From 98de1f93fd83fc6687b0dac0ae0f31b049d2c0c1 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Wed, 7 Jan 2026 01:15:28 +0100 Subject: [PATCH 01/49] template metrics from bombcell - use scipy findpeaks() to detect peaks and add more template metrics --- .gitignore | 6 + .../metrics/template/metrics.py | 641 ++++++++++++++++-- .../metrics/template/template_metrics.py | 32 +- 3 files changed, 630 insertions(+), 49 deletions(-) diff --git a/.gitignore b/.gitignore index 6a7edf06f8..398852fc77 100644 --- a/.gitignore +++ b/.gitignore @@ -145,3 +145,9 @@ test_folder/ # Mac OS .DS_Store test_data.json +analyzer_TDC_binary/ +CLAUDE.md +playground.ipynbd +playground.ipynb +analyzer_TDC_binary/ +spykingcircus2_output/ diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index a1af1de348..74e00d5714 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -2,32 +2,430 @@ import numpy as np from collections import namedtuple - +from scipy.signal import find_peaks, savgol_filter from spikeinterface.core.analyzer_extension_core import BaseMetric -def get_trough_and_peak_idx(template): +def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smooth=True, smooth_window_frac=0.1, smooth_polyorder=3): """ - Return the indices into the input template of the detected trough - (minimum of template) and peak (maximum of template, after trough). - Assumes negative trough and positive peak. + Detect troughs and peaks in a template waveform and return detailed information + about each detected feature. Parameters ---------- - template: numpy.ndarray + template : numpy.ndarray The 1D template waveform + min_thresh_detect_peaks_troughs : float, default: 0.4 + Minimum prominence threshold as a fraction of the template's absolute max value + smooth : bool, default: True + Whether to apply Savitzky-Golay smoothing before peak detection + smooth_window_frac : float, default: 0.1 + Smoothing window length as a fraction of template length (0.05-0.2 recommended) + smooth_polyorder : int, default: 3 + Polynomial order for Savitzky-Golay filter (must be < window_length) Returns ------- - trough_idx: int - The index of the trough - peak_idx: int - The index of the peak + troughs : dict + Dictionary containing: + - "indices": array of all trough indices + - "values": array of all trough values + - "prominences": array of all trough prominences + - "widths": array of all trough widths + - "main_idx": index of the main trough (most prominent) + - "main_loc": location (sample index) of the main trough in template + peaks_before : dict + Dictionary containing peaks detected before the main trough (initial peaks): + - "indices": array of all peak indices (in original template coordinates) + - "values": array of all peak values + - "prominences": array of all peak prominences + - "widths": array of all peak widths + - "main_idx": index of the main peak (most prominent) + - "main_loc": location (sample index) of the main peak in template + peaks_after : dict + Dictionary containing peaks detected after the main trough (repolarization peaks): + - "indices": array of all peak indices (in original template coordinates) + - "values": array of all peak values + - "prominences": array of all peak prominences + - "widths": array of all peak widths + - "main_idx": index of the main peak (most prominent) + - "main_loc": location (sample index) of the main peak in template """ assert template.ndim == 1 - trough_idx = np.argmin(template) - peak_idx = trough_idx + np.argmax(template[trough_idx:]) - return trough_idx, peak_idx + + # Save original for plotting + template_original = template.copy() + + # Smooth template to reduce noise while preserving peaks (Savitzky-Golay filter) + if smooth: + # Calculate window length from fraction, ensure odd, min 5 + window_length = int(len(template) * smooth_window_frac) // 2 * 2 + 1 + window_length = max(smooth_polyorder + 2, window_length) # Must be > polyorder + template = savgol_filter(template, window_length=window_length, polyorder=smooth_polyorder) + + # Initialize empty result dictionaries + empty_dict = { + "indices": np.array([], dtype=int), + "values": np.array([]), + "prominences": np.array([]), + "widths": np.array([]), + "main_idx": None, + "main_loc": None, + } + + # Get min prominence to detect peaks and troughs relative to template abs max value + min_prominence = min_thresh_detect_peaks_troughs * np.nanmax(np.abs(template)) + + # --- Find troughs (by inverting waveform and using find_peaks) --- + trough_locs, trough_props = find_peaks(-template, prominence=min_prominence, width=0) + + if len(trough_locs) == 0: + # Fallback: use global minimum + trough_locs = np.array([np.nanargmin(template)]) + trough_props = {"prominences": np.array([np.nan]), "widths": np.array([np.nan])} + + # Determine main trough (most prominent, or first if no valid prominences) + trough_prominences = trough_props.get("prominences", np.array([])) + if len(trough_prominences) > 0 and not np.all(np.isnan(trough_prominences)): + main_trough_idx = np.nanargmax(trough_prominences) + else: + main_trough_idx = 0 + + main_trough_loc = trough_locs[main_trough_idx] + + troughs = { + "indices": trough_locs, + "values": template[trough_locs], + "prominences": trough_props.get("prominences", np.full(len(trough_locs), np.nan)), + "widths": trough_props.get("widths", np.full(len(trough_locs), np.nan)), + "main_idx": main_trough_idx, + "main_loc": main_trough_loc, + } + + # --- Find peaks before the main trough --- + if main_trough_loc > 3: + template_before = template[:main_trough_loc] + + # Try with original prominence + peak_locs_before, peak_props_before = find_peaks( + template_before, prominence=min_prominence, width=0 + ) + + # If no peaks found, try with lower prominence (keep only max peak) + if len(peak_locs_before) == 0: + lower_prominence = 0.075 * min_thresh_detect_peaks_troughs * np.nanmax(np.abs(template)) + peak_locs_before, peak_props_before = find_peaks( + template_before, prominence=lower_prominence, width=0 + ) + # Keep only the most prominent peak when using lower threshold + if len(peak_locs_before) > 1: + prominences = peak_props_before.get("prominences", np.array([])) + if len(prominences) > 0 and not np.all(np.isnan(prominences)): + max_idx = np.nanargmax(prominences) + peak_locs_before = np.array([peak_locs_before[max_idx]]) + peak_props_before = { + "prominences": np.array([prominences[max_idx]]), + "widths": np.array([peak_props_before.get("widths", np.array([np.nan]))[max_idx]]), + } + + # If still no peaks found, fall back to argmax + if len(peak_locs_before) == 0: + peak_locs_before = np.array([np.nanargmax(template_before)]) + peak_props_before = {"prominences": np.array([np.nan]), "widths": np.array([np.nan])} + + peak_prominences_before = peak_props_before.get("prominences", np.array([])) + if len(peak_prominences_before) > 0 and not np.all(np.isnan(peak_prominences_before)): + main_peak_before_idx = np.nanargmax(peak_prominences_before) + else: + main_peak_before_idx = 0 + + peaks_before = { + "indices": peak_locs_before, + "values": template[peak_locs_before], + "prominences": peak_props_before.get("prominences", np.full(len(peak_locs_before), np.nan)), + "widths": peak_props_before.get("widths", np.full(len(peak_locs_before), np.nan)), + "main_idx": main_peak_before_idx, + "main_loc": peak_locs_before[main_peak_before_idx], + } + else: + peaks_before = empty_dict.copy() + + # --- Find peaks after the main trough (repolarization peaks) --- + if main_trough_loc < len(template) - 3: + template_after = template[main_trough_loc:] + + # Try with original prominence + peak_locs_after, peak_props_after = find_peaks( + template_after, prominence=min_prominence, width=0 + ) + + # If no peaks found, try with lower prominence (keep only max peak) + if len(peak_locs_after) == 0: + lower_prominence = 0.075 * min_thresh_detect_peaks_troughs * np.nanmax(np.abs(template)) + peak_locs_after, peak_props_after = find_peaks( + template_after, prominence=lower_prominence, width=0 + ) + # Keep only the most prominent peak when using lower threshold + if len(peak_locs_after) > 1: + prominences = peak_props_after.get("prominences", np.array([])) + if len(prominences) > 0 and not np.all(np.isnan(prominences)): + max_idx = np.nanargmax(prominences) + peak_locs_after = np.array([peak_locs_after[max_idx]]) + peak_props_after = { + "prominences": np.array([prominences[max_idx]]), + "widths": np.array([peak_props_after.get("widths", np.array([np.nan]))[max_idx]]), + } + + # If still no peaks found, fall back to argmax + if len(peak_locs_after) == 0: + peak_locs_after = np.array([np.nanargmax(template_after)]) + peak_props_after = {"prominences": np.array([np.nan]), "widths": np.array([np.nan])} + + # Convert to original template coordinates + peak_locs_after_abs = peak_locs_after + main_trough_loc + + peak_prominences_after = peak_props_after.get("prominences", np.array([])) + if len(peak_prominences_after) > 0 and not np.all(np.isnan(peak_prominences_after)): + main_peak_after_idx = np.nanargmax(peak_prominences_after) + else: + main_peak_after_idx = 0 + + peaks_after = { + "indices": peak_locs_after_abs, + "values": template[peak_locs_after_abs], + "prominences": peak_props_after.get("prominences", np.full(len(peak_locs_after), np.nan)), + "widths": peak_props_after.get("widths", np.full(len(peak_locs_after), np.nan)), + "main_idx": main_peak_after_idx, + "main_loc": peak_locs_after_abs[main_peak_after_idx], + } + else: + peaks_after = empty_dict.copy() + + # Quick visualization (set to True for debugging) + _plot = True + if _plot: + import matplotlib.pyplot as plt + + # Old simple method for comparison (argmin/argmax) + old_trough_idx = np.nanargmin(template) + old_peak_idx = np.nanargmax(template[old_trough_idx:]) + old_trough_idx + + fig, ax = plt.subplots(figsize=(10, 5)) + ax.plot(template_original, color="lightgray", lw=1, label="original (noisy)") + ax.plot(template, "k-", lw=1.5, label="smoothed") + + # Plot old method (simple argmin/argmax) + ax.axvline(old_trough_idx, color="gray", ls="--", alpha=0.5, label="old trough (argmin)") + ax.axvline(old_peak_idx, color="gray", ls=":", alpha=0.5, label="old peak (argmax after trough)") + + # Plot all detected troughs + ax.scatter(troughs["indices"], troughs["values"], c="blue", s=50, marker="v", zorder=5, label="troughs") + if troughs["main_loc"] is not None: + ax.scatter(troughs["main_loc"], template[troughs["main_loc"]], c="blue", s=150, marker="v", + edgecolors="red", linewidths=2, zorder=6, label="main trough") + + # Plot all peaks before + if len(peaks_before["indices"]) > 0: + ax.scatter(peaks_before["indices"], peaks_before["values"], c="green", s=50, marker="^", + zorder=5, label="peaks before") + if peaks_before["main_loc"] is not None: + ax.scatter(peaks_before["main_loc"], template[peaks_before["main_loc"]], c="green", s=150, + marker="^", edgecolors="red", linewidths=2, zorder=6, label="main peak before") + + # Plot all peaks after + if len(peaks_after["indices"]) > 0: + ax.scatter(peaks_after["indices"], peaks_after["values"], c="orange", s=50, marker="^", + zorder=5, label="peaks after") + if peaks_after["main_loc"] is not None: + ax.scatter(peaks_after["main_loc"], template[peaks_after["main_loc"]], c="orange", s=150, + marker="^", edgecolors="red", linewidths=2, zorder=6, label="main peak after") + + ax.axhline(0, color="gray", ls="-", alpha=0.3) + ax.set_xlabel("Sample") + ax.set_ylabel("Amplitude") + ax.legend(loc="best", fontsize=8) + ax.set_title(f"Trough/Peak Detection (prominence threshold: {min_thresh_detect_peaks_troughs})") + plt.tight_layout() + plt.show() + + return troughs, peaks_before, peaks_after + + +def get_waveform_duration(template, sampling_frequency, troughs, peaks_before, peaks_after, **kwargs): + """ + Calculate waveform duration from the main extremum to the next extremum. + + The duration is measured from the largest absolute feature (main trough or main peak) + to the next extremum. For typical negative-first waveforms, this is trough-to-peak. + For positive-first waveforms, this is peak-to-trough. + + Parameters + ---------- + template : numpy.ndarray + The 1D template waveform + sampling_frequency : float + The sampling frequency in Hz + troughs : dict + Trough detection results from get_trough_and_peak_idx + peaks_before : dict + Peak before trough results from get_trough_and_peak_idx + peaks_after : dict + Peak after trough results from get_trough_and_peak_idx + + Returns + ------- + waveform_duration_us : float + Waveform duration in microseconds + """ + + # Get main locations and values + trough_loc = troughs["main_loc"] + trough_val = template[trough_loc] if trough_loc is not None else None + + peak_before_loc = peaks_before["main_loc"] + peak_before_val = template[peak_before_loc] if peak_before_loc is not None else None + + peak_after_loc = peaks_after["main_loc"] + peak_after_val = template[peak_after_loc] if peak_after_loc is not None else None + + # Find the main extremum (largest absolute value) + candidates = [] + if trough_loc is not None and trough_val is not None: + candidates.append(("trough", trough_loc, abs(trough_val))) + if peak_before_loc is not None and peak_before_val is not None: + candidates.append(("peak_before", peak_before_loc, abs(peak_before_val))) + if peak_after_loc is not None and peak_after_val is not None: + candidates.append(("peak_after", peak_after_loc, abs(peak_after_val))) + + if len(candidates) == 0: + return np.nan + + # Sort by absolute value to find main extremum + candidates.sort(key=lambda x: x[2], reverse=True) + main_type, main_loc, _ = candidates[0] + + # Find the next extremum after the main one + if main_type == "trough": + # Main is trough, next is peak_after + if peak_after_loc is not None: + duration_samples = abs(peak_after_loc - main_loc) + elif peak_before_loc is not None: + duration_samples = abs(main_loc - peak_before_loc) + else: + return np.nan + elif main_type == "peak_before": + # Main is peak before, next is trough + if trough_loc is not None: + duration_samples = abs(trough_loc - main_loc) + else: + return np.nan + else: # peak_after + # Main is peak after, previous is trough + if trough_loc is not None: + duration_samples = abs(main_loc - trough_loc) + else: + return np.nan + + # Convert to microseconds + waveform_duration_us = (duration_samples / sampling_frequency) * 1e6 + + return waveform_duration_us + + +def get_waveform_ratios(template, troughs, peaks_before, peaks_after, **kwargs): + """ + Calculate various waveform amplitude ratios. + + Parameters + ---------- + template : numpy.ndarray + The 1D template waveform + troughs : dict + Trough detection results from get_trough_and_peak_idx + peaks_before : dict + Peak before trough results from get_trough_and_peak_idx + peaks_after : dict + Peak after trough results from get_trough_and_peak_idx + + Returns + ------- + ratios : dict + Dictionary containing: + - "peak_before_to_trough_ratio": ratio of peak before to trough amplitude + - "peak_after_to_trough_ratio": ratio of peak after to trough amplitude + - "peak_before_to_peak_after_ratio": ratio of peak before to peak after amplitude + - "main_peak_to_trough_ratio": ratio of larger peak to trough amplitude + """ + # Get absolute amplitudes + trough_amp = abs(template[troughs["main_loc"]]) if troughs["main_loc"] is not None else np.nan + peak_before_amp = abs(template[peaks_before["main_loc"]]) if peaks_before["main_loc"] is not None else np.nan + peak_after_amp = abs(template[peaks_after["main_loc"]]) if peaks_after["main_loc"] is not None else np.nan + + def safe_ratio(a, b): + if np.isnan(a) or np.isnan(b) or b == 0: + return np.nan + return a / b + + ratios = { + "peak_before_to_trough_ratio": safe_ratio(peak_before_amp, trough_amp), + "peak_after_to_trough_ratio": safe_ratio(peak_after_amp, trough_amp), + "peak_before_to_peak_after_ratio": safe_ratio(peak_before_amp, peak_after_amp), + "main_peak_to_trough_ratio": safe_ratio(max(peak_before_amp, peak_after_amp) if not (np.isnan(peak_before_amp) and np.isnan(peak_after_amp)) else np.nan, trough_amp), + } + + return ratios + + +def get_waveform_widths(template, sampling_frequency, troughs, peaks_before, peaks_after, **kwargs): + """ + Get the widths of the main trough and peaks in microseconds. + + Parameters + ---------- + template : numpy.ndarray + The 1D template waveform + sampling_frequency : float + The sampling frequency in Hz + troughs : dict + Trough detection results from get_trough_and_peak_idx + peaks_before : dict + Peak before trough results from get_trough_and_peak_idx + peaks_after : dict + Peak after trough results from get_trough_and_peak_idx + + Returns + ------- + widths : dict + Dictionary containing: + - "trough_width_us": width of main trough in microseconds + - "peak_before_width_us": width of main peak before trough in microseconds + - "peak_after_width_us": width of main peak after trough in microseconds + """ + def get_main_width(feature_dict): + if feature_dict["main_idx"] is None: + return np.nan + widths = feature_dict.get("widths", np.array([])) + if len(widths) == 0: + return np.nan + main_idx = feature_dict["main_idx"] + if main_idx < len(widths): + return widths[main_idx] + return np.nan + + # Convert from samples to microseconds + samples_to_us = 1e6 / sampling_frequency + + trough_width = get_main_width(troughs) + peak_before_width = get_main_width(peaks_before) + peak_after_width = get_main_width(peaks_after) + + widths = { + "trough_width_us": trough_width * samples_to_us if not np.isnan(trough_width) else np.nan, + "peak_before_width_us": peak_before_width * samples_to_us if not np.isnan(peak_before_width) else np.nan, + "peak_after_width_us": peak_after_width * samples_to_us if not np.isnan(peak_after_width) else np.nan, + } + + return widths ######################################################################################### @@ -53,7 +451,11 @@ def get_peak_to_valley(template_single, sampling_frequency, trough_idx=None, pea The peak to valley duration in seconds """ if trough_idx is None or peak_idx is None: - trough_idx, peak_idx = get_trough_and_peak_idx(template_single) + troughs, _, peaks_after = get_trough_and_peak_idx(template_single) + trough_idx = troughs["main_loc"] + peak_idx = peaks_after["main_loc"] + if trough_idx is None or peak_idx is None: + return np.nan ptv = (peak_idx - trough_idx) / sampling_frequency return ptv @@ -79,7 +481,11 @@ def get_peak_trough_ratio(template_single, sampling_frequency=None, trough_idx=N The peak to trough ratio """ if trough_idx is None or peak_idx is None: - trough_idx, peak_idx = get_trough_and_peak_idx(template_single) + troughs, _, peaks_after = get_trough_and_peak_idx(template_single) + trough_idx = troughs["main_loc"] + peak_idx = peaks_after["main_loc"] + if trough_idx is None or peak_idx is None: + return np.nan ptratio = template_single[peak_idx] / template_single[trough_idx] return ptratio @@ -105,9 +511,11 @@ def get_half_width(template_single, sampling_frequency, trough_idx=None, peak_id The half width in seconds """ if trough_idx is None or peak_idx is None: - trough_idx, peak_idx = get_trough_and_peak_idx(template_single) + troughs, _, peaks_after = get_trough_and_peak_idx(template_single) + trough_idx = troughs["main_loc"] + peak_idx = peaks_after["main_loc"] - if peak_idx == 0: + if peak_idx is None or peak_idx == 0: return np.nan trough_val = template_single[trough_idx] @@ -156,11 +564,12 @@ def get_repolarization_slope(template_single, sampling_frequency, trough_idx=Non The repolarization slope """ if trough_idx is None: - trough_idx, _ = get_trough_and_peak_idx(template_single) + troughs, _, _ = get_trough_and_peak_idx(template_single) + trough_idx = troughs["main_loc"] times = np.arange(template_single.shape[0]) / sampling_frequency - if trough_idx == 0: + if trough_idx is None or trough_idx == 0: return np.nan (rtrn_idx,) = np.nonzero(template_single[trough_idx:] >= 0) @@ -209,11 +618,12 @@ def get_recovery_slope(template_single, sampling_frequency, peak_idx=None, **kwa assert "recovery_window_ms" in kwargs, "recovery_window_ms must be given as kwarg" recovery_window_ms = kwargs["recovery_window_ms"] if peak_idx is None: - _, peak_idx = get_trough_and_peak_idx(template_single) + _, _, peaks_after = get_trough_and_peak_idx(template_single) + peak_idx = peaks_after["main_loc"] times = np.arange(template_single.shape[0]) / sampling_frequency - if peak_idx == 0: + if peak_idx is None or peak_idx == 0: return np.nan max_idx = int(peak_idx + ((recovery_window_ms / 1000) * sampling_frequency)) max_idx = np.min([max_idx, template_single.shape[0]]) @@ -222,9 +632,12 @@ def get_recovery_slope(template_single, sampling_frequency, peak_idx=None, **kwa return res.slope -def get_number_of_peaks(template_single, sampling_frequency, **kwargs): +def get_number_of_peaks(template_single, sampling_frequency, troughs, peaks_before, peaks_after, **kwargs): """ - Count the total number of peaks (positive + negative) in the template. + Count the total number of peaks (positive) and troughs (negative) in the template. + + Uses the pre-computed peak/trough detection from get_trough_and_peak_idx which + applies smoothing for more robust detection. Parameters ---------- @@ -232,28 +645,28 @@ def get_number_of_peaks(template_single, sampling_frequency, **kwargs): The 1D template waveform sampling_frequency : float The sampling frequency of the template - **kwargs: Required kwargs: - - peak_relative_threshold: the relative threshold to detect positive and negative peaks - - peak_width_ms: the width in samples to detect peaks + troughs : dict + Trough detection results from get_trough_and_peak_idx + peaks_before : dict + Peak before trough results from get_trough_and_peak_idx + peaks_after : dict + Peak after trough results from get_trough_and_peak_idx Returns ------- - number_of_peaks: int - the total number of peaks (positive + negative) - """ - from scipy.signal import find_peaks - - assert "peak_relative_threshold" in kwargs, "peak_relative_threshold must be given as kwarg" - assert "peak_width_ms" in kwargs, "peak_width_ms must be given as kwarg" - peak_relative_threshold = kwargs["peak_relative_threshold"] - peak_width_ms = kwargs["peak_width_ms"] - max_value = np.max(np.abs(template_single)) - peak_width_samples = int(peak_width_ms / 1000 * sampling_frequency) - - pos_peaks = find_peaks(template_single, height=peak_relative_threshold * max_value, width=peak_width_samples) - neg_peaks = find_peaks(-template_single, height=peak_relative_threshold * max_value, width=peak_width_samples) - num_positive = len(pos_peaks[0]) - num_negative = len(neg_peaks[0]) + num_positive_peaks : int + The number of positive peaks (peaks_before + peaks_after) + num_negative_peaks : int + The number of negative peaks (troughs) + """ + # Count peaks (positive) from peaks_before and peaks_after + num_peaks_before = len(peaks_before["indices"]) + num_peaks_after = len(peaks_after["indices"]) + num_positive = num_peaks_before + num_peaks_after + + # Count troughs (negative) + num_negative = len(troughs["indices"]) + return num_positive, num_negative @@ -626,11 +1039,18 @@ def _number_of_peaks_metric_function(sorting_analyzer, unit_ids, tmp_data, **met num_peaks_result = namedtuple("NumberOfPeaksResult", ["num_positive_peaks", "num_negative_peaks"]) num_positive_peaks_dict = {} num_negative_peaks_dict = {} - sampling_frequency = sorting_analyzer.sampling_frequency + sampling_frequency = tmp_data["sampling_frequency"] templates_single = tmp_data["templates_single"] + troughs_info = tmp_data["troughs_info"] + peaks_before_info = tmp_data["peaks_before_info"] + peaks_after_info = tmp_data["peaks_after_info"] for unit_index, unit_id in enumerate(unit_ids): template_single = templates_single[unit_index] - num_positive, num_negative = get_number_of_peaks(template_single, sampling_frequency, **metric_params) + num_positive, num_negative = get_number_of_peaks( + template_single, sampling_frequency, + troughs_info[unit_id], peaks_before_info[unit_id], peaks_after_info[unit_id], + **metric_params + ) num_positive_peaks_dict[unit_id] = num_positive num_negative_peaks_dict[unit_id] = num_negative return num_peaks_result(num_positive_peaks=num_positive_peaks_dict, num_negative_peaks=num_negative_peaks_dict) @@ -639,11 +1059,137 @@ def _number_of_peaks_metric_function(sorting_analyzer, unit_ids, tmp_data, **met class NumberOfPeaks(BaseMetric): metric_name = "number_of_peaks" metric_function = _number_of_peaks_metric_function - metric_params = {"peak_relative_threshold": 0.2, "peak_width_ms": 0.1} + metric_params = {} metric_columns = {"num_positive_peaks": int, "num_negative_peaks": int} metric_descriptions = { "num_positive_peaks": "Number of positive peaks in the template", - "num_negative_peaks": "Number of negative peaks in the template", + "num_negative_peaks": "Number of negative peaks (troughs) in the template", + } + needs_tmp_data = True + + +def _waveform_duration_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): + result = {} + templates_single = tmp_data["templates_single"] + troughs_info = tmp_data["troughs_info"] + peaks_before_info = tmp_data["peaks_before_info"] + peaks_after_info = tmp_data["peaks_after_info"] + sampling_frequency = tmp_data["sampling_frequency"] + for unit_index, unit_id in enumerate(unit_ids): + template_single = templates_single[unit_index] + value = get_waveform_duration( + template_single, sampling_frequency, + troughs_info[unit_id], peaks_before_info[unit_id], peaks_after_info[unit_id], + **metric_params + ) + result[unit_id] = value + return result + + +class WaveformDuration(BaseMetric): + metric_name = "waveform_duration" + metric_function = _waveform_duration_metric_function + metric_params = {} + metric_columns = {"waveform_duration": float} + metric_descriptions = { + "waveform_duration": "Waveform duration in microseconds from main extremum to next extremum." + } + needs_tmp_data = True + + +def _waveform_ratios_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): + waveform_ratios_result = namedtuple("WaveformRatiosResult", [ + "peak_before_to_trough_ratio", "peak_after_to_trough_ratio", + "peak_before_to_peak_after_ratio", "main_peak_to_trough_ratio" + ]) + peak_before_to_trough = {} + peak_after_to_trough = {} + peak_before_to_peak_after = {} + main_peak_to_trough = {} + templates_single = tmp_data["templates_single"] + troughs_info = tmp_data["troughs_info"] + peaks_before_info = tmp_data["peaks_before_info"] + peaks_after_info = tmp_data["peaks_after_info"] + for unit_index, unit_id in enumerate(unit_ids): + template_single = templates_single[unit_index] + ratios = get_waveform_ratios( + template_single, + troughs_info[unit_id], peaks_before_info[unit_id], peaks_after_info[unit_id], + **metric_params + ) + peak_before_to_trough[unit_id] = ratios["peak_before_to_trough_ratio"] + peak_after_to_trough[unit_id] = ratios["peak_after_to_trough_ratio"] + peak_before_to_peak_after[unit_id] = ratios["peak_before_to_peak_after_ratio"] + main_peak_to_trough[unit_id] = ratios["main_peak_to_trough_ratio"] + return waveform_ratios_result( + peak_before_to_trough_ratio=peak_before_to_trough, + peak_after_to_trough_ratio=peak_after_to_trough, + peak_before_to_peak_after_ratio=peak_before_to_peak_after, + main_peak_to_trough_ratio=main_peak_to_trough + ) + + +class WaveformRatios(BaseMetric): + metric_name = "waveform_ratios" + metric_function = _waveform_ratios_metric_function + metric_params = {} + metric_columns = { + "peak_before_to_trough_ratio": float, + "peak_after_to_trough_ratio": float, + "peak_before_to_peak_after_ratio": float, + "main_peak_to_trough_ratio": float, + } + metric_descriptions = { + "peak_before_to_trough_ratio": "Ratio of peak before amplitude to trough amplitude", + "peak_after_to_trough_ratio": "Ratio of peak after amplitude to trough amplitude", + "peak_before_to_peak_after_ratio": "Ratio of peak before amplitude to peak after amplitude", + "main_peak_to_trough_ratio": "Ratio of main peak amplitude to trough amplitude", + } + needs_tmp_data = True + + +def _waveform_widths_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): + waveform_widths_result = namedtuple("WaveformWidthsResult", [ + "trough_width", "peak_before_width", "peak_after_width" + ]) + trough_width_dict = {} + peak_before_width_dict = {} + peak_after_width_dict = {} + templates_single = tmp_data["templates_single"] + troughs_info = tmp_data["troughs_info"] + peaks_before_info = tmp_data["peaks_before_info"] + peaks_after_info = tmp_data["peaks_after_info"] + sampling_frequency = tmp_data["sampling_frequency"] + for unit_index, unit_id in enumerate(unit_ids): + template_single = templates_single[unit_index] + widths = get_waveform_widths( + template_single, sampling_frequency, + troughs_info[unit_id], peaks_before_info[unit_id], peaks_after_info[unit_id], + **metric_params + ) + trough_width_dict[unit_id] = widths["trough_width_us"] + peak_before_width_dict[unit_id] = widths["peak_before_width_us"] + peak_after_width_dict[unit_id] = widths["peak_after_width_us"] + return waveform_widths_result( + trough_width=trough_width_dict, + peak_before_width=peak_before_width_dict, + peak_after_width=peak_after_width_dict + ) + + +class WaveformWidths(BaseMetric): + metric_name = "waveform_widths" + metric_function = _waveform_widths_metric_function + metric_params = {} + metric_columns = { + "trough_width": float, + "peak_before_width": float, + "peak_after_width": float, + } + metric_descriptions = { + "trough_width": "Width of the main trough in microseconds", + "peak_before_width": "Width of the main peak before trough in microseconds", + "peak_after_width": "Width of the main peak after trough in microseconds", } needs_tmp_data = True @@ -655,6 +1201,9 @@ class NumberOfPeaks(BaseMetric): RepolarizationSlope, RecoverySlope, NumberOfPeaks, + WaveformDuration, + WaveformRatios, + WaveformWidths, ] diff --git a/src/spikeinterface/metrics/template/template_metrics.py b/src/spikeinterface/metrics/template/template_metrics.py index 83a9048a64..863f7687ea 100644 --- a/src/spikeinterface/metrics/template/template_metrics.py +++ b/src/spikeinterface/metrics/template/template_metrics.py @@ -9,6 +9,7 @@ import numpy as np import warnings from copy import deepcopy +from scipy.signal import find_peaks from spikeinterface.core.sortinganalyzer import register_result_extension from spikeinterface.core.analyzer_extension_core import BaseMetricExtension @@ -137,6 +138,10 @@ def _set_params( upsampling_factor=10, include_multi_channel_metrics=False, depth_direction="y", + min_thresh_detect_peaks_troughs=0.4, + smooth=True, + smooth_window_frac=0.1, + smooth_polyorder=3, ): # Auto-detect if multi-channel metrics should be included based on number of channels num_channels = self.sorting_analyzer.get_num_channels() @@ -165,6 +170,10 @@ def _set_params( upsampling_factor=upsampling_factor, include_multi_channel_metrics=include_multi_channel_metrics, depth_direction=depth_direction, + min_thresh_detect_peaks_troughs=min_thresh_detect_peaks_troughs, + smooth=smooth, + smooth_window_frac=smooth_window_frac, + smooth_polyorder=smooth_polyorder, ) def _prepare_data(self, sorting_analyzer, unit_ids): @@ -196,6 +205,9 @@ def _prepare_data(self, sorting_analyzer, unit_ids): templates_single = [] troughs = {} peaks = {} + troughs_info = {} + peaks_before_info = {} + peaks_after_info = {} templates_multi = [] channel_locations_multi = [] for unit_id in unit_ids: @@ -209,11 +221,22 @@ def _prepare_data(self, sorting_analyzer, unit_ids): else: template_upsampled = template_single sampling_frequency_up = sampling_frequency - trough_idx, peak_idx = get_trough_and_peak_idx(template_upsampled) + troughs_dict, peaks_before_dict, peaks_after_dict = get_trough_and_peak_idx( + template_upsampled, + min_thresh_detect_peaks_troughs=self.params['min_thresh_detect_peaks_troughs'], + smooth=self.params['smooth'], + smooth_window_frac=self.params['smooth_window_frac'], + smooth_polyorder=self.params['smooth_polyorder'], + ) templates_single.append(template_upsampled) - troughs[unit_id] = trough_idx - peaks[unit_id] = peak_idx + # Store main locations for backward compatibility + troughs[unit_id] = troughs_dict["main_loc"] + peaks[unit_id] = peaks_after_dict["main_loc"] + # Store full dicts for new metrics + troughs_info[unit_id] = troughs_dict + peaks_before_info[unit_id] = peaks_before_dict + peaks_after_info[unit_id] = peaks_after_dict if include_multi_channel_metrics: if sorting_analyzer.is_sparse(): @@ -238,6 +261,9 @@ def _prepare_data(self, sorting_analyzer, unit_ids): tmp_data["troughs"] = troughs tmp_data["peaks"] = peaks + tmp_data["troughs_info"] = troughs_info + tmp_data["peaks_before_info"] = peaks_before_info + tmp_data["peaks_after_info"] = peaks_after_info tmp_data["templates_single"] = np.array(templates_single) if include_multi_channel_metrics: From 71d35a43d68ef1a57a85efadd098308ea7817070 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Wed, 7 Jan 2026 11:25:14 +0100 Subject: [PATCH 02/49] template denoising - SVD option and bombcell baseline flatness metric --- in_container_params.json | 3 + in_container_recording.json | 15497 +++++++++++++++ in_container_sorter_script.py | 28 + .../spikeinterface_recording.json | 15607 ++++++++++++++++ playground2.ipynb | 322 + .../metrics/template/metrics.py | 155 +- .../metrics/template/template_metrics.py | 6 + 7 files changed, 31610 insertions(+), 8 deletions(-) create mode 100644 in_container_params.json create mode 100644 in_container_recording.json create mode 100644 in_container_sorter_script.py create mode 100644 kilosort4_output/spikeinterface_recording.json create mode 100644 playground2.ipynb diff --git a/in_container_params.json b/in_container_params.json new file mode 100644 index 0000000000..462dc67ed3 --- /dev/null +++ b/in_container_params.json @@ -0,0 +1,3 @@ +{ + "output_folder": "/Users/jf5479/Downloads/AL031_2019-12-02/spikeinterface_output/kilosort4_output" +} \ No newline at end of file diff --git a/in_container_recording.json b/in_container_recording.json new file mode 100644 index 0000000000..64f8f88c42 --- /dev/null +++ b/in_container_recording.json @@ -0,0 +1,15497 @@ +{ + "class": "spikeinterface.preprocessing.common_reference.CommonReferenceRecording", + "module": "spikeinterface", + "version": "0.103.3", + "kwargs": { + "recording": { + "class": "spikeinterface.preprocessing.phase_shift.PhaseShiftRecording", + "module": "spikeinterface", + "version": "0.103.3", + "kwargs": { + "recording": { + "class": "spikeinterface.core.channelslice.ChannelSliceRecording", + "module": "spikeinterface", + "version": "0.103.3", + "kwargs": { + "parent_recording": { + "class": "spikeinterface.preprocessing.filter.HighpassFilterRecording", + "module": "spikeinterface", + "version": "0.103.3", + "kwargs": { + "recording": { + "class": "spikeinterface.core.channelslice.ChannelSliceRecording", + "module": "spikeinterface", + "version": "0.103.3", + "kwargs": { + "parent_recording": { + "class": "spikeinterface.core.binaryrecordingextractor.BinaryRecordingExtractor", + "module": "spikeinterface", + "version": "0.103.3", + "kwargs": { + "file_paths": [ + "/Users/jf5479/Downloads/AL031_2019-12-02/AL031_2019-12-02_bank1_NatIm_g0_t0_bc_decompressed.imec0.ap.bin" + ], + "sampling_frequency": 30000.0, + "t_starts": null, + "num_channels": 385, + "dtype": " 0:\n print(f\"Bad channel IDs: {bad_channel_ids}\")\n rec_clean = rec_filtered.remove_channels(bad_channel_ids)\nelse:\n rec_clean = rec_filtered\n\n# Skip phase_shift - Kilosort4 handles this internally\n# Common median reference\nrec_preprocessed = si.common_reference(rec_clean, operator=\"median\", reference=\"global\")\n\nprint(f\"Preprocessed recording: {rec_preprocessed}\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Run Kilosort4 (if not already done)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "# Check if Kilosort output already exists\nif kilosort_output.exists() and (kilosort_output / \"spike_times.npy\").exists():\n print(f\"Kilosort output already exists at: {kilosort_output}\")\n print(\"Loading existing sorting results...\")\n sorting = si.read_sorter_folder(kilosort_output)\nelse:\n print(f\"Running Kilosort4, output will be saved to: {kilosort_output}\")\n print(f\"Installed sorters: {si.installed_sorters()}\")\n\n # Run Kilosort4\n sorting = si.run_sorter(\n sorter_name=\"kilosort4\",\n recording=rec_preprocessed,\n folder=kilosort_output,\n verbose=True,\n remove_existing_folder=True, # Remove any failed previous attempts\n )\n print(\"Kilosort4 completed!\")\n\nprint(f\"Sorting result: {sorting}\")\nprint(f\"Number of units: {len(sorting.unit_ids)}\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Create SortingAnalyzer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check if analyzer already exists\n", + "if analyzer_folder.exists():\n", + " print(f\"Loading existing analyzer from: {analyzer_folder}\")\n", + " analyzer = si.load_sorting_analyzer(analyzer_folder)\n", + "else:\n", + " print(f\"Creating new analyzer at: {analyzer_folder}\")\n", + " analyzer = si.create_sorting_analyzer(\n", + " sorting=sorting,\n", + " recording=rec_preprocessed,\n", + " sparse=True,\n", + " format=\"binary_folder\",\n", + " folder=analyzer_folder,\n", + " )\n", + "\n", + "analyzer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Compute Extensions for Template Metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Random spikes selection\n", + "if not analyzer.has_extension(\"random_spikes\"):\n", + " print(\"Computing random_spikes...\")\n", + " analyzer.compute(\"random_spikes\", method=\"uniform\", max_spikes_per_unit=500)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Waveforms\n", + "if not analyzer.has_extension(\"waveforms\"):\n", + " print(\"Computing waveforms...\")\n", + " analyzer.compute(\"waveforms\", ms_before=1.5, ms_after=2.0, **job_kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Templates\n", + "if not analyzer.has_extension(\"templates\"):\n", + " print(\"Computing templates...\")\n", + " analyzer.compute(\"templates\", operators=[\"average\", \"median\", \"std\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Noise levels\n", + "if not analyzer.has_extension(\"noise_levels\"):\n", + " print(\"Computing noise_levels...\")\n", + " analyzer.compute(\"noise_levels\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Compute Template Metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute template metrics with multi-channel metrics included\n", + "if not analyzer.has_extension(\"template_metrics\"):\n", + " print(\"Computing template_metrics...\")\n", + " analyzer.compute(\n", + " \"template_metrics\",\n", + " include_multi_channel_metrics=True,\n", + " )\n", + "\n", + "# Get the metrics as a DataFrame\n", + "template_metrics = analyzer.get_extension(\"template_metrics\").get_data()\n", + "template_metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Compute Quality Metrics (optional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Spike amplitudes\n", + "if not analyzer.has_extension(\"spike_amplitudes\"):\n", + " print(\"Computing spike_amplitudes...\")\n", + " analyzer.compute(\"spike_amplitudes\", **job_kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Correlograms\n", + "if not analyzer.has_extension(\"correlograms\"):\n", + " print(\"Computing correlograms...\")\n", + " analyzer.compute(\"correlograms\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Quality metrics\n", + "if not analyzer.has_extension(\"quality_metrics\"):\n", + " print(\"Computing quality_metrics...\")\n", + " analyzer.compute(\"quality_metrics\")\n", + "\n", + "quality_metrics = analyzer.get_extension(\"quality_metrics\").get_data()\n", + "quality_metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Total units: {len(sorting.unit_ids)}\")\n", + "print(f\"Analyzer saved to: {analyzer_folder}\")\n", + "print(f\"\\nAvailable extensions:\")\n", + "for ext_name in analyzer.get_loaded_extension_names():\n", + " print(f\" - {ext_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Combine metrics\n", + "combined_metrics = template_metrics.join(quality_metrics, how=\"outer\")\n", + "combined_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save metrics to CSV\n", + "output_folder.mkdir(parents=True, exist_ok=True)\n", + "metrics_csv = output_folder / \"combined_metrics.csv\"\n", + "combined_metrics.to_csv(metrics_csv)\n", + "print(f\"Metrics saved to: {metrics_csv}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index 74e00d5714..fb4f820699 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -6,7 +6,58 @@ from spikeinterface.core.analyzer_extension_core import BaseMetric -def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smooth=True, smooth_window_frac=0.1, smooth_polyorder=3): +def _svd_denoise(signal, n_components=3): + """ + Denoise a 1D signal using SVD on a Hankel matrix embedding. + + Parameters + ---------- + signal : numpy.ndarray + The 1D signal to denoise + n_components : int + Number of SVD components to keep for reconstruction + + Returns + ------- + denoised : numpy.ndarray + The denoised signal + """ + n = len(signal) + # Window size for Hankel matrix (roughly half the signal length) + window = n // 2 + if window < n_components + 1: + window = n_components + 1 + + # Build Hankel matrix + num_rows = n - window + 1 + hankel = np.zeros((num_rows, window)) + for i in range(num_rows): + hankel[i, :] = signal[i:i + window] + + # SVD decomposition + U, s, Vt = np.linalg.svd(hankel, full_matrices=False) + + # Keep only top n_components + n_components = min(n_components, len(s)) + s[n_components:] = 0 + + # Reconstruct + hankel_denoised = U @ np.diag(s) @ Vt + + # Average along anti-diagonals to get back the 1D signal + denoised = np.zeros(n) + counts = np.zeros(n) + for i in range(num_rows): + for j in range(window): + idx = i + j + denoised[idx] += hankel_denoised[i, j] + counts[idx] += 1 + denoised /= counts + + return denoised + + +def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smooth=True, smooth_method="savgol", smooth_window_frac=0.1, smooth_polyorder=3, svd_n_components=3): """ Detect troughs and peaks in a template waveform and return detailed information about each detected feature. @@ -18,11 +69,15 @@ def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smoot min_thresh_detect_peaks_troughs : float, default: 0.4 Minimum prominence threshold as a fraction of the template's absolute max value smooth : bool, default: True - Whether to apply Savitzky-Golay smoothing before peak detection + Whether to apply smoothing before peak detection + smooth_method : str, default: "savgol" + Smoothing method: "savgol" (Savitzky-Golay) or "svd" (SVD-based denoising) smooth_window_frac : float, default: 0.1 - Smoothing window length as a fraction of template length (0.05-0.2 recommended) + Smoothing window length as a fraction of template length (for savgol, 0.05-0.2 recommended) smooth_polyorder : int, default: 3 Polynomial order for Savitzky-Golay filter (must be < window_length) + svd_n_components : int, default: 3 + Number of SVD components to keep for reconstruction (for svd method) Returns ------- @@ -56,12 +111,18 @@ def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smoot # Save original for plotting template_original = template.copy() - # Smooth template to reduce noise while preserving peaks (Savitzky-Golay filter) + # Smooth template to reduce noise while preserving peaks if smooth: - # Calculate window length from fraction, ensure odd, min 5 - window_length = int(len(template) * smooth_window_frac) // 2 * 2 + 1 - window_length = max(smooth_polyorder + 2, window_length) # Must be > polyorder - template = savgol_filter(template, window_length=window_length, polyorder=smooth_polyorder) + if smooth_method == "savgol": + # Savitzky-Golay filter + window_length = int(len(template) * smooth_window_frac) // 2 * 2 + 1 + window_length = max(smooth_polyorder + 2, window_length) # Must be > polyorder + template = savgol_filter(template, window_length=window_length, polyorder=smooth_polyorder) + elif smooth_method == "svd": + # SVD-based denoising using Hankel matrix embedding + template = _svd_denoise(template, n_components=svd_n_components) + else: + raise ValueError(f"Unknown smooth_method: {smooth_method}. Use 'savgol' or 'svd'.") # Initialize empty result dictionaries empty_dict = { @@ -376,6 +437,61 @@ def safe_ratio(a, b): return ratios +def get_waveform_baseline_flatness(template, sampling_frequency, **kwargs): + """ + Compute the baseline flatness of the waveform. + + This metric measures the ratio of the max absolute amplitude in the baseline + window to the max absolute amplitude of the whole waveform. A lower value + indicates a flatter (quieter) baseline, which is expected for good units. + + Parameters + ---------- + template : numpy.ndarray + The 1D template waveform + sampling_frequency : float + The sampling frequency in Hz + **kwargs : Required kwargs: + - baseline_window_ms : tuple of (start_ms, end_ms) defining the baseline window + relative to waveform start. Default is (0, 0.5) for first 0.5ms. + + Returns + ------- + baseline_flatness : float + Ratio of max(abs(baseline)) / max(abs(waveform)). Lower = flatter baseline. + """ + baseline_window_ms = kwargs.get("baseline_window_ms", (0.0, 0.5)) + + if baseline_window_ms is None: + return np.nan + + start_ms, end_ms = baseline_window_ms + start_idx = int(start_ms / 1000 * sampling_frequency) + end_idx = int(end_ms / 1000 * sampling_frequency) + + # Clamp to valid range + start_idx = max(0, start_idx) + end_idx = min(len(template), end_idx) + + if end_idx <= start_idx: + return np.nan + + baseline_segment = template[start_idx:end_idx] + + if len(baseline_segment) == 0: + return np.nan + + max_baseline = np.nanmax(np.abs(baseline_segment)) + max_waveform = np.nanmax(np.abs(template)) + + if max_waveform == 0 or np.isnan(max_waveform): + return np.nan + + baseline_flatness = max_baseline / max_waveform + + return baseline_flatness + + def get_waveform_widths(template, sampling_frequency, troughs, peaks_before, peaks_after, **kwargs): """ Get the widths of the main trough and peaks in microseconds. @@ -1194,6 +1310,28 @@ class WaveformWidths(BaseMetric): needs_tmp_data = True +def _waveform_baseline_flatness_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): + result = {} + templates_single = tmp_data["templates_single"] + sampling_frequency = tmp_data["sampling_frequency"] + for unit_index, unit_id in enumerate(unit_ids): + template_single = templates_single[unit_index] + value = get_waveform_baseline_flatness(template_single, sampling_frequency, **metric_params) + result[unit_id] = value + return result + + +class WaveformBaselineFlatness(BaseMetric): + metric_name = "waveform_baseline_flatness" + metric_function = _waveform_baseline_flatness_metric_function + metric_params = {"baseline_window_ms": (0.0, 0.5)} + metric_columns = {"waveform_baseline_flatness": float} + metric_descriptions = { + "waveform_baseline_flatness": "Ratio of max baseline amplitude to max waveform amplitude. Lower = flatter baseline." + } + needs_tmp_data = True + + single_channel_metrics = [ PeakToValley, PeakToTroughRatio, @@ -1204,6 +1342,7 @@ class WaveformWidths(BaseMetric): WaveformDuration, WaveformRatios, WaveformWidths, + WaveformBaselineFlatness, ] diff --git a/src/spikeinterface/metrics/template/template_metrics.py b/src/spikeinterface/metrics/template/template_metrics.py index 863f7687ea..5cdb01c41a 100644 --- a/src/spikeinterface/metrics/template/template_metrics.py +++ b/src/spikeinterface/metrics/template/template_metrics.py @@ -140,8 +140,10 @@ def _set_params( depth_direction="y", min_thresh_detect_peaks_troughs=0.4, smooth=True, + smooth_method="savgol", smooth_window_frac=0.1, smooth_polyorder=3, + svd_n_components=3, ): # Auto-detect if multi-channel metrics should be included based on number of channels num_channels = self.sorting_analyzer.get_num_channels() @@ -172,8 +174,10 @@ def _set_params( depth_direction=depth_direction, min_thresh_detect_peaks_troughs=min_thresh_detect_peaks_troughs, smooth=smooth, + smooth_method=smooth_method, smooth_window_frac=smooth_window_frac, smooth_polyorder=smooth_polyorder, + svd_n_components=svd_n_components, ) def _prepare_data(self, sorting_analyzer, unit_ids): @@ -225,8 +229,10 @@ def _prepare_data(self, sorting_analyzer, unit_ids): template_upsampled, min_thresh_detect_peaks_troughs=self.params['min_thresh_detect_peaks_troughs'], smooth=self.params['smooth'], + smooth_method=self.params['smooth_method'], smooth_window_frac=self.params['smooth_window_frac'], smooth_polyorder=self.params['smooth_polyorder'], + svd_n_components=self.params['svd_n_components'], ) templates_single.append(template_upsampled) From 788e8be471240635c620806f3482c70b17637368 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Wed, 7 Jan 2026 11:26:20 +0100 Subject: [PATCH 03/49] woops remove kilosort4_output folder --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 398852fc77..ca12a50512 100644 --- a/.gitignore +++ b/.gitignore @@ -151,3 +151,4 @@ playground.ipynbd playground.ipynb analyzer_TDC_binary/ spykingcircus2_output/ +kilosort4_output/ From 4b79f5519251df8059dd7aa2fcffe291bda9124e Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Wed, 7 Jan 2026 11:28:05 +0100 Subject: [PATCH 04/49] woops remove kilosort4_output folder --- .../spikeinterface_recording.json | 15607 ---------------- 1 file changed, 15607 deletions(-) delete mode 100644 kilosort4_output/spikeinterface_recording.json diff --git a/kilosort4_output/spikeinterface_recording.json b/kilosort4_output/spikeinterface_recording.json deleted file mode 100644 index f62caed535..0000000000 --- a/kilosort4_output/spikeinterface_recording.json +++ /dev/null @@ -1,15607 +0,0 @@ -{ - "class": "spikeinterface.preprocessing.common_reference.CommonReferenceRecording", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "recording": { - "class": "spikeinterface.preprocessing.phase_shift.PhaseShiftRecording", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "recording": { - "class": "spikeinterface.core.channelslice.ChannelSliceRecording", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "parent_recording": { - "class": "spikeinterface.preprocessing.filter.HighpassFilterRecording", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "recording": { - "class": "spikeinterface.core.channelslice.ChannelSliceRecording", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "parent_recording": { - "class": "spikeinterface.core.binaryrecordingextractor.BinaryRecordingExtractor", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "file_paths": [ - "/Users/jf5479/Downloads/AL031_2019-12-02/AL031_2019-12-02_bank1_NatIm_g0_t0_bc_decompressed.imec0.ap.bin" - ], - "sampling_frequency": 30000.0, - "t_starts": null, - "num_channels": 385, - "dtype": " Date: Wed, 7 Jan 2026 15:13:45 +0100 Subject: [PATCH 05/49] remove SVD option - was not performing well - and add sane tested default params --- .gitignore | 1 + playground2.ipynb | 411 ++++++++++++------ .../metrics/template/metrics.py | 78 +--- 3 files changed, 284 insertions(+), 206 deletions(-) diff --git a/.gitignore b/.gitignore index ca12a50512..8481107d21 100644 --- a/.gitignore +++ b/.gitignore @@ -152,3 +152,4 @@ playground.ipynb analyzer_TDC_binary/ spykingcircus2_output/ kilosort4_output/ +playground2.ipynb diff --git a/playground2.ipynb b/playground2.ipynb index cfc8fdcdb4..aa821407fd 100644 --- a/playground2.ipynb +++ b/playground2.ipynb @@ -4,19 +4,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Playground2: Kilosort + Template Metrics\n", - "\n", - "This notebook:\n", - "1. Runs Kilosort4 (if not already done)\n", - "2. Loads data and sorting results\n", - "3. Computes template metrics" + "# Playground2: Kilosort + Template Metrics" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SpikeInterface version: 0.103.3\n" + ] + } + ], "source": [ "from pathlib import Path\n", "import spikeinterface.full as si\n", @@ -24,126 +27,61 @@ "print(f\"SpikeInterface version: {si.__version__}\")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configuration" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jf5479/Dropbox/Python/spikeinterface/src/spikeinterface/core/base.py:1117: UserWarning: Versions are not the same. This might lead to compatibility errors. Using spikeinterface==0.101.2 is recommended\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sorting result: SortingAnalyzer: 384 channels - 330 units - 1 segments - binary_folder - sparse\n", + "Loaded 11 extensions: template_metrics, random_spikes, unit_locations, quality_metrics, waveforms, spike_amplitudes, templates, spike_locations, correlograms, template_similarity, noise_levels\n", + "Number of units: 330\n" + ] + } + ], "source": [ - "# Data paths\n", - "data_folder = Path(\"/Users/jf5479/Downloads/AL031_2019-12-02\")\n", - "bin_file = data_folder / \"AL031_2019-12-02_bank1_NatIm_g0_t0_bc_decompressed.imec0.ap.bin\"\n", - "meta_file = data_folder / \"AL031_2019-12-02_bank1_NatIm_g0_t0.imec0.ap.meta\"\n", + "# Check if Kilosort output already exists\n", "\n", - "# Output paths\n", - "output_folder = data_folder / \"spikeinterface_output\"\n", - "kilosort_output = output_folder / \"kilosort4_output\"\n", - "analyzer_folder = output_folder / \"sorting_analyzer\"\n", + "# For kilosort/phy output files we can use the read_phy\n", + "# most formats will have a read_xx that can used.\n", + "analyzer = si.load_sorting_analyzer('/Users/jf5479/Downloads/kilosort4_sa/')\n", "\n", - "# Job kwargs for parallel processing\n", - "job_kwargs = dict(n_jobs=-1, chunk_duration=\"1s\", progress_bar=True)\n", + "# if kilosort_output.exists() and (kilosort_output / \"spike_times.npy\").exists():\n", + "# print(f\"Kilosort output already exists at: {kilosort_output}\")\n", + "# print(\"Loading existing sorting results...\")\n", + "# sorting = si.read_sorter_folder(kilosort_output)\n", + "# else:\n", + "# print(f\"Running Kilosort4, output will be saved to: {kilosort_output}\")\n", + "# print(f\"Installed sorters: {si.installed_sorters()}\")\n", "\n", - "print(f\"Data folder: {data_folder}\")\n", - "print(f\"Bin file exists: {bin_file.exists()}\")\n", - "print(f\"Meta file exists: {meta_file.exists()}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Load Recording" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": "# The bin and meta files have different names, so we need to load manually\nfrom neo.rawio.spikeglxrawio import read_meta_file\nfrom spikeinterface.extractors.cbin_ibl import extract_stream_info\nfrom spikeinterface.extractors.neuropixels_utils import get_neuropixels_sample_shifts\nimport probeinterface\n\n# Read meta file\nmeta = read_meta_file(meta_file)\ninfo = extract_stream_info(meta_file, meta)\n\n# Get parameters\nnum_channels = info[\"num_chan\"]\nsampling_frequency = info[\"sampling_rate\"]\nchannel_gains = info[\"channel_gains\"]\nchannel_offsets = info[\"channel_offsets\"]\nchannel_ids = info[\"channel_names\"]\n\n# Remove sync channel (last channel)\nnum_channels_no_sync = num_channels - 1\nchannel_gains_no_sync = channel_gains[:-1]\nchannel_offsets_no_sync = channel_offsets[:-1]\nchannel_ids_no_sync = channel_ids[:-1]\n\nprint(f\"Sampling frequency: {sampling_frequency} Hz\")\nprint(f\"Number of channels (without sync): {num_channels_no_sync}\")\n\n# Load as binary recording\nrecording = si.read_binary(\n file_paths=bin_file,\n sampling_frequency=sampling_frequency,\n num_channels=num_channels, # Include sync for reading, will remove later\n dtype=\"int16\",\n)\n\n# Remove sync channel using select_channels\nrecording = recording.select_channels(channel_ids=recording.channel_ids[:-1])\n\n# Set gains and offsets\nrecording.set_channel_gains(channel_gains_no_sync)\nrecording.set_channel_offsets(channel_offsets_no_sync)\n\n# Load and attach probe from meta file\nprobe = probeinterface.read_spikeglx(meta_file)\nrecording = recording.set_probe(probe)\n\n# Set inter_sample_shift property for phase correction (needed for Neuropixels)\nptype = probe.annotations.get(\"probe_type\", 0)\nif ptype in [21, 24]: # NP2.0\n num_channels_per_adc = 16\nelse: # NP1.0\n num_channels_per_adc = 12\n\nsample_shifts = get_neuropixels_sample_shifts(recording.get_num_channels(), num_channels_per_adc)\nrecording.set_property(\"inter_sample_shift\", sample_shifts)\n\nprint(f\"Loaded recording: {recording}\")" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": "print(f\"Duration: {recording.get_total_duration():.2f} s\")\nprint(f\"Probe: {recording.get_probe()}\")\nrecording" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": "# High-pass filter\nrec_filtered = si.highpass_filter(recording, freq_min=300.0)\n\n# Detect and remove bad channels\nbad_channel_ids, channel_labels = si.detect_bad_channels(rec_filtered)\nprint(f\"Bad channels detected: {len(bad_channel_ids)}\")\nif len(bad_channel_ids) > 0:\n print(f\"Bad channel IDs: {bad_channel_ids}\")\n rec_clean = rec_filtered.remove_channels(bad_channel_ids)\nelse:\n rec_clean = rec_filtered\n\n# Skip phase_shift - Kilosort4 handles this internally\n# Common median reference\nrec_preprocessed = si.common_reference(rec_clean, operator=\"median\", reference=\"global\")\n\nprint(f\"Preprocessed recording: {rec_preprocessed}\")" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Run Kilosort4 (if not already done)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": "# Check if Kilosort output already exists\nif kilosort_output.exists() and (kilosort_output / \"spike_times.npy\").exists():\n print(f\"Kilosort output already exists at: {kilosort_output}\")\n print(\"Loading existing sorting results...\")\n sorting = si.read_sorter_folder(kilosort_output)\nelse:\n print(f\"Running Kilosort4, output will be saved to: {kilosort_output}\")\n print(f\"Installed sorters: {si.installed_sorters()}\")\n\n # Run Kilosort4\n sorting = si.run_sorter(\n sorter_name=\"kilosort4\",\n recording=rec_preprocessed,\n folder=kilosort_output,\n verbose=True,\n remove_existing_folder=True, # Remove any failed previous attempts\n )\n print(\"Kilosort4 completed!\")\n\nprint(f\"Sorting result: {sorting}\")\nprint(f\"Number of units: {len(sorting.unit_ids)}\")" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Create SortingAnalyzer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check if analyzer already exists\n", - "if analyzer_folder.exists():\n", - " print(f\"Loading existing analyzer from: {analyzer_folder}\")\n", - " analyzer = si.load_sorting_analyzer(analyzer_folder)\n", - "else:\n", - " print(f\"Creating new analyzer at: {analyzer_folder}\")\n", - " analyzer = si.create_sorting_analyzer(\n", - " sorting=sorting,\n", - " recording=rec_preprocessed,\n", - " sparse=True,\n", - " format=\"binary_folder\",\n", - " folder=analyzer_folder,\n", - " )\n", + "# # Run Kilosort4\n", + "# sorting = si.run_sorter(\n", + "# sorter_name=\"kilosort4\",\n", + "# recording=rec_preprocessed,\n", + "# folder=kilosort_output,\n", + "# verbose=True,\n", + "# remove_existing_folder=True, # Remove any failed previous attempts\n", + "# )\n", + "# print(\"Kilosort4 completed!\")\n", "\n", - "analyzer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Compute Extensions for Template Metrics" + "print(f\"Sorting result: {sorting}\")\n", + "print(f\"Number of units: {len(sorting.unit_ids)}\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -155,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -167,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -179,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -193,33 +131,234 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 6. Compute Template Metrics" + "## Compute Template Metrics" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "ComputeTemplateMetrics._set_params() got an unexpected keyword argument 'smooth_method'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[58], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Compute template metrics with multi-channel metrics included\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m analyzer\u001b[38;5;241m.\u001b[39mcompute(\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtemplate_metrics\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4\u001b[0m smooth\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# Enable/disable smoothing\u001b[39;00m\n\u001b[1;32m 5\u001b[0m smooth_method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvd\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 6\u001b[0m svd_n_components\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[1;32m 7\u001b[0m smooth_window_frac\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;66;03m# Window as fraction of template length\u001b[39;00m\n\u001b[1;32m 8\u001b[0m smooth_polyorder\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, \u001b[38;5;66;03m# Polynomial order\u001b[39;00m\n\u001b[1;32m 9\u001b[0m min_thresh_detect_peaks_troughs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.4\u001b[39m\n\u001b[1;32m 10\u001b[0m )\n", + "File \u001b[0;32m~/Dropbox/Python/spikeinterface/src/spikeinterface/core/sortinganalyzer.py:1659\u001b[0m, in \u001b[0;36mSortingAnalyzer.compute\u001b[0;34m(self, input, save, extension_params, verbose, **kwargs)\u001b[0m\n\u001b[1;32m 1612\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1613\u001b[0m \u001b[38;5;124;03mCompute one extension or several extensiosn.\u001b[39;00m\n\u001b[1;32m 1614\u001b[0m \u001b[38;5;124;03mInternally calls compute_one_extension() or compute_several_extensions() depending on the input type.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1656\u001b[0m \n\u001b[1;32m 1657\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1658\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m-> 1659\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_one_extension(extension_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28minput\u001b[39m, save\u001b[38;5;241m=\u001b[39msave, verbose\u001b[38;5;241m=\u001b[39mverbose, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1660\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m 1661\u001b[0m params_, job_kwargs \u001b[38;5;241m=\u001b[39m split_job_kwargs(kwargs)\n", + "File \u001b[0;32m~/Dropbox/Python/spikeinterface/src/spikeinterface/core/sortinganalyzer.py:1742\u001b[0m, in \u001b[0;36mSortingAnalyzer.compute_one_extension\u001b[0;34m(self, extension_name, save, verbose, **kwargs)\u001b[0m\n\u001b[1;32m 1739\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m ok, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExtension \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mextension_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdependency_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to be computed first\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1741\u001b[0m extension_instance \u001b[38;5;241m=\u001b[39m extension_class(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m-> 1742\u001b[0m extension_instance\u001b[38;5;241m.\u001b[39mset_params(save\u001b[38;5;241m=\u001b[39msave, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m extension_class\u001b[38;5;241m.\u001b[39mneed_job_kwargs:\n\u001b[1;32m 1744\u001b[0m extension_instance\u001b[38;5;241m.\u001b[39mrun(save\u001b[38;5;241m=\u001b[39msave, verbose\u001b[38;5;241m=\u001b[39mverbose, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mjob_kwargs)\n", + "File \u001b[0;32m~/Dropbox/Python/spikeinterface/src/spikeinterface/core/sortinganalyzer.py:2724\u001b[0m, in \u001b[0;36mAnalyzerExtension.set_params\u001b[0;34m(self, save, **params)\u001b[0m\n\u001b[1;32m 2721\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save:\n\u001b[1;32m 2722\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset_extension_folder()\n\u001b[0;32m-> 2724\u001b[0m params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_params(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[1;32m 2725\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams \u001b[38;5;241m=\u001b[39m params\n\u001b[1;32m 2727\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msorting_analyzer\u001b[38;5;241m.\u001b[39mis_read_only():\n", + "\u001b[0;31mTypeError\u001b[0m: ComputeTemplateMetrics._set_params() got an unexpected keyword argument 'smooth_method'" + ] + } + ], "source": [ "# Compute template metrics with multi-channel metrics included\n", - "if not analyzer.has_extension(\"template_metrics\"):\n", - " print(\"Computing template_metrics...\")\n", - " analyzer.compute(\n", - " \"template_metrics\",\n", - " include_multi_channel_metrics=True,\n", - " )\n", - "\n", - "# Get the metrics as a DataFrame\n", - "template_metrics = analyzer.get_extension(\"template_metrics\").get_data()\n", - "template_metrics" + "analyzer.compute(\n", + " \"template_metrics\",\n", + " smooth=True, # Enable/disable smoothing\n", + " smooth_method='svd',\n", + " svd_n_components=3,\n", + " smooth_window_frac=0.1, # Window as fraction of template length\n", + " smooth_polyorder=3, # Polynomial order\n", + " min_thresh_detect_peaks_troughs=0.4\n", + ")\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
peak_to_valleypeak_trough_ratiohalf_widthrepolarization_sloperecovery_slopenum_positive_peaksnum_negative_peaks
00.001173-0.4400690.00174016271.944574-12828.34776712
10.000657-0.2009790.000163321619.078371-10908.85669311
20.001320-0.3746010.00064712679.478171-88050.93062412
30.000653-0.2421450.000220119110.411447-7646.45039711
40.000820-0.2754460.00026075484.461584-7431.03791921
........................
3250.001383-0.2506690.00051720844.603913-7716.30715511
3260.000603-0.6868210.00039772116.897018-21101.64470612
3270.001190-0.2640080.00054337373.373888-7624.69594021
3280.001090-0.3272310.00053028242.461115-9136.50051421
3290.000860-0.7884750.00042348524.639136-28883.58787212
\n", + "

330 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " peak_to_valley peak_trough_ratio half_width repolarization_slope \\\n", + "0 0.001173 -0.440069 0.001740 16271.944574 \n", + "1 0.000657 -0.200979 0.000163 321619.078371 \n", + "2 0.001320 -0.374601 0.000647 12679.478171 \n", + "3 0.000653 -0.242145 0.000220 119110.411447 \n", + "4 0.000820 -0.275446 0.000260 75484.461584 \n", + ".. ... ... ... ... \n", + "325 0.001383 -0.250669 0.000517 20844.603913 \n", + "326 0.000603 -0.686821 0.000397 72116.897018 \n", + "327 0.001190 -0.264008 0.000543 37373.373888 \n", + "328 0.001090 -0.327231 0.000530 28242.461115 \n", + "329 0.000860 -0.788475 0.000423 48524.639136 \n", + "\n", + " recovery_slope num_positive_peaks num_negative_peaks \n", + "0 -12828.347767 1 2 \n", + "1 -10908.856693 1 1 \n", + "2 -88050.930624 1 2 \n", + "3 -7646.450397 1 1 \n", + "4 -7431.037919 2 1 \n", + ".. ... ... ... \n", + "325 -7716.307155 1 1 \n", + "326 -21101.644706 1 2 \n", + "327 -7624.695940 2 1 \n", + "328 -9136.500514 2 1 \n", + "329 -28883.587872 1 2 \n", + "\n", + "[330 rows x 7 columns]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## 7. Compute Quality Metrics (optional)" + "\n", + "\n", + "# Get the metrics as a DataFrame\n", + "template_metrics = analyzer.get_extension(\"template_metrics\").get_data()\n", + "template_metrics" ] }, { @@ -265,7 +404,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 8. Summary" + "## Summary" ] }, { @@ -319,4 +458,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index fb4f820699..3d84dceadc 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -6,58 +6,7 @@ from spikeinterface.core.analyzer_extension_core import BaseMetric -def _svd_denoise(signal, n_components=3): - """ - Denoise a 1D signal using SVD on a Hankel matrix embedding. - - Parameters - ---------- - signal : numpy.ndarray - The 1D signal to denoise - n_components : int - Number of SVD components to keep for reconstruction - - Returns - ------- - denoised : numpy.ndarray - The denoised signal - """ - n = len(signal) - # Window size for Hankel matrix (roughly half the signal length) - window = n // 2 - if window < n_components + 1: - window = n_components + 1 - - # Build Hankel matrix - num_rows = n - window + 1 - hankel = np.zeros((num_rows, window)) - for i in range(num_rows): - hankel[i, :] = signal[i:i + window] - - # SVD decomposition - U, s, Vt = np.linalg.svd(hankel, full_matrices=False) - - # Keep only top n_components - n_components = min(n_components, len(s)) - s[n_components:] = 0 - - # Reconstruct - hankel_denoised = U @ np.diag(s) @ Vt - - # Average along anti-diagonals to get back the 1D signal - denoised = np.zeros(n) - counts = np.zeros(n) - for i in range(num_rows): - for j in range(window): - idx = i + j - denoised[idx] += hankel_denoised[i, j] - counts[idx] += 1 - denoised /= counts - - return denoised - - -def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smooth=True, smooth_method="savgol", smooth_window_frac=0.1, smooth_polyorder=3, svd_n_components=3): +def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smooth=True, smooth_window_frac=0.1, smooth_polyorder=3): """ Detect troughs and peaks in a template waveform and return detailed information about each detected feature. @@ -70,14 +19,10 @@ def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smoot Minimum prominence threshold as a fraction of the template's absolute max value smooth : bool, default: True Whether to apply smoothing before peak detection - smooth_method : str, default: "savgol" - Smoothing method: "savgol" (Savitzky-Golay) or "svd" (SVD-based denoising) smooth_window_frac : float, default: 0.1 - Smoothing window length as a fraction of template length (for savgol, 0.05-0.2 recommended) + Smoothing window length as a fraction of template length (0.05-0.2 recommended) smooth_polyorder : int, default: 3 Polynomial order for Savitzky-Golay filter (must be < window_length) - svd_n_components : int, default: 3 - Number of SVD components to keep for reconstruction (for svd method) Returns ------- @@ -111,18 +56,11 @@ def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smoot # Save original for plotting template_original = template.copy() - # Smooth template to reduce noise while preserving peaks + # Smooth template to reduce noise while preserving peaks using Savitzky-Golay filter if smooth: - if smooth_method == "savgol": - # Savitzky-Golay filter - window_length = int(len(template) * smooth_window_frac) // 2 * 2 + 1 - window_length = max(smooth_polyorder + 2, window_length) # Must be > polyorder - template = savgol_filter(template, window_length=window_length, polyorder=smooth_polyorder) - elif smooth_method == "svd": - # SVD-based denoising using Hankel matrix embedding - template = _svd_denoise(template, n_components=svd_n_components) - else: - raise ValueError(f"Unknown smooth_method: {smooth_method}. Use 'savgol' or 'svd'.") + window_length = int(len(template) * smooth_window_frac) // 2 * 2 + 1 + window_length = max(smooth_polyorder + 2, window_length) # Must be > polyorder + template = savgol_filter(template, window_length=window_length, polyorder=smooth_polyorder) # Initialize empty result dictionaries empty_dict = { @@ -263,7 +201,7 @@ def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smoot peaks_after = empty_dict.copy() # Quick visualization (set to True for debugging) - _plot = True + _plot = False if _plot: import matplotlib.pyplot as plt @@ -443,7 +381,7 @@ def get_waveform_baseline_flatness(template, sampling_frequency, **kwargs): This metric measures the ratio of the max absolute amplitude in the baseline window to the max absolute amplitude of the whole waveform. A lower value - indicates a flatter (quieter) baseline, which is expected for good units. + indicates a flat baseline (expected for good units). Parameters ---------- From c9306dfb828b34bf2f2bb29490bd6a5d8db7a131 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Wed, 7 Jan 2026 17:02:56 +0100 Subject: [PATCH 06/49] bombcell unit type classification logic and output plots - waveform overlay and histograms --- playground2.ipynb | 966 +++++++++++++++--- src/spikeinterface/comparison/__init__.py | 8 + .../comparison/unit_classification.py | 474 +++++++++ .../metrics/template/metrics.py | 148 ++- .../metrics/template/template_metrics.py | 4 - .../widgets/unit_classification.py | 519 ++++++++++ src/spikeinterface/widgets/widget_list.py | 11 + 7 files changed, 1971 insertions(+), 159 deletions(-) create mode 100644 src/spikeinterface/comparison/unit_classification.py create mode 100644 src/spikeinterface/widgets/unit_classification.py diff --git a/playground2.ipynb b/playground2.ipynb index aa821407fd..ab57eb72e1 100644 --- a/playground2.ipynb +++ b/playground2.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -39,15 +39,6 @@ "/Users/jf5479/Dropbox/Python/spikeinterface/src/spikeinterface/core/base.py:1117: UserWarning: Versions are not the same. This might lead to compatibility errors. Using spikeinterface==0.101.2 is recommended\n", " warnings.warn(\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sorting result: SortingAnalyzer: 384 channels - 330 units - 1 segments - binary_folder - sparse\n", - "Loaded 11 extensions: template_metrics, random_spikes, unit_locations, quality_metrics, waveforms, spike_amplitudes, templates, spike_locations, correlograms, template_similarity, noise_levels\n", - "Number of units: 330\n" - ] } ], "source": [ @@ -55,33 +46,12 @@ "\n", "# For kilosort/phy output files we can use the read_phy\n", "# most formats will have a read_xx that can used.\n", - "analyzer = si.load_sorting_analyzer('/Users/jf5479/Downloads/kilosort4_sa/')\n", - "\n", - "# if kilosort_output.exists() and (kilosort_output / \"spike_times.npy\").exists():\n", - "# print(f\"Kilosort output already exists at: {kilosort_output}\")\n", - "# print(\"Loading existing sorting results...\")\n", - "# sorting = si.read_sorter_folder(kilosort_output)\n", - "# else:\n", - "# print(f\"Running Kilosort4, output will be saved to: {kilosort_output}\")\n", - "# print(f\"Installed sorters: {si.installed_sorters()}\")\n", - "\n", - "# # Run Kilosort4\n", - "# sorting = si.run_sorter(\n", - "# sorter_name=\"kilosort4\",\n", - "# recording=rec_preprocessed,\n", - "# folder=kilosort_output,\n", - "# verbose=True,\n", - "# remove_existing_folder=True, # Remove any failed previous attempts\n", - "# )\n", - "# print(\"Kilosort4 completed!\")\n", - "\n", - "print(f\"Sorting result: {sorting}\")\n", - "print(f\"Number of units: {len(sorting.unit_ids)}\")" + "analyzer = si.load_sorting_analyzer('/Users/jf5479/Downloads/kilosort4_sa/')\n" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -105,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -136,22 +106,68 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 6, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "ComputeTemplateMetrics._set_params() got an unexpected keyword argument 'smooth_method'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[58], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Compute template metrics with multi-channel metrics included\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m analyzer\u001b[38;5;241m.\u001b[39mcompute(\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtemplate_metrics\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4\u001b[0m smooth\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# Enable/disable smoothing\u001b[39;00m\n\u001b[1;32m 5\u001b[0m smooth_method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvd\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 6\u001b[0m svd_n_components\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[1;32m 7\u001b[0m smooth_window_frac\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;66;03m# Window as fraction of template length\u001b[39;00m\n\u001b[1;32m 8\u001b[0m smooth_polyorder\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, \u001b[38;5;66;03m# Polynomial order\u001b[39;00m\n\u001b[1;32m 9\u001b[0m min_thresh_detect_peaks_troughs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.4\u001b[39m\n\u001b[1;32m 10\u001b[0m )\n", - "File \u001b[0;32m~/Dropbox/Python/spikeinterface/src/spikeinterface/core/sortinganalyzer.py:1659\u001b[0m, in \u001b[0;36mSortingAnalyzer.compute\u001b[0;34m(self, input, save, extension_params, verbose, **kwargs)\u001b[0m\n\u001b[1;32m 1612\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1613\u001b[0m \u001b[38;5;124;03mCompute one extension or several extensiosn.\u001b[39;00m\n\u001b[1;32m 1614\u001b[0m \u001b[38;5;124;03mInternally calls compute_one_extension() or compute_several_extensions() depending on the input type.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1656\u001b[0m \n\u001b[1;32m 1657\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1658\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m-> 1659\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_one_extension(extension_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28minput\u001b[39m, save\u001b[38;5;241m=\u001b[39msave, verbose\u001b[38;5;241m=\u001b[39mverbose, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1660\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m 1661\u001b[0m params_, job_kwargs \u001b[38;5;241m=\u001b[39m split_job_kwargs(kwargs)\n", - "File \u001b[0;32m~/Dropbox/Python/spikeinterface/src/spikeinterface/core/sortinganalyzer.py:1742\u001b[0m, in \u001b[0;36mSortingAnalyzer.compute_one_extension\u001b[0;34m(self, extension_name, save, verbose, **kwargs)\u001b[0m\n\u001b[1;32m 1739\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m ok, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExtension \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mextension_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdependency_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to be computed first\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1741\u001b[0m extension_instance \u001b[38;5;241m=\u001b[39m extension_class(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m-> 1742\u001b[0m extension_instance\u001b[38;5;241m.\u001b[39mset_params(save\u001b[38;5;241m=\u001b[39msave, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m extension_class\u001b[38;5;241m.\u001b[39mneed_job_kwargs:\n\u001b[1;32m 1744\u001b[0m extension_instance\u001b[38;5;241m.\u001b[39mrun(save\u001b[38;5;241m=\u001b[39msave, verbose\u001b[38;5;241m=\u001b[39mverbose, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mjob_kwargs)\n", - "File \u001b[0;32m~/Dropbox/Python/spikeinterface/src/spikeinterface/core/sortinganalyzer.py:2724\u001b[0m, in \u001b[0;36mAnalyzerExtension.set_params\u001b[0;34m(self, save, **params)\u001b[0m\n\u001b[1;32m 2721\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save:\n\u001b[1;32m 2722\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset_extension_folder()\n\u001b[0;32m-> 2724\u001b[0m params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_params(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[1;32m 2725\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams \u001b[38;5;241m=\u001b[39m params\n\u001b[1;32m 2727\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msorting_analyzer\u001b[38;5;241m.\u001b[39mis_read_only():\n", - "\u001b[0;31mTypeError\u001b[0m: ComputeTemplateMetrics._set_params() got an unexpected keyword argument 'smooth_method'" + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -159,8 +175,6 @@ "analyzer.compute(\n", " \"template_metrics\",\n", " smooth=True, # Enable/disable smoothing\n", - " smooth_method='svd',\n", - " svd_n_components=3,\n", " smooth_window_frac=0.1, # Window as fraction of template length\n", " smooth_polyorder=3, # Polynomial order\n", " min_thresh_detect_peaks_troughs=0.4\n", @@ -169,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -200,58 +214,136 @@ " recovery_slope\n", " num_positive_peaks\n", " num_negative_peaks\n", + " waveform_duration\n", + " peak_before_to_trough_ratio\n", + " peak_after_to_trough_ratio\n", + " peak_before_to_peak_after_ratio\n", + " main_peak_to_trough_ratio\n", + " trough_width\n", + " peak_before_width\n", + " peak_after_width\n", + " waveform_baseline_flatness\n", + " velocity_above\n", + " velocity_below\n", + " exp_decay\n", + " spread\n", " \n", " \n", " \n", " \n", " 0\n", - " 0.001173\n", - " -0.440069\n", - " 0.001740\n", - " 16271.944574\n", - " -12828.347767\n", - " 1\n", + " 0.001200\n", + " -0.412584\n", + " 0.001747\n", + " 16671.090016\n", + " -17191.329586\n", " 2\n", + " 1\n", + " 643.333333\n", + " 2.358820\n", + " 0.412584\n", + " 5.717192\n", + " 2.358820\n", + " 818.069771\n", + " 367.576821\n", + " 249.381750\n", + " 0.243320\n", + " 109.202199\n", + " NaN\n", + " 0.011540\n", + " 60.0\n", " \n", " \n", " 1\n", - " 0.000657\n", - " -0.200979\n", + " 0.000633\n", + " -0.195374\n", " 0.000163\n", " 321619.078371\n", - " -10908.856693\n", - " 1\n", + " -10477.415662\n", + " 2\n", " 1\n", + " 633.333333\n", + " 0.103992\n", + " 0.195374\n", + " 0.532271\n", + " 0.195374\n", + " 210.974071\n", + " NaN\n", + " 1035.993495\n", + " 0.104157\n", + " -1979.834401\n", + " NaN\n", + " 0.018553\n", + " 45.0\n", " \n", " \n", " 2\n", - " 0.001320\n", - " -0.374601\n", + " 0.001250\n", + " -0.331924\n", " 0.000647\n", - " 12679.478171\n", - " -88050.930624\n", - " 1\n", + " 12705.168897\n", + " -16207.805607\n", " 2\n", + " 1\n", + " 616.666667\n", + " 2.229546\n", + " 0.331924\n", + " 6.717041\n", + " 2.229546\n", + " 806.583590\n", + " 362.992828\n", + " 231.258891\n", + " 0.200324\n", + " NaN\n", + " NaN\n", + " 0.006662\n", + " 105.0\n", " \n", " \n", " 3\n", - " 0.000653\n", - " -0.242145\n", + " 0.000680\n", + " -0.234461\n", " 0.000220\n", - " 119110.411447\n", - " -7646.450397\n", - " 1\n", + " 118995.043916\n", + " -7777.377258\n", + " 2\n", " 1\n", + " 680.000000\n", + " 0.167090\n", + " 0.234461\n", + " 0.712656\n", + " 0.234461\n", + " 277.076137\n", + " 203.583558\n", + " 1012.443132\n", + " 0.177848\n", + " NaN\n", + " 1191.369764\n", + " 0.010610\n", + " 105.0\n", " \n", " \n", " 4\n", - " 0.000820\n", - " -0.275446\n", + " 0.000770\n", + " -0.267867\n", " 0.000260\n", - " 75484.461584\n", - " -7431.037919\n", + " 75660.685138\n", + " -7251.490738\n", " 2\n", " 1\n", + " 770.000000\n", + " 0.252582\n", + " 0.267867\n", + " 0.942940\n", + " 0.267867\n", + " 322.024228\n", + " 403.049068\n", + " 947.586212\n", + " 0.210937\n", + " 783.064501\n", + " 636.599008\n", + " 0.009241\n", + " 135.0\n", " \n", " \n", " ...\n", @@ -262,93 +354,236 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 325\n", - " 0.001383\n", - " -0.250669\n", + " 0.001350\n", + " -0.234132\n", " 0.000517\n", - " 20844.603913\n", - " -7716.307155\n", - " 1\n", + " 20857.109735\n", + " -7038.372419\n", + " 2\n", " 1\n", + " 516.666667\n", + " 1.357400\n", + " 0.234132\n", + " 5.797585\n", + " 1.357400\n", + " 613.535510\n", + " 475.232721\n", + " 629.538080\n", + " 0.253322\n", + " NaN\n", + " NaN\n", + " 0.000379\n", + " 180.0\n", " \n", " \n", " 326\n", - " 0.000603\n", - " -0.686821\n", + " 0.000620\n", + " -0.685191\n", " 0.000397\n", - " 72116.897018\n", - " -21101.644706\n", - " 1\n", + " 71418.515246\n", + " -21462.465005\n", " 2\n", + " 1\n", + " 620.000000\n", + " 0.147044\n", + " 0.685191\n", + " 0.214603\n", + " 0.685191\n", + " 445.745184\n", + " NaN\n", + " 809.107535\n", + " 0.168947\n", + " NaN\n", + " -1228.577175\n", + " NaN\n", + " 150.0\n", " \n", " \n", " 327\n", - " 0.001190\n", - " -0.264008\n", - " 0.000543\n", - " 37373.373888\n", - " -7624.695940\n", + " 0.001237\n", + " -0.263875\n", + " 0.000547\n", + " 36273.785365\n", + " -7774.517050\n", " 2\n", " 1\n", + " 703.333333\n", + " 1.185248\n", + " 0.263875\n", + " 4.491711\n", + " 1.185248\n", + " 624.165680\n", + " 629.583000\n", + " 454.026367\n", + " 0.389132\n", + " NaN\n", + " NaN\n", + " 0.000723\n", + " 120.0\n", " \n", " \n", " 328\n", - " 0.001090\n", - " -0.327231\n", - " 0.000530\n", - " 28242.461115\n", - " -9136.500514\n", + " 0.001137\n", + " -0.329055\n", + " 0.000537\n", + " 27766.452527\n", + " -9930.771106\n", " 2\n", " 1\n", + " 1136.666667\n", + " 0.994842\n", + " 0.329055\n", + " 3.023334\n", + " 0.994842\n", + " 648.431952\n", + " 667.718074\n", + " 488.826489\n", + " 0.353984\n", + " -400.589557\n", + " NaN\n", + " 0.000703\n", + " 105.0\n", " \n", " \n", " 329\n", - " 0.000860\n", - " -0.788475\n", - " 0.000423\n", - " 48524.639136\n", - " -28883.587872\n", - " 1\n", + " 0.000830\n", + " -0.794591\n", + " 0.000427\n", + " 50197.024838\n", + " -28537.288201\n", " 2\n", + " 1\n", + " 830.000000\n", + " 0.208674\n", + " 0.794591\n", + " 0.262618\n", + " 0.794591\n", + " 500.158495\n", + " NaN\n", + " 823.084749\n", + " 0.234050\n", + " NaN\n", + " 2112.898614\n", + " 0.000774\n", + " 90.0\n", " \n", " \n", "\n", - "

330 rows × 7 columns

\n", + "

330 rows × 20 columns

\n", "" ], "text/plain": [ " peak_to_valley peak_trough_ratio half_width repolarization_slope \\\n", - "0 0.001173 -0.440069 0.001740 16271.944574 \n", - "1 0.000657 -0.200979 0.000163 321619.078371 \n", - "2 0.001320 -0.374601 0.000647 12679.478171 \n", - "3 0.000653 -0.242145 0.000220 119110.411447 \n", - "4 0.000820 -0.275446 0.000260 75484.461584 \n", + "0 0.001200 -0.412584 0.001747 16671.090016 \n", + "1 0.000633 -0.195374 0.000163 321619.078371 \n", + "2 0.001250 -0.331924 0.000647 12705.168897 \n", + "3 0.000680 -0.234461 0.000220 118995.043916 \n", + "4 0.000770 -0.267867 0.000260 75660.685138 \n", ".. ... ... ... ... \n", - "325 0.001383 -0.250669 0.000517 20844.603913 \n", - "326 0.000603 -0.686821 0.000397 72116.897018 \n", - "327 0.001190 -0.264008 0.000543 37373.373888 \n", - "328 0.001090 -0.327231 0.000530 28242.461115 \n", - "329 0.000860 -0.788475 0.000423 48524.639136 \n", + "325 0.001350 -0.234132 0.000517 20857.109735 \n", + "326 0.000620 -0.685191 0.000397 71418.515246 \n", + "327 0.001237 -0.263875 0.000547 36273.785365 \n", + "328 0.001137 -0.329055 0.000537 27766.452527 \n", + "329 0.000830 -0.794591 0.000427 50197.024838 \n", + "\n", + " recovery_slope num_positive_peaks num_negative_peaks \\\n", + "0 -17191.329586 2 1 \n", + "1 -10477.415662 2 1 \n", + "2 -16207.805607 2 1 \n", + "3 -7777.377258 2 1 \n", + "4 -7251.490738 2 1 \n", + ".. ... ... ... \n", + "325 -7038.372419 2 1 \n", + "326 -21462.465005 2 1 \n", + "327 -7774.517050 2 1 \n", + "328 -9930.771106 2 1 \n", + "329 -28537.288201 2 1 \n", + "\n", + " waveform_duration peak_before_to_trough_ratio \\\n", + "0 643.333333 2.358820 \n", + "1 633.333333 0.103992 \n", + "2 616.666667 2.229546 \n", + "3 680.000000 0.167090 \n", + "4 770.000000 0.252582 \n", + ".. ... ... \n", + "325 516.666667 1.357400 \n", + "326 620.000000 0.147044 \n", + "327 703.333333 1.185248 \n", + "328 1136.666667 0.994842 \n", + "329 830.000000 0.208674 \n", "\n", - " recovery_slope num_positive_peaks num_negative_peaks \n", - "0 -12828.347767 1 2 \n", - "1 -10908.856693 1 1 \n", - "2 -88050.930624 1 2 \n", - "3 -7646.450397 1 1 \n", - "4 -7431.037919 2 1 \n", - ".. ... ... ... \n", - "325 -7716.307155 1 1 \n", - "326 -21101.644706 1 2 \n", - "327 -7624.695940 2 1 \n", - "328 -9136.500514 2 1 \n", - "329 -28883.587872 1 2 \n", + " peak_after_to_trough_ratio peak_before_to_peak_after_ratio \\\n", + "0 0.412584 5.717192 \n", + "1 0.195374 0.532271 \n", + "2 0.331924 6.717041 \n", + "3 0.234461 0.712656 \n", + "4 0.267867 0.942940 \n", + ".. ... ... \n", + "325 0.234132 5.797585 \n", + "326 0.685191 0.214603 \n", + "327 0.263875 4.491711 \n", + "328 0.329055 3.023334 \n", + "329 0.794591 0.262618 \n", "\n", - "[330 rows x 7 columns]" + " main_peak_to_trough_ratio trough_width peak_before_width \\\n", + "0 2.358820 818.069771 367.576821 \n", + "1 0.195374 210.974071 NaN \n", + "2 2.229546 806.583590 362.992828 \n", + "3 0.234461 277.076137 203.583558 \n", + "4 0.267867 322.024228 403.049068 \n", + ".. ... ... ... \n", + "325 1.357400 613.535510 475.232721 \n", + "326 0.685191 445.745184 NaN \n", + "327 1.185248 624.165680 629.583000 \n", + "328 0.994842 648.431952 667.718074 \n", + "329 0.794591 500.158495 NaN \n", + "\n", + " peak_after_width waveform_baseline_flatness velocity_above \\\n", + "0 249.381750 0.243320 109.202199 \n", + "1 1035.993495 0.104157 -1979.834401 \n", + "2 231.258891 0.200324 NaN \n", + "3 1012.443132 0.177848 NaN \n", + "4 947.586212 0.210937 783.064501 \n", + ".. ... ... ... \n", + "325 629.538080 0.253322 NaN \n", + "326 809.107535 0.168947 NaN \n", + "327 454.026367 0.389132 NaN \n", + "328 488.826489 0.353984 -400.589557 \n", + "329 823.084749 0.234050 NaN \n", + "\n", + " velocity_below exp_decay spread \n", + "0 NaN 0.011540 60.0 \n", + "1 NaN 0.018553 45.0 \n", + "2 NaN 0.006662 105.0 \n", + "3 1191.369764 0.010610 105.0 \n", + "4 636.599008 0.009241 135.0 \n", + ".. ... ... ... \n", + "325 NaN 0.000379 180.0 \n", + "326 -1228.577175 NaN 150.0 \n", + "327 NaN 0.000723 120.0 \n", + "328 NaN 0.000703 105.0 \n", + "329 2112.898614 0.000774 90.0 \n", + "\n", + "[330 rows x 20 columns]" ] }, - "execution_count": 54, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -387,9 +622,361 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
amplitude_mediansync_spike_2sync_spike_4sync_spike_8firing_ratesnramplitude_cv_medianamplitude_cv_rangepresence_ratioamplitude_cutoffsliding_rp_violationisi_violations_ratioisi_violations_countsd_ratiorp_contaminationrp_violationsnum_spikesfiring_range
0-10.5300000.1057950.0068020.0001906.1215790.9780101.1871011.1226011.00.000089NaN0.9518534601.3373941.02612631513.80
1-43.6800000.0630040.0021080.00003130.1286813.898206NaNNaN1.00.000002NaN0.45445453201.1094061.0407512951571.00
2-9.1650000.1249620.0094690.0001213.8571880.9449061.3981811.1074681.00.000083NaN0.7974231531.3014711.088165818.02
3-30.2249980.0863430.0083670.00024511.3991872.8399660.3412670.3241811.00.000136NaN0.4081766841.1207401.06054900217.20
4-27.8849980.1408250.0173890.0006234.4815582.428674NaNNaN1.00.000122NaN0.7605831971.3690031.01291926510.80
.........................................................
325-12.0900000.3360320.0585260.0039465.6600472.556509NaNNaN1.00.000011NaN4.15835917182.5443451.011762433115.82
326-15.7950000.2260840.0237520.0012393.1928053.4022700.8117861.2482981.00.000031NaN3.5599174682.7544591.0264137259.20
327-16.5749990.2976150.0377360.0014241.3068983.3737960.7613740.6439521.00.000079NaN1.952197432.5139391.03756184.00
328-14.2350000.3487250.0547680.00175111.4257062.804173NaNNaN1.00.000011NaN1.23013520712.3674201.011524911630.80
329-14.2350000.2587550.0229760.0003044.5967092.740247NaNNaN1.00.000177NaN2.9872348142.5234151.04411976013.00
\n", + "

330 rows × 18 columns

\n", + "
" + ], + "text/plain": [ + " amplitude_median sync_spike_2 sync_spike_4 sync_spike_8 firing_rate \\\n", + "0 -10.530000 0.105795 0.006802 0.000190 6.121579 \n", + "1 -43.680000 0.063004 0.002108 0.000031 30.128681 \n", + "2 -9.165000 0.124962 0.009469 0.000121 3.857188 \n", + "3 -30.224998 0.086343 0.008367 0.000245 11.399187 \n", + "4 -27.884998 0.140825 0.017389 0.000623 4.481558 \n", + ".. ... ... ... ... ... \n", + "325 -12.090000 0.336032 0.058526 0.003946 5.660047 \n", + "326 -15.795000 0.226084 0.023752 0.001239 3.192805 \n", + "327 -16.574999 0.297615 0.037736 0.001424 1.306898 \n", + "328 -14.235000 0.348725 0.054768 0.001751 11.425706 \n", + "329 -14.235000 0.258755 0.022976 0.000304 4.596709 \n", + "\n", + " snr amplitude_cv_median amplitude_cv_range presence_ratio \\\n", + "0 0.978010 1.187101 1.122601 1.0 \n", + "1 3.898206 NaN NaN 1.0 \n", + "2 0.944906 1.398181 1.107468 1.0 \n", + "3 2.839966 0.341267 0.324181 1.0 \n", + "4 2.428674 NaN NaN 1.0 \n", + ".. ... ... ... ... \n", + "325 2.556509 NaN NaN 1.0 \n", + "326 3.402270 0.811786 1.248298 1.0 \n", + "327 3.373796 0.761374 0.643952 1.0 \n", + "328 2.804173 NaN NaN 1.0 \n", + "329 2.740247 NaN NaN 1.0 \n", + "\n", + " amplitude_cutoff sliding_rp_violation isi_violations_ratio \\\n", + "0 0.000089 NaN 0.951853 \n", + "1 0.000002 NaN 0.454454 \n", + "2 0.000083 NaN 0.797423 \n", + "3 0.000136 NaN 0.408176 \n", + "4 0.000122 NaN 0.760583 \n", + ".. ... ... ... \n", + "325 0.000011 NaN 4.158359 \n", + "326 0.000031 NaN 3.559917 \n", + "327 0.000079 NaN 1.952197 \n", + "328 0.000011 NaN 1.230135 \n", + "329 0.000177 NaN 2.987234 \n", + "\n", + " isi_violations_count sd_ratio rp_contamination rp_violations \\\n", + "0 460 1.337394 1.0 261 \n", + "1 5320 1.109406 1.0 4075 \n", + "2 153 1.301471 1.0 88 \n", + "3 684 1.120740 1.0 605 \n", + "4 197 1.369003 1.0 129 \n", + ".. ... ... ... ... \n", + "325 1718 2.544345 1.0 1176 \n", + "326 468 2.754459 1.0 264 \n", + "327 43 2.513939 1.0 37 \n", + "328 2071 2.367420 1.0 1152 \n", + "329 814 2.523415 1.0 441 \n", + "\n", + " num_spikes firing_range \n", + "0 26315 13.80 \n", + "1 129515 71.00 \n", + "2 16581 8.02 \n", + "3 49002 17.20 \n", + "4 19265 10.80 \n", + ".. ... ... \n", + "325 24331 15.82 \n", + "326 13725 9.20 \n", + "327 5618 4.00 \n", + "328 49116 30.80 \n", + "329 19760 13.00 \n", + "\n", + "[330 rows x 18 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Quality metrics\n", "if not analyzer.has_extension(\"quality_metrics\"):\n", @@ -400,6 +987,129 @@ "quality_metrics" ] }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'spikeinterface.comparison' has no attribute 'print_threshold_failures'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[17], line 20\u001b[0m\n\u001b[1;32m 16\u001b[0m unit_type, labels \u001b[38;5;241m=\u001b[39m sc\u001b[38;5;241m.\u001b[39mclassify_units(metrics, thresholds)\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# plots!!!!!\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# Debug: see which thresholds are failing\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m sc\u001b[38;5;241m.\u001b[39mprint_threshold_failures(metrics)\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# Classify with default thresholds\u001b[39;00m\n\u001b[1;32m 23\u001b[0m unit_type, labels \u001b[38;5;241m=\u001b[39m sc\u001b[38;5;241m.\u001b[39mclassify_units(metrics)\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'spikeinterface.comparison' has no attribute 'print_threshold_failures'" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import spikeinterface.comparison as sc \n", + "# Get metrics from SortingAnalyzer\n", + "qm = analyzer.get_extension(\"quality_metrics\").get_data()\n", + "tm = analyzer.get_extension(\"template_metrics\").get_data()\n", + "metrics = pd.concat([qm, tm], axis=1)\n", + "\n", + "# Classify with default thresholds\n", + "unit_type, labels = sc.classify_units(metrics)\n", + "\n", + "# Or customize thresholds\n", + "thresholds = sc.get_default_thresholds() # probably not correct format. where should i put this? \n", + "thresholds[\"snr\"][\"min\"] = 3 # Lower threshold\n", + "thresholds[\"amplitude_median\"][\"min\"] = np.nan # Disable\n", + "unit_type, labels = sc.classify_units(metrics, thresholds)\n", + "\n", + "# plots!!!!!\n", + "# Debug: see which thresholds are failing\n", + "sc.print_threshold_failures(metrics)\n", + "\n", + "# Classify with default thresholds\n", + "unit_type, labels = sc.classify_units(metrics)\n", + "\n", + "# Get summary\n", + "summary = sc.get_classification_summary(unit_type, labels)\n", + "print(summary)\n", + "\n", + "# Plot histograms with threshold lines\n", + "plot_classification_histograms(metrics)\n", + "\n", + "# Plot waveform overlay by type\n", + "plot_waveform_overlay(analyzer, unit_type, labels)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", + " 'NOISE'], dtype=object)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unit_type\n", + "labels" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/src/spikeinterface/comparison/__init__.py b/src/spikeinterface/comparison/__init__.py index f4ada19f73..f4cc497916 100644 --- a/src/spikeinterface/comparison/__init__.py +++ b/src/spikeinterface/comparison/__init__.py @@ -40,3 +40,11 @@ create_hybrid_units_recording, create_hybrid_spikes_recording, ) + +from .unit_classification import ( + get_default_thresholds, + classify_units, + apply_thresholds, + get_classification_summary, + print_threshold_failures, +) diff --git a/src/spikeinterface/comparison/unit_classification.py b/src/spikeinterface/comparison/unit_classification.py new file mode 100644 index 0000000000..6b770e4e57 --- /dev/null +++ b/src/spikeinterface/comparison/unit_classification.py @@ -0,0 +1,474 @@ +""" +Unit classification based on quality metrics and user-defined thresholds. + +This module provides functionality to classify neural units based on quality metrics +(similar to BombCell). Each metric can have min and max thresholds - use NaN to +disable a threshold. + +Unit Types: + 0 (NOISE): Units failing waveform quality checks + 1 (GOOD): Units passing all quality thresholds + 2 (MUA): Multi-unit activity - units failing spike quality checks but not waveform checks + 3 (NON_SOMA): Non-somatic units (axonal, etc.) - optional classification +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +from typing import Optional + + +def get_default_thresholds() -> dict: + """ + Returns default thresholds for unit classification. + + Each threshold entry has 'min' and 'max' values. Use np.nan to disable + a threshold direction (e.g., if only a minimum matters, set max to np.nan). + + Thresholds are organized by category: + - waveform: Template/waveform shape checks (failures -> NOISE) + - spike_quality: Spike sorting quality checks (failures -> MUA) + - non_somatic: Non-somatic detection (optional, failures -> NON_SOMA) + + Returns + ------- + thresholds : dict + Dictionary of threshold parameters with min/max values. + + Notes + ----- + Metric names correspond to SpikeInterface metric column names: + + Template metrics (from template_metrics extension): + - num_positive_peaks: Number of positive peaks (repolarization peaks) + - num_negative_peaks: Number of negative peaks (troughs) + - waveform_duration: Duration in microseconds + - waveform_baseline_flatness: Baseline flatness metric + - peak_after_to_trough_ratio: Ratio of peak after trough to trough amplitude + - exp_decay: Exponential decay constant for spatial spread + + Quality metrics (from quality_metrics extension): + - amplitude_median: Median spike amplitude + - snr: Signal-to-noise ratio + - amplitude_cutoff: Estimated fraction of missing spikes + - num_spikes: Total spike count + - rp_contamination: Refractory period contamination + - isi_violations_ratio: ISI violations ratio + - presence_ratio: Fraction of recording where unit is present + - drift_mad: Median absolute deviation of drift + - nn_isolation: Nearest neighbor isolation score + - nn_noise_overlap: Nearest neighbor noise overlap + """ + thresholds = { + # ============================================================ + # WAVEFORM QUALITY THRESHOLDS (failures classify as NOISE) + # ============================================================ + + # Number of positive peaks (repolarization peaks after trough) + # Good units typically have 1-2 peaks + "num_positive_peaks": {"min": np.nan, "max": 2}, + + # Number of negative peaks (troughs) in waveform + # Good units typically have 1 main trough + "num_negative_peaks": {"min": np.nan, "max": 1}, + + # Waveform duration in MICROSECONDS (from template_metrics) + # Typical range: 100-1150 us + "waveform_duration": {"min": 100, "max": 1150}, + + # Baseline flatness - max deviation as fraction of peak amplitude + # Lower is better, typical threshold 0.3 + "waveform_baseline_flatness": {"min": np.nan, "max": 0.3}, + + # Peak after trough to trough ratio - helps detect noise + # High values indicate noise (ratio > 0.8 is suspicious) + "peak_after_to_trough_ratio": {"min": np.nan, "max": 0.8}, + + # Exponential decay constant for spatial spread + # Values outside typical range indicate noise + "exp_decay": {"min": 0.01, "max": 0.1}, + + # ============================================================ + # SPIKE QUALITY THRESHOLDS (failures classify as MUA) + # ============================================================ + + # Median spike amplitude (in uV typically) + # Lower bound ensures sufficient signal + "amplitude_median": {"min": 40, "max": np.nan}, + + # Signal-to-noise ratio + # Higher is better, minimum ensures reliable detection + "snr": {"min": 5, "max": np.nan}, + + # Amplitude cutoff - estimates fraction of missing spikes + # Lower is better (less missing), max 0.2 means <20% estimated missing + "amplitude_cutoff": {"min": np.nan, "max": 0.2}, + + # Minimum number of spikes + # Ensures sufficient data for reliable metrics + "num_spikes": {"min": 300, "max": np.nan}, + + # Refractory period contamination rate + # Lower is better, max typically 0.1 (10%) + "rp_contamination": {"min": np.nan, "max": 0.1}, + + # ISI violations ratio + # Lower is better, alternative to rp_contamination + "isi_violations_ratio": {"min": np.nan, "max": 0.1}, + + # Presence ratio - fraction of recording where unit is active + # Higher is better, ensures unit present throughout + "presence_ratio": {"min": 0.7, "max": np.nan}, + + # Drift MAD - median absolute deviation of drift in um + # Lower is better, ensures stable unit location + "drift_mad": {"min": np.nan, "max": 100}, + + # Nearest neighbor isolation (from PCA metrics) + # Higher is better, ensures good cluster separation + "nn_isolation": {"min": 0.8, "max": np.nan}, + + # Nearest neighbor noise overlap (from PCA metrics) + # Lower is better, ensures separation from noise + "nn_noise_overlap": {"min": np.nan, "max": 0.1}, + + # ============================================================ + # NON-SOMATIC DETECTION THRESHOLDS (optional) + # ============================================================ + + # These thresholds identify axonal/dendritic units by their waveform shape + # Non-somatic units have characteristic triphasic waveforms + + # Peak before to trough ratio - non-somatic have large initial peak + "peak_before_to_trough_ratio": {"min": np.nan, "max": 5}, # non-somatic if > max + + # Peak before width (in samples at sampling rate) + "peak_before_width": {"min": 4, "max": np.nan}, # non-somatic if < min + + # Trough width (in samples) + "trough_width": {"min": 5, "max": np.nan}, # non-somatic if < min + + # Peak before to peak after ratio + "peak_before_to_peak_after_ratio": {"min": np.nan, "max": 3}, # non-somatic if > max + + # Main peak to trough ratio + "main_peak_to_trough_ratio": {"min": np.nan, "max": 0.8}, # non-somatic if > max + } + + return thresholds + + +def classify_units( + quality_metrics: pd.DataFrame, + thresholds: Optional[dict] = None, + classify_non_somatic: bool = False, + split_non_somatic_good_mua: bool = False, +) -> tuple[np.ndarray, np.ndarray]: + """ + Classify units based on quality metrics and thresholds. + + Classification hierarchy: + 1. NOISE (0): Units failing waveform quality checks + 2. MUA (2): Units passing waveform checks but failing spike quality checks + 3. GOOD (1): Units passing all checks + 4. NON_SOMA (3/4): Optional - units with non-somatic waveform characteristics + + Parameters + ---------- + quality_metrics : pd.DataFrame + DataFrame with quality metrics. Index should be unit_ids. + Can contain metrics from quality_metrics, template_metrics, + and spiketrain_metrics extensions. + thresholds : dict or None, default: None + Threshold dictionary with format {"metric_name": {"min": val, "max": val}}. + Use np.nan to disable a threshold. If None, uses get_default_thresholds(). + classify_non_somatic : bool, default: False + If True, also classify non-somatic (axonal) units. + split_non_somatic_good_mua : bool, default: False + If True and classify_non_somatic is True, split non-somatic into + NON_SOMA_GOOD (3) and NON_SOMA_MUA (4). Only applies if + classify_non_somatic is True. + + Returns + ------- + unit_type : np.ndarray + Numeric classification: 0=NOISE, 1=GOOD, 2=MUA, 3=NON_SOMA (or NON_SOMA_GOOD), + 4=NON_SOMA_MUA (if split_non_somatic_good_mua=True) + unit_type_string : np.ndarray + String labels for each unit type. + + Examples + -------- + >>> import spikeinterface.comparison as sc + >>> import pandas as pd + >>> + >>> # Get metrics from SortingAnalyzer + >>> qm = analyzer.get_extension("quality_metrics").get_data() + >>> tm = analyzer.get_extension("template_metrics").get_data() + >>> metrics = pd.concat([qm, tm], axis=1) + >>> + >>> # Classify with default thresholds + >>> unit_type, unit_labels = sc.classify_units(metrics) + >>> + >>> # Classify with custom thresholds + >>> thresholds = sc.get_default_thresholds() + >>> thresholds["snr"]["min"] = 3 # Lower SNR threshold + >>> thresholds["amplitude_median"]["min"] = np.nan # Disable + >>> unit_type, unit_labels = sc.classify_units(metrics, thresholds=thresholds) + """ + if thresholds is None: + thresholds = get_default_thresholds() + + n_units = len(quality_metrics) + unit_type = np.full(n_units, np.nan) + + # Define which metrics go to which category + waveform_metrics = [ + "num_positive_peaks", + "num_negative_peaks", + "waveform_duration", + "waveform_baseline_flatness", + "peak_after_to_trough_ratio", + "exp_decay", + ] + + spike_quality_metrics = [ + "amplitude_median", + "snr", + "amplitude_cutoff", + "num_spikes", + "rp_contamination", + "isi_violations_ratio", + "presence_ratio", + "drift_mad", + "nn_isolation", + "nn_noise_overlap", + ] + + non_somatic_metrics = [ + "peak_before_to_trough_ratio", + "peak_before_width", + "trough_width", + "peak_before_to_peak_after_ratio", + "main_peak_to_trough_ratio", + ] + + # ======================================== + # NOISE classification (waveform failures) + # ======================================== + noise_mask = np.zeros(n_units, dtype=bool) + + for metric_name in waveform_metrics: + if metric_name not in quality_metrics.columns: + continue + if metric_name not in thresholds: + continue + + values = quality_metrics[metric_name].values + thresh = thresholds[metric_name] + + # NaN values in metrics are considered failures for waveform metrics + noise_mask |= np.isnan(values) + + # Check min threshold + if not np.isnan(thresh["min"]): + noise_mask |= values < thresh["min"] + + # Check max threshold + if not np.isnan(thresh["max"]): + noise_mask |= values > thresh["max"] + + unit_type[noise_mask] = 0 + + # ======================================== + # MUA classification (spike quality failures) + # ======================================== + mua_mask = np.zeros(n_units, dtype=bool) + + for metric_name in spike_quality_metrics: + if metric_name not in quality_metrics.columns: + continue + if metric_name not in thresholds: + continue + + values = quality_metrics[metric_name].values + thresh = thresholds[metric_name] + + # Only apply to units not yet classified as noise + valid_mask = np.isnan(unit_type) + + # Check min threshold (NaN values don't fail min threshold for spike quality) + if not np.isnan(thresh["min"]): + mua_mask |= valid_mask & ~np.isnan(values) & (values < thresh["min"]) + + # Check max threshold (NaN values don't fail max threshold for spike quality) + if not np.isnan(thresh["max"]): + mua_mask |= valid_mask & ~np.isnan(values) & (values > thresh["max"]) + + unit_type[mua_mask & np.isnan(unit_type)] = 2 + + # ======================================== + # GOOD classification (passed all checks) + # ======================================== + unit_type[np.isnan(unit_type)] = 1 + + # ======================================== + # NON-SOMATIC classification (optional) + # ======================================== + if classify_non_somatic: + is_non_somatic = np.zeros(n_units, dtype=bool) + + for metric_name in non_somatic_metrics: + if metric_name not in quality_metrics.columns: + continue + if metric_name not in thresholds: + continue + + values = quality_metrics[metric_name].values + thresh = thresholds[metric_name] + + # Non-somatic detection uses OPPOSITE logic: + # - Values BELOW min threshold -> non-somatic + # - Values ABOVE max threshold -> non-somatic + if not np.isnan(thresh["min"]): + is_non_somatic |= ~np.isnan(values) & (values < thresh["min"]) + + if not np.isnan(thresh["max"]): + is_non_somatic |= ~np.isnan(values) & (values > thresh["max"]) + + # Apply non-somatic classification + if split_non_somatic_good_mua: + # Split into NON_SOMA_GOOD (3) and NON_SOMA_MUA (4) + good_non_somatic = (unit_type == 1) & is_non_somatic + mua_non_somatic = (unit_type == 2) & is_non_somatic + unit_type[good_non_somatic] = 3 + unit_type[mua_non_somatic] = 4 + else: + # All non-noise non-somatic units get type 3 + unit_type[(unit_type != 0) & is_non_somatic] = 3 + + # ======================================== + # Create string labels + # ======================================== + if split_non_somatic_good_mua: + labels = { + 0: "NOISE", + 1: "GOOD", + 2: "MUA", + 3: "NON_SOMA_GOOD", + 4: "NON_SOMA_MUA", + } + else: + labels = { + 0: "NOISE", + 1: "GOOD", + 2: "MUA", + 3: "NON_SOMA", + } + + unit_type_string = np.array([labels.get(int(t), "UNKNOWN") for t in unit_type], dtype=object) + + return unit_type.astype(int), unit_type_string + + +def apply_thresholds( + quality_metrics: pd.DataFrame, + thresholds: Optional[dict] = None, +) -> pd.DataFrame: + """ + Apply thresholds to quality metrics and return pass/fail status for each. + + This is useful for debugging which metrics are causing units to fail. + + Parameters + ---------- + quality_metrics : pd.DataFrame + DataFrame with quality metrics. + thresholds : dict or None, default: None + Threshold dictionary. If None, uses get_default_thresholds(). + + Returns + ------- + threshold_results : pd.DataFrame + DataFrame with same index as quality_metrics, with columns: + - {metric}_pass: bool, True if metric passes threshold + - {metric}_fail_reason: str, reason for failure ("below_min", "above_max", "nan", or "") + """ + if thresholds is None: + thresholds = get_default_thresholds() + + results = {} + + for metric_name, thresh in thresholds.items(): + if metric_name not in quality_metrics.columns: + continue + + values = quality_metrics[metric_name].values + n_units = len(values) + + # Initialize + passes = np.ones(n_units, dtype=bool) + reasons = np.array([""] * n_units, dtype=object) + + # Check for NaN + nan_mask = np.isnan(values) + passes[nan_mask] = False + reasons[nan_mask] = "nan" + + # Check min threshold + if not np.isnan(thresh["min"]): + below_min = ~nan_mask & (values < thresh["min"]) + passes[below_min] = False + reasons[below_min] = "below_min" + + # Check max threshold + if not np.isnan(thresh["max"]): + above_max = ~nan_mask & (values > thresh["max"]) + passes[above_max] = False + # Only overwrite if not already failed + reasons[above_max & (reasons == "")] = "above_max" + # If both fail, indicate both + reasons[above_max & (reasons == "below_min")] = "below_min_and_above_max" + + results[f"{metric_name}_pass"] = passes + results[f"{metric_name}_fail_reason"] = reasons + + return pd.DataFrame(results, index=quality_metrics.index) + + +def get_classification_summary( + unit_type: np.ndarray, + unit_type_string: np.ndarray, +) -> dict: + """ + Get summary statistics of unit classification. + + Parameters + ---------- + unit_type : np.ndarray + Numeric unit type array from classify_units(). + unit_type_string : np.ndarray + String labels from classify_units(). + + Returns + ------- + summary : dict + Dictionary with counts and percentages for each unit type. + """ + n_total = len(unit_type) + unique_types, counts = np.unique(unit_type, return_counts=True) + + summary = { + "total_units": n_total, + "counts": {}, + "percentages": {}, + } + + # Get the label for each type + for utype, count in zip(unique_types, counts): + label = unit_type_string[unit_type == utype][0] + summary["counts"][label] = int(count) + summary["percentages"][label] = round(100 * count / n_total, 1) + + return summary diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index 3d84dceadc..3e3999dabd 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -760,7 +760,7 @@ def fit_velocity(peak_times, channel_dist): from sklearn.linear_model import TheilSenRegressor - theil = TheilSenRegressor() + theil = TheilSenRegressor(max_iter=1000) theil.fit(peak_times.reshape(-1, 1), channel_dist) slope = theil.coef_[0] intercept = theil.intercept_ @@ -843,7 +843,11 @@ def get_velocity_fits(template, channel_locations, sampling_frequency, **kwargs) def get_exp_decay(template, channel_locations, sampling_frequency=None, **kwargs): """ - Compute the exponential decay of the template amplitude over distance in units um/s. + Compute the spatial decay of the template amplitude over distance. + + Can fit either an exponential decay (with offset) or a linear decay model. Channels are first + filtered by x-distance tolerance from the max channel, then the closest channels + in y-distance are used for fitting. Parameters ---------- @@ -854,13 +858,18 @@ def get_exp_decay(template, channel_locations, sampling_frequency=None, **kwargs sampling_frequency : float The sampling frequency of the template **kwargs: Required kwargs: - - peak_function: the function to use to compute the peak amplitude for the exp decay ("ptp" or "min") - - min_r2: the minimum r2 to accept the exp decay fit + - peak_function: the function to use to compute the peak amplitude ("ptp" or "min") + - min_r2: the minimum r2 to accept the fit + - linear_fit: bool, if True use linear fit, otherwise exponential fit + - channel_tolerance: max x-distance (um) from max channel to include channels + - min_channels_for_fit: minimum number of valid channels required for fitting + - num_channels_for_fit: number of closest channels to use for fitting + - normalize_decay: bool, if True normalize amplitudes to max before fitting Returns ------- exp_decay_value : float - The exponential decay of the template amplitude + The spatial decay slope (decay constant for exp fit, negative slope for linear fit) """ from scipy.optimize import curve_fit from sklearn.metrics import r2_score @@ -868,41 +877,117 @@ def get_exp_decay(template, channel_locations, sampling_frequency=None, **kwargs def exp_decay(x, decay, amp0, offset): return amp0 * np.exp(-decay * x) + offset + def linear_fit_func(x, a, b): + return a * x + b + + # Extract parameters assert "peak_function" in kwargs, "peak_function must be given as kwarg" peak_function = kwargs["peak_function"] assert "min_r2" in kwargs, "min_r2 must be given as kwarg" min_r2 = kwargs["min_r2"] - # exp decay fit + + use_linear_fit = kwargs.get("linear_fit", False) + channel_tolerance = kwargs.get("channel_tolerance", None) + normalize_decay = kwargs.get("normalize_decay", False) + + # Set defaults based on fit type if not specified + min_channels_for_fit = kwargs.get("min_channels_for_fit") + if min_channels_for_fit is None: + min_channels_for_fit = 5 if use_linear_fit else 8 + + num_channels_for_fit = kwargs.get("num_channels_for_fit") + if num_channels_for_fit is None: + num_channels_for_fit = 6 if use_linear_fit else 10 + + # Compute peak amplitudes per channel if peak_function == "ptp": fun = np.ptp elif peak_function == "min": fun = np.min + else: + fun = np.ptp + peak_amplitudes = np.abs(fun(template, axis=0)) - max_channel_location = channel_locations[np.argmax(peak_amplitudes)] - channel_distances = np.array([np.linalg.norm(cl - max_channel_location) for cl in channel_locations]) - distances_sort_indices = np.argsort(channel_distances) + max_channel_idx = np.argmax(peak_amplitudes) + max_channel_location = channel_locations[max_channel_idx] - # longdouble is float128 when the platform supports it, otherwise it is float64 - channel_distances_sorted = channel_distances[distances_sort_indices].astype(np.longdouble) - peak_amplitudes_sorted = peak_amplitudes[distances_sort_indices].astype(np.longdouble) + # Channel selection based on tolerance (new bombcell-style) or use all channels (old style) + if channel_tolerance is not None: + # Calculate x-distances from max channel + x_dist = np.abs(channel_locations[:, 0] - max_channel_location[0]) - try: - amp0 = peak_amplitudes_sorted[0] - offset0 = np.min(peak_amplitudes_sorted) - - popt, _ = curve_fit( - exp_decay, - channel_distances_sorted, - peak_amplitudes_sorted, - bounds=([1e-5, amp0 - 0.5 * amp0, 0], [2, amp0 + 0.5 * amp0, 2 * offset0]), - p0=[1e-3, peak_amplitudes_sorted[0], offset0], + # Find channels within x-distance tolerance + valid_x_channels = np.argwhere(x_dist <= channel_tolerance).flatten() + + if len(valid_x_channels) < min_channels_for_fit: + return np.nan + + # Calculate y-distances for channel selection + y_dist = np.abs(channel_locations[:, 1] - max_channel_location[1]) + + # Set y distances to max for channels outside x tolerance (so they won't be selected) + y_dist_masked = y_dist.copy() + y_dist_masked[~np.isin(np.arange(len(y_dist)), valid_x_channels)] = y_dist.max() + 1 + + # Select the closest channels in y-distance + use_these_channels = np.argsort(y_dist_masked)[:num_channels_for_fit] + + # Calculate distances from max channel for selected channels + channel_distances = np.sqrt( + np.sum(np.square(channel_locations[use_these_channels] - max_channel_location), axis=1) ) - r2 = r2_score(peak_amplitudes_sorted, exp_decay(channel_distances_sorted, *popt)) - exp_decay_value = popt[0] + + # Get amplitudes for selected channels + spatial_decay_points = np.max(np.abs(template[:, use_these_channels]), axis=0) + + # Sort by distance + sort_idx = np.argsort(channel_distances) + channel_distances_sorted = channel_distances[sort_idx] + peak_amplitudes_sorted = spatial_decay_points[sort_idx] + + # Normalize if requested + if normalize_decay: + peak_amplitudes_sorted = peak_amplitudes_sorted / np.max(peak_amplitudes_sorted) + + # Ensure float64 for numerical stability + channel_distances_sorted = np.float64(channel_distances_sorted) + peak_amplitudes_sorted = np.float64(peak_amplitudes_sorted) + + else: + # Old style: use all channels sorted by distance + channel_distances = np.array([np.linalg.norm(cl - max_channel_location) for cl in channel_locations]) + distances_sort_indices = np.argsort(channel_distances) + + # longdouble is float128 when the platform supports it, otherwise it is float64 + channel_distances_sorted = channel_distances[distances_sort_indices].astype(np.longdouble) + peak_amplitudes_sorted = peak_amplitudes[distances_sort_indices].astype(np.longdouble) + + try: + if use_linear_fit: + # Linear fit: y = a*x + b + popt, _ = curve_fit(linear_fit_func, channel_distances_sorted, peak_amplitudes_sorted) + predicted = linear_fit_func(channel_distances_sorted, *popt) + r2 = r2_score(peak_amplitudes_sorted, predicted) + exp_decay_value = -popt[0] # Negative of slope + else: + # Exponential fit with offset: y = amp0 * exp(-decay * x) + offset + amp0 = peak_amplitudes_sorted[0] + offset0 = np.min(peak_amplitudes_sorted) + + popt, _ = curve_fit( + exp_decay, + channel_distances_sorted, + peak_amplitudes_sorted, + bounds=([1e-5, amp0 - 0.5 * amp0, 0], [2, amp0 + 0.5 * amp0, 2 * offset0]), + p0=[1e-3, peak_amplitudes_sorted[0], offset0], + ) + r2 = r2_score(peak_amplitudes_sorted, exp_decay(channel_distances_sorted, *popt)) + exp_decay_value = popt[0] if r2 < min_r2: exp_decay_value = np.nan - except: + + except Exception: exp_decay_value = np.nan return exp_decay_value @@ -1333,10 +1418,19 @@ def multi_channel_metric(unit_function, sorting_analyzer, unit_ids, tmp_data, ** class ExpDecay(BaseMetric): metric_name = "exp_decay" - metric_params = {"peak_function": "ptp", "min_r2": 0.2} + metric_params = { + "peak_function": "ptp", + "min_r2": 0.2, + "linear_fit": False, + "channel_tolerance": None, # None uses old style (all channels), set to e.g. 33 for bombcell-style + "min_channels_for_fit": None, # None means use default based on linear_fit (5 for linear, 8 for exp) + "num_channels_for_fit": None, # None means use default based on linear_fit (6 for linear, 10 for exp) + "normalize_decay": False, + } metric_columns = {"exp_decay": float} metric_descriptions = { - "exp_decay": ("Exponential decay of the template amplitude over distance from the extremum channel (1/um).") + "exp_decay": ("Spatial decay of the template amplitude over distance from the extremum channel (1/um). " + "Uses exponential or linear fit based on linear_fit parameter.") } needs_tmp_data = True diff --git a/src/spikeinterface/metrics/template/template_metrics.py b/src/spikeinterface/metrics/template/template_metrics.py index 5cdb01c41a..dfa5b6d69e 100644 --- a/src/spikeinterface/metrics/template/template_metrics.py +++ b/src/spikeinterface/metrics/template/template_metrics.py @@ -174,10 +174,8 @@ def _set_params( depth_direction=depth_direction, min_thresh_detect_peaks_troughs=min_thresh_detect_peaks_troughs, smooth=smooth, - smooth_method=smooth_method, smooth_window_frac=smooth_window_frac, smooth_polyorder=smooth_polyorder, - svd_n_components=svd_n_components, ) def _prepare_data(self, sorting_analyzer, unit_ids): @@ -229,10 +227,8 @@ def _prepare_data(self, sorting_analyzer, unit_ids): template_upsampled, min_thresh_detect_peaks_troughs=self.params['min_thresh_detect_peaks_troughs'], smooth=self.params['smooth'], - smooth_method=self.params['smooth_method'], smooth_window_frac=self.params['smooth_window_frac'], smooth_polyorder=self.params['smooth_polyorder'], - svd_n_components=self.params['svd_n_components'], ) templates_single.append(template_upsampled) diff --git a/src/spikeinterface/widgets/unit_classification.py b/src/spikeinterface/widgets/unit_classification.py new file mode 100644 index 0000000000..facf2056ed --- /dev/null +++ b/src/spikeinterface/widgets/unit_classification.py @@ -0,0 +1,519 @@ +""" +Widgets for visualizing unit classification results. + +These widgets provide summary plots for unit classification based on quality metrics, +similar to BombCell's plotting functionality. +""" + +from __future__ import annotations + +import numpy as np +from typing import Optional + +from .base import BaseWidget, to_attr + + +class UnitClassificationWidget(BaseWidget): + """ + Plot summary of unit classification results. + + This widget creates a multi-panel figure showing: + - Waveform overlays by unit type + - Classification summary bar chart + - Histogram of key metrics with threshold lines + + Parameters + ---------- + sorting_analyzer : SortingAnalyzer + The SortingAnalyzer object with computed template_metrics and quality_metrics. + unit_type : np.ndarray + Numeric unit type array from classify_units(). + unit_type_string : np.ndarray + String labels from classify_units(). + thresholds : dict, optional + Threshold dictionary used for classification. If None, uses default thresholds. + """ + + def __init__( + self, + sorting_analyzer, + unit_type: np.ndarray, + unit_type_string: np.ndarray, + thresholds: Optional[dict] = None, + backend=None, + **backend_kwargs, + ): + from spikeinterface.comparison import get_default_thresholds + + if thresholds is None: + thresholds = get_default_thresholds() + + sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) + + plot_data = dict( + sorting_analyzer=sorting_analyzer, + unit_type=unit_type, + unit_type_string=unit_type_string, + thresholds=thresholds, + ) + + BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) + + def plot_matplotlib(self, data_plot, **backend_kwargs): + import matplotlib.pyplot as plt + from .utils import get_unit_colors + + dp = to_attr(data_plot) + sorting_analyzer = dp.sorting_analyzer + unit_type = dp.unit_type + unit_type_string = dp.unit_type_string + + # Get unique types and counts + unique_types = np.unique(unit_type) + type_counts = {t: np.sum(unit_type == t) for t in unique_types} + type_labels = {t: unit_type_string[unit_type == t][0] for t in unique_types} + + # Create figure with subplots + fig, axes = plt.subplots(2, 2, figsize=(12, 10)) + + # Panel 1: Bar chart of classification counts + ax = axes[0, 0] + labels = [type_labels[t] for t in unique_types] + counts = [type_counts[t] for t in unique_types] + colors = ["red", "green", "orange", "blue", "purple"][: len(unique_types)] + bars = ax.bar(labels, counts, color=colors, alpha=0.7, edgecolor="black") + ax.set_ylabel("Number of units") + ax.set_title("Unit Classification Summary") + for bar, count in zip(bars, counts): + ax.text( + bar.get_x() + bar.get_width() / 2, + bar.get_height() + 0.5, + str(count), + ha="center", + va="bottom", + fontsize=10, + ) + + # Panel 2: Pie chart + ax = axes[0, 1] + ax.pie( + counts, + labels=labels, + autopct="%1.1f%%", + colors=colors, + startangle=90, + ) + ax.set_title("Unit Classification Distribution") + + # Panel 3 & 4: Placeholder for waveforms (would need templates) + ax = axes[1, 0] + ax.text( + 0.5, + 0.5, + "Waveform overlay\n(requires templates extension)", + ha="center", + va="center", + fontsize=12, + transform=ax.transAxes, + ) + ax.set_title("Template Waveforms by Type") + ax.axis("off") + + ax = axes[1, 1] + n_total = len(unit_type) + summary_text = "Classification Summary\n" + "=" * 30 + "\n" + for t in unique_types: + label = type_labels[t] + count = type_counts[t] + pct = 100 * count / n_total + summary_text += f"{label}: {count} ({pct:.1f}%)\n" + summary_text += "=" * 30 + f"\nTotal: {n_total} units" + ax.text( + 0.1, + 0.5, + summary_text, + ha="left", + va="center", + fontsize=11, + family="monospace", + transform=ax.transAxes, + ) + ax.axis("off") + + plt.tight_layout() + + self.figure = fig + self.axes = axes + + +class ClassificationHistogramsWidget(BaseWidget): + """ + Plot histograms of quality metrics with threshold lines. + + Shows the distribution of each metric with vertical lines indicating + the classification thresholds. + + Parameters + ---------- + quality_metrics : pd.DataFrame + DataFrame with quality metrics. + thresholds : dict, optional + Threshold dictionary. If None, uses default thresholds. + metrics_to_plot : list of str, optional + List of metric names to plot. If None, plots all metrics present in both + quality_metrics and thresholds. + """ + + def __init__( + self, + quality_metrics, + thresholds: Optional[dict] = None, + metrics_to_plot: Optional[list] = None, + backend=None, + **backend_kwargs, + ): + from spikeinterface.comparison import get_default_thresholds + + if thresholds is None: + thresholds = get_default_thresholds() + + # Determine which metrics to plot + if metrics_to_plot is None: + metrics_to_plot = [m for m in thresholds.keys() if m in quality_metrics.columns] + + plot_data = dict( + quality_metrics=quality_metrics, + thresholds=thresholds, + metrics_to_plot=metrics_to_plot, + ) + + BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) + + def plot_matplotlib(self, data_plot, **backend_kwargs): + import matplotlib.pyplot as plt + + dp = to_attr(data_plot) + quality_metrics = dp.quality_metrics + thresholds = dp.thresholds + metrics_to_plot = dp.metrics_to_plot + + n_metrics = len(metrics_to_plot) + if n_metrics == 0: + print("No metrics to plot") + return + + # Calculate grid layout + n_cols = min(4, n_metrics) + n_rows = int(np.ceil(n_metrics / n_cols)) + + fig, axes = plt.subplots(n_rows, n_cols, figsize=(4 * n_cols, 3 * n_rows)) + if n_metrics == 1: + axes = np.array([[axes]]) + elif n_rows == 1: + axes = axes.reshape(1, -1) + elif n_cols == 1: + axes = axes.reshape(-1, 1) + + colors = plt.cm.tab10(np.linspace(0, 1, 10)) + + for idx, metric_name in enumerate(metrics_to_plot): + row = idx // n_cols + col = idx % n_cols + ax = axes[row, col] + + values = quality_metrics[metric_name].values + values = values[~np.isnan(values)] + values = values[~np.isinf(values)] + + if len(values) == 0: + ax.set_title(f"{metric_name}\n(no valid data)") + continue + + # Plot histogram + color = colors[idx % 10] + ax.hist(values, bins=30, color=color, alpha=0.7, edgecolor="black", density=True) + + # Add threshold lines + thresh = thresholds.get(metric_name, {}) + min_thresh = thresh.get("min", np.nan) + max_thresh = thresh.get("max", np.nan) + + ylim = ax.get_ylim() + + if not np.isnan(min_thresh): + ax.axvline(min_thresh, color="red", linestyle="--", linewidth=2, label=f"min={min_thresh:.2g}") + + if not np.isnan(max_thresh): + ax.axvline(max_thresh, color="blue", linestyle="--", linewidth=2, label=f"max={max_thresh:.2g}") + + ax.set_xlabel(metric_name) + ax.set_ylabel("Density") + ax.legend(fontsize=8, loc="upper right") + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + + # Hide unused subplots + for idx in range(len(metrics_to_plot), n_rows * n_cols): + row = idx // n_cols + col = idx % n_cols + axes[row, col].set_visible(False) + + plt.tight_layout() + + self.figure = fig + self.axes = axes + + +class WaveformOverlayWidget(BaseWidget): + """ + Plot overlaid waveforms grouped by unit classification type. + + Parameters + ---------- + sorting_analyzer : SortingAnalyzer + The SortingAnalyzer object with computed templates. + unit_type : np.ndarray + Numeric unit type array from classify_units(). + unit_type_string : np.ndarray + String labels from classify_units(). + split_non_somatic : bool, default: False + If True, splits non-somatic into good/MUA. + """ + + def __init__( + self, + sorting_analyzer, + unit_type: np.ndarray, + unit_type_string: np.ndarray, + split_non_somatic: bool = False, + backend=None, + **backend_kwargs, + ): + sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) + + plot_data = dict( + sorting_analyzer=sorting_analyzer, + unit_type=unit_type, + unit_type_string=unit_type_string, + split_non_somatic=split_non_somatic, + ) + + BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) + + def plot_matplotlib(self, data_plot, **backend_kwargs): + import matplotlib.pyplot as plt + + dp = to_attr(data_plot) + sorting_analyzer = dp.sorting_analyzer + unit_type = dp.unit_type + unit_type_string = dp.unit_type_string + split_non_somatic = dp.split_non_somatic + + # Check if templates are available + if not sorting_analyzer.has_extension("templates"): + fig, ax = plt.subplots(1, 1, figsize=(8, 6)) + ax.text( + 0.5, + 0.5, + "Templates extension not computed.\nRun: analyzer.compute('templates')", + ha="center", + va="center", + fontsize=12, + ) + ax.axis("off") + self.figure = fig + self.axes = ax + return + + # Get templates + templates_ext = sorting_analyzer.get_extension("templates") + templates = templates_ext.get_templates(operator="average") + unit_ids = sorting_analyzer.unit_ids + + # Set up subplots based on split_non_somatic + if split_non_somatic: + labels = { + 0: "NOISE", + 1: "GOOD", + 2: "MUA", + 3: "NON_SOMA_GOOD", + 4: "NON_SOMA_MUA", + } + n_plots = 5 + nrows, ncols = 2, 3 + else: + labels = { + 0: "NOISE", + 1: "GOOD", + 2: "MUA", + 3: "NON_SOMA", + } + n_plots = 4 + nrows, ncols = 2, 2 + + fig, axes = plt.subplots(nrows, ncols, figsize=(5 * ncols, 4 * nrows)) + axes_flat = axes.flatten() + + for plot_idx in range(n_plots): + ax = axes_flat[plot_idx] + type_label = labels.get(plot_idx, "") + + # Get units of this type + mask = unit_type == plot_idx + n_units = np.sum(mask) + + if n_units > 0: + unit_indices = np.where(mask)[0] + alpha = max(0.05, min(0.3, 10 / n_units)) + + for unit_idx in unit_indices: + # Get template for this unit (best channel) + template = templates[unit_idx] # shape: (n_samples, n_channels) + # Find best channel (max amplitude) + best_chan = np.argmax(np.max(np.abs(template), axis=0)) + waveform = template[:, best_chan] + ax.plot(waveform, color="black", alpha=alpha, linewidth=0.5) + + ax.set_title(f"{type_label} (n={n_units})") + else: + ax.set_title(f"{type_label} (n=0)") + ax.text(0.5, 0.5, "No units", ha="center", va="center", transform=ax.transAxes) + + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + ax.spines["bottom"].set_visible(False) + ax.spines["left"].set_visible(False) + ax.set_xticks([]) + ax.set_yticks([]) + + # Hide unused subplots + for idx in range(n_plots, nrows * ncols): + axes_flat[idx].set_visible(False) + + plt.tight_layout() + + self.figure = fig + self.axes = axes + + +# Convenience functions for direct plotting +def plot_unit_classification( + sorting_analyzer, + unit_type, + unit_type_string, + thresholds=None, + backend=None, + **backend_kwargs, +): + """ + Plot summary of unit classification results. + + Parameters + ---------- + sorting_analyzer : SortingAnalyzer + The SortingAnalyzer object. + unit_type : np.ndarray + Numeric unit type array from classify_units(). + unit_type_string : np.ndarray + String labels from classify_units(). + thresholds : dict, optional + Threshold dictionary. + backend : str, optional + Backend to use for plotting. + **backend_kwargs + Additional kwargs for the backend. + + Returns + ------- + widget : UnitClassificationWidget + The widget object. + """ + widget = UnitClassificationWidget( + sorting_analyzer, + unit_type, + unit_type_string, + thresholds=thresholds, + backend=backend, + **backend_kwargs, + ) + return widget + + +def plot_classification_histograms( + quality_metrics, + thresholds=None, + metrics_to_plot=None, + backend=None, + **backend_kwargs, +): + """ + Plot histograms of quality metrics with threshold lines. + + Parameters + ---------- + quality_metrics : pd.DataFrame + DataFrame with quality metrics. + thresholds : dict, optional + Threshold dictionary. If None, uses default thresholds. + metrics_to_plot : list of str, optional + List of metric names to plot. + backend : str, optional + Backend to use for plotting. + **backend_kwargs + Additional kwargs for the backend. + + Returns + ------- + widget : ClassificationHistogramsWidget + The widget object. + """ + widget = ClassificationHistogramsWidget( + quality_metrics, + thresholds=thresholds, + metrics_to_plot=metrics_to_plot, + backend=backend, + **backend_kwargs, + ) + return widget + + +def plot_waveform_overlay( + sorting_analyzer, + unit_type, + unit_type_string, + split_non_somatic=False, + backend=None, + **backend_kwargs, +): + """ + Plot overlaid waveforms grouped by unit classification type. + + Parameters + ---------- + sorting_analyzer : SortingAnalyzer + The SortingAnalyzer object with computed templates. + unit_type : np.ndarray + Numeric unit type array from classify_units(). + unit_type_string : np.ndarray + String labels from classify_units(). + split_non_somatic : bool, default: False + If True, splits non-somatic into good/MUA. + backend : str, optional + Backend to use for plotting. + **backend_kwargs + Additional kwargs for the backend. + + Returns + ------- + widget : WaveformOverlayWidget + The widget object. + """ + widget = WaveformOverlayWidget( + sorting_analyzer, + unit_type, + unit_type_string, + split_non_somatic=split_non_somatic, + backend=backend, + **backend_kwargs, + ) + return widget diff --git a/src/spikeinterface/widgets/widget_list.py b/src/spikeinterface/widgets/widget_list.py index 6edba67c96..5dab36d773 100644 --- a/src/spikeinterface/widgets/widget_list.py +++ b/src/spikeinterface/widgets/widget_list.py @@ -37,12 +37,21 @@ from .comparison import AgreementMatrixWidget, ConfusionMatrixWidget from .gtstudy import StudyRunTimesWidget, StudyUnitCountsWidget, StudyPerformances, StudyAgreementMatrix, StudySummary from .collision import ComparisonCollisionBySimilarityWidget, StudyComparisonCollisionBySimilarityWidget +from .unit_classification import ( + UnitClassificationWidget, + ClassificationHistogramsWidget, + WaveformOverlayWidget, + plot_unit_classification, + plot_classification_histograms, + plot_waveform_overlay, +) widget_list = [ AgreementMatrixWidget, AllAmplitudesDistributionsWidget, AmplitudesWidget, AutoCorrelogramsWidget, + ClassificationHistogramsWidget, ConfusionMatrixWidget, ComparisonCollisionBySimilarityWidget, CrossCorrelogramsWidget, @@ -67,6 +76,7 @@ TemplateMetricsWidget, TemplateSimilarityWidget, TracesWidget, + UnitClassificationWidget, UnitDepthsWidget, UnitLocationsWidget, UnitPresenceWidget, @@ -75,6 +85,7 @@ UnitTemplatesWidget, UnitWaveformDensityMapWidget, UnitWaveformsWidget, + WaveformOverlayWidget, StudyRunTimesWidget, StudyUnitCountsWidget, StudyPerformances, From 44d81929e1244b64d917a4317580559b35a7dd93 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Wed, 7 Jan 2026 17:29:32 +0100 Subject: [PATCH 07/49] bombcell unit type classification logic and output plots - waveform overlay and histograms --- playground2.ipynb | 435 +++++++++++++++++- .../comparison/unit_classification.py | 54 +-- 2 files changed, 420 insertions(+), 69 deletions(-) diff --git a/playground2.ipynb b/playground2.ipynb index ab57eb72e1..24b2be94aa 100644 --- a/playground2.ipynb +++ b/playground2.ipynb @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -162,10 +162,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -183,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -583,7 +583,7 @@ "[330 rows x 20 columns]" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -989,19 +989,359 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "module 'spikeinterface.comparison' has no attribute 'print_threshold_failures'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[17], line 20\u001b[0m\n\u001b[1;32m 16\u001b[0m unit_type, labels \u001b[38;5;241m=\u001b[39m sc\u001b[38;5;241m.\u001b[39mclassify_units(metrics, thresholds)\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# plots!!!!!\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# Debug: see which thresholds are failing\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m sc\u001b[38;5;241m.\u001b[39mprint_threshold_failures(metrics)\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# Classify with default thresholds\u001b[39;00m\n\u001b[1;32m 23\u001b[0m unit_type, labels \u001b[38;5;241m=\u001b[39m sc\u001b[38;5;241m.\u001b[39mclassify_units(metrics)\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'spikeinterface.comparison' has no attribute 'print_threshold_failures'" - ] + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
amplitude_mediansync_spike_2sync_spike_4sync_spike_8firing_ratesnramplitude_cv_medianamplitude_cv_rangepresence_ratioamplitude_cutoffsliding_rp_violationisi_violations_ratioisi_violations_countsd_ratiorp_contaminationrp_violationsnum_spikesfiring_range
0-10.5300000.1057950.0068020.0001906.1215790.9780101.1871011.1226011.00.000089NaN0.9518534601.3373941.02612631513.80
1-43.6800000.0630040.0021080.00003130.1286813.898206NaNNaN1.00.000002NaN0.45445453201.1094061.0407512951571.00
2-9.1650000.1249620.0094690.0001213.8571880.9449061.3981811.1074681.00.000083NaN0.7974231531.3014711.088165818.02
3-30.2249980.0863430.0083670.00024511.3991872.8399660.3412670.3241811.00.000136NaN0.4081766841.1207401.06054900217.20
4-27.8849980.1408250.0173890.0006234.4815582.428674NaNNaN1.00.000122NaN0.7605831971.3690031.01291926510.80
.........................................................
325-12.0900000.3360320.0585260.0039465.6600472.556509NaNNaN1.00.000011NaN4.15835917182.5443451.011762433115.82
326-15.7950000.2260840.0237520.0012393.1928053.4022700.8117861.2482981.00.000031NaN3.5599174682.7544591.0264137259.20
327-16.5749990.2976150.0377360.0014241.3068983.3737960.7613740.6439521.00.000079NaN1.952197432.5139391.03756184.00
328-14.2350000.3487250.0547680.00175111.4257062.804173NaNNaN1.00.000011NaN1.23013520712.3674201.011524911630.80
329-14.2350000.2587550.0229760.0003044.5967092.740247NaNNaN1.00.000177NaN2.9872348142.5234151.04411976013.00
\n", + "

