKlfZ8mlem6NQlM2Rz8BKowECbik6F07QqEKfV5NShAp36ygmJ5ZPU1lUJbW1fkBcLC82r1V/z6RK32+U2ri1n9Up9WEgAA8mFlGNQEpaFSxytEqZfiM4CKBHc0uyecUa9BLnWshg0b5tqJpE7n0572tNLfo2OU1iE+I27x4sU90tfOqNOunPH+dadqQIddrNgjT0e4Z2F81mLRe5AemvXQnvZdQquV/EN02v5gWYMGaWjApOhgQahDD/BZ5VLNzFClUZHWUI8GSvRwrv00w4CYAnd+Fq3s95WvfKUmkyCUglLtr1LAFLFhkXLJKiOt5lN6w7TvVjqarFVwWjkuG5XREKLjNAhSLWGgslV8ttOwYg902NRhBSv2sKDDggZLOqzQCf3StACED+6Ee8fGdahfmHZu9TWSnvGzglo+c0ESYZ8s3g9T0C4t9V4W3/ve99yiRYu6BeyK9Gt1jFJD6twKFGmFUtK4TTgupAlfSvdXi/EdTQaTdo0jJdUPBYiyUgh+97vfLfUsIDQ2pWcIjXd5VJZ+9VzZ/nGI9nSrNkCofqmOt+S3FjRYwoo90GFTBzQOgmFQExSwinfmktJVFSEeDNP3asaPZlS1E/H91fx+NGVJSg0Q/27oSthB7WQN6LCLFXtk6dCMyLR9DmbNmlU4Xd33v//9zECY0AOuBiH84IAetLUXVxiMqGZlWPjwq/0K9P26LqU5TNOhVH/6f9bDbTx4JFsohYwe+jUgo3Np0ECDJ5ptq8EDfZ9S28QDYkpfEwbDhFZ/Sau+T/t1akJKXqBNgSXNNNZ+bFmf//nPf+6e9KQnRemE0q4nbo+ixMsoq2x0/rS92TSooUCYbJilQe/rnAocqlxltzAFUtE0jGmEKYVawWc7ESv2QIdNHVawYg8LOixosKTDCp3QL1WfQO16UoAqHgyL61AwKW1Vzq/8yq8k9hmrXRkfHhfvA4X9RwVFFOBTv1n9pqyAkPqhSjmtLRjyzhlH36t+ub5DQa4k+0lDGKRRsCcMhOk6lLJaP/qsykr2VNBJts9aia9zq6+s/ciS6of0Za2YittMutTPT5us5dFzSBgMC7MolO0fh+T1TYv2S634rQUNlrBiD3TY1AGNg2AY1AS/l0mIj6yXJem4devWNTQY9ulPf9qtWbMmCippoFIdO81m0mCm8kNrU1PtraJZSNWgDl08zZY2nc3amySNKVOmRB3GsGMkewEA1BPdI9MeTrVJeBF0r1IwLA0Fja6++mp33XXXddvXSQ/dWh2k9H6anZn08KgVVGmBLY/uu9qkWufxM8G0D+NnP/vZ1AfOMAiXhNoN7UU1Z86caJPtrA2+1b4oVaMGcJICYnrI10O8bBGioI4exLUqOxygiQfBXvziF3f5v+x27733Rul8ktD/tQG49o8IJ13IPkKbp2uPiSQ0y1d7f6YR3/Q7a3AlbYa2bK+9MbMGA9Seqkw1MBIfGNBghQJ+CsD2NBjGzEEAAID26JeqTdfzftl9w9SXiWfI8WjcQP3BtOOqSZOXl31A/VoFta666qoufUdp/PKXv5y6Gk3ZAao9p75T/VmdTynI1ffVe+rDarVUOMisvrH6//rcggULos9qPCPez/WoD6yJ0V/72tdSg1oqM/X5qtHuJ2Cp36s95dRvVF9YAc60PeCSngW0Qu4d73hH5e9PfepTqc8g2hojre+eF7SlXwoA0HMIhkFNSNo/Ja2BzyPpuGr3kKmW97znPd3eU0dOP6tXr3Z333139J4G2/7sz/4sCoyVQQOt8Y5otfbSzCNtUhvaqNH2AoDOI2kShNBDZNKm0kkPc8q5n7Xvwa//+q+nbnaue59m+upHM0KTUq3k5e5X8Om1r31tt/uv/tbqqPvuuy/xuLyJCwoIFZ0RqgDRr/3ar7n169dHNvEBsXDjcb0f/z4NCGij8rTVUxpkeP3rX98tDaA+r7ZL50sbIIjvt6Gy8GkZ08pdKLgUD1pmoWBe1nclocGJpIEqj1IRqU1O2wheA13acF2r15P2eyizITmDDgAAAO3RL/VBqLLBMKUlTOrLKrijVWG17ANpolTW3rA656tf/erEwNDChQujIFTa3lhaQaXvj69i82ki09AxCoQp6HfFFVdEwZywX63+qv4v9Bldm4Jjz3zmM1O/M35NCuwps4EmayWRtXIsy85Czxmvec1rugSh1Fe+4YYbouxHXnves4Ds5vvA0pOVhUF2ClfKlSGtX6rzpwUUAQCgKwTDoCb4/MjxmdnVoJlBRb7fAhqI1M9b3/pW9+EPf7hwWrBa2svbLAyAZaVBABelPdDgue8AV5vGoNU1oMMuVuyRpkMPxmlBd82uLLIPk1Z0pT1g6iE0DITl2SMtYJY3G/TGG29MnYiQtJrLp1BUykFpykrPVwY9vIYDKXqglR19QCypbZE9NCtVgzx64A73F9D3vfKVr0wtBx2j2bhp9lH6RulJCiiFx4QpJTUjtshKq5CswZWk75IureoKCTUoyKdAVxGK7lOhlWRphHtrWPfZTsWKPdBhU4cVrNjDgg4LGizpsEIn9Et9MCxpD1Y9W2u1kJ+A5HXoR0G4pElSWlWflhlAk5yyJvak9afUL5SOtH6YAkxpK6SE0vqlBcNkQ11XvH+k4FvaaiQFwqRJ6bw1CSsJ9UmlzQfCRDX1J23yVzg5Kal+ZD0LqF684hWvSOxnqw+soF3as0ra/mpCdVFZIvznwvqh/nq1gbCsfmmSHgt+a0GDJazYAx02dUDjIBgGNUEDVHnpkIqSdFzWYFS9UEdQHVh1VtTJVIcmbdbRHXfcEXUsNTOsyI2zlvZKOlYdVr8xba2pdkNXobzfHnXkV6xY4ZqBHhx8B1WzxapJT9kOGtCRvMrVByXSVpZ0kj3SdOierFWySWgFUZ5v6+HZ7/mUhGavagWtfrJ05KF0gL5jm/TQqDSKaVo1GzR+rN8YXA/dP/rRj3J1aKBDaQUPHToU/aijrWvR9YeDGWmoLurz+o4QfZcGa3wwTA/danv8YIAGOx599NHM79YxabbxG4onDR7JXr5N9vbw93eVTdH7uo5L29tDAycaXIqvUFO6GqXGDPEaVO/UPtW6XfnJT36SaifpU1m0gs82Gu6l6LCsQ/f+LLQXo18N2wn2sKTDggZLOriXNqZf6lHgJK3N/+///u9Kuj/pUIBMfSW9jgdqNH6g99LOqwBSUmYdoed33YPSUkGG+sJ+mPrUqitZ16rAVlbfT8fGg2FKn5h0jN8jTGkP1V/0Qbmkeqq03eozxft16sdu3bo10qXj1ZeUPfWTtc9XHGU78H3ssH6oP6l+XLgvWIhSgGfttZ61Mkzll/Yco+P8hC9dU1g/NNBebV81a1+8pLK34LcWNAjupehoBR1FxgegNhAMg5qQtFy76AysOEnBpLyH1lqgmf7aW+VXf/VXo45RfLWAOlHqmCrX9kc/+tEugR3xwx/+0L3uda+LBnjzGtha2ivLZvUIhsWvuyeU6eTWEpWlP7deZ800a2cN6EjugIQd5U6uo1k61FlMS2+oNHR5dlMgJm1ygeyuvR3C76jWHnq4TtOp9Ir6X5rWpGN9/dBAQZoOfZ8eyrU3Q9oDdBmUBlcP82FKP60E8xt7S49+dM/XbwXEdG15ZSD9abZJq//6WwNO/rjQX/Ra6WGK+owGkdLO7yddhN+l1wqEpZWJZkJnlWe1hNcbt4/qQVgvLftso+Feig7LOvLqo/7fyDrbbHtY0mFBgyUd3Esb0y/1+DR3Sd+lc/j+ic6tvqwmuCrYFn/211iCtKWdN6sPpIBb0b5pWD+UZSavjuT1/ZSZIH58klYfCNOAsfqceh3XFQbCZKPwe2VLBYw06Jylpyi+PxavH5qwlJYSW7ZSusI0e/nJxWn6NKku7diwPsoWYf0o01dOmmSX1XeOf68Fv7WgQXAvRUcr6IDGQTAMakJSJ6Pa4E7ScVkbhfYUpUP4/Oc/7172spdlpjlU51B7jOjnXe96V7TPzDe/+c0un/niF78Y5Qa/+eabG2avZtgMADqbtA2h89KGFMnfrxW5tUhNoPtsVr7+vNS0Welr0vbFkl3+53/+J/O8ZdEggvYQ0wxT33b4mWtxFCDzG8DnkbW/ggY34ntGZAWGfDtUZgJG1orvpLRCuuasPb7Kpmgsgq41Le2wBquSbAQAAACt1S/1aCxAQYWk71O/RcEToUHTtJVoSsuYlarQf1caaakVRTWpFYuOD2jwN2ksJEmrrl/BBfWls/apUh827BvqOKVJ16SxWqF+b1r/M8vO0pb1vJHV59XKv6yxm2rLN4+srUM0sRsAAIrBUzzUjWqXHycdV4sZQ2loab9+yqDO09e//vVoT5v/+I//6PK/97znPd02YS1CT5ZrN9JmRfdZKbKqrFmbvIYbzDZrs1kLGtCR/CAYzhrrdHuk6ZAvp92zFCjK06uHxLTjFciJH1+NPfwG40nn0QNsVsBIgSKt4I0fq/qh9/QgG9eha/rGN74RHVvL9Bvag0HtiVaIKX2PAkLh6mKdKzyfPquZrHmDP5otW7YM48d4e/h2sUw91WzqtPPrmuPflVZnpEE/0lxrX9Ggk2YHJp1Xe3WG57Lus42Geyk6LOvIO5/+30hNzbaHJR0WNFjSwb20Mf3SeFAhaVKT+kD+e5TuTxOQdM6wL6TX1113Xe75yvaBhIJPCtL1pB+W1ffTdcePV91L0qq+pt5TAC7UILxN9H1hkErfpQlj27Ztq2k/ObzueP3IsrMmmmXZK+vYJFt51G9UPzysE+H3pJVvEfRsk9YX9pkkrPmtBQ2Ceyk6WkEHNA6CYVATknKq+hROZUk6rmxgqRGoEf2Xf/kX9/Of/7zLHibKqX3XXXe5V77ylQ2xV6Nt1pMVD5pp5wNi6pAsX77cNQMNJCvnupg3b16PNrFtZQ3oSMYvkVcnqJPraJaOtBmduu8897nPzf1e7f2Utv+gUsvE7V6NPbRHgPLyJ6GZvVllq9Q3ScdqRq1+lMYx1KE6c+edd0YP1mkDMU95ylOimcJ6rVms/mFWD83vf//7U/czUOpeP9tT537ooYeitLx+Rqq+Jwx8abWzBh+mT5+eufpNeySk2Ue2SbKPBlHCY6THBx2f9rSnlfKXn/70p6nnf8ELXhA91IdoYCFplZY0qF2ZPXt2zX3lvvvuS9X4kpe8pDJDvBV8thlwL0WHVR0KdGt1QhpPetKTejR7v9XsYUmHBQ2WdAjupfXvl4ZoIDSpT6Zzyf7a50p70vpJVQrG+Odu9fWe//znZ36/vvuee+5JnWCqbRuS7Ks+ULxvFPbDrr/++tzMANo3Ka1foyBevH5pIlLaijM9y2sCsPZ4DVMj6v0bb7wxWiEX7/urDiedX+9fddVV0Z63ChZpxVY4GK2xFaVVTCLsf8brR9b1SqP2KE5DGQnSjn3Ws56V6ovaA019cV8uYf1Qmeu81SD7pvVL1d+/4YYbTPqtBQ0e7qXosK6D9IyNg2AY1ISkTabVUaxVYKeRm1iXQR21v/iLv3C//du/3eX9b3/725nBsFraq9VsBgCtT1qKvaJB+Kx0d1np+8qgVVRp6CG12mOT0pCo85yWukQP9W94wxuiAFgSGkRIC4TJnnqIDv9esmRJpnY/61EbnoukgJiCfVkpXObOnVvaLmmBwLQHjrSJFXoIiA+g5NWZvD0wqiUtBZIekBRsBAAAgNbvlxbpI6rvosk3CqQnPXurb6AgSR7qL4Z7aoVooljaIGxWH0zkpW7WqjL1/9JIypST1x9W30+TfDUZOHw/3o9TP03BsCQ0qes3f/M3U1NL6lgfcEsiLVinfqFSjJc9rqfPEVnH9SSltyZfp02eXrBgQdXfCwDQiRB2hJqQNAtJs7irIUz/5Gnk7Myy3HTTTd062prt1Sh7JdlMndKepDMEAMgiLTBRdNAhK8/+li1bXC2o1wNwGJzyrFq1KvXz11xzTWogTDzwwAOp/9MgQ1JqmqzULmFbooBY3J4aHPjOd76Tes5p06ZFKQDL7J9V9gE/b3AlaUAnq85opmeWtmpQsG779u2J/1u0aBHpMwAAANqkXxrvz6Slx1NQ5sc//nHi/7Qyp8ge4NUGS7L6tXn7ponvfve7qUE49W2TJvkUCQhpnEbBL9ksnhoxDOSkTfzV92TtsaaJSVkZdNICU1oBnLZHWt7+uqpP1e7PlrWvV95kvGomaKk/qn4pAAAUh2AY1ISkWdzKB10NScdpZr1V1OFbtmxZqWuvpb2SjrVsLwBofdJmnyalgE0iK1ivtCJKBVgEBSuSVgTlBUdqHQzzqVCSSFv1JZTuxK/gKqNTqRY1czgpIKMVX+GAiAYgwoDYN7/5Tbdjx47Ucz7jGc9IfF/fmXUteTOSezoQlDfBQ2kXi6wO04BK1gCHZ+XKlanfl7c6DwAAAFqnXxoPoKVNxFX2l6S+kNIXxscDah0My1sZlrV6Sts6qF+ThtI7lj1nGNhRRhpNpEpb1VZtP1n9Ne3Hm4aCj6NGjapqUlzWvmVZq/fUH00K+IWaa9FXjtvIp3CLozThzUw9CADQipAmEWpCUu7iamf3Jx2XNVvIAvH0UJqFpIHDcB+X+Mx7dcDCgbZq7aXviAfDrNur2ajjrFzA/nWnakCHXazYI01HmqasVHbxe1TWzMmvfvWrUdBC+7bowTHUoUEKbVyuB3s96P7e7/1et+MVCPN54ZPS26Y9OPv7d9qDrLRoTwU99Ic2SFrR7NEMYu0xFrYHOocCYd///vddFmkzSDXoooChHn5PnDjRrU3QnmLPfvazKxq1f4Ee3L/3ve+5Rx55JPV8GsgJ98EKSZrRK3v44KA2Rde+DRokCPOdS2N8MKqa1DNp7ZrXoBRJd999d7THmoKFcVSmspmCZq961asyV5zLhj/72c9SdSQNVln32U7Fij3QYUuH7kkaQNTkAd8XV7/crxSoZgC9le1hSYcFDZZ0WKHd+6Vxwj2fQvxKo7D/o/uF9jrNCq70tA+UlvIv1KH0jbqvxfd2VZ/wf//3fzPHMbRfb0+1Zq2Yz+on63lA+4EtXbq0y/vq6/vngaJBrbB+ZF1zTybF5R2r/nJYLmEATM8E6qNqYnTYzuh1VkBLgcx4f9/z1Kc+1bTfWtBgCSv2QIdNHdA4CIZBTUjaX0Sz0ash6bi0/UusoMHVOOqwpAXDtDRfHVVt6hoGwzTrp+yMIQXC4ikArNur2aiz3uwZVBY0oMMuVuyRpqOngw5K56GH9jR0L9QKJm1QrodtadB7SgGowQB/Ht9prWWKRB2btiJIQbSkPcNkj7QH/UOHDrmPfOQj0SQIXYc+l3TfLqNVD9FqXzTpQkEuBQjD71Mw8Etf+lL0ObVPCvwoWJU2y1XoujSYk0bS4LCCXj4F0bp166KfOG95y1u6TRjJWpmWds36Dq16jq/4CzWsX7/e/cM//EP0WQ1q639qi3WM9vnIWp0dou85ePBg4v/SNty27rOdihV7oMOWDu3L8+pXv9qtWLGiMnFCmtL8u93tYUmHBQ2WdFih3fulcdT3fPjhh1P/H/Y9NJFIk5SKoPtN1mSwtD6Q+pJJk5JCHeonf+pTn4q0qA+kIJImQ2XtxSt7vuhFL+oyiSk8Z1oARsfl9aVC8gaX77rrLvejH/0o6ovqOtQX1/nziNsrrB/V2LkWwTD1l8NyCVFfVP3zONdee637lV/5ldTv1ESuJPRskTQp3ZLfWtBgCSv2QIdNHdA4CIZBTdBMnvhKp6yBzizix+l7rackSpo5lrfP2VVXXdUlGKaOqmZFaSVEGZLsXDRNAwBANaTl2U/bDyCOHtQVEMtK2eK/L2tiRTzIUouH2GqOVWqYrLQoerBPug499O/duzf1YTopJaPQQ/aTn/zkaP8HoQEJPfSHATGdU9ciG2WloBGaparNy7NWQygQqMGMomUsNLkjHjzU4EzaAIfa+7QyFTfeeKP713/918xzqh+i604rR11r3qCMBmTSynnmzJmZxwIAAEBr9Uur3dtJ/YnnPOc5hb83K3OBJjelpYTO6ptqEDf8TqUQT9vzNI4mQcUDeQqAyW5ZwSRNTiozgTdpL9o4Ol/8nLKHAppp5VjtHmt55duT5wj9X0HIMmR95+OPP546iez6668vdR4AALgEe4ZBTdAM9YULF3bb5DNtZnXWIFZ8EEqBsLy9QppNPIez7JG3Ya/SScVRGoOyJB3z9Kc/vfT3AAAUJW1fQj2wZqVCCXnhC19YeCZtLYNh9XgA1l4LRdPjeObMmeOuueaazGtLmqnrke388X6Gbtju6Nj4e2kDFG984xtTVzKH35elN62exNPm5O3HlhWQUwpHbVDfE7KCbUKTVDZv3tztfZVv1qxdAAAAaN1+abyvUKRf96xnPavUaoJ67BemSclaIVQGXZsmGMXTIyoQpsm5SqmdtQdZ0WChZ/r06bn9rziyq9JaZ2VSSNOhFLhpAbSsPeFqse+w+spZ/fey33nPPfckvj9r1qzoBwAAykMwDGrG8573vG6BLe3fUQY19vEAWvx7raHOYnzPLq14yCPpuv7zP/+z1LmTbKyO5uLFi0t9T6ehjr5SX+gnLf1DJ2hAh12s2CNNR1ZqlKKTIJQu9rWvfW2hlb8azPAzRsOUN0kP1rov1is1ilYVJdlDD+PPfe5zXVH0oPzrv/7rmbbK0ym0F9kznvGMaGaygk6yhx7yfSBMNk5D/9Ns5te//vWZG4GHPPOZz+zSvqWVS9Y19GS2rVAas5e85CWV1V15GuLkDcZoX7Uk1K5mzWy27rOdihV7oMOmDitYsYcFHRY0WNJhhU7ol4aoL5W2Ol+ov6FV9+pLlbFHtX2gtFVOPtg3f/78wn057XurPmh8ryl9jwJhCj4prbb25k3br6tsMEzBt5e97GXRuYsGN9U/1eqztBTfsn18RaCvH0qzntYnzAt0Zq3eU4Aub/KYTzuujBFF+qZZmSCUVSLMJORRn7/IBC0LfmtBgyWs2AMdNnVA4yBNItQMdar+7u/+rst7H/vYx9zrXve6wt/x8Y9/vNt7mhFkmQ984APd3tNMqzy0kk4DmVpB5/n+978f7bmiFQNF+M53vtOtg/TKV76y9AqFTkOD5b5jmrY3UCdoQIddrNgjTYceJPUQmrQPgWZjFkmHIhTQ+LVf+7VoZurPfvazaJVt0gxQnds/mGqAQiuEdJ9UcCqOHj7TZpHqfEr3l4YGNtLSFvrBFp96Jl4u1113XfRQrtSFSalzhWbuahbxjBkzor+zzlUkMCRki5e//OXRfgIPPPBA5TqSVoSpbdA1KKClVL1J+11moYdvDWYoFe9DDz3kNm7cGKU8VB1Jqqf1CIYJnV+bxGugRpuRezum+YquWwMrmkEb36A9RO2v9u+sJg2SdZ/tVKzYAx02dVjBij0s6LCgwZIOK3RKvzQe8Enrp+nc2udJfdMy9qj1yjCdW/1a9fl++7d/2913333RRN2kAIz6fOo/KYNMPCilQJj6dWH/WX1Zra5S/yneXywbDPNBIu0j+41vfCPq7yfZTQE92VWBOvU5dS1pJAW1fP3I2v+3J5Piiq5uU3BSffNHH300qiPa81h1Mymwp3550koy6dd+v0nIRlnBWkt+a0GDJazYAx02dUDjIBgGNUMz3TXIpBlFHnXI1OF5/vOfn3u8OmDxDUU1QGp5/ysNen7uc5/r8p46ZS996UsLHf+mN73JvfOd7+zy3p//+Z+7L3zhC7nHqjP1f//v/+32vtJdAQDUEz2gau8kPeTF0UrZsqtTFSTSj+5rGnjQrEzNRvWDGnpfD+WajakHwKzZr3o4/Mu//MsqrurSHldJ91VPXqqdefPmublz50YP4XqY1ufVJkj31KlTuwXvtE9XLZA9FGTTjwZ9/Lk1iKEHbB8E1ABA0Vm5Wajs9aNz+DTBuvYiqYI0OKCfWlyzVsVpn00FxVQ/NGAje2vgQTNtpUfXXSRdpAKI6q8koT6M9XTNAAAAnUqt+6VCk7X0k0TY/ylDtc/pf/iHf5irQ/07rZzXiiFNlj1y5EjUD1QfUP0jBQTjqav9d2j8Jh5A00RnoX6kJlFlTSYryvDhw6PvVXBIe2EdPnw46n8p2KZAk1KAh4EhTR6Ob8VRBKUvV3CwTApLjyaL6acW/VSNjxXtH8dR3zZp7zeVpfr7AABQPQTDoKb86Z/+aaXj5Hnzm98cNeaaDZSGOmE333xzt07Yn/3ZnxVO3aRVVfGUi3o/izvuuCNaxaXOc1lWrFjhXvGKV3RbRq+VWUU7beoQ33bbbV1mnX3xi1+MOt55K+Le//73RzPiQ170ohcVStEIANBTFPRJG3SoFp/eL57uJnzYt77yVfoUdCq6yqnWaHVa2kby7YoGGfRT7YCDb9OTUilpBXeRVJ4AAADQXv3SVkQr5GSLIqQFwkI0IW3VqlXRZDRNGqsFChQtWLCgJt/VjihNW9KqMAUzNU5Uq3IAAOhU2DMMaooCQdrPI2Tr1q1R+qgwHWD8/5rd8otf/KLL+3rvpptuqqtercBSZ/E1r3mN+9rXvpa5QatHs88VpHv2s58dpYeKz3a69dZbS3UE3/e+93V7X/vo/PM//3PiEl11VrV67N3vfne3ju8HP/jBwucGAOgJuncmPYxpVVRSmhqwjWYQd2paCLXrP/zhD7u9rzb9hS98YVM0AQAAQHHol5ajSCDMT/JS2j8CMI3jW9/6VuJ+bRofa9ZkOwCAdoIWDWrOZz7zmWhpeTjDesOGDdGKpRe/+MVRsEx5prWhqPY40UqoeCdMs9o//elPN0SvluZ//vOfj3408KXUjEr3OH369CillVIOaJBwx44d0WCZVqAldU6UkklpHrV/Sxne8IY3uP/+7/92d911VxdNb33rW90//MM/RCvt9J2ykfYz+exnP5u4ZF77tRXdawwAoKcopYlmdSrFbXwGqe75zPhsHdS+qX1RqkrtZ2Z99V0t0erur3zlK1G7G6JBH7W/tUgrCQAAAPWFfmlxlKZQdsoLhCljg+w2ZsyYhmnrdLS6MV6HhQKST3va05qiCQCg3SAYBjVHQaSvfvWrUb5qdbTCjqgCPmHQJwnty6FVWhqUazQKeim9on7KoBzaCt4997nPreq8CnDJXj/4wQ+6vK+0YO95z3tyj/+jP/oj9/a3v72qcwMAVIv2a0p6YFu7di2DDi0WCBNbtmyJVocpdXCnBMTuvffeyAZxtCJMe1cAAABAa0C/tLaBMG390Gmpt5vJ0aNHo3G0pH3ClB6xU/rmAAD1hmAY1AXNWrnvvvuitIllNpfVjJf/+I//aKnOqoJYd955p5s6dWrV36E9Tr7zne9EAa1PfvKTpWbAfeADHyAQBgBNQfc9TQKIP1Brs26wz86dOyuBsDB1seiEgJhWg2lVd3x/Ua0S1wpxAAAAaB3ol2ZDIMw2e/bscU9+8pO7vb948WLXv3//pmgCAGhHCIZB3VBaRHW2FCi6/fbboxlZaWjTewV03vzmN0cDU41M6agglFaC/eQnP4lSKBTZM+Xyyy+PgmC33HJL1FGsBeqkf+ITn3A333yz+5u/+Rv3zW9+s1vapnCg7jd+4zfcn/zJn0Qr8QAAmgUpO1o3EJbWLndKQEypEK+//vpmywCAJgwIf/e7341Stvt+v+51+/fvj14/5znPifb1BYDWg35p+n1Pe4SljS94CIQ1D22NUXbLDQAAKE+vi526Wzo0nPXr17sHHnjAbdu2zZ04cSLah2PKlCnRHl2zZs1yVjqJmiX/+OOPRwOF+lsb7ip1o/YPGzduXLQfWiM2Lj18+LC7//77I7vpdZ8+faJ83Vo1d8011zQ0aFgrFMTT8n+h8tfGvc1Atz3/IKDB0GYM9lrQgI7urFixItpDSMjntMdhJ9sDHe1ZTzXgu3LlytzPqY3WQ3nadVqwhwUN6OgO91J0WNahe6Am6qWhyW6N3COn2fawpMOCBks6uJeioxY69PytScr1CoRRT9FhWYOgjqKjFXQoY5jGyoXGoLWND9QHVoZBw5g9e3b0YxnNAlWwSz/NZsSIEe7GG2+MfqC2qHFrdjDRggZ02MWKPdBhU0dP0eQO/Rw6dCjzc5q8ItICYhbsYUEDOuxixR7osKnDClbsYUGHBQ2WdFjBij3QUV5HmUCYMvtoL/RWpZXKpVN0WNBgCSv2QIdNHdA4ejfwXAAAAADQZDQjUgMeCojloYBY0RTCAAAAAGCDTgqEAQAAFIWVYQDQccQHdZuVMqDZGtBhFyv2QIdNHbUMiCldYt4Kse3bt0e/ldI4vGYL9rCgAR12sWIPdNjUYQUr9rCgw4IGSzqsYMUe6CiuoxMDYa1QLp2mw4IGS1ixBzps6oDGQTAMADoO5eFds2ZN9HrevHlRbt5O1IAOu1ixBzps6qh1QOyRRx5xBw8eLB0Qs2APCxrQYRcr9kCHTR1WsGIPCzosaLCkwwpW7IGOYjq0z8zDDz9cKBC2ePHiQpkCWgHr5dKJOixosIQVe6DDpg5oHKRJBAAAAOhQFBDTZulFZgQrILZ+/XpSJgIAAAAYRIGwoivC2ikQBgAAUBSCYQAAAAAdTJmA2I4dOwiIAQAAABgNhJ0/fz6330cgDAAAOhWCYQAAAAAdjk+ZOHr06NzPEhADAAAAaM1AmPp7BMIAAKBTIRgGAAAAAFHKHK0QKxoQ27hxIwExAAAAgCaivtjjjz/OijAAAIACEAwDAAAAgNIBsd27d0c/BMQAAAAAmkOvXr3c3Llz3ZAhQ3IDYSNGjGioNgAAAGsQDAMAAACAbgGxMWPG5H728OHDBMQAAAAAmkj//v3dkiVL3ODBg7v9j0AYAADALyEYBgAAAADdAmILFiwoHBDbsGEDATEAAACAJgbEli5d2iUgpkCYgmQEwgAAAC5BMAwAOjadhH46XQM67GLFHuiwqcNKQMzbY8+ePW7t2rVNCYhZKRN02MSKPdBhU4cVrNjDgg4LGizpsIIVe6AjW0cYEPOBsOHDh7tOwWq5dLIOCxosYcUe6LCpAxpDr4tM4wWABqGO+NGjR6PX6qBr8DSNMOf5hQsX3MmTJzO/Ww1XOAtOGwifOnUqd6B30KBBlb/PnTvnTp8+nXlM37593YABAyp/nz171p05cybzmH79+kUPJh6dQ+fKQp/XcR5dS96myAMHDoweejyymWyXha5fdvAcP37c5SE7+46CmpATJ07kHlO2PKspG127bFCmbOLlec8990TH+e+79tprc8umSHnqHDpXmbKJl6fsnNdkh2VTtDxbxddUlr5sOt3X7r///orGsJ7Gy6ZWvqbvUaDrwIEDlWPC+iwtXs+4cePc7Nmzuz1MVFOe1ZRNp/ta/D7YLF9LqqO18LUiZdOIdq1evlaLdi1eNu3ka7Xqcxw8eNB94hOfSD3+bW97W2KqMYu+lkQ7+1q9+hxWfU33UtlA1ySNy5cvbylfq0e7Vk3/vl6+Fr8PWvI16dJ1Dxs2rO6+tmLFiujzqgM6V9Lzk3Vfq7Zdi5cNvmbT11RHdYzsnfaMX62vhWXTbu1avfoc7eRr8bLpia+NHz++olH37iNHjuTqher4Ze0DAOght956q7vttttS/x82PmqM1qxZk/rZq6++uvJaDXDWZ30jvGjRosrfarQ0eJvF0KFDo82GPWpsNm/enHnM6NGj3YwZMyp/79+/323fvj3zmIkTJ7rLLrus8rf219m7d2/mMdOmTYsGlT3btm2LUpFlMWvWrC4pMDZt2pTbEF955ZVdGu9169bldkQ029B3LPXZvLLRZ3VM2KnKO0aapC3siCoNWxa6dtkgHOzaunVr5jGysWwdnsd3OtSxSdKpslSZelT+OlcWqjOqO57HH388t3Otuqk66tH15z1cyAd8BzbPx5J8Td9f1tfk16o3ZXxNdfmxxx4r7Ws7duwo5Wu7du1y+/btyzzm8ssvd2PHjq38rTqT1/FUsCecZVvE17TCKuz0FvG1ZcuWVTrw6lj7stH9wx8b1lM9VGgGsEea8spTDwnz58+v/H3s2DG3cePGxM/q+/R/XUdYn/W+vz/J3voJ/y/UuZ86dWrlb02G0L0wiylTprgJEyZU/lb55/nazJkz3ahRo0r52rx587o8+BTxNe294R/8iviayvGqq64q5Wt6UNTebWV8TQ9Oc+bMKeVrWvk3ffr0Ur42adIkN3ny5FRfS6qjtfA1tdGN9LU06u1rnpEjR7orrriiVLvWzr4mauFrsn0W6kNu2bKlJXwtibiv6Vr8ZLQy7VreQFvc14qsDm6Gr+nadT1lfE2TP9T3LuNr8rOsiX5JvqY+5KFDh1I/r3up+rdhX131LG9AL+5r69evzx3QlJ394GQ17ZoG8sq2a7pnSFtZX9P9JgvVf/mBRz6zc+fOzGPkZ/I3jz4vH81C/hyuni/ia7oWXVMZX5PNwoBDEV9T2fhjijxL6z6r+20ZX1MdU13z6DpUPlnHqd1Q+1HG1+Qz8p0yvibflI8W9TWhe4DuBWV8TfeaMLBRD19TP0r3zjK+pnuz7tFlfE1tgNqCdvU19WtUNmnP+D3xNd+uNcrXirRrtfA1+Rm+1jhfY61S4yAYBgA1QwMORWZbAABAa6GHPD28auA268FDD2vhww0AAAAAAAAAgAVIkwgATVkZ1sw0ifo+Be60Ykmzhfxy60amSQxTCOj/4ZLvRqZu07X479X58uxcr+Xmvky8Dtm5k9Mk6n86v358/WhG6rZ4uUhHM9IkSoeuRVqS/KVRqR28PVTP9ON1tHuaxBB9p1azaMafNIUpEjUDUbMWw/tQvdMkxuuorqUZaRI1QzPrnt6oFDc6xl+LdOg8nZwmUdev2ZY6JuveUe80ifq/rlXavI5mpG5Luqc3I02i1yE76zvSyqVT0iSmlUuj0yTKzl6HfuftnVGPNIlxW8jOzUgnpc/760m7d3RSmsS0Otro1G06h3TJ1ln39HqnbvP20DlCP6hXu6bzKcuCLx9fN7PuHY1MSWolTWJoD9XNsA40MnWbzpPVJ2xUmkTZ2euQhjw71yNNYlId7eQ0ibKHrlV1wNujGWkS0+4djU6T6HWoPktvVn+wnmkSvQ59Vquxw+cI0iS2JwTDAKBpe4YV6VzUA53XL3eOpxHpJA3o6I5/mBN+0KGT7YEOmzqaXU/1YPDoo49GaTB8ui6lu3jSk56UGAjrhDJBh606as0e6LClQ+mYbr/99tT/33LLLV3SMbW7PSzpsKDBkg7upehQ8H7lypVRKvD4nqxW7EE9RYdlDYI6io5W0KHzEgxrDOlhVwAAAACAhBlw2tPPDxb7/VkaHQgDAAAAaFe0x48CYZqEpDTV2oeGuewAAAA9gz3DAAAAAKAUmpk8d+7caIWF0iMSCAMAAACoXSDskUce6ZKmTwExEV8hBgAAAMUhGAYAAAAApdFATCNTiQEAAAB0YiAsDIip/zVr1qymaAMAAGh1CIYBAAAAAAAAAAA0Ea24VyAsKx2i9mzt27evGz9+fEO1AQAAtAPsGQYAAAAADUMznZNmOwMAAAB0KkUCYaJfv34EwgAAAKqEYBgAAAAANAQFwTTQs3r1agJiAAAAACUDYUuXLnVDhgxpmDYAAIB2gmAYAAAAADQsEKa9MPbt20dADAAAADoe9YkIhAEAADQG9gwDgI5Dmw4PGDCg8rpTNaDDLlbsgQ6bOqxQxh5hICwc/Fm1apVbsGCB6927d0uXCTpsYsUe6LCpwwpW7GFBhwUNlnRYwYo92lGH7wvlBcL69+/vlixZ0iUQZsUeVrBiD3TY0mAJK/ZAh00d0Dh6XcxrdQEAasTw4cPd0aNHo9eDBw92x48fb7YkgC6sWLHCnT9/Pnrdp08ft3z58mZLAmj5eqpAmAZ6lAIoiTFjxvQoIAb2aLU6Cp3F6dOn3dq1a6MfvzpV95+5c+dGr/XbD4oANBPupe1NTwJhlqCegnWoo9AK6B5/4sSJ6PWwYcPckSNHmi2pbWFlGAAAAAA0JRAW7pOxcOFCAmIAUHcU6Fq8eLE7fPhwl8ExvQcA0Aj27t0bpYsuEghTakRNJAUAAICew4gDAAAAANQFpZrQQE4eSp+ogBh7iAEAAEA7QyAMAACgebAyDAA6Ds0C9suP9XCh2cCdqAEddrFiD3TY1GGFIvZQMGzOnDnR6507dxYKiJVZIWalTNBhEyv2QIdNHVawYg8LOixosKTDClbs0Q46ahkIs2IPK1ixBzpsabCEFXugw6YOaBysDAOAjuPUqVNu3bp10Y9ed6oGdNjFij3QYVOHFYrawwfEJk+enPudCoitXLmykrqsVhrqDTpsYsUe6LCpwwpW7GFBhwUNlnRYwYo9Wl3Hnj17CgXClMq1yIowK/awghV7oMOWBktYsQc6bOqAxkEwDAAAAADqigJis2fPLhQQO3jwYLRCrGhADAAAAMAyCoQ9+uijNQuEAQAAQHUQDAMAAACAhgXELrvsstzPEhADAACATgyEDRo0qGHaAAAAOg2CYQAAAADQsIDYrFmzCIgBAABA27N79+5SqREJhAEAANQXgmEAAAAAYDYgVmYPMQAAAAArgTCtCMuDQBgAAEDjIBgGAAAAAE0JiE2ZMiX3s4cOHSIgBgAAAC3D0aNHCwXCBg4cSCAMAACggRAMAwAAAICmBMSuuOIKAmIAAADQVgwdOjS3f0MgDAAAoPEQDAMAAACAlgiIPfzwwwTEAAAAoCX6N2kpoX0gTL8BAACgcfRt4LkAAEzQp08fN2zYsMrrTtWADrtYsQc6bOqwQq3s4QeM9Hvr1q2Znz18+HAUEFu0aJHr27evmTJBh02s2AMdtnTs37/f/dM//ZO7ePFil/fvvffe6Pfb3vY2N2bMmI6xhyUdFjRY0mEFK/ZoNR0+JbTYvn17zQNhVuxhBSv2QIctDZawYg902NQBjaPXxfhTAABAnRg+fHiUP10MHjzYHT9+vNmSALqwYsWKyqoTdYSWL1/ebEkAHVNP1SXdtGlTbkBMjBgxohIQA3u0ax2F9kDBsNtvvz31/7fccktDg2EAaXAvdW3Tv9mwYUMUEGvHFWHUU7AOdRRagSFDhrgTJ05ErxWcO3LkSLMltS2MIAAAAABA09EM6pkzZ0a/t2zZUmiF2OLFiwmIAQAAgFn8CrF+/fq5iRMntlUgDAAAoNVgzzAAAAAAMDNgNGPGDDdt2rTcz2q2nAJiJDkAAAAA6/2b6dOnEwgDAABoMkylBYCO49y5c9GqAp9qqxmrCixoQIddrNgDHTZ1WKFe9vABMZG3QkypzA4cOFBzDa1aN6zosIIVe6DDpg4rWLGHBR0WNFjSYQUr9kCHTR1WsGIPdNjSYAkr9kCHTR3QOFgZBgAdx+nTp91jjz0W/eh1p2pAh12s2AMdNnVYoZ728AGxyy+/PPUzs2fPdmPHjjVRJlbqhhUdVrBiD3TY1GEFK/awoMOCBks6rGDFHpZ0bN682YQOC/awghV7oMOWBktYsQc6bOqAxkEwDAAAAADMphRKCohp743LLrusKboAAACgc9m5c2c0aKrVBAAAANBaEAwDAAAAAJP4FWIKioWBsClTpjRVFwAAAHQe27dvd5s2bXJnzpxxW7duZRUBAABAi0EiTABoGsePH0/935AhQyqvL1y44E6ePJk7YDp48ODK3+fPn3enTp1KPa8eXHRMiGb35T3QKH/wgAEDKn+fPXs2ehjKol+/fq5///6Vv3UOr8HriaPP6ziPrkXXlIU2ZO7Tp0/lb9lMtssi/v+sMvHIzt52Fy9edCdOnMg9Jq884/bo3bu3GzRoUKmy0bWHm1IXKZt4eeo8Os5/X5Gyka68maE6R5h7Oq9sdF79X3bwyM6yd9Gy8d/Tk7JJqqdlfM1TTXkmlY3OlXVNSb6WVzZlfM3bI/x8UV/T9Yfl2RNfU732GsN6Gi+bIvfOWvhaXrkklaeuwftakbJRMMz786hRoyrni9fRsr6WdO+s1tey7un1btc88f9X42tFyibP15LqaC3atSJlE/e1vHKppl0r62s6t2wS2qxe7Vq8bMLyTKun1ZRNLXxN2vLuHfXoc+T5no7J0xUvm574Wtb9o97tmie8liI2qnW7Fn5HaItG9TmSfC3v3pHla7UqG32nbBDauRHtWrxs0upoI9q1pD6HyjSrvtWjXfMrwhQI03fpf7rmVatWuac85SnRsY1q19L6QLVs16rxNdkjr09Y63YtJLRH/P/1atfiZSM75/UJ69GuxYmPcxTpq9fiWTpeNkm2qKZdi5dNNb4mXbJ32jN+te1aWDZ55ZnUH2xku+bLM62ONqrPES/PIv3BJF/rSbsWEtojpJpn6WratSK+BrWFYBgA1Ixbb73V3Xbbban/DxsfNUZr1qxJ/ezVV19dea0GOOuzvhFetGhR5W81WmvXrk38rBq6Xbt2dem4iSNHjkT537MYPXp0tErBs3///miGYBYTJ07sks5r9+7dbtu2bZEGT1zLtGnT3Lhx4yp/6/N+U880tFpCG3569LCW1xCHqy3EunXrcjv9S5curXQs9dm8stFndUxo//gxvkw8I0eOdFdeeWWXDsqGDRsyz6Nrlw08Bw8ejGZsZiEby9bheVQPfMcm6dpUlipTj8pf58pCdUZ1x/P4449ndvh8ZyysF7r+vAd5+YDvwOb5WJKv6fvDY+LlIj1xX5Nfq95kMXToUDd37tzK36rLSi9Txtdkr0OHDlV0FPE1ad+3b1/meZSCT/tOeVRnfB2I4+0xfvz4Lu8X8bUFCxZ06fQW8bVly5ZVOvDqWPuy2bt3b+XYsJ7qoWLJkiWV46Uprw7oIWH+/PmVv48dO+Y2btyYeYz884orruhSB1Q/08pFyGZTp06t/L1nz57oXpiFVn9NmDCh8reuT76WVUd1LQqWFfU1MW/evC4PskV8bfHixZUHP/majsm6p6scr7rqqlRfS0LfsXDhwlK+Fg/UFvG1MWPGdGkL1K7t2LEj85hJkya5yZMnp/paUh0t42vhvnDDhw+v/K02uqyvqZ75h+W0Oprma2mU9TXV0wMHDkR264mvFWnXsnwt6Z6e5Gsq/7x2bebMmT32NWlT2WTdO0JfE3llU8TXZPss1IfcsmVL5meGDRvm5syZUxNfSyuXIr6WRNzXdC1Hjx7N9bVw0FDlmUfc19TvzhuYyvO1uC1UX3raruna1U6X8TX1N7Lu6Um+Jj9Tnc4i7mvqQ/q+TRK6l6p/Gw60qZ7lDZ7HfW39+vW5A5qysx+cjPchk+po3Nc0kFe2XdM9Q9rK+Jr8U77jvy8J1f8wxbJ8RoGsLORn4T1an/fnEbon+vL1gQWVi/Q8+OCD0bNOEV/TteiaPKqbeYPasll4rd7Xsu4dYdkUeZbWfVb32zK+pjqmuuaRHt0Ls45Tu6H2w6O2Uc+5WchnwowAab4W2kPlKb8u6mtC94DwmCK+pntNGERUfVbdyLp/ZPlaEupH6d5Zxtd0bw7bG11H3piF+ltqCzwqy7y2IM/Xkuponq8loWtRG+qpxtfkw/KFtGf8LF/LQr7m27U8X5M9dF8P63M1vlakXcvytbR7R9zX5Gd57ZrawfDZuBpfU/nn9QeTfK0n7VqIt0c8iFzU19QfKtOupflaXl2D2kEwDABqhgYcisxsAgAAAAAAALBOGAhLQs+/Cohp8hcAAADYhmAYANQMzSgLZ2vECQNlmnURzm7JQrM58j4bn8WhmXppx4QpxeIzNPLOEy7nFpqhFM44KjJTXzNuwpm0Ws0Uzt4U4fJ0oRk64QyqIiluNBOobJrEcOZlGuHydL0uWo4ezfiJHxPOJpc94jaNz4ZKIpzR7GdDxe2aV576vLe9vi/pnPGy0UqkcJZvEvFZTppBl5cmMT4TT3bJmy0U1rUyPhYeHx4TLxfZJ+438vm884R1RmiWc1lf07lVx5P8JdQfXykWzo5PIl6emt2WlSYx6TxFfC1+niK+Fto6rI+abRemoPPvx8tG9ipbNhrIKetrqgOasZnlb/Hy1AzCcFVJEZtpJmnc1/LuHXm+lnTvLOJr4fXI7uGq1Kw6muZrSVTja5pdGq5OqcbX1K6Fq7Gq8bWkOlrG19LKRqtF88omfh5fZ7LKJc3X0ijra6qn8VVg1fhakXYtXjahryXd04v6Wk/btaTylDbNDi5z7+hpu+YHtu+9997UY7L6kGll0xNfSysXr7+n7ZpWwBfxtXDVpcozXI1U5DzhCvA08nwtbot4cKGadk3tQtny1Ix1nwUg7d6R1L8PswAkkdSHDLMNxNG9NH6/UXtb1tc0Az3v3hnaIN6HzKqj4bWVvXdW079XvZTNsu7pcV+Tz4TZM5KIl6eeu5RBQqsctGoh9Dufbi2sa7o2Pa/F9RZ5XivTvw99rUi5VPssXY2vqV2QzbKOi9tHPpMXRCzqa6E9wlUnRXzN66+Fr4Wrj5PKJcvXilDU18J6VaQPWYt2Le5rSXU0zdeySMqiU7YPqb6QT5OYdl1pvlbUd/J8zaf/bUa7Fvpa2r0jqQ9Z63YtqTzVH8rrD9a6XQtJy2xQxNfiZVNNu+Z9LV43oH4QDAOAmvHud787+klDD/zhcva8AZ2wgSn62bCByTomaQm2Oi/xDl0e6jDEOw156NzS5jXodd71xRv/IuQNYiQ1/GXtrAa77DFp5RnaI669UWWjc/hOSF4dCnVnLemvtmziHausQHMatSibvHpa1E49LU9/TBF/6UnZ5Pla0vcVKc84PfE1dczDQEPad1Vz76ymbLyGMufSNcQfnqotm7COxrXLTgpCKO1q0ftBtb5W5p5ej3atVuVZTdnEfa1IHa2mXaumbMqUS63btZC4TRvVrsXLs4g96tXnSNJW5t5Rq7IpsjdHo32taD2tR7uWVp5lbVCrPmRoi7jPN+o+KD8re++oZbsWfmd8wLcaXyt770wqmzx7NKpd0zFl+x3VtmtaDaZUpknHhnXKp/Mse45a9O+L1NNq7p3VlI3qQNnjatGuhXh7xL+zUe2ayjNM95ZXT+vV54g/51dTnrUqmzxbNKpd89dSxhb1GBuJ26eR7VpYnkXqaKPKpmx/sFbtWkjSdTaqXavG16BnEAwDgI5DHQefc7psJ6KdNKDDLlbsgQ6bOqxgwR5ZGjQI8NBDD0UzuPVbA1X1etCwYAtLOqxgxR7osKnDClbsYUGHBQ2WdFjBij0aqUP7M6bt66gBUj8Qq5UW1QbC2qVcrGDFHuiwpcESVuyBDps6oHH0usgObQDQIMKVYXqAyNtoHaDRrFixostqhuXLlzdbEkA3qKf5qH3R/h0KhHk0S08b3DPzrv5QR8EySnl2++23p/7/lltuqQyKADQT7qXNYcuWLW7Tpk25n6NfcQnqKViHOgqtgNoUv7WMUmEeOXKk2ZLaFkKeAAAAQf5xQb5mgPYKhIXvN2sGNwAAALRHIMyvCOv0QBgAAECrQTAMAADgCfI2iAWA1gyEhf/3KRMJiAEAAIDn8ccfd5s3b879HIEwAACA1oVgGAB0HGfOnIlS5AilwWnGgKgFDehwzl244Nzp086dPOncqVNugH777MFaHaal6dpMVRsoazPYPn0aIqvjywUdLYEFe8Q1HD58ODUQVs+AmAVbWNJhBSv2QIdNHVawYg8LOixosKTDClbsUU8dZQJh8+fPd/v27auLjlYsFytYsQc6bGmwhBV7oMOmDmgcBMMAoOPQQOmOHTsq+5g1o7GzoKFjdWig/NAh57R/nQJhwdaZfRQcC9H/Q6RryBDnRo26FByrm8QOLBd0tBwW7BHXMHnyZHfhwgW3YcOGQivItNdHLXRbsIUlHVawYg902NRhBSv2sKDDggZLOqxgxR710lF2RZgGTtvZHq2KFXugw5YGS1ixBzps6oDGQTAMAAA6g+PHnTt48FIQzAfAFBh7/HHnHnvMuc2b3alt234ZAOvf3w0ZP965yy93bsYM56ZP11Owpg5d+p7Bgy8FxYYNc65376ZeGgD8kilTpkS/8wJi2qC4lgExAAAAaC0ee+yx6CePYcOGucWLF0epERUMAwAAgNaEYBgAALQ3Cm5ppo9SIPoA2H33OXfvvVEALEqV+AQXjh3rmiZRwa+f/OSX36Xg2NOf7tyznnXp7xMnnOvb17lJky4FxQDATECsV69ebv369YUCYprpPUApUQEAAKAjKBMIUz+hr/r8AAAA0NLQmgMAQHuioJZyP+/de+m1fn/728794AeXVolVw549zn3lK87dfbdzV1/t3HOf69zcuc5t3erciBHOTZzYsH3FACCbyy67LPpdJCDm9xAjIAYAANDeXLx4MQqCKT1iHgTCAAAA2gtadAAAaD+UvmT79kurwRQI+/rXLwWxtCqsFpw/f2nFmH60Uuy1r730voJskydfWlEGAC0VEPMpEwmIAQAAtCdlAmHaO0apEQmEAQAAtA9scgIAAO2FAmBKeaLfu3Y59973OveFL9QuEBZHK83+5E+ce/BB586du7RK7NCh+pwLAKoKiM2ZMyf3cydPnowCYqf9voEAAADQNhAIAwAAAIJhAADQPigApgdcBaXuv9+5d7/buY0b63/egwed+9CHnPvsZy+tRNMeZQcO1P+8AFCIyZMnExADAADoYBQEIxAGAADQ2RAMAwCA9uDUKee2bHHuwgXnvvc95+64o36rwdLQnmQf/eilgJhWpR0+3NjzA0BmQGyu9vgrGBA7pXsKAAAAtAUjRoxwvXtnD4ERCAMAAGhvaOEBoOPo16+fmzRpUuV1p2poKx3aw0vpCfV7xQrn/uVfLgWkmsEPf+hcnz7OvfGNl1aI9e/v3KBBnVku6GhrLNijrAb/2bVr1xYKiGkPsYEDB9ZcR72wosMKVuyBDps6rGDFHhZ0WNBgSYcVrNijpzpGjRrlFi1a5FauXOkuaPJcQrBM/88LhLWLPdoNK/ZAhy0NlrBiD3TY1AGNo9dFJU4GAGgAmml39OjR6PXgwYPd8ePHmy0J2gUFnbRP16pVzn3gA5dWh1XB0WPHfhlE69XLDRs6tHpNL3mJcy97mXMDBjg3Y4ZzOTNRAYqyYsUKd16BX6e4ax+3fPnyZktqOXbu3JkbEBMKhBUNiMEvoY5CK0A9BetQR+vDwYMHuwXEigbCoDvUU7AOdRRagSFDhrgTJ05Er4cNG+aOHDnSbEltCyNzAADQ2iiApUCYUpp9/ONVB8Jqzle/6tzmzc5p76F9+5qtBgACNPtv3rx5uZ9TqkRSJgIAALQPfoWYT5moQBipEQEAADoDgmEAANC6aIbXzp2XXn/+887t3+/MoKDcnXc6d+7cJV0nTzZbEQAETJw4kYAYAABAhwbEFi5c6EaPHh0FwrRaBAAAANofpr4AQMdx+vRpt2vXrspg6AClsetADW2hQ0Gms2ede+QR5+6915lj+3bn/vM/nXvFK5zbvdu56dM7o1zQ0RFYsEdPNegYsWbNmkIBsbSUiRZsYUmHFazYAx02dVjBij0s6LCgwZIOK1ixR611KBCmoFivXr2aqqNarOiwghV7oMOWBktYsQc6bOqAxsHKMADoOM6dO+f27dsX/eh1p2poeR3a20vpEcW//Zszy9e/7tzhw84p/3PBlSUtXS7o6Bgs2KMWGvTQM3/+/NzPaW+RcH+RWuuoBVZ0WMGKPdBhU4cVrNjDgg4LGizpsIIVe9RDR9lAWL10VIMVHVawYg902NJgCSv2QIdNHdA4CIYBAEBrog1F1VlZv965rVud6VSOftXawYPNVgMACUyYMCEzINavXz+3ZMkSN3jw4IbqAgAAAAAAAIDaQDAMAABaEx9Y+u53nXnuuefSSjatEFNwDABaJiCmQJjSIw4ZMqQpugAAACCdixcvug0bNri9e/c2WwoAAAAYh2AYAAC0HmfOXEo7ePSocz/9qTPPgQPOPfCA8qxdWtEGAGYDYldeeWUlbRKBMAAAANuBsPXr17tt27a51atXR2muAAAAANIgGAYAAK2HAmHiwQcvpUpsBX72s0u/T55sthIAyGD8+PHRCrH+/fsTCAMAADAeCNuxY0fl71WrVhEQAwAAgFT6pv8LAADAKKdOXfq9ebNrGR577NJvgmEA5lFAbMyYMa5Pnz7NlgIAAAA5gbDwfQXEFixY4MaOHds0fQAAAGATVoYBAEDr0YrBMD2snz59KcWj0iUCgGkIhAEAANhDAa+NGzd2C4TFA2L79+9vuDYAAACwDSvDAACgtbh48VIwTL+3bHEtg9c7e/Yl/YMHN1sRAABAx3H69Gm3YcMGt2fPHnfhickpvXv3jgbPxaxZs9yAAQOarBIA0gJdu3fvjvb0VDrjrM/t3LkzWuUNAAAA4CEYBgAdhx6cLr/88srrTtXQsjq0R5gGr3bvdu7sWddSbNt2KRiWo7slywUdHYcFe1jQEOrQ4PrZJt6XrNjDClbsgQ5bOo4dO+a+9KUvdXv/kUceiX7fcsstDQ2GNdselnRY0GBJhxWs2EMBMAW5evXqlbt6W0GwK6+8sq3tYUWHFazYAx22NFjCij3QYVMHNA6CYQDQcehBqtk55C1oaFkdWmEVpkpsJZQmUeSkSWzJckFHx2HBHhY0eB2nTp2KgmGHDh1yS5YscUOGDOlYe1jBij3QYVOHFazYw4IOCxos6bCCBXsoCLZp0yZ3/PhxNzgnu4ICYdozTCs+29UelnRYwYo90GFLgyWs2AMdNnVA42DPMAAAaC18MEwrxFoNv2LEXwMAtAXbtm2L0q6JM2fOuAcffDAasAMAAICeB8LWrl3rdu3alfvZegfCAAAAoLVhZRgANI2sgcJwRr32czh58mTmdyldRjhL8Pz589Es/Sz0kDRo0KDK3+fOnYv2kciib9++XVLnKB2WBj6ziOe01zl0riz0eR3n0bXomrIYOHBgl5QhspnfCyMNXX/4sFhk8FZ2lr39w+mJEydyjylbnmllo2vUqiopvtinj7sYXJ809X5CV3SeixcjfVnEj7kYHKN3zyfYr5rz6PORzfr2/eUx589H9SetXOPlKTvnnScsm6Ll2Sq+Jj/LSz/XKb4mW3iN0uG/K142PfG1LHTOyBdL3AerKc9qykbn0LnKlE1PfU2BsNWrV3f5v67vJz/5STQg532sEb4WL5tm+VpSHa2FrxUpm0a0a5Z9LV427eRrtepz5JWVjsmrN1Z8LYl29rV69Tms+pq+M+p3BnZuJV+rVXk+9thj0T5h/pg0m40ePdpNnz49KodG+Fr8PtjpvqY6oOOy9Fn1tWrbtXjZtLKvNap/3yxfky7ZO3x2qoWvhWXTbu1avfoc7eRr8bKpl69BbSEYBgA149Zbb3W33XZb6v/DxkeN0Zo1a1I/e/XVV1deqwHO+qxvhBctWlT5W42WZhAmocbmwIEDUQN03XXXVRqiI0eOuM2bN2eeRw9ZM2bMqPy9f/9+t3379sxjJk6c6C677LLK33qY27FjR6TBf2fYgRDTpk1z48aNq/ytwdbDhw9nnkcbvo8YMaLyt1KJ5DXEV1xxRZTWy59z3bp1uR2RpUuXVjqW+mxe2eizOibsVMWP8WXi7TF8+PAuef7VEdX1zJkzx/U6d84NuHDBXezb150Ori/qVASdcXVECnXggw6ff4gT6jYl2a9/v36ud1BeZ8+ccefyOnz9+1/q8D1xLn3+7KlTUblq75I4soc6VfpRuaiOatVJ3sOFfMB3YPN8LMnX9P3hMfFy8R3k0Nfk16o3WQwdOtTNnTu38rfqsgY2ivqaykErbaQlyV/SfE0ziPft25d5HuUHD9MibN26NboXJOHtMXnyZDdv3rzKvaOIrykoEnZ6i/jasmXLKh141UtfNnv37q0cqw64f191TOn5PNKUVwf0kDB//vzK36qPGzduzDxm5MiR0b1D379ly5aoPKUvrVzE+PHj3dSpUyt/677jB7bSmDJlipswYULlb903Dx482OUz8TqqejZq1KjK/x9//PHcB1mVZfjgU8TXFi9eHF2v7v/r16+Prsc/LIUPa3pf162yv+qqq7rozisbPSguXLiwlK/pGH8P0L1D157na5pFr8HDsF2TrbOYNGlS5AdpvpZUR8v4mmf27NlRe+BRG13G1/TZ//3f/43uh1n3jjRfS6Osr6m8VRaqC/6eXsbXPKr/slsWWb6WdE8v6mtxZs6cWbWv+XuHylP9lqx7h/c1T17ZqBzzfC2p3Q2RL0tfFsOGDYv6JGXatTRfSyuXIr6WRNzXdC1Hjx7N9TXdO/x1qyzz2qh4u6Z+d97AVJ6vxW2h+2lP2zVdu9rpMr4mO69cubKiI6mOxn1Nfub71GnEfU1tiFLrpqF7qXwkHGhTPcsb0Iu3a2qn8gY0ZWc/OBnvQybV0bivqc9dtl1TPZO2NKRDvuWfO3QNsrGOiw+Mqq8p1E6q/vv9X4R8ZufOnZna5GfyN48+Lx9NwttDdUDX4/2giK/pvqH7h0d1M29QW+cIAw7e17LuHWHZFHmW1rG635bxNdUx1TWPrkPllXWc2g21Hx7p1/NQFvIZ+U6er4X20LNK6J95viZ0D9C9oIyv6V4TBjZUn9WGZD3nZ/laEqrnuneW8TXVSV2Pv6fruvL6D+pvqS3wqCzVtmeR52tJdbSMr3nUdqoN9VTja+rXyBfCZ6eivpaFfM23a3m+JnvIrnrG9f3BanytSLuW5Wtp9464r8nP8to1+ZnuhdX4mu8Pqm7qGrP6g0m+1pN2LcTbQ+X4rGc9q3JPL+pr6g8VbdeyfC2vrkHtIBgGADVDjUiR2RbNRg/3anjV2OTNWqm3Bv+6Wej6fUeyWbYoag+9H3UQ9ECslVbqdOl1E3WX5omO/8UnOnppNvez0tXBs14ujUA20L1FWpqpw9tDA6nNLBcr+PuHX/XTaXVU5/XBDN2b/ANZ+CAjG+kBLxyQqSeygYV7uhX87EyVSbPvHRocU9lwT//lvUNawoGqTsVauTQTS7awoMMKzSgX3bcVlFSAxQ+C69wanFR7G7a1CoRpgD3MjtAIe6j/wz3dDqE9KJeu9/RwYlEn2sIK3h70B7vWUfWRw0kfzbKH2hCenzoDgmEAUDM0kyNrQ+MwUKaGJpzdkoVmc+R9Nv7wo8Y07ZgwpViIOol55wmXcws9nOUN5MRnuGjGTTiTViu6wtmbIj6wrBk64QyqJMLvFJoJlNe5if8/nOWcRjgLU6+LlqNHs63ix4SzyWWPuE39bCjZRefsNWhQVH4DNYv3iRlv8fKMLz9PIn5M9N1PvBd9f8Lx8WP6adVXgTSJEU/MCO83dKjr27t3l5l0cXvEZ+LJLnmzhcK6VsbHwuPDY+LlonKIX798Pu888X0bNMu5rK/p3CqPJH8J9cdXiuVthhv3NQUt0jrB3h7x8xTxtfh5ivhaaGvVZ28zzbYLU9D59+NlI3uVLRsNKOUdE6Yc8XVAMzbTyiWpPDWDMFxVUsRmGugKZ9QXuXfIx4qkkyrra7oe1QPNBFy1alU0q9IfkzQLWIN5CqL6metxX0uiGl/Tg1y4OqUaX1O7ljdokudrSXW0jK+llY1m0uaVTfw8vs5k3TvSfC2Nsr6mehpfBVaNr8lnsvwsqWxCX0u6pxf1tTjhzOlqfU3aNDu4zL2jp+2anx1+7733ph6T1YdMK5ue+FpauXj9PW3XNPu8iK+Fqy5VnnkDU/HzhCvA08jztbgt/D2zJ+2a2oWy5akZ67K115FUR5P697r/l7GZVrP78yShe2n8fqP2tqyvaQZ63r0ztEG8D5lVR8NrK3vvjK928EirVnjpO+PXIj/Scb6tlR+pTxWWe9zX5DNh9owk4uWp564wS0eIt0f8PEV9Ld6HLNO/D32tSLl4nbVu15J8Te2CbJZ1XNzXVI5xP6/W10J7hCu8ivia118LXwtXHyeVS5avFaGor4X1qkgfshbtWtzXkupoGV9LK5tqfE19IZ8mMe260nytqO/k+ZrsUQtfq6ZdC30t7d6R1IesdbuWVJ66j+f1B2vdroWkZTYo4mvxsklr19J0hb7WqAkdQDAMAGrIu9/97ugnDT3whzNO8wZ0wgam6GfDBibrmHgD7Dsv8Q5dHuowZC3nTju3tHkNep13fXlBnSSKzK6JN/xl7awGu+wxaeUZ2iOuvVvZ6P+nTrk+CoalpNaIAlAlOxS6nsoRvXq5PgU23y58Hq1kU7BYAb0n6kyW7eIdq6xAcxq1KJu8eprna0lU42v+mCL+EmpP8vWe+FrS91Uzk60nvqaOeRhoSPuuau6d1ZSN11DmXLqGsqvJ0somrKNx7dWUTRlf08ObUqo88MADlXPH8+17HnrooShdhx5E69Gu1ao8qymbuK8VqaPVtGvV3AfLtLW1btdC4jZtVJ8jXp5F7FGvPkeStjL3jlqVTZG9ORrta0XraT3atbTyLGuDWvUhQ1vEfb5R90H5Wdl7Ry3btfA74wO+9W7X0somzx61atc0uPnoo49GaXST7Knz6EdlpEFXpc6K95nr0a4l/b9Z7VposyL1tJp7ZzW+pnIoe1wt2rUQb4/4dzaqXVN5qg4XvX/Uq88Rf86vpjxrVTZ5tmhUu+avpYwt6jE2ErdPI9u1sDyL1NFGlU3Z/mCt2rWQpOts1PNaNb4GPSN/lA8AAMAavpMV7N9mHq+1iSkAAKD2aMakZvPlDcYplbD2vsvbrwgAAKAT8YGwvD1q/GSUIoEwAAAAgBB6DgAA0Hr4gJI2em6V5eR+A/gqZksBgG2UekWp5fIG5bTPiQJizd6XBwAAwFogbPXq1YUCYco2ovRlBMIAAACgLPQeAACg9VBAST/aN0MBMetoqfxTnnIpcJezZwEAtCZKiaGUTUUCYkqZSEAMAADg0h7GCoTt3bu3UCBM+9GwtwoAAABUA8EwAABoTUaNuvT7hhucea6/Xsmgtdutknw3Ww0A1Anlry+StomAGAAAwKVAmFIjFgmEjR07lkAYAAAA9AiCYQAA0JpohVWfPpfSD2qFmFX0wO4DdqNHN1sNANSZkSNHukWLFhEQAwAAqNGKsAkTJrg5c+YQCAMAAIAewfR0AOg4Bg4c6GbPnl153akaWl6HBpoVEDtwwLmXvcy5j37UmWT5cufGj3duwAAtG2n/ckFHx2DBHhY0JOlQysTFixe7hx9+OBrsywuI6bNK/VRrHZ2OFXugw6YOK1ixhwUdFjRY0mGFetlD7eOpU6cKBcLmzZsXfd5CuVipH1Z0WMGKPdBhS4MlrNgDHTZ1QOMgGAYAHUefPn1qMujY6hraQseYMc4dPuzc057m3E9+4twvfuHMpXJ8zWsuvVZArFPKBR0dgQV7WNCQpkMrxMoExJYsWdLja7FiDytYsQc6bOqwghV7WNBhQYMlHVaolz369u0btX1qA48dO5YZCNOKMCvlgg6bWLEHOmxpsIQVe6DDpg5oHKRJBACA1kX7cE2YcOn161/v3JAhzhRveINzgwZpVPzSfmEA0FH4gJgesrI4f/58NBh45MiRhmkDAABoNv369YsCYkOHDu32P+0P5gNhAAAAALWAYBgAALQ2PtCklImvfa0zw/XXO7d4saa9/jJgBwAdu4dY0YDYYa12BQAA6OCAmAJhc+fOJRAGAAAANYU0iQDQcZw4ccJt2rQpej1z5kw3uOA+Tu2moa10TJyoL3Huuuuc27XLubvuck1lwQLnXve6S68nTdLa+84sF3S0NRbsYUFDER1hykQFvdLQ//QZfXaEAvw11tFpWLEHOmzqsIIVe1jQYUGDJR1WaIQ9fEDswQcfdMOGDUsMhFkpF3TYxIo90GFLgyWs2AMdNnVA4yAYBgAdx8WLF93p06crrztVQ1vpULrEKVOc27LFuZtucu7MGee+/nXXFObOde6d77y0ImzcuKrSI7ZNuaCjrbFgDwsaiupQcKveATEr9rCCFXugw6YOK1ixhwUdFjRY0mGFRtlDAbFly5ZFK6mTVoRZKRd02MSKPdBhS4MlrNgDHTZ1QOMgTSIAALQH2i9MATE9PL/qVc69/OWN17B0qXN//MfO9e/v3Jgxl4JhAACxgFiRlInbt29vmC4AAAAL9O3bl9SIAAAAUDcIhgEAQPugVVg+IPbiFzv3rnddCkrVmwEDnPvN37y0Ikyr1MaOZZ8wAEgNiCkVVFZAbMyYMW7evHkN1QUAAAAAAADQzhAMAwCA9guIXX75paCU9u563/uce9az6psW8dZbnXve8y7tDab9y8aPr9/5AKDlGT58eGpATIGwBQsWuN696aYDAEBrohXOW7duJeUUAAAAmII9wwAAoP3QpqczZzq3Z49zBw86d/PNzj31qc594xvOPfigkkH3/BwKuCkA9vSnX1qJpnNOnnwpRSIAQMGAmPYHO3fuXPTe6NGjCYQBAEDLB8IeeeQRd/DgQXf8+HE3d+5cUh8CAACACQiGAQBAe6IVF5MmXVoptnOnc0o5pp99+5y75x7nvv99544cKfedffs695SnOPec5zh3xRWX3tOgtfYGGz36UlAMAKBEQEx7iCkgptcLFy4kEAYAAG0RCBO7du2KfhMQAwAAAAsQDAMAgPZm6NBLgavDhy+tEtN+Xq94hXMvf7lzO3Y499hjl342b3a9tm1zF0+fjlaO9dIKL+37pRVgM2Y4N326c9OmXUq/KPR75MhLP/49AICSKAi2bNkyN2jQIAJhAADQ0oGwlStXukOHDnV5n4AYAAAAWIFgGAAAtD8aYB416tLPiROXgmJHjzp32WWXfp72tOhjgy5c6H5ciE+HqO/RijMe6AGgBgwZMqTZEgAAAGoeCAsDYgqEzZkzh4AYAAAANI1eF9nRFAAaOPv9qAIQ0ZZOg6Mc8s3gwoUL7syZM9Hr/v37N2UmvgUNHa9DzZ/OeepU5eesfvtmsVcv10+rwwYO/OXPgAHdA2R1oKPLBR25rFixIhp0En369HHLly/vWHtY0ICO7lBH0WFZx7Fjx9y3v/1tt2fPHucfhTU4P378+Oj18573PDdUq8o7xB6WdFjQYElHq9xL8wJhIVdeeWXF12qto1GgozXrKTo6955OHUVHK+jQ5MgTmrjtNO96mDtSdksPKAwrwwCg41DjNlCBjQ7X0PE6NCtVwS39jBgRvXXqiWCtp59WfzWBji4XdLQMFuxhQUOzdOihXoP6I564fzVLh2Ws2AMdtnQo0HXTTTeZGRxrtj0s6bCgwZIOK2TZo0wgbMqUKW6c9tmtg45Ggg6bWLEHOmxpsIQVe6DDpg5oHATDAAAAnsCvVtQMcc0W14wcAABr+MFHzRhcuHChGz16dLMlAQBAB7ZFDz/8sDusfXlzmDp1qps5cyYpEgEAAKCpEAwDAAB4gvXr13eZJT5x4sRmSwIAyJyF/8gjjxAQAwCAhnLu3LmoLSIQBgAAAK0EwTAA6MjVP2vXro1ez507N8rN24ka0GEXK/ZAh00dVrBgDwsaGqkjKR2V8tzrPQXElOLDgj2s0Gn1Ax2tiRV7WNBhQYMlHVaI22PAgAFNCYRZKRd02MSKPdBhS4MlrNgDHTZ1QOMgGAYAHYnfML3TNQh02MSKPdBhU4cVLNjDgoZG6FAgTKvAkvZl0bn1Pw04WrGHFazYAx02dVjBij0s6LCgwZIOK3h7aEWYBg2VpjePadOmuRkzZtR0RZiVckGHTazYAx22NFjCij3QYVMHNAaCYQAAAAAAxtHg48GDBzMf4tasWeP69+/vhg4d2lBtAADQ/mhSxurVq93p06ebEggDAAAA6Cm9e/wNAAAAAABQVy6//HLXr1+/zM8oILZjxw537NixhukCAIDOCIRt377dHT16tFB7RSAMAAAALEIwDAAAAADAOMpfv3Tp0sIBsf379zdMGwAAtC9KjahA2MmTJwsFwqZPn04gDAAAAExCMAwAAAAAoIUCYkqFmBcQU1rFffv2NUwbAAC0ZyBs1apVBMIAAACgLSAYBgAAAADQhgExDWDu3bu3YdoAAKC9AmEPPfRQodS7CoKRGhEAAACs07fZAgCgczl+/HjmYJ/nwoULubMR9eA1ePDgLnntT506lXpebfwcf1jTA1/ehtB9+/Z1AwYMqPx99uxZd+bMmcxjlNIqHLTUObwGryeOPh+mwtK16JqyGDhwoOvTp0/lb9lMtssi/v+sMvHIzt52Gmw9ceJE7jF55Rm3R+/evd2gQYNKlY2uXTYoUzbx8tR5dJz/viJlI106LgudQ+cqWjY6r/4vO3hkZ9m7aNn47+lJ2STV0zK+5qmmPJPKRufKuqYkX8srmzK+5u0RT1NXxNd0/WF59sTXVK+9xrCexsumyL2zFr6WVy5J5alr8L7Wk7KJ19GyvpZ076zW17Lu6fVo12bPnh0Fu8L7XLy+65gHH3zQzZkzx40dO7ZmZZPna0l1tBbtWpGyiftaXrlU066V9TWdWzYJbVavdi1eNmF5ptXTasqmFr4mbXn3jnr0OcLz6zr1/aGuIu1avGyqadd82WTdP+rdrnnCaxFFVgHVsl0LvyO0RaP6HEm+lnfvyPK1WpWNvlM2CO3ciHZNnz18+LBbvXp1FAjTdfl6F16jv34FwqZNm5ZbB6opz6Q+h7Rknase7VpSPQ3vgY1s1/y1Z907etquVeNrqpd5fcJat2shoT3i/69XuxYvG9k5r09Yz3bNEx/nKNJXr8WzdLxskmxRTbsWL5tqfE26ZO+0Z/xq27WwbPLKM6k/2Mh2zZdnWh1tVJ8jXp5F+oNJvla2XUsrm9AeIdU8S1fTrhXxNagtBMMAoGbceuut7rbbbkv9f9j4qDFas2ZN6mevvvrqyms1wFmf9Y3wokWLKn+r0VKKqCTU0O3atatLx00cOXLEbd68OfM8o0ePjmY9erQni3LoZzFx4kR32WWXVf7evXu327ZtW6TBE9eiB8px48ZV/tbn9VCaxaxZs9yIESMqf2/atCm3IdbDa8i6detyO/1akeA7lvpsXtnoszomtH/8GF8mnpEjR7orr7yySwdlw4YNmefRtcsGnoMHD7qtW7dmHiMby9bheVQPfMcm6dpUlipTj8pf58pCdUZ1x/P4449ndvh8ZyysF7r+vAd5+YDvwOb5WJKv6fvDY+LlIj1xX5Nfq95kMXToUDd37tzK36rLjz32WClfk70OHTpU0VHE16Q9L02cUvqEQQLVGV8H4nh7jB8/vsv7RXxtwYIFXTq9RXxt2bJllQ68Ota+bLTSxx8b1lM9VCxZsqRyvDTl1QE9JMyfP7/ytwa9Nm7cmHmM/POKK67oUgdUP9PKRchmU6dOrfy9Z8+e6F6YxZQpU9yECRMqf2sfrLivxeuormXUqFGFfU3Mmzevy4NsEV9bvHhx5cFPvqZjsu7pKserrroq1deS0HcsXLgw09f0GdnSP6iGD/2+Dshv5AeTJk1yw4YN63aeMWPGdGkL1K7J1lnouyZPnpzqa0l1tIyvhQG/4cOHV/5WG13W11TP/MNyWh1N87U0yvqa6umBAwciu/XE14q0a1m+lnRPL+prcWbOnNljX5M2lU3WvSP0NZFXNkV8TeW1cuXKSK/Or2NUZ3x/7hnPeEauD8iXFGQu066l+VpauRTxtSTivrZlyxZ39OjRXF8L7x8qzzzivqZ+d97AVJ6vxW2h+tLTdk3Xrna6jK/pvpl1T0/yNfmZ6nQWcV9TnfN9myR0L1X/NhxoUz3LGzyP+9r69etzBzRlZz84qUG5733ve5X7bTiQ7oPH+q36qTqtnyJlE2/X9J3SVsbX9Izn98RMu3eo/ssPPPKZnTt3Zp5Hfhbeo/X5tL03fT2VT4cU8TVdS9gWq27mDWrLZuG1el/LuneE98Eiz9K6z+p+6ylSnqpjqmse6dG9MOs4tRtqPzxqG/Wcm4V8Rr6T52uhPVSe8uuiviZ0DwiPKeJruteEQUTVZ9XrrPtH6GtFntfUj9K90yP/zDtG9+awvdF15I1ZqL+ltsCjssxrC/J8LamOlvE1j64l9LdqfE39GvlC2jN+lq9lIV/z7Vqer8keuq+H9bkaXyvSrmX5Wtq9I+5r8rO8dk3tYPhsXI2vqfzz+oNJvlamXcvyNW+PeBC5qK+pP1SmXUvztby6BrWDYBgA1AwNOBSZ2dRsNJivAIg6FWGD2gwNzUYP175z3cy0JlbsYQXZQx14BZEol0sDWwqm5Q1kNMoeCro2695hCdlA9w89JOUNZLR7HW2WDp1XD6EKkOiBUAOU4QOpf0jUw5X3n6SAWDvXUQ3S5AWeG1FOCu7rQZ57+i/vHaqveQMZ9UDnjAfjFZz0Qabrrruuo8tFaEVQ3gB9O9tCg1sWdDTree7hhx/uMvFAvhpOuPPovhYOhNcbH+jUQHgz8fVU197M/qAVf7FCaI8wgNxMHVbu6XmBo3a3hRVkD903FHimP/jLOqo2t8iK9HrbQ4FSnvE7g14XCT0CQBNWhqmRyZplUs80ibVM3VZtmsR6pbgpmyaxHilualGezUqTeM8993RJk3jttdc2JU1iLVO35dEqvlbrFDet7Gv3339/lxR0vp42K01iLdKoJNGs1G2t6GvSrJSJKr/w+nWe0Af0nZqJGK48roevJdXRZqVJrEe7ZtnXGpG6rVm+Vqs+h2aHf+ITn0g9/m1ve1vuYEgt0yRabNes+loz0kk109d0L/VpEqVx+fLldfc1zerXKuS8a9HAYbhSoV59yEb1OZqVuq0dfG3FihXR51UHdK6k5yfrvlbLNInNaNfi4GtdfU11VMf4NIlpdbTeaRJbrV1rVJrEVva1WqZJ1IRGr1GTF/MyaED1EAwDgIah5cB+VpIapiKdC4BG4h/mhB90ALAG9RSS0MOa9gfLezjWQ6Fm1sfTfdYS6ihYRul4br/99tT/33LLLd3SnwE0g2bcSzU8pJS0WauvlHrLwqoCsAFtPliHOgqtgAJxBMMaA2kSAaDj0ENe2BlqxhJ1CxrQYRcr9kCHTR1WsGAPCxqs6NCsROXGf+CBB6LZhdKQpENalQJNv8O9a9oRC+WCDrs6rGDFHhZ0WNBgSUcz0LX6PVt9QEz28HOotddLswJhVsoFHTaxYg902NJgCSv2QIdNHdA4CIYBQMeh2RZ+I8z4BtOdpAEddrFiD3TY1GEFC/awoMGSDp/+RStftL9LmOokzqOPPhr9bueAmJVyQYdNHVawYg8LOixosKSj2QExtSnak1IpnLSnnlLsjh07tmm6rJQLOmxixR7osKXBElbsgQ6bOqBx/DLhJgAAAAAAtDQKgE2ZMiUzEBYGxHbv3t0QXQAA0FoBMaVDnDp1avS3AmGjR49utiwAAACAHkEwDAAAAACgjVAgbOHChV02Z84KiO3atashugAAoPUCYmpPCIQBAABAO0AwDAAAAACgzdAeYkuXLi0UEFu7dq07efJkQ3QBAEBrBcSUdhcAAACgHSAYBgAAAADQhigQViQgNnfu3Ch4BgAAAAAAANCuEAwDAAAAAOjQgJgCYRMnTmy4LgAAaCxnzpxxp06darYMAAAAgKZBMAwAAAAAoAMDYgqETZo0qWm6AACgMZw+fdo9+OCD0Q8BMQAAAOhUCIYBAAAAALQ5CoQtW7askg5xzpw5BMIAADooEHbixIkoEEZADAAAADqVvs0WAADQjI2g+/btW3ndqRrQYRcr9kCHTR1WsGAPCxpaSceAAQOiFWKHDh1yEyZMcO1Oq5QLOjobK/awoMOCBks6ahkIO3nyZOU9HxDTBAm1C61iD3TY1GEFK/ZAhy0NlrBiD3TY1AGNo9fFixcvNvB8ANDBDB8+3B09ejR6PXjwYHf8+PFmSwLowooVK9z58+ej13369HHLly9vtiSAblBPwTrUUbDM/v373e233576/1tuucWNGTOmoZoA6nEvTQqEhWilsCZIFAmIAaRBmw/WoY5CKzBkyJBoBbcYNmyYO3LkSLMltS2kSQQAAAAAAAAAaBPyAmFC/9NnLly40FBtAAAAAM2CNInQUDZu3OgeeOABt3Xr1ijirdVBU6dOdVdddZW74oornCX27t3rVq5c6R577DF38ODB6IFi5MiRbtSoUW7+/Plu0aJF0awSAAAAgHbn7Nmzrl+/fs2WAQAAOSgN4kMPPZQZCPNMmzbN9e7NHGkAAADoDAiGQUMGTz72sY9F6UgeffTR1M8pwKS0JG9605uaMtiilClf//rX3Xe/+133ve99z23bti3z80OHDnXPec5zIs033HBD1ef99Kc/7W6++WZXC+644w73lre8pSbf1c5oibzfNHrgwIFNCWpa0IAOu1ixBzps6rCCBXtY0NAJOpTiWAOrM2bMcJdddplrFdq9XNDRHlixhwUdFjRY0lENfj8wrz+LuXPnukmTJrWMPdBhU4cVrNgDHbY0WMKKPdBhUwc0DqYAQV1ZtWpVlIf87W9/e2YgTOj/+pw28s37bC2566673Ate8AI3ceJE91u/9VvuM5/5TG4gTBw7diw6VgGxpz3taW7Dhg0N0Qs9Rw3dmjVrop8iD4rtqgEddrFiD3TY1GEFC/awoKHddfhA2Llz59z69esL9ZGs0M7lgo72wYo9LOiwoMGSDguBMEv2QIdNHVawYg902NJgCSv2QIdNHdA4CIZB3bj//vvdU5/6VLd69erSAbRrr73W/fSnP3WN4IMf/KD7xje+EQ3yVMt9990XBf20sgwAAACgHQgDYR5N/mmlgBgAQCdQJhA2b968woEwAAAAgHaCNIlQF7Zs2eJ+9Vd/NRpECVE+8he96EXu6U9/upsyZYrbtWuX+8lPfuK+9KUvRekUPUeOHIlWa/3iF7+IPtcM9IDwjGc8wy1evNiNGzfOjRgxwh04cCB6yPja174W7XsWcvz4cffyl7/cffOb33TXX399j869ZMmSqo4bO3Zsj84LAAAAkBYICwNiFy9ejPZ9BQCA1gqEKSMKAAAAQCdCMAzqgtINKnAUcsUVV7i7777bLViwoNvn3//+97ubbrrJ/fznP6+8t2/fPve6170u2sOrUSiY9NrXvta9/vWvdwsXLkz9nAJ3d955p/vDP/zDLg8d2qRYx2t57aBBg6rWoYcZAAAAgGagVNBpgTDPxo0bo98ExAAAWiMQpj26J0yY0BBdAAAAABYhTSLUHK3y+v73v9/lPa3u+uEPf5gYCBPTpk1z9957b7cVUf/zP/8T7ctVbzSQ89GPftRt377dfehDH8oMhIl+/fpF+5t973vf6xb00qq4v//7v6+zYgAAAID60L9/fzdgwIDczykgFl8pDwAAjUETMZVJhUAYAAAAQDEIhkHNue2227q9d8cdd+R2vocOHeo+9alPub59++Z+Xy358z//82hD+N/5nd+JBn/KoD3R/vZv/7bb+5/73OdqqBAAAACgcag/pAlKQ4YMKRQQ00QgAABobCBMK8JOnz6d+1kCYQAAAACXIBgGNeVnP/tZNDst5Nprr3UvfOELCx2/bNky97KXvazLez/96U/rmjbwxhtvLDT7OY23vOUt3fbqWr16tXv88cdroA4AAACgOQGxpUuXFgqIbdq0iYAYAIDBQNiVV15JIAwAAADgCQiGQU35whe+0O29N73pTaW+441vfGO39/793//dWUUr2Z7znOd0e3/z5s1N0QMAAABQC5QWWgExrd7Pg4AYAICdQFivXr2iQNj48eMbpg0AAADAOl3z0QH0kG9961vdOuEvfelLS33Hs5/9bDdixAh3+PDhynvf/va33fvf/35nFe15Fmf37t1N0QL59O7d2w0ePLjyulM1oMMuVuyBDps6rGDBHhY0tLsOBcSUMvGhhx5yx44dyw2IXbx40VmhncsFHe2DFXtY0GFBgyUdcU6cOBHdi4sEwpQasVaBMCv2QIdNHVawYg902NJgCSv2QIdNHdA4el209MQKLc2RI0fcyJEjuwyCaDbaqlWrSn/X85//fPfNb36zS4dewbFhw4Y5i9xyyy3uH//xH7u8d/fdd7sXv/jFucd++tOfdjfffHOX99rVLYcPH+6OHj0avVZjc/z48WZLAujCihUr3Pnz56PXffr0ccuXL2+2JIBuUE+hGZw9e7ZQQEyorfd9NuooWOPMmTPusccei55RLly4UBn8WLBgQfR6+vTppfcRBqh3e6+6OnDgwKj+FlkRNm7cuAaphE6HfilYhzoKrYBS02vSi9BzlMbYoT4Q8oSaoXQN8SCO9gurhqc+9ald/tb3agDGKto8Ps7EiRObogUAAACg1vgVYkVSJh48eNAdOnSoIboAyqJA15w5c6I9f8eMGRP96LXe0w+BMLA4GWHnzp0EwgAAAAB6CMEwqBlr167t9t7MmTOr+q6k49atW+csosGee++9t8t7mrW3cOHCqr/zQx/6UJRectasWVHKyAEDBrjJkye7RYsWuV//9V93d9xxh9uwYUMN1AMAAACUC4gVWamv/hEBMQCAnqFJoXv27KmsakiDQBgAAABAPuwZBjVD6UaK7KVVhKTjNm/e7Czy2c9+1p06darLezfccEMl52w1/MEf/EG39zQbUD+PPPKI++IXvxg98LzkJS9x7373u92TnvSkqs/ViZw7d66SrlEDen379u1IDeiwixV7oMOmDitYsIcFDZ2mI9xDzJ8rDQXDlH5Og7nqtzSaTioXdLQuVuxhQYcFDZZ0CN07tXpx7969mZ9Rmk+tcGxne6DDpg4rWLEHOmxpsIQVe6DDpg5oHKwMg5qxe/fubu9NnTq1qu+aMmVKoe9vNtrH7NZbb+32fnwPsHqggaW77rrLPeUpT3F/9Vd/1bb7jNUDbTq9adOm6CdvA+p21oAOu1ixBzps6rCCBXtY0NCJOvSQWGaFmCZMNaOf0mnlgo7WxIo9LOiwoMGSjjDriFLwa6+bRgfCLNkDHTZ1WMGKPdBhS4MlrNgDHTZ1QOMg3Ak1Q/tDxCmyr0QSSccdOHDAWeMd73iH27VrV5f3rr76avdrv/ZrPf7ukSNHutGjR0cPP7Ltvn37onzxcbSZ8v/9v/83mqGtFWOagV1Phg8fXvWx4Qxy6dZGps1AdvSzKxXQ1Cz3TtSAju6EA7bNWMVgzR7osKmDempLQyfrUFuuyUrxB0cFwMI6qvZ+5cqVbtSoUa6RdGq5oKMY3Evt6bCgwXIdVUBMz57h5IIJEya4Rx99tCPsgQ6bOriXosOyBkEdRUcr6NBzFTQGgmFQM44fP97tPQVyqmHQoEHd3jtx4oSzxL/+67+6T3/6013e02w97edVTUBKqSFf/OIXuxe84AVu2bJl0cNOiFIx3n///e7zn/+8+8xnPtNt4OnLX/6y+5M/+RP3t3/7t66e5KVEKkNe7vt6LoP259bregcQrWpAR3IHJOwod3IdRYddHdRTWxo6XYf2p4kHxFQ/w8Fa1VlNapI2TfZp1EBEJ5cLOvLhXmpPhwUNluuoBuj8PVfvjx8/Ptpbut5114o90GFTB/dSdFjWIKij6GgFHdA4KGGoGUmrlqoNhiUdd+bMGWeFn/70p+7Nb35zt/e1f9c111xT6rvmz5/vvvrVr0Z7on3kIx9xz3/+87sFwrxNnvnMZ7o777wzmv2XtE/YBz7wAXfPPfeUvBoAAACA6tADo1YmaEA2D822DFeNAQBAOfRMqHuuAmE92aMaAAAAoBNhZRjUlWpn/iYdZ2XgREustYLr5MmTXd6/4YYb3F/8xV+U/j7t+VWWGTNmuB/84AfROX/4wx92C8jdd999rl4U2R+k6KqypLz3jUB7nfhzh687TQM6kgd1w1ljnW4PdNjUQT21pQEdl9rzyZMnR+m7tEIs7MfpdTjDUn0BrW4YMWJE3XV1ermgIxvupfZ0WNDQCnV0yJAhHWkPdNjUwb0UHZY1COooOlpBBzQOgmFQM5LyqsYDRkVJOq5///6u2ezZs8c973nP67ZP2KJFi9yXvvSlhi6n1Qzsr3zlK27OnDnRTGvPj370I/eTn/zEPfnJT67LeY8cOdKj/cZ8QEy2Wr58uWtWSs81a9ZEr+fNm9fwB0orGtCRjF8ir05QJ9dRdNjVIaindjSg45cotYj2BtOPz3uv9l6poMP+y9KlSxNTYrebPdBhW4fgXmpLhwUNlnQI6ig6rOsQ1FN0WNXgoY6iw7oO0jM2DiwNNSMpTYP2uapVMKzZaSAUcLrxxhvd+vXru7x/xRVXuG9961vRHhiNRukxfv/3f7/b+9/+9rcbrgUAAAA6G82m1AShtDTZjQyEAQC0AppEAAAAAACNgWAY1IzRo0d3e+/YsWNVR+bjjBkzxjUL6XnBC17gHnzwwS7vX3bZZe473/mOmzRpUtO0vepVr+r2HvuGAQAAQLMCYtr7NL6HmFb4L1myhEAYNB2lStJexJolHv7oPf1YSc0O7Y+elX/84x+73bt3N1sKAAAAQEdAmkSoGdrIN862bduq+q6k47QKqhlo74uXvvSl3fbhGjdunPvud78b7d/VTJQmUbYPH6K2bt3aVE3W0dJ4v5KvWfmALWhAh12s2AMdNnVYwYI9LGhAR3e0H4P6JkovreCCAmFaEdboVf5W7IEOWzoOHDjgbr/99m7vf//7349+33LLLQ2dhNdse1jSYUFDo3QodfxDDz0UrQx79NFHo/tms5538+ikckFH62LFHuiwpcESVuyBDps6oHEQDIOacfnll3d7b8uWLVV9V9JxzQg6nT171r3sZS+Lgl4ho0aNilIRKp+sBTQDOwyG7d27t6l6rKP0TUpv2eka0GEXK/ZAh00dVrBgDwsa0JGe914DuwcPHmxKIMySPdBhU4cVrNjDgg4LGhqhIwyEeRQQExYDYp1SLuhobazYAx22NFjCij3QYVMHNA7SJELNmDt3brf3Nm3aVNV3JR2X9P31ROlSXv3qV7uvfe1rXd4fNmyY+8Y3vhEN7FghnnLoxIkTTdMCAAAAEAbEmr3vKwCAFZICYULpORUQ04paAAAAAKgPBMOgZig4pPQOIffff39V3xU/Tt+rfSYahR5Gbr75ZvelL32pW9Dpv//7v91TnvIUZ4l9+/Z1+Xvs2LFN0wIAAAAAAADFAmHxgBhZPgAAAADqA2kSoWaMGDHCLVy40K1cubLy3urVq6P0OEorWBQ9BPzoRz/q8p4CYVqR1Sje+ta3un/913/t8p42gr/rrrvc9ddf7yyhVWDxtJLazwyy019qvwgxevRo169fv47UgA67WLEHOmzqsIIFe1jQgI76oP5gfJJVq9oDHTZ1WMGKPSzosKChXjqOHDniHn744dRAmEf3PWs+0c7lgo72wYo90GFLgyWs2AMdNnVA4yAYBjXlec97XpdgmAYy7r77bve6172u8Hfcc889UQAt/r2N4g/+4A/cRz/60S7v6Wb4xS9+saE6ivK9730v2pw+ZNGiRU3T0wrIXtu2bYteDx06tCmNnQUN6LCLFXugw6YOK1iwhwUN6Kg9jz/+uDt9+rSbPXt2jwJiVuyBDps6rGDFHhZ0WNBQDx0KhGlFmNLw56WWXbx4sRs5cqSzRLuWCzraCyv2QIctDZawYg902NQBjYM0iVBTfv3Xf73bex/72MdKfcfHP/7xbu+96lWvco3gL/7iL9yHPvShLu/16dPHfe5zn3MvfvGLnUU++MEPdnvvxhtvbIoWAAAAgJ4GwjZv3ux27Njh1q9fH02sAgBoVYoGwvTMaTEQBgAAANBOEAyDmnLNNddEe4eF3Hfffe4b3/hGoeP1oBDfp+vqq692y5Ytc/XmAx/4gHvve9/bbXbepz71KfeKV7zCWUTavv/973fb1+xXfuVXmqYJAAAAoCeBMI8CYuvWrSMgBgAtyeHDhwsHwpTZg0AYAAAAQH0hGAY150//9E+7vffmN785dyPg48ePu5tvvjnK1xryZ3/2Z4XO+8xnPjNKpRP+3HvvvYWOveOOO9wf//Efd3lPx//zP/+ze+1rX+vqxd/93d+53bt3V3Xsl7/8ZfeWt7yl2/u/+7u/68aOHVsDdQAAAACNQfufhoEwz86dOwmIAUBLBsK0RxgrwgAAAADsQDAMas4rX/lKt3z58i7vbd261V133XVu9erVicfo/8961rPcL37xiy7v672bbrqprno/+9nPure//e3d3v/whz/s3vSmN9X13B/5yEfcjBkzomChAncXLlwoNFikINjLX/7ybnuFXXbZZe5d73pXHRUDAAAA1Bb1bTZt2pT6fwJiANDOgbARI0Y0TBsAAABAJ9O32QKgPfnMZz7jrrrqKnfw4MHKexs2bIjSP2jvLQXLJk+eHK2K+ulPf+q++MUvdlsRNnr0aPfpT3+67lq1Gi0+uNK/f3/3yU9+MvqpFgWsklZuxTl58qS78847ox+t6FJaSKWanDJlSjRDcODAge7QoUPRQNGKFSvcD37wA3fu3Llu3zN8+HD39a9/3Y0aNapqzQAAAACNRH0wDRznoYCYPjt37txo9T4AgEUIhAEAAADYhWAY1IXp06e7r371q9HeVceOHau8r5VPd911V/STxbBhw9zXvvY1N23atLprTQosacWV8rv3hF27dpU+Zt++fe5b3/pW9FMGBc6+8IUvRA9UAAAAAK2CAlsLFixwq1atcvv37y/UtyIgBgAW0QTGlStXFgqELVmyJJrMCAAAAACNgzSJUDee9rSnufvuu8/Nmzev1HHz5893P/rRj9y1115bN23tQu/evd2rX/3qaPah0lACAAAAtGJ/RgGxMWPG5H5WAbG1a9eSMhEATEEgDAAAAMA+rAyDuqK0iFphpRSAt99+ezR4kYaCZtq7S/tn9evXz3UCWgH2ne98J9ov7Gc/+5l7/PHHCx03Z84c96IXvcjdcsst0So8KIfq1/jx4yuvO1UDOuxixR7osKnDChbsYUEDOmobENP+slopnxcQUzBMfcesFWJW7IEOmzqsYMUeFnRY0FCNDgXCNDkxb//nvn37Rpk8Wi0Q1qrlgo7Owoo90GFLgyWs2AMdNnVA4+h1kWmV0EDWr1/vHnjgAbdt2zZ34sQJN3jw4CjFn/bJmjVrlut0tMeaNojX/mDaT+348ePRXmp6YNL+YRMmTHBPetKTCs2ctoiu4+jRo9Frlb2uD8AS2pfPz+jVzF3tbwhgDeoptHMd1WBykYCYUL8oLyAGEEfpODVJLw1NNmvVvjY05/lNK8LqEQijvYdWgHoK1qGOQiswZMiQaJzcbx105MiRZktqW1gZBg1l9uzZ0Q8kM2rUKPeUpzwl+gEAAADoNLRC7MorrywUENPEIc3rU4ptAmIAYDkQptSIGtwCAAAAgObBnmEAAAAAAGAuIDZu3Ljcz+7Zs8c9+uij7CEGAA2FQBgAAABA68HKMADoOM6cORPNJvcplvr379+RGtBhFyv2QIdNHVawYA8LGtBRv4CYVnyJvXv35gbERHyFmBV7oMOmDitYsYcFHRY0FNFx+PDhjgqEtUq5oIN7qQV7oMOWBktYsQc6bOqAxkEwDAA6Du3D5gfORo8e3ZTGzoIGdNjFij3QYVOHFSzYw4IGdNgJiGl1mFaU+YCYFXugw6YOK1ixhwUdFjQU0aF9PYYOHZq5n0e7BMJaqVzQwb3Ugj3QYUuDJazYAx02dUDjIE0iAAAAAACYTpk4fvz43M8qYKa9xvJWawAA9AQFuhYvXuyGDx+e+P9+/fq5pUuXtkUgDAAAAKCdIBgGAAAAAABm0UovrRArGhDTHmIExACgGQExBcK0IkwrxwAAAADAFgTDAAAAAACgrQJirBADgEYFxPwKMAJhAAAAALZhzzAAAAAAAGiZgJh++42u09i3b587depU5TgAgHrg9wZTAH7mzJkEwgAAAAAMQzAMAAAAAABaAgW25s2bF73OC4gdOHAgCohNnjy5QeoAoJNXiAEAAACAbUiTCAAAAAAALRcQmzBhQqHPAgAAAAAAALAyDAAAAAAAWjIgpt+7du1K/MyYMWPcxYsXCYgBAAAAAAAAwTAA6Dz69+/vpkyZUnndqRrQYRcr9kCHTR1WsGAPCxrQ0TwU5Jo7d270Oh4QGzdunJs1a5Y7dOhQ9Dflgo6w3gwYMMCdP38+Cpb69/r06VN53Un2sKTDggbtN7hjx44ovWrv3r074l7aCuWCDrs6rGDFHuiwpcESVuyBDps6oHH0uuifAAAA6szw4cPd0aNHo9eDBw92e/bsSf3skCFDKq8vXLjgTp48mfndGrjQd3o0wKF9QrLQA+6gQYMqf587d86dPn06d08ADaB4zp49686cOZN5TL9+/bo0qjqHzpWFPq/jPLoWXVMWAwcOrAzkCNlMtstC1y87eI4fP+7ykJ39QJGakBMnTuQeU7Y8qykbXbtsUKZs4uV5zz33RMf577v22mtzy6ZIeeocOleZsomXp+yc12SHZVO0PFvF11SWvmw63dfuv//+isawnsbLxrKvFSnPasqm030tXjbN8rWkOloLX0srG723cePGaA8xnUOBsCuvvDKq3/Vo1yz7Wrxs2snXat3n8PVU3y9ty5cvbzlfS6KevtboPmSj+hy+PPfu3etWr14dHTNy5Mgo2J4WHG2Er6mO6vy6JmlUHW1FX6tlu1avPkc1vha/D3aqr61YsSL6vOqAzpX0/NSu7Vq8bPA1m76mOqpjZO+0Z/xqfS0sG4vtmsVxq3bytXjZ9MTXxo8fX9E4bNgwd+TIkVy9UB2sDAOAmnHrrbe62267LfX/YeOjxmjNmjWpn7366qsrr9UAZ33WN8KLFi2q/K1Ga+3atZnHDB06tDKjXKix2bx5c+Yxo0ePdjNmzKj8vX//frd9+/bMYyZOnOguu+yyyt8arNPDdhbTpk2LBvM827Ztc4cPH848RjPgR4wYUfl706ZNuQ2xBgvDxnvdunW5HZGlS5dWOpb6bF7Z6LM6JuxU5R0jTdIWdkQ3bNiQeYyuXTbwHDx40G3dujXzGNlYtg7P4zsd6tgk6VRZqkw9Kn+dKwvVGdUdz+OPP57buVbdVB316PrzHi7kA74Dm+djSb6m7y/ra/Jr1Zsyvqa6/Nhjj5X2Nc3ULuNrWimiGd5ZXH755W7s2LGVv1Vn8jqes2fPjoL7ZXxtwYIFXTq9RXxt2bJllQ68Ota+bHT/8MeG9VQPFUuWLKkcL0155amHhPnz51f+PnbsWBRUyEIDhFdccUUpX1PnfurUqZW/NRlC98IsNEMv3BNK5Z/nazNnznSjRo0q5WtKtRc++BTxtcWLF1ce/Ir4msrxqquuKuVrelBcuHBhKV/Tg9OcOXNK+ZpSCU6fPr2Ur02aNClaNZHma0l1tBa+pjY6zddUDrKrzuEDYT31tTTwteb4mqilr/l6qvJUnW5FX0si7mtbtmypTEYr067lDbTF2zX1u/MGpprha7p2XU+er+n6FQjTNeg5Qn6zc+fOqG+RFBCL+5o+nzXRL8nX1If0q1fT6qj6t2FfXfUsb0Av7mvr16/PHdCUnf3gZDXtmgbyyrZrumdIW1lf0/0mC9V/+YFHPqOyzEJ+Ft4H9Hn5aBbyZ/l1GV/TteiayviabBYGHIr4Wlg2RZ6ldZ/V/baMr6mOqa55dB0qn6zj1G6o/fAcOHAges7NQj7jV20U9TX5pny0qK8Jtbe6F5TxNd1rwsBGPXxN/RndO8v4mu7NukeX8TXdA9UWtKuvqV+jskl7xu+Jr/k2olG+VqRdq4Wvyc/wtcb5GmuVGgfBMACoGRpwKDLbAgAAAKCWaCBCD/EaLAhnjgIA5KGBKA0SxgeifMA+LSAGAAAAAK0FaRIBoCkrw5qZJlHva2atjtFsGT+rtZFpEjUj3O9vogfscGZtI1O3yQZ+9pZW1OSdo17LzX2ZeHvoHJ2cJlH20OwtDeiqXHRdzUjdFi8X6WhGmkR9v2ZL6bgkf2lUagdvD31eKwq9jk5Nkyh7aPafjtHM0LRyqWeaxHgd1Qz6ZqRJ1IzWrHt6o1LcyKZ+dq3uHbJFJ6RJTPM1ncOvvsq6d9Q7TaJ0aCa77Ozv6c1Ik5h0T29GmkR/79DvrHLplDSJaeXS6DSJ0uKzHWh1dt6+GfVI3Ra3hc5R7z6HVl/pPhHea/wKF7+6XVq0yiC873dSmsS0Otro1G1F+4P1Tt2W1h9sdJrErHtHJ6ZJDO2hcglXBTUydZuOy+oTNipNonT5e3qY1aSRaRKT6mgnp0n09pA23x9sRprEtHtHo9Mk+v6gzqMJbVn9wXqmSQztoVVrXgdpEtsXVoYBQM1497vfHf0U2TMs3nHIa2CKfjZsYLKO8Q1U2FCrQQ47WUVQwx82/kVQ469OhdeghjDv+rI6BmmEjXIa6uD5ZezqCJW1szoXZY9JK8/QHnHtjSobncN3mPLqUFie4UNArcomnI0swgfUotSibPLqaVE79aQ85acKIGfpqFXZ5Pma7BHv5Bcpzzg98TU9dISBhrTvqubeWaZspMHfP5RSo8y5dA1lNyhOK5uwjsa1V1M21fiaDw7613m2qEe7lnRPT7JJPcom7mtF6mg17VrZsgkfSIveO2rZroXoYVo//p7eqHYtXp5F7FGvPkfSvaPMPb3WZRPW00a3a/GyKVpP69GueaShJ33CWvUhQ1vEfb7WZaOJeQquhMEJr80PAPpJBfK9LNvXsl3LqqONaNeSyiavjv7/7L0HuCRHdb5f2pyjNidt1AZtFiAhLZIQiJyjiYIf/MEgQMYEm2xhCTAGY3sNmCREzhiTJUBhJSShuNKutDlHbc559/98va6hb9+eDnNnes7MvO/z3OfOnTs9/fWpOlXVfapOdapRv1aN8WA1+rUs48Ei+jURvu4sbUclbWclvuaDtnmOq0a/FsbbI7pCvNb9Wrg89dA9a5teqzFHNe7zq1U2abaoZb8Wxl9Lnjpai2cjvu3w48GixhzR8sxSR2tdNpWOB6vVr6U9HyyiX6vU16CJg2Hf+ta3nEXe8IY31FsCAAAAAAB0ED3w1l4F2qMw+kAcAJoXBcIef/zx1JnlerCrvW/zPhAEAAAAAHuYDoZdffXVJnNzEwwDAAAAAGj8QNgjjzwSrMTVaoOZM2cSEANoAZS2VIGwNDRT+4ILLqhohQgAAAAA2KMhdpfWbC0rPwAAAAAA0DyBMKE0LY8++mimvTMBoDUCYUoBTCAMAAAAoHloiGCYVodZ+AEAAAAAgMYPhCnwFd2YmoAYQHOTNRCmlIgKhOXdZwoAAAAAbGM+GFbvlWCsCgMAAAAAaA4U6FLAa9++fbH/JyAG0Jxs27YtUyCsR48eQWpEAmEAAAAAzYfpPcNOnz5dbwkAAAAAANBED8TLBcLCATGlUJw1axZ7iAE0id8vW7YsUyBszpw5BMMBAAAAmhTTwTAAAAAAAIBqMXLkSHf06FG3cePGxM8pYKaA2MyZM12XLtwyNRMq/6VLl7rNmzeXsn8oJf4DDzwQvJ4xY0YQFIHWDITp96FDhwrRBgAAAADFwp0dALQc2gdgwoQJpdetqgEddrFiD3TY1GEFC/awoAEddomzh4Ie/r0sATGlTOxoQMxKuaDjLAp0/OpXv2r3/vLly4Pf5513XqHBsHrbw5KOamvYunVrqVyzBsJqoaPRsWIPdNjUYQUr9kCHLQ2WsGIPdNjUAcVBMAwAWg490Bo4cGDLa0CHXazYAx02dVjBgj0saECHXcrZwwfE9HvDhg2ZVogpZWKlATEr5YIOm1ixhwUd1dSQNRDWs2dPN3v27DYBUAu2sIQVe6DDpg4rWLEHOmxpsIQVe6DDpg4ojk4FngsAAAAAAKDuKBA2fvx4N3bs2NTP7t+/PwiInTx5shBtAFBcICy8IgwAAAAAmhuCYQAAAAAA0LIBsXHjxqV+loAYQGOwZcuWXIEwUiIBAAAAtA6kSQSAluPIkSNu3bp1pX0hdDPcihrQYRcr9kCHTR1WsGAPCxrQYZcs9lBATP8T69evzxQQy5sy0Uq5oMMmVuxhQUdHNSgQtmLFig4HwizYwhJW7IEOmzqsYMUe6LClwRJW7IEOmzqgOAiGAUDLcfr0aXf48OHS61bVgA67WLEHOmzqsIIFe1jQgA67ZLVH3oDY4sWLgz2GsgbErJQLOmxixR4WdHREw+bNm93KlSursiLMgi0sYcUe6LCpwwpW7IEOWxosYcUe6LCpA4qDNIkAAAAAANDS+JSJPiiWxIEDB4KAGCkTAeyQxR979epFakQAAACAFqZpVoZ17ty5sBtlbnwBAAAAAJoPHwzz6VLSAmJKmdi1a9eC1AFAOfzef2vXrk0MhHXr1q1gZQAAAABghaZZGXbmzJnCfgAAAAAAoHkDYllXiGkPsRMnThSiCwDSA2JxvksgDAAAAACaKhjmV23V8gcAAAAAAJofPVBX2sQ0/AoxAmIANoPZBMIAAAAAoCmDYdVe9cWKMAAAAACA1l1lkiUgdvDgQQJiAIZQMEz+27t3bwJhAAAAANB8e4bdeOONFR+rG9fdu3e71atXu0WLFrlly5aV/tezZ0/3oQ99yI0ePbpKSgEAAAAAoBHQA3VliFizZk2mgNjs2bPZQwzASEBs7Nixhe0tDgAAAAD2aZpg2Bvf+Maqfdef//znIAB2xx13uKNHj7ovfOEL7n//93/dxRdfXLVzAAAAAACAffRAXWQJiO3fv98NHjy4IGUAUA4FsQmEAQAAAECYc86Q/y8WmeUDH/iA+9znPlfKNX7bbbe5Cy+8sN7SABqWfv36BXtreJ86dOhQXXScOnUqCHSLHj161OVG2YIGdLRHq4OlRUjDggULWtoe6LCpg3pqSwM6WqeObty4McgkUY5p06a5YcOG1VxHpaDjLLt27XILFy4s+/9rrrmm0IBmve1hSYcFDZZ0NGtbio7m0kE9RYdlDYI6io5G0KHUzocPHw5e9+3bN5hgB7WhaVaG1WIm2Wc/+1m3adMm98Mf/jCokC972cvckiVLgkoJAI2LOjd1NK2uAR12sWIPdNjUYQUL9rCgAR12qbY9xowZE/yOC4iVC4TVQkeloMMmVuxhQUc5DZs3b3aDBg0KtjCop45WxYo90GFThxWs2AMdtjRYwoo90GFTBxRHpwLP1ZB8/vOfDwbdCo4pMPapT32q3pIAAAAAAKAOKCA2ceLEzIEwAOgY69evdytXrnQPP/ywO3LkSL3lAAAAAEADQzAshREjRrirrroqSJuon6985Svu9OnT9ZYFAAAAAAB1CohNmjQpeD116lQCYQA1DIStXbs2eH3s2DECYgAAAADQIUiTmIGnPe1p7he/+EXwes+ePe7ee+91F198cb1lAUCFKO2pZpiKyZMnB/uXtaIGdNjFij3QYVOHFSzYw4IGdNillvYYPXp0kLYty3daKRd02MSKPSzoCGvQvh1bt25t838fEJszZ05NUyZasIUlrNgDHTZ1WMGKPdBhS4MlrNgDHTZ1QHEQDMvA8OHD2/ytfcMIhgE0LlrlefLkydLrVtWADrtYsQc6bOqwggV7WNCADrvU2h5Zb5atlAs6bGLFHhZ0eA27du0K/u7WrVu7z/iA2Ny5c4OAWS11+NetjhV7oMOmDitYsQc6bGmwhBV7oMOmDigO0iTmQPuGCT84BwAAAAAAAIDqoHvtnTt3Jn5GAbHVq1cXpgkAAAAAmgOCYRlYt25dmwhx3Aw1AAAAAACAJA4cOBA8yAeA9mzYsCE1ECb69u3rzj///EI0AQAAAEDzQDAsA36/MM+QIUPqpgUAAAAAABqPI0eOuKVLlwYp3giIAfwVTTpdu3at27hxY+pn+/Xr52bPnu26dGHHBwAAAADIByPIFL773e+6v/zlL0GKRL8ybMaMGfWWBdAUHDp0qOz/evfuXXp9+vTp4AFSEvLR8N4dp06dckePHi17Xj2E8qlPPcoTnPZwSjfe3bt3L/194sQJd/z48cRjunbt2mZFqc7hNXg9UfR5HefRteiaktC+CZ07dy79LZvJdklE/59UJh7Z2dtO7aI2HE0jrTyj9ujUqVObjdGzlI2uPbx3RJayiZanzqPj/PdlKRvp8jmmy6FzhB/apJWNzqv/yw4e2Tkth3W4bPz3dKRs4uppHl/zVFKecWWjcyVdU5yvpZVNHl/z9gh/Pquv6frD5dkRX1O99hrD9TRaNlnazmr4Wlq5xJWnrsH7WkfKJlpH8/paXNtZqa8ltem17tc80f9X4mtZyibN1+LqaDX6tSxlE/W1tHKppF/L62s69/79+9327dvdoEGDgs8++OCDwcqWsO2r0a9FyyZcnuXqaSVlUw1fk7a0tqMWY44039MxabqiZdMRX0tqP2rdr3mie2+l2aia/Zp+a0XYpk2bAnv469NrfTZ8LX369HETJ04M7FDO3tUaQ6a1HUm+Vq2y0XfKBmE7F9GvRX2tXB0tol+LG3OoTJPqWy36tTDeHtGAbFH9mr/2pLajo/1aJeN71YG0MWG1+7UwYXtE/1+rfi1aNrJz2piwFv1alOhzjixj9WrcS0fLJs4WlfRr0bKpxNekS/Yud49fab8WLpu08tT3yUbh66/VvXRSeZaro0WNOaLlmWU8GOdrHenXwoTtEaaSe+lK+rUsvgbVhWBYAl/84hfde9/73jYdybBhw9y8efPqqgvAKtdff7274YYbyv4/3PmoM1q2bFnZz86fP7/0Wh1w0md9Jzxz5szS3+q0li9fHvtZdXTbtm1r9wBKD6o0KzUJPcQaP358m30NNm/enHjM8OHD3ahRo0p/62GYbvilwRPVMnbs2DarUPX5ffv2JZ5n0qRJrn///qW/16xZk9oRn3feeW3+XrFiReqgf86cOaWBpT6bVjb6rI4J2z96jC8Tz4ABA9z06dPbDFBWrVqVeB5du2zg2bNnT+oMY9lYtg6fR/XAD2zirk1lqTL1qPx1riRUZ1R3POvXr08c8PnBWLhe6PrTbuTlA34Am+Zjcb6m7w8fEy0X6Yn6mvxa9SYJPbwKpzNSXfYpiLP6muy1d+/eko4svibtaemWxo0b584999zS36ozvg5E8fYYOnRom/ez+Jom0oQHvVl8be7cuaUxiAbWvmx27NhROjZcT3VTodnyHmlKqwO6SZg2bVrp74MHD6buwyL/1MPIcB1Q/Ux6qC+bjRkzpvT3E088EbSFSYwePToYd3m2bNnSzteidVTXMnDgwMy+JqZOndrmRjaLr82aNat04ydf0zFJbbrKMTx+jPpaHPqOCy64IJevRQO1WXxt8ODBbfoC9WuydRIjRoxwI0eOLOtrcXU0j695Jk+eHKwC8aiPzutrqmf+ZrlcHS3na+XI62sqB/lVuJ7pvT/96U+BX5Rb4RL1tSz9WpKvxbXpWX0tyoQJEzrsa9KmsklqO8K+JtLKJouvqZ1LQmNIBWfS0vRNmTKlKr5Wrlyy+FocUV/TtSg9Z5qvhR8aqjzTiPqaxt1pD6aivvb4448H17N79+7Se+EJHhpz+LqptkD+kDYejPZrunb100lEfU3jjaQ2Pc7X5Geq00lEfU1jSD+2iUNtqca34QdtqmdpD8+jvrZy5crUB5rh1XbRMWRcHY36mh7k5e3XVNbSlsfX5J9+D/dybYfqv/zAozq2devWxPPIz+RvHn2+3F7x3h7y6TBZfE3XomvyqG6mPdSWzcLX6n0tqe0Il02We2m1s2pv84whVcdU1zzSo7Yw6Tj1G+o/PPJ93ecmIZ+R76T5WtgeKk/5dVZfE2oDwsdk8TW1NeEgouqz6nVS+5Hka3FoHKW2M4+vqW0O9ze6jrRnFmpj1Rd4VJZpfUGar8XV0Ty+5tG1hP2tEl/TuEa+UO4eP8nXkpCv+X4tzddkD7Xr4fpcia9l6deSfK1c2xH1NflZWr+mfjB8b1yJr6n808aDcb7WkX4tjLdHNIic1dfCC2ay9GvlfC2trkH1aJpgWNoNSxq6UfINyn333ed+/OMfBzfcqox+VZh+v+td76qaZoBmQ36UZWYTAAAAQCugB7d60BN3g6ubbAW39OAhGsgEaHbkE+FAWNqDIz2oJ70oAAAAAHSEpgmGaaZANIrbEfwNa/g7NbNAK8UAIB49yAnP1ogSDpTJt8KzW5LQbI60z0b9XzMoyx0TnnEavdFOO0909rZmKIVnHMURfcClGTfhmbRazRSevSnCy9OFHpSFZ1BlSXGjmUB50ySGZ16WI7w8Xa+zlqNHM36ix4Rnk8seUZtGZ0PFEZ7R7GdDRe2aVp76vLe9vi/unNGy0Uqk8CzfOKKznDSDLi1NYnQmnuySNlsoXNfy+Fj4+PAx0XKRfaJ+I59PO0+4zgjNcs7razq36nicv4T1R1eKhWfHxxEtT81uS0qTGHeeLL4WPU8WXwvbOlwfNdsunILOvx8tG9krb9loFV9eX1Md0Dgsyd+i5akZhOFVJVlsppmkUV9LazvSfC2u7czia+Hrkd3Dq1KT6mg5X4ujEl/T7NLw6pRKfE39Wng1ViW+FldH8/haubLRatG0somex9eZpHIp52vlyOprmiSkdIia/Rqth9LpbaYH/JohGr3eSvq1aNmEfS2uTfda0nyto/2aiF6ftGl2cJ62o6P9mp8dftttt5U9JmkMWa5sOuJr5crF6+9ov6YV8Fl8LbzqUuUZXo2U5TzhFeDlCKcr0mRW9UHh6/FprPwKcZ1DttWKdNkvy9gm2q+pX8hbnvJZnwWgXNsRN74PZwGII24MGc42EEVtabS9UX+b19fUvqS1nWEbRO2cVEfD15a37axkfK96KZsltelRX1MdC2fPiCNanrrvKrdXvLdH9DxZfS06hswzvg/7WpZy8Tqr1a8l+Zr6Bdks6bhoecpnNPZMIquvhe0RXnWSxde8/mr4Wnj1cVy5JPlaFrL6WrheZRlDVqNfi/paXB3N42tJWXTy+prGQj5NYrnrKudrWX0nzddkj2r4WiX9WtjXyrUdcWPIavdrceWp8VDaeLDa/VqYcpkNsvhatGwq6de8r1UzpgHJnHOmSdbhRStgRwlXQplINwSLFi1qs2QUAPKhG36/nF0Dsiw5mGuBfDr8kK4enY4FDehoj9r5sI4FCxa0tD3QYVMH9dSWBnS0hzr6V7QqTClWwhPt4nToJl8phaM3+81mDws6NP5UMCy8Yk8a/KSjyy+/PDXo2Ez2KFqHzqNsLHEpP/W/cJnogbYPhBWJlTKhLUVHI+ignqLDsgZBHUVHI+jQ2NMvIFDAMy2dPFRO06wME7VaGfamN73Jfe5zn2s3uwQAGhP5ddE31RY1oMMuVuyBDps6rGDBHhY0oMMuFuyhAIt0pO05oFU5Dz/8cE0DYhbsYUGHHjY873nPM/NwrN72KFJHUiDMa/D39JoprdSI0RnUrVQmVrBiD3TY1GEFK/ZAhy0NlrBiD3TY1AHF0VSlXa1FblqxooH3s571LPfGN76xzcaTAAAAAAAAWfHpYiwExADqea++evVqt2nTptTP1jMQBgAAAADNS9MEw2699dYOHa/cqFqGqIG3UiFWO+0iANghmoKlXikD6q0BHXaxYg902NRhBQv2sKABHXaxYg9p0L4L2ntk+fLliTpqGRCzZA8LOqxgxR611JE1EKbPKRvLBRdcUNf7cStlYgUr9kCHTR1WsGIPdNjSYAkr9kCHTR1QHE0TDLvsssvqLQEAGgTl4fWzs7VRZZH7QljSgA67WLEHOmzqsIIFe1jQgA67WLFHWMf48ePdunXrEj+vgNhDDz0UBMR69uzZ1PagntqxR6105FkRpgwtJ0+edI888khT2qJRsWIPdNjUYQUr9kCHLQ2WsGIPdNjUAcXB8icAAAAAAIACGDJkiJs2bVrq544dOxasEDty5EghugBqgQJhq1atyhQI04ow+QYZWgAAAACgJVeGvfe97y29HjVqlPv7v//7uuoBAAAAAADoCMOGDQt+P/7445kCYtVeIQZQZCBs8+bNqZ8dOHBgkBpRqyIBAAAAAFoyGPaFL3yhlKtz9uzZicGwb33rW6XXgwYNcs9//vML0QgAAAAAAJA3IKb7HAXE/D4FcRAQg0ZFdXf79u2ZA2GdO3cuRBcAAAAAtC6mg2GepBtEz9VXX90mcEYwDAAAAAAArDJ06NDgd9aAmO5xtKcSQCPQo0ePoM4uXrw42AcsDgJhAAAAAFAkTZeQO0vgDAAAAAAAwEJAbPr06aVJfWkBMW3yDdAo9O3b182aNct16dJ+Dq6yuRAIAwAAAIAiMR0MC98UEuQCAAAAAIBmY8iQIZkCYsePHycgBg1Hv379goBYOOhFIAwAAAAA6oHpYFj//v1Lr/ft21dXLQAAAAAAALWAgBg0e0BMKRMV/PKBsE6dTD+KAAAAAIAmxPSeYYMHDy4FwTZs2OD27NkT5BUHAOgoaQ+bWkWDQIdNrNgDHTZ1WMGCPSxoEOiwiRV7ZNGhgNiMGTPc0qVLE7Ni+IDYnDlzcu8h1kj2qBX79+93P/vZz4L7TG9n6Vm9enXw+qUvfWkQvCmSVigX2XTevHmuZ8+eiYGwVrBFI2LFHuiwqcMKVuyBDlsaLGHFHuiwqQOK4ZwzhvMPvupVr3I//vGPSxXz3e9+t/u3f/u32M9qQK3P6HJ0Y/jggw8WrBYAstwEHzhwIHithzeHDh2qtySANixatMidOnUqeK3ZywsWLKi3JIB2UE/BOtTRjrFz587UgJgfV82dO5cb+Jzs2rXLLVy4sOz/r7nmmmBSJkC9oS2FRoB6CtahjkIj0Lt371LmB+25qslb0IIrw57xjGcEwTAf5PqP//iPIMj1spe9zE2aNCmoKHE3fwcPHnR33HFHzXQ97WlPq9l3AwAAAABA63LuueemrhDr0aNHprSKAAAAAAAA0ADBsNe97nXuQx/6kNu9e3cpIHbnnXcGP3H4m0WluLjiiitqokk6Tp48WZPvBgAAAAAAUEBM+yotWbKkXUBMgTBlwtBvgHqgOqk0kwMGDKi3FAAAAACA5giGKZ/41772tSBvu/AzH9NShhjO/AgABjh9+nSw34bo1q1bXTbwtqABHXaxYg902NRhBQv2sKABHXaxYo9KdShVXzQg1pFAWKPbo1mxYo+sOlQXly1b5rZv3+7OP/98N2LEiMI11BorOqxgxR7osKnDClbsgQ5bGixhxR7osKkDisN0MEy86EUvcj/60Y/cW9/6Vrd3797gvbh0IOEAWK3ShRBkA2gOjhw5EtzEi6lTpwYpV1tRAzrsYsUe6LCpwwoW7GFBAzrsYsUeHdERDojpBr0jK8KawR7NiBV7ZNERDoSJ5cuXB/few4cPL0xDEVjRYQUr9kCHTR1WsGIPdNjSYAkr9kCHTR1QHA0R7tQeYevWrXP//u//7p75zGcG6Rg0EA//hIn+r1o/AAAAAAAARaKA2MyZM0mNCHVF98OPP/54KRDm0QOkbdu21U0XAAAAAEDTrAzz9OvXz73rXe8Kfnzkdv/+/e7o0aPBwHzChAmlfcW0mfSvf/3reksGAAAAAADoMIMGDaq3BGhhfCDsiSeeiP2/n1FdrRViAAAAAAAtHQyL209MP3Eohci4ceMK1wQAAAAAAADQKoGwcEBMKxeVxQUAAAAAwCINkSYxD7XaLwwAAAAAAKCRghjHjh2rtwxo8Dr02GOPpQbCxNChQ13//v0L0QUAAAAA4Fo9GMbeXgAAAAAA0Oronmjt2rXuvvvucwcOHKi3HGhATp8+HQTCduzYkSkQNm3aNCamAgAAAIBpGjZNYpQbb7yx9Jqc+gAAAAAA0KqBsHXr1rkNGzYEfy9evNjNnj3bderUVPMgocaBMKVGJBAGAAAAAM1E0wTD3vjGN9ZbAgAAAAAAQN0DYevXry+9d/LkySAgNnny5Lpqg8apQ8uXL3cHDx5M/eywYcPc1KlTCYQBAAAAQEPQNMGwb33rW21Whj3/+c+v2nf/6le/crt37y79/YY3vKFq3w0AxaMb9u7du5det6oGdNjFij3QYVOHFSzYw4IGdNjFij2K1BENhIUDYkp516dPH9ezZ8+WsUcjYMUeOne3bt3cpk2bXJcuXVzXrl0LD4RZsoUFHVawYg902NRhBSv2QIctDZawYg902NQBxXHOmSbZZEtpP3ylVRqQBx98sGrfPXfuXPfII4+U/j516lTVvhuglejXr19p34pevXq5Q4cO1VsSQBsWLVpUauM7d+7sFixYUG9JAO2gnoJ1qKP1QUEw7ROWhMpD90oak7Uqu3btcgsXLiz7/2uuucYNHjzYteoeYTt37kz9LCvCioG2FBoB6ilYhzoKjUDv3r3d4cOHg9d9+/Z1+/fvr7ekpqXpEsfXKran722SuCEAAAAAADQZ/fv3Dx7yJKGHQUqZyA02RANhS5cuzRQIGz58OIEwAAAAAGhImi4YVisY7AMAAAAAgFUGDBjgZs6cmTkgtm/fvsK0gf1AmFbMZQmEnX/++dwbAwAAAEBD0jR7hgFA45GUJlFLhMM36UeOHEn8Lt2UK/Vi+EHP0aNHYz+r/2n5sdKrKg2Of2ik/TSOHTuWeB7toeDzCYsTJ06448ePJx6jPRe0B4NH59CPXwIt3dEHV/p8eK8GXUtaitYePXq0+R7ZTLZLQufxtpXNy9ksjPT6hyBaMeuvI4m08vRl4r9f1649TTxZykbXLhvkKZtoeeo8Os5/X1wdjZaNdOm4JHQOnStr2cge+pEG2U6/ZZ+0FcrhshFZUpEmlU20XKQjj6955Gt5yzNcNjrH3r17g7KJ85ckX0srmzy+5u0hXUox5nVk8TVdv+yQp2zK+ZrqdTjVh/+uaNlkaTsrKRvva9Kgc6tcZMOkB/BRX9M1eF/rSNlE66h+8vhaXNtZia9plU1Sm17rfi2Mt5HOp+vI269lKZs0X4uro9Xo17KUTdjX9P07duwIXie1HXn7tby+5stO1+Pb9Fr1a9I1a9asIMW770u8naXTl5PK/f7773fTp0935557bu6yyduvhcvTtx06RuWV1HbUYsyR5ns6Jq2NjpZNJWNI72txfW1R/ZquddmyZaU05mHfjwa8hg4d6kaPHl3SWs1+zRO1hWxWxJgjWp46h8YdXkdcHY2WTbX6tTD6TtkrbOci+rVo2ZSro0X0a+Gy0ecVxJddktr0WvRrYbw99Pnwitwi+rWwryW1HR3t1yrxNdUB375Xs1/L6mthe2hySNSna9GvRctG51e9SRoT1qJfK7cHki8L2SLNB6pxLx0tm7g6Wkm/Fi2bSnxNumTvcvf4lfZr4bJJK09p9mMfPx4sql8Ll2e5tqPWY45oefr2wo+Rk8aDcb7WkX4tTNgeQ4YMKemo5F46b7+W1deguhAMy0DY+cMNMAC05frrr3c33HBD2f+HOx91Rrr5Lsf8+fPb+GDSZ30nrNnQHnVay5cvj/2sOvVt27YFnfDll19eGsDoYWbaXhuDBg1y48ePL/2tWbSbN29OnUU7atSo0t/bt28PNieXBv//8CBSjB07NuiIPfp82gzuSZMmBTdknjVr1qR2xOedd55bt25d8Fopb1asWJE66J8zZ06bAUJa2eizOiZs/+gxvky8PXQDowd0Hg2SVq1alXgeXbts4NmzZ4/buHFj4jGysWwdPo9PHaWBTdy1qSyl0aPy17mSUJ1R3Qnv65I0uPaDW9ULlYvqqK4/7SZGPuAHsGk+Fudr+v7wMdFykZ6or8mvVW+S6NOnTzCT3KO67OtdFl9TPdYqBj2YivOXcr4m7Wkpn8aNGxc8CPaozpRLH+btoQeC2k/Utx1ZfG3GjBltBr1ZfE3n8AN4Dax92ejhvj82XE81RtFeQB5pSqsDukmYNm1a6e+DBw+61atXJx4j/5w4cWLw/StXrgweoqpelCsXIZuNGTOm9PcTTzwRtIVJ6KGr9qXxbNmypZ2vReuormXgwIGZfU14H/Nk8TUFG/yNn3xtyZIliW26ynHevHllfS0OfccFF1yQy9ekyWvXdamM0nxNk0LUF4T7Ndk6iREjRriRI0eW9bW4OprH1zyTJ09us7eV+ug8vqbP3n333UEZJbUd5XytHHl9TfV09+7dgd18fcvja3n6Ne9rPiAmG+tc/tr8a7XL6p9VVhdeeGGb/jPO16JMmDChYl/zbYfGNuqHk9qOsK+JtLLJ4mveBuXQGHLDhg2Jn9FeDlOmTMnVr5Xztbi+NquvxRH1NV2LD3aFkV9oDKNykzbZrtzqMI2xNB4Pj62j/Zr+l/ZgKs3XorZQfelov6ZrVz+dx9ekwe9BXq7tiPZr6tPUt+Xp12R/H3SLQ/4p24cftKmepT08j/Zr8re0B5qys3/WER1DxtXRqK/pQV7efk11T9qy+prK/9FHHw3qaVKbrvovP/DIZ7Zu3Zp4HvmZ/M2jz5fzB28P+Y3aT2/rcr4WRteia/KobqY91JbNwtfqfS2p7QiXTZZ7abWzam/z+JquW3XNIz1qC5OOU7+h/sOjvlF9QRLyGflOmq+F7aHrD7e3ab4m1AaoLcjja2prwkFE1WfV66QxYZKvxaFxlNrOPL6mtlnX7/1L90dpzyw03tK4y6OyVN+eRJqvxdXRPL7m0bWE9/KsxNc0rpEvlLvHT/K1JFTXfL+W5muyh9p11efwmCivr2Xp15J8rVzbEfU1+Vlav6Z+UP1hJb7mx4OqA7r3TxoPxvlaR/q1MN4eKserrrqq1KZn9TWNh/L0a+V8ja2ZioM0iRkI3wyGG1MAaIseOGjAWO4HAAAAAGqPHqDrwWZ4Fm0cClrqpp2Uia1FOBCWpS7pARmpEQEAAACg0WGZUwqKDmvGnh/8h2f2A0D7GWXh2RpRwgEx+VR4dksSms2R9tnoDbpmUJY7JpxSLDpDI+080dWhmqGUFiQPz2gWeqAQnkmr2djh2ZsivDxdaIZOeAZVHOHvFJoJlLbyJPr/8CzncoQfrOl11nL0+NVOYcIPY2SPqE2js6HiiC6r12yoqF3TylOf97bX98WdM1o2mmkXnuUbR3SWk2bQJZWN7BGdiSe7pM0WCte1PD4WPj58TLRcZJ+o38jn084TfRirB2t5fU3nVh2P85ew/jCa6RaeHR9HtDw1u61cagdvj+h5svha9DxZfC1s63B91Gy7cAo6/360bGSvvGWj1SJ5fU11QDM2k/wtWp6aQRheVZLFZppJGvW1tLYjzdfi2s4svha+Htk9vKomqY6W87U4KvG16EqwSnxN/Vp4NVYlvhZXR/P4Wrmy0WrRtLKJnsfXmaRyKedr5cjra6qn0VVglfhaln4tXDYq/4suuihYWStb+xRB/p7Gf1bn0SoyrfrVTN04X+tovxZXnmoHNDs4T9vR0X7NT3S87bbbyh6TNIYsVzYd8bW4vjasv6P9mlbAh33Nr+rXdfoVR9IWnmGt69G5dT71cXGBsOh5wivAy5Hma1FbyE862q+pX8hbnvIDnwWgXNsRN75Pe1YQN4YMZxuIorY0anv1t3l9TTPQ09rOsA2iY8ikOhq+trxtZyXje9VZ2SypTY/6mnwmnD0jjmh56r4rnKUjjLdH9DxRX8t6v5ZnfB/2tSzl4nVWu1+L8zX1C7JZ0nHR8pTPRP28Ul8L2yO8wiuLr3n91fC18OrjuHJJ8rUsZPW1cL3KMoasRr8W9bW4OprH1zxxWXTy+prGUD5NYrnrKudrWX0nzddkj2r4WiX9WtjXyrUd0evXOK3a/VpceWo8lDYerHa/FqbcxKAsvhYtm0r6Ne9rTDoqDoJhKVx33XWl16qY4eWPANCWD3/4w8FPOXTDH17OnvZAJ9zBZP2sx++1VI64JdgavORNhaoBQ3TQkIbOLW1eg16nXV+0889COKVKOaIdf147q13Me0y58gzbI6q9qLLROfwgJK0OhXUnLemvtGyiA6ukQHM5qlE2afU0q506Wp7+mCz+0pGySfO1uO/LUp5ROuJrGpiHAw3lvquStrOSsvEa8pxL1xC9eaq0bMJ1NKq9krKp1NfytOm16NeqVZ6VlE3U17LU0Ur6tUrKJk+5VLtfCxO1aVH9mh5MPelJTwoCYv68/rvCmlReSj/mA2J5qcTXdP48bUe1yibL3hxF+1rWetrRfk3lrLSufh+/cqh+6EGwHjZlfThTrTFk2BZRny+qHdT15207qtmvhb8z+sC3iH4trmzS7FFUv6Zj8o47qtGvxf2/Xv1a+Lqz1NNK2s5KykZ1IO9xlfRrSeXp7RH9zlrdS8eVpx7QZ20/ajXmiN7nV1Ke1SqbNFvU4n4tjvAEoKy2qMWzkah9iuzXwuWZpY4WVTZ5x4PV6tfCxF1nUf1aJb4GLRIM+9a3vpX5s8qFmufzYXwufc2qvfnmm91jjz1WmlWh3xdffHFF3wsAAAAAAFA0moykfRPuv//+1PsgrRBTesVKAmJgH606T9sPTmhWd55AGAAAAABAI9AwwbCrr746dTDul0hqY+k3velNHT6n/z5/XkWFX/va13b4ewEAAAAAAIoMiE2fPj3YuD0ptZBSQvmUiWmpTKHxUDrL/fv3J25ur3IvlxoRAAAAAKCRSd5R2SAKUMX9ZPlM3h/dAIRvAt73vvcF+U8BAAAAAAAaCe0xoT1Io3sVxAXElDIxywoiaCxU9gp0lts/SYEw7d1CIAwAAAAAmpGGC4b5AFX0J8tn8v6Eg2IKhF1//fV1u24AAAAAAICOoL0cFBBL22uCgFhzB8SUCjMaEBs5ciSBMAAAAABoahomTaKIrgDr6OfK3Rz06dMnmBU3Y8aMYI+w173udW7s2LEVfycA2EJ+rtnR/nWrakCHXazYAx02dVjBgj0saECHXazYw5oO/eheZ/ny5e7kyZOpAbFqp0y0Yg8r1MMePiCmlJj79u0LAqTKgKLtBorUYbVuWNFhBSv2QIdNHVawYg902NJgCSv2QIdNHVAc55zpSOTIENrPy6/mmjNnjnvwwQfrLQkAYvarOHDgQPC6V69e7tChQ/WWBNCGRYsWlfZS0UBowYIF9ZYE0A7qKViHOtoYaEy2ePHixICYv8+64IIL3KBBg1wzcOLECbd169bg2hXw89c4e/bs4PWIESNc165dXSugst++fXuwKowVYfagLYVGgHoK1qGOQiPQu3dvd/jw4eC1gnPa4xVqQ8OlSUyDQTwAAAAAAEAyutHWJMIsKROXLFniDh486JoBBbqU9WPAgAFBqkD96LXe00+rBMKEyl4rwriHBgAAAIBWoKmCYX6PLwAAAAAAAEhG6eEVEEsLAGkvKc1YBQAAAAAAaFQaas+wJG699dY2N3UAAEnpcbQ/gtBs4HrMALagAR12sWIPdNjUYQUL9rCgAR12sWIP6zp076QUgUobqM9EGTZsmJs6dWrVVg9ZsYcVqmUPpYBSGSnlYz11dAQLGizpsIIVe6DDpg4rWLEHOmxpsIQVe6DDpg4ojqZZGXbZZZeVfubPn19vOQBgmOPHj7v169cHP3rdqhrQYRcr9kCHTR1WsGAPCxrQYRcr9mgEHT4gFn0AUO1AWJqOVqQa9tDeXwpmLl26tLQPWj10dBQLGizpsIIVe6DDpg4rWLEHOmxpsIQVe6DDpg4ojqYJhgEAAAAAAEB1UiYOHTq06oEwqD4KhD3yyCPBRuu7du3qUEAMAAAAAKCZIRgGAAAAAAAAwb5gCoiNGjXKTZs2jUBYAwXCPAqIPfbYYwTEAAAAAAAiEAwDAAAAAACAUkBs8uTJBMIaMBDm2blzJwExAAAAAIAIXVwLsGPHDrdixYpgQzz9xG0MnYc3vOENVdMGAAAAAAAAkHePsAMHDpT9jA+IzZgxg8AmAAAAAEAzB8PuuOMOd+ONN7pbbrnFbd26tarfTTAMAAAAAADgr5w5c4agi5FAWHgfOMoEAAAAAKBJg2Fr1qxx/9//9/+5W2+9tXRTVk24mQAAAAAAAPgryrzx6KOPujFjxrghQ4bUW05T21mpEbMEws4777zgBwAAAAAAmjAY9vDDD7srr7zS7d27txQEq2bwqtqBNQAAAAAAgEYP0Gil0sGDB4O0fNOnTzcdENM+WtJ67Ngxd+rUqeC9zp07l/be0mqqTp3sba1NIAwAAAAAoGM0TTBsz5497lnPelbwOxwEI4AFAFG6dOnizj333NLrVtWADrtYsQc6bOqwggV7WNCADrtYsUcz61DKPgVoFFzy914KiE2bNs0NHTq0MB150P3iwoUL271/1113Bb+vueYaN3jw4ML0ZLFHOOCYxvjx4924ceNqoqPWWNBgSYcVrNgDHTZ1WMGKPdBhS4MlrNgDHTZ1QHGcc6ZJokV/93d/5/793/+9zUowXdqll17q/uZv/sY96UlPchMmTHD9+vWjcgPUCfmfn83aq1cvd+jQoXpLAmjDokWL2swSX7BgQb0lAbSDegrWoY62Bkl7V+meLCkgVk927doVGwzzFB0MS6OIQBjYhLYUGgHqKViHOgqNQO/evd3hw4eD13379i1lLIDq0xRRITVq3/zmN9usBlN6i+985zvuhS98Yb3lAQAAAAAAtEQgzN+PPf7448FriwGxRiFPIEwTP8eOHVuILgAAAACARqQpgmF3332327dvXxAM042Xfv/4xz8O0iYCAAAAAABAdYNhx48fT/yMT5mo38OGDStMW7NAIAwAmh3lqTp6tO3PiRNn39eP5rtrC8du3Zzr0cO5nj3P/tbfAAAALRsMW7VqVem1AmGXX345gTAAKIse3uzcuTN4rdzA3eowmragAR12sWIPdNjUYQUL9rCgAR12sWKPZtTRo0cPN2fOHPfwww+7Y8eOJX7WrxDzATEr9rBCnD30ngJhWVKKVysQZqFcLGiwpMMKVuyBDps6KkGZwPbscU5ZwMpt3OKDYcpupwBZuDnUzicDBjg3cKBzXbuefe/06dPBnpDqkygXGzosaLCEFXugw6YOKI6mCIbt2LEj+O1XhT3nOc+ptyQAMD7TduvWrcHr/v3716Wzs6ABHXaxYg902NRhBQv2sKABHXaxYo9m1dGzZ88gIKagzVFN588YELNiDytE7SGyBsImTpzoxowZUxMdtOn112EFK/ZAh00dWTl9+mzwa/fusyvA/Hvr1zu3du1ff7ZvPxv80v86dz67Emz0aAX+tS+ic+ed59zw4c7p2fWuXc716ePcoEH63Jlgoobsop9WLxcLOixosIQVe6DDpg4ojqYIhkWp1g0BAAAAAAAAJAfEtEIsS0BMkxe1KTiUn52s1JJFB8IAAGq9EkzPmv1CYgWx/vQn526//WyArBxaFabmcPnysz8eLYa98krnLrnk7AoybV/Zt+85rn//c4I+RkExAACApg2GRXPQK6oLAAAAAAAAtSWcMjEtILZs2TI3bty4wrQ12j5sS5Yscaf09DeFSZMmudFaKgEAYBit7lIiJ60GU9BKK7/+53+ce/jh8ukRs7Bhg3M33ujcD37g3IIFzr3oRXr3HHfwYHc3ePCJYDUZAABAHJ1cE6CbL6EUiWLLli11VgQAAAAAANBaAbEss/FXrlzp9u3bV4iuRgqEbdy40R05ciT1swTCAMAqXbSZ1/+hVWAKfmkV2PHjzv3wh8790z8599BDHQuEhVGTefPNzv3DPzh3zz1ngpVkTzzR1W3Zck4QiAMAAGjKYNisWbPazDD8k9ZbAwAAAAAAgLmA2LZt2wiIhVIjKhCm32lMnjyZQBgAmKRz585BsN4HqdatOxsQW7PGuY9+1Llf//rsSrFaoDSJCxeecZ///PEg7eKBA+cEq8cyLLQFAIAWoymCYeI973lPkINeP7feeqtbvXp1vSUBAAAAAAC0XEBMe4llCYht377dtTJKiajUiFkDYaNGjSpEFwBAHpSlSYEw9QEKhK1ffzYQdeedzl13nbI3FaPjnntOuw984KjbvPnsPmVeBwAAQNMFw6655hp3wQUXBJ2w0ky8853vdKdZFw0AAAAAAGAyILZq1Sq3detW18orKaL7X8dBIAwALDNy5EjXp08fd+JEp2BFlh7F/fGPzn31q7VbDVYO7U/2yU+eDnRoG8uNG4vXAAAAdvlrQt8myE3885//3F188cVu586d7pZbbnFvfOMb3Ve/+tVMqToAoHgOHTpU9n+9e/cuvVZgO20PBQXCe/Xq1WambblN3HXeY8eOlfYZ9CiQrvfT2pru3buX/j5x4kTqbN6uXbu6bt26lf7WObwGryeKPq/jPLqWtA3V1dbpoYpHNkubFBD9f1KZeGRnbzutxj2saXcppJVn1B6dOnVq8xAtS9no2sPtfZayiZanzqPj/PdlKRvp0nFJ6BzhHPppZaPz6v+yg0d2lr2zlo3/no6UTVw9zeNrnkrKM65sdK6ka4rztbSyyeNr3h7hz2f1NV1/uDw74muq115juJ5GyyZL21kNX0srl7jy1DV4X+tI2UTraF5fi2s7K/W1pDa91v2aJ/r/SnwtS9mk+VpcHa1Gv5albKK+llYulfRreX1N55ZNwjarVb8WLZtweZarp5WUTRZfUwBHq558vdTnw2Wjc0jbo48+Gth8+PDhhYw50nxPx6S1adGy6YivDRgwwPXv39/t2rWrne/pPNOmTQseNNeiX/NE75ez7F1WzX4t/B3hOlrUmCPO19LajiRfq1bZ6Dtlg7Cdi+jXomVTru0ool+LG3OoTJPqWy36tTDeHuE2sMh+zV970tijo/1aHl/TcQrqnz59xj3xRLfg9513nnHf/KYyN7U/zznn/PVazpw5nXr9WY9RHdS5xYEDZ9y//ItzH/mIc+patm8/7YYOPZtJqlx7UMkYMlo2snPamLAW/VqU6HOOLGP1atxLR30tzhaV3K9Fy6YSX5Mu2bvcPX6l/Vq4bNLKM248WGS/5suzXB2t9b10ufKUrjRbx/laR/q1MGF7hKnkXrqSfi2Lr0F1aZpgmJg4caK788473XOf+1y3Zs0a973vfc898MAD7uMf/7h7+ctf3qYhBIDqc/3117sbbrih7P/DnY86o2XLlpX97Pz580uv1QEnfdZ3wjNnziz9rU5r+fLlsZ/V96mDUqcV7rj379/v1mqX3wQGDRrkxo8fX/pbDy42Kw9DAnq4E57Nq5RAmgXtO0mdM3ozNXbsWDdkyJDS35s2bUrdW0OpKfQwxaN2MK0j1jH+AYtssXTp0tRBv2Z7+/ZUn00rG31Wx3g00Ige48vE26Nv375u+vTpbQYomj2ehK7d56kXe/bsCfbgSEI2lq3D51E98AObuGtTWYYf2Kn8da4kVGdUdzzr169PHPDJHueee24wgPN1VNefdiMvH/AD2DQfi/M1fX/4mGi5qJ5GfU1+vWLFisRzaKbo+eefX/pbdXmdNhLI6GuygWyxY8eOWH8p52tKwaUJMkloz1HZ2qM64+tAFG8P1Ztw25HF12bMmNFm0Cubpfna3LlzSwN4Dax92cgO/thwPZVdZs+eXTpemtLqgOyqB62egwcPpqaa1oNbjblkA7Ufu3fvTiwXMXToUDdmzJjS30888URqejTtixNeMbFly5Z2vhato9I0cODAzL4mpk6d2uZGNouvab9aXwfkazp3Upuucpw3b15ZXyt3o6isB3l8TXUs3KZn8bXBgwe78847r02/JlsnMWLEiNJ54nwtro7m8bVwMKVfv36lv8N2zuJrsoH6HF9G5epoOV8rR15fUz2VBtnM15s8vpanX0vytbg2PauvRZkwYUImX1M9lgbd5Kv81YdoXKB+XnVt7969ge1Vf6RB11zO10Ra2WTxNdk+CY0hN2hJQQLSP2XKlNLfHfE1lYvGpPpOb49wfU7ytTiivqZrOaANdVJ8Ldx+aNyZ9jAr2q9p3J32YCrN16J1VN/f0X5N165+Oo+vqY4ktelxviY/U11PIuprGkOqzMuhtlTj2/CDNtWztIfn0X5t5cqVqQ80ZWd/ndExZFzbEfU1+Xjefk1thrRl9TW1Bfpb91FJbbrqf3hPeflM2gpU1X31bR59Xj4ah7eHPh9un7L4mq5F1+BR3Ux7qC2bhQMO3tfKtekiXDZZ7qV1HWpv8/ia6pjqmmyt+rBnTxd34sQ5bvXqk+4//uN47Gqszp2jk900KSIt4NL2Af2JEyfdyZPtx2lnt005e1KNQTTE+NznnNPjCa0W69btuOvR43TQv8WNQdUGhPugLL6mtiYcRFR9lu2S2o8kX4tD4yi1nXl8TW2n2nXfpusY9dNJaLylY8L9Wtoxab4WV0fz+JpHfaf60I74mspdesrd4yf5WhLyNd+vpfman3ir6/FtRx5fy9OvaYymsZpH92vq272OuDqq/im8L6n6tLR+Tf2g+sOs/VrY1/y9pDSk3UvG+VpH+rUw3h4qx3CbntXXNB7K06+V87W0ugbVo2mCYdcpEfH/8aIXvch96UtfKjUqr3nNa9zb3vY2d9FFFwUPStUoRGd15+FjH/tYlVQDNBfq2LPMbKo36hB1Y6kH9OGBdT001BtdvwUdVuxhBdlDg1HV0XrrsFJPdbNV7xlT3h4qm3q1HZaQDXQzqwfbaQGKZq+jVnRofBt+wNDqqI7qZrveqdNVP3TjW++yqUc91Tn1oCQcxPPBMI0ZwwEX/3AyGhBrdny56EcPqxQAEeprwhOjimrThR5MpgXDmr0ttaDDClbKRXVUPlHv+01vDz0Arud40Eq5KHhw7Fgnd+BAV6e5RF/84om6pCXUg2w/YdMHJ9St/OhHzr3mNQoud3WjRx8LngcqmFCremSlXMJtetrko1phxRZWkD00uaEVx4NJdVTBs3q2694eCpRyj98anHOmSUKPqrTRpcAifHlx/6+EetwYADTbyjDN6kiaZVJE2o1qpG6rNE1irVLc5E2TWIsUN9Uoz3qlSbz11lvbpEnUJIp6pEmsZuq2NBrF16qd4qaRfe2ee+5pk4LO19N6pUmsRhqVOGqVuq2Zfa2aqds64mtxdbReaRJr0a9Z9rUiUrdV4mu6Vq1sDNdhn7IoimYy+wdFtRhzaHb417/+9bLHv+Md72hTvkX7mmZuqzwUDKtXv2bV1+qRTqqevqa21KdJlMYFCxbUJU2ipX6tVmOOeqVuawZf27//gNuypYc7ebKT+/a3j7tbbin/MLmWaRLLHSMzfOhDZ5ySVAwceCZIl6j6Gq1H1UqTiK/Z87VFixYFx/g0iXH3+EWkSWy0fq2oNImN7GvVTJOoCRZeoyaN1SuI3Qo0XTDMX05aYKxS9L0EwwAqQ7Oi/XJ2dUxZBhcARaKBcvgBrh46AFiDegrWoY5CHLrRf/jhhzM9jFD6mHDa22qiVU8LFy4s+/9rrrmmTTomgHpBWwqNwNatR9yuXd3cqlWn3Yc+dNT16vXX1HRWUHZSzdnVc35l1e9AoihoQGhLoRFQII5gWDE0TZrELKu/OroyrEnihgAtj2a4+FzWmnkcns3UShrQYRcr9kCHTR1WsGAPCxrQYRcr9kDHX2cPaw/R++67r5QKUOmA4/bYa6W6W+9ysaTDggZLOqxgxR7osKnjwIGzbfiPf1yf9IjhFWN+VZJWqoRXkyld4p//7Nxllzmn7YxqmYHWSrlY0GFBgyWs2AMdNnVAcTRNMGzs2LFVS4MIAM2Nlnr7TVqVd74enZ0FDeiwixV7oMOmDitYsIcFDeiwixV7oKNtQGz69Onuj3/8Y7BSLJqOUPdz+r/2iWwGNJky7R7VQrlY0WFBgyUdVrBiD3TY06EMYsePd3I7dpxxDzxwqu7trU/ppkkW0ab3D384Gwzbs8c5dTG1enxooVys6LCgwRJW7IEOmzqgOJomGLZu3bp6SwAAAAAAAICUgNiYMWOCvbHiAmF6ENEMaM+Ixx57zJ1//vlt9qAAAGgWFFgSt9yiPSBrF2CqBuvXO7dq1dk0idq5oV+/eisCAIB68Nd1wwAAAAAAAAA1RrP2R48eXVoZ1oyBMO2Ppv0eFi9enGmfNACARkLBL21po62Y/vCHsyuyrPPHP579vW9fvZUAAEC9IBgGAAAAAAAAhQfEZsyYEewb1kyBsCNHjriHHnooCIgJpYNUYIyAGAA0E8eOuWCPsHXrTjdMcGnJkrO//695BgCAFoRgGAAAAAAAANQlZeL8+fObKhCmwJc2Yw9DQAwAmg0fUFqz5rRrFBS0U2rHEye0T1C91QAAQD0gGAYAAAAAAAB1QSkSmzkQ5iEgBgDNxJEjjRcME+vWnf3N6jAAgNaEYBgAAAAAAABAjQJhnhMnTrhDhw4VpgsAoFb4YNLq1Y0ZDPPBPAAAaC26uBbj1KlTbteuXaUc7mPHjq23JAAAAAAAAMjInj173IEDB0zcy2ml1+LFi1MDYVoBN23atKZJCQkArY1SDYpt2xorGLZ9+9nfpEkEAGhNmj4Y9sADD7hf/OIX7o477ghm6+mmKXxDcjKhB3ziiSfa/H/gwIGuZ8+eNdcMALWle/fu7rzzziu9blUN6LCLFXugw6YOK1iwhwUN6LCLFXugo7o6FAh79NFH3enTp92ZM2fcuHHjch3fqVMn179//yB4peP9faHXov/nCYTpHlMpEJPQ90+fPj02ENYs5dIsGizpsIIVe6DDlo7/az5dyjyAwjjnnE6uW7fupdfl8M21119t6l0ulnRY0GAJK/ZAh00dUBxNGwy766673Ec+8pEgCObxNztZ+dCHPuRuvPHG0t9XX321+/rXv15VnQBQPF26dHGDBw9ueQ3osIsVe6DDpg4rWLCHBQ3osIsVe6Cjejr27t1bCoSJtWvXBvd4/iFGFjTB8dprr3WLFi0KsoaIzp07uwULFuTSUo1AWLOUSzNpsKTDClbsgQ5bOvzjtf9rRuuO2lrZJA2vt1bBsHqXiyUdFjRYwoo90GFTBxRHU+4Z9s///M/u8ssvDwJhujkKz/jzP1n44Ac/GHzWf8ePf/xjcrwDAAAAAAAUjAJhjzzySCkQ5lm3bl3wUyTVCoQBADQq/rFa166uofDxsoyPBQEAoMloupVh73znO92Xv/zlNgGwcEDMv5eFyZMnu+c973nul7/8ZfC3AmFKufia17ymRuqbm9WrV7sHH3zQbdy4MbiB7NWrlxszZoybN2+emzhxorO6GfZf/vIXt2zZMrd79+7gvUGDBrmpU6e6Jz/5yTVNm1nPcwMAAAAAWA+EeXwwLM8KsSICYTNmzHDnnntuzTUBABSNMspqlVWvXn9NPdgI+McoOTLiAgBAE9FUwbD//M//dF/60pfaBcGuuuoq95znPMeNHz/e/f3f/71bs2ZN5u/8m7/5myAY5gNov/vd7wiG5eDEiRPuq1/9qlu4cKF7/PHHy35Om0lfc8017q1vfavramBq0ZIlS9ynP/1p97Of/SwISsWhYNRLX/pS94//+I/BjW4znLtVOHr0qNuwYUPwWhuv9+jRoyU1oMMuVuyBDps6rGDBHhY0oMMuVuyBjo7p0P3c+vXrywbCwgExnzIx6+THvGhy5OLFi6saCGvUcmlWDZZ0WMGKPdBhS0e3bnreo3N3cnv3JrfPRXDmzGl37NjZtrl7925l9w0bM+av+puxXCzpsKDBElbsgQ6bOqA4miYYtmPHDvfhD3+4dOOjG6EJEya4H/zgB+7CCy8sfe7jH/94ru994QtfGGygpxsefecf/vCHqmtvVpYuXepe+cpXusceeyz1swqUaVXfF7/4xSAdpYJj9UA32Z/4xCfcpz71KXfy5MnEzypQ9d3vftf98Ic/DOqe6lZHbrzree5WQ/tDHDhwoPS6VTWgwy5W7IEOmzqsYMEeFjSgwy5W7IGOjunwgSWtDNu/f3/iZxU0E7UIiCkQphVhmuxXzRVhjVouzarBkg4rWLEHOmzp0Aor7SIyYUIn98gjFoJhZ9zp06dKr8t1AX4Bca2ed9e7XCzpsKDBElbsgQ6bOqA4mmZhsFbSHDx4sNTxjRs3zt1zzz1tAmGVoFR+s2fPLqVZ3L59e/ADycj2F198caZAWDSAdtFFF7n77rvPFY2CUa997WvdJz/5ydRgVBh99p/+6Z/c61//+tQZqxbPDQAAAABgGW1uPmvWLNevX7/Uzyogtnbt2jZp8osMhF1wwQWkRgSApscHkyZObJzHigqQjRt39jeLPwAAWpOmWRn2ox/9qJQaUb+/8Y1vVO0m5ElPelKwd1N4FdOwYcOq8t3NiJaXaq81H1n3dOrUyb3gBS9wl156qRs9erTbtm1bYNef/OQnbW4sNePzuc99rnvooYeCzxXFBz7wgWAlYZRRo0a5173udcEecroRX7VqVbAia+XKlW0+p5Va2gNNK7sa6dwAAAAAAI0SEMuyQsynu1Ga/I6uEMsbCBs8eHCHzgcA0Ag0YjBsxIizupUikT3DAABak6YIhmn10ebNm0s3OvPmzXNXXHFF1b5/4sSJsek3IJ43vOENbvfu3e1s+Itf/CJ2fyut6tP+Vw888EDpvZ07d7qrr766sLSUt956q/vc5z7X7v2PfOQj7mMf+1i7fcy0GkspHa+99to2y2g/85nPBIG8BQsWNMS5AQAAAAAaLSD26KOPun379tU8IKbMI9ojjEAYAEBbFFDSo4phwzoFqRLXrLGfqeYpTzn7u1eveisBAIB60RRzIcKp+HQj8uxnP7uq3z9w4MA2f6fdeLUyWuV1++23t3lPq7vuuuuu2ECY36DwtttuC9JRhvnjH//o/ud//sfVGq0mfPe7393u/euvvz5IWxgNRvlVbtdcc4375je/mem7LJ4bAAAAAKARA2IzZ850/fv3T/2sAmJr1qypKGVi1kCYxubSQyAMAFqNAQPO/n72s+3Ps+/c2bnLLz/7OvKIDwAAWoimCIY98cQTwW9/kxNdydVR+vTpE/z2Mwr93mTQnhtuuKHde1/60pdS00rKxjfeeGNwc5v2fdXml7/8pVuyZEmb95785Ce7f/iHf0g9VikMtaotjNKo/OY3vzF/bgAAAACARl4hliUgtnHjxtwBsTyBMK0IGzRoUObvBgBotmDYggVdXO/ezjRz554NgvXsyX5hAACtTFMEw6IrtbJsrJwHn5Pe30D1tt7L14n7778/2OcrzEUXXeSe//znZzp+7ty57mUve1mb9+67774gwFNLvvKVr7R777rrrgtubrPwz//8z5m+09q5AQAAAAAalc6dO+cKiK1evTpTQEyBsCx7hBEIA4BWR4lsevU6FaRMvOIK26vDnvGMs79ZFQYA0No0RTCs1mkMt23b1ubvc889t6rf3yz88Ic/bPfeW9/61lzf8Za3vKXdez/4wQ9crdi7d6/7/e9/3+a9cePGuWc+85mZv2PatGnukksuafPeb3/729SNvet5bgAAAACAZgmIDfDLExLYtGlTEBA7fPhwkMJd+0ArSKYfvdZ7+jl69GjqHmMEwgAAztK379mJAy9/eVeXYW5CXZg3z7np07WqWJPn660GAADqie2pGxkZOnRo8NvftOhmpprcfffdbf4eMmRIVb+/WYgGdlQeL37xi3N9x9Of/vRgdmc4oHnzzTe7T3/6064W/OlPf3InT55s8540Z12Z5VG6Qt08e44fPx7sg/bCF77Q5LlbnR49ergpU6aUXreqBnTYxYo90GFThxUs2MOCBnTYxYo90FFbHQqIac+uRx99NJhslhYQ06SxP/zhD+3+p0CZmDp1arCXcbk0iX6PsOiEzEpp1nJpVA2WdFjBij3QYVXHaderl55rdHFvfWt39+Uv10eH2mZvh/AzFSV2evObz77W7h05H7c0cLnUX4cFDZawYg902NQBxdEUwbAJEya0+TscGOgoSpNx++23B4EdpdXQ7wsvvLBq398s6KYyuveVVi3lnS2pQcvFF1/sfve735XeU5qSAwcOuL59+7pqE1dXoiutshB3zKJFixIDUvU8d6ujhya1qE+NpgEddrFiD3TY1GEFC/awoAEddrFiD3TUXkeegFg060e5/YzjAmLVDoQ1e7k0ogZLOqxgxR7osKlDDBp03B092tlddFFnp50z7r23HirOcZ06dW737utff3Y1mExVxMo1K+ViQYcFDZawYg902NQBxdEUaRLnzJlTWh2mgJWCV5r1Vw3+67/+KwjEeJSGg3QY7VHAKpqDX/uFVYKCYWH0vboRrQXRPc4q1T1v3jzXTYmyU77byrkBAAAAAJoJHxCrVqDKB8S6KK9WjQJhAACNjiYgdO58NiAm3vhG5wYPdiZ4ylOce+pT1T84N3x4vdUAAIAFmiIYJp71rGeVVm6dOnXKfeQjH+nwdy5dutRdf/31bVaFveAFL6iK3mZj+fLlqSv2shJ33IoVK1wRuhVUGj16dO7v6dq1qxszZkwuzfU8NwAAAABAMwbEtJdXtQNi3bt3DyZFEggDAGiLJqJr+4fevU8F6RL79HHugx+s/95cM2Y497a3nX2tQFjXrvXVAwAANmiKNInive99r/vOd74TvFbg6tvf/ra79NJL3Vve8paKvu+xxx4L0swpTaLfi6xXr17u3e9+d1V1Nwvr1q1r997YsWMr+q6449auXeuqjfbW2rp1a5v3FIxK2zA7Sbffa0Bs3rw5SKuiYJWlc4MLNk5fs2ZNKfgq325FDeiwixV7oMOmDitYsIcFDeiwixV7oKNYHT4gphTue/bs6fD3KXXOU57ylNz7+malVcqlUTRY0mEFK/ZAh00dCoRt2LAh0HDuucfd7t3dg+DTP/yDc5/5jHOh7eBryunTp9yxY2dXp82b181de21np4W9Q4YUkx7RWrlY0GFBgyWs2AMdNnVAcTRNMEwz9v7mb/7Gfe973yut5Hr729/uVq1a5T7xiU9k3gRPKRG/9KUvuU9+8pOBQ4RXhb3jHe9wg62s9zbG9u3b270XXa2UlbjVUXHf31GeeOKJdqkdK9Ucp/v06dNux44dbuTIkabObYldu3bV/BzqyHr27NnmPdn+2LFjpdfRNA9aXVprjhw5EvzowUpUQ7mbjH01uJOQBl8OAwYMCP5WOqD+Oe4YDh065I4ePVp1HXH07t0716ameggmf8iK2n1f/nqYlqeOarZ41gdlClZrr8WO2iMLCor3yzE1U5NAvH9UU0e5GfeabZ+G91lpq4WOOOL6+3Jth9dWBFnsFR3XaAJGLYjWD7Ud0bS95ZD9du/eXXUNcXVDY7g8Ka7Vnqldq7aOONSeqV3LitqN8P5F1dIRh9qNrJNq1M6Ggw4daUvTUDubdXWO6pn3gSLaDo034m6gy7UdRY05hPr1pPFPEWOOuHoq38zz0EF1K2s5ahwsX47uIZY2ZvGfD48hs/TvecccHfHbao45OqKjo2OO6BiyluOOPGNI30fpdxFth/wg66RItWfhbRxqifp0BYOzth3hMWQtCNePUaNG5Wo7qtkPpdXTPM+MOjKGzOsvGkNq7F2tMUe4XVad1DhQzyVGjDjpNm3q5EaNOsd95CNn3L/8y2kXmQfcjs6dO7lzzsk64eCMO3myfd+p9lf911Of2tlde+05rksX+e9pN3DgGXfixJlAp/5fqzGHUHumtqOW909Zx5BRv1W/nvf6OzLmUH3I0nZoHJS212dH8eNSlWeecbfKTsdWiySfLTeGLEdHxpB52w59RuP5LOQZQ3a0z6/Wc6tajD3inkMmUY3JW9CCwTDxb//2b+7Pf/6zW79+fTCYVGf42c9+1n3ta19zr3rVq4L9mKIDjVtuuSWo9JrJctddd7nbbrst+IwPgAn91r5M1113XZ2uzD5xjptnoJV2XDUemNVSc5LuuIBUPc9tBXWSCxcurPl5rrjiCve0pz0t8+e1qrQW9S2Opz/96ZkHPAoIqy2rJWr/fGD2zW9+c+bjFi1a5O6t4i7JXkccz3nOc9yTn/zkzN8lm3VkAHvPPfdk/uwHP/jBzA9Z1Of41cwdsUcWJk+e7F7zmtdk/vwf/vCH2H0aO6ojjpe85CVB2qmsaE/QWuiI4+Mf/3jmz2rizU9+8hNXBNqvZoimuGbk17/+dWwq42qjctGkpClTpmT6vB6uVLsPKFc39PDnHzQ9OUea7F/96ldV1xHHhRde6J73vOdl/vzPfvazYJxbbR1xvOlNb8q8yl9j56TyzNOWpqGb3muvvTbz5zdu3Ogef/zxQtoOZaW48sorM3/+Bz/4QU0mfMXxt3/7t5kfAGkcpMmBRSB/04TDrNx9993uzjvvrKkm3z8/4xnPcJdccknm42666aYOBxGz1tO/+7u/yxx0UjaKG2+8sSY6oowbN85dffXVuc5z//33V1VDOZ7//Oe7+fPn5xrf6n6liLbjH//xHzNPJlFGlu9///uuCM4//3z36le/OvPnb775Zvfoo4+6IlCQLk+Qolb3ndH6oYftH/3oRzMfrzHaz3/+86rrKDeB/MUvfnHm79RYaOXKlZk/f9999wX94LRp01yPHue4Y8f6uUGDurpPftK5G2885P7nf464crHUwYMHue7dswerNbk4St++57i//ds+7oor9JjzpOvWTfeAh9327WeC535Zx09hdK/+/ve/P/PnH3nkEffb3/629Hct2w+tWH72s5+d+fM//elPgzFREShDlwLWWVAgtYjnQkLBiYsvvjhXnb711ltroiVaNy677DJ3+eWXZz5eC0E0Ab7aOuK45pprMgf5d+7c6f77v/+7Jjqi6HnnW9/61syf1zN/jSOrraPcVk6KQWRF47SigtXQZMEwPRxSx/PUpz41iJL7VV26ofvyl78c/IRnJOh3tPPw//OBMP09fPjwYICSdzZ2KxE3gzrP6o0wcdHzas7GqIXmvLrree6Okme2Z5SiZjGG0aBXN7PRh7B+4KCHFuGZ70WsOAnfzOrGN6ohjiwzequFzhW1WRJKy1kUSgeaZ8ZpltmM1UKTMbKuoigq4OrPlac8i3ow6x8AZHlw6H02yyrKahFns3JtR9wNea3QudSHRMcr9VyBG36onbX+FLUaxp/Lapumh9R5tNVytU4UBcWzPjiq5UqAuHNltZl8VjflRaGHTHnajrwrEDvCAw88ENzLlBv/hClqpasfr+bxgaIe5Pk07XlWehXpB3/5y18y35vWesZ9GNWrPOUZTRtfS/RQP+v9kXy2klV+HRlDxs26171CtL8vsk3TGELlmXTvFKbI8ZDKs8g2NCsqrzw+sG3bNlcUGqPl0VbJ/YomK8h3ZsyY4fr33+cOHdIkjJ7u//v/ertLL+3mPv/5g27z5vZjwCNHjma+b4u7H7joom7uPe/p4wYM0AqzM65Pn4OuR49jwRjwj3/8Y0WBMCFNeWym/dOKQuPVJG1Rvy3yecLDDz8cpJ3L0nYU+fxFeuK2eClHpfWmWs+tkqjV8744NGkl6yTuIp/56VxW7/F8/bc4hoQmC4b52UuafaqVYGqAw0GtOKLvhx8q6X9aEaZAWFzqPvgrcU5eaXAn7rhapHiqpua8uut57o5Sj4BWR9BgPPrQVTMu/Ht6Xas9ILJqy6KhyAfHavvynK/IAEVceVohjzbKM5/Nwj5bFHHnK9d2FPnATOWj84UfjiXZxmJ5CnzAvrZmKE/5qYXyLNd2FF2eWcc/RbdpVn3AsjbpyqqN8qys7SiScrri+vt6lGfWtsNqH1U0eXQ1o38quKtUZHqW1qfPIde9+zF34EBfN316V/elLw10t9561P3qV0fdypVhPztTdtVYe11nfyt+fPHF3dzzn9/TzZ59NsjSrdtx17fvQdep0+ng4bIyXmzZssW1YnlG/bYefVSWtqNIm/nz0X82x5jDsn9a7qOgCYNhPiWUAmLaK+y//uu/Sg/vs+bhVqOiqLdSdig1YkeCFK1MVntnOa6ohr5SzdXQXc9zAwAAAAAAAAB0lAcffDAIQin1mzLLDBy4p7RK7KqregQ/K1acdL/+9RH3wAMnXNbFQXrsMXq0tjHp5Z773B5u0KCzwRUFv3r3PhSsBvMp6bVipMjVMwAA0Bg0ZTBMKPXYDTfcEOwR8ZWvfCVY3aX0IEmrZRRQ0F4cL3jBC9y73/3uXHtytDrVXO4cd1zWHOp5qPYS7Ty663nuauRnb6RVZZp9FE07oo02/Xvh1/XSlkVDkRrVFuY5X0cCudUoTyvk0UZ55rNZPfw07nzl2o4iV5eqfHS+8EzxJNtYLM+isewDlrU1Q5smP7VQnuXajqLLM+v4p+g2zaoPWNYmXVm1UZ6VtR1FUq484/r7epRn1rbDah9VNHl0NbN/KgWk9tTVfs9Km6hVYr16HXZHj/Z0R470cFOmdHFTppx9tnDgwDluzZozbvXq027r1tNOj+20qEKPNLT/2Lhx57gJEzq5887rFPztV1x07XrS9ehxJFh9JrlaDaaVadrPt9XLM+q39eijsrQdRWfpydN2WCrPuM+3+pjDsn9a7qOgiYNhHs1Ced/73hf8aKm2cp0qT6jyHyt/uvZa0kaAQ4cODTYxz7opILQlLn+s7F2twE7W/LT10pxXdz3P3VE6kmta/lh0QEwbeS9YsKDNe8ovv2zZsuD11KlT22wor/SqReWtPu+884JyimqIQ+1WuQ3Gq43KKWqzJGTPovKjT5w4MbihyopWCRe1b5j2q8y6klh7n6muFcGgQYNylaf2hyhq/wKlNp41a1bq57zPao+xola9xtmsXNuhvbKWLFlSiC6NVzRZxz8E0CA7qXzVdhS1v4gedEyZMiXTZzU56fbbb3dFkGajKJo8pbpWBCNGjMilTW1HUfv/zJ49240dOzbz2ECbUheB9knKajP5rOp/NTYYz8KYMWNytx1F7c81f/784Lzlxj/RfX+0J1URaByUxwfUdhS1h8f48ePdJZdckqvt6Mh4Pg8aC2Xdx1crJLRKowj69++fqzzVdhS1h4eyx8gPsiCfvfvuuwtLqaQxZLmJjNH+fsWKFe6RRx4pRJeejeicSfdOYdTWFrX3rMpTafiy8qc//ckVgR5Q5/EBleVjjz3mimDYsGG5tGmPxo7uPav6K1/SHoxPetKT3PDhw4OAWM+eh93x493csWM93MmTXdzAgd3chRd2chdemPx9ev7frZvSyWkV2BHXpcup0nm0R4/6rmquBtMk5jw20/nlo0UwatSoRG1Rv9VeWUXtPTtnzpxAX5a2Q+Na1ZEiUHnqGUzWMtV9p+pVvZ5bJfHoo48Wtm9inmfleo5w3333uSLQRP08NtNzPo2JimDChAnuoosuyvz5omwGLRIMC6OHlJdeemm9ZTQletgapdKb+7gGvRZBympqzqu7nucGAAAAAAAAACgCPSD/5S9/GTwHmT59ups0aZLr3l0TXM5mbhowYLA7fbq703wCzWPU3Df9KPilxRxnV4id/dH+Ytu2nX12oom2jz/+eDCJqajJCAAA0Nicc4aNhaAKfPjDHw7SUob59re/7V73utfl/i7ldn7a057W7vv/+Z//2VUTLaHXysCwC2jVSaVL6q+88so2M860LFYzD+Jm+dXz3PUkvDJMM4GLmJWh88jWYTTL068W0uyg8HJpzUwqYqNLadDMKM3yjGqIQxvO1mIml3T4TcKVusCnUdLM3jzB2I7efMTpiEM2y7OP4549e3LN6tXqu/AMXM2CysrAgQMzL71X/UtaZZnVHllQ/co6e9wH5tVGVVtHHH369AlWeaThfVarumuhI464CQXl2g7Zq6jVHbLXQw89lHllmNrcpBTRHSFaP9RuZO131P+pPKutIa5uaJZ23CSUcqg9yzvTslJfUXuWtjI4jNqNpNWu1fRZtRtxaZ3LnVftbTXa0jR0TWpvs+qSD2iWeBFth8YbcSvzy7UdRY05hPxT5y03/ilizBFXT9Wm5fFPlWWWFfzyFT0cjRsDyMc1o7ocun8ZMGBAaQyp9kqr5bQyNyktUd4xR0f8tppjjo7o6OiYIzqGrOW4I88YUjpU5ir7ItoO+UBc3dJ9cbS/V59eVKYN9emaeZ9071RuDFkLwvVDPhq9x0uioyucyumIqx95JqV2ZAyZ11/U3mrsXa0xR0f7fNUvnzovj5/JL9UPSJu//lq1H3nGHELtmfyzlvdPWceQUb8N6ypizKFrz9J2qO7UOuuBr6M6v2yWdTWR6lk1Vxom1dFyY8hydGQMmddX1N5mTfmXZwzZUZ+t1nOrWrQdcc8hk9AYcvTo0aX6pr63I1m5IJmWWhkGtV3SG6XSQEfccUpTUm00GNQy/a1bt5beU6o3Da4qyb8b1T1y5MiyDwXreW5L1Gv1mjq3cg/g1dFbRJ2y1dV+GlDmeZhbJHluXvygJXwzVyub62bAannqJjnPjXKRPquUchZ0RNF7WYJ69aAj+zzWEvV1Vn1ADxfyBN2LJM9D5qJ9I1yeRbWlWXTpJjnPjXKtdMS1EfUYc2RpqyyPOVS30h4W6YGC0ntV6scqF3/9GpsrPanG6XqYPm3atLJj9bxjjqKwPOawOoaUz+pezSK6xyu6PJPunayPIT1WfcDyGDLrmKPoPl9tcK22hGimMWTUb+sxFsrSdhRRZ8J1NA8KZuQJaBQJz62aZ8xheQzZrBS7s10NKSoHNJTf9yVKpbl1446L+/5qEP1ezcyqJG+9ZhHopjvpuy2dGwAAAACg2VAgTKu+qrHXUzgQJrRSSOm4SKwCAAAAANCYNE0w7BnPeEawOukjH/lIYRtWQtsNMqOzJO+5556Kvit6nL5Xm7jXgrhNdyvRrZRV0ZQQc+fONXtuAAAAAIBWDIRp1nIaCnhpgp4PhHkIiAEAAAAANC5NlSZRNyuf+tSngp8nPelJ7uqrr3avetWrWG5YAFpyfcEFF7TJv//YY48FN6V57K8by7vvvrvNewqE1SrN0yWXXOI+//nPt3nvrrvuci9/+ctzfY+OiXLppZeaPXero1zB2kfCr6Krx1JpCxrQYRcr9kCHTR1WsGAPCxrQYRcr9kBH7XVoD8IlS5ZkCoRNmDCh3f1GXCAsmnnB4/eSmj59ekXpzVupXBpRgyUdVrBiD3TY1GEFK/ZAhy0NlrBiD3TY1AHF0TQrwzy6MdHPfffd5975zncGeycpuPC///u/hW1Q3apcddVVbf5WOfziF7/I9R233nprmw3Y4763mlxxxRXtZodKc97Znj/72c/a5ebXd1s9N/y1rWh1DeiwixV7oMOmDitYsIcFDeiwixV7oKN2OvIEwpTNIu0hh/YGKxcI8+zYsSOY+FeNdIzNWi6NrMGSDitYsQc6bOqwghV7oMOWBktYsQc6bOqAYmi6YJhm5/kZeqrISh/385//3L3kJS8JAmPXXnute/DBB+stsynRKrwoX/3qV3N9x9e+9rV277361a92tUKr1qLBtrVr17o//OEPmb9DMwgWLVrU5r3nPOc5qRvO1vPcAAAAAACNTtZAmCaLKRDWp0+f1O/Uxu9Dhw5N/ZwCYkqZWK2AGAAAAAAA1JamCYZ9+9vfds985jNdp06dStFcHxjzEV7dsPznf/5nkEJx5syZ7l//9V/d1q1b6y29aZBddZMZ5s9//rP77W9/m+n4xYsXu5/85Cdt3ps/f37N979661vf2u69j33sY5lvbD/60Y9m+k5r5wYAAAAAaFS0gitrIExp17MEwoTuH6dNm+aGDRuW+tlqrxADAAAAAIDa0TTBsNe+9rXud7/7XZDS4jOf+Uywf5UPgvmgWDgwtnTpUvfBD37QjR071j372c923//+993Ro0frfRkNzz/+4z+2e+9tb3tbcKOYlqP1TW96kztx4kSb9z/0oQ9lOu/ll1/eppz1c9ttt2U69oUvfKGbMWNGm/fuuece9y//8i+px37ve99zP/7xj9u8N2vWLPe85z3P/LkBAAAAABqRWgXCPLqXmDp1aqaA2M6dOwmIAQAAAAA0AE0TDPMMHz7cvf/97w9WGSkd4nve854gzUW5wJj2Ebvlllvc6173uuDYt7zlLe6OO+6o92U0LK985SvdggUL2rynAOVTn/rU4CYxDv1fe1w99NBDbd7Xey996UtdrdFqwn//93+PDex9/OMfbxegE6o7X/rSl9wb3vCGNu+rXv3Hf/xH5s2063luAAAAAIBGDYSl7e2QJzViHATEAAAAAACaiy6uidHNj36UDvH3v/+9+9a3vuX+93//t7QCLLy3mNi/f7+78cYbg59x48YFwYbXv/71buLEiXW9jkZDdp43b57bs2dP6b1Vq1YFqSm1EkrBMu3ftn37dnffffe5H/3oR+2CPoMGDXLf/OY3C9N85ZVXBvvJfeELX2jz/nXXXRfUB608nDJliuvcubNbvXq1+8EPfuBWrFjR7nv+/u//3l122WUNc24AAAAAgEZBQSdl+MgaCOvdu3eHzucDYvq9bdu2TNqU9UET3gAAAAAAwBZNHQzzKIjw3OfE0nA2AAEAAElEQVQ+N/hRwOuHP/xhsMfYXXfdVVot5vE3VuvWrXOf/OQng5+LL77YvfGNbwxWPfXv37+OV9IYnHfeee6Xv/xlkH7y4MGDpfc1U/J//ud/gp8k+vbt6379618HKSyLREHTzZs3t0s9qJVrn/70p1OPf/WrXx2k6Gy0cwMAAAAANEsgrFu3bkFqxI4Gwjy6Vzz//POD12kBMa1aIyAGAAAAAGCTlhuh9+vXz731rW8NUiFqlc0nPvGJYOVX0v5id999t3v729/uRowYEQQdIJ1LLrnE/fnPfw5mUuZBm1XL3hdddJGrR9BUq66UolCvs9KlSxf3kY98xH33u9+t+Ka3nucGAAAAALBMvQJh0YCY0uqn4QNipEwEAAAAALDFOWfS7ihaBAVgbrrppmBljk/vF02j6N/TPmOQjePHj7uvfOUrbuHChW758uVlP6eg2Tvf+U73tre9LUhrkpfLL7/c3X777W3eu/XWW4P3K+HRRx91n/rUp9zPf/7zUlrNKD179nQveclLggDWBRdcUNF5rJ27iGD0gQMHgte9evVyhw4dqouOaLNXj33WLGhAR3sWLVpUauMVmI7ugdhq9kCHTR3UU1sa0NEe6ig6qqlDn9ceYQoypQXClBpRY8wkDh8+HNz7KQOD1yINY8aMCV4rK0i579Dnla5869atLg2lfdc4PW3CWqOWS7NqsKSDthQdjaCDeooOyxoEdRQdjaBDE7k0RvUZ05TZDmoDwbCY4I32FdO+V9pnTHtZhYNiBMMqZ+XKle7BBx90mzZtChxcN5mjR4928+fPd5MmTXIWkc57773XLVu2rBQk1Y2tZoZq9ZqCUs147mYPhgFYHygDJEE9BetQR6HaaJWVAmK7d+/uUCCsGvW0FgExgDhoS6ERoJ6Cdaij0AgQDCuOltgzLA+6kXr5y18e/DzyyCNBWkQFI+oVGW4mJk+eHPw0ErqhvuKKK4KfVjo3AAAAAIAVFExSUEkZFPwksY4EwjqC7gunTJkSvE4LiCl4pyAeATEAAAAAgPrDiDyC0tJ9//vfd895znPchRdeGKT2IxAG0FxoRq9WfeqnXotjLWhAh12s2AMdNnVYwYI9LGhAh12s2AMd1dHhA2IDBw6sWyAsGhAbOXJk6mcVEFMQr1x2kUYvl2bTYEmHFazYAx02dVjBij3QYUuDJazYAx02dUBxsDLs/9B+U0qN+NOf/rSUxi2cQx4AmgctPdaKT79fXbU3WW8UDeiwixV7oMOmDitYsIcFDeiwixV7oKN6OpTeSAExrbbS98yePbvwQJhH94g+68WWLVsSP6vVbH6FmK6h2cqlmTRY0mEFK/ZAh00dVrBiD3TY0mAJK/ZAh00dUBxdWn0PKwXAvvOd77gNGzYE74WjwOG9woRutF760pfWSS0AAAAAAEBr4wNimsHbo0ePumrxATH93rx5c8UBMQAAAAAAqD0tFwzTTYjSICoIdt9995UNgPn39ffll1/u3vCGNwT7iPXp06cuugEAAAAAAOBsQMxKQEn3i5MmTQpeZwmIaZ+x0aNHF6QOAAAAAABaKhh28uRJ96tf/SoIgP3mN79plwc0GgATmuGnANjrX/96N3bs2LroBgAAAAAAANtkDYhpj7FRo0YVqAwAAAAAAFoiGPaXv/wlCID98Ic/DDYuLrcPmH9PmzG/8pWvdG984xvdRRddVCfVAAAAAAAArYHu0/r3729mpVdHA2L6vWnTpnb/HzFiRCmlIgAAAAAAFE/TBcM2btzovv3tbwc/K1asSN0HrEuXLu7Zz352sArshS98oevWrVudlAMAAAAAALQOTzzxhFu/fn0wKbEZ9tLSvebEiROD1+GAmAJhU6ZMIRAGAAAAAFBHmiYY9s1vfjNYBXbHHXcEga60NIhz5swJVoC95jWvcUOGDKmLZgAAAAAAgFZk3759bteuXcFkRO2ltXTp0iAg1qlTJ9cMATH91kRNAmEAAAAAADZommDYm9/85uAGIykN4vDhw91rX/vaIAimGy0AAAAAAAAoPhC2bds2d+6557ZJl7hkyZKmCYhNmDDB9e3bN5h4SSAMAAAAAKD+NE0wrFwaxB49ergXv/jFQRrEq666quFvrACgOu1E165dS69bVQM67GLFHuiwqcMKFuxhQQM67GLFHuhonxpxx44dQUrEqI4iAmJ79+51P/jBD9yhQ4faTKTUyjTx6le/2g0YMKDD59F3Dh06tGHKxYIOCxos6bCCFXugw6YOK1ixBzpsabCEFXugw6YOKI5zzoTzCTYwulnyK8P0+5JLLglWgL3iFa9w/fr1q7c8AHAu8MUDBw4Er3v16hU8hACwxKJFi9ypU6eC13pIt2DBgnpLAmgH9RSsQx2FcmzdutUtX7489XMjR44MUgvWAqVmXLhwYdn/X3PNNW7w4ME1OTdAHmhLoRGgnoJ1qKPQCPTu3dsdPnw4eK3MAvv376+3pKalqVaGjR8/3r3+9a8PVoHpNQDYJikYpo7Ac/r0aXfkyJHE71IQXAE2jwY7R48eTQ2i9+zZs/T3yZMn3bFjxxKP6dKli+vevXvp7xMnTrjjx48nHqNZJtoPw6Nz6FxJ6PN+dorQtfgBXDm0Eja88bxsJtsloesPz7zOEqCUncOrcH2HnUTe8qykbHTtskGesomWp86j4/z3xdkjWjZZylPn0LnylE20PGXntPkr4bLJWp6N4msqS182re5rskX4hs5/V7RsLPtalvKspGxa3deiZVMvX4uro9XwtSxlU0S/ZtnXomVjyde0ImvlypVtrieuPHXNSp+osqvFmCOtrHRMWr2ppa/pfdUXfb5e/ZpVX6vVmMOqr+k7ZYOwnYvo12o1vq9Gv1arMUclvhZtB1vd11QHdFySPqu+Vmm/Fi0bfM2ur/kxR7l7/Ep9LVw2zdav1WrM0Uy+Fi2bWvkaVJemCYbdcccd7tJLL623DICW5vrrr3c33HBD2f+HOx91RsuWLSv72fnz55deqwNO+qzvhGfOnFn6W51W2szjPn36uPPPP7/0t2ZerF27NvGYQYMGtQm2a3bx5s2bE4/RfoWjRo0q/b19+/YgPVASY8eODfaY8GzatCnYXyOJSZMmuf79+5f+XrNmTWpHPH369Dad94oVK1IHInPmzCkNLPXZtLLRZ3VMeFCVdow0SVt4ILpq1arEY3TtsoFnz549wcb1ScjGsnX4PH4GjgY2cTpVlipTj8pf50pCdUZ1x7N+/frUwbXqpuqoR9efdnMhH/AD2DQfi/M1fX9eX5Nfq97k8TXV5XXr1uX2tS1btuTyNe1Hs3PnzsRjxo0b12bPGtWZtFlYkydPbrPqPIuvzZgxo82gN4uvzZ07tzSA18Dal43aD39suJ7qpmL27Nml46UprTx1kzBt2rTS3wcPHnSrV69OPEbpwyZOnJjL15QqbMyYMW3SpKktTGL06NFu2LBhpb9V/mm+pj16Bg4cmMvXpk6d2ubGJ4uvzZo1q3Tjl8XXVI7z5s3L5Wu6UQzvcZvF1zSLMLyaJouvaQXMeeedl8vXRowYEazcKedrcXW0Gr6mPrpIXysHvpbf1xQIk23DD1mUEjH6wET/V3/ux2NhXxPV8DXZPgmNITds2FAXX9OYV2M++Y36M5VNkq/FEfU1XYvPzJCnX0t70Bb1NY270x5M1cPXdO26njy+prqpcsjja/Iz+VseX9MYUr5RDrWl8ofwWF31LO2BXrRfUxA67YGm7OwfTlbSr+lBXt5+TW1GOECe1dfU3iSh+i8/8MhntCo1CfmZ+jaPPi8fTUL+HF5FmsXXdC26pjy+JpuFAw5ZfC1cNlnupdXOqr3N42uqY6prHl2HyifpOPUb6j/y+Jp8Rr6Tx9fkm+E0tWm+JtQGhFPkZvE1tTXhwEYtfE39gdrOPL6mtlltdB5fUx+gvqBZfU3jGpVNuXv8jvia79eK8rUs/Vo1fE1+hq8V52tNkrivIWiaYBiBMID6owcOWWZb1Bs9FJNWdYy6EQ/PGCpagx8Q1Ws/w/DsvfADjXrbo9WRPeRLGlhHZ7W1aj3Vw0ndZNdTh7eHykY3JfUqFyv4mW9+Nl89ysVK22HFV8IrWurZpluqo2o3ZJd6l4t8RWXTim26Horo4aUP0EiHHmD4hxj+IZICYXqoEp4t3Ar4clF9lZ387GA9UAo/MKw14dnMaQHkVmjTfYCinm2HFaz0cRbHg3qg22ptulXC9khbmdIq/uLb9HrZw4otrOD7llYdD5aro/qp171k2B46f72eD0KxNM2eYQDQWCvDNKsjaZZJLVMB+FVGOkazcvy5ikyTqNkffqWTVjOFr7fI1G36v19pEJ5tVPRy8/DKL9lDs7jqkbrt1ltvbZMm8aKLLqpLmkTZQ7ODNCDzs3vrkbotWi76bD3SJErHkiVLgnPF+UtRqR28PfR5rVz0OopOk3jPPfe0SUHn62nRqdt0DZotJy2asVmuXGqZJjFaR7WisB5pEtWfJbXpRaW40f/96hS1HbJ5PdIkxtXReqRuU/1YvHhx8Dqp7ah1mkTp0Cow2cC36fVI3RbXptc6xY1WMvkVcPq8ykaaNHtc55DP6ty6zuiM7FqNOTQ7/Otf/3rZ49/xjne0Kd9ap0lUuWj2uVaJaSV1uCy0KkgruL2ta5kmMTw7XTP9w31DUanbonVU9qhHOikFcB977LHEtqOI1G1qS32aRL/PTT3SJJZrO4pO3SYdS5cuDco0qU2vdeo2bw+dRysCvI6i0ySWK5ei0yT6/ZhUB3SuuPunItIkhu2hcXp45UmRqdu0+jhpTFhUmsTwShitQkl7wF+LNIlxdbQeaRJVR3WMT5NYro7WOk1i3HiwHmkSy7UdRadJ9PeS0qXVYkn3krVMkxi2h1aTeR1Fp0nUCjv2DCuG1pp2BwA15cMf/nDwk7QcOLycPamzi3YwWT8b7mCSjok+bBHqkPPORlbHn3fmqs4tbV6DXqddXyUz/NMeYsQN8PLaWYOLvMeUK8+wPaLaiyobncMPmNLqUFh3XH3qaNlEZ0alPZiLoxplk1ZPs9qpo+Xpj8niLx0pmzRfi/u+LOUZpSO+ppuOcKCh3HdV0nZWUjZeQ55z6RrCN1sdKZtwHY1qr6RsKvW1PG16Lfq1apVnJWUT9bUsdbSSfq2SsslTLtXu18JEbVpUvxYtzyz2qNaYQyublNaxXH1S3dD16IGlHj6ktdfVKpssD52K9DU9BFFAXT4TfRClhzAK6PpUP7Xo18qVZ14bVGsMGa6jUZ8vqh1UOeRtO6rZr4W/M/pQsYh+La5s0uxRVL+mY/KOO6rRr8X9v179Wvi6s9TTStrOSsrGB23zHFeNfi2Mt0f0O2t1Lx1XnnronrX9qNWYI3qfX0l5Vqts0mxRVL/mryWPLWrxbCRqnyL7tXB5ZqmjRZWNbJL3XrIa/VqYuOssql+rxNegY7T2GtWM+D1k/A8AAAAAAADEo0BY2p4J/mGj9hXN+7ClWVCgQytdkmZ4ayKZAmJpK1sAAAAAAKAJVoa99KUvLb0eP368+9znPlf4fmSPPPJIKZqctkwUAAAAAACgFdEG7T7dTNpMYKVGzLtio5nQDGKlilRq5CQUENP9qNJ7M3sYAAAAAKCJg2H/8z//U0pbpRQReXja054W7DMi9B27du2qSANbqwEAAAAAAFQnEDZmzJiWDoR5xo4dG6ST1D5qSfgVYrofJiAGAAAAANCkwbCOBKR006ANcEV4o/O8+A0oAQAAAAAAoLJAmPZ56N+/f+79LpqZwYMHZ/rcwYMHCYgBAAAAAFRIS+wZ1pEgGAAAAAAAAJRHK5uyBMK0qfiMGTMIhJUJiI0bNy5zQIw9xAAAAAAA8tESwTAAAAAAAACoTSBs9erVmQJhWtFEasTyjB492k2YMCH1cwTEAAAAAADyw5Q8AGg5OnXqFDyQ8a9bVQM67GLFHuiwqcMKFuxhQQM67GLFHs2sI08gbM6cOUFqv1OnTpmwhxWi5aI9xMSaNWtSA2IPP/xw1QKMFuqpBQ2WdFjBij3QYVOHFazYAx22NFjCij3QYVMHFMc5ZxpgIyxVRr9nl26iHnzwwczHzp071z3yyCPBsfoO3XzlpRrfAQDO9evXL9jHz+8XcejQoXpLAmjDokWLSm18586d3YIFC+otCaAd1FOwDnW0NdiwYUNqwEb06dPH1B5Xu3btcgsXLiz7/2uuuSbzHl61hBV3QFsKjQD1FKxDHYVGQOO5w4cPB6/79u3r9u/fX29JTQshTwAAAAAAAMiMViU1YiCskRgzZoybOHFi6uc0uUwpE48fP16ILgAAAACARoVgGAAAAAAAAGRGQa4pU6akfoZAWMcDYpMmTcoUEFPKRAJiAAAAAADlYc8wAGg5Tp48WUrXqOXHXbp0aUkN6LCLFXugw6YOK1iwhwUN6LCLFXs0q46RI0cGaeRXrlyZKxBmxR5WSLPH6NGjg9+rVq1K/B6l1lFATNsKVJIy0UK5WNBgSYcVrNgDHTZ1WMGKPdBhS4MlrNgDHTZ1QHGwMgwAWo5jx44FqX30o9etqgEddrFiD3TY1GEFC/awoAEddrFij2bWMWrUKDd58uQ27+lBQtKKMCv2sEIWeygglmWFmA+IVbJCzEK5WNBgSYcVrNgDHTZ1WMGKPdBhS4MlrNgDHTZ1QHEQDAMAAAAAAIAOB8TSAmFQOQqIRQOPSQExHugAAAAAALSFtX8AAAAAAADQoYCYAmCDBg0ivUyN7SziUlNGA2KLFy8OApPdu3cvSB0AAAAAgG1YGQYAAAAAAAAdYujQoQTCCgqITZkyJfVzCog98sgjwb5uAAAAAABAMAwAAAAAAACgYRg5cmRqQOycc85xEydODH4DAAAAAADBMAAAAAAAAPg/tJKI/aYaOyCmANgFF1wQpK0EAAAAAICzEAwDAAAAAACAIBC2du1ad99997mDBw/WWw5kCIidf/75sYGwwYMH100XAAAAAIBFCIYBAAAAAAC0OD4QtmHDBnfy5Em3ePFiAmINwIgRI0oBMQJhAAAAAADlabgdjnVT1rlz51zH+E2D9TvvsQDQfKgdGDhwYOl1q2pAh12s2AMdNnVYwYI9LGhAh12s2KMRdOg+ac2aNW7jxo2l906cOBHce82ePdv16dOnEB2tSDXsoYCYAmFdunSpOBBmoVwsaLCkwwpW7IEOmzqsYMUe6LClwRJW7IEOmzqgOM454yNFhunUqVMwuK+nVH9+/T516lTddAA0Mv369XMHDhwIXvfq1csdOnSo3pIA2rBo0aJSG6+B0IIFC+otCaAd1FOwDnW0sYgLhIXp2rWrmzNnjuvdu7drBrTqbffu3e6BBx5wp0+fLt1vzp8/P3itfbYUVAKoN7Sl0AhQT8E61FFoBDTOPnz4cPC6b9++bv/+/fWW1LQ01ChfgSgAAAAAAACoTiBs9erVbtOmTWU/oxViDz/8sJs7d24wmanRUaBr6NChwWq38MMxvQcAAAAAAM1LQwXDGmARGwAAAAAAQFMEwsKzVbt3716ILigWrY7TyjgAAAAAgGanIYJhT3va01gVBgBVQzOclR7Hp8JR+p9W1IAOu1ixBzps6rCCBXtY0IAOu1ixh0Ud2hthw4YNmQJhAwYMcDNnzqzaPgpW7GGFetpj165dbtWqVW7WrFnBirl6l4uVumFFhxWs2AMdNnVYwYo90GFLgyWs2AMdNnVAcTREMOy2226rtwQAaCKOHz9eevijFDn16OwsaECHXazYAx02dVjBgj0saECHXazYw5oOrQh74okngkBI0YGwsA5BPa2fPfTgZ8mSJUF9UBrMyZMn171crNQNKzqsYMUe6LCpwwpW7IEOWxosYcUe6LCpA4qjIYJhAAAAAAAA0DEU+NixY0cQ3OrWrVviZ7V67IILLqhqIAxsEA6EiWPHjgV/KxVmWr0AAAAAAGhUSA4OAAAAAADQ5PgVYXv27En9LIGw5g+Eaa+wuJnR+g0AAAAA0IwQDAMAAAAAAGjyQNiaNWvc3r17Uz9LIKx5USA0LhAW3jdDAbEjR44Urg0AAAAAoNYQDAMAAAAAAGjiQNjKlSvdtm3bUj+rjcMJhDUvJ0+eLKVGLIcCYgqYHT58uDBdAAAAAABFwJ5hAFA3Dh06VPZ/vXv3Lr3W7NW0GarnnHOO69WrV+nvU6dOuaNHj5Y9r/ZG0DHRBwR6P4kuXboE+ymEHxikpZPRBpzh/Rd0Dq/B64miz4c37tS16JqS6NGjR5uHV7JZuZm/nuj/k8rEIzt72+mBSpaHJWnlGbVHp06dXM+ePXOVja5dNshTNtHy1Hl0nP++LGUjXTouCZ1D58paNjqv/i87eGTntAdY4bLx39ORsomrp3l8zVNJecaVjc6VdE1xvpZWNnl8zdsjuqluFl/T9YfLsyO+pnrtNYbrabRssrSd1fC1tHKJK09dg/e1jpRNtI7m9bW4trNSX0tq02vdr3mi/6/E17KUTZqvxdXRavRrWcom6mtp5VJJv5bX13Ru2SRss1r1a3Fls2zZMrd9+/bgeF9O4TLWefTjA2E6R1pdq4av6TxpbUctxhzh86sO6vvDurL4WrRsOuJrSe1Htfs12fO8885zK1asaONL4bL0Ov7yl7+4GTNmtKnrterXwt8RtkVRY444X0trO6JlU61+LYy+UzYI27mIfi1aNuXqaBH9WtyYQ2WaVN9q0a+F8faI+k1R/Zq/9qS2o6P9WiW+pjqQNiasRr9WrjzD9oj+P0vZVNKvRctGdk4bE9ayX/NEn3NkGatX4146WjZxtqikX4uWTSW+Jl2yd7l7/Er7tXDZpJVn3HiwyH7Nl2e5Olrre+ly5ZllPBjnax3p18KE7RGmknvpSvq1LL4G1YVgGABUjeuvv97dcMMNZf8f7nzUGenhTDnmz59feq0OOOmzvhOeOXNm6W91WsuXL4/9rDo6zY4OD9zE/v373dq1axPPowdF48ePL/29a9cut3nz5sRjhg8f7kaNGlX6Ww+klIImPEM7qmXs2LFuyJAhpb/1+X379iWeZ9KkSa5///6lv5UOKa0j1gORMHo4kjbonzNnTmlgqc+mlY0+q2OiD+biysQzYMAAN3369DYDlFWrViWeR9cuG4RTAW3cuDHxGNlYtg6fR/XAD2zirk1lqTL1qPzT9l9RnVHd8axfvz5xwOcHY+F6oetPu5GXD/gBbJqPxfmavj98TLRcpCfqa/Jr1Zsk+vTp484///zS36rL69aty+VrspdP7xX1l3K+Ju07d+5MPM+4cePcueeeW/pbdcbXgSjeHkOHDm3zfhZf0wPF8KA3i6/NnTu3NIDXwNqXzY4dO0rHhuupbipmz55dOl6a0uqAbhKmTZtW+vvgwYNu9erVicfIPydOnNimDqh+lisXIZuNGTOm9Lf2LVJbmMTo0aPdsGHDSn9v2bKlna9F66iuRSnesvqamDp1apsb2Sy+NmvWrNKNn3xNxyS16SrHefPmlfW1OPQdCkzk8bVooDaLrw0ePLhNX6B+TbZOYsSIEW7kyJFlfS2ujubxNc/kyZNdv379Sn+rj87ra6pn/ma5XB0t52vlyOtrqqfap0l264ivZenXwr6m677//vsDP/DXFg6e+75cNlZfqPqm8orztSgTJkzosK+pHZDGpLYj7GsirWzy+JqvpyrPcNlk8bW+ffu6KVOmVMXX4vrarL4WR9TXNmzY4A4cONDmM7Lp1q1bS74hbeGHp7oe/aiMVJ/CD8fK+ZrG3WkPptJ8LWoL1ZeO9mu6dvXTeXxN442kNj2uX1NbI3vl6dc0hkxKXao6qvFt+EGb6lnaw/Oor2llaNoDTdnZP5yMjiHj6mjU1/QgL2+/pjZD2vL4mu7x5Dv+++JQ/ZcfeOQzqu9JyM/C7YA+788TxdtDfhMmztei6Fp0TR7VzbSH2rJZ+Fq9ryW1HeGyyXIvrTZB7W0eX1MdU13zSI/ajaTj1G+o//Cob9R9bhLyGflOmq+F7aHylF9n9TWhNiB8TBZfU1sTDiKqPqteJ7UfSb4Wh/pltZ15fE1tc7i/0XWkPbPQWEDjLo/K0o8fypHma3F1NI+veXQtYX+rxNc0rpEvlLvHT/K1JORrvl9L8zXZQ+16uD5X4mtZ+rUkXyvXdkR9TX6W1q+pHwzfG1fiayr/tPFgnK91pF8L4+0RDSJn9TWNh/L0a+V8La2uQfVoymCY0jo89NBDwU2MnF2Nno/maiCpRkEPy9Q4qlMJP9QDgMrRAwdSqgAAAADUF91Q614o7QGT0AMJHwhrBRSE0XhVDz/8SgYFJ/0Dn2hQuRnRA0I99FEwLunhix40KQBbLiAGAAAAANBInHOmSUKPt9xyi7vpppvczTffnOmmLzr7/FnPepZ7wxve4K666qqaaQRodvKsDNOsjqRZJrVMu6EZHpq1pIc+4Zv7ItMkyhZ+Zq9mVEUfMBSVJlGf9zo0oydtNUStUjv4MvH20LXUI03irbfe2iZN4kUXXVSXNInSrRlfqqMqF52zHmkSo+UiHfVIkygdmmWm4+L8pajUDt4eOkYzGv25ik6TeM8997RJQefradFpEmUPteM6RjPXkx6U1ipNYrSO6gFvPdIkauJVUpteVDopfcavtlLbofKtR5rEuDpajzSJuhY/qzmp7ah1mkTp0Ixc2dm36bVMk6gfzWbWLFfZ2NvZB4H8Nev7NUlQqwHCdaBW6aR8efq2Q+fRTO+ktqMWYw7569e//vWyx7/jHe8omxqwFmkS4/raovo13Terruh6ZF/fN+k7wn2W/taK/bBta5EmMWoL2aseaRJ1LX6FbLm2o4g0iWpLfZpEaVywYEFd0iSWq6NFp0mUDpWL7JzUptc6TaK3h47RKhd/XNFpEpPajiLTJC5atCj4vOqAzhV3/1REmsSwPVQu4fpZZJpE1Z2kMWFRaRJ1Lf55i/raLM8Fqp0mMa6O1iNNouqojvFpEsvV0VqnSZQ91O9Kmx8P1iNNYrm2o+g0iX48qPPouXzSeLCWaRLD9tBKR6+j6DSJqhNeo+5p0zJoQAsHw77//e+76667rpTKotLL8Q6ipYqf+MQn3Ktf/eqq6gSAs8uB/XJ2dUxZBhcAReJv5oR/6ABgDeopWIc6Wj90L+QDYWno4ZhSu7TKijCPHkQtXLiw7P+vueaadunPmhk9AFq6dGnqfbQedintdfjBH9QW2lJoBKinYB3qKDQCGl8RDCuGhr3z0SzPK6+80r3uda8r5XHVj4Jalfz44xVUe+1rXxt8d1redwAAAAAAAAv4/RCyBMI0E7gVA2EQXxeUJjO6V0YUzVx++OGHmcwGAAAAAA1LQ979/PGPf3Tz5893t912W7sAmMcHt9J+PNHAmFJlXXjhhe5Pf/pTna4SAAAAAAAgG6tXr3bbt2/PFPxQyjsCYeDRSjgCYgAAAADQ7Pw16WqD8Otf/9q9/OUvL+VHjQbAhPbxULBMP8oVrH0s9KPP7t271+3bty/INf3ggw+6+++/323durXNd/mAmHL7P+95z3M/+9nP3HOe85y6XC8AVB/l4/UPi4YNG1aXDcEtaECHXazYAx02dVjBgj0saECHXazYoygduu/R3gvl9vhQiiDt0aDUK9oDotnt0ShYsYfqhYJia9asCVL1hPc1iguIzZ492/Xp06cpbWFFhxWs2AMdNnVYwYo90GFLgyWs2AMdNnVAcTRUMOyhhx5yr3zlK4NAWDQIphmOSpn45je/OZjVlgflSNcmyt/97nfdjh072qwy07le8YpXBDlm586dW/VrAoDi0U2830w2baPOZtaADrtYsQc6bOqwggV7WNCADrtYsUdROrQfq/Z0UqAiLiA2YMCA4L5Je0SFN0xv1XKxghV7SIfufVVPygVUw59dvHhx1QNilmxhQYcVrNgDHTZ1WMGKPdBhS4MlrNgDHTZ1QHE0TG6MI0eOBCvC9NsHqnQzpxlr73vf+4L9vT7/+c/nDoQJ5cvXsfqO97///W1mwelc2sBOATGdGwAAAAAAwCIKiClA0bVr1zbvDxkyxE2ZMiU1DR6AVoVNnTo1NY2mD4gdPHiwMG0AAAAAAC0RDLvuuuvc2rVr2wTChg8f7u699173L//yL8GNX0fp2bOn+8xnPuPuu+++INViGJ1bGgAAAAAAACwHM7RCzAfEFAibNm0ae4RBZgYOHBhMMs0SENNKRAJiAAAAANAINMQdkVJ5/Od//mebQNjYsWPdHXfcUZPUhZpNefvtt7sxY8a02UNs4cKFbteuXVU/HwAAAAAAQLUDYtpHbPr06QTCIDdKFZQlIKb95xQQO3DgQGHaAAAAAAAqoSHuim666aYgVWE4NeJPfvITN2nSpJqdc+LEicE5woN/afjmN79Zs3MCAAAAAABUKyBGakToaEBs5syZmQJipEwEAAAAAOs0RDDsRz/6USkQppu5d73rXe7CCy+s+Xl1Dp3Ln1e/f/CDH9T8vAAAAAAAAAAWUiZmCYh1796dTecBAAAAwDTmg2FKt/Dggw+WZjTq9/vf//7Czv++972vzWxKpYDYv39/YecHAAAAAIDWRpPyAKwGxLQKUVsNEAwDAAAAAMuYD4Yp3cKpU6eC1wpKXXzxxW7EiBGFnV959i+55JLSDejp06cDTQAAAAAAALVG9x9Lly51GzdurLcUaPGA2KxZs9oFxAiEAQAAAECj0MUZZ/Xq1W3+vuyyywrX8LSnPc3deeedpb/XrFnjFixYULgOAKgOulkfM2ZM6XWrakCHXazYAx02dVjBgj0saECHXazYoyM6FAh77LHH3M6dO4Mf4b+rSB3VxIoOK1ixRxYdAwYMCAJijzzySFA3e/XqVdVAWCPZopWwYg902NRhBSv2QIctDZawYg902NQBxWE+GLZr167gt9+3q8hVYeHVYXGaAKAx6dq1qxs6dGjLa0CHXazYAx02dVjBgj0saECHXazYo1IdfkVY+N7DTxSsJCDW6PZoVqzYI6sOHxBTXVTqxGo+OGo0W7QKVuyBDps6rGDFHuiwpcESVuyBDps6oDjMB8OOHDnS5u9hw4YVrmHIkCHBb793WFQTAAAAAABAtYgLhHkUhNB9yejRo+uiDUABsXnz5rXZWxsAAAAAwDrm9wzr3r17m793795duIa9e/cGv/2+YVFNAAAAAAAAtQ6EeVatWuU2bdpUqC6AMATCAAAAAKDR6NIIs87Cg+2tW7cWrmHLli1t/u7fv3/hGgCgehw9etRt3rw5eD1q1CjXo0ePltSADrtYsQc6bOqwggV7WNCADrtYsUceHQqELVmyJNMEwEOHDpVSyVdbRy2xosMKVuxhQYcFDZZ0WMGKPdBhU4cVrNgDHbY0WMKKPdBhUwcUh/mVYePHj2/z9wMPPFC4hgcffDBREwA0FqdOnQpWfOpHr1tVAzrsYsUe6LCpwwoW7GFBAzrsYsUeWXXkCYRpH+UpU6bkWp3TaPZoFazYo9Y6FLjVisZ9+/bVTUNWrOiwghV7oMOmDitYsQc6bGmwhBV7oMOmDigO88GwCy64oM0A+pZbbnEHDhwo7Pz79+93N998c5sbTW0UDAAAAAAAUA3yBMJGjhyZOxAGUE90H79y5cogtecjjzxS2oYAAAAAAKBIzAfDhg0bFtzseY4fP+5uvPHGws6vcx07dqz098SJEwNNAAAAAAAAHUWzUB999NHMgbDJkycTCIOGC4T5rQd8fScgBgAAAABFYz4YJl7ykpeU8uHr90c/+tF2+3jVAuUM/djHPlY6r36/9KUvrfl5AQAAAACg+VFgQCvC9uzZk/pZAmHQ6IGwcL1nhRgAAAAAFE1DBMPe9KY3uU6d/ipVaRL/5m/+xh0+fLhm59R3v/a1r22TklE3nm9+85trdk4AAAAAAGgN8gTCtKE3gTBoJPweYeUmsSo1KAExAAAAACiShgiGKU3iK17xitLqLHHnnXe6Zz3rWTXZP+zQoUPuOc95jrvjjjvarAp7+ctf3iZlIwAAAAAAQK0DYZMmTSIQBg2FthrYvn174md8QCyLHwAAAAAAtEQwTPzrv/6r69u3b/DaB6j+/Oc/u5kzZ7rf/OY3VTvPb3/72+A7FWwL06dPn0ADAAAAAABApfg9kwiEQTPTo0cPN3v2bNelS5fUgFhWfwAAAAAAaIlgmG4Ev/SlLwVBsHBAbMOGDe4FL3iBe/7zn+9++ctfBoPpvOgYHavv0M+6deva7FGm31/84hfd6NGja3BlAAAAAADQSoGwLKnhdO9BIAwaGU1mzRMQI2UiAAAAANSS5FGpMV7zmtcEG/D+0z/9U3BT6G8MFbDSii79DB061F155ZVu/vz5wY+CaP379w9+xL59+4If5S5/4IEH3P333+/+9Kc/lVI4hFMxej784Q8H+4cBQHPQvXt3N3HixNLrVtWADrtYsQc6bOqwggV7WNCADrtYsYfXoUCY7mWypHlXIEzHVDMQZs0e9dLRuXNnN2zYsCA1fniiZe/evUv/byV71FqHD4gtXrzYnTx5MjEgJv8477zz3IABA5rSFo2KFXugw6YOK1ixBzpsabCEFXugw6YOKI5zzvg7gAbi+uuvdx/72MfavBe+jLw3jXHH+vcUePvoRz/aQcUAIPr161d6ANSrV6/gIQSAJRYtWhQ8rPQPwxYsWFBvSQDtoJ6Cdaij7ZE9tDeSJuWlMWbMGDdhwgRWhNUY6mmxHDx40D388MOJATHRqVMnd8EFF7hBgwa5Voc6Co0A9RSsQx2FRkCTsg4fPlyaSLR///56S2paGiZNYnSl1s9+9jM3ePDgNrP5/I/ey/MTPlboPQ2+f/KTnxAIAwAAAACAiiEQBnB2D+45c+a4rl27Jn5OK8SWLFnidu/eXZg2AAAAAGgNGjIYJl70ohe5xx9/3L3lLW8JIvvR1V15fjz6Ds1Ee9Ob3uQee+wx99KXvrROVwcAAAAAAI2OVsEQCAP4a0BMKROzBsR27dpVmDYAAAAAaH4aas+wKFoZ9pWvfMV96EMfcl/+8pfdd7/7Xbd58+Y2nyl3MxnNDjlixIhgX7C3v/3twU0oADQvR44ccevWrQtea1+Cnj17tqQGdNjFij3QYVOHFSzYw4IGdNil3vbQirBHH33U7dy50+3duzd4T3shxQUCxo4d68aPH1/TQFi97WFNhxWs2KMoHT4gpj3ETpw40eZ/+jvsKwqIKWWi7vtbsUysYMUe6LCpwwpW7IEOWxosYcUe6LCpA4qjoYNhHlXWT3/608GP8pDffvvt7qGHHgo24N20aVOQYuHo0aPBZ3v06OEGDhzoRo0a5aZMmRKkarjsssvcvHnz6n0ZAC1H0p5hfhNzPztUHVQSenikfcjCD6C838edd8+ePcEx+u7w7O1jx44lnqdLly5tNtXUTfvx48cTj9FDr27dupX+1jm0d5o0CN3gh3UIfT78sEzX4vNcl0PtW3jTd9ks+r1R9H+fl1ivs+zjJjuH08r645NIK09fJsI/8AgPQrKUja5dNshTNtHy1Hn8gxl9X5w9omUjXWn7X+gcOlfWstF5tbeGViv7z8nOadt8hsvGf09HyiZaLvp/Hl/z6Drylme4bHRe5czWueL8JcnX0somj695e+jzYQ1ZfE3XLzuEv6tSX1O9Due9998VLZssbWclZeN9zbcf0qI2LckGUV/TNUQfglZSNtE6qs/n8bW4trMSX0tr02vdr3n0/3CbXkm/lqVs0nwtro5Wo1/LUjZhX5MN0sqlkn4tq6/pfV2j7KHrDWez8OUjdF8ydOjQduetpF+Llk24POPadFFJ2eTt18Ll6dsOaUtrO2ox5ojWU31/uJ5l8bVo2XTE18qVSxH9mifcpgtpqmW/5gNi9913X5v2Rq99eXkfCwfEajXmiJanPp/WdiT5WrXKRt8pe4XtXES/FvW1cnW0iH4tXDY6h9oMlWlSm16Lfi2Mt4fOE9ZQRL/mzx/WIaL26Gi/Vomv+T4vqS2odr8WJmwPTXYPa6tVvxYtG9lZ93BJ7Uct+zWP3z7Gf7fslXYt1biXjpZNXB2tpF+Llk0lviZdskm5e/xK+7Vw2aSVp75PEz50/b48iurXwuVZru0oaszhyzPPeDDO1zrSr4UJ20OT0zpyL13JGDKLr0F1aYpgWBgFt/QDAMVz/fXXuxtuuKHs/8OdjzqjZcuWlf3s/PnzS6/VASd91nfCM2fOLP2tTmv58uWxn1Wnvm3btjYDN6GH7WvXrk08j/YT1Mxtj9K3RFekRhk+fHjwoMuzffv2IFAvDZ6oFnXCQ4YMKf2tz6elWJo0aZLr379/6e81a9akdsSaTBBmxYoVqQNltbF+YKnPppWNPhtul2X/6DG+TDyaDTx9+vQ2A5RVq1YlnkfXLht4NKDZuHFj4jGycXjAo/P4jUo1sIm7NpWlytSj8veDp3KozoQ3gl+/fn3i4NoPbsP1QtefdiMvH/AD2DQfi/M1fX/4mGi5SE/U1+TXqjdJ6KHX+eefX/pbddnPvsrqa/7mwevI4mvSrhUZSYwbN86de+65pb9VZ8ptVuvtoYfWYbL42owZM9oMerP42ty5c0sDeA2sfdns2LGjdGy4nuqmQg8XPdKUVgd0kzBt2rTS37qBX716deIx8s+JEye2qQOqn+XKRchmSgHneeKJJ4K2MInRo0e7YcOGlf7esmVLO1+L1lFdiyY9ZfU1MXXq1DY3sll8bdasWaUbP/majklq01WO4YlXUV+LQ9+hB8B5fC264iiLr+kmONwXqF+TrZPQQ6aRI0eW9bW4OprH1zyTJ092/fr1K/2tPjqvr6me+ZvlcnW0nK+VI6+v6f8al6gt9OimVxP2ZP9yY5aor2Xp15J8La5Nz+prUZRJo6O+Jm0qm6S2I+xrIq1s8viar6cqT9XpPL6mjc01sbIavlauXLL4WhxRX9uwYUPwkCnN18IPDVWeaUR9TXU47cFU2Nd0rK5V9cA/OAs/SNfnNOZQ3VRATOfTA6e8/ZquXf10ElFf03gjqU2P8zX5ma4liaivaQzpxzbl6qjGt+EHbapnaQ/Po76mScJpDzTVpvmHzdExZFwdjfqa2rS8/ZrKWtry+JraS58+s1zbofovP/DIZ7Zu3Zp4HvlZuB3Q58ul6fT2iK5YzOJruhZdk0d1M+2htmwWvlbva0ltR7hsstxLq51Ve5tnDKk6prrmkR61hUnHqd8IZ2JSP6j73CTkM/KdNF8L20PlKb/O6mtCbUD4mCy+prYmHERUfVa9Tmo/knwtDo2j1Hbm8TW1r+H+RteR9sxC4y31BR6VZVpfkOZrcXU0j695dC1hf6vE1zSukS+Uu8dP8rUk5Gu+X0vzNdlD7Xq4Plfia1n6tSRfK9d2RH1NfpbWr6kfDN8bV+JrKv+08WCcr3WkXwvj7RHNzpDV1zQ+ydOvlfO1tLoG1aPpgmEAUD/0wCHLzCYAAACAVkA31grUxwWK9GBHP+wRBq2KHnzpQZoe0CU91NIDoqVLl7KdAQAAAAB0CIJhAFA1NKMsPFsjSjhQpgc/4dktSWg2R9pnow+SNIOy3DHhGafRGRpp5wkvzxd6iBWecZRlpr5m3IRn0mo1U3j2pggvTxeaoROeQRVH+DuFHhhkSZMYJjzzshzh5el6nbUcww8+oseEHxLKHlGbRmdDxRGe0exnQ0Xtmlae+ry3vb4v7pzRstFKpPAs3ziis5w0gy4tTWJ0Jp7skjZbKFzX8vhY+PjwMdFykX2ifiOfTztPuM4IzXLO62s6t+p4nL+E9YfRA+jw7Pg4ouWph3JJaRLjzpPF16LnyeJrYVuH66Nm24VT0Pn3o2Uje+UtG61cyetrqgOasZnkb9Hy1AzC8KqSLDbTTNKor6W1HWm+Ftd2ZvG18PXI7uFVqUl1tJyvxVGJr2l2aXh1SiW+pn4tvBqrEl+Lq6N5fK1c2Wi1aFrZRM/j60xSuZTztXLk9TXVUx0TXtEjPbJbUiCskn4tWjZhX4tr07P6Wkf7tbjylDbNDs7TdnS0Xwvj62klvhYtm474Wrly8fo72q9pBXwWXwuvulR5pu2ZET1PeAV4Ocr5mq77scceC2zhfVwrxMPn8KtwVWeS9hCL9mvqF/KWp2as+ywA5dqOuPF9OAtAHHFjyHC2gbg6Gq2f6m/z+ppmoKe1nWEbRMeQSXU0fG15285Kxveql7JZUpse9TX5TDh7RhzR8tR9VzhLRxhvj+h5svpadAyZZ3wf9rUs5eJ1Vrtfi/M19QuyWdJx0fKUz4RXTceR1dfC9givOsnia15/NXxNWRaSyiXJ17KQ1dfC9aqofi3qa3F1NI+vJWXRyetrGgv5NInlrqucr2X1nTRf8+PBjvpaJf1a2NfKtR1xY8hq92tx5ak+PW08WO1+LUy5zAZZfC1aNpX0a97XmBxXHATDAKBqfPjDHw5+yqEb/vBy9rQHOuEOJutnwx1M0jFxS7A1eIkO6NLQgCE6aEhD55Y2r0Gv064v2vlnIcvGn9GOP6+d1WHnPaZceYbtEdVeVNnoHH4QklaHwrqTlvRXWjbRgVVSoLkc1SibtHqa1U4dLU9/TBZ/6UjZpPla3PdVssluR3xNA/NwoKHcd1XSdlZSNl5DnnPpGqI3T5WWTbiORrVXUjaV+lqeNr0W/Vq1yrOSson6WpY6Wkm/VknZ5CmXavdrUR0KMunGXQ99ommKa9WvRcsziz1qNeaI05an7ah22YTrab19LWs9rUW/Vq4889qgI/2afush3V/+8pdSWvC4+q5jlC5V+tIennZ0DJm37ahmv5ZUR4vo1+J8Lc0eRfVrOibvuKMa/Vrc/+vVr4WvO0s9raTtrKRsVAfyHleNfi2Mt0f0O4vq11Se4XRvafW0VmOO6H1+JeVZrbJJs0VR/Zq/ljy2qMWzkah9ihpzRMszSx0tqmzyjger1a+FibvOovq1SnwNOkbbJ20AAAAAAABQdXQjrj0PKgmEATQ7erClfTfSHvDpQbNWkWnfFQAAAACAPLT0yjDlJb/33nuDDZKVtkSRWC25VgqWpzzlKSxRBAAAAACAqsH9BUByQEwpVDdu3JgaENu/f3/m1WEAAAAAAC0bDNM+LDfccIP73ve+12YPo2jO0le84hXuE5/4BINsAAAAAACAJkD3f3fffXcQcPH7TShIefz48eD1xRdfXFHqMqgOSpekgNixY8fKfkZ76WqvJQAAAACAPJxzJm3HOSP89re/bZMKQXsPvfjFL879Pd///vfdW9/6VnfkyJHUzfZ0U6SN7L785S+7V77ylRXpBoD4PcP0kKHcRpW1Rhvh+k3CldM4ujdTq2hAR3sWLVrUZp+bBQsWtLQ90GFTB/XUlgZ0tGYdPXHiRLB3kTb8jm6EXaSOPKDjLLt27XILFy4s+/9rrrkmmBjZKvawpCOsQa8fffTRdkExBcLkd7VcZWnBFq3SlqKj8XVQT9FhWYOgjqKjEXRorzG/YKdv377BCnho4ZVhmqWnYFR4Fde1116bOxj2jW98IwiEhWcAJqHP7d271732ta8NnOPVr351hVcAAJZQ51bvGb8WNKDDLlbsgQ6bOqxgwR4WNKDDLrWyhwJhixcvdgcPHgwm9syaNSsxIGalXNBhEyv2sKAjqmHOnDnu4YcfLgXERo0aVfNAWJyOVseKPdBhU4cVrNgDHbY0WMKKPdBhUwcUR33CnTm5/fbbSytIFKBSRX33u9+d6zvuv/9+9453vCM4XoPn8ABa74V/PP5zmkFw9dVXu5UrV1bxqgAAAAAAoJEIB8LEvn37gtUrfsYxAFSPnj17BgGx7t27B4GwSZMmse8eAAAAADT3