diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..337f16e
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,16 @@
+.git
+.gitignore
+.idea
+.vscode
+__pycache__
+.ruff_cache
+.venv
+*.pyc
+*.pyo
+*.pyd
+*.swp
+*.swo
+*.DS_Store
+dist
+build
+*.egg-info
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..2c07333
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.11
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..1c44413
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,49 @@
+# syntax=docker/dockerfile:1
+
+# Minimal Linux base (glibc) – Python will be installed by uv
+FROM debian:bookworm-slim
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    UV_INSTALL_DIR=/usr/local/bin \
+    UV_LINK_MODE=copy \
+    UV_PYTHON_DOWNLOADS=1 \
+    UV_PROJECT_ENVIRONMENT=/app/.venv \
+    PATH=/app/.venv/bin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+
+WORKDIR /app
+
+# System deps for building/using common scientific stack
+# Keep minimal; rely on wheels where possible
+RUN apt-get update && apt-get install -y --no-install-recommends \
+      ca-certificates curl git \
+      build-essential pkg-config \
+      libssl-dev libffi-dev \
+      libopenblas0 libstdc++6 \
+      libfreetype6 libpng16-16 libjpeg62-turbo \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install uv (static binary)
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Copy project metadata first for layer caching
+COPY pyproject.toml README.md ./
+
+# Install a managed Python via uv and create the project venv
+RUN uv python install 3.11 \
+    && uv venv /app/.venv --python 3.11
+
+# Resolve and install runtime deps into project venv
+# Use lockfile if present for reproducibility
+RUN if [ -f uv.lock ]; then uv sync --no-dev --no-install-project --frozen; else uv sync --no-dev --no-install-project; fi
+
+# Copy source code and optional templates
+COPY src ./src
+
+# Re-sync to ensure the local package is installed
+RUN uv sync --no-dev \
+    && rm -rf /root/.cache
+
+# Default command shows help; override in compose or docker run
+CMD ["ners", "--help"]
diff --git a/README.md b/README.md
index dc429bb..91886e7 100644
--- a/README.md
+++ b/README.md
@@ -10,37 +10,23 @@ million names from the Democratic Republic of Congo (DRC) annotated with gender
 
 ### Installation & Setup
 
-Instructions and command line snippets bellow are provided to help you set up the project environment quickly and
-efficiently.
-assuming you have Python 3.11 and Git installed and working on a Unix-like system (Linux, macOS, etc.).
-
-**Using Makefile (Recommended)**
-
+**Unix based**
 ```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
 git clone https://github.com/bernard-ng/drc-ners-nlp.git
 cd drc-ners-nlp
 
-# Setup environment
-make setup
-make activate
+uv sync
 ```
 
-**Manual Setup**
-
+**Macos & windows**
 ```bash
-git clone https://github.com/bernard-ng/drc-ners-nlp.git
-cd drc-ners-nlp
-
-# Setup environment
-python -m venv .venv
-.venv/bin/pip install --upgrade pip
-.venv/bin/pip install -r requirements.txt
-
-pip install --upgrade pip
-pip install -r requirements.txt
-pip install jupyter notebook ipykernel pytest black flake8 mypy
-
-source .venv/bin/activate
+docker compose build
+docker compose run --rm app
+docker compose run --rm app ners pipeline run --env=production
+docker compose run --rm app ners research train --name=lightgbm --type=baseline --env=production
+docker compose run --rm --service-ports app ners web run --env=production
 ```
 
 ## Data Processing
@@ -55,6 +41,7 @@ the `drc-ners-nlp/config/pipeline.yaml` file.
 ```yaml
 stages:
   - "data_cleaning"
+  - "data_selection"
   - "feature_extraction"
   - "data_splitting"
 ```
@@ -62,37 +49,7 @@ stages:
 **Running the Pipeline**
 
 ```bash
-python main.py --env production
-```
-
-## NER Processing (Optional)
-
-This project implements a custom named entity recognition (NER) pipeline tailored for Congolese names. 
-Its main objective is to accurately identify and tag the different components of a Congolese name, 
-specifically distinguishing between the native part and the surname.
-
-```bash
-python ner.py --env production
-```
-
-Once you've built and train the NER model you can use it to annotate **COMPOSE** name in the original dataset 
-
-**Running the Pipeline with NER Annotation**
-```yaml
-stages:
-  - "data_cleaning"
-  - "feature_extraction"
-  - "ner_annotation"
-  - "data_splitting"
-```
-
-**Running the Pipeline with LLM Annotation**
-```yaml
-stages:
-  - "data_cleaning"
-  - "feature_extraction"
-  - "llm_annotation"
-  - "data_splitting"
+uv run ners pipeline run --env="production"
 ```
 
 ## Experiments
@@ -105,54 +62,94 @@ you can define model features, training parameters, and evaluation metrics in th
 
 ```bash
 # bigru
-python train.py --name="bigru" --type="baseline" --env="production"
-python train.py --name="bigru_native" --type="baseline" --env="production"
-python train.py --name="bigru_surname" --type="baseline" --env="production"
+uv run ners research train --name="bigru" --type="baseline" --env="production"
+uv run ners research train --name="bigru_native" --type="baseline" --env="production"
+uv run ners research train --name="bigru_surname" --type="baseline" --env="production"
 
 # cnn
-python train.py --name="cnn" --type="baseline" --env="production"
-python train.py --name="cnn_native" --type="baseline" --env="production"
-python train.py --name="cnn_surname" --type="baseline" --env="production"
+uv run ners research train --name="cnn" --type="baseline" --env="production"
+uv run ners research train --name="cnn_native" --type="baseline" --env="production"
+uv run ners research train --name="cnn_surname" --type="baseline" --env="production"
 
 # lightgbm
-python train.py --name="lightgbm" --type="baseline" --env="production"
-python train.py --name="lightgbm_native" --type="baseline" --env="production"
-python train.py --name="lightgbm_surname" --type="baseline" --env="production"
+uv run ners research train --name="lightgbm" --type="baseline" --env="production"
+uv run ners research train --name="lightgbm_native" --type="baseline" --env="production"
+uv run ners research train --name="lightgbm_surname" --type="baseline" --env="production"
 
 # logistic regression
-python train.py --name="logistic_regression" --type="baseline" --env="production"
-python train.py --name="logistic_regression_native" --type="baseline" --env="production"
-python train.py --name="logistic_regression_surname" --type="baseline" --env="production"
+uv run ners research train --name="logistic_regression" --type="baseline" --env="production"
+uv run ners research train --name="logistic_regression_native" --type="baseline" --env="production"
+uv run ners research train --name="logistic_regression_surname" --type="baseline" --env="production"
 
 # lstm
-python train.py --name="lstm" --type="baseline" --env="production"
-python train.py --name="lstm_native" --type="baseline" --env="production"
-python train.py --name="lstm_surname" --type="baseline" --env="production"
+uv run ners research train --name="lstm" --type="baseline" --env="production"
+uv run ners research train --name="lstm_native" --type="baseline" --env="production"
+uv run ners research train --name="lstm_surname" --type="baseline" --env="production"
 
 # random forest
-python train.py --name="random_forest" --type="baseline" --env="production"
-python train.py --name="random_forest_native" --type="baseline" --env="production"
-python train.py --name="random_forest_surname" --type="baseline" --env="production"
+uv run ners research train --name="random_forest" --type="baseline" --env="production"
+uv run ners research train --name="random_forest_native" --type="baseline" --env="production"
+uv run ners research train --name="random_forest_surname" --type="baseline" --env="production"
 
 # svm
-python train.py --name="svm" --type="baseline" --env="production"
-python train.py --name="svm_native" --type="baseline" --env="production"
-python train.py --name="svm_surname" --type="baseline" --env="production"
+uv run ners research train --name="svm" --type="baseline" --env="production"
+uv run ners research train --name="svm_native" --type="baseline" --env="production"
+uv run ners research train --name="svm_surname" --type="baseline" --env="production"
 
 # naive bayes
-python train.py --name="naive_bayes" --type="baseline" --env="production"
-python train.py --name="naive_bayes_native" --type="baseline" --env="production"
-python train.py --name="naive_bayes_surname" --type="baseline" --env="production"
+uv run ners research train --name="naive_bayes" --type="baseline" --env="production"
+uv run ners research train --name="naive_bayes_native" --type="baseline" --env="production"
+uv run ners research train --name="naive_bayes_surname" --type="baseline" --env="production"
 
 # transformer
-python train.py --name="transformer" --type="baseline" --env="production"
-python train.py --name="transformer_native" --type="baseline" --env="production"
-python train.py --name="transformer_surname" --type="baseline" --env="production"
+uv run ners research train --name="transformer" --type="baseline" --env="production"
+uv run ners research train --name="transformer_native" --type="baseline" --env="production"
+uv run ners research train --name="transformer_surname" --type="baseline" --env="production"
 
 # xgboost
-python train.py --name="xgboost" --type="baseline" --env="production"
-python train.py --name="xgboost_native" --type="baseline" --env="production"
-python train.py --name="xgboost_surname" --type="baseline" --env="production"
+uv run ners research train --name="xgboost" --type="baseline" --env="production"
+uv run ners research train --name="xgboost_native" --type="baseline" --env="production"
+uv run ners research train --name="xgboost_surname" --type="baseline" --env="production"
+```
+
+## TensorFlow on macOS (Intel) with uv
+
+TensorFlow no longer publishes wheels for macOS Intel. To keep using uv and run TF reliably, use a Linux container with TF preinstalled and install project code with minimal extras inside the container.
+
+### One-time build
+
+```bash
+docker compose -f docker/compose.tf.yml build
+
+If you see a message like `tensorflow/tensorflow:<tag>: not found`, update `docker/Dockerfile.tf-cpu` to a tag that exists (e.g., `2.17.0`) and rebuild:
+
+```bash
+sed -n '1,20p' docker/Dockerfile.tf-cpu  # verify the FROM line
+docker pull tensorflow/tensorflow:2.17.0 # quick availability check
+docker compose -f docker/compose.tf.yml build
+```
+```
+
+### Start a shell with uv and TF available
+
+```bash
+docker compose -f docker/compose.tf.yml run --rm tf bash
+```
+
+Inside the container:
+
+```bash
+# Install project in editable mode without pulling full deps
+uv pip install -e . --no-deps
+
+# Install only what research needs alongside TensorFlow
+uv pip install typer pandas scikit-learn seaborn plotly
+
+# Sanity check
+uv run python -c "import tensorflow as tf; print(tf.__version__)"
+
+# Run an experiment
+uv run ners research train --name="lstm" --type="baseline" --env="production"
 ```
 
 ## Web Interface
@@ -163,60 +160,9 @@ experiments and make predictions without needing to understand the underlying co
 ### Running the Web Interface
 
 ```bash
-streamlit run web/app.py
+uv run ners web run --env="production"
 ```
 
-## GPU Acceleration
-
-This project can leverage GPUs for faster training when supported libraries and hardware are available.
-
-- TensorFlow/Keras models (BiGRU, LSTM, CNN, Transformer)
-  - Uses GPU automatically if a TensorFlow GPU build is installed.
-  - The code enables safe GPU memory growth by default; optionally enable mixed precision for additional speed:
-    - Add `mixed_precision: true` in the experiment `model_params` (e.g., in `config/research_templates.yaml`).
-  - The final layer outputs are set to float32 for numerical stability under mixed precision.
-
-- spaCy NER
-  - Automatically prefers GPU if available; otherwise falls back to CPU.
-  - Ensure a compatible CUDA-enabled spaCy/thinc stack is installed to use GPU.
-
-- XGBoost
-  - Enable GPU by adding to the experiment `model_params`:
-    - `use_gpu: true` (sets `tree_method: gpu_hist` and `predictor: gpu_predictor`).
-
-- LightGBM
-  - Enable GPU by adding to the experiment `model_params`:
-    - `use_gpu: true` (sets `device: gpu`). Optional: `gpu_platform_id`, `gpu_device_id`.
-
-Example template snippet (GPU on):
-
-```yaml
-- name: "lstm_gpu"
-  description: "LSTM with GPU + mixed precision"
-  model_type: "lstm"
-  features: ["full_name"]
-  model_params:
-    embedding_dim: 128
-    lstm_units: 64
-    epochs: 5
-    batch_size: 128
-    use_gpu: true
-    mixed_precision: true
-  tags: ["gpu", "mixed_precision"]
-
-- name: "xgboost_gpu"
-  description: "XGBoost with GPU"
-  model_type: "xgboost"
-  features: ["full_name"]
-  model_params:
-    n_estimators: 200
-    use_gpu: true
-```
-
-Notes:
-- Install CUDA‑enabled binaries for TensorFlow/spaCy/LightGBM/XGBoost to actually use GPU.
-- If GPU is requested but not available, training will proceed on CPU with a warning.
-
 ## Contributors
 