330 rows × 18 columns

\n", + "
" + ], + "text/plain": [ + " amplitude_median sync_spike_2 sync_spike_4 sync_spike_8 firing_rate \\\n", + "0 -10.530000 0.105795 0.006802 0.000190 6.121579 \n", + "1 -43.680000 0.063004 0.002108 0.000031 30.128681 \n", + "2 -9.165000 0.124962 0.009469 0.000121 3.857188 \n", + "3 -30.224998 0.086343 0.008367 0.000245 11.399187 \n", + "4 -27.884998 0.140825 0.017389 0.000623 4.481558 \n", + ".. ... ... ... ... ... \n", + "325 -12.090000 0.336032 0.058526 0.003946 5.660047 \n", + "326 -15.795000 0.226084 0.023752 0.001239 3.192805 \n", + "327 -16.574999 0.297615 0.037736 0.001424 1.306898 \n", + "328 -14.235000 0.348725 0.054768 0.001751 11.425706 \n", + "329 -14.235000 0.258755 0.022976 0.000304 4.596709 \n", + "\n", + " snr amplitude_cv_median amplitude_cv_range presence_ratio \\\n", + "0 0.978010 1.187101 1.122601 1.0 \n", + "1 3.898206 NaN NaN 1.0 \n", + "2 0.944906 1.398181 1.107468 1.0 \n", + "3 2.839966 0.341267 0.324181 1.0 \n", + "4 2.428674 NaN NaN 1.0 \n", + ".. ... ... ... ... \n", + "325 2.556509 NaN NaN 1.0 \n", + "326 3.402270 0.811786 1.248298 1.0 \n", + "327 3.373796 0.761374 0.643952 1.0 \n", + "328 2.804173 NaN NaN 1.0 \n", + "329 2.740247 NaN NaN 1.0 \n", + "\n", + " amplitude_cutoff sliding_rp_violation isi_violations_ratio \\\n", + "0 0.000089 NaN 0.951853 \n", + "1 0.000002 NaN 0.454454 \n", + "2 0.000083 NaN 0.797423 \n", + "3 0.000136 NaN 0.408176 \n", + "4 0.000122 NaN 0.760583 \n", + ".. ... ... ... \n", + "325 0.000011 NaN 4.158359 \n", + "326 0.000031 NaN 3.559917 \n", + "327 0.000079 NaN 1.952197 \n", + "328 0.000011 NaN 1.230135 \n", + "329 0.000177 NaN 2.987234 \n", + "\n", + " isi_violations_count sd_ratio rp_contamination rp_violations \\\n", + "0 460 1.337394 1.0 261 \n", + "1 5320 1.109406 1.0 4075 \n", + "2 153 1.301471 1.0 88 \n", + "3 684 1.120740 1.0 605 \n", + "4 197 1.369003 1.0 129 \n", + ".. ... ... ... ... \n", + "325 1718 2.544345 1.0 1176 \n", + "326 468 2.754459 1.0 264 \n", + "327 43 2.513939 1.0 37 \n", + "328 2071 2.367420 1.0 1152 \n", + "329 814 2.523415 1.0 441 \n", + "\n", + " num_spikes firing_range \n", + "0 26315 13.80 \n", + "1 129515 71.00 \n", + "2 16581 8.02 \n", + "3 49002 17.20 \n", + "4 19265 10.80 \n", + ".. ... ... \n", + "325 24331 15.82 \n", + "326 13725 9.20 \n", + "327 5618 4.00 \n", + "328 49116 30.80 \n", + "329 19760 13.00 \n", + "\n", + "[330 rows x 18 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1013,6 +1353,55 @@ "tm = analyzer.get_extension(\"template_metrics\").get_data()\n", "metrics = pd.concat([qm, tm], axis=1)\n", "\n", + "qm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'total_units': 330, 'counts': {'NOISE': 138, 'GOOD': 21, 'MUA': 171}, 'percentages': {'NOISE': 41.8, 'GOOD': 6.4, 'MUA': 51.8}}\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABjUAAAXSCAYAAABQKm2uAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVxU9foH8M/IMiCbIcuAKKKpqZQWeBHKNaWwzFwSs0uaS/LDndsimoWVkt3ikqmYXXLJjUwxu5KKt8RMLHEp0zJTFiWQRWQA2Tm/P7hMjczAgDNz5gyf9+t1Xjpnnjnn+c7y/XLOcxaZIAgCiIiIiIiIiIiIiIiITFwHsRMgIiIiIiIiIiIiIiLSBYsaREREREREREREREQkCSxqEBERERERERERERGRJLCoQUREREREREREREREksCiBhERERERERERERERSQKLGkREREREREREREREJAksahARERERERERERERkSSwqEFERERERERERERERJLAooaOBEGAUqmEIAhip0JERCaOYwYREemC4wUREemKYwYR0Z8sxU5AKkpLS+Hk5ISSkhI4Ojrq/LrYWECpBBwdgchIAyZIREQmoy1jBscLIqL2R+N4wQGBqFX4k6H2oq37pUg/2NcQmRaZwBKvTpRKZZsGDy8vICcH6NIFuH7dgAkSEZHJaMuYwfGCiKj90ThecEAgahX+ZKi9aOt+KdIP9jVEpoWXnyIionYjPj4eDzzwABwdHeHo6IjAwEB89dVXqucFQUB0dDQ8PT1ha2uL4cOH48KFCyJmTERERERE5qB79+6QyWRNprlz54qdGhGR5LCoQURE7YaXlxfeeecdpKenIz09HSNHjsS4ceNUhYt3330XsbGxWLt2LU6dOgWFQoHRo0ejtLRU5MyJiIiIiEjKTp06hdzcXNWUkpICAHjmmWdEzoyISHpY1CAionZj7NixGDNmDHr37o3evXtj5cqVsLe3x8mTJyEIAuLi4rBs2TJMmDABvr6+2LJlC27fvo0dO3aInToREREREUmYq6srFAqFavrPf/6Dnj17YtiwYWKnRkQkObxRuIE98QRw8ybg7Cx2JkTSIAgCamtrUVdXJ3YqkmNlZQULCwux05CMuro67N69G+Xl5QgMDERGRgby8vIQHBysipHL5Rg2bBhOnDiBOXPmGDQfjhdErVdXV4eamhqx05AcCwsLWFpaQiaTiZ2KztavX49//vOfyM3NRf/+/REXF4chQ4ZojU9NTUVkZCQuXLgAT09PvPLKKwgPD1eL2bNnD5YvX44rV66gZ8+eWLlyJcaPH696Pj4+HvHx8cjMzAQA9O/fH6+//jpCQkJUMdOnT8eWLVvUlhsQEICTJ0+2vbEcEEiCxOyPp04FSkoAJyegslKUFMwatzEMo7q6Gtu2bUNkZKTW8biqqgpVVVWqx0ql0ljpkQYcnqWP2w6GZ8wxQ/JFjZY2NgRBwIoVK7Bx40YUFxcjICAA69atQ//+/Y2S30cfGWU1RGahuroaubm5uH37ttipSJJMJoOXlxfs7e3FTsWknT9/HoGBgaisrIS9vT2SkpLQr18/nDhxAgDg7u6uFu/u7o6srKxml6mPDQ6OF0StU1ZWhuvXr0MQBLFTkaSOHTvCw8MD1tbWYqfSosTERCxatAjr16/Hww8/jI8++gghISG4ePEiunXr1iQ+IyMDY8aMwezZs7Ft2zZ89913iIiIgKurKyZOnAgASEtLQ2hoKN566y2MHz8eSUlJmDx5Mo4fP46AgAAAf16y8N577wUAbNmyBePGjcPZs2fVtiUef/xxbNq0SfX4rt9TDggkMWL3xy+88Of/MzJEScGscRvDMPbt24dbt25h+vTpWmNiYmKwYsUK4yVFzeLwLG1ij1XthTHHDJkg8U/zyy+/hIWFhdrGxj//+U/Vxsbq1auxcuVKbN68Gb1798bbb7+NY8eO4dKlS3BwcNB5PUqlEk5OTigpKYGjo6OhmkPUbtXX1+Py5cuwsLCAq6srrK2tJXUEqdgEQUBBQQFu376NXr168WiqZlRXVyM7Oxu3bt3Cnj178O9//xupqam4desWHn74Yfzxxx/w8PBQxc+ePRvXrl3DwYMHtS4zOjpa4wYHxwwiw6irq8Ply5fRsWNHuLq6crxoBUEQUF1djYKCAtTV1aFXr17o0MG0r0gbEBCAhx56CPHx8ap5ffv2xdNPP42YmJgm8a+++ir279+PX375RTUvPDwcP/74I9LS0gAAoaGhUCqV+Oqrr1Qxjz/+OO655x7s3LlTay7Ozs745z//iZkzZwJoOFPj1q1b2LdvX5vaxm0Mkjr2x+aN2xiG89hjj8Ha2hpffvml1hhNB0517dqVYwZRK3GsMg5jjxmSP1Nj7Nixao9XrlyJ+Ph4nDx5Ev369VO7PjrQUPRwd3fHjh07DH4pESLSXXV1Nerr69G1a1d07NhR7HQkydXVFZmZmaipqeEGRzOsra1VhXB/f3+cOnUKH3zwAV599VUAQF5enlpRIz8/v8nZG3eKiopCZGSk6nHjBgcRGUZNTQ0EQYCrqytsbW3FTkdybG1tYWVlhaysLFRXV8PGxkbslLSqrq7G6dOnsWTJErX5wcHBqjPs7pSWlqZ2KUGgYedRQkICampqYGVlhbS0NCxevLhJTFxcnMZl3nnJwr86evQo3Nzc0KlTJwwbNgwrV66Em5ubxuXwUiJkbtgfmz9uY+hfVlYWjhw5gr179zYbJ5fLIZfLjZQVkfniWGU8xhwzTPuwrFaqq6vDrl27dL4+enOqqqqgVCrVJiIyPFM/WtSU8WiDthEEAVVVVfDx8YFCoUBKSorquerqaqSmpiIoKKjZZcjlcjg6OqpNRGR47PfaTirjbWFhIerq6jReGjAvL0/ja/Ly8jTG19bWorCwsNmYO5d5/vx52NvbQy6XIzw8XHXJwkYhISHYvn07vv76a7z//vs4deoURo4cqVa4+KuYmBg4OTmpJhbAyVywPzZf/Gz1b9OmTXBzc8MTTzwhdipE7Qr7M8Mz5nss+TM1AMNcH11f1y709wf++KMOnTvXYN++HK1xjo6OcHV1vev1EZH5WbNmDTZu3IgOHTqgQ4cOiIqKQmhoqNhpSdLSpUsREhKCrl27orS0FLt27cLRo0dx8OBByGQyLFq0CKtWrUKvXr3Qq1cvrFq1Ch07dsTUqVMNnpuu4wXAMYOItDtw4ADeeOMNnD9/HvPnz8d7770ndkp6cecGkiAIzW40aYq/c74uy+zTpw/OnTunumThtGnTkJqaqips/HU89vX1hb+/P7y9vXHgwAHVmeJ/pdOZff7+QF4eoFAA6ela20i6KSgoaPEANY6r0nbxIlBTA1hZAX+pObYoPT0d//rXv7B9+3a95PHJJ5/gX//6F3755RfExcVh3rx5elluo4SEBLzzzjuor6/Ho48+ivXr18PS0hLnz5/H3LlzkZ+fDysrKwQGBuLDDz/kEf4mqr6+Hps2bcK0adNgaSm9XXLtuU/l8EzmqL6+HgsXLkRycjJkMhkiIyMRERGhMTY4OBh5eXno0KEDHBwc8OGHH2LgwIHGTfgvpNeDaqBtY6NRazeCAP1dSuSPP+qQm2uBwqKbmDIjXGucs0NHbNv0b7Ps+Ino7vTv3x/fffcdnJyccO3aNTz00EMYPHgwvL29xU5Ncm7cuIGwsDDk5ubCyckJDzzwAA4ePIjRo0cDAF555RVUVFQgIiICxcXFCAgIwOHDh1t1D6a2ahwviosKsWjG5GZj5Q6dEb9pO8cMImqiV69eSEhIwO7du1FZWSl2OnfNxcUFFhYWTc6gaO7SgAqFQmO8paUlOnfu3GzMncvUdsnCj7TcLdTDwwPe3t64fPmyxud1upRIXh6Q03xxm3RTUFCAsJlhuFl+s9k4ZztnfJrwKcdViaqpaZhay9/fX28FDQDw8/PDZ599pvFeP7qKjo5G9+7dm9w8OiMjA8uXL8fZs2fh5uaGcePGISEhAXPmzIGNjQ3Wrl2LBx54AHV1dZg6dSref/99LF269C5bRIZw5MgRZGdnY8aMGWKn0moFBQV48e9hqChuvk+1vccZG7eZX5/K4ZnM0bZt23Dx4kX89ttvKCkpwUMPPYSRI0fivvvuaxL72WefoVOnTgCAffv2YcaMGThz5oyRM/6TWRQ1DHF9dH1du7C+vuGosA7Wtuj+hOZKV/nNGyhI2wOlUml2nT6R1MlkMqxatQpJSUkoLCzExo0b8d///hcHDx5EdXU1PvvsM/Tv3x95eXl49tlnoVQqUVlZiUcffRQffPABZDIZZs+eDRsbG3z44Ye4efMmBg0ahC1btuCRRx7RKYdHH31U9f+uXbvC3d0d165dY1GjDRISEpp9XiaTITo6GtHR0cZJ6C8axwt7eQfEPa3QGnet6Dbe/7aIYwaRCTKFMaN3794AgKSkJEM21Wisra3h5+eHlJQUjB8/XjU/JSUF48aN0/iawMDAJjdePXz4MPz9/WFlZaWKSUlJUbuvxuHDh1u83GDjJQu1KSoqwrVr19S2PUg8SqUSN8tvosuYLrB3s9cYU5ZfhpzkHI6rZkTXvvjo0aN46aWXkJ6ejszMTPj7+yMiIgIHDhxASUkJ1qxZgzFjxui83gEDBgDQfHm/0tJSREZG4scff0RlZSWCgoLw4Ycfqvqklnz++ecYP368aj9GeHg43n33XcyZMwe9evVSxVlYWGDQoEH49ddfdc6bjCs4OFh19qDUKJVKVBTfxAyFAh52mvvU3PIyfJKXxz6VSAemsO2QmJiI8PBwWFhYwNnZGZMnT8auXbs07hNpLGgAQElJieiXszWLosadNF0f/cEHHwTw5/XRV69ebdScOnSwgKObl9bnC4yYC5GUxMY2TC156CFg/371eU89BWgrGkdGNky6cHR0xA8//IDdu3dj3LhxqiOw3n33XaxcuRI7duxAp06d8OWXX8Le3h51dXUYN24c9uzZg0mTJuHDDz9EYGAgdu/eja1bt2L27NmqAWbBggU4duyYxvV+9NFHCAgIUJt35MgRFBcXw8/PT7fkSXIsOsjQ013zRsKfeJ8nIk04ZgRofE7qIiMjERYWBn9/fwQGBmLjxo3Izs5GeHjDWdBRUVHIycnB1q1bATTs7Fu7di0iIyMxe/ZspKWlISEhATt37lQtc+HChRg6dChWr16NcePG4YsvvsCRI0dw/PhxVUxzlywEgLKyMkRHR2PixInw8PBAZmYmli5dChcXF7UCDInP3s0eTp5OYqfRfphAZ6xLX3ynoqIi+Pn54c0338TBgwexcOFCVVFj0qRJ+P333zWu68svv2zxqg7/+Mc/MHToUHz88ccQBAGzZ8/G2rVr1QqrzcnOzlY7oKl79+7Izs5uEldeXo5///vfRt/fQe2Lh509ujuxTyXpM4HhSvRtB03jS3oz11d7/vnn8c033wCA6m9isUi+qGHK10cnorunVOp2iqem7YiCAu2vbeEyoGoar5f90EMPoUOHDqobuvn5+WHv3r0AGq5D+Oqrr+L48eMQBAH5+fkYOHAgJk2aBBsbG+zevRt+fn4IDAxUnUUGNNwvQ1fnz5/HCy+8gMTERNja2ureACKidoJjhnkKDQ1FUVER3nzzTeTm5sLX1xfJycmqDbDc3Fy1nXs+Pj5ITk7G4sWLsW7dOnh6emLNmjWYOHGiKiYoKAi7du3Ca6+9huXLl6Nnz55ITExUKwy1dMlCCwsLnD9/Hlu3bsWtW7fg4eGBESNGIDEx0SiXLSQyWSbQGevSF9/Jzs5OdQZYYGAgrly5onru888/13ndmuzbtw8nT57E+++/DwCoqKiAtbU1AGDdunX4+OOPATRcZcLa2hpxcXEAgDfeeENVJP3rJbQ1HelfU1OD0NBQBAcHaz2TjYiI/mQCw5VJbDu0NL78VeNBRFu2bMHLL7+M5ORk3RurZ5Ivapjy9dGJ6O45OgJdurQcp+nMVldX7a91dNQ9BxsbGwANOy/+elk6CwsL1NbWAgBiY2NRVFSE77//HjY2NoiMjFS7lvmvv/4KOzs75Ofno7q6WrUcXY+6vXjxIp588kl88sknOp9GSETU3nDMMM8zNQAgIiJC600LN2/e3GTesGHDWrzG76RJkzBp0iStz7d0yUJbW1scOnSo2RiidskEOmNd+mJtr2mMq6urUz2+2zM1BEHAvn370KNHjybPzZ07F3PnzgWg/Z4a3bp1Q2ZmpupxVlYWunXrpnpcU1ODyZMnw8PDAx988EGzuRARUQMTGK5E33ZoHF8GDRoEoOn4os20adMQHh6OoqIi1T3rjE3yRQ1Tvj46Ed291py2d6c7Tw80pOLiYigUCtjY2ODGjRvYvXu3quKenZ2NiIgIHDlyBGvWrMGiRYsQHx8PQLfK+S+//IIxY8Zg48aNqoItERE1xTGDiMgESKUzboW7PVPjqaeewjvvvIP169fD0tISxcXFKCoqUt0btCUTJ07EI488gtdffx1ubm7YsGEDpkyZAgCora3FlClT4OzsjI0bN6odcUtERNpJZbgy5LbDM888g48++ggTJkxASUkJEhMTNV5WSqlUoqysDJ6engAa7t/XuXNnODs767GlrSPuHT2IiMzEggULcOLECQwcOBAzZszAqFGjAPy5kfHWW2+hX79+iIuLw8mTJ5GYmNiqZZeUlODVV1/FwIEDMXDgQB4ZSkQkYYYcM44ePQovLy/Exsbio48+gpeXF/ab6E5CIiJzsm3bNnh5eWH37t1Yvnw5vLy8cPbsWQBAXFwcLC0tMXDgQDzwwAMYNWqU2pkXLenRowdWrFiBhx9+GD179oSbmxtmzpwJoOEmr3v37kV6ejoefPBBDBw4UHXmBxERSZ8htx3CwsLQp08f9O7dG4MGDcLLL7+Mvn37AgD279+PWbNmAWi4MfjTTz+N+++/HwMGDMC6devwn//8R9RCukxo6WJZBKChIuXk5ISSkhI4tuI8IoWiFjduWMLWsQLPfvCz5mXnX0fmgfXY9ckG9OzZU18pE0lKZWUlMjIy4OPjo3bqN+mO76HpaMuY0TheuDsVIm+99g3RKzfKsGhfHuI++YxjBrVb7O/uHt9D06BxvPDyarhIc5cuwPXr4iYocVeuXMGzLz6LPtP7aL1ReMkfJbi0+RJ2btzJcbUNTKEv+fFHoKYGsLICBgwQJQWzZgqfMTVo634pfbly5QrmPvsslvW8V+uNwjNLSrDyyu9Yt9P8+lQOz9LFfsx4jPle80wNIiIiIiIiIiIiIiKSBMnfU8PUvfpqET6I3w53vyFip0JERCbs1VeLsDl+I6b/Td5yMBERma933wVu3wY6dhQ7EyJJ8PIC6uuBDjxkk4gMiMMzkWlhUcPAnnqqDDv2fYVuD/oA8BI7HSIiMlFPPVWGr/ftw1N+CgD2YqdDRERimTpV7AyIJKVzZ7EzIKL2gMMzkWnhsQxEZFLq6+vFTkGyeIskImpv2O+1HcdbIiIiImpPuO1geMZ8j3mmBhGZBGtra3To0AF//PEHXF1dYW1tDZlMJnZakiEIAgoKCiCTyWBlZSV2OkREBmVlZQWZTIaCggK4urpyvGgFQRBQXV2NgoICdOjQAdbW1mKnRERERERkMNx2MA5j75diUcPArl61QkW5N0oL7OHoJnY2RKarQ4cO8PHxQW5uLv744w+x05EkmUwGLy8vWFhYiJ0KtcHVq1YoLe+Bq/n3oKd7idjpEJk0CwsLeHl54fr168jMzBQ7HUnq2LEjunXrhg68CL3puXQJqK0FLC2BPn3EzobI5FVWAoIAyGSAjY3Y2RCRueLwLF3cdjAeY+6XYlHDwMLCPHHjxr9x9XIFnv3gZ7HTITJp1tbW6NatG2pra1FXVyd2OpJjZWXFgoaENYwXu/Hr74XIWz9X7HSITJ69vT169eqFmpoasVORHAsLC1haWvIoNVP16KNATg7QpQtw/brY2RCZvEuXgJoawMoKGDBA7GyIyFxxeJY2bjsYhzH3S7GoQUQmpfE0NV5CiYiIWmJhYcFiLhERmZwDBw7gjTfewPnz5zF//ny89957GuMqKysxZcoUXLx4ER07doRCocCGDRvQvXt3veRRX1+PhQsXIjk5GTKZDJGRkYiIiAAAJCUl4Y033kCHDh1QU1ODp59+Gm+//TYL3kRktrjtYF54vjkRERERERERkZ706tULCQkJePnll1uMffHFF3Hp0iWcO3cOTz75JF588cVWr2/48OEaL6mybds2XLx4Eb/99ht++OEHvPvuu/j1118BAKNGjcK5c+dw7tw5nD17FikpKfjyyy9bvW4iIiIxsKhBRERERERERGZBJpMhJiYGf/vb39CjRw8cOXIEUVFRePDBB9G/f39cuHABAJCXl4cRI0bAz88P/fv3x4IFCyAIAgBg9uzZmD9/PgDg5s2b6NmzJ44fP65zDr1798aAAQNgadn8xTFsbGwwZswY1dkRgwcPxtWrV1XPX758GU888QQGDRqEAQMGYP369a16LxITExEeHg4LCws4Oztj8uTJ2LVrFwDAwcFBdV+lyspKVFVV8T5LREQkGRyxiIiIiIiIiEgvYmMBL6+Wp6eeavrap57SHh8bq3sOjo6O+OGHH7B69WqMGzcOjzzyCM6ePYtp06Zh5cqVAIBOnTrhyy+/xOnTp/HTTz/h6tWr2LNnDwDgww8/xPHjx7F7925MmzYNs2fPxiOPPAIAWLBgAQYOHKhx+v777+/qvVuzZg3Gjh0LAKirq8PUqVPx/vvv49SpU0hLS8OGDRtw5swZnZeXnZ0Nb29v1ePu3bsjOztb9fjEiRN44IEH4ObmhkcffRRPPPHEXeVPRERkLLynBhEREREREbVbNdU1yMrKajbG0dERrq6uRspI2pTKhpvptqRr16bzCgq0v1ap1D2H0NBQAMBDDz2EDh06qHbW+/n5Ye/evQAa7jfx6quv4vjx4xAEAfn5+Rg4cCAmTZoEGxsb7N69G35+fggMDMSrr76qWvaaNWt0T6QVVq1ahcuXL2PDhg0AgEuXLuHChQuYMmWKKqa0tBQXL17EQw89hBdeeAFnz54FAPz+++8YM2YMrK2tAQBffvkluv7vDf7rPTIaz0RpFBQUhJ9++gkFBQWYMGECvv32WwwdOtQg7SMiItInFjWIiIiIiIioXapUViLzaiYiX4+Etdxaa5yznTM+TfiUhQ0dODoCXbq0HKfprXR11f5aR0fdc7CxsQHQcFNYuVyumm9hYYHa2loAQGxsLIqKivD999/DxsYGkZGRqKysVMX++uuvsLOzQ35+Pqqrq1XLWbBgAY4dO6ZxvR999BECAgJ0T/R/3nvvPezduxdHjhxBx44dATQUIFxcXHDu3DmNr9m0aZPq/8OHD8fmzZub3GC8W7duyMzMxKBBgwAAWVlZ6NatW5Nlubq64oknnsDu3btZ1CAiIklgUYOIiIiIiIjapZqKGtRb1EMRooBrN80Fi7L8MuQk50CpVLKooYPIyIapLfbv128uzSkuLoZCoYCNjQ1u3LiB3bt3q87wyM7ORkREBI4cOYI1a9Zg0aJFiI+PB6D/MzViY2Oxc+dOHDlyBJ06dVLN79OnDzp27IitW7fi+eefB9BwRoazszOcnZ11WvYzzzyDjz76CBMmTEBJSQkSExNx8OBBAA1ngvTq1QsdOnRAaWkp/vOf/2DatGl6bRsREZGh8J4aRERERERE1K7ZudjBydNJ42TvZi92emQACxYswIkTJzBw4EDMmDEDo0aNAgDU1tZiypQpeOutt9CvXz/ExcXh5MmTSExM1HnZR48ehZeXF2JjY/HRRx/By8sL+/9Xsdm/fz9mzZoFALh+/Tr+8Y9/4NatWxgxYgQGDhyoOtPD0tISX375JT777DM88MAD6N+/P2bNmoWKigqd8wgLC0OfPn3Qu3dvDBo0CC+//DL69u0LANi9ezd8fX0xYMAABAYGYtSoUaq8yDBycnLw97//HZ07d0bHjh0xcOBAnD59Wuy0iIgkiWdqEBEREREREZFZ+Ot9I7p3747CwkLV4+HDhyM9PR0A4O3tjR9++EHjMk6cOKH6v42NjereFboaPnw4rl+/rvG5p556Ck/97y7pXl5eTe5z8Ve9evXCf/7znxbXd/ToUY3zLSwssG7dOo3Pvfbaa3jttddaXDbpR3FxMR5++GGMGDECX331Fdzc3HDlyhW1s3OIiEh3LGoYWFLSdYQvjkLXUWEAeKoyERFplpR0HcsX/x/eCuksdipERCSmU6eAujrAwkLsTIgk4X8nHhCZtNWrV6Nr165q90K58x4oZNo4PBOZFl5+ysDc3OpgLS+ErWOV2KkQEZEJc3Org608H26OxWKnQkRkctavXw8fHx/Y2NjAz88P3377bbPxqamp8PPzg42NDXr06IENGzY0idmzZw/69esHuVyOfv36ISkpSe35+Ph4PPDAA3B0dISjoyMCAwPx1VdfqcUIgoDo6Gh4enrC1tYWw4cPx4ULF+6usR4egJdXw79E1CJr6z8nIlO1f/9++Pv745lnnoGbmxsefPBBfPzxx2KnRa3A4ZnItLCoQUREREREJisxMRGLFi3CsmXLcPbsWQwZMgQhISHIzs7WGJ+RkYExY8ZgyJAhOHv2LJYuXYoFCxZgz549qpi0tDSEhoYiLCwMP/74I8LCwjB58mR8//33qhgvLy+88847SE9PR3p6OkaOHIlx48apFS3effddxMbGYu3atTh16hQUCgVGjx6N0tJSw70hREQkOVevXkV8fDx69eqFQ4cOITw8HAsWLMDWrVu1vqaqqgpKpVJtIiKiBixqEBERERGRyYqNjcXMmTMxa9Ys9O3bF3FxcejatSvi4+M1xm/YsAHdunVDXFwc+vbti1mzZmHGjBl47733VDFxcXEYPXo0oqKicN999yEqKgqPPvoo4uLiVDFjx47FmDFj0Lt3b/Tu3RsrV66Evb09Tp48CaDhLI24uDgsW7YMEyZMgK+vL7Zs2YLbt29jx44dBn1PiExNc/eFIGnjZ6sf9fX1eOihh7Bq1So8+OCDmDNnDmbPnq11LAOAmJgYODk5qaauXbsaMWMiItPGe2oY2K5dDsi7PhH133fHwLG1YqdDRNSuxcTEYO/evfj1119ha2uLoKAgrF69Gn369FHFTJ8+HVu2bFF7XUBAgGonlqHs2uWAq9efw640Kyx7+rhB10VEJBXV1dU4ffo0lixZojY/ODhY7Ua+f5WWlobg4GC1eY899hgSEhJQU1MDKysrpKWlYfHixU1i/lrU+Ku6ujrs3r0b5eXlCAwMBNBwRkheXp7auuRyOYYNG4YTJ05gzpw5TZZTVVWFqqo/L0ur8ajbjRuBsjLA3h548UWN+RCZCisrK8hkMhQUFMDV1RUymczoOdy8+ed17p2djb56syYIAgoKCiCTyWBlZSV2OpLm4eGBfv36qc3r27ev2lmEd4qKikJkZKTqsVKpZGFDRByeiUwLixoG9uGHzrhxIxxFhRUYOPZnsdMhImrXUlNTMXfuXAwaNAi1tbVYtmwZgoODcfHiRdjZ2aniHn/8cbWb+Fkb4SLNDeNFJD4sKmRRg4jofwoLC1FXVwd3d3e1+e7u7sjLy9P4mry8PI3xtbW1KCwshIeHh9aYO5d5/vx5BAYGorKyEvb29khKSlLtlGqM1bScrKwsjbnFxMRgxYoVzTf6zTeBnBygSxfuNSGTZ2FhAS8vL1y/fh2ZmZmi5HD9+p9FDS8vUVIwazKZDF5eXrDg3ZHvysMPP4xLly6pzfvtt9/g7e2t9TVyuRxyudzQqZGOODwTmRYWNYiIqN04ePCg2uNNmzbBzc0Np0+fxtChQ1Xz5XI5FAqFsdMjIiIt7jz6WxCEZo8I1xR/53xdltmnTx+cO3cOt27dwp49ezBt2jSkpqaqHW3bmtx41C2ZI3t7e/Tq1Qs1NTWirH/6dODGDcDdHUhNFSUFs2ZlZcWChh4sXrwYQUFBWLVqFSZPnowffvgBGzduxMaNG8VOjYhIkiRf1DDlS4kQEZFpKykpAQA433GtgqNHj8LNzQ2dOnXCsGHDsHLlSri5uWldjk6XEyEiolZzcXGBhYVFkzMo8vPzm5wh0UihUGiMt7S0ROfOnZuNuXOZ1tbWuPfeewEA/v7+OHXqFD744AN89NFHquJ3Xl4ePDw8dMqNR92SubKwsBBtx3dOTsNUWwvY2IiSAlGLBg0ahKSkJERFReHNN9+Ej48P4uLi8Nxzz4mdGhGRJEn+RuGNlxI5efIkUlJSUFtbi+DgYJSXl6vFPf7448jNzVVNycnJImVMRESmQBAEREZG4pFHHoGvr69qfkhICLZv346vv/4a77//Pk6dOoWRI0eqFS3uxJv4EREZhrW1Nfz8/JCSkqI2PyUlBUFBQRpfExgY2CT+8OHD8Pf3V10TXluMtmU2EgRBNR74+PhAoVCoLae6uhqpqaktLoeIiNqfJ598EufPn0dlZSV++eUXzJ49W+yUiIgkS/JnavBSIkRE1Bbz5s3DTz/9hOPH1e9fERoaqvq/r68v/P394e3tjQMHDmDChAkal8XLiRARGU5kZCTCwsLg7++PwMBAbNy4EdnZ2QgPDwfQ0Afn5ORg69atAIDw8HCsXbsWkZGRmD17NtLS0pCQkICdO3eqlrlw4UIMHToUq1evxrhx4/DFF1/gyJEjamPC0qVLERISgq5du6K0tBS7du3C0aNHVdsfMpkMixYtwqpVq9CrVy/06tULq1atQseOHTF16lQjvkNERERERO2L5Isad+KlRIiIqCXz58/H/v37cezYMXi1cEdJDw8PeHt74/Lly1pjeDkRIiLDCQ0NRVFREd58803k5ubC19cXycnJqpur5ubmIjs7WxXv4+OD5ORkLF68GOvWrYOnpyfWrFmDiRMnqmKCgoKwa9cuvPbaa1i+fDl69uyJxMREBAQEqGJu3LiBsLAw5ObmwsnJCQ888AAOHjyI0aNHq2JeeeUVVFRUICIiAsXFxQgICMDhw4fh4OBghHeGiIiIiKh9MquiRnOXEnnmmWfg7e2NjIwMLF++HCNHjsTp06e17oSKiYnBihUrjJU6EREZgSAImD9/PpKSknD06FH4+Pi0+JqioiJcu3ZN7XrpRERkXBEREYiIiND43ObNm5vMGzZsGM6cOdPsMidNmoRJkyZpfT4hIaHFvGQyGaKjoxEdHd1iLBERERER6YdZFTV4KREiImrO3LlzsWPHDnzxxRdwcHBQ3STWyckJtra2KCsrQ3R0NCZOnAgPDw9kZmZi6dKlcHFxwfjx40XOnoiIiIiIiIiIzKaowUuJEBFRS+Lj4wEAw4cPV5u/adMmTJ8+HRYWFjh//jy2bt2KW7duwcPDAyNGjEBiYiIvJUJEREREREREZAIkX9Qw9UuJ+PjUoKT0Ouxd7Qy+LiIiap4gCM0+b2tri0OHDhkpG3U+PjUoL82Cj2uZKOsnIiIT0bs34OQEuLuLnQmRJPAnQ0TGwL6GyLRIvqhh6pcS2bbtD0yZEY7uT0QAaP4MEiIiar+2bfsDi2ZMRtzTCgD2YqdDRERi+fprsTMgkhT+ZIjIGNjXEJkWyRc1eCkRIiIiIiIiIiIiIqL2QfJFDVO+lAgREREREREREREREelPB7ETICIiIiIiIiIiIiIi0oXkz9QwdZGRbvjtfAxuKN0xemGe2OkQEZGJiox0w/fn1yKyrBJf/GOD2OkQEZFYnnsOKCwEXFyA7dvFzobI5PEnQ0TGwL6GyLSwqGFg339vC2WxP2rqKgCwqEFERJp9/70tCooD8X19odipEBGRmFJTgZwcoEsXsTMhkgT+ZIjIGNjXEJkWXn6KiIiIiIiIiIiIiIgkgUUNIiIiIiIiIiIiIiKSBBY1iIiIiIiIiIiIiIhIEljUICIiIiIiIiIiIiIiSWBRg4iIiIiIiIiIiIiIJIFFDSIiIiIiIiIiIiIikgQWNYiIiIiIiIiIiIiISBJY1CAiIiIiIiIiIiIiIkmwFDsBcxcaqkTinq/RuW9/sVMhIiITFhqqxJd7kjHWV+xMiIhIVLNnAyUlgJOT2JkQSQJ/MkRkDOxriEwLixoGtmBBMU6c+wjdR0cA8BI7HSIiMlELFhTj6rl/YcFjCgD2YqdDRERieeMNsTMgkhT+ZEgKoqOjsWLFCrV57u7uyMvLEykjai32NUSmhUUNIiIiIiIiIiIiA+rfvz+OHDmiemxhYSFiNkRE0sZ7ahARERERkUlbv349fHx8YGNjAz8/P3z77bfNxqempsLPzw82Njbo0aMHNmzY0CRmz5496NevH+RyOfr164ekpCS152NiYjBo0CA4ODjAzc0NTz/9NC5duqQWM336dMhkMrVp8ODBd99gIiIyO5aWllAoFKrJ1dVV7JSIiCSLRQ0iIiIiIjJZiYmJWLRoEZYtW4azZ89iyJAhCAkJQXZ2tsb4jIwMjBkzBkOGDMHZs2exdOlSLFiwAHv27FHFpKWlITQ0FGFhYfjxxx8RFhaGyZMn4/vvv1fFpKamYu7cuTh58iRSUlJQW1uL4OBglJeXq63v8ccfR25urmpKTk42zBtBRESSdvnyZXh6esLHxwdTpkzB1atXm42vqqqCUqlUm4iIqAGLGgb28MPeSD+WggMrHxc7FSIiMmEPP+yN/xw7jYdXbBE7FSIikxIbG4uZM2di1qxZ6Nu3L+Li4tC1a1fEx8drjN+wYQO6deuGuLg49O3bF7NmzcKMGTPw3nvvqWLi4uIwevRoREVF4b777kNUVBQeffRRxMXFqWIOHjyI6dOno3///hgwYAA2bdqE7OxsnD59Wm19crlc7chbZ2fnu2uwlxcgkzX8S0Qt4k+GpCAgIABbt27FoUOH8PHHHyMvLw9BQUEoKirS+pqYmBg4OTmppq5duxoxY7oT+xoi08KiBhERERERmaTq6mqcPn0awcHBavODg4Nx4sQJja9JS0trEv/YY48hPT0dNTU1zcZoWyYAlJSUAECTosXRo0fh5uaG3r17Y/bs2cjPz9e6DB51S0TUPoWEhGDixIm4//77MWrUKBw4cAAAsGWL9gOaoqKiUFJSopquXbtmrHSJiEweixpERERERGSSCgsLUVdXB3d3d7X57u7uyMvL0/iavLw8jfG1tbUoLCxsNkbbMgVBQGRkJB555BH4+vqq5oeEhGD79u34+uuv8f777+PUqVMYOXIkqqqqNC6HR90SEREA2NnZ4f7778fly5e1xsjlcjg6OqpNRETUwFLsBIiIiIiIiJojk8nUHguC0GReS/F3zm/NMufNm4effvoJx48fV5sfGhqq+r+vry/8/f3h7e2NAwcOYMKECU2WExUVhcjISNVjpVLJwgYRUTtUVVWFX375BUOGDBE7FSIiSWJRg4iIiIiITJKLiwssLCyanEGRn5/f5EyLRgqFQmO8paUlOnfu3GyMpmXOnz8f+/fvx7Fjx+DVwoW0PTw84O3trfXIW7lcDrlc3uwyiIjI/Lz00ksYO3YsunXrhvz8fLz99ttQKpWYNm2a2KkREUkSixpERERERGSSrK2t4efnh5SUFIwfP141PyUlBePGjdP4msDAQHz55Zdq8w4fPgx/f39YWVmpYlJSUrB48WK1mKCgINVjQRAwf/58JCUl4ejRo/Dx8Wkx36KiIly7dg0eHh6taicREZm369ev49lnn0VhYSFcXV0xePBgnDx5Et7e3mKnpnfVNTXIyspqMc7R0RGurq5GyIiIzBGLGkREREREZLIiIyMRFhYGf39/BAYGYuPGjcjOzkZ4eDiAhks65eTkYOvWrQCA8PBwrF27FpGRkZg9ezbS0tKQkJCAnTt3qpa5cOFCDB06FKtXr8a4cePwxRdf4MiRI2qXl5o7dy527NiBL774Ag4ODqozO5ycnGBra4uysjJER0dj4sSJ8PDwQGZmJpYuXQoXFxe1AgwREdGuXbvETsEoblVW4mpmJt5evBjWLZyZaHuPMzZu+5SFDSJqExY1iIio3YiJicHevXvx66+/wtbWFkFBQVi9ejX69OmjihEEAStWrMDGjRtRXFyMgIAArFu3Dv379xcxcyKi9is0NBRFRUV48803kZubC19fXyQnJ6uObs3NzUV2drYq3sfHB8nJyVi8eDHWrVsHT09PrFmzBhMnTlTFBAUFYdeuXXjttdewfPly9OzZE4mJiQgICFDFxMfHAwCGDx+uls+mTZswffp0WFhY4Pz589i6dStu3boFDw8PjBgxAomJiXBwcDDgO0JERGSaymtqYFVfj+nu7vDp7KI1Lre8DJ/k5UGpVLKoQURtwqIGERG1G6mpqZg7dy4GDRqE2tpaLFu2DMHBwbh48SLs7OwAAO+++y5iY2OxefNm9O7dG2+//TZGjx6NS5cucScVEZFIIiIiEBERofG5zZs3N5k3bNgwnDlzptllTpo0CZMmTdL6fOPNxbWxtbXFoUOHmo0hIiJqjxR2duju5CR2GkRkxjqIncDdiomJwaBBg+Dg4AA3Nzc8/fTTuHTpklqMIAiIjo6Gp6cnbG1tMXz4cFy4cEGkjImISCwHDx7E9OnT0b9/fwwYMACbNm1CdnY2Tp8+DaBhvIiLi8OyZcswYcIE+Pr6YsuWLbh9+zZ27NghcvZERERERERERCT5okbjUbcnT55ESkoKamtrERwcjPLyclVM41G3a9euxalTp6BQKDB69GiUlpYaPL/337+BXr5RGDQl3eDrIiKi1ikpKQEAODs7AwAyMjKQl5eH4OBgVYxcLsewYcNw4sQJrcupqqqCUqlUm1rr/fdv4G++8/D+c++1+rVERGRGtm0DDh5s+JeIWsSfDBEZA/saItMi+ctPHTx4UO3xpk2b4ObmhtOnT2Po0KFNjroFgC1btsDd3R07duzAnDlzDJrf4MGVcHJOh1vPvwHwMui6iIhId4IgIDIyEo888gh8fX0BQHUTWHd3d7VYd3d3ZGVlaV1WTEwMVqxYcVf5DB5ciV3OaRh8rwKA/V0ti4iIJOyOe3gQUfP4kyEiY2BfQ2RaJH+mxp1M6ahbIiIyXfPmzcNPP/2EnTt3NnlOJpOpPRYEocm8v4qKikJJSYlqunbtmt7zJSIiIiIiIiIiMytqtPao28bnNImJiYGTk5Nq6tq1q+ESJyIio5o/fz7279+Pb775Bl5ef55Fp1AoAKDJ+JCfn99kHPkruVwOR0dHtYmIiIiIiIiIiPTPrIoapnjU7cmTNii56Y/8Ky5tej0REemPIAiYN28e9u7di6+//ho+Pj5qz/v4+EChUCAlJUU1r7q6GqmpqQgKCjJobidP2iD/ZiBO/n6/QddDREQm7uhR4NChhn+JqEX8yRCRMbCvITItkr+nRqPGo26PHTum9ahbDw8P1XxdjrqVy+V3ndc//uGOGzdicD27AvcG/nzXyyMiorabO3cuduzYgS+++AIODg6qMzKcnJxga2sLmUyGRYsWYdWqVejVqxd69eqFVatWoWPHjpg6dapBc2sYL9biH9cK8dzDcw26LiIiMmF//zuQkwN06QJcvy52NkQmjz8ZIjIG9jVEpkXyZ2qY8lG3RERkWuLj41FSUoLhw4fDw8NDNSUmJqpiXnnlFSxatAgRERHw9/dHTk4ODh8+DAcHBxEzJyIiIiIiIiIiwAzO1DDlo26JiMi0CILQYoxMJkN0dDSio6MNnxAREZEBFRQUQKlUNhvj6OgIV1dXI2VERERERHT3JF/UiI+PBwAMHz5cbf6mTZswffp0AA1H3VZUVCAiIgLFxcUICAjgUbdERERERGS2CgoKEDYzDDfLbzYb52znjE8TPmVhg4ionWupEJ6VlYXa2lojZkREpJ3kixo86paIiIiIiEidUqnEzfKb6DKmC+zd7DXGlOWXISc5B0qlkkUNIqJ2rKCgAC/+PQwVxdoL4bcrK3Hj+nVU9+hpxMyIiDSTfFGDiIiIiIiINLN3s4eTp5PYaRARkQlTKpWoKL6JGQoFPOw0F8LP5t/Ah1lZqKvj2RpEJD5RixoZGRlNbuxNRESkCccMIiJpYb9N5qSmugZZWVnNxvD+JETmqT2NZx529ujupLkQnlNaauRsiIi0E7Woce+992Lo0KGYOXMmJk2aBBsbGzHTISIiE8Yxg4hIWthvk7moVFYi82omIl+PhLXcWmsc709CZJ44nhERmZ4OYq78xx9/xIMPPoh//OMfUCgUmDNnDn744QcxUyIiIhPFMYOISFrYb5O5qKmoQb1FPRQhCvSZ3kfj1GVMF9wsv9nsTXaJSJo4nhERmR5Rixq+vr6IjY1FTk4ONm3ahLy8PDzyyCPo378/YmNjUVBQIGZ6RERkQjhmEBFJC/ttMjd2LnZw8nTSOGm7GTsRSR/HMyIi0yNqUaORpaUlxo8fj88++wyrV6/GlStX8NJLL8HLywvPP/88cnNzxU6xzb77Lgv+Q0fjiWUHxU6FiMgsmOuY8d13WXhyqB++e2Oa2KkQEemVufbbBnP9OiAIDf8SUYv4kyFj4XjWvrGvITItJlHUSE9PR0REBDw8PBAbG4uXXnoJV65cwddff42cnByMGzdO7BSJiMhEcMwgIpIW9ttERGQOOJ4REZkOUW8UHhsbi02bNuHSpUsYM2YMtm7dijFjxqBDh4Zai4+PDz766CPcd999YqZJREQmgGMGEZG0sN8mIiJzYIjxLCYmBkuXLsXChQsRFxdnoMyJiMyXqEWN+Ph4zJgxAy+88AIUCoXGmG7duiEhIcHImRERkanhmEFEJC3st4mIyBzoezw7deoUNm7ciAceeECfaRIRtSuiXn4qJSUFr776apNBQRAEZGdnAwCsra0xbZp0ry++Zs09uHZlDi6m8Ag0IqK7Ye5jxpo19+DClcVYc+hZsVMhItILffbb69evh4+PD2xsbODn54dvv/222fjU1FT4+fnBxsYGPXr0wIYNG5rE7NmzB/369YNcLke/fv2QlJSk9nxMTAwGDRoEBwcHuLm54emnn8alS5eatCU6Ohqenp6wtbXF8OHDceHChRbb06wVK4DIyIZ/iahF/MmQoelzPCsrK8Nzzz2Hjz/+GPfcc49B8iXDYF9DZFpELWr07NkThYWFTebfvHkTPj4+ImSkf4mJjriRMwkZP3QXOxUiIkkz9zEjMdERGTl/R+LJx8VOhYhIL/TVbycmJmLRokVYtmwZzp49iyFDhiAkJES1I+lOGRkZGDNmDIYMGYKzZ89i6dKlWLBgAfbs2aOKSUtLQ2hoKMLCwvDjjz8iLCwMkydPxvfff6+KSU1Nxdy5c3Hy5EmkpKSgtrYWwcHBKC8vV8W8++67iI2Nxdq1a3Hq1CkoFAqMHj0apaWlOreviY8/Bv71r4Z/iahF/MmQoelzO2Tu3Ll44oknMGrUKH2lR0bCvobItIh6+SlBEDTOLysrg42NjZGzISIiU8Yxg4hIWvTVb8fGxmLmzJmYNWsWACAuLg6HDh1CfHw8YmJimsRv2LAB3bp1U12jvG/fvkhPT8d7772HiRMnqpYxevRoREVFAQCioqKQmpqKuLg47Ny5EwBw8OBBteVu2rQJbm5uOH36NIYOHQpBEBAXF4dly5ZhwoQJAIAtW7bA3d0dO3bswJw5c3RuIxERmS59jWe7du3CmTNncOrUKZ3iq6qqUFVVpXqsVCp1XhcRkbkTpagRGRkJAJDJZHj99dfRsWNH1XN1dXX4/vvvMXDgQDFSIyIiE8Mxg4hIWvTZb1dXV+P06dNYsmSJ2vzg4GCcOHFC42vS0tIQHBysNu+xxx5DQkICampqYGVlhbS0NCxevLhJTHM3ay0pKQEAODs7A2g4IyQvL09tXXK5HMOGDcOJEyc0FjW4g4qISDr0OZ5du3YNCxcuxOHDh3UuhMTExGAFr3VERKSRKEWNs2fPAmiodp8/fx7W1taq56ytrTFgwAC89NJLYqRGREQmhmMGEZG06LPfLiwsRF1dHdzd3dXmu7u7Iy8vT+Nr8vLyNMbX1taisLAQHh4eWmO0LVMQBERGRuKRRx6Br6+vaj2Nr7tzOVlZWRqXwx1URETSoc/x7PTp08jPz4efn59qXl1dHY4dO4a1a9eiqqoKFhYWaq+JiopSFVaAhkJ4165d76ZJRERmQ5SixjfffAMAeOGFF/DBBx/A0dFRjDSIiEgCOGYQEUmLIfptmUym9lgQhCbzWoq/c35rljlv3jz89NNPOH78+F3lxh1URETSoc/x7NFHH8X58+fV5r3wwgu477778OqrrzYpaAANZ//J5fI2r5OIyJyJek+NTZs2ibl6IiKSEI4ZRETSoo9+28XFBRYWFk3OoMjPz29yhkQjhUKhMd7S0hKdO3duNkbTMufPn4/9+/fj2LFj8PLyUlsP0HDGhoeHh065cQcVEZH06GM8c3BwUJ3p18jOzg6dO3duMp+IiFpm9KLGhAkTsHnzZjg6OqpuqKfN3r17jZQVERGZIo4ZRETSou9+29raGn5+fkhJScH48eNV81NSUjBu3DiNrwkMDMSXX36pNu/w4cPw9/eHlZWVKiYlJUXtvhqHDx9GUFCQ6rEgCJg/fz6SkpJw9OhR+Pj4qC3Tx8cHCoUCKSkpePDBBwE03AMkNTUVq1evbrFtRERkurgdQkRk2oxe1HByclKdju3k5GTs1RMRkYRwzCAikhZD9NuRkZEICwuDv78/AgMDsXHjRmRnZyM8PBxAwyWdcnJysHXrVgBAeHg41q5di8jISMyePRtpaWlISEjAzp07VctcuHAhhg4ditWrV2PcuHH44osvcOTIEbXLS82dOxc7duzAF198AQcHB9WZHU5OTrC1tYVMJsOiRYuwatUq9OrVC7169cKqVavQsWNHTJ06VS9tp+YVFBQ0e7P1rKws1NXWGTEjIjIXxtgOOXr0qEGWS0TUHhi9qPHX0/Z4KREiImoOxwwiImkxRL8dGhqKoqIivPnmm8jNzYWvry+Sk5Ph7e0NAMjNzUV2drYq3sfHB8nJyVi8eDHWrVsHT09PrFmzBhMnTlTFBAUFYdeuXXjttdewfPly9OzZE4mJiQgICFDFxMfHAwCGDx/epI3Tp08HALzyyiuoqKhAREQEiouLERAQgMOHD8PBwUEvbSftCgoKEDYzDDfLb2qNqbxdiZzcHNxbc68RMyMic8DtECIi0ybqPTUqKiogCAI6duwIoOFImqSkJPTr1w/BwcFipqY3AQEVOPrtJTh5a76uLhER6cbcx4yAgAqkffszAnwqxU6FiEgv9NlvR0REICIiQuNzmzdvbjJv2LBhOHPmTLPLnDRpEiZNmqT1+cabizdHJpMhOjoa0dHRLcbqbNgwoLAQcHHR3zLNkFKpxM3ym+gypgvs3ew1xty4eANZ27NQW1Nr5OzImPiTIUMz9+0Q0g37GiLTImpRY9y4cZgwYQLCw8Nx69Yt/O1vf4O1tTUKCwsRGxuL//u//xMzPb2Ijc3HlBlR6P5EBACvFuOJiEgzcx8zYmPzsWjGPMQ+rQCgeecMEZGUmHu/bTDbt4udgaTYu9nDyVPzpWFKb5QaORsSA38yZGgczwhgX0NkajqIufIzZ85gyJAhAIDPP/8cCoUCWVlZ2Lp1K9asWSNmakREZGI4ZhARSQv7bSIiMgccz4iITI+oRY3bt2+rrjd7+PBhTJgwAR06dMDgwYORlZUlZmpERGRiOGYQEUkL+20iIjIHHM+IiEyPqEWNe++9F/v27cO1a9dw6NAh1bUI8/Pz4ejoKGZqRERkYvQ1Zhw7dgxjx46Fp6cnZDIZ9u3bp/b89OnTIZPJ1KbBgwfrsylERO0C/9YnIiJzwPGMiMj0iFrUeP311/HSSy+he/fuCAgIQGBgIICGyveDDz4oZmp68/e/e+Ln9I+RuvERsVMhIpI0fY0Z5eXlGDBgANauXas15vHHH0dubq5qSk5Ovuv8W/L3v3viaPpn+Pv6VQZfFxGRMbSHv/UNYuRIoH//hn+JqEX8yZChcTwjgH0NkakR9UbhkyZNwiOPPILc3FwMGDBANf/RRx/F+PHjdV7OsWPH8M9//hOnT59Gbm4ukpKS8PTTT6uenz59OrZs2aL2moCAAJw8efKu29CSjAwrVN7uDllBhcHXRURkzvQ1ZoSEhCAkJKTZGLlcDoVC0eZc2yIjwwplt3sio6DQqOslIjIUffXb7c5vvwE5OUBJidiZEEkCfzJkaBzPCGBfQ2RqRC1qAIBCoWiy4+hvf/tbq5bReNTtCy+8gIkTJ2qMefzxx7Fp0ybVY2tr69YnS0REotLHmKGLo0ePws3NDZ06dcKwYcOwcuVKuLm56X09RETmzlj9NhERkSFxPCMiMi2iFjXKy8vxzjvv4L///S/y8/NRX1+v9vzVq1d1Wo6pHnVLRET6o68xoyUhISF45pln4O3tjYyMDCxfvhwjR47E6dOnIZfLNb6mqqoKVVVVqsdKpVIvuRARSZmx+m0iIiJD4nhGRGR6RC1qzJo1C6mpqQgLC4OHhwdkMpnB1tXao265g4qIyLQYa8wIDQ1V/d/X1xf+/v7w9vbGgQMHMGHCBI2viYmJwYoVKwySDxGRVBnzb30iIiJD4XhGRGR6RC1qfPXVVzhw4AAefvhhg66nLUfdcgcVEZFpMdaYcScPDw94e3vj8uXLWmOioqIQGRmpeqxUKtG1a1djpEdEZLLE6reJiIj0ieMZEZHpEbWocc8998DZ2dng62nLUbfcQUVEZFqMNWbcqaioCNeuXYOHh4fWGLlcrrVITkTUXonVbxMREekTxzMiItPTQcyVv/XWW3j99ddx+/Zto65Xl6Nu5XI5HB0d1SYiIhKPvsaMsrIynDt3DufOnQMAZGRk4Ny5c8jOzkZZWRleeuklpKWlITMzE0ePHsXYsWPh4uKC8ePH66EVRETth1h/6xMREekTxzMiItMj6pka77//Pq5cuQJ3d3d0794dVlZWas+fOXPGIOvV5ahbIiIyLfoaM9LT0zFixAjV48az8qZNm4b4+HicP38eW7duxa1bt+Dh4YERI0YgMTERDg4O+msMEVE7INbf+kRERPrE8YyIyPSIWtR4+umn9bKcsrIy/P7776rHjUfdOjs7w9nZGdHR0Zg4cSI8PDyQmZmJpUuX8qhbIiKJ0deYMXz4cAiCoPX5Q4cO6WU9RETtnb76bSIiIjFxPCMiMj2iFjXeeOMNvSzHlI+6nT//JjZ8shduAwMMvi4iInOmrzHDVM2ffxM7PtmGqX5WLQcTEUmAuffbBvP660BZGWBvL3YmRJLAnwwZGsczAtjXEJkaUYsaAHDr1i18/vnnuHLlCl5++WU4OzvjzJkzcHd3R5cuXXRahikfdTtlSin2Hd6D7gHuALxEy4OIyBzoY8wwVVOmlOLk4e2YEqgAwL+Uicg8mHO/bTAvvih2BkSSwp8MGQPHM2JfQ2RaRC1q/PTTTxg1ahScnJyQmZmJ2bNnw9nZGUlJScjKysLWrVvFTI+IiEwIxwwiImlhv01EROaA4xkRkenpIObKIyMjMX36dFy+fBk2Njaq+SEhITh27JiImRERkanhmEFEJC3st4mIyBxwPCMiMj2iFjVOnTqFOXPmNJnfpUsX5OXliZCR/uXnW6C6ygUVSrnYqRARSZq5jxn5+RaoqHJDvvIesVMhItILc++3DSY3F7h+veFfImoRfzJkaPoYz+Lj4/HAAw/A0dERjo6OCAwMxFdffaXvVMmA2NcQmRZRixo2NjZQKpVN5l+6dAmurq4iZKR/48d74afvd+LrD0e0HExERFqZ+5gxfrwX/vv9Vxj/rzixUyEi0gtz77cNZtAgoGvXhn+JqEX8yZCh6WM88/LywjvvvIP09HSkp6dj5MiRGDduHC5cuKDvdMlA2NcQmRZRixrjxo3Dm2++iZqaGgCATCZDdnY2lixZgokTJ4qZGhERmRiOGURE0qLPfnv9+vXw8fGBjY0N/Pz88O233zYbn5qaCj8/P9jY2KBHjx7YsGFDk5g9e/agX79+kMvl6NevH5KSktSeP3bsGMaOHQtPT0/IZDLs27evyTKmT58OmUymNg0ePLhVbSMiItOmj/Fs7NixGDNmDHr37o3evXtj5cqVsLe3x8mTJw2ZOhGR2RK1qPHee++hoKAAbm5uqKiowLBhw3DvvffCwcEBK1euFDM1IiIyMRwziIikRV/9dmJiIhYtWoRly5bh7NmzGDJkCEJCQpCdna0xPiMjA2PGjMGQIUNw9uxZLF26FAsWLMCePXtUMWlpaQgNDUVYWBh+/PFHhIWFYfLkyfj+++9VMeXl5RgwYADWrl3bbH6PP/44cnNzVVNycrLObSPzUlNdg6ysLFy5cqXZ6ZdffmkxpqCgQOzmENH/6Hs7pK6uDrt27UJ5eTkCAwMNkDERkfmzFHPljo6OOH78OL755hucPn0a9fX1eOihhzBq1Cgx0yIiIhPEMYOISFr01W/HxsZi5syZmDVrFgAgLi4Ohw4dQnx8PGJiYprEb9iwAd26dUNcXBwAoG/fvkhPT8d7772nOqI2Li4Oo0ePRlRUFAAgKioKqampiIuLw86dOwE03AA2JCSkxfzkcjkUCkWr2kTmp1JZicyrmYh8PRLWcmutcTXVNcjJykFXn66wsLTQGuds54xPEz7lpdqITIC+xrPz588jMDAQlZWVsLe3R1JSEvr166c1vqqqClVVVarHmi6BRUTUXolW1Kivr8fmzZuxd+9eZGZmQiaTwcfHBwqFAoIgQCaTiZUaERGZGI4ZRETSoq9+u7q6GqdPn8aSJUvU5gcHB+PEiRMaX5OWlobg4GC1eY899hgSEhJQU1MDKysrpKWlYfHixU1iGgshrXH06FG4ubmhU6dOGDZsGFauXAk3NzeNsdxBZb5qKmpQb1EPRYgCrt20FyJuXLyBq1lX4RrsqjWuLL8MOck5UCqVLGoQiUyf2yF9+vTBuXPncOvWLezZswfTpk1Damqq1sJGTEwMVqxYoa+mEBGZFVEuPyUIAp566inMmjULOTk5uP/++9G/f39kZWVh+vTpGD9+vBhpERGRCeKYQUQkLfrstwsLC1FXVwd3d3e1+e7u7sjLy9P4mry8PI3xtbW1KCwsbDZG2zK1CQkJwfbt2/H111/j/fffx6lTpzBy5Ei1wsVfxcTEwMnJSTV17dq1Vesj02fnYgcnTyetU8fOHVuMs3ezF7kVRATofzvE2toa9957L/z9/RETE4MBAwbggw8+0BofFRWFkpIS1XTt2rW7bRIRkdkQ5UyNzZs349ixY/jvf/+LESNGqD339ddf4+mnn8bWrVvx/PPPi5EeERGZEI4ZRETSYoh++84jYVs6OlZT/J3zW7tMTUJDQ1X/9/X1hb+/P7y9vXHgwAFMmDChSXxUVBQiIyNVj5VKJQsbREQmytDbIYIgaC2CAw2XN5TL5W1aNhGRuRPlTI2dO3di6dKlTQYFABg5ciSWLFmC7du3i5AZERGZGo4ZRETSos9+28XFBRYWFk3OoMjPz29ypkUjhUKhMd7S0hKdO3duNkbbMnXl4eEBb29vXL58WePzcrkcjo6OahMREZkmfY5nS5cuxbfffovMzEycP38ey5Ytw9GjR/Hcc8/pO20ionZBlKLGTz/9hMcff1zr8yEhIfjxxx+NmBEREZkqjhlERNKiz37b2toafn5+SElJUZufkpKCoKAgja8JDAxsEn/48GH4+/vDysqq2Rhty9RVUVERrl27Bg8Pj7taDhERiU+f49mNGzcQFhaGPn364NFHH8X333+PgwcPYvTo0fpKl4ioXRHl8lM3b95s9igod3d3FBcXGzEjIiIyVRwziIikRd/9dmRkJMLCwuDv74/AwEBs3LgR2dnZCA8PB9BwSaecnBxs3boVABAeHo61a9ciMjISs2fPRlpaGhISErBz507VMhcuXIihQ4di9erVGDduHL744gscOXIEx48fV8WUlZXh999/Vz3OyMjAuXPn4OzsjG7duqGsrAzR0dGYOHEiPDw8kJmZiaVLl8LFxYX3eyIiMgP6HM8SEhL0lRYREUGkokZdXR0sLbWv2sLCArW1tUbMyHA+/fQPLI56E14jQgF0EjsdIiLJaS9jxqef/oGVUf/AstH3iJ0KEdFd0Xe/HRoaiqKiIrz55pvIzc2Fr68vkpOT4e3tDQDIzc1Fdna2Kt7HxwfJyclYvHgx1q1bB09PT6xZswYTJ05UxQQFBWHXrl147bXXsHz5cvTs2ROJiYkICAhQxaSnp6tdcqTxXhjTpk3D5s2bYWFhgfPnz2Pr1q24desWPDw8MGLECCQmJsLBwUHn9jXx3/8CtbVAM+8hEf2JPxkylPayHUK6YV9DZFpE+SkKgoDp06drveFRczdKkpoePWpga5cFB9cysKhBRNR67WXM6NGjBg52V9HDTQHAXux0iIjazBD9dkREBCIiIjQ+t3nz5ibzhg0bhjNnzjS7zEmTJmHSpElanx8+fLjqBuOa2Nra4tChQ82uo0369NH/MonMGH8yZCjtZTuEdMO+hsi0iFLUmDZtWosxzz//vBEyISIiU8cxg4hIWthvExGROeB4RkRkukQpamzatEmM1RIRkQRxzCAikhb220REZA44nhERmS5eCc7A9u+3R0FuCDqc9YLvY2JnQ0REpmr/fntk5z6N/aflWDzmtNjpEBGRWHbsAG7fBjp2BKZOFTsbIpPHnwyRNFXX1CArK6vZGEdHR7i6uhopo+axryEyLSxqGNjq1Z1x40Yk8m9UwPexn8VOh4iITFTDeLEcq/MLWdQgImrPXnkFyMkBunThXhMiHfAnQyQ9tyorcTUzE28vXgxrLfcsAQDbe5yxcdunJlHYYF9DZFpY1CAiIiIiIiIiIiKjKK+pgVV9Paa7u8Ons4vGmNzyMnySlwelUmkSRQ0iMi0sahAREREREREREZFRKezs0N3JSew0iEiCOoidABERERERERERERERkS5Y1CAiIiIiIiIiIiIiIklgUYOIiIiIiIiIiIiIiCSBRQ0iImpXjh07hrFjx8LT0xMymQz79u1Te14QBERHR8PT0xO2trYYPnw4Lly4IE6yRERERERERESkxiyKGtxBRUREuiovL8eAAQOwdu1ajc+/++67iI2Nxdq1a3Hq1CkoFAqMHj0apaWlRs6UiIiIiIiIiIjuZBZFDe6gIiIiXYWEhODtt9/GhAkTmjwnCALi4uKwbNkyTJgwAb6+vtiyZQtu376NHTt2iJAtERERERERERH9laXYCehDSEgIQkJCND535w4qANiyZQvc3d2xY8cOzJkzx6C5ubrW4mZxMWwczOKtJiIyaxkZGcjLy0NwcLBqnlwux7Bhw3DixAmtY0ZVVRWqqqpUj5VKZavX7epai5LiIrg6tP61RERkRhQK9X+JqFn8yRCRMbCvITItZnGmRnNa2kGlTVVVFZRKpdrUFvv25WDA4Kl4dMHRNr2eiIiMJy8vDwDg7u6uNt/d3V31nCYxMTFwcnJSTV27dm31uvfty8GowWOwL3JRq19LRERmJD0duH694V8iahF/MkRkDOxriEyL2Rc1xNxBRURE0iSTydQeC4LQZN5fRUVFoaSkRDVdu3bN0CkSEREREREREbVLZl/UaMQdVERE1BLF/84lvrPonZ+f36Q4/ldyuRyOjo5qExERERERERER6Z/ZFzW4g4qIiHTl4+MDhUKBlJQU1bzq6mqkpqYiKChIxMyIiIiIiIiIiAhoB0UNsXdQvfaaC65cXI7TewYafF1ERNSysrIynDt3DufOnQPQcO+lc+fOITs7GzKZDIsWLcKqVauQlJSEn3/+GdOnT0fHjh0xdepUg+b12msuOH1xNV77bJ5B10NERCZuzhzgmWca/iWiFvEnQ0TGwL6GyLRYip2APpSVleH3339XPW7cQeXs7Ixu3bqpdlD16tULvXr1wqpVq4yygwoAvvnGDsWFQ1FZXQGg0ODrIyKi5qWnp2PEiBGqx5GRkQCAadOmYfPmzXjllVdQUVGBiIgIFBcXIyAgAIcPH4aDg4NB8/rmGzvcKByFb2oKAWw26LqIiMiEHTgA5OQAXbqInQmRJPAnQ0TGwL6GyLSYRVHDVHdQERGR6Rk+fDgEQdD6vEwmQ3R0NKKjo42XFBERERERma2YmBjs3bsXv/76K2xtbREUFITVq1ejT58+YqdGRCRJZnH5qcYdVHdOmzdvBvDnDqrc3FxUVlYiNTUVvr6+4iZNREREREQ6Wb9+PXx8fGBjYwM/Pz98++23zcanpqbCz88PNjY26NGjBzZs2NAkZs+ePejXrx/kcjn69euHpKQkteePHTuGsWPHwtPTEzKZDPv27WuyDEEQEB0dDU9PT9ja2mL48OG4cOHCXbWViIjMT2pqKubOnYuTJ08iJSUFtbW1CA4ORnl5udipERFJklkUNYiIiIiIyDwlJiZi0aJFWLZsGc6ePYshQ4YgJCQE2dnZGuMzMjIwZswYDBkyBGfPnsXSpUuxYMEC7NmzRxWTlpaG0NBQhIWF4ccff0RYWBgmT56M77//XhVTXl6OAQMGYO3atVpze/fddxEbG4u1a9fi1KlTUCgUGD16NEpLS/X3BhARkeQdPHgQ06dPR//+/TFgwABs2rQJ2dnZOH36tFHWX1BQgCtXrmidsrKyUFtba5RciIj0wSwuP0VEREREROYpNjYWM2fOxKxZswAAcXFxOHToEOLj4xETE9MkfsOGDejWrRvi4uIAAH379kV6ejree+89TJw4UbWM0aNHIyoqCgAQFRWF1NRUxMXFYefOnQCAkJAQhISEaM1LEATExcVh2bJlmDBhAgBgy5YtcHd3x44dOzCHdxIlIiItSkpKAADOzs5aY6qqqlBVVaV6rFQq27SugoICvPj3MFQU39Qac7uyEjeuX0d1j55tWgcRkbGxqEFERERERCapuroap0+fxpIlS9TmBwcH48SJExpfk5aWhuDgYLV5jz32GBISElBTUwMrKyukpaVh8eLFTWIaCyG6yMjIQF5entq65HI5hg0bhhMnTmgsauhrBxUREUmXIAiIjIzEI4880uyl0WNiYrBixYq7Xp9SqURF8U3MUCjgYWevMeZs/g18mJWFujqerUFE0sDLTxERERERkUkqLCxEXV0d3N3d1ea7u7sjLy9P42vy8vI0xtfW1qKwsLDZGG3L1LaextfpupyYmBg4OTmppq5du+q8PiIiMg/z5s3DTz/9pDozUJuoqCiUlJSopmvXrt3Vej3s7NHdyUnj5Gbb8a6WTURkbCxqEBERERGRSZPJZGqPBUFoMq+l+Dvnt3aZ+shN3zuoiIhIWubPn4/9+/fjm2++gZeXV7Oxcrkcjo6OahMRETXg5aeIiIiIiMgkubi4wMLCosmZD/n5+U3OkGikUCg0xltaWqJz587Nxmhbprb1AA1nbHh4eOi0HLlcDrlcrvM6iIjIPAiCgPnz5yMpKQlHjx6Fj4+P2CkREUkaz9QwsLFjy+Ci+ApdB14XOxUiIjJhY8eWoatiH8Y+mCp2KkREJsPa2hp+fn5ISUlRm5+SkoKgoCCNrwkMDGwSf/jwYfj7+8PKyqrZGG3L1MTHxwcKhUJtOdXV1UhNTW3Vcpp49llg5syGf4moRfzJkBTMnTsX27Ztw44dO+Dg4IC8vDzk5eWhoqJC7NRIR+xriEwLz9QwsCVLinDut1h0fyICQPOnFhIRUfu1ZEkR8n57C0ueUgDQfAM/IqL2KDIyEmFhYfD390dgYCA2btyI7OxshIeHA2i4pFNOTg62bt0KAAgPD8fatWsRGRmJ2bNnIy0tDQkJCWrXLl+4cCGGDh2K1atXY9y4cfjiiy9w5MgRHD9+XBVTVlaG33//XfU4IyMD586dg7OzM7p16waZTIZFixZh1apV6NWrF3r16oVVq1ahY8eOmDp1atsb/M9/tv21RO0QfzIkBfHx8QCA4cOHq83ftGkTpk+fbvyE2pmCggIolcpmY6qrq2Ftba31+f/92YHq6mpcuaI9DgAcHR3h6ura6jyJSHcsahARERERkckKDQ1FUVER3nzzTeTm5sLX1xfJycnw9vYGAOTm5iI7O1sV7+Pjg+TkZCxevBjr1q2Dp6cn1qxZg4kTJ6pigoKCsGvXLrz22mtYvnw5evbsicTERAQEBKhi0tPTMWLECNXjyMhIAMC0adOwefNmAMArr7yCiooKREREoLi4GAEBATh8+DAcHBwM+ZYQEZHENN7biYyvoKAAL/49DBXFN7XGVNfUIPN6Dnp09YKlpfZdpbrG2d7jjI3bPmVhg8iAWNQgIiIiIiKTFhERgYiICI3PNRYY/mrYsGE4c+ZMs8ucNGkSJk2apPX54cOHt7gTSiaTITo6GtHR0c3GUevockRtVlYW6mrrjJQRERFJlVKpREXxTcxQKOBhp/mM+LP5N/Dh1at43sUFPp1dtC5Ll7jc8jJ8kpcHpVLJogaRAbGoQURERERERCahoKAAYTPDcLNc+xG1AFB5uxI5uTm4t+ZeI2VGRERS5mFnj+5OThqfyyktBQAo7Oy0xrQmjogMj0UNAwsO7orMrH249Es9nnn3V7HTISIiExUc3BXZWakI/rUYV/71ktjpEBGRWO67D/jjD8DTE/i1/W0/KJVK3Cy/iS5jusDeTfs9pm5cvIGs7Vmorak1YnZkitr5T4aIjGRE4hv4o8wRtfXXAbwqdjpE7R6LGgZWXt4B9XV2qK2qEDsVIiIyYeXlHVBbZ4/yqkqxUyEiIjGVlQGlpQ3/tmP2bvZw8tR+FGzpjVIjZkOmjD8ZIjKG8ho5Kmo7wkJmJ3YqRAQWNYiIiIiIiCSnpftO8J4TRERERGSuWNQgIiIiIiKSEF3uO8F7ThARERGRuWJRg4iIiIiISEJ0ue8E7zlBREREROaKRQ0iIiIiIiIJau6+E7znBBERERGZqw5iJ0BERERERERERERERKQLFjWIiIiIiIiIiIiIiEgSWNQgIiIiIiIiIiIiIiJJYFGDiIiIiIiIiIiIiIgkgTcKN7C33irAO//6NxQBo8C3m4iItHnrrQJs+FccwoNsxU6FiIjEtGEDUFEB2HI8oOYVFBRAqVQ2G+Po6AhXV1cjZSQO/mSIyBhWDdmBH/NL8dkvZ8ROhYjAvewGN3LkbWzcdgyefX0BeImdDhERmaiRI29j/7YjGNlfAcBe7HSIiEgsTz4pdgYkAQUFBQibGYab5TebjXO2c8anCZ+adWGDPxkiMoZR3j/D1uI6Dvx+DMBIsdMhavdY1CAiIiIiIiKSEKVSiZvlN9FlTBfYu2k+GKIsvww5yTlQKpVmXdQgovatpbPWsrKyUFtba8SMiMgYWNQgIiL6i+joaKxYsUJtnru7O/Ly8kTKiIiIiEgzezd7OHk6iZ0GEZEoCgoK8OLfw1BRrP2stduVlbhx/Tqqe/Q0YmZEZGjtoqgh5g6qn3+2RpmyL4qvd4Kjm8FXR0REetC/f38cOXJE9djCwsLg6/z5Z2sUK+/Hz9ec0NOdBRQionbr9Gmguhqwtgb8/MTOhsjk8SdD1H4plUpUFN/EDIUCHnaaz1o7m38DH2Zloa7u7s7W+KmgG34rtkNlXfP3MiIi42gXRQ1AnB1UADBnjgdu3FiDrKsV8H7oZ6Osk4iI7o6lpSUUCoVR19kwXmzG7xmFGOc/16jrJiIiEzJuHJCTA3TpAly/LnY2RCaPPxki8rCzR3cnzWet5ZSW6mUdsw6FI6/8HljIcgDM08syiajt2k1RQ4wdVEREJE2XL1+Gp6cn5HI5AgICsGrVKvTo0UPstIiIiIiIiIiI2r0OYidgLI07qHx8fDBlyhRcvXq12fiqqioolUq1iYiIzF9AQAC2bt2KQ4cO4eOPP0ZeXh6CgoJQVFSk9TUcM4iIiIiIiIiIjKNdFDXasoMqJiYGTk5Oqqlr165GzJiIiMQSEhKCiRMn4v7778eoUaNw4MABAMCWLVu0voZjBhERERERNefYsWMYO3YsPD09IZPJsG/fPrFTIhEVFBTgypUrzU4FBQVip0lkstpFUaMtO6iioqJQUlKimq5du2asdImIyITY2dnh/vvvx+XLl7XGcMwgIjKs9evXw8fHBzY2NvDz88O3337bbHxqair8/PxgY2ODHj16YMOGDU1i9uzZg379+kEul6Nfv35ISkpq9XqnT58OmUymNg0ePPjuGktERGapvLwcAwYMwNq1a8VOhURWUFCAF/8ehrnPPtvs9OLfw1jYINKi3dxT46902UEll8shl8uNmBUREZmiqqoq/PLLLxgyZIjWGI4ZRESGk5iYiEWLFmH9+vV4+OGH8dFHHyEkJAQXL15Et27dmsRnZGRgzJgxmD17NrZt24bvvvsOERERcHV1xcSJEwEAaWlpCA0NxVtvvYXx48cjKSkJkydPxvHjxxEQENCq9T7++OPYtGmT6rG1tbWB3xEiIpKikJAQhISEiJ0GmQClUomK4puYoVDAw85eY0xueRk+ycuDUqmEq6urkTMkMn3tsqihyw4qIiJqn1566SWMHTsW3bp1Q35+Pt5++20olUpMmzZN7NSIiNql2NhYzJw5E7NmzQIAxMXF4dChQ4iPj0dMTEyT+A0bNqBbt26Ii4sDAPTt2xfp6el47733VEWNuLg4jB49GlFRUQAazrhLTU1FXFwcdu7c2ar1yuVyKBQKg7Wf2qea6hpkZWVpfT4rKwt1tXVGzIiIyPiqa1ruC2tra42YkX552Nmju5OT1udbaj8AODo6suhB7VK7KGpwBxUREenq+vXrePbZZ1FYWAhXV1cMHjwYJ0+ehLe3t9ipERG1O9XV1Th9+jSWLFmiNj84OBgnTpzQ+Jq0tDQEBwerzXvssceQkJCAmpoaWFlZIS0tDYsXL24S01gIac16jx49Cjc3N3Tq1AnDhg3DypUr4ebmpjG3qqoqVFVVqR4rlUrtjad2q1JZicyrmYh8PRLWcs1n/lTerkRObg7urbnXyNkRkbG09zHjVmUlrmZm4u3Fi2Gt5az425WVuHH9Oqp79DRydoanS/sBwPYeZ2zc9ikLG9TutIuiBndQERGRrnbt2iV2CkRE9D+FhYWoq6uDu7u72nx3d3fk5eVpfE1eXp7G+NraWhQWFsLDw0NrTOMydV1vSEgInnnmGXh7eyMjIwPLly/HyJEjcfr0aY2XJYyJicGKFSt0fwOoXaqpqEG9RT0UIQq4dtO8k+rGxRvI2p6F2hrpHqFMRM1r72NGeU0NrOrrMd3dHT6dXTTGnM2/gQ+zslBXZ359oS7t5yWqqD1rF0UN7qAiIiIiIpIumUym9lgQhCbzWoq/c74uy2wpJjQ0VPV/X19f+Pv7w9vbGwcOHMCECROa5BUVFYXIyEjVY6VSia5du2ptB7Vvdi52cPLUfFmS0hulRs6GiIyNY0YDhZ2d1ks05ZSaf1/YXPsB3S5RBfAyVWR+2kVRQ0yHDmXjhYjF6Pb4LAC81i4REWl26FA2Xo2YjtVPugGwEDsdIiKT4OLiAgsLiyZnZeTn5zc5i6KRQqHQGG9paYnOnTs3G9O4zLasFwA8PDzg7e2Ny5cva3xeLpdrPINDzS+/AIIANFO0IaI/8SdD5kqnMYOM5uvJK5CW8wdWpn0H4G9ipwNA90tUAbxMFZkfFjUMzN5egIXlbVjJze9UOCIi0h97ewFWluWwt6kAYC92OkREJsHa2hp+fn5ISUnB+PHjVfNTUlIwbtw4ja8JDAzEl19+qTbv8OHD8Pf3h5WVlSomJSVF7b4ahw8fRlBQUJvXCwBFRUW4du0aPDw8Wt/YRg4ObX8tUTvEnwxJRVlZGX7//XfV44yMDJw7dw7Ozs7o1q2biJmRLuytq9DRqgIdZGVip6KiyyWqAF6miswTixpERERERGSyIiMjERYWBn9/fwQGBmLjxo3Izs5GeHg4gIbLc+Tk5GDr1q0AgPDwcKxduxaRkZGYPXs20tLSkJCQgJ07d6qWuXDhQgwdOhSrV6/GuHHj8MUXX+DIkSM4fvy4zustKytDdHQ0Jk6cCA8PD2RmZmLp0qVwcXFRK4QQEREBQHp6OkaMGKF63HhpqWnTpmHz5s0iZUXmoKVLVBGZIxY1iIiIiIjIZIWGhqKoqAhvvvkmcnNz4evri+TkZHh7ewMAcnNzkZ2drYr38fFBcnIyFi9ejHXr1sHT0xNr1qzBxIkTVTFBQUHYtWsXXnvtNSxfvhw9e/ZEYmIiAgICdF6vhYUFzp8/j61bt+LWrVvw8PDAiBEjkJiYCAceOk5ERHcYPny46h5PZN5aus9FVlYWamt5RReiu8GihoElJDghJzMM1cfuhf+kSrHTISIiE5WQ4IRLmS8i4WgHrAo9InY6REQmJSIiAhERERqf03R067Bhw3DmzJlmlzlp0iRMmjSpzeu1tbXFoUOHmn19m8TGAkol4OgI/OUGsYZSU62/G4wWFBRAqVQ2G1NdXQ1ra2utz2dlZaGutq7FfIgaGfknQ0Tt1Mc/PYqLhdUorrofwC9a43S5z8XtykrcuH4d1T16GihbIvPHooaBffJJJ9y48Txu3aqA/6SfxU6HiIhMVMN4MQeflBSyqEFE1J7FxgI5OUCXLgbfQ1uprETm1UxEvh4Ja7n2QgMAONs549ME7TcYLSgoQNjMMNwsv6l1GTXVNcjJykFXn66wsLTQnNPtSuTk5uDemnt1bwi1a0b8yRBRO/bxT48ir/weWMhyAMzTGqfLfS7O5t/Ah1lZqKvj2RpEbcWiBhERERERUTtUU1GDeot6KEIUcO2m/SyMsvwy5CTnNHuDUaVSiZvlN9FlTBfYu9lrjLlx8QauZl2Fa7Cr1vXduHgDWduzUFvDHT1ERCRdzd3nIqe01MjZEJkfFjWIiIiIiIjaMTsXOzh56ucGo/Zu9lqXVXqjtMX1NcYQEREREWnDogYRERERERGRGdLlnim63C+FiIiIyJSwqEFERERERERkZnS9Z0pL90shIiIiMjUsahARERERERGZGV3umaLL/VKIiIiITA2LGkRERERERERmSp/3TCEiIiIyBR3EToCIiIiIiIiIiIiIiEgXLGoQEREREREREREREZEk8PJTBta/fxXKbv8Gxy73iJ0KERGZsP79q1B1+yL6e1WInQoREYnpoYeArl0B3t+ASCf8yRCRMfi6XIOD1Q3klF0VOxUiAosaBrdxYx6mzFiI7k9EAPASOx0iIjJRGzfmYdGMFxD3tAKAvdjpEBGRWPbvFzsDIknhT4aIjOGTx+Px3fXrWP7tMQAjxU6HqN1jUYOIiIiIiIiIiIjITFXX1CArK6vZGEdHR7jytDeSCBY1iIiIiIiIiIiIiMzQrcpKXM3MxNuLF8NaLtcaZ3uPMzZu+5SFDZIEFjWIiIiIiIiIiIiIzFB5TQ2s6usx3d0dPp1dNMbklpfhk7w8KJVKFjVIEljUMLAXX1Tgl7MfIKfgHoS8cl3sdIiIyES9+KICZ85uwotFFfjv0lix0yEiIrE89RRQUNBw12PeLICoRfzJEJExzDj4f8gqscYft68C+FjsdNpEYWeH7k5OYqdBpBcsahjYhQtylJf2Q31OBQAWNYiISLMLF+S4VfoALlwvFDsVIiIS05kzQE4O0KWL2Jmoqalu/lrcWVlZqKutM2JGpC8tfbaNTO1a6wUFBVAqlfjhB2/cuGEJd/daXLnStB2mljcRSdPPhV2RV34PLGQOYqdCRGBRg4iIiIiIiJpRqaxE5tVMRL4eCWu5teaY25XIyc3BvTX3Gjk7uhu6fLaNnO2c8WmCaVxrvaCgAGEzw3Cz/CZu3voSgDtu3irCsy8+2yTWlPImIiIi/WBRg4iIiIiIiLSqqahBvUU9FCEKuHbTvGP4xsUbyNqehdqaWiNnR3dDl88WAMryy5CTnGMy11pXKpW4WX4TXcZ0wYWzVqipAqw6WqHP9D5qcaaWNxGRKauuafnMPV3Ofms8k+5ul0PUHBY1iIiIiIiIqEV2LnZw8tR8Le7SG6VGzob0qbnP1pTZu9lDZiEDAMgsZJJsAxGRKbhVWYmrmZl4e/FiWMvlWuNs73HGxm3az34rKCjAi38PQ0XxzWbX19JyiFrCogYRERERkYTw6DciEoMu996orq6GtXXzl7HSx1G++r6Hizn3q+bcNiLSn/KaGljV12O6uzt8OrtojMktL8MneXnNnv2mVCpRUXwTMxQKeNjZt3k5RC1pV0WN9evX45///Cdyc3PRv39/xMXFYciQIWKnRUREJohjBhGZooKCAvzfC8+hqrSo2Ti5Q2fEb9puNhuKre2TU1NTERkZiQsXLsDT0xOvvPIKwsPD1WL27NmD5cuX48qVK+jZsydWrlyJ8ePHt2q9giBgxYoV2LhxI4qLixEQEIB169ahf//++n0DiESmy703aqprkJOVg64+XWFhaaF1WS3d4+Kv98vQmo8e7+Giy/oAad6bw5zbJlXcxiBTp7CzQ3cn7We9tXSJqqysLNTW1sLDzv6ulqOK01OxnHQjpUJ4uylqJCYmYtGiRVi/fj0efvhhfPTRRwgJCcHFixfRrVs3sdMjIiITwjGDiEyVUqlEVWkR/jHEEV07d9QYc63oNt7/tshsjn5rbZ+ckZGBMWPGYPbs2di2bRu+++47REREwNXVFRMnTgQApKWlITQ0FG+99RbGjx+PpKQkTJ48GcePH0dAQIDO63333XcRGxuLzZs3o3fv3nj77bcxevRoXLp0CQ4ODsZ7k4gMTNf7qlzNugrXYFetMbrc4+Kv98uwd9N8lK8+7+Giy/qkem8Oc26bFHEbg6ROl0tU3a6sxI3r11Hdo+ddLQdoKHxkXs9Bj65esLTUvgubl7LSD6ldOqzdFDViY2Mxc+ZMzJo1CwAQFxeHQ4cOIT4+HjExMSJnR0REpoRjBhGZuq6dO6Knu+YdVA2aP8JKSlrbJ2/YsAHdunVDXFwcAKBv375IT0/He++9pypqxMXFYfTo0YiKigIAREVFITU1FXFxcdi5c6dO6xUEAXFxcVi2bBkmTJgAANiyZQvc3d2xY8cOzJkzx6DvC5EYdLmvir7uz2HvZm/Ue7g0tz6pM+e2SQm3MUjqdLlE1dn8G/gwKwt1ddqLzrosR7Wsq1fxvIvLXV0Si3QjtUuHtYuiRnV1NU6fPo0lS5aozQ8ODsaJEyc0vqaqqgpVVVWqxyUlJQDQ4ik4d6qvrwVgibq6MhRl/6Yxprw4H1UVFbh48SJKS3mDPSLSrlOnTnB2dm716xwcHCCTyQyQkfkRa8xoHC9q6pQ4l1WsNS7nZgVuV1RxzCBqp65du4bKqmr8+ocSpZU1GmNyblagprYOpaWlrf7bFTCtMaMtfXJaWhqCg4PV5j322GNISEhATU0NrKyskJaWhsWLFzeJaSyE6LLejIwM5OXlqa1LLpdj2LBhOHHihMaihk7jRX39n/9q+fxKS0tRV1uH4uxiVFdUa4xR/qGEUC+g5HoJLGWaN/t0idHnssw5J+atW0x5QTmqWvg75tq1a6iurNb5+11fWwLAAvW1JSi4UmCQ9emyHFOka9vqzGTMMGVi7pcqLS1FbV0drtwqRnmN5r8dskuVqBcEZJSUoN5C8+9XXzHGXp++YmrqGvoaQSg1mZzEer8ramu1fpcqa2v1spy/Lqu5uNs1NbhdJb3+2RRdu3YNVdXVuF1T0+z7XVtnImOG0A7k5OQIAITvvvtObf7KlSuF3r17a3zNG2+8IQDgxIkTJ7OYSkpKjNHdmgWOGZw4cWrvkymNGW3pk3v16iWsXLlSbd53330nABD++OMPQRAEwcrKSti+fbtazPbt2wVra2ud19u4zJycHLWY2bNnC8HBwRpz43jBiRMnc5tMacwwZdzG4MSJEyf9jhnt4kyNRndWggRB0FodioqKQmRkpOpxfX09bt68ic6dO7eqoqRUKtG1a1dcu3YNjo6ObUvcRLFt0sS2SdfdtI/X9W49Y48Z/P5KF9smTWybdqY4ZrSmT9YWf+d8XZapr5hG+trG0Ddz+D2wDaaBbRCfsfM3xTHDlOlrzCgtLZX097StpP77bKv22m6g/bbdXNutzzGjXRQ1XFxcYGFhgby8PLX5+fn5cHd31/gauVwO+R03q+nUqVObc3B0dDSrL+FfsW3SxLZJl7m3T2xijxnm/vmac/vYNmli20xbW/pkhUKhMd7S0hKdO3duNqZxmbqsV6FQAADy8vLg4eGhU2763sbQN3P4zrANpoFtEJ/U8zc3+t7GaCyEtNfPme1uf9pr29tru3XRQewEjMHa2hp+fn5ISUlRm5+SkoKgoCCRsiIiIlPEMYOIyHS0pU8ODAxsEn/48GH4+/vDysqq2ZjGZeqyXh8fHygUCrWY6upqpKamcrwgIiI13MYgItKvdnGmBgBERkYiLCwM/v7+CAwMxMaNG5GdnY3w8HCxUyMiIhPDMYOIyHS01CdHRUUhJycHW7duBQCEh4dj7dq1iIyMxOzZs5GWloaEhATs3LlTtcyFCxdi6NChWL16NcaNG4cvvvgCR44cwfHjx3Ver0wmw6JFi7Bq1Sr06tULvXr1wqpVq9CxY0dMnTrViO8QERFJAbcxiIj0p90UNUJDQ1FUVIQ333wTubm58PX1RXJyMry9vQ26XrlcjjfeeKPJKYPmgG2TJrZNusy9faZEjDHD3D9fc24f2yZNbJt0tNQn5+bmIjs7WxXv4+OD5ORkLF68GOvWrYOnpyfWrFmDiRMnqmKCgoKwa9cuvPbaa1i+fDl69uyJxMREBAQE6LxeAHjllVdQUVGBiIgIFBcXIyAgAIcPH5bcNebN4TvDNpgGtkF8Us/fnOlzG6O9fs5sd/tqN9B+295e290aMqHxrnlEREREREREREREREQmrF3cU4OIiIiIiIiIiIiIiKSPRQ0iIiIiIiIiIiIiIpIEFjWIiIiIiIiIiIiIiEgSWNQgIiIiIiIiIiIiIiJJYFHjLsTExGDQoEFwcHCAm5sbnn76aVy6dKnF16WmpsLPzw82Njbo0aMHNmzYYIRsW6ctbdu7dy9Gjx4NV1dXODo6IjAwEIcOHTJSxrpr6+fW6LvvvoOlpSUGDhxouCTbqK1tq6qqwrJly+Dt7Q25XI6ePXvik08+MULGumtr27Zv344BAwagY8eO8PDwwAsvvICioiIjZNw68fHxeOCBB+Do6Kj6/Xz11VfNvkYKfQn96dixYxg7diw8PT0hk8mwb9++Fl8jlc+4tW2TyngBtO1za2TK4wXQtrZJYbxo1Jb2SWHMMOe/P0m/dPmuTJ8+HTKZTG0aPHiwWkxVVRXmz58PFxcX2NnZ4amnnsL169eN0obo6Ogm+SkUCtXzgiAgOjoanp6esLW1xfDhw3HhwgWTyR8Aunfv3qQNMpkMc+fOBWCan0FL/ae+3vfi4mKEhYXByckJTk5OCAsLw61btwyaf01NDV599VXcf//9sLOzg6enJ55//nn88ccfassYPnx4k89lypQpRsm/pTYA+vveGLINdPfWr18PHx8f2NjYwM/PD99++22z8eYy1rem3VLarmhJaz/vRqa+zdGS1rZbStsjLWlt26WwrWJMLGrchdTUVMydOxcnT55ESkoKamtrERwcjPLycq2vycjIwJgxYzBkyBCcPXsWS5cuxYIFC7Bnzx4jZt6ytrTt2LFjGD16NJKTk3H69GmMGDECY8eOxdmzZ42Yecva0rZGJSUleP755/Hoo48aIdPWa2vbJk+ejP/+979ISEjApUuXsHPnTtx3331Gylo3bWnb8ePH8fzzz2PmzJm4cOECdu/ejVOnTmHWrFlGzFw3Xl5eeOedd5Ceno709HSMHDkS48aNa7KB2EgqfQn9qby8HAMGDMDatWt1ipfSZ9zatkllvABa37ZGpj5eAG1rmxTGi0atbZ9Uxgxz/vuT9EvX78rjjz+O3Nxc1ZScnKz2/KJFi5CUlIRdu3bh+PHjKCsrw5NPPom6ujqjtKN///5q+Z0/f1713LvvvovY2FisXbsWp06dgkKhwOjRo1FaWmoy+Z86dUot/5SUFADAM888o4oxtc+gpf5TX+/71KlTce7cORw8eBAHDx7EuXPnEBYWZtD8b9++jTNnzmD58uU4c+YM9u7di99++w1PPfVUk9jZs2erfS4fffSR2vOGyr+lNjTSx/fGkG2gu5OYmIhFixZh2bJlOHv2LIYMGYKQkBBkZ2drjDeXsb617ZbSdkVzWtvuRlLY5mhOW9otpe2R5rS27VLZVjEqgfQmPz9fACCkpqZqjXnllVeE++67T23enDlzhMGDBxs6vbuiS9s06devn7BixQoDZaUfrWlbaGio8NprrwlvvPGGMGDAAMMnd5d0adtXX30lODk5CUVFRUbM7O7p0rZ//vOfQo8ePdTmrVmzRvDy8jJ0enpxzz33CP/+9781PifVvoQaABCSkpKajZHqZ6xL2zSRwnjRmrZJbbzQpW1SHS8EQbf2SXXMMOe/P0m/NH1Xpk2bJowbN07ra27duiVYWVkJu3btUs3LyckROnToIBw8eNCQ6QqCIDTbh9bX1wsKhUJ45513VPMqKysFJycnYcOGDYIgiJ+/JgsXLhR69uwp1NfXC4Jg+p/Bnf2nvt73ixcvCgCEkydPqmLS0tIEAMKvv/5qsPw1+eGHHwQAQlZWlmresGHDhIULF2p9jbHyFwTNbdDH98aYbaDW+9vf/iaEh4erzbvvvvuEJUuWaIw3l7G+te3WRArbFXdqa7ults1xp9a2W8rbI3dqbduluq1iSDxTQ49KSkoAAM7Ozlpj0tLSEBwcrDbvscceQ3p6Ompqagya393QpW13qq+vR2lpaateIwZd27Zp0yZcuXIFb7zxhjHS0gtd2rZ//374+/vj3XffRZcuXdC7d2+89NJLqKioMFaabaJL24KCgnD9+nUkJydDEATcuHEDn3/+OZ544gljpdkmdXV12LVrF8rLyxEYGKgxRqp9CemuPX3GUhkvdCXF8UIXUh0vdCXVMcOc//4k/dL2XTl69Cjc3NzQu3dvzJ49G/n5+arnTp8+jZqaGrXvj6enJ3x9fXHixAmj5H358mV4enrCx8cHU6ZMwdWrVwE0HJWcl5enlptcLsewYcNUuZlC/n9VXV2Nbdu2YcaMGZDJZKr5pv4Z/JW+3ve0tDQ4OTkhICBAFTN48GA4OTkZvV0lJSWQyWTo1KmT2vzt27fDxcUF/fv3x0svvaR2Joop5H+33xtTaANpVl1djdOnTzcZu4ODg7V+NuYw1rel3XeS4nZFW9st9W2OtrTbXLZH2tJ2qW6rGJKl2AmYC0EQEBkZiUceeQS+vr5a4/Ly8uDu7q42z93dHbW1tSgsLISHh4ehU201Xdt2p/fffx/l5eWYPHmyAbO7O7q27fLly1iyZAm+/fZbWFpK42eja9uuXr2K48ePw8bGBklJSSgsLERERARu3rxpstcl1LVtQUFB2L59O0JDQ1FZWYna2lo89dRT+PDDD42Yre7Onz+PwMBAVFZWwt7eHklJSejXr5/GWCn2JdQ67ekzlsJ4oSspjhe6kuJ40RpSGzMA8/77k/RL23clJCQEzzzzDLy9vZGRkYHly5dj5MiROH36NORyOfLy8mBtbY177rlHbXnu7u7Iy8szeN4BAQHYunUrevfujRs3buDtt99GUFAQLly4oFq/pu92VlYWAIie/5327duHW7duYfr06ap5pv4Z3Elf73teXh7c3NyaLN/Nzc2o7aqsrMSSJUswdepUODo6quY/99xz8PHxgUKhwM8//4yoqCj8+OOPqsuHiZ2/Pr43YreBtCssLERdXZ3G35m2z8Ycxvq2tPtOUtyuaEu7zWGboy3tNpftkba0XYrbKoYmzW++CZo3bx5++uknHD9+vMXYvx6VAzRsZGiabypa07ZGO3fuRHR0NL744guNfyiZCl3aVldXh6lTp2LFihXo3bu3EbO7O7p+bvX19ZDJZNi+fTucnJwAALGxsZg0aRLWrVsHW1tbY6TbKrq27eLFi1iwYAFef/11PPbYY8jNzcXLL7+M8PBwJCQkGClb3fXp0wfnzp3DrVu3sGfPHkybNg2pqalaCxtS60uo9drDZyyV8UIXUh0vdCXF8aI1pDZmAOb99yfpl7bvSmhoqOr/vr6+8Pf3h7e3Nw4cOIAJEyZoXZ4gCEb57oSEhKj+f//99yMwMBA9e/bEli1bVDdF1vTdbik3Y+V/p4SEBISEhMDT01M1z9Q/A2308b5rijdmu2pqajBlyhTU19dj/fr1as/Nnj1b9X9fX1/06tUL/v7+OHPmDB566CEA4uavr++N2J8BNa+1vzNzGevb0r8A0t+u0LXd5rbN0ZrP29y2R1rTdiluqxgaLz+lB/Pnz8f+/fvxzTffwMvLq9lYhULRpOqWn58PS0tLdO7c2ZBptklr2tYoMTERM2fOxGeffYZRo0YZOMO207VtpaWlSE9Px7x582BpaQlLS0u8+eab+PHHH2FpaYmvv/7aiFnrpjWfm4eHB7p06aIaEACgb9++EAQB169fN3SqrdaatsXExODhhx/Gyy+/jAceeACPPfYY1q9fj08++QS5ublGylh31tbWuPfee+Hv74+YmBgMGDAAH3zwgcZYqfUl1Hrt4TOWynihKymOF60htfGitaQ2Zpjz35+kX639u9Db2xuXL18G0PDdqa6uRnFxsVpcfn5+k6MLjcHOzg73338/Ll++DIVCAQAav9uNuZlS/llZWThy5EiLN/Q09c9AX++7QqHAjRs3miy/oKDAKO2qqanB5MmTkZGRgZSUFLWzNDR56KGHYGVlpfa5iJn/ndryvTG1NtCfXFxcYGFh0ezv7E7mMNa3pd2NpLxd0dp2m8s2R1s+b3PZHmlL26W2rWIMLGrcBUEQMG/ePOzduxdff/01fHx8WnxNYGCg6pTVRocPH4a/vz+srKwMlWqrtaVtQENlfPr06dixY4fJXtettW1zdHTE+fPnce7cOdUUHh6uOqr+r9cgFVtbPreHH34Yf/zxB8rKylTzfvvtN3To0EHnQpYxtKVtt2/fRocO6t2chYWFanmmThAEVFVVaXxOKn0JtZ25f8ZSGC9aS0rjRVtIZbxoK6mMGeb89yfpV1u+K0VFRbh27ZrqMiV+fn6wsrJS+/7k5ubi559/RlBQkMFy16aqqgq//PILPDw8VJcG+mtu1dXVSE1NVeVmSvlv2rQJbm5uLY55pv4Z6Ot9DwwMRElJCX744QdVzPfff4+SkhKDt6uxoHH58mUcOXJEpx2+Fy5cQE1NjepzETN/TdryvTG1NtCfrK2t4efn12TsTklJ0frZmMNY35Z2A9Lfrmhtu81lm6Mtn7e5bI+0pe1S2VYxKsPcf7x9+L//+z/ByclJOHr0qJCbm6uabt++rYpZsmSJEBYWpnp89epVoWPHjsLixYuFixcvCgkJCYKVlZXw+eefi9EErdrSth07dgiWlpbCunXr1F5z69YtMZqgVVvadqc33nhDGDBggBGybZ22tK20tFTw8vISJk2aJFy4cEFITU0VevXqJcyaNUuMJmjVlrZt2rRJsLS0FNavXy9cuXJFOH78uODv7y/87W9/E6MJzYqKihKOHTsmZGRkCD/99JOwdOlSoUOHDsLhw4cFQZBuX0J/Ki0tFc6ePSucPXtWACDExsYKZ8+eFbKysgRBkPZn3Nq2SWW8EITWt+1OpjpeCELr2yaV8aJRa9snlTHDnP/+JP1q6btSWloq/OMf/xBOnDghZGRkCN98840QGBgodOnSRVAqlarlhIeHC15eXsKRI0eEM2fOCCNHjhQGDBgg1NbWGrwN//jHP4SjR48KV69eFU6ePCk8+eSTgoODg5CZmSkIgiC88847gpOTk7B3717h/PnzwrPPPit4eHiYTP6N6urqhG7dugmvvvqq2nxT/Qxa6j/19b4//vjjwgMPPCCkpaUJaWlpwv333y88+eSTBs2/pqZGeOqppwQvLy/h3Llzar+NqqoqQRAE4ffffxdWrFghnDp1SsjIyBAOHDgg3HfffcKDDz5olPxbaoM+vzeGbAPdnV27dglWVlZCQkKCcPHiRWHRokWCnZ2dqv8z17G+te2W0nZFc1rb7juZ8jZHc1rbbqltjzSntW2XyraKMbGocRcAaJw2bdqkipk2bZowbNgwtdcdPXpUePDBBwVra2uhe/fuQnx8vHET10Fb2jZs2DCNr5k2bZrR829OWz+3vzLVAaOtbfvll1+EUaNGCba2toKXl5cQGRmptnPEFLS1bWvWrBH69esn2NraCh4eHsJzzz0nXL9+3bjJ62DGjBmCt7e3YG1tLbi6ugqPPvqoqqAhCNLtS+hP33zzTbN9pJQ/49a2TSrjhSC07XP7K1MdLwShbW2TwnjRqC3tk8KYYc5/f5J+tfRduX37thAcHCy4uroKVlZWQrdu3YRp06YJ2dnZasupqKgQ5s2bJzg7Owu2trbCk08+2STGUEJDQwUPDw/ByspK8PT0FCZMmCBcuHBB9Xx9fb3wxhtvCAqFQpDL5cLQoUOF8+fPm0z+jQ4dOiQAEC5duqQ231Q/g5b6T32970VFRcJzzz0nODg4CA4ODsJzzz0nFBcXGzT/jIwMrb+Nb775RhAEQcjOzhaGDh0qODs7C9bW1kLPnj2FBQsWCEVFRUbJv6U26PN7Y8g20N1bt26dahvxoYceElJTU1XPmfNY35p2S2m7oiWt/bz/ypS3OVrS2nZLaXukJa1tuxS2VYxJJgjt9RwVIiIiIiIiIiIiIiKSEt5Tg4iIiIiIiIiIiIiIJIFFDSIiIiIiIiIiIiIikgQWNYiIiIiIiIiIiIiISBJY1CAiIiIiIiIiIiIiIklgUYOIiIiIiIiIiIiIiCSBRQ0iIiIiIiIiIiIiIpIEFjWIiIiIiIiIiIiIiEgSWNQgIiIiIiIiIiIiIiJJYFGDyEiOHj0KmUyGW7duNRvXvXt3xMXFGSUnY9G17URE1DbR0dEYOHCg2GnonTmOiUTUPuXl5WH06NGws7NDp06dxE6nRaYwrshkMuzbt0/UHIiofTPlvtsU+unNmzervS9i5LRv3z7ce++9sLCwwKJFi5rkROaLRQ0iIwkKCkJubi6cnJwANO38G506dQovvviikbMjIiKp0LST56WXXsJ///tfcRIiIqIW/etf/0Jubi7OnTuH3377Tex0TIq2nWC5ubkICQkxfkJERP/Dvrt1xNgmmTNnDiZNmoRr167hrbfeatMyeCCVNFmKnQBRe2FtbQ2FQtFinKurqxGyISIic2Jvbw97e3ux0yAiIi2uXLkCPz8/9OrVq83LqKmpgZWVlR6zMqzq6mpYW1u3+fW6bDsRERlSe+y774axt0nKysqQn5+Pxx57DJ6enkZbL5kGnqlBohg+fDgWLFiAV155Bc7OzlAoFIiOjgYAZGZmQiaT4dy5c6r4W7duQSaT4ejRowD+vJzRoUOH8OCDD8LW1hYjR45Efn4+vvrqK/Tt2xeOjo549tlncfv2bZ1zmjdvHubNm4dOnTqhc+fOeO211yAIgiqmuLgYzz//PO655x507NgRISEhuHz5sur5rKwsjB07Fvfccw/s7OzQv39/JCcnq+V869YtHD16FC+88AJKSkogk8kgk8lU7f9rhfjZZ5/FlClT1PKsqamBi4sLNm3aBAAQBAHvvvsuevToAVtbWwwYMACff/65Tm1uzOnAgQMYMGAAbGxsEBAQgPPnz6vFnThxAkOHDoWtrS26du2KBQsWoLy8XPX8tm3b4O/vDwcHBygUCkydOhX5+fla11tRUYEnnngCgwcPxs2bN1FdXY158+bBw8MDNjY26N69O2JiYnRqAxGZH1MdI7Tl1KikpAQvvvgi3Nzc4OjoiJEjR+LHH39Ui3n77bfh5uYGBwcHzJo1C0uWLFE7OvXUqVMYPXo0XFxc4OTkhGHDhuHMmTOq57t37w4AGD9+PGQymerxX49yPXToEGxsbJpc8m/BggUYNmyY6nFLfXtzunfvjrfeegtTp06Fvb09PD098eGHH7bq/bhy5QrGjRsHd3d32NvbY9CgQThy5Eiz6920aROcnJyQkpICAPj8889x//33w9bWFp07d8aoUaN0bgMRma8vv/wSnTp1Qn19PQDg3LlzkMlkePnll1Uxc+bMwbPPPouioiI8++yz8PLyQseOHXH//ff/P3v3HRbFtf4B/LuALCgsCiJFAbG32MAolqBGMZhr7Br1Gk3UyMVoDGkaU9RESVEkxoZeg3ptxKsm8adRiYm9RBCvXo2dJoIUkQWlc35/eN24ssCCyw7sfD/Ps4/OzJnZd47jOTPz7pzBtm3bNOXCwsLQuHFjzbYee+WVVzBp0iSt7/Ty8oKVlRWaNWuGBQsWoKioCMCjNnPnzp3YtGkTFAoFJk+eDABISEjA0KFDYWNjA5VKhTFjxuDu3buabT5u27///ns0a9YMSqUSQggoFAqEhYXhb3/7G+rWrYu2bdvi1KlTuHHjBvr27Yt69erBx8cHN2/e1LvOvvzySzg5OcHW1hZTpkxBXl6e1vK+ffti9uzZWvOGDRum2ZfH+/nFF19g8uTJsLOzw7Rp0wAAH374IVq1aoW6deuiWbNm+OSTT1BYWAjg0dPrCxYswH/+8x/NddGGDRsAlH4y8eLFi+jfv7+mzX/zzTeRk5OjWT558mQMGzYMS5YsgYuLCxwcHDBjxgzNdxFRzca2u/Jt9+N9dXNzQ926dTF69Gita4CKri0ex+vu7g6lUglXV1fMmjVLs6ygoAAffPABGjdujHr16qF79+6aay5dnn7yTp92ubLf8djhw4dha2sLAOjfv7/W9eCTKrrm6Nu3L+Lj4/HOO+9o+iHgr9FVDhw4gLZt28LGxgYvvfQSkpOTtbYfHh6Otm3bwsrKCm3atMGqVau09q28e13l1T3pQRBJwNfXV6hUKjF//nxx7do1sXHjRqFQKMTBgwdFbGysACBiYmI05TMzMwUA8fvvvwshhPj9998FANGjRw9x/Phxce7cOdGiRQvh6+sr/Pz8xLlz58TRo0eFg4OD+PLLL/WOycbGRrz99tviypUrYvPmzaJu3bpi7dq1mjKvvPKKaNu2rTh69Kg4f/68GDRokGjRooUoKCgQQgjx8ssvi4EDB4oLFy6Imzdvij179ogjR45oxZyZmSny8/NFaGioUKlUIjk5WSQnJ4vs7GwhhBAeHh5i2bJlQggh9uzZI6ytrTXLHs+zsrISWVlZQgghPvroI9GmTRuxf/9+cfPmTREeHi6USqU4fPhwhfv8OKa2bduKgwcPigsXLoi//e1vomnTppp9unDhgrCxsRHLli0T165dEydOnBBdunQRkydP1mxn/fr1Yt++feLmzZvi1KlTokePHsLf37/U92RmZor79++L3r17iwEDBoicnBwhhBDffPONcHNzE0ePHhVxcXHi2LFjYuvWrXr9uxGR6ampfURZMQkhRElJiejVq5cYMmSIOHv2rLh27Zp49913hYODg8jIyBBCCLF582ZhZWUlvv/+e3H16lWxYMECoVKpRKdOnTTfc+jQIfGvf/1LXL58WVy+fFlMmTJFODk5CbVaLYQQIjU1VQAQ4eHhIjk5WaSmpgohhPjss8802ykqKhJOTk7in//8p2a7j+eFhYUJIfRr28vj4eEhbG1tRXBwsLh69apYvny5MDc3r1R9nD9/XqxZs0ZcuHBBXLt2TcybN09YWVmJ+Ph4re953Cd+8803wt7eXpw6dUoIIcSdO3eEhYWFCAkJEbGxseLChQti5cqVWn0mEcnT/fv3hZmZmYiKihJCCBEaGioaNmwounXrpinTqlUrsXr1anH79m3xzTffiJiYGHHz5k1Ne3b69GkhhBAZGRnC0tJS/Prrr5p17927JywtLcWBAweEEELs379fqFQqsWHDBnHz5k1x8OBB0bRpUzF//nwhxKO2+6WXXhJjxowRycnJ4v79+6KkpER06dJF9O7dW0RFRYnTp0+Lrl27Cl9fX833fPbZZ6JevXpi0KBB4ty5c+I///mPKCkpEQBE48aNRUREhLh69aoYNmyYaNq0qejfv7/Yv3+/uHz5sujRo4d46aWX9KqviIgIYWlpKdatWyeuXLki5s2bJ2xtbbX6J19fX/H2229rrTd06FAxadIkzbSHh4dQqVTim2++EdevXxfXr18XQgjx+eefixMnTojY2Fjx888/CycnJ/HVV18JIYR4+PChePfdd0X79u0110UPHz4UQggBQOzevVsIIcSDBw+Eq6urGDFihLh48aI4dOiQ8PT01Pr+SZMmCZVKJQICAsSff/4p9uzZU+p6johqLrbdlWu7H39P//79RUxMjDhy5Iho0aKFGD9+vKZMRdcWO3bsECqVSuzbt0/Ex8eLM2fOaLWZ48ePFz179hRHjx4VN27cEN98841QKpXi2rVrQgghwsPDhZ2dnVZMT/Yd+rTLFX1HWfLz88XVq1cFALFz506RnJws8vPzS8VU0TVHRkaGaNKkiVi4cKGmH3q8b3Xq1BEDBgwQZ8+eFdHR0aJt27Za9bt27Vrh4uIidu7cKW7duiV27twp7O3txYYNG4QQ5d/rqqjuqWJMapAkfH19Re/evbXmdevWTXz44YeVumH1ZAcVHBwsAIibN29q5k2fPl0MGjRI75jatm0rSkpKNPM+/PBD0bZtWyGEENeuXRMAxIkTJzTL09PThbW1tfjhhx+EEEI899xzmg7waU/e2BeidOP/2JM3cAoKCkTDhg3Fpk2bNMvHjRsnRo8eLYQQIicnR1hZWYmTJ09qbWPKlCli3LhxFe7z45i2b9+umZeRkSGsra1FRESEEEKIiRMnijfffFNrvWPHjgkzMzORm5urc7t//PGHAKC5sfT4e65cuSI6deokRowYIfLz8zXlZ86cKfr3769V90QkXzW1jygrJiEeXTCoVCqRl5enVaZ58+aaREL37t3FjBkztJb36tVL68T/aUVFRcLW1lbs2bNHM+/JmzyPPX0BMWvWLNG/f3/N9IEDB4SlpaW4d++eEKJqbfuTPDw8Sl1wjR07VpPQ1qc+dGnXrp347rvvtL5n2bJlYs6cOcLFxUVcuHBBsyw6OloAEHFxcRXGS0Ty07VrV7FkyRIhhBDDhg0TixYtEpaWlkKtVovk5GQBQPz555861x08eLB49913NdOvvPKKeOONNzTTYWFhwtnZWRQVFQkhhOjTp49YvHix1jb+9a9/CRcXF8300wmAgwcPCnNzc5GQkKCZd+nSJQFA/PHHH0KIR217nTp1NAnsxwCIjz/+WDN96tQpAUCsX79eM2/btm3Cysqq/Er6Hx8fHxEQEKA1r3v37lVKagwbNqzC7/v666+Fl5eXZvrpPuyxJ/u7tWvXigYNGmh+FCWEEHv37hVmZmYiJSVFCPHo5pmHh4fm30UIIUaPHi3Gjh1bYUxEVDOw7da/7f7ss8+Eubm5SExM1Mz75ZdfhJmZmebG/NOevrZYunSpaNWqleZHrU+6ceOGUCgUIikpSWv+iy++KObOnSuE0C+pUV67rM93lOfp60BdMelS1jXHk8LDwwUAcePGDc28lStXCicnJ820m5tbqR/kfv7558LHx0cIUf69rvLqnvTD4adIMh07dtSadnFxKXfIooq24eTkpHmk+cl5ldlmjx49NI+aAYCPjw+uX7+O4uJi/Pnnn7CwsED37t01yx0cHNC6dWv8+eefAB4N7fHFF1+gV69e+Oyzz3DhwoVK7c/T6tSpg9GjR2PLli0AgAcPHuCnn37ChAkTAACXL19GXl4eBg4cqBm70MbGBps2barUI4s+Pj6av9vb22vtU3R0NDZs2KC1/UGDBqGkpASxsbEAgJiYGAwdOhQeHh6wtbVF3759ATx6LPNJAwYMQLNmzfDDDz9oja87efJknD9/Hq1bt8asWbNw8ODBylcWEZmUmthHlBdTdHQ0cnJy4ODgoNVexsbGatrjq1ev4vnnn9faxtPTqampCAgIQKtWrWBnZwc7Ozvk5OSUak8rMmHCBBw+fBh37twBAGzZsgWDBw9GgwYNNPFW1LZX5Mm+4/H0k31HRfXx4MEDfPDBB2jXrh3q168PGxsbXLlypdS+Ll26FGFhYTh+/Diee+45zfxOnTrhxRdfxHPPPYfRo0dj3bp1yMzMrFQ9EZHp6tu3Lw4fPgwhBI4dO4ahQ4eiQ4cOOH78OH7//Xc4OTmhTZs2KC4uxqJFi9CxY0dNm3Xw4EGttmjChAnYuXMn8vPzATxqU1999VWYm5sDeNTmLVy4UKu9mzZtGpKTk8sc5vDPP/+Em5sb3NzcNPMet4eP21IA8PDw0PnOvaf7OABabaSTkxPy8vKgVqsrrKs///xTZ5teFd7e3qXm/fvf/0bv3r3h7OwMGxsbfPLJJ5Xu1/7880906tQJ9erV08zr1asXSkpKcPXqVc289u3ba/5dgKqdPxCRdNh26992A4C7uzuaNGmimfbx8dFqFyu6thg9ejRyc3PRrFkzTJs2Dbt379YMv3Xu3DkIIdCqVSutOjpy5Eil7jeV1y4b6jvKo+81hy5169ZF8+bNdcaelpaGxMRETJkyRSv2L774QhN7efe6yqt70g9fFE6SefpFSQqFAiUlJTAze5RrE0+8y6KscVCf3IZCoShzm4bwZDxPz3+cCJk6dSoGDRqEvXv34uDBgwgODsbSpUsxc+bMKn/vhAkT4Ovri9TUVERGRsLKygr+/v4AoNm3vXv3onHjxlrrKZXKKn8nAM0+lZSUYPr06TrH9nN3d8eDBw/g5+cHPz8/bN68GY6OjkhISMCgQYNQUFCgVf7ll1/Gzp07cfnyZa2Ou2vXroiNjcUvv/yCX3/9FWPGjMGAAQP0fjcIEZmemthHlLd+SUkJXFxcdI7jWr9+fa11nvR03zJ58mSkpaUhNDQUHh4eUCqV8PHxKdWeVuT5559H8+bNsX37dvzjH//A7t27Ne9iehxveW17VT3Zd1RUH++//z4OHDiAJUuWoEWLFrC2tsaoUaNK7WufPn2wd+9e/PDDD5gzZ45mvrm5OSIjI3Hy5EkcPHgQ3333HebNm4czZ87A09OzyvtARKahb9++WL9+Pf7zn//AzMwM7dq1g6+vL44cOYLMzEzNO4aWLl2KZcuWITQ0FM899xzq1auH2bNna7VFQ4YMQUlJCfbu3Ytu3brh2LFjCAkJ0SwvKSnBggULMGLEiFJxWFlZ6YzvyWuI8uY/eRP/SU/3cWXNM9S1kJmZWak+S1f/+3S8p0+fxquvvooFCxZg0KBBsLOzw/bt27F06dJKfX9Z9QVo963VeT1IRNWPbfeztd2P13/8Z0XXFm5ubrh69SoiIyPx66+/IjAwEN988w2OHDmCkpISmJubIzo6WispAaBSLwOv6BrKEN9RHn2vOfSN/XFf+Hgf1q1bp/XjZwCafSnvXld5dS+XF8s/KyY1qMZ5nM1OTk5Gly5dAEDrhbDV6fTp06WmW7ZsCXNzc7Rr1w5FRUU4c+YMevbsCQDIyMjAtWvX0LZtW806bm5uCAgIQEBAAObOnYt169bpTGpYWlqiuLi4wph69uwJNzc3RERE4JdffsHo0aM1Tzm0a9cOSqUSCQkJWi9/rcp+P76JlZmZiWvXrqFNmzYAHjXCly5dQosWLXSue/HiRaSnp+PLL7/U/FohKipKZ9kvv/wSNjY2ePHFF3H48GG0a9dOs0ylUmHs2LEYO3YsRo0ahZdeegn37t2Dvb19lfeLiEyPlH1Eebp27YqUlBRYWFhoXt79tNatW+OPP/7AxIkTNfOebi+PHTuGVatWYfDgwQCAxMREpKena5WpU6eOXv3H+PHjsWXLFjRp0gRmZmZ4+eWXteItr23Xh64+88m+o6L6OHbsGCZPnozhw4cDAHJychAXF1eq3PPPP4+ZM2di0KBBMDc313pZpEKhQK9evdCrVy98+umn8PDwwO7duxEUFFTl/SIi0/DCCy8gOzsboaGh8PX1hUKhgK+vL4KDg5GZmYm3334bADS/BP773/8O4NFNiuvXr2ud31tbW2PEiBHYsmULbty4gVatWsHLy0uzvGvXrrh69Wql2tR27dohISEBiYmJmnPoy5cvIysrS+u7jaFt27Y4ffo0XnvtNc28p9t4R0dHrZejFhcX47///S/69etX7rZPnDgBDw8PzJs3TzMvPj5eq4w+10Xt2rXDxo0b8eDBA83NwhMnTsDMzAytWrUqfweJqNZg2105CQkJuHPnDlxdXQEAp06d0moX9bm2sLa2xiuvvIJXXnkFM2bMQJs2bXDx4kV06dIFxcXFSE1NRZ8+faolfmN8hz7XHPren3uSk5MTGjdujFu3bmlGU9GlvHtdZdV9165dK72fcsSkBtU41tbW6NGjB7788ks0bdoU6enp+Pjjj43y3YmJiQgKCsL06dNx7tw5fPfdd5pfEbVs2RJDhw7FtGnTEBYWBltbW8yZMweNGzfG0KFDAQCzZ8+Gv78/WrVqhczMTPz2229ldmxNmzZFTk4ODh06hE6dOqFu3bqoW7duqXIKhQLjx4/HmjVrcO3aNfz++++aZba2tnjvvffwzjvvoKSkBL1794ZarcbJkydhY2ODSZMm6bXfCxcuhIODA5ycnDBv3jw0bNgQw4YNAwB8+OGH6NGjB2bMmIFp06ahXr16+PPPPxEZGYnvvvsO7u7usLS0xHfffYeAgAD897//xeeff17mdy1ZsgTFxcXo378/Dh8+jDZt2mDZsmVwcXFB586dYWZmhh07dsDZ2Vnr181ERIC0fUR5BgwYAB8fHwwbNgxfffUVWrdujTt37mDfvn0YNmwYvL29MXPmTEybNg3e3t7o2bMnIiIicOHCBa0hsVq0aIF//etf8Pb2hlqtxvvvvw9ra2ut72ratCkOHTqEXr16QalUaoaUetqECROwYMECLFq0CKNGjdL6xVlFbbs+Tpw4ga+//hrDhg1DZGQkduzYgb179+pdHy1atMCuXbswZMgQKBQKfPLJJ2X+Ks3Hxwe//PILXnrpJVhYWOCdd97BmTNncOjQIfj5+aFRo0Y4c+YM0tLSJLmgJKKax87ODp07d8bmzZvx7bffAnh0s2z06NEoLCzUDJfaokUL7Ny5EydPnkSDBg0QEhKClJSUUm3JhAkTMGTIEFy6dElzE+2xTz/9FH/729/g5uaG0aNHw8zMDBcuXMDFixfxxRdf6IxvwIAB6NixIyZMmIDQ0FAUFRUhMDAQvr6+Oodwqk5vv/02Jk2aBG9vb/Tu3RtbtmzBpUuXtPqn/v37IygoCHv37kXz5s2xbNky3L9/v8Jtt2jRAgkJCdi+fTu6deuGvXv3Yvfu3VplmjZtitjYWJw/fx5NmjSBra1tqafOJ0yYgM8++wyTJk3C/PnzkZaWhpkzZ2LixImaIVyIqPZj2105VlZWmDRpEpYsWQK1Wo1Zs2ZhzJgxcHZ2BlDxtcWGDRtQXFyM7t27o27duvjXv/4Fa2treHh4wMHBARMmTMBrr72GpUuXokuXLkhPT8dvv/2G5557TpMoeRatWrWq9u/Q55qjadOmOHr0KF599VUolUo0bNhQr23Pnz8fs2bNgkqlgr+/P/Lz8xEVFYXMzEwEBQWVe6+rvLon/fCdGlQjff/99ygsLIS3tzfefvvtMjsUQ3vttdeQm5uL559/HjNmzMDMmTPx5ptvapaHh4fDy8sLf/vb3+Dj4wMhBPbt26d5NKy4uBgzZsxA27Zt8dJLL6F169ZYtWqVzu/q2bMnAgICMHbsWDg6OuLrr78uM64JEybg8uXLaNy4MXr16qW17PPPP8enn36K4OBgtG3bFoMGDcKePXsqNfTGl19+ibfffhteXl5ITk7Gzz//rHkapGPHjjhy5AiuX7+OPn36oEuXLvjkk0/g4uIC4NGvtjZs2IAdO3agXbt2+PLLL7FkyZJyv2/ZsmUYM2YM+vfvj2vXrsHGxgZfffUVvL290a1bN8TFxWHfvn2aYWaIiJ4kVR9RHoVCgX379uGFF17AG2+8gVatWuHVV19FXFyc5mbLhAkTMHfuXLz33nuaR5EnT56slWz4/vvvkZmZiS5dumDixImYNWsWGjVqpPVdS5cuRWRkJNzc3DRPq+jSsmVLdOvWDRcuXCj166GK2nZ9vPvuu4iOjkaXLl3w+eefY+nSpRg0aJDe9bFs2TI0aNAAPXv2xJAhQzBo0KByf5XUq1cv7N27F5988gmWL18OlUqFo0ePYvDgwWjVqhU+/vhjLF26VDNEIxFRv379UFxcrLkJ1qBBA7Rr1w6Ojo6aG1+ffPIJunbtikGDBqFv375wdnbW/LjnSf3794e9vT2uXr2K8ePHay0bNGgQ/u///g+RkZHo1q0bevTogZCQkHJvTCgUCvz4449o0KABXnjhBc275yIiIgy2//oaO3YsPv30U3z44Yfw8vJCfHw8/vGPf2iVeeONNzBp0iS89tpr8PX1haenZ4VPaQDA0KFD8c477+Ctt95C586dcfLkSXzyySdaZUaOHImXXnoJ/fr1g6OjI7Zt21ZqO3Xr1sWBAwdw7949dOvWDaNGjcKLL76IFStWPNvOE1GNw7Zbfy1atMCIESMwePBg+Pn5oUOHDlr3oCq6tqhfvz7WrVuHXr16oWPHjjh06BD27NkDBwcHAI/ugb322mt499130bp1a7zyyis4c+aM1jtFnlV1f4c+1xwLFy5EXFwcmjdvrvNdKGWZOnUq/vnPf2LDhg147rnn4Ovriw0bNmjux5V3r6uiuqeKKURZLwogkpm+ffuic+fOCA0NlToUozl8+DD69euHzMxMPhVBRCSBgQMHwtnZGf/617+kDqVSmjZtitmzZ2P27NlSh0JEREREREQyw+GniIiIiIzg4cOHWLNmjebdENu2bcOvv/6KyMhIqUMjIiIiIiIiqjU4tgvJQkJCAmxsbMr8JCQkSB1itQgICChznwMCAqQOj4ioRjBWH/F4SKY+ffrAy8sLe/bswc6dOzFgwACDbN9Qjh07Vm59EBFR7dK+ffsy2/QtW7ZIHR4REenAthvw9/cvsw4WL14sdXgkMQ4/RbJQVFSEuLi4Mpc3bdoUFham9+BSamoq1Gq1zmUqlarUOO1ERHIk1z6iLLm5uUhKSipzeYsWLYwYDRERPav4+HgUFhbqXObk5ARbW1sjR0RERBVh2w0kJSUhNzdX5zJ7e3vY29sbOSKqSZjUICIiIiIiIiIiIiKiWoHDTxERERERERERERERUa3ApAYREREREREREREREdUKTGoQEREREREREREREVGtwKQGERERERERERERERHVCkxqEBERERERERERERFRrcCkBhERERERERERERER1QpMahARERERERERERERUa3ApAYREREREREREREREdUKTGoQEREREREREREREVGtwKQGERERERERERERERHVCkxqEBERERERERERERFRrcCkBhERERERERERERER1QpMahARERERERERERERUa3ApAYREREREREREREREdUKTGoQEREREREREREREVGtwKQGERERERERERERERHVCkxqEBERERERERERERFRrcCkBhERERERERERERER1QpMahARERERERERERERUa3ApAYREREREREREREREdUKTGoQEREREREREREREVGtwKQGERERERERERERERHVCkxqEBERERERERERERFRrcCkBhERERERERERERER1QpMahARERERERERERERUa3ApAYREREREREREREREdUKTGoQEREREREREREREVGtwKSGnoQQUKvVEEJIHQoREdVw7DOIiEgf7C+IiEhf7DOIiP5iIXUAtUV2djbs7OyQlZUFlUoldThUBSEhgFoNqFRAUJDU0RCRKTN4n8EGjFVARCaJ1xhEJDmeZNUa7DP0xGOaSBYUgilevajVanYetVyTJkBSEtC4MXD7ttTREJEpM3ifwQaMVUBEJonXGEQkOZ5k1RrsM/TEY5pIFjj8FBERERERERERERER1QpMahAREREREREREVXCqlWr4OnpCSsrK3h5eeHYsWN6rXfixAlYWFigc+fO1RsgEZEJY1KDiIiIiIiIiIhITxEREZg9ezbmzZuHmJgY9OnTB/7+/khISCh3vaysLLz22mt48cUXjRQpEZFp4ovCSTZefhm4dw+wt5c6EnkqLi5GYWGh1GGYtDp16sDc3FzqMGq01atXY/Xq1YiLiwMAtG/fHp9++in8/f0BAJMnT8bGjRu11unevTtOnz5t7FC1sQFjFZg49hHGZW5uDgsLCygUCqlDISKqNuxb9DR+PJCVBdjZAXl5pRbzGkO3kJAQTJkyBVOnTgUAhIaG4sCBA1i9ejWCg4PLXG/69OkYP348zM3N8eOPPxopWpnhhQMZGPsT/Rmzz2BSg2QjLEzqCOQrJycHt2/fhhBC6lBMmkKhQJMmTWBjYyN1KDVWkyZN8OWXX6JFixYAgI0bN2Lo0KGIiYlB+/btAQAvvfQSwsPDNetYWlpKEqsWNmCsAhPGPkIadevWhYuLS81o44iIDIx9SyW8/vpff4+NLbWY1xilFRQUIDo6GnPmzNGa7+fnh5MnT5a5Xnh4OG7evInNmzfjiy++qO4w5YsXDmRA7E8qx5h9BpMaRFStiouLcfv2bdStWxeOjo78VWg1EUIgLS0Nt2/fRsuWLflrqjIMGTJEa3rRokVYvXo1Tp8+rUlqKJVKODs7SxEekeywjzA+IQQKCgqQlpaG2NhYtGzZEmZmHJGWiEwH+xbD4TWGbunp6SguLoaTk5PWfCcnJ6SkpOhc5/r165gzZw6OHTsGCwv9bsXl5+cjPz9fM61Wq6seNBFVGvuTyjF2n8GkBhFVq8LCQggh4OjoCGtra6nDMWmOjo6Ii4tDYWEhLzj0UFxcjB07duDBgwfw8fHRzD98+DAaNWqE+vXrw9fXF4sWLUKjRo0kjJTIdLGPkIa1tTXq1KmD+Ph4FBQUwMrKSuqQiIgMhn2LYfEao2xP3+AUQui86VlcXIzx48djwYIFaNWqld7bDw4OxoIFC545TiKqGvYnlWfMPoNJDSIyCma0qx/rWD8XL16Ej48P8vLyYGNjg927d6Ndu3YAAH9/f4wePRoeHh6IjY3FJ598gv79+yM6OhpKpbLMbfJXVETPhu2X8fHpDCIydexbDIP1WFrDhg1hbm5e6qmM1NTUUk9vAEB2djaioqIQExODt956CwBQUlICIQQsLCxw8OBB9O/fv9R6c+fORVBQkGZarVbDzc3NwHtDRBVhO6g/Y9YVr2ZqkbS0NNy8ebPcT1pamtRh1lje3kCTJo/+JJJaSUkJZs6ciebNm6NFixZYtWpVmWUPHDgALy8vdOnSBR06dCj1ImuqnNatW+P8+fM4ffo0/vGPf2DSpEm4fPkyAGDs2LF4+eWX0aFDBwwZMgS//PILrl27hr1795a7zeDgYNjZ2Wk+Br/YeKoBk2N/wDacapqoqChMmDDBYNt7+PAhxo0bhxYtWqBVq1bYtWtXmWXPnDmDzp07o1WrVnjxxReRnJysWTZr1iw0bdoUCoUC//3vfw0WHxFRTVHeeVBtP/8xdN/y2OHDh2Fubo4VK1Zo5mmuR9zd0cLdHas++cTg32uqLC0t4eXlhcjISK35kZGR6NmzZ6nyKpUKFy9exPnz5zWfgIAAzXVJ9+7ddX6PUqmESqXS+tQG+lyrVOv/V144EBm8P5k8eTKaNGmCzp07o3Pnznj//fcNtu2q4pMatURaWhqmTJ6GHPXDcsvZqOpi/YZ1cHR0NFJktUdKCpCUJHUURI9s3rwZly9fxrVr15CVlYWuXbuif//+aNOmjVY5IQTGjx+P33//HR07dkRcXBzatGmDESNGwNbWVqLoazdLS0vNi8K9vb1x9uxZfPvttwjT8UI5FxcXeHh44Pr16+Vus9p/RfVEA5aWloY3Jr2GnKysclexsbPD9xs3mUx/wDacahpvb29s2bLFYNtbsmQJlEolbty4gdjYWPj4+KBfv35o0KCBVjkhBCZMmIB//vOf6Nu3L5YsWYKgoCBs27YNADBq1Ch88MEH6N27t8FiIyKqKdLS0jD99WnIy9Z9XWxlWxcr1pb9Y6GaztB9C/DoKYEPP/wQ/v7+WvM11yM//oiszEx0nTgR/SdMKHU9QroFBQVh4sSJ8Pb2ho+PD9auXYuEhAQEBAQAeHR9kJSUhE2bNsHMzAwdOnTQWr9Ro0awsrIqNb+2S0tLw+uvv47s7OwKy9ra2iI8PNzw1yu8cCCqlv5kzpw5mqfNagImNWoJtVqNHPVDDOw6Bo4NdL/ANi0zBZHnfoBarTaZm1hE1UGhUGDx4sXYvXs30tPTsXbtWhw6dAj79+9HQUEBfvjhB7Rv3x4pKSkYN24c1Go18vLy8OKLL+Lbb7+FQqHAtGnTYGVlhe+++w737t1Dt27dsHHjRr1vIkVERCAgIADm5uawt7fHmDFjsH37dsyfP19n+fv37wN41BY4ODiUOxQSVY4QQmvoqCdlZGQgMTERLi4u5W5DqVQa7d9ErVYjJysL/du3QsP6djrLpN/Pwm+XrrE/IKoCffuIw4cP47333kNUVBTi4uLg7e2NwMBA7N27F1lZWVi+fDkGDx6s9/dGRERgw4YNAABPT0+88MIL+OmnnzB58mStclFRUVAqlejbty8AYPr06WjUqBEKCwtRp04dvPDCCwaqCSKimketViMv+yGm+4yDq732dfGdeykIO7UNOTk5EkVXNqn6FuDRzff3338f//d//6c1X+t6xM4OY/z8yr0eIW1jx45FRkYGFi5ciOTkZHTo0AH79u2Dh4cHACA5ORkJCQkSR2l8arUa2dnZ6NOnDxwcHMosl5GRgWPHjvF6haiSpOxPahomNWoZxwbOcHHkGIpUu4WEPPpUpGtX4Oeftee98gpw7pzu8kFBjz76UKlU+OOPP7Bjxw4MHToUP/zwA4KDg/H1119j0aJF2Lp1K+rXr489e/bAxsYGxcXFGDp0KHbu3IlRo0bhu+++g4+PD3bs2IFNmzZh2rRpmoTGrFmzcPToUZ3fGxYWhu7duyMhIUFzwgsATZs2RVRUVKnyCoUCP/zwA0aMGIF69eohMzMTu3btgqWlpX47Slo++ugj+Pv7w83NDdnZ2di+fTsOHz6M/fv3IycnB/Pnz8fIkSPh4uKCuLg4fPTRR2jYsCGGDx8udeilNKxvB2cHe6nDIDK8GtBJ6NNHPC0jIwNeXl5YuHAh9u/fj7fffltzoTBq1CjcuHFD53ft2bMHbm5uOvsFXTdDni5na2sLW1tbJCcnw93dXa/9IyKq7VztneHZqBJtXnX1LYDe/YsUfcsvv/yC+/fvY9SoUaWSGqX6HVdXRMnwJvyzCAwMRGBgoM5lj3+oUJb58+ebdALJwcFB5/tFiGo9mV6rAEBISAjWrl0Ld3d3fPHFF+jcubNe8VYXJjVMTEFhAeLj48sto1KpmAknSanV+j0Nqmv0nrS0stetzLuZx44dCwDo2rUrzMzM8PLLLwMAvLy8NOOYl5SU4MMPP8Tx48chhEBqaio6d+6MUaNGwcrKCjt27ICXlxd8fHzw4Ycfara9fPlyvWJ48gVKQgidZYqKihAcHIyffvoJvXr1wtmzZzFs2DBcvHgR9va8oV1Zd+/excSJE5GcnAw7Ozt07NgR+/fvx8CBA5Gbm4uLFy9i06ZNuH//PlxcXNCvXz9ERERwqC8iY6oBnYQ+fcTT6tWrh6FDhwIAfHx8cPPmTc2yf//733p9rz79wtPlKipLRESovr7l8bb1YOy+5f79+5gzZ06p9z48Sd9+h4iI/kem1yqLFi2Ci4sLzMzMsHv3bvj7++P69euwsbHRO25DY1LDhGQ/yMKtW7fwyZwF5Q6DwvdukNRUKqBx44rL6TpEHR3LXrcy702zsrICAJibm2v9fzE3N0dRURGAR1nojIwMnDlzBlZWVggKCkJeXp6m7JUrV1CvXj2kpqaioKBAsx19ntRwd3dHXFwcunXrBgCIj4/X+Qvb8+fP486dO+jVqxcAoFu3bnB1dcV//vMf9OvXT/8dJgDA+vXry1xmbW2NAwcOGDEaItKpBnQS+vQRZa3zuFxxcbFmWp9fPz3uFx6fn8XHx+t8JPxxuceys7ORnZ1d4TB5RESyVl19y+Nt68HYfUt8fDySk5Px/PPPAwDS09OxZ88epKWlYcGCBX9dj7RqBQCIT06Ge9Omeu0LEZFsyfRapfETcQ8fPhxz5szB1atX4eXlpXfchsakhgnJzX8Ic4UFBnQdAzdnD51l+N4NqgkqM0zU055+eq86ZWZmwtnZGVZWVrh79y527NihyYgnJCQgMDAQv/76K5YvX47Zs2dj9erVAPR7UmP06NEICwvDiBEjkJWVhYiICOzfv79UOTc3N9y+fRtXr15F69atcePGDdy8eROt/nfxQURkcmpLJ1EJ+vz6afTo0Vi5ciU2bNiA2NhYHDlyBGvWrClVzsvLC3l5eTh8+DD69u2LsLAwDBs2DHXq1KmO0ImITIMM+xY3NzekpqZqpidPngxvb2/NS1411yNff/3oeuTgQew/dKhaYyYiqvVk2J8AwO3bt9GkSRMAwOnTp5GRkYEWLVpUd2jlYlLDBDnUb8T3bhAZwKxZszB69Gh07twZjRs3xoABAwA8GhLq1Vdfxeeff4527dohNDQUPj4+iIiI0CQ9KjJx4kScPXtWk5x4//330bZtWwDAzz//jJ9//hn//Oc/4eTkhLCwMIwaNQpmZmYQQmDVqlVaWXIiIqr93n//fbzxxhto0aIFzMzMsHLlSs0wg2vWrMGdO3ewcOFCmJmZYfPmzQgICEBubi4aN26MzZs3a7YzY8YM/PTTT0hJScGAAQNgY2NT5i+viIhIvjTXI0OHAkLg/UmTNNcjRERET5o8eTLu3r0Lc3NzWFtbY8eOHbCzs5M0JiY1iEh2nhwvtmnTpkhPT9dM9+3bV/PCbg8PD/zxxx86t3Hy5EnN362srBATE1OpGMzNzbFy5Uqdy1555RW88sormulx48Zh3Lhxldo+ERFVjb59xJN/f7qcjY1Npccmr1evHiIiInQuCwgI0Jr28fHBf/7zH51lV65cWWb/QkRE0pCqb3nS0y+u1lyPvPkmUFgI8Ik/IqIaT6r+5Ndff32WsKuFmdQBEBERERERERERERER6YNPapBsfP018PAhULeu1JEQEVUSGzBWAREREVF1aNIEKCkBzPibVzIRvHAgkgUmNUg2xo+XOgIiovKlpaVBrVaXXtC9u+av8fHxKCwqMmJUNQPbcCIiIqJq4OAgdQREhsULByJZYFKDiIziWcZ/Jf2wjmu3tLQ0vDHpNeRkZZVbLjcvD3eSbqOg0NtIkRERERHVPjw3NgzWIxHJHdtB/RmzrpjUIKJqVadOHSgUCqSlpcHR0REKhULqkEySEAJpaWlQKBSow5f81UpqtRo5WVno374VGta3K7PctYRE/JAQjyIZPq1BREREVBEzMzOUlJTw+sMAeI1BRHLG+1mVY+w+g0kNko2rV4GiIsDCAmjdWupo5MPc3BxNmjTB7du3ERcXJ3U4Jk2hUKBJkyYwNzeXOhR6Bg3r28HZwV5rnk1aOhQlJRBmZkiztZUoMmmxDSciIiJ9PD4n5vWHngoL//q7jptQvMagWocXDmQgvJ9VecbsM5jUINl48UUgKQlo3Bi4fVvqaOTFxsYGLVu2ROGTJ8xkcHXq1OHFhonqvW4jrNXZyFXZ4tSY4VKHIwm24URERKQvXn9Ugq8vcPcu4OQEHDlSajGvMajW4YUDGRD7k8oxZp/BpAYRGYW5uTlPhomIiPRQUlKCt99+G/v27YNCoUBQUBACAwN1lp01axZ+/vlnxMfH4+LFi+jQoYORoyUiqpl4/aGnpKRHn6IiwMpK6miIiGoc9ic1k5nUARARERER0V82b96My5cv49q1a/jjjz/w9ddf48qVKzrLjho1CsePH4eHh4eRoyQiIiIiIpIGkxpERERERP+jUCgQHByM559/Hs2aNcOvv/6KuXPnokuXLmjfvj0uXboEAEhJSUG/fv3g5eWF9u3bY9asWRBCAACmTZuGmTNnAgDu3buH5s2b4/jx43rHEBERgYCAAJibm8Pe3h5jxozB9u3bdZZ94YUX0KRJk2fcayIiIiIiotpDlkmN4OBgKBQKzJ49W+pQiIiIiOgJISFAkyYVf155pfS6r7xSdvmQEP1jUKlU+OOPP/DVV19h6NCh6N27N2JiYjBp0iQsWrQIAFC/fn3s2bMH0dHRuHDhAm7duoWdO3cCAL777jscP34cO3bswKRJkzBt2jT07t0bwKPhojp37qzzc+bMGQBAQkKC1pMXTZs2RUJCQhVrlIiIiIiIyLTI7p0aZ8+exdq1a9GxY0epQyEiIiKip6jVj4b2roibW+l5aWllr6tW6x/D2LFjAQBdu3aFmZkZXn75ZQCAl5cXdu3aBeDRey8+/PBDHD9+HEIIpKamonPnzhg1ahSsrKywY8cOeHl5wcfHBx9++KFm28uXL9crBoVCofn74ydAiIiIiIiISGZJjZycHEyYMAHr1q3DF198IXU4RERERPQUlQpo3Ljico6OuueVta5KpX8MVv97Uaq5uTmUSqVmvrm5OYqKigAAISEhyMjIwJkzZ2BlZYWgoCDk5eVpyl65cgX16tVDamoqCgoKNNuZNWsWjh49qvN7w8LC0L17d7i7uyMuLg7dunUDAMTHx8Pd3V3/HSAiIiIiIjJhshp+asaMGXj55ZcxYMCACsvm5+dDrVZrfYiIiIioegUFAbdvV/z5+efS6/78c9nlg4IMG2dmZiacnZ1hZWWFu3fvYseOHZplCQkJCAwMxK+//ornn39ea8jT5cuX4/z58zo/3bt3BwCMHj0aYWFhKC4uxr179xAREaF5eoSqbtWqVfD09ISVlRW8vLxw7NixcssfOXIEXl5esLKyQrNmzbBmzZoyy27fvh0KhQLDhg0zcNRERERERPQ02SQ1tm/fjnPnziE4OFiv8sHBwbCzs9N83HSNcUBEREREsjRr1iycPHkSnTt3xhtvvKH50UxRURFeffVVfP7552jXrh1CQ0Nx+vRpRERE6L3tiRMnonXr1mjVqhW6deuG999/H23btgUA/Pzzz5g6daqm7IwZM9CkSRPcvn0bAwYMQIsWLQy7oyYiIiICs2fPxrx58xATE4M+ffrA39+/zHeVxMbGYvDgwejTpw9iYmLw0UcfYdasWZr3pjwpPj4e7733Hvr06VPdu0FERERERJDJ8FOJiYl4++23cfDgQc1wAhWZO3cugp74SZ9arWZig4iIiMjEPfn+iqZNmyI9PV0z3bdvX0RFRQEAPDw88Mcff+jcxsmTJzV/t7KyQkxMTKViMDc3x8qVK3Uue+WVV/DKE29JX7lyZZll6S8hISGYMmWKJiEUGhqKAwcOYPXq1Tp/9LRmzRq4u7sjNDQUANC2bVtERUVhyZIlGDlypKZccXExJkyYgAULFuDYsWO4f/++MXaHiIiIiEjWZJHUiI6ORmpqKry8vDTziouLcfToUaxYsQL5+fkwNzfXWkepVGqNoUy139mzQHEx8NQ/NRFRjff7W29CIUogFGZAaprU4UiCbTgRVVVBQQGio6MxZ84crfl+fn5aCagnnTp1Cn5+flrzBg0ahPXr16OwsBB16tQBACxcuBCOjo6YMmVKhcNZAY+GuM3Pz9dMc4hbIpIcT7LI1PCYJpIFWQw/9eKLL+LixYtaYxZ7e3tjwoQJOH/+fKmEBpkmFxegSZNHfxKRPK1evRodO3aESqWCSqWCj48PfvnlF81yIQTmz58PV1dXWFtbo2/fvrh06ZKEET+Sr7JFnp0d8lW2UociGbbhRFRV6enpKC4uhpOTk9Z8JycnpKSk6FwnJSVFZ/mioiLN0zsnTpzA+vXrsW7dOr1j4RC3RFTj8CSLTA2PaSJZkEVSw9bWFh06dND61KtXDw4ODujQoYPU4RERkZE0adIEX375JaKiohAVFYX+/ftj6NChmsTF119/jZCQEKxYsQJnz56Fs7MzBg4ciOzsbIkjJyKiZ6VQKLSmhRCl5lVU/vH87Oxs/P3vf8e6devQsGFDvWOYO3cusrKyNJ/ExMRK7AEREREREQEyGX6KiIgIAIYMGaI1vWjRIqxevRqnT5/WvNB33rx5GDFiBABg48aNcHJywtatWzF9+nQpQiaShSffY0HGUVJSInUIRtOwYUOYm5uXeiojNTW11NMYjzk7O+ssb2FhAQcHB1y6dAlxcXFa/crjOrWwsMDVq1fRvHnzUtvlELdERERERM9OtkmNw4cPSx0CGdnatUBODmBjA7z5ptTREJHUiouLsWPHDjx48AA+Pj6IjY1FSkqK1hjqSqUSvr6+OHnypKRJjaZnomBRUIAiS0tcdLCXLA4psQ03TXXq1IFCoUBaWhocHR3L/dU8GYYQAgUFBUhLS4OZmRksLS2lDqnaWVpawsvLC5GRkRg+fLhmfmRkJIYOHapzHR8fH+zZs0dr3sGDB+Ht7Y06deqgTZs2uHjxotbyjz/+GNnZ2fj22285rBQR1R48ySJTw2OaSBZkm9Qg+Vm4EEhKAho3Zr9GJGcXL16Ej48P8vLyYGNjg927d6Ndu3aal8XqGkM9Pj6+3G1W94tf2xw6Amt1NnJVttgzZnjFK5ggtuGmydzcHE2aNMHt27cRFxcndTiyUrduXbi7u8PMTBaj0SIoKAgTJ06Et7c3fHx8sHbtWiQkJCAgIADAo2GhkpKSsGnTJgBAQEAAVqxYgaCgIEybNg2nTp3C+vXrsW3bNgCAlZVVqWFs69evDwAc3paIaheeZJGp4TFNJAtMahARkay0bt0a58+fx/3797Fz505MmjQJR44c0Syv7JjrwKMXvy5YsKBa4iUydTY2NmjZsiUKCwulDkU2zM3NYWFhIasnY8aOHYuMjAwsXLgQycnJ6NChA/bt2wcPDw8AQHJyMhISEjTlPT09sW/fPrzzzjtYuXIlXF1dsXz5cowcOVKqXSAiIiIiov9hUoOIiGTF0tISLVq0AAB4e3vj7Nmz+Pbbb/Hhhx8CAFJSUuDi4qIpX96Y64/NnTsXQUFBmmm1Ws2hR4gqwdzcHObm5lKHQSYuMDAQgYGBOpdt2LCh1DxfX1+cO3dO7+3r2gYRERERERmePJ43JyIiKoMQAvn5+fD09ISzszMiIyM1ywoKCnDkyBH07Nmz3G0olUqoVCqtDxERERERERERGR6f1CAiItn46KOP4O/vDzc3N2RnZ2P79u04fPgw9u/fD4VCgdmzZ2Px4sVo2bIlWrZsicWLF6Nu3boYP3681KETERERERERERGY1CAiIhm5e/cuJk6ciOTkZNjZ2aFjx47Yv38/Bg4cCAD44IMPkJubi8DAQGRmZqJ79+44ePAgbG1tJY6ciIiIiIiIiIgAJjWIiEhG1q9fX+5yhUKB+fPnY/78+cYJiIiIiIiIiIiIKoXv1CAiIiIiIiIiIiIiolqBSQ0iIiIiIiIiIiIiIqoVOPwUyUarVoCdHeDkJHUkRESVk9PQAYVWVsi3qSd1KJJhG05ERERUDXiSRaaGxzSRLDCpQbLx229SR0BEVDXH35z818SNW5LFISW24URERETVgCdZZGp4TBPJAoefIiIiIiIiIiIiIiKiWoFJDSIiIiIiIiIiIiIiqhWY1CAiIiIiIiIiIiIiolqB79Qg2ZgwAUhPBxo2BLZskToaIiL9eW/fCcsHD1FQry4ueneROhxJsA0nIiIiqgY8ySJTw2OaSBaY1CDZOHIESEoCGjeWOhIiosppeCsO1ups5KpsAZkmNdiGExEREVUDnmSRqeExTSQLHH6KiIiIiIiIiIiIiIhqBSY1iIiIiIiIiIiIiIioVmBSg4iIiIiIiIiIqBJWrVoFT09PWFlZwcvLC8eOHSuz7PHjx9GrVy84ODjA2toabdq0wbJly4wYLRGRaeE7NYiIiIiIiIiIiPQUERGB2bNnY9WqVejVqxfCwsLg7++Py5cvw93dvVT5evXq4a233kLHjh1Rr149HD9+HNOnT0e9evXw5ptvSrAHRES1G5/UICIiIiIiIiIi0lNISAimTJmCqVOnom3btggNDYWbmxtWr16ts3yXLl0wbtw4tG/fHk2bNsXf//53DBo0qNynO4iIqGxMahAREREREREREemhoKAA0dHR8PPz05rv5+eHkydP6rWNmJgYnDx5Er6+vtURIhGRyePwU0RERERERERERHpIT09HcXExnJyctOY7OTkhJSWl3HWbNGmCtLQ0FBUVYf78+Zg6dWqZZfPz85Gfn6+ZVqvVzxY4EZEJ4ZMaRERERERERERElaBQKLSmhRCl5j3t2LFjiIqKwpo1axAaGopt27aVWTY4OBh2dnaaj5ubm0HiJiIyBXxSg2Rj2jQgKwuws5M6EiKiyol73gt18vJQaGUldSiSYRtOREREVA14klVpDRs2hLm5eamnMlJTU0s9vfE0T09PAMBzzz2Hu3fvYv78+Rg3bpzOsnPnzkVQUJBmWq1WM7GhDx7TRLLApAbJxmefSR0BEVHVXBnQ96+JG7cki0NKbMOJiIiIqgFPsirN0tISXl5eiIyMxPDhwzXzIyMjMXToUL23I4TQGl7qaUqlEkql8plilSUe00SywKQGERERERERERGRnoKCgjBx4kR4e3vDx8cHa9euRUJCAgICAgA8esoiKSkJmzZtAgCsXLkS7u7uaNOmDQDg+PHjWLJkCWbOnCnZPhAR1WZMahARkWwEBwdj165duHLlCqytrdGzZ0989dVXaN26tabM5MmTsXHjRq31unfvjtOnTxs7XCIiIiIiqoHGjh2LjIwMLFy4EMnJyejQoQP27dsHDw8PAEBycjISEhI05UtKSjB37lzExsbCwsICzZs3x5dffonp06dLtQtERLUakxpERCQbR44cwYwZM9CtWzcUFRVh3rx58PPzw+XLl1GvXj1NuZdeegnh4eGaaUtLSynCJSIiIiKiGiowMBCBgYE6l23YsEFreubMmXwqg4jIgJjUINlo0gRISgIaNwZu35Y6GiKSwv79+7Wmw8PD0ahRI0RHR+OFF17QzFcqlXB2djZ2eGV6afFSWKuzkauyxcUxwytewQSxDSciIiKqBjzJIlPDY5pIFsykDoCIiEgqWVlZAAB7e3ut+YcPH0ajRo3QqlUrTJs2DampqVKER0RERERERERET+GTGkREJEtCCAQFBaF3797o0KGDZr6/vz9Gjx4NDw8PxMbG4pNPPkH//v0RHR0NpVKpc1v5+fnIz8/XTKvV6mqPn4iIiIiIiIhIjpjUICIiWXrrrbdw4cIFHD9+XGv+2LFjNX/v0KEDvL294eHhgb1792LEiBE6txUcHIwFCxZUa7xERERERERERMSkBhERydDMmTPx888/4+jRo2jSpEm5ZV1cXODh4YHr16+XWWbu3LkICgrSTKvVari5uRksXiIiIiK5SEtLK/Op1/j4eBQVFhk5IiIiIqppmNQgIiLZEEJg5syZ2L17Nw4fPgxPT88K18nIyEBiYiJcXFzKLKNUKsscmoqIiIiI9JOWlobpr09DXvZDncsf5D7E3aQUFBYWGDkyIiIiqkmY1CAiItmYMWMGtm7dip9++gm2trZISUkBANjZ2cHa2ho5OTmYP38+Ro4cCRcXF8TFxeGjjz5Cw4YNMXz4cImjJyIiIjJtarUaedkPMd1nHFztnUstj755AcsSw1BUVCxBdERERFRTMKlBRESysXr1agBA3759teaHh4dj8uTJMDc3x8WLF7Fp0ybcv38fLi4u6NevHyIiImBraytBxERERETy42rvDM9G7qXm385IliAaIiIiqmlkkdRYvXo1Vq9ejbi4OABA+/bt8emnn8Lf31/awIiIyKiEEOUut7a2xoEDB4wUDRERERERERERVZaZ1AEYQ5MmTfDll18iKioKUVFR6N+/P4YOHYpLly5JHRoREREREREREREREelJFkmNIUOGYPDgwWjVqhVatWqFRYsWwcbGBqdPn5Y6NDKizZuB/fsf/UlEVJtEjR2BE2/8HVFjR0gdimTYhhPRs1q1ahU8PT1hZWUFLy8vHDt2rNzyR44cgZeXF6ysrNCsWTOsWbNGa/muXbvg7e2N+vXro169eujcuTP+9a9/VecuEBEZHk+yyNTwmCaSBVkMP/Wk4uJi7NixAw8ePICPj4/U4ZARPTWEPhFRrZHe3POviRu3pAtEQmzDiehZREREYPbs2Vi1ahV69eqFsLAw+Pv74/Lly3B3Lz1uf2xsLAYPHoxp06Zh8+bNOHHiBAIDA+Ho6IiRI0cCAOzt7TFv3jy0adMGlpaW+L//+z+8/vrraNSoEQYNGmTsXSQiqhqeZJGp4TFNJAuySWpcvHgRPj4+yMvLg42NDXbv3o127dqVWT4/Px/5+fmaabVabYwwiYiIiIjIwEJCQjBlyhRMnToVABAaGooDBw5g9erVCA4OLlV+zZo1cHd3R2hoKACgbdu2iIqKwpIlSzRJjb5P3TR5++23sXHjRhw/fpxJDSIiIiKiaiSL4acAoHXr1jh//jxOnz6Nf/zjH5g0aRIuX75cZvng4GDY2dlpPm5ubkaMloiIiIiIDKGgoADR0dHw8/PTmu/n54eTJ0/qXOfUqVOlyg8aNAhRUVEoLCwsVV4IgUOHDuHq1at44YUXDBc8ERERERGVIpsnNSwtLdGiRQsAgLe3N86ePYtvv/0WYWFhOsvPnTsXQUFBmmm1Ws3ERi13+DCQnw8olXwakYhql4Y3Y2FWXIwSc3OpQ5EM23Aiqqr09HQUFxfDyclJa76TkxNSUlJ0rpOSkqKzfFFREdLT0+Hi4gIAyMrKQuPGjZGfnw9zc3OsWrUKAwcOLDMWPg1ORDUOT7LI1PCYJpIF2SQ1niaE0LqgeJpSqYRSqTRiRFTd/v53ICkJaNwYuH1b6miIiPTnHbEL1ups5Kps8fuY4VKHIwm24UT0rBQKhda0EKLUvIrKPz3f1tYW58+fR05ODg4dOoSgoCA0a9as1NBUjwUHB2PBggVV3AMiqkh+QQHi4+PLXK5SqeDo6GjEiGoBnmSRqeExTSQLskhqfPTRR/D394ebmxuys7Oxfft2HD58GPv375c6NCIiIiIiqkYNGzaEubl5qacyUlNTSz2N8Zizs7PO8hYWFnBwcNDMMzMz0zwN3rlzZ/z5558IDg4uM6nBp8GJqk9mThZuxd7Coo8WlPkDRSvbuggLX8fEBhERUS0ni6TG3bt3MXHiRCQnJ8POzg4dO3bE/v37y300nIiIiIiIaj9LS0t4eXkhMjISw4f/9bRbZGQkhg4dqnMdHx8f7NmzR2vewYMH4e3tjTp16pT5XXwanEg6D/IfwlJhgWk9XkVz16allt+5l4KwU9ugVquZ1CAiIqrlZJHUWL9+vdQhEBERERGRRIKCgjBx4kR4e3vDx8cHa9euRUJCAgICAgA8eoIiKSkJmzZtAgAEBARgxYoVCAoKwrRp03Dq1CmsX78e27Zt02wzODgY3t7eaN68OQoKCrBv3z5s2rQJq1evlmQfiegR1wZO8GzkLnUYREREVI1kkdQgIiIiIiL5Gjt2LDIyMrBw4UIkJyejQ4cO2LdvHzw8PAAAycnJSEhI0JT39PTEvn378M4772DlypVwdXXF8uXLMXLkSE2ZBw8eIDAwELdv34a1tTXatGmDzZs3Y+zYsUbfPyIiIiIiOWFSg4iIiIiITF5gYCACAwN1LtuwYUOpeb6+vjh37lyZ2/viiy/wxRdfGCo8IiIiIiLSk5nUAVQkNjZW6hCIiKgGYH9ARCQ/bPuJiMiQ2K8QEZmGGp/UaNGiBfr164fNmzcjLy9P6nCIiEgi7A+IiOSHbT8RERkS+xUiItNQ45Ma//nPf9ClSxe8++67cHZ2xvTp0/HHH39IHRYRERkZ+wMiIvlh209ERIbEfoWIyDTU+KRGhw4dEBISgqSkJISHhyMlJQW9e/dG+/btERISgrS0NKlDJCIiI2B/QEQkP2z7iYjIkNivEBGZhhqf1HjMwsICw4cPxw8//ICvvvoKN2/exHvvvYcmTZrgtddeQ3JystQhUg13+zYgxKM/iaj2kmN/sP+jd7H7y/nY/9G7UociGbbhRPImx7afiMgoZHqSxX5FegUFBYiPj8fNmzfL/FQpySTTY5pIbiykDkBfUVFR+P7777F9+3bUq1cP7733HqZMmYI7d+7g008/xdChQ/nIIBGRDLA/ICKSH7b9RERkSOxXpJWTk4PY2FjMmzcPlpaWZZaztbVFeHg4HB0djRgdEdUGNT6pERISgvDwcFy9ehWDBw/Gpk2bMHjwYJiZPXrIxNPTE2FhYWjTpo3EkRIRUXVif0BEJD9s+4mIyJDYr9QMeXl5MDMzQ69evdC4cWOdZTIyMnDs2DGo1WomNYiolBqf1Fi9ejXeeOMNvP7663B2dtZZxt3dHevXrzdyZEREZEzsD4iI5IdtPxERGRL7lZqlQYMGcHJykjoMIqqFanxSIzIyEu7u7pqs+WNCCCQmJsLd3R2WlpaYNGmSRBFSbbFgAZCVBdjZAZ99JnU0RFRZhugPgoODsWvXLly5cgXW1tbo2bMnvvrqK7Ru3VprewsWLMDatWuRmZmJ7t27Y+XKlWjfvn217VtF2vx6GHXy8lBoZYWLTd0li0NKbMOJ5InXAkRE1UxmJ1nsV2RAZsc0kVzV+BeFN2/eHOnp6aXm37t3D56enhJERLXVunXAsmWP/iSi2scQ/cGRI0cwY8YMnD59GpGRkSgqKoKfnx8ePHigKfP1118jJCQEK1aswNmzZ+Hs7IyBAwciOzvbYPtSWU3/iEaL46fR9I9oyWKQGttwInnitQARUTWT2UkW+xUZkNkxTSRXNf5JDSGEzvk5OTmwsrIycjRERCQVQ/QH+/fv15oODw9Ho0aNEB0djRdeeAFCCISGhmLevHkYMWIEAGDjxo1wcnLC1q1bMX369GfbCSIiqhReCxARkSGxXyEiMg01NqkRFBQEAFAoFPj0009Rt25dzbLi4mKcOXMGnTt3lig6IiIylursD7KysgAA9vb2AIDY2FikpKTAz89PU0apVMLX1xcnT55kUoOIyEh4LUBERIbEfoWIyLTU2KRGTEwMgEdZ9IsXL8LS0lKzzNLSEp06dcJ7770nVXhERGQk1dUfCCEQFBSE3r17o0OHDgCAlJQUACj1sjonJyfEx8eXua38/Hzk5+drptVqdaXjISKiv/BagIiIDIn9ChGRaamxSY3ff/8dAPD666/j22+/hUqlkjgiIiKSQnX1B2+99RYuXLiA48ePl1qmUCi0poUQpeY9KTg4GAsWLDBIXERExGsBIiIyLPYrRESmpca/KDw8PJydDRERGbQ/mDlzJn7++Wf8/vvvaNKkiWa+s7MzgL+e2HgsNTW11NMbT5o7dy6ysrI0n8TERIPESUQkd7wWICIiQ2K/QkRkGmrkkxojRozAhg0boFKpNC9qLcuuXbuMFBURERmbofsDIQRmzpyJ3bt34/Dhw/D09NRa7unpCWdnZ0RGRqJLly4AgIKCAhw5cgRfffVVmdtVKpVQKpV67BEREVWE1wJERGRI7FeIiExPjUxq2NnZaYb5sLOzkzgaIiKSiqH7gxkzZmDr1q346aefYGtrq3kiw87ODtbW1lAoFJg9ezYWL16Mli1bomXLlli8eDHq1q2L8ePHP/P3ExFRxXgtQEREhsR+hYjI9NTIpEZ4eLjOvxMRkbwYuj9YvXo1AKBv376lvmfy5MkAgA8++AC5ubkIDAxEZmYmunfvjoMHD8LW1vaZv5+IiCrGawEiIjIk9itERKanRiY1npSbmwshBOrWrQsAiI+Px+7du9GuXTv4+flJHB3VJr6+QHo60LCh1JEQUVUYoj8QQlRYRqFQYP78+Zg/f/6zhGtQ6c2awvLBQxTUqyt1KJJhG04kT7wWICJDyi8oQHx8fJnLVSoVHB0djRhRDSCzkyz2KzIgs2OaSK5qfFJj6NChGDFiBAICAnD//n08//zzsLS0RHp6OkJCQvCPf/xD6hCpltiyReoIiOhZyLk/iHp15F8TN25JF4iE2IYTyZOc234iMqzMnCzcir2FRR8tKPNdaFa2dREWvk5eiQ2ZnWSxX5EBmR3TRHJlJnUAFTl37hz69OkDAPj3v/8NZ2dnxMfHY9OmTVi+fLnE0RERkbGwPyAikh+2/URkKA/yH8JSYYFpPV7FgpffKfWZ7jMOedkPoVarpQ6VqhH7FSIi01Djn9R4+PChZhzzgwcPYsSIETAzM0OPHj3KfWyUiIhMC/sDIiL5YdtPRIbm2sAJno3cpQ6DJMJ+hYjINNT4JzVatGiBH3/8EYmJiThw4IBmjMPU1FSoVCqJoyMiImNhf0BEJD9s+4mIyJDYrxARmYYan9T49NNP8d5776Fp06bo3r07fHx8ADzKqHfp0kXi6Kg26d8faN/+0Z9EVPvIuT/ovXYDXgxZid5rN0gdimTYhhPJk5zbfiIio5DZSZYh+5VVq1bB09MTVlZW8PLywrFjx8osu2vXLgwcOBCOjo5QqVTw8fHBgQMHnmlfqAwyO6aJ5KrGDz81atQo9O7dG8nJyejUqZNm/osvvojhw4dLGBnVNteuAUlJQFaW1JEQUVXIuT+wSc+AtTobuXl5UociGbbhRPIk57afiMgoZHaSZah+JSIiArNnz8aqVavQq1cvhIWFwd/fH5cvX4a7e+nhzY4ePYqBAwdi8eLFqF+/PsLDwzFkyBCcOXOGSXpDk9kxTSRXNT6pAQDOzs5wdnbWmvf8889LFA0REUmF/QERkfyw7SciIkMyRL8SEhKCKVOmYOrUqQCA0NBQHDhwAKtXr0ZwcHCp8qGhoVrTixcvxk8//YQ9e/YwqUFEVAU1Pqnx4MEDfPnllzh06BBSU1NRUlKitfzWrVsSRUZERMbE/oCISH7Y9hMRkSEZol8pKChAdHQ05syZozXfz88PJ0+e1CuOkpISZGdnw97evswy+fn5yM/P10yr1Wq9tk1EJAc1PqkxdepUHDlyBBMnToSLiwsUCoXUIRERkQTYHxARyQ/bfiIiMiRD9Cvp6ekoLi6Gk5OT1nwnJyekpKTotY2lS5fiwYMHGDNmTJllgoODsWDBgkrHR0QkBzU+qfHLL79g79696NWrl9ShEBGRhNgfEBHJD9t+IiIyJEP2K08nRIQQeiVJtm3bhvnz5+Onn35Co0aNyiw3d+5cBAUFaabVajXc3NyqHjARkQmp8UmNBg0alPs4HhERyQP7AyIi+WHbT0REhmSIfqVhw4YwNzcv9VRGampqqac3nhYREYEpU6Zgx44dGDBgQLlllUollErlM8VKRGSqzKQOoCKff/45Pv30Uzx8+FDqUIiISELsD4iI5IdtPxERGZIh+hVLS0t4eXkhMjJSa35kZCR69uxZ5nrbtm3D5MmTsXXrVrz88stV/n4iIqoFT2osXboUN2/ehJOTE5o2bYo6depoLT937pxEkRERkTGxPyAikh+2/UREZEiG6leCgoIwceJEeHt7w8fHB2vXrkVCQgICAgIAPBo6KikpCZs2bQLwKKHx2muv4dtvv0WPHj00T3lYW1vDzs7OgHtIRCQPNT6pMWzYMKlDICKiGoD9ARGR/LDtJyIiQzJUvzJ27FhkZGRg4cKFSE5ORocOHbBv3z54eHgAAJKTk5GQkKApHxYWhqKiIsyYMQMzZszQzJ80aRI2bNhgkJiIiOSkxic1PvvsM6lDIBPx6adATg5gYyN1JERUFXLuD6686AuLggIUWVpKHYpk2IYTyZOc234iIqOQ2UmWIfuVwMBABAYG6lz2dKLi8OHDBvteqoDMjmkiuarxSQ0AuH//Pv7973/j5s2beP/992Fvb49z587ByckJjRs3ljo8qiXefFPqCIjoWcm1P4jr7v3XxI1b0gUiIbbhRPIl17afiMgoZHiSxX7FxMnwmCaSoxqf1Lhw4QIGDBgAOzs7xMXFYdq0abC3t8fu3bsRHx+vGZ+wPMHBwdi1axeuXLkCa2tr9OzZE1999RVat25thD0gIiJDMER/QEREtQvbfiIiMiT2K0REpsFM6gAqEhQUhMmTJ+P69euwsrLSzPf398fRo0f12saRI0cwY8YMnD59GpGRkSgqKoKfnx8ePHhQXWETEZGBGaI/ICKi2oVtPxERGRL7FSIi01Djkxpnz57F9OnTS81v3LgxUlJS9NrG/v37MXnyZLRv3x6dOnVCeHg4EhISEB0dbehwqQZLTgZu3370JxHVPoboD2orpTobVllZUKqzpQ5FMmzDieTJkG3/qlWr4OnpCSsrK3h5eeHYsWPllj9y5Ai8vLxgZWWFZs2aYc2aNVrL161bhz59+qBBgwZo0KABBgwYgD/++KNSMRERSU5mJ1lyvqaQDZkd00RyVeOTGlZWVlCr1aXmX716FY6OjlXaZlZWFgDA3t7+mWKj2qVbN8DN7dGfRFT7VEd/UFv0W7EW/sHL0G/FWqlDkQzbcCJ5MlTbHxERgdmzZ2PevHmIiYlBnz594O/vj4SEBJ3lY2NjMXjwYPTp0wcxMTH46KOPMGvWLOzcuVNT5vDhwxg3bhx+//13nDp1Cu7u7vDz80NSUlLld5SISCoyO8mS8zWFbMjsmCaSqxqf1Bg6dCgWLlyIwsJCAIBCoUBCQgLmzJmDkSNHVnp7QggEBQWhd+/e6NChQ5nl8vPzoVartT5ERCQdQ/UHR48exZAhQ+Dq6gqFQoEff/xRa/nkyZOhUCi0Pj169DDkrhARkZ4M1faHhIRgypQpmDp1Ktq2bYvQ0FC4ublh9erVOsuvWbMG7u7uCA0NRdu2bTF16lS88cYbWLJkiabMli1bEBgYiM6dO6NNmzZYt24dSkpKcOjQoWfbaSIiqjaGvsdERETSqPFJjSVLliAtLQ2NGjVCbm4ufH190aJFC9ja2mLRokWV3t5bb72FCxcuYNu2beWWCw4Ohp2dnebj5uZW1V0gIiIDMFR/8ODBA3Tq1AkrVqwos8xLL72E5ORkzWffvn2G2AUiIqokQ7T9BQUFiI6Ohp+fn9Z8Pz8/nDx5Uuc6p06dKlV+0KBBiIqK0twIe9rDhw9RWFhY7tPg/OEUEZG0DH2PiYiIpGEhdQAVUalUOH78OH7//XdER0ejpKQEXbt2xYABAyq9rZkzZ+Lnn3/G0aNH0aRJk3LLzp07F0FBQZpptVrNxAYRkYQM1R/4+/vD39+/3DJKpRLOzs7PEi4RERmAIdr+9PR0FBcXw8nJSWu+k5NTmeOnp6Sk6CxfVFSE9PR0uLi4lFpnzpw5aNy4cbmxBQcHY8GCBXrHTkREhmXIe0xERCSdGp3UKCkpwYYNG7Br1y7ExcVBoVDA09MTzs7OEEJAoVDotR0hBGbOnIndu3fj8OHD8PT0rHAdpVIJpVL5rLtAREQGYKj+QF+HDx9Go0aNUL9+ffj6+mLRokVo1KiRQb+DiIjKZ+i2/+nyFW1DV3ld8wHg66+/xrZt23D48GFYWVmVuU3+cIqISDrGvqYgIqLqU2OHnxJC4JVXXsHUqVORlJSE5557Du3bt0d8fDwmT56M4cOH672tGTNmYPPmzdi6dStsbW2RkpKClJQU5ObmVuMeEBGRIRiyP9CHv78/tmzZgt9++w1Lly7F2bNn0b9/f+Tn55e5DocTISIyLEO2/Q0bNoS5uXmppzJSU1NLPY3xmLOzs87yFhYWcHBw0Jq/ZMkSLF68GAcPHkTHjh3LjUWpVEKlUml9iIio+hn7moKIiKpXjX1SY8OGDTh69CgOHTqEfv36aS377bffMGzYMGzatAmvvfZahdt6/ALAvn37as0PDw/H5MmTDRUyERFVA0P2B/oYO3as5u8dOnSAt7c3PDw8sHfvXowYMULnOhxOhIjIsAzZ9ltaWsLLywuRkZFaN60iIyMxdOhQnev4+Phgz549WvMOHjwIb29v1KlTRzPvm2++wRdffIEDBw7A29u7MrtIRERGZOxrClOVlpZW7g+44uPjUVRUZMSIiEiuamxSY9u2bfjoo49KdTYA0L9/f8yZMwdbtmzRq8N5/Kg4ERHVPobsD6rCxcUFHh4euH79epllOJwIEZFhGbrtDwoKwsSJE+Ht7Q0fHx+sXbsWCQkJCAgIAPCoHU9KSsKmTZsAAAEBAVixYgWCgoIwbdo0nDp1CuvXr8e2bds02/z666/xySefYOvWrWjatKnmyQ4bGxvY2Ng8axUQEZEBSX1NYQrS0tLw+uuvIzs7u8wyubm5uHPnDgoLC40YGRHJUY1Naly4cAFff/11mcv9/f2xfPlyI0ZERERSkLo/yMjIQGJios6Xwj7G9zARERmWodv+sWPHIiMjAwsXLkRycjI6dOiAffv2wcPDAwCQnJyMhIQETXlPT0/s27cP77zzDlauXAlXV1csX74cI0eO1JRZtWoVCgoKMGrUKK3v+uyzzzB//ny9YyMiouon9TWFKVCr1cjOzkafPn1KDcX42I0bN7B7924mNYio2tXYpMa9e/fKHOMWAJycnJCZmWnEiIiISAqG7g9ycnJw48YNzXRsbCzOnz8Pe3t72NvbY/78+Rg5ciRcXFwQFxeHjz76CA0bNuQ4u0RERlQd1wKBgYEIDAzUuWzDhg2l5vn6+uLcuXNlbi8uLq5S309ERNLhPSbDcXBwKLMu09PTjRwNEclVjU1qFBcXw8Ki7PDMzc05Th9VyqFDQFERUM5hRUQ1kKH7g6ioKK3Hzh8PGzVp0iSsXr0aFy9exKZNm3D//n24uLigX79+iIiIgK2tbdV34hkdnzYJipISCDMzIEueLyFnG04kL7wWICIyEpmcZLFfkRGZHNNEcldj/4cLITB58uQyh/PIz883ckRU27VuLXUERFQVhu4P+vbtW+67lg4cOFCp7RlDjmPDvyZkmtRgG04kL7wWICIyEpmcZLFfkRGZHNNEcldjkxqTJk2qsAxf4EREZPrYHxARyQ/bfiIiMiT2K0REpqXGJjXCw8OlDoGIiGoA9gdERPLDtp+IiAyJ/QoRkWmpsUkNIkPbuhV4+BCoWxcYP17qaIiI9Nfk/AWYFxSi2LIOLtrYSB2OJNiGExEREVUDnmSRqeExTSQLTGqQbHzwAZCUBDRuzH6NiGqXDvsiYa3ORq7KFr+MGS51OJJgG05ERERUDXiSRaaGxzSRLJhJHQAREREREREREREREZE++KRGDZGWlga1Wl3m8vj4eBQWFRkxIiIiIiIiIiIiIiKimoVJjRogLS0NUyZPQ476YZllcnMf4s6dFBQUFhoxMiIiIiIiIiIiIiKimoNJjRpArVYjR/0QA7uOgWMDZ51lrsReQETiP1FUzKc1iIiIiIiIiIiIiEiemNSoQRwbOMPF0U3nstR7yUaOhoiIiIiIiIiI5ECfYdGLOCw6EdUQTGrIUEFhAeLj4yssp1Kp4OjoaISIiIiIiIiIiIhICmlpaXj99deRnZ1dZpnc3FzcuXMHhRwWnYhqACY1ZCb7QRZu3bqFT+YsgFKpLLesjaou1m9Yx8QGEREREREREVEtpc9TGJmZmejXrx8cHBx0lrlx4wZ2797NpAYR1QhMashMbv5DmCssMKDrGLg5e5RZLi0zBZHnfoBarWZSg4iIiIiIiIioFqrMUxgqlQpOTk46y6Snp1dXiERElcakhkw51G9U5vs7TJWzs/afRES1RZ6tjdafcsQ2nIiIiKga8CTL5KnVamRnZ6NPnz7yeAqDxzSRLDCpQbIRFSV1BEREVXN45vS/Jm7cki4QCbENJyIiIqoGPMmSDQcHB3k8hcFjmkgWzKQOgIiIiIiIiIiIiIiISB9MahARERERERERERERUa3ApAYREREREREREREREdUKfKcGycb06cC9e4C9PRAWJnU0RET667xrDyxzc1FgbY2LHdtLHY4k2IYTERGZhrS0NKjVap3L4uPjUVRYZOSIZI4nWWRqeEwTyQKTGiQbe/cCSUlA48ZSR0JEVDnOV67BWp2NXJUtINOkBttwIiKi2i8tLQ3TX5+GvOyHOpc/yH2Iu0kpKCwsMHJkMsaTLDI1PKaJZIFJDSIiIiIiIiKqdmq1GnnZDzHdZxxc7Z1LLY++eQHLEsNQVFQsQXRERERUW/CdGkREJCtHjx7FkCFD4OrqCoVCgR9//FFruRAC8+fPh6urK6ytrdG3b19cunRJmmCJiIiITJCrvTM8G7mX+jjVd5Q6NCIiIqoFmNQgIiJZefDgATp16oQVK1boXP71118jJCQEK1aswNmzZ+Hs7IyBAwciOzvbyJESEREREREREdHTOPwUERHJir+/P/z9/XUuE0IgNDQU8+bNw4gRIwAAGzduhJOTE7Zu3Yrp06cbM1QiIiIiIiIiInoKn9QgIiL6n9jYWKSkpMDPz08zT6lUwtfXFydPnpQwMiIiIiIiIiIiAvikBhERkUZKSgoAwMnJSWu+k5MT4uPjy1wvPz8f+fn5mmm1Wl09ARIRERERERERyRyf1CAiInqKQqHQmhZClJr3pODgYNjZ2Wk+bm5u1R0iEREREREREZEsMalBRET0P87OzgD+emLjsdTU1FJPbzxp7ty5yMrK0nwSExOrNU4iIiIiIiIiIrni8FMkG+PGAZmZQIMGUkdCRDWVp6cnnJ2dERkZiS5dugAACgoKcOTIEXz11VdlrqdUKqFUKqstrtudn0Odh7korGtdbd9R07ENJyIiIqoGPMmqslWrVuGbb75BcnIy2rdvj9DQUPTp00dn2eTkZLz77ruIjo7G9evXMWvWLISGhho3YLngMU0kC0xqkGx8843UERBRTZCTk4MbN25opmNjY3H+/HnY29vD3d0ds2fPxuLFi9GyZUu0bNkSixcvRt26dTF+/HjJYv7v4L9eXI4btySLQ0psw4mIiIiqAU+yqiQiIgKzZ8/GqlWr0KtXL4SFhcHf3x+XL1+Gu7t7qfL5+flwdHTEvHnzsGzZMgkilhEe00SywKQGERHJSlRUFPr166eZDgoKAgBMmjQJGzZswAcffIDc3FwEBgYiMzMT3bt3x8GDB2FraytVyEREREREVIOEhIRgypQpmDp1KgAgNDQUBw4cwOrVqxEcHFyqfNOmTfHtt98CAL7//nujxkpEZIqY1CAiIlnp27cvhBBlLlcoFJg/fz7mz59vvKCIiIiIiKhWKCgoQHR0NObMmaM138/PDydPnpQoKiIieWFSg4iIiIiIiIiISA/p6ekoLi6Gk5OT1nwnJyekpKQY7Hvy8/ORn5+vmVar1QbbNhFRbWcmdQBExtKmDaBSPfqTiKg2GbD0O/zts8UYsPQ7qUORDNtwIiIiomrAk6wqUygUWtNCiFLznkVwcDDs7Ow0Hzc3N4Nt26TxmCaSBSY1SDZycoDs7Ed/EhHVJhb5BaiTXwCL/AKpQ5EM23AiIiKiasCTrEpr2LAhzM3NSz2VkZqaWurpjWcxd+5cZGVlaT6JiYkG27ZJ4zFNJAuySWocPXoUQ4YMgaurKxQKBX788UepQyIiIiIiIiNZtWoVPD09YWVlBS8vLxw7dqzc8keOHIGXlxesrKzQrFkzrFmzRmv5pUuXMHLkSDRt2hQKhQKhoaHVGD0REdUUlpaW8PLyQmRkpNb8yMhI9OzZ02Dfo1QqoVKptD5yU1BQgPj4eNy8ebPMT1pamtRhEpEEZPNOjQcPHqBTp054/fXXMXLkSKnDISIiIiIiI4mIiMDs2bOxatUq9OrVC2FhYfD398fly5fh7u5eqnxsbCwGDx6MadOmYfPmzThx4gQCAwPh6OiouZZ4+PAhmjVrhtGjR+Odd94x9i4REZGEgoKCMHHiRHh7e8PHxwdr165FQkICAgICADx6yiIpKQmbNm3SrHP+/HkAQE5ODtLS0nD+/HlYWlqiXbt2UuxCjZeTk4PY2FjMmzcPlpaWZZaztbVFeHg4HB0djRgdEUlNNkkNf39/+Pv7Sx0GEREREREZWUhICKZMmYKpU6cCAEJDQ3HgwAGsXr0awcHBpcqvWbMG7u7umqcv2rZti6ioKCxZskST1OjWrRu6desGAJgzZ45xdoSIqlX+/34VrotKpeJNU9IYO3YsMjIysHDhQiQnJ6NDhw7Yt28fPDw8AADJyclISEjQWqdLly6av0dHR2Pr1q3w8PBAXFycMUOvNfLy8mBmZoZevXqhcePGOstkZGTg2LFjUKvV/P9JJDOySWpUVn5+PvLz8zXTarVawmiIiIiIiKgqCgoKEB0dXSrx4Ofnh5MnT+pc59SpU/Dz89OaN2jQIKxfvx6FhYWoU6dOtcVLRNLIzMnCrdhbWPTRAiiVylLLrWzrIix8HW+ckkZgYCACAwN1LtuwYUOpeUKIao7INDVo0MCg7yohItPApEYZgoODsWDBAqnDICIiIiKiZ5Ceno7i4uJSN0ScnJxKveT1sZSUFJ3li4qKkJ6eDhcXlyrFwh9OEdVcD/IfwlJhgWk9XkVz16Zay+7cS0HYqW38NTgREVENIZsXhVfW3LlzkZWVpfkkJiZKHRIREREREVWRQqHQmhZClJpXUXld8ysjODgYdnZ2mo+bm1uVt0VE1cO1gRM8G7lrfVztnaUOi4iIiJ7ApEYZlEolVCqV1oeIiIiIiGqXhg0bwtzcvNRTGampqWUOZ+Hs7KyzvIWFBRwcHKocC384RURERET07JjUICIiIiIik2VpaQkvLy9ERkZqzY+MjETPnj11ruPj41Oq/MGDB+Ht7f1M79PgD6eIiIiIiJ6dbN6pkZOTgxs3bmimY2Njcf78edjb28Pd3V3CyMhY1qwBcnMBa2upIyEiqpzzw/8Gs8IilNSRTbddCttwInoWQUFBmDhxIry9veHj44O1a9ciISEBAQEBAB49QZGUlIRNmzYBAAICArBixQoEBQVh2rRpOHXqFNavX49t27ZptllQUIDLly9r/p6UlITz58/DxsYGLVq0MP5OEhFVBU+yyNTwmCaSBdncHYmKikK/fv0000FBQQCASZMmYcOGDRJFRcb0t79JHQERUdWktG3918SNW9IFIiG24UT0LMaOHYuMjAwsXLgQycnJ6NChA/bt2wcPDw8AQHJyMhISEjTlPT09sW/fPrzzzjtYuXIlXF1dsXz5cowcOVJT5s6dO+jSpYtmesmSJViyZAl8fX1x+PBho+0bEdEz4UkWmRoe00SyIJukRt++fTUv9yMiIjJ1BYWFiI+PL7eMSqWCo6OjkSIiIpJWYGAgAgMDdS7T9SMnX19fnDt3rsztNW3alNcXREREREQSkE1Sg4iISC6yHz5EbGwsPv7gfSiVyjLL2djZ4fuNm5jYICIiIiIiIqJag0kNko3oaKCgALC0BLy8pI6GiEh/9W/fgVlxMUrMzfUqn5dfAAUE+rVvhSZOjXSWSb+fhd8uXYNara4VSQ224URERETVgCdZZGp4TBPJApMaRpCWlga1Wl3m8vj4eBQWFRkxInkaOhRISgIaNwZu35Y6GiIi/fXYtA3W6mzkqmxxbMxwvdezV9nC2cG+GiMzHrbhRERERNWAJ1lkanhME8kCkxrVLC0tDVMmT0OO+mGZZXJzH+LOnRQUFBYaMTIiIiIiIiIiIiIiotqFSY1qplarkaN+iIFdx8CxgbPOMldiLyAi8Z8oKubTGkREREREREREREREZWFSw0gcGzjDxdFN57LUe8lGjkY/BYUFiI+PL7eMSqWqFWOxExERERERkXGUNQRzfHw8igr5Yz4iIiJ6NkxqkE7ZD7Jw69YtfDJnAZRKZZnlbFR1sX7DOiY2iMhkzJ8/HwsWLNCa5+TkhJSUFIkiIiIiIqo90tLSMP31acjLLj0E84Pch7iblILCwgIJIiMiIiJTwaQG6ZSb/xDmCgsM6DoGbs4eOsukZaYg8twPUKvVTGoQkUlp3749fv31V820ubm5hNEQERER1R5qtRp52Q8x3WccXO21h2COvnkByxLDUFRULFF0REREZAqY1KByOdRvVOawWUREpsrCwgLOzrrfg0REREREFXO1d4ZnI3etebczaubQy0RERFS7mEkdABERUU1z/fp1uLq6wtPTE6+++ipu3boldUhERERERERERAQ+qUFERKSle/fu2LRpE1q1aoW7d+/iiy++QM+ePXHp0iU4ODjoXCc/Px/5+fmaaV0vxiQiIiKqTcp62TcAqFQqDkFMRDVCQUEB4uPjNdMeRUWwAFBUVIT4mzcBsM0iMkVMahARET3B399f8/fnnnsOPj4+aN68OTZu3IigoCCd6wQHB5d6uTgRERFRbVXey74BwMq2LsLC1/EmIRFJKicnB7GxsZg3bx4sLS0BADsyM9EIwL3MTLzxxhsAAFtbW4SHh7PNIjIhTGqQbPz5JyAEoFBIHQkR1Sb16tXDc889h+vXr5dZZu7cuVoJD7VaDTc3w72P6Nd33/qrAUtMMth2axO24URERMZT3su+79xLQdipbVCr1bxBaAp4kkW1WF5eHszMzNCrVy80btwYAPDDSy9BAUAAGGZlhYyMDBw7doxtFpGJYVKDZMPWVuoIiKg2ys/Px59//ok+ffqUWUapVEKpVFZbDEXVuO3agm04ERGR8el62TeZGJ5kkQlo0KABnJycpA6DiIyILwonIiJ6wnvvvYcjR44gNjYWZ86cwahRo6BWqzFp0iSpQyMiIiIiIiIikj0+qUFERPSE27dvY9y4cUhPT4ejoyN69OiB06dPw8PDQ+rQiIiIiIiIiIhkj0kNko2QEECtBlQqoIx3/RIRYfv27VKHUEqLYydhkZePIislLro4V7yCCWIbTkRERFQNeJJFJqbNvn2ok5uLQmtrXBk8WOpwiKiaMKlBshESAiQlAY0b81yNiGqXFsdOwVqdjVyVLTBmuNThSIJtOBEREVE14EkWmZg2+/ahbmYmHjZowKQGkQnjOzWIiIiIiIiIiIiIiKhW4JMaRERERERERDKTlpYGtVqtc1l8fDyKCouMHBERUc1XXtv5mEqlgqOjo5EiIpInJjWo2unT4BcUFMDS0rLcMuwUiIiIiIiInl1aWhqmvz4NedkPdS5/kPsQd5NSUFhYYOTIiIhqrrS0NLz++uvIzs4ut5ytrS3Cw8N5D4uoGjGpQdUqLS0NUyZPQ45a98kyABQUFiAhIR4eHp6oY1H2IWmjqov1G9axUyAiIiIiInoGarUaedkPMd1nHFztnUstj755AcsSw1BUVKxz/fyCAsTHx+tcxqc8iMhUqdVqZGdno0+fPnBwcNBZJiMjA8eOHYNareb9K6JqxKQGVSu1Wo0c9UMM7DoGjg1KnywDwJXYC4iP/Sf6dxoJN2cPnWXSMlMQee4HdgpEREREREQG4mrvDM9G7qXm385ILnOdzJws3Iq9hUUfLYBSqSy1nE95EFFNU1BOIvbJMhWNIBIfH4+ioiI4ODjAycnJkCESUSUxqUFG4djAGS6ObjqXpd57dMLsUL9RmWWIiIiIiIhIeg/yH8JSYYFpPV5Fc9empZZX9JQHEZEx5eTkIDY2FvPmzSszaVFQUIDExER4eHjAopwRRHJzc3Hnzh0UFhZWV7hEpCcmNYiIiIiIiIhMTHW/CNy1gVOln/IgIjK2vLw8mJmZoVevXmjcuLHOMjdu3EB8fDx8fHzKLPO43O7du5nUIKoBmNQgIiIiIiIiMiF8ETgRkbYGDRqUOWRUenp6hWWeLEdE0mNSg2Sja1fAzQ3gKzmIqLa539gFufXtkF+vrtShSIZtOBERkf6e9UXgJCM8ySITc8/TEw8dHJCnUkkWgz7v8FCpVHxnLNEzYFKDnklBYfkNdXx8PAqLnu2xZkP5+WepIyAiqprTk8b/NXHjlnSBSIhtOBERUeVV5UXgJDM8ySITc/TddyX9fn3e4QEAtra2CA8PZ2KDqIqY1KAqy36QhVu3buGTOQugVCp1lsnNfYg7d1JQwPEGiYiIiIiIiIjIhOnzDo+MjAwcO3YMarWaSQ2iKmJSg6osN/8hzBUWGNB1DNycPXSWuRJ7ARGJ/0RRcc14WoOIiIiIiIiIiKg6VfR+DiJ6Nkxq0DNzqN8ILo5uOpel3uNjzURERERERERERERkGExqUK1R0fs7NOUKCnSOW/jmm864d88c9vbF2L79IR/xI6Jao8fGrVA+eIj8enVxsVcPg223oLCw1rzA7pVXgLS0R++w5NDPRERERAbCkywyMS8sXQortRp5KpXk79cgourDpAbVCvq8vwN4lPhISIiHh4cn6lhoH94n/9iC/AJHKC3TMGXyNKzfsK5G3KgjIqpI/aRkWKuzkauyNdg2sx8+RGxsLD7+4P1y21UbOzt8v3GT5O3luXNAUhJQxrC0RERERFQVPMkiE2MfG4u6mZl42KCB1KEQUTViUoNqBX3e3wE8eodHfOw/0b/TyFLlYv5jg/wCQFmnLnLUD/lCJiKStbz8Aigg0K99KzRxaqSzTPr9LPx26RrbSyIiIiIiIiKqMZjUoFqlvPd3AH+9w0NXOXNzcwCAmZlZ9QVIRFTL2Kts4exgL3UYRERERERERER6YVKDiIiIiIiolklLS4NarS5zeU15J5IxlVcnplofZe1zfHw8igqLJIiIiIiIqPoxqUGypM9Lx031woeIiIiIare0tDRMf30a8rIfllnGyrYuwsLl8w65iurEFOujvH1+kPsQd5NSUFhYIEFkRERkLBX9yOEx3uOqGOtSvzqoKfsvq6TGqlWr8M033yA5ORnt27dHaGgo+vTpI3VYZGRCCL1eOm6jqsuXiRPJGPuMmkufE62CggJYWlqWW6amnIwRkXFUtl0/cuQIgoKCcOnSJbi6uuKDDz5AQECAVpmdO3fik08+wc2bN9G8eXMsWrQIw4cPr+5dgVqtRl72Q0z3GQdXe+dSy+/cS0HYqW2yeidSeXViqvVR3j5H37yAZYlhKCoqlig6ItNXHf0KUWWkpaXh9ddfR3Z2doVlbW1tER4eblL9oCGxLvWvg5qy/7JJakRERGD27NlYtWoVevXqhbCwMPj7++Py5ctwd3eXOjwyIiFKKnzpeFpmCiLP/WByFz41mbGzwTUt+8xfBNQs7DNqrpKSYrwx6TXkZGWVWaagsBAJiYnwcHdHHYuyT3Vs7Ozw/cZN/D9FJAOVbddjY2MxePBgTJs2DZs3b8aJEycQGBgIR0dHjBw5EgBw6tQpjB07Fp9//jmGDx+O3bt3Y8yYMTh+/Di6d+9ulP1ytXeGZyPT6ZcqOh8qL2H9eLilmlgn5e1XRUn4qu7z7YzkqgdMOuUXlP+0P8/T5aU6+hWSl4IK2pTHZcrrI+Lj45GZmYl+/frBwcGhzHIZGRk4dOgQLl68CA8P3ffB9Pk+oGa2dYb40Vtl6vLYsWMmeb9QrVYjOzsbffr0KbMOatL+yyapERISgilTpmDq1KkAgNDQUBw4cACrV69GcHCwxNGRFCp66TgZT1paGqZMnoYcddlDKACGe3rG2N9nqHiMGZPcsc/4S0FhoVGH6yvrhLSoyAOABQoLC5GZkYFBndqhYX07ndu4lpCIuNhb8G3THE2cGuksk34/C/vPX6rwxB7Qb/9qWqKUTBuPt8qrbLu+Zs0auLu7IzQ0FADQtm1bREVFYcmSJZqbT6GhoRg4cCDmzp0LAJg7dy6OHDmC0NBQbNu2zTg7ZkIqGj4qv6AAcQnxaO7hCYs6pS9ja+pwS+XtV0X7VFv32RRl5mThVuwtLPqo7Kf9TXGIMypbdfQrJB85OTmIjY3FvHnzyrzRXlBQgMTERHh4eMCijB9q5ebm4s6dO1CpVHBycqr27wNqzq/0H9Pn6QJD1qUcODg41Io6kEVSo6CgANHR0ZgzZ47WfD8/P5w8eVLnOvn5+cjPz9dMZ/3vF6H6/JL6SdnZ2SgqKkJiSixy83WfoCenJaKkpAR3UuOhUAjJy9TEmAwRd1GRMwBrFJeoK9xWeuZd5Obm4vLly3o9ekbPJjExEemp9+DVoi9UtvV1llFn30fU9d9x+vRpuLk9WzLK2N9niHgex/SfuONISkoqd+g0XWxtbaFQKJ4xUnmQqs943F/cTk1D7hPbAoDM4mIUAsgpLkbKvXsoKRFITr8HhZmZzm0ZqkxiShpu3ryJOe8GQVnOr1osra0x79PPYG9vr9/OluHevXtY/PnnyH/4QMeybQAccf9+KuJibyGnpSfqWVvp3E5+YSFKSgTyCwpL1aVme1nZeu0bUPH+lRd3ZbZDpA99j7d6KjusCgtDw4YNK/0dptZnVKVdP3XqFPz8/LTmDRo0COvXr0dhYSHq1KmDU6dO4Z133ilV5vENK10MeY1RWFSEG8mxyMkrfY2RknkXD2vZuWxiYiLup2Xib237wcG2QanlV+/cRPytOPRv2h0uDqWH3Lp65yZ+iP8ZV27fRH6x9guypayP8vZLn32q6j7HpSaiuKQEN1LiUKzjmudZlku1rpTbvpR4FebCDAOb9dT5b5GRnYlfrh+t0nm65EpK/vqzkm0RYHp9hj6qq195mqHvS925cwd5eXk6y6SmpqKkpAQpKSkwK+PawFBlqvv7MouLUQQgu7gY8fHxNTLuhIQEAECLFi3KPFdLSkpCbGwsmjVrVm6ZhIQEJCYmori47OEGDfV92dnZuHDhglHuk+grMTER6enp6NChA2xtbXWWMWRd3rt3z2TvFyYmJiI/P7/ctuLevXsoKipCdnZ2pdsiwMB9hpCBpKQkAUCcOHFCa/6iRYtEq1atdK7z2WefCQD88MMPPybxycrKMkZzaxLYZ/DDDz9y/5han1GVdr1ly5Zi0aJFWvNOnDghAIg7d+4IIYSoU6eO2LJli1aZLVu2CEtLyzJjYX/BDz/8mNrH1PoMfVRXv/I09hn88MOPqX0M2WfI4kmNx57OBAkhyswOzZ07F0FBQZrp+/fvw8PDAwkJCbCzs6vWOGsbtVoNNzc3JCYmQqVSSR1OjcF6KRvrpmzVVTdl/WKByvYsfUZJSQnu3bsHBwcHvX+FwP8X5WP9lI/1Uz7WT8WerCNT7TMq066XVf7p+ZXdpiH6iyfx2C4f66d8rJ/ysX70Y6p9hj6qo195UlX6DB63rAOAdfAY66Hm1YEh+wxZJDUaNmwIc3NzpKSkaM1PTU0tc4wwpVKp87FROzu7GnEQ1EQqlYp1owPrpWysm7KxbqRjqD6jfv36Vfp+/tuXj/VTPtZP+Vg/FVOpVCY3jEhV2nVnZ2ed5S0sLDQvTiyrTHljEBuyv3gSj+3ysX7Kx/opH+uHnlZd/crTnqXP4HHLOgBYB4+xHkyzDsoeKM6EWFpawsvLC5GRkVrzIyMj0bNnT4miIiKimoh9BhGRaalKu+7j41Oq/MGDB+Ht7a0Z97ysMuwriIhMW3X1K0REpD9ZPKkBAEFBQZg4cSK8vb3h4+ODtWvXIiEhAQEBAVKHRkRENQz7DCIi01JRuz537lwkJSVh06ZNAICAgACsWLECQUFBmDZtGk6dOoX169dj27Ztmm2+/fbbeOGFF/DVV19h6NCh+Omnn/Drr7/i+PHjkuwjEREZT3X0K0REpD/ZJDXGjh2LjIwMyRJjeQABAABJREFULFy4EMnJyejQoQP27dsHDw8PvdZXKpX47LPPdA5JJXesG91YL2Vj3ZSNdVMzPGufURX8ty8f66d8rJ/ysX4qZup1VFG7npycjISEBE15T09P7Nu3D++88w5WrlwJV1dXLF++HCNHjtSU6dmzJ7Zv346PP/4Yn3zyCZo3b46IiAh0797daPtl6v9uz4r1Uz7WT/lYP1Se6uhXDIHHLesAYB08xnow7TpQiMdvJiIiIiIiIiIiIiIiIqrBZPFODSIiIiIiIiIiIiIiqv2Y1CAiIiIiIiIiIiIiolqBSQ0iIiIiIiIiIiIiIqoVmNQgIiIiIiIiIiIiIqJagUmNJ6xatQqenp6wsrKCl5cXjh07Vm75I0eOwMvLC1ZWVmjWrBnWrFljpEiNqzL1cvjwYSgUilKfK1euGDFi4zh69CiGDBkCV1dXKBQK/PjjjxWuI5djprJ1I5fjJjg4GN26dYOtrS0aNWqEYcOG4erVqxWuJ5fjRg7Yz5SvMvWTnJyM8ePHo3Xr1jAzM8Ps2bONF6hEKlM/u3btwsCBA+Ho6AiVSgUfHx8cOHDAiNEaX2Xq5/jx4+jVqxccHBxgbW2NNm3aYNmyZUaM1vgq2/48duLECVhYWKBz587VGyCVa9GiRejZsyfq1q2L+vXr6yyj61zq6X7j4sWL8PX1hbW1NRo3boyFCxdCCGGEPah++tRRQkIChgwZgnr16qFhw4aYNWsWCgoKtMqYch09qWnTpqWOlzlz5miV0ae+TFlV200iKcn5uJ0/f36pds3Z2VnqsKpVRfdehBCYP38+XF1dYW1tjb59++LSpUvSBFtNKqqDyZMnlzouevToIU2w1USfe02meCwwqfE/ERERmD17NubNm4eYmBj06dMH/v7+SEhI0Fk+NjYWgwcPRp8+fRATE4OPPvoIs2bNws6dO40cefWqbL08dvXqVSQnJ2s+LVu2NFLExvPgwQN06tQJK1as0Ku8XI4ZoPJ185ipHzdHjhzBjBkzcPr0aURGRqKoqAh+fn548OBBmevI6bgxdexnylfZ+snPz4ejoyPmzZuHTp06GTla46ts/Rw9ehQDBw7Evn37EB0djX79+mHIkCGIiYkxcuTGUdn6qVevHt566y0cPXoUf/75Jz7++GN8/PHHWLt2rZEjN46qns9lZWXhtddew4svvmikSKksBQUFGD16NP7xj3+UWy48PFzrXGrSpEmaZWq1GgMHDoSrqyvOnj2L7777DkuWLEFISEh1h28UFdVRcXExXn75ZTx48ADHjx/H9u3bsXPnTrz77ruaMqZeR09buHCh1vHy8ccfa5bpU1+mrKrtJpGUeNwC7du312rXLl68KHVI1aqiey9ff/01QkJCsGLFCpw9exbOzs4YOHAgsrOzjRxp9dHn/tNLL72kdVzs27fPiBFWP33uNZnksSBICCHE888/LwICArTmtWnTRsyZM0dn+Q8++EC0adNGa9706dNFjx49qi1GKVS2Xn7//XcBQGRmZhohupoDgNi9e3e5ZeRyzDxNn7qR63GTmpoqAIgjR46UWUaux40pYj9TvsrWz5N8fX3F22+/XU2R1QzPUj+PtWvXTixYsMDQodUIhqif4cOHi7///e+GDq1GqGr9jB07Vnz88cfis88+E506darGCElf4eHhws7OTueyis65Vq1aJezs7EReXp5mXnBwsHB1dRUlJSUGjlQ6ZdXRvn37hJmZmUhKStLM27Ztm1AqlSIrK0sIIZ86EkIIDw8PsWzZsjKX61NfpswQ/QqRscn9uJX7+crT5wElJSXC2dlZfPnll5p5eXl5ws7OTqxZs0aCCKufrnOhSZMmiaFDh0oSj1SevtdkqscCn9TAo1/1REdHw8/PT2u+n58fTp48qXOdU6dOlSo/aNAgREVFobCwsNpiNaaq1MtjXbp0gYuLC1588UX8/vvv1RlmrSGHY+ZZye24ycrKAgDY29uXWYbHjWlgP1O+Z+lv5MAQ9VNSUoLs7Oxy25vayhD1ExMTg5MnT8LX17c6QpRUVesnPDwcN2/exGeffVbdIZIBvfXWW2jYsCG6deuGNWvWoKSkRLPs1KlT8PX1hVKp1MwbNGgQ7ty5g7i4OAmiNa5Tp06hQ4cOcHV11cwbNGgQ8vPzER0drSkjpzr66quv4ODggM6dO2PRokVaQ0vpU1+miuclVBvxuH3k+vXrcHV1haenJ1599VXcunVL6pAkExsbi5SUFK1jQqlUwtfXV1bHBPBoyPNGjRqhVatWmDZtGlJTU6UOqVo9fa/JVI8FJjUApKeno7i4GE5OTlrznZyckJKSonOdlJQUneWLioqQnp5ebbEaU1XqxcXFBWvXrsXOnTuxa9cutG7dGi+++CKOHj1qjJBrNDkcM1Ulx+NGCIGgoCD07t0bHTp0KLMcjxvTwH6mfFWpHzkxRP0sXboUDx48wJgxY6ojREk9S/00adIESqUS3t7emDFjBqZOnVqdoUqiKvVz/fp1zJkzB1u2bIGFhYUxwiQD+Pzzz7Fjxw78+uuvePXVV/Huu+9i8eLFmuVl9SuPl5k6XfvfoEEDWFpaavZfTnX09ttvY/v27fj999/x1ltvITQ0FIGBgZrl+tSXqeJ5CdVGPG6B7t27Y9OmTThw4ADWrVuHlJQU9OzZExkZGVKHJonH/+5yPiYAwN/fH1u2bMFvv/2GpUuX4uzZs+jfvz/y8/OlDq1a6LrXZKrHAq9SnqBQKLSmhRCl5lVUXtf82q4y9dK6dWu0bt1aM+3j44PExEQsWbIEL7zwQrXGWRvI5ZipLDkeN2+99RYuXLiA48ePV1iWx43pYD9TvsrWj9xUtX62bduG+fPn46effkKjRo2qKzzJVaV+jh07hpycHJw+fRpz5sxBixYtMG7cuOoMUzL61k9xcTHGjx+PBQsWoFWrVsYKT5bmz5+PBQsWlFvm7Nmz8Pb21mt7T74P4fGL3RcuXKg1v7b1K4auI137+fT/hdpWR0+qTH298847mnkdO3ZEgwYNMGrUKM3TG4B+9WXKeF5CtZGcj1t/f3/N35977jn4+PigefPm2LhxI4KCgiSMTFpyPiYAYOzYsZq/d+jQAd7e3vDw8MDevXsxYsQICSOrHuXdazK1Y4FJDQANGzaEubl5qexUampqqSzWY87OzjrLW1hYaE4Ca7uq1IsuPXr0wObNmw0dXq0jh2PGkEz5uJk5cyZ+/vlnHD16FE2aNCm3LI8b08B+pnyG6m9M1bPUT0REBKZMmYIdO3ZgwIAB1RmmZJ6lfjw9PQE8uvC9e/cu5s+fb3JJjcrWT3Z2NqKiohATE4O33noLwKPhy4QQsLCwwMGDB9G/f3+jxG7q3nrrLbz66qvllmnatGmVt9+jRw+o1WrcvXsXTk5OZfYrQOlf7tUUhqwjZ2dnnDlzRmteZmYmCgsLNftfG+voSc9SXz169AAA3LhxAw4ODnrVl6nieQnVRjxuS6tXrx6ee+45XL9+XepQJOHs7Azg0a/0XVxcNPPlfEwAj0YK8fDwMMnjoqx7TaZ6LHD4KQCWlpbw8vJCZGSk1vzIyEj07NlT5zo+Pj6lyh88eBDe3t6oU6dOtcVqTFWpF11iYmK0/tPIlRyOGUMyxeNGCIG33noLu3btwm+//aa5mVYeHjemgf1M+QzV35iqqtbPtm3bMHnyZGzduhUvv/xydYcpGUMdP0IIk3wMvbL1o1KpcPHiRZw/f17zCQgIQOvWrXH+/Hl0797dWKGbvIYNG6JNmzblfqysrKq8/ZiYGFhZWaF+/foAHvUrR48e1XpvwsGDB+Hq6vpMyZPqZMg68vHxwX//+18kJydr5h08eBBKpRJeXl6aMrWtjp70LPUVExMDAJrzb33qy1TxvIRqIx63peXn5+PPP/80ufsK+vL09ISzs7PWMVFQUIAjR47I9pgAgIyMDCQmJprUcVHRvSaTPRaM9kryGm779u2iTp06Yv369eLy5cti9uzZol69eiIuLk4IIcScOXPExIkTNeVv3bol6tatK9555x1x+fJlsX79elGnTh3x73//W6pdqBaVrZdly5aJ3bt3i2vXron//ve/Ys6cOQKA2Llzp1S7UG2ys7NFTEyMiImJEQBESEiIiImJEfHx8UII+R4zQlS+buRy3PzjH/8QdnZ24vDhwyI5OVnzefjwoaaMnI8bU8d+pnyVrR8hhKad8fLyEuPHjxcxMTHi0qVLUoRf7SpbP1u3bhUWFhZi5f+zd9/xUVX5/8ffIZWSBBJIgxCqSJMSkKIIKAaCShEFy1JcRFlUQGBVxAKKxAIYBUFFBBEFdkX86srSVECkKAEUARExhGJCQoAECKSe3x/8mGVIYRImmZnk9Xw87kPm3jP3fO6deM6985lzzzvvWLU3p0+fdtQhlKrinp/Zs2ebL7/80vz+++/m999/Nx9++KHx8/MzkyZNctQhlKqS/P91uRdffNG0atWqjKJFQRISEszOnTvNlClTTLVq1Szt35kzZ4wxxnz55Zfm/fffN7t37zZ//PGHmTdvnvHz8zOjR4+27OP06dMmODjY3H///Wb37t3m888/N35+fmb69OmOOiy7uto5ysnJMS1atDC33Xab2bFjh1m3bp2pU6eOefzxxy37KO/n6JLNmzdbrs///PNPs2zZMhMWFmb69OljKWPL+SrPrtZuAs6oov/djh8/3qxfv978+eefZuvWrebOO+80vr6+5fr4r/bdy6uvvmr8/f3N559/bnbv3m3uv/9+ExoaatLT0x0cuf0UdQ7OnDljxo8fbzZv3mzi4+PNd999Zzp16mRq165drs6BLd81lce/BZIal3nnnXdMRESE8fLyMm3btjUbNmywbBs6dKjp2rWrVfn169ebNm3aGC8vL1OvXj0zd+7cMo64bBTnvLz22mumYcOGxsfHx9SoUcPcfPPN5uuvv3ZA1KXvu+++M5LyLUOHDjXGVOy/meKem4ryd1PQOZFkFixYYClTkf9uKgL6maIV9/wU9P9TRERE2QZdhopzfrp27VpkO1weFef8vP3226Z58+amSpUqxs/Pz7Rp08bMmTPH5ObmOiDyslHc/78uR1LD8YYOHVrg/9PfffedMcaY//73v6Z169amWrVqpkqVKqZFixYmNjbWZGdnW+3nl19+MV26dDHe3t4mJCTETJ482eTl5TngiOzvaufImIuJjzvuuMNUrlzZBAQEmMcff9xcuHDBaj/l+RxdEhcXZzp06GD8/f2Nj4+PadKkiXnxxRfNuXPnrMrZcr7Ks6LaTcBZVeS/20GDBpnQ0FDj6elpwsLCzN13311uf/B0ydW+e8nLyzMvvviiCQkJMd7e3uaWW24xu3fvdmzQdlbUOcjIyDBRUVGmVq1axtPT09StW9cMHTrUHD582NFh25Ut3zWVx78FN2P+/8xnAAAAAAAAAAAATow5NQAAAAAAAAAAgEsgqQEAAAAAAAAAAFwCSQ0AAAAAAAAAAOASSGoAAAAAAAAAAACXQFIDAAAAAAAAAAC4BJIaAAAAAAAAAADAJZDUAAAAAAAAAAAALoGkBgAAAAAAAAAAcAkkNeAw3bp109ixY0tt/0lJSbr99ttVtWpVVa9evdTqqcgWLlzoVOe2tP+mAMBZOFv7CwDI78pr03r16ik2NrZM6nIVV/ZnkydPVuvWrR0WDwAAcA0kNVBuvfnmm0pMTNSuXbv0+++/a/369XJzc9Pp06dLve7SuBjnAv9/CvssP//8c7388suOCQoAAAAowk8//aRHHnnE8trNzU1ffPGF4wJyQhMmTNA333zj6DAAAICTI6mBcuvgwYOKjIxU48aNFRQUZLf9GmOUk5Njt/3ZW3Z2tqNDKLGsrKxren9AQIB8fX3tFA0AAABgP7Vq1VKVKlUcHYZTq1atmgIDAx0dBgCgCNf63Q1gDyQ1UKBu3brp8ccf1+OPP67q1asrMDBQzz33nIwxki42YE899ZRq166tqlWrqkOHDlq/fr3l/ampqbr//vtVp04dValSRS1bttSSJUuKrHPVqlXy9/fXokWLrhrfTz/9pNtvv101a9aUv7+/unbtqh07dli216tXT8uXL9eiRYvk5uamYcOGqXv37pKkGjVqWNZJF5MUr7/+uho0aKDKlSurVatW+uyzzyz7ujQqYPXq1WrXrp28vb31/fffFxrbwoULNWXKFP38889yc3OTm5ubFi5cKEk6fPiw+vbtq2rVqsnPz08DBw7U8ePHr3q8Re3Tzc1N7777rvr27auqVatq6tSpkqS5c+eqYcOG8vLyUpMmTfTxxx9b9nfo0CG5ublp165dlnWnT5+Wm5ub1ef45ZdfqnHjxqpcubK6d++ujz76qMAREqtXr1bTpk1VrVo19erVS4mJiVc9JkkaNmyY+vXrp5iYGIWFhem6666TJC1evFjt2rWTr6+vQkJC9MADDyg5OdkSe2Gf5ZXD7k+dOqUhQ4aoRo0aqlKliqKjo3XgwAGbYgOAyxXWVxhj1KNHD/Xq1cvSR54+fVp169bVpEmTJP2vH/n666/VqlUr+fj4qEOHDtq9e7fN9S9cuFB169ZVlSpV1L9/f6WmpuYr89VXXykyMlI+Pj5q0KCBpkyZYpWEP336tB555BEFBwfLx8dHLVq00H/+8x9JV++3Fy1apMDAQGVmZlrVOWDAAA0ZMsT2EwkADrBq1SrdfPPNlvuaO++8UwcPHpT0v+vif/3rX+rSpYsqV66s9u3b6/fff9dPP/2kdu3aWa5xU1JSLPu8dB07ZcoUBQUFyc/PT48++miRX/Rc/vipevXqSZL69+8vNzc3y+tL+73c2LFj1a1bN8vrc+fOaciQIapWrZpCQ0M1Y8aMfHVd7X6tKJceCfWf//xHTZo0UZUqVXTPPffo3Llz+uijj1SvXj3VqFFDTzzxhHJzc4tV59X6sytHp1/tvk+6eD/0wQcfqH///qpSpYoaN26sL7/80qZjBYCK4rPPPlPLli1VuXJlBQYGqkePHjp37pyl35k+fbpCQ0MVGBioxx57zOoHs/Xq1dPUqVM1bNgw+fv7a8SIEQ48EuD/M0ABunbtaqpVq2bGjBljfvvtN7N48WJTpUoV8/777xtjjHnggQdM586dzcaNG80ff/xh3njjDePt7W1+//13Y4wxR48eNW+88YbZuXOnOXjwoHn77beNu7u72bp1q1UdY8aMMcYYs2TJEuPr62u++OILm+L75ptvzMcff2z27t1r9u7da4YPH26Cg4NNenq6McaY5ORk06tXLzNw4ECTmJhoTp8+bZYvX24kmf3791vWGWPMs88+a66//nqzatUqc/DgQbNgwQLj7e1t1q9fb4wx5rvvvjOSzA033GDWrFlj/vjjD3PixIlCY8vIyDDjx483zZs3N4mJiSYxMdFkZGSYvLw806ZNG3PzzTeb7du3m61bt5q2bduarl27XvV4C9unMcZIMkFBQWb+/Pnm4MGD5tChQ+bzzz83np6e5p133jH79+83M2bMMO7u7ubbb781xhgTHx9vJJmdO3da6jh16pSRZL777jtLGU9PTzNhwgTz22+/mSVLlpjatWsbSebUqVPGGGMWLFhgPD09TY8ePcxPP/1k4uLiTNOmTc0DDzxg0+c4dOhQU61aNTN48GDz66+/mt27dxtjjJk/f75ZuXKlOXjwoNmyZYvp2LGjiY6ONsYYk5OTU+hnefnflDHG9OnTxzRt2tRs3LjR7Nq1y/Ts2dM0atTIZGVl2RQfAFxSVF9x9OhRU6NGDRMbG2uMMWbQoEGmXbt2lrbmUj/StGlTs2bNGvPLL7+YO++809SrV8+m9mjr1q3Gzc3NxMTEmP3795u33nrLVK9e3fj7+1vKrFq1yvj5+ZmFCxeagwcPmjVr1ph69eqZyZMnG2OMyc3NNR07djTNmzc3a9asMQcPHjRfffWVWblypTHm6v12RkaG8ff3N//6178sdaakpBgvLy9L3wIAzuqzzz4zy5cvN7///rvZuXOnueuuu0zLli1Nbm6u5br4Uhu/d+9e07FjR9O2bVvTrVs3s2nTJrNjxw7TqFEjM3LkSMs+L13HDho0yPz666/mP//5j6lVq5Z59tlnLWWuvDaNiIgwb775pjHm4v2KJLNgwQKTmJhokpOTLfvt27evVfxjxoyxumf4xz/+YerUqWPVp1y6d7vkavdrRbl0jX/77bebHTt2mA0bNpjAwEATFRVlBg4caPbs2WO++uor4+XlZZYuXWpznbb0Zy+++KJp1aqV5fXV7vuMuXg/VKdOHfPpp5+aAwcOmNGjR5tq1aqZ1NTUqx4rAFQEf/31l/Hw8DAzZ8408fHx5pdffjHvvPOOOXPmjBk6dKjx8/MzI0eONPv27TNfffWV1fd/xlzsv/z8/Mwbb7xhDhw4YA4cOODAowEuIqmBAnXt2tU0bdrU5OXlWdY9/fTTpmnTpuaPP/4wbm5u5tixY1bvue2228zEiRML3Wfv3r3N+PHjreoYM2aMeeedd4y/v/81fSmSk5NjfH19zVdffWVZ17dvXzN06FDL60tfKl36Qt4YY86ePWt8fHzM5s2brfY3fPhwc//991u9z9aEizH5L8aNMWbNmjXG3d3dHD582LJuz549RpL58ccfS7RPYy5exI8dO9ZqXefOnc2IESOs1t17772md+/exhjbkhpPP/20adGihdU+Jk2alC+pIcn88ccfljLvvPOOCQ4OvurxGHPxpi04ONhkZmYWWe7HH380ksyZM2eMMQV/lsZY3zj+/vvvRpL54YcfLNtPnDhhKleubPWlHABcjS19xb/+9S/j7e1tJk6caKpUqWL2799vKXepzbr8i5/U1FRTuXJls2zZsqvWf//995tevXpZrRs0aJDVl0BdunQx06ZNsyrz8ccfm9DQUGOMMatXrzaVKlWyiutqruy3//GPf1gSzMYYExsbaxo0aGB1rQAAruBSQmH37t2W6+IPPvjAsn3JkiVGkvnmm28s62JiYkyTJk0sr4cOHWoCAgLMuXPnLOvmzp1rqlWrZnJzc40xRSc1jLl4Hb9ixQqr2K6W1Dhz5ky+ZMKlPuVSXSW9X7ukoGv8Rx991FSpUsVyPW6MMT179jSPPvqozXXa0p8Vds9zSUH3fZLMc889Z3l99uxZ4+bmZv773/9e9VgBoCKIi4szksyhQ4fybRs6dKiJiIgwOTk5lnX33nuvGTRokOV1RESE6devX5nECtjKoyxGg8A1dezYUW5ubpbXnTp10owZM7R9+3YZYyyPCrokMzPT8vzT3Nxcvfrqq1q2bJmOHTumzMxMZWZmqmrVqlbvWb58uY4fP65NmzbpxhtvtDm25ORkvfDCC/r22291/Phx5ebmKiMjQ4cPHy7WMe7du1cXLlzQ7bffbrU+KytLbdq0sVrXrl27Yu37Svv27VN4eLjCw8Mt65o1a6bq1atr3759at++fYn3fWVs+/bts5qEUJJuuukmvfXWWzbvc//+/fliKugzqlKliho2bGh5HRoaanlUlC1atmwpLy8vq3U7d+7U5MmTtWvXLp08eVJ5eXmSLj6+q1mzZjbtd9++ffLw8FCHDh0s6wIDA9WkSRPt27fP5vgAwJa+4t5779WKFSsUExOjuXPn5usjpYv96CUBAQE2t0f79u1T//798+1r1apVltdxcXH66aef9Morr1jW5ebm6sKFC8rIyNCuXbtUp06dAuO6VPZq/faIESPUvn17HTt2TLVr19aCBQs0bNgwq2sFAHBGBw8e1PPPP6+tW7fqxIkTBV5b3nDDDZbywcHBki5ep16+7spr3FatWlnNkdGpUyedPXtWR44cUURERKkdS1ZWVoF9yiU7duy46v3a1Vx5jR8cHKx69eqpWrVqVusunRNb6rSlP7uSrfd9l39+VatWla+vb7HuSQCgPGvVqpVuu+02tWzZUj179lRUVJTuuece1ahRQ5LUvHlzubu7W8qHhobme1TutX4nBtgbSQ2UiLu7u+Li4qwaPUmWi9wZM2bozTffVGxsrFq2bKmqVatq7Nix+Z4x27p1a+3YsUMLFixQ+/btbf5iZNiwYUpJSVFsbKwiIiLk7e2tTp06FXuyoks3NF9//bVq165ttc3b29vq9ZUJmeIyxhR4fIWtL46CYrtyn5fXU6lSJcu6S66cYLyguC4vf4mnp2e+egsqZ2vs586dU1RUlKKiorR48WLVqlVLhw8fVs+ePYv1+RYWgz3ON4CKxZa+IiMjw9IvFmfuHlvaI1va1Ly8PE2ZMkV33313vm0+Pj6qXLlyke+3pd9u06aNWrVqpUWLFqlnz57avXu3vvrqq6vGBgCOdtdddyk8PFzz5s1TWFiY8vLy1KJFC6s27vJr2ktt85XrLvUHV3Mt15qVKlXK1+5ffp1ua59wtfu1qynoGr+gdZfOiS11Fuce4RJb7/uKig0AKjp3d3etXbtWmzdv1po1azRr1ixNmjRJ27Ztk2RbG3qt34kB9kZSA4XaunVrvteNGzdWmzZtlJubq+TkZHXp0qXA937//ffq27ev/va3v0m6eJF74MABNW3a1Kpcw4YNNWPGDHXr1k3u7u6aPXu2TbF9//33mjNnjnr37i1JOnLkiE6cOFHkey6NBrh8MrtmzZrJ29tbhw8fVteuXW2q2xZeXl5W9Vyq6/Dhwzpy5IhltMbevXuVlpaW77zYus/CNG3aVJs2bbKavHXz5s2WemrVqiVJSkxMtPzK+PJJwyXp+uuv18qVK63Wbd++3ab6r8Vvv/2mEydO6NVXX7WcpyvrLeizvFKzZs2Uk5Ojbdu2qXPnzpIuToT7+++/23S+AeASW/qK8ePHq1KlSvrvf/+r3r1764477tCtt95qVWbr1q2qW7euJOnUqVP6/fffdf3119tUf0F98uXatm2r/fv3q1GjRgXu44YbbtDRo0f1+++/Fzhaw9Z+++GHH9abb76pY8eOqUePHlajDwHAGaWmpmrfvn167733LPcumzZtssu+f/75Z50/f96SON66dauqVaumOnXq2PR+T0/PfNeztWrV0q+//mq1bteuXZYvnBo1aiRPT88C+5RLfZQt92v2ZkudtvRnVyrJfR8AID83NzfddNNNuummm/TCCy8oIiJCK1ascHRYQImR1EChjhw5onHjxunRRx/Vjh07NGvWLM2YMUPXXXedHnzwQQ0ZMkQzZsxQmzZtdOLECX377bdq2bKlevfurUaNGmn58uXavHmzatSooZkzZyopKanAL5Ovu+46fffdd+rWrZs8PDwUGxt71dgaNWqkjz/+WO3atVN6err++c9/XvVXqBEREXJzc9N//vMf9e7dW5UrV5avr68mTJigJ598Unl5ebr55puVnp6uzZs3q1q1aho6dGiJzl29evUUHx9vedyHr6+vevTooRtuuEEPPvigYmNjlZOTo1GjRqlr1642DeMraJ9Xjia55J///KcGDhyotm3b6rbbbtNXX32lzz//XOvWrZMkVa5cWR07dtSrr76qevXq6cSJE3ruuees9vHoo49q5syZevrppzV8+HDt2rVLCxculHRtvz67mrp168rLy0uzZs3SyJEj9euvv+rll1+2KlPQZ3nlr84aN26svn37asSIEXrvvffk6+urZ555RrVr11bfvn1LLX4A5c/V+oqaNWvqww8/1JYtW9S2bVs988wzGjp0qH755RfLkG5JeumllxQYGKjg4GBNmjRJNWvWVL9+/a5a/+jRo9W5c2e9/vrr6tevn9asWZPvUR0vvPCC7rzzToWHh+vee+9VpUqV9Msvv2j37t2aOnWqunbtqltuuUUDBgzQzJkz1ahRI/32229yc3NTr169bO63H3zwQU2YMEHz5s3TokWL7HJ+AaA01ahRQ4GBgXr//fcVGhqqw4cP65lnnrHLvrOysjR8+HA999xzSkhI0IsvvqjHH3/cMir6aurVq6dvvvlGN910k7y9vVWjRg3deuuteuONN7Ro0SJ16tRJixcv1q+//mr5IVK1atU0fPhw/fOf/7TqUy6v05b7NXuzpU5b+rMrleS+DwBgbdu2bfrmm28UFRWloKAgbdu2TSkpKWratKl++eUXR4cHlIhtV1uokIYMGaLz58/rxhtv1GOPPaYnnnjCMk/DggULNGTIEI0fP15NmjRRnz59tG3bNssvNp9//nm1bdtWPXv2VLdu3RQSElLkFzdNmjTRt99+qyVLlmj8+PFXje3DDz/UqVOn1KZNGw0ePFijR49WUFBQke+pXbu2pkyZomeeeUbBwcF6/PHHJUkvv/yyXnjhBcXExKhp06bq2bOnvvrqK9WvX9/GM5XfgAED1KtXL3Xv3l21atXSkiVL5Obmpi+++EI1atTQLbfcoh49eqhBgwZatmxZifdZmH79+umtt97SG2+8oebNm+u9997TggUL1K1bN0uZDz/8UNnZ2WrXrp3GjBmjqVOnWu2jfv36+uyzz/T555/rhhtu0Ny5czVp0iRJ+R/NZU+1atXSwoUL9e9//1vNmjXTq6++qunTp1uVKeyzvNKCBQsUGRmpO++8U506dZIxRitXrsw3tBIArqawvqJevXoaPny4Jk+erLZt20qSXnzxRYWFhWnkyJFW+3j11Vc1ZswYRUZGKjExUV9++WW+OYUK0rFjR33wwQeaNWuWWrdurTVr1uRLRPfs2VP/+c9/tHbtWrVv314dO3bUzJkzrZ7pvnz5crVv317333+/mjVrpqeeesryC2Fb+20/Pz8NGDBA1apVsykhAwCOVqlSJS1dulRxcXFq0aKFnnzySb3xxht22fdtt92mxo0b65ZbbtHAgQN11113afLkyTa/f8aMGVq7dq3Cw8MtSYuePXvq+eef11NPPaX27dvrzJkzVqOvJemNN97QLbfcoj59+qhHjx66+eabFRkZaVXmavdrpeFqddrSn12pJPd9AABrfn5+2rhxo3r37q3rrrtOzz33nGbMmKHo6GhHhwaUmJspyYMtUe5169ZNrVu3tmnUBCqOV155Re+++66OHDni6FAAwGWsX79e3bt316lTp1S9enVHh3PNbr/9djVt2lRvv/22o0MBAIcZNmyYTp8+rS+++MLRoQAAAFQ4PH4KQKHmzJmj9u3bKzAwUD/88IPeeOONQkdFAADKt5MnT2rNmjX69ttvbZ4DCwAAAAAAe+PxU3BK1apVK3T5/vvvHR2emjdvXmh8n3zyidPs81odOHBAffv2VbNmzfTyyy9r/PjxxRpS7+yfIwA4g+jo6ELbymnTpjk6PIu2bdvq0Ucf1WuvvaYmTZo4OhwAQAm5Sr8DAABQGB4/Baf0xx9/FLqtdu3aDp8cLiEhQdnZ2QVuCw4Olq+vr1Ps09Gc/XMEAGdw7NgxnT9/vsBtAQEBCggIKOOIAADlGf0OAABwdSQ1AAAAAAAAAACAS+DxUwAAAAAAAAAAwCWQ1AAAAAAAAAAAAC6BpAYAAAAAAAAAAHAJJDUAAAAAAAAAAIBLIKkBAAAAAAAAAABcAkkNAAAAAAAAAADgEkhqAAAAAAAAAAAAl0BSAwAAAAAAAAAAuASSGgAAAAAAAAAAwCWQ1AAAAAAAAAAAAC6BpAYAAAAAAAAAAHAJJDUAAAAAAAAAAIBLIKkBAAAAAAAAAABcAkkNAAAAAAAAAADgEkhqAABQSjZu3Ki77rpLYWFhcnNz0xdffFGq9U2ePFlubm5WS0hISKnWCQAAAAAAUJZIagAAUErOnTunVq1aafbs2WVWZ/PmzZWYmGhZdu/eXWZ1AwAAAAAAlDYPRwcAAEB5FR0drejo6EK3Z2Vl6bnnntMnn3yi06dPq0WLFnrttdfUrVu3Etfp4eHB6AwAAAAAAFBuMVIDAAAHeeihh/TDDz9o6dKl+uWXX3TvvfeqV69eOnDgQIn3eeDAAYWFhal+/fq677779Oeff9oxYgAAAAAAAMdyM8YYRwcBAEB55+bmphUrVqhfv36SpIMHD6px48Y6evSowsLCLOV69OihG2+8UdOmTSt2Hf/973+VkZGh6667TsePH9fUqVP122+/ac+ePQoMDLTXoQAAAAAAADgMIzUAAHCAHTt2yBij6667TtWqVbMsGzZs0MGDByVJhw4dyjfx95XL448/btlndHS0BgwYoJYtW6pHjx76+uuvJUkfffSRQ44RAAAAAADA3phTAwAAB8jLy5O7u7vi4uLk7u5uta1atWqSpNq1a2vfvn1F7qdGjRqFbqtatapatmx5TY+zAgAAAAAAcCYkNQAAcIA2bdooNzdXycnJ6tKlS4FlPD09df3115e4jszMTO3bt6/Q/QMAAAAAALgakhoAAJSSs2fP6o8//rC8jo+P165duxQQEKDrrrtODz74oIYMGaIZM2aoTZs2OnHihL799lu1bNlSvXv3LnZ9EyZM0F133aW6desqOTlZU6dOVXp6uoYOHWrPwwIAAAAAAHAYJgoHAKCUrF+/Xt27d8+3fujQoVq4cKGys7M1depULVq0SMeOHVNgYKA6deqkKVOmqGXLlsWu77777tPGjRt14sQJ1apVSx07dtTLL7+sZs2a2eNwAAAAAAAAHI6kBgAAAAAAAAAAcAmVHB0AAAAAAAAAAACALUhqAAAAAAAAAAAAl0BSw0bGGKWnp4undQEAroY+AwBgC/oLAICt6DMA4H88HB2Aqzhz5oz8/f2VlpYmPz8/R4cDoAKaOVNKT5f8/KRx4xwdDYpS4j6DDxkAKhRXucegewIAxytpn0EbDqA8YqJwG6Wnp7vEDQeA8qtOHenYMal2benoUUdHg6KUuM/gQwaACsVV7jHongDA8UraZ9CGAyiPePwUAAAAAAAAAABwCSQ1AAAAAAAAAACASyCpAQAAAAAAAAAAXAIThQMoE7m5ucrOznZ0GC6tdm3Jw0MKDpYuXMi/3dPTU+7u7mUfGABchvbe+bm7u8vDw0Nubm6ODgUArokxRjk5OcrNzXV0KOUW9xgAygvuU0pfWfYZJDUAlLqzZ8/q6NGjMsY4OhSXNmOGlJcnVaokxcfn3+7m5qY6deqoWrVqZR8c7OOOO6STJ6WAAEdHApQI7b3rqFKlikJDQ+Xl5eXoUOAC6J7gjLKyspSYmKiMjAxHh1KucY/h+mjDAe5TykpZ9hluhk/TJunp6fL391daWpr8/PwcHQ7gMnJzc3XgwAFVqVJFtWrV4lehpcQYo5SUFGVkZKhx48b8msrB6DNQEdHeuwZjjLKyspSSkqLc3Fw1btxYlSrxRFpHob8ASiYvL08HDhyQu7u7atWqJS8vL/qdUsA9hnOhzwBKhvuUslHWfQYjNQCUquzsbBljVKtWLVWuXNnR4ZRrtWrV0qFDh5Sdnc0NB4AyR3vvOipXrixPT08lJCQoKytLPj4+jg4JAIolKytLeXl5Cg8PV5UqVRwdTrnGPQYAV8d9Stkpyz6Dn2UBKBNkwksf5xiAM6Atcg2MzgBQHtCWlT76dQDlBe1Z6SvLc8wVAAA4QF5enp544gk1bNhQjRo10pw5cwosd+HCBfXr10/XXXedWrdurV69eunQoUNlGyycTkpKig4ePFjkkpKS4ugwAZexfft2Pfjgg3bb36RJk9SyZUu1bt1arVu31rJly+y2b0maP3++GjdurIYNG+qRRx5RTk6OJGn37t265ZZbdP3116tly5Z65JFHlJmZade6AQDO7cr7jFmzZunChQsFLqNGjVJERITc3Nz066+/Ojp0lCLuHwAUxNbvpiRp9OjRqlevntP0GTx+qpSdPHlSq1atUq9evRTArEwA/r/Fixdr7969+v3335WWlqa2bdvq1ltv1fXXX5+v7COPPKLo6Gjt2+emjz+erQcffEQ//LDGAVGj1LVrJyUlSSEh0vbtBRZJSUnR30Y8otSM80XuKrBKZS2e975q1apVGpEC5Uq7du30ySef2G1///znP/XKK69Ikv766y9df/31ioqKUo0aNWzex+TJk1WvXj0NGzbMan18fLyef/557dy5U0FBQerbt6/mz5+vRx99VD4+Ppo9e7ZuuOEG5ebm6oEHHtCMGTP07LPP2u3YUDHZ0D0BcBKX32ecOHFCkZGRqt+wkRo0aJivbMdON+megfdpyN8eUHZ2tgOiRVlo3Tpbv++XvDwy1aXt4ELLefsGau6CT7h/ACqQ4nw3dc899+ipp57SzTff7IBI82OkRik7deqUlixZolOnTjk6FAD/n5ubm2JiYnTjjTeqQYMGWrdunSZOnKg2bdqoefPm2rNnjyQpKSlJ3bt3V2RkpJo3b67Ro0fLGCNJGjFihJ544glJF5OXDRs21KZNm2yOYdmyZRo5cqTc3d0VEBCggQMHaunSpfnK+fj4qHfv3nJzc1N2ttS0aUcdOfKnHc4CnFJSknTs2MX/FiI9PV2pGedVY9AQ1RnzdIFLjUFDlJpxXunp6WUYPOB8bG3v169fr3bt2kmSDh06pJo1a+qFF15QZGSkGjVqpJUrVxar3urVq1v+febMGbm5uSkvL8/yesSIEbrxxht1ww03aOTIkcX6Iumzzz5T//79FRwcLDc3N40cOVJLliyRJDVu3Fg33HCDJMnd3V3t27fXn3/SZ+Da2dA9AZDz3Wf4+/srqmcvrV73rbyr18q33HR7b4U3ul7GyNJPofxJSnLT+Qu15OlWS7H9QgpcxnfxU+aZVO4fgDLkbH1GUd9NSdItt9yiOnXqXONR2w9JDQBlbuZMqU6dqy99+uR/b58+hZefOdP2GPz8/PTjjz/qtddeU9++fXXzzTdr586dGjp0qOXXtdWrV9dXX32luLg4/fLLL/rzzz+1fPlySdKsWbO0adMm/fvf/9bQoUM1YsQIS7Z69OjRlkeOXLls27ZNknT48GFFRERY4qlXr54OHz581biXLXtbt9xyl+0HinKrakiofMMjClyqhoQ6OjzgIido8G1p76+UmpqqyMhIxcXFafbs2XryySct2+65555C2/gjR45Yyr399ttq0qSJ2rZtq/fff1+BgYGSpPHjx+uWW27Rjz/+qJ9//lk5OTmaPXu2zcdja/9x7tw5ffDBB7rrLvoMABWHE3Q7TnefUbt2bSUl/iV3D68Cl0oeniU823A17pXc1DC4WoFLeGAVR4cHlDn6jJJ/N+UMePwUgDKXnn7x135XEx6ef11KSuHvLc6PSgYNGiRJatu2rSpVqqQ77rhDkhQZGanPP/9c0sVfKz399NPatGmTjDFKTk5W69atdc8998jHx0f//ve/FRkZqU6dOunpp5+27Pvtt9+2KYbLJ1C6lGUvygcfTNORIwf0/PPv2nycAOBQTtDg29LeX6lq1arq27evJKlTp046ePCgZdtnn31mU72jR4/W6NGj9fPPP+tvf/ubevToocDAQH3xxRfaunWrZsyYIUk6f/68vLy8JEnvvPOO5s2bJ+niL7K8vLwUGxsrSXrxxRfVv39/SVfvP7KzszVo0CBFRUVZjgMAKgIn6Hac8D7D9tgBoCKhz7iouN9NOQuSGgDKnJ+fVLv21csV9CjPWrUKf6+fn+0x+Pj4SLr4eA5vb2/Lend3d8uEqzNnzlRqaqq2bdsmHx8fjRs3ThcuXLCU/e2331S1alUlJycrKyvLsp/Ro0dr48aNBdb73nvvqUOHDqpbt64OHTqk9u3bS5ISEhJUt27dQuOdPn26vv32c82evU6VK/MrGgAuwgkafFva+8Lec6lcbm6u5fU999yjP/74o8D3ffXVVwq/4q6nVatWql27ttavX68BAwbIGKMvvvhCDRo0yPf+xx57TI899pikwufUuNR/XHJl/5Gdna2BAwcqNDRUb731VoFxAkB55QTdjtPdZ/z11zGF1naex4UAgLOgzyj+d1POhKQGgDI3btzFpSS+/NK+sRTl1KlTCgkJkY+Pj44fP65///vfliz64cOHNWrUKK1bt05vv/22xo4dq7lz50qyLRt+77336r333tPdd9+ttLQ0LVu2TKtWrSqw7MyZM7VkyRK9++46Va5c3W7HBwClzlUa/GKwZaTGvn371LRpU0nSwYMHtXPnTjVr1kyS1KdPH7366quaM2eOPDw8dOrUKaWmpqpRo0Y21T9gwADdfPPNeuGFFxQUFKR3331X9913nyQpJydH9913nwICAvT+++9b/eoKACoCV+l2yuo+4+TJk1r13/9qwdLlpXo8AOCK6DOK992Us2FODQAoxOjRo7V582a1bt1af//739WjRw9J//vS6OWXX1azZs0UGxurrVu3atmyZTbve/DgwWrSpImuu+46tW/fXv/85z8tX4B9+eWXevjhhyVJR48e1fjx43X69Gk9/HB3PfBAa/3tbx3sf7AAALt55pln1Lx5c7Vu3VqDBg3S7NmzLW18bGysPDw81Lp1a91www3q0aOH1ciLq2nQoIGmTJmim266SQ0bNlRQUJCGDx8u6eJEf59//rm2b9+uNm3aqHXr1paRHwAA51FW9xk333yzHho+XI2uayJJWrvqaz099n/9wvNPPamb2jbX8eNJuuOOO2xOsAMAyo4zfDclXRxVXqdOHR09elQ9evRweJ/hZlzpYVkOlJ6eLn9/f6WlpcmvGOOIDh48qLFjxyo2NlYNGzYsxQgB53ThwgXFx8erfv36Vo/zQPH9/LOUnS15ekqtWuXfzrl2HiXtM1SnzsUHc9auLR09WmCRgwcPatA/HlOdMU/LNzyiwDJnjiTo6Fuvadncd+h7UGZog1wLn5dzKHF/UcZs6J6AMkUbZpsLFy7oz0OH5V29ltw9vAosk5uTpczTKWpQr26B55Jz7TxK2meEhOTo+HEPBfufUNKcgn/scPD4WY39IkmxH/6L+weUO7RjZacszzUjNQAAAAAAAFAhzJkzx/KFW2RkpL7//vsiy2/YsEGRkZHy8fFRgwYN9O6771ptnzdvnrp06aIaNWqoRo0a6tGjh3788cdrrhcAUDiSGgAAAAAAACj3li1bprFjx2rSpEnauXOnunTpoujoaB0+fLjA8vHx8erdu7e6dOminTt36tlnn9Xo0aO1fPn/5ilZv3697r//fn333XfasmWL6tatq6ioKB07dqzE9QIAisZE4QDgIurUkfLypEqko8uv11+XMjKkKlUcHQkAABZ0TwDKi5kzZ2r48OGW58THxsZq9erVmjt3rmJiYvKVf/fdd1W3bl3FxsZKkpo2bart27dr+vTpGjBggCTpk08+sXrPvHnz9Nlnn+mbb77RkCFDSlSvPT39dKoWzn1fw270LtV6AKAskdQAUCaYvufaBQYWvZ1zXA488ICjIwCuGW2Ra+BzQnHQPcFZ0ZaVvvJ0jrOyshQXF6dnnnnGan1UVJQ2b95c4Hu2bNmiqKgoq3U9e/bU/PnzlZ2dLU9Pz3zvycjIUHZ2tgICAkpcryRlZmYqMzPT8jo9Pb3oAyxEnz5n9e0XX6hPZIikaiXaB1AelKf2zFmV5Tnm974ASpW7u7ukixdyKF2XzvGlcw4AZYn23rVkZGRIUoFfxgCAs7vUdl1qy1B6ytM9xokTJ5Sbm6vg4GCr9cHBwUpKSirwPUlJSQWWz8nJ0YkTJwp8zzPPPKPatWurR48eJa5XkmJiYuTv729ZwsPDr3qMAPLjPqXslGWf4dCRGhs3btQbb7yhuLg4JSYmasWKFerXr59l+7Bhw/TRRx9ZvadDhw7aunWr5XVmZqYmTJigJUuW6Pz587rttts0Z84c1alTx1Lm1KlTGj16tL788ktJUp8+fTRr1ixVr169VI8PgOTh4aEqVaooJSVFnp6eqsSzk0pFXl6eUlJSVKVKFXl4MAgPQNmjvXcNxhhlZGQoOTlZ1atXLxdfUgGoeNzd3VW9enUlJydLkqpUqSI3NzcHR+V8MjMzlZeXq9zsLKmQX8/m5mQrLy/XakTAJeX1HuPKvxVjTJF/PwWVL2i9JL3++utasmSJ1q9fLx8fn2uqd+LEiRo3bpzldXp6OokNoAS4TykbZd1nOLRXOnfunFq1aqWHHnrI8izCK/Xq1UsLFiywvPby8rLaPnbsWH311VdaunSpAgMDNX78eN15552Ki4uz3KQ98MADOnr0qFatWiVJeuSRRzR48GB99dVXpXRkAC5xc3NTaGio4uPjlZCQ4OhwXFp29v/+XdAPaytVqqS6detyQ+fK9u+XcnIkDw+pSRNHRwMUC+29a6levbpCQkIcHQZcBN0TnNGlNuxSYgP5ZWdn60TqSXmkZ6hSIUnsvNxc5Zw/o7ycgh+lVJ7uMWrWrCl3d/d8oyOSk5PzjaK4JCQkpMDyHh4eCrzi+cDTp0/XtGnTtG7dOt1www3XVK8keXt7y9v72ufB+PNPT50510B/JtdQw+C0a94f4Gq4Tyk7ZdlnODSpER0drejo6CLLeHt7F3rDlZaWpvnz5+vjjz+2DOtbvHixwsPDtW7dOvXs2VP79u3TqlWrtHXrVnXo0EHSxUmbOnXqpP3796sJV+VAqfPy8lLjxo0Z6neNunaVjh+XgoOlDRvyb/fy8uIXB67uttukY8ek2rWlo0cdHQ1QbLT3rsHT05MRGigWuic4o0tfUgUFBSn78l//wOLw4cOaOed91e56v6oFFvy9ytnUJB3bsEQzX5msunXr5ttenu4xvLy8FBkZqbVr16p///6W9WvXrlXfvn0LfE+nTp3y/SB2zZo1ateunVUS6I033tDUqVO1evVqtWvX7prrtafBg8N0/Pi/9dsfJ5Q057FSrw9wRtynlI2y7DOcfvzg+vXrFRQUpOrVq6tr16565ZVXFBQUJEmKi4tTdna21aRNYWFhatGihTZv3qyePXtqy5Yt8vf3tyQ0JKljx47y9/fX5s2bC01q2GtCJgAXVapUKd/wWxTPsWMXl5wciVMJwFnR3qO8iImJ0eeff67ffvtNlStXVufOnfXaa69Z3T8YYzRlyhS9//77OnXqlDp06KB33nlHzZs3d2DkQMXi7u5OorYQ7u7uOp6cosoZOcopZH7o9IwcHU9Okbu7e4Xov8eNG6fBgwerXbt26tSpk95//30dPnxYI0eOlHTxkU/Hjh3TokWLJEkjR47U7NmzNW7cOI0YMUJbtmzR/PnztWTJEss+X3/9dT3//PP69NNPVa9ePcuIjGrVqqlatWo21Qug9HGfUr44dbo9Ojpan3zyib799lvNmDFDP/30k2699VZLsiEpKUleXl6qUaOG1fsun2wpKSnJkgS5XFBQEBMyAQAAACjQhg0b9Nhjj2nr1q1au3atcnJyFBUVpXPnzlnKvP7665o5c6Zmz56tn376SSEhIbr99tt15swZB0YOACjMoEGDFBsbq5deekmtW7fWxo0btXLlSkVEREiSEhMTdfjwYUv5+vXra+XKlVq/fr1at26tl19+WW+//bbVI9TnzJmjrKws3XPPPQoNDbUs06dPt7leAEDxOPVIjUGDBln+3aJFC7Vr104RERH6+uuvdffddxf6visnWyroOV5MyAQAAACgMJfm47tkwYIFCgoKUlxcnG655RYZYxQbG6tJkyZZ7k0++ugjBQcH69NPP9Wjjz7qiLABAFcxatQojRo1qsBtCxcuzLeua9eu2rFjR6H7O3To0DXXCwAoHqceqXGl0NBQRURE6MCBA5IuTtiUlZWlU6dOWZW7fLKlkJAQHT9+PN++UlJSrjohk5+fn9UCAAAAoGJKS7s4uWpAQIAkKT4+XklJSVaPwvX29lbXrl21efPmAveRmZmp9PR0qwUAAABA8bhUUiM1NVVHjhxRaGioJCkyMlKenp5au3atpUxiYqJ+/fVXde7cWdLFSZ3S0tL0448/Wsps27ZNaWlpljIAAFwyefJkubm5WS0hIQVPrAgAqBiMMRo3bpxuvvlmtWjRQpIsj7K98odSlz8K90o84hYAAAC4dg59/NTZs2f1xx9/WF7Hx8dr165dCggIUEBAgCZPnqwBAwYoNDRUhw4d0rPPPquaNWuqf//+kiR/f38NHz5c48ePV2BgoAICAjRhwgS1bNlSPXr0kCQ1bdpUvXr10ogRI/Tee+9Jkh555BHdeeedhU4SDgCo2Jo3b65169ZZXjP5JABUbI8//rh++eUXbdq0Kd+2Kx9pW9RjbnnELQAAAHDtHJrU2L59u7p37255fekCf+jQoZo7d652796tRYsW6fTp0woNDVX37t21bNky+fr6Wt7z5ptvysPDQwMHDtT58+d12223aeHChVZfQH3yyScaPXq0ZWh4nz59NHv27DI6SgCAq/Hw8GB0BgBAkvTEE0/oyy+/1MaNG1WnTh3L+kv9RFJSkmUkuWT9KNwreXt7y9vbu3QDBgAAAMo5hyY1unXrJmNModtXr1591X34+Pho1qxZmjVrVqFlAgICtHjx4hLFCACoeA4cOKCwsDB5e3urQ4cOmjZtmho0aODosAAAZcgYoyeeeEIrVqzQ+vXrVb9+favt9evXV0hIiNauXas2bdpIkrKysrRhwwa99tprjggZAAAAqBAcmtQAAMDZdOjQQYsWLdJ1112n48ePa+rUqercubP27NmjwMDAAt+TmZmpzMxMy2smfgUA1/fYY4/p008/1f/93//J19fXMk+Gv7+/KleuLDc3N40dO1bTpk1T48aN1bhxY02bNk1VqlTRAw884ODoAQAAgPKLpAYAuIiffpJycyWmdyhd0dHRln+3bNlSnTp1UsOGDfXRRx9ZPQf9cjExMZoyZcq1V86HDABOY+7cuZIuji6/3IIFCzRs2DBJ0lNPPaXz589r1KhROnXqlDp06KA1a9ZYPS63PKB7AgDXtWLFUT3/5D/0cnTBP9ACAFdEUgMAXMRlj+tGGapatapatmypAwcOFFrGbhO/8iEDgNMo6jG5l7i5uWny5MmaPHly6QfkQHRPAOC6goJyVdk7WUF+lSRVc3Q4AGAXJDUAAChCZmam9u3bpy5duhRaholfAQAAAAAAykYlRwcAAIAzmTBhgjZs2KD4+Hht27ZN99xzj9LT0zV06FBHhwYAAAAAAFDhMVIDAFzE++9LZ89K1apJjzzi6GjKr6NHj+r+++/XiRMnVKtWLXXs2FFbt25VRERE6VfOhwwAcEJ0TwDgupYu9dWfRx/U0i2emtRvk6PDAQC7IKkBAC7ipZekY8ek2rX5QqE0LV261HGV8yEDAJwQ3RMAuK5ZswJ0/Pg4zUo9QVIDQLnB46cAAAAAAAAAAIBLIKkBAAAAAAAAAABcAkkNAAAAAAAAAADgEkhqAAAAAAAAAAAAl0BSAwAAAAAAAAAAuASSGgAAAAAAAAAAwCWQ1AAAAAAAAAAAAC6BpAYAAAAAAAAAAHAJHo4OAABgm+uuk/z9peBgR0eCUsOHDABwQnRPAOC66tfP1rkzCapf66yjQwEAuyGpAQAu4ttvHR0BSh0fMgDACdE9AYDrWrz4L439+0DF9guRVM3R4QCAXfD4KQAAAAAAAAAA4BJIagAAAAAAAAAAAJdAUgMAAAAAAAAAALgE5tQAABfx4IPSiRNSzZrSJ584OhqUCj5kAIATonsCANc1blyQtu2erXFnL+j/xr/r6HAAwC5IagCAi9iwQTp2TKpd29GRoNTwIQMAnBDdEwC4rm3bKivlVCdtyzvh6FAAwG4c+vipjRs36q677lJYWJjc3Nz0xRdfWLZlZ2fr6aefVsuWLVW1alWFhYVpyJAh+uuvv6z20a1bN7m5uVkt9913n1WZU6dOafDgwfL395e/v78GDx6s06dPl8ERAgAAAAAAAAAAe3FoUuPcuXNq1aqVZs+enW9bRkaGduzYoeeff147duzQ559/rt9//119+vTJV3bEiBFKTEy0LO+9957V9gceeEC7du3SqlWrtGrVKu3atUuDBw8uteMCAAAAAAAAAAD259DHT0VHRys6OrrAbf7+/lq7dq3VulmzZunGG2/U4cOHVbduXcv6KlWqKCQkpMD97Nu3T6tWrdLWrVvVoUMHSdK8efPUqVMn7d+/X02aNLHT0QAAAAAAAAAAgNLk0JEaxZWWliY3NzdVr17dav0nn3yimjVrqnnz5powYYLOnDlj2bZlyxb5+/tbEhqS1LFjR/n7+2vz5s2F1pWZman09HSrBQAAAAAAAAAAOI7LTBR+4cIFPfPMM3rggQfk5+dnWf/ggw+qfv36CgkJ0a+//qqJEyfq559/tozySEpKUlBQUL79BQUFKSkpqdD6YmJiNGXKFPsfCAAAAAAAAAAAKBGXSGpkZ2frvvvuU15enubMmWO1bcSIEZZ/t2jRQo0bN1a7du20Y8cOtW3bVpLk5uaWb5/GmALXXzJx4kSNGzfO8jo9PV3h4eHXeigAAAAAAAAAAKCEnD6pkZ2drYEDByo+Pl7ffvut1SiNgrRt21aenp46cOCA2rZtq5CQEB0/fjxfuZSUFAUHBxe6H29vb3l7e19z/AAAAAAAAAAAwD6cek6NSwmNAwcOaN26dQoMDLzqe/bs2aPs7GyFhoZKkjp16qS0tDT9+OOPljLbtm1TWlqaOnfuXGqxAwAAAAAAAAAA+3LoSI2zZ8/qjz/+sLyOj4/Xrl27FBAQoLCwMN1zzz3asWOH/vOf/yg3N9cyB0ZAQIC8vLx08OBBffLJJ+rdu7dq1qypvXv3avz48WrTpo1uuukmSVLTpk3Vq1cvjRgxQu+9954k6ZFHHtGdd96pJk2alP1BA0AJjRghpaVJ/v6OjgSlhg8ZAOCE6J4AwHUNGpSur5av1F0tHB0JANiPQ5Ma27dvV/fu3S2vL81hMXToUE2ePFlffvmlJKl169ZW7/vuu+/UrVs3eXl56ZtvvtFbb72ls2fPKjw8XHfccYdefPFFubu7W8p/8sknGj16tKKioiRJffr00ezZs0v56ADAvl580dERoNTxIQMAnBDdEwC4rtGjT+nPXW9qdM8QSdUcHQ4A2IVDkxrdunWTMabQ7UVtk6Tw8HBt2LDhqvUEBARo8eLFxY4PAAAAAAAAAAA4D6eeUwMAAAAAAAAAAOASkhoAABQhJiZGbm5uGjt2rKNDAQAAAAAAqPBIagCAi6hTR3Jzu/hflI2ffvpJ77//vm644YayqZAPGQDghOieAJQnc+bMUf369eXj46PIyEh9//33RZbfsGGDIiMj5ePjowYNGujdd9+12r5nzx4NGDBA9erVk5ubm2JjY/PtY/LkyXJzc7NaQkJC7HlYhbrppgj9Z2OcbpryUZnUBwBlgaQGAAAFOHv2rB588EHNmzdPNWrUcHQ4AAAAAK7RsmXLNHbsWE2aNEk7d+5Uly5dFB0drcOHDxdYPj4+Xr1791aXLl20c+dOPfvssxo9erSWL19uKZORkaEGDRro1VdfLTJR0bx5cyUmJlqW3bt32/34AKCiIKkBAEABHnvsMd1xxx3q0aOHo0MBAAAAYAczZ87U8OHD9fDDD6tp06aKjY1VeHi45s6dW2D5d999V3Xr1lVsbKyaNm2qhx9+WH//+981ffp0S5n27dvrjTfe0H333Sdvb+9C6/bw8FBISIhlqVWrlt2PDwAqCpIaAABcYenSpdqxY4diYmJsKp+Zman09HSrBQAAAIDzyMrKUlxcnKKioqzWR0VFafPmzQW+Z8uWLfnK9+zZU9u3b1d2dnax6j9w4IDCwsJUv3593Xffffrzzz+LLM89BgAUjqQGAACXOXLkiMaMGaPFixfLx8fHpvfExMTI39/fsoSHh5dylAAAAACK48SJE8rNzVVwcLDV+uDgYCUlJRX4nqSkpALL5+Tk6MSJEzbX3aFDBy1atEirV6/WvHnzlJSUpM6dOys1NbXQ93CPAQCF83B0AAAAOJO4uDglJycrMjLSsi43N1cbN27U7NmzlZmZKXd3d6v3TJw4UePGjbO8Tk9Pd/hNR3ZWlhISEoos4+fnx7B3AAAAVChubm5Wr40x+dZdrXxB64sSHR1t+XfLli3VqVMnNWzYUB999JHVfcTlnPEeAwCcBUkNAAAuc9ttt+WbtO+hhx7S9ddfr6effjpfQkOSvL29i3x+blnLTDuthD8PasyUl4uMK7BKZS2e9z6JDQAowMaNG/XGG28oLi5OiYmJWrFihfr162fZPmzYMH300UdW7+nQoYO2bt1axpECAGxRs2ZNubu75xuVkZycnG80xiUhISEFlvfw8FBgYGCJY6latapatmypAwcOFFrG2e4xAMCZkNQAAOAyvr6+atGihdW6qlWrKjAwMN96Z5WTcU65Hp6qMXCwAurVL7DMuaREpS5bpPT0dJIaAFCAc+fOqVWrVnrooYc0YMCAAsv06tVLCxYssLz28vIqq/AAAMXk5eWlyMhIrV27Vv3797esX7t2rfr27Vvgezp16qSvvvrKat2aNWvUrl07eXp6ljiWzMxM7du3T126dCnxPgCgIiOpAQBAOVU5KES+4RGFbj9VhrEAgKuJjo62elxIQby9vRUSElJGEQEArtW4ceM0ePBgtWvXTp06ddL777+vw4cPa+TIkZIuPvLp2LFjWrRokSRp5MiRmj17tsaNG6cRI0Zoy5Ytmj9/vpYsWWLZZ1ZWlvbu3Wv597Fjx7Rr1y5Vq1ZNjRo1kiRNmDBBd911l+rWravk5GRNnTpV6enpGjp0aBmfAQAoH0hqAABwFevXr3d0CAAAJ7R+/XoFBQWpevXq6tq1q1555RUFBQUVWj4zM1OZmZmW1+np6WURJgDg/xs0aJBSU1P10ksvKTExUS1atNDKlSsVEXHxh0CJiYk6fPiwpXz9+vW1cuVKPfnkk3rnnXcUFhamt99+22oE319//aU2bdpYXk+fPl3Tp09X165dLfcRR48e1f33368TJ06oVq1a6tixo7Zu3WqpFwBQPCQ1AMBFLF4sZWZKPFa1HONDBgCXER0drXvvvVcRERGKj4/X888/r1tvvVVxcXGFPgM9JiZGU6ZMKeNIrx3dE4DyZNSoURo1alSB2xYuXJhvXdeuXbVjx45C91evXj3L5OGFWbp0abFitKcZM47r7VdjNPqWag6LAQDsjaQGALiIbt0cHQFKHR8yALiMQYMGWf7dokULtWvXThEREfr666919913F/ieiRMnaty4cZbX6enpCg8PL/VYrxXdEwC4ro4dL2hpwBZ1bBQiicQGgPKBpAYAAAAAXKPQ0FBFRETowIEDhZbx9vYudBQHAAAAANtUcnQAAAAAAODqUlNTdeTIEYWGhjo6FAAAAKBcY6QGALiI9ev/9zxrHgNRTvEhA4DTOHv2rP744w/L6/j4eO3atUsBAQEKCAjQ5MmTNWDAAIWGhurQoUN69tlnVbNmTfXv39+BUZcOuicAcF1bt/oo+WQnbf2jmhoGxzs6HACwC5IaAOAi/vY36dgxqXZt6ehRR0eDUsGHDABOY/v27erevbvl9aW5MIYOHaq5c+dq9+7dWrRokU6fPq3Q0FB1795dy5Ytk6+vr6NCLjV0TwDgusaPD9bx47M1/sgJPXjTY44OBwDsgqQGAAAAAFyhW7duMsYUun316tVlGA0AAACAS5hTAwAAAAAAAAAAuASSGgAAAAAAAAAAwCWQ1AAAAAAAAAAAAC6hREmN+Ph4u1S+ceNG3XXXXQoLC5Obm5u++OILq+3GGE2ePFlhYWGqXLmyunXrpj179liVyczM1BNPPKGaNWuqatWq6tOnj45eMXvdqVOnNHjwYPn7+8vf31+DBw/W6dOn7XIMAADnYa/+CQDg2ugPAKB8oV0HAFyuREmNRo0aqXv37lq8eLEuXLhQ4srPnTunVq1aafbs2QVuf/311zVz5kzNnj1bP/30k0JCQnT77bfrzJkzljJjx47VihUrtHTpUm3atElnz57VnXfeqdzcXEuZBx54QLt27dKqVau0atUq7dq1S4MHDy5x3AAA52Sv/gkA4NroDwCgfKFdBwBcrkRJjZ9//llt2rTR+PHjFRISokcffVQ//vhjsfcTHR2tqVOn6u677863zRij2NhYTZo0SXfffbdatGihjz76SBkZGfr0008lSWlpaZo/f75mzJihHj16qE2bNlq8eLF2796tdevWSZL27dunVatW6YMPPlCnTp3UqVMnzZs3T//5z3+0f//+khw+AMBJ2at/AgC4NvoDAChfaNcBAJcrUVKjRYsWmjlzpo4dO6YFCxYoKSlJN998s5o3b66ZM2cqJSXlmgOLj49XUlKSoqKiLOu8vb3VtWtXbd68WZIUFxen7OxsqzJhYWFq0aKFpcyWLVvk7++vDh06WMp07NhR/v7+ljIFyczMVHp6utUCAHBuZdE/AQCcH/0BAJQvtOsAgMtd00ThHh4e6t+/v/71r3/ptdde08GDBzVhwgTVqVNHQ4YMUWJiYon3nZSUJEkKDg62Wh8cHGzZlpSUJC8vL9WoUaPIMkFBQfn2HxQUZClTkJiYGMscHP7+/goPDy/xsQAAylZp9k8AANdBfwAA5QvtOgBAusakxvbt2zVq1CiFhoZq5syZmjBhgg4ePKhvv/1Wx44dU9++fa85QDc3N6vXxph86650ZZmCyl9tPxMnTlRaWpplOXLkSDEjBwD7OnpUMubif1G0suifSkPKzp06+McfOrhhgw4ePFjgkpCQoJycHEeHCgAuwVX7A2fDNQgAZ0G7Xnw//JCgO2+J1A8vDnV0KABgNx4ledPMmTO1YMEC7d+/X71799aiRYvUu3dvVap0MUdSv359vffee7r++utLHFhISIikiyMtQkNDLeuTk5MtozdCQkKUlZWlU6dOWY3WSE5OVufOnS1ljh8/nm//KSkp+UaBXM7b21ve3t4ljh8AUPbKon8qLSkpKfrbiEeUmnG+yHKZGRk6kpSk2tnZZRQZALgeV+4PAAD50a4DAC5XoqTG3Llz9fe//10PPfSQJflwpbp162r+/PklDqx+/foKCQnR2rVr1aZNG0lSVlaWNmzYoNdee02SFBkZKU9PT61du1YDBw6UJCUmJurXX3/V66+/Lknq1KmT0tLS9OOPP+rGG2+UJG3btk1paWmWxAcAoHwoi/6ptKSnpys147xqDBqiqiGhhZZL+WWXcue9o5xsRmsAQGFcuT8AAORHuw4AuFyJkhpr165V3bp1LRnxS4wxOnLkiOrWrSsvLy8NHVr00LazZ8/qjz/+sLyOj4/Xrl27FBAQoLp162rs2LGaNm2aGjdurMaNG2vatGmqUqWKHnjgAUmSv7+/hg8frvHjxyswMFABAQGaMGGCWrZsqR49ekiSmjZtql69emnEiBF67733JEmPPPKI7rzzTjVp0qQkhw8AcFL26p8cqWpIqHzDIwrdfi7xWBlGAwCuqTz0BwCA/6FdBwBcrkRJjYYNGyoxMTHfBNwnT55U/fr1lZuba9N+tm/fru7du1tejxs3TpI0dOhQLVy4UE899ZTOnz+vUaNG6dSpU+rQoYPWrFkjX19fy3vefPNNeXh4aODAgTp//rxuu+02LVy4UO7u7pYyn3zyiUaPHq2oqChJUp8+fTR79uySHDoAOMyUKVJamuTvL734oqOjcU726p8cZcTBPxQyb45yw2prxcjRjg4HAFyWq/cHzoZrEACORrtecm+/XUN7Dj6pt1dLbw35ytHhAIBdlCipYYwpcP3Zs2fl4+Nj8366detW6L6kixN8T548WZMnTy60jI+Pj2bNmqVZs2YVWiYgIECLFy+2OS4AcEbz5knHjkm1a/OFQmHs1T85Sr9jRxX850GdDAohqQEA18DV+wNnwzUIAEejXS+5Zcv8dPz437Ts7AmSGgDKjWIlNS6NpHBzc9MLL7ygKlWqWLbl5uZq27Ztat26tV0DBADgauifAAAS/QEAlDe06wCAghQrqbFz505JFzPku3fvlpeXl2Wbl5eXWrVqpQkTJtg3QgAAroL+CQAg0R8AQHlDuw4AKEixkhrfffedJOmhhx7SW2+9JT8/v1IJCgCA4qB/AgBI9AcAUN7QrgMAClKiOTUWLFhg7zgAALhm9E8AAIn+AADKG9p1AMDlbE5q3H333Vq4cKH8/Px09913F1n2888/v+bAAACwBf0TAECiPwCA8oZ2HQBQGJuTGv7+/nJzc7P8GwAAZ0D/BACQ6A8AoLyhXQcAFMbmpMblQ/0Y9gcAcBb27p/mzp2ruXPn6tChQ5Kk5s2b64UXXlB0dPQ17xsAUHq4XwGA8oV2HQBQmEoledP58+eVkZFheZ2QkKDY2FitWbPGboEBAFBc9uif6tSpo1dffVXbt2/X9u3bdeutt6pv377as2dPaYQMACgF3K8AQPlCuw4AuFyJJgrv27ev7r77bo0cOVKnT5/WjTfeKC8vL504cUIzZ87UP/7xD3vHCQAVXteu0okTUs2ajo7Eedmjf7rrrrusXr/yyiuaO3eutm7dqubNm5dW6JKkHTUCFBJaW+dDw0q1HgAo77hfsS+uQQA4Gu16yXXocF5bvv9VHepfcHQoAGA3JRqpsWPHDnXp0kWS9NlnnykkJEQJCQlatGiR3n77bbsGCAC46JNPpNWrL/4XBbN3/5Sbm6ulS5fq3Llz6tSpk73Dzef5ljdoysuva27Mm6VeFwCUZ9yv2BfXIAAcjXa95GbOTFaHlo9r5t+mOzoUALCbEo3UyMjIkK+vryRpzZo1uvvuu1WpUiV17NhRCQkJdg0QAABb2at/2r17tzp16qQLFy6oWrVqWrFihZo1a1Zo+czMTGVmZlpep6enl/wgAADXjPsVAChfaNcBAJcr0UiNRo0a6YsvvtCRI0e0evVqRUVFSZKSk5Pl5+dn1wABALCVvfqnJk2aaNeuXdq6dav+8Y9/aOjQodq7d2+h5WNiYuTv729ZwsPDr/lYAAAlx/0KAJQvtOsAgMuVKKnxwgsvaMKECapXr546dOhgeSTHmjVr1KZNG7sGCACArezVP3l5ealRo0Zq166dYmJi1KpVK7311luFlp84caLS0tIsy5EjR675WAAAJcf9CgCUL7TrAIDLlejxU/fcc49uvvlmJSYmqlWrVpb1t912m/r372+34AAA/3PrrdLx41JwsPTtt46OxjmVVv9kjLF6vNSVvL295e3tXeL9XzJ3+08KHvWQzgaHKuYDHlwOACXF/Yp9cQ0CwNFo10vub38L06+//Et/++ustkyZ5uhwAMAuSpTUkKSQkBCFhIRYrbvxxhuvOSAAQMF+/106dkxKS3N0JM7tWvunZ599VtHR0QoPD9eZM2e0dOlSrV+/XqtWrbJ3qPnUzTin4FMndfJC4QkUAIBtuF+xH65BADgD2vWSiY/31NmMhopPOeHoUADAbkqU1Dh37pxeffVVffPNN0pOTlZeXp7V9j///NMuwQEAUBz26J+OHz+uwYMHKzExUf7+/rrhhhu0atUq3X777aUVNgDAzrhfAYDyhXYdAHC5EiU1Hn74YW3YsEGDBw9WaGio3Nzc7B0XAADFZo/+af78+aUQGQCgLHG/AgDlC+06AOByJUpq/Pe//9XXX3+tm266yd7xAABQYvRPAACJ/gAAyhvadQDA5SqV5E01atRQQECAvWMBAOCa0D8BACT6AwAob2jXAQCXK1FS4+WXX9YLL7ygjIwMe8cDAECJ0T8BACT6AwAob+zZrs+ZM0f169eXj4+PIiMj9f333xdZfsOGDYqMjJSPj48aNGigd99912r7nj17NGDAANWrV09ubm6KjY21S70AgMKV6PFTM2bM0MGDBxUcHKx69erJ09PTavuOHTvsEhwAAMVB/wQAkOgPAKC8sVe7vmzZMo0dO1Zz5szRTTfdpPfee0/R0dHau3ev6tatm698fHy8evfurREjRmjx4sX64YcfNGrUKNWqVUsDBgyQJGVkZKhBgwa699579eSTT9qlXgBA0UqU1OjXr5+dwwAA4NrRPwEAJPoDAChv7NWuz5w5U8OHD9fDDz8sSYqNjdXq1as1d+5cxcTE5Cv/7rvvqm7dupbRF02bNtX27ds1ffp0S1Kjffv2at++vSTpmWeesUu9AICilSip8eKLL9o7jkLVq1dPCQkJ+daPGjVK77zzjoYNG6aPPvrIaluHDh20detWy+vMzExNmDBBS5Ys0fnz53Xbbbdpzpw5qlOnTqnHDwAoO2XZPwEAnBf9AQCUL/Zo17OyshQXF5cv8RAVFaXNmzcX+J4tW7YoKirKal3Pnj01f/58ZWdn5xsxYq96AQBFK9GcGpJ0+vRpffDBB5o4caJOnjwp6eJwv2PHjtktOEn66aeflJiYaFnWrl0rSbr33nstZXr16mVVZuXKlVb7GDt2rFasWKGlS5dq06ZNOnv2rO68807l5ubaNVYAKE0vvCDNmHHxvyhcWfVPpeGDBg314fB/6ItHH3d0KADg8q61P9i4caPuuusuhYWFyc3NTV988YXVdmOMJk+erLCwMFWuXFndunXTnj177H0YToFrEADO4Frb9RMnTig3N1fBwcFW64ODg5WUlFTge5KSkgosn5OToxMnTpRavdLFH+imp6dbLSXxxBMn1azBTD0RtaRE7wcAZ1SikRq//PKLevToIX9/fx06dEgjRoxQQECAVqxYoYSEBC1atMhuAdaqVcvq9auvvqqGDRuqa9eulnXe3t4KCQkp8P1paWmaP3++Pv74Y/Xo0UOStHjxYoWHh2vdunXq2bOn3WIFgNL0yCOOjsD5lWX/VBpW1AlXnf73yjc8wtGhAIBLs0d/cO7cObVq1UoPPfSQ5REjl3v99dc1c+ZMLVy4UNddd52mTp2q22+/Xfv375evr29pHJbDcA0CwNHseZ3v5uZm9doYk2/d1coXtN7e9cbExGjKlCnFqqMg9913RlvXfKL7OoVIqnbN+wMAZ1CikRrjxo3TsGHDdODAAfn4+FjWR0dHa+PGjXYL7kpZWVlavHix/v73v1s1/OvXr1dQUJCuu+46jRgxQsnJyZZtcXFxys7OthouGBYWphYtWhQ5zM9eGXEAQNlxVP8EAHAu9ugPoqOjNXXqVN199935thljFBsbq0mTJunuu+9WixYt9NFHHykjI0Offvqp3Y4DAHCRPdr1mjVryt3dPd/oiOTk5HyjKC4JCQkpsLyHh4cCAwNLrV5JmjhxotLS0izLkSNHbKoPACqCEiU1fvrpJz366KP51teuXbvIoXPX6osvvtDp06c1bNgwy7ro6Gh98skn+vbbbzVjxgz99NNPuvXWW5WZmSnp4lBBLy8v1ahRw2pfVxvmFxMTI39/f8sSHh5eKscEALAfR/VPAADnUtr9QXx8vJKSkqx+OOXt7a2uXbvyfHQAKAX2aNe9vLwUGRlpeaz5JWvXrlXnzp0LfE+nTp3ylV+zZo3atWtn03waJa1Xutiv+Pn5WS0AgItK9PgpHx+fAkcu7N+/P9/jouxp/vz5io6OVlhYmGXdoEGDLP9u0aKF2rVrp4iICH399dcF/qrqkqsN85s4caLGjRtneZ2enk5iA4BDJSZKubmSu7sUGuroaJyTo/onewnMzFTgiRRV9qmstFpBjg4HAFxWafcHl75AK+j56AkJCYW+LzMz0/LjK0kuMxqcaxAAjmavdn3cuHEaPHiw2rVrp06dOun999/X4cOHNXLkSEkXvws6duyY5XFWI0eO1OzZszVu3DiNGDFCW7Zs0fz587Vkyf/mp8jKytLevXst/z527Jh27dqlatWqqVGjRjbVW5qSk911PjNIyek11DA4u9TrA4CyUKKRGn379tVLL72k7OyLjaGbm5sOHz6sZ555psDnzdpDQkKC1q1bp4cffrjIcqGhoYqIiNCBAwckXRwqmJWVpVOnTlmVu9owPzLiAJxN+/ZSePjF/6Jgjuif7GnRti2aP2yQXnqgv6NDAQCXVlb9QUmej+6Ko8G5BgHgaPZq1wcNGqTY2Fi99NJLat26tTZu3KiVK1cqIuLinHaJiYk6fPiwpXz9+vW1cuVKrV+/Xq1bt9bLL7+st99+26rOv/76S23atFGbNm2UmJio6dOnq02bNlbfX12t3tLUv38dfbPtv+r/Zmyp1wUAZaVESY3p06crJSVFQUFBOn/+vLp27apGjRrJ19dXr7zyir1jlCQtWLBAQUFBuuOOO4osl5qaqiNHjij0//+EKDIyUp6enlbD/BITE/Xrr78WOcwPAOB6HNE/AQCcT2n3ByEhIZLE89EBoIzYs10fNWqUDh06pMzMTMXFxemWW26xbFu4cKHWr19vVb5r167asWOHMjMzFR8fn290Rb169WSMybdcuZ+i6gUAFE+JHj/l5+enTZs26bvvvlNcXJzy8vLUtm1b9ejRw97xSZLy8vK0YMECDR06VB4e/wv57Nmzmjx5sgYMGKDQ0FAdOnRIzz77rGrWrKn+/S/+ytXf31/Dhw/X+PHjFRgYqICAAE2YMEEtW7YstXgBAI5R1v0TAMA5lXZ/UL9+fYWEhGjt2rVq06aNpIuPHNmwYYNee+21Qt/n7e0tb29vu8QAABUJ1/kAgMsVO6mRl5enhQsX6vPPP9ehQ4fk5uZmuai/2nDrklq3bp0OHz6sv//971br3d3dtXv3bi1atEinT59WaGiounfvrmXLlsnX19dS7s0335SHh4cGDhyo8+fP67bbbtPChQvl7u5u91gBAI7hiP4JAOB87NUfnD17Vn/88YfldXx8vHbt2qWAgADVrVtXY8eO1bRp09S4cWM1btxY06ZNU5UqVfTAAw+U1qEBQIXEdT4A4ErFSmoYY9SnTx+tXLlSrVq1UsuWLWWM0b59+zRs2DB9/vnn+uKLL+weZFRUlIwx+dZXrlxZq1evvur7fXx8NGvWLM2aNcvusQEAHM9R/RMAwLnYsz/Yvn27unfvbnk9btw4SdLQoUO1cOFCPfXUUzp//rxGjRqlU6dOqUOHDlqzZo3Vj6sAANeG63wAQEGKldRYuHChNm7cqG+++cbqAl+Svv32W/Xr10+LFi3SkCFD7BokAABFoX8CAEj27Q+6detW4A+rLnFzc9PkyZM1efLkaw0bAFAIrvMBAAUp1kThS5Ys0bPPPpuvI5GkW2+9Vc8884w++eQTuwUHAIAt6J8AABL9AQCUN7TrAICCFCup8csvv6hXr16Fbo+OjtbPP/98zUEBAFAc9E8AAIn+AADKG9p1AEBBipXUOHnypIKDgwvdHhwcrFOnTl1zUAAAFAf9EwBAoj8AgPKGdh0AUJBiJTVyc3Pl4VH4NBzu7u7Kycm55qAAACgO+icAgER/AADlDe06AKAgxZoo3BijYcOGydvbu8DtmZmZdgkKAJDfN99IOTlSEdf0FVZ56Z9GRbZX2IMPyadOXUeHAgAuqbz0B86GaxAAjkK7fu0+/vgvvTJxvCbdXsPRoQCA3RTrsnTo0KFXLTNkyJASBwMAKFyTJo6OwHmVl/4poWpV5UbUl294hKNDAQCXVF76A2fDNQgAR6Fdv3YNGmTLt+qfahAUIqmao8MBALsoVlJjwYIFpRUHAAAlRv8EAJDoDwCgvKFdBwAUpFhzagAAAAAAAAAAADgKT0UFABfx6adSRoZUpYr0wAOOjgaloWfiXwpd/bUqhdXRlt59HB0OAACSuAYBAFf25ZfVdDixn76M89aTveMcHQ4A2AVJDQBwEU89JR07JtWuzRcK5dXoA78r+NfdOhkUQlIDAOA0uAYBANf12muBOn78eb2WfIKkBoByg8dPAQAAAAAAAAAAl0BSAwCAy8TExKh9+/by9fVVUFCQ+vXrp/379zs6LAAAAAAAAIikBgAAVjZs2KDHHntMW7du1dq1a5WTk6OoqCidO3fO0aEBAAAAAABUeMypAQDAZVatWmX1esGCBQoKClJcXJxuueUWB0UFAAAAAAAAiaQGAABFSktLkyQFBAQUWiYzM1OZmZmW1+np6aUeFwAAAAAAQEXE46cAACiEMUbjxo3TzTffrBYtWhRaLiYmRv7+/pYlPDy8DKMEAAAAAACoOEhqAABQiMcff1y//PKLlixZUmS5iRMnKi0tzbIcOXKkjCIEAAAAAACoWHj8FAAABXjiiSf05ZdfauPGjapTp06RZb29veXt7V1GkQEAAAAAAFRcJDUAwEWEhFj/F6XDGKMnnnhCK1as0Pr161W/fv0yqzvVy1vu1XyVXrNmmdUJAMDVcA0CAK6rVq0cpZ1KVS1f5v0DUH6Q1AAAF7F9u6MjqBgee+wxffrpp/q///s/+fr6KikpSZLk7++vypUrl2rdQzp2Up0xT8s3PKJU6wEAoDi4BgEA1/XFF8c09u8DFdsvRFI1R4cDAHbBnBoAAFxm7ty5SktLU7du3RQaGmpZli1b5ujQAAAAAAAAKjynTmpMnjxZbm5uVkvIZWOejTGaPHmywsLCVLlyZXXr1k179uyx2kdmZqaeeOIJ1axZU1WrVlWfPn109OjRsj4UAICLMMYUuAwbNszRoQEAAAAAAFR4Tp3UkKTmzZsrMTHRsuzevduy7fXXX9fMmTM1e/Zs/fTTTwoJCdHtt9+uM2fOWMqMHTtWK1as0NKlS7Vp0yadPXtWd955p3Jzcx1xOAAAAAAAAAAAoIScfk4NDw8Pq9EZlxhjFBsbq0mTJunuu++WJH300UcKDg7Wp59+qkcffVRpaWmaP3++Pv74Y/Xo0UOStHjxYoWHh2vdunXq2bNnmR4LAFyLRx+VTp6UAgKk995zdDQoDc/u3aPgmMnKDAnTghdecXQ4AABI4hoEAFzZc8/VVNze1/RcVraWPLHQ0eEAgF04/UiNAwcOKCwsTPXr19d9992nP//8U5IUHx+vpKQkRUVFWcp6e3ura9eu2rx5syQpLi5O2dnZVmXCwsLUokULS5nCZGZmKj093WoBAEf6+mvps88u/hfl000nUnTTDxvV+vv1jg4FAAALrkEAwHV9911VJZ7ooe/2tXd0KABgN06d1OjQoYMWLVqk1atXa968eUpKSlLnzp2VmpqqpKQkSVJwcLDVe4KDgy3bkpKS5OXlpRo1ahRapjAxMTHy9/e3LOHh4XY8MgAAAAAAAAAAUFxOndSIjo7WgAED1LJlS/Xo0UNf//+fBn300UeWMm5ublbvMcbkW3clW8pMnDhRaWlpluXIkSMlPAoAAAAAAAAAAGAPTp3UuFLVqlXVsmVLHThwwDLPxpUjLpKTky2jN0JCQpSVlaVTp04VWqYw3t7e8vPzs1oAAAAAAAAAAIDjuFRSIzMzU/v27VNoaKjq16+vkJAQrV271rI9KytLGzZsUOfOnSVJkZGR8vT0tCqTmJioX3/91VIGAAAAAAAAAAC4Bg9HB1CUCRMm6K677lLdunWVnJysqVOnKj09XUOHDpWbm5vGjh2radOmqXHjxmrcuLGmTZumKlWq6IEHHpAk+fv7a/jw4Ro/frwCAwMVEBCgCRMmWB5nBQAAAAAAAAAAXIdTJzWOHj2q+++/XydOnFCtWrXUsWNHbd26VREREZKkp556SufPn9eoUaN06tQpdejQQWvWrJGvr69lH2+++aY8PDw0cOBAnT9/XrfddpsWLlwod3d3Rx0WAAAAAAAAAAAoAadOaixdurTI7W5ubpo8ebImT55caBkfHx/NmjVLs2bNsnN0AAAAAAAAAACgLDl1UgMAAJSe7KwsJSQkFFnGz89PtWrVKqOIAAAAAAAAikZSAwBcxP33S6dOSTVqODoSlJbVIaEKbdJMWaFhpV5XZtppJfx5UGOmvCxvb+9CywVWqazF894nsQEAFRjXIADguu6666xW/2e9el6f6+hQAMBuSGoAgIt44w1HR4DS9vZ1TVRn9AT5hkeUel05GeeU6+GpGgMHK6Be/QLLnEtKVOqyRUpPTyepAQAVGNcgAOC6nnkmVUm/v6xn+oRIqubocADALkhqAABQgVUOCikyiXKqDGMBAAAAAAC4mkqODgAAAAAAAAAoC3PmzFH9+vXl4+OjyMhIff/990WW37BhgyIjI+Xj46MGDRro3XffzVdm+fLlatasmby9vdWsWTOtWLHCavvkyZPl5uZmtYSEhNj1uACgIiGpAQAAAAAAgHJv2bJlGjt2rCZNmqSdO3eqS5cuio6O1uHDhwssHx8fr969e6tLly7auXOnnn32WY0ePVrLly+3lNmyZYsGDRqkwYMH6+eff9bgwYM1cOBAbdu2zWpfzZs3V2JiomXZvXt3qR4rAJRnJDUAwEVcf73k53fxvyifPvthkz6990691vd2R4cCALBBRfnlLdcgAMqLmTNnavjw4Xr44YfVtGlTxcbGKjw8XHPnzi2w/Lvvvqu6desqNjZWTZs21cMPP6y///3vmj59uqVMbGysbr/9dk2cOFHXX3+9Jk6cqNtuu02xsbFW+/Lw8FBISIhlKas566KiwrXqhw2KejX/CBMAcFUkNQDARZw9K505c/G/KJ8q5+aoyvkM+WRkODoUAICNKsIvb7kGAVAeZGVlKS4uTlFRUVbro6KitHnz5gLfs2XLlnzle/bsqe3btys7O7vIMlfu88CBAwoLC1P9+vV133336c8//7zWQ7LJuXOVlJNbTecyK5dJfQBQFpgoHAAAXJOUlBSlp6dftZyfn1+Z/SINAMrKpV/eAgCc24kTJ5Sbm6vg4GCr9cHBwUpKSirwPUlJSQWWz8nJ0YkTJxQaGlpomcv32aFDBy1atEjXXXedjh8/rqlTp6pz587as2ePAgMDC6w7MzNTmZmZlte2XG8DQEVBUgMAAJRYSkqK/jbiEaVmnL9q2cAqlbV43vskNgCUK5d+eevt7a0OHTpo2rRpatCggaPDAgAUws3Nzeq1MSbfuquVv3L91fYZHR1t+XfLli3VqVMnNWzYUB999JHGjRtXYL0xMTGaMmXKVY4GAComkhoAAKDE0tPTlZpxXjUGDVHVkNBCy51LSlTqskVKT08nqQGg3CjuL2/51S0AOE7NmjXl7u6eb1RGcnJyvpEWl4SEhBRY3sPDw9LOF1amsH1KUtWqVdWyZUsdOHCg0DITJ060Snikp6crPDy80PIAUJEwpwYAALhmVUNC5RseUehSVMIDAFxVdHS0BgwYoJYtW6pHjx76+uuvJUkfffRRgeVjYmLk7+9vWfhyCgDKjpeXlyIjI7V27Vqr9WvXrlXnzp0LfE+nTp3ylV+zZo3atWsnT0/PIssUtk/pYpJ73759Cg0t/BrZ29tbfn5+VgsA4CKSGgAAAABgB1f75e3EiROVlpZmWY4cOVLGEQJAxTZu3Dh98MEH+vDDD7Vv3z49+eSTOnz4sEaOHCnpYjs9ZMgQS/mRI0cqISFB48aN0759+/Thhx9q/vz5mjBhgqXMmDFjtGbNGr322mv67bff9Nprr2ndunUaO3aspcyECRO0YcMGxcfHa9u2bbrnnnuUnp6uoUOHltmxA0B5wuOnAAAAAMAOLv3ytkuXLgVu9/b2lre3dxlHBQC4ZNCgQUpNTdVLL72kxMREtWjRQitXrlRERIQkKTExUYcPH7aUr1+/vlauXKknn3xS77zzjsLCwvT2229rwIABljKdO3fW0qVL9dxzz+n5559Xw4YNtWzZMnXo0MFS5ujRo7r//vt14sQJ1apVSx07dtTWrVst9QIAioekBgAAAACUwIQJE3TXXXepbt26Sk5O1tSpU/nlLQA4uVGjRmnUqFEFblu4cGG+dV27dtWOHTuK3Oc999yje+65p9DtS5cuLVaMAICikdQAAAAAgBLgl7cAAABA2SOpAQAu4t13pfPnpcqVHR0JSktM02YK63WX3MOYOBYAXEFF+eUt1yAA4LpefjlF774Zq5GdacQBlB8kNQDARdx5p6MjqBg2btyoN954Q3FxcUpMTNSKFSvUr1+/Mql7U60g1bm5m3zD+YUvAMB5cA0CAK7r1lsz9OXidbq1eYikao4OBwDsopKjAwAAwJmcO3dOrVq10uzZsx0dCgAAAAAAAK7ASA0AAC4THR2t6OhoR4cBAAAAAACAApDUAAAXERcnZWVJXl5SZKSjo8HlMjMzlZmZaXmdnp5eov1cn56m2vv2yOtMug41a2mv8AAAuCZcgwCA6/r1Vy+dSm+pX4/4q2FwkqPDAQC7IKlRBjIzM5WamqqGDRs6OhQALqxvX+nYMal2benoUUdHg8vFxMRoypQp17yfGbt2KnjbVp0MCtGYtT/YITIAAK4d1yAA4LoefTRUx48v1B/xJ9S33WOODgcA7MKp59SIiYlR+/bt5evrq6CgIPXr10/79++3KjNs2DC5ublZLR07drQqk5mZqSeeeEI1a9ZU1apV1adPHx0to6vx1NRU/f77z3r++QlKSUkpkzoBAGVr4sSJSktLsyxHjhxxdEgAAAAAAADlklMnNTZs2KDHHntMW7du1dq1a5WTk6OoqCidO3fOqlyvXr2UmJhoWVauXGm1fezYsVqxYoWWLl2qTZs26ezZs7rzzjuVm5tb6sdw9uxZubvn6MKFtBI/jgQA4Ny8vb3l5+dntQAAAAAAAMD+nPrxU6tWrbJ6vWDBAgUFBSkuLk633HKLZb23t7dCQkIK3EdaWprmz5+vjz/+WD169JAkLV68WOHh4Vq3bp169uxZegcAAAAAAAAAAADsxqmTGldKS0uTJAUEBFitX79+vYKCglS9enV17dpVr7zyioKCgiRJcXFxys7OVlRUlKV8WFiYWrRooc2bN5PUAABYOXv2rP744w/L6/j4eO3atUsBAQGqW7euAyNzjOysLCUkJBS6PSEhQTk5OWUYEQAAAAAAqMhcJqlhjNG4ceN08803q0WLFpb10dHRuvfeexUREaH4+Hg9//zzuvXWWxUXFydvb28lJSXJy8tLNWrUsNpfcHCwkpKSCq0vMzNTmZmZltc8OgoAKobt27ere/fultfjxo2TJA0dOlQLFy50UFSOkZl2Wgl/HtSYKS/L29u74DIZGTqSlKTa2dllHB0AAAAAAKiIXCap8fjjj+uXX37Rpk2brNYPGjTI8u8WLVqoXbt2ioiI0Ndff62777670P0ZY+Tm5lbo9piYGE2ZMuXaAwcAuJRu3brJGOPoMJxCTsY55Xp4qsbAwQqoV7/AMim/7FLuvHeUk81oDQAAAAAAUPqceqLwS5544gl9+eWX+u6771SnTp0iy4aGhioiIkIHDhyQJIWEhCgrK0unTp2yKpecnKzg4OBC9zNx4kSlpaVZliNHjlz7gQAA4IIqB4XINzyiwKVKrVqODg8AAAAAAFQgTj1SwxijJ554QitWrND69etVv37BvxK9XGpqqo4cOaLQ0FBJUmRkpDw9PbV27VoNHDhQkpSYmKhff/1Vr7/+eqH78fb2LvRRGwAAoPiuNj+HJPn5+akWiRIAAAAAAFAIp05qPPbYY/r000/1f//3f/L19bXMgeHv76/KlSvr7Nmzmjx5sgYMGKDQ0FAdOnRIzz77rGrWrKn+/ftbyg4fPlzjx49XYGCgAgICNGHCBLVs2VI9evRw5OEBAFBh2DI/hyQFVqmsxfPeJ7EBAAAAAAAK5NRJjblz50q6+Hzzyy1YsEDDhg2Tu7u7du/erUWLFun06dMKDQ1V9+7dtWzZMvn6+lrKv/nmm/Lw8NDAgQN1/vx53XbbbVq4cKHc3d3L8nAA4Jrs2ycZIxUxHRBc3L2db1btf4xVtTp1HR2K3dkyP8e5pESlLluk9PR0khoA4ES4BgEA17V69WE9PWqYXrszSBLfgwEoH5w6qXG1iVorV66s1atXX3U/Pj4+mjVrlmbNmmWv0ACgzF2Wq0U5leHhofNVqsqjajVHh1JqLs3PUZhThW4BADgK1yAA4LqqVTPy9Dinaj7nJZXf+wwAFYtLTBQOAAAAAAAAAABAUgMAAAAAAAAAALgEp378FADgf2bOlNLTJT8/adw4R0eD0vBgwiGFfrJQuWF1tGrIcEeHAwCAJK5BAMCVzZ/vr/2HHtH89ZU0bdA6R4cDAHZBUgMAXMTMmdKxY1Lt2nyhUF49kHBIwb/v18mgEJIaAACnwTUIALiuDz+sruPHH9WHaSdIagAoN0hqAAAAAICLSUlJUXp6epFl/Pz8VKtWrTKKCAAAACgbJDUAAAAAwIWkpKTobw89rJNnMoosF+BbRYsXfEBiAwAAAOUKSQ0AAOA0srOylJCQUGQZfnkMoKJLT0/XyTMZqtVpgKoGBBdY5tzJ40rZslzp6em0mQAAAChXSGoAAACnkJl2Wgl/HtSYKS/L29u70HKBVSpr8bz3+ZIOQIVXNSBYfkF1Ct2eUoaxAAAAAGWFpAYAAHAKORnnlOvhqRoDByugXv0Cy5xLSlTqskX88hgAAAAAgAqKpAYAAHAqlYNC5BseUej2Uzbsgwl0AQAAAAAon0hqAACAciUlJUV/G/GIUjPOF1mOx1gBAAAAAOB6SGoAAIByJT09XakZ51Vj0BBVDQktsMy5pEQlffyBdu/erYiIwkeFSIzoAAAAAADAmZDUAAAX0batFB4u8d1q+bXf10+n6tVSRiFfxKN4qoaEFvoYK1snJZcY0QEAXIMAgOtq3jxTmRl71bxO0aOYAcCVkNQAABfx5ZeOjgClbVybtqoz5uki55OAlJ2VpYSEhEK3JyQkKCcnp8h92DIpucTE5AAgcQ0CAK7s/feTNPbvDym2X4ikao4OBwDsgqQGAABwGbaMsMjMyNCRpCTVzs6+6v6uNim5ZNvE5AAAAAAAoGyQ1AAAAC7DlhEWKb/sUu68d5STXfRoDQAAAAAA4HpIagAAAJdT1AiLc4nHyjgaKSUlRenp6UWWsWXCcVv2k5WVJS8vr6vGxATnAAAAAIDyiKRGGcnKylFqaqoaNmzo6FAAuKg+faSUlIuTdPJs6/Jp5s4dChr/uDJCQvXm2+87OhzYKCUlRX8b8YhSM4qefPFqE47bsp/srCwdSzikOvUbyMOj6Ms4JjgHYAtbkqmPPBKitDQvhYV5cg0CAC7mkUdCtGPnAj2Sel7fPDvT0eEAgF2Q1CgDubl5OnLkqJ5/foIWL17OlwsASmTHDunYMal2bUdHgtLS5Ey6gk+k6OSpk44OBcWQnp6u1IzzqjFoiKqGhBZY5lxSopI+/kC7d+9WRETBI0wSEhJ0PP2Maj3490L3k/LLLmXOe0d+Ax5ggnMA1ywlJUV/e+hhnTyTUWS5n7d+quysqvrrr1xJ7mUTHADALvbs8dbpMzdoz9ETjg4FAOyGpEYZyMsz8vDI04ULaXy5AABAOVU1JLTQR2IVa4LzwJpXfbQWE5wDsEV2VpYSEhIK3Z6QkKDkk+kKvWWQqgYEF1pu947KUtbF+xpnYq9H/wEAAMC1kNQAAAAoZUxwDqCsZZ5N06H4PzX22cmFJlMvnM/Q0WOJqusbIL+gOoXuq1Il5xudYesokwDfKlq84AMSGwAAAOUISQ0AAIAyUpYTnF/tF9qS/X7BzK+lAeeTnXleeW4eqtnxbgWGFdzuJB/8VQlHPlRuTnYZR3ft0tPTdfJMhmp1GlDoKJNzJ48rZctyRssDAACUMxUqqTFnzhy98cYbSkxMVPPmzRUbG6suXbqUWf1MFg4ArsPRfQZwLWx53JVkn8nE7TVROuDKnLnPqFKjVqGjMM6mJhVrX7m5OTp4sPBkqSOSl1UDgoscZZJiwz5IzAIVS3Hb7A0bNmjcuHHas2ePwsLC9NRTT2nkyJFWZZYvX67nn39eBw8eVMOGDfXKK6+of//+11RvWcvMyi6zH8QAwLWqMEmNZcuWaezYsZozZ45uuukmvffee4qOjtbevXtVt27dUq8/N9cwWTgAuAhH9xlwLrY8kz4nx7keGWXL465smbxckrKysuTl5VXodlsmOLe1Llvqk5zzhtqWL0VtOTZ7lXHGc1ReVYQ+w5g8SdLptHTd9/eRhZaz16OebPn/KSEhwabH9V2tDU9NTdXTz03W2cyiR6vY8zFWztZe2FJGol1B+VDcNjs+Pl69e/fWiBEjtHjxYv3www8aNWqUatWqpQEDBkiStmzZokGDBunll19W//79tWLFCg0cOFCbNm1Shw4dSlRvWUs9m6k/4xP06qQx8i6iPfD2DdTcBZ/QFgBwuAqT1Jg5c6aGDx+uhx9+WJIUGxur1atXa+7cuYqJiSn1+nNy8uTunqtz504y/BkAnJyj+ww4j2JN8J3tfI9vKepxV7YcW3ZWlo4lHFKd+g3k4VHwZaMtE5zbOnLElvok5xv1YctoFVuOzV5lJOc7R+VZRegzjLk4Qbi7d2XVu2NUgWVsfdTT1b7QtzXJYJkPpIi2tzjzirS770lVDy54xIc9H2Nly1wg2VlZOnY4QXUi6svDs4i2oIzKXFLNy12vvfKSAgMDCy1D4gPOrrht9rvvvqu6desqNjZWktS0aVNt375d06dPtyQ1YmNjdfvtt2vixImSpIkTJ2rDhg2KjY3VkiVLSlRvWTt7IUdelXL15E3VdF3tgALLHEnN0IzvU/lOC4BTqBBJjaysLMXFxemZZ56xWh8VFaXNmzeXSQx5eUbG5OrChcxregRVSsrFAdR0IABQOpyhz4DzKM8TfNt6bJnz3pHfgAeu6fhtqcvW+s4lJSp12SKnuqFOT09XasZ51Rg0pNDRKraeS3uUccZzVF5VtD7Dzc39mh71ZMsX+rYkGSTb5gMpzrwi3n5FT5Zur8dYJSQkKPlkukJvGVToXCDJB3/Vn4c+VI0b+xYZd1mVkaSTR/9Q3L/e1sOjJxSZnC7L0Tr2HGFSlqNnSPw4Tkna7C1btigqKspqXc+ePTV//nxlZ2fL09NTW7Zs0ZNPPpmvzKVEiCv1FXVq+KhhcLUiShT9/wkg2fexjjwismy50vmuEEmNEydOKDc3V8HB1heNwcHBSkoq+DmymZmZyszMtLxOS0uTpKt+sFc6d+6c8vLM/09q5CkxMUnPPDNWo0c/pX379qlz587y9/e3aV8nT57UzJnT5OXloenT56hmzZrFigWAa8vL+99/i9kUydfXV25ubvYPqhxyVJ9x5swZGWNUWVJadpZO7t9bYLn0I0dk8vJ0JiFeHspziTLOGFNx4845f17ZGecKLJNz4YLTxm3Lvmw5Nnsdf1H7sbm+8xnKPJ+hvXv36syZM4XuqywdOXJEWRcuKOd8hl3O5TWXOZ+h3JwcnTlzptjXrhJ9RnEUt8+w1z3GmTNnlJuTo9OJh5R9oeAEQXryUZm8PKUnHZFHIR+nLWUkKS8vVFKe8vLOKPXw7wWWOXcqWZnnzxf5/+aRI0eUlHJS1Zp2UWXf6gXX9Ve8chKOKjPjXKHHJkk5WRdsPr6czAuF7suW/dhybCdPntTkV2J09kLRCe4LFzL011/HVb1turyq+BYZky1xl0UZSbpw5rRyTSV5NbhR/oFBBZY5f+a0Evdu1NatWxUeHl7ovq7GlnOZnZWlv44eUe3wiKuPMPH20OTnJiogoOBfn9urPltjulo89nSpfyqqrTh3KrnC9Bkluc5PSkoqsHxOTo5OnDih0NDQQstc2mdJ6pXs12fk5eVI8lB2brp2JZwqsMzB5LPKzTP6Pemscit5Fljm2Mnzyjif6VTXYHA+J0+e1PRpLyrvfNF/I27evvrnc1OKbAvtuS9cna3n27tagN6c+0GJvpe2a59hKoBjx44ZSWbz5s1W66dOnWqaNGlS4HtefPFFI4mFhYWlXCxpaWll0dyWC/QZLCwsFX2hz7BdcfsM+gsWFpbytrhSn1GS6/zGjRubadOmWa3btGmTkWQSExONMcZ4enqaTz/91KrM4sWLjbe3d4nrNYY+g4WFpfwt9uwzKsRIjZo1a8rd3T1fBjw5OTlfpvySiRMnaty4cZbXeXl5OnnypAIDA4uVUUpPT1d4eLiOHDkiPz+/kh1AOcM5yY9zkh/nJL9rOSe+vgX/EhD5OarPcPW/eVePX3L9YyB+xypP8dNn2K64fQb3GFdXno9NKt/Hx7G5pms9NlfqM0pynR8SElJgeQ8PD8v8MoWVubTPktQr0WdcieNwLhyHc3GV47Bnn1EhkhpeXl6KjIzU2rVr1b9/f8v6tWvXqm/fvgW+x9vbO9+zQqtXr17iGPz8/Jz6j8oROCf5cU7y45zkxzkpXY7uM1z983X1+CXXPwbid6zyEL+rPEbEGRS3z+Aew3bl+dik8n18HJtrKs/HdklJrvM7deqkr776ymrdmjVr1K5dO3l6elrKrF271mpejTVr1qhz584lrleizygMx+FcOA7nUl6OwxYVIqkhSePGjdPgwYPVrl07derUSe+//74OHz6skSNHOjo0AICToc8AANiKPgMAXMfV2uyJEyfq2LFjWrRokSRp5MiRmj17tsaNG6cRI0Zoy5Ytmj9/vpYsWWLZ55gxY3TLLbfotddeU9++ffV///d/WrdunTZt2mRzvQCA4qkwSY1BgwYpNTVVL730khITE9WiRQutXLlSERERjg4NAOBk6DMAALaizwAA13G1NjsxMVGHDx+2lK9fv75WrlypJ598Uu+8847CwsL09ttva8CAAZYynTt31tKlS/Xcc8/p+eefV8OGDbVs2TJ16NDB5noBAMVTYZIakjRq1CiNGjWqTOv09vbWiy++mG/IYEXGOcmPc5If5yQ/zknZKus+w9U/X1ePX3L9YyB+xyL+io0+w37K87FJ5fv4ODbXVJ6PrTBFtdkLFy7Mt65r167asWNHkfu85557dM8995S43tJUXj5jjsO5cBzOpbwcR3G4GWOMo4MAAAAAAAAAAAC4mkqODgAAAAAAAAAAAMAWJDUAAAAAAAAAAIBLIKkBAAAAAAAAAABcAkmNYpozZ47q168vHx8fRUZG6vvvvy+y/IYNGxQZGSkfHx81aNBA7777br4yy5cvV7NmzeTt7a1mzZppxYoVpRV+qbD3OVm4cKHc3NzyLRcuXCjNw7Cr4pyTxMREPfDAA2rSpIkqVaqksWPHFliuIv2d2HJOKtrfyeeff67bb79dtWrVkp+fnzp16qTVq1fnK+fqfycVWXHb0msVExOj9u3by9fXV0FBQerXr5/2799vVWbYsGH5/h/r2LGjVZnMzEw98cQTqlmzpqpWrao+ffro6NGjVmVOnTqlwYMHy9/fX/7+/ho8eLBOnz5tVebw4cO66667VLVqVdWsWVOjR49WVlZWkccwefLkfPGFhIRYthtjNHnyZIWFhaly5crq1q2b9uzZ4zTx16tXr8B27LHHHpPkfOd/48aNuuuuuxQWFiY3Nzd98cUXVtud7Xzv3r1bXbt2VeXKlVW7dm39/e9/LzT+7OxsPf3002rZsqWqVq2qsLAwDRkyRH/99ZfVPrt165bvM7nvvvscHr/kfH8vV8b/0ksvian8iqc07jucRWlcAzmLkvbnP/zwgzw8PNS6devSDfAaFff4MjMzNWnSJEVERMjb21sNGzbUhx9+WEbRFk9xj+2TTz5Rq1atVKVKFYWGhuqhhx5SampqGUVru6v13wVxpfYERSvre4ziKst7ktJUVvclpa2s7k/szZnuU0rrOMr6fsUlGNhs6dKlxtPT08ybN8/s3bvXjBkzxlStWtUkJCQUWP7PP/80VapUMWPGjDF79+418+bNM56enuazzz6zlNm8ebNxd3c306ZNM/v27TPTpk0zHh4eZuvWrWV1WNekNM7JggULjJ+fn0lMTLRaXEVxz0l8fLwZPXq0+eijj0zr1q3NmDFj8pWpaH8ntpyTivZ3MmbMGPPaa6+ZH3/80fz+++9m4sSJxtPT0+zYscNSxtX/Tiqy4v492EPPnj3NggULzK+//mp27dpl7rjjDlO3bl1z9uxZS5mhQ4eaXr16Wf0/lpqaarWfkSNHmtq1a5u1a9eaHTt2mO7du5tWrVqZnJwcS5levXqZFi1amM2bN5vNmzebFi1amDvvvNOyPScnx7Ro0cJ0797d7Nixw6xdu9aEhYWZxx9/vMhjePHFF03z5s2t4ktOTrZsf/XVV42vr69Zvny52b17txk0aJAJDQ016enpThF/cnKyVexr1641ksx3333nlOd/5cqVZtKkSWb58uVGklmxYoXVdmc632lpaSY4ONjcd999Zvfu3Wb58uWmcuXK5rbbbisw/tOnT5sePXqYZcuWmd9++81s2bLFdOjQwURGRlodY9euXc2IESOsPpPTp09blXFE/MY4199LQfH7+vqa6dOnG9imNK6xnUVpXAM5i5L256dPnzYNGjQwUVFRplWrVmUTbAmU5Pj69OljOnToYNauXWvi4+PNtm3bzA8//FCGUdumuMf2/fffm0qVKpm33nrL/Pnnn+b77783zZs3N/369SvjyK/uav33lVypPUHRHHGPUVxleU9SmsrqvqS0ldX9ib05y31KaR5HWd6vuAqSGsVw4403mpEjR1qtu/76680zzzxTYPmnnnrKXH/99VbrHn30UdOxY0fL64EDB5pevXpZlenZs6e577777BR16SqNc7JgwQLj7+9v91jLSnHPyeW6du1a4Bf4Fe3v5HKFnZOK/HdySbNmzcyUKVMsr13976Qis8ffw7VKTk42ksyGDRss64YOHWr69u1b6HtOnz5tPD09zdKlSy3rjh07ZipVqmRWrVpljDFm7969RpJVcm3Lli1Gkvntt9+MMRcv3ipVqmSOHTtmKbNkyRLj7e1t0tLSCq3/xRdfLPTLn7y8PBMSEmJeffVVy7oLFy4Yf39/8+677zpF/FcaM2aMadiwocnLyzPGOPf5v/Ii29nO95w5c4y/v7+5cOGCpUxMTIwJCwszeXl5Nn2p8+OPPxpJVjf+hfVJlzgyfmf6e7la/Li60rjGdhalcQ3kLEp6bIMGDTLPPfdckf2aMyju8f33v/81/v7++b7wckbFPbY33njDNGjQwGrd22+/berUqVNqMdqDLf2fK7UnKJoz3GMUV2ndk5S2srgvcYTSuD8pbY68TynN4yhIad2vuAoeP2WjrKwsxcXFKSoqymp9VFSUNm/eXOB7tmzZkq98z549tX37dmVnZxdZprB9OpPSOieSdPbsWUVERKhOnTq68847tXPnTvsfQCkoyTmxRUX7O7FVRf47ycvL05kzZxQQEGBZ58p/JxVZaf4/UhxpaWmSZPU3JUnr169XUFCQrrvuOo0YMULJycmWbXFxccrOzraKPSwsTC1atLDEvmXLFvn7+6vD/2PvzuOiqtc/gH9GdmVRFJhBAdHUFFzBCExxxbC6uJC0XJVUblwyFzKT0lxayFIvmXuZaJpiIVk3UzAFU8kE19xTBCWQRWXEhQE8vz/8cS4jMzDgMBuf9+s1L5tznnPOc87QeebM93y/x89PjHn66afh4OCgFOPt7Q1XV1cxZvjw4SgrK0NmZmateV+8eBGurq7w9PTESy+9hMuXLwMAsrKykJ+fr5SblZUVAgMDxe0aQv5VFAoFNm3ahIkTJ0IikYjTDf34VzG0452eno7AwEBYWVkpxfz999+4cuWKRvtUUlICiUSCli1bKk3fvHkz2rRpAy8vL8ycORO3b98W5+k7f0P5e9HG8W/KGvM7tr411ncgQ9DQfVu/fj0uXbqEefPmNXaKj6Uh+/fjjz/C19cXn376Kdq2bYvOnTtj5syZuHfvni5S1lhD9i0gIADXrl3Dzp07IQgCrl+/ju+//x7PPfecLlJuVMZyPqHaGco1Rn011jWJLjT2dYmuNdb1ia7p8jpF1xrresVYsFFDQ0VFRaisrISLi4vSdBcXF+Tn56tcJj8/X2V8RUUFioqKao1Rt05D0ljH5Mknn0R8fDx+/PFHbNmyBdbW1ujXrx8uXrzYODuiRQ05Jppoan8nmmjqfydLlizBnTt3MHbsWHGaMf+dNGWN9f9IfQiCgOjoaDzzzDPw9vYWpwcHB2Pz5s3Yu3cvlixZgiNHjmDw4MEoKysD8PBvztLSEq1atVKbe35+PpydnWts09nZWSnm0f1v1aoVLC0taz0Gfn5+2LhxI3bv3o0vv/wS+fn5CAgIQHFxsbhcbcdV3/lX98MPP+DWrVsIDw8Xpxn68a/O0I63uvNh9Vxrc//+fcyePRuvvPIK7O3txemvvvoqtmzZgtTUVMydOxeJiYkYPXq00nHQV/6G9PfyuMe/qWus79iGoLG+AxmChuzbxYsXMXv2bGzevBnm5ua6SLPBGrJ/ly9fxoEDB/Dnn38iKSkJcXFx+P7778Wx2Q1FQ/YtICAAmzdvRlhYGCwtLSGVStGyZUt88cUXuki5URnL+YRqZwjXGPXVmNckjU0X1yW61ljXJ7qmy+sUXWrM6xVjYdjfnAxQ9dZJ4OFJ99FpdcU/Or2+6zQ02j4mTz/9tNKDhvr164c+ffrgiy++wLJly7SVdqNqjM+0qf2d1KUp/51s2bIF8+fPx44dO2oUI2P/O2nK9PnZTZkyBSdPnsSBAweUpoeFhYn/7e3tDV9fX3h4eODnn39W+mL0qEdzV7UfDYl5VHBwsPjf3bt3h7+/Pzp27IgNGzaI54eGHFdd5V/dunXrEBwcrHT3u6Eff1UM6Xhr8h1MlfLycrz00kt48OABVq5cqTQvIiJC/G9vb2906tQJvr6+OHr0KPr06aPX/A3t76Whx5/+pzGuOwxFY3wHMhSa7ltlZSVeeeUVLFiwAJ07d9ZVeo+tPp/dgwcPIJFIsHnzZjg4OAAAli5ditDQUKxYsQI2NjaNnm991Gffzpw5g6lTp+L999/H8OHDkZeXh7fffhuRkZFYt26dLtJtVMZ0PqHaGdP1YWNfkzQmXV2X6FJjXp/og66uU3RBF9crxoA9NTTUpk0bmJmZ1Wi1KigoqNHaV0UqlaqMNzc3R+vWrWuNUbdOQ9JYx+RRzZo1Q9++fY3iDvyGHBNNNLW/k4ZoKn8nCQkJmDRpErZt24ahQ4cqzTPmv5OmTFf/j6jz5ptv4scff8S+ffvQrl27WmNlMhk8PDzE/8+kUikUCgVu3rypFFc9d6lUiuvXr9dYV2FhoVLMo/t/8+ZNlJeX1+sYtGjRAt27d8fFixchlUoB1Lwr/NHcDCH/7Oxs7NmzB5MnT641zpCPv6Edb3XnQ6DmXVrVlZeXY+zYscjKykJKSorSXU+q9OnTBxYWFkqfiT7zr06ffy/ayL8p09V3bH1orO9AhqC++3b79m1kZGRgypQpMDc3h7m5ORYuXIgTJ07A3Nwce/fu1VXqGmnIZyeTydC2bVuxQQMAunbtCkEQcO3atUbNtz4asm+xsbHo168f3n77bfTo0QPDhw/HypUr8fXXXyMvL08XaTcaYzmfUO30fY1RX419TaJrjXFdokuNeX2ia7q8TtEFXVyvGAs2amjI0tISPj4+SElJUZqekpKCgIAAlcv4+/vXiE9OToavry8sLCxqjVG3TkPSWMfkUYIg4Pjx45DJZNpJvBE15Jhooqn9nTREU/g72bJlC8LDw/Htt9+qHK/XmP9OmjJd/T/yKEEQMGXKFGzfvh179+6Fp6dnncsUFxfj6tWr4v9nPj4+sLCwUMo9Ly8Pf/75p5i7v78/SkpK8Mcff4gxhw8fRklJiVLMn3/+qfQjQHJyMqysrODj46PxPpWVleHs2bOQyWTw9PSEVCpVyk2hUCAtLU3crqHkv379ejg7O9c5DrchH39DO97+/v7Yv38/FAqFUoyrqyvat2+vch+qLhAuXryIPXv2aPTDzenTp1FeXi5+JvrM/1H6/HvRRv5Nma6+Y+tDY30HMgT13Td7e3ucOnUKx48fF1+RkZHo0qULjh8/rjTWtSFoyGfXr18//P333ygtLRWnXbhwAc2aNavzR0tdasi+3b17F82aKf+cYmZmBuB/vRqMlbGcT6h2+rrGqC9dXZPoWmNcl+hSY16f6Jour1Mam66uV4yGtp443hRs3bpVsLCwENatWyecOXNGmD59utCiRQvhypUrgiAIwuzZs4Vx48aJ8ZcvXxaaN28uzJgxQzhz5oywbt06wcLCQvj+++/FmIMHDwpmZmbCJ598Ipw9e1b45JNPBHNzc6Wn0Buyxjgm8+fPF3bt2iVcunRJOHbsmPDaa68J5ubmwuHDh3W+fw1R32MiCIJw7Ngx4dixY4KPj4/wyiuvCMeOHRNOnz4tzm9qfyeCUPcxaWp/J99++61gbm4urFixQsjLyxNft27dEmOM/e+kKavr76Ex/Pvf/xYcHByE1NRUpb+pu3fvCoIgCLdv3xbeeust4dChQ0JWVpawb98+wd/fX2jbtq0gl8vF9URGRgrt2rUT9uzZIxw9elQYPHiw0LNnT6GiokKMefbZZ4UePXoI6enpQnp6utC9e3fh+eefF+dXVFQI3t7ewpAhQ4SjR48Ke/bsEdq1aydMmTKl1n146623hNTUVOHy5cvC77//Ljz//POCnZ2deNw++eQTwcHBQdi+fbtw6tQp4eWXXxZkMpnB5C8IglBZWSm4u7sL77zzjtJ0Qzz+t2/fFs/NAISlS5cKx44dE7Kzsw3ueN+6dUtwcXERXn75ZeHUqVPC9u3bBTs7O2HGjBkq8y8vLxf+8Y9/CO3atROOHz+u9P9EWVmZIAiC8NdffwkLFiwQjhw5ImRlZQk///yz8OSTTwq9e/fWe/6G9veiKn97e3th8eLFAmmmMb5jG4rG+A5kKBrynbe6efPmCT179tRRtvVX3/27ffu20K5dOyE0NFQ4ffq0kJaWJnTq1EmYPHmyvnZBrfru2/r16wVzc3Nh5cqVwqVLl4QDBw4Ivr6+wlNPPaWvXVCrrvptzOcTqp0+rjHqS5fXJI1JV9cluqCL6xNtM5TrlMbcD11erxgLNmrU04oVKwQPDw/B0tJS6NOnj5CWlibOmzBhghAYGKgUn5qaKvTu3VuwtLQU2rdvL6xatarGOr/77juhS5cugoWFhfDkk08KiYmJjb0bWqXtYzJ9+nTB3d1dsLS0FJycnISgoCDh0KFDutgVranvMQFQ4+Xh4aEU09T+Tuo6Jk3t7yQwMFDlMZkwYYLSOo3976Qpq+3voTGo+nsCIKxfv14QBEG4e/euEBQUJDg5OQkWFhaCu7u7MGHCBCEnJ0dpPffu3ROmTJkiODo6CjY2NsLzzz9fI6a4uFh49dVXBTs7O8HOzk549dVXhZs3byrFZGdnC88995xgY2MjODo6ClOmTBHu379f6z6EhYUJMplMsLCwEFxdXYXRo0crNX4+ePBAmDdvniCVSgUrKythwIABwqlTpwwmf0EQhN27dwsAhPPnzytNN8Tjv2/fvlrPQ4Z2vE+ePCn0799fsLKyEqRSqTBhwgS1+WdlZan9f2Lfvn2CIAhCTk6OMGDAAMHR0VGwtLQUOnbsKEydOlUoLi7We/6G+PfyaP7z588XHjx4IJDmGuO6w1A0xncgQ1Hfz606Q2/UEIT679/Zs2eFoUOHCjY2NkK7du2E6Oho8cdKQ1PffVu2bJnQrVs3wcbGRpDJZMKrr74qXLt2TcdZ162u+m3s5xOqna6vMepLl9ckjUlX1yW6oIvrE20zpOuUxtoPXV+vGAOJIBh530giIiIiIiIiIiIiImoS+EwNIiIiIiIiIiIiIiIyCmzUICIiIiIiIiIiIiIio8BGDSIiIiIiIiIiIiIiMgps1CAiIiIiIiIiIiIiIqPARg0iIiIiIiIiIiIiIjIKbNQgIiIiIiIiIiIiIiKjwEYNIiIiIiIiIiIiIiIyCmzUICIiIiIiIiIiIiIio8BGDaJaDBw4ENOnTxfft2/fHnFxcTrZlrHKz8/HsGHD0KJFC7Rs2VLtNCIi0i1d1jQiIjJe4eHhGDlypE62xVpERIZG099mtPkbTn3Pu6mpqZBIJLh169ZjbVdb6zFEV65cgUQiwfHjx/WdCjUSc30nQGRMjhw5ghYtWojvJRIJkpKSdPalX5caum//+c9/kJeXh+PHj8PBwUHtNCIi0q3t27fDwsJC32kQEVE9paamYtCgQbh586ZObhD6/PPPIQiCVtcZHx+P6dOn1/jh7NHrKyIifdP0O7M2v1s3xnn3UQMHDkSvXr2UGpIDAgKQl5dn9L/ThIeH49atW/jhhx/EaW5ubsjLy0ObNm30lxg1KjZqENWDk5OTvlMweJcuXYKPjw86depU6zQiItItR0dHfadARGRwFAoFLC0t9Z2GQdHlj1u8viIiQ6Ppd2ZtfrfWV6OCpaUlpFKpXratifLy8gY3HJmZmRn0vtHj4/BTZBR27dqFZ555Bi1btkTr1q3x/PPP49KlSwD+16Vs27Zt6N+/P2xsbNC3b19cuHABR44cga+vL2xtbfHss8+isLBQXGdV974FCxbA2dkZ9vb2eP3116FQKNTmUb17dPv27QEAo0aNgkQiEd+r6jY4ffp0DBw4UHx/584djB8/Hra2tpDJZFiyZEmNbSkUCsyaNQtt27ZFixYt4Ofnh9TUVI2P2cGDBxEYGIjmzZujVatWGD58OG7evFljP6r06tUL8+fPr3XfAGDVqlXo2LEjLC0t0aVLF3zzzTdKxycxMREbN26ERCJBeHi4ymlERIZu4MCBmDp1KmbNmgVHR0dIpVLxHKmqK/OtW7cgkUjE83RVV+7du3ejd+/esLGxweDBg1FQUIBffvkFXbt2hb29PV5++WXcvXtXo5y+//57dO/eHTY2NmjdujWGDh2KO3fuANCsptXVRX79+vVwcHBASkoKAODMmTMYMWIEbG1t4eLignHjxqGoqEijfIiIDNXAgQMxZcoUREdHo02bNhg2bBgkEglWrVqF4OBg2NjYwNPTE999953G67x27RpeeuklODo6okWLFvD19cXhw4fF+bV9fwYe9pD+6quvMGrUKDRv3hydOnXCjz/+COBhzRk0aBAAoFWrVkrfp2u7Rqpa9nGuk6ofM3U1scrSpUvRvXt3tGjRAm5uboiKikJpaSmAhzXxtddeQ0lJCSQSCSQSidJ1R/XrkpycHISEhMDW1hb29vYYO3Ysrl+/Ls6fP38+evXqhW+++Qbt27eHg4MDXnrpJdy+fVvjz4uIqDbVvzOvXLkSnTp1grW1NVxcXBAaGqoyrjYxMTF4+umna0zv0aMH5s2bB6DmebesrAxTp06Fs7MzrK2t8cwzz+DIkSNqt1FcXIyXX34Z7dq1Q/PmzdG9e3ds2bJFnB8eHo60tDR8/vnn4nn4ypUrKoefSkxMhJeXF6ysrNC+ffsav1e1b98eH3/8MSZOnAg7Ozu4u7tj7dq14nyFQoEpU6ZAJpPB2toa7du3R2xsbJ3HCXhYD1evXo2QkBC0aNECH374ISorKzFp0iR4enrCxsYGXbp0weeffy4uM3/+fGzYsAE7duwQ9y01NVXlNVtaWhqeeuopWFlZQSaTYfbs2aioqNAoNzI8bNQgo3Dnzh1ER0fjyJEj+PXXX9GsWTOMGjUKDx48EGPmzZuHOXPm4OjRozA3N8fLL7+MWbNm4fPPP8dvv/2GS5cu4f3331da76+//oqzZ89i37592LJlC5KSkrBgwQKNcqoqKOvXr0deXl6tBeZRb7/9Nvbt24ekpCQkJycjNTUVmZmZSjGvvfYaDh48iK1bt+LkyZN48cUX8eyzz+LixYt1rv/48eMYMmQIvLy8kJ6ejgMHDuCFF15AZWXlY+1bUlISpk2bhrfeegt//vknXn/9dbz22mvYt2+fuNyzzz6LsWPHIi8vD59//rnKaURExmDDhg1o0aIFDh8+jE8//RQLFy4Uf/DX1Pz587F8+XIcOnQIV69exdixYxEXF4dvv/0WP//8M1JSUvDFF1/UuZ68vDy8/PLLmDhxIs6ePYvU1FSMHj1aqZv649S0xYsXY+bMmdi9ezeGDRuGvLw8BAYGolevXsjIyMCuXbtw/fp1jB07VuN8iIgM1YYNG2Bubo6DBw9izZo1AIC5c+dizJgxOHHiBP75z3/i5ZdfxtmzZ+tcV2lpKQIDA/H333/jxx9/xIkTJzBr1izxOqWu789VFixYgLFjx+LkyZMYMWIEXn31Vdy4cQNubm5ITEwEAJw/f17p+7Qm10hAw66TVB2z2mpis2bNsGzZMvz555/YsGED9u7di1mzZgF4OLxJXFwc7O3tkZeXh7y8PMycObPGNgRBwMiRI3Hjxg2kpaUhJSUFly5dQlhYmFLcpUuX8MMPP+C///0v/vvf/yItLQ2ffPJJnZ8VEVF9ZGRkYOrUqVi4cCHOnz+PXbt2YcCAAfVez6uvvorDhw8rNTqfPn0ap06dwquvvqpymVmzZiExMREbNmzA0aNH8cQTT2D48OG4ceOGyvj79+/Dx8cH//3vf/Hnn3/iX//6F8aNGyc2sH/++efw9/dHRESEeB52c3OrsZ7MzEyMHTsWL730Ek6dOoX58+dj7ty5iI+PV4pbsmQJfH19cezYMURFReHf//43zp07BwBYtmwZfvzxR2zbtg3nz5/Hpk2blG6Urcu8efMQEhKCU6dOYeLEiXjw4AHatWuHbdu24cyZM3j//ffx7rvvYtu2bQCAmTNnYuzYsXj22WfFfQsICKix3tzcXIwYMQJ9+/bFiRMnsGrVKqxbtw4ffvihxrmRgRGIjFBBQYEAQDh16pSQlZUlABC++uorcf6WLVsEAMKvv/4qTouNjRW6dOkivp8wYYLg6Ogo3LlzR5y2atUqwdbWVqisrBQEQRACAwOFadOmifM9PDyE//znP+J7AEJSUpJSbhMmTBBCQkKUpk2bNk0IDAwUBEEQbt++LVhaWgpbt24V5xcXFws2Njbitv766y9BIpEIubm5SusZMmSIEBMTU+fxefnll4V+/fqpnf/ofgiCIPTs2VOYN29erfsWEBAgREREKE178cUXhREjRojvQ0JChAkTJijFqJpGRGTIAgMDhWeeeUZpWt++fYV33nlHrDvHjh0T5928eVMAIOzbt08QBEHYt2+fAEDYs2ePGBMbGysAEC5duiROe/3114Xhw4fXmU9mZqYAQLhy5YrK+Y9T02bPni3IZDLh5MmT4ry5c+cKQUFBStu4evWqAEA4f/58nfkQERmqwMBAoVevXkrTAAiRkZFK0/z8/IR///vfda5vzZo1gp2dnVBcXKxyvibfnwEIc+bMEd+XlpYKEolE+OWXXwRB+F9NuXnzZq25VL9GEgThsa6Tql/P1FYT1dm2bZvQunVr8f369esFBweHGnHVr0uSk5MFMzMzIScnR5x/+vRpAYDwxx9/CIIgCPPmzROaN28uyOVyMebtt98W/Pz81OZCRFQfVd+ZExMTBXt7e6Xzjao4TfTo0UNYuHCh+D4mJkbo27ev+L76ebe0tFSwsLAQNm/eLM5XKBSCq6ur8OmnnwqCoFldGDFihPDWW2/Vmu+j63nllVeEYcOGKcW8/fbbQrdu3cT3Hh4ewj//+U/x/YMHDwRnZ2dh1apVgiAIwptvvikMHjxYePDgQS1HRDUAwvTp0+uMi4qKEsaMGSO+V/U73KPXbO+++67QpUsXpbxWrFihdL1ExoU9NcgoXLp0Ca+88go6dOgAe3t7eHp6AnjYPblKjx49xP92cXEBAHTv3l1pWkFBgdJ6e/bsiebNm4vv/f39UVpaiqtXrzbKfgAP90WhUMDf31+c5ujoiC5duojvjx49CkEQ0LlzZ9ja2oqvtLQ0pdZ9dap6amjb2bNn0a9fP6Vp/fr10+guNiIiY1O9rgCATCarUUfqsw4XFxc0b94cHTp0UJqmyTp79uyJIUOGoHv37njxxRfx5ZdfikMKVo+pb01bsmQJ1qxZgwMHDijVzMzMTOzbt0+pBj355JMAHtYxTfIhIjJUvr6+NaZV/25e9V6T77jHjx9H79691Y6trun35+r1okWLFrCzs6uzPmhyjfToujW9TnpUXTVx3759GDZsGNq2bQs7OzuMHz8excXF9RqW8OzZs3Bzc1O6e7hbt25o2bKl0vFq37497Ozs1OZCRKQNw4YNg4eHBzp06IBx48Zh8+bNGg8b+6hXX30VmzdvBvCwV9qWLVvU9tK4dOkSysvLlWqHhYUFnnrqKbV1qbKyEh999BF69OiB1q1bw9bWFsnJyTXqQV3U1ayLFy8qjfxRvSZIJBJIpVLxPBweHo7jx4+jS5cumDp1KpKTk+uVg6oavXr1avj6+sLJyQm2trb48ssvG7Rv/v7+kEgkSvtWWlqKa9eu1WtdZBjYqEFG4YUXXkBxcTG+/PJLHD58WOxCV32s8OoPD6o6ST067dGu2OpUP8nVV7NmzWoMv1FeXi7+96PzVHnw4AHMzMyQmZmJ48ePi6+zZ89qNHyTjY3NY+VYm0ePjSAIj3W8iIgM1aMPpauqI82aPfz6VP08qu4c+mgdUrfOupiZmSElJQW//PILunXrhi+++AJdunRBVlZWncvWdo7u378/Kisrxe7bVR48eIAXXnhBqQYdP34cFy9exIABAx4rHyIifWvRooVGcZp8x63re7eq9aj6/tyQ+qDJNdKj627odVJt+WVnZ2PEiBHw9vZGYmIiMjMzsWLFCgCaX2MA6q8rHp3e0FpKRFQfdnZ2OHr0KLZs2QKZTIb3338fPXv2VHr+hKZeeeUVXLhwAUePHhWHpX3ppZdUxlZdY9Tnt5clS5bgP//5D2bNmoW9e/fi+PHjGD58eK3PjFW3bVXbfVRt5+E+ffogKysLH3zwAe7du4exY8cqPYukLo/W6G3btmHGjBmYOHEikpOTcfz4cbz22mta3Tf+pmWc2KhBBq+4uBhnz57FnDlzMGTIEHTt2lVrd4OeOHEC9+7dE9///vvvsLW1Rbt27TRa3sLCosZzKpycnJCXl6c0rfqDiZ544glYWFjg999/F6fdvHkTFy5cEN/37t0blZWVKCgowBNPPKH0kkqldebVo0cP/Prrr2rnP5qjXC6v8UOUqn3r2rUrDhw4oDTt0KFD6Nq1a505ERGZCicnJwBQOo9WP883FolEgn79+mHBggU4duwYLC0tkZSUJM5vSE176qmnsGvXLnz88cf47LPPxOl9+vTB6dOn0b59+xp1qOpCo658iIiMSfXv5lXvq3qo1aZHjx44fvy42nHOtfH92dLSEgCUvps35jVSfWVkZKCiogJLlizB008/jc6dO+Pvv/9WirG0tKzz+X7dunVDTk6OUg/DM2fOoKSkhNcbRKQX5ubmGDp0KD799FOcPHkSV65cwd69e+u9nnbt2mHAgAHYvHkzNm/ejKFDh4o95x71xBNPwNLSUql2lJeXIyMjQ+258LfffkNISAj++c9/omfPnujQoUON57Fqeh5WVbM6d+4MMzMzTXYVAGBvb4+wsDB8+eWXSEhIQGJioto6WZfffvsNAQEBiIqKQu/evfHEE0/UGMFE0307dOiQUiPNoUOHYGdnh7Zt2zYoN9IvNmqQwWvVqhVat26NtWvX4q+//sLevXsRHR2tlXUrFApMmjQJZ86cwS+//IJ58+ZhypQp4l24dWnfvj1+/fVX5OfnixcRgwcPRkZGBjZu3IiLFy9i3rx5+PPPP8VlbG1tMWnSJLz99tv49ddf8eeffyI8PFxpm507d8arr76K8ePHY/v27cjKysKRI0ewaNEi7Ny5s868YmJicOTIEURFReHkyZM4d+4cVq1ahaKiIjHHb775Br/99hv+/PNPTJgwoUaBUrVvb7/9NuLj47F69WpcvHgRS5cuxfbt21U+6I+IyFTZ2Njg6aefxieffIIzZ85g//79mDNnTqNu8/Dhw/j444+RkZGBnJwcbN++HYWFhUoXNg2taf7+/vjll1+wcOFC/Oc//wEAvPHGG7hx4wZefvll/PHHH7h8+TKSk5MxceJEVFZWapQPEZEx+e677/D111/jwoULmDdvHv744w9MmTKlzuVefvllSKVSjBw5EgcPHsTly5eRmJiI9PR0ANr5/uzh4QGJRIL//ve/KCwsRGlpaaNeI9VXx44dUVFRgS+++AKXL1/GN998g9WrVyvFtG/fHqWlpfj1119RVFSkcgiXoUOHokePHnj11Vdx9OhR/PHHHxg/fjwCAwNVDkdCRNSY/vvf/2LZsmU4fvw4srOzsXHjRjx48EBp6PD6ePXVV7F161Z89913+Oc//6k2rkWLFvj3v/+Nt99+G7t27cKZM2cQERGBu3fvYtKkSSqXeeKJJ5CSkoJDhw7h7NmzeP3115Gfn68U0759exw+fBhXrlxBUVGRyh5ub731Fn799Vd88MEHuHDhAjZs2IDly5fXq2b95z//wdatW3Hu3DlcuHAB3333HaRSKVq2bKnxOh7dt4yMDOzevRsXLlzA3LlzceTIkRr7dvLkSZw/fx5FRUUqewlGRUXh6tWrePPNN3Hu3Dns2LED8+bNQ3R0tMa/AZJh4adGBq9Zs2bYunUrMjMz4e3tjRkzZijdTfo4hgwZgk6dOmHAgAEYO3YsXnjhBcyfP1/j5ZcsWYKUlBS4ubmhd+/eAIDhw4dj7ty5mDVrFvr27Yvbt29j/PjxSst99tlnGDBgAP7xj39g6NCheOaZZ+Dj46MUs379eowfPx5vvfUWunTpgn/84x84fPiw0hiz6nTu3BnJyck4ceIEnnrqKfj7+2PHjh0wNzcH8LDRY8CAAXj++ecxYsQIjBw5Eh07dqxz30aOHInPP/8cn332Gby8vLBmzRqsX78eAwcO1PiYERGZgq+//hrl5eXw9fXFtGnT8OGHHzbq9uzt7bF//36MGDECnTt3xpw5c7BkyRIEBweLMY9T0/r164eff/4Zc+fOxbJly+Dq6oqDBw+isrISw4cPh7e3N6ZNmwYHBwc0a9ZMo3yIiIzJggULsHXrVvTo0QMbNmzA5s2b0a1btzqXs7S0RHJyMpydnTFixAh0794dn3zyiXjDkDa+P7dt2xYLFizA7Nmz4eLiIjZYN9Y1Un316tULS5cuxaJFi+Dt7Y3NmzcjNjZWKSYgIACRkZEICwuDk5MTPv300xrrkUgk+OGHH9CqVSsMGDAAQ4cORYcOHZCQkKCrXSEiErVs2RLbt2/H4MGD0bVrV6xevRpbtmyBl5dXg9b34osvori4GHfv3sXIkSNrjf3kk08wZswYjBs3Dn369MFff/2F3bt3o1WrVirj586diz59+mD48OEYOHCg2Nhe3cyZM2FmZoZu3brByclJ5TMp+vTpg23btmHr1q3w9vbG+++/j4ULFyI8PFzj/bS1tcWiRYvg6+uLvn374sqVK9i5c2eDGw4iIyMxevRohIWFwc/PD8XFxYiKilKKiYiIQJcuXcTnbhw8eLDGetq2bYudO3fijz/+QM+ePREZGYlJkyY1+s1p1HgkgiYD/BOZoPDwcNy6dQs//PCDvlMhIiJ6LKxpREQNJ5FIkJSUVOePTERERERkGNhTg4iIiIiIiIiIiIiIjAIbNYiMUHBwMGxtbVW+Pv74Y32nR0RE9ZSTk6P2vG5ra6uyezgRETW+jz/+WO25mUPuERHRb7/9Vuv3eHpo8+bNao9RQ4f0oqaNw08RGaHc3Fzcu3dP5TxHR0c4OjrqOCMiInocFRUVuHLlitr57du3F5+LREREunPjxg3cuHFD5TwbGxu0bdtWxxkREZEhuXfvHnJzc9XOf+KJJ3SYjeG6ffs2rl+/rnKehYUFPDw8dJwRGTs2ahARERERERERERERkVHg8FNERERERERERERERGQU2KhBRERERERERERERERGgY0aRERERERERERERERkFNioQURERERERERERERERoGNGkREREREREREREREZBTYqEFEREREREREREREREaBjRpERERERERERERERGQU2KhBRERERERERERERERGgY0aRERERERERERERERkFNioQURERERERERERERERoGNGkREREREREREREREZBTYqEFEREREREREREREREaBjRpERERERERERERERGQU2KhBRERERERERERERERGgY0aRERERERERERERERkFNioQURERERERERERERERoGNGkREREREREREREREZBTYqEFEREREREREREREREaBjRpERERERERERERERGQU2KhBRERERERERERERERGgY0aRERERERERERERERkFNioQURERERERERERERERoGNGkREREREREREREREZBTYqEFEREREREREREREREaBjRpERERERERERERERGQU2KhBRERERERERERERERGgY0aRERERERERERERERkFNiooSFBECCXyyEIgr5TISIiA8eaQUREmmC9ICIiTbFmEBH9Dxs1NHT79m04ODjg9u3b+k6FSPeWLgXmz3/4L5ERiY2NRd++fWFnZwdnZ2eMHDkS58+fr3WZ1NRUSCSSGq9z585pvF3WDCLSJ5Zt49HgesEPmYioyWHNICL6H4nAJl6NyOVyODg4oKSkBPb29vpOh0i32rUDcnOBtm2Ba9f0nQ2Rxp599lm89NJL6Nu3LyoqKvDee+/h1KlTOHPmDFq0aKFymdTUVAwaNAjnz59XOt87OTnBzMxMo+2yZhCRPrFsG48G1wt+yERETQ5rBhHR/5jrOwEiIqLGsmvXLqX369evh7OzMzIzMzFgwIBal3V2dkbLli0bMTsiIiIiIiIiIqovDj9FRERNRklJCQDA0dGxztjevXtDJpNhyJAh2LdvX2OnRkREREREREREGmBPDSIiahIEQUB0dDSeeeYZeHt7q42TyWRYu3YtfHx8UFZWhm+++QZDhgxBamqq2t4dZWVlKCsrE9/L5XKt509ERERERERERGzUICI9qKysRHl5ub7TMDkWFhYaP/OhKZoyZQpOnjyJAwcO1BrXpUsXdOnSRXzv7++Pq1evYvHixWobNWJjY7FgwQKt5ktEdWM9Ua1tW8DcHHBxAe7frznfzMwM5ubmkEgkuk+OiMgAsH5ozhSvMVauXInPPvsMeXl58PLyQlxcHPr37682Pi0tDdHR0Th9+jRcXV0xa9YsREZGKsUkJiZi7ty5uHTpEjp27IiPPvoIo0aNEufPnz+/xvWCi4sL8vPztbtzRKR1rBma02XNYKMGEdXtueeAGzcADYbsqUtpaSmuXbsGQRC0kBhVJ5FI0K5dO9ja2uo7FYPz5ptv4scff8T+/fvRrl27ei//9NNPY9OmTWrnx8TEIDo6Wnwvl8vh5ubWoFyJSDOsJ+otWQI8eAA0awZkZamOad68OWQyGSwtLXWbHGmHFr+bETU1rB/1Y2rXGAkJCZg+fTpWrlyJfv36Yc2aNQgODsaZM2fg7u5eIz4rKwsjRoxAREQENm3ahIMHDyIqKgpOTk4YM2YMACA9PR1hYWH44IMPMGrUKCQlJWHs2LE4cOAA/Pz8xHV5eXlhz5494nudNRaxZhA1GGtG/eiyZkgEfioakcvlcHBwQElJCezt7fWdDpFRqqysxMWLF9G8eXM4OTnxDlEtEgQBhYWFuHv3Ljp16mRyd1M1lCAIePPNN5GUlITU1FR06tSpQesJDQ3FjRs3sHfvXo3iWTOIGhfrScMJggCFQoHCwkJUVlaiU6dOaNaMj9nTF9YLIt1i/agfU7zG8PPzQ58+fbBq1SpxWteuXTFy5EjExsbWiH/nnXfw448/4uzZs+K0yMhInDhxAunp6QCAsLAwyOVy/PLLL2LMs88+i1atWmHLli0AHvbU+OGHH3D8+PEG586aQaRbrBn1o+uawZ4aRKQz5eXlEAQBTk5OsLGx0Xc6JsfJyQlXrlxBeXm5SVxwaMMbb7yBb7/9Fjt27ICdnZ3YvdvBwUH8G4yJiUFubi42btwIAIiLi0P79u3h5eUFhUKBTZs2ITExEYmJiXrbDyJSxnryeGxsbGBhYYHs7GwoFApYW1vrOyUiIp1g/ag/U7rGUCgUyMzMxOzZs5WmBwUF4dChQyqXSU9PR1BQkNK04cOHY926dSgvL4eFhQXS09MxY8aMGjFxcXFK0y5evAhXV1dYWVnBz88PH3/8MTp06PD4O0ZEjYI1o/50WTPYqEFEOsfW7cbB41pT1R1YAwcOVJq+fv16hIeHAwDy8vKQk5MjzlMoFJg5cyZyc3NhY2MDLy8v/PzzzxgxYoSu0iYiDfG813DsnUFETRnrh+ZM6VgVFRWhsrISLi4uStNre7ZFfn6+yviKigoUFRVBJpOpjam+Tj8/P2zcuBGdO3fG9evX8eGHHyIgIACnT59G69atVW67rKwMZWVl4nu5XF6v/SUi7TCl82Bj0+WxYqMGEZEGMjIy8J///AebN2/Wyvru3r2LSZMm4ciRI2jWrBk+/PBD/OMf/6gRd/bsWfHHdwAoKSmBXC7HjRs3tJKHqdNkhMX4+Hil97NmzcKsWbMaKaPaFRYWqrxYsbe3h5OTkx4yIiJjM3/+fKxcuRKurq4AHo7fra3aRfQodXWrOtYwIu3R9jVJldTUVAwZMgSff/45pkyZotV1G6JHf3QTBKHWH+JUxT86va51BgcHi//dvXt3+Pv7o2PHjtiwYYPSs/mqi42NrfFwcdKeumoY6xcZM23Xi/DwcOzZswdt2rQBAAwbNgyfffaZVtbdUGzUIKK6+foC+fmAVApkZOg7G73w9fXV6sXD4sWLYWVlhb/++gsXLlxAv3794N7eHQ4ODkpx1s2tsXXbVvH9Rx98VCOGTENhYSH+9c9xuHezZoOVTStHrN30Db9UE5FGnntuPKZPXwwLC6BbN31nQ43CAL6bFRYWYlJ4BErld2uNs7VvjnXxX7KGEWmBtq9JAOD27dt45513lH50N1Vt2rSBmZlZjV4ZBQUFNXpaVJFKpSrjzc3NxR4W6mLUrRMAWrRoge7du+PixYtqY2JiYpQaPORyOdzc3NTGq2UANcPQFBYWYuKE8SgtKVEbY+vggK83bGT9IqPUGPVi9uzZBtXwbfT9zletWoUePXrA3t4e9vb28Pf3V3o4kyAImD9/PlxdXWFjY4OBAwfi9OnTesyYyAjl5wO5uQ//NTESiQSxsbF46qmn0KFDB+zZswcxMTHo3bs3vLy8xPNFamoqfH19AQBXrlxBmzZt8P7778PHxwdPPPEEdu7cWa/tJiQk4I033gAAuLu7w8fXB2npabBqY6X29cD6AX76708YP368dg8CGQS5XI57N29golSK9zo+Ib4mSqW4d/MGu5sTGQFNa0p+fj4GDRoEHx8feHl5YerUqeJdnxEREXjzzTcBADdu3EDHjh1x4MCBeuXx4AFQXv7wRSbKAL6byeVylMrvYlifsXhlyFSVr2F9xqJUfpc1jKgO+romAYDo6Gi8/fbb4t23pszS0hI+Pj5ISUlRmp6SkoKAgACVy/j7+9eIT05Ohq+vLywsLGqNUbdO4OHQUmfPnoVMJlMbY2VlJf7WVfVqEAOoGYZGLpejtKQEg706Y2y/vjVeg706o/T/R0kgMiT6rBeGxugbNdq1a4dPPvkEGRkZyMjIwODBgxESEiJ+iJ9++imWLl2K5cuX48iRI5BKpRg2bBhu376t58yJCACwdCnQrl3dLxVDM+Ef/6h9maVLNUrB3t4ef/zxBxYtWoSQkBA888wzOHbsGCZMmICPPvpI5TLFxcXw8fFBZmYmli9frvRguNDQUPTq1Uvl6+rVqwCAnJwceHh4iMu0bdsW+Xn5MLcwV/tK2Z2Cdm3boWfPnvU4wGRsZC1s0d7BQXzJWtjqOyUio9FYJUXDcgJAs5rSsmVL/PTTT8jMzMTJkydx+fJlJCYmAgC++OILHDhwAN999x0mTJiAiIgIPPPMMwCAqVOnqq0vhw8fFnP45ZcteOWVnoiIGIx9+/Y1/IASacCplRQyJzeVL6dWUn2nR6QZAygg+rgm+eWXX3Dr1i2EhobW73gZsejoaHz11Vf4+uuvcfbsWcyYMQM5OTmIjIwE8LB3RPWbyCIjI5GdnY3o6GicPXsWX3/9NdatW4eZM2eKMdOmTUNycjIWLVqEc+fOYdGiRdizZw+mT58uxsycORNpaWnIysrC4cOHERoaCrlcjgkTJuhs36mmNi0dIG3tWOPVpiVHRyA1mmi9eLjrS9GjRw88//zzOH78uMb5NhajH37qhRdeUHr/0UcfYdWqVfj999/RrVs3xMXF4b333sPo0aMBABs2bICLiwu+/fZbvP766/pImYiqk8sf3jVSF1XdbAsLa19Ww7sqwsLCAAB9+vRBs2bN8NxzzwEAfHx8sH37dpXLtGjRAiEhIQAe3plz6dIlcd7333+v0Xarj7GqybMfvv/2e4wJHaPRuomImqLGKin1uUlPk5ry4MEDvPPOOzhw4AAEQUBBQQF69eqF0NBQWFtb47vvvoOPjw/8/f3xzjvviOtetmxZnduPjIzE88+/B8ACf/55EGFho3DkyBGlhnQiInqEARQQXV+T3Lp1C7Nnz67Rw8DUhYWFobi4GAsXLkReXh68vb2xc+dOsU7m5eUhJydHjPf09MTOnTsxY8YMrFixAq6urli2bBnGjPnfdVlAQAC2bt2KOXPmYO7cuejYsSMSEhLg5+cnxly7dg0vv/wyioqK4OTkhKeffhq///476zORsWmC9QJ4+Hu7TCZDs2bNkJSUhODgYFy8eBG2tvq7CdPoGzWqq6ysxHfffYc7d+7A398fWVlZyM/PR1BQkBhjZWWFwMBAHDp0iI0aRIbA3h5o27buOFXjWDo51b6sht1zra2tAQBmZmawsrISp5uZmaGioqLWZariKisrxfehoaH466+/VC73008/wc3NDe7u7rhy5Yo4Pufff/+NJ3s/qTbHaznXcPTIUSz5bIlG+0RE1BQ1Vkmpz2gPmtSUpUuXori4GIcPH4a1tTWio6Nx//59MfbcuXNo0aIFCgoKoFAoxPVMnToV+/fvV7ndNWvWwM/PD1KpFNevPxx6qnfvfujduzcyMjKM/keTlStX4rPPPkNeXh68vLwQFxeH/v37q41PS0tDdHQ0Tp8+DVdXV8yaNUu8C7dKYmIi5s6di0uXLqFjx4746KOPMGrUKHH+qlWrsGrVKly5cgXAw4euv//++0rjzguCgAULFmDt2rW4efMm/Pz8sGLFCnh5eWn3ABBR4zKAAqLra5Ls7Gzk5eXhqaeeAgAUFRXhp59+QmFhock/nDoqKgpRUVEq58XHx9eYFhgYiKNHj9a6ztDQ0Fp7vGzdulXtPCIyIk2wXri5uaFttbxHjRqF2bNn4/z58/Dx8dE4b20ziUaNU6dOwd/fH/fv34etrS2SkpLQrVs3HDp0CABqPJzJxcUF2dnZta6zrKwMZWVl4nuOo0fUSKKjH74a4scftZuLlmjSyv3iiy9ixYoViI+Px5UrV5BxJAMff/Gx2vjvNn+HoBFBDR9HlYioCTCWknLz5k1IpVJYW1vj+vXr+O6778Q7rnJychAVFYU9e/Zg2bJlmD59OlatWgVAs54a165dA9AOAJCdfRHHjx9H9+7dG21fdCEhIQHTp0/HypUr0a9fP6xZswbBwcE4c+YM3N3da8RnZWVhxIgRiIiIwKZNm3Dw4EFERUXByclJvLM2PT0dYWFh+OCDDzBq1CgkJSVh7NixOHDggHhnbdUwt0888QSAhz2+Q0JCcOzYMbHRomqo2/j4eHTu3Bkffvghhg0bhvPnz8POzk5HR4iIHpuxFJB6qOuaxM3NDQUFBeL78PBw+Pr6GtRDYImIDE4TrBfAw2uMdu0eXmP8/vvvKC4uFr8j64tJNGp06dIFx48fx61bt5CYmIgJEyYgLS1NnF99iBfg4R1Vj057VGxsrMnfnUBE+vP2229j4sSJeOKJJyCRSDBn7hy0bNUSALBp3SZcz7+Ot957C8DDc9b3336PTz7/RI8ZExGRtkydOhUvvvgievXqhbZt22Lo0KEAgIqKCrz00kv44IMPxGFU/f39kZCQIDZ61OW9997DwYOZaNbMHObmZlixYgU6d+7cmLvT6JYuXYpJkyZh8uTJAIC4uDjs3r0bq1atQmxsbI341atXw93dHXFxcQCArl27IiMjA4sXLxYbNeLi4jBs2DDExMQAeDiGelpaGuLi4rBlyxYAtQ9z6+XlBUEQONQtEREREZm88PBwXL9+HWZmZrCxscF3330HBwf9PnvGJBo1LC0txdYhX19fHDlyBJ9//rk4BnF+fj5kMpkYX1BQUKP3xqNiYmIQXa3lTS6Xw03VeGhEZNSqP8uiffv2KCoqEt8PHDgQGRkZNf770ThbW1uNnolRXYsWLZCQkAAAuH//PrKys8R5/5z0T6VYiUSCg6cOoqK8AmVFZSAiIsOkaU3x8PDAH3/8oXIdVT2NgYfdxI8dO1avHDZs2IATJx4OP2VhAfTsWa/FDY5CoUBmZiZmz56tND0oKEjpWFWXnp6uNPwsAAwfPhzr1q1DeXk5LCwskJ6ervSAxKqYqoaQRz06zC0ADnVLRFqjr2uS6lQNu0RERIZFX/Viz549j5N2o2im7wQagyAIKCsrg6enJ6RSqdKDrxQKBdLS0hAQEFDrOqysrGBvb6/0IiIiIiIi3SkqKkJlZaXK4WTz8/NVLpOfn68yvqKiQrygUxfz6DpPnToFW1tbWFlZITIyUhzmtmodVctpmltZWRnkcrnSi4iIiIiI6sfoe2q8++67CA4OhpubG27fvo2tW7ciNTUVu3btgkQiwfTp0/Hxxx+jU6dO6NSpEz7++GM0b94cr7zyir5TJyIiIiIiDdR3OFlV8Y9O12Sd6oa5rWrYqG9uHOKWiIiIiOjxGX2jxvXr1zFu3Djk5eXBwcEBPXr0wK5duzBs2DAAwKxZs3Dv3j1ERUXh5s2b8PPzQ3JyMh/cR1Qfn34K3L0LNG+u70yIiIioDu3aAQ8eAM1MoE92mzZtYGZmVqPnQ23DyUqlUpXx5ubmaN26da0xj65T3TC3a9asgVQqBVC/oW61NsStFr6bFRYW1tpTxN7eHk5OTg1ePxERGQhezxORCTL6Ro1169bVOl8ikWD+/PmYP3++bhIiMkVa7tn0OGO9kno8rkTU1PC8p9r//25fK2M5dpaWlvDx8UFKSgpGjRolTk9JSUFISIjKZfz9/fHTTz8pTUtOToavry8sLCzEmJSUFKXnaiQnJ9c5RG3VMLcAlIa67d27N4D/DXW7aNEilctbWVnBysqqjr3WwGN+NyssLMSk8AiUyu+qjbG1b4518V+yYYNMkrGcAw0Bj5UJ4EglRI+F50HN6fJYGX2jBhEZDzMzMwAPL/htbGz0nI3pUSgUAP53nImITBXryeO7e/fhj9lVP/IbsujoaIwbNw6+vr7w9/fH2rVrkZOTg8jISAAPez/k5uZi48aNAIDIyEgsX74c0dHRiIiIQHp6OtatW4ctW7aI65w2bRoGDBiARYsWISQkBDt27MCePXtw4MABMaa2YW4BGPVQt3K5HKXyuxjWZyycWklrzC+8mY+Uo9sgl8vZqEEmhfWj/niNQURNFWtG/emyZrBRg4h0xtzcHM2bN0dhYSEsLCzQzBTGxdCCsrIyPHjwAJWKSqCWRu3K8ko8ePBAvEO0ugcPHqCwsBDNmzeHuTlP7URk2lhPGk4QBNy9excFBQVo2bKlUfxIFRYWhuLiYixcuBB5eXnw9vbGzp074eHhAQDIy8tDTk6OGO/p6YmdO3dixowZWLFiBVxdXbFs2TKMGTNGjAkICMDWrVsxZ84czJ07Fx07dkRCQgL8/PzEmLqGuQWMf6hbp1ZSyJwaMPwVkZFi/agfXmOQMattmMXs7GyUV1ToOCMyNqwZ9aPrmsGqRER1O38eqKgAzM2BLl0avBqJRAKZTIasrCxkZ2drMUHjVl5ejqIbRbAotUAzc/VF8kHFA5TfKUdleaXKO2ubNWsGd3f3Wh+cSkRkClhPalde/r//VtcRo2XLluIzIYxBVFQUoqKiVM6Lj4+vMS0wMBBHjx6tdZ2hoaEIDQ1VO7+uYW4BPQ51q6XvZkRNDetH/fEawwQ0wZpRWFiIiRPGo7SkROX8e/fv4+/ca1CU++o4MzImrBn1p8uawUYNIqrbkCFAbi7Qti1w7dpjrcrS0hKdOnUSu6QRkJOTg/+s/g88R3nCzln9nZ23C24jKykLSxYugbu7e435lpaWvHOAiJoM1hP1AgOB69cBFxcgLa3mfAsLC6PooUG10OJ3M6KmhvWjfniNYQKaYM2Qy+UoLSnBYK/OaNPSocb8CzlXsS0nGxXsrUF1YM2oH13WDDZqEJHONWvWDNbW1vpOw2CYmZnheuF1tFS0xIMHD9TGlShKcL3wOszMzHj8iIjAeqJObu7DV0UFwMNDRFQT6wdR09CmpQOkrR1rTC+8eUv3yZDRYs0wTGxuJyIiIiIiIiIiIiIio8BGDSIiIiIiIiIiIiIiMgps1CAiIiIiIiIiIiIiIqPARg0iIiIiIiIiIiIiIjIKbNQgIiIiIiIiIiIiIiKjwEYNIiIiIiIiIiIiIiIyCmzUICIiIiIiIiIiIiIio8BGDSIiIiIiIiIiIiIiMgrm+k6AiIzAkSNAZSVgZqbvTIiIiKgOLNtNAD9kIiLSFGsGEZkgNmoQUd1kMn1nQERERBpi2W4C+CETEZGmWDOIyARx+CkiIiIiIiIiIiIiIjIKbNQgIiIiIiIiIiIiIiKjwOGniKhua9cCpaWArS3wr3/pOxsiIiKqBct2E8APmYiINMWaQUQmiI0aRFS3hQuB3FygbVt+CSIiIjJwLNtNAD9kIiLSFGsGEZkgNmoQEREZOEV5ObKzs2tMt7e3h5OTkx4yIiIi0oyiXKGyhlVhLdOvwsJCyOXyWmP4GREREZGhYaMGERGRAbt1/z4uX7mCD2fMgKWVldI8m1aOWLvpG/7QQEREBun2nRJcvnwZc2cvgNUjNayKrX1zrIv/krVMDwoLCzEpPAKl8ru1xvEzIiIiIkPDRg0iIjJZsbGx2L59O86dOwcbGxsEBARg0aJF6NKlS63LpaWlITo6GqdPn4arqytmzZqFyMhIHWWt7E55OSwePEC4iws8W7cRp+fdKcXX+fmQy+X8kYGIiAzSvbK7MJOYY2ifsXCTetSYX3gzHylHt7GW6YlcLkep/C6G9RkLp1ZSlTH8jIiIiMgQsVGDiIhMVlpaGt544w307dsXFRUVeO+99xAUFIQzZ86gRYsWKpfJysrCiBEjEBERgU2bNuHgwYOIioqCk5MTxowZo+M9+B9pixZo7+Cgt+0TERE1VOuWzpA5uek7DVLDqZWUnw8REREZFTZqEBGRydq1a5fS+/Xr18PZ2RmZmZkYMGCAymVWr14Nd3d3xMXFAQC6du2KjIwMLF68WK+NGkREREREREREBDTTdwKPKzY2Fn379oWdnR2cnZ0xcuRInD9/XikmPDwcEolE6fX000/rKWMiItKXkpISAICjo6PamPT0dAQFBSlNGz58ODIyMlBeXt6o+RERERERERERUe2MvlGjamiR33//HSkpKaioqEBQUBDu3LmjFPfss88iLy9PfO3cuVNPGRMRkT4IgoDo6Gg888wz8Pb2VhuXn58PFxcXpWkuLi6oqKhAUVGRymXKysogl8uVXkREREREREREpH1G36ixa9cuhIeHw8vLCz179sT69euRk5ODzMxMpTgrKytIpVLxVdtdukREZHqmTJmCkydPYsuWLXXGSiQSpfeCIKicXiU2NhYODg7iy82N41ITERERERmilStXwtPTE9bW1vDx8cFvv/1Wa3xaWhp8fHxgbW2NDh06YPXq1TViEhMT0a1bN1hZWaFbt25ISkpSu77Y2FhIJBJMnz79cXeFiKjJMvpGjUepG1okNTUVzs7O6Ny5MyIiIlBQUKCP9IiMU+fOQLduD/8lMkJvvvkmfvzxR+zbtw/t2rWrNVYqlSI/P19pWkFBAczNzdG6dWuVy8TExKCkpER8Xb16VWu5ExHVlymWbX38AGXQw9ya4odMRKQDCQkJmD59Ot577z0cO3YM/fv3R3BwMHJyclTGZ2VlYcSIEejfvz+OHTuGd999F1OnTkViYqIYk56ejrCwMIwbNw4nTpzAuHHjMHbsWBw+fLjG+o4cOYK1a9eiR48ejbaPNbBmEJEJMqlGDXVDiwQHB2Pz5s3Yu3cvlixZgiNHjmDw4MEoKytTuy4OJUJUzd69wOnTD/8lMiKCIGDKlCnYvn079u7dC09PzzqX8ff3R0pKitK05ORk+Pr6wsLCQuUyVlZWsLe3V3oREemLqZVtff0AZdDD3Jrah0xEpCNLly7FpEmTMHnyZHTt2hVxcXFwc3PDqlWrVMavXr0a7u7uiIuLQ9euXTF58mRMnDgRixcvFmPi4uIwbNgwxMTE4Mknn0RMTAyGDBmCuLg4pXWVlpbi1VdfxZdffolWrVo15m4qY80gIhNkUo0a6oYWCQsLw3PPPQdvb2+88MIL+OWXX3DhwgX8/PPPatfFoUSIiIzfG2+8gU2bNuHbb7+FnZ0d8vPzkZ+fj3v37okxMTExGD9+vPg+MjIS2dnZiI6OxtmzZ/H1119j3bp1mDlzpj52gYioydPXD1Ac5paIyLQoFApkZmYiKChIaXpQUBAOHTqkcpn09PQa8cOHD0dGRgbKy8trjXl0nW+88Qaee+45DB06VKN8ebMtEZF65vpOQFuqhhbZv39/nUOLyGQyeHh44OLFi2pjYmJiEB0dLb6Xy+Vs2CAiMjJVP3gNHDhQafr69esRHh4OAMjLy1O629fT0xM7d+7EjBkzsGLFCri6umLZsmUYM2aMrtImIqL/V/UD1OzZs5WmN+QHqHXr1qG8vBwWFhZIT0/HjBkzasQ8eldtdXUNc9uyZUsEBgbio48+grOzs8p1lJWVKfUWN+QfqBTlCmRnZ6udn52djfKKikbfDvDw78DS0rLWGHt7ezg5OT12PlRTXZ8Rjz0Zi6KiIlRWVsLFxUVpuouLS43hZ6vk5+erjK+oqEBRURFkMpnamOrr3Lp1K44ePYojR45onG9sbCwWLFigcTwRUVNi9I0agiDgzTffRFJSElJTUzUaWqS4uBhXr16FTCZTG2NlZQUrKyttpkpERDpW9YDv2sTHx9eYFhgYiKNHjzZCRkREVB/6/AGqutqGuX3xxRfh4eGBrKwszJ07F4MHD0ZmZqbKawlj+YHq9p0SXL58GXNnL1B7TXTv3l38/Xc+FP9/p3JjbUdRrkBOTjY8PDxhYa7+8tXWvjnWxX/JH9e1TJPPiMeejI1EIlF6LwhCjWl1xT86vbZ1Xr16FdOmTUNycjKsra01zpM32xIRqWf0jRpvvPEGvv32W+zYsUMcWgQAHBwcYGNjg9LSUsyfPx9jxoyBTCbDlStX8O6776JNmzYYNWqUnrMnMhKvvgoUFQFt2gCbN+s7GyIiIqqFKZZtXf8A9aiqYW4PHDigND0sLEz8b29vb/j6+sLDwwM///wzRo8eXWM9WvuBqpE/5Htld2EmMcfQPmPhJvVQGXMu6yQSrn6FisqG99bQdDvZWV9hcM8xamMKb+Yj5eg2yOVy/rCuZXV9Rjz2ZEzatGkDMzOzGg3YBQUFNRq6q0ilUpXx5ubmaN26da0xVevMzMxEQUEBfHx8xPmVlZXYv38/li9fjrKyMpiZmdXYttZutjXFLwZE1OQZfaNGXUOLmJmZ4dSpU9i4cSNu3boFmUyGQYMGISEhAXZ2dnrImMgIpaUBublA27b6zoSIiIjqYEplW18/QFWnzWFutfYDlY4+5NYtnSFzUt3oUnAjT6fbqS2GGh+PP5kCS0tL+Pj4ICUlRekm15SUFISEhKhcxt/fHz/99JPStOTkZPj6+sLCwkKMSUlJURrWMDk5GQEBAQCAIUOG4NSpU0rreO211/Dkk0/inXfeUdmgoVWm9MWAiOj/GX2jRl1Di9jY2GD37t06yoaIiIiIiLRFXz9AAY03zC0REelPdHQ0xo0bB19fX/j7+2Pt2rXIyclBZGQkgIc96nJzc7Fx40YAQGRkJJYvX47o6GhEREQgPT0d69atw5YtW8R1Tps2DQMGDMCiRYsQEhKCHTt2YM+ePWLvPjs7O6WhCwGgRYsWaN26dY3p9D+FhYW1PnuKz/MhatqMvlGDiIiIiIhMlz5+gAI4zC0RkSkKCwtDcXExFi5ciLy8PHh7e2Pnzp3w8Hg4vFpeXh5ycnLEeE9PT+zcuRMzZszAihUr4OrqimXLlmHMmDFiTEBAALZu3Yo5c+Zg7ty56NixIxISEuDn56fz/TMVhYWFmDhhPEpLStTG2Do44OsNG9mwQdREsVGDiIiIiIgMlr5+gOIwt0REpikqKgpRUVEq58XHx9eYFhgYiKNHj9a6ztDQUISGhmqcQ2pqqsaxTZFcLkdpSQkGe3VGm5YONeYX3SrB3tMX+DwfoiaMjRpERERERGTQ9PEDFIe5JSIi0q82LR0gbe2o7zSIyAA103cCREREREREREREREREmmCjBhERERERERERERERGQU2ahARERERERERERERkVFgowYRERERERERERERERkFPiiciOoWEQGUlAAODvrOhIiIiOrAst0E8EMmIiJNsWYQkQliowYR1W3ePH1nQERERBpi2W4C+CETEZGmWDOIyARx+CkiIiIiIiIiIiIiIjIKbNQgIiIiIiIiIiIiIiKjwEYNIiIiIiIiIiIiIiIyCmzUIKK6tWsHSCQP/yUiIiKDxrLdBPBDJiIiTbFmEJEJYqMGEREREREREREREREZBTZqEBERERERERERERGRUWCjBhERERERERERERERGQU2ahARERERERERERERkVFgowYRERERERERERERERkFNmoQEREREREREREREZFRYKMGEREREREREREREREZBTZqEBERERERERERERGRUWCjBhERERERERERERERGQVzfSdAREZg0yagrAywstJ3JkRERFQHlu0mgB8yERFpijWDiEwQGzWIqG4DB+o7AyIiItIQy3YTwA+ZiIg0xZpBRCbI6Iefio2NRd++fWFnZwdnZ2eMHDkS58+fV4oRBAHz58+Hq6srbGxsMHDgQJw+fVpPGRMRERERERERERERUUMYfaNGWloa3njjDfz+++9ISUlBRUUFgoKCcOfOHTHm008/xdKlS7F8+XIcOXIEUqkUw4YNw+3bt/WYORERERERERERERER1YfRN2rs2rUL4eHh8PLyQs+ePbF+/Xrk5OQgMzMTwMNeGnFxcXjvvfcwevRoeHt7Y8OGDbh79y6+/fZbPWdPZCRSU4Hdux/+S0RERAbNFMv2ypUr4enpCWtra/j4+OC3336rNT4tLQ0+Pj6wtrZGhw4dsHr16hoxiYmJ6NatG6ysrNCtWzckJSUpzTfoHuGm+CETEVHjYM0gIhNk9I0ajyopKQEAODo6AgCysrKQn5+PoKAgMcbKygqBgYE4dOiQXnIkMjr//Cfw7LMP/yUiIiKDZmplOyEhAdOnT8d7772HY8eOoX///ggODkZOTo7K+KysLIwYMQL9+/fHsWPH8O6772Lq1KlITEwUY9LT0xEWFoZx48bhxIkTGDduHMaOHYvDhw+LMQbdI9zUPmQiImo8rBlEZIJMqlFDEARER0fjmWeegbe3NwAgPz8fAODi4qIU6+LiIs5TpaysDHK5XOlFRERERES6tXTpUkyaNAmTJ09G165dERcXBzc3N6xatUpl/OrVq+Hu7o64uDh07doVkydPxsSJE7F48WIxJi4uDsOGDUNMTAyefPJJxMTEYMiQIYiLixNj2COciIiIiMgwmVSjxpQpU3Dy5Els2bKlxjyJRKL0XhCEGtOqi42NhYODg/hyc3PTer5ERERERKSeQqFAZmamUq9rAAgKClLb6zo9Pb1G/PDhw5GRkYHy8vJaY2rryc0e4UREREREhsFkGjXefPNN/Pjjj9i3bx/atWsnTpdKpQBQo1dGQUFBjd4b1cXExKCkpER8Xb16tXESJyIiIiIilYqKilBZWVmvXtf5+fkq4ysqKlBUVFRrjLp1aqtHOHuDExERERE9PqNv1BAEAVOmTMH27duxd+9eeHp6Ks339PSEVCpFSkqKOE2hUCAtLQ0BAQFq12tlZQV7e3ulFxERGZf9+/fjhRdegKurKyQSCX744Yda41NTUyGRSGq8zp07p5uEiYhIpfr2ulYV/+j0+qxTWz3C2RuciIiIiOjx6bVRIysr67HX8cYbb2DTpk349ttvYWdnh/z8fOTn5+PevXsAHl5kTJ8+HR9//DGSkpLw559/Ijw8HM2bN8crr7zy2NsnIiLt00Z9AIA7d+6gZ8+eWL58eb2WO3/+PPLy8sRXp06dtJIPEVFToo1zeZs2bWBmZlavXtdSqVRlvLm5OVq3bl1rjKp1arNHOHuDExE1jLauD8h0KMrLkZ2djUuXLtV4ZWdno7yiQt8pElEjMtfnxp944gkMGDAAkyZNQmhoKKytreu9jqoHBA4cOFBp+vr16xEeHg4AmDVrFu7du4eoqCjcvHkTfn5+SE5Ohp2d3ePuAhERNQJt1AcACA4ORnBwcL2Xc3Z2RsuWLRu0TSIiekgb53JLS0v4+PggJSUFo0aNEqenpKQgJCRE5TL+/v746aeflKYlJyfD19cXFhYWYkxKSgpmzJihFFO9J7cgCHjzzTeRlJSE1NTUWnuE9+7dG8D/eoQvWrRIZW5WVlawsrKqxxEgIiJAe9cHZBpu372LrKwszJn1tsq6eu/+ffydew2Kcl89ZEdEuqDXnhonTpxA79698dZbb0EqleL111/HH3/8Ua91CIKg8lXVoAE87K0xf/585OXl4f79+0hLSxPHwiUiIsOjjfrwOHr37g2ZTIYhQ4Zg3759dcZzjHQiopq0dS6Pjo7GV199ha+//hpnz57FjBkzkJOTg8jISAAPez+MHz9ejI+MjER2djaio6Nx9uxZfP3111i3bh1mzpwpxkybNg3JyclYtGgRzp07h0WLFmHPnj2YPn26GMMe4UREhkOb1wcrV66Ep6cnrK2t4ePjg99++63W+LS0NPj4+MDa2hodOnTA6tWra8QkJiaiW7dusLKyQrdu3ZCUlKQ0f9WqVejRo4c4vLm/vz9++eWXBuVPwP0yBSQQMMirM8b261vj5d+pPSorK1HB3hpEJkuvjRre3t5YunQpcnNzsX79euTn5+OZZ56Bl5cXli5disLCQn2mR0REeqKv+iCTybB27VokJiZi+/bt6NKlC4YMGYL9+/fXuhzHSCciqklb5/KwsDDExcVh4cKF6NWrF/bv34+dO3fCw8MDAJCXl4ecnBwx3tPTEzt37kRqaip69eqFDz74AMuWLcOYMWPEmICAAGzduhXr169Hjx49EB8fj4SEBPj5+Ykxq1atQklJCQYOHAiZTCa+EhISxJhZs2Zh+vTpiIqKgq+vL3Jzc9kjnIioEWirpiQkJGD69Ol47733cOzYMfTv3x/BwcFKdaS6rKwsjBgxAv3798exY8fw7rvvYurUqUhMTBRj0tPTERYWhnHjxuHEiRMYN24cxo4di8OHD4sx7dq1wyeffIKMjAxkZGRg8ODBCAkJwenTpx/vwDRxjvZ2kLZ2rPFqxTpMZPIM4kHh5ubmGDVqFLZt24ZFixbh0qVLmDlzJtq1a4fx48cjLy9P3ykSEZEe6Lo+dOnSBREREejTpw/8/f2xcuVKPPfcc1i8eHGty3GMdCIi9bRxLo+KisKVK1dQVlaGzMxMDBgwQJwXHx+P1NRUpfjAwEAcPXoUZWVlyMrKEnt1VBcaGopz585BoVDg7NmzGD16tNJ89ggnIjI8j1tTli5dikmTJmHy5Mno2rUr4uLi4ObmJg5t/qjVq1fD3d0dcXFx6Nq1KyZPnoyJEycqXR/ExcVh2LBhiImJwZNPPomYmBgMGTIEcXFxYswLL7yAESNGoHPnzujcuTM++ugj2Nra4vfff9fKcSEiamoMolEjIyMDUVFRkMlkWLp0KWbOnIlLly5h7969yM3NVTteLhHpyLVrgCA8/JdIhwyhPjz99NO4ePFirTFWVlZiV/KqFxGRvhha2TaEc7nJMbQPmYhIRx6npigUCmRmZiIoKEhpelBQEA4dOqRymfT09Brxw4cPR0ZGBsrLy2uNUbfOyspKbN26FXfu3IG/v3+d+/zYWDOIyATp9UHhS5cuxfr163H+/HmMGDECGzduxIgRI9Cs2cO2Fk9PT6xZswZPPvmkPtMkIiIdM6T6cOzYMchkskbfDhGRqTGkczmZPkW5AtnZ2bXG2Nvbw8nJSUcZEZE2aaOmFBUVobKyEi4uLkrTXVxckJ+fr3KZ/Px8lfEVFRUoKiqCTCZTG/PoOk+dOgV/f3/cv38ftra2SEpKQrdu3dTmW1ZWhrKyMvE9n9tHRPQ/em3UWLVqFSZOnIjXXnsNUqlUZYy7uzvWrVun48yIiEiftFUfSktL8ddff4nvs7KycPz4cTg6OsLd3R0xMTHIzc3Fxo0bATzsOt6+fXt4eXlBoVBg06ZNSExMVBozl4iINMPv+qQrt++U4PLly5g7ewGsrKzUxtnaN8e6+C/ZsEFkhLRZUyQSidJ7QRBqTKsr/tHpmqyzS5cuOH78OG7duoXExERMmDABaWlpahs2YmNjsWDBgjr3h4ioKdJro0ZKSgrc3d3FlvUqgiDg6tWrcHd3h6WlJSZMmKCnDImISB+0VR8yMjIwaNAg8X10dDQAYMKECYiPj6/xcFmFQoGZM2ciNzcXNjY28PLyws8//4wRI0Zoce+IiJoGftcnXblXdhdmEnMM7TMWblIPlTGFN/ORcnQb5HI5GzWIjJA2akqbNm1gZmZWowdFQUFBjZ4WVaRSqcp4c3NztG7dutaYR9dpaWmJJ554AgDg6+uLI0eO4PPPP8eaNWtUbjsmJka8fgEe9tRwc3NTu39ERE2JXhs1OnbsiLy8PDg7OytNv3HjBjw9PVFZWamnzIhIyYIFQEkJ4OAAzJun72yoCdBWfRg4cKB4J5Uq8fHxSu9nzZqFWbNm1TtfIiJDYihlm9/1G5GhfMgGpnVLZ8ic+IMfkSnSRk2xtLSEj48PUlJSMGrUKHF6SkqK2mdx+Pv746efflKalpycDF9fX1hYWIgxKSkpmDFjhlJMQEBArfkIgqA0vNSjrKysau19pjHWDCIyQXpt1FD3Q1NpaSmsra11nA0RqfXll0BuLtC2Lb8EkU6wPhARNZyhlG2eyxuRoXzIREQ6oq2aEh0djXHjxsHX1xf+/v5Yu3YtcnJyEBkZCQA1hqeNjIzE8uXLER0djYiICKSnp2PdunXYsmWLuM5p06ZhwIABWLRoEUJCQrBjxw7s2bMHBw4cEGPeffddBAcHw83NDbdv38bWrVuRmpqKXbt2NeRw1A9rBhGZIL00alR1n5NIJHj//ffRvHlzcV5lZSUOHz6MXr166SM1IiLSI9YHIiLjx3M5ERFpi7ZrSlhYGIqLi7Fw4ULk5eXB29sbO3fuhIfHw6HrHh2e1tPTEzt37sSMGTOwYsUKuLq6YtmyZRgzZowYExAQgK1bt2LOnDmYO3cuOnbsiISEBPj5+Ykx169fx7hx45CXlwcHBwf06NEDu3btwrBhwxp6aIiImjS9NGocO3YMwMOW9lOnTsHS0lKcZ2lpiZ49e2LmzJn6SI2IiPSI9YGIyPjxXE5ERNrSGDUlKioKUVFRKuc9OjwtAAQGBuLo0aO1rjM0NBShoaFq52vyAHMiItKcXho19u3bBwB47bXX8Pnnn8Pe3l4faRARkYFhfSAiMn48lxMRkbawphARkSp6fabG+vXr9bl5IiIyUKwPRETGj+dyIiLSFtYUIiKqTueNGqNHj0Z8fDzs7e0xevToWmO3b9+uo6yIiEjfWB+IiIwfz+VERKQtrClERKSOzhs1HBwcIJFIxP8mIiICWB+IiEwBz+VERKQtrClERKSOzhs1qncZZPdBIiKqwvpARGT8eC4nQ6YoVyA7O1vtfHt7ezg5OekwIyKqDWsKERGpo9dnaty7dw+CIKB58+YAgOzsbCQlJaFbt24ICgrSZ2pERKRHrA9ERMaP53IyJLfvlODy5cuYO3sBrKysVMbY2jfHuvgv2bBBZIBYU4iIqDq9NmqEhIRg9OjRiIyMxK1bt/DUU0/B0tISRUVFWLp0Kf7973/rMz0iqhIYCBQVAW3a6DsTaiJYH4iIGs5QyjbP5Y3IUD5kI3Kv7C7MJOYY2mcs3KQeNeYX3sxHytFtkMvlbNQgMkCsKY+BNYOITFAzfW786NGj6N+/PwDg+++/h1QqRXZ2NjZu3Ihly5bpMzUiqm7zZmD37of/EukA6wMRUcMZStnmubwRGcqHbIRat3SGzMmtxsuplVTfqRFRLVhTHgNrBhGZIL02aty9exd2dnYAgOTkZIwePRrNmjXD008/XetYp0REZNpYH4iIjB/P5UREpC2sKUREVJ1eGzWeeOIJ/PDDD7h69Sp2794tjoNYUFAAe3t7faZGRER6xPpARGT8eC4nIiJtYU0hIqLq9Nqo8f7772PmzJlo3749/Pz84O/vD+Bhq3vv3r31mRoREekR6wMRkfHjuZyIiLSFNYWIiKrT64PCQ0ND8cwzzyAvLw89e/YUpw8ZMgSjRo3SY2ZEpGTwYOD6dcDFBdi7V9/ZUBPA+kBE1HCGUrZ5Lm9EhvIhExHpCGvKY2DNICITpNdGDQCQSqWQSpUfyvbUU0/pKRsiUunCBSA3Fygp0Xcm1ISwPhARNYwhlW2eyxuJIX3IREQ6wprSQKwZRGSC9NqocefOHXzyySf49ddfUVBQgAcPHijNv3z5sp4yIyIifWJ9ICIyfjyXExGRtrCmEBFRdXpt1Jg8eTLS0tIwbtw4yGQySCSSBq1n//79+Oyzz5CZmYm8vDwkJSVh5MiR4vzw8HBs2LBBaRk/Pz/8/vvvj5M+ERE1Em3VByIi0h+ey4mISFtYU4iIqDq9Nmr88ssv+Pnnn9GvX7/HWs+dO3fQs2dPvPbaaxgzZozKmGeffRbr168X31taWj7WNomIqPFoqz4QEZH+8FxOTVVhYSHkcrna+QqFos7rUXt7ezg5OWk7NSKjxZpCRETV6bVRo1WrVnB0dHzs9QQHByM4OLjWGCsrqxpjLxIRkWHSVn0gIiL90ea5fOXKlfjss8+Ql5cHLy8vxMXFoX///mrj09LSEB0djdOnT8PV1RWzZs1CZGSkUkxiYiLmzp2LS5cuoWPHjvjoo4+UHjZbV29wgD3CqabCwkJMCo9AqfyuyvmKcgVycrLh4eEJC3P1l+O29s2xLv5LNmwQ/T9eHxARUXV6bdT44IMP8P7772PDhg1o3rx5o24rNTUVzs7OaNmyJQIDA/HRRx/B2dm5UbdJREQNo8v6QEREjUNb5/KEhARMnz4dK1euRL9+/bBmzRoEBwfjzJkzcHd3rxGflZWFESNGICIiAps2bcLBgwcRFRUFJycnsVd3eno6wsLC8MEHH2DUqFFISkrC2LFjceDAAfj5+QHQrDc4wB7hpEwul6NUfhfD+oyFU6uaN9WdyzqJ7KyvMLjnGLhJPVSuo/BmPlKOboNcLmejBtH/4/UBERFVp9dGjSVLluDSpUtwcXFB+/btYWFhoTT/6NGjWtlOcHAwXnzxRXh4eCArKwtz587F4MGDkZmZCSsrK5XLlJWVoaysTHxfW/dhIiLSLl3VByIiajzaOpcvXboUkyZNwuTJkwEAcXFx2L17N1atWoXY2Nga8atXr4a7uzvi4uIAAF27dkVGRgYWL14sNk7ExcVh2LBhiImJAQDExMQgLS0NcXFx2LJlCwDNeoMD7BFOqjm1kkLm5FZjesGNPABA65bOKucTkWq8PiAiour02qjxaPftxhIWFib+t7e3N3x9feHh4YGff/4Zo0ePVrlMbGwsFixYoJP8iIhIma7qAxERNR5tnMsVCgUyMzMxe/ZspelBQUE4dOiQymXS09MRFBSkNG348OFYt24dysvLYWFhgfT0dMyYMaNGTFVDSH3Up0c4b5wiImoYXh8QEVF1em3UmDdvnl62K5PJ4OHhgYsXL6qNiYmJQXR0tPheLpfDzY130hAR6YK+6gMREWmPNs7lRUVFqKyshIuLi9J0FxcX5Ofnq1wmPz9fZXxFRQWKioogk8nUxqhbpzr17RHOG6eIiBqG1wdERFSdXhs1AODWrVv4/vvvcenSJbz99ttwdHTE0aNH4eLigrZt2zbKNouLi3H16lXIZDK1MVZWVmqHpiJqct5/HygtBWxt9Z0JNSH6qA9ERKbAkMq2ts7lEolE6b0gCDWm1RX/6PT6rlOV+vYI19qNU4b0IRMR6QivDxqINYOITJBeGzVOnjyJoUOHwsHBAVeuXEFERAQcHR2RlJSE7OxsbNy4UaP1lJaW4q+//hLfZ2Vl4fjx43B0dISjoyPmz5+PMWPGQCaT4cqVK3j33XfRpk0bjBo1qrF2jci0/Otf+s6Amhht1QcioqbIUMq2Ns7lbdq0gZmZWY0eFAUFBTV6WlSRSqUq483NzdG6detaY9StU1N19QjX2o1ThvIhExHpCK8PHgNrBhGZoGb63Hh0dDTCw8Nx8eJFWFtbi9ODg4Oxf/9+jdeTkZGB3r17o3fv3uJ6e/fujffffx9mZmY4deoUQkJC0LlzZ0yYMAGdO3dGeno67OzstL5PRET0+LRVH4iISH+0cS63tLSEj48PUlJSlKanpKQgICBA5TL+/v414pOTk+Hr6ys+WFZdjLp1akqTHuFERFR/vD4gIqLq9NpT48iRI1izZk2N6W3btq3XeLYDBw4Uu5Srsnv37gblR0RE+qGt+kBERPqjrXN5dHQ0xo0bB19fX/j7+2Pt2rXIyclBZGQkgIdDOuXm5op36UZGRmL58uWIjo5GREQE0tPTsW7dOmzZskVc57Rp0zBgwAAsWrQIISEh2LFjB/bs2YMDBw6IMbX1Bnd3d0dpaSl7hBMR6QivD4iIqDq9NmpYW1tDLpfXmH7+/Hk4OTnpISMiUikvD6isBMzMAN55SDrA+kBE1HCGUra1dS4PCwtDcXExFi5ciLy8PHh7e2Pnzp3w8PAAAOTl5SEnJ0eM9/T0xM6dOzFjxgysWLECrq6uWLZsGcaMGSPGBAQEYOvWrZgzZw7mzp2Ljh07IiEhAX5+fmJMRkYGBg0aJL6vehbGhAkTEB8fL/YI37hxI27dugWZTIZBgwYhISGh8XuEG8qH3AQVFhaq/Luukp2djfKKCoPIRaFQwNLSUu18XeZK9Lh4ffAYWDOIyATptVEjJCQECxcuxLZt2wA8fFhfTk4OZs+erXTRQUR61rcvkJsLtG0LXLum72yoCWB9ICJqOEMp29o8l0dFRSEqKkrlvPj4+BrTAgMDcfTo0VrXGRoaitDQULXz6+oNbmNjo78e4YbyITcxhYWFmBQegVL5XbUx9+7dxd9/50NRXq7XXBTlCuTkZMPDwxMW5qov+3WVK5E28PrgMbBmEJEJ0mujxuLFizFixAg4Ozvj3r17CAwMRH5+Pvz9/fHRRx/pMzUiItIj1gfNKMrLkZ2drXKevb0971ojIr3iuZxMjVwuR6n8Lob1GQunVlKVMeeyTiLh6leoqGzcHhB15XIu6ySys77C4J5j4Cb10GuuRNrAmkJERNXptVHD3t4eBw4cwL59+5CZmYkHDx6gT58+GDp0qD7TIiIiPWN9qNut+/dx+coVfDhjBiytrGrMt2nliLWbvmHDBhHpDc/lZKqcWkkhc3JTOa/gRp5B5FKVR+uWzgaTK9HjYE0hIqLq9Nao8eDBA8THx2P79u24cuUKJBIJPD09IZVKIQgCJBKJvlIjIiI9Yn3QzJ3yclg8eIBwFxd4tm6jNC/vTim+zs+HXC5nowYR6QXP5UREpC2sKURE9Khm+tioIAj4xz/+gcmTJyM3Nxfdu3eHl5cXsrOzER4ejlGjRukjLSIi0jNt14f9+/fjhRdegKurKyQSCX744Yc6l0lLS4OPjw+sra3RoUMHrF69uoF7oxvSFi3Q3sFB6SVrYavvtIioCeN3fSIi0hbWFCIiUkUvPTXi4+Oxf/9+/Prrrxg0aJDSvL1792LkyJHYuHEjxo8fr4/0iIhIT7RdH+7cuYOePXvitdde0+gBgllZWRgxYgQiIiKwadMmHDx4EFFRUXBycuIDCImINMTv+kREpC2sKUREpIpeemps2bIF7777bo2CBACDBw/G7NmzsXnzZj1kRkRE+qTt+hAcHIwPP/wQo0eP1ih+9erVcHd3R1xcHLp27YrJkydj4sSJWLx4scbbJCJq6vhdn4iItKUxasrKlSvh6ekJa2tr+Pj44Lfffqs1XpOe3ImJiejWrRusrKzQrVs3JCUlKc2PjY1F3759YWdnB2dnZ4wcORLnz5+vV95ERPQ/emnUOHnyJJ599lm184ODg3HixAkdZkRERIZA3/UhPT0dQUFBStOGDx+OjIwMlJeXN9p2iYhMib7P5UREZDq0XVMSEhIwffp0vPfeezh27Bj69++P4OBg5OTkqIyv6sndv39/HDt2DO+++y6mTp2KxMREMSY9PR1hYWEYN24cTpw4gXHjxmHs2LE4fPiwGJOWloY33ngDv//+O1JSUlBRUYGgoCDcuXNH49yJiOh/9DL81I0bN+Di4qJ2vouLC27evKnDjIiIyBDouz7k5+fX2L6LiwsqKipQVFQEmUymcrmysjKUlZWJ7+VyeaPlSERk6PR9LiciItOh7ZqydOlSTJo0CZMnTwYAxMXFYffu3Vi1ahViY2NrxFfvyQ0AXbt2RUZGBhYvXiwOTxsXF4dhw4YhJiYGABATE4O0tDTExcVhy5YtAIBdu3YprXf9+vVwdnZGZmYmBgwYoHH+RET0kF56alRWVsLcXH17ipmZGSoqKnSYERERGQJDqA8SiUTpvSAIKqdXFxsbCwcHB/Hl5ubWqDkSERkyQziXExGRadBmTVEoFMjMzKzRMzsoKAiHDh1SuYwmPbnVxahbJwCUlJQAABwdHTXKnYiIlOmlp4YgCAgPD4eVlZXK+dXvdiUiA/Drr0BFBVDLl0kibdB3fZBKpcjPz1eaVlBQAHNzc7Ru3VrtcjExMYiOjhbfy+VyNmwQkd7ou2zr+1zeJOj7Q6ZGpyhXIDs7W+387OxslLNxkJoAbdaUoqIiVFZWquyZ/eg1QBVNenKri1G3TkEQEB0djWeeeQbe3t5q89Vab3DWDCIyQXo5o02YMKHOmPHjx+sgEyLSSJcu+s6Amgh91wd/f3/89NNPStOSk5Ph6+sLCwsLtctZWVmpvdAiItI1fZdtfZ/LmwR9f8jUqG7fKcHly5cxd/YCtd8v7t27i7//zoeCz/wiE9cYNUVVz+zaemVr0pO7PuucMmUKTp48iQMHDtSaZ2xsLBYsWFBrjEZYM4jIBOmlUWP9+vX62CwRERk4bdeH0tJS/PXXX+L7rKwsHD9+HI6OjnB3d0dMTAxyc3OxceNGAEBkZCSWL1+O6OhoREREID09HevWrRPHwiUiorrxuz7R47lXdhdmEnMM7TMWblIPlTHnsk4i4epXqKhkbw0ybdqsKW3atIGZmZnKntnqntuhSU9udTGq1vnmm2/ixx9/xP79+9GuXbta82VvcP1SlJfX2mPO3t4eTk5OOsyIiKpj3zMiIjJZGRkZGDRokPi+6qJgwoQJiI+PR15eHnJycsT5np6e2LlzJ2bMmIEVK1bA1dUVy5YtEx8CSERERKQrrVs6Q+ak+gfMght5Os6GyPhZWlrCx8cHKSkpGDVqlDg9JSUFISEhKpfRpCe3v78/UlJSMGPGDKWYgIAA8b0gCHjzzTeRlJSE1NRUeHp61pkve4Prz+27d5GVlYU5s95W+xnYOjjg6w0b2bBBpCds1CCiun37LXD3LtC8OfDKK/rOhkhjAwcOFLuHqxIfH19jWmBgII4ePdqIWRERNS6W7SaAHzIRUYNER0dj3Lhx8PX1hb+/P9auXYucnBxERkYCQIN6ck+bNg0DBgzAokWLEBISgh07dmDPnj1Kw0u98cYb+Pbbb7Fjxw7Y2dmJPTscHBxgY2PTuDvNmlFv98sUkEDAIK/OaOfiXGN+0a0S7D19AXK5nI0aRHrCRg0iqtusWUBuLtC2Lb8EERERGTiW7SaAHzIRUYOEhYWhuLgYCxcuRF5eHry9vbFz5054eDwc6q0hPbkDAgKwdetWzJkzB3PnzkXHjh2RkJAAPz8/MWbVqlUAHt50Vd369esRHh7eeDsMsGY8Bkd7O0hbO+o7DSJSgY0aRERERERERETUJERFRSEqKkrlvIb25A4NDUVoaKja+bX1Hiciovprpu8EiIiIiIiIiIiIiIiINMFGDSIiIiIiIiIiIiIiMgps1CAiIiIiIiIiIiIiIqPARg0iIiIiIiIiIiIiIjIKbNQgIiIiIiIiIiIiIiKjYK7vBIiIiIiIiIiIiIiI6lJYWAi5XK52vr29PZycnHSYEemDSTRq7N+/H5999hkyMzORl5eHpKQkjBw5UpwvCAIWLFiAtWvX4ubNm/Dz88OKFSvg5eWlv6SJiIiIiIiIiIiISCOFhYWYOGE8SktK1MbYOjjg6w0b2bBh4kxi+Kk7d+6gZ8+eWL58ucr5n376KZYuXYrly5fjyJEjkEqlGDZsGG7fvq3jTImMlFQKtG378F8iIiIyaKZYtleuXAlPT09YW1vDx8cHv/32W63xaWlp8PHxgbW1NTp06IDVq1fXiElMTES3bt1gZWWFbt26ISkpSWn+/v378cILL8DV1RUSiQQ//PBDjXUIgoD58+fD1dUVNjY2GDhwIE6fPv1Y+6oRU/yQiYiocbBmkAmRy+UoLSnBYK/OGNuvb43XYK/OKC0pqbUnB5kGk2jUCA4OxocffojRo0fXmCcIAuLi4vDee+9h9OjR8Pb2xoYNG3D37l18++23esiWyAhlZADXrj38l4iIiAyaqZXthIQETJ8+He+99x6OHTuG/v37Izg4GDk5OSrjs7KyMGLECPTv3x/Hjh3Du+++i6lTpyIxMVGMSU9PR1hYGMaNG4cTJ05g3LhxGDt2LA4fPizG1HXjFKDHm6dM7UMmIqLGw5pBJqhNSwdIWzvWeLVp6aDv1EhHTGL4qdpkZWUhPz8fQUFB4jQrKysEBgbi0KFDeP311/WYHRERERER1Wbp0qWYNGkSJk+eDACIi4vD7t27sWrVKsTGxtaIX716Ndzd3REXFwcA6Nq1KzIyMrB48WKMGTNGXMewYcMQExMDAIiJiUFaWhri4uKwZcsWAA9vnAoODlab16M3TwHAhg0b4OLigm+//ZbXGUREREQNUNszM7Kzs1FeUaHjjMgQmXyjRn5+PgDAxcVFabqLiwuys7PVLldWVoaysjLxPbstERERERHplkKhQGZmJmbPnq00PSgoCIcOHVK5THp6utINTQAwfPhwrFu3DuXl5bCwsEB6ejpmzJhRI6aqIUQTvHmKiIiISLvqembGvfv38XfuNSjKfXWcGRkak2/UqCKRSJTeC4JQY1p1sbGxWLBgQWOnRUREREREahQVFaGyslLlDUpVNy89Kj8/X2V8RUUFioqKIJPJ1MaoW6e67VQt9+h61N08xRuniIiIiNSr/swMVUNJXci5im052ahgb40mz+QbNaT//yCk/Px8yGQycXpBQUGNC5DqYmJiEB0dLb6Xy+Vwc3NrvESJDNnrrwM3bgCOjsCaNfrOhoiIiGphimW7vjcoqYp/dHp91yy7zzsAAKNBSURBVKmN3LR245QpfshERNQ4WDPICFU9M+NRhTdv6T4ZMkgm36jh6ekJqVSKlJQU9O7dG8DDbuxpaWlYtGiR2uWsrKxgZWWlqzSJDNvPPwO5uUDbtvrOhIiIiOpgSmW7TZs2MDMzq9GDorYblKRSqcp4c3NztG7dutaY2m56UrUdoH43T2ntxilT+pCJiKhxsWYQkQlqpu8EtKG0tBTHjx/H8ePHATwc3/b48ePIycmBRCLB9OnT8fHHHyMpKQl//vknwsPD0bx5c7zyyiv6TZyIiIiIiNSytLSEj48PUlJSlKanpKQgICBA5TL+/v414pOTk+Hr6wsLC4taY9StU5XqN09Vqbp5St16rKysYG9vr/QiIiJqigoLC3Hp0iWVLz4MmojqYhI9NTIyMjBo0CDxfdXdTxMmTEB8fDxmzZqFe/fuISoqCjdv3oSfnx+Sk5NhZ2enr5SJiIiIiEgD0dHRGDduHHx9feHv74+1a9ciJycHkZGRAB72fsjNzcXGjRsBAJGRkVi+fDmio6MRERGB9PR0rFu3Dlu2bBHXOW3aNAwYMACLFi1CSEgIduzYgT179uDAgQNiTGlpKf766y/xfdWNU46OjnB3d1e6eapTp07o1KkTPv74Y948RdRAhYWFtT5nxt7eHk5OTkazHSJSjw+DJqLHZRKNGgMHDhTHyVVFIpFg/vz5mD9/vu6SIiIiIiKixxYWFobi4mIsXLgQeXl58Pb2xs6dO+Hh4QEAyMvLQ05Ojhjv6emJnTt3YsaMGVixYgVcXV2xbNkyjBkzRowJCAjA1q1bMWfOHMydOxcdO3ZEQkIC/Pz8xJi6bpwCwJuniLSksLAQk8IjUCq/qzbG1r451sV/+VgNDrraDhHVjg+DJqLHZRKNGkREREREZLqioqIQFRWlcl5VA0N1gYGBOHr0aK3rDA0NRWhoqNr5dd04BfDmKSJtkcvlKJXfxbA+Y+HUSlpjfuHNfKQc3Qa5XP5YjQ262g4RaYYPgyaihmKjBhERERERERHpnVMrKWRObiazHSIiImocJvGgcCIiIiIiIiIiIiIiMn1s1CAiIiIiIiIiIiIiIqPARg0iIiIiIiIiIiIiIjIKfKYGEdXt5ZeBmzeBVq30nQkRERHVgWW7CeCHTEREmmLNICITxEYNIqrbZ5/pOwMiIiLSEMt2E8APmYiINMWaoReFhYWQy+Vq59vb28PJyanRlicydWzUICIiIiIiIiIiItKCwsJCTJwwHqUlJWpjbB0c8PWGjSobJh53eaKmgI0aRERERERERERERFogl8tRWlKCwV6d0aalQ435RbdKsPf0BcjlcpWNEo+7PFFTwEYNIiIiIiIiavIU5QpkZ2ernZ+dnY3yigodZmQc6jpugHENk6Kt/dHG0DF1rUOXuRBR/bVp6QBpa0e9LU9kytioQUR1e/JJ4O+/AVdX4Nw5fWdDREREtWDZbgL4IWvd7TsluHz5MubOXgArKyuVMffu3cXff+dDUV6u4+wMlybHDQBs7ZtjXfyXBv/Dubb2p7CwEJPCI1Aqv9uo69BVLmTkWDOIyASxUYOI6lZaCty+/fBfIiIiMmgs200AP2Stu1d2F2YScwztMxZuUg+VMeeyTiLh6leoqGRvjSqaHLfCm/lIObrNKIZJ0db+yOVylMrvYlifsXBqJW2UdegyFzJyrBlNEntokaljowYRERERERERgNYtnSFzclM5r+BGno6zMR61HTdjpK39cWolfez1aGMd2lwPERk+PmicmgI2ahARERERERERERGZAD5onJoCNmoQERERERERERERmRA+aJxMWTN9J0BERNTYVq5cCU9PT1hbW8PHxwe//fab2tjU1FRIJJIar3N8qB4RERERERERkd6xUYOIiExaQkICpk+fjvfeew/Hjh1D//79ERwcjJycnFqXO3/+PPLy8sRXp06ddJSxdijKy5GdnY1Lly4pvQoLC/WdGhERERGR3tTnhicASEtLg4+PD6ytrdGhQwesXr26RkxiYiK6desGKysrdOvWDUlJSUrz9+/fjxdeeAGurq6QSCT44YcftLlLRFpXWFhY41qS15VkSDj8FBERmbSlS5di0qRJmDx5MgAgLi4Ou3fvxqpVqxAbG6t2OWdnZ7Rs2VJHWWrXrfv3cfnKFXw4YwYsrayU5tm0csTaTd9w7FQiIiIianKqbnhauXIl+vXrhzVr1iA4OBhnzpyBu7t7jfisrCyMGDECERER2LRpEw4ePIioqCg4OTlhzJgxAID09HSEhYXhgw8+wKhRo5CUlISxY8fiwIED8PPzAwDcuXMHPXv2xGuvvSYuR2So+KBxMgZs1CAiIpOlUCiQmZmJ2bNnK00PCgrCoUOHal22d+/euH//Prp164Y5c+Zg0KBBamPLyspQVlYmvpfL5Y+X+GO6U14OiwcPEO7iAs/WbcTpeXdK8XV+Ph8IR0RERERNUn1veFq9ejXc3d0RFxcHAOjatSsyMjKwePFisXEiLi4Ow4YNQ0xMDAAgJiYGaWlpiIuLw5YtWwAAwcHBCA4O1sEeEj0+PmicjAEbNYiIyGQVFRWhsrISLi4uStNdXFyQn5+vchmZTIa1a9fCx8cHZWVl+OabbzBkyBCkpqZiwIABKpeJjY3FggULtJ7/45K2aIH2DjW/hBIRERHpkqJcgezsbLXzs7OzUV5R8VjrAB7e0GJpaflY29EVbRwTqp+G3PCUnp6OoKAgpWnDhw/HunXrUF5eDgsLC6Snp2PGjBk1YqoaQhrK0G6coqaHDxonQ8ZGDSKq2+rVwL17gI2NvjMhahCJRKL0XhCEGtOqdOnSBV26dBHf+/v74+rVq1i8eLHaRo2YmBhER0eL7+VyOdzc3LSQORFR/bFsNwH8kMmI3L5TgsuXL2Pu7AWwemRYzCr37t3F33/nQ1Fe3uB1KMoVyMnJhoeHJyzMVf/UUdd2dEUbx4TqryE3POXn56uMr6ioQFFREWQymdoYdevUlNZunGLNMEhVz0BURReNmvrefmFhocqGOjbokqbYqEFEdXv+eX1nQNQgbdq0gZmZWY0LioKCghoXHrV5+umnsWnTJrXzrays1F6Q0kPqvrTa29uzyzKRlrFsNwH8kMmI3Cu7CzOJOYb2GQs3qYfKmHNZJ5Fw9StUVKr+IUvTdWRnfYXBPcc0eDu6oo1jQg1Xnxue1MU/Or2+69SE1m6cYs0wOLfv3kVWVhbmzHpb5XXkvfv38XfuNSjKfU1y+7U9s6Oxt02mg40aRERksiwtLeHj44OUlBSMGjVKnJ6SkoKQkBCN13Ps2DHIZLLGSLFJKCwsxL/+OQ73bt6oMY8PLiciImoaWrd0hsxJ9Q+yBTfytLYObWxHV4wpV1PQkBuepFKpynhzc3O0bt261pj63ESlCm+cMl33yxSQQMAgr85o5+JcY/6FnKvYlpONikbqsaDv7df2zI7G3jaZDjZqEBGRSYuOjsa4cePg6+sLf39/rF27Fjk5OYiMjATw8A6o3NxcbNy4EcDDB/21b98eXl5eUCgU2LRpExITE5GYmKjP3TBqcrkc927ewESpFLIWtuJ0PriciIiIiHSlITc8+fv746efflKalpycDF9fX1hYWIgxKSkpSs/VSE5ORkBAQCPsBZkSR3s7lc+sKLx5q0lsX9UzO3S1bTJ+TaJRY/78+TXGIdTG+IZETUZmJqBQAJaWgI+PvrMhqpewsDAUFxdj4cKFyMvLg7e3N3bu3AkPj4dd/fPy8pCTkyPGKxQKzJw5E7m5ubCxsYGXlxd+/vlnjBgxQl+7YDJkLWz54HIiHWDZbgL4IRMRNUh9b3iKjIzE8uXLER0djYiICKSnp2PdunXYsmWLuM5p06ZhwIABWLRoEUJCQrBjxw7s2bMHBw4cEGNKS0vx119/ie+zsrJw/PhxODo6wt3dvXF3mjWDiExQM30noCteXl7Iy8sTX6dOndJ3SkTGIyQECAh4+C+REYqKisKVK1dQVlaGzMxMpQd+x8fHIzU1VXw/a9Ys/PXXX7h37x5u3LiB3377jQ0aRGRUTLFsr1y5Ep6enrC2toaPjw9+++23WuPT0tLg4+MDa2trdOjQAatXr64Rk5iYiG7dusHKygrdunVDUlJSvbcbHh4OiUSi9Hr66acfb2c1YYofMhGRDoSFhSEuLg4LFy5Er169sH///lpvePL09MTOnTuRmpqKXr164YMPPsCyZcswZswYMSYgIABbt27F+vXr0aNHD8THxyMhIQF+fn5iTEZGBnr37o3evXsDeNi40rt3b7z//vuNv9OsGURkgppETw0AMDc3h1Qq1XcaRERERERUDwkJCZg+fTpWrlyJfv36Yc2aNQgODsaZM2dU3t2alZWFESNGICIiAps2bcLBgwcRFRUFJycn8Ueo9PR0hIWF4YMPPsCoUaOQlJSEsWPH4sCBA+KPUJpu99lnn8X69evF95aWlo18RIiI6HFERUUhKipK5bz4+Pga0wIDA3H06NFa1xkaGorQ0FC18wcOHCg+YJyIiB5fk+mpcfHiRbi6usLT0xMvvfQSLl++rO+UiIiIiIioDkuXLsWkSZMwefJkdO3aFXFxcXBzc8OqVatUxq9evRru7u6Ii4tD165dMXnyZEycOBGLFy8WY+Li4jBs2DDExMTgySefRExMDIYMGYK4uLh6b9fKygpSqVR8OTrWHJuaiIiIiIi0p0k0avj5+WHjxo3YvXs3vvzyS+Tn5yMgIADFxcVqlykrK4NcLld6ERERERGR7igUCmRmZiIoKEhpelBQEA4dOqRymfT09Brxw4cPR0ZGBsrLy2uNqVpnfbabmpoKZ2dndO7cGRERESgoKFC7P7zGICIiIiJ6fE2iUSM4OBhjxoxB9+7dMXToUPz8888AgA0bNqhdJjY2Fg4ODuLLzc1NV+kSERERERGAoqIiVFZWwsXFRWm6i4sL8vPzVS6Tn5+vMr6iogJFRUW1xlStU9PtBgcHY/Pmzdi7dy+WLFmCI0eOYPDgwSgrK1OZG68xiIiIiIgeX5N5pkZ1LVq0QPfu3XHx4kW1MTExMYiOjhbfy+VyXnQQEREREemBRCJRei8IQo1pdcU/Ol2TddYVExYWJv63t7c3fH194eHhgZ9//hmjR4+ukRevMYhMg6JcgezsbLXzs7OzUV5RocOMiIiImpYm2ahRVlaGs2fPon///mpjrKysYGVlpcOsiIiIiIioujZt2sDMzKxGr4yCgoIavSiqSKVSlfHm5uZo3bp1rTFV62zIdgFAJpPBw8ND7c1TvMYgMn6375Tg8uXLmDt7gdr/n+/du4u//86H4v+HvCMiIiLtahKNGjNnzsQLL7wAd3d3FBQU4MMPP4RcLseECRP0nRoREREREalhaWkJHx8fpKSkYNSoUeL0lJQUhISEqFzG398fP/30k9K05ORk+Pr6wsLCQoxJSUnBjBkzlGICAgIavF0AKC4uxtWrVyGTyeq/s0RkFO6V3YWZxBxD+4yFm9RDZcy5rJNIuPoVKirZW4OIiKgxNIlGjWvXruHll19GUVERnJyc8PTTT+P333+Hh4fqLyBERERERGQYoqOjMW7cOPj6+sLf3x9r165FTk4OIiMjATwc0ik3NxcbN24EAERGRmL58uWIjo5GREQE0tPTsW7dOmzZskVc57Rp0zBgwAAsWrQIISEh2LFjB/bs2YMDBw5ovN3S0lLMnz8fY8aMgUwmw5UrV/Duu++iTZs2Sg0hRGSaWrd0hsxJ9fBxBTfydJwNERFR09IkGjW2bt2q7xSIiIiIiKgBwsLCUFxcjIULFyIvLw/e3t7YuXOneINSXl4ecnJyxHhPT0/s3LkTM2bMwIoVK+Dq6oply5ZhzJgxYkxAQAC2bt2KOXPmYO7cuejYsSMSEhLg5+en8XbNzMxw6tQpbNy4Ebdu3YJMJsOgQYOQkJAAOzs7HR0dIiIi0gdFebnaZ+s0hefq1Lb/AGBvbw8nJycdZkRNTZNo1CCix3T2LCAIQC0P5CQiIiLDYIplOyoqClFRUSrnxcfH15gWGBiIo0eP1rrO0NBQhIaGNni7NjY22L17d63LNxpT/JCJiKhxsGZo3e27d5GVlYU5s95W+Wyde/fv4+/ca1CU++ohu8ZX1/4DgK2DA77esJENG9Ro2KhBRHXj3YZERERGg2W7CeCHTEREmmLN0Lr7ZQpIIGCQV2e0c3GuMf9CzlVsy8lGhYn21qhr/4tulWDv6QuQy+Vs1KBGw0YNIiIiIiIiIiIionpwtLeDtLVjjemFN2/pPhk9ULf/RLrQTN8JEBERERERERERERERaYI9NYiobkuXAnI5YG8PREfrOxsiIiKqBct2E8APmYiINMWaYZKa+oPKidioQUR1W7oUyM0F2rbllyAiI6fuy6+9vb1exjtVl49CoYClpWWN6frKk8iYsGw3AfyQiYhIU6wZJqepP6icCGCjBhERUZNx6/59XL5yBR/OmAHLR7782rRyxNpN3+i0wUBdPorycly5losObu1gbq78VUUfeRIREREZKkW5Qu3d2gDv2CYyRU39QeVEABs1iIiImow75eWwePAA4S4u8GzdRpyed6cUX+fnQy6X67SxQF0+xwqu44vLlzG+TRuDyJOIiIjIEN2+U4LLly9j7uwFKu/WBoB79+7i77/zoSgv13F2RNTYmvqDyqlpY6MGERFREyNt0QLtHRz0nYbo0Xxyb99WOR0wvOGziIiIiPTlXtldmEnMMbTPWLhJPVTGnMs6iYSrX6GikndsExGR6WCjBhERERkFQxs+i4iIiMgQtG7pDJmTm8p5BTfydJwNEZF+H2Re27YB/d8QV1hYCLlcrna+uudLVtF3/oaCjRpERERkFD0gDG34LCIiIiIiIlKmzweZ17VtALB1cMDXGzbq5dqxsLAQEyeMR2lJicr5ivJy5Fy9Cg93d1iYq/7ZXp/5GxI2ahARETVxxtYDwtCGzyIiIiIiIqKH9Pkg87q2XXSrBHtPX9DbDXFyuRylJSUY7NUZbVrWvKa9kHMVV7IuI/DJjgaZvyFhowYREVETxx4QREREREREpE36fJC5um0bijYtHWo9NoaevyFgowYREREBYA8IIiIiIiIiIjJ8bNQgorr16QO4uQG8U5uIiMjgsWw3AfyQiYhIU6wZRGSC2KhBRHX78Ud9Z0BEeqLuAeIKhQKWlpY1puvrweLaetB5YWEh5HK5xutRF9+QbRNpC8t2E8APmYiINMWaQUQmiI0aREREpJK6B4grystx5VouOri1g7m58lcJfTxYXFsPOi8sLMS//jkO927eqDFP1Xpqi6/vtomIiIiIiIhIM2zUICIiIpXUPUD8WMF1fHH5Msa3aWMQDxbX1oPO5XI57t28gYlSKWQtbOtcj7r4hmybiIiIiIiIiDTDRg0iIiKq1aMPEM+9fVvldED1MFDZ2dmoqKjQeZ4NJWthW6/11DeeiIiIiIiIDFNtQwwDdQ8zXNvy2dnZKNfBtXFTwEYNIqrbP/4BFBY+fLAYx+MkIjXUDQN19/59XL92DYoOHfWYHVHTwbLdBPBDJiIiTbFmEGmssLAQEyeMR2lJidoYWwcHfL1ho9pnLta2/L379/F37jUoyn21lnNTxUYNIvo/9u47Pqoq///4e9KBFCBACiWA0pEiKIZiQAWEXUXBlVWXosjXLCpgVhDQFXBVrBjdBQQXQRZFVgErIqgQqqs0YeklJCEmhtCSAOnn9wc/ZhnSJm0mk7yej8c8YO6ce+/nnkzOJ3M/c+8p2c6dUmKi1LixsyMBUIUVe7uquDjl5VWdb6QU9u2Z4q4oKcsVKKWdZL2qTb4O10XargH4IQMA7EXOAOyWlpamjPPndVuH1mpQt+DV+KnnzuuHfYeLvM1wSesfjk/Qv+MdcyeD6o6iBgAAqFBF3a6qqihqgu+irigpyxUopZ1kvapNvg4AAAAANVWDugEKDqxf4eufOnuuHFHhahQ1AABAjVLUBN9FXVFSlitQSjvJelWbfB0AAAAAgKqKogYAAKi2irtt1LUTfJd0RUlZrkCxd5L14iZfryhFTVjH7a0AAAAAwD5F3WZYcsxE4MXtXyrfROZS0bdFtnf7jlKjihpz587V66+/rqSkJHXo0EHR0dHq06ePs8MCAFSy0o7/MTExioqK0r59+xQaGqrJkycrMjLSgRGjIjBx+f8UdcstidtbwTVUxji+YsUK/fWvf9WxY8d03XXX6aWXXtK9995bqv0aYzRz5kwtWLBAZ8+eVY8ePTRnzhx16NChYjsAAFBhqmpOAVD1pV+8qNjYWD03eZK8r/qMeUVlTwRe0v6l8k1knp2To/iEBIU1ayZPj8LLBsVt35FqTFFj+fLlmjhxoubOnatevXpp/vz5GjRokPbv369mzZo5O7xqraQKoFR1qnw1hT0/k6srs2G5ufKQlJubq7hjx2zaOfJnV9q4i2NP3BX13i1pO3FxccrLzSt2Gyi70o7/sbGxGjx4sMaOHaulS5dqy5YtGjdunBo2bKhhw4Y54QhQVq40cXlxivomTmnG36JuuVWRt7fiShBUlsoYx7dt26bhw4frb3/7m+69916tWrVK999/vzZv3qwePXrYvd/XXntNs2fP1uLFi9W6dWu9+OKL6t+/vw4dOiQ/Pz/HdRIAwC5VOacAqPoys7JlkVG/Dq3VJKhRgdcreyLwkvZfEROZn4g9roi215Vp+45UY4oas2fP1pgxY/Too49KkqKjo/Xtt99q3rx5mjVrlpOjq75OnTqlEWNG6MyFgt8MvVr9OvX1r4V8S7QkFXFS//Tp05ry/BRl5GQU2SYnO0eJcYlq2qKp3D3c9eW5MwqSdPrcGT3wfw/YtPX18NUrf3tFgYGBRW6vIk5o2fNeujbu4pT0nquo964928m8mKnEpERdn3N9sftC2ZR2/H/33XfVrFkzRUdHS5LatWun7du364033qCo4aKq+sTlxSnqahOpbFdYXHvLrYrClSCoTJUxjkdHR6t///6aOnWqJGnq1KmKiYlRdHS0li1bZtd+jTGKjo7Ws88+q6FDh0qSPvjgAwUFBemjjz7SY489Vqn9AgAovaqaUwC4lvr+fk6dCLyo/durpInMy7t9R6gRRY3s7Gzt2LFDU6ZMsVk+YMAAbd26tdL376hve1fkdiT7vvFeUpu4uDidOn9Kze5uJt9GvoW2yUjJ0InPTmjv3r0KCwsrVzz2tnPF/i5LMaIwV06g3/LnW1Q/tPAB6rf9v+l43HE1HNBQDZs1lOeufVJWjjxre6rN6Db/i+n4aW1bsE3/F/V/8vIuOvaKKHykpaXpzIUzajy4cZHvpWvjLkpGSoYSVycWW1m2Z3/2vHft+R34bf9vivswTrk5rvGtcVdSlvF/27ZtGjBggM2ygQMHauHChcrJyZGnp2elxQtcq6irTaraBOKOuBIENVNljePbtm3TU089VaDNlZNW9uw3NjZWycnJNvvy9vZWRESEtm7dSlEDAKqYqpxTAAClUyOKGqmpqcrLy1NQUJDN8qCgICUnJxe6TlZWlrKysqzPz///e43Zc3L62n3/3+P/p7MXzxbbro5nHc2YNkP16xd+kvnMmTOaOWumMrKLPqFdkdvJyc7Rrwm/qklYkyJPjtvTJvNippJ+S1JgRqC8/Ao/6Z2RmqHYo7GaMG1CkSfG7dlXadq5Yn9f6cv297SXX2DhtzM4E3dGmbGZ8u7orYBGhX8T90zcGeUk5CgrI0vZl7ILbZOblSuTb5SbmavsS9lKM0YektKMsVkn83ym8ix5qt25dpH7S0tK087Pd2rM+DHFFj5K6suEhARlZ2YrJzPH7riLkpOZo6xLWdq/f7/Si/jGtj37s+e9a8/vwJW4z588Lw9L0cPyhVMXlJebp/T09FKPRX5+frJYLKVapzooy/ifnJxcaPvc3FylpqYqJCSkwDoVkTPS09OVm5enY+fO6kJOjnV5fHqa8o1R7Pnzynf3KHF5WdZhecUur4x9XMrNtXlfXMzJ0cWs4sexqyUkJCgrO7vA+yv5QkaptlPS9i/m5BSIMzevbOMWyi4//3//lqXbq1LOqKxxvKg2V7Zpz36v/FtYm6Imb6yozxjl/SGnp6crNzdXCcmxupR1scDrSacSlJ+fr19T4mSxmEK3URFtHLWfqhQL+2E/jtyPPW1Sz/6m3NzcMufqqpQzSlKVc0phqkrOqAxX8tDJlFO6dNUxXpF85ozy842SUs/I4ubG6zXo9cre9+nzabqUmVnk55+EhARlZmW57Huzso/v9Pm0qpMzTA2QmJhoJJmtW7faLH/xxRdNmzZtCl1n+vTpRhIPHjx4VIvH+fPnHTHcVjllGf9btWplXn75ZZtlmzdvNpJMUlJSoeuQM3jw4FGdHlUpZ1TWOO7p6Wk++ugjmzZLly413t7edu93y5YtRpL59ddfbdo8+uijZuDAgYXGRr7gwYNHdXtUpZxRkqqcUwpDzuDBg0d1e1RkzqgRV2o0aNBA7u7uBSrgKSkpBSrlV0ydOlVRUVHW5/n5+Tpz5owCAwMd8i2EtLQ0NW3aVAkJCfL396/0/VVV9AN9INEHV5SnH2rqZKVlGf+Dg4MLbe/h4VHkLdQqImdU9/d5dT4+js01cWxFq0o5o7LG8aLaXNmmPfsNDg6WdPlbvFdfxVfZnzGq83u3otFX9qOvSof++p+qlDNKUpVzSmHKmzNqwvuUY6weOEbXZ+/xVWTOqBFFDS8vL3Xr1k3r1q3Tvffea12+bt06DRkypNB1vL295X3NhJx169atzDAL5e/vXy3f7KVFP9AHEn1wBf1gv7KM/+Hh4fryyy9tlq1du1bdu3cvcj6NiswZ1f3nW52Pj2NzTRxb1VZZ43h4eLjWrVtncw/0tWvXqmfPnnbvt0WLFgoODta6devUtWtXSZfvmx4TE6NXX3210NjIF85BX9mPviod+su1VOWcUpiKyhk14X3KMVYPHKPrc+Tx1YiihiRFRUVpxIgR6t69u8LDw7VgwQLFx8crMjLS2aEBACpRSeP/1KlTlZiYqCVLlkiSIiMj9Y9//ENRUVEaO3astm3bpoULF2rZsmXOPAwAqLEqYxyfMGGCbr31Vr366qsaMmSIPv/8c3333XfavHmz3fu1WCyaOHGiXn75ZbVq1UqtWrXSyy+/rNq1a+vBBx90YA8BAOxVVXMKAKB0akxRY/jw4Tp9+rReeOEFJSUlqWPHjlq9erXCwsKcHRoAoBKVNP4nJSUpPj7e2r5FixZavXq1nnrqKc2ZM0ehoaF65513NGzYMGcdAgDUaJUxjvfs2VMff/yxnnvuOf31r3/Vddddp+XLl6tHjx5271eSJk+erEuXLmncuHE6e/asevToobVr17rU7VgAoCapyjkFAFAKFTY7BypUZmammT59usnMzHR2KE5FP9AHxtAHV9AP1Vt1//lW5+Pj2FwTxwZXxc/XfvSV/eir0qG/4ApqwvuUY6weOEbX54zjsxhjjLMLKwAAAAAAAAAAACVxc3YAAAAAAAAAAAAA9qCoAQAAAAAAAAAAXAJFDQAAAAAAAAAA4BIoajjR3Llz1aJFC/n4+Khbt27atGlTkW1Xrlyp/v37q2HDhvL391d4eLi+/fZbB0ZbOUrTB1fbsmWLPDw81KVLl8oN0EFK2w9ZWVl69tlnFRYWJm9vb1133XV6//33HRRt5ShtH3z44Yfq3LmzateurZCQED388MM6ffq0g6KteBs3btRdd92l0NBQWSwWffbZZyWuExMTo27dusnHx0ctW7bUu+++W/mBotKUdTys6sry3nYFs2bN0k033SQ/Pz81atRI99xzjw4dOuTssCrMvHnz1KlTJ/n7+1v/7vjmm2+cHVaFmzVrliwWiyZOnOjsUCrEjBkzZLFYbB7BwcHODgsVqLrmitKwZ/w1xmjGjBkKDQ1VrVq11LdvX+3bt8+mTVZWlp588kk1aNBAderU0d13362TJ0868lAcrrAxj76ylZiYqD/96U8KDAxU7dq11aVLF+3YscP6Ov0FV+KqOaOmjfPVeWyuzmNqbm6unnvuObVo0UK1atVSy5Yt9cILLyg/P9/axtWOr6TP7hV1PGfPntWIESMUEBCggIAAjRgxQufOnSt9wA6bkhw2Pv74Y+Pp6Wnee+89s3//fjNhwgRTp04dExcXV2j7CRMmmFdffdX89NNP5vDhw2bq1KnG09PT7Ny508GRV5zS9sEV586dMy1btjQDBgwwnTt3dkywlags/XD33XebHj16mHXr1pnY2Fjzn//8x2zZssWBUVes0vbBpk2bjJubm3n77bfN8ePHzaZNm0yHDh3MPffc4+DIK87q1avNs88+a1asWGEkmVWrVhXb/vjx46Z27dpmwoQJZv/+/ea9994znp6e5tNPP3VMwKhQZR0PXUFp39uuYuDAgWbRokXmv//9r9m9e7f53e9+Z5o1a2YyMjKcHVqF+OKLL8zXX39tDh06ZA4dOmSmTZtmPD09zX//+19nh1ZhfvrpJ9O8eXPTqVMnM2HCBGeHUyGmT59uOnToYJKSkqyPlJQUZ4eFClKdc0Vp2DP+vvLKK8bPz8+sWLHC7N271wwfPtyEhISYtLQ0a5vIyEjTuHFjs27dOrNz507Tr18/07lzZ5Obm+uMw6p0RY159NX/nDlzxoSFhZnRo0eb//znPyY2NtZ899135ujRo9Y29BdchSvnjJo0zlfnsbm6j6kvvviiCQwMNF999ZWJjY01n3zyifH19TXR0dHWNq52fCV9dq+o47nzzjtNx44dzdatW83WrVtNx44dze9///tSx0tRw0luvvlmExkZabOsbdu2ZsqUKXZvo3379mbmzJkVHZrDlLUPhg8fbp577jkzffr0alHUKG0/fPPNNyYgIMCcPn3aEeE5RGn74PXXXzctW7a0WfbOO++YJk2aVFqMjmTPid/Jkyebtm3b2ix77LHHzC233FKJkaGyVEROcAXVqahxrZSUFCPJxMTEODuUSlOvXj3zz3/+09lhVIj09HTTqlUrs27dOhMREVGtihrV4W8jFK6m5IrSunb8zc/PN8HBweaVV16xtsnMzDQBAQHm3XffNcZc/pKUp6en+fjjj61tEhMTjZubm1mzZo1jD8ABihrz6CtbzzzzjOndu3eRr9NfcCXVKWdU13G+uo/N1X1M/d3vfmceeeQRm2VDhw41f/rTn4wxrn981352r6jj2b9/v5FkfvzxR2ubbdu2GUnm4MGDpYqR2085QXZ2tnbs2KEBAwbYLB8wYIC2bt1q1zby8/OVnp6u+vXrV0aIla6sfbBo0SIdO3ZM06dPr+wQHaIs/fDFF1+oe/fueu2119S4cWO1bt1aTz/9tC5duuSIkCtcWfqgZ8+eOnnypFavXi1jjH777Td9+umn+t3vfueIkKuEbdu2FeizgQMHavv27crJyXFSVCiLisgJcL7z589Lksvm5eLk5eXp448/1oULFxQeHu7scCrE448/rt/97ne64447nB1KhTty5IhCQ0PVokUL/fGPf9Tx48edHRIqALmiaNeOv7GxsUpOTrbpK29vb0VERFj7aseOHcrJybFpExoaqo4dO1bL/ixqzKOvbF35nPWHP/xBjRo1UteuXfXee+9ZX6e/4CqqW86oruN8dR+bq/uY2rt3b33//fc6fPiwJOmXX37R5s2bNXjwYEmuf3zXqqjj2bZtmwICAtSjRw9rm1tuuUUBAQGlPmaP8hwQyiY1NVV5eXkKCgqyWR4UFKTk5GS7tvHmm2/qwoULuv/++ysjxEpXlj44cuSIpkyZok2bNsnDo3q8dcvSD8ePH9fmzZvl4+OjVatWKTU1VePGjdOZM2dccl6NsvRBz5499eGHH2r48OHKzMxUbm6u7r77bv397393RMhVQnJycqF9lpubq9TUVIWEhDgpMpRWReQEOJcxRlFRUerdu7c6duzo7HAqzN69exUeHq7MzEz5+vpq1apVat++vbPDKrePP/5YO3fu1M8//+zsUCpcjx49tGTJErVu3Vq//fabXnzxRfXs2VP79u1TYGCgs8NDOZArClfY+HulPwrrq7i4OGsbLy8v1atXr0Cb6tafxY159JWt48ePa968eYqKitK0adP0008/afz48fL29tbIkSPpL7iM6pQzqus4XxPG5uo+pj7zzDM6f/682rZtK3d3d+Xl5emll17SAw88IKn6/ByvqKjjSU5OVqNGjQpsv1GjRqU+5upxZthFWSwWm+fGmALLCrNs2TLNmDFDn3/+eaFvBFdibx/k5eXpwQcf1MyZM9W6dWtHhecwpXkv5Ofny2Kx6MMPP1RAQIAkafbs2brvvvs0Z84c1apVq9LjrQyl6YP9+/dr/Pjxev755zVw4EAlJSVp0qRJioyM1MKFCx0RbpVQWJ8Vthyuoaw5Ac73xBNPaM+ePdq8ebOzQ6lQbdq00e7du3Xu3DmtWLFCo0aNUkxMjEsXNhISEjRhwgStXbtWPj4+zg6nwg0aNMj6/xtuuEHh4eG67rrr9MEHHygqKsqJkaGikCtsFTf+lqWvqlt/2jvm0VeX5efnq3v37nr55ZclSV27dtW+ffs0b948jRw50tqO/oKrqA45ozqO8zVlbK7uY+ry5cu1dOlSffTRR+rQoYN2796tiRMnKjQ0VKNGjbK2c9XjK0pFHE9h7ctyzNx+ygkaNGggd3f3AhWolJSUAhWvay1fvlxjxozRv//9b5e+ZUJp+yA9PV3bt2/XE088IQ8PD3l4eOiFF17QL7/8Ig8PD/3www+OCr1CleW9EBISosaNG1sLGpLUrl07GWN08uTJSo23MpSlD2bNmqVevXpp0qRJ6tSpkwYOHKi5c+fq/fffV1JSkiPCdrrg4OBC+8zDw4Nv47qY8uQEON+TTz6pL774QuvXr1eTJk2cHU6F8vLy0vXXX6/u3btr1qxZ6ty5s95++21nh1UuO3bsUEpKirp162b9eyImJkbvvPOOPDw8lJeX5+wQK1SdOnV0ww036MiRI84OBeVEriioqPE3ODhYkortq+DgYGVnZ+vs2bNFtqkOShrzrhwrfXVZSEhIgcJ9u3btFB8fL4n3FlxHdckZ1XWcryljc3UfUydNmqQpU6boj3/8o2644QaNGDFCTz31lGbNmiXJ9Y/vWhV1PMHBwfrtt98KbP/UqVOlPmaKGk7g5eWlbt26ad26dTbL161bp549exa53rJlyzR69Gh99NFHLj93QGn7wN/fX3v37tXu3butj8jISOu3SK++F5srKct7oVevXvr111+VkZFhXXb48GG5ubm55Am1svTBxYsX5eZmO3y5u7tL+t/VCtVdeHh4gT5bu3atunfvLk9PTydFhbIoa06Acxlj9MQTT2jlypX64Ycf1KJFC2eHVOmMMcrKynJ2GOVy++23F/h7onv37nrooYe0e/duay6pLrKysnTgwAFuSVgNkCv+p6Txt0WLFgoODrbpq+zsbMXExFj7qlu3bvL09LRpk5SUpP/+97/Vqj9LGvNatmxJX12lV69eOnTokM2yw4cPKywsTBLvLbgOV88Z1X2cryljc3UfU4s6L5Wfny/J9Y/vWhV1POHh4Tp//rx++ukna5v//Oc/On/+fOmPuVTTiqPCfPzxx8bT09MsXLjQ7N+/30ycONHUqVPHnDhxwhhjzJQpU8yIESOs7T/66CPj4eFh5syZY5KSkqyPc+fOOesQyq20fXCt6dOnm86dOzso2spT2n5IT083TZo0Mffdd5/Zt2+fiYmJMa1atTKPPvqosw6h3ErbB4sWLTIeHh5m7ty55tixY2bz5s2me/fu5uabb3bWIZRbenq62bVrl9m1a5eRZGbPnm127dpl4uLijDEF++D48eOmdu3a5qmnnjL79+83CxcuNJ6enubTTz911iGgHEr6HXBlJb23XdWf//xnExAQYDZs2GCTly9evOjs0CrE1KlTzcaNG01sbKzZs2ePmTZtmnFzczNr1651dmgVLiIiwkyYMMHZYVSIv/zlL2bDhg3m+PHj5scffzS///3vjZ+fX7UYS1C9c0Vp2DP+vvLKKyYgIMCsXLnS7N271zzwwAMmJCTEpKWlWdtERkaaJk2amO+++87s3LnT3HbbbaZz584mNzfXGYflMNeOefTV//z000/Gw8PDvPTSS+bIkSPmww8/NLVr1zZLly61tqG/4CpcOWfUxHG+Oo7N1X1MHTVqlGncuLH56quvTGxsrFm5cqVp0KCBmTx5srWNqx1fSZ/dK+p47rzzTtOpUyezbds2s23bNnPDDTeY3//+96WOl6KGE82ZM8eEhYUZLy8vc+ONN5qYmBjra6NGjTIRERHW5xEREUZSgceoUaMcH3gFKk0fXKu6FDWMKX0/HDhwwNxxxx2mVq1apkmTJiYqKsrlT6SVtg/eeecd0759e1OrVi0TEhJiHnroIXPy5EkHR11x1q9fX+zveGF9sGHDBtO1a1fj5eVlmjdvbubNm+f4wFFhivsdcGUlvbddVWHHJMksWrTI2aFViEceecT6fmzYsKG5/fbbq2VBw5jqVdQYPny4CQkJMZ6eniY0NNQMHTrU7Nu3z9lhoQJV11xRGvaMv/n5+Wb69OkmODjYeHt7m1tvvdXs3bvXZjuXLl0yTzzxhKlfv76pVauW+f3vf2/i4+MdfDSOd+2YR1/Z+vLLL03Hjh2Nt7e3adu2rVmwYIHN6/QXXImr5oyaOM5X17G5Oo+paWlpZsKECaZZs2bGx8fHtGzZ0jz77LMmKyvL2sbVjq+kz+4VdTynT582Dz30kPHz8zN+fn7moYceMmfPni11vBZjasi9WgAAAAAAAAAAgEtjTg0AAAAAAAAAAOASKGoAAAAAAAAAAACXQFEDAAAAAAAAAAC4BIoaAAAAAAAAAADAJVDUAAAAAAAAAAAALoGiBgAAAAAAAAAAcAkUNQAAAAAAAAAAgEugqAEAAAAAAAAAAFwCRQ0AJZoxY4a6dOni7DAA1AB9+/bVxIkTy7Tu6NGjdc8995Q7hi1btuiGG26Qp6dnhWwPxauon1tFsVgs+uyzz5wdBgAXQu4qm8WLF6tu3brFtrHnc8iJEydksVi0e/fuCosNAKojR/7dbc/4bU88GzZskMVi0blz5yosNlQPFDUA2CjsZM7TTz+t77//3jkBAYCDRUVFqUuXLoqNjdXixYudFkd5TpI5cpuuqqgPWklJSRo0aJDjAwKAcqgquas0hg8frsOHD5dqnapWCAeA4tTkv73Lch6pJvcXSo+iBmqc7OxsZ4fgcHl5ecrPzy/z+r6+vgoMDKzAiACg6jp27Jhuu+02NWnSpMRvkBbFlXONMUa5ubnODqPMytv3wcHB8vb2rqBoAMAxXDF31apVS40aNXLoPgGgKnH1v7uLw3kkVDaKGnB5ffv21RNPPKEnnnhCdevWVWBgoJ577jkZYyRJzZs314svvqjRo0crICBAY8eOlSRt3bpVt956q2rVqqWmTZtq/PjxunDhgnW7c+fOVatWreTj46OgoCDdd9991teMMXrttdfUsmVL1apVS507d9ann35qff3K5XHff/+9unfvrtq1a6tnz546dOiQTexffPGFunfvLh8fHzVo0EBDhw61vpadna3JkyercePGqlOnjnr06KENGzbY1SdXLuX+6quv1L59e3l7eysuLk4///yz+vfvrwYNGiggIEARERHauXOndb3mzZtLku69915ZLBbr82u/zZqfn68XXnhBTZo0kbe3t7p06aI1a9bYFRsA11XSeFvSuHX69Gk98MADatKkiWrXrq0bbrhBy5YtK3afa9asUUBAgJYsWWJ3nDNnzlSjRo3k7++vxx57zOYkTXHj95XbV5w+fVqPPPKILBaL9duuMTExuvnmm+Xt7a2QkBBNmTLF5gPIlb6JiopSgwYN1L9/f0nS/v37NXjwYPn6+iooKEgjRoxQampqiccwevRoxcTE6O2335bFYpHFYtGJEyfsiqW027ySs7799lt1795d3t7e2rRpk7KysjR+/Hg1atRIPj4+6t27t37++Wfr9gq7bchnn30mi8Vis+zFF19Uo0aN5Ofnp0cffVRTpkwp9AqJN954QyEhIQoMDNTjjz+unJycEo9JKjrPP/PMM2rdurVq166tli1b6q9//at1m4sXL9bMmTP1yy+/WPviys/62isW9+7dq9tuu021atVSYGCg/u///k8ZGRl2xQbA+chdjstdX375perWrWv9MtXu3btlsVg0adIka5vHHntMDzzwgKTC88grr7yioKAg+fn5acyYMcrMzLS+NmPGDH3wwQf6/PPPrWP31T+r48ePq1+/fqpdu7Y6d+6sbdu22dHzAFA5Cvvbe/HixS77d/ff//533XDDDQX2P2fOHOuygQMHaurUqZIKnkfKy8tTVFSUNRdPnjzZmouL6q8rn38kaceOHcWeX0MNZAAXFxERYXx9fc2ECRPMwYMHzdKlS03t2rXNggULjDHGhIWFGX9/f/P666+bI0eOmCNHjpg9e/YYX19f89Zbb5nDhw+bLVu2mK5du5rRo0cbY4z5+eefjbu7u/noo4/MiRMnzM6dO83bb79t3ee0adNM27ZtzZo1a8yxY8fMokWLjLe3t9mwYYMxxpj169cbSaZHjx5mw4YNZt++faZPnz6mZ8+e1m189dVXxt3d3Tz//PNm//79Zvfu3eall16yvv7ggw+anj17mo0bN5qjR4+a119/3Xh7e5vDhw+X2CeLFi0ynp6epmfPnmbLli3m4MGDJiMjw3z//ffmX//6l9m/f7/Zv3+/GTNmjAkKCjJpaWnGGGNSUlKMJLNo0SKTlJRkUlJSjDHGTJ8+3XTu3Nm6/dmzZxt/f3+zbNkyc/DgQTN58mTj6elpV2wAXFdJ421J49bJkyfN66+/bnbt2mWOHTtm3nnnHePu7m5+/PFHm31MmDDBGGPMsmXLjJ+fn/nss8/sim/UqFHG19fXDB8+3Pz3v/81X331lWnYsKGZNm2atU1x43dubq5JSkoy/v7+Jjo62iQlJZmLFy+akydPmtq1a5tx48aZAwcOmFWrVpkGDRqY6dOnF+ibSZMmmYMHD5oDBw6YX3/91TRo0MBMnTrVHDhwwOzcudP079/f9OvXr8RjOXfunAkPDzdjx441SUlJJikpyeTm5toVS2m3eSVnderUyaxdu9YcPXrUpKammvHjx5vQ0FCzevVqs2/fPjNq1ChTr149c/r0aWPM5VwTEBBgs49Vq1aZq/+8XLp0qfHx8THvv/++OXTokJk5c6bx9/e3ySmjRo0y/v7+JjIy0hw4cMB8+eWXNu+rkhSW540x5m9/+5vZsmWLiY2NNV988YUJCgoyr776qjHGmIsXL5q//OUvpkOHDta+uHjxojHGGElm1apVxhhjLly4YEJDQ83QoUPN3r17zffff29atGhhRo0aZVdsAJyP3OXY3OXm5ma2b99ujDEmOjraNGjQwNx0003WNq1btzbz5s0zxhTMI8uXLzdeXl7mvffeMwcPHjTPPvus8fPzs+aM9PR0c//995s777zTOnZnZWWZ2NhYI8m0bdvWfPXVV+bQoUPmvvvuM2FhYSYnJ8eunwMAVLTC/vb+7rvvXPbv7j179hiLxWJOnTpljDFm4sSJpkGDBuYPf/iDMcaYnJwc4+vra7755htjTMHzSK+++qoJCAgwn376qfV8lJ+fnxkyZEiR/XX1Z5Xizq+hZqKoAZcXERFh2rVrZ/Lz863LnnnmGdOuXTtjzOWTHffcc4/NOiNGjDD/93//Z7Ns06ZNxs3NzVy6dMmsWLHC+Pv7W0/2Xy0jI8P4+PiYrVu32iwfM2aMeeCBB4wx/ytqfPfdd9bXv/76ayPJXLp0yRhjTHh4uHnooYcKPaajR48ai8ViEhMTbZbffvvtZurUqcX2hzGXE54ks3v37mLb5ebmGj8/P/Pll19al119MueKa5NRaGioTQHGGGNuuukmM27cuBJjA+C6ihtvyzpuDR482PzlL3+x2ceECRPMnDlzTEBAgPnhhx/sjm/UqFGmfv365sKFC9Zl8+bNM76+viYvL8+u8dsYYwICAsyiRYusz6dNm2batGljc9xz5syxbvdK3F26dLHZ7l//+lczYMAAm2UJCQlGkjl06FCJx3P1SbLSxFLabV7JWVefgMvIyDCenp7mww8/tC7Lzs42oaGh5rXXXjPG2PfhqkePHubxxx+3adOrV68CH67CwsJMbm6uddkf/vAHM3z48BKPx5jC83xhXnvtNdOtWzfr82tz2xVX58EFCxaYevXqmYyMDOvrX3/9tXFzczPJycl2xQfAuchdjs1dN954o3njjTeMMcbcc8895qWXXjJeXl4mLS3NJCUlGUnmwIEDxpiCeSQ8PNxERkbabK9Hjx4FcsaVE2BXXClq/POf/7Qu27dvn82+AMAZrv3b25X/7s7PzzcNGjQwn376qTHGmC5duphZs2aZRo0aGWOM2bp1q/Hw8DDp6enGmIJ/a4eEhJhXXnnF+jwnJ8c0adLEZkwv7rNKcefXUDNx+ylUC7fccovNZXfh4eE6cuSI8vLyJEndu3e3ab9jxw4tXrxYvr6+1sfAgQOVn5+v2NhY9e/fX2FhYWrZsqVGjBihDz/8UBcvXpR0+XLszMxM9e/f32b9JUuW6NixYzb76dSpk/X/ISEhkqSUlBRJly/Hvv322ws9np07d8oYo9atW9vsIyYmpsA+iuLl5WWz/yv7joyMVOvWrRUQEKCAgABlZGQoPj7erm1KUlpamn799Vf16tXLZnmvXr104MABu7cDwDUVNd5u3769xHErLy9PL730kjp16qTAwED5+vpq7dq1BcagFStWaOLEiVq7dq369etXqvg6d+6s2rVr28SXkZGhhISEUo3fVztw4IDCw8NtjrtXr17KyMjQyZMnrcsKyzXr16+32Vfbtm0lye6xvKyxlMXV8R87dkw5OTk2Y72np6duvvnmUo31hw4d0s0332yz7NrnktShQwe5u7tbn4eEhFjzZWljv+LTTz9V7969FRwcLF9fX/31r38tVb6TLvd3586dVadOHeuyXr16KT8/n0veARdC7rrMEbmrb9++2rBhg4wx2rRpk4YMGaKOHTtq8+bNWr9+vYKCgqzbKyrmq137vDjFffYCgKrEFf/utlgsuvXWW7VhwwadO3dO+/btU2RkpPLy8nTgwAFt2LBBN954o3x9fQuse/78eSUlJdmM6R4eHoX+DV8Uxnhcy8PZAQCOcPXJCOnynBCPPfaYxo8fX6Bts2bN5OXlpZ07d2rDhg1au3atnn/+ec2YMUM///yz9R6xX3/9tRo3bmyz7rUTi3p6elr/f+UDxZX1a9WqVWS8+fn5cnd3144dO2ySjaRCE0RhatWqVeD+iqNHj9apU6cUHR2tsLAweXt7Kzw8vEyTAl67bWNMgWUAapaSxq0333xTb731lqKjo3XDDTeoTp06mjhxYoExqEuXLtq5c6cWLVqkm266qULGFovFUqrx+2qFjW/m/9//9erlheWau+66S6+++mqBbV75Q7y07I2lLK6Ov6htXr1/Nzc3m/vgSir0frxFxXu1q/PllXWu/LxKG7sk/fjjj/rjH/+omTNnauDAgQoICNDHH3+sN9980+5tXom1qH4l5wHVA7mrYnNX3759tXDhQv3yyy9yc3NT+/btFRERoZiYGJ09e1YRERElbqOsivvsBQBViav+3d23b18tWLBAmzZtUufOnVW3bl3deuutiomJ0YYNG9S3b1+7tlMWjPG4FldqoFr48ccfCzxv1apVgQ8nV9x4443at2+frr/++gIPLy8vSZerxnfccYdee+017dmzRydOnNAPP/xgnXg7Pj6+wLpNmza1O+ZOnTrp+++/L/S1rl27Ki8vTykpKQX2ERwcbPc+rrVp0yaNHz9egwcPVocOHeTt7V1g0j9PT0/rFS6F8ff3V2hoqDZv3myzfOvWrWrXrl2ZYwPgGooab+0Zt658Y/NPf/qTOnfurJYtW+rIkSMF9nHddddp/fr1+vzzz/Xkk0+WKr5ffvlFly5dsonP19dXTZo0KfP43b59e23dutXmQ8HWrVvl5+dX4ATT1a7kmubNmxfY37UnkQrj5eVVYDwuayzFbbMwV/Lh1WN9Tk6Otm/fbh3rGzZsqPT0dF24cMHaZvfu3TbbadOmjX766SebZdu3by9x/+W1ZcsWhYWF6dlnn1X37t3VqlUrxcXF2bSxpy/at2+v3bt32xzjli1b5ObmptatW1dK7AAqHrnrMkfkrltvvVXp6emKjo5WRESELBaLIiIitGHDBm3YsKHYoka7du0K/Vldzd48BgBVgT1jliv93d23b1/t27dPn376qbWAERERoe+++05bt24tcowPCAhQSEiIzZiem5urHTt22LRjjEdpUNRAtZCQkKCoqCgdOnRIy5Yt09///ndNmDChyPbPPPOMtm3bpscff1y7d+/WkSNH9MUXX1g/gHz11Vd65513tHv3bsXFxWnJkiXKz89XmzZt5Ofnp6efflpPPfWUPvjgAx07dky7du3SnDlz9MEHH9gd8/Tp07Vs2TJNnz5dBw4c0N69e/Xaa69Jklq3bq2HHnpII0eO1MqVKxUbG6uff/5Zr776qlavXl3mfrr++uv1r3/9SwcOHNB//vMfPfTQQwWuGGnevLm+//57JScn6+zZs4VuZ9KkSXr11Ve1fPlyHTp0SFOmTNHu3buL7XMA1UNR460949b111+vdevWaevWrTpw4IAee+wxJScnF7qf1q1ba/369dbbedgrOztbY8aM0f79+/XNN99o+vTpeuKJJ+Tm5lbm8XvcuHFKSEjQk08+qYMHD+rzzz/X9OnTFRUVJTe3ov+Uevzxx3XmzBk98MAD+umnn3T8+HGtXbtWjzzyiF1/rDdv3lz/+c9/dOLECaWmpio/P7/MsRS3zcLUqVNHf/7znzVp0iStWbNG+/fv19ixY3Xx4kWNGTNGktSjRw/Vrl1b06ZN09GjR/XRRx9p8eLFNtt58skntXDhQn3wwQc6cuSIXnzxRe3Zs6fSr3K4/vrrFR8fr48//ljHjh3TO++8o1WrVtm0ad68uWJjY7V7926lpqYqKyurwHYeeugh+fj4aNSoUfrvf/+r9evX68knn9SIESMUFBRUqccAoOKQuxyXuwICAtSlSxctXbrUesLr1ltv1c6dO3X48OFiv8U7YcIEvf/++3r//fd1+PBhTZ8+Xfv27bNp07x5c+3Zs0eHDh1Sampqod9UBoCqwp6/vV3p7+6OHTsqMDBQH374oXU879u3rz777DNdunRJvXv3LnLdCRMm6JVXXtGqVat08OBBjRs3TufOnbNpY+9nFUDSVTPKAC4qIiLCjBs3zkRGRhp/f39Tr149M2XKFOukeGFhYeatt94qsN5PP/1k+vfvb3x9fU2dOnVMp06drJNfb9q0yURERJh69eqZWrVqmU6dOpnly5db183Pzzdvv/22adOmjfH09DQNGzY0AwcONDExMcaY/01kdPbsWes6u3btMpJMbGysddmKFStMly5djJeXl2nQoIEZOnSo9bXs7Gzz/PPPm+bNmxtPT08THBxs7r33XrNnz54S+6SwSaSMMWbnzp2me/fuxtvb27Rq1cp88sknBfrniy++MNdff73x8PAwYWFhxpiCEzzl5eWZmTNnmsaNGxtPT0/TuXNn880335QYFwDXVtJ4W9K4dfr0aTNkyBDj6+trGjVqZJ577jkzcuTIYieH279/v2nUqJGJiooqMb4rk4c+//zzJjAw0Pj6+ppHH33UZGZmWtuUNH4bU3CyVWOM2bBhg7npppuMl5eXCQ4ONs8884zJyckpMu4rDh8+bO69915Tt25dU6tWLdO2bVszceJEm4lbi3Lo0CFzyy23mFq1atnkj5JiKe02C8tZxhhz6dIl8+STT5oGDRoYb29v06tXL/PTTz/ZtFm1apW5/vrrjY+Pj/n9739vFixYYK798/KFF14wDRo0ML6+vuaRRx4x48ePN7fccov19cImfZ0wYYKJiIiw65iKyvOTJk2yvg+GDx9u3nrrLZvcmJmZaYYNG2bq1q1rJFl/5rpqonBjjNmzZ4/p16+f8fHxMfXr1zdjx461ToAIoOojdzk2dxljzF/+8hcjyfz3v/+1LuvcubNp2LChzTYK+8zy0ksvWXPGqFGjzOTJk20+h6SkpFg/w0ky69evt04UvmvXLmu7s2fPWl8HAGe59m/vRYsWufTf3cYYM2zYMOPu7m7Onz9vjLmco+rXr2+6d+9u0+7a80g5OTlmwoQJxt/f39StW9dERUUVyKf2flYp7Pwaah6LMYXcYA1wIX379lWXLl0UHR3t7FAAoFpjvEVF6N+/v4KDg/Wvf/3L2aEAqAHIXQCAmoq/u1GdMVE4AAAAKsXFixf17rvvauDAgXJ3d9eyZcv03Xffad26dc4ODQAAAKg2+LsbNQ1zagAuaNCgQfL19S308fLLLzs7PACoFEWNe76+vtq0aZOzwyuV+Pj4Yo8nPj6+SmyzvCwWi1avXq0+ffqoW7du+vLLL7VixQrdcccddq2/adOmYo8JAKo6chcAwBH4uxs1DbefAlxQYmKiLl26VOhr9evXV/369R0cEQBUvqNHjxb5WuPGjVWrVi0HRlM+ubm5OnHiRJGvN2/eXB4epbugtjK26WyXLl1SYmJika9ff/31DowGAEqP3AUAcAX83Q1XQ1EDAAAAAAAAAAC4BG4/BQAAAAAAAAAAXAJFDQAAAAAAAAAA4BIoagAAAAAAAAAAAJdAUQMAAAAAAAAAALgEihoAAAAAAAAAAMAlUNQAAAAAAAAAAAAugaIGAAAAAAAAAABwCRQ1AAAAAAAAAACAS6CoAQAAAAAAAAAAXAJFDQAAAAAAAAAA4BIoagAAAAAAAAAAAJdAUQMAAAAAAAAAALgEihoAAAAAAAAAAMAlUNQAAAAAAAAAAAAugaIGAAAAAAAAAABwCRQ1AAAAAAAAAACAS6CoAQAAAAAAAAAAXAJFDQAAAAAAAAAA4BIoagAAAAAAAAAAAJdAUQMAAAAAAAAAALgEihoAAAAAAAAAAMAlUNQAAAAAAAAAAAAugaIGAAAAAAAAAABwCRQ1AAAAAAAAAACAS6CoAQAAAAAAAAAAXAJFDQAAAAAAAAAA4BIoagAAAAAAAAAAAJdAUcNOxhilpaXJGOPsUAAAVRw5AwAAAAAAoHJ4ODsAV5Genq6AgACdP39e/v7+zg6n0syeLaWlSf7+UlSUs6MBANdUU3JGaZFjAAAAAABAeVkMXyO1S1paWo04QdWkiZSYKDVuLJ086exoAMA11ZScUVrkGAAAAAAAUF7cfgoAAAAAAAAAALgEihoAAAAAAAAAAMAlUNQAAAAAAAAAAAAugYnCAVQpxhjl5uYqLy/P2aG4HE9PT7m7uzs7DAAot7y8POXk5Dg7jGrL3d1dHh4eslgszg4FAAAAAEqNogZs/O530pkzUv36zo4ENVF2draSkpJ08eJFZ4fikiwWi5o0aSJfX19nhwIUihwDe2RkZOjkyZMyxjg7lGqtdu3aCgkJkZeXl7NDAQAAAIBSsRg+MdolLS1NAQEBOn/+vPz9/Z0dDlDt5Ofn68iRI3J3d1fDhg3l5eXFN0hLwRijU6dO6eLFi2rVqhVXbDgZOQMom7y8PB05ckS1a9dWw4YNyQOVwBij7OxsnTp1Snl5eWrVqpXc3LgjLQAAAADXwZUaAKqE7Oxs5efnq2nTpqpdu7azw3FJDRs21IkTJ5STk0NRA4BLysnJkTFGDRs2VK1atZwdTrVVq1YteXp6Ki4uTtnZ2fLx8XF2SAAAAABgN76WBaBK4duiZcc3mgFUF4xnlY98CwAAAMBVcaWGA5w6dUppaWnFtvH391fDhg0dFBEAVzJnzhy9++67cnd3V15ensaOHavx48c7Oyw4EXkFqFny8/M1YcIErV69WhaLRVFRURo3blyhbb/99ltNmzZN+fn5ysnJ0aRJkzRq1CgHRwwAAAAAlcdlixpz587V66+/rqSkJHXo0EHR0dHq06dPke0//PBDvfbaazpy5IgCAgJ055136o033lBgYGClxnnq1Ck99vBYZaYXP/Gxj19tzV/0ntNPQHXvLiUnS8HB0vbtTg0FwP/3pz/9SY8//riky3M1dOzYUX379lWnTp2cHBmc4dSpU3r44YeVnp5ebDs/Pz8tWrTI6XnlauQYoGyWLl2q/fv36/Dhwzp//rxuvPFG3XbbbWrbtq1NO2OMHnzwQa1fv16dOnXSiRMn1LZtWw0dOlR+fn5Oih4AAAAAKpZLFjWWL1+uiRMnau7cuerVq5fmz5+vQYMGaf/+/WrWrFmB9ps3b9bIkSP11ltv6a677lJiYqIiIyP16KOPatWqVZUaa1pamjLTL+qx8AcUWj+40Da/nknW/G3LlJaW5vSTT8nJUmKiU0MAqhSLxaKXX35Zq1atUmpqqhYsWKDvv/9ea9asUXZ2tv7973+rQ4cOSk5O1gMPPHD5dz4zU7fffrvefvttWSwWjR07Vj4+Pvr73/+uM2fO6KabbtIHH3yg3r172xVDQECA9f8XL15Ubm4ut2apwdLS0pSenq4+ffoUWZg/ffq0Nm3aVCXyytXIMXBFVSEPLF++XJGRkXJ3d1f9+vV1//336+OPP9aMGTMKbX/u3DlJl8eLwMBAeXt7V1BvAAAAAIDzuWRRY/bs2RozZoweffRRSVJ0dLS+/fZbzZs3T7NmzSrQ/scff1Tz5s2tt2tp0aKFHnvsMb322msOizm0frBaNCpYcAFQvNmzLz9KcuON0hdf2C67+25p587C20dFXX7Yw9/fXz/99JM++eQTDRkyRP/+9781a9Ysvfbaa3rppZf00UcfqW7duvryyy/l6+urvLw8DRkyRCtWrNB9992nv//97woPD9cnn3yiJUuWaOzYsdYTWePHj9fGjRsL3e/8+fPVo0cPSdKnn36q6dOn6+jRo3rllVd0ww032Bc8qq3AwEAFBQU5Owyg0pEHeig+Pl5hYWHW5c2bN9f2Qi53slgs+ve//62hQ4eqTp06Onv2rFauXCkvLy/7DhQAAAAAXIDLFTWys7O1Y8cOTZkyxWb5gAEDtHXr1kLX6dmzp5599lmtXr1agwYNUkpKij799FP97ne/c0TIAMohLc2+b3Y3bVpw2alTRa9bwnQENoYPHy5JuvHGG+Xm5mYdO7p166aVK1dKuny/82eeeUabN2+WMUYpKSnq0qWL7rvvPvn4+OiTTz5Rt27dFB4ermeeeca67XfeeceuGO677z7dd999OnHihO69914NHjxYbdq0sf8gAMBFkQcuu/oKPWNMoW1yc3M1a9Ysff755+rVq5d+/vln3XPPPdq7d6/q169v/wEDAAAAQBXmckWN1NRU5eXlFfh2alBQkJKTkwtdp2fPnvrwww81fPhwZWZmKjc3V3fffbf+/ve/F7mfrKwsZWVlWZ+XNCErgMrh7y81blxyu8LusNOwYdHr+vvbH4OPj48kyd3d3eYWHu7u7srNzZV0+Qqy06dP6z//+Y98fHwUFRWlzMxMa9uDBw+qTp06SklJUXZ2tnU79l6pcUXz5s3Vo0cPffXVVxQ1ANQI5IEeatasmU6cOKGbbrpJkhQXF1foLVd3796tX3/9Vb169ZIk3XTTTQoNDdUvv/yifv362X/AAAAAAFCFuVxR44pr7ydvjCnyHvP79+/X+PHj9fzzz2vgwIFKSkrSpEmTFBkZqYULFxa6zqxZszRz5swKjxtA6ZTm9iDXuvY2JJXp7NmzCg4Olo+Pj3777Td98skn1m/2xsfHa9y4cfruu+/0zjvvaOLEiZo3b54k+76he+DAAbVr107S5Umiv//+ew0bNqzyDgYAqhDygPSHP/xB8+fP19ChQ3X+/HktX75ca9asKdCuadOmOnnypA4dOqQ2bdro6NGjOnbsmFq3bl2xBwsAAAAATuRyRY0GDRrI3d29wFUZKSkpRd5bfNasWerVq5cmTZokSerUqZPq1KmjPn366MUXX1RISEiBdaZOnaqoqz5Bp6WlqWlh9zUAAF3+pu0f/vAHdenSRY0bN9Ydd9wh6fKtQP74xz/qb3/7m9q3b6/o6GiFh4dr+fLl1pNdJfn73/+umJgYeXp6yhijp556Sv3796/MwwEAlFJl5oERI0bo559/thYnJk2aZC12f/HFF/riiy/0z3/+U0FBQZo/f77uu+8+ubm5yRijuXPnqrE9l7oAAAAAgItwuaKGl5eXunXrpnXr1unee++1Ll+3bp2GDBlS6DoXL16Uh4ftobq7u0sq+p7E3t7eNrcXAFAzXT1GNG/eXKmpqdbnffv2tU7UGhYWpp9++qnQbVw934+Pj4927dpVqhjmzp1bqvYAgIpTFfKAu7u75syZU+hrd999t+6++27r8wceeEAPPPBAqbYPAAAAAK7EzdkBlEVUVJT++c9/6v3339eBAwf01FNPKT4+XpGRkZIuX2UxcuRIa/u77rpLK1eu1Lx583T8+HFt2bJF48eP180336zQ0FBnHQYAoJLNmzdPnTp1kr+/v/z9/RUeHq5vvvmm2HViYmLUrVs3+fj4qGXLlnr33XcdFC0AAAAAAABK4nJXakjS8OHDdfr0ab3wwgtKSkpSx44dtXr1aoWFhUmSkpKSFB8fb20/evRopaen6x//+If+8pe/qG7durrtttv06quvOusQAAAO0KRJE73yyiu6/vrrJUkffPCBhgwZol27dqlDhw4F2sfGxmrw4MEaO3asli5dqi1btmjcuHFq2LAh85gAAAAAAABUAS5Z1JCkcePGady4cYW+tnjx4gLLnnzyST355JOVHJXre+016eJFqXZtZ0cCAOV311132Tx/6aWXNG/ePP3444+FFjXeffddNWvWTNHR0ZKkdu3aafv27XrjjTcoalQAcgwAAAAAACgvly1qoHI8+KCzI0BNV9Q8NygZfVe8vLw8ffLJJ7pw4YLCw8MLbbNt2zYNGDDAZtnAgQO1cOFC5eTkyNPTs9D1srKylJWVZX2elpZWcYFXI+QY2IvxrPLRxwAAAABclUvOqQGg+rlysvjixYtOjsR1ZWdnS7o8oSz+Z+/evfL19ZW3t7ciIyO1atUqtW/fvtC2ycnJCgoKslkWFBSk3Nxcm8mBrzVr1iwFBARYH02bNq3QYwBqiivj15XxDJXnSr4tqlgLAAAAAFUVV2oAqBLc3d1Vt25dpaSkSJJq164ti8Xi5KhcR35+vk6dOqXatWvLw4Oh/Wpt2rTR7t27de7cOa1YsUKjRo1STExMkYWNa993V77NXNz7cerUqYqKirI+T0tLo7ABlIGHh4dq166tU6dOydPTU25ufP+mohljdPHiRaWkpKhu3boUwgEAAAC4HM58wcahQ1JuruThIbVp4+xoUNMEBwdLkrWwgdJxc3NTs2bNKAZdw8vLyzpRePfu3fXzzz/r7bff1vz58wu0DQ4OVnJyss2ylJQUeXh4KDAwsMh9eHt7y9vbu2IDr4bIMSiJxWJRSEiIYmNjFRcX5+xwqrW6deta8y4AAAAAuBKKGrBx++1SYqLUuLF08qSzo0FNc+VkVqNGjZSTk+PscFyOl5cX32q2gzHGZv6Lq4WHh+vLL7+0WbZ27Vp1796dW7RUAHIM7OHl5aVWrVpxC6pK5OnpyRUaAAAAAFwWRQ0AVY67uzsnW1Ahpk2bpkGDBqlp06ZKT0/Xxx9/rA0bNmjNmjWSLt82KjExUUuWLJEkRUZG6h//+IeioqI0duxYbdu2TQsXLtSyZcuceRhAjePm5iYfHx9nhwEAAAAAqIIoagAAqq3ffvtNI0aMUFJSkgICAtSpUyetWbNG/fv3lyQlJSUpPj7e2r5FixZavXq1nnrqKc2ZM0ehoaF65513NGzYMGcdAgAAAAAAAK5CUQMAUG0tXLiw2NcXL15cYFlERIR27txZSREBAAAAAACgPLj5OgAAAAAAAAAAcAkUNQAAAAAAAAAAgEugqAEAAAAAAAAAAFwCRQ0AAAAAAAAAAOASKGoAAAAAAAAAAACXQFEDAAAAAAAAAAC4BA9nB4Cq5eefpbw8yd3d2ZEAAKobcgwAAAAAACgvihqwERLi7AgAANUVOQYAAAAAAJQXt58CAAAAAAAAAAAugaIGAAAAAAAAAABwCdx+CjYWLJAyMiRfX+n//s/Z0QAAqhNyDAAAAAAAKC+KGrDxwgtSYqLUuDEnnAAAFYscAwAAAAAAyovbTwEAAAAAAAAAAJdAUQMAAAAAAAAAALgEihoAAAAAAAAAAMAlUNQAAAAAAAAAAAAugaIGAAAAAAAAAABwCRQ1AAAAAAAAAACAS6CoAQAAAAAAAAAAXAJFDQAAAAAAAAAA4BI8nB0AqpbWraWAACkoyNmRAACqG3IMAAAAAAAoL4oasPHDD86OAABQXZFjAAAAAABAeXH7KQAAAAAAAAAA4BIoagAAqq1Zs2bppptukp+fnxo1aqR77rlHhw4dKnadDRs2yGKxFHgcPHjQQVEDAAAAAACgKBQ1AADVVkxMjB5//HH9+OOPWrdunXJzczVgwABduHChxHUPHTqkpKQk66NVq1YOiBgAAAAAAADFYU4N2HjoISk1VWrQQPrwQ2dHAwDls2bNGpvnixYtUqNGjbRjxw7deuutxa7bqFEj1a1btxKjq3nIMQAAAAAAoLwoasBGTIyUmCg1buzsSACg4p0/f16SVL9+/RLbdu3aVZmZmWrfvr2ee+459evXr8i2WVlZysrKsj5PS0srf7DVEDkGAAAAAACUF7efAgDUCMYYRUVFqXfv3urYsWOR7UJCQrRgwQKtWLFCK1euVJs2bXT77bdr48aNRa4za9YsBQQEWB9NmzatjEMAAAAAAACo8bhSAwBQIzzxxBPas2ePNm/eXGy7Nm3aqE2bNtbn4eHhSkhI0BtvvFHkLaumTp2qqKgo6/O0tDQKGwAAAAAAAJWAKzUAANXek08+qS+++ELr169XkyZNSr3+LbfcoiNHjhT5ure3t/z9/W0eAAAAAAAAqHhcqQEAqLaMMXryySe1atUqbdiwQS1atCjTdnbt2qWQkJAKjg4AAAAAAAClRVEDAFBtPf744/roo4/0+eefy8/PT8nJyZKkgIAA1apVS9LlW0clJiZqyZIlkqTo6Gg1b95cHTp0UHZ2tpYuXaoVK1ZoxYoVTjsOAAAAAAAAXEZRAwBQbc2bN0+S1LdvX5vlixYt0ujRoyVJSUlJio+Pt76WnZ2tp59+WomJiapVq5Y6dOigr7/+WoMHD3ZU2AAAAAAAACgCRQ0AQLVljCmxzeLFi22eT548WZMnT66kiAAAAAAAAFAeLjtR+Ny5c9WiRQv5+PioW7du2rRpU7Hts7Ky9OyzzyosLEze3t667rrr9P777zsoWgAAAAAAAAAAUF4ueaXG8uXLNXHiRM2dO1e9evXS/PnzNWjQIO3fv1/NmjUrdJ37779fv/32mxYuXKjrr79eKSkpys3NdXDkVd/YsdL581JAgLMjAQBUN+QYAAAAAABQXi5Z1Jg9e7bGjBmjRx99VNLlSV2//fZbzZs3T7NmzSrQfs2aNYqJidHx48dVv359SVLz5s0dGbLLmD7d2REAAKorcgwAAAAAACgvl7v9VHZ2tnbs2KEBAwbYLB8wYIC2bt1a6DpffPGFunfvrtdee02NGzdW69at9fTTT+vSpUtF7icrK0tpaWk2DwAAAAAAAAAA4Dwud6VGamqq8vLyFBQUZLM8KChIycnJha5z/Phxbd68WT4+Plq1apVSU1M1btw4nTlzpsh5NWbNmqWZM2dWePwAAAAAAAAAAKBsXO5KjSssFovNc2NMgWVX5Ofny2Kx6MMPP9TNN9+swYMHa/bs2Vq8eHGRV2tMnTpV58+ftz4SEhIq/BgAAAAAAAAAAID9XK6o0aBBA7m7uxe4KiMlJaXA1RtXhISEqHHjxgq4ambSdu3ayRijkydPFrqOt7e3/P39bR41QZMmksVy+V8AACoSOQYAAAAAAJSXyxU1vLy81K1bN61bt85m+bp169SzZ89C1+nVq5d+/fVXZWRkWJcdPnxYbm5uasKZFQAAAAAAAAAAXILLFTUkKSoqSv/85z/1/vvv68CBA3rqqacUHx+vyMhISZdvHTVy5Ehr+wcffFCBgYF6+OGHtX//fm3cuFGTJk3SI488olq1ajnrMAAAAAAAAAAAQCm43EThkjR8+HCdPn1aL7zwgpKSktSxY0etXr1aYWFhkqSkpCTFx8db2/v6+mrdunV68skn1b17dwUGBur+++/Xiy++6KxDAAAAAAAAAAAApeSSRQ1JGjdunMaNG1foa4sXLy6wrG3btgVuWQUAAAAAAAAAAFyHS95+CgAAAAAAAAAA1DwUNQAAAAAAAAAAgEugqAEAAAAAAAAAAFwCRQ0AAAAAAAAAAOASKGoAAAAAAAAAAACX4OHsAFC1LF0qZWVJ3t7OjgQAUN2QYwAAAAAAQHlR1ICNvn2dHQEAoLoixwAAAAAAgPLi9lMAAAAAAAAAAMAlUNQAAAAAAAAAAAAugdtPwcaGDf+73zm3CQEAVCRyDAAAAAAAKC+KGrDxpz9JiYlS48bSyZPOjgYAUJ2QYwAAAAAAQHlx+ykAAAAAAAAAAOASKGoAAKqtWbNm6aabbpKfn58aNWqke+65R4cOHSpxvZiYGHXr1k0+Pj5q2bKl3n33XQdECwAAAAAAgJJQ1AAAVFsxMTF6/PHH9eOPP2rdunXKzc3VgAEDdOHChSLXiY2N1eDBg9WnTx/t2rVL06ZN0/jx47VixQoHRg4AAAAAAIDCMKcGAKDaWrNmjc3zRYsWqVGjRtqxY4duvfXWQtd599131axZM0VHR0uS2rVrp+3bt+uNN97QsGHDKjtkAAAAAAAAFMOhV2rExsY6cncAABdVWfni/PnzkqT69esX2Wbbtm0aMGCAzbKBAwdq+/btysnJKXSdrKwspaWl2TwAAAAAAABQ8Rxa1Lj++uvVr18/LV26VJmZmY7cNQDAhVRGvjDGKCoqSr1791bHjh2LbJecnKygoCCbZUFBQcrNzVVqamqh68yaNUsBAQHWR9OmTSskZgAAAAAAANhyaFHjl19+UdeuXfWXv/xFwcHBeuyxx/TTTz85MgQAgAuojHzxxBNPaM+ePVq2bFmJbS0Wi81zY0yhy6+YOnWqzp8/b30kJCSUK1YAAAAAAAAUzqFFjY4dO2r27NlKTEzUokWLlJycrN69e6tDhw6aPXu2Tp065chwAABVVEXniyeffFJffPGF1q9fryZNmhTbNjg4WMnJyTbLUlJS5OHhocDAwELX8fb2lr+/v80DAAAAAAAAFc+hRY0rPDw8dO+99+rf//63Xn31VR07dkxPP/20mjRpopEjRyopKckZYQEAqpjy5gtjjJ544gmtXLlSP/zwg1q0aFHiPsPDw7Vu3TqbZWvXrlX37t3l6elZruMBAAAAAABA+TilqLF9+3aNGzdOISEhmj17tp5++mkdO3ZMP/zwgxITEzVkyBBnhAVJJ09Kxlz+FwCcrbz54vHHH9fSpUv10Ucfyc/PT8nJyUpOTtalS5esbaZOnaqRI0dan0dGRiouLk5RUVE6cOCA3n//fS1cuFBPP/10pR1nTUGOAQAAAAAA5eXhyJ3Nnj1bixYt0qFDhzR48GAtWbJEgwcPlpvb5dpKixYtNH/+fLVt29aRYQEAqpiKyhfz5s2TJPXt29dm+aJFizR69GhJUlJSkuLj462vtWjRQqtXr9ZTTz2lOXPmKDQ0VO+8846GDRtWcQcIAAAAAACAMnFoUWPevHl65JFH9PDDDys4OLjQNs2aNdPChQsdGRYAoIqpqHxxZYLv4ixevLjAsoiICO3cudOuWAEAAAAAAOA4Di1qrFu3Ts2aNbN+0/YKY4wSEhLUrFkzeXl5adSoUY4MCwBQxZAvAAAAAAAAUBiHFjWuu+46JSUlqVGjRjbLz5w5oxYtWigvL8+R4aAQM2dK589LAQHS9OnOjgZATUW+qJ7IMQAAAAAAoLwcWtQo6jYgGRkZ8vHxcWQoKMJ770mJiVLjxpxwAuA85IvqiRwDAAAAAADKyyFFjaioKEmSxWLR888/r9q1a1tfy8vL03/+8x916dLFEaEAAKow8gUAAAAAAACK45Cixq5duyRd/ubt3r175eXlZX3Ny8tLnTt31tNPP+2IUAAAVRj5AgAAAAAAAMVxSFFj/fr1kqSHH35Yb7/9tvz9/R2xWwCAiyFfAAAAAAAAoDgOnVNj0aJFjtwdAMBFkS8AAAAAAABQmEovagwdOlSLFy+Wv7+/hg4dWmzblStXVnY4AIAqinwBAAAAAACAklR6USMgIEAWi8X6fwAACkO+AAAAAAAAQEkqvahx9S1EuJ0IAKAo5AsAAAAAAACUxM2RO7t06ZIuXrxofR4XF6fo6GitXbvWkWEAAKo48gUAAAAAAAAK49CJwocMGaKhQ4cqMjJS586d08033ywvLy+lpqZq9uzZ+vOf/+zIcFCIiAgpNVVq0MDZkQCoycgX1RM5BgAAAAAAlJdDr9TYuXOn+vTpI0n69NNPFRwcrLi4OC1ZskTvvPOOI0NBET78UPr228v/AoCzkC+qJ3IMAAAAAAAoL4cWNS5evCg/Pz9J0tq1azV06FC5ubnplltuUVxcnCNDAQBUYeQLAAAAAAAAFMahRY3rr79en332mRISEvTtt99qwIABkqSUlBT5+/s7MhQAQBVGvgAAAAAAAEBhHFrUeP755/X000+refPm6tGjh8LDwyVd/hZu165dHRkKAKAKI18AAAAAAACgMA6dKPy+++5T7969lZSUpM6dO1uX33777br33nsdGQqKcNtt0m+/SUFB0g8/ODsaADUV+aJ6IscAAAAAAIDycmhRQ5KCg4MVHBxss+zmm292dBgowuHDUmKidP68syMBUNORL6ofcgwAAAAAACgvhxY1Lly4oFdeeUXff/+9UlJSlJ+fb/P68ePHHRkOAKCKIl8AAAAAAACgMA4tajz66KOKiYnRiBEjFBISIovFUuZtzZ07V6+//rqSkpLUoUMHRUdHq0+fPiWut2XLFkVERKhjx47avXt3mfcPAKg8FZkvAAAAAAAAUH04tKjxzTff6Ouvv1avXr3KtZ3ly5dr4sSJmjt3rnr16qX58+dr0KBB2r9/v5o1a1bkeufPn9fIkSN1++2367fffitXDACAylNR+QIAAAAAAADVi5sjd1avXj3Vr1+/3NuZPXu2xowZo0cffVTt2rVTdHS0mjZtqnnz5hW73mOPPaYHH3xQ4eHh5Y4BAFB5KipfAAAAAAAAoHpxaFHjb3/7m55//nldvHixzNvIzs7Wjh07NGDAAJvlAwYM0NatW4tcb9GiRTp27JimT59u136ysrKUlpZm8wAAOEZF5AtJ2rhxo+666y6FhobKYrHos88+K7b9hg0bZLFYCjwOHjxYrjgAAAAAAABQMRx6+6k333xTx44dU1BQkJo3by5PT0+b13fu3FniNlJTU5WXl6egoCCb5UFBQUpOTi50nSNHjmjKlCnatGmTPDzsO+RZs2Zp5syZdrUFAFSsisgX0uUJxzt37qyHH35Yw4YNs3v/hw4dkr+/v/V5w4YN7V4XAAAAAAAAlcehRY177rmnwrZ17aSxxphCJ5LNy8vTgw8+qJkzZ6p169Z2b3/q1KmKioqyPk9LS1PTpk3LHjAAwG4VlS8GDRqkQYMGlXq9Ro0aqW7duhUSAwAAAAAAACqOQ4sa9t76qTgNGjSQu7t7gasyUlJSCly9IUnp6enavn27du3apSeeeEKSlJ+fL2OMPDw8tHbtWt12220F1vP29pa3t3e54wUAlF5F5Ivy6Nq1qzIzM9W+fXs999xz6tevn1PjAQAAAAAAwGUOLWpI0rlz5/Tpp5/q2LFjmjRpkurXr6+dO3cqKChIjRs3LnF9Ly8vdevWTevWrdO9995rXb5u3ToNGTKkQHt/f3/t3bvXZtncuXP1ww8/6NNPP1WLFi3Kf1DVyPPPSxkZkq+vsyMBUNOVN1+URUhIiBYsWKBu3bopKytL//rXv3T77bdrw4YNuvXWW4tcLysrS1lZWdbnzMNUOHIMAAAAAAAoL4cWNfbs2aM77rhDAQEBOnHihMaOHav69etr1apViouL05IlS+zaTlRUlEaMGKHu3bsrPDxcCxYsUHx8vCIjIyVdvnVUYmKilixZIjc3N3Xs2NFm/UaNGsnHx6fAckj/93/OjgAAKi5flFabNm3Upk0b6/Pw8HAlJCTojTfeKLaowTxM9iHHAAAAAACA8nJz5M6ioqI0evRoHTlyRD4+PtblgwYN0saNG+3ezvDhwxUdHa0XXnhBXbp00caNG7V69WqFhYVJkpKSkhQfH1/h8QMAHKOi8kVFuOWWW3TkyJFi20ydOlXnz5+3PhISEhwUHQAAAAAAQM3i0Cs1fv75Z82fP7/A8saNGxeYI6Mk48aN07hx4wp9bfHixcWuO2PGDM2YMaNU+wMAOE5F5ovy2rVrl0JCQoptwzxMAAAAAAAAjuHQooaPj0+h9xk/dOiQGjZs6MhQUISkJCkvT3J3l0o4hwcAlaai8kVGRoaOHj1qfR4bG6vdu3erfv36atasmc3tCiUpOjpazZs3V4cOHZSdna2lS5dqxYoVWrFiRfkPCuQYAAAAAABQbg69/dSQIUP0wgsvKCcnR5JksVgUHx+vKVOmaNiwYY4MBUW46SapadPL/wKAs1RUvti+fbu6du2qrl27Srp8W6uuXbvq+eefl1TwdoXZ2dl6+umn1alTJ/Xp00ebN2/W119/raFDh1bg0dVc5BgAAAAAAFBeDr1S44033tDgwYPVqFEjXbp0SREREUpOTlZ4eLheeuklR4YCAKjCKipf9O3bV8aYIl+/9naFkydP1uTJk8saNgAAAAAAACqZQ4sa/v7+2rx5s9avX68dO3YoPz9fN954o+644w5HhgEAqOLIFwAAAAAAACiMw4oa+fn5Wrx4sVauXKkTJ07IYrGoRYsWCg4OljFGFovFUaEAAKow8gUAAAAAAACK4pA5NYwxuvvuu/Xoo48qMTFRN9xwgzp06KC4uDiNHj1a9957ryPCAABUceQLAAAAAAAAFMchV2osXrxYGzdu1Pfff69+/frZvPbDDz/onnvu0ZIlSzRy5EhHhAMAqKLIFwAAAAAAACiOQ67UWLZsmaZNm1bgBJUk3XbbbZoyZYo+/PBDR4QCAKjCyBcAAAAAAAAojkOKGnv27NGdd95Z5OuDBg3SL7/84ohQAABVGPkCAAAAAAAAxXFIUePMmTMKCgoq8vWgoCCdPXvWEaEAAKow8gUAAAAAAACK45CiRl5enjw8ip6+w93dXbm5uY4IBQBQhZEvAAAAAAAAUByHTBRujNHo0aPl7e1d6OtZWVmOCAN2+P57KTdXKuacIgBUGvJF9UaOAQAAAAAA5eWQ0wqjRo0qsc3IkSMdEAlK0qaNsyMAUJORL6o3cgwAAAAAACgvhxQ1Fi1a5IjdAABcXE3PF6dOnVJaWlqxbeLi4rgFFwAAAAAAqLG4AQQAAFXAqVOn9PDDDys9Pb3YdpcuXdKvv/6qnJwcB0UGAAAAAABQdVDUgI2PPpIuXpRq15YefNDZ0QBAzZGWlqb09HT16dNHgYGBRbY7evSoVq1a5ZJFDXIMAAAAAAAoL4oasDF5spSYKDVuzAknAHCGwMBABQUFFfl6amqqA6OpWOQYAAAAAABQXm7ODgAAAAAAAAAAAMAeFDUAAAAAAAAAAIBLoKgBAAAAAAAAAABcAkUNAAAAAAAAAADgEpgoHACAaig7O1txcXHFtvH391fDhg0dFBEAAAAAAED5UdQAAKCaycjIUGxsrJ599ll5eXkV2c7Pz0+LFi2isAEAAAAAAFwGRQ0AAKqZzMxMubm5qVevXmrcuHGhbU6fPq1NmzYpLS2NogYAAAAAAHAZFDUAAKim6tWrp6CgIGeHAQAAAAAAUGEoasBGcLDtvwAAVBRyDAAAAAAAKC83ZweAqmX7dunkycv/AoCr27hxo+666y6FhobKYrHos88+K3GdmJgYdevWTT4+PmrZsqXefffdyg+0hiDHAAAAAACA8qKoAQCoti5cuKDOnTvrH//4h13tY2NjNXjwYPXp00e7du3StGnTNH78eK1YsaKSIwUAAAAAAIA9uP0UAKDaGjRokAYNGmR3+3fffVfNmjVTdHS0JKldu3bavn273njjDQ0bNqySogQAAAAAAIC9uFIDAID/b9u2bRowYIDNsoEDB2r79u3KyclxUlQAAAAAAAC4gis1YOOxx6QzZ6T69aX5850dDQA4VnJysoKCgmyWBQUFKTc3V6mpqQoJCSl0vaysLGVlZVmfp6WlVWqcroocAwAAAAAAyouiBmx8/bWUmCg1buzsSADAOSwWi81zY0yhy682a9YszZw5s1Ljqg7IMQAAAAAAoLy4/RQAAP9fcHCwkpOTbZalpKTIw8NDgYGBRa43depUnT9/3vpISEio7FABAAAAAABqJK7UAADg/wsPD9eXX35ps2zt2rXq3r27PD09i1zP29tb3t7elR0eAAAAAABAjceVGgCAaisjI0O7d+/W7t27JUmxsbHavXu34uPjJV2+wmLkyJHW9pGRkYqLi1NUVJQOHDig999/XwsXLtTTTz/tjPABAAAAAABwDa7UqCKysrMVFxdXbBt/f381bNjQQREBgOvbvn27+vXrZ30eFRUlSRo1apQWL16spKQka4FDklq0aKHVq1frqaee0pw5cxQaGqp33nlHw4YNc3jsAAAAAAAAKIiiRhVwNuO8jsce10vTZhZ7+xIfv9qav+g9ChsAYKe+fftaJ/ouzOLFiwssi4iI0M6dOysxKgAAAAAAAJQVRY0q4ELWRXlZPDT2lj/qutDmhbb59Uyy5m9bprS0NIoaAAAAAAAAAIAaiaJGFRJaL0gtGjVzdhgAAAAAAAAAAFRJTBQOAAAAAAAAAABcAldqwMYDD0hnz0r16jk7EgBAdUOOAQAAAAAA5eWyV2rMnTtXLVq0kI+Pj7p166ZNmzYV2XblypXq37+/GjZsKH9/f4WHh+vbb791YLSu4/XXpX/+8/K/AABUJHIMAAAAAAAoL5csaixfvlwTJ07Us88+q127dqlPnz4aNGiQ4uPjC22/ceNG9e/fX6tXr9aOHTvUr18/3XXXXdq1a5eDIwcAAAAAAAAAAGXlkkWN2bNna8yYMXr00UfVrl07RUdHq2nTppo3b16h7aOjozV58mTddNNNatWqlV5++WW1atVKX375pYMjBwAAAAAAAAAAZeVyRY3s7Gzt2LFDAwYMsFk+YMAAbd261a5t5OfnKz09XfXr16+MEAEAAAAAAAAAQCVwuaJGamqq8vLyFBQUZLM8KChIycnJdm3jzTff1IULF3T//fcX2SYrK0tpaWk2j5qgbVvJ3//yvwAAVCRyDAAAAAAAKC+XK2pcYbFYbJ4bYwosK8yyZcs0Y8YMLV++XI0aNSqy3axZsxQQEGB9NG3atNwxu4KMDCk9/fK/AABUJHIMAAAAAAAoL5crajRo0EDu7u4FrspISUkpcPXGtZYvX64xY8bo3//+t+64445i206dOlXnz5+3PhISEsodOwAAAAAAAAAAKDuXK2p4eXmpW7duWrdunc3ydevWqWfPnkWut2zZMo0ePVofffSRfve735W4H29vb/n7+9s8AAAAAAAAAACA83g4O4CyiIqK0ogRI9S9e3eFh4drwYIFio+PV2RkpKTLV1kkJiZqyZIlki4XNEaOHKm3335bt9xyi/Uqj1q1aikgIMBpxwEAAAAAAAAAAOznkkWN4cOH6/Tp03rhhReUlJSkjh07avXq1QoLC5MkJSUlKT4+3tp+/vz5ys3N1eOPP67HH3/cunzUqFFavHixo8MHAAAAAAAAAABl4JJFDUkaN26cxo0bV+hr1xYqNmzYUPkBAQAAAAAAAACASuVyc2oAAAAAAAAAAICaiaIGAAAAAAAAAABwCRQ1AAAAAAAAAACAS3DZOTVQOd59V7p0SapVy9mRAACqG3IMAAAAAAAoL4oasPH73zs7AgBAdUWOAQAAAAAA5cXtpwAAAAAAAAAAgEugqAEAAAAAAAAAAFwCt5+CjR07pOxsyctL6tbN2dEAAKoTcgwAAAAAACgvrtSAjSFDpJ49L/8LANXF3Llz1aJFC/n4+Khbt27atGlTkW03bNggi8VS4HHw4EEHRlw9kWMAAAAAAEB5UdQAAFRry5cv18SJE/Xss89q165d6tOnjwYNGqT4+Phi1zt06JCSkpKsj1atWjkoYgAAAAAAABSF208BAKq12bNna8yYMXr00UclSdHR0fr22281b948zZo1q8j1GjVqpLp16zooyqrr1KlTSktLK7aNv7+/GjZs6KCIAAAAAABATUZRAwBQbWVnZ2vHjh2aMmWKzfIBAwZo69atxa7btWtXZWZmqn379nruuefUr1+/ygy1Sjp16pQefvhhpaenF9vOz89PixYtorABAAAAAAAqHUUNAEC1lZqaqry8PAUFBdksDwoKUnJycqHrhISEaMGCBerWrZuysrL0r3/9S7fffrs2bNigW2+9tdB1srKylJWVZX1e0pUNriItLU3p6enq06ePAgMDC21z+vRpbdq0SWlpaRQ1AAAAAABApaOoAQCo9iwWi81zY0yBZVe0adNGbdq0sT4PDw9XQkKC3njjjSKLGrNmzdLMmTMrLuAqJjAwsEBhCAAAAAAAwBkoagAAqq0GDRrI3d29wFUZKSkppTpJf8stt2jp0qVFvj516lRFRUVZn6elpalp06alDxh2YZ4PAAAAAABqLooaAIBqy8vLS926ddO6det07733WpevW7dOQ4YMsXs7u3btUkhISJGve3t7y9vbu1yxwj7M8wEAAAAAQM1GUQMAUK1FRUVpxIgR6t69u8LDw7VgwQLFx8crMjJS0uWrLBITE7VkyRJJUnR0tJo3b64OHTooOztbS5cu1YoVK7RixQpnHkalyM7OVlxcXJGvx8XFKTc314ERlYx5PgAAAAAAqNkoagAAqrXhw4fr9OnTeuGFF5SUlKSOHTtq9erVCgsLkyQlJSUpPj7e2j47O1tPP/20EhMTVatWLXXo0EFff/21Bg8e7KxDqBQZGRmKjY3Vs88+Ky8vr0LbXLp0Sb/++qtycnIcHF3JmOcDAAAAAICaiaIGbBw4IBkjFTF/LgC4pHHjxmncuHGFvrZ48WKb55MnT9bkyZMdEJVzZWZmys3NTb169VLjxo0LbXP06FGtWrWqwooa5BgAAAAAAFBeFDVgw8/P2REAABypXr16RV7xkJqaWqH7IscAAAAAAIDycnN2AAAAAAAAAAAAAPagqAEAAAAAAAAAAFwCt5+CjdmzpbQ0yd9fiopydjQAgOqEHAMAAAAAAMqLogZszJ4tJSZKjRtzwgkAULHIMQAAAAAAoLwoaqDSnTp1SmlpacW28ff3V8OGDR0UEQCgImVnZysuLq7Edvn5zSW5V3o8AAAAAACg+qKo4UKy7DhpVNWKA6dOndJjD49VZvrFYttZvNw1/aWZCgwMLLJNVTs2AICUkZGh2NhYPfvss/Ly8iq27dmzKyQ1cExgAAAAAACgWqKo4SLOZpzX8djjemnaTHl7exfZzsevtuYveq/KnPxPS0tTZvpFPRb+gELrBxfa5sDJw5r+7zc1dfwklzo2AICUmZkpNzc39erVS40bNy6y3enTp/Xjj/kOjKxk9lxJKFFUBwAAAACgKqGo4SIuZF2Ul8VDY2/5o64LbV5om1/PJGv+tmVKS0urcidfQusHq0WjZoW+dvJ0kksfGwBAqlevnoKCgpwdht1OnTqlhx9+WOnp6SW29fPz06JFi8g/AAAAAABUARQ1XExovaAiiwOurjofGwDAsUqa5yMuLk5nz55Vv379ir314enTp7Vp0yaK6gAAAAAAVBEUNQAAQLVizzwfly5d0q+//ip/f3+XusIEAAAAAICajqIGyqWk+5HHxcUpNyfXgREBAKq63NxcHTtW+FUUFTF/hT3zfBw9elSrVq1STk5OufYFAAAAAAAci6IGyuzUqVN67OGxyky/WGSbC5cu6rfEZOXkZDswMgBAVZORkaHc3MtF7rNnz+iRRx4ptF1Fzl9R3Dwfqamp5d4+AAAAAABwPIoasHHjjVLTppI955LS0tKUmX5Rj4U/oND6wYW22XFsj95KmK/c3LwKjhQA4EoyMzPl6flf1a+frfr183TPPfcUaMP8FfYp6SrJKyriqhcAAAAAAKoaihqw8cUXpV8ntH5wkRN8nzydVM6IAADVRf36o/X444+rRYsWkpjHoixOnTqlhx9+WOnp6SW2rcirXgAAAAAAqCooaqDasecbrHx7FQBgr+zsbMXFFT4HyNVtipqU/IqKyD1paWlKT09Xnz59FBgYWGQ7rnoBAAAAAFRXFDWqmSw7TrxU5xP69szzIUk+frU1f9F71bYfAAAVIyMjQ7GxsXr22WeLLFpkZ2crISFBYWFh8vAo+k+rirxyIjAwsMj5QgAAAAAAqM4oalQjZzPO63jscb00baa8vb2LbFedT+jbM8/Hr2eSNX/bMr69CgAoUWZmptzc3NSrVy81bty40DZHjx5VXFycwsPDi2zjyldOcAUkAAAAAKAqoahRjVzIuigvi4fG3vJHXRfavNA2JZ3Qv/tu6dSpyxOFl2V+jaqiuHk+7MVJHACoWGfOLNb8+e3VsKH0l79sdHY4pVKvXr0ir4xITU0tsY2rsncOD+bvAAAAAAA4CkWNaii0XlCxJ/SLu0XVTz+F6bffPBQUlKtTp87W2JMT3MYKACpeTk5HJSSEKiOj6LG1pPkr4uLilJubWxnh1UglFfDj4uJ09uxZ9evXr8g5PCr6KhS+VAAAAAAAKA5FjRqmpFtUnTv7oaSGOnf2rB57eGyVOmFvz3whcXFxys0p/8kubmMFAI5nz/wVly5d0q+//qqcnBwHR1f92HMVxpX+9vf3d8hVKFwZAgAAAAAoCUWNGqakW1Tt2O2nlGzJ16u2MtMvVpkT9vbOF3Lh0kX9lpisnJzsCtlvRdzGCgBgH3vnr1i1apVLFjVKugpFqtgrEOy56qWkqzDs7e+KOra0tDSlp6erT58+DrsyBAAAAADgWihq1FBF3aLK3d398r9u7o4OqVj2zBciSTuO7dFbCfOVm5vnuOAAABXKnvkrXI09V6FIFXcFQmmueinuKgx7+rsyji0wMLDazU8CAAAAAKgYLlvUmDt3rl5//XUlJSWpQ4cOio6OVp8+fYpsHxMTo6ioKO3bt0+hoaGaPHmyIiMjHRix6ynpdk8Vdaun0ihpvpCTp5McGI19t8Tivt+A85Ez4Gz2XIVy+vRpff/999q7d6/CwsIKbWPvnCKOvOrF3mPj6goAAAAAQEVwyaLG8uXLNXHiRM2dO1e9evXS/PnzNWjQIO3fv1/NmhU84R0bG6vBgwdr7NixWrp0qbZs2aJx48apYcOGGjZsmBOOoOrLN/kl3u6pom/15EgVUbCx95ZYFi93TX9pZpG30biiqhU/7JmoVap6cQPXImegKinuKpTKmFPEkVe9FLcvAAAAAAAqiksWNWbPnq0xY8bo0UcflSRFR0fr22+/1bx58zRr1qwC7d999101a9ZM0dHRkqR27dpp+/bteuONNzhBVYR8Y0q83ZOr3urJnmKEPQUbe26JdeDkYU3/95uaOn5SsYUPyb7iR3Z2drG39pAqpshw6tQpPfbwWGWmXyyxrY9f7RInlLenQOKoY6uqKCJVHnIGXAVzith/JUpFqaj8JDl2fLYnbvIFAAAAgOrK5Yoa2dnZ2rFjh6ZMmWKzfMCAAdq6dWuh62zbtk0DBgywWTZw4EAtXLhQOTk58vT0rLR4XV1xt3ty9K2eKoo9xYjSFGxK6iN75gKxp/iRlZ2tE/Fxui6shTw8i/7VtafIUJK0tDRlpl/UY+EPKLR+cJHtfj2TrPnblhV7OxF7CiSOPLaqqKKLSPgfcgZcUU2eU6S0V6KUx6lTp/Twww8rPT29yDbZ2dlKSEhQWFiYPDyK/7O5ouZDKYk9cTsyHgAAAABwNJcraqSmpiovL6/Ah/2goCAlJycXuk5ycnKh7XNzc5WamqqQkJAC62RlZSkrK8v6/Pz585Jk1zepr5aenq6c3FwdTYpVRmbhJyxPpCQoLz9fR5NPKM9inNomJ7ehJB/l5adXmZjsbVPabV3MulTkz+RSdmaFxl3cviTpdPo5uRs39W/ZUyGBhRcRDv16THHHT+i25j2KbHM6/ay+2P+9fvzxRzVt2rTI/ZUkISFBmZlZJcZ9MeuSLl66pP379xd5ciUhIUHnTp3V79v1U6BfvULbOPLYqiJ7+ki63AffHNmoxMTEEq/8uZafn58sFkt5Q3U5rpQz0tPTlZubq19//VWZmZlFtktJSVF+fr6Sk5Pl5ubmEm2utDMmXVKa8vLSC/3GflWNuyrF5Kpxx8fHS5Kuv/56NWjQoMi4ExMTFR8fr4SEBOXlFf7FgjNnzuhSCbnHHgkJCUpNTVXHjh3l5+dXZDyxsbFq2bJlsXGnp6drz549DslR9sSdnp6uAwcOlClfSDU3ZwAAAABwDRZjTNFniKugX3/9VY0bN9bWrVsVHh5uXf7SSy/pX//6lw4ePFhgndatW+vhhx/W1KlTrcu2bNmi3r17KykpScHBBU+izpgxQzNnzqycgwAABzt//rz8/f2dHYbDkTMAoPRqas4AAAAA4Bpc7kqNBg0ayN3dvcA3bFNSUoq8VUNwcHCh7T08PIqcv2Dq1KmKioqyPs/Pz9eZM2cUGBhYqm+upaWlqWnTpkpISODDYTHoJ/vQT/ahnwoq6tu81Z2zcsa5c+cUFham+Ph4BQQElPMoKp+r/c4Qb+Ui3srlCvHW1JwBAAAAwDW4XFHDy8tL3bp107p163Tvvfdal69bt05DhgwpdJ3w8HB9+eWXNsvWrl2r7t27F3lvdG9v7wKX69etW7fMcfv7+1fZD65VCf1kH/rJPvQTnJkzJCkgIMCl3oOu9jtDvJWLeCuXq8ULAAAAAFVF0TdjrsKioqL0z3/+U++//74OHDigp556SvHx8YqMjJR0+RuzI0eOtLaPjIxUXFycoqKidODAAb3//vtauHChnn76aWcdAgDAQcgZAAAAAAAA1YfLXakhScOHD9fp06f1wgsvKCkpSR07dtTq1asVFhYmSUpKSrJOSClJLVq00OrVq/XUU09pzpw5Cg0N1TvvvKNhw4Y56xAAAA5CzgAAAAAAAKg+XLKoIUnjxo3TuHHjCn1t8eLFBZZFRERo586dlRxVQd7e3po+fXqhtyXB/9BP9qGf7EM/4VqOzhmu9h4k3spFvJWLeAEAAACgZrEYY4yzgwAAAAAAAAAAACiJS86pAQAAAAAAAAAAah6KGgAAAAAAAAAAwCVQ1AAAAAAAAAAAAC6BokYlmjt3rlq0aCEfHx9169ZNmzZtcnZIVcqMGTNksVhsHsHBwc4Oy+k2btyou+66S6GhobJYLPrss89sXjfGaMaMGQoNDVWtWrXUt29f7du3zznBOlFJ/TR69OgC769bbrnFOcGiWirtGB8TE6Nu3brJx8dHLVu21LvvvuugSC8rTbwbNmwo8PtjsVh08ODBSo+zpN/twjizb0sbrzP7VpJmzZqlm266SX5+fmrUqJHuueceHTp0qMT1nNXHZYnXmX08b948derUSf7+/vL391d4eLi++eabYtdx9tgAAAAAAK6GokYlWb58uSZOnKhnn31Wu3btUp8+fTRo0CDFx8c7O7QqpUOHDkpKSrI+9u7d6+yQnO7ChQvq3Lmz/vGPfxT6+muvvabZs2frH//4h37++WcFBwerf//+Sk9Pd3CkzlVSP0nSnXfeafP+Wr16tQMjRHVW2jE+NjZWgwcPVp8+fbRr1y5NmzZN48eP14oVK6pkvFccOnTI5neoVatWlR6rPb/bV3N235Y23iuc0bfS5RPojz/+uH788UetW7dOubm5GjBggC5cuFDkOs7s47LEe4Uz+rhJkyZ65ZVXtH37dm3fvl233XabhgwZUuSXD5z9/gUAAAAAl2RQKW6++WYTGRlps6xt27ZmypQpToqo6pk+fbrp3Lmzs8Oo0iSZVatWWZ/n5+eb4OBg88orr1iXZWZmmoCAAPPuu+86IcKq4dp+MsaYUaNGmSFDhjglHlR/pR3jJ0+ebNq2bWuz7LHHHjO33HJLpcV4tdLGu379eiPJnD171gHRFa2w3+1rObtvr2ZPvFWlb69ISUkxkkxMTEyRbapSH9sTb1Xr43r16pl//vOfhb5WlfoWAAAAAFwFV2pUguzsbO3YsUMDBgywWT5gwABt3brVSVFVTUeOHFFoaKhatGihP/7xjzp+/LizQ6rSYmNjlZycbPPe8vb2VkREBO+tQmzYsEGNGjVS69atNXbsWKWkpDg7JFQDZRnjt23bVqD9wIEDtX37duXk5FRarFL5clLXrl0VEhKi22+/XevXr6/MMMvMmX1bHlWlb8+fPy9Jql+/fpFtqlIf2xPvFc7u47y8PH388ce6cOGCwsPDC21TlfoWAAAAAFwFRY1KkJqaqry8PAUFBdksDwoKUnJyspOiqnp69OihJUuW6Ntvv9V7772n5ORk9ezZU6dPn3Z2aFXWlfcP762SDRo0SB9++KF++OEHvfnmm/r555912223KSsry9mhwcWVZYxPTk4utH1ubq5SU1MrLVapbPGGhIRowYIFWrFihVauXKk2bdro9ttv18aNGys11rJwZt+WRVXqW2OMoqKi1Lt3b3Xs2LHIdlWlj+2N19l9vHfvXvn6+srb21uRkZFatWqV2rdvX2jbqtK3AAAAAOBKPJwdQHVmsVhsnhtjCiyryQYNGmT9/w033KDw8HBdd911+uCDDxQVFeXEyKo+3lslGz58uPX/HTt2VPfu3RUWFqavv/5aQ4cOdWJkqC5K+3tYWPvClleW0sTbpk0btWnTxvo8PDxcCQkJeuONN3TrrbdWapxl4ey+LY2q1LdPPPGE9uzZo82bN5fYtir0sb3xOruP27Rpo927d+vcuXNasWKFRo0apZiYmCILG1WhbwEAAADAlXClRiVo0KCB3N3dC3wDNiUlpcC38fA/derU0Q033KAjR444O5QqKzg4WJJ4b5VBSEiIwsLCeH+h3MoyxgcHBxfa3sPDQ4GBgZUWq1RxOemWW26pkr8/zuzbiuKMvn3yySf1xRdfaP369WrSpEmxbatCH5cm3sI4so+9vLx0/fXXq3v37po1a5Y6d+6st99+u9C2VaFvAQAAAMDVUNSoBF5eXurWrZvWrVtns3zdunXq2bOnk6Kq+rKysnTgwAGFhIQ4O5Qqq0WLFgoODrZ5b2VnZysmJob3VglOnz6thIQE3l8ot7KM8eHh4QXar127Vt27d5enp2elxSpVXE7atWtXlfz9cWbfVhRH9q0xRk888YRWrlypH374QS1atChxHWf2cVniLYwz37/GmCJvfVgd3r8AAAAA4GjcfqqSREVFacSIEerevbvCw8O1YMECxcfHKzIy0tmhVRlPP/207rrrLjVr1kwpKSl68cUXlZaWplGjRjk7NKfKyMjQ0aNHrc9jY2O1e/du1a9fX82aNdPEiRP18ssvq1WrVmrVqpVefvll1a5dWw8++KATo3a84vqpfv36mjFjhoYNG6aQkBCdOHFC06ZNU4MGDXTvvfc6MWpUFyWN8VOnTlViYqKWLFkiSYqMjNQ//vEPRUVFaezYsdq2bZsWLlyoZcuWVcl4o6Oj1bx5c3Xo0EHZ2dlaunSpVqxYoRUrVlR6rCWNgVWtb0sbrzP7VpIef/xxffTRR/r888/l5+dnvUogICBAtWrVklS13r9lideZfTxt2jQNGjRITZs2VXp6uj7++GNt2LBBa9asKTRWZ79/AQAAAMAlGVSaOXPmmLCwMOPl5WVuvPFGExMT4+yQqpThw4ebkJAQ4+npaUJDQ83QoUPNvn37nB2W061fv95IKvAYNWqUMcaY/Px8M336dBMcHGy8vb3Nrbfeavbu3evcoJ2guH66ePGiGTBggGnYsKHx9PQ0zZo1M6NGjTLx8fHODhvVSHFj/KhRo0xERIRN+w0bNpiuXbsaLy8v07x5czNv3rwqG++rr75qrrvuOuPj42Pq1atnevfubb7++muHxFnSGFjV+ra08Tqzb40xhcYqySxatMjapir1cVnidWYfP/LII9bfs4YNG5rbb7/drF27tshYjXH+2AAAAAAArsZizP+fjRAAAAAAAAAAAKAKY04NAAAAAAAAAADgEihqAAAAAAAAAAAAl0BRAwAAAAAAAAAAuASKGgAAAAAAAAAAwCVQ1AAAAAAAAAAAAC6BogYAAAAAAAAAAHAJFDUAAAAAAAAAAIBLoKgBAAAAAAAAAABcAkUN2K1v376aOHFimdYdPXq07rnnnnLHsGXLFt1www3y9PSskO3VROX5OdojOTlZ/fv3V506dVS3bt1K248jVNT7FkDpuOrvnsVi0WeffebsMKq0ys5BpXHixAlZLBbt3r3b2aEAAAAAAEqBogZcSlRUlLp06aLY2FgtXrzYaXFUpZMyVc1bb72lpKQk7d69W4cPH9aGDRtksVh07tw5Z4dWpKJObL399ttOfZ8BNVVN/N1r3ry5oqOjq/w2XVVhhbKmTZsqKSlJHTt2dE5QAAAAAIAyoagBl3Ls2DHddtttatKkSZmvAsjOzq7YoGDj2LFj6tatm1q1aqVGjRpV2HaNMcrNzS3VOjk5OeXaZ0BAgMtfbQK4In73HCcvL0/5+fnODqPMyjPOu7u7Kzg4WB4eHhUYEQAAAACgslHUqCb69u2rJ554Qk888YTq1q2rwMBAPffcczLGSLp8In/y5Mlq3Lix6tSpox49emjDhg3W9U+fPq0HHnhATZo0Ue3atXXDDTdo2bJlxe5zzZo1CggI0JIlS+yOc+bMmWrUqJH8/f312GOP2RQYjDF67bXX1LJlS9WqVUudO3fWp59+Kul/36Q/ffq0HnnkEVksFuu3eGNiYnTzzTfL29tbISEhmjJlis3J7yt9ExUVpQYNGqh///6SpP3792vw4MHy9fVVUFCQRowYodTU1BKPYfTo0YqJidHbb78ti8Uii8WiEydO2BVLcUr6GUrO/zn+/PPP6t+/vxo0aKCAgABFRERo586d1tebN2+uFStWaMmSJbJYLBo9erT69esnSapXr551mVT8z1uS9QqPb7/9Vt27d5e3t7c2bdpUbHwzZsxQly5d9P7776tly5by9vaWMUZr1qxR7969rf36+9//XseOHbOu16JFC0lS165dZbFY1LdvX0kFv9mblZWl8ePHq1GjRvLx8VHv3r31888/l9hvQHXWt29fPfnkk5o4caLq1aunoKAgLViwQBcuXNDDDz8sPz8/XXfddfrmm28kXT6JPmbMGLVo0UK1atVSmzZt9Pbbb9ts89rfvb59+2r8+PGaPHmy6tevr+DgYM2YMcPuGC0Wi+bNm6dBgwapVq1aatGihT755BObNomJiRo+fLjq1aunwMBADRkyxDq2SyWPf4V54YUXFBQUVOLtjfr27au4uDg99dRT1rxyxYoVK9ShQwd5e3urefPmevPNN+065qK2uXjxYtWtW1dfffWV2rdvL29vb8XFxens2bMaOXKk6tWrp9q1a2vQoEE6cuSIdXtXxterRUdHq3nz5tbnubm5Gj9+vHWsfeaZZzRq1KgCV0jk5+eX62f57rvvasiQIapTp45efPHFEt9TM2bM0AcffKDPP//c2hcbNmwo9Cq98uRxAAAAAICDGFQLERERxtfX10yYMMEcPHjQLF261NSuXdssWLDAGGPMgw8+aHr27Gk2btxojh49al5//XXj7e1tDh8+bIwx5uTJk+b11183u3btMseOHTPvvPOOcXd3Nz/++KPNPiZMmGCMMWbZsmXGz8/PfPbZZ3bFN2rUKOPr62uGDx9u/vvf/5qvvvrKNGzY0EybNs3aZtq0aaZt27ZmzZo15tixY2bRokXG29vbbNiwweTm5pqkpCTj7+9voqOjTVJSkrl48aI5efKkqV27thk3bpw5cOCAWbVqlWnQoIGZPn16gb6ZNGmSOXjwoDlw4ID59ddfTYMGDczUqVPNgQMHzM6dO03//v1Nv379SjyWc+fOmfDwcDN27FiTlJRkkpKSTG5url2xFKekn6Exzv85fv/99+Zf//qX2b9/v9m/f78ZM2aMCQoKMmlpacYYY1JSUsydd95p7r//fpOUlGTOnTtnVqxYYSSZQ4cOWZcZU/zP2xhj1q9fbySZTp06mbVr15qjR4+a1NTUYuObPn26qVOnjhk4cKDZuXOn+eWXX0x+fr759NNPzYoVK8zhw4fNrl27zF133WVuuOEGk5eXZ4wx5qeffjKSzHfffWeSkpLM6dOnjTGX37dDhgyxbn/8+PEmNDTUrF692uzbt8+MGjXK1KtXz9oeqIkiIiKMn5+f+dvf/mYOHz5s/va3vxk3NzczaNAgs2DBAnP48GHz5z//2QQGBpoLFy6Y7Oxs8/zzz5uffvrJHD9+3DrWLV++3LrNa3/3IiIijL+/v5kxY4Y5fPiw+eCDD4zFYjFr1661K0ZJJjAw0Lz33nvm0KFD5rnnnjPu7u5m//79xhhjLly4YFq1amUeeeQRs2fPHrN//37z4IMPmjZt2pisrCxjTMnj35X9rFq1yuTn55vx48ebZs2aWcfn4pw+fdo0adLEvPDCC9a8Yowx27dvN25ubuaFF14whw4dMosWLTK1atUyixYtKvM2Fy1aZDw9PU3Pnj3Nli1bzMGDB01GRoa5++67Tbt27czGjRvN7t27zcCBA831119vsrOzjTGXx9fOnTvb7OOtt94yYWFh1ucvvviiqV+/vlm5cqU5cOCAiYyMNP7+/hX+s2zUqJFZuHChOXbsmDlx4kSJ76n09HRz//33mzvvvNPaF1lZWSY2NtZIMrt27TLGmHLncQAAAACAY1DUqCYiIiJMu3btTH5+vnXZM888Y9q1a2eOHj1qLBaLSUxMtFnn9ttvN1OnTi1ym4MHDzZ/+ctfbPYxYcIEM2fOHBMQEGB++OEHu+MbNWqUqV+/vrlw4YJ12bx584yvr6/Jy8szGRkZxsfHx2zdutVmvTFjxpgHHnjA+jwgIMDmZM60adNMmzZtbI57zpw51u1eibtLly422/3rX/9qBgwYYLMsISHBevK9JFcXBkoTS0nbLOpnaIypEj/Ha+Xm5ho/Pz/z5ZdfWpcNGTLEjBo1yvr8SnHi7Nmz1mX2/LyvrGdvwcWYyyfdPD09TUpKSrHtUlJSjCSzd+9eY4wpcGLriqtPrGZkZBhPT0/z4YcfWl/Pzs42oaGh5rXXXrM7RqC6iYiIML1797Y+z83NNXXq1DEjRoywLktKSjKSzLZt2wrdxrhx48ywYcOszwsraly9D2OMuemmm8wzzzxjV4ySTGRkpM2yHj16mD//+c/GGGMWLlxYYPzOysoytWrVMt9++22h2yxs/JNkPvnkE/OnP/3JtG3b1iQkJNgVnzHGhIWFmbfeestm2YMPPmj69+9vs2zSpEmmffv2Zd7mokWLjCSze/du67LDhw8bSWbLli3WZampqaZWrVrm3//+tzHGvqJGUFCQef31163Pc3NzTbNmzSr8Zzlx4sQS25X0njKm4Nhf3jwOAAAAAHAMbiJcjdxyyy02t6wIDw/Xm2++qe3bt8sYo9atW9u0z8rKUmBgoKTLtwN55ZVXtHz5ciUmJiorK0tZWVmqU6eOzTorVqzQb7/9ps2bN+vmm28uVXydO3dW7dq1beLLyMhQQkKCUlJSlJmZab011BXZ2dn6f+3dbUxT1x8H8G9bHsakMwSBSad0kYcU8YHiQyrZJFkWkmXIpoubIo2TsDmVIQ/L9mLAlBfOGTKygZnbImsMgTlxMToUt2FmkdAqGrfEDkYHFE1EdMbM2M3Z8n/BnxtKW7gt7Qrs+0lM7H0499f7u5wTzuGek5qa6rZMk8kEjUbj8L3T09Nx//59XL9+HQsXLgQArFixwuG8zs5OnDt3DuHh4U5lms1mp3slhthYJuIuhzabDZcvXw54Hm/duoXy8nK0trZicHAQNpsNDx48gMViEV0GMDL1l9h8j8/dZOLi4hAVFeWwzWw2o6ysDB0dHbh9+7Ywf7zFYhG9QKzZbMY///yD9PR0YVtwcDBWrVoFk8nkUYxEs83SpUuF/8tkMkRGRmLJkiXCtpiYGAAjdQgAfPbZZ/jyyy/R398Pq9WKhw8fOk1tNNE1AGD+/PlCeWJoNBqnz6PTDnV2dqKnpwdyudzhmL/++kuYqk5s/VdUVITQ0FB0dHRg3rx5ouNzxWQyITs722Fbeno6qqurYbPZIJPJvCo3JCTE4X6aTCYEBQVh9erVwrbIyEgkJSWJrt/u3buHwcFBhzZFJpMhLS3Nac2OqebSVbvgzTM1ni/acSIiIiIiIvI/Dmr8R8hkMnR2djp1gIx26ldVVeHjjz9GdXU1lixZgjlz5mD37t1Oi2ovX74cly9fRl1dHVauXOnwi7+3JBKJ0OHx3XffQaFQOOwPDQ11e+7w8LBTDMP/X4Ni7Pbxnfp2ux1ZWVnYv3+/U5nz58/37At4GIu37HZ7wPO4detWDA0Nobq6GnFxcQgNDYVGo/F48XVP8j0+d5NxdXxWVhYWLFiAL774ArGxsbDb7UhJSfEobne5dJV3ov+a4OBgh88SicRh2+jPiN1ux9GjR1FUVISqqipoNBrI5XIcOHAABoPB42tMdYHrsXGlpaWhvr7e6ZjRQVKx9d/zzz+PhoYGtLS0ICcnZ0rxTdSuTEVYWJhDue7KHHt9qVTqdJyrRbrFxDvVXI6v5719psbzdztOREREREREvsFBjVmko6PD6XNCQgJSU1Nhs9lw69YtPPPMMy7P1ev1yM7OxpYtWwCMdPD89ttvUKlUDsctWrQIVVVVyMjIgEwmQ01Njej4rl69CqvVirCwMCG+8PBwPPXUU4iIiEBoaCgsFgvWrl0ruszk5GQ0NTU5dES0t7dDLpc7dZaPpVar0dTUBKVSiaAgz38MQkJCYLPZfBLLWO5yKJPJpkUe9Xo9Dh48iBdeeAEAMDAwMOni6iEhIQDgcL9GF6f1NN/euHPnDkwmEw4dOiTct7a2tkljHC8+Ph4hISFoa2vD5s2bAYx06F26dAm7d+/2T/BEs5Ber8eaNWuwY8cOYdvo2xD+1NHRAa1W6/B59M0wtVqNr7/+GtHR0XjiiSdcni+2/lu3bh2ysrKwefNmyGQyvPbaa6Lic9eujK+v2tvbkZiYKOotDVdlupKcnIxHjx7BYDBgzZo1AEbqzu7ubqH9iIqKws2bNx3auLELbM+dOxcxMTEwGo1CXWuz2XDlyhWP35jwlJhnSsy98EU7TkRERERERP4nDXQA5DsDAwMoLi5GV1cXGhoa8Omnn6KwsBCJiYnIycmBVqvF8ePH0dvbi4sXL2L//v1obm4GMNJh+/3336O9vR0mkwlvvvkmbt686fI6iYmJOHfuHJqamjzqzH348CHy8vJw7do1nD59GhUVFdi1axekUinkcjlKS0tRVFQEnU4Hs9mMK1euoLa2Fjqdzm2ZO3bswMDAAAoKCvDrr7/ixIkTqKioQHFxMaRS94/3zp078ccff2DTpk0wGo34/fffcfbsWWzbtk1UB5BSqYTBYEBfX58wnZG3sYzlLocApkUe4+PjceTIEZhMJhgMBuTk5AiDVO7ExcVBIpHg1KlTGBoawv37973OtzciIiIQGRmJzz//HD09PWhtbUVxcbHDMdHR0QgLC8OZM2cwODiIe/fuOZUzZ84cvPXWW3jnnXdw5swZXLt2Dfn5+Xjw4AHy8vJ8GjPRbBYfH49Lly6hpaUF3d3dKCsrw8WLF/1+3W+++QaHDx9Gd3c3KioqYDQasWvXLgBATk4O5s2bh+zsbOj1evT29uKnn35CYWEhrl+/LsQttv57+eWXceTIEbz++us4duyYqPiUSiXOnz+PGzduCIMlJSUl+PHHH1FZWYnu7m7odDrU1NSgtLTU6zJdSUhIQHZ2NvLz89HW1oarV69iy5YtUCgUwvRXGRkZGBoawkcffQSz2Yza2lqcPn3aoZyCggLs27cPJ06cQFdXFwoLC3H37l2/v+Ug5plSKpX4+eef0dXVhdu3b7t8y8QX7TgRERERERH5H39Dm0W0Wi2sVitWrVqFnTt3oqCgAG+88QYAoK6uDlqtFiUlJUhKSsK6detgMBiwYMECAEBZWRnUajUyMzORkZGBJ598Ei+99JLbayUlJaG1tRUNDQ0oKSkRFd9zzz2HhIQEPPvss9i4cSOysrLwwQcfCPsrKytRXl6Offv2QaVSITMzEydPnsTTTz/ttkyFQoHm5mYYjUYsW7YM27dvR15eHt5///0JY4mNjcWFCxdgs9mQmZmJlJQUFBYWYu7cuaI6LkpLSyGTyZCcnIyoqChYLBavYxlrohwCgc/j4cOHcffuXaSmpiI3Nxdvv/02oqOjJzxHoVBgz549eO+99xATEyN0InqTb29IpVI0Njais7MTKSkpKCoqwoEDBxyOCQoKwieffIJDhw4hNjbWaQ77UR9++CE2bNiA3NxcqNVq9PT0oKWlBRERET6NmWg22759O9avX49XX30Vq1evxp07dxz+wt5f9uzZg8bGRixduhQ6nQ719fVITk4GADz++OM4f/48Fi5ciPXr10OlUmHbtm2wWq3Cmxue1n+vvPIKdDodcnNzcfz48Unj27t3L/r6+rBo0SJhyiu1Wo2jR4+isbERKSkpKC8vx969e7F161ZR39lVme7U1dUhLS0NL774IjQaDYaHh9Hc3CxMFaVSqXDw4EHU1tZi2bJlMBqNToMr7777LjZt2gStVguNRoPw8HBkZmbiscceExWvt8Q8U/n5+UhKSsKKFSsQFRWFCxcuOJXji3aciIiIiIiI/E8y7IvJmSngMjIysHz5clRXVwc6FPISc0hE5B8SiQTffvvthIO85Ht2ux0qlQobN25EZWVloMMhIiIiIiKiWYJrahARERHRlPX39+Ps2bNYu3Yt/v77b9TU1KC3t1dYh4iIiIiIiIjIFzj9FPlEeHi42396vT7Q4XnEYrFM+H0sFsu0KNMfpnseFy9e7Da++vr6QIdHRAFQX1/vtl5YvHhxoMMDMLKQ9UT163Qpc6qkUim++uorrFy5Eunp6fjll1/www8/CIuNT2Ym5JKIiIiIiIgCj9NPkU/09PS43adQKCZdTHo6efToEfr6+tzuVyqVCAry7CUnf5TpD9M9j/39/S4XdwWAmJgYyOXyfzkiIgq0P//8E4ODgy73BQcHIy4u7l+OyJnVasWNGzfc7o+Pj58WZQbaTMglERERERERBR4HNYiIiIiIiIiIiIiIaEbg9FNERERERERERERERDQjcFCDiIiIiIiIiIiIiIhmBA5qEBERERERERERERHRjMBBDSIiIiIiIiIiIiIimhE4qEFERERERERERERERDMCBzWIiIiIiIiIiIiIiGhG4KAGERERERERERERERHNCBzUICIiIiIiIiIiIiKiGeF/Ps52SX07wAwAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", "# Classify with default thresholds\n", "unit_type, labels = sc.classify_units(metrics)\n", "\n", @@ -1020,24 +1409,22 @@ "thresholds = sc.get_default_thresholds() # probably not correct format. where should i put this? \n", "thresholds[\"snr\"][\"min\"] = 3 # Lower threshold\n", "thresholds[\"amplitude_median\"][\"min\"] = np.nan # Disable\n", - "unit_type, labels = sc.classify_units(metrics, thresholds)\n", "\n", - "# plots!!!!!\n", - "# Debug: see which thresholds are failing\n", - "sc.print_threshold_failures(metrics)\n", - "\n", - "# Classify with default thresholds\n", - "unit_type, labels = sc.classify_units(metrics)\n", + "unit_type, labels = sc.classify_units(metrics, thresholds)\n", "\n", + "# plots!\n", "# Get summary\n", "summary = sc.get_classification_summary(unit_type, labels)\n", "print(summary)\n", "\n", + "import spikeinterface.widgets as sw\n", "# Plot histograms with threshold lines\n", - "plot_classification_histograms(metrics)\n", + "sw.plot_classification_histograms(metrics)\n", "\n", "# Plot waveform overlay by type\n", - "plot_waveform_overlay(analyzer, unit_type, labels)\n", + "sw.plot_waveform_overlay(analyzer, unit_type, labels)\n", + "\n", + "\n", "\n", "\n" ] diff --git a/src/spikeinterface/comparison/unit_classification.py b/src/spikeinterface/comparison/unit_classification.py index 6b770e4e57..9f973e2a0c 100644 --- a/src/spikeinterface/comparison/unit_classification.py +++ b/src/spikeinterface/comparison/unit_classification.py @@ -54,11 +54,8 @@ def get_default_thresholds() -> dict: - amplitude_cutoff: Estimated fraction of missing spikes - num_spikes: Total spike count - rp_contamination: Refractory period contamination - - isi_violations_ratio: ISI violations ratio - presence_ratio: Fraction of recording where unit is present - - drift_mad: Median absolute deviation of drift - - nn_isolation: Nearest neighbor isolation score - - nn_noise_overlap: Nearest neighbor noise overlap + - drift_ptp: Peak-to-peak drift in um """ thresholds = { # ============================================================ @@ -113,25 +110,13 @@ def get_default_thresholds() -> dict: # Lower is better, max typically 0.1 (10%) "rp_contamination": {"min": np.nan, "max": 0.1}, - # ISI violations ratio - # Lower is better, alternative to rp_contamination - "isi_violations_ratio": {"min": np.nan, "max": 0.1}, - # Presence ratio - fraction of recording where unit is active # Higher is better, ensures unit present throughout "presence_ratio": {"min": 0.7, "max": np.nan}, # Drift MAD - median absolute deviation of drift in um # Lower is better, ensures stable unit location - "drift_mad": {"min": np.nan, "max": 100}, - - # Nearest neighbor isolation (from PCA metrics) - # Higher is better, ensures good cluster separation - "nn_isolation": {"min": 0.8, "max": np.nan}, - - # Nearest neighbor noise overlap (from PCA metrics) - # Lower is better, ensures separation from noise - "nn_noise_overlap": {"min": np.nan, "max": 0.1}, + "drift_ptp": {"min": np.nan, "max": 100}, # ============================================================ # NON-SOMATIC DETECTION THRESHOLDS (optional) @@ -141,12 +126,12 @@ def get_default_thresholds() -> dict: # Non-somatic units have characteristic triphasic waveforms # Peak before to trough ratio - non-somatic have large initial peak - "peak_before_to_trough_ratio": {"min": np.nan, "max": 5}, # non-somatic if > max + "peak_before_to_trough_ratio": {"min": np.nan, "max": 3}, # non-somatic if > max - # Peak before width (in samples at sampling rate) + # Peak before width (in samples at sampling rate) #QQ should be microseconds or something! "peak_before_width": {"min": 4, "max": np.nan}, # non-somatic if < min - # Trough width (in samples) + # Trough width (in samples) #QQ should be microseconds or something! "trough_width": {"min": 5, "max": np.nan}, # non-somatic if < min # Peak before to peak after ratio @@ -198,24 +183,6 @@ def classify_units( unit_type_string : np.ndarray String labels for each unit type. - Examples - -------- - >>> import spikeinterface.comparison as sc - >>> import pandas as pd - >>> - >>> # Get metrics from SortingAnalyzer - >>> qm = analyzer.get_extension("quality_metrics").get_data() - >>> tm = analyzer.get_extension("template_metrics").get_data() - >>> metrics = pd.concat([qm, tm], axis=1) - >>> - >>> # Classify with default thresholds - >>> unit_type, unit_labels = sc.classify_units(metrics) - >>> - >>> # Classify with custom thresholds - >>> thresholds = sc.get_default_thresholds() - >>> thresholds["snr"]["min"] = 3 # Lower SNR threshold - >>> thresholds["amplitude_median"]["min"] = np.nan # Disable - >>> unit_type, unit_labels = sc.classify_units(metrics, thresholds=thresholds) """ if thresholds is None: thresholds = get_default_thresholds() @@ -239,11 +206,8 @@ def classify_units( "amplitude_cutoff", "num_spikes", "rp_contamination", - "isi_violations_ratio", "presence_ratio", - "drift_mad", - "nn_isolation", - "nn_noise_overlap", + "drift_ptp", ] non_somatic_metrics = [ @@ -255,7 +219,7 @@ def classify_units( ] # ======================================== - # NOISE classification (waveform failures) + # NOISE classification # ======================================== noise_mask = np.zeros(n_units, dtype=bool) @@ -282,7 +246,7 @@ def classify_units( unit_type[noise_mask] = 0 # ======================================== - # MUA classification (spike quality failures) + # MUA classification # ======================================== mua_mask = np.zeros(n_units, dtype=bool) @@ -314,7 +278,7 @@ def classify_units( unit_type[np.isnan(unit_type)] = 1 # ======================================== - # NON-SOMATIC classification (optional) + # NON-SOMATIC classification # ======================================== if classify_non_somatic: is_non_somatic = np.zeros(n_units, dtype=bool) From a29d3e1338937c2067c716e83a6b1312f840032a Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Wed, 7 Jan 2026 18:16:24 +0100 Subject: [PATCH 08/49] bombcell snr --- src/spikeinterface/comparison/__init__.py | 1 - .../metrics/quality/misc_metrics.py | 261 +++++++++++++++++- 2 files changed, 259 insertions(+), 3 deletions(-) diff --git a/src/spikeinterface/comparison/__init__.py b/src/spikeinterface/comparison/__init__.py index f4cc497916..f2b80909c7 100644 --- a/src/spikeinterface/comparison/__init__.py +++ b/src/spikeinterface/comparison/__init__.py @@ -46,5 +46,4 @@ classify_units, apply_thresholds, get_classification_summary, - print_threshold_failures, ) diff --git a/src/spikeinterface/metrics/quality/misc_metrics.py b/src/spikeinterface/metrics/quality/misc_metrics.py index c6b07da52e..2af4583f13 100644 --- a/src/spikeinterface/metrics/quality/misc_metrics.py +++ b/src/spikeinterface/metrics/quality/misc_metrics.py @@ -182,6 +182,114 @@ class SNR(BaseMetric): depend_on = ["noise_levels", "templates"] +def compute_snrs_bombcell( + sorting_analyzer, + unit_ids=None, + peak_sign: str = "neg", + baseline_window_ms: float = 0.5, +): + """ + Compute signal to noise ratio using BombCell method. + + This differs from the standard SNR by using: + - Signal: Max absolute value of raw waveforms on peak channel + - Noise: MAD (Median Absolute Deviation) of baseline samples from waveforms + + Parameters + ---------- + sorting_analyzer : SortingAnalyzer + A SortingAnalyzer object. + unit_ids : list or None + The list of unit ids to compute the SNR. If None, all units are used. + peak_sign : "neg" | "pos" | "both", default: "neg" + The sign of the template to compute best channels. + baseline_window_ms : float, default: 0.5 + Duration in ms at the start of the waveform to use as baseline for noise calculation. + + Returns + ------- + snrs : dict + Computed signal to noise ratio for each unit. + + Notes + ----- + This implementation follows the BombCell methodology: + - Signal is the maximum absolute amplitude of raw waveforms on the peak channel + - Noise is computed as MAD of baseline samples (first N samples of each waveform) + + Requires the "waveforms" extension to be computed. + """ + if not sorting_analyzer.has_extension("waveforms"): + raise ValueError( + "The 'waveforms' extension is required for compute_snrs_bombcell. " + "Please compute it first with: analyzer.compute('waveforms')" + ) + + if unit_ids is None: + unit_ids = sorting_analyzer.unit_ids + + waveforms_ext = sorting_analyzer.get_extension("waveforms") + nbefore = waveforms_ext.nbefore + sampling_frequency = sorting_analyzer.sampling_frequency + + # Calculate baseline samples from ms + baseline_samples = int(baseline_window_ms / 1000 * sampling_frequency) + baseline_samples = min(baseline_samples, nbefore) # Can't exceed nbefore + + # Get peak channel for each unit from templates + extremum_channels_ids = get_template_extremum_channel(sorting_analyzer, peak_sign=peak_sign) + + snrs = {} + for unit_id in unit_ids: + # Get waveforms for this unit (num_spikes, num_samples, num_channels) + waveforms = waveforms_ext.get_waveforms_one_unit(unit_id, force_dense=False) + + if waveforms is None or len(waveforms) == 0: + snrs[unit_id] = np.nan + continue + + # Get peak channel index + peak_chan_id = extremum_channels_ids[unit_id] + if sorting_analyzer.is_sparse(): + chan_ids = sorting_analyzer.sparsity.unit_id_to_channel_ids[unit_id] + if peak_chan_id not in chan_ids: + snrs[unit_id] = np.nan + continue + peak_chan_idx = np.where(chan_ids == peak_chan_id)[0][0] + else: + peak_chan_idx = sorting_analyzer.channel_ids_to_indices([peak_chan_id])[0] + + # Extract waveforms on peak channel + waveforms_peak = waveforms[:, :, peak_chan_idx] # (num_spikes, num_samples) + + # Signal: max absolute value across all spikes + signal = np.max(np.abs(waveforms_peak)) + + # Noise: MAD of baseline samples (first N samples of each waveform) + baseline_samples_all = waveforms_peak[:, :baseline_samples].flatten() + median_baseline = np.median(baseline_samples_all) + noise = np.median(np.abs(baseline_samples_all - median_baseline)) + + # Calculate SNR (avoid division by zero) + if noise > 0: + snrs[unit_id] = signal / noise + else: + snrs[unit_id] = np.nan + + return snrs + + +class SNRBombcell(BaseMetric): + metric_name = "snr_bombcell" + metric_function = compute_snrs_bombcell + metric_params = {"peak_sign": "neg", "baseline_window_ms": 0.5} + metric_columns = {"snr_bombcell": float} + metric_descriptions = { + "snr_bombcell": "Signal to noise ratio using BombCell method (raw waveform max / baseline MAD)." + } + depend_on = ["waveforms", "templates"] + + def compute_isi_violations(sorting_analyzer, unit_ids=None, isi_threshold_ms=1.5, min_isi_ms=0): """ Calculate Inter-Spike Interval (ISI) violations. @@ -752,6 +860,7 @@ def compute_amplitude_cutoffs( num_histogram_bins=500, histogram_smoothing_value=3, amplitudes_bins_min_ratio=5, + plot_details=True, # Hardcoded ON for debugging ): """ Calculate approximate fraction of spikes missing from a distribution of amplitudes. @@ -770,6 +879,9 @@ def compute_amplitude_cutoffs( The minimum ratio between number of amplitudes for a unit and the number of bins. If the ratio is less than this threshold, the amplitude_cutoff for the unit is set to NaN. + plot_details : bool, default: True + If True, generate diagnostic plots for each unit showing amplitude histogram + and gaussian fit. Hardcoded ON for debugging. Returns ------- @@ -807,13 +919,38 @@ def compute_amplitude_cutoffs( amplitudes_by_units = extension.get_data(outputs="by_unit", concatenated=True) + # Get spike times for scatter plots if plot_details is enabled + spike_times_by_units = None + if plot_details: + sorting = sorting_analyzer.sorting + fs = sorting_analyzer.sampling_frequency + # Get spike times by unit (concatenated across segments) + spike_times_by_units = {} + for unit_id in unit_ids: + all_spike_times = [] + time_offset = 0.0 + for seg_idx in range(sorting_analyzer.get_num_segments()): + spike_train = sorting.get_unit_spike_train(unit_id=unit_id, segment_index=seg_idx) + spike_times_s = spike_train / fs + time_offset + all_spike_times.append(spike_times_s) + time_offset += sorting_analyzer.get_num_samples(seg_idx) / fs + spike_times_by_units[unit_id] = np.concatenate(all_spike_times) if all_spike_times else np.array([]) + for unit_id in unit_ids: amplitudes = amplitudes_by_units[unit_id] if invert_amplitudes: amplitudes = -amplitudes + spike_times = spike_times_by_units[unit_id] if spike_times_by_units is not None else None + all_fraction_missing[unit_id] = amplitude_cutoff( - amplitudes, num_histogram_bins, histogram_smoothing_value, amplitudes_bins_min_ratio + amplitudes, + num_histogram_bins, + histogram_smoothing_value, + amplitudes_bins_min_ratio, + spike_times=spike_times, + unit_id=unit_id, + plot_details=plot_details, ) if np.any(np.isnan(list(all_fraction_missing.values()))): @@ -829,6 +966,7 @@ class AmplitudeCutoff(BaseMetric): "num_histogram_bins": 100, "histogram_smoothing_value": 3, "amplitudes_bins_min_ratio": 5, + "plot_details": True, # Hardcoded ON for debugging } metric_columns = {"amplitude_cutoff": float} metric_descriptions = { @@ -1295,6 +1433,7 @@ class SDRatio(BaseMetric): FiringRate, PresenceRatio, SNR, + SNRBombcell, ISIViolation, RPViolation, SlidingRPViolation, @@ -1421,7 +1560,17 @@ def isi_violations(spike_trains, total_duration_s, isi_threshold_s=0.0015, min_i return isi_violations_ratio, isi_violations_rate, isi_violations_count -def amplitude_cutoff(amplitudes, num_histogram_bins=500, histogram_smoothing_value=3, amplitudes_bins_min_ratio=5): +def amplitude_cutoff( + amplitudes, + num_histogram_bins=500, + histogram_smoothing_value=3, + amplitudes_bins_min_ratio=5, + spike_times=None, + unit_id=None, + plot_details=True, # Hardcoded ON for debugging + ax_scatter=None, + ax_hist=None, +): """ Calculate approximate fraction of spikes missing from a distribution of amplitudes. @@ -1439,6 +1588,18 @@ def amplitude_cutoff(amplitudes, num_histogram_bins=500, histogram_smoothing_val The minimum ratio between number of amplitudes for a unit and the number of bins. If the ratio is less than this threshold, the amplitude_cutoff for the unit is set to NaN. + spike_times : ndarray_like or None, default: None + The spike times (in seconds) for this unit. Used for plotting scatter plot. + unit_id : any, default: None + The unit ID for labeling plots. + plot_details : bool, default: True + If True, generate diagnostic plots showing amplitude histogram and gaussian fit. + Hardcoded ON for debugging. + ax_scatter : matplotlib axis or None, default: None + Axis for scatter plot (spike times vs amplitudes). If None and plot_details=True, + a new figure is created. + ax_hist : matplotlib axis or None, default: None + Axis for histogram plot. If None and plot_details=True, uses same figure. Returns ------- @@ -1471,6 +1632,102 @@ def amplitude_cutoff(amplitudes, num_histogram_bins=500, histogram_smoothing_val fraction_missing = np.sum(pdf[G:]) * bin_size fraction_missing = np.min([fraction_missing, 0.5]) + # Plot details for debugging (similar to MATLAB BombCell) + if plot_details: + import matplotlib.pyplot as plt + + # Create figure if no axes provided + if ax_scatter is None and ax_hist is None: + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + ax_scatter = axes[0] + ax_hist = axes[1] + created_figure = True + else: + created_figure = False + + # Colors matching MATLAB BombCell style + main_color = [0, 0.35, 0.71] # Blue + cutoff_color = [0.5430, 0, 0.5430] # Purple + fit_color = "red" + + # Plot 1: Scatter plot of spike times vs amplitudes (if spike_times provided) + if ax_scatter is not None and spike_times is not None: + ax_scatter.scatter(spike_times, amplitudes, s=4, c=[main_color], alpha=0.5) + + # Add outlier threshold line (using IQR method like MATLAB) + q1, q3 = np.percentile(amplitudes, [25, 75]) + iqr = q3 - q1 + iqr_threshold = 4 # Same as MATLAB default + outlier_line = q3 + iqr_threshold * iqr + + ylims = ax_scatter.get_ylim() + xlims = ax_scatter.get_xlim() + + ax_scatter.axhline(outlier_line, color=cutoff_color, linewidth=1.5) + ax_scatter.text( + xlims[1] * 0.98, + outlier_line * 1.02, + "Outlier Threshold", + ha="right", + va="bottom", + color=cutoff_color, + fontweight="bold", + fontsize=8, + ) + + ax_scatter.set_xlabel("Time (s)") + ax_scatter.set_ylabel("Amplitude scaling factor") + title_str = f"Unit {unit_id}" if unit_id is not None else "Amplitudes over time" + ax_scatter.set_title(title_str) + ax_scatter.spines["top"].set_visible(False) + ax_scatter.spines["right"].set_visible(False) + + elif ax_scatter is not None: + ax_scatter.text( + 0.5, + 0.5, + "Spike times not provided", + ha="center", + va="center", + transform=ax_scatter.transAxes, + ) + ax_scatter.set_title("Scatter plot requires spike_times") + + # Plot 2: Histogram with gaussian fit + if ax_hist is not None: + # Plot histogram as horizontal bars (like MATLAB) + bin_centers = (b[:-1] + b[1:]) / 2 + ax_hist.barh(bin_centers, h, height=bin_size * 0.9, color=main_color, alpha=0.7, label="Histogram") + + # Plot smoothed PDF (gaussian fit) + ax_hist.plot(pdf, support, color=fit_color, linewidth=2, label="Smoothed PDF") + + # Mark the cutoff point G + cutoff_amplitude = support[G] + ax_hist.axhline(cutoff_amplitude, color=cutoff_color, linestyle="--", linewidth=1.5, label="Cutoff") + + # Mark the peak + peak_amplitude = support[peak_index] + ax_hist.axhline(peak_amplitude, color="green", linestyle=":", linewidth=1.5, label="Peak") + + ax_hist.set_xlabel("Density") + ax_hist.set_ylabel("Amplitude") + + # Add percent missing text + rounded_p = f"{fraction_missing * 100:.1f}%" + title_str = f"% missing spikes: {rounded_p}" + if unit_id is not None: + title_str = f"Unit {unit_id}\n{title_str}" + ax_hist.set_title(title_str, color=[0.7, 0.7, 0.7]) + + ax_hist.legend(loc="upper right", fontsize=8) + ax_hist.spines["top"].set_visible(False) + ax_hist.spines["right"].set_visible(False) + + if created_figure: + plt.tight_layout() + plt.show() + return fraction_missing From 4514a51f003650b0679b10942778865c195bc3c4 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Wed, 7 Jan 2026 18:57:18 +0100 Subject: [PATCH 09/49] fix: use peak_valley code to get duration, rename to peak_to_trough_duration, add amplitude_median, bombcell_snr and fix non-somatic classification rules --- playground2.ipynb | 1669 +++++++++-------- .../comparison/unit_classification.py | 110 +- .../metrics/quality/misc_metrics.py | 6 +- .../metrics/template/metrics.py | 14 +- .../widgets/unit_classification.py | 6 + 5 files changed, 988 insertions(+), 817 deletions(-) diff --git a/playground2.ipynb b/playground2.ipynb index 24b2be94aa..78e3131f5d 100644 --- a/playground2.ipynb +++ b/playground2.ipynb @@ -46,7 +46,7 @@ "\n", "# For kilosort/phy output files we can use the read_phy\n", "# most formats will have a read_xx that can used.\n", - "analyzer = si.load_sorting_analyzer('/Users/jf5479/Downloads/kilosort4_sa/')\n" + "analyzer = si.load_sorting_analyzer('/Users/jf5479/Downloads/M25_D18/kilosort4_sa')\n" ] }, { @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -156,34 +156,45 @@ "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", " warnings.warn(\n", "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute template metrics with multi-channel metrics included\n", + "# Delete the old cached extension first\n", + "\n", + "# Then recompute\n", "analyzer.compute(\n", " \"template_metrics\",\n", - " smooth=True, # Enable/disable smoothing\n", - " smooth_window_frac=0.1, # Window as fraction of template length\n", - " smooth_polyorder=3, # Polynomial order\n", + " smooth=True,\n", + " smooth_window_frac=0.1,\n", + " smooth_polyorder=3,\n", " min_thresh_detect_peaks_troughs=0.4\n", ")\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -207,7 +218,7 @@ " \n", " \n", " \n", - " peak_to_valley\n", + " peak_to_trough_duration\n", " peak_trough_ratio\n", " half_width\n", " repolarization_slope\n", @@ -232,118 +243,118 @@ " \n", " \n", " 0\n", - " 0.001200\n", - " -0.412584\n", - " 0.001747\n", - " 16671.090016\n", - " -17191.329586\n", + " 0.000930\n", + " -0.391377\n", + " 0.000217\n", + " 48304.384356\n", + " -11509.478867\n", " 2\n", " 1\n", - " 643.333333\n", - " 2.358820\n", - " 0.412584\n", - " 5.717192\n", - " 2.358820\n", - " 818.069771\n", - " 367.576821\n", - " 249.381750\n", - " 0.243320\n", - " 109.202199\n", + " 930.000000\n", + " 0.281250\n", + " 0.391377\n", + " 0.718618\n", + " 0.391377\n", + " 339.416196\n", " NaN\n", - " 0.011540\n", - " 60.0\n", + " 996.954546\n", + " 0.320548\n", + " NaN\n", + " 1968.672345\n", + " 0.011912\n", + " 180.0\n", " \n", " \n", " 1\n", - " 0.000633\n", - " -0.195374\n", - " 0.000163\n", - " 321619.078371\n", - " -10477.415662\n", + " 0.000993\n", + " -0.559453\n", + " 0.000607\n", + " 19480.348714\n", + " -30323.760290\n", " 2\n", " 1\n", - " 633.333333\n", - " 0.103992\n", - " 0.195374\n", - " 0.532271\n", - " 0.195374\n", - " 210.974071\n", - " NaN\n", - " 1035.993495\n", - " 0.104157\n", - " -1979.834401\n", - " NaN\n", - " 0.018553\n", - " 45.0\n", + " 890.000000\n", + " 1.005980\n", + " 0.559453\n", + " 1.798148\n", + " 1.005980\n", + " 804.599484\n", + " 752.785561\n", + " 299.439215\n", + " 0.445078\n", + " -1150.976105\n", + " 395.389870\n", + " 0.017526\n", + " 180.0\n", " \n", " \n", " 2\n", - " 0.001250\n", - " -0.331924\n", - " 0.000647\n", - " 12705.168897\n", - " -16207.805607\n", + " 0.000467\n", + " -0.196339\n", + " 0.000210\n", + " 54427.516428\n", + " -3144.791432\n", " 2\n", " 1\n", - " 616.666667\n", - " 2.229546\n", - " 0.331924\n", - " 6.717041\n", - " 2.229546\n", - " 806.583590\n", - " 362.992828\n", - " 231.258891\n", - " 0.200324\n", - " NaN\n", - " NaN\n", - " 0.006662\n", - " 105.0\n", + " 466.666667\n", + " 0.166725\n", + " 0.196339\n", + " 0.849167\n", + " 0.196339\n", + " 250.587237\n", + " 567.387928\n", + " 828.894898\n", + " 0.180007\n", + " 70.256344\n", + " -850.000000\n", + " 0.022914\n", + " 75.0\n", " \n", " \n", " 3\n", - " 0.000680\n", - " -0.234461\n", - " 0.000220\n", - " 118995.043916\n", - " -7777.377258\n", + " 0.000837\n", + " -0.331457\n", + " 0.000203\n", + " 87707.717569\n", + " -9623.441262\n", " 2\n", " 1\n", - " 680.000000\n", - " 0.167090\n", - " 0.234461\n", - " 0.712656\n", - " 0.234461\n", - " 277.076137\n", - " 203.583558\n", - " 1012.443132\n", - " 0.177848\n", + " 836.666667\n", + " 0.241981\n", + " 0.331457\n", + " 0.730053\n", + " 0.331457\n", + " 284.207039\n", " NaN\n", - " 1191.369764\n", - " 0.010610\n", - " 105.0\n", + " 1051.925666\n", + " 0.273670\n", + " 1104.061445\n", + " 1500.544772\n", + " 0.016225\n", + " 180.0\n", " \n", " \n", " 4\n", - " 0.000770\n", - " -0.267867\n", - " 0.000260\n", - " 75660.685138\n", - " -7251.490738\n", + " 0.000597\n", + " -0.431754\n", + " 0.000343\n", + " 57011.717748\n", + " -11030.357085\n", " 2\n", " 1\n", - " 770.000000\n", - " 0.252582\n", - " 0.267867\n", - " 0.942940\n", - " 0.267867\n", - " 322.024228\n", - " 403.049068\n", - " 947.586212\n", - " 0.210937\n", - " 783.064501\n", - " 636.599008\n", - " 0.009241\n", - " 135.0\n", + " 596.666667\n", + " 0.179789\n", + " 0.431754\n", + " 0.416416\n", + " 0.431754\n", + " 377.873485\n", + " 412.239605\n", + " 584.726183\n", + " 0.184279\n", + " -303.266685\n", + " NaN\n", + " 0.034594\n", + " 60.0\n", " \n", " \n", " ...\n", @@ -369,221 +380,221 @@ " ...\n", " \n", " \n", - " 325\n", - " 0.001350\n", - " -0.234132\n", - " 0.000517\n", - " 20857.109735\n", - " -7038.372419\n", + " 371\n", + " 0.000927\n", + " -0.640045\n", + " 0.000503\n", + " 32619.897138\n", + " -16025.030223\n", " 2\n", " 1\n", - " 516.666667\n", - " 1.357400\n", - " 0.234132\n", - " 5.797585\n", - " 1.357400\n", - " 613.535510\n", - " 475.232721\n", - " 629.538080\n", - " 0.253322\n", + " 926.666667\n", + " 0.351054\n", + " 0.640045\n", + " 0.548484\n", + " 0.640045\n", + " 602.278842\n", + " 253.236408\n", + " 727.313733\n", + " 0.366593\n", " NaN\n", + " 1285.624797\n", " NaN\n", - " 0.000379\n", " 180.0\n", " \n", " \n", - " 326\n", - " 0.000620\n", - " -0.685191\n", - " 0.000397\n", - " 71418.515246\n", - " -21462.465005\n", + " 372\n", + " 0.000653\n", + " -0.174707\n", + " 0.000150\n", + " 230009.965415\n", + " -14782.371126\n", " 2\n", " 1\n", - " 620.000000\n", - " 0.147044\n", - " 0.685191\n", - " 0.214603\n", - " 0.685191\n", - " 445.745184\n", - " NaN\n", - " 809.107535\n", - " 0.168947\n", - " NaN\n", - " -1228.577175\n", + " 653.333333\n", + " 0.075224\n", + " 0.174707\n", + " 0.430571\n", + " 0.174707\n", + " 196.679473\n", + " 80.050997\n", + " 647.211641\n", + " 0.098756\n", " NaN\n", - " 150.0\n", + " 371.169649\n", + " 0.032790\n", + " 45.0\n", " \n", " \n", - " 327\n", - " 0.001237\n", - " -0.263875\n", - " 0.000547\n", - " 36273.785365\n", - " -7774.517050\n", + " 373\n", + " 0.000773\n", + " -0.351117\n", + " 0.000240\n", + " 33768.217742\n", + " -4993.292180\n", " 2\n", " 1\n", - " 703.333333\n", - " 1.185248\n", - " 0.263875\n", - " 4.491711\n", - " 1.185248\n", - " 624.165680\n", - " 629.583000\n", - " 454.026367\n", - " 0.389132\n", - " NaN\n", - " NaN\n", - " 0.000723\n", - " 120.0\n", + " 773.333333\n", + " 0.220369\n", + " 0.351117\n", + " 0.627622\n", + " 0.351117\n", + " 302.944762\n", + " 200.943300\n", + " 724.343500\n", + " 0.226603\n", + " -732.014874\n", + " -387.998015\n", + " 0.012231\n", + " 75.0\n", " \n", " \n", - " 328\n", - " 0.001137\n", - " -0.329055\n", - " 0.000537\n", - " 27766.452527\n", - " -9930.771106\n", + " 374\n", + " 0.000723\n", + " -0.373636\n", + " 0.000240\n", + " 23095.225222\n", + " -5458.678401\n", " 2\n", " 1\n", - " 1136.666667\n", - " 0.994842\n", - " 0.329055\n", - " 3.023334\n", - " 0.994842\n", - " 648.431952\n", - " 667.718074\n", - " 488.826489\n", - " 0.353984\n", - " -400.589557\n", + " 723.333333\n", + " 0.111326\n", + " 0.373636\n", + " 0.297953\n", + " 0.373636\n", + " 356.978504\n", + " 157.402821\n", + " 777.102803\n", + " 0.330019\n", " NaN\n", - " 0.000703\n", - " 105.0\n", + " -128.602566\n", + " 0.011870\n", + " 60.0\n", " \n", " \n", - " 329\n", - " 0.000830\n", - " -0.794591\n", - " 0.000427\n", - " 50197.024838\n", - " -28537.288201\n", + " 375\n", + " 0.000937\n", + " -0.237736\n", + " 0.000443\n", + " 20061.279064\n", + " -3433.231163\n", " 2\n", " 1\n", - " 830.000000\n", - " 0.208674\n", - " 0.794591\n", - " 0.262618\n", - " 0.794591\n", - " 500.158495\n", + " 936.666667\n", + " 0.769391\n", + " 0.237736\n", + " 3.236320\n", + " 0.769391\n", + " 563.349356\n", + " 836.898954\n", + " 525.617333\n", + " 0.515874\n", " NaN\n", - " 823.084749\n", - " 0.234050\n", " NaN\n", - " 2112.898614\n", - " 0.000774\n", + " 0.001666\n", " 90.0\n", " \n", " \n", "\n", - "