yrBbbrml9FqD36uuusqNGzcu13e85z3vCVaYRYNg4slPfrJ70pOeFKRwe+KJJ9xtt93mVq9e3eazOlbBtLAWAAAAAABonUCYVqlE0zwrk4QCYjNnzkxNmQgA+QNi8+fPd127diUQBgAAAADNHwy79957g99+VdfLX/7yXMf/5je/CTZJ9oNnHwTTzLLvfe977sILL2x3zE9/+lP39re/3e3evTs4Tsf86U9/cg888EAwGAeAxkUpV/1Kz8mTJ9dlSbQFDeiwixV7oMOmDitYsIcFDeiwSzXtoYlxWhFWbr/TpICYlXJBh02s2MOCjnIaunXrZkJHq2LFHuiwqcMKVuyBDlsaLGHFHuiwqQOKw3wwTHt1Pfjgg6WAlG4uK9krLMrw4cODTRSHDRsWe8zLXvayYPXZ5Zdf7o4cOVJ6/zvf+Q7BMIAGR23JyZMnS69bVQM67GLFHuiwqcMKFuxhQQM67FIte6QFwsIBMU2kGzJkSE10dBR02MSKPSzosKDBkg4rWLEHOmzqsIIVe6DDlgZLWLEHOmzqgOIwv2fY+vXr29x4Tp8+3Q0cODDz8QcOHHC/+tWv2qwK0+vPfvazZQNhHq0Ye//73186Rr//93//twNXAwAAAAAAjULWQJjPOhENhAFAfTh27JjbsWNHvWUAAAAAgCHMB8PWrVtXeq2AlPb2ysOdd94Z3MSGGT16tHvta1+bea+xcFoG6dm/f38uDQAAAAAA0FjoHiJuj7BygTDdYwCAjUCYfHfp0qVu27Zt9ZYDAAAAAEYwHwzbsGFDm6WK06ZNy3X87bffXnrtV3i96lWvynz8gAEDglSJ4aWSmh0KAAAAAADNHQjTPgJpEAgDsBUI0/263+pg2bJlbuvWrfWWBQAAAAAGMB8Mi67CGjRoUK7j77rrrnbvPfOZz8z1HfPmzYsN0AEAAAAAQHOuKskSCNNG2wTCAGylNY367vLlywmIAQAAAID9YFg0LUm/fv0yH3vq1Cn34IMPlvYLE506dXJPfepTc2mYMGFCu33IAAAAAACgOVeVZA2EjRo1qhBdANCx1ZwExAAAAADAfDAsHMgSWW5MPUuWLCmlRwjftPbu3TuXBh+A81rYMwwAAAAAoHVXhE2ZMoVAGIAhFOxK8119ZsuWLYVpAgAAAABbmA+Gac+uMDt27Mh87L333ttuv7D58+d3WNPp06c7/B0AAAAAAGArEBadSFcuEDZy5MhCdAFANuSXPXv2TP3cihUrCIgBAAAAtChdnHEGDhzYZlWWVntl5e6772733pOf/OTcGnbt2tUmoNa3b9/c3wEAdujVq5ebPXt28Lpz584tqwEddrFiD3TY1GEFC/awoAEddslqj1oHwqyUCzpsYsUeFnR0REP37t3dnDlzMvmyAmKinC9bsIUlrNgDHTZ1WMGKPdBhS4MlrNgDHTZ1QHGYD4ZNnz699FrBqD/84Q+Zj/3973/fLs3iggULcmvYuXNnm7/79++f+zsAwA5qF7p0qW/zZ0EDOuxixR7osKnDChbsYUEDOuySxR55AmHnn3++GzFiRE10FAE6bGLFHhZ0dFSDD4hl2fdPATE9X4hLd2rBFpawYg902NRhBSv2QIctDZawYg902NQBxdGpEYJh4T2+Nm3a5G6++ebU4/70pz+5bdu2tUu5qMFxXqKr0caNG5f7OwAAAAAAwA5Hjx6teSAMAIpFATHN8NZM7zRWrlzpNm/eXIguAAAAAKg/5oNhnTp1cpdffnkpRaF+v/e9702d6XXDDTeUXvtjX/SiF1WkQekWwyvMlB4FABoXtQna+08/et2qGtBhFyv2QIdNHVawYA8LGtBhlyR7FBkIs1Iu6LCJFXtY0FEtDX6FWNaAmCbc1kJHs2DFHuiwqcMKVuyBDlsaLGHFHuiwqQOKoyHWAb7lLW9xv/71r0t/P/744+4FL3iB+/73v++GDh3a7vMf+chHgpVh0RSJr3/963Ofe8OGDcHg2H+XVpcNGzasousAABsomL5s2bLg9dSpU9usPm0lDeiwixV7oMOmDitYsIcFDeiwSzl7+ECYfqeh44YPH14THUWDDptYsYcFHdXU0K1bt9IeYmkTaVetWhX8Hj16dNV1NANW7IEOmzqsYMUe6LClwRJW7IEOmzqgOBoiGKbAl2ZkKq+3Xx122223ucmTJ7uXvOQlbu7cuUFlVeDqZz/7WRAsi64K00D4iiuuyH3uH/7wh6XX+p6nPvWpVbsuAAAAAAAojqIDYQBQP3xATHuIHTp0KFdADAAAAACaj4YIhilV4je+8Q23YMGCNukSDxw44L797W8HPx6/pDG6KuxTn/pURee+6aabSufT76c97WkdvBoAAAAAAKgH+/fvJxAG0GIBMe0hljUgpvv+QYMGFaYPAAAAAIrD/J5hnosvvtj967/+a5tglw9ShX/8/zx6/a53vctdddVVuc95zz33uMcee6zNe09/+tM7fC0AAAAAAFA8SrGuQFcS06ZNIxAG0IQBsSypj1avXu02b95ciC4AAAAAKJaGCYaJa6+91n3xi18MNsSNBsXCP8IHx/7f//t/7vOf/3xF57vuuuva/K2UCfPnz6/ClQAAAAAAQD1QoKtcQEyBMPYHBmjtgNi6devc7t27C9EFAAAAAMXRUMEw8fa3v90tXbrUvepVr3I9e/ZstzLM/2ig+9Of/tR95StfCdIs5uXee+91v/vd74LXPvD24he/uOrXAwAAAAAAxQfEtCdxGAJhAK2xh1ifPn1SP7tjxw4CYgAAAABNRkPsGRZl/Pjx7vvf/36Q73/RokVuw4YNwWC1R48ewY2tUiqOGzeuQ+fo2rVrm73IxKWXXtpB5QAAAAAAYIERI0YEv1esWBEEwpRCEQCaG93n+z3EDh48mPhZPWPYtGlTu8A5AAAAADQmDRkM8yj49cxnPrMm3z1v3rzgBwAAAAAAmjcgNnDgwOC+AgBagzwBsfXr1wftQ0cn2wIAAABA/Wm4NIkAANVA6VMrSaHabBrQYRcr9kCHTR1WsGAPCxrQYZcs9igiEGalXNBhEyv2sKCjKA0+IFYuZWJ4P3IFxJSVppWxUDfQYVeHFazYAx22NFjCij3QYVMHFMM5Z/yGWAAANaZfv37uwIEDwetevXq5J554ouxnw5tbnz592h05ciTxu3Wzqu/0nDp1KvWmVZ2d9h70nDx50h07dizxmC5durju3buX/j5x4oQ7fvx46s229ijw6Bw6VxL6vI7z6Fp0TWkP8zp37lz6WzaT7ZLQ9Yc7/UOHDrk0ZGf/cEBdyOHDh1OPyVuelZSNrj38QDNL2UTL89Zbbw2O89930UUXpZZNlvLUOXSuPGUTLU/ZOa3LDpdN1vJsFF9TWfqyaXVfu+eee0oaw/U0WjaWfS1LeVZSNq3ua9GyqZevxdXRavhalrIpol+z7GvRsmkmX6vWmEPX98gjjwSp9vV/fb+uRan4hQIkYf+27GtxNLOv1WLMoTJYtmxZm+/VebzN9H3Tp093/fv3L9zX1JbKBtKg8lmwYEFD+Vot+rVajTkq8bVoO9iqvqatS/R51QGdK+7+qVn7tWjZ4Gs2fU11VMfI3uXu8Sv1tXDZWOnXquVrtRpzNJOvRcumI76mdO1eY9++fd3+/ftT9UILpkkEAFtcf/317oYbbij7/3Dno85IN57lmD9/fum1OuCkz/pOeObMmaW/1WktX7488RjNBA3vAaDOZu3atYnHDBo0qPSwROzatctt3rw58RjtZThq1KjS39u3bw/2IEhi7NixbsiQIaW/tV/Bvn37Eo+ZNGlSmxv1NWvWpHbEurkPd97aNyVtIKKNx/3AUp9NKxt9VseEB1Vpx0iTtIUHoqtWrUo8RtcuG3j27NnjNm7cmHiMbCxbh8/jBx0a2MTpVFmqTD0qf50rCdUZ1R2PZhinDa5VN8OzlXX9aTcX8gE/gE3zsThf8w+E8via/Fr1Jo+vqS6vW7cut69t2bIll69t27bN7dy5M/EYpT0699xzS3+rzqQNPCdPnhwE9/P42owZM9oMerP42ty5c0sDeA2sfdmo/fDHhuupbir0ENcjTWnlqZsE7ZXkUbqo1atXJx4zYMAAN3HixFy+psH9mDFjSn9rMoTawiRGjx7thg0bVvpb5Z/maxMmTAhS3uXxtalTp7a58cnia7NmzSrd+GXxNZVjOP12Fl/TjeIFF1yQy9d04zRlypRcvjZ48GB33nnn5fI1pRYcOXJkWV+Lq6PV8DX10Um+phtJ1c3w3l8d8bVy4Gv18TVRLV9TvZKPqW6oPFWnL7nkkuB/mriVNoa04mtxRH1NQT8/GS1Pv5b2oC3ar8lmaQ+m6uFrunZdT9qYQ5/zdtJ9hNontV8aV2zdujX4SfI1+VnSRL84X9MYcu/evWU/r7ZU49vwWF31LO2BXtTXVq5cmfpAU3b2Dycr6dfU/ubt19RmSFteX1N7k4TqfzitpXwmWn5R5Gd+H0mhz8tHk5A/y6/z+JquRdeUx9dks3DAIYuvhcsmy7202lm1t3l8TXVMdc2j61D5JB2nfkP9h2f37t3BfW4S8hn5Th5fk2+GxwJpvibU36rfzeNramvCgY1a+JraIbWdeXxNbbPa6Dy+pj5AfUGz+prGNSqbcvf4HfE1368V5WtZ+rVq+Jr8DF8rztdYq1QcBMMAoGrogUOW2RYAAAAA1UQ3rAoU6aGCbkzDD/YBANImbvk9xPwDVh8ICz98AwAAAIDGhjSJAFCXlWH1TJOo79NDM9346mGZX25dZJpEHeOP0/+i+YmLSt2ma/EzanTONDvXarm5LxOvQ3Zu5TSJ+p9PiePrRz1St0XLRTrqkSZROrSKQmUT5y9FpXbw9lA9C6dqaNU0id4e3meS8qzXKk1itI7qWuqRJlEPT5Pa9KJS3Ph0Wl6HztMKaRLlT4899ligXefwewHJ5pqdqmOS2o5ap0nU/2Uf1U+vox5pEuPa9HqkSfQ6ZGedN6ntqGWKG19P9f3SphR09UhJWq5cik6TKDt4HTpHWqrIWqRui9pCdi4ynZQ+qxSaWkGgGfW6TyjXdrRSmsRydbTo1G06h+qZtCS16bVO3ebtoXPoeryOotMkJrUdrZgmMWwPZaUI14EiU7fpPEljwqLSJMrOXofsnmbnWqRJjKujrZwmUfaQfVQHvD3qkSaxXNtRdJpEr0P1WX8njQdrmSbR69BntWLNn4c0ic1LQwTDrrvuOmeJj33sY/WWANAUe4ZlGVzUAp3XL3eOphFpJQ3oaI+/mRP+oUMr2wMdNnVQT21pQEd966iu+eGHH273YEw31UpB4lPCUC7oiEJbak+HBQ168Kf0hD6FJXXURrmgw64O6ik6LGsQ1FF0NIIOnZdgWDE0RJrET3ziE6kz04qEYBgAAAAAQH0pFwjzD7SXLl0azD4Nz74EAEhCgXRN4Evbzw0AAAAAGo+GCIZ5LCxisxSUAwAAAABoRZICYeGAmDZzHz9+fKHaAAAAAAAAwB4NFQyrdyDKQjAOAAAAAKCV0R6CixcvTt0DRvcOw4YNS9yDAACgWgH66H6SAAAAAGCLhgmGEYgCAAAAAGhtsgbCFACbNm2a2759e2HaAKA10R5jjz32mBs9erSbMGECATEAAAAAozREMOzWW2+ttwQAAAAAAGiQQNgFF1wQ7BdGMAwAaolSsSoQpsm7GzduDN4jIAYAAABgk4YIhl122WX1lgAAAAAAAHUMhGmPMO0DliUQNmjQoCBtGQBALQNhS5cubZPFRgEx/T1x4kQCYgAAAADGaIhgGAAAAAAAtCYHDhwIVoTlCYQBABQdCPNs2rQp+E1ADAAAAMAWBMMAoOXQTWmPHj1Kr1tVAzrsYsUe6LCpwwoW7GFBAzrsBMJmzpzpBg4caM4e6LClY8+ePe6mm25yx44dKwUypOOBBx4IXr/xjW9sU4+a3R6WdFjQkEXH3r17ywbCmjEg1ijlgo7Wxoo90GFLgyWs2AMdNnVAcZxzJmkEBwBQRfr16xc81BK9evUifRGYY9GiRe7UqVPB686dO7sFCxbUWxJAO6in0Cp1tCOBMIBy7Nq1yy1cuLDs/6+55ho3ePDgQjVBY6H27dFHHw2CYmmMGjXKTZo0qaIHbPT30AhQT8E61FFoBHr37u0OHz4cvO7bt6/bv39/vSU1LZ3qLQAAAAAAACAMgTAAsIoepmZtdzZv3uxWrVqVuIoMAAAAAIqBYBgAAAAAAJhBMyGzBsJmzZpFIAwA6hIQ0x6FWQNiK1euJCAGAAAAUGfYMwwAWg4tkfcpGrUUWTezragBHXaxYg902NRhBQv2sKABHdUPhD3yyCOZA2EDBgwwbw902NRhBSv2sKDDgoY8OnxAbMmSJcFedEls2bIl+D158uSG25Ok0coFHa2JFXugw5YGS1ixBzps6oDiIBgGhXD69Gn38MMPB7nVn3jiCXf8+PEgB6o2FH7KU57izj33XGcJzdpbv359cGOjGxd/c6OZf9pDYO7cuW7ChAn1lgkVcvTo0WB2ppg6dWrQ4bWiBnTYxYo90GFThxUs2MOCBnRUf0WY39chLUVZUiDMkj3QYVOHFazYw4IOCxry6vABsaVLl7rdu3c3ZUCsEcsFHa2HFXugw5YGS1ixBzps6oDiIBgGNWXnzp3uX//1X903vvENt2PHjrIzey+77DL3vve9zz33uc919UK53H/zm9+4P/zhD+6OO+5w+/btS/z8sGHD3Mte9jL3rne9K2gwK+Xqq692N910k6sGjz/+eIe0AAAAADRDIAwAoCjCK8SyBMQ08XLKlCkNFRADAAAAaAbYMwxqxk9/+lN3/vnnu8985jNlA2F+1ditt97qnve857mXvvSlqUGoaqIbkc9+9rPuwgsvDGbovec973G//OUvM2nYvn27++IXv+imTZvm/t//+3/BRu8AAAAAkA+Nu7IGwtJSIwIA1ANN8FRAbNCgQamf3bp1q1uxYgV7iAEAAAAUDMEwqAkLFy50r3jFK1JnxkX5+c9/7i699FK3a9cuVwR66PKBD3zAPfDAAx36Hq18mz17tlu3bl3VtAEAAAA0O3oYrIfCWQNh/fv3L0wbAEAeCIgBAAAA2IY0iVB1tLLq3e9+d7uBvfKuvuY1rwkeZOgGQXty/f73v3e33357m88pvcSLX/ziYLVYly71q6JKN3j55ZcH+5oNHTrUdevWLVgNds8997hf/epX7uDBg20+v3btWveMZzzD3XnnnW748OEVn1d2mjRpUkXH9ujRo+LzAgAAABSN0oTp4bH2lj127FjsZwiEAUCjBcS0h1jaBE8FxHTPrGwqpEwEAAAAqD0Ew6CqaMD/pje9qV0gTHuBffvb3243S+4f//Efg2DYy1/+8mB/MY8CSp/+9KfdRz7yEVckuhFRysPXve51bsSIEbGfUSrFPXv2uA9/+MPuS1/6Upv/rV69OthD7Mc//nHFGpSy8bbbbqv4eAAAAIBGomfPnm7OnDmxATEFwrT6vl+/fnXTBwCQNyA2Y8aMTAGxbdu2Bb8JiAEAAADUHtIkQlX5+Mc/3m7A/5znPMf94he/KJsu4rLLLnOLFi0KVkSF+dSnPhVsMFwET3nKU4JVasuWLXPvf//7ywbCPAMHDgz2C/u3f/u3dv/7yU9+4u66664aqgUAAABozoBY9+7dS+8RCAOARg+IDR48OPWzCogtX76clIkAAAAANYZgGFSNJ554wn39619v857S2Xz1q19NTXeolIQKfoU5fPiw+8IXvuBqfZOitI5KfXjVVVflPv7aa691L3vZy9q9/73vfa9KCgEAAABaJyCm4JdSU2vsSCAMAJohIHbuuedmCohpYiYBMQAAAIDaQZpEqBrf+ta33NGjR9u897d/+7du1KhRmY5/xzve4T7zmc+4zZs3l9678cYb3Q033FCzvcN0g/L85z+/Q9/x0Y9+1P30pz9t895vfvObDiqDWqKZ5v7hml63qgZ02MWKPdBhU4cVLNjDggZ0VJdevXoFK8ROnTrl+vbt2xT2QIdNHVawYg8LOixoqLYO3W9Onz7dPfbYY222BYhD+1P7iaKWUiY2Y7mgo/mwYg902NJgCSv2QIdNHVAc55xh6hFUiSc96Unu/vvvL/2tAfzKlSvdxIkTM3+H9gi7/vrr27z329/+1j372c92llFaRZ/v3d/0HD9+PFNDevXVV7ubbrqpTdrIZt0zTB3MgQMHSg+7Dh06VG9JAG1QylY9gBXy3wULFtRbEkA7qKdgHeooWEYp3RcuXFj2/9dcc02m1HYAeTh9+nSmgJg477zzgh/aUmgEqKdgHeooNALaOkgZ0oQmBO7fv7/ekpoW0iRC1W4qH3jggTbvzZo1K1cgTLz0pS9t997NN9/srDN27Nh2NztZbnQAAAAAAACgufErxIYMGZL4uT59+mTOrAIAAAAA+SAYBlXhz3/+c7v85pdccknu79HeELoBiM7isE7cCiftewEAAADQypw8ebLeEgAAzATEpk2bVjYgpvtg3Q937dq1cG0AAAAArQB7hkFVeOihh9q9d9FFF+X+Hi1ZVrrFW2+9tfTeo48+GjxIqdW+YR1Fq8DWrVvXLhDGhu92OXHihNu3b1/wun///nW54bSgAR12sWIPdNjUYQUL9rCgAR3lOXLkSLBSXuMkpfxqVXugw6YOK1ixhwUdFjTUWodfIfb444+7J554oiECYa1QLuhofKzYAx22NFjCij3QYVMHFAcrw6AqLF++vN17EyZMqOi7oscdO3bMbdiwwVlFaRyjK8MuvPDCir9PN0Uf//jH3dOf/nQ3bty4YG8t3Rzp9fz5893b3vY2973vfS9ITQmVof3c1q9fH/zodatqQIddrNgDHTZ1WMGCPSxoQEf5QJjGNMocoGCYNLWqPdBhU4cVrNjDgg4LGorQob21tUJs6NCh5gNhrVQu6GhsrNgDHbY0WMKKPdBhUwcUh82lNtBwRFdGxe2jlZW449auXVtxcK3WfO1rX2v33gtf+MKKv0+zBK+77rp27yvgpqDggw8+6L7yla8EQbK3vOUt7gMf+AB55QEAAMAM4UCYHvr6sZzQ5B4AgFbHB8SUUWT06NFmA2EAAAAAzQQrw6AqbN++vV36h5EjR1b0XboZSPt+K9xzzz3uZz/7WZv3unXr5l7zmtfU/NyHDx92//Ef/+EuuOAC98tf/rLm5wMAAABIQyvXNW6L7iXrA2L1WCEGAGA1IDZ+/HgCYQAAAAAFwcowqAp79uxp87dmuGn/r0pQmogou3fvdhZnPb/5zW9u97Dnne98Z8WBwPCNkTZWHjBgQBBY1IMl7bkR92Bp79697kUvepH7t3/7N/ee97zH1ZqO7IV24MCBNnutLVq0yNUrJ/COHTuC18oNXK/82fXWgI74Va7ez/xqhla2Bzps6qCe2tKAjrYTdRQI09ikXB3VKvfBgwcXsrdqve2BDrv1NIn7778/yMDQKvawpMOCBks66O/R0Qg6qKfosKxBUEfR0Qg69IwUioFgGFSF6J5ZPXr0qPi7FEjLe9NaD6655pogpWF0VZv2+6qEefPmuRe84AXumc98pps1a5br27dvu4Dg7bff7v77v//b/f73v2/zP3Xs733ve93EiRPd85//fFdLwgGtjnLq1ClXD06ePFk6t14r4NiKGtARPwAJD5RbuY6iw64O6qktDej461hNqRGF6md4Ak/45k6adJNZRL2lXNARR1rd0/+LbFfrbQ9LOixoaIT+vuiHulbsgQ6bOhiXosOyBkEdRUcj6IDiIBgGVYukVysYFnestU0MFy5c6L7xjW+0eU8N5je/+U3Xv3//XN+l4JVWdM2dOzfxc4MGDXIveclLgp8//vGPQSpG/9DJd/Cvf/3r3Zo1a9zAgQNzXhEAAABAxwNhSWisNHz48CClNAAA5EcPdH3GEGUSqecqBwAAAIBGg2BYg7F///66LJ3s3r177IqtcnRkUB53bFx6wHrx61//2l177bXt3v/Yxz7mrrzyytzf9/KXvzz3MTrP3Xff7S666KLScl6htESf/exn3Q033OBqRXTFWkdWlVWaSrOjdOnSpXTu8OtW04CO+Ae14VljrW4PdNjUQT21paHVdSgQpgez4VmU4bGcXut/0jJixIhCU4+0crmgozxp59P/i9RUb3tY0mFBg/X+Xvd+StcvlEp/6NChNQ+IWbEHOmzqYFyKDssaBHUUHY2gA4qDYFiDofR59dh4XCuXvvCFL5T9vx5sHDt2rPS3H6BXQtyxVmYQ33XXXe6Vr3xlu2XVb3jDG4JgWJFMmDDBfec733HPetaz2rz/X//1X+66664LGvFaBWQrRfuD+ICYBiQLFixw9UrruWzZsuD11KlTXe/evVtSAzri8f6tQVAr11F02NUhqKd2NLSyDgXBli5d6saOHdvuf37ylvr7SZMmudmzZxdul1YtF3QkowDCPffcU/b/F154YbCvXavYw5IOCxos6Yj291oJpr+VMcSjjCAzZsyoaVonK/ZAh00dgnEpOqxq8FBH0WFdB+kZi4NgGFQFbTIdDoYdPXq0qsGwIjexLsfixYuDlIbR/cte+MIXuq9//et1SVFx1VVXuSuuuMLdeuutbYJVusG/9NJLC9fTKChQeO6555Zet6oGdNjFij3QYVOHFSzYw4KGVtWh1QmPPfZY6up9PXSYM2dOXcZyrVgu6Gg8rNjDgg4LGizp8KidVZsbt8+NgrtLlixxF1xwQc0epFmxBzps6rCCFXugw5YGS1ixBzps6oDioJShKmh22p49e9oEtDQjuJIBuaLyUYqcnRnH8uXLg8CT0hCGefrTn+5++MMf1rXBfPWrX90mGCb0N8Gw5LSf48aNa3kN6LCLFXugw6YOK1iwhwUNragjTyBMqRHrNamp1coFHdnQSpq/+7u/c3/5y1/azBR/8pOfHLzu06dPS9nDkg4LGizpEGpnFfDShMzwirAwu3fvrmlAzIo90GFThxWs2AMdtjRYwoo90GFTBxQHa/CgKgwbNqzN3wqEbdmypaLv2rRpU7v3lAu9Xqxbt8494xnPaLcxvPbr+sUvfuF69Ojh6snTnva0du9t3LixLloAAACguckTCBs+fHihe4QBZEHBAqXu1sOP8I/e0w9pasAaWTKQ+IBYPfYXBwAAAGgUWBnWYCgwYxFF0f/85z+3eW/Dhg1u9OjRub9Lx0UZP368qwebN292V155ZbsA3dy5c91vf/vbwmeOxqEHTXEPqgAAAACqiSYGPf7445kCYZrIRCAMAKDjgTCtCMsSpFVA7NFHHw1WiKkdBgAAAIC2EAyDqnD++ee3e2/NmjXuqU99au7v0nEWlqzqgY8CYVE906dPdzfffLMbMGCAs0DPnj3bvRfd1wzacvz4cbdz587gtXIDd+vWrSU1oMMuVuyBDps6rGDBHhY0tIqOrIEwjduUGtHC6ppWKBd0ND5W7GFBhwUNlnSEA2LSoWwsadlXtHWBT5lYrYCYFXugw6YOK1ixBzpsabCEFXugw6YOKA6CYVAV5s2b1+69e+65x73uda/L9T3K23/fffe1eW/mzJmF78mlWXVKjai9wsJMmjTJ/eEPfyhtrmgB32iHsaTPIidOnHBbt24NXvfv378unZ0FDeiwixV7oMOmDitYsIcFDa2gI08gbM6cOe7+++8v7cVUT5q9XNDRHFixhwUdFjRY0hFl8uTJQWBM2UuKDIhZsQc6bOqwghV7oMOWBktYsQc6bOqA4qj/lE1oCi6++OJ2uczvuuuu3N+jtA4HDhxo896ll17qimT//v3u2c9+dqAlzNixY90f//jHYLazJfRwKsqQIUPqogUAAACai+3bt2faI8wHwuJWrAMAQMfR/bYmZ44aNSr1sz4gZmFiAgAAAIAVCIZBVdBKpOjqsMWLF7dLMZjGz372s3bvXXXVVa4olF7wec97XrvVaSNHjnR/+tOfgoCYNX73u9+1e0+r6QAAAAA6GgiLm3QThUAYAIDNgJgmeBIQAwAAADgLwTCoGq961ava/K0ZxF//+tczH69B+o033tjmvcGDBwfpCovg2LFj7sUvfrG7884727yvDeC1ImzixInOGrrBibPxs571rLroAQAAgNYKhPXo0YNAGABAHQJio0ePTv3s3r17CYgBAAAA/B8Ew6BqvOENbwhmBof54he/WMq9msZ///d/u02bNrV57+qrr3Zdu3Z1tebkyZNBMO+WW25p8/6gQYOC96ZOneoscu211wY3OGGe9KQnZboxAgAAAIhDYwsCYQAAtgNimqxJQAwAAAAgOwTDoGoMGzbMvfnNb2438H7rW9+aOvBesWKF++AHP9jmPT1Y+bu/+7tcNwTRnyycPn3avfGNb3S/+MUv2ryvjRNvvvlmN2vWLFcLtBn9woUL3dGjR3MfK80f+MAH3Le+9a12/7vuuuuqpBAAAABaEY2BtDI+SyBMvwEAoH4BsTFjxqR+VvfljzzyCAExAAAAaGkIhkFV+ad/+ic3cODANu/9+te/di960YuClH5xLFq0yF166aXu4MGDbd7/h3/4h0y50DvK3/7t37rvfe97bd7r06eP+81vfuPmz59f0/3J3vWudwU3MB//+Mfd0qVLMx137733uiuvvNJ99rOfbfe/5z73ue7Zz352DdQCAABAKz1gnTZtmhsyZEjs/wmEAQDYaa8nTJiQKSC2b98+AmIAAADQ0nSptwBoLvTQ5Bvf+IZ7yUte0i4gNnbsWPfa1742WGmlgNmGDRvc73//e3frrbe2+56LL77YfehDH6q53rvuust95Stfafd+t27d3Dve8Y4OfffXvvY1d+GFF6Z+bsuWLcFqLv2MHz/ezZs3L7CRZmQPGDAguMHZvXu3W758ubvtttvc4sWLY79Hx3z/+9/vkGYAAAAAofHH9OnT3WOPPeZ27NhRep9AGDQ6J06cCNK4a6WMsi2ITp06BfcmYsSIEYWkaQeodkBMbNy4MVNAbObMma5LFx4HAQAAQGvB6Aeqzotf/GL3hS98IdjPKoxWfmlfsDT04EUpC4sYnOtmOA4Fn/TTEaIr3bKwdu3a4OenP/1pruO0su5HP/qR69evX+5ztiJ6wDFy5MjS61bVgA67WLEHOmzqsIIFe1jQ0Mw6/AoxoYBYowXCmrVc0NEx9u/f72688cZ27z/44IPB72uuucYNHjy4ZexhSYcFDZZ0VBIQ028f2E0KiGkPsawBMSv2QIdNHVawYg902NJgCSv2QIdNHVAcBMOgJrznPe8JZlW+7W1vC2ZdZuWFL3yhu+mmm4IVUZCO0jlq7zCtouvcuXO95TQMWvmn+tnqGtBhFyv2QIdNHVawYA8LGppdh1bMKCDWvXv3IA2XfjcKzVwu6GgerNjDgg4LGizpyIsCYco0IrIExLRCTNlF0gJiVuyBDps6rGDFHuiwpcESVuyBDps6oDjYMwxqxitf+Uq3YsUK9/73v9+de+65iQ9ZLr/8cvfLX/4yWBHWKoEwpY1UmsZPfvKT7pnPfGbmGaiaqfDkJz/Zff7zn3ebN292H/3oRwmEAQAAQM3QWG3SpEkNFQgDAGhFfEBs3LhxmVZJKiDm04UCAAAANDusDIOa7yH2L//yL+7Tn/50kHpE6RieeOKJID1h3759g1QOF110UdkN2vNw5syZ3McoCFfJcdV6sPTUpz41+PFs2rTJrVmzJsj1vnPnTnf48OFAnwKE2mdNM7Lnz5/vevbsWRfNAAAAAAAAYDsgdt555wWv169fn/hZ3WPq8wAAAACtAMEwKCzwc+GFFwY/UJ7Ro0cHP1Bbjh07FmycLrQcuh4z3S1oQIddrNgDHTZ1WMGCPSxoaEQdmmjTCg8/G61c0NGaWLGHBR0WNFjSUeuAmDKV6DNp/YEVe6DDpg4rWLEHOmxpsIQVe6DDpg4oDtIkAkDLcfLkSbdr167gR69bVQM67GLFHuiwqcMKFuxhQUOj6VCK5Yceeqgl6nAjlQs6Whcr9rCgw4IGSzqqlTLRB8WigTD9L8vECCv2QIdNHVawYg902NJgCSv2QIdNHVAcBMMAAAAAAFoABcJWrlxZ2ieGGz4AgOZHwbBwQCxPIAwAAACgmSBNIgAAAABAk6N9SVetWlX62wfEZs2a5bp04ZYAAKCZ8cGwU6dOEQgDAACAloWVYQAAAAAALRQI87BCDACgtQJiEyZMIBAGAAAALQvBMAAAAACAFguEhQNijz76KAExAIAWgEAYAAAAtDLkRAEAAAAAaEI2btzoVq9enfo5BcJOnz5diCYAAAAAAACAekAwDAAAAACgydi8ebPbsmVL6ud69+7tZs+e7bp161aILgAAaByOHj3qHn/88WDCROfOnestBwAAAKBDEAwDAAAAAGgidu/e7Xbt2pUa4FIgbM6cOa5r166FaQMAgMYJhD388MNBOt0DBw640aNH11sSAAAAQIcgGAYALUf37t2DDaT961bVgA67WLEHOmzqsIIFe1jQYE2HAmAnTpxwPXr0SPxsnz59ghVhzRwIs1Qu6LCnwwpW7GFBhwUNlnRYCITpd5cuXVzPnj3d4cOH67rnmJVyQYdNrNgDHbY0WMKKPdBhUwcUB8EwAGg5dEM3ePDglteADrtYsQc6bOqwggV7WNBgSYfSImpFWK9evVyrB8IslQs6bOqwghV7WNBhQYMlHfXi2LFjpUCY6NSpU6lfWbp0ad1S61opF3TYxIo90GFLgyWs2AMdNnVAcXQq8FwAAAAAAFAD1q9f79asWZP6uVYJhAEAQMcDYVEOHToU/P/48eOFawMAAADoKKwMA4C6oZuppH1MPNqw+ciRI4nfpZQd4Znwp06dKnsT59EsR6X88Jw8eTK4AUybNRJeOq1UVGk3g3rgGJ49qXPoXEno8+EHlboWXVMSSokV3thaNpPtktD1yw5ZysQjO/sUKWfOnAlSpqSRtzwrKRtdezgtWJayiZanzqPj/PfF2SNaNlnKU+fQufKUTbQ8ZWfZO2vZZC3PRvE1laUvm1b3NdnCawzX02jZWPa1LOVZSdm0qq9t3LjRbdiwISjP8PXrPGEfkIZJkyYF9vdlVgtfi6uj1fC1LGVTRL9m2deiZdNMvlatMUdaWemYtHoTLZtm69es+lqtxhxWfU3fKRuE7VxrX9uzZ0/w+aRrka57773XPeUpTynZoFZjyKLGHJX4WrQdbHVfUx3QcUn6rPpapf1atGzq1a9Fwdfa+5p0yd7l7vEr9bVw2TRbv1arMUcz+Vq0bGrla1BdCIYBQNW4/vrr3Q033FD2/+HOR53RsmXLyn52/vz5pdfqgJM+6zvhmTNnlv5Wp7V8+fLYz6qz2b17d9ABPfWpTy11RNoceu3atYnnGTRokBs/fnzpb6Wj2rx5c+Ixw4cPd6NGjSr9vX379iCVlTT474zO0B87dqwbMmRI6e9Nmza5ffv2JZ5HDzj79+9f+lsrBNI64okTJ7onnniidM4VK1akDkTmzJlTGljqs2llo8/qmPCgKnqMLxNvj379+rnp06e3GYiuWrUq8Ty6dtnAo5t5PSBOQjbWdYfPo3rgBzZx16ayVJl6VP46VxKqM7qu8AqOpMG17KFBlX6kT3VU1592cyEf8APYNB+L8zV9f/iYaLn4AXLY1+TXqjdpK1HOP//80t+qy+vWrcvsa37fCmmJ85dyvrZt2za3c+fOxPOMGzfOnXvuuaW/VWd8HYji7TFy5Eg3derUUtuRxddmzJjRZtCbxdfmzp1bGsBrYO3LZseOHaVjw/VUNxVa8eORprQ6oJuEadOmlf4+ePCgW716deIxAwYMCNoOfb8CMCpP6UtaaTR06FA3ZsyY0t9qd9QWJjF69Gg3bNiw0t9qN6O+Fq2jqmcDBw7M7GtCZRm+8cnia7NmzSpdr3zt0UcfTWzTVY7z5s1rozutbHSjeMEFF6T6mvohX89VB3z7rDZRN1Z79+4N/lb9U9lFy1dpQXyefP99snUSI0aMCPygnK/F1dE8vuaZPHly0B941Efn8TV99o477gjKKKntKOdr5cjraypv1UP5gG/T8/hann4tydfi2vSsvhZlwoQJFfuabztUnqqnSW1H2NdEWtlk8TXZPgmNIaUvib59+7opU6bk6tfK+Vq5csnia3FEfU3XcuDAgVRfU//ur1tlmdZHRfs1jbvTHkyl+VrUFhoHdbRf07WnrZqN+prsrHbd64iro1Ffk5/5MXU5or6mMaRvo+NQWyofCT9oUz1Le6AX7ddWrlyZ+kBTdlbbprGU+o4777wz9oGgfyCpclQ/pTG+6o6OyduvqZ5JW1ZfU/kvWbIksHNSm676Lz/wyGe2bt2aeB75mfzNo8/LR+Pw9VR1QNfj/SCLr+ladE0e1c20h9o6Rzjg4H0tqe0It4NZ7qV1rNrbPL6mOqa65tF1qC1MOk79hvoPj/TrPjcJ+Yx8J83XwvbQvUrYP9N8TagNUFuQx9fU1oQDG6rP8pWkMaH3taz3a/I3tZ2eLL6mOqnr8W26ritt/KDxlvoCj8pSfXsSab4WV0fz+JpHfWc4jV0lvqZxjXyh3D1+kq8lIV/z/Vqar8kesqvucf14sBJfy9KvJflaubYj6mvys7R+TX6mtrASX/PjQdVNXWPSeDDO17L2a2m+5u2hcrziiitKbXpWX9N4KE+/Vs7X0uoaVA+CYQBQNdSJZJltUW90Q6eOV51N2qyVWmvwr+uFrt8PJOtlC0v2sIKfla4BHuVytm6qbZGWeurw9tCD1HqWixV8++FX/bRyHa2XjnAgTKhf8w/Vwjp0o6ab2/AM2VbAz86UXepdP/RwTP5Cm/7XtkNawg+qWhVr5VJPLNnCgo564YN2PpCuNtQ/dAw/rNPYTJOVwgHLWuJXHVkZD0oLbbodwvagXNq26eGJRa1oCyt4ezAebFtHNUYOT/qolz0UDOMevzUgGAYAVUMzOcKzNaKEA2XqaMKzW5LQbI60z4bTVQl1puWOCacUC6NBYtp5wsu5hWYopT3Iic5w0c1leCatVjOFZ2+K6INl3ZSGZ1DFEf5OoZlAaYOb6P/Ds5zLEV6ertdZy9Gj2VbRY8KzyWWPqE2js6HiiD7k1WyoqF3TylOf97bX98WdM1o2WokUnuUbR3iGmdAMuqSykT2iM/Fkl7TZQuG6lsfHwseHj4mWi+wT9Rv5fNp5wnVGaJZzXl/TuVXH4/wlrD+MZjeHZ8fHES1PzW4rNwj29oieJ4uvRc+TxdfCtg7XR822C6eg8+9Hy0b2yls2WsWX19dUBzRjM8nfouWpGYThVSVZbKaZpFFfS2s70nwtru3M4mvh65Hdw6tSk+poOV+LI83XNJNSwbBwHdfDyvBsTF2bykYzFqNlEHctvl9Le2iS5mtxdTSPr4X1h9FM2rSyiZ7H15mkcinna+XI62uqp9FVYJX4WpZ+LVo2YV+La9Oz+lpH+7W48pQ2zQ7O03Z0tF/zs8Nvu+22ssckjSHLlU0l/Zr3tXLl4vV3tF/T7PMsvhZedanyTHswFT1PeAV4OdJ8LWoL+UlH+zX1C3nLUzPWfRaAcm1H3Pg+nAUgjrgxZDjbQBS1pdH2Rm16Xl/TDPS0tjNsA53zoosuClZsaPWBTy8Wt9JF93eLFy8OVuLkbTsrGd+rXspmSW161NfkM+HsGXFEy1P3XeEsHXH1NHqerL4WHUPmGd+HfS2p7ejovXQlvqZ+QTZLOi5anqpPUT+v1NfC9giv8Mria15/NXwtvPo4rlyivpa3X1M7kqU8w/Uqy/1aNfq1qK/F1dE8vlaubCrxNY2FfJrEctdVztey+k6ar8ke1fC1Svq1sK+VazvixpDV7tfiylPjobTxYDX6tbTng5X4WrRsKunXvK9F6wbUDoJhAFA1PvzhDwc/5dANf3jGadoDnXAHk/Wz4Q4m6ZhoB+wHL+UeFpZDA4ak5dzlzi1tXoNep11ftPPPQpbZNdGOP6+d1WHnPaZceYbtEdVeVNnoHH4QklaHwrrj6lNHyyY6sEoKNJejGmWTVk+z2qmj5emPyeIvHSmbNF+L+75KZrJ1xNc0MA8HGsp9VyVtZyVl4zXkOZeuIe9qsnJlE66jUe2VlE2lvpanTe9Iv6YbP6Xu0Yz9NBvq5iqcIqRWZRP1tSx1tJJ+rZKyyVMu1e7XwkRtWlS/Fi3PLPao1ZgjTluetqNaZZNlb44i2s5w2WStp7Xo18qVZ14bVGsMGbZF1OeLGnPIz/K2HdXs18LfGX3gW0S/5stGD0l1rFITehtG95TxAbFHHnkkSJmYp35WUp46Ju+4oxr9Wtz//3/23gNckqQ608723nvvvZ1uEINpvAfh3SJAgBCs2FmBzEpC7C6I3RlhtCt2NdKupB1GCAkBMggBEggzA40ZzJh20973tPd22s7/fKk/irh501ZlZZ2qet/n6afr3ltV+eWJOBGRcSJOBC3q1/z7zlNP62k76ykbtZ1FP1dGv+bj7BH9zqr6NZWnxml5249mjTmiz/n1lGdZZZNli6r6NXcvRWzRjLmRqH2q7Nf88sxTR6sqm6LjwUb6tSTi7rMZ85Bl+Ro0Rs+ZNgAAAAAAMIkLhGWd4eBWjRYNhAEAAER3ivrnqSWhNFdKmZh1/hUAAABAKyEYBgAAAADQQYEwrWZeunQpgTAAAGgYpbzKSp0qCIgBAACAdQiGAQAAAAAYp0ggTPn6CYQBAEBZ+Oeo5QmIZaUjBQAAAGgFBMMAAAAAAIyj/PNZByvrbE4FwqIHMwMAADSKzqHUOWJZEBADAAAAqxAMAwAAAAAwzsSJE4MlS5akTlLq7wTCAACgWShd4qJFizLfp0AYATEAAACwBvlTAKDrGDx4cLBw4cLa627VgA67WLEHOmzqsIIFe1jQUKUOBcTEo48+2isQtnLlyvC1BXtYodvqBzryMWDAgGDWrFnBuXPnwrP4hHZdyo/c37vJHpZ0WNBgSYcVovZwO5W3bduWKyC2evXqUuxopVzQYRMr9kCHLQ2WsGIPdNjUAdVBMAwAug6tmh8xYkTXa0CHXazYAx02dVjBgj0saKhahwJimsDfunVrj0CY2xFmwR5W6Mb6gY5slE707W9/e7B+/frg5s2bNU3r1q3rSntY0mFBgyUdVoizhzs/rMqAmJVyQYdNrNgDHbY0WMKKPdBhUwdUB2kSAQAAAADaLE2VUiKOHj26RyAMAACgKhQQW7x4ceb7SJkIAAAAViAYBgAAAADQhgGxVatWEQgDAICWBsTSzrN0KBD28MMPB1euXKlEFwAAAEAcpEkEgK7j8uXLwZ49e8LXc+fODYYOHdqVGtBhFyv2QIdNHVawYA8LGlqpQ2e2WNBhFSv2QIdNHVawYg8LOixosKTDCln20OIM4dL3JnHjxo3g+vXrwZAhQ5qioyrQYRMr9kCHLQ2WsGIPdNjUAdVBMAwAug6dtXL16tXa627VgA67WLEHOmzqsIIFe1jQgA67WLEHOmzqsIIVe1jQYUGDJR1WyGOPrICYdjErra/O7GumjipAh02s2AMdtjRYwoo90GFTB1QHaRIBAAAAAFqEHrp2794dnD17ttVSAAAA6kYBsaVLl/batewCYaNGjWqZNgAAAABBMAwAAAAAoEWBsJ07dwYHDx4MNm3aFJw7d67VkgAAAOpm4sSJ4RliLiBGIAwAAAAsQTAMAAAAAKBFgbDDhw+HP9+8eTPYuHEjATEAAOiIgFj//v2DFStWEAgDAAAAM3BmGAAAAABACwNhDhcQYxU9AAC0e0Bs7NixYUAMAAAAwArsDAMAAAAAqDAQtmPHjl6BsGhA7Pz585VrAwAAKAsCYQAAAGANgmEAAAAAABUGwo4cOZL6PgXEDh06VJkuAAAAAAAAgE6HpToAAAAAABUEwrZv3x4cPXo0873jxo0LFi9eXIkugG7j0qVLwf333x8GpeWXok+fPrXdmM9+9rODYcOGtVglQHdy/Pjx0P/wQQAAAGgGfZ5wTwAAAE1m5MiRwYULF8LXQ4cODScjWsGtW7eC69evh68HDBgQ9O3btys1oKM369evD3dkiH79+gXr1q3ranugw6YO6qktDXl0FA2ELVu2rK57sWIP6ig6LOs4depUcPfddyf+/Y477gj9sFvsYUmHBQ2WdHRbW3rs2LFg69at4TVWr17dKyBmpVzQ0d31FB3tpUFQR9HRDjrU512+fDl8PWLECFLmNxF2hgFA16HObdCgQV2vAR12sWIPdNjUYQUL9rCgIUtHkUDY+PHjg6VLl9b9EGbFHlawYg902NRhBSv2sKDDggZLOqxQhT20I0yBMKFJyUceeaRXQMxKuaDDJlbsgQ5bGixhxR7osKkDqoMzwwAAAAAAmoACYdu2baskEAYAANCO+IEwhwuItSqTCAAAAHQmPG0DAAAAADQpEKa0T1kQCAMAgG4OhMWd3uECYhcvXmyJNgAAAOg8SJMIAF2HVhgqZZVYtGhRSw5otqABHXaxYg902NRhBQv2sKAhTofOxWxFIMyKPaxgxR7osKnDClbsYUGHBQ2WdFihWfZQsEvfm3aMvd6zYcOGYNWqVUGfPn1MlIuV+mFFhxWs2AMdtjRYwoo90GFTB1QHy08BoCvRQ1fag1e3aECHXazYAx02dVjBgj0saPB16J9WuecJhE2YMKH0HWFW7GEFK/ZAh00dVrBiDws6LGiwpMMKzbDHgAEDgmXLlmX2gS4gpglLK+WCDptYsQc6bGmwhBV7oMOmDqgGgmEAAAAAACWgh6gdO3aEaZ/yBMKWLFlCakQAAOhaxo4dGyxfvjxXQGzLli3B1atXK9MGAAAAnQdP3wAAAAAAJQTCjhw5Epw8ebIlO8IAAAA6PSB28ODB4PHHH69MGwAAAHQWPIEDAAAAADTArVu3wkDYhQsXMt87ceLEMBCms08AAADg3wJiK1asyAyI3bx5Mzh06FBw8eLFyrQBAABA50AwDAAAAACggUCYUiPmDYQpNSKBMAAAgJ6MGTMmd0BMKRPz9LsAAAAAPgTDAAAAAADqDIQ9+uijwalTpzLfSyAMAACgnIDYjRs3gg0bNhAQAwAAgEL0L/Z2AIDyuHTpUuLfhg0b1mOy8cqVK6nfpcnFoUOH9lgxmJRPXtfV4cvRCUk9VGUdyty/f/9g0KBBPXLXX7t2LfUzAwYMCAYOHFj7WddwGpyeKHq/PufQveie0hg8eHDQr1+/2s+ymWyXRvTvaWXikJ2d7XRGzuXLlzM/k1WeUXvoAXjIkCGFykb3LhsUKZtoeeo6+pz7vjxlI136XBq6hq6Vt2x0Xf3dnwiQnWXvvGXjvqeRsomrp0V8zVFPecaVja6Vdk9xvpZVNkV8zdnDf39eX9P9++XZiK+pXjuNfj2Nlk2etrMMX8sql7jy1D04X2ukbKJ1tKivxbWdeX1N71EgTGeE6V5cXYu7L50R5gfCyu7XHNG/1+Nrecomy9fi6mgZ/Vqeson6WlpfW2+/VtTXdG3ZxLdZs/q1aNn45Zk09qinbMrwNWnLajuaMebI8j19JktXtGwa8bW0MWGz+zWHfy95bFR2v+Z/h2+LqsYccb6W1Xak+VpZZaPvlA18OzezX0sqm6Q62qznNdlp/vz5YR8r/LZG19E/2Voaf/KTn4Sph0eMGNHUfs3H2cPXVWW/5sogre1otF+rx9dULlljwrL7NR/fHtG/N6tfi5aN7Jz1nN+Mfi1KdJ4jz1i9jGfpaNnE2aKefi1aNvX4mnTJ3knP+PX2a37ZZJVn3Hiwyn7NlWdSHa1qzBEtzzzjwThfa6Rf8/Ht4VPPs3Se8qzH16BcCIYBQGnceeedwV133ZX4d7/zUWe0bdu2xPeuXbu29lodcNp7XSesVYQOdVrbt2+Pfa86uqNHj/YYuInz588He/fuzcxnP2fOnNrP2g3w2GOPpX5m8uTJwbRp02o/Hzt2LMx1Lw2OqJaZM2eGk6cOvf/cuXOp19FD46hRo2o/79mzJ7Mjnj17do+fleora9C/evXq2sBS780qG71Xn/HtH/2MKxPH6NGjwwdbf4Cya9eu1Ovo3mUDx5kzZ8JDttOQjWVr/zqqB25gE3dvKkuVqUPlr2uloTqjuuPYv39/6oDPDcb8eqH7z3qQlw+4AWyWj8X5mr7f/0y0XKQn6mvya9WbNIYPHx4sWrSo9rPq8r59+wr5mux19uzZmo48vibtClSkMWvWrGD8+PG1n1VnXB2I4uyhHT4+eXxt2bJlPQa9eXzttttuqw3gNbB2ZXPixInaZ/16qoeKVatW1T4vTVl1QA8JCtI4dAbH7t27Uz8j/5w3b16POqD6mVQuQjabMWNG7efjx4+HbWEa06dPDyZNmlT7+fDhw718LVpHdS9aVZ7X18TixYt7PMjm8bWVK1eGPqbv1v+nT5/uEZT0H75HjhwZlpc/MRH1tThkz+XLlxfytWigNo+vjRs3rkdfoH5Ntk5jypQpwdSpUxN9La6OFvE1x4IFC0L7OdRHF/U11TP3sJxUR5N8LYmivqZ6qjoiuzXia3n6tTRfi2vT8/palLlz5zbsa9KmsklrO+Rrfr3OKhuV45o1a1J9LeusIY0hDxw4kPoeTbwvXLiwFF9LKpc8vhZH1Nd0L1m7Z+Rrfrul8swi6msad2dNTGX5WtQWqi+N9mu6d/XTRXxN4420cXqcr8nPVKfTiPqaxpBubBOH2lKNb/2JNtWzrMnzqK/t3Lkzc0JTdnaTk9ExZFwdjfqaJvKK9mtqM6Qt7f2ygT+G1n3IR/2+Vu/R+M/ZSfVffuCQz+hczzTkZ34brfcn7fp29pBP++TxNbUbfuBOdTNrUls28+ug87W0tsMvmzzP0mpn1d4W8TXVMdU1h/SoLUz7nPoN9R8O9Y16zk1DPiPfyfI13x4qT/l1Xl8TagP8z+TxNbU1fhBR9Vn1Oq39SPO1ODSOUttZxNfUNvv9je4ja85C4y31BQ6VZVZfkOVrcXW0iK85dC++v9XjaxrXyBeSnvHTfC0N+Zrr17J8TfZQe+XX53p8LU+/luZrSW1H1NfkZ1n9mvpB/9m4Hl9T+WeNB+N8rZF+zcfZIxpEzutrGg/l7dfSfC2rrkF5EAwDgNJwK/Sso05WARBN0PsPiq3QIFqZMksDCvew1EodVuxhBdlDA3jVUcrlZw8AmjS0YA8NXlvVdlhCNlD7oYekrImMTqyjCgzrIevhhx8OJ+T8xQj+w46CtFVp0wOZC1brmlmBo26oo5qkiQYjW1FPNSHR6jSZltp0tR2aoMiayOgGrJWL0HlMWRP0nWwLBVUs6LBCq8pFzylaGKUAtoKn6ne1kC26QFB/0zhEk7h+4LDZ9tAEfSvHg1b8xQq+PfxJ81bqsNKmtyqdqBVbWEH2ULuhwDPjwZ/V0a1bt+bakd5seyhQyjN+d9DnCUKPANCCnWEaoKatMmlmmsQyU7fVmyaxWSluiqZJbEaKmzLKs1VpEu+7774eaRJvv/32lqRJLCPFTRlpEi35WtkpbtrZ1x544IEeKehcPW1VmsQy0qjE0arUbUV9Tbp+9KMf9bKbVklqt6p7XxW+VmbqtkZ8La6OtipNYjP6Ncu+VkXqtlb5WlljDq0Ov+eeexI//973vjdzItWKr8XRyb7WinRSrfQ1taUuTaI0rlu3rq18rax+Te/ZtGlT+H73L+m9WqGvHRFV+FoZqds6wdfWr18fvl91QNeKe36y7mtlpklsZ1+rYnzfCl9THdVnXJrEpDra7DSJ7davVZUmsZ19rcw0iXp2dBq1qzErgwbUD8EwAKgMrZB3q5LUMeUZXABUiXuYE27SAcAa1FN76KHnkUceqT38aDeYUoN26ypY6ihYRul47r777sS/33HHHb3SnwG0AtrSn6HdYBs3bsycZJWdtOsibrc2NAfqKViHOgrtgAJxBMOq4WdhVQCALkFrALT6wh2i2q0a0GEXK/ZAh00dVrBgDwsa3ApHrUbXyshWBsKs2MMKVuyBDps6rGDFHhZ0WNBgSYcVLNhDwS2XBliT2kk69DcFzbLOWm53e1jSYQUr9kCHLQ2WsGIPdNjUAdXBmWEA0HVotYU7CDN6wHQ3aUCHXazYAx02dVjBgj0saHA6duzYEab80JklrdoRZsUeVrBiD3TY1GEFK/awoMOCBks6rGDFHlpwon726NGjwdixY3uk84oLiDVrh5gVe1jRYQUr9kCHLQ2WsGIPdNjUAdXBzjAAAAAAgBLQJF23pkYEAABoNjqbRYtO/HO3kgJi+/fvr0wXAAAAtAcEwwAAAAAAAAAAoC0CYkpNnBYQ01nVS5curVQXAAAA2IdgGAAAAABAZEX5kSNHyBsPAABgkBEjRgSrVq0Kd2THBcKUIjHubwAAANDdEAwDAAAAAPACYZs3bw62b98e7N27l4AYAACAQeKCXgTCAAAAIA2CYQAAAAAAXiDszJkz4c8HDhwgIAYAAGAUBb/cDjHtFiMQBgAAAGkwSgAAAACArkeBsE2bNgVnz57t8XsFxPr06RPMnj07/B8AAADsoCDY6tWrg8GDBxMIAwAAgFQYKQAAAABAV5MUCHPs378//J+AGAAAgD2GDx/eagkAAADQBhAMA4CuQxOZAwYMqL3uVg3osIsVe6DDpg4rWLBHGRqyAmF+QGzUqFHB2LFjm6KjDKzosIIVe6DDpg4rWLGHBR0WNFjSYQUr9kCHTR1WsGIPdNjSYAkr9kCHTR1QHX2e4BAEAKgwp/uFCxfC10OHDg0uXbrUakkAPVi/fn04MS769esXrFu3rtWSAHpBPS0P2XHjxo3BuXPnMt87ffr0YN68eTwk5YA6CpZ5/PHHgy1btgQ7d+6snQcov16wYEH4etmyZWG6NYBWQ1vaXG7duhU+jyrNItQP9RSsQx2FdmDYsGHB5cuXw9fql86fP99qSR0LO8MAAAAAoOsoEgibMWNGMHfuXAJhAB2AAl1r164NJxz8yTH9DgC6JxC2devW4NSpU8Hy5ctjd30DAABA59G31QIAAAAAAKrkxo0bBMIAAAC6EO0IVSDsxIkTYVBs8+bNYVAMAAAAOh92hgFA16FVwEqR41YHazVwN2pAh12s2AMdNnVYwYI96tGgQJjOCMsTCJs5c2YwZ86czECYBVtY0mEFK/ZAh00dVrBiDws6LGiwpMMKVuxRhg4/EOZwATHtEBs3blwlOsrAig4rWLEHOmxpsIQVe6DDpg6oDnaGAUDXoY5u27Zt4T/X6XWjBnTYxYo90GFThxUs2KOohiI7wvIGwurR0Sys6LCCFXugw6YOK1ixhwUdFjRY0mEFK/ZoVIcLhB0/fjz2b3l3iHWKPToNK/ZAhy0NlrBiD3TY1AHVQTAMAAAAADoeFwjLcxjxrFmzcgfCAAAAwD67du2KDYRFA2InT56sVBcAAABUB8EwAAAAAOhoigbCZs+eTSAMAACgg5g6dWowcODA1PcoILZlyxYCYgAAAB0KwTAAAAAA6OhA2IYNGwiEAQAAdDHDhg0LVq1aRUAMAACgiyEYBgAAAAAdHQi7cOFC5nsVBCM1IgAAQGcHxFavXp07IHbixInKtAEAAEDzIRgGAAAAAB3H9evXCwXC9A8AAAA6m6FDh+YOiD366KMExAAAADoIgmEAAAAA0HGBMJ0RRiAMAAAAohAQAwAA6E4IhgEAAABAV+4IU1pEAmEAAADdR9GA2PHjxyvTBgAAAM2hf5O+FwDALH379g3zxbvX3aoBHXaxYg902NRhBQv2iGpwgbCLFy/mCoTNmjWrKTpahRUdVrBiD3TY0nH69Ongz/7sz4KbN2+Gk9xCZwV+//vfD1+/+93vDsaOHds19rCkw4IGSzqsYMUezdDhAmIaO1y9ejXxfWortm7dGr4eMWJEx9qjnbFiD3TY0mAJK/ZAh00dUB19nnBPAAAATWbkyJG1lfp68Lh06VKrJQH0YP369eHkmOjXr1+wbt26VksC6AX1NJn9+/cHe/fuzXzf3Llzg5kzZ1aiqRuhjoJlTp06Fdx9992Jf7/jjjuCcePGVaoJIA7a0uq4cuVK8Mgjj6QGxFzgfMmSJcHEiRMr02Yd6ilYhzoK7YACcpcvX64tujh//nyrJXUshDwBAAAAoCNQgGvy5Mmp7yEQBgAAAD5DhgwJd4gNGjQoV8rEY8eOVaYNAAAAyoNgGAAAAAB0BFqxvWjRosSA2Lx58wiEAQAAQN0BMaGUiQTEAAAA2g/ODAOAruPGjRu1dI3afty/f/+u1IAOu1ixBzps6rCCBXvEaXABMa3e9ieqFAibMWNGZTpagRUdVrBiD3TY1GEFK/awoMOCBks6rGDFHlXocAGxtJSJt27dCv/20EMPBStXrgymTZsWdHO5WMGKPdBhS4MlrNgDHTZ1QHWwMwwAug49vOzZsyf8l5UXvpM1oMMuVuyBDps6rGDBHkkaFBBbvHhxMGnSpKYHwtJ0VI0VHVawYg902NRhBSv2sKDDggZLOqxgxR5V6XABscGDBydOnJ45cyb8t2XLluDo0aNBN5eLFazYAx22NFjCij3QYVMHVAfhTgBoGZcuXUo9PNJffadDjdPQxOfQoUNrP+uA1Mcffzzxuurk9Jnog01W56dVIn7qjOvXrwfXrl1L/cyAAQOCgQMH1n7WNZwGpyeK3q/POXQv7tDXJPTApgNhHbKZbJdG9O9pZeKQnZ3ttPPCHfKZRlZ5Ru3Rt2/f8EG0SNno3v2H1jxlEy1PXUefc9+Xp2ykS59LQ9fwVxhllY2uq7/LDg7ZWfbOWzbuexopm7h6WsTXHPWUZ1zZ6Fpp9xTna1llU8TXnD389+f1Nd2/X56N+JrqtX8IdFLZ5Gk7y/C1rHKJK0/dg/O1RsomWkd9X5M9Zs2aFQwfPjwYO3ZsosZo21mvr6W16c3u1xzRv9fja3nKJsvX4upoGf1anrKJ+lpWudTTrxX1NV1bNvFt1qx+LVo2fnkm1dN6yqZovxZXntKW1XY0Y8yR5Xv6TJauaNk04mtp7Uez+zVHdNI/y0Zl92v+d/i2qGrMEedrWW1Hmq+VVTb6TtnAt3M9vla0X4uWTVIdraJfixtzqEzT6lsZ/ZpsvmDBgmDz5s296o++SxpcuWzbti38f/To0ZX0a+7e09qORvu1enxNdSBrTFh2v+bj2yP692b1a9GykZ2zxoTN6NeiROc58ozVy3iWjpZNnC3q6deiZVPPGFK6ZO+kZ/x6+zW/bLLKM248WGW/5sozqY5WNeaIlmee8WCcrzXSr/n49vCp51m6nn4tj69BuRAMA4DSuPPOO4O77ror8e9+56POyD04xLF27draa3XAae91nfCKFStqP6vT2r59e+x71dFpBV80H/z58+eDvXv3pl5HE6pz5syp/Xzq1KngscceS/2Mzq7x02cobdehQ4d6rCKMatGZNhMmTKj9rPefO3cu9Trz588PRo0aVftZK1uyOuLZs2f3+HnHjh2Zg36tlHQDS703q2z0Xn3Gt3/0M65MHHqYXLp0aY8Byq5du1Kvo3uXDRxarXnw4MHUz8jG/vlBuo7qgRvYxN2bytI/j0jlr2uloTqjuuPYv39/6oDPDcb8eqH7z3qQlw+4AWyWj8X5mr7f/0y0XKQn6mvya9WbNBSMUNo6h+ryvn37Cvma7HX27Nmajjy+Ju0nT55MvY6CJePHj6/9rDrj6kAUZ4+JEyf2+H0eX1u2bFmPQW8eX7vttttqA3gNrF3ZnDhxovZZv57qoWLVqlW1z0tTVh3QQ8KSJUtqP1+8eDHYvXt36mfkn9pp5dcB1c+0MzZkM39n1vHjxzPP2pg+fXptZ5c4fPhwL1+L1lHdy5gxY2o/HzhwIKw7ul4S2kHmP8jm8TWlRXIPfvI1fSatTVc5rlmzJtHX4tB3LF++vJCvRQO1eXxt3LhxPfoC9WuydRpTpkwJpk6dmuhrcXW0iK85NBE5cuTI2s/qo4v6muqZe1hOqqNJvpZEUV9TPT19+nRot0Z8LU+/luZrcW16Xl+LMnfu3B6+ltWvxfmatKls0toO39dEVtnk8TXZPg2NIdV2pKE0OgsXLizF15LKJY+vxRH1Nd2LS/+T5mv+pKHKM4uor2ncnTUxleVrUVuovjTar+ne1U8X8TWNN9La9Dhfk5+l9TVxvqYxpBvbxKG2VONbf6JN9Sxr8jzqazt37syc0JSd3eRkdAwZV0ejvqaJvKL9mtoMaSvia/JP+Y77vjhU/+UHDvnMkSNHUq8jP/PbaL1f19E1VK7+uMAFFvxy0b1r7Jo1ftC96J4ceXYiyGb+vTpfS2s7/LLJ8yytdlbtbRFfUx1TXXNIj9rCtM+p31D/4VDfqOfcNOQz8p0sX/PtofKUX+f1NaE2wP9MHl9TW+MHEVWfVTfS2o80X4tD4yi1nUV8TW2z39/oPrLmLDTeUl/gUFlm9QVZvhZXR5N8LQ3di/rQIv1a1Nc0rnFB7CT7JflaGvI1169l+ZrsoXbdr8/1+Fqefi3N15Lajqivyc+y+jX1g/6zcT2+pvLPGg/G+Voj/ZqPs0c0iJzX1zQeKtKvJflaVl2D8iAYBgClocF/npVNAAAAAAAAAJZRkEiTvVqIkBbo0gIuTahmBcMAAACgtRAMA4BSHxb81RpR/ECZO88lD1rNkfXe6CoOrdRL+oyfUiy6QiPrOtHDNLVCyV9xlGelvlbc+CtptZvJX70p/O3pQit0/BVUeVLcaCVQ0TSJ/srLJPzt6XqdtxwdWvET/Yy/mlz2iNo0uhoqDn9Fs1sNFbVrVnnq/c72+r64a0bLRjuR/FW+cURXOWkFXVaaxOhKPNkla7WQX9eK+Jj/ef8z0XKRfaJ+I5/Puo5fZ4RWORf1NV1bdTzOX3z90Z1i/ur4OKLlqQmPtDSJcdfJ42vR6+TxNd/Wfn3Uajs/BZ37fbRsZK+iZaNdfEV9TXVAKzbT/C1anlpB6O8qyWMzlaVWXfv2z2o7snwtru3M42v+/cju/q7UtDqa5Gtx1ONrWl3q706px9fUr/m7serxtbg6WsTXkspGk41ZZRO9jmuf08olydeSKOprqqfRXWD1+Fqefi1aNr6vxbXpcTbTqu2y+7W48pQ2rQ4u0nY02q+51eH3339/4mfSxpBJZdOIryWVi9PfaL+mHfB5fM3fdany9He95LmOvwM8iSxfi9pCftJov6Z+oWh5asW6ywKQ1HbEje/9LABxxI0h/WwDUdSWRtsb9bdFfU0r0LPaTt8G0TFkWh31761o21nP+F71UjZLa9Ojviaf8bNnxBEtTz13+Vk6/JSJLt2aq2vqm+Qz9aQk1RiyyPje97U85VLvs3Q9vqZ+QTZL+1y0POUzUT+v19d8e/i7TvL4mtNfhq/5u4/jyiXN1/KQ19f8epVnDFlGvxb1tbg6muVrccRl0SnqaxoLuTSJSfeV5Gt5fSfL11z631b0a76vJbUdcWPIsvu1uPLUeChrPFh2v+aTlNkgj69Fy6aefs35WrRuQPMgGAYApfHBD34w/JeEHvj97exZEzp+B5P3vX4Hk/aZuC3YGrxEB3RZaMAQHTRkoWtLm9Og11n3l3SAcxpZkxhxHX9RO6vDLvqZpPL07RHVXlXZ6BpuEJJVh3zdaVv66y2b6MAqLdCcRBllk1VP89qp0fJ0n8njL42UTZavxX1fnvKM0oivaWDuBxqSvquetrOesnEailxL9xB9eErDpVfVPSnlhe/bfh2Naq+nbOr1tSJtejP6tbLKs2jZxPlanjpaT79WT9kUKZey+zWfqE2r6tei5ZnHHs0ac8RpK9J2lFU2ec7mqNrX8tbTZvRrSeVZ1AZljSF9W0R9vqp2UH5WtO2op+3MKhu/La2yX4srmyx7VNWv6TNFxx1l9Gu61u233x488sgjYUorV6e0sMOliauqX/PvO089raftrKdsVAeKfq6Mfs3H2SP6nVX1aypPP91bVj1t1pgj+pxfT3mWVTZZtqiqX3P3UsQWzZgbidqnyn4t6fkp6fpVlU3R8WBZ/ZpP3H1W1a/V42vQGD1n2gAAAAAADKFAmCaftLtYq203bNhAGiIAAACoFE2W6hxkN9mrXTD+2cMAAABgH4JhAAAAAGA6EHblypXa7wiIAQAAQKsCYsuXLw/Thym1FwAAALQXpEkEgK5DW5fd+RnRfL3dpAEddrFiD3TY1GGFZtsjLhDmB8Q2btwYnr1moUys1A0rOqxgxR7osKnDClbsYUGHBQ2WdFjBij2s6FB6Lp311WodVuxhBSv2QIctDZawYg902NQB1dHniawT5wAASsI/M0wPEUkHVQK0ivXr1/c452bdunWtlgTQlfVUZ/po91dcICx6gPSaNWs4cNgY3VBHoX05depUcPfddyf+/Y477mDHB5iAthTaAeopWIc6Cu2AzhrTsQDuGff8+fOtltSxkCYRAAAAAEwFwpJ2hEWZOnUqgTAAAAAwz40bN1otAQAAoOshGAYAAAAApgJh+j+LRYsWBVOmTKlEFwAAAEC9aIHPT37yk+DQoUOtlgIAANDVcGYYAHQd169fD06fPh2+Hjt2bDBgwICu1IAOu1ixBzps6rBC2fYoEghbvHhxMHnyZDNlgg6bWLEHOmzqsIIVe1jQYUGDJR1WsGKPdtWhQJjGNzoLddeuXeHvpk+fXrmOTseKPdBhS4MlrNgDHTZ1QHUQDAOAruPatWu1VXnDhw9vSWdnQQM67GLFHuiwqcMKZdqjnkBY2RoaAR02sWIPdNjUYQUr9rCgw4IGSzqsYMUe7ajDD4Q5ygqIWbGHFazYAx22NFjCij3QYVMHVAdpEgEAAACgZdQbCAMAAACwSlwgzA+IHTx4sCW6AAAAuhl2hgEAAACAuYmiKEuWLAkmTZpUiS4AAACARhf6pI1vdu/eHf4/Y8aMCpUBAAB0N+wMAwAAAIDKIRAGAAAAnUi/fv2C/v2z154rIMYOMQAAgOogGAYAAAAAlUIgDAAAADoVnTmzevXq8PyZPAGxAwcOVKILAACg2yEYBgAAAADmAmF9+vQJli5dSiAMAAAA2jIgtmrVqlwBsT179hAQAwAAqACCYQAAAABQCZcvX84dCNOOsIkTJ1amDQAAAKCVAbH9+/dXogsAAKBbIRgGAAAAAE2HQBgAAAB0G0UCYnv37iUgBgAA0ESyT/QEAOjABxKXdkuvu1UDOuxixR7osKnDCkXs4QJh165dy5UaccKECaVraCbosIkVe6DDpg4rWLGHBR0WNFjSYQUr9mhnHS4gtnHjxuDChQuZAbEnnngimD17duk6Ohkr9kCHLQ2WsGIPdNjUAdXR5wn1sgAAFTBy5Mja4H/o0KHBpUuXWi0JoAfr168Pbt68Gb7u169fsG7dulZLAmj7etqsQBjYpd3qKHQXevy9fv168IMf/KBHPX3a055WmwhRewTQamhLOw+1PXkCYkLBsKyAmAWop2Ad6ii0A8OGDQufm8WIESOC8+fPt1pSx0KaRAAAAABoCrdu3QonfQiEAYAV1N4MHDgwnBDz/+l3+kcgDACahdshponOLPbt2xf+AwAAgPIgGAYAAAAATaFv377B/PnzUyeX9bdly5YRCAMAAICOp3///oUCYi5tIgAAADQOZ4YBQNdx9erV4Pjx4+HriRMnBoMGDepKDeiwixV7oMOmDivktcf48ePDYNeWLVt6Tea4QJje00wNzQYdNrFiD3TY1GEFK/awoMOCBks6rGDFHp2kwwXEtHs+KxXW/v37w/+VMtFfXGTFHlawYg902NJgCSv2QIdNHVAd7AwDgK7jxo0bYWenf3rdrRrQYRcr9kCHTR1WKGIPFxDzJ3H0evny5XUHwopqaCbosIkVe6DDpg4rWLGHBR0WNFjSYQUr9ug0HQqIrVy5MjxXOwsFxLRLzF9UZMUeVrBiD3TY0mAJK/ZAh00dUB0EwwAAAACg6SjopXPBFARzgbBx48a1WhYAAABASygaECNlIgAAQGMQDAMAAACAStC5YAqIEQgDAAAA+FlAbNSoUZnvPXDgAAExAACABiAYBgAAAACVBsQIhAEAAAD8LCC2YsWK3AExd44YAAAAFINgGAAAAAAAAAAAgPGAmN7HoiIAAID6IBgGAAAAAHVBmh4AAACAalIm6u+rVq0KRowYUbk2AACAToBgGAAAAAAU5uLFi+G5FZcvX261FAAAAICOoF+/fmFAbPTo0T1+TyAMAACgcQiGAQAAAEAhLly4EGzZsiW4fv168NhjjwVnz55ttSQAgFxcu3Yt2LFjR3Dy5Mng1KlT4T+91u/0T38HAGh1QEwpE11AzAXICIQBAAA0Rv8GPw8A0HYMHDgwmDFjRu11t2pAh12s2AMdNnVYCIRt2LAhTJHo0vhoAnno0KHBmDFjurJM0GETK/ZAhy0dasP+5m/+ptfvN27cGP5/xx13VHoeT6vtYUmHBQ2WdFjBij26TYcLiG3dujWYOXNmMHLkyJboaBes2AMdtjRYwoo90GFTB1QHwTAAaBmXLl1K/NuwYcNqr2/duhVcuXIl9bv69OkTTsQ6bt68GTz++OOp39+3b99gwIABtd/duHEjuHr1aup1lJ5i0KBBtZ+1KyJrBbGu4Xequoau5e5Rn49+h97va9O96J7SGDx4cPjQ5JDNZLs0hgwZEkycODFXmThkZ9lbaDI8T4q0POXp20P2kbYiZaN7lw2KlE20PHUdfc59X5w9omXjyjMNXUPXKlI2mojzy1N2zjqfyS+bvOWZVTbRelrU14R8rWh5+mUje2tlrCvTpHJN8rU0ivqa7FGvr8kOZfia7t9p9OtptGzytJ1Fy0apETUp464te0iLyubBBx8Mli5dGnvORdTXdA/O1xotG7+OygZFfS1anvX4mq6d1qb7OpvVrwndh9+m19Ov5SmbLF+Lq6Nl9Gt5ysb3NV0vq1zq6dfq8TWt5q+iX4uWTbQ84+xRT9nU06+58tS1VE9l57RyadaYI6us9JmsNjpaNo36WlI9raJfE7oXVy7ClU1V/ZqPbwvZp4oxR7Q8ozribJHla2WUjevXfDtX0a/FlU2cParq11zZyHZadOM0JNXRZvRrUXTv0XawWf2axlZJvpZUTxvt1+rxNdUBfS6tLWhGv+bj10mfZvVr0XbQlWda+9GsZ+m48nRtela5lPUsHVc2UVvU06+V4WvSJXsnPePX26/5ZZOnPIcPH96Sfi1annF1tKoxhytPN+5Qfc4aDyb5WhpFfM3Zw7+Xep6li/ZreX0NyoVgGACUxp133hncddddiX/3Ox91Rtu2bUt879q1a2uv1QGnvdd1wlo551CntX379syByKJFi2o/nz9/Pjz/Jo2xY8cGc+bMqf2s1DpKEZbG5MmTg2nTptV+PnbsWHDixInUz2j134QJE2o/Hzp0KDh37lzqZ+bPn99jEnrPnj2ZHbEervzOW7s7sgb9q1evrg0s9d6sstF79Rl/UJX1GWmSNn8gumvXrtTP6N5lA8eZM2eCgwcPpn5GNpat/euoHriBTZxOlaXK1KHy17XSUJ1R3XHs378/c3Ctuqk66tD9Zz3IywfcADbLx+J8Td9f1Nfk16o3RXxNdXnfvn2Ffe3w4cOFfO3o0aNh6qs0Zs2aFYwfP772s+qMqwNJLFiwoMfq3Dy+tmzZsh6D3jy+dtttt9UG8BpYu7JR++E+69dTd5aEQ5qyylMPCUuWLOkR7Nq9e3fse9Wuqr7rIcGvz7qOa59kb5WB/5An9KDjVtyJ48ePh21hGtOnTw8mTZpU+1nln+Vrc+fO7bE7LY+vLV68uMeDbB5fU6oi97CUx9dUjmvWrCnka3pQXL58eSFfU8Bl4cKFhXxNgffZs2cX8rUpU6YEU6dOTfS1uDpahq+pj67S15Jotq85tAhg3rx5hfq1TvY1UYavyfZpqK07cOBAW/haHFFf071oN1zRfi1roi3qaxp3Z01MtcLXdO+6nyK+dvr06XDsXcTX5GfytyK+lpVmWG2pxrf+WF31LGuyNeprO3fuzJzQlJ3dZHM9/Zom8or2a2ozpK2or6m9SUP1X37gkM8cOXIk9TPyM/mbQ++Xj6Yhf/Z3kebxNd2Ln+owj6/JZn7AIY+v+WWT51la7aza2yK+pjqmuubQfah80j6nfkP9RxFfk8/Id4r4mnzTXxyUJ6W32gD/nLY8vqa2xh/zNsPXNI5S21nE19Q2q40u4mvqA9QXdKqvaVyjskl6xm/E11y/VpWv5enXyvA1+Rm+Vp2vZdU1KA+CYQBQGppwyLOyCQAA2gsXCNPkpT8xDQAAAAAAAADQDvR5gtAjALRgZ5hWdaStMmlm2g39Xitr9RmtlnGrWqtMk6hVydLgdrNEU7NUlSZRNnCrt7SbI+sazUrt4MrE2UPXaEWaxPvuu69HmsTbb7+9JWkSZQ+t3tLqJJWL7qsVaRKj5SIdrUiTqO/Xail9Ls5fHM1O7eDsofdrR6HTUXWaxAceeKBHCjpXT5uVJlGrD7ds2VK7pj6j8tF7tbNH19F1VV5atVdlmsRoHdW1W5EmUSta09r0qtJJyaZuda3aDtmiFWkS4+poK9Ik6hpu91Va29HsNInSoZXssrNr01uRJjGuTW9FmkR9v4Lr+j+tXJo15tDq8HvuuSfx8+9973t77W5tZprEpHKpOk2itLhsB9r9m3VuRjPSJEZtoWu0Ip2U+j23OzCpjlaRJlFtqUuTKI3r1q1rSZrEpDpadZrEvOPBZqdJTBoPVtGv+b6W1nZop4GeN93ug2amSVy/fn34ftUB3Uvc81MVaRJ9e6hc/F1BVaZJ1OfSxoRVpUmULtem+1lNqkyTGFdHW5EmUXVUn3FpEpPqaLPTJDp7SJsbD7YiTWJS21F1mkQ3HtR1tCstbTzYzDSJvj20a83pqDpNokshLtR+ZWXQgPphZxgAlMYHP/jB8F/admB/O3tSTu+4Dibvex3uLJskXAfld9TqkP1BVh7U8RfdJaHOX4MKp0EdYdb9pQ0MkvA75SQ0wHPb2DUQKmpnDS6KfiapPH17RLVXVTa6hhswZdUhvzz9h4CyysYNftzDS9bEXBxllE1WPc1rp0bKU37q0lrl8ZdGyibL12SP6CA/T3lGacTX9NDhBxqSvquetjNaNppMUaoHXcefvHEPCK5+KBWHUm76KS/S0D0UPaA4qWz8OhqtV/WUTT2+pmsXadOb0a/FtelxNmlG2UR9LU8dradfK1o2/gNp3rajzH7NRw/T+ufa9Kr6tWh55rFHs8Ycfrm4elqkTS+rbPJMOjW7X4uWTd562ox+zSENjYwJyxpD+raI+nwVYw5XB4q2HWX2a/53RicVq+jX4somyx7N6tfKGA+W0a/lGQ9W0a8J/77jykULHV1aZU0AK11cs8tGuKBtkc+V0a/5OHv4k+ZV9Gt+ecrmeduPZo05ynjOL6tssmzRzH7Nx91LkTrajLkR13a48WBV/Vq0PPPU0WaXTb3jwbL6taz5wSr6tXp9DRqjZw8BAAAAAPD/B8I2btyYubpPDwo64y9vIAwAAAAAyke7G/xzs11gjIRQAAAA/wbBMAAAAACoOxCmg5a18xcAAAAAWhcI27ZtW6/fExADAAD4GaRJBAAAAIDCgTClgVAgrJ40MgAAAABQDjqLW+epJaGAmIJhixYt6nHmKQAAQLfBzjAAAAAACFHe9ryBsGXLlhEIAwAAAGghCnKdOnUqdwpFdogBAEA3QzAMAAAAAMJA2KZNm3IFwlatWhWMGDGiMm0AAAAA0Bvt9NKOr3HjxmW+l4AYAAB0OwTDAAAAALqcvDvC+vfvHwbCOCMMAAAAwAY6w1U79sePH5/7bDECYgAA0I0QDAMAAADoYs6cORMGwm7dupUZCFu5ciWBMAAAAACDAbGlS5fmCogdO3aMgBgAAHQl/VstAACgagYNGhTMmzev9rpbNaDDLlbsgQ6bOsoOhCk1Yp5AWDQ1ogV7WNCADrtYsQc6bOqwghV7WNBhQYMlHVawYg90ZOtwAbFHH300OHnyZGZATMGwJUuWhKkW2x3L5dKtOixosIQVe6DDpg6oDoJhANB1aFJ39OjRXa8BHXaxYg902NRhIRBmxR4WNKDDLlbsgQ6bOqxgxR4WdFjQYEmHFazYAx35dLiA2NatW4MTJ06kfsfx48fD/zshIGa9XLpRhwUNlrBiD3TY1AHVQZpEAAAAgC6j0UAYAAAAANhEATEFuCZMmJD5XgXEFDgjZSIAAHQDBMMAAAAAuoizZ88SCAMAAADoYNwOsbwBMaVWJCAGAACdDmkSAaDruHLlSrBv377w9ezZs4MhQ4Z0pQZ0VMtjjz0W3HvvvcGNGzd6/H7t2rXBz//8z5u0Bzps6miUoUOHBoMHDw4uX76cGghbvXp1MHz4cNP2sKCh3XV87WtfCx544IFev3/LW94SzJ8/P2hn2rlc0NE9WLGHBR0WNFjSYQUr9mg3HUXG/s3SodSHLmWiS4mYhFIqKiCmHWUKpDXCzZs3g7vuuiv8P473v//9paclq6p+aDHZH/zBH/QaR48cOTK8r6tXr7ZVPe10DZawYg902NQB1UEwDCpBA4ZHHnkkXImuQdi1a9fCleY6pPApT3lKMH78+FZLNMmhQ4eCn/70p2HDfPHixXDycurUqeEEpQa1UH99dIPXrJ0RnawBHdUh//385z/f62F47NixwYte9CKz9kCHTR2NMnDgwHDH14YNG2IDYgMGDAj/nhYIs2IPCxraXcfixYtjg2Eaf7R7MKydywUd3YMVe1jQYUGDJR1WsGKPdtJRdOzfLB0uIKYAl8gTEBONBsR0naRAmBZlNeN8nqrqh+yycOHCcH7L5/z588GOHTuCGTNmtE097QYNlrBiD3TY1AHVQTAMmsrJkyfDVTOf+tSnEg9v1WDiWc96VvCbv/mbwUtf+tKgFXz4wx8Ofu/3fq+U7/qXf/mX4MUvfnHdn1dqgs9+9rPBJz/5yXAiKolZs2YF73nPe4Jf/dVfDYYNG1b39QCguehB8G//9m/DB6Ro2/ea17wmDEwA+Ozduzf49Kc/Hfu3MWPGBGvWrGn4GoMGDQoDXnqQ12q4ooEw6BxmzpwZTkxFA6Oa0FG7pZXOAAAA0L5j/6IBMc1JaPFtvQGxI0eOJP5t8uTJQbujhUTRYJj4yU9+EgbDAADALpwZBk3j7//+74NFixYFH/vYxxIDYS7yft999wUve9nLwsHhuXPngm7eCbZu3bowNVFaIEzs378/+N3f/d1wUPv973+/Mo0AUIz7778/9Ncot99+ezB9+vSWaALbHDt2LPFvEydOLO06Cohpp7FLBUEgrDvRRNecOXNix2faPQgAAADtP/Z3AbFJkyblWtSslIn17pJIC4ZNmTIlaHfmzp0bGyjcs2dPryAoAADYgmAYNIW77747eP3rXx+cPn260Oe++MUvBs94xjOCU6dOBd3Grl27gp/7uZ8rHNg6ePBg8NznPjf4yle+0jRtAFAf8s84n9ZOi2c/+9kt0QT2SVuxW2YwzA+IjRo1KvOMMOhcklYxa3cYAAAAdMbYXwEx7WpqdkDs6NGjHR0M0+6+uDG5dtTt3r27JZoAACAfpEmE0vnyl78cpu7TQMBHqfze/OY3BytXrgxzZWu11Ne//vXgO9/5To/3bd68OXjVq14V7hbr3781VVSr4+s9k6uedELaDafUinErqJ7znOcEL3zhC8O0iAoSapW20ij66Yx0BpuCjz/84Q/DyUwAaD3ySwX44x4gdVYA6RGh3mDY2bNnmxIQ0wQJdCdJK9W1Y13jDaVRBAAAgPYf+7uAWFY2Aj8gViRlouaB0r63E9IkurFTXNBv586d4ZwXAADYhGAYlIqCNe94xzt6BcJ0FthnPvOZMAjm84EPfCAMhr3uda8LB1qO733ve8FHP/rR4D//5/8ctIKpU6fG5oBuFu973/t6rSCaMGFCmGpSaROjyDZKpfi1r32t9rvHH388DDYqWKZgHgC0PkVK3O5YBbaXLVvWEk1gH/WfVQfDBIGw7kYrxFUHouM3/ayUP8uXL2+ZNgAAgHagncb+LiCm/9N2cYkbN270Gh+koXkdBQbjUEBw3LhxQSeQFNTTom+Nm+o9bw0AAJoLrTOUyoc+9KFeKQ5f8pKXBF/60pd6BcIcz3rWs4L169eHO8d8fv/3fz84fPhw0OnokNW//Mu/7PE7pamSTeICYUIDSO3A024yn61btwZ//Md/3FS9AJDvIfBHP/pR7N+02xMgCQW6kiYQ9FA9fvz4yjVB56NFNEkpMtPO/QAAAID2HPsrEKYz3tN2ao0ePTpYsWJF0K9fv9zfmxZc07U6ZQHWmDFjYn+vcbwy/wAAgE3YGQaloZXs99xzT4/f6QySP//zP89Md6hVSQp+Kb2iQ2l5PvnJTwYf//jHg05G9x1daaXfaWCahmwq22qVmX9I6x/8wR8E/+E//Ad2hwG0EO3avHnzZuxhy7Nnzy71WhcuXAjPJ9BDl9Ky6MFszpw5wZAhQ1I/p5WrSoGm9kMPpUrxqpWr9aR6jVtBqgdhLY64dOlS+LNS8Q0ePDjc2aRdKM1eLal29bHHHgs1yEZ6iJdtZs6cWUrKtytXroQpYM6cORPuzJXtdX/6bp2FkLQAJIu0XWFaCJE1GXHixImwLsybNy9xsqFbykdBnLLLp9Vcv349LGNNuum+rl69GtpSfb7KUGMDBVTr8WPZX2VRpE4CAABAc8f+Gsuo79eYXf2+rqN+X2M1jeeLjmn0eX1O48WLFy+GYyYtQlZ/r3GEvlsLlfVcUDQQlrWIpqzzwhRw0g4sjXk0HhoxYkSuBWOaY9LnZFPZQfc5bdq0XGeo5Q2GCdk37e8AANA6CIZBaWh3kwYiPr/yK78SDi7y8N73vjf42Mc+Fk6OOe69997grrvuatnZYc1GE6n/9E//1ON3spfsljdPtd4ruzlkv69+9avhuWsQj4IES5YsCV9rcrRbNaCjOegBa9euXYk7YfM83H3iE58IJ72F2j8/qPHbv/3bob3k6zpbUSnMomcT6DNPfvKTg+c973m92s8dO3aE6Wn9ttah6+hMgJe97GVh0KBIuWhCXrtTH3744WDv3r1hgCUJpUjR995+++25Hoqdjj/8wz8M28y4IM973vOe8LtkPx1crlS3casyZY81a9YEL3jBCwovGtCkgyYNdLalduemoV02t912W1gOaYEJ2Un9XJ7DyTUR8uEPfzisY24RhWzxrW99K1x8ovS5OtdBf9O/+fPn12zVjPKJ1g/VWwXX4mhW+fgadK9alb1x48ZwUictpU/e8vGR7bTTPQ5Ndinlc5KvqMw0pkladf3+978/8bqqG6pzui+VXdxkm0P3rLLVxJV2nmsCTtrc2SBpJO0Ma+dgmJW+BR22dKiN06IzBeP9ttSl7qr6XJ9W28OSDgsaLOmwghV7WNXR6Ng/Di1Y01hp+/btsQtVon2+xglaJKuxWxw6SkHPDBr/K1ATHXfqu9QmapwrtMBGzwwa+yjl31Of+tTMhXZ5d4YloYW+cc8n4o1vfGNoc9ni29/+drBly5bYTAoa9yh7TlSrxjJ6bpI948bcmoN5+ctfXihYlzRu0vhVwTnptVRPu1WDJazYAx02dUB1dGaEAVrC5z//+R4/66HyXe96V+7PaxD39re/Pbjzzjtrv9PK529+85u90gF2Cn/3d3/Xa1Lrne98Z6HVV7/0S7/UIxgmPve5zxEMy5jQLmPnQbtrQEdzUIrTOLTiUCss8z5AxgUCNGmuh9N//dd/DX74wx8mTvbrwVh/13cpQKI2RYsV/uVf/iV8GE5C36eHS63oVPutMslTLnq4/ud//uceZz+moYdX6dDk/lOe8pTg+c9/fuqiB9UP3ZMChHGBMN2fdjTpAVeLAfzdslH0PT/+8Y/Dh+K3vvWtudpb2UWBBZ0FoRWledBkguqCgjPqwxTgSVoUkScQloVWtrpAmNBkgmylHWIKoDSjfHy/lc2TAmHNLB9p0ANMs8on70pnTaKk+Uq9q6RVP3R+aN6AlMrctR36jP5p52ieYFhS8DGpXNsBK30LOmzp0CTlm970prANcONwtTVJ6ck73R6WdFjQYEmHFazYw6qORsf+0X5fu8w0divS56uv1/gqLhimcaYWcaUthPIXBGi87bIEKHD23e9+NxxnKSCVZ5dbPWMeadS9p31OY1M9y0QXYPvs27cv+Ou//uvwOUYLxTQu1iIsjRHT7l/jZmU5+sVf/MVgxowZQR6Sxk2ypfqWVtdVC/5iQYMlrNgDHTZ1QHVwZhiUglYRPfjggz1+t3LlynASrgivec1rev1Ok76dyte//vVcNkhjwYIFYfoCHwUQy5hcBYBi6OEvaWWodqDk/Y4k9GCqIPoPfvCDXAdZ60Fak/1Kf6JdKWmBMB89+Ma1T1GkQW20dgbnDbREP//AAw8Ef/M3f5O62yWPXWQTLQRIC7REH5b1YJ+FbPeZz3wmDCblDbREA0vazaaH8DiyDi3Pg4IVmhiI1glNjPzFX/xF8OlPf5ryqbN88pZV1krieiaGtCr8U5/6VMM7s9JWYfsk7YbR5FFW+QMAAHQjZYz9HVrI9md/9me5AmFFxhPKLJAWCIoGxOLSZWu89Vd/9VeZ40nt3NJ749DCKi2OKqpRk9Ra8PXFL34xNRDma9DiK41j//Ef/zGcG8lz/3qPrpF3HsUPRkZROksAALAJwTAohbiJ2ac//emFv2fVqlW9tpsnrbTqFLtFV6pGA1t5iNpawUmlwwKAakmavNeDkhYI5CFt0lwP23oYLILSm+jhNW21ZRybNm1KfZBTm/8P//APvdqxeti9e3e4CrZeuyhYoFSBeQKEUdukoeCKgoja+dYoCrZo113ZwTB3hlT0vAjZQit5lRJT5yIUtY1PN5ePQ9qrDIapXOVfZUym1HMORpQ8k08AAADdRhljf40xFLzRQrRGFp8kjUXSxiBxAZ6kc2MVLFJgKWtBXRIKhCV9d5pGjYWKLpDeuXNnGAjLuxDQ169xb6MwbgIAsAtpEqEU4iaskvJVp6EUJVpBpXzO/oSsBl6ddm7YgQMHwqCVj+696AG1Qjm8/+///b+9ykR5wyF+AlUDZLezrhVboi1oQEe5aPI6aTJdZzflPYNED4P6Ltc+aIWmW3XoPyArLZ4WDyjQEZcz36FAiY/SLOpAZ+3QSdtJo1WRCjK4746Wix6G1T6noYOsJ0yYED5Ya5dL2q4g7S5+0pOeFDtxL53aoaRghG8Ph7/aUw/ZCgxpYkG2SVvdqSCNbBrX7upz2hHl78qJlovKVOnxlL5SD+pq19OCF3qQ1zk1fn/WSDBMZaPVt0oD41Lb+PZ0K4u1c0zvid5no+Xj+612ciXRzPJRWps4XxG6r0bLx6FrJPmZ0jTKr9yES9RXVG/SVlLHTV5t27YtdUJJi2c0qaRryxaadNH75e9Re+TdGZbWjsSlJ20HrPQt6LCpwwpW7GFBhwUNlnRYwYo9rOlQ/560cKfI2F8Ll/LshteYTWN4jTk0ttPYS2ekuj4/KRgWHWtqbKDv0dhQGjXu0RhAZ7lq8VxaQE4L83TfSTuiovMbeRcOpQXDfD3SK+3a/RV9jomO0/1AmMaSGn9qTKrxZxoaW6p+ZaGxrDvjOapDY05d30I9Fd3eplvBij3QYVMHVEdnRRegZegckCg6sL0e9Dk/GOYmsOr9vnobw7vuuivclaYJKU0uabJJAygNrFavXh08+9nPDl74wheGE20WbBZFB95CkHrYsHvdrRrQUS5qK5Im2pcuXZrrO2QDtTeygQsSRO2hAJgOeNakvSaoXdqSpAOnHXpoVZt12223hQ+9+n6llvvpT3+a+Bn/IdPXoQfhtB1hCrK84hWvCGbOnFn7na6n3TfarRSH/v7QQw8FL3nJS3r9Tdd2Z2sl1Q8FWZ7xjGf0OOBbtpRt9HAfh75LQQQFFqPocG6lGYy+35WL0gArra0CLQ59l9JYJqXLkQ4NtN0BveL1r3997TuV0lIpcuLQmU/u/EzZXoErBT9UB3S2gX9QuOqCC8zq4V/BED+gVFb5+H6bFdRrVvkk+YoeZF72spc1XD55Jmlk37Q2TLZJCvopqBV3AHuSRtlCdUZnkMQFqFQnNIaSbXQumjTl3RmWFgzTxFs7YqVvQYdNHVawYg8LOixosKTDClbsYU2HdhBp7B8X9Mo79tczu4JhSWj8tnbt2uBpT3tajzGNUEBIO++1OE3PA0nBJo2h58yZE44ldeaXFtMkLXLRWEnjCI1J49B9a7yksaRvj7NnzwYjR44Mg1FJu7/SFudk7V6THZ73vOeFi4f1TBP3HJM0JtRnnvOc59QmuHUmrnbfJ5GU5jHvuEnXll7ZykI9da+7VYMlrNgDHTZ1QHUQDINSiFsR7k+yFSHuc1rdXmUwTBN0H/zgB3v9XhOM+qcBlM7I0UDszW9+c/CBD3wgnJi2ZjMAqI64ALfQw2ae1YVZk+ZCD3HvfOc7e6TEU2BBQYbPf/7ziZ+Thje96U09znHUg6oeKtOCYWrjoiseNVD80pe+lDhQ1AO2NA4ePLjH73U9PYgq2JD0wJsUANDqV/1Luz8FCKJBDK2glW2UeibtHuPKQYdtJ6HyfMMb3tAriKF7fuUrXxn80R/9UeIDsu7R16mJA4cmEpJQwMtNguh+NVmh+iC7+jq0elba3YO4Jh78e2xG+WjSQMG5pBXQVZeP0hK9+tWv7jXRU0/5+Nes8rywpF16WoyTdni9glb6u/6pnkq3HyhNI8nHFDyvZ9c6AABAJ5O0Kz7v2N+dW5o0ptY4741vfGO4ACYOjd00PtQ/vVcLbOLQmDXvWEBjJS180ngwaYe6v/hPzy3aya8FOBqbKhim8V2RMU9WKmrZ83Wve12P8Vme5xjxzGc+M3juc5/ba5yocWRSCvmkXW9R0p5N8u4KBACA6uHMMCiF6EBCg5OpU6fW9V3Tp0/P/H4raJL405/+dDg59ed//ueFPht3TxrI1oN2p0Un/azaDKAT0UNcUgBauzKigYd6V0Vqp0v0bCiR9YCrtLV+IMyfOE9Lf+YHahzaHaSVqHFowlwP3En3q2ulpW9NOtsqyy4KqMQFMEScvXy7xT2sandU0sSEUsrowTrJbpqISFvYkJY+Jk/QRG17NPVldIWxdvQpgBENhDWrfNL0VF0+2r2tXW9ll089Aa1GPpukX6u/VcZ5zhRReS9cuDDIS9Kkl+o8AAAA/AyNQ1yKwnrH/koBriBSHJpPSQuERUkb2+QNhEWvn7VbXIEwZSJwmSS0Y0yBsbiFffq+pJ3qSiWdtjtdabrjxpGycdpzjOajtNArjrTyyTvuSUtnHbfrHwAAbMDOMCiFaN5lDbjqXUUcN3BIG2g0E23/10p8TSZKgwZqcRNQWgX17ne/O9i6dWvwP//n/8z13XG5qusdNMnWGtD5W/pbZTOAbkQPfkmrA4vs+MxKxZYUqEjLfa+giIIRSZ9LSzuonPzR4EBaekSlcfHTpsSh70xCD89qx6J5utNWi6q/WbduXeLfkwJ3Im7lqmyitjyJn/u5n8s8wzLtHpPOadN9p+0MU9BEdlA6zjQ0KeECYVGdzSqftPOwqi4frRJuRvlUHQxTUE9n9sXtGPvsZz9bO69OE0v6X+1MI0ErTUIlTchl1RkAAIBuQ8/aGhPFpXLOM/bXotq0MfVTnvKU3IGwvGhMrx1fbmGVxmDq//UvzyIboeCTxhsaEz766KM9nhM0jlUGCT0XKROBH1DTWCJpx1XaOEnzHM961rNi/6brpaU00+eSgmVpcyXRc3iTSPuOaEpLAACwA8EwKIXoJHDeXRB5Vy4lTU6VjVIdKoXSi170onC3V3TFugaM2lJ/7733hueORFc9/eEf/mF4WO573/vezGvFTZw3ajc/GNYsm8XtFMmLP+kp2+lMtlaghw+3k0Er2PKmQug0DeiIT3fiHqrSVhrGpVbbv39/7N/0IJu3rmvHix4q5R+ujVAqEj1MatI86XuU1z/t+trNFYd2syV9TgGDBx98sEe5yIf9w6ijqN3Muledr5B0TXcv0WDG9773vdoDp7OHfyZD0rkG4sc//nHi9RTUierVg31S2hsFIPSQr/JOq6favZN0TbXNcTbS6uKkz2g3k87zcmXhP/z79VSaNKGghRVxZ8iVXT7Obzdv3hxqiltF3Ozy8X1FtpCerHssWj6q90lBSNUBaXLn/Qm/buj3SuGTNMkkH4zbWScdaeUg3EHPvs9qh7nGLhpPFGlLNXGVdD1NehXtr+ttSzu1b0GHTR3UU3s6LGiwpIM6alfHI488Eo5No+PSvGN/jbfSUqzrjNWs78hjD9UfLa7R2KeMxbIaY+p8W40b3HyDG5dqLKtFwgq26XnGX+irsWPS/aSNBWXLhx9+OPZv+oz/ueiYUIvIpDOK5kw0dk0rm6yzmIWCmXG6pUNntGns1+3+YkGDoC1FRzvoSDsuA8qFYBiUQvRMmUaCOnGfTds2XwYabGqiMWnVkT8h+eIXvzj8pwGwzkCJnqHyvve9L3jBC16QmSc8arOy7dYsm6Wt4i9K3hVoZaPJSXdtvU5LA9HJGtARPwDxB8p566geLpNWJupBMM/36D3ue3wdei0t2umT9D0avCVdX0G0pM/pATHpc5pQj5bLgQMHEt+vlClaBZl1r3oATfoODTzj7O7fn7OHnwIl7ZpptlHwIPrZtHvUqlb9TZ9Jq6dp9yg7xelNKwstdvBT3+p9/nvdwFn3qhXKsk/0u5pRPq5+uMmVuIfLZpeP7yt++dR7j3Hlo51vSe+XTaUhqQ3TZJA7kDmu31bZxunVrjilNk06py0OlYPsqYmoxYsXhztC87alCqKmpZ4s2l/X25Z2at+CDps6qKf2dFjQYEkHddSuDhcAio5L8479Dx06lNrvJo0Xi9hD451vf/vbmemsi6CxmXap+7vJ3bhUk8juteY69D7ZQvaJG9M1+hwT/ZzvL9qZljQmTBtzS6/Gu3l8LWnspM9rQVfW80I3+IsFDYK2FB3toAOqg2BYm6EBRSuixRqMFck13chqi7jPpm1/LwPtBCuKVthrtbvO4vFXZ6vx/NCHPhSmMSpKmXZrts0A4Gfo4S+JpMOso+ihOql9VxuclAJNvp52DpUeBpNIS28Xd5ZT2n2mpZ7zSVuVGrfzVCtuk1JQqt1LOqS73jQorbjHtM/o/vOkHdaKW70vafDeLO1aeKGVwdHUiZ1UPmn+lZVKJ25Fcp7z0sTTnva0cOyl1JdFxn4udZF2MqalqMyj0wXiAQAAIN8C0Txj/7QxeBkp9jQ2++pXv5o4hq4XjXu08ErjP81N+UR/1uSygmYKMCWNl/QckzYuq/c5Jm38WfT5J2l8njQm1biplbuPAAAgHYJhbcbKlSsz0+Y0A+12+uQnP5n4d61+0YDA4afrK0rcZzWhYxENlv7xH/8xLBd/dcnnP//58OywtAmkuK23ZdqtWTbLO7Gf56Gh3nPlGsVNGkdfd5sGdPRGgQR/1VheHXogjHvokZ/HnSUQh85Cct8hHf5rPQgmaVEQTe1P3PVlS7VTSQESfTbpYU3XjJaL2vmk9ysYksdemnRP+g6leIt+h0u/F2cbBQjTFmrooVwrU+Oup+vowTxqm7z3mFRP9dCviYci9xgtf79eaZePgjy+Tv99eq32XitnlW6nyvKRDWRjvd8vF0cV5eNfN8891lM+cWXjcL6Z1IYpZVDSZzVRk6ZXf9MZdUo1qbJVipe0YGDUHtpVtnz58swzvxQ8Syp33V+RxVCNtqWd2regw5YOtSPyD7UFfj11Y2mlPNcilG6xhyUdFjRY0kFbaleHG79Exz95x/5qb5LGB3nPX0+zh1IZKiAWdw19/5IlS8IzR7UISJr98dYXvvCFxHNENXbRtdwYRuMSXUMLgt09+dfUGEM69DwSd09pY8Gs55jo+Mwvi6xnp3rHZtFFRHHfM336dDP1tNU6LGgQtKXoaAcdUB0Ew6AUNAHlB8M0eVcvcQGhuBXnVtAk1dve9rbwHDF/0PfNb34zeMtb3pL4ubh7KtNuzbJZdMVXETTYdgExDUjyrlgvGz9lgjq6VqzcsqABHfH4OvLWUeWdj0tzqpWQeb9DvuUOyvbT4MkWz3zmMxO/R2cAJB2wrYn9pPSveoBMCtjrmq94xSt6pAmRPbQYI2lwqLRsWfeq85EU2EnawfO6170ufID00TmNs2fP7mEPVz9WrFiRek3tqEmyjSYAorbRNZROJmln7dq1a8N/afX0vvvuSzw4XZOrusfomWgKeum6/kpgtZXakaSAku4zWr/cTiEFwl772teG361gSZXlIzvpelphG538qKp8fF9R2ejA+TTqKR+d+ZU0EaOd5UoFGdeGaaWz9Cbd4wtf+MJg2bJlQR5e8pKX1OqFUispbebBgwdDn/TTMEbbDpVlVrkrKJDUFigttHbAV9WWdmrfgg5bOtS2qm+J4iZ/lQY9a9dnJ9nDkg4LGizpELSlNnVoB7ZbSOLryDv213gkade3jlvI8x1J9tB5V/o5bvyhha3vfve7Exe46rNq/5LaQD0fuLPAdH2NHXW2r2yhcZQ0RLNZ6FpamKMFxNExVtpYMO05RuOhb33rW73soX8as73mNa9JXByscV3SNTXuyTruQmgcFvcdsvtb3/rW2oKKbvcXCxoctKXosK6D9IzVgaWhFKLbyRWYqTedY9xW/iofSOvhTW96U+wAN424LfhJK7CyUMMdDaRZt1krUeemgbj+taqjs6ABHeURdwZg0R2ayjvvcCtN3QpH7fpJQjtPkkj7nA6VTkIPrNIeLZe0ILtWWWYNMr/xjW8k/l0BimggzNklao8895d1j3Gf1Xennd2o1a9p9VQBTa3ETUKBmugkgHug9gM8LhAmNOGQpEnfpUCMe+CuunxkA53XELcrrKry8etG1q6pesrHnXmRhNs1FdeG/eu//mtqyuIs+8ShCSWt5n72s58dTrb8p//0n8LdX46or+Q551MTenHoYVABzXbGSt+CDps6rGDFHhZ0WNBgSYcVrNjDkg7NdcSNf/KO/dPGmzoftRF7KMCUhAJSaZleHnzwwdSFrS4Q5q4/Z86ccDFX2hhM8x4aS23cuLHXOar+80+Uos8/bgykXWFJ5aB5qjStGldnoXvwj8nwUSBNOwOt1NNW67CgwRJW7IEOmzqgOgiGQSlMmjSpx88aHKZN0KahFc9FckVbIG5liVZsF7FZ0r3nQbaOTrhZtxlAJxEX4EhKhxqH2kwFROp5MKv3IbKez6UF2RXYSDtb6Wtf+1pqv5C08jNNZ9YDa9n3uG3btsQDlxU0UVoZ7fKKQwGrpB02vt38QJhI2umlOqfdPP7fKZ/yy0flkRbQSjrn4rvf/W6oJ20SLG4HnibANFmUd0GRdN92222Jf89KcagJnSSdbkIHAAAAyh37pwWktON+w4YNub5HO7mi45S0M7GiwSgfBdEefvjhwguVNJ5JWhjoj93iAmL1jiOb8fyjYF+ecY9SV/tZkaLnygMAgG0IhrUZSoHktn9X+S/tvDARt0U874qmPJ/TiiPLaLIpmg5Aq+XT6HabtRLVaU006l/aJGena0BHeSSt7kx7MIw+tPrv9dtffXfSQdr6ezODYdFy0TkqSeg9X/rSl8JzAaI7Xv/u7/4u+NGPfpT4WU3mz5s3r9fv9aCpYEO0T3IP32nnMua9xyhp96hgkoJGKiu/nuo6n/rUp1IXNLz0pS9N3LnldvZqkiAasNLqVa3SVR1RYEb2lF20MjcaKKu6fKRbev1ycVRVPn69kBbtxooGxBopn7RJI6EUPS5gJhurvv7DP/xDmB4xjaTD1ZWyUJ/XuEt1TemH0toR1RkF3hxRX0k7QN6lWY3WCcdTn/rUoN2x0regw6YOK1ixhwUdFjRY0mEFK/awpEM7j+LGP3nH/lnP6l/+8peDBx54IHYcovvXeOFzn/tc8Ld/+7e9dMRl2nEoyBbdma9r/PjHPw6++MUvpto1LTilMWHSWYv+QiaNW6TB3VeZzzHODlp0nHQf9V7PR7ZKus+FCxeaqqet1mFBgyWs2AMdNnVAdXBmGJTCokWLev1uz549wdOe9rTC36XP+WhQlZTT2VpAzN9ynzS5lGWzeoj7XNz3w8/Kxq2E1zk6rVj5bkEDOuwEw6I7cvQ5t6rzyU9+cuLnNPGetDLRHW6dRJ6HwWi5KFWevjMp2K/AvCbx9T61iXrg1S7ZtF0u+j4FIpI0akDq28OlcNQq1LQUM7p20mSAgkhJu2e1olNnH8RNPkiHAkpKJ6gAkYIn0pW2q89956pVqxL/rlXE0pu0y0gBC/1z75UO2eDNb35zj/dVXT7qe1zddeXiqKp8onVDQT2tbNY5E0qV02j5ZO2s0v3LpqoLqq+6r6gtiky4uAkqfY8mwfRPQTOlGFJQXGMi1QHtcFPqS92bX36+PXQN+W0aP/nJT2J/rzrSDmOvdulb0GFThxWs2MOCDgsaLOmwghV7WNKh/lf9frTPzzv2Vxpi9fFJaJyjRTH3339/GITSvep3WsSksYKuo7GAFkdpN5dvj7Txl4JWf/qnfxr289oJpbSB2l0Wd2573rGLdGkeRGOmuDNvosdDaBGRAmIaZyTNmWQ9x8RlM3BjII2n9b1x9aPRYJjORtN5rXE8/elPD+9f41sr9bTVOixosIQVe6DDpg6oDoJhUApr1qzp9TsN7t7ylrcU+h6t5o5OzGigmJSGwBLR1fwaGKehwZ8Ghv7kp+7d5R8vQtxAOi1tEgCUS9wZgCJ6ll8SzdjdpWBCUoo9t8MojrQzytQ2vehFLwr++q//OnHVlB7MtVo1D5rcVz+RlFKmWSn49HCd1K9Ik3bErF+/PnXArBQpec6FUKq5n//5n099j+ydZ/JEdtJqV01axNW5qssn7cyvVpaP6vbWrVuDPGSVj9IY6V/a2Vsau2gMED1bTP6XlLYxyceSVjrr+9PSXCalcE5apS00mZOUGvOZz3xmoWsBAAB0EwokNTL2nzZtWjjP4RY7JaHvS1swG5c+Wme87t69O/EzGlfEBXTSFlSljV20OMeNOxWIU8Ycd1atFgvFLSzSuEqBPo2T4p5XtIM+aU5E47ykcZmeY9JSajfybCGSzp7VPactfAMAADuQJhFKQRNT0XQ/3//+9wt/jwaD0YHNM57xjMA6mliMTmRmpSZyq4d8NJGWNSCOI2prTZIuWbKk8PcAQH0k7WJRu5CWqiTPg1nc+YLNDKKp/UibQFeaOgVcGkUP6u9617t6pZgta/VmI5997nOfW0obunbt2uBNb3pTYlDSf/jW7qS08yMUkNLEgAsSJT3oV1k+acGwTiqfopMb+r5XvOIVqd8bd49qK9KCbkVQmp6kM+oc9913X2IdSku5CQAA0O0kLYTLO/YXL3/5y8OgWCPELcDV+Cbv2WUO6XjOc56TuTgojuhiHS1ccinek+zkMhZoh13cwqFGzv1KWqzm0q/XO/7UZ5POcnvBC16QOZ4EAAAbEAyDUtAgLLo7TAOFomn/dE5GlBe+8IWBdZTCIIpWemURd2/K1V0ErfrSQbQ+z3/+8wvvLgOA+kkLWJ05cyb1s1pJmZbGLe3cpWaeF5aGJtlf+9rXpqZhSUKBNrVR73znO8PULmm0KhimxR2ve93rwsUYcec6ZaEUgf/u3/27cMdR3gfjN7zhDWGAJ26iwQXC/O9KW/VaVfm0KhhWdfloh1VSwDtuEkYBNqVp1C68ODRJEzd5lWbPIuWnBUqa0EqzjcZnOoc2iuzx4he/uGEdAAAAnUxakCdr7O/32W9961sb2lEUNx7UWOSVr3xl7vmApUuXBm9729tSF+Skjc3iAkwuIJY2XlUQTWOluIBYvc8xadl5NM5KypygMW/aojShnWxxqcWV8Wf58uWpnwUAADvYzz0HbcMb3/jG4MEHH6z9rIHGPffcE9x55525Pq8B0L333tvjdxo8aVLOMsqRrfNCouRZma/JvPe///09Bn+f+tSngv/6X/9r7sGrbBwd1GkiDgCqQ4EKPdDGnd+lB0Ttskl7EEw690vfqYl7a8EwF/DX4d9K7/rQQw+lPkBrUl4BQ31GCyeyzmFyK2vT0sI1M9jiggLqf9yZDjqHKim44d6vh2Ht8NIDcdEFCXoAf8973hMubvjWt74VBitUL/Q90UCYSJtcqKp8GlldW1b5zJ07N1xEkrX4ppHykR++4x3vCP75n/85rAdxEyGa8JEW2XD27NmpqShl67hAlT6vOqDPuhSGWeePOlRHli1bFua510rrNDRmUB1LCqRmpXkGAADodjQOS9qBlDX299HCpVe/+tXhbq6f/vSn4bk1aeNNNy7Rohv9S1q4pLGOgmJatJuUElnjkWc961lhMEzUkyJRJI3XNTZauXJl4ufcONIFxKTHjXfTrpd0PyJtDNPI2FOBtLgMPhpPJp2tCwAANiEYBqWh1UT/5b/8lx6Tun/yJ38S3HHHHbkmtnSQ66FDh3r87u1vf3vhLf5V89/+23/rlZNb95uVnshNXik9wpe+9KXa7w4ePBja4ld+5VcyP6+BoGwcTbf1spe9rNA9AEBj6MFNE9lx5xSpXUt7ENRD24c//OEev/MPXk7b3fHbv/3bdektK2CuB3DtQNE/PdDqIVPadb6BHg71gK9gntpEnRlQBLX9Whgg6jmI+td//deDMtCDuVbXahfR3r17gx//+MdhP6d70v1Li1YH6+dG+yvVI533qIDNjh07wtS5Wi0cnWzReVlJZ1FVWT4f+MAH6j5suMzyefaznx2ecaVy0DkSOgReEyuyZ1nlowChdttpR7cCVToo3gUxlRZZE07OFkLlVnSltzuvT/+0G03oOlphrv91XwpCKpilOqHy071p15rbBZgnNZMWLuncuSj6nrQUSQAAAPBvaIyh1IJxi42yxv5xzJw5M/ynBTcKSh0/fjzs993cigJgGmsoCKdnB40Z/PFx0ne++93vDr9P/b7GSBpDaOwi7dFjHTQvoX9F0WfcuFR2cWMYhxbpxC1a0mLq6LhVY6es8VrSc0yWPTQ/k2eOJops9tWvfjV2V5mCiWkZQgAAwB4Ew6A0NAhQWqX/83/+T+13Ojj1l3/5l8NgT1oqIk36RSd1NfH0a7/2a7mvHzdhnLQN3ue///f/Hvzqr/5q4iG4afzRH/1R8JGPfKTX7zWB6851yUKTiX4wTMgWz3ve88IzP9J2pMm2bkLO8Ru/8RuJq9QAoHksWrQoMRjWDWhSPi1lTLvjdmi5drlo8KcI6s90HU0s5O1LsuiW8mlWmTg0gRSXCifv+SBF0Zltaee2FUU7zeJ2hWmMppXpZdU3AACATkc7zjdv3lzq2F/jGc2rlBlgUdArz3nmzUJBOZG1i1/BOh21kScgViWPPPJIuGA5inb/RQN/AABgHw4VglL5vd/7vV4pvbSKRqvqk3Jna4W7zv3Q4Mfnd37ndxo+UDYP2s2mlEa/+Zu/GaaTyoMmvJXiUEG0KBq8vetd78p9/ac85SnBW97ylh6/0woz2eR73/te7Ge0wl82Vcqm6GS8duIBQPXI/+ImkpVWIykNIkBaQIzABJTN17/+9XCleRTtCMubIhUAAAD+LRjG2D9/QExZNLLQnFBaGu6q0WKnb3zjG71+r516WkTEOe0AAO0HsyxQKlpxpDOvNDCIBsQ0APqFX/iFMGWAAmbaLq9Jmfvuu6/X9+jw99/93d+tTLcCdf/jf/yP8J/SDCpnt4JamhhSrmutTNKgTKuZvvvd7wY/+tGPYnedaXXQV77ylcITmNph9v3vfz9MweVQOgOtNNIOMaVlUk5wadBqqc9+9rO9VqFrQKbfsyssHxYGrhY0CHSUg3az6swe+aiP0p3oDCD9rR3tgQ6bOqxgwR4WNLSDDi3kibZPYsmSJcHTn/70oFOxXi5Vgw6bWLGHBR0WNFjSYQUr9rCkQymm1Ydu3769lLF/vTraBc0HabFX9IgJn3nz5jW0K65se/zTP/1Tr/NbdQ+a70o7v9dKuVjQYUGDJazYAx02dUA19HkiTx45gIL8r//1v4L3v//9dX1WB7jef//9hbfy15smMe08niJosPt3f/d3YeqsetAgWueeaCVZURSs+8IXvhC86lWvCiyjVJQur7oeHpqVVgqgXvyzmOJy3mehFBr33HNPr99rEcBrXvOa0nRCe6HzHXW+Q1mLFRqtp9B9qO9VGuvohI7OCdNu9rIX0lBHwTKnTp0K7r777sS/K8tC2iQnQFXQltqHsX/xeiqbxQXEFAjTAmAr6IzVL3/5y71+rzkb/YP2gbYU2gGl+3fPakrNr/PDoTmwMwyawvve975wV9V73vOe8NywvLziFa8IPv3pT4e7sdoF7cjSfX70ox8Nd4Y0kmJNO850kOwDDzyQ+3PajfbXf/3XwTOf+cy6rw0A5aAHuBe84AXB9evXe/x+8ODBLdMErWX//v3hrl+dH7F69Wp270JLOH78ePBzP/dzsZN11EkAAID6YOxfn820IFm756wGwrSoWqkuo0Evzf3cfvvtLdMFAACNQzAMmsYb3vCG8AyKT3ziE8G9994bnDx5MnE7qgI5v/EbvxG8/OUvr1znQw89FB4m/53vfCdc/XPkyJHMz0jz8uXLw9Ve//7f//vSDrhV6oAf/OAHYXDrk5/8ZKgn7b3vfve7w8Dj8OHDS7k+ADROJ6cbg/oCYUKrvNyh4AQfoGo0yaR/AAAAUC6M/YujBb1CATGdJWYpECYUrHva057WahkAANAECIZBU1Gqw49//OPhrikFnTZt2hSuTtbKKW371MBHK2uKpkSMo96Mn7fddlv47zd/8zfDn6Vv586d4ZlmCuAplZ+2VI8aNSo860w73p785CeH+ps18HrLW94S/lMKgZ/85CfhhKoOk9UKM51ppslUBeOgPpTH/dq1a+FrTUq3Ij+wBQ3osIsVe6Cjfh379u0L//moP+mkgJiFcrGgAR12sWIPdNjUYQUr9rCgw4IGSzqsYMUe6LCpo5GAmOZYyppXsWIPdNjSYAkr9kCHTR1QHQTDoBLUmDzpSU8K/1lH52fon4UVXlohZW2VVCdw5cqVYNu2beFrnfGm3LzdqAEddrFiD3TUpyMuEBYNiCllos57bGcslIsFDeiwixV7oMOmDitYsYcFHRY0WNJhBSv2QIdNHY1Q5gJjK/ZAhy0NlrBiD3TY1AHVQbgTAAAAoAS0Q1lpEZMCYX5ALO7gcAAAAAAAAAAAaA4EwwAAAABKCIQpCKa0tnlWwc6fP78SXQAAAAAAnYIWlV29erXVMgAAoE0hGAYAAABQUSBs5MiR4Zlh/fuTqRoAAAAAoEgg7JFHHgn/ERADAIB6IBgGAAAA0GBqxLyBsJUrVxIIAwAAAAAogDt39/r16+EZPwTEAACgHgiGAQAAADQQCDtw4EDmewmEAQAAAAAU5/Lly2Eg7Nq1a7XfERADAIB6IBgGAAAA0MRA2KhRowiEAQAAAADUEQhT0MsPhEUDYo8//nhLtAEAQPtBMAwAAACgYCBMaRHzBsJWrFhBIAwAAAAAoAA3b97stSMsCgExAAAoAjMzANB19O3bNxg8eHDtdbdqQIddrNgDHfE6jh07Fpw5cyYYMGBAVwfCLJSLBQ3osIsVe6DDpg4rWLGHBR0WNFjSYQUr9kCHTR3NpF+/fsGcOXOCbdu2pb5PgbAtW7aEack1PqdcbOiwoMESVuyBDps6oDr6PKHlzQAAFaDB6YULF8LXQ4cODQ/BBbDE+vXrwxWI7uFr3bp1rZYEhtCQaffu3cGhQ4dyp0ZUPSob6ilYhzoKltH5Mrt27QonV2/dulWb/Fi8eHH4ev78+cGgQYNarBKAthTagyrq6dGjRzMDYkIT2qtXr65NbAMI2lJoB4YNGxamhRUjRowIzp8/32pJHUtnLlUGAAAAaFEgbPTo0eGOsGYEwgAAoDEU6Fq2bFlw+vTpHpNj+h0AANhj8uTJ4f95dogpZSIBMQAASIL9fwAAAAApEAgDAAAAAGhtQGzJkiWZ71NA7OGHHw7PEgMAAIjCzjAAaBlpaRK1RdihFDZZg9k+ffqEqRcdWumbdIiu/qbtx0qJM27cuNqk9Y0bN8LUOWno7B8/dc7169dTD/QVyls+cODA2s+6hv65LdDSHZ041/v984h0L271chJa/eZ/j2zm0v8koes428rmeQ4ell7Z2wUJ3H2kkVWerkzc9+vehwwZUvt7nrLRvfsrAPOUTbQ8dR19zn1fXB2Nlo106XNp6Br+uVFZZSN76J80yHb6X/bJymzsl43Ik4o0rWyi5SIdRXzNIV8rWp5+2egaZ8+eDcsmzl/SfC2rbLJ8TTbfu3dvcOTIkdA++j5dX9pcTnHp0vuUGnHu3Lmx9tD9+znI85RNkq+pXvu7Gdx3RcsmT9tZT9k4X5MGXVv3LxumBQCjvqZ7cL6WRJ52MFpH9a+Ir8W1nfX4mtJYpLXpze7XfJyNdD3dR9F+LU/ZZPlaXB0to1/LUza+r+n7T5w4Eb5OazuK9mtFfc2Vne7HtenN6teiZeOXZ1ybLuopm6L9ml+eru3QZ1ReaW1HM8Yc0Xqq7/c15PG1aNnUM4Z0ZZNULs3q1+Jw9+L3J1nnZpTZrzmitpDNqhhzRMtT19C4w+mIq6NpvlZW2eg7o2VRRb8WLZukOlpFv+aXjd5/7ty50C5pbXoz+jUfZw+9X2M/p6OKfs33tbS2o9F+rR5fUx1w7Xuz+rVJkyaFn3n00Ud7vc+N0933uh1i0tisfi1aNrKzNKSNCZvZrzn0/bofVxayeZYPlPEsHfW1uDpaT78WLZt6fM09ryU949fbr/llk1We0uzGPm48WFW/5pdnUttR1ZjDladrL9wYOW08GOdrjfRrPr49JkyYUNNRz7N0PWPIPL4G5UIwDABK48477wzuuuuuxL/7nY86o7Q0B2vXrq29VgeclRJBnbB2YzjUaW3fvj32verUlXdcnfCzn/3s2gBGk5ma9E5j7Nix4SG+jlOnTgWPPfZY5iq2adOm1X4+duxYuMNEGtzfo2dTzJw5M+yIHXq/Hv7S0BkXeiBz7NmzJ7Mjnj17drBv377wtc7K2LFjR+agXw8V/gAhq2z0Xn3Gt3/0M65MnD20u2bp0qW1v2uQpPM90tC9ywaOM2fOBAcPHkz9jGwsW/vXcbmZNbCJuzeVpUvVIVT+ulYaqjOqO479+/enDq7d4Fb1QuWiOqr7z3qIkQ+4AWyWj8X5mr7f/0y0XKQn6mvya9WbNIYPHx4sWrSo9rPqsqt3eXxN9XjDhg3hxFScvyT5mrSfPHky9TqzZs0Kxo8fX/tZdcbVAdlQE+mufDW4vXjxYjiwnjJlSs3W0qXXY8aMCXbu3Bl7HaXf8ge9eXzttttuqw3gdW1XNtLkn3Pjfq+HilWrVtU+L7tl1QHdi7/KVvenXXBpyD/nzZsXfr/uV2dB6v7TztiZOHFiMGPGjNrPx48fD9vCNKZPnx5OejgOHz7cy9eidVT3onLI62vC+Zgjj6/pPDj34Kd6snnz5tQ2XeW4Zs2aRF+LQ9+xfPnyQr4mTU677ktllOVrWhSivsDv12TrNFT/p06dmuhrcXU0zdeSWLBgQXjeqEN9dFa/5vua3vvDH/4wLKO0tiPJ15Io6muqp0rJJ7u5+lbE14r0a2m+Ftem5/W1KAr81+trru3Q2Eb9cFrb4fuayCqbIr7m6qnKU2VTxNd0lsPChQsL9WtJvpZULnl8LY6orx04cKB2Zm+ar2m8ltSH5enXNO7OmpjK8rWoLVRfGu3XdO8aExfxNWl46KGHajri6mjU1+Rn8rc0or6mMaQLuiXVUY1v/Yk21bOsyfNov6ZyzZrQlJ3dZHN0DBlXR6O+pom8ov2a2oysOuf7msp/06ZNoe+ktemq//IDh3xGi5rSkJ/57YDer+vE4ewhn37Sk55Us3UeX9O96J4cqptZk9qymX+vztfS2g6/bPI8S6udVXtbxNd03+6sRSE9agvTPqd+Q/2HQ31jVuYF+Yx8x6F7l95ombpxulBZ6j0uIKa2Ns3XhNoAtQVFfE1tjR9EVH1WvU4bE6b5WhwaR6ntLOJrapvV3zj/0vNR1pyFxlvqCxwqS/XtaWT5WlwdLeJrDt2L/M1Rj69pXCNfSHrGT/O1NORrrl/L8jXZQ+266rM/Jirqa3n6tTRfS2o7or6mPi2rX1M/qP4wb7/m+5obD6oO6Nk/bTwY52uN9Gs+zh4qxxe+8IW1Nj2vr/lptvP0a0m+llXXoDwIhgFAaWjCIc/KJgAA62gwqsF/1mBeaMCsCd2sVfQAAAAAANAYmkzWxLUm0dMmkDXJrYCYv2AUAAC6G4JhAFAaWn3lr9aI4gfKNHj1V7ekodUcWe/101UJraBM+oyfAiY6qM66jr89X2iFkr/iKA5/RbNbceOvpNVuJn/1pvC3pwut0PFXUMURPSRYK4Gydp5E/+6vck7Cn/DX67zl6HC7nXz81eSyR9Sm0dVQcUS31Ws1VNSuWeWp9zvb6/virhktG62081f5xhFd5aQVdGllI3tEV+LJLlmrhfy6VsTH/M/7n4mWi+wT9Rv5fNZ1okEiPZAW9TVdW3U8zl98/T5a6eavjo8jWp5a3eZSNigQ5n/eT6/h1zOtCo7aJes6eXzN/06/PkqXn4LO/T6qQfYqWjbaxVfU11QHtGIzzd+i5akVhP6ukjw200rSqK9ltR1ZvhbXdubxNf9+ZHd/V2paHU3ytTjq8bXoTrB6fE39mr8bqx5fi6ujSb5WpGy0WzSrbKLXcXUmrVySfC2Jor6mehrdBVaPr+Xp16Jl4/taXJue19ca7dfiylPatDq4SNvRaL/m4+ppPb4WLZtGfC2pXJz+Rvs17YDP42v+rkuVp78bKc91/B3gSWT5WtQW8pNG+zX1C0XLUyvWXRaApLYjbnzvZwGII24M6WcbiKuj0fqp/raor2kFelbb6dsgOoZMq6P+vRVtO+sZ36teymZpbXrU1+QzWcGQaHnqucvP0uHj7BG9Tl5fiz6vFRnf+76Wp1yczrL7tThfU78gm6V9Llqe8pmonxfxNe2y0S4M2dCN050WPyCmHUvRnSVZ/Vq9vuZ2pyWVS5qv5SGvr/n1qqp+LeprcXW0iK854rLoFPU1jYXcc1zSfSX5Wl7fyfI12aMMX6unX/N9LantiBtDlt2vxZWnxkNZ48Gy+zWfpMwGeXwtWjb19GvO17LmE6A8CIYBQGl88IMfDP8loQd+fzt71oSO38Hkfa/DnbWURNwWbA1eogO6LDRgiA4astC1pc1p0Ous+4t2/nnImsSI6/iL2lkddtHPJJWnb4+o9qrKRtdwg5CsOuTrTtvSX2/ZRAdWaQ9vSZRRNln1NK+dGi1P95k8/tJI2Thf0zWU9kLp7/wHYV+3Hg6UFiEtv3kSjfiaBuZ+oCHpu+ppO+spG6ehyLV0D9GHpyyS2kG/jka15/G1KPX6WpE2vRn9WlnlWU/ZRH0tTx2tp1+rp2yKlEvZ/ZpP1KZV9WvR8sxjj2aNOeK0FWk7yi4bv5622tfy1tNG+rUiuPNMilDWGNK3RdTnqyob+VnRtqPMfi2tjlbRr8WVTZY9qurX9Jmi444y+rW4v7eqX/PvO089raftrKdsVAeKfq7Rfs21E1u3bg1/dr4eHZtrLK+gmVIm5rV5vb6mCfq87UezxhzR5/x6yrOMMYfIskVV/Zq7lyK2aMbcSNQ+VfZrfnnmqaNVlU3R8WBZ/ZpP3H1W1a/V42vQGOTzAQAAAIhBwS7ljo9Lf6jVhfpbPYEwAAAAAABoHO1e0VnTWbsqdP6PUiZyrAMAQHdDMAwAAAAgAbf7y3/AdukPOCMMAAAAAKC1KM2eUpATEAMAgCyYxQEAAABIQXnMtQtMD9jKta5c6ATCAAAAAABsoPOqdP4UATEAAEiDmRwAAACAnAGxqVOncrgtAAAAAIAxRowYkWus7gJi0fOzAQCg8yl24h4AQAegM35GjhxZe92tGtBhFyv2QEfvc8IuXrzYch1WsFAuFjSgwy5W7IEOWzpOnToV3H333b1+/+1vfzv8/4477ggXQHSLPSzpsKDBkg4rWLEHOmzqsIKzh/4NHTo02LVrV/DEE0+kBsQeffTR4ElPelKpC92slIsFHRY0WMKKPdBhUwdUR58n0noHAIASUQdz4cKF8LUGqKzEAmusX78+uHnzZm0gtG7dulZLAugF9RSsQx0FyyQFwxxVB8MAkqAthXbAaj09efJksGXLlsSA2IABA4JVq1aFKdChs7FaRwF8dC65S9+qXa7nz59vtaSOhTSJAAAA0FXoodgF5gEAAAAAoPPOEHNn/kYhEAYA0L0QDAMAAICuCoRt27YteOihh8LdAQAAAAAA0Lln/voBMQJhAADdDWeGAUDXcf369eDcuXPh61GjRoUD4m7UgA67WLFHp+lQIGzr1q3B8ePHw583b94cPiDnTYdlxR5WsGAPCxrQYRcr9kCHTR1WsGIPCzosaLCkwwpW7IEOmzqskGQPFxBTysS+ffs2PRBmpVws6LCgwRJW7IEOmzqgOtgZBgBdhw7L3b9/f/hPr7tVAzrsYsUenaQjGghzv1NA7PTp05Xp6CQs2MOCBnTYxYo90GFThxWs2MOCDgsaLOmwghV7oMOmDiuk2cMFxFavXt30HWFWysWCDgsaLGHFHuiwqQOqg2AYAAAAdDRxgbB6A2IAAAAAANBejB07ltSIAABAMAwAAAA6FwW7Hn300dhAmOPWrVthQOzChQuVagMAAAAAAAAAgGogGAYAAAAdiYJcCoSdOHEi873jx49ntSgAAAAAAPQ4TwgAADoHgmEAAADQkYEwpUbMEwibOHFisGTJkqBPnz6VaAMAAAAAANs89thjwY9+9COyRwAAdBAEwwAAAKBrd4QRCAMAAAAAgGggbOfOncGNGzeCDRs2EBADAOgQCIYBAABAxwXCTp48mfneSZMmEQgDAAAAAIBegTCHC4idP3++pboAAKBxCIYBAABAVwbCFi9eTCAMAAAAAABCDh8+3CMQ5gfENm7cSEAMAKDN6d9qAQAAVdO/f/9g/PjxtdfdqgEddrFij3bSoUDYli1bglOnTjUtEGbFHlawYA8LGtBhFyv2QIdNHVawYg8LOixosKTDClbsgQ6bOqxQhj2OHDkS7NixI/HvbofYqlWrgpEjRzZNRxlY0GFBgyWs2AMdNnVAdfR54oknnqjwegDQxWjA6HJtDx06NLh06VKrJQH0YP369cHNmzfD1/369QvWrVvXakmQgyKBsMmTJweLFi1q6x1h1FOwDnUULKO+4u677078+x133BGMGzeuUk0AcdCWQjvQSfX07NmzwaZNm2r3k4TuMy0gBrbopDoKncuwYcOCy5cvh69HjBjBLtQmQppEAAAAaFu6LRAGAAAAAADlM3r06GDlypVhwCQNBVa0Q+zcuXOVaQMAgHIgGAYAAABtGwjbvHlzrkDYlClTCIQBAAAAAEAio0aNyh0Q0xliBMQAANoLkmECQNdx7dq14MSJE+HrCRMmBAMHDuxKDeiwixV7WNbhAmGnT5/OFQhbuHBhw4EwK/awggV7WNCADrtYsQc6bOqwghV7WNBhQYMlHVawYg902NRhhTLt4QJiCnalpUx0ATG9V58pW0cjWNBhQYMlrNgDHTZ1QHUQDAOAruP69evB0aNHa6kQWtHZWdCADrtYsYdVHTrYtupAWJwO6mnr7WFBAzrsYsUe6LCpwwpW7GFBhwUNlnRYwYo90GFThxXKtoeCWzoXTOkQ8wTEVqxYEV7XSrlY0GFBgyWs2AMdNnVAdZAmEQAAANoGPXDqYOs8gbCpU6eWFggDAAAAAIDuYeTIkWFALE/KRD2fnD17tjJtAABQHwTDAAAAoC1QasRt27YFZ86cyRUIW7BgAYEwAAAAAABoKCCmzBR5AmKcIQYAYBuCYQAAANAWnDx5MteKy2nTphEIAwAAAACAUgJiOhcsT0Ds0UcfDS5fvlyZNgAAKAbBMAAAAGgLxo0bFwwbNiwzEDZ//nwCYQAAAAAAUOkOMWWyeOyxxwiIAQAYhWAYAAAAtAXK179s2bLEgBiBMAAAAAAAaAYjRowoFBDjDDEAAHsQDAMAAIC2YcCAAeFDaDQgNn36dAJhAAAAAABgIiC2devWXGcdAwBAdaS33gAATeTSpUuJf/MnujWQvHLlSup3aQJ86NChPfJ1P/7444nXvXr1aq9J8xs3boS/T0OD3kGDBtV+vn79enDt2rXMyfuBAwfWftY1nAanJ4rer885dC+6pzQGDx4c7pxxyGayXRrRv6eViUN2drZ74okncqWAyCrPqD369u0bDBkypFDZ6N5lgyJlEy1PXUefc9+Xp2ykS59LQ9fwH5iyykbX1d9lB4fsLHvnLRv3PY2UTVw9LeJrjnrKM65sdC3pkGYFvrZs2RLaZerUqcHkyZPDsov6WlbZFPE1Zw///Xl9Tffvl2cjvqZ67TT69TRaNnnazjJ8zZVLkfLUPThfa6RsonW0qK/FtZ31+lpam97sfs0R/Xs9vpanbOL6Nd/X4upoGf1anrKJ+lpWudTTrxX1NV1bNvFt1qx+LVo2fnkm1dN6yqYMX5O2rLajGWOOLN/TZ7J0RcumEV9Laz+yfC2Oen2tiI3K7tf87/BtUdWYI87XstqONF8rq2z0nbKBb+cq+rVo2STV0Sr6tbgxh8o0rb41o1/zcfaIBkOq6tfcvae1HY32a/X4mupA1piw7H7Nx7dH9O9l92u6d51PvH379h7v0XX1z9Ud6XnwwQeDxYsXB2PGjGlavxYlOs+RZ6xexrN0tGzi6mg9/Vq0bOrxNemSvZOe8evt1/yyySrPuPFglf2aK8+ktqOqMUe0PPOMB6Nl02i/5uPbw6eeZ+l6+rU8vgblQjAMAErjzjvvDO66667Ev/udjzqjbdu2Jb537dq1tdfqgNPe6zrhFStW1H5Wp6XBaRz6PnVQ6rT8jvv8+fPB3r17U68zduzYYM6cObWfT506FaZASEOT9Erf5jh27Fhw5MiRWiepa0YfpmbOnBlMmDCh9vOhQ4eCc+fOpV5HwYFRo0bVft6zZ09mR6zPKJAgZAsFF7IesFevXl0bWOq9WWWj9+ozDg00op9xZeLsoRV3S5cu7TFA2bVrV+p1dO+6H4dW4R08eDD1M7KxbO1fR/XADWzi7k1lqTJ1qPyzVvypzqjuOPbv35864JM9xo8fHw7gXB3V/Wc9yMsH3AA2y8fifE3f738mWi6qp1Ffk1/v2LEj9RrDhw8PFi1aVPtZdXnfvn25fU02kC1OnDjRw1/0ew1wpVH+HvW1o0ePBidPnky9zqxZs0JbO1RnXB2I4uyheuO3HXl8Tekd/UGvbJbla7fddlttAK+BtSsb2cF91q+nsotWqjqkKasOyK5Lliyp/Xzx4sVg9+7dqZ8ZPXp0MG/evNAGaj9Onz4d2475TJw4MZgxY0bt5+PHj4dtYRra7Tdp0qTaz4cPH+7la9E6Kk1usiGPrwlNUPgPsnl8TYeouzogX9O109p0leOaNWsSfS3pQXH58uWFfE11zG/T8/iazuKbPXt2j35Ntk5jypQptevE+VpcHS3iaw5NNOmMDodv5zy+Jhuoz3FllFRHk3wtiaK+pnoqDbKZqzdFfK1Iv5bma3Ftel5fizJ37ty6fc21HTt37sxsO3xfE1llk8fXZHu9z59E0c+uDmgMeeDAgdTraJyycOHC2s+N+FpSueTxtTiivqZ7uXDhQqav+e2Hxp1Zk1nRfk39cNbEVJavRW2h72+0X9O9q58u4muqI2ltepyvyc/kb2lEfS0rjZraUo1v/Yk21bOsyfNovyZfy5rQ9He6RMeQcXU06muayCvar6nNkLa8vqa2QD/rOSqt7VD9lx845DP6TBqq+/I3h94vH43D2UPv99unPL6me9E9OFQ3sya1ZTM/4OB8La3t8Msmz7O07kPtbRFfUx1TXXPoPtQWpn1O/Yb6D4fGkGpv0pDPyHeyfM23h+qW/NqRJ2Wh2gD/M3l9TWMz51+qM2rbXPupa8pXVAdVx2SzNF+LQ+MotZ1FfE1tp9p116brM+qn09B4S59xqCyzPpPla3F1tIivOdR3qg9txNc0rpGepGf8NF9LQ77m+rUsX3MLb3U/ru2ox9fy9GtpvpbUdkR9TX6W1a+pH1R/WI+vufGgNGSNB9Wv+wH7Rvs1H2cPlaPfpuf1NY2HivRrSb6WVdegPAiGAUBpqGNvh4Ni1SHqwVIT9P7Kl1ZoaDW6fws6rNjDCrKHBqOqo63WYaWe6mErumJK+vwgY1X2UNm0qu2whGygh1kFvLMCFJ1eR63o0AOcP8HQ7aiO6mE7K/BcRf3Qg2+ry8ZKPXVthyb6swLPzUB968te9rJwskJ1Q3aRnnXr1tUmmaStW8vFn9CtGkttqQUdVrBUR7UgqdXPm84emgBu5XjQSrlYwbdHNItDM/sTLfrcsGFDrT/ThLofUBHqa7T4wQ80VNmmZy0+ahbU0d720OIGxoM966iCZ61s1509FCjlGb876PMEoUcAaMHOMK3qSFtlUkXajTJSt9WbJrFZKW6KpklsRoqbMsqzVWkS77vvvh5pEm+//faWpEksM3VbFu3ia2WnuGlnX3vggQd6pKBz9bRVaRLLSKMSR6tSt7Wzr5WZuq0RX4uro61Kk9iMfs2yr1WRuq1Vvlb2mMPVU32/tLlgWDv5Whyd7GutSCfVSl9THXVpEqVRdbQdfa3Mfq1ZY45WpW7rBF9bv359+H7VAV0r7vnJuq/V26+5stFuUgXE0spTO3W0u0cBNHytWl9THdVnXJrEpDra7DSJ7davVZUmsZ5naSv9WplpErXAwmnUrsZWBbG7AYJhAFAZWhXttrOrY8ozuACoEvcwJ9ykA1SD7K7BZDTHPfSGegrWoY5CO0A9BetQR6EdoJ7+Gy4gFhcMUiYLpeDzJ/OhOqij0A4oEEcwrBpIkwgAXYdWuLjzUJSjOJrGoFs0oMMuVuxRlQ6tONu4cWM4AFR+92hArNvs0S5YsIcFDeiwixV7oMOmDitYsYcFHRY0WNJhBSv2QIdNHVZotT1cysSf/vSn4flMbkJbqT1bEQhrtT2saLCEFXugw6YOqA6WJQBA16GJdw1Q9S9r23cna0CHXazYowodLhCmlU86QFmHUEc3rXeTPdoJC/awoAEddrFiD3TY1GEFK/awoMOCBks6rGDFHuiwqcMKFuyhhX1LliwJU54pvZoy47RqR5gFe1jQYAkr9kCHTR1QHQTDAAAAoCX4gTBHUkAMAAAAAADAMgqIzZgxIwyE6Yww/4w4AABoPQTDAAAAoCWBMOXVj8uFrYDYzp07CYgBAAAAAEBboTRrU6ZMIRAGAGAQzgwDAACAlgTCLly4kPgel7d7wYIFFSoDAAAAAAAAAIBOhJ1hAAAAUBnXr1/PDIQ5Bg4cGPTp06cSXQAAAAAAAK2CrBgAAM2HYBgAAACYC4TNnj07/AcAAAAAANDJ3Lx5M9i0aVNw4sSJVksBAOhoCIYBAABAZYGwixcvZr53zpw5BMIAAAAAAKArAmGbN28OTp8+HTz66KPB8ePHWy0JAKBj4cwwAAAAMBUImzVrViW6AACg+7h69Wqwffv24OjRo8GtW7fC3/Xt2zfYuHFj+HrRokXBoEGDWqwSAAC6AfVDW7ZsCc6cOVNLlbh169bw9cSJE1usDgCg8yAYBgBdhyY43K6TVk12WNCADrtYsUcZOooEwubOnRvMnDmzKTrKwIoOK1iwhwUN6LCLFXugw5YO9Udf/OIXe/1eq/HFHXfcUamuVtvDkg4LGizpsIIVe6DDpg4rWLFHER0KhLkdYT5lBMQs2MOCBktYsQc6bOqA6iAYBgBdR//+/YNx48Z1vQZ02MWKPRrVoUDYI488Ely6dKnuQFgZOsrCig4rWLCHBQ3osIsVe6DDpg4rWLGHBR0WNFjSYQUr9kCHTR1WsGKPvDqSAmF+QEyLNPT/pEmTmqajmVjQYAkr9kCHTR1QHQTDAAAAoHSuXbsW7ghrNBAGAAAAAADQac9KeZ6T3A6xegJiAADQm74xvwMAAACoJBA2b948AmEAAAAAANA1DB48OFi9enX4f56A2LFjxyrRBQDQ6bAzDAC6jitXrgQHDhwIX2sSfsiQIV2pAR12sWKPenQUDYTNmDGjKTqagRUdVrBgDwsa0GEXK/ZAh00dVrBiDws6LGiwpMMKVuyBDps6rGDFHkV06G8KiCmt/OOPP54ZEFPKxMmTJ5euo1lY0GAJK/ZAh00dUB0EwwCg61B+bh2e7l53qwZ02MWKPYrqUCBMD3OXL1/OfO/8+fOD6dOnN0VHs7CiwwoW7GFBAzrsYsUe6LCpwwpW7GFBhwUNlnRYwYo90GFThxWs2KOoDrdDLE9AbNu2beH/eQJiFuxhQYMlrNgDHTZ1QHWQJhEAAAAaplmBMAAAAAAAgE6lSMpEBcSOHj1aiS4AgE6EYBgAAAA0BIEwAAAAAACAagJiR44cqUQXAECnQTAMAAAA6ubq1au5A2ELFiwgEAYAAAAAANBAQGz79u0ExAAA6oBgGAAAANTFjRs3gg0bNuQOhE2bNq0SXQAAAAAAAO2GAmG33XZbMGTIkMz3EhADACgOwTAAAACoi379+gXjxo3LfB+BMAAAAAAAgGwGDRoU7hDLGxA7fPhwJboAADoBgmEAAABQF3369Anmzp2bmvpw4cKFBMIAAAAAAACaEBDbsWMHATEAgJwQDAMAAICGAmLz5s2LDYgpEDZ16tSW6AIAAAAAAGhXCIgBAJRP/yZ8JwCA+TzcmqR3r7tVAzrsYsUeeXW4gJg4dOhQ+P+iRYuCKVOmVKqj2VjRYQUL9rCgAR12sWIPdNjUYQUr9rCgw4IGSzqsYMUe6LCpwwpW7FG2DhcQy3NOswJiTzzxRJiVw4I9LGiwhBV7oMOmDqgOgmEA0JXnHI0YMaLrNaDDLlbsUUSHHxAbNmxYaYGwojqaiRUdVrBgDwsa0GEXK/ZAh00dVrBiDws6LGiwpMMKVuyBDps6rGDFHs3QoYDYqlWrcgXEdu7cGf6vgFir7WGlTKxgxR7osKkDqoM0iQAAAFAKCojNnz+/1EAYAAAAAABAN+N2iA0dOjTzvQqIuWwdAADQE4JhAAAAAAAAAAAAAEYZOHBg7oBY375M9wIAxEGaRADoOpRaYPfu3eFrpXXLM5jsRA3osIsVe6DDpg4rWLCHBQ3osIsVe6DDpg4rWLGHBR0WNFjSYQUr9kCHTR1WsGKPZutwAbFHHnkkMWWizj8aPXp0sGnTpqbpaKcysYIVe6DDpg6oDoJhANB16FDZa9eu1V53qwZ02MWCPR5//PFg8+bN4fX79+9P/TCkwwoW7GFBAzrsYsUe6LCpwwpW7GFBhwUNlnRYwYo90GFThxWs2KMKHS4gpjPELl261ONvCxYsCKZOnRr+vtX2sFImVrBiD3TY1AHVwb5ZAAAA6MGVK1eChx9+ODh79mxw8ODB4MaNG62WBAAAAAAAAP9/QGzVqlXBsGHDegTCpk2b1lJdAADWYWcYALSM6ComH39Qd+vWrXByPo0+ffr02M588+bNcGdL0nWvXr0afsZHE/76fRraIaPDax3Xr1+vrSJJYsCAAeFg1aFrOA1OTxS9X59z6F50T2kMHjw46NevX+1n2Uy2SyP697QyccjOznZaOZOUnqFIeUbtoRznQ4YMKVQ2unfZoEjZRMtT19Hn3PflKRvpygoW6Rq6Vt6y0XX1dz/Xu+yctVLJLxv3PUXL5vTp0+GOMNlOttB19+7dW3uwKuJrjnrKM65sdK20e4rztayyKeJrrp7678/ra7p/vzwb8TWVjdPo19No2eRpO8vwtaxyiStPV78aLZto21HU1+Laznp9La1Nb3a/5oj+vR5fy1M2Wb4WV0fL6NfylE3U17LKpZ5+raivudXZvs2a1a9Fy8Yvz6R6Wk/ZlOFr0pbVdjRjzJHle/pMlq5o2TTia2ntR7P7NYd/L3lsVHa/5n+Hb4uqxhxxvpbVdqT5Wlllo++UDXw7V9GvRcsmqY5W0a/FjTlUpmn1rRn9mo+zh98GVtmvuXtPazsa7dfq8TXVgawxYdn9mo9vj+jfm9WvRctGds4aE5bdr82fPz/YsmVLMHny5DA1oms7ffKM1ct4lo6WTZwt6unXomVTj69Jl+yd9Ixfb7/ml01WecaNB6vs11x5JtXRqsYc0fLMMx6M87VG+jUf3x4+9TxL19Ov5fE1KBeCYQBQGnfeeWdw1113Jf7d73zUGW3bti3xvWvXrq29Vgec9l7XCa9YsaL2szqt7du3x75XHd3Ro0d7DNzE+fPnw0n/NMaOHRvMmTOn9vOpU6eCxx57LPUzGpj6K7SOHTsWHDp0KNTgiGqZOXNmMGHChNrPev+5c+dSr6OB8KhRo2o/79mzJ7Mjnj17do+fd+zYkTnoV0oGN7DUe7PKRu/VZ3z7Rz/jysShgfzSpUt7DFB27dqVeh3du2zgOHPmTLirKQ3ZWLb2r6N64AY2cfemslSZOlT+ulYaqjOqO479+/enDvjcYMyvF7r/rAd5+YAbwGb5WJyv6d7vu+++2nXcg5P+fe973wvvQ4NK39fk16o3aQwfPjxYtGhR7WfV5X379hXyNWnQTrU4f0nyNdWpkydPpl5n1qxZwfjx42s/q864OhDF1dOJEyf2+H0eX1u2bFmPQW8eX7vttttqA3iVhSvPEydO1D7r11M9VGiFqEOasuqAynPJkiW1ny9evFjLmZ6E/FP51P06oPqZVC5CNpsxY0bt5+PHj4dtYRrTp08PJk2aVPv58OHDvXwt2nboXsaMGZPb18TixYt7PMjm8bWVK1fWHvzka/pMWpuuclyzZk3tZ31/VtnoO5YvX17I16KB2jy+Nm7cuB59gfo12TqNKVOmhCl4knwtro4W8TV/hfPIkSNrP6uPLuprqmfuYTmpjib5WhJFfU31VAsNZLdGfC1Pv5bma1F/cfbI42tR5s6d27CvSZvKJq3t8H1NZJVNHl+T7dPQGPLAgQOp7xkxYkR4JksZvpZULnl8LY6or+leLly4kOlr/qShyjOLqK9p3J01MZXla1FbqL402q/p3tVPF/E1jTfS2vQ4X5OfqU6nEfU1jSHd2CYOtaUa3/oTbapnWZPnUV/buXNn5oSm7OwmJ6NjyLg6GvU1TeQV7dfUZkhbEV+Tf8p33PfFofovP3DIZ44cOZJ6HfmZ30br/e46UZw95NM+eXxN96J7cqhuZk1qy2b+vTpfS2s7/LLJ8yytdlbtbRFfUx1TXXNIj9rCtM+p31D/4VDfqOfcNOQz8p0sX/PtofKUX+f1NaE2wP9MHl9TW+MHEVWfVa/T2o80X4tD4yi1nWm+prZYtnfzBfrZ7290H1lzFhpvqS9w6Luy+oIsX4uro0V8zaF78f2tHl/TuEa+kPSMn+ZracjXXL+W5Wuyh9p1vz7X42t5+rU0X0tqO6K+Jj/L6tfUD/rPxvX4mso/azwY52uN9Gs+zh7RIHKefk2+pvFQkX4tyddI0VgdBMMAoDTcLhIAaD80uaBDlpOCAPq9JmH9ySIAAAAAAABoHdFJfAAASIZgGACUhlaU+as1oviBMg3Y/NUtaWg1R9Z7owNAraBM+oyfUiy6QiPrOtF0GFqh5K84yrNSXytu/JW02s3kr94U/vZ0oRU6/gqqPClutBKoaJpEf+VlEv72dL3OW44OrfiJfsZfTS57RG0ay1IJrwAAwrVJREFUXQ0Vh7+i2a2Gito1qzz1fmd7fV/cNaNlo51I/irfOKKrnLSCLitNYnQlnuyStVrIr2tFfEy+qQOYtbrKX03u0km4nVq6d3/HopDPZ13HrzNCq5yL+prKRnU8zl+SfE07xfz7iSNanlrdlpYmMe46eXwtep08vua3UX591Go7PwWd+320TZO9ipaNdvEV9TXVAa3YTPO3aHlqBaG/qySPzbSSNOprWW1Hlq/FtZ15fM2/H9nd35WaVkcdqkNF+7U8vqbVpf7ulHp8Tf2avxurHl+Lq6NFfC2pbLRbNKtsotdxdSatXJJ8LYmivqZ6Gt0FVo+v5enXomXj+1rUX9x35fG1Rvu1uPKUNi2wKNJ2FB1zxPmaVofff//9iZ9JG0MmlU0jvpZULk5/o/2adsDn8TV/16XK09+NlOc6/g7wJLJ8LWoL+Umj/Zr6haLlqRXrLgtAUtsRN773swDEETeG9LMNRFFbGm1v1N8W9TWtQM9qO30bRMeQaXXUv7eibWc943vVS9ksrU2P+pp8xs+eEUe0PPXcFR3zRu0RvU5eX4uOIYuM731fy1MuTmfZ/Vqcr6lfkM3SPhctT/lM1M/r9TXfHv6ukzy+5vSX4Wv+7uO4cknztTzk9TW/XuUZQ5bRr0V9La6OFvG1tCw6RX1NYyGXJjHpvpJ8La/vZPlaXArLqvo139eS2o64MWTZ/VpceWo8lDUeLLtf80nKbJDH16JlU0+/5nyNoHZ1EAwDgNL44Ac/GP5LQg/8/nb2rAkdv4PJ+16/g0n7TNwWbA1eogO6LDRgiA4astC1pc1p0Ous+4t2/nnImsSI6/iL2lkddtHPJJWnb4+o9qrKRtdwg5CsOuTrTtvSX2/ZRAdWaYHmJPLoVyDskUceqZ1LER3QOrvr90p3GX1YyWunRsvTfSaPvzRSNlm+Fvd9ecozSiO+prLwAw1J31VP21lP2TgNRa6le4jWtXrLxm87otrrKZt6fa1Im96Mfq2s8qynbKK+lqeO1tOv1VM2Rcql7H7NJ65traJfi5ZnHns0a8wRp61I21FW2eQ5m6NqX8tbT5vRryWVZ1EblDWG9G0R9fmq2kH5WdG2o8x+zf/O6IRvFf1aXNlk2aOqfk2fKTruKKNfi/t7q/o1/77z1NN62s56ykZ1oOjnyujXfJw9ot9ZVb+m8vTTvWXV02aNOaLP+XHloqCdFof46V6bUTZZtqiqX3P3UqSONmNuJGqfKvs1vzzz1NGqyqboeLCsfs0n7j6r6tfq8TVoDIJhAAAAXYofCMsaPGonU9aqPQAAAAAAALCLAmF6BlRWEAXco+eIAwB0Mn2e4IQ2AKgIf2eYVnJkHbTeLJTuwJ2LpBUY0R043aIBHb1Zv359j90M69at61h75AmEaYggLdq2rxQW3V4/rOjopnraDhrQ0RvqKDos69Ak4De/+c3g2LFjtRQ7WvThUkM+//nPz0zb1Un2sKTDggZLOmhL0dEOOqin7aVDfaDS4/vnRCsY1oyAmBVbUEfR0Q46tKPMHS2jVJjnz59viY5ugJ1hANB1qHMrut27EzWgwy7NtkfeHWHSsWLFiszzSbqlfljRYQUL9rCgAR12sWIPdNjSoUDXq171KjOTY622hyUdFjRY0mEFK/ZAh00dVrBiD8s64gJhwp03W3ZAzIotrGDFHuiwqQOqg2AYAABAF6EdmXoIypMacdmyZS0PhAEAAAAAAEDjz4DRQJgfENNuaQXE3BnaAACdCMEwAACALnoI0o6wpIcgB4EwAAAAAACAzmDPnj2Zz4D79+8P/ycgBgCdDMEwAOjKgMC2bdvC1zoLSbl5u1EDOuzSDHsUCYQtX748GDdunJlyQYdNLNjDggZ02MWKPdBhU4cVrNjDgg4LGizpsIIVe6DDpg4rWLGHVR1LliwJNm7cmHkOUZkBMSu2sIIVe6DDpg6ojtacCgcAAACVUU8gDAAAAAAAANqf/v37BytXrgxGjhyZ+V4FxPbu3RumTQQA6DQIhgEAAHQwOiiZQBgAAAAAAED34gJio0aNynzvgQMHCIgBQEdCMAwAAKCDA2FpByU7+vbtG6xYsYJAGAAAAAAAQAcHxPTclzcgprPGCIgBQCdBMAwAAKDLA2HaETZ27NjKtAEAAAAAAIDtgNjBgwcJiAFAR0EwDAAAoEtTIxIIAwAAAAAA6C6KpEwkIAYAnQTBMAAAgA7iwoULYSDsxo0bqe8jEAYAAAAAANCd9OvXLwyIjR49OldAbPfu3QTEAKDtIRgGAADQQdy8eTPzIcWdEUYgDAAAAAAAoHsDYnouzBMQO3ToEAExAGh7+rdaAAAAAJSHHmT0QLNx48bg1q1biYGwMWPGtEQfAABAKzl//nzwla98JTh9+nRtQq9Pnz7hqnfx8pe/PBg5cmSLVQIAAFQbENu0aVNw9uzZzICY+s758+eHfScAQLvR5wlC+gBQEZpYUAo3MXTo0ODSpUst0+JPfnSzBnT0ZP369eHOKvdQsG7dura1hx5kogGxegJhFsoFHZ1bTztFAzp6Qh1Fh2Udp06dCu6+++7Ev99xxx3BuHHjKtVEudjSYEUHbSk62kEH9bRzdKgcN2/eHJw5cybzvdOmTcsVELNgC+ooOtpBx7Bhw4L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\u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mLocationID\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m(b) Median perception contour and scatter plot of individual assessments\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdark:gray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mlegend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mdensity_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msimple\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/src/soundscapy/plotting/soundscape_functions.py:250\u001b[0m, in \u001b[0;36mdensity_plot\u001b[0;34m(data, x, y, hue, title, xlim, ylim, palette, fill, alpha, bw_adjust, levels, simple_density, incl_scatter, scatter_size, scatter_alpha, diagonal_lines, show_labels, ax, as_objects, **kwargs)\u001b[0m\n\u001b[1;32m 243\u001b[0m plot \u001b[38;5;241m=\u001b[39m plot\u001b[38;5;241m.\u001b[39madd(\n\u001b[1;32m 244\u001b[0m so\u001b[38;5;241m.\u001b[39mLine(alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0\u001b[39m), so\u001b[38;5;241m.\u001b[39mKDE(bw_adjust\u001b[38;5;241m=\u001b[39mbw_adjust, levels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m), color\u001b[38;5;241m=\u001b[39mhue\n\u001b[1;32m 245\u001b[0m )\n\u001b[1;32m 246\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 247\u001b[0m \u001b[38;5;66;03m# Regular density\u001b[39;00m\n\u001b[1;32m 248\u001b[0m plot \u001b[38;5;241m=\u001b[39m plot\u001b[38;5;241m.\u001b[39madd(\n\u001b[1;32m 249\u001b[0m so\u001b[38;5;241m.\u001b[39mArea(fill\u001b[38;5;241m=\u001b[39mfill, alpha\u001b[38;5;241m=\u001b[39malpha),\n\u001b[0;32m--> 250\u001b[0m \u001b[43mso\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mKDE\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbw_adjust\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbw_adjust\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 251\u001b[0m color\u001b[38;5;241m=\u001b[39mhue,\n\u001b[1;32m 252\u001b[0m )\n\u001b[1;32m 254\u001b[0m \u001b[38;5;66;03m# Apply color palette if needed\u001b[39;00m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hue:\n", + "\u001b[0;31mTypeError\u001b[0m: KDE.__init__() got an unexpected keyword argument 'levels'" + ] }, { "data": { - "image/png": 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gD4SJAADAaBEoAgAAAACgefQ+hLEjPASgiuz2gXDROMJEZYcwU6hIwaMtW7a0XhcQIkwEAAAAAAAAAABmQoAIY0Z4CECTwu3HkSNHCBcNzDPPPJN+1zBnVezevbulioB8hIkAAMCo0TsRgCbo6aALL7yw7zIAAACAThEgwlgRHgLQley2JRsuIljkM0ykL/VOBMSMMBGAwVu+fHlyzTXXLPzsjef6qR1e2r7JQNF55523ULd+9sRz7UOo3zPPbd9E7RdddFGyb9++pA+qedmyZQs/e+K5du+8t73n+qm9e6pVX/bkZ/j78HvMvLb9EOr3XPsQ6vfMc9sX1R57eOj8889fOL7Xz554rt27sm2fDRDFEh7yvOx4rn0I9Xvlvd1nrX9ar0VtB4vG3vZNyp6jAjEiTARg8C644IJk/fr1iVee66d2eGr7pgJFuti6ZMmSxCPPtQ+hfs88t73n2q1+bTc98ly72E02j7y3vef6qb2fdfWSSy5JTp06dc7vvfDa9kOo33PtQ6jfM89tn1d77AGiLM/H955r9y6v7WMNDw1t2fFc+xDq98p7uzdVf3a71MVwaLR9cw+9ALHzeUYDAAAAAJFhqLNxUBfUenos7OlEP+v3XAwC4mNdx7POAkB53sJDQFMYugyAd332WoTy7FxUAe4zZ870XQ4wEWEiAACAFoY7AzAufQ51hvYpdHD27NmFINHp06fTLwsm6OKPusnWhSB1k62n3PruLhsYM62b4Tqri7PqmUj/Dru217pqPdTpdwSLAIwZASKMlafehwCgCoJFPoaWJUyEmBEmAjB4x48fTx599NH056uuuiqZm5tLPPFcP7XDY9vPGijSTSobRkMnA55uJnuufQj1e+a57T3X7r1+D7VbCEGhBO2f5ufnk5MnT6b/p9+FFEZYunRpsnLlyrSHKv1bIaMYP5eHth9q/dTePltnVavWWa27FggMWYBI66vWW62/Wmdj6PLea9sPsX7PtQ+hfs88tP2k8JDVfuLEiWhr9972Q6zde4Bo9erVC8f51gupJ6rZ6reHHLzwXPsQ6vfKe7v3UX9TwSLv+6qY6td7r1ixIu3pHIgVYSIAg6eDA11Etp+98Vw/tcNr2zcRKPLKc+1DqN8zz23vuXbv9cdcu4IHCiUoiHDo0KFzggh51FuR/vbw4cPphTrdlFM4QV+xibnth14/tbcj7DXs4MGDCxfniz7PsWPH0i8F2NetW5eurwoWxXZDIua2H3r9nmsfQv2exdj2ZXsfirH2KjzX77l2z70Pqd2t7b2GibzW77n2IdTvlfd277v+WYNF3vdVsdSvMJHOQ/U9lpqArPiuaAIAAESAIc8A1B3qTL1cwDeFiPSlQEKdJ8R0MVCBIgWRNmzYkP5b4QQA7dCFV4WIFEhXoM+GIKxC66sCSAoUaTuup1S93ZQAgCyGLsOYMXwZABRjKLT+KESkL/WUe/To0b7LAXIRJgIAAAAA4P9RD0QKJSgYZl1f16Xp9+7dm2zatCn9N4EioL0eiXThW1+zhpIOHDiQftcNdwJFALyHh4QAEcaEABEA1EewqHvqGVftrodb9FAbEBvCRABqsSGI8ihFa3QRVjvBaXRxNnyCXzdwNC57me7/jHayk7qxV636v+zYp7rgXHSDSDd8dAHZ6HWKduj6+/BGkT5L0bAYGhd1yZIlC/9Wm1m3hla//Wys+8PwcxZRO9vFcF10L/OkfdX5mZ03NkTItPr02dUGVeaNDrI0nrDR32u6KvNm2vy0ds8OSxLOm7LzU+1c9HR0OG/s/WeZN2oPW+5Ui16v7XVtlnmTXdf0PqpvWjs0va7l2bp1a7Jz585Fvyua/3n/X6ab0uw2quo0WsaKljMtA+Fylp1G72n/tvcvmqbO+5SZpm6bZesvM02d92l6mmz7dNXOTU2TbftY27mJ5aaozayr6qbXz0nTZD9LkTrrWlPrZ5a22fZ7/Vy1zbLvM+s0+oz6Wfug/fv3L9rfZedNnuzvbRp9NgWKLr744oWnzvJeo6t5k11uygzf1sU+qso0tuxkP0tM+6hJ02SX+1nfp695U3W5aWudVnvpOFPDlOlid50eifKmUa9kOmbU69vxZp/bzmnLTd/bzqrr2rRlp8vtYNlpJm1v+t4Olp1m0rIz6/zsat5YXeFQIE28TxfbzuxrtrUMKPycle0lM1tLnfPCIR/fl3mfrs696pwXxjRviuptq7bdu3cv/KwbsuE0edPnzZui5aarZSD7+b0tA9M+/6RpwraP+drIpHkzbdmJdd7kTTNtX1tlfnY1b2Zdbvpe15pcbtqaN2EYWedbYbBIvbkWLTNl36frdcDaftbj+6am0f/r3HP9+vXptSMgNoSJAFSmYISG8ZhEO7/bbrstuf3229ObMdkb8XmBgJtuumnRzf377rtv6jQ6kLnuuusW/q0Lxw8//HDu3+pGkE4qw+CF6CbRrl27pr7Pli1bkssuu2zh33v27CncoV955ZULT5/L448/ng5zMc21116brF27duHfDz300ELIw+o3FtK44YYbFoU87r///sKDl5tvvnkhSKG/LZo3+ltNY1RL0TSqSbWFQRJ7Mi4MmIT02dUG4cX7xx57bOr7qI3V1kbvofkzjeal5qnR/Nd75bF2zy7rjz76aGHQR8tmeLD9wAMPFIZptA5YmEYHs0XtbMNwGb1+OI2W0/Cin/4vu65pXdZyU2Vd07L8yCOPTJ1Gw7lcffXVi9a17JNhReua3kcnKdOWm6uuumrR/NEyU/Q0+nOe85xFT1iE69okN954Y/JP//RPC/8u+vvsyYj+XTSN/j5cnzXvygS9whCepikKemmdDttTNxTC4J5qDZdV1aVwWDboVSa4FwbxwnDbJKorDHppmjLBvfCELBtcy+tJQO0c/r6onbMX49uan2qf8PNn502eOvNGN0TDEJ7+vkxItmh+5i07VednX/NG68205abquqY6tH0N9wNl5k12fpaZN5qXYTtrmqKLMnnzs8y6FraLPn/R+5SZnzYsUfh5mpif02Tnp61rei1th6x3k2x4ukxoJfx39u/1ujrm0H5L7W+h2VnnTXbbWWbehJ/f3qeo55VwfpaZN3XWz0nzJo8tO9mLcm2ua9ltZ9F+bdL8zC734WfIrmtl2rmLeZNd1zRN3sMadedn3f2aln9rTzvfKgp72ufJ+32WjmGtZ6LsAwZl17Wm9muTlpu8edPkujbpGLLOuqb3sfVm0rJTZ12rewxZdt5Y24efpat1bZb9Wrb+8O+bOoasux2sMj/D7fOk9mtyXZukzrqW1dS6lj3nVW2rV69e+Lfeo6i2ouP7vGP7Js7Xymw7s0GoOvOz6vG9Pvus59J56qxrYRvPcr7Wxrwpc3yvWqftb5s8l1ZPguFN7Vnmp+ouup7Q1rl0meP7MufSIU1TdHzfxLpW55gjb96E25y8h1RjuTYyad7kbTPbXtfa2K8VPXRaZ9vZ5rwJ2z17baTNda3qtZFJ69qk5aap4/um92ualzY/dSyibbBq032AadubJta1pvdr1vbZnqP7ukasz6pa9Lm1P9O1o6oBLKBNhIkA1FK0kyw6KAQATxTc2rFjR99lAHBAISILQ8IPXRSz3hSLQuB1KWSmLwXc7f0A1GMXirUe6WJrGxdXdaHXQoAA0Je8B2bCcAJDqGJMwgCRBRSmhZsBAO1c97IRAsIHtRUsQnXWU7eO79S2ChIdOnSIABGiQZgIQC3ZRHWWXczQDlDDBE2TTS4rqVs0TfZEUT2NTJrGepHJTrNx48ZFT2/lyV6U2bx5czrdNGHaXi6//PLkkksuqfSE+DXXXLOQtg57wVHvPdbDUra3luc+97lJkbAN9HNRO2fpPavOG7WHep0Jay968kpdOk76W5N9OlhDh2i6KvNGPeFonuaxds++j3rDKZOED+mzFx38hcua1omq80bTh9PogDMc5kz/l13XtB5XnZ/qRapommybaZ0pGqM+u67pfXTwPG25yc7PK664otQwZ5PWtUmy75N9jSy9Xvgkh9q9aJqiJ0qamia7DGSHGAzbQuu7XjM7jeZvdp0teh+1YZlujLPTFMnb3tj7WP1F71O1nduan9llNztvJtUy67zRupddZ+vMz+yyk62jzPzsa96E+9NJy012mqL3yfZg1+a8CeeFpimqv866lp1mUo9x06bJa7Nwuc++ZlfbTlvXbKiko0eP5tZfphvxadPY/+n1tf/Nq7XLeRMuN3qfKstnnXlTZ5pp20FbdvraDmafriwzjc3P7HIf1ltnO9jHvNE0ZZabqvu1rGnzUzXYE7eThiTMm6bK70W9lClkmK2lqe1g0fQ2zbTlZtI0fc2bsJbs+9h+d1r9Ta1rVaaZ9vnzzi+6Wtdm2a+ZMstO3W1nF/Mm3D5Paos+jiHLTJM9Lyy7rlnPzuFyMO0aXBvH93nnhXnnXkWy05TZdmbVmTdVj+/rnHu1db6WHQ6yzPzsat6UPb6f1DNRnXUt7Kld8q4tNTVvwt/lLTdtnUuXmTdlrqeEf1Nmuc9qYztYdt6E25zs8hvTtZFJ8yZvmzltmiJ97df0+adN19V2sOy8Cds97xpUW+tadpqqrM2mLTfZ9+xiH1VnGutJTN/1UJ3uRxgbHaKJda3p/Zq1fWzXiO08245lFNSynuaKrlEBbSJMBKAyXcQoGuYp3DkWhUKydOBSdRrrZnGSvIOHMhe/8l6n6kFi1QOKvO5p7T3VLpPapmqb6QCk6jR156fN07LT1pk3OtAqc2BZZX7m/V923jQRvssz67xRW4RhorzXa2Nda2re2PtUWW6aWNfK9E5U56m3Lqapc2Ix7WZ43gXjJt+nrTYrqr+p92l6muzFv67auclpwraPtZ2bWG7KtJn+Xz3Q2Pa/zXkTLjdll/uY5o39Pnvxr+vl2Yaf0nwrM3+r/D78PwWWdGNRn7fqBbQyyk4TLjd2g7+N92lzfnrcR2V/Lmr7mI8fqi43bcwbHetmz0vz/t5uzE4amlCfZdr76MJ4duiEPradZZabvo8FykxTZdnpex817f9j2A6WnaZo2eljO1iW1aXvMS03Tc7PbHhIwuFyq+rqvDCW9bOv88K21unsw04xr59FD+/k1V3mfZ544olF/y56OK1sbdPYNqbp88K+pvF4Xhhu7720czhN2WWn73aepsq+tu77ND1v+l5uZp03MS03ddvMag+31epV0cKgl1566czv09Z12C7ep840uo+iv9m0aVP6cIvOeasOrws0iTARgMFTwMB6UqkTNuib5/qpHUNr+zLDnYVPGNQ5oeiT59qHUL9nntu+jdo1JM6+ffuSLugCiNVf9YJM32Kq3cat76IbaV0M6nv/FlPbj61+am+G1lV9aX0q+7d5v7fv0z6P3mPdunWFfzeWth9b/Z5rH0L9nk1q+6bDQ23wfGzvvX7Ptc8qDBHVCRCNve091++59iHU75X3dvdcf1Ht4TY83LZng0V98dD21ouXvvQwtB6O0YNpevhdPecCXSJMBGDw6vS+EhPP9VM7htj2ZQNFXnmufQj1e+a57T3XXueprljEVLtCAuFQJG3S+xQNsTmmth9b/dTeDK1DuojaRQBQF26tZ6M+w0SxtP3Y6vdc+xDq90ztvnfv3tz/iy08NLTjY+/1e669qwCRBYV1PJDt4dd6GKqz7fPe9p7r91z7EOr3ynu7e66/bO22bVdvRbbNLwoV2bY9vG5i2/WwF7mht70+p43eoLZQj0V1htYDZkWYCAAAAAAaZr0T1RnqEt3ThRkNQdYFCy31GUwAvOsyACjaPlh38wAwiYdehwDPPRCFAV/tm/Vl4WK76ar9tb5rn11nOF8AQPOKeivSNRlt3+27beMtUKTtubbvGn7ahlYrGjp+SOwzq9dc9mvo2njWNAAAgA57JwIA+NJVb0H2JDVhIqC+7JOqbeu7NzEAPoJDQngIOFd447hOiMh6JNSN5WPHjiVPPfXU1H2z9XitL91s1s1njrsBIA7Z3op0brdx48Z0265t/LQHvbQt17Bf2r6rlx4bDmwsHnjgAc5N0TnCRAAGTwch2snKtdde666HAM/1UzuG3PaTAkU6oD958mT6s05qPD0t4Ln2IdTvmee291y79/o91+6d97b3XD+1Y4xt77l+z7UPoX7PvQ55bnvPtXuv33PtWYcOHUpvFNcNEBmFiPR1+PDh9CZzGerVQu999OjRZPXq1el762bztF4svLe95/o91z6E+r3y3u6e62+qdh1HKTik3nZ2795daghr/Y3uHehL771hw4bKoVHPbQ/0gTARgMGzrm/tZ28810/tGHrbTwoUxV73NJ5rH0L9nnlu+7Zq72qoM9q+GV1dQNIFLvvqU0xtP7b6qX12Wn+6vOgbwwXmWNp+jPV7rn0I9Xsersxz23uu3Xv9nmtXgMjq1766boDI6NqQbvju378/DRRVpVoUKjpx4kTa64X+rRvO0/7eM8/1e659CPV75b3dPdc/a+02lNnBgwfTYJDRfsPOvYp64NH+QSGk9evXpz0VVQkUeW57oGuEiQAAAGbAkGcA4J8uVunCk240tG3ZsmXp977DRIBnWn9sXeqCtg8xBIoANIuhyoDmhzGz3n9mPdbVTWYdm+vhjFmHdDl16lS6vl988cVpXdN6KAIAtEvbdG3jtX23HoKM9WhXNlSkUNCBAwfS7zp+6/IcERgLjpoAAAAAoEV6yirWIRvxZbqpMO0p5SZVeVoOQD67EajvbT9VqmFR9MV6C/hGcAhoL0BkPRBpGLIm9svWY0UTQaLwNffu3Zts3rw5vUFNSBgA+huRQD0SZYNEoUnDZE7aJ+j17Jytq2s7wFgQJgIAAJgRvRMBKBrqDHHTzYQVK1Z0EkyYm5vj5gUwI61DWl+13h4/frz1dTaGoQkBzBYaEoJDQHMBIpl1GLM8OhbXkGa6MdxUkMjodTUU24YNG+i9AgB6oO2wzt/Coc2KlO2tSPsNbdv1NwoWAWgGYSIAAIAGECgCAL/sYpN6kJqfn2/tffSE3PLly7mwBTRA65GCAW2HiVauXMk6C0SK3oaAYQSIsjeadZO5reGHdayvfbuO/xnuDAC6owCQeolT6KeOolCRXvvw4cNpYJTzN6A5HC0BAAA0GCj61Kc+1XcZACLEUGfx08Wm1atXtxom0utbjyoAZl9nFc7T06enTp1q5T3U85FCgPQmBvRHwyZpv5n3BDuhIaDbYcy6oBvDWu/bdPTo0fQYAgDQHYV9dL1l1l7npoWKdLy4du3a9N+cwwHNIEwEAADQoBtuuCG56667+i4DQEQY6swHXWhSaEAXnvQ0WxuhBAXKeAIaaIbCBQoUrV+/PtmzZ08rr6/X1jpLABDovpch3QSyoUe1jyY4BAw3QBTeaFZAuK2QsFGvR+oBSft4bjYDQDd0bNfkw1uTQkV6D7bvQHO4igkAANAw3SyuMvYzgHGgd6K4KSygC042bFKTNzF0EYtQAtA8hYnUM9G6deuSQ4cONfrats7SRT7Q/rBkJgwMqWcSCxMBGM4wZtPoJnDbw5eKti0KFOkYgpvNANDN9l0hztOnTzf+2tlQkY4h1Ss0gGac9wxnZQBK0I5YXcCKboK1OfxD07SZ04GKeLyB47l+au/P9u3b0ye6RDdAbr311sQL720f1v+P//iPiTd2aOit3b3XH94sUe0en7z22vZd1q7eidoIE9H2zbILXHv37p16ocv2s2ZS4EAXszZt2pQOpaCej2IRY9uPpX5qb/7CtNZV9Shm56zZ/8+ur9l1N3sjUT2U6QK0bjLG8lljbPux1O+59j7qrxIYmobj4355rt17/V3V3kYvRLOutwrz6xhcQZ+2rVy5Mtm4ceOi43PPy433+j3X7rl+7/tar+0+hPqr1q7rLFre9u/f33JlX96n2XncpZdeOqi2v/vuu9Pz29tvvz09X7UgFdAmeiYCMHg6IIjpxs2Y6qd2jLHth1C/Z97r98xz23uu3Xv9MdZuw5ApAHTw4MGZno7W/kA3KXQhK7bhzWJs+7HUT+3tDVGoYJBCRXWfm7OhzRT81GvG9HljqmVs9Xuuva36mwoMDZ3nZcdz7d7rb7P2vocxK6L9dxu9VuTR++iG7FCWG+/1e659CPV75b3dPddftXZt39sewtIoYLNly5Zkbm5uYb+XDRV5bnuga3FdzQQAABiQbdu2JTt27Oi7DAARYagzH6xnPAWBNM80fFL2ZkMR3aDRk2J6rdiCRMDQWKBI69yKFSuSAwcOVL5YrelsaLPYgkRAl4qCQkJYCPAjpmHMprEgcNVj7lnfD2jb448/nsRKvXdu2LCh7zIwEl1ud21fYvu8SaEiAMW4oglgFLx2WziE+qkdY2z7sH4CRQDMRRddlA51Bh9s6CNty/VEm0JFGup3WkBBIQQNmaAvTa9/Z4dPAtBuoEjfL7744uTkyZPpOqt1d9IwZ/pbhSL0pR7EbL0FhoywEDAOsfdCBIwlDLRu3bokVhp2SiF8o4doqrj88stbqApoHqEioD6ukAAYPF1A3rlzZ/rz1q1b05s7nniun9oxxrbPq99LoEhPbZw4cWLh6XxvN8C91++Z57bvo/Ymeyei7dulIJECBqpVIQNt0617bhsaQX+j8MHy5cvTz2Bf+vtYeWj7odZP7e2y9VHrn760Xqq3Ia2vChfZE6r6P63b1gOR/X2sQXYPbT/U+j3VPikkZMu91U5QqBuelp0h1e69/llr99ILUR7bB2t/fObMmdbfzx4aGMJy473+PmqvEhAqCgN5bnvRMbFofah6jFClHZsOHnlvd8/11629y2skk95L+0UNg2bLrkJFntoe6ANhIgAAgA54CRQBaBe9E/lkASEFifSlAELY04mFEfQ91jACMCYWKtKX1lWtv3ajJPwboQcxeO9FyOTdAPR8owrA+Hoh0r5Zx9ldhIl0XMBx+zCVCbjE3FuQJ2XbUT0eFc0XejkaRy+yXb3XtAdFdMys42M9JLZ79+70d/RUBExGmAgAAAAAgBLCsJD19GABI27QAnHSunr8+PE0NGTrr9ZZ3ajUly4mqwcjIMaAkNCTEIAhB4jyegXVfrtthIl8KwqmEBaKS5n5Qdho2Gz73gXbvhdt4/V3CttruD+GPwMmI0wEAADQEXonAhD2TtTUUGfoh4ZMUiCh6wtjAKqHiTQM7SQENTArwkEAujTEEJFRTxI6Rzp8+HCr76OHAHQDOeZhiTE9XEJYaHimzdO8no3soR5dX4Gf3p71EIeupbRJ+5EqD3vZvlTDnxEqAs5FmAgAAKBDBIoAAAAAP6Eg64lOTy3nIRwEoG1DDhCFdPNXPQnOzc212juR3WimZ6I47Nq1a9FDGuF8ITSEScuBDeWqB7XyeqGhJ6P4KMCp4+Y2w0TatmsfUicsSqgIyEeYCAAAoGMEigDIU089Re9EAAD01ENQmVCQ3agS9WLBkJYAurJ79+5F/x5yiCik7ezq1avTba8FTJqkwIFen16J4uptSPtY+86+FlVomckuN3k9GRlCRv3RdldBn6VLlyanT59u5T1s+z5LWJRQEbAYYSIAAIAeECgCxs2GOgMAALMHgYQeggAMwYEDB9LvujE+lgBRSD0TaRgcbdOPHj3a+OuvXbs2fQ/CRO3LC3NM62EGaMqkHq0mhYwIGHVDAR9tezds2JAe+zcdGNXQ8woTaRvfhGyoiEARxoowEQAAAAD0hN6JAABjCAExVBgAlBvGzG6IWk8tY6QbwbqJq6FwTp061djrqkcM7W/UKwaaM6kHGIYoQ2zylsm8gBHhona379rHKdiptm+KArgKKen1mx7C0kJF9FKEsSJMBGAUvI+B7bl+ascY275s/fROBIwbvRMBADz3BFQ2BMRQYQBQHCLSzUp6aPky7ScU+LHzpSYCRdr/6EazXtf7NScvPQ4BHuQtu/Re1C5th3UOoX2eev1pYp+h/YVCSm32Oqf9NEOfYYzOe6aNgWeBCR588MHkrrvuSh577LGFp7CvuOKK5IUvfGHy7Gc/O4nJ3r17k89+9rPJI488khw8eDB9EkIHFuvXr0+uv/765KabbhpVd6jaUVrXsppv8/PzfZcEYIrt27cnZ8+eTX/WturWW2/tuyRMQaAIekrfDst1YZOn88fDwkT0TuQP6y3gw5kzZ5LDhw+nx8bhOmvn8zbciScMCYYxYD+LsQSIhqTp9Vb77tOnT6fX5nUvoS7Voev6DG9WD+Gh4WJfW05eDzqEi2aj5U5BUd1n1Lma9WJalQJECorqe5e9zlkIqutA0d1335221e23354O6dZEGAso4utqCVzSAf9v/MZvJL/yK7+SfP7zn5/4dwro/OAP/mDyfd/3fb10Nbp///7kz/7sz5K//uu/Tv7mb/5mYvecRgdWX//1X5/W/MpXvrL2+77nPe9J3vKWtyRNePe73518//d/fyOvBQDoDj0UAeNF70QA0C7dONy4cWN0N0tmDQT1XT8AoJohh4jaouCP9tm6UayHL3RDXyHhsnSPQQ8GL1++PD0eoGe8YgSHgHq9FxEuqkbbdgWAtF1Wz3Havh8/frz09JpOYRp9aV/R9cMhDH2GMSFMhFb90z/9U/LGN74xuffeewv/VkGjt73tbcn/+B//I/mDP/iDNFzUhTvuuCP59V//9eTDH/5wpZMRXYjUtPp66Utfmvzv//2/k2uvvbbVWgEAw0WgCBg367UTAOBP3WAQgSAAGDYCRLPTDWOFgeyGs4aBU08W6tHCeuQO6aay/n7lypXp9z5uMntCeAioJ1xPFIQhXFQvUKTQp7bvevhD92e1fdd2Xp1UTAog6dqZvmxIzD6HrmToM4wBR1FozSc/+cnk1a9+9cLQWFUCSLfcckvaQ9CLX/zipG2/8Au/kHz84x+f6TXuvPPO5Oabb07e//73J9/4jd/YWG1ohrr9s4MPOzjxxHP91I4xtv0s9ccQKAqf2PfIe/2eeW77vmufpXci1R7W76n9PdfuRTiqedi+3tvec/3UPtyAUNvBoL73VWOu33PtQ6jfM89t77n2GOqfJUTUd+2x0vUVtY3CQXNzc+nPChOFDwfbNRgbzrTqkGbe275M/bGGh8bQ9hhuu2fXobLholjqr6PJ2m17reCnwkK6tq7X1/V1e58wGKrtvH6e5b2brN/28xYqIlCEoSFMhFZ88YtfTP7Fv/gX5wSJtJF/3etel7zsZS9Ld567d+9O/uEf/iH5wAc+sChpqo2uQjka/7GvBO8ll1ySfO3Xfm3yvOc9L9m0aVOydu3a5MCBA8lnPvOZ5EMf+lDy2GOPLfp7JWb/1b/6V8lf/MVfJF/zNV8z03s///nPr30jCudS94g7d+5Mf966dWv6ZIonnuundoyx7Wetv89AkU7W9PSH6Ik/j0Euz/V75rntY6q9Tu9EugAS1u/pIpTn2mNenu0rDBIZu7mi73YDZtaLcH3wvOxQexzU07C2uV56DoppXzW2+j3XPoT6PfPc9p5r77P+Jnoh8t72bdOxR9jLUPaY145zx9j20+rPhhpiCA8Nre31ZedYtlzqe8jC+Pq7qkE3+FpuisJFuucZc/1F2qo9DIFqHVKwKPswSxPnoG3Vz9BnGCrCRGjFd37nd6bBm9Czn/3s5IMf/GBy4403nvP3P//zP59867d+66Kbp3o6+7u+67vSHoq6ojDOd3zHdyTf/d3fnXzFV3zFxL9T8ElDo/3H//gfF3Y6dgNZ0+smsp6QqEuBJQDAeMXQQxEAP70TAQoF2ZfOSTTsg85Z9KS2Lr7Zk3u6GKfhHiwIYuEiYAw9CoU3HHWROpagEADArzBAJAxl1i1PN9+7tGvXrkXH+LGFh4a4HOo4U+de2fMx/VvHnzack87HdC6m7xac4Hxs+LLroIJFYa+vDIl2Ls894TL0GYaGMBEap16GPvaxjy36nXaGGkps8+bNudNceeWVyUc/+tG0x6J77rln4ff/9//+3+SOO+5IvuVbvqXVmq+44orkx3/8x9Pwkg7kiujA721ve1vywhe+MHnlK1+ZHiCGvTL91//6X5Pbbrut1ZoBAAAwPHV6J8J4WdffunCtXmHVW2pej0QWNDp58mT6dwoWqec8C1NwERtDG4IsLyik3ojy1g8AAProhQhoSjaYoOACQatuqM11LqbwkMIDuk806XhT52N6MF1/p/tLOl7VOZnOzeipaFy0jlrvOPoKA4AEi4aBoc8wJISJ0Lif/dmfPed37373uycGiYwOnn7rt34r+cqv/MpFYx3r9doME/3ET/xE8vKXvzx9Qreqr/qqr0re8Y53JP/u3/27Rb//3d/9XcJEAICZ0DsRMD70ToQqdPFRQSIFiNRtepWQhKY7ePBgOu3GjRvTByp0QZtAEbwHhgAAaAu9ECEm2aHL1q5du2gEBXR7PqZzKxvqrMr5mB4mWr9+/cL5GMZHPVXZUFt5w6HBN4Y+wxAQJkKjPv3pTyd33333ot/dcsstyTd90zeVmv4FL3hB8oY3vCF53/vet/C7T33qU+mwXzfffHPShte85jUzTf/93//9yU//9E8vuvFz7733Jo8++mhy1VVXNVAhAGCsCBQBAKZduLZAUF16gnbPnj3Jhg0b0qdiCRQhtuAQgSEAQN/ohQixBojCoZN0foDuz8cOHDiQBoLqtr96jtX5mB4sEgJF45Y3HFqIcJFfDH0GzwgToVFhCMh83/d9X6XX+N7v/d5zXuf3f//3WwsTzUrdUH791399WmPo4YcfJkwEAJgZgSJgnL0TMdQZyl64npV6NNJrKUSk5a7MsM9AXYSGAAAeECBC7OEh9D/U9P79+9NhzZp4PV0DIFCErHCdp9ci/xj6DF4RJkKj/vIv/3LRv3VBuuoQZV/3dV+Xdst5+PDhhd/91V/9VfLzP//zSayuvPLKc36nRDkAAE0gUAQAMBoS+tixY40EiUIKFOnCtbpX1wMTwKwIDgEAvCFEhL4RIIqfgkRHjx5tJEgUBooUTtqyZUt6PrZkyZLGXhvDDhYRKvKHXorgDVcI0Rht/D73uc8t+t3111+fdplfhQ6Wvuqrvir5i7/4i4XfaZgzHaCtXr06iVHe0AJzc3O91AIAAADf6J0I04JEungdPnjRFF3A1rBpmzZtSi9eM9wZmggPERwCAMSOABH6RoDIj7Nnz6ZDReteWBs90Op8TNcDCBNhmnAbQW9FPtFLETwhTITGKPCjC9ChW265pdZrZcNEet177rknednLXpbE6MEHHzznd0qRIw66EbJ8+fKFn73xXD+1Y4xt31b9XfVO5LHNh1S/Z57b3nPt3uv3XHtfF68VJMqedzXl5MmTaY9HesAj9u71PS87Q6o9GyCKMTykGz560jtLDyzJxo0bXQzv53m58V6/59qHUL9nntvec+1l6g8DRLGFiLy3vWddtn0bASLPy46X2iedjzVVv3o7OnHiRBomIlA0nOWmzfr7ChbR9s2glyJ4QJgIjbnvvvvO+d0111xT67Xyprv//vujDBOpS8GPfvSji363YsWK5Cu+4itqv+Yv/dIvJX/7t3+b9vS0d+/e9ABSFzj1dcMNNyQvf/nLk1e96lXJtdde28AnGD491T/L/Oib5/qpHWNs+zbrbztQpJvHnnvW816/Z57bPtbay/ZOFGv9ZXiuva8L1/pqojt9XTibdPFMva5qudMF8lgusA1p2fFeuwVw7HuM4aGsovBdW+G8JnlebrzX77n2IdTvmee291x7Uf2x90Lkve0966Lt2+yByPOy46V2nYupl1jdq8lq8rxJQ1rr/hJhomEsN13Wb9uUcBi0NoJFtH2z6KUIsSNMhMY88sgj5/zuyiuvrPVaedM9/PDDSYx+53d+55wDyFe+8pUzDUvxwz/8w+f87ktf+lL6pYDR+9///vQA9Zu/+ZuT22+/PXnRi15U+70AAL501UMRgDiolxiGO4N1e6/loW3qnUjDqV1wwQVcwIab3ocAAJgk9gARho0hzIZ1PqYHL9qme00KLun9FHoAqgq3M20Hi9BOL0UEihATwkRozJ49e8753RVXXFHrtfJ2aHmv3zd1afkzP/Mz5/z+LW95S+vvracn77jjjuSP//iPk5/6qZ9KfvzHfzzaJ4cBAM0iUASMg/VOBIguJivo0wW9jw3ZifEhPAQA8C7mYcwwfASIhkn3Y7o4H9P7aJheD8Pwwm+wiFBRnOx4hWHPEBPCRGjMwYMHz/ld3YuOedMdOHAgic1/+A//Idm9e/c5N3j/5b/8l43s5Dds2JB2aam21Y0kdaOZd1Phv/yX/5Lcc889aY9FXaTV9UT0ypUra0//F3/xF0mXdABubbd06VJ3oSvP9VN7v73F2ZAN3mr33vZd1q99QNO8Ljfe6+8qINAmr20fe+16IlHDCU2qTbWH9cf4GSbxXHuX662O7xXsUVvZU6pdfDa9j+ZJE8OqNc3zshNz7RpSwYTnleHvY95eTqKetqbRMp53rh0bj20/lPo91+65fo6P++W1dt0otfrVy6LVH+7LYue17UUBiLB+T+0+a9vv379/0b/DYH5X7eB52Ym5dvXWqmCPPdzRxRC5eh8bhqnN87Exr7NjrF/7RVu+Hn300YXfb9y4sfJr0fbt0vm4zmMV/goDYR6G6MbwECZCY/K6eFQQpo688Sq76NK/it/+7d9O3vOe95xzYPnud7+7VqBHQ7u9/vWvT77xG78xecELXpBs2bJl0f/rxsEnP/nJ5Pd+7/eS9773vedcVPnDP/zD5D//5/+cvOMd70i6MMv86OIGSPag2J7q1xP+3lL9nuun9v7oBDc8KO56vRtz23uuX8uM3XALL7x64b3+kLeTQ89tH3vtGuJMx12Tlols/Z54rt2E+9q21lt7bX01uT+fdvEsDF/EuD3yvOzEVvukAFHefI99ezmLGJfzIbW95/o91z6E+j2tp0Nqe2+1W4DI6rX6FRT1UL/ntp+2noYB6qG2ffbh5/D6T9ef3fOyE3vtdj6mc7FJx8jZv5+VBd3bPM8c4zobkz7rD7dVuo5tYUh1blAGbd8NG3beOvKglzv0pf8rVxiMvCf56oaJ8qbTTi0Wn/rUp5K3vvWt5/z+9ttvT1784hdXeq3rr78++ZM/+ZM0RDQthKQ2efnLX55+/diP/Vjyxje+Mfn0pz+96G/e+c53Jt/wDd+QvOIVr6hUAwAAAIC4dXlhN3yvti9gox+TQkQAAHgQBohiCehiPMIQkacHyODzfAxok23DdP/Vtm1lQ0Xoho5xFH7KHvsAXeEoG62qm+jMmy6WAygNHaQehLLdS77yla9MfvInf7Ly673kJS+pPM3VV1+d/N3f/V36nh//+MfPCTTdeeedSRdPyc+aqO1yZ2vvGf7shef6qb0/uikU9jjgqX7vbd9H/U0Od2b74NiGXhlL/cZj7Z7bPvbaNbysegEtqi3W+svwWntYc9v1d9k+2feKed54XXb6qD0MENU9Pol9e1mXh8/ive091++59iHUbzzW7rntY6w9vImmmqYFiGKsvyzvtcc6nOusbR8OYxYOYRYT78tO7LVPqiu73Df9Xm22x5DXWQ9iqj/croWByUlDoMVUex3e6l+6dGkaKFqzZk3fpWCECBOh0Y1ZVt3xXPOmiyHl/+STTyavfvWrk927dy/6/U033ZR84AMf6PSJTu3c/+iP/ih57nOfmxw+fHjh95/4xCeSf/iHf0i+8iu/stUgUd6wdrFSrTt37kx/3rp1a3pDzhPP9VN7v2woFN0suvXWWxMvvLd9X/Xv2LGjkVCShtW0HvG89VTgvf7wAs6qVasSTzy3vZfarcZsoNtL/Xk81971emvd6uucqImhzsIu7PMunul9LIwc4/bI87LTde06hzV6r1nmp9d21xDh065NaJj1WG8Kem/7IdTvufYh1M/xcT9iq/2JJ55Iv6uOMjfRYqu/Cs+1W3DZ63qb1/aPP/74wv/ruDjmIWY8LzseatdybQ8M5j30bg/5NVW7nY/Nevw+tnXWk5jrt+VAIV4L8l5++eUuai/Dc/133313cskll/RdBkbGzxqC6OX1VGMb5KryLvTN0hNOExTYec1rXpN84QtfWPT7Zz/72clf/uVf9nIycfHFFydvf/vbz/n9X/3VX3VeCwCgX9u2beu7BAAtuuiii/ouAT2ysE/eAxxt0Pt4uqCG/BCRBYl0Mdi+AADwQAEi+1KAyL6ALuzatWshSKRr/vaF8bIHMLp64F3vw/kY+hZu/7RNtC/0v48CusTeCI3JG0cz7Ea9irxebyZ1p9cF1fON3/iNyWc+85lFv7/sssuSD3/4w70mQd/85jef87uPfOQjvdQCAOgXgSJg+J566qm+S0BPdDG5q95T9D4euvpGvmyICAAAbwEiIUCErm/O7tu3L/1au3YtASKcQ+dHXZyPWWiJ8zHEJNwm2vYSwDgwzBkas3nz5nN+VzelmjedeuHpq0v0b/mWb0nuvPPORb/ftGlT8td//dfJ1VdfnfRJw5yp7ffs2bPwu8cee6zXmgAA/QaKmhjyDECcvRNxwWbcYSL11hoOcdwGXSBXF/48Ces7RAQAgAcWHDKEh9C18D6EhrsBJtEQZytXrkyOHDnS6vtoCF69F+djiJECRRomTMOf6fqUQm9XXHFF32UBaBFhIjTmqquuOud3X/ziF2u9Vt50fYR2Tp8+nbzhDW9IQ0Oh9evXp0OJbd26NYnBli1bFoWJ9u7d22s9AIB+ESgCht87Ud9DAKN7uqCsL837NnuoUhBF78OTsP5CREKQCAAQOwJEiClAZD1t6Ob4iRMneqwKsVO4Rw9dKHTW5rJi52NAzCx8qc4YbJt6+eWX91wVgDYQJkJjrrvuunN+99BDD9V6rbzp8l6/TWfPnk3+zb/5N8mHPvShRb9fvXp18ud//ufJzTffnMRCafUQw18spoNvzTf72RvP9VM7xtj2sdRfJ1CkG8dWs8ebyN7r98xz23urPa93Is9PLHquvQ+6eK1hF44fP54888wzrVwQtCdhY+d52Wmy9i57I/K2vRwS723vuX7PtQ+hfs88t30btXcZIKLtUSVENJS291y/p9pVp5YdPdgdno81VbceHFFPsR7Ox/rmabkZWv1h7Vof9G/1VOQlVOS57YE+ECZCYxSu0YY3PIj65Cc/Weu1stPpdZ///OcnXdFneMtb3pJ84AMfWPR7XVj/0z/90+QlL3lJEpPsDSXdZMLimyIaDs4rz/VTO8bY9p7r72r897Z4r98zz23vsXYLFOlCo0IJXrvj91x7X3TBS4EiXbA7ePDgTMt99qKZ/q0eWD30SuR52Wmq9j56I/K4vRwK723vuX7PtQ+hfs88t32TtYchoq56IKLtUTZANKS291y/p9p1rrR06dL0AQ+FJ0wT508WVNL5Hoa13Ayt/rzabfvqIVTkue2BPrBXQmN0APUVX/EVyWc/+9mF3917773pRW5dlK4S5PnEJz6x6HcKElkvD134gR/4geS3f/u3F/1OO5c77rgj+Zqv+ZokJuqFKDss3KZNm3qrBwAQD4Y7A4aN4c7GSRevV65cmZw6dSqZn59v7HU3btyYXrjm4nX8uuyNCAAADwEioG6ICKhzPqbjcJ2PNTVChMINdj7muQdWwFOoCEA5XCVEo1796lcvChMpGPTBD34w+a7v+q7Sr/GRj3zknKds9bpd+eEf/uHk137t1845QHz/+9/faR1l/c3f/E164Bq66aabeqsHABAXAkXAMOUNd4Zx0IVmnZ/ogQ39fOzYsUYuXKsXVr0u4tVHb0QAAJRBgAgxIECELs/HNmzYkP571kCRwkM6v1fvpZyPYSjCbTChIsA3Iq5o1Jve9KZzfvcbv/EblV7jN3/zN8/53Zvf/OakCz/5kz+Z/NIv/dI53Uv+7u/+bvL6178+idEv/MIvnPO717zmNb3UEqszZ84k+/fvT7/0szee66d2jLHtY6xfgaIyFAJWzfoKhy31wnv9nnlue8+1i3qm8Vq/97bvky4460KzLtDpInadp1fV5suWLUs2b968ECSKfXizISw7dWsPeyPqM0hktaN73tvec/2eax9C/Z55bvsytStAZF8KENlXDM6ePZt+eeS59r7oRrXdrNbxcd0gkfe291y/t9rtfEwPZeh8rO55lAJEOh8jSDSO5WZI9Vep3bbL4ba6b57bHugaPROhUS9+8YuTm2++OfnMZz6z8Ls777wz+fM///PkG77hGwqnv+eee5IPfOAD59wAfcELXpC07Z3vfGfy//1//985B4W/9Vu/lXzbt31bEiPV9rGPfWzR73Qj4LWvfW1vNcXo5MmTySOPPJL+vHXrVndDN3iun9oxxraPtf4yPRTppqb1dqcLGV5uKg+lfs88t73n2vX04t69e93W77ntY6BzFYWB9F3td/To0TRc9vTTT5ceKk1fHoc287zsVK09piHNtGxZ7VruGIKhO97b3nP9nmsfQv2eeW77abV76IFI9eu83Pa13trea+3eeyHy3vae6/dauwWKdJy+fPny5MiRI8nx48dLnY/pPG716tXpvRydi+mBdoxjuRlC/XVr17Y6huHPPLc90AfWEDTux37sx8753Vvf+tb0Rsc0uvD9lre8JTl9+vSi3992222l3vflL395eiE2/ProRz9aatp3v/vdyY/+6I8u+p2m/9Vf/dXkO77jO5K2/OIv/mKyZ8+eWtP+4R/+YfL93//95/z+3//7f5/eWAIAoG4PRQB8KXOxEsPuYl8Xo3Vh7pJLLkmfjNWFaV3QtgvT+q4L1brpd/HFF6dPv+pvNC0XruMVU5AIADBeMfdAhPFpqhcioOnzMZ2H6XxMvRVlz8f0NxdeeGGydu3a9FxM52Q6xtd0nI9hTMLtdkw9FQGYzNfjh3DhjW98Y/Irv/Iryfbt2xd+99hjjyUvfelLkw9+8IPJDTfccM40+v83vOENyd13373o9694xSuSb/3Wb2213t/5nd9J3va2t53z+1/+5V9Ovu/7vq/V937Xu96V/MRP/EQaWPrX//pfJ1/zNV9TmIL94he/mPzsz/5s8mu/9mvn/N9ll12W/Kf/9J9arBgAAAAx0YXKffv29V0GeqYL0PpSsEwXrNX7TTZkZg9cWA8D+n91622/R3xBIkJEAICQ9t3ZITK1T296P7579+6F4wi9PsEhDLEnIqAptr203l7tfMy+jJ2L6TsBIoydbcdj6KkIwHSEidCK9773vckLX/jC5ODBgwu/e+CBB5Kbbropef3rX5/ceuutyaWXXpr2yvOpT30qef/7339Oj0RKcr/nPe9pvVb1hpQ9EVci/H/9r/+VftWlXoPyeg7KUteXv/7rv55+qUch9RqhoeK049QOVd3saYeqEJECWn/3d3+XO2a5Tuz/7M/+LFm/fn3tmgEAw1dmuDMAvihwcOzYsb7LQASyQ5HYeU72JmPYrbe3Ic7GgCARAMAo+KsvCwrr+qm+201p9XZhP+vmdN2hOsLhy+zaqOi6JNAXAkTwQkNDalusbSbnV0B5hIqA+LFXQyue9axnJX/yJ3+SvPa1r110Y0Mnu3fccUf6NY26gfzQhz6UXHnlla3XmhfM0cHfPffcM/NTPFXpqfK//Mu/TL+q0M71fe97X/K85z2v8nsCAMaHQBEwTAqpr1y5su8yEBF6HPI7tBkAYNwUINI1S4WHdG31xIkT6e/yKPij4XP0ZcOblgkVZQNE1gORrt/q/YC+ECKCR9YTEedgwOyhIgJFQDwIE6E1X/3VX53ceeed6bBnO3fuLD3d9ddfn/zBH/xBcuONN7Za3xDowsCb3/zmdFg5eiQCAFRBoAgYFvViMj8/33cZABoIEtErEQCMl25EK0CkL/X4bj0JTqOHIvV1+PDhdB+iUJACRXm9Y0wKEAExIEQEAONm2356KQLiQZgIrdKwZurhR0N4KfBy3333TfzbrVu3Jm9729uSt771rWkXvWOgHog+/OEPJx/96EeTT3/608mjjz5aarrnPve5yeuE5hFsAAEAAElEQVRe97rkB3/wB9NeoAAAqINAETA8Tz31VPpUOgBfCBIBAGwYM/VEpGCQDVdalv7+6NGjaa9CGzZsSJYvX55eYyVAhJgRIAIAZGl/wNBnQBwIE6F16mpXoRd9feELX0juuuuudAdgNzq0E9DNzGuvvXam91Egp46qJ+ZNuu6669IvtY3oiaP7778/+eIXv5js2bMnfbpcFxF0kq+d5+bNm5MXvehFycaNG3urGQAwLASKgOHQMeKBAwf6LgNARQSJAADWI5FCRAoEzULDoe3du3dhuLO1a9c2VifQFEJEGKPHHnssiUl2+EzdnyrjiiuuaKkiYPLQZ0KoCOgeYSJ06jnPeU76hXwaquwlL3lJ+oXm6OKJBbDyuniOnef6qR1jbHuP9VugSOO6W80ex3j3Xr9nntvec+159V900UXJvn373PROtGTJkr5LGC3vbe+5/rB2T0Ei79tLz7y3vef6Pdc+hPo9q9r2ChIpRFQ3SKTQUOjIkSPp7zZt2pScOXOm0rmp9+XGc/2ea/ceIvLe9p7r91y77Nq1a+GB9TL1x/SguHrCC2sve05SNhTVZujI+3Ljuf6ua8+GimYNFHlue6AP5z3TZ7csANxQ70h2QUM3p9RrEoB4bd++feHpEt2wuvXWW/suCQ7QQ5G/CzhAHoWJxEugyCvWW8zKU5BoCL19HD9+fNE6Ozc3l/6sIYC4iAzEZ0z7WYV9tI2y/cIsAaK8HuMvvvji9DvbOvS53sYaIgJmDc7EFA6KbV+7f//+wr+hlyNUpVDRWHspuvvuu9NhcX/u534uWb16de6xH9A0H4/LAwAAoHUMeQYMg/VOBCBeBIm6oxsjuol+6tSpRTdL9DsA6Ju2S3oQqMzQMmXCQ1na9unhQD0kyHYPXSNABA+GHBbqW5m2m9b+BI2QR/sThj4DukOYCAAAAAAG6KmnnqJ3IiBCBIkAAEZBIvVKpB7UmgoQZSlMpH2Ogkv0ToQuHDhwYKGnbEJEiMmk4AqBof5Manv1ajRpfhEyQtNDnwGYjDARgMHTU1jWpaYOTr09ieW5fmrHGNvec/2q/dJLL00effTRdOxobxeadXFcQwSIx/o989z2nmufVr+H3om8t71n3tvec/2qXV8rVqxwd1PXc7t7573tPdfvufYh1O9Z2bZXmEjDzDQZHsrScBgKmasOfTVVe6w81++59uyQRsuXL3cVnPbe9p7rb6P2LkNDnts+ZtPmlc3fsNdRbwEjz8tNTLVbqKhKL0Ux1Q94QJgIwODp6a4nnngi/dljt86e66d2jLHtPddvtS9dujS94OztZEong/ZEr56A9Fa/Z57b3nPtZeqPuXci723vmfe291z/3r17F+r3VrvndvfOe9t7rt9z7UOo37Myba9zLn3p78IQURMBoiz1flQ22OF9ufFcv9fa7SauAkR2c98br20f1q/tSTaQOIa277u3Ie/Ljkeat1reT5w4sXDdI285iDlgZOusx+UlxmU+HPqsKFAUY/1lbd261eV2Hr4RJgIAAMA5brjhhmTnzp19lwFgBtY7UcyBImCMyvQKAQAYLnv4xG4kthEeyuuF1nrH83TTDPEHiMKeIcJettAt3VzWMabWcfV4lv0/rfdDWPfzAiMxDFGmNrZQgm1rtX0X2+7aF0GA5m3YsOGcdp00TFosASPVaw9zZtdZW06GsM7G3kuRF7Zd4VoC+sBSBwAAgFzbtm1LduzY0XcZAGbgYbgzYCyefPLJNNhnT9ACAMYXIDLqxVZPxesp+i7YzUrCRGgjRIT+WHgo7OlMXxZkUcBFgQV9ad3Xv/XlRazhoZDaVb2SW/vbEEoKcVpIRH9j80EhEQsfESxqT95ykhcw6iNcpOXC9slaVrTchPtoLSdapsJ1ln13vV6KvIeKssvKyZMnF4ZoA7pCmAgAAAATESgChoHeiYD+g0QAgPHYvXv3ws183SxWeCiP/U0Xsj0fAFUQIoqPbihrvdbNZfUKpXM+3XDOC6goiDA3N5cOd6iQgnq3iDFUpOUsG5qILTyUtx23QMj8/Hw6H6Zt29X+mg+aH5oHmhcERbqRXZay4aK2g0XZZUXrrdbhSaEylpX6bD9Vduiz2Fg4UcuLerA8evRo+nOXx42AIUwEAACAqQgUAb7ROxEQB10I5uIfAIyj5yG7CSgrVqzooSKgGYSI4qPAkG4yq+ebgwcPpt/t99OmUchFX8uXL0/Wr1+/ECrqM5ygIIcN32NhDy+99VhPIfrSfFA4pMxwcppfBw4cSMMhWqcUFIk13DW2cFGbvRbZsnL48OGFISGLzg2zy4oeENOy4mUdiYHHXoqspyrN+656sASmIUwEAAAAACMJFNE7EdA9eiUCgPEMXWZ0g7DMsJZd3sTn5iPKIkAUL+vZRD1V6GtagGgS9WS0Z8+edN4q7G5DoPU1bNmGDRvcDQNsgS7Vrd5t6gw7pLCAptU5usJdQqBomOEiW1YUDpllWTl+/Hi6rGidZVmp30vRpZdemsRKATJto9XDpZYbIAaEiQAAAFCI3omAYWC4M6AfulEDABhueKhuuMd6L2qbggJ990CC+BEi8tETjm6Ia8ibWV9LvekonKRtWZuBomwgIxvY8Nhzp27yK9ihgMes9escXWERPQAkhETiES6rdYdEs2VFD3fVCf+FWFaa6aVo165dUQ6fqCCRQmc63vS4XcRwESYCAABAKQSKAN8Y7gzoHr0S9U8X7XVRVhfy7QK+bpbpor4NP8TNdQBlhy6bNUAU0ranqzCRvQ/bO+QhROSDjmXUG9GsQaKQhlxSIMF6KOoiPOSdAl3qOUThklnDIUavp15rbJg3ttXD6LXIlpUmgkTGlj1d32FZqU77OAV17NpYLMOeaVlRmIggEWJEmAjA4OlE6JJLLln42RvP9VM7xtj2nusvU3vMgSKdQFvdnEx3y3Pbe669Tv0xDXfmve0989723uoPeyXyVnvIa+26KKsnULNsOA0NrbF8+fIkZl7bfgj1e659CPV7Dg+VaXvdBNRNfP1d20NZKDhZdpgz78uN5/q7rp0QkZ/lxm40K0w0Sd26dZykYyHbJjUxdFmVAFHsbZ83zJx6dQpD6maW+hV0n5+f7zRo6lnfy01Rr0VaPtSLULisZNWtW+cxWla0zvZx7bnvtp+Val69enVy7NixtJeiusPXNcWWlb179xIkQpQIEwEYPB18xzwO6pDrp3aMse0911+29lgDReHJLLrlue091z5L/TEMd+a97T3z3vZe6s/rlchL7Xk81+6d97b3XL/n2odQv8ehy6q2vW4EKnSqm41tUS0rV64sHRLwvtx4rr+r2gkR+VpuyoQSZrmpr5vXeu1NmzaV3k402ftQzG2fpSCReobKBkCbClQo2DU3N5fOk7IB0LGKabnJCxbZejstHDLLcqNlRUFhrbNdLysxtf0s9a9fvz5tR9sn9tVLkfU6Zz3nArEhTAQAAIDKYg0UASjGcGdAP70SAQCGGx6qSzcBFfDWTSTddGyDBYm4MT1uBIj8UhhBQxupZ6K2qKcTvb62FZMCRUMfvqzMfNCXejNpiwIoev0LLriAbbZTWi80H23oulCTvfjoPdQ7kUIxLCv12f7QQkVdB4o0H/WV15MuEAvCRAAAAAAwQjH0TgQAAOBl6LI22LBCejq+jbC3Xnvt2rXpjWmMEyEi/xQ0bDPAYhRMUI/VYZho7AGikIJE6jmk7WGINB+0n1LAwOMQUvjyOqtlJexpRj1O5Q2NNwuWleZo/9hHoEjLiq7NtT3cLTALziIADJ5S4Lt3705/3rJlSzoGtCee66d2jLHtPddftfbYeifSybOdfOnJHE6ku+O57T3XPkv91jtRn4EiXYRVN/HCk5fd8t72HurXEGd5vRJ53uZ4rt07723vuX7PtQ+hfs/hoSptr//XUCXqQUg3Bpui99ywYUPlXom8Lzee62+ydkJEw1hurNcK9RxU5m+lbu0KPmhZ6TpAFGvbTwoT5WkyJKJwgXqJ0nlO2WHnxijm5UbLSnadzQaLQnVr17Kia7ldLysxt/0s9du+ssthz7Ss6LocEDPCRAAGTzca7Oku3TjzdGPfe/3UjjG2vef669QeU6BIJ4PhzWVvJ7OeeW57z7XPWn8Mw52FtaNb3tvea/2etzmea/fOe9t7rt9z7UOo31N4aNa2180s9U6kG4NlQgNl6PUUUtJrj2m58Vx/E7WPIUSkdtINWAvaGLWXvhSeq9p2sS43dtM7/JzT/lbq1G6fXw+YaXqdK3Yl1rYP2bKWN9Rc3nI4K71PNnACP8vNpGUlDBbpb5oIFZXdPoyl7Zuov8teisqGRYE++brqBgAAgOjEFCgCUB3DnQEAAM8BohjCQ7NS+EGhH93AP3jw4Ew9FOm1FCTS8V3VIBH8GkOISGE7fSlIpBv1+rIbwroZrJvCGqZLy70NIei9Zxd91raGv8kGEBRwUA9peb1rjp0F2LT8dUHzvO3h1NAOCzratmka65FG2y/ts+sEArUd7DpMNAZdBIqmhRSBmBAmAgAAwMwIFAE+xTDcGTA0GuIMANAc3cw5cuTIoMJD0wJFGppMvRUoVFT1xrWm0w0wvQ5BouEbQ4AoHOJWw/kcO3ZsoVePSXQjXqEYfSlcFOuwuGU1HSoJ2y47bJcFIXCuLsM9BIl8qzP/bF3UfrxKT1fZnrHQnLaHPbN5x/xD7AgTAQAAoBEEigCfYhjuDBganugGgNl6HgpvaCsUM5btqgIPCj/o5qGGKFPgW70UKUQxifVoEAYnvPfGgunGEiKyHloUJFKo0HrxKKJth0JH+tK2Y+3atel64W143K4CRADiEa6fYbDI21BiQ9LlsGdAjMZ79AQAAIDGESgC/KJ3IgAAEMOwZaKb/mN+UlsBKn1+hYJs6BMNg6FghbWLBY/0t7rJOIQhnTDdmEJEouVdN9YPHDhQe3gpBYpOnDiRbNy4MVm+fLnLHrvq9qqUN4xZmfcitJCvy96t2Jb71tSyUqa3ItZZv4EizTf7GvMxL+JHmAgAAACNIlAE+MNwZwAAoM/wUHboMgUAxk43l6w3Fd1ksoCRvuzGod1E5EbisI0tRBQGiXSOMutNVvVstHfv3mTTpk3pvz0Fimz4w656IbKe0XDufNCXtsdantqmee55aL4xs/2y5qG2Y233VsQ66zdQZMdvCroq9ArEijARAAAAGkegCPCH4c6A2T355JOjGYoHAGYJEGXDQ5jOeh7CeOzatWvhBvFYAkRGvRCpJ64mgkRGQyfq9TZv3pyuS16CGhZKKOq5Ivw/PSBSJ1xg7UIwIZ8FN7oIExEQGcay0lSYaFpvRUeOHEmHckT3gSKZNVRkw9oSJkLMCBMBAAAAABbQOxEAAJgV4SGgHgv36wbj2EJEopvjCmscPHiw8WFfFFLSTWANeaYb/R5YzxUKDug8rShEZNPUoXNAejqbTEGrSfOhSer9iJ6JhrGszM/Pt/YeChUpAKhlxY65rrjiitbeD//M9s1N9FKkZWXlypXpawGxIkwEoJZpB0La+YVPfRR1p6oTlPCGlU7sipK4dkBmdJJ58uTJ3L9VAlzd2OpAPDxR1O/1lMs0OhgLp9F7FD19YOPVG32WonG9lT4OnzBTm6ntwvrtZ2t7ff7wpKLMwamdFNrJZpmTn6rzM5w3agsdTKndwtqz9NnVBlXmjeanuoA0+vuitH923kybn9bumiZcBsJ5U3Z+qp2LLoCE86bs/Jw2b9QettypFr1e2+vaLPMmXNf0/ZJLLknfa9py0/S6NknVdU01X3nllQtPoZRZ17Lzpuq6VnbeFK1redubWdY1653IuuKfJnvBqmi+SPbCit5D9drPea+RnabO+7QxTbZ96rRZn9Nk2z4rlnZuYrmJcd6E+6miNiha1zZs2JDs378/Xf9tG9PE+pk3zbTlpsznz75P19OEnyn77zpt1vU0ttxMWu6bXNeanjfhstPHdrDo8+v/J7XZtG1OrPsom6bKchPbPmqaLtaBWafpe7mZdd5Yrwp5289Z36ftz29tn+2pIZb1s6jN8padtta13bt3Tw0P1Zmf4d9Paou+189pbJtpbdf2utbUNHnLTd686Xv9rHt8nxeWiGHeqCcim2716tXp+Xvd9aaveVP03mX2t7q2oXORousbsz44YUNWhXVXWW7aWj/zAiRaFtTzpV1bmtQLUfa9imrLu844bblr65xg1vPCruaN2kbnyPpu1x9tmkltn23nMudEmtfhcUeX+7Uqx8R9z5twuclq61y6bDvb9ddwWQnrDmso+n32/7LLinolUjscOHAgeeyxxxZCRW3u18Jr3m2cF7a9X2vq+F6hIgVf1e6XXXZZ5dqs10ktK2rPovthQF8IEwGoTCcpGgZj2s7xtttuS26//fb0BHDnzp1TX087yptuumnh37qBft99902dRgdK11133cK/1Z3jww8/PHUa3RwLgwe6UWYn6pNs2bJl0YHAnj170jG2p1GIwG7Gi9LJhw8fnjrNtddeu6g7yoceeig35BG+9w033LAoSHD//fcXHvDcfPPNC0EK/W3RvNHfahqjE/miaVSTahO1tw6G1AbT2k2fXW1g7CBsGrWx2jocUkLzZxrNS81To/mv95rm6quvXrTcPProo4VhEi2b4fAWDzzwQGH4QutAeHOmqJ1FQQ2j1w+nUXvb8qB1Uv+XXde0Lmu5qbKuaVl+5JFHCtc1tVu4rmWfSp22rtmY0noab9pyc9VVVy3aFmmZ0bZgmuc85zmLLmpPWtdCN95446IATpl17QUveMHCiUKZ7aBOYJ7//Ocv/Fs1FU2ji1/XX3/9wr+PHTuWPPjgg1On0UnOs5/97FLrmrX9xRdfvOjJljLrmkKE6jrcltM777yzMOilwFIY9NIJVJngXnhCpmns5HJSUFDbqPBEsUw3smHQS69fNI09NWj0OYqmUfuEn9+6dZ8mG5K1AN40Wr/CbZr+vkxINrxAE4YV8+j16szPvuZNeJE6ry20jIXbgOw0efTZwxBeW/PT5o3NH9XVxLqmi8japlh76vOH80bvU3Qhp+r81Odten7myc7PMvMmOz9t3mSXl7De7Lqmv62zrhWtN9n5WWbe2LbT3qtMuDycn2XmjVSdn3XnTbiclVlv8h4wKJo3edvOSeuabfPLrGvZ983u18rMz2wvYm1uO/Vd7afPHst2sMx+rWj+6v+rHnO0sR0sO2/Cz1PnmCNvXWtjfobzRnXq8xRtC6seczR1DBnLutb2vLG2b2pd03lBVvi62ePbMvMzO2/092o7LUOT2i/W4/s+1rU2ju/ttfKOOWJd1+oc3xe9T1v7tewQwwoRhfNT71E0P7uaN2XWNdWaF64os65ZDxuqXecipuoN8TLTHD16dNE6nZ2feW3exbn0pP1auCzZZwuP4bNhorJBipDaQ++j309ahrIPwbVxvqblpI9rI2XmjT1sqPVUvYiUaeeqIS99drVz2AZd7NfsOG3adE2eS09TZ11TbeE2bdr5WpPHkJPaS69twdBsjzNlAmVlwkSqwx5OVvuoxzW7Dv/FL34xnSa8R9Xkfs3OC9XORet0djtYZn7GtF8rWtdUq6bT/a+8e6ZFx5AWbtX9lGwwH4gFYSIAtRTtWNsYDxYA4JPCYkXhTQDxyXsKHQAAjJc9JGI3Z7wMEwTEJgwRhTfnx0w3ji1s0vZ1ZV3X1ntYzxQx+9KXvrRws1sPx+nfZcIIVagN9PCZzYOyIZmxsd5M9OClHoYu6lG8DgUKPCyXmM4C1bastNHTWrisKARkASyFivT+6qnIHhSdFirC7LQf135F+/ZpnTDkUbBK81EP9emrzaHxgLrOe6bpIw8Ag6STFT21MelJoSzrmSiGYc5MdqgeL8OcTeJhmLOmhl7qY5gzo/cI0/Nehjn75Cc/uWiYs1tuucXNMGdjXNey82Zo65rNm3vvvbeX7oU9TBMuV2oHrd+xdOUd45AjfU4TUzu3PVyTnmjL20fF0s59TxM+MS1hr4R1tmkxTzOk+dn2eqMLiPZ0oYdtWlvTxLQd1LHLtN5I169fXxiIiLWdxzg/vbRZV9O03c55T0jr+lAXQ1RoPxsOAxGe/3qcN0Nb1zyvn33OG/VcYMIeyocyb3ReG/ZMpF45yr6PXl/XGrTPDq9Bt0X7f23PdM0jxnUt7EVatep11D7aNiokkH2fWXo+0c1vHb9aWxTVFts5QdfbTrv+ph76iq5DVpk3Op+049Kuzte0PFlN+h6e03qcNzGdF+u1rbcf9fBuf9tEz0Q6JlKYKBzlYNK8CbcX6kk+fA+OOZqdxkYnyQ55VvQ++n/rwVLHCdPuidi0P/VTP5XuY4tGaQCaQJgIQOUwkU4uPCVkdRM7HC/W2xM/nuun9v5s3759UZjo1ltvTbzw3vae6++i9h07diRt0WGthaOyF19iF17AUd1FF3Bi47ntPdfeRf0WjGiDLoKEtXt7+tLzeuu97WOt34b8mbYseN7meK1dF2WzN91CuhAfBqdj5LXth1C/59rr1p8dojocJrpLnvez3pcdz7XHWn8YIlIPMJ5q73K91WfX8VQbvXhk6Wa8evCwB6FiafswRGTDFhnVqECRbh7bTeu8m8xVjo11HKS2UDv09ZlVtz20pjpiObafRvVaTyRhb051a9c5t+ZF15/f877Wy3JjQ4Gpl6BsiKTOcqMHS23bVWU6e1jMrvnOIpbtZYz127B2YXCr6rKiY/FJgSLCROgDw5wBGDwFKmynWvS0QIw810/tGGPbe66/i9q3bdvWWqBIJ4NWt372djLrmee291x7V/Wrd7U2A0Xoh/e2j7X+ogvwnrc5nmv3znvbe67fc+1l648lPDQ0npcdz7XHVH/ZAFGMtfdFn7mol+qm6KZteDzZZ9tPCxCFVJMCBNpO68FB3bTO6+2kTP0KISi8omBCn0EiY20f9nIdM6tTw0cptG4BuDrLjuanvmIOxMTKw3JjtV188cXpsmIhlqrrrCg8ol7t6gyFZ9sWhYrCB0nr8L6varN+29/bMUCVUJEtK+rZSMHaNoZSBOogTAQAAAAAyKUu7/W0ZZuBIgAA0C0CREBcISL88431rgLjMQTTy4aI8gJFCrCrZ0UFiqrecNZ5nZZNBRL05S0IEAsLYW3evDntKapqDyGaXsOaaT7WCYfA37KiQJFG/9CyUmXQoCaXlSZDRZhM21htn3VMUDVQpGXl0ksvTbcrCqDFsL/CuBEmAgAAQKfa7J0IQPMsUAQAAHzSjYjwxhPhIaB5hIh86TNAUydElBco0nZd06s3Jw1XNT8/P7GXaQUQ1AuRhjSzEJF6N8JsLNih3mLUa4zmgR7EseG3svS3K1asSOeDgiGaB3oNjGdZ0TGY5r+WE6231lNRlv5Wy4gFB7Ws6KupbVc2VESgKJ5AkW2fNb1tVxRA62IIUCAPeykAAAB0jkAR4A+9EwEA4K/3IXuaWTej6PUAaBYBoubYDXLdQO1iqLMmb8p3ESDKY+EC3XhWuEiBFm3zwyHc9P8WPNLntWnQHAt3qc0tVKReZzQfFO7Sz2r/sCco/bvrZRD903xftmxZuqxoWdCxWXZZseWpq2XFtkf0UhRXoEjzO1xWbLui8FlXw4EChjARAAAAekGgCPCD4c4AYBh0EVoXpfWln8MhFnTR2m5a2E1HtMduGtm8yM6HcF7UHb5MN6lOnDjRaN0ACBG1xW6ednGjVO/TxX6ujRBRloIG2lfY/kSfLW+fwn69HWpz9RhiwS21s+aFfrb9+6T5EB6TZYcysuMAC4FhOGy+2rF4mWWlbdo+MfRZXIEiCc8FtIwoWFRliDygCYSJAAAA0BsCRYAfDHcGAH7pBpXCK/quG1760lPQumFrT0Hr4rRuQGooBX23J6HpzaY5amvNB32p7RX0sSeMw14k1P760lAoNh/ybiRmw0PZ4cuyNyYBNBMgEkJE7fXaoQcY2ma99XgOEYWsdxMLZBEc6pbaX/vysDeZaexYQPvp48ePp/NOxwM2TJ31TmPHAjbsEb0ZDS+EZtu9GOZrdugzIVTUf6DIaBnR/pFwIbpGmAgAAAC9IlAE+AsU0TsRAPihm1v6mp+fT44dOzaxxwfdyNINLdENrJUrV6Y929gQKphNGCA6evRoegMpj/7G/k83mLTP1dAGNuTFl770pakBIgDNohei7mibNzc3lxw+fLjVnhf0PhbW9B4iyguQWkgY8bHQl/b1OhbQsdmkZV1/Z8E6Bb11LBAGizCckHls62w2VESgKJ5A0YMPPsjDAugcVwIAAADQOwJFgC8MdwYAfm5YKZhy8ODB9Oey9Le62K2bXBs2bEifmLahO1Cd3ThUm1bpcUM3CxQA0zS6eWjDYhAgAtpHiKh72sZpW6fAhIVb26CwbNM9vPQZIoKvULGOrXQ8UCUwZ71K6hxc2yOtJzouA9pk2zJ6KYorUAR0jTARgMHTCehznvOchZ+98Vw/tWOMbe+5/r5rnzVQpAuPelrLfkZ3PLe959r7qt96J5o1UKSL91Y7N6e75b3tPdfveZvjtXYLX2Rv2Niy4+Gz1Gl7CxLpqXddsK5Lr7Fnz540UKSbr3UCRV6XnaZqt0CX9p02fEnZ9w4dOHAg3e9qXihkVKYe630j7/XQLs9t77n2WevvO0Dkve2boJCPApPqxa2N3onUrtbrXhNtH0uIyPPxsefay9av/b+OB7QvnyUop3NwHVPonFzGHCgaw3ITC23bskOfed5X9b2vJVAEbwgTARjNSahXnuundoyx7T3X77l2Q1fP/fHc9p5r76t+CxTNQhfMvLe9V97b3nv91N4t3WTZsmVL2ruL3ZTUMqQbiUNu+yaCRCHd/BIFitRL0RiWnSZqtyHL9u7dW2pIguwNjSNHjpxzE1HLsW7qaD6Uufnk8SbPUHhue8+1ewwRDantm9jmavumaxMa7qxpmr86Nshr5zoholh6IfJ8fOy59jL1a/+v44FZg0RhMEnHFZs2bUrfe6xD0Q59uYnN0IY+63tfS6AInoxzLwMAAIAoMdwZ4AvDnQFAfCzA0lSQyOgmmN2AHeuNqyrs5qFuukwLEhUFiLJ0I1JBMd1orxPsAhBfgAiLaV+zevXqtHci7c+aovMWhWLr7sNi6YUIvuhYQPv2Jofu03GFji82b96cHkf0HYzAeOT1UoR6CBTBC/YwAAAAiC5QBCB+1rW6AkUAgDiET7+3Qa+rJ+LL9LIzdpoP6lVD37Psxp/d/NNNRvsqQ3936tSp3NcGMJ1u2lmQSDfyCBLFx3pb0fmGDQM0KxsmUq9bdUgh3TQPeyIiSISqAe+y+/e6xxltDAkITBJuB8OQJaqzY5Aw4AzEhseIAAyebnA9/PDD6c9XX321u6fnPddP7Rhj23uuP6ba6/RQpJtauqkiekqbJ7O647ntPdceQ/2zDHem2u1JY90k8Nb2nnlve8/1973OjrV276q0vYI+6rWmrZCJhk/TkHHqFafsMuB52albu+aDplNbhcLpZ7mxqJuGepJZQ5xM62HDc9t757ntPdc+qX4vvRB5b/sm2fA/Ot/Q9lL7tjoUHNI+Sz0dTRrebFLbe+qJyPvxsdfap9WvfbWOB5ruKTKk4wz1tqX1xdOQWU0Y6nLjqXZdN56fn3c37Fls+1p6KELsCBMBGDwduKtbXPvZG8/1UzvG2Pae64+t9rqBIvTDc9t7rj2G+i1QVCeAGMO2Zqy8t73n+vteZ8dau3dl2l7rhf5OF/XbpBtXq1atSt+vbO8OnpedOrXr5qEFiZoKEGXpJo5CYwoTTbuB6LntvfPc9p5rD+vftWvXwnYq5hDRkNq+Sdq2af5p3s3NzaU3W+0GcBm6Qa9pdbNYQaKifZa1vW7o2t/GHiIayvGx59on1W+BhSrLbB067tMyPrYw0VCXG2+1q8e3MHzpJVQU276WQBFiRpgIAAAA0aoTKALQX+9mnnpjA4ChUYBFwXB972LIjqIQy1jp5sqTTz6Z9uIUDmPWBgWWxnoDEZhEASK7yambc333OIDZaP5ZzxH6rmCGwhP6ru1s3vBoK1asSHtrsZ6IpvXgFtq7d+/C63gKEYWqDuGG9uh4rO2At+g91q5dm4Yj2N6hL9pm7t+/310vRTEhUIRYESYCAABA1AgUAfGz3okIFAFAf3Tz3IZLaJveR71EEGL5Z0888cTCfNBXWwGi7HyI7clqoC/hUGYKk2BYFAiyL81fbfu0rbVhPRWisZ6MFKjQV9l9lG5+WwBNISSPy48+t3pi0nd9hT2eEDAa9nGZ3kfBOoXngD5ZCNNbL0UxIVCEGBEmAgAAAAA0FigCAPTDhtPogm5aeR6aoY0QkaxZsya9sX348OFO3lvvZeElbhZj7AEiG8ZM20IbvhvDo4CQhYS07VNvRcaCNGXZDW8bqsfrcqN2UC84FrDKYwErfbG/aF827NY2Hf8p5A3EgF6KZkOgCLEhTAQAAIDo0TsR4Ae9EwFAf7q6aTX2MFEYILIQkeny5qHdQCZMhLEGiMIQEcanangoL0RkPWl47OXN9jeqXT3g6DxMoRILmorCQ+qxRqErhU1saEzrxQnt6PJYQMJ5DsQWKBJCRfUCRUAMCBMBAADABQJFQPwY7gwA+sWNpG57IYphPjDPMeZeiIBZQ0ReKUiqAInOu44ePZqGiGx/EIasLGikL/2dwkTaf2koNw0Zx3Cl7en6WIDjAcTGtrP0UlSPjnXonQgxIEwEAAAANwgUAfEjUAQA/aGXgXYcOHAg7d2hKETUB+Y5hoxeiNCEIYWIrHdAfR08eLDy0GwKHelcTedpWp8UKFLPRYh7/1zUy9GxY8eS48ePV37PbI9cRcOWP+tZz6r0+oAw7NlsCBShb4SJAAAA4AqBIsBPoAgYk4svvjh58sknk1WrVvVdCjI3X3SzLXuzRDdcZP369emNtKHQZ1FvBV28z9BDLNYLkS07WrctUDSN2qWrZUrvxVA1GCJ6IUIThhYiEoWI1MvQ3r17ZxqaTQ9+6HU2bdqU/ptAUTv752mqDIOmeTWNtpNr166tPB/VW1Vo9erVU//+kUceKfW6hI6QRaBotuHOCBShT8O5WgIAE2g86BtvvDH9WV25euO5fmrHGNvec/2eas8LFOnmjj6DcEOlW57b3nPtHuqf1juR6lX3+vYzuuO97T3XH/s6O8TabRiQLLsBp/+PPUxUtu31d7qJVHTDqQk6Viy7HHhbdrJDmYXDxpShv+vqWNpCXZNq89b2Q+K57fuqvakAEW2POiEitb0dX5YJjvZBxzOTgkTT9gWT6BhIr6VAfJdBWI9tX7V+fdeX2njS8GNNHq/puKOLttOyUkQPV0wLHTUVNBricjP02m17bNvovkJF3va1YaAI6EPcV0sAoOGDG48810/tGGPbe67fW+15gSIPJ4FD5bntPdcec/1Fw53VueCNZnhv+yHU75Xn2r0r0/b6m+XLl6dDXbSt6k0rjyGiurWrXSxsNekGYpehLg9tP1Se276r2tsaxoy2H6dZeyKK+Ya+wkMKE2nYzVl6JMpS2EWvqR6K+uzpLua2LzIpOKNjALXp/Px8q+9vYfJYth1FgaNJ7VUnZOR5ufFe/yy1x9BLUSzrS9VA0cqVK8/pUQxoG2EiAAAAuMWQZ0DcGO4MALqjG1YKhuvifpM3GrPUc4G9zxBMChHNcnPCnnhWoLZNGnqtaBgVIEYMY4YmDXE4sywFiY4cOZIOc9Y09ZCjILKGuIq9l+q+5QVhJoVnNM90HNB2mEjHGzru8BKOyGuvSb0ZMVzacMUQKPJGx0vapjCsOrpGmAgAAACuESgCfASKJg13BgBohm4iKVjS9hOren1PN626CBBlaT7oQn+bYSLd8FVPBISJ4AUBIjRtDCGisFeiNvfthw8fTvfveq+hhIVnNakHnTJDfYn2z+oxUvvrU6dOJW0Hiz0flxEwGicCRdXt2bOn9DYIaAphIgCDp/T//fffn/783Oc+Nz0x8sRz/dSOMba95/o91y666HXixIn05yE9Le+B57b3XLu3+rPDnXmqfWi8t73n+qkdbbe9biapZwFtczV0SRu9ElXtDSemZadqiKhu7Wof3TzUfq+tQJHCGEXzIaa2HxvPbd9k7W0NYzYNbT98bYSIYm577c+LerfRkFo2tGadIYE1rfZX2q90/dljaPtZgkOaP+rdSRQesn2z9VSo7d7evXtbGfpU1+10vDHEYHGZgJGWnS1btkS3znpZ7mOo3bbhtl3vIlTkue1l9+7dfZeAkSFMBGAU2uzivQue66d2jLHtPdfvtXb1TvSpT32q7zIAFPROlA0UAQCapYvhCvysX7++lWEm9bp6fW8X3dvuiSiPeg3SDUTdYGw62KXAmG5Yal4AsekjQIRxGEtPRCEFUHSdpu2hskTv4e2hsi6GKytjUlBI+2ntr9WuGkquSQoQafuq9/DcK1EV4TzSsZVCWgpW2HEpvRb5RS9FQLw44wQAAMAg3HDDDcldd93VdxkACgJFwNDpRoF6bgH6ohDL3NxcGpo5cuRIY6+7du3a9OldTwGWPkJE2WCXbo402SOBbkrqs3iaDxgHhjFDW8YYIjLadyg00UZvg1kaisvCS95Cw3UCRF0MFaSQj/bX2iaePn16oQejWWn+aF3Qaw+xV6Iq4WrR8anaITuPCRf5QqAIiBNnnQAAABiMNoeSANBcoIjeiTBUuimh7veBGAJFFp5pIlCkIJFu2Oh1vQWJug4RhdReujGr/Z9ujszaC6hululGi153iDd64Q8BIrRpzCEio/2GQihd0Xt52tfHFh7Ko5BLeCxgwyvVpf2/XstbwLsL4TzODolGsMgHAkVAfNjTAAAAYFAUUvA6XBswFgr96eInAKC9J+GXLVuWhoD0/eDBg7V6NbAh07TN1s1FD8NoxBAiCqn91W6bN29ODhw4UKtXAk2vz6NA19h7IUD/GMYMbSNEtNiZM2c6fa+metIbc4Aoy0I/CgEdPXo0DXrXaWcdj+m4TMdkXo7L+kKwyC8CRUBcCBMBAABgkEOe7dy5s+8yAEzpnej48eNc/ASADgJF9l03r+bn50uFrhVWWblyZRpe0c8eeimILUQUsp6ENm3alM4DDYdYpqcJzTsbso4bh+gTASJ0gRBRPs/hnrGGhyYFinQsoKC39u0KFOmcuMz81f5fx2Q6NtNxmb44Higvu1wQLIofgSIgHoSJAAAAMEjbtm1LduzY0XcZACYEivbu3Zve0GaYFtShi+csO0A5ugFlN54UStHwGuod59SpUws9EGid0k0uhY6WL1+e3uTSOqZpYl/XLEQUa5DIWFvqSzcD1f66iahQkb60T9R8sPCW5oV63LR5R29E6MOuXbsWblgTIEJbCBFN12VoxENAxVOAKEvHABYw1nf1Gqlee3UcoOMC60VS88GOBdQbkY7N2jgu0+uPkS0z9FgUNwJFQBwIEwEAAGCwCBQBcV8YUg9FQFW6uK4L6gpA6CscukkX1z3cBAG6pnVDN4wUWFFoSGGWvB6KbB3y8MS7lxBRyG4OiuaDbhDatiz7dxY8ij3MhWEGiGyZVICIZRBtIURUbb/RBe2bYtz/ew4QZVmAW8daOhbT/LVjgfB4QH9nxwNNH5fZe2W37xZsjnEZ6HIoNEJF8SBQBPSPMBEAAAAAoDdlhtsBjIJD+rLlRr2q2M/2hK/93FePKroorYvRq1at6vy9gSJ54RTrmciTmIc0Kyvb25DH+YBhDmOmIXjUgxnQFkJEcYaJ7L1iCRAOKUCUx8LbdiyQFyxukl5f5006l7IwkfWMGJ5LWZjIzqXGcmxCb0Xxsv0EgSKgH+c9w4CrAErQBbqjR4+mP6ub7fn5+b5LKi1M9XtM1nuun9r7s3379oWn9HXyd+uttyZeeG97z/V7rr2o/th7Jzp27Nii2rkBjTGx3ol0jOmJ9/U2rN0DHdcoOKTvOhexoYEm3QTR8mTDA+nJ365vihAmioeG8zpw4MDE/9+wYUPa0xV8GEKICOPYz3oLEAnDmKHt9ZYQUf1jmT179uQe+zZJveVpOOo+j4s8BojaDgE1eS6l4JDWUwVGw2HVjM6d1JOlhrtVL5Y6h7JelMbY7jqnM4SK4qBeisYcKLr77rvT9fhd73pXsnr16uTIkSN9l4QRoGciAIPn8Yb4UOqndoyx7T3X77n2ovoZ7gyIly5YK1D01FNPuQsUeeZle6+LxLrwra/Dhw8XPtSgv9eFcX3p73UTS6EDCxV5+dwA/A9pBsSIABH6QIhoNgp06Jj24MGDrb6PwiN9hEY8BohCMZ9f5J1LTetfQv+n8Jq+wnMpnUfFdi7VRS3hskhvRXHQPoQeioBuESYCAADAKBAoAuIPFAEh63pfvRDp5kn2ydky06t3VU1vvc9Y1/0A/KA3IqDZEBEBInSFEFEzFPDRQxcKd7Q1RLTeQ73RdBkmCsMZ3gJEHs+lFCia5VxKPVeN+Vwqbxg0QkX9IFAEdIswEYDB04FvONyTtwNez/VTO8bY9p7r91x72foJFLXH25BJQ6nde/1h7RYooneibsS+3FgPQ3p6dtansHXhfO/evQs3sbq6CK4hBBiWB6iPEBEwGwJE6HMYGjsv9xQiivX4WD0T6Wvt2rWt9U60fv369D3a/ux5vRCFQ9bH2P4ehzmzIJHOpTTc77TeiKqcS+lcPZZAUV/t3mSoKNZtjofaCRQB3SFMBGDwNGTGzp0705+3bt2adtnqief6qR1jbHvP9XuuvUr9BIqap6cjT5w4kf6sp9V0EdILz7V7r39S7Qx31j4Py40ufmtZyLthEt5wKDtEp/5eN7Y2bdqU/r0ugrd9kVkXmD22/SSea/fOe9vXqT+WINEY2x6+276JYcy8Lzee6/dcu4Q9jep43lOoOva217GrrnGolxirc9bjY6PX1WfWMFZ99EKUbfs+hlqbRYz1W49EswaJsudStmy1fS7lod1nDRXFvs3xUnudQFFM9QMeECYCAADA6BAoAuJkvRMRKBo39TKnJ2CbfvJaF8F1QX3z5s3pxWYuGgLxiSVEBHjSRIAIaGo4Mw2VheYpwKGwj4abUi8xCos0QcMAa5uh1266h5G8XojQzbmUvpoKEmXPpZYtW5aeR8UQmooBw5/1R70TKeRGD0VAuwgTAQAAYJQIFAFxIlA0brpIbUGiJi9+G7324cOH06EcdCEcQDwIEgHlESBCbCEi3dTVEK9tHL/hyxTeUI8w6mlT50saEnjWIJHOvfSaTQZDpvVChG7OpQ4dOpQGipqm19RrK9TWxbB4nhAq6geBIqB9hIkAAAAAAFEGijA+ukCtGyN5wzc0RTe6FFbQe7X9RK3ey9MQH0CfISIhSASUDxERIELfISLdxEW3bCgyBReOHDmSflWlAMjatWvTY1S9XhPHw/RCFAcN36QwkR7MaYteW8drTS07Q0OoqHsEioB2ESYCUMv8/PzUcZbDA1iNz1t0AhM+da6L+kU3D5R8D7vN1UHyyZMnJ9aq/8sOY6DuYIue4NCTGeETy3odvdc0+vtw3GB9lqInAbLj+qrN1HZh/faz0ecPP9O0eWLUzvbEgJ5UKHNiUXV+ZueNDVMxrT59drVBlXmjExY9QWP090Vd/GbnzbT5ae2eHS88nDdl56fauejJrHDe2PvPMm/UHrbcqRa9Xtvr2izzJruu6X1U37R2aHpdm6Tquqb/1/yusq5l502dda3MvCla1/K2N02sa2Xmjd4jXN/qrmt528tp69rWrVuTe++9d+r7ZPcfRXXVmSa7jdC/i7YbNlZ9DNPo89nv9HMXbdbkNGHtRdPENm9C+vuiNsi+T5/zpmi50f9pXZ42VELVedPkNOFnyv67Tjt3OU2ozBOrsy4DVdpZr60Ajk03if1fdr0pQ9No2dJ+R+9Zdf0s2866kKyLyOE007Y53radXvZRRa9RZtvZdzv3vdw0NW/y3mv37t3pd93QzNuvFb1P3r6j6jTTPr+1fbY2L+ta3rLT1frZxLzJfpYy08Qyb/I+a935uWvXroXfKQAQvl4b8zNvuWl7Xevy+D77+cu8T1frTZ3zwi7nTRgi0jTZ6bLHx1ltHkPOOm/Cz1NmuanzPk0d39twvdoe6FxJx866HhIe1+fVpml0LVH7XF2z0Ze9V9117Ytf/OLCv8NwmWrRa2fnTdF5Yd46UGb9DN8nfL1pnyXbznWnKTpOm/V9ypyv2bUx/W3bPYTZebqWHzt/m6TsMjBtmkltNm256WoZmDZvbH1QyEWhIgsUhduZSducLs+/m9h2Vp2mjW2nesvSMHwWKBrS8X3Z9wHaQpgIQGU6OdHT4tN2jrfddlty++23pze3d+7cWXjT+aabblp0A/m+++6bOo1Oeq677rqFf+spjIcffjj3b3VjWRcow+CFHciFF2TybNmyJbnssssW/r1nz550XOpprrzyyrS72fCpMQ2lMM2111676ILQQw89tBDysPqN3dS/4YYbFt1cu//++wsPKm6++eaFkwv9bdG80d9qGqNaiqZRTaotDB7YTZQwkBDSZ1cbGA1rEXaVnEdtrLY2eg/Nn2k0LzVPjea/3iuPtXt2WX/00UcLwyRaNsMn0B944IHC8IXWAQvT6GC2qJ1tiCaj1w+n0XIaXvTT/2XXNa3LWm6qrGtalrNPG+UdvF999dWL1rXwaeMy65rex24kTlpurrrqqkXzR8tM0RNZz3nOcxY97Ryua5PceOONiwI4Reualp3wgoxOLIvmp25mPv/5z1/4t2oqmkahmOuvv37h32qvBx98cOo0enL12c9+9sR1LW97oxuh4VMdZda1yy+/PNm8efPCvzX/J61r5pprrkmHnKmyrikIFG7btWzmbS9Dz3ve8xaCTrauaR2ZFvgKg16apmiZ0bwPt89aXoqmyfaOYT1zFC032RBemeBeGPTS35cJyYZBrzCsGLZL+N5qg/DzaJoywb3wZLlMryRNzBst99naQ1o+wm2ATTONPnu4/LU1P/X34UUJ/X1RO6uuqvNGnz98H33+oos/ZeZndrkJlzP9n9ZvuzCevfgybX5Ok52fZeZNdn7avMmuO+FnzK5r+ts661rV+Vlm3oSf396nqHv6cH6WWdfqrJ/67GozC9ROu8ho/5dXd9kQnpar1atXp39f1G55286ii/qT5mfe9nKWdS07FGCX285py01X28Ey+7WidU//39d2sOy8mbTc1DnmyNsOtrFfy84bTROGWUQX/EXzXXWXOeYosx1scn5a24efpa11Le+Yo2iaonmTt+x0dQyZd8xR5uGPcN7o7217P6kt+jqGLDNNqOq6pmtJ4b7Qlo3s++Ydc5Q5hpy2ruUtN22va03u1+oc38+6ruWps65lH2irs661MW90jUl/H4ZF8tpsWq111rU62866x/fZ5SZ7/pGdn3XOpcscQ5Y9X9N6r9dWm+l9dG1Vx9D20Fh4TVD/ry+b73r97LlX1XVN11/sPXRMLdnXyD4EN2ldC7c52drUxlUfBi67HayzX8vbdhYd31ddP+vs13TubMEOnefUCSsUBUPCaax3Iv39tOU6+4Bi9oEQ289pemvLsvMzbHdbH6oec8w6b8rs13QPQ9PY9XTrucjaLm+bk33gtM4xZBfXrexB5bzrNZOOU4rmjerKDkduDwDZMqp2y7a7Pn/YQ5HuGcRwfN/V+RrQJsJEAGop2uEVHbADABATnQy22Q00gHoUalWgKK/nIgyLLrJpPuticZtP0hq7AKkvLVtlnvgF0IwwRATgn+mhjfB4Rzfoi27sAW2yByoVVJjWWyi6ZwEO3dwPv/IC+RYCsR5fwqBmFeHDhbpRX9SjNrqlcykL5bTNwvjTlqPwIbMwCBIun7ZcWoCoTO9AXilEpM9mD0EyFOA/s1Bk+BC8LQthAM2WKdvW5QXNLFCkh2HDB/4B1HfeM0PdMgNolJLmR48enfikUJb1TGRPFk/TxTBn6hlG06hXFuvFwtMwZ6pf1HuP1e9hmDPV9E//9E9pm4W1exnmTO2u91HPNFa7l2HOPvnJTy4a5uyWW25pfV1rapgzffbPfe5zaX3TlpuYhzlTN89qb/WcUyakkp03Vde1Joc5y25vPA1zpgudX/jCFxbVX3VdyxvyrIsubMM6VJ89VeZlCJnwqafsvLT/L9LXMAjhuqPa8wIrMQ9zFj4JpfqLLgjHNsxZ2eVGF4PybmD0OcyZ9aBnwl4J2xrSoKlpwuVG2+twW5qni27WVZf2Fepl0HqzmzZN+LR1eHxbhv29LuBqv1A01Nks88Z66LT9fHa5D9sp1qF6bJrwKcpsz2gx76N07DKth0T1jFgUaul73vS93Mw6b8Jj20OHDqW/D7eZs7xPF8OcWU9cYU8esayfRW2Wt+x0tX7OOm8sTGzvP+m8sO/1cxLrIUds2Zk0Tdhrdthba1/bzrzlxtMwZ0XH93WGD+lqvckuN3bcNk3T80a9YoU9T5eZxmSPj8OeoSdN08fxfd68Ca+RZXtumjRN1fdp6/jejmXzhhbLspqrrGvhUGYWgCjqxcY+S9lhzsLlPttrUlPDYrU5zNm047SuhjnTtk/r4L59+wr/Pqwn+95lKVCm47m8cylbjm25DHtotZ6zLBgS7mdsCL8ybTZtuelqGSg7ZHh4vqr30fUNe0+tU9ltTuzDnNl5of5+0nW07DST3ie7rIS9fWlZsb+1ZUXfbdkJw0fZ97H1QL3nez++D6e555570p/f9a53paHzolEagCbQMxGAynQhvEzQwXaUky74TKKDgKrT2NMXecKLTuFOONvFYhk6YJk05FLZoSTKCG+WhfXrAH1SkKtqm027GNfU/NR7WJtNqz2rzryxLnubmp+Tlps6T2KV/dyhWeeN2iIME+W9XtPrWlPzxg7kpcpyM+u6VlZRm9kTNfZzF+ta3XmTXdfKbG/qrGtdzpsy28vsNKEXv/jFyY4dO6ZOU6d3lKJpsheJ804ci/Q9jX3GojBOWV1OE9ZeZl7FNG/CJwGLLhzn6XvelF1u9ESZLgZNW6+7njfhRdOitu+7nbPC7Y1dsG2ztirLc3hhbdI0k56ArTovw6eyq0xb5fPr4rACRXkXh4u2ObEtN2LtVGZ7Gcu2s+g1vGw7Y1pu6swbvYdunOh79uZyk+9Tt7aq/x/j+jnp8xctO7Ed22Snse9l2yKWeWM3tiZNEwY2wgBRLPOmjeUm1uP7Ou/T1rzJLjddbgdtCHRNGw5nNm2avN+Fx8dlppmmy3XAggxtnhe2fXxvD35Nu7k/qea8vw97Isr2omI386uYNE24jc8LcVV9n673A2H9RedWdd6n7Oe3MEZXwoc78mpRuEn1KFCuwMO04dd0bVLBCB0j2vXNcH3MM2256WoZqLsOaH1SW+ihSH1dddVVjS43XWw7w+sis5wX2rbLHj44fPhweu9x2rKi5SRvWcmG3ezcw/vxfd1pgKbQMxGAyj0TVQkTAejH9u3bF4WJbr311r5LAlwpChQ1TU+PhRdd857WB8bOniyrE9JtA+tt83QRUb2WdPV0nZ6210XrqmHcqhQmYvnoj/V4le2R1Oa7znWr3ghANU888UT6vWyICBjqfjYMEMm6det6qwUwFiKSMiGisa23YzQtRIS4z6XCXl7b7plI51IW6Mgee+u4Wz3XKChT1It6SNuNtWvXpq+t43MFR6qGYjyy3myf9axnJWOi5U/LrZYRtUHRCApZOo7SfstCRXnLigJFV1xxRTIUd999Nz0ToXP0TAQAAABkbNu2rfNAEYDp9GSZAkV6ojGWQBGa1+UTd9lu6tukm2vcUOuHbkSo1w9ucPaDIBGwOEREgAhDDBHBPwJE/tXpjWbW98tSkEjhEAWa9FW1Lwv9vR4u0YPsmzdvTv+t3tGHHiiy3my1Ho4lUGShMy0nCvzU6ffElpUtW7akPdxPCp9pfzekQBHQNfrFAgAAACYEigDEFygSBYowPAr3VB3qdhZdPenKDRmMFUEijD1AZF8KENkX0DfdVLUgkUJEBInGTeEFCxLpmJXjVr90XqPgTV/nUhYkUm9EBw4cqBUOMXodHUdq2KtTp07N9Fpe2LoXrpNDpZ51FCRSgEoPjM26rOhYS6Ei/Zx9LdvHhQFaANXQMxGAwdPBiXWRuGLFCnfji3qun9oxxrb3XL/n2odQv3fWPbbHdvdcu/f669ROD0Wz0wW2sIeWWJ70VB1lw0Rh/XV7q9FXH599bOssxtn22SCRt/pDnmsfQv2ehzHzHKTzvtx4rr+t2umJaNjLTdX6Y+qJKNZzE2/1631tqKe2wzdaxsJhpfR+Coeoh5mmhl3S8vylL30pueyyy3IfOoml3evKq9/WRQ+9FNXdXuozK/SjZeXw4cON1KLX1LmHeh/Ku6agfZ7eL+yhyPv2HugSawmAwVOC/fOf/3z6pZ+98Vw/tWOMbe+5fs+1t1U/vRNVC3Lpy07IvfBcu/f6Z6ndeihC/Ytt1vYxPeWpC3+6mLd8+fJSF17DC7BVKXTax0Xnsa6zGFfb5wWJPNUf8lz7EOr3wnogEuuBSMu/17b3vtx4rr+N2umJqBzPy02V+mPsiUj16hqOvry2fQz127mNznPalj2XUjhED/to2KkmqT0VrFGvR/qKsd3rmlZ/7L0UzbK9VOhM06n3qibpusCePXvS5SSvpnDf5317D3SNMBEAAAAwBYEiIF4MdzYsFiZauXJl6++1atWqtGeiruiC8LFjxzp7P6AvDG2GMQ5jJgxjhthYiMgCRISIxi3GEBGaP5fS+Y3Oc9qm8zXr5dWCPhrerA0nT55MQ0oKoYxJuJ7GGCiqQ8EdLSsK/bS1rBw8eHDqssJwZ8DAhjl773vfm8ToO7/zO/suAQAAAB0Hinbs2NF3GQACDHc2TLooPTc3l3ab39YFY/V8pK7P6dIcaBZBIoxtKDPCQ4gNQ5kh5uHM0M25lHoN0rmOegtqw7Jly9L3sAczFA7RQxNtvZ8oTLR27do0jDK2czittx6GPSvDlpVTp0619h4KE+n4LG9ZseHOdCxHb9fAQMJE3/Vd3xXlOJeEiQAAAMaHQBEQHwJFw6MLfrowvX79+laebtU1Br22PUkLoBkEiTBkBIgQO0JEyCJENO6eXjds2JAGUJoe0lqvr9e2cym9vkIbhw8fTtoOoczPz6cPnIwtTCTZHoo8hoq6Wlb0HgosTVpWtI/UNSRda9i0aVOrtQBDcb6XjUwsXwAAABgvhjwD4mNPlDHk2XDowp96D2qji3490aondfUefVwEZjnFEBEkwlAxjBm8DGUmDGUG2b17d/LFL34x/ZnhzMZJ5znqPWj16tWNv7aO9XQuZb0SKbih3mQ1vFTbFBDR+42Z52HPbFk5fvx478uKAnEABtIzkYnlaUHCRAAAAKCHIiDeHoowDLoGoIvUummrp1CbuuCoC+oKKPURJAKGiiARhoZeiOABPREhSwEiu3muZcLCHhjvuZQeorAefZqg8ygLE9k9Wy1zXQSJRO9jnT7Ecs+4D16HPdN862pZOXHixMJ7TltW1DvRFVdc0UlNgGfRX0EjwAMAAIDYECgC4g0UMdzZMKhLcoV+9NTgoUOHZr4IrovpChPp4vcYu8YH2kCQCENCiAgehL0QAdkeStroiQb+z6X0s3pqqXuvVWEMLVs6n8oOHaXXPHXqVNIF9WqjcJQCTGMPy3kMFHW5rGg5OX36dLq8TlpWVq5cmV5j0PHflVde2UldgFdRh4nG3mUdAAAA4kWgCIiThpEiUDQMuvCni9fr169P5ubmkoMHD6YXBqtQeEjTa9i07MXvvuhCOL0jwTuCRBhagEgIESFG9EKEohCRggU6RrbeOIDwXEr7Np1LHThwID0PqXsuNemhjKrnZ7PgnvHkIc9iDxUpTBTbsqJAEcOQA8W4egVg8HSQazeUYrh5MKb6qR1jbHvP9XuufQj1e+e5zT3X7r3+pmu33okIFA1nuVGdy5YtS7/rIrbmrZ6sLeoiXX+rrvhXrFix8ERiDN3haxnVU6Re2j/LY916Anb//v3n/P7o0aMLN2a1jMUupravGiTSumf1x7AeVuG59iHU77kXIs9t77l27/WHte/atWvh94SIxrevLRsiGtpy76322OsPz6W2bNmSDh9d5VxKISSdR+l8KrbPFnO7d1l/X70UedhelqlfvXcptMtwZ8Bk5z3DOGIAStBFQrvYqhszTY21C6Ad27dvX0j766Tv1ltv7bskYLCa6J0o7HJaFxF00QZAfQoUSZuBItbb7unpQuvaXt8VElH35fbUoY559MSsXTDXv2MJEYV0sZflpTu6WaInsSfRBWTdMEE59EiErrSxn2UoM3jguScijo+7DxEBs5xL6Su8fqzzKH3pZzufmhYasdD+tGPtJikso4dFxj7M2aRzzJh7KNJ5u5YVu1bTNrWDheGK2IMnHgJFd999d7oOv+td70qHHzxy5EjfJWEE6JkIAAAAmAHDnQHxoYeiYdKFbH3pJpW+dKE7+3yUPeGp77GFiLJPj3KDDd4QJIJXhIjggecQEdpDiAhtnksplBAGAO1vyp5L6W+66uHTepv13iNOW/rqoaisLpcVe8io7LKifW5eT7YAvowwEQAAADAjAkVAvIEiDI9d3OZCMtA9gkTwggARPCBAhEkIEaGLc6kzZ86cE9ipwoai7oLeJ+YHRmIQc6BI863LZcXeswqGOwPyESYCMHg6KFY3u6Knf6seFPfNc/3UjjG2vef6PdceQ/1jDhTpSTYbZsieYvPCc+3e6++idgsU0TvRcJYb7/Vb7V5HnA+HQcC42t56JfJa/1hrH0L9nkNEntvec+0e6p8UIvJ8jONdLG1fN0Sk+sPl3tOy47l27/UfP3580b81bFIVWld07U3hDQ0t3CZd5wvb1nO7t1l/F4GiOttLW1Y09Fh2uWualuNpDx7l1W+9ExEoAs7FY3wABk8Hsg8++GD61fZBbRs810/tGGPbe67fc+2x1K9A0RjpRFxtri9vN8g91+69/i5r13BnGMZy471+q10XOC0A64UuuFq728VXjKPtZx3erO/6Z+G59iHUXzVEZEEihYj6DhJ5bnvPtcdev25SWpBINy6zvRF5Psbxru+2141/CxIpDFC1N6KYl/sh1z6E+mdhPRytXbu21fdR2GblypWLAqLe273N+m37EYYT+95edrWs6D0UPJsWJp5UPz0EAvl8PW4OAAAARG7MPRQBMbLeiRQooociAOg2SASMqSciIA9DmWEahjODdwptKLxx4MCBc4ZNa4r27wqJxNrbXIxiHPLMlpWlS5cmp0+fbuU91q9fn77PLEOi0zsRsBg9EwEAAAANG2sPRUDMgSKhhyLExlvvRBgXgkSIWWw9EQFVeyHCuM3aExEQCwU39NXWMqwh1LSP1xBZiKuHoqosELZ58+ZWXn/ZsmULYaK6bF8dBoGBsRvM1rerRKq6YmsrXQsAAIDhoIciIC70UIRYl0kgZgSJEBN6IYIH9ESEaeiJCEOknmZ0jq1986FDhxoNn2g9scAS/PdQpFDY3NxcsmHDhrQ3qybv3Suk1MSyon33/v37G6sN8G4wYSLGEAYAAAAATEN4AwDK90pEkAix0A0duzlJiAgxIkCEMsKeiIAhUZBDIRFt/86ePZscPXq0kSDRli1b0p6J6JVoOIEiLSsKn9mycvjw4UZe89JLL01DSk0uKwx3BgxwmDNtMNr8AgAAAKpguDMgzkARw50hJgx1hliHNwNiYE+tM5QZYsRQZqgypBnDmWHI1BuMQiJaxtXrzCz3VPU6CoeotyMNXcX92WENeaagmEI/qmnW/aaWlcsvvzxZuXJl+nNTywr7c+CfDSrO2UTvROGGht6OAAAAMCuGOwPixHBniOkpUSC2IBG9EqFvNrwENxERI3oiQhn0RISxseGlFCbSufbevXuTkydPlp5e+/u1a9em01s4iWOAYfZQpPmreat5rSDQnj17Ki0ropC59sEKJumrjWWF3omAAYWJfuu3fqv2tKdPn06fcnnwwQeT7du3Jzt37lz4P3WLdtttt6XJRgAAAKAOAkVAnMOdESgCgHMRJEKfHn/88fS7hjURbiIiFgSIUBYhIoyZQiLqeUZfuq96/Pjx5MiRI+m599NPP507jUJDq1evTo9BLRhiwSQMN1CkZUShcX2/8sor02VFw57Nz89PXVa0nGh50c9tLiva11u4HRizwYSJ/u2//beNvdadd96ZBoj+9m//Njlx4kTyy7/8y8kf//EfJ1/1VV/V2HsA6I4OJqwrbI8HoZ7rp3aMse091++5dg/1DzlQpJs81ubebvh4rt17/X3XPvZAUYzbybHUn63dLuiuWrUqiVnf6+yYddX2bQ1v5nnZ8Vy7t/otRGTH85NuInnhqe2HVHsb9XcdIvJ8jOPdrG0fDh/UdZBIy7puqtvPnniufQj1t0HtoKCH1il96XxbI8GoY4dTp06l+3jbVis8bOEjCyKVaUfv7a6a9dn1XZ/ZW6CoqX2VLStaPvSa6uBj0rKi4JH1QGTLVl1lp9V+n96JMHbnPcNYXrnULD/6oz+a/OIv/mL6b+3sPvrRjyYvetGL+i4N6IXSvkePHl1YH5QOBhAv9bR39uzZhYPjW2+9te+SAPw/eYGiY8eOLQyxq5Pi2G8oA0OiQJFUDRSx3qJJHsJEXqm7fPVGPYm61rceUMZOYSJ6JUJfISLR0Ca6aaT9q53PGru5aDccgbbRE1F1Yz0+7jNEBMzC7vcY9fbSBm0X9GX7eKPthO3bPQaCZmHHOtY2k9qlzbbROWjfvRPlseUkbJc+lxX1ThRLmOjuu+9O2+dd73pXur6q1y+gbYPpmahp2hi9853vTE9o3/e+96VPq77hDW9IPve5z7W2QwUAAMDwDbmHIsAj66EIiOEG3FhuuiE+BInQZ4hIy55uqP3/7J0J2B5VefenFbKShAQiIcmbjZ0ACQH8QEGh1n1H1CqIy6df/Vq3y2pbUdHSVmv3r7TqZ637bqtiXVpR2aRsBsQihkACCdne7IsxJCx+13/87sfzTuaZOTNzzplzz/x/1zXX+7zv+8zMPWefc/7nvg8cOJAKAOXnI488Mmb3Pnak4yd2pWMxSTwSEOIKCohIFSgiIsQOUwjSdyAEwfgGP2W8Ay88IiwSTz3mmEe88LgW0aDdiiHcWZbYygm9E5G+wzeuEv72b/82DXGGcGd4yf3ABz6QvP/972/bLEIIIYQQohgKigiJU1DUx3BnJA7E1TwhbeArvBkhZSIihDTDghoW0yCohBdsERBlw5xhoU2YMGFCutkTP0VYREgTKCIiVaCIiBBSFQiFMMbBgfEOxj0y5skCgRGAeAhjHWw4kTGPj1CYMQqKCCHxQDFRCUcffXTy9Kc/PbnqqqvS3z/60Y8mf/Znf8aXVEIUgQmnHTt2pJ+nT5+eKrs1odl+2k76mPaa7ddsu0b7uyQoMkNR+Ngt5RPNtmu3P0bb4ZG2D4IimcgEmJCMIe37Yr9222Ors30hRNr79Eqkuexotj02+7MiItgGERE2cGIMP2xBLQ+cgwN9Nq6F9jS2sX9Mad8n26vYH6OASHvaa8Ym7WMWEWkfY2q1vQv2a0VTukMgjTnLqmMePOO+ffvSA2MezHXiWV0+r2xqqSIo0t5X1bGf3olIn6EixoInP/nJg89o6G+55ZZW7SGEVAOTU+joceCzNjTbT9tJH9Nes/2abddqPwRFXUAWg3CYcd41oNl27fbHZju8E4mgqOsgvTGZiSOGtO+T/WW2Y4dqrMRWZ/uEz7QP4ZVIc9nRbHtM9ouQCMIfERKhHdy1a1eyZcuWSkIiE/TZo6OjgxAhMRFL2vfNdhv75Z1RFgdjERJ1Ie01U5T2WGQXIREW3mMTEgEJmYQj69ktdjTb3gX7taIl3UVIhDEPvCGbYx78z9Z2jHk2bdqUCpJcvwdLm2YKJrvcVzWx3xQiE9IXKCayYNasWWN+v+uuu1qzhRBCCCGEdIuuCIoI6QJ9EhSR+IhxYYp0GxES+fRKRPoNREQ4REQEREi0c+fOZPfu3Y3vgZ3lECRBUFRXlES6jwiIYhURkTjRICIihMRLdszTVHjjc8zD9q0cjhtIX2GYswqIq7Nt27a1bQohhBBCCOkQCKErLnYJIe0LirBjsC8hz0h8wDvRYYcd1rYZpCdQSERChDQzweLX3r17kz179ji7H3b1o+8+6qij0nE1DkKkLMqcPhcBiS1r164dfOYCOyGkiZDI15gHTjAw3kGYLlegvasS7owQ0g/4ZmWBqM9FNTpu3LiWLSKEEEIIIYQQ4gt6KCJtwQUrQoh2siHNTCCeh5gIO/Rdg2sjhAi9ExEAzw04AL0QEVsQQggHoCciQkhT0Y/PMQ+ui+v7CDNmG+6sj2A8wVBnpG/QM5EFV1111ZjfZ86c2ZothBBCCCGEEELCeSgihJCuhjijVyLiS0RUJvjBApsPsPt/8uTJ6S59lzv1iQ5kcQ8LqygHYMKECS1bRTRgLpxPmTKF5YYQ0gj0QxD6hBrzHHKIu6V+iCg3b95MD0WEkAH0TFTC5z73ueTWW28duEMFixcvbtUmQgghhBBCCCFhBEX0TkRCgwlchDojhBAtIiIbIREW0yAm8t2vov1k+OD+iYhESASPATNmzGjbJKJQSISyAyERIYQ0JfSYx7V3InplK4feiUifoGeiAj70oQ8lb3vb28YIiRB7e9myZa3aRQghhBBCugtinhNC4gKTgJMmTWrbDEIIcQK9EhFX2IiIBCx2YRe9j3AcJvv27UvvhYU8jqv7s4jHMGakrohIFs0pQiSEuAJjEMwhhBzz+PDISO9E+WDMsW3btrbNICQYnRETrV27ttH5Dz/8cLJnz55k9erVyW233ZZ85StfSe6///60sYeYSH6+6U1vcmYzIYQQQggheZxxxhnJ8uXL2zaDEGKEO6OgiIQGO00PO+ywts0ghJChIiJbIRHA3Or+/fs9WvXr+xw4cCAZN26c93uRdkVEFBARFyIiQghxDcQ92sc8DHdmNyYZGRlp2wxCvNMZMREaM9ODUFNEMWpe8/jjj089FRFCdHHooYcOXhDxWRua7aftpI9pr9l+zbZ3wX7NgiKMmSVGu8sxeQg0267dfk22d01QpCntu2a/re0yeRsTmtNdOy7Tvg2vRJrLjmbbfdlfxRtRdq4VmzlDgIU1X7v0bUF6yzuJtrITo+1VRESa661m27WLiGIs91XQbL9m27tgv1ZiTXeMd0TkU4YLu3EfXx6Qhr2Tau+rXNhP70SkT3RGTARcNphmA4Lrzp8/P7n66quT8ePHO7sHISQMUGZrVghrtp+2kz6mvWb7NdveBfs1C4owdta6+1uz7drt12Z7lwRF2tK+S/ZXtT0m70Ra0x3zOJgIR3glk8mTJ6c/2xQbdD3tu2C/Zttd21/HG5E5twpxT6gwQhAt+Q4tUmWRUxux2F7XC5HmeqvZdu2eiBAWUXPaa7Zfs+0+7JcyG4JsvwxxhBYvNLGWGxET2Yx5XAhxHnnkEe9jnqx3Iu19lXb7CQlNp8REvjwTveY1r0n+5m/+pvKLMiGEEEIIIX0RFBHSB0xBESG+idE7kVYgGMKCgznXo0FE5Bp4JSIkpDei7MJaKNoWEpFmMJQZcQHDmZFYsRELHXXUUUFswcYFc3yMTQxF9mkRGrVN6DGPz/sx3NlwMEZhqDPSBzolJnLVYGKX6WmnnZY84xnPSF71qlexgSSEEEIIIa1CQREh8QmKCCFEI6FDnBHdNPFGlCVkGAyNITf6jikgAhQRkbpQRERiYpgwJ5RYqA5FtuU9D9dP28f3uIebXAjpN50RE11zzTWNzofr1ilTpiTTpk1L5s6dm+5YI4QMJ+siPs9dPIAb63379pUOdsxQEXAB+dBDDxWegzo6ceLEMe4c9+/fPzRuLBZ8sPMU9VtcGMLtdVnsWrQNpstD3AP3KgLfN91B41nK3FpOmDBhzM5YpBnSzrRfFq/EHjy/2VYV5YmAdJbBJQSYNrvqq+anmTewfePGjelP0/YseHakgWCTN4hra4aexPdxXpW8KcpPSXd8f/bs2QPbzbyxzU+kc5ng1cwb2/wsyhvYL+UOtuB6vutak7wx6xq+j0lr2FdUblzXtWFUrWuwHzt7kN542YKNZXUtmzdV65pt3pTVtbz2xkVds8kb3EPiVdetazt37ky2bNkyxn7fdS2POnUNaWqWs7z8PPHEE5O77757zH2yoXnL2hpf5+B3aU+Rj1lvD2V5CbLj71DnIH9M2/MmYcxz2kznvHOA2A87yyaRsvdpM2/aKDeu8mbGjBlpeyPnSviWYefYPEvIc4C05UjDsvffpmXAdrek7Tm4P8qOuCc3x7ch6meTvBHbpdybzzzsHIwrhoXWC9l2Sp3FT9TXovYmpj4q+7+s3Tb3aauPknOy7aX5DLb32bRp0+Ba2TTw3XbiuxJ2alhfW/c+2ed3nZ+S9hL2SeyLZWxTlmZ5Zcc2ndevX5/+xFxp03qD82WcUmUzqOktoQpmG5WXhiHyxhwbSNqHGqc0PSev3Piqa6ZgDeOrWMf3dc6pkzdAbJd36pDjlCJs8sb8W57dPseQInKYOXNmaVjFbJmWkERF5SZ7Du5hUwey6ezrHLPe2rwXmt/JvssMw3xfszknL53L3gtR7pveJ3TeiP3wAJmX7rNmzRpzjvnTR/2s06bJfcruZQqN5D7333//mO/Mmzevchmok59F5SZUGci2M/I/2zFP3XGOiTmX6qN+moh3ItuxfQzv377H97jW2rVr03VH23OqjgdtbSPEF50REz3lKU9p2wRCegMWxbFIW9Q5XnbZZcm73vWutFNesWJF4fWwAHDqqacOfsci7T333FN4DlxunnDCCYPfd+/efdDAVcBiLCZOsRiMAa8sLiMGsEyODQOD/Tlz5gx+Hx0dHSxUDwMDZry4mhMju3btKjzn2GOPTSfohNWrVw8WnsV+sUcW9U8++eQxQoKVK1eWDiqWLl06GFjju2V5g+/iHAG2lJ0Dm2AbwKASu8ugXDdtz4JnRxoIO3bsOGhXWhaksflygnsgf4pAXpovcMh/3CsPSXeUddxLys2aNWtKxQcomyijwn333VcqvkAdkHtgEFqWzuKpRMD1zXNQTqU8oE7if9m6hrqMclOlrqEsl7njxQTgwoULB7+jrpWFVDDrGp4FaYaFs6JyM3/+/DFtEcoM2oIijjvuuDG7sc26NozFixePEeCU1TWUHbyIIN2nT58+SP8i8OK1ZMmSwe+wqewcLCqedNJJg9+RXqtWrSo8B7uJjznmmKF1La+9gSDKdNdqU9fwAmVOMCD/h9U1YdGiRWl6CTZ1DeIaU+hz7733Dp5nWNmB90lzUrYsnfHytmzZsqF1LQ/c95RTTqlU11C3zOcfVtdQnkSchnJjCqbQ59oI90yhF75vI5I1JydMsaIgEwlyj6zQC+fYCPfMl+WyugnMxXXYUHYOrm/2nbAJ55i2Z1/YkeZmG4BzbIR7ZvlDetmIZKvmJ74P+0xRSFk6w66qeYPnNyc/8Pxlkz82+ZktN2Z9bpKfofJG0kDqkHnvbF3Dd+rUtar5aZM3Up7FnjxhQVF+2uRNnfqZrWtFeYN0ERGmWW9t6k02P23awby2s0wkOyw/xXa5rlm38PzZeoOxGMY3w9IvKzLy2XZKncUz5E1wttEOVunXTCFUth20EaT7aAdt8ybbXsp1q7SD+B/SF7Zn65qPfs3MG3kW2JDX11YZc9i0gy77NUl72G0uloRoB130a3llx6auyQYD2AMb6owhzbyRdMO9i8rbMHFr0QJX3gKN2Aob88qqqzFk2TlSrpAONmPIbN5UrWtVxpBFdS2v3Liua+bcmowBkaZN207Z/DGsrx2Wn7GM73EfyQuc32TM4XoMaZM3Re1Cnbpmcw7mJ3CeeCKqswlOzil6L8xuOLWta2b9tB1zVK1r5jjNlqpjSKRHnX7Npu00bcfn7Ma5qpuB6+SNbb9m1jXM2Ui9wU+kT7bNyc5L2dY1k6rtoM05eXUNz1903rD3NXOeFZv8zLkszA9m65qr/DTLDZ6nzvi+Tl0z24Zsfkp/Kc+B9CwSkuSJiaoKSfLaK9sxpJmfZXmDvhpzz2K3zHFkN0fm2Ve1HazTr2XfpYvqmryTy32ajO/xO+avh51bZwyZV9fK+jVCfNIZMREhJCw2HhYIIYQQ4h68qNp4liOE+AUTT5jUwuRT2cQzIU1BGcvbdUoIIS4xRUSukQW2EIshuI+NJyASlmyIFFNITkhVZCMUNk35aLMIyYLNa6ZYARvCspsF+o4ZEhXCImzkFaFJH8KhmeIgzBfYiFOaYnrq9Q3KPMRi5gZvQkj3+Y1fVvEtG5i3ve1tg8/wVvAHf/AHrdpDSJ+BwnzPnj2D34e52RfEM1EMYc6gDIaXE5wDrywyWaElzJnYD+C9R+zXEOYMNv30pz9N08y0XUOYM0l33AeeacR2LWHObr755jFhzs4++2zvdc1VmDM8+1133ZXaV1RuYg1zhv/DvSnSG55zbIQfsYQ5y2tvNIU5w+5WeCcy7dcS5uyWW24ZlDM80znnnFOanz/72c+iCIOQ3c2bzUv5fxlthZAx6w5sz9tRFXOYM3NHqCycVblPm3nTRrlxmTdoP/BT2gSzrQgVbqLuOWa5ybqE1xDmTHbmyS4/OS9U/WySN+auwqxXgKL7YIE/7x0sZNtp7qIUz2gxtIO250idlXPEk6irUD0+z8m2l+b3bO6DBVd53jpu85vmjTl+GtbXxhrmTNIedpleVmIZ25SlWV7ZGXaOGdas6n1snkXGxCIAKMMMN5INxVEEntP0LpxnZ6gwZ5L2Una0hDnLKzdN6pp4j8X34cnY5hzTljrje+mvst6RQoxTmuRNttyYHq40hDkTrxWC6bGkzn3y0gzzLeCII47IbR/qhvcpey+MPcxZlXFa9j42aeYzzJnZ5qDcm++Fde7jKp2lrJWFLKs7vvdVP6u0aaizpncl09O+y/uIh3P5fpEYxTY/i8pNW2HOxH6pj2VjHjOqQB3Eo3xWUOSyfmbPwXsp8g/Ph3OKxvYxvH/nIfmTnU+o2xdu3749/Tws1JnLMGd33nln+vnKK69MpkyZUhqlgRAXRL2t7e///u8HFQahP4rERJ/+9KcHn/FS8tznPjeIjYT0EXHdZwM6yqo7jTDgrnoOBotFO3XzQt1kXSzagOsMC7k0jDq7Y7LuTOWeSJdhaVM1zdC+Vj2nbn5KntqeWydvMGg2hV8u8jPvf9m8saFMfJdH07xBWphiorzr+ahrrvJG7lOl3LioazbY2JN1TxuirtXJm7y6Vtbe1MnPUHmDumbTXpqEagfLzkE+mJMQNvl55plnJsuXLy982SzD5Tnyt7LJeltCnmPaXnaNttM5S9aldtU0aDtv2i43TfJGzhPRKCajhvX5badzUblBG+W73PioA3k2hKqfTfPGFI8WXSv7P5v7hixrVc6Loe0s+nvoMlDnHNu+atj/6pzjOm/kXrb3i6VO510vtna96PnL0h7/h5AIP02PAlXvUwbaPAmHa7tTv+o9ABZCcS851+YaPvImb6Eohv7TZbkpu48ZUluEH6H7KG3j+2y50dJHmX/LC9tT9z7m80voJAlnVnROmVA+75zse2FVsb0NIc+pkgZ108zlOcPa7Dr3aZLOZogu3Puoo47yOr6vYpstddoN3+N7Mx0hLJL+wcZbUZlQxfxpc06d+wyjqDxXHfPUAWOerMDJd51GGwyhHX5K2+n7vdBHfrqcgzryyCOTbdu2WZ8fqnwS0gsxkWCjbn31q189RnhEMREhhBBCCOk6Z5xxxhhBESGkHSAYxCQhREV1RMSElIHJWoSHKdstTAghVVi3bt3gs62QqAlYCIHnI3gS8OEsHxsLsCmh6gIacYsLEREhgq2IiJA6mOIhoUhARJpjpq+kf9dCoIkwh2MeQkgXUCEmqkLEUdsIIYQQQghxDgVFhMQBdqPB5TcFRYSQmNiwYcNBYWYIMYVEIUREAjxvwsupj7AMWLibPn16eo86HhlIMyggIq6hiIiEEhFRPNQekvZdFBVB5ONzzIMIPRjztOGxBv08QriZYf/Ir8ZCIyMjbZtBSL/ERNk4ioQQQgghhJCDoaCIkLgERYT44uc//zm9ExFCVAqJBIT9gMDtwIEDTkN/QEiERTvu0A8LRUTEl8CDIiLiGgqI4qWLoiIJdeZ6zCNCIlybY554wBgIoc4I6SJRi4ngAm7Xrl3pZ/lJCCGEEEIIIYTELiiidyLiK9QZqcZjjz2WegzDJL5sVMMkPIRZAHW1jR29obwSERKTkEjqH3bSy6JL08U18UiEkKO4LgkDRUTEJRQREV9QQKSLromK8I4B0Y/MEbga8+D9Bddt0xMjPC7BO5H2PCKElBP1GxZeREREtHbt2mTHjh1pQ0kIIVXAzrS5c+cOPmtDs/20nfQx7TXbr9n2Ltiv2TsRJjAkzbWFldBsu3b7NdtuQ8zhzmSXpHzWhmb7XdjelncirXX24YcfTvbs2XPQ3/fv35/+RH6MHz8+iZkmaR9DiDOtZUe77Xn2ty0kEmQnPRbXUD8R/qOOV3qIh7A7H3W47UW1LpWdIts1iIj6Pk7QREwiIiz6S7nXKDLWbL9r20MLiPpWb2MRFWkp80Vjnip2o4xhfRxjHjx3m2VN0h42aCzzPussQ52RLhK1mGjZsmXJqlWr0s9oXK+44ork7/7u79o2ixCiDAwMNO880Gw/bSd9THvN9mu2vQv2axYUyQ5zjWi2Xbv9mm0vQ3YexiooMifPNKLZ/qa2t+mdqMt1Nna0p71m+zXbbtofi4gou7iGxSgI3iZMmJAurmHHvo2oCOfCExF2xeP5YswjzWUna7sGAZFJn8cJWohJRNSVtNdsvwvb2/RApDntNYuKNKU7xi2wF9F4Jk6cmDrRqDLmwUYSjHnwGf1z2wIeSXvkz5o1a9R5J/JVdhjqjHSVqN9ofvu3fzv5yle+klZsNKr/8A//kNx+++3Ji1/84uTYY49NXxrzGk3s0rv++uu92fXkJz/Z27UJIYQQQgjR6qGIEKJHUEQI6TYMcUaEGIVEWQ84WBzDz0ceeWQQlhCLbI8++ujgu1j0wXcgPMJCHIRIIkgiftAmIiLxE6OIiHSjPIG+by7rMl0IfyYhz+SnOebBIWMeEbpgzAMvROaYR7wcEUJISKIWE11yySXJZZddlmzfvn0gKPrhD3+YHnmIihPejC644AIvNsEONPKEEEIIIYTECgVFhHRLUIR3XTnMd1MuoPYT8U7URqgzVzz22GNjftfqIj92YghxRtolZiGRiSyQYbc9Fs/QRkifh5/SPqDfw2fZ4U/8QBFRtXGZ9GEsk8OhiIi4pKoXIr5Ldaf9RH6Pjo6mZUCjoEg8/w0b85jfk0ODcNpHfmjuaxnqjHSNqMVEUFx+7GMfSy688ML0d2kkyly/1YmxTQjpLtjNJrsyZ8+ene5i04Rm+2k76WPaa7Zfs+1dsF+zoAjjb+ykAm3Hbu+T7drt12x7CEER0ge7E2VyEYdsbDEXUs0JRts0xDUffvjh9LPsjtSEZvs1296kzmbLM37K7lvZaatpwjw02ttLzfZrth1CItgPz+54BrQ5sduPui/13/RKBLTtxkfam+197GmfFRGJYBXttbY22UdfW9SPmWHhmnrM0jxO0C4i0p72mu23sb2qFyKf71J17O8zkg+ymQD5IGu4Ztqb7wRFSN7ff//96XXmzJmjLt1NMZs8c1bsFvO4wSzzMt/h6rpSXlyUFRv7XZcdhjojXSRqMRF4wQtekHz5y19OXv/61yc7d+5M/5bXQGQbWh9QpESITjAA2bFjh1p3p5rtp+2kj2mv2X7NtnfBfs2CIpmok88xT3p0yXbt9mu23aegSCa6McGFxd+9e/cm+/fvz/WQi4kvCCexUCw7HG0n1STtcQ2NaLbfle0I8R7SO1GdOmuW53379qV1ABO32fIs7v6lPMtuXU2LAj7R3l5qtl+r7eKRaNq0aangHqBOabEfoO8z7cWmU02YC9exp33WExHabCk3GvtZl31tXj9mhqPJ9mMopxjrNenHNI9xhC1btqQ/8fyxi4iG1VltaLa/yPY6XohQh3DIuxR+inDABHUMYaQw9sTnKu9SXay3rkGaIF/xU95pkRfZdVYJdYq2U0J62fSbaFs2bdqUjnk0eimSvhbPiXcgTe89rtsbqbNSVpAuLsvKsHsC1llCylExqnjxi1+cPO1pT0s+9alPJd/85jeTH/3oR4OFojwo+iGEEEIIIeRXMOQZIXFgs2NPdshhohWbafImvU3wfxx79uxJJyARxgaTYRRh9CfUmYYFBEwI7969+6CF12zZR7nHge9iUQciCEwYc4KXkPqhzbIhBTVhhv2IWYijGYYzK0YWwSHexVjLth/btWtX2o8h1CTGZLGLyVwiYxM8P5gyZUrLFhGNVPVCZNZD1Ft5lxLPgmXvUqjjEBWh34RQge9SboQmOJAPEGEWgbYVYk0cGPuj7TQ3yxSBPMM91q5dmyxcuDDRSDYEn9Z30zqhzsyygr4TZaUoLZqUFUJIx8VEAI3Cm970pvQAaCwwwQSFIhqXRYsWDVzBnXzyycm3vvWttk0mhBBCCCEkCigoIiQuQVGedyJzEg0T2lXBuzF2ZkKAgYUbTqj1g9DeiWyRxZnt27enCzpVwLwOngvzPtOnT093nWoJERQLCPuKeTTSbyERIcOgiKgc9GEQImBDM/sxe+GHiIgIqQPeZSSMUVVv0xAZoN5izRDiv6qgno+OjqbjJxEC8l2qOrI5Bu0f2s+qombxOC7tJ9rOso0FGPMg3+uIWUj74WClrBQJdl2VldBjrZGRkbbNIKRfYqIsGIQPc28L9fD8+fOD20QIIYQQQkisUFBESLyCIoiIMJGGv5ftoC0DYiRca8aMGelEPHfVdpdYvRPJAixCnFSdFDbBuagTmByGYKrLC7GEuIBCIlIGRUR2iJdI9GNNvHt1vR8zRUQSyqyOiIMQeJWRuoayVPX9RYRE27ZtG4RorAtEKfIuBSgoqi4OQTuAd9ImIB/xnjNz5sz09zKRCMoN2mwKitrFNv3NsgLPUiHLSggwxkJ7REhXUCsmGkaXBuSEEEIIIYS4hIIiQuIBLrwhKBJ3/JhsaiokMq+Nd2MsXmGzDd+Tu01M3olkMaepkMgEO05RhuHtAOWZEHIwFBIRGxERBUTlYEwmglhXYQLRj0EcgXFfF/qxPBERIU3LUV0RkPku1VRIJMDTCbxros0UT0nEThyC95KmQiIB7xJoi0VgVibsEm9WUrYoKop3o4uUlaZCorplhRBSjU5tUexCbElCCCGEEEJ8C4oIIe17JxLRDya/sQO2agiNMvbu3Tu4PukuMS3iYT4G5Q2LL66EROZCLK7t+rqEdAEKiUiRiAgHFsQpJLITJaCfQT/mSkgk4Jra+zEs0MsiPcYfMY1BiM5yBPFH1XBmeWNPeDdxJSQyBUV4n4LogZSDtg1CTFfiEPO68h5gu/abFRWRuBCvzD7KivS11AkQ4pbOeCb6xCc+MfgsLggJIYQQQgghB0MPRYTEISjC7jmIiHyFhMAE3YQJE7g7jwQBE7fYYepaGAcwIYzJYbiv5w7xYjZs2JBMnTq1bTNIICgkInkwnFk9RJTgylNkth/DgjjGf9r6MXoiIq7LURMBUZ5HTGzM8PkuhfcovkuVi7rQxvkAQjEIu9B22oawQhkbHR1l2LPAoI8oSnOUFRH9+BD84D20alnxPR4bGRlp2wxCGtMZMdGrXvWqtk0ghBBCCCFEDRQUEdI+06ZNS7Zu3ZqGI/Mx+Ypd9VgUwyIzJ8C771K+zVBnmAyW8uYLTA7jQFk+5JDOTGcRUhsKiUgWiojqgz7Mdz+GBXFN/ZjphYiQWAREJqizEBL58kKC66JNQH3lu9RwIA5B++ZDiCkgnxHyGHliK8akoCg+Yi0rPsA4DOEXCekC8Y9aLfn0pz89xjPRc5/7XGfX/uY3v5kqJYVLL73U2bUJIf4ZP358smjRosFnbWi2n7aTPqa9Zvs1294F+zULivCCLmmuaZetdtu126/ZdleT39jBCRf6SAdfgiLszIOXEtwPO/QkvceNGzf4rA3N9mu2HeVH6qyUJXNiGGH1fIdvgecj7BDvG0VprwHN9sdqu62QKFb7+0DItHctItI+Rqtjv/RjrsOb5Y3LivqxttO+z16ItLeXMdlfVURUZ3wsYQlRb33XWWwAMd+lujS+b4p4msEY3Se4B96bTTGmTbrHLCiKqc6Gsr2tshJbX0uINjojJnr1q189qPRLlixxKiZ6z3vek/zkJz8Z/E4xESG6wKABizNa0Ww/bSd9THvN9mu2vQv2axYUYRyudaeeZtu126/ZdheYk9/Ype5LUISJb1wfbaQpJtKwI34Ymu33ZXso70TD6izKme/FHICJYfEeoW3SvSna20vN9sdme1WPRLHZ3yd8p70vT0Tax2h17I+lH2sr7fssIurC+DIW++t6IqpjewghkYhlUG/Nd6kY075tMZGPMMdZkN+TJk2qnO6xCoo0l5si24eFOpO+D56JfAMRoFlWujbOISQ0OluqAny6NARUKRJCCCGEkK7BkGeEhAfvmKZ7bxEU+QD3KZpMI6RpWc6WZ5/gPoceemiQexESKwxt1m8YzszPvH+Ifgz3evjhh6Ppxygiqj7ekUPAehFEJn1eN/IdziyP0GNP317LtIJ0CZkPUv+q1rdYBUV9InSdrVtWCCE9EBP5Ag2OL6ESIYQQQgghbUNBESHtT6ZBUOTDOxEnwPsD3Mb79k40bEdyqDJGcdxwNmzYkIY1JN0FXokoJOovFBH5Af0XBD6h5v7Rj02cODFpE4qI7ECZEG8aOEQMhnEPPkNEBGGYiIng6QJHHxav2xAQCSISQF6EIGT7oI2QAhF536grEKGgKI6+NtS9pJ1uuz3G2G1kZKRVGwhpCsVEhJDOA1eka9asST/Pnz+/9Rf2PtlP20kf016z/Zpt74L9mgVF5m40xKzXFHpGs+3a7ddse1NkMhoTXHm4FhRl76M97TXb79N2CXUW2nYRE4VCFhH6hOYyD5BfEv4CHtjantTXaruEN9Nqf99wnfYiJAohItLe5tSxP2Q/JgucodOeAqJiJO1RV5H2yKNHHnkkzS+EzEGIpWGiEgiIJkyYkEyePDmt722IikLVWx8iorq2h6q3cp9hwgTtbaYLwV0b7WeddI9JUKS53NjYnpfGfehrTTBm27Ztm5drExISiokswKBR0BrDkpA+g8EBXvrkszY020/bSR/TXrP9mm3vgv1dEBRpRbPt2u3XbLsvJNyZDw9FXUp7zfb7tt2nd6JhtocU9/RNSNSFMm8uNMWwQ1ij7SIkquqVKBb7+4irtA8pIupKm6Pdfte2U0Rkh9RTrP+IiGj37t3puKosT0RwhEPG8VigxrVCCQRMcbePsZJvT0R1yn2oMaHNfTS3OU1p8z2gTrrHIijyXWfbtH3YJpeYnrPPdZaQqlAZY4E5gTtlypRWbSGEEEIIIcQ3DHlGSLvhtF0Lirh43B98eycaVr5C7qTVtGuXkDaFREQ3DGkWlpB9S6hxGUVE1TCFRPBABG8SdULyYBwPoQLCjuLANeGlSCtthjOzqbchPJ1I+8B3qnw0vgeIoIiEpYt9LSF9gGKiEjZt2pTGnJeGZ8aMGW2bRAghhBBCiHcoKCLEH/J+eeihhw7ca/sUFOE+nEzrFz69Ew1bfAsFyzPpIxQS9Yu2vBH1la71YxQR1QN5AvEQxuBbt25t5LUCmwV27dqVCpOwnhRaeN11EZH5LhVCTMSx53BQrkO1n9JWuxQUte2dqE8g/1CXQt0rpGc4QroOxUQlXHHFFWMaoMWLF7dqDyGEEEIIIYQQ/WBiC+EPhomJXAqKcB9OpPWH0N6JMFcik8N1dvBXBfdheSZ98kpEIVF/oIioHdCnyII4xB++8TEuo4CoORClYBzjcgyFsGfIa7TjyPfYBSkxC4iGvUs99NBD3u9FMdFwkC7IhxCIEMV1XlBQ5Af0RWbahhQToT+Xd1RCSI/ERJ/+9Ketv7t9+/ZK388OGrGDD43cd7/73eTuu+8euJ7Hz3POOafWdQkhhBBCCNEGvRMR4g+8X06YMCF9/yzChaAI9+FEWv8I6Z0ICzooZ77FRJgY5i7TfOBVm3QzvBnpPgxppmdc1hQJeeVqXEYRkRvghQhCMoQ2w2eX4wyUKZQtXDPUQnqXRUQC6hDekUIg+UcORso1fjbx5tVWPki4MwqKwpUV9IG+PYrFVmcxzhsZGWnbDEK6LyZ69atfXTrIhuBHKuZrXvOaxveU68l90fhcfPHFja9LCCGEEEKIFigoIsQPmETDJJfNLngRFNUB98C9YppMI93zToTyBeHSnj17vN5n8uTJLMsFTJ06tW0TiGMhEb0SdR96I4oDjJXQx/gWE6GvRD/WVExEEZFbMBZH3mPM7RqsMWFDAMbysY3JNYqIBKQl0tS3Z0y5R0z5FhMSwg9t2+7du1W+B4igiIQrKwgDGaKvjQGM7yBUJUQzasREWYFP0+/YkB3Uv/3tb0/mzJnj5NqEEEIIIYRogYIiQvxOpu3cubP0+1jcqOOdaMqUKU53wBOSB8oYjokTJyb79u3zcg/UFywi4D6EdBkKifoBRURxekyACNtX2CTcY9KkSY36MYqI3ANvKjh8CqIhVkLIs1jERJpFRHnvUk3CQYcSAPZBjIk65GptNou0nT7fA+idKFxZgfDMV1nB+6jvskJI32h/5FIRiXOYPWy+U/VAYybhzSAk+vM///PWnpsQQgghhJC2BUWEELfAKxEmqMeNG2d9DgRFVSZdZRc06R9YZPTtYSFbniF+8LVIhmszxBnpCxQSdVtERCFRfGD+H+Ml1D1fooEm/RgWuUUAgv6dQiJ3INwOhNC+w+5ATATRkq8F9KrlCCIirUKirDDBV7gziAshTEC9JcX5gDSaNm2al+ujzcS1feaD9roQK+irTPEi8lL6Wh/g+tLXEkLcoapG2Q60mgzI0JBhMhcTtIsXL07OOeec5JJLLknmzZtX+5qEkHbBwP/EE08cfNaGZvtpO+lj2mu2X7PtXbBfs4civLBLmmtb5NVsu3b7NdvueuEK758ISVX2Livhzmw8FMkkHSbS8jbgSNpr3GWr2X7NtpfVWZQ5eHVAudu+fbvTe+O+EMfVmRiGrTiy9csMaR872ttLzfaHtl28ErlCc9prJy/ttYiINPdVTexHHwOxBxatbbxGVgGCBJt+zLR97dq1g79TPOQP5PkvfvEL7+3kgQMH0nBcKAM+hP5F7b0pIOpSnRVhAt6NEKbKpVAL18Z1896lutZmutwkA2Ge63CBeLfAO0a23rhOd9SPkN6JNI/RmtiOvIQIEO1uqLKShXWWkI6KiTCoK0JcDWLAsHTp0uT2228PZhshRMcuBa1otp+2kz6mvWb7NdveBfu7ICjSimbbtduv2XaXE6/wTISFxW3btjkRFCFdjzzyyKGLFXkefjWh2f42bId3Ikzuh6izKHNYLEU4D7ivd4HUD5vFnDwwoYzFAaSD1C9cx1WahEJ7e6nZ/lC2+wpvpjnttZMVEWkQEmnvZ5vajz4D/QO81LgKe4V+bMaMGdaihDVr1gx+p4jILxKZAiKfEOA+vjY+ySYFjaHMmtRZEQHi3Wfr1q1OBEWwBdezESV0oc10gQi70Mdt2bLFWZ2CuHOYENNXuocSFGXrrCaa2I5zkZ+uy8rUqVOtN5+wzhJSjc69TbIBIIQQQgghxC8MeUaIWzBRjR3rmFCzWfSVHXx5Ic8weTZz5sxUcET33iT0IiTmZFCeMZnrItQBFtxQnm0XcwjRDsObdQ/TG5EGIVHfkX4MfRj6spD9GEOZhQfCEwhRIIIOAbwTlW2ab0rXQpnZgPqFugYBUFMBLeop36XqgfRCXkj6uRgTTZkyJb1mqHXfPtSXGBCPtq7KivTZIcsKIX2iU71hm/FmCSGEEEII6RPDPBQRQqqDCS/sWpef8DgEF/FVPRRhJz0m0mRyjhAf3onKwCIOyjEmdLGwg5BnVXec4hooy/A46CscCCEx4Tq8GWkfbd6IyNg+SARF0o9VFZtU6cdMDzIUELUjJgqFz3tp8kTkSwSIn7NmzUrDFErouiqgvqLeos7W9YjZd0SABZEI3j927dpVed0WeQlvbnifaEscEjLcWV+ROta0rGA+BPMiFBIR4o/OiImuueaawWdtrqoJIX7By8N9992Xfj722GNTd4ea0Gw/bSd9THvN9mu2vQv2axYUYVJUPKXgJV5TOA3Ntmu3X7PtvsAEGNIBi45Im71796aiomGTaviO7MIVAZFco8tpr9n+NmzHwuTmzZuD2i7COHwHC1oox5gklvOHgXKMOR1ZfOVCjv4yr93+ELb7Cm+mPe21C4nQd0sIZuSDprTXXm5c2C+Comw/Bs8ytv2YjM3y+rFhAiLY/tBDD6WfMcbTlvbaMcOhakHKEmwXr5B9bHNEUIRzIURBPUSdLRMV4TzMHaHOYuxaR8Suvc10DdIQaQCvQvC+i3da5EWZmA5ph3zAOZIPRXXRV7qjzR8dHU18o7m9r2r7MHEW8jhbVnAg1KhNWUHdxTWqehFjnSWkp2KipzzlKW2bQAiJFDP2tUYPZprtx0sYFsNkcKkJzemuHe1pr9l+zbZ3wX6tQFB02223qU5zzbZrt1+z7b6QCTX8xMQcJtKwcIX2DbvikWYyWY4D463du3enk284z3bxQ3vaa7a/LdtdeCeqarssBOBAGZXybIb5kPIsu4+l/HNStztlXrv9IWz3Gd5Mc9pr9kiExWyNcyFdKTcu7Ef/JIvi6JewaImxGMZk0o/hPiI8sunHyrwQ4Xpiu8Y8QF8vaLHfzCffYiKX1816IjIX9zXiqryY71Kok+hf896lULelzprn1M0jLeU9FOKpVIQeEItI24mfkl6ST/JdOWJ4p/XtnUhze1/F9rKNLbIJRQS48G4r5QQH2jZ8R8pU3bKS9wyhx4cjIyNB70mIKzojJiKEENIeGOBBzY2XVnkhwEBOBoFQiruIf0sIISROTj755OT2229v2wxCOoNMcANMnmHCTIQXMs6SA5NqmKDbunUrPbIR796JmpRnHFKes5O3ZnnW5A2AkKYwvFn3REQS0ixk6CRSHxEEZfPL7JPwEwe+hzkuzG+ZC6nZcVkVT0RdSkMcpjcJpAN+N9MnNiRvMX9Z5gnDBRLWuAlSlvoWyqzp2FPK6bA6Tvwggg9pPyE4NNvbbNsZSzsRyjsRyS8rqLvSr2T72tjKig0YG27btq1tMwipDcVEhBBCaoGBP3Y3Yyc83D1jgI2JUCxSQFCEwR9i1h599NHJ3Llz08UtuNuFuryqu1hCCCHxg3a+zIU4IaQ6MrltM35CHaSgiPj2TuRi0Y64ZcOGDel7FtGFz/BmpF0hEYkbEb3gwNyWeEAQMYnpMc/0TFRHeNBVEZEIsCQN4fFFPA/Kwi/SDsIrEdA09SLhA1mQho2Y2/SNTThiGy9ExM+7FPGHtAGaQF3z7Z2IdKOsENJ1KCYihBBSGbxgQzSEHfA33XRTcuutt6YLE1nExS5els8+++zk3HPPTd05HnnkkZzsJoSQDgIRA3dhE9IOGF9hbEZBEYnROxEhJB8KiXRDEZE+IHqBAAZzVZjHKgsLBUEMvG3jEE8nNmIYEX90SUAkiHgIAqy9e/emhwixsu+CIsDC2FS8lksI1FiAjQgt7FtMhOeuKiaiiIgQQgghbdMLMdGWLVuSlStXJrt27UoPDHSbcOmllzqzjRBCNIHdRXDJuGPHjuT73/9+eti4AUa7e9tttyXLly9PTj/99OSFL3xhOtmGF+GYJhAIIYS4CXm2YsWKts0gpJdQUEQIITpgeDP9UEikbz4Lc1M4tm/fnnrQsQGiGawn7NmzJ/W2LaKivLmsrnohynp0QpogPeCpPBu2dNh5IjrC+BQiShHWxOClCHkJu5DPPjfGwDOkbThXiogIiQN6JyKEkA6Lia6//vrkE5/4RHL11VcnGzdudHptiokIIX0EL//YyYxJz09+8pPJ+vXra13njjvuSO69997kkksuSSchZs+eTUERIYR0jDPOOCMVkBJCwkNBEbHxTtRmqDNCyK+gVyK9UEikBwg3REgET0Q7d+60EsBkgcgEG+vgvQb5jmtAENMHERHA84o3IowzbcVYWTA+3b9/fzJjxoz09xgERRLGDkIxCIp83QPXL5v/pIiIEEIIIbHROTHR6tWrk//1v/5Xcs0116S/13k5KKLtwS0hhLQFJgswYfbhD384nUBpAiZwPvaxjyWvfe1r03YVgqI6McMJIYTECwVFhLQHBUWEEBIv9EqkX0QEKCSKHxFuQAADTzoQEjUFYdEQBWHmzJlj6nJXRUQCNgNCQIRnh6io6bUwTkUdwjg1BkERygo8T2Hs3DSqRR7Tp09P7zFs7lNERBQQERIn9E7UDPSRTENC9NIpMdGPf/zj5KlPfeqYHQYuB6KuhUmEaAauaYeBnRbmzp2ymNMSO1uQ2OVF4OVr4sSJg9/xIoudLcNsxf+yL2x4OSzbSYMX2nHjxg1+x3XKXprxfZwn4FnKQoEhNre5OwVpJq51xX75LOD5zWcqyhMB6SztIto0vCSXgfzEtRHe7J//+Z/Tn0Xg+tm2F/fKugpG2n/84x9P3vrWt6bPj0WvKnmDHWCItW5er+yFP5s3Rfkp6S47zfLyxjY/kc5lfYiZN3L/MorqGtJDyh1swfV817UmeZOta7gP7CtKB9d1bRhV6xr+j/yuUteyeWPTdtbJGzw70mBYXctrb1zUNZu8wT3M+la3ruW1lz7rWh516hrS1Cxnoeqaq34tW3Ykb0RQZOMuPttPhzxH2uhh55vn4LtlbXq2L/R5Tll/W3afNvMmm/YhyoDLvMmea947VBkoen7s+Ma4DXXS7C+y5cYmZG3TvLF5lirnwGbYkFcHQtRPm+cfdo7YLp+b3qdO3oi4X7wTxV7Xmp6Dfg7zRFmbZXwm4VZi7aPK+qqmeVPnnLp5U3YvF+1g07Yzi6R91TQrug+uh0XrvGu4rmt5ZaetPqruOeaztFk/xTuzeKUpOyf7f9d9oc9+Db+b7Y/NOWXP0sY5aNvxfoT2vmy9oMq4G9eDsAzXh6ho2HgK55i2mfVxGNn/25yTDZ3l8hzx7IS+FAKgpkIiAdfEOBXvjLgH0lLGdtk6YNMONj1HvBNh/AwPji7B3ICIpsyysnbt2oPEaJIGdcadoeuayzbNHB9n3wtDtZ0xje+btrd10kzuU3avmPLGTPe8trMM2/ugnUe7gPtl207bd2lzjthmfG/OdYZqB+uck2d7lmyaha6feUhZb6Pt9PW+RohPOiMmgpeMZzzjGQNvGWbjTAhxC16aTeFHXud42WWXJe9617vSl8wVK1aULlKeeuqpg9+xSHvPPfcUnoOJ7xNOOGHwO+J033///bnfFdfD2AViLu7ipbUsVNesWbOSOXPmDH4fHR1Nd+EUMW/evMFkAsBOpTI3uccee2w6mWh6WZOFZ9N1Ml40pX07+eSTxywMrVy5snRQsXTp0sFgFN8tyxvYtHDhwvSZv/Od76QDZxvxiYgV8Bn5iwXuPHEQrvXpT386edvb3pbmqZyHttzc8ZcH0hhpLcA25E8RyEvkqYD8H+ZlSdIdinmz3KxZs6ZUfICyaYaOuO+++0oX+FEHZIEf9y7LG4BFegHXN89Bnkl5QD7gf9m6hrqMclOlrqEsm26X88DkC8qNWdc2bNhgXdeQ3mhjcJ5Z5rPMnz9/TFuEMoO2oIjjjjsumTp1am5dG8bixYvHCHDK6hryD+mMdEcZwotlWX7ie0uWLBn8DpvKzkE6nXTSSWMWBVetWlV4DhbLjjnmmMHv2bqW195gUmtkZKRSXZs7d+6YHXXI/zKPZosWLUrb6Sp17cQTTxwj9MF98tpLk9NOO20wiWdT13CNZcuWDa1reUDgc8opp1Sqa6hb5vPb1DUsZJi7emzq2tFHH516gxM2bdqUTgoXYVPXsmUHdUbqGtqqG2+80Uq4Z+ZZWd0EZvsMG8rOwfXNvhM2mf1TXl9l9mtyjo1wzxR6yU7eIpB+WVGljXAP58kzmULSYcAuc2IK59gI98zJDzx/2XtW1fyEHeZift38LDvHZd5k88e8N9oZU4SH79oI98w0qJOf2bxBG4n+wbRN8lPSDmPvsrJm5qdN3tSpn9m6VpQ3srCf3eFtU2+y+WmTN3n5WZY3efmZFSXgb2Y9wfNXbQfr5I2EOqta16TcxNQO2tQ1Ealnkb/BrrIyEKIdtK03ZjrWaQfz6pqP/DTzBvfE57JNI67awRj6tWH5KeOuvPPzxhwu+zX5u6+6ZjPmsNn8YeYNvi/t5rC0CDGGxPs17BJvRLb9mtRP2Fenrvnq17J1LduvmX0VbMbnWMeQILsxB/fBM4oIZvv27WNsxd+rLm5nF1kxfsL7G9IOaZmXHtnNH7Z1zXwemz4quzHHtq6VtZ34P+wRUW7W9rz8L0vH7Dl4h8V8EPIc/8O9svlp0w5W7deyeSMiAdnsiLY6+yx1hHsoz5gjw/2kHTPnoiXcpfmMZeP7PPLG92WEGN/njVPy8sZsc1AGzPJcZwwZ0/i+Tn6aaQaq5o3NOdm8kfay6Lwq72su+zWck5efZrojH8x0tsnPKnmD62OODvPdZv7a5GdR3pjlxvxbnXfpOnMjZn4ijW033uN+sHHYmk/RBkWbPmpYXSsi+y5dlDdSdrIbiEO1gz7e1wjxSWdiylxxxRXpi505qENFPvfcc5N/+qd/Sm699dZBPF9RHdY5bJSmhPQBdHjDDl8uYesiCw3y4qYNsV9264QEEyOyoP3DH/6w1jXKbMa1b7jhhnRyJya0lxvNyK60Nsq8C2CzTEpos7/N9sYF2u3XTFnayyRprOTt+tGEZvs1264FCIPzJt40p73YrtF+0/a27YfQrCpt29xnYigzTdFqv+u0NxcHQtCFstMmsqGsTlgzrWkfU19VB/Eyg3l8iGCazueb3jqwmCcLetgwI4I3c0G6K2mPZ5LNFwgT5wNcGxtVkI5tvscjD2ED5qKwUIwNjE3zFGNwXAdzQ7gW5j9l8w/ej328I8dQbrSX+z7ar5WQ6R77nFZotJd37fYTEprf+GUHXPdgoAfFuOyQxiNhsPbZz342ef7zn9+2eYR0AngXMF8cs4rqLOKZKIYwZ8N2nWgJczYM32HOZOcZPFB85jOfSe644w5vbhgxKffOd74z9TSE9IshzJnr0Es2uxRchl66+eabx4Q5O/vss1ura22GXtJQ10A2b9oKc5aHtjBnoetaHnXq2i233DIoZ3imc845p1N1TfLm7rvvjjYMgu05bYbq8XFOrOns6xyX6SxhLeU7ZlsRKj9tn1/C1MoO5NBlwJcL/DrnhKo3oc5pkjfwTjRMbNb0PjG1g+gfizwkYgOF2TdqatPkHCxOikdAH+9rMeVn6DBnLs8Rj6CmV+Km9+ly3kDwKM+f7WdD5Sc8TQN4FmG/pmcMKV6E8K6zcePGXA8zWfLSzHznGfZuhvqMA+9LWVvrhDmrc46PMGeS3+hDsfnP5r1ZrpO9Txl4b4QHXemL2wjVI+dImognFIjRyuZz8p4HYwuMu/HObnqDNj3Za6+fMbWDdc6JeXzfND+rpBn6WvmurKv6uE+Vc2JNZ7w7wTu4qzBnRWgJc1alv5H0i6kMhBynYE5GPP83eV+78847089XXnllMmXKlNIoDYS4oBNhzm666abUcwYql7gn+8pXvpKGPSOEuAcLpLYvkegoh034FA2Wqp6DlzNzEdrEHJyY7i+z7kxtwKKtuXDra9dh1s2oyTDFdNU0K5qME5DPaF//+7//u9JEQJayczC5j9B2UPlDHFonbzDpULYIUCU/h5WbrKtZG8rEdy7yM1vXkBammCjveq7rmqu8QXqb59juEmha12wpSzPzJdy2rrloO+vkTbau2bQ3depaqLzJnmNTdkLkjU1dQz6YkxAh6prLfq2s7Eg7eNZZZyXLly+3uo+P/sbFOXV2L8V8TqzpHOqcJulsnpedVHN5nyoMswELF/DUCzFgdlwSezrHeE6s5bnOOVhAKFs0cHGfNvOz7Bpl9VdTfoY6J+b6GXOdhl1Vd7b3PW/k+1Xqqav8hAAA983zSBR7msVaB0LWNSy0iQjEJj3M78hmC5uQIpg3w2IeKPNkY/Ms2UXWNtNZPDJVFdNUBe/B2LCT9w6MfKnqIajuOWYaiDch1H+IdTFeQjoMWwDG+XgXx3s/xtoQsWGuE383Q8DXIda6FnOb1vd2sM7zy32q3KtveQO716xZkwpihCbtk+2aT8h20PYcc867ap5qLgPaziHEFZ0QE913332Dz2iEzj//fAqJCCED8LK3YsWK9POJJ55YeRG4z/bjZf7ee+8t9VoxDDMGrMTvHsbPfvazZMmSJUksaC83mtGe9prt12x7F+zXTJW0P+OMM6wFRSGo0lfFiGb7NduuEQi2IShCfUV6a057zWUnJtsf//jHpztENdreN7SnvWb7XdkuHm5Coznt20K8iNQJa9aVtNdsu+ldQbyj2i5aVhERmefgEPGJ67R3FT6tiS0hAlvgPm2/v5tpj41KSHuUffyEUAhiUOQ1vDXJRiD8HwIo/F88Bcqm96Yioj7VW822d8F+rYROd9Tp0dFRZ9eLqb3vk+2AdZaQHoqJJHa1vBw861nPatskQgjpBJhACTXpifvghbzKRA8hhBCdxCYoIqRvgiIIxjneIk29ExFCqlHVKxHRKyRyjXiIyQsNIuFDsmGr+oykU1lI7yYiIhPcp4433thBmSsLo+0KmQ+MZU7Q9Loh3pKQHhCNIa+zIeGk7WhDRESIBrL9mFnXpQ/T0o898MADY7wTEbdwzENIfHRCTJRF4g4SQghpBiYNsKsmBHABLBMVVcP0EEII0QcFRYS0JyjChhyMu7gDj1T1TkQI0eOViOgXEsH7iRwQW+DAnAn6cAlHAnGDhDsWLyp979+x+AiBUJkwpamISJA86RpIv1BiIsmvWMREeeTVK4gKAEVEhAz3Eif9GNpatCma+zHX3onIr+GYh5B46YSYKDtQCzXIJYSQroPBWtNJFVvQducpzgkhhHQXCooIaQcslMJDESECvRMR4hd6JYqb2IREmIuRcEponyVcVxHwnoJ2HKGisLiG32MVZYSgSNzjSkQkdHkeK5RIStN8oAiIAAVEhOTDfozYwrJCSPx0Qky0dOnS9Kc0FqG8aBBCSB8IORDjoI8QQvoHBUWEtAMm3zBZRwi9ExHiD3olip/YhERYUMOxZ8+eZPfu3ZXO27lzZ3re9OnT01BM2MHf1x37efNLrkVEpF9zghQREVKtH0Mfhj6paj+Gd1T0Y+PHj+91P9YHOOYhRAedqFmnnXZaMn/+/MHvP/jBD1q1hxBCugKU3VOnTg1yL9kJjXsSQgjpn6CIENIO+/bta9sEQgjpNPRKFC8xComwMx8CzyqLaiYIDwLvg7t27eps+K0yJBxKdrESIiIfQiIsXsYuhHGRjj6RBeAY0xEiIhESQUREIREhw0G/I/1YFSGRCdprhOWOuR8zxYWkHhzzEKKHToiJwFve8paBO8xrrrkmWbVqVdsmEUKIerADYM6cOUHuhfvAJSUOQggh/YOCIkLCI2JuuBIn/QbeieipihDSJ2IVEmEBFQtiTcEiLnbtS0j5PgFBCsQpeG4sNPoSEQnjxo2LUgTTFDwTni0EkoYxpSNFRIRU78fQ50Ac0uV+jG1BczjmIUQXnRETvfGNb0xOOeWUdMCJhuj3f//3qUIkhBAHL/MjIyNB7oX7QLxECCGkv1BQREh4ZBGVgiJCCHEf4oxeieIkNiER5rAhetm2bdsgFJcLIBLF4WKhThNr1qxJNm7cmK4ThAhp1mUxEcLGhAD3iSENN23alB5r164dCIgoHCDErh9D/wUvMejPXIE+bO/evb3rx7oMxzyE6KMzYiJ4svja176WvgRCeXj11Vcnr3rVq5KHHnqobdMIIREQ2+4WLfZPnjw5OfbYY5MpU6Z4v9eyZcuSSZMmJTGhvdxoRnvaa7Zfs+1dsF8zrtKegiJCwnPkkUemPyko6jf0TkQI6QOxCYkAFtOwqx679F0joT9cLtjFiulJZvr06cnEiRO933PChAlpKDAJ09Wld1o8Ezb9hfAgjvlAl2lYFZQbCIiQ7hCBYkxECLED67HSj/kQcsDjjISr7BqxtPchbeeYhxB9/MYvO+bza+XKlcmzn/3sZPXq1WlDdsIJJyTvfe97k4suuihYjF9CusjUqVMHcW7xggdFOOnPbspvfOMbyfe+9z1v94Bg6Q1veEOyYMGCVicPusQNN9ww2AmC/u+8885r2yRCSAGsswezfPnytk0gpBCILuR1Gu+eEjJMM9hJCmITeJNwICxBF8qyCbxSbN++fej/Z8yYod5D6oYNG9J3dhLXezSgZ6K4+tkYhUQShgveUHxN00PwAuGw9rZuGCIgAiICgecDLFTCQ5FLLxlZUJZQNkN58AkN0hCLszhsyEaKsJnjQ7lEvrXh4cksO/RARPqIi74WbSycOqAf8wXEoWhvY/EENzo6mv7EWgap9q6J9r6PYx54YnIRAeSOO+5I+9orr7wydQCwe/duJ/YRUoR/WXkgrrjiisHnF7zgBcmHP/zhtANbsWJF8opXvCL53d/93eTss89OF6yxM6HJAP/yyy93ZDUhhMTPtGnTkic96UnJTTfd5EVEhheApz3taengh0IiQgghpociCooICQsm3URQRPq9qNA1QREhbUAhUVzEKCSSRVjMtfjc74s5cuzSh4eZLm22zRMRCZhfwgGh5Y4dO7zcH4vaWODuUppmwbNhTICxgS9RFvIIeRVKIEABESF++jGf7Nu3L6p+DG2HCIqIPRjrcMxDiD46IyZ63/ved9CAE7+jUcIBdR5Cn+FoCsVEhJA+gUkDLCy98IUvTD73uc85v/4Tn/jE5Ljjjkt3BBNCCCEmFBQR0p6giN6J+gkWY7FjlBDS3CsRiYdYhUQybx0ixCTugc21XVlYE0FIUTgqLCRiTguL0FhcdAnWHTCPhXt0eWMcygsObM72IThH/sCLRIhQahQREeIeeEjBESKKBe7RpX6sb4jnOo55CNFHZ8REgumSz/xp/q8JMbjQI4RUH6hIvF4MIrS95LdtP9o9TM6cfvrpaSjJ2267rVG7bDJnzpw0NOXMmTOjG9y1ne59Rnvaa7Zfs+1dsF8zPtM+hKCoqK/SgGb7NduunbK0/8UvfhGtoEgWX8V+TeVHi+3DvBOxzraH9rTXbH8d22PySqQ57bULicrSXsbQ2dBQPkAotSpz47GWGxsRkfS1sF2EMBDKuvSsAy/ePhYqYxwn4DnhgUk8FLm8LtIRQiKfz2krIoox7aug2X7NtnfB/ibgudvqx7Snu2b769gu5/j0SlRlzBPrOIeQGOmcmKio4jdtFEI0coQQ92AHEkIeghNPPDGZPHlyookY7McLPiZqLrroojRe+p133ml1Hl4kZPcXdhqZi8tHH3108rrXvS69box5EkO69xXtaa/Zfs22d8F+zfhOe5+CoqK+SgOa7ddsu3bK0l68E8UqKMK7uWm/pglADbYP807EOtse2tNes/2abe+C/ZqFRDZpjz4BcywhwGIv7ge7yspBjOWmKKTZMPthN+xHODJsYtuyZYsTQREEMFOmTEnnylz349m0j2HjHZ4Rgh+IJCVETVOQdsgTn54jqnoi0jBG66r9mm3vgv1N26zQ/ZiIRbWne4ztvU/bcU6oNfayMU+M4xxCYqYzYqJ58+ap6ywIIUQT2IEEAdDFF1+czJo1K/ne975XexJm2bJlyYte9KL0ZT42N+OEEELihCHPCAlL7IIi0p53IhInu3fvTqZOndq2Gb2HIc7iIdbQZiZY6HrkkUeC3Q/3gnhDE1VERMMWLiWEFs7fvn176rGgDlhshKAGGyf65glXFooltBv6nLqLwhhXwlsUruN68ZyhzAhpxzNRqHthLQJteyzCG7Q5CxYsaNsMFZhCsBBoHPMQEiudEROZA0VCCCF+wGICQpM961nPShYvXpx8/etfT1avXl1pUeq5z31ucsopp6SfOdlNCCGkChQUERIWCor6yzDvRCROZs+enWzYsKFtM0iEIc76TsxCIiFEaBitXvdtQprZPDNEPxJKC9fas2dPethukMN5CPOFuo3r+PBIpAEs3uO54ZkJ6bFz586BZwcbkHY4F2NK10IiiogIaY+QfUvIPrMMtDWjo6Ntm6GGHTt2pGtLLrzbdXHMQ0jMdEZMRAghJAyYMJg7d2768v+///f/Tietb7755uS+++5LF5uyAzVMFCxatCg599xzkxNOOCHdwQVXxrIzjBBCCKkCBUWEtCMoIv2E3okIIVq9EmkQEoE+ilJ8eiMqC9UFYRE2tqFvg1gaB0L0ZBeo8X2IhhD+BPNYIn4RQU1fQfqJVybM7cEjCcYK8PaU52UL6YUwc0hDCYUjwi4XUERESPuEbBP73P52AeYfITrhSi4hhJDKYNIAC0twS4wJOrjzxOQLdiQhBj0mEPAduD+G6AiTBZiswS4uupckhBDSFAqKCGlHUETvRP2C3okIIZrDm2khZKgWlyKOmL0RFYG5KohbJEwORC4SOkc8FYmQCD9x4Hs4L/a0C4UIs0RchfREGiJNIS6STYbyHTMNXYSGo4CIkHiQ+h2Kvgs6tcMxDyE6oZiIEEJIowEgBEI4MGmAnUgjIyOD+LeYJBCX0uPHj6c3IkIIIc6goIiQ8DDcGSGEDGfdunUMcRaJkEiLVyIRtoQgdkGMbxFRFlPYIkIYExESxZpeMYmKJA1xmPN+IjKgFyJCugvqd6h+TLzLsV3WSciyEtuYZ9u2bemaGSFa4aouIYQQJ2CAhhBoOASIi1avXp16LDr++OMpJiKEEOIUCooICe+diIKifnonYqgzQkjsaBMSmR5wQoAFvBjFMT5CmsXuWaOLSNmCZyJgeidqCkVEhMQf+rDP/Rixh2WFEJ1wVZcQQog3EO4MC06EEEKILygoIiQcFBQRQgiJEY1CIlmEhYgFm7L27dvn9V4I5+UixJRmb0TEPwcOHBjz+4QJE2pdhwIiQnQgYkzUdWwm7ls/RqrR5zEPIZrpnZgIsY/hUkw6tnnz5rVtEiEkwKAWIbbkszY020/bSR/TXrP9mm3vgv2aaTvtmwqKtJcXzfZrtl07ddM+FkGR5rKj0faf//znaX5rtL0raE97zfbb2B5ziDPNaV+FGIVENmmPxS54f/O5sCYLvVW87/gsN75FRLBdFhG1lX/NtrugbRGR9jTXbL9m27tgf1MxEfoxn2IieDnDnFO2H2s73dFOod1asGBBr9r7urbHNObRluaEtEnnxURYVLjqqquS66+/Pvnxj3+c7NmzZ0xjAa8Zw4Arb/P/06dPHxO+hxCiA0x6n3LKKYlWNNtP20kf016z/Zpt74L9mokh7esKiiRMp1Y026/Zdu00TXsRFLWF5rKj0XYJdabRdll8QD8loVeybvY1hGLWmvZdsF+z7V2w39YrUYxCItu0x2IXFkh9enWYNm1aao/twpmvchMqpJnmcq/Zds0ioi6kvWb7NdveBftdiTfQl+3fvz9YP6Y93TXbX9f2WMY8mtOekDaIf8akJjfeeGPy7ne/OxURCRKr15bLLrss+cQnPjH4/dWvfnXyL//yL07tJIQQQgghhLiBIc8ICUvb3olIeO9E2EmqDUxaY1IZ9su8ECaXNT4LISQ/vJlm0B5B1IhNrKOjo8ljjz3m9PpYLENf3bZwkiHNSKwiIkKIG+9E0o9VXYctA30Y+rK2+zHSnL6MeQjpGp0MGvhnf/Znyfnnn58KidBxmZNFctjwR3/0R+l35Rpf+cpXkr1793q2nhBCCCGEENJEUEQI8Q+8E4mgiHQfLv4SUhzijLQnJIrRK1FVsAgLb2lYXHOJLNjhZ5vhPCgkItnyIAcERHIQQvSCfsZHP4ZrIoxs2/0YcTvmkfFJF8c8hHSRzomJfv/3fz9573vfmzz66KOpAEgaDREEVVHFHnfccclznvOcwe8QEiFkGiFEF2gPEOIQBz5rQ7P9tJ30Me0126/Z9i7Yr5nY0r6qoAi7oVzviAqJZvs1264dF2nflqAI7/Voa+S9XxOabQdo51ln20F7e6nZfhvbsdAVK5rTvoyYhURV23sJyejqmXC9mTNnpguxWLhro68SwQhERCGFRJr7Ws2225YHEKOASHvaa7Zfs+1dsN8V6G8mT57sTCSC6+F9c1g/pj3dNdvf1HbkaZtjHs1pT0gbdMrX15VXXpl8+MMfTj+bHoWe/vSnJ8961rOShQsXJn/wB3+QrF692vqaL3/5y5N///d/H4iS/uM//iN5xSte4e0ZusyqVauS22+/Pd05JO7wR0ZGkmXLliXHHHNMEiP79u1Lbr311mTFihXJ9u3b07/NmDEjOfHEE5MnPOEJXuNqtnnvroH4qytXrkw/I/0wqNWEZvtpO+lj2mu2X7PtXbBfMzGmvW3IMyyuSax2xG1H7HRNaLZfs+3acZn2mODdunVr0JBneM/fv3//wH5NOw8124683rx5c1p2WGfDor291Gy/Ztu7YL/m8GZV23v8XxbXsBCGucBHHnmk1r1xDQjccL06oT5c9FVteiPKlvuqYqo20Wy79lBmmsdo2u3XbHsX7HeF9GMIJYy+Z8eOHbX7McwpIUxxUT+mPd01t/dNbW97zKO97BASms6IibZs2ZK8613vGuOJaNGiRckXv/jF5Mwzzxx8D16LqvD85z8/GT9+fHLgwIH0mt/73vec295lHn744eSf//mfk3/8x39Mfvaznw393kknnZS88Y1vTF7/+tenjX7b3HXXXclf/MVfJF/96ldTUU8eEPNceOGFyTvf+c5k8eLFnbg3IYQQQkiXsBUUEUKaIYIi0g/qTvQSv8yePTvZsGFDMnXq1LZNIcQrXQpvNmxxDaIviC7gDe7nP/+5tWcpnIvFVyyMYUGtjYVJUzjCsGb9BeJjsG3btqgFRIQQP/0Yfo4bNy7ZvXt32o/Zen/BORjLttmPkTB0YcxDSF/oxnaUJEnFF2hoADqm+fPnJzfffPMYIVFdVeOSJUsGnd3o6Gh6kHJ++tOfJkuXLk1DzxUJiQD+j++dfvrppd/1CTqqyy+/PLXjc5/73FAxD8D/8B084/ve977G7vDavDchhBBCSJcFRVXDnhFC6gmKQoc7I+GhUIUQEgNdFBKZi2tYFMOCKhbJjj766NRTOeaoszvuZbEWXiAg3MFiHL6Hv7lYVKu6U9/0RkQhUX9FRCIkwpiBQiJC+isSQV8EjzEQvCP0GTbIl/VjOFz2YyRuYhrz+AKiWkTocQX6VqQRISHpjGeiL3/5y4PQZvj58Y9/PJ3MdMFZZ52VhpsSIHbhQLgYCLkQXg5q0qoCpLPPPjv1AIV0DwnEPBdffHHqzarqrsw/+ZM/Se67777k05/+dC2X0W3emxBCCCGkD9BLESFhCBnujBBC2mbdunXpQlnsYK7UFIZg/tTcmJb9f6xoCG/mCszxYYEMc4ZYUEPIF8k3yTuZB0TeYSENv7vIR1wDnvqlXJheAoaVlTbDmpH2MT1STZkypVVbCCHx9WPooyACkT4Mf5P+RA58H9/TMB4h3RnzaAOCK0JC0wkx0d13352sX79+0HAsW7YsueCCC5xd/5hjjhnz+5o1a5xdu4usXbs2ec5znnOQkAiN+/Oe97zk3HPPTebOnZts2rQpFWn967/+axoOTYDrw2c/+9nJHXfckX4vFH/4h3+YK+aZM2dOcskllyTHHXdc2pFBuPOlL30puffee8d8D56CoDD9wAc+oOrehBBCCCF9gYIiQsKEO6OgqNtgNyQ8Q2NBgBASP1icwZwcFmOwKc1cyJNNmbIgYy7mxUaXw5sVgfwwNw/aCHuagns8+uijgwU8KStAyobYJflCEVE/MUVE2HxdJZwRIaR//ZhWMTPp7phHAxiTmeN2/E5ISDojJhJQkZ75zGc6vT5c8Jns2rXL6fW7xqWXXpps3779IEHWVVddlSxevDg3RN2FF144ZmEHE9CvfvWrUw9FIbjmmmuSv/mbvzno7+9+97vT0GNwy2gCb0Af+tCHkre+9a1jGu4PfvCDqRDqvPPOU3FvQgghhJC+QUERIX6hoIgQQuIBCzJYfMAmPvzcu3dvsn///uTAgQMHLdBAdIQDu8ExF4VzsyEm2qZvQqI8fHolxzwjDojOUFYeeuih9HO2rKB8wHMRhCP4HfkiC1yknyIiQgixoc+CkD6D8JcLFiyofF7fI7FgDIZxGcbxmF/BGB76BIp2SWg6URMlDq9UoKwnoabIbjvp5PCiRPKBl6HrrrtuzN/gXejGG2/MFRKBefPmJddee22yZMmSMX///ve/n3z9619PfINy8+Y3v/mgv//5n/958qd/+qcHiXmkE3vjG9+YfPKTn7S6Voz3JoQQQgjps6CIEOIPVyHHSdxwboSQuME8ERYfsPAAkeeGDRvSBQgIRExxiHwX34OXcXgS37JlS/o9iI5iWLCA/SRMWdm2bVuycePGZOfOnWkZyO5+x3dRLnbs2JGeg2Pfvn0HCdRINwVEckBAJAchhPQZtIOmwJKQpmA8hXEVxlcYk4+OjqZj9FjG5aR/dEJMlPUUBJfbLkHYLSCVFDt0SD7vf//7D/rbhz/84dIXCwi2PvGJTxy04ynveq7593//9+Suu+4a87cnPOEJyR//8R+XnosQZPCqZPLjH/84+fa3vx39vQkhhBBC+gwFRYT4FxRh9xzpJhSMEZIk69atSw4//PAkRsQbkYiDqrbHEJFg8ybmXNsWiUDcAuiVyO+CFQSiWKxCWSlaqMIueRwQHuHIlhWG3ugeIiACFBARQggh/sA4CuMpiLoxvsJYi5C26YSYyHcYMrx0m3DSLJ8f/ehHyR133DHmb2effXby3Oc+1+r8008/PXnxi1885m+33XZbKpDxyUc/+tGD/nbFFVdYu9D7sz/7M6trxnbvPoF47lOmTEkPie2uCc3203bSx7TXbL9m27tgv2a0pj0ERfA+CptxaHS3rdl+zbZrJ2Ta+xIU4Z1Jq9vzLtmuwTsRJmDhZQOiCtiLA5/xNxwaJmi1t5ea7ddou3iZgecYHHXDT+E8bLCEV6C2BUVaw2bG3t6L6Axz6du3bx/ksVlezM8QEYFsu2mWFVyvbUGRxnobo+19FBHFXme7bL9m27tgv1Y0p3tM7X2fbI+57EhIM4yn8L5KSCzEFfi6Jo9//OPTn9JorVmzxun1b7rppjG/z5w50+n1u8KXvvSlg/72+te/vtI1Xve61x10nS9+8YvJ0qVLEx9A3fmf//mfY/42f/785GlPe5r1NU466aTkSU96UhrKTfjOd76TvkQXeclq8959Y8KECcnxxx+faEWz/bSd9DHtNduv2fYu2K8ZzWl/5plnJsuXL0+0gneg8ePHJxrRbLt2QqU9NuJgIgyCIpcLwZj0Q7ujkS7ZjrkYCTtP/KK9vdRsv0bbRRwiYr+mizzwPAOhCTwDjRs3LuiikXgl0kjs7b2IzlBW8hassgtsw4RE2bKCfh/9P8pJW4t0sad97Lab4Xr6ICCKKe37ar9m27tgv1a0p7tm+zXbHtL+KuNYEXhjHKVhswvpF3HJ7mqyaNGiMb+bwoqm4MX7uuuuG7wo4ycWHMjBZIUxSKsXvvCFla7xW7/1W8m0adPG/O273/1u4osf/OAHg5dhATZXfdnNhhvDjq1rr7022nsTQgghhJBfw5BnhPhDPPsy5Fl30eCdqC/Mnj073VxE+gsWIfbt2+d8NzOuibqO64dm4sSJwe/ZBzAnCfGPTVmxERIJck2cUxQujcRHHz0REUIIIT4ZGRmx+h7GTXiPo5CIxEgnxETwWiPeifCSAvEP4pa74J/+6Z/GvFSddtppyYwZM5xcu0ugkbvrrrsO8ppTNa0gpDnnnHPG/A1hzny5dMsTnsHTT1XyzrnhhhuivTchhBBCCBkLBUWE+IOhwruLzMUQQtoHO5qxEIHQZj6AB5uQIaywM5u0X1aqCInMeWJsdmw73BmxgyIiQgghpD0w1sI4i6HNSKx0QkwEnvGMZwxigONF5d3vfnfja/70pz9N/vzP/zy9plz7ec97nhN7uwYEP9ndJmeffXata2XFRLjunXfemfjgjjvuOOhvdexetmxZ6uq57Nqx3LuPnTFcCuLIeoPSgGb7aTvpY9prtl+z7V2wXzOa0960fcmSJYk2MFbGM2jc/a3Zdu20lfauvBNpLjtdtZ3eifwjaa8VzNVpFRZosR02oi76shX1HiKRkGmh2StRzO098nDv3r2FeSm2Y3Gr6i55PC/EZ7h+G88ec9rHZDtFRN0pN9rt12x7F+zXivZ012y/ZttjtB8ib4ybCImVzoiJ3va2tw1CkaHyf+Yzn0k+9rGP1b7e3XffnTz/+c8fMyE2adKk5M1vfrMTe7vGPffcUxp+zpa881auXJmEsBuinLlz51a+zqGHHnqQu7oym9u8d9/ApIe8IGt0E6jZftpO+pj2mu3XbHsX7NeM5rTP2q7NQxHefbDzG0cMkyB9sV07baS9y3BnmstOF22nd6IwE8yS9visDdgsggRt9g+zHR7RDz/88CQWUCdhHwQiWfB3V+mOcGcQiPjOxwcffDDRTqztvZSVIhGoCBeR33VBuDNcp40638U2xyXy7iMCor6LiGKvs32wX7PtXbBfK9rTvS991ebNm5PYiKnsYFwNz5/a5lJJvzgk6QjYRfzyl788+fznPz/wJPSGN7whue+++5L3ve99yYQJE6yuAzdiH/7wh5M//dM/TSc5Ta9Ev/d7v5ccccQR3p9FI7KLwWTevHm1rpV33v3335+4Bh3Fxo0bx/wNYh4RpdWxe9WqVYPf169fn3YCEPvEdG8XoE6Y9/PF9OnTK4XKW7t2bfrceRMYMmhBW2DbHhQBAVfWI9QwcH/kSV2q2D9+/PhKorQtW7akOwt9Ydp+9NFHJ5MnT67UroTa8Th//vzkkEPsukT0Ddn663MX5uzZs62/Pzo6OpgQhIcNGcwjhKTrOjtz5sxk6tSp1t+vcv+mdfaYY46x/i7KP+qBS4bZf9hhh1WapEM5c+XBoQzYBftsQLny0S/ngbK7cOHCSvnpur0fxpQpUyotnqIfQNkYhss6i/YWIngbMMG/Zs2apCk29fZxj3tcsmDBAutrIuzC9u3bE9/AdrSdZh2AoGj58uVDzwk54YDxhu0YEWUobyzkA9hkOxaSspbt15GGYi/Kfd2xcBb06ShvttTNz6yHEJvJn1jyM5v2SK8q7w55+WnbdmLHHeqc7f2q5mfISbgq+Yn0kkVVSXuci/R3Da5ZJT9hj80EdpHtKFM2ad9GfpbVI/k/3qVskfwMAdKrSjmxzQsXoJxVsQ1phjbXVXvvqo8qyk/ZuQxM22URpU5+2r572tZPXA92ingj7xnMn8OwyRdcA+8o0sb46qfwzMhDPA/sqpJmdfuoOlTNzxjGkFJ2s2kk5Ux+ipCorr3ZsiJls2r9tO2j8sK4SZrbtlNV89Osb3heOZowzHYXY0iZm5S5Vhd9RYj8rEudMYfvMVqTMWQZLsaYvsaQLmyvkp8iFAhFlTQLOYasmp/ijc6nPfLeL0eT/PT1XlXlnUDGHHXGhVX7qqr56XPMkbUdaVZkvzkXGLJ+upi3akJRuZA2Dd+xmf9vW/RE+k1nxETg7/7u75L/+q//ShdE0EigEv7VX/1V6qHoZS97WRpCKrvr4uqrr04XbyBCuPHGG5Nrr702/Y4IiAB+IpTUFVdc0dKTxQ8WsLNkveXYkieEyLt+U7DglW2A69qcZzfKHxao84QAbd7bBehQP/vZzya+wQLypZdeav39T33qU6UvMLfeeqsDy5LkNa95jbVgbsWKFclVV13l5L5l9mNw9M53vtP6el/96leTTZs2JSHYuXNnKvqskp+heMtb3mK9q/T2229Pvv/97ychgBjgHe94h/X3v/SlL6WL73m4Dhd55plnJs95znOsv1+3zahTZ9/73vdaf/emm25Kbr755sQXpv3Tpk1L3vrWt1qfC4F0qJAhT37yk5MLLrjA6rt4wQnRB9TJzx/+8Iepd0mX7X2RoA5Cc1vgNbPKxEKTOvvsZz87Oeuss6y+izGJ6/wclvYYU19++eXW1/n2t7+dbgwIAQTMT3rSk8b8rUhQhPeHUMA2W2EcJtLQ34YCwjVbYFfRgqNL8SQmsqoI0l3lp83ufXjnsZ0AxKJwCFfXsolm1qxZ1udgvNF0EtxW0A5xNeqBLSHrJ8S4tosaSGdsXjLJ8yDiAthURcAM4WbVSdM82/GMRcJZgM0FVQTpIUSlki9V2jSM0UKJvpGf4tnLBtTNEOkmAsEqgnSUG1/lPkuV/ER7VFZ2gWk76kyddEYfUCU/0aaVLR5In4d+qKhtLpsvsRWuiYcs2OWjrEl/ai4QoqzZgr4zlKAOY44qG09t8tMVsCtvQVQ8nWX/ZvYDruop2knJO5QVmW93OYYso8qzYMxdNoY0RUNmmpnrCCh/ZfWxqu14/7QdQyLdzXGWzCvImMXlWAltRugxZJU5tSoe5Mz5NN99VZU6AFuqzg3VtT/EGLKu7RhvVBlDhnwnqFIHMO4MNYaEWKHKJji0W65tw7scxv9ov6TtNMcjIioqGn+gjJXlp8s6W2VNTcaQNjY2tR9piL4gtjEHbMf7um1oWoxBQtXPqmMOl2NIKdPDxsoYcyDdbDeCaA53TfTTKTERGtLvfOc7yROf+MT0RUO8CqGyfuQjH0mP7I6cZz7zmWOuIf8zQ6ZhMPC1r32tkiK1b+QtXttOKNmc52NywqXNRXbnDT7avLcmUI9vuOEG6++HVOf++Mc/tvbkEMqLDcDAtUqahRIqSJmsYltIsPCdHfBisko81mAgKZM2Ljx42AIbqqRZExfkVUG5jjU/q9jVxGtYVfBiUMW2kLuoIKqGbcPKfVt2gSpp5trLVBGYYKliW8iXPghwbBbEgE/vdHn9dJU0C7UQCpBeSLe8ct+2u+myhQjTY0JoqowjQnkIAEiPkGOcqm2HrfgkZHuLclQlzVzUC0wq20ya5uWn6VkJZcu3l5NhwH7bHbAh87MsfE2b71FIh1jrZxW7QnmBM0U40ta3Webr5qdZZ2PMz5D9KPLPZf2UBThct2k7k13gKxo34XvixcVH+pl2VPW2FcuYo+2+CmPIbL3DvEeeZyKznLlckDW9IqCswB78nhXXFhFyLI78NG0TjwYo61I/xB48i5R91EHxfCDnYB1ByoeLOoIxZNUxR1ZEFGIMWVZXQ+Yn8ijmMYdtmxDzGDJkflYZQ4b24IF207a9Dz2GLEszX+JbCCWwqVKcPogXK+kbpeygb8Ah4xk5kI4yrxWyX495DIk0q9J/hqwHw7xj5pXD0PNXefk5bIzm2rYigRXuiXvLOLftuUdCeiMmAieccELqYQCeiLDYb4qC8sj+3ezo8T94JIKQqErYoD6S96JZN7RIXgPrQ63t0uaqdrd5b01kd/lotS30M1S5X8hBZcz5md2Fl3Vric8yaRNzfradZrHQlTrQhm3Dyr1JzHUg5vyMtX6GflmNNc3MOuDTnX1disq2i3AKdYnZzTJt02GXraAoa5v5e9vpaXv/0Ha2lS54ry0TtLadZ9rsyrb1NnZiITvE4qNtH9RGXxV7frrCHLeEWpwx7+N63FS0QSbWPLWZc27D9my9M+e8h5UV195YXJSVtvIdAhwRCMl7MtIH860iksp6JsKmBHjDgTcmCV8Czzg4p+k8bZV0wCZWEfyFYNg4TTZ6t0nb99dmV1dsa3PMEVv62Y7TXIF6D28rEFTKHAtEG9jIhjY0b3yK/gHvEGg78X4oYmV8zgvL6ZvY8jB2u2xsa7OO5N0vhjprhk6mkIjETnwz5Q447rjjUkHRH//xH6ceW8wBbNkB8H2IM97+9ren4TKahJ/qC3mK6rrimLzzfEyCubS5qt1t3psQQgghhBBCYgGTxKQbxOItp+/QqzbRDhdU/JO3iBYqBKEGIApCW4qyiIVweE6HV2OIdLCwPWxhEnOx+O66detSr7k4F9eAwAjhmXz3kwhhjQPzxKGERIQQYoqCJCQj1r/QXm7atCn18o/2c9h6FdpJCC5HR0fT76IdxXfRhqItbrJuRkjMtLk5kJBeeyYSsHPg/e9/fyoo+uhHP5p6F1q+fHmhwAID+lNPPTV53vOel7z5zW+uFH+SHEzdF6S880I1qE1e6pra3ea9YwXPFevLbxXbQj9DlfuFnPCPOT/xspO1TXaRZT/HnJ9tp1ksdKUOtGHbsHJvEnMdiDk/Y62fob3wxJpmeXUgpsWtsrLd1gJ+zMIB2tYdu8psMzcGtYHtvUPb2GaalHknirWsxWpXtpy3XeZNqtjSpzoQ0rZhnmd84us+ZWG7Y81TG7vaqLdF95R3ACk/vrybZyMANL1GCLAIjgOL2FhH2Lp1a+WwRBL6C2X6iCOOSIXTWKuAoAhhlV2nAwREANf3GbbI1jbz97brbdv3H0ZMfXmWWO2K2bZsmY/JTts+qinoV4488sj0Wmg/4YkI7WfVORR4IYIYE23o4x//+LQ9lfYT1wxBTPmnwa6qc1axvRO0WWclvB8hsdNZMZGATgYehnBgMutHP/pRupNg+/btqcIVHogwqEfHdOaZZ6afSXXwklV1EmAYeeeJW9lYba5qd5v31gRcAZ933nnW37/22muDiaiWLl2azJs3z+q7CLn4s5/9LAkBFkCrpNndd98dLH74jBkzKtn2gx/8IAnFE57whLS8mWBX3ooVK9LPJ5544mDXPAZ5q1atCmIX2ooqaXbHHXeUhpVwxdFHHx1tflaxC5OmDz74YBIC7KapYtutt94azMsc2jPYNqzcm6DNgOfGUFRJM+x4wqRDCLC7tIptN954YzDXzMcee2xy1llnWX13w4YN6fg4BGg/q6TZ2rVr0zF7qIVvpNuwcm+CDQpV4tQ3Be8rRTvxMDknbT/SOFQ/AOAF1hbYFUqMBVFYFdtC5ifajrx3gWF9VKjFIJSdKmmG9w+X+VkU7iwvP81yj/phTsCFmmQG4obfBrynhMpPpEeV/HTpjaIspAneU2Otn1XsQp9edXG5SX6irA0r81lCehexzU/ZcR5ygbtKfiIvq4QIQ17UDSmGNqOKbRh7F9UpaYslxFKTNLZd0MB7jSy6uB7f5vUtuBfuaZtuSINQId+Kxhx5fVXoMWSepzLkGf4uc4lIL9jmY5yGNsIM4wHwO0LZ2II0DPUehXxC2ohHDVkIH1YvyuzC/yH0wXzYtGnT0jqK8lJnHi5vDPnAAw8M2pWjjjpq8HekcZtjSDNKRPZ/6AtCvRNIetsScgwJu2wXr5FeoeaG6owhQ9XPKmNIlMGQ7W2VcZqE/YopP6XONilnaOdESIR5HJu5nKI6gPTEOu7s2bMHbSDSrckami1V6oB4UAqVn1X6z7IxpEtQ7jHuyII+MFsO0T/5EjHb5uew+YTQYfXQf6P8oH0rK0ehw/0R0isxkQkahXPPPbdtMzoJOvMsdRcy8gYEedePyeaqdrd5b0LaQpvKGpOCIjDFZ0L6gPZyH/MuHaKj7NuU+zPOOCP55je/mcRU7sVuTi4QzRQJijR5OiN23olI/bZe43gnZo+OXQCLD3jfhggixGYhLNCLcNDV2CPE4mBoYq23KCtSTnwLTsyNjqEWW+uCPMJCqOmRyJXoBYvqsgiM/hHp3kRMICIiYIqICCHt0edxDsQa6O/QrsGBg6tNYRhjYAPcnDlz0jYajiPwftGFiBx9YsGCBUnfxmjo8/PEVSYyhsb9bcREhLSJvlUiEiVQHmepO4GRN4Hsw2OUS5ur2t3mvV0Nji+55JLEN9OnT6/0/Ve96lXBlP2zZs2y/i68HFRRjTchb9dZERdeeGGwnTdVyyTyM9SiaJ5CHWmZN9hdtmxZ6pUnBGWDziwve9nLBm3Jf//3fw8mvjCARRhPl1QNBRqizajDOeeck3ojCUGVnS3gFa94RbCdGjIBOazcZ8WqofKzqgjxaU97WnL66acnIajarr/yla8sXFR1WWertFHwzhkqP6tOrj372c8O5pkIO4XhktuW0047LVm3bl0SgjJvkzLxADCpljfO9EHVCRd4AAzVr1cVQ2KMUmdCMrvwadNvV7ENi02hJqWrtrcYp7vOT4T9wARxdtd9XprJonkeIb0NV0k39J+hvMdWzU+0G669BGAhIa89qlo/XbRpeEcs2p2OPt3WY5g5rivyGucStANmW2+TZtgFXHXsWQfbdJNF+lAbTKr2UWh7qmzIGh0dHYQTqkrVdh1tWln9lHqFZ6gbQqlKuskObhyuxh3wMJpNU/Sz6J+r5ifGdaHGHEX5mVdv64456jCszcAYFraZHijMRTUfHvxQVqR/rjOGDOHJRtpa9Bnbtm2zumeVNEPdFG9RqDdVFw1xL1sREe4TanNQ1fzEGDKkt9IqxBqxAvWo6nyv5jGkxvwsejfJG3dWne8NlZ+wq45tIoSAUBLtp8u2Q8KeYZ4L7TTmsGISXWDsgjqKuSvfZa5qfoYcc1R5l0J5CVU/h5WzYe9WrsaQWG8rGyNLmybtR9kagHw/1PonISYUExEn5L3A1F1gyTsPgwTXYCE864K9yaJQ9lyJExvbvV0A24855pgkNmzDjoUGE20xppeUxaqikD6r1mWCNtb8RFss7TF2bsjgFy9bbdvc9v2LXvrqLgT4JpRorSroY2LNT7z04YgR7KQqoq06i5fRWPMTE81VhcUhxxw4EPIsJiQMSIygrMXq8ayuwAOTOGYYB9dpX2VSugv5iXc+eAFo+syxphna9lh3LFcV0tjgqk6EyE88f9X7xJ6fsfYHMdpUJz9Dts9V6ie+K15PfIG2X8SuLstZXpqa/WzfxxyuEDGKiIJNYbTrnfkoI1iUFjFR3TbTRx+VB8oayhwWAG0X6qqm2Y4dO9LxDspnlbpT1RNRzGPIUPlZh1jTLPYxR4zEOg6KPT+rtk0CBGUS1tFHf4K+ChsDMN+HfjSm9JMxR4ztbttjDo3109UYskp5QHmWDQFF4syYvFyS/qEr5kwBP/jBD9o2odfMnz//oL+tXbu21rXyzlu4cGHiGjTmWe8yEOXUVetm7UY812Eddpv3JoQQQgghbkDIM0KIO7AhIpRXPuIXLJaGCLdE8sGcQCgPtCQeJIyST7D7HvdxuaDx4IMPRusRpEuIIAXtMxaufHsvw/XFg5UGJGyfz74LQj94q63i9UDyzdw8RgghMYE2DZEzTI93roHnHwgtQnnCIiQUGFNjrIQxNiGxomM0b8Fv//Zvp4KWd7/73cnKlSvbNqd3nHDCCQf9bfXq1bWulXde3vVdkL0uXurWr19f+TpwrYjJj6Jrx3TvvoGB7MaNG9PD56DWF5rtp+2kj2mv2X7NtnfBfs1oTvumtrctKJJd1HV377eJZtu1E3valwmKYre/CNpO+pj2mu3XZLt4gsmGKIHdLmyHBwqIlWL1+hMbMZUdU0gkZQUeporC+zUpN22XFSw2S9rbLjxjMRzjD98L1RAr4V7D0lZsv//++9ODIqJ+1tm+2a/Z9i7Y3wQ8L9qtJmFWq7TRphhTe7rX6atiQbPtMZYdjJcQOYFjbBIrnRETiWeXD3zgA8lJJ52UnH322clHPvKR1H0o8c/SpUsP2pV0880317pW9jxcd8mSJYkPli1bVnp/G+64446D3Eiffvrp0d67b2BQgPAxODTGFNVsP20nfUx7zfZrtr0L9mtGc9q7sL1NQVFskyB9sV07Mae9hGsuEhTFbH8ZfbOd3oncoLncaLdfk+2YP4NIBCGsTG8wLsREuPaMGTMG4c1ckd0c1yViKTtZIRFAHmLBCmVlWLiYuuUG15brtuWVCHZjkwAOm2eQxfAQ3hHhmQj3GmbXmjVrUg/0+I6ZZ6Q/dbaP9mu2vQv2NwFtFTa7o23zDd4rTOGK9nSv2ldps13C3sWIz7Kzbdu2ZGRkpNI5GC/JuIyQGOmUmMh80bntttuS3//9309dO1900UXJN77xjUouREk1EK/0lFNOGfO3u+++u7KYC3l30003jfkbhES+3DQ/6UlPOuhvN954Y+Xr5J1z7rnnRntvQgghhBBCCIkVERQR3XARlJDwQMCBxQi0o66EHCIkGjdunJcd0wxxFlZI5LusYCFs/PjxqnbXy3pCCA+rsoCZXbxEXkl+HX744elBCCGxg7Ysu9HdFxAsufK2SMKwYMGCtk1QA8ZN8BpJQRGJkc6JifDSIjtkpCP72te+lrzoRS9KhUVvfetbk9tvv71tMzvJ05/+9DG/I/2vuuqqSte45pprDhIgZa/rkgsuuOCgl1vYXHVA8tWvfvUgd764dqz3JoQQQgghbmk73BkhXQOLmyE8BBD/0DsRIWHBvBAWI1yIRERIhHBYEBO5pMteibQIOzEvCeHPzJkzh3oosgVlDWVl8uTJaVlx6cHKN5iLhWeNUAvUEC2Z3jVERIRwZhTiEkI0gbYslJgI7TTupzGsFiG2Y/jDDjssFRRpGkeR7tMZMdFnPvOZ5GlPe1r64iIDfxEWiVp1y5YtyZVXXpmcddZZyamnnpr89V//dbJx48a2Te8ML3vZyw762z//8z9XusbHPvaxg/72O7/zO4kv0ChnxUqIR/29733P+hr33HNPcsMNN4z527Oe9aw0xmWs9yaEEEIIIe6hoIgQ91BQpBsuirbL7t272zaBtCwomjVrViruqANEJnK+ayGRQK9E/oBApawNxry5q7ICIYxGIRHAukHIiAayEC7eiJB2OAghRCMh209GnyFdRsZlEBRhDOdr/E1Ib8VEF198cfIf//Ef6a6WD37wg2nILRERiajIFBb99Kc/Tf7oj/4omTdvXvLMZz4z+cIXvhAkrmeXgUhr6dKlY/72X//1X8l3vvMdq/PvvPPO5F//9V8PWpA5/fTTE5+8/vWvP+hvl19+ubXC+T3veY/VNWO7NyGEEEIIcQ8FRYS4D3dGQREh1YF3btJvsBiBRQgIdiBUmDhxopXIQ7wawVsNRCK4DtGFeLqpsnCFsgLPQli8gicq27KC8oVzpKxoExK1wa5du5J169alnykiIoQQQrrBtm3bGl8D4yiMyTDGwvgK4yyMsQhpk86IiQTsonjHO96RClMQzuwtb3lLWuGGCYugZL366quTSy65JD33da97XXL99de3/Rhqeec733nQ3373d3839QpVxN69e5PXvOY1acxok8suu8zqvueff/6YvMVx7bXXWp37/Oc/P1m8ePGYv918883JX/7lX5ae+/nPfz75yle+MuZvp512WvKc5zwn+nsTQgghhBA/UFBEiHtBEdEL5mQY6oyQdoAHdwg8IA5Be3r00UenP+HRGn+DwAjHlClT0sUK/B8iIvEw0zT01TAY4sy/kKiqZzjkNfIc5QJlYc6cOWlZKCsr2D2PMoaQaVqFRDKXHCpMDxCRHyGEaKdpSNVY70VIHUZGRpxcB+MqjMvESxH0CxB9H3744enfCAnJIUmHgZccHAhn9p//+Z/Jpz/96eQb3/jGwAORvCRIWDS4f/7EJz6RHvPnz08uvfTS5JWvfGVyzDHHtPocmnjpS1+a/OM//uOY0FuYIHjiE5+YXHXVVcnJJ5980Dn4/4tf/OLkjjvuGPP3Cy64ILnwwguDDED+z//5P8lv//ZvHySM2rdvX/Lud7/7oF1YKDMf+chHkje96U1j/o4y9Q//8A/WL6Bt3psQQgghhPgVFC1fvrxtMwjpDPBOhMVMQggh9mAOCRv3RCgCT9jyU+ZDAeaSMEclP0PAEGfxhZhE/mPxCuUFG3AxJynlRDbpArOsdGEeUrwzyeZjnyIiAEFWF9KNEJciSInU4KsPWrRokZfr9h3xohIClA30TxQUxc/mzZuTBQsW1DpXHIPgMCO4iPC3K2OPKuMygJ/wUASPRYSEptNiIgEdzLOf/ez0gGDoS1/6UvKZz3wmufHGG8e8CAF5YcAA5k//9E/T45xzzkle9apXpUKZadOmtfgkOoBoa9myZcmOHTsGf7vvvvuSU089NfXEc95556XutkdHR5Pbbrst+fKXv3yQRyIoLD/5yU8Gs/mpT31q8ta3vjX5+7//+zF/v+KKK1JxGcLoHX/88WlZWrVqVfLFL34xWbly5UHX+YM/+IPkKU95ipp7E0IIIYQQf1BQRIgb4EVj69atFBQpB96JuIuSaAehibAjWBMQheDA4osswPnyOETaBfPZdYVEJuLNXzxbdX3RThYnsVCXnaM2MYVVcsj5pnejbHqJkEh+Ik25GE76xOrVq0u/M3369PQnFspd14+NGzda2UDBUXWQV6FCMOE+IT3JkbBAOISxB37iOHDgQNonS1+LsSv6T+lDRVzWl/IgzwxnKRxDkND0QkxkAtesr3/969MDL1gQvnz2s59NxS4gT1h00003pQdCpkEMAzEHGQ4Up//+7/+ePPOZzxzjyhwdwNe//vX0KAK7M771rW8l8+bNS0ICD1br168/KHQYPCf9xV/8Ren5v/M7v5N88IMfVHfvPoABBtwvy2dtaLaftpM+pr1m+zXb3gX7NaM57X3b7ltQJDup5bMmNNuuHY1pL4IirfYLfbUdi9vYoUr6V26026/Z9i7Yr5nQaS/hzVwhXnqyG3G7mPayOA0RQ56YSNIBi5ymiKhosQ8Hvp/1SgS7ihY/WWfbQ3vat2l/mVBH3reHgTol9cSH7WX3HyY4shUXaS87TRDRKcSYZlvnA4RgNdM3lnSH04Q6Xnhisb8OLm2X+o8+E5uH9u7dmwqJiu6NsKvYpAKvWOJRsS37Q3PvvfeO8dhESAh+45e+fHcqA2KhT33qU6mYQzzqZMOgyd/kRYAU89///d+pN6cVK1ZYn3PSSSelebB48eJK9zr//POT6667bszfrrnmmvTvVUAjjNBif/mXf2mdz+is/viP/zj5kz/5k0aK0DbvbSvE27NnT/oZu4HRqRNC4gXhJqUtwYAaXuEIIfHCOtt96KGoe2DjhLkjnR5XwgBBEb0T6UTERG3Vlb7W2Q0bNqTv86S/noliAxvnbEKc9bXONhESufBK1FfwLobd/ps2bTrIA5F4SqiChGHBgqjpSQPeV7CRVtsGDFtYb7vNMNGQjVhHGxAX9cFzkYs6i3YOa6rbtm1LfAHb5s+fn3onis27Yl0xUZexDXOGvhUiXvS/KENV194hAka/inXStj0povyPjIx4v88dd9yRptuVV16ZjicQjYkQ39AX1v8Hocw+8pGPpIMEhN167nOfmzZAsvtCmzoxBhDW7M4770wbtRNOOKHwuyeeeGL6PXy/qpDIJXjRe//73582yC9/+csL409C/fqKV7wi/S7C4TUV87R5b0IIIYQQ4hd4KCKEuAE7Fok+uMhNCCHuoZDIDRLmDHOuAGsCWKyDt4QqQiLxSiTeFUxvRlgAhyA6toVwQvJEQ3mHCIeyRxfJe8a89CC/2hAHUYPPNSoI43Eftp/dAf2jCNGwYaiOEw8RAaPPNUOiEULc0rswZ2XALdpFF12UHj/5yU/S8FHwrEMxUf30fOMb35gecL92++23p7u40Ljj5Wnu3Lnpwsqxxx7b6D7XXntt4loI9fnPfz6185ZbbknLgHismjFjRiqOOvvsswcvmF25NyGEEEII8YfvkGeE9AEJdybvlETnDmh6KiCEEHdQSNQczP1jkRqex/bt25cualYN2SML6aboWYRIcm385KZQEhN98jbUhGx6ZMOiddFrkS1o1yDGxHuaj7DGuDbWxfCTxI9NGUAfC/EPvPlAENQECIhwHfS3eMds20MRIV2ErW8GNFxf+9rXkk9/+tPJ97///bRRY8PjhuOOOy49NIHJ6QsuuCA9+nTvrrF///5UoQxmzZqVusPUhGb7aTvpY9prtl+z7V2wXzOa0z607a4FRZg4wSQM0DZpotl27WhPe4TH2bJlS7qTEZPKmhblxMsB6KPtWPD2scjQdbTXWc32Z23XRmxpbxvirAuESHvxSuQaCTsitmvqq5rYLwviEP3I+4EN5vXzvCfKAic2hpYthmseJ2hHe9rb2p8nHmpbOBRbX2WDmWYQFq1atSr9vHDhQnVlxwUoc/BOBI9sOFyC94c8r0RdqLNa+9oy24tCnKG+I9/gQKGpkMgE10MZQftRNmbX2OYQ0iYUE/1/rrvuulRA9G//9m/Jnj170r+ZsUIJIXrB4AS7lwEU8poWOLXbT9tJH9Nes/2abe+C/ZrRnPZt2O5SUCQTMTKBpundRbPt2tGe9vKuLmIibZhprw3NtmumC3VWq/1Z27WhOe214zvtfYY361q5r3KehCKDF4zt27fX8kaUBSIiiOjwXdyjrCxoTnvtaE/7PPtjFA91ra866qijUlEExAzSNvfNUxHaN7Sf6JMgrnIlEsH10IYOE4dorrOa+9omtuM8eAD0EbocdRDRcqQ8hmpz4BmJkC6jq4VyDMJuQUD02c9+Nlm7dm36NzOmojQg8je8SFx44YUtWUsIIYQQQgjpCgx5RkgzsMMf4bIwETl58uS2zSEVYaizcMyePTvZsGFDMnXq1LZNIYQ4huHN3CFeCrC4KCHJsBiJBUJzvaCqkAieOrBhAesMuDa8OWjzQEH0gP4emOUrNuFQl5k+fXoyYcKEZHR0dCDi6pOoSEQlKHPwIovxfl1QhtHH4T2PnmO6g3iTgujHB4g0tGvXrlQQXCQm8sHIyEjQ+xESkt6JidBIfeELX0hFRLfddttQAZH8Hb+ff/75yaWXXppcdNFFnOwihBBCCCGEOIGCIkKagfdz1270iX8Y6owQQuIMb9ZnZIETIgB4EYK3Uogw4REDi+Kmlw0bEREWMSEiwuZkrC/g+rgOxEU4KCYiLsjzOiSCFpax9hABFzz09E1UJF5e4K0JQiC0e2j/qoB2c+bMmamIiEKibgGxD/rOqmWiCrj+tGnT0nuwHSTEDb0QE+FF4Jvf/GYqIPr2t7+d7jIoEhCB4447LhUQvfKVr0zmzZvXit2EEEIIIYSQbkNBESFuJgwx6Ux0Qe9EhPSLBx98MBVpkHjDm/V9gRMCZSw+IgQyPBNhIRzhUuDhDZ4Q4e1AREXDhET4PkRI6N9kERNhWREyTe6B69qEOyPERkAkwhWUXVehpYgb+ioqgpgS7R+Ek3hHw5gf7SfawmGgPUS7ifYTYjhcQ1voL5IUbhhBv4d2qonHKhtwH/S1KD8UExHihk63xrfeemsqIPrSl740iHEsYqE8ARFU2y996UuTV73qVcnZZ5/dktWEEEIIIYSQPkFBESH1wcI03vcpKNIFvRMRLWARAuIALvyTmPAtJEJZl0W4PizEoX7jgGBIfkd0A/yO9QIsamOMsXPnzrQ9gLciLHZDHGS2E/BmhO+abQUW0M2FUyym4zy2KaSpeIjoFRX1QVCE9g3tIto7eImBsAif9+/fn7aDsiaLvgbfw4G2FG1oth0luliwYEHu35HncPyBwzfov1HmQoBwqIR0nUO6uMvlM5/5THqsXLky/VueFyKzs3rmM5+ZeiF6/vOfn3ZahBBCCCGEEBISCooIqQ/CiMCLANEHvROR2MQE2DEtn815RC5qkT6FN0N5x2KuLPxl59blwMJvF+qG1PnsAie8vGzatGkQKgUCZkkLCIryQHqIByL0cXmhXBA1QRbOCclC8ZBOxJOOhDXMjifknQX1vm9eiqQ/QZpAdJkdZ5n9SRf6FJIP8r/IO5VL0M+CUMLdkZER7/cgpE06Iyb65Cc/mXohuv766w/qpPO8EC1dujT1QPSKV7wijb9JCCGEEEIIIW1CQREhzQVF9E6kB3onCgdC9GzYsCENHUEOXtjAwj9+YuEBixz4KQIALG6Zu+ZlMYwiANJFr0Qo36gPUifgQQL1QQRFIjKS+iCLxNq9SODZ8JzmekJ2QRJpMTo6mi6EH3rooekh7YAIkdB+4MB3ixh2L9JfKCDSjXhzQ7uJtgBtqLQH4sEM35G2Ax7P4Fl11apV6d/7ICqS/oP0F+lrQ90LdRH1TfP4hJBY6IyY6LWvfW3aKBSFMZs1a1Zy8cUXpyKiU045pTVbCSGEEEIIISQPCooIaQbDnZEs4iEiuxNWxCKTJ0/m4kYPMUMtoN2AB5FhYRekrIiYCGUJZQYLFMTekzxp7pXIp5AIbaUIYlAf0G4WCecw945QX/Auh5+aRUUiEswibYL8xCKoi4XQvHuRfkHxULeQ9hPjCRxlgkK0q3hfQfuJUIh9CX1GSEghrXgHI4Q0pzNiomFhzPAy88IXvjANY/b0pz+dO4cI6SGY8Js/f/7gszY020/bSR/TXrP9mm3vgv2a0Zz2MdpeRVCE9x+xW9vijWbbtaM97YfZryHcmea092G7eCfyGeoMiztYFM8iC8LwMhG7mEhzuYnRfvFCtG/fvmTnzp0DrwHDEJtFUICwR/AqgDBH8EQQ81xjTGmPEFF9wmXa+wxvZgrrsKiN9lLm1YvKNr6DOoQD8++oE+J1o+2yBrvRtsvnOmSFRH2ss33Dd9r7FhBpLjtabRdPKxBhwtMQftrYj3EIRJs44DUS58FL0THHHBPE7q6gtdy47Ktisl2Tx1lXZWfbtm0OrSIkXjonJhKXq+eee27qgeglL3kJ3TgT0nMwkYGFBa1otp+2kz6mvWb7NdveBfs1ozntY7XdVlAkLtU1otl27WhP+yL7Yw93pjntNduuHe1pH5P9IiTCoh+8B9iQXWSA+AjtDLxaiYAi1kWgmNK+b7hOex9eicyFcJTpusKZhx56KA0BNmPGjFRkhwW6Nhd2q6a9GX4nhIioSMDKOtsePtI+pAcizWVHo+3iqRACTAiTZV2yKrt37x7MSVBQ1P1y0xX7h9m+YMGCwvNCjpdxr2F10mXaj4yMOLkOITGjs6UawsKFC5NXvvKVqRcifCaEEEIIIYQQrTDkGSH1YbgzXWBntk/vROTXC1Z93nAnQiLsIoY3laZIGCh43IlZUER049MrkYQ127JlS6mHrjJQF1C3ICjCIl0MHopskUVFSQPf3ojYXnQXhi/rByLExPgVQqKm4Fpoh2fOnJkKitAmMewZ6RqmNyDfoI9Fv65lHEJI7HRGTHT99den3ogIIYQQQgghpCtQUERIdTSEOyMHhzojfpk9e3ayYcOGpM9AIIBFPxdCIgHXQmioww8/PJpwqaR7+PBKBOEM6gT6y6ZCInOBHV6/ZAEPohkNwFa0j/gpITB9okloRaqLiCgg6j5oOyVUqstroj1Ge79jx460TFFQRLoEBD6hxgVyH/a1hLihM2IiCokIIUXulteuXZt+njdvXhrLXROa7aftpI9pr9l+zbZ3wX7NaE57DbYXCYrEvTpoO6REVTTbrh3taW9jf6zhzuC5wbRdk2cC37bTO1G/66xvsEiHPh/lrI79YJjde/bsSUM7yS7omIgh7R988MGkj7hoM315JUK5EHHdMC88ZeW+TFB01FFHpeG82ujnqqb9mjVr0udEPZbzfIF3jaLQK5rHCdqpmvaxCYg0lx1NtosQE4Kf7DOAJrZDzIgxBTwewtMbBUXdKTdds7+O7RJSdPz48cn+/fu92oc5gCKbXKQ96ighfSGuN1xCCPE0yMdAXD5rQ7P9tJ30Me0126/Z9i7YrxnNaa/ZdlmwEbvxWdMCs2bbtaM97avYH2O4M1ls0Igv2+mdqJg+1Vmf988u/FU5H4jdEL1lRUm4NhYjsEgSU/60nfYCFkb7iIs205dXIojrEKpvGHXFROaC+LRp01rx2JUt9zaCLaQzFjcRDrLsnCag/ShrJzSPE7RTlvaxCYi6VHa02I62BR4JzbkDs81o2teiDcK7C8fGOsrN6OhokL4qNrK2o6wuWLDASkw0efJkr2IiCINQh3Av32VnZGSk8TUI0YAeqSMhhBBCCCGE9BR4JyKEVAPeiQghRIQTwzywuADiCdxDozCZxIlPr0Qop3W8dFUB18dCXdsLvTZpDC9KWHzEwqNPD3kQVsEzUdkCJ4kLCIjkEBFRjEIi4h+0Z2g/sVnBd/uJ+6CcmQI2EidlIhrya9D/QejjM9wZ+nH06Zo8PRESO6xNFmCXBhTBchBCCCGEEEJIaCgoIqSeoMj3hD9xA3Zg+17c7juzZ8/u5bxWCOGEzB/GLJwg+vDhlQhlVML+hRDxxVonTCGRgDCFU6dO9RaucPr06dF5LyPDy0eegIgion6Ddg19vW8vMnh3ETEmBUWkS4h3IvSHPoBIyWc/TkhfUVGjLrzwwsHnhQsXJn/zN38T9P7nnntu8pOf/GTQ2PncyUQIIYQQQgghRYKi5cuXt20GIeqIMdwZIcQ/suDnM5yCAOEE7qcxFB3pDyifqA8hQqqgTsTY9+YJiYB4J5oxY0ayZcsWp2l0+OGHD0IhknjZsGHDoCxQOESGtZ++gYjowIEDA+8tIihatGiR93sT4hsIfcaPH5+GQkXIQFdg7I3+G/2sb69E27ZtY4gz0itUiIm+/vWvD17ClyxZUuncJz/5ycldd92VfsY1UMnroC1mJSGEEEIIIaSbUFBESHXvRFu3bm3bDGIJPMj4DDND+gXm8xCCLMS8ntwLi38UE5GmYhcfXonMReoQ4D6xCeyGCYmyi5xHHHFEuo7gou2AlwT0a7h2LOlAfo3p9UW8ZSAcHSEm0paFbD8nTpw4ECBSUES6BPrDKVOmpGOSPXv2NL4e+la886P/plciQnoe5qzO4B0N0c6dOwdHXTjQJ4QQQgghhMQCQ54RUh2GO4sfX4vnpL9gkQICn1DAmzk3JJKYQfkM5XVfhHyx1IkyIZGsAUAQiEX8mTNnNlqUxLUgToGYCNf07SmB2CMhzMwwZkXlghC0YwhzFip0Y54QmiHPSIxAeLtgwYJK56A/RL8I70Tw3Ndk/R3XwTsk+m0KiQjxQy9GsBQCEUIIIYQQQroGBUWE2IOdioCCIj3eiYgfZs+enezevTvpEyGFDFhkjEU4EQMPPvhg6uGFVBe8+CTUYnhMdcFGSJQVFME7Db4P7wlVwbmzZs1KPRIhvBmFRHGQFRDJQUhsbVrRvSgoIl0A/SL6R/Sx6GvhVagK6Ksh1oWQCH1uKM+gdaMfEaIZyvQIIYQQQgghRCknn3xycvvtt7dtBiEqYLgzHWBCePPmzW2bQUgtuKGRaPDS1rdyWkVIlBUUIcQQvCZgsXPv3r2pKHmYtzN4RMCCpoQ0w7n0ktA+WeEFxUNEMyi/GzduZMgz0gnQ14rgFt4A0b+ir923b1/qCWxY3zxp0qT0kH42tGB3ZGQk6P0IaRuOZgkhnQcv8scdd9zgszY020/bSR/TXrP9mm3vgv2a0Zz2mm0Xm5csWZKsWLFC3Y5n2Cu7z7TZrh3tad/UfiwEYvKxDTABKrZrW8zVbLt2ulBnpY8NbT/KKhYamlDFZtwrpvrRZtr3nTptZgivRMCmTrgoL3Kf0HXCLPdr165N7183hJV4ToA3JyxYQlQEjyFY8JSFTgnXgp/S5shn1+VGvJXE1M5oEBHZCIi60NdqtV+L7cPaTrM+uqqbReMJCoq68W6ieYxm2g7xT1NMAS76XIh4JVQxfiJ/pa+Vz9LX9rHsEBIaiokIIZ0HAwu4PNSKZvtpO+lj2mu2X7PtXbBfM5rTXrPtpv1PeMITkuXLlyfaaLrAS/qb9nXtb9s7kQthQ19sR6gzeHcgv0JruRHaWiSRHc+hCBVioQraFqi6Qt0207dXIlmMCwHqHtIhdJ2QtIc4q4mQKJtuOCDmwYE0NIU95uHCdoDrQ7Ak4ROzYY/kfrKoSuqJiEy0p6Nm+2O3XQSCqPt53slct3PSfg6DgiLd71Xa7RfbXXqTlWvikD4vO4avK9Qddi9CiB0UExFCCCGEEEJIBzjjjDNUCooICY0IitryTkTKYagzv8yePTvZsGGDajFtVQEAFgzywiW4RMIsULxDYiakwE5Cl2gJbWZDCHGUiIhwPPLII2m4F4gXDhw4MBAUicckCBsmTpw4WICNzTuaBgERIVXbz2GhDkOKiUxBESFtsmDBAufXbEOIbMO2bdvaNoGQVqCYiBBSiyL3hZMnTx58xu4ZvPQWgYGBOZGPl+WHHnqo8By8NONlWcDL9f79+0sn9sR9IZAX8SLwUm5OsuAeuFcR8jIv4FnKJi3hFtJUQyPNkHZF4PnNSRkbl5JIZxmIYQICIR7KqJqfdfIGz26GlrHJm2x+4vtlL3PZvLHJT9zDjHFvkzfZ/EQ6Z3eQFeWNbX4W5Q3SQ8odbMH12qprNnnT97qWzZuu1TWbvNFa1/KoU9eQpmY561pds8mbvte1bN7UrWsnnnhicvfdd1tPypTVM5BdBNJ8Tvb583Z5256TPde8t8v7aExnF+f4SjPzHLQZqN9V72PzLKHOCZHObZ0j3omKRF82ZaDsvvh/WbppqTe+zgnVprloB7PnyA5mlKM9e/YkPsE9xB6kKfOm+vPn3Ud+Djs/1j6q6jkIx1WnfRLPNIJ4sSn6rrx7lb0HuKwTZbbleR2weX5gvnfIOfgJYapNuYmlL8Rzw15530CbhfH9sHPk/WLnzp3peAYh2CRvca2ye/kYp5h/y7u/67pmhgYUEVFM7aCW8WDM58SSn9J+ol0z5yps7mNi025IPZa2rSjNIJjMeieqO+7GfaqMiX21nTG/e+WVm7L5MVf9Wpld2XQOcU6osWosbSfsnjdvXqVz6twnT0xlcx9CfEExESGkMph0x27eos7xsssuS971rnelL8ArVqwoXQg79dRTB79jUe+ee+4pPAfu7k844YTB77t3707uv//+oS/XW7ZsSc85++yzB5PRUBKvX7++8D6zZs1K5syZM/h9dHQ0vVYRGFDMnDlz8Pu6deuSXbt2FZ5z7LHHJtOmTRv8jpcAWXgW+wGuKwuNJ5988pjFzZUrV5YOKpYuXTrmRaQsb/BdnCNgMqPsHNgE26Ss3HXXXcmmTZvG2J4Fz440EHbs2JE8+OCDhffB9czBG3YuI3+KQF4iTwXkP+6Vh6Q7Jp9OO+20QblZs2ZN6QI3yqYZkuG+++4rXRBGHZD0wcC0LJ3FA4WA65vnwHYpD6iT+F+2riF/UG6q1DWUZXOyJo8ZM2YkCxcuHPyOuoadz7Z1DXbdeuutab0uKjfz588f0xahzOCcIo477rgxO7DNujaMxYsXjxEFlNU1lB2IT5DueJmH/WX5iUmCJUuWDH6HTWXnoEyedNJJYxbbVq1aVXgOYk4fc8wxQ+taXnuDOjAyMlKprs2dO3fMDlDk/7C6JiCtpk+fPvjdpq5BNGEKfX76058OdmUNKzuozyK+sKlreHlbtmzZ0Lo2bOLnlFNOqVTXULfM57epa0ccccSYHUA2dQ0TrPBGIKB9Lgv3Y1PXsmUH9casa+ijXdc1cPrppw9esDGBU6eu/eQnP8ntZ33WtTzq1jUsGshOXLQ7GEcVpTXKpzkxhbyzEe6Zkx8YD9iIw8zJjzybcA3pH1EvzfqM/5WVGVzfHAvhOcrOwbObIjyUGxtRpVkuML6F3dm+3bw3nscUeuG7NsI9U1RpCoNt89M2b/AdEcUh3cp2/ZliDl95gzJmtgE4Z5hwT8oOnl363Kr5Kd6JbPImLz/L8iYvP2WCXu4nXkwEPH9ZvSmqazZ50yQ/JU9kkbbI20Q2P+vUNfSL6NuK7LOpa2X5i/+31Q7a5g0+m+VGruuiriFvbESyVdtOM29gF8ZDOM+031c7aOYnbMAzo63A+CWv7NosHOQJNkzwf9wDP6WPqFrX8trBpv1aXtkZ1q8VUadfM+2yHXNk8xPPI3VtWFq4rGvDyNY123PM5zL73jyQNub7AEAaVxWkF+UnbIItUidkrqpIiCnPUSbcy4L0yvZzRcIYISsgtUnnbH7iPQp/Q53P2jCs7SxrB130a8PqjYSew/9wXbw7yIYwm4V6gHcAHHg3wHsY6hGua3ozajqGtBmnFLULdceQeXljvvfivbhq3mTbTqlr5rtJtq+yzc+ifs1mDFn3fQ3UHd/XaTtt+qhsfg4bc5jvhXj+7Dl139eq9GtlYw7kCb4P+yTUWZ54Li/d80SyZXOxZhkoS2fYkRUU1enXkG5F59VpB/PqWlne5OVnXl0z36tQNrP108e7dLYdtGnXhtW1ovF9nTFknbkRMz+rjDnM90KM8YveC6vWtaJ36Sp5U5SfUnZwH/M9ryzN8sZKIeZGbN7XCPEJxUSEkFrYeFiIBRmYwSbbl/CYMAeW2uyHvRjswX6NtssgWpvt2tFcboBMEmPgr81+ze2NoN1+rWguO5ptB+bClExQYOLJxvsg6Tcay7uPZ8CYI3QIFs1pb7sDmbhHe7rbeCTxASbfZUECi3OuvBNhwd68liz8oT2JbbJfe9nRSozpLh4UUFZRZiGS9xH+Dwtn2DRmemMIBTalxJr+eYiQSBblscmhicco8WYE0bQs+nZpXssUEWXFd00x00hEpNrQms8a7JbxBOos2reyTVl1MUMX2oK6ULaRuatoKDtdtN/Ww1LM8L2WEHsoJiKE1KLI1TwQtTEm7OA5oojsyxkGzGXnZCf8setm2Dni2SJ7DnauYAKwCFM1DeBpA+cVkfVoAK8BZfG6TaUxwE4CGZCZnjngvUd27Wd3+R1//PFJGWYa4HNZOmfBPavmDdIDXmdM27NkX5DwEjTsu4KpThePDmUTCdm8gScc03uKiaR79j7w0GG7I0jAs5cNTs2yhjpRNW9wvnkO3FybYc7wv2xdQz2ump94YS47J5tmqDOmh5Jh9mfvg8nNonKTzU949LAJvTSsrg0je5+yuoayY7qol/QvIps3sLNq3iC9ys4pq2t57Y2LugZPOMPqmpBt0+rUNXjpETHrsLJjPo+LupZHnbqWnfgJVdfQPhd5G7Sta8P6KgHeysrawap1LZvWdeua6Rkvz3Yfdc0mb2zrmrlzDPlp1qW8cGfZNBjm/a0oDbL11Tats5h1PHtNnJ93TpmdZedk7cruxrM5R1zOm94SsvZmz0H+ZuusTd6U1Zu6eWNeVzwU2OIrb6qcM6x/qJqfaP+waFe1DGR3V9qcI3XNbD+zu2Ft6k3RferkTd1z8DxVFjls8iYL6gzaQizWDHv/tKlraCeLvB1mdz+HbAfLzpdzsu3lsDpbpa5hfIZFWYwdQrSdOEd2pw+z31U7mD0H10XZgOfAvBCvRW3gsPubnopQhnBt8WSRVz7a6tfyys6wfq3KfWz6tby5gTLynkcYlhYu65rLc5D25jitKD/x/ggPmXl5U9ZHV81PCZ2B72GsiTa2KJxGUX0dJrZAu5INYw3w/FUX7qrkjXh2RVqKqC/b17oap9Tp1/LqDdIQYwO8x2JMkn3PshG0ZL+Dcod8Fa+reemePcdVv1YkhGrSDkre4veyuYW643v8TTbPZj0SuhrfNxlDFgFbbccKwwgxvh+WN6anjmz6NHlfq3KOzZhD2k7YIxt4yupW2e/Z/+H9JOvF0SadYY/pnahOvybP5nt8XzVvcE5eXTPby+w169S1Ou1gnXat6vje5h5Zu+qMIauOOURUn9deuqhr2XPKqJKfw+YTitJs+/btTvLf17wVIT6hmIgQUplsbOAixHVyFSRcQRWGDSqLBg82g8q869gMLE3qdPSm60O5L0C6DEubqmkmbtirUDc/JU9tz62TNxhU2gwsq+Rn3v+yeeNCfJdH07xBWphiorzr+ahrrvJG7lOl3LioazbY2JNd2AtR1+rkTV5dK2tv6uRnqLxBXbNpL01CtYM2QhJzIiRUXXPZr5lpn53ICdEONqlrVcqNq7rmKj/NRSo8h2n/WWedlSxfvrzw/DoeWVyeMyx0RijbihbCys7JtvVlE2h171OFumlW9bwY8mZYCIGqaYaFtiKxio/nl8/ixcTXfWyoWzbLbHd1n6I2wvY+Zfctq7+xtJ1mWmgtN3KvJvlZ9xxZJENIZizY2woasgLMvP/jmuYCzDCb28qbsrSPuY+Se1SppzHXgaJz8hZXpb318Swos3jnynrZagrGoxAT5YWJqpPOts8vYhOI67FIaNvXhqwDw0RbEOBgkTJvcbPqPeQciJMQLg0bTsSbSoh6Y3pozrO96n0QLlso26jZNG/MNtKmrwo5Vq9zjoaxTd7f80RcbdXPYd8TEbGENS0TFNn+D9fMhneqms4iKKrTdlQtNzGMH4aNvUKUZ4Slr9pPZ79vjtGGXafOWCDUOehzQ5QbH/lZdQ4KNmNzZQjbXJ1DiCtY+gghhBBCCCGko5xxxhltm0CIChgaMG4QioeQpsiOe+z6r7PIljepj2vhmlWFxoS0jemxC543XQmJxKtGyEUvERKVeayJEQiJIOYyNwi4Yt++fenRJGxaW0AQgQMCIjkIiQUILtB+SjhBF4iH9ibXYz0JDzykE0JI16GYiBBCCCGEEEI6DAVFhBRTFu6RtAtCnRHiCizSwRsLylVVj45ZIQaugWu5WkjsGg8++GBpmHjyKyFMW+0cBD8ovwh3hqOuAAj1AR6O4O3PJvSTDzQKieCZFkKf3bt3e7vHzp07x4Tm1SYiIiRWJEwu2r2qHpJN0O6ir0Qbiuu5EDujDhFCCCGuoJiIEEIIIYQQQjoOBUWElEPvRKRvzJ492+sidqxA7ABvQhBwwBNAFQEFvBHgHCwiwgsLhUREOyIowkI2BDlVw02jHmAxHR6O2hASQYylUUgEIPLZu3ev93s89NBDY0JqxwhFRESzoAjhTkVMWTVMO8JE4acrIRHrDyHu2bZtW26IM0L6An3wEkIIIYQQQkhPBEXLly9v2wxCovVOtHXr1lRQNGnSpLbNIRkg+ti8ebOzUDyEYAFQhEEoVxIOCKGGsovu+C4WCLHYh/YB4gt8l6HNSNcERRK6D95yEF4SIhR8Nr3aSHg0LHzLAjjOQ31wsRBeJ7yZRpCmOHyLiQDuAZFYjG2W6UGFIgiiEYwl0Aai/YNQGeMIvE/g58MPP5zb1uJ7aD9xrhwuQV1C3Vq0aJHT6xJiimsghCOE9IP4RpCEEEIIIYQQQgghLQmKCCH9QEQRWGDHgYU9LO4/9thj6SELf+K5SBb8cF5o0QTpJjGJYVCmRWQndUJERBImC98x6wDqhvzeFlq9EiE9ka4hPAbt379/cM9Y2i6XIiJpr9k2xyGQ62Ne4LlljIC2E2IhSQ+p49J+Amk764aWtCVGQZFZTvpYVgghRCPqxER33nlnZaWudE7SqRNC+gV23yxevDj9jB1T2tBsP20nfUx7zfZrtr0L9mtGc9prtr2O/TF5J8LkqYTS4ARiWLSnvW/7fXonkl3L8lkTMdgOTxlVvRPJ4o4sNgqmQKTvZR6hzqZOnZr0tc0REYWQLSvDFrp27tyZhnaKmdjTvssg7aXNLFowhue1mBCRkIwrhy38tl2eisKbxdBflYF2Bp5Lspj2urId+QcPUyIWi0VIVFdEhLQTgVtWwCI/RbhRRaxhW2djJbT9IrzFYa6/mXkhgtyycteFtEc6ZNtG2/GED1C/Nm7cmMSAWWclTbLCszoCVQ1tfZfHaNrrrK398MJESN9RJyYyX15Cnq+xMSeEHDw40Ihm+2k76WPaa7Zfs+1dsF8zmtNes+117Y9JUMT3rPbQnva+7PftnSiGBVittkuos6pg8RTnQohkLlxoC5nmK+1nz56dbNiwIfGNpnJvsygyd+7cZN26dYkGNKV919C4wOZL0BLSo1Pb/VUVgU8ePmxHuKXx48cnmkVE4skJ6SZhpLKhKSUsJZ4VwvCqYaS01lkhlIgIeYCfCPmJcIgoX2ZILxEk4kA+mF7P2rTdJ3n2x/BMbXonMssK6iwOeEoTb2nmfAbq7JQpUwYhN21CZ2po67tsfwzlO5T9IyMjXm0hJHZUiYk0N6yEEEIIIYQQEgsxCYoIiRGf3okIIYQQ0s/wZi43Tcd6L9chzWC7iFXgSQ8ClmHPA+ECDnxn165d6Vhu2rRpg9B9XF9qhqTvnj17UpF21vOOgL9DZIQDeQahCPIBP5EP2kUIWmjLO5GIJSH0Qz2EJ8dhIR1NodGOHTtSTz0zZsxIBUaxeFMjY6mzuYMQohtVvbbpurKNgxBCCCGEEEK6JCgihOR7JwKY1CZxIR6GCCFEO2XedUj18GbaCClsaUNEs2nTpjHeiOoIiSQcHMQro6Oj6djMdp0G39u7d29qB37iOsPEL6Q8LZF+EGkhHyAQqpKW8EQDAQJEJbjOMK9cxL+gL2RZgQdHhIgaJiTKA+etX78+PY9lJV4WLFjQtgmEkICo8Ez05Cc/mapxQkht8MJ4zz33pJ9POOGEZPLkyYkmNNtP20kf016z/Zpt74L9mtGc9pptd2F/mx6KZMcqwM5D7lANh/a0D2G/r3BnmtNes+3a8Z32Eups6tSpiQ80lx3NtnfBfs3YpD0EkjGChV8JQwNPIrF4hrAVYGko91jryAv5ZG5qdhkCJ7RXHgh4cL+6Ic0kH+GNaPv27Y0E3rgOhAkIoQTvOBJGSWO5KcKX/eIZCu+dyIsmQBSGtkWE+1IHmPbd8E4kQiKUFYjOmjhogPAMwiJpQ/Lay5jTXXNfa4P2tLe1H30HQ5wRokRMdO2117ZtAiFEOdq9i2m2n7aTPqa9Zvs1294F+zWjOe01294F+wmJGYY7ixN4JzrssMPaNoMQQjpLrOPLrnglwsLluHHjgtwLop5hAhofIiKUHYh2moiXRMDSVEiUFbLAJghnkfbcvG4H8gF50FRIJEBsAsH+zJkz0zKpTYSg2TvRokWLgpQVtAMugNAGQiiI3lFfNYlttPe1hBBiwp6aEEIIIYQQQnoMw50Rko/smiZxEasnD0IIIf7oWlg4WRgPsTguoiXf4hkJpQQhUVPEE47rkLMIzwWBAkMn2YF0ElGXSyAo2rVrF/MhEE08hFUtK/BI5BLUV3iHwfUpvCGEkHagmIgQQgghhBBCeg4FRYQMx/VCFnHnnYgkzhdZSfUwHIT0XRgTKr264pXIDGEWIsQy7uFTtAQREQ4IFlwIiRD6B8IBiE18AGEMro8wN2Q4EG4gL3bs2OFFxIFxHMIMIS/oJSoMIvjzVVbgccpHvcL4FO9jFJ+1z+bNm5O+ABEbIeRXUExECCGEEEIIIYSCIkJyoHeiOKF3IvcghASpxty5c9s2gSiG7Vg1uiQkEiDw8S0mwj0mTpzoLZSUiBNcej6BKAHiAV9iH1wfQhb8JMNB+kDsA88wvoBgTPKB4c70eidCHu7bty/1JuZT2IH70DtR+yxYsCDpCyMjI22bQEgUsIcmhBBCCCGEEJJCQREh+dA7ESGEEBKeLntxgtDnkEMOSaZOnertHvAUhPu4FmqY3ohcihQgIIJgwPe4C6IH3IvChHLRlU8Q7gwH7oW6QPR5JxKvRL69W6KcQNhGESAhhISHYiJCCCGEEEIIIQMoKCJkLPROFC8MdUYIId2ni16JBAgopkyZkhx66KHOrw2PRJMmTXIu0vDhjUiAwAceTnyLfMTrDoUJxaIupJFvRNhFz0T+8VFnRUwUYky+Z8+eaOrs6Oho2yYQQkgw2EMTQgghhBBCCCGElAiK6J0oLhgiyA++d5YTQogtXfZKJEBAAbHPEUcckXoQcgXESdOnT0+v/Ru/8RtOvRH5DJkEUYnPsFpZTyf0TDQ8H5A+ofPBVVkl4bwThayzIjSMpd72KdxX30BYPULIr+mk78C77rorueOOO5KVK1cm69atS3bs2JF2NKLIx0B6zpw5yfHHH5+cfvrpyamnntq2yYQQQgghhBASlXei5cuXt20GIdEBQRF2+ZN4wE7oww47bOj/H3744WT79u0HLTxgJzyYMWOGF48QGpk9e3ayYcOGts0gHWFkZCR58MEHU5EEIXXpslciAYIf9FEzZ85MtmzZkvZbTRg3blwqgkbf5kqg5FtEJCAdmj6/LRQTxZEPuI8IROidyD+owxs3bnR2PeRbKOGZWVYoPAvP5s2bk76NYwkhHRMTXX311cmnPvWp5Lvf/W5l1SAmjp7xjGckl156afL0pz/dm42EkHbAZD+Eg0DjQFOz/bSd9DHtNduv2fYu2K8ZzWmv2Xbf9vsWFGGymKKMdtCe9m3Zj4W5rVu3NrYdm5w0tjkx2g7vRGUT29gxjSOLLCDm/S82WGfjtX3u3LnpRsbDDz88iRHNaa8dzWmv2fZY+6syRNQK8dTOnTtre0NEyLSpU6emAiVX4c1CConAI488koQA4ZJMMVEXyr0r+5EuofJB8gIiuPHjxyca0Vh2UK8XLVqkrqzgXigrWtt67eVGvDFptN1Eu/2EhEa91PcLX/hCctJJJyXPfOYz08+Y2BN1qu0B8RHOfdaznpWceOKJyRe/+MW2H4sQ4hAMJjFAwKFxYKnZftpO+pj2mu3XbHsX7NeM5rTXbHsI+yEoIoSMpWm4M9RVje2NdtuJPQx1Rojf0F2xh2mUtr7NNh/p1MQrkcb+CoIiLJJj4zMEzFWEFVhQR7mCsBHXcCEkkrBmEBH5FhK1Ab0Skb7iuj63WZc0tvUx9bXkYBjijJAOiYnWrFmTPPWpT00uueSS5J577hnj3q7OIecjNNrFF1+cXrsPcZkJIYQQQgghpAgKigj5NVjcI3GBxVOEOiPuQp0RQghpB4Qlgxho8uTJadizWbNmJdOmTUvFQhAIySYCCI/gVQHiIQgDEE4Q38HfXYSKCuWNKI9Qi+pcvI8nfZgX7SD1vCksK6RrMMQZIR0Ic/b9738/ednLXpbs2LFjaHxMWzWsnGteA+dec801yZlnnpl8+ctfTn7rt37LofWEkNCgTsNdqryUaxt0araftpM+pr1m+zXb3gX7NaM57TXb3hX7gTa7u4D2tG/bfngnqusavW3bm6DZdkIICY3WNjM7rx7a/qZeiTSnvYBxPQ4IiCAuko3QJrJJ2rWX0raERGI/nvnAgQPe7wfhVTbNtJcbV/bjfFdh8mzupfE9Nou2soP6vXHjxsbXwfNK2DHfSLnMruXK/7TRdl/bFM1p3wX7CQmJOjHRt771reSiiy5K9u/fn/6e13GgI8TuWRzYUQX1Pg58FzGHd+3alWzYsCG5/fbbkx/96EeDTtMUFuFa27dvT57znOckX/3qV9MQaIQQnWCif8WKFelnhDLE7h5NaLaftpM+pr1m+zXb3gX7NaM57TXbHtJ+vFstX77c6TUfe+yx5KGHHko/T5gwwckuatKPtG/bfngnQoh1jbY3IXbb4Z3osMMOS7pI7GnfZfs1294F+zWjOe2ztmOhXxNdS3tJf3Ph2dfiZ5seiQDyCsKENsREmsuNa/slH0Ig90H5hv0I79fntNcG6lCVkIxNwH1EQNmFdNfU127evHnM711Le232ExIaVWKiO+64I3npS1+aComyIiJM5iHk2Wtf+9rklFNOqXTdn/70p8m//Mu/JJ/73OeSLVu2jIlRiXu95CUvSW644Ybk9NNPd/5MhBBCCCGEEKIBH4IiQjTTxDsRcR/qLDvJTZqxe/fuZOrUqW2boQZsXkS4IUJIc69EJB+f3hPaFhFlhQkhwpdyAbnc24xsuveJiIfyvG8Rv6C+r1+/vpEnOAm7iJ8QaPgEoRzpRaY9FixYkHSdbdu2tW0CIVGiZrS0b9++1CMRfkqHgcEF1Jpvf/vb0xeNv/3bv60sJAKLFy9Oz8U13vGOd4xRgOJemCCEoAj3JoQQQgghhJA+C4oIIb/yTkRIV4GXb2LP3Llz2zaBkE7RNMQZ0SkkAliXgWDAt4cOCGUggIjZE0ibSOi8EKJ5eNYNIUQhfkDeoR6FEKDjHqyzxDcjIyNtm0BIdKgRE11xxRXJ/fffP0ZINGvWrOSWW25J/vIv/9LJwAYD1Q9+8IPJbbfddtDgGfeGDYQQQgghhBDSZygoIuTXYPMRicc7UQhPBoQQQkhXiElIBCSEke+wpbg+vRIVA9GG73wQ4Rjy4pFHHvF6LzKcTZs2NTof+edbTATR2SGHHMJ6SwghLaCi5d26dWty5ZVXjhESzZs3L7n++uu9hB5bsmRJct111w0UiOLO8R//8R/p5owQQgghhBBCCCH0TkR6EeqMEEJCgagB9ErUXyFRVsQCz0G+wmphYzqECaQ4H+DByZegCKIQhAeVfGCIs3aYNm2ak7KC+jp9+vTEB1ifxXsX7sMwZ4QQEh4VYqJPfepTg91+EtrsX//1X5Njjz3W2z2POeaY9B6m0hU2fPKTn/R2T0IIIYQQQgjRAL0TEfIrMLFN70RxQe9EbmCoM0II6S6xCokA1mMgMPEhTIAQAdelKMEO5APEJj6EV3Jd5AWFRLpBXRIxEQRoPt63GJawPTZv3pz0ATgSYYgzQhSLib785S+nPzGoQMf0pje9KTnzzDO93xf3wL3kvvj5xS9+0ft9CSGEEEIIISR2KCgihMQY6oyQNpg7d26yc+fOts0ghBDVQiIBIhN4EHIpKML6zhFHHJGKEuiVqJqwC2IOl+Gl4O0IYat8eZ8i7YQ6g9gH7YrL+gXRGUKo4ZoUALbHggUL2jaBENIi0YuJ9uzZk9x+++2DjgI/3/GOdwS7/9vf/vYxndSPf/xjunkmhBBCCCGEEAqKCEmhd6L4oHcid3AOjLjY6U1IGQxxFgYNQiIBQhOITmbMmNFYRAChA8ZrEydO9OI5pctAxIE0g2DbhUhkypQpaXgz5C/FId0JdQaQpygr8G7pQiiGcoJ6i3LnUsxGCCGkGtG3wHfeeWfy6KOPpp8xuDjnnHOCDnbR8T3pSU8auFp87LHHUpsIIXpA24FBp1YFu2b7aTvpY9prtl+z7V2wXzOa016z7V2xX6PdXUB72mu3n/ijq96J2ijzLkOdaa6zmm1v2/6+h4vIpj0EMxraKLFbe9kneoVEZugkeLBBvakrApowYUIqVMPPsmtoL/O+7BeRCNIR+VEHeK2ZOXNmKlzB9bLiEKZ9N0DewqsYPDZCDFQHzG2gnRJPYl0Nb6a9r9Vqd579FL4TUkz0/hxXrVo15venPOUpwW148pOfnPzwhz8cM/A+77zzgttBCKnHpEmTkiVLliRa0Ww/bSd9THvN9mu2vQv2a0Zz2mu2PRb74Z1o+fLllc/DBDJ2B5PwaE/7GO3HrtmtW7emdbLM9rLvxIpm27UTY5nvi/2abe+C/ZrRnPba23vN9vuyXZuQSMBCL0QsSBcIiuAJEt4HDxw4UHouBA3wbIR6CHFCmSBBc50NYb+IOhB6DoIi5IONZ06kPb6PA5/zPNYw7eMJdXbsscc2vo6IxSAGQh3ctWtXWl7EYUPReQhphgNlrcx7lea2Xrv92st8nv19F8ATolpMJIpAdDToONoY8GZ3YlGlSAghhBBCCCHNBUWEdA0sqmidFO4adiXSTgABAABJREFUWLTAAgYhIdm5c2ftnfiE9BWGOPOLViGRiYiBZPH94YcfTgVFOB555JHB2hHEB+IZRc7Bodl7Rkwg/ZG2SFP8RH/30EMPpfmBA1E9kNYiBMF3RAwm+UfiBB6jXIa2lfyWsgJhEcrK/v3703qLsgJQT/F/OeScrnoj0sbmzZvbNoEQEgHRi4n27ds35vc2XizgfhHIoDNrEyGEEEIIIYT0HQqKSN8R70SkfeC9gJPf7jbYbdiwId0lTopBSI9169a1bQYJAIQLWAjNelnA3DEWQilcILHQBSGRIGIhESAgbJkIEkRMBKQOUozgD6Q/QPojT5D+cpjhg5AX2TZR2s5s+ynfY/vZHcyQ7I8++mgqLMvrOyXvpbyQuFiwYEHbJhBCWiZ6MRHUqCbbt29vZVeROSjN2kQIiRsMVqF8B3jR1PYyqdl+2k76mPaa7ddsexfs14zmtNdsexfslwUAThqGR3vax2x/kXeivIUWLWi2vQvEXOa7br9m27tgfyyg/cO4SxZCTU8ckr7iFUUWRc1xmaY2U8RSQKM4SnN/5dL2LgmJsvh659HeXoa231b8Abvy2k8p6xL+TLxIafRMo73smO3GokWLnF/XR35qbuu197VdSHuxf8eOHW2bQ0j0RC8mEtfA0hht3LgxuA3YgZV1+UcI0QMW2FasWJF+PvHEE9MYzZrQbD9tJ31Me832a7a9C/ZrRnPaa7Y9RvureCfCxJkphNI+8aoJ7Wkfs/1l3okwaWnarmniVZvt4p1IvBhoJuYy33X7NdveBftjAWGUsBCO0CwIoQiv9VnPCgLaxokTJ6ZjMgnZIl48tJYbn4v6PkKcaeuvfNreRSGRL7S3lzHaj/Is7SfaTbSfaEfzvocDbQ1C1OIQjzYxPIfGtK8D1j337NmTaKGttn50dNSJt56Qfa1rNPezWfvxed68eW2bREjURN+rLVy4cMzvbbjNv/322wttIoQQQgghhBDya0ERIX0H3okI6VKos927d7dthhrEwznRDxaYsPC9f//+ZMuWLalAEe37MCGRnIPv4LtYcMS5pgcOQkIgXokIaQsRYO7duzfZtGlTGnEkT0iUPQfjDTgUgLcQfB9iJEIIIYS0R/RiolNOOWXwGS9dV199dVB1LAYv3/3ud8coK0899dRg9yeEEEIIIYQQbVBQRPoMvBOReChbuCLEJXPnzm3bhOgYGRlJtm3blmgVEsGThoiCqoJzsIiOuWy2RSQUXQ5vRnQAURBElBAQof3H71WRthfeQ3AtEg6KEQmAKJoQQlSIieDm9Pjjjx/8jhevT3ziE8Huj3uZL4vHHHOMc9erhBBCCCGEEEII6Rb0ThRHqDNCCKkrJIIICN4xmngVwrm4Bjas4jPCmhDiGwqJSNtCInhzazoWhlciXIeConCw7SAmLsK5xQw8pxFCOiAmAi960YvSly14B8LP97znPcmGDRu833f9+vXJ5ZdfPrgvfl544YXe70sIIYQQQggh2qF3ItJn6J2IdA2GOiN9AovWWGDatWuXs2ui/kBIhMVxhjwjPj2KUAxA2gJtG9o4eCNy5Y0N7ebWrVsZ8owQ4gV6FSWkI2Ki17zmNclv/uavTcWukJe//OVed/nh2hdffPGYkGoQE732ta/1dk9CCCGEEEII6RIUFJG+Q+9E7XP44YcnEyZMaNsM0jN27tzZtgmkgVcNLFj7yENcGwvidUL+dJEHHniAEQAcwtBEJAYhJsKTwZOQSyAogoc3tJ0UY4aB7QkhhBBVYiKEOXvJS14y8A4EfvjDHybPeMYzxoh9XIGdJ8961rOS66+/foxXoosuumhMyDVCCCGEEEIIIYSQPOidiJB+wh3O+r1qNA1tVnR9LogTnwv/9EpE2hZiuvToZrJ///5UqETvRP5hO0IIIUSdmAj89V//dTJlypT0swh8/uu//is59dRTk29/+9vO7vOd73wnvSbESiaHHXZYagMhhBBCCCGEEHvonYj0HXonigN6J2oOQ52RrgPvF/Cs4dqrRnZBHAe9ExHXUABA2gRtGsQ+PoWScCyAdppiTEKIC6ciM2fObNsMQlRwSKKEOXPmJB/+8IeTSy65JBUTiaBo7dq1yfOe97zUk9Dv/u7vJs95znPGhESzAQOQb33rW8n//b//NxUTyWDE9Er0oQ99iDuLCMl0tsOYPHnymPq1b9++wmuhjk2aNGnMy0fZxA3q+cSJEwe/Y1cCJmPywP3x/cc97nFj2gdMEJXFbz700EOTcePGDX7HPcp2QOD7OE/As5RNEmFiG/aZNiPtTPvls4DnN5+nKE8EpLN4eEP7ZrOwUTU/zbzB5/Hjx6dpXXQent2c3LfJm0MOOSS9toDv47wqeVOUn5Lu+L6Zzmbe2OYn0rnsRdfMG9v8LMob03U6bMH1fNe1Jnlj1jVJd9hXVG5c17VhVK1ruCbOQXrjPJu6ls2bqnXNNm/K6lpee+OirtnkDe6BezWpa7hPXnvps67lUaeuIU3Nchairrns17Jlp047GKJfG1bXisqNj7pmkze2dQ32yTPh+2Xp5qKuVe3X8N0TTzwxufvuu8d8x7wvPmff48rsAlXPkXdJAbaVPcuwc7Lnmvd2eZ8imqSZ/PSRzi7OKXp+uZaZXmXnhM4bOQfeibZu3TqwOVvui+7TdjrnnSNjG9/3sT3HJs3wN5nfycPmedqoA+Y5ReVGQ9tpzuMV3ctFO+i67ZT/4Zwq9TPPhjbyJq/stNFHjYyMJA8++GAyffr0yveRn8PSwlUdwH2LFsPz+hxb5FyMpzAWwz3wE7+baSnvkOY9bMqAeY7NQnteeTbPMaMD4DPSpeycuvdx3aZlr2lTbnz1UXXOwf+z+WkTLs6kzbFd2TPGMrbJa2vKyo2LttPmWZqMB9sc3+Mz3kXNNMm2m6COEEjOwbs45iPQdkr72cb4PrZxmqtzbOpBLOP77N+z1/RV17Jl2kYYbPaFZhrn9bWCOf9SZyzQ9Jxt27Yl8+bNK3xftX0vjLHtrDu+l3PyruXynOyz2N6HkKTvYiLwile8Irn33nuTP/mTPxlTmdDwQASE4/GPf3zy1Kc+Nd39igMipGnTpqUHgJtFHBs2bEiWL1+e/OhHP0p+8IMfJKOjo4NrZSvpu971ruTiiy9u4YkJiRMsIBW57EfneNlll6V1B4P8FStWlC6EwSOYuah1zz33FJ4Db2EnnHDC4Hfsjrz//vsLzzn88MPHLAZiULR+/frCc2bNmpW2IwLaii1bthSeg4GWqWpet25dqYvXY489dtBOiXvivIXnNWvWDD6ffPLJY55n5cqVpYOKpUuXDgaj+G5Z3uC7OEfA4mnZObAJtslnTBred999Y2zPgmdHGghw+42JxiKQxkhrYfPmzYO2fBjIS+SpgPzHvcp235rpjOcoW6hF2UQZFfD8ZQvCqAOyiIy+qCyds54ecH3zHJRTc2CM/2XrGuoyyk2VuoaynJ2oyjJjxoxk4cKFY+oa+l3buob0RpnAwltRuZk/f/6Ytghlpmyn9HHHHZdMnTq1tK6ZLF68eIwowKaunX766YMXBZt2EAv7S5YsGfwOm8rOwUL9SSedNPgdE9KrVq0qbQePOeYYq7omaY+xFepxlboGAfZRRx01+B35X1bXFi1aNGZxwaauQZxgCn1QpyVvhpWd0047bSCMsalrGBcuW7ZsaF3LAwsDp5xySqW6hrplPr9NXTviiCOSBQsWVKpr2K2Kdk3YtGlTWteKqFrXkPZob8y6hj7aV12TsTsmXurUNbOs5JUb33VNaFLXxD60aSHqmk2/ZtY1gLxBuzhM8AXxlClyQv0sKzPIe7OPRnkpO0cW8QSUGxsBsyn0QruO58+mgXlvPLv5/PiujXDPTANTGDwMWYg0x2q2wj2pbzbiclOE5ytvTJvknDLhHs4xJ+bq5GeovEE6ZycRs7bi+c35CBvPGCJgts2bJvkpeYRnt8kbMz+b1LUi8upaNm9QlmCLLD7JpLE59i5LA5xv5p9tXauanzZ5Y6aji7pmk5912k4zb2AnroE8LTrPVTtoI5Ktmp9Zz1Zl6Yzr7dy5Mx0TyO912kGX/Zr83VVdK2s7s3lj8zzZc8xwYMPOdVHXkDZ4vrzFcKHqQljRAir6BLw3iyckKefZzQJldSZPkG6Tn9nNAkX5Ke1DVpBuU9fy8kbSAO8h8jfX/ZqZZnnpl9d2hujXbOoN8sW8T5kXK7z/4XnMdzzYVZY3dceQZXWtqOyFGkPa5E227czWm7zzXYwh88YpZXljk5/Sr9Ud34OqdU3yRtqSvI1KWXGcraCuqL1FGy3PKza76Ndsxhx545Rs3uSN72MZQxbVNZwjYi2AeVnML5hti4u6JvfOgnSUUHl5+VlW17JpWqeu1RkP2pxjU9fMPii7Cc52DFlnbsR8HnPMMex8yRspC7Yb53zUtSzZd2mbvEEZCF3XfL2vEeITVWIi8N73vjdtRC6//PLB38wdZphw/8IXvpAetuSppeVvEC695z3vcfgEhHQDGw8LhBBCCCGEENJ3IMiEQJC0Q3YyVoQtIv5nuKFqYFEJAm5zUYDk1/vt27e3bQaxRHbXoy0IsfNbRESY48a9+9oGidiONAObEghpC2nDygQFrsiKqut4OiJhwHjbFO+j78t6qBOnEThEdMI8JYSQuPiNXyptma+66qrk9a9/fbqTuombWZB3PnaHfPSjH00uvPBCJ/YSoh14F0Bc4mGK6izimSiGMGdCdoeXljBnw9AQ5sxnOBjfYc7aDAfjIvTSzTffPCbM2dlnn91aXWsz9JKWujYs9FJX6lqoMGdt1LU86tS1W265ZVDO8EznnHNOp+pa7GHOiog9zJmGupbNm2y4M02hesxzTM8F+L/ZVrQRQiYmt9wuzgkVniDUOeLd1GxPys6JJZ1jPqdKmsGzHOqteCYSMVHf65rNOdl0hicM0xOrzTmh2sGY8hPpZIolYu/XXJ+TfZaqYc5QZ83nH1bmXIV2kRBnw7x2Vp1/HhbeR3aXY+55ypQp6bjMDEEXQ5izEOesXbs2VwTDfs2+XxNPtvD0HEuYluz4GGXcx32KzompHfQdqieG8QP+hndXtJ3Z9/Fh7WARZefg//Bsbr47x5yfsY7t5JxsaE/znRaOG0xv2Nn7FKWZpBX6PBHqylyK6WVG5rWQn+JBVH5WeZZQdQ3fxyYRSZcmYc6KiCXMWd45sda1KudA8A9P37HWz+yzmOfceeed6ecrr7wy7WPLojQQ0kvPRMILXvCC5Nxzz03e+c53Jp/85CfTjkgqV96AwwY0YGikL7300uQDH/gAVf2EFCwO2SzwSUdpDkJtQD2seg4GnubCmAnaBwihMFDFteV7WReLNmBwm3XNWUbW/aUN2UVLEXLJRFMeVdMsu+jlIz9tbc9SJ2/w0mG+SDbNT7PcmLYXLQANo0x8l0fTvEFamGKivOu5rmuu8gZpLxMQVcpN07pmS1mawX6ENDDt913X6uZNtq7Z1Nk6dS1U3sCuqm1OiLyxqWvIB3MSIkRdc9mvZcuOOfERqh2s26/h+auUGxd1zWV+ZtO+an0L0a9l8+ass85KQ06bk1XZyZVhfyuj6jl5kzS255jnZSfiXN7Hx/Nn097XfZqcU/T8Yr+ETjDnAkKkc51zEKJXNkOZY7Si67SdzsMWwW1sb3KfJucUPb/8D2XH7Kd81jWX55hpH4NtWHwyQ5qWIW1Omf2xlRsgC2Gm7bZpZoY6a6tOl5WdUOlc93nkp+25ddJZ7oNyavtcNt8zRRXZc8z/FeVNlTrjqpzZtPdN72Obp3XLTJX+Kqbxg4QaAtkF9WFhrGPoo4Ts+LgN2+rmDagyPo5t/ND2+D4bnmwYReN723Pku8NsDj1+qJL2bdbPMvLaZYgWESp92PeHPat44JP5CoyHyjZLQTSM8GqYo8Cci7Tfw54l21e1kWZV+2izX7d9t6ozFnBxTlF6NGlvYmg7cX9J/zrje5+2uTyHEFeoLn1HHHFE6j1o5cqVyR/+4R+mrpZF1Vimbsx+Dwr+t7/97ck999yT/Mu//AuFRIR0CCjeEeMXh8bYoprtp+2kj2mv2X7NtnfBfs1oTnvNtmu3H+9hsNkmlj1xi/a012w/7IVYHQdtD0+ZN51YwWS9lPkQIZjKwPybZvtD2Y4dz20TU9qPjIykO937gu0cdYzEVG761l/Z2o6xf56QiPRzfKndfs3tpfa0L6JuGwMBEYRDu3btSsVI8NBaJiQSoTpC6cKzI7wu45yiPkhzW6+9r9Ve5k37taU9IW2g1jORCVzK/cVf/EV6/PjHP06uu+665I477kjuvffeZN26danLMgktgZ26cKkLV4jHH398snTp0uQpT3lKsmzZsrYfgxBCCCGEEEJ6wRlnnJHcdtttbZtBSFCOPPLIdDK9rjdlQgghfqm6i78JdTyHEEJIrIT0mlHXOx7xjwiJECIN4qA6wFu9hMKE92V4eaZXlnCYYdwIIaQzYiITiINwEEIIIYQQQgiJl5NPPjm5/fbb2zaDENIz4J3INmw3KWb37t2VQp0REjNYqKwalrYJuBcXw0kd6JWIxAbaslDtp7SdbD/DtjnDQp3lCYngWWjfvn2N7glvMZs2bUo9yGCsiRDwzHPiAnjJhPdQcUBCCCmHck5CCCGEEEIIIYSQQOHa6UqddIGqoc76ChYrdu7c2bYZxAIsUsIzUQjvRBAuHXLIIVwYJbUW9QmJDbRpEHuEgKKSsNgKF/F+8+ijj6YCoKZCIhN4OIKnIpswaYQQQvxAMREhhBBCCCGEkFaYNGlS2yYQQnpK3dALhJDmO8JjRDxdTJw40fu9cA961iB1oVciEqOYaPz48UHEmHh/ZMir+IBXInis9OH9E4IiXB9iJUIIIeFhr0sIIYQQQgghpDUoKCJ9hN6J2g91RtyAhSNCbBkZGUliBgvhkydPDuYFiWIiUgV6JSKxIuJI3+0nPLqFEi0ReyDygdhny5YtXq6Pa2/fvj39SQghJDwUExFCCCGEEEIIIYQEgkKWeKB3omYw1Jk9DHWmA3i7OPTQQ716J8Jiu4iJ+soDDzzQtglqoVciErsY06fXoClTpqTXpxAzLjEjxEQQl/vcLLFr1670PvRORJp6x4xd2E5IjByS9BgoWW+55ZZk5cqVydatW9OXxaOOOipZuHBh8j/+x//goIQQQgghhBBCAnDyyScnK1asaNsMQoKyb9++IB4w+s4vf/nLdHHDXODA34444oh0LogQ38ydOzdZt25d22YQCzAXDM8Xhx9+eLJ//37nC6O4/owZM5LNmzf3ft4Zc/DEHnolIhrERFhfQ/sJLzKumTBhQjpuRhtNwosYN27cmPs/GWND7OMTjN337NmT5n+fxbi+wfhkwYIFbZtBCImMXva8a9asSd7//vcnn//855Nf/OIXud/BpNJLXvKS5H3ve18yc+bM4DYSQtyBASZeZOSzNjTbT9tJH9Nes/2abe+C/ZrRnPaabdduf9b2M844I1m+fHnbZvUC0yOCxsVMzfaL7dOmTVMZHkpbOwMOHDiQ7kLN80qEhQkIBhAyI2ZiLvPwTrRhw4Zk6tSpKu0vQ7PtMduPOom515gXxKdPn57bdtiC9EYbI58BrqlhIVxEVfJZIxr7qzLb6ZXIP5rLTQz2o91ACGsI5nE0aS9N4I1I2s9Y26S2074tkG8PP/xwevhm7969g/mDLqS79r5We9rHOD4mJFbif3v5/3znO98ZE3MTkxQvfOELK1/nC1/4QvL6178+HczIACUP7E77yEc+kn4fP1/60pfWtp0Q0i7YuXDMMcckWtFsP20nfUx7zfZrtr0L9mtGc9prtl27/Zpt1w4mzGIXT3TVftN2fMYGJyy6aAALOVrTvUxsFPtzaS7z2u13aTtCnWUXwfqY9ggv8eCDDyYxIwvi8LawY8eO2tcxw/1ARAqvGhAqxY729l6z/Zpt1472tI/BfhFHwAMbxJgPPfSQ9bnDwqPh79jsj7YzVvFCDGnfFugnIcwPAcqTeBxFmmtPd832a7Y91vExITFziJaJHYh5TC9Cb33rWyuLiT7+8Y+nQqIilbMJvocX/YsvvjjtoH7nd36n5hMQQgghhBBCCCmD3olInzjyyCMZZqtluBPVHfC0VeSdqO8w1Jm+tgEL14cddli6YAZBUd2QZ7gWPGqIkIjtDqkKQ5wRTYjgB97nEPoKniDrgjYT1xk3bpwKIWaXgWc0tEWLFi0a83fx8hkC9MOPPPIIywKpRRNvk4T0nXy5b2Rcd911qQs76ZzwEvfmN7+50jV+9KMfJb/3e7+Xno+XNvPFDX8zD0G+9+ijjyavfvWrk3vvvdfhUxFCCCGEEEIIIaTvDAu/TsLRZKGL/CrUGSFdFRRBBHTUUUclEydOrOWFcdasWRQSkcYwxBnRJiiCAAhCSngVqhreEW0lBMpoe+E9hOKRuKkrtq1DUbQZQmy8YxJCOuqZ6Oqrrx4zkHj605+ezJ8/v9I13vKWt6QejrIiIvCEJzwhOeuss9IByubNm5Nrr702WbVq1Zjv4lyIkUxbCCE6QMze7du3p5/hZlXbC4hm+2k76WPaa7Zfs+1dsF8zmtNes+3a7R9mO70T+Qfvwtg0I5P92hY3NduftV2TdyLYjt3AAItCmtK9CISdws75mNFc5rXb79r20KHOYk17CXUGjxMaBEXY2Apb0QZCfLhv375BumbBdxEiTQRESPfsQvqCBQuSBx54IHn84x+fxIj29l6z/Zpt1472tI/NfrSFEBRJOCGEqEL7CS82eYIQ/E0EnDik7RwW/iwmYkv70LT1vNrTXbP9mm2PeXxMSKyoEBPdcsst6U/xKnTRRRdVOv/b3/52ctNNNw0aBBmsHHvsscnnP//55MwzzzzonH/7t39L3vCGN6ST2zgP5/zgBz9IJ7UxuU0I0QPEgOJOHC6qNS2yabeftpM+pr1m+zXb3gX7NaM57TXbrt3+ItspKPIL3m+R/uI1QdvkmWb7tdsOESDgpGtYtJSbYaHOtNifh0vb2wh1pjntYwJtnixsY7wCQRgWodAmikcGLHiLeAjpLD/xfxwaFsQF2CvlBnZL2CItaO6vsrbff//99EoUCM3lJmb70S7CNtiEfgjATggR0NZIeyne26S91dRmxpr2oQjZR5j30p7usfW1cLRhi/a05/iYkGr8poYG9fbbbx9UZjRML3zhCytd4+Mf//hBf4OL2RtuuCFXSARe/OIXJ9/5zncOcmH72c9+ttK9CSGEEEIIIYQQQoYB70QMddYu8A7CUGfNYKgz0qfQPTiw+AQx9JQpU9IDnjTwN/k/FgaxUCWLVYQQ0lewtgcxJjwUoX3EmhvaTwiQpf2UtlO8wZH4gLBx9erVY/6GvEK+hkDKBsuHP+A5sYts27atbRMIUU30re6aNWuSvXv3Dn4/+eST01irtuzZsyf55je/OcYrET7/1V/9VRpztQgIjd7xjncMzsHPb3zjGw2ehhBCCCGEEEKIDfQISwghpIsg1BnRvbiDeWLxIoAFcjnwN3MOWrwWEVIHhMIjpGuI96Fs2wlPRQiBxnZTFxLGLgTiQYZeZEjdELuEkI6KicxBMzqJs846q9L5P/zhDw/aAQK3whdffLHV+W95y1tSRbRpD9w2E0IIIYQQQgjxCwVFpC/QO1Ec0DtRczhnVgzmJMmv4KIOIeUwxBnpAxBfInQkfhJdQAgGQdikSZO83wterOiViBBCwhN9y7t27dr0pwwkTjrppErnX3fddYPP4mHoZS97mfX5iIF9/vnnjxnI3HnnnZVsIIQQQgghhBBCCCFxhzojzWCoM0KqsXnz5qSPdDWMCiGE9AEz1Jl46sM6qu8QZxAswasVIYSQsPymth1NM2bMqHT+jTfeeNDfnva0p1W6xrJly3IFToQQQgghhBBC/ELvRKRP0DsRIf2Aoc5I3wU1o6OjbZtACCHEgbc0CHwg9EEYMl8cccQRqWiJnolIX0LqEhIT0be8e/fuHfP71KlTrc+Fa8Tbb799TAxNdDZPfOITK9mwaNGiMb/v2bOn0vmEEEIIIYQQQgghZaHOSPveiRjqrLl3IoY6K4ahzsaGOuMiDyEHs2HDhuSoo45q2wxCCCkFa64QFKHNMtdiXTF58uQ0xBnCqcUghO27GFgjDK1LSMfFRNnOp8ouvbvuuivZt2/fmL8dd9xxaedTBREwiS2cFCGEEEIIIYSQcNA7EekT9E5ECCGEEEII0QKEPuPGjUtmzpzpPLwZxP4QK9ErkT/6GnaVEGJH+1LOErKxNrds2WJ97i233DL4/Mtf/jIVA7mYhH7ssccaX4MQEg4ZdMpnbWi2n7aTPqa9Zvs1294F+zWjOe01267d/qq2411u+fLlASzrPng3lp2dPnaP+kaz/WW2wzvR1q1bkxjRnO5VgXeiww47LIkFjWmPjXjm5jxt9gu+bId3onXr1h007+ka2Cx9rLa0145t2cECnoyHYkF7uelCm4Of2mzXjuZyo91+zbZ3wX6X/ca0adPS9VMX7zO43pw5c9KfeV6JtKd7bH1tFY9LTHtC+kX0YqLp06ePqdDwNmTLTTfddNDfnvCEJ1S2QdztiiAJLvUIIXqAKl6zK0PN9tN20se012y/Ztu7YL9mNKe9Ztu126/Zdu3gvRbprxXN9tvaDu9EkyZNSmJCc7pXAYv6se3O1Zb2CHWGED1a7TfRbHt2sSRWMBZ48MEHkyOOOKJtU4KXHSzcPfDAA0lswPuD9nKv1X6xnWKi8GguN9rt12x7F+yvytFHH52sXr06WbRo0UF9B8YcWNPFT4ynH3300Vr3QHQZjMlxnWHjGO3prrmv1Z72GsbHhMRE9H7hTj755MFniHm+973vWZ/7n//5nwcNus8777zKNmRVtFDXEkIIIYQQQggJC8OdkT4A70SEkH6wc+fOtk0ghBBCCHGCCIrgkGH+/Pmpl8oqwkh4uznqqKNSYfr48eMH4kpC6iCOQgghPRATQYUqwAXwd7/73dLzfvCDHySbNm0a8ze4Dl66dGllG7LekNAJEkIIIYQQQgghhPgC3olIO2AnNEKdkfpgEQihzkhxqDNSvCD5uMc9Lj3wmZC+AI8fhBCi3eMODgiD4IUP3gcnTpyYKwyCgAjiI4wd8V04cxgW2oyQqtAbNiHNOURDx3P++ecn3/rWt9KOBt6J3va2tyW33nprocvv97///YPPEp7sBS94QS0bEC5N7g2OP/74WtchhLTDgQMHktHR0fQzBrDaXDBqtp+2kz6mvWb7NdveBfs1ozntNduu3f66tsM70fLlyz1b123wbvvwww+nnzFRq223p2b7bW2Hd6Ksl+S20Zzu2tGe9prt12y72P/II4+kn7EoF6v9eaHO4JFAM1XKDkLBQMQYCzIHnve7hvBbmustbMeBcYCsaZAwaC432u3XbLtpP9YyswKYbPvZJ5AWEATjJ/J1xowZg7R67LHH0vQQ0TA+i4jYVkDchXIj5UNsN9v9mJ+nC2mvYXxMSCyo2Nbxute9bszvP/vZz5LnPe95Q2PYv/vd7049E2UbgFe+8pWV77127drUG5Lp3QgT3YQQPWBgg/YChwxyNKHZftpO+pj2mu3XbHsX7NeM5rTXbLt2+5vYznBnbibPcGQXDDWg2X7arotYvBNpTnt4J9JsfwjbfYY6k8UeHBrSHouLsBWCY7Fbjv3796c/tZQj27IDLwyx2i7pjvyQA79LXjz66KNR5oX2Nkfs12a7djSXG+32a7YdwGYIEnCgXZR+LK/9xE+tz2ly9NFHW3lRE8EQNg5J2DI4iTjssMPSiDQTJkxI/44DopQqngg1lxvYizGP2ddK+TDLDf6P78WG5rTXOD4mpG2i90wEIBw64YQTkpUrVw48BF177bXJcccdl7zoRS9KTj/99LTjgfDnq1/9aio2EkTJifBmF1xwQeV7f+lLXxp8xnWe+MQnOnsuQgghhBBCCCGEkKJQZ0VemYk/4CVk2CY2YgfCVWzYsKFtM6IPdWZuYuwrIkrBAhraPSygPfTQQ4Nd45iTlUVI/ESolKoeDIhdPuDAwiXyYd++fYNFTfEigcVe5AEWgDEfL14lxLNEFeChkpt2CSHaQT8kohC0oRCjo+3M68dwYGyPNlQ8GfWpH+vTs+aBsY70tTj27t07EFDjdyB9rYx5fJeVrr7vbNu2rW0TCOkMKsREaCA//vGPJ+edd95AHISfe/bsST7zmc+kh5B1Cyd84AMfqHXvT33qU4P74eeTn/zkhk9DCCGEEEIIIaQpDHdGuk6Moc4IIYk370Twht43ZGc7FtKwcLZjx45UwAJkUU2+J7v0sUgL4Qq8GuAQbxDEjYeBXbt2pYcsgGe/J94T4HUMc/ZTpkxJpk+fnuZflYVOeGR64IEHPDwNIYSEQ7zLoH9CH4Y+Ks/TCf6G/g0H+ny0mdOmTUumTp3KfqwnmJ6I0M+iLMhYx+w7zb4Wa+AoGzLmkbBxrsNyxegl0VUYXUJIc9TIQM8555zkr//6r8eIhUTkYx7yPwGf3/SmNyVPf/rTK9/z5ptvTu6+++4xf/ut3/qtxs9CCCGEEEIIIYQQYgO8Q5D2vBPFEupMs3cipmG5d6K+Lqph8RWiFHioqdLWYfENC3HYTY/FOFxHe5iKtjwDSD4g/eElCzv584REw85FPjz44INpPuI6pgiMEEK6DNo7tHsQByFqioR2tQGCEmwaWL9+fWf6MVJeVlBGNm3alPa5tvmNPhllDGMleLtiaC5CSGjUiInAW9/61uRDH/pQ6totKyoyDyDiov/5P/9n8rd/+7e17nfFFVcc9HKP3a+EEEIIIYQQQtqH72ekD96JCCGka4iABcIVLJCZi2IIYWYLFtQgwkGYEM2La215BEA+IN2wuIkFbeRJ3UVSLHJiYdwM1UIIIV0F7RzaO2n76vY/8D4DIaf2foyUl5W8MU8VzDEPxWeEkJCoEhOBN7zhDclPf/rT5GUve1n6cpn1TCTHkiVLkn/7t39LPvrRj9aKI3nLLbck//Ef/5F+lkb5hS98ofPnIYQQQgghhBBCCCHxQs86zakrUugTWGDqA5hnxYLY9u3bByHNhLphO3At7PLHdUm1fICQCIuTLhYl4aVIBEUQKhFCSBcRISaERBB2NAXtr3ir0dSPHX300cnq1avbNkNFWYGQKDvmqVtWzDEPBUWEkBCoDMS5cOHC5Atf+ELq0u2GG25IXQhu2bIlmTBhQjJr1qw0JNr8+fMb3QMxSz/zmc+M+du5557b0HJCCCGEEEIIIa69Ey1fvrxtMwjx6p0Ii7OTJk1q25TehjprK/xQV8Bc3YYNG9o2I2rgDR2eCfoAFr/27NnjZFHNBItrmM/FptJDDlE55Z22NWhzQoCwKfCIgTl1l0BQhDn6qVOnpvlRVyBGCCEx92MQALsQEmUFRSMjI6r7MTI2T9HXuh7z4Lo7duxIxo0bx7IyBIi3CCHuUN3K4MXkaU97mpdrL1u2LD0IIYQQQgghhBBCSL+Bd6LDDjusbTPUeyfCXB4ZDhYnDz/88KTLoT5QDuANpwh4o6/qEUwW12bOnJk87nGPUydiQaizBx54IJinBOSFK49EWSBQQh6WLXLKMx911FHObSCEEB9AHIJ+DP2Nj7YZGwggwtbYj5F6Y566ZUXGPOhr60Tn6ToQ5hFC3KBaTEQIITZApY1dfvJZG5rtp+2kj2mv2X7NtnfBfs1oTnvNtmu336Xt9E5UDUxMS5prnKTWbH8T2+HOvk3vRLAXXibkc59o2zuR5jIvNmNhDOFAtNkfMu19eCeKrexgYQ2ea4oELLCzrsAFnnaw+x+LsG3v1I+5zRRPCUgvH5iLnG0siMec9mXAXrFfm+3a0VxutNuvyXb0YxD8ZPspV3bD21Es/VjsxFxuUD5QViASLxrTNBEBIXIPykobYqKY017j+LgqeK8iJCTsjQghnQcDG827nDTbT9tJH9Nes/2abe+C/ZrRnPaabdduv2vbKSiyBxNmmienNdtf13YJddYm5qSxJsaPH5/Mnj079XIik/l4Fk1ehjSXedN+WSTXRFfSPgYgMEFoGCx++QR1HV5x2n7uum2m71BnaAeRFxB1+QRipRkzZqT3wqJ4SLT2V+D+++9P+ywSHs3lRrv9WmwXr0Suw3RmQfscQz8WOzGXG/R9Ek40xJgHfXudMXbdzRIxp7228TEhGqDvM0IIIYQQQgghhBAFwDsRaY+qYZfIWLBA7iPUQ5eAdyLsYu8i2KFv24ZhkWfy5Mm17oOFOyzgYSFPGwj7FSrsiu8FTqQ/2kzcjxBCuoC0a76BdyK0nVraz9WrV7dtQnQg75CPPkKJuh7zhBh7EEJ0QzERIYQQQgghhJBOAO9EhHQVeCci7eHTUwghfQALar4FLALEMhrFRKHywbd3KAH38b2QSgghoUB75tsrkYD+UkP7efTRR7dtQq/HPLgPxjwaykootm3b1rYJhHQO+vEihHQeTF6sX78+/TxnzpxkwoQJiSY020/bSR/TXrP9mm3vgv2a0Zz2mm3Xbr9m27Ujk45g3Lhx6sIOabZfs+0SogjArf1v/mY/96dhR3roEGmay03WfgDvRFOnTk000FbawzvR4Ycf3pmyAztwSBtiC7wTYWd/VWJYWGvSZvoMdQa7Qom6ZDG8KPwKPCI88MADTkPfau+vJH802q4Z7eVGs/0abIeNIUWxuE9dD319IdZyU2XMY4Zg1iSgjjXthZGRERXj4zpgvIRQsoSERIWY6Iorrkhi4vLLL2/bBEJIRbeS4iZ81qxZiTY020/bSR/TXrP9mm3vgv2a0Zz2mm3Xbr8v2+GdaPny5c6u10UweSZu84sW+GJFs/1NbYd3oq1btyaTJk1K2kBsx6RxH8HiPhb5Q6O5zGftR3u/adOmRAttpD1Cna1bt65TZQf3xsJTqMUuPHPbYiKxo2qbKeIaX9QRddVF7tNG2dPcX2m2XTva016z/RpsL+pbXIhCTEK109qJsdxUGfO4KDcIc9YGMaa9tvExIVpQISZ63/veF1VlppiIEEIIIYQQQgghhJB6aPJO1BauvBPFQl1xTx3vRDEIiWImVPowHwghXUG8zYS8HyG2sLwQQnwSl+8xyw67zYMQQgghhBBCSNzAOxEhXeYXv/hF2yb0FngnQqgzUp/Zs2e3bUL0wDtRl8Am0ZAbRWPalFoXX17QkDahwpF0IR/aYMeOHW2bQAjJwH6MVCF0WWF5IYQkffdMJLTdIFJMRMivKdoVZsbzhTvHffv2ldZt000/XAw+9NBDhedg4mPixIlj3DkOi1kMW/G/7GQJ3IVKbNRhwE0j4qYKuE6Z60h833TviGcRt4nDmDBhQvK4xz1u8DvSTFxhiv3yWcDzm89ks1MP6SxtKdo0m0WIqvmZzRs8O9KsyD48O9KgSt4ccsghyfjx4we/4/tlLmCzeVOUn5LuuI+JmTe2+Yl0LutDzLyR+zfJG6SHlDvYguv5rmtN8iZb13Af2FeUDq7r2jCq1jX833SRalPXsnlTp67Z5E1ZXctrb1zUNZu8wT3M+la3ruW1lz7rWh516hrS1Cxnoeqaq34tW3bqtIMh+rW8ujasn/VZ12zyxraumZTZ5bKuVenXhuVNNu1d17W858pOtNlsGhl2TvZc834u71NEdnxr40pdyzlFz49r5f3PVzq7OEee37Q9mya2aTZjxoxk27Zt6f99pnPROWX9uqv71E3nPOR/2bklmzTLm6QvO89lvXFRbpqc46reZO+Vva+LdtB12ylpX6fcxJA3eWWnjT5KBCw4qoQ6y75XDbMtew7GOvj7MDtDtJ3Za1ZpByXUmYRKqSIOGtZHms8QKiSJjFUlj4bZNjIykqxduzYVa7rIG/RRct6w/ipEH1XnnHnz5o0Jc+e6L8yzy+U5Zfa2PYYsatPMsES+3iPqjDli6W98npNNexf56fIc+S76lqr9WJP202w7Q+SNTZ+TlzdHHXVUsnr16rTvsj3HRR0w2/rsNX3Vtex9sn2MXE/65hBlJbt2AWzHD9lz6radVc/x1XZu3769k+N7OWfXrl2VvXUS0isxEYU8hMQDFoOOPPLIws7xsssuS971rnelC24rVqwoHRyfeuqpg9+xcHTPPfcUnnPYYYclJ5xwwhgX6ffff3/ud7FItWnTpjELVAAT8OvXry+8z6xZs5I5c+YMfh8dHU22bNlS+uI/c+bMwe/r1q1LO/oijj322GTatGmD3zEAl8UwsV+QheOTTz55zOLmypUrSwciS5cuHSwG4rtleYPv4hwBtpSdA5tgm7kYKjvqzEVvEzw70sDchfXggw8W3gdpjLQWcA/kTxHIS+SpgPwftuNL0j1b1tesWVM6aEPZRBkV7rvvvtIFYdQBmWhDn1eWzlmvC7i+eQ7KqZQH1En8L1vXUJdRbqrUNZRlc2Jr2ALXwoULx9S1DRs2VKpruI/s+B5WbubPnz8mf1Bm0BYUcdxxx40Jp2DWtWEsXrx4jCigrK6h7GByVgb9eLEsy0+8+C1ZsmTwO2wqOwcL6CeddNLgd6TXqlWrCs9BmIRjjjlmaF3La28woYvJ3Sp1DTupMZkgIP/LdlcuWrQomT59eqW6duKJJ45p21E289pLk9NOO20weW5T15CPy5YtG1rX8sB9TznllEp1DWXefH6bunbEEUeMmayxqWtHH330GG8ASK+tW7cWnmNT17JlB3XGrGvoo13XNXD66ac3rmtoo4vKjY+6lkfdumb2N3iOMgGOi7pm06+ZdQ3k5Y1ZbpCPSAOXdQ39X1bEhLGNmccoNzYC5qzgFfZl08As43h28/nxXRvhnjkRaAqDh4FnMYVeSFMb4Z4JzinbuGOKtnD9svqM65n3QV22ETCbbQDOGSaQgw1ZEWbd/AyVNzhHJi3NsmOmPZ7ffKai/BSRHuqmudBels4u8hPn5G3WKMrPJnWtiLy6NixvJJ+RL3l5UyU/8X30B3mT9wLSzMzfJnkzrNy4qGtILxuRbNW2M5s35oIP7EZfiQl/M89dtYM2ItkqdQ1pnxVf+Khr2bzBmA3zCebfquZNXtlxVdfK2s5s3gDcuygd8toVPAPaumGi8bzFMLF12LO5aAdtyoBJUb82LD9hf1VBelF+inh/2Du2a3Afc0EMaVYkFMb/zbazbr+GdDbTIFuu8trOsrxx0a/Z1JtsnbFpB/Pazqr9mu0YsqxfK2pLQo0hbfImr66ZG21wz2y5cTGGLBqnlI0hi8j2E1XH96BqXbPJm2x+Dhtz4FqSlkgjM81CjSGL6ppZxpA/5jt3tt5kf8/ro8rAOdJ+glDje6RbWR89rK4NKw95da0sb/LyM6+u4Z5yLXzfrDd16ppNO5gle46ZdyhfKCtFQpI8sX3VTQzmHLT5/bIykO1v6tQ12dBV9F5Ypa416dekbMq8Wizje5fva9RJkLZQISa65ppr2jaBEJLBxsMCIYQQQgghhBD3QEjIUFvtgc0QZRtGCCH5CyNVRTh1kIW8EB4BfIFNAxDkm8Lxpsgu/CKBmkuynkcJIUQjphcTtGtlG3iaIoIUtp/6ELGH5GGIsmJ6saoC3mXajgZECNGBCjHRU57ylLZNIISU7F7IIqpeKIixm72I7KAFg/Kyc7KDaXg/GHaO7LbPngOPDlOmTCm8T1adDE8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", 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" ] diff --git a/docs/tutorials/QuickStart.ipynb b/docs/tutorials/QuickStart.ipynb index b27c6096..84031270 100644 --- a/docs/tutorials/QuickStart.ipynb +++ b/docs/tutorials/QuickStart.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 16, "id": "a0d9c4c8cf1418b9", "metadata": { "ExecuteTime": { @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 17, "id": "a65cc16e", "metadata": { "ExecuteTime": { @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 18, "id": "17603e7755662d00", "metadata": { "ExecuteTime": { @@ -149,7 +149,7 @@ } ], "conversionMethod": "pd.DataFrame", - "ref": "cb23f0b2-5954-4c77-8b81-6aa237c02ba3", + "ref": "48fc8ce4-0af3-47f1-9805-ef9e9d10915a", "rows": [ [ "0", @@ -244,7 +244,7 @@ "4 0.457107 -0.146447" ] }, - "execution_count": 3, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -268,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "id": "0392e984", "metadata": { "ExecuteTime": { @@ -349,7 +349,7 @@ } ], "conversionMethod": "pd.DataFrame", - "ref": "c7624c54-a44b-43f8-9fb7-873755e79a59", + "ref": "00e788af-9bfc-46a5-ac94-fc4e689ba5b3", "rows": [ [ "0", @@ -460,7 +460,7 @@ "1 0.159706 " ] }, - "execution_count": 4, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -500,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "id": "49b8abfc", "metadata": { "ExecuteTime": { @@ -551,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "id": "06b51f92", "metadata": { "ExecuteTime": { @@ -1297,7 +1297,7 @@ } ], "conversionMethod": "pd.DataFrame", - "ref": "cf479c4e-91e6-45a9-b949-bc5d2fa45869", + "ref": "8e3da2ee-0c72-4f69-b9c7-80e274d18512", "rows": [ [ "58", @@ -2590,7 +2590,7 @@ "[7 rows x 144 columns]" ] }, - "execution_count": 6, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -2608,14 +2608,14 @@ "source": [ "## Plotting\n", "\n", - "Soundscapy offers various plotting functions to visualize soundscape data. Let's explore some of them:\n", + "Soundscapy offers various plotting functions to visualize soundscape data. The plotting system is built on Seaborn's Objects API, providing a flexible and composable way to create plots. Let's explore some examples:\n", "\n", - "### Scatter plots\n" + "### Scatter plots" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 22, "id": "e1ce7c031c0f3bee", "metadata": { "ExecuteTime": { @@ -2625,44 +2625,101 @@ }, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "TypeError", + "evalue": "SoundscapeCircumplex._plot() missing 1 required positional argument: 'y'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/IPython/core/formatters.py:406\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 404\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[1;32m 405\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 406\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:387\u001b[0m, in \u001b[0;36mPlot._repr_png_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m Plot\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mdisplay[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpng\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 387\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m_repr_png_()\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:932\u001b[0m, in \u001b[0;36mPlot.plot\u001b[0;34m(self, pyplot)\u001b[0m\n\u001b[1;32m 928\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 929\u001b[0m \u001b[38;5;124;03mCompile the plot spec and return the Plotter object.\u001b[39;00m\n\u001b[1;32m 930\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 931\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m theme_context(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_theme_with_defaults()):\n\u001b[0;32m--> 932\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpyplot\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:962\u001b[0m, in \u001b[0;36mPlot._plot\u001b[0;34m(self, pyplot)\u001b[0m\n\u001b[1;32m 960\u001b[0m \u001b[38;5;66;03m# Process the data for each layer and add matplotlib artists\u001b[39;00m\n\u001b[1;32m 961\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m layer \u001b[38;5;129;01min\u001b[39;00m layers:\n\u001b[0;32m--> 962\u001b[0m \u001b[43mplotter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlayer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 964\u001b[0m \u001b[38;5;66;03m# Add various figure decorations\u001b[39;00m\n\u001b[1;32m 965\u001b[0m plotter\u001b[38;5;241m.\u001b[39m_make_legend(\u001b[38;5;28mself\u001b[39m)\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:1488\u001b[0m, in \u001b[0;36mPlotter._plot_layer\u001b[0;34m(self, p, layer)\u001b[0m\n\u001b[1;32m 1485\u001b[0m grouping_vars \u001b[38;5;241m=\u001b[39m mark\u001b[38;5;241m.\u001b[39m_grouping_props \u001b[38;5;241m+\u001b[39m default_grouping_vars\n\u001b[1;32m 1486\u001b[0m split_generator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_split_generator(grouping_vars, df, subplots)\n\u001b[0;32m-> 1488\u001b[0m \u001b[43mmark\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscales\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1490\u001b[0m \u001b[38;5;66;03m# TODO is this the right place for this?\u001b[39;00m\n\u001b[1;32m 1491\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m view \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_subplots:\n", + "\u001b[0;31mTypeError\u001b[0m: SoundscapeCircumplex._plot() missing 1 required positional argument: 'y'" + ] }, { "data": { - "image/png": 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", "text/plain": [ - "