 <a href="https://github.com/bernard-ng/drc-ners-nlp/graphs/contributors" title="show all contributors">
diff --git a/osm/provinces.cpg b/assets/osm/provinces.cpg
similarity index 100%
rename from osm/provinces.cpg
rename to assets/osm/provinces.cpg
diff --git a/osm/provinces.dbf b/assets/osm/provinces.dbf
similarity index 100%
rename from osm/provinces.dbf
rename to assets/osm/provinces.dbf
diff --git a/osm/provinces.prj b/assets/osm/provinces.prj
similarity index 100%
rename from osm/provinces.prj
rename to assets/osm/provinces.prj
diff --git a/osm/provinces.shp b/assets/osm/provinces.shp
similarity index 100%
rename from osm/provinces.shp
rename to assets/osm/provinces.shp
diff --git a/osm/provinces.shx b/assets/osm/provinces.shx
similarity index 100%
rename from osm/provinces.shx
rename to assets/osm/provinces.shx
diff --git a/compose.yml b/compose.yml
new file mode 100644
index 0000000..640b4cb
--- /dev/null
+++ b/compose.yml
@@ -0,0 +1,21 @@
+services:
+  app:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    image: drc-ners:uv
+    working_dir: /app
+    tty: true
+    stdin_open: true
+    environment:
+      NERS_ENV: production
+      STREAMLIT_SERVER_ADDRESS: 0.0.0.0
+    # expose Streamlit for `ners web run`
+    ports:
+      - "8501:8501"
+    volumes:
+      - ./assets:/app/assets
+      - ./config:/app/config
+      - ./data:/app/data
+    # default command shows CLI help; override per run
+    command: ["ners", "--help"]
diff --git a/monitor.py b/monitor.py
deleted file mode 100755
index a173fcc..0000000
--- a/monitor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-#!.venv/bin/python3
-import argparse
-import sys
-import traceback
-from pathlib import Path
-
-from core.config import setup_config
-from processing.monitoring.pipeline_monitor import PipelineMonitor
-
-
-def main():
-    choices = [
-        "data_cleaning",
-        "data_selection",
-        "feature_extraction",
-        "ner_annotation",
-        "llm_annotation",
-        "data_splitting",
-    ]
-
-    parser = argparse.ArgumentParser(description="DRC NERS Processing Monitoring")
-    parser.add_argument("--config", type=Path, help="Path to configuration file")
-    parser.add_argument("--env", type=str, default="development", help="Environment")
-    subparsers = parser.add_subparsers(dest="command", help="Available commands")
-
-    # Clean command
-    clean_parser = subparsers.add_parser("clean", help="Clean checkpoint files")
-    clean_parser.add_argument("--step", type=str, choices=choices, help="default: all")
-    clean_parser.add_argument("--keep-last", type=int, default=1, help="(default: 1)")
-    clean_parser.add_argument("--force", action="store_true", help="Clean without confirmation")
-
-    # Reset command
-    reset_parser = subparsers.add_parser("reset", help="Reset pipeline step")
-    reset_parser.add_argument("--step", type=str, choices=choices, help="(default: all)")
-    reset_parser.add_argument("--all", action="store_true", help="Reset all steps")
-    reset_parser.add_argument("--force", action="store_true", help="Reset without confirmation")
-    args = parser.parse_args()
-
-    try:
-        setup_config(config_path=args.config, env=args.env)
-        monitor = PipelineMonitor()
-
-        if not args.command:
-            parser.print_help()
-            monitor.print_status(detailed=True)
-            return 1
-
-        elif args.command == "clean":
-            checkpoint_info = monitor.count_checkpoint_files()
-            print(f"Current checkpoint storage: {checkpoint_info['total_size_mb']:.1f} MB")
-
-            if not args.force:
-                response = input("Are you sure you want to clean checkpoints? (y/N): ")
-                if response.lower() != "y":
-                    print("Cancelled")
-                    return 0
-
-            if args.step:
-                monitor.clean_step_checkpoints(args.step, args.keep_last)
-            else:
-                for step in monitor.steps:
-                    monitor.clean_step_checkpoints(step, args.keep_last)
-
-            print("Checkpoint cleaning completed")
-
-        elif args.command == "reset":
-            if not args.force:
-                response = input(
-                    f"Are you sure you want to reset {args.step}? This will delete all checkpoints. (y/N): "
-                )
-                if response.lower() != "y":
-                    print("Cancelled")
-                    return 0
-
-            if args.step:
-                monitor.reset_step(args.step)
-            else:
-                for step in monitor.steps:
-                    monitor.reset_step(step)
-
-            print(f"Reset completed")
-
-    except Exception as e:
-        print(f"Monitoring failed: {e}")
-        traceback.print_exc()
-        return 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/notebooks/analysis.ipynb b/notebooks/analysis.ipynb
deleted file mode 100644
index 32874cb..0000000
--- a/notebooks/analysis.ipynb
+++ /dev/null
@@ -1,499 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2025-09-21T12:06:07.309139Z",
-     "start_time": "2025-09-21T12:06:07.086638Z"
-    }
-   },
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import sys\n",
-    "import os\n",
-    "\n",
-    "sys.path.append(os.path.abspath(\"..\"))\n",
-    "from core.utils.data_loader import DataLoader\n",
-    "from core.config.pipeline_config import PipelineConfig"
-   ],
-   "outputs": [],
-   "execution_count": 1
-  },
-  {
-   "cell_type": "code",
-   "id": "74a8a262bc55d976",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-21T12:06:14.601682Z",
-     "start_time": "2025-09-21T12:06:14.598078Z"
-    }
-   },
-   "source": [
-    "config = PipelineConfig(\n",
-    "    paths={\n",
-    "        \"root_dir\": \"../data\",\n",
-    "        \"data_dir\": \"../data/dataset\",\n",
-    "        \"models_dir\": \"../models\",\n",
-    "        \"outputs_dir\": \"../data/processed\",\n",
-    "        \"logs_dir\": \"../logs\",\n",
-    "        \"configs_dir\": \"../configs\",\n",
-    "        \"checkpoints_dir\": \"../checkpoints\"\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "loader = DataLoader(config)"
-   ],
-   "outputs": [],
-   "execution_count": 2
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-21T12:07:02.954766Z",
-     "start_time": "2025-09-21T12:06:29.300866Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")",
-   "id": "171054a786856d23",
-   "outputs": [],
-   "execution_count": 3
-  },
-  {
-   "cell_type": "code",
-   "id": "295eda175081129c",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-21T12:08:17.955798Z",
-     "start_time": "2025-09-21T12:08:17.165693Z"
-    }
-   },
-   "source": [
-    "df.describe().T"
-   ],
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "                count       mean       std  min   25%   50%   75%   max\n",
-       "words       7901815.0   2.872912  0.466004  1.0   3.0   3.0   3.0  11.0\n",
-       "length      7901815.0  20.161653  3.796664  0.0  18.0  21.0  23.0  60.0\n",
-       "ner_tagged  6150395.0   0.998319   0.04097  0.0   1.0   1.0   1.0   1.0\n",
-       "annotated   6150395.0   0.998319   0.04097  0.0   1.0   1.0   1.0   1.0"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>words</th>\n",
-       "      <td>7901815.0</td>\n",
-       "      <td>2.872912</td>\n",
-       "      <td>0.466004</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>11.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>length</th>\n",
-       "      <td>7901815.0</td>\n",
-       "      <td>20.161653</td>\n",
-       "      <td>3.796664</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>60.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ner_tagged</th>\n",
-       "      <td>6150395.0</td>\n",
-       "      <td>0.998319</td>\n",
-       "      <td>0.04097</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>annotated</th>\n",
-       "      <td>6150395.0</td>\n",
-       "      <td>0.998319</td>\n",
-       "      <td>0.04097</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 6
-  },
-  {
-   "cell_type": "code",
-   "id": "c6f41484d947a77a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-21T12:08:26.820519Z",
-     "start_time": "2025-09-21T12:08:26.426014Z"
-    }
-   },
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import numpy as np\n",
-    "from scipy.interpolate import make_interp_spline\n",
-    "\n",
-    "# Step 1: Count frequencies\n",
-    "word_counts = df[\"words\"].value_counts().sort_index()\n",
-    "x = word_counts.index.values       # distinct word counts\n",
-    "y = word_counts.values             # their frequencies\n",
-    "\n",
-    "# Step 2: Create smoother X values (interpolation)\n",
-    "x_smooth = np.linspace(x.min(), x.max(), 300)\n",
-    "spline = make_interp_spline(x, y, k=3)  # cubic spline\n",
-    "y_smooth = spline(x_smooth)\n",
-    "\n",
-    "# Step 3: Plot\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.plot(x_smooth, y_smooth, color='blue', label='Smoothed Curve')\n",
-    "plt.scatter(x, y, color='black', zorder=5, label='Actual Counts')\n",
-    "plt.title(\"Smoothed Word Count Distribution\")\n",
-    "plt.xlabel(\"Number of Words in Name\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "plt.show()\n"
-   ],
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 7
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2533054dd18b57c7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-07-08T16:02:00.928164Z",
-     "start_time": "2025-07-08T16:02:00.666031Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import numpy as np\n",
-    "from scipy.interpolate import make_interp_spline\n",
-    "\n",
-    "# Step 1: Count frequencies\n",
-    "word_counts = df[\"length\"].value_counts().sort_index()\n",
-    "x = word_counts.index.values       # distinct word counts\n",
-    "y = word_counts.values             # their frequencies\n",
-    "\n",
-    "# Step 2: Create smoother X values (interpolation)\n",
-    "x_smooth = np.linspace(x.min(), x.max(), 300)\n",
-    "spline = make_interp_spline(x, y, k=3)  # cubic spline\n",
-    "y_smooth = spline(x_smooth)\n",
-    "\n",
-    "# Step 3: Plot\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.plot(x_smooth, y_smooth, color='blue', label='Smoothed Curve')\n",
-    "plt.scatter(x, y, color='black', zorder=5, label='Actual Counts')\n",
-    "plt.title(\"Smoothed Word Count Distribution\")\n",
-    "plt.xlabel(\"Number of Words in Name\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "af313d893f31698f",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-07-08T16:08:48.468235Z",
-     "start_time": "2025-07-08T16:08:47.627659Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      ">> Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
-      ">> Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gender_by_year = df.groupby(['year', 'sex']).size().reset_index(name='count')\n",
-    "\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "sns.barplot(data=gender_by_year, x='year', y='count', hue='sex')\n",
-    "plt.title('Gender Count by Year')\n",
-    "plt.xlabel('Year')\n",
-    "plt.ylabel('Count')\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.legend(title='Gender')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "93232202305056a3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-07-08T16:09:45.349954Z",
-     "start_time": "2025-07-08T16:09:45.086546Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sex_counts = df['sex'].value_counts()\n",
-    "\n",
-    "plt.figure(figsize=(8, 8))\n",
-    "plt.pie(sex_counts, labels=sex_counts.index, autopct='%1.1f%%', startangle=90, colors=['lightblue', 'lightpink'])\n",
-    "plt.title('Distribution of Sex')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "f7b8afb3bc565875",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-07-08T16:13:37.061494Z",
-     "start_time": "2025-07-08T16:13:36.764013Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAIjCAYAAAA0vUuxAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAARsdJREFUeJzt3XlcVdX+//H3QeAACijJ4IBKivOsqag5pEbq9UqDlVmoqd26mJrTI7u3zLQwzemmOZRKZV7LcuhnToSiOeZEOeWcmAJqqQgqIuzfHz0833sC3IDgQXg9H4/9yL322mt/9vGUvtt7LSyGYRgCAAAAAOTIydEFAAAAAEBRR3ACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAeqVq2a+vXr5+gyir3JkyfrwQcfVKlSpdS4cWNHl5Nnb7/9tiwWi6PLAIASjeAEAAUkKipKFotFu3fvzvZ4hw4dVL9+/bu+zurVq/X222/f9Tglxfr16zV69Gi1adNGCxcu1HvvvZdtv3/+859ycnLSH3/8Ydf+xx9/yMnJSVarVTdu3LA7dvLkSVksFr3xxhuFVn9+xMbG6oknnlBAQIBcXV3l5+enHj16aNmyZY4uTZJ07do1vf3224qNjXV0KQCQawQnAHCgI0eO6OOPP87TOatXr9a4ceMKqaLiZ8OGDXJyctL8+fMVHh6ubt26Zduvbdu2MgxDW7dutWvftm2bnJyclJ6eniUU3+7btm3bwik+H8aOHauOHTvqwIED+sc//qE5c+Zo1KhRSklJ0ZNPPqnFixc7ukRdu3ZN48aNIzgBuK84O7oAACjJrFaro0vIs9TUVJUuXdrRZeTa+fPn5e7uLldX1zv2ux1+tmzZoh49etjat27dqoYNG+r69evasmWLXUjasmWLnJyc1Lp167uq8datW8rMzDSt0czXX3+td955R0899ZQWL14sFxcX27FRo0Zp3bp1Sk9Pv6trAEBJxRMnAHCgv85xSk9P17hx4xQcHCw3Nzc98MADatu2raKjoyVJ/fr106xZsyRJFovFtt2WmpqqESNGKDAwUFarVbVq1dIHH3wgwzDsrnv9+nUNGTJE5cuXl6enp/7+97/r7Nmzslgsdq8B3p5bc+jQIT333HMqV66cLTj8/PPP6tevnx588EG5ubkpICBAL774on7//Xe7a90e4+jRo3r++efl7e0tX19fvfnmmzIMQ2fOnFHPnj3l5eWlgIAATZkyJVef3a1btzR+/HhVr15dVqtV1apV0xtvvKG0tDRbH4vFooULFyo1NdX2WUVFRWU7XpUqVRQYGJjlidPWrVvVpk0btW7dOttj9erVU9myZSX9GdIGDBggf39/ubm5qVGjRvr000/tzvn1119lsVj0wQcfaPr06bb6Dx06JOnPMPbQQw/Jzc1N1atX19y5c3P1eUjSm2++KR8fHy1YsMAuNN0WGhqqv/3tb7b93NQbGxsri8WS5enQ7fv438+zX79+KlOmjM6ePauwsDCVKVNGvr6+GjlypDIyMmzn+fr6SpLGjRtn+325/b1LTExU//79VblyZVmtVlWoUEE9e/bUr7/+muvPAQAKA0+cAKCAXblyRRcvXszSnpv/0//2228rMjJSAwcOVIsWLZScnKzdu3dr79696tKli/7xj3/o3Llzio6O1ueff253rmEY+vvf/66NGzdqwIABaty4sdatW6dRo0bp7NmzmjZtmq1vv3799NVXX+mFF15Qq1attGnTJnXv3j3Hunr16qXg4GC99957thAWHR2tkydPqn///goICNDBgwc1b948HTx4UDt27MiymMEzzzyjOnXqaOLEifruu+80YcIE+fj4aO7cuXrkkUf0/vvv64svvtDIkSP10EMPqV27dnf8rAYOHKhPP/1UTz31lEaMGKGdO3cqMjJShw8f1vLlyyVJn3/+uebNm6cff/xRn3zyiSTd8elQ27ZttWzZMqWlpclqtermzZvatWuXXnnlFV27dk2jR4+WYRiyWCy6dOmSDh06pJdfflnSn2G0Q4cOOn78uAYPHqygoCAtXbpU/fr10+XLlzV06FC7ay1cuFA3btzQSy+9JKvVKh8fH+3fv1+PPvqofH199fbbb+vWrVsaO3as/P397/hZSNKxY8f0yy+/6MUXX5Snp6dp/7zWm1sZGRkKDQ1Vy5Yt9cEHH+j777/XlClTVL16db3yyivy9fXV7Nmz9corr+jxxx/XE088IUlq2LChJOnJJ5/UwYMH9eqrr6patWo6f/68oqOjFR8fr2rVquWrJgAoEAYAoEAsXLjQkHTHrV69enbnVK1a1ejbt69tv1GjRkb37t3veJ2IiAgju/98r1ixwpBkTJgwwa79qaeeMiwWi3H8+HHDMAxjz549hiRj2LBhdv369etnSDLGjh1raxs7dqwhyejdu3eW6127di1L23//+19DkrF58+YsY7z00ku2tlu3bhmVK1c2LBaLMXHiRFv7pUuXDHd3d7vPJDtxcXGGJGPgwIF27SNHjjQkGRs2bLC19e3b1yhduvQdx7tt1qxZhiTjhx9+MAzDMLZv325IMk6fPm0cOnTIkGQcPHjQMAzDWLVqlSHJ+OKLLwzDMIzp06cbkoxFixbZxrt586YREhJilClTxkhOTjYMwzBOnTplSDK8vLyM8+fP210/LCzMcHNzM06fPm1rO3TokFGqVKlsf8//18qVKw1JxrRp03J1r7mtd+PGjYYkY+PGjXbn376PhQsX2tr69u1rSDLeeecdu75NmjQxmjVrZtu/cOFClu+aYfz5+y/JmDx5cq7uAQDuJV7VA4ACNmvWLEVHR2fZbv8f9TspW7asDh48qGPHjuX5uqtXr1apUqU0ZMgQu/YRI0bIMAytWbNGkrR27VpJf64i979effXVHMe+/VTlf7m7u9t+fePGDV28eFGtWrWSJO3duzdL/4EDB9p+XapUKTVv3lyGYWjAgAG29rJly6pWrVo6efJkjrVIf96rJA0fPtyufcSIEZKk77777o7n5+R/5zlJf76KV6lSJVWpUkW1a9eWj4+P7XW9vy4MsXr1agUEBKh379628VxcXDRkyBClpKRo06ZNdtd68sknba+sSX8+qVm3bp3CwsJUpUoVW3udOnUUGhpqWntycrIk5eppU37qzYu/fl8efvhh099TSba5aLGxsbp06VK+rw8AhaFEB6fNmzerR48eqlixoiwWi1asWJHnMQzD0AcffKCaNWvKarWqUqVKevfddwu+WAD3jRYtWqhz585ZtnLlypme+8477+jy5cuqWbOmGjRooFGjRunnn3/O1XVPnz6tihUrZvmLc506dWzHb//TyclJQUFBdv1q1KiR49h/7Sv9uUz30KFD5e/vL3d3d/n6+tr6XblyJUv//w0DkuTt7S03NzeVL18+S7vZX5pv38Nfaw4ICFDZsmVt95pX9evXV9myZe3CUZs2bST9OV8qJCTE7lhgYKDtvk6fPq3g4GA5Odn/0frXz/+2v36mFy5c0PXr1xUcHJylrlq1apnW7uXlJUm6evWqad/81Jtbbm5udoFQksqVK5erIGS1WvX+++9rzZo18vf3V7t27TRp0iQlJibmqxYAKEglOjilpqaqUaNGtonW+TF06FB98skn+uCDD/TLL7/o22+/VYsWLQqwSgAlSbt27XTixAktWLBA9evX1yeffKKmTZva5uc4yv8+Xbrt6aef1scff6yXX35Zy5Yt0/r1621PszIzM7P0L1WqVK7aJGVZzCInBf1DYZ2cnBQSEqJt27bZlib/3zlRrVu31pYtW2xzn+5mGfLsPtO7Ubt2bUnS/v37C3TcnD7j24s9/FVOv6e5NWzYMB09elSRkZFyc3PTm2++qTp16mjfvn13NS4A3K0SHZy6du2qCRMm6PHHH8/2eFpamkaOHKlKlSqpdOnSatmypd2qQocPH9bs2bO1cuVK/f3vf1dQUJCaNWumLl263KM7AFAc+fj4qH///vrvf/+rM2fOqGHDhnYr3eX0F9mqVavq3LlzWZ44/PLLL7bjt/+ZmZmpU6dO2fU7fvx4rmu8dOmSYmJi9Prrr2vcuHF6/PHH1aVLFz344IO5HuNu3L6Hv77SmJSUpMuXL9vuNT/atm2rP/74Q99++63Onz9ve+Ik/RmcTpw4odWrV+v69et2walq1ao6duxYltD4188/J76+vnJ3d8/2Nc0jR46Y1l2zZk3VqlVLK1euVEpKimn/3NZ7+0np5cuX7frl94mUZB54q1evrhEjRmj9+vU6cOCAbt68mevVFgGgsJTo4GRm8ODB2r59u5YsWaKff/5ZvXr10mOPPWb7Q+3//b//pwcffFCrVq1SUFCQqlWrpoEDB2b5qfMAkFt/Xcq7TJkyqlGjht0S27d/htJf/yLbrVs3ZWRkaObMmXbt06ZNk8ViUdeuXSXJNl/mo48+suv34Ycf5rrO208V/vpkaPr06bke427c/iG2f73e1KlTJemOKwSauR2G3n//fXl4eKhx48a2Yy1atJCzs7MmTZpk1/d2TYmJifryyy9tbbdu3dKHH36oMmXKqH379ne8bqlSpRQaGqoVK1YoPj7e1n748GGtW7cuV7WPGzdOv//+uwYOHKhbt25lOb5+/XqtWrUqT/VWrVpVpUqV0ubNm+3G+uv3Jy88PDwkZf0OX7t2TTdu3LBrq169ujw9Pe3+HQAAR2A58hzEx8dr4cKFio+PV8WKFSVJI0eO1Nq1a7Vw4UK99957OnnypE6fPq2lS5fqs88+U0ZGhl577TU99dRT2rBhg4PvAMD9qG7duurQoYOaNWsmHx8f7d69W19//bUGDx5s69OsWTNJ0pAhQxQaGqpSpUrp2WefVY8ePdSxY0f961//0q+//qpGjRpp/fr1WrlypYYNG6bq1avbzn/yySc1ffp0/f7777blyI8ePSopd6+/eXl52eafpKenq1KlSlq/fn2Wp1iFpVGjRurbt6/mzZuny5cvq3379vrxxx/16aefKiwsTB07dsz32C1atJCrq6u2b9+uDh06yNn5//6o9PDwUKNGjbR9+3aVLVtW9evXtx176aWXNHfuXPXr10979uxRtWrV9PXXX2vr1q2aPn16rhZtGDdunNauXauHH35Y//znP21Bpl69erma6/bMM89o//79evfdd7Vv3z717t1bVatW1e+//661a9cqJiZGixcvzlO93t7e6tWrlz788ENZLBZVr15dq1at0vnz5/P60dq4u7urbt26+vLLL1WzZk35+Piofv36unXrljp16qSnn35adevWlbOzs5YvX66kpCQ9++yz+b4eABQIRy7pV5RIMpYvX27bv73MbOnSpe02Z2dn4+mnnzYMwzAGDRpkSDKOHDliO+/2Mr+//PLLvb4FAA52eznyXbt2ZXu8ffv2psuRT5gwwWjRooVRtmxZw93d3ahdu7bx7rvvGjdv3rT1uXXrlvHqq68avr6+hsVisVum+urVq8Zrr71mVKxY0XBxcTGCg4ONyZMnG5mZmXbXTU1NNSIiIgwfHx+jTJkyRlhYmHHkyBFDkt3y4LeXEr9w4UKW+/ntt9+Mxx9/3Chbtqzh7e1t9OrVyzh37lyOS5r/dYyclgnP7nPKTnp6ujFu3DgjKCjIcHFxMQIDA40xY8YYN27cyNV17iQkJMSQZLzxxhtZjg0ZMsSQZHTt2jXLsaSkJKN///5G+fLlDVdXV6NBgwZ2y3Ubxv8t453TktubNm0ymjVrZri6uhoPPvigMWfOHNtnmFsxMTFGz549DT8/P8PZ2dnw9fU1evToYaxcuTLP9RrGn8uHP/nkk4aHh4dRrlw54x//+Idx4MCBbJcjz+6zzq7+bdu22e7z9nfm4sWLRkREhFG7dm2jdOnShre3t9GyZUvjq6++yvW9A0BhsRhGLmfgFnMWi0XLly9XWFiYJOnLL79Unz59dPDgwSwTXcuUKaOAgACNHTtW7733nt0Ptbx+/bo8PDy0fv165joBuK/ExcWpSZMmWrRokfr06ePocgAAKFJ4VS8HTZo0UUZGhs6fP6+HH3442z5t2rTRrVu3dOLECdsrMLdfdbmbickAUNiuX7+eZVW36dOny8nJSe3atXNQVQAAFF0lOjilpKTYrSJ16tQpxcXFycfHRzVr1lSfPn0UHh6uKVOmqEmTJrpw4YJiYmLUsGFDde/eXZ07d1bTpk314osvavr06crMzFRERIS6dOmimjVrOvDOAODOJk2apD179qhjx45ydnbWmjVrtGbNGr300ksKDAx0dHkAABQ5JfpVvdjY2GwnEPft21dRUVFKT0/XhAkT9Nlnn+ns2bMqX768WrVqpXHjxqlBgwaSpHPnzunVV1/V+vXrVbp0aXXt2lVTpkyRj4/Pvb4dAMi16OhojRs3TocOHVJKSoqqVKmiF154Qf/617/sFkMAAAB/KtHBCQAAAAByg5/jBAAAAAAmCE4AAAAAYKLEvciemZmpc+fOydPTM1c/5BEAAABA8WQYhq5evaqKFSvKyenOz5RKXHA6d+4cK0YBAAAAsDlz5owqV658xz4lLjh5enpK+vPD8fLycnA1AAAAABwlOTlZgYGBtoxwJyUuON1+Pc/Ly4vgBAAAACBXU3hYHAIAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMCEs6MLAJA38fHxunjxYqGNX758eVWpUqXQxgcAALgfEZyA+0h8fLxq16mj69euFdo13D089Mvhw4QnAACA/0FwAu4jFy9e1PVr1/T0hNnyCwou8PHPnzqmr/79ii5evEhwAgAA+B8EJ+A+5BcUrEp1Gjm6DAAAgBKDxSEAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMFJngNHHiRFksFg0bNuyO/ZYuXaratWvLzc1NDRo00OrVq+9NgQAAAABKrCIRnHbt2qW5c+eqYcOGd+y3bds29e7dWwMGDNC+ffsUFhamsLAwHThw4B5VCgAAAKAkcnhwSklJUZ8+ffTxxx+rXLlyd+w7Y8YMPfbYYxo1apTq1Kmj8ePHq2nTppo5c+Y9qhYAAABASeTw4BQREaHu3burc+fOpn23b9+epV9oaKi2b9+e4zlpaWlKTk622wAAAAAgL5wdefElS5Zo79692rVrV676JyYmyt/f367N399fiYmJOZ4TGRmpcePG3VWdAAAAAEo2hz1xOnPmjIYOHaovvvhCbm5uhXadMWPG6MqVK7btzJkzhXYtAAAAAMWTw5447dmzR+fPn1fTpk1tbRkZGdq8ebNmzpyptLQ0lSpVyu6cgIAAJSUl2bUlJSUpICAgx+tYrVZZrdaCLR4AAABAieKwJ06dOnXS/v37FRcXZ9uaN2+uPn36KC4uLktokqSQkBDFxMTYtUVHRyskJORelQ0AAACgBHLYEydPT0/Vr1/frq106dJ64IEHbO3h4eGqVKmSIiMjJUlDhw5V+/btNWXKFHXv3l1LlizR7t27NW/evHtePwAAAICSw+Gr6t1JfHy8EhISbPutW7fW4sWLNW/ePDVq1Ehff/21VqxYkSWAAQAAAEBBcuiqen8VGxt7x31J6tWrl3r16nVvCgIAAAAAFfEnTgAAAABQFBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATDg0OM2ePVsNGzaUl5eXvLy8FBISojVr1uTYPyoqShaLxW5zc3O7hxUDAAAAKImcHXnxypUra+LEiQoODpZhGPr000/Vs2dP7du3T/Xq1cv2HC8vLx05csS2b7FY7lW5AAAAAEoohwanHj162O2/++67mj17tnbs2JFjcLJYLAoICLgX5QEAAACApCI0xykjI0NLlixRamqqQkJCcuyXkpKiqlWrKjAwUD179tTBgwfvOG5aWpqSk5PtNgAAAADIC4cHp/3796tMmTKyWq16+eWXtXz5ctWtWzfbvrVq1dKCBQu0cuVKLVq0SJmZmWrdurV+++23HMePjIyUt7e3bQsMDCysWwEAAABQTDk8ONWqVUtxcXHauXOnXnnlFfXt21eHDh3Ktm9ISIjCw8PVuHFjtW/fXsuWLZOvr6/mzp2b4/hjxozRlStXbNuZM2cK61YAAAAAFFMOneMkSa6urqpRo4YkqVmzZtq1a5dmzJhxxzB0m4uLi5o0aaLjx4/n2MdqtcpqtRZYvQAAAABKHoc/cfqrzMxMpaWl5apvRkaG9u/frwoVKhRyVQAAAABKMoc+cRozZoy6du2qKlWq6OrVq1q8eLFiY2O1bt06SVJ4eLgqVaqkyMhISdI777yjVq1aqUaNGrp8+bImT56s06dPa+DAgY68DQAAAADFnEOD0/nz5xUeHq6EhAR5e3urYcOGWrdunbp06SJJio+Pl5PT/z0Uu3TpkgYNGqTExESVK1dOzZo107Zt23JcTAIAAAAACoJDg9P8+fPveDw2NtZuf9q0aZo2bVohVgQAAAAAWRW5OU4AAAAAUNQQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEw4NDjNnj1bDRs2lJeXl7y8vBQSEqI1a9bc8ZylS5eqdu3acnNzU4MGDbR69ep7VC0AAACAksqhwaly5cqaOHGi9uzZo927d+uRRx5Rz549dfDgwWz7b9u2Tb1799aAAQO0b98+hYWFKSwsTAcOHLjHlQMAAAAoSRwanHr06KFu3bopODhYNWvW1LvvvqsyZcpox44d2fafMWOGHnvsMY0aNUp16tTR+PHj1bRpU82cOTPHa6SlpSk5OdluAwAAAIC8KDJznDIyMrRkyRKlpqYqJCQk2z7bt29X586d7dpCQ0O1ffv2HMeNjIyUt7e3bQsMDCzQugEAAAAUfw4PTvv371eZMmVktVr18ssva/ny5apbt262fRMTE+Xv72/X5u/vr8TExBzHHzNmjK5cuWLbzpw5U6D1AwAAACj+nB1dQK1atRQXF6crV67o66+/Vt++fbVp06Ycw1NeWa1WWa3WAhkLAAAAQMnk8ODk6uqqGjVqSJKaNWumXbt2acaMGZo7d26WvgEBAUpKSrJrS0pKUkBAwD2pFQAAAEDJ5PBX9f4qMzNTaWlp2R4LCQlRTEyMXVt0dHSOc6IAAAAAoCA49InTmDFj1LVrV1WpUkVXr17V4sWLFRsbq3Xr1kmSwsPDValSJUVGRkqShg4dqvbt22vKlCnq3r27lixZot27d2vevHmOvA0AAAAAxZxDg9P58+cVHh6uhIQEeXt7q2HDhlq3bp26dOkiSYqPj5eT0/89FGvdurUWL16sf//733rjjTcUHBysFStWqH79+o66BQAAAAAlgEOD0/z58+94PDY2Nktbr1691KtXr0KqCAAAAACyKnJznAAAAACgqCE4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAICJfAWnkydPFsjFIyMj9dBDD8nT01N+fn4KCwvTkSNH7nhOVFSULBaL3ebm5lYg9QAAAABAdvIVnGrUqKGOHTtq0aJFunHjRr4vvmnTJkVERGjHjh2Kjo5Wenq6Hn30UaWmpt7xPC8vLyUkJNi206dP57sGAAAAADCTr+C0d+9eNWzYUMOHD1dAQID+8Y9/6Mcff8zzOGvXrlW/fv1Ur149NWrUSFFRUYqPj9eePXvueJ7FYlFAQIBt8/f3z89tAAAAAECu5Cs4NW7cWDNmzNC5c+e0YMECJSQkqG3btqpfv76mTp2qCxcu5KuYK1euSJJ8fHzu2C8lJUVVq1ZVYGCgevbsqYMHD+bYNy0tTcnJyXYbAAAAAOTFXS0O4ezsrCeeeEJLly7V+++/r+PHj2vkyJEKDAxUeHi4EhIScj1WZmamhg0bpjZt2qh+/fo59qtVq5YWLFiglStXatGiRcrMzFTr1q3122+/Zds/MjJS3t7eti0wMDDP9wkAAACgZLur4LR7927985//VIUKFTR16lSNHDlSJ06cUHR0tM6dO6eePXvmeqyIiAgdOHBAS5YsuWO/kJAQhYeHq3Hjxmrfvr2WLVsmX19fzZ07N9v+Y8aM0ZUrV2zbmTNn8nSPAAAAAOCcn5OmTp2qhQsX6siRI+rWrZs+++wzdevWTU5Of+awoKAgRUVFqVq1arkab/DgwVq1apU2b96sypUr56kWFxcXNWnSRMePH8/2uNVqldVqzdOYAAAAAPC/8hWcZs+erRdffFH9+vVThQoVsu3j5+en+fPn33EcwzD06quvavny5YqNjVVQUFCea8nIyND+/fvVrVu3PJ8LAAAAALmRr+B07Ngx0z6urq7q27fvHftERERo8eLFWrlypTw9PZWYmChJ8vb2lru7uyQpPDxclSpVUmRkpCTpnXfeUatWrVSjRg1dvnxZkydP1unTpzVw4MD83AoAAAAAmMpXcFq4cKHKlCmjXr162bUvXbpU165dMw1Mt82ePVuS1KFDhyzj9+vXT5IUHx9vewVQki5duqRBgwYpMTFR5cqVU7NmzbRt2zbVrVs3P7cCAAAAAKbyFZwiIyOzXYzBz89PL730Uq6Dk2EYpn1iY2Pt9qdNm6Zp06blanwAAAAAKAj5WlUvPj4+2/lIVatWVXx8/F0XBQAAAABFSb6Ck5+fn37++ecs7T/99JMeeOCBuy4KAAAAAIqSfAWn3r17a8iQIdq4caMyMjKUkZGhDRs2aOjQoXr22WcLukYAAAAAcKh8zXEaP368fv31V3Xq1EnOzn8OkZmZqfDwcL333nsFWiAAAAAAOFq+gpOrq6u+/PJLjR8/Xj/99JPc3d3VoEEDVa1ataDrAwAAAACHy1dwuq1mzZqqWbNmQdUCAAAAAEVSvoJTRkaGoqKiFBMTo/PnzyszM9Pu+IYNGwqkOAAAAAAoCvIVnIYOHaqoqCh1795d9evXl8ViKei6AAAAAKDIyFdwWrJkib766it169atoOsBAAAAgCInX8uRu7q6qkaNGgVdCwAAAAAUSfkKTiNGjNCMGTNkGEZB1wMAAAAARU6+XtXbsmWLNm7cqDVr1qhevXpycXGxO75s2bICKQ4AAAAAioJ8BaeyZcvq8ccfL+haAAAAAKBIyldwWrhwYUHXAQAAAABFVr7mOEnSrVu39P3332vu3Lm6evWqJOncuXNKSUkpsOIAAAAAoCjI1xOn06dP67HHHlN8fLzS0tLUpUsXeXp66v3331daWprmzJlT0HUCAAAAgMPk64nT0KFD1bx5c126dEnu7u629scff1wxMTEFVhwAAAAAFAX5euL0ww8/aNu2bXJ1dbVrr1atms6ePVsghQEAAABAUZGvJ06ZmZnKyMjI0v7bb7/J09PzrosCAAAAgKIkX8Hp0Ucf1fTp0237FotFKSkpGjt2rLp161ZQtQEAAABAkZCvV/WmTJmi0NBQ1a1bVzdu3NBzzz2nY8eOqXz58vrvf/9b0DUCAAAAgEPlKzhVrlxZP/30k5YsWaKff/5ZKSkpGjBggPr06WO3WAQAAAAAFAf5Ck6S5OzsrOeff74gawEAAACAIilfwemzzz674/Hw8PB8FQMAAAAARVG+gtPQoUPt9tPT03Xt2jW5urrKw8OD4AQAAACgWMnXqnqXLl2y21JSUnTkyBG1bduWxSEAAAAAFDv5Ck7ZCQ4O1sSJE7M8jQIAAACA+12BBSfpzwUjzp07V5BDAgAAAIDD5WuO07fffmu3bxiGEhISNHPmTLVp06ZACgMAAACAoiJfwSksLMxu32KxyNfXV4888oimTJlSEHUBAAAAQJGRr+CUmZlZ0HUAAAAAQJFVoHOcAAAAAKA4ytcTp+HDh+e679SpU/NzCQAAAAAoMvIVnPbt26d9+/YpPT1dtWrVkiQdPXpUpUqVUtOmTW39LBZLwVQJAAAAAA6Ur+DUo0cPeXp66tNPP1W5cuUk/flDcfv376+HH35YI0aMKNAiAQAAAMCR8jXHacqUKYqMjLSFJkkqV66cJkyYwKp6AAAAAIqdfAWn5ORkXbhwIUv7hQsXdPXq1bsuCgAAAACKknwFp8cff1z9+/fXsmXL9Ntvv+m3337TN998owEDBuiJJ54o6BoBAAAAwKHyNcdpzpw5GjlypJ577jmlp6f/OZCzswYMGKDJkycXaIEAAAAA4Gj5Ck4eHh766KOPNHnyZJ04cUKSVL16dZUuXbpAiwMAAACAouCufgBuQkKCEhISFBwcrNKlS8swjIKqCwAAAACKjHwFp99//12dOnVSzZo11a1bNyUkJEiSBgwYkKelyCMjI/XQQw/J09NTfn5+CgsL05EjR0zPW7p0qWrXri03Nzc1aNBAq1evzs9tAAAAAECu5Cs4vfbaa3JxcVF8fLw8PDxs7c8884zWrl2b63E2bdqkiIgI7dixQ9HR0UpPT9ejjz6q1NTUHM/Ztm2bevfurQEDBmjfvn0KCwtTWFiYDhw4kJ9bAQAAAABT+ZrjtH79eq1bt06VK1e2aw8ODtbp06dzPc5fQ1ZUVJT8/Py0Z88etWvXLttzZsyYoccee0yjRo2SJI0fP17R0dGaOXOm5syZk8c7AQAAAABz+XrilJqaavek6bY//vhDVqs138VcuXJFkuTj45Njn+3bt6tz5852baGhodq+fXu2/dPS0pScnGy3AQAAAEBe5Cs4Pfzww/rss89s+xaLRZmZmZo0aZI6duyYr0IyMzM1bNgwtWnTRvXr18+xX2Jiovz9/e3a/P39lZiYmG3/yMhIeXt727bAwMB81QcAAACg5MrXq3qTJk1Sp06dtHv3bt28eVOjR4/WwYMH9ccff2jr1q35KiQiIkIHDhzQli1b8nV+TsaMGaPhw4fb9pOTkwlPAAAAAPIkX8Gpfv36Onr0qGbOnClPT0+lpKToiSeeUEREhCpUqJDn8QYPHqxVq1Zp8+bNWeZN/VVAQICSkpLs2pKSkhQQEJBtf6vVelevDwIAAABAnoNTenq6HnvsMc2ZM0f/+te/7urihmHo1Vdf1fLlyxUbG6ugoCDTc0JCQhQTE6Nhw4bZ2qKjoxUSEnJXtQAAAABATvIcnFxcXPTzzz8XyMUjIiK0ePFirVy5Up6enrZ5St7e3nJ3d5ckhYeHq1KlSoqMjJQkDR06VO3bt9eUKVPUvXt3LVmyRLt379a8efMKpCYAAAAA+Kt8LQ7x/PPPa/78+Xd98dmzZ+vKlSvq0KGDKlSoYNu+/PJLW5/4+HjbD9iVpNatW2vx4sWaN2+eGjVqpK+//lorVqy444ISAAAAAHA38jXH6datW1qwYIG+//57NWvWTKVLl7Y7PnXq1FyNYxiGaZ/Y2Ngsbb169VKvXr1ydQ0AAAAAuFt5Ck4nT55UtWrVdODAATVt2lSSdPToUbs+Foul4KoDAAAAgCIgT8EpODhYCQkJ2rhxoyTpmWee0X/+858sP1cJAAAAAIqTPM1x+uurdWvWrFFqamqBFgQAAAAARU2+Foe4LTdzlAAAAADgfpen4GSxWLLMYWJOEwAAAIDiLk9znAzDUL9+/WS1WiVJN27c0Msvv5xlVb1ly5YVXIUAAAAA4GB5Ck59+/a123/++ecLtBgAAAAAKIryFJwWLlxYWHUAAAAAQJF1V4tDAAAAAEBJQHACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAw4dDgtHnzZvXo0UMVK1aUxWLRihUr7tg/NjZWFosly5aYmHhvCgYAAABQIjk0OKWmpqpRo0aaNWtWns47cuSIEhISbJufn18hVQgAAAAAkrMjL961a1d17do1z+f5+fmpbNmyBV8QAAAAAGTjvpzj1LhxY1WoUEFdunTR1q1b79g3LS1NycnJdhsAAAAA5MV9FZwqVKigOXPm6JtvvtE333yjwMBAdejQQXv37s3xnMjISHl7e9u2wMDAe1gxAAAAgOLAoa/q5VWtWrVUq1Yt237r1q114sQJTZs2TZ9//nm254wZM0bDhw+37ScnJxOeAAAAAOTJfRWcstOiRQtt2bIlx+NWq1VWq/UeVgQAAACguLmvXtXLTlxcnCpUqODoMgAAAAAUYw594pSSkqLjx4/b9k+dOqW4uDj5+PioSpUqGjNmjM6ePavPPvtMkjR9+nQFBQWpXr16unHjhj755BNt2LBB69evd9QtAAAAACgBHBqcdu/erY4dO9r2b89F6tu3r6KiopSQkKD4+Hjb8Zs3b2rEiBE6e/asPDw81LBhQ33//fd2YwAAAABAQXNocOrQoYMMw8jxeFRUlN3+6NGjNXr06EKuCgAAAADs3fdznAAAAACgsBGcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATDg0OG3evFk9evRQxYoVZbFYtGLFCtNzYmNj1bRpU1mtVtWoUUNRUVGFXicAAACAks3ZkRdPTU1Vo0aN9OKLL+qJJ54w7X/q1Cl1795dL7/8sr744gvFxMRo4MCBqlChgkJDQ+9BxbiX4uPjdfHixUIZu3z58qpSpUqhjA0AAIDix6HBqWvXruratWuu+8+ZM0dBQUGaMmWKJKlOnTrasmWLpk2bRnAqZuLj41W7Th1dv3atUMZ39/DQL4cPE54AAACQKw4NTnm1fft2de7c2a4tNDRUw4YNy/GctLQ0paWl2faTk5MLqzwUoIsXL+r6tWt6esJs+QUFF+jY508d01f/fkUXL14kOAEAACBX7qvglJiYKH9/f7s2f39/JScn6/r163J3d89yTmRkpMaNG3evSkQB8wsKVqU6jRxdBgAAAEq4Yr+q3pgxY3TlyhXbdubMGUeXBAAAAOA+c189cQoICFBSUpJdW1JSkry8vLJ92iRJVqtVVqv1XpQHAAAAoJi6r544hYSEKCYmxq4tOjpaISEhDqoIAAAAQEng0OCUkpKiuLg4xcXFSfpzufG4uDjFx8dL+vM1u/DwcFv/l19+WSdPntTo0aP1yy+/6KOPPtJXX32l1157zRHlAwAAACghHBqcdu/erSZNmqhJkyaSpOHDh6tJkyZ66623JEkJCQm2ECVJQUFB+u677xQdHa1GjRppypQp+uSTT1iKHAAAAEChcugcpw4dOsgwjByPR0VFZXvOvn37CrEqAAAAALB3X81xAgAAAABHIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYKBLBadasWapWrZrc3NzUsmVL/fjjjzn2jYqKksVisdvc3NzuYbUAAAAAShqHB6cvv/xSw4cP19ixY7V37141atRIoaGhOn/+fI7neHl5KSEhwbadPn36HlYMAAAAoKRxeHCaOnWqBg0apP79+6tu3bqaM2eOPDw8tGDBghzPsVgsCggIsG3+/v73sGIAAAAAJY1Dg9PNmze1Z88ede7c2dbm5OSkzp07a/v27Tmel5KSoqpVqyowMFA9e/bUwYMHc+yblpam5ORkuw0AAAAA8sKhwenixYvKyMjI8sTI399fiYmJ2Z5Tq1YtLViwQCtXrtSiRYuUmZmp1q1b67fffsu2f2RkpLy9vW1bYGBggd8HAAAAgOLN4a/q5VVISIjCw8PVuHFjtW/fXsuWLZOvr6/mzp2bbf8xY8boypUrtu3MmTP3uGIAAAAA9ztnR168fPnyKlWqlJKSkuzak5KSFBAQkKsxXFxc1KRJEx0/fjzb41arVVar9a5rBQAAAFByOfSJk6urq5o1a6aYmBhbW2ZmpmJiYhQSEpKrMTIyMrR//35VqFChsMoEAAAAUMI59ImTJA0fPlx9+/ZV8+bN1aJFC02fPl2pqanq37+/JCk8PFyVKlVSZGSkJOmdd95Rq1atVKNGDV2+fFmTJ0/W6dOnNXDgQEfeBgAAAIBizOHB6ZlnntGFCxf01ltvKTExUY0bN9batWttC0bEx8fLyen/HoxdunRJgwYNUmJiosqVK6dmzZpp27Ztqlu3rqNuAQAAAEAx5/DgJEmDBw/W4MGDsz0WGxtrtz9t2jRNmzbtHlQFAAAAAH+671bVAwAAAIB7jeAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgwtnRBQDA/SA+Pl4XL14stPHLly+vKlWqFNr4AADg7hCcAMBEfHy8atepo+vXrhXaNdw9PPTL4cOEJwAAiiiCEwCYuHjxoq5fu6anJ8yWX1BwgY9//tQxffXvV3Tx4kWCEwAARRTBCQByyS8oWJXqNHJ0GQAAwAFYHAIAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMBEkQhOs2bNUrVq1eTm5qaWLVvqxx9/vGP/pUuXqnbt2nJzc1ODBg20evXqe1QpAAAAgJLI2dEFfPnllxo+fLjmzJmjli1bavr06QoNDdWRI0fk5+eXpf+2bdvUu3dvRUZG6m9/+5sWL16ssLAw7d27V/Xr13fAHQBAyRYfH6+LFy8Wytjly5dXlSpVCmVsAADywuHBaerUqRo0aJD69+8vSZozZ46+++47LViwQK+//nqW/jNmzNBjjz2mUaNGSZLGjx+v6OhozZw5U3PmzLmntRcU/tIB4H4VHx+v2nXq6Pq1a4UyvruHh345fJj/jgEAHM6hwenmzZvas2ePxowZY2tzcnJS586dtX379mzP2b59u4YPH27XFhoaqhUrVmTbPy0tTWlpabb9K1euSJKSk5PvsvqCcebMGTV/6CHduH69UMa3urnp888+k7+/f4GP7eTkpMzMzAIfV5KOHDkiSTp7+GfdvJZaoGNfOH1CkrRnzx6lpKQU6Ni3FdZnU5ifi1T4n01hfmcKc3w+95wdOXJE169d08PhESobUKlAx76ceFY/fDZL69atU61atQp07NsK87O5X7/vhT12YY9P7Y4Zn9odM/79XHtAQIACAgIKZey8uJ0JDMMw72w40NmzZw1JxrZt2+zaR40aZbRo0SLbc1xcXIzFixfbtc2aNcvw8/PLtv/YsWMNSWxsbGxsbGxsbGxsbNluZ86cMc0uDn9Vr7CNGTPG7glVZmam/vjjDz3wwAOyWCwOrAx3Izk5WYGBgTpz5oy8vLwcXQ6KOb5vuNf4zuFe4vuGe60ofecMw9DVq1dVsWJF074ODU7ly5dXqVKllJSUZNeelJSU46O7gICAPPW3Wq2yWq12bWXLls1/0ShSvLy8HP4vHEoOvm+41/jO4V7i+4Z7rah857y9vXPVz6HLkbu6uqpZs2aKiYmxtWVmZiomJkYhISHZnhMSEmLXX5Kio6Nz7A8AAAAAd8vhr+oNHz5cffv2VfPmzdWiRQtNnz5dqamptlX2wsPDValSJUVGRkqShg4dqvbt22vKlCnq3r27lixZot27d2vevHmOvA0AAAAAxZjDg9MzzzyjCxcu6K233lJiYqIaN26stWvX2laBi4+Pl5PT/z0Ya926tRYvXqx///vfeuONNxQcHKwVK1bwM5xKGKvVqrFjx2Z5DRMoDHzfcK/xncO9xPcN99r9+p2zGEZu1t4DAAAAgJLLoXOcAAAAAOB+QHACAAAAABMEJwAAAAAwQXACAAAAABMEJ9w3IiMj9dBDD8nT01N+fn4KCwvTkSNHHF0WSpCJEyfKYrFo2LBhji4FxdTZs2f1/PPP64EHHpC7u7saNGig3bt3O7osFFMZGRl68803FRQUJHd3d1WvXl3jx48X64ahoGzevFk9evRQxYoVZbFYtGLFCrvjhmHorbfeUoUKFeTu7q7OnTvr2LFjjik2FwhOuG9s2rRJERER2rFjh6Kjo5Wenq5HH31Uqampji4NJcCuXbs0d+5cNWzY0NGloJi6dOmS2rRpIxcXF61Zs0aHDh3SlClTVK5cOUeXhmLq/fff1+zZszVz5kwdPnxY77//viZNmqQPP/zQ0aWhmEhNTVWjRo00a9asbI9PmjRJ//nPfzRnzhzt3LlTpUuXVmhoqG7cuHGPK80dliPHfevChQvy8/PTpk2b1K5dO0eXg2IsJSVFTZs21UcffaQJEyaocePGmj59uqPLQjHz+uuva+vWrfrhhx8cXQpKiL/97W/y9/fX/PnzbW1PPvmk3N3dtWjRIgdWhuLIYrFo+fLlCgsLk/Tn06aKFStqxIgRGjlypCTpypUr8vf3V1RUlJ599lkHVps9njjhvnXlyhVJko+Pj4MrQXEXERGh7t27q3Pnzo4uBcXYt99+q+bNm6tXr17y8/NTkyZN9PHHHzu6LBRjrVu3VkxMjI4ePSpJ+umnn7RlyxZ17drVwZWhJDh16pQSExPt/mz19vZWy5YttX37dgdWljNnRxcA5EdmZqaGDRumNm3aqH79+o4uB8XYkiVLtHfvXu3atcvRpaCYO3nypGbPnq3hw4frjTfe0K5duzRkyBC5urqqb9++ji4PxdDrr7+u5ORk1a5dW6VKlVJGRobeffdd9enTx9GloQRITEyUJPn7+9u1+/v7244VNQQn3JciIiJ04MABbdmyxdGloBg7c+aMhg4dqujoaLm5uTm6HBRzmZmZat68ud577z1JUpMmTXTgwAHNmTOH4IRC8dVXX+mLL77Q4sWLVa9ePcXFxWnYsGGqWLEi3zkgG7yqh/vO4MGDtWrVKm3cuFGVK1d2dDkoxvbs2aPz58+radOmcnZ2lrOzszZt2qT//Oc/cnZ2VkZGhqNLRDFSoUIF1a1b166tTp06io+Pd1BFKO5GjRql119/Xc8++6waNGigF154Qa+99poiIyMdXRpKgICAAElSUlKSXXtSUpLtWFFDcMJ9wzAMDR48WMuXL9eGDRsUFBTk6JJQzHXq1En79+9XXFycbWvevLn69OmjuLg4lSpVytElohhp06ZNlh+xcPToUVWtWtVBFaG4u3btmpyc7P8qWKpUKWVmZjqoIpQkQUFBCggIUExMjK0tOTlZO3fuVEhIiAMryxmv6uG+ERERocWLF2vlypXy9PS0vf/q7e0td3d3B1eH4sjT0zPLHLrSpUvrgQceYG4dCtxrr72m1q1b67333tPTTz+tH3/8UfPmzdO8efMcXRqKqR49eujdd99VlSpVVK9ePe3bt09Tp07Viy++6OjSUEykpKTo+PHjtv1Tp04pLi5OPj4+qlKlioYNG6YJEyYoODhYQUFBevPNN1WxYkXbyntFDcuR475hsViybV+4cKH69et3b4tBidWhQweWI0ehWbVqlcaMGaNjx44pKChIw4cP16BBgxxdFoqpq1ev6s0339Ty5ct1/vx5VaxYUb1799Zbb70lV1dXR5eHYiA2NlYdO3bM0t63b19FRUXJMAyNHTtW8+bN0+XLl9W2bVt99NFHqlmzpgOqNUdwAgAAAAATzHECAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACABQbHTp00LBhwxxdBgCgGCI4AQAKxJw5c+Tp6albt27Z2lJSUuTi4qIOHTrY9Y2NjZXFYtGJEyfucZXSzZs3NWnSJDVq1EgeHh4qX7682rRpo4ULFyo9Pf2e1kLQA4D7h7OjCwAAFA8dO3ZUSkqKdu/erVatWkmSfvjhBwUEBGjnzp26ceOG3NzcJEkbN25UlSpVVL169TxfxzAMZWRkyNk573+E3bx5U6Ghofrpp580fvx4tWnTRl5eXtqxY4c++OADNWnSRI0bN87zuACA4o8nTgCAAlGrVi1VqFBBsbGxtrbY2Fj17NlTQUFB2rFjh117x44dJUlpaWkaMmSI/Pz85ObmprZt22rXrl12fS0Wi9asWaNmzZrJarVqy5YtSk1NVXh4uMqUKaMKFSpoypQppjVOnz5dmzdvVkxMjCIiItS4cWM9+OCDeu6557Rz504FBwfnqqaoqCiVLVvWbuwVK1bIYrHY9t9++201btxYn3/+uapVqyZvb289++yzunr1qiSpX79+2rRpk2bMmCGLxSKLxaJff/1Vly5dUp8+feTr6yt3d3cFBwdr4cKFuf+NAAAUCoITAKDAdOzYURs3brTtb9y4UR06dFD79u1t7devX9fOnTttwWn06NH65ptv9Omnn2rv3r2qUaOGQkND9ccff9iN/frrr2vixIk6fPiwGjZsqFGjRmnTpk1auXKl1q9fr9jYWO3du/eO9X3xxRfq3LmzmjRpkuWYi4uLSpcunaeazJw4cUIrVqzQqlWrtGrVKm3atEkTJ06UJM2YMUMhISEaNGiQEhISlJCQoMDAQL355ps6dOiQ1qxZo8OHD2v27NkqX758nq4LACh4BCcAQIHp2LGjtm7dqlu3bunq1avat2+f2rdvr3bt2tmeRG3fvl1paWnq2LGjUlNTNXv2bE2ePFldu3ZV3bp19fHHH8vd3V3z58+3G/udd95Rly5dVL16dbm6umr+/Pn64IMP1KlTJzVo0ECffvqp3fyq7Bw7dky1a9e+Y5+81GQmMzNTUVFRql+/vh5++GG98MILiomJkSR5e3vL1dVVHh4eCggIUEBAgEqVKqX4+Hg1adJEzZs3V7Vq1dS5c2f16NEjT9cFABQ8ghMAoMB06NBBqamp2rVrl3744QfVrFlTvr6+at++vW2eU2xsrB588EFVqVJFJ06cUHp6utq0aWMbw8XFRS1atNDhw4ftxm7evLnt1ydOnNDNmzfVsmVLW5uPj49q1ap1x/oMwzC9h7zUZKZatWry9PS07VeoUEHnz5+/4zmvvPKKlixZosaNG2v06NHatm1bnq4JACgcBCcAQIGpUaOGKleurI0bN2rjxo1q3769JKlixYoKDAzUtm3btHHjRj3yyCN5Hvv2a3R3o2bNmvrll1/uehwnJ6csISy7FflcXFzs9i0WizIzM+84dteuXXX69Gm99tprOnfunDp16qSRI0fedc0AgLtDcAIAFKiOHTsqNjZWsbGxdsuQt2vXTmvWrNGPP/5om990+7W7rVu32vqlp6dr165dqlu3bo7XqF69ulxcXLRz505b26VLl3T06NE71vbcc8/p+++/1759+7IcS09PV2pqaq5q8vX11dWrV5WammrrExcXd8drZ8fV1VUZGRlZ2n19fdW3b18tWrRI06dP17x58/I8NgCgYBGcAAAFqmPHjtqyZYvi4uJsT5wkqX379po7d65u3rxpC06lS5fWK6+8olGjRmnt2rU6dOiQBg0apGvXrmnAgAE5XqNMmTIaMGCARo0apQ0bNujAgQPq16+fnJzu/MfasGHD1KZNG3Xq1EmzZs3STz/9pJMnT+qrr75Sq1atdOzYsVzV1LJlS3l4eOiNN97QiRMntHjxYkVFReX5s6pWrZp27typX3/9VRcvXlRmZqbeeustrVy5UsePH9fBgwe1atUq1alTJ89jAwAKFj/HCQBQoDp27Kjr16+rdu3a8vf3t7W3b99eV69etS1bftvEiROVmZmpF154QVevXlXz5s21bt06lStX7o7XmTx5slJSUtSjRw95enpqxIgRunLlyh3PsVqtio6O1rRp0zR37lyNHDlSHh4eqlOnjoYMGaL69evnqiYfHx8tWrRIo0aN0scff6xOnTrp7bff1ksvvZSnz2rkyJHq27ev6tatq+vXr+vUqVNydXXVmDFj9Ouvv8rd3V0PP/ywlixZkqdxAQAFz2LkZqYsAAAAAJRgvKoHAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACb+P3XBtmhRM6pKAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.hist(df['words'], bins=30, color='skyblue', edgecolor='black')\n",
-    "plt.title('Histogram of Word Counts')\n",
-    "plt.xlabel('Word Counts')\n",
-    "plt.ylabel('Frequency')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "f5c322d32b2d60fa",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-07-08T16:13:42.022402Z",