330 rows × 20 columns

\n", + "

376 rows × 20 columns

\n", "" ], "text/plain": [ - " peak_to_valley peak_trough_ratio half_width repolarization_slope \\\n", - "0 0.001200 -0.412584 0.001747 16671.090016 \n", - "1 0.000633 -0.195374 0.000163 321619.078371 \n", - "2 0.001250 -0.331924 0.000647 12705.168897 \n", - "3 0.000680 -0.234461 0.000220 118995.043916 \n", - "4 0.000770 -0.267867 0.000260 75660.685138 \n", - ".. ... ... ... ... \n", - "325 0.001350 -0.234132 0.000517 20857.109735 \n", - "326 0.000620 -0.685191 0.000397 71418.515246 \n", - "327 0.001237 -0.263875 0.000547 36273.785365 \n", - "328 0.001137 -0.329055 0.000537 27766.452527 \n", - "329 0.000830 -0.794591 0.000427 50197.024838 \n", + " peak_to_trough_duration peak_trough_ratio half_width \\\n", + "0 0.000930 -0.391377 0.000217 \n", + "1 0.000993 -0.559453 0.000607 \n", + "2 0.000467 -0.196339 0.000210 \n", + "3 0.000837 -0.331457 0.000203 \n", + "4 0.000597 -0.431754 0.000343 \n", + ".. ... ... ... \n", + "371 0.000927 -0.640045 0.000503 \n", + "372 0.000653 -0.174707 0.000150 \n", + "373 0.000773 -0.351117 0.000240 \n", + "374 0.000723 -0.373636 0.000240 \n", + "375 0.000937 -0.237736 0.000443 \n", "\n", - " recovery_slope num_positive_peaks num_negative_peaks \\\n", - "0 -17191.329586 2 1 \n", - "1 -10477.415662 2 1 \n", - "2 -16207.805607 2 1 \n", - "3 -7777.377258 2 1 \n", - "4 -7251.490738 2 1 \n", - ".. ... ... ... \n", - "325 -7038.372419 2 1 \n", - "326 -21462.465005 2 1 \n", - "327 -7774.517050 2 1 \n", - "328 -9930.771106 2 1 \n", - "329 -28537.288201 2 1 \n", + " repolarization_slope recovery_slope num_positive_peaks \\\n", + "0 48304.384356 -11509.478867 2 \n", + "1 19480.348714 -30323.760290 2 \n", + "2 54427.516428 -3144.791432 2 \n", + "3 87707.717569 -9623.441262 2 \n", + "4 57011.717748 -11030.357085 2 \n", + ".. ... ... ... \n", + "371 32619.897138 -16025.030223 2 \n", + "372 230009.965415 -14782.371126 2 \n", + "373 33768.217742 -4993.292180 2 \n", + "374 23095.225222 -5458.678401 2 \n", + "375 20061.279064 -3433.231163 2 \n", "\n", - " waveform_duration peak_before_to_trough_ratio \\\n", - "0 643.333333 2.358820 \n", - "1 633.333333 0.103992 \n", - "2 616.666667 2.229546 \n", - "3 680.000000 0.167090 \n", - "4 770.000000 0.252582 \n", - ".. ... ... \n", - "325 516.666667 1.357400 \n", - "326 620.000000 0.147044 \n", - "327 703.333333 1.185248 \n", - "328 1136.666667 0.994842 \n", - "329 830.000000 0.208674 \n", + " num_negative_peaks waveform_duration peak_before_to_trough_ratio \\\n", + "0 1 930.000000 0.281250 \n", + "1 1 890.000000 1.005980 \n", + "2 1 466.666667 0.166725 \n", + "3 1 836.666667 0.241981 \n", + "4 1 596.666667 0.179789 \n", + ".. ... ... ... \n", + "371 1 926.666667 0.351054 \n", + "372 1 653.333333 0.075224 \n", + "373 1 773.333333 0.220369 \n", + "374 1 723.333333 0.111326 \n", + "375 1 936.666667 0.769391 \n", "\n", " peak_after_to_trough_ratio peak_before_to_peak_after_ratio \\\n", - "0 0.412584 5.717192 \n", - "1 0.195374 0.532271 \n", - "2 0.331924 6.717041 \n", - "3 0.234461 0.712656 \n", - "4 0.267867 0.942940 \n", + "0 0.391377 0.718618 \n", + "1 0.559453 1.798148 \n", + "2 0.196339 0.849167 \n", + "3 0.331457 0.730053 \n", + "4 0.431754 0.416416 \n", ".. ... ... \n", - "325 0.234132 5.797585 \n", - "326 0.685191 0.214603 \n", - "327 0.263875 4.491711 \n", - "328 0.329055 3.023334 \n", - "329 0.794591 0.262618 \n", + "371 0.640045 0.548484 \n", + "372 0.174707 0.430571 \n", + "373 0.351117 0.627622 \n", + "374 0.373636 0.297953 \n", + "375 0.237736 3.236320 \n", "\n", " main_peak_to_trough_ratio trough_width peak_before_width \\\n", - "0 2.358820 818.069771 367.576821 \n", - "1 0.195374 210.974071 NaN \n", - "2 2.229546 806.583590 362.992828 \n", - "3 0.234461 277.076137 203.583558 \n", - "4 0.267867 322.024228 403.049068 \n", + "0 0.391377 339.416196 NaN \n", + "1 1.005980 804.599484 752.785561 \n", + "2 0.196339 250.587237 567.387928 \n", + "3 0.331457 284.207039 NaN \n", + "4 0.431754 377.873485 412.239605 \n", ".. ... ... ... \n", - "325 1.357400 613.535510 475.232721 \n", - "326 0.685191 445.745184 NaN \n", - "327 1.185248 624.165680 629.583000 \n", - "328 0.994842 648.431952 667.718074 \n", - "329 0.794591 500.158495 NaN \n", + "371 0.640045 602.278842 253.236408 \n", + "372 0.174707 196.679473 80.050997 \n", + "373 0.351117 302.944762 200.943300 \n", + "374 0.373636 356.978504 157.402821 \n", + "375 0.769391 563.349356 836.898954 \n", "\n", " peak_after_width waveform_baseline_flatness velocity_above \\\n", - "0 249.381750 0.243320 109.202199 \n", - "1 1035.993495 0.104157 -1979.834401 \n", - "2 231.258891 0.200324 NaN \n", - "3 1012.443132 0.177848 NaN \n", - "4 947.586212 0.210937 783.064501 \n", + "0 996.954546 0.320548 NaN \n", + "1 299.439215 0.445078 -1150.976105 \n", + "2 828.894898 0.180007 70.256344 \n", + "3 1051.925666 0.273670 1104.061445 \n", + "4 584.726183 0.184279 -303.266685 \n", ".. ... ... ... \n", - "325 629.538080 0.253322 NaN \n", - "326 809.107535 0.168947 NaN \n", - "327 454.026367 0.389132 NaN \n", - "328 488.826489 0.353984 -400.589557 \n", - "329 823.084749 0.234050 NaN \n", + "371 727.313733 0.366593 NaN \n", + "372 647.211641 0.098756 NaN \n", + "373 724.343500 0.226603 -732.014874 \n", + "374 777.102803 0.330019 NaN \n", + "375 525.617333 0.515874 NaN \n", "\n", " velocity_below exp_decay spread \n", - "0 NaN 0.011540 60.0 \n", - "1 NaN 0.018553 45.0 \n", - "2 NaN 0.006662 105.0 \n", - "3 1191.369764 0.010610 105.0 \n", - "4 636.599008 0.009241 135.0 \n", + "0 1968.672345 0.011912 180.0 \n", + "1 395.389870 0.017526 180.0 \n", + "2 -850.000000 0.022914 75.0 \n", + "3 1500.544772 0.016225 180.0 \n", + "4 NaN 0.034594 60.0 \n", ".. ... ... ... \n", - "325 NaN 0.000379 180.0 \n", - "326 -1228.577175 NaN 150.0 \n", - "327 NaN 0.000723 120.0 \n", - "328 NaN 0.000703 105.0 \n", - "329 2112.898614 0.000774 90.0 \n", + "371 1285.624797 NaN 180.0 \n", + "372 371.169649 0.032790 45.0 \n", + "373 -387.998015 0.012231 75.0 \n", + "374 -128.602566 0.011870 60.0 \n", + "375 NaN 0.001666 90.0 \n", "\n", - "[330 rows x 20 columns]" + "[376 rows x 20 columns]" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -598,19 +609,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Spike amplitudes\n", "if not analyzer.has_extension(\"spike_amplitudes\"):\n", " print(\"Computing spike_amplitudes...\")\n", - " analyzer.compute(\"spike_amplitudes\", **job_kwargs)" + " analyzer.compute(\"spike_amplitudes\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -624,6 +635,48 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jf5479/Dropbox/Python/spikeinterface/src/spikeinterface/core/analyzer_extension_core.py:1032: UserWarning: Metric sd_ratio requires a recording. Since the SortingAnalyzer has no recording, the metric will not be computed.\n", + " warnings.warn(\n", + "/Users/jf5479/Dropbox/Python/spikeinterface/src/spikeinterface/core/analyzer_extension_core.py:1040: UserWarning: The following metrics will not be computed due to missing dependencies: ['mahalanobis', 'd_prime', 'sd_ratio', 'silhouette', 'nearest_neighbor']\n", + " warnings.warn(\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n", + " arrmean = um.true_divide(arrmean, div, out=arrmean,\n", + "/Users/jf5479/anaconda3/lib/python3.12/site-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/Users/jf5479/Dropbox/Python/spikeinterface/src/spikeinterface/metrics/quality/misc_metrics.py:957: UserWarning: Some units have too few spikes : amplitude_cutoff is set to NaN\n", + " warnings.warn(f\"Some units have too few spikes : amplitude_cutoff is set to NaN\")\n", + "/Users/jf5479/Dropbox/Python/spikeinterface/src/spikeinterface/metrics/quality/misc_metrics.py:1903: UserWarning: Only one bin is selected as the reference region, and thus the standard deviation cannot be computed. Please increase high_quantile. Setting noise cutoff to NaN\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analyzer.compute(\n", + " \"quality_metrics\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, "outputs": [ { "data": { @@ -646,131 +699,149 @@ " \n", " \n", " \n", - " amplitude_median\n", - " sync_spike_2\n", - " sync_spike_4\n", - " sync_spike_8\n", + " num_spikes\n", " firing_rate\n", - " snr\n", - " amplitude_cv_median\n", - " amplitude_cv_range\n", " presence_ratio\n", - " amplitude_cutoff\n", - " sliding_rp_violation\n", + " snr\n", + " snr_bombcell\n", " isi_violations_ratio\n", " isi_violations_count\n", - " sd_ratio\n", " rp_contamination\n", " rp_violations\n", - " num_spikes\n", - " firing_range\n", + " sliding_rp_violation\n", + " ...\n", + " amplitude_cv_median\n", + " amplitude_cv_range\n", + " amplitude_cutoff\n", + " noise_cutoff\n", + " noise_ratio\n", + " amplitude_median\n", + " drift_ptp\n", + " drift_std\n", + " drift_mad\n", + " sd_ratio\n", " \n", " \n", " \n", " \n", " 0\n", - " -10.530000\n", - " 0.105795\n", - " 0.006802\n", - " 0.000190\n", - " 6.121579\n", - " 0.978010\n", - " 1.187101\n", - " 1.122601\n", - " 1.0\n", - " 0.000089\n", + " 70860\n", + " 16.569565\n", + " 1.000000\n", + " 2.997902\n", + " 21.794119\n", + " 1.482813\n", + " 5223\n", + " 1.000000\n", + " 4899\n", + " NaN\n", + " ...\n", + " NaN\n", " NaN\n", - " 0.951853\n", - " 460\n", - " 1.337394\n", - " 1.0\n", - " 261\n", - " 26315\n", - " 13.80\n", + " 0.000089\n", + " -0.129627\n", + " 0.041261\n", + " -17.355000\n", + " 2.781601\n", + " 0.623276\n", + " 0.467753\n", + " 2.223717\n", " \n", " \n", " 1\n", - " -43.680000\n", - " 0.063004\n", - " 0.002108\n", - " 0.000031\n", - " 30.128681\n", - " 3.898206\n", - " NaN\n", - " NaN\n", - " 1.0\n", - " 0.000002\n", + " 35219\n", + " 8.235443\n", + " 1.000000\n", + " 1.515432\n", + " 19.924999\n", + " 0.935490\n", + " 814\n", + " 1.000000\n", + " 627\n", " NaN\n", - " 0.454454\n", - " 5320\n", - " 1.109406\n", - " 1.0\n", - " 4075\n", - " 129515\n", - " 71.00\n", + " ...\n", + " 2.064498\n", + " 1.489093\n", + " 0.000082\n", + " -0.225793\n", + " 0.024113\n", + " -5.264999\n", + " 3.083042\n", + " 0.672090\n", + " 0.520758\n", + " 1.821735\n", " \n", " \n", " 2\n", - " -9.165000\n", - " 0.124962\n", - " 0.009469\n", - " 0.000121\n", - " 3.857188\n", - " 0.944906\n", - " 1.398181\n", - " 1.107468\n", - " 1.0\n", - " 0.000083\n", - " NaN\n", - " 0.797423\n", - " 153\n", - " 1.301471\n", - " 1.0\n", - " 88\n", - " 16581\n", - " 8.02\n", + " 22971\n", + " 5.371429\n", + " 1.000000\n", + " 2.459971\n", + " 22.950001\n", + " 0.083747\n", + " 31\n", + " 0.107035\n", + " 25\n", + " 0.105\n", + " ...\n", + " 0.461843\n", + " 0.458329\n", + " 0.000011\n", + " -0.038763\n", + " 0.015673\n", + " -13.844999\n", + " 1.776146\n", + " 0.446181\n", + " 0.384374\n", + " 1.009472\n", " \n", " \n", " 3\n", - " -30.224998\n", - " 0.086343\n", - " 0.008367\n", - " 0.000245\n", - " 11.399187\n", - " 2.839966\n", - " 0.341267\n", - " 0.324181\n", - " 1.0\n", - " 0.000136\n", + " 38556\n", + " 9.015752\n", + " 1.000000\n", + " 3.150455\n", + " 17.388889\n", + " 1.845931\n", + " 1925\n", + " 1.000000\n", + " 1794\n", " NaN\n", - " 0.408176\n", - " 684\n", - " 1.120740\n", - " 1.0\n", - " 605\n", - " 49002\n", - " 17.20\n", + " ...\n", + " 0.933989\n", + " 0.471161\n", + " 0.000179\n", + " -0.046065\n", + " 0.042872\n", + " -18.719999\n", + " 3.046326\n", + " 0.677080\n", + " 0.502092\n", + " 2.171387\n", " \n", " \n", " 4\n", - " -27.884998\n", - " 0.140825\n", - " 0.017389\n", - " 0.000623\n", - " 4.481558\n", - " 2.428674\n", + " 25600\n", + " 5.986182\n", + " 1.000000\n", + " 3.088286\n", + " 25.347828\n", + " 0.746076\n", + " 343\n", + " 1.000000\n", + " 285\n", " NaN\n", + " ...\n", " NaN\n", - " 1.0\n", - " 0.000122\n", " NaN\n", - " 0.760583\n", - " 197\n", - " 1.369003\n", - " 1.0\n", - " 129\n", - " 19265\n", - " 10.80\n", + " 0.000058\n", + " -0.221197\n", + " 0.018106\n", + " -17.160000\n", + " 4.121138\n", + " 0.547034\n", + " 0.892043\n", + " 1.144326\n", " \n", " \n", " ...\n", @@ -792,187 +863,205 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 325\n", - " -12.090000\n", - " 0.336032\n", - " 0.058526\n", - " 0.003946\n", - " 5.660047\n", - " 2.556509\n", + " 371\n", + " 685\n", + " 0.160177\n", + " 0.464789\n", + " 2.362186\n", + " 48.705883\n", + " 689.625782\n", + " 227\n", + " 1.000000\n", + " 204\n", " NaN\n", + " ...\n", " NaN\n", - " 1.0\n", - " 0.000011\n", " NaN\n", - " 4.158359\n", - " 1718\n", - " 2.544345\n", - " 1.0\n", - " 1176\n", - " 24331\n", - " 15.82\n", + " 0.000445\n", + " -0.391426\n", + " 0.013178\n", + " -9.945000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1.711817\n", " \n", " \n", - " 326\n", - " -15.795000\n", - " 0.226084\n", - " 0.023752\n", - " 0.001239\n", - " 3.192805\n", - " 3.402270\n", - " 0.811786\n", - " 1.248298\n", - " 1.0\n", - " 0.000031\n", + " 372\n", + " 31850\n", + " 7.447653\n", + " 1.000000\n", + " 10.522679\n", + " 28.590910\n", + " 0.001405\n", + " 1\n", + " 0.002110\n", + " 1\n", + " 0.005\n", + " ...\n", + " NaN\n", " NaN\n", - " 3.559917\n", - " 468\n", - " 2.754459\n", - " 1.0\n", - " 264\n", - " 13725\n", - " 9.20\n", + " 0.000108\n", + " -0.090904\n", + " 0.056504\n", + " -57.719997\n", + " 4.326493\n", + " 0.647887\n", + " 0.567893\n", + " 1.075722\n", " \n", " \n", - " 327\n", - " -16.574999\n", - " 0.297615\n", - " 0.037736\n", - " 0.001424\n", - " 1.306898\n", - " 3.373796\n", - " 0.761374\n", - " 0.643952\n", - " 1.0\n", - " 0.000079\n", - " NaN\n", - " 1.952197\n", - " 43\n", - " 2.513939\n", - " 1.0\n", - " 37\n", - " 5618\n", - " 4.00\n", + " 373\n", + " 13935\n", + " 3.258494\n", + " 1.000000\n", + " 1.511464\n", + " 32.826088\n", + " 0.249594\n", + " 34\n", + " 0.168328\n", + " 14\n", + " 0.175\n", + " ...\n", + " 1.168065\n", + " 1.922004\n", + " 0.000028\n", + " -0.169045\n", + " 0.013893\n", + " -7.605000\n", + " 1.844368\n", + " 0.225273\n", + " 0.309508\n", + " 1.390354\n", " \n", " \n", - " 328\n", - " -14.235000\n", - " 0.348725\n", - " 0.054768\n", - " 0.001751\n", - " 11.425706\n", - " 2.804173\n", - " NaN\n", - " NaN\n", - " 1.0\n", - " 0.000011\n", - " NaN\n", - " 1.230135\n", - " 2071\n", - " 2.367420\n", - " 1.0\n", - " 1152\n", - " 49116\n", - " 30.80\n", + " 374\n", + " 13567\n", + " 3.172443\n", + " 1.000000\n", + " 1.298798\n", + " 33.615387\n", + " 0.727996\n", + " 94\n", + " 0.449561\n", + " 30\n", + " 0.225\n", + " ...\n", + " 1.558193\n", + " 3.098256\n", + " 0.000019\n", + " -0.133455\n", + " 0.007452\n", + " -7.994999\n", + " 1.490345\n", + " 0.200480\n", + " 0.246163\n", + " 1.959414\n", " \n", " \n", - " 329\n", - " -14.235000\n", - " 0.258755\n", - " 0.022976\n", - " 0.000304\n", - " 4.596709\n", - " 2.740247\n", - " NaN\n", + " 375\n", + " 12910\n", + " 3.018813\n", + " 1.000000\n", + " 2.027381\n", + " 32.628571\n", + " 1.710591\n", + " 200\n", + " 1.000000\n", + " 81\n", " NaN\n", - " 1.0\n", - " 0.000177\n", - " NaN\n", - " 2.987234\n", - " 814\n", - " 2.523415\n", - " 1.0\n", - " 441\n", - " 19760\n", - " 13.00\n", + " ...\n", + " 1.848594\n", + " 2.540619\n", + " 0.000179\n", + " -0.011103\n", + " 0.014755\n", + " -5.264999\n", + " 2.983435\n", + " 0.413590\n", + " 0.364231\n", + " 2.308002\n", " \n", " \n", "\n", - "

330 rows × 18 columns

\n", + "

376 rows × 24 columns

\n", "" ], "text/plain": [ - " amplitude_median sync_spike_2 sync_spike_4 sync_spike_8 firing_rate \\\n", - "0 -10.530000 0.105795 0.006802 0.000190 6.121579 \n", - "1 -43.680000 0.063004 0.002108 0.000031 30.128681 \n", - "2 -9.165000 0.124962 0.009469 0.000121 3.857188 \n", - "3 -30.224998 0.086343 0.008367 0.000245 11.399187 \n", - "4 -27.884998 0.140825 0.017389 0.000623 4.481558 \n", - ".. ... ... ... ... ... \n", - "325 -12.090000 0.336032 0.058526 0.003946 5.660047 \n", - "326 -15.795000 0.226084 0.023752 0.001239 3.192805 \n", - "327 -16.574999 0.297615 0.037736 0.001424 1.306898 \n", - "328 -14.235000 0.348725 0.054768 0.001751 11.425706 \n", - "329 -14.235000 0.258755 0.022976 0.000304 4.596709 \n", + " num_spikes firing_rate presence_ratio snr snr_bombcell \\\n", + "0 70860 16.569565 1.000000 2.997902 21.794119 \n", + "1 35219 8.235443 1.000000 1.515432 19.924999 \n", + "2 22971 5.371429 1.000000 2.459971 22.950001 \n", + "3 38556 9.015752 1.000000 3.150455 17.388889 \n", + "4 25600 5.986182 1.000000 3.088286 25.347828 \n", + ".. ... ... ... ... ... \n", + "371 685 0.160177 0.464789 2.362186 48.705883 \n", + "372 31850 7.447653 1.000000 10.522679 28.590910 \n", + "373 13935 3.258494 1.000000 1.511464 32.826088 \n", + "374 13567 3.172443 1.000000 1.298798 33.615387 \n", + "375 12910 3.018813 1.000000 2.027381 32.628571 \n", "\n", - " snr amplitude_cv_median amplitude_cv_range presence_ratio \\\n", - "0 0.978010 1.187101 1.122601 1.0 \n", - "1 3.898206 NaN NaN 1.0 \n", - "2 0.944906 1.398181 1.107468 1.0 \n", - "3 2.839966 0.341267 0.324181 1.0 \n", - "4 2.428674 NaN NaN 1.0 \n", - ".. ... ... ... ... \n", - "325 2.556509 NaN NaN 1.0 \n", - "326 3.402270 0.811786 1.248298 1.0 \n", - "327 3.373796 0.761374 0.643952 1.0 \n", - "328 2.804173 NaN NaN 1.0 \n", - "329 2.740247 NaN NaN 1.0 \n", + " isi_violations_ratio isi_violations_count rp_contamination \\\n", + "0 1.482813 5223 1.000000 \n", + "1 0.935490 814 1.000000 \n", + "2 0.083747 31 0.107035 \n", + "3 1.845931 1925 1.000000 \n", + "4 0.746076 343 1.000000 \n", + ".. ... ... ... \n", + "371 689.625782 227 1.000000 \n", + "372 0.001405 1 0.002110 \n", + "373 0.249594 34 0.168328 \n", + "374 0.727996 94 0.449561 \n", + "375 1.710591 200 1.000000 \n", "\n", - " amplitude_cutoff sliding_rp_violation isi_violations_ratio \\\n", - "0 0.000089 NaN 0.951853 \n", - "1 0.000002 NaN 0.454454 \n", - "2 0.000083 NaN 0.797423 \n", - "3 0.000136 NaN 0.408176 \n", - "4 0.000122 NaN 0.760583 \n", - ".. ... ... ... \n", - "325 0.000011 NaN 4.158359 \n", - "326 0.000031 NaN 3.559917 \n", - "327 0.000079 NaN 1.952197 \n", - "328 0.000011 NaN 1.230135 \n", - "329 0.000177 NaN 2.987234 \n", + " rp_violations sliding_rp_violation ... amplitude_cv_median \\\n", + "0 4899 NaN ... NaN \n", + "1 627 NaN ... 2.064498 \n", + "2 25 0.105 ... 0.461843 \n", + "3 1794 NaN ... 0.933989 \n", + "4 285 NaN ... NaN \n", + ".. ... ... ... ... \n", + "371 204 NaN ... NaN \n", + "372 1 0.005 ... NaN \n", + "373 14 0.175 ... 1.168065 \n", + "374 30 0.225 ... 1.558193 \n", + "375 81 NaN ... 1.848594 \n", "\n", - " isi_violations_count sd_ratio rp_contamination rp_violations \\\n", - "0 460 1.337394 1.0 261 \n", - "1 5320 1.109406 1.0 4075 \n", - "2 153 1.301471 1.0 88 \n", - "3 684 1.120740 1.0 605 \n", - "4 197 1.369003 1.0 129 \n", - ".. ... ... ... ... \n", - "325 1718 2.544345 1.0 1176 \n", - "326 468 2.754459 1.0 264 \n", - "327 43 2.513939 1.0 37 \n", - "328 2071 2.367420 1.0 1152 \n", - "329 814 2.523415 1.0 441 \n", + " amplitude_cv_range amplitude_cutoff noise_cutoff noise_ratio \\\n", + "0 NaN 0.000089 -0.129627 0.041261 \n", + "1 1.489093 0.000082 -0.225793 0.024113 \n", + "2 0.458329 0.000011 -0.038763 0.015673 \n", + "3 0.471161 0.000179 -0.046065 0.042872 \n", + "4 NaN 0.000058 -0.221197 0.018106 \n", + ".. ... ... ... ... \n", + "371 NaN 0.000445 -0.391426 0.013178 \n", + "372 NaN 0.000108 -0.090904 0.056504 \n", + "373 1.922004 0.000028 -0.169045 0.013893 \n", + "374 3.098256 0.000019 -0.133455 0.007452 \n", + "375 2.540619 0.000179 -0.011103 0.014755 \n", "\n", - " num_spikes firing_range \n", - "0 26315 13.80 \n", - "1 129515 71.00 \n", - "2 16581 8.02 \n", - "3 49002 17.20 \n", - "4 19265 10.80 \n", - ".. ... ... \n", - "325 24331 15.82 \n", - "326 13725 9.20 \n", - "327 5618 4.00 \n", - "328 49116 30.80 \n", - "329 19760 13.00 \n", + " amplitude_median drift_ptp drift_std drift_mad sd_ratio \n", + "0 -17.355000 2.781601 0.623276 0.467753 2.223717 \n", + "1 -5.264999 3.083042 0.672090 0.520758 1.821735 \n", + "2 -13.844999 1.776146 0.446181 0.384374 1.009472 \n", + "3 -18.719999 3.046326 0.677080 0.502092 2.171387 \n", + "4 -17.160000 4.121138 0.547034 0.892043 1.144326 \n", + ".. ... ... ... ... ... \n", + "371 -9.945000 NaN NaN NaN 1.711817 \n", + "372 -57.719997 4.326493 0.647887 0.567893 1.075722 \n", + "373 -7.605000 1.844368 0.225273 0.309508 1.390354 \n", + "374 -7.994999 1.490345 0.200480 0.246163 1.959414 \n", + "375 -5.264999 2.983435 0.413590 0.364231 2.308002 \n", "\n", - "[330 rows x 18 columns]" + "[376 rows x 24 columns]" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -989,7 +1078,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1013,131 +1102,149 @@ " \n", " \n", " \n", - " amplitude_median\n", - " sync_spike_2\n", - " sync_spike_4\n", - " sync_spike_8\n", + " num_spikes\n", " firing_rate\n", - " snr\n", - " amplitude_cv_median\n", - " amplitude_cv_range\n", " presence_ratio\n", - " amplitude_cutoff\n", - " sliding_rp_violation\n", + " snr\n", + " snr_bombcell\n", " isi_violations_ratio\n", " isi_violations_count\n", - " sd_ratio\n", " rp_contamination\n", " rp_violations\n", - " num_spikes\n", - " firing_range\n", + " sliding_rp_violation\n", + " ...\n", + " amplitude_cv_median\n", + " amplitude_cv_range\n", + " amplitude_cutoff\n", + " noise_cutoff\n", + " noise_ratio\n", + " amplitude_median\n", + " drift_ptp\n", + " drift_std\n", + " drift_mad\n", + " sd_ratio\n", " \n", " \n", " \n", " \n", " 0\n", - " -10.530000\n", - " 0.105795\n", - " 0.006802\n", - " 0.000190\n", - " 6.121579\n", - " 0.978010\n", - " 1.187101\n", - " 1.122601\n", - " 1.0\n", - " 0.000089\n", + " 70860\n", + " 16.569565\n", + " 1.000000\n", + " 2.997902\n", + " 21.794119\n", + " 1.482813\n", + " 5223\n", + " 1.000000\n", + " 4899\n", + " NaN\n", + " ...\n", " NaN\n", - " 0.951853\n", - " 460\n", - " 1.337394\n", - " 1.0\n", - " 261\n", - " 26315\n", - " 13.80\n", + " NaN\n", + " 0.000089\n", + " -0.129627\n", + " 0.041261\n", + " -17.355000\n", + " 2.781601\n", + " 0.623276\n", + " 0.467753\n", + " 2.223717\n", " \n", " \n", " 1\n", - " -43.680000\n", - " 0.063004\n", - " 0.002108\n", - " 0.000031\n", - " 30.128681\n", - " 3.898206\n", - " NaN\n", - " NaN\n", - " 1.0\n", - " 0.000002\n", + " 35219\n", + " 8.235443\n", + " 1.000000\n", + " 1.515432\n", + " 19.924999\n", + " 0.935490\n", + " 814\n", + " 1.000000\n", + " 627\n", " NaN\n", - " 0.454454\n", - " 5320\n", - " 1.109406\n", - " 1.0\n", - " 4075\n", - " 129515\n", - " 71.00\n", + " ...\n", + " 2.064498\n", + " 1.489093\n", + " 0.000082\n", + " -0.225793\n", + " 0.024113\n", + " -5.264999\n", + " 3.083042\n", + " 0.672090\n", + " 0.520758\n", + " 1.821735\n", " \n", " \n", " 2\n", - " -9.165000\n", - " 0.124962\n", - " 0.009469\n", - " 0.000121\n", - " 3.857188\n", - " 0.944906\n", - " 1.398181\n", - " 1.107468\n", - " 1.0\n", - " 0.000083\n", - " NaN\n", - " 0.797423\n", - " 153\n", - " 1.301471\n", - " 1.0\n", - " 88\n", - " 16581\n", - " 8.02\n", + " 22971\n", + " 5.371429\n", + " 1.000000\n", + " 2.459971\n", + " 22.950001\n", + " 0.083747\n", + " 31\n", + " 0.107035\n", + " 25\n", + " 0.105\n", + " ...\n", + " 0.461843\n", + " 0.458329\n", + " 0.000011\n", + " -0.038763\n", + " 0.015673\n", + " -13.844999\n", + " 1.776146\n", + " 0.446181\n", + " 0.384374\n", + " 1.009472\n", " \n", " \n", " 3\n", - " -30.224998\n", - " 0.086343\n", - " 0.008367\n", - " 0.000245\n", - " 11.399187\n", - " 2.839966\n", - " 0.341267\n", - " 0.324181\n", - " 1.0\n", - " 0.000136\n", + " 38556\n", + " 9.015752\n", + " 1.000000\n", + " 3.150455\n", + " 17.388889\n", + " 1.845931\n", + " 1925\n", + " 1.000000\n", + " 1794\n", " NaN\n", - " 0.408176\n", - " 684\n", - " 1.120740\n", - " 1.0\n", - " 605\n", - " 49002\n", - " 17.20\n", + " ...\n", + " 0.933989\n", + " 0.471161\n", + " 0.000179\n", + " -0.046065\n", + " 0.042872\n", + " -18.719999\n", + " 3.046326\n", + " 0.677080\n", + " 0.502092\n", + " 2.171387\n", " \n", " \n", " 4\n", - " -27.884998\n", - " 0.140825\n", - " 0.017389\n", - " 0.000623\n", - " 4.481558\n", - " 2.428674\n", + " 25600\n", + " 5.986182\n", + " 1.000000\n", + " 3.088286\n", + " 25.347828\n", + " 0.746076\n", + " 343\n", + " 1.000000\n", + " 285\n", " NaN\n", + " ...\n", " NaN\n", - " 1.0\n", - " 0.000122\n", " NaN\n", - " 0.760583\n", - " 197\n", - " 1.369003\n", - " 1.0\n", - " 129\n", - " 19265\n", - " 10.80\n", + " 0.000058\n", + " -0.221197\n", + " 0.018106\n", + " -17.160000\n", + " 4.121138\n", + " 0.547034\n", + " 0.892043\n", + " 1.144326\n", " \n", " \n", " ...\n", @@ -1159,187 +1266,205 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 325\n", - " -12.090000\n", - " 0.336032\n", - " 0.058526\n", - " 0.003946\n", - " 5.660047\n", - " 2.556509\n", + " 371\n", + " 685\n", + " 0.160177\n", + " 0.464789\n", + " 2.362186\n", + " 48.705883\n", + " 689.625782\n", + " 227\n", + " 1.000000\n", + " 204\n", " NaN\n", + " ...\n", " NaN\n", - " 1.0\n", - " 0.000011\n", " NaN\n", - " 4.158359\n", - " 1718\n", - " 2.544345\n", - " 1.0\n", - " 1176\n", - " 24331\n", - " 15.82\n", + " 0.000445\n", + " -0.391426\n", + " 0.013178\n", + " -9.945000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1.711817\n", " \n", " \n", - " 326\n", - " -15.795000\n", - " 0.226084\n", - " 0.023752\n", - " 0.001239\n", - " 3.192805\n", - " 3.402270\n", - " 0.811786\n", - " 1.248298\n", - " 1.0\n", - " 0.000031\n", + " 372\n", + " 31850\n", + " 7.447653\n", + " 1.000000\n", + " 10.522679\n", + " 28.590910\n", + " 0.001405\n", + " 1\n", + " 0.002110\n", + " 1\n", + " 0.005\n", + " ...\n", " NaN\n", - " 3.559917\n", - " 468\n", - " 2.754459\n", - " 1.0\n", - " 264\n", - " 13725\n", - " 9.20\n", + " NaN\n", + " 0.000108\n", + " -0.090904\n", + " 0.056504\n", + " -57.719997\n", + " 4.326493\n", + " 0.647887\n", + " 0.567893\n", + " 1.075722\n", " \n", " \n", - " 327\n", - " -16.574999\n", - " 0.297615\n", - " 0.037736\n", - " 0.001424\n", - " 1.306898\n", - " 3.373796\n", - " 0.761374\n", - " 0.643952\n", - " 1.0\n", - " 0.000079\n", - " NaN\n", - " 1.952197\n", - " 43\n", - " 2.513939\n", - " 1.0\n", - " 37\n", - " 5618\n", - " 4.00\n", + " 373\n", + " 13935\n", + " 3.258494\n", + " 1.000000\n", + " 1.511464\n", + " 32.826088\n", + " 0.249594\n", + " 34\n", + " 0.168328\n", + " 14\n", + " 0.175\n", + " ...\n", + " 1.168065\n", + " 1.922004\n", + " 0.000028\n", + " -0.169045\n", + " 0.013893\n", + " -7.605000\n", + " 1.844368\n", + " 0.225273\n", + " 0.309508\n", + " 1.390354\n", " \n", " \n", - " 328\n", - " -14.235000\n", - " 0.348725\n", - " 0.054768\n", - " 0.001751\n", - " 11.425706\n", - " 2.804173\n", - " NaN\n", - " NaN\n", - " 1.0\n", - " 0.000011\n", - " NaN\n", - " 1.230135\n", - " 2071\n", - " 2.367420\n", - " 1.0\n", - " 1152\n", - " 49116\n", - " 30.80\n", + " 374\n", + " 13567\n", + " 3.172443\n", + " 1.000000\n", + " 1.298798\n", + " 33.615387\n", + " 0.727996\n", + " 94\n", + " 0.449561\n", + " 30\n", + " 0.225\n", + " ...\n", + " 1.558193\n", + " 3.098256\n", + " 0.000019\n", + " -0.133455\n", + " 0.007452\n", + " -7.994999\n", + " 1.490345\n", + " 0.200480\n", + " 0.246163\n", + " 1.959414\n", " \n", " \n", - " 329\n", - " -14.235000\n", - " 0.258755\n", - " 0.022976\n", - " 0.000304\n", - " 4.596709\n", - " 2.740247\n", - " NaN\n", - " NaN\n", - " 1.0\n", - " 0.000177\n", + " 375\n", + " 12910\n", + " 3.018813\n", + " 1.000000\n", + " 2.027381\n", + " 32.628571\n", + " 1.710591\n", + " 200\n", + " 1.000000\n", + " 81\n", " NaN\n", - " 2.987234\n", - " 814\n", - " 2.523415\n", - " 1.0\n", - " 441\n", - " 19760\n", - " 13.00\n", + " ...\n", + " 1.848594\n", + " 2.540619\n", + " 0.000179\n", + " -0.011103\n", + " 0.014755\n", + " -5.264999\n", + " 2.983435\n", + " 0.413590\n", + " 0.364231\n", + " 2.308002\n", " \n", " \n", "\n", - "

330 rows × 18 columns

\n", + "

376 rows × 24 columns

\n", "" ], "text/plain": [ - " amplitude_median sync_spike_2 sync_spike_4 sync_spike_8 firing_rate \\\n", - "0 -10.530000 0.105795 0.006802 0.000190 6.121579 \n", - "1 -43.680000 0.063004 0.002108 0.000031 30.128681 \n", - "2 -9.165000 0.124962 0.009469 0.000121 3.857188 \n", - "3 -30.224998 0.086343 0.008367 0.000245 11.399187 \n", - "4 -27.884998 0.140825 0.017389 0.000623 4.481558 \n", - ".. ... ... ... ... ... \n", - "325 -12.090000 0.336032 0.058526 0.003946 5.660047 \n", - "326 -15.795000 0.226084 0.023752 0.001239 3.192805 \n", - "327 -16.574999 0.297615 0.037736 0.001424 1.306898 \n", - "328 -14.235000 0.348725 0.054768 0.001751 11.425706 \n", - "329 -14.235000 0.258755 0.022976 0.000304 4.596709 \n", + " num_spikes firing_rate presence_ratio snr snr_bombcell \\\n", + "0 70860 16.569565 1.000000 2.997902 21.794119 \n", + "1 35219 8.235443 1.000000 1.515432 19.924999 \n", + "2 22971 5.371429 1.000000 2.459971 22.950001 \n", + "3 38556 9.015752 1.000000 3.150455 17.388889 \n", + "4 25600 5.986182 1.000000 3.088286 25.347828 \n", + ".. ... ... ... ... ... \n", + "371 685 0.160177 0.464789 2.362186 48.705883 \n", + "372 31850 7.447653 1.000000 10.522679 28.590910 \n", + "373 13935 3.258494 1.000000 1.511464 32.826088 \n", + "374 13567 3.172443 1.000000 1.298798 33.615387 \n", + "375 12910 3.018813 1.000000 2.027381 32.628571 \n", "\n", - " snr amplitude_cv_median amplitude_cv_range presence_ratio \\\n", - "0 0.978010 1.187101 1.122601 1.0 \n", - "1 3.898206 NaN NaN 1.0 \n", - "2 0.944906 1.398181 1.107468 1.0 \n", - "3 2.839966 0.341267 0.324181 1.0 \n", - "4 2.428674 NaN NaN 1.0 \n", - ".. ... ... ... ... \n", - "325 2.556509 NaN NaN 1.0 \n", - "326 3.402270 0.811786 1.248298 1.0 \n", - "327 3.373796 0.761374 0.643952 1.0 \n", - "328 2.804173 NaN NaN 1.0 \n", - "329 2.740247 NaN NaN 1.0 \n", + " isi_violations_ratio isi_violations_count rp_contamination \\\n", + "0 1.482813 5223 1.000000 \n", + "1 0.935490 814 1.000000 \n", + "2 0.083747 31 0.107035 \n", + "3 1.845931 1925 1.000000 \n", + "4 0.746076 343 1.000000 \n", + ".. ... ... ... \n", + "371 689.625782 227 1.000000 \n", + "372 0.001405 1 0.002110 \n", + "373 0.249594 34 0.168328 \n", + "374 0.727996 94 0.449561 \n", + "375 1.710591 200 1.000000 \n", "\n", - " amplitude_cutoff sliding_rp_violation isi_violations_ratio \\\n", - "0 0.000089 NaN 0.951853 \n", - "1 0.000002 NaN 0.454454 \n", - "2 0.000083 NaN 0.797423 \n", - "3 0.000136 NaN 0.408176 \n", - "4 0.000122 NaN 0.760583 \n", - ".. ... ... ... \n", - "325 0.000011 NaN 4.158359 \n", - "326 0.000031 NaN 3.559917 \n", - "327 0.000079 NaN 1.952197 \n", - "328 0.000011 NaN 1.230135 \n", - "329 0.000177 NaN 2.987234 \n", + " rp_violations sliding_rp_violation ... amplitude_cv_median \\\n", + "0 4899 NaN ... NaN \n", + "1 627 NaN ... 2.064498 \n", + "2 25 0.105 ... 0.461843 \n", + "3 1794 NaN ... 0.933989 \n", + "4 285 NaN ... NaN \n", + ".. ... ... ... ... \n", + "371 204 NaN ... NaN \n", + "372 1 0.005 ... NaN \n", + "373 14 0.175 ... 1.168065 \n", + "374 30 0.225 ... 1.558193 \n", + "375 81 NaN ... 1.848594 \n", "\n", - " isi_violations_count sd_ratio rp_contamination rp_violations \\\n", - "0 460 1.337394 1.0 261 \n", - "1 5320 1.109406 1.0 4075 \n", - "2 153 1.301471 1.0 88 \n", - "3 684 1.120740 1.0 605 \n", - "4 197 1.369003 1.0 129 \n", - ".. ... ... ... ... \n", - "325 1718 2.544345 1.0 1176 \n", - "326 468 2.754459 1.0 264 \n", - "327 43 2.513939 1.0 37 \n", - "328 2071 2.367420 1.0 1152 \n", - "329 814 2.523415 1.0 441 \n", + " amplitude_cv_range amplitude_cutoff noise_cutoff noise_ratio \\\n", + "0 NaN 0.000089 -0.129627 0.041261 \n", + "1 1.489093 0.000082 -0.225793 0.024113 \n", + "2 0.458329 0.000011 -0.038763 0.015673 \n", + "3 0.471161 0.000179 -0.046065 0.042872 \n", + "4 NaN 0.000058 -0.221197 0.018106 \n", + ".. ... ... ... ... \n", + "371 NaN 0.000445 -0.391426 0.013178 \n", + "372 NaN 0.000108 -0.090904 0.056504 \n", + "373 1.922004 0.000028 -0.169045 0.013893 \n", + "374 3.098256 0.000019 -0.133455 0.007452 \n", + "375 2.540619 0.000179 -0.011103 0.014755 \n", "\n", - " num_spikes firing_range \n", - "0 26315 13.80 \n", - "1 129515 71.00 \n", - "2 16581 8.02 \n", - "3 49002 17.20 \n", - "4 19265 10.80 \n", - ".. ... ... \n", - "325 24331 15.82 \n", - "326 13725 9.20 \n", - "327 5618 4.00 \n", - "328 49116 30.80 \n", - "329 19760 13.00 \n", + " amplitude_median drift_ptp drift_std drift_mad sd_ratio \n", + "0 -17.355000 2.781601 0.623276 0.467753 2.223717 \n", + "1 -5.264999 3.083042 0.672090 0.520758 1.821735 \n", + "2 -13.844999 1.776146 0.446181 0.384374 1.009472 \n", + "3 -18.719999 3.046326 0.677080 0.502092 2.171387 \n", + "4 -17.160000 4.121138 0.547034 0.892043 1.144326 \n", + ".. ... ... ... ... ... \n", + "371 -9.945000 NaN NaN NaN 1.711817 \n", + "372 -57.719997 4.326493 0.647887 0.567893 1.075722 \n", + "373 -7.605000 1.844368 0.225273 0.309508 1.390354 \n", + "374 -7.994999 1.490345 0.200480 0.246163 1.959414 \n", + "375 -5.264999 2.983435 0.413590 0.364231 2.308002 \n", "\n", - "[330 rows x 18 columns]" + "[376 rows x 24 columns]" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1358,29 +1483,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'total_units': 330, 'counts': {'NOISE': 138, 'GOOD': 21, 'MUA': 171}, 'percentages': {'NOISE': 41.8, 'GOOD': 6.4, 'MUA': 51.8}}\n" + "{'total_units': 376, 'counts': {'NOISE': 112, 'GOOD': 55, 'MUA': 183, 'NON_SOMA': 26}, 'percentages': {'NOISE': 29.8, 'GOOD': 14.6, 'MUA': 48.7, 'NON_SOMA': 6.9}}\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1390,7 +1515,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1407,7 +1532,7 @@ "\n", "# Or customize thresholds\n", "thresholds = sc.get_default_thresholds() # probably not correct format. where should i put this? \n", - "thresholds[\"snr\"][\"min\"] = 3 # Lower threshold\n", + "thresholds[\"snr_bombcell\"][\"min\"] = 3 # Lower threshold\n", "thresholds[\"amplitude_median\"][\"min\"] = np.nan # Disable\n", "\n", "unit_type, labels = sc.classify_units(metrics, thresholds)\n", diff --git a/src/spikeinterface/comparison/unit_classification.py b/src/spikeinterface/comparison/unit_classification.py index 9f973e2a0c..35bbd2f02c 100644 --- a/src/spikeinterface/comparison/unit_classification.py +++ b/src/spikeinterface/comparison/unit_classification.py @@ -43,14 +43,14 @@ def get_default_thresholds() -> dict: Template metrics (from template_metrics extension): - num_positive_peaks: Number of positive peaks (repolarization peaks) - num_negative_peaks: Number of negative peaks (troughs) - - waveform_duration: Duration in microseconds + - peak_to_trough_duration: Duration in seconds from trough to peak - waveform_baseline_flatness: Baseline flatness metric - peak_after_to_trough_ratio: Ratio of peak after trough to trough amplitude - exp_decay: Exponential decay constant for spatial spread Quality metrics (from quality_metrics extension): - - amplitude_median: Median spike amplitude - - snr: Signal-to-noise ratio + - amplitude_median: Median spike amplitude (in uV) + - snr_bombcell: Signal-to-noise ratio (BombCell method: raw waveform max / baseline MAD) - amplitude_cutoff: Estimated fraction of missing spikes - num_spikes: Total spike count - rp_contamination: Refractory period contamination @@ -70,13 +70,13 @@ def get_default_thresholds() -> dict: # Good units typically have 1 main trough "num_negative_peaks": {"min": np.nan, "max": 1}, - # Waveform duration in MICROSECONDS (from template_metrics) - # Typical range: 100-1150 us - "waveform_duration": {"min": 100, "max": 1150}, + # Peak to trough duration in SECONDS (from template_metrics) + # Typical range: 0.0001-0.00115 s (100-1150 μs) + "peak_to_trough_duration": {"min": 0.0001, "max": 0.00115}, # Baseline flatness - max deviation as fraction of peak amplitude # Lower is better, typical threshold 0.3 - "waveform_baseline_flatness": {"min": np.nan, "max": 0.3}, + "waveform_baseline_flatness": {"min": np.nan, "max": 0.5}, # Peak after trough to trough ratio - helps detect noise # High values indicate noise (ratio > 0.8 is suspicious) @@ -94,9 +94,9 @@ def get_default_thresholds() -> dict: # Lower bound ensures sufficient signal "amplitude_median": {"min": 40, "max": np.nan}, - # Signal-to-noise ratio + # Signal-to-noise ratio (BombCell method: raw waveform max / baseline MAD) # Higher is better, minimum ensures reliable detection - "snr": {"min": 5, "max": np.nan}, + "snr_bombcell": {"min": 5, "max": np.nan}, # Amplitude cutoff - estimates fraction of missing spikes # Lower is better (less missing), max 0.2 means <20% estimated missing @@ -123,21 +123,26 @@ def get_default_thresholds() -> dict: # ============================================================ # These thresholds identify axonal/dendritic units by their waveform shape - # Non-somatic units have characteristic triphasic waveforms + # Non-somatic (axonal) units have: large initial peak, narrow widths, small repolarization - # Peak before to trough ratio - non-somatic have large initial peak + # Peak before to trough ratio - non-somatic have large initial peak relative to trough + # If peak_before/trough > max, classify as non-somatic "peak_before_to_trough_ratio": {"min": np.nan, "max": 3}, # non-somatic if > max - # Peak before width (in samples at sampling rate) #QQ should be microseconds or something! - "peak_before_width": {"min": 4, "max": np.nan}, # non-somatic if < min + # Peak before width in MICROSECONDS - non-somatic have narrow initial peaks + # If width < min, classify as non-somatic + "peak_before_width": {"min": 150, "max": np.nan}, # non-somatic if < 150 μs - # Trough width (in samples) #QQ should be microseconds or something! - "trough_width": {"min": 5, "max": np.nan}, # non-somatic if < min + # Trough width in MICROSECONDS - non-somatic have narrow troughs + # If width < min, classify as non-somatic + "trough_width": {"min": 200, "max": np.nan}, # non-somatic if < 200 μs - # Peak before to peak after ratio + # Peak before to peak after ratio - non-somatic have large initial peak vs small repolarization + # If peak_before/peak_after > max, classify as non-somatic "peak_before_to_peak_after_ratio": {"min": np.nan, "max": 3}, # non-somatic if > max - # Main peak to trough ratio + # Main peak to trough ratio - non-somatic have peak almost as large as trough + # If max_peak/trough > max, classify as non-somatic (somatic units have trough >> peaks) "main_peak_to_trough_ratio": {"min": np.nan, "max": 0.8}, # non-somatic if > max } @@ -147,7 +152,7 @@ def get_default_thresholds() -> dict: def classify_units( quality_metrics: pd.DataFrame, thresholds: Optional[dict] = None, - classify_non_somatic: bool = False, + classify_non_somatic: bool = True, split_non_somatic_good_mua: bool = False, ) -> tuple[np.ndarray, np.ndarray]: """ @@ -194,7 +199,7 @@ def classify_units( waveform_metrics = [ "num_positive_peaks", "num_negative_peaks", - "waveform_duration", + "peak_to_trough_duration", "waveform_baseline_flatness", "peak_after_to_trough_ratio", "exp_decay", @@ -202,7 +207,7 @@ def classify_units( spike_quality_metrics = [ "amplitude_median", - "snr", + "snr_bombcell", "amplitude_cutoff", "num_spikes", "rp_contamination", @@ -218,6 +223,10 @@ def classify_units( "main_peak_to_trough_ratio", ] + # Metrics that should use absolute values for comparison + # (amplitude values are typically negative in extracellular recordings) + absolute_value_metrics = ["amplitude_median"] + # ======================================== # NOISE classification # ======================================== @@ -230,6 +239,9 @@ def classify_units( continue values = quality_metrics[metric_name].values + # Use absolute values for amplitude-based metrics + if metric_name in absolute_value_metrics: + values = np.abs(values) thresh = thresholds[metric_name] # NaN values in metrics are considered failures for waveform metrics @@ -257,6 +269,9 @@ def classify_units( continue values = quality_metrics[metric_name].values + # Use absolute values for amplitude-based metrics + if metric_name in absolute_value_metrics: + values = np.abs(values) thresh = thresholds[metric_name] # Only apply to units not yet classified as noise @@ -281,25 +296,50 @@ def classify_units( # NON-SOMATIC classification # ======================================== if classify_non_somatic: - is_non_somatic = np.zeros(n_units, dtype=bool) + # Non-somatic (axonal) units require BOTH ratio AND width criteria + # Logic from BombCell: + # is_non_somatic = (ratio_conditions & width_conditions) | standalone_ratio_condition + + # Helper to get metric values safely + def get_metric(name): + if name in quality_metrics.columns: + return quality_metrics[name].values + return np.full(n_units, np.nan) + + # Width conditions (ALL must be met) + peak_before_width = get_metric("peak_before_width") + trough_width = get_metric("trough_width") + + width_thresh_peak = thresholds.get("peak_before_width", {}).get("min", np.nan) + width_thresh_trough = thresholds.get("trough_width", {}).get("min", np.nan) + + narrow_peak = ~np.isnan(peak_before_width) & (peak_before_width < width_thresh_peak) if not np.isnan(width_thresh_peak) else np.zeros(n_units, dtype=bool) + narrow_trough = ~np.isnan(trough_width) & (trough_width < width_thresh_trough) if not np.isnan(width_thresh_trough) else np.zeros(n_units, dtype=bool) + + width_conditions = narrow_peak & narrow_trough + + # Ratio conditions + peak_before_to_trough = get_metric("peak_before_to_trough_ratio") + peak_before_to_peak_after = get_metric("peak_before_to_peak_after_ratio") + main_peak_to_trough = get_metric("main_peak_to_trough_ratio") + + ratio_thresh_pbt = thresholds.get("peak_before_to_trough_ratio", {}).get("max", np.nan) + ratio_thresh_pbpa = thresholds.get("peak_before_to_peak_after_ratio", {}).get("max", np.nan) + ratio_thresh_mpt = thresholds.get("main_peak_to_trough_ratio", {}).get("max", np.nan) - for metric_name in non_somatic_metrics: - if metric_name not in quality_metrics.columns: - continue - if metric_name not in thresholds: - continue + # Large initial peak relative to trough + large_initial_peak = ~np.isnan(peak_before_to_trough) & (peak_before_to_trough > ratio_thresh_pbt) if not np.isnan(ratio_thresh_pbt) else np.zeros(n_units, dtype=bool) - values = quality_metrics[metric_name].values - thresh = thresholds[metric_name] + # Large initial peak relative to repolarization peak + large_peak_ratio = ~np.isnan(peak_before_to_peak_after) & (peak_before_to_peak_after > ratio_thresh_pbpa) if not np.isnan(ratio_thresh_pbpa) else np.zeros(n_units, dtype=bool) - # Non-somatic detection uses OPPOSITE logic: - # - Values BELOW min threshold -> non-somatic - # - Values ABOVE max threshold -> non-somatic - if not np.isnan(thresh["min"]): - is_non_somatic |= ~np.isnan(values) & (values < thresh["min"]) + # Main peak almost as large as trough (standalone condition) + large_main_peak = ~np.isnan(main_peak_to_trough) & (main_peak_to_trough > ratio_thresh_mpt) if not np.isnan(ratio_thresh_mpt) else np.zeros(n_units, dtype=bool) - if not np.isnan(thresh["max"]): - is_non_somatic |= ~np.isnan(values) & (values > thresh["max"]) + # Combined logic: (ratio AND width conditions) OR standalone ratio + # Requires at least one ratio condition AND both width conditions, OR the standalone ratio + ratio_conditions = large_initial_peak | large_peak_ratio + is_non_somatic = (ratio_conditions & width_conditions) | large_main_peak # Apply non-somatic classification if split_non_somatic_good_mua: diff --git a/src/spikeinterface/metrics/quality/misc_metrics.py b/src/spikeinterface/metrics/quality/misc_metrics.py index 2af4583f13..fdcf501d08 100644 --- a/src/spikeinterface/metrics/quality/misc_metrics.py +++ b/src/spikeinterface/metrics/quality/misc_metrics.py @@ -860,7 +860,7 @@ def compute_amplitude_cutoffs( num_histogram_bins=500, histogram_smoothing_value=3, amplitudes_bins_min_ratio=5, - plot_details=True, # Hardcoded ON for debugging + plot_details=False, ): """ Calculate approximate fraction of spikes missing from a distribution of amplitudes. @@ -966,7 +966,7 @@ class AmplitudeCutoff(BaseMetric): "num_histogram_bins": 100, "histogram_smoothing_value": 3, "amplitudes_bins_min_ratio": 5, - "plot_details": True, # Hardcoded ON for debugging + "plot_details": False, } metric_columns = {"amplitude_cutoff": float} metric_descriptions = { @@ -1567,7 +1567,7 @@ def amplitude_cutoff( amplitudes_bins_min_ratio=5, spike_times=None, unit_id=None, - plot_details=True, # Hardcoded ON for debugging + plot_details=False, ax_scatter=None, ax_hist=None, ): diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index 3e3999dabd..d142b029c1 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -1064,17 +1064,17 @@ def single_channel_metric(unit_function, sorting_analyzer, unit_ids, tmp_data, * return result -class PeakToValley(BaseMetric): - metric_name = "peak_to_valley" +class PeakToTroughDuration(BaseMetric): + metric_name = "peak_to_trough_duration" metric_params = {} - metric_columns = {"peak_to_valley": float} + metric_columns = {"peak_to_trough_duration": float} metric_descriptions = { - "peak_to_valley": "Duration in s between the trough (minimum) and the peak (maximum) of the spike waveform." + "peak_to_trough_duration": "Duration in seconds between the trough (minimum) and the peak (maximum) of the spike waveform." } needs_tmp_data = True @staticmethod - def _peak_to_valley_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): + def _peak_to_trough_duration_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): return single_channel_metric( unit_function=get_peak_to_valley, sorting_analyzer=sorting_analyzer, @@ -1083,7 +1083,7 @@ def _peak_to_valley_metric_function(sorting_analyzer, unit_ids, tmp_data, **metr **metric_params, ) - metric_function = _peak_to_valley_metric_function + metric_function = _peak_to_trough_duration_metric_function class PeakToTroughRatio(BaseMetric): @@ -1356,7 +1356,7 @@ class WaveformBaselineFlatness(BaseMetric): single_channel_metrics = [ - PeakToValley, + PeakToTroughDuration, PeakToTroughRatio, HalfWidth, RepolarizationSlope, diff --git a/src/spikeinterface/widgets/unit_classification.py b/src/spikeinterface/widgets/unit_classification.py index facf2056ed..f031314026 100644 --- a/src/spikeinterface/widgets/unit_classification.py +++ b/src/spikeinterface/widgets/unit_classification.py @@ -216,12 +216,18 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): colors = plt.cm.tab10(np.linspace(0, 1, 10)) + # Metrics that should use absolute values (amplitude values are negative in extracellular recordings) + absolute_value_metrics = ["amplitude_median"] + for idx, metric_name in enumerate(metrics_to_plot): row = idx // n_cols col = idx % n_cols ax = axes[row, col] values = quality_metrics[metric_name].values + # Use absolute values for amplitude-based metrics + if metric_name in absolute_value_metrics: + values = np.abs(values) values = values[~np.isnan(values)] values = values[~np.isinf(values)] From 5b4cafb715f5400b8f3fcce19c6cfa08e0819ada Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Thu, 8 Jan 2026 01:55:30 +0100 Subject: [PATCH 10/49] upset plots --- .../comparison/unit_classification.py | 2 +- .../widgets/unit_classification.py | 324 ++++++++++++++++++ src/spikeinterface/widgets/widget_list.py | 3 + 3 files changed, 328 insertions(+), 1 deletion(-) diff --git a/src/spikeinterface/comparison/unit_classification.py b/src/spikeinterface/comparison/unit_classification.py index 35bbd2f02c..79dde2d1ae 100644 --- a/src/spikeinterface/comparison/unit_classification.py +++ b/src/spikeinterface/comparison/unit_classification.py @@ -297,7 +297,7 @@ def classify_units( # ======================================== if classify_non_somatic: # Non-somatic (axonal) units require BOTH ratio AND width criteria - # Logic from BombCell: + # Logic from bombcell: # is_non_somatic = (ratio_conditions & width_conditions) | standalone_ratio_condition # Helper to get metric values safely diff --git a/src/spikeinterface/widgets/unit_classification.py b/src/spikeinterface/widgets/unit_classification.py index f031314026..948f4a9123 100644 --- a/src/spikeinterface/widgets/unit_classification.py +++ b/src/spikeinterface/widgets/unit_classification.py @@ -402,6 +402,272 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): self.axes = axes +class UpsetPlotWidget(BaseWidget): + """ + Plot UpSet plots showing which metrics fail together for each unit type. + + UpSet plots visualize set intersections, showing which combinations of + metric failures are most common for units classified as NOISE, MUA, etc. + + Each unit type shows only the relevant metrics: + - NOISE: waveform quality metrics (num_positive_peaks, peak_to_trough_duration, etc.) + - MUA: spike quality metrics (amplitude_median, snr_bombcell, rp_contamination, etc.) + - NON_SOMA: non-somatic detection metrics (peak_before_to_trough_ratio, widths, etc.) + + Parameters + ---------- + quality_metrics : pd.DataFrame + DataFrame with quality metrics. + unit_type : np.ndarray + Numeric unit type array from classify_units(). + unit_type_string : np.ndarray + String labels from classify_units(). + thresholds : dict, optional + Threshold dictionary. If None, uses default thresholds. + unit_types_to_plot : list of str, optional + Which unit types to create upset plots for. + Default: ["NOISE", "MUA", "NON_SOMA"] or with split: ["NOISE", "MUA", "NON_SOMA_GOOD", "NON_SOMA_MUA"] + split_non_somatic : bool, default: False + If True, uses split non-somatic labels. + min_subset_size : int, default: 1 + Minimum size of subsets to show in the plot. + + Notes + ----- + Requires the `upsetplot` package to be installed. If not installed, displays + a message instructing the user to install it. + """ + + # Define metric categories + WAVEFORM_METRICS = [ + "num_positive_peaks", + "num_negative_peaks", + "peak_to_trough_duration", + "waveform_baseline_flatness", + "peak_after_to_trough_ratio", + "exp_decay", + ] + + SPIKE_QUALITY_METRICS = [ + "amplitude_median", + "snr_bombcell", + "amplitude_cutoff", + "num_spikes", + "rp_contamination", + "presence_ratio", + "drift_ptp", + ] + + NON_SOMATIC_METRICS = [ + "peak_before_to_trough_ratio", + "peak_before_width", + "trough_width", + "peak_before_to_peak_after_ratio", + "main_peak_to_trough_ratio", + ] + + def __init__( + self, + quality_metrics, + unit_type: np.ndarray, + unit_type_string: np.ndarray, + thresholds: Optional[dict] = None, + unit_types_to_plot: Optional[list] = None, + split_non_somatic: bool = False, + min_subset_size: int = 1, + backend=None, + **backend_kwargs, + ): + from spikeinterface.comparison import get_default_thresholds + + if thresholds is None: + thresholds = get_default_thresholds() + + if unit_types_to_plot is None: + if split_non_somatic: + unit_types_to_plot = ["NOISE", "MUA", "NON_SOMA_GOOD", "NON_SOMA_MUA"] + else: + unit_types_to_plot = ["NOISE", "MUA", "NON_SOMA"] + + plot_data = dict( + quality_metrics=quality_metrics, + unit_type=unit_type, + unit_type_string=unit_type_string, + thresholds=thresholds, + unit_types_to_plot=unit_types_to_plot, + min_subset_size=min_subset_size, + ) + + BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) + + def _get_metrics_for_unit_type(self, unit_type_label): + """Get the relevant metrics for a given unit type.""" + if unit_type_label == "NOISE": + return self.WAVEFORM_METRICS + elif unit_type_label == "MUA": + return self.SPIKE_QUALITY_METRICS + elif unit_type_label in ("NON_SOMA", "NON_SOMA_GOOD", "NON_SOMA_MUA"): + return self.NON_SOMATIC_METRICS + else: + return None # Show all metrics + + def plot_matplotlib(self, data_plot, **backend_kwargs): + import matplotlib.pyplot as plt + import pandas as pd + + dp = to_attr(data_plot) + quality_metrics = dp.quality_metrics + unit_type_string = dp.unit_type_string + thresholds = dp.thresholds + unit_types_to_plot = dp.unit_types_to_plot + min_subset_size = dp.min_subset_size + + # Check if upsetplot is available + try: + from upsetplot import UpSet, from_memberships + except ImportError: + # Display message to install upsetplot + fig, ax = plt.subplots(1, 1, figsize=(10, 6)) + ax.text( + 0.5, + 0.5, + "UpSet plots require the 'upsetplot' package.\n\n" + "Please install it with:\n\n" + " pip install upsetplot\n\n" + "Then re-run this plot.", + ha="center", + va="center", + fontsize=14, + family="monospace", + bbox=dict(boxstyle="round", facecolor="lightyellow", edgecolor="orange"), + ) + ax.axis("off") + ax.set_title("UpSet Plot - Package Not Installed", fontsize=16) + self.figure = fig + self.axes = ax + self.figures = [fig] + return + + # Build failure table for ALL metrics once + failure_table = self._build_failure_table(quality_metrics, thresholds) + + figures = [] + axes_list = [] + + for unit_type_label in unit_types_to_plot: + # Get units of this type + mask = unit_type_string == unit_type_label + n_units = np.sum(mask) + + if n_units == 0: + continue + + # Get relevant metrics for this unit type + relevant_metrics = self._get_metrics_for_unit_type(unit_type_label) + + # Filter failure table to relevant metrics only + if relevant_metrics is not None: + available_metrics = [m for m in relevant_metrics if m in failure_table.columns] + if len(available_metrics) == 0: + # No relevant metrics available, skip this unit type + continue + unit_failure_table = failure_table[available_metrics] + else: + unit_failure_table = failure_table + + # Get failure data for these units + unit_failures = unit_failure_table.loc[mask] + + # Build membership list for upsetplot + memberships = [] + for idx in unit_failures.index: + failed_metrics = unit_failures.columns[unit_failures.loc[idx]].tolist() + if len(failed_metrics) > 0: + memberships.append(failed_metrics) + + if len(memberships) == 0: + continue + + # Create upset data + upset_data = from_memberships(memberships) + + # Filter by min_subset_size + upset_data = upset_data[upset_data >= min_subset_size] + + if len(upset_data) == 0: + continue + + # Create figure + fig = plt.figure(figsize=(12, 6)) + upset = UpSet( + upset_data, + subset_size="count", + show_counts=True, + sort_by="cardinality", + sort_categories_by="cardinality", + ) + upset.plot(fig=fig) + fig.suptitle(f"{unit_type_label} (n={n_units})", fontsize=14, y=1.02) + + figures.append(fig) + axes_list.append(fig.axes) + + if len(figures) == 0: + fig, ax = plt.subplots(1, 1, figsize=(8, 6)) + ax.text( + 0.5, + 0.5, + "No units found for the specified unit types\nor no metric failures detected.", + ha="center", + va="center", + fontsize=12, + ) + ax.axis("off") + figures = [fig] + axes_list = [ax] + + self.figures = figures + self.figure = figures[0] if figures else None + self.axes = axes_list + + def _build_failure_table(self, quality_metrics, thresholds): + """Build a boolean DataFrame indicating which metrics failed for each unit.""" + import pandas as pd + + # Metrics that should use absolute values + absolute_value_metrics = ["amplitude_median"] + + failure_data = {} + + for metric_name, thresh in thresholds.items(): + if metric_name not in quality_metrics.columns: + continue + + values = quality_metrics[metric_name].values.copy() + + # Use absolute values for amplitude-based metrics + if metric_name in absolute_value_metrics: + values = np.abs(values) + + # Check failures + failed = np.zeros(len(values), dtype=bool) + + # NaN is a failure + failed |= np.isnan(values) + + # Check min threshold + if not np.isnan(thresh.get("min", np.nan)): + failed |= values < thresh["min"] + + # Check max threshold + if not np.isnan(thresh.get("max", np.nan)): + failed |= values > thresh["max"] + + failure_data[metric_name] = failed + + return pd.DataFrame(failure_data, index=quality_metrics.index) + + # Convenience functions for direct plotting def plot_unit_classification( sorting_analyzer, @@ -523,3 +789,61 @@ def plot_waveform_overlay( **backend_kwargs, ) return widget + + +def plot_upset( + quality_metrics, + unit_type, + unit_type_string, + thresholds=None, + unit_types_to_plot=None, + split_non_somatic=False, + min_subset_size=1, + backend=None, + **backend_kwargs, +): + """ + Plot UpSet plots showing which metrics fail together for each unit type. + + UpSet plots visualize set intersections, showing which combinations of + metric failures are most common for units classified as NOISE, MUA, etc. + + Parameters + ---------- + quality_metrics : pd.DataFrame + DataFrame with quality metrics. + unit_type : np.ndarray + Numeric unit type array from classify_units(). + unit_type_string : np.ndarray + String labels from classify_units(). + thresholds : dict, optional + Threshold dictionary. If None, uses default thresholds. + unit_types_to_plot : list of str, optional + Which unit types to create upset plots for. + Default: ["NOISE", "MUA", "NON_SOMA"] or with split: ["NOISE", "MUA", "NON_SOMA_GOOD", "NON_SOMA_MUA"] + split_non_somatic : bool, default: False + If True, uses split non-somatic labels. + min_subset_size : int, default: 1 + Minimum size of subsets to show in the plot. + backend : str, optional + Backend to use for plotting. + **backend_kwargs + Additional kwargs for the backend. + + Returns + ------- + widget : UpsetPlotWidget + The widget object. Access individual figures via widget.figures. + """ + widget = UpsetPlotWidget( + quality_metrics, + unit_type, + unit_type_string, + thresholds=thresholds, + unit_types_to_plot=unit_types_to_plot, + split_non_somatic=split_non_somatic, + min_subset_size=min_subset_size, + backend=backend, + **backend_kwargs, + ) + return widget diff --git a/src/spikeinterface/widgets/widget_list.py b/src/spikeinterface/widgets/widget_list.py index 5dab36d773..a5728ebf30 100644 --- a/src/spikeinterface/widgets/widget_list.py +++ b/src/spikeinterface/widgets/widget_list.py @@ -41,9 +41,11 @@ UnitClassificationWidget, ClassificationHistogramsWidget, WaveformOverlayWidget, + UpsetPlotWidget, plot_unit_classification, plot_classification_histograms, plot_waveform_overlay, + plot_upset, ) widget_list = [ @@ -85,6 +87,7 @@ UnitTemplatesWidget, UnitWaveformDensityMapWidget, UnitWaveformsWidget, + UpsetPlotWidget, WaveformOverlayWidget, StudyRunTimesWidget, StudyUnitCountsWidget, From 515ed36b0a28eeecf03404c125cea04693b93c92 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 8 Jan 2026 01:09:58 +0000 Subject: [PATCH 11/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- in_container_params.json | 2 +- in_container_recording.json | 2 +- .../comparison/unit_classification.py | 51 +++--- .../metrics/quality/misc_metrics.py | 2 +- .../metrics/template/metrics.py | 155 ++++++++++++------ .../metrics/template/template_metrics.py | 10 +- 6 files changed, 140 insertions(+), 82 deletions(-) diff --git a/in_container_params.json b/in_container_params.json index 462dc67ed3..01ccea40b6 100644 --- a/in_container_params.json +++ b/in_container_params.json @@ -1,3 +1,3 @@ { "output_folder": "/Users/jf5479/Downloads/AL031_2019-12-02/spikeinterface_output/kilosort4_output" -} \ No newline at end of file +} diff --git a/in_container_recording.json b/in_container_recording.json index 64f8f88c42..6738af6b0b 100644 --- a/in_container_recording.json +++ b/in_container_recording.json @@ -15494,4 +15494,4 @@ "physical_unit": null }, "relative_paths": false -} \ No newline at end of file +} diff --git a/src/spikeinterface/comparison/unit_classification.py b/src/spikeinterface/comparison/unit_classification.py index 79dde2d1ae..c312e465a7 100644 --- a/src/spikeinterface/comparison/unit_classification.py +++ b/src/spikeinterface/comparison/unit_classification.py @@ -61,86 +61,65 @@ def get_default_thresholds() -> dict: # ============================================================ # WAVEFORM QUALITY THRESHOLDS (failures classify as NOISE) # ============================================================ - # Number of positive peaks (repolarization peaks after trough) # Good units typically have 1-2 peaks "num_positive_peaks": {"min": np.nan, "max": 2}, - # Number of negative peaks (troughs) in waveform # Good units typically have 1 main trough "num_negative_peaks": {"min": np.nan, "max": 1}, - # Peak to trough duration in SECONDS (from template_metrics) # Typical range: 0.0001-0.00115 s (100-1150 μs) "peak_to_trough_duration": {"min": 0.0001, "max": 0.00115}, - # Baseline flatness - max deviation as fraction of peak amplitude # Lower is better, typical threshold 0.3 "waveform_baseline_flatness": {"min": np.nan, "max": 0.5}, - # Peak after trough to trough ratio - helps detect noise # High values indicate noise (ratio > 0.8 is suspicious) "peak_after_to_trough_ratio": {"min": np.nan, "max": 0.8}, - # Exponential decay constant for spatial spread # Values outside typical range indicate noise "exp_decay": {"min": 0.01, "max": 0.1}, - # ============================================================ # SPIKE QUALITY THRESHOLDS (failures classify as MUA) # ============================================================ - # Median spike amplitude (in uV typically) # Lower bound ensures sufficient signal "amplitude_median": {"min": 40, "max": np.nan}, - # Signal-to-noise ratio (BombCell method: raw waveform max / baseline MAD) # Higher is better, minimum ensures reliable detection "snr_bombcell": {"min": 5, "max": np.nan}, - # Amplitude cutoff - estimates fraction of missing spikes # Lower is better (less missing), max 0.2 means <20% estimated missing "amplitude_cutoff": {"min": np.nan, "max": 0.2}, - # Minimum number of spikes # Ensures sufficient data for reliable metrics "num_spikes": {"min": 300, "max": np.nan}, - # Refractory period contamination rate # Lower is better, max typically 0.1 (10%) "rp_contamination": {"min": np.nan, "max": 0.1}, - # Presence ratio - fraction of recording where unit is active # Higher is better, ensures unit present throughout "presence_ratio": {"min": 0.7, "max": np.nan}, - # Drift MAD - median absolute deviation of drift in um # Lower is better, ensures stable unit location "drift_ptp": {"min": np.nan, "max": 100}, - # ============================================================ # NON-SOMATIC DETECTION THRESHOLDS (optional) # ============================================================ - # These thresholds identify axonal/dendritic units by their waveform shape # Non-somatic (axonal) units have: large initial peak, narrow widths, small repolarization - # Peak before to trough ratio - non-somatic have large initial peak relative to trough # If peak_before/trough > max, classify as non-somatic "peak_before_to_trough_ratio": {"min": np.nan, "max": 3}, # non-somatic if > max - # Peak before width in MICROSECONDS - non-somatic have narrow initial peaks # If width < min, classify as non-somatic "peak_before_width": {"min": 150, "max": np.nan}, # non-somatic if < 150 μs - # Trough width in MICROSECONDS - non-somatic have narrow troughs # If width < min, classify as non-somatic "trough_width": {"min": 200, "max": np.nan}, # non-somatic if < 200 μs - # Peak before to peak after ratio - non-somatic have large initial peak vs small repolarization # If peak_before/peak_after > max, classify as non-somatic "peak_before_to_peak_after_ratio": {"min": np.nan, "max": 3}, # non-somatic if > max - # Main peak to trough ratio - non-somatic have peak almost as large as trough # If max_peak/trough > max, classify as non-somatic (somatic units have trough >> peaks) "main_peak_to_trough_ratio": {"min": np.nan, "max": 0.8}, # non-somatic if > max @@ -313,8 +292,16 @@ def get_metric(name): width_thresh_peak = thresholds.get("peak_before_width", {}).get("min", np.nan) width_thresh_trough = thresholds.get("trough_width", {}).get("min", np.nan) - narrow_peak = ~np.isnan(peak_before_width) & (peak_before_width < width_thresh_peak) if not np.isnan(width_thresh_peak) else np.zeros(n_units, dtype=bool) - narrow_trough = ~np.isnan(trough_width) & (trough_width < width_thresh_trough) if not np.isnan(width_thresh_trough) else np.zeros(n_units, dtype=bool) + narrow_peak = ( + ~np.isnan(peak_before_width) & (peak_before_width < width_thresh_peak) + if not np.isnan(width_thresh_peak) + else np.zeros(n_units, dtype=bool) + ) + narrow_trough = ( + ~np.isnan(trough_width) & (trough_width < width_thresh_trough) + if not np.isnan(width_thresh_trough) + else np.zeros(n_units, dtype=bool) + ) width_conditions = narrow_peak & narrow_trough @@ -328,13 +315,25 @@ def get_metric(name): ratio_thresh_mpt = thresholds.get("main_peak_to_trough_ratio", {}).get("max", np.nan) # Large initial peak relative to trough - large_initial_peak = ~np.isnan(peak_before_to_trough) & (peak_before_to_trough > ratio_thresh_pbt) if not np.isnan(ratio_thresh_pbt) else np.zeros(n_units, dtype=bool) + large_initial_peak = ( + ~np.isnan(peak_before_to_trough) & (peak_before_to_trough > ratio_thresh_pbt) + if not np.isnan(ratio_thresh_pbt) + else np.zeros(n_units, dtype=bool) + ) # Large initial peak relative to repolarization peak - large_peak_ratio = ~np.isnan(peak_before_to_peak_after) & (peak_before_to_peak_after > ratio_thresh_pbpa) if not np.isnan(ratio_thresh_pbpa) else np.zeros(n_units, dtype=bool) + large_peak_ratio = ( + ~np.isnan(peak_before_to_peak_after) & (peak_before_to_peak_after > ratio_thresh_pbpa) + if not np.isnan(ratio_thresh_pbpa) + else np.zeros(n_units, dtype=bool) + ) # Main peak almost as large as trough (standalone condition) - large_main_peak = ~np.isnan(main_peak_to_trough) & (main_peak_to_trough > ratio_thresh_mpt) if not np.isnan(ratio_thresh_mpt) else np.zeros(n_units, dtype=bool) + large_main_peak = ( + ~np.isnan(main_peak_to_trough) & (main_peak_to_trough > ratio_thresh_mpt) + if not np.isnan(ratio_thresh_mpt) + else np.zeros(n_units, dtype=bool) + ) # Combined logic: (ratio AND width conditions) OR standalone ratio # Requires at least one ratio condition AND both width conditions, OR the standalone ratio diff --git a/src/spikeinterface/metrics/quality/misc_metrics.py b/src/spikeinterface/metrics/quality/misc_metrics.py index fdcf501d08..8c6339b773 100644 --- a/src/spikeinterface/metrics/quality/misc_metrics.py +++ b/src/spikeinterface/metrics/quality/misc_metrics.py @@ -966,7 +966,7 @@ class AmplitudeCutoff(BaseMetric): "num_histogram_bins": 100, "histogram_smoothing_value": 3, "amplitudes_bins_min_ratio": 5, - "plot_details": False, + "plot_details": False, } metric_columns = {"amplitude_cutoff": float} metric_descriptions = { diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index d142b029c1..abc56d04dd 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -6,7 +6,9 @@ from spikeinterface.core.analyzer_extension_core import BaseMetric -def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smooth=True, smooth_window_frac=0.1, smooth_polyorder=3): +def get_trough_and_peak_idx( + template, min_thresh_detect_peaks_troughs=0.4, smooth=True, smooth_window_frac=0.1, smooth_polyorder=3 +): """ Detect troughs and peaks in a template waveform and return detailed information about each detected feature. @@ -106,16 +108,12 @@ def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smoot template_before = template[:main_trough_loc] # Try with original prominence - peak_locs_before, peak_props_before = find_peaks( - template_before, prominence=min_prominence, width=0 - ) + peak_locs_before, peak_props_before = find_peaks(template_before, prominence=min_prominence, width=0) # If no peaks found, try with lower prominence (keep only max peak) if len(peak_locs_before) == 0: lower_prominence = 0.075 * min_thresh_detect_peaks_troughs * np.nanmax(np.abs(template)) - peak_locs_before, peak_props_before = find_peaks( - template_before, prominence=lower_prominence, width=0 - ) + peak_locs_before, peak_props_before = find_peaks(template_before, prominence=lower_prominence, width=0) # Keep only the most prominent peak when using lower threshold if len(peak_locs_before) > 1: prominences = peak_props_before.get("prominences", np.array([])) @@ -154,16 +152,12 @@ def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smoot template_after = template[main_trough_loc:] # Try with original prominence - peak_locs_after, peak_props_after = find_peaks( - template_after, prominence=min_prominence, width=0 - ) + peak_locs_after, peak_props_after = find_peaks(template_after, prominence=min_prominence, width=0) # If no peaks found, try with lower prominence (keep only max peak) if len(peak_locs_after) == 0: lower_prominence = 0.075 * min_thresh_detect_peaks_troughs * np.nanmax(np.abs(template)) - peak_locs_after, peak_props_after = find_peaks( - template_after, prominence=lower_prominence, width=0 - ) + peak_locs_after, peak_props_after = find_peaks(template_after, prominence=lower_prominence, width=0) # Keep only the most prominent peak when using lower threshold if len(peak_locs_after) > 1: prominences = peak_props_after.get("prominences", np.array([])) @@ -220,24 +214,65 @@ def get_trough_and_peak_idx(template, min_thresh_detect_peaks_troughs=0.4, smoot # Plot all detected troughs ax.scatter(troughs["indices"], troughs["values"], c="blue", s=50, marker="v", zorder=5, label="troughs") if troughs["main_loc"] is not None: - ax.scatter(troughs["main_loc"], template[troughs["main_loc"]], c="blue", s=150, marker="v", - edgecolors="red", linewidths=2, zorder=6, label="main trough") + ax.scatter( + troughs["main_loc"], + template[troughs["main_loc"]], + c="blue", + s=150, + marker="v", + edgecolors="red", + linewidths=2, + zorder=6, + label="main trough", + ) # Plot all peaks before if len(peaks_before["indices"]) > 0: - ax.scatter(peaks_before["indices"], peaks_before["values"], c="green", s=50, marker="^", - zorder=5, label="peaks before") + ax.scatter( + peaks_before["indices"], + peaks_before["values"], + c="green", + s=50, + marker="^", + zorder=5, + label="peaks before", + ) if peaks_before["main_loc"] is not None: - ax.scatter(peaks_before["main_loc"], template[peaks_before["main_loc"]], c="green", s=150, - marker="^", edgecolors="red", linewidths=2, zorder=6, label="main peak before") + ax.scatter( + peaks_before["main_loc"], + template[peaks_before["main_loc"]], + c="green", + s=150, + marker="^", + edgecolors="red", + linewidths=2, + zorder=6, + label="main peak before", + ) # Plot all peaks after if len(peaks_after["indices"]) > 0: - ax.scatter(peaks_after["indices"], peaks_after["values"], c="orange", s=50, marker="^", - zorder=5, label="peaks after") + ax.scatter( + peaks_after["indices"], + peaks_after["values"], + c="orange", + s=50, + marker="^", + zorder=5, + label="peaks after", + ) if peaks_after["main_loc"] is not None: - ax.scatter(peaks_after["main_loc"], template[peaks_after["main_loc"]], c="orange", s=150, - marker="^", edgecolors="red", linewidths=2, zorder=6, label="main peak after") + ax.scatter( + peaks_after["main_loc"], + template[peaks_after["main_loc"]], + c="orange", + s=150, + marker="^", + edgecolors="red", + linewidths=2, + zorder=6, + label="main peak after", + ) ax.axhline(0, color="gray", ls="-", alpha=0.3) ax.set_xlabel("Sample") @@ -369,7 +404,14 @@ def safe_ratio(a, b): "peak_before_to_trough_ratio": safe_ratio(peak_before_amp, trough_amp), "peak_after_to_trough_ratio": safe_ratio(peak_after_amp, trough_amp), "peak_before_to_peak_after_ratio": safe_ratio(peak_before_amp, peak_after_amp), - "main_peak_to_trough_ratio": safe_ratio(max(peak_before_amp, peak_after_amp) if not (np.isnan(peak_before_amp) and np.isnan(peak_after_amp)) else np.nan, trough_amp), + "main_peak_to_trough_ratio": safe_ratio( + ( + max(peak_before_amp, peak_after_amp) + if not (np.isnan(peak_before_amp) and np.isnan(peak_after_amp)) + else np.nan + ), + trough_amp, + ), } return ratios @@ -455,6 +497,7 @@ def get_waveform_widths(template, sampling_frequency, troughs, peaks_before, pea - "peak_before_width_us": width of main peak before trough in microseconds - "peak_after_width_us": width of main peak after trough in microseconds """ + def get_main_width(feature_dict): if feature_dict["main_idx"] is None: return np.nan @@ -1186,9 +1229,12 @@ def _number_of_peaks_metric_function(sorting_analyzer, unit_ids, tmp_data, **met for unit_index, unit_id in enumerate(unit_ids): template_single = templates_single[unit_index] num_positive, num_negative = get_number_of_peaks( - template_single, sampling_frequency, - troughs_info[unit_id], peaks_before_info[unit_id], peaks_after_info[unit_id], - **metric_params + template_single, + sampling_frequency, + troughs_info[unit_id], + peaks_before_info[unit_id], + peaks_after_info[unit_id], + **metric_params, ) num_positive_peaks_dict[unit_id] = num_positive num_negative_peaks_dict[unit_id] = num_negative @@ -1217,9 +1263,12 @@ def _waveform_duration_metric_function(sorting_analyzer, unit_ids, tmp_data, **m for unit_index, unit_id in enumerate(unit_ids): template_single = templates_single[unit_index] value = get_waveform_duration( - template_single, sampling_frequency, - troughs_info[unit_id], peaks_before_info[unit_id], peaks_after_info[unit_id], - **metric_params + template_single, + sampling_frequency, + troughs_info[unit_id], + peaks_before_info[unit_id], + peaks_after_info[unit_id], + **metric_params, ) result[unit_id] = value return result @@ -1237,10 +1286,15 @@ class WaveformDuration(BaseMetric): def _waveform_ratios_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): - waveform_ratios_result = namedtuple("WaveformRatiosResult", [ - "peak_before_to_trough_ratio", "peak_after_to_trough_ratio", - "peak_before_to_peak_after_ratio", "main_peak_to_trough_ratio" - ]) + waveform_ratios_result = namedtuple( + "WaveformRatiosResult", + [ + "peak_before_to_trough_ratio", + "peak_after_to_trough_ratio", + "peak_before_to_peak_after_ratio", + "main_peak_to_trough_ratio", + ], + ) peak_before_to_trough = {} peak_after_to_trough = {} peak_before_to_peak_after = {} @@ -1253,8 +1307,10 @@ def _waveform_ratios_metric_function(sorting_analyzer, unit_ids, tmp_data, **met template_single = templates_single[unit_index] ratios = get_waveform_ratios( template_single, - troughs_info[unit_id], peaks_before_info[unit_id], peaks_after_info[unit_id], - **metric_params + troughs_info[unit_id], + peaks_before_info[unit_id], + peaks_after_info[unit_id], + **metric_params, ) peak_before_to_trough[unit_id] = ratios["peak_before_to_trough_ratio"] peak_after_to_trough[unit_id] = ratios["peak_after_to_trough_ratio"] @@ -1264,7 +1320,7 @@ def _waveform_ratios_metric_function(sorting_analyzer, unit_ids, tmp_data, **met peak_before_to_trough_ratio=peak_before_to_trough, peak_after_to_trough_ratio=peak_after_to_trough, peak_before_to_peak_after_ratio=peak_before_to_peak_after, - main_peak_to_trough_ratio=main_peak_to_trough + main_peak_to_trough_ratio=main_peak_to_trough, ) @@ -1288,9 +1344,9 @@ class WaveformRatios(BaseMetric): def _waveform_widths_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): - waveform_widths_result = namedtuple("WaveformWidthsResult", [ - "trough_width", "peak_before_width", "peak_after_width" - ]) + waveform_widths_result = namedtuple( + "WaveformWidthsResult", ["trough_width", "peak_before_width", "peak_after_width"] + ) trough_width_dict = {} peak_before_width_dict = {} peak_after_width_dict = {} @@ -1302,17 +1358,18 @@ def _waveform_widths_metric_function(sorting_analyzer, unit_ids, tmp_data, **met for unit_index, unit_id in enumerate(unit_ids): template_single = templates_single[unit_index] widths = get_waveform_widths( - template_single, sampling_frequency, - troughs_info[unit_id], peaks_before_info[unit_id], peaks_after_info[unit_id], - **metric_params + template_single, + sampling_frequency, + troughs_info[unit_id], + peaks_before_info[unit_id], + peaks_after_info[unit_id], + **metric_params, ) trough_width_dict[unit_id] = widths["trough_width_us"] peak_before_width_dict[unit_id] = widths["peak_before_width_us"] peak_after_width_dict[unit_id] = widths["peak_after_width_us"] return waveform_widths_result( - trough_width=trough_width_dict, - peak_before_width=peak_before_width_dict, - peak_after_width=peak_after_width_dict + trough_width=trough_width_dict, peak_before_width=peak_before_width_dict, peak_after_width=peak_after_width_dict ) @@ -1429,8 +1486,10 @@ class ExpDecay(BaseMetric): } metric_columns = {"exp_decay": float} metric_descriptions = { - "exp_decay": ("Spatial decay of the template amplitude over distance from the extremum channel (1/um). " - "Uses exponential or linear fit based on linear_fit parameter.") + "exp_decay": ( + "Spatial decay of the template amplitude over distance from the extremum channel (1/um). " + "Uses exponential or linear fit based on linear_fit parameter." + ) } needs_tmp_data = True diff --git a/src/spikeinterface/metrics/template/template_metrics.py b/src/spikeinterface/metrics/template/template_metrics.py index dfa5b6d69e..11f2a57df1 100644 --- a/src/spikeinterface/metrics/template/template_metrics.py +++ b/src/spikeinterface/metrics/template/template_metrics.py @@ -9,7 +9,7 @@ import numpy as np import warnings from copy import deepcopy -from scipy.signal import find_peaks +from scipy.signal import find_peaks from spikeinterface.core.sortinganalyzer import register_result_extension from spikeinterface.core.analyzer_extension_core import BaseMetricExtension @@ -225,10 +225,10 @@ def _prepare_data(self, sorting_analyzer, unit_ids): sampling_frequency_up = sampling_frequency troughs_dict, peaks_before_dict, peaks_after_dict = get_trough_and_peak_idx( template_upsampled, - min_thresh_detect_peaks_troughs=self.params['min_thresh_detect_peaks_troughs'], - smooth=self.params['smooth'], - smooth_window_frac=self.params['smooth_window_frac'], - smooth_polyorder=self.params['smooth_polyorder'], + min_thresh_detect_peaks_troughs=self.params["min_thresh_detect_peaks_troughs"], + smooth=self.params["smooth"], + smooth_window_frac=self.params["smooth_window_frac"], + smooth_polyorder=self.params["smooth_polyorder"], ) templates_single.append(template_upsampled) From 8467177298a7597dec3ab358ec6987d654888d97 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Thu, 8 Jan 2026 02:13:30 +0100 Subject: [PATCH 12/49] cleanup --- .gitignore | 9 +- playground2.ipynb | 1683 --------------------------------------------- 2 files changed, 1 insertion(+), 1691 deletions(-) delete mode 100644 playground2.ipynb diff --git a/.gitignore b/.gitignore index 8481107d21..4bf7f07949 100644 --- a/.gitignore +++ b/.gitignore @@ -145,11 +145,4 @@ test_folder/ # Mac OS .DS_Store test_data.json -analyzer_TDC_binary/ -CLAUDE.md -playground.ipynbd -playground.ipynb -analyzer_TDC_binary/ -spykingcircus2_output/ -kilosort4_output/ -playground2.ipynb + diff --git a/playground2.ipynb b/playground2.ipynb deleted file mode 100644 index 78e3131f5d..0000000000 --- a/playground2.ipynb +++ /dev/null @@ -1,1683 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Playground2: Kilosort + Template Metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SpikeInterface version: 0.103.3\n" - ] - } - ], - "source": [ - "from pathlib import Path\n", - "import spikeinterface.full as si\n", - "\n", - "print(f\"SpikeInterface version: {si.__version__}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jf5479/Dropbox/Python/spikeinterface/src/spikeinterface/core/base.py:1117: UserWarning: Versions are not the same. This might lead to compatibility errors. Using spikeinterface==0.101.2 is recommended\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# Check if Kilosort output already exists\n", - "\n", - "# For kilosort/phy output files we can use the read_phy\n", - "# most formats will have a read_xx that can used.\n", - "analyzer = si.load_sorting_analyzer('/Users/jf5479/Downloads/M25_D18/kilosort4_sa')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Random spikes selection\n", - "if not analyzer.has_extension(\"random_spikes\"):\n", - " print(\"Computing random_spikes...\")\n", - " analyzer.compute(\"random_spikes\", method=\"uniform\", max_spikes_per_unit=500)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Waveforms\n", - "if not analyzer.has_extension(\"waveforms\"):\n", - " print(\"Computing waveforms...\")\n", - " analyzer.compute(\"waveforms\", ms_before=1.5, ms_after=2.0, **job_kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Templates\n", - "if not analyzer.has_extension(\"templates\"):\n", - " print(\"Computing templates...\")\n", - " analyzer.compute(\"templates\", operators=[\"average\", \"median\", \"std\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "# Noise levels\n", - "if not analyzer.has_extension(\"noise_levels\"):\n", - " print(\"Computing noise_levels...\")\n", - " analyzer.compute(\"noise_levels\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute Template Metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_theil_sen.py:127: ConvergenceWarning: Maximum number of iterations 1000 reached in spatial median for TheilSen regressor.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Compute template metrics with multi-channel metrics included\n", - "# Delete the old cached extension first\n", - "\n", - "# Then recompute\n", - "analyzer.compute(\n", - " \"template_metrics\",\n", - " smooth=True,\n", - " smooth_window_frac=0.1,\n", - " smooth_polyorder=3,\n", - " min_thresh_detect_peaks_troughs=0.4\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
peak_to_trough_durationpeak_trough_ratiohalf_widthrepolarization_sloperecovery_slopenum_positive_peaksnum_negative_peakswaveform_durationpeak_before_to_trough_ratiopeak_after_to_trough_ratiopeak_before_to_peak_after_ratiomain_peak_to_trough_ratiotrough_widthpeak_before_widthpeak_after_widthwaveform_baseline_flatnessvelocity_abovevelocity_belowexp_decayspread
00.000930-0.3913770.00021748304.384356-11509.47886721930.0000000.2812500.3913770.7186180.391377339.416196NaN996.9545460.320548NaN1968.6723450.011912180.0
10.000993-0.5594530.00060719480.348714-30323.76029021890.0000001.0059800.5594531.7981481.005980804.599484752.785561299.4392150.445078-1150.976105395.3898700.017526180.0
20.000467-0.1963390.00021054427.516428-3144.79143221466.6666670.1667250.1963390.8491670.196339250.587237567.387928828.8948980.18000770.256344-850.0000000.02291475.0
30.000837-0.3314570.00020387707.717569-9623.44126221836.6666670.2419810.3314570.7300530.331457284.207039NaN1051.9256660.2736701104.0614451500.5447720.016225180.0
40.000597-0.4317540.00034357011.717748-11030.35708521596.6666670.1797890.4317540.4164160.431754377.873485412.239605584.7261830.184279-303.266685NaN0.03459460.0
...............................................................
3710.000927-0.6400450.00050332619.897138-16025.03022321926.6666670.3510540.6400450.5484840.640045602.278842253.236408727.3137330.366593NaN1285.624797NaN180.0
3720.000653-0.1747070.000150230009.965415-14782.37112621653.3333330.0752240.1747070.4305710.174707196.67947380.050997647.2116410.098756NaN371.1696490.03279045.0
3730.000773-0.3511170.00024033768.217742-4993.29218021773.3333330.2203690.3511170.6276220.351117302.944762200.943300724.3435000.226603-732.014874-387.9980150.01223175.0
3740.000723-0.3736360.00024023095.225222-5458.67840121723.3333330.1113260.3736360.2979530.373636356.978504157.402821777.1028030.330019NaN-128.6025660.01187060.0
3750.000937-0.2377360.00044320061.279064-3433.23116321936.6666670.7693910.2377363.2363200.769391563.349356836.898954525.6173330.515874NaNNaN0.00166690.0
\n", - "

376 rows × 20 columns

\n", - "
" - ], - "text/plain": [ - " peak_to_trough_duration peak_trough_ratio half_width \\\n", - "0 0.000930 -0.391377 0.000217 \n", - "1 0.000993 -0.559453 0.000607 \n", - "2 0.000467 -0.196339 0.000210 \n", - "3 0.000837 -0.331457 0.000203 \n", - "4 0.000597 -0.431754 0.000343 \n", - ".. ... ... ... \n", - "371 0.000927 -0.640045 0.000503 \n", - "372 0.000653 -0.174707 0.000150 \n", - "373 0.000773 -0.351117 0.000240 \n", - "374 0.000723 -0.373636 0.000240 \n", - "375 0.000937 -0.237736 0.000443 \n", - "\n", - " repolarization_slope recovery_slope num_positive_peaks \\\n", - "0 48304.384356 -11509.478867 2 \n", - "1 19480.348714 -30323.760290 2 \n", - "2 54427.516428 -3144.791432 2 \n", - "3 87707.717569 -9623.441262 2 \n", - "4 57011.717748 -11030.357085 2 \n", - ".. ... ... ... \n", - "371 32619.897138 -16025.030223 2 \n", - "372 230009.965415 -14782.371126 2 \n", - "373 33768.217742 -4993.292180 2 \n", - "374 23095.225222 -5458.678401 2 \n", - "375 20061.279064 -3433.231163 2 \n", - "\n", - " num_negative_peaks waveform_duration peak_before_to_trough_ratio \\\n", - "0 1 930.000000 0.281250 \n", - "1 1 890.000000 1.005980 \n", - "2 1 466.666667 0.166725 \n", - "3 1 836.666667 0.241981 \n", - "4 1 596.666667 0.179789 \n", - ".. ... ... ... \n", - "371 1 926.666667 0.351054 \n", - "372 1 653.333333 0.075224 \n", - "373 1 773.333333 0.220369 \n", - "374 1 723.333333 0.111326 \n", - "375 1 936.666667 0.769391 \n", - "\n", - " peak_after_to_trough_ratio peak_before_to_peak_after_ratio \\\n", - "0 0.391377 0.718618 \n", - "1 0.559453 1.798148 \n", - "2 0.196339 0.849167 \n", - "3 0.331457 0.730053 \n", - "4 0.431754 0.416416 \n", - ".. ... ... \n", - "371 0.640045 0.548484 \n", - "372 0.174707 0.430571 \n", - "373 0.351117 0.627622 \n", - "374 0.373636 0.297953 \n", - "375 0.237736 3.236320 \n", - "\n", - " main_peak_to_trough_ratio trough_width peak_before_width \\\n", - "0 0.391377 339.416196 NaN \n", - "1 1.005980 804.599484 752.785561 \n", - "2 0.196339 250.587237 567.387928 \n", - "3 0.331457 284.207039 NaN \n", - "4 0.431754 377.873485 412.239605 \n", - ".. ... ... ... \n", - "371 0.640045 602.278842 253.236408 \n", - "372 0.174707 196.679473 80.050997 \n", - "373 0.351117 302.944762 200.943300 \n", - "374 0.373636 356.978504 157.402821 \n", - "375 0.769391 563.349356 836.898954 \n", - "\n", - " peak_after_width waveform_baseline_flatness velocity_above \\\n", - "0 996.954546 0.320548 NaN \n", - "1 299.439215 0.445078 -1150.976105 \n", - "2 828.894898 0.180007 70.256344 \n", - "3 1051.925666 0.273670 1104.061445 \n", - "4 584.726183 0.184279 -303.266685 \n", - ".. ... ... ... \n", - "371 727.313733 0.366593 NaN \n", - "372 647.211641 0.098756 NaN \n", - "373 724.343500 0.226603 -732.014874 \n", - "374 777.102803 0.330019 NaN \n", - "375 525.617333 0.515874 NaN \n", - "\n", - " velocity_below exp_decay spread \n", - "0 1968.672345 0.011912 180.0 \n", - "1 395.389870 0.017526 180.0 \n", - "2 -850.000000 0.022914 75.0 \n", - "3 1500.544772 0.016225 180.0 \n", - "4 NaN 0.034594 60.0 \n", - ".. ... ... ... \n", - "371 1285.624797 NaN 180.0 \n", - "372 371.169649 0.032790 45.0 \n", - "373 -387.998015 0.012231 75.0 \n", - "374 -128.602566 0.011870 60.0 \n", - "375 NaN 0.001666 90.0 \n", - "\n", - "[376 rows x 20 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "\n", - "# Get the metrics as a DataFrame\n", - "template_metrics = analyzer.get_extension(\"template_metrics\").get_data()\n", - "template_metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Spike amplitudes\n", - "if not analyzer.has_extension(\"spike_amplitudes\"):\n", - " print(\"Computing spike_amplitudes...\")\n", - " analyzer.compute(\"spike_amplitudes\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Correlograms\n", - "if not analyzer.has_extension(\"correlograms\"):\n", - " print(\"Computing correlograms...\")\n", - " analyzer.compute(\"correlograms\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jf5479/Dropbox/Python/spikeinterface/src/spikeinterface/core/analyzer_extension_core.py:1032: UserWarning: Metric sd_ratio requires a recording. Since the SortingAnalyzer has no recording, the metric will not be computed.\n", - " warnings.warn(\n", - "/Users/jf5479/Dropbox/Python/spikeinterface/src/spikeinterface/core/analyzer_extension_core.py:1040: UserWarning: The following metrics will not be computed due to missing dependencies: ['mahalanobis', 'd_prime', 'sd_ratio', 'silhouette', 'nearest_neighbor']\n", - " warnings.warn(\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n", - " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n", - " arrmean = um.true_divide(arrmean, div, out=arrmean,\n", - "/Users/jf5479/anaconda3/lib/python3.12/site-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in divide\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/Users/jf5479/Dropbox/Python/spikeinterface/src/spikeinterface/metrics/quality/misc_metrics.py:957: UserWarning: Some units have too few spikes : amplitude_cutoff is set to NaN\n", - " warnings.warn(f\"Some units have too few spikes : amplitude_cutoff is set to NaN\")\n", - "/Users/jf5479/Dropbox/Python/spikeinterface/src/spikeinterface/metrics/quality/misc_metrics.py:1903: UserWarning: Only one bin is selected as the reference region, and thus the standard deviation cannot be computed. Please increase high_quantile. Setting noise cutoff to NaN\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "analyzer.compute(\n", - " \"quality_metrics\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
num_spikesfiring_ratepresence_ratiosnrsnr_bombcellisi_violations_ratioisi_violations_countrp_contaminationrp_violationssliding_rp_violation...amplitude_cv_medianamplitude_cv_rangeamplitude_cutoffnoise_cutoffnoise_ratioamplitude_mediandrift_ptpdrift_stddrift_madsd_ratio
07086016.5695651.0000002.99790221.7941191.48281352231.0000004899NaN...NaNNaN0.000089-0.1296270.041261-17.3550002.7816010.6232760.4677532.223717
1352198.2354431.0000001.51543219.9249990.9354908141.000000627NaN...2.0644981.4890930.000082-0.2257930.024113-5.2649993.0830420.6720900.5207581.821735
2229715.3714291.0000002.45997122.9500010.083747310.107035250.105...0.4618430.4583290.000011-0.0387630.015673-13.8449991.7761460.4461810.3843741.009472
3385569.0157521.0000003.15045517.3888891.84593119251.0000001794NaN...0.9339890.4711610.000179-0.0460650.042872-18.7199993.0463260.6770800.5020922.171387
4256005.9861821.0000003.08828625.3478280.7460763431.000000285NaN...NaNNaN0.000058-0.2211970.018106-17.1600004.1211380.5470340.8920431.144326
..................................................................
3716850.1601770.4647892.36218648.705883689.6257822271.000000204NaN...NaNNaN0.000445-0.3914260.013178-9.945000NaNNaNNaN1.711817
372318507.4476531.00000010.52267928.5909100.00140510.00211010.005...NaNNaN0.000108-0.0909040.056504-57.7199974.3264930.6478870.5678931.075722
373139353.2584941.0000001.51146432.8260880.249594340.168328140.175...1.1680651.9220040.000028-0.1690450.013893-7.6050001.8443680.2252730.3095081.390354
374135673.1724431.0000001.29879833.6153870.727996940.449561300.225...1.5581933.0982560.000019-0.1334550.007452-7.9949991.4903450.2004800.2461631.959414
375129103.0188131.0000002.02738132.6285711.7105912001.00000081NaN...1.8485942.5406190.000179-0.0111030.014755-5.2649992.9834350.4135900.3642312.308002
\n", - "

376 rows × 24 columns

\n", - "
" - ], - "text/plain": [ - " num_spikes firing_rate presence_ratio snr snr_bombcell \\\n", - "0 70860 16.569565 1.000000 2.997902 21.794119 \n", - "1 35219 8.235443 1.000000 1.515432 19.924999 \n", - "2 22971 5.371429 1.000000 2.459971 22.950001 \n", - "3 38556 9.015752 1.000000 3.150455 17.388889 \n", - "4 25600 5.986182 1.000000 3.088286 25.347828 \n", - ".. ... ... ... ... ... \n", - "371 685 0.160177 0.464789 2.362186 48.705883 \n", - "372 31850 7.447653 1.000000 10.522679 28.590910 \n", - "373 13935 3.258494 1.000000 1.511464 32.826088 \n", - "374 13567 3.172443 1.000000 1.298798 33.615387 \n", - "375 12910 3.018813 1.000000 2.027381 32.628571 \n", - "\n", - " isi_violations_ratio isi_violations_count rp_contamination \\\n", - "0 1.482813 5223 1.000000 \n", - "1 0.935490 814 1.000000 \n", - "2 0.083747 31 0.107035 \n", - "3 1.845931 1925 1.000000 \n", - "4 0.746076 343 1.000000 \n", - ".. ... ... ... \n", - "371 689.625782 227 1.000000 \n", - "372 0.001405 1 0.002110 \n", - "373 0.249594 34 0.168328 \n", - "374 0.727996 94 0.449561 \n", - "375 1.710591 200 1.000000 \n", - "\n", - " rp_violations sliding_rp_violation ... amplitude_cv_median \\\n", - "0 4899 NaN ... NaN \n", - "1 627 NaN ... 2.064498 \n", - "2 25 0.105 ... 0.461843 \n", - "3 1794 NaN ... 0.933989 \n", - "4 285 NaN ... NaN \n", - ".. ... ... ... ... \n", - "371 204 NaN ... NaN \n", - "372 1 0.005 ... NaN \n", - "373 14 0.175 ... 1.168065 \n", - "374 30 0.225 ... 1.558193 \n", - "375 81 NaN ... 1.848594 \n", - "\n", - " amplitude_cv_range amplitude_cutoff noise_cutoff noise_ratio \\\n", - "0 NaN 0.000089 -0.129627 0.041261 \n", - "1 1.489093 0.000082 -0.225793 0.024113 \n", - "2 0.458329 0.000011 -0.038763 0.015673 \n", - "3 0.471161 0.000179 -0.046065 0.042872 \n", - "4 NaN 0.000058 -0.221197 0.018106 \n", - ".. ... ... ... ... \n", - "371 NaN 0.000445 -0.391426 0.013178 \n", - "372 NaN 0.000108 -0.090904 0.056504 \n", - "373 1.922004 0.000028 -0.169045 0.013893 \n", - "374 3.098256 0.000019 -0.133455 0.007452 \n", - "375 2.540619 0.000179 -0.011103 0.014755 \n", - "\n", - " amplitude_median drift_ptp drift_std drift_mad sd_ratio \n", - "0 -17.355000 2.781601 0.623276 0.467753 2.223717 \n", - "1 -5.264999 3.083042 0.672090 0.520758 1.821735 \n", - "2 -13.844999 1.776146 0.446181 0.384374 1.009472 \n", - "3 -18.719999 3.046326 0.677080 0.502092 2.171387 \n", - "4 -17.160000 4.121138 0.547034 0.892043 1.144326 \n", - ".. ... ... ... ... ... \n", - "371 -9.945000 NaN NaN NaN 1.711817 \n", - "372 -57.719997 4.326493 0.647887 0.567893 1.075722 \n", - "373 -7.605000 1.844368 0.225273 0.309508 1.390354 \n", - "374 -7.994999 1.490345 0.200480 0.246163 1.959414 \n", - "375 -5.264999 2.983435 0.413590 0.364231 2.308002 \n", - "\n", - "[376 rows x 24 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Quality metrics\n", - "if not analyzer.has_extension(\"quality_metrics\"):\n", - " print(\"Computing quality_metrics...\")\n", - " analyzer.compute(\"quality_metrics\")\n", - "\n", - "quality_metrics = analyzer.get_extension(\"quality_metrics\").get_data()\n", - "quality_metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
num_spikesfiring_ratepresence_ratiosnrsnr_bombcellisi_violations_ratioisi_violations_countrp_contaminationrp_violationssliding_rp_violation...amplitude_cv_medianamplitude_cv_rangeamplitude_cutoffnoise_cutoffnoise_ratioamplitude_mediandrift_ptpdrift_stddrift_madsd_ratio
07086016.5695651.0000002.99790221.7941191.48281352231.0000004899NaN...NaNNaN0.000089-0.1296270.041261-17.3550002.7816010.6232760.4677532.223717
1352198.2354431.0000001.51543219.9249990.9354908141.000000627NaN...2.0644981.4890930.000082-0.2257930.024113-5.2649993.0830420.6720900.5207581.821735
2229715.3714291.0000002.45997122.9500010.083747310.107035250.105...0.4618430.4583290.000011-0.0387630.015673-13.8449991.7761460.4461810.3843741.009472
3385569.0157521.0000003.15045517.3888891.84593119251.0000001794NaN...0.9339890.4711610.000179-0.0460650.042872-18.7199993.0463260.6770800.5020922.171387
4256005.9861821.0000003.08828625.3478280.7460763431.000000285NaN...NaNNaN0.000058-0.2211970.018106-17.1600004.1211380.5470340.8920431.144326
..................................................................
3716850.1601770.4647892.36218648.705883689.6257822271.000000204NaN...NaNNaN0.000445-0.3914260.013178-9.945000NaNNaNNaN1.711817
372318507.4476531.00000010.52267928.5909100.00140510.00211010.005...NaNNaN0.000108-0.0909040.056504-57.7199974.3264930.6478870.5678931.075722
373139353.2584941.0000001.51146432.8260880.249594340.168328140.175...1.1680651.9220040.000028-0.1690450.013893-7.6050001.8443680.2252730.3095081.390354
374135673.1724431.0000001.29879833.6153870.727996940.449561300.225...1.5581933.0982560.000019-0.1334550.007452-7.9949991.4903450.2004800.2461631.959414
375129103.0188131.0000002.02738132.6285711.7105912001.00000081NaN...1.8485942.5406190.000179-0.0111030.014755-5.2649992.9834350.4135900.3642312.308002
\n", - "