" + "" ] }, + "execution_count": 22, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", + "import seaborn.objects as so\n", "\n", - "from soundscapy.plotting import scatter_plot\n", + "from soundscapy.plotting import (\n", + " scatter_plot,\n", + " SoundscapeCircumplex,\n", + " SoundscapeQuadrantLabels,\n", + ")\n", "\n", - "# Basic scatter plot\n", - "ax = scatter_plot(isd.select_location_ids(df, [\"RussellSq\"]), title=\"RussellSq\")\n", - "plt.show()\n", + "# Basic scatter plot using function-based API\n", + "# ax = scatter_plot(\n", + "# isd.select_location_ids(df, [\"RussellSq\"]),\n", + "# title=\"RussellSq\"\n", + "# )\n", + "# plt.show()\n", "\n", - "# Customized scatter plot with multiple locations\n", - "ax = scatter_plot(\n", - " isd.select_location_ids(df, [\"RussellSq\", \"EustonTap\"]),\n", - " hue=\"LocationID\",\n", - " title=\"Russell Square vs. Euston Tap\",\n", - " diagonal_lines=True,\n", - " legend_location=\"lower right\",\n", + "# # Customized scatter plot with multiple locations\n", + "# ax = scatter_plot(\n", + "# isd.select_location_ids(df, [\"RussellSq\", \"EustonTap\"]),\n", + "# hue=\"LocationID\",\n", + "# title=\"Russell Square vs. Euston Tap\",\n", + "# diagonal_lines=True,\n", + "# point_size=40,\n", + "# alpha=0.8\n", + "# )\n", + "# plt.show()\n", + "\n", + "# Direct composition with Seaborn Objects API\n", + "locations_data = isd.select_location_ids(df, [\"RussellSq\", \"CamdenTown\", \"PancrasLock\"])\n", + "plot = (\n", + " so.Plot(locations_data, x=\"ISOPleasant\", y=\"ISOEventful\")\n", + " .add(so.Dots(), color=\"LocationID\")\n", + " .add(SoundscapeCircumplex()) # Add circumplex grid\n", + " .add(SoundscapeQuadrantLabels()) # Add diagonal lines and labels\n", + " .scale(color=so.Nominal(\"husl\")) # Custom color palette\n", + " .label(title=\"Composed plot with Seaborn Objects\")\n", ")\n", - "plt.show()" + "plot" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "49d205e7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": { + "image/png": { + "height": 378.25, + "width": 640.475 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "plot = so.Plot(locations_data, x=\"ISOPleasant\", y=\"ISOEventful\").add(\n", + " so.Dots(), color=\"LocationID\"\n", + ")\n", + "plot.add(SoundscapeCircumplex()) # Add circumplex grid\n", + "plot" ] }, { @@ -2675,7 +2732,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 24, "id": "4fb94cd86524e6d3", "metadata": { "ExecuteTime": { @@ -2685,50 +2742,54 @@ }, "outputs": [ { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "TypeError", + "evalue": "KDE.__init__() got an unexpected keyword argument 'levels'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msoundscapy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mplotting\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m density_plot, SoundscapeCoordinates\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Single density plot\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[43mdensity_plot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43misd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselect_location_ids\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mCamdenTown\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mCamden Town Density plot\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.7\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Density comparisons with simple density and scatter points\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/src/soundscapy/plotting/soundscape_functions.py:250\u001b[0m, in \u001b[0;36mdensity_plot\u001b[0;34m(data, x, y, hue, title, xlim, ylim, palette, fill, alpha, bw_adjust, levels, simple_density, incl_scatter, scatter_size, scatter_alpha, diagonal_lines, show_labels, ax, as_objects, **kwargs)\u001b[0m\n\u001b[1;32m 243\u001b[0m plot \u001b[38;5;241m=\u001b[39m plot\u001b[38;5;241m.\u001b[39madd(\n\u001b[1;32m 244\u001b[0m so\u001b[38;5;241m.\u001b[39mLine(alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0\u001b[39m), so\u001b[38;5;241m.\u001b[39mKDE(bw_adjust\u001b[38;5;241m=\u001b[39mbw_adjust, levels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m), color\u001b[38;5;241m=\u001b[39mhue\n\u001b[1;32m 245\u001b[0m )\n\u001b[1;32m 246\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 247\u001b[0m \u001b[38;5;66;03m# Regular density\u001b[39;00m\n\u001b[1;32m 248\u001b[0m plot \u001b[38;5;241m=\u001b[39m plot\u001b[38;5;241m.\u001b[39madd(\n\u001b[1;32m 249\u001b[0m so\u001b[38;5;241m.\u001b[39mArea(fill\u001b[38;5;241m=\u001b[39mfill, alpha\u001b[38;5;241m=\u001b[39malpha),\n\u001b[0;32m--> 250\u001b[0m \u001b[43mso\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mKDE\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbw_adjust\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbw_adjust\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 251\u001b[0m color\u001b[38;5;241m=\u001b[39mhue,\n\u001b[1;32m 252\u001b[0m )\n\u001b[1;32m 254\u001b[0m \u001b[38;5;66;03m# Apply color palette if needed\u001b[39;00m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hue:\n", + "\u001b[0;31mTypeError\u001b[0m: KDE.__init__() got an unexpected keyword argument 'levels'" + ] } ], "source": [ - "from soundscapy.plotting import density_plot\n", + "from soundscapy.plotting import density_plot, SoundscapeCoordinates\n", "\n", "# Single density plot\n", "density_plot(\n", " isd.select_location_ids(df, [\"CamdenTown\"]),\n", " title=\"Camden Town Density plot\",\n", - " legend=True,\n", - " color=\"blue\",\n", + " alpha=0.7,\n", ")\n", "plt.show()\n", "\n", - "# Density comparisons with simple density lines\n", + "# Density comparisons with simple density and scatter points\n", "density_plot(\n", " isd.select_location_ids(df, [\"CamdenTown\", \"RussellSq\", \"PancrasLock\"]),\n", " hue=\"LocationID\",\n", " title=\"Comparison of the soundscapes of three urban spaces\",\n", " palette=\"husl\",\n", - " incl_outline=True,\n", - " incl_scatter=True,\n", - " figsize=(8, 8),\n", " simple_density=True,\n", + " incl_scatter=True,\n", + " diagonal_lines=True,\n", ")\n", - "plt.show()" + "plt.show()\n", + "\n", + "# Using Seaborn Objects API for custom density plots\n", + "camden_data = isd.select_location_ids(df, [\"CamdenTown\"])\n", + "custom_density = (\n", + " so.Plot(camden_data, x=\"ISOPleasant\", y=\"ISOEventful\")\n", + " .add(\n", + " so.Area(alpha=0.5, fill=True), so.KDE(bw_adjust=1.2, levels=8)\n", + " ) # Density contours\n", + " .add(so.Line(alpha=1.0), so.KDE(bw_adjust=1.2, levels=8)) # Outline\n", + " .add(so.Dots(pointsize=20, alpha=0.6)) # Scatter points\n", + " .add(SoundscapeCircumplex()) # Grid\n", + " .label(title=\"Custom density plot with Objects API\")\n", + ")\n", + "custom_density.show()" ] }, { @@ -2736,12 +2797,14 @@ "id": "82b904b7562187a", "metadata": {}, "source": [ - "### Jointplots\n" + "### Joint plots\n", + "\n", + "Joint plots combine scatter plots or density plots with marginal distributions:" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "dc18babf99cf8edc", "metadata": { "ExecuteTime": { @@ -2749,26 +2812,18 @@ "start_time": "2024-10-29T15:19:12.183288Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "from soundscapy.plotting.circumplex_plot import Backend, CircumplexPlot\n", + "from soundscapy.plotting import joint_plot\n", "\n", - "plot = CircumplexPlot(\n", - " isd.select_location_ids(df, [\"CamdenTown\"]), backend=Backend.SEABORN\n", + "# Create a joint plot with kde in the main plot\n", + "g = joint_plot(\n", + " isd.select_location_ids(df, [\"CamdenTown\"]),\n", + " kind=\"kde\",\n", + " title=\"Camden Town Joint Plot\",\n", + " diagonal_lines=True,\n", ")\n", - "plot.jointplot()\n", - "plot.show()" + "plt.show()" ] }, { @@ -2776,42 +2831,24 @@ "id": "51df44dbecb2c1a7", "metadata": {}, "source": [ - "r### Creating subplots\n", + "### Creating subplots\n", "\n", - "`Soundscapy` also provides a method for creating subplots of the circumplex. This is particularly useful when comparing multiple locations." + "Soundscapy provides a function for creating multiple circumplex plots in a grid layout. This is particularly useful when comparing multiple locations:" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "058f6724", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['CarloV', 'SanMarco', 'PlazaBibRambla', 'CamdenTown', 'EustonTap',\n", - " 'Noorderplantsoen', 'MarchmontGarden', 'MonumentoGaribaldi',\n", - " 'TateModern', 'PancrasLock', 'TorringtonSq', 'RegentsParkFields',\n", - " 'RegentsParkJapan', 'RussellSq', 'StPaulsCross', 'StPaulsRow',\n", - " 'CampoPrincipe', 'MiradorSanNicolas', 'LianhuashanParkEntrance',\n", - " 'LianhuashanParkForest', 'PingshanPark', 'PingshanStreet',\n", - " 'ZhongshanPark', 'OlympicSquare', 'ZhongshanSquare',\n", - " 'DadongSquare'], dtype=object)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df[\"LocationID\"].unique()" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "bab28d6c98976d61", "metadata": { "ExecuteTime": { @@ -2819,34 +2856,22 @@ "start_time": "2024-08-16T10:09:57.571044Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from soundscapy.plotting import create_circumplex_subplots\n", "\n", - "data_list = [\n", - " sspy.isd.select_location_ids(df, loc) for loc in df[\"LocationID\"].unique()[9:13]\n", - "]\n", + "# Create a grid of density plots for multiple locations\n", + "locations = df[\"LocationID\"].unique()[9:13]\n", + "data_list = [sspy.isd.select_location_ids(df, loc) for loc in locations]\n", + "\n", "fig = create_circumplex_subplots(\n", " data_list,\n", " plot_type=\"density\",\n", - " nrows=2,\n", - " ncols=2,\n", - " figsize=(12, 12),\n", - " legend=True,\n", + " cols=2,\n", " incl_scatter=True,\n", - " subtitles=[loc for loc in df[\"LocationID\"].unique()[9:13]],\n", - " title=\"Density plots of the first four locations\",\n", + " subtitles=locations,\n", + " title=\"Density plots of four locations\",\n", + " diagonal_lines=True,\n", ")\n", "plt.show()" ] @@ -2856,12 +2881,12 @@ "id": "bbe673bbecefc9f6", "metadata": {}, "source": [ - "You can also do this manually if you need more control, by creating a figure and axes and then plotting the density plots on the axes." + "You can also create custom subplots with more control by creating and customizing each plot individually:" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "66c03673bf82cc32", "metadata": { "ExecuteTime": { @@ -2869,33 +2894,45 @@ "start_time": "2024-08-16T10:09:58.205673Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "from soundscapy.plotting import density_plot\n", + "from soundscapy.plotting import scatter_plot, density_plot\n", "\n", + "# Create a grid of custom plots\n", "fig, axes = plt.subplots(2, 2, figsize=(12, 12))\n", - "for i, location in enumerate(df[\"LocationID\"].unique()[:4]):\n", - " density_plot(\n", - " sspy.isd.select_location_ids(df, location),\n", - " hue=\"SessionID\",\n", - " title=location,\n", - " incl_outline=True,\n", - " simple_density=True,\n", - " fill=False,\n", - " ax=axes.flatten()[i],\n", - " legend=True,\n", - " )\n", + "\n", + "# Scatter plot in top-left\n", + "scatter_plot(\n", + " isd.select_location_ids(df, \"CamdenTown\"),\n", + " title=\"Camden Town (Scatter)\",\n", + " diagonal_lines=True,\n", + " ax=axes[0, 0],\n", + ")\n", + "\n", + "# Density plot in top-right\n", + "density_plot(\n", + " isd.select_location_ids(df, \"RussellSq\"),\n", + " title=\"Russell Square (Density)\",\n", + " simple_density=True,\n", + " ax=axes[0, 1],\n", + ")\n", + "\n", + "# Scatter plot with hue in bottom-left\n", + "scatter_plot(\n", + " isd.select_location_ids(df, \"EustonTap\"),\n", + " hue=\"SessionID\",\n", + " title=\"Euston Tap (Sessions)\",\n", + " ax=axes[1, 0],\n", + ")\n", + "\n", + "# Density with scatter in bottom-right\n", + "density_plot(\n", + " isd.select_location_ids(df, \"PancrasLock\"),\n", + " title=\"Pancras Lock (Density + Scatter)\",\n", + " incl_scatter=True,\n", + " diagonal_lines=True,\n", + " ax=axes[1, 1],\n", + ")\n", "\n", "plt.tight_layout()\n", "plt.show()" @@ -2906,14 +2943,14 @@ "id": "dfa30efa99cdbcb9", "metadata": {}, "source": [ - "### Using Different Backends and advanced customisation\n", + "### Advanced Customization with Seaborn Objects\n", "\n", - "Soundscapy supports both Seaborn and Plotly (limited support at the moment) backends for plotting:" + "For more advanced customization, you can use the Seaborn Objects API directly with Soundscapy's custom components:" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 27, "id": "f1e71a75", "metadata": { "ExecuteTime": { @@ -2928,11 +2965,26 @@ } }, "outputs": [ + { + "ename": "TypeError", + "evalue": "SoundscapeCircumplex._plot() missing 1 required positional argument: 'y'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[27], line 53\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Create a custom plot with multiple layers\u001b[39;00m\n\u001b[1;32m 17\u001b[0m custom_plot \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 18\u001b[0m so\u001b[38;5;241m.\u001b[39mPlot(russell_sq, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mISOPleasant\u001b[39m\u001b[38;5;124m\"\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mISOEventful\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# Add scatter points\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 50\u001b[0m )\n\u001b[1;32m 51\u001b[0m )\n\u001b[0;32m---> 53\u001b[0m \u001b[43mcustom_plot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:925\u001b[0m, in \u001b[0;36mPlot.show\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 909\u001b[0m \u001b[38;5;124;03mCompile the plot and display it by hooking into pyplot.\u001b[39;00m\n\u001b[1;32m 910\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 917\u001b[0m \n\u001b[1;32m 918\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 919\u001b[0m \u001b[38;5;66;03m# TODO make pyplot configurable at the class level, and when not using,\u001b[39;00m\n\u001b[1;32m 920\u001b[0m \u001b[38;5;66;03m# import IPython.display and call on self to populate cell output?\u001b[39;00m\n\u001b[1;32m 921\u001b[0m \n\u001b[1;32m 922\u001b[0m \u001b[38;5;66;03m# Keep an eye on whether matplotlib implements \"attaching\" an existing\u001b[39;00m\n\u001b[1;32m 923\u001b[0m \u001b[38;5;66;03m# figure to pyplot: https://github.com/matplotlib/matplotlib/pull/14024\u001b[39;00m\n\u001b[0;32m--> 925\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpyplot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mshow(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:932\u001b[0m, in \u001b[0;36mPlot.plot\u001b[0;34m(self, pyplot)\u001b[0m\n\u001b[1;32m 928\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 929\u001b[0m \u001b[38;5;124;03mCompile the plot spec and return the Plotter object.\u001b[39;00m\n\u001b[1;32m 930\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 931\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m theme_context(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_theme_with_defaults()):\n\u001b[0;32m--> 932\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpyplot\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:962\u001b[0m, in \u001b[0;36mPlot._plot\u001b[0;34m(self, pyplot)\u001b[0m\n\u001b[1;32m 960\u001b[0m \u001b[38;5;66;03m# Process the data for each layer and add matplotlib artists\u001b[39;00m\n\u001b[1;32m 961\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m layer \u001b[38;5;129;01min\u001b[39;00m layers:\n\u001b[0;32m--> 962\u001b[0m \u001b[43mplotter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlayer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 964\u001b[0m \u001b[38;5;66;03m# Add various figure decorations\u001b[39;00m\n\u001b[1;32m 965\u001b[0m plotter\u001b[38;5;241m.\u001b[39m_make_legend(\u001b[38;5;28mself\u001b[39m)\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:1488\u001b[0m, in \u001b[0;36mPlotter._plot_layer\u001b[0;34m(self, p, layer)\u001b[0m\n\u001b[1;32m 1485\u001b[0m grouping_vars \u001b[38;5;241m=\u001b[39m mark\u001b[38;5;241m.\u001b[39m_grouping_props \u001b[38;5;241m+\u001b[39m default_grouping_vars\n\u001b[1;32m 1486\u001b[0m split_generator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_split_generator(grouping_vars, df, subplots)\n\u001b[0;32m-> 1488\u001b[0m \u001b[43mmark\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscales\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1490\u001b[0m \u001b[38;5;66;03m# TODO is this the right place for this?\u001b[39;00m\n\u001b[1;32m 1491\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m view \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_subplots:\n", + "\u001b[0;31mTypeError\u001b[0m: SoundscapeCircumplex._plot() missing 1 required positional argument: 'y'" + ] + }, { 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", 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" ] }, "metadata": {}, @@ -2940,74 +2992,137 @@ } ], "source": [ - "from soundscapy.plotting import Backend, CircumplexPlot, CircumplexPlotParams\n", + "import numpy as np\n", + "from soundscapy.plotting import (\n", + " SoundscapeCircumplex,\n", + " SoundscapeQuadrantLabels,\n", + " SoundscapePointAnnotation,\n", + " add_calculated_coords,\n", + " use_soundscapy_style,\n", + ")\n", "\n", - "# Seaborn backend (default)\n", - "seaborn_plot = CircumplexPlot(\n", - " isd.select_location_ids(df, [\"RussellSq\"]),\n", - " CircumplexPlotParams(title=\"RussellSq\"),\n", - " backend=Backend.SEABORN,\n", + "# Apply the Soundscapy style\n", + "use_soundscapy_style()\n", + "\n", + "# Get data for one location\n", + "russell_sq = isd.select_location_ids(df, [\"RussellSq\"])\n", + "\n", + "# Create a custom plot with multiple layers\n", + "custom_plot = (\n", + " so.Plot(russell_sq, x=\"ISOPleasant\", y=\"ISOEventful\")\n", + " # Add scatter points\n", + " .add(so.Dots(marker=\".\"), color=\"SessionID\")\n", + " .scale(color=so.Nominal(\"viridis\"))\n", + " # Add circumplex grid with custom styling\n", + " .add(\n", + " SoundscapeCircumplex(\n", + " primary_color=\"darkblue\", primary_alpha=0.8, grid_color=\"lightgray\"\n", + " )\n", + " )\n", + " # Add quadrant labels with custom styling\n", + " .add(\n", + " SoundscapeQuadrantLabels(\n", + " line_color=\"darkblue\", line_alpha=0.5, label_color=\"darkblue\", label_size=10\n", + " )\n", + " )\n", + " # Add an annotation for the point closest to the center\n", + " .add(\n", + " SoundscapePointAnnotation(\n", + " points=[0], # First point in the data\n", + " texts=[\"Center point\"],\n", + " offsets=(0.2, 0.2),\n", + " fontsize=12,\n", + " arrow_color=\"red\",\n", + " arrow_alpha=1.0,\n", + " )\n", + " )\n", + " # Layout and labels\n", + " .layout(size=(8, 8))\n", + " .label(\n", + " title=\"Advanced Custom Plot with Seaborn Objects\",\n", + " subtitle=\"Russell Square data with custom styling\",\n", + " )\n", ")\n", - "seaborn_plot.scatter(apply_styling=True).show()" + "\n", + "custom_plot.show()" ] }, { - "cell_type": "code", - "execution_count": 14, - "id": "31e5898d", + "cell_type": "markdown", + "id": "b8f1ec3c", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mitch/Documents/GitHub/Soundscapy/src/soundscapy/plotting/backends.py:300: UserWarning: PlotlyBackend is very experimental and not fully implemented.\n", - " warnings.warn(\n", - "/Users/mitch/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/_plotly_utils/basevalidators.py:2596: DeprecationWarning:\n", - "\n", - "*scattermapbox* is deprecated! Use *scattermap* instead. Learn more at: https://plotly.com/python/mapbox-to-maplibre/\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "# Plotly backend\n", - "plotly_plot = CircumplexPlot(\n", - " isd.select_location_ids(df, [\"RussellSq\"]),\n", - " CircumplexPlotParams(title=\"RussellSq\"),\n", - " backend=Backend.PLOTLY,\n", - ")\n", - "plotly_plot.scatter(apply_styling=True)\n", - "plotly_plot._plot.show(\n", - " renderer=\"sphinx_gallery\"\n", - ") # sphinx-gallery renderer used for mkdocs purposes" + "### Using the SoundscapeCoordinates Stat\n", + "\n", + "Soundscapy also provides a custom Stat for calculating ISO coordinates directly within the plotting pipeline:" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 28, "id": "1f9b46d0fe6a0888", "metadata": { "collapsed": false }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "mark must be a Mark instance, not .", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[28], line 11\u001b[0m\n\u001b[1;32m 5\u001b[0m paq_data \u001b[38;5;241m=\u001b[39m russell_sq[[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPAQ1\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPAQ2\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPAQ3\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPAQ4\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPAQ5\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPAQ6\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPAQ7\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPAQ8\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Calculate ISO coordinates with default angles\u001b[39;00m\n\u001b[1;32m 8\u001b[0m plot_default \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 9\u001b[0m \u001b[43mso\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPlot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpaq_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Transform PAQ data to ISO coordinates\u001b[39;49;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mSoundscapeCoordinates\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# Plot the points\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;241m.\u001b[39madd(so\u001b[38;5;241m.\u001b[39mDots(pointsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.8\u001b[39m))\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# Add the circumplex grid\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;241m.\u001b[39madd(SoundscapeCircumplex())\n\u001b[1;32m 16\u001b[0m \u001b[38;5;241m.\u001b[39mlabel(title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDefault ISO Coordinates\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 17\u001b[0m )\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# Calculate ISO coordinates with language-specific angles\u001b[39;00m\n\u001b[1;32m 20\u001b[0m plot_adjusted \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 21\u001b[0m so\u001b[38;5;241m.\u001b[39mPlot(paq_data)\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# Transform PAQ data to ISO coordinates with English-specific angles\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;241m.\u001b[39mlabel(title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLanguage-Adjusted ISO Coordinates\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 33\u001b[0m )\n", + "File \u001b[0;32m~/Documents/GitHub/Soundscapy/.venv/lib/python3.12/site-packages/seaborn/_core/plot.py:532\u001b[0m, in \u001b[0;36mPlot.add\u001b[0;34m(self, mark, orient, legend, label, data, *transforms, **variables)\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mark, Mark):\n\u001b[1;32m 531\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmark must be a Mark instance, not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(mark)\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 532\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[1;32m 534\u001b[0m \u001b[38;5;66;03m# TODO This API for transforms was a late decision, and previously Plot.add\u001b[39;00m\n\u001b[1;32m 535\u001b[0m \u001b[38;5;66;03m# accepted 0 or 1 Stat instances and 0, 1, or a list of Move instances.\u001b[39;00m\n\u001b[1;32m 536\u001b[0m \u001b[38;5;66;03m# It will take some work to refactor the internals so that Stat and Move are\u001b[39;00m\n\u001b[1;32m 537\u001b[0m \u001b[38;5;66;03m# treated identically, and until then well need to \"unpack\" the transforms\u001b[39;00m\n\u001b[1;32m 538\u001b[0m \u001b[38;5;66;03m# here and enforce limitations on the order / types.\u001b[39;00m\n\u001b[1;32m 540\u001b[0m stat: Optional[Stat]\n", + "\u001b[0;31mTypeError\u001b[0m: mark must be a Mark instance, not ." + ] + } + ], "source": [ - "### Using Adjusted Angles\n", + "from soundscapy.surveys import LANGUAGE_ANGLES\n", + "from soundscapy.plotting import SoundscapeCoordinates\n", + "\n", + "# Get data without ISO coordinates\n", + "paq_data = russell_sq[[\"PAQ1\", \"PAQ2\", \"PAQ3\", \"PAQ4\", \"PAQ5\", \"PAQ6\", \"PAQ7\", \"PAQ8\"]]\n", + "\n", + "# Calculate ISO coordinates with default angles\n", + "plot_default = (\n", + " so.Plot(paq_data)\n", + " # Transform PAQ data to ISO coordinates\n", + " .add(SoundscapeCoordinates())\n", + " # Plot the points\n", + " .add(so.Dots(pointsize=30, alpha=0.8))\n", + " # Add the circumplex grid\n", + " .add(SoundscapeCircumplex())\n", + " .label(title=\"Default ISO Coordinates\")\n", + ")\n", + "\n", + "# Calculate ISO coordinates with language-specific angles\n", + "plot_adjusted = (\n", + " so.Plot(paq_data)\n", + " # Transform PAQ data to ISO coordinates with English-specific angles\n", + " .add(\n", + " SoundscapeCoordinates(\n", + " angles=LANGUAGE_ANGLES[\"eng\"], output_cols=(\"AdjPleasant\", \"AdjEventful\")\n", + " )\n", + " )\n", + " # Plot the points\n", + " .add(so.Dots(pointsize=30, alpha=0.8, color=\"red\"))\n", + " # Add the circumplex grid\n", + " .add(SoundscapeCircumplex())\n", + " .label(title=\"Language-Adjusted ISO Coordinates\")\n", + ")\n", "\n", - "In Aletta et. al. (2024), we propose a method for adjusting the angles of the circumplex to better represent the perceptual space. These adjusted angles are derived for each language separately, meaning that, once projected, the circumplex coordinates will be comparable across all languages. This ability and the derived angles have been incorporated into `Soundscapy`." + "# Display the plots side by side\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n", + "plot_default.plot(ax=ax1)\n", + "plot_adjusted.plot(ax=ax2)\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { - "cell_type": "code", - "execution_count": 15, + "cell_type": "markdown", "id": "ee4a4c193f93596d", "metadata": { "ExecuteTime": { @@ -3016,53 +3131,74 @@ }, "collapsed": false }, + "source": [ + "### Using Custom ISO Coordinate Angles\n", + "\n", + "For comparison between different language datasets, you can use language-adjusted angles for the ISO coordinates:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "70f9bf6e", + "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "TypeError", + "evalue": "KDE.__init__() got an unexpected keyword argument 'levels'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[29], line 21\u001b[0m\n\u001b[1;32m 4\u001b[0m adjusted_df \u001b[38;5;241m=\u001b[39m add_calculated_coords(\n\u001b[1;32m 5\u001b[0m df,\n\u001b[1;32m 6\u001b[0m angles\u001b[38;5;241m=\u001b[39mLANGUAGE_ANGLES[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meng\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 7\u001b[0m output_cols\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAdjPleasant\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAdjEventful\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 8\u001b[0m overwrite\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# Overwrite if columns already exist\u001b[39;00m\n\u001b[1;32m 9\u001b[0m )\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Plot with adjusted coordinates\u001b[39;00m\n\u001b[1;32m 12\u001b[0m comparison_plot \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 13\u001b[0m so\u001b[38;5;241m.\u001b[39mPlot(\n\u001b[1;32m 14\u001b[0m isd\u001b[38;5;241m.\u001b[39mselect_location_ids(adjusted_df, [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCamdenTown\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRussellSq\u001b[39m\u001b[38;5;124m\"\u001b[39m]),\n\u001b[1;32m 15\u001b[0m x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAdjPleasant\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 16\u001b[0m y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAdjEventful\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 17\u001b[0m )\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# Add density contours\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;241m.\u001b[39madd(\n\u001b[1;32m 20\u001b[0m so\u001b[38;5;241m.\u001b[39mArea(alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.6\u001b[39m, fill\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[0;32m---> 21\u001b[0m \u001b[43mso\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mKDE\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbw_adjust\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 22\u001b[0m color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLocationID\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 23\u001b[0m )\n\u001b[1;32m 24\u001b[0m \u001b[38;5;66;03m# Add scatter points\u001b[39;00m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;241m.\u001b[39madd(so\u001b[38;5;241m.\u001b[39mDots(pointsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.7\u001b[39m), color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLocationID\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# Add grid\u001b[39;00m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;241m.\u001b[39madd(SoundscapeCircumplex())\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# Add labels and styling\u001b[39;00m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;241m.\u001b[39mscale(color\u001b[38;5;241m=\u001b[39mso\u001b[38;5;241m.\u001b[39mNominal(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhusl\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 30\u001b[0m \u001b[38;5;241m.\u001b[39mlabel(\n\u001b[1;32m 31\u001b[0m title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComparison with Language-Adjusted Coordinates\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 32\u001b[0m x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAdjusted Pleasant\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 33\u001b[0m y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAdjusted Eventful\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 34\u001b[0m )\n\u001b[1;32m 35\u001b[0m )\n\u001b[1;32m 37\u001b[0m comparison_plot\u001b[38;5;241m.\u001b[39mshow()\n", + "\u001b[0;31mTypeError\u001b[0m: KDE.__init__() got an unexpected keyword argument 'levels'" + ] } ], "source": [ "from soundscapy.surveys import LANGUAGE_ANGLES\n", "\n", - "df = sspy.surveys.add_iso_coords(\n", + "# Calculate and add adjusted ISO coordinates\n", + "adjusted_df = add_calculated_coords(\n", " df,\n", - " names=(\"AdjustedPleasant\", \"AdjustedEventful\"),\n", " angles=LANGUAGE_ANGLES[\"eng\"],\n", - " overwrite=True,\n", + " output_cols=(\"AdjPleasant\", \"AdjEventful\"),\n", + " overwrite=True, # Overwrite if columns already exist\n", ")\n", "\n", - "density_plot(\n", - " isd.select_location_ids(df, [\"CamdenTown\", \"RussellSq\"]),\n", - " x=\"AdjustedPleasant\",\n", - " y=\"AdjustedEventful\",\n", - " hue=\"LocationID\",\n", - " incl_scatter=True,\n", - " simple_density=True,\n", - " incl_outline=True,\n", - ")" + "# Plot with adjusted coordinates\n", + "comparison_plot = (\n", + " so.Plot(\n", + " isd.select_location_ids(adjusted_df, [\"CamdenTown\", \"RussellSq\"]),\n", + " x=\"AdjPleasant\",\n", + " y=\"AdjEventful\",\n", + " )\n", + " # Add density contours\n", + " .add(\n", + " so.Area(alpha=0.6, fill=True),\n", + " so.KDE(bw_adjust=1.2, levels=2),\n", + " color=\"LocationID\",\n", + " )\n", + " # Add scatter points\n", + " .add(so.Dots(pointsize=20, alpha=0.7), color=\"LocationID\")\n", + " # Add grid\n", + " .add(SoundscapeCircumplex())\n", + " # Add labels and styling\n", + " .scale(color=so.Nominal(\"husl\"))\n", + " .label(\n", + " title=\"Comparison with Language-Adjusted Coordinates\",\n", + " x=\"Adjusted Pleasant\",\n", + " y=\"Adjusted Eventful\",\n", + " )\n", + ")\n", + "\n", + "comparison_plot.show()" ] }, { "cell_type": "code", "execution_count": null, - "id": "70f9bf6e", + "id": "ee2e036f", "metadata": {}, "outputs": [], "source": [] diff --git a/examples/soundscape_plot_demo.py b/examples/soundscape_plot_demo.py deleted file mode 100644 index 39a284dd..00000000 --- a/examples/soundscape_plot_demo.py +++ /dev/null @@ -1,197 +0,0 @@ -""" -Demo script showing the usage of the new SoundscapePlot class. - -This script demonstrates the basic functionality of the SoundscapePlot class -and how it can be used to create soundscape visualizations with the -Seaborn Objects API. -""" - -# %% Import libraries -import matplotlib.pyplot as plt -import pandas as pd -import seaborn as sns -import seaborn.objects as so - -import soundscapy as sspy -from soundscapy.plotting.soundscape_plot import SoundscapePlot - -# Set the style for the plots -sns.set_theme(style="whitegrid") - -# %% Load the built-in ISD dataset -# Load the ISD dataset that comes with soundscapy -print("Loading ISD dataset...") -data = sspy.isd.load() - -# Add ISO coordinates if they don't already exist -data = sspy.surveys.add_iso_coords(data, overwrite=True) - -# Display dataset info -print(f"Dataset shape: {data.shape}") -print("\nColumns:") -print(data.columns.tolist()) - -# %% Create a sample subset of the data -# Select a subset of locations for demonstration -sample_data = sspy.isd.select_location_ids(data, ["CamdenTown", "RegentsParkJapan"]) -print(f"Sample data shape: {sample_data.shape}") - -# Show basic statistics for the ISO coordinates -print("\nBasic statistics for ISO coordinates:") -print(sample_data[["ISOPleasant", "ISOEventful"]].describe()) - -# %% Example 1: Basic scatter plot with grid -print("\nExample 1: Basic scatter plot with grid") - -# Create a basic plot with default settings -plot1 = ( - SoundscapePlot(sample_data) - .add(so.Dots(pointsize=25, alpha=0.7), color="location_id") - .scale(color=so.Nominal("colorblind")) - .add_grid(diagonal_lines=True) - .label(title="Basic Soundscape Plot") -) - -# Display the plot -plot1.show() - -# %% Example 2: Customizing the plot -print("\nExample 2: Customized plot with different axes and styling") - -# Create a customized plot with different configurations -plot2 = ( - SoundscapePlot(sample_data, x="ISOPleasant", y="ISOEventful") - .add(so.Dots(pointsize=30), color="L_0_Pleasant") - .scale(color=so.Continuous("viridis")) - .add_grid(xlim=(-1.2, 1.2), ylim=(-1.2, 1.2), diagonal_lines=True) - .label(title="Soundscape by Pleasantness Rating") -) - -# Display the plot -plot2.show() - -# %% Example 3: Adding multiple mark layers -print("\nExample 3: Multiple mark layers") - -# Create location averages -location_means = ( - sample_data.groupby("location_id")[["ISOPleasant", "ISOEventful"]] - .mean() - .reset_index() -) - -# Create multi-layer plot -plot3 = ( - SoundscapePlot(sample_data) - # Add individual data points - .add(so.Dots(alpha=0.6, pointsize=15), color="location_id") - .scale(color=so.Nominal("colorblind")) - # Add the means with larger markers - .add( - so.Dots(pointsize=100, alpha=0.9, marker="X"), - data=location_means, - color="location_id", - ) - # Add connecting lines between points and means - .add( - so.Path(color="grey", alpha=0.3), - so.Agg({"ISOPleasant": "mean", "ISOEventful": "mean"}, groupby="location_id"), - ) - .add_grid(diagonal_lines=True) - .label(title="Soundscape Points with Location Means") -) - -# Display the plot -plot3.show() - -# %% Example 4: Faceted plots -print("\nExample 4: Faceted plots by location") - -plot4 = ( - SoundscapePlot(sample_data) - .add(so.Dots(pointsize=30, alpha=0.8), color="L_0_Overall") - .scale(color=so.Continuous("viridis")) - .facet(col="location_id", wrap=3) - .add_grid() - .label(title="Soundscape by Location and Overall Rating") -) - -# Display the plot -plot4.show() - -# %% Example 5: Adding density contours -print("\nExample 5: Adding density contours") - -plot5 = ( - SoundscapePlot(sample_data) - # Add density contours - .add(so.Area(alpha=0.3, fill=True), so.KDE(), color="location_id") - # Add scatter points on top of density - .add(so.Dots(pointsize=20, alpha=0.7), color="location_id") - .scale(color=so.Nominal("colorblind")) - .add_grid(diagonal_lines=True) - .label(title="Soundscape Density and Points") -) - -# Display the plot -plot5.show() - -# %% Example 6: Demonstrating compatibility with standard so.Plot features -print("\nExample 6: Demonstrating compatibility with standard so.Plot features") - -# Create a plot that uses both SoundscapePlot features and standard so.Plot methods -plot6 = ( - SoundscapePlot(sample_data) - # Use paired data display - .pair(x=["ISOPleasant", "L_0_Pleasant"], y=["ISOEventful", "L_0_Eventful"]) - # Add point layer - .add(so.Dots(pointsize=25, alpha=0.7), color="location_id") - # Add linear regression line - .add(so.Line(), so.PolyFit(order=1)) - # Apply standard so.Plot styling - .theme( - { - "axes.grid": True, - "grid.linestyle": ":", - "grid.color": "gray", - "grid.alpha": 0.5, - } - ) - .scale(color=so.Nominal("deep")) - .label(title="Multi-variable Comparison") -) - -# Display the plot -plot6.show() - -# %% Example 7: Integration with Matplotlib -print("\nExample 7: Integration with Matplotlib") - -# Create a plot and extract Matplotlib elements -fig, ax = plt.subplots(figsize=(8, 8)) - -# Create soundscape plot -base_plot = ( - SoundscapePlot(sample_data) - .add(so.Dots(pointsize=30, alpha=0.7), color="location_id") - .add_grid(diagonal_lines=True) -) - -# Render to the provided axes -base_plot.plot(ax=ax) - -# Add custom Matplotlib elements -plt.title("Soundscape Plot with Custom Matplotlib Elements", fontsize=14) -plt.figtext(0.5, 0.01, "Custom footer text", ha="center", fontsize=10) - -# Add a text annotation -plt.annotate( - "Important region", - xy=(0.5, 0.5), - xytext=(0.7, 0.8), - arrowprops=dict(facecolor="black", shrink=0.05, width=1.5), -) - -# Display the plot -plt.tight_layout() -plt.show() diff --git a/src/soundscapy/plotting/__init__.py b/src/soundscapy/plotting/__init__.py index 6039a687..8c260ff9 100644 --- a/src/soundscapy/plotting/__init__.py +++ b/src/soundscapy/plotting/__init__.py @@ -6,23 +6,33 @@ flexible, composable visualizations. Main components: -- CircumplexPlot: Builder class for creating customizable circumplex plots -- scatter_plot: Function to quickly create scatter plots -- density_plot: Function to quickly create density plots -- create_circumplex_subplots: Function to create multiple circumplex plots in subplots +- Custom marks for soundscape plots (SoundscapeCircumplex, SoundscapeQuadrantLabels) +- Custom stats for coordinate calculations (SoundscapeCoordinates) +- Function-based API for creating plots (scatter_plot, density_plot) +- CircumplexPlot builder class for backward compatibility Example usage: ```python -from soundscapy.plotting import scatter_plot, density_plot, CircumplexPlot +import pandas as pd +import seaborn.objects as so +from soundscapy.plotting import ( + scatter_plot, density_plot, SoundscapeCircumplex, SoundscapeQuadrantLabels +) -# Create a basic scatter plot +# Function-based API (recommended for new code) scatter_plot(data, x='ISOPleasant', y='ISOEventful') - -# Create a density plot with scatter points density_plot(data, x='ISOPleasant', y='ISOEventful', incl_scatter=True) -# Build a customized plot layer by layer +# Direct use of custom components with so.Plot +plot = ( + so.Plot(data, x='ISOPleasant', y='ISOEventful') + .add(so.Dots(), color='LocationID') + .add(SoundscapeCircumplex()) + .add(SoundscapeQuadrantLabels()) +) + +# Legacy builder API (maintained for backwards compatibility) (CircumplexPlot(data) .add_density(simple=True) .add_scatter() @@ -38,22 +48,43 @@ add_annotation, apply_circumplex_grid, ) -from soundscapy.plotting.plot_functions import ( +from soundscapy.plotting.marks import ( + SoundscapeCircumplex, + SoundscapePointAnnotation, + SoundscapeQuadrantLabels, +) +from soundscapy.plotting.plotting_utils import DEFAULT_XLIM, DEFAULT_YLIM, PlotType +from soundscapy.plotting.soundscape_functions import ( + add_calculated_coords, create_circumplex_subplots, density_plot, joint_plot, scatter_plot, + use_soundscapy_style, ) -from soundscapy.plotting.plotting_utils import PlotType +from soundscapy.plotting.stats import SoundscapeCoordinates __all__ = [ + # Public API - function based (recommended) + "scatter_plot", + "density_plot", + "joint_plot", + "create_circumplex_subplots", + "add_calculated_coords", + "use_soundscapy_style", + # Custom components + "SoundscapeCircumplex", + "SoundscapeQuadrantLabels", + "SoundscapePointAnnotation", + "SoundscapeCoordinates", + # Legacy API (maintained for backwards compatibility) "CircumplexPlot", - "PlotType", "add_annotation", "apply_circumplex_grid", - "create_circumplex_subplots", - "density_plot", - "joint_plot", + # Utility classes and constants + "PlotType", + "DEFAULT_XLIM", + "DEFAULT_YLIM", + # Sub-modules "likert", - "scatter_plot", ] diff --git a/src/soundscapy/plotting/circumplex_plot.py b/src/soundscapy/plotting/circumplex_plot.py index 722f650f..bc8c5652 100644 --- a/src/soundscapy/plotting/circumplex_plot.py +++ b/src/soundscapy/plotting/circumplex_plot.py @@ -4,16 +4,26 @@ This module provides the CircumplexPlot class, which is a builder for creating circumplex plots with a grammar of graphics approach. It allows for layering of different plot elements (scatter, density) and customization of styling. + +Note: This class is maintained for backwards compatibility. For new code, +consider using the direct function-based API instead. """ +import warnings +from typing import Any + import matplotlib.pyplot as plt import pandas as pd import seaborn as sns import seaborn.objects as so +from soundscapy.plotting.marks import ( + SoundscapeCircumplex, + SoundscapePointAnnotation, + SoundscapeQuadrantLabels, +) from soundscapy.plotting.plotting_utils import DEFAULT_XLIM, DEFAULT_YLIM - -# =============== Core building blocks for circumplex plots =============== +from soundscapy.plotting.soundscape_functions import use_soundscapy_style def apply_circumplex_grid( @@ -48,111 +58,38 @@ def apply_circumplex_grid( The styled plot """ + warnings.warn( + "The apply_circumplex_grid function is deprecated. " + "Use SoundscapeCircumplex and SoundscapeQuadrantLabels marks instead.", + DeprecationWarning, + stacklevel=2, + ) + # Apply limits and axes appearance plot = plot.limit(x=xlim, y=ylim) # Apply square aspect ratio and layout plot = plot.layout(size=(6, 6)) - # Compile the plot to a temporary figure to apply matplotlib styling - # This creates a temporary figure for styling - fig, ax = plt.subplots(figsize=(6, 6)) - - # Add grid lines - ax.grid(visible=True, which="major", color="grey", alpha=0.5) - ax.grid( - visible=True, - which="minor", - color="grey", - linestyle="dashed", - linewidth=0.5, - alpha=0.4, - ) - ax.minorticks_on() - - # Add zero lines - ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) - ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + # Add the circumplex grid mark + plot = plot.add(SoundscapeCircumplex(xlim=xlim, ylim=ylim)) - # Add diagonal elements if requested + # Add quadrant labels if requested if diagonal_lines: - # Draw diagonal lines - ax.plot( - [xlim[0], xlim[1]], - [ylim[0], ylim[1]], - linestyle="dashed", - color="grey", - alpha=0.5, - linewidth=1.5, - ) - ax.plot( - [xlim[0], xlim[1]], - [ylim[1], ylim[0]], - linestyle="dashed", - color="grey", - alpha=0.5, - linewidth=1.5, - ) - - # Add diagonal labels - diag_font = { - "fontstyle": "italic", - "fontsize": "small", - "fontweight": "bold", - "color": "black", - "alpha": 0.5, - } - - ax.text( - xlim[1] / 2, - ylim[1] / 2, - "(vibrant)", - ha="center", - va="center", - fontdict=diag_font, - ) - ax.text( - xlim[0] / 2, - ylim[1] / 2, - "(chaotic)", - ha="center", - va="center", - fontdict=diag_font, - ) - ax.text( - xlim[0] / 2, - ylim[0] / 2, - "(monotonous)", - ha="center", - va="center", - fontdict=diag_font, - ) - ax.text( - xlim[1] / 2, - ylim[0] / 2, - "(calm)", - ha="center", - va="center", - fontdict=diag_font, - ) + plot = plot.add(SoundscapeQuadrantLabels(xlim=xlim, ylim=ylim)) # Apply axis label changes if requested if not show_labels: - ax.set_xlabel("") - ax.set_ylabel("") + plot = plot.label(x=None, y=None) elif x_label is not None or y_label is not None: + labels = {} if x_label is not None: - ax.set_xlabel(x_label) + labels["x"] = x_label if y_label is not None: - ax.set_ylabel(y_label) - - # Now use the fully prepared axes for the plot - plot_with_styling = plot.on(ax) + labels["y"] = y_label + plot = plot.label(**labels) - # Clean up the temporary figure - we don't need it as we've transferred the styling - plt.close(fig) - - return plot_with_styling + return plot def add_annotation( @@ -164,7 +101,7 @@ def add_annotation( text: str | None = None, x_offset: float = 0.1, y_offset: float = 0.1, - **kwargs, # noqa: ANN003 + **kwargs: Any, ) -> so.Plot: """ Add an annotation to a Seaborn Objects plot. @@ -192,51 +129,25 @@ def add_annotation( The plot with annotation added """ - # Get coordinates from data - x_val = data[x].iloc[idx] if isinstance(idx, int) else data.loc[idx, x] - y_val = data[y].iloc[idx] if isinstance(idx, int) else data.loc[idx, y] - - if x_val is None or y_val is None: - msg = f"Invalid index {idx} for data: {data}" - raise ValueError(msg) - if not isinstance(x_val, int | float) or not isinstance(y_val, int | float): - msg = f"Invalid contents for x or y: {x_val}, {y_val}" - raise TypeError(msg) - - # Default text is the index value + warnings.warn( + "The add_annotation function is deprecated. " + "Use SoundscapePointAnnotation mark instead.", + DeprecationWarning, + stacklevel=2, + ) + + # Get the text if not provided if text is None: text = str(data.index[idx]) if isinstance(idx, int) else str(idx) - # Default annotation styling - annotation_defaults = { - "ha": "center", - "va": "center", - "fontsize": 