-     "start_time": "2025-07-08T16:13:41.781236Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAIjCAYAAAA0vUuxAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAR8VJREFUeJzt3Xt8z/X///H7e2MHp4nxnsOY08QHo4kkCctCPuhTCWMWOiFaOuxTGZGRQ1QyHZwqhxSqjxyX6VcpmeTQnHKYsLEcZjMb2+v3h4v3t3cbr+3tPe+N2/VyeV3yfr6er+fr8X57tbrv+Xo93xbDMAwBAAAAAK7KzdUFAAAAAEBxR3ACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACgCIWEBCggQMHurqMm97kyZNVt25dubu7q3nz5q4uB4UwcOBAlStXztVlAMA1EZwAoBDmzZsni8WiLVu25Lv/vvvuU5MmTa77PN98843GjBlz3ePcKtauXasXX3xRbdu21dy5czVhwoSr9h04cKAsFouaNWsmwzDy7LdYLBo2bFhRllsk4uPjZbFY9Pnnn7u6lHydP39eY8aMUXx8vKtLAQCHlHJ1AQBws9uzZ4/c3Ar3e6pvvvlGM2fOJDwV0Lfffis3Nzd99NFH8vDwKNAxO3bs0LJly/Sf//yniKuDdDk4jR07VtLlXzAAQEnDjBMAFDFPT0+VLl3a1WUUSkZGhqtLKJQTJ07I29u7wKHJ29tbgYGBev311/OddQIA4J8ITgBQxP75jNPFixc1duxYNWjQQF5eXqpcubLuuecerVu3TtLlW8lmzpwp6fJtY1e2KzIyMvT888/L399fnp6eatiwoaZMmZInAGRmZurZZ5+Vr6+vypcvr3//+986evSoLBaL3UzWmDFjZLFY9Pvvv6tv37667bbbdM8990iStm/froEDB6pu3bry8vKSn5+fHn/8cf31119257oyxt69exUWFiYfHx9VqVJFr732mgzD0JEjR9SjRw9VqFBBfn5+mjp1aoE+u0uXLmncuHGqV6+ePD09FRAQoP/+97/Kysqy9bFYLJo7d64yMjJsn9W8efOuOa6bm5teffVVbd++XcuXL79m3+zsbI0ePVrBwcHy8fFR2bJl1a5dO23YsMGu36FDh2SxWDRlyhTNnDlTdevWVZkyZdS5c2cdOXJEhmFo3Lhxqlmzpry9vdWjRw+dOnUqz/lWrVqldu3aqWzZsipfvry6deumXbt2FejzKogzZ85o5MiRtuunfv36mjRpknJzc/N9L++//77t87/zzjv1yy+/5Blz6dKlaty4sby8vNSkSRMtX75cAwcOVEBAgG28KlWqSJLGjh1r+3v654zq0aNH1bNnT5UrV05VqlTRqFGjlJOTY9dn8eLFCg4OVvny5VWhQgU1bdpUM2bMcNrnAwBXw616AOCAs2fPKjU1NU/7xYsXTY8dM2aMYmJiNHjwYLVq1UppaWnasmWLtm7dqvvvv19PPvmkjh07pnXr1unjjz+2O9YwDP373//Whg0bNGjQIDVv3lxr1qzRCy+8oKNHj+qtt96y9R04cKA+++wz9e/fX3fddZc2btyobt26XbWuRx55RA0aNNCECRNsIWzdunU6cOCAIiIi5Ofnp127dun999/Xrl279NNPP9kFOknq3bu3GjVqpIkTJ2rlypUaP368KlWqpNmzZ6tjx46aNGmSPv30U40aNUp33nmn7r333mt+VoMHD9b8+fP18MMP6/nnn9fPP/+smJgYJSYm2gLPxx9/rPfff1+bN2/Whx9+KEm6++67Tf8e+vbtq3Hjxun1119Xr1698ryXK9LS0vThhx+qT58+GjJkiM6dO6ePPvpIoaGh2rx5c56FKD799FNlZ2dr+PDhOnXqlN588009+uij6tixo+Lj4/XSSy9p//79eueddzRq1CjNmTPHduzHH3+s8PBwhYaGatKkSTp//rxmzZqle+65R7/++qstiDjq/Pnzat++vY4ePaonn3xStWrV0o8//qioqCgdP35c06dPt+u/cOFCnTt3Tk8++aQsFovefPNNPfTQQzpw4IBtFnXlypXq3bu3mjZtqpiYGJ0+fVqDBg1SjRo1bONUqVJFs2bN0tNPP61evXrpoYcekiQ1a9bM1icnJ0ehoaFq3bq1pkyZovXr12vq1KmqV6+enn76aUmXr8c+ffqoU6dOmjRpkiQpMTFRP/zwg0aMGHFdnw0AmDIAAAU2d+5cQ9I1t3/96192x9SuXdsIDw+3vQ4KCjK6det2zfMMHTrUyO9H9IoVKwxJxvjx4+3aH374YcNisRj79+83DMMwEhISDEnGyJEj7foNHDjQkGRER0fb2qKjow1JRp8+ffKc7/z583naFi1aZEgyvvvuuzxjPPHEE7a2S5cuGTVr1jQsFosxceJEW/vp06cNb29vu88kP9u2bTMkGYMHD7ZrHzVqlCHJ+Pbbb21t4eHhRtmyZa85Xn5958+fb0gyli1bZtsvyRg6dKjd+8jKyrIb4/Tp04bVajUef/xxW9vBgwcNSUaVKlWMM2fO2NqjoqIMSUZQUJBx8eJFW3ufPn0MDw8P48KFC4ZhGMa5c+eMihUrGkOGDLE7V3JysuHj45On/Z82bNhgSDKWLl161T7jxo0zypYta+zdu9eu/eWXXzbc3d2NpKQku/dSuXJl49SpU7Z+X375pSHJ+Prrr21tTZs2NWrWrGmcO3fO1hYfH29IMmrXrm1rO3nyZJ5r74rw8HBDkvH666/btbdo0cIIDg62vR4xYoRRoUIF49KlS9f8LACgKHCrHgA4YObMmVq3bl2e7e+/Qb+aihUrateuXdq3b1+hz/vNN9/I3d1dzz77rF37888/L8MwtGrVKknS6tWrJUnPPPOMXb/hw4dfdeynnnoqT5u3t7ftzxcuXFBqaqruuusuSdLWrVvz9B88eLDtz+7u7mrZsqUMw9CgQYNs7RUrVlTDhg114MCBq9YiXX6vkhQZGWnX/vzzz0u6PNNxvfr166cGDRpc81knd3d327NTubm5OnXqlC5duqSWLVvm+xk88sgj8vHxsb1u3bq1JCksLEylSpWya8/OztbRo0clXZ5NOXPmjPr06aPU1FTb5u7urtatW+e5NdARS5cuVbt27XTbbbfZnSMkJEQ5OTn67rvv7Pr37t1bt912m+11u3btJMn2d3fs2DHt2LFDAwYMsFtOvH379mratGmh6/vnNdiuXTu766RixYrKyMiw3dYKADfSLR2cvvvuO3Xv3l3Vq1eXxWLRihUrCj2GYRiaMmWKAgMD5enpqRo1auiNN95wfrEAipVWrVopJCQkz/b3/8m8mtdff11nzpxRYGCgmjZtqhdeeEHbt28v0HkPHz6s6tWrq3z58nbtjRo1su2/8k83NzfVqVPHrl/9+vWvOvY/+0rSqVOnNGLECFmtVnl7e6tKlSq2fmfPns3Tv1atWnavfXx85OXlJV9f3zztp0+fvmotf38P/6zZz89PFStWtL3X6+Hu7q5XX31V27Ztu+Z/A+bPn69mzZrZnkmrUqWKVq5cWeDPQJL8/f3zbb/yOVwJ0h07dlSVKlXstrVr1+rEiRMOv88r9u3bp9WrV+cZPyQkRJLynOOf7+XK9X2l5it/B/ldV9e61vLj5eVlew7q7+f7+3XyzDPPKDAwUF26dFHNmjX1+OOP235JAABF7ZZ+xikjI0NBQUF6/PHHbfdbF9aIESO0du1aTZkyRU2bNtWpU6fyfdgXAK6499579ccff+jLL7/U2rVr9eGHH+qtt95SbGys3YzNjfb32aUrHn30Uf3444964YUX1Lx5c5UrV065ubl64IEH7BYTuMLd3b1AbZIKvJrd1Z49cpZ+/frZnnXq2bNnnv2ffPKJBg4cqJ49e+qFF15Q1apV5e7urpiYGP3xxx95+l/t/Zp9Dlc+z48//lh+fn55+v19tspRubm5uv/++/Xiiy/muz8wMNDu9fX+3RXG1c71d1WrVtW2bdu0Zs0arVq1SqtWrdLcuXM1YMAAzZ8/3+k1AcDf3dLBqUuXLurSpctV92dlZemVV17RokWLdObMGTVp0kSTJk2yff9EYmKiZs2apZ07d6phw4aS8v+NLQD8U6VKlRQREaGIiAilp6fr3nvv1ZgxY2zB6WphoXbt2lq/fr3OnTtnN+u0e/du2/4r/8zNzdXBgwfVoEEDW7/9+/cXuMbTp08rLi5OY8eO1ejRo23tjtxi6Igr72Hfvn22GTVJSklJ0ZkzZ2zv9XpdmXUaOHCgvvzyyzz7P//8c9WtW1fLli2z+3uJjo52yvmvqFevnqTL4eDKDJCz1atXT+np6U4b/8rfQX7X1T/bnBWAPTw81L17d3Xv3l25ubl65plnNHv2bL322muFnuUCgMK4pW/VMzNs2DBt2rRJixcv1vbt2/XII4/ogQcesP1Pw9dff626devqf//7n+rUqaOAgAANHjyYGScA1/TPpbzLlSun+vXr2y2xXbZsWUmXl47+u65duyonJ0fvvvuuXftbb70li8Vi+2VQaGioJOm9996z6/fOO+8UuM4rMwD/nF3458prRaVr1675nm/atGmSdM0VAgsrLCxM9evXt31B69/l9zn8/PPP2rRpk9POL13+O6tQoYImTJiQ7+qMJ0+evO5zPProo9q0aZPWrFmTZ9+ZM2d06dKlQo1XvXp1NWnSRAsWLFB6erqtfePGjdqxY4dd3zJlytjO46h//rvj5uZme67w7//+AEBRuKVnnK4lKSlJc+fOVVJSkqpXry5JGjVqlFavXq25c+dqwoQJOnDggA4fPqylS5dqwYIFysnJ0XPPPaeHH35Y3377rYvfAYDiqnHjxrrvvvsUHBysSpUqacuWLfr88881bNgwW5/g4GBJ0rPPPqvQ0FC5u7vrscceU/fu3dWhQwe98sorOnTokIKCgrR27Vp9+eWXGjlypG3WIjg4WP/5z380ffp0/fXXX7blyPfu3SupYL/9r1Chgu699169+eabunjxomrUqKG1a9fq4MGDRfCp5BUUFKTw8HC9//77OnPmjNq3b6/Nmzdr/vz56tmzpzp06OC0c7m7u+uVV15RREREnn0PPvigli1bpl69eqlbt246ePCgYmNj1bhxY7uwcL0qVKigWbNmqX///rrjjjv02GOPqUqVKkpKStLKlSvVtm3bPIE5P1988YVtBvLvwsPD9cILL+irr77Sgw8+qIEDByo4OFgZGRnasWOHPv/8cx06dCjP82hmJkyYoB49eqht27aKiIjQ6dOn9e6776pJkyZ2n4+3t7caN26sJUuWKDAwUJUqVVKTJk3UpEmTAp/ryi8nO3bsqJo1a+rw4cN655131Lx5c7tZSQAoCgSnq9ixY4dycnLy3O+dlZWlypUrS7p8r3hWVpYWLFhg6/fRRx8pODhYe/bssd2+BwB/9+yzz+qrr77S2rVrlZWVpdq1a2v8+PF64YUXbH0eeughDR8+XIsXL9Ynn3wiwzD02GOPyc3NTV999ZVGjx6tJUuWaO7cuQoICNDkyZNtq81dsWDBAvn5+WnRokVavny5QkJCtGTJEjVs2FBeXl4FqnXhwoUaPny4Zs6cKcMw1LlzZ61atcr2C6Wi9uGHH6pu3bqaN2+eli9fLj8/P0VFRTn9Njnp8qzT+PHj8zy3NHDgQCUnJ2v27Nlas2aNGjdurE8++URLly5VfHy8U2vo27evqlevrokTJ2ry5MnKyspSjRo11K5du3xDXX4WL16cb/t9990nf39/bdy4URMmTLD90q9ChQoKDAzU2LFj7VYDLKju3btr0aJFGjNmjF5++WU1aNBA8+bN0/z58/N8ce+HH36o4cOH67nnnlN2draio6MLFZzCwsL0/vvv67333tOZM2fk5+en3r17a8yYMXJz4yYaAEXLYhTFE54lkMVi0fLly20PBi9ZskT9+vXTrl278jywWq5cOfn5+Sk6OjrPLRWZmZkqU6aM1q5dq/vvv/9GvgUAMLVt2za1aNFCn3zyifr16+fqcnATa968uapUqcLS4QBuGsw4XUWLFi2Uk5OjEydO2L634p/atm2rS5cu6Y8//rDdHnPlNhhnPbQMAI7KzMzMs1Le9OnT5ebmpnvvvddFVeFmc/HiRVksFrtV/+Lj4/Xbb79p/PjxLqwMAJzrlp5xSk9Pt63606JFC02bNk0dOnRQpUqVVKtWLYWFhemHH37Q1KlT1aJFC508eVJxcXFq1qyZunXrptzcXN15550qV66cpk+frtzcXA0dOlQVKlTQ2rVrXfzuANzqxo4dq4SEBHXo0EGlSpWyLd/8xBNPaPbs2a4uDzeJQ4cOKSQkRGFhYapevbp2796t2NhY+fj4aOfOnbbb2wGgpLulg1N8fHy+DxeHh4dr3rx5unjxosaPH68FCxbo6NGj8vX11V133aWxY8favhH92LFjGj58uNauXauyZcuqS5cumjp1qipVqnSj3w4A2Fm3bp3Gjh2r33//Xenp6apVq5b69++vV155xSnfCQRIl78I+YknntAPP/ygkydPqmzZsurUqZMmTpxouxsDAG4Gt3RwAgAAAICCYAkaAAAAADBBcAIAAAAAE7fcTe65ubk6duyYypcvX6AvgAQAAABwczIMQ+fOnVP16tVNvw/ulgtOx44dk7+/v6vLAAAAAFBMHDlyRDVr1rxmn1suOJUvX17S5Q+nQoUKLq4GAAAAgKukpaXJ39/flhGu5ZYLTlduz6tQoQLBCQAAAECBHuFhcQgAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATpVxdAABASkpKUmpqapGM7evrq1q1ahXJ2AAA3CoITgDgYklJSbq90e3KPJ9ZJON7l/HW7sTdhCcAAK4DwQkAXCw1NVWZ5zMVNjtM1kCrU8dO2ZuiT578RKmpqQQnAACuA8EJAIoJa6BV/kH+ri4DAADkg8UhAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMBEKVcXAAAlQVJSklJTU4tk7MTExCIZFwAAOA/BCQBMJCUl6fZGtyvzfKarSwEAAC5CcAIAE6mpqco8n6mw2WGyBlqdPv7v63/XqjdWOX1cAADgPAQnACgga6BV/kH+Th83ZW+K08cEAADOxeIQAAAAAGDCpcHpu+++U/fu3VW9enVZLBatWLHimv2XLVum+++/X1WqVFGFChXUpk0brVmz5sYUCwAAAOCW5dLglJGRoaCgIM2cObNA/b/77jvdf//9+uabb5SQkKAOHTqoe/fu+vXXX4u4UgAAAAC3Mpc+49SlSxd16dKlwP2nT59u93rChAn68ssv9fXXX6tFixZOrg4AAAAALivRi0Pk5ubq3LlzqlSp0lX7ZGVlKSsry/Y6LS3tRpQGAAAA4CZSoheHmDJlitLT0/Xoo49etU9MTIx8fHxsm7+/81fEAgAAAHBzK7HBaeHChRo7dqw+++wzVa1a9ar9oqKidPbsWdt25MiRG1glAAAAgJtBibxVb/HixRo8eLCWLl2qkJCQa/b19PSUp6fnDaoMAAAAwM2oxM04LVq0SBEREVq0aJG6devm6nIAAAAA3AJcOuOUnp6u/fv3214fPHhQ27ZtU6VKlVSrVi1FRUXp6NGjWrBggaTLt+eFh4drxowZat26tZKTkyVJ3t7e8vHxccl7AAAAAHDzc+mM05YtW9SiRQvbUuKRkZFq0aKFRo8eLUk6fvy4kpKSbP3ff/99Xbp0SUOHDlW1atVs24gRI1xSPwAAAIBbg0tnnO677z4ZhnHV/fPmzbN7HR8fX7QFAQAAAEA+StwzTgAAAABwoxGcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATLg0OH333Xfq3r27qlevLovFohUrVpgeEx8frzvuuEOenp6qX7++5s2bV+R1AgAAALi1uTQ4ZWRkKCgoSDNnzixQ/4MHD6pbt27q0KGDtm3bppEjR2rw4MFas2ZNEVcKAAAA4FZWypUn79Kli7p06VLg/rGxsapTp46mTp0qSWrUqJG+//57vfXWWwoNDS2qMgEAAADc4krUM06bNm1SSEiIXVtoaKg2bdp01WOysrKUlpZmtwEAAABAYZSo4JScnCyr1WrXZrValZaWpszMzHyPiYmJkY+Pj23z9/e/EaUCAAAAuImUqODkiKioKJ09e9a2HTlyxNUlAQAAAChhXPqMU2H5+fkpJSXFri0lJUUVKlSQt7d3vsd4enrK09PzRpQHAAAA4CZVomac2rRpo7i4OLu2devWqU2bNi6qCAAAAMCtwKXBKT09Xdu2bdO2bdskXV5ufNu2bUpKSpJ0+Ta7AQMG2Po/9dRTOnDggF588UXt3r1b7733nj777DM999xzrigfAAAAwC3CpcFpy5YtatGihVq0aCFJioyMVIsWLTR69GhJ0vHjx20hSpLq1KmjlStXat26dQoKCtLUqVP14YcfshQ5AAAAgCLl0mec7rvvPhmGcdX98+bNy/eYX3/9tQirAgAAAAB7JeoZJwAAAABwBYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACZcHp5kzZyogIEBeXl5q3bq1Nm/efM3+06dPV8OGDeXt7S1/f38999xzunDhwg2qFgAAAMCtyKXBacmSJYqMjFR0dLS2bt2qoKAghYaG6sSJE/n2X7hwoV5++WVFR0crMTFRH330kZYsWaL//ve/N7hyAAAAALcSlwanadOmaciQIYqIiFDjxo0VGxurMmXKaM6cOfn2//HHH9W2bVv17dtXAQEB6ty5s/r06WM6SwUAAAAA18NlwSk7O1sJCQkKCQn5v2Lc3BQSEqJNmzble8zdd9+thIQEW1A6cOCAvvnmG3Xt2vWq58nKylJaWprdBgAAAACFUcpVJ05NTVVOTo6sVqtdu9Vq1e7du/M9pm/fvkpNTdU999wjwzB06dIlPfXUU9e8VS8mJkZjx451au0AAAAAbi0OzTgdOHDA2XUUSHx8vCZMmKD33ntPW7du1bJly7Ry5UqNGzfuqsdERUXp7Nmztu3IkSM3sGIAAAAANwOHZpzq16+v9u3ba9CgQXr44Yfl5eVV6DF8fX3l7u6ulJQUu/aUlBT5+fnle8xrr72m/v37a/DgwZKkpk2bKiMjQ0888YReeeUVubnlzYGenp7y9PQsdH0AAAAAcIVDM05bt25Vs2bNFBkZKT8/Pz355JOFXqDBw8NDwcHBiouLs7Xl5uYqLi5Obdq0yfeY8+fP5wlH7u7ukiTDMAr5LgAAAACgYBwKTs2bN9eMGTN07NgxzZkzR8ePH9c999yjJk2aaNq0aTp58mSBxomMjNQHH3yg+fPnKzExUU8//bQyMjIUEREhSRowYICioqJs/bt3765Zs2Zp8eLFOnjwoNatW6fXXntN3bt3twUoAAAAAHC261ocolSpUnrooYfUrVs3vffee4qKitKoUaP03//+V48++qgmTZqkatWqXfX43r176+TJkxo9erSSk5PVvHlzrV692rZgRFJSkt0M06uvviqLxaJXX31VR48eVZUqVdS9e3e98cYb1/M2AAAAAOCaris4bdmyRXPmzNHixYtVtmxZjRo1SoMGDdKff/6psWPHqkePHqa38A0bNkzDhg3Ld198fLx9saVKKTo6WtHR0ddTNgAAAAAUikPBadq0aZo7d6727Nmjrl27asGCBeratattdqhOnTqaN2+eAgICnFkrAAAAALiEQ8Fp1qxZevzxxzVw4MCr3opXtWpVffTRR9dVHAAAAAAUBw4Fp3379pn28fDwUHh4uCPDAwAAAECx4tCqenPnztXSpUvztC9dulTz58+/7qIAAAAAoDhxKDjFxMTI19c3T3vVqlU1YcKE6y4KAAAAAIoTh4JTUlKS6tSpk6e9du3aSkpKuu6iAAAAAKA4cSg4Va1aVdu3b8/T/ttvv6ly5crXXRQAAAAAFCcOBac+ffro2Wef1YYNG5STk6OcnBx9++23GjFihB577DFn1wgAAAAALuXQqnrjxo3ToUOH1KlTJ5UqdXmI3NxcDRgwgGecAAAAANx0HApOHh4eWrJkicaNG6fffvtN3t7eatq0qWrXru3s+gAAAADA5RwKTlcEBgYqMDDQWbUAAAAAQLHkUHDKycnRvHnzFBcXpxMnTig3N9du/7fffuuU4gAAAACgOHAoOI0YMULz5s1Tt27d1KRJE1ksFmfXBQAAAADFhkPBafHixfrss8/UtWtXZ9cDAAAAAMWOQ8uRe3h4qH79+s6uBQAAAACKJYeC0/PPP68ZM2bIMAxn1wMAAAAAxY5Dt+p9//332rBhg1atWqV//etfKl26tN3+ZcuWOaU4AAAAACgOHApOFStWVK9evZxdCwAAAAAUSw4Fp7lz5zq7DgAAAAAothx6xkmSLl26pPXr12v27Nk6d+6cJOnYsWNKT093WnEAAAAAUBw4NON0+PBhPfDAA0pKSlJWVpbuv/9+lS9fXpMmTVJWVpZiY2OdXScAAAAAuIxDM04jRoxQy5Ytdfr0aXl7e9vae/Xqpbi4OKcVBwAAAADFgUMzTv/v//0//fjjj/Lw8LBrDwgI0NGjR51SGAAAAAAUFw7NOOXm5ionJydP+59//qny5ctfd1EAAAAAUJw4FJw6d+6s6dOn215bLBalp6crOjpaXbt2dVZtAAAAAFAsOHSr3tSpUxUaGqrGjRvrwoUL6tu3r/bt2ydfX18tWrTI2TUCAAAAgEs5FJxq1qyp3377TYsXL9b27duVnp6uQYMGqV+/fnaLRQAAAADAzcCh4CRJpUqVUlhYmDNrAQAAAIBiyaHgtGDBgmvuHzBggEPFAAAAAEBx5FBwGjFihN3rixcv6vz58/Lw8FCZMmUITgAAAABuKg6tqnf69Gm7LT09XXv27NE999zD4hAAAAAAbjoOBaf8NGjQQBMnTswzGwUAAAAAJZ3TgpN0ecGIY8eOOXNIAAAAAHA5h55x+uqrr+xeG4ah48eP691331Xbtm2dUhgAAAAAFBcOBaeePXvavbZYLKpSpYo6duyoqVOnOqMuAAAAACg2HApOubm5zq4DAAAAAIotpz7jBAAAAAA3I4dmnCIjIwvcd9q0aY6cAgAAAACKDYeC06+//qpff/1VFy9eVMOGDSVJe/fulbu7u+644w5bP4vF4pwqAQAAAMCFHApO3bt3V/ny5TV//nzddtttki5/KW5ERITatWun559/3qlFAgAAAIArOfSM09SpUxUTE2MLTZJ02223afz48ayqBwAAAOCm41BwSktL08mTJ/O0nzx5UufOnbvuogAAAACgOHEoOPXq1UsRERFatmyZ/vzzT/3555/64osvNGjQID300EPOrhEAAAAAXMqhZ5xiY2M1atQo9e3bVxcvXrw8UKlSGjRokCZPnuzUAgEAAADA1RwKTmXKlNF7772nyZMn648//pAk1atXT2XLlnVqcQAAAABQHFzXF+AeP35cx48fV4MGDVS2bFkZhuGsugAAAACg2HAoOP3111/q1KmTAgMD1bVrVx0/flySNGjQIJYiBwAAAHDTcSg4PffccypdurSSkpJUpkwZW3vv3r21evVqpxUHAAAAAMWBQ884rV27VmvWrFHNmjXt2hs0aKDDhw87pTAAAAAAKC4cmnHKyMiwm2m64tSpU/L09LzuogAAAACgOHEoOLVr104LFiywvbZYLMrNzdWbb76pDh06OK04AAAAACgOHLpV780331SnTp20ZcsWZWdn68UXX9SuXbt06tQp/fDDD86uEQAAAABcyqEZpyZNmmjv3r2655571KNHD2VkZOihhx7Sr7/+qnr16jm7RgAAAABwqULPOF28eFEPPPCAYmNj9corrxRFTQAAAABQrBR6xql06dLavn17UdQCAAAAAMWSQ7fqhYWF6aOPPnJKATNnzlRAQIC8vLzUunVrbd68+Zr9z5w5o6FDh6patWry9PRUYGCgvvnmG6fUAgAAAAD5cWhxiEuXLmnOnDlav369goODVbZsWbv906ZNK9A4S5YsUWRkpGJjY9W6dWtNnz5doaGh2rNnj6pWrZqnf3Z2tu6//35VrVpVn3/+uWrUqKHDhw+rYsWKjrwNAAAAACiQQgWnAwcOKCAgQDt37tQdd9whSdq7d69dH4vFUuDxpk2bpiFDhigiIkKSFBsbq5UrV2rOnDl6+eWX8/SfM2eOTp06pR9//FGlS5eWJAUEBBTmLQAAAABAoRUqODVo0EDHjx/Xhg0bJEm9e/fW22+/LavVWugTZ2dnKyEhQVFRUbY2Nzc3hYSEaNOmTfke89VXX6lNmzYaOnSovvzyS1WpUkV9+/bVSy+9JHd393yPycrKUlZWlu11WlpaoWsFAAAAcGsr1DNOhmHYvV61apUyMjIcOnFqaqpycnLyhC6r1ark5OR8jzlw4IA+//xz5eTk6JtvvtFrr72mqVOnavz48Vc9T0xMjHx8fGybv7+/Q/UCAAAAuHU5tDjEFf8MUkUtNzdXVatW1fvvv6/g4GD17t1br7zyimJjY696TFRUlM6ePWvbjhw5cgMrBgAAAHAzKNStehaLJc8zTIV5punvfH195e7urpSUFLv2lJQU+fn55XtMtWrVVLp0abvb8ho1aqTk5GRlZ2fLw8MjzzGenp7y9PR0qEYAAAAAkAoZnAzD0MCBA21B5MKFC3rqqafyrKq3bNky07E8PDwUHBysuLg49ezZU9LlGaW4uDgNGzYs32Patm2rhQsXKjc3V25ulyfL9u7dq2rVquUbmgAAAADAGQoVnMLDw+1eh4WFXdfJIyMjFR4erpYtW6pVq1aaPn26MjIybKvsDRgwQDVq1FBMTIwk6emnn9a7776rESNGaPjw4dq3b58mTJigZ5999rrqAAAAAIBrKVRwmjt3rlNP3rt3b508eVKjR49WcnKymjdvrtWrV9sWjEhKSrLNLEmSv7+/1qxZo+eee07NmjVTjRo1NGLECL300ktOrQsAAAAA/s6hL8B1pmHDhl311rz4+Pg8bW3atNFPP/1UxFUBAAAAwP+5rlX1AAAAAOBW4PIZJwBAyZaUlKTU1NQiGdvX11e1atUqkrEBACgMghMAwGFJSUm6vdHtyjyfWSTje5fx1u7E3YQnAIDLEZwAAA5LTU1V5vlMhc0OkzXQ6tSxU/am6JMnP1FqairBCQDgcgQnAMB1swZa5R/k7+oyAAAoMiwOAQAAAAAmCE4AAAAAYILgBAAAAAAmeMYJwA1TlMtWSyxdDQAAig7BCcANUdTLVkssXQ0AAIoOwQnADVGUy1ZLLF0NAACKFsEJwA3FstUAAKAkYnEIAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBRytUFAACKXmJiYokaFwCA4obgBAA3sbSUNFncLAoLC3N1KQAAlGgEJwC4iWWezZSRayhsdpisgVanj//7+t+16o1VTh8XAIDihuAEALcAa6BV/kH+Th83ZW+K08cEAKA4YnEIAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBRytUFAIAzJSYmlogxAQBAyUJwAnBTSEtJk8XNorCwMFeXAgAAbkIEJwA3hcyzmTJyDYXNDpM10OrUsX9f/7tWvbHKqWMCAICSheAE4KZiDbTKP8jfqWOm7E1x6ngAAKDkYXEIAAAAADBBcAIAAAAAE8UiOM2cOVMBAQHy8vJS69attXnz5gIdt3jxYlksFvXs2bNoCwQAAABwS3N5cFqyZIkiIyMVHR2trVu3KigoSKGhoTpx4sQ1jzt06JBGjRqldu3a3aBKAQAAANyqXB6cpk2bpiFDhigiIkKNGzdWbGysypQpozlz5lz1mJycHPXr109jx45V3bp1rzl+VlaW0tLS7DYAAAAAKAyXBqfs7GwlJCQoJCTE1ubm5qaQkBBt2rTpqse9/vrrqlq1qgYNGmR6jpiYGPn4+Ng2f3/nrrYFAAAA4Obn0uCUmpqqnJwcWa3237litVqVnJyc7zHff/+9PvroI33wwQcFOkdUVJTOnj1r244cOXLddQMAAAC4tZSo73E6d+6c+vfvrw8++EC+vr4FOsbT01Oenp5FXBkAAACAm5lLg5Ovr6/c3d2VkmL/5ZIpKSny8/PL0/+PP/7QoUOH1L17d1tbbm6uJKlUqVLas2eP6tWrV7RFAwAAALjluPRWPQ8PDwUHBysuLs7Wlpubq7i4OLVp0yZP/9tvv107duzQtm3bbNu///1vdejQQdu2beP5JQAAAABFwuW36kVGRio8PFwtW7ZUq1atNH36dGVkZCgiIkKSNGDAANWoUUMxMTHy8vJSkyZN7I6vWLGiJOVpBwAAAABncXlw6t27t06ePKnRo0crOTlZzZs31+rVq20LRiQlJcnNzeWrpgMAAAC4hbk8OEnSsGHDNGzYsHz3xcfHX/PYefPmOb8gAAAAAPgbpnIAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwEQpVxcAoHCSkpKUmppaZOP7+vqqVq1aRTY+AABASURwAkqQpKQk3d7odmWezyyyc3iX8dbuxN2EJwAAgL8hOAElSGpqqjLPZypsdpisgVanj5+yN0WfPPmJUlNTCU4AAAB/Q3ACSiBroFX+Qf6uLgMAAOCWweIQAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJkq5ugBJmjlzpiZPnqzk5GQFBQXpnXfeUatWrfLt+8EHH2jBggXauXOnJCk4OFgTJky4an8AhZeYmFgixgQAALhRXB6clixZosjISMXGxqp169aaPn26QkNDtWfPHlWtWjVP//j4ePXp00d33323vLy8NGnSJHXu3Fm7du1SjRo1XPAOgJtHWkqaLG4WhYWFuboUAACAYsXlwWnatGkaMmSIIiIiJEmxsbFauXKl5syZo5dffjlP/08//dTu9YcffqgvvvhCcXFxGjBgwA2pGbhZZZ7NlJFrKGx2mKyBVqeO/fv637XqjVVOHRMAAOBGcWlwys7OVkJCgqKiomxtbm5uCgkJ0aZNmwo0xvnz53Xx4kVVqlQp3/1ZWVnKysqyvU5LS7u+ooFbgDXQKv8gf6eOmbI3xanjAQAA3EguXRwiNTVVOTk5slrtf7NttVqVnJxcoDFeeuklVa9eXSEhIfnuj4mJkY+Pj23z93fu/wwCAAAAuPmV6FX1Jk6cqMWLF2v58uXy8vLKt09UVJTOnj1r244cOXKDqwQAAABQ0rn0Vj1fX1+5u7srJcX+Fp6UlBT5+fld89gpU6Zo4sSJWr9+vZo1a3bVfp6envL09HRKvQAAAABuTS6dcfLw8FBwcLDi4uJsbbm5uYqLi1ObNm2uetybb76pcePGafXq1WrZsuWNKBUAAADALczlq+pFRkYqPDxcLVu2VKtWrTR9+nRlZGTYVtkbMGCAatSooZiYGEnSpEmTNHr0aC1cuFABAQG2Z6HKlSuncuXKuex9AAAAALh5uTw49e7dWydPntTo0aOVnJys5s2ba/Xq1bYFI5KSkuTm9n8TY7NmzVJ2drYefvhhu3Gio6M1ZsyYG1k6AAAAgFuEy4OTJA0bNkzDhg3Ld198fLzd60OHDhV9QQAAAADwNyV6VT0AAAAAuBEITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACaKxfc4AQDgCklJSUpNTS2SsX19fVWrVq0iGRsAcOMRnAAAt6SkpCTd3uh2ZZ7PLJLxvct4a3fibsITANwkCE4AgFtSamqqMs9nKmx2mKyBVqeOnbI3RZ88+YlSU1MJTgBwkyA4AQCKtcTExCId1xpolX+Qf5GcAwBw8yA4AQCKpbSUNFncLAoLC3N1KQAAEJwAAMVT5tlMGblGkdxKJ0m/r/9dq95Y5fRxAQA3J4ITAKBYK6pb6VL2pjh9TADAzYvvcQIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBRytUFADejpKQkpaamOn3cxMREp48JAAAAcwQnwMmSkpJ0e6PblXk+09WlAAAAwEkIToCTpaamKvN8psJmh8kaaHXq2L+v/12r3ljl1DEBAABgjuAEFBFroFX+Qf5OHTNlb4pTxwMAAEDBsDgEAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACAiVKuLgAAgJtVYmJikY3t6+urWrVqFdn4AAB7BCcAAJwsLSVNFjeLwsLCiuwc3mW8tTtxN+EJAG4QghMAAE6WeTZTRq6hsNlhsgZanT5+yt4UffLkJ0pNTSU4AcANQnACAKCIWAOt8g/yd3UZAAAnYHEIAAAAADBRLGacZs6cqcmTJys5OVlBQUF655131KpVq6v2X7p0qV577TUdOnRIDRo00KRJk9S1a9cbWDFKuqSkJKWmphbJ2EX5MDgAAABcw+XBacmSJYqMjFRsbKxat26t6dOnKzQ0VHv27FHVqlXz9P/xxx/Vp08fxcTE6MEHH9TChQvVs2dPbd26VU2aNHHBO0BJk5SUpNsb3a7M85muLgUAAAAlhMuD07Rp0zRkyBBFRERIkmJjY7Vy5UrNmTNHL7/8cp7+M2bM0AMPPKAXXnhBkjRu3DitW7dO7777rmJjY29o7c5SlLMfJXm52qL6XBITE5V5PrPIHtr+ff3vWvXGKqePCwAAANdxaXDKzs5WQkKCoqKibG1ubm4KCQnRpk2b8j1m06ZNioyMtGsLDQ3VihUr8u2flZWlrKws2+uzZ89KktLS0q6zeuc4cuSIWt7ZUhcyLxTJ+J5envp4wceyWp0fENzc3JSbm+v0cSUpJSVF/Qf0V9aFLPPODsrOzFZWhvPHv5h1UZJ05LcjTh8/eV9ykY1d1ONTu2vGp3bXjF/UtZ/Yf0KSlJCQoPT0dKePX5Q/34t6fGp3zfjU7prxS3Ltfn5+8vPzK5KxC+NKJjAMw7yz4UJHjx41JBk//vijXfsLL7xgtGrVKt9jSpcubSxcuNCubebMmUbVqlXz7R8dHW1IYmNjY2NjY2NjY2Njy3c7cuSIaXZx+a16RS0qKspuhio3N1enTp1S5cqVZbFYnHKOtLQ0+fv768iRI6pQoYJTxsStiWsJzsB1BGfhWoKzcC3BWZx9LRmGoXPnzql69eqmfV0anHx9feXu7q6UlBS79pSUlKtO3fn5+RWqv6enpzw9Pe3aKlas6HjR11ChQgV+GMApuJbgDFxHcBauJTgL1xKcxZnXko+PT4H6ufR7nDw8PBQcHKy4uDhbW25uruLi4tSmTZt8j2nTpo1df0lat27dVfsDAAAAwPVy+a16kZGRCg8PV8uWLdWqVStNnz5dGRkZtlX2BgwYoBo1aigmJkaSNGLECLVv315Tp05Vt27dtHjxYm3ZskXvv/++K98GAAAAgJuYy4NT7969dfLkSY0ePVrJyclq3ry5Vq9ebVsFLikpSW5u/zcxdvfdd2vhwoV69dVX9d///lcNGjTQihUrXPodTp6enoqOjs5zSyBQWFxLcAauIzgL1xKchWsJzuLKa8liGAVZew8AAAAAbl0ufcYJAAAAAEoCghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4XaeZM2cqICBAXl5eat26tTZv3uzqklDMfffdd+revbuqV68ui8WiFStW2O03DEOjR49WtWrV5O3trZCQEO3bt881xaJYi4mJ0Z133qny5curatWq6tmzp/bs2WPX58KFCxo6dKgqV66scuXK6T//+U+eLxHHrW3WrFlq1qyZ7csk27Rpo1WrVtn2cw3BURMnTpTFYtHIkSNtbVxPKIgxY8bIYrHYbbfffrttv6uuI4LTdViyZIkiIyMVHR2trVu3KigoSKGhoTpx4oSrS0MxlpGRoaCgIM2cOTPf/W+++abefvttxcbG6ueff1bZsmUVGhqqCxcu3OBKUdxt3LhRQ4cO1U8//aR169bp4sWL6ty5szIyMmx9nnvuOX399ddaunSpNm7cqGPHjumhhx5yYdUobmrWrKmJEycqISFBW7ZsUceOHdWjRw/t2rVLEtcQHPPLL79o9uzZatasmV071xMK6l//+peOHz9u277//nvbPpddRwYc1qpVK2Po0KG21zk5OUb16tWNmJgYF1aFkkSSsXz5ctvr3Nxcw8/Pz5g8ebKt7cyZM4anp6exaNEiF1SIkuTEiROGJGPjxo2GYVy+dkqXLm0sXbrU1icxMdGQZGzatMlVZaIEuO2224wPP/yQawgOOXfunNGgQQNj3bp1Rvv27Y0RI0YYhsHPJBRcdHS0ERQUlO8+V15HzDg5KDs7WwkJCQoJCbG1ubm5KSQkRJs2bXJhZSjJDh48qOTkZLvrysfHR61bt+a6gqmzZ89KkipVqiRJSkhI0MWLF+2up9tvv121atXiekK+cnJytHjxYmVkZKhNmzZcQ3DI0KFD1a1bN7vrRuJnEgpn3759ql69uurWrat+/fopKSlJkmuvo1JFOvpNLDU1VTk5ObJarXbtVqtVu3fvdlFVKOmSk5MlKd/r6so+ID+5ubkaOXKk2rZtqyZNmki6fD15eHioYsWKdn25nvBPO3bsUJs2bXThwgWVK1dOy5cvV+PGjbVt2zauIRTK4sWLtXXrVv3yyy959vEzCQXVunVrzZs3Tw0bNtTx48c1duxYtWvXTjt37nTpdURwAoCbwNChQ7Vz5067e8CBgmrYsKG2bdums2fP6vPPP1d4eLg2btzo6rJQwhw5ckQjRozQunXr5OXl5epyUIJ16dLF9udmzZqpdevWql27tj777DN5e3u7rC5u1XOQr6+v3N3d86zgkZKSIj8/PxdVhZLuyrXDdYXCGDZsmP73v/9pw4YNqlmzpq3dz89P2dnZOnPmjF1/rif8k4eHh+rXr6/g4GDFxMQoKChIM2bM4BpCoSQkJOjEiRO64447VKpUKZUqVUobN27U22+/rVKlSslqtXI9wSEVK1ZUYGCg9u/f79KfSwQnB3l4eCg4OFhxcXG2ttzcXMXFxalNmzYurAwlWZ06deTn52d3XaWlpennn3/mukIehmFo2LBhWr58ub799lvVqVPHbn9wcLBKly5tdz3t2bNHSUlJXE+4ptzcXGVlZXENoVA6deqkHTt2aNu2bbatZcuW6tevn+3PXE9wRHp6uv744w9Vq1bNpT+XuFXvOkRGRio8PFwtW7ZUq1atNH36dGVkZCgiIsLVpaEYS09P1/79+22vDx48qG3btqlSpUqqVauWRo4cqfHjx6tBgwaqU6eOXnvtNVWvXl09e/Z0XdEoloYOHaqFCxfqyy+/VPny5W33dvv4+Mjb21s+Pj4aNGiQIiMjValSJVWoUEHDhw9XmzZtdNddd7m4ehQXUVFR6tKli2rVqqVz585p4cKFio+P15o1a7iGUCjly5e3PWN5RdmyZVW5cmVbO9cTCmLUqFHq3r27ateurWPHjik6Olru7u7q06ePa38uFemafbeAd955x6hVq5bh4eFhtGrVyvjpp59cXRKKuQ0bNhiS8mzh4eGGYVxekvy1114zrFar4enpaXTq1MnYs2ePa4tGsZTfdSTJmDt3rq1PZmam8cwzzxi33XabUaZMGaNXr17G8ePHXVc0ip3HH3/cqF27tuHh4WFUqVLF6NSpk7F27Vrbfq4hXI+/L0duGFxPKJjevXsb1apVMzw8PIwaNWoYvXv3Nvbv32/b76rryGIYhlG00QwAAAAASjaecQIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAoBiIj4+XxWLRmTNnXF0KACAfBCcAgEMGDhwoi8WiiRMn2rWvWLFCFovFRVVdW3EJJ/fdd59Gjhzp0hoAAIVDcAIAOMzLy0uTJk3S6dOnXV0KAABFiuAEAHBYSEiI/Pz8FBMTc9U+f/31l/r06aMaNWqoTJkyatq0qRYtWmTX57777tPw4cM1cuRI3XbbbbJarfrggw+UkZGhiIgIlS9fXvXr19eqVavsjtu5c6e6dOmicuXKyWq1qn///kpNTXX4/WRlZWnUqFGqUaOGypYtq9atWys+Pt62f968eapYsaLWrFmjRo0aqVy5cnrggQd0/PhxW59Lly7p2WefVcWKFVW5cmW99NJLCg8PV8+ePSVdnqnbuHGjZsyYIYvFIovFokOHDtmOT0hIUMuWLVWmTBndfffd2rNnj23fb7/9pg4dOqh8+fKqUKGCgoODtWXLFoffLwCg4AhOAACHubu7a8KECXrnnXf0559/5tvnwoULCg4O1sqVK7Vz50498cQT6t+/vzZv3mzXb/78+fL19dXmzZs1fPhwPf3003rkkUd09913a+vWrercubP69++v8+fPS5LOnDmjjh07qkWLFtqyZYtWr16tlJQUPfroow6/n2HDhmnTpk1avHixtm/frkceeUQPPPCA9u3bZ+tz/vx5TZkyRR9//LG+++47JSUladSoUbb9kyZN0qeffqq5c+fqhx9+UFpamlasWGHbP2PGDLVp00ZDhgzR8ePHdfz4cfn7+9v2v/LKK5o6daq2bNmiUqVK6fHHH7ft69evn2rWrKlffvlFCQkJevnll1W6dGmH3y8AoBAMAAAcEB4ebvTo0cMwDMO46667jMcff9wwDMNYvny5Yfafl27duhnPP/+87XX79u2Ne+65x/b60qVLRtmyZY3+/fvb2o4fP25IMjZt2mQYhmGMGzfO6Ny5s924R44cMSQZe/bsyfe8GzZsMCQZp0+fzrPv8OHDhru7u3H06FG79k6dOhlRUVGGYRjG3LlzDUnG/v37bftnzpxpWK1W22ur1WpMnjzZ7r3UqlXL9lldeb8jRozIt7b169fb2lauXGlIMjIzMw3DMIzy5csb8+bNy/e9AQCKVikXZjYAwE1i0qRJ6tixo93MyxU5OTmaMGGCPvvsMx09elTZ2dnKyspSmTJl7Po1a9bM9md3d3dVrlxZTZs2tbVZrVZJ0okTJyRdvm1tw4YNKleuXJ5z/vHHHwoMDCzUe9ixY4dycnLyHJeVlaXKlSvbXpcpU0b16tWzva5WrZqtprNnzyolJUWtWrWyey/BwcHKzc0tUB1//xyqVasm6fJ7rlWrliIjIzV48GB9/PHHCgkJ0SOPPGJXCwCg6BCcAADX7d5771VoaKiioqI0cOBAu32TJ0/WjBkzNH36dDVt2lRly5bVyJEjlZ2dbdfvn7ecWSwWu7YrK/VdCSDp6enq3r27Jk2alKeeK4GjMNLT0+Xu7q6EhAS5u7vb7ft7OMuvTsMwCn2+q7nWex4zZoz69u2rlStXatWqVYqOjtbixYvVq1cvp50fAJA/ghMAwCkmTpyo5s2bq2HDhnbtP/zwg3r06KGwsDBJl0PA3r171bhx4+s63x133KEvvvhCAQEBKlXq+v9z1qJFC+Xk5OjEiRNq166dQ2P4+PjIarXql19+0b333ivp8ozb1q1b1bx5c1s/Dw8P5eTkOHSOwMBABQYG6rnnnlOfPn00d+5cghMA3AAsDgEAcIqmTZuqX79+evvtt+3aGzRooHXr1unHH39UYmKinnzySaWkpFz3+YYOHapTp06pT58++uWXX/THH39ozZo1ioiIMA0lO3bs0LZt22zbb7/9psDAQPXr108DBgzQsmXLdPDgQW3evFkxMTFauXJlgesaPny4YmJi9OWXX2rPnj0aMWKETp8+bffdVgEBAfr555916NAhpaamFug2vszMTA0bNkzx8fE6fPiwfvjhB/3yyy9q1KhRgWsDADiOGScAgNO8/vrrWrJkiV3bq6++qgMHDig0NFRlypTRE088oZ49e+rs2bPXda7q1avrhx9+0EsvvaTOnTsrKytLtWvX1gMPPCA3t2v/XvDKbNAV7u7uunTpkubOnavx48fr+eef19GjR+Xr66u77rpLDz74YIHreumll5ScnKwBAwbI3d1dTzzxhEJDQ+1u/xs1apTCw8PVuHFjZWZm6uDBg6bjuru766+//tKAAQOUkpIiX19fPfTQQxo7dmyBawMAOM5iOPPGbAAAYCc3N1eNGjXSo48+qnHjxrm6HACAg5hxAgDAiQ4fPqy1a9eqffv2ysrK0rvvvquDBw+qb9++ri4NAHAdeMYJAAAncnNz07x583TnnXeqbdu22rFjh9avX8+zSABQwnGrHgAAAACYYMYJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADAxP8HMC3D6eylx5oAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.hist(df['length'], bins=30, color='lightgreen', edgecolor='black')\n",
-    "plt.title('Histogram of Name Lengths')\n",
-    "plt.xlabel('Name Lengths')\n",
-    "plt.ylabel('Frequency')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "cc849ca9d9d43ae3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-07-08T16:14:08.587040Z",
-     "start_time": "2025-07-08T16:13:47.209322Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.boxplot(data=df[['words', 'length']])\n",
-    "plt.title('Boxplots of Word Counts and Name Lengths')\n",
-    "plt.ylabel('Values')\n",
-    "plt.xticks(ticks=[0, 1], labels=['Words', 'Length'])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "f27c59dd8b8eaf4b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-07-08T18:18:51.166573Z",
-     "start_time": "2025-07-08T18:18:42.805191Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(data=df, x='length', y='words', alpha=0.7)\n",
-    "plt.title('Scatterplot: Name Length vs Word Counts')\n",
-    "plt.xlabel('Name Length')\n",
-    "plt.ylabel('Word Counts')\n",
-    "plt.grid(True)\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..b339851
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,41 @@
+[project]
+name = "ners"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "geopandas>=1.1.1",
+    "joblib>=1.5.2",
+    "lightgbm>=4.6.0",
+    "matplotlib>=3.10.6",
+    "numpy>=2.3.3",
+    "ollama>=0.6.0",
+    "pandas>=2.3.3",
+    "plotly>=6.3.1",
+    "psutil>=7.1.0",
+    "pydantic>=2.11.10",
+    "pyyaml>=6.0.3",
+    "scikit-learn>=1.7.2",
+    "seaborn>=0.13.2",
+    "spacy>=3.8.7",
+    "streamlit>=1.50.0",
+    "tqdm>=4.67.1",
+    "typer>=0.19.2",
+    "xgboost>=3.0.5",
+]
+
+[project.scripts]
+ners = "ners.cli:app"
+
+[build-system]
+requires = ["uv_build>=0.8.12,<0.9.0"]
+build-backend = "uv_build"
+
+[dependency-groups]
+dev = [
+    "ruff>=0.13.3",
+]
+
+[tool.uv]
+required-environments = ["sys_platform == 'linux' and platform_machine == 'x86_64'"]
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 3037ffc..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,170 +0,0 @@
-absl-py==2.3.0
-altair==5.1.2
-annotated-types==0.7.0
-anyio==4.9.0
-appnope==0.1.4
-argon2-cffi==25.1.0
-argon2-cffi-bindings==21.2.0
-arrow==1.3.0
-asttokens==3.0.0
-astunparse==1.6.3
-async-lru==2.0.5
-attrs==25.3.0
-babel==2.17.0
-beautifulsoup4==4.13.4
-black==25.1.0
-bleach==6.2.0
-blinker==1.9.0
-cachetools==6.1.0
-certifi==2025.6.15
-cffi==1.17.1
-charset-normalizer==3.4.2
-click==8.2.1
-comm==0.2.2
-contourpy==1.3.2
-cycler==0.12.1
-debugpy==1.8.14
-decorator==5.2.1
-defusedxml==0.7.1
-executing==2.2.0
-fastjsonschema==2.21.1
-flake8==7.3.0
-flatbuffers==25.2.10
-fonttools==4.58.4
-fqdn==1.5.1
-gast==0.6.0
-gitdb==4.0.12
-GitPython==3.1.45
-google-pasta==0.2.0
-grpcio==1.73.0
-h11==0.16.0
-h5py==3.14.0
-httpcore==1.0.9
-httpx==0.28.1
-idna==3.10
-imbalanced-learn==0.13.0
-ipykernel==6.29.5
-ipython>=8.0,<9.0
-ipython_pygments_lexers==1.1.1
-isoduration==20.11.0
-jedi==0.19.2
-Jinja2==3.1.6
-joblib==1.5.1
-json5==0.12.0
-jsonpointer==3.0.0
-jsonschema==4.24.0
-jsonschema-specifications==2025.4.1
-jupyter-events==0.12.0
-jupyter-lsp==2.2.5
-jupyter_client==8.6.3
-jupyter_core==5.8.1
-jupyter_server==2.16.0
-jupyter_server_terminals==0.5.3
-jupyterlab==4.4.4
-jupyterlab_pygments==0.3.0
-jupyterlab_server==2.27.3
-keras==3.10.0
-kiwisolver==1.4.8
-libclang==18.1.1
-lightgbm~=4.6.0
-Markdown==3.8.2
-markdown-it-py==3.0.0
-MarkupSafe==3.0.2
-matplotlib==3.10.3
-matplotlib-inline==0.1.7
-mccabe==0.7.0
-mdurl==0.1.2
-mistune==3.1.3
-ml-dtypes==0.3.2
-mypy==1.17.0
-mypy_extensions==1.1.0
-namex==0.1.0
-narwhals==2.0.1
-nbclient==0.10.2
-nbconvert==7.16.6
-nbformat==5.10.4
-nest-asyncio==1.6.0
-nltk==3.9.1
-notebook==7.4.4
-notebook_shim==0.2.4
-numpy==1.26.4
-ollama~=0.5.1
-opt_einsum==3.4.0
-optree==0.16.0
-overrides==7.7.0
-packaging==25.0
-pandas==2.3.0
-pandocfilters==1.5.1
-parso==0.8.4
-pathspec==0.12.1
-pexpect==4.9.0
-pillow==11.2.1
-platformdirs==4.3.8
-plotly~=6.2.0
-prometheus_client==0.22.1
-prompt_toolkit==3.0.51
-protobuf==4.25.8
-psutil==7.0.0
-ptyprocess==0.7.0
-pure_eval==0.2.3
-pyarrow==21.0.0
-pycodestyle==2.14.0
-pycparser==2.22
-pydantic~=2.11.7
-pydantic_core==2.33.2
-pydeck==0.9.1
-pyflakes==3.4.0
-Pygments==2.19.1
-pyparsing==3.2.3
-python-dateutil==2.9.0.post0
-python-json-logger==3.3.0
-pytz==2025.2
-PyYAML~=6.0.2
-pyzmq==27.0.0
-referencing==0.36.2
-regex==2024.11.6
-requests==2.32.4
-rfc3339-validator==0.1.4
-rfc3986-validator==0.1.1
-rich==14.0.0
-rpds-py==0.26.0
-scikit-learn~=1.6.1
-scipy==1.15.3
-seaborn==0.13.2
-Send2Trash==1.8.3
-six==1.17.0
-sklearn-compat==0.1.3
-smmap==5.0.2
-sniffio==1.3.1
-soupsieve==2.7
-spacy~=3.8.7
-stack-data==0.6.3
-streamlit~=1.47.1
-tenacity==9.1.2
-tensorboard==2.16.2
-tensorboard-data-server==0.7.2
-tensorflow==2.16.2
-tensorflow-io-gcs-filesystem==0.37.1
-termcolor==3.1.0
-terminado==0.18.1
-threadpoolctl==3.6.0
-tinycss2==1.4.0
-toml==0.10.2
-toolz==1.0.0
-tornado==6.5.1
-tqdm==4.67.1
-traitlets==5.14.3
-types-python-dateutil==2.9.0.20250516
-types-PyYAML==6.0.12.20250516
-typing-inspection==0.4.1
-typing_extensions==4.14.0
-tzdata==2025.2
-uri-template==1.3.0
-urllib3==2.5.0
-wcwidth==0.2.13
-webcolors==24.11.1
-webencodings==0.5.1
-websocket-client==1.8.0
-Werkzeug==3.1.3
-wrapt==1.17.2
-xgboost~=3.0.3
\ No newline at end of file
diff --git a/src/ners/__init__.py b/src/ners/__init__.py
new file mode 100644
index 0000000..c9a6010
--- /dev/null
+++ b/src/ners/__init__.py
@@ -0,0 +1,3 @@
+"""DRC NERS NLP package."""
+
+__all__: list[str] = []
diff --git a/src/ners/cli.py b/src/ners/cli.py
new file mode 100644
index 0000000..d30cab3
--- /dev/null
+++ b/src/ners/cli.py
@@ -0,0 +1,226 @@
+from __future__ import annotations
+
+import os
+import subprocess
+import sys
+from pathlib import Path
+from typing import Optional
+
+import typer
+
+from ners.core.config import setup_config, PipelineConfig
+
+app = typer.Typer(help="DRC NERS command-line interface", no_args_is_help=True)
+
+
+# -------------------------
+# Pipeline commands
+# -------------------------
+pipeline_app = typer.Typer(help="Data processing pipeline")
+app.add_typer(pipeline_app, name="pipeline")
+
+
+@pipeline_app.command("run")
+def pipeline_run(
+    config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
+    env: str = typer.Option("development", help="Environment name"),
+) -> None:
+    """Run the full processing pipeline."""
+    from ners.main import run_pipeline as _run_pipeline
+
+    cfg = setup_config(config_path=config, env=env)
+    code = _run_pipeline(cfg)
+    raise typer.Exit(code)
+
+
+# -------------------------
+# NER commands
+# -------------------------
+ner_app = typer.Typer(help="NER dataset and model")
+app.add_typer(ner_app, name="ner")
+
+
+def _load_config(config: Optional[Path], env: str) -> PipelineConfig:
+    return setup_config(config_path=config, env=env)
+
+
+@ner_app.command("feature")
+def ner_feature(
+    config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
+    env: str = typer.Option("development", help="Environment name"),
+) -> None:
+    from ners.ner import feature as _feature
+
+    cfg = _load_config(config, env)
+    _feature(cfg)
+
+
+@ner_app.command("build")
+def ner_build(
+    config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
+    env: str = typer.Option("development", help="Environment name"),
+) -> None:
+    from ners.ner import build as _build
+
+    cfg = _load_config(config, env)
+    _build(cfg)
+
+
+@ner_app.command("train")
+def ner_train(
+    config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
+    env: str = typer.Option("development", help="Environment name"),
+) -> None:
+    from ners.ner import train as _train
+
+    cfg = _load_config(config, env)
+    _train(cfg)
+
+
+@ner_app.command("run")
+def ner_run(
+    config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
+    env: str = typer.Option("development", help="Environment name"),
+    reset: bool = typer.Option(
+        False, help="Reset intermediate outputs and rerun all steps"
+    ),
+) -> None:
+    from ners.ner import run_pipeline as _ner_pipeline
+
+    cfg = _load_config(config, env)
+    code = _ner_pipeline(cfg, reset)
+    raise typer.Exit(code)
+
+
+# -------------------------
+# Research commands
+# -------------------------
+research_app = typer.Typer(help="Research experiments and training")
+app.add_typer(research_app, name="research")
+
+
+@research_app.command("train")
+def research_train(
+    name: str = typer.Option(..., "--name", help="Model name to train"),
+    type: str = typer.Option(..., "--type", help="Experiment type"),
+    templates: str = typer.Option(
+        "research_templates.yaml", help="Templates file path"
+    ),
+    config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
+    env: str = typer.Option("development", help="Environment name"),
+) -> None:
+    from ners.research.experiment.experiment_builder import ExperimentBuilder
+    from ners.research.model_trainer import ModelTrainer
+
+    cfg = _load_config(config, env)
+    exp_builder = ExperimentBuilder(cfg)
+    tmpl = exp_builder.load_templates(templates)
+    exp_cfg = exp_builder.find_template(tmpl, name, type)
+
+    trainer = ModelTrainer(cfg)
+    trainer.train_single_model(
+        model_name=exp_cfg.get("name"),
+        model_type=exp_cfg.get("model_type"),
+        features=exp_cfg.get("features"),
+        model_params=exp_cfg.get("model_params", {}),
+        tags=exp_cfg.get("tags", []),
+    )
+
+
+# -------------------------
+# Monitor commands
+# -------------------------
+monitor_app = typer.Typer(help="Monitor pipeline checkpoints")
+app.add_typer(monitor_app, name="monitor")
+
+
+@monitor_app.command("status")
+def monitor_status(
+    config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
+    env: str = typer.Option("development", help="Environment name"),
+    detailed: bool = typer.Option(
+        False, help="Show detailed status (failed batch IDs)"
+    ),
+) -> None:
+    _ = _load_config(config, env)
+    from ners.processing.monitoring.pipeline_monitor import PipelineMonitor
+
+    PipelineMonitor().print_status(detailed=detailed)
+
+
+@monitor_app.command("clean")
+def monitor_clean(
+    step: Optional[str] = typer.Option(None, help="Step to clean; default all"),
+    keep_last: int = typer.Option(1, help="Number of latest checkpoint files to keep"),
+    force: bool = typer.Option(False, help="Do not ask for confirmation"),
+    config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
+    env: str = typer.Option("development", help="Environment name"),
+) -> None:
+    _ = _load_config(config, env)
+    from ners.processing.monitoring.pipeline_monitor import PipelineMonitor
+
+    mon = PipelineMonitor()
+    if not force:
+        typer.confirm("Clean checkpoints?", abort=True)
+
+    if step:
+        mon.clean_step_checkpoints(step, keep_last)
+    else:
+        for s in mon.steps:
+            mon.clean_step_checkpoints(s, keep_last)
+
+
+@monitor_app.command("reset")
+def monitor_reset(
+    step: Optional[str] = typer.Option(None, help="Step to reset; default all"),
+    force: bool = typer.Option(False, help="Do not ask for confirmation"),
+    config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
+    env: str = typer.Option("development", help="Environment name"),
+) -> None:
+    _ = _load_config(config, env)
+    from ners.processing.monitoring.pipeline_monitor import PipelineMonitor
+
+    mon = PipelineMonitor()
+    if not force:
+        msg = f"Reset {step or 'all steps'}? This deletes checkpoints."
+        typer.confirm(msg, abort=True)
+
+    if step:
+        mon.reset_step(step)
+    else:
+        for s in mon.steps:
+            mon.reset_step(s)
+
+
+# -------------------------
+# Web commands
+# -------------------------
+web_app = typer.Typer(help="Web UI wrapper")
+app.add_typer(web_app, name="web")
+
+
+@web_app.command("run")
+def web_run(
+    config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
+    env: str = typer.Option("development", help="Environment name"),
+) -> None:
+    """Launch the Streamlit web app via subprocess."""
+    app_path = Path(__file__).parent / "web" / "app.py"
+    cmd = [
+        sys.executable,
+        "-m",
+        "streamlit",
+        "run",
+        str(app_path),
+    ]
+    # Pass configuration via environment variables to avoid argparse in Streamlit
+    env_vars = os.environ.copy()
+    if config is not None:
+        env_vars["NERS_CONFIG"] = str(config)
+    env_vars["NERS_ENV"] = env
+
+    raise typer.Exit(subprocess.call(cmd, env=env_vars))
+
+
+if __name__ == "__main__":  # pragma: no cover
+    app()
diff --git a/core/__init__.py b/src/ners/core/__init__.py
similarity index 100%
rename from core/__init__.py
rename to src/ners/core/__init__.py
diff --git a/core/config/__init__.py b/src/ners/core/config/__init__.py
similarity index 88%
rename from core/config/__init__.py
rename to src/ners/core/config/__init__.py
index 322aded..4169761 100644
--- a/core/config/__init__.py
+++ b/src/ners/core/config/__init__.py
@@ -2,10 +2,10 @@ import logging
 from pathlib import Path
 from typing import Optional, Union
 