376 rows × 24 columns

\n", - "
" - ], - "text/plain": [ - " num_spikes firing_rate presence_ratio snr snr_bombcell \\\n", - "0 70860 16.569565 1.000000 2.997902 21.794119 \n", - "1 35219 8.235443 1.000000 1.515432 19.924999 \n", - "2 22971 5.371429 1.000000 2.459971 22.950001 \n", - "3 38556 9.015752 1.000000 3.150455 17.388889 \n", - "4 25600 5.986182 1.000000 3.088286 25.347828 \n", - ".. ... ... ... ... ... \n", - "371 685 0.160177 0.464789 2.362186 48.705883 \n", - "372 31850 7.447653 1.000000 10.522679 28.590910 \n", - "373 13935 3.258494 1.000000 1.511464 32.826088 \n", - "374 13567 3.172443 1.000000 1.298798 33.615387 \n", - "375 12910 3.018813 1.000000 2.027381 32.628571 \n", - "\n", - " isi_violations_ratio isi_violations_count rp_contamination \\\n", - "0 1.482813 5223 1.000000 \n", - "1 0.935490 814 1.000000 \n", - "2 0.083747 31 0.107035 \n", - "3 1.845931 1925 1.000000 \n", - "4 0.746076 343 1.000000 \n", - ".. ... ... ... \n", - "371 689.625782 227 1.000000 \n", - "372 0.001405 1 0.002110 \n", - "373 0.249594 34 0.168328 \n", - "374 0.727996 94 0.449561 \n", - "375 1.710591 200 1.000000 \n", - "\n", - " rp_violations sliding_rp_violation ... amplitude_cv_median \\\n", - "0 4899 NaN ... NaN \n", - "1 627 NaN ... 2.064498 \n", - "2 25 0.105 ... 0.461843 \n", - "3 1794 NaN ... 0.933989 \n", - "4 285 NaN ... NaN \n", - ".. ... ... ... ... \n", - "371 204 NaN ... NaN \n", - "372 1 0.005 ... NaN \n", - "373 14 0.175 ... 1.168065 \n", - "374 30 0.225 ... 1.558193 \n", - "375 81 NaN ... 1.848594 \n", - "\n", - " amplitude_cv_range amplitude_cutoff noise_cutoff noise_ratio \\\n", - "0 NaN 0.000089 -0.129627 0.041261 \n", - "1 1.489093 0.000082 -0.225793 0.024113 \n", - "2 0.458329 0.000011 -0.038763 0.015673 \n", - "3 0.471161 0.000179 -0.046065 0.042872 \n", - "4 NaN 0.000058 -0.221197 0.018106 \n", - ".. ... ... ... ... \n", - "371 NaN 0.000445 -0.391426 0.013178 \n", - "372 NaN 0.000108 -0.090904 0.056504 \n", - "373 1.922004 0.000028 -0.169045 0.013893 \n", - "374 3.098256 0.000019 -0.133455 0.007452 \n", - "375 2.540619 0.000179 -0.011103 0.014755 \n", - "\n", - " amplitude_median drift_ptp drift_std drift_mad sd_ratio \n", - "0 -17.355000 2.781601 0.623276 0.467753 2.223717 \n", - "1 -5.264999 3.083042 0.672090 0.520758 1.821735 \n", - "2 -13.844999 1.776146 0.446181 0.384374 1.009472 \n", - "3 -18.719999 3.046326 0.677080 0.502092 2.171387 \n", - "4 -17.160000 4.121138 0.547034 0.892043 1.144326 \n", - ".. ... ... ... ... ... \n", - "371 -9.945000 NaN NaN NaN 1.711817 \n", - "372 -57.719997 4.326493 0.647887 0.567893 1.075722 \n", - "373 -7.605000 1.844368 0.225273 0.309508 1.390354 \n", - "374 -7.994999 1.490345 0.200480 0.246163 1.959414 \n", - "375 -5.264999 2.983435 0.413590 0.364231 2.308002 \n", - "\n", - "[376 rows x 24 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import spikeinterface.comparison as sc \n", - "# Get metrics from SortingAnalyzer\n", - "qm = analyzer.get_extension(\"quality_metrics\").get_data()\n", - "tm = analyzer.get_extension(\"template_metrics\").get_data()\n", - "metrics = pd.concat([qm, tm], axis=1)\n", - "\n", - "qm" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'total_units': 376, 'counts': {'NOISE': 112, 'GOOD': 55, 'MUA': 183, 'NON_SOMA': 26}, 'percentages': {'NOISE': 29.8, 'GOOD': 14.6, 'MUA': 48.7, 'NON_SOMA': 6.9}}\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABjoAAAXRCAYAAAAntUSNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVxUZfs/8M+wDfsoCgzI5oZ75hailWSKkrlkLmUZlI/Lo2lGVpqlWCpqpZaZaY+5oWlmmv40FCswc0lRH00NTQGB2FRkANm5f3/wZR5HBphhmfXzfr3OK2fOPedc9+F0rjlznXNuiRBCgIiIiIiIiIiIiIiIyAhZ6DsAIiIiIiIiIiIiIiKi+mKhg4iIiIiIiIiIiIiIjBYLHUREREREREREREREZLRY6CAiIiIiIiIiIiIiIqPFQgcRERERERERERERERktFjqIiIiIiIiIiIiIiMhosdBBRERERERERERERERGi4UOIiIiIiIiIiIiIiIyWix0EBERERERERERERGR0WKho56EEFAoFBBC6DsUIiIyIswfRERUX8whRERUH8wfRGQOWOiop7y8PMhkMuTl5Wn1uZUrgYiIyv8SEZFhWbduHR555BE4OzvD2dkZgYGB+Omnn5TzhRCIiIiAp6cn7OzsEBQUhMuXL2u1DuYPIiKqr3rlECYQ0hPuekSGo77nIES6xLxBDSURLOfWi0KhgEwmQ25uLpydnTX+nJcXkJYGtGoFpKY2YYBERKS1AwcOwNLSEu3atQMAbNmyBR9//DHOnz+PLl26YPny5ViyZAk2b94Mf39/LF68GMeOHUNCQgKcnJw0WgfzBxER1Ve9cggTCOkJdz0iw1HfcxAiXWLeoIbiHR1ERET/Z/jw4XjmmWfg7+8Pf39/LFmyBI6Ojjh16hSEEFi9ejXmz5+P0aNHo2vXrtiyZQvu37+PHTt26Dt0IiIiIiIiIiKzxUIHERGRGuXl5di5cycKCgoQGBiIxMREZGRkIDg4WNlGKpViwIABOHHiRI3LKS4uhkKhUJmIiIiIiIiIiKjxsNBBRET0gEuXLsHR0RFSqRTTpk3D3r170blzZ2RkZAAA3N3dVdq7u7sr56kTGRkJmUymnLy9vZs0fiIiIiIiIiIic2Ol7wDMzbBhwN27gIuLviMhMh5CCJSVlaG8vFzfoRgda2trWFpa6jsMo9KhQwdcuHAB9+7dw549exAaGoq4uDjlfIlEotJeCFHtvQfNmzcP4eHhytcKhaJexQ7mD6L6KS8vR2lpqb7DMDrMHyaECYT0pKG7Ho/fxon5g4jqy9C/sjAvNb2G5hCTHIx83bp1WLduHZKSkgAAXbp0wYIFCxASEgKg8kepRYsWYcOGDcjJyUFAQADWrl2LLl26aLwODuREpBslJSVIT0/H/fv39R2KUZJIJPDy8oKjo6O+QzFagwYNQtu2bfHuu++ibdu2OHfuHHr06KGcP3LkSDRr1gxbtmzRaHnMH0S6k5+fj9TUVJjg190mx/xhmJhDyFzw+G28mD8ME/MHUcMwL+lGQ3OISd7R4eXlhWXLlqFdu3YAgC1btmDkyJE4f/48unTpghUrVmDlypXYvHkz/P39sXjxYgwePBgJCQlwcnLSc/REVKWiogKJiYmwtLSEp6cnbGxsar1ynlQJIZCdnY3U1FS0b9+eV1bVkxACxcXFaN26NeRyOWJiYpSFjpKSEsTFxWH58uV6jpKIHlZeXo7U1FTY29vD1dWV+UMLzB9EpE88fhsv5g8iMkXMS7rRGDnEJAsdw4cPV3m9ZMkSrFu3DqdOnULnzp2xevVqzJ8/H6NHjwZQWQhxd3fHjh07MHXqVH2ETERqlJSUoKKiAt7e3rC3t9d3OEbJ1dUVSUlJKC0t5YmGBt577z2EhITA29sbeXl52LlzJ2JjYxEdHQ2JRILZs2dj6dKlaN++Pdq3b4+lS5fC3t4eEyZM0HfoRPSQ0tJSCCHg6uoKOzs7fYdjdMwpf0RGRuKHH37AX3/9BTs7O/Tr1w/Lly9Hhw4dlG00uSO8uLgYc+bMwbfffovCwkI8/fTT+PLLL+Hl5aVsk5OTg1mzZmH//v0AgBEjRmDNmjVo1qyZzvpLZOh4/DZu5pQ/iMg8MC/pTkNziMkPRl5eXo6dO3eioKAAgYGBSExMREZGBoKDg5VtpFIpBgwYgBMnTtS4nOLiYigUCpWJiHTDwsLkD1VNhlcaaCczMxMTJ05Ehw4d8PTTT+P06dOIjo7G4MGDAQDvvPMOZs+ejenTp6N3795IS0vDkSNHeDcgkQHjcbB+zGm7xcXFYcaMGTh16hRiYmJQVlaG4OBgFBQUKNtU3RH+xRdf4MyZM5DL5Rg8eDDy8vKUbWbPno29e/di586dOH78OPLz8/Hss8+qjDE2YcIEXLhwAdHR0YiOjsaFCxcwceJEnfaXyFiY03HIlPDvRkSmise3ptfQbWySd3QAwKVLlxAYGIiioiI4Ojpi79696Ny5s7KY4e7urtLe3d0dycnJNS4vMjISixYtanBcvXsD//xTjhYtSrFvX1qN7ZydneHq6trg9RERkeY2btxY63yJRIKIiAhEREToJqAHaJo/AOYQIiJtREdHq7zetGkT3NzcEB8fjyeffBJCiDrvCM/NzcXGjRuxbds2DBo0CAAQFRUFb29vHD16FEOGDMHVq1cRHR2NU6dOISAgAADw9ddfIzAwEAkJCSp3kDSq3r2BjAxALgfOnm2adRiB7OzsOi9WY/5sXNz1iKg+eLw2X8wb1FAmW+jo0KEDLly4gHv37mHPnj0IDQ1FXFyccv7DFSIhRK1Vo3nz5iE8PFz5WqFQwNvbW+u4/vmnHOnplrh95y5eeG1aje1cnOwRtek/PHATkVqff/45NmzYAAsLC1hYWGDevHkYP368vsOiJlSVP3Lu3Mbs18bV2lbq1ALrNm1nDiGiag4ePIiFCxfi0qVLmDlzJj755BN9h2RwcnNzAQAuLi4AUOcd4VOnTkV8fDxKS0tV2nh6eqJr1644ceIEhgwZgpMnT0ImkymLHADQt29fyGQynDhxQm2ho7i4GMXFxcrX9bqrPCMDSKu9QG7qsrOzMXHSRNwtuFtrOxcHF2zbuI35s5GY66539uxZrFq1Ctu3b2+U5d2/fx+TJk3CmTNnYGFhgWXLlimLrg87ffo0pk6divv378Pb2xtRUVHw8PAAAFy/fh2hoaG4ffs2mjVrhs2bN6Nz584AgKysLLzyyiu4ceMGpFIpvvrqKzz++OMAgG+++QarVq3C1atXsXr1arz++uuN0i8idbKzszHl5YkozKn9eG3X3AUboni8NjXmmjeMVUVFBd544w0cOnQIEokE4eHhmD59utq2fn5+sLW1ha2tLQA02W9YJlvosLGxUQ5G3rt3b5w5cwafffYZ3n33XQBARkaGMuEDlYn94bs8HiSVSiGVShscV0WFAABY2NjBb5j6P37B3Uxkn9wDhULBgzYRqdWlSxf8/vvvkMlkSElJQc+ePdG3b1/4+vrqOzRqIlX5w1FqgdWj5DW2S7lzH5/+doc5hIjUat++PTZu3Ijdu3ejqKhI3+EYHCEEwsPD8fjjj6Nr164AKs8bgNrvCM/IyICNjQ2aN29erU3V5zMyMuDm5lZtnW5ubso2D2usu8rNnUKhwN2Cu2j1TCs4ujmqbZOflY+0Q2nMn9RgvXv3brQiBwB88sknkEql+Pvvv5GYmIjAwEA89dRT1Y43Qgi89NJL+M9//oOgoCB88sknCA8Px7fffgsAmDp1KqZMmYKwsDB8//33mDRpEk6ePAkAmDt3Lvr27Yvo6GicOXMGY8aMwY0bN2BlZYVevXrhu+++Q2RkZKP1iagmCoUChTl38ZpcDg8H9cfr9IJ8fJORweM1kZ5FRUXhypUruHbtGnJzc9GzZ08MHDgQHTt2VNv++++/V36/bipm8+B7IQSKi4vRunVryOVyxMTEKOeVlJQgLi4O/fr101k8FhaWcHbzUjs5uNRccCEiYOVKwMur7mnEiOqfHTGi5vYrV2q2folEgsjISDz22GNo06YNjh49innz5qFHjx7o0qULLl++DKDyB42nnnoKvXr1QpcuXTBr1iwIUflj9eTJkzFz5kwAwN27d9G2bVscP35c423w9NNPQyaTAQC8vb3h7u6OlJQUjT9PxsvSQoK27o41Tt4t7PUdIpHBYv4A/P390b17d1hZmez1Tg3y+uuv4+LFi8ofBh+k7R3h6tqoa1/bcubNm4fc3FzlxFzfMI5ujpB5ytRONRVAyEA01QFci4O4psfw2NhY9O7dGwCQlJSEli1bYsGCBejVqxfatWuHQ4cOadX1Xbt2YcaMGQCA1q1b48knn8SPP/5Yrd3Zs2chlUoRFBQEoLKwsW/fPpSWliIrKwvnzp3Dyy+/DAB4/vnnkZiYiKSkJADAd999p1xHnz594O7urswt3bt3R6dOnThuIumUh4Mj/GQytVNNBRAiXeJ5RWV+mjZtGiwtLeHi4oJx48Zh586dGn++KZjkGc57772HkJAQeHt7Iy8vDzt37kRsbCyio6MhkUgwe/ZsLF26FO3bt0f79u2xdOlS2NvbY8KECfoOnYg0oFBodjujuqfLZWfX/Fltngbh7OyMP/74A7t378bIkSOVVzmtWLECS5YswY4dO9CsWTMcOHAAjo6OKC8vx8iRI7Fnzx6MGTMGa9asQWBgIHbv3o2tW7di8uTJytvDZ82ahWPHjqld7/r161UeeQEAR48eRU5ODnr16qV5B4iIzBDzR4DaeVRp5syZ2L9/P44dOwYvLy/l+3J55V10td0RLpfLUVJSgpycHJWrrLOyspQXU8nlcmRmZlZbb3Z2do13ljfWXeVERq+pDuBVy9aQJsfwh925cwe9evXChx9+iOjoaLzxxht45plnAABjxozB33//rXZdBw4cgLe3N27duqVy17afnx9u3bpVrf3D7ZycnODk5IT09HRkZ2fD09NTWeSWSCTw8fHBrVu34OTkhIqKCpUr42taBxERVeJ5RYDa/HS2lsFVXnrpJVRUVCAgIACRkZFNckeWSRY6MjMzMXHiRKSnp0Mmk+GRRx5BdHQ0Bg8eDAB45513UFhYiOnTpyMnJwcBAQE4cuQInJyc9Bw5EWnC2Rlo1aruduqOma6uNX/W2VnzGKqeJdizZ09YWFhg2LBhAIBevXrhhx9+AFD5vMJ3330Xx48fhxACWVlZePTRRzFmzBjY2tpi9+7d6NWrFwIDA5WP1QMqx9/Q1KVLl/Dqq69i165dsLOz07wDRERmiPmD1BFCYObMmdi7dy9iY2PRunVrlfkP3hHeo0cPAP+7I3z58uUAKv9+1tbWiImJwbhxleMopaen488//8SKFSsAAIGBgcjNzcUff/yBxx57DEDl8/Rzc3N1emc51ay0pFT5OLLaaDIILgfTbWRNdQCvWraGNDmGP8zBwQEjR44EUHkcuHHjhnLe999/r9F6H7zrq+pK3LraPdy2vvOIiKg6nldU0jQ/HTt2DD4+PigtLcX777+P0NBQre9w1IRJFjo2btxY63yJRIKIiAhEREToJiAialTh4ZVTfezf3zgxVA2gZGlpqXKlpaWlJcrKygAAK1euxJ07d3D69GnY2toiPDxc5Xnof/31FxwcHJCVlYWSkhLlcjS9IvfKlSt49tln8c033yir7kREVDPmD97Roc6MGTOwY8cO/Pjjj3ByclKOlyGTyWBnZ6fRHeEymQyTJk3CW2+9hRYtWsDFxQVz5sxBt27dMGjQIABAp06dMHToUEyePBnr168HAEyZMgXPPvus2oHISbeKFEVIupmE8AXhsJHa1Nq2rkHLOfh5EzCEAzg0O4bX9JmqduXl5crXmtzR4ePjg6SkJOV+kpycrLwj5EFV7ark5eUhLy8PHh4esLW1RWpqKsrKymBlZQUhBFJSUuDj44MWLVoAqNxvH1yHj4+PJpuEiMgsGUJa0vd5RVXe6dOnD4Dac0fV+9bW1pg9ezb8/f0b2Hv1TLLQQURkCHJyciCXy2Fra4vMzEzs3r1bWXG/desWpk+fjqNHj+Lzzz/H7NmzsW7dOgCaVc6vXr2KZ555Bhs2bFDerUZERKahKfMHVVe1/aqea19l06ZNCAsLA6DZHeGrVq2ClZUVxo0bh8LCQjz99NPYvHkzLC0tlW22b9+OWbNmITg4GAAwYsQIfPHFF03bQdJIaWEpKiwrIA+Rw9Wn5sKDJoOWc/Bz0pQmd3SMHTsWa9euxebNm5GYmIi4uDh89dVX1dr16tULRUVFiI2NRVBQENavX49Ro0bB2toabm5u6NGjB6KiohAWFoY9e/bAz88Pfn5+KuuIiIjAmTNnkJGRwQupiIhMQFOeV4wdOxbr16/H6NGjkZubi127diE6Orpau4KCApSWlqJZs2YAgG+//VZ5l3RjY6GDiKiJzJo1C2PHjsWjjz6KVq1aKa/oLCsrwwsvvICPPvoInTt3xurVqxEYGIhdu3YpE44my87NzcW7776rvL1w+fLlGDJkSJP1h4iIdKMp80dsbCxefvllKBQKCCGwc+dOfPnllxihbqREM6HJI1o0uSPc1tYWa9aswZo1a2ps4+LigqioqPqESTri0NIBMk9ZoyyravBzooZ4++238dprr6Fdu3awsLDA2rVr4eLiAgD46quv8M8//+DDDz+EhYUFoqKiMG3aNBQWFqJVq1Yqx5v169cjLCwMS5cuhbOzM7Zs2aKct3z5ckycOBHt27eHjY0Ntm3bphzPIyoqCnPnzkVOTg5+/PFHLFu2DAcOHGiyH6mIiKjxNOV5xcSJE3HmzBnl3Rlvv/02OnXqBADYv38/9u/fj//85z/IzMzE888/j/Lycggh0KZNG2zdurVJ+stCBxGRlh78QcTPzw+3b99Wvg4KClIOvuTr64s//vhD7TJOnDih/LetrS3Onz+vVQwxMTFatSciIv0zhPwRFBSE1NRUrT5DRESaH8Mf/PfD7RwdHbUe/8LBwQG7du1SO2/atGkqrwMDA/Hf//5XbdsOHTrg5MmTaue5u7vjyJEjaue9/PLLePnll7WImIiImpohnFdYWlpi7dq1aueNGDFCeSFVmzZttF52fbHQoWPvvnsHn63bDvdeT+g7FCIiMiLvvnsHm9dtQNhj0robExERVVmxArh/H7C313ckZGa46xERkTaYN6ihWOjQsREj8rFj30/w6dEagJe+wyEiIiMxYkQ+ftm3DyN6yQGof943ERFRNf83YDqRrnHXIyIibTBvUENZ6DsAIqK6aHt7N/0Ptx0RmTseB+uH242IiIiI6H/4/bjpNXQbs9BBRAbL2toaAHD//n09R2K8SkpKAFQ+O5GIyJxUHfeqjoOkHeYPIiIiIiKeV+hSQ89B+OgqHbt50xqFBb7Iy3aEs5u+oyEybJaWlmjWrBmysrIAAPb29pBIJHqOynhUVFQgOzsb9vb2sLLi4d7Y3bxpjbyCNriZ1Rxt3XP1HQ6RwbOysoK9vT2ys7NhbW0NCwte36Mp5g8Tk5AAlJUBVlZAhw76jobMCHc9IiLShqHmDZ5X6EZjnIPwzEXHJk70RGbmf3DzeiFe/OxPfYdDZPDkcjkAKIsdpB0LCwv4+PiwQGQCKvPHbvz1921kfDlD3+EQGTyJRAIPDw8kJiYiOTlZ3+EYHeYPE/L000BaGtCqFZCaqu9oyIxw1yMiIm0Yat7geYXuNPQchIUOIjJoVQnFzc0NpaWl+g7H6NjY2PBqAyIyWzY2Nmjfvj1vM68H5g8iIsNTUVGBN954A4cOHYJEIkF4eDimT5+utu3169cRGhqK27dvo1mzZti8eTM6d+4MoPIisldeeQU3btyAVCrFV199hccffxwA8M0332DVqlW4evUqVq9ejddff125zIMHD2LhwoW4dOkSZs6ciU8++aTpO01EZAB4XqEbDT0HYaGDiIyCpaUlnxNORERas7CwgK2trb7DICIiarCoqChcuXIF165dQ25uLnr27ImBAweiY8eO1dpOnToVU6ZMQVhYGL7//ntMmjQJJ0+eBADMnTsXffv2RXR0NM6cOYMxY8bgxo0bsLKyQq9evfDdd98hMjKy2jLbt2+PjRs3Yvfu3SgqKmry/hIRGRKeVxg+XqZFRERERERERFQLiUSCyMhIPPbYY2jTpg2OHj2KefPmoUePHujSpQsuX74MAMjIyMBTTz2FXr16oUuXLpg1axaEEACAyZMnY+bMmQCAu3fvom3btjh+/LjGMezatQvTpk2DpaUlXFxcMG7cOOzcubNau6ysLJw7dw4vv/wyAOD5559HYmIikpKSAADfffcdZsyofBRqnz594O7uroyje/fu6NSpk9orav39/dG9e3eO30RERAaJhQ4iIiIiIiIiMlgrVwJeXnVPI0ZU/+yIEbV/ZuVKzeNwdnbGH3/8geXLl2PkyJF4/PHHcf78eYSGhmLJkiUAgGbNmuHAgQOIj4/HxYsXcfPmTezZswcAsGbNGhw/fhy7d+9GaGgoJk+erHxk1KxZs/Doo4+qnU6fPg0AuHXrFnx9fZXx+Pn54datW9XiTElJgaenp7IgIZFI4OPjg1u3buHOnTuoqKiAq6trncshIiIyJizDExEREREREZHBUigqB6iti7d39feys2v/rEKheRzjx48HAPTs2RMWFhYYNmwYAKBXr1744YcfAFSOo/Huu+/i+PHjEEIgKysLjz76KMaMGQNbW1vs3r0bvXr1QmBgIN59913lsj///HONYnhwgNaqO0Xqavdw29rmERERGSsWOoiIiIiIiIjIYDk7A61a1d3ugZsUVN6r7bPOzprHUfVsdktLS0ilUuX7lpaWKCsrAwCsXLkSd+7cwenTp2Fra4vw8HCV8Sz++usvODg4ICsrCyUlJcrlzJo1C8eOHVO73vXr1yMgIAA+Pj5ISkpCnz59AADJycnw8fGp1t7b2xupqakoKyuDlZUVhBBISUmBj48PWrRoAQDIzs5W3tVR03LIcERERGDRokUq77m7uyMjI0NPERERGR4WOoiIiIiIiIjIYIWHV071sX9/48ZSl5ycHMjlctja2iIzMxO7d+9W3gly69YtTJ8+HUePHsXnn3+O2bNnY926dQA0u6Nj7NixWL9+PUaPHo3c3Fzs2rUL0dHR1dq5ubmhR48eiIqKQlhYGPbs2QM/Pz/4+fkpl7N27VpERETgzJkzyMjIUD5CiwxXly5dcPToUeVrS0tLPUZDRGR4OEYHEREREREREVEjmDVrFk6cOIFHH30Ur732GgYNGgQAKCsrwwsvvICPPvoInTt3xurVq3Hq1Cns2rVL42VPnDgRHTp0gL+/P/r06YO3334bnTp1AgDs378f//rXv5Rt169fj/Xr18Pf3x/Lli3Dxo0blfOWL1+OEydOoH379ggLC8O2bduU43lERUXBy8sLu3fvxgcffAAvLy+cP38eABAbGwsvLy+sXLkS69evh5eXF/brupJkxqysrCCXy5WTq7pbmIiIzBjv6CAiIiIiIiIiqsWD41j4+fnh9u3bytdBQUE4e/YsAMDX1xd//PGH2mWcOHFC+W9bW1tlAUFTlpaWWLt2rdp5I0aMwIgHRmPv0KEDTp48qbatu7s7jhw5onbeyy+/jJdfflntvKCgIKSmpmoVMzWe69evw9PTE1KpFAEBAVi6dCnatGmjtm1xcTGKi4uVrxXaDEZDRGSkWOjQsb17UzHtzXnwHjQRAKvvRESkmb17U/HBm//GRyEt9B0KEREZkzNngPJygI84IR3jrkfUeAICArB161b4+/sjMzMTixcvRr9+/XD58mXluCsPioyMrDamB5GhY96ghmKhQ8fc3MphI70NO+fiuhsTERH9Hze3cthJs+DmbAHAUd/hEBGRsfDw0HcEZKa46xE1npCQEOW/u3XrhsDAQLRt2xZbtmxBuJoBbObNm6fyvkKhgLe3t05iJaov5g1qKI7RQURE9H8iIyPRp08fODk5wc3NDaNGjUJCQoJKm7CwMEgkEpWpb9++eoqYiIiIiIjMjYODA7p164br16+rnS+VSuHs7KwyERGZOhY6iIiI/k9cXBxmzJiBU6dOISYmBmVlZQgODkZBQYFKu6FDhyI9PV05HTp0SE8RExEREZmmB8fEIOPBv5tuFBcX4+rVq/DgJfBEREp8dJWO7dzphIzU51Fx2g+PDi/TdzhERPSA6OholdebNm2Cm5sb4uPj8eSTTyrfl0qlkMvlOo1t504n3Ex9CTtPWmP+qOM6XTcRERmxDRuA/HzA0RGYMkXf0ZAZqe+uZ21tDYlEguzsbLi6ukIikTRdkNSohBDIzs6GRCKBtbW1vsMxKXPmzMHw4cPh4+ODrKwsLF68GAqFAqGhofoOjajR8CsLNRQLHTq2Zo0LMjOn4c7tQjw6/E99h0NERLXIzc0FALi4uKi8HxsbCzc3NzRr1gwDBgzAkiVL4ObmpnYZxcXFKC7+37hMCoWiXrFU5o9wrLlzm4UOIiLS3IcfAmlpQKtW/NWAdKq+u56lpSW8vLyQmpqKpKSkJouPmoZEIoGXlxcsOZpwo0pNTcWLL76I27dvw9XVFX379sWpU6fg6+ur79CIGg2/slBDsdBBRESkhhAC4eHhePzxx9G1a1fl+yEhIRg7dix8fX2RmJiIDz74AAMHDkR8fDykUmm15URGRmLRokW6DJ2IiIjIqDk6OqJ9+/YoLS3VdyikJWtraxY5msDOnTv1HQIRkcEzyUJHZGQkfvjhB/z111+ws7NDv379sHz5cnTo0EHZJiwsDFu2bFH5XEBAAE6dOqXrcImIyAC9/vrruHjxIo4fV717Yvz48cp/d+3aFb1794avry8OHjyI0aNHV1vOvHnzEB4ernytUCjg7e3ddIETERERmQBLS0v+YE5EREQaM8lCR9Vgsn369EFZWRnmz5+P4OBgXLlyBQ4ODsp2Q4cOxaZNm5SvbWxs9BEuEREZmJkzZ2L//v04duwYvLy8am3r4eEBX19fXL9+Xe18qVSq9k4PIiIiIiIiIiJqHCZZ6DDkwWSJiMhwCSEwc+ZM7N27F7GxsWjdunWdn7lz5w5SUlLg4eGhgwiJiIiIiIiIiOhhFvoOQBfqGkzW398fkydPRlZWVo3LKC4uhkKhUJmIiMi0zJgxA1FRUdixYwecnJyQkZGBjIwMFBYWAgDy8/MxZ84cnDx5EklJSYiNjcXw4cPRsmVLPPfcc3qOnoiIiIiIiIjIPJl8oaO2wWS3b9+OX375BZ9++inOnDmDgQMHori4WO1yIiMjIZPJlBOfr05EZHrWrVuH3NxcBAUFwcPDQznt2rULQOWzoi9duoSRI0fC398foaGh8Pf3x8mTJ+Hk5KTn6ImIiIiIiIiIzJNJPrrqQRxMloiINCWEqHW+nZ0dDh8+rKNoiIiIiIiIiIhIEyZd6OBgskREREREREREREREps0kCx2GPJhs69alyM1LhaOrQ5Ouh4iITEvr1qUoyEtGa9d8fYdCRETGxN8fkMkAd3d9R0JmhrseERFpg3mDGsokCx0zZszAjh078OOPPyoHkwUAmUwGOzs75OfnIyIiAs8//zw8PDyQlJSE9957TyeDyUZF/YMXXpsGv2HTAdR+lwkREVGVqKh/MPu1cVg9Sg7AUd/hEBGRsfjlF31HQGaKux4REWmDeYMayiQLHevWrQMABAUFqby/adMmhIWFKQeT3bp1K+7duwcPDw889dRT2LVrFweTJSIiIiIiIiIiIiIyIiZZ6OBgskRERERERNrJzs6GQqGotY2zszNcXV11FBERERERkWZMstBBREREREREmsvOzsbESRNxt+Bure1cHFywbeM2FjuIiIiIyKCw0KFj4eFuuHYpEpkKdwx+I0Pf4RARkZEID3fD6UtfIDy/CD++9ZW+wyEiImPx0kvA7dtAy5bA9u01NlMoFLhbcBetnmkFRzf1Y0HlZ+Uj7VAaFAoFCx1UJw13PSIiIgDMG9RwLHTo2OnTdlDk9EZpeSEAFjqIiEgzp0/bITsnEKcrbus7FCIiMiZxcUBaGtCqlUbNHd0cIfOUNXFQZA603PWIiMjMMW9QQ1noOwAiIiIiIiIiIiIiIqL6YqGDiIiIiIiIiIiIiIiMFgsdRERERERERERERERktFjoICIiIiIiIiIiIiIio8XByImIiIiIiIiIiKjJZGdnQ6FQ1NomOTkZZWVlOoqIiEwNCx1ERERERERERETUJLKzszHl5YkozLlba7v7RUXITE1FSZu2OoqMiEwJH11FRERERERm69ixYxg+fDg8PT0hkUiwb98+lflhYWGQSCQqU9++fVXaFBcXY+bMmWjZsiUcHBwwYsQIpKamqrTJycnBxIkTIZPJIJPJMHHiRNy7d6+Je0dERKR/CoUChTl38Zpcjvlt29U4jXF1RUV5OcrLeVcHEWmPhQ4iIiIiIjJbBQUF6N69O7744osa2wwdOhTp6enK6dChQyrzZ8+ejb1792Lnzp04fvw48vPz8eyzz6K8vFzZZsKECbhw4QKio6MRHR2NCxcuYOLEiU3WLyIiIkPj4eAIP5msxsnNzl7fIRKREeOjq3Rs/HgFdu35BS06ddF3KEREZETGj1fgwJ5DGN5V35EQEZmWkJAQhISE1NpGKpVCLpernZebm4uNGzdi27ZtGDRoEAAgKioK3t7eOHr0KIYMGYKrV68iOjoap06dQkBAAADg66+/RmBgIBISEtChQ4fG7dSDJk8GcnMBmazp1kGkBnc9IvNR1/gbHHuDNMG8QQ3FQoeOzZqVgxMX1sNv8HQAXvoOh4iIjMSsWTm4eWEVZg2RA3DUdzhERGYlNjYWbm5uaNasGQYMGIAlS5bAzc0NABAfH4/S0lIEBwcr23t6eqJr1644ceIEhgwZgpMnT0ImkymLHADQt29fyGQynDhxosZCR3FxMYqLi5Wv6xrEVa2FC7X/DFEj4K5HZB40GX+DY2+QJpg3qKFY6CAiIiIiIqpBSEgIxo4dC19fXyQmJuKDDz7AwIEDER8fD6lUioyMDNjY2KB58+Yqn3N3d0dGRgYAICMjQ1kYeZCbm5uyjTqRkZFYtGhR43aIiIioET04/oaHg/oLss5nZWJNcjLH3iCiJsVCBxERERERUQ3Gjx+v/HfXrl3Ru3dv+Pr64uDBgxg9enSNnxNCQCKRKF8/+O+a2jxs3rx5CA8PV75WKBTw9vbWtgtERERNrmr8DXXS8vJ0HA0RmSMORk5ERERERKQhDw8P+Pr64vr16wAAuVyOkpIS5OTkqLTLysqCu7u7sk1mZma1ZWVnZyvbqCOVSuHs7KwyERERERFRdSx06Fj//r44eywGB5cM1XcoRERkRPr398X/OxaP/ou26DsUIiKzdufOHaSkpMDDwwMA0KtXL1hbWyMmJkbZJj09HX/++Sf69esHAAgMDERubi7++OMPZZvTp08jNzdX2abJeHkBEknlf4l0iLseUdOIjIyERCLB7Nmz9R0KUaNi3qCG4qOriIiIiIjIbOXn5+Pvv/9Wvk5MTMSFCxfg4uICFxcXRERE4Pnnn4eHhweSkpLw3nvvoWXLlnjuuecAADKZDJMmTcJbb72FFi1awMXFBXPmzEG3bt0waNAgAECnTp0wdOhQTJ48GevXrwcATJkyBc8++2yNA5ETERE97MyZM9iwYQMeeeQRfYdCRGRweEcHERERERGZrbNnz6JHjx7o0aMHACA8PBw9evTAggULYGlpiUuXLmHkyJHw9/dHaGgo/P39cfLkSTg5OSmXsWrVKowaNQrjxo1D//79YW9vjwMHDsDS0lLZZvv27ejWrRuCg4MRHByMRx55BNu2bdN5f4mIyDjl5+fjpZdewtdff43mzZvrOxwiIoPDOzqIiIiIiMhsBQUFQQhR4/zDhw/XuQxbW1usWbMGa9asqbGNi4sLoqKi6hUjERHRjBkzMGzYMAwaNAiLFy+utW1xcTGKi4uVrxUKRVOHR0Skdyx0EBERERERERERGaidO3fi3LlzOHPmjEbtIyMjsWjRoiaOiojIsPDRVURERP8nMjISffr0gZOTE9zc3DBq1CgkJCSotBFCICIiAp6enrCzs0NQUBAuX76sp4iJiIiIiMiUpaSk4I033kBUVBRsbW01+sy8efOQm5urnFJSUpo4SiIi/WOhg4iI6P/ExcVhxowZOHXqFGJiYlBWVobg4GAUFBQo26xYsQIrV67EF198gTNnzkAul2Pw4MHIy8vTY+RERERERGSK4uPjkZWVhV69esHKygpWVlaIi4vD559/DisrK5SXl1f7jFQqhbOzs8pERGTq+OgqIiKi/xMdHa3yetOmTXBzc0N8fDyefPJJCCGwevVqzJ8/H6NHjwYAbNmyBe7u7tixYwemTp2qj7CJiIiIiMhEPf3007h06ZLKe6+++io6duyId999F5aWlnqKjIjIsLDQQUREVIPc3FwAlQPIAkBiYiIyMjIQHBysbCOVSjFgwACcOHFCbaGDAwESEREREVF9OTk5oWvXrirvOTg4oEWLFtXeJyIyZyb56Co+Y52IiBpKCIHw8HA8/vjjyhOIjIwMAIC7u7tKW3d3d+W8h0VGRkImkyknb2/vpg2ciIiIiIiIiMjMmOQdHVXPWO/Tpw/Kysowf/58BAcH48qVK3BwcADwv2esb968Gf7+/li8eDEGDx6MhIQEODk5NVlsn36aiUXLvoBHv2cAaDaIFBER6d7rr7+Oixcv4vjx49XmSSQSlddCiGrvVZk3bx7Cw8OVrxUKRb2KHZ9+monPl0Vi1pOOWn+WiIjMWFQUUFwMSKX6joTMDHc9oqYTGxur7xCIGh3zBjWUSRY6DPkZ6337FkHmchZubR8D4NVk6yEiovqbOXMm9u/fj2PHjsHL63/HarlcDqDyzg4PDw/l+1lZWdXu8qgilUohbYRvan37FmGny0n0bScHwGIHERFpKChI3xGQmeKuR0RE2mDeoIYyyUdXPUzbZ6yrU1xcDIVCoTIREZFpEULg9ddfxw8//IBffvkFrVu3VpnfunVryOVyxMTEKN8rKSlBXFwc+vXrp+twiYiIiIiIiIgIZlDo4DPWiYhIUzNmzEBUVBR27NgBJycnZGRkICMjA4WFhQAqH1k1e/ZsLF26FHv37sWff/6JsLAw2NvbY8KECXqOnoiIiIiIiIjIPJnko6seZGjPWD91yha5d3sj60ZLOLtp/XEiImpC69atAwAEPXTP7KZNmxAWFgYAeOedd1BYWIjp06cjJycHAQEBOHLkSJOO7wRU5o+su4E49bcj2ronNum6iIjIhMTG/u+B13wmBOkQdz0iItIG8wY1lEkXOgzxGetvveWOzMxIpN4qRLvAPxu8PCIiajxCiDrbSCQSREREICIioukDekBl/vgCb6Xcxkv9Z+h03UREZMRefhlISwNatQJSU/UdDZkR7npERKQN5g1qKJMsdAghMHPmTOzduxexsbG1PmO9R48eAP73jPXly5frI2QiIiIiIiIyAqUlpUhOTq5xfnJyMsrLynUYERERERGZZKFjxowZ2LFjB3788UflM9YBQCaTwc7OTuUZ6+3bt0f79u2xdOlSPmOdiIiIiIiIalSkKELSzSSELwiHjdRGfZv7RUhLT0O70nY6jo6IiIjIfJlkocOQn7FORERERERExqm0sBQVlhWQh8jh6uOqtk3mlUwkb09GWWmZjqMjIiIiMl8mWegw5GesExERERERkXFzaOkAmadM7by8zDwdR0NEREREFvoOgIiIiIiIiIiIiIiIqL4MrtCRmJio7xCIiMgIMX8QEZkXHveJiMjQMVcREemOwRU62rVrh6eeegpRUVEoKirSdzhERGQkmD+IiMwLj/tERGTomKuIiHTH4Aod//3vf9GjRw+89dZbkMvlmDp1Kv744w99h0VERAaO+YOIyLzwuE9ERIaOuYqISHcMrtDRtWtXrFy5Emlpadi0aRMyMjLw+OOPo0uXLli5ciWys7P1HSIRERkg5g8iIvPC4z4RERk65ioiIt0xuEJHFSsrKzz33HP47rvvsHz5cty4cQNz5syBl5cXXnnlFaSnp+s7xHr5/fdk9H5yMIbNj9Z3KEREJsmU88ezT/bC7wtD9R0KEZFBMdXjfqNJTQWEqPyvicrOzsaNGzdqnJKTk1FeVq7vMM2OGex6RBpjriKqG/MGNZSVvgOoydmzZ/HNN99g586dcHBwwJw5czBp0iT8888/WLBgAUaOHMnb/YiIqBrmDyIi88LjvnnLzs7GxEkTcbfgbo1tiu4XIS09De1K2+kwMiKi/2GuIiJqegZX6Fi5ciU2bdqEhIQEPPPMM9i6dSueeeYZWFhU3nzSunVrrF+/Hh07dtRzpEREZEiYP4iIzAuP+wQACoUCdwvuotUzreDo5qi2TeaVTCRvT0ZZaZmOoyMic8dc1TRKSkuRnJxcaxtnZ2e4urrqKCIiMgQGV+hYt24dXnvtNbz66quQy+Vq2/j4+GDjxo06joyIiAwZ8wcRkXnhcZ8e5OjmCJmnTO28vMw8HUdDRFSJuarx3Ssqws2kJCx+803YSKU1trNr7oINUdtY7CAyIwZX6IiJiYGPj4+yul1FCIGUlBT4+PjAxsYGoaHG+Yzyzz9vjpQbU3E/piP6vpSv73CIiEyGOeSPyzfexOeHgc9eOaDvcIiI9M7Uj/uNZtEiIDcXkMmAhQv1HQ2ZEe56RMxVTaGgtBTWFRUIc3dH6xYt1bZJL8jHNxkZUCgULHQYEeYNaiiDK3S0bdsW6enpcHNzU3n/7t27aN26NcrLjXsQuV27nJGZOQaKvEL0felPfYdDRGQyzCN/vIxd+bdZ6CAigukf9xvN118DaWlAq1aN8qtBaQkfF0KaaeRdj8goMVc1HbmDA/xk6u/kI+PEvEENZXCFDiGE2vfz8/Nha2ur42iIiMhYMH8QEZkXHvd1r0hRhKSbSQhfEA4bqU2N7VwcXLBtIx8XQkTEXEVEpDsGU+gIDw8HAEgkEixYsAD29vbKeeXl5Th9+jQeffRRPUVHRESGivmDiMi88LivP6WFpaiwrIA8RA5XH/VFjPysfKQdSuPjQojIrDFXERHpnsEUOs6fPw+gstp96dIl2Nj87wohGxsbdO/eHXPmzNFXeEREZKCYP4iIzAuP+/rn0NKhxoG/iYiIuYqISB8MptDx66+/AgBeffVVfPbZZ3B2dtZzREREZAyYP4iIzAuP+0REZOiYq4iIdM9gCh1VNm3apO8QiIjICDF/EBGZFx73iYjI0DFXERHpjkEUOkaPHo3NmzfD2dkZo0ePrrXtDz/8oKOoiIjI0DF/EBGZFx73iYjI0DFXERHph0EUOmQyGSQSifLfREREmmD+ICIyLzzuExGRoWOuIiLSD4ModDx4Kx9v6yMiIk0xfxARmRce94mIyNA1Ra5at24d1q1bh6SkJABAly5dsGDBAoSEhDTK8omITIFBFDoeVFhYCCEE7O3tAQDJycnYu3cvOnfujODgYD1H13ABAYWI/S0BMl93fYdCRGRSzCF/nPztTwS0LtJ3KEREBsHUj/uNZsAA4PZtoGVLfUdCZoa7HlHj5SovLy8sW7YM7dq1AwBs2bIFI0eOxPnz59GlS5cmiZ1I15g3qKEMrtAxcuRIjB49GtOmTcO9e/fw2GOPwcbGBrdv38bKlSvx73//W98hNsjKlVl44bV58Bs2HYCXvsMhIjIZ5pA/Zr/2OlaOkgNw1Hc4RER6Z+rH/Uazfbu+IyAzxV2PqPFy1fDhw1VeL1myBOvWrcOpU6dY6CCTwbxBDWWh7wAedu7cOTzxxBMAgO+//x5yuRzJycnYunUrPv/8cz1HR0REhor5g4jIvPC4T0REhq4pclV5eTl27tyJgoICBAYGqm1TXFwMhUKhMtVXdnY2bty4UeOUnJyMsrKyei+fiKixGNwdHffv34eTkxMA4MiRIxg9ejQsLCzQt29fJCcn6zk6IiIyVI2VP44dO4aPP/4Y8fHxSE9Px969ezFq1Cjl/LCwMGzZskXlMwEBATh16lSj9IOIiDTD8wYiIjJ0jZmrLl26hMDAQBQVFcHR0VH5CCx1IiMjsWjRogbHn52djSkvT0Rhzt0a29wvKkJmaipK2rRt8PqIiBrC4O7oaNeuHfbt24eUlBQcPnxY+czCrKwsODs76zk6IiIyVI2VPwoKCtC9e3d88cUXNbYZOnQo0tPTldOhQ4caHD8REWmH5w1ERGToGjNXdejQARcuXMCpU6fw73//G6Ghobhy5YratvPmzUNubq5ySklJqVf8CoUChTl38Zpcjvlt26mdxri6oqK8HOXlvKuDiPTL4O7oWLBgASZMmIA333wTTz/9tPI2vCNHjqBHjx56jq7hXn7ZE39e/BrJaQ4Y/n6SvsMhIjIZjZU/QkJCEBISUmsbqVQKuVzeoHi1VZk/vsPL/+Tj5KKlOl03EZEhMvXzhkYzcCCQmQm4uwO//KLvaMiMcNcjatxcZWNjoxyMvHfv3jhz5gw+++wzrF+/vlpbqVQKqVTa8A78Hw8HR/jJZGrnpeXlNdp6yLwxb1BDGdwdHWPGjMGtW7dw9uxZREdHK99/+umnsWrVKo2WcezYMQwfPhyenp6QSCTYt2+fyvywsDBIJBKVqW/fvo3ZjRolJlqj6L4f8rM5kCwRUWNqjPyhqdjYWLi5ucHf3x+TJ09GVlZWjW0b6/m4iYnWyL/fFonZreobNhGRSdHlcd+oXbsGXLlS+V8iHeKuR9S0uUoIgeLi4oaGSGQwmDeooQyu0AEAcrkcPXr0gIXF/8J77LHH0LFjR40+z8eOEBGZp4bmD02EhIRg+/bt+OWXX/Dpp5/izJkzGDhwYI0nGZGRkZDJZMrJ29u70WIhIjJ3jXHcr+siKSEEIiIi4OnpCTs7OwQFBeHy5csqbYqLizFz5ky0bNkSDg4OGDFiBFJTU1Xa5OTkYOLEicp8MHHiRNy7d0/rPhMRkXFpjFz13nvv4bfffkNSUhIuXbqE+fPnIzY2Fi+99FJThExEZJQM7tFVBQUFWLZsGX7++WdkZWWhoqJCZf7NmzfrXIahPnaEiIiaTmPkD02MHz9e+e+uXbuid+/e8PX1xcGDBzF69Ohq7efNm4fw8HDla4VCwWIHEVEjaKzjftVFUq+++iqef/75avNXrFiBlStXYvPmzfD398fixYsxePBgJCQkKAeYnT17Ng4cOICdO3eiRYsWeOutt/Dss88iPj4elpaWAIAJEyYgNTVVeUXvlClTMHHiRBw4cKAhm4GIiAxYY+WqzMxMTJw4Eenp6ZDJZHjkkUcQHR2NwYMHN0XYRERGyeAKHf/6178QFxeHiRMnwsPDAxKJpEnWU/XYkWbNmmHAgAFYsmQJ3NzcamxfXFyscrVufR89QkRETUNX+eNhHh4e8PX1xfXr19XOb+zn4xIRUaXGOu7XdpGUEAKrV6/G/PnzlcXsLVu2wN3dHTt27MDUqVORm5uLjRs3Ytu2bRg0aBAAICoqCt7e3jh69CiGDBmCq1evIjo6GqdOnUJAQAAA4Ouvv0ZgYCASEhLQoUOHesVORESGrbFy1caNGxs5MiIi02NwhY6ffvoJBw8eRP/+/ZtsHSEhIRg7dix8fX2RmJiIDz74AAMHDkR8fHyNP0ZFRkZi0aJFTRYTERE1jC7yhzp37txBSkoKPDw8dLpeIiJzp4vjfmJiIjIyMhAcHKx8TyqVYsCAAThx4gSmTp2K+Ph4lJaWqrTx9PRE165dceLECQwZMgQnT56ETCZTFjkAoG/fvpDJZDhx4kSNhQ5ebEVEZNz0dY5CRGSODG6MjubNm8PFxaVJ1zF+/HgMGzYMXbt2xfDhw/HTTz/h2rVrOHjwYI2fmTdvHnJzc5VTSkpKk8ZIRETaaaz8kZ+fjwsXLuDChQsAKn/kunDhAm7duoX8/HzMmTMHJ0+eRFJSEmJjYzF8+HC0bNkSzz33XIPXTUREmtPFeUNGRgYAwN3dXeV9d3d35byMjAzY2NigefPmtbZRd/e4m5ubso06HOeJiMi46SJXERFRJYMrdHz00UdYsGAB7t+/r7N11vXYEaDyyi1nZ2eViYiIDEdj5Y+zZ8+iR48e6NGjBwAgPDwcPXr0wIIFC2BpaYlLly5h5MiR8Pf3R2hoKPz9/XHy5Enlc9qJiEg3dHne8PCjRoQQdT5+5OE26trXtRxebEVEZNz08RsXEZG5MrhHV3366ae4ceMG3N3d4efnB2tra5X5586da/R18rEjRETGr7HyR1BQEIQQNc4/fPhwg+IkIqLGoYvzBrlcDqDyjowHzxWysrKUd3nI5XKUlJQgJydH5a6OrKws9OvXT9kmMzOz2vKzs7Or3S3yII7zRERk3PTxGxcRkbkyuELHqFGjGryM/Px8/P3338rXVY8dcXFxgYuLCyIiIvD888/Dw8MDSUlJeO+99/jYESIiI9cY+YOIiIyHLo77rVu3hlwuR0xMjPJOv5KSEsTFxWH58uUAgF69esHa2hoxMTEYN24cACA9PR1//vknVqxYAQAIDAxEbm4u/vjjDzz22GMAgNOnTyM3N1dZDCEiItPDcxQiIt0xuELHwoULG7yMs2fP4qmnnlK+Dg8PBwCEhoZi3bp1uHTpErZu3Yp79+7Bw8MDTz31FHbt2qWTx47MnHkXX33zA9weDai7MRERaawx8ochmznzLnZ8E4UJvazrbkxEZAYa67hf20VSPj4+mD17NpYuXYr27dujffv2WLp0Kezt7TFhwgQAgEwmw6RJk/DWW2+hRYsWcHFxwZw5c9CtWzcMGjQIANCpUycMHToUkydPxvr16wEAU6ZMwbPPPlvjQOSNZsECID8fcHRs2vUQPYS7HpHpn6MQNSbmDWoogyt0AMC9e/fw/fff48aNG3j77bfh4uKCc+fOwd3dHa1atarz84b82JEXXsjDviN74BfgDsBLb3EQEZmihuYPQ/bCC3k4dWQ7XgiUA+A3PyIioHGO+7VdJLV582a88847KCwsxPTp05GTk4OAgAAcOXJE5SKpVatWwcrKCuPGjUNhYSGefvppbN68GZaWlso227dvx6xZsxAcHAwAGDFiBL744ovG2Ay1mzKl6ddBpAZ3PaJKpnyOQtSYmDeooQyu0HHx4kUMGjQIMpkMSUlJmDx5MlxcXLB3714kJydj69at+g6RiIgMEPMHEZF5aazjfl0XSUkkEkRERCAiIqLGNra2tlizZg3WrFlTYxsXFxdERUVpFBMREZkGnqPoT0lpKZKTk2tt4+zsDFdXVx1FRERNzeAKHeHh4QgLC8OKFStUrpIKCQlR3h5ORET0MOYPIiLzwuM+EREZOuYq/bhXVISbSUlY/OabsJFKa2xn19wFG6K2sdhBZCIMrtBx5swZ5XNrH9SqVStkZGToIaLGlZVliZLilihUSOHspu9oiIhMhznkj8JiN2QpmqOte6m+wyEi0jtTP+43mvR0oLwcsLQEPDz0HQ2ZEe56RMxV+lJQWgrrigqEubujdYuWatukF+Tjm4wMKBQKFjoMBPMGNZTBFTpsbW2hUCiqvZ+QkGASB57nnvNCZua3uH61EC9+9qe+wyEiMhnmkT9+wp9/3UbGlzP0HQ4Rkd6Z+nG/0fTpA6SlAa1aAamp+o6GzAh3PSLmKn2TOzjATybTdxikIeYNaigLfQfwsJEjR+LDDz9EaWnl1aoSiQS3bt3C3Llz8fzzz+s5OiIiMlTMH0RE5oXHfSIiMnTMVUREumNwhY5PPvkE2dnZcHNzQ2FhIQYMGIB27drByckJS5Ys0Xd4RERkoJg/iIjMC4/7RERk6JiriIh0x+AeXeXs7Izjx4/j119/RXx8PCoqKtCzZ08MGjRI36EREZEBY/4gIjIvPO4TEZGhY64iItIdgyp0VFRUYPPmzfjhhx+QlJQEiUSC1q1bQy6XQwgBiUSi7xCJiMgAMX8QEZkXHvfJFJSWlCI5ObnWNs7OznyOP5GRYq4iItItgyl0CCEwYsQIHDp0CN27d0e3bt0ghMDVq1cRFhaGH374Afv27dN3mEREZGCYP4iIzAuP+2QKihRFSLqZhPAF4bCR2tTYzsXBBds2bmOxg8jIMFcREemewRQ6Nm/ejGPHjuHnn3/GU089pTLvl19+wahRo7B161a88soreoqQiIgMEfMHEZF54XGfTEFpYSkqLCsgD5HD1Ud9ESM/Kx9ph9KgUChY6CAyMsxVRES6ZzCDkX/77bd47733qiUAABg4cCDmzp2L7du36yEyIiIyZMwfRETmhcd9MiUOLR0g85SpnRzdHPUdHhHVE3MVEZHuGUyh4+LFixg6dGiN80NCQvDf//5XhxEREZExYP4gIjIvPO4TEZGhY64iItI9gyl03L17F+7u7jXOd3d3R05Ojg4jIiIiY8D8QURkXnjcJyIiQ8dcRUSkewYzRkd5eTmsrGoOx9LSEmVlZTqMqGls2/YP3pz3IbyeGg+gmb7DISIyeuaUP5bMewvzBzfXdyhERHplLsf9RvPzz0BZGVDLNiNqCtz1yJwxVxFpj3mDGspgdh0hBMLCwiCVStXOLy4u1nFETaNNm1LYOSTDyTUfLHQQETWcOeUPJ4ebaOMmB8BndhOR+TKX436j6dBB3xGQmeKuR+aMucq0ZGdnQ6FQ1NrG2dkZrq6uOorINDFvUEMZTKEjNDS0zjavvPKKDiIhIiJjwvxBRGReeNwnIiJDx1xlOrKzszHl5YkozLlbazu75i7YELWNxQ4iPTKYQsemTZv0HQIRERkh5g8iIvPC4z4RERk65irToVAoUJhzF6/J5fBwUH9nfXpBPr7JyIBCoWChg0iPDKbQYS7273dEdnoILM57oesQfUdDRETGYv9+R9xKH4X98VK8+Uy8vsMhIiJjsWMHcP8+YG8PTJig72jIjHDXIyJT4uHgCD+ZTN9hmDTmDWooFjp0bPnyFsjMDEdWZiG6DvlT3+EQEZGRqMwfH2B51m0WOoiISHPvvAOkpQGtWvFXA9Ip7npERKQN5g1qKAt9B0BERERERERERERERFRfLHQQEREREREREREREZHRYqGDiIiIiIiIiIjIQEVGRqJPnz5wcnKCm5sbRo0ahYSEBH2HRURkUFjoICIiIiIiIiIiMlBxcXGYMWMGTp06hZiYGJSVlSE4OBgFBQX6Do2IyGCw0EFERPSAY8eOYfjw4fD09IREIsG+fftU5gshEBERAU9PT9jZ2SEoKAiXL1/WT7BERERERGTyoqOjERYWhi5duqB79+7YtGkTbt26hfj4eH2HRkRkMFjoICIiekBBQQG6d++OL774Qu38FStWYOXKlfjiiy9w5swZyOVyDB48GHl5eTqOlIiIiIiIzFFubi4AwMXFRe384uJiKBQKlYmIyNSZZKGDV+MSEVF9hYSEYPHixRg9enS1eUIIrF69GvPnz8fo0aPRtWtXbNmyBffv38eOHTv0EC0RERGZstKSUiQnJ+PGjRs1TtnZ2foOk4h0SAiB8PBwPP744+jatavaNpGRkZDJZMrJ29tbx1ESEemelb4DaApVV+O++uqreP7556vNr7oad/PmzfD398fixYsxePBgJCQkwMnJSQ8RExGRMUhMTERGRgaCg4OV70mlUgwYMAAnTpzA1KlTq32muLgYxcXFyte8moqIiIg0UaQoQtLNJIQvCIeN1KbGdi4OLti2cRtcXV11GB0R6cvrr7+Oixcv4vjx4zW2mTdvHsLDw5WvFQoFix1EZPJMstAREhKCkJAQtfMevhoXALZs2QJ3d3fs2LFD7Y9UjcnVtQx3c3Jg62SSm56IyKRlZGQAANzd3VXed3d3R3JystrPREZGYtGiRQ1et6trGXJz7sDViYUSIiLSglyu+l8yGqWFpaiwrIA8RA5XH/VFjPysfKQdSoNCoTC4Qgd3PaLGN3PmTOzfvx/Hjh2Dl5dXje2kUimkUqkOIyNqOOYNaiiz+7W9PlfjAo13Re6+fWl44bVp8Bs2HUDNSYmIiAyXRCJReS2EqPZelca6mmrfvjTMfm0cVo+SA3DU+vNERGSmzp7VdwTUQA4tHSDzlOk7DK1x1yNqPEIIzJw5E3v37kVsbCxat26t75CIGh3zBjWUSY7RUZvarsatmqcOn29IRETy/7u05OF8kZWVVS2vVJFKpXB2dlaZiIiIiIiINDVjxgxERUVhx44dcHJyQkZGBjIyMlBYWKjv0IiIDIbZFTqqaHM1LlB5RW5ubq5ySklJaeoQiYjIwLRu3RpyuRwxMTHK90pKShAXF4d+/frpMTIiIiIiIjJV69atQ25uLoKCguDh4aGcdu3ape/QiIgMhtk9uurBq3E9PDyU79d2NS7A5xsSEZmL/Px8/P3338rXiYmJuHDhAlxcXODj44PZs2dj6dKlaN++Pdq3b4+lS5fC3t4eEyZM0GPURERERERkqoQQ+g6BiMjgmV2h48GrcXv06AHgf1fjLl++vMnX//77LXHjyge4U+SNp/59u8nXR0RE2jl79iyeeuop5euq8TVCQ0OxefNmvPPOOygsLMT06dORk5ODgIAAHDlyBE5OTk0a1/vvt0T8leV4v6QU387c3KTrIiIiEzJ1KnD3LuDiAqxfr+9oyIxw1yMiIm0wb1BDmWShw5Cvxv31Vwfk3H4SRSWFAFjoICIyNEFBQbVeMSWRSBAREYGIiAjdBYXK/JF5exB+Lb0NYLNO101EREbs4EEgLQ1o1UrfkZCZ4a5HRETaYN6ghjLJQoehXo1LRERERERERERERESNyyQLHYZ6NS4RERERERERERERETUuC30HQEREREREREREREREVF8sdBARERERERERERERkdFioYOIiIiIiIiIiIiIiIyWSY7RQURERERERMYvOzsbCoWixvnJyckoLyvXYURERGQqSkpLkZycXGub5ORklJWV6SgiImoIFjqIiIiIiIjI4GRnZ2PipIm4W3C3xjZF94uQlp6GdqXtdBgZEREZu3tFRbiZlITFb74JG6m0xnb3i4qQmZqKkjZtdRgdEdUHCx1ERERERERkcBQKBe4W3EWrZ1rB0c1RbZvMK5lI3p6MslJebUtERJorKC2FdUUFwtzd0bpFyxrbnc/KxJrkZJSXM88QGToWOnRs+PB87Pt/J+HizyuOiIhIc8OH5+Pw/4vFkI58PAcREWnhxReBnBygeXN9R1Jvjm6OkHnK1M7Ly8zTcTSkKRPY9YjIDMgdHOAnU59jACAtj3lGV5g3qKFY6NCxuXPv4MK1lfAbNh2Al77DISIiIzF37h1kXPsIc0fIAai/qpWIiKiajz/WdwRkprjrERGRNpg3qKEs9B0AERERERGRIYuIiIBEIlGZ5HK5cr4QAhEREfD09ISdnR2CgoJw+fJllWUUFxdj5syZaNmyJRwcHDBixAikpqbquitkgkpLKgfTvXHjRq1Tdna2vkMlIiIiajK8o4OIiIiIiKgOXbp0wdGjR5WvLS0tlf9esWIFVq5cic2bN8Pf3x+LFy/G4MGDkZCQACcnJwDA7NmzceDAAezcuRMtWrTAW2+9hWeffRbx8fEqyyLSRpGiCEk3kxC+IBw2Upta27o4uGDbxm1wdXXVUXREREREusNCBxERERERUR2srKxU7uKoIoTA6tWrMX/+fIwePRoAsGXLFri7u2PHjh2YOnUqcnNzsXHjRmzbtg2DBg0CAERFRcHb2xtHjx7FkCFDdNoXMh2lhaWosKyAPEQOV5+aCxj5WflIO5QGhULBQgcRERGZJD66SseCg71x7vd9OPzJIH2HQkRERiQ42BvRv8cheNlX+g6FiMgsXb9+HZ6enmjdujVeeOEF3Lx5EwCQmJiIjIwMBAcHK9tKpVIMGDAAJ06cAADEx8ejtLRUpY2npye6du2qbKNOcXExFAqFyqS1jh0BZ+fK/5LJcmjpAJmnrMbJ0U3343tx1yMiIm0wb1BDsdChYwUFFqgod0BZMW+mISIizRUUWKCs3BEFxXb6DoWIyOwEBARg69atOHz4ML7++mtkZGSgX79+uHPnDjIyMgAA7u7uKp9xd3dXzsvIyICNjQ2aN29eYxt1IiMjIZPJlJO3t7f2wefnA3l5lf8l0iHuekREpA3mDWooFjqIiIiIiIhqERISgueffx7dunXDoEGDcPDgQQCVj6iqIpFIVD4jhKj23sPqajNv3jzk5uYqp5SUlAb0goiIiIjIdPG2AiIiIiIiIi04ODigW7duuH79OkaNGgWg8q4NDw8PZZusrCzlXR5yuRwlJSXIyclRuasjKysL/fr1q3E9UqkUUqm0aTpBREREjaaktBTJycm1tnF2duY4SURNiIUOIiIiIiIiLRQXF+Pq1at44okn0Lp1a8jlcsTExKBHjx4AgJKSEsTFxWH58uUAgF69esHa2hoxMTEYN24cACA9PR1//vknVqxYobd+EBERUcPdKyrCzaQkLH7zTdjUcoGCXXMXbIjaxmIHURNhoYOIiIiIiKgWc+bMwfDhw+Hj44OsrCwsXrwYCoUCoaGhkEgkmD17NpYuXYr27dujffv2WLp0Kezt7TFhwgQAgEwmw6RJk/DWW2+hRYsWcHFxwZw5c5SPwiIiIiLjVVBaCuuKCoS5u6N1i5Zq26QX5OObjAwoFAoWOoiaCAsdREREREREtUhNTcWLL76I27dvw9XVFX379sWpU6fg6+sLAHjnnXdQWFiI6dOnIycnBwEBAThy5AicnJyUy1i1ahWsrKwwbtw4FBYW4umnn8bmzZthaWmpr24RVZOdnQ2FQlFrGz56hYhIPbmDA/xkMn2HQWS2WOggIiIiIiKqxc6dO2udL5FIEBERgYiIiBrb2NraYs2aNVizZk0jR0fUOLKzszFx0kTcLbhbazsXBxds28hHrxARaUuTcTxKSkpgY2NTaxsWnInUY6GDiIiIiIiIyMwpFArcLbiLVs+0gqObo9o2+Vn5SDuUxkevEBFpSZNxPEpKS5GUmoY23l6wsqr5J1uO9UGkHgsdRERERERERAQAcHRzhMyTj14hImpMmozjcT4rE2tu3sQrLVtyrA+iemChQ8c++igby1b9B/KAQeDmJyIiTX30UTa+WrUa0/rZ6TsUIiIyJl99BRQWAnbMH6Rb3PWIiKqrbRyPtLy8OtuYMuYNaij+0q5jAwfex4aoY/Ds1BWAl77DISIiIzFw4H3sjzqKgV3kANQ/ToKIiKiaZ5/VdwRkIEpLan82fHJyMsrLyhttfY2163GAdCIi88CvLNRQLHQQERERERERmbAiRRGSbiYhfEE4bKTqB7ktul+EtPQ0tCttp+PoasYB0okqHTt2DB9//DHi4+ORnp6OvXv3YtSoUfoOi4jIoLDQQUREpIWIiAgsWrRI5T13d3dkZGToKSIiIiKi2pUWlqLCsgLyEDlcfdQXAzKvZCJ5ezLKSst0HF3NOEA6UaWCggJ0794dr776Kp5//nl9h0NEZJDMttChrx+q/vzTBvmKTshJbQZntyZdFRERNZEuXbrg6NGjyteWlpZNvs4//7RBjqIb/kyRoa07iypERKSh+HigpASwsQF69dJ3NKRnDi0dahxoPC8zr1HX1Zi7HgdIJ3MXEhKCkJAQfYdB1KT4lYUaymwLHYB+fqiaOtUDmZmfI/lmIXx7/tnk6yMiosZnZWUFuVyu03VW5o/N+DvxNkb2nqHTdRMRkREbORJISwNatQJSU/UdDZkR7npE+lNcXIzi4mLl67rGuSEyBMwb1FBmXejQxw9VRERk/K5fvw5PT09IpVIEBARg6dKlaNOmjdq2PMkgIiJzU9eg11U4gDQRUdOIjIys9hQTIiJTZ9aFDv5QRURE2goICMDWrVvh7++PzMxMLF68GP369cPly5fRokWLau15kkFEROZEk0Gvq3AAaSKipjFv3jyEh4crXysUCnh7e+sxIjJU2dnZdf7GyQsTyFiYbaGDP1QREVF9PPhs3G7duiEwMBBt27bFli1bVE4mqvAkg4iIzIkmg14DHECaiKgpSaVSSKVSfYdBBi47OxtTXp6Iwpy7tbaza+6CDVG8MIEMn9kWOvhDFRERNQYHBwd069YN169fVzufJxlERGSOahv0moiIiPRPoVCgMOcuXpPL4eHgqLZNekE+vsnI4IUJZBTMttDxMP5QRURE9VFcXIyrV6/iiSee0HcoRERERERkgvLz8/H3338rXycmJuLChQtwcXGBj4+PHiMjU+Dh4Ag/GS9OIONnoe8ADEXVD1UeHh76DoWIiAzYnDlzEBcXh8TERJw+fRpjxoyBQqFAaGiovkMjIiIiIiITdPbsWfTo0QM9evQAAISHh6NHjx5YsGCBniMjIjIcZntHx5w5czB8+HD4+PggKysLixcv5g9VRERUp9TUVLz44ou4ffs2XF1d0bdvX5w6dQq+vr76Do2IiIiIiExQUFAQhBD6DoOIyKCZbaGDP1QREVF97Ny5U98hEBERERERERHRA8y20MEfqoiIiIiIiIiIiIiIjJ/ZFjr05fDhW3h1+pvwGfovAHJ9h0NEREbi8OFbeHd6GJY/6wbAUt/hEBGRsbh6FRACkEj0HQmZGe56RESkDeYNaigWOnTM0VHA0uo+rKVl+g6FiIiMiKOjgLVVARxtCwE46jscIiIyFk5O+o6AzBR3PSIi0gbzBjWUhb4DICIiIiIiIiIiIiIiqi/e0UFERERERERERERkBEpKS5GcnFx3u5IS2NjY1Dg/OTkZZWV84gyZDhY6dGzjRhnSkiai5Fg79B5TpO9wiIjISGzcKENC0hRsjLXA0vFH9R0OEREZi5UrAYUCcHYGwsP1HQ2ZEe56RESN715REW4mJWHxm2/CRiqtsV1JaSmSUtPQxtsLVlbqf/69X1SEzNRUlLRp21ThaoV5gxqKhQ4d++abZsjMfAX37hWi95g/9R0OEREZicr8MRXf5N5moYOIiDS3ciWQlga0asVfDahRlJbUfSVxSUkJVqxoj8xMK7i7l2HkSPXtnZ2d4erq2hRhEhGZpILSUlhXVCDM3R2tW7Sssd35rEysuXkTr7RsWWO781mZWJOcjPJyw7irg19ZqKFY6CAiIiIiIiKiOhUpipB0MwnhC8JhI1X/OJTSklKkJafhXs4lAO64e+8OXpzyotq2Lg4u2LZxG4sdRERakjs4wE8mq3F+Wl5ene2q2hCZChY6iIiIiIiIiKhOpYWlqLCsgDxEDlcf9cWJzCuZuJl8Exa2FkAJYG1vjQ5hHaq1y8/KR9qhNCgUChY6iIiIqMFY6CAiIiIiIiIijTm0dIDMU/0VwnmZlVcISywsKv9rKamxLREREVFjsdB3AERERERERERERERERPXFQgcRERERERERERERERktFjqIiIiIiIiIiIiIiMhosdBBRERERERERERERERGi4UOIiIiIiIiIiIiIiIyWlb6DsDcdOlSjPz71+Dcqrm+QyEiIiPSpUsxiu9fQRevQn2HQkRExqRnT8DbG3B11Xck1ZSWlCI5ObnG+cnJySgvK9dhRNSYmvnchPBQwK5Zvr5DISKiBigprT1fA0BJSQlsbGwa1KZDBzlcXS3h7i4BIK1PqGTmWOjQsQ0bMvDCa2/Ab9h0AF76DoeIiIzEhg0ZmP3aq1g9Sg7AUd/hEBGRsdi/X98RqFWkKELSzSSELwiHjVT9jx5F94uQlp6GdqXtdBwdNYZ+05fBo52HvsMgIqIGuFdUhJtJSVj85puwkaovPpSUliIpNQ1tvL1gZaX+p2ZN2lgDcLcG7IQLsrO3wdUAL9Igw8ZCBxEREREREelUaWEpKiwrIA+Rw9VH/Q8ZmVcykbw9GWWlZTqOjoiIiACgoLQU1hUVCHN3R+sWLdW2OZ+ViTU3b+KVli0b1AYA0gvy8U1GBhQKBQsdpDUWOoiIiIiIiEgvHFo6QOYpUzsvLzNPx9EQERGROnIHB/jJ1OfrtLy8RmlD1FAsdBARERERERERERER1VN2djYUCkWtbZydnXmnShNioUPHpkyR4+r5z5CW3Rwh76TqOxwiIjISU6bIce78Jky5U4if31up73CIiMhYjBgBZGejWCZD6tq1NTbjwN/U2E58OReitCXsmuVj2Efr9R2ORjT5kQrgD1VERE3hteh/Iz3fDlnl6foORWvZ2dmY8vJEFObcrbWdXXMXbIji+CNNhYUOHbt8WYqCvM6oSCsEwEIHERFp5vJlKe7lPYLLqbf1HQoRERmTc+eAtDQobKV4ccqLNTbjwN/U2O7daoOiey3g0DJH36FoJDs7GxMnTcTdgtp/pAIAFwcXbNvIH6qIiBrTn7e9kVHQHFJrZwD5+g5HKwqFAoU5d/GaXA4PB0e1bTj+SNNjoYOIiIiIyMTw1nl6WIWoQKtnWsHRTf3JNwf+JnOnUChwt+Burf+fAEB+Vj7SDqXxhyoioiaUnJxc6/ySkhLY2NjU2kaT77qNfSefh4MjxyDRIxY6iIiIiIhMSHZ2Nv796ksozrtTazupUwus27SdP9SZEUc3Rw78TVSH2v4/ISKipldWVobFb74JG6lU7fyS0lIkpaahjbcXrKxq/mm7rsdEafq4KU2WRYaBhQ4iIiIiIhOiUChQnHcHbz3hDO8W9mrbpNy5j09/u8MrkomIiIjI4IS5u6N1i5Zq553PysSamzfxSsuWNbbR5DFRmjxuStNlkWFgoYOIiIiIyAR5t7BHW/eaT9qAum/TJyJqSqUlpbU+niQ5ORnlZeWNsq66Hk+izbrqihsw3scD8tGHRGQI5A4ONT4CKi0vr8422uDjpkyHWRc6vvzyS3z88cdIT09Hly5dsHr1ajzxxBP6DouIiIwAcwgREdUH8wdRpSJFEZJuJiF8QThspOqfs150vwhp6WloV9quQevSZKBxTdelSdyAcQ5YrumA7MbYN1PA/EFk/EpKDa9Q3lgF7sYe76Q+zLbQsWvXLsyePRtffvkl+vfvj/Xr1yMkJARXrlyBj4+PvsMjIiIDxhxCRET1wfxB9D+lhaWosKyAPEQOVx/1P3hkXslE8vZklJWWNWhdmgw0rum6NInbWAcs12Q7GWvfjB3zB5Hxu1dUhJtJSbWOPwLodjwQTccpMZbxTsy20LFy5UpMmjQJ//rXvwAAq1evxuHDh7Fu3TpERkbqOToiIsOohpN6zCFERFQfzB9E1Tm0dKhx8O+8zLxGXVdtA41ru67a4jZ2HJDd8DB/EBm/gtJSWFdU1Dr+iK7HA9FknBJjGu/ELAsdJSUliI+Px9y5c1XeDw4OxokTJ9R+pri4GMXFxcrXubm5AKDRj5APqqgoA2CF8vJ83Ll1TW2bgpwsFBcW4sqVK8jLa9wvdkRkHO7evYuIJZHIL6r76rXmjnb4et0XaNlSfaKsiZOTEyQSSX1DNFva5pDGzh+l5QpcSM6psV3a3ULcLyxmDiEyYykpKSgqLsFf/yiQV1Sqtk3a3UKUlpUjLy9P6+MRwBxSH3o7B6moAADkCYGcWzkoKSxR20zxjwKiQiA3NRdWEvWniY3VRtfrY9z6iamiPBeANSrKcpF9I7vJ1leQXYDiOr77pKSkoKSopMH/DzRmTIZIk+1UkF2AcuYPndLnb1h5eXkoKy/HjXs5KChV/53iVp4CFUIgMTcXFZbq/5/QpE1jLsuUYzLluEvLcwFYQoi8Bi8royAf94vrzg3FJSW17t+NuayquAvLympsc7+0tM51NaaquO+XljYoJk2WU7WssvL65RCN8ocwQ2lpaQKA+P3331XeX7JkifD391f7mYULFwoAnDhx4mQyU25uri4OuSZH2xzC/MGJEydTnJhDtMdzEE6cOHFi/qgP5g9OnDhx0ix/mOUdHVUergIJIWqsDM2bNw/h4eHK1xUVFbh79y5atGih1dUICoUC3t7eSElJgbOzc/0CN1Cm3DfAtPvHvhmvhvTPycmpiaIyD5rmEOYPzZhy/9g342XK/Wto35hD6k8f5yDmxpT/321s3Fba4fbSjrrtxfxRf02ZP7hvcxsA3AYAtwFguNtAk/xhloWOli1bwtLSEhkZGSrvZ2Vlwd3dXe1npFIppA8NFNOsWbN6x+Ds7GxQO0tjMuW+AabdP/bNeJl6/wyJtjmE+UM7ptw/9s14mXL/TLlvhsYQzkHMDfdvzXFbaYfbSzvcXg2jy/zBvxW3AcBtAHAbAMa5DSz0HYA+2NjYoFevXoiJiVF5PyYmBv369dNTVEREZAyYQ4iIqD6YP4iIqD6YP4iINGOWd3QAQHh4OCZOnIjevXsjMDAQGzZswK1btzBt2jR9h0ZERAaOOYSIiOqD+YOIiOqD+YOIqG5mW+gYP3487ty5gw8//BDp6eno2rUrDh06BF9f3yZdr1QqxcKFC6vdQmgKTLlvgGn3j30zXqbeP0Oljxxi6n9rU+4f+2a8TLl/ptw3Q6avcxBzw/1bc9xW2uH20g63V+Np6vzBvxW3AcBtAHAbAMa9DSRCCKHvIIiIiIiIiIiIiIiIiOrDLMfoICIiIiIiIiIiIiIi08BCBxERERERERERERERGS0WOoiIiIiIiIiIiIiIyGix0EFEREREREREREREREaLhQ4iIiIiIiIiIiIiIjJaLHQ0osjISPTp0wdOTk5wc3PDqFGjkJCQUOfn4uLi0KtXL9ja2qJNmzb46quvdBCtdurTtx9++AGDBw+Gq6srnJ2dERgYiMOHD+soYu3U929X5ffff4eVlRUeffTRpguynurbt+LiYsyfPx++vr6QSqVo27YtvvnmGx1ErLn69m379u3o3r077O3t4eHhgVdffRV37tzRQcTaWbduHR555BE4Ozsr/x/66aefav2MMRxPSL1jx45h+PDh8PT0hEQiwb59++r8jLH8vbXtmzHlj/r83aoYcu6oUp/+GUP+AOrXN2PJH6b8nZRMz5dffonWrVvD1tYWvXr1wm+//VZre0320z179qBz586QSqXo3Lkz9u7dq/V6w8LCIJFIVKa+ffs2rLONQB/bS5PjpRACERER8PT0hJ2dHYKCgnD58uUG9bUxGOr2MsT9Sx/bSpN8Zaj7ljFqir+xsdFmGxjTOYk2tN0PqhjDuYumtN0GxnJ+ow1tt4GxnAdBUKMZMmSI2LRpk/jzzz/FhQsXxLBhw4SPj4/Iz8+v8TM3b94U9vb24o033hBXrlwRX3/9tbC2thbff/+9DiOvW3369sYbb4jly5eLP/74Q1y7dk3MmzdPWFtbi3Pnzukwcs3Up39V7t27J9q0aSOCg4NF9+7dmz5YLdW3byNGjBABAQEiJiZGJCYmitOnT4vff/9dR1Frpj59++2334SFhYX47LPPxM2bN8Vvv/0munTpIkaNGqXDyDWzf/9+cfDgQZGQkCASEhLEe++9J6ytrcWff/6ptr2xHE9IvUOHDon58+eLPXv2CABi7969tbY3pr+3tn0zpvyhbd+qGHruqFKf/hlD/hBC+74ZU/4w5e+kZFp27twprK2txddffy2uXLki3njjDeHg4CCSk5PVttdkPz1x4oSwtLQUS5cuFVevXhVLly4VVlZW4tSpU1qtNzQ0VAwdOlSkp6crpzt37jTdxtCAvraXJsfLZcuWCScnJ7Fnzx5x6dIlMX78eOHh4SEUCkWjbwdNGfL2MrT9S1/bSpN8ZYj7ljFqir+xsdF2GxjTOYmmtN0GVYzl3EUT9dkGxnJ+oyltt4ExnQex0NGEsrKyBAARFxdXY5t33nlHdOzYUeW9qVOnir59+zZ1eA2iSd/U6dy5s1i0aFETRdV4tOnf+PHjxfvvvy8WLlxoFAd8Tfr2008/CZlMpveTOW1p0rePP/5YtGnTRuW9zz//XHh5eTV1eI2iefPm4j//+Y/aecZ6PKHqNPnR1Vj/3toUAx5kDPlDm74ZW+4QQrP+GWv+0KRvxpw/TPk7KRm3xx57TEybNk3lvY4dO4q5c+eqba/Jfjpu3DgxdOhQlTZDhgwRL7zwglbrDQ0NFSNHjtSqP01NX9vrQeqOlxUVFUIul4tly5Yp3ysqKhIymUx89dVXdfarqRjq9hLC8PYvQ9hWQlTPV4a6bxmjpvgbGxttt4E6xnBOUpv6bgNjPHepibbbwFjPb2qj7TYwpvMgPrqqCeXm5gIAXFxcamxz8uRJBAcHq7w3ZMgQnD17FqWlpU0aX0No0reHVVRUIC8vT6vP6Ium/du0aRNu3LiBhQsX6iKsRqFJ3/bv34/evXtjxYoVaNWqFfz9/TFnzhwUFhbqKsx60aRv/fr1Q2pqKg4dOgQhBDIzM/H9999j2LBhugqzXsrLy7Fz504UFBQgMDBQbRtjPZ5Q/ZjT39uY8ocmjDF3aMpY84cmjDV/AKb9nZSMV0lJCeLj46vtd8HBwThx4oTaz2iyn9bUpmqZ2qw3NjYWbm5u8Pf3x+TJk5GVlaV9RxuJvraXJhITE5GRkaGyHKlUigEDBmi1nMZkyNuriqHsX4a0rR7OV4a4bxmjpvobG5P6bIOHGfs5SX23gSmdu9RnG5ja+U19toExnQdZ6TsAUyWEQHh4OB5//HF07dq1xnYZGRlwd3dXec/d3R1lZWW4ffs2PDw8mjpUrWnat4d9+umnKCgowLhx45owuobTtH/Xr1/H3Llz8dtvv8HKyjj+V9K0bzdv3sTx48dha2uLvXv34vbt25g+fTru3r1rsM8h1LRv/fr1w/bt2zF+/HgUFRWhrKwMI0aMwJo1a3QYreYuXbqEwMBAFBUVwdHREXv37kXnzp3VtjXG4wnVnzn9vY0lf2jCGHOHNowxf2jK2PJHFVP+TkrG7fbt2ygvL1e732VkZKj9jCb7aU1tqpap6XpDQkIwduxY+Pr6IjExER988AEGDhyI+Ph4SKXSeve7vvS1vTRR1VbdcpKTkzVeTmMy5O0FGNb+ZSjbSl2+MsR9yxg11d/YmNRnGzzM2M9J6rMNTO3cpT7bwNTOb+qzDYzpPMj491ID9frrr+PixYs4fvx4nW0lEonKayGE2vcNhTZ9q/Ltt98iIiICP/74I9zc3JowuobTpH/l5eWYMGECFi1aBH9/fx1G1zCa/u0qKiogkUiwfft2yGQyAMDKlSsxZswYrF27FnZ2droIVyua9u3KlSuYNWsWFixYgCFDhiA9PR1vv/02pk2bho0bN+ooWs116NABFy5cwL1797Bnzx6EhoYiLi6uxmKHsR1PqGHM4e9tTPmjLsaaO7RhjPlDU8aWP6qY8ndSMg3q9rva9jlN9lNNlllXm/Hjxyv/3bVrV/Tu3Ru+vr44ePAgRo8eXVuXmpS+tldTxKYLhrq9DHH/0ve2qi1fGeK+ZYya4m9sbOq7L5nSOYmm28CUz1202Q9M9fxGm21gTOdBLHQ0gZkzZ2L//v04duwYvLy8am0rl8urVcyysrJgZWWFFi1aNGWY9aJN36rs2rULkyZNwu7duzFo0KAmjrBhNO1fXl4ezp49i/Pnz+P1118HUHnwE0LAysoKR44cwcCBA3UVtka0+dt5eHigVatWyoM4AHTq1AlCCKSmpqJ9+/ZNHa5WtOlbZGQk+vfvj7fffhsA8Mgjj8DBwQFPPPEEFi9ebHBXptjY2KBdu3YAgN69e+PMmTP47LPPsH79+mptje14Qg1jDn9vY8ofmjDG3KEtY8sf2jC2/AGY9ndSMn4tW7aEpaWl2v3u4asMq2iyn9bUpmqZ9VkvUHl88/X1xfXr1zXrYCPT1/bShFwuB1B5BfiDx0Jtl9OYDHl7qaPP/csQtlVN+coQ9y1j1FR/Y2NS32M/YDrnJNpuA1M8d6nPfmBq5zf12QbGdB7EMToakRACr7/+On744Qf88ssvaN26dZ2fCQwMRExMjMp7R44cQe/evWFtbd1UoWqtPn0DKqveYWFh2LFjh0E+u62Ktv1zdnbGpUuXcOHCBeU0bdo05dX3AQEBOoq8bvX52/Xv3x///PMP8vPzle9du3YNFhYWGhe4dKE+fbt//z4sLFQPfZaWlsrlGTohBIqLi9XOM5bjCTUOU/97G0v+0IYx5Y76Mpb8UR/GlD9M+TspmQ4bGxv06tWr2n4XExODfv36qf2MJvtpTW2qllmf9QLAnTt3kJKSoreTeX1tL020bt0acrlcZTklJSWIi4vTajmNyZC3lzr63L/0ua3qyleGuG8Zo6b6GxuT+h77TemcRNttYIrnLvXZD0zt/KY+28CYzoPQuGObm7d///vfQiaTidjYWJGenq6c7t+/r2wzd+5cMXHiROXrmzdvCnt7e/Hmm2+KK1euiI0bNwpra2vx/fff66MLNapP33bs2CGsrKzE2rVrVT5z7949fXShVvXp38MWLlwounfvroNotVOfvuXl5QkvLy8xZswYcfnyZREXFyfat28v/vWvf+mjCzWqT982bdokrKysxJdffilu3Lghjh8/Lnr37i0ee+wxfXShVvPmzRPHjh0TiYmJ4uLFi+K9994TFhYW4siRI0II4z2ekHp5eXni/Pnz4vz58wKAWLlypTh//rxITk4WQhj331vbvhlT/tC2bw8z1NxRRdv+GUv+EEL7vhlT/jDl76RkWnbu3Cmsra3Fxo0bxZUrV8Ts2bOFg4ODSEpKEkLUbz/9/fffhaWlpVi2bJm4evWqWLZsmbCyshKnTp3SeL15eXnirbfeEidOnBCJiYni119/FYGBgaJVq1ZCoVDoaOtUp6/tVdfxUgghli1bJmQymfjhhx/EpUuXxIsvvig8PDy4vdRsL0Pcv/S1rTTJV4a4bxmjpvgbGxttt4ExnZNoSttt8DBDP3fRhLbbwJjObzSl7TYwpvMgFjoaEQC106ZNm5RtQkNDxYABA1Q+FxsbK3r06CFsbGyEn5+fWLdunW4D10B9+jZgwAC1nwkNDdV5/HWp79/uQYZ6wK9v365evSoGDRok7OzshJeXlwgPD1f5wmkI6tu3zz//XHTu3FnY2dkJDw8P8dJLL4nU1FTdBq+B1157Tfj6+gobGxvh6uoqnn76aWWRQwjjPZ6Qer/++mutx0xj/ntr2zdjyh/1+bs9yFBzR5X69M8Y8ocQ9eubseQPU/5OSqZn7dq1yu87PXv2FHFxccp59d1Pd+/eLTp06CCsra1Fx44dxZ49e7Ra7/3790VwcLBwdXUV1tbWwsfHR4SGhopbt241XsfrSR/bq67jpRBCVFRUiIULFwq5XC6kUql48sknxaVLlxq17/VhiNvLUPcvfWwrTfKVoe5bxqgp/sbGRpttYEznJNrQdj94kKGfu2hK221gLOc32tB2GxjLeZBECEO7x4SIiIiIiIiIiIiIiEgzHKODiIiIiIiIiIiIiIiMFgsdRERERERERERERERktFjoICIiIiIiIiIiIiIio8VCBxERERERERERERERGS0WOoiIiIiIiIiIiIiIyGix0EFEREREREREREREREaLhQ4iIiIiIiIiIiIiIjJaLHQQ6UlsbCwkEgnu3btXazs/Pz+sXr1aJzHpiqZ9JyKixhEREYFHH31U32E0OlPMkURkOIKCgjB79mx9h2HQmiK/6Hu781yFiBpDRkYGBg8eDAcHBzRr1kzf4agwhHODzZs3q2wXfcS0b98+tGvXDpaWlpg9e3a1mMj4sNBBpCf9+vVDeno6ZDIZgOoH+SpnzpzBlClTdBwdEREZK4lEgn379qm8N2fOHPz888/6CYiIyAyFhYVh1KhRBr9Mc6euqPLweRoRUX2sWrUK6enpuHDhAq5du6bvcAyePs5Xpk6dijFjxiAlJQUfffRRvZbBC68Mi5W+AyAyVzY2NpDL5XW2c3V11UE0RERkyhwdHeHo6KjvMIiISAdKS0thbW2t7zD0qiHbQNPzNCKi2ty4cQO9evVC+/bt670Mczqe6/p8JT8/H1lZWRgyZAg8PT11tl5qWryjgwxCUFAQZs2ahXfeeQcuLi6Qy+WIiIgAACQlJUEikeDChQvK9vfu3YNEIkFsbCyA/91efPjwYfTo0QN2dnYYOHAgsrKy8NNPP6FTp05wdnbGiy++iPv372sc0+uvv47XX38dzZo1Q4sWLfD+++9DCKFsk5OTg1deeQXNmzeHvb09QkJCcP36deX85ORkDB8+HM2bN4eDgwO6dOmCQ4cOqcR87949xMbG4tVXX0Vubi4kEgkkEomy/w9Wh1988UW88MILKnGWlpaiZcuW2LRpEwBACIEVK1agTZs2sLOzQ/fu3fH9999r1OeqmA4ePIju3bvD1tYWAQEBuHTpkkq7EydO4Mknn4SdnR28vb0xa9YsFBQUKOdHRUWhd+/ecHJyglwux4QJE5CVlVXjegsLCzFs2DD07dsXd+/eRUlJCV5//XV4eHjA1tYWfn5+iIyM1KgPRGT6DDVn1BRTldzcXEyZMgVubm5wdnbGwIED8d///lelzeLFi+Hm5gYnJyf861//wty5c1Vu4T5z5gwGDx6Mli1bQiaTYcCAATh37pxyvp+fHwDgueeeg0QiUb5+8Fbww4cPw9bWttojOWbNmoUBAwYoX9d1rK+Nn58fPvroI0yYMAGOjo7w9PTEmjVrtNoeN27cwMiRI+Hu7g5HR0f06dMHR48erXW9mzZtgkwmQ0xMDADg+++/R7du3WBnZ4cWLVpg0KBBGveBiAxbXd/VS0pK8M4776BVq1ZwcHBAQECAMg8AwJ07d/Diiy/Cy8sL9vb26NatG7799tta1xkdHQ2ZTIatW7fW2i4iIgJbtmzBjz/+qPxuX7XuS5cuYeDAgcrj0pQpU5Cfn19nf2taZlXe++677xAUFARbW1tERUWhoqICH374Iby8vCCVSvHoo48iOjpauTx1j2e6cOECJBIJkpKSlO99/fXX8Pb2hr29PZ577jmsXLlS7V3o27Ztg5+fH2QyGV544QXk5eXV2ScAKCgowCuvvAJHR0d4eHjg008/rdZG3Z2KzZo1w+bNmwGgxm1Q1984LCwMcXFx+Oyzz5TbNCkpSe222bNnD7p06QKpVAo/P79qcfr5+WHp0qV47bXX4OTkBB8fH2zYsEGjbUBEDXfgwAE0a9YMFRUVAP53PHv77beVbaZOnYoXX3yxzmPD+vXr0apVK+WyqowYMQKhoaEq6+zVqxdsbW3Rpk0bLFq0CGVlZQAqjwl79uzB1q1bIZFIEBYWBgC4desWRo4cCUdHRzg7O2PcuHHIzMxULrPqO/s333yDNm3aQCqVQggBiUSC9evX49lnn4W9vT06deqEkydP4u+//0ZQUBAcHBwQGBiIGzduaLXd1q9frzzGjx07VuW4V9d5R1W8Pj4+kEql8PT0xKxZs5Tz6srDD3v40VVVdzF+8skn8PDwQIsWLTBjxgyUlpbWex1VYmNj4eTkBAAYOHCgSp5+UF3nI0FBQUhOTsabb76pzCPA/57YcvjwYXTq1AmOjo4YOnQo0tPTVZa/adMmdOrUCba2tujYsSO+/PJLlb7V9rtYbdverAkiAzBgwADh7OwsIiIixLVr18SWLVuERCIRR44cEYmJiQKAOH/+vLJ9Tk6OACB+/fVXIYQQv/76qwAg+vbtK44fPy7OnTsn2rVrJwYMGCCCg4PFuXPnxLFjx0SLFi3EsmXLNI7J0dFRvPHGG+Kvv/4SUVFRwt7eXmzYsEHZZsSIEaJTp07i2LFj4sKFC2LIkCGiXbt2oqSkRAghxLBhw8TgwYPFxYsXxY0bN8SBAwdEXFycSsw5OTmiuLhYrF69Wjg7O4v09HSRnp4u8vLyhBBC+Pr6ilWrVgkhhDhw4ICws7NTzqt6z9bWVuTm5gohhHjvvfdEx44dRXR0tLhx44bYtGmTkEqlIjY2ts4+V8XUqVMnceTIEXHx4kXx7LPPCj8/P2WfLl68KBwdHcWqVavEtWvXxO+//y569OghwsLClMvZuHGjOHTokLhx44Y4efKk6Nu3rwgJCam2npycHHHv3j3x+OOPi0GDBon8/HwhhBAff/yx8Pb2FseOHRNJSUnit99+Ezt27NDo70ZEps9Qc0ZNMQkhREVFhejfv78YPny4OHPmjLh27Zp46623RIsWLcSdO3eEEEJERUUJW1tb8c0334iEhASxaNEi4ezsLLp3765cz88//yy2bdsmrly5Iq5cuSImTZok3N3dhUKhEEIIkZWVJQCITZs2ifT0dJGVlSWEEGLhwoXK5ZSVlQl3d3fxn//8R7ncqvfWr18vhNDsWF8bX19f4eTkJCIjI0VCQoL4/PPPhaWlpVbb48KFC+Krr74SFy9eFNeuXRPz588Xtra2Ijk5WWU9VTny448/Fi4uLuLkyZNCCCH++ecfYWVlJVauXCkSExPFxYsXxdq1a1VyKBEZr7q+q0+YMEH069dPHDt2TPz999/i448/FlKpVFy7dk0IIURqaqr4+OOPxfnz58WNGzeUx6lTp06prOONN94QQgjx7bffCicnJ7Fv3746Y8vLyxPjxo0TQ4cOVX63Ly4uFgUFBcLT01OMHj1aXLp0Sfz888+idevWIjQ0tN7LrMp7fn5+Ys+ePeLmzZsiLS1NrFy5Ujg7O4tvv/1W/PXXX+Kdd94R1tbWyv4/+H28yvnz5wUAkZiYKIQQ4vjx48LCwkJ8/PHHIiEhQaxdu1a4uLgImUym/MzChQuFo6Ojsk/Hjh0TcrlcvPfee3X2SQgh/v3vfwsvLy+Vc4+qv2sVAGLv3r0qn5PJZGLTpk1CCFHjNqjrb3zv3j0RGBgoJk+erNymZWVl1bbN2bNnhYWFhfjwww9FQkKC2LRpk7Czs1OuX4jKfOTi4iLWrl0rrl+/LiIjI4WFhYW4evWqRtuBiBrm3r17wsLCQpw9e1YIIcTq1atFy5YtRZ8+fZRt/P39xbp16+o8Nty5c0fY2NiIo0ePKj979+5dYWNjIw4fPiyEECI6Olo4OzuLzZs3ixs3bogjR44IPz8/ERERIYSo/E4+dOhQMW7cOJGeni7u3bsnKioqRI8ePcTjjz8uzp49K06dOiV69uwpBgwYoFzPwoULhYODgxgyZIg4d+6c+O9//ysqKioEANGqVSuxa9cukZCQIEaNGiX8/PzEwIEDRXR0tLhy5Yro27evGDp0qEbbq2o9AwcOFOfPnxdxcXGiXbt2YsKECco2dZ137N69Wzg7O4tDhw6J5ORkcfr0aZXfy+rKw5s2baqWTx487wkNDRXOzs5i2rRp4urVq+LAgQPVfpOrax01KS4uFgkJCQKA2LNnjzKnPhxTXecjd+7cEV5eXuLDDz9U5pGqvllbW4tBgwaJM2fOiPj4eNGpUyeV7bthwwbh4eGhzFt79uwRLi4uYvPmzUKI2n8Xq2vbmzMWOsggDBgwQDz++OMq7/Xp00e8++67Wv1o9WAiioyMFADEjRs3lO9NnTpVDBkyROOYOnXqJCoqKpTvvfvuu6JTp05CCCGuXbsmAIjff/9dOf/27dvCzs5OfPfdd0IIIbp166ZMdA97+Av0wwfUKg/+iFNSUiJatmwptm7dqpz/4osvirFjxwohhMjPzxe2trbixIkTKsuYNGmSePHFF+vsc1VMO3fuVL53584dYWdnJ3bt2iWEEGLixIliypQpKp/77bffhIWFhSgsLFS73D/++EMAUP64VLWev/76S3Tv3l2MHj1aFBcXK9vPnDlTDBw4UGXbExFVMdScUVNMQlSeKDg7O4uioiKVNm3btlUWFwICAsSMGTNU5vfv31/lC//DysrKhJOTkzhw4IDyPXU/CD184jBr1iwxcOBA5evDhw8LGxsbcffuXSFE/Y71D/L19a12ojV+/Hhl0VuT7aFO586dxZo1a1TWs2rVKjF37lzh4eEhLl68qJwXHx8vAIikpKQ64yUi41Pbd/W///5bSCQSkZaWpvKZp59+WsybN6/GZT7zzDPirbfeUlnHG2+8IdauXStkMpn45ZdfNI4vNDRUjBw5UuW9DRs2iObNmysv7hFCiIMHDwoLCwuRkZFRr2VW5b3Vq1ervO/p6SmWLFmi8l6fPn3E9OnThRCaFTrGjx8vhg0bprKMl156qdoPU/b29sofvoQQ4u233xYBAQF19icvL0/Y2NioPfeoT6Hj4W2gTk1/4wc9vG0mTJggBg8erNLm7bffFp07d1a+9vX1FS+//LLydUVFhXBzcxPr1q2rMyYiahw9e/YUn3zyiRBCiFGjRoklS5YIGxsboVAoRHp6ugBQY/Hx4WPDiBEjxGuvvaZ8vX79eiGXy0VZWZkQQognnnhCLF26VGUZ27ZtEx4eHsrXI0eOVClkHzlyRFhaWopbt24p37t8+bIAIP744w8hROUx1draWnmxUhUA4v3331e+PnnypAAgNm7cqHzv22+/Fba2trVvpP+zcOFCYWlpKVJSUpTv/fTTT8LCwkL5Y/3DHj7v+PTTT4W/v7/yotgHaZKHNSl0+Pr6Kre5EEKMHTtWjB8/XuN11Obhc0R1MalT0/nIgzZt2iQAiL///lv53tq1a4W7u7vytbe3d7ULej/66CMRGBgohKj9d7Hatr2546OryGA88sgjKq89PDxqfdxRXctwd3eHvb092rRpo/KeNsvs27ev8tYzAAgMDMT169dRXl6Oq1evwsrKCgEBAcr5LVq0QIcOHXD16lUAlY8BWbx4Mfr374+FCxfi4sWLWvXnYdbW1hg7diy2b98OoPJW7x9//BEvvfQSAODKlSsoKirC4MGDlc83dHR0xNatW7W6hTEwMFD5bxcXF5U+xcfHY/PmzSrLHzJkCCoqKpCYmAgAOH/+PEaOHAlfX184OTkhKCgIQOVtmg8aNGgQ2rRpg++++w42NjbK98PCwnDhwgV06NABs2bNwpEjR7TfWERk0gwxZ9QWU3x8PPLz89GiRQuV42diYqLy+JyQkIDHHntMZRkPv87KysK0adPg7+8PmUwGmUyG/Pz8asfXurz00kuIjY3FP//8AwDYvn07nnnmGTRv3lwZb13H+ro8mEuqXj+YS+raHgUFBXjnnXfQuXNnNGvWDI6Ojvjrr7+q9fXTTz/F+vXrcfz4cXTr1k35fvfu3fH000+jW7duGDt2LL7++mvk5ORotZ2IyLDV9F397NmzEELA399f5RgTFxenPMaUl5djyZIleOSRR5THoiNHjlQ7xuzZswezZ8/GkSNH8NRTTzUo3qtXr6J79+5wcHBQvte/f39UVFQgISGhQcvu3bu38t8KhQL//PMP+vfvr9Kmf//+yuOwJjTJS0DlI1qqHgECaJ6Tb9y4gZKSErXnHvXx4DYANP8b1+Xq1atqt2XVeWGVB78HSCQSyOVyrb+bEFH9BQUFITY2FkII/Pbbbxg5ciS6du2K48eP49dff4W7uzs6duyo0bHhpZdewp49e1BcXAyg8rvyCy+8AEtLSwCV32U//PBDlRwzefJkpKen1/jo26tXr8Lb2xve3t7K96q+5z54bPb19VU7VuvD5y4AVL77uru7o6ioCAqFQqPt5ePjAy8vL+XrwMBAlXxU13nH2LFjUVhYiDZt2mDy5MnYu3ev8tFd586dqzMPa6JLly7KbQ6o5pfGWkdtND0fUcfe3h5t27ZVG3t2djZSUlIwadIkldgXL16sjL2238Vq2/bmjoORk8F4eIAliUSCiooKWFhU1uPEA2NjPPhMvpqWIZFIalxmY3gwnoffrzrh+te//oUhQ4bg4MGDOHLkCCIjI/Hpp59i5syZ9V7vSy+9hAEDBiArKwsxMTGwtbVFSEgIACj7dvDgQbRq1Urlc1KptN7rBKDsU0VFBaZOnar2+X8+Pj4oKChAcHAwgoODERUVBVdXV9y6dQtDhgxBSUmJSvthw4Zhz549uHLlikqC7tmzJxITE/HTTz/h6NGjGDduHAYNGqTxWCNEZPoMMWfU9vmKigp4eHioffbrg886f/AHO6B6rgkLC0N2djZWr14NX19fSKVSBAYGVju+1uWxxx5D27ZtsXPnTvz73//G3r17lWM9VcVb27G+vh7MJXVtj7fffhuHDx/GJ598gnbt2sHOzg5jxoyp1tcnnngCBw8exHfffYe5c+cq37e0tERMTAxOnDiBI0eOYM2aNZg/fz5Onz6N1q1b17sPRGQcLC0tER8fr/IDCQDlQKeffvopVq1ahdWrV6Nbt25wcHDA7Nmzqx1jHn30UZw7dw6bNm1Cnz59qh2ntfHgecLDGrJcACrFk5qW+eD6NcmX6uJVdw5U3/xZ0/nUwyQSSbW26nL7w9tA07+xJnE25XYgosYRFBSEjRs34r///S8sLCzQuXNnDBgwAHFxccjJyVGORafJsWH48OGoqKjAwYMH0adPH/z2229YuXKlcn5FRQUWLVqE0aNHV4vD1tZWbXw15YCH31d3PAeqn7vU9F59jztVn6/6b13nHd7e3khISEBMTAyOHj2K6dOn4+OPP0ZcXBwqKirqzMOaqOv8qjHWURtNz0c0jb0qd1T14euvv1a5eBqAsi+1/S5W27Y3l8Hra8JCBxm8qkp2eno6evToAQAqg8w2pVOnTlV73b59e1haWqJz584oKyvD6dOn0a9fPwCVgxpeu3YNnTp1Un7G29sb06ZNw7Rp0zBv3jx8/fXXagsdNjY2KlcE1aRfv37w9vbGrl278NNPP2Hs2LHKuyE6d+4MqVSKW7duqQwoW59+V/2QlZOTg2vXrqFjx44AKg+2ly9fRrt27dR+9tKlS7h9+zaWLVumvFLh7NmzatsuW7YMjo6OePrppxEbG4vOnTsr5zk7O2P8+PEYP348xowZg6FDh+Lu3btwcXGpd7+IyPTpM2fUpmfPnsjIyICVlZVygPCHdejQAX/88QcmTpyofO/h4+dvv/2GL7/8Es888wwAICUlBbdv31ZpY21trVE+mTBhArZv3w4vLy9YWFhg2LBhKvHWdqzXhLoc+mAuqWt7/PbbbwgLC8Nzzz0HAMjPz1cZILfKY489hpkzZ2LIkCGwtLRUGXRSIpGgf//+6N+/PxYsWABfX1/s3bsX4eHh9e4XERmOmr6r9+jRA+Xl5cjKysITTzyh9rNVV/u+/PLLACp/dLh+/brK93gAaNu2LT799FMEBQXB0tISX3zxhUaxqftu37lzZ2zZsgUFBQXKH7J+//13WFhYwN/fv17LVMfZ2Rmenp44fvw4nnzySeX7J06cUN6R8WC+rLqb7+F82bFjR/zxxx8q79X0vb4+2rVrB2tra7XnHg+ey7i6uqoM4Hr9+vUar5h+kCZ/Y022aefOnXH8+HGV906cOAF/f/9qP64Rkf48+eSTyMvLw+rVqzFgwABIJBIMGDAAkZGRyMnJwRtvvAFAs2ODnZ0dRo8eje3bt+Pvv/+Gv78/evXqpZzfs2dPJCQkaPVduXPnzrh16xZSUlKUv5VcuXIFubm51XKPLty6dQv//PMPPD09AQAnT55UyUeanHfY2dlhxIgRGDFiBGbMmIGOHTvi0qVLGuXhhtLFOjQ5H9E0Nz/I3d0drVq1ws2bN5VPaFGntt/Fatr2PXv21LqfpoSFDjJ4dnZ26Nu3L5YtWwY/Pz/cvn0b77//vk7WnZKSgvDwcEydOhXnzp3DmjVr8OmnnwIA2rdvj5EjR2Ly5MlYv349nJycMHfuXLRq1QojR44EAMyePRshISHw9/dHTk4OfvnllxoTmJ+fH/Lz8/Hzzz+je/fusLe3h729fbV2EokEEyZMwFdffYVr167h119/Vc5zcnLCnDlz8Oabb6KiogKPP/44FAoFTpw4AUdHR4SGhmrU7w8//BAtWrSAu7s75s+fj5YtW2LUqFEAgHfffRd9+/bFjBkzMHnyZDg4OODq1auIiYnBmjVr4OPjAxsbG6xZswbTpk3Dn3/+iY8++qjGdX3yyScoLy/HwIEDERsbi44dO2LVqlXw8PDAo48+CgsLC+zevRtyuVzlqmciInX0mTNqM2jQIAQGBmLUqFFYvnw5OnTogH/++QeHDh3CqFGj0Lt3b8ycOROTJ09G79690a9fP+zatQsXL15UeZxWu3btsG3bNvTu3RsKhQJvv/027OzsVNbl5+eHn3/+Gf3794dUKlX+gPWwl156CYsWLcKSJUswZswYlavP6jrWa+L333/HihUrMGrUKMTExGD37t04ePCgxtujXbt2+OGHHzB8+HBIJBJ88MEHNV6hFhgYiJ9++glDhw6FlZUV3nzzTZw+/f/Zu+/4qOrs/+PvkEpJAoGQAgkEkCKoYMJiUASRsuAqCiqii7AKa35RKREVsAECsSCbZemIlEUBFXR1ZYFYQJRYaMoiokIKhEQSSgYIpHF/f7DMl5BJMgmTTHs9H495wNw5c+/5zGTuuXfO3Hu/1WeffaZ+/fqpadOm+vbbb5WTk2OXHUkANaO8bfW2bdvqoYce0sMPP6w33nhDXbp0UW5urj7//HNdd911GjhwoNq0aaN169Zp+/btatSokWbPnq3s7GyL64i2bdvqiy++UK9eveTl5aWkpKRKc2vZsqU2bdqkAwcOqHHjxgoMDNRDDz2kl156SSNGjNCUKVOUk5OjJ598UsOHDzefhqSq8yzP008/rZdeekmtW7dW586dtWzZMu3Zs8d8Ctw2bdooIiJCU6ZM0fTp0/Xrr7+a93MuefLJJ3Xrrbdq9uzZuvPOO/X555/rP//5z1UffXJJgwYN9Oijj+rpp58ute9x6WiTS3r37q25c+fqpptu0oULF/Tss89a9YtVa97jli1b6ttvv1VaWpoaNGhg8UdVTz31lLp27aqXX35ZQ4cOVUpKiubOnav58+df/YsAwGYCAwPVuXNnrVq1Sn//+98lXWx+3HfffSoqKjKfUtva9f9DDz2kO++8U/v27TM3RS558cUX9ac//UkRERG67777VKdOHf3444/au3evpk+fbjG/Pn366Prrr9dDDz2kpKQkFRcXKz4+Xj179ixz6r3a4OfnpxEjRmjWrFkymUwaM2aM7r//foWGhkqqfL9j+fLlKikpUbdu3VSvXj3985//VN26ddWiRQs1bty40jp8tayp9VfLmv2Rli1b6ssvv9QDDzwgX19fNWnSxKp5T5kyRWPGjFFAQIAGDBiggoIC7dixQydPnlRCQkKF34tV9Nq7O67RAafw1ltvqaioSDExMRo7dmy5hcPWHn74YZ07d05/+MMf9Pjjj+vJJ5/UX//6V/Pjy5YtU3R0tP70pz8pNjZWhmFow4YN5g3vkpISPf744+rQoYP++Mc/ql27duVuEHfv3l1xcXEaOnSogoOD9dprr5Wb10MPPaSffvpJzZo1K3O+2JdfflkvvviiEhMT1aFDB/Xv318ff/xxlU7T8corr2js2LGKjo5WVlaWPvroI/NRI9dff722bt2qX3/9VT169FCXLl30wgsvKCwsTNLFX1wtX75c7733nq699lq98sormjVrVoXL+9vf/qb7779fvXv31i+//KIGDRro1VdfVUxMjLp27aq0tDRt2LChzE4PAFhir5pREQ8PD23YsEG33nqrHnnkEbVt21YPPPCA0tLSzF9uPfTQQ5o0aZImTJhgPlR55MiRpRoQb731lk6ePKkuXbpo+PDhGjNmjJo2bVpqWW+88YaSk5MVERFhPqrFkmuuuUZdu3bVjz/+WOaXRJWt663x1FNPaefOnerSpYtefvllvfHGG+rfv7/Vr8ff/vY3NWrUSN27d9edd96p/v37V/gLpZtvvlmffPKJXnjhBc2ZM0cBAQH68ssvNXDgQLVt21bPP/+83njjDfPpHgE4v4q21ZctW6aHH35YTz31lNq1a6e77rpL3377rflXtC+88IJuvPFG9e/fX7169VJoaKj5hz2WtGvXTp9//rlWr16tp556qtLcRo8erXbt2ikmJkbBwcH6+uuvVa9ePW3atEknTpxQ165dde+99+r222+3+igRS/Msz5gxY/TUU0/pqaee0nXXXaeNGzfqo48+0jXXXCPp4tF/q1ev1s8//6wbbrhBr776apl6efPNN2vhwoWaPXu2brjhBm3cuFHjx48v97Qs1fH666/r1ltv1V133aU+ffrolltuKfWraeliXYuIiNCtt96qBx98UBMmTLD4g7ArWfMeT5gwwXy0/qVT7l7pxhtv1Lvvvqs1a9aoU6dOevHFFzVt2jSNHDnyaoYOoAbcdtttKikpMTc1GjVqZP58X2pkWLv+7927t4KCgnTgwAE9+OCDpR7r37+//v3vfys5OVldu3bVTTfdpNmzZ1f4RbOHh4c+/PBDNWrUSLfeeqv5mqVr16612firok2bNho8eLAGDhyofv36qVOnTqW+r6psv6Nhw4ZasmSJbr75Zl1//fX67LPP9PHHH6tx48aSKq/DtlDTy7Bmf2TatGlKS0tT69atLV5bpTyjRo3Sm2++qeXLl+u6665Tz549tXz5cvN3dxV9L1bZa+/OPAxrT4wJuJlevXqpc+fOVv1iy1Vs2bJFt912m06ePMnREwDgAPr27avQ0FD985//tHcqVdKyZUuNGzdO48aNs3cqAFyUO26rO4LRo0fr559/1rZt2+ydCgAAQCmcugoAAMAB5Ofna+HCheZrTaxevVqffvqpkpOT7Z0aAMBNzZo1S3379lX9+vX1n//8RytWrOCUTQAAwCFxHhi4pYyMDDVo0KDcm6VDll1BXFxcuWOOi4uzd3oA4JBqq2ZcOp1Tjx49FB0drY8//ljr1q1Tnz59bDJ/W9m2bVuFrwcAuIOK1oPVPdqhJuZ5tb777jv17dtX1113nRYuXKg5c+Zo1KhRVj3XXfe5AMDeOnbsWO6699K1mlzdgAEDyn0NZs6cae/0UEM4dRXcUnFxsdLS0sp9vGXLlvLycr0Dno4dOyaTyWTxsYCAgDLneQcAuG/NKM+5c+eUmZlZ7uNt2rSpxWwAwD5+++23ch9r1qxZqQu22nOe9kT9BAD7SE9PV1FRkcXHQkJC5O/vX8sZ1b7MzEydO3fO4mNBQUEKCgqq5YxQG2h0AAAAAAAAAAAAp8WpqwAAAAAAAAAAgNOi0QEAAAAAAAAAAJwWjQ4AAAAAAAAAAOC0aHQAAAAAAAAAAACnRaMDAAAAAAAAAAA4LRodAAAAAAAAAADAadHoAAAAAAAAAAAATotGBwAAAAAAAAAAcFo0OgAAAAAAAAAAgNOi0QEAAAAAAAAAAJwWjQ4AAAAAAAAAAOC0aHQAAAAAAAAAAACnRaMDAAAAAAAAAAA4LRodAAAAAAAAAADAadHoAAAAAAAAAAAATotGBwAAAAAAAAAAcFo0OgAAAAAAAAAAgNOi0QEAAAAAAAAAAJwWjQ4AAAAAAAAAAOC0aHQAAAAAAAAAAACnRaMDAAAAAAAAAAA4LRodAAAAAAAAAADAadHoAAAAAAAAAAAATotGBwAAAAAAAAAAcFo0OgAAAAAAAAAAgNOi0QEAAAAAAAAAAJwWjQ4AAP5nwYIFuv766xUQEKCAgADFxsbqP//5j/nxkSNHysPDo9TtpptusmPGAAAAAAAAoNFRTYZhyGQyyTAMe6cCALCR5s2b65VXXtGOHTu0Y8cO9e7dW4MGDdK+ffvMMX/84x+VlZVlvm3YsKFKy6B+AACqixoCAKgO6gcAd+BhsJarFpPJpMDAQOXl5SkgIMDe6aAcs2dLJpMUECAlJNg7GwDOKCgoSK+//roeffRRjRw5UqdOndKHH35Y7fk5XP1gRQmUi48HHI3D1RDUPlZMAKqhWvWD9Q0AJ0Ojo5rYyXAOzZtLmZlSs2bSkSP2zgaAMykpKdF7772nESNGaPfu3br22ms1cuRIffjhh/Lx8VHDhg3Vs2dPzZgxQ02bNi13PgUFBSooKDDfN5lMioiIcJz6wYoSKBcfDzga9kHAiglAdVSrfrC+AeBkOHUVAACX2bt3rxo0aCBfX1/FxcXpgw8+0LXXXitJGjBggN5++219/vnneuONN/T999+rd+/epRoZV0pMTFRgYKD5FhERUVtDAQAAAAAAcAte9k4AAABH0q5dO+3Zs0enTp3SunXrNGLECG3dulXXXnuthg4dao7r1KmTYmJi1KJFC33yyScaPHiwxflNmjRJCZcd6n3piA4AAAAAAADYBo0OAHZz4cIFFRYW2jsNl+bj46M6dTh4ryp8fHzUpk0bSVJMTIy+//57/f3vf9eiRYvKxIaFhalFixb69ddfy52fr6+vfH19ayxfwB3Yq140ayZ5eUkhIdL587W+eLuifgBwN+yb2Ab1AwBKKykpUVFRkb3TcApXW0NodMCl3XGHdOKEFBRk70xwpcLCQqWmpurChQv2TsWl1alTR1FRUfLx8bF3Kk7LMIxyT011/PhxHT58WGFhYbWclQ2xooSDs2e9eOMN6cIFqU4dKTW11hdvV9QPwEFRt2sE+ya2Q/1wIaxvgKtiGIays7N16tQpe6fiNK62hnAx8mriQoBA9RmGoYyMDBUVFSk8PJxf/NSQCxcu6OjRo/L29lZkZKQ8PDzsnZLDmzx5sgYMGKCIiAidPn1aa9as0SuvvKKNGzcqNjZWU6ZM0ZAhQxQWFqa0tDRNnjxZGRkZ2r9/v/z9/a1aBvUDsB71wj6oH46LGgLYHrXGdqgfjov6AdS+rKwsnTp1Sk2bNlW9evVYJ1bCFjWEIzoA1Lri4mLl5+crPDxc9erVs3c6Li04OFhHjx5VcXGxvL297Z2Ow/v99981fPhwZWVlKTAwUNdff702btyovn376ty5c9q7d69WrlypU6dOKSwsTLfddpvWrl1rdZMDQNVQL+yH+gHAXVBrbIv6AQAXT1d1qcnRuHFje6fjNK62htDoAFDrSkpKJInDmWvBpde4pKSEHQ0rLF26tNzH6tatq02bNtViNgCoF/ZD/QDgLqg1tkX9AACZr8lBA71qrraGcEwmALvhsL2ax2sMwBWwLqt9vOYA3A3rPdvgdQSA/8M6sWqu9vWi0eEGcnJydPDgwQpvOTk59k6zRsTESM2bX/wXcHQXLlzQk08+qdatW6tNmzaaP39+ubGbNm1SdHS0unTpok6dOmnFihW1mClcigOtKN25XsEx/fST9MMPF/+tjh07duihhx6yWT75+fkaNmyY2rRpo7Zt22r9+vXlxn777bfq3Lmz2rZtq9tvv11ZWVnmx8aMGaOWLVvKw8ND//3vf22WH1ATqA1XcKC6Dcdg61pzyZYtW+Tp6am5c+eap1VlfwUu4H/rm6LOnVkPA27G1rVl5MiRat68uTp37qzOnTvr6aefttm8L8epq1xcTk6OHh05WmdM+RXGNQiop6XLlyg4OLiWMqsd2dlSZqa9swCss2rVKv3000/65ZdflJeXpxtvvFG9e/dW+/btS8UZhqEHH3xQX3zxha6//nqlpaWpffv2Gjx4MNeKQNU5yIoyJydHj4x4WGfy8iqMaxAYqLdWrHS5egXHVFR08VZdMTExevvtt22Wz6xZs+Tr66vffvtNqampio2N1W233aZGjRqVijMMQw899JDefPNN9erVS7NmzVJCQoJWr14tSbr33nv1zDPP6JZbbrFZbkBNyMnJ0WN/Ga3zpyvel/Hzr6dFy1xvX8YiB6nbcBy2rjWSdPr0aT377LMaMGBAqenW7q/ARfxvfZN3/LgeeeSRCkP9/f21bNky91gPA26gJmrLxIkT9cQTT9h0nlei0eHiTCaTzpjy1ffG+xXcKNRiTM7JbCXvelcmk4miBLfl4eGhmTNn6oMPPlBubq4WL16szz77TBs3blRhYaHeffdddezYUdnZ2Ro2bJhMJpPOnz+v22+/XX//+9/l4eGh0aNHy8/PT//4xz904sQJde3aVStWrLD6i6S1a9cqLi5Onp6eCgoK0v333681a9ZoypQpFuNPnTol6eLnvHHjxvL19bXRqwHUPpPJpDN5eerdsa2aNAy0GJN7Kk+f7/uFegW7s7ZmbNmyRRMmTNCOHTuUlpammJgYxcfH65NPPlFeXp7mzJmjgQMHWr3ctWvXavny5ZKkqKgo3XrrrfrXv/6lkSNHlorbsWOHfH191atXL0nSY489pqZNm6qoqEje3t669dZbbfRKADXLZDLp/Ol8PRY7TOFBlvdljp7I1qKU1dQGuBx71RpJSkhI0NNPP61///vfpaZXdX8FruHChQvq0aNHuRdUPn78uLZt28Z6GHAC9qwttYFGh5sIbhSqsOAIe6cBlGv27Iu3ytx4o/TRR6Wn3XWXtGuX5fiEhIs3awQEBOi7777Te++9p0GDBundd99VYmKiXnvtNc2YMUPvvPOOGjZsqI8//lgNGjRQSUmJBg0apHXr1unee+/VP/7xD8XGxuq9997TypUrNXr0aHOTY8yYMfryyy8tLnfRokXq1q2bMjIy1KJFC/P0li1baseOHWXiPTw89O6772rw4MGqX7++Tp48qfXr13MBRbiEJg0DFdo4yN5pwJHVVMGQqlQ0rKkZVzp+/Liio6M1bdo0bdy4UWPHjjXvINx777367bffLC7r448/VkREhMU6kZGRUSb+yjh/f3/5+/srKytLkZGRVo0PcCThQaGKasrfLmqRG9ea//znPzp16pTuvffeMo0Oa/dX4HoaN26skJAQe6cBOD8H+PLLHrVFkmbPnq3FixcrMjJS06dPV+fOna3KtypodABwCCaTdUfhR1jo1+XklP9ck8n6HIYOHSpJuvHGG1WnTh3dcccdkqTo6GjzedAvXLigZ599Vl999ZUMw9CxY8fUuXNn3XvvvfLz89N7772n6OhoxcbG6tlnnzXPe86cOVblcPmFlwzDsBhTXFysxMRE/etf/9LNN9+s77//Xnfffbf27t2roCC+IAbg4mqqYFyat5WsqRlXql+/vgYNGiRJio2N1cGDB82Pvf/++1Yt15o6cWVcZbEAgCu4aa05deqUJk6cqOTk5HJjrK1DAAALHODLL3vsx8yYMUNhYWGqU6eOPvjgAw0YMEC//vqrGjRoYHXe1qDRAcAhBARIzZpVHmfpSNjg4PKfGxBgfQ5+fn6SJE9Pz1KngfL09FRxcbGkix3o48eP69tvv5Wfn58SEhJ0/vx5c+zPP/+s+vXr69ixYyosLDTPx5ojOiIjI5WWlqauXbtKktLT0y3+8nbPnj06evSobr75ZklS165dFR4erh9++EG33Xab9QMGAGdUUwXj0rytZE3NKO85l+JKSkrM9635JdSlOnHptBDp6ekWDxm/FHfJ6dOndfr0aYWFhVk9Pkjz58/X66+/rqysLHXs2FFJSUnq0aNHufFbt25VQkKC9u3bp/DwcD3zzDOKi4uzGLtmzRoNGzZMgwYN0ocfflhDIwBQbW5aa9LT05WVlaU//OEPkqTc3Fx9/PHHysnJ0dSpU63eXwEAlMMBvvyyx35Ms8vyvueeezRx4kQdOHBA0dHRVudtDRodABxCVU4xdaUrj+arSSdPnlRoaKj8/Pz0+++/67333jN3wzMyMhQfH69PP/1Uc+bM0bhx47RgwQJJ1h3Rcd9992nRokUaPHiw8vLytHbtWm3cuLFMXEREhI4cOaIDBw6oXbt2+u2333Tw4EG1bdvWtoMFAEfkLAWjiqz5JdR9992nefPmafny5UpNTdXWrVu1cOHCMnHR0dE6f/68tmzZol69emnRokW6++675e3tXROpu6S1a9dq3Lhxmj9/vm6++WYtWrRIAwYM0E8//WTxS73U1FQNHDhQo0eP1qpVq/T1118rPj5ewcHBGjJkSKnY9PR0TZgwocKmCQA7c9NaExERoWPHjpnvjxw5UjExMeaLx1q7vwIAKIcL1hdr9mOOHDmi5s2bS5K++eYbHT9+XG3atLF5LjQ6AKAKxowZo/vuu0+dO3dWs2bN1KdPH0kXTyf1wAMP6OWXX9a1116rpKQkxcbGau3ateZGSGWGDx+u77//3tywePrpp9WhQwdJ0kcffaSPPvpIb775pkJCQrRo0SLde++9qlOnjgzD0Pz580t1yAEArufpp5/WI488ojZt2qhOnTqaN2+e+ZSFCxcu1NGjRzVt2jTVqVNHq1atUlxcnM6dO6dmzZpp1apV5vk8/vjj+te//qXs7Gz16dNHDRo0KPdXWO5q9uzZevTRRzVq1ChJUlJSkjZt2qQFCxYoMTGxTPzChQsVGRmppKQkSVKHDh20Y8cOzZo1q1Sjo6SkRA899JCmTp2qbdu26dSpU7UxHACwiYr2VwAAKM/IkSP1+++/y9PTU3Xr1tV7772nwMBAmy+HRgcAqPT5ZVu2bKnc3Fzz/V69epkvsteiRQt99913Fuexfft28//9/Py0e/fuKuXg6empefPmWXzsrrvu0l133WW+P2zYMA0bNqxK8wcA2Ia1NePy/18Z16BBgyqf27x+/fpau3atxceuPEVSbGysfvjhB4ux8+bNK7feQCosLNTOnTs1ceLEUtP79etXqtZfLiUlRf369Ss1rX///lq6dKmKiorMR9NMmzZNwcHBevTRR7Vt27ZKcykoKFBBQYH5vqkqFx8D4NTsVWsut3z58lL3K9pfAQA4PnvVlk8//fRq0rYajQ4AAFCjcnJyKv1yLj09XUXlnA8UAGpTbm6uSkpKFBISUmp6SEiIsrOzLT4nOzvbYnxxcbFyc3MVFhamr7/+WkuXLtWePXusziUxMVFTp06t8hgAAAAAd0OjAy7ttdek/HypXj17ZwIADqqGV5Q5OTl6ZMTDOpOXV2HcufPndTTziAqLYmokD6A6mjeXLlyQ6tSxdyawBw8Pj1L3DcMoM62y+EvTT58+rT//+c9asmSJmjRpYnUOkyZNUsJl53E2mUyKiIiw+vlwQezgAKgtr72mY2lpWvTPf6qBvXMBACvQ6IBLe/BBe2cAAA6uhleUJpNJZ/Ly1LtjWzVpWP45OH/JOKx3M9JVzFEdcCCNG9s7A9hDkyZN5OnpWebojWPHjpU5auOS0NBQi/FeXl5q3Lix9u3bp7S0NN15553mxy9cuCBJ8vLy0oEDB9S6desy8/X19ZWvr+/VDgmuhB0cALXlwQd1+uBBfbppk+62dy4AYAV+nwbAbq7mfLGwDq8xHEWThoEKbRxU7q2Rv7+9UwQASZKPj4+io6OVnJxcanpycrK6d+9u8TmxsbFl4jdv3qyYmBh5e3urffv22rt3r/bs2WO+3XXXXbrtttu0Z88ejtIAHADbzbbh6q/j/PnzFRUVJT8/P0VHR1d6vaWtW7cqOjpafn5+atWqlRYuXFjq8fXr1ysmJkYNGzZU/fr11blzZ/3zn/+86uUCcAyXftgC61xtDeGIDgC1ztvbWx4eHsrJyVFwcHCFp4FA9RmGoZycHHl4eJgvggoAACqXkJCg4cOHKyYmRrGxsVq8eLEyMjLMF32fNGmSMjMztXLlSkkXLwY/d+5cJSQkaPTo0UpJSdHSpUu1evVqSZKfn586depUahkNGzaUpDLTAdQu9k1sx9X3P9auXatx48Zp/vz5uvnmm7Vo0SINGDBAP/30kyIjI8vEp6amauDAgRo9erRWrVqlr7/+WvHx8QoODtaQIUMkSUFBQXruuefUvn17+fj46N///rf+8pe/qGnTpurfv3+1lgvA/nx8fFSnTh0dPXpUwcHB8vHxob5UwhY1hEYHXNqBA1JxseTlJbVrZ+9scImnp6eaN2+uI0eOKC0tzd7puDQPDw81b95cnp6e9k4FjooVJVCu8+clw5A8PCQ/P3tng9o0dOhQHT9+XNOmTVNWVpY6deqkDRs2qEWLFpKkrKwsZWRkmOOjoqK0YcMGjR8/XvPmzVN4eLjmzJlj/iILsBnqts2xb2Jbrrz/MXv2bD366KMaNWqUJCkpKUmbNm3SggULlJiYWCZ+4cKFioyMVFJSkiSpQ4cO2rFjh2bNmmWuD7169Sr1nLFjx2rFihX66quvzI2Oqi7XZg4ckPehQ4rIz6+5ZQAuqk6dOoqKilJWVpaOHj1q73ScxtXWEBodcGm33y5lZkrNmklHjtg7G1yuQYMGuuaaa1RUVGTvVFyat7e3S+5kwIZYUQLlOnBAKiqSvL2lG26wdzaobfHx8YqPj7f42PLly8tM69mzp3bt2mX1/C3NA6gUdbtGsG9iO666/1FYWKidO3dq4sSJpab369dP27dvt/iclJQU9evXr9S0/v37a+nSpSoqKirzi2XDMPT555/rwIEDevXVV6u9XEkqKChQQUGB+b7JZKp8kFe6/XZFZmZqto+P3ub6QECV+fj4KDIyUsXFxSopKbF3Ok7hamsIjQ4AduPp6emSG8EAANS0CxcuaOzYsdqwYYM8PDyUkJBQ7pfyY8aM0UcffaT09HTt3buXUyUBgAXsm6Aiubm5KikpUUhISKnpISEhys7Otvic7Oxsi/HFxcXKzc1VWFiYJCkvL0/NmjVTQUGBPD09NX/+fPXt27fay5WkxMRETZ06tcrjBGBbl07D5Iqn83NENDoAAAAAJ7Nq1Sr99NNP+uWXX5SXl6cbb7xRvXv3Vvv27cvE3nvvvXrmmWd0yy232CFTOJOcnJxKf/UbEBCg4ODgWsoIABzLlefYNwyjwvPuW4q/crq/v7/27NmjM2fO6LPPPlNCQoJatWpV6rRWVV3upEmTlJCQYL5vMpkUERFR/sAAwAXQ6AAAAACqwMPDQzNnztQHH3yg3NxcLV68WJ999pk2btyowsJCvfvuu+rYsaOys7M1bNgwmUwmnT9/Xrfffrv+/ve/y8PDQ6NHj5afn5/+8Y9/6MSJE+ratatWrFhhdTNi7dq1iouLk6enp4KCgnT//fdrzZo1mjJlSpnYW2+91cavAFxRTk6OHvvLaJ0/XfG52P3862nRsiU0OwC4lSZNmsjT07PMURTHjh0rc7TFJaGhoRbjvby81LhxY/O0OnXqqE2bNpKkzp07a//+/UpMTFSvXr2qtVxJ8vX1la+vb5XGCADOjkYHAAAAnMbs2RdvlbnxRumjj0pPu+suqaJLKCQkXLxZIyAgQN99953ee+89DRo0SO+++64SExP12muvacaMGXrnnXfUsGFDffzxx2rQoIFKSko0aNAgrVu3Tvfee6/+8Y9/KDY2Vu+9955Wrlyp0aNHm5scY8aM0ZdffmlxuYsWLVK3bt2UkZFhvjC2JLVs2VI7duywLnnAApPJpPOn8/VY7DCFB4VajDl6IluLUlbLZDLR6ADgVnx8fBQdHa3k5GTdc8895unJyckaNGiQxefExsbq448/LjVt8+bNiomJqfA0NoZhmK+vUZ3lAoC7otGhi+cunDx5ssaOHaukpCR7pwMAsJMFCxZowYIFSktLkyR17NhRL774ogYMGCDp4k7H1KlTtXjxYp08eVLdunXTvHnz1LFjRztmDbgXk+nidXgrY+nsDDk5FT+3KtfpHDp0qCTpxhtvVJ06dXTHHXdIkqKjo7V+/XpJF6+j8eyzz+qrr76SYRg6duyYOnfurHvvvVd+fn567733FB0drdjYWD377LPmec+ZM8eqHC4/ZcWlU2EAVys8KFRRTSPtnQYAOJyEhAQNHz5cMTExio2N1eLFi5WRkaG4uDhJF08XlZmZqZUrV0qS4uLiNHfuXCUkJGj06NFKSUnR0qVLtXr1avM8ExMTFRMTo9atW6uwsFAbNmzQypUrtWDBAquXCwC4yO0bHd9//70WL16s66+/3t6pAADsrHnz5nrllVfMh46vWLFCgwYN0u7du9WxY0e99tprmj17tpYvX662bdtq+vTp6tu3rw4cOCB/f387Zw+4h4AAqVmzyuMs/dg8OLji5wYEWJ+Hn5+fpIsXr7381BCenp4qLi6WJM2ePVvHjx/Xt99+Kz8/PyUkJOj8+fPm2J9//ln169fXsWPHVFhYaJ6PNUd0REZGKi0tTV27dpUkpaenKzKSL6cBAKgpQ4cO1fHjxzVt2jRlZWWpU6dO2rBhg/kIy6ysLGVkZJjjo6KitGHDBo0fP17z5s1TeHi45syZoyFDhphjzp49q/j4eB05ckR169ZV+/bttWrVKvMPKqxZLgDgIrdudJw5c0YPPfSQlixZounTp9s7HQCAnd15552l7s+YMUMLFizQN998o2uvvVZJSUl67rnnNHjwYEkXGyEhISF655139Nhjj9kjZcDtVOX0Ule68lRWNe3kyZMKDQ2Vn5+ffv/9d7333nvmLy4yMjIUHx+vTz/9VHPmzNG4cePMv9605oiO++67T4sWLdLgwYOVl5entWvXauPGjTU6HgAA3F18fLzi4+MtPrZ8+fIy03r27KldFZw3c/r06VZ9H1XRcgEAF9WxdwL29Pjjj+uOO+5Qnz59Ko0tKCiQyWQqdQMAuK6SkhKtWbNGZ8+eVWxsrFJTU5Wdna1+/fqZY3x9fdWzZ09t37693Pm4ev3IycnRwYMHy72lp6er6H+/bgfczZgxY7R9+3Z17txZjzzyiHmbs7i4WA888IBefvllcxP1m2++0dq1a62e9/Dhw9WuXTu1bdtWXbt21dNPP60OHTpIkj766CONGjXKHPv444+refPmOnLkiPr06WM+ag0AAAAAXIXbHtGxZs0a7dq1S99//71V8YmJiZo6dWoNZwUAsLe9e/cqNjZW58+fV4MGDfTBBx/o2muvNTczQkJCSsWHhIQoPT293Pm5cv3IycnRIyMe1pm8vHJjzp0/r6OZR1RYFFOLmQE16/LrYbRs2VK5ubnm+7169TJfFLxFixb67rvvLM7j8gapn5+fdu/eXaUcPD09NW/ePIuP3XXXXbrrrrvM9+fNm1duLAAAAAC4ArdsdBw+fFhjx47V5s2bzedXrsykSZOUcNl5EkwmkyIsXeUSDuX776WSEsnT096ZAHAW7dq10549e3Tq1CmtW7dOI0aM0NatW82PX37xX+niF55XTrucw9ePq1hRmkwmncnLU++ObdWkYaDFmF8yDuvdjHTzNQsAZ/K/AyQAwHGwgwOgtnz/vdIOHtT4CRN0q71zAQAruGWjY+fOnTp27Jiio6PN00pKSvTll19q7ty5KigokOcVG46+vr6lLjQJ5xAWZu8MADgbHx8f82ldYmJi9P333+vvf/+7nn32WUlSdna2wi5buRw7dqzMUR6Xc/j6YYMVZZOGgQptHGTxsZyTp656/oC9+PjYOwMAuAI7OABqS1iYSvLzdcKR92UA4DJueY2O22+/XXv37tWePXvMt5iYGD300EPas2dPmSYHAMB9GYahgoICRUVFKTQ0VMnJyebHCgsLtXXrVnXv3t2OGQIAAAAAALg3tzyiw9/fX506dSo1rX79+mrcuHGZ6QAA9zF58mQNGDBAEREROn36tNasWaMtW7Zo48aN8vDw0Lhx4zRz5kxdc801uuaaazRz5kzVq1dPDz74oL1TB1za5dfEQO3gNQcAAADgTNyy0QH3sXixdOaM1KCB9Ne/2jsbAI7u999/1/Dhw5WVlaXAwEBdf/312rhxo/r27StJeuaZZ3Tu3DnFx8fr5MmT6tatmzZv3ix/f387Z34VWFHCgXl7e8vDw0M5OTkKDg6u8Ho4NeHEif87FX6Q5bOzuSTDMJSTkyMPDw95e3vbOx0Al6NuA6gtixcrMC1Nd2Zl2TsTALAKjY7/2bJli71TQA2YNk3KzJSaNWM/AEDlli5dWuHjHh4emjJliqZMmVI7CdUGVpRwYJ6enmrevLmOHDmitLS0Wl/+kSP/1+ho3rzWF29XHh4eat68Oad0BRwNdRtAbZk2TU0yM/Wwj4/etncuAGAFGh0AAABwWA0aNNA111yjoqKiWl/2yJHS779LISHS1q21vni78vb2pskBAAAAwGnQ6AAAAIBD8/T0tMuX7pmZF2/FxZKfX60vHgAAAABgpTr2TgAAAAAAAAAAAKC6aHQAAAAAAAAAAACnRaMDAAAAAAAAAAA4LRodAAAAAAAAAADAadHoAAAAAAAAAAAATotGBwAAAAAAAAAAcFpe9k4AqElt20qBgVJIiL0zAQAHxYoSKBcfDwAOhxUTgNrStq0K69bVkZwce2cCAFah0QGX9vnn9s4AABwcK0qgXHw8ADgcVkwAasvnn+vwwYMa/8gjutveuQCAFTh1FQAAAAAAAAAAcFo0OgAAAAAAAAAAgNPi1FUAAAAAAKdVUFio9PT0CmMCAgIUHBxcSxkBAACgttHogEt76CEpN1dq0kR6+217ZwMADogVJVAuPh6A4zt5Jk+HUg9pxuSp8vX1LTfOz7+eFi1b4vzNDlZMAGrLQw8pLCNDzx84oP/efbe9swGAStHogEvbulXKzJSaNbN3JgDgoFhRAuXi4wE4vrMF+fLx8NLomx5Q6/CWFmOOnsjWopTVMplMzt/oYMUEoLZs3ap6mZm6wcdH/7V3LgBgBa7RAQAAAABwauGNQhTVNNLiLTwo1N7pAXAR8+fPV1RUlPz8/BQdHa1t27ZVGL9161ZFR0fLz89PrVq10sKFC0s9vmTJEvXo0UONGjVSo0aN1KdPH3333XelYqZMmSIPD49St9BQ1msAcCWO6AAAAAAAWIXrYQBwV2vXrtW4ceM0f/583XzzzVq0aJEGDBign376SZGRkWXiU1NTNXDgQI0ePVqrVq3S119/rfj4eAUHB2vIkCGSpC1btmjYsGHq3r27/Pz89Nprr6lfv37at2+fml125FbHjh316aefmu97enrW/IABwMnQ6AAAAAAAVMrtrocBAJeZPXu2Hn30UY0aNUqSlJSUpE2bNmnBggVKTEwsE79w4UJFRkYqKSlJktShQwft2LFDs2bNMjc63r7iWjtLlizR+++/r88++0wPP/ywebqXl1eVjuIoKChQQUGB+b7JZLL6uQDgrGh0AAAAAAAq5XbXwwCA/yksLNTOnTs1ceLEUtP79eun7du3W3xOSkqK+vXrV2pa//79tXTpUhUVFcnb27vMc/Lz81VUVKSgoKBS03/99VeFh4fL19dX3bp108yZM9WqVaty801MTNTUqVOtHR4AuAQaHQAAuKGcnByZTCa1KC6Wl6Ti4mKlHzxYKqawsFA+Pj7lziM9PV1FxcU1nCkAwNFcuh4GALiL3NxclZSUKCQkpNT0kJAQZWdnW3xOdna2xfji4mLl5uYqLCyszHMmTpyoZs2aqU+fPuZp3bp108qVK9W2bVv9/vvvmj59urp37659+/apcePGFpc9adIkJSQkmO+bTCZFRERYPV4AcEY0OgAAcDM5OTl6ZMTDOpOXp3dPnlSwpJMnT2rUw8PNMYVFRco4fFgtIiPl7WV5c+Hc+fM6mnlEhUUxtZQ5AAAAYD8eHh6l7huGUWZaZfGWpkvSa6+9ptWrV2vLli3y8/MzTx8wYID5/9ddd51iY2PVunVrrVixolQz43K+vr4VnmIQAFwRjQ4AAP4nMTFR69ev188//6y6deuqe/fuevXVV9WuXTtzzMiRI7VixYpSz+vWrZu++eab2k632kwmk87k5al3x7aq++OPUmGh6vp46/6bu5pjfsk4rLTUQ+rZvrWahzS1OJ9fMg7r3Yx0FXNUBwAAAFxYkyZN5OnpWebojWPHjpU5auOS0NBQi/FeXl5ljsSYNWuWZs6cqU8//VTXX399hbnUr19f1113nX799ddqjAQAXFcdeycAAICj2Lp1qx5//HF98803Sk5OVnFxsfr166ezZ8+WivvjH/+orKws823Dhg12yvjqNGkYKM86FzcFPOvUUWjjIPOtkb+/JCkowL/UdEsxAAAAgCvz8fFRdHS0kpOTS01PTk5W9+7dLT4nNja2TPzmzZsVExNT6vocr7/+ul5++WVt3LhRMTGVHyldUFCg/fv3Wzz1FQC4M47ogEsbPVrKy5MCA+2dCQBnsHHjxlL3ly1bpqZNm2rnzp269dZbzdN9fX0VGhpa2+nViLQ/RMv7/HkVXXZ4PICL2I4A4HBYMQF2k5CQoOHDhysmJkaxsbFavHixMjIyFBcXJ+nidTEyMzO1cuVKSVJcXJzmzp2rhIQEjR49WikpKVq6dKlWr15tnudrr72mF154Qe+8845atmxpPgKkQYMGatCggSRpwoQJuvPOOxUZGaljx45p+vTpMplMGjFiRM0OePRonUpP178//7xmlwMANkKjAy7tpZfsnQEAZ5aXlydJCgoKKjV9y5Ytatq0qRo2bKiePXtqxowZatrU8umdCgoKVFBQYL5vMplqLuFq+LlPL3unADgstiMAOBxWTIDdDB06VMePH9e0adOUlZWlTp06acOGDWrRooUkKSsrSxkZGeb4qKgobdiwQePHj9e8efMUHh6uOXPmaMiQIeaY+fPnq7CwUPfee2+pZb300kuaMmWKJOnIkSMaNmyYcnNzFRwcrJtuuknffPONebk15qWXdPzgQa04eFB31+ySAMAmaHQAAGCBYRhKSEjQLbfcok6dOpmnDxgwQPfdd59atGih1NRUvfDCC+rdu7d27txp8YJ/iYmJmjp1am2mDgAAAKAGxMfHKz4+3uJjy5cvLzOtZ8+e2rVrV7nzS0tLq3SZa9assTY9AHBrXKMDAAALnnjiCf3444+lDi2XLv6S64477lCnTp1055136j//+Y9++eUXffLJJxbnM2nSJOXl5Zlvhw8fro30AQBXaf78+YqKipKfn5+io6O1bdu2CuO3bt2q6Oho+fn5qVWrVlq4cGGpx9evX6+YmBg1bNhQ9evXV+fOnfXPf/6zJocAAAAAuA0aHQAAXOHJJ5/URx99pC+++ELNmzevMDYsLEwtWrTQr7/+avFxX19fBQQElLoBABzb2rVrNW7cOD333HPavXu3evTooQEDBpQ6JcnlUlNTNXDgQPXo0UO7d+/W5MmTNWbMGK1bt84cExQUpOeee04pKSn68ccf9Ze//EV/+ctftGnTptoaFgAAAOCyaHTApTVvLnl4XPwXACpjGIaeeOIJrV+/Xp9//rmioqIqfc7x48d1+PBhhYWF1UKGtvfHmW/onolT9MeZb9g7FcDhsB3hvmbPnq1HH31Uo0aNUocOHZSUlKSIiAgtWLDAYvzChQsVGRmppKQkdejQQaNGjdIjjzyiWbNmmWN69eqle+65Rx06dFDr1q01duxYXX/99frqq69qa1hwBayYANSW5s3Vuk0bvffNN/bOBACsQqMDAID/efzxx7Vq1Sq988478vf3V3Z2trKzs3Xu3DlJ0pkzZzRhwgSlpKQoLS1NW7Zs0Z133qkmTZronnvusXP2AABbKCws1M6dO9WvX79S0/v166ft27dbfE5KSkqZ+P79+2vHjh0qKioqE28Yhj777DMdOHBAt956a7m5FBQUyGQylboBAAAAKItGBwAA/7NgwQLl5eWpV69eCgsLM9/Wrl0rSfL09NTevXs1aNAgtW3bViNGjFDbtm2VkpIif39/O2cPALCF3NxclZSUKCQkpNT0kJAQZWdnW3xOdna2xfji4mLl5uaap+Xl5alBgwby8fHRHXfcoX/84x/q27dvubkkJiYqMDDQfIuIiLiKkQEAAACuy8veCQAA4CgMw6jw8bp163IudQBwEx4eHqXuG4ZRZlpl8VdO9/f31549e3TmzBl99tlnSkhIUKtWrdSrVy+L85w0aZISEhLM900mE80OAAAAwAIaHQAAAADwP02aNJGnp2eZozeOHTtW5qiNS0JDQy3Ge3l5qXHjxuZpderUUZs2bSRJnTt31v79+5WYmFhuo8PX11e+vr5XMRoAAADAPdDoAAAAAID/8fHxUXR0tJKTk0tdfyk5OVmDBg2y+JzY2Fh9/PHHpaZt3rxZMTEx8vb2LndZhmGooKDANok7kILCQqWnp1caFxAQoODg4FrICAAAAK6ORgcAAAAAXCYhIUHDhw9XTEyMYmNjtXjxYmVkZCguLk7SxVNKZWZmauXKlZKkuLg4zZ07VwkJCRo9erRSUlK0dOlSrV692jzPxMRExcTEqHXr1iosLNSGDRu0cuVKLViwwC5jrCknz+TpUOohzZg8tdKjUfz862nRsiU0OwAAAHDVaHQAAAAAwGWGDh2q48ePa9q0acrKylKnTp20YcMGtWjRQpKUlZWljIwMc3xUVJQ2bNig8ePHa968eQoPD9ecOXM0ZMgQc8zZs2cVHx+vI0eOqG7dumrfvr1WrVqloUOH1vr4atLZgnz5eHhp9E0PqHV4y3Ljjp7I1qKU1TKZTDQ6AAAAcNVodECSVFhU+eHlHFoOAAAAdxEfH6/4+HiLjy1fvrzMtJ49e2rXrl3lzm/69OmaPn26rdJzeOGNQhTVNNLeaQAAAMBNuG2jY8GCBVqwYIHS0tIkSR07dtSLL76oAQMG2DcxOzh9Nk+HDh3SCxMrPry8QUA9LV3OoeUAAAAAAAAAAMfhto2O5s2b65VXXlGbNm0kSStWrNCgQYO0e/dudezY0c7Z1a5zBfny9PBSnxvvV0RoC4sxOSezlbzrXac7tHzVKqmgQKrk9MAA4LZ2DB2sOiUluuDpae9UAIfDdgQAh8OKCUBtWbVKR1NT9WpSklraOxcAsILbNjruvPPOUvdnzJihBQsW6JtvvnG7RscljRs2VVhwhL3TsKleveydAQA4ttzWUfZOAXBYbEcAcDismADUll69dC4iQnuWL6fRAcApuG2j43IlJSV67733dPbsWcXGxlqMKSgoUEFBgfm+yWSqrfQAAAAAAAAAAEA56tg7AXvau3evGjRoIF9fX8XFxemDDz7QtddeazE2MTFRgYGB5ltEhGsd+QAAAAAAAAAAgDNy6yM62rVrpz179ujUqVNat26dRowYoa1bt1psdkyaNEkJCQnm+yaTiWaHE9iy5f9OYctR3gBQVpODqeZrdHAaK6A0tiMAOBxWTABqy5Ytqpuaqs6nTtk7EwCwils3Onx8fMwXI4+JidH333+vv//971q0aFGZWF9fX/lywTen8+c/S5mZUrNm0pEj9s4GABxPzNr1qms6rXMB/to4+Sl7pwM4FLYjADgcVkwAasuf/6zwzEw95+Ojt+2dCwBYwa1PXXUlwzBKXYcDAAAAAAAAAAA4Nrc9omPy5MkaMGCAIiIidPr0aa1Zs0ZbtmzRxo0b7Z0aAAAAAAAAAACwkts2On7//XcNHz5cWVlZCgwM1PXXX6+NGzeqb9++9k4NAAAAAFxeQWGh0tPTy308PT1dxUXFtZgRAAAAnJXbNjqWLl1q7xQAAAAAwC2dPJOnQ6mHNGPy1HKvhXj2XL5+z8xWUVFhLWcHAAAAZ8M1OgAAAAAAtepsQb58PLw0+qYHNPWO8RZvw7rcKePCBRUXl9g7XQCQJM2fP19RUVHy8/NTdHS0tm3bVmH81q1bFR0dLT8/P7Vq1UoLFy4s9fiSJUvUo0cPNWrUSI0aNVKfPn303XffXfVyAcAdOV2jIzU11d4pAAAcEPUBAEAtcD7hjUIU1TTS4i2kYbC90wPgAmxVG9auXatx48bpueee0+7du9WjRw8NGDBAGRkZ5S534MCB6tGjh3bv3q3JkydrzJgxWrdunTlmy5YtGjZsmL744gulpKQoMjJS/fr1U2ZmZrWXCwDuyukaHW3atNFtt92mVatW6fz58/ZOBwDgIKgPAABqAQDgSraqDbNnz9ajjz6qUaNGqUOHDkpKSlJERIQWLFhgMX7hwoWKjIxUUlKSOnTooFGjRumRRx7RrFmzzDFvv/224uPj1blzZ7Vv315LlizRhQsX9Nlnn1V7uQDgrpyu0fHDDz+oS5cueuqppxQaGqrHHnvM4mF9AAD3Qn0AAFALAABXskVtKCws1M6dO9WvX79S0/v166ft27dbfE5KSkqZ+P79+2vHjh0qKiqy+Jz8/HwVFRUpKCio2suVpIKCAplMplI3AHB1Ttfo6NSpk2bPnq3MzEwtW7ZM2dnZuuWWW9SxY0fNnj1bOTk59k4RAGAH1AcAALUAAHAlW9SG3NxclZSUKCQkpNT0kJAQZWdnW3xOdna2xfji4mLl5uZafM7EiRPVrFkz9enTp9rLlaTExEQFBgaabxEREZWOEQCcndM1Oi7x8vLSPffco3fffVevvvqqDh48qAkTJqh58+Z6+OGHlZWVZe8U4QCOHJEM4+K/ANzD1dSHxMREde3aVf7+/mratKnuvvtuHThwoFSMYRiaMmWKwsPDVbduXfXq1Uv79u2r6WHVmI2Tn9IHr0zRxslP2TsVwOGwHeG82FeAy2LFBFSbLWqDh4dHqfuGYZSZVlm8pemS9Nprr2n16tVav369/Pz8rmq5kyZNUl5envl2+PDhcmPLdeSIDv72m+676aaqPxcA7MBpGx07duxQfHy8wsLCNHv2bE2YMEEHDx7U559/rszMTA0aNMjeKQIA7OBq6sPWrVv1+OOP65tvvlFycrKKi4vVr18/nT171hzz2muvafbs2Zo7d66+//57hYaGqm/fvjp9+nRtDA8AYAX2FQAAV7qa2tCkSRN5enqWOYri2LFjZY62uCQ0NNRivJeXlxo3blxq+qxZszRz5kxt3rxZ119//VUtV5J8fX0VEBBQ6gYArs7L3glU1ezZs7Vs2TIdOHBAAwcO1MqVKzVw4EDVqXOxZxMVFaVFixapffv2ds4UAFCbbFEfNm7cWOr+smXL1LRpU+3cuVO33nqrDMNQUlKSnnvuOQ0ePFiStGLFCoWEhOidd97RY489VnMDBABUin0FAMCVbFEbfHx8FB0dreTkZN1zzz3m6cnJyeU2SGJjY/Xxxx+XmrZ582bFxMTI29vbPO3111/X9OnTtWnTJsXExFz1cgHAXTldo2PBggV65JFH9Je//EWhoaEWYyIjI7V06dJazgwAYE81UR/y8vIkyXwxwNTUVGVnZ5e6GKCvr6969uyp7du3W2x0FBQUqKCgwHyfCwECQM1hXwEAcCVb1YaEhAQNHz5cMTExio2N1eLFi5WRkaG4uDhJF08XlZmZqZUrV0qS4uLiNHfuXCUkJGj06NFKSUnR0qVLtXr1avM8X3vtNb3wwgt655131LJlS/ORGw0aNFCDBg2sWi4A4CKna3QkJycrMjLS3Hm/xDAMHT58WJGRkfLx8dGIESPslCEcydSpUl6eFBgovfSSvbMBUJNsXR8Mw1BCQoJuueUWderUSZLMOx6WLgaYnp5ucT6JiYmaOnVqVYdTa9p/ukXe58+ryM9PP/fpZe90AIfCdoTzYV8BLo8VE1BltqoNQ4cO1fHjxzVt2jRlZWWpU6dO2rBhg1q0aCFJysrKUkZGhjk+KipKGzZs0Pjx4zVv3jyFh4drzpw5GjJkiDlm/vz5Kiws1L333ltqWS+99JKmTJli1XJrzNSpapyerhHp6cqr2SUBgE04XaOjdevWysrKUtOmTUtNP3HihKKiolRSUmKnzOCIliyRMjOlZs3YDwBcna3rwxNPPKEff/xRX331VZnHqnIxwEmTJikhIcF832QyKSIiokq51KSW3+1UXdNpnQvwp9EBXIHtCOfDvgJcHismoMpsWRvi4+MVHx9v8bHly5eXmdazZ0/t2rWr3PmlpaVd9XJrzJIlapiZqT/5+Ojt2l0yAFSL0zU6DMOwOP3MmTPy8/Or5WwAAI7ClvXhySef1EcffaQvv/xSzZs3N0+/dKh7dna2wsLCzNMruhigr6+vfH19q7R8AED1sK8AALgStQEA3IPTNDou/RrWw8NDL774ourVq2d+rKSkRN9++606d+5sp+wAAPZiy/pgGIaefPJJffDBB9qyZYuioqJKPR4VFaXQ0FAlJyerS5cukqTCwkJt3bpVr776qm0GBACoMvYVAABXojYAgHtxmkbH7t27JV38Emrv3r3y8fExP+bj46MbbrhBEyZMsFd6AAA7sWV9ePzxx/XOO+/oX//6l/z9/c3X5AgMDFTdunXl4eGhcePGaebMmbrmmmt0zTXXaObMmapXr54efPBB2w8OAGAV9hUAAFeiNgCAe3GaRscXX3whSfrLX/6iv//97woICLBzRgAAR2DL+rBgwQJJUq9evUpNX7ZsmUaOHClJeuaZZ3Tu3DnFx8fr5MmT6tatmzZv3ix/f/9qLxcAcHXYVwAAXInaAADuxWkaHZcsW7bM3ikAAByQLepDeefvvZyHh4emTJmiKVOmXPXyAAC2xb4CylNQWKj09PQKYwICAhQcHFxLGQGoLdQGAHAPTtHoGDx4sJYvX66AgAANHjy4wtj169fXUlYAAHujPgAAqAWozMkzeTqUekgzJk+Vr69vuXF+/vW0aNkSmh2AC6A2AID7cYpGR2BgoDw8PMz/BwBAcr/6kJOTI5PJVGmcK/8itbCoiF/kAijF3WoBqu5sQb58PLw0+qYH1Dq8pcWYoyeytShltUwmEzUEcAHUBgBwP07R6Lj8MEMOOQQAXOJO9SEnJ0ePjHhYZ/LyKo1tEBiot1asdLkvak7n5ys1NVXPP/N0hb/IddXxA7DMnWoBrk54oxBFNY20dxoAagG1AQDcj1M0Oi537tw5GYahevXqSZLS09P1wQcf6Nprr1W/fv3snB0cTc+eUm6u1KSJvTMBUNNcvT6YTCadyctT745t1aRh+b9Kyz2Vp8/3/WL1L1JzW7WUz9l8FdavZ8t0a8T5gkJ5yNBtHduqeUhTizFVHT9QEbYjnI+r1wKAFRNQddSGaurZU/kZGfrhwAF7ZwIAVnG6RsegQYM0ePBgxcXF6dSpU/rDH/4gHx8f5ebmavbs2fp//+//2TtFOJC337Z3BgBqi7vUhyYNAxXaOMhm89vxwBCbzau2BAX42/Q1AMrDdoTzcZdaADfGigmoMmpDNb39trIOHtT0Rx7R3fbOBQCsUMfeCVTVrl271KNHD0nS+++/r9DQUKWnp2vlypWaM2eOnbMDANgL9QEAQC0AAFyJ2gAA7sHpGh35+fny9/eXJG3evFmDBw9WnTp1dNNNN1V6cVIAgOuiPgAAqAUAgCtRGwDAPThdo6NNmzb68MMPdfjwYW3atMl8PsVjx44pICDAztkBAOyF+gAAoBYAAK5EbQAA9+B0jY4XX3xREyZMUMuWLdWtWzfFxsZKutiV79Kli52zg6Pp3Vvq2PHivwBcG/Whem5ZvFy3z56nWxYvt3cqgMNhO8L5UAvg8lgxAVVGbaim3r0V8cc/6m8//GDvTADAKk53MfJ7771Xt9xyi7KysnTDDTeYp99+++2655577JgZHNEvv0iZmVJenr0zAVDTqA/V0yD3uOqaTuvc+fP2TgVwOGxHOB9qAVweKyagyqgN1fTLL/LJzFRzHx97ZwIAVnG6RockhYaGKjQ0tNS0P/zhD3bKBgDgKKgPAABqAQDgStQGAHB9TtfoOHv2rF555RV99tlnOnbsmC5cuFDq8UOHDtkpMwCAPVEfAADUAgDAlagNAOAenK7RMWrUKG3dulXDhw9XWFiYPDw87J0SAMABUB8AANQCAMCVqA0A4B6crtHxn//8R5988oluvvlme6cCAHAg1AcAALUAAHAlagMAuIc69k6gqho1aqSgoCB7pwEAcDDUBwCALWvB/PnzFRUVJT8/P0VHR2vbtm0Vxm/dulXR0dHy8/NTq1attHDhwlKPL1myRD169FCjRo3UqFEj9enTR999951NcgUAlI/9BABwD07X6Hj55Zf14osvKj8/396p2F1OTo4OHjxY4S09PV1FxcX2ThUAahz1AQBgq1qwdu1ajRs3Ts8995x2796tHj16aMCAAcrIyLAYn5qaqoEDB6pHjx7avXu3Jk+erDFjxmjdunXmmC1btmjYsGH64osvlJKSosjISPXr10+ZmZlXlSsAoGLsJwCAe3C6U1e98cYbOnjwoEJCQtSyZUt5e3uXenzXrl12yqx25eTk6NGRo3XGVHGhPncuX0ePZquwqKiWMgMA+6A+AABsVQtmz56tRx99VKNGjZIkJSUladOmTVqwYIESExPLxC9cuFCRkZFKSkqSJHXo0EE7duzQrFmzNGTIEEnS22+/Xeo5S5Ys0fvvv6/PPvtMDz/8cFWHCgCwEvsJAOAenK7Rcffdd9s7BYdgMpl0xpSvvjfer+BGoeXG/Zz6o9YeflPFJRzVAcC1UR8AALaoBYWFhdq5c6cmTpxYanq/fv20fft2i89JSUlRv379Sk3r37+/li5dqqKiojJfqklSfn6+ioqKKjydSkFBgQoKCsz3TSZTVYYCAJBt9xPmz5+v119/XVlZWerYsaOSkpLUo0ePcuO3bt2qhIQE7du3T+Hh4XrmmWcUFxdnfnzfvn168cUXtXPnTqWnp+tvf/ubxo0bV2oeU6ZM0dSpU0tNCwkJUXZ2ts3GBQCuwOkaHS+99JK9U3AowY1CFRYcUe7jx05k1WI2jufFF6UzZ6QGDeydCYCaRn2onp9v7ymvwkIV+/jYOxXA4bAd4XxsUQtyc3NVUlKikJCQUtMr+lIpOzvbYnxxcbFyc3MVFhZW5jkTJ05Us2bN1KdPn3JzSUxMLPPlFtwcKyagymy1n3DptIbz58/XzTffrEWLFmnAgAH66aefFBkZWSb+0mkNR48erVWrVunrr79WfHy8goODzUf75efnq1WrVrrvvvs0fvz4cpfdsWNHffrpp+b7np6eNhlThV58UblpaVr5/vuqhaUBwFVzukaHJJ06dUrvv/++Dh48qKefflpBQUHatWuXQkJC1KxZM3unBwfy17/aOwMAtYn6cFFhUZHS09PLffzy6zeldYuprbQAp8N2hHOyVS3w8PAodd8wjDLTKou3NF2SXnvtNa1evVpbtmyRn59fufOcNGmSEhISzPdNJpMiIsr/kRPcACsmoFpsURtq4rSGXbt2VdeuXSWpzJGEl/Py8lJoaPln86gRf/2r8g4e1Mdff627a3fJAFAtTtfo+PHHH9WnTx8FBgYqLS1No0ePVlBQkD744AOlp6dr5cqV9k4RAGAH1IeLTufnKzU1Vc8/87R8fX0txpw7f15HM4+osIgmBwDXYota0KRJE3l6epY5euPYsWNljtq4JDQ01GK8l5eXGjduXGr6rFmzNHPmTH366ae6/vrrK8zF19e33HU5AMA6tqgNtXVaw/L8+uuvCg8Pl6+vr7p166aZM2eqVatW5cZz6kMA7qiOvROoqoSEBI0cOVK//vprqV8/DRgwQF9++aXV80lMTFTXrl3l7++vpk2b6u6779aBAwdqImUAQC2wVX348ssvdeeddyo8PFweHh768MMPSz0+cuRIeXh4lLrddNNNthrGVTtfUCgPGbqtY1vdf3NXi7fYa1qqpKRExcVcvwmAa7FFLfDx8VF0dLSSk5NLTU9OTlb37t0tPic2NrZM/ObNmxUTE1Pqi6zXX39dL7/8sjZu3KiYGJrNAFAbbFEbauK0htbq1q2bVq5cqU2bNmnJkiXKzs5W9+7ddfz48XKfk5iYqMDAQPONowEBuAOna3R8//33euyxx8pMb9asWZUuxLR161Y9/vjj+uabb5ScnKzi4mL169dPZ8+etWW6sLOsLOnIkYv/AnBttqoPZ8+e1Q033KC5c+eWG/PHP/5RWVlZ5tuGDRuqlXNNCgrwV2jjIIu3Rv7+5jhf02n55eXJ13TajtkCjontCOdjq1qQkJCgN998U2+99Zb279+v8ePHKyMjw3wB2UmTJunhhx82x8fFxSk9PV0JCQnav3+/3nrrLS1dulQTJkwwx7z22mt6/vnn9dZbb6lly5bKzs5Wdna2zpw5cxUjhtthxQRUma1qg1SzpzUsz4ABAzRkyBBdd9116tOnjz755BNJ0ooVK8p9zqRJk5SXl2e+HT582OrlmWVlyTMrS0GXHRkCAI7M6U5d5efnZ/GQuwMHDig4ONjq+WzcuLHU/WXLlqlp06bauXOnbr311qvOE46ha1cpM1Nq1uzi/gAA12Wr+jBgwAANGDCgwhhfX9/aP0duDblt7mLVNZ3WuQB/bZz8lL3TARwK2xHOx1a1YOjQoTp+/LimTZumrKwsderUSRs2bFCLFi0kSVlZWcrIyDDHR0VFacOGDRo/frzmzZun8PBwzZkzx3wOdkmaP3++CgsLde+995Za1ksvvaQpU6ZUcaRwW6yYgCqzRW2o6dMaVkX9+vV13XXX6ddffy03xianPuzaVS0zM7XIx0dvDx16dfMCgFrgdI2OQYMGadq0aXr33XclXeyCZ2RkaOLEiaV2JKoqLy9PkhQUFGTxcc5vCACOrabqgyVbtmxR06ZN1bBhQ/Xs2VMzZsxQ06ZNLcZSP2pfZRdjl6SAgIAqfekJwDnYshbEx8crPj7e4mPLly8vM61nz57atWtXufNLS0ur0vIBALZhi9pw+WkN77nnHvP05ORkDRo0yOJzYmNj9fHHH5eaZum0hlVVUFCg/fv3q0ePHtWeBwC4IqdrdMyaNUsDBw5U06ZNde7cOfXs2VPZ2dmKjY3VjBkzqjVPwzCUkJCgW265RZ06dbIYk5iYqKlTp15N6gCAGlQT9cGSAQMG6L777lOLFi2UmpqqF154Qb1799bOnTst/mqK+lG7rLkYuyQ1CAzUWytW0uwAXExt1QIAgPOwVW1ISEjQ8OHDFRMTo9jYWC1evLjMaQ0zMzPNFzePi4vT3LlzlZCQoNGjRyslJUVLly7V6tWrzfMsLCzUTz/9ZP5/Zmam9uzZowYNGqhNmzaSpAkTJujOO+9UZGSkjh07punTp8tkMmnEiBG2eokAwCU4XaMjICBAX331lb744gvt3LlTFy5c0I033qg+ffpUe55PPPGEfvzxR3311VflxkyaNEkJCQnm+yaTiYs5AYADqYn6YMnQyw7b7tSpk2JiYtSiRQt98sknGjx4cJl46kftuvxi7M1DLB9lk3sqT5/v+0Umk4lGB+BiaqsWAACch61qQ02c1vDo0aPq0qWL+f6sWbM0a9Ys9ezZU1u2bJEkHTlyRMOGDVNubq6Cg4N100036ZtvvjEvFwBwkVM1Oi5cuKDly5dr/fr1SktLk4eHh6KiohQaGlrpBaDK8+STT+qjjz7Sl19+qebNm5cbZ5PzGwIAakRN1AdrhYWFqUWLFuWeI5f6YR+XLsYOwH3YsxYAAByTrWuDrU9r2LJlS/MFysuzZs2aKuUIAO6qjr0TsJZhGLrrrrs0atQoZWZm6rrrrlPHjh2Vnp6ukSNHljpHorXze+KJJ7R+/Xp9/vnnioqKqqHMAQA1ydb1oaqOHz+uw4cPKywsrEaXAwAon71rAQDA8VAbAMC9OM0RHcuXL9eXX36pzz77TLfddlupxz7//HPdfffdWrlypR5++GGr5vf444/rnXfe0b/+9S/5+/srOztbkhQYGKi6devaPH8AQM2wdX04c+aMfvvtN/P91NRU7dmzR0FBQQoKCtKUKVM0ZMgQhYWFKS0tTZMnT1aTJk3YUQIAO7J1LQAAOD9qAwC4F6c5omP16tWaPHlymeIkSb1799bEiRP19ttvWz2/BQsWKC8vT7169VJYWJj5tnbtWlumDQCoYbauDzt27FCXLl3M58pNSEhQly5d9OKLL8rT01N79+7VoEGD1LZtW40YMUJt27ZVSkqK/P39bTYmAEDV2LoWAACcH7UBANyL0zQ6fvzxR/3xj38s9/EBAwbohx9+sHp+hmFYvI0cOdIG2QIAaout60OvXr0s1ofly5erbt262rRpk44dO6bCwkKlp6dr+fLlXFwcAOzM1rUAAOD8qA0A4F6cptFx4sQJhYSElPt4SEiITp48WYsZAQAcAfUBAEAtAABcidoAAO7Faa7RUVJSIi+v8tP19PRUcXFxLWYEZ/DZZ1JxsVTBnw4AJ0d9uDpfjR4hjwsXZNRxmt8+ALWG7QjnQS2A22DFBFiN2nCVPvtMGYcOafKLLyra3rkAgBWcZuvo0mmlfH19LT5eUFBQyxnBGbRrZ+8MANQ06sPVORPcxN4pAA6L7QjnQS2A22DFBFiN2nCV2rVTkZeXDterR6MDgFNwmkbHiBEjKo15+OGHayETAIAjoT4AAKgFAIArURsAwL04TaNj2bJl9k4BAOCAqA8AAGoBAOBK1AYAcC9O0+gAquOdd6T8fKlePenBB+2dDQA4nuZ7fpRnYZFKfLx1pPP19k4HcChsRwBwOKyYANSWd96Rf1qa+hw7Zu9MAMAqNDrg0p55RsrMlJo1Yz8AACzptCFZdU2ndS7An0YHcAW2IwA4HFZMAGrLM8+oaWamHvPx0dv2zgUArFDH3gkAAAAAAAAAAABUF40OAAAAAAAAAADgtGh0AAAAAAAAAAAAp8U1OgAAAAAAsKGcnByZTKYKYwICAhQcHFxLGQEAALg2Gh0AAAAAANhITk6OHvvLaJ0/nV9hnJ9/PS1atoRmBwAAgA3Q6AAAAAAAwEZMJpPOn87XY7HDFB4UajHm6IlsLUpZLZPJRKMDAADABmh0AAAAAABgY+FBoYpqGmnvNAAAANwCFyMHAAAAAAAAAABOiyM64NJCQ0v/CwAo7bx/g1L/Avg/bEcAcDismADUltBQFRcX68TZs/bOBACsQqMDLm3HDntnAACObcuTj9k7BcBhsR0BwOGwYgJQW3bsUPrBg3rskUd0t71zAQArcOoqAAAAAAAAAADgtGh0AAAAAAAAAJWYP3++oqKi5Ofnp+joaG3btq3C+K1btyo6Olp+fn5q1aqVFi5cWOrxffv2aciQIWrZsqU8PDyUlJRkk+UCgDui0QEAAAAALi4nJ0cHDx4s95aenq7iomJ7pwkADmvt2rUaN26cnnvuOe3evVs9evTQgAEDlJGRYTE+NTVVAwcOVI8ePbR7925NnjxZY8aM0bp168wx+fn5atWqlV555RWFlnPtnaouFwDcFdfogEt77DHpxAkpKEhatMje2QCA4+m8/mP5nDunwrp1tWfwnfZOB3AobEfAVeTk5Oixv4zW+dP55cacPZev3zOzVVRUWIuZocpYMQF2M3v2bD366KMaNWqUJCkpKUmbNm3SggULlJiYWCZ+4cKFioyMNB+l0aFDB+3YsUOzZs3SkCFDJEldu3ZV165dJUkTJ060yXJt5rHHFJKRoad++UUHa24pAGAzNDrg0j75RMrMlJo1s3cmAJzFl19+qddff107d+5UVlaWPvjgA919993mxw3D0NSpU7V48WKdPHlS3bp107x589SxY0f7JX0VQn/+RXVNp3UuwN/eqQAOh+0IuAqTyaTzp/P1WOwwhQdZ/sXwzoM/6m+HF6m4uKSWs0OVsGIC7KKwsFA7d+4s04zo16+ftm/fbvE5KSkp6tevX6lp/fv319KlS1VUVCRvb+8aWa4kFRQUqKCgwHzfZDJVuqwyPvlEDTIzdZOPD40OAE6BU1cBAHCZs2fP6oYbbtDcuXMtPv7aa69p9uzZmjt3rr7//nuFhoaqb9++On36dC1nCgBA1YQHhSqqaaTFW0jDYHunBwAOKzc3VyUlJQoJCSk1PSQkRNnZ2Rafk52dbTG+uLhYubm5NbZcSUpMTFRgYKD5FhERYdXyAMCZ0egAAOAyAwYM0PTp0zV48OAyjxmGoaSkJD333HMaPHiwOnXqpBUrVig/P1/vvPOOHbIFAAAAUFs8PDxK3TcMo8y0yuItTbf1cidNmqS8vDzz7fDhw1VaHgA4IxodAABYKTU1VdnZ2aUOQff19VXPnj3LPXS8oKBAJpOp1A0AAACA82jSpIk8PT3LHEVx7NixMkdbXBIaGmox3svLS40bN66x5UoX91ECAgJK3QDA1dHoAADASpd2MKpy6DiHjQMAAADOzcfHR9HR0UpOTi41PTk5Wd27d7f4nNjY2DLxmzdvVkxMjFXX56jucgHAXdHoAACgiqpy6DiHjQMAAADOLyEhQW+++abeeust7d+/X+PHj1dGRobi4uIkXdzuf/jhh83xcXFxSk9PV0JCgvbv36+33npLS5cu1YQJE8wxhYWF2rNnj/bs2aPCwkJlZmZqz549+u2336xeLgDgIi97JwAAgLMIDQ2VdPHIjrCwMPP0ig4d9/X1la+vb63kBwAAAKBmDB06VMePH9e0adOUlZWlTp06acOGDWrRooUkKSsrSxkZGeb4qKgobdiwQePHj9e8efMUHh6uOXPmaMiQIeaYo0ePqkuXLub7s2bN0qxZs9SzZ09t2bLFquUCAC6i0QEAgJWioqIUGhqq5ORk8w5JYWGhtm7dqldffdXO2QEAgKtRUFio9PT0SuMCAgIUHBxcCxkBcDTx8fGKj4+3+Njy5cvLTOvZs6d27dpV7vxatmxpvkB5dZcLALiIRgcAAJc5c+ZMqUPFU1NTtWfPHgUFBSkyMlLjxo3TzJkzdc011+iaa67RzJkzVa9ePT344IN2zBoAAFyNk2fydCj1kGZMnlrpkZh+/vW0aNkSmh0AAAAOhEYHXNqwYdLJk1KjRvbOBICz2LFjh2677Tbz/YSEBEnSiBEjtHz5cj3zzDM6d+6c4uPjdfLkSXXr1k2bN2+Wv7+/vVK+Kkc6Xyfv/HMqqlfX3qkADoftCMB9nC3Il4+Hl0bf9IBah7csN+7oiWwtSlktk8lkn0YHKyYAtWXYMJnS0/VZSoq9MwEAq9DogEt7/XV7ZwDA2fTq1avCw8c9PDw0ZcoUTZkypfaSqkH/HdjP3ikADovtCMD9hDcKUVTTSHunUT5WTABqy+uvK+fgQS185BHdbe9cAMAKdeydAAAAAAAAAAAAQHXR6AAAAACAK8yfP19RUVHy8/NTdHS0tm3bVmH81q1bFR0dLT8/P7Vq1UoLFy4s9fi+ffs0ZMgQtWzZUh4eHkpKSqrB7AEAAAD3QqMDAAAAAC6zdu1ajRs3Ts8995x2796tHj16aMCAAcrIyLAYn5qaqoEDB6pHjx7avXu3Jk+erDFjxmjdunXmmPz8fLVq1UqvvPKKQkNDa2soAAAAgFug0QGX1r69FBBw8V8AQFl93viH/vTSTPV54x/2TgVwOGxHuK/Zs2fr0Ucf1ahRo9ShQwclJSUpIiJCCxYssBi/cOFCRUZGKikpSR06dNCoUaP0yCOPaNasWeaYrl276vXXX9cDDzwgX1/f2hoKXA0rJgC1pX17Rd1wg1Z+/729MwEAq9DogEs7c0Y6ffrivwCAsrwKCuVdUCivgkJ7pwI4HLYj3FNhYaF27typfv36lZrer18/bd++3eJzUlJSysT3799fO3bsUFFRUbVzKSgokMlkKnWDm2PFBKC2nDmjOmfPqm5Jib0zAQCruG2j48svv9Sdd96p8PBweXh46MMPP7R3SgAAAADsLDc3VyUlJQoJCSk1PSQkRNnZ2Rafk52dbTG+uLhYubm51c4lMTFRgYGB5ltERES15wUAAAC4MrdtdJw9e1Y33HCD5s6da+9UAAAAADgYDw+PUvcNwygzrbJ4S9OrYtKkScrLyzPfDh8+XO15AQAAAK7My94J2MuAAQM0YMAAe6cBAAAAwIE0adJEnp6eZY7eOHbsWJmjNi4JDQ21GO/l5aXGjRtXOxdfX1+u5wEAAABYwW2P6Kgqzo8LAAAAuD4fHx9FR0crOTm51PTk5GR1797d4nNiY2PLxG/evFkxMTHy9vausVwBAAAAXESjw0qcHxcAAABwDwkJCXrzzTf11ltvaf/+/Ro/frwyMjIUFxcn6eIppR5++GFzfFxcnNLT05WQkKD9+/frrbfe0tKlSzVhwgRzTGFhofbs2aM9e/aosLBQmZmZ2rNnj3777bdaHx8AAADgatz21FVVNWnSJCUkJJjvm0wmmh0AAACACxo6dKiOHz+uadOmKSsrS506ddKGDRvUokULSVJWVpYyMjLM8VFRUdqwYYPGjx+vefPmKTw8XHPmzNGQIUPMMUePHlWXLl3M92fNmqVZs2apZ8+e2rJlS62NDQAAAHBFNDqsxPlxAQAAAPcRHx+v+Ph4i48tX768zLSePXtq165d5c6vZcuW5guUAwAAALAtTl0FAAAAAAAAAACcltse0XHmzJlS58NNTU3Vnj17FBQUpMjISDtmBltauFA6d06qW9femQCAY9pzz59Up6hYF7zddpMAKBfbEQAcDismALVl4UJlp6YqadEihdg7FwCwgtt+q7Fjxw7ddttt5vuXrr8xYsQIi4eiwzn96U/2zgAAHFt2h3b2TgFwWGxHAHA4rJgA1JY//UlnDx5Uyvvv62575wIAVnDbRkevXr04Ry4AAG6qsKhI6enplcYFBAQoODi4FjICAAAAAADV5baNDgAA4J5O5+crNTVVzz/ztHx9fSuMbRAYqLdWrKTZAQAAAACAA6PRAZe2c6dUWCj5+EjR0fbOBgAcT8MjR1WnpEQXPD11qnm4vdOpFecLCuUhQ7d1bKvmIU3Ljcs9lafP9/0ik8lEo8NNsR0BoCYVFBZWenRhmSMLWTEBqC07d8r30CG1PX260tDC6qzPAMDGaHTApQ0aJGVmSs2aSUeO2DsbAHA8N61crbqm0zoX4K+Nk5+ydzq1KijAX6GNg+ydBhwY2xEAasrJM3k6lHpIMyZPrfDoQj//elq0bMn/fTnIiglAbRk0SM0zMzXDx0dvVxB25swZpaam6rnnnpOPj0+5cf7+/lq2bBnNDgA1hkYHAAAAAAC16GxBvnw8vDT6pgfUOrylxZijJ7K1KGU1RxYCcGjnz59XnTp1dPPNN6tZs2YWY44fP65t27axPgNQo+rYOwEAAAAAANxReKMQRTWNtHgLDwq1d3oArjB//nxFRUXJz89P0dHR2rZtW4XxW7duVXR0tPz8/NSqVSstXLiwTMy6det07bXXytfXV9dee60++OCDUo9PmTJFHh4epW6hoY63fmjUqJFCQkIs3ho3bmzv9AC4ARodAABUgbPsaAAAAACwnbVr12rcuHF67rnntHv3bvXo0UMDBgxQRkaGxfjU1FQNHDhQPXr00O7duzV58mSNGTNG69atM8ekpKRo6NChGj58uH744QcNHz5c999/v7799ttS8+rYsaOysrLMt71799boWAHAGXHqKgeVk5Mjk8lU7uPp6ekqKi6uxYwAAJd07NhRn376qfm+p6enHbMBAAAAUNNmz56tRx99VKNGjZIkJSUladOmTVqwYIESExPLxC9cuFCRkZFKSkqSJHXo0EE7duzQrFmzNGTIEPM8+vbtq0mTJkmSJk2apK1btyopKUmrV682z8vLy4sfVwFAJWh0OKCcnBw9OnK0zpjyy405dy5fR49mq7CoqBYzAwBI7GgAAAAA7qSwsFA7d+7UxIkTS03v16+ftm/fbvE5KSkp6tevX6lp/fv319KlS1VUVCRvb2+lpKRo/PjxZWIuNUcu+fXXXxUeHi5fX19169ZNM2fOVKtWrcrNt6CgQAUFBeb7Ff2QFgBcBY0OB2QymXTGlK++N96v4EaWv0j7OfVHrT38popLOKoDAGpbVXY02MkAAAAAnFtubq5KSkoUEhJSanpISIiys7MtPic7O9tifHFxsXJzcxUWFlZuzOXz7Natm1auXKm2bdvq999/1/Tp09W9e3ft27ev3GtfJCYmaurUqdUZKgA4LRodDiy4UajCgiMsPnbsRFYtZyMVFhUqPT29wpiAgAAFBwfXUkYAUPuquqPBTgYAAADgGjw8PErdNwyjzLTK4q+cXtk8BwwYYP7/ddddp9jYWLVu3VorVqxQQkKCxeVOmjSp1GMmk0kREZa/XwIAV0GjA1Y5fTZPhw4d0gsTp8rX17fcuAYB9bR0+RKaHQBcVlV3NNjJAADAtRQUVvwDsPT0dBUXOd6R95VdB1Lih2tAeZo0aSJPT88yR28cO3aszBEZl4SGhlqM9/LyMv9AqryY8uYpSfXr19d1112nX3/9tdwYX1/fCr+7AQBXRKMDVjlXkC9PDy/1ufF+RYS2sBiTczJbybvelclkYuMYgNuobEeDnQwAAFzHyTN5OpR6SDMml/8DsLPn8vV7ZraKigprObvy5eTk6LG/jNb50+VfB1KS/PzradEyfrgGXMnHx0fR0dFKTk7WPffcY56enJysQYMGWXxObGysPv7441LTNm/erJiYGHl7e5tjkpOTS12nY/PmzerevXu5uRQUFGj//v3q0aPH1QwJAFwOjQ5USeOGTcs9nZYj2r9fMgypgiNJAeCqOPuOxqdPPcGK8irxC1nXxXYEgCudLciXj4eXRt/0gFqHt7QYs/Pgj/rb4UUqLi6xfQLVXDGZTCadP52vx2KHKTzI8nUgj57I1qKU1fxwDShHQkKChg8frpiYGMXGxmrx4sXKyMhQXFycpItHcmdmZmrlypWSpLi4OM2dO1cJCQkaPXq0UlJStHTpUq1evdo8z7Fjx+rWW2/Vq6++qkGDBulf//qXPv30U3311VfmmAkTJujOO+9UZGSkjh07punTp8tkMmnEiBE1O+D9+3Xo4EHFP/64+tfskgDAJmh0wKX5+9s7AwCuxm47GjWkmKNNrkpOTo4eGfGwzuTlVRjXIDBQb61YyRdHTobtCADlCW8UoqimkRYfO3K8Bq+neJUrpvCg0HLzBlCxoUOH6vjx45o2bZqysrLUqVMnbdiwQS1aXDzrRVZWljIyMszxUVFR2rBhg8aPH6958+YpPDxcc+bM0ZAhQ8wx3bt315o1a/T888/rhRdeUOvWrbV27Vp169bNHHPkyBENGzZMubm5Cg4O1k033aRvvvnGvNwa4+8vw99f57z46hCAc2BtZQeV/fIzPT1dRcWOd05XAIAddzTgkEwmk87k5al3x7Zq0jDQYkzuqTx9vu8XfiELAKiyyq4HIkmFhYXy8fGpMMZRrxsCOJv4+HjFx8dbfGz58uVlpvXs2VO7du2qcJ733nuv7r333nIfX7NmTZVyBAB3RaOjluXk5OjRkaN1xlT+uVHPncvX0aPZKiwqqsXMAADWYEcDljRpGKjQxkH2TgMA4EKsuR5IQWGh0jLS1bpFlLy8y9+9d8TrhgAAANgSjY5aZjKZdMaUr7433q/gRpbPjfpz6o9ae/hNFZfwi5urNXu2ZDJJAQFSQoK9swEAx9Nm23Z5nS9QsZ+vfutR/kUPAXfEdgQAe7J0PZCQL9fLsyBfJb719Putgy9eDyRtkR75w33lXjNEquHrhgBwTbNnq1Famu4/ckS0SAE4AxoddhLcKLTci3ofO1GD53R1M7NnS5mZUrNmfEEBAJa02ZaiuqbTOhfgT6PDgsKiogpPGcLpJl0b2xEAHMHl1wNptP1jeZ78XSWNQlTv3nHm64FUdM0QqYavGwLANc2eraDMTN3n46O37Z0LAFiBRgcAAIAFp/PzlZqaquefebrcU4acO39eRzOPqLAoppazAwAAVVHZtTIlKSAggOtpAQDgpGh0AAAAWHC+oFAeMnRbx7ZqHtLUYswvGYf1bka6ijmqAwAAh5WTk6PH/jJa50+Xf61MSfLzr6dFy5bQ7AAAwAnR6AAAAKhAUIB/uRcazzl5qnaTAQAAVWYymXT+dL4eix2m8CDL18o8eiJbi1JWy2Qy0egAAMAJ0egAAAAAAAAuLzwotMJrmQAAAOdVx94JAAAAAAAAAAAAVBdHdAAAAAAAABUUFio9Pb3CGGsu2G3Nhb+tnRcAAIA1aHQAAAAAAODmTp7J06HUQ5oxeap8fX3Ljavsgt3WXvjbmnkBAABYi0YHAAAAAABu7mxBvnw8vDT6pgfUOrylxRhrLthtzYW/rZ0XAACAtWh0wKXdeKMUESGx3QwAlp1qFqZzDQNVUL+evVNxaYVFRZWeCqSwsFA+Pj5XHWNtHKcLqRzbEQAcTXGLDroQFKoL/o1qbBnhjUJscsFuLvwNOLkbb9T54GD9mpFh70wAwCo0OuDSPvrI3hkAgGP7ZsSD9k7B5Z3Oz1dqaqqef+bpck8FUlhUpIzDh9UiMlLeXpY3z6yJqUpcg8BAvbViJc2OCrAdAcDRnB47194pAHAXH32kzIMHNfmRR3S3vXMBACvQ6AAAAKhB5wsK5SFDt3Vsq+YhTS3G/JJxWGmph9SzfeurirE2LvdUnj7f9wunCwEAAAAAuAQaHbCpwqLCSk/NIXFKDQCA+wkK8Fdo4yCLj+WcPGWTmKrEAQAAAADgKmh0wGZOn83ToUOH9MLEqeWemkO62AzJyEhXixZRFZ9SI6Celi5fQrMDAAAAAADAiRUWWvfDWH70CqC6aHTAZs4V5MvTw0t9brxfEaEtyo37OfVHpae+qd43DCk3LudktpJ3vXvVp9S46y4pJ+fiRUQ5zzYAlHXTinfkezZfBfXrcb0O4ApsRwBwNP5/f0J1Tp/UBf9GdrteR0ElX1amp6eruKjYJvOS+NITsJu77lKzw4c1MyND391991XN6syZM0pNTdVzzz1X6dk9/P39tWzZMj73AKqMRgdsrnHDpgoLjij38WMnsqyKs4Vdu6TMTKlZsxpdDAA4rYaZWaprOq1zAf72TgVwOGxHAHA0Xun75Xnyd5U0CrHL8k+eydOh1EOaMbn8o/jPnsvX75nZKioqvOp5SZKffz0tWsaR/kCt27VLfpmZusbHR99d5azOnz+vOnXq6Oabb1azCjasjh8/rm3btnEdOQDVQqMDAAAAAABU6mxBvnw8vDT6pgfUOrylxZidB3/U3w4vUnFxyVXP6+iJbC1KWc2XnoCLaNSokUJC7NOoBeD6aHQAAAAAAACrhTcKUVTTSIuPHTmeZbN5AQAAWItGB5xaTk6OTCZTuY8XF7cQf+YAAJRVWFRU6XnRCwsLKz2PMudOd0yVbSNJvHcAAAAAXAffAMNp5eTk6NGRo3XGlF9uzKmTb0sK1oULJZI8ay03AAAc2en8fKWmpur5Z54u97zohUVFyjh8WC0iI+XtVf4mY4PAQL21YiVfmDuQnJwcPTLiYZ3Jy6swjvcOgDOw5oLllTXmq3KBdFuxpuFsyx8U2KrBTaMcAOCs3LrRMX/+fL3++uvKyspSx44dlZSUpB49etg7LfxPYVHFG7Tp6ek6ecKkgX94UMGNQi3G7NxdTwWFUlFRkQ4eTCt3XrbcUKvNDVprlmXtvABUDTUEzux8QaE8ZOi2jm3VPKSpxZhfMg4rLfWQerZvXW5M7qk8fb7vF86d7mBMJpPO5OWpd8e2atIw0GIM713lqrqe37p1qxISErRv3z6Fh4frmWeeUVxcXKmYdevW6YUXXtDBgwfVunVrzZgxQ/fcc09NDwVwWtZcsLygsFBpGelq3SJKXt6Wv+Kw9gLptpKTk6PH/jJa50+X/6M8a/KWrLsYuzXLs2ZetpqPK7NXbWDfAwAq57aNjrVr12rcuHGaP3++br75Zi1atEgDBgzQTz/9pMhIzg9qb6fP5unQoUN6YWL5G7TnzuXr6NFsBfZurLDgCIsxHh4ekiRTnkmjR/6/cpfXIKCeli6/+g01a44yKSwqVEZGulq0iKr4F7KV5GTNsqydly3RfLEdXkvHRQ2BqwgK8Fdo4yCLj+WcPFVpDBxbk4aBvHfVVNX1fGpqqgYOHKjRo0dr1apV+vrrrxUfH6/g4GANGTJEkpSSkqKhQ4fq5Zdf1j333KMPPvhA999/v7766it169attocIOAWrL36etkiP/OG+q75Auq2YTCadP52vx2KHKTyonB/lWZG3tRdjt2Z51szLVvNxVfaqDe6271FoxVFcttoHZp8bcC1u2+iYPXu2Hn30UY0aNUqSlJSUpE2bNmnBggVKTEy0c3Y4V5AvTw8v9bnxfkWEtrAY83Pqj1p7+E0Vl5R/CLJhXJAk+frU04O3j7EYk3MyW8m73rXJhprJZNIZU7763nh/uUeZ/Jz6o9JT31TvG4aUOzZrcrJmWdbOy1YctfnijHgtHRs1BABcW1XX8wsXLlRkZKSSkpIkSR06dNCOHTs0a9Ys85dZSUlJ6tu3ryZNmiRJmjRpkrZu3aqkpCStXr26dgYGOClrLn5uywuk20p4UOhV5W3L5dljPq7GXrXBnfY9zpw5o9TUVD333HMVngXD399fy5Ytq/RIp4qaGMePH9cLL7yg8+fPV5qXNcsD3J0jNA7dstFRWFionTt3auLEiaWm9+vXT9u3b7f4nIKCAhUUFJjv5/3vnMfWvIGXO336tIqLi3U4O1XnCix/gZmVc1gXLlzQ0WPp8vAwqh1jy3nZK6eCwnPlvk4FhecrnU/JhcaSLsgwzpQ7n/MF53Tu3Dn99NNPOn36dLl5W+Pw4cM6f/78xXlWkndFY7MmJ2uWZe28bOXw4cPKPXZC0W16KcC/YblxptOntOPXL/TNN98oIsLy0Tjuriqv5Q9pXykzM7Pco5/K4+/vbz7qCdarag2xdf04cixH5y6b35WyT5zQhQuGsnJPyKNOnUpjTpaUqEjSmZISpR7NqvZ8riamtpfnrDk5Yt7H80w6d/58rdQYeygsjJTkpcLCYu3Zk2HvdKx2+PBhnS8oqHB9cTzPpOLiYp0+fbrK6yPJtWtIdfYVUlJS1K9fv1LT+vfvr6VLl6qoqEje3t5KSUnR+PHjy8Rc+gLMElvWkKLiYv2Wlaoz5y1vN6YdO6ySCxf0W3aaSsrZtrZVTG0vz5Xyvq64UD6SCosLtTfjZ6fJ297LsyYm++TvyrfpfmHBVX/mrM3JmuVZMy9r51PkhvXDXrWhOsuVbFQ/Llz84ehpw9DRo0fLbQgcO3ZMFy5cUHZ2tuqUs81oTYwkZWRc3N5q06aNmjRpYjHm9OnT+vHHHyv8PuHEiROaOXOmzp07V+6yzp8/r6ysLPXr10+NGjUqN86a5QHuzprP3CUNGjTQggULyv2Ml8eq+mG4oczMTEOS8fXXX5eaPmPGDKNt27YWn/PSSy8Zkrhx48bNZW55eXm1scp1OVWtIdQPbty4ueLNlWtIdfYVrrnmGmPGjBmlpn399deGJOPo0aOGYRiGt7e38fbbb5eKefvttw0fH59yc6GGcOPGzdVuzlo/7FUbqrNcw6B+cOPGzfVu1tQPtzyi45Iru0CGYZTbGZo0aZISEhLM9y9cuKATJ06ocePGVv8awWQyKSIiQocPH1ZAQED1E3dwjNO1ME7Xcvk4/f397Z2OU7O2htiifkjO9zfqbPlKzpcz+dY8Z8u5NvN1hxpSlX2F8uKvnF7VebprDakOdxijxDhdjTuO09nrh71qQ23WD3f5u7xavE7W47WyDq9TxaypH27Z6GjSpIk8PT2VnZ1davqxY8cUEhJi8Tm+vr5lTgvTsGHDai0/ICDALf5gGadrYZyuJSAgwCkPGXcEVa0htqwfkvP9jTpbvpLz5Uy+Nc/Zcna2fB1NdfYVQkNDLcZ7eXmpcePGFcaUN0+JGlId7jBGiXG6Gncap7Pug9irNlRnuZJt6oe7/F1eLV4n6/FaWYfXqfrKPzGeC/Px8VF0dLSSk5NLTU9OTlb37t3tlBUAwBlQQwDAtVVnPR8bG1smfvPmzYqJiZG3t3eFMdQOAHB89qoN7HsAgPXc8ogOSUpISNDw4cMVExOj2NhYLV68WBkZGYqLi7N3agAAB0cNAQDXVtl6ftKkScrMzNTKlSslSXFxcZo7d64SEhI0evRopaSkaOnSpVq9erV5nmPHjtWtt96qV199VYMGDdK//vUvffrpp/rqq6/sMkYAQNXYqzaw7wEA1nHbRsfQoUN1/PhxTZs2TVlZWerUqZM2bNigFi1a1NgyfX199dJLL5U5fNDVME7Xwjhdi7uMs6ZRQyrnbPlKzpcz+dY8Z8vZ2fJ1ZJWt57OyspSRkWGOj4qK0oYNGzR+/HjNmzdP4eHhmjNnjoYMGWKO6d69u9asWaPnn39eL7zwglq3bq21a9eqW7duNT4ed/jbcIcxSozT1TBO52Kv2lDb+x6u8n7VNF4n6/FaWYfX6ep5GJeuhAQAAAAAAAAAAOBk3PIaHQAAAAAAAAAAwDXQ6AAAAAAAAAAAAE6LRgcAAAAAAAAAAHBaNDoAAAAAAAAAAIDTotFhY/Pnz1dUVJT8/PwUHR2tbdu2VRi/detWRUdHy8/PT61atdLChQtrKdOrU5VxbtmyRR4eHmVuP//8cy1mXHVffvml7rzzToWHh8vDw0Mffvhhpc9xxvezquN0xvczMTFRXbt2lb+/v5o2baq7775bBw4cqPR5zvZ+Vmeczvh+uoqaqBfr1q3TtddeK19fX1177bX64IMPHDrnJUuWqEePHmrUqJEaNWqkPn366LvvvnPYfC+3Zs0aeXh46O6773bofE+dOqXHH39cYWFh8vPzU4cOHbRhwwaHzjkpKUnt2rVT3bp1FRERofHjx+v8+fO1nm9WVpYefPBBtWvXTnXq1NG4ceMsxtXk587W+db0Zw6OqaqfU0dnzfaOYRiaMmWKwsPDVbduXfXq1Uv79u2zU8ZXLzExUR4eHqU+164yxszMTP35z39W48aNVa9ePXXu3Fk7d+40P+4K4ywuLtbzzz+vqKgo1a1bV61atdK0adN04cIFc4wzjrOy/UhrxlRQUKAnn3xSTZo0Uf369XXXXXfpyJEjtTgKXMnVakZV2arGuNvfdnXrlLu8Traode7yWl01AzazZs0aw9vb21iyZInx008/GWPHjjXq169vpKenW4w/dOiQUa9ePWPs2LHGTz/9ZCxZssTw9vY23n///VrOvGqqOs4vvvjCkGQcOHDAyMrKMt+Ki4trOfOq2bBhg/Hcc88Z69atMyQZH3zwQYXxzvp+VnWczvh+9u/f31i2bJnx3//+19izZ49xxx13GJGRkcaZM2fKfY4zvp/VGaczvp+uoCbqxfbt2w1PT09j5syZxv79+42ZM2caXl5exjfffOOwOT/44IPGvHnzjN27dxv79+83/vKXvxiBgYHGkSNHHDLfS9LS0oxmzZoZPXr0MAYNGnTVudZUvgUFBUZMTIwxcOBA46uvvjLS0tKMbdu2GXv27HHYnFetWmX4+voab7/9tpGammps2rTJCAsLM8aNG1fr+aamphpjxowxVqxYYXTu3NkYO3ZsmZia/NzVRL41+ZmDY6rq35EzsGZ755VXXjH8/f2NdevWGXv37jWGDh1qhIWFGSaTyY6ZV893331ntGzZ0rj++utLfa5dYYwnTpwwWrRoYYwcOdL49ttvjdTUVOPTTz81fvvtN3OMK4xz+vTpRuPGjY1///vfRmpqqvHee+8ZDRo0MJKSkswxzjjOyvYjrRlTXFyc0axZMyM5OdnYtWuXcdtttxk33HAD+yJ24oo1o6psVWPc6W/7auqUO7xOtqp17vBa2QKNDhv6wx/+YMTFxZWa1r59e2PixIkW45955hmjffv2paY99thjxk033VRjOdpCVcd56YvUkydP1kJ2NcOaBoCzvp+Xq0qjw5nfz2PHjhmSjK1bt5Yb4wrvpzXjdIX30xnVRL24//77jT/+8Y+lYvr372888MADDpvzlYqLiw1/f39jxYoVDptvcXGxcfPNNxtvvvmmMWLECJs1Omoi3wULFhitWrUyCgsLbZLjlWoi58cff9zo3bt3qZiEhATjlltuqfV8L9ezZ0+LjYOa/NzVRL5XsuVnDo7pav6OnMWV2zsXLlwwQkNDjVdeecUcc/78eSMwMNBYuHChvdKsltOnTxvXXHONkZycXOpz7SpjfPbZZytcv7vKOO+44w7jkUceKTVt8ODBxp///GfDMFxjnFfuR1ozplOnThne3t7GmjVrzDGZmZlGnTp1jI0bN9Za7vg/7lAzqqo6Ncad/ravpk65y+tki1rnLq+VLXDqKhspLCzUzp071a9fv1LT+/Xrp+3bt1t8TkpKSpn4/v37a8eOHSoqKqqxXK9GdcZ5SZcuXRQWFqbbb79dX3zxRU2maRfO+H5eDWd+P/Py8iRJQUFB5ca4y/DNegABAABJREFUwvtpzTgvceb309nUVL0oL6aydbM9c75Sfn6+ioqKrPqbtVe+06ZNU3BwsB599NGryrE28v3oo48UGxurxx9/XCEhIerUqZNmzpypkpISh835lltu0c6dO82nUzp06JA2bNigO+64o9bztUZNfe5qKt8r2eozB8dUW39H9nbl9k5qaqqys7NLjdvX11c9e/Z0unE//vjjuuOOO9SnT59S011ljB999JFiYmJ03333qWnTpurSpYuWLFliftxVxnnLLbfos88+0y+//CJJ+uGHH/TVV19p4MCBklxnnJezZkw7d+5UUVFRqZjw8HB16tTJacftzNylZlRVdWqMO/1tX02dcpfXyRa1zl1eK1ug0WEjubm5KikpUUhISKnpISEhys7Otvic7Oxsi/HFxcXKzc2tsVyvRnXGGRYWpsWLF2vdunVav3692rVrp9tvv11ffvllbaRca5zx/awOZ38/DcNQQkKCbrnlFnXq1KncOGd/P60dp7O/n86opupFeTHlzdMRcr7SxIkT1axZszIbyo6S79dff62lS5eW2jC1hZrK99ChQ3r//fdVUlKiDRs26Pnnn9cbb7yhGTNmOGzODzzwgF5++WXdcsst8vb2VuvWrXXbbbdp4sSJtZ6vNWrqc1dT+V7JVp85OKba+juyJ0vbO5fG5uzjXrNmjXbt2qXExMQyj7nKGA8dOqQFCxbommuu0aZNmxQXF6cxY8Zo5cqVklxnnM8++6yGDRum9u3by9vbW126dNG4ceM0bNgwSa4zzstZM6bs7Gz5+PioUaNG5cag9rhDzaiq6tYYd/nbvto65S6vky1qnbu8VrbgZe8EXI2Hh0ep+4ZhlJlWWbyl6Y6mKuNs166d2rVrZ74fGxurw4cPa9asWbr11ltrNM/a5qzvZ1U4+/v5xBNP6Mcff9RXX31Vaawzv5/WjtPZ309nVhP1oqrzrKqarHGvvfaaVq9erS1btsjPz88G2do239OnT+vPf/6zlixZoiZNmtgkP2uWfzWv74ULF9S0aVMtXrxYnp6eio6O1tGjR/X666/rxRdfdMict2zZohkzZmj+/Pnq1q2bfvvtN40dO1ZhYWF64YUXaj1fe82zNuZdE585OKaarg32VNH2jjOP+/Dhwxo7dqw2b95c4efTmccoXaxTMTExmjlzpqSLRxjv27dPCxYs0MMPP2yOc/Zxrl27VqtWrdI777yjjh07as+ePRo3bpzCw8M1YsQIc5yzj9OS6ozJFcbtzFzx77C6bF1jXOm1rMk65Uqvk1Sztc7VXitb4IgOG2nSpIk8PT3LdNKOHTtWpit3SWhoqMV4Ly8vNW7cuMZyvRrVGaclN910k3799Vdbp2dXzvh+2oqzvJ9PPvmkPvroI33xxRdq3rx5hbHO/H5WZZyWOMv76axqql6UF1OVdXNt53zJrFmzNHPmTG3evFnXX3+9Q+Z78OBBpaWl6c4775SXl5e8vLy0cuVKffTRR/Ly8tLBgwcdKl/p4hFbbdu2laenpzmmQ4cOys7OVmFhYbXzrcmcX3jhBQ0fPlyjRo3Sddddp3vuuUczZ85UYmKiLly4UKv5WqOmPnc1le8ltv7MwTHV9N+RvZW3vRMaGipJTj3unTt36tixY4qOjjbXnK1bt2rOnDny8vIyj8OZxyhdrFPXXnttqWkdOnRQRkaGJNd4LyXp6aef1sSJE/XAAw/ouuuu0/DhwzV+/Hjzr6BdZZyXs2ZMoaGhKiws1MmTJ8uNQe1x9ZpRVVdTY9zhb9sWdcodXifJNrXOXV4rW6DRYSM+Pj6Kjo5WcnJyqenJycnq3r27xefExsaWid+8ebNiYmLk7e1dY7lejeqM05Ldu3crLCzM1unZlTO+n7bi6O+nYRh64okntH79en3++eeKioqq9DnO+H5WZ5yWOPr76exqql6UF1OVdXNt5yxJr7/+ul5++WVt3LhRMTExV51rTeXbvn177d27V3v27DHf7rrrLt12223as2ePIiIiHCpfSbr55pv122+/lWoQ/PLLLwoLC5OPj0+1863JnPPz81WnTunNU09PTxmGYT76o7bytUZNfe5qKl+pZj5zcEw1+XdkT5Vt70RFRSk0NLTUuAsLC7V161anGfftt99epubExMTooYce0p49e9SqVSunH6N0sU4dOHCg1LRffvlFLVq0kOQa76VUfm27VJ9dZZyXs2ZM0dHR8vb2LhWTlZWl//73v047bmfmqjWjqmxRY9zhb9sWdcodXifJNrXOXV4rm6ihi5y7pTVr1hje3t7G0qVLjZ9++skYN26cUb9+fSMtLc0wDMOYOHGiMXz4cHP8oUOHjHr16hnjx483fvrpJ2Pp0qWGt7e38f7779trCFap6jj/9re/GR988IHxyy+/GP/973+NiRMnGpKMdevW2WsIVjl9+rSxe/duY/fu3YYkY/bs2cbu3buN9PR0wzBc5/2s6jid8f38f//v/xmBgYHGli1bjKysLPMtPz/fHOMK72d1xumM76crqIl68fXXXxuenp7GK6+8Yuzfv9945ZVXDC8vL+Obb75x2JxfffVVw8fHx3j//fdL/c2ePn3aIfO90ogRI4xBgwZdda41lW9GRobRoEED44knnjAOHDhg/Pvf/zaaNm1qTJ8+3WFzfumllwx/f39j9erVxqFDh4zNmzcbrVu3Nu6///5az9cwDHN9jI6ONh588EFj9+7dxr59+8yP1+TnribyrcnPHBxTZX9Hzsia7Z1XXnnFCAwMNNavX2/s3bvXGDZsmBEWFmaYTCY7Zn51evbsaYwdO9Z83xXG+N133xleXl7GjBkzjF9//dV4++23jXr16hmrVq0yx7jCOEeMGGE0a9bM+Pe//22kpqYa69evN5o0aWI888wz5hhnHGdl+5HWjCkuLs5o3ry58emnnxq7du0yevfubdxwww1GcXGxvYbl1lyxZlSVrWqMO/5tV6dOucPrZKta5w6vlS3Q6LCxefPmGS1atDB8fHyMG2+80di6dav5sREjRhg9e/YsFb9lyxajS5cuho+Pj9GyZUtjwYIFtZxx9VRlnK+++qrRunVrw8/Pz2jUqJFxyy23GJ988okdsq6aL774wpBU5jZixAjDMFzn/azqOJ3x/bQ0PknGsmXLzDGu8H5WZ5zO+H66ipqoF++9957Rrl07w9vb22jfvr3NG1a2zrlFixYW/2Zfeuklh8z3SrZsdNRUvtu3bze6detm+Pr6Gq1atTJmzJhh041hW+dcVFRkTJkyxbxeioiIMOLj442TJ0/aJV9Lf58tWrQoFVOTnztb51vTnzk4por+jpyRNds7Fy5cMF566SUjNDTU8PX1NW699VZj79699kvaBq78AslVxvjxxx8bnTp1Mnx9fY327dsbixcvLvW4K4zTZDIZY8eONSIjIw0/Pz+jVatWxnPPPWcUFBSYY5xxnJXtR1ozpnPnzhlPPPGEERQUZNStW9f405/+ZGRkZNhhNLjE1WpGVdmqxrjj33Z16pS7vE62qHXu8lpdLQ/DuIrzAAAAAAAAAAAAANgR1+gAAAAAAAAAAABOi0YHAAAAAAAAAABwWjQ6AAAAAAAAAACA06LRAQAAAAAAAAAAnBaNDgAAAAAAAAAA4LRodAAAAAAAAAAAAKdFowMAAAAAAAAAADgtGh0AAAAAAAAAAMBp0eiAQ+nVq5fGjRtXY/PPzs5W3759Vb9+fTVs2LDGluPOli9f7lCvbU3/TQGAI3K0dTEAoHJXbre2bNlSSUlJtbIsZ3FlfZsyZYo6d+5st3wAwBWkpaXJw8NDe/bsqfVlb9myRR4eHjp16lStLYN9JddFowNu5W9/+5uysrK0Z88e/fLLL7WyQr2kJjbC2bD/P+W9l+vXr9fLL79sn6QAAACAavr+++/117/+1Xzfw8NDH374of0SckATJkzQZ599Zu80AACAA6DRAbdy8OBBRUdH65prrlHTpk1tNl/DMFRcXGyz+dlaUVGRvVOotsLCwqt6flBQkPz9/W2UDQAAAFA7goODVa9ePXun4dAaNGigxo0b2zsNAHBoV/u9CuAsaHTAar169dITTzyhJ554Qg0bNlTjxo31/PPPyzAMSRdXnM8884yaNWum+vXrq1u3btqyZYv5+cePH9ewYcPUvHlz1atXT9ddd51Wr15d4TI3btyowMBArVy5stL8vv/+e/Xt21dNmjRRYGCgevbsqV27dpkfb9mypdatW6eVK1fKw8NDI0eO1G233SZJatSokXmadLFx8dprr6lVq1aqW7eubrjhBr3//vvmeV06emDTpk2KiYmRr6+vtm3bVm5uy5cv19SpU/XDDz/Iw8NDHh4eWr58uSQpIyNDgwYNUoMG/5+9e4+zsdz/P/5e5mQwM8wwJzOGCmEkOUshwpRTKUpb9JVSjhsdpAO7Mp1IUYrdHtkkexdtdiVKSA4xCBFyphnjMGYc53j9/vCztmVOa2bWzFpr5vV8PO5HrXtd674+9z3L/bnu+7Pu+64if39/9e3bVydOnChwffNbpsVi0UcffaRevXqpcuXKeu211yRJM2fO1I033ihvb2/Vr19f//znP63Ly+1SxbNnz8pisdj8HZcsWaK6devK19dXHTt21KeffprrlRTfffedGjRooCpVqqhbt25KSEgocJ0kadCgQerdu7diY2MVHh6uevXqSZLmzZun5s2by8/PT6Ghoerfv7+SkpKssef1t7z+svzk5GQ9+uijqlatmipVqqSYmBjt27fPrtgAIC955Q1jjDp37qxu3bpZ8+XZs2dVq1YtTZgwQdL/csrXX3+tJk2aqGLFimrVqpV27Nhhd/9z5sxRrVq1VKlSJd133306ffp0jjZLly5Vs2bNVLFiRd1www2aNGmSTZH+7NmzeuKJJxQSEqKKFSsqOjpa//3vfyUVnMPnzp2roKAgpaWl2fTZp08fPfroo/ZvSABwAcuWLVO7du2sxzzdu3fX/v37Jf1vzPyvf/1Ld9xxh3x9fdWiRQvt3btXmzZtUvPmza3j35MnT1qXeXWMO2nSJAUHB8vf319PPvlkviefrr11Ve3atSVJ9913nywWi/X11eVea/To0erQoYP19YULF/Too4+qSpUqCgsL05QpU3L0VdCxXH6u3gLkv//9r+rXr69KlSrpgQce0IULF/Tpp5+qdu3aqlatmkaMGKGsrKxC9VlQfrv+CveCjgmlK8dKf//733XfffepUqVKqlu3rpYsWWLXugJAafniiy/UuHFj+fr6KigoSJ07d9aFCxes+/133nlHYWFhCgoK0rBhw2x+4Fq7dm299tprGjRokAICAjRkyBC7+vz999/Vtm1bVaxYUY0aNcqxT169erVatmwpHx8fhYWF6fnnn7c5nujQoYNGjBih0aNHq1q1agoJCdGsWbN04cIFPfbYY/Lz89ONN96ob7/9NkffP//8c77HQj///LPat2+vSpUqqVq1auratauSk5MlFXwOD+WIAezUvn17U6VKFTNq1Cjz+++/m3nz5plKlSqZWbNmGWOM6d+/v2nbtq1Zs2aN+eOPP8zbb79tfHx8zN69e40xxhw7dsy8/fbbZuvWrWb//v3m/fffNx4eHmbDhg02fYwaNcoYY8yCBQuMn5+f+eqrr+yK74cffjD//Oc/za5du8yuXbvM4MGDTUhIiElNTTXGGJOUlGS6detm+vbtaxISEszZs2fNl19+aSSZPXv2WOcZY8wLL7xgbr75ZrNs2TKzf/9+ExcXZ3x8fMyqVauMMcb8+OOPRpK55ZZbzPLly80ff/xhTp06lWdsFy9eNGPHjjWNGjUyCQkJJiEhwVy8eNFkZ2ebpk2bmnbt2pnNmzebDRs2mNtuu820b9++wPXNa5nGGCPJBAcHm08++cTs37/fHDp0yCxatMh4eXmZDz74wOzZs8dMmTLFeHh4mJUrVxpjjDl48KCRZLZu3WrtIzk52UgyP/74o7WNl5eXGTdunPn999/NggULTM2aNY0kk5ycbIwxJi4uznh5eZnOnTubTZs2mfj4eNOgQQPTv39/u/6OAwcONFWqVDEDBgwwO3fuNDt27DDGGPPJJ5+Yb775xuzfv9+sX7/etG7d2sTExBhjjMnMzMzzb3ntd8oYY3r27GkaNGhg1qxZY7Zt22a6du1qbrrpJpOenm5XfACQm/zyxrFjx0y1atXMtGnTjDHG9OvXzzRv3ty637maUxo0aGCWL19utm/fbrp3725q165t175pw4YNxmKxmNjYWLNnzx7z3nvvmapVq5qAgABrm2XLlhl/f38zZ84cs3//frN8+XJTu3ZtM3HiRGOMMVlZWaZ169amUaNGZvny5Wb//v1m6dKl5ptvvjHGFJzDL168aAICAsy//vUva58nT5403t7e1jwDAO7iiy++MF9++aXZu3ev2bp1q+nRo4dp3LixycrKso6Zr+7zd+3aZVq3bm1uu+0206FDB7N27VqzZcsWc9NNN5mhQ4dal3l1jNuvXz+zc+dO89///tfUqFHDvPDCC9Y2149bo6KizLvvvmuMuXIsI8nExcWZhIQEk5SUZF1ur169bOIfNWqUzfHEU089ZSIiImxyzNXjuqsKOpbLz9Xx/9133222bNliVq9ebYKCgkyXLl1M3759zW+//WaWLl1qvL29zeeff253n/bkt1deecU0adLE+rqgY0JjrhwrRUREmM8++8zs27fPjBw50lSpUsWcPn26wHUFgNLw559/Gk9PTzN16lRz8OBBs337dvPBBx+Yc+fOmYEDBxp/f38zdOhQs3v3brN06VKbc3PGXMkf/v7+5u233zb79u0z+/bty7e/q7ktIiLCfPHFF2bXrl3m8ccfN35+ftZzXceOHTOVKlUyTz/9tNm9e7dZvHixqV69unnllVesy2nfvr3x8/Mzr776qtm7d6959dVXTYUKFUxMTIyZNWuW2bt3r3nqqadMUFCQuXDhgjHGvmOhrVu3Gh8fH/PUU0+Zbdu2mZ07d5rp06ebkydPGmPsP4d37Xmra3MJyg4KHbBb+/btTYMGDUx2drZ13nPPPWcaNGhg/vjjD2OxWMzx48dtPtOpUyczfvz4PJd5zz33mLFjx9r0MWrUKPPBBx+YgICAYp0cyczMNH5+fmbp0qXWeb169TIDBw60vr5+Z2eMMefPnzcVK1Y069ats1ne4MGDzcMPP2zzOXuLMMbkHIQbY8zy5cuNh4eHOXLkiHXeb7/9ZiSZX375pUjLNObK4H306NE289q2bWuGDBliM+/BBx8099xzjzHGvkLHc889Z6Kjo22WMWHChBwJQ5L5448/rG0++OADExISUuD6GHPlYC0kJMSkpaXl2+6XX34xksy5c+eMMbn/LY2xPWDcu3evkWR+/vln6/unTp0yvr6+NifnAKAw7Mkb//rXv4yPj48ZP368qVSpktmzZ4+13dX917Unf06fPm18fX3NwoULC+z/4YcfNt26dbOZ169fP5vB+x133GEmT55s0+af//ynCQsLM8YY891335kKFSrYxFWQ63P4U089ZS1AG2PMtGnTzA033GAzbgAAd3S1yLBjxw7rmPnvf/+79f0FCxYYSeaHH36wzouNjTX169e3vh44cKAJDAy0ntgxxpiZM2eaKlWqmKysLGNM/oUOY66M8RcvXmwTW0GFjnPnzuUoMFzNMVf7Kuqx3FW5jf+ffPJJU6lSJetY3Rhjunbtap588km7+7Qnv+V1PHRVbseEksyLL75ofX3+/HljsVjMt99+W+C6AkBpiI+PN5LMoUOHcrw3cOBAExUVZTIzM63zHnzwQdOvXz/r66ioKNO7d2+7+7ua29544w3rvIyMDBMREWHefPNNY8yVYkL9+vVtxvYffPBBjjzWrl076/uZmZmmcuXKZsCAAdZ5CQkJRpJZv369Mca+Y6GHH37Y3H777bnGXphzeBQ6yj7PEr1cBGVO69atZbFYrK/btGmjKVOmaPPmzTLGWG8zdFVaWpr1nqlZWVl64403tHDhQh0/flxpaWlKS0tT5cqVbT7z5Zdf6sSJE1q7dq1atmxpd2xJSUl6+eWXtXLlSp04cUJZWVm6ePGijhw5Uqh13LVrly5fvqy7777bZn56erqaNm1qM6958+aFWvb1du/ercjISEVGRlrnNWzYUFWrVtXu3bvVokWLIi/7+th2795t8zBDSbr99tv13nvv2b3MPXv25Igpt79RpUqVdOONN1pfh4WFWW8zZY/GjRvL29vbZt7WrVs1ceJEbdu2TWfOnFF2drakK7f+atiwoV3L3b17tzw9PdWqVSvrvKCgINWvX1+7d++2Oz4AuJY9eePBBx/U4sWLFRsbq5kzZ+bIl9KVnHpVYGCg3fum3bt367777suxrGXLlllfx8fHa9OmTXr99det87KysnT58mVdvHhR27ZtU0RERK5xXW1bUA4fMmSIWrRooePHj6tmzZqKi4vToEGDbMYNAOAO9u/fr5deekkbNmzQqVOnch133nLLLdb2ISEhkq6MYa+dd/34t0mTJjbP3GjTpo3Onz+vo0ePKioqqsTWJT09Pdccc9WWLVsKPJYryPXj/5CQENWuXVtVqlSxmXd1m9jTpz357Xr2HhNe+/erXLmy/Pz8CnW8AgAlqUmTJurUqZMaN26srl27qkuXLnrggQdUrVo1SVKjRo3k4eFhbR8WFpbjVk9FOV91ba7w9PRU8+bNrccju3fvVps2bWzG9rfffrvOnz+vY8eOqVatWpJs968eHh4KCgrKkR8l5djn5ncstG3bNj344IO5xlyYc3go+yh0wGE8PDwUHx9vs7OVZB3cTpkyRe+++66mTZumxo0bq3Llyho9enSO+9Leeuut2rJli+Li4tSiRQu7T5AMGjRIJ0+e1LRp0xQVFSUfHx+1adOm0A9dunog8/XXX6tmzZo27/n4+Ni8vr5IU1jGmFzXL6/5hZFbbNcv89p+KlSoYJ131fUPMc8trmvbX+Xl5ZWj39za2Rv7hQsX1KVLF3Xp0kXz5s1TjRo1dOTIEXXt2rVQf9+8YnDE9gZQftmTNy5evGjNkYV5LpA9+yZ79q/Z2dmaNGmS7r///hzvVaxYUb6+vvl+3p4c3rRpUzVp0kRz585V165dtWPHDi1durTA2ADA1fTo0UORkZGaPXu2wsPDlZ2drejoaJt93rXj3av76uvnXc0PBSnOOLRChQo58sC1Y3h7c0RBx3IFyW38n9u8q9vEnj4Lc/xwlb3HhPnFBgDO5uHhoRUrVmjdunVavny5pk+frgkTJmjjxo2S7NuHFfd81bXLlvI/H3Tt/ILywdW29uxzr7bN71ilMOfwUPZR6EChbNiwIcfrunXrqmnTpsrKylJSUpLuuOOOXD/7008/qVevXvrLX/4i6crOaN++fWrQoIFNuxtvvFFTpkxRhw4d5OHhoRkzZtgV208//aQPP/xQ99xzjyTp6NGjOnXqVL6fuXrVwLUPxWvYsKF8fHx05MgRtW/f3q6+7eHt7W3Tz9W+jhw5oqNHj1qv6ti1a5dSUlJybBd7l5mXBg0aaO3atTYPhV23bp21nxo1akiSEhISrFXvax9MLkk333yzvvnmG5t5mzdvtqv/4vj999916tQpvfHGG9btdH2/uf0tr9ewYUNlZmZq48aNatu2raQrD9jdu3evXdsbAHJjT94YO3asKlSooG+//Vb33HOP7r33Xt111102bTZs2GD9JVRycrL27t2rm2++2a7+c8vP17rtttu0Z88e3XTTTbku45ZbbtGxY8e0d+/eXK/qsDeHP/7443r33Xd1/Phxde7c2eaKRQBwB6dPn9bu3bv18ccfW49r1q5d65Bl//rrr7p06ZL1hM2GDRtUpUoVRURE2PV5Ly+vHGPdGjVqaOfOnTbztm3bZj2pdNNNN8nLyyvXHHM1Z9lzLOdo9vRpT367XlGOCQHAFVksFt1+++26/fbb9fLLLysqKkqLFy8u0T43bNigO++8U5KUmZmp+Ph4DR8+XNKVffKXX35pU/BYt26d/Pz8chQYitp3XsdCt9xyi3744QdNmjQpx+dK6hwe3BOFDhTK0aNHNWbMGD355JPasmWLpk+frilTpqhevXp65JFH9Oijj2rKlClq2rSpTp06pZUrV6px48a65557dNNNN+nLL7/UunXrVK1aNU2dOlWJiYm5nmCuV6+efvzxR3Xo0EGenp6aNm1agbHddNNN+uc//6nmzZsrNTVVzzzzTIG/UI2KipLFYtF///tf3XPPPfL19ZWfn5/GjRunv/71r8rOzla7du2UmpqqdevWqUqVKho4cGCRtl3t2rV18OBB6+1B/Pz81LlzZ91yyy165JFHNG3aNGVmZurpp59W+/bt7brMMLdl5lWxfuaZZ9S3b1/ddttt6tSpk5YuXapFixbp+++/l3SlQt66dWu98cYbql27tk6dOqUXX3zRZhlPPvmkpk6dqueee06DBw/Wtm3bNGfOHEnF+yVaQWrVqiVvb29Nnz5dQ4cO1c6dO/Xqq6/atMntb3n9L9Dq1q2rXr16aciQIfr444/l5+en559/XjVr1lSvXr1KLH4AZVtBeaN69er6xz/+ofXr1+u2227T888/r4EDB2r79u3Wy88l6W9/+5uCgoIUEhKiCRMmqHr16urdu3eB/Y8cOVJt27bVW2+9pd69e2v58uU5buvx8ssvq3v37oqMjNSDDz6oChUqaPv27dqxY4dee+01tW/fXnfeeaf69OmjqVOn6qabbtLvv/8ui8Wibt262Z3DH3nkEY0bN06zZ8/W3LlzHbJ9AaA0VatWTUFBQZo1a5bCwsJ05MgRPf/88w5Zdnp6ugYPHqwXX3xRhw8f1iuvvKLhw4dbr6wuSO3atfXDDz/o9ttvl4+Pj6pVq6a77rpLb7/9tubOnas2bdpo3rx52rlzp/WHS1WqVNHgwYP1zDPP2OSYa/u051jO0ezp0578dr2iHBMCgKvZuHGjfvjhB3Xp0kXBwcHauHGjTp48qQYNGmj79u0l1u8HH3ygunXrqkGDBnr33XeVnJys//u//5MkPf3005o2bZpGjBih4cOHa8+ePXrllVc0ZswYu/NYfvI7Fho/frwaN26sp59+WkOHDpW3t7d+/PFHPfjgg6pevXqJnMODeyr+NxHlyqOPPqpLly6pZcuWGjZsmEaMGGF97kNcXJweffRRjR07VvXr11fPnj21ceNG6685X3rpJd12223q2rWrOnTooNDQ0HxP4NSvX18rV67UggULNHbs2AJj+8c//qHk5GQ1bdpUAwYM0MiRIxUcHJzvZ2rWrKlJkybp+eefV0hIiLVS/eqrr+rll19WbGysGjRooK5du2rp0qWqU6eOnVsqpz59+qhbt27q2LGjatSooQULFshiseirr75StWrVdOedd6pz58664YYbtHDhwiIvMy+9e/fWe++9p7fffluNGjXSxx9/rLi4OHXo0MHa5h//+IcyMjLUvHlzjRo1Sq+99prNMurUqaMvvvhCixYt0i233KKZM2dqwoQJkkr2ksAaNWpozpw5+ve//62GDRvqjTfe0DvvvGPTJq+/5fXi4uLUrFkzde/eXW3atJExRt98802OyysBoDDyyhu1a9fW4MGDNXHiRN12222SpFdeeUXh4eEaOnSozTLeeOMNjRo1Ss2aNVNCQoKWLFmS43lFuWndurX+/ve/a/r06br11lu1fPnyHIXqrl276r///a9WrFihFi1aqHXr1po6darNPeG//PJLtWjRQg8//LAaNmyoZ5991vrLYXtzuL+/v/r06aMqVarYVaQBAFdToUIFff7554qPj1d0dLT++te/6u2333bIsjt16qS6devqzjvvVN++fdWjRw9NnDjR7s9PmTJFK1asUGRkpLWQ0bVrV7300kt69tln1aJFC507d87mCm5Jevvtt3XnnXeqZ8+e6ty5s9q1a6dmzZrZtCnoWK4kFNSnPfntekU5JgQAV+Pv7681a9bonnvuUb169fTiiy9qypQpiomJKdF+33jjDb355ptq0qSJfvrpJ/3nP/9R9erVJV055/LNN9/ol19+UZMmTTR06FBr8d5Rfed1LFSvXj0tX75cv/76q1q2bKk2bdroP//5jzw9r/x+vyTO4cE9WUxRbnyJcqlDhw669dZb7bq6AuXH66+/ro8++khHjx51digA4JZWrVqljh07Kjk5WVWrVnV2OMV29913q0GDBnr//fedHQoAuIxBgwbp7Nmz+uqrr5wdCgAAQJnErasAFMqHH36oFi1aKCgoSD///LPefvvtPK+eAACUH2fOnNHy5cu1cuVKu5+vBQAAAACAI3DrKriNKlWq5Dn99NNPzg5PjRo1yjO++fPnu8wyi2vfvn3q1auXGjZsqFdffVVjx44t1CX3rv53BABXExMTk+d+c/Lkyc4Oz+q2227Tk08+qTfffFP169d3djgAAAdxlzwEAMjd5MmT89yPl/TtsIDSxK2r4Db++OOPPN+rWbOm0x8yd/jwYWVkZOT6XkhIiPz8/Fximc7m6n9HAHA1x48f16VLl3J9LzAwUIGBgaUcEQCgPCEPAYB7O3PmjM6cOZPre76+vqpZs2YpRwSUDAodAAAAAAAAAADAbXHrKgAAAAAAAAAA4LYodAAAAAAAAAAAALdFoQMAAAAAAAAAALgtCh0AAAAAAAAAAMBtUegAAAAAAAAAAABui0IHAAAAAAAAAABwWxQ6AAAAAAAAAACA26LQAQAAAAAAAAAA3BaFDgAAAAAAAAAA4LYodAAAAAAAAAAAALdFoQMAAAAAAAAAALgtCh0AAAAAAAAAAMBtUegAAAAAAAAAAABui0IHAAAAAAAAAABwWxQ6AABwkjVr1qhHjx4KDw+XxWLRV199VaL9TZw4URaLxWYKDQ0t0T4BAAAAAABKGoUOAACc5MKFC2rSpIlmzJhRan02atRICQkJ1mnHjh2l1jcAAAAAAEBJ8HR2AAAAlFcxMTGKiYnJ8/309HS9+OKLmj9/vs6ePavo6Gi9+eab6tChQ5H79PT05CoOAAAAAABQpnBFBwAALuqxxx7Tzz//rM8//1zbt2/Xgw8+qG7dumnfvn1FXua+ffsUHh6uOnXq6KGHHtKBAwccGDEAAAAAAEDpsxhjjLODAACgvLNYLFq8eLF69+4tSdq/f7/q1q2rY8eOKTw83Nquc+fOatmypSZPnlzoPr799ltdvHhR9erV04kTJ/Taa6/p999/12+//aagoCBHrQoAAAAAAECp4ooOAABc0JYtW2SMUb169VSlShXrtHr1au3fv1+SdOjQoRwPF79+Gj58uHWZMTEx6tOnjxo3bqzOnTvr66+/liR9+umnTllHAAAAAAAAR+AZHQAAuKDs7Gx5eHgoPj5eHh4eNu9VqVJFklSzZk3t3r073+VUq1Ytz/cqV66sxo0bF+tWWAAAAAAAAM5GoQMAABfUtGlTZWVlKSkpSXfccUeubby8vHTzzTcXuY+0tDTt3r07z+UDAAAAAAC4AwodAAA4yfnz5/XHH39YXx88eFDbtm1TYGCg6tWrp0ceeUSPPvqopkyZoqZNm+rUqVNauXKlGjdurHvuuafQ/Y0bN049evRQrVq1lJSUpNdee02pqakaOHCgI1cLAAAAAACgVPEwcgAAnGTVqlXq2LFjjvkDBw7UnDlzlJGRoddee01z587V8ePHFRQUpDZt2mjSpElq3Lhxoft76KGHtGbNGp06dUo1atRQ69at9eqrr6phw4aOWB0AAAAAAACnoNABAAAAAAAAAADcVgVnBwAAAAAAAAAAAFBUFDqKyBij1NRUcUEMAKAwyB8AgKIihwAAioL8AaA84GHkRXTu3DkFBAQoJSVF/v7+zg4HQDk1daqUmir5+0tjxjg7GtijWPmDPzgAlGvucAxCqgIA1+MO+QNwOQxq3A7P6Cii1NRUkgQAp4uIkI4fl2rWlI4dc3Y0sEex8gd/cAAo19zhGIRUBQCuxx3yB+ByGNS4HW5dBQAAAAAAAAAA3BaFDgAAAAAAAAAA4LYodAAAAAAAAAAAALfFw8gBOE1WVpYyMjKcHYZb699fSkmRAgKky5dzvu/l5SUPD4/SDwwl4957pTNnpMBAZ0cCFJkxRpmZmcrKynJ2KMgH+QNFRaqCKyMHlTzyR+HFxsZq0aJF+v333+Xr66u2bdvqzTffVP369a1tBg0apE8//dTmc61atdKGDRtKO1yg/MhnUMP5rJJR3BzCw8iLiAc5AcVz/vx5HTt2TOyCSpbFYlFERISqVKni7FDw/5E/UJ6lp6crISFBFy9edHYoKAD5wzWRQ4CiIweVDvJH4XXr1k0PPfSQWrRooczMTE2YMEE7duzQrl27VLlyZUlXCh0nTpxQXFyc9XPe3t4KtLOqTP4AHIfzWSWnuDmEKzoAlLqsrCwdO3ZMlSpVUo0aNWSxWJwdUplkjNHJkyd17Ngx1a1bl19WAXCq7OxsHTx4UB4eHgoPD5e3tzf7fxdF/gBQ1pCDSgf5o2iWLVtm8zouLk7BwcGKj4/XnXfeaZ3v4+Oj0NDQ0g4PwDU4n1VyHJFDKHQAKHUZGRkyxqhGjRry9fV1djhlWo0aNXTo0CFlZGRwoAHAqdLT05Wdna3IyEhVqlTJ2eGgAOQPAGUJOaj0kD+KLyUlRZJyXK2xatUqBQcHq2rVqmrfvr1ef/11BQcH57qMtLQ0paWlWV+npqaWXMBAOcL5rJJV3BzCw8gBOA2V75LHNi55sbGxslgsGj16tLNDAdxChQoMP90B+QNAWUQOKnnkj+IxxmjMmDFq166doqOjrfNjYmI0f/58rVy5UlOmTNGmTZt011132RQzrhUbG6uAgADrFBkZWVqrAJQL7OtKRnG3K1d0AIAb27VLysiQvLykhg2dHU35s2nTJs2aNUu33HJLqfSXceutsiQmKrNGDR3/6qs82/n7+6tGjRqlEhMAANdq3lxKTJRCQ6XNm50dDYDCysjIKPBh7R4eHvLy8iqliMqX4cOHa/v27Vq7dq3N/H79+ln/Pzo6Ws2bN1dUVJS+/vpr3X///TmWM378eI0ZM8b6OjU1lWIHUFgMagrFnvwhlWwOodBRys6cOaNly5apW7dudj80CkD5kJ2drVGjRumbb76RxWLRmDFj9PTTT+dod/nyZT300EPatWuXLJZKCgwM1UsvfaSGDWuXftDl2Pnz5/XII49o9uzZeu2110q8v5MnT0p79qjG5cs6ffas+j01LM+2QZV8NW/2LIodgANt3rxZ7777rubPn++Q5U2YMEFLliyxXpI9fvx4m5MYxfXJJ5/ojTfeUHZ2tjp16qQPP/xQnp6e2rFjh4YNG6akpCR5eXmpTZs2mj59unx8fBzWN8q3xETp+HFnRwHAXtcfgzzylwHq99DDubad/Ppr+nHlSv3553Ft2bJFTZs2LeVoy7YRI0ZoyZIlWrNmjSIiIvJtGxYWpqioKO3bty/X9318fMjtQHGVsUGNo49nLl68qMGDB2vTpk2yWCz666hRurvzXTna/fHHHxrzzLPW1+fOndfFixd15swZh8RxLQodpSw5OVkLFixQq1atKHQAsDFv3jzt2rVLe/fuVUpKim677Tbddddduvnmm3O0feKJJxQTE6Pt2y2aP3+GXn31Cd1zz3InRF1+DRs2TPfee686d+6cb6HDUffHTU1NVRVjJEkeVfwUMeq5XNtdSEzQ6YVzlZqaSqEDcKDmzZs77KBAkp555hm9/vrrkqQ///xTN998s7p06aJq1arZvYyJEyeqdu3aGjRokM38gwcP6qWXXtLWrVsVHBysXr166ZNPPtGTTz6pihUrasaMGbrllluUlZWl/v37a8qUKXrhhRcctm4AAPdx7THIiRMn1KJFS7Xp0Fl1b855ufi99/fTkyPH6KHe3ZWdne2EaMsmY4xGjBihxYsXa9WqVapTp06Bnzl9+rSOHj2qsLCwUogQQFng6OOZd955Rz4+Pvrjjz/0+++/64472ql7x5YKrm57PBPZooG2rloqScrIzNZTz06Sn3+Aw+K4FjeoBABduQ9gbGysWrZsqRtuuEHff/+9xo8fr6ZNm6pRo0b67bffJEmJiYnq2LGjmjVrpkaNGmnkyJEy///k85AhQzRixAhJV67euvHGG3NccpyfhQsXaujQofLw8FBgYKD69u2rzz//PEe7ihUr6p577rHeuzA6urWOHz9Q3E2AQvj888+1ZcsWxcbGFti2JO6PW8HDU36RUblOlUM52AEKw979/6pVq9S8eXNJ0qFDh1S9enW9/PLLatasmW666SZ98803heq3atWq1v8/d+6cLBaL9aTRuXPnNGTIELVs2VK33HKLhg4dqoyMDLuX/cUXX+i+++5TSEiILBaLhg4dqgULFkiS6tata73dnoeHh1q0aKEDB8ghAOAMrngM0rVbN33z3yXy8PTOMbW5o4PCI6McvyHKuWHDhmnevHn67LPP5Ofnp8TERCUmJurSpUuSrlxJPm7cOK1fv16HDh3SqlWr1KNHD1WvXl333Xefk6MH4GzOOp5ZuHChhg27cqeJ2rVrq0Xz5vru+x9V0bNCnlN2Vqb++/XXOX6o5SgUOgC4hKlTpYiIgqeePXN+tmfPvNtPnWp/DP7+/vrll1/05ptvqlevXmrXrp22bt2qgQMHWn91W7VqVS1dulTx8fHavn27Dhw4oC+//FKSNH36dK1du1b//ve/NXDgQA0ZMkTt2rWTJI0cOVK33nprrtPGjRslSUeOHFFU1P8OHGrXrq0jR44UGPfChe/rzjt72L+iKJajR49q1KhRmjdvnipWrFhg+/HjxyslJcU6HT16tBSiBNyICyQAe/b/1zt9+rSaNWum+Ph4zZgxQ3/961+t7z3wwAN57vOv3Qe8//77ql+/vm677TbNmjVLQUFBkqSxY8fqzjvv1C+//KJff/1VmZmZmjFjht3rY28+uXDhgv7+97+rRw9yCIDyyQVSkMsdg9SsWVMJx48VcYuiKGbOnKmUlBR16NBBYWFh1mnhwoWSrvwwYceOHerVq5fq1aungQMHql69elq/fr38/PycHD0AV0gmzjieyZE/wsN19Pif+cb51TfLFVEzQk2aNLF73QqDW1cBcAmpqfbd+jC3H8OfPJn3Zwtzl6Cr90a/7bbbVKFCBd17772SpGbNmmnRokWSrtzD9rnnntPatWtljFFSUpJuvfVWPfDAA6pYsaL+/e9/q1mzZmrTpo2ee+5/txZ6//337Yrh6lUakqy/0srP3/8+WUeP7tNLL31k93qieOLj45WUlKRmzZpZ52VlZWnNmjWaMWOG0tLSrPfcl7g/LlAgF0gA9uz/r1e5cmX16tVLktSmTRvt37/f+t4XX3xhV78jR47UyJEj9euvv+ovf/mLOnfurKCgIH311VfasGGDpkyZIkm6dOmSvL29JUkffPCBZs+eLenKL3y9vb01bdo0SdIrr7xi/WVnQfkkIyND/fr1U5cuXazrAQDljQukIBc8BrE/djhGQcd9vr6++u6770opGgCF5gLJxFnHMzb5w472cxd8oQf63G/XsouCQgcAl+DvL9WsWXC73B45UKNG3p/197c/hqu/zvfw8LA5Me3h4aHMzExJ0tSpU3X69Glt3LhRFStW1JgxY3T58mVr299//12VK1dWUlKS0tPTrcsZOXKk1qxZk2u/H3/8sVq1aqVatWrp0KFDatGihSTp8OHDqlWrVp7xvvPOO1q5cpFmzPhevr6V7F9RFEunTp20Y8cOm3mPPfaYbr75Zj333HM2RQ4AdnCBBGDP/j+vz1xtl5WVZX39wAMP6I8//sj1c0uXLs1xC7smTZqoZs2aWrVqlfr06SNjjL766ivdcMMNOT4/bNgw6yXieT2j42o+uer6fJKRkaG+ffsqLCxM7733Xq5xAkB54AIpyOWOQf7887jCaub/IGwAwDVcIJk443jmav64+mzQP//8U61u6ZhnjIePHteGTVv11ttT7F6vwqLQAcAljBlzZSqKJUscG0t+kpOTFRoaqooVK+rEiRP697//ba2cHzlyRE8//bS+//57vf/++xo9erRmzpwpyb5fUz344IP6+OOPdf/99yslJUULFy7UsmXLcm07depULViwQB999L18fas6bP1QMD8/P0VHR9vMq1y5soKCgnLMB2AHd0kAhWDPL6B2796tBg0aSJL279+vrVu3qmHDKw9+7dmzp9544w19+OGH8vT0VHJysk6fPq2bbrrJrv779Omjdu3a6eWXX1ZwcLA++ugjPfTQQ5KkzMxMPfTQQwoMDNSsWbNsfoUFAOWNu6Sg0joGOXPmjJZ9+63iPv+yRNcHAMoUd0kmhWDP8cyDDz6oDz74QHPmzNGhQ4f0y6bN+vu7r+bZPu6zL9TznrvlX5hfAxSSSz2jIzY2Vi1atJCfn5+Cg4PVu3dv7dmzx6bNoEGDZLFYbKbWrVvbtElLS9OIESNUvXp1Va5cWT179tSxY7b3mExOTtaAAQOsD4cdMGCAzp49W9KrCMDNjRw5UuvWrdOtt96q//u//1Pnzp0l/e/E0auvvqqGDRtq2rRp2rBhg/W+qvYYMGCA6tevr3r16qlFixZ65plnrCfBlixZoscff1ySdOzYMY0dO1Znz57V4493VP/+t+ovf2nl+JUFAJSY559/Xo0aNdKtt96qfv36acaMGdZ9/rRp0+Tp6albb71Vt9xyizp37mxzhUZBbrjhBk2aNEm33367brzxRgUHB2vw4MGSrjw0cNGiRdq8ebOaNm2qW2+91XqFCADANZXWMUi7du302ODBuqlefUnSimVf67nR/8sRLz37V91+WyOdOJGoe++91+4CPACg7HnmmWd06dIl3XTTTerRo4deeWmCAqtVlSR9FPeZXo6dZm1rjNGczxdp0MMPlGhMFmPPTeBLSbdu3fTQQw+pRYsWyszM1IQJE7Rjxw7t2rVLlStXlnSl0HHixAnFxcVZP+ft7a3AwEDr66eeekpLly7VnDlzFBQUpLFjx+rMmTOKj4+33lIkJiZGx44d06xZsyRJTzzxhGrXrq2lS5faFWtqaqoCAgKUkpJSqErU/v37NXr0aE2bNk033nij3Z8DypLLly/r4MGDqlOnjl0Pc0befv1VysiQvLyk3J7lxLZ2PcXJH1UaNVJIWprOBIdq1Iqfc2137uhhHXvvTS2c+QF5Bi6F/ZF74e9VeGvWrNHbb7+t+Ph4JSQkaPHixerdu3eubZ988knNmjVL7777rkaPHm13H0XNIaUpIuLKraZr1pSO8TxjuAj2afa5fPmyDhw6Ip+qNeTh6Z1rm6zMdKWdPakbatfKdVuyrV2TO+QPwOXkMqhhH5e7y5cv6+ihA4qs5qWKnnlfV3E5M1tHkzMUWfuGEskhLnXrqutv0RIXF6fg4GDFx8frzjvvtM738fFRaGhorstISUnRJ598on/+85/WXznMmzdPkZGR+v7779W1a1ft3r1by5Yt04YNG9Sq1ZVfQc+ePVtt2rTRnj17VL9+/RJaQwBwrIgIKTtbquBS1+ehpLxft57COnZRhXDumwwArubChQtq0qSJHnvsMfXp0yfPdl999ZU2btyo8PDwUoyu9Lz1lnTxolSJx4cBAAB3xqDG7bhUoeN6KSkpkmRztYYkrVq1SsHBwapatarat2+v119/XcHBwZKk+Ph4ZWRkqEuXLtb24eHhio6O1rp169S1a1etX79eAQEB1iKHJLVu3VoBAQFat25droWOtLQ0paWlWV+npqY6dF0BoCiCgpwdAUrTd2Hhiuh6r/wio5wdCgDgOjExMYqJicm3zfHjxzV8+HB99913uvfee0spstLVv7+zIwAAAHAABjVux2V/A2yM0ZgxY9SuXTubh7vGxMRo/vz5WrlypaZMmaJNmzbprrvushYhEhMT5e3trWrVqtksLyQkRImJidY2Vwsj1woODra2uV5sbKz1eR4BAQGKjIx01KoCAAAAKOOys7M1YMAAPfPMM2rUqJFdn0lLS1NqaqrNBAAAACAnly10DB8+XNu3b9eCBQts5vfr10/33nuvoqOj1aNHD3377bfau3evvv7663yXZ4yRxWKxvr72//Nqc63x48crJSXFOh09erQIawXgWi70iKAyi20MwNVkZ2c7OwTYgfzheG+++aY8PT01cuRIuz/Dj60AxyIHlTzyB4DygH1dySjudnXJW1eNGDFCS5Ys0Zo1axQRkf99yMPCwhQVFaV9+/ZJkkJDQ5Wenq7k5GSbqzqSkpLUtm1ba5sTJ07kWNbJkycVEhKSaz8+Pj7y8fEp6ioBuIaXl5csFotOnjypGjVq5FlgRMHS0iRjJItFun4XZYzRyZMnZbFY5OXl5ZwA4VBRFy4o/PBBVczKUmLtG5wdDlAo3t7eqlChgv7880/VqFFD3t7e7P9dFPnD8eLj4/Xee+9py5Ythfrejx8/XmPGjLG+Tk1Ndflix549Umam5Okp8ehDuApykH3S0tKUnZ2lrIz0KwcZucjKzFB2dpbNrb2vIn8AKFNyGdRwPit3aWlpysrOVlpGlpSdd7EiLSv7SrsSyiEuVegwxmjEiBFavHixVq1apTp16hT4mdOnT+vo0aMKCwuTJDVr1kxeXl5asWKF+vbtK0lKSEjQzp079dZbb0mS2rRpo5SUFP3yyy9q2bKlJGnjxo1KSUmxFkMAlBwPDw9FRETo2LFjOnTokLPDcWvHjklZWZKHx5UHk1/PYrEoIiJCHh4epR8cHO7D+E0KWbdWZ4JDNWrFz84OByiUChUqqE6dOkpISNCff/7p7HBQAPKHY/30009KSkpSrVq1rPOysrI0duxYTZs2Lc/xkDv+2KpTJ+n4calmzSvjFMAVkIPsk5GRoVOnz8gz9aIq5LH/z87KUualc8rOzMj1RBT5A0CZkcughvNZucvIyFDy6VNKP+chL4+8iz8ZWUbJF7OUnpldIjnEpQodw4YN02effab//Oc/8vPzsz4vIyAgQL6+vjp//rwmTpyoPn36KCwsTIcOHdILL7yg6tWr67777rO2HTx4sMaOHaugoCAFBgZq3Lhxaty4sTp37ixJatCggbp166YhQ4bo448/liQ98cQT6t69e64PIgfgeFWqVFHdunWVkZHh7FDc2qBB0okTUkiItHp1zve9vLw4yADgMry9vVWrVi1lZmYqKyvL2eEgH+QPxxowYID1WOSqrl27asCAAXrsscecFBVQvpCDCnbkyBFN/XCWarZ/WFWCQnNtc/50oo6vXqCpr0+0Kd5eRf4AUNZxPiunI0eOaM6HUzS+U3XVql4p73anLmrOD6c0/vV3SySHuFShY+bMmZKkDh062MyPi4vToEGD5OHhoR07dmju3Lk6e/aswsLC1LFjRy1cuFB+fn7W9u+++648PT3Vt29fXbp0SZ06ddKcOXNsNtT8+fM1cuRIdenSRZLUs2dPzZgxo+RXEoCVh4cHg+BiOn78ypSZKVWs6OxoAKBgVy9F5pYWKGvOnz+vP/74w/r64MGD2rZtmwIDA1WrVi0FBQXZtPfy8lJoaCg/tAJKETkofx4eHjqRdFK+FzOVWSX3NqkXM3Ui6aQ8PDxUkQMQAOUU57NseXh46FRSojwuSRXT80ggkjwunb/SroRyiEsVOgp64Iivr6++++67ApdTsWJFTZ8+XdOnT8+zTWBgoObNm1foGAEAAADgeps3b1bHjh2tr68+W2PgwIGaM2eOk6ICAAAAygeXKnQAAAAAgDvq0KFDgT/cuhb3dQYAAAAcp4KzAwAAAAAAAAAAACgqCh0AAAAAAAAAAMBtUegAAAAAAAAAAABui0IHAAAAAAAAAABwWxQ6AAAAAAAAAACA2/J0dgAAgKLbtEnKypI8PJwdCUrDo63aKOL/npJvRC1nhwIAQK4YmwAAgDKBQY3bodABAG4sLMzZEaA0nfbxkW/1GvKrEezsUAAAyBVjEwAAUCYwqHE73LoKAAAAAAAAAAC4LQodAAAAAAAAAADAbXHrKgBwY7NmSefPS1WqSE884exoUNLuO3ZUYYv/LYXX1I8PPOzscAAAyIGxCQAAKBMY1LgdCh0A4Mb+9jfp+HGpZk3ybnnw+IH9Ctm9S2eCQyl0AABcEmMTAABQJjCocTvcugoAAAAAAAAAALgtCh0AAAAAAAAAAMBtUegAAAAAAAAAAABui0IHAAAAAAAAAABwWxQ6AAAAAAAAAACA26LQAQAAAAAAAAAA3BaFDgAAAAAAAAAA4LYodAAAAABAMa1Zs0Y9evRQeHi4LBaLvvrqK+t7GRkZeu6559S4cWNVrlxZ4eHhevTRR/Xnn386L2AAAACgDKHQAQBurF49qWHDK/9F2XekUmUdqRWlxKjazg4FAHCdCxcuqEmTJpoxY0aO9y5evKgtW7bopZde0pYtW7Ro0SLt3btXPXv2dEKkJYuxCQAUTmxsrFq0aCE/Pz8FBwerd+/e2rNnj00bY4wmTpyo8PBw+fr6qkOHDvrtt9+cFDFQTjCocTuezg4AAFB0K1c6OwKUpqeat1DEqOfkFxnl7FAAANeJiYlRTExMru8FBARoxYoVNvOmT5+uli1b6siRI6pVq1ZphFgqGJsAQOGsXr1aw4YNU4sWLZSZmakJEyaoS5cu2rVrlypXrixJeuuttzR16lTNmTNH9erV02uvvaa7775be/bskZ+fn5PXACijGNS4HQodAAAAAFDKUlJSZLFYVLVq1TzbpKWlKS0tzfo6NTW1FCIDAJSmZcuW2byOi4tTcHCw4uPjdeedd8oYo2nTpmnChAm6//77JUmffvqpQkJC9Nlnn+nJJ590RtgA4HK4dRUAAAAAlKLLly/r+eefV//+/eXv759nu9jYWAUEBFinyMjIUowSAOAMKSkpkqTAwEBJ0sGDB5WYmKguXbpY2/j4+Kh9+/Zat25drstIS0tTamqqzQQAZR2FDgAAAAAoJRkZGXrooYeUnZ2tDz/8MN+248ePV0pKinU6evRoKUUJAHAGY4zGjBmjdu3aKTo6WpKUmJgoSQoJCbFpGxISYn3vehTKAZRH3LoKANzYI49Ip05J1atL8+c7OxqUtFd3bFfoS8/qUli4Zsa+6+xwAACFlJGRob59++rgwYNauXJlvldzSFd+sevj41NK0TkGYxMAKLrhw4dr+/btWrt2bY73LBaLzWtjTI55V40fP15jxoyxvk5NTaXYARQWgxq3Q6EDANzY6tXS8eNSzZrOjgSl4bbkMwpJTNCZ46HODgUAUEhXixz79u3Tjz/+qKCgIGeHVCIYmwBA0YwYMUJLlizRmjVrFBERYZ0fGnpl7J+YmKiwsDDr/KSkpBxXeVzljoVywOUwqHE73LoKAAAAAIrp/Pnz2rZtm7Zt2ybpyj3Vt23bpiNHjigzM1MPPPCANm/erPnz5ysrK0uJiYlKTExUenq6cwMHADiVMUbDhw/XokWLtHLlStWpU8fm/Tp16ig0NFQrVqywzktPT9fq1avVtm3b0g4XAFwWV3QAAAAAQDFt3rxZHTt2tL6+esuQgQMHauLEiVqyZIkk6dZbb7X53I8//qgOHTqUVpgAABczbNgwffbZZ/rPf/4jPz8/63M3AgIC5OvrK4vFotGjR2vy5MmqW7eu6tatq8mTJ6tSpUrq37+/k6MHANdBoQMAAAAAiqlDhw4yxuT5fn7vAQDKr5kzZ0pSjqJ3XFycBg0aJEl69tlndenSJT399NNKTk5Wq1attHz5cvn5+ZVytADgulzq1lWxsbFq0aKF/Pz8FBwcrN69e2vPnj02bYwxmjhxosLDw+Xr66sOHTrot99+s2mTlpamESNGqHr16qpcubJ69uypY8eO2bRJTk7WgAEDFBAQoICAAA0YMEBnz54t6VUEAAAAAAAAJF05z5XbdLXIIV15EPnEiROVkJCgy5cva/Xq1YqOjnZe0ADgglyq0LF69WoNGzZMGzZs0IoVK5SZmakuXbrowoUL1jZvvfWWpk6dqhkzZmjTpk0KDQ3V3XffrXPnzlnbjB49WosXL9bnn3+utWvX6vz58+revbuysrKsbfr3769t27Zp2bJlWrZsmbZt26YBAwaU6voCAAAAAAAAAIDicalbVy1btszmdVxcnIKDgxUfH68777xTxhhNmzZNEyZM0P333y9J+vTTTxUSEqLPPvtMTz75pFJSUvTJJ5/on//8pzp37ixJmjdvniIjI/X999+ra9eu2r17t5YtW6YNGzaoVatWkqTZs2erTZs22rNnj+rXr1+6Kw4AAAAAAAAAAIrEpa7ouF5KSookKTAwUJJ08OBBJSYmqkuXLtY2Pj4+at++vdatWydJio+PV0ZGhk2b8PBwRUdHW9usX79eAQEB1iKHJLVu3VoBAQHWNtdLS0tTamqqzQQAKH9mzpypW265Rf7+/vL391ebNm307bffOjssAAAAAACAcstlCx3GGI0ZM0bt2rWz3ncwMTFRkhQSEmLTNiQkxPpeYmKivL29Va1atXzbBAcH5+gzODjY2uZ6sbGx1ud5BAQEKDIysngrCABwSxEREXrjjTe0efNmbd68WXfddZd69eqV43lRAAAAAAAAKB0udeuqaw0fPlzbt2/X2rVrc7xnsVhsXhtjcsy73vVtcmuf33LGjx+vMWPGWF+npqZS7ADgdEOGSCkpUkCAsyMpP3r06GHz+vXXX9fMmTO1YcMGNWrUqET7/qpmhEIbN1VWeM0S7QcAgKJibAIAAMoEBjVuxyULHSNGjNCSJUu0Zs0aRUREWOeHhoZKunJFRlhYmHV+UlKS9SqP0NBQpaenKzk52eaqjqSkJLVt29ba5sSJEzn6PXnyZI6rRa7y8fGRj49P8VcOABzolVecHUH5lpWVpX//+9+6cOGC2rRpk2ubtLQ0paWlWV8X59aHs2+8SRFDnpZfZFSRlwEAQElibAIAAMoEBjVux6VuXWWM0fDhw7Vo0SKtXLlSderUsXm/Tp06Cg0N1YoVK6zz0tPTtXr1amsRo1mzZvLy8rJpk5CQoJ07d1rbtGnTRikpKfrll1+sbTZu3KiUlBRrGwAA8rJjxw5VqVJFPj4+Gjp0qBYvXqyGDRvm2pZbHwIAAAAAAJQsl7qiY9iwYfrss8/0n//8R35+ftbnZQQEBMjX11cWi0WjR4/W5MmTVbduXdWtW1eTJ09WpUqV1L9/f2vbwYMHa+zYsQoKClJgYKDGjRunxo0bq3PnzpKkBg0aqFu3bhoyZIg+/vhjSdITTzyh7t27q379+s5ZeQCA26hfv762bdums2fP6ssvv9TAgQO1evXqXIsd3PoQAAAAAACgZLlUoWPmzJmSpA4dOtjMj4uL06BBgyRJzz77rC5duqSnn35aycnJatWqlZYvXy4/Pz9r+3fffVeenp7q27evLl26pE6dOmnOnDny8PCwtpk/f75GjhypLl26SJJ69uypGTNmlOwKAgDKBG9vb910002SpObNm2vTpk167733rMXza3HrQwAAAAAAgJLlUoUOY0yBbSwWiyZOnKiJEyfm2aZixYqaPn26pk+fnmebwMBAzZs3ryhhAoDLiIiQjh+XataUjh1zdjTllzHG5jkcJeXrNasUsuI7nQkO1agVP5d4fwAAFBZjEwAAUCYwqHE7LlXoAADA1b3wwguKiYlRZGSkzp07p88//1yrVq3SsmXLnB0aAAAAAABAuUShAwCAQjhx4oQGDBighIQEBQQE6JZbbtGyZct09913Ozs0AAAAAACAcolCBwAAhfDJJ584OwQAAAAAAABco4KzAwAAAAAAAAAAACgqCh0AAAAAAAAAAMBtUegAAAAAAAAAAABui0IHAAAAAAAAAABwWxQ6AAAAAKCY1qxZox49eig8PFwWi0VfffWVzfvGGE2cOFHh4eHy9fVVhw4d9NtvvzknWAAAAKCModABAAAAAMV04cIFNWnSRDNmzMj1/bfeektTp07VjBkztGnTJoWGhuruu+/WuXPnSjlSAAAAoOzxdHYAAICimzdPSkuTfHycHQlKw8vRtyis5wPyqhnh7FAAANeJiYlRTExMru8ZYzRt2jRNmDBB999/vyTp008/VUhIiD777DM9+eSTpRlqiWJsAgAAygQGNW6HQgcAuLEOHZwdAUpTfGCgIpq1kF9klLNDAQAUwsGDB5WYmKguXbpY5/n4+Kh9+/Zat25dnoWOtLQ0paWlWV+npqaWeKzFxdgEAACUCQxq3A63rgIAAACAEpSYmChJCgkJsZkfEhJifS83sbGxCggIsE6RkZElGicAAADgrih0AAAAAEApsFgsNq+NMTnmXWv8+PFKSUmxTkePHi3pEAEAAAC3xK2rAMCNrVr1v1tGclVl2dfszBmFxW+SV2KCfm/R2tnhAADsFBoaKunKlR1hYWHW+UlJSTmu8riWj4+PfNzsvtCMTQAAQJnAoMbtUOgAADf2l79Ix49LNWtKx445OxqUtL/t3K6Q+E06ExyqUSt+dnY4AAA71alTR6GhoVqxYoWaNm0qSUpPT9fq1av15ptvOjk6x2JsAgAAygQGNW6HQgcAAAAAFNP58+f1xx9/WF8fPHhQ27ZtU2BgoGrVqqXRo0dr8uTJqlu3rurWravJkyerUqVK6t+/vxOjBgAAAMoGCh0AAAAAUEybN29Wx44dra/HjBkjSRo4cKDmzJmjZ599VpcuXdLTTz+t5ORktWrVSsuXL5efn5+zQgYAAADKDAodAAAAAFBMHTp0kDEmz/ctFosmTpyoiRMnll5QAAAAQDlRwdkBAAAAAAAAAAAAFBWFDgAAAAAAAAAA4LYcVug4ePCgoxYFAECJIFcBAK5HbgAAFAX5AwBci8MKHTfddJM6duyoefPm6fLly45aLAAADkOuAgBcj9wAACgK8gcAuBaHFTp+/fVXNW3aVGPHjlVoaKiefPJJ/fLLL45aPAAAxUauAgBcj9wAACgKR+WPNWvWqEePHgoPD5fFYtFXX31l8/6gQYNksVhsptatWztoLQCg7HBYoSM6OlpTp07V8ePHFRcXp8TERLVr106NGjXS1KlTdfLkSUd1BQBAkZCrAADXIzcAAIrCUfnjwoULatKkiWbMmJFnm27duikhIcE6ffPNN45aDQAoMxz+MHJPT0/dd999+te//qU333xT+/fv17hx4xQREaFHH31UCQkJju4SAMqtY8ckY678F/Zz11x1750d1Pu/KzVqxc/ODgUAyhx3zQ2uhrEJgPKmuPkjJiZGr732mu6///482/j4+Cg0NNQ6BQYGOno1AFyPQY3bcXihY/PmzXr66acVFhamqVOnaty4cdq/f79Wrlyp48ePq1evXo7uEgCAQiFXAQCuR24AABRFaeSPVatWKTg4WPXq1dOQIUOUlJSUb/u0tDSlpqbaTABQ1nk6akFTp05VXFyc9uzZo3vuuUdz587VPffcowoVrtRS6tSpo48//lg333yzo7oEAKBQyFUAgOuRGwAARVFa+SMmJkYPPvigoqKidPDgQb300ku66667FB8fLx8fn1w/Exsbq0mTJhWrXwBwNw4rdMycOVP/93//p8cee0yhoaG5tqlVq5Y++eQTR3UJAEChkKsAANcjNwAAiqK08ke/fv2s/x8dHa3mzZsrKipKX3/9dZ63uxo/frzGjBljfZ2amqrIyMhixQEArs5hhY4VK1aoVq1a1sr1VcYYHT16VLVq1ZK3t7cGDhzoqC4BoNybNElKSZECAqRXXnF2NK7P3XPVkP1/KHT2h8oKr6nFQ0c6OxwAKBPcPTe4GsYmAMoLZ+WPsLAwRUVFad++fXm28fHxyfNqDwB2YlDjdhz2jI4bb7xRp06dyjH/zJkzqlOnjqO6AQBcY/Zs6d13r/wXBXP3XNX7+DH1+s8X6vDlQmeHAgBlhrvnBlfD2ARAeeGs/HH69GkdPXpUYWFhJdYHADGocUMOK3QYY3Kdf/78eVWsWNGuZaxZs0Y9evRQeHi4LBaLvvrqK5v3Bw0aJIvFYjO1bt3apk1aWppGjBih6tWrq3LlyurZs6eOHTtm0yY5OVkDBgxQQECAAgICNGDAAJ09e9budQUAuCdH5CoAQNlCbgAAFIWj8sf58+e1bds2bdu2TZJ08OBBbdu2TUeOHNH58+c1btw4rV+/XocOHdKqVavUo0cPVa9eXffdd58jVgMAyoxi37rq6j3/LBaLXn75ZVWqVMn6XlZWljZu3Khbb73VrmVduHBBTZo00WOPPaY+ffrk2qZbt26Ki4uzvvb29rZ5f/To0Vq6dKk+//xzBQUFaezYserevbvi4+Pl4eEhSerfv7+OHTumZcuWSZKeeOIJDRgwQEuXLrV7vQEA7sORuQoAUDaQGwAAReHo/LF582Z17Ngxx/IHDhyomTNnaseOHZo7d67Onj2rsLAwdezYUQsXLpSfn59jVggAyohiFzq2bt0q6Uole8eOHTaFB29vbzVp0kTjxo2za1kxMTGKiYnJt42Pj0+eD3lKSUnRJ598on/+85/q3LmzJGnevHmKjIzU999/r65du2r37t1atmyZNmzYoFatWkmSZs+erTZt2mjPnj2qX7++XbECANyHI3MVAKBsIDcAAIrC0fmjQ4cOeV4dIknfffdd0YMFgHKk2IWOH3/8UZL02GOP6b333pO/v3+xg8rPqlWrFBwcrKpVq6p9+/Z6/fXXFRwcLEmKj49XRkaGunTpYm0fHh6u6OhorVu3Tl27dtX69esVEBBgLXJIUuvWrRUQEKB169blWehIS0tTWlqa9XVqamoJrSEAwNFKO1cBAFwfuQEAUBTkDwBwTcUudFx17e2kSkpMTIwefPBBRUVF6eDBg3rppZd01113KT4+Xj4+PkpMTJS3t7eqVatm87mQkBAlJiZKkhITE62FkWsFBwdb2+QmNjZWkyZNcuwKAQBKVWnkKgCAeyE3AACKgvwBAK6lWIWO+++/X3PmzJG/v7/uv//+fNsuWrSoOF1Jkvr162f9/+joaDVv3lxRUVH6+uuv8+3fGCOLxWJ9fe3/59XmeuPHj7feJ1G6ckVHZGRkYVcBAFDKSjtXAQBcH7kBAFAU5A8AcF3FKnQEBARYiwMBAQEOCagwwsLCFBUVpX379kmSQkNDlZ6eruTkZJurOpKSktS2bVtrmxMnTuRY1smTJxUSEpJnXz4+PvLx8XHwGgAASpqzcxUAwPU4IzdkZmZq4sSJmj9/vhITExUWFqZBgwbpxRdfVIUKFUolBgBA8XBsAQCuq1iFjmsv03PGJXunT5/W0aNHFRYWJklq1qyZvLy8tGLFCvXt21eSlJCQoJ07d+qtt96SJLVp00YpKSn65Zdf1LJlS0nSxo0blZKSYi2GAADKDmfnKgCA63FGbnjzzTf10Ucf6dNPP1WjRo20efNmPfbYYwoICNCoUaNKJQYAQPFwbAEArsthz+i4dOmSjDGqVKmSJOnw4cNavHixGjZsaPNw8PycP39ef/zxh/X1wYMHtW3bNgUGBiowMFATJ05Unz59FBYWpkOHDumFF15Q9erVdd9990m6Uk0fPHiwxo4dq6CgIAUGBmrcuHFq3LixOnfuLElq0KCBunXrpiFDhujjjz+WJD3xxBPq3r17ng8iBwBX1b69dOqUVL26syNxD47IVc60pVqgQsNq6lJYuLNDAYAyo7Ryw/r169WrVy/de++9kqTatWtrwYIF2rx5s8P6cAWMTQCUF+5+bAGgAAxq3I7DCh29evXS/fffr6FDh+rs2bNq2bKlvL29derUKU2dOlVPPfVUgcvYvHmzOnbsaH199ZkYAwcO1MyZM7Vjxw7NnTtXZ8+eVVhYmDp27KiFCxfKz8/P+pl3331Xnp6e6tu3ry5duqROnTppzpw58vDwsLaZP3++Ro4caU08PXv21IwZMxy1KQCg1Myf7+wI3IsjcpUzvdT4FkWMek5+kVHODgUAyozSyg3t2rXTRx99pL1796pevXr69ddftXbtWk2bNi3Pz6SlpSktLc36OjU11SGxlCTGJgDKC3c/tgBQAAY1bsdhN4PdsmWL7rjjDknSF198odDQUB0+fFhz587V+++/b9cyOnToIGNMjmnOnDny9fXVd999p6SkJKWnp+vw4cOaM2dOjgeCV6xYUdOnT9fp06d18eJFLV26NEebwMBAzZs3T6mpqUpNTdW8efNUtWpVh2wHAIDrckSuAgCULaWVG5577jk9/PDDuvnmm+Xl5aWmTZtq9OjRevjhh/P8TGxsrAICAqzT9cc1AADn4dgCAFyLwwodFy9etF5ZsXz5ct1///2qUKGCWrdurcOHDzuqGwAAioxcBQC4XmnlhoULF2revHn67LPPtGXLFn366ad655139Omnn+b5mfHjxyslJcU6HT161GHxAACKh2MLAHAtDit03HTTTfrqq6909OhRfffdd9bbQiUlJcnf399R3QAAUGTkKgDA9UorNzzzzDN6/vnn9dBDD6lx48YaMGCA/vrXvyo2NjbPz/j4+Mjf399mAgC4Bo4tAMC1OKzQ8fLLL2vcuHGqXbu2WrVqpTZt2ki6UtVu2rSpo7oBAFzjrrukRo2u/BcFc/dcNXPzJr3/9GMa//gjzg4FAMqM0soNFy9eVIUKtodfHh4eys7OdlgfroCxCYDywt2PLQAUgEGN23HYw8gfeOABtWvXTgkJCWrSpIl1fqdOnXTfffc5qhsAwDX27pWOH5dSUpwdiXtw91xV6+IFhSSf0ZnLaQU3BgDYpbRyQ48ePfT666+rVq1aatSokbZu3aqpU6fq//7v/xzWhytgbAKgvHD3YwsABWBQ43YcVuiQpNDQUIWGhtrMa9mypSO7AACgWMhVAIDrlUZumD59ul566SU9/fTTSkpKUnh4uJ588km9/PLLDu0HAFB6OLYAANfhsELHhQsX9MYbb+iHH35QUlJSjkuwDxw44KiuAAAoEkfkqtjYWC1atEi///67fH191bZtW7355puqX79+SYUNAChBpXUc4+fnp2nTpmnatGkOWR4AwLk4DwYArsVhhY7HH39cq1ev1oABAxQWFiaLxeKoRQMA4BCOyFWrV6/WsGHD1KJFC2VmZmrChAnq0qWLdu3apcqVK5dA1ACAksRxDACgKMgfAOBaHFbo+Pbbb/X111/r9ttvd9QiAQBwKEfkqmXLltm8jouLU3BwsOLj43XnnXcWN0QAQCnjOAYAUBTkDwBwLQ4rdFSrVk2BgYGOWhwAAA5XErkq5f8/mCyv5aalpSkt7X8PD09NTXVo/wCA4uE4BgBQFOQPAHAtFRy1oFdffVUvv/yyLl686KhFAgDgUI7OVcYYjRkzRu3atVN0dHSubWJjYxUQEGCdIiMjHdI3AMAxOI4BABQF+QMAXIvDruiYMmWK9u/fr5CQENWuXVteXl4272/ZssVRXQEAUCSOzlXDhw/X9u3btXbt2jzbjB8/XmPGjLG+Tk1NpdgBAC6E4xgAQFGQPwDAtTis0NG7d29HLQoAgBLhyFw1YsQILVmyRGvWrFFERESe7Xx8fOTj4+OwfgEAjsVxDACgKMgfAOBaHFboeOWVVxy1KACAnV5+WTp/XqpSxdmRuAdH5CpjjEaMGKHFixdr1apVqlOnjgMis8/fb7hRYW3bS+E1S61PACjrOI5xLMYmAMoL8gdQxjGocTsOK3RI0tmzZ/XFF19o//79euaZZxQYGKgtW7YoJCRENWtyUgYAHO2JJ5wdgfspbq4aNmyYPvvsM/3nP/+Rn5+fEhMTJUkBAQHy9fUt0dgXR0Qq4r4H5RcZVaL9AEB5w3GM4zA2AVCekD+AMoxBjdtxWKFj+/bt6ty5swICAnTo0CENGTJEgYGBWrx4sQ4fPqy5c+c6qisAAIrEEblq5syZkqQOHTrYzI+Li9OgQYNKIGoAQEniOAYAUBTkDwBwLRUctaAxY8Zo0KBB2rdvnypWrGidHxMTozVr1jiqGwAAiswRucoYk+tEkQMA3BPHMQCAoiB/AIBrcdgVHZs2bdLHH3+cY37NmjWtt/UAADhWQoKUlSV5eEhhYc6OxvW5e64KSktT0KmT8q3oq5Qawc4OBwDKBHfPDa6GsQmA8oL8AZRxDGrcjsMKHRUrVlRqamqO+Xv27FGNGjUc1Q0A4BotWkjHj0s1a0rHjjk7Gtfn7rlq7sb1ClmzSmeCQzVqxc/ODgcAygR3zw2uhrEJgPKC/AGUcQxq3I7Dbl3Vq1cv/e1vf1NGRoYkyWKx6MiRI3r++efVp08fR3UDAECRlZdclZGersOHD2v//v15TidPnnR2mADgEspLbgAAOBb5AwBci8Ou6HjnnXd0zz33KDg4WJcuXVL79u2VmJioNm3a6PXXX3dUNwAAFFl5yFVpKWd1+MB+jZr0qnx8fPJsF1TJV/Nmz+LXZgDKvfKQGwAAjkf+AADX4rBCh7+/v9auXasff/xR8fHxys7O1m233abOnTs7qgsAAIqlPOSqzIsXlOXppWp9Byiwdp1c21xITNDphXOVmppKoQNAuVcecgMAwPHIHwDgWhxS6MjOztacOXO0aNEiHTp0SBaLRXXq1FFoaKiMMbJYLI7oBgCAIitvuco3OFR+kVF5vp9cirEAgKsqb7kBAOAY5A8AcD3FfkaHMUY9e/bU448/ruPHj6tx48Zq1KiRDh8+rEGDBum+++5zRJwAABQZuQoAcD1yAwCgKMgfAOCain1Fx5w5c7RmzRr98MMP6tixo817K1euVO/evTV37lw9+uijxe0KAIAiIVcBAK5HbgAAFAX5AwBcU7Gv6FiwYIFeeOGFHDt3Sbrrrrv0/PPPa/78+cXtBgCAIiNXAQCu54zccPz4cf3lL39RUFCQKlWqpFtvvVXx8fEO7QMAULI4tgAA11TsQsf27dvVrVu3PN+PiYnRr7/+WtxuAAAoMnIVAOB6pZ0bkpOTdfvtt8vLy0vffvutdu3apSlTpqhq1aoO6wMAUPIcnT/WrFmjHj16KDw8XBaLRV999ZXN+8YYTZw4UeHh4fL19VWHDh3022+/FTV8ACizil3oOHPmjEJCQvJ8PyQkRMnJPPIUAOA85CoAwPVKOze8+eabioyMVFxcnFq2bKnatWurU6dOuvHGGx3WBwCg5Dk6f1y4cEFNmjTRjBkzcn3/rbfe0tSpUzVjxgxt2rRJoaGhuvvuu3Xu3LlCxw4AZVmxn9GRlZUlT8+8F+Ph4aHMzMzidgMAyMUPP0iZmVI+u2Go7OSqp5u1UPgjj6liRC1nhwIAbq+0c8OSJUvUtWtXPfjgg1q9erVq1qypp59+WkOGDMnzM2lpaUpLS7O+Tk1NdVg8JYWxCYCyztH5IyYmRjExMbm+Z4zRtGnTNGHCBN1///2SpE8//VQhISH67LPP9OSTTxYueAD2Y1Djdor9lzLGaNCgQfLx8cn1/WsH5gAAx6pf39kRuIeykqsOV66srKg68ouMcnYoAOD2Sjs3HDhwQDNnztSYMWP0wgsv6JdfftHIkSPl4+OT5wNrY2NjNWnSJIfGUdIYmwAo60ozfxw8eFCJiYnq0qWLdZ6Pj4/at2+vdevW5VnocMdCOeByGNS4nWIXOgYOHFhgm7wG7gAAlAZyFQDgeqWdG7Kzs9W8eXNNnjxZktS0aVP99ttvmjlzZp79jB8/XmPGjLG+Tk1NVWRkpMNiAgAUXmnmj8TEREnKcauskJAQHT58OM/PuWOhHACKq9iFjri4OEfEIenKA5jefvttxcfHKyEhQYsXL1bv3r2t7xtjNGnSJM2aNUvJyclq1aqVPvjgAzVq1MjaJi0tTePGjdOCBQt06dIlderUSR9++KEiIiKsbZKTkzVy5EgtWbJEktSzZ09Nnz6dBwECQBnlyFwFACgbSjs3hIWFqWHDhjbzGjRooC+//DLPz/j4+OT5i2EAgHM449jCYrHYvDbG5Jh3LQrlAMqjYj+M3JEc8QCm0aNHa/Hixfr888+1du1anT9/Xt27d1dWVpa1Tf/+/bVt2zYtW7ZMy5Yt07Zt2zRgwIASXz8AcLTPPpP+/vcr/0XZ1zXhT3X+7mu1+WaJs0MBABTS7bffrj179tjM27t3r6KiytbtCBmbAIDjhIaGSvrflR1XJSUl5ftAdB8fH/n7+9tMAAqJQY3bcamnqRT3AUwpKSn65JNP9M9//lOdO3eWJM2bN0+RkZH6/vvv1bVrV+3evVvLli3Thg0b1KpVK0nS7Nmz1aZNG+3Zs0f1uf8aADfy7LPS8eNSzZpS//7OjgYlbeS+vQrZuUNngkO1/p6ezg4HAFAIf/3rX9W2bVtNnjxZffv21S+//KJZs2Zp1qxZzg7NoRibAIDj1KlTR6GhoVqxYoWaNm0qSUpPT9fq1av15ptvOjk6oIxjUON2XOqKjvwU9AAmSYqPj1dGRoZNm/DwcEVHR1vbrF+/XgEBAdYihyS1bt1aAQEB1ja5SUtLU2pqqs0EAAAAAPZo0aKFFi9erAULFig6Olqvvvqqpk2bpkceecTZoQEAnOj8+fPatm2btm3bJunK+a9t27bpyJEjslgsGj16tCZPnqzFixdr586dGjRokCpVqqT+nHgFABsudUVHfux5AFNiYqK8vb1VrVq1HG2ufj4xMVHBwcE5lh8cHJzjUsBr8SAnAAAAAMXRvXt3de/e3dlhAABcyObNm9WxY0fr66vP1hg4cKDmzJmjZ599VpcuXdLTTz9tfV7t8uXL5efn56yQAcAluU2h46rCPoAptza5tedBTgAAAAAAAChNHTp0kDEmz/ctFosmTpyoiRMnll5QAOCG3ObWVfY8gCk0NFTp6elKTk7Ot82JEydyLP/kyZM8yAkAAAAAAAAAADfjNoWOax/AdNXVBzC1bdtWktSsWTN5eXnZtElISNDOnTutbdq0aaOUlBT98ssv1jYbN25USkqKtQ0AAAAAAAAAAHAPLnXrqvPnz+uPP/6wvr76AKbAwEDVqlXL+gCmunXrqm7dupo8ebLNA5gCAgI0ePBgjR07VkFBQQoMDNS4cePUuHFjde7cWZLUoEEDdevWTUOGDNHHH38sSXriiSfUvXt31a9fv/RXGgAAAAAAAAAAFJlLFToc8QCmd999V56enurbt68uXbqkTp06ac6cOfLw8LC2mT9/vkaOHKkuXbpIknr27KkZM2aU0loCAAAAAAAAAABHcalChyMewFSxYkVNnz5d06dPz7NNYGCg5s2bV5xQAQAAAAAAAACAC3CpQgcAoHBCQ23/i7LttLePPKr4KbV6dWeHAgBArhibAACAMoFBjduh0AEAbmzzZmdHgNL0aOs2ihj1nPwio5wdCgAAuWJsAgAAygQGNW6ngrMDAAAAAAAAAAAAKCoKHQAAAAAAAAAAwG1R6AAAAAAAAAAAAG6LZ3QAgBt78knpzBkpMFD6+GNnR4OS9sKu3xQSO1FpoeGKe/l1Z4cDAEAOjE0AAECZwKDG7VDoAAA39vXX0vHjUs2azo4EpeH2UycVcvyYzgSHOjsUAAByxdgEAACUCQxq3A63rgIAAAAAAAAAAG6LQgcAAAAAAAAAAHBbFDoAAAAAAAAAAIDbotABAAAAAAAAAADcFoUOAAAAAAAAAADgtih0AAAAAEApi42NlcVi0ejRo50dCgAAAOD2KHQAAAAAQCnatGmTZs2apVtuucXZoQAAAABlgqezAwAAAACA8uL8+fN65JFHNHv2bL322mul1u/JkyeVmpqabxt/f3/VqFGjlCICAAAAHIdCBwC4sYcflpKTpWrVnB0JSsN3oWEKq99Q6WHhzg4FAFBEw4YN07333qvOnTsXWOhIS0tTWlqa9XVBhYq8nDx5Un957HGdOXcx33aBfpU0L+7vxSp2MDYBAABlAoMat0OhAwDc2NtvOzsClKb369VXxMhx8ouMcnYoAIAi+Pzzz7VlyxZt2rTJrvaxsbGaNGlSsftNTU3VmXMXVaNNH1UODMm1zYUzJ3Ry/ZdKTU0tVqGDsQkAACgTGNS4HQodAAAUwpo1a/T2228rPj5eCQkJWrx4sXr37u3ssAotIz1dhw8fzrcNtzABAMc5evSoRo0apeXLl6tixYp2fWb8+PEaM2aM9XVqaqoiIyOLHEPlwBD5B0fk+f7JIi8ZAAAAcC4KHQAAFMKFCxfUpEkTPfbYY+rTp4+zwymStJSzOnxgv0ZNelU+Pj55tguq5Kt5s2dR7AAAB4iPj1dSUpKaNWtmnZeVlaU1a9ZoxowZSktLk4eHh81nfHx88t1PAwAAALiCQgcAAIUQExOjmJgYZ4dRLJkXLyjL00vV+g5QYO06uba5kJig0wvnFvsWJgCAKzp16qQdO3bYzHvsscd0880367nnnstR5AAAAABgPwodAODGbr5Z+vNPKTxc+v13Z0eD3DjqQbKS9MXPaxW89iedDQnVc/9ZUezYfIND833eR3KxewAAXOXn56fo6GibeZUrV1ZQUFCO+e6MsQkAACgTGNS4nQrODgAAUHTnz0vnzl35L1xTbGysAgICrFNx7q3um5WpSpcuquLFiw6MEAAAx2FsAgAAygQGNW6HKzoAAChBjn6QLACg7Fi1apWzQwAAAADKBAodAACUIB4kCwAAAAAAULK4dRUAAAAAAAAAAHBbXNEBAEAhnD9/Xn/88Yf19cGDB7Vt2zYFBgaqVq1aTowMAAAAAACgfKLQAQBAIWzevFkdO3a0vr76/I2BAwdqzpw5TooKAAAAAACg/OLWVQAAFEKHDh1kjMkxUeQAAAAAUBImTpwoi8ViM4WGhjo7LABwKVzRAQAAAAAAALiwRo0a6fvvv7e+9vDwcGI0AOB6KHQ4QVpamk6fPq0bb7zR2aEAAAAAAADAxXl6enIVBwDkw+1uXVXQ5XrGGE2cOFHh4eHy9fVVhw4d9Ntvv9ksIy0tTSNGjFD16tVVuXJl9ezZU8eOHSuV+E+fPq29e3/VSy+N08mTJ0ulTwBl10cfSf/615X/ouyLbdBQbz3/suJefNXZoQAAkCvGJgBQMvbt26fw8HDVqVNHDz30kA4cOJBn27S0NKWmptpMAAqJQY3bccsrOvK7XO+tt97S1KlTNWfOHNWrV0+vvfaa7r77bu3Zs0d+fn6SpNGjR2vp0qX6/PPPFRQUpLFjx6p79+6Kj48v8Uv/zp8/Lw+PTF2+nKLU1FTVqFGjRPsDULZ17+7sCFCa1tYIVkS7DvKLjHJ2KAAA5IqxCQA4XqtWrTR37lzVq1dPJ06c0Guvvaa2bdvqt99+U1BQUI72sbGxmjRpkhMiBcoQBjVux+2u6JD+d7ne1elqscAYo2nTpmnChAm6//77FR0drU8//VQXL17UZ599JklKSUnRJ598oilTpqhz585q2rSp5s2bpx07dtgUT65HNRwAAAAAAAClLSYmRn369FHjxo3VuXNnff3115KkTz/9NNf248ePV0pKinU6evRoaYYLAE7hloWOvC7XO3jwoBITE9WlSxdrWx8fH7Vv317r1q2TJMXHxysjI8OmTXh4uKKjo61tchMbG6uAgADrFBkZWUJrBwAAAAAAAOSucuXKaty4sfbt25fr+z4+PvL397eZAKCsc7tbV+V3uV5iYqIkKSQkxOYzISEhOnz4sCQpMTFR3t7eqlatWo42Vz+fm/Hjx2vMmDHW16mpqRQ7ADhdfLyUni55e0vNmjk7GpS0m1NTVHP3b/I+l6pDDRuXeH8Z6enW/JkXf39/bsMIALBibAIAJS8tLU27d+/WHXfc4exQgLKLQY3bcbtCR0xMjPX/GzdurDZt2ujGG2/Up59+qtatW0uSLBaLzWeMMTnmXa+gNj4+PvLx8SlG5ADgeL16ScePSzVrSseOOTsalLQp27YqZOMGnQkO1agVP5doX2kpZ3X4wH6NmvRqvvkvqJKv5s2eRbEDACCJsQkAlIRx48apR48eqlWrlpKSkvTaa68pNTVVAwcOdHZoQNnFoMbtuF2h43rXXq7Xu3dvSVeu2ggLC7O2SUpKsl7lERoaqvT0dCUnJ9tc1ZGUlKS2bduWauwAALiqzIsXlOXppWp9Byiwdp1c21xITNDphXOVmppKoQMAAAAoIceOHdPDDz+sU6dOqUaNGmrdurU2bNigqKgoZ4cGAC7D7Qsd116uV6dOHYWGhmrFihVq2rSpJCk9PV2rV6/Wm2++KUlq1qyZvLy8tGLFCvXt21eSlJCQoJ07d+qtt95y2noAAOCKfIND5ReZ9wFUcinGAgAAAJRHn3/+ubNDAACX53aFjvwu17NYLBo9erQmT56sunXrqm7dupo8ebIqVaqk/v37S5ICAgI0ePBgjR07VkFBQQoMDNS4cePUuHFjde7c2clrBwAAAAAAAAAACsPtCh0FXa737LPP6tKlS3r66aeVnJysVq1aafny5fLz87Mu491335Wnp6f69u2rS5cuqVOnTpozZ448PDyctVoAAAAAAAAAAKAI3K7QUdDlehaLRRMnTtTEiRPzbFOxYkVNnz5d06dPd3B0AAAAAAAAAACgNFVwdgAAAAAAUNbFxsaqRYsW8vPzU3BwsHr37q09e/Y4OywAAACgTKDQAQAAAAAlbPXq1Ro2bJg2bNigFStWKDMzU126dNGFCxecHRoAAADg9tzu1lUAAMB1ZKSn6/Dhw/m28ff3V40aNUopIgBwTcuWLbN5HRcXp+DgYMXHx+vOO+90UlQAAABA2UChAwAAFElaylkdPrBfoya9Kh8fnzzbBVXy1bzZsyh2AMA1UlJSJEmBgYF5tklLS1NaWpr1dWpqaonHBQAAALgjCh0A4MZ275aMkSwWZ0eC0vBg23aq+dRoVYmo5exQJEmZFy8oy9NL1foOUGDtOrm2uZCYoNML5yo1NZVCBwD8f8YYjRkzRu3atVN0dHSe7WJjYzVp0qRSjKz4GJsAAIAygUGN26HQAQBuzM/P2RGgNF309NSlSpXlWbmKs0Ox4RscKr/IqDzfTy7FWADAHQwfPlzbt2/X2rVr8203fvx4jRkzxvo6NTVVkZGRJR1esTA2AQAAZQKDGrdDoQMAAAAASsmIESO0ZMkSrVmzRhEREfm29fHxyffWgAAAAACuoNABAAAAACXMGKMRI0Zo8eLFWrVqlerUyf2WfwAAAAAKj0IHALixqVOl1FTJ31+65s4WKKMeOXxIYfPnKCs8QsseHezscAAAhTBs2DB99tln+s9//iM/Pz8lJiZKkgICAuTr6+vk6ByHsQkAACgTGNS4HQodAODGpk6Vjh+XatYk75YH/Q8fUsjePToTHEqhAwDczMyZMyVJHTp0sJkfFxenQYMGlX5AJYSxCQAAKBMY1LgdCh0AAAAAUMKMMc4OAQAAACizKjg7AAAAAAAAAAAAgKKi0OEEWVnZSkpK1tmzZ50dCgAAAAAAAAAAbo1ChxNkZxslJZ1VSkqKs0MBAAAAAAAAAMCtUegAAAAAAAAAAABui4eRAwAAAACUkZ6uw4cP59vG399fNWrUKKWIAAAAAPtQ6AAAACXKnhNnEifPAMCZ0s6n6NDBAxr9wkT5+Pjk2S7Qr5Lmxf2d/TUAAABcCoUOAABQYtJSzurwgf0aNenVfE+cSVJQJV/Nmz2Lk2cA4AQZaZeUbfFU9db3Kyg8Ktc2F86c0J+rF2jHjh2Kisq9TWZmlDjMBAAAQGljBAoAbuy226TISInzwuXDHj9/JdeuoYuhYc4OxW6ZFy8oy9NL1foOUGDtOnm2u5CYoNML5yo1NZVCBwA4UaVqNeQfHJHre/Zc9XE5428KqFpN0dF1JOVf4AYAAHBZnHBxOxQ6AMCNLVni7AhQmsY0vU0Ro56TX2Tuv6J1Zb7BoQXGnWTHLa7S09Pl7e2dbxtugQUAJcOeqz5qtPlVJ9d/qQ8++EjSjaUbYD5Onjyp1NTUfNuQPwAAgBUnXNwOhQ4AAOB09tziKiM9XccPH1JEnRvk6Zn3EIZbYAFAycrvqg9JOunAvhxRoDh58qT+8tjjOnPuYr7L4fkjAAAA7otCBwAAcDp7bnF1cvs2pc3+QP59+ufZhltgAUDZ4agCRWpqqs6cu6gabfqocmBIrm0unDmhk+u/JH8AAAC4KQodTpKdna2zZ886OwwAAFxKfre4upBwvMA2kpRcIpEBAOyVYcetCKWCr8RwdIGicmCIQ65E4TZYAAAArodChxNkZWXLmHRNmxarO+64gwEwgCLr2VM6efLKs7G4fWTZN3XrFgWPHa6LoWF69/1Zzg4HAIAcfvp7M506NFW9+1xW/VuG5tvW3ltFFVSg+LOAwsrhw4eVmZGZf+B2svcqkyreHnrz9b8pKCgozzaOeu6UPYUXe5cFAAD+P064uB0KHU6QmZktb2/p8uUULo0GUCxbtkjHj0s1azo7EpSG+udSFXLqpM4kn3F2KAAA5OpsQqCyMmopPfOcat/7dJ7tLpw5oT9XL9COHTsUFZX7VXr2FCjSzqfo0MEDGv3CxDyf8XT50kUdO56gWhkZ9q9IHuy5yuTMsT8U/6/39fjIcfk/d+rIYUVE1ZGnV96H5QUVg+wtvNizLAAAcA1OuLgdCh1OYIyRMdnKzs52digAAAAA4HAWi0e+V2E4qkCRkXZJ2RZPVW99v4LCcy+YJO3fqcNH/6GszPwLHfbccutq8SW/q0zOn060K6YDh/6hai175dnGntty2VN4sXdZAAAA7oxCBwAAAACgVDmyQCFJlarVyLfwUBB7Ci9S4a4OsSem/NpI9j83pKDbexVmWQAAAO6IQoeTZGcbnTlzjgeSAwDgYPb8Itee+6JL3M+cB+4CKGnFLVA4ij2FF6lwxRcAAACUHgodTmKMdOrUOR0+fFjNmjVzdjgAAJQJaSlndfjAfo2a9Gr+90U/fEgRdW6Qp2f+Q6GgSr6aN3tWqZzId2RRwRHLOnnypP4y5Amdvngp3+WU5jYCgJJW0BUWpVl8cWc8IB0AioYfGgFFV64LHR9++KHefvttJSQkqFGjRpo2bZruuOOOUuk7O9vIYsnQtGmxuuOOO9hBAYCbcWYOQd4yL15QlqeXqvUdoMDadXJtc3L7NqXN/kD+ffrn2UaSLiQm6PTCuaVyP3NHFhUctazU1FSdvnhJ1fo9qsqhYbm2Kc1tBJQV5A8URkFXKdrzwHZ7lyU55uSZox+Q7q4n/dw1brgu8odrc9QPjZ567BGlnTud73J8/II0M26+S+0/XO1HWyifym2hY+HChRo9erQ+/PBD3X777fr4448VExOjXbt2qVatWiXef1aWkY+P0ZEjh/Xuu+9q0KBBqlevXon3CwAoPmfnEBTMNzhUfpF5PNw14XiBba5KKqWTQo4sKji6QFE5NCzf7ZSc76cBXIv8gcJw1APb7V2WZF/hoSCFeUD6n6sXaMeOHYqKyj3PnD59Ws+9OFHn0/JfP1crmNhb7HHE9i5sXPltg8IUzlC6nJk/HPVvpyxf6eWoAkVqaqrSzp3W2Dv8FRlUKdc2R09f1JSfTrvUD40cWaAp68Uee26hbE8bRxaNHBHT4cOHlZnp/PxRbgsdU6dO1eDBg/X4449LkqZNm6bvvvtOM2fOVGxsbKnEkJmZrYSEZMXFfaj161fqww/j1KBBg1LpGwBQdK6QQ1Dy7LkNlmT/VRYFnljIzHRoUaG0ChSl9QvhwnDUgF5yz4NtiV/CuSryBwrDkQ9st2dZF86c0Mn1XxZ48szek+UFPSC9MIWc5g/9VVVDcl9WaRdM7GFPscfe7W2vgv4u9mwDewtnKH3Oyh+OOuls73LsWZYz2LPfu5B8Qs91DMy3QDH5h8R891VXjwkigyrpxpAqefaXln66wPG3VHpjPXsLNAWtv2T/trSn2FPcY4KozEx5SsrMzFTyyZPFLtCkpWfo4NE/dVNUzTxvoWxPG0mSt59eev0tBQUF5dnk9OnTeu2lZ2UunyvxmC5cStOJP48qLSM475hLQbksdKSnpys+Pl7PP/+8zfwuXbpo3bp1uX4mLS1NaWlp1tcpKSmSZFc1+loXLlxQdraRMVduX5WenqmMjIvavn2bevfurhdfnKhjx44pOjpaBw4cUK9evRQYGFjINQRQXmRn/++/hdwdyc/PTxaLxfFBlXGFzSGOyh/nzp2TMUa+klIy0nVmz65c26UePSqTna1zhw/KU9lu0cZVYzqz53dlVvCQV5s7VTkk9ysjLief0Z8/LtOGDRsUGRmZ+3LOnNHEN97UucysPPtKu3hRx5NOyG/fXmVcvJBrm4snEpV26aJ27dqlc+fyHqwePXpU6ZcvK+Xg/mIty57lnN2/T4f+2KfhL72SbzHIz6OCJo5/vlTGNPZs74z0dP159IhqRtUu8DktpRm7o9izDSQp0LeiZk9/X9WrVy90H+SQwnPmMci5c+eUlZmpswmHlHE591+YpyYdk8nOVmriUXnm8ae1p012dpikbGVnn9PpI3vzjMlR/ZVmG2fFlJl2Oc+/W2b65ULFnd+yMtIuKe3SpXxzw5kzZzTx9Vidv5z3rzYvX76oP/88oapH9uXZlySdPrpPWaaCvG9oqYCg3E+MZP95UJmHjynt4oU8l3Xh7Ekd3P+HRjz7Yt4Fk/8fU/1O/eRXLfeTQpfOnVXCrjX55nR7Xc2hGWmXirW97VWYv0t+2+Dq9k4+dkCWrNyLHReSk5SVmalz584Vel8kkT+Kwpn54/jx4zqfnKQ+DTxV3S/3f1+nzqXp8+1/5vtv5+jRo0o5laC+0T55LsfeZZW2M2fO6J3Jryj7Ut7/Ti9eTlNSwnElt/RV1cpeubb588xF/bH/kF57bnieJ9avLmf7EV+du5z7v8FdR1MKXM5VFh8/PfPipBIfxx49elSX09J1IS0zz7jtWX/Jvm15IS1TFy+lFZivCvq7padn6PDxBN0QGS6PXI4JZp45rSBJp8+c1tP97s93W9rzHd91PF2HD17SvTdkq2b1ikVucyDpgj5YsUXPDxtk17Yc2eUGhVWrXKIx7TqerrlHMrX76FmlZ+W9jz9+5pIyMrOKlEPsyh+mHDp+/LiRZH7++Web+a+//rqpV69erp955ZVXjCQmJiamMjOlpKSUxi63zClsDiF/MDExlcWJHFJ4HIMwMTExkT+KgvzBxMTEZF/+KJdXdFx1fRXIGJNnZWj8+PEaM2aM9XV2drbOnDmjoKCgQv0aITU1VZGRkTp69Kj8/f2LFngZwzbJiW2SE9skd8XZLn5+fiUUVflgbw4pz/nD3WJ2t3gl94vZ3eKV3C/m0oqXHFJ0HIM4Fuvmvsry+rFueSN/FF1p5I+y/N2VWD93x/q5v5I+h1UuCx3Vq1eXh4eHEhMTbeYnJSUpJCT3+2f6+PjkuAy2atWqRY7B39+/zH5pi4ptkhPbJCe2Se7YLqWnsDmE/OF+MbtbvJL7xexu8UruF7O7xVsecAxSslg391WW1491gyM4I3+U9b8v6+feWD/3V1LrWMHhS3QD3t7eatasmVasWGEzf8WKFWrbtq2TogIAuANyCACgKMgfAICiIH8AgH3K5RUdkjRmzBgNGDBAzZs3V5s2bTRr1iwdOXJEQ4cOdXZoAAAXRw4BABQF+QMAUBTkDwAoWLktdPTr10+nT5/W3/72NyUkJCg6OlrffPONoqKiSrRfHx8fvfLKKzkuISzP2CY5sU1yYpvkju3iHM7IIe74t3a3mN0tXsn9Yna3eCX3i9nd4i1vOAZxPNbNfZXl9WPd4GillT/K+t+X9XNvrJ/7K+l1tBhjTIksGQAAAAAAAAAAoISVy2d0AAAAAAAAAACAsoFCBwAAAAAAAAAAcFsUOgAAAAAAAAAAgNui0AEAAAAAAAAAANwWhQ4AAAAAAAAAAOC2KHQU04cffqg6deqoYsWKatasmX766ad8269evVrNmjVTxYoVdcMNN+ijjz7K0ebLL79Uw4YN5ePjo4YNG2rx4sUlFX6JcPQ2mTNnjiwWS47p8uXLJbkaDleY7ZKQkKD+/furfv36qlChgkaPHp1ru/L0XbFnm5SF70phtsmiRYt09913q0aNGvL391ebNm303Xff5Wjn7t8TXFHYfWtRTJw4Mce/n9DQUOv7xhhNnDhR4eHh8vX1VYcOHfTbb7/ZLCMtLU0jRoxQ9erVVblyZfXs2VPHjh2zaZOcnKwBAwYoICBAAQEBGjBggM6ePWvT5siRI+rRo4cqV66s6tWra+TIkfrhhx/Uo0cPhYeHy2Kx6KuvvrL5jLPjS09Pt2mzY8cONWnSRB4eHvLw8JDFYsnx72/QoEE5tnnr1q2dFvOoUaPk5+cni8UiDw8PNWjQQL///rvLbufY2FhFR0fL09NTFotFvr6+GjlypIwxLruNX3zxRVWpUkUWi0UVKlRQZGSkvvnmG5fcvtKV73H79u3l6+urmjVr6m9/+5vN9oVrKIljEldSEuMjV1HU/P7zzz/L09NTt956a8kGWAyFXbe0tDRNmDBBUVFR8vHx0Y033qh//OMfpRRt4RV2/ebPn68mTZqoUqVKCgsL02OPPabTp0+XUrT2W7NmTb7jrdy42z6lvCNn/I+75QypbOcNidxxPXfJHZKL5A+DIvv888+Nl5eXmT17ttm1a5cZNWqUqVy5sjl8+HCu7Q8cOGAqVapkRo0aZXbt2mVmz55tvLy8zBdffGFts27dOuPh4WEmT55sdu/ebSZPnmw8PT3Nhg0bSmu1iqUktklcXJzx9/c3CQkJNpM7Kex2OXjwoBk5cqT59NNPza233mpGjRqVo015+67Ys03c/btS2G0yatQo8+abb5pffvnF7N2714wfP954eXmZLVu2WNu4+/cEVxT2u1FUr7zyimnUqJHNv5+kpCTr+2+88Ybx8/MzX375pdmxY4fp16+fCQsLM6mpqdY2Q4cONTVr1jQrVqwwW7ZsMR07djRNmjQxmZmZ1jbdunUz0dHRZt26dWbdunUmOjradO/e3fp+ZmamiY6ONh07djRbtmwxK1asMOHh4aZHjx5mwoQJ5ssvvzSSzOLFi23id3Z8w4cPt7ZJSUkxISEh5s477zRDhgwxzzzzjJFkBg0aZBPzwIEDTbdu3Wy2+enTp23alGbM3t7epmXLlmbx4sVmypQpxsPDw1StWtWcP3/eJbdzp06djL+/v4mJiTFffPGFadasmbFYLOb111932W1ctWpVc+edd5qlS5ea6dOnG29vb+Ph4WF27tzpctv36vf4oYceMjt27DBffvml8fPzM++8846B6yiJ8bcrKYnxkasoan4/e/asueGGG0yXLl1MkyZNSifYQirKuvXs2dO0atXKrFixwhw8eNBs3LjR/Pzzz6UYtf0Ku34//fSTqVChgnnvvffMgQMHzE8//WQaNWpkevfuXcqRF+ybb77Jd7x1PXfbp5R35Axb7pQzjCnbecMYcsf13Cl3GOMa+YNCRzG0bNnSDB061GbezTffbJ5//vlc2z/77LPm5ptvtpn35JNPmtatW1tf9+3b13Tr1s2mTdeuXc1DDz3koKhLVklsk7i4OBMQEODwWEtTYbfLtdq3b5/rSf3y9l25Vl7bxN2/K8XZJlc1bNjQTJo0yfra3b8nuMIR3w17vPLKK3kOfLOzs01oaKh54403rPMuX75sAgICzEcffWSMuTKA9vLyMp9//rm1zfHjx02FChXMsmXLjDHG7Nq1y0iyKbatX7/eSDK///67MebKAKlChQrm+PHj1jYLFiwwPj4+JiUlxRhjcgycXC2+Dz/80AQEBJjLly9b20gy1apVM9nZ2dZ5AwcONL169cp1m7tCzBMmTDCSzKpVq9xiOyclJRlJJigoyLqdXX0bx8bGGovFYmbPnu3y2/dqvOHh4TbfYzhXSYy/XUlJjI9cRVHXrV+/fubFF1/MN287W2HX7dtvvzUBAQE5CtGuqrDr9/bbb5sbbrjBZt77779vIiIiSixGR7DnRJW77VPKO3JGwVw1ZxhTtvOGMeSO67lr7jDGefmDW1cVUXp6uuLj49WlSxeb+V26dNG6dety/cz69etztO/atas2b96sjIyMfNvktUxXUlLbRJLOnz+vqKgoRUREqHv37tq6davjV6CEFGW72KO8fVfs5a7fFUdsk+zsbJ07d06BgYHWee78PcEVJfnvJTf79u1TeHi46tSpo4ceekgHDhyQJB08eFCJiYk2cfj4+Kh9+/bWOOLj45WRkWHTJjw8XNHR0dY269evV0BAgFq1amVt07p1awUEBNi0iY6OVnh4uLVN165dlZaWpvj4+FzjdrX41q9fr/bt28vHx8cmzuTkZB06dMhm3qpVqxQcHKx69eppyJAhSkpKsr7n7JhbtGghSbp06ZJbbOeUlBRJ0unTp222s6tu46ysLElXbldVq1Ytl9++V9v8+eefOb7HcI6SHH+7gpIaH7mCoq5bXFyc9u/fr1deeaWkQyyyoqzbkiVL1Lx5c7311luqWbOm6tWrp3Hjxlnzjyspyvq1bdtWx44d0zfffCNjjE6cOKEvvvhC9957b2mEXKLcaZ9S3pEzCuaqOUMq23lDInfkpiznDqlk9i8UOoro1KlTysrKUkhIiM38kJAQJSYm5vqZxMTEXNtnZmbq1KlT+bbJa5mupKS2yc0336w5c+ZoyZIlWrBggSpWrKjbb79d+/btK5kVcbCibBd7lLfvij3c+bviiG0yZcoUXbhwQX379rXOc+fvCa4oqX8vuWnVqpXmzp2r7777TrNnz1ZiYqLatm2r06dPW/vKL47ExER5e3urWrVq+bYJDg7O0XdwcLBNm+v7qVatmry9vfPNJ64UX25tro9VkmJiYjR//nytXLlSU6ZM0aZNm3TXXXcpLS3N6TEbY/TBBx9IkgICAmxid8XtbIzRmDFj1LJlS5tYXXEb79ixQ1WqVJGPj49iY2MlSX5+fi69fa/t5+p7cL6SGn+7ipIaH7mCoqzbvn379Pzzz2v+/Pny9PQsjTCLpCjrduDAAa1du1Y7d+7U4sWLNW3aNH3xxRcaNmxYaYRcKEVZv7Zt22r+/Pnq16+fvL29FRoaqqpVq2r69OmlEXKJcqd9SnlHziiYq+YMqWznDYnckZuynDukktm/UOgoJovFYvPaGJNjXkHtr59f2GW6Gkdvk9atW+svf/mLmjRpojvuuEP/+te/VK9ePbf7h10Sf9fy9l0pSFn4rhR1myxYsEATJ07UwoULc5zccvfvCa4ojb9jTEyM+vTpo8aNG6tz5876+uuvJUmffvppseK4vk1u7YvSJjeuFF9e/V47v1+/frr33nsVHR2tHj166Ntvv9XevXut296ZMQ8fPly7d+/O9bOuuJ2HDx+u7du3a8aMGTbzXXEb169fX9u2bdOGDRs0YMAASbK5QsIVt++17+f1WThPSRyTuJKSGB+5CnvXLSsrS/3799ekSZNUr1690gqvWArzd8vOzpbFYtH8+fPVsmVL3XPPPZo6darmzJnjkr/MlQq3frt27dLIkSP18ssvKz4+XsuWLdPBgwc1dOjQ0gi1xLnbPqW8I2fkzh1yhlS284ZE7rhWWc8dkuP3LxQ6iqh69ery8PDIUXVLSkrK8xecoaGhubb39PRUUFBQvm3yWqYrKaltcr0KFSqoRYsWbvErfalo28Ue5e27UhTu9F0pzjZZuHChBg8erH/961/q3LmzzXvu/D3BFaX17yU3lStXVuPGjbVv3z6FhoZKyvkr7mvjCA0NVXp6upKTk/Ntc+LEiRx9nTx50qbN9f0kJycrIyMj33ziSvHl1uaq/P5uYWFhioqKsu63nBXziBEjtGTJEs2cOdMmZlfdzlfj/fHHH62/VstrO7vCNvb29tZNN92k5s2ba/DgwZKkpUuXuuz2vb4fKf/vMUpPaY2/naWkxkeuoLDrdu7cOW3evFnDhw+Xp6enPD099be//U2//vqrPD09tXLlytIKvUBF+buFhYWpZs2a1isIJalBgwYyxujYsWMlGm9hFWX9YmNjdfvtt+uZZ57RLbfcoq5du+rDDz/UP/7xDyUkJJRG2CXGnfYp5R05I2+unjOksp03JHJHbspy7pBKZv9CoaOIvL291axZM61YscJm/ooVK9S2bdtcP9OmTZsc7ZcvX67mzZvLy8sr3zZ5LdOVlNQ2uZ4xRtu2bVNYWJhjAi9hRdku9ihv35WicKfvSlG3yYIFCzRo0CB99tlnud6n0Z2/J7iitP695CYtLU27d+9WWFiY6tSpo9DQUJs40tPTtXr1amsczZo1k5eXl02bhIQE7dy509qmTZs2SklJ0S+//GJts3HjRqWkpNi02blzp83gbfny5fLx8VGzZs1yjdXV4mvTpo3WrFmj9PR0mzirVaum2rVr577BdeXZEkePHrXut0o75tWrV+upp57SokWLtHLlSv32228KDw+3xuxq27l169ZatmyZNd46depo+fLlNjG72ja+/nuxfPlyeXt7y9vb2+W2b17x5rd9UbpKa/ztLCU1PnIFhV03f39/7dixQ9u2bbNOQ4cOtV4hdu0zeZytKH+322+/XX/++afOnz9vnbd3715VqFBBERERJRpvYRVl/S5evKgKFWxPv3h4eEj6369X3ZU77VPKO3JG7twhZ0hlO29I5I7clOXcIZXQ/qXIjzGH+fzzz42Xl5f55JNPzK5du8zo0aNN5cqVzaFDh4wxxjz//PNmwIAB1vYHDhwwlSpVMn/961/Nrl27zCeffGK8vLzMF198YW3z888/Gw8PD/PGG2+Y3bt3mzfeeMN4enqaDRs2lPr6FUVJbJOJEyeaZcuWmf3795utW7eaxx57zHh6epqNGzeW+voVVWG3izHGbN261WzdutU0a9bM9O/f32zdutX89ttv1vfL23fFmIK3ibt/Vwq7TT777DPj6elpPvjgA5OQkGCdzp49a23j7t8TXFHQd8NRxo4da1atWmUOHDhgNmzYYLp37278/Pys/bzxxhsmICDALFq0yOzYscM8/PDDJiwszKSmplqXMXToUBMREWG+//57s2XLFnPXXXeZJk2amMzMTGubbt26mVtuucWsX7/erF+/3jRu3Nh0797d+n5mZqaJjo42nTp1Mlu2bDHff/+9iYiIME888YR1PyDJTJ061WzdutUcPnzYJeIbPny4tc3Zs2dNSEiIeeCBB8y///1vM2XKFCPJ9OzZ0xrzuXPnzNixY826devMwYMHzY8//mjatGljatas6bSYfX19jZeXl/nHP/5hPvnkE1OlShXz8ssvm4sXL1rbudJ2Hjx4sLFYLKZTp07mxx9/tMYcGxtrjDEuuY0rVapkOnXqZJYtW2beffdd4+3tbSwWi1m+fLnLbd+r3+OHH37Y7NixwyxatMj4+/ubd955x8B1lMT425WUxPjIVRRlPHytV155xTRp0qSUoi2cwq7buXPnTEREhHnggQfMb7/9ZlavXm3q1q1rHn/8cWetQr4Ku35xcXHG09PTfPjhh2b//v1m7dq1pnnz5qZly5bOWoU8nTt3Lt/xlrvvU8o7cob75gxjynbeMIbc4c65wxjXyB8UOorpgw8+MFFRUcbb29vcdtttZvXq1db3Bg4caNq3b2/TftWqVaZp06bG29vb1K5d28ycOTPHMv/973+b+vXrGy8vL3PzzTebL7/8sqRXw6EcvU1Gjx5tatWqZby9vU2NGjVMly5dzLp160pjVRyqsNtFUo4pKirKpk15+64UtE3KwnelMNukffv2uW6TgQMH2izT3b8nuCK/74aj9OvXz4SFhRkvLy8THh5u7r//fptiYnZ2tnnllVdMaGio8fHxMXfeeafZsWOHzTIuXbpkhg8fbgIDA42vr6/p3r27OXLkiE2b/8fencdFVe5/AP+MLAPKosiugGguLG5BKSjiikHXSCW5LailFD80FzITlVJbyFTuZApqkei1lIrQTFLQFDcyQVCv2mYISkMsCiOorOf3B5e5jszAoMAw8Hm/XvPSOed7nvme0TnPzPme8zzFxcXCiy++KBgbGwvGxsbCiy++KNy6dUshJicnR3j66acFQ0NDwczMTJg/f75w6NChRv/Pazq/e/fuKcRcuHBBGDx4sMqc79y5I/j4+AgWFhaCnp6eYG9vL8yaNatBPm2Zs7JcAQjbt2+Xx7Sn91lVvp9//rkgCEK7fI+nTp0qiMViAYDQpUsXwdHRUTh06FC7fH8Foe7/sZeXlyAWiwVra2th1apVQm1trUDtS2v8JmlPWuP7UXvR3H+7+7X3E1bN3bcrV64IEydOFAwNDYXevXsLYWFhCoX29qa5+7dx40bB2dlZMDQ0FGxsbIQXX3xRuHHjRhtn3bSjR482+hnqCMeUzo59hrf8ubb1GYLQsfsNQWDfoa19hyC0j/5DJAgd4F4XIiIiIiIiIiIiIiLqlDhHBxERERERERERERERaS0WOoiIiIiIiIiIiIiISGux0EFERERERERERERERFqLhQ4iIiIiIiIiIiIiItJaLHQQEREREREREREREZHWYqGDiIiIiIiIiIiIiIi0FgsdRERERERERERERESktVjoIGqGsWPHYtGiRfLnffr0gUQiaZPX0lb5+fmYNGkSunXrhu7du6tcRkREmtWWfRwREXUcs2fPxrPPPtsmr8W+iYiImmvVqlUYNmyYptOgNsBCB9EjOHv2LF599VX5c5FIhL1792ouoVb0sPv2r3/9C1KpFFlZWfjtt99ULiMiIs369ttv8e6772o6DSIiekTHjh2DSCRCSUlJm7zexx9/jLi4uBZtMy4uTukFUQ/+/iIiIrqfsnNXS5YswZEjRzSTELUpXU0nQKTNLCwsNJ1Cu3f16lW4ubmhf//+jS4jIiLNMjMz03QKRETtXmVlJfT19TWdRrtiamraZq/F319E1Nl1xn6opqYGIpEIXbo83PX6RkZGMDIyauGsqD3iHR2klQ4ePIjRo0eje/fu6NmzJ/7xj3/g6tWrAIBr165BJBLhq6++gpeXFwwNDfHEE0/gt99+w9mzZ+Hu7g4jIyM89dRTKCwslLdZf8v16tWrYWlpCRMTE7z22muorKxUmcf9t0736dMHADB16lSIRCL5c2W3ci9atAhjx46VPy8vL8fMmTNhZGQEGxsbbNiwocFrVVZWYunSpejVqxe6deuGESNG4NixY2q/Z6dOnYK3tze6du2KHj16YPLkybh161aD/ag3bNgwrFq1qtF9A4CYmBj069cP+vr6GDhwIP79738rvD8JCQnYuXMnRCIRZs+erXQZEZG2GTt2LBYsWIClS5fCzMwM1tbW8mNmfT+UlZUljy8pKYFIJJIft+uvtj106BCGDx8OQ0NDjB8/HgUFBfjhhx/g5OQEExMTPP/887hz545aOX3zzTcYPHgwDA0N0bNnT0ycOBHl5eUA1Ovjmhoycfv27TA1NUVKSgoA4PLly/Dz84ORkRGsrKwQFBSEoqIitfIhItIWY8eOxfz58xEWFgZzc3NMmjQJIpEIMTEx8PX1haGhIRwdHfH111+r3eaNGzfwz3/+E2ZmZujWrRvc3d1x5swZ+frGvl8DdVerfvbZZ5g6dSq6du2K/v3747vvvgNQ1weNGzcOANCjRw+F79uN/Yaq3/ZRfkfd/56p6iPrRUVFYfDgwejWrRvs7OwQGhqKsrIyAHV95Msvv4zS0lKIRCKIRCKF3yX3/27Jzc2Fv78/jIyMYGJighkzZuDvv/+Wr68fruTf//43+vTpA1NTU/zzn//E7du31f73IiJqTfX9zPz58+XH55UrV0IQBAB1x7333nsPs2fPhqmpKYKDgwEAp0+fxpgxY2BoaAg7OzssWLBA4bt2dHQ0+vfvDwMDA1hZWSEgIEC+ThAEfPTRR+jbty8MDQ0xdOhQfPPNN/L19b9Vjhw5And3d3Tt2hWenp749ddfFXL/7rvv4O7uDgMDA5ibm2PatGnydY9yDqv+rr7vv/8ezs7OEIvFyMnJwdmzZzFp0iSYm5vD1NQU3t7eOHfunHw7VeeuHhy6qra2FmvWrEHv3r0hFosxbNgwHDx4UK3cqH1joYO0Unl5OcLCwnD27FkcOXIEXbp0wdSpU1FbWyuPeeedd7By5UqcO3cOurq6eP7557F06VJ8/PHHOHHiBK5evYq3335bod0jR47gypUrOHr0KHbv3o3ExESsXr1arZzOnj0LoO5EkFQqlT9Xx5tvvomjR48iMTERycnJOHbsGDIyMhRiXn75ZZw6dQp79uzBhQsX8Nxzz+Gpp57C77//3mT7WVlZmDBhAlxcXJCWloaTJ09iypQpqKmpeaR9S0xMxMKFC/HGG2/gP//5D1577TW8/PLLOHr0qHy7p556CjNmzIBUKsXHH3+sdBkRkTbasWMHunXrhjNnzuCjjz7CmjVr5EUAda1atQqbNm3C6dOncf36dcyYMQMSiQRffvklDhw4gJSUFHzyySdNtiOVSvH888/jlVdewZUrV3Ds2DFMmzZN/gMJeLQ+bv369ViyZAkOHTqESZMmQSqVwtvbG8OGDUN6ejoOHjyIv//+GzNmzFA7HyIibbFjxw7o6uri1KlT2Lp1KwAgIiIC06dPx/nz5/HSSy/h+eefx5UrV5psq6ysDN7e3vjrr7/w3Xff4fz581i6dKn8d0xT36/rrV69GjNmzMCFCxfg5+eHF198ETdv3oSdnR0SEhIAAL/++qvC9211fkMBD/c7Stl71lgf2aVLF2zcuBH/+c9/sGPHDvz4449YunQpAMDT0xMSiQQmJiaQSqWQSqVYsmRJg9cQBAHPPvssbt68idTUVKSkpODq1asIDAxUiLt69Sr27t2L77//Ht9//z1SU1Px4YcfNvlvRUTUVur7mTNnzmDjxo3417/+hc8++0y+ft26dXB1dUVGRgYiIiJw8eJFTJ48GdOmTcOFCxcQHx+PkydPYv78+QCA9PR0LFiwAGvWrMGvv/6KgwcPYsyYMfL2Vq5cie3btyMmJgaXLl3C4sWL8dJLLyE1NVUhrxUrVmDDhg1IT0+Hrq4uXnnlFfm6AwcOYNq0aXj66aeRmZkpL4rUe5RzWABw584dREZG4rPPPsOlS5dgaWmJ27dvY9asWThx4gR++ukn9O/fH35+fvLitbrn5T7++GNs2LAB69evx4ULFzB58mQ888wzaudG7ZhA1AEUFBQIAISLFy8K2dnZAgDhs88+k6/fvXu3AEA4cuSIfFlkZKQwcOBA+fNZs2YJZmZmQnl5uXxZTEyMYGRkJNTU1AiCIAje3t7CwoUL5esdHByEf/3rX/LnAITExESF3GbNmiX4+/srLFu4cKHg7e0tCIIg3L59W9DX1xf27NkjX19cXCwYGhrKX+uPP/4QRCKRkJeXp9DOhAkThPDw8Cbfn+eff14YNWqUyvUP7ocgCMLQoUOFd955p9F98/T0FIKDgxWWPffcc4Kfn5/8ub+/vzBr1iyFGGXLiIi0ibe3tzB69GiFZU888YTw1ltvyfuhzMxM+bpbt24JAISjR48KgiAIR48eFQAIhw8flsdERkYKAISrV6/Kl7322mvC5MmTm8wnIyNDACBcu3ZN6fpH6eOWLVsm2NjYCBcuXJCvi4iIEHx8fBRe4/r16wIA4ddff20yHyIibeHt7S0MGzZMYRkAISQkRGHZiBEjhP/7v/9rsr2tW7cKxsbGQnFxsdL16ny/BiCsXLlS/rysrEwQiUTCDz/8IAjC//qYW7duNZrL/b+hBEF4pN9R9//eaayPVOWrr74SevbsKX++fft2wdTUtEHc/b9bkpOTBR0dHSE3N1e+/tKlSwIA4eeffxYEQRDeeecdoWvXroJMJpPHvPnmm8KIESNU5kJE1Ja8vb0FJycnoba2Vr7srbfeEpycnARBqDvuPfvsswrbBAUFCa+++qrCshMnTghdunQR7t69KyQkJAgmJiYKx756ZWVlgoGBgXD69GmF5XPmzBGef/55QRCU/1Y5cOCAAEC4e/euIAiC4OHhIbz44otK9+lRz2Ft375dACBkZWU1GlddXS0YGxsL+/fvly9Tdu7qnXfeEYYOHSp/bmtrK7z//vsKMU888YQQGhraZG7UvvGODtJKV69exQsvvIC+ffvCxMQEjo6OAOpuXa43ZMgQ+d+trKwAAIMHD1ZYVlBQoNDu0KFD0bVrV/lzDw8PlJWV4fr1662yH0DdvlRWVsLDw0O+zMzMDAMHDpQ/P3fuHARBwIABA+RjCxoZGSE1NVXhdnNV6u/oaGlXrlzBqFGjFJaNGjVKravZiIi03f39DADY2Ng06Fea04aVlRW6du2Kvn37KixTp82hQ4diwoQJGDx4MJ577jl8+umn8uEJ749pbh+3YcMGbN26FSdPnlToQzMyMnD06FGFPmnQoEEA6vo1dfIhItIW91+hWu/+7+71z9X5DpyVlYXhw4ernBdJ3e/X9/cf3bp1g7GxcZP9hTq/oR5sW93fUQ9qqo88evQoJk2ahF69esHY2BgzZ85EcXFxs4Y4vHLlCuzs7GBnZydf5uzsjO7duyu8X3369IGxsbHKXIiING3kyJEQiUTy5x4eHvj999/lo3A82A9lZGQgLi5O4bv45MmTUVtbi+zsbEyaNAkODg7o27cvgoKC8MUXX8iHw718+TLu3buHSZMmKWy/c+fOBueX7j+W29jYAID8+NnYeaZHPYcFAPr6+g36koKCAoSEhGDAgAEwNTWFqakpysrKGvRjjZHJZPjrr794LquD4mTkpJWmTJkCOzs7fPrpp7C1tUVtbS1cXV0VxhrX09OT/72+w3hw2YO3aatyf4fTXF26dGkwVEdVVZX87w+uU6a2thY6OjrIyMiAjo6Owjp1JlQyNDR8pBwb8+B7IwjCI71fRETa4v4+Bfhfv1I/Sd79x1VVx9QH+yVVbTZFR0cHKSkpOH36NJKTk/HJJ59gxYoVOHPmjPxEliqNHbO9vLxw4MABfPXVV1i2bJl8eW1tLaZMmYK1a9c22MbGxuaR8iEiam+6deumVpw634Gb+l6urB1l368fpr9Q5zfUg20/7O+oxvLLycmBn58fQkJC8O6778LMzAwnT57EnDlz1P4NAqj+3fHg8oftW4mI2osH+6Ha2lq89tprWLBgQYNYe3t76Ovr49y5czh27BiSk5Px9ttvY9WqVTh79qz8+HfgwAH06tVLYVuxWKzwXFl/UL99Y/3Zo57Dqm//wWP87NmzUVhYCIlEAgcHB4jFYnh4eDQ6t64qPJfVMfGODtI6xcXFuHLlClauXIkJEybAycmpxa4SPX/+PO7evSt//tNPP8HIyAi9e/dWa3s9Pb0G815YWFhAKpUqLLt/gtrHHnsMenp6+Omnn+TLbt26hd9++03+fPjw4aipqUFBQQEee+wxhYe1tXWTeQ0ZMgRHjhxRuf7BHGUyGbKzs5vcNycnJ5w8eVJh2enTp+Hk5NRkTkREHZWFhQUAKBxX7z/utxaRSIRRo0Zh9erVyMzMhL6+PhITE+XrH6aPe/LJJ3Hw4EF88MEHWLdunXz5448/jkuXLqFPnz4N+qX6H2JN5UNEpM3u/+5e/7z+zrbGDBkyBFlZWbh586bS9S3x/VpfXx8AFL67t+ZvqOZKT09HdXU1NmzYgJEjR2LAgAH466+/FGL09fWbnE/Q2dkZubm5CncmXr58GaWlpfw9QkRaRVmf0r9//wZFgnr138Uf/B7+2GOPyfsAXV1dTJw4ER999BEuXLiAa9eu4ccff5RP7p2bm9tg2/vvkGtKY+eZHvUclionTpzAggUL4OfnBxcXF4jFYhQVFSnEKDt3dT8TExPY2tryXFYHxTs6SOv06NEDPXv2xLZt22BjY4Pc3FyFq0wfRWVlJebMmYOVK1ciJycH77zzDubPny+/Orcpffr0wZEjRzBq1CiIxWL06NED48ePx7p167Bz5054eHhg165d+M9//oPhw4cDqKtmz5kzB2+++SZ69uwJKysrrFixQuE1BwwYgBdffBEzZ87Ehg0bMHz4cBQVFeHHH3/E4MGD4efn12he4eHhGDx4MEJDQxESEgJ9fX0cPXoUzz33HMzNzTF+/HjExcVhypQp6NGjByIiIhp0qMr27c0338SMGTPw+OOPY8KECdi/fz++/fZbHD58uJnvPBFRx2FoaIiRI0fiww8/RJ8+fVBUVISVK1e26mueOXMGR44cgY+PDywtLXHmzBkUFhYqfFl/2D7Ow8MDP/zwA5566ino6upi8eLFmDdvHj799FM8//zzePPNN2Fubo4//vgDe/bswaeffor09PQm8yEi0mZff/013N3dMXr0aHzxxRf4+eefERsb2+R2zz//PD744AM8++yziIyMhI2NDTIzM2FrawsPD48W+X7t4OAAkUiE77//Hn5+fjA0NGzV31DN1a9fP1RXV+OTTz7BlClTcOrUKWzZskUhpk+fPigrK8ORI0fkQy/eP/wiAEycOBFDhgzBiy++CIlEgurqaoSGhsLb21vpcGNERO3V9evXERYWhtdeew3nzp3DJ598gg0bNqiMf+uttzBy5EjMmzcPwcHB6NatG65cuYKUlBR88skn+P777/Hnn39izJgx6NGjB5KSklBbW4uBAwfC2NgYS5YsweLFi1FbW4vRo0dDJpPh9OnTMDIywqxZs9TK+Z133sGECRPQr18//POf/0R1dTV++OEHLF269JHPYany2GOP4d///jfc3d0hk8nw5ptvNrizRNm5qwe9+eabeOedd9CvXz8MGzYM27dvR1ZWFr744ouHyovaD97RQVqnS5cu2LNnDzIyMuDq6orFixcrXGX6KCZMmID+/ftjzJgxmDFjBqZMmYJVq1apvf2GDRuQkpICOzs7eSFj8uTJiIiIwNKlS/HEE0/g9u3bmDlzpsJ269atw5gxY/DMM89g4sSJGD16NNzc3BRitm/fjpkzZ+KNN97AwIED8cwzz+DMmTNqVdwHDBiA5ORknD9/Hk8++SQ8PDywb98+6OrW1TrDw8MxZswY/OMf/4Cfnx+effZZ9OvXr8l9e/bZZ/Hxxx9j3bp1cHFxwdatW7F9+3aMHTtW7feMiKgj+vzzz1FVVQV3d3csXLgQ7733Xqu+nomJCY4fPw4/Pz8MGDAAK1euxIYNG+Dr6yuPeZQ+btSoUThw4AAiIiKwceNG2Nra4tSpU6ipqcHkyZPh6uqKhQsXwtTUFF26dFErHyIibbZ69Wrs2bMHQ4YMwY4dO/DFF1/A2dm5ye309fWRnJwMS0tL+Pn5YfDgwfjwww/lFxm1xPfrXr16YfXq1Vi2bBmsrKzkRe3W+g3VXMOGDUNUVBTWrl0LV1dXfPHFF4iMjFSI8fT0REhICAIDA2FhYYGPPvqoQTsikQh79+5Fjx49MGbMGEycOBF9+/ZFfHx8W+0KEVGLmDlzJu7evYsnn3wS8+bNw+uvv45XX31VZfyQIUOQmpqK33//HV5eXhg+fDgiIiLk82h0794d3377LcaPHw8nJyds2bIFu3fvhouLCwDg3Xffxdtvv43IyEg4OTlh8uTJ2L9/f7OGmB07diy+/vprfPfddxg2bBjGjx+PM2fOyNc/yjksVT7//HPcunULw4cPR1BQEBYsWABLS0uFGGXnrh60YMECvPHGG3jjjTcwePBgHDx4EN999x369+//0LlR+yAS1JkggKgTmD17NkpKSrB3715Np0JERNSi2McREbUckUiExMREPPvss5pOhYiItNzYsWMxbNgwSCQSTadCpPV4RwcREREREREREREREWktFjqIOgBfX18YGRkpfXzwwQeaTo+IiB5Rbm6uyuO8kZERcnNzNZ0iEREB+OCDD1Qeqzl8HxEREc9hUevh0FVEHUBeXh7u3r2rdJ2ZmRnMzMzaOCMiImpJ1dXVuHbtmsr1ffr0kc+7REREmnPz5k3cvHlT6TpDQ0P06tWrjTMiIiJqX3gOi1oLCx1ERERERERERERERKS1OHQVERERERERERERERFpLRY6iIiIiIiIiIiIiIhIa7HQQUREREREREREREREWouFDiIiIiIiIiIiIiIi0losdBARERERERERERERkdZioYOIiIiIiIiIiIiIiLQWCx1ERERERERERERERKS1WOggIiIiIiIiIiIiIiKtxUIHERERERERERERERFpLRY6iIiIiIiIiIiIiIhIa7HQQUREREREREREREREWouFDiIiIiIiIiIiIiIi0losdBARERERERERERERkdZioYOIiIiIiIiIiIiIiLQWCx1ERERERERERERERKS1WOggIiIiIiIiIiIiIiKtxUIHERERERERERERERFpLRY6iIiIiIiIiIiIiIhIa7HQQUREREREREREREREWouFDiIiIiIiIiIiIiIi0losdBARERERERERERERkdZioYOIiIiIiIiIiIiIiLQWCx1ERERERERERERERKS1WOggIiIiIiIiIiIiIiKtxUIHERERERERERERERFpLRY6iIiIiIiIiIiIiIhIa7HQQUREREREREREREREWouFjockCAJkMhkEQdB0KkREpEXYfxAR0cNiH0JERA+D/QcRdQa6mk5AW92+fRumpqYoLS2FiYmJptMhok4qKgqQyQATEyAsTNPZkDrYf7QtfkaIqCNpl30ID7RERO1eu+w/GsO+hYgegkhgOfehyGQy7eokiKhD6t0byMsDevUCbtzQdDakDvYfbYufESLqSNplH8IDLRFRu9cu+4/GsG8hoofAoauIiIiIiIiIiIiIiEhrsdBBRERERERERERERERai4UOIiIiIiIiIiIiIiLSWpyMnIjUVlNTg6qqKk2nQfd54QWgtBQwNQXu3Wu4Xk9PDzo6Om2fGFE78fTTwM2bgJmZpjMhTWHf9XDYf5DaeKClR8TjdMfC/oNaBPsWamHsa7TDo/YhnIz8IWndRE5Ej6isrAw3btwADxnaRSQSoXfv3jAyMtJ0KvRf7D+I2g77rofH/qN9Yh9CHQ2P0x0P+4/2if0HdWbsa7THo/YhvKODiJpUU1ODGzduoGvXrrCwsIBIJNJ0SqQGQRBQWFiIGzduoH///ryyiog6FfZdD4/9BxG1BR6nOx72H0TU3rCv0R4t0Yew0EFETaqqqoIgCLCwsIChoaGm06FmsLCwwLVr11BVVcUfGkTUqbDvejTsP4iotfE43TGx/yCi9oR9jXZ51D6Ek5ETkdpY+dY+/Dcjos6Ox8GHw/eNiNoKjzcdC/89iag94rFJOzzqvxPv6CAi0mKXLwNVVYCeHuDsrOlsqLUVFhZCJpM1GWdiYgILC4s2yKj9c3cH8vMBa2sgPV3T2RARab8H+6Jezz4L3cJCVFtYIG/vXgDsh4iI6BE18SVend9FlZWV0NfXbzSG/RVRx8JCh4bcvHkTBw8exFNPPQUzMzNNp0NED+HAgQN45513cPHiRbz++utYv359i7b//fffY8mSJaiursbQoUOxY8cOGBkZ4a+//sLLL7+Ma9euQRDEcHAYhIiILQB4LOnICgsL8epLQbh762aTsYY9zLBt17/5pR11v4/y8jSdBVH7sXnzZmzZsgU6OjqoqalBcHAwFixYoOm0SEsUFhZizuxglMnuyJfF//Y7LCorcOtWCYJn/x8AwMikK2LjPmU/RB1aeno6/vWvf+GLL75okfY+//xz/Otf/8KVK1cgkUgwf/58lbEikQiDBw9Gly51g3R88skn8PLyapE87ty5gzlz5uDs2bPo0qULPvzwQ0ybNg0AsHHjRmzbtg1dunRBly5dEB4ejsDAwBZ5XSIFjXyJLywsxCuzZqKstFTl5pVVVci9fh0O9vbQ01V96tPI1BSf79jJ/orapZbuZ5YvX47ExETo6+tDLBbjww8/xPjx41ukbQCIjY3Fhx9+iNraWkyYMAHR0dHQ1dXFxYsXMW/ePBQUFEBPTw8eHh745JNPIBaLW+y167HQoSG3bt3C7t27MWLECBY6iLRU//79ERsbi6+//hr37t17qDbi4uJw7do1rFq1SmF5WVkZ5syZg9TUVAwaNAjz58/H+++/j8jISOjo6CAiIgKjR4/G+fPA+vVv4uOPl2HMmG0tsFfUXslkMty9dROvWFvDppuRyjhpeRk+z8+HTCbjF3YiauCll17CvHnzANQdV1xdXTF27FgMGTJEw5mRNpDJZCiT3cGkx2fAooc1AKDr+XNAZQW6io3wwoQFKLyVj5RzX7Efog7P3d29xU4+AYCbmxu++uorREZGqhV/+vRpGBmp/k7YlD59+uDatWsNlq9fvx5isRh//PEHsrOz4eHhgXHjxqFHjx5wcXHBqVOnYGpqiuvXr+Pxxx/HyJEj4eDg8NB5EDWXTCZDWWkpxrsMgHl3U6Uxv+Vex7XsP+E9qB96W1kqjSkqKcWPl35jf0XtVkv3M15eXoiIiIChoSHOnz+PsWPHQiqVwsDAQO02Vq1ahT59+mD27NkKy7OzsxEREYHMzExYWlrC398fsbGxeO2112BgYIBNmzZhyJAhqKmpwQsvvIANGzZg+fLlLbZv9TrMHB3R0dFwdHSEgYEB3NzccOLECbW2O3XqFHR1dTFs2LDWTZCog4mKAnr3bvrxzDMNt33mGdXxUVHqvb5IJEJkZCSefPJJ9O3bF4cPH0Z4eDiGDx8OFxcXXLp0CQCQn5+PcePGwc3NDS4uLliwYAEEQQAABAcH4/XXXwdQd5dVv379cPLkSbXfgwEDBmDo0KHQVXKFSFVVFZYtW4Ynn3wSw4YNwz//+U+UlJSo3fYPP/wAd3d3DBo0CAAQGhqK3bt3AwCsrKwwevRoeayr6wjk5f2pdtuk3Wy6GaGPqanKR2NFEKLOjn0XYGr6vxMCd+7cQXV1Nccspmaz6GENGws72FjYoct/J4rsoqMDGws7eQGE6KG01oG6GQdrdY/Vx44dg7u7OwDg2rVrMDc3x9tvvw03Nzc89thjSEpKatauDx06FE5OTvK7NB7W2bNnMX78eLi7u+Pxxx9HQkJCs7aPj4+XF8QdHR0xZswY7Nu3DwAwYcIEeT9iZ2cHKysrXL9+/ZHyJXpY5t1NYd3TTOmjh7ExAMDMxFhljKoiCXUCGu5rNNXP+Pr6yidkHzx4MGpqalBUVAQAuH37NoKDg/Hkk09iyJAhCAkJQVVVldptf/PNN5g6dSqsrKwgEokQEhIiP4fVv39/+UVVOjo6eOKJJ/Dnn61zDqtDFDri4+OxaNEirFixApmZmfDy8oKvry9yc3Mb3a60tBQzZ87EhAkT2ihToo5DJqu7k7SpR2Fhw20LC1XHqzH9gJyJiQl+/vlnrF27Fv7+/hg9ejQyMzMxa9YsvP/++wCA7t27Y//+/cjIyMCFCxfw559/yr/sf/LJJzh58iS+/vprzJo1C8HBwfICwoIFCzBs2DCljzNnzjSZ27p162BkZISff/4ZWVlZcHFxwTvvvKP2vuXm5ipcGdWnTx/k5eWhtrZWIa6mpgZff70ZY8ZMUbttIqLOin1XnW+++QYuLi5wcHDAm2++icGDBz/8m0pE1JJa60DdzIO1OsfqBxUXF8PNzQ0ZGRnYtGkTFi9eLF8XEBCg8vj8sIWCsWPHYujQoQgLC0N5eTkAoKSkBK+99hq++OILpKenIzk5GWFhYcjPz1e7XWW/Q5SdWzl8+DBu3boFNze3h8pfWzX3ItvU1FS4ubnBwMAAffv2xZYtWxrEJCQkwNnZGWKxGM7OzkhMTFRYv2rVKohEIoWHtTWLykQPrR30NZruZ7Zv345+/fqhd+/eAIA33ngDY8aMwc8//4zz58+juroamzZtUmtfAPX7jvLycnz22WeYMqV1zmF1iKGroqKiMGfOHMydOxcAIJFIcOjQIcTExDR62+drr72GF154ATo6Otj734nziEg9JiZAr15Nxym7A9TCQvW2Jibq51A/Huzjjz+OLl264OmnnwZQd9v3t99+CwCora3FW2+9hZMnT0IQBBQUFGDYsGEICAiAgYEBvv76a7i5ucHDwwNvvfWWvO2NGzeqn4gSe/fuhUwmwzfffAOgbiK0fv36AQASExOxevVqAHVX41ZWVsqPQcHBwfIrqJq6wlYQBHzwQSiMjbvj+edff6R8iYg6A/ZddQICAhAQEIBr165h6tSp8PPzw8CBA9XfCSKi1tJaB+r6ttWkzrH6Qd26dYO/vz8AwMPDA1evXpWvq/9N0FJycnJgb2+P8vJyhISE4M0330R0dDROnz6NP//8E76+vvJYQRDw66+/wtraGpMnT8bff/8NAPjrr78URrbIyMiAzn/v0Lr/d0j9HYX3u3jxIl5++WXEx8fLrw7uDOovso2OjsaoUaOwdetW+Pr64vLly7C3t28Qn52dDT8/PwQHB2PXrl04deoUQkNDYWFhgenTpwMA0tLSEBgYiHfffRdTp05FYmIiZsyYgZMnT2LEiBHytlxcXHD48GH58/p/KyJ6CO2gr9FkP3PkyBGsXr0aKSkp8mV79+7FTz/9hA0bNgAA7t69C319fQB1c/x9+umnAOruPNfX14dEIgEAvPPOO5g6dSqApvuOqqoqBAYGwsfHR74fLU3rCx2VlZXIyMjAsmXLFJb7+Pjg9OnTKrfbvn07rl69il27duG9995r8nUqKipQUVEhfy5rzqV7RB1QWFjd42F8913L5FA/jqCOjo7CJEY6Ojqorq4GUFcILS4uxpkzZ2BgYICwsDCF+TR++eUXdOvWDQUFBaisrJS3s2DBAhw/flzp627dulXhS6cygiAgOjpa6cROU6dOlXcEqubosLe3x48//ih/fu3aNfTq1UvhVvYFCxYgP/861q3b+8i3uBMRdQbsuxT7rj59+mDEiBH4/vvvO1ShIzo6GuvWrYNUKoWLiwskEkmjk/SmpqYiLCwMly5dgq2tLZYuXYqQkBCFmISEBERERODq1avo168f3n//fXlfDgAxMTGIiYmRj3fv4uKCt99+W+Fk4+zZs7Fjxw6FdkeMGIGffvqpBfaaqINoDwdqqHesVrVNfVxNTY38eUBAAP744w+l2+3fvx92dnbNyq/+pHq3bt0QGhqKV199FUDdb5AhQ4ao7AsOHTok/3ufPn2QlZWltO1r167J5yzIycmBn5+ffP3ly5fxj3/8A59//rnCcLqdQXMvst2yZQvs7e3lJwSdnJyQnp6O9evXywsdEokEkyZNQnh4OAAgPDwcqampkEgk8mFfAEBXV5d3cRC1lHbQ12iqn0lNTcXLL7+M/fv3K3z/FwQBe/fuRd++fRtsP2/ePPkFuarm6KjvO+rVF+TrVVVVYcaMGbCxscHHH3+sNM+WoPVnxoqKilBTUwMrKyuF5VZWVipvz/z999+xbNkyfPHFF0rH1lcmMjISpqam8kdzv4gQkWbcunUL1tbWMDAwwN9//42vv/5avi43NxehoaE4fPgwnnzySSxatEi+buPGjcjKylL6aKrIAQDPPPMMoqKicOfOHQB146DXj7Oojqeeegpnz57FL7/8AqDupM0///lP+foFCxbgjz/+wL/+lQg9PX212yUiovavNfuuK1euyOMLCwtx5MiRDjUReXOHtK2/2tbLywuZmZlYvnw5FixYoDCmff3VtkFBQTh//jyCgoIwY8YMheHAevfujQ8//BDp6elIT0/H+PHj4e/v36Dvf+qppyCVSuWP5o6tTETa6ZtvvlF5fG7uuYVbt27Jf2PU1tYiPj4ew4cPBwB4enri999/V7hgKisrC5WVlWq3/9xzz2Hz5s0A6o6RqampeOa/49RfuXIFfn5+2LZtGyZNmtSsvLVd/UW2Pj4+Cssbu8g2LS2tQfzkyZORnp4uH/teVcyDbf7++++wtbWFo6Mj/vnPfzY5vn1FRQVkMpnCg4g6LnX6mePHjyMoKAj79u3D0KFDFbZ/5pln8OGHH8qLLLdu3VJZOFFm+vTpSExMxN9//w1BELBlyxb5Oazq6mr885//hJmZGbZt29aq8wNqfaGj3oNvkiAISt+4+tndV69ejQEDBqjdfnh4OEpLS+UPTrhFpB0WLFiA06dPY9iwYXjllVcwceJEAP870L777rtwdnaGRCLBTz/9hPj4eLXbPnbsGHr37o2oqChs3boVvXv3xnf/re4vW7YMw4YNw4gRIzBkyBCMHDlS6RVTqhgbG+Ozzz7Ds88+i8ceewx5eXlYvnw5AODUqVP45JNPcO3aNbz00gi88MIwLF48tYkWiYhIW7Rm3/XJJ5/AxcUFw4YNw8SJE7F48eIOdbLq/qttnZycIJFIYGdnh5iYGKXx919t6+TkhLlz5+KVV17B+vXr5TH3X207aNAghIeHY8KECfIrdAFgypQp8PPzw4ABAzBgwAC8//77MDIyanC3hlgshrW1tfxhZmbWKu8DEWmfXbt2oXfv3vj6668RERGB3r17IzMzE0Ddsertt98GUHdX38iRIzF06FAMHjwYxcXF8uNRjx49sH//frz77rsYOnQonJ2dsWzZsgbz/DXmzTffxN27d/HYY49h8uTJ2Lx5s/xYtWDBApSWluKtt96Sj/1+/10iHdnDXGSbn5+vNL66ulo+AbCqmPvbHDFiBHbu3IlDhw7h008/RX5+Pjw9PVFcXKwyX16sS0QPmjNnDioqKvDyyy/Lj+EXL14EUPd9V1dXF8OGDcOQIUMwceJEhTs0mtK3b1+sXr0ao0aNQr9+/WBpaYk5c+YAqLsQ6dtvv0V6ejqGDx+OYcOGye8QaWlaP3SVubk5dHR0GnQsBQUFDToLoG4W+fT0dGRmZmL+/PkA6q6CEAQBurq6SE5OVjrUjFgsVriViIg06/7x/vr06SP/ogjUTcyXnp4OAHBwcMDPP/+stI37r5IxMDCQ/5BQ19ixY3Hjxg2l6/T09PDee+81OTTeg7f73e+ZZ56RXz11v1GjRsn3//x5oKoK0NNTP2/6n8jISHz77bf45ZdfYGhoCE9PT6xdu7bJIVzUGeaEiOhB7aHvio6Obla8NnmYIW1VXUkbGxuLqqoq6OnpIS0tTWGyx/qY+wsd96upqcHXX3+N8vJyeHh4KKw7duwYLC0t0b17d3h7e+P999+HpaWlyn3i8LlEbU/dY/X9f38wzsjISOn45I156aWX8NJLLyldd//3TA8PD1y4cEFlO+7u7jh69GiTr6fqBFa3bt1UFtDvH8+9s1L3ItvG4h9c3lSb9w+DOHjwYHh4eKBfv37YsWMHwlQMvxMeHq6wTiaTsdhB1E5oqp/5/fffVa4zNjZW63fCg8Ou3y84OBjBwcENlr/44ot48cUX1crxUWl9oUNfXx9ubm5ISUlRGCc3JSVF6cQmJiYm8mpVvejoaPz444/45ptv4Ojo2Oo5ExG1lN69gdpagFN0PJzU1FTMmzcPTzzxBKqrq7FixQr4+Pjg8uXL6Natm9Jt1JlUkNqPjz4C7twBunbVdCZE1Npa42pbGxsbta62Beom5/Xw8MC9e/dgZGSExMREODs7y9f7+vriueeeg4ODA7KzsxEREYHx48cjIyND5QVVkZGRWL16tdrvgSb84r8IOlX3UKNn0HQwEZGWau5FtgBgbW2tNF5XVxc9e/ZsNEZVm0BdMWrw4MGNnrTU+ot1+SWeiB6C1hc6ACAsLAxBQUFwd3eHh4cHtm3bhtzcXPlVD+Hh4cjLy8POnTvRpUsXuLq6KmxvaWkJAwODBsuJiNq7/34/pod08OBBhefbt2+HpaUlMjIyMGbMGKXbqDOpILUfL7yg6QyIqK1p4mpbABg4cCCysrJQUlKChIQEzJo1C6mpqfJiR2BgoDzW1dUV7u7ucHBwwIEDBzBt2jSluWnDFbl/PeHXdBARkZZr7kW2QN3dN/v371dYlpycDHd3d+j995Z8Dw8PpKSkKNw5mJycDE9PT5W5VFRU4MqVK/Dy8nqUXWrf+CWeiB5Chyh0BAYGori4GGvWrIFUKoWrqyuSkpLg4OAAAJBKpSonICQi9TX3tjjSPP6bNU9paSkANDpmujrDnNyPw44QaRaPgw9HG983TV9tq6+vj8ceewxA3dAxZ8+exccff4ytW7cqfW0bGxs4ODh07CtyidSgjccbUq0j/3s25yJboG7IsU2bNiEsLAzBwcFIS0tDbGwsdu/eLW9z4cKFGDNmDNauXQt/f3/s27cPhw8fxsmTJ+UxS5YswZQpU2Bvb4+CggK89957kMlkmDVrVtu+AURarCMfmzqSR/136hCFDgAIDQ1FaGio0nVxcXGNbrtq1apGxxgj6uz09PQgEolQWFgICwuLRq+KpPZDEAQUFhZCJBI1OPlODQmCgLCwMIwePbrRO/zUGebkftow7AhRR8S+6+Fpa//Rnq62Berex/sL3Q8qLi7G9evXG/QbRJ0Fj9Mdj7b2H+pq7kW2jo6OSEpKwuLFi7F582bY2tpi48aNCneBe3p6Ys+ePVi5ciUiIiLQr18/xMfHY8SIEfKYGzdu4Pnnn0dRUREsLCwwcuRI/PTTT/LXJSLV2Ndoj5boQzpMoYOIWo+Ojg569+6NGzduqJy0jjSjqup/f1fWD4hEIvTu3Rs6Ojptl5SWmj9/Pi5cuKBw9ZQq6gxzUk8bhh3pyH79FaiuBnR1gSbmmKcOhn3Xo9HW/kNTV9suX74cvr6+sLOzw+3bt7Fnzx4cO3ZMPkRiWVkZVq1ahenTp8PGxgbXrl3D8uXLYW5urlCUaU2FhYVN3lVoYmICCwuLZrXb7e9rENVWQ+iii3KrPo+QIXU2PE53TNraf6iruRfZent749y5c422GRAQgICAAJXr9+zZ06wcOwR+iacWwr5GuzxqH8JCBxGpxcjICP3790fV/WfWSeO8vYG//wasrIDU1Ibr9fT0OuyPjJb0+uuv47vvvsPx48fRu3fvRmPVGebkfhx2RLMmTADy8oBevYAbNzSdDbU19l0PT1v7D01dbfv3338jKCgIUqkUpqamGDJkCA4ePIhJkyYBqPuRffHiRezcuRMlJSWwsbHBuHHjEB8fD2Nj41Z/XwoLCzFndjDKZHcajTMy6YrYuE+bVewY8cmrMCwpwN3ulvjxveRHTZU6GR6nOx5t7T+oneGXeGpB7Gu0x6P2ISx0tAOFhYUA0Oyrp4jamo6ODr+0tjN5eXWP6mrAwEDT2WgfQRDw+uuvIzExEceOHYOjo2OT26gzzAkRtR/suzofTVxtGxsb2+j2hoaGOHToUKMxrUkmk6FMdgeTHp8Bix7WSmMKb+Uj5dxXkMlk/F1CbYrHaSIiam3sazoHFjo0rLi4GPMWzwMA/Dv23/xRQUTUhubNm4cvv/wS+/btg7GxsfxODVNTUxgaGgJ4uGFOiIiI2iOLHtawseDwiURERETU8XTRdAKdXVlZGW6W38TN8ptNjplLREQtKyYmBqWlpRg7dixsbGzkj/j4eHmMqmFOjh07hmHDhuHdd99tMMwJERERERERERG1Hd7RQUREnVb9JOKNedhhToiIiIiIiIiIqG3wjg4iIiIiIiIiIiIiItJaLHQQEREREREREREREZHWYqGDiIiIiIiIiIiIiIi0FgsdRERERERERERERESktVjoICIiIiIiIiIiIiIiraWr6QQ6s4qKCpSUlGg6DSLSYmfPAjU1gI6OpjMhap/4GSEial2n3vwCotpaCF14DR0REbUQfoknoofAQoeGFBcX4/wv5xG5IRK1urXQ0eXBm4iaz8ZG0xkQtW/8jBARta4KUwtNp0BERB0Nv8QT0UPgZTcaUlZWhpouNSi7V4aamhpNp0NEREREREREREREpJVY6CAiIiIiIiIiIiIiIq3FoauIiLTYtm1AWRlgZAS8+qqmsyFqf/gZISJqXXYnv4FuxV1Uiw1xfXSAptMhIqKOgF/iieghsNBBRKTF1qwB8vKAXr34/Y9IGX5GiIhaV/+D22BYUoC73S1Z6CAiopbBL/FE9BA4dBUREREREREREREREWktFjqIiIiIiIiIiIiIiEhrsdBBRERERERERERERERai4UOIiIiIiIiIiIiIiLSWix0EBERERFRhxEdHQ1HR0cYGBjAzc0NJ06caDQ+NTUVbm5uMDAwQN++fbFly5YGMQkJCXB2doZYLIazszMSExMV1sfExGDIkCEwMTGBiYkJPDw88MMPPyjECIKAVatWwdbWFoaGhhg7diwuXbr06DtMREREREQsdBARERERUccQHx+PRYsWYcWKFcjMzISXlxd8fX2Rm5urND47Oxt+fn7w8vJCZmYmli9fjgULFiAhIUEek5aWhsDAQAQFBeH8+fMICgrCjBkzcObMGXlM79698eGHHyI9PR3p6ekYP348/P39FQoZH330EaKiorBp0yacPXsW1tbWmDRpEm7fvt16bwgRERERUSfBQgcREREREXUIUVFRmDNnDubOnQsnJydIJBLY2dkhJiZGafyWLVtgb28PiUQCJycnzJ07F6+88grWr18vj5FIJJg0aRLCw8MxaNAghIeHY8KECZBIJPKYKVOmwM/PDwMGDMCAAQPw/vvvw8jICD/99BOAurs5JBIJVqxYgWnTpsHV1RU7duzAnTt38OWXX7bqe0JERERE1Bmw0EFERERERFqvsrISGRkZ8PHxUVju4+OD06dPK90mLS2tQfzkyZORnp6OqqqqRmNUtVlTU4M9e/agvLwcHh4eAOruHMnPz1doRywWw9vbW2U7AFBRUQGZTKbwICIiIiKihnQ1nQARET28AQMAU1PAykrTmRC1T/yMEHUeRUVFqKmpgdUDH3grKyvk5+cr3SY/P19pfHV1NYqKimBjY6My5sE2L168CA8PD9y7dw9GRkZITEyEs7Oz/HXqt3uwnZycHJX7FBkZidWrVzey15pXbumAagMjVJj01HQqRETUUfBLPBE9BBY6iIi02I8/ajoDovaNnxGizkckEik8FwShwbKm4h9crk6bAwcORFZWFkpKSpCQkIBZs2YhNTVVXux4mNzCw8MRFhYmfy6TyWBnZ6cyXhPOLPhU0ykQEVFHwy/xRPQQWOggIiIiIiKtZ25uDh0dnQZ3WhQUFDS4k6KetbW10nhdXV307Nmz0ZgH29TX18djjz0GAHB3d8fZs2fx8ccfY+vWrbC2tgZQd2eHjY2NWrkBdcNbicXixnabiIiIiIjAOTqIiIiIiKgD0NfXh5ubG1JSUhSWp6SkwNPTU+k2Hh4eDeKTk5Ph7u4OPT29RmNUtVlPEARUVFQAABwdHWFtba3QTmVlJVJTU5tsh4iIiIiImsY7OoiIiIiIqEMICwtDUFAQ3N3d4eHhgW3btiE3NxchISEA6oaCysvLw86dOwEAISEh2LRpE8LCwhAcHIy0tDTExsZi9+7d8jYXLlyIMWPGYO3atfD398e+fftw+PBhnDx5Uh6zfPly+Pr6ws7ODrdv38aePXtw7NgxHDx4EEDdkFWLFi3CBx98gP79+6N///744IMP0LVrV7zwwgtt+A4REREREXVMLHQQEWmxF18EiooAc3Pgiy80nQ1R+8PPCFHnEhgYiOLiYqxZswZSqRSurq5ISkqCg4MDAEAqlSI3N1ce7+joiKSkJCxevBibN2+Gra0tNm7ciOnTp8tjPD09sWfPHqxcuRIRERHo168f4uPjMWLECHnM33//jaCgIEilUpiammLIkCE4ePAgJk2aJI9ZunQp7t69i9DQUNy6dQsjRoxAcnIyjI2N2+CdaT3D4sKhX1aCSqPuyJodqel0iIioI+CXeCJ6CCx0EBFpsdRUIC8P6NVL05kQtU/8jBB1PqGhoQgNDVW6Li4ursEyb29vnDt3rtE2AwICEBAQoHJ9bGxsk3mJRCKsWrUKq1atajJWm5j9kQHDkgLc7W6p6VSIiKij4Jd4InoInKODiIiIiIiIiIioCdHR0XB0dISBgQHc3Nxw4sSJRuNTU1Ph5uYGAwMD9O3bF1u2bGkQk5CQAGdnZ4jFYjg7OyMxMVFle5GRkfLhEImISBELHURERERERERERI2Ij4/HokWLsGLFCmRmZsLLywu+vr4KQyLeLzs7G35+fvDy8kJmZiaWL1+OBQsWICEhQR6TlpaGwMBABAUF4fz58wgKCsKMGTNw5syZBu2dPXsW27Ztw5AhQ1ptH4mItBkLHURERERERERERI2IiorCnDlzMHfuXDg5OUEikcDOzg4xMTFK47ds2QJ7e3tIJBI4OTlh7ty5eOWVV7B+/Xp5jEQiwaRJkxAeHo5BgwYhPDwcEyZMgEQiUWirrKwML774Ij799FP06NGjNXeTiEhrsdChYTXVNaiurtZ0GkREREREREREpERlZSUyMjLg4+OjsNzHxwenT59Wuk1aWlqD+MmTJyM9PR1VVVWNxjzY5rx58/D0009j4sSJauVbUVEBmUym8NAWhYWF8vNk1dXVuHr1qsIjJycHVTyPRkRKcDJyDaqtqYX0Lym66HeBmYkZSkpKNJ0SERERERERERHdp6ioCDU1NbCyslJYbmVlhfz8fKXb5OfnK42vrq5GUVERbGxsVMbc3+aePXtw7tw5nD17Vu18IyMjsXr1arXj24vCwkK8MmsmPr91CxYAbt26hbkzgxRi7t67h7/ybqCyyl0zSRJRu8VChwYJtQIEXQE1tTW4+fdNlJaWajolIiIiIiIiIiJSQiQSKTwXBKHBsqbiH1zeWJvXr1/HwoULkZycDAMDA7XzDA8PR1hYmPy5TCaDnZ2d2ttrikwmQ1lpKcR6ukBlJQz19TBj1BMKMb/lXsdXuTkcHYWIGmChg4iIiIiIiIiISAVzc3Po6Og0uHujoKCgwR0Z9aytrZXG6+rqomfPno3G1LeZkZGBgoICuLm5ydfX1NTg+PHj2LRpEyoqKqCjo9PgtcViMcRicfN3tJ3o8t9Cj06XLrDuaaawrvBWiQYyIiJtwDk6iIiIiIiIiIiIVNDX14ebmxtSUlIUlqekpMDT01PpNh4eHg3ik5OT4e7uDj09vUZj6tucMGECLl68iKysLPnD3d0dL774IrKyspQWOYiIOive0UFEpMWCg4HSUsDUVNOZELVP/IwQEbWu657ToHu3DNWGRppOhYioVYWFhSEoKAju7u7w8PDAtm3bkJubi5CQEAB1w0Xl5eVh586dAICQkBBs2rQJYWFhCA4ORlpaGmJjY7F79255mwsXLsSYMWOwdu1a+Pv7Y9++fTh8+DBOnjwJADA2Noarq6tCHt26dUPPnj0bLO9IrgxxRY8uIlQ1Y7guIqIOU+iIjo7GunXrIJVK4eLiAolEAi8vL6WxJ0+exFtvvYVffvkFd+7cgYODA1577TUsXry4jbMmIno077yj6QyI2jd+RoiIWtfvfiGaToGIqE0EBgaiuLgYa9asgVQqhaurK5KSkuDg4AAAkEqlyM3Nlcc7OjoiKSkJixcvxubNm2Fra4uNGzdi+vTp8hhPT0/s2bMHK1euREREBPr164f4+HiMGDGizfevPUkfNaLBkFVERE3pEIWO+Ph4LFq0CNHR0Rg1ahS2bt0KX19fXL58Gfb29g3iu3Xrhvnz52PIkCHo1q0bTp48iddeew3dunXDq6++qoE9ICIiIiIiIiKi9iw0NBShoaFK18XFxTVY5u3tjXPnzjXaZkBAAAICAtTO4dixY2rHEhF1Jh1ijo6oqCjMmTMHc+fOhZOTEyQSCezs7BATE6M0fvjw4Xj++efh4uKCPn364KWXXsLkyZNx4sSJNs6ciIiIiIiIiIiIiIgehdYXOiorK5GRkQEfHx+F5T4+Pjh9+rRabWRmZuL06dPw9vZWGVNRUQGZTKbwaEm1tbUoKSlp0TaJiIiIiIiIiIiIiDo6rS90FBUVoaamBlZWVgrLrayskJ+f3+i2vXv3hlgshru7O+bNm4e5c+eqjI2MjISpqan8YWdn1yL5A4BQK6CythIfRn2IwsLCFmuXiDq+3r0BkajuTyJqiJ8RIqLWNX6lD56ePwzjV/o0HUxERKSGoJhYTF22Ck99sEHTqRCRFtH6Qkc9kUik8FwQhAbLHnTixAmkp6djy5YtkEgk2L17t8rY8PBwlJaWyh/Xr19vkbyBukKHSCxCWWVZi98pQkREqh0/fhxTpkyBra0tRCIR9u7d22j8sWPHIBKJGjx++eWXtkmYiIiIiIiIiIga0PrJyM3NzaGjo9Pg7o2CgoIGd3k8yNHREQAwePBg/P3331i1ahWef/55pbFisRhisbhlkiYionahvLwcQ4cOxcsvv4zp06ervd2vv/4KExMT+XMLC4vWSI+IiIiIiIiIiNSg9YUOfX19uLm5ISUlBVOnTpUvT0lJgb+/v9rtCIKAioqK1kiRiIjaKV9fX/j6+jZ7O0tLS3Tv3r3lEyIiIiIiIiIiombT+kIHAISFhSEoKAju7u7w8PDAtm3bkJubi5CQEAB1w07l5eVh586dAIDNmzfD3t4egwYNAgCcPHkS69evx+uvv66xfSAiIu0xfPhw3Lt3D87Ozli5ciXGjRunMraiokKhkM4hComIiIiIiDSvsqoKOTk5TcaZmJjwLn4iLdAhCh2BgYEoLi7GmjVrIJVK4erqiqSkJDg4OAAApFIpcnNz5fG1tbUIDw9HdnY2dHV10a9fP3z44Yd47bXXNLULRESkBWxsbLBt2za4ubmhoqIC//73vzFhwgQcO3YMY8aMUbpNZGQkVq9e3caZEhERERERkSq379xBdnY2Vi59s8mh6o1MTfH5jp0sdhC1cx1mMvLQ0FBcu3YNFRUVyMjIUDjhFBcXh2PHjsmfv/766/jPf/6D8vJylJaW4ty5c/i///s/dOnSYd4OIiJqBQMHDkRwcDAef/xxeHh4IDo6Gk8//TTWr1+vcpvw8HCUlpbKH9evX2/DjImIOp/o6Gg4OjrCwMAAbm5uOHHiRKPxqampcHNzg4GBAfr27YstW7Y0iElISICzszPEYjGcnZ2RmJiosD4yMhJPPPEEjI2NYWlpiWeffRa//vqrQszs2bMhEokUHiNHjnz0HSYiIqJmu1dRCREEjHMZgBmjnlD5GO8yAGWlpbwzn0gL8Mx+O1JdVY3i4mJNp0FERM0wcuRI/P777yrXi8VimJiYKDyIiKh1xMfHY9GiRVixYgUyMzPh5eUFX19fhbu775ednQ0/Pz94eXkhMzMTy5cvx4IFC5CQkCCPSUtLQ2BgIIKCgnD+/HkEBQVhxowZOHPmjDwmNTUV8+bNw08//YSUlBRUV1fDx8cH5eXlCq/31FNPQSqVyh9JSUmt80YQERGRWsxMjGHd00zlw7y7qaZTJCI1dYihqzoCoVZAvjQfS1YsQcKXCbwdjohIS2RmZsLGxkbTaRAREYCoqCjMmTMHc+fOBQBIJBIcOnQIMTExiIyMbBC/ZcsW2NvbQyKRAACcnJyQnp6O9evXY/r06fI2Jk2ahPDwcAB1d+qlpqZCIpFg9+7dAICDBw8qtLt9+3ZYWlo2uNNcLBbD2tq6xfebiIhIkwoLC5u844HzXBBRa2Oho70QAEFHgOyuDDKZjAd/IqI2UFZWhj/++EP+PDs7G1lZWTAzM4O9vT3Cw8ORl5eHnTt3Aqg72dWnTx+4uLigsrISu3btQkJCgsKVv0REpBmVlZXIyMjAsmXLFJb7+Pjg9OnTSrdJS0uDj4+PwrLJkycjNjYWVVVV0NPTQ1paGhYvXtwgpr44okxpaSkAwMzMTGH5sWPHYGlpie7du8Pb2xvvv/8+LC0tVbZTUVGBiooK+XMOm0FERO1NYWEhXpk1E2X/7ftU4TwXRNTaWOggItJiu3YBFRVAE3OnkQrp6ekYN26c/HlYWBgAYNasWYiLi4NUKlUY7qSyshJLlixBXl4eDA0N4eLiggMHDsDPz6/Ncyf18DNC1HkUFRWhpqYGVlZWCsutrKyQn5+vdJv8/Hyl8dXV1SgqKoKNjY3KGFVtCoKAsLAwjB49Gq6urvLlvr6+eO655+Dg4IDs7GxERERg/PjxyMjIUDkJamRkJFavXt3kvmtS1sz30aW6CrW6eppOhYiINEAmk6GstBTjXQaoHOapqKQUP176Te0Le488PRnm3bqiVkenpdMlog6MhQ4iIi02dqymM9BuY8eOhSAIKtfHxcUpPF+6dCmWLl3ayllRS+JnhKjzEYlECs8FQWiwrKn4B5c3p8358+fjwoULOHnypMLywMBA+d9dXV3h7u4OBwcHHDhwANOmTVPaVnh4uLwID9SdTLKzs1O5L5pwc8ATmk6BiIjaAfPuprDuadZ0oBr+su+N2hZqi4g6DxY6iIiIiIhI65mbm0NHR6fBnRYFBQUN7sioZ21trTReV1cXPXv2bDRGWZuvv/46vvvuOxw/fhy9e/duNF8bGxs4ODjg999/VxkjFotV3u1BRERERET/w0IHERERERFpPX19fbi5uSElJQVTp06VL09JSYG/v7/SbTw8PLB//36FZcnJyXB3d4eenp48JiUlRWGejuTkZHh6esqfC4KA119/HYmJiTh27BgcHR2bzLe4uBjXr1+HjY1Ns/aTiIhIG1VWVSEnJ6fRmJycHFRVV7dRRkTU0bDQQUSkxY4d+9/8Axyih6ghfkaIOpewsDAEBQXB3d0dHh4e2LZtG3JzcxESEgKgbiiovLw87Ny5EwAQEhKCTZs2ISwsDMHBwUhLS0NsbCx2794tb3PhwoUYM2YM1q5dC39/f+zbtw+HDx9WGJpq3rx5+PLLL7Fv3z4YGxvL7wAxNTWFoaEhysrKsGrVKkyfPh02Nja4du0ali9fDnNzc4WijDYy++2sfI4ODmNFRETK3L5zB9nZ2Vi59M1G71S8e+8e/sq7AYs/bWBZfBO1Ojoo6tf0xQNERAALHe1ScXExTExM1JqgiYg6t5deAvLygF69gBs3NJ0NUfvDzwhR5xIYGIji4mKsWbMGUqkUrq6uSEpKgoODAwBAKpUiNzdXHu/o6IikpCQsXrwYmzdvhq2tLTZu3Ijp06fLYzw9PbFnzx6sXLkSERER6NevH+Lj4zFixAh5TExMDIC6uZ/ut337dsyePRs6Ojq4ePEidu7ciZKSEtjY2GDcuHGIj4+HsbFxK74jrW/YzhUwLCnA3e6W+PG9ZE2nQ0RE7dC9ikqIIGCcywD0trJUGfdb7nV8lZuDyQePwLi8HHdNjHFw+RttmCkRaTMWOtqZ6qpqLFmxBF0Nu+Lfsf9msYOIiIiIqBlCQ0MRGhqqdF1cXFyDZd7e3jh37lyjbQYEBCAgIEDl+voJzFUxNDTEoUOHGo0hIiLq6MxMjBudsLzwVknbJUNEHU4XTSdAimqqayC7K8PN8puQyWSaToeIiIiIiIiIiIiIqF1joYOIiIiIiIiIiIiIiLQWCx1ERERERERERERERKS1WOggIiIiIiIiIiIiIiKtpdFCR3Z2tiZfnoiItBT7DyKijoXHdSIiai3sY4iIOgeNFjoee+wxjBs3Drt27cK9e/c0mQoREWkR9h9ERB0Lj+tERNRa2McQEXUOGi10nD9/HsOHD8cbb7wBa2trvPbaa/j55581mRIREWkB9h9ERB0Lj+tERNRa2McQEXUOGi10uLq6IioqCnl5edi+fTvy8/MxevRouLi4ICoqCoWFhZpMj4iI2in2H0REHQuP60RE1FrYxxARdQ7tYjJyXV1dTJ06FV999RXWrl2Lq1evYsmSJejduzdmzpwJqVSq6RSJiNqlGzcAQaj7szNi/0FN6eyfESJtw+O69vnxvWQc2JSFH99L1nQqRESNYh+jPba9/AISP1yFg8vf0HQqRKRF2kWhIz09HaGhobCxsUFUVBSWLFmCq1ev4scff0ReXh78/f01nSIREbVD7D+IiDoWHteJiKi1sI8hIurYdDX54lFRUdi+fTt+/fVX+Pn5YefOnfDz80OXLnX1F0dHR2zduhWDBg3SZJpERNTOsP8gIupYeFwnIqLWwj6GiKhz0OgdHTExMXjhhReQm5uLvXv34h//+Ie8o6lnb2+P2NhYDWVIRETtEfsPIqKOhcd1IiJqLS3Zx0RHR8PR0REGBgZwc3PDiRMnGo1PTU2Fm5sbDAwM0LdvX2zZsqVBTEJCApydnSEWi+Hs7IzExMQG+Q8ZMgQmJiYwMTGBh4cHfvjhBzX2nIioc9HoHR0pKSmwt7dv0MEIgoDr16/D3t4e+vr6mDVrloYyJCJq31avBkpLAVNT4J13NJ1N22H/QerqrJ8RIm3D47r26p+0Bbp3y1BtaITf/UI0nQ4RUQMt1cfEx8dj0aJFiI6OxqhRo7B161b4+vri8uXLsLe3bxCfnZ0NPz8/BAcHY9euXTh16hRCQ0NhYWGB6dOnAwDS0tIQGBiId999F1OnTkViYiJmzJiBkydPYsSIEQCA3r1748MPP8Rjjz0GANixYwf8/f2RmZkJFxeXlniL2h2PnzNgmXURVQYG+GXiWE2nQ0RaQqN3dPTr1w9FRUUNlt+8eROOjo4ayIiISLt8+inwr3/V/dmZsP8gdXXWzwiRtuFxXXvZnf4WfY/ugt3pbzWdChGRUi3Vx0RFRWHOnDmYO3cunJycIJFIYGdnh5iYGKXxW7Zsgb29PSQSCZycnDB37ly88sorWL9+vTxGIpFg0qRJCA8Px6BBgxAeHo4JEyZAIpHIY6ZMmQI/Pz8MGDAAAwYMwPvvvw8jIyP89NNP6r8JWmbwpV/x2Mmf0OfnDE2nQkRaRKOFDkEQlC4vKyuDgYFBG2dDRETagv0HEVHHwuM6ERG1lpboYyorK5GRkQEfHx+F5T4+Pjh9+rTSbdLS0hrET548Genp6aiqqmo0RlWbNTU12LNnD8rLy+Hh4aEy34qKCshkMoUHEVFHp5Ghq8LCwgAAIpEIb7/9Nrp27SpfV1NTgzNnzmDYsGGaSI2IiNox9h9ERB0Lj+tERNRaWrKPKSoqQk1NDaysrBSWW1lZIT8/X+k2+fn5SuOrq6tRVFQEGxsblTEPtnnx4kV4eHjg3r17MDIyQmJiIpydnVXmGxkZidWrV6u1b0REHYVGCh2ZmZkA6qrqFy9ehL6+vnydvr4+hg4diiVLlmgiNSIiasfYfxARdSw8rhMRUWtpjT5GJBIpPBcEocGypuIfXK5OmwMHDkRWVhZKSkqQkJCAWbNmITU1VWWxIzw8XF7oAQCZTAY7O7tG9oyISPtppNBx9OhRAMDLL7+Mjz/+GCYmJppIg4iItAz7DyKijoXHdSIiai0t2ceYm5tDR0enwZ0WBQUFDe7IqGdtba00XldXFz179mw05sE29fX15ZORu7u74+zZs/j444+xdetWpa8tFoshFovV30Eiog5Ao3N0bN++nT9miIio2dh/EBF1LC15XI+OjoajoyMMDAzg5uaGEydONBqfmpoKNzc3GBgYoG/fvtiyZUuDmISEBDg7O0MsFsPZ2RmJiYkK6yMjI/HEE0/A2NgYlpaWePbZZ/Hrr78qxAiCgFWrVsHW1haGhoYYO3YsLl269Og7TEREjWqJPkZfXx9ubm5ISUlRWJ6SkgJPT0+l23h4eDSIT05Ohru7O/T09BqNUdVmPUEQUFFR0dzdICLq0Nr8jo5p06YhLi4OJiYmmDZtWqOx3377bRtlRURE7R37DyKijqU1juvx8fFYtGgRoqOjMWrUKGzduhW+vr64fPky7O3tG8RnZ2fDz88PwcHB2LVrF06dOoXQ0FBYWFhg+vTpAOomig0MDMS7776LqVOnIjExETNmzMDJkycxYsQIAHXFknnz5uGJJ55AdXU1VqxYAR8fH1y+fBndunUDAHz00UeIiopCXFwcBgwYgPfeew+TJk3Cr7/+CmNj4+a8dURE1ITW6GPCwsIQFBQEd3d3eHh4YNu2bcjNzUVISAiAuuGi8vLysHPnTgBASEgINm3ahLCwMAQHByMtLQ2xsbHYvXu3vM2FCxdizJgxWLt2Lfz9/bFv3z4cPnwYJ0+elMcsX74cvr6+sLOzw+3bt7Fnzx4cO3YMBw8ebO7bQkTUobV5ocPU1FQ+1qCpqWlbvzwREWkp9h9ERB1LaxzXo6KiMGfOHMydOxcAIJFIcOjQIcTExCAyMrJB/JYtW2Bvbw+JRAIAcHJyQnp6OtavXy8vdEgkEkyaNAnh4eEA6k5kpaamQiKRyE9WPXiyafv27bC0tERGRgbGjBkDQRAgkUiwYsUK+Qm3HTt2wMrKCl9++SVee+21Ftl/IiKq0xp9TGBgIIqLi7FmzRpIpVK4uroiKSkJDg4OAACpVIrc3Fx5vKOjI5KSkrB48WJs3rwZtra22Lhxo7x/AQBPT0/s2bMHK1euREREBPr164f4+Hh5IR0A/v77bwQFBUEqlcLU1BRDhgzBwYMHMWnSpBbZLyKijqLNCx3bt29X+nciIqLGsP8gIupYWvq4XllZiYyMDCxbtkxhuY+PD06fPq10m7S0NPj4+Cgsmzx5MmJjY1FVVQU9PT2kpaVh8eLFDWLqiyPKlJaWAgDMzMwA1N05kp+fr/BaYrEY3t7eOH36tMpCR0VFhcLQJDKZTOVrEhHR/7TWb4fQ0FCEhoYqXRcXF9dgmbe3N86dO9domwEBAQgICFC5PjY2tlk5EhF1VhqZjLze3bt3IQgCunbtCgDIyclBYmIinJ2dG/zgICKihry9gaIiwNxc05m0LfYfpK7O+hkh0jYtcVwvKipCTU1NgwlcraysGkz0Wi8/P19pfHV1NYqKimBjY6MyRlWbgiAgLCwMo0ePhqurq/x16rd7sJ2cnByV+xQZGYnVq1erXN8e3HzMDfplJag06q7pVIiIlOJvB+1zo5c1zASgsltXTadCRFpEo4UOf39/TJs2DSEhISgpKcGTTz4JfX19FBUVISoqCv/3f/+nyfSIiNq9L77QdAaawf6D1NVZPyNE2qYlj+v1Q5XUEwShwbKm4h9c3pw258+fjwsXLiiMr/6wuYWHhyMsLEz+XCaTwc7OTmW8JmTNbjgkGBFRe8LfDtonyWc8+vbupek0iEjLdNHki587dw5eXl4AgG+++QbW1tbIycnBzp07sXHjRk2mRkRE7Rj7DyKijqUljuvm5ubQ0dFpcKdFQUFBgzsp6llbWyuN19XVRc+ePRuNUdbm66+/ju+++w5Hjx5F7969FV4HQLNyA+qGtzIxMVF4EBFR8/C3AxFR56DRQsedO3dgbGwMAEhOTsa0adPQpUsXjBw5stFbuImIqHNj/0FE1LG0xHFdX18fbm5uSElJUViekpICT09Ppdt4eHg0iE9OToa7uzv09PQajbm/TUEQMH/+fHz77bf48ccf4ejoqBDv6OgIa2trhXYqKyuRmpqqMjciImoZ/O1ARNQ5aHToqsceewx79+7F1KlTcejQIfkkfwUFBbxaiYiIVGL/QUTUsbTUcT0sLAxBQUFwd3eHh4cHtm3bhtzcXISEhACoGwoqLy8PO3fuBACEhIRg06ZNCAsLQ3BwMNLS0hAbG4vdu3fL21y4cCHGjBmDtWvXwt/fH/v27cPhw4cVhqaaN28evvzyS+zbtw/GxsbyOzdMTU1haGgIkUiERYsW4YMPPkD//v3Rv39/fPDBB+jatSteeOGFR37/tFVhYWGTE6ybmJjAwsKi3eQDtG1ORPTo+NuBiKhz0Gih4+2338YLL7yAxYsXY8KECfDw8ABQV2EfPny4JlMjItIK48cDf/8NWFkBP/6o6WzaDvsPUldn/YwQaZuWOq4HBgaiuLgYa9asgVQqhaurK5KSkuDg4AAAkEqlyM3Nlcc7OjoiKSkJixcvxubNm2Fra4uNGzdi+vTp8hhPT0/s2bMHK1euREREBPr164f4+HiMGDFCHhMTEwMAGDt2rEI+27dvx+zZswEAS5cuxd27dxEaGopbt25hxIgRSE5Oll9lrK1GbAyGWFaMCpOeOLPgU7W3KywsxJzZwSiT3Wk0zsikK2LjPm31woK6+bRlTkTUMvjbQfs8l/g9uldVo8KoG06+OlvT6RCRltBooSMgIACjR4+GVCrF0KFD5csnTJiAqVOnajAzIiLt8NtvQF4eUFqq6UzaFvsPUldn/YwQaZuWPK6HhoYiNDRU6bq4uLgGy7y9vXHu3Lkm8wsICFC5vn4C88aIRCKsWrUKq1atajJWm3QryIFhSQF075U1azuZTIYy2R1MenwGLHpYK40pvJWPlHNfQSaTtXpRQZ182jonImoZ/O2gfXqUyGBcXo679+5pOhUi0iIaLXQAdRPz1U/OV+/JJ5/UUDZERKQt2H8QEXUsPK53ThY9rGFjYafpNOTaWz5E1DLYxxARdXwaLXSUl5fjww8/xJEjR1BQUIDa2lqF9X/++aeGMiMiovaM/QcRUcfC4zoREbUW9jFERJ2DRgsdc+fORWpqKoKCgmBjYwORSKTJdIiISEuw/yAi6lh4XCdtUllViZycnEZjOGE5UfvBPoaIqHPQaKHjhx9+wIEDBzBq1KhHbis6Ohrr1q2DVCqFi4sLJBIJvLy8lMZ+++23iImJQVZWFioqKuDi4oJVq1Zh8uTJj5wHERG1vpbsP4iISPN4XCdtcbu8FH/++Scilq2GWCxWGccJy4naD/YxRESdg0YLHT169ICZmdkjtxMfH49FixYhOjoao0aNwtatW+Hr64vLly/D3t6+Qfzx48cxadIkfPDBB+jevTu2b9+OKVOm4MyZMxg+fPgj50NERK2rpfoPIiJqH3hcJ21xt+IOdES6mPj4DNhZOyiN4YTlRO0L+xgios6hiyZf/N1338Xbb7+NO3fuPFI7UVFRmDNnDubOnQsnJydIJBLY2dkhJiZGabxEIsHSpUvxxBNPoH///vjggw/Qv39/7N+//5HyICKittFS/QcREbUPPK6TtunZ3RI2FnZKHxY9rJtugIjaDPsYIqLOQaN3dGzYsAFXr16FlZUV+vTpAz09PYX1586da7KNyspKZGRkYNmyZQrLfXx8cPr0abXyqK2txe3btxut8FdUVKCiokL+XCaTqdU2ERG1vJboP4C6O/zWrVuHjIwMSKVSJCYm4tlnn210m9TUVISFheHSpUuwtbXF0qVLERIS8rC7QkREaLnjOhER0YPYxxARdQ4aLXQ0dTJJHUVFRaipqYGVlZXCcisrK+Tn56vVxoYNG1BeXo4ZM2aojImMjMTq1asfKVciImoZLdF/AEB5eTmGDh2Kl19+GdOnT28yPjs7G35+fggODsauXbtw6tQphIaGwsLCQq3tiYhIuZY6rhMRET2IfQwRUeeg0ULHO++802JtiUQiheeCIDRYpszu3buxatUq7Nu3D5aWlirjwsPDERYWJn8uk8lgZ2f38AkTEbWAt98GysoAIyNNZ9K2Wqr/8PX1ha+vr9rxW7Zsgb29PSQSCQDAyckJ6enpWL9+vcpCB+8I1KzO+hkh0jYt+buA2tbvT70K3Yq7qBYbajoVIiKl2Mdon7QnhsOmW1dU6+trOhUi0iIaLXQAQElJCb755htcvXoVb775JszMzHDu3DlYWVmhV69eTW5vbm4OHR2dBndvFBQUNLjL40Hx8fGYM2cOvv76a0ycOLHRWLFYDLFY3PQOERG1oVdf1XQGmvOo/cfDSEtLg4+Pj8KyyZMnIzY2FlVVVQ1ugwd4R6CmdebPCJG20cRxnR7d9dEBmk6BiKhJ7GO0y0VXJ5T35r8LETWPRicjv3DhAgYMGIC1a9di/fr1KCkpAQAkJiYiPDxcrTb09fXh5uaGlJQUheUpKSnw9PRUud3u3bsxe/ZsfPnll3j66acfeh+IiKjttUT/8TDy8/OVDpVYXV2NoqIipduEh4ejtLRU/rh+/Xqr5UdEpK00dVwnIqKOj30MEVHnoNFCR1hYGGbPno3ff/8dBgYG8uW+vr44fvx4s9r57LPP8Pnnn+PKlStYvHgxcnNz5ZPDhoeHY+bMmfL43bt3Y+bMmdiwYQNGjhyJ/Px85Ofno7S0tOV2joiIWk1L9R8PQ9lQicqW1xOLxTAxMVF4EBGRIk0e14mIqGNjH0NE1DlodOiqs2fPYuvWrQ2W9+rVS+2JxAEgMDAQxcXFWLNmDaRSKVxdXZGUlAQHBwcAgFQqRW5urjx+69atqK6uxrx58zBv3jz58lmzZiEuLu7hd4iIqI1JpUBNDaCjA9jYaDqbttNS/UdzWVtbKx0qUVdXFz179my116WH11k/I0TaRlPHdXp04tJCiGprIXTpggpTC02nQ0TUAPsY7dOt/A4MSkshiLqgwsRY0+kQkZbQaKHDwMBA6aSsv/76KywsmvclOTQ0FKGhoUrXPVi8OHbsWLPaJiJqr554AsjLA3r1Am7c0HQ2bacl+4/m8PDwwP79+xWWJScnw93dXen8HKR5nfUzQqRtNHVcp0c3at2LMCwpwN3ulvjxvWRNp0NE1AD7GO3z4ld7YVxejrsmxji4/A1Np0NEWkKjQ1f5+/tjzZo1qKqqAlA37Edubi6WLVuG6dOnazI1IiJqx1qq/ygrK0NWVhaysrIAANnZ2cjKypLfBfjg0IchISHIyclBWFgYrly5gs8//xyxsbFYsmRJy+0cEVEnxN8FRETUWtjHEBF1DhotdKxfvx6FhYWwtLTE3bt34e3tjcceewzGxsZ4//33NZkaERG1Yy3Vf6Snp2P48OEYPnw4gLrxe4cPH463334bQMOhDx0dHZGUlIRjx45h2LBhePfdd7Fx40b+QCIiekT8XUBERK2FfQwRUeeg0aGrTExMcPLkSRw9ehQZGRmora3F448/jokTJ2oyLSIiaudaqv8YO3asfDJxZZTN2+Tt7Y1z5841N2UiImoEfxcQEVFrYR9DRNQ5aKzQUVtbi7i4OHz77be4du0aRCIRHB0dYW1tDUEQIBKJNJUaERG1Y+w/iIg6Fh7X24/Kqkrk5OSoXJ+Tk4Oq6uo2zIiI6NGwjyEi6jw0UugQBAHPPPMMkpKSMHToUAwePBiCIODKlSuYPXs2vv32W+zdu1cTqRERUTvG/oOIqGPhcb39uF1eij///BMRy1ZDLBYrjbl79w7++isflf8d556IqD1jH0NE1LloZI6OuLg4HD9+HEeOHEFmZiZ2796NPXv24Pz58zh8+DB+/PFH7Ny5UxOpERFRO8b+g4ioY2mN43p0dDQcHR1hYGAANzc3nDhxotH41NRUuLm5wcDAAH379sWWLVsaxCQkJMDZ2RlisRjOzs5ITExUWH/8+HFMmTIFtra2EIlESk+czZ49GyKRSOExcuTIZu1ba7pbcQc6Il1MfHwGXpiwQOljlIsfamtqUV3DuzqIqP3jbwcios5FI4WO3bt3Y/ny5Rg3blyDdePHj8eyZcvwxRdfaCCz9qG6qhrF+cUoKSnRdCpERO0K+w8ioo6lpY/r8fHxWLRoEVasWIHMzEx4eXnB19cXubm5SuOzs7Ph5+cHLy8vZGZmYvny5ViwYAESEhLkMWlpaQgMDERQUBDOnz+PoKAgzJgxA2fOnJHHlJeXY+jQodi0aVOj+T311FOQSqXyR1JSktr71lZ6dreEjYWd0oeZqYWm0yMiUht/OxARdS4aKXRcuHABTz31lMr1vr6+OH/+fBtm1L7UVNeg+O9ilJaWajoVIqJ2hf0HEVHH0tLH9aioKMyZMwdz586Fk5MTJBIJ7OzsEBMTozR+y5YtsLe3h0QigZOTE+bOnYtXXnkF69evl8dIJBJMmjQJ4eHhGDRoEMLDwzFhwgRIJBKFPN977z1Mmzat0fzEYjGsra3lDzMzM7X3jYiImoe/HYiIOheNFDpu3rwJKysrleutrKxw69atNsyIiIi0AfsPIqKOpSWP65WVlcjIyICPj4/Cch8fH5w+fVrpNmlpaQ3iJ0+ejPT0dFT9dx4KVTGq2mzMsWPHYGlpiQEDBiA4OBgFBQWNxldUVEAmkyk8iIhIPfztQETUuWhkMvKamhro6qp+aR0dHVRXc9xXIqKmHDkCVFcDjRxSOxT2H9Rcne0zQqRtWvK4XlRUhJqamgYntaysrJCfn690m/z8fKXx1dXVKCoqgo2NjcoYVW2q4uvri+eeew4ODg7Izs5GREQExo8fj4yMDJWTf0dGRmL16tXNep22dub1bRDVVkPowgMtEbUv/O2gvb5+1g/2lhYQumjk+mwi0lIa+TYqCAJmz56t8gt9RUVFG2dERKSdBg7UdAZti/0HNVdn+4wQaZvWOK6LRKIGr/HgsqbiH1ze3DaVCQwMlP/d1dUV7u7ucHBwwIEDB1QOeRUeHo6wsDD5c5lMBjs7u2a9bmsrt+qj6RSIiJRqjT4mOjoa69atg1QqhYuLCyQSCby8vFTGp6amIiwsDJcuXYKtrS2WLl2KkJAQhZiEhARERETg6tWr6NevH95//31MnTpVvj4yMhLffvstfvnlFxgaGsLT0xNr167FwA78RfdWj+7oYWWp6TSISMtopNAxa9asJmNmzpzZBpkQEZE2Yf9BRNSxtORx3dzcHDo6Og3utCgoKFA5dIm1tbXSeF1dXfTs2bPRmMaGQ1GHjY0NHBwc8Pvvv6uMEYvFKk/QERFR41r6t0N8fDwWLVqE6OhojBo1Clu3boWvry8uX74Me3v7BvHZ2dnw8/NDcHAwdu3ahVOnTiE0NBQWFhaYPn06gLrhEQMDA/Huu+9i6tSpSExMxIwZM3Dy5EmMGDECQF2xZN68eXjiiSdQXV2NFStWwMfHB5cvX0a3bt3Uzp+IqKPTSKFj+/btmnhZIiLScuw/iIg6lpY8ruvr68PNzQ0pKSkKV8KmpKTA399f6TYeHh7Yv3+/wrLk5GS4u7tDT09PHpOSkoLFixcrxHh6ej5SvsXFxbh+/TpsbGweqR0iIlKupX87REVFYc6cOZg7dy4AQCKR4NChQ4iJiUFkZGSD+C1btsDe3h4SiQQA4OTkhPT0dKxfv15e6JBIJJg0aRLCw8MB1N3Jl5qaColEgt27dwMADh482GC/LC0tkZGRgTFjxrToPhIRaTMOpEpEpMW+/BK4cwfo2hV44QVNZ0PU/vAzQtS5hIWFISgoCO7u7vDw8MC2bduQm5srHyYkPDwceXl52LlzJwAgJCQEmzZtQlhYGIKDg5GWlobY2Fj5ySUAWLhwIcaMGYO1a9fC398f+/btw+HDh3Hy5El5TFlZGf744w/58+zsbGRlZcHMzAz29vYoKyvDqlWrMH36dNjY2ODatWtYvnw5zM3NFYoy2sj2bBJ0qu6hRs8Afz3hp+l0iIhaRWVlJTIyMrBs2TKF5T4+Pjh9+rTSbdLS0uDj46OwbPLkyYiNjUVVVRX09PSQlpamUEivj6kvjihTWloKADAzM1MZU1FRoTA0l0wmUxnbHg369Q/Y/JWPGn093Bg2RNPpEJGWYKGDiEiLLV0K5OUBvXrxJC6RMvyMEHUugYGBKC4uxpo1ayCVSuHq6oqkpCQ4ODgAAKRSKXJzc+Xxjo6OSEpKwuLFi7F582bY2tpi48aN8ittAcDT0xN79uzBypUrERERgX79+iE+Pl4+pAgApKenY9y4cfLn9fNqzJo1C3FxcdDR0cHFixexc+dOlJSUwMbGBuPGjUN8fDyMjY1b+21pVYP2SWBYUoC73S1Z6CCiDquoqAg1NTUNhi20srJqMLxhvfz8fKXx1dXVKCoqgo2NjcoYVW0KgoCwsDCMHj0arq6uKvONjIzE6tWr1dm1dmnM6Z9hXF6OuybGLHQQkdpY6CAiIiIiog4jNDQUoaGhStfFxcU1WObt7Y1z58412mZAQAACAgJUrh87dqx8EnNlDA0NcejQoUZfg4iI2j+RSKTwXBCEBsuain9weXPanD9/Pi5cuKBwV6Ey4eHh8qI7UHdHh52dXaPbkGqVVVXIyclpNMbExAQWFhZtlBERKcNCBxERERERERERkQrm5ubQ0dFpcKdFQUFBgzsy6llbWyuN19XVRc+ePRuNUdbm66+/ju+++w7Hjx9H7969G81XLBZDLBY3uV/UtNt37iA7Oxsrl77Z6HtqZGqKz3fsZLGDSINY6CAiIiIiIiIiIlJBX18fbm5uSElJUZhbKSUlBf7+/kq38fDwwP79+xWWJScnw93dHXp6evKYlJQUhXk6kpOT4enpKX8uCAJef/11JCYm4tixY3B0dGzJXaMm3KuohAgCxrkMQG8rS6UxRSWl+PHSb5DJZCx0EGkQCx1ERERERETUYiqrKpsc4iMnJwdV1dVtlBER0aMLCwtDUFAQ3N3d4eHhgW3btiE3NxchISEA6oaLysvLw86dOwEAISEh2LRpE8LCwhAcHIy0tDTExsZi9+7d8jYXLlyIMWPGYO3atfD398e+fftw+PBhhaGp5s2bhy+//BL79u2DsbGx/A4QU1NTGBoatuE70LmZmRjDuqfqCeCJSPNY6CAiIiIiIqIWcbu8FH/++Scilq1udIiPu3fv4K+/8lFZVdWG2RERPbzAwEAUFxdjzZo1kEqlcHV1RVJSEhwcHAAAUqkUubm58nhHR0ckJSVh8eLF2Lx5M2xtbbFx40ZMnz5dHuPp6Yk9e/Zg5cqViIiIQL9+/RAfH48RI0bIY2JiYgDUzQd1v+3bt2P27Nmtt8NERFqGhQ4iIiIiIiJqEXcr7kBHpIuJj8+AnbWDyrhfsi8g/vpnqK7hXR1EpD1CQ0MRGhqqdF1cXFyDZd7e3jh37lyjbQYEBCAgIEDl+voJzImIqHEsdGhISUkJaoXaBstrqmsAkQYSIiIiIiIiaiE9u1vCxsJO5fqCm9I2zIaIiIiIOjoWOjSgsLAQkesica/iHnT1/vdPINQIkP4lhUhPBJHAagcRERERERERERERUVNY6NAAmUwG2T0ZIKDu8V+CIAC6dX/qCDoay4+ItIe1teKfRKSInxEiotZVYWyu8GdLU2di88rKSujr6z9SDCdHJyJqP8q7GkJXpwvuGRtpOhUi0iIsdBARabH0dE1nQNS+8TNCRNS6Tr31Zau1rc7E5pVVlcjNzYGDgyP0dJX/vFUnhpOjExG1H18ETkXf3r00nQYRaRkWOoiIiIiIiKjdUWdi81+yLyAn+zOMHzr9kWM4OToRERGR9mKhg4iIiIiIiNqtxiY2r5/UvKViiIiIiEg7ddF0AkRERERERERERERERA+Ld3QQEWmx114Dbt4EzMyArVs1nQ1R+8PPCBFR63Ld/S7075Sisqsp/vN8hKbTaTfUmUTdxMQEFhYWbZQREZH2mHj0BMy7dEGloSGypk3RdDpEpCVY6CAi0mIHDgB5eUAvztNGpBQ/I0RErcvy0gkYlhTgbndLTafSbqgziToAGJl0RWzcpyx2EBE9oO+16zAuL8ddE2NNp0JEWoSFjnaqtrYWJSUlmk6DiIiIiIiImkGdSdQLb+Uj5dxXkMlkLHQQERERtQAWOtohoVZAVW0VPoz6EF5eXvziS0REREREpGUam/yciIiIiFoWJyNvh4RaASKxCGWVZZDJZJpOh4iIiIiIiIiIiIio3WKhg4iIiIiIiIiIiIiItBYLHURERERE1GFER0fD0dERBgYGcHNzw4kTJxqNT01NhZubGwwMDNC3b19s2bKlQUxCQgKcnZ0hFovh7OyMxMREhfXHjx/HlClTYGtrC5FIhL179zZoQxAErFq1Cra2tjA0NMTYsWNx6dKlR9pXIiIiIlLUyx4AAKSjSURBVCKqw0IHERERERF1CPHx8Vi0aBFWrFiBzMxMeHl5wdfXF7m5uUrjs7Oz4efnBy8vL2RmZmL58uVYsGABEhIS5DFpaWkIDAxEUFAQzp8/j6CgIMyYMQNnzpyRx5SXl2Po0KHYtGmTytw++ugjREVFYdOmTTh79iysra0xadIk3L59u+XeACIiIiKiToqFDiIiIiIi6hCioqIwZ84czJ07F05OTpBIJLCzs0NMTIzS+C1btsDe3h4SiQROTk6YO3cuXnnlFaxfv14eI5FIMGnSJISHh2PQoEEIDw/HhAkTIJFI5DG+vr547733MG3aNKWvIwgCJBIJVqxYgWnTpsHV1RU7duzAnTt38OWXX7boe0BERERE1Bmx0EFERERERFqvsrISGRkZ8PHxUVju4+OD06dPK90mLS2tQfzkyZORnp6OqqqqRmNUtalMdnY28vPzFdoRi8Xw9vZutJ2KigrIZDKFB3UclVWVyMnJwdWrV1U+CgsLNZ0mERERkVbQ1XQCRET08J5/Hrh1C+jRQ9OZELVP/IwQdR5FRUWoqamBlZWVwnIrKyvk5+cr3SY/P19pfHV1NYqKimBjY6MyRlWbql6nfrsH28nJyVG5XWRkJFavXq3262jCX25PQe/ObVR1NdZ0Klrldnkp/vzzT0QsWw2xWKwyzsikK2LjPoWFhUUbZkdEpFm/DOgHCx0dVHU11HQqaqusqmq0TwcAExMTHs+JWhELHUREWmzdOk1nQNS+8TNC1PmIRCKF54IgNFjWVPyDy5vbZkvlFh4ejrCwMPlzmUwGOzu7Zr9ua/plaljTQdTA3Yo70BHpYuLjM2Bn7aA0pvBWPlLOfQWZTMYTY0TUqRwfNQJ9e/fSdBpqu33nDrKzs7Fy6ZuNF69NTfH5jp08phO1EhY6iIiIiIhI65mbm0NHR6fBnRYFBQUN7qSoZ21trTReV1cXPXv2bDRGVZuqXgeou7PDxsZG7XbEYnGjJ0xI+/Xsbgkbi/ZVvCIioua5V1EJEQSMcxmA3laWSmOKSkrx46XfWLwmakUdZo6O6OhoODo6wsDAAG5ubjhx4oTKWKlUihdeeAEDBw5Ely5dsGjRorZLlIiIiIiIWpy+vj7c3NyQkpKisDwlJQWenp5Kt/Hw8GgQn5ycDHd3d+jp6TUao6pNZRwdHWFtba3QTmVlJVJTU5vVDhEREbVfZibGsO5ppvRh3t1U0+kRdXgdotARHx+PRYsWYcWKFcjMzISXlxd8fX2Rm5urNL6iogIWFhZYsWIFhg4d2sbZEhFRe9OcYvmxY8cgEokaPH755Zc2zJiIiJQJCwvDZ599hs8//xxXrlzB4sWLkZubi5CQEAB1Q0HNnDlTHh8SEoKcnByEhYXhypUr+PzzzxEbG4slS5bIYxYuXIjk5GSsXbsWv/zyC9auXYvDhw8rXCxVVlaGrKwsZGVlAaibfDwrK0v+e0QkEmHRokX44IMPkJiYiP/85z+YPXs2unbtihdeeKH13xgiIiLSuPp5PK5evaryUVhYqOk0ibRWhxi6KioqCnPmzMHcuXMBABKJBIcOHUJMTAwiIyMbxPfp0wcff/wxAODzzz9v01yJiFrSoEHAX38BtrYAz7M/nPpieXR0NEaNGoWtW7fC19cXly9fhr29vcrtfv31V5iYmMif8/bj9omfEaLOJTAwEMXFxVizZg2kUilcXV2RlJQEB4e6ORCkUqnCxVCOjo5ISkrC4sWLsXnzZtja2mLjxo2YPn26PMbT0xN79uzBypUrERERgX79+iE+Ph4jRoyQx6Snp2PcuHHy5/XzasyaNQtxcXEAgKVLl+Lu3bsIDQ3FrVu3MGLECCQnJ8PYWLsn8fZ+91mISwtRYWqB1Ii9mk6HiIg6gJd3fQXju3dxz8QYh994XdPptAjO40HU+rS+0FFZWYmMjAwsW7ZMYbmPjw9Onz7dYq9TUVGBiooK+XOZTNZibRMRPayyMuD27bo/6eE0t1hez9LSEt27d2+jLOlh8TNC1PmEhoYiNDRU6br6osP9vL29ce7cuUbbDAgIQEBAgMr1Y8eOlU9iropIJMKqVauwatWqRuO0jU7FHejdK0e1QTdNp0JERB2EXlU19CoqUV1RqelUWgzn8SBqfVo/dFVRURFqamoaTOJnZWXVYNLARxEZGQlTU1P5w86OE8YREWm7+mK5j4+PwnJ1iuXDhw+HjY0NJkyYgKNHj6qMq6iogEwmU3gQERERERFR58N5PIhaj9YXOuqJRCKF54IgNFj2KMLDw1FaWip/XL9+vcXaJiIizXiYYrmNjQ22bduGhIQEfPvttxg4cCAmTJiA48ePK41noZyIiIiIiIiIqHVp/dBV5ubm0NHRaXBCqqCgoMGJq0chFosbHUOPiIi0V3OK5QMHDsTAgQPlzz08PHD9+nWsX78eY8aMaRAfHh4uH6sdqBv6kMUOIiIiIiIi0rTCwsImRx0wMTHhUFqkFbS+0KGvrw83NzekpKRg6tSp8uUpKSnw9/fXYGZERNTetVSxfOTIkdi1a5fSdSyUExERERERUXtTWFiIV2bNRFlpaaNxnCCdtIXWFzoAICwsDEFBQXB3d4eHhwe2bduG3NxchISEAKi7mjYvLw87d+6Ub5OVlQUAKCsrQ2FhIbKysqCvrw9nZ2dN7AIREWlASxXLMzMzYWNj0xopEhEREREREbU4mUyGstJSjHcZoHJ+EE6QTtqkQxQ6AgMDUVxcjDVr1kAqlcLV1RVJSUlwcHAAAEilUuTm5ipsM3z4cPnfMzIy8OWXX8LBwQHXrl1ry9SJiEjDmlssl0gk6NOnD1xcXFBZWYldu3YhISEBCQkJmtwNIiIiIiIiomYz724K655mmk6D6JF1mMnIQ0NDce3aNVRUVCAjI0NhnPS4uDgcO3ZMIV4QhAYPFjmIiDqfwMBASCQSrFmzBsOGDcPx48cbLZZXVlZiyZIlGDJkCLy8vHDy5EkcOHAA06ZN09QuEBERERFRG4iOjoajoyMMDAzg5uaGEydONBqfmpoKNzc3GBgYoG/fvtiyZUuDmISEBDg7O0MsFsPZ2RmJiYkK648fP44pU6bA1tYWIpEIe/fubcldIiLqMDpMoYOIiOhhNadYvnTpUvzxxx+4e/cubt68iRMnTsDPz08DWRMRERERUVuJj4/HokWLsGLFCmRmZsLLywu+vr4NRhCpl52dDT8/P3h5eSEzMxPLly/HggULFO4ET0tLQ2BgIIKCgnD+/HkEBQVhxowZOHPmjDymvLwcQ4cOxaZNm1p9H4mItFmHGLqKiKiz2rIFuHsXMDTUdCZE7RM/I0REres/gSvRpeoeavUMNJ0KEVGrioqKwpw5czB37lwAdUPaHjp0CDExMYiMjGwQv2XLFtjb20MikQAAnJyckJ6ejvXr12P69OnyNiZNmoTw8HAAdcPmpqamQiKRYPfu3QAAX19f+Pr6tsEeth+Hx46CrakpavV42pKI1McjBhGRFvvHPzSdAVH7xs8IEVHrKhg8pukgIiItV1lZiYyMDCxbtkxhuY+PD06fPq10m7S0NPj4+Cgsmzx5MmJjY1FVVQU9PT2kpaVh8eLFDWLqiyMPq6KiAhUVFfLnMpnskdpra386OgC9e2k6DSLSMhy6SgOKi4sh1AiaToOIiIiIiIiIiJpQVFSEmpoaWFlZKSy3srJCfn6+0m3y8/OVxldXV6OoqKjRGFVtqisyMhKmpqbyh52d3SO1R0SkDXhHRxsrLCzEkvAlkP4t1XQqRERERERERESkJpFIpPBcEIQGy5qKf3B5c9tUR3h4OMLCwuTPZTIZix1aorKqCjk5OY3GmJiYwMLCoo0yItIeLHS0MZlMhpI7JagVajWdChF1ABkZQGUloK8PuLlpOhui9oefESKi1mWSexldqqtQq6sHmb2zptMhImoV5ubm0NHRaXCnRUFBQYM7MupZW1srjdfV1UXPnj0bjVHVprrEYjHEYvEjtaFJlgWFMKupRa2ODkp622o6nTZz+84dZGdnY+XSNxv99zMyNcXnO3ay2EH0ABY6iIi0mL8/kJcH9OoF3Lih6WyI2h9+RoiIWpf7tkUwLCnA3e6W+PG9ZE2nQ0TUKvT19eHm5oaUlBRMnTpVvjwlJQX+/v5Kt/Hw8MD+/fsVliUnJ8Pd3R16enrymJSUFIV5OpKTk+Hp6dkKe6E9nj2QAuPyctw1McbB5W9oOp02c6+iEiIIGOcyAL2tLJXGFJWU4sdLv0Emk7HQQfQAFjqIiIiIiIiIiIgaERYWhqCgILi7u8PDwwPbtm1Dbm4uQkJCANQNF5WXl4edO3cCAEJCQrBp0yaEhYUhODgYaWlpiI2Nxe7du+VtLly4EGPGjMHatWvh7++Pffv24fDhwzh58qQ8pqysDH/88Yf8eXZ2NrKysmBmZgZ7e/s22ntqS2YmxrDuaabpNIi0DgsdREREREREREREjQgMDERxcTHWrFkDqVQKV1dXJCUlwcHBAQAglUqRm5srj3d0dERSUhIWL16MzZs3w9bWFhs3bsT06dPlMZ6entizZw9WrlyJiIgI9OvXD/Hx8RgxYoQ8Jj09HePGjZM/r597Y9asWYiLi2vlva6ba1Ymk6lcn5OTg6rq6lbPg7RfU/+X6nEOEnpYLHQQERERERERERE1ITQ0FKGhoUrXKSs6eHt749y5c422GRAQgICAAJXrx44dK5/EvK0VFhbilVkzUVZaqjLm7r17+CvvBiqr3NswM9I26vxfqsc5SOhhsdBBRERERERE1A5VVlUiJyen8ZjKSujr6zfZFq+QJaLmkslkKCstxXiXATDvbqo05rfc6/gqNwfVvKuDGqHO/yWAc5DQo2Ghg4iIiIiIiKiduV1eij///BMRy1ZDLBYrjamsqkRubg4cHByhp9v4z3sjk66IjfuUJ46IqNnMu5uqnDOi8FZJ2yZDWq2x/0tEj4qFDiIiIiIi6jCio6Oxbt06SKVSuLi4QCKRwMvLS2V8amoqwsLCcOnSJdja2mLp0qXyiWXrJSQkICIiAlevXkW/fv3w/vvvY+rUqc163dmzZ2PHjh0K24wYMQI//fRTC+w1dUR3K+5AR6SLiY/PgJ21g9KYX7IvICf7M4wfOl1lDAAU3spHyrmveIUsERERdVgsdBARERERUYcQHx+PRYsWITo6GqNGjcLWrVvh6+uLy5cvw97evkF8dnY2/Pz8EBwcjF27duHUqVMIDQ2FhYWFfLLYtLQ0BAYG4t1338XUqVORmJiIGTNm4OTJk/LJYtV93aeeegrbt2+XP1dnuCGint0tYWNhp3RdwU1pkzFEREREnUEXTSdARERERETUEqKiojBnzhzMnTsXTk5OkEgksLOzQ0xMjNL4LVu2wN7eHhKJBE5OTpg7dy5eeeUVrF+/Xh4jkUgwadIkhIeHY9CgQQgPD8eECRMgkUia/bpisRjW1tbyh5kZh24gIiIiImoJLHQQEREREZHWq6ysREZGBnx8fBSW+/j44PTp00q3SUtLaxA/efJkpKeno6qqqtGY+jab87rHjh2DpaUlBgwYgODgYBQUFDS6TxUVFZDJZAoPoodVP7H51atXVT4KCws1nSYRERHRQ+HQVUREWuzKFUAQAJFI05kQtU/8jBB1HkVFRaipqYGVlZXCcisrK+Tn5yvdJj8/X2l8dXU1ioqKYGNjozKmvk11X9fX1xfPPfccHBwckJ2djYiICIwfPx4ZGRkqJ5qOjIzE6tWr1XsDNCR1ZSJEggCBB9p2TZ2JzQFOWE5E7cP2FwPgaGvDL/FE1CwsdBARaTFjY01nQNS+8TNC1PmIHjgpIghCg2VNxT+4XJ02m4oJDAyU/93V1RXu7u5wcHDAgQMHMG3aNKW5hYeHIywsTP5cJpPBzq59zcNQY9BN0ymQGtSZ2JwTlhNRe1Glr49qAwNNp9FuVVZVIScnp+m4yspG5wPLyclBVXV1S6ZG7UxhYWGTdwSbmJh0mH6fhQ4iIiIiItJ65ubm0NHRaXD3RkFBQYO7LepZW1srjdfV1UXPnj0bjalv82FeFwBsbGzg4OCA33//XWWMWCxu9Op7oubipOVERNrt9p07yM7Oxsqlbzb6HaGyqgq516/Dwd4eerrKT//evXcPf+XdQGWVe2ulSxpUWFiIV2bNRFlpaaNxRqam+HzHzg5R7GCho50rLi5GZWUlzM3NO8R/OCIiIiKi1qCvrw83NzekpKRg6tSp8uUpKSnw9/dXuo2Hhwf279+vsCw5ORnu7u7Q09OTx6SkpGDx4sUKMZ6eng/9ukDd9/zr16/Dxsam+TtLREREndK9ikqIIGCcywD0trJUGfdb7nVcy/4T3oP6qYz7Lfc6vsrNQTXv6uiQZDIZykpLMd5lAMy7myqNKSopxY+Xfuswd3Oy0NGOVVdVY8GbC/Bn9p9wdXZF/L/jO8R/OiJqOVFRgEwGmJgA941sQUT/xc8IUecSFhaGoKAguLu7w8PDA9u2bUNubi5CQkIA1A0FlZeXh507dwIAQkJCsGnTJoSFhSE4OBhpaWmIjY3F7t275W0uXLgQY8aMwdq1a+Hv7499+/bh8OHDOHnypNqvW1ZWhlWrVmH69OmwsbHBtWvXsHz5cpibmysUR7SR45F/Q/deGaoNjJA9IUjT6RARUQfglnkBVld+Q7WBGH94eWo6nXbJzMQY1j3NVK4vvFXSZFx9DHVs5t1NG/2/0pGw0NGO1dbUorSsFBWowM2ymx2mukZELScqCsjLA3r14klcImX4GSHqXAIDA1FcXIw1a9ZAKpXC1dUVSUlJcHCom5NAKpUiNzdXHu/o6IikpCQsXrwYmzdvhq2tLTZu3Ijp06fLYzw9PbFnzx6sXLkSERER6NevH+Lj4zFixAi1X1dHRwcXL17Ezp07UVJSAhsbG4wbNw7x8fEw1vLJhByP/huGJQW4292ShQ4iImoRbln/gXF5Oe6aGLPQQURqY6GDiIiIiIg6jNDQUISGhipdFxcX12CZt7c3zp0712ibAQEBCAgIeOjXNTQ0xKFDhxrdnoiIiIhaTmebiJtY6CAiIiIiIiIiIiKiDqIzTsRNLHQQERERERERERERUQfRGSfiJhY6iIiIiIiIiIiIiKiD6UwTcRMLHURERERERERERESkRGVVFf6fvTuPj6JK9z/+bbICCQFCyAIEggsgIGCiGLbgQhhwF0dkHAQF7mQiCkREEB1QUVAYboaRRRwG5KLAVdBxYZTgSGSJIpsiICIGAjERwpKwZj2/P/jRlzZbJ3TSqeTzfr36BX36VNVzKkk91f101Tl06FDZffLy5O3tXerrhw4dUn5Bgcu2Z9W5NWrivCGu2t/OjM3ZdVUWhY4arKCgQPXq1XN3GAAAi6nNJ4YAAAAAgOpx+tw5paam6rkJT8vHx6fEPnn5+Uo7fFitw8Pl5VnyR83nL1zQL+lHlJcfdcXbk6w5t0ZNnDfEVfvb2bE5s64rQaGjhjJFRllHs1TPu55kk04eO6lTp065OywAQA136sIF/XzwoKaNGyfvMk5U6jdpqoXL/sdSJ4YAAAAAgOpzITdPNhnd0vFatQxuXmKfH9MO62Dqz4ppf1WZff437ZAKyrmqw5ntWXVujZo4b4ir9rczY3N2XVeCQkdNZSTjaVRkiiQjnTp2StlOVMUAAHXb2fx8eRUVaXhwsCICm5XYJ+PsGf0zM9NyJ4YAAAAAgOrXtJF/qXNdHDt5yuk+rtie1dXEeUNctb/dPTYKHQBgYTfcILVqJfFZNX4rpGFDtQko/ZsUdeX2VvyNAEDVymnZQRcahyjPr4m7QwEA1BJHgwKVH9hEuQ0buDsUABZCoQMALOzDD90dAazI2dtb1Wvop6mvzlBgYGCpfWp6MYS/EQCoWlvj/ubuEAAAtcwHd/ZX25Yt3B0G6gBnvgAolT/ZujN9KjIhOyqHQgcAAHWMM7e32nfiuKZt2aKJo0Yx1wcAAAAAoFZxdiJuZyZbd+WE7Kg8Ch0AANRRZd3eKv30aeb6AAAAAADUSs5MxC05P9m6qyZkR+VR6AAAAKUqb64PAABQtxw7dkw5OTll9nH21pauXBcAAJVR3kTcFZls3ZUTsqPiKHQAgIXdfbd07NjFiZaZiwAojr8RAKhaUQvGyPvMSeX5NWG+jjrg2LFjGjF8lM7knCuzn1+jBlq05M0yCxSuXBeA2uXejz9Tk6Ii5TZsoK+G/cHd4aAGKm9uDebDqJsodFjIqVOndOzYMYcTvGPHjkmSve23zwHUbtu3S+npUgvmaYObODN5mzu/icnfCABUrUZH9qr+qaM637j0Wz6g9sjJydGZnHPqd8ODCmoSUmKfYyczlbT9f8u9taUr1wWgdml+7Lj8z57V+Ub+7g4FNZAzc2swH0bdRKHDIoqKijRj9gy9vvB1PXjvg3rwwQdVWFiowUMHK+tolt5b8Z6aNGmioSOGSpL+ZxETwwIAqtapCxf088GDmjZuHBOWAwBQhwQ1CVFoUKsaty4AQO3nzNwazIdRN1HosAojnck7owvZF/S///u/6tevnyQpKztLaWlpOnLkiDw8PHTi7AlJ4hsvAIAqdzY/nwnLAQAAAADVjvkw8FsUOgAAwBVhwnIAAGqHvPw87nlewzgzYbsk5eXlydvbu8w+ztxOlAniAQBWRaEDAABUKWfm8ZB40wwAgDudPputn3/+Wc9PfKH0e56fP6dffslUXn5+NUdXNzk7YXtefp7S0g6pdesIeXmW/jFPeRO7M0E8ALifM++fnSluu/LLCVaZ/L3WFDrmzZunmTNnKiMjQx07dlRiYqJ69+5dav/k5GQlJCRo9+7dCgsL04QJExQXF1eNEQMAagpySNVxdh4Pibk8ALhGVRzTV61apeeff14HDhzQVVddpZdffln33XdfhbZrjNELL7yghQsX6uTJk+revbvmzp2rjh07unYHAJV0PvecPGyeuv2GB9UqpHWJfX5I/U4rD/9DBYXu/zCjLnBmwnbp4s/lUOo/dGuXQaX+7JyZ2J0J4stXU3MMgNrBmYnW8/LzlXb4sFqHh5dZ3HbVhOxWmvy9VhQ6Vq5cqbFjx2revHnq2bOn3njjDQ0YMEB79uxReHh4sf6pqakaOHCgRo0apWXLlmnTpk2Kj49XUFCQBg0a5IYRAADchRxStZyZx0O6OJfHgsOHtWvXLrVuXfIbdKl6b8vgynVxtQpQParimJ6SkqLBgwfrpZde0n333af3339fDz74oDZu3Kju3bs7vd3XXntNs2fP1pIlS3Tttddq2rRp6tevn/bt2yd/f//q20lAOQIbNy91cvCjJzKqORpI5U/YfunnUtbPzpXbq6tqco4BUDs4O9H6wdSfFdP+qlL7XOrnignZrTT5e60odMyePVsjRozQyJEjJUmJiYn67LPPNH/+fE2fPr1Y/wULFig8PFyJiYmSpA4dOmjr1q2aNWsWH1IBQB1DDqke5c3j4cyVH3n5+Tp4JF1tW7WUZxnfXCnvypBjx47pv/44VOdPnig3bletq15DP019dYYCAwNL7ePKAo2rCkLVjaKR69TVfVkVx/TExET169dPkyZNkiRNmjRJycnJSkxM1PLly53arjFGiYmJmjx5su6//35J0ltvvaXg4GC98847+tOf/lSl+wUAcOVqao4BUPs4M9F6WX0u71edMbmb5QsdeXl52rZtmyZOnOjQHhsbq82bN5e4TEpKimJjYx3a+vfvr0WLFik/P19eXl7FlsnNzVVubq79eXZ2tiQ59WHD5U6fPq3CgkKZIiNjzMV/L/u/pGLPJckUGuXl5amoXpFOF5zWnj17JEn5efkqLCzUTz/9pLNnzyrvQp48PD10+vTpCscGwHqKiv7v34r+yfv7+8tms7k+KAupjhziyvxRUFioA6dO6mwZ98VOO52jImOUmp2tIo+S03xN6yNJe08cl0dhofrWr6/QRiUXRPafPKFDuRfUx9u71D4nLlzQJ5kZ+uqrr9SqVSvl5YVL8lReXoF27kyTJB0+fFinfs3U7xoFqKmvb6kx/XZdJXFmXWk5OXprx3aNf+yxMm/fVa9hQ02cOlVNm5Z88njixAm9+sILKjxzptR1SP//Uub0X9SmZYsyC0Llba+6OTu+mhZ3TeTsvvRt3ERz3lyoZs1Kv9qqNDUxh1TVMT0lJUXjxo0r1ufSB1fObDc1NVWZmZkO2/Lx8VFMTIw2b95caqHDpTmkoECHM1N1Prfke+9nHDusoqIi/XL0kGw243SfGwrylS/pbEG+fj6yz6n1XMn23NmnJsZUE+POOvmrzp8/rz179uj06dOlxnT48GFduHChzN9LZ9dVWzmzjyTX/Vyc/ZkUFBRU+jOHmpg/nFGTc0xJXJ0/jhw9pvOXre9ymSdOqKjIKCPrhGz16lW6z+X9ThUVykg6U1io1F8ySuxzpduraEzVsT3iJqbaHLckHc/OqXQOcSp/GItLT083ksymTZsc2l9++WVz7bXXlrjMNddcY15++WWHtk2bNhlJ5pdffilxmSlTphhJPHjw4FFrHtnZ2a45EFtYdeQQ8gcPHjxq46Mm5pCqOqZ7eXmZt99+26HP22+/bby9vZ3e7qV1pqenO/QZNWqUiY2NLXVM5BAePHjUtkdNzB/OqMk5piTkDx48eNS2hzP5w/JXdFzy24qOMabMKk9J/Utqv2TSpElKSEiwPy8qKtKJEycUGBhYbjUpJydHrVq10uHDh9WoUaMy+1oR47M2xmdtVzI+7gf+f6oyh1xJ/rhcbftdZjw1V20ai8R4qkpNziFVcUx3Zp2u6nO58nJITfl9cBbxVi3irTpWilWq2fHW5PzhjJqcYy7nqvcg7lKTf4drEvZT+dhHzrHCfnImf1i+0NGsWTN5eHgoMzPTof3o0aMKDg4ucZmQkJAS+3t6epZ672wfH59iM8s3bty4QrE2atSoxv6yuALjszbGZ221fXxVpTpyiCvyx+Vq28+a8dRctWksEuOpC6rqmF5an0vrdGa7ISEhkqTMzEyFhoY6FZvkfA6x2u8D8VYt4q06VopVsl68NVlNzjElcfV7EHfhd9g57KfysY+cY/X9VPpNsyzC29tbkZGRSkpKcmhPSkpSjx49SlwmOjq6WP+1a9cqKiqqxPk5AAC1EzkEAGqPqjqml9bn0jqd2W5ERIRCQkIc+uTl5Sk5ObnU2AAANUdNzjEAgP+v3JtbWcCKFSuMl5eXWbRokdmzZ48ZO3asadiwoTl48KAxxpiJEyeaoUOH2vv//PPPpkGDBmbcuHFmz549ZtGiRcbLy8u89957VRJfdna20/cSsyLGZ22Mz9pq+/iqQ03PIZfUtp8146m5atNYjGE8dU1VHNM3bdpkPDw8zIwZM8zevXvNjBkzjKenp/nqq6+c3q4xxsyYMcMEBASY1atXm127dpkhQ4aY0NBQk5OTU+nxWu33gXirFvFWHSvFaoz14rWKmpxjaht+h53Dfiof+8g5tWU/1YpChzHGzJ0717Ru3dp4e3ubG264wSQnJ9tfGzZsmImJiXHov379etOtWzfj7e1t2rRpY+bPn19lsV24cMFMmTLFXLhwocq24U6Mz9oYn7XV9vFVl5qcQy6pbT9rxlNz1aaxGMN46qKqOKa/++67pl27dsbLy8u0b9/erFq1qkLbNcaYoqIiM2XKFBMSEmJ8fHxMnz59zK5du65orFb7fSDeqkW8VcdKsRpjvXitpKbmmNqG32HnsJ/Kxz5yTm3ZTzZj/v9MSAAAAAAAAAAAABZj+Tk6AAAAAAAAAABA3UWhAwAAAAAAAAAAWBaFDgAAAAAAAAAAYFkUOgAAAAAAAAAAgGVR6KgG8+bNU0REhHx9fRUZGakNGza4O6QKmz59um688Ub5+/urefPmuvfee7Vv3z6HPsYYTZ06VWFhYapfv7769u2r3bt3uyniKzN9+nTZbDaNHTvW3mb18aWnp+uPf/yjAgMD1aBBA3Xt2lXbtm2zv27l8RUUFOi5555TRESE6tevr7Zt2+rFF19UUVGRvY+Vxvfll1/qrrvuUlhYmGw2mz744AOH150ZS25urp544gk1a9ZMDRs21N13360jR45U4yjgarUhl0jO5ROrKil3WE15ucJKnMkNNZkrcgFqt5qSF1z1PsEd5y6VPeevzlhdcQ5fXfG66py8quKtrnPskydPaujQoQoICFBAQICGDh2qU6dOuTTe/Px8PfPMM+rcubMaNmyosLAwPfLII/rll1/cFi9QFivnCneyQp5yFyvlR3ep6Xm5ShhUqRUrVhgvLy/z5ptvmj179pgxY8aYhg0bmkOHDrk7tArp37+/Wbx4sfn+++/Nzp07zR133GHCw8PNmTNn7H1mzJhh/P39zapVq8yuXbvM4MGDTWhoqMnJyXFj5BW3ZcsW06ZNG3P99debMWPG2NutPL4TJ06Y1q1bm+HDh5uvv/7apKammnXr1pmffvrJ3sfK45s2bZoJDAw0H3/8sUlNTTXvvvuu8fPzM4mJifY+VhrfmjVrzOTJk82qVauMJPP+++87vO7MWOLi4kyLFi1MUlKS2b59u7nllltMly5dTEFBQTWPBq5QW3KJMc7lEysqLXdYiTO5wkqcyQ01mStyAWqvmpQXXPU+obrPXa7knL+6YnXVOXx1xeuqc/Kqire6zrF/97vfmU6dOpnNmzebzZs3m06dOpk777zTpfGeOnXK3H777WblypXmhx9+MCkpKaZ79+4mMjLSYR3VGS9QFqvmCneyQp5yF6vlR3ep6Xm5KlDoqGI33XSTiYuLc2hr3769mThxopsico2jR48aSSY5OdkYY0xRUZEJCQkxM2bMsPe5cOGCCQgIMAsWLHBXmBV2+vRpc80115ikpCQTExNjTyZWH98zzzxjevXqVerrVh/fHXfcYR577DGHtvvvv9/88Y9/NMZYe3y/fVPjzFhOnTplvLy8zIoVK+x90tPTTb169cynn35abbHDdWprLjGmeD6xotJyh9WUlyusprzcYCWVyQWo3WpyXqjM+4TqPne5knP+6ozVFefw1RmvK87JqyveqjrH3rNnj5FkvvrqK3uflJQUI8n88MMPLou3JFu2bDGS7AVPd8YLlMcKucKdrJKn3MVq+dFdrJSXXYVbV1WhvLw8bdu2TbGxsQ7tsbGx2rx5s5uico3s7GxJUtOmTSVJqampyszMdBirj4+PYmJiLDXWxx9/XHfccYduv/12h3arj+/DDz9UVFSUfv/736t58+bq1q2b3nzzTfvrVh9fr1699Pnnn+vHH3+UJH377bfauHGjBg4cKMn647ucM2PZtm2b8vPzHfqEhYWpU6dOlhsvancukYrnEysqLXdYTXm5wmrKyw1WVpvyGiqupueFyrxPqO5zlys556/OWF1xDl+d8brinNxd57Guii0lJUUBAQHq3r27vc/NN9+sgICAKv/7zM7Ols1mU+PGjS0RL+o2K+QKd7JKnnIXq+VHd7FyXq4sT3cHUJtlZWWpsLBQwcHBDu3BwcHKzMx0U1RXzhijhIQE9erVS506dZIk+3hKGuuhQ4eqPcbKWLFihbZv365vvvmm2GtWH9/PP/+s+fPnKyEhQc8++6y2bNmiJ598Uj4+PnrkkUcsP75nnnlG2dnZat++vTw8PFRYWKiXX35ZQ4YMkWT9n9/lnBlLZmamvL291aRJk2J9rHzsqatqay6RSs4nVlNW7rCa8nKF1ZSXG6ysNuU1VFxNzguVfZ9QnecuV3rOX52xuuIcvjrjdcU5ubvOY10VW2Zmppo3b15s/c2bN6/S+C9cuKCJEyfqD3/4gxo1alTj40XdZoVc4U5WylPuYrX86C5WzsuVRaGjGthsNofnxphibVYyevRofffdd9q4cWOx16w61sOHD2vMmDFau3atfH19S+1n1fEVFRUpKipKr7zyiiSpW7du2r17t+bPn+/w4ZVVx7dy5UotW7ZM77zzjjp27KidO3dq7NixCgsL07Bhw+z9rDq+klRmLFYeL2rX7+8lZeUTK3A2d1iFs7nCKpzNDVZWG48LcF5N/Pm7+n2Cq8dUlef8VbH/q/Icvirircpz8ur6/XZFbCX1r8r48/Pz9dBDD6moqEjz5s0rt7+74wVqeq5wJ6vlKXexWn50l9qQlyuKW1dVoWbNmsnDw6NYhevo0aPFqmVW8cQTT+jDDz/UF198oZYtW9rbQ0JCJMmyY922bZuOHj2qyMhIeXp6ytPTU8nJyZozZ448PT3tY7Dq+EJDQ3Xdddc5tHXo0EFpaWmSrP/ze/rppzVx4kQ99NBD6ty5s4YOHapx48Zp+vTpkqw/vss5M5aQkBDl5eXp5MmTpfaBddTGXCKVnk+spLzcUVhY6O4QK6S8XGE15eUGK6tNeQ0VV1PzwpW8T6iucxdXnPNX53mWK87hqzNeV5yTu+s81lWxhYSE6Ndffy22/mPHjlVJ/Pn5+XrwwQeVmpqqpKQk+9UcNTVewAq5wp2slqfcxWr50V2snJcri0JHFfL29lZkZKSSkpIc2pOSktSjRw83RVU5xhiNHj1aq1ev1n/+8x9FREQ4vB4REaGQkBCHsebl5Sk5OdkSY73tttu0a9cu7dy50/6IiorSww8/rJ07d6pt27aWHl/Pnj21b98+h7Yff/xRrVu3lmT9n9+5c+dUr57j4czDw0NFRUWSrD++yzkzlsjISHl5eTn0ycjI0Pfff2+58aJ25RKp/HxiJeXlDg8PD3eHWCHl5QqrKS83WFltymuouJqWF1zxPqG6zl1ccc5fnedZrjiHr854XXFO7q7zWFfFFh0drezsbG3ZssXe5+uvv1Z2drbL479U5Ni/f7/WrVunwMBAh9drWryo26yUK9zJannKXayWH93Fynm50qpwonMYY1asWGG8vLzMokWLzJ49e8zYsWNNw4YNzcGDB90dWoX8+c9/NgEBAWb9+vUmIyPD/jh37py9z4wZM0xAQIBZvXq12bVrlxkyZIgJDQ01OTk5boy88mJiYsyYMWPsz608vi1bthhPT0/z8ssvm/3795u3337bNGjQwCxbtszex8rjGzZsmGnRooX5+OOPTWpqqlm9erVp1qyZmTBhgr2PlcZ3+vRps2PHDrNjxw4jycyePdvs2LHDHDp0yBjj3Fji4uJMy5Ytzbp168z27dvNrbfearp06WIKCgrcNSxcgdqSS4xxLp9Y2W9zh5U4kyusxJncUJO5Iheg9qpJecFV7xPcde5SmXP+6orVVefw1RWvq87Jqyre6jrH/t3vfmeuv/56k5KSYlJSUkznzp3NnXfe6dJ48/Pzzd13321atmxpdu7c6fC3l5ub65Z4gbJYPVe4U03OU+5itfzoLjU9L1cFCh3VYO7cuaZ169bG29vb3HDDDSY5OdndIVWYpBIfixcvtvcpKioyU6ZMMSEhIcbHx8f06dPH7Nq1y31BX6HfJhOrj++jjz4ynTp1Mj4+PqZ9+/Zm4cKFDq9beXw5OTlmzJgxJjw83Pj6+pq2bduayZMnO5zkW2l8X3zxRYl/b8OGDTPGODeW8+fPm9GjR5umTZua+vXrmzvvvNOkpaW5YTRwldqQS4xxLp9YmZULHcaUnyusxJncUJO5IhegdqspecFV7xPcde5SmXP+6ozVFefw1RWvq87Jqyre6jrHPn78uHn44YeNv7+/8ff3Nw8//LA5efKkS+NNTU0t9W/viy++cEu8QFmsnivcqabnKXexUn50l5qel6uCzRhjXHmFCAAAAAAAAAAAQHVhjg4AAAAAAAAAAGBZFDoAAAAAAAAAAIBlUegAAAAAAAAAAACWRaEDAAAAAAAAAABYFoUOAAAAAAAAAABgWRQ6AAAAAAAAAACAZVHoAAAAAAAAAAAAlkWhAwAAAAAAAAAAWBaFDuAK9e3bV2PHji319YMHD8pms2nnzp32tk2bNqlz587y8vLSvffeW+UxAkBtUd4xtyzDhw93yTGXY3j1ctXPzVVsNps++OADd4cBwKLIY5WzZMkSNW7cuMw+U6dOVdeuXcvsU9J7MwBA+arznNyZ47kz8axfv142m02nTp1yWWyo2Sh0AFWsVatWysjIUKdOnextCQkJ6tq1q1JTU7VkyRKnDuIVxUk8AFSN3x7D3eVKPiyrznVaVWm5OSMjQwMGDKj+gADARWpKHquIwYMH68cff6zQMjWtUA4AFVGXz8vHjx+vzz//vELL1OX9hf9DoQOoQnl5efLw8FBISIg8PT3t7QcOHNCtt96qli1blvvNJABAzeKKY3heXp5rg6pGxhgVFBS4O4xKu9J9HxISIh8fHxdFAwDVz4p5rH79+mrevHm1bhMAajKrn5OXxc/PT4GBge4OAxZEoQOogLNnz+qRRx6Rn5+fQkND9de//tXh9TZt2mjatGkaPny4AgICNGrUKIcrKy79//jx43rsscdks9m0ZMkSvfDCC/r2229ls9nsbeWx2WyaP3++BgwYoPr16ysiIkLvvvuu/fWIiAhJUrdu3WSz2dS3b19J//fNphdeeEHNmzdXo0aN9Kc//cnSH7oBqBn69u2r0aNHa/To0WrcuLECAwP13HPPyRgj6eKHIhMmTFCLFi3UsGFDde/eXevXr7cvf/z4cQ0ZMkQtW7ZUgwYN1LlzZy1fvrzMbX766acKCAjQ0qVLnY6zrOOfMUavvfaa2rZtq/r166tLly567733JKnUY7gkJScn66abbpKPj49CQ0M1ceJEhzcel/ZNQkKCmjVrpn79+kmS9uzZo4EDB8rPz0/BwcEaOnSosrKyyh3D8OHDlZycrL/97W/23HHw4EGnYqnoOi9d8v3ZZ58pKipKPj4+2rBhg3Jzc/Xkk0+qefPm8vX1Va9evfTNN9/Y11fSbUY++OAD2Ww2h7Zp06apefPm8vf318iRIzVx4sQSr6SYNWuWQkNDFRgYqMcff1z5+fnljkkqOTdL0jPPPKNrr71WDRo0UNu2bfX888/b11lWbv7trat27dqlW2+9VfXr11dgYKD+67/+S2fOnHEqNgA1C3ms+vLYRx99pMaNG6uoqEiStHPnTtlsNj399NP2Pn/60580ZMgQSSXnlBkzZig4OFj+/v4aMWKELly4YH9t6tSpeuutt/Svf/3Lfhy//Gf1888/65ZbblGDBg3UpUsXpaSkOLHnAaB6lHRevmTJEsuek//9739X586di21/7ty59rb+/ftr0qRJkopfWV1YWKiEhAR7bp4wYYI9N5e2vy69N5Kkbdu2KSoqSg0aNFCPHj20b9++cmOGRRkATvvzn/9sWrZsadauXWu+++47c+eddxo/Pz8zZswYY4wxrVu3No0aNTIzZ840+/fvN/v37zepqalGktmxY4cpKCgwGRkZplGjRiYxMdFkZGSYc+fOmaeeesp07NjRZGRk2NvKI8kEBgaaN9980+zbt88899xzxsPDw+zZs8cYY8yWLVuMJLNu3TqTkZFhjh8/bowxZtiwYcbPz88MHjzYfP/99+bjjz82QUFB5tlnn62y/QagboiJibEfE3/44QezbNky06BBA7Nw4UJjjDF/+MMfTI8ePcyXX35pfvrpJzNz5kzj4+NjfvzxR2OMMUeOHDEzZ840O3bsMAcOHDBz5swxHh4e5quvvnLYxqVj7vLly42/v7/54IMPnIrPmePfs88+a9q3b28+/fRTc+DAAbN48WLj4+Nj1q9fX+ox/MiRI6ZBgwYmPj7e7N2717z//vumWbNmZsqUKcX2zdNPP21++OEHs3fvXvPLL7+YZs2amUmTJpm9e/ea7du3m379+plbbrml3LGcOnXKREdHm1GjRtlzR0FBgVOxVHSdX3zxhZFkrr/+erN27Vrz008/maysLPPkk0+asLAws2bNGrN7924zbNgw06RJE3u+Wbx4sQkICHDYxvvvv28uP/1ctmyZ8fX1Nf/85z/Nvn37zAsvvGAaNWpkunTp4vBza9SokYmLizN79+41H330kcPvVXlKys3GGPPSSy+ZTZs2mdTUVPPhhx+a4OBg8+qrrxpjTJm5WZJ5//33jTHGnD171oSFhZn777/f7Nq1y3z++ecmIiLCDBs2zKnYANQs5LHqzWP16tUzW7duNcYYk5iYaJo1a2ZuvPFGe59rr73WzJ8/3xhTPKesXLnSeHt7mzfffNP88MMPZvLkycbf39+eP06fPm0efPBB87vf/c5+HM/NzbW/N2vfvr35+OOPzb59+8wDDzxgWrdubfLz8536OQBAVSvpvHzdunWWPSf/7rvvjM1mM8eOHTPGGDN27FjTrFkz8/vf/94YY0x+fr7x8/Mz//73v40xxkyZMsVh26+++qoJCAgw7733ntmzZ48ZMWKE8ff3N/fcc0+p++vy9zHdu3c369evN7t37za9e/c2PXr0qOiPBBZBoQNw0unTp423t7dZsWKFve348eOmfv36DoWOe++912G5ywsdlwQEBJjFixfbn//2IO4MSSYuLs6hrXv37ubPf/5zqds15mJyatq0qTl79qy9bf78+cbPz88UFhZWKAYAuFxMTIzp0KGDKSoqsrc988wzpkOHDuann34yNpvNpKenOyxz2223mUmTJpW6zoEDB5qnnnrKYRtjxowxc+fONQEBAeY///mP0/GVd/w7c+aM8fX1NZs3b3ZYbsSIEWbIkCH25789hj/77LOmXbt2DuOeO3euw3E1JibGdO3a1WG9zz//vImNjXVoO3z4sJFk9u3bV+54Lv+wrCKxVHSdl94gXP5B3JkzZ4yXl5d5++237W15eXkmLCzMvPbaa8YY595Ude/e3Tz++OMOfXr27FnsTVXr1q1NQUGBve33v/+9GTx4cLnjMabk3FyS1157zURGRtqfl5abLy90LFy40DRp0sScOXPG/vonn3xi6tWrZzIzM52KD0DNQR6r3jx2ww03mFmzZhljjLn33nvNyy+/bLy9vU1OTo7JyMgwkszevXuNMcVzSnR0dInvhX6bPy59CHbJpfdI//jHP+xtu3fvdtgWANQEvz0vt/I5eVFRkWnWrJl57733jDHGdO3a1UyfPt00b97cGGPM5s2bjaenpzl9+rQxpvh5eGhoqJkxY4b9eX5+vmnZsqXDMb6s9zHr1q2zt33yySdGkjl//ny5ccN6uHUV4KQDBw4oLy9P0dHR9ramTZuqXbt2Dv2ioqKqLabLY7n0fO/eveUu16VLFzVo0MBhuTNnzujw4cMujxFA3XLzzTc7XAYdHR2t/fv3a+vWrTLG6Nprr5Wfn5/9kZycrAMHDki6eEnyyy+/rOuvv16BgYHy8/PT2rVrlZaW5rCNVatWaezYsVq7dq1uueWWCsVX1vFvz549unDhgvr16+cQ49KlS+0xlmTv3r2Kjo52GHfPnj115swZHTlyxN722/ywbds2ffHFFw7bat++vSSVub2yOBtLZVwe/4EDB5Sfn6+ePXva27y8vHTTTTc5lYcu2bdvn2666SaHtt8+l6SOHTvKw8PD/jw0NFRHjx6tVOyXvPfee+rVq5dCQkLk5+en559/vtjvWnn27t2rLl26qGHDhva2nj17qqioiEviAYsij11UHXmsb9++Wr9+vYwx2rBhg+655x516tRJGzdu1BdffKHg4GD7+kqL+XK/fV6W66+/3v7/0NBQSapQXgEAd7HiObnNZlOfPn20fv16nTp1Srt371ZcXJwKCwu1d+9erV+/XjfccIP8/PyKLZudna2MjAyHY7ynp2eFPnvjmF93eJbfBYAkh/v/leXyDzvc4bf3WayuZQGgPB4eHtq2bZvDybEk+wntX//6V/33f/+3EhMT1blzZzVs2FBjx44tNodQ165dtX37di1evFg33nijS45dNpvNfp/wTz75RC1atHB4vazJp40xxWK4lDMub/9tfigqKtJdd92lV199tdg6L52AV5SzsVTG5fGXts7Lt1+vXr1iubOke/iWFu/lvLy8ii1z6edV0dgl6auvvtJDDz2kF154Qf3791dAQIBWrFhRbO6t8pS0vy+PEUDtQh5zbR7r27evFi1apG+//Vb16tXTddddp5iYGCUnJ+vkyZOKiYkpdx2VdXleuTTGiuQVAHAXq56T9+3bVwsXLtSGDRvUpUsXNW7cWH369FFycrLWr19vn1e2KnDMrzu4ogNw0tVXXy0vLy999dVX9raTJ0/qxx9/vOJ1e3t7q7CwsMLLXR7LpeeXvvXk7e0tSSWu99tvv9X58+cdlvPz81PLli0rHAMAXK6k49I111yjbt26qbCwUEePHtXVV1/t8AgJCZEk+7c5//jHP6pLly5q27at9u/fX2wbV111lb744gv961//0hNPPFGh+Mo6/l133XXy8fFRWlpasRhbtWpV6jqvu+46bd682eHNwObNm+Xv71/sg6bL3XDDDdq9e7fatGlTbHvOFM1Lyh2VjaWsdZbk6quvlre3tzZu3Ghvy8/P19atW9WhQwdJUlBQkE6fPq2zZ8/a++zcudNhPe3atdOWLVsc2rZu3Vru9q/Upk2b1Lp1a02ePFlRUVG65pprdOjQIYc+zuyL6667Tjt37nQY46ZNm1SvXj1de+21VRI7gKpFHruoOvJYnz59dPr0aSUmJiomJkY2m00xMTFav3691q9fX2aho0OHDiX+rC5X2fdYAFATOHMMs9I5ed++fbV7926999579qJGTEyM1q1bp82bN5d6zA8ICFBoaKjDMb6goEDbtm1z6McxHxKFDsBpfn5+GjFihJ5++ml9/vnn+v777zV8+HDVq3flf0Zt2rRRamqqdu7cqaysLOXm5jq13Lvvvqt//vOf+vHHHzVlyhRt2bJFo0ePliQ1b95c9evX16effqpff/1V2dnZ9uXy8vI0YsQI7dmzR//+9781ZcoUjR492iVjAVC3HT58WAkJCdq3b5+WL1+uv//97xozZoyuvfZaPfzww3rkkUe0evVqpaam6ptvvtGrr76qNWvWSLp4op6UlKTNmzdr7969+tOf/qTMzMwSt3Pttdfqiy++sN/+w1llHf/8/f01fvx4jRs3Tm+99ZYOHDigHTt2aO7cuXrrrbdKXWd8fLwOHz6sJ554Qj/88IP+9a9/acqUKUpISCjzuPr444/rxIkTGjJkiLZs2aKff/5Za9eu1WOPPebUSXqbNm309ddf6+DBg8rKylJRUVGlYylrnSVp2LCh/vznP+vpp5/Wp59+qj179mjUqFE6d+6cRowYIUnq3r27GjRooGeffVY//fST3nnnHS1ZssRhPU888YQWLVqkt956S/v379e0adP03XffVfnVEFdffbXS0tK0YsUKHThwQHPmzNH777/v0MeZ3Pzwww/L19dXw4YN0/fff68vvvhCTzzxhIYOHarg4OAqHQOAqkEeq748FhAQoK5du2rZsmX2D7369Omj7du368cffyzz271jxozRP//5T4f3Qrt373bo06ZNG3333Xfat2+fsrKySvwGMwDUVM6cl1vpnLxTp04KDAzU22+/bT++9+3bVx988IHOnz+vXr16lbrsmDFjNGPGDL3//vv64YcfFB8fr1OnTjn0cfZ9DGo3PtUEKmDmzJnq06eP7r77bt1+++3q1auXIiMjr3i9gwYN0u9+9zvdcsstCgoK0vLly51a7oUXXtCKFSt0/fXX66233tLbb7+t6667TtLFexbOmTNHb7zxhsLCwnTPPffYl7vtttt0zTXXqE+fPnrwwQd11113aerUqVc8DgB45JFHdP78ed100016/PHH9cQTT+i//uu/JEmLFy/WI488oqeeekrt2rXT3Xffra+//tr+LdPnn39eN9xwg/r376++ffsqJCRE9957b6nbateunf7zn/9o+fLleuqpp5yKr7zj30svvaS//OUvmj59ujp06KD+/fvro48+UkRERKnrbNGihdasWaMtW7aoS5cuiouL04gRI/Tcc8+VGUtYWJg2bdqkwsJC9e/fX506ddKYMWMUEBDgVFFi/Pjx8vDw0HXXXaegoCClpaVVOpay1lmaGTNmaNCgQRo6dKhuuOEG/fTTT/rss8/UpEkTSRfnsVq2bJnWrFmjzp07a/ny5cVyzcMPP6xJkyZp/PjxuuGGG5Samqrhw4fL19fXqXgr65577tG4ceM0evRode3aVZs3b9bzzz/v0MeZ3NygQQN99tlnOnHihG688UY98MADuu222/T6669XafwAqg55rPrymCTdcsstKiwstH/o1aRJE3sOuvRt5JIMHjxYf/nLX/TMM88oMjJShw4d0p///GeHPqNGjVK7du0UFRWloKAgbdq0yamYAKAmcPa83Crn5Jeu2pOk3r17S7o4d0ZAQIC6deumRo0albrsU089pUceeUTDhw9XdHS0/P39dd999zn0qcj7GNReNuPsxAMAahSbzab333+/zDdPJRk+fLhOnTqlDz74oEriAlB39e3bV127dlViYqK7Q4GF9evXTyEhIfqf//kfd4cCoI4hjwEAcBHn5LAiJiMHAACAW5w7d04LFixQ//795eHhoeXLl2vdunVKSkpyd2gAAABAncA5OWoLbl0F1EBvv/22/Pz8Snx07NjR3eEBQI1U2nHTz89PGzZscHd4FZKWllbmeCpzKXZVrPNK2Ww2rVmzRr1791ZkZKQ++ugjrVq1SrfffrtTy2/YsKHMMQGAlZDHAADuwDk5agtuXQXUQKdPn9avv/5a4mteXl5q3bp1NUcEADXfTz/9VOprLVq0UP369asxmitTUFCggwcPlvp6mzZt5OlZsQtzq2Kd7nb+/Hmlp6eX+vrVV19djdEAwJUhjwEArIhzctQUFDoAAAAAAAAAAIBlcesqAAAAAAAAAABgWRQ6AAAAAAAAAACAZVHoAAAAAAAAAAAAlkWhAwAAAAAAAAAAWBaFDgAAAAAAAAAAYFkUOgAAAAAAAAAAgGVR6AAAAAAAAAAAAJZFoQMAAAAAAAAAAFgWhQ4AAAAAAAAAAGBZFDoAAAAAAAAAAIBlUegAAAAAAAAAAACWRaEDAAAAAAAAAABYFoUOAAAAAAAAAABgWRQ6AAAAAAAAAACAZVHoAAAAAAAAAAAAlkWhAwAAAAAAAAAAWBaFDgAAAAAAAAAAYFkUOgAAAAAAAAAAgGVR6AAAAAAAAAAAAJZFoQMAAAAAAAAAAFgWhQ4AAAAAAAAAAGBZFDoAAAAAAAAAAIBlUegAAAAAAAAAAACWRaEDAAAAAAAAAABYFoUOAAAAAAAAAABgWRQ6AAAAAAAAAACAZVHoAAAAAAAAAAAAlkWho5KMMcrJyZExxt2hAAAshPwBAAAAAADgWp7uDsCqTp8+rYCAAGVnZ6tRo0YVWnb2bCknR2rUSEpIqKIAAQA10pXkD1cgBwEAAAAAgNrGZvhKaaXk5ORU+oOqli2l9HSpRQvpyJEqChAAUCNdSf5wBXIQAAAAAACobbh1FQAAAAAAAAAAsCwKHQAAAAAAAAAAwLKYowNAjWeMUUFBgQoLC90diuV4eXnJw8PD3WEAgEsVFhYqPz/f3WHUauQPAAAAAFZCocMN7rhDOnFCatrU3ZEANV9eXp4yMjJ07tw5d4diSTabTS1btpSfn5+7Q0ENQQ6C1Z05c0ZHjhwR08xVLfIHAAAAACthMvJKcvdkskBdUFRUpP3798vDw0NBQUHy9vaWzWZzd1iWYYzRsWPHdO7cOV1zzTV8M7eGIH8AlVdYWKj9+/erQYMGCgoKIidUEfIHAAAAAKvhig4ANVZeXp6KiorUqlUrNWjQwN3hWFJQUJAOHjyo/Px8PqgCYHn5+fkyxigoKEj169d3dzi1GvkDAAAAgJUwGTmAGq9ePQ5VlcW3nQHURhzbqh77GAAAAICVcEWHGxw7dkw5OTll9mnUqJGCgoKqKSIAgBU4kz8kcggAAAAAAKhb3F7omDdvnmbOnKmMjAx17NhRiYmJ6t27d6n9k5OTlZCQoN27dyssLEwTJkxQXFyc/fXVq1frlVde0U8//aT8/Hxdc801euqppzR06FB7n6lTp+qFF15wWG9wcLAyMzNdP8DfOHbsmK69JlvnzwfIx/uket4wusR+vv4N9MbiN/mgCkCJ5s6dqwULFsjDw0OFhYUaNWqUnnzySXeHhSp07NgxPfroozp9+nS5ff39/bV48eISc0hUlJSZKYWESFu3VkWkANyhqKhIY8aM0Zo1a2Sz2ZSQkKD4+PgS+3722Wd69tlnVVRUpPz8fD399NMaNmxYNUcMAAAAAK7j1kLHypUrNXbsWM2bN089e/bUG2+8oQEDBmjPnj0KDw8v1j81NVUDBw7UqFGjtGzZMm3atEnx8fEKCgrSoEGDJElNmzbV5MmT1b59e3l7e+vjjz/Wo48+qubNm6t///72dXXs2FHr1q2zP6+uew/n5OTo/PkA5eYFKcDXXy/cMa5Yn19OZOqNlOXKycmh0AGgRH/84x/1+OOPS7p4XOnUqZP69u2r66+/3s2Roark5OTo9OnT6t27twIDA0vtd/z4cW3YsKHUHJKZKaWnV2WkANxh2bJl2rNnj3788UdlZ2frhhtu0K233qr27ds79DPG6A9/+IO++OILXX/99Tp48KDat2+v+++/X/7+/m6KHgAAAACujFtvfD979myNGDFCI0eOVIcOHZSYmKhWrVpp/vz5JfZfsGCBwsPDlZiYqA4dOmjkyJF67LHHNGvWLHufvn376r777lOHDh101VVXacyYMbr++uu1ceNGh3V5enoqJCTE/nBHQcHDw0MRzcOLPcKahlR7LACcZ7PZNH36dN10001q27at1q1bp0mTJqlbt27q2LGjdu/eLUnKzMzULbfcosjISHXs2FFPPvmkjDGSpFGjRumJJ56QJJ04cUJXXXVVseNUWQICAuz/P3funAoKCrifeh0RGBio4ODgUh9lFUEAVI2akBdWrlypuLg4eXh4qGnTpnrwwQe1YsWKUvufOnVK0sUiamBgoHx8fCo5egAAAABwP7cVOvLy8rRt2zbFxsY6tMfGxmrz5s0lLpOSklKsf//+/bV161bl5+cX62+M0eeff659+/apT58+Dq/t379fYWFhioiI0EMPPaSff/65zHhzc3OVk5Pj8ADgHrNnSy1blv+4++7iy959d+n9Z892PoZGjRppy5YtevXVV3XPPfeoV69e2rFjh4YNG6aXX35ZktS4cWN99NFH2rZtm7777jv9/PPPWrVqlSTp73//uzZu3Kh3331Xw4YN06hRo9SrVy9J0pNPPqmuXbuW+Pj666/tMbz33nvq2LGjWrduraefflqdO3eu/E4FAIuqqpxgtbyQlpam1q1b2+Np06aN0tLSisVps9n0v//7v7r//vvVunVr9erVS2+99Za8vb2dHywAAAAA1DBuu3VVVlaWCgsLFRwc7NBe1lwZmZmZJfYvKChQVlaWQkNDJUnZ2dlq0aKFcnNz5eHhoXnz5qlfv372Zbp3766lS5fq2muv1a+//qpp06apR48e2r17d6nfhJ0+fXqxeT0AuEdOjnO33mnVqnjbsWOlL1uR+uXgwYMlSTfccIPq1aunO+64Q5IUGRmp1atXS7p4v/RnnnlGGzdulDFGR48eVdeuXfXAAw/I19dX7777riIjIxUdHa1nnnnGvu45c+Y4FcMDDzygBx54QAcPHtR9992ngQMHql27ds4PAgBqgarKCZfW7ayakBcuv7Lv0pUiv1VQUKDp06frX//6l3r27KlvvvlG9957r3bt2qWmTZs6P2AAAAAAqEHcPhn5b2+1Yowp8/YrJfX/bbu/v7927typM2fO6PPPP1dCQoLatm2rvn37SpIGDBhg79u5c2dFR0frqquu0ltvvaWEhIQStztp0iSH13JyctSqpHfMAKpco0ZSixbl9yvpjnRBQaUv26iR8zH4+vpKungLustv9+Hh4aGCggJJF2/Pd/z4cX399dfy9fVVQkKCLly4YO/7ww8/qGHDhjp69Kjy8vLs63nyySf15ZdflrjdN954Q927d3doa9Omjbp3766PP/6YQgeAOqeqcsKldTvL3XkhPDxcBw8e1I033ihJOnToUIlz3u3cuVO//PKLevbsKUm68cYbFRYWpm+//Va33HKL8wMGAAAAgBrEbYWOZs2aycPDo9jVG0ePHi121cYlISEhJfb39PR0uBKjXr16uvrqqyVJXbt21d69ezV9+nR7oeO3GjZsqM6dO2v//v2lxuvj48O9i4EaIiHh4qMyPvzQtbGU5eTJkwoJCZGvr69+/fVXvfvuu/Zv/KalpSk+Pl7r1q3TnDlzNHbsWPv8RM58c3fv3r3q0KGDJOnYsWP6/PPPNWjQoKobDADUUFbJCVLV5oXf//73euONN3T//fcrOztbK1eu1KefflqsX6tWrXTkyBHt27dP7dq1008//aQDBw7o2muvde1gAQAAAKAauW2ODm9vb0VGRiopKcmhPSkpST169Chxmejo6GL9165dq6ioKHl5eZW6LWOMcnNzS309NzdXe/futd/6CgBc4cknn9TmzZvVtWtXPfbYY7r99tslXbxtyEMPPaSXXnpJ1113nRITE/XVV19p5cqVTq/773//uzp27KiuXbvq9ttv17hx4xxu0QcAqHmqMi8MHTpU7dq107XXXqsbb7xRTz/9tL0g/uGHH2rkyJGSLt729Y033tADDzygLl266P7779e8efPUwpnLYgAAAACghnLrrasSEhI0dOhQRUVFKTo6WgsXLlRaWpri4uIkXbxdVHp6upYuXSpJiouL0+uvv66EhASNGjVKKSkpWrRokZYvX25f5/Tp0xUVFaWrrrpKeXl5WrNmjZYuXWr/RpwkjR8/XnfddZfCw8N19OhRTZs2TTk5ORo2bFj17gAAlnT5fc/btGmjrKws+/O+fftq69atkqTWrVtry5YtJa5j8+bN9v/7+vpqx44dFYph3rx5FeqPkk2fPl2rV6/WDz/8oPr166tHjx569dVXy70FWHJyshISErR7926FhYVpwoQJ9twFoO6pCXnBw8NDc+fOLfG1u+++W3dfNhv7kCFDNGTIkAqtHwAAAABqMrcWOgYPHqzjx4/rxRdfVEZGhjp16qQ1a9aodevWkqSMjAylpaXZ+0dERGjNmjUaN26c5s6dq7CwMM2ZM8fhdi1nz55VfHy8jhw5ovr166t9+/ZatmyZ/bYAknTkyBENGTJEWVlZCgoK0s0336yvvvrKvl0AQN2QnJysxx9/XDfeeKMKCgo0efJkxcbGas+ePWrYsGGJy6SmpmrgwIEaNWqUli1bpk2bNik+Pl5BQUHcPgwAAAAAAMAN3D4ZeXx8vOLj40t8bcmSJcXaYmJitH379lLXN23aNE2bNq3Mba5YsaJCMbpa+7b/0B0d71R4YBO3xgEAdd1v71+/ePFiNW/eXNu2bVOfPn1KXGbBggUKDw9XYmKiJKlDhw7aunWrZs2aZYlCx2uvSefOSQ0auDsSAAAAAAAA13B7oaMuCmv+he69qasimrttihQAQAmys7MlSU2bNi21T0pKimJjYx3a+vfvr0WLFik/P7/YnFG5ubkO80Tl5OS4MOKK+8Mf3Lp5AAAAAAAAl+OTdgA1XlFRkbtDsKzL7xuPshljlJCQoF69eqlTp06l9svMzFRwcLBDW3BwsAoKChzuy3/J9OnTFRAQYH+0atXK5bEDdQ3HtqrHPgYAAABgJVzRAaDG8vb2Vr169fTLL78oKChI3t7estls7g7LMowxOnbsmGw2W7GrDFDc6NGj9d1332njxo3l9v3t7+GlDwRL+v2cNGmSEhIS7M9zcnIodgCV5OXlJZvNpmPHjikoKIicUEXIHwAAAACshkKHG5w511IHMv1UWOitq0Pz3B0OUGPVq1dPERERysjI0C+//OLucCzJZrOpZcuW8vDwcHcoNdoTTzyhDz/8UF9++aVatmxZZt+QkBBlZmY6tB09elSenp4KDAws1t/Hx0c+Pj4ujfdK7NsnFRRInp5Su3bujgaoGA8PD7Vs2VJHjhzRwYMH3R1OrUb+AAAAAGAlFDrcYMt3r2rD1iCFNsnXd7N/dHc4QI3m7e2t8PBwFRQUqLCw0N3hWI6XlxcfUpXBGKMnnnhC77//vtavX6+IiIhyl4mOjtZHH33k0LZ27VpFRUVZ4pvPt90mpadLLVpIR464Oxqg4vz8/HTNNdcoPz/f3aHUauQPAAAAAFZCoQNAjXfp1hlW+BAZ1vL444/rnXfe0b/+9S/5+/vbr9QICAhQ/fr1JV289VR6erqWLl0qSYqLi9Prr7+uhIQEjRo1SikpKVq0aJGWL1/utnEAdY2HhwcfwgMAAAAA7JiMHABQZ82fP1/Z2dnq27evQkND7Y+VK1fa+2RkZCgtLc3+PCIiQmvWrNH69evVtWtXvfTSS5ozZ44GDRrkjiEAAAAAAADUeVzRAQCosy5NIl6WJUuWFGuLiYnR9u3bqyAiAAAAAAAAVBRXdAAAAAAAAAAAAMui0AEAAAAAAAAAACyLQgcAAAAAAAAAALAsCh0AAAAAAAAAAMCyKHQAAAAAAAAAAADLotABAAAAAAAAAAAsy9PdAdRFPbo9oYRb/6Q2zcPcHQoAoI755hupsFDy8HB3JAAAAAAAAK5BocMNfH1OKLjxBYU0LnB3KACAOiY01N0RAAAAAAAAuBa3rgIAAAAAAAAAAJZFoQMAAAAAAAAAAFgWt65yg7SMgXr7yzYKbxagR/qedHc4AIA6ZOFC6cwZyc9P+q//cnc0AAAAAAAAV87tV3TMmzdPERER8vX1VWRkpDZs2FBm/+TkZEVGRsrX11dt27bVggULHF5fvXq1oqKi1LhxYzVs2FBdu3bV//zP/1zxdl3pp0MP62+fdNCsD4OqbZsAAEjSiy9KTz118V8AAAAAAIDawK2FjpUrV2rs2LGaPHmyduzYod69e2vAgAFKS0srsX9qaqoGDhyo3r17a8eOHXr22Wf15JNPatWqVfY+TZs21eTJk5WSkqLvvvtOjz76qB599FF99tlnld4uAAAAAAAAAAComdxa6Jg9e7ZGjBihkSNHqkOHDkpMTFSrVq00f/78EvsvWLBA4eHhSkxMVIcOHTRy5Eg99thjmjVrlr1P3759dd9996lDhw666qqrNGbMGF1//fXauHFjpbcLAAAAAAAAAABqJrcVOvLy8rRt2zbFxsY6tMfGxmrz5s0lLpOSklKsf//+/bV161bl5+cX62+M0eeff659+/apT58+ld6uJOXm5ionJ8fhAQAAAAAAAAAA3MtthY6srCwVFhYqODjYoT04OFiZmZklLpOZmVli/4KCAmVlZdnbsrOz5efnJ29vb91xxx36+9//rn79+lV6u5I0ffp0BQQE2B+tWrWq0HgBAAAAAAAAAIDruX0ycpvN5vDcGFOsrbz+v2339/fXzp079c033+jll19WQkKC1q9ff0XbnTRpkrKzs+2Pw4cPlzkuAAAAAAAAAABQ9TzdteFmzZrJw8Oj2FUUR48eLXa1xSUhISEl9vf09FRgYKC9rV69err66qslSV27dtXevXs1ffp09e3bt1LblSQfHx/5+PhUaIwAAAAAAAAAAKBque2KDm9vb0VGRiopKcmhPSkpST169Chxmejo6GL9165dq6ioKHl5eZW6LWOMcnNzK71dAAAAAAAAAABQM7ntig5JSkhI0NChQxUVFaXo6GgtXLhQaWlpiouLk3TxdlHp6elaunSpJCkuLk6vv/66EhISNGrUKKWkpGjRokVavny5fZ3Tp09XVFSUrrrqKuXl5WnNmjVaunSp5s+f7/R2AQAAAAAAAACANbi10DF48GAdP35cL774ojIyMtSpUyetWbNGrVu3liRlZGQoLS3N3j8iIkJr1qzRuHHjNHfuXIWFhWnOnDkaNGiQvc/Zs2cVHx+vI0eOqH79+mrfvr2WLVumwYMHO73dqtawfrrCGvuqRdPS5wQBAKAqXHutFBAglXG3RgAAAAAAAEtxa6FDkuLj4xUfH1/ia0uWLCnWFhMTo+3bt5e6vmnTpmnatGlXtN2q1r3LBL1wxzhFNA93y/YBAHXXf/7j7ggAAAAAAABcy21zdAAAAAAAAAAAAFwpCh0AAAAAAAAAAMCyKHQAAAAAAAAAAADLcvscHXXRzr0T9URGZ7UM9NKCP6W7OxwAQB3y8MNSVpbUrJn09tvujgYAAAAAAODKUehwgxPZnZVxLEhpWfnuDgUAUMckJ0vp6VKLFu6OBAAAAAAAwDW4dRUAAAAAAAAAALAsCh0AAAAAAAAAAMCyKHQAAAAAAAAAAADLotABAAAAAAAAAAAsi0IHAAAAAAAAAACwLAodAAAAAAAAAADAsih0AAAAAAAAAAAAy6LQAQAAAAAAAAAALMvT3QHURa1C/60bW/VWq8CG7g4FAFDHjBolZWdLAQHujgQAAAAAAMA1KHS4wTWtl2ncHUGKaB7u7lAAAHXMlCnujgAAAAAAAMC1uHUVAAAAAAAAAACwLAodAAAAAAAAAADAsih0AAAAAAAAAAAAy6LQ4Qb/+ept3fTMAF2fcK27QwGAOu3LL7/UXXfdpbCwMNlsNn3wwQdl9l+/fr1sNluxxw8//FA9AbtAy5aSzXbxXwAAAAAAgNrA7YWOefPmKSIiQr6+voqMjNSGDRvK7J+cnKzIyEj5+vqqbdu2WrBggcPrb775pnr37q0mTZqoSZMmuv3227VlyxaHPlOnTi32IVVISIjLxwYAqNnOnj2rLl266PXXX6/Qcvv27VNGRob9cc0111RRhAAAAAAAACiPpzs3vnLlSo0dO1bz5s1Tz5499cYbb2jAgAHas2ePwsPDi/VPTU3VwIEDNWrUKC1btkybNm1SfHy8goKCNGjQIEkXv207ZMgQ9ejRQ76+vnrttdcUGxur3bt3q0WLFvZ1dezYUevWrbM/9/DwqPoBAwBqlAEDBmjAgAEVXq558+Zq3Lix6wMCAAAAAABAhbn1io7Zs2drxIgRGjlypDp06KDExES1atVK8+fPL7H/ggULFB4ersTERHXo0EEjR47UY489plmzZtn7vP3224qPj1fXrl3Vvn17vfnmmyoqKtLnn3/usC5PT0+FhITYH0FBQVU6VgBA7dGtWzeFhobqtttu0xdffFFm39zcXOXk5Dg8AAAAAAAA4DpuK3Tk5eVp27Ztio2NdWiPjY3V5s2bS1wmJSWlWP/+/ftr69atys/PL3GZc+fOKT8/X02bNnVo379/v8LCwhQREaGHHnpIP//8c5nx8kEVACA0NFQLFy7UqlWrtHr1arVr10633Xabvvzyy1KXmT59ugICAuyPVq1aVWPEAAAAAAAAtZ/bbl2VlZWlwsJCBQcHO7QHBwcrMzOzxGUyMzNL7F9QUKCsrCyFhoYWW2bixIlq0aKFbr/9dntb9+7dtXTpUl177bX69ddfNW3aNPXo0UO7d+9WYGBgiduePn26XnjhhYoOEwBQi7Rr107t2rWzP4+Ojtbhw4c1a9Ys9enTp8RlJk2apISEBPvznJwcih0AAAAAAAAu5PbJyG02m8NzY0yxtvL6l9QuSa+99pqWL1+u1atXy9fX194+YMAADRo0SJ07d9btt9+uTz75RJL01ltvlbrdSZMmKTs72/44fPhw+YMDANR6N998s/bv31/q6z4+PmrUqJHDAwAAAAAAAK7jtis6mjVrJg8Pj2JXbxw9erTYVRuXhISElNjf09Oz2JUYs2bN0iuvvKJ169bp+uuvLzOWhg0bqnPnzuV+UOXj41PmegAAdc+OHTtKvKIQAAAAAAAA1cNthQ5vb29FRkYqKSlJ9913n709KSlJ99xzT4nLREdH66OPPnJoW7t2raKiouTl5WVvmzlzpqZNm6bPPvtMUVFR5caSm5urvXv3qnfv3pUcDQDAis6cOaOffvrJ/jw1NVU7d+5U06ZNFR4erkmTJik9PV1Lly6VJCUmJqpNmzbq2LGj8vLytGzZMq1atUqrVq1y1xAAAAAAAADqPLcVOiQpISFBQ4cOVVRUlKKjo7Vw4UKlpaUpLi5Okop9wBQXF6fXX39dCQkJGjVqlFJSUrRo0SItX77cvs7XXntNzz//vN555x21adPGfgWIn5+f/Pz8JEnjx4/XXXfdpfDwcB09elTTpk1TTk6Ohg0bVs17AADgTlu3btUtt9xif35pLo1hw4ZpyZIlysjIUFpamv31vLw8jR8/Xunp6apfv746duyoTz75RAMHDqz22AEAAAAAAHCRWwsdgwcP1vHjx/Xiiy8qIyNDnTp10po1a9S6dWtJKvYBU0REhNasWaNx48Zp7ty5CgsL05w5czRo0CB7n3nz5ikvL08PPPCAw7amTJmiqVOnSpKOHDmiIUOGKCsrS0FBQbr55pv11Vdf2bdb1bq0f1UPRw1W66Bm1bI9AEDJ+vbta5/rqSRLlixxeD5hwgRNmDChiqOqWsuWSbm5EndjBAAAAAAAtYVbCx2SFB8fr/j4+BJf++0HTJIUExOj7du3l7q+gwcPlrvNFStWOBtelQhs/J2i292miOYN3BoHAKDu6dvX3REAAAAAAAC4Vj13BwAAAAAAAAAAAFBZFDoAAAAAAAAAAIBluf3WVXXR8VPXK2VfM/1yooF6tj/n7nAAAHXI+vX/N0cHt7ECAAAAAAC1AYUON/j2h2e05bsghTbJ13ezf3R3OACAOuSPf5TS06UWLaQjR9wdDQAAAAAAwJXj1lUAAAAAAAAAAMCyKHQAAAAAAAAAAADLotABAAAAAAAAAAAsi0IHAAAAAAAAAACwLAodAAAAAAAAAADAsipV6EhNTXV1HAAAOI08BAAAAAAAgEsqVei4+uqrdcstt2jZsmW6cOGCq2MCAKBM5CEAAAAAAABcUqlCx7fffqtu3brpqaeeUkhIiP70pz9py5Ytro4NAIASkYcAAAAAAABwSaUKHZ06ddLs2bOVnp6uxYsXKzMzU7169VLHjh01e/ZsHTt2zNVxAgBgRx4CAAAAAADAJVc0Gbmnp6fuu+8+/e///q9effVVHThwQOPHj1fLli31yCOPKCMjw1Vx1iq33vywtrz6b303+0d3hwIAlkYeqrgjRyRjLv4LAAAAAABQG1xRoWPr1q2Kj49XaGioZs+erfHjx+vAgQP6z3/+o/T0dN1zzz2uihMAgGLIQwAAAAAAAPCszEKzZ8/W4sWLtW/fPg0cOFBLly7VwIEDVa/exbpJRESE3njjDbVv396lwQIAIJGHAAAAAAAA8H8qVeiYP3++HnvsMT366KMKCQkpsU94eLgWLVp0RcEBAFAS8hAAAAAAAAAuqVShIykpSeHh4fZvzl5ijNHhw4cVHh4ub29vDRs2zCVB1jb7D/1R//1Re7UKbKin72XCXACoKPJQ5b3wgpSdLQUESFOmuDsaAAAAAACAK1epOTquuuoqZWVlFWs/ceKEIiIirjio2u5wxgAt3xih//myibtDAQBLIg9V3ptvSv/93xf/BQAAAAAAqA0qVegwxpTYfubMGfn6+l5RQAAAlIc8BAAAAAAAgEsqdOuqhIQESZLNZtNf/vIXNWjQwP5aYWGhvv76a3Xt2rVCAcybN08zZ85URkaGOnbsqMTERPXu3bvU/snJyUpISNDu3bsVFhamCRMmKC4uzv76m2++qaVLl+r777+XJEVGRuqVV17RTTfddEXbBQC4X1XkIQAAAAAAAFhbhQodO3bskHTxm7S7du2St7e3/TVvb2916dJF48ePd3p9K1eu1NixYzVv3jz17NlTb7zxhgYMGKA9e/YoPDy8WP/U1FQNHDhQo0aN0rJly7Rp0ybFx8crKChIgwYNkiStX79eQ4YMUY8ePeTr66vXXntNsbGx2r17t1q0aFGp7QIAagZX5yEAAAAAAABYX4UKHV988YUk6dFHH9Xf/vY3NWrU6Io2Pnv2bI0YMUIjR46UJCUmJuqzzz7T/PnzNX369GL9FyxYoPDwcCUmJkqSOnTooK1bt2rWrFn2Qsfbb7/tsMybb76p9957T59//rkeeeSRSm0XAFAzuDoPAQAAAAAAwPoqNUfH4sWLr/jDpby8PG3btk2xsbEO7bGxsdq8eXOJy6SkpBTr379/f23dulX5+fklLnPu3Dnl5+eradOmld6uJOXm5ionJ8fhAQBwD1fkIQAAAAAAANQOTl/Rcf/992vJkiVq1KiR7r///jL7rl69utz1ZWVlqbCwUMHBwQ7twcHByszMLHGZzMzMEvsXFBQoKytLoaGhxZaZOHGiWrRoodtvv73S25Wk6dOn64UXXih3XACAquHqPAQAAAAAAIDawelCR0BAgGw2m/3/rnJpnZcYY4q1lde/pHZJeu2117R8+XKtX79evr6+V7TdSZMm2SfBlaScnBy1atWq1P4AANeqqjwEAAAAAAAAa3O60LF48eIS/19ZzZo1k4eHR7GrKI4ePVrsaotLQkJCSuzv6empwMBAh/ZZs2bplVde0bp163T99ddf0XYlycfHRz4+Pk6NDQDgeq7OQwAAAAAAAKgdKjVHx/nz53Xu3Dn780OHDikxMVFr1651eh3e3t6KjIxUUlKSQ3tSUpJ69OhR4jLR0dHF+q9du1ZRUVHy8vKyt82cOVMvvfSSPv30U0VFRV3xdl2tacAudb/mmHq0O1st2wOA2sYVeaiuiomRYmMv/gsAAAAAAFAbOH1Fx+Xuuece3X///YqLi9OpU6d00003ydvbW1lZWZo9e7b+/Oc/O7WehIQEDR06VFFRUYqOjtbChQuVlpamuLg4SRdvF5Wenq6lS5dKkuLi4vT6668rISFBo0aNUkpKihYtWqTly5fb1/naa6/p+eef1zvvvKM2bdrYr9zw8/OTn5+fU9utal07zNALd4xTRPPwatkeANQ2rspDddHbb7s7AgAAAAAAANeq1BUd27dvV+/evSVJ7733nkJCQnTo0CEtXbpUc+bMcXo9gwcPVmJiol588UV17dpVX375pdasWaPWrVtLkjIyMpSWlmbvHxERoTVr1mj9+vXq2rWrXnrpJc2ZM0eDBg2y95k3b57y8vL0wAMPKDQ01P6YNWuW09sFANRsrspDAAAAAAAAsL5KXdFx7tw5+fv7S7p466j7779f9erV080336xDhw5VaF3x8fGKj48v8bUlS5YUa4uJidH27dtLXd/BgweveLsAgJrNlXkIAAAAAAAA1lapKzquvvpqffDBBzp8+LA+++wzxcbGSro4oXejRo1cGiAAAL9FHgIAAAAAAMAllSp0/OUvf9H48ePVpk0bde/eXdHR0ZIufqu2W7duLg2wNvr629c0+K+9dN+r3CoLACqDPFR5t94qdex48V8AAAAAAIDaoFK3rnrggQfUq1cvZWRkqEuXLvb22267Tffdd5/Lgqutzp5voRPZ/rqQn+/uUADAkshDlffjj1J6upSd7e5IAAAAAAAAXKNShQ5JCgkJUUhIiEPbTTfddMUBAQDgDPIQAAAAAAAApEoWOs6ePasZM2bo888/19GjR1VUVOTw+s8//+yS4AAAKAl5CAAAAAAAAJdUqtAxcuRIJScna+jQoQoNDZXNZnN1XAAAlIo8BAAAAAAAgEsqVej497//rU8++UQ9e/Z0dTwAAJSLPAQAAAAAAIBL6lVmoSZNmqhp06aujgUAAKeQhwAAAAAAAHBJpQodL730kv7yl7/o3Llzro4HAIByuSoPffnll7rrrrsUFhYmm82mDz74oNxlkpOTFRkZKV9fX7Vt21YLFiy4ohgAAAAAAABwZSp166q//vWvOnDggIKDg9WmTRt5eXk5vL59+3aXBAcAQElclYfOnj2rLl266NFHH9WgQYPK7Z+amqqBAwdq1KhRWrZsmTZt2qT4+HgFBQU5tTwAAAAAAABcr1KFjnvvvdfFYQAA4DxX5aEBAwZowIABTvdfsGCBwsPDlZiYKEnq0KGDtm7dqlmzZlHoAAAAAAAAcJNKFTqmTJni6jjqlKtbv63br+2v8GYB7g4FACzJXXkoJSVFsbGxDm39+/fXokWLlJ+fX+zKEknKzc1Vbm6u/XlOTk6Vx1mWv/xFOnNG8vNzaxgAAAAAAAAuU6lChySdOnVK7733ng4cOKCnn35aTZs21fbt2xUcHKwWLVq4MsZaJzx0jR7u004RzcPdHQoAWJY78lBmZqaCg4Md2oKDg1VQUKCsrCyFhoYWW2b69Ol64YUXqiSeyviv/3J3BAAAAAAAAK5VqULHd999p9tvv10BAQE6ePCgRo0apaZNm+r999/XoUOHtHTpUlfHCQCAnTvzkM1mc3hujCmx/ZJJkyYpISHB/jwnJ0etWrWqsvgAAAAAAADqmnqVWSghIUHDhw/X/v375evra28fMGCAvvzyS5cFBwBASdyVh0JCQpSZmenQdvToUXl6eiowMLDEZXx8fNSoUSOHBwAAAAAAAFynUld0fPPNN3rjjTeKtbdo0aLYB0Ao7kJuU/16ylf1vT0V0rjA3eEAgOW4Kw9FR0fro48+cmhbu3atoqKiSpyfoybKyJAKCyUPD6mEO20BAAAAAABYTqWu6PD19S1xMtV9+/YpKCjoioOq7Tbv+Lvumn6LYl9s6+5QAMCSXJWHzpw5o507d2rnzp2SpNTUVO3cuVNpaWmSLt526pFHHrH3j4uL06FDh5SQkKC9e/fqn//8pxYtWqTx48df2YCq0Y03Sq1aXfwXAAAAAACgNqhUoeOee+7Riy++qPz8fEkX70uelpamiRMnatCgQS4NEACA33JVHtq6dau6deumbt26Sbp4S6xu3brpL3/5iyQpIyPDXvSQpIiICK1Zs0br169X165d9dJLL2nOnDnkPgAAAAAAADeq1K2rZs2apYEDB6p58+Y6f/68YmJilJmZqejoaL388suujhEAAAeuykN9+/a1TyZekiVLlhRri4mJ0fbt2ysTNgAAAAAAAKpApa7oaNSokTZu3KjVq1drxowZGj16tNasWaPk5GQ1bNiwQuuaN2+eIiIi5Ovrq8jISG3YsKHM/snJyYqMjJSvr6/atm2rBQsWOLy+e/duDRo0SG3atJHNZlNiYmKxdUydOlU2m83hERISUqG4AQDu48o8BAAAAAAAAGur8BUdRUVFWrJkiVavXq2DBw/KZrMpIiJCISEhMsbIZrM5va6VK1dq7Nixmjdvnnr27Kk33nhDAwYM0J49exQeHl6sf2pqqgYOHKhRo0Zp2bJl2rRpk+Lj4xUUFGS/bci5c+fUtm1b/f73v9e4ceNK3XbHjh21bt06+3MPD48K7AUAgLu4Mg8BAAAAAADA+ip0RYcxRnfffbdGjhyp9PR0de7cWR07dtShQ4c0fPhw3XfffRXa+OzZszVixAiNHDlSHTp0UGJiolq1aqX58+eX2H/BggUKDw9XYmKiOnTooJEjR+qxxx7TrFmz7H1uvPFGzZw5Uw899JB8fHxK3banp6dCQkLsDyZRB4Caz9V5CAAAAAAAANZXoSs6lixZoi+//FKff/65brnlFofX/vOf/+jee+/V0qVL9cgjj5S7rry8PG3btk0TJ050aI+NjdXmzZtLXCYlJUWxsbEObf3799eiRYuUn58vLy8vp8eyf/9+hYWFycfHR927d9crr7yitm3blto/NzdXubm59uc5OTlObwsA4BquzEMAAAAAAACoHSp0Rcfy5cv17LPPFvtwSZJuvfVWTZw4UW+//bZT68rKylJhYaGCg4Md2oODg5WZmVniMpmZmSX2LygoUFZWlpOjkLp3766lS5fqs88+05tvvqnMzEz16NFDx48fL3WZ6dOnKyAgwP5o1aqV09sDALiGK/MQAAAAAAAAaocKFTq+++47/e53vyv19QEDBujbb7+tUAC/vZd6efdXL6l/Se1lGTBggAYNGqTOnTvr9ttv1yeffCJJeuutt0pdZtKkScrOzrY/Dh8+7PT2AACuURV5CAAAAAAAANZWoVtXnThxotgVFZcLDg7WyZMnnVpXs2bN5OHhUezqjaNHj5a6jZCQkBL7e3p6KjAw0KntlqRhw4bq3Lmz9u/fX2ofHx+fMuf8AABUPVfmIQAAAAAAANQOFbqio7CwUJ6epddGPDw8VFBQ4NS6vL29FRkZqaSkJIf2pKQk9ejRo8RloqOji/Vfu3atoqKiKjQ/x2/l5uZq7969Cg0NrfQ6KuKm65/R8nEbtPrpg9WyPQCoLVyZh+qqzz+Xvv/+4r8AAAAAAAC1QYWu6DDGaPjw4aVe2XD5ZN3OSEhI0NChQxUVFaXo6GgtXLhQaWlpiouLk3TxdlHp6elaunSpJCkuLk6vv/66EhISNGrUKKWkpGjRokVavny5fZ15eXnas2eP/f/p6enauXOn/Pz8dPXVV0uSxo8fr7vuukvh4eE6evSopk2bppycHA0bNqxC8VeWX4MjuirkjCKa51XL9gCgtnB1HqqL2rVzdwQAAAAAAACuVaFChzOFgEceecTp9Q0ePFjHjx/Xiy++qIyMDHXq1Elr1qxR69atJUkZGRlKS0uz94+IiNCaNWs0btw4zZ07V2FhYZozZ44GDRpk7/PLL7+oW7du9uezZs3SrFmzFBMTo/Xr10uSjhw5oiFDhigrK0tBQUG6+eab9dVXX9m3CwComVydhwAAAAAAAGB9FSp0LF682OUBxMfHKz4+vsTXlixZUqwtJiZG27dvL3V9bdq0sU9QXpoVK1ZUKEYAQM1QFXkIAAAAAAAA1lahQgdc45ejt+iDLS0VHhigQdHZ7g4HAFCHvPOOdO6c1KCB9Ic/uDsaAAAAAACAK0ehww1++Hmkvv0hSKFN8il0AACq1YQJUnq61KIFhQ4AAAAAAFA71HN3AAAAAAAAAAAAAJVFoQMAAAAAAAAAAFgWhQ4AAAAAAAAAAGBZFDoAAAAAAAAAAIBlUegAAAAAAAAAAACWRaEDAAAAAAAAAABYFoUOAAAAAAAAAABgWRQ6AAAAAAAAAACAZXm6O4C6yMf7pAJ8/dU8wLg7FABAHRMS4vgvAAAAAACA1VHocIOeN4zWC3eMU0TzcHeHAgCoY7ZudXcEAAAAAAAArsWtqwAAAAAAAAAAgGVR6AAAAAAAAAAAAJZFoQMAAAAAAAAAAFgWc3S4wfc/jtHEk13VsqmP/jo8w93hAADqkD/9STpxQmraVHrjDXdHAwAAAAAAcOUodLjB0RM36XBmkEKb5Eui0AEAqD6ffCKlp0stWrg7EgAAAAAAANfg1lUAAAAAAAAAAMCyKHQAAAAAAAAAAADLcnuhY968eYqIiJCvr68iIyO1YcOGMvsnJycrMjJSvr6+atu2rRYsWODw+u7duzVo0CC1adNGNptNiYmJLtkuAAAAAAAAAACoedxa6Fi5cqXGjh2ryZMna8eOHerdu7cGDBigtLS0EvunpqZq4MCB6t27t3bs2KFnn31WTz75pFatWmXvc+7cObVt21YzZsxQSEiIS7YLAAAAAAAAAABqJrcWOmbPnq0RI0Zo5MiR6tChgxITE9WqVSvNnz+/xP4LFixQeHi4EhMT1aFDB40cOVKPPfaYZs2aZe9z4403aubMmXrooYfk4+Pjku0CAAAAAAAAAICayW2Fjry8PG3btk2xsbEO7bGxsdq8eXOJy6SkpBTr379/f23dulX5+flVtl1Jys3NVU5OjsMDAAAAAAAAAAC4l9sKHVlZWSosLFRwcLBDe3BwsDIzM0tcJjMzs8T+BQUFysrKqrLtStL06dMVEBBgf7Rq1cqp7QEAAAAAAAAAgKrj9snIbTabw3NjTLG28vqX1O7q7U6aNEnZ2dn2x+HDhyu0PQAAAAAAAAAA4Hqe7tpws2bN5OHhUewqiqNHjxa72uKSkJCQEvt7enoqMDCwyrYrST4+PqXO+VFRYc3Xq3PozWoVWN8l6wMAwFlDhkgnT0pNmrg7EgAAAAAAANdw2xUd3t7eioyMVFJSkkN7UlKSevToUeIy0dHRxfqvXbtWUVFR8vLyqrLtulr7tm/quQe+19TBv1bL9gAAuGTmTOkf/7j4LwAAAAAAQG3gtis6JCkhIUFDhw5VVFSUoqOjtXDhQqWlpSkuLk7SxdtFpaena+nSpZKkuLg4vf7660pISNCoUaOUkpKiRYsWafny5fZ15uXlac+ePfb/p6ena+fOnfLz89PVV1/t1HYBAAAAAAAAAIA1uLXQMXjwYB0/flwvvviiMjIy1KlTJ61Zs0atW7eWJGVkZCgtLc3ePyIiQmvWrNG4ceM0d+5chYWFac6cORo0aJC9zy+//KJu3brZn8+aNUuzZs1STEyM1q9f79R2AQAAAAAAAACANbi10CFJ8fHxio+PL/G1JUuWFGuLiYnR9u3bS11fmzZt7BOUV3a7AIC6Zd68eZo5c6YyMjLUsWNHJSYmqnfv3iX2Xb9+vW655ZZi7Xv37lX79u2rOlQAAAAAAAD8htvm6KjLvvxmkW75Sz9FT7ra3aEAQJ23cuVKjR07VpMnT9aOHTvUu3dvDRgwwOGKwpLs27dPGRkZ9sc111xTTRFfmfbtpUaNLv4LAAAAAABQG1DocIOCQl+dzfXU2Vx2PwC42+zZszVixAiNHDlSHTp0UGJiolq1aqX58+eXuVzz5s0VEhJif3h4eFRTxFfmzBnp9OmL/wIAAAAAANQGfNIOAKiz8vLytG3bNsXGxjq0x8bGavPmzWUu261bN4WGhuq2227TF198UWq/3Nxc5eTkODwAAAAAAADgOhQ6AAB1VlZWlgoLCxUcHOzQHhwcrMzMzBKXCQ0N1cKFC7Vq1SqtXr1a7dq102233aYvv/yyxP7Tp09XQECA/dGqVSuXjwMAAAAAAKAuc/tk5AAAuJvNZnN4bowp1nZJu3bt1K5dO/vz6OhoHT58WLNmzVKfPn2K9Z80aZISEhLsz3Nycih2AAAAAAAAuBBXdAAA6qxmzZrJw8Oj2NUbR48eLXaVR1luvvlm7d+/v8TXfHx81KhRI4cHAAAAAAAAXIdCBwCgzvL29lZkZKSSkpIc2pOSktSjRw+n17Njxw6Fhoa6OjwAAAAAAAA4gVtXAQDqtISEBA0dOlRRUVGKjo7WwoULlZaWpri4OEkXbz2Vnp6upUuXSpISExPVpk0bdezYUXl5eVq2bJlWrVqlVatWuXMYAAAAAAAAdRaFDgBAnTZ48GAdP35cL774ojIyMtSpUyetWbNGrVu3liRlZGQoLS3N3j8vL0/jx49Xenq66tevr44dO+qTTz7RwIED3TUEAAAAAACAOo1CBwCgzouPj1d8fHyJry1ZssTh+YQJEzRhwoRqiAoAAAAAAADOoNDhBp2umaNBXe9X62ZN3R0KAKCOWbBAOn9eql/f3ZEAAAAAAAC4BoUON2ge+LVuv76HIpp7uzsUAEAdc+ed7o4AAAAAAADAteq5OwAAAAAAAAAAAIDKotABAAAAAAAAAAAsi1tXuUH26Wv03aHGyjnnqy5tLrg7HABAHbJtm5SXJ3l7S5GR7o4GAAAAAADgylHocINtu6dq844ghTbJ13ezf3R3OACAOuSee6T0dKlFC+nIEXdHAwAAAAAAcOW4dRUAAAAAAAAAALAsCh0AAAAAAAAAAMCyKHQAAAAAAAAAAADLcnuhY968eYqIiJCvr68iIyO1YcOGMvsnJycrMjJSvr6+atu2rRYsWFCsz6pVq3TdddfJx8dH1113nd5//32H16dOnSqbzebwCAkJcem4AAAAAAAAAABA1XNroWPlypUaO3asJk+erB07dqh3794aMGCA0tLSSuyfmpqqgQMHqnfv3tqxY4eeffZZPfnkk1q1apW9T0pKigYPHqyhQ4fq22+/1dChQ/Xggw/q66+/dlhXx44dlZGRYX/s2rWrSscKAAAAAAAAAABcz62FjtmzZ2vEiBEaOXKkOnTooMTERLVq1Urz588vsf+CBQsUHh6uxMREdejQQSNHjtRjjz2mWbNm2fskJiaqX79+mjRpktq3b69JkybptttuU2JiosO6PD09FRISYn8EBQVV5VABAAAAAAAAAEAVcFuhIy8vT9u2bVNsbKxDe2xsrDZv3lziMikpKcX69+/fX1u3blV+fn6ZfX67zv379yssLEwRERF66KGH9PPPP5cZb25urnJychweAAAAAAAAAADAvdxW6MjKylJhYaGCg4Md2oODg5WZmVniMpmZmSX2LygoUFZWVpl9Ll9n9+7dtXTpUn322Wd68803lZmZqR49euj48eOlxjt9+nQFBATYH61atarQeAEAAAAAAAAAgOu5fTJym83m8NwYU6ytvP6/bS9vnQMGDNCgQYPUuXNn3X777frkk08kSW+99Vap2500aZKys7Ptj8OHD5czMgAAAAAAAAAAUNU83bXhZs2aycPDo9jVG0ePHi12RcYlISEhJfb39PRUYGBgmX1KW6ckNWzYUJ07d9b+/ftL7ePj4yMfH58yx+SsPlEjNan/aEUEtXTJ+gAAcNbevZIxUhnfKQAAAAAAALAUt13R4e3trcjISCUlJTm0JyUlqUePHiUuEx0dXaz/2rVrFRUVJS8vrzL7lLZO6eL8G3v37lVoaGhlhlJhnp7n5edbIL/6RdWyPQAALvH3lxo1uvgvAAAAAABAbeDWW1clJCToH//4h/75z39q7969GjdunNLS0hQXFyfp4u2iHnnkEXv/uLg4HTp0SAkJCdq7d6/++c9/atGiRRo/fry9z5gxY7R27Vq9+uqr+uGHH/Tqq69q3bp1Gjt2rL3P+PHjlZycrNTUVH399dd64IEHlJOTo2HDhlXb2AEAAAAAAAAAwJVz262rJGnw4ME6fvy4XnzxRWVkZKhTp05as2aNWrduLUnKyMhQWlqavX9ERITWrFmjcePGae7cuQoLC9OcOXM0aNAge58ePXpoxYoVeu655/T888/rqquu0sqVK9W9e3d7nyNHjmjIkCHKyspSUFCQbr75Zn311Vf27QIAAAAAAAAAAGtwa6FDkuLj4xUfH1/ia0uWLCnWFhMTo+3bt5e5zgceeEAPPPBAqa+vWLGiQjG6WuqRQVqYdLXCm/nrz/2PuzUWAEDdMnu2lJNz8fZVCQnujgYAAAAAAODKub3QURelHrlfP/wcpNAm+RQ6AADVavZsKT1datGCQgcAAAAAAKgd3DpHBwAAAAAAAAAAwJXgig4AAOqgoqJCHThwsMw+jRo1UlBQUPUEBAAAAAAAUEkUOgAAqGXy8vJ06NChEl8rKGgtyVMnTpzQY489VuZ6/P39tXjxYoodAAAAAACgRqPQAQBALXLmzBmlpqZq8uTJ8vb2Lvb6yZPvSmqu/PwCRUdHKzg4uMT1HD9+XBs2bFBOTg6FDgAAAAAAUKNR6AAAoBa5cOGC6tWrp549e6pFixbFXv/22/rKy5MkI39//1ILHQAAAAAAAFZBoQMAgFqoSZMmJRYx6tXzcEM0AAAAAAAAVaeeuwMAAAAAAAAAAACoLAodAAAAAAAAAADAsrh1lRsE+P2k5n6eatGU24cAAKpXRMQJNWiQpaysH9wdCgAAAAAAgEtwRYcbRHaaon8+/pWWjTns7lAAAHXMU099qWHD3lDTpsPdHQoAAAAAAIBLUOgAAAAAAAAAAACWRaEDAAAAAAAAAABYFnN0AACAEuXl5enQoUNl9mnUqJGCgoKqKSIAAAAAAIDiKHS4wbbvX9BjB9urRVMP5ukAAFSrv/61j379tZdOnIiR9E2p/c6cOaPU1FRNnjxZ3t7epfbz9/fX4sWLKXYAAAAAAAC3odDhBtlnrtbRE010/HS+u0MBANQxqalNdfJkA9Wr56myCh0XLlxQvXr11LNnT7Vo0aLEPsePH9fnn3+uXbt2qXXr1qWui6s+AAAAAABAVaLQAQAAStWkSRMFBweX+BpXfQAAAAAAgJqAQkcNlct90QEANZyzV31s2LBBOTk55CwAAAAAAFAlKHTUQCfPZOvn1J/18rMvyMfHp9R+vv4N9MbiN/ngCADgVmVd9QEAAAAAAFDVKHTUQGdzz8nb5qlRNz+kq8LalNjnlxOZeiNlOd+QBQAAAAAAAADUaW4vdMybN08zZ85URkaGOnbsqMTERPXu3bvU/snJyUpISNDu3bsVFhamCRMmKC4uzqHPqlWr9Pzzz+vAgQO66qqr9PLLL+u+++67ou26Q1iTYEU0D3d3GABQ61VFLsL/yXPidoyX+pU114ezfZy5teOxY8eUk5NTbkzcJhIAAAAAgJrPrYWOlStXauzYsZo3b5569uypN954QwMGDNCePXsUHl78A/7U1FQNHDhQo0aN0rJly7Rp0ybFx8crKChIgwYNkiSlpKRo8ODBeumll3Tffffp/fff14MPPqiNGzeqe/fuldpuTeXMPB6S6z6kceZDIT4QAmA1VZGL8H+cnbA8Ly9Phw8fVuvWreXpWfLpiTN9pPInPz927JgeffRRnT59utz4vb299fLLLyswMLDUPuQ+AAAAAADcy62FjtmzZ2vEiBEaOXKkJCkxMVGfffaZ5s+fr+nTpxfrv2DBAoWHhysxMVGS1KFDB23dulWzZs2yf7iUmJiofv36adKkSZKkSZMmKTk5WYmJiVq+fHmltlsTOTuPh+SauTyOHTumPz06ShdOn6vybV2+zfIKK676Zi9QE/GN8+pRFbkI/8eZCcsl6aefftKhQ4cUHR1daj9n+jgz+XlOTo5Onz6t3r17l1nASEtL0/LlyzVmzJgyc40zxZDqvhLFqvmRL1W4DvsSAAAAQF3itkJHXl6etm3bpokTJzq0x8bGavPmzSUuk5KSotjYWIe2/v37a9GiRcrPz5eXl5dSUlI0bty4Yn0ufSBVme1KUm5urnJzc+3Ps7OzJcmpDxsud/r0aRljk+Sj/IIL2pX2Q7E+B48eVmFRkX7KPKhCmylxPbsP75OHqad+bXsoNDCk1O0dP31SH+75XF999ZVatWpVoVgvd/jwYZ06dlJ3drhFgf5NqnRbknTixAm99vIMFV4oKLVPXl6e0o4cVkSr1vLwKv1XuZ6Ph555bpKaNm16RTEB1cmZv4FLfP3q62/z/65mzZpVaBv+/v6y2WyVDbFWqKpcdDlX5o+CggL98ssvunDhQqn9jh49qqKiImVmZqpevXrFXi8sPCmpQMacLrWPM+upaJ/c3Nwy487Lyyu3nzN9cnNzdf78ee3Zs6fUKzYOHz5s/7mUFdOZM2ckSVdffXWpf19Hjx5VUlKSHn/88VILC3l5eUpPT1erVq3KvBLFx8dHzz33XKn56sSJE3rllVd0/vz5Utfhyu1VN2fHV9Piromc3Zd+fn6aP39+hfOHRA4BAAAAULPYjDElf5JexX755Re1aNFCmzZtUo8ePeztr7zyit566y3t27ev2DLXXnuthg8frmeffdbetnnzZvXs2VO//PKLQkND5e3trSVLlugPf/iDvc8777yjRx99VLm5uZXariRNnTpVL7zwgiuGDgA1QnZ2tho1auTuMNyqqnLR5cgfAGojcggAAACAmsTtk5H/9ptgxpgyvx1WUv/ftjuzzopud9KkSUpISLA/Lyoq0okTJxQYGFihb7Pl5OSoVatWOnz4MG8Oxf4oCfvEEfujOFftE39/fxdGZW1VkYsuuZL8Udt+/2vbeKTaNybGU/PVlDGRQwAAAADUJG4rdDRr1kweHh7KzMx0aD969KiCg4NLXCYkJKTE/p6envb7YpfW59I6K7Nd6eJtEn47F0bjxo1LH2A5GjVqVGvecLsC+6M49okj9kdx7JMrV1W56HKuyB+17Wdd28Yj1b4xMZ6arzaOCQAAAAAqq+QbalcDb29vRUZGKikpyaE9KSnJ4fYhl4uOji7Wf+3atYqKirLfE720PpfWWZntAgBqp6rKRQAAAAAAAKg+br11VUJCgoYOHaqoqChFR0dr4cKFSktLU1xcnKSLt/tIT0/X0qVLJUlxcXF6/fXXlZCQoFGjRiklJUWLFi3S8uXL7escM2aM+vTpo1dffVX33HOP/vWvf2ndunXauHGj09sFANQdVZGLAAAAAAAAUH3cWugYPHiwjh8/rhdffFEZGRnq1KmT1qxZo9atW0uSMjIylJaWZu8fERGhNWvWaNy4cZo7d67CwsI0Z84cDRo0yN6nR48eWrFihZ577jk9//zzuuqqq7Ry5Up1797d6e1WJR8fH02ZMqXYbUzqKvZHcewTR+yP4tgnrlUVuchVatvPuraNR6p9Y2I8NV9tHBMAAAAAXCmbuTSDKgAAAAAAAAAAgMW4bY4OAAAAAAAAAACAK0WhAwAAAAAAAAAAWBaFDgAAAAAAAAAAYFkUOgAAAAAAAAAAgGVR6KhG8+bNU0REhHx9fRUZGakNGza4O6QqMX36dN14443y9/dX8+bNde+992rfvn0OfYwxmjp1qsLCwlS/fn317dtXu3fvduiTm5urJ554Qs2aNVPDhg11991368iRI9U5lCoxffp02Ww2jR071t5WF/dHenq6/vjHPyowMFANGjRQ165dtW3bNvvrdW2fFBQU6LnnnlNERITq16+vtm3b6sUXX1RRUZG9T13bJ3VdbcoZzuQFKyvpuG415R2TrcaZY2pN9uWXX+quu+5SWFiYbDabPvjgA4fXnckHAAAAAFCXUOioJitXrtTYsWM1efJk7dixQ71799aAAQOUlpbm7tBcLjk5WY8//ri++uorJSUlqaCgQLGxsTp79qy9z2uvvabZs2fr9ddf1zfffKOQkBD169dPp0+ftvcZO3as3n//fa1YsUIbN27UmTNndOedd6qwsNAdw3KJb775RgsXLtT111/v0F7X9sfJkyfVs2dPeXl56d///rf27Nmjv/71r2rcuLG9T13bJ6+++qoWLFig119/XXv37tVrr72mmTNn6u9//7u9T13bJ3VZbcsZzuQFqyrtuG4lzhyTrcaZY2pNdvbsWXXp0kWvv/56ia87kw8AAAAAoE4xqBY33XSTiYuLc2hr3769mThxopsiqj5Hjx41kkxycrIxxpiioiITEhJiZsyYYe9z4cIFExAQYBYsWGCMMebUqVPGy8vLrFixwt4nPT3d1KtXz3z66afVOwAXOX36tLnmmmtMUlKSiYmJMWPGjDHG1M398cwzz5hevXqV+npd3Cd33HGHeeyxxxza7r//fvPHP/7RGFM390ldVttzxm/zglWVdly3mvKOyVZU3jHVSiSZ999/3/7cmXwAAAAAAHUNV3RUg7y8PG3btk2xsbEO7bGxsdq8ebOboqo+2dnZkqSmTZtKklJTU5WZmemwP3x8fBQTE2PfH9u2bVN+fr5Dn7CwMHXq1Mmy++zxxx/XHXfcodtvv92hvS7ujw8//FBRUVH6/e9/r+bNm6tbt25688037a/XxX3Sq1cvff755/rxxx8lSd9++602btyogQMHSqqb+6Suqgs547d5wapKO65bTXnHZCsq75hqZc7kAwAAAACoazzdHUBdkJWVpcLCQgUHBzu0BwcHKzMz001RVQ9jjBISEtSrVy916tRJkuxjLml/HDp0yN7H29tbTZo0KdbHivtsxYoV2r59u7755ptir9XF/fHzzz9r/vz5SkhI0LPPPqstW7boySeflI+Pjx555JE6uU+eeeYZZWdnq3379vLw8FBhYaFefvllDRkyRFLd/D2pq2p7zigpL1hRWcd1qynvmGxF5R1TrcyZfAAAAAAAdQ2Fjmpks9kcnhtjirXVNqNHj9Z3332njRs3FnutMvvDivvs8OHDGjNmjNauXStfX99S+9WV/SFJRUVFioqK0iuvvCJJ6tatm3bv3q358+c7fKhWl/bJypUrtWzZMr3zzjvq2LGjdu7cqbFjxyosLEzDhg2z96tL+6Suq605o6y8YBXOHtetwtljspU4e0y1stp6jAAAAACAyuDWVdWgWbNm8vDwKPZN3KNHjxb7Nl5t8sQTT+jDDz/UF198oZYtW9rbQ0JCJKnM/RESEqK8vDydPHmy1D5WsW3bNh09elSRkZHy9PSUp6enkpOTNWfOHHl6etrHU1f2hySFhobquuuuc2jr0KGDfaLluvY7IklPP/20Jk6cqIceekidO3fW0KFDNW7cOE2fPl1S3dwndVVtzhml5QWrKe+4XlhY6O4QK6S8Y7IVlXdMtTJn8gEAAAAA1DUUOqqBt7e3IiMjlZSU5NCelJSkHj16uCmqqmOM0ejRo7V69Wr95z//UUREhMPrERERCgkJcdgfeXl5Sk5Otu+PyMhIeXl5OfTJyMjQ999/b7l9dtttt2nXrl3auXOn/REVFaWHH35YO3fuVNu2bevU/pCknj17at++fQ5tP/74o1q3bi2p7v2OSNK5c+dUr57jIdnDw0NFRUWS6uY+qatqY84oLy9YTXnHdQ8PD3eHWCHlHZOtqLxjqpU5kw8AAAAAoM5xwwToddKKFSuMl5eXWbRokdmzZ48ZO3asadiwoTl48KC7Q3O5P//5zyYgIMCsX7/eZGRk2B/nzp2z95kxY4YJCAgwq1evNrt27TJDhgwxoaGhJicnx94nLi7OtGzZ0qxbt85s377d3HrrraZLly6moKDAHcNyqZiYGDNmzBj787q2P7Zs2WI8PT3Nyy+/bPbv32/efvtt06BBA7Ns2TJ7n7q2T4YNG2ZatGhhPv74Y5OammpWr15tmjVrZiZMmGDvU9f2SV1W23KGM3nB6n57XLcSZ47JVuPMMbUmO336tNmxY4fZsWOHkWRmz55tduzYYQ4dOmSMcS4fAAAAAEBdQqGjGs2dO9e0bt3aeHt7mxtuuMEkJye7O6QqIanEx+LFi+19ioqKzJQpU0xISIjx8fExffr0Mbt27XJYz/nz583o0aNN06ZNTf369c2dd95p0tLSqnk0VeO3H4jVxf3x0UcfmU6dOhkfHx/Tvn17s3DhQofX69o+ycnJMWPGjDHh4eHG19fXtG3b1kyePNnk5uba+9S1fVLX1aac4UxesDorFzqMKf+YbDXOHFNrsi+++KLEv5lhw4YZY5zLBwAAAABQl9iMMaa6ryIBAAAAAAAAAABwBeboAAAAAAAAAAAAlkWhAwAAAAAAAAAAWBaFDgAAAAAAAAAAYFkUOgAAAAAAAAAAgGVR6AAAAAAAAAAAAJZFoQMAAAAAAAAAAFgWhQ4AAAAAAAAAAGBZFDoAAAAAAAAAAIBlUejAFenbt6/Gjh1bqWWHDx+ue++994pj2LRpkzp37iwvLy+XrK8uupKfozMyMzPVr18/NWzYUI0bN66y7VQHV/3eAqg8q/4d2mw2ffDBB+4Oo0ar6nxUEQcPHpTNZtPOnTvdHQoAAAAAoBwUOmB5CQkJ6tq1q1JTU7VkyRK3xVGTPpypaf77v/9bGRkZ2rlzp3788UetX79eNptNp06dcndopSrtA66//e1vbv09A1A3/w7btGmjxMTEGr9OqyqpeNaqVStlZGSoU6dO7gkKAAAAAOA0Ch2wvAMHDujWW29Vy5YtK321QF5enmuDgoMDBw4oMjJS11xzjZo3b+6y9RpjVFBQUKFl8vPzr2ibAQEBlr8qBbA6/g6rT2FhoYqKitwdRqVdyTHfw8NDISEh8vT0dGFEAAAAAICqQKGjFuvbt69Gjx6t0aNHq3HjxgoMDNRzzz0nY4ykix/uT5gwQS1atFDDhg3VvXt3rV+/3r788ePHNWTIELVs2VINGjRQ586dtXz58jK3+emnnyogIEBLly51Os4XXnhBzZs3V6NGjfSnP/3JoehgjNFrr72mtm3bqn79+urSpYvee+89Sf/3jfvjx4/rsccek81ms3/DNzk5WTfddJN8fHwUGhqqiRMnOnwgfmnfJCQkqFmzZurXr58kac+ePRo4cKD8/PwUHBysoUOHKisrq9wxDB8+XMnJyfrb3/4mm80mm82mgwcPOhVLWcr7GUru/zl+88036tevn5o1a6aAgADFxMRo+/bt9tfbtGmjVatWaenSpbLZbBo+fLhuueUWSVKTJk3sbVLZP29J9itBPvvsM0VFRcnHx0cbNmwoM76pU6eqa9eu+uc//6m2bdvKx8dHxhh9+umn6tWrl32/3nnnnTpw4IB9uYiICElSt27dZLPZ1LdvX0nFv/Wbm5urJ598Us2bN5evr6969eqlb775ptz9BtQVffv21RNPPKGxY8eqSZMmCg4O1sKFC3X27Fk9+uij8vf311VXXaV///vfki5+sD5ixAhFRESofv36ateunf72t785rPO3f4d9+/bVk08+qQkTJqhp06YKCQnR1KlTnY7RZrNp/vz5GjBggOrXr6+IiAi9++67Dn3S09M1ePBgNWnSRIGBgbrnnnvsx3mp/GNhSV588UUFBweXe2ukvn376tChQxo3bpw9x1yyatUqdezYUT4+PmrTpo3++te/OjXm0ta5ZMkSNW7cWB9//LGuu+46+fj46NChQzp58qQeeeQRNWnSRA0aNNCAAQO0f/9++/ouHWsvl5iYqDZt2tifFxQU6Mknn7Qfd5955hkNGzas2JUURUVFV/SzXLBgge655x41bNhQ06ZNK/d3aurUqXrrrbf0r3/9y74v1q9fX+KVfVeS0wEAAAAAVcig1oqJiTF+fn5mzJgx5ocffjDLli0zDRo0MAsXLjTGGPOHP/zB9OjRw3z55Zfmp59+MjNnzjQ+Pj7mxx9/NMYYc+TIETNz5kyzY8cOc+DAATNnzhzj4eFhvvrqK4dtjBkzxhhjzPLly42/v7/54IMPnIpv2LBhxs/PzwwePNh8//335uOPPzZBQUHm2Weftfd59tlnTfv27c2nn35qDhw4YBYvXmx8fHzM+vXrTUFBgcnIyDCNGjUyiYmJJiMjw5w7d84cOXLENGjQwMTHx5u9e/ea999/3zRr1sxMmTKl2L55+umnzQ8//GD+X3v3HlN1/f8B/AlHQAR0iEKCAk2QwDveQipozjAXUtosQdFkFF4RLzOboEKbpmO6EktNiTnmFZ2zUEhxCRIgFy+LIycQBA1BSE0UIQ6v3x/++MwjtwPKF7XnY2vr8z6fz/vzOuf14f1e73ef91utVstff/0l/fr1kzVr1oharZbc3FyZPHmyvPvuu+1+l7t374qHh4cEBwdLeXm5lJeXS0NDg16xtKW9HIp0fx7PnDkj+/btk/z8fMnPz5egoCCxsbGRf/75R0REKisrZcqUKTJz5kwpLy+Xu3fvSkJCggCQgoICpUyk7XyLiJw9e1YAyIgRIyQ5OVkKCwulqqqqzfjWrVsnZmZm4uPjI7m5uXLp0iVpbGyUI0eOSEJCgmg0GsnLyxNfX18ZPny4aLVaERHJysoSAHL69GkpLy+X6upqEXn83Pr5+Sn1L126VGxtbSUxMVH++OMPmTt3rlhaWirnE/3XeXl5iYWFhURFRYlGo5GoqCgxNDSU999/X3bt2iUajUYWLFggVlZW8uDBA6mvr5eIiAjJysqSa9euKe3ewYMHlTqf/jv08vKS3r17y/r160Wj0UhcXJwYGBhIcnKyXjECECsrK9m9e7cUFBTI2rVrRaVSSX5+voiIPHjwQJydnWX+/Ply+fJlyc/PF39/f3FxcZG6ujoRab8tbLrPsWPHpLGxUZYuXSr29vZKW92W6upqGThwoERGRip9jIhIdna2GBoaSmRkpBQUFEhsbKyYmppKbGxsp+uMjY0VIyMjmThxopw/f16uXr0qNTU1Mm3aNHF1dZVz587JxYsXxcfHR5ycnKS+vl5EHre1I0eO1LnH1q1bxcHBQTn++uuvpW/fvnL06FFRq9USEhIivXv3fu65tLa2lj179khRUZGUlJS0+0zdv39fZs6cKVOmTFF+i7q6OikuLhYAkpeXJyLyzH06ERERERERdR1OdLzCvLy8xNXVVRobG5Wy1atXi6urqxQWFoqBgYHcvHlT55pJkybJmjVrWq1z6tSpsmLFCp17hIaGSkxMjPTp00dSUlL0jm/u3LnSt29fefDggVL2/fffi7m5uWi1WqmpqZGePXtKenq6znVBQUEya9Ys5bhPnz46gzpfffWVuLi46HzvmJgYpd6muEeNGqVTb3h4uLz33ns6ZWVlZcqAfHuenCzoSCzt1dlaDkXkhcjj0xoaGsTCwkJOnDihlPn5+cncuXOV46YJizt37ihl+uS76Tp9J2FEHg++GRkZSWVlZZvnVVZWCgC5cuWKiEizAa4mTw6w1tTUiJGRkcTHxyuf19fXi62trWzevFnvGIleZV5eXvLWW28pxw0NDWJmZiZz5sxRysrLywWA/P777y3WsXDhQpkxY4Zy3NJEx5P3EBEZN26crF69Wq8YAUhISIhO2YQJE2TBggUiIrJnz55mbXldXZ2YmppKUlJSi3W21BYCkMOHD8vs2bPljTfekLKyMr3iExFxcHCQrVu36pT5+/vL5MmTdcpWrVolbm5una4zNjZWAMjFixeVMo1GIwDk/PnzSllVVZWYmprKoUOHRES/iQ4bGxvZsmWLctzQ0CD29vbPPZfLli1r97z2nimR5v3As/bpRERERERE1HW46PAr7s0339RZ4sLDwwPR0dHIzs6GiGDIkCE659fV1cHKygrA4+VDNm3ahIMHD+LmzZuoq6tDXV0dzMzMdK5JSEhARUUF0tLSMH78+A7FN3LkSPTq1UsnvpqaGpSVlaGyshKPHj1SlpVqUl9fj9GjR7dap1qthoeHh8739vT0RE1NDW7cuAF7e3sAwNixY3Wuy8nJwdmzZ2Fubt6szqKioma/lT70jaUtreVQq9UiNze32/NYWVmJiIgIpKSkoKKiAlqtFg8fPkRpaanedQCPlw3TN99P5649Dg4O6N+/v05ZUVERwsPDkZGRgaqqKmUN+tLSUr03ni0qKsK///4LT09PpczIyAjjx4+HWq3uUIxEr7IRI0Yo/65SqWBlZYXhw4crZTY2NgAetycA8MMPP+DHH3/E9evXUVtbi/r6+mbLIrV1DwAYMGCAUp8+PDw8mh03LVmUk5ODwsJCWFhY6Jzz6NEjZck7fdvCsLAwmJiYICMjA/369dM7vpao1Wr4+fnplHl6emLbtm3QarVQqVSdqtfY2Fjn91Sr1ejRowcmTJiglFlZWcHFxUXvtu7evXuoqKjQ6V9UKhXGjBnTbA+QZ81lS31EZ56ppz2PPp2IiIiIiIi6Bic6/sNUKhVycnKaDYQ0DfRHR0dj69at2LZtG4YPHw4zMzMsW7as2cbdo0aNQm5uLmJjYzFu3DidAYDOMjAwUAY+fvnlF9jZ2el8bmJi0uq1ItIsBvn/PS2eLH96oL+xsRG+vr745ptvmtU5YMCAjn2BDsbSWY2Njd2ex3nz5uH27dvYtm0bHBwcYGJiAg8Pjw5v8N6RfD+du/a0dL6vry8GDRqE3bt3w9bWFo2NjRg2bFiH4m4tly3lnei/zMjISOfYwMBAp6zp76WxsRGHDh1CWFgYoqOj4eHhAQsLC2zZsgWZmZkdvsezbqL9ZFxjxoxBfHx8s3OaJlH1bQsnT56M/fv3IykpCQEBAc8UX1t9zLMwNTXVqbe1Op+8v6GhYbPzWtoIXJ94nzWXT7f5nX2mntbVfToRERERERF1Hic6XnEZGRnNjp2dnTF69GhotVpUVlbi7bffbvHa1NRU+Pn5Yfbs2QAeD/T8+eefcHV11Tlv8ODBiI6Ohre3N1QqFbZv3653fJcuXUJtbS1MTU2V+MzNzTFw4EBYWlrCxMQEpaWl8PLy0rtONzc3JCQk6AxIpKenw8LCotkA+pPc3d2RkJAAR0dH9OjR8T8NY2NjaLXa5xLLk1rLoUqleiHymJqaih07dmDq1KkAgLKysnY3cDc2NgYAnd+radPbjua7M6qrq6FWq7Fz507ld0tLS2s3xqc5OTnB2NgYaWlp8Pf3B/B4YC87OxvLli3rmuCJXnGpqamYOHEiFi5cqJQ1vTXRlTIyMhAYGKhz3PQ2mbu7Ow4ePAhra2v07t27xev1bQunTZsGX19f+Pv7Q6VS4dNPP9Urvtb6mKfbrvT0dAwZMkSvtzlaqrMlbm5uaGhoQGZmJiZOnAjgcTuq0WiUvqR///64deuWTn/35Cbeffr0gY2NDbKyspR2V6vVIi8vr8NvVnSUPs+UPr/F8+jTiYiIiIiIqGsYdncA1LXKysqwfPlyFBQUYP/+/fjuu+8QGhqKIUOGICAgAIGBgTh69CiKi4tx4cIFfPPNN0hMTATweBD3119/RXp6OtRqNb744gvcunWrxfsMGTIEZ8+eRUJCQocGeOvr6xEUFIT8/HycPHkS69atw+LFi2FoaAgLCwusXLkSYWFhiIuLQ1FREfLy8hATE4O4uLhW61y4cCHKysqwZMkSXL16FcePH8e6deuwfPlyGBq2/sgvWrQIf//9N2bNmoWsrCxcu3YNycnJmD9/vl4DQY6OjsjMzERJSYmyFFJnY3lSazkE8ELk0cnJCfv27YNarUZmZiYCAgKUiavWODg4wMDAAD///DNu376NmpqaTue7MywtLWFlZYVdu3ahsLAQKSkpWL58uc451tbWMDU1xalTp1BRUYF79+41q8fMzAwLFizAqlWrcOrUKeTn5yM4OBgPHz5EUFDQc42Z6L/CyckJ2dnZSEpKgkajQXh4OC5cuNDl9z18+DD27t0LjUaDdevWISsrC4sXLwYABAQEoF+/fvDz80NqaiqKi4vx22+/ITQ0FDdu3FDi1rct/Oijj7Bv3z589tlnOHLkiF7xOTo64ty5c7h586YygbJixQqcOXMGUVFR0Gg0iIuLw/bt27Fy5cpO19kSZ2dn+Pn5ITg4GGlpabh06RJmz54NOzs7Zeksb29v3L59G5s3b0ZRURFiYmJw8uRJnXqWLFmCjRs34vjx4ygoKEBoaCju3LnT5W9D6PNMOTo64vLlyygoKEBVVVWLb6M8jz6diIiIiIiIugb/q+wVFxgYiNraWowfPx6LFi3CkiVL8PnnnwMAYmNjERgYiBUrVsDFxQXTpk1DZmYmBg0aBAAIDw+Hu7s7fHx84O3tjddeew0ffvhhq/dycXFBSkoK9u/fjxUrVugV36RJk+Ds7Ix33nkHM2fOhK+vL9avX698HhUVhYiICGzcuBGurq7w8fHBiRMn8Prrr7dap52dHRITE5GVlYWRI0ciJCQEQUFBWLt2bZux2Nra4vz589BqtfDx8cGwYcMQGhqKPn366DWAsXLlSqhUKri5uaF///4oLS3tdCxPaiuHQPfnce/evbhz5w5Gjx6NOXPmYOnSpbC2tm7zGjs7O2zYsAFffvklbGxslMHEzuS7MwwNDXHgwAHk5ORg2LBhCAsLw5YtW3TO6dGjB7799lvs3LkTtra2zdbBb7Jp0ybMmDEDc+bMgbu7OwoLC5GUlARLS8vnGjPRf0VISAimT5+OTz75BBMmTEB1dbXO/4nfVTZs2IADBw5gxIgRiIuLQ3x8PNzc3AAAvXr1wrlz52Bvb4/p06fD1dUV8+fPR21trfKGR0fbwo8//hhxcXGYM2cOjh492m58kZGRKCkpweDBg5Xlstzd3XHo0CEcOHAAw4YNQ0REBCIjIzFv3jy9vnNLdbYmNjYWY8aMwQcffAAPDw+ICBITE5VlplxdXbFjxw7ExMRg5MiRyMrKajbhsnr1asyaNQuBgYHw8PCAubk5fHx80LNnT73i7Sx9nqng4GC4uLhg7Nix6N+/P86fP9+snufRpxMREREREVHXMJDnsZgzvZC8vb0xatQobNu2rbtDoU5iDomIup6BgQGOHTvW5iQwPX+NjY1wdXXFzJkzERUV1d3hEBERERER0UuMe3QQERERUZe7fv06kpOT4eXlhbq6Omzfvh3FxcXKHkdEREREREREncWlq6jLmJubt/pPampqd4fXIaWlpW1+n9LS0heizq7woudx6NChrcYXHx/f3eERUTeLj49vtY0YOnRod4cH4PFm2W21tS9Knc/K0NAQP/30E8aNGwdPT09cuXIFp0+fVjY0b8/LkEsiIiIiIiLqHly6irpMYWFhq5/Z2dm1u2H1i6ShoQElJSWtfu7o6IgePTr2glRX1NkVXvQ8Xr9+vcVNYwHAxsYGFhYW/+OIiOhFcv/+fVRUVLT4mZGRERwcHP7HETVXW1uLmzdvtvq5k5PTC1Fnd3sZcklERERERETdgxMdRERERERERERERET00uLSVURERERERERERERE9NLiRAcREREREREREREREb20ONFBREREREREREREREQvLU50EBERERERERERERHRS4sTHURERERERERERERE9NLiRAcREREREREREREREb20ONFBREREREREREREREQvrf8D15FhUfwrVn0AAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "\n", - "# Classify with default thresholds\n", - "unit_type, labels = sc.classify_units(metrics)\n", - "\n", - "# Or customize thresholds\n", - "thresholds = sc.get_default_thresholds() # probably not correct format. where should i put this? \n", - "thresholds[\"snr_bombcell\"][\"min\"] = 3 # Lower threshold\n", - "thresholds[\"amplitude_median\"][\"min\"] = np.nan # Disable\n", - "\n", - "unit_type, labels = sc.classify_units(metrics, thresholds)\n", - "\n", - "# plots!\n", - "# Get summary\n", - "summary = sc.get_classification_summary(unit_type, labels)\n", - "print(summary)\n", - "\n", - "import spikeinterface.widgets as sw\n", - "# Plot histograms with threshold lines\n", - "sw.plot_classification_histograms(metrics)\n", - "\n", - "# Plot waveform overlay by type\n", - "sw.plot_waveform_overlay(analyzer, unit_type, labels)\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE', 'NOISE',\n", - " 'NOISE'], dtype=object)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unit_type\n", - "labels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Total units: {len(sorting.unit_ids)}\")\n", - "print(f\"Analyzer saved to: {analyzer_folder}\")\n", - "print(f\"\\nAvailable extensions:\")\n", - "for ext_name in analyzer.get_loaded_extension_names():\n", - " print(f\" - {ext_name}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Combine metrics\n", - "combined_metrics = template_metrics.join(quality_metrics, how=\"outer\")\n", - "combined_metrics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Save metrics to CSV\n", - "output_folder.mkdir(parents=True, exist_ok=True)\n", - "metrics_csv = output_folder / \"combined_metrics.csv\"\n", - "combined_metrics.to_csv(metrics_csv)\n", - "print(f\"Metrics saved to: {metrics_csv}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 2e8d6ea3dff544431770944003a3ac87bda4b2b0 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Thu, 8 Jan 2026 02:28:45 +0100 Subject: [PATCH 13/49] cleanup --- src/spikeinterface/comparison/__init__.py | 3 + .../comparison/unit_classification.py | 358 +++-------- .../widgets/unit_classification.py | 558 +++--------------- 3 files changed, 156 insertions(+), 763 deletions(-) diff --git a/src/spikeinterface/comparison/__init__.py b/src/spikeinterface/comparison/__init__.py index f2b80909c7..f7a3b0a80d 100644 --- a/src/spikeinterface/comparison/__init__.py +++ b/src/spikeinterface/comparison/__init__.py @@ -42,6 +42,9 @@ ) from .unit_classification import ( + WAVEFORM_METRICS, + SPIKE_QUALITY_METRICS, + NON_SOMATIC_METRICS, get_default_thresholds, classify_units, apply_thresholds, diff --git a/src/spikeinterface/comparison/unit_classification.py b/src/spikeinterface/comparison/unit_classification.py index c312e465a7..ff3ccf2c34 100644 --- a/src/spikeinterface/comparison/unit_classification.py +++ b/src/spikeinterface/comparison/unit_classification.py @@ -1,15 +1,11 @@ """ -Unit classification based on quality metrics and user-defined thresholds. - -This module provides functionality to classify neural units based on quality metrics -(similar to BombCell). Each metric can have min and max thresholds - use NaN to -disable a threshold. +Unit classification based on quality metrics (similar to BombCell). Unit Types: - 0 (NOISE): Units failing waveform quality checks - 1 (GOOD): Units passing all quality thresholds - 2 (MUA): Multi-unit activity - units failing spike quality checks but not waveform checks - 3 (NON_SOMA): Non-somatic units (axonal, etc.) - optional classification + 0 (NOISE): Failed waveform quality checks + 1 (GOOD): Passed all thresholds + 2 (MUA): Failed spike quality checks + 3 (NON_SOMA): Non-somatic units (axonal) """ from __future__ import annotations @@ -19,114 +15,64 @@ from typing import Optional +WAVEFORM_METRICS = [ + "num_positive_peaks", + "num_negative_peaks", + "peak_to_trough_duration", + "waveform_baseline_flatness", + "peak_after_to_trough_ratio", + "exp_decay", +] + +SPIKE_QUALITY_METRICS = [ + "amplitude_median", + "snr_bombcell", + "amplitude_cutoff", + "num_spikes", + "rp_contamination", + "presence_ratio", + "drift_ptp", +] + +NON_SOMATIC_METRICS = [ + "peak_before_to_trough_ratio", + "peak_before_width", + "trough_width", + "peak_before_to_peak_after_ratio", + "main_peak_to_trough_ratio", +] + + def get_default_thresholds() -> dict: """ Returns default thresholds for unit classification. - Each threshold entry has 'min' and 'max' values. Use np.nan to disable - a threshold direction (e.g., if only a minimum matters, set max to np.nan). - - Thresholds are organized by category: - - waveform: Template/waveform shape checks (failures -> NOISE) - - spike_quality: Spike sorting quality checks (failures -> MUA) - - non_somatic: Non-somatic detection (optional, failures -> NON_SOMA) - - Returns - ------- - thresholds : dict - Dictionary of threshold parameters with min/max values. - - Notes - ----- - Metric names correspond to SpikeInterface metric column names: - - Template metrics (from template_metrics extension): - - num_positive_peaks: Number of positive peaks (repolarization peaks) - - num_negative_peaks: Number of negative peaks (troughs) - - peak_to_trough_duration: Duration in seconds from trough to peak - - waveform_baseline_flatness: Baseline flatness metric - - peak_after_to_trough_ratio: Ratio of peak after trough to trough amplitude - - exp_decay: Exponential decay constant for spatial spread - - Quality metrics (from quality_metrics extension): - - amplitude_median: Median spike amplitude (in uV) - - snr_bombcell: Signal-to-noise ratio (BombCell method: raw waveform max / baseline MAD) - - amplitude_cutoff: Estimated fraction of missing spikes - - num_spikes: Total spike count - - rp_contamination: Refractory period contamination - - presence_ratio: Fraction of recording where unit is present - - drift_ptp: Peak-to-peak drift in um + Each metric has 'min' and 'max' values. Use np.nan to disable a threshold. """ - thresholds = { - # ============================================================ - # WAVEFORM QUALITY THRESHOLDS (failures classify as NOISE) - # ============================================================ - # Number of positive peaks (repolarization peaks after trough) - # Good units typically have 1-2 peaks + return { + # Waveform quality (failures -> NOISE) "num_positive_peaks": {"min": np.nan, "max": 2}, - # Number of negative peaks (troughs) in waveform - # Good units typically have 1 main trough "num_negative_peaks": {"min": np.nan, "max": 1}, - # Peak to trough duration in SECONDS (from template_metrics) - # Typical range: 0.0001-0.00115 s (100-1150 μs) - "peak_to_trough_duration": {"min": 0.0001, "max": 0.00115}, - # Baseline flatness - max deviation as fraction of peak amplitude - # Lower is better, typical threshold 0.3 + "peak_to_trough_duration": {"min": 0.0001, "max": 0.00115}, # seconds "waveform_baseline_flatness": {"min": np.nan, "max": 0.5}, - # Peak after trough to trough ratio - helps detect noise - # High values indicate noise (ratio > 0.8 is suspicious) "peak_after_to_trough_ratio": {"min": np.nan, "max": 0.8}, - # Exponential decay constant for spatial spread - # Values outside typical range indicate noise "exp_decay": {"min": 0.01, "max": 0.1}, - # ============================================================ - # SPIKE QUALITY THRESHOLDS (failures classify as MUA) - # ============================================================ - # Median spike amplitude (in uV typically) - # Lower bound ensures sufficient signal - "amplitude_median": {"min": 40, "max": np.nan}, - # Signal-to-noise ratio (BombCell method: raw waveform max / baseline MAD) - # Higher is better, minimum ensures reliable detection + # Spike quality (failures -> MUA) + "amplitude_median": {"min": 40, "max": np.nan}, # uV "snr_bombcell": {"min": 5, "max": np.nan}, - # Amplitude cutoff - estimates fraction of missing spikes - # Lower is better (less missing), max 0.2 means <20% estimated missing "amplitude_cutoff": {"min": np.nan, "max": 0.2}, - # Minimum number of spikes - # Ensures sufficient data for reliable metrics "num_spikes": {"min": 300, "max": np.nan}, - # Refractory period contamination rate - # Lower is better, max typically 0.1 (10%) "rp_contamination": {"min": np.nan, "max": 0.1}, - # Presence ratio - fraction of recording where unit is active - # Higher is better, ensures unit present throughout "presence_ratio": {"min": 0.7, "max": np.nan}, - # Drift MAD - median absolute deviation of drift in um - # Lower is better, ensures stable unit location - "drift_ptp": {"min": np.nan, "max": 100}, - # ============================================================ - # NON-SOMATIC DETECTION THRESHOLDS (optional) - # ============================================================ - # These thresholds identify axonal/dendritic units by their waveform shape - # Non-somatic (axonal) units have: large initial peak, narrow widths, small repolarization - # Peak before to trough ratio - non-somatic have large initial peak relative to trough - # If peak_before/trough > max, classify as non-somatic - "peak_before_to_trough_ratio": {"min": np.nan, "max": 3}, # non-somatic if > max - # Peak before width in MICROSECONDS - non-somatic have narrow initial peaks - # If width < min, classify as non-somatic - "peak_before_width": {"min": 150, "max": np.nan}, # non-somatic if < 150 μs - # Trough width in MICROSECONDS - non-somatic have narrow troughs - # If width < min, classify as non-somatic - "trough_width": {"min": 200, "max": np.nan}, # non-somatic if < 200 μs - # Peak before to peak after ratio - non-somatic have large initial peak vs small repolarization - # If peak_before/peak_after > max, classify as non-somatic - "peak_before_to_peak_after_ratio": {"min": np.nan, "max": 3}, # non-somatic if > max - # Main peak to trough ratio - non-somatic have peak almost as large as trough - # If max_peak/trough > max, classify as non-somatic (somatic units have trough >> peaks) - "main_peak_to_trough_ratio": {"min": np.nan, "max": 0.8}, # non-somatic if > max + "drift_ptp": {"min": np.nan, "max": 100}, # um + # Non-somatic detection + "peak_before_to_trough_ratio": {"min": np.nan, "max": 3}, + "peak_before_width": {"min": 150, "max": np.nan}, # us + "trough_width": {"min": 200, "max": np.nan}, # us + "peak_before_to_peak_after_ratio": {"min": np.nan, "max": 3}, + "main_peak_to_trough_ratio": {"min": np.nan, "max": 0.8}, } - return thresholds - def classify_units( quality_metrics: pd.DataFrame, @@ -137,158 +83,75 @@ def classify_units( """ Classify units based on quality metrics and thresholds. - Classification hierarchy: - 1. NOISE (0): Units failing waveform quality checks - 2. MUA (2): Units passing waveform checks but failing spike quality checks - 3. GOOD (1): Units passing all checks - 4. NON_SOMA (3/4): Optional - units with non-somatic waveform characteristics - Parameters ---------- quality_metrics : pd.DataFrame - DataFrame with quality metrics. Index should be unit_ids. - Can contain metrics from quality_metrics, template_metrics, - and spiketrain_metrics extensions. - thresholds : dict or None, default: None - Threshold dictionary with format {"metric_name": {"min": val, "max": val}}. - Use np.nan to disable a threshold. If None, uses get_default_thresholds(). - classify_non_somatic : bool, default: False - If True, also classify non-somatic (axonal) units. - split_non_somatic_good_mua : bool, default: False - If True and classify_non_somatic is True, split non-somatic into - NON_SOMA_GOOD (3) and NON_SOMA_MUA (4). Only applies if - classify_non_somatic is True. + DataFrame with quality metrics (index = unit_ids). + thresholds : dict or None + Threshold dict: {"metric": {"min": val, "max": val}}. Use np.nan to disable. + classify_non_somatic : bool + If True, detect non-somatic (axonal) units. + split_non_somatic_good_mua : bool + If True, split non-somatic into NON_SOMA_GOOD (3) and NON_SOMA_MUA (4). Returns ------- unit_type : np.ndarray - Numeric classification: 0=NOISE, 1=GOOD, 2=MUA, 3=NON_SOMA (or NON_SOMA_GOOD), - 4=NON_SOMA_MUA (if split_non_somatic_good_mua=True) + Numeric: 0=NOISE, 1=GOOD, 2=MUA, 3=NON_SOMA unit_type_string : np.ndarray - String labels for each unit type. - + String labels. """ if thresholds is None: thresholds = get_default_thresholds() n_units = len(quality_metrics) unit_type = np.full(n_units, np.nan) - - # Define which metrics go to which category - waveform_metrics = [ - "num_positive_peaks", - "num_negative_peaks", - "peak_to_trough_duration", - "waveform_baseline_flatness", - "peak_after_to_trough_ratio", - "exp_decay", - ] - - spike_quality_metrics = [ - "amplitude_median", - "snr_bombcell", - "amplitude_cutoff", - "num_spikes", - "rp_contamination", - "presence_ratio", - "drift_ptp", - ] - - non_somatic_metrics = [ - "peak_before_to_trough_ratio", - "peak_before_width", - "trough_width", - "peak_before_to_peak_after_ratio", - "main_peak_to_trough_ratio", - ] - - # Metrics that should use absolute values for comparison - # (amplitude values are typically negative in extracellular recordings) absolute_value_metrics = ["amplitude_median"] - # ======================================== - # NOISE classification - # ======================================== + # NOISE: waveform failures noise_mask = np.zeros(n_units, dtype=bool) - - for metric_name in waveform_metrics: - if metric_name not in quality_metrics.columns: - continue - if metric_name not in thresholds: + for metric_name in WAVEFORM_METRICS: + if metric_name not in quality_metrics.columns or metric_name not in thresholds: continue - values = quality_metrics[metric_name].values - # Use absolute values for amplitude-based metrics if metric_name in absolute_value_metrics: values = np.abs(values) thresh = thresholds[metric_name] - - # NaN values in metrics are considered failures for waveform metrics noise_mask |= np.isnan(values) - - # Check min threshold if not np.isnan(thresh["min"]): noise_mask |= values < thresh["min"] - - # Check max threshold if not np.isnan(thresh["max"]): noise_mask |= values > thresh["max"] - unit_type[noise_mask] = 0 - # ======================================== - # MUA classification - # ======================================== + # MUA: spike quality failures mua_mask = np.zeros(n_units, dtype=bool) - - for metric_name in spike_quality_metrics: - if metric_name not in quality_metrics.columns: + for metric_name in SPIKE_QUALITY_METRICS: + if metric_name not in quality_metrics.columns or metric_name not in thresholds: continue - if metric_name not in thresholds: - continue - values = quality_metrics[metric_name].values - # Use absolute values for amplitude-based metrics if metric_name in absolute_value_metrics: values = np.abs(values) thresh = thresholds[metric_name] - - # Only apply to units not yet classified as noise valid_mask = np.isnan(unit_type) - - # Check min threshold (NaN values don't fail min threshold for spike quality) if not np.isnan(thresh["min"]): mua_mask |= valid_mask & ~np.isnan(values) & (values < thresh["min"]) - - # Check max threshold (NaN values don't fail max threshold for spike quality) if not np.isnan(thresh["max"]): mua_mask |= valid_mask & ~np.isnan(values) & (values > thresh["max"]) - unit_type[mua_mask & np.isnan(unit_type)] = 2 - # ======================================== - # GOOD classification (passed all checks) - # ======================================== + # GOOD: passed all checks unit_type[np.isnan(unit_type)] = 1 - # ======================================== - # NON-SOMATIC classification - # ======================================== + # NON-SOMATIC if classify_non_somatic: - # Non-somatic (axonal) units require BOTH ratio AND width criteria - # Logic from bombcell: - # is_non_somatic = (ratio_conditions & width_conditions) | standalone_ratio_condition - - # Helper to get metric values safely def get_metric(name): if name in quality_metrics.columns: return quality_metrics[name].values return np.full(n_units, np.nan) - # Width conditions (ALL must be met) peak_before_width = get_metric("peak_before_width") trough_width = get_metric("trough_width") - width_thresh_peak = thresholds.get("peak_before_width", {}).get("min", np.nan) width_thresh_trough = thresholds.get("trough_width", {}).get("min", np.nan) @@ -302,10 +165,8 @@ def get_metric(name): if not np.isnan(width_thresh_trough) else np.zeros(n_units, dtype=bool) ) - width_conditions = narrow_peak & narrow_trough - # Ratio conditions peak_before_to_trough = get_metric("peak_before_to_trough_ratio") peak_before_to_peak_after = get_metric("peak_before_to_peak_after_ratio") main_peak_to_trough = get_metric("main_peak_to_trough_ratio") @@ -314,64 +175,39 @@ def get_metric(name): ratio_thresh_pbpa = thresholds.get("peak_before_to_peak_after_ratio", {}).get("max", np.nan) ratio_thresh_mpt = thresholds.get("main_peak_to_trough_ratio", {}).get("max", np.nan) - # Large initial peak relative to trough large_initial_peak = ( ~np.isnan(peak_before_to_trough) & (peak_before_to_trough > ratio_thresh_pbt) if not np.isnan(ratio_thresh_pbt) else np.zeros(n_units, dtype=bool) ) - - # Large initial peak relative to repolarization peak large_peak_ratio = ( ~np.isnan(peak_before_to_peak_after) & (peak_before_to_peak_after > ratio_thresh_pbpa) if not np.isnan(ratio_thresh_pbpa) else np.zeros(n_units, dtype=bool) ) - - # Main peak almost as large as trough (standalone condition) large_main_peak = ( ~np.isnan(main_peak_to_trough) & (main_peak_to_trough > ratio_thresh_mpt) if not np.isnan(ratio_thresh_mpt) else np.zeros(n_units, dtype=bool) ) - # Combined logic: (ratio AND width conditions) OR standalone ratio - # Requires at least one ratio condition AND both width conditions, OR the standalone ratio + # (ratio AND width) OR standalone main_peak_to_trough ratio_conditions = large_initial_peak | large_peak_ratio is_non_somatic = (ratio_conditions & width_conditions) | large_main_peak - # Apply non-somatic classification if split_non_somatic_good_mua: - # Split into NON_SOMA_GOOD (3) and NON_SOMA_MUA (4) - good_non_somatic = (unit_type == 1) & is_non_somatic - mua_non_somatic = (unit_type == 2) & is_non_somatic - unit_type[good_non_somatic] = 3 - unit_type[mua_non_somatic] = 4 + unit_type[(unit_type == 1) & is_non_somatic] = 3 + unit_type[(unit_type == 2) & is_non_somatic] = 4 else: - # All non-noise non-somatic units get type 3 unit_type[(unit_type != 0) & is_non_somatic] = 3 - # ======================================== - # Create string labels - # ======================================== + # String labels if split_non_somatic_good_mua: - labels = { - 0: "NOISE", - 1: "GOOD", - 2: "MUA", - 3: "NON_SOMA_GOOD", - 4: "NON_SOMA_MUA", - } + labels = {0: "NOISE", 1: "GOOD", 2: "MUA", 3: "NON_SOMA_GOOD", 4: "NON_SOMA_MUA"} else: - labels = { - 0: "NOISE", - 1: "GOOD", - 2: "MUA", - 3: "NON_SOMA", - } + labels = {0: "NOISE", 1: "GOOD", 2: "MUA", 3: "NON_SOMA"} unit_type_string = np.array([labels.get(int(t), "UNKNOWN") for t in unit_type], dtype=object) - return unit_type.astype(int), unit_type_string @@ -380,58 +216,35 @@ def apply_thresholds( thresholds: Optional[dict] = None, ) -> pd.DataFrame: """ - Apply thresholds to quality metrics and return pass/fail status for each. - - This is useful for debugging which metrics are causing units to fail. - - Parameters - ---------- - quality_metrics : pd.DataFrame - DataFrame with quality metrics. - thresholds : dict or None, default: None - Threshold dictionary. If None, uses get_default_thresholds(). - - Returns - ------- - threshold_results : pd.DataFrame - DataFrame with same index as quality_metrics, with columns: - - {metric}_pass: bool, True if metric passes threshold - - {metric}_fail_reason: str, reason for failure ("below_min", "above_max", "nan", or "") + Apply thresholds and return pass/fail status for each metric. + Useful for debugging classification results. """ if thresholds is None: thresholds = get_default_thresholds() results = {} - for metric_name, thresh in thresholds.items(): if metric_name not in quality_metrics.columns: continue values = quality_metrics[metric_name].values n_units = len(values) - - # Initialize passes = np.ones(n_units, dtype=bool) reasons = np.array([""] * n_units, dtype=object) - # Check for NaN nan_mask = np.isnan(values) passes[nan_mask] = False reasons[nan_mask] = "nan" - # Check min threshold if not np.isnan(thresh["min"]): below_min = ~nan_mask & (values < thresh["min"]) passes[below_min] = False reasons[below_min] = "below_min" - # Check max threshold if not np.isnan(thresh["max"]): above_max = ~nan_mask & (values > thresh["max"]) passes[above_max] = False - # Only overwrite if not already failed reasons[above_max & (reasons == "")] = "above_max" - # If both fail, indicate both reasons[above_max & (reasons == "below_min")] = "below_min_and_above_max" results[f"{metric_name}_pass"] = passes @@ -440,35 +253,12 @@ def apply_thresholds( return pd.DataFrame(results, index=quality_metrics.index) -def get_classification_summary( - unit_type: np.ndarray, - unit_type_string: np.ndarray, -) -> dict: - """ - Get summary statistics of unit classification. - - Parameters - ---------- - unit_type : np.ndarray - Numeric unit type array from classify_units(). - unit_type_string : np.ndarray - String labels from classify_units(). - - Returns - ------- - summary : dict - Dictionary with counts and percentages for each unit type. - """ +def get_classification_summary(unit_type: np.ndarray, unit_type_string: np.ndarray) -> dict: + """Get counts and percentages for each unit type.""" n_total = len(unit_type) unique_types, counts = np.unique(unit_type, return_counts=True) - summary = { - "total_units": n_total, - "counts": {}, - "percentages": {}, - } - - # Get the label for each type + summary = {"total_units": n_total, "counts": {}, "percentages": {}} for utype, count in zip(unique_types, counts): label = unit_type_string[unit_type == utype][0] summary["counts"][label] = int(count) diff --git a/src/spikeinterface/widgets/unit_classification.py b/src/spikeinterface/widgets/unit_classification.py index 948f4a9123..916e271322 100644 --- a/src/spikeinterface/widgets/unit_classification.py +++ b/src/spikeinterface/widgets/unit_classification.py @@ -1,9 +1,4 @@ -""" -Widgets for visualizing unit classification results. - -These widgets provide summary plots for unit classification based on quality metrics, -similar to BombCell's plotting functionality. -""" +"""Widgets for visualizing unit classification results.""" from __future__ import annotations @@ -14,25 +9,7 @@ class UnitClassificationWidget(BaseWidget): - """ - Plot summary of unit classification results. - - This widget creates a multi-panel figure showing: - - Waveform overlays by unit type - - Classification summary bar chart - - Histogram of key metrics with threshold lines - - Parameters - ---------- - sorting_analyzer : SortingAnalyzer - The SortingAnalyzer object with computed template_metrics and quality_metrics. - unit_type : np.ndarray - Numeric unit type array from classify_units(). - unit_type_string : np.ndarray - String labels from classify_units(). - thresholds : dict, optional - Threshold dictionary used for classification. If None, uses default thresholds. - """ + """Plot summary of unit classification (bar chart, pie chart, text summary).""" def __init__( self, @@ -49,34 +26,28 @@ def __init__( thresholds = get_default_thresholds() sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) - plot_data = dict( sorting_analyzer=sorting_analyzer, unit_type=unit_type, unit_type_string=unit_type_string, thresholds=thresholds, ) - BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) def plot_matplotlib(self, data_plot, **backend_kwargs): import matplotlib.pyplot as plt - from .utils import get_unit_colors dp = to_attr(data_plot) - sorting_analyzer = dp.sorting_analyzer unit_type = dp.unit_type unit_type_string = dp.unit_type_string - # Get unique types and counts unique_types = np.unique(unit_type) type_counts = {t: np.sum(unit_type == t) for t in unique_types} type_labels = {t: unit_type_string[unit_type == t][0] for t in unique_types} - # Create figure with subplots fig, axes = plt.subplots(2, 2, figsize=(12, 10)) - # Panel 1: Bar chart of classification counts + # Bar chart ax = axes[0, 0] labels = [type_labels[t] for t in unique_types] counts = [type_counts[t] for t in unique_types] @@ -85,40 +56,22 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): ax.set_ylabel("Number of units") ax.set_title("Unit Classification Summary") for bar, count in zip(bars, counts): - ax.text( - bar.get_x() + bar.get_width() / 2, - bar.get_height() + 0.5, - str(count), - ha="center", - va="bottom", - fontsize=10, - ) - - # Panel 2: Pie chart + ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.5, + str(count), ha="center", va="bottom", fontsize=10) + + # Pie chart ax = axes[0, 1] - ax.pie( - counts, - labels=labels, - autopct="%1.1f%%", - colors=colors, - startangle=90, - ) + ax.pie(counts, labels=labels, autopct="%1.1f%%", colors=colors, startangle=90) ax.set_title("Unit Classification Distribution") - # Panel 3 & 4: Placeholder for waveforms (would need templates) + # Placeholder ax = axes[1, 0] - ax.text( - 0.5, - 0.5, - "Waveform overlay\n(requires templates extension)", - ha="center", - va="center", - fontsize=12, - transform=ax.transAxes, - ) + ax.text(0.5, 0.5, "Waveform overlay\n(requires templates extension)", + ha="center", va="center", fontsize=12, transform=ax.transAxes) ax.set_title("Template Waveforms by Type") ax.axis("off") + # Text summary ax = axes[1, 1] n_total = len(unit_type) summary_text = "Classification Summary\n" + "=" * 30 + "\n" @@ -128,41 +81,17 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): pct = 100 * count / n_total summary_text += f"{label}: {count} ({pct:.1f}%)\n" summary_text += "=" * 30 + f"\nTotal: {n_total} units" - ax.text( - 0.1, - 0.5, - summary_text, - ha="left", - va="center", - fontsize=11, - family="monospace", - transform=ax.transAxes, - ) + ax.text(0.1, 0.5, summary_text, ha="left", va="center", + fontsize=11, family="monospace", transform=ax.transAxes) ax.axis("off") plt.tight_layout() - self.figure = fig self.axes = axes class ClassificationHistogramsWidget(BaseWidget): - """ - Plot histograms of quality metrics with threshold lines. - - Shows the distribution of each metric with vertical lines indicating - the classification thresholds. - - Parameters - ---------- - quality_metrics : pd.DataFrame - DataFrame with quality metrics. - thresholds : dict, optional - Threshold dictionary. If None, uses default thresholds. - metrics_to_plot : list of str, optional - List of metric names to plot. If None, plots all metrics present in both - quality_metrics and thresholds. - """ + """Plot histograms of quality metrics with threshold lines.""" def __init__( self, @@ -176,8 +105,6 @@ def __init__( if thresholds is None: thresholds = get_default_thresholds() - - # Determine which metrics to plot if metrics_to_plot is None: metrics_to_plot = [m for m in thresholds.keys() if m in quality_metrics.columns] @@ -186,7 +113,6 @@ def __init__( thresholds=thresholds, metrics_to_plot=metrics_to_plot, ) - BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) def plot_matplotlib(self, data_plot, **backend_kwargs): @@ -202,10 +128,8 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): print("No metrics to plot") return - # Calculate grid layout n_cols = min(4, n_metrics) n_rows = int(np.ceil(n_metrics / n_cols)) - fig, axes = plt.subplots(n_rows, n_cols, figsize=(4 * n_cols, 3 * n_rows)) if n_metrics == 1: axes = np.array([[axes]]) @@ -215,42 +139,28 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): axes = axes.reshape(-1, 1) colors = plt.cm.tab10(np.linspace(0, 1, 10)) - - # Metrics that should use absolute values (amplitude values are negative in extracellular recordings) absolute_value_metrics = ["amplitude_median"] for idx, metric_name in enumerate(metrics_to_plot): - row = idx // n_cols - col = idx % n_cols + row, col = idx // n_cols, idx % n_cols ax = axes[row, col] values = quality_metrics[metric_name].values - # Use absolute values for amplitude-based metrics if metric_name in absolute_value_metrics: values = np.abs(values) - values = values[~np.isnan(values)] - values = values[~np.isinf(values)] + values = values[~np.isnan(values) & ~np.isinf(values)] if len(values) == 0: ax.set_title(f"{metric_name}\n(no valid data)") continue - # Plot histogram - color = colors[idx % 10] - ax.hist(values, bins=30, color=color, alpha=0.7, edgecolor="black", density=True) + ax.hist(values, bins=30, color=colors[idx % 10], alpha=0.7, edgecolor="black", density=True) - # Add threshold lines thresh = thresholds.get(metric_name, {}) - min_thresh = thresh.get("min", np.nan) - max_thresh = thresh.get("max", np.nan) - - ylim = ax.get_ylim() - - if not np.isnan(min_thresh): - ax.axvline(min_thresh, color="red", linestyle="--", linewidth=2, label=f"min={min_thresh:.2g}") - - if not np.isnan(max_thresh): - ax.axvline(max_thresh, color="blue", linestyle="--", linewidth=2, label=f"max={max_thresh:.2g}") + if not np.isnan(thresh.get("min", np.nan)): + ax.axvline(thresh["min"], color="red", ls="--", lw=2, label=f"min={thresh['min']:.2g}") + if not np.isnan(thresh.get("max", np.nan)): + ax.axvline(thresh["max"], color="blue", ls="--", lw=2, label=f"max={thresh['max']:.2g}") ax.set_xlabel(metric_name) ax.set_ylabel("Density") @@ -258,33 +168,16 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) - # Hide unused subplots for idx in range(len(metrics_to_plot), n_rows * n_cols): - row = idx // n_cols - col = idx % n_cols - axes[row, col].set_visible(False) + axes[idx // n_cols, idx % n_cols].set_visible(False) plt.tight_layout() - self.figure = fig self.axes = axes class WaveformOverlayWidget(BaseWidget): - """ - Plot overlaid waveforms grouped by unit classification type. - - Parameters - ---------- - sorting_analyzer : SortingAnalyzer - The SortingAnalyzer object with computed templates. - unit_type : np.ndarray - Numeric unit type array from classify_units(). - unit_type_string : np.ndarray - String labels from classify_units(). - split_non_somatic : bool, default: False - If True, splits non-somatic into good/MUA. - """ + """Plot overlaid waveforms grouped by unit classification type.""" def __init__( self, @@ -296,14 +189,12 @@ def __init__( **backend_kwargs, ): sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) - plot_data = dict( sorting_analyzer=sorting_analyzer, unit_type=unit_type, unit_type_string=unit_type_string, split_non_somatic=split_non_somatic, ) - BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) def plot_matplotlib(self, data_plot, **backend_kwargs): @@ -312,50 +203,26 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): dp = to_attr(data_plot) sorting_analyzer = dp.sorting_analyzer unit_type = dp.unit_type - unit_type_string = dp.unit_type_string split_non_somatic = dp.split_non_somatic - # Check if templates are available if not sorting_analyzer.has_extension("templates"): fig, ax = plt.subplots(1, 1, figsize=(8, 6)) - ax.text( - 0.5, - 0.5, - "Templates extension not computed.\nRun: analyzer.compute('templates')", - ha="center", - va="center", - fontsize=12, - ) + ax.text(0.5, 0.5, "Templates extension not computed.\nRun: analyzer.compute('templates')", + ha="center", va="center", fontsize=12) ax.axis("off") self.figure = fig self.axes = ax return - # Get templates templates_ext = sorting_analyzer.get_extension("templates") templates = templates_ext.get_templates(operator="average") - unit_ids = sorting_analyzer.unit_ids - # Set up subplots based on split_non_somatic if split_non_somatic: - labels = { - 0: "NOISE", - 1: "GOOD", - 2: "MUA", - 3: "NON_SOMA_GOOD", - 4: "NON_SOMA_MUA", - } - n_plots = 5 - nrows, ncols = 2, 3 + labels = {0: "NOISE", 1: "GOOD", 2: "MUA", 3: "NON_SOMA_GOOD", 4: "NON_SOMA_MUA"} + n_plots, nrows, ncols = 5, 2, 3 else: - labels = { - 0: "NOISE", - 1: "GOOD", - 2: "MUA", - 3: "NON_SOMA", - } - n_plots = 4 - nrows, ncols = 2, 2 + labels = {0: "NOISE", 1: "GOOD", 2: "MUA", 3: "NON_SOMA"} + n_plots, nrows, ncols = 4, 2, 2 fig, axes = plt.subplots(nrows, ncols, figsize=(5 * ncols, 4 * nrows)) axes_flat = axes.flatten() @@ -363,41 +230,30 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): for plot_idx in range(n_plots): ax = axes_flat[plot_idx] type_label = labels.get(plot_idx, "") - - # Get units of this type mask = unit_type == plot_idx n_units = np.sum(mask) if n_units > 0: unit_indices = np.where(mask)[0] alpha = max(0.05, min(0.3, 10 / n_units)) - for unit_idx in unit_indices: - # Get template for this unit (best channel) - template = templates[unit_idx] # shape: (n_samples, n_channels) - # Find best channel (max amplitude) + template = templates[unit_idx] best_chan = np.argmax(np.max(np.abs(template), axis=0)) - waveform = template[:, best_chan] - ax.plot(waveform, color="black", alpha=alpha, linewidth=0.5) - + ax.plot(template[:, best_chan], color="black", alpha=alpha, linewidth=0.5) ax.set_title(f"{type_label} (n={n_units})") else: ax.set_title(f"{type_label} (n=0)") ax.text(0.5, 0.5, "No units", ha="center", va="center", transform=ax.transAxes) - ax.spines["top"].set_visible(False) - ax.spines["right"].set_visible(False) - ax.spines["bottom"].set_visible(False) - ax.spines["left"].set_visible(False) + for spine in ax.spines.values(): + spine.set_visible(False) ax.set_xticks([]) ax.set_yticks([]) - # Hide unused subplots for idx in range(n_plots, nrows * ncols): axes_flat[idx].set_visible(False) plt.tight_layout() - self.figure = fig self.axes = axes @@ -406,64 +262,21 @@ class UpsetPlotWidget(BaseWidget): """ Plot UpSet plots showing which metrics fail together for each unit type. - UpSet plots visualize set intersections, showing which combinations of - metric failures are most common for units classified as NOISE, MUA, etc. - - Each unit type shows only the relevant metrics: - - NOISE: waveform quality metrics (num_positive_peaks, peak_to_trough_duration, etc.) - - MUA: spike quality metrics (amplitude_median, snr_bombcell, rp_contamination, etc.) - - NON_SOMA: non-somatic detection metrics (peak_before_to_trough_ratio, widths, etc.) - - Parameters - ---------- - quality_metrics : pd.DataFrame - DataFrame with quality metrics. - unit_type : np.ndarray - Numeric unit type array from classify_units(). - unit_type_string : np.ndarray - String labels from classify_units(). - thresholds : dict, optional - Threshold dictionary. If None, uses default thresholds. - unit_types_to_plot : list of str, optional - Which unit types to create upset plots for. - Default: ["NOISE", "MUA", "NON_SOMA"] or with split: ["NOISE", "MUA", "NON_SOMA_GOOD", "NON_SOMA_MUA"] - split_non_somatic : bool, default: False - If True, uses split non-somatic labels. - min_subset_size : int, default: 1 - Minimum size of subsets to show in the plot. - - Notes - ----- - Requires the `upsetplot` package to be installed. If not installed, displays - a message instructing the user to install it. + Requires `upsetplot` package. Each unit type shows relevant metrics: + NOISE -> waveform metrics, MUA -> spike quality metrics, NON_SOMA -> non-somatic metrics. """ - # Define metric categories WAVEFORM_METRICS = [ - "num_positive_peaks", - "num_negative_peaks", - "peak_to_trough_duration", - "waveform_baseline_flatness", - "peak_after_to_trough_ratio", - "exp_decay", + "num_positive_peaks", "num_negative_peaks", "peak_to_trough_duration", + "waveform_baseline_flatness", "peak_after_to_trough_ratio", "exp_decay", ] - SPIKE_QUALITY_METRICS = [ - "amplitude_median", - "snr_bombcell", - "amplitude_cutoff", - "num_spikes", - "rp_contamination", - "presence_ratio", - "drift_ptp", + "amplitude_median", "snr_bombcell", "amplitude_cutoff", + "num_spikes", "rp_contamination", "presence_ratio", "drift_ptp", ] - NON_SOMATIC_METRICS = [ - "peak_before_to_trough_ratio", - "peak_before_width", - "trough_width", - "peak_before_to_peak_after_ratio", - "main_peak_to_trough_ratio", + "peak_before_to_trough_ratio", "peak_before_width", "trough_width", + "peak_before_to_peak_after_ratio", "main_peak_to_trough_ratio", ] def __init__( @@ -482,7 +295,6 @@ def __init__( if thresholds is None: thresholds = get_default_thresholds() - if unit_types_to_plot is None: if split_non_somatic: unit_types_to_plot = ["NOISE", "MUA", "NON_SOMA_GOOD", "NON_SOMA_MUA"] @@ -497,19 +309,16 @@ def __init__( unit_types_to_plot=unit_types_to_plot, min_subset_size=min_subset_size, ) - BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) def _get_metrics_for_unit_type(self, unit_type_label): - """Get the relevant metrics for a given unit type.""" if unit_type_label == "NOISE": return self.WAVEFORM_METRICS elif unit_type_label == "MUA": return self.SPIKE_QUALITY_METRICS elif unit_type_label in ("NON_SOMA", "NON_SOMA_GOOD", "NON_SOMA_MUA"): return self.NON_SOMATIC_METRICS - else: - return None # Show all metrics + return None def plot_matplotlib(self, data_plot, **backend_kwargs): import matplotlib.pyplot as plt @@ -522,25 +331,14 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): unit_types_to_plot = dp.unit_types_to_plot min_subset_size = dp.min_subset_size - # Check if upsetplot is available try: from upsetplot import UpSet, from_memberships except ImportError: - # Display message to install upsetplot fig, ax = plt.subplots(1, 1, figsize=(10, 6)) - ax.text( - 0.5, - 0.5, - "UpSet plots require the 'upsetplot' package.\n\n" - "Please install it with:\n\n" - " pip install upsetplot\n\n" - "Then re-run this plot.", - ha="center", - va="center", - fontsize=14, - family="monospace", - bbox=dict(boxstyle="round", facecolor="lightyellow", edgecolor="orange"), - ) + ax.text(0.5, 0.5, + "UpSet plots require 'upsetplot' package.