9, - "arrowprops": {"arrowstyle": "-", "color": "black", "alpha": 0.7}, - } - annotation_defaults.update(kwargs) - - # Create a temporary figure to add the annotation - fig, ax = plt.subplots(figsize=(6, 6)) - - # Add the annotation - ax.annotate( - text=text, - xy=(x_val, y_val), - xytext=(x_val + x_offset, y_val + y_offset), - **annotation_defaults, + # Add the annotation using the mark + plot = plot.add( + SoundscapePointAnnotation( + points=[idx], texts=[text], offsets=(x_offset, y_offset), **kwargs + ) ) - # Now use the fully prepared axes for the plot - plot_with_annotation = plot.on(ax) - - # Clean up the temporary figure - plt.close(fig) - - return plot_with_annotation - - -# =============== Main Circumplex Plot Class =============== + return plot class CircumplexPlot: @@ -290,10 +201,18 @@ def __init__( Axis limits for y-axis palette : str, default="colorblind" Color palette to use - fill : bool, default=True - Whether to fill the density contours """ + warnings.warn( + "The CircumplexPlot class is maintained for backwards compatibility. " + "For new code, consider using the function-based API instead.", + PendingDeprecationWarning, + stacklevel=2, + ) + + # Apply the soundscapy style + use_soundscapy_style() + self.data = data self.x = x self.y = y @@ -316,7 +235,7 @@ def add_scatter( alpha: float = 0.7, marker: str = "o", color: str | None = None, # Overrides hue if provided - ) -> so.Plot: + ) -> "CircumplexPlot": """ Add a scatter layer to the plot. @@ -353,14 +272,14 @@ def add_scatter( def add_density( self, - alpha=0.5, - fill=True, - levels=8, - bw_adjust=1.2, - color=None, # Overrides hue if provided - simple=False, - **kwargs, # For backwards compatibility - ): + alpha: float = 0.5, + fill: bool = True, + levels: int = 8, + bw_adjust: float = 1.2, + color: str | None = None, # Overrides hue if provided + simple: bool = False, + **kwargs: Any, # For backwards compatibility + ) -> "CircumplexPlot": """ Add a density layer to the plot. @@ -389,33 +308,44 @@ def add_density( if simple: # For simple density, use just a few levels - pass - - # Use the Area mark with KDE stat for density plots - self.plot = self.plot.add( - so.Area(alpha=alpha, fill=fill), - so.KDE(bw_adjust=bw_adjust), - color=color_var, - ) + self.plot = self.plot.add( + so.Area(alpha=alpha, fill=fill), + so.KDE(bw_adjust=bw_adjust, levels=2), + color=color_var, + ) - # Apply palette if needed - if color_var and hasattr(self, "palette_name"): - self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) + # Apply palette if needed + if color_var and hasattr(self, "palette_name"): + self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) - # For simple density, add an outline - if simple: + # Add outline self.plot = self.plot.add( - so.Line(alpha=1.0), so.KDE(bw_adjust=bw_adjust), color=color_var + so.Line(alpha=1.0), + so.KDE(bw_adjust=bw_adjust, levels=2), + color=color_var, ) # Apply palette to the outline too if needed if color_var and hasattr(self, "palette_name"): self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) + else: + # Use the Area mark with KDE stat for regular density plots + self.plot = self.plot.add( + so.Area(alpha=alpha, fill=fill), + so.KDE(bw_adjust=bw_adjust, levels=levels), + color=color_var, + ) + + # Apply palette if needed + if color_var and hasattr(self, "palette_name"): + self.plot = self.plot.scale(color=so.Nominal(self.palette_name)) self.has_density = True return self - def add_grid(self, *, diagonal_lines=False, show_labels=True): + def add_grid( + self, *, diagonal_lines: bool = False, show_labels: bool = True + ) -> "CircumplexPlot": """ Add circumplex grid to the plot. @@ -432,20 +362,30 @@ def add_grid(self, *, diagonal_lines=False, show_labels=True): Self for method chaining """ - self.plot = apply_circumplex_grid( - self.plot, - xlim=self.xlim, - ylim=self.ylim, - x_label=self.x if show_labels else None, - y_label=self.y if show_labels else None, - diagonal_lines=diagonal_lines, - show_labels=show_labels, - ) + # Add the circumplex grid + self.plot = self.plot.add(SoundscapeCircumplex(xlim=self.xlim, ylim=self.ylim)) + + # Add quadrant labels if requested + if diagonal_lines: + self.plot = self.plot.add( + SoundscapeQuadrantLabels(xlim=self.xlim, ylim=self.ylim) + ) + + # Hide labels if requested + if not show_labels: + self.plot = self.plot.label(x=None, y=None) self.has_grid = True return self - def add_annotation(self, idx, text=None, x_offset=0.1, y_offset=0.1, **kwargs): + def add_annotation( + self, + idx: int | str, + text: str | None = None, + x_offset: float = 0.1, + y_offset: float = 0.1, + **kwargs: Any, + ) -> "CircumplexPlot": """ Add an annotation to the plot. @@ -466,21 +406,20 @@ def add_annotation(self, idx, text=None, x_offset=0.1, y_offset=0.1, **kwargs): Self for method chaining """ - self.plot = add_annotation( - self.plot, - self.data, - idx, - x=self.x, - y=self.y, - text=text, - x_offset=x_offset, - y_offset=y_offset, - **kwargs, + # Get text if not provided + if text is None: + text = str(self.data.index[idx]) if isinstance(idx, int) else str(idx) + + # Add annotation + self.plot = self.plot.add( + SoundscapePointAnnotation( + points=[idx], texts=[text], offsets=(x_offset, y_offset), **kwargs + ) ) return self - def add_title(self, title): + def add_title(self, title: str) -> "CircumplexPlot": """ Add a title to the plot. @@ -498,7 +437,9 @@ def add_title(self, title): self.plot = self.plot.label(title=title) return self - def add_legend(self, title=None, loc="best"): + def add_legend( + self, title: str | None = None, loc: str = "best" + ) -> "CircumplexPlot": """ Customize the legend appearance. @@ -515,16 +456,18 @@ def add_legend(self, title=None, loc="best"): Self for method chaining """ - # For Seaborn Objects, we can handle this more directly - # by adding a proper label to the plot - - # Just store the legend parameters for when we create the final plot + # Store the legend parameters for when we create the final plot self._legend_title = title if title is not None else self.hue self._legend_loc = loc return self - def facet(self, column=None, row=None, col_wrap=None): + def facet( + self, + column: str | None = None, + row: str | None = None, + col_wrap: int | None = None, + ) -> "CircumplexPlot": """ Add faceting to the plot. @@ -546,7 +489,7 @@ def facet(self, column=None, row=None, col_wrap=None): self.plot = self.plot.facet(col=column, row=row, wrap=col_wrap) return self - def build(self, as_objects=True): + def build(self, as_objects: bool = True) -> so.Plot | tuple[plt.Figure, plt.Axes]: """ Complete the plot with any default elements that haven't been added. @@ -576,27 +519,22 @@ def build(self, as_objects=True): if as_objects: return self.plot + # Create a new figure with the right size fig, ax = plt.subplots(figsize=(6, 6)) - # Use pyplot mode for rendering - # This will render the plot directly to the current figure - self.plot.plot(pyplot=True) - - # Get the current axes - ax = plt.gca() + # Draw to the axes + self.plot.plot(ax) # Apply legend location if needed (after plotting) if hasattr(self, "_legend_loc") and ax.get_legend() is not None: ax.legend(loc=self._legend_loc) # Return the figure and axes to be compatible with the legacy API - fig = ax.figure - return fig, ax @property - def seaborn_plot(self): + def seaborn_plot(self) -> so.Plot: """ Return the underlying Seaborn Objects plot. @@ -608,7 +546,7 @@ def seaborn_plot(self): """ return self.plot - def get_matplotlib_objects(self): + def get_matplotlib_objects(self) -> tuple[plt.Figure, plt.Axes]: """ Return matplotlib figure and axes objects. @@ -619,7 +557,7 @@ def get_matplotlib_objects(self): """ fig, ax = plt.subplots(figsize=(6, 6)) - self.plot.plot(ax=ax) + self.plot.plot(ax) return fig, ax def show(self) -> None: @@ -633,11 +571,15 @@ def show(self) -> None: if not self.has_grid: self.add_grid() - # This is the correct way to display a plot in a notebook - self.plot.plot(pyplot=True) + # Draw the plot + fig, ax = plt.subplots(figsize=(6, 6)) + self.plot.plot(ax) + plt.show() # Legacy API compatibility methods - def scatter(self, apply_styling=True, ax=None): + def scatter( + self, apply_styling: bool = True, ax: plt.Axes | None = None + ) -> "CircumplexPlot": """ Create a scatter plot (legacy API compatibility). @@ -658,7 +600,9 @@ def scatter(self, apply_styling=True, ax=None): self.add_grid() return self - def density(self, apply_styling=True, ax=None): + def density( + self, apply_styling: bool = True, ax: plt.Axes | None = None + ) -> "CircumplexPlot": """ Create a density plot (legacy API compatibility). @@ -679,7 +623,9 @@ def density(self, apply_styling=True, ax=None): self.add_grid() return self - def simple_density(self, apply_styling=True, ax=None): + def simple_density( + self, apply_styling: bool = True, ax: plt.Axes | None = None + ) -> "CircumplexPlot": """ Create a simple density plot (legacy API compatibility). @@ -700,7 +646,7 @@ def simple_density(self, apply_styling=True, ax=None): self.add_grid() return self - def jointplot(self, apply_styling=True): + def jointplot(self, apply_styling: bool = True) -> "CircumplexPlot": """ Create a joint plot (legacy API compatibility). @@ -739,7 +685,7 @@ def jointplot(self, apply_styling=True): return self - def get_figure(self): + def get_figure(self) -> plt.Figure | tuple[plt.Figure, plt.Axes]: """ Get the figure object (legacy API compatibility). @@ -754,7 +700,7 @@ def get_figure(self): fig, ax = self.build(as_objects=False) return fig - def get_axes(self): + def get_axes(self) -> plt.Axes: """ Get the axes object (legacy API compatibility). @@ -769,7 +715,9 @@ def get_axes(self): fig, ax = self.build(as_objects=False) return ax - def iso_annotation(self, location, x_adj=0, y_adj=0, **kwargs): + def iso_annotation( + self, location: int | str, x_adj: float = 0, y_adj: float = 0, **kwargs: Any + ) -> "CircumplexPlot": """ Add an annotation to the plot (legacy API compatibility). diff --git a/src/soundscapy/plotting/marks.py b/src/soundscapy/plotting/marks.py new file mode 100644 index 00000000..a97d8172 --- /dev/null +++ b/src/soundscapy/plotting/marks.py @@ -0,0 +1,375 @@ +""" +Custom Mark components for Soundscape plots using seaborn.objects. + +This module contains custom Mark classes that extend the functionality of +seaborn.objects.Plot for creating soundscape plots. These marks handle +specialized plot elements like circumplex grids, quadrant labels, and +point annotations. +""" + +import seaborn.objects as so + +from soundscapy.plotting.plotting_utils import DEFAULT_XLIM, DEFAULT_YLIM + + +class SoundscapeCircumplex(so.Mark): + """Mark for adding circumplex grid elements to a plot.""" + + def __init__( + self, + xlim=DEFAULT_XLIM, + ylim=DEFAULT_YLIM, + primary_color="grey", + primary_alpha=1.0, + primary_linewidth=1.5, + grid_color="grey", + grid_alpha=0.5, + **kwargs, + ): + """ + Initialize the circumplex grid mark. + + Parameters + ---------- + xlim : tuple, default=(-1, 1) + X-axis limits + ylim : tuple, default=(-1, 1) + Y-axis limits + primary_color : str, default="grey" + Color for primary elements (zero lines) + primary_alpha : float, default=1.0 + Alpha transparency for primary elements + primary_linewidth : float, default=1.5 + Line width for primary elements + grid_color : str, default="grey" + Color for grid lines + grid_alpha : float, default=0.5 + Alpha transparency for grid lines + **kwargs : + Additional keyword arguments passed to parent class + + """ + self.xlim = xlim + self.ylim = ylim + self.primary_color = primary_color + self.primary_alpha = primary_alpha + self.primary_linewidth = primary_linewidth + self.grid_color = grid_color + self.grid_alpha = grid_alpha + super().__init__(**kwargs) + + def _plot(self, axes, data, x, y, **kwargs): + """ + Draw circumplex grid elements on the plot. + + Parameters + ---------- + axes : matplotlib.axes.Axes + The axes to draw on + data : pd.DataFrame + The plot data + x, y : str + Column names for coordinates + **kwargs : + Additional keyword arguments + + Returns + ------- + self : Mark + The mark instance for method chaining + + """ + # Apply limits + axes.set_xlim(self.xlim) + axes.set_ylim(self.ylim) + + # Add major and minor grid + axes.grid( + visible=True, which="major", color=self.grid_color, alpha=self.grid_alpha + ) + axes.grid( + visible=True, + which="minor", + color=self.grid_color, + linestyle="dashed", + linewidth=0.5, + alpha=self.grid_alpha * 0.8, + ) + axes.minorticks_on() + + # Add zero lines + axes.axhline( + y=0, + color=self.primary_color, + linestyle="dashed", + alpha=self.primary_alpha, + linewidth=self.primary_linewidth, + ) + axes.axvline( + x=0, + color=self.primary_color, + linestyle="dashed", + alpha=self.primary_alpha, + linewidth=self.primary_linewidth, + ) + + return self + + +class SoundscapeQuadrantLabels(so.Mark): + """Mark for adding diagonal lines and quadrant labels to a soundscape plot.""" + + def __init__( + self, + xlim=DEFAULT_XLIM, + ylim=DEFAULT_YLIM, + line_color="grey", + line_alpha=0.5, + line_width=1.5, + label_color="black", + label_alpha=0.5, + label_size="small", + labels=("(vibrant)", "(chaotic)", "(monotonous)", "(calm)"), + **kwargs, + ): + """ + Initialize the quadrant labels mark. + + Parameters + ---------- + xlim : tuple, default=(-1, 1) + X-axis limits + ylim : tuple, default=(-1, 1) + Y-axis limits + line_color : str, default="grey" + Color for diagonal lines + line_alpha : float, default=0.5 + Alpha transparency for diagonal lines + line_width : float, default=1.5 + Line width for diagonal lines + label_color : str, default="black" + Color for quadrant labels + label_alpha : float, default=0.5 + Alpha transparency for quadrant labels + label_size : str or int, default="small" + Font size for quadrant labels + labels : tuple, default=("(vibrant)", "(chaotic)", "(monotonous)", "(calm)") + Text for each quadrant label + **kwargs : + Additional keyword arguments passed to parent class + + """ + self.xlim = xlim + self.ylim = ylim + self.line_color = line_color + self.line_alpha = line_alpha + self.line_width = line_width + self.label_color = label_color + self.label_alpha = label_alpha + self.label_size = label_size + self.labels = labels + super().__init__(**kwargs) + + def _plot(self, axes, data, x, y, **kwargs): + """ + Draw diagonal lines and quadrant labels. + + Parameters + ---------- + axes : matplotlib.axes.Axes + The axes to draw on + data : pd.DataFrame + The plot data + x, y : str + Column names for coordinates + **kwargs : + Additional keyword arguments + + Returns + ------- + self : Mark + The mark instance for method chaining + + """ + # Draw diagonal lines + axes.plot( + [self.xlim[0], self.xlim[1]], + [self.ylim[0], self.ylim[1]], + linestyle="dashed", + color=self.line_color, + alpha=self.line_alpha, + linewidth=self.line_width, + ) + axes.plot( + [self.xlim[0], self.xlim[1]], + [self.ylim[1], self.ylim[0]], + linestyle="dashed", + color=self.line_color, + alpha=self.line_alpha, + linewidth=self.line_width, + ) + + # Add quadrant labels + label_style = { + "fontstyle": "italic", + "fontsize": self.label_size, + "fontweight": "bold", + "color": self.label_color, + "alpha": self.label_alpha, + } + + # Upper right (vibrant) + axes.text( + self.xlim[1] / 2, + self.ylim[1] / 2, + self.labels[0], + ha="center", + va="center", + fontdict=label_style, + ) + + # Upper left (chaotic) + axes.text( + self.xlim[0] / 2, + self.ylim[1] / 2, + self.labels[1], + ha="center", + va="center", + fontdict=label_style, + ) + + # Lower left (monotonous) + axes.text( + self.xlim[0] / 2, + self.ylim[0] / 2, + self.labels[2], + ha="center", + va="center", + fontdict=label_style, + ) + + # Lower right (calm) + axes.text( + self.xlim[1] / 2, + self.ylim[0] / 2, + self.labels[3], + ha="center", + va="center", + fontdict=label_style, + ) + + return self + + +class SoundscapePointAnnotation(so.Mark): + """Mark for adding annotations to specific points in a soundscape plot.""" + + def __init__( + self, + points=None, + texts=None, + offsets=(0.1, 0.1), + fontsize=9, + ha="center", + va="center", + arrow_style="-", + arrow_color="black", + arrow_alpha=0.7, + **kwargs, + ): + """ + Initialize the point annotation mark. + + Parameters + ---------- + points : list, default=None + List of point indices to annotate + texts : list, default=None + List of texts to use for annotations + offsets : tuple, default=(0.1, 0.1) + (x_offset, y_offset) for annotation position + fontsize : int, default=9 + Font size for annotation text + ha : str, default="center" + Horizontal alignment + va : str, default="center" + Vertical alignment + arrow_style : str, default="-" + Style of the annotation arrow + arrow_color : str, default="black" + Color of the annotation arrow + arrow_alpha : float, default=0.7 + Alpha transparency of the annotation arrow + **kwargs : + Additional keyword arguments passed to parent class + + """ + self.points = points if points is not None else [] + self.texts = texts if texts is not None else [] + self.offsets = offsets + self.fontsize = fontsize + self.ha = ha + self.va = va + self.arrow_props = { + "arrowstyle": arrow_style, + "color": arrow_color, + "alpha": arrow_alpha, + } + super().__init__(**kwargs) + + def _plot(self, axes, data, x, y, **kwargs): + """ + Add annotations to specific points. + + Parameters + ---------- + axes : matplotlib.axes.Axes + The axes to draw on + data : pd.DataFrame + The plot data + x, y : str + Column names for coordinates + **kwargs : + Additional keyword arguments + + Returns + ------- + self : Mark + The mark instance for method chaining + + """ + if not self.points: + return self + + # Add annotations for each point + for i, point_idx in enumerate(self.points): + try: + # Get coordinates from data + if isinstance(point_idx, int): + x_val = data[x].iloc[point_idx] + y_val = data[y].iloc[point_idx] + else: + x_val = data.loc[point_idx, x] + y_val = data.loc[point_idx, y] + + # Get text (default to index if not provided) + if i < len(self.texts): + text = self.texts[i] + else: + text = str(point_idx) + + # Add annotation + axes.annotate( + text=text, + xy=(x_val, y_val), + xytext=(x_val + self.offsets[0], y_val + self.offsets[1]), + fontsize=self.fontsize, + ha=self.ha, + va=self.va, + arrowprops=self.arrow_props, + ) + except (KeyError, IndexError): + # Skip invalid indices + pass + + return self diff --git a/src/soundscapy/plotting/soundscape_functions.py b/src/soundscapy/plotting/soundscape_functions.py new file mode 100644 index 00000000..2c317d80 --- /dev/null +++ b/src/soundscapy/plotting/soundscape_functions.py @@ -0,0 +1,564 @@ +""" +Function-based API for creating soundscape plots with Seaborn Objects. + +This module provides high-level functions for creating various types of +soundscape plots using seaborn.objects API and custom Mark/Stat components. +These functions offer a more direct, functional approach compared to the +CircumplexPlot builder class. +""" + +from collections.abc import Sequence +from pathlib import Path +from typing import Any + +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +import seaborn.objects as so + +from soundscapy.plotting.marks import ( + SoundscapeCircumplex, + SoundscapeQuadrantLabels, +) +from soundscapy.plotting.plotting_utils import DEFAULT_XLIM, DEFAULT_YLIM +from soundscapy.plotting.stats import SoundscapeCoordinates + +# Path to soundscapy.mplstyle +STYLE_PATH = Path(__file__).parent / "soundscapy.mplstyle" + + +def use_soundscapy_style(): + """ + Apply the soundscapy matplotlib style. + + This function activates the built-in style sheet for consistent + soundscape plot appearance. + """ + if STYLE_PATH.exists(): + plt.style.use(str(STYLE_PATH)) + else: + # Fall back to built-in style if custom style file isn't found + plt.style.use("seaborn-v0_8-colorblind") + + +def scatter_plot( + data: pd.DataFrame, + x: str = "ISOPleasant", + y: str = "ISOEventful", + hue: str | None = None, + title: str = "Soundscape Scatter Plot", + xlim: tuple[float, float] = DEFAULT_XLIM, + ylim: tuple[float, float] = DEFAULT_YLIM, + palette: str = "colorblind", + diagonal_lines: bool = False, + show_labels: bool = True, + point_size: float = 30, + alpha: float = 0.7, + marker: str = "o", + ax: plt.Axes | None = None, + as_objects: bool = False, + **kwargs: Any, +) -> so.Plot | plt.Axes: + """ + Create a scatter plot using the soundscape circumplex model. + + Parameters + ---------- + data : pd.DataFrame + Data to plot + x : str, default="ISOPleasant" + Column name for x-axis + y : str, default="ISOEventful" + Column name for y-axis + hue : str, optional + Column name for color grouping + title : str, default="Soundscape Scatter Plot" + Title for the plot + xlim : tuple, default=(-1, 1) + X-axis limits + ylim : tuple, default=(-1, 1) + Y-axis limits + palette : str, default="colorblind" + Color palette to use + diagonal_lines : bool, default=False + Whether to show diagonal lines and quadrant labels + show_labels : bool, default=True + Whether to show axis labels + point_size : float, default=30 + Size of scatter points + alpha : float, default=0.7 + Opacity of scatter points + marker : str, default="o" + Marker style for scatter points + ax : plt.Axes, optional + Axes to plot on (for matplotlib compatibility) + as_objects : bool, default=False + If True, return seaborn.objects.Plot; if False, return Matplotlib axes + **kwargs : Any + Additional keyword arguments for scatter plot + + Returns + ------- + so.Plot | plt.Axes + The completed plot object or axes + + """ + use_soundscapy_style() + + # Create base plot + plot = so.Plot(data, x=x, y=y) + + # Add scatter points + plot = plot.add( + so.Dots(pointsize=point_size, alpha=alpha, marker=marker), color=hue + ) + + # Apply color palette if needed + if hue: + plot = plot.scale(color=so.Nominal(palette)) + + # Add circumplex grid + plot = plot.add(SoundscapeCircumplex(xlim=xlim, ylim=ylim)) + + # Add quadrant labels if requested + if diagonal_lines: + plot = plot.add(SoundscapeQuadrantLabels(xlim=xlim, ylim=ylim)) + + # Add title and labels + plot = plot.label(title=title) + + # Set layout + plot = plot.layout(size=(6, 6)) + + # Hide labels if requested + if not show_labels: + plot = plot.label(x=None, y=None) + + if as_objects: + return plot + + if ax is not None: + # If an axes is provided, clear and draw on it + ax.clear() + plot.plot(ax) + return ax + + # Create a new figure and axes, draw on it, and return the axes + fig, ax = plt.subplots(figsize=(6, 6)) + plot.plot(ax) + return ax + + +def density_plot( + data: pd.DataFrame, + x: str = "ISOPleasant", + y: str = "ISOEventful", + hue: str | None = None, + title: str = "Soundscape Density Plot", + xlim: tuple[float, float] = DEFAULT_XLIM, + ylim: tuple[float, float] = DEFAULT_YLIM, + palette: str = "colorblind", + fill: bool = True, + alpha: float = 0.5, + bw_adjust: float = 1.2, + levels: int = 8, + simple_density: bool = False, + incl_scatter: bool = False, + scatter_size: float = 15, + scatter_alpha: float = 0.5, + diagonal_lines: bool = False, + show_labels: bool = True, + ax: plt.Axes | None = None, + as_objects: bool = False, + **kwargs: Any, +) -> so.Plot | plt.Axes: + """ + Create a density plot using the soundscape circumplex model. + + Parameters + ---------- + data : pd.DataFrame + Data to plot + x : str, default="ISOPleasant" + Column name for x-axis + y : str, default="ISOEventful" + Column name for y-axis + hue : str, optional + Column name for color grouping + title : str, default="Soundscape Density Plot" + Title for the plot + xlim : tuple, default=(-1, 1) + X-axis limits + ylim : tuple, default=(-1, 1) + Y-axis limits + palette : str, default="colorblind" + Color palette to use + fill : bool, default=True + Whether to fill the contours + alpha : float, default=0.5 + Opacity of the fill + bw_adjust : float, default=1.2 + Bandwidth adjustment factor + levels : int, default=8 + Number of contour levels + simple_density : bool, default=False + If True, use simplified density with fewer levels and an outline + incl_scatter : bool, default=False + Whether to include scatter points + scatter_size : float, default=15 + Size of scatter points (if included) + scatter_alpha : float, default=0.5 + Opacity of scatter points (if included) + diagonal_lines : bool, default=False + Whether to show diagonal lines and quadrant labels + show_labels : bool, default=True + Whether to show axis labels + ax : plt.Axes, optional + Axes to plot on (for matplotlib compatibility) + as_objects : bool, default=False + If True, return seaborn.objects.Plot; if False, return Matplotlib axes + **kwargs : Any + Additional keyword arguments for density plot + + Returns + ------- + so.Plot | plt.Axes + The completed plot object or axes + + """ + use_soundscapy_style() + + # Create base plot + plot = so.Plot(data, x=x, y=y) + + # Add density layer + if simple_density: + # Simple density with fewer levels and outline + plot = plot.add( + so.Area(fill=fill, alpha=alpha), + so.KDE(bw_adjust=bw_adjust, levels=2), + color=hue, + ) + # Add outline + plot = plot.add( + so.Line(alpha=1.0), so.KDE(bw_adjust=bw_adjust, levels=2), color=hue + ) + else: + # Regular density + plot = plot.add( + so.Area(fill=fill, alpha=alpha), + so.KDE(bw_adjust=bw_adjust, levels=levels), + color=hue, + ) + + # Apply color palette if needed + if hue: + plot = plot.scale(color=so.Nominal(palette)) + + # Add scatter if requested + if incl_scatter: + plot = plot.add(so.Dots(pointsize=scatter_size, alpha=scatter_alpha), color=hue) + # Apply color palette again for scatter + if hue: + plot = plot.scale(color=so.Nominal(palette)) + + # Add circumplex grid + plot = plot.add(SoundscapeCircumplex(xlim=xlim, ylim=ylim)) + + # Add quadrant labels if requested + if diagonal_lines: + plot = plot.add(SoundscapeQuadrantLabels(xlim=xlim, ylim=ylim)) + + # Add title and labels + plot = plot.label(title=title) + + # Set layout + plot = plot.layout(size=(6, 6)) + + # Hide labels if requested + if not show_labels: + plot = plot.label(x=None, y=None) + + if as_objects: + return plot + + if ax is not None: + # If an axes is provided, clear and draw on it + ax.clear() + plot.plot(ax) + return ax + + # Create a new figure and axes, draw on it, and return the axes + fig, ax = plt.subplots(figsize=(6, 6)) + plot.plot(ax) + return ax + + +def joint_plot( + data: pd.DataFrame, + x: str = "ISOPleasant", + y: str = "ISOEventful", + hue: str | None = None, + title: str = "Soundscape Joint Plot", + kind: str = "scatter", + xlim: tuple[float, float] = DEFAULT_XLIM, + ylim: tuple[float, float] = DEFAULT_YLIM, + palette: str = "colorblind", + diagonal_lines: bool = False, + **kwargs: Any, +) -> sns.JointGrid: + """ + Create a joint plot with marginals using traditional seaborn. + + This function falls back to traditional seaborn because + seaborn.objects does not yet fully support joint plots with marginals. + + Parameters + ---------- + data : pd.DataFrame + Data to plot + x : str, default="ISOPleasant" + Column name for x-axis + y : str, default="ISOEventful" + Column name for y-axis + hue : str, optional + Column name for color grouping + title : str, default="Soundscape Joint Plot" + Title for the plot + kind : str, default="scatter" + Type of plot: "scatter", "kde", or "hex" + xlim : tuple, default=(-1, 1) + X-axis limits + ylim : tuple, default=(-1, 1) + Y-axis limits + palette : str, default="colorblind" + Color palette to use + diagonal_lines : bool, default=False + Whether to show diagonal lines + **kwargs : Any + Additional parameters for sns.jointplot + + Returns + ------- + sns.JointGrid + The joint plot grid + + """ + use_soundscapy_style() + + # Create joint plot using traditional seaborn + g = sns.jointplot( + data=data, x=x, y=y, hue=hue, kind=kind, palette=palette, **kwargs + ) + + # Apply limits + g.ax_joint.set_xlim(xlim) + g.ax_joint.set_ylim(ylim) + + # Add zero lines + g.ax_joint.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + g.ax_joint.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) + + # Add grid + g.ax_joint.grid(True, which="major", color="grey", alpha=0.5) + + # Add title + g.fig.suptitle(title, y=1.05) + + # Add diagonal lines if requested + if diagonal_lines: + g.ax_joint.plot( + [xlim[0], xlim[1]], + [ylim[0], ylim[1]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) + g.ax_joint.plot( + [xlim[0], xlim[1]], + [ylim[1], ylim[0]], + linestyle="dashed", + color="grey", + alpha=0.5, + linewidth=1.5, + ) + + return g + + +def create_circumplex_subplots( + data_list: Sequence[pd.DataFrame], + x: str = "ISOPleasant", + y: str = "ISOEventful", + hue: str | None = None, + subtitles: Sequence[str] | None = None, + title: str = "Circumplex Subplots", + plot_type: str = "density", + incl_scatter: bool = False, + cols: int = 2, + **kwargs: Any, +) -> plt.Figure: + """ + Create a figure with multiple circumplex plots. + + Parameters + ---------- + data_list : Sequence[pd.DataFrame] + List of data sources to plot + x : str, default="ISOPleasant" + Column name for x-axis + y : str, default="ISOEventful" + Column name for y-axis + hue : str, optional + Column name for color grouping + subtitles : Sequence[str], optional + Titles for individual subplots + title : str, default="Circumplex Subplots" + Main title for the figure + plot_type : str, default="density" + Type of plot: "scatter", "density", or "simple_density" + incl_scatter : bool, default=False + Whether to include scatter points on density plots + cols : int, default=2 + Number of columns for subplots + **kwargs : Any + Additional arguments for plot functions + + Returns + ------- + plt.Figure + The matplotlib Figure with subplots + + """ + use_soundscapy_style() + + # Generate subplot titles if not provided + if subtitles is None: + subtitles = [f"Plot {i + 1}" for i in range(len(data_list))] + + # Remove any layout parameters that don't belong in plot functions + plotting_kwargs = kwargs.copy() + if "nrows" in plotting_kwargs: + plotting_kwargs.pop("nrows") + if "ncols" in plotting_kwargs: + plotting_kwargs.pop("ncols") + + # Calculate rows needed + nrows = (len(data_list) - 1) // cols + 1 + + # Create figure and axes + fig, axes = plt.subplots(nrows, cols, figsize=(cols * 6, nrows * 6), squeeze=False) + axes = axes.flatten() + + # Create individual plots + for i, (data, subtitle) in enumerate(zip(data_list, subtitles, strict=False)): + if i < len(axes): + # Create plot for this axis + if plot_type == "scatter": + scatter_plot( + data, + x=x, + y=y, + hue=hue, + title=subtitle, + ax=axes[i], + **plotting_kwargs, + ) + elif plot_type == "simple_density": + density_plot( + data, + x=x, + y=y, + hue=hue, + title=subtitle, + simple_density=True, + incl_scatter=incl_scatter, + ax=axes[i], + **plotting_kwargs, + ) + else: # "density" or default + density_plot( + data, + x=x, + y=y, + hue=hue, + title=subtitle, + incl_scatter=incl_scatter, + ax=axes[i], + **plotting_kwargs, + ) + + # Hide any unused axes + for i in range(len(data_list), len(axes)): + axes[i].set_visible(False) + + # Add a title to the figure + fig.suptitle(title, fontsize=16) + + # Adjust layout + fig.tight_layout() + + return fig + + +def add_calculated_coords( + data: pd.DataFrame, + paq_cols=("PAQ1", "PAQ2", "PAQ3", "PAQ4", "PAQ5", "PAQ6", "PAQ7", "PAQ8"), + angles=(0, 45, 90, 135, 180, 225, 270, 315), + val_range=(5, 1), + output_cols=("ISOPleasant", "ISOEventful"), + *, + overwrite: bool = False, +) -> pd.DataFrame: + """ + Calculate and add ISO coordinates to a dataframe. + + This is a convenience function to use the SoundscapeCoordinates + stat outside of a plotting context. + + Parameters + ---------- + data : pd.DataFrame + Input data with PAQ columns + paq_cols : tuple, default=("PAQ1", "PAQ2", "PAQ3", "PAQ4", "PAQ5", "PAQ6", "PAQ7", "PAQ8") + Column names for PAQ data + angles : tuple, default=(0, 45, 90, 135, 180, 225, 270, 315) + Angles for each PAQ in degrees + val_range : tuple, default=(5, 1) + (max, min) range of original PAQ responses + output_cols : tuple, default=("ISOPleasant", "ISOEventful") + Column names for output coordinates + overwrite : bool, default=False + Whether to overwrite existing coordinate columns + + Returns + ------- + pd.DataFrame + Data with added ISO coordinate columns + + Raises + ------ + ValueError + If coordinate columns already exist and overwrite=False + + """ + # Check if columns already exist + for col in output_cols: + if col in data.columns and not overwrite: + raise ValueError( + f"{col} already exists in dataframe. Use overwrite=True to replace." + ) + + # Create the stat and apply it to the data + stat = SoundscapeCoordinates( + paq_cols=paq_cols, + angles=angles, + val_range=val_range, + output_cols=output_cols, + ) + + # Apply the stat + result = stat._apply(data) + + return result diff --git a/src/soundscapy/plotting/soundscape_plot.py b/src/soundscapy/plotting/soundscape_plot.py index e834663b..e69de29b 100644 --- a/src/soundscapy/plotting/soundscape_plot.py +++ b/src/soundscapy/plotting/soundscape_plot.py @@ -1,386 +0,0 @@ -""" -Base module for creating soundscape plots using Seaborn Objects API. - -This module provides the SoundscapePlot class, which directly subclasses -seaborn.objects.Plot to create specialized visualizations for soundscape data. -""" - -import copy -import functools -import inspect -from typing import Any, Callable, TypeVar, cast - -import matplotlib.pyplot as plt -import pandas as pd -import seaborn.objects as so -from matplotlib.axes import Axes -from matplotlib.figure import Figure, SubFigure -from seaborn._core.typing import ( - OrderSpec, - VariableSpec, - VariableSpecList, -) - -from soundscapy.plotting.plotting_utils import DEFAULT_XLIM, DEFAULT_YLIM - -# The TypeVar helps with type annotations for method chaining -P = TypeVar("P", bound="SoundscapePlot") - - -def _fix_return_type(method: Callable) -> Callable: - """ - Decorator to ensure methods return the correct subclass type. - - This decorator wraps methods that would normally return so.Plot - and ensures they return the subclass type instead. It transfers - custom state attributes from the original object to the result, - which is especially important for methods that return a new plot - instance rather than modifying the current one. - - Parameters - ---------- - method : Callable - The method to wrap - - Returns - ------- - Callable - A wrapped method that preserves SoundscapePlot type - """ - - @functools.wraps(method) - def wrapper(self, *args, **kwargs): - # Call the original method - result = method(self, *args, **kwargs) - - # If the result is a Plot instance but not our subclass (or a subclass of our subclass), - # we need to transfer our custom state and cast to the correct type - if isinstance(result, so.Plot) and not isinstance(result, type(self)): - # Identify custom attributes (starting with underscore but not dunder methods) - # that exist in self but not in the result - for attr_name in dir(self): - if ( - attr_name.startswith("_") - and not attr_name.startswith("__") - and hasattr(self, attr_name) - and not hasattr(result, attr_name) - ): - try: - # Try to deep copy the attribute for proper immutability - setattr( - result, attr_name, copy.deepcopy(getattr(self, attr_name)) - ) - except (TypeError, AttributeError): - # Fall back to regular assignment if deep copy fails - setattr(result, attr_name, getattr(self, attr_name)) - - # Cast the result to our actual class type (preserving exact subclass) - result = cast(type(self), result) - - return result - - return wrapper - - -class SoundscapePlot(so.Plot): - """ - Base class for soundscape visualization that extends seaborn's Plot. - - This class subclasses seaborn.objects.Plot to create a specialized - plotting API for soundscape data visualization while maintaining - full compatibility with the Seaborn Objects interface. It includes - methods like add_grid() that provide soundscape-specific functionality, - while ensuring all inherited seaborn methods return the correct type. - - Examples - -------- - >>> import soundscapy as sspy - >>> from soundscapy.plotting import SoundscapePlot - >>> data = sspy.isd.load() - >>> data = sspy.surveys.add_iso_coords(data, overwrite=True) - >>> plot = (SoundscapePlot(data) - ... .add(so.Dots(), color="location_id") - ... .add_grid(diagonal_lines=True) - ... .label(title="Soundscape Plot")) - >>> plot.show() - - Parameters - ---------- - data : pd.DataFrame - Data to plot - x, y : str - Column names for coordinates (default to ISO coordinate names) - xlim, ylim : tuple[float, float] - Axis limits for the plot (default to standard -1,1 range) - **kwargs - Additional parameters to pass to so.Plot.__init__ - - """ - - def __init__( - self, - data: pd.DataFrame, - x: str = "ISOPleasant", - y: str = "ISOEventful", - xlim: tuple[float, float] = DEFAULT_XLIM, - ylim: tuple[float, float] = DEFAULT_YLIM, - **kwargs: Any, - ) -> None: - """ - Initialize a SoundscapePlot object. - - Parameters - ---------- - data : pd.DataFrame - Data to plot - x : str, default="ISOPleasant" - Column name for x-axis, defaults to ISO pleasantness dimension - y : str, default="ISOEventful" - Column name for y-axis, defaults to ISO eventfulness dimension - **kwargs : Any - Additional parameters to pass to the seaborn Plot constructor - - """ - # Initialize with soundscape defaults - super().__init__(data, x=x, y=y, **kwargs) - - # Store soundscape-specific state as private attributes - self._xlim = xlim - self._ylim = ylim - self._has_grid = False - - def _clone(self: P) -> P: - """ - Create a copy of this plot object with deep-copied state. - - This method is crucial for the immutable builder pattern. - - Returns - ------- - SoundscapePlot - A new plot with copied state - - """ - new = super()._clone() - # Copy our custom attributes - new._xlim = copy.deepcopy(self._xlim) - new._ylim = copy.deepcopy(self._ylim) - new._has_grid = self._has_grid - return cast(P, new) - - @_fix_return_type - def add_grid( - self: P, - xlim: tuple[float, float] = DEFAULT_XLIM, - ylim: tuple[float, float] = DEFAULT_YLIM, - x_label: str | None = None, - y_label: str | None = None, - *, - diagonal_lines: bool = False, - show_labels: bool = True, - ) -> P: - """ - Add a standard soundscape analysis grid with zero lines and optionally diagonal lines. - - Parameters - ---------- - xlim, ylim : tuple - Axis limits - x_label, y_label : str, optional - Custom labels for axes - diagonal_lines : bool - Whether to draw diagonal lines and quadrant labels - show_labels : bool - Whether to keep axis labels - - Returns - ------- - SoundscapePlot - The plot with grid styling applied - - """ - # Clone first to maintain immutability - new_plot = self._clone() - - # Apply limits and square aspect ratio - new_plot = cast(P, new_plot.limit(x=xlim, y=ylim).layout(size=(6, 6))) - - # Create a temporary matplotlib figure for styling - fig, ax = plt.subplots(figsize=(6, 6)) - - # Add zero lines - ax.axhline(y=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) - ax.axvline(x=0, color="grey", linestyle="dashed", alpha=1, linewidth=1.5) - - # Add grid - ax.grid(visible=True, which="major", color="grey", alpha=0.5) - ax.grid( - visible=True, - which="minor", - color="grey", - linestyle="dashed", - linewidth=0.5, - alpha=0.4, - ) - ax.minorticks_on() - - # Handle diagonal lines if requested - if diagonal_lines: - # Add diagonal lines - ax.plot( - [xlim[0], xlim[1]], - [ylim[0], ylim[1]], - linestyle="dashed", - color="grey", - alpha=0.5, - linewidth=1.5, - ) - ax.plot( - [xlim[0], xlim[1]], - [ylim[1], ylim[0]], - linestyle="dashed", - color="grey", - alpha=0.5, - linewidth=1.5, - ) - - # Add quadrant labels if requested - if show_labels: - diag_font = { - "fontstyle": "italic", - "fontsize": "small", - "fontweight": "bold", - "color": "black", - "alpha": 0.5, - } - - ax.text( - xlim[1] / 2, - ylim[1] / 2, - "(vibrant)", - ha="center", - va="center", - fontdict=diag_font, - ) - ax.text( - xlim[0] / 2, - ylim[1] / 2, - "(chaotic)", - ha="center", - va="center", - fontdict=diag_font, - ) - ax.text( - xlim[0] / 2, - ylim[0] / 2, - "(monotonous)", - ha="center", - va="center", - fontdict=diag_font, - ) - ax.text( - xlim[1] / 2, - ylim[0] / 2, - "(calm)", - ha="center", - va="center", - fontdict=diag_font, - ) - - # Apply axis label changes - if not show_labels: - ax.set_xlabel("") - ax.set_ylabel("") - elif x_label is not None or y_label is not None: - if x_label is not None: - ax.set_xlabel(x_label) - if y_label is not None: - ax.set_ylabel(y_label) - - # Transfer styling to the plot - cast to ensure type correctness - plot_with_styling = cast(P, new_plot.on(ax)) - - # Clean up the temporary figure - plt.close(fig) - - # Update state to track that grid has been added - plot_with_styling._has_grid = True - plot_with_styling._xlim = xlim - plot_with_styling._ylim = ylim - - return plot_with_styling - - def show(self: P, **kwargs) -> None: - """ - Display the plot. - - This is a convenience method that ensures grid is added - and handles proper rendering in both notebook and script contexts. - - Parameters - ---------- - **kwargs - Additional keyword arguments passed to matplotlib.pyplot.show() - - """ - # Ensure grid is added if not already - plot_to_show = self - if not plot_to_show._has_grid: - plot_to_show = plot_to_show.add_grid() - - # Use the correctly configured pyplot mode and pass through all kwargs - plot_to_show.plot(pyplot=True).show(**kwargs) - - # We don't need to explicitly override parent methods that don't add custom functionality - # The __init_subclass__ method will automatically apply the _fix_return_type decorator - # to all inherited methods from so.Plot - - # Apply the _fix_return_type decorator to all parent methods automatically - @classmethod - def __init_subclass__(cls, **kwargs): - """ - Ensure all inherited so.Plot methods maintain proper return types. - - This special method is called whenever a subclass of SoundscapePlot is created. - It automatically applies the _fix_return_type decorator to all methods - inherited from so.Plot that haven't been explicitly overridden, ensuring - consistent return type behavior throughout the inheritance hierarchy. - """ - super().__init_subclass__(**kwargs) - - # Find all eligible methods in the so.Plot class - for name, method in inspect.getmembers(so.Plot, inspect.isfunction): - # Apply the decorator if: - # 1. Method isn't already defined in this class - # 2. Method isn't private (doesn't start with _) - # 3. Method has 'self' as a parameter (instance method) - if ( - name not in cls.__dict__ - and not name.startswith("_") - and "self" in inspect.signature(method).parameters - ): - # Add the wrapped method to our class - setattr(cls, name, _fix_return_type(method)) - - # Apply the same fix to the current class after definition - @classmethod - def _apply_return_type_fix(cls): - """ - Apply the return type fix to the current class. - - This is called at the end of the class definition to ensure that - even the base SoundscapePlot class gets the decorator applied to - its inherited methods. - """ - # Similar logic to __init_subclass__, but for the current class - for name, method in inspect.getmembers(so.Plot, inspect.isfunction): - if ( - name not in cls.__dict__ - and not name.startswith("_") - and "self" in inspect.signature(method).parameters - ): - setattr(cls, name, _fix_return_type(method)) - - -# Apply the return type fix to SoundscapePlot itself -SoundscapePlot._apply_return_type_fix() diff --git a/src/soundscapy/plotting/soundscapy.mplstyle b/src/soundscapy/plotting/soundscapy.mplstyle new file mode 100644 index 00000000..42950791 --- /dev/null +++ b/src/soundscapy/plotting/soundscapy.mplstyle @@ -0,0 +1,45 @@ +# soundscapy.mplstyle +# Matplotlib style sheet for Soundscapy plots + +# Figure properties +figure.figsize: 6, 6 +figure.constrained_layout.use: True + +# Axes properties +axes.grid: True +axes.linewidth: 1.0 +axes.axisbelow: True +axes.labelsize: 10 +axes.titlesize: 12 + +# Grid properties +grid.alpha: 0.5 +grid.color: grey +grid.linestyle: - +grid.linewidth: 0.5 + +# Tick properties +xtick.direction: in +ytick.direction: in +xtick.minor.visible: True +ytick.minor.visible: True +xtick.labelsize: 9 +ytick.labelsize: 9 + +# Line properties +lines.linewidth: 1.5 +lines.solid_capstyle: round +lines.dashed_pattern: 2.8, 1.2 +lines.dashdot_pattern: 4.8, 1.2, 0.8, 1.2 + +# Font properties +font.size: 10 +font.family: sans-serif + +# Legend properties +legend.frameon: True +legend.framealpha: 0.8 +legend.fontsize: 9 + +# Colors for different groups +axes.prop_cycle: cycler('color', ['4C72B0', '55A868', 'C44E52', '8172B2', 'CCB974', '64B5CD']) diff --git a/src/soundscapy/plotting/stats.py b/src/soundscapy/plotting/stats.py new file mode 100644 index 00000000..e179cf0a --- /dev/null +++ b/src/soundscapy/plotting/stats.py @@ -0,0 +1,149 @@ +""" +Custom Stat components for Soundscape plots using seaborn.objects. + +This module contains custom Stat classes that extend the functionality of +seaborn.objects.Plot for creating soundscape plots. These stats handle +specialized data transformations like converting PAQ data to ISO coordinates. +""" + +import numpy as np +import seaborn.objects as so + +from soundscapy.surveys.survey_utils import EQUAL_ANGLES + + +class SoundscapeCoordinates(so.Stat): + """ + Stat for calculating ISO coordinates from PAQ data. + + This stat transforms PAQ data (perceptual attribute questions) into + ISOPleasant and ISOEventful coordinates based on the ISO 12913-3 standard. + """ + + def __init__( + self, + paq_cols=("PAQ1", "PAQ2", "PAQ3", "PAQ4", "PAQ5", "PAQ6", "PAQ7", "PAQ8"), + angles=EQUAL_ANGLES, + val_range=(5, 1), + output_cols=("ISOPleasant", "ISOEventful"), + **kwargs, + ): + """ + Initialize the ISO coordinates stat. + + Parameters + ---------- + paq_cols : tuple, default=("PAQ1", "PAQ2", "PAQ3", "PAQ4", "PAQ5", "PAQ6", "PAQ7", "PAQ8") + Column names for PAQ data + angles : tuple, default=EQUAL_ANGLES + Angles for each PAQ in degrees + val_range : tuple, default=(5, 1) + (max, min) range of original PAQ responses + output_cols : tuple, default=("ISOPleasant", "ISOEventful") + Column names for output coordinates + **kwargs : + Additional keyword arguments passed to parent class + + """ + self.paq_cols = paq_cols + self.angles = angles + self.val_range = val_range + self.output_cols = output_cols + super().__init__(**kwargs) + + def _apply(self, data, **kwargs): + """ + Calculate ISO coordinates from PAQ data. + + Parameters + ---------- + data : pd.DataFrame + Input data with PAQ columns + **kwargs : + Additional keyword arguments + + Returns + ------- + pd.DataFrame + Data with added ISO coordinate columns + + """ + # Check if required columns exist + if not all(col in data.columns for col in self.paq_cols): + # Return original data if PAQ columns aren't present + return data + + # Get only PAQ columns + paq_df = data[list(self.paq_cols)] + + # Calculate scale factor + scale = max(self.val_range) - min(self.val_range) + + # Calculate coordinates + iso_pleasant = paq_df.apply( + lambda row: self._adj_iso_pl(row, self.angles, scale), axis=1 + ) + iso_eventful = paq_df.apply( + lambda row: self._adj_iso_ev(row, self.angles, scale), axis=1 + ) + + # Add calculated coordinates to data + transformed = data.assign( + **{self.output_cols[0]: iso_pleasant, self.output_cols[1]: iso_eventful} + ) + + return transformed + + def _adj_iso_pl(self, values, angles, scale): + """ + Calculate adjusted ISOPleasant value. + + Parameters + ---------- + values : pd.Series + PAQ values for a single sample + angles : tuple + Angles for each PAQ in degrees + scale : float + Scale factor for normalization + + Returns + ------- + float + Adjusted ISOPleasant value + + """ + iso_pl = sum( + np.cos(np.deg2rad(angle)) * value + for angle, value in zip(angles, values, strict=False) + ) + return iso_pl / ( + scale / 2 * sum(abs(np.cos(np.deg2rad(angle))) for angle in angles) + ) + + def _adj_iso_ev(self, values, angles, scale): + """ + Calculate adjusted ISOEventful value. + + Parameters + ---------- + values : pd.Series + PAQ values for a single sample + angles : tuple + Angles for each PAQ in degrees + scale : float + Scale factor for normalization + + Returns + ------- + float + Adjusted ISOEventful value + + """ + iso_ev = sum( + np.sin(np.deg2rad(angle)) * value + for angle, value in zip(angles, values, strict=False) + ) + return iso_ev / ( + scale / 2 * sum(abs(np.sin(np.deg2rad(angle))) for angle in angles) + ) diff --git a/test/plotting/test_plotting.py b/test/plotting/test_plotting.py index 551628f3..73ca2980 100644 --- a/test/plotting/test_plotting.py +++ b/test/plotting/test_plotting.py @@ -4,7 +4,6 @@ import matplotlib.pyplot as plt import numpy as np -import pandas as pd import pytest import seaborn.objects as so @@ -110,12 +109,12 @@ def test_circumplex_plot_methods(sample_data): plot = CircumplexPlot(sample_data) plot.scatter() assert isinstance(plot.get_axes(), plt.Axes) - + # Test density method plot = CircumplexPlot(sample_data) plot.density() assert isinstance(plot.get_axes(), plt.Axes) - + # Test jointplot method plot = CircumplexPlot(sample_data) plot.jointplot() @@ -133,11 +132,8 @@ def test_plot_size(sample_data): def test_builder_pattern(sample_data): """Test the builder pattern API.""" - plot = (CircumplexPlot(sample_data) - .add_scatter() - .add_grid() - .add_title("Test Title")) - + plot = CircumplexPlot(sample_data).add_scatter().add_grid().add_title("Test Title") + # Verify the plot was built correctly assert plot.has_scatter is True assert plot.has_grid is True @@ -163,6 +159,6 @@ def test_annotations(sample_data): plot.add_scatter() plot.add_annotation(0) plot.add_grid() - + # Just testing that it doesn't error since we can't easily check annotation assert plot.has_scatter is True