-from core.utils import ensure_directories
-from .config_manager import ConfigManager
-from .logging_config import LoggingConfig
-from .pipeline_config import PipelineConfig
+from ners.core.utils import ensure_directories
+from ners.core.config.config_manager import ConfigManager
+from ners.core.config.logging_config import LoggingConfig
+from ners.core.config.pipeline_config import PipelineConfig
 
 config_manager = ConfigManager()
 
@@ -22,7 +22,9 @@ def load_config(config_path: Optional[Union[str, Path]] = None) -> PipelineConfi
     return config_manager.get_config()
 
 
-def setup_config(config_path: Optional[Path] = None, env: str = "development") -> PipelineConfig:
+def setup_config(
+    config_path: Optional[Path] = None, env: str = "development"
+) -> PipelineConfig:
     """
     Unified configuration loading and logging setup for all entrypoint scripts.
 
diff --git a/core/config/annotation_config.py b/src/ners/core/config/annotation_config.py
similarity index 100%
rename from core/config/annotation_config.py
rename to src/ners/core/config/annotation_config.py
diff --git a/core/config/config_manager.py b/src/ners/core/config/config_manager.py
similarity index 91%
rename from core/config/config_manager.py
rename to src/ners/core/config/config_manager.py
index 3939405..8e6c650 100644
--- a/core/config/config_manager.py
+++ b/src/ners/core/config/config_manager.py
@@ -5,8 +5,8 @@ from typing import Optional, Union, Dict, Any
 
 import yaml
 
-from core.config.pipeline_config import PipelineConfig
-from core.config.project_paths import ProjectPaths
+from ners.core.config.pipeline_config import PipelineConfig
+from ners.core.config.project_paths import ProjectPaths
 
 
 class ConfigManager:
@@ -36,7 +36,7 @@ class ConfigManager:
 
     def _setup_default_paths(self):
         """Setup default project paths"""
-        root_dir = Path(__file__).parent.parent.parent
+        root_dir = Path(__file__).parent.parent.parent.parent.parent
         self.default_paths = ProjectPaths(
             root_dir=root_dir,
             configs_dir=root_dir / "config",
@@ -53,7 +53,9 @@ class ConfigManager:
             self.config_path = config_path
 
         if not self.config_path.exists():
-            logging.warning(f"Config file not found: {self.config_path}. Using defaults.")
+            logging.warning(
+                f"Config file not found: {self.config_path}. Using defaults."
+            )
             return self._create_default_config()
 
         try:
@@ -122,7 +124,11 @@ class ConfigManager:
     def _deep_update(self, base_dict: Dict, update_dict: Dict):
         """Recursively update nested dictionaries"""
         for key, value in update_dict.items():
-            if key in base_dict and isinstance(base_dict[key], dict) and isinstance(value, dict):
+            if (
+                key in base_dict
+                and isinstance(base_dict[key], dict)
+                and isinstance(value, dict)
+            ):
                 self._deep_update(base_dict[key], value)
             else:
                 base_dict[key] = value
diff --git a/core/config/data_config.py b/src/ners/core/config/data_config.py
similarity index 100%
rename from core/config/data_config.py
rename to src/ners/core/config/data_config.py
diff --git a/core/config/logging_config.py b/src/ners/core/config/logging_config.py
similarity index 100%
rename from core/config/logging_config.py
rename to src/ners/core/config/logging_config.py
diff --git a/core/config/pipeline_config.py b/src/ners/core/config/pipeline_config.py
similarity index 68%
rename from core/config/pipeline_config.py
rename to src/ners/core/config/pipeline_config.py
index 43bf1a8..d20ac36 100644
--- a/core/config/pipeline_config.py
+++ b/src/ners/core/config/pipeline_config.py
@@ -1,10 +1,10 @@
 from pydantic import BaseModel
 
-from core.config.annotation_config import AnnotationConfig
-from core.config.data_config import DataConfig
-from core.config.logging_config import LoggingConfig
-from core.config.processing_config import ProcessingConfig
-from core.config.project_paths import ProjectPaths
+from ners.core.config.annotation_config import AnnotationConfig
+from ners.core.config.data_config import DataConfig
+from ners.core.config.logging_config import LoggingConfig
+from ners.core.config.processing_config import ProcessingConfig
+from ners.core.config.project_paths import ProjectPaths
 
 
 class PipelineConfig(BaseModel):
diff --git a/core/config/processing_config.py b/src/ners/core/config/processing_config.py
similarity index 75%
rename from core/config/processing_config.py
rename to src/ners/core/config/processing_config.py
index 5d1d705..4bf6034 100644
--- a/core/config/processing_config.py
+++ b/src/ners/core/config/processing_config.py
@@ -10,6 +10,8 @@ class ProcessingConfig(BaseModel):
     max_workers: int = 4
     checkpoint_interval: int = 5
     use_multiprocessing: bool = False
-    encoding_options: list = field(default_factory=lambda: ["utf-8", "utf-16", "latin1"])
+    encoding_options: list = field(
+        default_factory=lambda: ["utf-8", "utf-16", "latin1"]
+    )
     chunk_size: int = 100_000
     epochs: int = 2
diff --git a/core/config/project_paths.py b/src/ners/core/config/project_paths.py
similarity index 100%
rename from core/config/project_paths.py
rename to src/ners/core/config/project_paths.py
diff --git a/core/utils/__init__.py b/src/ners/core/utils/__init__.py
similarity index 92%
rename from core/utils/__init__.py
rename to src/ners/core/utils/__init__.py
index b452b86..65e387a 100644
--- a/core/utils/__init__.py
+++ b/src/ners/core/utils/__init__.py
@@ -4,13 +4,13 @@ from pathlib import Path
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from core.config import PipelineConfig
+    from ners.core.config import PipelineConfig
 
 
 @contextmanager
 def temporary_config_override(**overrides):
     """Context manager for temporarily overriding configuration"""
-    from core.config import get_config
+    from ners.core.config import get_config
 
     config = get_config()
     original_values = {}
diff --git a/core/utils/data_loader.py b/src/ners/core/utils/data_loader.py
similarity index 91%
rename from core/utils/data_loader.py
rename to src/ners/core/utils/data_loader.py
index d429e88..6f64ade 100644
--- a/core/utils/data_loader.py
+++ b/src/ners/core/utils/data_loader.py
@@ -5,7 +5,7 @@ from typing import Optional, Union, Iterator, Dict
 
 import pandas as pd
 
-from core.config.pipeline_config import PipelineConfig
+from ners.core.config.pipeline_config import PipelineConfig
 
 OPTIMIZED_DTYPES = {
     # Numeric columns with appropriate bit-width
@@ -113,7 +113,9 @@ class DataLoader:
         sex_values = df["sex"].dropna().unique()
 
         if len(sex_values) == 0:
-            logging.warning(f"No valid values found in sex column 'sex', using random sampling")
+            logging.warning(
+                "No valid values found in sex column 'sex', using random sampling"
+            )
             return df.sample(n=max_size, random_state=self.config.data.random_seed)
 
         # Calculate samples per sex category
@@ -140,18 +142,22 @@ class DataLoader:
                 logging.info(f"Sampled {current_samples} records for sex '{sex}'")
 
         if not balanced_samples:
-            logging.warning("No balanced samples could be created, using random sampling")
+            logging.warning(
+                "No balanced samples could be created, using random sampling"
+            )
             return df.sample(n=max_size, random_state=self.config.data.random_seed)
 
         # Create result using iloc with indices (no copying until final step)
         result = df.iloc[balanced_samples].copy()
 
         # Shuffle the final result
-        result = result.sample(frac=1, random_state=self.config.data.random_seed).reset_index(
-            drop=True
-        )
+        result = result.sample(
+            frac=1, random_state=self.config.data.random_seed
+        ).reset_index(drop=True)
 
-        logging.info(f"Created balanced dataset with {len(result)} records from {len(df)} total")
+        logging.info(
+            f"Created balanced dataset with {len(result)} records from {len(df)} total"
+        )
         return result
 
     @classmethod
diff --git a/core/utils/prompt_manager.py b/src/ners/core/utils/prompt_manager.py
similarity index 93%
rename from core/utils/prompt_manager.py
rename to src/ners/core/utils/prompt_manager.py
index fecd8c5..fecee35 100644
--- a/core/utils/prompt_manager.py
+++ b/src/ners/core/utils/prompt_manager.py
@@ -1,4 +1,4 @@
-from core.config.pipeline_config import PipelineConfig
+from ners.core.config.pipeline_config import PipelineConfig
 
 
 class PromptManager:
diff --git a/core/utils/rate_limiter.py b/src/ners/core/utils/rate_limiter.py
similarity index 100%
rename from core/utils/rate_limiter.py
rename to src/ners/core/utils/rate_limiter.py
diff --git a/core/utils/region_mapper.py b/src/ners/core/utils/region_mapper.py
similarity index 100%
rename from core/utils/region_mapper.py
rename to src/ners/core/utils/region_mapper.py
diff --git a/core/utils/state_manager.py b/src/ners/core/utils/state_manager.py
similarity index 95%
rename from core/utils/state_manager.py
rename to src/ners/core/utils/state_manager.py
index f650d12..1397d96 100644
--- a/core/utils/state_manager.py
+++ b/src/ners/core/utils/state_manager.py
@@ -2,7 +2,7 @@ import json
 import logging
 from typing import Dict, Any
 
-from core.config.pipeline_config import PipelineConfig
+from ners.core.config.pipeline_config import PipelineConfig
 
 
 class StateManager:
diff --git a/core/utils/text_cleaner.py b/src/ners/core/utils/text_cleaner.py
similarity index 100%
rename from core/utils/text_cleaner.py
rename to src/ners/core/utils/text_cleaner.py
diff --git a/main.py b/src/ners/main.py
similarity index 60%
rename from main.py
rename to src/ners/main.py
index 2b53c6e..3939ed2 100755
--- a/main.py
+++ b/src/ners/main.py
@@ -1,21 +1,17 @@
 #!.venv/bin/python3
-import argparse
 import logging
-import sys
-import traceback
-
-from core.config import setup_config
-from core.utils.data_loader import DataLoader
-from processing.batch.batch_config import BatchConfig
-from processing.pipeline import Pipeline
-from processing.steps.data_cleaning_step import DataCleaningStep
-from processing.steps.data_selection_step import DataSelectionStep
-from processing.steps.data_splitting_step import DataSplittingStep
-from processing.steps.feature_extraction_step import FeatureExtractionStep
+from ners.core.utils.data_loader import DataLoader
+from ners.processing.batch.batch_config import BatchConfig
+from ners.processing.pipeline import Pipeline
+from ners.processing.steps.data_cleaning_step import DataCleaningStep
+from ners.processing.steps.data_selection_step import DataSelectionStep
+from ners.processing.steps.data_splitting_step import DataSplittingStep
+from ners.processing.steps.llm_annotation_step import LLMAnnotationStep
+from ners.processing.steps.ner_annotation_step import NERAnnotationStep
+from ners.processing.steps.feature_extraction_step import FeatureExtractionStep
 
 
 def create_pipeline(config) -> Pipeline:
-    """Create pipeline from configuration"""
     batch_config = BatchConfig(
         batch_size=config.processing.batch_size,
         max_workers=config.processing.max_workers,
@@ -23,14 +19,13 @@ def create_pipeline(config) -> Pipeline:
         use_multiprocessing=config.processing.use_multiprocessing,
     )
 
-    # Add steps based on configuration
     pipeline = Pipeline(batch_config)
     steps = [
         DataCleaningStep(config),
         FeatureExtractionStep(config),
         DataSelectionStep(config),
-        # NERAnnotationStep(config),
-        # LLMAnnotationStep(config),
+        NERAnnotationStep(config),
+        LLMAnnotationStep(config),
     ]
 
     for stage in config.stages:
@@ -42,7 +37,6 @@ def create_pipeline(config) -> Pipeline:
 
 
 def run_pipeline(config) -> int:
-    """Run the complete pipeline"""
     try:
         logging.info(f"Starting pipeline: {config.name} v{config.version}")
 
@@ -79,27 +73,3 @@ def run_pipeline(config) -> int:
     except Exception as e:
         logging.error(f"Pipeline failed: {e}", exc_info=True)
         return 1
-
-
-def main():
-    """Main entry point with unified configuration loading"""
-    parser = argparse.ArgumentParser(
-        description="DRC NERS Processing Pipeline",
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-    )
-    parser.add_argument("--config", type=str, help="Path to configuration file")
-    parser.add_argument("--env", type=str, default="development", help="Environment name")
-    args = parser.parse_args()
-
-    try:
-        config = setup_config(config_path=args.config, env=args.env)
-        return run_pipeline(config)
-
-    except Exception as e:
-        print(f"Pipeline failed: {e}")
-        traceback.print_exc()
-        return 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/src/ners/monitor.py b/src/ners/monitor.py
new file mode 100755
index 0000000..e207a10
--- /dev/null
+++ b/src/ners/monitor.py
@@ -0,0 +1,14 @@
+#!.venv/bin/python3
+from ners.processing.monitoring.pipeline_monitor import PipelineMonitor
+
+
+def status(*, detailed: bool = False) -> None:
+    PipelineMonitor().print_status(detailed=detailed)
+
+
+def clean_step(step: str, *, keep_last: int = 1) -> None:
+    PipelineMonitor().clean_step_checkpoints(step, keep_last)
+
+
+def reset_step(step: str) -> None:
+    PipelineMonitor().reset_step(step)
diff --git a/ner.py b/src/ners/ner.py
similarity index 62%
rename from ner.py
rename to src/ners/ner.py
index d671c35..fe83d57 100755
--- a/ner.py
+++ b/src/ners/ner.py
@@ -1,29 +1,24 @@
 #!/usr/bin/env python3
-import argparse
 import logging
 import os
-import sys
 import traceback
 from pathlib import Path
 
-from core.config import setup_config, PipelineConfig
-from processing.ner.name_builder import NameBuilder
-from processing.ner.name_engineering import NameEngineering
-from processing.ner.name_model import NameModel
+from ners.core.config import PipelineConfig
+from ners.processing.ner.name_builder import NameBuilder
+from ners.processing.ner.name_engineering import NameEngineering
+from ners.processing.ner.name_model import NameModel
 
 
 def feature(config: PipelineConfig):
-    """Apply feature engineering to create position-independent NER dataset."""
     NameEngineering(config).compute()
 
 
 def build(config: PipelineConfig):
-    """Build NER dataset using NERDataBuilder."""
     NameBuilder(config).build()
 
 
 def train(config: PipelineConfig):
-    """Train the NER model."""
     name_model = NameModel(config)
 
     data_path = Path(config.paths.data_dir) / config.data.output_files["ner_data"]
@@ -37,7 +32,9 @@ def train(config: PipelineConfig):
     split_idx = int(len(data) * 0.9)
     train_data, eval_data = data[:split_idx], data[split_idx:]
 
-    logging.info(f"Training with {len(train_data)} examples, evaluating on {len(eval_data)}")
+    logging.info(
+        f"Training with {len(train_data)} examples, evaluating on {len(eval_data)}"
+    )
     name_model.train(
         data=train_data,
         epochs=config.processing.epochs,
@@ -75,21 +72,9 @@ def run_pipeline(config: PipelineConfig, reset: bool = False):
 
 
 def main():
-    parser = argparse.ArgumentParser(description="NER model management for DRC names")
-    parser.add_argument("--config", type=str, help="Path to configuration file")
-    parser.add_argument("--env", type=str, default="development", help="Environment name")
-    parser.add_argument("--reset", action="store_true", help="Reset all steps")
-    args = parser.parse_args()
-
     try:
-        config = setup_config(config_path=args.config, env=args.env)
-        return run_pipeline(config, args.reset)
-
-    except Exception as e:
-        print(f"Pipeline failed: {e}")
+        logging.error("This module is no longer a CLI. Use 'ners ner ...' instead.")
+        return 1
+    except Exception:
         traceback.print_exc()
         return 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/processing/__init__.py b/src/ners/processing/__init__.py
similarity index 100%
rename from processing/__init__.py
rename to src/ners/processing/__init__.py
diff --git a/processing/batch/__init__.py b/src/ners/processing/batch/__init__.py
similarity index 100%
rename from processing/batch/__init__.py
rename to src/ners/processing/batch/__init__.py
diff --git a/processing/batch/batch_config.py b/src/ners/processing/batch/batch_config.py
similarity index 67%
rename from processing/batch/batch_config.py
rename to src/ners/processing/batch/batch_config.py
index df60987..036320d 100644
--- a/processing/batch/batch_config.py
+++ b/src/ners/processing/batch/batch_config.py
@@ -8,4 +8,6 @@ class BatchConfig:
     batch_size: int = 1000
     max_workers: int = 4
     checkpoint_interval: int = 5  # Save checkpoint every N batches
-    use_multiprocessing: bool = False  # Use ProcessPoolExecutor instead of ThreadPoolExecutor
+    use_multiprocessing: bool = (
+        False  # Use ProcessPoolExecutor instead of ThreadPoolExecutor
+    )
diff --git a/processing/batch/batch_processor.py b/src/ners/processing/batch/batch_processor.py
similarity index 88%
rename from processing/batch/batch_processor.py
rename to src/ners/processing/batch/batch_processor.py
index e6773ed..43f886e 100644
--- a/processing/batch/batch_processor.py
+++ b/src/ners/processing/batch/batch_processor.py
@@ -4,9 +4,9 @@ from typing import Iterator
 
 import pandas as pd
 
-from processing.batch.batch_config import BatchConfig
-from processing.batch.memory_monitor import MemoryMonitor
-from processing.steps import PipelineStep
+from ners.processing.batch.batch_config import BatchConfig
+from ners.processing.batch.memory_monitor import MemoryMonitor
+from ners.processing.steps import PipelineStep
 
 
 class BatchProcessor:
@@ -33,7 +33,9 @@ class BatchProcessor:
 
         for batch_num, (batch, batch_id) in enumerate(self.create_batches(df)):
             if step.batch_exists(batch_id):
-                logging.info(f"Batch {batch_id} already processed, loading from checkpoint")
+                logging.info(
+                    f"Batch {batch_id} already processed, loading from checkpoint"
+                )
                 processed_batch = step.load_batch(batch_id)
             else:
                 try:
@@ -80,7 +82,9 @@ class BatchProcessor:
     def process_concurrent(self, step: PipelineStep, df: pd.DataFrame) -> pd.DataFrame:
         """Memory-optimized concurrent processing"""
         executor_class = (
-            ProcessPoolExecutor if self.config.use_multiprocessing else ThreadPoolExecutor
+            ProcessPoolExecutor
+            if self.config.use_multiprocessing
+            else ThreadPoolExecutor
         )
         results = {}
 
@@ -89,7 +93,9 @@ class BatchProcessor:
             future_to_batch = {}
             for batch, batch_id in self.create_batches(df):
                 if step.batch_exists(batch_id):
-                    logging.info(f"Batch {batch_id} already processed, loading from checkpoint")
+                    logging.info(
+                        f"Batch {batch_id} already processed, loading from checkpoint"
+                    )
                     results[batch_id] = step.load_batch(batch_id)
                 else:
                     # Only copy if necessary for concurrent processing
@@ -121,7 +127,9 @@ class BatchProcessor:
         del results
         self.memory_monitor.cleanup_memory()
 
-        result = self._safe_concat(ordered_results) if ordered_results else pd.DataFrame()
+        result = (
+            self._safe_concat(ordered_results) if ordered_results else pd.DataFrame()
+        )
 
         # Final cleanup
         del ordered_results
@@ -131,7 +139,9 @@ class BatchProcessor:
 
     def process(self, step: PipelineStep, df: pd.DataFrame) -> pd.DataFrame:
         """Process data using the configured strategy"""
-        step.state.total_batches = (len(df) + self.config.batch_size - 1) // self.config.batch_size
+        step.state.total_batches = (
+            len(df) + self.config.batch_size - 1
+        ) // self.config.batch_size
         step.load_state()
 
         logging.info(f"Starting {step.name} with {step.state.total_batches} batches")
diff --git a/processing/batch/memory_monitor.py b/src/ners/processing/batch/memory_monitor.py
similarity index 100%
rename from processing/batch/memory_monitor.py
rename to src/ners/processing/batch/memory_monitor.py
diff --git a/processing/monitoring/__init__.py b/src/ners/processing/monitoring/__init__.py
similarity index 100%
rename from processing/monitoring/__init__.py
rename to src/ners/processing/monitoring/__init__.py
diff --git a/processing/monitoring/pipeline_monitor.py b/src/ners/processing/monitoring/pipeline_monitor.py
similarity index 90%
rename from processing/monitoring/pipeline_monitor.py
rename to src/ners/processing/monitoring/pipeline_monitor.py
index 1c17da3..0e5d5d1 100644
--- a/processing/monitoring/pipeline_monitor.py
+++ b/src/ners/processing/monitoring/pipeline_monitor.py
@@ -4,8 +4,8 @@ import shutil
 from datetime import datetime
 from typing import Optional, Dict
 
-from core.config.config_manager import ConfigManager
-from core.config.project_paths import ProjectPaths
+from ners.core.config.config_manager import ConfigManager
+from ners.core.config.project_paths import ProjectPaths
 
 
 class PipelineMonitor:
@@ -97,7 +97,10 @@ class PipelineMonitor:
 
         avg_completion = total_completion / len(self.steps)
 
-        if avg_completion >= 100 and overall_status not in ["error", "completed_with_errors"]:
+        if avg_completion >= 100 and overall_status not in [
+            "error",
+            "completed_with_errors",
+        ]:
             overall_status = "completed"
 
         return {
@@ -121,7 +124,9 @@ class PipelineMonitor:
             print(f"{step_name.replace('_', ' ').title()}:")
             print(f"  Status: {step_status['status']}")
             print(f"  Progress: {step_status['completion_percentage']:.1f}%")
-            print(f"  Batches: {step_status['processed_batches']}/{step_status['total_batches']}")
+            print(
+                f"  Batches: {step_status['processed_batches']}/{step_status['total_batches']}"
+            )
 
             if step_status["failed_batches"] > 0:
                 print(f"  Failed Batches: {step_status['failed_batches']}")
@@ -141,7 +146,10 @@ class PipelineMonitor:
             if step_dir.exists():
                 csv_files = list(step_dir.glob("*.csv"))
                 step_size = sum(f.stat().st_size for f in csv_files)
-                counts[step] = {"files": len(csv_files), "size_mb": step_size / (1024 * 1024)}
+                counts[step] = {
+                    "files": len(csv_files),
+                    "size_mb": step_size / (1024 * 1024),
+                }
                 total_size += step_size
             else:
                 counts[step] = {"files": 0, "size_mb": 0}
@@ -160,7 +168,9 @@ class PipelineMonitor:
         csv_files = sorted(step_dir.glob("batch_*.csv"))
 
         if len(csv_files) <= keep_last:
-            logging.info(f"Only {len(csv_files)} checkpoint files for {step_name}, keeping all")
+            logging.info(
+                f"Only {len(csv_files)} checkpoint files for {step_name}, keeping all"
+            )
             return
 
         files_to_delete = csv_files[:-keep_last] if keep_last > 0 else csv_files
diff --git a/processing/ner/__init__.py b/src/ners/processing/ner/__init__.py
similarity index 100%
rename from processing/ner/__init__.py
rename to src/ners/processing/ner/__init__.py
diff --git a/processing/ner/formats/__init__.py b/src/ners/processing/ner/formats/__init__.py
similarity index 85%
rename from processing/ner/formats/__init__.py
rename to src/ners/processing/ner/formats/__init__.py
index d2a39e8..94fa2c7 100644
--- a/processing/ner/formats/__init__.py
+++ b/src/ners/processing/ner/formats/__init__.py
@@ -3,7 +3,7 @@ from typing import List, Tuple, Dict
 
 import pandas as pd
 
-from processing.steps.feature_extraction_step import NameCategory
+from ners.processing.steps.feature_extraction_step import NameCategory
 
 
 class BaseNameFormatter(ABC):
@@ -12,7 +12,9 @@ class BaseNameFormatter(ABC):
     Contains common logic for NER tagging and attribute computation.
     """
 
-    def __init__(self, connectors: List[str] = None, additional_surnames: List[str] = None):
+    def __init__(
+        self, connectors: List[str] = None, additional_surnames: List[str] = None
+    ):
         self.connectors = connectors or ["wa", "ya", "ka", "ba"]
         self.additional_surnames = additional_surnames or [
             "jean",
@@ -46,7 +48,9 @@ class BaseNameFormatter(ABC):
             end_pos = current_pos + len(word)
 
             # Determine tag based on word content
-            if word in native_parts or any(connector in word for connector in self.connectors):
+            if word in native_parts or any(
+                connector in word for connector in self.connectors
+            ):
                 tag = "NATIVE"
             elif word == surname or word in self.additional_surnames:
                 tag = "SURNAME"
@@ -72,7 +76,9 @@ class BaseNameFormatter(ABC):
             "words": words_count,
             "length": length,
             "identified_category": (
-                NameCategory.SIMPLE.value if words_count == 3 else NameCategory.COMPOSE.value
+                NameCategory.SIMPLE.value
+                if words_count == 3
+                else NameCategory.COMPOSE.value
             ),
         }
 
diff --git a/processing/ner/formats/connectors_format.py b/src/ners/processing/ner/formats/connectors_format.py
similarity index 95%
rename from processing/ner/formats/connectors_format.py
rename to src/ners/processing/ner/formats/connectors_format.py
index 66a6795..4bf192d 100644
--- a/processing/ner/formats/connectors_format.py
+++ b/src/ners/processing/ner/formats/connectors_format.py
@@ -3,7 +3,7 @@ from typing import Dict
 
 import pandas as pd
 
-from processing.ner.formats import BaseNameFormatter
+from ners.processing.ner.formats import BaseNameFormatter
 
 
 class ConnectorFormatter(BaseNameFormatter):
diff --git a/processing/ner/formats/extended_surname_format.py b/src/ners/processing/ner/formats/extended_surname_format.py
similarity index 76%
rename from processing/ner/formats/extended_surname_format.py
rename to src/ners/processing/ner/formats/extended_surname_format.py
index cc94388..a4c4df7 100644
--- a/processing/ner/formats/extended_surname_format.py
+++ b/src/ners/processing/ner/formats/extended_surname_format.py
@@ -3,13 +3,15 @@ from typing import Dict
 
 import pandas as pd
 
-from processing.ner.formats import BaseNameFormatter
+from ners.processing.ner.formats import BaseNameFormatter
 
 
 class ExtendedSurnameFormatter(BaseNameFormatter):
     def transform(self, row: pd.Series) -> Dict:
         native_parts = self.parse_native_components(row["probable_native"])
-        original_surname = row["probable_surname"] if pd.notna(row["probable_surname"]) else ""
+        original_surname = (
+            row["probable_surname"] if pd.notna(row["probable_surname"]) else ""
+        )
 
         # Add random additional surname
         additional_surname = random.choice(self.additional_surnames)
@@ -22,7 +24,9 @@ class ExtendedSurnameFormatter(BaseNameFormatter):
             "identified_name": row["probable_native"],
             "probable_surname": combined_surname,
             "identified_surname": combined_surname,
-            "ner_entities": str(self.create_ner_tags(full_name, native_parts, combined_surname)),
+            "ner_entities": str(
+                self.create_ner_tags(full_name, native_parts, combined_surname)
+            ),
             "transformation_type": self.transformation_type,
             **self.compute_numeric_features(full_name),
         }
diff --git a/processing/ner/formats/native_only_format.py b/src/ners/processing/ner/formats/native_only_format.py
similarity index 93%
rename from processing/ner/formats/native_only_format.py
rename to src/ners/processing/ner/formats/native_only_format.py
index 8e98163..8f0f82c 100644
--- a/processing/ner/formats/native_only_format.py
+++ b/src/ners/processing/ner/formats/native_only_format.py
@@ -2,7 +2,7 @@ from typing import Dict
 
 import pandas as pd
 
-from processing.ner.formats import BaseNameFormatter
+from ners.processing.ner.formats import BaseNameFormatter
 
 
 class NativeOnlyFormatter(BaseNameFormatter):
diff --git a/processing/ner/formats/original_format.py b/src/ners/processing/ner/formats/original_format.py
similarity index 94%
rename from processing/ner/formats/original_format.py
rename to src/ners/processing/ner/formats/original_format.py
index ffcced9..5985d01 100644
--- a/processing/ner/formats/original_format.py
+++ b/src/ners/processing/ner/formats/original_format.py
@@ -2,7 +2,7 @@ from typing import Dict
 
 import pandas as pd
 
-from processing.ner.formats import BaseNameFormatter
+from ners.processing.ner.formats import BaseNameFormatter
 
 
 class OriginalFormatter(BaseNameFormatter):
diff --git a/processing/ner/formats/position_flipped_format.py b/src/ners/processing/ner/formats/position_flipped_format.py
similarity index 94%
rename from processing/ner/formats/position_flipped_format.py
rename to src/ners/processing/ner/formats/position_flipped_format.py
index 9702640..ad53ead 100644
--- a/processing/ner/formats/position_flipped_format.py
+++ b/src/ners/processing/ner/formats/position_flipped_format.py
@@ -2,7 +2,7 @@ from typing import Dict
 
 import pandas as pd
 
-from processing.ner.formats import BaseNameFormatter
+from ners.processing.ner.formats import BaseNameFormatter
 
 
 class PositionFlippedFormatter(BaseNameFormatter):
diff --git a/processing/ner/formats/reduced_native_format.py b/src/ners/processing/ner/formats/reduced_native_format.py
similarity index 74%
rename from processing/ner/formats/reduced_native_format.py
rename to src/ners/processing/ner/formats/reduced_native_format.py
index 2f992f1..3bd1b50 100644
--- a/processing/ner/formats/reduced_native_format.py
+++ b/src/ners/processing/ner/formats/reduced_native_format.py
@@ -2,7 +2,7 @@ from typing import Dict
 
 import pandas as pd
 
-from processing.ner.formats import BaseNameFormatter
+from ners.processing.ner.formats import BaseNameFormatter
 
 
 class ReducedNativeFormatter(BaseNameFormatter):
@@ -11,7 +11,9 @@ class ReducedNativeFormatter(BaseNameFormatter):
         surname = row["probable_surname"] if pd.notna(row["probable_surname"]) else ""
 
         # Keep only first native component + surname
-        reduced_native = native_parts[0] if len(native_parts) > 1 else row["probable_native"]
+        reduced_native = (
+            native_parts[0] if len(native_parts) > 1 else row["probable_native"]
+        )
         full_name = f"{reduced_native} {surname}".strip()
 
         return {
@@ -20,7 +22,9 @@ class ReducedNativeFormatter(BaseNameFormatter):
             "identified_name": reduced_native,
             "probable_surname": surname,
             "identified_surname": surname,
-            "ner_entities": str(self.create_ner_tags(full_name, [reduced_native], surname)),
+            "ner_entities": str(
+                self.create_ner_tags(full_name, [reduced_native], surname)
+            ),
             "transformation_type": self.transformation_type,
             **self.compute_numeric_features(full_name),
         }
diff --git a/processing/ner/name_builder.py b/src/ners/processing/ner/name_builder.py
similarity index 66%
rename from processing/ner/name_builder.py
rename to src/ners/processing/ner/name_builder.py
index f724b3f..a2e1783 100644
--- a/processing/ner/name_builder.py
+++ b/src/ners/processing/ner/name_builder.py
@@ -4,8 +4,8 @@ import logging
 import spacy
 from spacy.tokens import DocBin
 
-from core.config import PipelineConfig
-from core.utils.data_loader import DataLoader
+from ners.core.config import PipelineConfig
+from ners.core.utils.data_loader import DataLoader
 from .name_tagger import NameTagger
 
 
@@ -20,7 +20,9 @@ class NameBuilder:
         self.tagger = NameTagger()
 
     def build(self) -> int:
-        filepath = self.config.paths.get_data_path(self.config.data.output_files["engineered"])
+        filepath = self.config.paths.get_data_path(
+            self.config.data.output_files["engineered"]
+        )
         df = self.data_loader.load_csv_complete(filepath)
         df = df[["name", "ner_tagged", "ner_entities"]]
 
@@ -38,7 +40,9 @@ class NameBuilder:
 
         # Use NERNameTagger for parsing and validation
         parsed_entities = self.tagger.parse_entities(ner_df["ner_entities"])
-        validated_entities = self.tagger.validate_entities(ner_df["name"], parsed_entities)
+        validated_entities = self.tagger.validate_entities(
+            ner_df["name"], parsed_entities
+        )
 
         # Drop rows with no valid entities
         mask = validated_entities.map(bool)
@@ -51,22 +55,33 @@ class NameBuilder:
 
         # Prepare training data
         training_data = list(
-            zip(ner_df["name"].tolist(), [{"entities": ents} for ents in validated_entities])
+            zip(
+                ner_df["name"].tolist(),
+                [{"entities": ents} for ents in validated_entities],
+            )
         )
 
         # Use NERNameTagger to create spaCy DocBin
-        docs = self.tagger.create_docs(nlp, ner_df["name"].tolist(), validated_entities.tolist())
+        docs = self.tagger.create_docs(
+            nlp, ner_df["name"].tolist(), validated_entities.tolist()
+        )
         doc_bin = DocBin(docs=docs)
 
         # Save
-        json_path = self.config.paths.get_data_path(self.config.data.output_files["ner_data"])
-        spacy_path = self.config.paths.get_data_path(self.config.data.output_files["ner_spacy"])
+        json_path = self.config.paths.get_data_path(
+            self.config.data.output_files["ner_data"]
+        )
+        spacy_path = self.config.paths.get_data_path(
+            self.config.data.output_files["ner_spacy"]
+        )
 
         with open(json_path, "w", encoding="utf-8") as f:
             json.dump(training_data, f, ensure_ascii=False, separators=(",", ":"))
         doc_bin.to_disk(spacy_path)
 
-        logging.info(f"Processed: {len(training_data)}, Skipped: {total_rows - len(training_data)}")
+        logging.info(
+            f"Processed: {len(training_data)}, Skipped: {total_rows - len(training_data)}"
+        )
         logging.info(f"Saved NER JSON to {json_path}")
         logging.info(f"Saved NER spacy to {spacy_path}")
         return 0
diff --git a/processing/ner/name_engineering.py b/src/ners/processing/ner/name_engineering.py
similarity index 62%
rename from processing/ner/name_engineering.py
rename to src/ners/processing/ner/name_engineering.py
index 0b300f6..e78c3e6 100644
--- a/processing/ner/name_engineering.py
+++ b/src/ners/processing/ner/name_engineering.py
@@ -6,14 +6,14 @@ import numpy as np
 import pandas as pd
 from tqdm import tqdm
 
-from core.config import PipelineConfig
-from core.utils.data_loader import DataLoader
-from processing.ner.formats.connectors_format import ConnectorFormatter
-from processing.ner.formats.extended_surname_format import ExtendedSurnameFormatter
-from processing.ner.formats.native_only_format import NativeOnlyFormatter
-from processing.ner.formats.original_format import OriginalFormatter
-from processing.ner.formats.position_flipped_format import PositionFlippedFormatter
-from processing.ner.formats.reduced_native_format import ReducedNativeFormatter
+from ners.core.config import PipelineConfig
+from ners.core.utils.data_loader import DataLoader
+from ners.processing.ner.formats.connectors_format import ConnectorFormatter
+from ners.processing.ner.formats.extended_surname_format import ExtendedSurnameFormatter
+from ners.processing.ner.formats.native_only_format import NativeOnlyFormatter
+from ners.processing.ner.formats.original_format import OriginalFormatter
+from ners.processing.ner.formats.position_flipped_format import PositionFlippedFormatter
+from ners.processing.ner.formats.reduced_native_format import ReducedNativeFormatter
 
 
 class NameEngineering:
@@ -44,42 +44,60 @@ class NameEngineering:
         # Initialize format classes
         self.formatters = {
             "original": OriginalFormatter(self.connectors, self.additional_surnames),
-            "native_only": NativeOnlyFormatter(self.connectors, self.additional_surnames),
-            "position_flipped": PositionFlippedFormatter(self.connectors, self.additional_surnames),
-            "reduced_native": ReducedNativeFormatter(self.connectors, self.additional_surnames),
-            "connector_added": ConnectorFormatter(self.connectors, self.additional_surnames),
-            "extended_surname": ExtendedSurnameFormatter(self.connectors, self.additional_surnames),
+            "native_only": NativeOnlyFormatter(
+                self.connectors, self.additional_surnames
+            ),
+            "position_flipped": PositionFlippedFormatter(
+                self.connectors, self.additional_surnames
+            ),
+            "reduced_native": ReducedNativeFormatter(
+                self.connectors, self.additional_surnames
+            ),
+            "connector_added": ConnectorFormatter(
+                self.connectors, self.additional_surnames
+            ),
+            "extended_surname": ExtendedSurnameFormatter(
+                self.connectors, self.additional_surnames
+            ),
         }
 
     def load_data(self) -> pd.DataFrame:
         """Load and filter NER-tagged data from CSV file"""
 
-        filepath = self.config.paths.get_data_path(self.config.data.output_files["featured"])
+        filepath = self.config.paths.get_data_path(
+            self.config.data.output_files["featured"]
+        )
         df = self.data_loader.load_csv_complete(filepath)
 
         # Filter only NER-tagged rows
         ner_data = df[df["ner_tagged"] == 1].copy()
-        logging.info(f"Loaded {len(ner_data)} NER-tagged records from {len(df)} total records")
+        logging.info(
+            f"Loaded {len(ner_data)} NER-tagged records from {len(df)} total records"
+        )
 
         return ner_data
 
     def compute(self) -> None:
         logging.info("Applying feature engineering transformations...")
-        input_filepath = self.config.paths.get_data_path(self.config.data.output_files["featured"])
+        input_filepath = self.config.paths.get_data_path(
+            self.config.data.output_files["featured"]
+        )
         output_filepath = self.config.paths.get_data_path(
             self.config.data.output_files["engineered"]
         )
 
         df = self.data_loader.load_csv_complete(input_filepath)
         ner_df = df[df["ner_tagged"] == 1].copy()
-        logging.info(f"Loaded {len(ner_df)} NER-tagged records from {len(df)} total records")
+        logging.info(
+            f"Loaded {len(ner_df)} NER-tagged records from {len(df)} total records"
+        )
 
         del df  # No need to keep in memory
         gc.collect()
 
-        ner_df = ner_df.sample(frac=1, random_state=self.config.data.random_seed).reset_index(
-            drop=True
-        )
+        ner_df = ner_df.sample(
+            frac=1, random_state=self.config.data.random_seed
+        ).reset_index(drop=True)
         total_rows = len(ner_df)
 
         # Calculate split points
@@ -94,7 +112,11 @@ class NameEngineering:
             (0, split_25_1, "original"),  # First 25%: original format
             (split_25_1, split_25_2, "native_only"),  # Second 25%: remove surname
             (split_25_2, split_25_3, "position_flipped"),  # Third 25%: flip positions
-            (split_25_3, split_10_1, "reduced_native"),  # Fourth 10%: reduce native components
+            (
+                split_25_3,
+                split_10_1,
+                "reduced_native",
+            ),  # Fourth 10%: reduce native components
             (split_10_1, split_10_2, "connector_added"),  # Fifth 10%: add connectors
             (split_10_2, total_rows, "extended_surname"),  # Last 5%: extend surnames
         ]
diff --git a/processing/ner/name_model.py b/src/ners/processing/ner/name_model.py
similarity index 87%
rename from processing/ner/name_model.py
rename to src/ners/processing/ner/name_model.py
index bc4901e..b47beb2 100644
--- a/processing/ner/name_model.py
+++ b/src/ners/processing/ner/name_model.py
@@ -11,7 +11,7 @@ from spacy.training import Example
 from spacy.util import minibatch
 from tqdm import tqdm
 
-from core.config.pipeline_config import PipelineConfig
+from ners.core.config.pipeline_config import PipelineConfig
 
 
 class NameModel:
@@ -87,7 +87,9 @@ class NameModel:
 
                 # Handle different annotation formats from NERNameTagger
                 if not isinstance(annotations, dict) or "entities" not in annotations:
-                    logging.warning(f"Skipping invalid annotations at index {i}: {annotations}")
+                    logging.warning(
+                        f"Skipping invalid annotations at index {i}: {annotations}"
+                    )
                     skipped_count += 1
                     continue
 
@@ -124,7 +126,9 @@ class NameModel:
                 valid_entities = []
                 for entity in entities:
                     if not isinstance(entity, (list, tuple)) or len(entity) != 3:
-                        logging.warning(f"Skipping invalid entity format in '{text}': {entity}")
+                        logging.warning(
+                            f"Skipping invalid entity format in '{text}': {entity}"
+                        )
                         continue
 
                     start, end, label = entity
@@ -138,21 +142,30 @@ class NameModel:
                         or start < 0
                         or end > len(text)
                     ):
-                        logging.warning(f"Skipping invalid entity bounds in '{text}': {entity}")
+                        logging.warning(
+                            f"Skipping invalid entity bounds in '{text}': {entity}"
+                        )
                         continue
 
                     # Check for overlaps with already validated entities
                     has_overlap = any(
-                        start < v_end and end > v_start for v_start, v_end, _ in valid_entities
+                        start < v_end and end > v_start
+                        for v_start, v_end, _ in valid_entities
                     )
 
                     if has_overlap:
-                        logging.warning(f"Skipping overlapping entity in '{text}': {entity}")
+                        logging.warning(
+                            f"Skipping overlapping entity in '{text}': {entity}"
+                        )
                         continue
 