\n\npip install upsetplot", + ha="center", va="center", fontsize=14, family="monospace", + bbox=dict(boxstyle="round", facecolor="lightyellow", edgecolor="orange")) ax.axis("off") ax.set_title("UpSet Plot - Package Not Installed", fontsize=16) self.figure = fig @@ -548,80 +346,51 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): self.figures = [fig] return - # Build failure table for ALL metrics once failure_table = self._build_failure_table(quality_metrics, thresholds) - figures = [] axes_list = [] for unit_type_label in unit_types_to_plot: - # Get units of this type mask = unit_type_string == unit_type_label n_units = np.sum(mask) - if n_units == 0: continue - # Get relevant metrics for this unit type relevant_metrics = self._get_metrics_for_unit_type(unit_type_label) - - # Filter failure table to relevant metrics only if relevant_metrics is not None: available_metrics = [m for m in relevant_metrics if m in failure_table.columns] if len(available_metrics) == 0: - # No relevant metrics available, skip this unit type continue unit_failure_table = failure_table[available_metrics] else: unit_failure_table = failure_table - # Get failure data for these units unit_failures = unit_failure_table.loc[mask] - - # Build membership list for upsetplot memberships = [] for idx in unit_failures.index: - failed_metrics = unit_failures.columns[unit_failures.loc[idx]].tolist() - if len(failed_metrics) > 0: - memberships.append(failed_metrics) + failed = unit_failures.columns[unit_failures.loc[idx]].tolist() + if failed: + memberships.append(failed) - if len(memberships) == 0: + if not memberships: continue - # Create upset data upset_data = from_memberships(memberships) - - # Filter by min_subset_size upset_data = upset_data[upset_data >= min_subset_size] - if len(upset_data) == 0: continue - # Create figure fig = plt.figure(figsize=(12, 6)) - upset = UpSet( - upset_data, - subset_size="count", - show_counts=True, - sort_by="cardinality", - sort_categories_by="cardinality", - ) - upset.plot(fig=fig) + UpSet(upset_data, subset_size="count", show_counts=True, + sort_by="cardinality", sort_categories_by="cardinality").plot(fig=fig) fig.suptitle(f"{unit_type_label} (n={n_units})", fontsize=14, y=1.02) - figures.append(fig) axes_list.append(fig.axes) - if len(figures) == 0: + if not figures: fig, ax = plt.subplots(1, 1, figsize=(8, 6)) - ax.text( - 0.5, - 0.5, - "No units found for the specified unit types\nor no metric failures detected.", - ha="center", - va="center", - fontsize=12, - ) + ax.text(0.5, 0.5, "No units found or no metric failures detected.", + ha="center", va="center", fontsize=12) ax.axis("off") figures = [fig] axes_list = [ax] @@ -631,219 +400,50 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): self.axes = axes_list def _build_failure_table(self, quality_metrics, thresholds): - """Build a boolean DataFrame indicating which metrics failed for each unit.""" import pandas as pd - # Metrics that should use absolute values absolute_value_metrics = ["amplitude_median"] - failure_data = {} for metric_name, thresh in thresholds.items(): if metric_name not in quality_metrics.columns: continue - values = quality_metrics[metric_name].values.copy() - - # Use absolute values for amplitude-based metrics if metric_name in absolute_value_metrics: values = np.abs(values) - # Check failures - failed = np.zeros(len(values), dtype=bool) - - # NaN is a failure - failed |= np.isnan(values) - - # Check min threshold + failed = np.isnan(values) if not np.isnan(thresh.get("min", np.nan)): failed |= values < thresh["min"] - - # Check max threshold if not np.isnan(thresh.get("max", np.nan)): failed |= values > thresh["max"] - failure_data[metric_name] = failed return pd.DataFrame(failure_data, index=quality_metrics.index) -# Convenience functions for direct plotting -def plot_unit_classification( - sorting_analyzer, - unit_type, - unit_type_string, - thresholds=None, - backend=None, - **backend_kwargs, -): - """ - Plot summary of unit classification results. - - Parameters - ---------- - sorting_analyzer : SortingAnalyzer - The SortingAnalyzer object. - unit_type : np.ndarray - Numeric unit type array from classify_units(). - unit_type_string : np.ndarray - String labels from classify_units(). - thresholds : dict, optional - Threshold dictionary. - backend : str, optional - Backend to use for plotting. - **backend_kwargs - Additional kwargs for the backend. - - Returns - ------- - widget : UnitClassificationWidget - The widget object. - """ - widget = UnitClassificationWidget( - sorting_analyzer, - unit_type, - unit_type_string, - thresholds=thresholds, - backend=backend, - **backend_kwargs, - ) - return widget +# Convenience functions +def plot_unit_classification(sorting_analyzer, unit_type, unit_type_string, thresholds=None, backend=None, **kwargs): + """Plot summary of unit classification results.""" + return UnitClassificationWidget(sorting_analyzer, unit_type, unit_type_string, + thresholds=thresholds, backend=backend, **kwargs) -def plot_classification_histograms( - quality_metrics, - thresholds=None, - metrics_to_plot=None, - backend=None, - **backend_kwargs, -): - """ - Plot histograms of quality metrics with threshold lines. - - Parameters - ---------- - quality_metrics : pd.DataFrame - DataFrame with quality metrics. - thresholds : dict, optional - Threshold dictionary. If None, uses default thresholds. - metrics_to_plot : list of str, optional - List of metric names to plot. - backend : str, optional - Backend to use for plotting. - **backend_kwargs - Additional kwargs for the backend. - - Returns - ------- - widget : ClassificationHistogramsWidget - The widget object. - """ - widget = ClassificationHistogramsWidget( - quality_metrics, - thresholds=thresholds, - metrics_to_plot=metrics_to_plot, - backend=backend, - **backend_kwargs, - ) - return widget - - -def plot_waveform_overlay( - sorting_analyzer, - unit_type, - unit_type_string, - split_non_somatic=False, - backend=None, - **backend_kwargs, -): - """ - Plot overlaid waveforms grouped by unit classification type. - - Parameters - ---------- - sorting_analyzer : SortingAnalyzer - The SortingAnalyzer object with computed templates. - unit_type : np.ndarray - Numeric unit type array from classify_units(). - unit_type_string : np.ndarray - String labels from classify_units(). - split_non_somatic : bool, default: False - If True, splits non-somatic into good/MUA. - backend : str, optional - Backend to use for plotting. - **backend_kwargs - Additional kwargs for the backend. - - Returns - ------- - widget : WaveformOverlayWidget - The widget object. - """ - widget = WaveformOverlayWidget( - sorting_analyzer, - unit_type, - unit_type_string, - split_non_somatic=split_non_somatic, - backend=backend, - **backend_kwargs, - ) - return widget - - -def plot_upset( - quality_metrics, - unit_type, - unit_type_string, - thresholds=None, - unit_types_to_plot=None, - split_non_somatic=False, - min_subset_size=1, - backend=None, - **backend_kwargs, -): - """ - Plot UpSet plots showing which metrics fail together for each unit type. +def plot_classification_histograms(quality_metrics, thresholds=None, metrics_to_plot=None, backend=None, **kwargs): + """Plot histograms of quality metrics with threshold lines.""" + return ClassificationHistogramsWidget(quality_metrics, thresholds=thresholds, + metrics_to_plot=metrics_to_plot, backend=backend, **kwargs) - UpSet plots visualize set intersections, showing which combinations of - metric failures are most common for units classified as NOISE, MUA, etc. - - Parameters - ---------- - quality_metrics : pd.DataFrame - DataFrame with quality metrics. - unit_type : np.ndarray - Numeric unit type array from classify_units(). - unit_type_string : np.ndarray - String labels from classify_units(). - thresholds : dict, optional - Threshold dictionary. If None, uses default thresholds. - unit_types_to_plot : list of str, optional - Which unit types to create upset plots for. - Default: ["NOISE", "MUA", "NON_SOMA"] or with split: ["NOISE", "MUA", "NON_SOMA_GOOD", "NON_SOMA_MUA"] - split_non_somatic : bool, default: False - If True, uses split non-somatic labels. - min_subset_size : int, default: 1 - Minimum size of subsets to show in the plot. - backend : str, optional - Backend to use for plotting. - **backend_kwargs - Additional kwargs for the backend. - - Returns - ------- - widget : UpsetPlotWidget - The widget object. Access individual figures via widget.figures. - """ - widget = UpsetPlotWidget( - quality_metrics, - unit_type, - unit_type_string, - thresholds=thresholds, - unit_types_to_plot=unit_types_to_plot, - split_non_somatic=split_non_somatic, - min_subset_size=min_subset_size, - backend=backend, - **backend_kwargs, - ) - return widget + +def plot_waveform_overlay(sorting_analyzer, unit_type, unit_type_string, split_non_somatic=False, backend=None, **kwargs): + """Plot overlaid waveforms grouped by unit classification type.""" + return WaveformOverlayWidget(sorting_analyzer, unit_type, unit_type_string, + split_non_somatic=split_non_somatic, backend=backend, **kwargs) + + +def plot_upset(quality_metrics, unit_type, unit_type_string, thresholds=None, + unit_types_to_plot=None, split_non_somatic=False, min_subset_size=1, backend=None, **kwargs): + """Plot UpSet plots showing which metrics fail together for each unit type.""" + return UpsetPlotWidget(quality_metrics, unit_type, unit_type_string, thresholds=thresholds, + unit_types_to_plot=unit_types_to_plot, split_non_somatic=split_non_somatic, + min_subset_size=min_subset_size, backend=backend, **kwargs) From ed770bb90e9404e460e20c1dbd312c3dd006aa50 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Thu, 8 Jan 2026 02:29:35 +0100 Subject: [PATCH 14/49] cleanup --- in_container_params.json | 3 - in_container_recording.json | 15497 -------------------------------- in_container_sorter_script.py | 28 - 3 files changed, 15528 deletions(-) delete mode 100644 in_container_params.json delete mode 100644 in_container_recording.json delete mode 100644 in_container_sorter_script.py diff --git a/in_container_params.json b/in_container_params.json deleted file mode 100644 index 01ccea40b6..0000000000 --- a/in_container_params.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "output_folder": "/Users/jf5479/Downloads/AL031_2019-12-02/spikeinterface_output/kilosort4_output" -} diff --git a/in_container_recording.json b/in_container_recording.json deleted file mode 100644 index 6738af6b0b..0000000000 --- a/in_container_recording.json +++ /dev/null @@ -1,15497 +0,0 @@ -{ - "class": "spikeinterface.preprocessing.common_reference.CommonReferenceRecording", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "recording": { - "class": "spikeinterface.preprocessing.phase_shift.PhaseShiftRecording", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "recording": { - "class": "spikeinterface.core.channelslice.ChannelSliceRecording", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "parent_recording": { - "class": "spikeinterface.preprocessing.filter.HighpassFilterRecording", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "recording": { - "class": "spikeinterface.core.channelslice.ChannelSliceRecording", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "parent_recording": { - "class": "spikeinterface.core.binaryrecordingextractor.BinaryRecordingExtractor", - "module": "spikeinterface", - "version": "0.103.3", - "kwargs": { - "file_paths": [ - "/Users/jf5479/Downloads/AL031_2019-12-02/AL031_2019-12-02_bank1_NatIm_g0_t0_bc_decompressed.imec0.ap.bin" - ], - "sampling_frequency": 30000.0, - "t_starts": null, - "num_channels": 385, - "dtype": " Date: Thu, 8 Jan 2026 11:54:07 +0100 Subject: [PATCH 15/49] cleanup old template metric functions and ensure backward compaiblity for name changes --- .../unit_classification.py | 12 +++-- .../metrics/template/metrics.py | 52 ------------------- .../metrics/template/template_metrics.py | 14 ++++- .../widgets/unit_classification.py | 2 +- 4 files changed, 20 insertions(+), 60 deletions(-) rename src/spikeinterface/{comparison => curation}/unit_classification.py (96%) diff --git a/src/spikeinterface/comparison/unit_classification.py b/src/spikeinterface/curation/unit_classification.py similarity index 96% rename from src/spikeinterface/comparison/unit_classification.py rename to src/spikeinterface/curation/unit_classification.py index ff3ccf2c34..412d4020c3 100644 --- a/src/spikeinterface/comparison/unit_classification.py +++ b/src/spikeinterface/curation/unit_classification.py @@ -1,5 +1,5 @@ """ -Unit classification based on quality metrics (similar to BombCell). +Unit classification based on quality metrics (Bombcell). Unit Types: 0 (NOISE): Failed waveform quality checks @@ -43,12 +43,14 @@ ] -def get_default_thresholds() -> dict: +def get_default_thresholds() -> dict: """ - Returns default thresholds for unit classification. + Bombcell - Returns default thresholds for unit classification. - Each metric has 'min' and 'max' values. Use np.nan to disable a threshold. + Each metric has 'min' and 'max' values. Use np.nan to disable a threshold (e.g. to ignore a metric completly + or to only have a min or a max threshold) """ + # QQ need to make it so user can change this! return { # Waveform quality (failures -> NOISE) "num_positive_peaks": {"min": np.nan, "max": 2}, @@ -81,7 +83,7 @@ def classify_units( split_non_somatic_good_mua: bool = False, ) -> tuple[np.ndarray, np.ndarray]: """ - Classify units based on quality metrics and thresholds. + Bombcell - classify units based on quality metrics and thresholds. Parameters ---------- diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index abc56d04dd..fe21fc8c56 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -557,35 +557,6 @@ def get_peak_to_valley(template_single, sampling_frequency, trough_idx=None, pea return ptv -def get_peak_trough_ratio(template_single, sampling_frequency=None, trough_idx=None, peak_idx=None, **kwargs) -> float: - """ - Return the peak to trough ratio of input waveforms. - - Parameters - ---------- - template_single: numpy.ndarray - The 1D template waveform - sampling_frequency : float - The sampling frequency of the template - trough_idx: int, default: None - The index of the trough - peak_idx: int, default: None - The index of the peak - - Returns - ------- - ptratio: float - The peak to trough ratio - """ - if trough_idx is None or peak_idx is None: - troughs, _, peaks_after = get_trough_and_peak_idx(template_single) - trough_idx = troughs["main_loc"] - peak_idx = peaks_after["main_loc"] - if trough_idx is None or peak_idx is None: - return np.nan - ptratio = template_single[peak_idx] / template_single[trough_idx] - return ptratio - def get_half_width(template_single, sampling_frequency, trough_idx=None, peak_idx=None, **kwargs) -> float: """ @@ -1129,28 +1100,6 @@ def _peak_to_trough_duration_metric_function(sorting_analyzer, unit_ids, tmp_dat metric_function = _peak_to_trough_duration_metric_function -class PeakToTroughRatio(BaseMetric): - metric_name = "peak_trough_ratio" - metric_params = {} - metric_columns = {"peak_trough_ratio": float} - metric_descriptions = { - "peak_trough_ratio": "Ratio of the amplitude of the peak (maximum) to the trough (minimum) of the spike waveform." - } - needs_tmp_data = True - - @staticmethod - def _peak_to_trough_ratio_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): - return single_channel_metric( - unit_function=get_peak_trough_ratio, - sorting_analyzer=sorting_analyzer, - unit_ids=unit_ids, - tmp_data=tmp_data, - **metric_params, - ) - - metric_function = _peak_to_trough_ratio_metric_function - - class HalfWidth(BaseMetric): metric_name = "half_width" metric_params = {} @@ -1414,7 +1363,6 @@ class WaveformBaselineFlatness(BaseMetric): single_channel_metrics = [ PeakToTroughDuration, - PeakToTroughRatio, HalfWidth, RepolarizationSlope, RecoverySlope, diff --git a/src/spikeinterface/metrics/template/template_metrics.py b/src/spikeinterface/metrics/template/template_metrics.py index 11f2a57df1..4c4e8aa811 100644 --- a/src/spikeinterface/metrics/template/template_metrics.py +++ b/src/spikeinterface/metrics/template/template_metrics.py @@ -47,8 +47,8 @@ def get_template_metric_names(): class ComputeTemplateMetrics(BaseMetricExtension): """ Compute template metrics including: - * peak_to_valley - * peak_trough_ratio + * peak_to_trough_duration + * peak_to_trough_ratio * halfwidth * repolarization_slope * recovery_slope @@ -126,6 +126,16 @@ def _handle_backward_compatibility_on_load(self): self.params["metric_names"].remove("velocity_below") if "velocity_fits" not in self.params["metric_names"]: self.params["metric_names"].append("velocity_fits") + # peak to valley -> peak_to_trough_duration + if "peak_to_valley" in self.params["metric_names"]: + self.params["metric_names"].remove("peak_to_valley") + if "peak_to_trough_duration" not in self.params["metric_names"]: + self.params["metric_names"].append("peak_to_trough_duration") + # peak to trough ratio -> main peak to trough ratio + if "peak_to_trough_ratio" in self.params["metric_names"]: + self.params["metric_names"].remove("peak_to_trough_ratio") + if "main_peak_to_trough_ratio" not in self.params["metric_names"]: + self.params["metric_names"].append("main_peak_to_trough_ratio") def _set_params( self, diff --git a/src/spikeinterface/widgets/unit_classification.py b/src/spikeinterface/widgets/unit_classification.py index 916e271322..ecba824c46 100644 --- a/src/spikeinterface/widgets/unit_classification.py +++ b/src/spikeinterface/widgets/unit_classification.py @@ -291,7 +291,7 @@ def __init__( backend=None, **backend_kwargs, ): - from spikeinterface.comparison import get_default_thresholds + from spikeinterface.comparison import get_default_thresholds #QQ need to change to user thresholds! should be in some self ? if thresholds is None: thresholds = get_default_thresholds() From 71063dcb4e8ed229b8b698957ac5b73b0b69e1f9 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Thu, 8 Jan 2026 12:07:35 +0100 Subject: [PATCH 16/49] move bombcell functions to curation and rename bombcell ones to bombcell_ --- src/spikeinterface/comparison/__init__.py | 9 --------- src/spikeinterface/curation/__init__.py | 10 ++++++++++ src/spikeinterface/curation/unit_classification.py | 10 +++++----- src/spikeinterface/widgets/unit_classification.py | 12 ++++++------ 4 files changed, 21 insertions(+), 20 deletions(-) diff --git a/src/spikeinterface/comparison/__init__.py b/src/spikeinterface/comparison/__init__.py index f7a3b0a80d..d8c3b0c55c 100644 --- a/src/spikeinterface/comparison/__init__.py +++ b/src/spikeinterface/comparison/__init__.py @@ -41,12 +41,3 @@ create_hybrid_spikes_recording, ) -from .unit_classification import ( - WAVEFORM_METRICS, - SPIKE_QUALITY_METRICS, - NON_SOMATIC_METRICS, - get_default_thresholds, - classify_units, - apply_thresholds, - get_classification_summary, -) diff --git a/src/spikeinterface/curation/__init__.py b/src/spikeinterface/curation/__init__.py index af7fb90f94..2913bd7693 100644 --- a/src/spikeinterface/curation/__init__.py +++ b/src/spikeinterface/curation/__init__.py @@ -21,5 +21,15 @@ from .sortingview_curation import apply_sortingview_curation # automated curation +from .unit_classification import ( + WAVEFORM_METRICS, + SPIKE_QUALITY_METRICS, + NON_SOMATIC_METRICS, + bombcell_get_default_thresholds, + bombcell_classify_units, + apply_thresholds, + get_classification_summary, +) + from .model_based_curation import auto_label_units, load_model from .train_manual_curation import train_model, get_default_classifier_search_spaces diff --git a/src/spikeinterface/curation/unit_classification.py b/src/spikeinterface/curation/unit_classification.py index 412d4020c3..6dc2b1cb61 100644 --- a/src/spikeinterface/curation/unit_classification.py +++ b/src/spikeinterface/curation/unit_classification.py @@ -43,14 +43,14 @@ ] -def get_default_thresholds() -> dict: +def bombcell_get_default_thresholds() -> dict: """ Bombcell - Returns default thresholds for unit classification. Each metric has 'min' and 'max' values. Use np.nan to disable a threshold (e.g. to ignore a metric completly or to only have a min or a max threshold) """ - # QQ need to make it so user can change this! + # bombcell return { # Waveform quality (failures -> NOISE) "num_positive_peaks": {"min": np.nan, "max": 2}, @@ -76,7 +76,7 @@ def get_default_thresholds() -> dict: } -def classify_units( +def bombcell_classify_units( quality_metrics: pd.DataFrame, thresholds: Optional[dict] = None, classify_non_somatic: bool = True, @@ -104,7 +104,7 @@ def classify_units( String labels. """ if thresholds is None: - thresholds = get_default_thresholds() + thresholds = bombcell_get_default_thresholds() n_units = len(quality_metrics) unit_type = np.full(n_units, np.nan) @@ -222,7 +222,7 @@ def apply_thresholds( Useful for debugging classification results. """ if thresholds is None: - thresholds = get_default_thresholds() + thresholds = bombcell_get_default_thresholds() results = {} for metric_name, thresh in thresholds.items(): diff --git a/src/spikeinterface/widgets/unit_classification.py b/src/spikeinterface/widgets/unit_classification.py index ecba824c46..2bbb2413da 100644 --- a/src/spikeinterface/widgets/unit_classification.py +++ b/src/spikeinterface/widgets/unit_classification.py @@ -20,10 +20,10 @@ def __init__( backend=None, **backend_kwargs, ): - from spikeinterface.comparison import get_default_thresholds + from spikeinterface.curation import bombcell_get_default_thresholds if thresholds is None: - thresholds = get_default_thresholds() + thresholds = bombcell_get_default_thresholds() sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) plot_data = dict( @@ -101,10 +101,10 @@ def __init__( backend=None, **backend_kwargs, ): - from spikeinterface.comparison import get_default_thresholds + from spikeinterface.curation import bombcell_get_default_thresholds if thresholds is None: - thresholds = get_default_thresholds() + thresholds = bombcell_get_default_thresholds() if metrics_to_plot is None: metrics_to_plot = [m for m in thresholds.keys() if m in quality_metrics.columns] @@ -291,10 +291,10 @@ def __init__( backend=None, **backend_kwargs, ): - from spikeinterface.comparison import get_default_thresholds #QQ need to change to user thresholds! should be in some self ? + from spikeinterface.curation import bombcell_get_default_thresholds #QQ need to change to user thresholds! should be in some self ? if thresholds is None: - thresholds = get_default_thresholds() + thresholds = bombcell_get_default_thresholds() if unit_types_to_plot is None: if split_non_somatic: unit_types_to_plot = ["NOISE", "MUA", "NON_SOMA_GOOD", "NON_SOMA_MUA"] From 5a2416e523156fe1b12a3a99ed2ae0ed683ccb2e Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Thu, 8 Jan 2026 12:15:02 +0100 Subject: [PATCH 17/49] remove upset plot warnings for now --- .../metrics/template/metrics.py | 2 +- .../widgets/unit_classification.py | 27 ++++++++++++------- 2 files changed, 19 insertions(+), 10 deletions(-) diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index fe21fc8c56..09ede61da7 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -195,7 +195,7 @@ def get_trough_and_peak_idx( peaks_after = empty_dict.copy() # Quick visualization (set to True for debugging) - _plot = False + _plot = True #QQ set to false if _plot: import matplotlib.pyplot as plt diff --git a/src/spikeinterface/widgets/unit_classification.py b/src/spikeinterface/widgets/unit_classification.py index 2bbb2413da..bfbd3b98a0 100644 --- a/src/spikeinterface/widgets/unit_classification.py +++ b/src/spikeinterface/widgets/unit_classification.py @@ -157,14 +157,18 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): ax.hist(values, bins=30, color=colors[idx % 10], alpha=0.7, edgecolor="black", density=True) thresh = thresholds.get(metric_name, {}) + has_thresh = False if not np.isnan(thresh.get("min", np.nan)): ax.axvline(thresh["min"], color="red", ls="--", lw=2, label=f"min={thresh['min']:.2g}") + has_thresh = True if not np.isnan(thresh.get("max", np.nan)): ax.axvline(thresh["max"], color="blue", ls="--", lw=2, label=f"max={thresh['max']:.2g}") + has_thresh = True ax.set_xlabel(metric_name) ax.set_ylabel("Density") - ax.legend(fontsize=8, loc="upper right") + if has_thresh: + ax.legend(fontsize=8, loc="upper right") ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) @@ -321,6 +325,7 @@ def _get_metrics_for_unit_type(self, unit_type_label): return None def plot_matplotlib(self, data_plot, **backend_kwargs): + import warnings import matplotlib.pyplot as plt import pandas as pd @@ -332,7 +337,9 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): min_subset_size = dp.min_subset_size try: - from upsetplot import UpSet, from_memberships + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=FutureWarning, module="upsetplot") + from upsetplot import UpSet, from_memberships except ImportError: fig, ax = plt.subplots(1, 1, figsize=(10, 6)) ax.text(0.5, 0.5, @@ -375,14 +382,16 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): if not memberships: continue - upset_data = from_memberships(memberships) - upset_data = upset_data[upset_data >= min_subset_size] - if len(upset_data) == 0: - continue + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=FutureWarning, module="upsetplot") + upset_data = from_memberships(memberships) + upset_data = upset_data[upset_data >= min_subset_size] + if len(upset_data) == 0: + continue - fig = plt.figure(figsize=(12, 6)) - UpSet(upset_data, subset_size="count", show_counts=True, - sort_by="cardinality", sort_categories_by="cardinality").plot(fig=fig) + fig = plt.figure(figsize=(12, 6)) + UpSet(upset_data, subset_size="count", show_counts=True, + sort_by="cardinality", sort_categories_by="cardinality").plot(fig=fig) fig.suptitle(f"{unit_type_label} (n={n_units})", fontsize=14, y=1.02) figures.append(fig) axes_list.append(fig.axes) From afe4e1be16841e05d9a27fb10c3ff1d0f4ad33f1 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Thu, 8 Jan 2026 12:31:40 +0100 Subject: [PATCH 18/49] bombcell plot wrapper --- .../widgets/unit_classification.py | 83 +++++++++++++++++++ src/spikeinterface/widgets/widget_list.py | 1 + 2 files changed, 84 insertions(+) diff --git a/src/spikeinterface/widgets/unit_classification.py b/src/spikeinterface/widgets/unit_classification.py index bfbd3b98a0..993e509bc4 100644 --- a/src/spikeinterface/widgets/unit_classification.py +++ b/src/spikeinterface/widgets/unit_classification.py @@ -456,3 +456,86 @@ def plot_upset(quality_metrics, unit_type, unit_type_string, thresholds=None, return UpsetPlotWidget(quality_metrics, unit_type, unit_type_string, thresholds=thresholds, unit_types_to_plot=unit_types_to_plot, split_non_somatic=split_non_somatic, min_subset_size=min_subset_size, backend=backend, **kwargs) + + +def plot_unit_classification_all( + sorting_analyzer, + unit_type: np.ndarray, + unit_type_string: np.ndarray, + quality_metrics=None, + thresholds: Optional[dict] = None, + split_non_somatic: bool = False, + include_upset: bool = True, + backend=None, + **kwargs, +): + """ + Generate all unit classification plots. + + Parameters + ---------- + sorting_analyzer : SortingAnalyzer + The sorting analyzer object. + unit_type : np.ndarray + Array of unit type codes (0=NOISE, 1=GOOD, 2=MUA, 3=NON_SOMA, etc.). + unit_type_string : np.ndarray + Array of unit type labels as strings. + quality_metrics : pd.DataFrame, optional + Quality metrics DataFrame. If None, attempts to get from sorting_analyzer. + thresholds : dict, optional + Threshold dictionary. If None, uses default thresholds. + split_non_somatic : bool, default: False + Whether to split NON_SOMA into NON_SOMA_GOOD and NON_SOMA_MUA. + include_upset : bool, default: True + Whether to include UpSet plots (requires upsetplot package). + backend : str, optional + Plotting backend. + **kwargs + Additional arguments passed to plot functions. + + Returns + ------- + dict + Dictionary with keys 'summary', 'histograms', 'waveforms', 'upset' containing widget objects. + """ + from spikeinterface.curation import bombcell_get_default_thresholds + + if thresholds is None: + thresholds = bombcell_get_default_thresholds() + + if quality_metrics is None: + if sorting_analyzer.has_extension("quality_metrics"): + quality_metrics = sorting_analyzer.get_extension("quality_metrics").get_data() + if sorting_analyzer.has_extension("template_metrics"): + tm = sorting_analyzer.get_extension("template_metrics").get_data() + if quality_metrics is not None: + quality_metrics = quality_metrics.join(tm, how="outer") + else: + quality_metrics = tm + + results = {} + + # Summary plot + results["summary"] = plot_unit_classification( + sorting_analyzer, unit_type, unit_type_string, thresholds=thresholds, backend=backend, **kwargs + ) + + # Histograms + if quality_metrics is not None: + results["histograms"] = plot_classification_histograms( + quality_metrics, thresholds=thresholds, backend=backend, **kwargs + ) + + # Waveform overlay + results["waveforms"] = plot_waveform_overlay( + sorting_analyzer, unit_type, unit_type_string, split_non_somatic=split_non_somatic, backend=backend, **kwargs + ) + + # UpSet plots + if include_upset and quality_metrics is not None: + results["upset"] = plot_upset( + quality_metrics, unit_type, unit_type_string, thresholds=thresholds, + split_non_somatic=split_non_somatic, backend=backend, **kwargs + ) + + return results diff --git a/src/spikeinterface/widgets/widget_list.py b/src/spikeinterface/widgets/widget_list.py index a5728ebf30..72a8c01028 100644 --- a/src/spikeinterface/widgets/widget_list.py +++ b/src/spikeinterface/widgets/widget_list.py @@ -46,6 +46,7 @@ plot_classification_histograms, plot_waveform_overlay, plot_upset, + plot_unit_classification_all, ) widget_list = [ From 6fb5b13f71539bd2f3bf6b81ce8a5eae35088458 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 8 Jan 2026 11:36:54 +0000 Subject: [PATCH 19/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .gitignore | 1 - src/spikeinterface/comparison/__init__.py | 1 - .../curation/unit_classification.py | 5 +- .../metrics/template/metrics.py | 3 +- .../metrics/template/template_metrics.py | 2 +- .../widgets/unit_classification.py | 143 +++++++++++++----- 6 files changed, 111 insertions(+), 44 deletions(-) diff --git a/.gitignore b/.gitignore index 4bf7f07949..6a7edf06f8 100644 --- a/.gitignore +++ b/.gitignore @@ -145,4 +145,3 @@ test_folder/ # Mac OS .DS_Store test_data.json - diff --git a/src/spikeinterface/comparison/__init__.py b/src/spikeinterface/comparison/__init__.py index d8c3b0c55c..f4ada19f73 100644 --- a/src/spikeinterface/comparison/__init__.py +++ b/src/spikeinterface/comparison/__init__.py @@ -40,4 +40,3 @@ create_hybrid_units_recording, create_hybrid_spikes_recording, ) - diff --git a/src/spikeinterface/curation/unit_classification.py b/src/spikeinterface/curation/unit_classification.py index 6dc2b1cb61..e9433d1413 100644 --- a/src/spikeinterface/curation/unit_classification.py +++ b/src/spikeinterface/curation/unit_classification.py @@ -43,14 +43,14 @@ ] -def bombcell_get_default_thresholds() -> dict: +def bombcell_get_default_thresholds() -> dict: """ Bombcell - Returns default thresholds for unit classification. Each metric has 'min' and 'max' values. Use np.nan to disable a threshold (e.g. to ignore a metric completly or to only have a min or a max threshold) """ - # bombcell + # bombcell return { # Waveform quality (failures -> NOISE) "num_positive_peaks": {"min": np.nan, "max": 2}, @@ -147,6 +147,7 @@ def bombcell_classify_units( # NON-SOMATIC if classify_non_somatic: + def get_metric(name): if name in quality_metrics.columns: return quality_metrics[name].values diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index 09ede61da7..3fad4c827d 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -195,7 +195,7 @@ def get_trough_and_peak_idx( peaks_after = empty_dict.copy() # Quick visualization (set to True for debugging) - _plot = True #QQ set to false + _plot = True # QQ set to false if _plot: import matplotlib.pyplot as plt @@ -557,7 +557,6 @@ def get_peak_to_valley(template_single, sampling_frequency, trough_idx=None, pea return ptv - def get_half_width(template_single, sampling_frequency, trough_idx=None, peak_idx=None, **kwargs) -> float: """ Return the half width of input waveforms in seconds. diff --git a/src/spikeinterface/metrics/template/template_metrics.py b/src/spikeinterface/metrics/template/template_metrics.py index 4c4e8aa811..f00e870c30 100644 --- a/src/spikeinterface/metrics/template/template_metrics.py +++ b/src/spikeinterface/metrics/template/template_metrics.py @@ -126,7 +126,7 @@ def _handle_backward_compatibility_on_load(self): self.params["metric_names"].remove("velocity_below") if "velocity_fits" not in self.params["metric_names"]: self.params["metric_names"].append("velocity_fits") - # peak to valley -> peak_to_trough_duration + # peak to valley -> peak_to_trough_duration if "peak_to_valley" in self.params["metric_names"]: self.params["metric_names"].remove("peak_to_valley") if "peak_to_trough_duration" not in self.params["metric_names"]: diff --git a/src/spikeinterface/widgets/unit_classification.py b/src/spikeinterface/widgets/unit_classification.py index 993e509bc4..e77b16ebd6 100644 --- a/src/spikeinterface/widgets/unit_classification.py +++ b/src/spikeinterface/widgets/unit_classification.py @@ -56,8 +56,14 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): ax.set_ylabel("Number of units") ax.set_title("Unit Classification Summary") for bar, count in zip(bars, counts): - ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.5, - str(count), ha="center", va="bottom", fontsize=10) + ax.text( + bar.get_x() + bar.get_width() / 2, + bar.get_height() + 0.5, + str(count), + ha="center", + va="bottom", + fontsize=10, + ) # Pie chart ax = axes[0, 1] @@ -66,8 +72,15 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): # Placeholder ax = axes[1, 0] - ax.text(0.5, 0.5, "Waveform overlay\n(requires templates extension)", - ha="center", va="center", fontsize=12, transform=ax.transAxes) + ax.text( + 0.5, + 0.5, + "Waveform overlay\n(requires templates extension)", + ha="center", + va="center", + fontsize=12, + transform=ax.transAxes, + ) ax.set_title("Template Waveforms by Type") ax.axis("off") @@ -81,8 +94,7 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): pct = 100 * count / n_total summary_text += f"{label}: {count} ({pct:.1f}%)\n" summary_text += "=" * 30 + f"\nTotal: {n_total} units" - ax.text(0.1, 0.5, summary_text, ha="left", va="center", - fontsize=11, family="monospace", transform=ax.transAxes) + ax.text(0.1, 0.5, summary_text, ha="left", va="center", fontsize=11, family="monospace", transform=ax.transAxes) ax.axis("off") plt.tight_layout() @@ -211,8 +223,14 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): if not sorting_analyzer.has_extension("templates"): fig, ax = plt.subplots(1, 1, figsize=(8, 6)) - ax.text(0.5, 0.5, "Templates extension not computed.\nRun: analyzer.compute('templates')", - ha="center", va="center", fontsize=12) + ax.text( + 0.5, + 0.5, + "Templates extension not computed.\nRun: analyzer.compute('templates')", + ha="center", + va="center", + fontsize=12, + ) ax.axis("off") self.figure = fig self.axes = ax @@ -271,16 +289,28 @@ class UpsetPlotWidget(BaseWidget): """ WAVEFORM_METRICS = [ - "num_positive_peaks", "num_negative_peaks", "peak_to_trough_duration", - "waveform_baseline_flatness", "peak_after_to_trough_ratio", "exp_decay", + "num_positive_peaks", + "num_negative_peaks", + "peak_to_trough_duration", + "waveform_baseline_flatness", + "peak_after_to_trough_ratio", + "exp_decay", ] SPIKE_QUALITY_METRICS = [ - "amplitude_median", "snr_bombcell", "amplitude_cutoff", - "num_spikes", "rp_contamination", "presence_ratio", "drift_ptp", + "amplitude_median", + "snr_bombcell", + "amplitude_cutoff", + "num_spikes", + "rp_contamination", + "presence_ratio", + "drift_ptp", ] NON_SOMATIC_METRICS = [ - "peak_before_to_trough_ratio", "peak_before_width", "trough_width", - "peak_before_to_peak_after_ratio", "main_peak_to_trough_ratio", + "peak_before_to_trough_ratio", + "peak_before_width", + "trough_width", + "peak_before_to_peak_after_ratio", + "main_peak_to_trough_ratio", ] def __init__( @@ -295,7 +325,9 @@ def __init__( backend=None, **backend_kwargs, ): - from spikeinterface.curation import bombcell_get_default_thresholds #QQ need to change to user thresholds! should be in some self ? + from spikeinterface.curation import ( + bombcell_get_default_thresholds, + ) # QQ need to change to user thresholds! should be in some self ? if thresholds is None: thresholds = bombcell_get_default_thresholds() @@ -342,10 +374,16 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): from upsetplot import UpSet, from_memberships except ImportError: fig, ax = plt.subplots(1, 1, figsize=(10, 6)) - ax.text(0.5, 0.5, - "UpSet plots require 'upsetplot' package.\n\npip install upsetplot", - ha="center", va="center", fontsize=14, family="monospace", - bbox=dict(boxstyle="round", facecolor="lightyellow", edgecolor="orange")) + ax.text( + 0.5, + 0.5, + "UpSet plots require 'upsetplot' package.\n\npip install upsetplot", + ha="center", + va="center", + fontsize=14, + family="monospace", + bbox=dict(boxstyle="round", facecolor="lightyellow", edgecolor="orange"), + ) ax.axis("off") ax.set_title("UpSet Plot - Package Not Installed", fontsize=16) self.figure = fig @@ -390,16 +428,20 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): continue fig = plt.figure(figsize=(12, 6)) - UpSet(upset_data, subset_size="count", show_counts=True, - sort_by="cardinality", sort_categories_by="cardinality").plot(fig=fig) + UpSet( + upset_data, + subset_size="count", + show_counts=True, + sort_by="cardinality", + sort_categories_by="cardinality", + ).plot(fig=fig) fig.suptitle(f"{unit_type_label} (n={n_units})", fontsize=14, y=1.02) figures.append(fig) axes_list.append(fig.axes) if not figures: fig, ax = plt.subplots(1, 1, figsize=(8, 6)) - ax.text(0.5, 0.5, "No units found or no metric failures detected.", - ha="center", va="center", fontsize=12) + ax.text(0.5, 0.5, "No units found or no metric failures detected.", ha="center", va="center", fontsize=12) ax.axis("off") figures = [fig] axes_list = [ax] @@ -434,28 +476,50 @@ def _build_failure_table(self, quality_metrics, thresholds): # Convenience functions def plot_unit_classification(sorting_analyzer, unit_type, unit_type_string, thresholds=None, backend=None, **kwargs): """Plot summary of unit classification results.""" - return UnitClassificationWidget(sorting_analyzer, unit_type, unit_type_string, - thresholds=thresholds, backend=backend, **kwargs) + return UnitClassificationWidget( + sorting_analyzer, unit_type, unit_type_string, thresholds=thresholds, backend=backend, **kwargs + ) def plot_classification_histograms(quality_metrics, thresholds=None, metrics_to_plot=None, backend=None, **kwargs): """Plot histograms of quality metrics with threshold lines.""" - return ClassificationHistogramsWidget(quality_metrics, thresholds=thresholds, - metrics_to_plot=metrics_to_plot, backend=backend, **kwargs) + return ClassificationHistogramsWidget( + quality_metrics, thresholds=thresholds, metrics_to_plot=metrics_to_plot, backend=backend, **kwargs + ) -def plot_waveform_overlay(sorting_analyzer, unit_type, unit_type_string, split_non_somatic=False, backend=None, **kwargs): +def plot_waveform_overlay( + sorting_analyzer, unit_type, unit_type_string, split_non_somatic=False, backend=None, **kwargs +): """Plot overlaid waveforms grouped by unit classification type.""" - return WaveformOverlayWidget(sorting_analyzer, unit_type, unit_type_string, - split_non_somatic=split_non_somatic, backend=backend, **kwargs) + return WaveformOverlayWidget( + sorting_analyzer, unit_type, unit_type_string, split_non_somatic=split_non_somatic, backend=backend, **kwargs + ) -def plot_upset(quality_metrics, unit_type, unit_type_string, thresholds=None, - unit_types_to_plot=None, split_non_somatic=False, min_subset_size=1, backend=None, **kwargs): +def plot_upset( + quality_metrics, + unit_type, + unit_type_string, + thresholds=None, + unit_types_to_plot=None, + split_non_somatic=False, + min_subset_size=1, + backend=None, + **kwargs, +): """Plot UpSet plots showing which metrics fail together for each unit type.""" - return UpsetPlotWidget(quality_metrics, unit_type, unit_type_string, thresholds=thresholds, - unit_types_to_plot=unit_types_to_plot, split_non_somatic=split_non_somatic, - min_subset_size=min_subset_size, backend=backend, **kwargs) + return UpsetPlotWidget( + quality_metrics, + unit_type, + unit_type_string, + thresholds=thresholds, + unit_types_to_plot=unit_types_to_plot, + split_non_somatic=split_non_somatic, + min_subset_size=min_subset_size, + backend=backend, + **kwargs, + ) def plot_unit_classification_all( @@ -534,8 +598,13 @@ def plot_unit_classification_all( # UpSet plots if include_upset and quality_metrics is not None: results["upset"] = plot_upset( - quality_metrics, unit_type, unit_type_string, thresholds=thresholds, - split_non_somatic=split_non_somatic, backend=backend, **kwargs + quality_metrics, + unit_type, + unit_type_string, + thresholds=thresholds, + split_non_somatic=split_non_somatic, + backend=backend, + **kwargs, ) return results From 52a58b250146f51a7d18fb3e280956354514c56b Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Thu, 8 Jan 2026 15:25:09 +0100 Subject: [PATCH 20/49] users can input bombcell parameters as JSON --- src/spikeinterface/curation/__init__.py | 2 + .../curation/default_thresholds.json | 74 +++++++++++++++++++ .../curation/unit_classification.py | 59 +++++++++++++++ 3 files changed, 135 insertions(+) create mode 100644 src/spikeinterface/curation/default_thresholds.json diff --git a/src/spikeinterface/curation/__init__.py b/src/spikeinterface/curation/__init__.py index 2913bd7693..537c1d370b 100644 --- a/src/spikeinterface/curation/__init__.py +++ b/src/spikeinterface/curation/__init__.py @@ -29,6 +29,8 @@ bombcell_classify_units, apply_thresholds, get_classification_summary, + save_thresholds, + load_thresholds, ) from .model_based_curation import auto_label_units, load_model diff --git a/src/spikeinterface/curation/default_thresholds.json b/src/spikeinterface/curation/default_thresholds.json new file mode 100644 index 0000000000..6023cb1880 --- /dev/null +++ b/src/spikeinterface/curation/default_thresholds.json @@ -0,0 +1,74 @@ +{ + "num_positive_peaks": { + "min": null, + "max": 2 + }, + "num_negative_peaks": { + "min": null, + "max": 1 + }, + "peak_to_trough_duration": { + "min": 0.0001, + "max": 0.00115 + }, + "waveform_baseline_flatness": { + "min": null, + "max": 0.5 + }, + "peak_after_to_trough_ratio": { + "min": null, + "max": 0.8 + }, + "exp_decay": { + "min": 0.01, + "max": 0.1 + }, + "amplitude_median": { + "min": 40, + "max": null + }, + "snr_bombcell": { + "min": 5, + "max": null + }, + "amplitude_cutoff": { + "min": null, + "max": 0.2 + }, + "num_spikes": { + "min": 300, + "max": null + }, + "rp_contamination": { + "min": null, + "max": 0.1 + }, + "presence_ratio": { + "min": 0.7, + "max": null + }, + "drift_ptp": { + "min": null, + "max": 100 + }, + "peak_before_to_trough_ratio": { + "min": null, + "max": 3 + }, + "peak_before_width": { + "min": 150, + "max": null + }, + "trough_width": { + "min": 200, + "max": null + }, + "peak_before_to_peak_after_ratio": { + "min": null, + "max": 3 + }, + "main_peak_to_trough_ratio": { + "min": null, + "max": 0.8 + } +} \ No newline at end of file diff --git a/src/spikeinterface/curation/unit_classification.py b/src/spikeinterface/curation/unit_classification.py index 6dc2b1cb61..e121aa8078 100644 --- a/src/spikeinterface/curation/unit_classification.py +++ b/src/spikeinterface/curation/unit_classification.py @@ -267,3 +267,62 @@ def get_classification_summary(unit_type: np.ndarray, unit_type_string: np.ndarr summary["percentages"][label] = round(100 * count / n_total, 1) return summary + + +def save_thresholds(thresholds: dict, filepath) -> None: + """ + Save thresholds to a JSON file. + + Parameters + ---------- + thresholds : dict + Threshold dictionary from bombcell_get_default_thresholds() or modified version. + filepath : str or Path + Path to save the JSON file. + """ + import json + from pathlib import Path + + # Convert np.nan to None for JSON serialization + json_thresholds = {} + for metric_name, thresh in thresholds.items(): + json_thresholds[metric_name] = { + "min": None if (isinstance(thresh["min"], float) and np.isnan(thresh["min"])) else thresh["min"], + "max": None if (isinstance(thresh["max"], float) and np.isnan(thresh["max"])) else thresh["max"], + } + + filepath = Path(filepath) + with open(filepath, "w") as f: + json.dump(json_thresholds, f, indent=4) + + +def load_thresholds(filepath) -> dict: + """ + Load thresholds from a JSON file. + + Parameters + ---------- + filepath : str or Path + Path to the JSON file. + + Returns + ------- + thresholds : dict + Threshold dictionary compatible with bombcell_classify_units(). + """ + import json + from pathlib import Path + + filepath = Path(filepath) + with open(filepath, "r") as f: + json_thresholds = json.load(f) + + # Convert None to np.nan + thresholds = {} + for metric_name, thresh in json_thresholds.items(): + thresholds[metric_name] = { + "min": np.nan if thresh["min"] is None else thresh["min"], + "max": np.nan if thresh["max"] is None else thresh["max"], + } + + return thresholds From aa35ac8cc6fc882d9cea51813215906a9e85259d Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 8 Jan 2026 14:26:33 +0000 Subject: [PATCH 21/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/curation/default_thresholds.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/spikeinterface/curation/default_thresholds.json b/src/spikeinterface/curation/default_thresholds.json index 6023cb1880..8e39d89179 100644 --- a/src/spikeinterface/curation/default_thresholds.json +++ b/src/spikeinterface/curation/default_thresholds.json @@ -71,4 +71,4 @@ "min": null, "max": 0.8 } -} \ No newline at end of file +} From eb130e997241c4077e1a3f58f3d02e004dbd9a7b Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Thu, 8 Jan 2026 15:58:03 +0100 Subject: [PATCH 22/49] optionally save plots and metrics, explicit inputs to functions to have template and quality metrics (this way it is clear what to input) --- src/spikeinterface/curation/__init__.py | 7 +- ...it_classification.py => unit_labelling.py} | 133 ++++++++-- .../metrics/template/metrics.py | 2 +- ...it_classification.py => unit_labelling.py} | 231 +++++++----------- src/spikeinterface/widgets/widget_list.py | 13 +- 5 files changed, 217 insertions(+), 169 deletions(-) rename src/spikeinterface/curation/{unit_classification.py => unit_labelling.py} (71%) rename src/spikeinterface/widgets/{unit_classification.py => unit_labelling.py} (74%) diff --git a/src/spikeinterface/curation/__init__.py b/src/spikeinterface/curation/__init__.py index 537c1d370b..944dd59338 100644 --- a/src/spikeinterface/curation/__init__.py +++ b/src/spikeinterface/curation/__init__.py @@ -21,16 +21,17 @@ from .sortingview_curation import apply_sortingview_curation # automated curation -from .unit_classification import ( +from .unit_labelling import ( WAVEFORM_METRICS, SPIKE_QUALITY_METRICS, NON_SOMATIC_METRICS, bombcell_get_default_thresholds, - bombcell_classify_units, + bombcell_label_units, apply_thresholds, - get_classification_summary, + get_labelling_summary, save_thresholds, load_thresholds, + save_labelling_results, ) from .model_based_curation import auto_label_units, load_model diff --git a/src/spikeinterface/curation/unit_classification.py b/src/spikeinterface/curation/unit_labelling.py similarity index 71% rename from src/spikeinterface/curation/unit_classification.py rename to src/spikeinterface/curation/unit_labelling.py index 4895ff7521..7b4f985d67 100644 --- a/src/spikeinterface/curation/unit_classification.py +++ b/src/spikeinterface/curation/unit_labelling.py @@ -1,5 +1,5 @@ """ -Unit classification based on quality metrics (Bombcell). +Unit labelling based on quality metrics (Bombcell). Unit Types: 0 (NOISE): Failed waveform quality checks @@ -45,7 +45,7 @@ def bombcell_get_default_thresholds() -> dict: """ - Bombcell - Returns default thresholds for unit classification. + Bombcell - Returns default thresholds for unit labelling. Each metric has 'min' and 'max' values. Use np.nan to disable a threshold (e.g. to ignore a metric completly or to only have a min or a max threshold) @@ -76,22 +76,36 @@ def bombcell_get_default_thresholds() -> dict: } -def bombcell_classify_units( - quality_metrics: pd.DataFrame, +def _combine_metrics(quality_metrics, template_metrics): + """Combine quality_metrics and template_metrics into a single DataFrame.""" + if quality_metrics is None and template_metrics is None: + return None + if quality_metrics is None: + return template_metrics + if template_metrics is None: + return quality_metrics + return quality_metrics.join(template_metrics, how="outer") + + +def bombcell_label_units( + quality_metrics=None, + template_metrics=None, thresholds: Optional[dict] = None, - classify_non_somatic: bool = True, + label_non_somatic: bool = True, split_non_somatic_good_mua: bool = False, ) -> tuple[np.ndarray, np.ndarray]: """ - Bombcell - classify units based on quality metrics and thresholds. + Bombcell - label units based on quality metrics and thresholds. Parameters ---------- - quality_metrics : pd.DataFrame + quality_metrics : pd.DataFrame, optional DataFrame with quality metrics (index = unit_ids). + template_metrics : pd.DataFrame, optional + DataFrame with template metrics (index = unit_ids). thresholds : dict or None Threshold dict: {"metric": {"min": val, "max": val}}. Use np.nan to disable. - classify_non_somatic : bool + label_non_somatic : bool If True, detect non-somatic (axonal) units. split_non_somatic_good_mua : bool If True, split non-somatic into NON_SOMA_GOOD (3) and NON_SOMA_MUA (4). @@ -103,19 +117,23 @@ def bombcell_classify_units( unit_type_string : np.ndarray String labels. """ + combined_metrics = _combine_metrics(quality_metrics, template_metrics) + if combined_metrics is None: + raise ValueError("At least one of quality_metrics or template_metrics must be provided") + if thresholds is None: thresholds = bombcell_get_default_thresholds() - n_units = len(quality_metrics) + n_units = len(combined_metrics) unit_type = np.full(n_units, np.nan) absolute_value_metrics = ["amplitude_median"] # NOISE: waveform failures noise_mask = np.zeros(n_units, dtype=bool) for metric_name in WAVEFORM_METRICS: - if metric_name not in quality_metrics.columns or metric_name not in thresholds: + if metric_name not in combined_metrics.columns or metric_name not in thresholds: continue - values = quality_metrics[metric_name].values + values = combined_metrics[metric_name].values if metric_name in absolute_value_metrics: values = np.abs(values) thresh = thresholds[metric_name] @@ -129,9 +147,9 @@ def bombcell_classify_units( # MUA: spike quality failures mua_mask = np.zeros(n_units, dtype=bool) for metric_name in SPIKE_QUALITY_METRICS: - if metric_name not in quality_metrics.columns or metric_name not in thresholds: + if metric_name not in combined_metrics.columns or metric_name not in thresholds: continue - values = quality_metrics[metric_name].values + values = combined_metrics[metric_name].values if metric_name in absolute_value_metrics: values = np.abs(values) thresh = thresholds[metric_name] @@ -146,11 +164,11 @@ def bombcell_classify_units( unit_type[np.isnan(unit_type)] = 1 # NON-SOMATIC - if classify_non_somatic: + if label_non_somatic: def get_metric(name): - if name in quality_metrics.columns: - return quality_metrics[name].values + if name in combined_metrics.columns: + return combined_metrics[name].values return np.full(n_units, np.nan) peak_before_width = get_metric("peak_before_width") @@ -256,7 +274,7 @@ def apply_thresholds( return pd.DataFrame(results, index=quality_metrics.index) -def get_classification_summary(unit_type: np.ndarray, unit_type_string: np.ndarray) -> dict: +def get_labelling_summary(unit_type: np.ndarray, unit_type_string: np.ndarray) -> dict: """Get counts and percentages for each unit type.""" n_total = len(unit_type) unique_types, counts = np.unique(unit_type, return_counts=True) @@ -327,3 +345,84 @@ def load_thresholds(filepath) -> dict: } return thresholds + + +def save_labelling_results( + quality_metrics: pd.DataFrame, + unit_type: np.ndarray, + unit_type_string: np.ndarray, + thresholds: dict, + folder, + save_narrow: bool = True, + save_wide: bool = True, +) -> None: + """ + Save labelling results to CSV files. + + Parameters + ---------- + quality_metrics : pd.DataFrame + DataFrame with quality metrics (index = unit_ids). + unit_type : np.ndarray + Numeric unit type codes. + unit_type_string : np.ndarray + String labels for each unit. + thresholds : dict + Threshold dictionary used for labelling. + folder : str or Path + Folder to save the CSV files. + save_narrow : bool, default: True + Save narrow/tidy format (one row per unit-metric). + save_wide : bool, default: True + Save wide format (one row per unit, metrics as columns). + """ + from pathlib import Path + + folder = Path(folder) + folder.mkdir(parents=True, exist_ok=True) + + unit_ids = quality_metrics.index.values + + # Wide format: one row per unit + if save_wide: + wide_df = quality_metrics.copy() + wide_df.insert(0, "label", unit_type_string) + wide_df.insert(1, "label_code", unit_type) + wide_df.to_csv(folder / "labelling_results_wide.csv") + + # Narrow format: one row per unit-metric combination + if save_narrow: + rows = [] + for i, unit_id in enumerate(unit_ids): + label = unit_type_string[i] + label_code = unit_type[i] + for metric_name in quality_metrics.columns: + if metric_name not in thresholds: + continue + value = quality_metrics.loc[unit_id, metric_name] + thresh = thresholds[metric_name] + thresh_min = thresh.get("min", np.nan) + thresh_max = thresh.get("max", np.nan) + + # Determine pass/fail + passed = True + if np.isnan(value): + passed = False + elif not np.isnan(thresh_min) and value < thresh_min: + passed = False + elif not np.isnan(thresh_max) and value > thresh_max: + passed = False + + rows.append({ + "unit_id": unit_id, + "label": label, + "label_code": label_code, + "metric_name": metric_name, + "value": value, + "threshold_min": None if np.isnan(thresh_min) else thresh_min, + "threshold_max": None if np.isnan(thresh_max) else thresh_max, + "passed": passed, + }) + + narrow_df = pd.DataFrame(rows) + narrow_df.to_csv(folder / "labelling_results_narrow.csv", index=False) diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index 3fad4c827d..9f55cb6fc6 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -195,7 +195,7 @@ def get_trough_and_peak_idx( peaks_after = empty_dict.copy() # Quick visualization (set to True for debugging) - _plot = True # QQ set to false + _plot = False # QQ set to false if _plot: import matplotlib.pyplot as plt diff --git a/src/spikeinterface/widgets/unit_classification.py b/src/spikeinterface/widgets/unit_labelling.py similarity index 74% rename from src/spikeinterface/widgets/unit_classification.py rename to src/spikeinterface/widgets/unit_labelling.py index e77b16ebd6..ca9ca5e939 100644 --- a/src/spikeinterface/widgets/unit_classification.py +++ b/src/spikeinterface/widgets/unit_labelling.py @@ -1,4 +1,4 @@ -"""Widgets for visualizing unit classification results.""" +"""Widgets for visualizing unit labelling results.""" from __future__ import annotations @@ -8,106 +8,24 @@ from .base import BaseWidget, to_attr -class UnitClassificationWidget(BaseWidget): - """Plot summary of unit classification (bar chart, pie chart, text summary).""" - - def __init__( - self, - sorting_analyzer, - unit_type: np.ndarray, - unit_type_string: np.ndarray, - thresholds: Optional[dict] = None, - backend=None, - **backend_kwargs, - ): - from spikeinterface.curation import bombcell_get_default_thresholds - - if thresholds is None: - thresholds = bombcell_get_default_thresholds() - - sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) - plot_data = dict( - sorting_analyzer=sorting_analyzer, - unit_type=unit_type, - unit_type_string=unit_type_string, - thresholds=thresholds, - ) - BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) - - def plot_matplotlib(self, data_plot, **backend_kwargs): - import matplotlib.pyplot as plt - - dp = to_attr(data_plot) - unit_type = dp.unit_type - unit_type_string = dp.unit_type_string - - unique_types = np.unique(unit_type) - type_counts = {t: np.sum(unit_type == t) for t in unique_types} - type_labels = {t: unit_type_string[unit_type == t][0] for t in unique_types} - - fig, axes = plt.subplots(2, 2, figsize=(12, 10)) - - # Bar chart - ax = axes[0, 0] - labels = [type_labels[t] for t in unique_types] - counts = [type_counts[t] for t in unique_types] - colors = ["red", "green", "orange", "blue", "purple"][: len(unique_types)] - bars = ax.bar(labels, counts, color=colors, alpha=0.7, edgecolor="black") - ax.set_ylabel("Number of units") - ax.set_title("Unit Classification Summary") - for bar, count in zip(bars, counts): - ax.text( - bar.get_x() + bar.get_width() / 2, - bar.get_height() + 0.5, - str(count), - ha="center", - va="bottom", - fontsize=10, - ) - - # Pie chart - ax = axes[0, 1] - ax.pie(counts, labels=labels, autopct="%1.1f%%", colors=colors, startangle=90) - ax.set_title("Unit Classification Distribution") - - # Placeholder - ax = axes[1, 0] - ax.text( - 0.5, - 0.5, - "Waveform overlay\n(requires templates extension)", - ha="center", - va="center", - fontsize=12, - transform=ax.transAxes, - ) - ax.set_title("Template Waveforms by Type") - ax.axis("off") - - # Text summary - ax = axes[1, 1] - n_total = len(unit_type) - summary_text = "Classification Summary\n" + "=" * 30 + "\n" - for t in unique_types: - label = type_labels[t] - count = type_counts[t] - pct = 100 * count / n_total - summary_text += f"{label}: {count} ({pct:.1f}%)\n" - summary_text += "=" * 30 + f"\nTotal: {n_total} units" - ax.text(0.1, 0.5, summary_text, ha="left", va="center", fontsize=11, family="monospace", transform=ax.transAxes) - ax.axis("off") - - plt.tight_layout() - self.figure = fig - self.axes = axes +def _combine_metrics(quality_metrics, template_metrics): + """Combine quality_metrics and template_metrics into a single DataFrame.""" + if quality_metrics is None and template_metrics is None: + return None + if quality_metrics is None: + return template_metrics + if template_metrics is None: + return quality_metrics + return quality_metrics.join(template_metrics, how="outer") -class ClassificationHistogramsWidget(BaseWidget): +class LabellingHistogramsWidget(BaseWidget): """Plot histograms of quality metrics with threshold lines.""" def __init__( self, - quality_metrics, + quality_metrics=None, + template_metrics=None, thresholds: Optional[dict] = None, metrics_to_plot: Optional[list] = None, backend=None, @@ -115,13 +33,17 @@ def __init__( ): from spikeinterface.curation import bombcell_get_default_thresholds + combined_metrics = _combine_metrics(quality_metrics, template_metrics) + if combined_metrics is None: + raise ValueError("At least one of quality_metrics or template_metrics must be provided") + if thresholds is None: thresholds = bombcell_get_default_thresholds() if metrics_to_plot is None: - metrics_to_plot = [m for m in thresholds.keys() if m in quality_metrics.columns] + metrics_to_plot = [m for m in thresholds.keys() if m in combined_metrics.columns] plot_data = dict( - quality_metrics=quality_metrics, + quality_metrics=combined_metrics, thresholds=thresholds, metrics_to_plot=metrics_to_plot, ) @@ -193,7 +115,7 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): class WaveformOverlayWidget(BaseWidget): - """Plot overlaid waveforms grouped by unit classification type.""" + """Plot overlaid waveforms grouped by unit label type.""" def __init__( self, @@ -315,9 +237,10 @@ class UpsetPlotWidget(BaseWidget): def __init__( self, - quality_metrics, unit_type: np.ndarray, unit_type_string: np.ndarray, + quality_metrics=None, + template_metrics=None, thresholds: Optional[dict] = None, unit_types_to_plot: Optional[list] = None, split_non_somatic: bool = False, @@ -325,9 +248,11 @@ def __init__( backend=None, **backend_kwargs, ): - from spikeinterface.curation import ( - bombcell_get_default_thresholds, - ) # QQ need to change to user thresholds! should be in some self ? + from spikeinterface.curation import bombcell_get_default_thresholds + + combined_metrics = _combine_metrics(quality_metrics, template_metrics) + if combined_metrics is None: + raise ValueError("At least one of quality_metrics or template_metrics must be provided") if thresholds is None: thresholds = bombcell_get_default_thresholds() @@ -338,7 +263,7 @@ def __init__( unit_types_to_plot = ["NOISE", "MUA", "NON_SOMA"] plot_data = dict( - quality_metrics=quality_metrics, + quality_metrics=combined_metrics, unit_type=unit_type, unit_type_string=unit_type_string, thresholds=thresholds, @@ -474,33 +399,34 @@ def _build_failure_table(self, quality_metrics, thresholds): # Convenience functions -def plot_unit_classification(sorting_analyzer, unit_type, unit_type_string, thresholds=None, backend=None, **kwargs): - """Plot summary of unit classification results.""" - return UnitClassificationWidget( - sorting_analyzer, unit_type, unit_type_string, thresholds=thresholds, backend=backend, **kwargs - ) - - -def plot_classification_histograms(quality_metrics, thresholds=None, metrics_to_plot=None, backend=None, **kwargs): +def plot_labelling_histograms( + quality_metrics=None, template_metrics=None, thresholds=None, metrics_to_plot=None, backend=None, **kwargs +): """Plot histograms of quality metrics with threshold lines.""" - return ClassificationHistogramsWidget( - quality_metrics, thresholds=thresholds, metrics_to_plot=metrics_to_plot, backend=backend, **kwargs + return LabellingHistogramsWidget( + quality_metrics=quality_metrics, + template_metrics=template_metrics, + thresholds=thresholds, + metrics_to_plot=metrics_to_plot, + backend=backend, + **kwargs, ) def plot_waveform_overlay( sorting_analyzer, unit_type, unit_type_string, split_non_somatic=False, backend=None, **kwargs ): - """Plot overlaid waveforms grouped by unit classification type.""" + """Plot overlaid waveforms grouped by unit label type.""" return WaveformOverlayWidget( sorting_analyzer, unit_type, unit_type_string, split_non_somatic=split_non_somatic, backend=backend, **kwargs ) def plot_upset( - quality_metrics, unit_type, unit_type_string, + quality_metrics=None, + template_metrics=None, thresholds=None, unit_types_to_plot=None, split_non_somatic=False, @@ -510,9 +436,10 @@ def plot_upset( ): """Plot UpSet plots showing which metrics fail together for each unit type.""" return UpsetPlotWidget( - quality_metrics, unit_type, unit_type_string, + quality_metrics=quality_metrics, + template_metrics=template_metrics, thresholds=thresholds, unit_types_to_plot=unit_types_to_plot, split_non_somatic=split_non_somatic, @@ -522,19 +449,21 @@ def plot_upset( ) -def plot_unit_classification_all( +def plot_unit_labelling_all( sorting_analyzer, unit_type: np.ndarray, unit_type_string: np.ndarray, quality_metrics=None, + template_metrics=None, thresholds: Optional[dict] = None, split_non_somatic: bool = False, include_upset: bool = True, + save_folder=None, backend=None, **kwargs, ): """ - Generate all unit classification plots. + Generate all unit labelling plots and optionally save to folder. Parameters ---------- @@ -545,13 +474,17 @@ def plot_unit_classification_all( unit_type_string : np.ndarray Array of unit type labels as strings. quality_metrics : pd.DataFrame, optional - Quality metrics DataFrame. If None, attempts to get from sorting_analyzer. + Quality metrics DataFrame. If None, loads from sorting_analyzer. + template_metrics : pd.DataFrame, optional + Template metrics DataFrame. If None, loads from sorting_analyzer. thresholds : dict, optional Threshold dictionary. If None, uses default thresholds. split_non_somatic : bool, default: False Whether to split NON_SOMA into NON_SOMA_GOOD and NON_SOMA_MUA. include_upset : bool, default: True Whether to include UpSet plots (requires upsetplot package). + save_folder : str or Path, optional + If provided, saves all plots and CSV results to this folder. backend : str, optional Plotting backend. **kwargs @@ -560,34 +493,32 @@ def plot_unit_classification_all( Returns ------- dict - Dictionary with keys 'summary', 'histograms', 'waveforms', 'upset' containing widget objects. + Dictionary with keys 'histograms', 'waveforms', 'upset' containing widget objects. """ - from spikeinterface.curation import bombcell_get_default_thresholds + from pathlib import Path + from spikeinterface.curation import bombcell_get_default_thresholds, save_labelling_results if thresholds is None: thresholds = bombcell_get_default_thresholds() - if quality_metrics is None: - if sorting_analyzer.has_extension("quality_metrics"): - quality_metrics = sorting_analyzer.get_extension("quality_metrics").get_data() - if sorting_analyzer.has_extension("template_metrics"): - tm = sorting_analyzer.get_extension("template_metrics").get_data() - if quality_metrics is not None: - quality_metrics = quality_metrics.join(tm, how="outer") - else: - quality_metrics = tm + # Load metrics from sorting_analyzer if not provided + if quality_metrics is None and sorting_analyzer.has_extension("quality_metrics"): + quality_metrics = sorting_analyzer.get_extension("quality_metrics").get_data() + if template_metrics is None and sorting_analyzer.has_extension("template_metrics"): + template_metrics = sorting_analyzer.get_extension("template_metrics").get_data() - results = {} + combined_metrics = _combine_metrics(quality_metrics, template_metrics) - # Summary plot - results["summary"] = plot_unit_classification( - sorting_analyzer, unit_type, unit_type_string, thresholds=thresholds, backend=backend, **kwargs - ) + results = {} # Histograms - if quality_metrics is not None: - results["histograms"] = plot_classification_histograms( - quality_metrics, thresholds=thresholds, backend=backend, **kwargs + if combined_metrics is not None: + results["histograms"] = plot_labelling_histograms( + quality_metrics=quality_metrics, + template_metrics=template_metrics, + thresholds=thresholds, + backend=backend, + **kwargs, ) # Waveform overlay @@ -596,15 +527,35 @@ def plot_unit_classification_all( ) # UpSet plots - if include_upset and quality_metrics is not None: + if include_upset and combined_metrics is not None: results["upset"] = plot_upset( - quality_metrics, unit_type, unit_type_string, + quality_metrics=quality_metrics, + template_metrics=template_metrics, thresholds=thresholds, split_non_somatic=split_non_somatic, backend=backend, **kwargs, ) + # Save to folder if requested + if save_folder is not None: + save_folder = Path(save_folder) + save_folder.mkdir(parents=True, exist_ok=True) + + # Save plots + if "histograms" in results and results["histograms"].figure is not None: + results["histograms"].figure.savefig(save_folder / "labelling_histograms.png", dpi=150, bbox_inches="tight") + if "waveforms" in results and results["waveforms"].figure is not None: + results["waveforms"].figure.savefig(save_folder / "waveform_overlay.png", dpi=150, bbox_inches="tight") + if "upset" in results and hasattr(results["upset"], "figures"): + for i, fig in enumerate(results["upset"].figures): + fig.savefig(save_folder / f"upset_plot_{i}.png", dpi=150, bbox_inches="tight") + + # Save CSV results + if combined_metrics is not None: + save_labelling_results(combined_metrics, unit_type, unit_type_string, thresholds, save_folder) + return results + diff --git a/src/spikeinterface/widgets/widget_list.py b/src/spikeinterface/widgets/widget_list.py index 72a8c01028..d8f2f46856 100644 --- a/src/spikeinterface/widgets/widget_list.py +++ b/src/spikeinterface/widgets/widget_list.py @@ -37,16 +37,14 @@ from .comparison import AgreementMatrixWidget, ConfusionMatrixWidget from .gtstudy import StudyRunTimesWidget, StudyUnitCountsWidget, StudyPerformances, StudyAgreementMatrix, StudySummary from .collision import ComparisonCollisionBySimilarityWidget, StudyComparisonCollisionBySimilarityWidget -from .unit_classification import ( - UnitClassificationWidget, - ClassificationHistogramsWidget, +from .unit_labelling import ( + LabellingHistogramsWidget, WaveformOverlayWidget, UpsetPlotWidget, - plot_unit_classification, - plot_classification_histograms, + plot_labelling_histograms, plot_waveform_overlay, plot_upset, - plot_unit_classification_all, + plot_unit_labelling_all, ) widget_list = [ @@ -54,7 +52,7 @@ AllAmplitudesDistributionsWidget, AmplitudesWidget, AutoCorrelogramsWidget, - ClassificationHistogramsWidget, + LabellingHistogramsWidget, ConfusionMatrixWidget, ComparisonCollisionBySimilarityWidget, CrossCorrelogramsWidget, @@ -79,7 +77,6 @@ TemplateMetricsWidget, TemplateSimilarityWidget, TracesWidget, - UnitClassificationWidget, UnitDepthsWidget, UnitLocationsWidget, UnitPresenceWidget, From 01480b38a7ff99c5d61147286bfcbc91396dfe60 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 8 Jan 2026 14:58:51 +0000 Subject: [PATCH 23/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/curation/unit_labelling.py | 22 ++++++++++--------- .../metrics/template/metrics.py | 2 +- src/spikeinterface/widgets/unit_labelling.py | 1 - 3 files changed, 13 insertions(+), 12 deletions(-) diff --git a/src/spikeinterface/curation/unit_labelling.py b/src/spikeinterface/curation/unit_labelling.py index 7b4f985d67..814632da6e 100644 --- a/src/spikeinterface/curation/unit_labelling.py +++ b/src/spikeinterface/curation/unit_labelling.py @@ -413,16 +413,18 @@ def save_labelling_results( elif not np.isnan(thresh_max) and value > thresh_max: passed = False - rows.append({ - "unit_id": unit_id, - "label": label, - "label_code": label_code, - "metric_name": metric_name, - "value": value, - "threshold_min": None if np.isnan(thresh_min) else thresh_min, - "threshold_max": None if np.isnan(thresh_max) else thresh_max, - "passed": passed, - }) + rows.append( + { + "unit_id": unit_id, + "label": label, + "label_code": label_code, + "metric_name": metric_name, + "value": value, + "threshold_min": None if np.isnan(thresh_min) else thresh_min, + "threshold_max": None if np.isnan(thresh_max) else thresh_max, + "passed": passed, + } + ) narrow_df = pd.DataFrame(rows) narrow_df.to_csv(folder / "labelling_results_narrow.csv", index=False) diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index 9f55cb6fc6..53148fac85 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -195,7 +195,7 @@ def get_trough_and_peak_idx( peaks_after = empty_dict.copy() # Quick visualization (set to True for debugging) - _plot = False # QQ set to false + _plot = False # QQ set to false if _plot: import matplotlib.pyplot as plt diff --git a/src/spikeinterface/widgets/unit_labelling.py b/src/spikeinterface/widgets/unit_labelling.py index ca9ca5e939..0c01b7f528 100644 --- a/src/spikeinterface/widgets/unit_labelling.py +++ b/src/spikeinterface/widgets/unit_labelling.py @@ -558,4 +558,3 @@ def plot_unit_labelling_all( save_labelling_results(combined_metrics, unit_type, unit_type_string, thresholds, save_folder) return results - From af362595defdb688f38cf33e5140d313d151f1a6 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Thu, 8 Jan 2026 16:21:51 +0100 Subject: [PATCH 24/49] example jupyter notebook --- .../example_bombcell_unit_labelling.ipynb | 262 ++++++++++++++++++ 1 file changed, 262 insertions(+) create mode 100644 examples/get_started/example_bombcell_unit_labelling.ipynb diff --git a/examples/get_started/example_bombcell_unit_labelling.ipynb b/examples/get_started/example_bombcell_unit_labelling.ipynb new file mode 100644 index 0000000000..08e29e9f65 --- /dev/null +++ b/examples/get_started/example_bombcell_unit_labelling.ipynb @@ -0,0 +1,262 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bombcell unit labelling\n", + "\n", + "With this notebook you can:\n", + "- load a SortingAnalyzer\n", + "- compute required extensions\n", + "- label units based on quality thresholds\n", + "- generating and save summary plots\n", + "- save metrics and results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import spikeinterface as si\n", + "from spikeinterface.curation import (\n", + " bombcell_get_default_thresholds,\n", + " bombcell_label_units,\n", + " save_thresholds,\n", + " load_thresholds,\n", + ")\n", + "from spikeinterface.widgets import plot_unit_labelling_all" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### load a SortingAnalyzer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Change this to your analyzer path\n", + "analyzer_path = \"/Users/jf5479/Downloads/M25_D18/kilosort4_sa\"\n", + "output_folder = Path(analyzer_path) / \"bombcell\"\n", + "\n", + "analyzer = si.load_sorting_analyzer(analyzer_path)\n", + "analyzer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### compute required extensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Templates (required for template_metrics)\n", + "if not analyzer.has_extension(\"templates\"):\n", + " analyzer.compute(\"templates\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Template metrics\n", + "if not analyzer.has_extension(\"template_metrics\"):\n", + " analyzer.compute(\"template_metrics\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Quality metrics (and dependencies)\n", + "if not analyzer.has_extension(\"spike_amplitudes\"):\n", + " analyzer.compute(\"spike_amplitudes\")\n", + "\n", + "if not analyzer.has_extension(\"noise_levels\"):\n", + " analyzer.compute(\"noise_levels\")\n", + "\n", + "if not analyzer.has_extension(\"quality_metrics\"):\n", + " analyzer.compute(\"quality_metrics\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### get metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qm = analyzer.get_extension(\"quality_metrics\").get_data()\n", + "tm = analyzer.get_extension(\"template_metrics\").get_data()\n", + "\n", + "print(f\"Quality metrics: {list(qm.columns)}\")\n", + "print(f\"Template metrics: {list(tm.columns)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### set labelling thresholds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use default thresholds\n", + "thresholds = bombcell_get_default_thresholds()\n", + "\n", + "# Or load from file:\n", + "# thresholds = load_thresholds(\"my_thresholds.json\")\n", + "\n", + "thresholds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Optionally modify thresholds\n", + "# thresholds[\"amplitude_median\"][\"min\"] = 50 # stricter\n", + "# thresholds[\"rp_contamination\"][\"max\"] = 0.05 # stricter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Optionally set and load thresholds from a JSON file \n", + "# Load thresholds from saved JSON\n", + "thresholds = load_thresholds(output_folder / \"thresholds.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The JSON file format looks like:\n", + "```json\n", + "{\n", + " \"amplitude_median\": {\"min\": 40, \"max\": null},\n", + " \"num_positive_peaks\": {\"min\": null, \"max\": 2},\n", + " \"peak_to_trough_duration\": {\"min\": 0.0001, \"max\": 0.00115}\n", + "}\n", + "```\n", + "`null` in JSON becomes `np.nan` (threshold disabled)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### label units" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "unit_type, unit_type_string = bombcell_label_units(\n", + " quality_metrics=qm,\n", + " template_metrics=tm,\n", + " thresholds=thresholds,\n", + " label_non_somatic=True,\n", + " split_non_somatic_good_mua=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### generate summary plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plots = plot_unit_labelling_all(\n", + " analyzer,\n", + " unit_type,\n", + " unit_type_string,\n", + " quality_metrics=qm,\n", + " template_metrics=tm,\n", + " thresholds=thresholds,\n", + " save_folder=output_folder,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### save labelling thresholds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "save_thresholds(thresholds, output_folder / \"thresholds.json\")\n", + "\n", + "print(f\"Results saved to: {output_folder.absolute()}\")\n", + "print(\"\\nFiles:\")\n", + "for f in sorted(output_folder.glob(\"*\")):\n", + " print(f\" - {f.name}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 7045c432a0031bde31fe630baa28ef59c049bb4e Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Thu, 8 Jan 2026 16:21:54 +0100 Subject: [PATCH 25/49] example jupyter notebook --- examples/get_started/example_bombcell_unit_labelling.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/get_started/example_bombcell_unit_labelling.ipynb b/examples/get_started/example_bombcell_unit_labelling.ipynb index 08e29e9f65..8b18aaec90 100644 --- a/examples/get_started/example_bombcell_unit_labelling.ipynb +++ b/examples/get_started/example_bombcell_unit_labelling.ipynb @@ -45,7 +45,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Change this to your analyzer path\n", + "# Change this to your analyzer path - you need to have already generated a sorting analyzer. see quickstart.py for how to do this\n", "analyzer_path = \"/Users/jf5479/Downloads/M25_D18/kilosort4_sa\"\n", "output_folder = Path(analyzer_path) / \"bombcell\"\n", "\n", From adac68ee12d8d6ad22581820b1c4a15de4e85d17 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 20:14:21 -0500 Subject: [PATCH 26/49] labelling -> labeling and unit_labeling.py -> bombcell_curation.py --- doc/how_to/import_kilosort_data.rst | 2 +- .../example_bombcell_unit_labelling.ipynb | 262 ------ .../comparison/comparisontools.py | 2 +- .../test_channelsaggregationrecording.py | 2 +- src/spikeinterface/curation/__init__.py | 6 +- .../{unit_labelling.py => unit_labeling.py} | 16 +- src/spikeinterface/exporters/__init__.py | 1 + .../exporters/tests/test_to_methods.py | 154 ++++ src/spikeinterface/exporters/to_methods.py | 796 ++++++++++++++++++ .../metrics/quality/misc_metrics.py | 11 +- .../quality/tests/test_metrics_functions.py | 2 +- .../{unit_labelling.py => unit_labeling.py} | 20 +- src/spikeinterface/widgets/widget_list.py | 10 +- 13 files changed, 987 insertions(+), 297 deletions(-) delete mode 100644 examples/get_started/example_bombcell_unit_labelling.ipynb rename src/spikeinterface/curation/{unit_labelling.py => unit_labeling.py} (96%) create mode 100644 src/spikeinterface/exporters/tests/test_to_methods.py create mode 100644 src/spikeinterface/exporters/to_methods.py rename src/spikeinterface/widgets/{unit_labelling.py => unit_labeling.py} (97%) diff --git a/doc/how_to/import_kilosort_data.rst b/doc/how_to/import_kilosort_data.rst index dad522334a..ed7bf7b6c0 100644 --- a/doc/how_to/import_kilosort_data.rst +++ b/doc/how_to/import_kilosort_data.rst @@ -49,7 +49,7 @@ If you'd like to store the information you've computed, you can save the analyze ) You now have a fully functional ``SortingAnalyzer`` - congrats! You can now use `spikeinterface-gui `__. to view the results -interactively, or start manually labelling your units to `create an automated curation model `__. +interactively, or start manually labeling your units to `create an automated curation model `__. Note that if you have access to the raw recording, you can attach it to the analyzer, and re-compute extensions from the raw data. E.g. diff --git a/examples/get_started/example_bombcell_unit_labelling.ipynb b/examples/get_started/example_bombcell_unit_labelling.ipynb deleted file mode 100644 index 8b18aaec90..0000000000 --- a/examples/get_started/example_bombcell_unit_labelling.ipynb +++ /dev/null @@ -1,262 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Bombcell unit labelling\n", - "\n", - "With this notebook you can:\n", - "- load a SortingAnalyzer\n", - "- compute required extensions\n", - "- label units based on quality thresholds\n", - "- generating and save summary plots\n", - "- save metrics and results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "import spikeinterface as si\n", - "from spikeinterface.curation import (\n", - " bombcell_get_default_thresholds,\n", - " bombcell_label_units,\n", - " save_thresholds,\n", - " load_thresholds,\n", - ")\n", - "from spikeinterface.widgets import plot_unit_labelling_all" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### load a SortingAnalyzer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Change this to your analyzer path - you need to have already generated a sorting analyzer. see quickstart.py for how to do this\n", - "analyzer_path = \"/Users/jf5479/Downloads/M25_D18/kilosort4_sa\"\n", - "output_folder = Path(analyzer_path) / \"bombcell\"\n", - "\n", - "analyzer = si.load_sorting_analyzer(analyzer_path)\n", - "analyzer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### compute required extensions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Templates (required for template_metrics)\n", - "if not analyzer.has_extension(\"templates\"):\n", - " analyzer.compute(\"templates\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Template metrics\n", - "if not analyzer.has_extension(\"template_metrics\"):\n", - " analyzer.compute(\"template_metrics\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Quality metrics (and dependencies)\n", - "if not analyzer.has_extension(\"spike_amplitudes\"):\n", - " analyzer.compute(\"spike_amplitudes\")\n", - "\n", - "if not analyzer.has_extension(\"noise_levels\"):\n", - " analyzer.compute(\"noise_levels\")\n", - "\n", - "if not analyzer.has_extension(\"quality_metrics\"):\n", - " analyzer.compute(\"quality_metrics\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### get metrics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qm = analyzer.get_extension(\"quality_metrics\").get_data()\n", - "tm = analyzer.get_extension(\"template_metrics\").get_data()\n", - "\n", - "print(f\"Quality metrics: {list(qm.columns)}\")\n", - "print(f\"Template metrics: {list(tm.columns)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### set labelling thresholds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use default thresholds\n", - "thresholds = bombcell_get_default_thresholds()\n", - "\n", - "# Or load from file:\n", - "# thresholds = load_thresholds(\"my_thresholds.json\")\n", - "\n", - "thresholds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Optionally modify thresholds\n", - "# thresholds[\"amplitude_median\"][\"min\"] = 50 # stricter\n", - "# thresholds[\"rp_contamination\"][\"max\"] = 0.05 # stricter" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Optionally set and load thresholds from a JSON file \n", - "# Load thresholds from saved JSON\n", - "thresholds = load_thresholds(output_folder / \"thresholds.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The JSON file format looks like:\n", - "```json\n", - "{\n", - " \"amplitude_median\": {\"min\": 40, \"max\": null},\n", - " \"num_positive_peaks\": {\"min\": null, \"max\": 2},\n", - " \"peak_to_trough_duration\": {\"min\": 0.0001, \"max\": 0.00115}\n", - "}\n", - "```\n", - "`null` in JSON becomes `np.nan` (threshold disabled)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### label units" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "unit_type, unit_type_string = bombcell_label_units(\n", - " quality_metrics=qm,\n", - " template_metrics=tm,\n", - " thresholds=thresholds,\n", - " label_non_somatic=True,\n", - " split_non_somatic_good_mua=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### generate summary plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plots = plot_unit_labelling_all(\n", - " analyzer,\n", - " unit_type,\n", - " unit_type_string,\n", - " quality_metrics=qm,\n", - " template_metrics=tm,\n", - " thresholds=thresholds,\n", - " save_folder=output_folder,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### save labelling thresholds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "save_thresholds(thresholds, output_folder / \"thresholds.json\")\n", - "\n", - "print(f\"Results saved to: {output_folder.absolute()}\")\n", - "print(\"\\nFiles:\")\n", - "for f in sorted(output_folder.glob(\"*\")):\n", - " print(f\" - {f.name}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/spikeinterface/comparison/comparisontools.py b/src/spikeinterface/comparison/comparisontools.py index dd06e60458..1771ba5c16 100644 --- a/src/spikeinterface/comparison/comparisontools.py +++ b/src/spikeinterface/comparison/comparisontools.py @@ -569,7 +569,7 @@ def make_hungarian_match(agreement_scores, min_score): def do_score_labels(sorting1, sorting2, delta_frames, unit_map12, label_misclassification=False): """ - Makes the labelling at spike level for each spike train: + Makes the labeling at spike level for each spike train: * TP: true positive * CL: classification error * FN: False negative diff --git a/src/spikeinterface/core/tests/test_channelsaggregationrecording.py b/src/spikeinterface/core/tests/test_channelsaggregationrecording.py index 119ab1d598..d5ba74cfd9 100644 --- a/src/spikeinterface/core/tests/test_channelsaggregationrecording.py +++ b/src/spikeinterface/core/tests/test_channelsaggregationrecording.py @@ -189,7 +189,7 @@ def test_aggregation_labeling_for_lists(): assert np.all(user_group_property == [6, 6, 7, 7]) -def test_aggretion_labelling_for_dicts(): +def test_aggretion_labeling_for_dicts(): """Aggregated dicts of recordings get different labels depending on their underlying `property`s""" recording1 = generate_recording(num_channels=4, durations=[20], set_probe=False) diff --git a/src/spikeinterface/curation/__init__.py b/src/spikeinterface/curation/__init__.py index 944dd59338..65fa02880d 100644 --- a/src/spikeinterface/curation/__init__.py +++ b/src/spikeinterface/curation/__init__.py @@ -21,17 +21,17 @@ from .sortingview_curation import apply_sortingview_curation # automated curation -from .unit_labelling import ( +from .unit_labeling import ( WAVEFORM_METRICS, SPIKE_QUALITY_METRICS, NON_SOMATIC_METRICS, bombcell_get_default_thresholds, bombcell_label_units, apply_thresholds, - get_labelling_summary, + get_labeling_summary, save_thresholds, load_thresholds, - save_labelling_results, + save_labeling_results, ) from .model_based_curation import auto_label_units, load_model diff --git a/src/spikeinterface/curation/unit_labelling.py b/src/spikeinterface/curation/unit_labeling.py similarity index 96% rename from src/spikeinterface/curation/unit_labelling.py rename to src/spikeinterface/curation/unit_labeling.py index 814632da6e..88870e46c1 100644 --- a/src/spikeinterface/curation/unit_labelling.py +++ b/src/spikeinterface/curation/unit_labeling.py @@ -1,5 +1,5 @@ """ -Unit labelling based on quality metrics (Bombcell). +Unit labeling based on quality metrics (Bombcell). Unit Types: 0 (NOISE): Failed waveform quality checks @@ -45,7 +45,7 @@ def bombcell_get_default_thresholds() -> dict: """ - Bombcell - Returns default thresholds for unit labelling. + Bombcell - Returns default thresholds for unit labeling. Each metric has 'min' and 'max' values. Use np.nan to disable a threshold (e.g. to ignore a metric completly or to only have a min or a max threshold) @@ -274,7 +274,7 @@ def apply_thresholds( return pd.DataFrame(results, index=quality_metrics.index) -def get_labelling_summary(unit_type: np.ndarray, unit_type_string: np.ndarray) -> dict: +def get_labeling_summary(unit_type: np.ndarray, unit_type_string: np.ndarray) -> dict: """Get counts and percentages for each unit type.""" n_total = len(unit_type) unique_types, counts = np.unique(unit_type, return_counts=True) @@ -347,7 +347,7 @@ def load_thresholds(filepath) -> dict: return thresholds -def save_labelling_results( +def save_labeling_results( quality_metrics: pd.DataFrame, unit_type: np.ndarray, unit_type_string: np.ndarray, @@ -357,7 +357,7 @@ def save_labelling_results( save_wide: bool = True, ) -> None: """ - Save labelling results to CSV files. + Save labeling results to CSV files. Parameters ---------- @@ -368,7 +368,7 @@ def save_labelling_results( unit_type_string : np.ndarray String labels for each unit. thresholds : dict - Threshold dictionary used for labelling. + Threshold dictionary used for labeling. folder : str or Path Folder to save the CSV files. save_narrow : bool, default: True @@ -388,7 +388,7 @@ def save_labelling_results( wide_df = quality_metrics.copy() wide_df.insert(0, "label", unit_type_string) wide_df.insert(1, "label_code", unit_type) - wide_df.to_csv(folder / "labelling_results_wide.csv") + wide_df.to_csv(folder / "labeling_results_wide.csv") # Narrow format: one row per unit-metric combination if save_narrow: @@ -427,4 +427,4 @@ def save_labelling_results( ) narrow_df = pd.DataFrame(rows) - narrow_df.to_csv(folder / "labelling_results_narrow.csv", index=False) + narrow_df.to_csv(folder / "labeling_results_narrow.csv", index=False) diff --git a/src/spikeinterface/exporters/__init__.py b/src/spikeinterface/exporters/__init__.py index 97d0f64126..027ffe3222 100644 --- a/src/spikeinterface/exporters/__init__.py +++ b/src/spikeinterface/exporters/__init__.py @@ -2,3 +2,4 @@ from .report import export_report from .to_ibl import export_to_ibl_gui from .to_pynapple import to_pynapple_tsgroup +from .to_methods import export_to_methods diff --git a/src/spikeinterface/exporters/tests/test_to_methods.py b/src/spikeinterface/exporters/tests/test_to_methods.py new file mode 100644 index 0000000000..c914fdff8e --- /dev/null +++ b/src/spikeinterface/exporters/tests/test_to_methods.py @@ -0,0 +1,154 @@ +"""Tests for export_to_methods function.""" + +from __future__ import annotations + +import pytest +from pathlib import Path + +from spikeinterface.exporters import export_to_methods +from spikeinterface.exporters.tests.common import make_sorting_analyzer + + +class TestExportToMethods: + """Test the export_to_methods function.""" + + @pytest.fixture(scope="class") + def sorting_analyzer(self): + """Create a sorting analyzer for testing.""" + return make_sorting_analyzer(sparse=False) + + def test_export_to_methods_markdown(self, sorting_analyzer): + """Test markdown output format.""" + result = export_to_methods(sorting_analyzer, format="markdown") + + assert isinstance(result, str) + assert len(result) > 0 + # Check for markdown header and prose content + assert "## Spike Sorting Methods" in result + assert "Extracellular recordings were acquired" in result + assert "### References" in result + + def test_export_to_methods_latex(self, sorting_analyzer): + """Test LaTeX output format.""" + result = export_to_methods(sorting_analyzer, format="latex") + + assert isinstance(result, str) + assert len(result) > 0 + # Check for LaTeX sections + assert "\\section{Spike Sorting Methods}" in result + assert "Extracellular recordings were acquired" in result + assert "\\subsection*{References}" in result + + def test_export_to_methods_text(self, sorting_analyzer): + """Test plain text output format.""" + result = export_to_methods(sorting_analyzer, format="text") + + assert isinstance(result, str) + assert len(result) > 0 + assert "SPIKE SORTING METHODS" in result + assert "Extracellular recordings were acquired" in result + + def test_export_to_methods_invalid_format(self, sorting_analyzer): + """Test that invalid format raises ValueError.""" + with pytest.raises(ValueError, match="format must be"): + export_to_methods(sorting_analyzer, format="invalid") + + def test_export_to_methods_invalid_detail_level(self, sorting_analyzer): + """Test that invalid detail_level raises ValueError.""" + with pytest.raises(ValueError, match="detail_level must be"): + export_to_methods(sorting_analyzer, detail_level="invalid") + + def test_export_to_methods_detail_levels(self, sorting_analyzer): + """Test different detail levels produce different output lengths.""" + brief = export_to_methods(sorting_analyzer, detail_level="brief") + standard = export_to_methods(sorting_analyzer, detail_level="standard") + detailed = export_to_methods(sorting_analyzer, detail_level="detailed") + + # Brief should be shortest, detailed should be longest + assert len(brief) <= len(standard) + assert len(standard) <= len(detailed) + + def test_export_to_methods_with_citations(self, sorting_analyzer): + """Test that citations are included when requested.""" + with_citations = export_to_methods(sorting_analyzer, include_citations=True) + without_citations = export_to_methods(sorting_analyzer, include_citations=False) + + # With citations should be longer + assert len(with_citations) > len(without_citations) + # Should include SpikeInterface citation + assert "SpikeInterface" in with_citations or "spikeinterface" in with_citations.lower() + + def test_export_to_methods_bombcell_citation(self, sorting_analyzer): + """Test that Bombcell citation is included when quality metrics are present.""" + # The sorting_analyzer from make_sorting_analyzer has quality_metrics computed + result = export_to_methods(sorting_analyzer, include_citations=True) + + # Should include Bombcell citation since quality_metrics is present + assert "Bombcell" in result or "bombcell" in result.lower() + assert "Fabre" in result # First author of Bombcell paper + + def test_export_to_methods_contains_recording_info(self, sorting_analyzer): + """Test that recording information is included.""" + result = export_to_methods(sorting_analyzer) + + # Should contain sampling frequency + assert "Hz" in result + # Should contain channel count + assert "channels" in result.lower() or "channel" in result.lower() + + def test_export_to_methods_contains_extensions(self, sorting_analyzer): + """Test that computed extensions are listed.""" + result = export_to_methods(sorting_analyzer) + + # The sorting_analyzer from make_sorting_analyzer has these extensions + assert "waveforms" in result.lower() or "Waveforms" in result + assert "templates" in result.lower() or "Templates" in result + assert "quality" in result.lower() or "Quality" in result + + def test_export_to_methods_write_to_file(self, sorting_analyzer, tmp_path): + """Test writing output to a file.""" + output_file = tmp_path / "methods.md" + result = export_to_methods(sorting_analyzer, output_file=output_file) + + # File should be created + assert output_file.exists() + + # File content should match returned string + file_content = output_file.read_text(encoding="utf-8") + assert file_content == result + + def test_export_to_methods_write_to_nested_path(self, sorting_analyzer, tmp_path): + """Test writing to a nested path that doesn't exist.""" + output_file = tmp_path / "nested" / "path" / "methods.md" + result = export_to_methods(sorting_analyzer, output_file=output_file) + + # File and parent directories should be created + assert output_file.exists() + assert output_file.read_text(encoding="utf-8") == result + + +class TestExportToMethodsWithoutSortingInfo: + """Test export_to_methods when sorting_info is not available.""" + + @pytest.fixture(scope="class") + def sorting_analyzer_no_info(self): + """Create a sorting analyzer without sorting_info.""" + analyzer = make_sorting_analyzer(sparse=False) + # The sorting from generate_ground_truth_recording doesn't have sorting_info + return analyzer + + def test_handles_missing_sorting_info(self, sorting_analyzer_no_info): + """Test that missing sorting_info is handled gracefully.""" + result = export_to_methods(sorting_analyzer_no_info) + + assert isinstance(result, str) + assert len(result) > 0 + # Should mention that info is not available + assert "not available" in result.lower() or "Spike Sorting" in result + + +if __name__ == "__main__": + # Quick manual test + analyzer = make_sorting_analyzer(sparse=False) + result = export_to_methods(analyzer, detail_level="detailed") + print(result) diff --git a/src/spikeinterface/exporters/to_methods.py b/src/spikeinterface/exporters/to_methods.py new file mode 100644 index 0000000000..af1032bd73 --- /dev/null +++ b/src/spikeinterface/exporters/to_methods.py @@ -0,0 +1,796 @@ +""" +Export a methods section for academic papers from a SortingAnalyzer. +""" + +from __future__ import annotations + +from pathlib import Path +from datetime import datetime + +import spikeinterface + + +# Citations for SpikeInterface, sorters, and analysis tools +CITATIONS = { + "spikeinterface": ( + "Buccino, A. P., Hurwitz, C. L., Garcia, S., Magland, J., Siegle, J. H., Hurwitz, R., & Hennig, M. H. " + "(2020). SpikeInterface, a unified framework for spike sorting. eLife, 9, e61834. " + "https://doi.org/10.7554/eLife.61834" + ), + "bombcell": ( + "Fabre, J. M. J., van Beest, E. H., Peters, A. J., Carandini, M., & Harris, K. D. (2023). " + "Bombcell: automated curation and cell classification of spike-sorted electrophysiology data. " + "Zenodo. https://doi.org/10.5281/zenodo.8172821" + ), + "kilosort": ( + "Pachitariu, M., Steinmetz, N. A., Kadir, S. N., Carandini, M., & Harris, K. D. (2016). " + "Fast and accurate spike sorting of high-channel count probes with KiloSort. " + "Advances in Neural Information Processing Systems, 29, 4448-4456." + ), + "kilosort2": ( + "Pachitariu, M., Steinmetz, N. A., Kadir, S. N., Carandini, M., & Harris, K. D. (2016). " + "Fast and accurate spike sorting of high-channel count probes with KiloSort. " + "Advances in Neural Information Processing Systems, 29, 4448-4456." + ), + "kilosort2_5": ( + "Pachitariu, M., Steinmetz, N. A., Kadir, S. N., Carandini, M., & Harris, K. D. (2016). " + "Fast and accurate spike sorting of high-channel count probes with KiloSort. " + "Advances in Neural Information Processing Systems, 29, 4448-4456." + ), + "kilosort3": ( + "Pachitariu, M., Steinmetz, N. A., Kadir, S. N., Carandini, M., & Harris, K. D. (2016). " + "Fast and accurate spike sorting of high-channel count probes with KiloSort. " + "Advances in Neural Information Processing Systems, 29, 4448-4456." + ), + "kilosort4": ( + "Pachitariu, M., Sridhar, S., Pennington, J., & Stringer, C. (2024). " + "Spike sorting with Kilosort4. Nature Methods. https://doi.org/10.1038/s41592-024-02232-7" + ), + "mountainsort4": ( + "Chung, J. E., Magland, J. F., Barnett, A. H., Tolosa, V. M., Tooker, A. C., Lee, K. Y., ... & Greengard, L. F. " + "(2017). A fully automated approach to spike sorting. Neuron, 95(6), 1381-1394." + ), + "mountainsort5": ( + "Magland, J., Jun, J. J., Lovero, E., Morber, A. J., Barnett, A. H., Greengard, L. F., & Chung, J. E. (2020). " + "SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters. eLife, 9, e55167." + ), + "spykingcircus": ( + "Yger, P., Spampinato, G. L., Esposito, E., Lefebvre, B., Deny, S., Gardella, C., ... & Marre, O. (2018). " + "A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. " + "eLife, 7, e34518." + ), + "spykingcircus2": ( + "Yger, P., Spampinato, G. L., Esposito, E., Lefebvre, B., Deny, S., Gardella, C., ... & Marre, O. (2018). " + "A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. " + "eLife, 7, e34518." + ), + "tridesclous": ( + "Garcia, S., & Bhumbra, G. S. (2020). Tridesclous: a free, easy-to-use and lightweight spike sorter. " + "FENS Forum 2020." + ), + "tridesclous2": ( + "Garcia, S., & Bhumbra, G. S. (2020). Tridesclous: a free, easy-to-use and lightweight spike sorter. " + "FENS Forum 2020." + ), + "herdingspikes": ( + "Hilgen, G., Sorbaro, M., Pirber, S., Zber, J. E., Resber, M. E., Hennig, M. H., & Sernagor, E. (2017). " + "Unsupervised spike sorting for large-scale, high-density multielectrode arrays. Cell Reports, 18(10), 2521-2532." + ), + "ironclust": ( + "Jun, J. J., Steinmetz, N. A., Siegle, J. H., Denman, D. J., Bauza, M., Barbarits, B., ... & Harris, T. D. (2017). " + "Fully integrated silicon probes for high-density recording of neural activity. Nature, 551(7679), 232-236." + ), +} + +# Human-readable names for preprocessing classes +PREPROCESSING_NAMES = { + "BandpassFilterRecording": "Bandpass Filter", + "HighpassFilterRecording": "Highpass Filter", + "LowpassFilterRecording": "Lowpass Filter", + "NotchFilterRecording": "Notch Filter", + "FilterRecording": "Filter", + "CommonReferenceRecording": "Common Reference", + "WhitenRecording": "Whitening", + "NormalizeByQuantileRecording": "Normalize by Quantile", + "ScaleRecording": "Scale", + "CenterRecording": "Center", + "ZScoreRecording": "Z-Score", + "RectifyRecording": "Rectify", + "ClipRecording": "Clip", + "BlankSaturationRecording": "Blank Saturation", + "RemoveArtifactsRecording": "Remove Artifacts", + "RemoveBadChannelsRecording": "Remove Bad Channels", + "InterpolateBadChannelsRecording": "Interpolate Bad Channels", + "DepthOrderRecording": "Depth Order", + "ResampleRecording": "Resample", + "DecimateRecording": "Decimate", + "PhaseShiftRecording": "Phase Shift", + "AsTypeRecording": "Convert Data Type", + "UnsignedToSignedRecording": "Unsigned to Signed", + "AverageAcrossDirectionRecording": "Average Across Direction", + "DirectionalDerivativeRecording": "Directional Derivative", + "HighpassSpatialFilterRecording": "Highpass Spatial Filter", + "GaussianBandpassFilterRecording": "Gaussian Bandpass Filter", + "SilencedPeriodsRecording": "Silenced Periods", + "CorrectMotionRecording": "Motion Correction", + "InterpolateBadChannelsRecording": "Interpolate Bad Channels", +} + +# Key parameters to show for each preprocessing step (for standard detail level) +PREPROCESSING_KEY_PARAMS = { + "BandpassFilterRecording": ["freq_min", "freq_max", "filter_order"], + "HighpassFilterRecording": ["freq_min", "filter_order"], + "LowpassFilterRecording": ["freq_max", "filter_order"], + "NotchFilterRecording": ["freq", "q"], + "FilterRecording": ["band", "btype", "filter_order"], + "CommonReferenceRecording": ["reference", "operator"], + "WhitenRecording": ["mode", "radius_um"], + "NormalizeByQuantileRecording": ["q1", "q2"], + "ScaleRecording": ["gain", "offset"], + "ResampleRecording": ["resample_rate"], + "DecimateRecording": ["decimation_factor"], + "PhaseShiftRecording": ["inter_sample_shift"], + "RemoveBadChannelsRecording": ["bad_channel_ids"], + "CorrectMotionRecording": ["spatial_interpolation_method"], +} + + +def _trace_preprocessing_chain(recording) -> list[dict]: + """ + Walk the recording parent chain and extract preprocessing step info. + + Parameters + ---------- + recording : BaseRecording + The recording to trace + + Returns + ------- + list[dict] + List of dicts with 'class_name' and 'kwargs' for each step, + ordered from original recording to most recent preprocessing + """ + chain = [] + current = recording + + while current is not None: + class_name = current.__class__.__name__ + kwargs = getattr(current, "_kwargs", {}) + + # Filter out the 'recording' key as it's the parent reference + filtered_kwargs = {k: v for k, v in kwargs.items() if k != "recording"} + + chain.append({"class_name": class_name, "kwargs": filtered_kwargs}) + current = current.get_parent() + + # Reverse so original recording is first + chain.reverse() + return chain + + +def _get_sorter_info(sorting) -> dict | None: + """ + Extract sorter name, version, and parameters from a sorting. + + Parameters + ---------- + sorting : BaseSorting + The sorting object + + Returns + ------- + dict | None + Dict with sorter info, or None if not available + """ + sorting_info = sorting.sorting_info + if sorting_info is None: + return None + + info = {} + + # Get sorter name and params + params = sorting_info.get("params", {}) + info["sorter_name"] = params.get("sorter_name", "Unknown") + info["sorter_params"] = params.get("sorter_params", {}) + + # Get log info + log = sorting_info.get("log", {}) + info["sorter_version"] = log.get("sorter_version", "Unknown") + info["run_time"] = log.get("run_time") + info["datetime"] = log.get("datetime") + + return info + + +def _format_value(value) -> str: + """Format a parameter value for display.""" + if value is None: + return "None" + elif isinstance(value, bool): + return str(value) + elif isinstance(value, float): + if value == float("inf"): + return "infinity" + elif value == float("-inf"): + return "-infinity" + else: + # Format with reasonable precision + return f"{value:g}" + elif isinstance(value, (list, tuple)): + if len(value) <= 5: + return ", ".join(_format_value(v) for v in value) + else: + return f"[{len(value)} items]" + elif isinstance(value, dict): + return f"{{...}}" + else: + return str(value) + + +def _format_params_markdown(params: dict, detail_level: str, key_params: list | None = None) -> str: + """Format parameters as markdown list.""" + lines = [] + + if detail_level == "brief": + return "" + + if detail_level == "standard" and key_params: + # Only show key parameters + for key in key_params: + if key in params: + lines.append(f" - {key}: {_format_value(params[key])}") + else: + # Show all parameters (detailed) + for key, value in params.items(): + lines.append(f" - `{key}`: {_format_value(value)}") + + return "\n".join(lines) + + +def _format_params_text(params: dict, detail_level: str, key_params: list | None = None) -> str: + """Format parameters as plain text.""" + lines = [] + + if detail_level == "brief": + return "" + + if detail_level == "standard" and key_params: + for key in key_params: + if key in params: + lines.append(f" {key}: {_format_value(params[key])}") + else: + for key, value in params.items(): + lines.append(f" {key}: {_format_value(value)}") + + return "\n".join(lines) + + +def _format_params_latex(params: dict, detail_level: str, key_params: list | None = None) -> str: + """Format parameters as LaTeX itemize.""" + lines = [] + + if detail_level == "brief": + return "" + + if detail_level == "standard" and key_params: + params_to_show = {k: v for k, v in params.items() if k in key_params} + else: + params_to_show = params + + if params_to_show: + lines.append(" \\begin{itemize}") + for key, value in params_to_show.items(): + escaped_key = key.replace("_", "\\_") + lines.append(f" \\item \\texttt{{{escaped_key}}}: {_format_value(value)}") + lines.append(" \\end{itemize}") + + return "\n".join(lines) + + +def _get_probe_description(sorting_analyzer) -> str: + """Get a description of the probe.""" + try: + probe = sorting_analyzer.get_probe() + if probe is not None: + manufacturer = probe.annotations.get("manufacturer", "") + probe_name = probe.annotations.get("probe_name", "") + if manufacturer and probe_name: + return f"{manufacturer} {probe_name}" + elif probe_name: + return probe_name + else: + return "electrode array" + except Exception: + pass + return "electrode array" + + +def _get_recording_duration(sorting_analyzer) -> float | None: + """Get total recording duration in seconds.""" + try: + total_samples = sum(sorting_analyzer.get_num_samples(i) for i in range(sorting_analyzer.get_num_segments())) + return total_samples / sorting_analyzer.sampling_frequency + except Exception: + return None + + +def _describe_preprocessing_step(class_name: str, kwargs: dict, detail_level: str) -> str: + """Generate a prose description of a preprocessing step.""" + human_name = PREPROCESSING_NAMES.get(class_name, class_name.replace("Recording", "")) + + # Build description based on the preprocessing type + if "Filter" in class_name: + freq_min = kwargs.get("freq_min") or kwargs.get("band", [None, None])[0] if isinstance(kwargs.get("band"), (list, tuple)) else None + freq_max = kwargs.get("freq_max") or kwargs.get("band", [None, None])[1] if isinstance(kwargs.get("band"), (list, tuple)) else None + order = kwargs.get("filter_order", kwargs.get("order")) + ftype = kwargs.get("ftype", "butterworth") + + if freq_min and freq_max: + desc = f"bandpass filtered ({freq_min}-{freq_max} Hz" + elif freq_min: + desc = f"highpass filtered (>{freq_min} Hz" + elif freq_max: + desc = f"lowpass filtered (<{freq_max} Hz" + else: + desc = f"filtered (" + + if detail_level == "detailed" and order: + desc += f", {order}th order {ftype})" + else: + desc += ")" + return desc + + elif "CommonReference" in class_name: + ref = kwargs.get("reference", "global") + operator = kwargs.get("operator", "median") + if detail_level == "detailed": + return f"re-referenced using {ref} {operator} referencing" + return f"common {operator} referenced" + + elif "Whiten" in class_name: + mode = kwargs.get("mode", "global") + if detail_level == "detailed": + radius = kwargs.get("radius_um") + if radius: + return f"whitened ({mode} mode, {radius} µm radius)" + return "whitened" + + elif "Normalize" in class_name or "ZScore" in class_name: + return "normalized" + + elif "RemoveBadChannels" in class_name or "InterpolateBadChannels" in class_name: + return "with bad channels removed/interpolated" + + elif "Resample" in class_name: + rate = kwargs.get("resample_rate") + if rate: + return f"resampled to {rate} Hz" + return "resampled" + + elif "CorrectMotion" in class_name: + method = kwargs.get("spatial_interpolation_method", "") + if detail_level == "detailed" and method: + return f"motion corrected (using {method} interpolation)" + return "motion corrected" + + elif "PhaseShift" in class_name: + return "phase shift corrected" + + elif "InjectTemplates" in class_name or "NoiseGenerator" in class_name: + # These are used for synthetic/test data generation, not real preprocessing + return None + + else: + return human_name.lower() + + +def _describe_sorter_params(sorter_name: str, params: dict, detail_level: str) -> str: + """Generate a prose description of key sorter parameters.""" + if not params or detail_level == "brief": + return "" + + # Define key parameters for each sorter + key_params_by_sorter = { + "kilosort4": ["Th_universal", "Th_learned", "do_CAR", "batch_size", "nblocks"], + "kilosort3": ["Th", "ThPre", "lam", "AUCsplit", "minFR"], + "kilosort2": ["Th", "ThPre", "lam", "AUCsplit", "minFR"], + "kilosort2_5": ["Th", "ThPre", "lam", "AUCsplit", "minFR"], + "mountainsort5": ["scheme", "detect_threshold", "snippet_T1", "snippet_T2"], + "spykingcircus2": ["detection", "selection", "clustering", "matching"], + "tridesclous2": ["detection", "selection", "clustering"], + } + + sorter_key = sorter_name.lower().replace("-", "").replace("_", "") + key_params = key_params_by_sorter.get(sorter_key, []) + + if detail_level == "standard" and key_params: + # Only describe key parameters in prose + parts = [] + for key in key_params: + if key in params: + parts.append(f"{key}={_format_value(params[key])}") + if parts: + return " (" + ", ".join(parts) + ")" + return "" + elif detail_level == "detailed": + # List all parameters + parts = [f"{k}={_format_value(v)}" for k, v in params.items()] + if parts: + return ". Key parameters: " + ", ".join(parts) + return "" + return "" + + +def _describe_quality_metrics(params: dict, detail_level: str) -> str: + """Generate a prose description of quality metrics computed.""" + metric_names = params.get("metric_names") or params.get("metrics_to_compute", []) + if isinstance(metric_names, (list, tuple)): + if detail_level == "brief": + return "quality metrics" + elif len(metric_names) <= 5 or detail_level == "detailed": + return f"quality metrics ({', '.join(metric_names)})" + else: + return f"quality metrics ({len(metric_names)} metrics including {', '.join(metric_names[:3])}, etc.)" + return "quality metrics" + + +def export_to_methods( + sorting_analyzer, + output_file: str | Path | None = None, + format: str = "markdown", + include_citations: bool = True, + detail_level: str = "detailed", + sorter_name: str | None = None, + sorter_version: str | None = None, + probe_name: str | None = None, + probe_manufacturer: str | None = None, + preprocessing_description: str | None = None, +) -> str: + """ + Generate a methods section describing the spike sorting pipeline. + + This function extracts information from a SortingAnalyzer about the + preprocessing steps, spike sorting parameters, and post-processing + analyses that were performed, and formats them as a methods section + suitable for academic papers. + + Parameters + ---------- + sorting_analyzer : SortingAnalyzer + A SortingAnalyzer object containing the sorting results and metadata + output_file : str | Path | None, default: None + If provided, write the methods section to this file + format : str, default: "markdown" + Output format: "markdown", "latex", or "text" + include_citations : bool, default: True + If True, include citation references at the end + detail_level : str, default: "detailed" + Level of detail: "brief" (just step names), "standard" (key parameters), + or "detailed" (all parameters) + sorter_name : str | None, default: None + Override the sorter name if not available in sorting_info. + Use this when loading sorted data from Phy/Kilosort output directly. + Examples: "Kilosort4", "Kilosort2.5", "MountainSort5", "SpykingCircus2" + sorter_version : str | None, default: None + Override the sorter version if not available in sorting_info. + probe_name : str | None, default: None + Override the probe name if not set in probe annotations. + Examples: "Neuropixels 1.0", "Neuropixels 2.0", "Cambridge NeuroTech H2" + probe_manufacturer : str | None, default: None + Override the probe manufacturer if not set in probe annotations. + Examples: "IMEC", "Cambridge NeuroTech", "NeuroNexus" + preprocessing_description : str | None, default: None + Manual description of preprocessing if not done via SpikeInterface. + Example: "bandpass filtered (300-6000 Hz) and common median referenced" + + Returns + ------- + str + The generated methods section text + + Notes + ----- + For best results, ensure your data has complete metadata: + + - **Probe info**: Set via `recording.set_probe()` with annotations, or use + `probe_name` and `probe_manufacturer` parameters + - **Sorter info**: Automatically captured when using `spikeinterface.sorters.run_sorter()`. + When loading from Phy/Kilosort output directly, use `sorter_name` parameter + - **Preprocessing**: Automatically tracked when using `spikeinterface.preprocessing`. + Otherwise, use `preprocessing_description` parameter + + Examples + -------- + >>> # When sorter info is captured automatically (via run_sorter) + >>> export_to_methods(sorting_analyzer) + + >>> # When loading from Kilosort output directly + >>> export_to_methods( + ... sorting_analyzer, + ... sorter_name="Kilosort4", + ... sorter_version="4.0.1", + ... probe_name="Neuropixels 1.0", + ... probe_manufacturer="IMEC" + ... ) + """ + if format not in ("markdown", "latex", "text"): + raise ValueError(f"format must be 'markdown', 'latex', or 'text', got '{format}'") + if detail_level not in ("brief", "standard", "detailed"): + raise ValueError(f"detail_level must be 'brief', 'standard', or 'detailed', got '{detail_level}'") + + paragraphs = [] + citations_to_include = ["spikeinterface"] + missing_info = [] # Track what info is missing + + si_version = spikeinterface.__version__ + + # === Gather all information first === + fs = sorting_analyzer.sampling_frequency + n_channels = sorting_analyzer.get_num_channels() + duration = _get_recording_duration(sorting_analyzer) + n_units = sorting_analyzer.get_num_units() + + # Get probe description - use override or extract from data + if probe_name: + if probe_manufacturer: + probe_desc = f"{probe_manufacturer} {probe_name}" + else: + probe_desc = probe_name + else: + probe_desc = _get_probe_description(sorting_analyzer) + if probe_desc == "electrode array": + missing_info.append("probe_name") + + # Get preprocessing chain + preprocessing_steps = [] + if sorting_analyzer.has_recording(): + recording = sorting_analyzer.recording + chain = _trace_preprocessing_chain(recording) + preprocessing_steps = [step for step in chain if step["class_name"].endswith("Recording") and step["kwargs"]] + + # Get sorter info - use overrides if provided + sorter_info = _get_sorter_info(sorting_analyzer.sorting) + + # Apply overrides to sorter info + if sorter_name: + if sorter_info is None: + sorter_info = {"sorter_name": sorter_name, "sorter_version": sorter_version or "", "sorter_params": {}, "run_time": None} + else: + sorter_info["sorter_name"] = sorter_name + if sorter_version: + sorter_info["sorter_version"] = sorter_version + elif sorter_info is None: + missing_info.append("sorter_name") + + # Get extensions + if sorting_analyzer.format == "memory": + extensions = sorting_analyzer.get_loaded_extension_names() + else: + extensions = sorting_analyzer.get_saved_extension_names() + + # Check for quality/template metrics for Bombcell citation + has_quality_metrics = "quality_metrics" in extensions or "template_metrics" in extensions + if has_quality_metrics: + citations_to_include.append("bombcell") + + # === Build the methods section as prose === + + # Title/Header + if format == "markdown": + paragraphs.append("## Spike Sorting Methods\n") + elif format == "latex": + paragraphs.append("\\section{Spike Sorting Methods}\n") + else: + paragraphs.append("SPIKE SORTING METHODS\n") + + # === First paragraph: Data acquisition and preprocessing === + para1_parts = [] + + # Data acquisition sentence + acq_sentence = f"Extracellular recordings were acquired at {fs:.0f} Hz using a {probe_desc} ({n_channels} channels" + if duration is not None: + if duration >= 60: + acq_sentence += f", {duration/60:.1f} minutes of data" + else: + acq_sentence += f", {duration:.1f} seconds of data" + acq_sentence += ")." + para1_parts.append(acq_sentence) + + # Preprocessing sentence(s) - use manual description if provided + if preprocessing_description: + para1_parts.append(f"Raw voltage traces were {preprocessing_description}.") + elif preprocessing_steps: + prep_descriptions = [] + for step in preprocessing_steps: + desc = _describe_preprocessing_step(step["class_name"], step["kwargs"], detail_level) + if desc: + prep_descriptions.append(desc) + + if prep_descriptions: + if len(prep_descriptions) == 1: + prep_sentence = f"Raw voltage traces were {prep_descriptions[0]}." + elif len(prep_descriptions) == 2: + prep_sentence = f"Raw voltage traces were {prep_descriptions[0]} and {prep_descriptions[1]}." + else: + prep_sentence = f"Raw voltage traces were {', '.join(prep_descriptions[:-1])}, and {prep_descriptions[-1]}." + para1_parts.append(prep_sentence) + else: + missing_info.append("preprocessing") + else: + missing_info.append("preprocessing") + + paragraphs.append(" ".join(para1_parts)) + paragraphs.append("") + + # === Second paragraph: Spike sorting === + para2_parts = [] + + if sorter_info: + sorter_name = sorter_info["sorter_name"] + sorter_version = sorter_info["sorter_version"] + sorter_params = sorter_info["sorter_params"] + + # Add citation for this sorter + sorter_key = sorter_name.lower().replace("-", "").replace("_", "") + if sorter_key in CITATIONS: + citations_to_include.append(sorter_key) + + # Build sorter description + if format == "markdown": + sort_sentence = f"Spike sorting was performed using **{sorter_name}**" + elif format == "latex": + sort_sentence = f"Spike sorting was performed using \\textbf{{{sorter_name}}}" + else: + sort_sentence = f"Spike sorting was performed using {sorter_name}" + + if sorter_version and sorter_version != "Unknown": + sort_sentence += f" (version {sorter_version})" + + # Add parameter description + param_desc = _describe_sorter_params(sorter_name, sorter_params, detail_level) + sort_sentence += param_desc + + if not sort_sentence.endswith("."): + sort_sentence += "." + para2_parts.append(sort_sentence) + + # Add runtime info if available + if detail_level == "detailed" and sorter_info.get("run_time") is not None: + run_time = sorter_info["run_time"] + if run_time >= 60: + para2_parts.append(f"Sorting completed in {run_time/60:.1f} minutes.") + else: + para2_parts.append(f"Sorting completed in {run_time:.1f} seconds.") + else: + para2_parts.append("Spike sorting was performed (sorter parameters not recorded).") + + # Add unit count + para2_parts.append(f"A total of {n_units} units were identified.") + + paragraphs.append(" ".join(para2_parts)) + paragraphs.append("") + + # === Third paragraph: Post-processing and quality control === + if extensions: + para3_parts = [] + + # Categorize extensions + waveform_exts = [e for e in extensions if e in ("waveforms", "templates", "random_spikes")] + location_exts = [e for e in extensions if "location" in e] + metric_exts = [e for e in extensions if "metric" in e] + other_exts = [e for e in extensions if e not in waveform_exts + location_exts + metric_exts] + + # Waveforms and templates + if waveform_exts: + wf_ext = sorting_analyzer.get_extension("waveforms") + if wf_ext: + ms_before = wf_ext.params.get("ms_before", 1) + ms_after = wf_ext.params.get("ms_after", 2) + para3_parts.append(f"Spike waveforms were extracted ({ms_before} ms before to {ms_after} ms after each spike) and averaged to compute unit templates.") + + # Quality metrics + if "quality_metrics" in extensions: + qm_ext = sorting_analyzer.get_extension("quality_metrics") + if qm_ext: + qm_desc = _describe_quality_metrics(qm_ext.params, detail_level) + para3_parts.append(f"Unit {qm_desc} were computed to assess sorting quality.") + + if "template_metrics" in extensions: + para3_parts.append("Template-based metrics were computed for each unit.") + + # Locations + if "unit_locations" in extensions: + loc_ext = sorting_analyzer.get_extension("unit_locations") + method = loc_ext.params.get("method", "center_of_mass") if loc_ext else "center_of_mass" + para3_parts.append(f"Unit locations were estimated using the {method.replace('_', ' ')} method.") + + # Other notable extensions + if "principal_components" in extensions: + pc_ext = sorting_analyzer.get_extension("principal_components") + if pc_ext and detail_level == "detailed": + n_comp = pc_ext.params.get("n_components", 5) + para3_parts.append(f"Principal component analysis was performed ({n_comp} components).") + + if "correlograms" in extensions: + para3_parts.append("Auto- and cross-correlograms were computed.") + + if "spike_amplitudes" in extensions: + para3_parts.append("Spike amplitudes were extracted for each spike.") + + if para3_parts: + paragraphs.append(" ".join(para3_parts)) + paragraphs.append("") + + # === Software attribution paragraph === + software_para = f"All spike sorting and analysis was performed using SpikeInterface version {si_version}" + if has_quality_metrics: + software_para += ", with quality metrics following the Bombcell framework" + software_para += "." + paragraphs.append(software_para) + paragraphs.append("") + + # === Missing Info Warning === + if missing_info: + paragraphs.append("") + if format == "markdown": + paragraphs.append("---") + paragraphs.append("**Note**: Some information could not be extracted automatically and should be added manually:") + for info in missing_info: + if info == "probe_name": + paragraphs.append("- Probe type/name (use `probe_name` parameter)") + elif info == "sorter_name": + paragraphs.append("- Spike sorter name and version (use `sorter_name` and `sorter_version` parameters)") + elif info == "preprocessing": + paragraphs.append("- Preprocessing steps (use `preprocessing_description` parameter)") + paragraphs.append("") + elif format == "latex": + paragraphs.append("\\textit{Note: Some information could not be extracted automatically. See function documentation for how to specify missing metadata.}") + paragraphs.append("") + else: + paragraphs.append("NOTE: Missing information that should be added manually:") + for info in missing_info: + if info == "probe_name": + paragraphs.append(" - Probe type/name") + elif info == "sorter_name": + paragraphs.append(" - Spike sorter name and version") + elif info == "preprocessing": + paragraphs.append(" - Preprocessing steps") + paragraphs.append("") + + # === Citations Section === + if include_citations: + if format == "markdown": + paragraphs.append("### References\n") + elif format == "latex": + paragraphs.append("\\subsection*{References}\n") + else: + paragraphs.append("References\n") + + # Remove duplicates while preserving order + seen = set() + unique_citations = [] + for c in citations_to_include: + if c not in seen: + seen.add(c) + unique_citations.append(c) + + for citation_key in unique_citations: + if citation_key in CITATIONS: + citation = CITATIONS[citation_key] + if format == "markdown": + paragraphs.append(f"- {citation}\n") + elif format == "latex": + paragraphs.append(f"\\bibitem{{{citation_key}}} {citation}\n") + else: + paragraphs.append(f"- {citation}\n") + + # Join all paragraphs + result = "\n".join(paragraphs) + + # Write to file if requested + if output_file is not None: + output_path = Path(output_file) + output_path.parent.mkdir(parents=True, exist_ok=True) + output_path.write_text(result, encoding="utf-8") + + return result diff --git a/src/spikeinterface/metrics/quality/misc_metrics.py b/src/spikeinterface/metrics/quality/misc_metrics.py index 8c6339b773..8049d4f173 100644 --- a/src/spikeinterface/metrics/quality/misc_metrics.py +++ b/src/spikeinterface/metrics/quality/misc_metrics.py @@ -192,7 +192,7 @@ def compute_snrs_bombcell( Compute signal to noise ratio using BombCell method. This differs from the standard SNR by using: - - Signal: Max absolute value of raw waveforms on peak channel + - Signal: Max absolute value of the median waveform on peak channel - Noise: MAD (Median Absolute Deviation) of baseline samples from waveforms Parameters @@ -214,7 +214,7 @@ def compute_snrs_bombcell( Notes ----- This implementation follows the BombCell methodology: - - Signal is the maximum absolute amplitude of raw waveforms on the peak channel + - Signal is the maximum absolute amplitude of the median waveform on the peak channel - Noise is computed as MAD of baseline samples (first N samples of each waveform) Requires the "waveforms" extension to be computed. @@ -262,8 +262,9 @@ def compute_snrs_bombcell( # Extract waveforms on peak channel waveforms_peak = waveforms[:, :, peak_chan_idx] # (num_spikes, num_samples) - # Signal: max absolute value across all spikes - signal = np.max(np.abs(waveforms_peak)) + # Signal: max absolute value of the median waveform + median_waveform = np.median(waveforms_peak, axis=0) # median across spikes + signal = np.max(np.abs(median_waveform)) # Noise: MAD of baseline samples (first N samples of each waveform) baseline_samples_all = waveforms_peak[:, :baseline_samples].flatten() @@ -285,7 +286,7 @@ class SNRBombcell(BaseMetric): metric_params = {"peak_sign": "neg", "baseline_window_ms": 0.5} metric_columns = {"snr_bombcell": float} metric_descriptions = { - "snr_bombcell": "Signal to noise ratio using BombCell method (raw waveform max / baseline MAD)." + "snr_bombcell": "Signal to noise ratio using BombCell method (median waveform max / baseline MAD)." } depend_on = ["waveforms", "templates"] diff --git a/src/spikeinterface/metrics/quality/tests/test_metrics_functions.py b/src/spikeinterface/metrics/quality/tests/test_metrics_functions.py index c0dd6c6033..e900599e96 100644 --- a/src/spikeinterface/metrics/quality/tests/test_metrics_functions.py +++ b/src/spikeinterface/metrics/quality/tests/test_metrics_functions.py @@ -258,7 +258,7 @@ def test_unit_structure_in_output(small_sorting_analyzer): def test_unit_id_order_independence(small_sorting_analyzer): """ Takes two almost-identical sorting_analyzers, whose unit_ids are in different orders and have different labels, - and checks that their calculated quality metrics are independent of the ordering and labelling. + and checks that their calculated quality metrics are independent of the ordering and labeling. """ recording = small_sorting_analyzer.recording diff --git a/src/spikeinterface/widgets/unit_labelling.py b/src/spikeinterface/widgets/unit_labeling.py similarity index 97% rename from src/spikeinterface/widgets/unit_labelling.py rename to src/spikeinterface/widgets/unit_labeling.py index 0c01b7f528..ccdb9128f2 100644 --- a/src/spikeinterface/widgets/unit_labelling.py +++ b/src/spikeinterface/widgets/unit_labeling.py @@ -1,4 +1,4 @@ -"""Widgets for visualizing unit labelling results.""" +"""Widgets for visualizing unit labeling results.""" from __future__ import annotations @@ -19,7 +19,7 @@ def _combine_metrics(quality_metrics, template_metrics): return quality_metrics.join(template_metrics, how="outer") -class LabellingHistogramsWidget(BaseWidget): +class LabelingHistogramsWidget(BaseWidget): """Plot histograms of quality metrics with threshold lines.""" def __init__( @@ -399,11 +399,11 @@ def _build_failure_table(self, quality_metrics, thresholds): # Convenience functions -def plot_labelling_histograms( +def plot_labeling_histograms( quality_metrics=None, template_metrics=None, thresholds=None, metrics_to_plot=None, backend=None, **kwargs ): """Plot histograms of quality metrics with threshold lines.""" - return LabellingHistogramsWidget( + return LabelingHistogramsWidget( quality_metrics=quality_metrics, template_metrics=template_metrics, thresholds=thresholds, @@ -449,7 +449,7 @@ def plot_upset( ) -def plot_unit_labelling_all( +def plot_unit_labeling_all( sorting_analyzer, unit_type: np.ndarray, unit_type_string: np.ndarray, @@ -463,7 +463,7 @@ def plot_unit_labelling_all( **kwargs, ): """ - Generate all unit labelling plots and optionally save to folder. + Generate all unit labeling plots and optionally save to folder. Parameters ---------- @@ -496,7 +496,7 @@ def plot_unit_labelling_all( Dictionary with keys 'histograms', 'waveforms', 'upset' containing widget objects. """ from pathlib import Path - from spikeinterface.curation import bombcell_get_default_thresholds, save_labelling_results + from spikeinterface.curation import bombcell_get_default_thresholds, save_labeling_results if thresholds is None: thresholds = bombcell_get_default_thresholds() @@ -513,7 +513,7 @@ def plot_unit_labelling_all( # Histograms if combined_metrics is not None: - results["histograms"] = plot_labelling_histograms( + results["histograms"] = plot_labeling_histograms( quality_metrics=quality_metrics, template_metrics=template_metrics, thresholds=thresholds, @@ -546,7 +546,7 @@ def plot_unit_labelling_all( # Save plots if "histograms" in results and results["histograms"].figure is not None: - results["histograms"].figure.savefig(save_folder / "labelling_histograms.png", dpi=150, bbox_inches="tight") + results["histograms"].figure.savefig(save_folder / "labeling_histograms.png", dpi=150, bbox_inches="tight") if "waveforms" in results and results["waveforms"].figure is not None: results["waveforms"].figure.savefig(save_folder / "waveform_overlay.png", dpi=150, bbox_inches="tight") if "upset" in results and hasattr(results["upset"], "figures"): @@ -555,6 +555,6 @@ def plot_unit_labelling_all( # Save CSV results if combined_metrics is not None: - save_labelling_results(combined_metrics, unit_type, unit_type_string, thresholds, save_folder) + save_labeling_results(combined_metrics, unit_type, unit_type_string, thresholds, save_folder) return results diff --git a/src/spikeinterface/widgets/widget_list.py b/src/spikeinterface/widgets/widget_list.py index d8f2f46856..46c16898a4 100644 --- a/src/spikeinterface/widgets/widget_list.py +++ b/src/spikeinterface/widgets/widget_list.py @@ -37,14 +37,14 @@ from .comparison import AgreementMatrixWidget, ConfusionMatrixWidget from .gtstudy import StudyRunTimesWidget, StudyUnitCountsWidget, StudyPerformances, StudyAgreementMatrix, StudySummary from .collision import ComparisonCollisionBySimilarityWidget, StudyComparisonCollisionBySimilarityWidget -from .unit_labelling import ( - LabellingHistogramsWidget, +from .unit_labeling import ( + LabelingHistogramsWidget, WaveformOverlayWidget, UpsetPlotWidget, - plot_labelling_histograms, + plot_labeling_histograms, plot_waveform_overlay, plot_upset, - plot_unit_labelling_all, + plot_unit_labeling_all, ) widget_list = [ @@ -52,7 +52,7 @@ AllAmplitudesDistributionsWidget, AmplitudesWidget, AutoCorrelogramsWidget, - LabellingHistogramsWidget, + LabelingHistogramsWidget, ConfusionMatrixWidget, ComparisonCollisionBySimilarityWidget, CrossCorrelogramsWidget, From 291bca43890055c72b86d1959a6ac04ea1dc3a3f Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 20:19:05 -0500 Subject: [PATCH 27/49] WAVEFORM_METRICS -> NOISE_METRICS --- src/spikeinterface/curation/__init__.py | 4 ++-- .../curation/{unit_labeling.py => bombcell_curation.py} | 4 ++-- .../widgets/{unit_labeling.py => bombcell_curation.py} | 4 ++-- src/spikeinterface/widgets/widget_list.py | 2 +- 4 files changed, 7 insertions(+), 7 deletions(-) rename src/spikeinterface/curation/{unit_labeling.py => bombcell_curation.py} (99%) rename src/spikeinterface/widgets/{unit_labeling.py => bombcell_curation.py} (99%) diff --git a/src/spikeinterface/curation/__init__.py b/src/spikeinterface/curation/__init__.py index 65fa02880d..2c55e7edee 100644 --- a/src/spikeinterface/curation/__init__.py +++ b/src/spikeinterface/curation/__init__.py @@ -21,8 +21,8 @@ from .sortingview_curation import apply_sortingview_curation # automated curation -from .unit_labeling import ( - WAVEFORM_METRICS, +from .bombcell_curation import ( + NOISE_METRICS, SPIKE_QUALITY_METRICS, NON_SOMATIC_METRICS, bombcell_get_default_thresholds, diff --git a/src/spikeinterface/curation/unit_labeling.py b/src/spikeinterface/curation/bombcell_curation.py similarity index 99% rename from src/spikeinterface/curation/unit_labeling.py rename to src/spikeinterface/curation/bombcell_curation.py index 88870e46c1..55de1f3b72 100644 --- a/src/spikeinterface/curation/unit_labeling.