                     # Validate that the span doesn't contain spaces (matching tagger validation)
                     span_text = text[start:end]
-                    if not span_text or span_text != span_text.strip() or " " in span_text:
+                    if (
+                        not span_text
+                        or span_text != span_text.strip()
+                        or " " in span_text
+                    ):
                         logging.warning(
                             f"Skipping entity with spaces in '{text}': {entity} -> '{span_text}'"
                         )
@@ -161,7 +174,9 @@ class NameModel:
                     valid_entities.append((start, end, label))
 
                 if not valid_entities:
-                    logging.warning(f"Skipping training example with no valid entities: '{text}'")
+                    logging.warning(
+                        f"Skipping training example with no valid entities: '{text}'"
+                    )
                     skipped_count += 1
                     continue
 
@@ -219,7 +234,9 @@ class NameModel:
             batches = minibatch(examples, size=batch_size)
             for batch in batches:
                 batch_losses = {}
-                self.nlp.update(batch, losses=batch_losses, drop=dropout_rate, sgd=optimizer)
+                self.nlp.update(
+                    batch, losses=batch_losses, drop=dropout_rate, sgd=optimizer
+                )
                 logging.info(
                     f"Training batch with {len(batch)} examples, current losses: {batch_losses}"
                 )
@@ -230,7 +247,7 @@ class NameModel:
 
             del batches  # free memory
             losses_history.append(losses.get("ner", 0))
-            logging.info(f"Epoch {epoch+1}/{epochs}, Total Loss: {losses['ner']:.4f}")
+            logging.info(f"Epoch {epoch + 1}/{epochs}, Total Loss: {losses['ner']:.4f}")
 
         # Store training statistics
         self.training_stats = {
@@ -242,7 +259,9 @@ class NameModel:
             "dropout_rate": dropout_rate,
         }
 
-        logging.info(f"Training completed. Final loss: {self.training_stats['final_loss']:.4f}")
+        logging.info(
+            f"Training completed. Final loss: {self.training_stats['final_loss']:.4f}"
+        )
 
     def evaluate(self, test_data: List[Tuple[str, Dict]]) -> Dict[str, Any]:
         """Evaluate the trained model on test data"""
@@ -291,10 +310,14 @@ class NameModel:
                     entity_stats[label]["fp"] += 1
 
         # Calculate overall metrics
-        precision = correct_entities / predicted_entities if predicted_entities > 0 else 0
+        precision = (
+            correct_entities / predicted_entities if predicted_entities > 0 else 0
+        )
         recall = correct_entities / actual_entities if actual_entities > 0 else 0
         f1_score = (
-            2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
+            2 * (precision * recall) / (precision + recall)
+            if (precision + recall) > 0
+            else 0
         )
 
         # Calculate per-label metrics
@@ -304,7 +327,11 @@ class NameModel:
             label_precision = tp / (tp + fp) if (tp + fp) > 0 else 0
             label_recall = tp / (tp + fn) if (tp + fn) > 0 else 0
             label_f1 = (
-                (2 * (label_precision * label_recall) / (label_precision + label_recall))
+                (
+                    2
+                    * (label_precision * label_recall)
+                    / (label_precision + label_recall)
+                )
                 if (label_precision + label_recall) > 0
                 else 0
             )
@@ -394,7 +421,9 @@ class NameModel:
                     "label": ent.label_,
                     "start": ent.start_char,
                     "end": ent.end_char,
-                    "confidence": getattr(ent, "score", None),  # If confidence scores are available
+                    "confidence": getattr(
+                        ent, "score", None
+                    ),  # If confidence scores are available
                 }
             )
 
diff --git a/processing/ner/name_tagger.py b/src/ners/processing/ner/name_tagger.py
similarity index 91%
rename from processing/ner/name_tagger.py
rename to src/ners/processing/ner/name_tagger.py
index 6251ac0..7ab7280 100644
--- a/processing/ner/name_tagger.py
+++ b/src/ners/processing/ner/name_tagger.py
@@ -48,7 +48,9 @@ class NameTagger:
             # Find the first occurrence of this native word that doesn't overlap
             start_pos = 0
             while True:
-                pos = name_lower.find(native_word_lower, start_pos)  # Case-insensitive search
+                pos = name_lower.find(
+                    native_word_lower, start_pos
+                )  # Case-insensitive search
                 if pos == -1:
                     break
 
@@ -78,7 +80,9 @@ class NameTagger:
             # Find the first occurrence that doesn't overlap
             start_pos = 0
             while True:
-                pos = name_lower.find(surname_lower, start_pos)  # Case-insensitive search
+                pos = name_lower.find(
+                    surname_lower, start_pos
+                )  # Case-insensitive search
                 if pos == -1:
                     break
 
@@ -120,8 +124,13 @@ class NameTagger:
                 continue
 
             # Check for overlaps with already validated entities
-            if any(start < v_end and end > v_start for v_start, v_end, _ in validated_entities):
-                logging.warning(f"Overlapping span ({start}, {end}, '{label}') in '{name}'")
+            if any(
+                start < v_end and end > v_start
+                for v_start, v_end, _ in validated_entities
+            ):
+                logging.warning(
+                    f"Overlapping span ({start}, {end}, '{label}') in '{name}'"
+                )
                 continue
 
             # CRITICAL VALIDATION: Check that the span contains only the expected word (no spaces)
@@ -200,10 +209,16 @@ class NameTagger:
             elif entities_str.startswith("[[") and entities_str.endswith("]]"):
                 return [tuple(e) for e in ast.literal_eval(entities_str)]
             elif entities_str.startswith("[{") and entities_str.endswith("}]"):
-                return [(e["start"], e["end"], e["label"]) for e in json.loads(entities_str)]
+                return [
+                    (e["start"], e["end"], e["label"]) for e in json.loads(entities_str)
+                ]
             else:
                 parsed = ast.literal_eval(entities_str)
-                return [tuple(e) for e in parsed if isinstance(e, (list, tuple)) and len(e) == 3]
+                return [
+                    tuple(e)
+                    for e in parsed
+                    if isinstance(e, (list, tuple)) and len(e) == 3
+                ]
         except (ValueError, SyntaxError, json.JSONDecodeError):
             return []
 
@@ -251,7 +266,9 @@ class NameTagger:
                 last_end = e
         return filtered
 
-    def validate_entities(self, texts: pd.Series, entities_series: pd.Series) -> pd.Series:
+    def validate_entities(
+        self, texts: pd.Series, entities_series: pd.Series
+    ) -> pd.Series:
         """Vectorized entity validation."""
         return pd.Series(map(self.validate, texts, entities_series), index=texts.index)
 
diff --git a/processing/pipeline.py b/src/ners/processing/pipeline.py
similarity index 90%
rename from processing/pipeline.py
rename to src/ners/processing/pipeline.py
index e93c54e..4d4bd5b 100644
--- a/processing/pipeline.py
+++ b/src/ners/processing/pipeline.py
@@ -4,9 +4,9 @@ from typing import Dict, Any
 
 import pandas as pd
 
-from processing.batch.batch_config import BatchConfig
-from processing.batch.batch_processor import BatchProcessor
-from processing.steps import PipelineStep
+from ners.processing.batch.batch_config import BatchConfig
+from ners.processing.batch.batch_processor import BatchProcessor
+from ners.processing.steps import PipelineStep
 
 
 class Pipeline:
diff --git a/processing/steps/__init__.py b/src/ners/processing/steps/__init__.py
similarity index 93%
rename from processing/steps/__init__.py
rename to src/ners/processing/steps/__init__.py
index c8483c1..8873645 100644
--- a/processing/steps/__init__.py
+++ b/src/ners/processing/steps/__init__.py
@@ -8,9 +8,9 @@ from typing import List, Optional
 import pandas as pd
 from pydantic import BaseModel
 
-from core.config.pipeline_config import PipelineConfig
-from core.utils.data_loader import DataLoader
-from processing.batch.batch_config import BatchConfig
+from ners.core.config.pipeline_config import PipelineConfig
+from ners.core.utils.data_loader import DataLoader
+from ners.processing.batch.batch_config import BatchConfig
 
 
 @dataclass
@@ -38,7 +38,10 @@ class PipelineStep(ABC):
     """Abstract base class for pipeline steps"""
 
     def __init__(
-        self, name: str, pipeline_config: PipelineConfig, batch_config: Optional[BatchConfig] = None
+        self,
+        name: str,
+        pipeline_config: PipelineConfig,
+        batch_config: Optional[BatchConfig] = None,
     ):
         self.name = name
         self.pipeline_config = pipeline_config
diff --git a/processing/steps/data_cleaning_step.py b/src/ners/processing/steps/data_cleaning_step.py
similarity index 84%
rename from processing/steps/data_cleaning_step.py
rename to src/ners/processing/steps/data_cleaning_step.py
index 1443984..6284f78 100644
--- a/processing/steps/data_cleaning_step.py
+++ b/src/ners/processing/steps/data_cleaning_step.py
@@ -2,9 +2,9 @@ import logging
 
 import pandas as pd
 
-from core.config.pipeline_config import PipelineConfig
-from core.utils.text_cleaner import TextCleaner
-from processing.steps import PipelineStep
+from ners.core.config.pipeline_config import PipelineConfig
+from ners.core.utils.text_cleaner import TextCleaner
+from ners.processing.steps import PipelineStep
 
 
 class DataCleaningStep(PipelineStep):
diff --git a/processing/steps/data_selection_step.py b/src/ners/processing/steps/data_selection_step.py
similarity index 85%
rename from processing/steps/data_selection_step.py
rename to src/ners/processing/steps/data_selection_step.py
index 5b07104..8ae202d 100644
--- a/processing/steps/data_selection_step.py
+++ b/src/ners/processing/steps/data_selection_step.py
@@ -2,8 +2,8 @@ import logging
 
 import pandas as pd
 
-from core.config.pipeline_config import PipelineConfig
-from processing.steps import PipelineStep
+from ners.core.config.pipeline_config import PipelineConfig
+from ners.processing.steps import PipelineStep
 
 
 class DataSelectionStep(PipelineStep):
@@ -31,8 +31,12 @@ class DataSelectionStep(PipelineStep):
                 )
 
         # Check which columns exist in the batch
-        available_columns = [col for col in self.selected_columns if col in batch.columns]
-        missing_columns = [col for col in self.selected_columns if col not in batch.columns]
+        available_columns = [
+            col for col in self.selected_columns if col in batch.columns
+        ]
+        missing_columns = [
+            col for col in self.selected_columns if col not in batch.columns
+        ]
 
         if missing_columns:
             logging.warning(f"Missing columns in batch {batch_id}: {missing_columns}")
diff --git a/processing/steps/data_splitting_step.py b/src/ners/processing/steps/data_splitting_step.py
similarity index 77%
rename from processing/steps/data_splitting_step.py
rename to src/ners/processing/steps/data_splitting_step.py
index 953e72a..27b97fd 100644
--- a/processing/steps/data_splitting_step.py
+++ b/src/ners/processing/steps/data_splitting_step.py
@@ -1,11 +1,11 @@
 import numpy as np
 import pandas as pd
 
-from core.config.pipeline_config import PipelineConfig
-from core.utils.region_mapper import RegionMapper
-from processing.batch.batch_config import BatchConfig
-from processing.steps import PipelineStep
-from processing.steps.feature_extraction_step import Gender
+from ners.core.config.pipeline_config import PipelineConfig
+from ners.core.utils.region_mapper import RegionMapper
+from ners.processing.batch.batch_config import BatchConfig
+from ners.processing.steps import PipelineStep
+from ners.processing.steps.feature_extraction_step import Gender
 
 
 class DataSplittingStep(PipelineStep):
@@ -26,7 +26,9 @@ class DataSplittingStep(PipelineStep):
         if self.eval_indices is None:
             np.random.seed(self.pipeline_config.data.random_seed)
             eval_size = int(total_size * self.pipeline_config.data.evaluation_fraction)
-            self.eval_indices = set(np.random.choice(total_size, size=eval_size, replace=False))
+            self.eval_indices = set(
+                np.random.choice(total_size, size=eval_size, replace=False)
+            )
         return self.eval_indices
 
     def process_batch(self, batch: pd.DataFrame, batch_id: int) -> pd.DataFrame:
@@ -45,7 +47,9 @@ class DataSplittingStep(PipelineStep):
             df_evaluation = df[eval_mask]
             df_featured = df[~eval_mask]
 
-            self.data_loader.save_csv(df_evaluation, data_dir / output_files["evaluation"])
+            self.data_loader.save_csv(
+                df_evaluation, data_dir / output_files["evaluation"]
+            )
             self.data_loader.save_csv(df_featured, data_dir / output_files["featured"])
         else:
             self.data_loader.save_csv(df, data_dir / output_files["featured"])
@@ -53,7 +57,9 @@ class DataSplittingStep(PipelineStep):
         if self.pipeline_config.data.split_by_province:
             for province in RegionMapper.get_provinces():
                 df_region = df[df.province == province]
-                self.data_loader.save_csv(df_region, data_dir / "provinces" / f"{province}.csv")
+                self.data_loader.save_csv(
+                    df_region, data_dir / "provinces" / f"{province}.csv"
+                )
 
         if self.pipeline_config.data.split_by_gender:
             df_males = df[df.sex == Gender.MALE.value]
diff --git a/processing/steps/feature_extraction_step.py b/src/ners/processing/steps/feature_extraction_step.py
similarity index 95%
rename from processing/steps/feature_extraction_step.py
rename to src/ners/processing/steps/feature_extraction_step.py
index 5ed8aa3..30192ed 100644
--- a/processing/steps/feature_extraction_step.py
+++ b/src/ners/processing/steps/feature_extraction_step.py
@@ -5,10 +5,10 @@ from typing import Dict, Any
 
 import pandas as pd
 
-from core.config.pipeline_config import PipelineConfig
-from core.utils.region_mapper import RegionMapper
-from processing.ner.name_tagger import NameTagger
-from processing.steps import PipelineStep
+from ners.core.config.pipeline_config import PipelineConfig
+from ners.core.utils.region_mapper import RegionMapper
+from ners.processing.ner.name_tagger import NameTagger
+from ners.processing.steps import PipelineStep
 
 
 class Gender(Enum):
@@ -64,10 +64,14 @@ class FeatureExtractionStep(PipelineStep):
 
         self._assign_probable_names(result)
         self._process_simple_names(result)
-        result["identified_category"] = self._assign_identified_category(result["words"])
+        result["identified_category"] = self._assign_identified_category(
+            result["words"]
+        )
 
         if "year" in result.columns:
-            result["year"] = pd.to_numeric(result["year"], errors="coerce").astype("Int16")
+            result["year"] = pd.to_numeric(result["year"], errors="coerce").astype(
+                "Int16"
+            )
 
         if "region" in result.columns:
             result["province"] = self.region_mapper.map(result["region"]).str.lower()
diff --git a/processing/steps/llm_annotation_step.py b/src/ners/processing/steps/llm_annotation_step.py
similarity index 88%
rename from processing/steps/llm_annotation_step.py
rename to src/ners/processing/steps/llm_annotation_step.py
index 357df1f..c819a19 100644
--- a/processing/steps/llm_annotation_step.py
+++ b/src/ners/processing/steps/llm_annotation_step.py
@@ -7,12 +7,12 @@ import ollama
 import pandas as pd
 from pydantic import ValidationError
 
-from core.config.pipeline_config import PipelineConfig
-from core.utils.prompt_manager import PromptManager
-from core.utils.rate_limiter import RateLimitConfig
-from core.utils.rate_limiter import RateLimiter
-from processing.batch.batch_config import BatchConfig
-from processing.steps import PipelineStep, NameAnnotation
+from ners.core.config.pipeline_config import PipelineConfig
+from ners.core.utils.prompt_manager import PromptManager
+from ners.core.utils.rate_limiter import RateLimitConfig
+from ners.core.utils.rate_limiter import RateLimiter
+from ners.processing.batch.batch_config import BatchConfig
+from ners.processing.steps import PipelineStep, NameAnnotation
 
 
 class LLMAnnotationStep(PipelineStep):
@@ -24,7 +24,8 @@ class LLMAnnotationStep(PipelineStep):
         batch_config = BatchConfig(
             batch_size=pipeline_config.processing.batch_size,
             max_workers=min(
-                self.llm_config.max_concurrent_requests, pipeline_config.processing.max_workers
+                self.llm_config.max_concurrent_requests,
+                pipeline_config.processing.max_workers,
             ),
             checkpoint_interval=pipeline_config.processing.checkpoint_interval,
             use_multiprocessing=pipeline_config.processing.use_multiprocessing,
@@ -33,7 +34,9 @@ class LLMAnnotationStep(PipelineStep):
 
         self.prompt = PromptManager(pipeline_config).load_prompt()
         self.rate_limiter = (
-            self._create_rate_limiter() if self.llm_config.enable_rate_limiting else None
+            self._create_rate_limiter()
+            if self.llm_config.enable_rate_limiting
+            else None
         )
 
         # Statistics
@@ -76,7 +79,9 @@ class LLMAnnotationStep(PipelineStep):
                         f"Request took {elapsed_time:.2f}s, exceeding {self.llm_config.timeout_seconds}s timeout"
                     )
 
-                annotation = NameAnnotation.model_validate_json(response.message.content)
+                annotation = NameAnnotation.model_validate_json(
+                    response.message.content
+                )
                 result = {
                     **annotation.model_dump(),
                     "annotated": 1,
@@ -119,7 +124,9 @@ class LLMAnnotationStep(PipelineStep):
             logging.info(f"Batch {batch_id}: No entries to annotate")
             return batch
 
-        logging.info(f"Batch {batch_id}: Annotating {len(unannotated_entries)} entries with LLM")
+        logging.info(
+            f"Batch {batch_id}: Annotating {len(unannotated_entries)} entries with LLM"
+        )
 
         batch = batch.copy()
         client = ollama.Client()
diff --git a/processing/steps/ner_annotation_step.py b/src/ners/processing/steps/ner_annotation_step.py
similarity index 91%
rename from processing/steps/ner_annotation_step.py
rename to src/ners/processing/steps/ner_annotation_step.py
index f2c92c3..9f71a8b 100644
--- a/processing/steps/ner_annotation_step.py
+++ b/src/ners/processing/steps/ner_annotation_step.py
@@ -5,9 +5,9 @@ from typing import Dict
 
 import pandas as pd
 
-from core.config.pipeline_config import PipelineConfig
-from processing.ner.name_model import NameModel
-from processing.steps import PipelineStep, NameAnnotation
+from ners.core.config.pipeline_config import PipelineConfig
+from ners.processing.ner.name_model import NameModel
+from ners.processing.steps import PipelineStep, NameAnnotation
 
 
 class NERAnnotationStep(PipelineStep):
@@ -39,7 +39,9 @@ class NERAnnotationStep(PipelineStep):
                 logging.info("NER model loaded successfully")
             else:
                 logging.warning(f"NER model not found at {self.model_path}")
-                logging.warning("NER annotation will be skipped. Train the model first.")
+                logging.warning(
+                    "NER annotation will be skipped. Train the model first."
+                )
                 self.name_model.nlp = None
         except Exception as e:
             logging.error(f"Failed to load NER model: {e}")
@@ -80,7 +82,9 @@ class NERAnnotationStep(PipelineStep):
                 # Create annotation result in same format as LLM step
                 annotation = NameAnnotation(
                     identified_name=" ".join(native_parts) if native_parts else None,
-                    identified_surname=" ".join(surname_parts) if surname_parts else None,
+                    identified_surname=" ".join(surname_parts)
+                    if surname_parts
+                    else None,
                 )
 
                 result = {
@@ -124,7 +128,9 @@ class NERAnnotationStep(PipelineStep):
             logging.info(f"Batch {batch_id}: No entries to annotate")
             return batch
 
-        logging.info(f"Batch {batch_id}: Annotating {len(unannotated_entries)} entries with NER")
+        logging.info(
+            f"Batch {batch_id}: Annotating {len(unannotated_entries)} entries with NER"
+        )
 
         batch = batch.copy()
 
diff --git a/research/__init__.py b/src/ners/research/__init__.py
similarity index 100%
rename from research/__init__.py
rename to src/ners/research/__init__.py
diff --git a/research/base_model.py b/src/ners/research/base_model.py
similarity index 91%
rename from research/base_model.py
rename to src/ners/research/base_model.py
index 8fff57d..35cb599 100644
--- a/research/base_model.py
+++ b/src/ners/research/base_model.py
@@ -7,7 +7,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 
-from research.experiment import ExperimentConfig
+from ners.research.experiment import ExperimentConfig
 
 
 class BaseModel(ABC):
@@ -103,16 +103,25 @@ class BaseModel(ABC):
             feature_names = self._get_feature_names()
             return dict(zip(feature_names, coefficients))
 
-        elif hasattr(self.model, "named_steps") and "classifier" in self.model.named_steps:
+        elif (
+            hasattr(self.model, "named_steps")
+            and "classifier" in self.model.named_steps
+        ):
             # For sklearn pipelines (like LogisticRegression with vectorizer)
             classifier = self.model.named_steps["classifier"]
             if hasattr(classifier, "coef_"):
                 coefficients = np.abs(classifier.coef_[0])
-                if hasattr(self.model.named_steps["vectorizer"], "get_feature_names_out"):
-                    feature_names = self.model.named_steps["vectorizer"].get_feature_names_out()
+                if hasattr(
+                    self.model.named_steps["vectorizer"], "get_feature_names_out"
+                ):
+                    feature_names = self.model.named_steps[
+                        "vectorizer"
+                    ].get_feature_names_out()
                     # Take top features to avoid too many n-grams
                     top_indices = np.argsort(coefficients)[-20:]
-                    return dict(zip(feature_names[top_indices], coefficients[top_indices]))
+                    return dict(
+                        zip(feature_names[top_indices], coefficients[top_indices])
+                    )
 
         return None
 
@@ -143,7 +152,7 @@ class BaseModel(ABC):
         model_data = joblib.load(path)
 
         # Recreate the model instance
-        from research.experiment import ExperimentConfig
+        from ners.research.experiment import ExperimentConfig
 
         config = ExperimentConfig.from_dict(model_data["config"])
         instance = cls(config)
@@ -221,7 +230,9 @@ class BaseModel(ABC):
         if "accuracy" in self.training_history:
             axes[0].plot(self.training_history["accuracy"], label="Training Accuracy")
             if "val_accuracy" in self.training_history:
-                axes[0].plot(self.training_history["val_accuracy"], label="Validation Accuracy")
+                axes[0].plot(
+                    self.training_history["val_accuracy"], label="Validation Accuracy"
+                )
             axes[0].set_title("Model Accuracy")
             axes[0].set_xlabel("Epoch")
             axes[0].set_ylabel("Accuracy")
diff --git a/research/experiment/__init__.py b/src/ners/research/experiment/__init__.py
similarity index 88%
rename from research/experiment/__init__.py
rename to src/ners/research/experiment/__init__.py
index b948a9c..2cbd36d 100644
--- a/research/experiment/__init__.py
+++ b/src/ners/research/experiment/__init__.py
@@ -18,7 +18,9 @@ class ExperimentConfig:
     tags: List[str] = field(default_factory=list)
 
     # Model configuration
-    model_type: str = "logistic_regression"  # logistic_regression, lstm, transformer, etc.
+    model_type: str = (
+        "logistic_regression"  # logistic_regression, lstm, transformer, etc.
+    )
     model_params: Dict[str, Any] = field(default_factory=dict)
 
     # Feature configuration
@@ -26,7 +28,9 @@ class ExperimentConfig:
     feature_params: Dict[str, Any] = field(default_factory=dict)
 
     # Data configuration
-    train_data_filter: Optional[Dict[str, Any]] = None  # Filter criteria for training data
+    train_data_filter: Optional[Dict[str, Any]] = (
+        None  # Filter criteria for training data
+    )
     test_data_filter: Optional[Dict[str, Any]] = None
     target_column: str = "sex"
 
@@ -36,7 +40,9 @@ class ExperimentConfig:
     cross_validation_folds: int = 5
 
     # Evaluation configuration
-    metrics: List[str] = field(default_factory=lambda: ["accuracy", "precision", "recall", "f1"])
+    metrics: List[str] = field(
+        default_factory=lambda: ["accuracy", "precision", "recall", "f1"]
+    )
 
     def to_dict(self) -> Dict[str, Any]:
         """Convert to dictionary for serialization"""
diff --git a/research/experiment/experiement_result.py b/src/ners/research/experiment/experiement_result.py
similarity index 90%
rename from research/experiment/experiement_result.py
rename to src/ners/research/experiment/experiement_result.py
index 0e47654..4db6e26 100644
--- a/research/experiment/experiement_result.py
+++ b/src/ners/research/experiment/experiement_result.py
@@ -2,7 +2,7 @@ from dataclasses import dataclass, field, asdict
 from datetime import datetime
 from typing import Optional, Dict, List, Any
 
-from research.experiment import ExperimentConfig, ExperimentStatus
+from ners.research.experiment import ExperimentConfig, ExperimentStatus
 
 
 @dataclass
@@ -51,6 +51,8 @@ class ExperimentResult:
         """Create from dictionary"""
         data["config"] = ExperimentConfig.from_dict(data["config"])
         data["start_time"] = datetime.fromisoformat(data["start_time"])
-        data["end_time"] = datetime.fromisoformat(data["end_time"]) if data["end_time"] else None
+        data["end_time"] = (
+            datetime.fromisoformat(data["end_time"]) if data["end_time"] else None
+        )
         data["status"] = ExperimentStatus(data["status"])
         return cls(**data)
diff --git a/research/experiment/experiment_builder.py b/src/ners/research/experiment/experiment_builder.py
similarity index 93%
rename from research/experiment/experiment_builder.py
rename to src/ners/research/experiment/experiment_builder.py
index fb09956..e9efe0c 100644
--- a/research/experiment/experiment_builder.py
+++ b/src/ners/research/experiment/experiment_builder.py
@@ -3,9 +3,9 @@ from typing import List, Dict
 
 import yaml
 
-from core.config.pipeline_config import PipelineConfig
-from research.experiment import ExperimentConfig
-from research.experiment.feature_extractor import FeatureType
+from ners.core.config.pipeline_config import PipelineConfig
+from ners.research.experiment import ExperimentConfig
+from ners.research.experiment.feature_extractor import FeatureType
 
 
 class ExperimentBuilder:
@@ -27,7 +27,9 @@ class ExperimentBuilder:
             raise
 
     @classmethod
-    def find_template(cls, templates: dict, name: str, experiment_type: str = "baseline") -> dict:
+    def find_template(
+        cls, templates: dict, name: str, experiment_type: str = "baseline"
+    ) -> dict:
         """Find experiment configuration by name and type"""
 
         # Map type to section in templates
diff --git a/research/experiment/experiment_runner.py b/src/ners/research/experiment/experiment_runner.py
similarity index 85%
rename from research/experiment/experiment_runner.py
rename to src/ners/research/experiment/experiment_runner.py
index 7cc3201..293a3a4 100644
--- a/research/experiment/experiment_runner.py
+++ b/src/ners/research/experiment/experiment_runner.py
@@ -9,12 +9,16 @@ import pandas as pd
 from sklearn.metrics import confusion_matrix
 from sklearn.model_selection import train_test_split
 
-from core.config import PipelineConfig
-from core.utils.data_loader import DataLoader
-from research.base_model import BaseModel
-from research.experiment import ExperimentConfig, ExperimentStatus, calculate_metrics
-from research.experiment.experiment_tracker import ExperimentTracker
-from research.model_registry import create_model
+from ners.core.config import PipelineConfig
+from ners.core.utils.data_loader import DataLoader
+from ners.research.base_model import BaseModel
+from ners.research.experiment import (
+    ExperimentConfig,
+    ExperimentStatus,
+    calculate_metrics,
+)
+from ners.research.experiment.experiment_tracker import ExperimentTracker
+from ners.research.model_registry import create_model
 
 
 class ExperimentRunner:
@@ -32,10 +36,14 @@ class ExperimentRunner:
 
         try:
             logging.info(f"Starting experiment: {experiment_id}")
-            self.tracker.update_experiment(experiment_id, status=ExperimentStatus.RUNNING)
+            self.tracker.update_experiment(
+                experiment_id, status=ExperimentStatus.RUNNING
+            )
 
             # Load data
-            filepath = self.config.paths.get_data_path(self.config.data.output_files["featured"])
+            filepath = self.config.paths.get_data_path(
+                self.config.data.output_files["featured"]
+            )
             df = self.data_loader.load_csv_complete(filepath)
 
             # Apply data filters if specified
@@ -63,8 +71,12 @@ class ExperimentRunner:
             test_pred = model.predict(X_test)
 
             # Calculate metrics
-            train_metrics = calculate_metrics(y_train, train_pred, experiment_config.metrics)
-            test_metrics = calculate_metrics(y_test, test_pred, experiment_config.metrics)
+            train_metrics = calculate_metrics(
+                y_train, train_pred, experiment_config.metrics
+            )
+            test_metrics = calculate_metrics(
+                y_test, test_pred, experiment_config.metrics
+            )
 
             # Cross-validation if requested
             cv_metrics = {}
@@ -125,7 +137,9 @@ class ExperimentRunner:
         experiment_ids = []
 
         for i, config in enumerate(experiments):
-            logging.info(f"Running experiment {i + 1}/{len(experiments)}: {config.name}")
+            logging.info(
+                f"Running experiment {i + 1}/{len(experiments)}: {config.name}"
+            )
             try:
                 exp_id = self.run_experiment(config)
                 experiment_ids.append(exp_id)
@@ -136,7 +150,9 @@ class ExperimentRunner:
         return experiment_ids
 
     @classmethod
-    def _apply_data_filters(cls, df: pd.DataFrame, config: ExperimentConfig) -> pd.DataFrame:
+    def _apply_data_filters(
+        cls, df: pd.DataFrame, config: ExperimentConfig
+    ) -> pd.DataFrame:
         """Apply data filters specified in experiment config"""
         filtered_df = df.copy()
 
@@ -148,9 +164,13 @@ class ExperimentRunner:
                         filtered_df = filtered_df[filtered_df[column].isin(criteria)]
                     elif isinstance(criteria, dict):
                         if "min" in criteria:
-                            filtered_df = filtered_df[filtered_df[column] >= criteria["min"]]
+                            filtered_df = filtered_df[
+                                filtered_df[column] >= criteria["min"]
+                            ]
                         if "max" in criteria:
-                            filtered_df = filtered_df[filtered_df[column] <= criteria["max"]]
+                            filtered_df = filtered_df[
+                                filtered_df[column] <= criteria["max"]
+                            ]
                     else:
                         filtered_df = filtered_df[filtered_df[column] == criteria]
 
@@ -231,7 +251,9 @@ class ExperimentRunner:
                 return model
 
             except Exception as e:
-                logging.error(f"Failed to load model for experiment {experiment_id}: {e}")
+                logging.error(
+                    f"Failed to load model for experiment {experiment_id}: {e}"
+                )
                 return None
 
         return None
diff --git a/research/experiment/experiment_tracker.py b/src/ners/research/experiment/experiment_tracker.py
similarity index 91%
rename from research/experiment/experiment_tracker.py
rename to src/ners/research/experiment/experiment_tracker.py
index cbdab93..8e50177 100644
--- a/research/experiment/experiment_tracker.py
+++ b/src/ners/research/experiment/experiment_tracker.py
@@ -6,9 +6,9 @@ from typing import Optional, Dict, List
 
 import pandas as pd
 
-from core.config import PipelineConfig, get_config
-from research.experiment import ExperimentConfig, ExperimentStatus
-from research.experiment.experiement_result import ExperimentResult
+from ners.core.config import PipelineConfig, get_config
+from ners.research.experiment import ExperimentConfig, ExperimentStatus
+from ners.research.experiment.experiement_result import ExperimentResult
 
 
 class ExperimentTracker:
@@ -97,7 +97,10 @@ class ExperimentTracker:
         return sorted(results, key=lambda x: x.start_time, reverse=True)
 
     def get_best_experiment(
-        self, metric: str = "accuracy", dataset: str = "test", filters: Optional[Dict] = None
+        self,
+        metric: str = "accuracy",
+        dataset: str = "test",
+        filters: Optional[Dict] = None,
     ) -> Optional[ExperimentResult]:
         """Get the best experiment based on a metric"""
         experiments = self.list_experiments()
@@ -106,7 +109,9 @@ class ExperimentTracker:
             # Apply additional filters
             if "model_type" in filters:
                 experiments = [
-                    e for e in experiments if e.config.model_type == filters["model_type"]
+                    e
+                    for e in experiments
+                    if e.config.model_type == filters["model_type"]
                 ]
             if "features" in filters:
                 experiments = [
@@ -118,7 +123,9 @@ class ExperimentTracker:
         valid_experiments = []
         for exp in experiments:
             if exp.status == ExperimentStatus.COMPLETED:
-                metrics_dict = exp.test_metrics if dataset == "test" else exp.train_metrics
+                metrics_dict = (
+                    exp.test_metrics if dataset == "test" else exp.train_metrics
+                )
                 if metric in metrics_dict:
                     valid_experiments.append((exp, metrics_dict[metric]))
 
diff --git a/research/experiment/feature_extractor.py b/src/ners/research/experiment/feature_extractor.py
similarity index 96%
rename from research/experiment/feature_extractor.py
rename to src/ners/research/experiment/feature_extractor.py
index 916c69d..cff71e2 100644
--- a/research/experiment/feature_extractor.py
+++ b/src/ners/research/experiment/feature_extractor.py
@@ -24,7 +24,9 @@ class FeatureType(Enum):
 class FeatureExtractor:
     """Extract different types of features from name data"""
 
-    def __init__(self, feature_types: List[FeatureType], feature_params: Dict[str, Any] = None):
+    def __init__(
+        self, feature_types: List[FeatureType], feature_params: Dict[str, Any] = None
+    ):
         self.feature_types = feature_types
         self.feature_params = feature_params or {}
 
diff --git a/research/model_registry.py b/src/ners/research/model_registry.py
similarity index 50%
rename from research/model_registry.py
rename to src/ners/research/model_registry.py
index 4e1bb49..8ca2948 100644
--- a/research/model_registry.py
+++ b/src/ners/research/model_registry.py
@@ -1,18 +1,18 @@
 from typing import List
 
-from research.base_model import BaseModel
-from research.experiment import ExperimentConfig
-from research.models.bigru_model import BiGRUModel
-from research.models.cnn_model import CNNModel
-from research.models.ensemble_model import EnsembleModel
-from research.models.lightgbm_model import LightGBMModel
-from research.models.logistic_regression_model import LogisticRegressionModel
-from research.models.lstm_model import LSTMModel
-from research.models.naive_bayes_model import NaiveBayesModel
-from research.models.random_forest_model import RandomForestModel
-from research.models.svm_model import SVMModel
-from research.models.transformer_model import TransformerModel
-from research.models.xgboost_model import XGBoostModel
+from ners.research.base_model import BaseModel
+from ners.research.experiment import ExperimentConfig
+from ners.research.models.bigru_model import BiGRUModel
+from ners.research.models.cnn_model import CNNModel
+from ners.research.models.ensemble_model import EnsembleModel
+from ners.research.models.lightgbm_model import LightGBMModel
+from ners.research.models.logistic_regression_model import LogisticRegressionModel
+from ners.research.models.lstm_model import LSTMModel
+from ners.research.models.naive_bayes_model import NaiveBayesModel
+from ners.research.models.random_forest_model import RandomForestModel
+from ners.research.models.svm_model import SVMModel
+from ners.research.models.transformer_model import TransformerModel
+from ners.research.models.xgboost_model import XGBoostModel
 
 MODEL_REGISTRY = {
     "bigru": BiGRUModel,
diff --git a/research/model_trainer.py b/src/ners/research/model_trainer.py
similarity index 86%
rename from research/model_trainer.py
rename to src/ners/research/model_trainer.py
index 5d908ee..ad77c61 100644
--- a/research/model_trainer.py
+++ b/src/ners/research/model_trainer.py
@@ -5,12 +5,12 @@ from typing import List, Dict, Any
 
 import pandas as pd
 
-from core.config import get_config
-from core.utils.data_loader import DataLoader
-from research.experiment import FeatureType, ExperimentConfig
-from research.experiment.experiment_runner import ExperimentRunner
-from research.experiment.experiment_tracker import ExperimentTracker
-from research.model_registry import MODEL_REGISTRY
+from ners.core.config import get_config
+from ners.core.utils.data_loader import DataLoader
+from ners.research.experiment import FeatureType, ExperimentConfig
+from ners.research.experiment.experiment_runner import ExperimentRunner
+from ners.research.experiment.experiment_tracker import ExperimentTracker
+from ners.research.model_registry import MODEL_REGISTRY
 
 
 class ModelTrainer:
@@ -66,7 +66,9 @@ class ModelTrainer:
         if experiment and experiment.test_metrics:
             logging.info("Training completed successfully!")
             logging.info(f"Experiment ID: {experiment_id}")
-            logging.info(f"Test Accuracy: {experiment.test_metrics.get('accuracy', 0):.4f}")
+            logging.info(
+                f"Test Accuracy: {experiment.test_metrics.get('accuracy', 0):.4f}"
+            )
             logging.info(f"Test F1-Score: {experiment.test_metrics.get('f1', 0):.4f}")
 
             if save_artifacts:
@@ -144,13 +146,17 @@ class ModelTrainer:
 
         try:
             # Load data for learning curve generation
-            data_path = self.config.paths.get_data_path(self.config.data.output_files["featured"])
+            data_path = self.config.paths.get_data_path(
+                self.config.data.output_files["featured"]
+            )
             if data_path.exists():
                 df = self.data_loader.load_csv_complete(data_path)
 
                 # Generate learning curve
                 logging.info("Generating learning curve...")
-                trained_model.generate_learning_curve(df, df[experiment.config.target_column])
+                trained_model.generate_learning_curve(
+                    df, df[experiment.config.target_column]
+                )
 
                 # Plot and save learning curve
                 learning_curve_path = model_dir / "learning_curve.png"
@@ -187,8 +193,12 @@ class ModelTrainer:
             "model_path": str(model_path),
             "config_path": str(config_path),
             "results_path": str(results_path),
-            "learning_curve_plot": str(learning_curve_path) if learning_curve_path else None,
-            "training_history_plot": str(training_history_path) if training_history_path else None,
+            "learning_curve_plot": str(learning_curve_path)
+            if learning_curve_path
+            else None,
+            "training_history_plot": str(training_history_path)
+            if training_history_path
+            else None,
             "has_learning_curve": bool(trained_model.learning_curve_data),
             "has_training_history": bool(trained_model.training_history),
         }
@@ -215,8 +225,12 @@ class ModelTrainer:
             "config_path": str(config_path),
             "results_path": str(results_path),
             "metadata_path": str(metadata_path),
-            "learning_curve_plot": str(learning_curve_path) if learning_curve_path else None,
-            "training_history_plot": str(training_history_path) if training_history_path else None,
+            "learning_curve_plot": str(learning_curve_path)
+            if learning_curve_path
+            else None,
+            "training_history_plot": str(training_history_path)
+            if training_history_path
+            else None,
         }
 
     def load_trained_model(self, experiment_id: str):
@@ -227,7 +241,9 @@ class ModelTrainer:
         model_path = model_dir / "complete_model.joblib"
 
         if not model_path.exists():
-            raise FileNotFoundError(f"Model artifacts not found for experiment {experiment_id}")
+            raise FileNotFoundError(
+                f"Model artifacts not found for experiment {experiment_id}"
+            )
 
         # Load the model class dynamically
         metadata_path = model_dir / "metadata.json"
@@ -261,7 +277,9 @@ class ModelTrainer:
                             metadata = json.load(f)
                         models_data.append(metadata)
                     except Exception as e:
-                        logging.warning(f"Could not read metadata for {model_dir.name}: {e}")
+                        logging.warning(
+                            f"Could not read metadata for {model_dir.name}: {e}"
+                        )
 
         if not models_data:
             logging.info("No saved models found.")
diff --git a/research/models/__init__.py b/src/ners/research/models/__init__.py
similarity index 100%
rename from research/models/__init__.py
rename to src/ners/research/models/__init__.py
diff --git a/research/models/bigru_model.py b/src/ners/research/models/bigru_model.py
similarity index 93%
rename from research/models/bigru_model.py
rename to src/ners/research/models/bigru_model.py
index 5c9c347..daabfcf 100644
--- a/research/models/bigru_model.py
+++ b/src/ners/research/models/bigru_model.py
@@ -7,7 +7,7 @@ from tensorflow.keras.models import Sequential
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras.preprocessing.text import Tokenizer
 
-from research.neural_network_model import NeuralNetworkModel
+from ners.research.neural_network_model import NeuralNetworkModel
 
 
 class BiGRUModel(NeuralNetworkModel):
@@ -53,7 +53,9 @@ class BiGRUModel(NeuralNetworkModel):
         )
 
         model.compile(
-            loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"]
+            loss="sparse_categorical_crossentropy",
+            optimizer="adam",
+            metrics=["accuracy"],
         )
         return model
 
diff --git a/research/models/cnn_model.py b/src/ners/research/models/cnn_model.py
similarity index 87%
rename from research/models/cnn_model.py
rename to src/ners/research/models/cnn_model.py
index d1b1257..c7097a2 100644
--- a/research/models/cnn_model.py
+++ b/src/ners/research/models/cnn_model.py
@@ -15,7 +15,7 @@ from tensorflow.keras.models import Sequential
 from tensorflow.keras.preprocessing.text import Tokenizer
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 
-from research.neural_network_model import NeuralNetworkModel
+from ners.research.neural_network_model import NeuralNetworkModel
 
 
 class CNNModel(NeuralNetworkModel):
@@ -29,7 +29,9 @@ class CNNModel(NeuralNetworkModel):
             [
                 # Learn char/subword embeddings; spatial dropout regularizes across channels
                 # to make the model robust to noisy characters and transliteration.
-                Embedding(input_dim=vocab_size, output_dim=params.get("embedding_dim", 64)),
+                Embedding(
+                    input_dim=vocab_size, output_dim=params.get("embedding_dim", 64)
+                ),
                 SpatialDropout1D(rate=params.get("embedding_dropout", 0.1)),
                 # Small kernels capture short n-gram like patterns; padding='same' keeps
                 # sequence length stable for simpler pooling behavior.
@@ -59,7 +61,9 @@ class CNNModel(NeuralNetworkModel):
         )
 
         model.compile(
-            loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"]
+            loss="sparse_categorical_crossentropy",
+            optimizer="adam",
+            metrics=["accuracy"],
         )
         return model
 
@@ -75,6 +79,8 @@ class CNNModel(NeuralNetworkModel):
             self.tokenizer.fit_on_texts(text_data)
 
         sequences = self.tokenizer.texts_to_sequences(text_data)
-        max_len = self.config.model_params.get("max_len", 20)  # Longer for character level
+        max_len = self.config.model_params.get(
+            "max_len", 20
+        )  # Longer for character level
 
         return pad_sequences(sequences, maxlen=max_len, padding="post")
diff --git a/research/models/ensemble_model.py b/src/ners/research/models/ensemble_model.py
similarity index 77%
rename from research/models/ensemble_model.py
rename to src/ners/research/models/ensemble_model.py
index 7ccf801..4f758b3 100644
--- a/research/models/ensemble_model.py
+++ b/src/ners/research/models/ensemble_model.py
@@ -8,8 +8,8 @@ from sklearn.linear_model import LogisticRegression
 from sklearn.naive_bayes import MultinomialNB
 from sklearn.pipeline import Pipeline
 
-from research.experiment import ExperimentConfig
-from research.traditional_model import TraditionalModel
+from ners.research.experiment import ExperimentConfig
+from ners.research.traditional_model import TraditionalModel
 
 
 class EnsembleModel(TraditionalModel):
@@ -40,22 +40,28 @@ class EnsembleModel(TraditionalModel):
                     [
                         (
                             "vectorizer",
-                            CountVectorizer(analyzer="char", ngram_range=(2, 4), max_features=5000),
+                            CountVectorizer(
+                                analyzer="char", ngram_range=(2, 4), max_features=5000
+                            ),
                         ),
                         (
                             "classifier",
-                            LogisticRegression(max_iter=1000, random_state=self.config.random_seed),
+                            LogisticRegression(
+                                max_iter=1000, random_state=self.config.random_seed
+                            ),
                         ),
                     ]
                 )
-                estimators.append((f"logistic_regression", model))
+                estimators.append(("logistic_regression", model))
 
             elif model_type == "random_forest":
                 model = Pipeline(
                     [
                         (
                             "vectorizer",
-                            TfidfVectorizer(analyzer="char", ngram_range=(2, 3), max_features=3000),
+                            TfidfVectorizer(
+                                analyzer="char", ngram_range=(2, 3), max_features=3000
+                            ),
                         ),
                         (
                             "classifier",
@@ -65,19 +71,21 @@ class EnsembleModel(TraditionalModel):
                         ),
                     ]
                 )
-                estimators.append((f"rf", model))
+                estimators.append(("rf", model))
 
             elif model_type == "naive_bayes":
                 model = Pipeline(
                     [
                         (
                             "vectorizer",
-                            CountVectorizer(analyzer="char", ngram_range=(1, 3), max_features=4000),
+                            CountVectorizer(
+                                analyzer="char", ngram_range=(1, 3), max_features=4000
+                            ),
                         ),
                         ("classifier", MultinomialNB()),
                     ]
                 )
-                estimators.append((f"nb", model))
+                estimators.append(("nb", model))
 
         # Soft voting averages probabilities (preferred when members are calibrated);
         # hard voting uses majority class. Parallelize member predictions.
diff --git a/research/models/lightgbm_model.py b/src/ners/research/models/lightgbm_model.py
similarity index 97%
rename from research/models/lightgbm_model.py
rename to src/ners/research/models/lightgbm_model.py
index deb895f..1da50b0 100644
--- a/research/models/lightgbm_model.py
+++ b/src/ners/research/models/lightgbm_model.py
@@ -5,7 +5,7 @@ from sklearn.base import BaseEstimator
 from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.preprocessing import LabelEncoder
 
-from research.traditional_model import TraditionalModel
+from ners.research.traditional_model import TraditionalModel
 
 
 class LightGBMModel(TraditionalModel):
@@ -106,7 +106,9 @@ class LightGBMModel(TraditionalModel):
                             lambda x: x if x in known_classes else default_class
                         )
 
-                        encoded = self.label_encoders[feature_key].transform(column_mapped)
+                        encoded = self.label_encoders[feature_key].transform(
+                            column_mapped
+                        )
 
                     features.append(encoded.reshape(-1, 1))
 
diff --git a/research/models/logistic_regression_model.py b/src/ners/research/models/logistic_regression_model.py
similarity index 97%
rename from research/models/logistic_regression_model.py
rename to src/ners/research/models/logistic_regression_model.py
index dc2e54b..452397a 100644
--- a/research/models/logistic_regression_model.py
+++ b/src/ners/research/models/logistic_regression_model.py
@@ -5,7 +5,7 @@ from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import Pipeline
 
-from research.traditional_model import TraditionalModel
+from ners.research.traditional_model import TraditionalModel
 
 
 class LogisticRegressionModel(TraditionalModel):
diff --git a/research/models/lstm_model.py b/src/ners/research/models/lstm_model.py
similarity index 93%
rename from research/models/lstm_model.py
rename to src/ners/research/models/lstm_model.py
index 1e65aa1..792c7a4 100644
--- a/research/models/lstm_model.py
+++ b/src/ners/research/models/lstm_model.py
@@ -7,7 +7,7 @@ from tensorflow.keras.models import Sequential
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras.preprocessing.text import Tokenizer
 
-from research.neural_network_model import NeuralNetworkModel
+from ners.research.neural_network_model import NeuralNetworkModel
 
 
 class LSTMModel(NeuralNetworkModel):
@@ -50,7 +50,9 @@ class LSTMModel(NeuralNetworkModel):
         )
 
         model.compile(
-            loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"]
+            loss="sparse_categorical_crossentropy",
+            optimizer="adam",
+            metrics=["accuracy"],
         )
         return model
 
diff --git a/research/models/naive_bayes_model.py b/src/ners/research/models/naive_bayes_model.py
similarity index 96%
rename from research/models/naive_bayes_model.py
rename to src/ners/research/models/naive_bayes_model.py
index d377d7f..cf8f027 100644
--- a/research/models/naive_bayes_model.py
+++ b/src/ners/research/models/naive_bayes_model.py
@@ -5,7 +5,7 @@ from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.naive_bayes import MultinomialNB
 from sklearn.pipeline import Pipeline
 
-from research.traditional_model import TraditionalModel
+from ners.research.traditional_model import TraditionalModel
 
 
 class NaiveBayesModel(TraditionalModel):
diff --git a/research/models/random_forest_model.py b/src/ners/research/models/random_forest_model.py
similarity index 87%
rename from research/models/random_forest_model.py
rename to src/ners/research/models/random_forest_model.py
index 14ef0bc..7d6f5a1 100644
--- a/research/models/random_forest_model.py
+++ b/src/ners/research/models/random_forest_model.py
@@ -6,7 +6,7 @@ from sklearn.base import BaseEstimator
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import LabelEncoder
 
-from research.traditional_model import TraditionalModel
+from ners.research.traditional_model import TraditionalModel
 
 
 class RandomForestModel(TraditionalModel):
@@ -18,7 +18,6 @@ class RandomForestModel(TraditionalModel):
         self.label_encoders: Dict[str, LabelEncoder] = {}
 
     def build_model(self) -> BaseEstimator:
-
         params = self.config.model_params
 
         # Tree ensemble is robust to mixed numeric/categorical encodings; parallelize
@@ -56,10 +55,14 @@ class RandomForestModel(TraditionalModel):
                         column_clean = column.fillna("unknown").astype(str)
                         known_classes = set(encoder.classes_)
                         default_class = (
-                            "unknown" if "unknown" in known_classes else encoder.classes_[0]
+                            "unknown"
+                            if "unknown" in known_classes
+                            else encoder.classes_[0]
                         )
                         column_mapped = column_clean.apply(
-                            lambda value: value if value in known_classes else default_class
+                            lambda value: value
+                            if value in known_classes
+                            else default_class
                         )
                         encoded = encoder.transform(column_mapped)
 
diff --git a/research/models/svm_model.py b/src/ners/research/models/svm_model.py
similarity index 96%
rename from research/models/svm_model.py
rename to src/ners/research/models/svm_model.py
index b980f36..2d56a9e 100644
--- a/research/models/svm_model.py
+++ b/src/ners/research/models/svm_model.py
@@ -5,7 +5,7 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.pipeline import Pipeline
 from sklearn.svm import SVC
 
-from research.traditional_model import TraditionalModel
+from ners.research.traditional_model import TraditionalModel
 
 
 class SVMModel(TraditionalModel):
diff --git a/research/models/transformer_model.py b/src/ners/research/models/transformer_model.py
similarity index 82%
rename from research/models/transformer_model.py
rename to src/ners/research/models/transformer_model.py
index f5bdcbe..2fc460f 100644
--- a/research/models/transformer_model.py
+++ b/src/ners/research/models/transformer_model.py
@@ -16,7 +16,7 @@ from tensorflow.keras.models import Model
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras.preprocessing.text import Tokenizer
 
-from research.neural_network_model import NeuralNetworkModel
+from ners.research.neural_network_model import NeuralNetworkModel
 
 
 class TransformerModel(NeuralNetworkModel):
@@ -37,7 +37,8 @@ class TransformerModel(NeuralNetworkModel):
         # Add positional encoding
         positions = tf.range(start=0, limit=params.get("max_len", 8), delta=1)
         pos_embedding = Embedding(
-            input_dim=params.get("max_len", 8), output_dim=params.get("embedding_dim", 64)
+            input_dim=params.get("max_len", 8),
+            output_dim=params.get("embedding_dim", 64),
         )(positions)
         x = x + pos_embedding
 
@@ -49,7 +50,9 @@ class TransformerModel(NeuralNetworkModel):
 
         model = Model(inputs, outputs)
         model.compile(
-            optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
+            optimizer="adam",
+            loss="sparse_categorical_crossentropy",
+            metrics=["accuracy"],
         )
         return model
 
@@ -62,11 +65,15 @@ class TransformerModel(NeuralNetworkModel):
             key_dim=cfg_params.get("transformer_head_size", 64),
             dropout=cfg_params.get("attn_dropout", 0.1),
         )(x, x)
-        x = LayerNormalization(epsilon=1e-6)(x + Dropout(cfg_params.get("dropout", 0.1))(attn))
+        x = LayerNormalization(epsilon=1e-6)(
+            x + Dropout(cfg_params.get("dropout", 0.1))(attn)
+        )
 
         ff = Dense(cfg_params.get("transformer_ff_dim", 128), activation="relu")(x)
         ff = Dense(x.shape[-1])(ff)
-        return LayerNormalization(epsilon=1e-6)(x + Dropout(cfg_params.get("dropout", 0.1))(ff))
+        return LayerNormalization(epsilon=1e-6)(
+            x + Dropout(cfg_params.get("dropout", 0.1))(ff)
+        )
 
     def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
         text_data = self._collect_text_corpus(X)
diff --git a/research/models/xgboost_model.py b/src/ners/research/models/xgboost_model.py
similarity index 97%
rename from research/models/xgboost_model.py
rename to src/ners/research/models/xgboost_model.py
index c642203..eaf34e1 100644
--- a/research/models/xgboost_model.py
+++ b/src/ners/research/models/xgboost_model.py
@@ -5,7 +5,7 @@ from sklearn.base import BaseEstimator
 from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.preprocessing import LabelEncoder
 
-from research.traditional_model import TraditionalModel
+from ners.research.traditional_model import TraditionalModel
 
 
 class XGBoostModel(TraditionalModel):
@@ -106,7 +106,9 @@ class XGBoostModel(TraditionalModel):
                             lambda x: x if x in known_classes else default_class
                         )
 
-                        encoded = self.label_encoders[feature_key].transform(column_mapped)
+                        encoded = self.label_encoders[feature_key].transform(
+                            column_mapped
+                        )
 
                     features.append(encoded.reshape(-1, 1))
 
diff --git a/research/neural_network_model.py b/src/ners/research/neural_network_model.py
similarity index 91%
rename from research/neural_network_model.py
rename to src/ners/research/neural_network_model.py
index 64f6445..6181367 100644
--- a/research/neural_network_model.py
+++ b/src/ners/research/neural_network_model.py
@@ -10,8 +10,10 @@ from sklearn.model_selection import StratifiedKFold
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 
-from research.base_model import BaseModel
-from research.experiment.feature_extractor import FeatureExtractor
+import tensorflow as tf
+
+from ners.research.base_model import BaseModel
+from ners.research.experiment.feature_extractor import FeatureExtractor
 
 
 class NeuralNetworkModel(BaseModel):
@@ -34,8 +36,6 @@ class NeuralNetworkModel(BaseModel):
         # - Enables memory growth to avoid pre-allocating all VRAM
         # - Optionally enables mixed precision if requested via model params
         try:
-            import tensorflow as tf  # Imported lazily to avoid dependency for non-NN runs
-
             requested_gpu = bool(self.config.model_params.get("use_gpu", False))
             enable_mixed = bool(self.config.model_params.get("mixed_precision", False))
 
@@ -49,15 +49,15 @@ class NeuralNetworkModel(BaseModel):
 
                 if enable_mixed:
                     try:
-                        from tensorflow.keras import mixed_precision
-
-                        mixed_precision.set_global_policy("mixed_float16")
+                        tf.keras.mixed_precision.set_global_policy("mixed_float16")
                         logging.info("Enabled TensorFlow mixed precision (float16)")
                     except Exception as e:
                         logging.warning(f"Could not enable mixed precision: {e}")
             else:
                 if requested_gpu:
-                    logging.warning("Requested GPU but no TensorFlow GPU device is available.")
+                    logging.warning(
+                        "Requested GPU but no TensorFlow GPU device is available."
+                    )
         except Exception as e:
             # Keep silent in non-TF environments / non-NN workflows
             logging.debug(f"TensorFlow GPU setup skipped: {e}")
@@ -86,7 +86,9 @@ class NeuralNetworkModel(BaseModel):
         logging.info(f"Vocabulary size: {vocab_size}")
 
         # Get additional model parameters
-        self.model = self.build_model_with_vocab(vocab_size=vocab_size, **self.config.model_params)
+        self.model = self.build_model_with_vocab(
+            vocab_size=vocab_size, **self.config.model_params
+        )
 
         # Train the neural network
         logging.info(
@@ -143,7 +145,7 @@ class NeuralNetworkModel(BaseModel):
                 # Keep zeros (padding) untouched; clamp negatives and > max_idx to OOV
                 invalid_mask = (arr < 0) | (arr > max_idx)
                 # Avoid turning zeros into OOV
-                invalid_mask &= (arr != 0)
+                invalid_mask &= arr != 0
                 if invalid_mask.any():
                     arr[invalid_mask] = oov_index
 
@@ -157,10 +159,14 @@ class NeuralNetworkModel(BaseModel):
         """Combine configured textual features into one string per record."""
 
         column_names = [
-            feature.value for feature in self.config.features if feature.value in X.columns
+            feature.value
+            for feature in self.config.features
+            if feature.value in X.columns
         ]
         if not column_names:
-            raise ValueError("No configured text features found in the provided DataFrame.")
+            raise ValueError(
+                "No configured text features found in the provided DataFrame."
+            )
 
         text_frame = X[column_names].fillna("").astype(str)
 
@@ -193,9 +199,7 @@ class NeuralNetworkModel(BaseModel):
                         pass
                 if enable_mixed:
                     try:
-                        from tensorflow.keras import mixed_precision
-
-                        mixed_precision.set_global_policy("mixed_float16")
+                        tf.keras.mixed_precision.set_global_policy("mixed_float16")
                     except Exception:
                         pass
             else:
@@ -208,7 +212,9 @@ class NeuralNetworkModel(BaseModel):
         X_prepared = self._sanitize_sequences(X_prepared)
         y_encoded = self.label_encoder.transform(y)
 
-        cv = StratifiedKFold(n_splits=cv_folds, shuffle=True, random_state=self.config.random_seed)
+        cv = StratifiedKFold(
+            n_splits=cv_folds, shuffle=True, random_state=self.config.random_seed
+        )
 
         accuracies = []
         precisions = []
@@ -280,14 +286,14 @@ class NeuralNetworkModel(BaseModel):
                         pass
                 if enable_mixed:
                     try:
-                        from tensorflow.keras import mixed_precision
-
-                        mixed_precision.set_global_policy("mixed_float16")
+                        tf.keras.mixed_precision.set_global_policy("mixed_float16")
                     except Exception:
                         pass
             else:
                 if requested_gpu:
-                    logging.warning("Requested GPU for learning curve but none is available.")
+                    logging.warning(
+                        "Requested GPU for learning curve but none is available."
+                    )
         except Exception:
             pass
 
@@ -342,7 +348,7 @@ class NeuralNetworkModel(BaseModel):
 
                 # Train model
                 if hasattr(model, "fit"):
-                    history = model.fit(
+                    model.fit(
                         X_train_subset,
                         y_train_subset,
                         epochs=self.config.model_params.get("epochs", 10),
diff --git a/research/statistics/__init__.py b/src/ners/research/statistics/__init__.py
similarity index 100%
rename from research/statistics/__init__.py
rename to src/ners/research/statistics/__init__.py
diff --git a/research/statistics/plots.py b/src/ners/research/statistics/plots.py
similarity index 73%
rename from research/statistics/plots.py
rename to src/ners/research/statistics/plots.py
index 051feb1..86014ff 100644
--- a/research/statistics/plots.py
+++ b/src/ners/research/statistics/plots.py
@@ -3,12 +3,16 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
 
-from research.statistics.utils import LETTERS, build_letter_frequencies
+from ners.research.statistics.utils import LETTERS, build_letter_frequencies
 
 
 def plot_transition_matrix(ax, df_probs, title=""):
     hm = sns.heatmap(
-        df_probs.loc[list(LETTERS), list(LETTERS)], cmap="Reds", annot=False, cbar=False, ax=ax
+        df_probs.loc[list(LETTERS), list(LETTERS)],
+        cmap="Reds",
+        annot=False,
+        cbar=False,
+        ax=ax,
     )
     ax.set_title(title, fontsize=12)
     return hm
@@ -31,8 +35,12 @@ def plot_letter_frequencies(males, females, sort_values=False, title=None):
     x = np.arange(len(df_plot))
     w = 0.4
     fig, ax = plt.subplots(figsize=(16, 6))
-    ax.bar(x - w / 2, df_plot["Male"], width=w, label="Male", color="steelblue", alpha=0.8)
-    ax.bar(x + w / 2, df_plot["Female"], width=w, label="Female", color="salmon", alpha=0.8)
+    ax.bar(
+        x - w / 2, df_plot["Male"], width=w, label="Male", color="steelblue", alpha=0.8
+    )
+    ax.bar(
+        x + w / 2, df_plot["Female"], width=w, label="Female", color="salmon", alpha=0.8
+    )
 
     ax.set_xticks(x)
     ax.set_xticklabels(df_plot["letter"])
diff --git a/research/statistics/utils.py b/src/ners/research/statistics/utils.py
similarity index 92%
rename from research/statistics/utils.py
rename to src/ners/research/statistics/utils.py
index 25f2248..b27c135 100644
--- a/research/statistics/utils.py
+++ b/src/ners/research/statistics/utils.py
@@ -5,8 +5,6 @@ import numpy as np
 import pandas as pd
 from scipy.spatial.distance import euclidean
 from scipy.stats import entropy
-from scipy.spatial.distance import euclidean
-from scipy.stats import entropy
 from typing import Dict, Any
 
 LETTERS = "abcdefghijklmnopqrstuvwxyz"
@@ -49,7 +47,12 @@ def build_words_token(df: pd.DataFrame, source: str, target: str) -> pd.DataFram
 
 def build_letter_frequencies(series: pd.Series) -> pd.DataFrame:
     # Normalize: lowercase, remove non-letters, concatenate all into one string
-    s = series.astype(str).str.lower().str.replace(r"[^a-z]", "", regex=True).str.cat(sep="")
+    s = (
+        series.astype(str)
+        .str.lower()
+        .str.replace(r"[^a-z]", "", regex=True)
+        .str.cat(sep="")
+    )
 
     # Convert string into Series of characters
     chars = pd.Series(list(s))
@@ -150,8 +153,12 @@ def build_transition_comparisons(
     kl_names_mf = entropy(prepared_names["m"] + 1e-12, prepared_names["f"] + 1e-12)
     kl_names_fm = entropy(prepared_names["f"] + 1e-12, prepared_names["m"] + 1e-12)
 
-    kl_surnames_mf = entropy(prepared_surnames["m"] + 1e-12, prepared_surnames["f"] + 1e-12)
-    kl_surnames_fm = entropy(prepared_surnames["f"] + 1e-12, prepared_surnames["m"] + 1e-12)
+    kl_surnames_mf = entropy(
+        prepared_surnames["m"] + 1e-12, prepared_surnames["f"] + 1e-12
+    )
+    kl_surnames_fm = entropy(
+        prepared_surnames["f"] + 1e-12, prepared_surnames["m"] + 1e-12
+    )
 
     jsd_names = 0.5 * (kl_names_mf + kl_names_fm)
     jsd_surnames = 0.5 * (kl_surnames_mf + kl_surnames_fm)
@@ -163,7 +170,9 @@ def build_transition_comparisons(
         P_f = transitions["f"]["probs"].flatten()
 
         # Calculate the observed JSD (our test statistic)
-        observed_jsd = 0.5 * (entropy(P_m + 1e-12, P_f + 1e-12) + entropy(P_f + 1e-12, P_m + 1e-12))
+        observed_jsd = 0.5 * (
+            entropy(P_m + 1e-12, P_f + 1e-12) + entropy(P_f + 1e-12, P_m + 1e-12)
+        )
 
         # Concatenate male and female counts
         counts_m = transitions["m"]["counts"]
@@ -194,10 +203,12 @@ def build_transition_comparisons(
 
             permuted_jsd = 0.5 * (
                 entropy(
-                    permuted_probs_m.mean(axis=1) + 1e-12, permuted_probs_f.mean(axis=1) + 1e-12
+                    permuted_probs_m.mean(axis=1) + 1e-12,
+                    permuted_probs_f.mean(axis=1) + 1e-12,
                 )
                 + entropy(
-                    permuted_probs_f.mean(axis=1) + 1e-12, permuted_probs_m.mean(axis=1) + 1e-12
+                    permuted_probs_f.mean(axis=1) + 1e-12,
+                    permuted_probs_m.mean(axis=1) + 1e-12,
                 )
             )
             permuted_jsds.append(permuted_jsd)
diff --git a/research/traditional_model.py b/src/ners/research/traditional_model.py
similarity index 88%
rename from research/traditional_model.py
rename to src/ners/research/traditional_model.py
index 8f97c0a..bce368c 100644
--- a/research/traditional_model.py
+++ b/src/ners/research/traditional_model.py
@@ -8,8 +8,8 @@ from sklearn.model_selection import StratifiedKFold, cross_val_score
 from sklearn.model_selection import learning_curve
 from sklearn.preprocessing import LabelEncoder
 
-from research.base_model import BaseModel
-from research.experiment.feature_extractor import FeatureExtractor
+from ners.research.base_model import BaseModel
+from ners.research.experiment.feature_extractor import FeatureExtractor
 
 
 class TraditionalModel(BaseModel):
@@ -52,7 +52,9 @@ class TraditionalModel(BaseModel):
         # Train model
         if len(X_prepared.shape) == 1:
             # For text-based features (like LogisticRegression with vectorization)
-            logging.info(f"Fitting model with {X_prepared.shape[0]} samples (text features)")
+            logging.info(
+                f"Fitting model with {X_prepared.shape[0]} samples (text features)"
+            )
         else:
             # For numerical features
             logging.info(
@@ -74,12 +76,16 @@ class TraditionalModel(BaseModel):
 
         return self
 
-    def cross_validate(self, X: pd.DataFrame, y: pd.Series, cv_folds: int = 5) -> Dict[str, float]:
+    def cross_validate(
+        self, X: pd.DataFrame, y: pd.Series, cv_folds: int = 5
+    ) -> Dict[str, float]:
         features_df = self.feature_extractor.extract_features(X)
         X_prepared = self.prepare_features(features_df)
         y_encoded = self.label_encoder.transform(y)
 
-        cv = StratifiedKFold(n_splits=cv_folds, shuffle=True, random_state=self.config.random_seed)
+        cv = StratifiedKFold(
+            n_splits=cv_folds, shuffle=True, random_state=self.config.random_seed
+        )
 
         # Calculate different metrics
         results = {}
@@ -95,7 +101,11 @@ class TraditionalModel(BaseModel):
         for metric in ["precision", "recall", "f1"]:
             if metric in self.config.metrics:
                 scores = cross_val_score(
-                    self.model, X_prepared, y_encoded, cv=cv, scoring=f"{metric}_weighted"
+                    self.model,
+                    X_prepared,
+                    y_encoded,
+                    cv=cv,
+                    scoring=f"{metric}_weighted",
                 )
                 results[metric] = scores.mean()
                 results[f"{metric}_std"] = scores.std()
diff --git a/src/ners/train.py b/src/ners/train.py
new file mode 100755
index 0000000..eee5c18
--- /dev/null
+++ b/src/ners/train.py
@@ -0,0 +1,46 @@
+#!.venv/bin/python3
+import logging
+import traceback
+
+from ners.core.config import setup_config
+from ners.research.experiment.experiment_builder import ExperimentBuilder
+from ners.research.model_trainer import ModelTrainer
+
+
+def train_from_template(
+    name: str,
+    type: str,
+    *,
+    templates: str = "research_templates.yaml",
+    config: str | None = None,
+    env: str = "development",
+) -> int:
+    try:
+        cfg = setup_config(config_path=config, env=env)
+        experiment_builder = ExperimentBuilder(cfg)
+
+        logging.info(f"Loading research templates from: {templates}")
+        tmpl = experiment_builder.load_templates(templates)
+
+        logging.info(f"Looking for experiment: name='{name}', type='{type}'")
+        experiment_config = experiment_builder.find_template(tmpl, name, type)
+
+        logging.info(f"Found experiment: {experiment_config.get('name')}")
+        logging.info(f"Description: {experiment_config.get('description')}")
+        logging.info(f"Features: {experiment_config.get('features')}")
+
+        trainer = ModelTrainer(cfg)
+        trainer.train_single_model(
+            model_name=experiment_config.get("name"),
+            model_type=experiment_config.get("model_type"),
+            features=experiment_config.get("features"),
+            model_params=experiment_config.get("model_params", {}),
+            tags=experiment_config.get("tags", []),
+        )
+
+        logging.info("Training completed successfully!")
+        return 0
+    except Exception as e:
+        logging.error(f"Training failed: {e}")
+        traceback.print_exc()
+        return 1
diff --git a/core/utils/log_reader.py b/src/ners/web/__init__.py
similarity index 100%
rename from core/utils/log_reader.py
rename to src/ners/web/__init__.py
diff --git a/web/app.py b/src/ners/web/app.py
similarity index 66%
rename from web/app.py
rename to src/ners/web/app.py
index 7cde28b..8488c7d 100644
--- a/web/app.py
+++ b/src/ners/web/app.py
@@ -1,19 +1,13 @@
 #!.venv/bin/python3
-import argparse
-import sys
-from pathlib import Path
+import os
 
 import streamlit as st
 
-# Add parent directory to Python path to access core modules
-parent_dir = Path(__file__).parent.parent
-sys.path.insert(0, str(parent_dir))
-
-from core.config import setup_config, PipelineConfig
-from core.utils.data_loader import DataLoader
-from processing.monitoring.pipeline_monitor import PipelineMonitor
-from research.experiment.experiment_runner import ExperimentRunner
-from research.experiment.experiment_tracker import ExperimentTracker
+from ners.core.config import setup_config, PipelineConfig
+from ners.core.utils.data_loader import DataLoader
+from ners.processing.monitoring.pipeline_monitor import PipelineMonitor
+from ners.research.experiment.experiment_runner import ExperimentRunner
+from ners.research.experiment.experiment_tracker import ExperimentTracker
 
 # Page configuration
 st.set_page_config(
@@ -65,19 +59,9 @@ class StreamlitApp:
         )
 
 
-def main():
-    parser = argparse.ArgumentParser(
-        description="DRC NERS Platform",
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-    )
-    parser.add_argument("--config", type=str, help="Path to configuration file")
-    parser.add_argument("--env", type=str, default="development", help="Environment name")
-    args = parser.parse_args()
-
-    config = setup_config(args.config, env=args.env)
-    app = StreamlitApp(config)
-    app.run()
-
-
-if __name__ == "__main__":
-    main()
+# Initialize app using environment variables when launched via Typer
+_config_path = os.environ.get("NERS_CONFIG")
+_env = os.environ.get("NERS_ENV", "development")
+_cfg = setup_config(_config_path, env=_env)
+_app = StreamlitApp(_cfg)
+_app.run()
diff --git a/web/interfaces/__init__.py b/src/ners/web/interfaces/__init__.py
similarity index 100%
rename from web/interfaces/__init__.py
rename to src/ners/web/interfaces/__init__.py
diff --git a/web/interfaces/configuration.py b/src/ners/web/interfaces/configuration.py
similarity index 100%
rename from web/interfaces/configuration.py
rename to src/ners/web/interfaces/configuration.py
diff --git a/web/interfaces/dashboard.py b/src/ners/web/interfaces/dashboard.py
similarity index 81%
rename from web/interfaces/dashboard.py
rename to src/ners/web/interfaces/dashboard.py
index 435db1c..371dd5b 100644
--- a/web/interfaces/dashboard.py
+++ b/src/ners/web/interfaces/dashboard.py
@@ -1,7 +1,7 @@
 import pandas as pd
 import streamlit as st
 
-from core.utils.data_loader import OPTIMIZED_DTYPES
+from ners.core.utils.data_loader import OPTIMIZED_DTYPES
 
 
 @st.cache_data
@@ -25,7 +25,9 @@ class Dashboard:
 
         # Load basic statistics
         try:
-            data_path = self.config.paths.get_data_path(self.config.data.output_files["featured"])
+            data_path = self.config.paths.get_data_path(
+                self.config.data.output_files["featured"]
+            )
             if data_path.exists():
                 df = load_dataset(str(data_path))
 
@@ -37,13 +39,17 @@ class Dashboard:
                     st.metric("Annotated Names", f"{annotated:,}")
 
                 with col3:
-                    provinces = df["province"].nunique() if "province" in df.columns else 0
+                    provinces = (
+                        df["province"].nunique() if "province" in df.columns else 0
+                    )
                     st.metric("Provinces", provinces)
 
                 with col4:
                     if "sex" in df.columns:
                         gender_dist = df["sex"].value_counts()
-                        ratio = gender_dist.get("f", 0) / max(gender_dist.get("m", 1), 1)
+                        ratio = gender_dist.get("f", 0) / max(
+                            gender_dist.get("m", 1), 1
+                        )
                         st.metric("F/M Rate", f"{ratio:.2%}")
                 with col5:
                     if "annotated" in df.columns:
@@ -79,4 +85,6 @@ class Dashboard:
 
             st.dataframe(pd.DataFrame(exp_data), use_container_width=True)
         else:
-            st.info("No experiments found. Create your first experiment in the Experiments tab!")
+            st.info(
+                "No experiments found. Create your first experiment in the Experiments tab!"
+            )
diff --git a/src/ners/web/interfaces/data_overview.py b/src/ners/web/interfaces/data_overview.py
new file mode 100644
index 0000000..8cac3b8
--- /dev/null
+++ b/src/ners/web/interfaces/data_overview.py
@@ -0,0 +1,52 @@
+from datetime import datetime
+
+import pandas as pd
+import streamlit as st
+
+from ners.core.utils.data_loader import OPTIMIZED_DTYPES
+
+
+@st.cache_data
+def load_dataset(file_path: str) -> pd.DataFrame:
+    try:
+        return pd.read_csv(file_path, dtype=OPTIMIZED_DTYPES)
+    except Exception as e:
+        st.error(f"Error loading dataset: {e}")
+        return pd.DataFrame()
+
+
+class DataOverview:
+    def __init__(self, config):
+        self.config = config
+
+    def index(self):
+        st.title("Data Overview")
+        data_files = {
+            "Names": self.config.data.input_file,
+            "Featured Dataset": self.config.data.output_files["featured"],
+            "Evaluation Dataset": self.config.data.output_files["evaluation"],
+            "Male Names": self.config.data.output_files["males"],
+            "Female Names": self.config.data.output_files["females"],
+        }
+
+        st.write("Available Data Files:")
+        for name, rel_path in data_files.items():
+            file_path = self.config.paths.get_data_path(rel_path)
+            exists = file_path.exists()
+            size = file_path.stat().st_size if exists else 0
+            stats = (
+                f"Size: {size / (1024 * 1024):.1f} MB, Last Modified: {datetime.fromtimestamp(file_path.stat().st_mtime)}"
+                if exists
+                else "Not found"
+            )
+            st.write(f"- {name}: {file_path} ({stats})")
+
+        # Preview featured dataset if available
+        data_path = self.config.paths.get_data_path(
+            self.config.data.output_files["featured"]
+        )
+        if data_path.exists():
+            df = load_dataset(str(data_path))
+            st.subheader("Featured Dataset Preview")
+            st.dataframe(df.head(), use_container_width=True)
+            st.write(f"Rows: {len(df):,}")
diff --git a/web/interfaces/data_processing.py b/src/ners/web/interfaces/data_processing.py
similarity index 89%
rename from web/interfaces/data_processing.py
rename to src/ners/web/interfaces/data_processing.py
index 1f82d53..0b738ad 100644
--- a/web/interfaces/data_processing.py
+++ b/src/ners/web/interfaces/data_processing.py
@@ -2,8 +2,8 @@ import pandas as pd
 import plotly.express as px
 import streamlit as st
 
-from core.utils.data_loader import OPTIMIZED_DTYPES
-from web.interfaces.log_reader import LogReader
+from ners.core.utils.data_loader import OPTIMIZED_DTYPES
+from ners.web.interfaces.log_reader import LogReader
 
 
 @st.cache_data
@@ -31,7 +31,9 @@ class DataProcessing:
 
         # Step details
         for step_name, step_status in status["steps"].items():
-            with st.expander(f"{step_name.replace('_', ' ').title()} - {step_status['status']}"):
+            with st.expander(
+                f"{step_name.replace('_', ' ').title()} - {step_status['status']}"
+            ):
                 col1, col2, col3 = st.columns(3)
 
                 with col1:
@@ -63,14 +65,20 @@ class DataProcessing:
 
             with col2:
                 num_entries = st.number_input(
-                    "Number of entries", min_value=5, max_value=50, value=10, key="num_log_entries"
+                    "Number of entries",
+                    min_value=5,
+                    max_value=50,
+                    value=10,
+                    key="num_log_entries",
                 )
 
             # Get log entries based on filter
             if log_level_filter == "All":
                 log_entries = log_reader.read_last_entries(num_entries)
             else:
-                log_entries = log_reader.read_entries_by_level(log_level_filter, num_entries)
+                log_entries = log_reader.read_entries_by_level(
+                    log_level_filter, num_entries
+                )
 
             if log_entries:
                 for entry in log_entries:
diff --git a/web/interfaces/experiments.py b/src/ners/web/interfaces/experiments.py
similarity index 79%
rename from web/interfaces/experiments.py
rename to src/ners/web/interfaces/experiments.py
index 968f29f..e9550fa 100644
--- a/web/interfaces/experiments.py
+++ b/src/ners/web/interfaces/experiments.py
@@ -2,13 +2,13 @@ from typing import List, Dict
 
 import streamlit as st
 
-from core.config.pipeline_config import PipelineConfig
-from research.experiment import ExperimentConfig, ExperimentStatus
-from research.experiment.experiment_builder import ExperimentBuilder
-from research.experiment.experiment_runner import ExperimentRunner
-from research.experiment.experiment_tracker import ExperimentTracker
-from research.experiment.feature_extractor import FeatureType
-from research.model_registry import list_available_models
+from ners.core.config.pipeline_config import PipelineConfig
+from ners.research.experiment import ExperimentConfig, ExperimentStatus
+from ners.research.experiment.experiment_builder import ExperimentBuilder
+from ners.research.experiment.experiment_runner import ExperimentRunner
+from ners.research.experiment.experiment_tracker import ExperimentTracker
+from ners.research.experiment.feature_extractor import FeatureType
+from ners.research.model_registry import list_available_models
 
 
 class Experiments:
@@ -46,13 +46,19 @@ class Experiments:
             available_experiments = self.experiment_builder.get_templates()
 
             # Create tabs for different experiment types
-            exp_tabs = st.tabs(["Baseline", "Advanced", "Feature Studies", "Hyperparameter Tuning"])
+            exp_tabs = st.tabs(
+                ["Baseline", "Advanced", "Feature Studies", "Hyperparameter Tuning"]
+            )
 
             with exp_tabs[0]:
-                self._show_experiments_by_type(available_experiments["baseline"], "baseline")
+                self._show_experiments_by_type(
+                    available_experiments["baseline"], "baseline"
+                )
 
             with exp_tabs[1]:
-                self._show_experiments_by_type(available_experiments["advanced"], "advanced")
+                self._show_experiments_by_type(
+                    available_experiments["advanced"], "advanced"
+                )
 
             with exp_tabs[2]:
                 self._show_experiments_by_type(
@@ -60,7 +66,9 @@ class Experiments:
                 )
 
             with exp_tabs[3]:
-                self._show_experiments_by_type(available_experiments["tuning"], "tuning")
+                self._show_experiments_by_type(
+                    available_experiments["tuning"], "tuning"
+                )
 
         except Exception as e:
             st.error(f"Error loading experiment templates: {e}")
@@ -79,7 +87,9 @@ class Experiments:
         # Show available experiments
         for i, exp_template in enumerate(experiments):
             exp_name = exp_template.get("name", f"Experiment {i + 1}")
-            exp_description = exp_template.get("description", "No description available")
+            exp_description = exp_template.get(
+                "description", "No description available"
+            )
 
             with st.expander(f"📊 {exp_name} - {exp_description}"):
                 col1, col2 = st.columns([2, 1])
@@ -88,7 +98,7 @@ class Experiments:
                     st.json(exp_template)
 
                 with col2:
-                    if st.button(f"🚀 Run Experiment", key=f"run_{experiment_type}_{i}"):
+                    if st.button("🚀 Run Experiment", key=f"run_{experiment_type}_{i}"):
                         self._run_template_experiment(exp_template)
 
     def _run_template_experiment(self, exp_template: Dict):
@@ -100,7 +110,9 @@ class Experiments:
 
                 # Run the experiment
                 experiment_id = self.experiment_runner.run_experiment(experiment_config)
-                st.success(f"Experiment '{experiment_config.name}' completed successfully!")
+                st.success(
+                    f"Experiment '{experiment_config.name}' completed successfully!"
+                )
                 st.info(f"Experiment ID: `{experiment_id}`")
 
                 # Show results
@@ -130,13 +142,17 @@ class Experiments:
             )
 
         with col2:
-            model_filter = st.selectbox("Filter by Model", ["All"] + list_available_models())
+            model_filter = st.selectbox(
+                "Filter by Model", ["All"] + list_available_models()
+            )
 
         with col3:
             tag_filter = st.text_input("Filter by Tags (comma-separated)")
 
         # Get and filter experiments
-        experiments = self._get_filtered_experiments(status_filter, model_filter, tag_filter)
+        experiments = self._get_filtered_experiments(
+            status_filter, model_filter, tag_filter
+        )
 
         if not experiments:
             st.info("No experiments found matching the filters.")
@@ -149,20 +165,28 @@ class Experiments:
             ):
                 self._display_experiment_details(exp, i)
 
-    def _get_filtered_experiments(self, status_filter: str, model_filter: str, tag_filter: str):
+    def _get_filtered_experiments(
+        self, status_filter: str, model_filter: str, tag_filter: str
+    ):
         """Get experiments with applied filters"""
         experiments = self.experiment_tracker.list_experiments()
 
         # Apply filters
         if status_filter != "All":
-            experiments = [e for e in experiments if e.status == ExperimentStatus(status_filter)]
+            experiments = [
+                e for e in experiments if e.status == ExperimentStatus(status_filter)
+            ]
 
         if model_filter != "All":
-            experiments = [e for e in experiments if e.config.model_type == model_filter]
+            experiments = [
+                e for e in experiments if e.config.model_type == model_filter
+            ]
 
         if tag_filter:
             tags = [tag.strip() for tag in tag_filter.split(",")]
-            experiments = [e for e in experiments if any(tag in e.config.tags for tag in tags)]
+            experiments = [
+                e for e in experiments if any(tag in e.config.tags for tag in tags)
+            ]
 
         return experiments
 
@@ -173,7 +197,9 @@ class Experiments:
 
         with col1:
             st.write(f"**Model:** {exp.config.model_type}")
-            st.write(f"**Features:** {', '.join([f.value for f in exp.config.features])}")
+            st.write(
+                f"**Features:** {', '.join([f.value for f in exp.config.features])}"
+            )
             st.write(f"**Tags:** {', '.join(exp.config.tags)}")
 
         with col2:
@@ -185,7 +211,7 @@ class Experiments:
             st.write(f"**Train Size:** {exp.train_size:,}")
             st.write(f"**Test Size:** {exp.test_size:,}")
 
-            if st.button(f"View Details", key=f"details_{index}"):
+            if st.button("View Details", key=f"details_{index}"):
                 st.session_state.selected_experiment = exp.experiment_id
                 st.rerun()
 
@@ -198,7 +224,9 @@ class Experiments:
         st.write("Run multiple experiments with different parameter combinations.")
 
         # Add option to run template batch experiments
-        batch_type = st.radio("Batch Type", ["Template Batch", "Custom Parameter Sweep"])
+        batch_type = st.radio(
+            "Batch Type", ["Template Batch", "Custom Parameter Sweep"]
+        )
 
         if batch_type == "Template Batch":
             self._show_template_batch_experiments()
@@ -227,10 +255,13 @@ class Experiments:
                     if experiments:
                         st.write(f"**{exp_type.title()} Experiments:**")
                         exp_names = [
-                            exp.get("name", f"Exp {i}") for i, exp in enumerate(experiments)
+                            exp.get("name", f"Exp {i}")
+                            for i, exp in enumerate(experiments)
                         ]
                         selected_names = st.multiselect(
-                            f"Select {exp_type} experiments", exp_names, key=f"select_{exp_type}"
+                            f"Select {exp_type} experiments",
+                            exp_names,
+                            key=f"select_{exp_type}",
                         )
 
                         for name in selected_names:
@@ -258,13 +289,17 @@ class Experiments:
                     experiment_configs.append(config)
 
                 # Run batch experiments
-                experiment_ids = self.experiment_runner.run_experiment_batch(experiment_configs)
+                experiment_ids = self.experiment_runner.run_experiment_batch(
+                    experiment_configs
+                )
 
                 st.success(f"Completed {len(experiment_ids)} template experiments!")
 