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -15,7 +15,7 @@ from typing import Optional -WAVEFORM_METRICS = [ +NOISE_METRICS = [ "num_positive_peaks", "num_negative_peaks", "peak_to_trough_duration", @@ -130,7 +130,7 @@ def bombcell_label_units( # NOISE: waveform failures noise_mask = np.zeros(n_units, dtype=bool) - for metric_name in WAVEFORM_METRICS: + for metric_name in NOISE_METRICS: if metric_name not in combined_metrics.columns or metric_name not in thresholds: continue values = combined_metrics[metric_name].values diff --git a/src/spikeinterface/widgets/unit_labeling.py b/src/spikeinterface/widgets/bombcell_curation.py similarity index 99% rename from src/spikeinterface/widgets/unit_labeling.py rename to src/spikeinterface/widgets/bombcell_curation.py index ccdb9128f2..4fca92772e 100644 --- a/src/spikeinterface/widgets/unit_labeling.py +++ b/src/spikeinterface/widgets/bombcell_curation.py @@ -210,7 +210,7 @@ class UpsetPlotWidget(BaseWidget): NOISE -> waveform metrics, MUA -> spike quality metrics, NON_SOMA -> non-somatic metrics. """ - WAVEFORM_METRICS = [ + NOISE_METRICS = [ "num_positive_peaks", "num_negative_peaks", "peak_to_trough_duration", @@ -274,7 +274,7 @@ def __init__( def _get_metrics_for_unit_type(self, unit_type_label): if unit_type_label == "NOISE": - return self.WAVEFORM_METRICS + return self.NOISE_METRICS elif unit_type_label == "MUA": return self.SPIKE_QUALITY_METRICS elif unit_type_label in ("NON_SOMA", "NON_SOMA_GOOD", "NON_SOMA_MUA"): diff --git a/src/spikeinterface/widgets/widget_list.py b/src/spikeinterface/widgets/widget_list.py index 46c16898a4..2327dd922e 100644 --- a/src/spikeinterface/widgets/widget_list.py +++ b/src/spikeinterface/widgets/widget_list.py @@ -37,7 +37,7 @@ from .comparison import AgreementMatrixWidget, ConfusionMatrixWidget from .gtstudy import StudyRunTimesWidget, StudyUnitCountsWidget, StudyPerformances, StudyAgreementMatrix, StudySummary from .collision import ComparisonCollisionBySimilarityWidget, StudyComparisonCollisionBySimilarityWidget -from .unit_labeling import ( +from .bombcell_curation import ( LabelingHistogramsWidget, WaveformOverlayWidget, UpsetPlotWidget, From 6b21830b2a81b92d75b0b73872cf4fff50ae9c33 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 20:44:18 -0500 Subject: [PATCH 28/49] use sorting analyzer rather than inputing template_metrics and qualty_metrics in bombcell functions --- .../curation/bombcell_curation.py | 125 +++++++++------ .../widgets/bombcell_curation.py | 150 +++++++++++++----- 2 files changed, 180 insertions(+), 95 deletions(-) diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index 55de1f3b72..57729ff1d9 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -47,32 +47,32 @@ def bombcell_get_default_thresholds() -> dict: """ Bombcell - Returns default thresholds for unit labeling. - Each metric has 'min' and 'max' values. Use np.nan to disable a threshold (e.g. to ignore a metric completly + Each metric has 'min' and 'max' values. Use None to disable a threshold (e.g. to ignore a metric completely or to only have a min or a max threshold) """ # bombcell return { # Waveform quality (failures -> NOISE) - "num_positive_peaks": {"min": np.nan, "max": 2}, - "num_negative_peaks": {"min": np.nan, "max": 1}, + "num_positive_peaks": {"min": None, "max": 2}, + "num_negative_peaks": {"min": None, "max": 1}, "peak_to_trough_duration": {"min": 0.0001, "max": 0.00115}, # seconds - "waveform_baseline_flatness": {"min": np.nan, "max": 0.5}, - "peak_after_to_trough_ratio": {"min": np.nan, "max": 0.8}, + "waveform_baseline_flatness": {"min": None, "max": 0.5}, + "peak_after_to_trough_ratio": {"min": None, "max": 0.8}, "exp_decay": {"min": 0.01, "max": 0.1}, # Spike quality (failures -> MUA) - "amplitude_median": {"min": 40, "max": np.nan}, # uV - "snr_bombcell": {"min": 5, "max": np.nan}, - "amplitude_cutoff": {"min": np.nan, "max": 0.2}, - "num_spikes": {"min": 300, "max": np.nan}, - "rp_contamination": {"min": np.nan, "max": 0.1}, - "presence_ratio": {"min": 0.7, "max": np.nan}, - "drift_ptp": {"min": np.nan, "max": 100}, # um + "amplitude_median": {"min": 40, "max": None}, # uV + "snr_bombcell": {"min": 5, "max": None}, + "amplitude_cutoff": {"min": None, "max": 0.2}, + "num_spikes": {"min": 300, "max": None}, + "rp_contamination": {"min": None, "max": 0.1}, + "presence_ratio": {"min": 0.7, "max": None}, + "drift_ptp": {"min": None, "max": 100}, # um # Non-somatic detection - "peak_before_to_trough_ratio": {"min": np.nan, "max": 3}, - "peak_before_width": {"min": 150, "max": np.nan}, # us - "trough_width": {"min": 200, "max": np.nan}, # us - "peak_before_to_peak_after_ratio": {"min": np.nan, "max": 3}, - "main_peak_to_trough_ratio": {"min": np.nan, "max": 0.8}, + "peak_before_to_trough_ratio": {"min": None, "max": 3}, + "peak_before_width": {"min": 150, "max": None}, # us + "trough_width": {"min": 200, "max": None}, # us + "peak_before_to_peak_after_ratio": {"min": None, "max": 3}, + "main_peak_to_trough_ratio": {"min": None, "max": 0.8}, } @@ -87,28 +87,41 @@ def _combine_metrics(quality_metrics, template_metrics): return quality_metrics.join(template_metrics, how="outer") +def _is_threshold_disabled(value): + """Check if a threshold value is disabled (None or np.nan).""" + if value is None: + return True + if isinstance(value, float) and np.isnan(value): + return True + return False + + def bombcell_label_units( - quality_metrics=None, - template_metrics=None, + sorting_analyzer=None, thresholds: Optional[dict] = None, label_non_somatic: bool = True, split_non_somatic_good_mua: bool = False, + quality_metrics=None, + template_metrics=None, ) -> tuple[np.ndarray, np.ndarray]: """ Bombcell - label units based on quality metrics and thresholds. Parameters ---------- - quality_metrics : pd.DataFrame, optional - DataFrame with quality metrics (index = unit_ids). - template_metrics : pd.DataFrame, optional - DataFrame with template metrics (index = unit_ids). + sorting_analyzer : SortingAnalyzer, optional + SortingAnalyzer with computed quality_metrics and/or template_metrics extensions. + If provided, metrics are extracted automatically using get_metrics_extension_data(). thresholds : dict or None - Threshold dict: {"metric": {"min": val, "max": val}}. Use np.nan to disable. + Threshold dict: {"metric": {"min": val, "max": val}}. Use None to disable. label_non_somatic : bool If True, detect non-somatic (axonal) units. split_non_somatic_good_mua : bool If True, split non-somatic into NON_SOMA_GOOD (3) and NON_SOMA_MUA (4). + quality_metrics : pd.DataFrame, optional + DataFrame with quality metrics (index = unit_ids). Deprecated, use sorting_analyzer instead. + template_metrics : pd.DataFrame, optional + DataFrame with template metrics (index = unit_ids). Deprecated, use sorting_analyzer instead. Returns ------- @@ -117,9 +130,19 @@ def bombcell_label_units( unit_type_string : np.ndarray String labels. """ - combined_metrics = _combine_metrics(quality_metrics, template_metrics) - if combined_metrics is None: - raise ValueError("At least one of quality_metrics or template_metrics must be provided") + if sorting_analyzer is not None: + combined_metrics = sorting_analyzer.get_metrics_extension_data() + if combined_metrics.empty: + raise ValueError( + "SortingAnalyzer has no metrics extensions computed. " + "Compute quality_metrics and/or template_metrics first." + ) + else: + combined_metrics = _combine_metrics(quality_metrics, template_metrics) + if combined_metrics is None: + raise ValueError( + "Either sorting_analyzer or at least one of quality_metrics/template_metrics must be provided" + ) if thresholds is None: thresholds = bombcell_get_default_thresholds() @@ -138,9 +161,9 @@ def bombcell_label_units( values = np.abs(values) thresh = thresholds[metric_name] noise_mask |= np.isnan(values) - if not np.isnan(thresh["min"]): + if not _is_threshold_disabled(thresh["min"]): noise_mask |= values < thresh["min"] - if not np.isnan(thresh["max"]): + if not _is_threshold_disabled(thresh["max"]): noise_mask |= values > thresh["max"] unit_type[noise_mask] = 0 @@ -154,9 +177,9 @@ def bombcell_label_units( values = np.abs(values) thresh = thresholds[metric_name] valid_mask = np.isnan(unit_type) - if not np.isnan(thresh["min"]): + if not _is_threshold_disabled(thresh["min"]): mua_mask |= valid_mask & ~np.isnan(values) & (values < thresh["min"]) - if not np.isnan(thresh["max"]): + if not _is_threshold_disabled(thresh["max"]): mua_mask |= valid_mask & ~np.isnan(values) & (values > thresh["max"]) unit_type[mua_mask & np.isnan(unit_type)] = 2 @@ -173,17 +196,17 @@ def get_metric(name): peak_before_width = get_metric("peak_before_width") trough_width = get_metric("trough_width") - width_thresh_peak = thresholds.get("peak_before_width", {}).get("min", np.nan) - width_thresh_trough = thresholds.get("trough_width", {}).get("min", np.nan) + width_thresh_peak = thresholds.get("peak_before_width", {}).get("min", None) + width_thresh_trough = thresholds.get("trough_width", {}).get("min", None) narrow_peak = ( ~np.isnan(peak_before_width) & (peak_before_width < width_thresh_peak) - if not np.isnan(width_thresh_peak) + if not _is_threshold_disabled(width_thresh_peak) else np.zeros(n_units, dtype=bool) ) narrow_trough = ( ~np.isnan(trough_width) & (trough_width < width_thresh_trough) - if not np.isnan(width_thresh_trough) + if not _is_threshold_disabled(width_thresh_trough) else np.zeros(n_units, dtype=bool) ) width_conditions = narrow_peak & narrow_trough @@ -192,23 +215,23 @@ def get_metric(name): peak_before_to_peak_after = get_metric("peak_before_to_peak_after_ratio") main_peak_to_trough = get_metric("main_peak_to_trough_ratio") - ratio_thresh_pbt = thresholds.get("peak_before_to_trough_ratio", {}).get("max", np.nan) - ratio_thresh_pbpa = thresholds.get("peak_before_to_peak_after_ratio", {}).get("max", np.nan) - ratio_thresh_mpt = thresholds.get("main_peak_to_trough_ratio", {}).get("max", np.nan) + ratio_thresh_pbt = thresholds.get("peak_before_to_trough_ratio", {}).get("max", None) + ratio_thresh_pbpa = thresholds.get("peak_before_to_peak_after_ratio", {}).get("max", None) + ratio_thresh_mpt = thresholds.get("main_peak_to_trough_ratio", {}).get("max", None) large_initial_peak = ( ~np.isnan(peak_before_to_trough) & (peak_before_to_trough > ratio_thresh_pbt) - if not np.isnan(ratio_thresh_pbt) + if not _is_threshold_disabled(ratio_thresh_pbt) else np.zeros(n_units, dtype=bool) ) large_peak_ratio = ( ~np.isnan(peak_before_to_peak_after) & (peak_before_to_peak_after > ratio_thresh_pbpa) - if not np.isnan(ratio_thresh_pbpa) + if not _is_threshold_disabled(ratio_thresh_pbpa) else np.zeros(n_units, dtype=bool) ) large_main_peak = ( ~np.isnan(main_peak_to_trough) & (main_peak_to_trough > ratio_thresh_mpt) - if not np.isnan(ratio_thresh_mpt) + if not _is_threshold_disabled(ratio_thresh_mpt) else np.zeros(n_units, dtype=bool) ) @@ -257,12 +280,12 @@ def apply_thresholds( passes[nan_mask] = False reasons[nan_mask] = "nan" - if not np.isnan(thresh["min"]): + if not _is_threshold_disabled(thresh["min"]): below_min = ~nan_mask & (values < thresh["min"]) passes[below_min] = False reasons[below_min] = "below_min" - if not np.isnan(thresh["max"]): + if not _is_threshold_disabled(thresh["max"]): above_max = ~nan_mask & (values > thresh["max"]) passes[above_max] = False reasons[above_max & (reasons == "")] = "above_max" @@ -306,8 +329,8 @@ def save_thresholds(thresholds: dict, filepath) -> None: json_thresholds = {} for metric_name, thresh in thresholds.items(): json_thresholds[metric_name] = { - "min": None if (isinstance(thresh["min"], float) and np.isnan(thresh["min"])) else thresh["min"], - "max": None if (isinstance(thresh["max"], float) and np.isnan(thresh["max"])) else thresh["max"], + "min": None if (isinstance(thresh["min"], float) and _is_threshold_disabled(thresh["min"])) else thresh["min"], + "max": None if (isinstance(thresh["max"], float) and _is_threshold_disabled(thresh["max"])) else thresh["max"], } filepath = Path(filepath) @@ -401,16 +424,16 @@ def save_labeling_results( continue value = quality_metrics.loc[unit_id, metric_name] thresh = thresholds[metric_name] - thresh_min = thresh.get("min", np.nan) - thresh_max = thresh.get("max", np.nan) + thresh_min = thresh.get("min", None) + thresh_max = thresh.get("max", None) # Determine pass/fail passed = True if np.isnan(value): passed = False - elif not np.isnan(thresh_min) and value < thresh_min: + elif not _is_threshold_disabled(thresh_min) and value < thresh_min: passed = False - elif not np.isnan(thresh_max) and value > thresh_max: + elif not _is_threshold_disabled(thresh_max) and value > thresh_max: passed = False rows.append( @@ -420,8 +443,8 @@ def save_labeling_results( "label_code": label_code, "metric_name": metric_name, "value": value, - "threshold_min": None if np.isnan(thresh_min) else thresh_min, - "threshold_max": None if np.isnan(thresh_max) else thresh_max, + "threshold_min": thresh_min, + "threshold_max": thresh_max, "passed": passed, } ) diff --git a/src/spikeinterface/widgets/bombcell_curation.py b/src/spikeinterface/widgets/bombcell_curation.py index 4fca92772e..fd2847c9ba 100644 --- a/src/spikeinterface/widgets/bombcell_curation.py +++ b/src/spikeinterface/widgets/bombcell_curation.py @@ -19,23 +19,43 @@ def _combine_metrics(quality_metrics, template_metrics): return quality_metrics.join(template_metrics, how="outer") +def _is_threshold_disabled(value): + """Check if a threshold value is disabled (None or np.nan).""" + if value is None: + return True + if isinstance(value, float) and np.isnan(value): + return True + return False + + class LabelingHistogramsWidget(BaseWidget): """Plot histograms of quality metrics with threshold lines.""" def __init__( self, - quality_metrics=None, - template_metrics=None, + sorting_analyzer=None, thresholds: Optional[dict] = None, metrics_to_plot: Optional[list] = None, + quality_metrics=None, + template_metrics=None, backend=None, **backend_kwargs, ): from spikeinterface.curation import bombcell_get_default_thresholds - combined_metrics = _combine_metrics(quality_metrics, template_metrics) - if combined_metrics is None: - raise ValueError("At least one of quality_metrics or template_metrics must be provided") + if sorting_analyzer is not None: + combined_metrics = sorting_analyzer.get_metrics_extension_data() + if combined_metrics.empty: + raise ValueError( + "SortingAnalyzer has no metrics extensions computed. " + "Compute quality_metrics and/or template_metrics first." + ) + else: + combined_metrics = _combine_metrics(quality_metrics, template_metrics) + if combined_metrics is None: + raise ValueError( + "Either sorting_analyzer or at least one of quality_metrics/template_metrics must be provided" + ) if thresholds is None: thresholds = bombcell_get_default_thresholds() @@ -92,10 +112,10 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): thresh = thresholds.get(metric_name, {}) has_thresh = False - if not np.isnan(thresh.get("min", np.nan)): + if not _is_threshold_disabled(thresh.get("min", None)): ax.axvline(thresh["min"], color="red", ls="--", lw=2, label=f"min={thresh['min']:.2g}") has_thresh = True - if not np.isnan(thresh.get("max", np.nan)): + if not _is_threshold_disabled(thresh.get("max", None)): ax.axvline(thresh["max"], color="blue", ls="--", lw=2, label=f"max={thresh['max']:.2g}") has_thresh = True @@ -239,20 +259,31 @@ def __init__( self, unit_type: np.ndarray, unit_type_string: np.ndarray, - quality_metrics=None, - template_metrics=None, + sorting_analyzer=None, thresholds: Optional[dict] = None, unit_types_to_plot: Optional[list] = None, split_non_somatic: bool = False, min_subset_size: int = 1, + quality_metrics=None, + template_metrics=None, backend=None, **backend_kwargs, ): from spikeinterface.curation import bombcell_get_default_thresholds - combined_metrics = _combine_metrics(quality_metrics, template_metrics) - if combined_metrics is None: - raise ValueError("At least one of quality_metrics or template_metrics must be provided") + if sorting_analyzer is not None: + combined_metrics = sorting_analyzer.get_metrics_extension_data() + if combined_metrics.empty: + raise ValueError( + "SortingAnalyzer has no metrics extensions computed. " + "Compute quality_metrics and/or template_metrics first." + ) + else: + combined_metrics = _combine_metrics(quality_metrics, template_metrics) + if combined_metrics is None: + raise ValueError( + "Either sorting_analyzer or at least one of quality_metrics/template_metrics must be provided" + ) if thresholds is None: thresholds = bombcell_get_default_thresholds() @@ -389,9 +420,9 @@ def _build_failure_table(self, quality_metrics, thresholds): values = np.abs(values) failed = np.isnan(values) - if not np.isnan(thresh.get("min", np.nan)): + if not _is_threshold_disabled(thresh.get("min", None)): failed |= values < thresh["min"] - if not np.isnan(thresh.get("max", np.nan)): + if not _is_threshold_disabled(thresh.get("max", None)): failed |= values > thresh["max"] failure_data[metric_name] = failed @@ -400,14 +431,21 @@ def _build_failure_table(self, quality_metrics, thresholds): # Convenience functions def plot_labeling_histograms( - quality_metrics=None, template_metrics=None, thresholds=None, metrics_to_plot=None, backend=None, **kwargs + sorting_analyzer=None, + thresholds=None, + metrics_to_plot=None, + quality_metrics=None, + template_metrics=None, + backend=None, + **kwargs, ): """Plot histograms of quality metrics with threshold lines.""" return LabelingHistogramsWidget( - quality_metrics=quality_metrics, - template_metrics=template_metrics, + sorting_analyzer=sorting_analyzer, thresholds=thresholds, metrics_to_plot=metrics_to_plot, + quality_metrics=quality_metrics, + template_metrics=template_metrics, backend=backend, **kwargs, ) @@ -425,12 +463,13 @@ def plot_waveform_overlay( def plot_upset( unit_type, unit_type_string, - quality_metrics=None, - template_metrics=None, + sorting_analyzer=None, thresholds=None, unit_types_to_plot=None, split_non_somatic=False, min_subset_size=1, + quality_metrics=None, + template_metrics=None, backend=None, **kwargs, ): @@ -438,12 +477,13 @@ def plot_upset( return UpsetPlotWidget( unit_type, unit_type_string, - quality_metrics=quality_metrics, - template_metrics=template_metrics, + sorting_analyzer=sorting_analyzer, thresholds=thresholds, unit_types_to_plot=unit_types_to_plot, split_non_somatic=split_non_somatic, min_subset_size=min_subset_size, + quality_metrics=quality_metrics, + template_metrics=template_metrics, backend=backend, **kwargs, ) @@ -501,25 +541,36 @@ def plot_unit_labeling_all( if thresholds is None: thresholds = bombcell_get_default_thresholds() - # Load metrics from sorting_analyzer if not provided - if quality_metrics is None and sorting_analyzer.has_extension("quality_metrics"): - quality_metrics = sorting_analyzer.get_extension("quality_metrics").get_data() - if template_metrics is None and sorting_analyzer.has_extension("template_metrics"): - template_metrics = sorting_analyzer.get_extension("template_metrics").get_data() + # Use sorting_analyzer directly if no explicit metrics provided + use_analyzer = quality_metrics is None and template_metrics is None - combined_metrics = _combine_metrics(quality_metrics, template_metrics) + # Get combined metrics for checking and saving + if use_analyzer: + combined_metrics = sorting_analyzer.get_metrics_extension_data() + if combined_metrics.empty: + combined_metrics = None + else: + combined_metrics = _combine_metrics(quality_metrics, template_metrics) results = {} # Histograms if combined_metrics is not None: - results["histograms"] = plot_labeling_histograms( - quality_metrics=quality_metrics, - template_metrics=template_metrics, - thresholds=thresholds, - backend=backend, - **kwargs, - ) + if use_analyzer: + results["histograms"] = plot_labeling_histograms( + sorting_analyzer=sorting_analyzer, + thresholds=thresholds, + backend=backend, + **kwargs, + ) + else: + results["histograms"] = plot_labeling_histograms( + quality_metrics=quality_metrics, + template_metrics=template_metrics, + thresholds=thresholds, + backend=backend, + **kwargs, + ) # Waveform overlay results["waveforms"] = plot_waveform_overlay( @@ -528,16 +579,27 @@ def plot_unit_labeling_all( # UpSet plots if include_upset and combined_metrics is not None: - results["upset"] = plot_upset( - unit_type, - unit_type_string, - quality_metrics=quality_metrics, - template_metrics=template_metrics, - thresholds=thresholds, - split_non_somatic=split_non_somatic, - backend=backend, - **kwargs, - ) + if use_analyzer: + results["upset"] = plot_upset( + unit_type, + unit_type_string, + sorting_analyzer=sorting_analyzer, + thresholds=thresholds, + split_non_somatic=split_non_somatic, + backend=backend, + **kwargs, + ) + else: + results["upset"] = plot_upset( + unit_type, + unit_type_string, + quality_metrics=quality_metrics, + template_metrics=template_metrics, + thresholds=thresholds, + split_non_somatic=split_non_somatic, + backend=backend, + **kwargs, + ) # Save to folder if requested if save_folder is not None: From 8673ebc008ebc5a0c1a8e53a2a81b3cca7eb30e0 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 20:49:36 -0500 Subject: [PATCH 29/49] remove unused apply_thresholds() function --- src/spikeinterface/curation/__init__.py | 1 - .../curation/bombcell_curation.py | 42 ------------------- 2 files changed, 43 deletions(-) diff --git a/src/spikeinterface/curation/__init__.py b/src/spikeinterface/curation/__init__.py index 2c55e7edee..af24c9e862 100644 --- a/src/spikeinterface/curation/__init__.py +++ b/src/spikeinterface/curation/__init__.py @@ -27,7 +27,6 @@ NON_SOMATIC_METRICS, bombcell_get_default_thresholds, bombcell_label_units, - apply_thresholds, get_labeling_summary, save_thresholds, load_thresholds, diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index 57729ff1d9..b41492546e 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -255,48 +255,6 @@ def get_metric(name): return unit_type.astype(int), unit_type_string -def apply_thresholds( - quality_metrics: pd.DataFrame, - thresholds: Optional[dict] = None, -) -> pd.DataFrame: - """ - Apply thresholds and return pass/fail status for each metric. - Useful for debugging classification results. - """ - if thresholds is None: - thresholds = bombcell_get_default_thresholds() - - results = {} - for metric_name, thresh in thresholds.items(): - if metric_name not in quality_metrics.columns: - continue - - values = quality_metrics[metric_name].values - n_units = len(values) - passes = np.ones(n_units, dtype=bool) - reasons = np.array([""] * n_units, dtype=object) - - nan_mask = np.isnan(values) - passes[nan_mask] = False - reasons[nan_mask] = "nan" - - if not _is_threshold_disabled(thresh["min"]): - below_min = ~nan_mask & (values < thresh["min"]) - passes[below_min] = False - reasons[below_min] = "below_min" - - if not _is_threshold_disabled(thresh["max"]): - above_max = ~nan_mask & (values > thresh["max"]) - passes[above_max] = False - reasons[above_max & (reasons == "")] = "above_max" - reasons[above_max & (reasons == "below_min")] = "below_min_and_above_max" - - results[f"{metric_name}_pass"] = passes - results[f"{metric_name}_fail_reason"] = reasons - - return pd.DataFrame(results, index=quality_metrics.index) - - def get_labeling_summary(unit_type: np.ndarray, unit_type_string: np.ndarray) -> dict: """Get counts and percentages for each unit type.""" n_total = len(unit_type) From e55cde73ade4fe2d685fa88714dd20b54729bcef Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 20:53:57 -0500 Subject: [PATCH 30/49] get_labeling_summary -> get_bombcell_labeling_summary --- src/spikeinterface/curation/__init__.py | 2 +- src/spikeinterface/curation/bombcell_curation.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/curation/__init__.py b/src/spikeinterface/curation/__init__.py index af24c9e862..907905f9de 100644 --- a/src/spikeinterface/curation/__init__.py +++ b/src/spikeinterface/curation/__init__.py @@ -27,7 +27,7 @@ NON_SOMATIC_METRICS, bombcell_get_default_thresholds, bombcell_label_units, - get_labeling_summary, + get_bombcell_labeling_summary, save_thresholds, load_thresholds, save_labeling_results, diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index b41492546e..db95a6f896 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -255,7 +255,7 @@ def get_metric(name): return unit_type.astype(int), unit_type_string -def get_labeling_summary(unit_type: np.ndarray, unit_type_string: np.ndarray) -> dict: +def get_bombcell_labeling_summary(unit_type: np.ndarray, unit_type_string: np.ndarray) -> dict: """Get counts and percentages for each unit type.""" n_total = len(unit_type) unique_types, counts = np.unique(unit_type, return_counts=True) From b04ff268aa4dfca3b6340606cb04d18fe1b15b28 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 20:56:08 -0500 Subject: [PATCH 31/49] Removed save_thresholds and load_thresholds. can now use standard json.dump()/json.load() since None serializes directly to JSON null. --- src/spikeinterface/curation/__init__.py | 2 - .../curation/bombcell_curation.py | 59 ------------------- 2 files changed, 61 deletions(-) diff --git a/src/spikeinterface/curation/__init__.py b/src/spikeinterface/curation/__init__.py index 907905f9de..b047d6d6d7 100644 --- a/src/spikeinterface/curation/__init__.py +++ b/src/spikeinterface/curation/__init__.py @@ -28,8 +28,6 @@ bombcell_get_default_thresholds, bombcell_label_units, get_bombcell_labeling_summary, - save_thresholds, - load_thresholds, save_labeling_results, ) diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index db95a6f896..e98cf279b8 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -269,65 +269,6 @@ def get_bombcell_labeling_summary(unit_type: np.ndarray, unit_type_string: np.nd return summary -def save_thresholds(thresholds: dict, filepath) -> None: - """ - Save thresholds to a JSON file. - - Parameters - ---------- - thresholds : dict - Threshold dictionary from bombcell_get_default_thresholds() or modified version. - filepath : str or Path - Path to save the JSON file. - """ - import json - from pathlib import Path - - # Convert np.nan to None for JSON serialization - json_thresholds = {} - for metric_name, thresh in thresholds.items(): - json_thresholds[metric_name] = { - "min": None if (isinstance(thresh["min"], float) and _is_threshold_disabled(thresh["min"])) else thresh["min"], - "max": None if (isinstance(thresh["max"], float) and _is_threshold_disabled(thresh["max"])) else thresh["max"], - } - - filepath = Path(filepath) - with open(filepath, "w") as f: - json.dump(json_thresholds, f, indent=4) - - -def load_thresholds(filepath) -> dict: - """ - Load thresholds from a JSON file. - - Parameters - ---------- - filepath : str or Path - Path to the JSON file. - - Returns - ------- - thresholds : dict - Threshold dictionary compatible with bombcell_classify_units(). - """ - import json - from pathlib import Path - - filepath = Path(filepath) - with open(filepath, "r") as f: - json_thresholds = json.load(f) - - # Convert None to np.nan - thresholds = {} - for metric_name, thresh in json_thresholds.items(): - thresholds[metric_name] = { - "min": np.nan if thresh["min"] is None else thresh["min"], - "max": np.nan if thresh["max"] is None else thresh["max"], - } - - return thresholds - - def save_labeling_results( quality_metrics: pd.DataFrame, unit_type: np.ndarray, From 538372810bb6a5546eeceb11ac439df875050e62 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 20:57:48 -0500 Subject: [PATCH 32/49] get_labeling_results -> get_bombcell_results --- src/spikeinterface/curation/__init__.py | 2 +- src/spikeinterface/curation/bombcell_curation.py | 2 +- src/spikeinterface/widgets/bombcell_curation.py | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/spikeinterface/curation/__init__.py b/src/spikeinterface/curation/__init__.py index b047d6d6d7..acd936be54 100644 --- a/src/spikeinterface/curation/__init__.py +++ b/src/spikeinterface/curation/__init__.py @@ -28,7 +28,7 @@ bombcell_get_default_thresholds, bombcell_label_units, get_bombcell_labeling_summary, - save_labeling_results, + save_bombcell_results, ) from .model_based_curation import auto_label_units, load_model diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index e98cf279b8..e756f61e13 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -269,7 +269,7 @@ def get_bombcell_labeling_summary(unit_type: np.ndarray, unit_type_string: np.nd return summary -def save_labeling_results( +def save_bombcell_results( quality_metrics: pd.DataFrame, unit_type: np.ndarray, unit_type_string: np.ndarray, diff --git a/src/spikeinterface/widgets/bombcell_curation.py b/src/spikeinterface/widgets/bombcell_curation.py index fd2847c9ba..3f4cad6296 100644 --- a/src/spikeinterface/widgets/bombcell_curation.py +++ b/src/spikeinterface/widgets/bombcell_curation.py @@ -536,7 +536,7 @@ def plot_unit_labeling_all( Dictionary with keys 'histograms', 'waveforms', 'upset' containing widget objects. """ from pathlib import Path - from spikeinterface.curation import bombcell_get_default_thresholds, save_labeling_results + from spikeinterface.curation import bombcell_get_default_thresholds, save_bombcell_results if thresholds is None: thresholds = bombcell_get_default_thresholds() @@ -617,6 +617,6 @@ def plot_unit_labeling_all( # Save CSV results if combined_metrics is not None: - save_labeling_results(combined_metrics, unit_type, unit_type_string, thresholds, save_folder) + save_bombcell_results(combined_metrics, unit_type, unit_type_string, thresholds, save_folder) return results From 7491a6adfccdf275c9b3d30169d724b1480ecc0b Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 21:09:53 -0500 Subject: [PATCH 33/49] woops, that was tracked and committed too early - reverting --- .../curation/bombcell_curation.py | 10 +- .../curation/default_thresholds.json | 2 +- src/spikeinterface/exporters/__init__.py | 1 - .../exporters/tests/test_to_methods.py | 154 ---- src/spikeinterface/exporters/to_methods.py | 796 ------------------ .../metrics/quality/misc_metrics.py | 28 +- .../widgets/bombcell_curation.py | 2 +- 7 files changed, 23 insertions(+), 970 deletions(-) delete mode 100644 src/spikeinterface/exporters/tests/test_to_methods.py delete mode 100644 src/spikeinterface/exporters/to_methods.py diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index e756f61e13..3830e6a727 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -1,5 +1,5 @@ """ -Unit labeling based on quality metrics (Bombcell). +Unit labeling based on quality metrics (bombcell). Unit Types: 0 (NOISE): Failed waveform quality checks @@ -26,7 +26,7 @@ SPIKE_QUALITY_METRICS = [ "amplitude_median", - "snr_bombcell", + "snr_baseline", "amplitude_cutoff", "num_spikes", "rp_contamination", @@ -45,7 +45,7 @@ def bombcell_get_default_thresholds() -> dict: """ - Bombcell - Returns default thresholds for unit labeling. + bombcell - Returns default thresholds for unit labeling. Each metric has 'min' and 'max' values. Use None to disable a threshold (e.g. to ignore a metric completely or to only have a min or a max threshold) @@ -61,7 +61,7 @@ def bombcell_get_default_thresholds() -> dict: "exp_decay": {"min": 0.01, "max": 0.1}, # Spike quality (failures -> MUA) "amplitude_median": {"min": 40, "max": None}, # uV - "snr_bombcell": {"min": 5, "max": None}, + "snr_baseline": {"min": 5, "max": None}, "amplitude_cutoff": {"min": None, "max": 0.2}, "num_spikes": {"min": 300, "max": None}, "rp_contamination": {"min": None, "max": 0.1}, @@ -105,7 +105,7 @@ def bombcell_label_units( template_metrics=None, ) -> tuple[np.ndarray, np.ndarray]: """ - Bombcell - label units based on quality metrics and thresholds. + bombcell - label units based on quality metrics and thresholds. Parameters ---------- diff --git a/src/spikeinterface/curation/default_thresholds.json b/src/spikeinterface/curation/default_thresholds.json index 8e39d89179..0f8dde710e 100644 --- a/src/spikeinterface/curation/default_thresholds.json +++ b/src/spikeinterface/curation/default_thresholds.json @@ -27,7 +27,7 @@ "min": 40, "max": null }, - "snr_bombcell": { + "snr_baseline": { "min": 5, "max": null }, diff --git a/src/spikeinterface/exporters/__init__.py b/src/spikeinterface/exporters/__init__.py index 027ffe3222..97d0f64126 100644 --- a/src/spikeinterface/exporters/__init__.py +++ b/src/spikeinterface/exporters/__init__.py @@ -2,4 +2,3 @@ from .report import export_report from .to_ibl import export_to_ibl_gui from .to_pynapple import to_pynapple_tsgroup -from .to_methods import export_to_methods diff --git a/src/spikeinterface/exporters/tests/test_to_methods.py b/src/spikeinterface/exporters/tests/test_to_methods.py deleted file mode 100644 index c914fdff8e..0000000000 --- a/src/spikeinterface/exporters/tests/test_to_methods.py +++ /dev/null @@ -1,154 +0,0 @@ -"""Tests for export_to_methods function.""" - -from __future__ import annotations - -import pytest -from pathlib import Path - -from spikeinterface.exporters import export_to_methods -from spikeinterface.exporters.tests.common import make_sorting_analyzer - - -class TestExportToMethods: - """Test the export_to_methods function.""" - - @pytest.fixture(scope="class") - def sorting_analyzer(self): - """Create a sorting analyzer for testing.""" - return make_sorting_analyzer(sparse=False) - - def test_export_to_methods_markdown(self, sorting_analyzer): - """Test markdown output format.""" - result = export_to_methods(sorting_analyzer, format="markdown") - - assert isinstance(result, str) - assert len(result) > 0 - # Check for markdown header and prose content - assert "## Spike Sorting Methods" in result - assert "Extracellular recordings were acquired" in result - assert "### References" in result - - def test_export_to_methods_latex(self, sorting_analyzer): - """Test LaTeX output format.""" - result = export_to_methods(sorting_analyzer, format="latex") - - assert isinstance(result, str) - assert len(result) > 0 - # Check for LaTeX sections - assert "\\section{Spike Sorting Methods}" in result - assert "Extracellular recordings were acquired" in result - assert "\\subsection*{References}" in result - - def test_export_to_methods_text(self, sorting_analyzer): - """Test plain text output format.""" - result = export_to_methods(sorting_analyzer, format="text") - - assert isinstance(result, str) - assert len(result) > 0 - assert "SPIKE SORTING METHODS" in result - assert "Extracellular recordings were acquired" in result - - def test_export_to_methods_invalid_format(self, sorting_analyzer): - """Test that invalid format raises ValueError.""" - with pytest.raises(ValueError, match="format must be"): - export_to_methods(sorting_analyzer, format="invalid") - - def test_export_to_methods_invalid_detail_level(self, sorting_analyzer): - """Test that invalid detail_level raises ValueError.""" - with pytest.raises(ValueError, match="detail_level must be"): - export_to_methods(sorting_analyzer, detail_level="invalid") - - def test_export_to_methods_detail_levels(self, sorting_analyzer): - """Test different detail levels produce different output lengths.""" - brief = export_to_methods(sorting_analyzer, detail_level="brief") - standard = export_to_methods(sorting_analyzer, detail_level="standard") - detailed = export_to_methods(sorting_analyzer, detail_level="detailed") - - # Brief should be shortest, detailed should be longest - assert len(brief) <= len(standard) - assert len(standard) <= len(detailed) - - def test_export_to_methods_with_citations(self, sorting_analyzer): - """Test that citations are included when requested.""" - with_citations = export_to_methods(sorting_analyzer, include_citations=True) - without_citations = export_to_methods(sorting_analyzer, include_citations=False) - - # With citations should be longer - assert len(with_citations) > len(without_citations) - # Should include SpikeInterface citation - assert "SpikeInterface" in with_citations or "spikeinterface" in with_citations.lower() - - def test_export_to_methods_bombcell_citation(self, sorting_analyzer): - """Test that Bombcell citation is included when quality metrics are present.""" - # The sorting_analyzer from make_sorting_analyzer has quality_metrics computed - result = export_to_methods(sorting_analyzer, include_citations=True) - - # Should include Bombcell citation since quality_metrics is present - assert "Bombcell" in result or "bombcell" in result.lower() - assert "Fabre" in result # First author of Bombcell paper - - def test_export_to_methods_contains_recording_info(self, sorting_analyzer): - """Test that recording information is included.""" - result = export_to_methods(sorting_analyzer) - - # Should contain sampling frequency - assert "Hz" in result - # Should contain channel count - assert "channels" in result.lower() or "channel" in result.lower() - - def test_export_to_methods_contains_extensions(self, sorting_analyzer): - """Test that computed extensions are listed.""" - result = export_to_methods(sorting_analyzer) - - # The sorting_analyzer from make_sorting_analyzer has these extensions - assert "waveforms" in result.lower() or "Waveforms" in result - assert "templates" in result.lower() or "Templates" in result - assert "quality" in result.lower() or "Quality" in result - - def test_export_to_methods_write_to_file(self, sorting_analyzer, tmp_path): - """Test writing output to a file.""" - output_file = tmp_path / "methods.md" - result = export_to_methods(sorting_analyzer, output_file=output_file) - - # File should be created - assert output_file.exists() - - # File content should match returned string - file_content = output_file.read_text(encoding="utf-8") - assert file_content == result - - def test_export_to_methods_write_to_nested_path(self, sorting_analyzer, tmp_path): - """Test writing to a nested path that doesn't exist.""" - output_file = tmp_path / "nested" / "path" / "methods.md" - result = export_to_methods(sorting_analyzer, output_file=output_file) - - # File and parent directories should be created - assert output_file.exists() - assert output_file.read_text(encoding="utf-8") == result - - -class TestExportToMethodsWithoutSortingInfo: - """Test export_to_methods when sorting_info is not available.""" - - @pytest.fixture(scope="class") - def sorting_analyzer_no_info(self): - """Create a sorting analyzer without sorting_info.""" - analyzer = make_sorting_analyzer(sparse=False) - # The sorting from generate_ground_truth_recording doesn't have sorting_info - return analyzer - - def test_handles_missing_sorting_info(self, sorting_analyzer_no_info): - """Test that missing sorting_info is handled gracefully.""" - result = export_to_methods(sorting_analyzer_no_info) - - assert isinstance(result, str) - assert len(result) > 0 - # Should mention that info is not available - assert "not available" in result.lower() or "Spike Sorting" in result - - -if __name__ == "__main__": - # Quick manual test - analyzer = make_sorting_analyzer(sparse=False) - result = export_to_methods(analyzer, detail_level="detailed") - print(result) diff --git a/src/spikeinterface/exporters/to_methods.py b/src/spikeinterface/exporters/to_methods.py deleted file mode 100644 index af1032bd73..0000000000 --- a/src/spikeinterface/exporters/to_methods.py +++ /dev/null @@ -1,796 +0,0 @@ -""" -Export a methods section for academic papers from a SortingAnalyzer. -""" - -from __future__ import annotations - -from pathlib import Path -from datetime import datetime - -import spikeinterface - - -# Citations for SpikeInterface, sorters, and analysis tools -CITATIONS = { - "spikeinterface": ( - "Buccino, A. P., Hurwitz, C. L., Garcia, S., Magland, J., Siegle, J. H., Hurwitz, R., & Hennig, M. H. " - "(2020). SpikeInterface, a unified framework for spike sorting. eLife, 9, e61834. " - "https://doi.org/10.7554/eLife.61834" - ), - "bombcell": ( - "Fabre, J. M. J., van Beest, E. H., Peters, A. J., Carandini, M., & Harris, K. D. (2023). " - "Bombcell: automated curation and cell classification of spike-sorted electrophysiology data. " - "Zenodo. https://doi.org/10.5281/zenodo.8172821" - ), - "kilosort": ( - "Pachitariu, M., Steinmetz, N. A., Kadir, S. N., Carandini, M., & Harris, K. D. (2016). " - "Fast and accurate spike sorting of high-channel count probes with KiloSort. " - "Advances in Neural Information Processing Systems, 29, 4448-4456." - ), - "kilosort2": ( - "Pachitariu, M., Steinmetz, N. A., Kadir, S. N., Carandini, M., & Harris, K. D. (2016). " - "Fast and accurate spike sorting of high-channel count probes with KiloSort. " - "Advances in Neural Information Processing Systems, 29, 4448-4456." - ), - "kilosort2_5": ( - "Pachitariu, M., Steinmetz, N. A., Kadir, S. N., Carandini, M., & Harris, K. D. (2016). " - "Fast and accurate spike sorting of high-channel count probes with KiloSort. " - "Advances in Neural Information Processing Systems, 29, 4448-4456." - ), - "kilosort3": ( - "Pachitariu, M., Steinmetz, N. A., Kadir, S. N., Carandini, M., & Harris, K. D. (2016). " - "Fast and accurate spike sorting of high-channel count probes with KiloSort. " - "Advances in Neural Information Processing Systems, 29, 4448-4456." - ), - "kilosort4": ( - "Pachitariu, M., Sridhar, S., Pennington, J., & Stringer, C. (2024). " - "Spike sorting with Kilosort4. Nature Methods. https://doi.org/10.1038/s41592-024-02232-7" - ), - "mountainsort4": ( - "Chung, J. E., Magland, J. F., Barnett, A. H., Tolosa, V. M., Tooker, A. C., Lee, K. Y., ... & Greengard, L. F. " - "(2017). A fully automated approach to spike sorting. Neuron, 95(6), 1381-1394." - ), - "mountainsort5": ( - "Magland, J., Jun, J. J., Lovero, E., Morber, A. J., Barnett, A. H., Greengard, L. F., & Chung, J. E. (2020). " - "SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters. eLife, 9, e55167." - ), - "spykingcircus": ( - "Yger, P., Spampinato, G. L., Esposito, E., Lefebvre, B., Deny, S., Gardella, C., ... & Marre, O. (2018). " - "A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. " - "eLife, 7, e34518." - ), - "spykingcircus2": ( - "Yger, P., Spampinato, G. L., Esposito, E., Lefebvre, B., Deny, S., Gardella, C., ... & Marre, O. (2018). " - "A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. " - "eLife, 7, e34518." - ), - "tridesclous": ( - "Garcia, S., & Bhumbra, G. S. (2020). Tridesclous: a free, easy-to-use and lightweight spike sorter. " - "FENS Forum 2020." - ), - "tridesclous2": ( - "Garcia, S., & Bhumbra, G. S. (2020). Tridesclous: a free, easy-to-use and lightweight spike sorter. " - "FENS Forum 2020." - ), - "herdingspikes": ( - "Hilgen, G., Sorbaro, M., Pirber, S., Zber, J. E., Resber, M. E., Hennig, M. H., & Sernagor, E. (2017). " - "Unsupervised spike sorting for large-scale, high-density multielectrode arrays. Cell Reports, 18(10), 2521-2532." - ), - "ironclust": ( - "Jun, J. J., Steinmetz, N. A., Siegle, J. H., Denman, D. J., Bauza, M., Barbarits, B., ... & Harris, T. D. (2017). " - "Fully integrated silicon probes for high-density recording of neural activity. Nature, 551(7679), 232-236." - ), -} - -# Human-readable names for preprocessing classes -PREPROCESSING_NAMES = { - "BandpassFilterRecording": "Bandpass Filter", - "HighpassFilterRecording": "Highpass Filter", - "LowpassFilterRecording": "Lowpass Filter", - "NotchFilterRecording": "Notch Filter", - "FilterRecording": "Filter", - "CommonReferenceRecording": "Common Reference", - "WhitenRecording": "Whitening", - "NormalizeByQuantileRecording": "Normalize by Quantile", - "ScaleRecording": "Scale", - "CenterRecording": "Center", - "ZScoreRecording": "Z-Score", - "RectifyRecording": "Rectify", - "ClipRecording": "Clip", - "BlankSaturationRecording": "Blank Saturation", - "RemoveArtifactsRecording": "Remove Artifacts", - "RemoveBadChannelsRecording": "Remove Bad Channels", - "InterpolateBadChannelsRecording": "Interpolate Bad Channels", - "DepthOrderRecording": "Depth Order", - "ResampleRecording": "Resample", - "DecimateRecording": "Decimate", - "PhaseShiftRecording": "Phase Shift", - "AsTypeRecording": "Convert Data Type", - "UnsignedToSignedRecording": "Unsigned to Signed", - "AverageAcrossDirectionRecording": "Average Across Direction", - "DirectionalDerivativeRecording": "Directional Derivative", - "HighpassSpatialFilterRecording": "Highpass Spatial Filter", - "GaussianBandpassFilterRecording": "Gaussian Bandpass Filter", - "SilencedPeriodsRecording": "Silenced Periods", - "CorrectMotionRecording": "Motion Correction", - "InterpolateBadChannelsRecording": "Interpolate Bad Channels", -} - -# Key parameters to show for each preprocessing step (for standard detail level) -PREPROCESSING_KEY_PARAMS = { - "BandpassFilterRecording": ["freq_min", "freq_max", "filter_order"], - "HighpassFilterRecording": ["freq_min", "filter_order"], - "LowpassFilterRecording": ["freq_max", "filter_order"], - "NotchFilterRecording": ["freq", "q"], - "FilterRecording": ["band", "btype", "filter_order"], - "CommonReferenceRecording": ["reference", "operator"], - "WhitenRecording": ["mode", "radius_um"], - "NormalizeByQuantileRecording": ["q1", "q2"], - "ScaleRecording": ["gain", "offset"], - "ResampleRecording": ["resample_rate"], - "DecimateRecording": ["decimation_factor"], - "PhaseShiftRecording": ["inter_sample_shift"], - "RemoveBadChannelsRecording": ["bad_channel_ids"], - "CorrectMotionRecording": ["spatial_interpolation_method"], -} - - -def _trace_preprocessing_chain(recording) -> list[dict]: - """ - Walk the recording parent chain and extract preprocessing step info. - - Parameters - ---------- - recording : BaseRecording - The recording to trace - - Returns - ------- - list[dict] - List of dicts with 'class_name' and 'kwargs' for each step, - ordered from original recording to most recent preprocessing - """ - chain = [] - current = recording - - while current is not None: - class_name = current.__class__.__name__ - kwargs = getattr(current, "_kwargs", {}) - - # Filter out the 'recording' key as it's the parent reference - filtered_kwargs = {k: v for k, v in kwargs.items() if k != "recording"} - - chain.append({"class_name": class_name, "kwargs": filtered_kwargs}) - current = current.get_parent() - - # Reverse so original recording is first - chain.reverse() - return chain - - -def _get_sorter_info(sorting) -> dict | None: - """ - Extract sorter name, version, and parameters from a sorting. - - Parameters - ---------- - sorting : BaseSorting - The sorting object - - Returns - ------- - dict | None - Dict with sorter info, or None if not available - """ - sorting_info = sorting.sorting_info - if sorting_info is None: - return None - - info = {} - - # Get sorter name and params - params = sorting_info.get("params", {}) - info["sorter_name"] = params.get("sorter_name", "Unknown") - info["sorter_params"] = params.get("sorter_params", {}) - - # Get log info - log = sorting_info.get("log", {}) - info["sorter_version"] = log.get("sorter_version", "Unknown") - info["run_time"] = log.get("run_time") - info["datetime"] = log.get("datetime") - - return info - - -def _format_value(value) -> str: - """Format a parameter value for display.""" - if value is None: - return "None" - elif isinstance(value, bool): - return str(value) - elif isinstance(value, float): - if value == float("inf"): - return "infinity" - elif value == float("-inf"): - return "-infinity" - else: - # Format with reasonable precision - return f"{value:g}" - elif isinstance(value, (list, tuple)): - if len(value) <= 5: - return ", ".join(_format_value(v) for v in value) - else: - return f"[{len(value)} items]" - elif isinstance(value, dict): - return f"{{...}}" - else: - return str(value) - - -def _format_params_markdown(params: dict, detail_level: str, key_params: list | None = None) -> str: - """Format parameters as markdown list.""" - lines = [] - - if detail_level == "brief": - return "" - - if detail_level == "standard" and key_params: - # Only show key parameters - for key in key_params: - if key in params: - lines.append(f" - {key}: {_format_value(params[key])}") - else: - # Show all parameters (detailed) - for key, value in params.items(): - lines.append(f" - `{key}`: {_format_value(value)}") - - return "\n".join(lines) - - -def _format_params_text(params: dict, detail_level: str, key_params: list | None = None) -> str: - """Format parameters as plain text.""" - lines = [] - - if detail_level == "brief": - return "" - - if detail_level == "standard" and key_params: - for key in key_params: - if key in params: - lines.append(f" {key}: {_format_value(params[key])}") - else: - for key, value in params.items(): - lines.append(f" {key}: {_format_value(value)}") - - return "\n".join(lines) - - -def _format_params_latex(params: dict, detail_level: str, key_params: list | None = None) -> str: - """Format parameters as LaTeX itemize.""" - lines = [] - - if detail_level == "brief": - return "" - - if detail_level == "standard" and key_params: - params_to_show = {k: v for k, v in params.items() if k in key_params} - else: - params_to_show = params - - if params_to_show: - lines.append(" \\begin{itemize}") - for key, value in params_to_show.items(): - escaped_key = key.replace("_", "\\_") - lines.append(f" \\item \\texttt{{{escaped_key}}}: {_format_value(value)}") - lines.append(" \\end{itemize}") - - return "\n".join(lines) - - -def _get_probe_description(sorting_analyzer) -> str: - """Get a description of the probe.""" - try: - probe = sorting_analyzer.get_probe() - if probe is not None: - manufacturer = probe.annotations.get("manufacturer", "") - probe_name = probe.annotations.get("probe_name", "") - if manufacturer and probe_name: - return f"{manufacturer} {probe_name}" - elif probe_name: - return probe_name - else: - return "electrode array" - except Exception: - pass - return "electrode array" - - -def _get_recording_duration(sorting_analyzer) -> float | None: - """Get total recording duration in seconds.""" - try: - total_samples = sum(sorting_analyzer.get_num_samples(i) for i in range(sorting_analyzer.get_num_segments())) - return total_samples / sorting_analyzer.sampling_frequency - except Exception: - return None - - -def _describe_preprocessing_step(class_name: str, kwargs: dict, detail_level: str) -> str: - """Generate a prose description of a preprocessing step.""" - human_name = PREPROCESSING_NAMES.get(class_name, class_name.replace("Recording", "")) - - # Build description based on the preprocessing type - if "Filter" in class_name: - freq_min = kwargs.get("freq_min") or kwargs.get("band", [None, None])[0] if isinstance(kwargs.get("band"), (list, tuple)) else None - freq_max = kwargs.get("freq_max") or kwargs.get("band", [None, None])[1] if isinstance(kwargs.get("band"), (list, tuple)) else None - order = kwargs.get("filter_order", kwargs.get("order")) - ftype = kwargs.get("ftype", "butterworth") - - if freq_min and freq_max: - desc = f"bandpass filtered ({freq_min}-{freq_max} Hz" - elif freq_min: - desc = f"highpass filtered (>{freq_min} Hz" - elif freq_max: - desc = f"lowpass filtered (<{freq_max} Hz" - else: - desc = f"filtered (" - - if detail_level == "detailed" and order: - desc += f", {order}th order {ftype})" - else: - desc += ")" - return desc - - elif "CommonReference" in class_name: - ref = kwargs.get("reference", "global") - operator = kwargs.get("operator", "median") - if detail_level == "detailed": - return f"re-referenced using {ref} {operator} referencing" - return f"common {operator} referenced" - - elif "Whiten" in class_name: - mode = kwargs.get("mode", "global") - if detail_level == "detailed": - radius = kwargs.get("radius_um") - if radius: - return f"whitened ({mode} mode, {radius} µm radius)" - return "whitened" - - elif "Normalize" in class_name or "ZScore" in class_name: - return "normalized" - - elif "RemoveBadChannels" in class_name or "InterpolateBadChannels" in class_name: - return "with bad channels removed/interpolated" - - elif "Resample" in class_name: - rate = kwargs.get("resample_rate") - if rate: - return f"resampled to {rate} Hz" - return "resampled" - - elif "CorrectMotion" in class_name: - method = kwargs.get("spatial_interpolation_method", "") - if detail_level == "detailed" and method: - return f"motion corrected (using {method} interpolation)" - return "motion corrected" - - elif "PhaseShift" in class_name: - return "phase shift corrected" - - elif "InjectTemplates" in class_name or "NoiseGenerator" in class_name: - # These are used for synthetic/test data generation, not real preprocessing - return None - - else: - return human_name.lower() - - -def _describe_sorter_params(sorter_name: str, params: dict, detail_level: str) -> str: - """Generate a prose description of key sorter parameters.""" - if not params or detail_level == "brief": - return "" - - # Define key parameters for each sorter - key_params_by_sorter = { - "kilosort4": ["Th_universal", "Th_learned", "do_CAR", "batch_size", "nblocks"], - "kilosort3": ["Th", "ThPre", "lam", "AUCsplit", "minFR"], - "kilosort2": ["Th", "ThPre", "lam", "AUCsplit", "minFR"], - "kilosort2_5": ["Th", "ThPre", "lam", "AUCsplit", "minFR"], - "mountainsort5": ["scheme", "detect_threshold", "snippet_T1", "snippet_T2"], - "spykingcircus2": ["detection", "selection", "clustering", "matching"], - "tridesclous2": ["detection", "selection", "clustering"], - } - - sorter_key = sorter_name.lower().replace("-", "").replace("_", "") - key_params = key_params_by_sorter.get(sorter_key, []) - - if detail_level == "standard" and key_params: - # Only describe key parameters in prose - parts = [] - for key in key_params: - if key in params: - parts.append(f"{key}={_format_value(params[key])}") - if parts: - return " (" + ", ".join(parts) + ")" - return "" - elif detail_level == "detailed": - # List all parameters - parts = [f"{k}={_format_value(v)}" for k, v in params.items()] - if parts: - return ". Key parameters: " + ", ".join(parts) - return "" - return "" - - -def _describe_quality_metrics(params: dict, detail_level: str) -> str: - """Generate a prose description of quality metrics computed.""" - metric_names = params.get("metric_names") or params.get("metrics_to_compute", []) - if isinstance(metric_names, (list, tuple)): - if detail_level == "brief": - return "quality metrics" - elif len(metric_names) <= 5 or detail_level == "detailed": - return f"quality metrics ({', '.join(metric_names)})" - else: - return f"quality metrics ({len(metric_names)} metrics including {', '.join(metric_names[:3])}, etc.)" - return "quality metrics" - - -def export_to_methods( - sorting_analyzer, - output_file: str | Path | None = None, - format: str = "markdown", - include_citations: bool = True, - detail_level: str = "detailed", - sorter_name: str | None = None, - sorter_version: str | None = None, - probe_name: str | None = None, - probe_manufacturer: str | None = None, - preprocessing_description: str | None = None, -) -> str: - """ - Generate a methods section describing the spike sorting pipeline. - - This function extracts information from a SortingAnalyzer about the - preprocessing steps, spike sorting parameters, and post-processing - analyses that were performed, and formats them as a methods section - suitable for academic papers. - - Parameters - ---------- - sorting_analyzer : SortingAnalyzer - A SortingAnalyzer object containing the sorting results and metadata - output_file : str | Path | None, default: None - If provided, write the methods section to this file - format : str, default: "markdown" - Output format: "markdown", "latex", or "text" - include_citations : bool, default: True - If True, include citation references at the end - detail_level : str, default: "detailed" - Level of detail: "brief" (just step names), "standard" (key parameters), - or "detailed" (all parameters) - sorter_name : str | None, default: None - Override the sorter name if not available in sorting_info. - Use this when loading sorted data from Phy/Kilosort output directly. - Examples: "Kilosort4", "Kilosort2.5", "MountainSort5", "SpykingCircus2" - sorter_version : str | None, default: None - Override the sorter version if not available in sorting_info. - probe_name : str | None, default: None - Override the probe name if not set in probe annotations. - Examples: "Neuropixels 1.0", "Neuropixels 2.0", "Cambridge NeuroTech H2" - probe_manufacturer : str | None, default: None - Override the probe manufacturer if not set in probe annotations. - Examples: "IMEC", "Cambridge NeuroTech", "NeuroNexus" - preprocessing_description : str | None, default: None - Manual description of preprocessing if not done via SpikeInterface. - Example: "bandpass filtered (300-6000 Hz) and common median referenced" - - Returns - ------- - str - The generated methods section text - - Notes - ----- - For best results, ensure your data has complete metadata: - - - **Probe info**: Set via `recording.set_probe()` with annotations, or use - `probe_name` and `probe_manufacturer` parameters - - **Sorter info**: Automatically captured when using `spikeinterface.sorters.run_sorter()`. - When loading from Phy/Kilosort output directly, use `sorter_name` parameter - - **Preprocessing**: Automatically tracked when using `spikeinterface.preprocessing`. - Otherwise, use `preprocessing_description` parameter - - Examples - -------- - >>> # When sorter info is captured automatically (via run_sorter) - >>> export_to_methods(sorting_analyzer) - - >>> # When loading from Kilosort output directly - >>> export_to_methods( - ... sorting_analyzer, - ... sorter_name="Kilosort4", - ... sorter_version="4.0.1", - ... probe_name="Neuropixels 1.0", - ... probe_manufacturer="IMEC" - ... ) - """ - if format not in ("markdown", "latex", "text"): - raise ValueError(f"format must be 'markdown', 'latex', or 'text', got '{format}'") - if detail_level not in ("brief", "standard", "detailed"): - raise ValueError(f"detail_level must be 'brief', 'standard', or 'detailed', got '{detail_level}'") - - paragraphs = [] - citations_to_include = ["spikeinterface"] - missing_info = [] # Track what info is missing - - si_version = spikeinterface.__version__ - - # === Gather all information first === - fs = sorting_analyzer.sampling_frequency - n_channels = sorting_analyzer.get_num_channels() - duration = _get_recording_duration(sorting_analyzer) - n_units = sorting_analyzer.get_num_units() - - # Get probe description - use override or extract from data - if probe_name: - if probe_manufacturer: - probe_desc = f"{probe_manufacturer} {probe_name}" - else: - probe_desc = probe_name - else: - probe_desc = _get_probe_description(sorting_analyzer) - if probe_desc == "electrode array": - missing_info.append("probe_name") - - # Get preprocessing chain - preprocessing_steps = [] - if sorting_analyzer.has_recording(): - recording = sorting_analyzer.recording - chain = _trace_preprocessing_chain(recording) - preprocessing_steps = [step for step in chain if step["class_name"].endswith("Recording") and step["kwargs"]] - - # Get sorter info - use overrides if provided - sorter_info = _get_sorter_info(sorting_analyzer.sorting) - - # Apply overrides to sorter info - if sorter_name: - if sorter_info is None: - sorter_info = {"sorter_name": sorter_name, "sorter_version": sorter_version or "", "sorter_params": {}, "run_time": None} - else: - sorter_info["sorter_name"] = sorter_name - if sorter_version: - sorter_info["sorter_version"] = sorter_version - elif sorter_info is None: - missing_info.append("sorter_name") - - # Get extensions - if sorting_analyzer.format == "memory": - extensions = sorting_analyzer.get_loaded_extension_names() - else: - extensions = sorting_analyzer.get_saved_extension_names() - - # Check for quality/template metrics for Bombcell citation - has_quality_metrics = "quality_metrics" in extensions or "template_metrics" in extensions - if has_quality_metrics: - citations_to_include.append("bombcell") - - # === Build the methods section as prose === - - # Title/Header - if format == "markdown": - paragraphs.append("## Spike Sorting Methods\n") - elif format == "latex": - paragraphs.append("\\section{Spike Sorting Methods}\n") - else: - paragraphs.append("SPIKE SORTING METHODS\n") - - # === First paragraph: Data acquisition and preprocessing === - para1_parts = [] - - # Data acquisition sentence - acq_sentence = f"Extracellular recordings were acquired at {fs:.0f} Hz using a {probe_desc} ({n_channels} channels" - if duration is not None: - if duration >= 60: - acq_sentence += f", {duration/60:.1f} minutes of data" - else: - acq_sentence += f", {duration:.1f} seconds of data" - acq_sentence += ")." - para1_parts.append(acq_sentence) - - # Preprocessing sentence(s) - use manual description if provided - if preprocessing_description: - para1_parts.append(f"Raw voltage traces were {preprocessing_description}.") - elif preprocessing_steps: - prep_descriptions = [] - for step in preprocessing_steps: - desc = _describe_preprocessing_step(step["class_name"], step["kwargs"], detail_level) - if desc: - prep_descriptions.append(desc) - - if prep_descriptions: - if len(prep_descriptions) == 1: - prep_sentence = f"Raw voltage traces were {prep_descriptions[0]}." - elif len(prep_descriptions) == 2: - prep_sentence = f"Raw voltage traces were {prep_descriptions[0]} and {prep_descriptions[1]}." - else: - prep_sentence = f"Raw voltage traces were {', '.join(prep_descriptions[:-1])}, and {prep_descriptions[-1]}." - para1_parts.append(prep_sentence) - else: - missing_info.append("preprocessing") - else: - missing_info.append("preprocessing") - - paragraphs.append(" ".join(para1_parts)) - paragraphs.append("") - - # === Second paragraph: Spike sorting === - para2_parts = [] - - if sorter_info: - sorter_name = sorter_info["sorter_name"] - sorter_version = sorter_info["sorter_version"] - sorter_params = sorter_info["sorter_params"] - - # Add citation for this sorter - sorter_key = sorter_name.lower().replace("-", "").replace("_", "") - if sorter_key in CITATIONS: - citations_to_include.append(sorter_key) - - # Build sorter description - if format == "markdown": - sort_sentence = f"Spike sorting was performed using **{sorter_name}**" - elif format == "latex": - sort_sentence = f"Spike sorting was performed using \\textbf{{{sorter_name}}}" - else: - sort_sentence = f"Spike sorting was performed using {sorter_name}" - - if sorter_version and sorter_version != "Unknown": - sort_sentence += f" (version {sorter_version})" - - # Add parameter description - param_desc = _describe_sorter_params(sorter_name, sorter_params, detail_level) - sort_sentence += param_desc - - if not sort_sentence.endswith("."): - sort_sentence += "." - para2_parts.append(sort_sentence) - - # Add runtime info if available - if detail_level == "detailed" and sorter_info.get("run_time") is not None: - run_time = sorter_info["run_time"] - if run_time >= 60: - para2_parts.append(f"Sorting completed in {run_time/60:.1f} minutes.") - else: - para2_parts.append(f"Sorting completed in {run_time:.1f} seconds.") - else: - para2_parts.append("Spike sorting was performed (sorter parameters not recorded).") - - # Add unit count - para2_parts.append(f"A total of {n_units} units were identified.") - - paragraphs.append(" ".join(para2_parts)) - paragraphs.append("") - - # === Third paragraph: Post-processing and quality control === - if extensions: - para3_parts = [] - - # Categorize extensions - waveform_exts = [e for e in extensions if e in ("waveforms", "templates", "random_spikes")] - location_exts = [e for e in extensions if "location" in e] - metric_exts = [e for e in extensions if "metric" in e] - other_exts = [e for e in extensions if e not in waveform_exts + location_exts + metric_exts] - - # Waveforms and templates - if waveform_exts: - wf_ext = sorting_analyzer.get_extension("waveforms") - if wf_ext: - ms_before = wf_ext.params.get("ms_before", 1) - ms_after = wf_ext.params.get("ms_after", 2) - para3_parts.append(f"Spike waveforms were extracted ({ms_before} ms before to {ms_after} ms after each spike) and averaged to compute unit templates.") - - # Quality metrics - if "quality_metrics" in extensions: - qm_ext = sorting_analyzer.get_extension("quality_metrics") - if qm_ext: - qm_desc = _describe_quality_metrics(qm_ext.params, detail_level) - para3_parts.append(f"Unit {qm_desc} were computed to assess sorting quality.") - - if "template_metrics" in extensions: - para3_parts.append("Template-based metrics were computed for each unit.") - - # Locations - if "unit_locations" in extensions: - loc_ext = sorting_analyzer.get_extension("unit_locations") - method = loc_ext.params.get("method", "center_of_mass") if loc_ext else "center_of_mass" - para3_parts.append(f"Unit locations were estimated using the {method.replace('_', ' ')} method.") - - # Other notable extensions - if "principal_components" in extensions: - pc_ext = sorting_analyzer.get_extension("principal_components") - if pc_ext and detail_level == "detailed": - n_comp = pc_ext.params.get("n_components", 5) - para3_parts.append(f"Principal component analysis was performed ({n_comp} components).") - - if "correlograms" in extensions: - para3_parts.append("Auto- and cross-correlograms were computed.") - - if "spike_amplitudes" in extensions: - para3_parts.append("Spike amplitudes were extracted for each spike.") - - if para3_parts: - paragraphs.append(" ".join(para3_parts)) - paragraphs.append("") - - # === Software attribution paragraph === - software_para = f"All spike sorting and analysis was performed using SpikeInterface version {si_version}" - if has_quality_metrics: - software_para += ", with quality metrics following the Bombcell framework" - software_para += "." - paragraphs.append(software_para) - paragraphs.append("") - - # === Missing Info Warning === - if missing_info: - paragraphs.append("") - if format == "markdown": - paragraphs.append("---") - paragraphs.append("**Note**: Some information could not be extracted automatically and should be added manually:") - for info in missing_info: - if info == "probe_name": - paragraphs.append("- Probe type/name (use `probe_name` parameter)") - elif info == "sorter_name": - paragraphs.append("- Spike sorter name and version (use `sorter_name` and `sorter_version` parameters)") - elif info == "preprocessing": - paragraphs.append("- Preprocessing steps (use `preprocessing_description` parameter)") - paragraphs.append("") - elif format == "latex": - paragraphs.append("\\textit{Note: Some information could not be extracted automatically. See function documentation for how to specify missing metadata.}") - paragraphs.append("") - else: - paragraphs.append("NOTE: Missing information that should be added manually:") - for info in missing_info: - if info == "probe_name": - paragraphs.append(" - Probe type/name") - elif info == "sorter_name": - paragraphs.append(" - Spike sorter name and version") - elif info == "preprocessing": - paragraphs.append(" - Preprocessing steps") - paragraphs.append("") - - # === Citations Section === - if include_citations: - if format == "markdown": - paragraphs.append("### References\n") - elif format == "latex": - paragraphs.append("\\subsection*{References}\n") - else: - paragraphs.append("References\n") - - # Remove duplicates while preserving order - seen = set() - unique_citations = [] - for c in citations_to_include: - if c not in seen: - seen.add(c) - unique_citations.append(c) - - for citation_key in unique_citations: - if citation_key in CITATIONS: - citation = CITATIONS[citation_key] - if format == "markdown": - paragraphs.append(f"- {citation}\n") - elif format == "latex": - paragraphs.append(f"\\bibitem{{{citation_key}}} {citation}\n") - else: - paragraphs.append(f"- {citation}\n") - - # Join all paragraphs - result = "\n".join(paragraphs) - - # Write to file if requested - if output_file is not None: - output_path = Path(output_file) - output_path.parent.mkdir(parents=True, exist_ok=True) - output_path.write_text(result, encoding="utf-8") - - return result diff --git a/src/spikeinterface/metrics/quality/misc_metrics.py b/src/spikeinterface/metrics/quality/misc_metrics.py index 8049d4f173..7e78cb54e9 100644 --- a/src/spikeinterface/metrics/quality/misc_metrics.py +++ b/src/spikeinterface/metrics/quality/misc_metrics.py @@ -182,14 +182,14 @@ class SNR(BaseMetric): depend_on = ["noise_levels", "templates"] -def compute_snrs_bombcell( +def compute_snrs_versus_baseline( sorting_analyzer, unit_ids=None, peak_sign: str = "neg", baseline_window_ms: float = 0.5, ): """ - Compute signal to noise ratio using BombCell method. + Compute signal to noise ratio versus baseline. This differs from the standard SNR by using: - Signal: Max absolute value of the median waveform on peak channel @@ -213,15 +213,19 @@ def compute_snrs_bombcell( Notes ----- - This implementation follows the BombCell methodology: + This implementation follows the bombcell methodology [1]: - Signal is the maximum absolute amplitude of the median waveform on the peak channel - Noise is computed as MAD of baseline samples (first N samples of each waveform) Requires the "waveforms" extension to be computed. + + References + ---------- + [1] https://github.com/Julie-Fabre/bombcell """ if not sorting_analyzer.has_extension("waveforms"): raise ValueError( - "The 'waveforms' extension is required for compute_snrs_bombcell. " + "The 'waveforms' extension is required for compute_snrs_versus_baseline. " "Please compute it first with: analyzer.compute('waveforms')" ) @@ -280,13 +284,13 @@ def compute_snrs_bombcell( return snrs -class SNRBombcell(BaseMetric): - metric_name = "snr_bombcell" - metric_function = compute_snrs_bombcell +class SNRBaseline(BaseMetric): + metric_name = "snr_baseline" + metric_function = compute_snrs_versus_baseline metric_params = {"peak_sign": "neg", "baseline_window_ms": 0.5} - metric_columns = {"snr_bombcell": float} + metric_columns = {"snr_baseline": float} metric_descriptions = { - "snr_bombcell": "Signal to noise ratio using BombCell method (median waveform max / baseline MAD)." + "snr_baseline": "Signal to noise ratio versus baseline (median waveform max / baseline MAD). Based on bombcell." } depend_on = ["waveforms", "templates"] @@ -1434,7 +1438,7 @@ class SDRatio(BaseMetric): FiringRate, PresenceRatio, SNR, - SNRBombcell, + SNRBaseline, ISIViolation, RPViolation, SlidingRPViolation, @@ -1633,7 +1637,7 @@ def amplitude_cutoff( fraction_missing = np.sum(pdf[G:]) * bin_size fraction_missing = np.min([fraction_missing, 0.5]) - # Plot details for debugging (similar to MATLAB BombCell) + # Plot details for debugging (similar to MATLAB bombcell) if plot_details: import matplotlib.pyplot as plt @@ -1646,7 +1650,7 @@ def amplitude_cutoff( else: created_figure = False - # Colors matching MATLAB BombCell style + # Colors matching MATLAB bombcell style main_color = [0, 0.35, 0.71] # Blue cutoff_color = [0.5430, 0, 0.5430] # Purple fit_color = "red" diff --git a/src/spikeinterface/widgets/bombcell_curation.py b/src/spikeinterface/widgets/bombcell_curation.py index 3f4cad6296..eed6aee5b4 100644 --- a/src/spikeinterface/widgets/bombcell_curation.py +++ b/src/spikeinterface/widgets/bombcell_curation.py @@ -240,7 +240,7 @@ class UpsetPlotWidget(BaseWidget): ] SPIKE_QUALITY_METRICS = [ "amplitude_median", - "snr_bombcell", + "snr_baseline", "amplitude_cutoff", "num_spikes", "rp_contamination", From 3b1d819290a8d6beec5099c38b65a7b6d8bb2033 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 22:39:00 -0500 Subject: [PATCH 34/49] removed debugging plots --- .../metrics/quality/misc_metrics.py | 140 ------------------ 1 file changed, 140 deletions(-) diff --git a/src/spikeinterface/metrics/quality/misc_metrics.py b/src/spikeinterface/metrics/quality/misc_metrics.py index 7e78cb54e9..4320845bfd 100644 --- a/src/spikeinterface/metrics/quality/misc_metrics.py +++ b/src/spikeinterface/metrics/quality/misc_metrics.py @@ -865,7 +865,6 @@ def compute_amplitude_cutoffs( num_histogram_bins=500, histogram_smoothing_value=3, amplitudes_bins_min_ratio=5, - plot_details=False, ): """ Calculate approximate fraction of spikes missing from a distribution of amplitudes. @@ -884,9 +883,6 @@ def compute_amplitude_cutoffs( The minimum ratio between number of amplitudes for a unit and the number of bins. If the ratio is less than this threshold, the amplitude_cutoff for the unit is set to NaN. - plot_details : bool, default: True - If True, generate diagnostic plots for each unit showing amplitude histogram - and gaussian fit. Hardcoded ON for debugging. Returns ------- @@ -924,38 +920,16 @@ def compute_amplitude_cutoffs( amplitudes_by_units = extension.get_data(outputs="by_unit", concatenated=True) - # Get spike times for scatter plots if plot_details is enabled - spike_times_by_units = None - if plot_details: - sorting = sorting_analyzer.sorting - fs = sorting_analyzer.sampling_frequency - # Get spike times by unit (concatenated across segments) - spike_times_by_units = {} - for unit_id in unit_ids: - all_spike_times = [] - time_offset = 0.0 - for seg_idx in range(sorting_analyzer.get_num_segments()): - spike_train = sorting.get_unit_spike_train(unit_id=unit_id, segment_index=seg_idx) - spike_times_s = spike_train / fs + time_offset - all_spike_times.append(spike_times_s) - time_offset += sorting_analyzer.get_num_samples(seg_idx) / fs - spike_times_by_units[unit_id] = np.concatenate(all_spike_times) if all_spike_times else np.array([]) - for unit_id in unit_ids: amplitudes = amplitudes_by_units[unit_id] if invert_amplitudes: amplitudes = -amplitudes - spike_times = spike_times_by_units[unit_id] if spike_times_by_units is not None else None - all_fraction_missing[unit_id] = amplitude_cutoff( amplitudes, num_histogram_bins, histogram_smoothing_value, amplitudes_bins_min_ratio, - spike_times=spike_times, - unit_id=unit_id, - plot_details=plot_details, ) if np.any(np.isnan(list(all_fraction_missing.values()))): @@ -971,7 +945,6 @@ class AmplitudeCutoff(BaseMetric): "num_histogram_bins": 100, "histogram_smoothing_value": 3, "amplitudes_bins_min_ratio": 5, - "plot_details": False, } metric_columns = {"amplitude_cutoff": float} metric_descriptions = { @@ -1570,11 +1543,6 @@ def amplitude_cutoff( num_histogram_bins=500, histogram_smoothing_value=3, amplitudes_bins_min_ratio=5, - spike_times=None, - unit_id=None, - plot_details=False, - ax_scatter=None, - ax_hist=None, ): """ Calculate approximate fraction of spikes missing from a distribution of amplitudes. @@ -1593,18 +1561,6 @@ def amplitude_cutoff( The minimum ratio between number of amplitudes for a unit and the number of bins. If the ratio is less than this threshold, the amplitude_cutoff for the unit is set to NaN. - spike_times : ndarray_like or None, default: None - The spike times (in seconds) for this unit. Used for plotting scatter plot. - unit_id : any, default: None - The unit ID for labeling plots. - plot_details : bool, default: True - If True, generate diagnostic plots showing amplitude histogram and gaussian fit. - Hardcoded ON for debugging. - ax_scatter : matplotlib axis or None, default: None - Axis for scatter plot (spike times vs amplitudes). If None and plot_details=True, - a new figure is created. - ax_hist : matplotlib axis or None, default: None - Axis for histogram plot. If None and plot_details=True, uses same figure. Returns ------- @@ -1637,102 +1593,6 @@ def amplitude_cutoff( fraction_missing = np.sum(pdf[G:]) * bin_size fraction_missing = np.min([fraction_missing, 0.5]) - # Plot details for debugging (similar to MATLAB bombcell) - if plot_details: - import matplotlib.pyplot as plt - - # Create figure if no axes provided - if ax_scatter is None and ax_hist is None: - fig, axes = plt.subplots(1, 2, figsize=(12, 5)) - ax_scatter = axes[0] - ax_hist = axes[1] - created_figure = True - else: - created_figure = False - - # Colors matching MATLAB bombcell style - main_color = [0, 0.35, 0.71] # Blue - cutoff_color = [0.5430, 0, 0.5430] # Purple - fit_color = "red" - - # Plot 1: Scatter plot of spike times vs amplitudes (if spike_times provided) - if ax_scatter is not None and spike_times is not None: - ax_scatter.scatter(spike_times, amplitudes, s=4, c=[main_color], alpha=0.5) - - # Add outlier threshold line (using IQR method like MATLAB) - q1, q3 = np.percentile(amplitudes, [25, 75]) - iqr = q3 - q1 - iqr_threshold = 4 # Same as MATLAB default - outlier_line = q3 + iqr_threshold * iqr - - ylims = ax_scatter.get_ylim() - xlims = ax_scatter.get_xlim() - - ax_scatter.axhline(outlier_line, color=cutoff_color, linewidth=1.5) - ax_scatter.text( - xlims[1] * 0.98, - outlier_line * 1.02, - "Outlier Threshold", - ha="right", - va="bottom", - color=cutoff_color, - fontweight="bold", - fontsize=8, - ) - - ax_scatter.set_xlabel("Time (s)") - ax_scatter.set_ylabel("Amplitude scaling factor") - title_str = f"Unit {unit_id}" if unit_id is not None else "Amplitudes over time" - ax_scatter.set_title(title_str) - ax_scatter.spines["top"].set_visible(False) - ax_scatter.spines["right"].set_visible(False) - - elif ax_scatter is not None: - ax_scatter.text( - 0.5, - 0.5, - "Spike times not provided", - ha="center", - va="center", - transform=ax_scatter.transAxes, - ) - ax_scatter.set_title("Scatter plot requires spike_times") - - # Plot 2: Histogram with gaussian fit - if ax_hist is not None: - # Plot histogram as horizontal bars (like MATLAB) - bin_centers = (b[:-1] + b[1:]) / 2 - ax_hist.barh(bin_centers, h, height=bin_size * 0.9, color=main_color, alpha=0.7, label="Histogram") - - # Plot smoothed PDF (gaussian fit) - ax_hist.plot(pdf, support, color=fit_color, linewidth=2, label="Smoothed PDF") - - # Mark the cutoff point G - cutoff_amplitude = support[G] - ax_hist.axhline(cutoff_amplitude, color=cutoff_color, linestyle="--", linewidth=1.5, label="Cutoff") - - # Mark the peak - peak_amplitude = support[peak_index] - ax_hist.axhline(peak_amplitude, color="green", linestyle=":", linewidth=1.5, label="Peak") - - ax_hist.set_xlabel("Density") - ax_hist.set_ylabel("Amplitude") - - # Add percent missing text - rounded_p = f"{fraction_missing * 100:.1f}%" - title_str = f"% missing spikes: {rounded_p}" - if unit_id is not None: - title_str = f"Unit {unit_id}\n{title_str}" - ax_hist.set_title(title_str, color=[0.7, 0.7, 0.7]) - - ax_hist.legend(loc="upper right", fontsize=8) - ax_hist.spines["top"].set_visible(False) - ax_hist.spines["right"].set_visible(False) - - if created_figure: - plt.tight_layout() - plt.show() - return fraction_missing From 02c9a536403669fffd9a6db03b17621c5446c05a Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 22:48:15 -0500 Subject: [PATCH 35/49] imports to individual functions --- src/spikeinterface/metrics/template/metrics.py | 12 ++++++++++-- .../metrics/template/template_metrics.py | 11 ++++++++--- 2 files changed, 18 insertions(+), 5 deletions(-) diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index 53148fac85..71193f7022 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -1,8 +1,6 @@ from __future__ import annotations import numpy as np -from collections import namedtuple -from scipy.signal import find_peaks, savgol_filter from spikeinterface.core.analyzer_extension_core import BaseMetric @@ -53,6 +51,8 @@ def get_trough_and_peak_idx( - "main_idx": index of the main peak (most prominent) - "main_loc": location (sample index) of the main peak in template """ + from scipy.signal import find_peaks, savgol_filter + assert template.ndim == 1 # Save original for plotting @@ -1166,6 +1166,8 @@ def _recovery_slope_metric_function(sorting_analyzer, unit_ids, tmp_data, **metr def _number_of_peaks_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): + from collections import namedtuple + num_peaks_result = namedtuple("NumberOfPeaksResult", ["num_positive_peaks", "num_negative_peaks"]) num_positive_peaks_dict = {} num_negative_peaks_dict = {} @@ -1234,6 +1236,8 @@ class WaveformDuration(BaseMetric): def _waveform_ratios_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): + from collections import namedtuple + waveform_ratios_result = namedtuple( "WaveformRatiosResult", [ @@ -1292,6 +1296,8 @@ class WaveformRatios(BaseMetric): def _waveform_widths_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): + from collections import namedtuple + waveform_widths_result = namedtuple( "WaveformWidthsResult", ["trough_width", "peak_before_width", "peak_after_width"] ) @@ -1374,6 +1380,8 @@ class WaveformBaselineFlatness(BaseMetric): def _get_velocity_fits_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): + from collections import namedtuple + velocity_above_result = namedtuple("Velocities", ["velocity_above", "velocity_below"]) velocity_above_dict = {} velocity_below_dict = {} diff --git a/src/spikeinterface/metrics/template/template_metrics.py b/src/spikeinterface/metrics/template/template_metrics.py index f00e870c30..a4528261b0 100644 --- a/src/spikeinterface/metrics/template/template_metrics.py +++ b/src/spikeinterface/metrics/template/template_metrics.py @@ -7,9 +7,6 @@ from __future__ import annotations import numpy as np -import warnings -from copy import deepcopy -from scipy.signal import find_peaks from spikeinterface.core.sortinganalyzer import register_result_extension from spikeinterface.core.analyzer_extension_core import BaseMetricExtension @@ -35,6 +32,8 @@ def get_template_metric_list(): def get_template_metric_names(): + import warnings + warnings.warn( "get_template_metric_names is deprecated and will be removed in a version 0.105.0. " "Please use get_template_metric_list instead.", @@ -97,6 +96,8 @@ class ComputeTemplateMetrics(BaseMetricExtension): metric_list = single_channel_metrics + multi_channel_metrics def _handle_backward_compatibility_on_load(self): + from copy import deepcopy + # For backwards compatibility - this reformats metrics_kwargs as metric_params if (metrics_kwargs := self.params.get("metrics_kwargs")) is not None: @@ -189,6 +190,8 @@ def _set_params( ) def _prepare_data(self, sorting_analyzer, unit_ids): + import warnings + from scipy.signal import resample_poly # compute templates_single and templates_multi (if include_multi_channel_metrics is True) @@ -310,6 +313,8 @@ def get_default_tm_params(metric_names=None): metric_params : dict Dictionary with default parameters for template metrics. """ + import warnings + warnings.warn( "get_default_tm_params is deprecated and will be removed in a version 0.105.0. " "Please use get_default_template_metrics_params instead.", From 96d8bb6c5ef6cc846ec1edd8fc60af9443b75bed Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 22:50:32 -0500 Subject: [PATCH 36/49] remove more debugging plots --- .../metrics/template/metrics.py | 91 ------------------- 1 file changed, 91 deletions(-) diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index 71193f7022..89b20971a3 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -55,9 +55,6 @@ def get_trough_and_peak_idx( assert template.ndim == 1 - # Save original for plotting - template_original = template.copy() - # Smooth template to reduce noise while preserving peaks using Savitzky-Golay filter if smooth: window_length = int(len(template) * smooth_window_frac) // 2 * 2 + 1 @@ -194,94 +191,6 @@ def get_trough_and_peak_idx( else: peaks_after = empty_dict.copy() - # Quick visualization (set to True for debugging) - _plot = False # QQ set to false - if _plot: - import matplotlib.pyplot as plt - - # Old simple method for comparison (argmin/argmax) - old_trough_idx = np.nanargmin(template) - old_peak_idx = np.nanargmax(template[old_trough_idx:]) + old_trough_idx - - fig, ax = plt.subplots(figsize=(10, 5)) - ax.plot(template_original, color="lightgray", lw=1, label="original (noisy)") - ax.plot(template, "k-", lw=1.5, label="smoothed") - - # Plot old method (simple argmin/argmax) - ax.axvline(old_trough_idx, color="gray", ls="--", alpha=0.5, label="old trough (argmin)") - ax.axvline(old_peak_idx, color="gray", ls=":", alpha=0.5, label="old peak (argmax after trough)") - - # Plot all detected troughs - ax.scatter(troughs["indices"], troughs["values"], c="blue", s=50, marker="v", zorder=5, label="troughs") - if troughs["main_loc"] is not None: - ax.scatter( - troughs["main_loc"], - template[troughs["main_loc"]], - c="blue", - s=150, - marker="v", - edgecolors="red", - linewidths=2, - zorder=6, - label="main trough", - ) - - # Plot all peaks before - if len(peaks_before["indices"]) > 0: - ax.scatter( - peaks_before["indices"], - peaks_before["values"], - c="green", - s=50, - marker="^", - zorder=5, - label="peaks before", - ) - if peaks_before["main_loc"] is not None: - ax.scatter( - peaks_before["main_loc"], - template[peaks_before["main_loc"]], - c="green", - s=150, - marker="^", - edgecolors="red", - linewidths=2, - zorder=6, - label="main peak before", - ) - - # Plot all peaks after - if len(peaks_after["indices"]) > 0: - ax.scatter( - peaks_after["indices"], - peaks_after["values"], - c="orange", - s=50, - marker="^", - zorder=5, - label="peaks after", - ) - if peaks_after["main_loc"] is not None: - ax.scatter( - peaks_after["main_loc"], - template[peaks_after["main_loc"]], - c="orange", - s=150, - marker="^", - edgecolors="red", - linewidths=2, - zorder=6, - label="main peak after", - ) - - ax.axhline(0, color="gray", ls="-", alpha=0.3) - ax.set_xlabel("Sample") - ax.set_ylabel("Amplitude") - ax.legend(loc="best", fontsize=8) - ax.set_title(f"Trough/Peak Detection (prominence threshold: {min_thresh_detect_peaks_troughs})") - plt.tight_layout() - plt.show() - return troughs, peaks_before, peaks_after From 0e7fb401321bdf0e88553035e5f2a62866fa8c91 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 23:00:59 -0500 Subject: [PATCH 37/49] rename waveform_duration --- .../metrics/template/metrics.py | 28 +++++++++---------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index 89b20971a3..74f03699c2 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -194,9 +194,9 @@ def get_trough_and_peak_idx( return troughs, peaks_before, peaks_after -def get_waveform_duration(template, sampling_frequency, troughs, peaks_before, peaks_after, **kwargs): +def get_main_to_next_peak_duration(template, sampling_frequency, troughs, peaks_before, peaks_after, **kwargs): """ - Calculate waveform duration from the main extremum to the next extremum. + Calculate duration from the main extremum to the next extremum. The duration is measured from the largest absolute feature (main trough or main peak) to the next extremum. For typical negative-first waveforms, this is trough-to-peak. @@ -217,8 +217,8 @@ def get_waveform_duration(template, sampling_frequency, troughs, peaks_before, p Returns ------- - waveform_duration_us : float - Waveform duration in microseconds + main_to_next_peak_duration_us : float + Duration in microseconds from main extremum to next extremum """ # Get main locations and values @@ -270,9 +270,9 @@ def get_waveform_duration(template, sampling_frequency, troughs, peaks_before, p return np.nan # Convert to microseconds - waveform_duration_us = (duration_samples / sampling_frequency) * 1e6 + main_to_next_peak_duration_us = (duration_samples / sampling_frequency) * 1e6 - return waveform_duration_us + return main_to_next_peak_duration_us def get_waveform_ratios(template, troughs, peaks_before, peaks_after, **kwargs): @@ -1112,7 +1112,7 @@ class NumberOfPeaks(BaseMetric): needs_tmp_data = True -def _waveform_duration_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): +def _main_to_next_peak_duration_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_params): result = {} templates_single = tmp_data["templates_single"] troughs_info = tmp_data["troughs_info"] @@ -1121,7 +1121,7 @@ def _waveform_duration_metric_function(sorting_analyzer, unit_ids, tmp_data, **m sampling_frequency = tmp_data["sampling_frequency"] for unit_index, unit_id in enumerate(unit_ids): template_single = templates_single[unit_index] - value = get_waveform_duration( + value = get_main_to_next_peak_duration( template_single, sampling_frequency, troughs_info[unit_id], @@ -1133,13 +1133,13 @@ def _waveform_duration_metric_function(sorting_analyzer, unit_ids, tmp_data, **m return result -class WaveformDuration(BaseMetric): - metric_name = "waveform_duration" - metric_function = _waveform_duration_metric_function +class MainToNextPeakDuration(BaseMetric): + metric_name = "main_to_next_peak_duration" + metric_function = _main_to_next_peak_duration_metric_function metric_params = {} - metric_columns = {"waveform_duration": float} + metric_columns = {"main_to_next_peak_duration": float} metric_descriptions = { - "waveform_duration": "Waveform duration in microseconds from main extremum to next extremum." + "main_to_next_peak_duration": "Duration in microseconds from main extremum to next extremum." } needs_tmp_data = True @@ -1281,7 +1281,7 @@ class WaveformBaselineFlatness(BaseMetric): RepolarizationSlope, RecoverySlope, NumberOfPeaks, - WaveformDuration, + MainToNextPeakDuration, WaveformRatios, WaveformWidths, WaveformBaselineFlatness, From a5ab4a613144fc296aff953fd76ec595aa3ad15d Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 23:04:31 -0500 Subject: [PATCH 38/49] more combine -> sorting_analyzer --- .../widgets/bombcell_curation.py | 152 +++++------------- 1 file changed, 41 insertions(+), 111 deletions(-) diff --git a/src/spikeinterface/widgets/bombcell_curation.py b/src/spikeinterface/widgets/bombcell_curation.py index eed6aee5b4..ad84a054d6 100644 --- a/src/spikeinterface/widgets/bombcell_curation.py +++ b/src/spikeinterface/widgets/bombcell_curation.py @@ -8,17 +8,6 @@ from .base import BaseWidget, to_attr -def _combine_metrics(quality_metrics, template_metrics): - """Combine quality_metrics and template_metrics into a single DataFrame.""" - if quality_metrics is None and template_metrics is None: - return None - if quality_metrics is None: - return template_metrics - if template_metrics is None: - return quality_metrics - return quality_metrics.join(template_metrics, how="outer") - - def _is_threshold_disabled(value): """Check if a threshold value is disabled (None or np.nan).""" if value is None: @@ -33,29 +22,21 @@ class LabelingHistogramsWidget(BaseWidget): def __init__( self, - sorting_analyzer=None, + sorting_analyzer, thresholds: Optional[dict] = None, metrics_to_plot: Optional[list] = None, - quality_metrics=None, - template_metrics=None, backend=None, **backend_kwargs, ): from spikeinterface.curation import bombcell_get_default_thresholds - if sorting_analyzer is not None: - combined_metrics = sorting_analyzer.get_metrics_extension_data() - if combined_metrics.empty: - raise ValueError( - "SortingAnalyzer has no metrics extensions computed. " - "Compute quality_metrics and/or template_metrics first." - ) - else: - combined_metrics = _combine_metrics(quality_metrics, template_metrics) - if combined_metrics is None: - raise ValueError( - "Either sorting_analyzer or at least one of quality_metrics/template_metrics must be provided" - ) + sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) + combined_metrics = sorting_analyzer.get_metrics_extension_data() + if combined_metrics.empty: + raise ValueError( + "SortingAnalyzer has no metrics extensions computed. " + "Compute quality_metrics and/or template_metrics first." + ) if thresholds is None: thresholds = bombcell_get_default_thresholds() @@ -257,33 +238,25 @@ class UpsetPlotWidget(BaseWidget): def __init__( self, + sorting_analyzer, unit_type: np.ndarray, unit_type_string: np.ndarray, - sorting_analyzer=None, thresholds: Optional[dict] = None, unit_types_to_plot: Optional[list] = None, split_non_somatic: bool = False, min_subset_size: int = 1, - quality_metrics=None, - template_metrics=None, backend=None, **backend_kwargs, ): from spikeinterface.curation import bombcell_get_default_thresholds - if sorting_analyzer is not None: - combined_metrics = sorting_analyzer.get_metrics_extension_data() - if combined_metrics.empty: - raise ValueError( - "SortingAnalyzer has no metrics extensions computed. " - "Compute quality_metrics and/or template_metrics first." - ) - else: - combined_metrics = _combine_metrics(quality_metrics, template_metrics) - if combined_metrics is None: - raise ValueError( - "Either sorting_analyzer or at least one of quality_metrics/template_metrics must be provided" - ) + sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) + combined_metrics = sorting_analyzer.get_metrics_extension_data() + if combined_metrics.empty: + raise ValueError( + "SortingAnalyzer has no metrics extensions computed. " + "Compute quality_metrics and/or template_metrics first." + ) if thresholds is None: thresholds = bombcell_get_default_thresholds() @@ -431,21 +404,17 @@ def _build_failure_table(self, quality_metrics, thresholds): # Convenience functions def plot_labeling_histograms( - sorting_analyzer=None, + sorting_analyzer, thresholds=None, metrics_to_plot=None, - quality_metrics=None, - template_metrics=None, backend=None, **kwargs, ): """Plot histograms of quality metrics with threshold lines.""" return LabelingHistogramsWidget( - sorting_analyzer=sorting_analyzer, + sorting_analyzer, thresholds=thresholds, metrics_to_plot=metrics_to_plot, - quality_metrics=quality_metrics, - template_metrics=template_metrics, backend=backend, **kwargs, ) @@ -461,29 +430,25 @@ def plot_waveform_overlay( def plot_upset( + sorting_analyzer, unit_type, unit_type_string, - sorting_analyzer=None, thresholds=None, unit_types_to_plot=None, split_non_somatic=False, min_subset_size=1, - quality_metrics=None, - template_metrics=None, backend=None, **kwargs, ): """Plot UpSet plots showing which metrics fail together for each unit type.""" return UpsetPlotWidget( + sorting_analyzer, unit_type, unit_type_string, - sorting_analyzer=sorting_analyzer, thresholds=thresholds, unit_types_to_plot=unit_types_to_plot, split_non_somatic=split_non_somatic, min_subset_size=min_subset_size, - quality_metrics=quality_metrics, - template_metrics=template_metrics, backend=backend, **kwargs, ) @@ -493,8 +458,6 @@ def plot_unit_labeling_all( sorting_analyzer, unit_type: np.ndarray, unit_type_string: np.ndarray, - quality_metrics=None, - template_metrics=None, thresholds: Optional[dict] = None, split_non_somatic: bool = False, include_upset: bool = True, @@ -508,15 +471,11 @@ def plot_unit_labeling_all( Parameters ---------- sorting_analyzer : SortingAnalyzer - The sorting analyzer object. + The sorting analyzer object with computed metrics extensions. unit_type : np.ndarray Array of unit type codes (0=NOISE, 1=GOOD, 2=MUA, 3=NON_SOMA, etc.). unit_type_string : np.ndarray Array of unit type labels as strings. - quality_metrics : pd.DataFrame, optional - Quality metrics DataFrame. If None, loads from sorting_analyzer. - template_metrics : pd.DataFrame, optional - Template metrics DataFrame. If None, loads from sorting_analyzer. thresholds : dict, optional Threshold dictionary. If None, uses default thresholds. split_non_somatic : bool, default: False @@ -541,36 +500,19 @@ def plot_unit_labeling_all( if thresholds is None: thresholds = bombcell_get_default_thresholds() - # Use sorting_analyzer directly if no explicit metrics provided - use_analyzer = quality_metrics is None and template_metrics is None - - # Get combined metrics for checking and saving - if use_analyzer: - combined_metrics = sorting_analyzer.get_metrics_extension_data() - if combined_metrics.empty: - combined_metrics = None - else: - combined_metrics = _combine_metrics(quality_metrics, template_metrics) + combined_metrics = sorting_analyzer.get_metrics_extension_data() + has_metrics = not combined_metrics.empty results = {} # Histograms - if combined_metrics is not None: - if use_analyzer: - results["histograms"] = plot_labeling_histograms( - sorting_analyzer=sorting_analyzer, - thresholds=thresholds, - backend=backend, - **kwargs, - ) - else: - results["histograms"] = plot_labeling_histograms( - quality_metrics=quality_metrics, - template_metrics=template_metrics, - thresholds=thresholds, - backend=backend, - **kwargs, - ) + if has_metrics: + results["histograms"] = plot_labeling_histograms( + sorting_analyzer, + thresholds=thresholds, + backend=backend, + **kwargs, + ) # Waveform overlay results["waveforms"] = plot_waveform_overlay( @@ -578,28 +520,16 @@ def plot_unit_labeling_all( ) # UpSet plots - if include_upset and combined_metrics is not None: - if use_analyzer: - results["upset"] = plot_upset( - unit_type, - unit_type_string, - sorting_analyzer=sorting_analyzer, - thresholds=thresholds, - split_non_somatic=split_non_somatic, - backend=backend, - **kwargs, - ) - else: - results["upset"] = plot_upset( - unit_type, - unit_type_string, - quality_metrics=quality_metrics, - template_metrics=template_metrics, - thresholds=thresholds, - split_non_somatic=split_non_somatic, - backend=backend, - **kwargs, - ) + if include_upset and has_metrics: + results["upset"] = plot_upset( + sorting_analyzer, + unit_type, + unit_type_string, + thresholds=thresholds, + split_non_somatic=split_non_somatic, + backend=backend, + **kwargs, + ) # Save to folder if requested if save_folder is not None: @@ -616,7 +546,7 @@ def plot_unit_labeling_all( fig.savefig(save_folder / f"upset_plot_{i}.png", dpi=150, bbox_inches="tight") # Save CSV results - if combined_metrics is not None: + if has_metrics: save_bombcell_results(combined_metrics, unit_type, unit_type_string, thresholds, save_folder) return results From a345e422b614c4f382a6766467961e2d95ed3a88 Mon Sep 17 00:00:00 2001 From: Julie-Fabre Date: Sat, 17 Jan 2026 23:12:42 -0500 Subject: [PATCH 39/49] converted all durations (and defualt thresholds) to seconds --- .../curation/bombcell_curation.py | 4 +- .../curation/default_thresholds.json | 4 +- .../metrics/template/metrics.py | 42 +++++++++---------- 3 files changed, 25 insertions(+), 25 deletions(-) diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index 3830e6a727..fcce065e3d 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -69,8 +69,8 @@ def bombcell_get_default_thresholds() -> dict: "drift_ptp": {"min": None, "max": 100}, # um # Non-somatic detection "peak_before_to_trough_ratio": {"min": None, "max": 3}, - "peak_before_width": {"min": 150, "max": None}, # us - "trough_width": {"min": 200, "max": None}, # us + "peak_before_width": {"min": 0.00015, "max": None}, # seconds + "trough_width": {"min": 0.0002, "max": None}, # seconds "peak_before_to_peak_after_ratio": {"min": None, "max": 3}, "main_peak_to_trough_ratio": {"min": None, "max": 0.8}, } diff --git a/src/spikeinterface/curation/default_thresholds.json b/src/spikeinterface/curation/default_thresholds.json index 0f8dde710e..329114741f 100644 --- a/src/spikeinterface/curation/default_thresholds.json +++ b/src/spikeinterface/curation/default_thresholds.json @@ -56,11 +56,11 @@ "max": 3 }, "peak_before_width": { - "min": 150, + "min": 0.00015, "max": null }, "trough_width": { - "min": 200, + "min": 0.0002, "max": null }, "peak_before_to_peak_after_ratio": { diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index 74f03699c2..e66b6a8971 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -217,8 +217,8 @@ def get_main_to_next_peak_duration(template, sampling_frequency, troughs, peaks_ Returns ------- - main_to_next_peak_duration_us : float - Duration in microseconds from main extremum to next extremum + main_to_next_peak_duration : float + Duration in seconds from main extremum to next extremum """ # Get main locations and values @@ -269,10 +269,10 @@ def get_main_to_next_peak_duration(template, sampling_frequency, troughs, peaks_ else: return np.nan - # Convert to microseconds - main_to_next_peak_duration_us = (duration_samples / sampling_frequency) * 1e6 + # Convert to seconds + main_to_next_peak_duration = duration_samples / sampling_frequency - return main_to_next_peak_duration_us + return main_to_next_peak_duration def get_waveform_ratios(template, troughs, peaks_before, peaks_after, **kwargs): @@ -383,7 +383,7 @@ def get_waveform_baseline_flatness(template, sampling_frequency, **kwargs): def get_waveform_widths(template, sampling_frequency, troughs, peaks_before, peaks_after, **kwargs): """ - Get the widths of the main trough and peaks in microseconds. + Get the widths of the main trough and peaks in seconds. Parameters ---------- @@ -402,9 +402,9 @@ def get_waveform_widths(template, sampling_frequency, troughs, peaks_before, pea ------- widths : dict Dictionary containing: - - "trough_width_us": width of main trough in microseconds - - "peak_before_width_us": width of main peak before trough in microseconds - - "peak_after_width_us": width of main peak after trough in microseconds + - "trough_width": width of main trough in seconds + - "peak_before_width": width of main peak before trough in seconds + - "peak_after_width": width of main peak after trough in seconds """ def get_main_width(feature_dict): @@ -418,17 +418,17 @@ def get_main_width(feature_dict): return widths[main_idx] return np.nan - # Convert from samples to microseconds - samples_to_us = 1e6 / sampling_frequency + # Convert from samples to seconds + samples_to_seconds = 1.0 / sampling_frequency trough_width = get_main_width(troughs) peak_before_width = get_main_width(peaks_before) peak_after_width = get_main_width(peaks_after) widths = { - "trough_width_us": trough_width * samples_to_us if not np.isnan(trough_width) else np.nan, - "peak_before_width_us": peak_before_width * samples_to_us if not np.isnan(peak_before_width) else np.nan, - "peak_after_width_us": peak_after_width * samples_to_us if not np.isnan(peak_after_width) else np.nan, + "trough_width": trough_width * samples_to_seconds if not np.isnan(trough_width) else np.nan, + "peak_before_width": peak_before_width * samples_to_seconds if not np.isnan(peak_before_width) else np.nan, + "peak_after_width": peak_after_width * samples_to_seconds if not np.isnan(peak_after_width) else np.nan, } return widths @@ -1139,7 +1139,7 @@ class MainToNextPeakDuration(BaseMetric): metric_params = {} metric_columns = {"main_to_next_peak_duration": float} metric_descriptions = { - "main_to_next_peak_duration": "Duration in microseconds from main extremum to next extremum." + "main_to_next_peak_duration": "Duration in seconds from main extremum to next extremum." } needs_tmp_data = True @@ -1228,9 +1228,9 @@ def _waveform_widths_metric_function(sorting_analyzer, unit_ids, tmp_data, **met peaks_after_info[unit_id], **metric_params, ) - trough_width_dict[unit_id] = widths["trough_width_us"] - peak_before_width_dict[unit_id] = widths["peak_before_width_us"] - peak_after_width_dict[unit_id] = widths["peak_after_width_us"] + trough_width_dict[unit_id] = widths["trough_width"] + peak_before_width_dict[unit_id] = widths["peak_before_width"] + peak_after_width_dict[unit_id] = widths["peak_after_width"] return waveform_widths_result( trough_width=trough_width_dict, peak_before_width=peak_before_width_dict, peak_after_width=peak_after_width_dict ) @@ -1246,9 +1246,9 @@ class WaveformWidths(BaseMetric): "peak_after_width": float, } metric_descriptions = { - "trough_width": "Width of the main trough in microseconds", - "peak_before_width": "Width of the main peak before trough in microseconds", - "peak_after_width": "Width of the main peak after trough in microseconds", + "trough_width": "Width of the main trough in seconds", + "peak_before_width": "Width of the main peak before trough in seconds", + "peak_after_width": "Width of the main peak after trough in seconds", } needs_tmp_data = True From 1b1877538d506b558fe869db13d79445ef5d606a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 18 Jan 2026 04:26:45 +0000 Subject: [PATCH 40/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/metrics/template/metrics.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index e66b6a8971..f8f9331ab7 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -1138,9 +1138,7 @@ class MainToNextPeakDuration(BaseMetric): metric_function = _main_to_next_peak_duration_metric_function metric_params = {} metric_columns = {"main_to_next_peak_duration": float} - metric_descriptions = { - "main_to_next_peak_duration": "Duration in seconds from main extremum to next extremum." - } + metric_descriptions = {"main_to_next_peak_duration": "Duration in seconds from main extremum to next extremum."} needs_tmp_data = True From e5964939ba871e777051f4a456c0056939c74636 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 21 Jan 2026 12:24:35 +0100 Subject: [PATCH 41/49] Use external metrics and remove json --- .../curation/bombcell_curation.py | 25 ++++--- .../curation/default_thresholds.json | 74 ------------------- 2 files changed, 13 insertions(+), 86 deletions(-) delete mode 100644 src/spikeinterface/curation/default_thresholds.json diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index fcce065e3d..3201a4718d 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -76,7 +76,7 @@ def bombcell_get_default_thresholds() -> dict: } -def _combine_metrics(quality_metrics, template_metrics): +def _combine_metrics(metrics: list[pd.DataFrame]) -> pd.DataFrame: """Combine quality_metrics and template_metrics into a single DataFrame.""" if quality_metrics is None and template_metrics is None: return None @@ -101,8 +101,7 @@ def bombcell_label_units( thresholds: Optional[dict] = None, label_non_somatic: bool = True, split_non_somatic_good_mua: bool = False, - quality_metrics=None, - template_metrics=None, + external_metrics: Optional[pd.DataFrame | list[pd.DataFrame]] = None, ) -> tuple[np.ndarray, np.ndarray]: """ bombcell - label units based on quality metrics and thresholds. @@ -118,10 +117,8 @@ def bombcell_label_units( If True, detect non-somatic (axonal) units. split_non_somatic_good_mua : bool If True, split non-somatic into NON_SOMA_GOOD (3) and NON_SOMA_MUA (4). - quality_metrics : pd.DataFrame, optional - DataFrame with quality metrics (index = unit_ids). Deprecated, use sorting_analyzer instead. - template_metrics : pd.DataFrame, optional - DataFrame with template metrics (index = unit_ids). Deprecated, use sorting_analyzer instead. + external_metrics: Optional[pd.DataFrame | list[pd.DataFrame]] = None + External metrics DataFrame(s) (index = unit_ids) to use instead of those from SortingAnalyzer. Returns ------- @@ -138,11 +135,15 @@ def bombcell_label_units( "Compute quality_metrics and/or template_metrics first." ) else: - combined_metrics = _combine_metrics(quality_metrics, template_metrics) - if combined_metrics is None: - raise ValueError( - "Either sorting_analyzer or at least one of quality_metrics/template_metrics must be provided" - ) + if external_metrics is None: + raise ValueError("Either sorting_analyzer or external_metrics must be provided") + if isinstance(external_metrics, list): + assert all( + isinstance(df, pd.DataFrame) for df in external_metrics + ), "All items in external_metrics must be DataFrames" + combined_metrics = pd.concat(external_metrics, axis=1) + else: + combined_metrics = external_metrics if thresholds is None: thresholds = bombcell_get_default_thresholds() diff --git a/src/spikeinterface/curation/default_thresholds.json b/src/spikeinterface/curation/default_thresholds.json deleted file mode 100644 index 329114741f..0000000000 --- a/src/spikeinterface/curation/default_thresholds.json +++ /dev/null @@ -1,74 +0,0 @@ -{ - "num_positive_peaks": { - "min": null, - "max": 2 - }, - "num_negative_peaks": { - "min": null, - "max": 1 - }, - "peak_to_trough_duration": { - "min": 0.0001, - "max": 0.00115 - }, - "waveform_baseline_flatness": { - "min": null, - "max": 0.5 - }, - "peak_after_to_trough_ratio": { - "min": null, - "max": 0.8 - }, - "exp_decay": { - "min": 0.01, - "max": 0.1 - }, - "amplitude_median": { - "min": 40, - "max": null - }, - "snr_baseline": { - "min": 5, - "max": null - }, - "amplitude_cutoff": { - "min": null, - "max": 0.2 - }, - "num_spikes": { - "min": 300, - "max": null - }, - "rp_contamination": { - "min": null, - "max": 0.1 - }, - "presence_ratio": { - "min": 0.7, - "max": null - }, - "drift_ptp": { - "min": null, - "max": 100 - }, - "peak_before_to_trough_ratio": { - "min": null, - "max": 3 - }, - "peak_before_width": { - "min": 0.00015, - "max": null - }, - "trough_width": { - "min": 0.0002, - "max": null - }, - "peak_before_to_peak_after_ratio": { - "min": null, - "max": 3 - }, - "main_peak_to_trough_ratio": { - "min": null, - "max": 0.8 - } -} From 90b5a51e8e5a0e52078353b2bfc9043b7f3bab5b Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 21 Jan 2026 13:07:45 +0100 Subject: [PATCH 42/49] clean up imports --- src/spikeinterface/curation/__init__.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/spikeinterface/curation/__init__.py b/src/spikeinterface/curation/__init__.py index acd936be54..5c0fb31cdd 100644 --- a/src/spikeinterface/curation/__init__.py +++ b/src/spikeinterface/curation/__init__.py @@ -22,9 +22,6 @@ # automated curation from .bombcell_curation import ( - NOISE_METRICS, - SPIKE_QUALITY_METRICS, - NON_SOMATIC_METRICS, bombcell_get_default_thresholds, bombcell_label_units, get_bombcell_labeling_summary, From 7d4747d8dfb9e0d9cc090f6347ef38ab2dba6d79 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 21 Jan 2026 15:24:44 +0100 Subject: [PATCH 43/49] Add polarity inversion for old peak to trough --- .../core/analyzer_extension_core.py | 26 +++++++++++ .../metrics/template/metrics.py | 9 ++-- .../metrics/template/template_metrics.py | 45 +++++++++++++++---- 3 files changed, 69 insertions(+), 11 deletions(-) diff --git a/src/spikeinterface/core/analyzer_extension_core.py b/src/spikeinterface/core/analyzer_extension_core.py index 74ef52e258..ee5eb6dee9 100644 --- a/src/spikeinterface/core/analyzer_extension_core.py +++ b/src/spikeinterface/core/analyzer_extension_core.py @@ -959,6 +959,25 @@ def get_metric_column_descriptions(cls, metric_names=None): ) return metric_column_descriptions + def get_computed_metric_names(self): + """ + Get the list of already computed metric names. + + Returns + ------- + computed_metric_names : list[str] + List of computed metric names. + """ + if self.data is None or len(self.data) == 0: + return [] + else: + computed_metric_columns = self.data["metrics"].columns.tolist() + computed_metric_names = [] + for m in self.metric_list: + if all(col in computed_metric_columns for col in m.metric_columns.keys()): + computed_metric_names.append(m.metric_name) + return computed_metric_names + def _cast_metrics(self, metrics_df): metric_dtypes = {} for m in self.metric_list: @@ -1119,6 +1138,13 @@ def _compute_metrics( metric = [m for m in self.metric_list if m.metric_name == metric_name][0] column_names_dtypes.update(metric.metric_columns) + # drop metric that don't map to any metric names + possible_metric_names = [m.metric_name for m in self.metric_list] + wrong_metric_names = [m for m in metric_names if m not in possible_metric_names] + if len(wrong_metric_names) > 0: + warnings.warn(f"The following metric names are not recognized and will be ignored: {wrong_metric_names}") + metric_names = [m for m in metric_names if m in possible_metric_names] + metrics = pd.DataFrame(index=unit_ids, columns=list(column_names_dtypes.keys())) for metric_name in metric_names: diff --git a/src/spikeinterface/metrics/template/metrics.py b/src/spikeinterface/metrics/template/metrics.py index f8f9331ab7..fc22f09006 100644 --- a/src/spikeinterface/metrics/template/metrics.py +++ b/src/spikeinterface/metrics/template/metrics.py @@ -1,5 +1,6 @@ from __future__ import annotations +import warnings import numpy as np from spikeinterface.core.analyzer_extension_core import BaseMetric @@ -682,8 +683,10 @@ def fit_velocity(peak_times, channel_dist): from sklearn.linear_model import TheilSenRegressor - theil = TheilSenRegressor(max_iter=1000) - theil.fit(peak_times.reshape(-1, 1), channel_dist) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message=".*Maximum number of iterations.*") + theil = TheilSenRegressor(max_iter=1000) + theil.fit(peak_times.reshape(-1, 1), channel_dist) slope = theil.coef_[0] intercept = theil.intercept_ score = theil.score(peak_times.reshape(-1, 1), channel_dist) @@ -1370,7 +1373,7 @@ def _exp_decay_metric_function(sorting_analyzer, unit_ids, tmp_data, **metric_pa class Spread(BaseMetric): metric_name = "spread" - metric_params = {"spread_threshold": 0.5, "spread_smooth_um": 20, "column_range": None} + metric_params = {"spread_threshold": 0.2, "spread_smooth_um": 20, "column_range": None} metric_columns = {"spread": float} metric_descriptions = { "spread": ( diff --git a/src/spikeinterface/metrics/template/template_metrics.py b/src/spikeinterface/metrics/template/template_metrics.py index a4528261b0..56d64ddda0 100644 --- a/src/spikeinterface/metrics/template/template_metrics.py +++ b/src/spikeinterface/metrics/template/template_metrics.py @@ -6,6 +6,7 @@ from __future__ import annotations +import warnings import numpy as np from spikeinterface.core.sortinganalyzer import register_result_extension @@ -109,34 +110,52 @@ def _handle_backward_compatibility_on_load(self): del self.params["metrics_kwargs"] # handle metric names change: - # num_positive_peaks/num_negative_peaks merged into number_of_peaks if "num_positive_peaks" in self.params["metric_names"]: self.params["metric_names"].remove("num_positive_peaks") if "number_of_peaks" not in self.params["metric_names"]: self.params["metric_names"].append("number_of_peaks") + if "num_positive_peaks" in self.params["metric_params"]: + del self.params["metric_params"]["num_positive_peaks"] if "num_negative_peaks" in self.params["metric_names"]: self.params["metric_names"].remove("num_negative_peaks") if "number_of_peaks" not in self.params["metric_names"]: self.params["metric_names"].append("number_of_peaks") + if "num_negative_peaks" in self.params["metric_params"]: + del self.params["metric_params"]["num_negative_peaks"] # velocity_above/velocity_below merged into velocity_fits if "velocity_above" in self.params["metric_names"]: self.params["metric_names"].remove("velocity_above") if "velocity_fits" not in self.params["metric_names"]: self.params["metric_names"].append("velocity_fits") + self.params["metric_params"]["velocity_fits"] = self.params["metric_params"]["velocity_above"] + self.params["metric_params"]["velocity_fits"]["min_channels"] = self.params["metric_params"][ + "velocity_above" + ]["min_channels_for_velocity"] + self.params["metric_params"]["velocity_fits"]["min_r2"] = self.params["metric_params"]["velocity_above"][ + "min_r2_velocity" + ] + del self.params["metric_params"]["velocity_above"] if "velocity_below" in self.params["metric_names"]: self.params["metric_names"].remove("velocity_below") if "velocity_fits" not in self.params["metric_names"]: self.params["metric_names"].append("velocity_fits") + # parameters are already updated from velocity_above + if "velocity_below" in self.params["metric_params"]: + del self.params["metric_params"]["velocity_below"] # peak to valley -> peak_to_trough_duration if "peak_to_valley" in self.params["metric_names"]: self.params["metric_names"].remove("peak_to_valley") if "peak_to_trough_duration" not in self.params["metric_names"]: self.params["metric_names"].append("peak_to_trough_duration") - # peak to trough ratio -> main peak to trough ratio - if "peak_to_trough_ratio" in self.params["metric_names"]: - self.params["metric_names"].remove("peak_to_trough_ratio") - if "main_peak_to_trough_ratio" not in self.params["metric_names"]: - self.params["metric_names"].append("main_peak_to_trough_ratio") + # peak_trough ratio -> main peak to trough ratio + # note that the new implementation correctly uses the absolute peak values, + # which is different from the old implementation. + # we make a flag to invert the polarity of old values if needed + if "peak_trough_ratio" in self.params["metric_names"]: + self.params["metric_names"].remove("peak_trough_ratio") + if "waveform_ratios" not in self.params["metric_names"]: + self.params["metric_names"].append("waveform_ratios") + self.params["metric_params"]["invert_peak_to_trough"] = True def _set_params( self, @@ -151,10 +170,8 @@ def _set_params( depth_direction="y", min_thresh_detect_peaks_troughs=0.4, smooth=True, - smooth_method="savgol", smooth_window_frac=0.1, smooth_polyorder=3, - svd_n_components=3, ): # Auto-detect if multi-channel metrics should be included based on number of channels num_channels = self.sorting_analyzer.get_num_channels() @@ -289,6 +306,18 @@ def _prepare_data(self, sorting_analyzer, unit_ids): return tmp_data + def get_data(self, *args, **kwargs): + """Override to handle deprecated polarity of 'peak_trough_ratio' metric.""" + metrics = super().get_data(*args, **kwargs) + if self.params["metric_params"].get("invert_peak_to_trough", False): + if "peak_trough_ratio" in metrics.columns: + warnings.warn( + "The 'peak_trough_ratio' metric has been deprecated and replaced by 'main_peak_to_trough_ratio'. " + "The values have been inverted to maintain consistency with previous versions." + ) + metrics["peak_trough_ratio"] = -metrics["peak_trough_ratio"] + return metrics + register_result_extension(ComputeTemplateMetrics) compute_template_metrics = ComputeTemplateMetrics.function_factory() From 81dc7d6660ff3b407f462be9f42e500ae5c7deb7 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 21 Jan 2026 16:36:38 +0100 Subject: [PATCH 44/49] ported general label widhet to unit labels and backward comp for unitrefine --- .../curation/bombcell_curation.py | 6 +- .../curation/model_based_curation.py | 23 ++- .../widgets/bombcell_curation.py | 187 ++---------------- src/spikeinterface/widgets/unit_labels.py | 114 +++++++++++ src/spikeinterface/widgets/widget_list.py | 10 +- 5 files changed, 155 insertions(+), 185 deletions(-) create mode 100644 src/spikeinterface/widgets/unit_labels.py diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index 3201a4718d..db618c40f5 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -248,11 +248,11 @@ def get_metric(name): # String labels if split_non_somatic_good_mua: - labels = {0: "NOISE", 1: "GOOD", 2: "MUA", 3: "NON_SOMA_GOOD", 4: "NON_SOMA_MUA"} + labels = {0: "NOISE", 1: "good", 2: "mua", 3: "non_soma_good", 4: "non_soma_mua"} else: - labels = {0: "NOISE", 1: "GOOD", 2: "MUA", 3: "NON_SOMA"} + labels = {0: "noise", 1: "good", 2: "mua", 3: "non_soma"} - unit_type_string = np.array([labels.get(int(t), "UNKNOWN") for t in unit_type], dtype=object) + unit_type_string = np.array([labels.get(int(t), "unknown") for t in unit_type], dtype=object) return unit_type.astype(int), unit_type_string diff --git a/src/spikeinterface/curation/model_based_curation.py b/src/spikeinterface/curation/model_based_curation.py index e779e13182..99822d64f6 100644 --- a/src/spikeinterface/curation/model_based_curation.py +++ b/src/spikeinterface/curation/model_based_curation.py @@ -3,11 +3,10 @@ import json import warnings import re +from packaging.version import parse from spikeinterface.core import SortingAnalyzer from spikeinterface.curation.train_manual_curation import ( - try_to_get_metrics_from_analyzer, - _get_computed_metrics, _format_metric_dataframe, ) from copy import deepcopy @@ -81,11 +80,12 @@ def predict_labels( # Get metrics DataFrame for classification if input_data is None: - input_data = _get_computed_metrics(self.sorting_analyzer) + input_data = self.sorting_analyzer.get_metrics_extension_data() else: if not isinstance(input_data, pd.DataFrame): raise ValueError("Input data must be a pandas DataFrame") + input_data = self.handle_backwards_compatibility_in_metrics(input_data, model_info=model_info) input_data = self._check_required_metrics_are_present(input_data) if model_info is not None: @@ -127,8 +127,23 @@ def predict_labels( return classified_units - def _check_required_metrics_are_present(self, calculated_metrics): + def handle_backwards_compatibility_in_metrics(self, calculated_metrics, model_info): + si_version = model_info["requirements"].get("spikeinterface", None) + if si_version is not None and parse(si_version) < parse("0.103.2"): + # if the model was trained with SI version < 0.103.2, we need to rename some metrics + calculated_metrics = calculated_metrics.copy() + # peak_to_trough_duration was named peak_to_valley + if "peak_to_trough_duration" in calculated_metrics.columns: + calculated_metrics = calculated_metrics.rename(columns={"peak_to_trough_duration": "peak_to_valley"}) + # main_peak_to_trough_ratio was named peak_trough_ratio and had inverted sign + if "main_peak_to_trough_ratio" in calculated_metrics.columns: + calculated_metrics = calculated_metrics.rename( + columns={"main_peak_to_trough_ratio": "peak_trough_ratio"} + ) + calculated_metrics["peak_trough_ratio"] = -1 * calculated_metrics["peak_trough_ratio"] + return calculated_metrics + def _check_required_metrics_are_present(self, calculated_metrics): # Check all the required metrics have been calculated required_metrics = set(self.required_metrics) if required_metrics.issubset(set(calculated_metrics)): diff --git a/src/spikeinterface/widgets/bombcell_curation.py b/src/spikeinterface/widgets/bombcell_curation.py index ad84a054d6..5dd87dda0a 100644 --- a/src/spikeinterface/widgets/bombcell_curation.py +++ b/src/spikeinterface/widgets/bombcell_curation.py @@ -7,6 +7,8 @@ from .base import BaseWidget, to_attr +from .unit_labels import WaveformOverlayByLabelWidget + def _is_threshold_disabled(value): """Check if a threshold value is disabled (None or np.nan).""" @@ -115,94 +117,6 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): self.axes = axes -class WaveformOverlayWidget(BaseWidget): - """Plot overlaid waveforms grouped by unit label type.""" - - def __init__( - self, - sorting_analyzer, - unit_type: np.ndarray, - unit_type_string: np.ndarray, - split_non_somatic: bool = False, - backend=None, - **backend_kwargs, - ): - sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) - plot_data = dict( - sorting_analyzer=sorting_analyzer, - unit_type=unit_type, - unit_type_string=unit_type_string, - split_non_somatic=split_non_somatic, - ) - BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) - - def plot_matplotlib(self, data_plot, **backend_kwargs): - import matplotlib.pyplot as plt - - dp = to_attr(data_plot) - sorting_analyzer = dp.sorting_analyzer - unit_type = dp.unit_type - split_non_somatic = dp.split_non_somatic - - if not sorting_analyzer.has_extension("templates"): - fig, ax = plt.subplots(1, 1, figsize=(8, 6)) - ax.text( - 0.5, - 0.5, - "Templates extension not computed.\nRun: analyzer.compute('templates')", - ha="center", - va="center", - fontsize=12, - ) - ax.axis("off") - self.figure = fig - self.axes = ax - return - - templates_ext = sorting_analyzer.get_extension("templates") - templates = templates_ext.get_templates(operator="average") - - if split_non_somatic: - labels = {0: "NOISE", 1: "GOOD", 2: "MUA", 3: "NON_SOMA_GOOD", 4: "NON_SOMA_MUA"} - n_plots, nrows, ncols = 5, 2, 3 - else: - labels = {0: "NOISE", 1: "GOOD", 2: "MUA", 3: "NON_SOMA"} - n_plots, nrows, ncols = 4, 2, 2 - - fig, axes = plt.subplots(nrows, ncols, figsize=(5 * ncols, 4 * nrows)) - axes_flat = axes.flatten() - - for plot_idx in range(n_plots): - ax = axes_flat[plot_idx] - type_label = labels.get(plot_idx, "") - mask = unit_type == plot_idx - n_units = np.sum(mask) - - if n_units > 0: - unit_indices = np.where(mask)[0] - alpha = max(0.05, min(0.3, 10 / n_units)) - for unit_idx in unit_indices: - template = templates[unit_idx] - best_chan = np.argmax(np.max(np.abs(template), axis=0)) - ax.plot(template[:, best_chan], color="black", alpha=alpha, linewidth=0.5) - ax.set_title(f"{type_label} (n={n_units})") - else: - ax.set_title(f"{type_label} (n=0)") - ax.text(0.5, 0.5, "No units", ha="center", va="center", transform=ax.transAxes) - - for spine in ax.spines.values(): - spine.set_visible(False) - ax.set_xticks([]) - ax.set_yticks([]) - - for idx in range(n_plots, nrows * ncols): - axes_flat[idx].set_visible(False) - - plt.tight_layout() - self.figure = fig - self.axes = axes - - class UpsetPlotWidget(BaseWidget): """ Plot UpSet plots showing which metrics fail together for each unit type. @@ -211,31 +125,6 @@ class UpsetPlotWidget(BaseWidget): NOISE -> waveform metrics, MUA -> spike quality metrics, NON_SOMA -> non-somatic metrics. """ - NOISE_METRICS = [ - "num_positive_peaks", - "num_negative_peaks", - "peak_to_trough_duration", - "waveform_baseline_flatness", - "peak_after_to_trough_ratio", - "exp_decay", - ] - SPIKE_QUALITY_METRICS = [ - "amplitude_median", - "snr_baseline", - "amplitude_cutoff", - "num_spikes", - "rp_contamination", - "presence_ratio", - "drift_ptp", - ] - NON_SOMATIC_METRICS = [ - "peak_before_to_trough_ratio", - "peak_before_width", - "trough_width", - "peak_before_to_peak_after_ratio", - "main_peak_to_trough_ratio", - ] - def __init__( self, sorting_analyzer, @@ -277,12 +166,18 @@ def __init__( BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) def _get_metrics_for_unit_type(self, unit_type_label): + from spikeinterface.curation.bombcell_curation import ( + NOISE_METRICS, + SPIKE_QUALITY_METRICS, + NON_SOMATIC_METRICS, + ) + if unit_type_label == "NOISE": - return self.NOISE_METRICS + return NOISE_METRICS elif unit_type_label == "MUA": - return self.SPIKE_QUALITY_METRICS + return SPIKE_QUALITY_METRICS elif unit_type_label in ("NON_SOMA", "NON_SOMA_GOOD", "NON_SOMA_MUA"): - return self.NON_SOMATIC_METRICS + return NON_SOMATIC_METRICS return None def plot_matplotlib(self, data_plot, **backend_kwargs): @@ -402,58 +297,6 @@ def _build_failure_table(self, quality_metrics, thresholds): return pd.DataFrame(failure_data, index=quality_metrics.index) -# Convenience functions -def plot_labeling_histograms( - sorting_analyzer, - thresholds=None, - metrics_to_plot=None, - backend=None, - **kwargs, -): - """Plot histograms of quality metrics with threshold lines.""" - return LabelingHistogramsWidget( - sorting_analyzer, - thresholds=thresholds, - metrics_to_plot=metrics_to_plot, - backend=backend, - **kwargs, - ) - - -def plot_waveform_overlay( - sorting_analyzer, unit_type, unit_type_string, split_non_somatic=False, backend=None, **kwargs -): - """Plot overlaid waveforms grouped by unit label type.""" - return WaveformOverlayWidget( - sorting_analyzer, unit_type, unit_type_string, split_non_somatic=split_non_somatic, backend=backend, **kwargs - ) - - -def plot_upset( - sorting_analyzer, - unit_type, - unit_type_string, - thresholds=None, - unit_types_to_plot=None, - split_non_somatic=False, - min_subset_size=1, - backend=None, - **kwargs, -): - """Plot UpSet plots showing which metrics fail together for each unit type.""" - return UpsetPlotWidget( - sorting_analyzer, - unit_type, - unit_type_string, - thresholds=thresholds, - unit_types_to_plot=unit_types_to_plot, - split_non_somatic=split_non_somatic, - min_subset_size=min_subset_size, - backend=backend, - **kwargs, - ) - - def plot_unit_labeling_all( sorting_analyzer, unit_type: np.ndarray, @@ -507,7 +350,7 @@ def plot_unit_labeling_all( # Histograms if has_metrics: - results["histograms"] = plot_labeling_histograms( + results["histograms"] = LabelingHistogramsWidget( sorting_analyzer, thresholds=thresholds, backend=backend, @@ -515,13 +358,11 @@ def plot_unit_labeling_all( ) # Waveform overlay - results["waveforms"] = plot_waveform_overlay( - sorting_analyzer, unit_type, unit_type_string, split_non_somatic=split_non_somatic, backend=backend, **kwargs - ) + results["waveforms"] = WaveformOverlayByLabelWidget(sorting_analyzer, unit_type_string, backend=backend, **kwargs) # UpSet plots if include_upset and has_metrics: - results["upset"] = plot_upset( + results["upset"] = UpsetPlotWidget( sorting_analyzer, unit_type, unit_type_string, diff --git a/src/spikeinterface/widgets/unit_labels.py b/src/spikeinterface/widgets/unit_labels.py new file mode 100644 index 0000000000..03ee0b2391 --- /dev/null +++ b/src/spikeinterface/widgets/unit_labels.py @@ -0,0 +1,114 @@ +"""Widgets for visualizing unit labeling results.""" + +from __future__ import annotations + +import numpy as np + +from .base import BaseWidget, to_attr + + +class WaveformOverlayByLabelWidget(BaseWidget): + """Plot overlaid waveforms grouped by unit label type. + + Parameters + ---------- + sorting_analyzer : SortingAnalyzer + A SortingAnalyzer object with 'templates' extension computed. + unit_labels : np.ndarray + Array of unit type labels corresponding to each unit in the sorting. + labels_order : list, optional + List specifying the order of labels to display. If None, unique labels in unit_labels are + used in the order they appear. + max_columns : int, default: 3 + Maximum number of columns in the plot grid. + """ + + def __init__( + self, + sorting_analyzer, + unit_labels: np.ndarray, + labels_order=None, + max_columns: int = 3, + backend=None, + **backend_kwargs, + ): + sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) + self.check_extensions(sorting_analyzer, "templates") + if labels_order is not None: + assert len(labels_order) == len(np.unique(unit_labels)), "labels_order length must match unique unit types" + assert all( + [label in np.unique(unit_labels) for label in labels_order] + ), "All labels in labels_order must be present in unit_labels" + else: + labels_order = np.unique(unit_labels) + plot_data = dict( + sorting_analyzer=sorting_analyzer, + labels_order=labels_order, + unit_labels=unit_labels, + max_columns=max_columns, + ) + BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) + + def plot_matplotlib(self, data_plot, **backend_kwargs): + import matplotlib.pyplot as plt + from .utils_matplotlib import make_mpl_figure + + dp = to_attr(data_plot) + sorting_analyzer = dp.sorting_analyzer + unit_labels = dp.unit_labels + labels_order = dp.labels_order + + if not sorting_analyzer.has_extension("templates"): + fig, ax = plt.subplots(1, 1, figsize=(8, 6)) + ax.text( + 0.5, + 0.5, + "Templates extension not computed.\nRun: analyzer.compute('templates')", + ha="center", + va="center", + fontsize=12, + ) + ax.axis("off") + self.figure = fig + self.axes = ax + return + + templates_ext = sorting_analyzer.get_extension("templates") + templates = templates_ext.get_templates(operator="average") + + backend_kwargs["num_axes"] = len(labels_order) + if len(labels_order) <= dp.max_columns: + ncols = len(labels_order) + else: + ncols = int(np.ceil(len(labels_order) / 2)) + nrows = int(np.ceil(len(labels_order) / ncols)) + backend_kwargs["ncols"] = ncols + if "figsize" not in backend_kwargs: + backend_kwargs["figsize"] = (5 * ncols, 4 * nrows) + self.figure, self.axes, self.ax = make_mpl_figure(**backend_kwargs) + + axes_flat = self.axes.flatten() + for index, label in enumerate(labels_order): + ax = axes_flat[index] + mask = unit_labels == label + n_units = np.sum(mask) + + if n_units > 0: + unit_indices = np.where(mask)[0] + alpha = max(0.05, min(0.3, 10 / n_units)) + for unit_idx in unit_indices: + template = templates[unit_idx] + best_chan = np.argmax(np.max(np.abs(template), axis=0)) + ax.plot(template[:, best_chan], color="black", alpha=alpha, linewidth=0.5) + ax.set_title(f"{label} (n={n_units})") + else: + ax.set_title(f"{label} (n=0)") + ax.text(0.5, 0.5, "No units", ha="center", va="center", transform=ax.transAxes) + + for spine in ax.spines.values(): + spine.set_visible(False) + ax.set_xticks([]) + ax.set_yticks([]) + + for idx in range(len(labels_order), len(axes_flat)): + axes_flat[idx].set_visible(False) diff --git a/src/spikeinterface/widgets/widget_list.py b/src/spikeinterface/widgets/widget_list.py index 2327dd922e..fe191d4450 100644 --- a/src/spikeinterface/widgets/widget_list.py +++ b/src/spikeinterface/widgets/widget_list.py @@ -37,13 +37,10 @@ from .comparison import AgreementMatrixWidget, ConfusionMatrixWidget from .gtstudy import StudyRunTimesWidget, StudyUnitCountsWidget, StudyPerformances, StudyAgreementMatrix, StudySummary from .collision import ComparisonCollisionBySimilarityWidget, StudyComparisonCollisionBySimilarityWidget +from .unit_labels import WaveformOverlayByLabelWidget from .bombcell_curation import ( LabelingHistogramsWidget, - WaveformOverlayWidget, UpsetPlotWidget, - plot_labeling_histograms, - plot_waveform_overlay, - plot_upset, plot_unit_labeling_all, ) @@ -86,7 +83,7 @@ UnitWaveformDensityMapWidget, UnitWaveformsWidget, UpsetPlotWidget, - WaveformOverlayWidget, + WaveformOverlayByLabelWidget, StudyRunTimesWidget, StudyUnitCountsWidget, StudyPerformances, @@ -160,6 +157,9 @@ plot_template_similarity = TemplateSimilarityWidget plot_traces = TracesWidget plot_unit_depths = UnitDepthsWidget +plot_unit_labels = WaveformOverlayByLabelWidget +plot_unit_labeling_upset = UpsetPlotWidget +plot_unit_labeling_histograms = LabelingHistogramsWidget plot_unit_locations = UnitLocationsWidget plot_unit_presence = UnitPresenceWidget plot_unit_probe_map = UnitProbeMapWidget From 702f5c7b4f2e838d44575e0059e80e91f871de20 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Thu, 22 Jan 2026 11:05:30 +0100 Subject: [PATCH 45/49] labels to lower case --- src/spikeinterface/widgets/bombcell_curation.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/spikeinterface/widgets/bombcell_curation.py b/src/spikeinterface/widgets/bombcell_curation.py index 5dd87dda0a..b35b38ea4b 100644 --- a/src/spikeinterface/widgets/bombcell_curation.py +++ b/src/spikeinterface/widgets/bombcell_curation.py @@ -151,9 +151,9 @@ def __init__( thresholds = bombcell_get_default_thresholds() if unit_types_to_plot is None: if split_non_somatic: - unit_types_to_plot = ["NOISE", "MUA", "NON_SOMA_GOOD", "NON_SOMA_MUA"] + unit_types_to_plot = ["noise", "mua", "non_soma_good", "non_soma_mua"] else: - unit_types_to_plot = ["NOISE", "MUA", "NON_SOMA"] + unit_types_to_plot = ["noise", "mua", "non_soma"] plot_data = dict( quality_metrics=combined_metrics, @@ -172,11 +172,11 @@ def _get_metrics_for_unit_type(self, unit_type_label): NON_SOMATIC_METRICS, ) - if unit_type_label == "NOISE": + if unit_type_label == "noise": return NOISE_METRICS - elif unit_type_label == "MUA": + elif unit_type_label == "mua": return SPIKE_QUALITY_METRICS - elif unit_type_label in ("NON_SOMA", "NON_SOMA_GOOD", "NON_SOMA_MUA"): + elif unit_type_label in ("non_soma", "non_soma_good", "non_soma_mua"): return NON_SOMATIC_METRICS return None @@ -316,13 +316,13 @@ def plot_unit_labeling_all( sorting_analyzer : SortingAnalyzer The sorting analyzer object with computed metrics extensions. unit_type : np.ndarray - Array of unit type codes (0=NOISE, 1=GOOD, 2=MUA, 3=NON_SOMA, etc.). + Array of unit type codes (0=noise, 1=good, 2=mua, 3=non_soma, etc.). unit_type_string : np.ndarray Array of unit type labels as strings. thresholds : dict, optional Threshold dictionary. If None, uses default thresholds. split_non_somatic : bool, default: False - Whether to split NON_SOMA into NON_SOMA_GOOD and NON_SOMA_MUA. + Whether to split "non_soma" into "non_soma_good" and "non_soma_mua". include_upset : bool, default: True Whether to include UpSet plots (requires upsetplot package). save_folder : str or Path, optional From 2983204096ea32edcbc0cdd05d592cf31db61864 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Thu, 22 Jan 2026 12:31:34 +0100 Subject: [PATCH 46/49] Use peak_sign='both' as default for SNR --- src/spikeinterface/curation/bombcell_curation.py | 2 +- src/spikeinterface/metrics/quality/misc_metrics.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index db618c40f5..48455b3e4f 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -26,7 +26,7 @@ SPIKE_QUALITY_METRICS = [ "amplitude_median", - "snr_baseline", + "snr", "amplitude_cutoff", "num_spikes", "rp_contamination", diff --git a/src/spikeinterface/metrics/quality/misc_metrics.py b/src/spikeinterface/metrics/quality/misc_metrics.py index 4320845bfd..e94e822a15 100644 --- a/src/spikeinterface/metrics/quality/misc_metrics.py +++ b/src/spikeinterface/metrics/quality/misc_metrics.py @@ -121,7 +121,7 @@ class PresenceRatio(BaseMetric): def compute_snrs( sorting_analyzer, unit_ids=None, - peak_sign: str = "neg", + peak_sign: str = "both", peak_mode: str = "extremum", ): """ From 2d8c126a84e0306c35a67fd39d3926ce71475e3f Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 22 Jan 2026 15:32:09 +0000 Subject: [PATCH 47/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/curation/bombcell_curation.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index 48455b3e4f..ff45911c36 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -14,7 +14,6 @@ import pandas as pd from typing import Optional - NOISE_METRICS = [ "num_positive_peaks", "num_negative_peaks", From 7e8e644616889f354994ecc291ec0855ecf56619 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Thu, 22 Jan 2026 16:53:38 +0100 Subject: [PATCH 48/49] Lazy import of pandas and basic tests --- .../curation/bombcell_curation.py | 20 +++++-------------- .../curation/tests/test_bombcell_curation.py | 17 ++++++++++++++++ .../metrics/quality/misc_metrics.py | 2 +- 3 files changed, 23 insertions(+), 16 deletions(-) create mode 100644 src/spikeinterface/curation/tests/test_bombcell_curation.py diff --git a/src/spikeinterface/curation/bombcell_curation.py b/src/spikeinterface/curation/bombcell_curation.py index 48455b3e4f..84a954ffa6 100644 --- a/src/spikeinterface/curation/bombcell_curation.py +++ b/src/spikeinterface/curation/bombcell_curation.py @@ -11,10 +11,8 @@ from __future__ import annotations import numpy as np -import pandas as pd from typing import Optional - NOISE_METRICS = [ "num_positive_peaks", "num_negative_peaks", @@ -76,17 +74,6 @@ def bombcell_get_default_thresholds() -> dict: } -def _combine_metrics(metrics: list[pd.DataFrame]) -> pd.DataFrame: - """Combine quality_metrics and template_metrics into a single DataFrame.""" - if quality_metrics is None and template_metrics is None: - return None - if quality_metrics is None: - return template_metrics - if template_metrics is None: - return quality_metrics - return quality_metrics.join(template_metrics, how="outer") - - def _is_threshold_disabled(value): """Check if a threshold value is disabled (None or np.nan).""" if value is None: @@ -101,7 +88,7 @@ def bombcell_label_units( thresholds: Optional[dict] = None, label_non_somatic: bool = True, split_non_somatic_good_mua: bool = False, - external_metrics: Optional[pd.DataFrame | list[pd.DataFrame]] = None, + external_metrics: Optional["pd.DataFrame | list[pd.DataFrame]"] = None, ) -> tuple[np.ndarray, np.ndarray]: """ bombcell - label units based on quality metrics and thresholds. @@ -127,6 +114,8 @@ def bombcell_label_units( unit_type_string : np.ndarray String labels. """ + import pandas as pd + if sorting_analyzer is not None: combined_metrics = sorting_analyzer.get_metrics_extension_data() if combined_metrics.empty: @@ -271,7 +260,7 @@ def get_bombcell_labeling_summary(unit_type: np.ndarray, unit_type_string: np.nd def save_bombcell_results( - quality_metrics: pd.DataFrame, + quality_metrics: "pd.DataFrame", unit_type: np.ndarray, unit_type_string: np.ndarray, thresholds: dict, @@ -300,6 +289,7 @@ def save_bombcell_results( Save wide format (one row per unit, metrics as columns). """ from pathlib import Path + import pandas as pd folder = Path(folder) folder.mkdir(parents=True, exist_ok=True) diff --git a/src/spikeinterface/curation/tests/test_bombcell_curation.py b/src/spikeinterface/curation/tests/test_bombcell_curation.py new file mode 100644 index 0000000000..a867453064 --- /dev/null +++ b/src/spikeinterface/curation/tests/test_bombcell_curation.py @@ -0,0 +1,17 @@ +import pytest +from pathlib import Path +from spikeinterface.curation.tests.common import sorting_analyzer_for_curation, trained_pipeline_path +from spikeinterface.curation.bombcell_curation import bombcell_label_units + + +def test_bombcell_label_units(sorting_analyzer_for_curation): + """Test bombcell_label_units function on a sorting_analyzer with computed quality metrics.""" + + sorting_analyzer = sorting_analyzer_for_curation + sorting_analyzer.compute("quality_metrics") + sorting_analyzer.compute("template_metrics") + + unit_type, unit_type_string = bombcell_label_units(sorting_analyzer=sorting_analyzer) + + assert len(unit_type) == sorting_analyzer.unit_ids.size + assert set(unit_type_string).issubset({"somatic", "non-somatic", "good", "mua", "noise"}) diff --git a/src/spikeinterface/metrics/quality/misc_metrics.py b/src/spikeinterface/metrics/quality/misc_metrics.py index 2b1763b9f6..c5f29ac329 100644 --- a/src/spikeinterface/metrics/quality/misc_metrics.py +++ b/src/spikeinterface/metrics/quality/misc_metrics.py @@ -1508,7 +1508,7 @@ class SDRatio(BaseMetric): FiringRate, PresenceRatio, SNR, - SNRBaseline, + # SNRBaseline, ISIViolation, RPViolation, SlidingRPViolation, From 6af9617d023dc5a9f9db4a7eb23e3fc4cbc23195 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Fri, 23 Jan 2026 10:53:38 +0100 Subject: [PATCH 49/49] fix docs and tests --- doc/modules/metrics.rst | 43 +++++++++++++++---- .../template/tests/test_template_metrics.py | 2 +- .../widgets/bombcell_curation.py | 18 +++----- 3 files changed, 43 insertions(+), 20 deletions(-) diff --git a/doc/modules/metrics.rst b/doc/modules/metrics.rst index d3f3512670..fe5b631f19 100644 --- a/doc/modules/metrics.rst +++ b/doc/modules/metrics.rst @@ -28,14 +28,32 @@ metric information. For example, you can get the list of available metrics using .. code-block:: Available metric columns: - ['peak_to_valley', 'peak_trough_ratio', 'half_width', 'repolarization_slope', - 'recovery_slope', 'num_positive_peaks', 'num_negative_peaks', 'velocity_above', - 'velocity_below', 'exp_decay', 'spread'] + [ + 'peak_to_trough_duration', + 'half_width', + 'repolarization_slope', + 'recovery_slope', + 'num_positive_peaks', + 'num_negative_peaks', + 'main_to_next_peak_duration', + 'peak_before_to_trough_ratio', + 'peak_after_to_trough_ratio', + 'peak_before_to_peak_after_ratio', + 'main_peak_to_trough_ratio', + 'trough_width', + 'peak_before_width', + 'peak_after_width', + 'waveform_baseline_flatness', + 'velocity_above', + 'velocity_below', + 'exp_decay', + 'spread' + ] .. code-block:: python - metric_descriptions = ComputeTemplateMetrics.get_metric_descriptions() + metric_descriptions = ComputeTemplateMetrics.get_metric_column_descriptions() print("Metric descriptions: ") print(metric_descriptions) @@ -44,21 +62,30 @@ metric information. For example, you can get the list of available metrics using Metric descriptions: { - 'peak_to_valley': 'Duration in s between the trough (minimum) and the peak (maximum) of the spike waveform.', - 'peak_trough_ratio': 'Ratio of the amplitude of the peak (maximum) to the trough (minimum) of the spike waveform.', + 'peak_to_trough_duration': 'Duration in seconds between the trough (minimum) and the peak (maximum) of the spike waveform.', 'half_width': 'Duration in s at half the amplitude of the trough (minimum) of the spike waveform.', 'repolarization_slope': 'Slope of the repolarization phase of the spike waveform, between the trough (minimum) and return to baseline in uV/s.', 'recovery_slope': 'Slope of the recovery phase of the spike waveform, after the peak (maximum) returning to baseline in uV/s.', 'num_positive_peaks': 'Number of positive peaks in the template', - 'num_negative_peaks': 'Number of negative peaks in the template', + 'num_negative_peaks': 'Number of negative peaks (troughs) in the template', + 'main_to_next_peak_duration': 'Duration in seconds from main extremum to next extremum.', + 'peak_before_to_trough_ratio': 'Ratio of peak before amplitude to trough amplitude', + 'peak_after_to_trough_ratio': 'Ratio of peak after amplitude to trough amplitude', + 'peak_before_to_peak_after_ratio': 'Ratio of peak before amplitude to peak after amplitude', + 'main_peak_to_trough_ratio': 'Ratio of main peak amplitude to trough amplitude', + 'trough_width': 'Width of the main trough in seconds', + 'peak_before_width': 'Width of the main peak before trough in seconds', + 'peak_after_width': 'Width of the main peak after trough in seconds', + 'waveform_baseline_flatness': 'Ratio of max baseline amplitude to max waveform amplitude. Lower = flatter baseline.', 'velocity_above': 'Velocity of the spike propagation above the max channel in um/ms', 'velocity_below': 'Velocity of the spike propagation below the max channel in um/ms', - 'exp_decay': 'Exponential decay of the template amplitude over distance from the extremum channel (1/um).', + 'exp_decay': 'Spatial decay of the template amplitude over distance from the extremum channel (1/um). Uses exponential or linear fit based on linear_fit parameter.', 'spread': 'Spread of the template amplitude in um, calculated as the distance between channels whose templates exceed the spread_threshold.' } + .. toctree:: :caption: Metrics submodules :maxdepth: 1 diff --git a/src/spikeinterface/metrics/template/tests/test_template_metrics.py b/src/spikeinterface/metrics/template/tests/test_template_metrics.py index 8f1bf05b85..66437b156e 100644 --- a/src/spikeinterface/metrics/template/tests/test_template_metrics.py +++ b/src/spikeinterface/metrics/template/tests/test_template_metrics.py @@ -83,7 +83,7 @@ def test_metric_names_in_same_order(small_sorting_analyzer): """ Computes sepecified template metrics and checks order is propagated. """ - specified_metric_names = ["peak_trough_ratio", "half_width", "peak_to_valley"] + specified_metric_names = ["main_peak_to_trough_ratio", "half_width", "peak_to_valley"] small_sorting_analyzer.compute( "template_metrics", metric_names=specified_metric_names, delete_existing_metrics=True ) diff --git a/src/spikeinterface/widgets/bombcell_curation.py b/src/spikeinterface/widgets/bombcell_curation.py index b35b38ea4b..030cb67327 100644 --- a/src/spikeinterface/widgets/bombcell_curation.py +++ b/src/spikeinterface/widgets/bombcell_curation.py @@ -53,6 +53,7 @@ def __init__( BaseWidget.__init__(self, plot_data, backend=backend, **backend_kwargs) def plot_matplotlib(self, data_plot, **backend_kwargs): + from .utils_matplotlib import make_mpl_figure import matplotlib.pyplot as plt dp = to_attr(data_plot) @@ -67,17 +68,15 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): n_cols = min(4, n_metrics) n_rows = int(np.ceil(n_metrics / n_cols)) - fig, axes = plt.subplots(n_rows, n_cols, figsize=(4 * n_cols, 3 * n_rows)) - if n_metrics == 1: - axes = np.array([[axes]]) - elif n_rows == 1: - axes = axes.reshape(1, -1) - elif n_cols == 1: - axes = axes.reshape(-1, 1) + backend_kwargs["ncols"] = n_cols + if "figsize" not in backend_kwargs: + backend_kwargs["figsize"] = (4 * n_cols, 3 * n_rows) + self.figure, self.ax, self.axes = make_mpl_figure(n_rows, n_cols, **backend_kwargs) colors = plt.cm.tab10(np.linspace(0, 1, 10)) absolute_value_metrics = ["amplitude_median"] + axes = self.axes for idx, metric_name in enumerate(metrics_to_plot): row, col = idx // n_cols, idx % n_cols ax = axes[row, col] @@ -112,10 +111,6 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): for idx in range(len(metrics_to_plot), n_rows * n_cols): axes[idx // n_cols, idx % n_cols].set_visible(False) - plt.tight_layout() - self.figure = fig - self.axes = axes - class UpsetPlotWidget(BaseWidget): """ @@ -181,6 +176,7 @@ def _get_metrics_for_unit_type(self, unit_type_label): return None def plot_matplotlib(self, data_plot, **backend_kwargs): + from .utils_matplotlib import make_mpl_figure import warnings import matplotlib.pyplot as plt import pandas as pd