                 # Show summary
                 if experiment_ids:
-                    comparison = self.experiment_runner.compare_experiments(experiment_ids)
+                    comparison = self.experiment_runner.compare_experiments(
+                        experiment_ids
+                    )
                     st.write("**Template Batch Results:**")
                     st.dataframe(
                         comparison[["name", "model_type", "test_accuracy"]],
@@ -285,7 +320,9 @@ class Experiments:
             with col1:
                 base_name = st.text_input("Base Experiment Name", "parameter_sweep")
                 model_types = st.multiselect(
-                    "Model Types", list_available_models(), default=["logistic_regression"]
+                    "Model Types",
+                    list_available_models(),
+                    default=["logistic_regression"],
                 )
 
                 # N-gram ranges for logistic regression
@@ -301,13 +338,20 @@ class Experiments:
                     default=["full_name", "native_name", "surname"],
                 )
 
-                test_sizes = st.text_input("Test Sizes (comma-separated)", "0.15,0.2,0.25")
+                test_sizes = st.text_input(
+                    "Test Sizes (comma-separated)", "0.15,0.2,0.25"
+                )
 
                 tags = st.text_input("Common Tags", "parameter_sweep,batch")
 
             if st.form_submit_button("🚀 Run Parameter Sweep"):
                 self.run_batch_experiments(
-                    base_name, model_types, ngram_ranges, feature_combinations, test_sizes, tags
+                    base_name,
+                    model_types,
+                    ngram_ranges,
+                    feature_combinations,
+                    test_sizes,
+                    tags,
                 )
 
     def run_batch_experiments(
@@ -369,13 +413,17 @@ class Experiments:
                                 exp_count += 1
 
                 # Run experiments
-                experiment_ids = self.experiment_runner.run_experiment_batch(experiments)
+                experiment_ids = self.experiment_runner.run_experiment_batch(
+                    experiments
+                )
 
                 st.success(f"Completed {len(experiment_ids)} batch experiments")
 
                 # Show summary
                 if experiment_ids:
-                    comparison = self.experiment_runner.compare_experiments(experiment_ids)
+                    comparison = self.experiment_runner.compare_experiments(
+                        experiment_ids
+                    )
                     st.write("**Batch Results Summary:**")
                     st.dataframe(
                         comparison[["name", "model_type", "test_accuracy"]],
diff --git a/src/ners/web/interfaces/log_reader.py b/src/ners/web/interfaces/log_reader.py
new file mode 100644
index 0000000..dffc7bf
--- /dev/null
+++ b/src/ners/web/interfaces/log_reader.py
@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import List
+
+
+@dataclass
+class LogEntry:
+    timestamp: datetime
+    level: str
+    message: str
+
+
+class LogReader:
+    def __init__(self, log_file_path: Path):
+        self.log_file_path = Path(log_file_path)
+
+    def read_last_entries(self, num_entries: int = 20) -> List[LogEntry]:
+        entries = []
+        if not self.log_file_path.exists():
+            return entries
+
+        with open(self.log_file_path, "r") as f:
+            lines = f.readlines()[-num_entries:]
+
+        for line in lines:
+            entry = self._parse_log_line(line)
+            if entry:
+                entries.append(entry)
+
+        return entries
+
+    def read_entries_by_level(
+        self, level: str, num_entries: int = 20
+    ) -> List[LogEntry]:
+        entries = []
+        if not self.log_file_path.exists():
+            return entries
+
+        with open(self.log_file_path, "r") as f:
+            for line in reversed(f.readlines()):
+                entry = self._parse_log_line(line)
+                if entry and entry.level == level:
+                    entries.append(entry)
+                    if len(entries) >= num_entries:
+                        break
+
+        return list(reversed(entries))
+
+    def get_log_stats(self) -> dict:
+        if not self.log_file_path.exists():
+            return {}
+
+        stats = {"total_lines": 0}
+        with open(self.log_file_path, "r") as f:
+            for line in f:
+                stats["total_lines"] += 1
+                entry = self._parse_log_line(line)
+                if entry:
+                    stats[entry.level] = stats.get(entry.level, 0) + 1
+
+        return stats
+
+    @staticmethod
+    def _parse_log_line(line: str) -> LogEntry | None:
+        try:
+            # Expected format from logging config: [timestamp] - LEVEL - message
+            parts = line.strip().split(" - ")
+            if len(parts) >= 3:
+                timestamp_str = parts[0].strip("[]")
+                timestamp = datetime.fromisoformat(timestamp_str)
+                level = parts[1].strip()
+                message = " - ".join(parts[2:])
+                return LogEntry(timestamp, level, message)
+        except Exception:
+            return None
+
+        return None
diff --git a/web/interfaces/ner_testing.py b/src/ners/web/interfaces/ner_testing.py
similarity index 83%
rename from web/interfaces/ner_testing.py
rename to src/ners/web/interfaces/ner_testing.py
index f4984c2..102e68c 100644
--- a/web/interfaces/ner_testing.py
+++ b/src/ners/web/interfaces/ner_testing.py
@@ -1,10 +1,8 @@
-from pathlib import Path
-
 import streamlit as st
 from spacy import displacy
 
-from core.config import PipelineConfig
-from processing.ner.name_model import NameModel
+from ners.core.config import PipelineConfig
+from ners.processing.ner.name_model import NameModel
 
 
 class NERTesting:
@@ -56,12 +54,15 @@ class NERTesting:
 
             with col1:
                 st.metric(
-                    "Training Examples", f"{self.training_stats.get('training_examples', 0):,}"
+                    "Training Examples",
+                    f"{self.training_stats.get('training_examples', 0):,}",
                 )
             with col2:
                 st.metric("Epochs", self.training_stats.get("epochs", 0))
             with col3:
-                st.metric("Final Loss", f"{self.training_stats.get('final_loss', 0):.2f}")
+                st.metric(
+                    "Final Loss", f"{self.training_stats.get('final_loss', 0):.2f}"
+                )
             with col4:
                 st.metric("Batch Size", f"{self.training_stats.get('batch_size', 0):,}")
 
@@ -99,9 +100,11 @@ class NERTesting:
 
         if names_input.strip():
             if st.button("Analyze All Names", type="primary"):
-                names = [name.strip() for name in names_input.split("\n") if name.strip()]
+                names = [
+                    name.strip() for name in names_input.split("\n") if name.strip()
+                ]
                 for i, name in enumerate(names):
-                    st.markdown(f"**Name {i+1}: {name}**")
+                    st.markdown(f"**Name {i + 1}: {name}**")
                     self.analyze_and_display(name)
                     if i < len(names) - 1:
                         st.markdown("---")
@@ -127,7 +130,9 @@ class NERTesting:
 
             else:
                 st.warning("No entities detected in the input text.")
-                st.info("Try using traditional Congolese names or ensure the spelling is correct.")
+                st.info(
+                    "Try using traditional Congolese names or ensure the spelling is correct."
+                )
 
         except Exception as e:
             st.error(f"Error analyzing text: {e}")
@@ -139,14 +144,21 @@ class NERTesting:
             ents = []
             for entity in entities:
                 ents.append(
-                    {"start": entity["start"], "end": entity["end"], "label": entity["label"]}
+                    {
+                        "start": entity["start"],
+                        "end": entity["end"],
+                        "label": entity["label"],
+                    }
                 )
 
             # Create doc-like structure for displacy
             doc_data = {"text": text, "ents": ents, "title": None}
 
             # Custom colors for our labels
-            colors = {"NATIVE": "#74C0FC", "SURNAME": "#69DB7C"}  # Light blue  # Light green
+            colors = {
+                "NATIVE": "#74C0FC",
+                "SURNAME": "#69DB7C",
+            }  # Light blue  # Light green
 
             options = {"colors": colors, "distance": 90}
 
diff --git a/src/ners/web/interfaces/predictions.py b/src/ners/web/interfaces/predictions.py
new file mode 100644
index 0000000..685f59d
--- /dev/null
+++ b/src/ners/web/interfaces/predictions.py
@@ -0,0 +1,215 @@
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+from ners.core.utils.data_loader import OPTIMIZED_DTYPES
+from ners.research.experiment.experiment_runner import ExperimentRunner
+from ners.research.experiment.experiment_tracker import ExperimentTracker
+
+
+class Predictions:
+    def __init__(
+        self,
+        config,
+        experiment_tracker: ExperimentTracker,
+        experiment_runner: ExperimentRunner,
+    ):
+        self.config = config
+        self.experiment_tracker = experiment_tracker
+        self.experiment_runner = experiment_runner
+
+    def index(self):
+        st.title("Predictions")
+
+        # Load available models
+        experiments = self.experiment_tracker.list_experiments()
+        completed_experiments = [
+            e for e in experiments if e.status.value == "completed" and e.model_path
+        ]
+
+        if not completed_experiments:
+            st.warning(
+                "No trained models available. Please run some experiments first."
+            )
+            return
+
+        # Model selection
+        model_options = {
+            f"{exp.config.name} (Acc: {exp.test_metrics.get('accuracy', 0):.3f})": exp
+            for exp in completed_experiments
+            if exp.test_metrics
+        }
+
+        selected_model_name = st.selectbox("Select Model", list(model_options.keys()))
+
+        if not selected_model_name:
+            return
+
+        selected_experiment = model_options[selected_model_name]
+
+        # Prediction modes
+        prediction_mode = st.radio(
+            "Prediction Mode", ["Single Name", "Batch Upload", "Dataset Prediction"]
+        )
+
+        if prediction_mode == "Single Name":
+            self.show_single_prediction(selected_experiment)
+        elif prediction_mode == "Batch Upload":
+            self.show_batch_prediction(selected_experiment)
+        elif prediction_mode == "Dataset Prediction":
+            self.show_dataset_prediction(selected_experiment)
+
+    def show_single_prediction(self, experiment):
+        """Show single name prediction interface"""
+        name_input = st.text_input(
+            "Enter a name:", placeholder="e.g., Jean Baptiste Mukendi"
+        )
+        if name_input and st.button("Predict Gender"):
+            try:
+                # Load the model
+                model = self.experiment_runner.load_experiment_model(
+                    experiment.experiment_id
+                )
+
+                if model is None:
+                    st.error("Failed to load model")
+                    return
+
+                # Create a DataFrame with the input
+                input_df = self._prepare_single_input(name_input)
+
+                # Make prediction
+                prediction = model.predict(input_df)[0]
+
+                # Get prediction probability if available
+                confidence = self._get_prediction_confidence(model, input_df)
+
+                # Display results
+                self._display_single_prediction_results(
+                    prediction, confidence, experiment, name_input
+                )
+
+            except Exception as e:
+                st.error(f"Error making prediction: {e}")
+
+    def _prepare_single_input(self, name_input: str) -> pd.DataFrame:
+        """Prepare single name input for prediction"""
+        return pd.DataFrame(
+            {
+                "name": [name_input],
+                "words": [len(name_input.split())],
+                "length": [len(name_input.replace(" ", ""))],
+                "province": ["unknown"],  # Default values
+                "identified_name": [None],
+                "identified_surname": [None],
+                "probable_native": [None],
+                "probable_surname": [None],
+            }
+        )
+
+    def _get_prediction_confidence(
+        self, model, input_df: pd.DataFrame
+    ) -> Optional[float]:
+        """Get prediction confidence if available"""
+        try:
+            probabilities = model.predict_proba(input_df)[0]
+            return max(probabilities)
+        except:
+            return None
+
+    def _display_single_prediction_results(
+        self, prediction: str, confidence: Optional[float], experiment, name_input: str
+    ):
+        """Display single prediction results"""
+        col1, col2 = st.columns(2)
+
+        with col1:
+            gender_label = "Female" if prediction == "f" else "Male"
+            st.success(f"**Predicted Gender:** {gender_label}")
+
+        with col2:
+            if confidence:
+                st.metric("Confidence", f"{confidence:.2%}")
+
+        # Additional info
+        st.info(f"Model used: {experiment.config.name}")
+        st.info(
+            f"Features used: {', '.join([f.value for f in experiment.config.features])}"
+        )
+
+    def show_batch_prediction(self, experiment):
+        uploaded_file = st.file_uploader("Upload CSV file with names", type="csv")
+        if uploaded_file is not None:
+            try:
+                df = pd.read_csv(uploaded_file, dtype=OPTIMIZED_DTYPES)
+
+                st.write("**Uploaded Data Preview:**")
+                st.dataframe(df.head(), use_container_width=True)
+
+                # Column selection
+                df = self._prepare_batch_data(df)
+
+                if st.button("Run Batch Prediction"):
+                    self._run_batch_prediction(df, experiment)
+
+            except Exception as e:
+                st.error(f"Error processing file: {e}")
+
+    def _prepare_batch_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Prepare batch data for prediction"""
+        # Column selection
+        if "name" not in df.columns:
+            name_column = st.selectbox("Select the name column:", df.columns)
+            df = df.rename(columns={name_column: "name"})
+
+        # Add missing columns with defaults
+        required_columns = [
+            "words",
+            "length",
+            "province",
+            "identified_name",
+            "identified_surname",
+            "probable_native",
+            "probable_surname",
+        ]
+
+        for col in required_columns:
+            if col not in df.columns:
+                if col == "words":
+                    df[col] = df["name"].str.split().str.len()
+                elif col == "length":
+                    df[col] = df["name"].str.replace(" ", "").str.len()
+                else:
+                    df[col] = None
+
+        return df
+
+    def _run_batch_prediction(self, df: pd.DataFrame, experiment):
+        """Run batch prediction and display results"""
+        with st.spinner("Making predictions..."):
+            # Load model
+            model = self.experiment_runner.load_experiment_model(
+                experiment.experiment_id
+            )
+
+            if model is None:
+                st.error("Failed to load model")
+                return
+
+            # Make predictions
+            predictions = model.predict(df)
+            df["predicted_gender"] = predictions
+            df["gender_label"] = df["predicted_gender"].map(
+                {"f": "Female", "m": "Male"}
+            )
+
+            # Try to get probabilities
+            try:
+                probabilities = model.predict_proba(df)
+                df["confidence"] = np.max(probabilities, axis=1)
+            except:
+                df["confidence"] = None
+
+        st.success("Predictions completed!")
diff --git a/web/interfaces/results_analysis.py b/src/ners/web/interfaces/results_analysis.py
similarity index 57%
rename from web/interfaces/results_analysis.py
rename to src/ners/web/interfaces/results_analysis.py
index aa3d52c..19bb2ec 100644
--- a/web/interfaces/results_analysis.py
+++ b/src/ners/web/interfaces/results_analysis.py
@@ -1,18 +1,20 @@
 from typing import List
 
-import numpy as np
 import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
 import streamlit as st
 
-from research.experiment.experiment_runner import ExperimentRunner
-from research.experiment.experiment_tracker import ExperimentTracker
+from ners.research.experiment.experiment_runner import ExperimentRunner
+from ners.research.experiment.experiment_tracker import ExperimentTracker
 
 
 class ResultsAnalysis:
     def __init__(
-        self, config, experiment_tracker: ExperimentTracker, experiment_runner: ExperimentRunner
+        self,
+        config,
+        experiment_tracker: ExperimentTracker,
+        experiment_runner: ExperimentRunner,
     ):
         self.config = config
         self.experiment_tracker = experiment_tracker
@@ -38,7 +40,9 @@ class ResultsAnalysis:
         st.subheader("Compare Experiments")
 
         experiments = self.experiment_tracker.list_experiments()
-        completed_experiments = [e for e in experiments if e.status.value == "completed"]
+        completed_experiments = [
+            e for e in experiments if e.status.value == "completed"
+        ]
 
         if not completed_experiments:
             st.warning("No completed experiments found.")
@@ -78,10 +82,14 @@ class ResultsAnalysis:
 
         # Select columns to display
         metric_columns = [
-            col for col in comparison_df.columns if col.startswith("test_") or col.startswith("cv_")
+            col
+            for col in comparison_df.columns
+            if col.startswith("test_") or col.startswith("cv_")
         ]
         display_columns = ["name", "model_type", "features"] + metric_columns
-        available_columns = [col for col in display_columns if col in comparison_df.columns]
+        available_columns = [
+            col for col in display_columns if col in comparison_df.columns
+        ]
 
         st.dataframe(comparison_df[available_columns], use_container_width=True)
 
@@ -102,11 +110,15 @@ class ResultsAnalysis:
 
         # Metric comparison across multiple metrics
         metric_columns = [
-            col for col in comparison_df.columns if col.startswith("test_") or col.startswith("cv_")
+            col
+            for col in comparison_df.columns
+            if col.startswith("test_") or col.startswith("cv_")
         ]
 
         if len(metric_columns) > 1:
-            metric_to_plot = st.selectbox("Select Metric for Detailed Comparison", metric_columns)
+            metric_to_plot = st.selectbox(
+                "Select Metric for Detailed Comparison", metric_columns
+            )
 
             if metric_to_plot in comparison_df.columns:
                 fig = px.bar(
@@ -163,7 +175,10 @@ class ResultsAnalysis:
 
         with col1:
             # Accuracy vs Training Size
-            if "accuracy" in analysis_df.columns and "train_size" in analysis_df.columns:
+            if (
+                "accuracy" in analysis_df.columns
+                and "train_size" in analysis_df.columns
+            ):
                 fig = px.scatter(
                     analysis_df,
                     x="train_size",
@@ -176,7 +191,10 @@ class ResultsAnalysis:
 
         with col2:
             # Feature Count vs Performance
-            if "accuracy" in analysis_df.columns and "feature_count" in analysis_df.columns:
+            if (
+                "accuracy" in analysis_df.columns
+                and "feature_count" in analysis_df.columns
+            ):
                 fig = px.scatter(
                     analysis_df,
                     x="feature_count",
@@ -201,133 +219,65 @@ class ResultsAnalysis:
                 go.Bar(
                     x=model_performance["model_type"],
                     y=model_performance["mean"],
-                    error_y=dict(type="data", array=model_performance["std"]),
-                    name="Average Accuracy",
+                    error_y=dict(type="data", array=model_performance["std"].fillna(0)),
+                    name="Accuracy",
                 )
             )
-            fig.update_layout(title="Average Accuracy by Model Type", yaxis_title="Accuracy")
+
             st.plotly_chart(fig, use_container_width=True)
 
     def _display_top_experiments(self, analysis_df: pd.DataFrame):
-        """Display top performing experiments"""
-        st.subheader("Top Performing Experiments")
-
+        """Display top-performing experiments"""
         if "accuracy" in analysis_df.columns:
-            display_columns = ["name", "model_type", "features", "accuracy"]
+            top_n = st.slider("Select Top N Experiments", 3, 20, 5)
+            top_experiments = analysis_df.nlargest(top_n, "accuracy")
 
-            # Add other metrics if available
-            for metric in ["precision", "recall", "f1"]:
-                if metric in analysis_df.columns:
-                    display_columns.append(metric)
-
-            top_experiments = analysis_df.nlargest(5, "accuracy")[display_columns]
-            st.dataframe(top_experiments, use_container_width=True)
+            st.write("**Top Performing Experiments:**")
+            st.dataframe(
+                top_experiments[
+                    [
+                        "name",
+                        "model_type",
+                        "features",
+                        "train_size",
+                        "test_size",
+                        "accuracy",
+                    ]
+                ],
+                use_container_width=True,
+            )
 
     def show_model_analysis(self):
-        """Show detailed model analysis"""
+        """Show detailed model analysis interface"""
         st.subheader("Model Analysis")
 
         experiments = self.experiment_tracker.list_experiments()
-        completed_experiments = [e for e in experiments if e.status.value == "completed"]
+        completed_experiments = [
+            e for e in experiments if e.status.value == "completed"
+        ]
 
         if not completed_experiments:
-            st.warning("No completed experiments found.")
+            st.warning("No completed experiments found for analysis.")
             return
 
-        # Select experiment for detailed analysis
+        # Model selection
         exp_options = {
-            f"{exp.config.name} ({exp.experiment_id[:8]})": exp for exp in completed_experiments
+            f"{exp.config.name} ({exp.experiment_id[:8]})": exp.experiment_id
+            for exp in completed_experiments
         }
-
         selected_exp_name = st.selectbox(
-            "Select Experiment for Detailed Analysis", list(exp_options.keys())
+            "Select Model for Analysis", list(exp_options.keys())
         )
-
         if not selected_exp_name:
             return
 
-        selected_exp = exp_options[selected_exp_name]
+        exp_id = exp_options[selected_exp_name]
+        experiment = self.experiment_tracker.get_experiment(exp_id)
 
-        self._display_experiment_details(selected_exp)
-        self._display_confusion_matrix(selected_exp)
-        self._display_feature_importance(selected_exp)
-        self._display_prediction_examples(selected_exp)
+        if not experiment or not experiment.test_metrics:
+            st.warning("Selected experiment has no evaluation metrics.")
+            return
 
-    def _display_experiment_details(self, experiment):
-        """Display experiment configuration and metrics"""
-        col1, col2 = st.columns(2)
-
-        with col1:
-            st.write("**Experiment Configuration**")
-            st.json(
-                {
-                    "name": experiment.config.name,
-                    "model_type": experiment.config.model_type,
-                    "features": [f.value for f in experiment.config.features],
-                    "model_params": experiment.config.model_params,
-                }
-            )
-
-        with col2:
-            st.write("**Performance Metrics**")
-            if experiment.test_metrics:
-                for metric, value in experiment.test_metrics.items():
-                    st.metric(metric.title(), f"{value:.4f}")
-
-    def _display_confusion_matrix(self, experiment):
-        """Display confusion matrix if available"""
-        if experiment.confusion_matrix:
-            st.write("**Confusion Matrix**")
-            cm = np.array(experiment.confusion_matrix)
-
-            fig = px.imshow(cm, text_auto=True, aspect="auto", title="Confusion Matrix")
-            st.plotly_chart(fig, use_container_width=True)
-
-    def _display_feature_importance(self, experiment):
-        """Display feature importance if available"""
-        if experiment.feature_importance:
-            st.write("**Feature Importance**")
-
-            importance_data = sorted(
-                experiment.feature_importance.items(), key=lambda x: x[1], reverse=True
-            )[:20]
-
-            features, importances = zip(*importance_data)
-
-            fig = px.bar(
-                x=list(importances),
-                y=list(features),
-                orientation="h",
-                title="Top 20 Feature Importances",
-            )
-            fig.update_layout(height=600)
-            st.plotly_chart(fig, use_container_width=True)
-
-    def _display_prediction_examples(self, experiment):
-        """Display prediction examples if available"""
-        if experiment.prediction_examples:
-            st.write("**Prediction Examples**")
-
-            examples_df = pd.DataFrame(experiment.prediction_examples)
-
-            # Separate correct and incorrect predictions
-            correct_examples = examples_df[examples_df["correct"] == True]
-            incorrect_examples = examples_df[examples_df["correct"] == False]
-
-            col1, col2 = st.columns(2)
-
-            with col1:
-                st.write("**Correct Predictions**")
-                if not correct_examples.empty:
-                    st.dataframe(
-                        correct_examples[["name", "true_label", "predicted_label"]],
-                        use_container_width=True,
-                    )
-
-            with col2:
-                st.write("**Incorrect Predictions**")
-                if not incorrect_examples.empty:
-                    st.dataframe(
-                        incorrect_examples[["name", "true_label", "predicted_label"]],
-                        use_container_width=True,
-                    )
+        # Display detailed metrics
+        st.write("**Detailed Metrics:**")
+        st.json(experiment.test_metrics)
diff --git a/web/pages/1_📊_Dashboard.py b/src/ners/web/pages/1_📊_Dashboard.py
similarity index 67%
rename from web/pages/1_📊_Dashboard.py
rename to src/ners/web/pages/1_📊_Dashboard.py
index f3b3f34..b256f03 100644
--- a/web/pages/1_📊_Dashboard.py
+++ b/src/ners/web/pages/1_📊_Dashboard.py
@@ -1,13 +1,6 @@
-import sys
-from pathlib import Path
-
 import streamlit as st
 
-# Add parent directory to Python path to access core modules
-parent_dir = Path(__file__).parent.parent.parent
-sys.path.insert(0, str(parent_dir))
-
-from web.interfaces.dashboard import Dashboard
+from ners.web.interfaces.dashboard import Dashboard
 
 st.set_page_config(page_title="Dashboard", page_icon="📊", layout="wide")
 
diff --git a/web/pages/2_📋_Data_Overview.py b/src/ners/web/pages/2_📋_Data_Overview.py
similarity index 61%
rename from web/pages/2_📋_Data_Overview.py
rename to src/ners/web/pages/2_📋_Data_Overview.py
index aae723c..12c00a7 100644
--- a/web/pages/2_📋_Data_Overview.py
+++ b/src/ners/web/pages/2_📋_Data_Overview.py
@@ -1,13 +1,6 @@
-import sys
-from pathlib import Path
-
 import streamlit as st
 
-# Add parent directory to Python path to access core modules
-parent_dir = Path(__file__).parent.parent.parent
-sys.path.insert(0, str(parent_dir))
-
-from web.interfaces.data_overview import DataOverview
+from ners.web.interfaces.data_overview import DataOverview
 
 st.set_page_config(page_title="Data Overview", page_icon="📋", layout="wide")
 
diff --git a/src/ners/web/pages/3_⚙️_Data_Processing.py b/src/ners/web/pages/3_⚙️_Data_Processing.py
new file mode 100644
index 0000000..6fa4d34
--- /dev/null
+++ b/src/ners/web/pages/3_⚙️_Data_Processing.py
@@ -0,0 +1,14 @@
+import streamlit as st
+
+from ners.web.interfaces.data_processing import DataProcessing
+
+st.set_page_config(page_title="Data Processing", page_icon="⚙️", layout="wide")
+
+if "config" in st.session_state:
+    data_processing = DataProcessing(
+        st.session_state.config, st.session_state.pipeline_monitor
+    )
+    data_processing.index()
+else:
+    st.error("Please run the main app first to initialize the configuration.")
+    st.markdown("Go back to the [main page](/) to start the application.")
diff --git a/web/pages/4_🧪_Experiments.py b/src/ners/web/pages/4_🧪_Experiments.py
similarity index 67%
rename from web/pages/4_🧪_Experiments.py
rename to src/ners/web/pages/4_🧪_Experiments.py
index 29bb680..de5df32 100644
--- a/web/pages/4_🧪_Experiments.py
+++ b/src/ners/web/pages/4_🧪_Experiments.py
@@ -1,13 +1,6 @@
-import sys
-from pathlib import Path
-
 import streamlit as st
 
-# Add parent directory to Python path to access core modules
-parent_dir = Path(__file__).parent.parent.parent
-sys.path.insert(0, str(parent_dir))
-
-from web.interfaces.experiments import Experiments
+from ners.web.interfaces.experiments import Experiments
 
 st.set_page_config(page_title="Experiments", page_icon="🧪", layout="wide")
 
diff --git a/web/pages/5_📈_Results_Analysis.py b/src/ners/web/pages/5_📈_Results_Analysis.py
similarity index 67%
rename from web/pages/5_📈_Results_Analysis.py
rename to src/ners/web/pages/5_📈_Results_Analysis.py
index 04525ee..5febe8b 100644
--- a/web/pages/5_📈_Results_Analysis.py
+++ b/src/ners/web/pages/5_📈_Results_Analysis.py
@@ -1,13 +1,6 @@
-import sys
-from pathlib import Path
-
 import streamlit as st
 
-# Add parent directory to Python path to access core modules
-parent_dir = Path(__file__).parent.parent.parent
-sys.path.insert(0, str(parent_dir))
-
-from web.interfaces.results_analysis import ResultsAnalysis
+from ners.web.interfaces.results_analysis import ResultsAnalysis
 
 st.set_page_config(page_title="Results & Analysis", page_icon="📈", layout="wide")
 
diff --git a/web/pages/6_🔮_Predictions.py b/src/ners/web/pages/6_🔮_Predictions.py
similarity index 67%
rename from web/pages/6_🔮_Predictions.py
rename to src/ners/web/pages/6_🔮_Predictions.py
index b414613..636c535 100644
--- a/web/pages/6_🔮_Predictions.py
+++ b/src/ners/web/pages/6_🔮_Predictions.py
@@ -1,13 +1,6 @@
-import sys
-from pathlib import Path
-
 import streamlit as st
 
-# Add parent directory to Python path to access core modules
-parent_dir = Path(__file__).parent.parent.parent
-sys.path.insert(0, str(parent_dir))
-
-from web.interfaces.predictions import Predictions
+from ners.web.interfaces.predictions import Predictions
 
 st.set_page_config(page_title="Predictions", page_icon="🔮", layout="wide")
 
diff --git a/web/pages/7_⚙️_Configuration.py b/src/ners/web/pages/7_⚙️_Configuration.py
similarity index 61%
rename from web/pages/7_⚙️_Configuration.py
rename to src/ners/web/pages/7_⚙️_Configuration.py
index c5e0e47..ff2c249 100644
--- a/web/pages/7_⚙️_Configuration.py
+++ b/src/ners/web/pages/7_⚙️_Configuration.py
@@ -1,13 +1,6 @@
-import sys
-from pathlib import Path
-
 import streamlit as st
 
-# Add parent directory to Python path to access core modules
-parent_dir = Path(__file__).parent.parent.parent
-sys.path.insert(0, str(parent_dir))
-
-from web.interfaces.configuration import Configuration
+from ners.web.interfaces.configuration import Configuration
 
 st.set_page_config(page_title="Configuration", page_icon="⚙️", layout="wide")
 
diff --git a/web/pages/8_🏷️_NER_Testing.py b/src/ners/web/pages/8_🏷️_NER_Testing.py
similarity index 61%
rename from web/pages/8_🏷️_NER_Testing.py
rename to src/ners/web/pages/8_🏷️_NER_Testing.py
index 1f9eab0..1430879 100644
--- a/web/pages/8_🏷️_NER_Testing.py
+++ b/src/ners/web/pages/8_🏷️_NER_Testing.py
@@ -1,13 +1,6 @@
-import sys
-from pathlib import Path
-
 import streamlit as st
 
-# Add parent directory to Python path to access core modules
-parent_dir = Path(__file__).parent.parent.parent
-sys.path.insert(0, str(parent_dir))
-
-from web.interfaces.ner_testing import NERTesting
+from ners.web.interfaces.ner_testing import NERTesting
 
 st.set_page_config(page_title="NER Testing", page_icon="🏷️", layout="wide")
 
diff --git a/notebooks/eda.ipynb b/src/notebooks/eda.ipynb
similarity index 99%
rename from notebooks/eda.ipynb
rename to src/notebooks/eda.ipynb
index cd049c8..9f7e652 100644
--- a/notebooks/eda.ipynb
+++ b/src/notebooks/eda.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "c93a55c8",
    "metadata": {
     "ExecuteTime": {
@@ -20,8 +20,8 @@
     "import os\n",
     "\n",
     "sys.path.append(os.path.abspath(\"..\"))\n",
-    "from core.utils.data_loader import DataLoader\n",
-    "from core.config.pipeline_config import PipelineConfig"
+    "from ners.core.utils.data_loader import DataLoader\n",
+    "from ners.core.config.pipeline_config import PipelineConfig"
    ]
   },
   {
@@ -44,7 +44,7 @@
     "        \"outputs_dir\": \"../data/processed\",\n",
     "        \"logs_dir\": \"../logs\",\n",
     "        \"configs_dir\": \"../configs\",\n",
-    "        \"checkpoints_dir\": \"../checkpoints\"\n",
+    "        \"checkpoints_dir\": \"../checkpoints\",\n",
     "    }\n",
     ")\n",
     "\n",
@@ -65,7 +65,7 @@
    "source": [
     "gdf = gpd.read_file(\"../osm/provinces.shp\")\n",
     "gdf_proj = gdf.to_crs(epsg=32732)\n",
-    "gdf['centroid'] = gdf_proj.geometry.centroid.to_crs(gdf.crs)\n",
+    "gdf[\"centroid\"] = gdf_proj.geometry.centroid.to_crs(gdf.crs)\n",
     "\n",
     "df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")"
    ]
@@ -92,25 +92,31 @@
    "source": [
     "import unicodedata\n",
     "\n",
+    "\n",
     "# Helper function for cleaning province names\n",
     "def clean_province(s):\n",
     "    return (\n",
     "        s.str.upper()\n",
     "        .str.strip()\n",
-    "        .apply(lambda x: unicodedata.normalize(\"NFKD\", x)\n",
-    "               .encode(\"ascii\", errors=\"ignore\")\n",
-    "               .decode(\"utf-8\") if isinstance(x, str) else x)\n",
+    "        .apply(\n",
+    "            lambda x: unicodedata.normalize(\"NFKD\", x)\n",
+    "            .encode(\"ascii\", errors=\"ignore\")\n",
+    "            .decode(\"utf-8\")\n",
+    "            if isinstance(x, str)\n",
+    "            else x\n",
+    "        )\n",
     "    )\n",
     "\n",
+    "\n",
     "# Apply to both DataFrames\n",
-    "df['province'] = clean_province(df['province'])\n",
-    "gdf['province'] = clean_province(gdf['name'])\n",
+    "df[\"province\"] = clean_province(df[\"province\"])\n",
+    "gdf[\"province\"] = clean_province(gdf[\"name\"])\n",
     "\n",
     "# Group and merge\n",
-    "counts = df['province'].value_counts().reset_index()\n",
-    "counts.columns = ['province', 'count']\n",
+    "counts = df[\"province\"].value_counts().reset_index()\n",
+    "counts.columns = [\"province\", \"count\"]\n",
     "\n",
-    "gdf = gdf.merge(counts, left_on='province', right_on='province', how='left')\n"
+    "gdf = gdf.merge(counts, left_on=\"province\", right_on=\"province\", how=\"left\")"
    ]
   },
   {
@@ -325,9 +331,6 @@
        "<Figure size 1000x1000 with 2 Axes>"
       ]
      },
-     "jetTransient": {
-      "display_id": null
-     },
      "metadata": {},
      "output_type": "display_data"
     }
@@ -335,18 +338,20 @@
    "source": [
     "fig, ax = plt.subplots(figsize=(10, 10))\n",
     "\n",
-    "gdf.plot(column='count', cmap='Blues', edgecolor='black', legend=True, ax=ax)\n",
-    "labels = gdf.loc[gdf['count'].notna(), ['province', 'centroid']]\n",
+    "gdf.plot(column=\"count\", cmap=\"Blues\", edgecolor=\"black\", legend=True, ax=ax)\n",
+    "labels = gdf.loc[gdf[\"count\"].notna(), [\"province\", \"centroid\"]]\n",
     "\n",
     "for _, row in labels.iterrows():\n",
     "    ax.annotate(\n",
-    "        text=row['province'],\n",
-    "        xy=(row['centroid'].x, row['centroid'].y),\n",
-    "        ha='center', va='center',\n",
-    "        fontsize=8, color='black'\n",
+    "        text=row[\"province\"],\n",
+    "        xy=(row[\"centroid\"].x, row[\"centroid\"].y),\n",
+    "        ha=\"center\",\n",
+    "        va=\"center\",\n",
+    "        fontsize=8,\n",
+    "        color=\"black\",\n",
     "    )\n",
     "\n",
-    "ax.axis('off')\n",
+    "ax.axis(\"off\")\n",
     "plt.show()"
    ]
   },
@@ -376,18 +381,15 @@
        "<Figure size 700x700 with 1 Axes>"
       ]
      },
-     "jetTransient": {
-      "display_id": null
-     },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "provinces = df['province'].value_counts()\n",
+    "provinces = df[\"province\"].value_counts()\n",
     "plt.figure(figsize=(7, 7))\n",
-    "plt.pie(provinces, labels=provinces.index, autopct='%1.1f%%', startangle=140)\n",
-    "plt.axis('equal')\n",
+    "plt.pie(provinces, labels=provinces.index, autopct=\"%1.1f%%\", startangle=140)\n",
+    "plt.axis(\"equal\")\n",
     "plt.show()"
    ]
   },
@@ -409,16 +411,13 @@
        "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "jetTransient": {
-      "display_id": null
-     },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
-    "provinces.plot(kind='bar', color='skyblue')\n",
+    "provinces.plot(kind=\"bar\", color=\"skyblue\")\n",
     "plt.xlabel(\"Province\")\n",
     "plt.ylabel(\"Count\")\n",
     "plt.xticks(rotation=45)\n",
@@ -444,20 +443,17 @@
        "<Figure size 1200x600 with 1 Axes>"
       ]
      },
-     "jetTransient": {
-      "display_id": null
-     },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.show()\n",
-    "data = df.groupby(['province', 'sex'], observed=False).size().reset_index(name='count')\n",
-    "data = data.sort_values('count', ascending=False).drop_duplicates(['province', 'sex'])\n",
+    "data = df.groupby([\"province\", \"sex\"], observed=False).size().reset_index(name=\"count\")\n",
+    "data = data.sort_values(\"count\", ascending=False).drop_duplicates([\"province\", \"sex\"])\n",
     "\n",
     "plt.figure(figsize=(12, 6))\n",
-    "sns.barplot(data=data, x='province', y='count', hue='sex')\n",
+    "sns.barplot(data=data, x=\"province\", y=\"count\", hue=\"sex\")\n",
     "\n",
     "plt.xlabel(\"Region\")\n",
     "plt.ylabel(\"Count\")\n",
@@ -485,18 +481,15 @@
        "<Figure size 700x700 with 1 Axes>"
       ]
      },
-     "jetTransient": {
-      "display_id": null
-     },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sex = df['sex'].value_counts()\n",
+    "sex = df[\"sex\"].value_counts()\n",
     "plt.figure(figsize=(7, 7))\n",
-    "plt.pie(sex, labels=sex.index, autopct='%1.1f%%', startangle=140)\n",
-    "plt.axis('equal')\n",
+    "plt.pie(sex, labels=sex.index, autopct=\"%1.1f%%\", startangle=140)\n",
+    "plt.axis(\"equal\")\n",
     "plt.show()"
    ]
   },
@@ -518,26 +511,25 @@
        "<Figure size 1500x2000 with 12 Axes>"
       ]
      },
-     "jetTransient": {
-      "display_id": null
-     },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df_provinces_sex_counts = df.groupby(['province', 'sex'], observed=False).size().reset_index(name='count')\n",
-    "provinces = df_provinces_sex_counts['province'].unique()\n",
+    "df_provinces_sex_counts = (\n",
+    "    df.groupby([\"province\", \"sex\"], observed=False).size().reset_index(name=\"count\")\n",
+    ")\n",
+    "provinces = df_provinces_sex_counts[\"province\"].unique()\n",
     "cols = 3\n",
     "rows = (len(provinces) + cols - 1) // cols\n",
     "\n",
     "plt.figure(figsize=(cols * 5, rows * 5))\n",
     "for i, province in enumerate(provinces, 1):\n",
-    "    data = df_provinces_sex_counts[df_provinces_sex_counts['province'] == province]\n",
+    "    data = df_provinces_sex_counts[df_provinces_sex_counts[\"province\"] == province]\n",
     "    plt.subplot(rows, cols, i)\n",
-    "    plt.pie(data['count'], labels=data['sex'], autopct='%1.1f%%')\n",
+    "    plt.pie(data[\"count\"], labels=data[\"sex\"], autopct=\"%1.1f%%\")\n",
     "    plt.title(province)\n",
-    "    plt.axis('equal')\n",
+    "    plt.axis(\"equal\")\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -644,16 +636,18 @@
     }
    ],
    "source": [
-    "#breakdown of names into surname, middle name and first name\n",
-    "#retrieve all rows whose name is identified as simple, and delete rows whose identified_name is null or empty\n",
+    "# breakdown of names into surname, middle name and first name\n",
+    "# retrieve all rows whose name is identified as simple, and delete rows whose identified_name is null or empty\n",
     "\n",
-    "df_simple = df[df['identified_category'] == 'simple'].copy()\n",
-    "df_simple['surname'] = df_simple['identified_name'].str.split().str[0]\n",
-    "df_simple['middle_name'] = df_simple['identified_name'].str.split().str[1]\n",
-    "df_simple['first_name'] = df_simple['identified_surname']\n",
-    "df_simple = df_simple[df_simple['first_name'].notna() & (df_simple['first_name'].str.strip() != '')]\n",
+    "df_simple = df[df[\"identified_category\"] == \"simple\"].copy()\n",
+    "df_simple[\"surname\"] = df_simple[\"identified_name\"].str.split().str[0]\n",
+    "df_simple[\"middle_name\"] = df_simple[\"identified_name\"].str.split().str[1]\n",
+    "df_simple[\"first_name\"] = df_simple[\"identified_surname\"]\n",
+    "df_simple = df_simple[\n",
+    "    df_simple[\"first_name\"].notna() & (df_simple[\"first_name\"].str.strip() != \"\")\n",
+    "]\n",
     "\n",
-    "df_simple[['name', 'surname', 'middle_name', 'first_name']].head()"
+    "df_simple[[\"name\", \"surname\", \"middle_name\", \"first_name\"]].head()"
    ]
   },
   {
@@ -680,7 +674,10 @@
    ],
    "source": [
     "# Simple distribution of first names (display of the 10 most popular first names)\n",
-    "sns.countplot(y=df_simple['first_name'], order=df_simple['first_name'].value_counts().iloc[:10].index)\n",
+    "sns.countplot(\n",
+    "    y=df_simple[\"first_name\"],\n",
+    "    order=df_simple[\"first_name\"].value_counts().iloc[:10].index,\n",
+    ")\n",
     "plt.title(\"Top 10 first names (simple names only)\")\n",
     "plt.xlabel(\"Count\")\n",
     "plt.ylabel(\"First name\")\n",
@@ -799,9 +796,9 @@
     }
    ],
    "source": [
-    "firstnames = df_simple['first_name'].value_counts()\n",
+    "firstnames = df_simple[\"first_name\"].value_counts()\n",
     "df_firstnames = firstnames.reset_index()\n",
-    "df_firstnames.columns = ['First name', 'quantity']\n",
+    "df_firstnames.columns = [\"First name\", \"quantity\"]\n",
     "df_firstnames.head(10)"
    ]
   },
@@ -823,26 +820,37 @@
     }
    ],
    "source": [
-    "#Most common first name by province\n",
-    "top_names = df_simple.groupby(['province_clean', 'first_name']).size().reset_index(name='count')\n",
-    "top_names = top_names.sort_values(['province_clean', 'count'], ascending=[True, False])\n",
-    "top_names = top_names.drop_duplicates(subset='province_clean', keep='first')\n",
-    "gdf_named = gdf_merged.merge(top_names[['province_clean', 'first_name']], \n",
-    "                             left_on='province_upper', right_on='province_clean', \n",
-    "                             how='left')\n",
+    "# Most common first name by province\n",
+    "top_names = (\n",
+    "    df_simple.groupby([\"province_clean\", \"first_name\"]).size().reset_index(name=\"count\")\n",
+    ")\n",
+    "top_names = top_names.sort_values([\"province_clean\", \"count\"], ascending=[True, False])\n",
+    "top_names = top_names.drop_duplicates(subset=\"province_clean\", keep=\"first\")\n",
+    "gdf_named = gdf_merged.merge(\n",
+    "    top_names[[\"province_clean\", \"first_name\"]],\n",
+    "    left_on=\"province_upper\",\n",
+    "    right_on=\"province_clean\",\n",
+    "    how=\"left\",\n",
+    ")\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(12, 12))\n",
-    "gdf_named.plot(column='count', cmap='Blues', edgecolor='black', legend=True, ax=ax)\n",
+    "gdf_named.plot(column=\"count\", cmap=\"Blues\", edgecolor=\"black\", legend=True, ax=ax)\n",
     "\n",
     "for idx, row in gdf_named.iterrows():\n",
-    "    if pd.notna(row['first_name']):\n",
-    "        centroid = row['geometry'].centroid\n",
+    "    if pd.notna(row[\"first_name\"]):\n",
+    "        centroid = row[\"geometry\"].centroid\n",
     "        label = f\"{row['first_name']}\"\n",
-    "        plt.text(centroid.x, centroid.y, label, \n",
-    "                 horizontalalignment='center', fontsize=8, color='black')\n",
+    "        plt.text(\n",
+    "            centroid.x,\n",
+    "            centroid.y,\n",
+    "            label,\n",
+    "            horizontalalignment=\"center\",\n",
+    "            fontsize=8,\n",
+    "            color=\"black\",\n",
+    "        )\n",
     "\n",
     "plt.title(\"Most common first name by province\")\n",
-    "plt.axis('off')\n",
+    "plt.axis(\"off\")\n",
     "plt.show()"
    ]
   },
@@ -870,17 +878,25 @@
    ],
    "source": [
     "# Total number of distinct first names\n",
-    "total_firstnames = df_simple['first_name'].notna().sum()\n",
+    "total_firstnames = df_simple[\"first_name\"].notna().sum()\n",
     "print(f\">> Total number of first names registered : {total_firstnames}\")\n",
     "\n",
     "# categorization of first names\n",
-    "firstnames_by_sex = df_simple.groupby(['first_name', 'sex']).size().unstack(fill_value=0)\n",
-    "male_firstnames = firstnames_by_sex[(firstnames_by_sex['m'] > 0) & (firstnames_by_sex['f'] == 0)]\n",
+    "firstnames_by_sex = (\n",
+    "    df_simple.groupby([\"first_name\", \"sex\"]).size().unstack(fill_value=0)\n",
+    ")\n",
+    "male_firstnames = firstnames_by_sex[\n",
+    "    (firstnames_by_sex[\"m\"] > 0) & (firstnames_by_sex[\"f\"] == 0)\n",
+    "]\n",
     "print(f\">> Number of all-male first names : {len(male_firstnames)}\")\n",
-    "female_firstnames = firstnames_by_sex[(firstnames_by_sex['f'] > 0) & (firstnames_by_sex['m'] == 0)]\n",
+    "female_firstnames = firstnames_by_sex[\n",
+    "    (firstnames_by_sex[\"f\"] > 0) & (firstnames_by_sex[\"m\"] == 0)\n",
+    "]\n",
     "print(f\">> Number of all-female first names : {len(female_firstnames)}\")\n",
-    "neutral_firstnames = firstnames_by_sex[(firstnames_by_sex['m'] > 0) & (firstnames_by_sex['f'] > 0)]\n",
-    "print(f\">> Number of all-neutral first names : {len(neutral_firstnames)}\")\n"
+    "neutral_firstnames = firstnames_by_sex[\n",
+    "    (firstnames_by_sex[\"m\"] > 0) & (firstnames_by_sex[\"f\"] > 0)\n",
+    "]\n",
+    "print(f\">> Number of all-neutral first names : {len(neutral_firstnames)}\")"
    ]
   },
   {
@@ -906,15 +922,21 @@
     }
    ],
    "source": [
-    "# data for graphic interpretation \n",
-    "labels = ['Male', 'Female', 'Neutral']\n",
+    "# data for graphic interpretation\n",
+    "labels = [\"Male\", \"Female\", \"Neutral\"]\n",
     "values = [len(male_firstnames), len(female_firstnames), len(neutral_firstnames)]\n",
     "\n",
     "# circular\n",
-    "plt.figure(figsize=(6,6))\n",
-    "plt.pie(values, labels=labels, autopct='%1.1f%%', startangle=140, colors=['skyblue', 'lightgreen', 'lightgray'])\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.pie(\n",
+    "    values,\n",
+    "    labels=labels,\n",
+    "    autopct=\"%1.1f%%\",\n",
+    "    startangle=140,\n",
+    "    colors=[\"skyblue\", \"lightgreen\", \"lightgray\"],\n",
+    ")\n",
     "plt.title(\"Distribution of first names by sex\")\n",
-    "plt.axis('equal')\n",
+    "plt.axis(\"equal\")\n",
     "plt.show()"
    ]
   },
@@ -937,31 +959,42 @@
    ],
    "source": [
     "# Assign first name sex category\n",
-    "df_simple['first_name_sex_category'] = df_simple['first_name'].map(\n",
-    "    lambda x: 'Male' if x in male_firstnames else (\n",
-    "        'Female' if x in female_firstnames else (\n",
-    "            'Neutral' if x in neutral_firstnames else None\n",
-    "        )\n",
+    "df_simple[\"first_name_sex_category\"] = df_simple[\"first_name\"].map(\n",
+    "    lambda x: \"Male\"\n",
+    "    if x in male_firstnames\n",
+    "    else (\n",
+    "        \"Female\"\n",
+    "        if x in female_firstnames\n",
+    "        else (\"Neutral\" if x in neutral_firstnames else None)\n",
     "    )\n",
     ")\n",
     "\n",
-    "df_cat = df_simple.dropna(subset=['first_name_sex_category'])\n",
-    "category_counts = df_cat.groupby(['province_clean', 'first_name_sex_category']).size().unstack(fill_value=0)\n",
-    "category_counts['dominant_category'] = category_counts.idxmax(axis=1)\n",
-    "map_df = gdf.set_index('province_upper').join(category_counts['dominant_category'])\n",
+    "df_cat = df_simple.dropna(subset=[\"first_name_sex_category\"])\n",
+    "category_counts = (\n",
+    "    df_cat.groupby([\"province_clean\", \"first_name_sex_category\"])\n",
+    "    .size()\n",
+    "    .unstack(fill_value=0)\n",
+    ")\n",
+    "category_counts[\"dominant_category\"] = category_counts.idxmax(axis=1)\n",
+    "map_df = gdf.set_index(\"province_upper\").join(category_counts[\"dominant_category\"])\n",
     "\n",
-    "color_map = {'Male': 'skyblue', 'Female': 'lightgreen', 'Neutral': 'lightgray'}\n",
-    "map_df['color'] = map_df['dominant_category'].map(color_map)\n",
+    "color_map = {\"Male\": \"skyblue\", \"Female\": \"lightgreen\", \"Neutral\": \"lightgray\"}\n",
+    "map_df[\"color\"] = map_df[\"dominant_category\"].map(color_map)\n",
     "\n",
     "fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n",
-    "map_df.plot(color=map_df['color'], edgecolor='black', ax=ax)\n",
+    "map_df.plot(color=map_df[\"color\"], edgecolor=\"black\", ax=ax)\n",
     "for idx, row in map_df.iterrows():\n",
-    "    plt.annotate(text=row['dominant_category'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),\n",
-    "                 horizontalalignment='center', fontsize=8, color='black')\n",
+    "    plt.annotate(\n",
+    "        text=row[\"dominant_category\"],\n",
+    "        xy=(row.geometry.centroid.x, row.geometry.centroid.y),\n",
+    "        horizontalalignment=\"center\",\n",
+    "        fontsize=8,\n",
+    "        color=\"black\",\n",
+    "    )\n",
     "\n",
     "plt.title(\"Dominant first name category by province\")\n",
-    "plt.axis('off')\n",
-    "plt.show()\n"
+    "plt.axis(\"off\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -983,8 +1016,8 @@
    ],
    "source": [
     "# Histogram\n",
-    "plt.figure(figsize=(6,4))\n",
-    "plt.bar(labels, values, color=['skyblue', 'lightpink', 'lightgray'])\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "plt.bar(labels, values, color=[\"skyblue\", \"lightpink\", \"lightgray\"])\n",
     "plt.title(\"Distribution of first names by sex\")\n",
     "plt.ylabel(\"Number of first names\")\n",
     "plt.show()"
@@ -1117,14 +1150,16 @@
    ],
    "source": [
     "# Count of unique first names per region (province)\n",
-    "distinct_regions = df_simple['province'].nunique()\n",
+    "distinct_regions = df_simple[\"province\"].nunique()\n",
     "print(f\">> Number of distinct regions: {distinct_regions}\")\n",
-    "firstnames_by_region = df_simple.groupby('province')['first_name'].nunique().sort_values(ascending=False)\n",
+    "firstnames_by_region = (\n",
+    "    df_simple.groupby(\"province\")[\"first_name\"].nunique().sort_values(ascending=False)\n",
+    ")\n",
     "\n",
     "print(\"Number of unique first names per region\")\n",
     "df_firstnames_region = firstnames_by_region.reset_index()\n",
-    "df_firstnames_region.columns = ['Region', 'Unique first names']\n",
-    "df_firstnames_region.head(12)\n"
+    "df_firstnames_region.columns = [\"Region\", \"Unique first names\"]\n",
+    "df_firstnames_region.head(12)"
    ]
   },
   {
@@ -1145,21 +1180,43 @@
     }
    ],
    "source": [
-    "unique_first_names = df_simple.groupby('province')['first_name'].nunique().reset_index(name='unique_count')\n",
-    "unique_first_names['province_clean'] = unique_first_names['province'].str.upper().str.strip() \\\n",
-    "    .str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')\n",
-    "gdf_diversity = gdf_merged.merge(unique_first_names[['province_clean', 'unique_count']], on='province_clean', how='left')\n",
+    "unique_first_names = (\n",
+    "    df_simple.groupby(\"province\")[\"first_name\"]\n",
+    "    .nunique()\n",
+    "    .reset_index(name=\"unique_count\")\n",
+    ")\n",
+    "unique_first_names[\"province_clean\"] = (\n",
+    "    unique_first_names[\"province\"]\n",
+    "    .str.upper()\n",
+    "    .str.strip()\n",
+    "    .str.normalize(\"NFKD\")\n",
+    "    .str.encode(\"ascii\", errors=\"ignore\")\n",
+    "    .str.decode(\"utf-8\")\n",
+    ")\n",
+    "gdf_diversity = gdf_merged.merge(\n",
+    "    unique_first_names[[\"province_clean\", \"unique_count\"]],\n",
+    "    on=\"province_clean\",\n",
+    "    how=\"left\",\n",
+    ")\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(10, 10))\n",
-    "gdf_diversity.plot(column='unique_count', cmap='viridis', edgecolor='black', legend=True, ax=ax)\n",
+    "gdf_diversity.plot(\n",
+    "    column=\"unique_count\", cmap=\"viridis\", edgecolor=\"black\", legend=True, ax=ax\n",
+    ")\n",
     "\n",
     "for idx, row in gdf_diversity.iterrows():\n",
-    "    centroid = row['geometry'].centroid\n",
-    "    plt.text(centroid.x, centroid.y, row['province_upper'], \n",
-    "             horizontalalignment='center', fontsize=8, color='black')\n",
+    "    centroid = row[\"geometry\"].centroid\n",
+    "    plt.text(\n",
+    "        centroid.x,\n",
+    "        centroid.y,\n",
+    "        row[\"province_upper\"],\n",
+    "        horizontalalignment=\"center\",\n",
+    "        fontsize=8,\n",
+    "        color=\"black\",\n",
+    "    )\n",
     "\n",
     "plt.title(\"First name diversity by province\")\n",
-    "plt.axis('off')\n",
+    "plt.axis(\"off\")\n",
     "plt.show()"
    ]
   },
@@ -1238,15 +1295,15 @@
    ],
    "source": [
     "# top 3 most popular first names by region\n",
-    "grouped = df_simple.groupby(['province', 'first_name']).size().reset_index(name='count')\n",
-    "grouped = grouped.sort_values(['province', 'count'], ascending=[True, False])\n",
-    "top3_by_region = grouped.groupby('province').head(3)\n",
+    "grouped = df_simple.groupby([\"province\", \"first_name\"]).size().reset_index(name=\"count\")\n",
+    "grouped = grouped.sort_values([\"province\", \"count\"], ascending=[True, False])\n",
+    "top3_by_region = grouped.groupby(\"province\").head(3)\n",
     "\n",
-    "for province, group in top3_by_region.groupby('province'):\n",
+    "for province, group in top3_by_region.groupby(\"province\"):\n",
     "    print(f\"{province.upper()}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"    {row['first_name']:<15} {row['count']}\")\n",
-    "    print()\n"
+    "    print()"
    ]
   },
   {
@@ -1430,20 +1487,27 @@
     }
    ],
    "source": [
-    "#Group by region, gender and first name, then count\n",
+    "# Group by region, gender and first name, then count\n",
     "def determine_sex_category(row):\n",
-    "    if row['first_name'] in neutral_firstnames:\n",
-    "        return 'Neutral'\n",
-    "    return row['sex']\n",
+    "    if row[\"first_name\"] in neutral_firstnames:\n",
+    "        return \"Neutral\"\n",
+    "    return row[\"sex\"]\n",
     "\n",
-    "df_simple['sex_category'] = df_simple.apply(determine_sex_category, axis=1)\n",
+    "\n",
+    "df_simple[\"sex_category\"] = df_simple.apply(determine_sex_category, axis=1)\n",
     "\n",
     "# Groupement\n",
-    "grouped = df_simple.groupby(['province', 'sex_category', 'first_name']).size().reset_index(name='count')\n",
-    "grouped = grouped.sort_values(['province', 'sex_category', 'count'], ascending=[True, True, False])\n",
-    "top3_by_region_sex = grouped.groupby(['province', 'sex_category']).head(3)\n",
+    "grouped = (\n",
+    "    df_simple.groupby([\"province\", \"sex_category\", \"first_name\"])\n",
+    "    .size()\n",
+    "    .reset_index(name=\"count\")\n",
+    ")\n",
+    "grouped = grouped.sort_values(\n",
+    "    [\"province\", \"sex_category\", \"count\"], ascending=[True, True, False]\n",
+    ")\n",
+    "top3_by_region_sex = grouped.groupby([\"province\", \"sex_category\"]).head(3)\n",
     "\n",
-    "for (province, sex), group in top3_by_region_sex.groupby(['province', 'sex_category']):\n",
+    "for (province, sex), group in top3_by_region_sex.groupby([\"province\", \"sex_category\"]):\n",
     "    print(f\"{province.upper()} - {sex}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"    {row['first_name']:<15} {row['count']}\")\n",
@@ -1489,11 +1553,11 @@
    ],
    "source": [
     "# Grouping surnames by sex\n",
-    "surnames_by_sex = df_simple.groupby(['surname', 'sex']).size().reset_index(name='count')\n",
-    "surnames_by_sex = surnames_by_sex.sort_values(['sex', 'count'], ascending=[True, False])\n",
-    "top10_per_sex = surnames_by_sex.groupby('sex').head(10)\n",
+    "surnames_by_sex = df_simple.groupby([\"surname\", \"sex\"]).size().reset_index(name=\"count\")\n",
+    "surnames_by_sex = surnames_by_sex.sort_values([\"sex\", \"count\"], ascending=[True, False])\n",
+    "top10_per_sex = surnames_by_sex.groupby(\"sex\").head(10)\n",
     "\n",
-    "for sex, group in top10_per_sex.groupby('sex'):\n",
+    "for sex, group in top10_per_sex.groupby(\"sex\"):\n",
     "    print(f\"Top 10 surnames for sex: {sex}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"    {row['surname']:<15} {row['count']}\")\n",
@@ -1575,11 +1639,15 @@
    ],
    "source": [
     "# Grouping surnames by region\n",
-    "surnames_by_region = df_simple.groupby(['province', 'surname']).size().reset_index(name='count')\n",
-    "surnames_by_region = surnames_by_region.sort_values(['province', 'count'], ascending=[True, False])\n",
-    "top3_surnames_per_region = surnames_by_region.groupby('province').head(3)\n",
+    "surnames_by_region = (\n",
+    "    df_simple.groupby([\"province\", \"surname\"]).size().reset_index(name=\"count\")\n",
+    ")\n",
+    "surnames_by_region = surnames_by_region.sort_values(\n",
+    "    [\"province\", \"count\"], ascending=[True, False]\n",
+    ")\n",
+    "top3_surnames_per_region = surnames_by_region.groupby(\"province\").head(3)\n",
     "\n",
-    "for province, group in top3_surnames_per_region.groupby('province'):\n",
+    "for province, group in top3_surnames_per_region.groupby(\"province\"):\n",
     "    print(f\"{province.upper()}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"    {row['surname']:<15} {row['count']}\")\n",
@@ -1721,11 +1789,15 @@
    ],
    "source": [
     "# Grouping surnames by region and sex\n",
-    "grouped = df_simple.groupby(['province', 'sex', 'surname']).size().reset_index(name='count')\n",
-    "grouped = grouped.sort_values(['province', 'sex', 'count'], ascending=[True, True, False])\n",
-    "top3_surnames_by_region_sex = grouped.groupby(['province', 'sex']).head(3)\n",
+    "grouped = (\n",
+    "    df_simple.groupby([\"province\", \"sex\", \"surname\"]).size().reset_index(name=\"count\")\n",
+    ")\n",
+    "grouped = grouped.sort_values(\n",
+    "    [\"province\", \"sex\", \"count\"], ascending=[True, True, False]\n",
+    ")\n",
+    "top3_surnames_by_region_sex = grouped.groupby([\"province\", \"sex\"]).head(3)\n",
     "\n",
-    "for (province, sex), group in top3_surnames_by_region_sex.groupby(['province', 'sex']):\n",
+    "for (province, sex), group in top3_surnames_by_region_sex.groupby([\"province\", \"sex\"]):\n",
     "    print(f\"{province.upper()} - {sex}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"    {row['surname']:<15} {row['count']}\")\n",
@@ -1760,15 +1832,23 @@
    "source": [
     "# Find surnames common to both sexes per region - the most popular per region\n",
     "\n",
-    "common_surnames = df_simple.groupby(['province', 'surname', 'sex']).size().unstack(fill_value=0)\n",
-    "common_surnames = common_surnames[(common_surnames.get('m', 0) > 0) & (common_surnames.get('f', 0) > 0)]\n",
-    "common_surnames['total'] = common_surnames['m'] + common_surnames['f']\n",
+    "common_surnames = (\n",
+    "    df_simple.groupby([\"province\", \"surname\", \"sex\"]).size().unstack(fill_value=0)\n",
+    ")\n",
+    "common_surnames = common_surnames[\n",
+    "    (common_surnames.get(\"m\", 0) > 0) & (common_surnames.get(\"f\", 0) > 0)\n",
+    "]\n",
+    "common_surnames[\"total\"] = common_surnames[\"m\"] + common_surnames[\"f\"]\n",
     "common_surnames = common_surnames.reset_index()\n",
-    "top_common_surnames = common_surnames.sort_values(['province', 'total'], ascending=[True, False])\n",
-    "top_common_surnames = top_common_surnames.groupby('province').first().reset_index()\n",
+    "top_common_surnames = common_surnames.sort_values(\n",
+    "    [\"province\", \"total\"], ascending=[True, False]\n",
+    ")\n",
+    "top_common_surnames = top_common_surnames.groupby(\"province\").first().reset_index()\n",
     "\n",
     "for _, row in top_common_surnames.iterrows():\n",
-    "    print(f\"{row['province'].upper()} → {row['surname']} ({row['m']} M / {row['f']} F) - Total: {row['total']}\")"
+    "    print(\n",
+    "        f\"{row['province'].upper()} → {row['surname']} ({row['m']} M / {row['f']} F) - Total: {row['total']}\"\n",
+    "    )"
    ]
   },
   {
@@ -1810,15 +1890,19 @@
    ],
    "source": [
     "# Grouping middle names by sex\n",
-    "middlenames_by_sex = df_simple.groupby(['middle_name', 'sex']).size().reset_index(name='count')\n",
-    "middlenames_by_sex = middlenames_by_sex.sort_values(['sex', 'count'], ascending=[True, False])\n",
-    "top10_middlenames_per_sex = middlenames_by_sex.groupby('sex').head(10)\n",
+    "middlenames_by_sex = (\n",
+    "    df_simple.groupby([\"middle_name\", \"sex\"]).size().reset_index(name=\"count\")\n",
+    ")\n",
+    "middlenames_by_sex = middlenames_by_sex.sort_values(\n",
+    "    [\"sex\", \"count\"], ascending=[True, False]\n",
+    ")\n",
+    "top10_middlenames_per_sex = middlenames_by_sex.groupby(\"sex\").head(10)\n",
     "\n",
-    "for sex, group in top10_middlenames_per_sex.groupby('sex'):\n",
+    "for sex, group in top10_middlenames_per_sex.groupby(\"sex\"):\n",
     "    print(f\"Top 10 middle names for sex: {sex}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"    {row['middle_name']:<15} {row['count']}\")\n",
-    "    print()\n"
+    "    print()"
    ]
   },
   {
@@ -1896,15 +1980,19 @@
    ],
    "source": [
     "# Grouping middle names by region\n",
-    "middlenames_by_region = df_simple.groupby(['province', 'middle_name']).size().reset_index(name='count')\n",
-    "middlenames_by_region = middlenames_by_region.sort_values(['province', 'count'], ascending=[True, False])\n",
-    "top3_middlenames_per_region = middlenames_by_region.groupby('province').head(3)\n",
+    "middlenames_by_region = (\n",
+    "    df_simple.groupby([\"province\", \"middle_name\"]).size().reset_index(name=\"count\")\n",
+    ")\n",
+    "middlenames_by_region = middlenames_by_region.sort_values(\n",
+    "    [\"province\", \"count\"], ascending=[True, False]\n",
+    ")\n",
+    "top3_middlenames_per_region = middlenames_by_region.groupby(\"province\").head(3)\n",
     "\n",
-    "for province, group in top3_middlenames_per_region.groupby('province'):\n",
+    "for province, group in top3_middlenames_per_region.groupby(\"province\"):\n",
     "    print(f\"{province.upper()}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"    {row['middle_name']:<20} {row['count']}\")\n",
-    "    print()    "
+    "    print()"
    ]
   },
   {
@@ -2042,11 +2130,21 @@
    ],
    "source": [
     "# Grouping middle names by province and sex\n",
-    "middlenames_by_province_sex = df_simple.groupby(['province', 'sex', 'middle_name']).size().reset_index(name='count')\n",
-    "middlenames_by_province_sex = middlenames_by_province_sex.sort_values(['province', 'sex', 'count'], ascending=[True, True, False])\n",
-    "top3_middlenames_by_province_sex = middlenames_by_province_sex.groupby(['province', 'sex']).head(3)\n",
+    "middlenames_by_province_sex = (\n",
+    "    df_simple.groupby([\"province\", \"sex\", \"middle_name\"])\n",
+    "    .size()\n",
+    "    .reset_index(name=\"count\")\n",
+    ")\n",
+    "middlenames_by_province_sex = middlenames_by_province_sex.sort_values(\n",
+    "    [\"province\", \"sex\", \"count\"], ascending=[True, True, False]\n",
+    ")\n",
+    "top3_middlenames_by_province_sex = middlenames_by_province_sex.groupby(\n",
+    "    [\"province\", \"sex\"]\n",
+    ").head(3)\n",
     "\n",
-    "for (province, sex), group in top3_middlenames_by_province_sex.groupby(['province', 'sex']):\n",
+    "for (province, sex), group in top3_middlenames_by_province_sex.groupby(\n",
+    "    [\"province\", \"sex\"]\n",
+    "):\n",
     "    print(f\"{province.upper()} - {sex}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"    {row['middle_name']:<20} {row['count']}\")\n",
@@ -2128,14 +2226,20 @@
    ],
    "source": [
     "# Find middle names common to both sexes per region\n",
-    "common_middlenames = df_simple.groupby(['province', 'middle_name', 'sex']).size().unstack(fill_value=0)\n",
-    "common_middlenames = common_middlenames[(common_middlenames.get('m', 0) > 0) & (common_middlenames.get('f', 0) > 0)]\n",
-    "common_middlenames['total'] = common_middlenames['m'] + common_middlenames['f']\n",
+    "common_middlenames = (\n",
+    "    df_simple.groupby([\"province\", \"middle_name\", \"sex\"]).size().unstack(fill_value=0)\n",
+    ")\n",
+    "common_middlenames = common_middlenames[\n",
+    "    (common_middlenames.get(\"m\", 0) > 0) & (common_middlenames.get(\"f\", 0) > 0)\n",
+    "]\n",
+    "common_middlenames[\"total\"] = common_middlenames[\"m\"] + common_middlenames[\"f\"]\n",
     "common_middlenames = common_middlenames.reset_index()\n",
-    "common_middlenames = common_middlenames.sort_values(['province', 'total'], ascending=[True, False])\n",
-    "top3_common_middlenames = common_middlenames.groupby('province').head(3)\n",
+    "common_middlenames = common_middlenames.sort_values(\n",
+    "    [\"province\", \"total\"], ascending=[True, False]\n",
+    ")\n",
+    "top3_common_middlenames = common_middlenames.groupby(\"province\").head(3)\n",
     "\n",
-    "for province, group in top3_common_middlenames.groupby('province'):\n",
+    "for province, group in top3_common_middlenames.groupby(\"province\"):\n",
     "    print(f\"{province.upper()}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"    {row['middle_name']:<20} {row['total']}\")\n",
@@ -2166,10 +2270,10 @@
    ],
    "source": [
     "# Pie chart of surnames by sex\n",
-    "sex = df['sex'].value_counts()\n",
+    "sex = df[\"sex\"].value_counts()\n",
     "plt.figure(figsize=(7, 7))\n",
-    "plt.pie(sex, labels=sex.index, autopct='%1.1f%%', startangle=140)\n",
-    "plt.axis('equal')\n",
+    "plt.pie(sex, labels=sex.index, autopct=\"%1.1f%%\", startangle=140)\n",
+    "plt.axis(\"equal\")\n",
     "plt.show()"
    ]
   },
@@ -2218,7 +2322,7 @@
    ],
    "source": [
     "table = df.groupby([\"year\", \"sex\"]).size().unstack(fill_value=0)\n",
-    "print(table)\n"
+    "print(table)"
    ]
   },
   {
@@ -2253,19 +2357,13 @@
    ],
    "source": [
     "plt.show()\n",
-    "#Bar chart middle name by region and sex\n",
-    "data = df.groupby(['province', 'sex']).size().reset_index(name='count')\n",
-    "dataa = data.sort_values('count', ascending=False).drop_duplicates(\n",
-    "    ['province', 'sex'])\n",
+    "# Bar chart middle name by region and sex\n",
+    "data = df.groupby([\"province\", \"sex\"]).size().reset_index(name=\"count\")\n",
+    "dataa = data.sort_values(\"count\", ascending=False).drop_duplicates([\"province\", \"sex\"])\n",
     "\n",
     "# Bar chart\n",
     "plt.figure(figsize=(12, 6))\n",
-    "sns.barplot(\n",
-    "    data=dataa,\n",
-    "    x='province',\n",
-    "    y='count',\n",
-    "    hue='sex'\n",
-    ")\n",
+    "sns.barplot(data=dataa, x=\"province\", y=\"count\", hue=\"sex\")\n",
     "plt.xlabel(\"Region\")\n",
     "plt.ylabel(\"Count\")\n",
     "plt.xticks(rotation=45)\n",
@@ -2293,18 +2391,25 @@
    ],
    "source": [
     "# Pie chart of surnames by sex and region\n",
-    "prov_sex_counts = df_simple.groupby(['province', 'sex']).size().reset_index(name='count')\n",
-    "provinces = prov_sex_counts['province'].unique()\n",
+    "prov_sex_counts = (\n",
+    "    df_simple.groupby([\"province\", \"sex\"]).size().reset_index(name=\"count\")\n",
+    ")\n",
+    "provinces = prov_sex_counts[\"province\"].unique()\n",
     "cols = 3\n",
     "rows = (len(provinces) + cols - 1) // cols\n",
     "\n",
     "plt.figure(figsize=(cols * 5, rows * 5))\n",
     "for i, prov in enumerate(provinces, 1):\n",
-    "    data = prov_sex_counts[prov_sex_counts['province'] == prov]\n",
+    "    data = prov_sex_counts[prov_sex_counts[\"province\"] == prov]\n",
     "    plt.subplot(rows, cols, i)\n",
-    "    plt.pie(data['count'], labels=data['sex'], autopct='%1.1f%%', colors=['lightpink', 'skyblue'])\n",
+    "    plt.pie(\n",
+    "        data[\"count\"],\n",
+    "        labels=data[\"sex\"],\n",
+    "        autopct=\"%1.1f%%\",\n",
+    "        colors=[\"lightpink\", \"skyblue\"],\n",
+    "    )\n",
     "    plt.title(f\"Distribution of Surnames by Sex\\nin {prov}\")\n",
-    "    plt.axis('equal')\n",
+    "    plt.axis(\"equal\")\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -2329,13 +2434,19 @@
    ],
    "source": [
     "# surnames per region (aggregated)\n",
-    "top_surnames_region = surnames_by_region.sort_values('count', ascending=False).drop_duplicates('province').head(12)\n",
-    "plt.figure(figsize=(10,6))\n",
-    "sns.barplot(y='province', x='count', data=top_surnames_region, hue='surname', dodge=False)\n",
+    "top_surnames_region = (\n",
+    "    surnames_by_region.sort_values(\"count\", ascending=False)\n",
+    "    .drop_duplicates(\"province\")\n",
+    "    .head(12)\n",
+    ")\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(\n",
+    "    y=\"province\", x=\"count\", data=top_surnames_region, hue=\"surname\", dodge=False\n",
+    ")\n",
     "plt.title(\"Most Common Surname per Region\")\n",
     "plt.xlabel(\"Count\")\n",
     "plt.ylabel(\"Region\")\n",
-    "plt.legend(title=\"Surname\", bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.legend(title=\"Surname\", bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
     "plt.show()"
    ]
   },
@@ -2358,26 +2469,41 @@
    ],
    "source": [
     "# Map of the most common surname by province in DRC\n",
-    "top_surnames = df_simple.groupby(['province', 'surname']).size().reset_index(name='count')\n",
-    "top_surnames = top_surnames.sort_values(['province', 'count'], ascending=[True, False])\n",
-    "top_surnames = top_surnames.drop_duplicates(subset='province', keep='first')\n",
-    "top_surnames['province_clean'] = top_surnames['province'].str.upper().str.strip() \\\n",
-    "    .str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')\n",
+    "top_surnames = (\n",
+    "    df_simple.groupby([\"province\", \"surname\"]).size().reset_index(name=\"count\")\n",
+    ")\n",
+    "top_surnames = top_surnames.sort_values([\"province\", \"count\"], ascending=[True, False])\n",
+    "top_surnames = top_surnames.drop_duplicates(subset=\"province\", keep=\"first\")\n",
+    "top_surnames[\"province_clean\"] = (\n",
+    "    top_surnames[\"province\"]\n",
+    "    .str.upper()\n",
+    "    .str.strip()\n",
+    "    .str.normalize(\"NFKD\")\n",
+    "    .str.encode(\"ascii\", errors=\"ignore\")\n",
+    "    .str.decode(\"utf-8\")\n",
+    ")\n",
     "\n",
-    "gdf_named = gdf_merged.merge(top_surnames[['province_clean', 'surname']], \n",
-    "                             on='province_clean', how='left')\n",
+    "gdf_named = gdf_merged.merge(\n",
+    "    top_surnames[[\"province_clean\", \"surname\"]], on=\"province_clean\", how=\"left\"\n",
+    ")\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(12, 12))\n",
-    "gdf_named.plot(column='count', cmap='PuBu', edgecolor='black', legend=True, ax=ax)\n",
+    "gdf_named.plot(column=\"count\", cmap=\"PuBu\", edgecolor=\"black\", legend=True, ax=ax)\n",
     "\n",
     "for idx, row in gdf_named.iterrows():\n",
-    "    if pd.notna(row['surname']):\n",
-    "        centroid = row['geometry'].centroid\n",
-    "        plt.text(centroid.x, centroid.y, row['surname'], \n",
-    "                 horizontalalignment='center', fontsize=8, color='black')\n",
+    "    if pd.notna(row[\"surname\"]):\n",
+    "        centroid = row[\"geometry\"].centroid\n",
+    "        plt.text(\n",
+    "            centroid.x,\n",
+    "            centroid.y,\n",
+    "            row[\"surname\"],\n",
+    "            horizontalalignment=\"center\",\n",
+    "            fontsize=8,\n",
+    "            color=\"black\",\n",
+    "        )\n",
     "\n",
     "plt.title(\"Most common surname by province\")\n",
-    "plt.axis('off')\n",
+    "plt.axis(\"off\")\n",
     "plt.show()"
    ]
   },
@@ -2400,14 +2526,14 @@
    ],
    "source": [
     "# Bar chart surnames by region and sex\n",
-    "top10_regions = df_simple['province'].value_counts().head(12).index.tolist()\n",
-    "df_top10 = df_simple[df_simple['province'].isin(top10_regions)]\n",
-    "plt.figure(figsize=(12,6))\n",
-    "sns.countplot(data=df_top10, y='province', hue='sex', order=top10_regions)\n",
+    "top10_regions = df_simple[\"province\"].value_counts().head(12).index.tolist()\n",
+    "df_top10 = df_simple[df_simple[\"province\"].isin(top10_regions)]\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.countplot(data=df_top10, y=\"province\", hue=\"sex\", order=top10_regions)\n",
     "plt.title(\"Surnames Distribution by Region and Sex\")\n",
     "plt.xlabel(\"Number of entries\")\n",
     "plt.ylabel(\"Region\")\n",
-    "plt.legend(title='Sex')\n",
+    "plt.legend(title=\"Sex\")\n",
     "plt.show()"
    ]
   },
@@ -2430,11 +2556,16 @@
    ],
    "source": [
     "# Pie chart of middle names by sex\n",
-    "middlenames_sex_count = middlenames_by_province_sex.groupby('sex')['count'].sum()\n",
-    "plt.figure(figsize=(6,6))\n",
-    "plt.pie(middlenames_sex_count, labels=middlenames_sex_count.index, autopct='%1.1f%%', colors=['lightpink', 'skyblue'])\n",
+    "middlenames_sex_count = middlenames_by_province_sex.groupby(\"sex\")[\"count\"].sum()\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.pie(\n",
+    "    middlenames_sex_count,\n",
+    "    labels=middlenames_sex_count.index,\n",
+    "    autopct=\"%1.1f%%\",\n",
+    "    colors=[\"lightpink\", \"skyblue\"],\n",
+    ")\n",
     "plt.title(\"Distribution of Middle Names by Sex\")\n",
-    "plt.axis('equal')\n",
+    "plt.axis(\"equal\")\n",
     "plt.show()"
    ]
   },
@@ -2457,13 +2588,19 @@
    ],
    "source": [
     "# most popular middle name per region\n",
-    "top_middlename_region = middlenames_by_region.sort_values('count', ascending=False).drop_duplicates('province').head(12)\n",
-    "plt.figure(figsize=(10,6))\n",
-    "sns.barplot(y='province', x='count', data=top_middlename_region, hue='middle_name', dodge=False)\n",
+    "top_middlename_region = (\n",
+    "    middlenames_by_region.sort_values(\"count\", ascending=False)\n",
+    "    .drop_duplicates(\"province\")\n",
+    "    .head(12)\n",
+    ")\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(\n",
+    "    y=\"province\", x=\"count\", data=top_middlename_region, hue=\"middle_name\", dodge=False\n",
+    ")\n",
     "plt.title(\"Most Common Middle Name per Region\")\n",
     "plt.xlabel(\"Count\")\n",
     "plt.ylabel(\"Region\")\n",
-    "plt.legend(title=\"Middle Name\", bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.legend(title=\"Middle Name\", bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
     "plt.show()"
    ]
   },
@@ -2486,26 +2623,41 @@
    ],
    "source": [
     "# Map of the most common middle by province in DRC\n",
-    "top_surnames = df_simple.groupby(['province', 'middle_name']).size().reset_index(name='count')\n",
-    "top_surnames = top_surnames.sort_values(['province', 'count'], ascending=[True, False])\n",
-    "top_surnames = top_surnames.drop_duplicates(subset='province', keep='first')\n",
-    "top_surnames['province_clean'] = top_surnames['province'].str.upper().str.strip() \\\n",
-    "    .str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')\n",
+    "top_surnames = (\n",
+    "    df_simple.groupby([\"province\", \"middle_name\"]).size().reset_index(name=\"count\")\n",
+    ")\n",
+    "top_surnames = top_surnames.sort_values([\"province\", \"count\"], ascending=[True, False])\n",
+    "top_surnames = top_surnames.drop_duplicates(subset=\"province\", keep=\"first\")\n",
+    "top_surnames[\"province_clean\"] = (\n",
+    "    top_surnames[\"province\"]\n",
+    "    .str.upper()\n",
+    "    .str.strip()\n",
+    "    .str.normalize(\"NFKD\")\n",
+    "    .str.encode(\"ascii\", errors=\"ignore\")\n",
+    "    .str.decode(\"utf-8\")\n",
+    ")\n",
     "\n",
-    "gdf_named = gdf_merged.merge(top_surnames[['province_clean', 'middle_name']], \n",
-    "                             on='province_clean', how='left')\n",
+    "gdf_named = gdf_merged.merge(\n",
+    "    top_surnames[[\"province_clean\", \"middle_name\"]], on=\"province_clean\", how=\"left\"\n",
+    ")\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(12, 12))\n",
-    "gdf_named.plot(column='count', cmap='PuBu', edgecolor='black', legend=True, ax=ax)\n",
+    "gdf_named.plot(column=\"count\", cmap=\"PuBu\", edgecolor=\"black\", legend=True, ax=ax)\n",
     "\n",
     "for idx, row in gdf_named.iterrows():\n",
-    "    if pd.notna(row['middle_name']):\n",
-    "        centroid = row['geometry'].centroid\n",
-    "        plt.text(centroid.x, centroid.y, row['middle_name'], \n",
-    "                 horizontalalignment='center', fontsize=8, color='black')\n",
+    "    if pd.notna(row[\"middle_name\"]):\n",
+    "        centroid = row[\"geometry\"].centroid\n",
+    "        plt.text(\n",
+    "            centroid.x,\n",
+    "            centroid.y,\n",
+    "            row[\"middle_name\"],\n",
+    "            horizontalalignment=\"center\",\n",
+    "            fontsize=8,\n",
+    "            color=\"black\",\n",
+    "        )\n",
     "\n",
     "plt.title(\"Most common middle name by province\")\n",
-    "plt.axis('off')\n",
+    "plt.axis(\"off\")\n",
     "plt.show()"
    ]
   },
@@ -2527,18 +2679,19 @@
     }
    ],
    "source": [
-    "#Bar chart middle name by region and sex\n",
-    "middlenames_by_sex_region = df_simple.groupby(['province', 'sex', 'middle_name']).size().reset_index(name='count')\n",
-    "top_middlename_sex_region = middlenames_by_sex_region.sort_values('count', ascending=False).drop_duplicates(['province', 'sex'])\n",
+    "# Bar chart middle name by region and sex\n",
+    "middlenames_by_sex_region = (\n",
+    "    df_simple.groupby([\"province\", \"sex\", \"middle_name\"])\n",
+    "    .size()\n",
+    "    .reset_index(name=\"count\")\n",
+    ")\n",
+    "top_middlename_sex_region = middlenames_by_sex_region.sort_values(\n",
+    "    \"count\", ascending=False\n",
+    ").drop_duplicates([\"province\", \"sex\"])\n",
     "\n",
     "# Bar chart\n",
     "plt.figure(figsize=(12, 6))\n",
-    "sns.barplot(\n",
-    "    data=top_middlename_sex_region,\n",
-    "    x='province',\n",
-    "    y='count',\n",
-    "    hue='sex'\n",
-    ")\n",
+    "sns.barplot(data=top_middlename_sex_region, x=\"province\", y=\"count\", hue=\"sex\")\n",
     "plt.title(\"Most Common Middle Name Count by Region and Sex\")\n",
     "plt.xlabel(\"Region\")\n",
     "plt.ylabel(\"Count\")\n",
@@ -2578,7 +2731,12 @@
    ],
    "source": [
     "# Crossover 1: Firstname vs Surname\n",
-    "crossover_firstname_surname = df_simple.groupby(['first_name', 'surname']).size().sort_values(ascending=False).head(10)\n",
+    "crossover_firstname_surname = (\n",
+    "    df_simple.groupby([\"first_name\", \"surname\"])\n",
+    "    .size()\n",
+    "    .sort_values(ascending=False)\n",
+    "    .head(10)\n",
+    ")\n",
     "print(\"--- Top 10 Firstname vs Surname Crossovers ---\")\n",
     "print(crossover_firstname_surname)\n",
     "print(\"\\n\")"
@@ -2614,11 +2772,15 @@
    ],
    "source": [
     "# Crossover: Firstname vs Surname vs Sex\n",
-    "crossover_fname_sname_sex = df_simple.groupby(['sex', 'first_name', 'surname']).size().reset_index(name='count')\n",
-    "crossover_fname_sname_sex = crossover_fname_sname_sex.sort_values('count', ascending=False).head(10)\n",
+    "crossover_fname_sname_sex = (\n",
+    "    df_simple.groupby([\"sex\", \"first_name\", \"surname\"]).size().reset_index(name=\"count\")\n",
+    ")\n",
+    "crossover_fname_sname_sex = crossover_fname_sname_sex.sort_values(\n",
+    "    \"count\", ascending=False\n",
+    ").head(10)\n",
     "\n",
     "print(\"--- Top 10 Firstname vs Surname Crossovers by Sex ---\")\n",
-    "for sex, group in crossover_fname_sname_sex.groupby('sex'):\n",
+    "for sex, group in crossover_fname_sname_sex.groupby(\"sex\"):\n",
     "    print(f\"\\nSEX: {sex}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"  {row['first_name']} {row['surname']} — {row['count']}\")"
@@ -2643,22 +2805,24 @@
    ],
    "source": [
     "# frequency of joint appearance\n",
-    "top_firstnames = df_simple['first_name'].value_counts().head(10).index\n",
-    "top_surnames = df_simple['surname'].value_counts().head(10).index\n",
+    "top_firstnames = df_simple[\"first_name\"].value_counts().head(10).index\n",
+    "top_surnames = df_simple[\"surname\"].value_counts().head(10).index\n",
     "\n",
     "filtered_df = df_simple[\n",
-    "    df_simple['first_name'].isin(top_firstnames) &\n",
-    "    df_simple['surname'].isin(top_surnames)\n",
+    "    df_simple[\"first_name\"].isin(top_firstnames)\n",
+    "    & df_simple[\"surname\"].isin(top_surnames)\n",
     "]\n",
     "\n",
-    "cooccurrence = filtered_df.groupby(['first_name', 'surname']).size().unstack(fill_value=0)\n",
+    "cooccurrence = (\n",
+    "    filtered_df.groupby([\"first_name\", \"surname\"]).size().unstack(fill_value=0)\n",
+    ")\n",
     "\n",
-    "#heatmap\n",
+    "# heatmap\n",
     "plt.figure(figsize=(12, 10))\n",
-    "sns.heatmap(cooccurrence, annot=True, fmt='d', cmap='Blues')\n",
-    "plt.title('Heatmap of First Name vs Surname Cooccurrence')\n",
-    "plt.xlabel('Surname')\n",
-    "plt.ylabel('First Name')\n",
+    "sns.heatmap(cooccurrence, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
+    "plt.title(\"Heatmap of First Name vs Surname Cooccurrence\")\n",
+    "plt.xlabel(\"Surname\")\n",
+    "plt.ylabel(\"First Name\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -2693,7 +2857,12 @@
    ],
    "source": [
     "# Crossover 2: Firstname vs Middle Name\n",
-    "crossover_firstname_middlename = df_simple.groupby(['first_name', 'middle_name']).size().sort_values(ascending=False).head(10)\n",
+    "crossover_firstname_middlename = (\n",
+    "    df_simple.groupby([\"first_name\", \"middle_name\"])\n",
+    "    .size()\n",
+    "    .sort_values(ascending=False)\n",
+    "    .head(10)\n",
+    ")\n",
     "print(\"--- Top 10 Firstname vs Middle Name Crossovers ---\")\n",
     "print(crossover_firstname_middlename)\n",
     "print(\"\\n\")"
@@ -2718,20 +2887,22 @@
    ],
    "source": [
     "# frequency of joint appearance\n",
-    "top_middlenames = df_simple['middle_name'].value_counts().head(10).index\n",
+    "top_middlenames = df_simple[\"middle_name\"].value_counts().head(10).index\n",
     "\n",
     "filtered_df = df_simple[\n",
-    "    df_simple['first_name'].isin(top_firstnames) &\n",
-    "    df_simple['middle_name'].isin(top_middlenames)\n",
+    "    df_simple[\"first_name\"].isin(top_firstnames)\n",
+    "    & df_simple[\"middle_name\"].isin(top_middlenames)\n",
     "]\n",
     "\n",
-    "cooccurrence = filtered_df.groupby(['first_name', 'middle_name']).size().unstack(fill_value=0)\n",
+    "cooccurrence = (\n",
+    "    filtered_df.groupby([\"first_name\", \"middle_name\"]).size().unstack(fill_value=0)\n",
+    ")\n",
     "\n",
     "plt.figure(figsize=(12, 10))\n",
-    "sns.heatmap(cooccurrence, annot=True, fmt='d', cmap='Greens')\n",
-    "plt.title('Heatmap of First Name vs Middle Name Cooccurrence')\n",
-    "plt.xlabel('Middle Name')\n",
-    "plt.ylabel('First Name')\n",
+    "sns.heatmap(cooccurrence, annot=True, fmt=\"d\", cmap=\"Greens\")\n",
+    "plt.title(\"Heatmap of First Name vs Middle Name Cooccurrence\")\n",
+    "plt.xlabel(\"Middle Name\")\n",
+    "plt.ylabel(\"First Name\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -2764,11 +2935,17 @@
    ],
    "source": [
     "# Crossover: Firstname vs Middle Name vs Sex\n",
-    "crossover_fname_middlename_sex = df_simple.groupby(['sex', 'first_name', 'middle_name']).size().reset_index(name='count')\n",
-    "crossover_fname_middlename_sex = crossover_fname_middlename_sex.sort_values('count', ascending=False).head(10)\n",
+    "crossover_fname_middlename_sex = (\n",
+    "    df_simple.groupby([\"sex\", \"first_name\", \"middle_name\"])\n",
+    "    .size()\n",
+    "    .reset_index(name=\"count\")\n",
+    ")\n",
+    "crossover_fname_middlename_sex = crossover_fname_middlename_sex.sort_values(\n",
+    "    \"count\", ascending=False\n",
+    ").head(10)\n",
     "\n",
     "print(\"--- Top 10 Firstname vs Middle Name Crossovers by Sex ---\")\n",
-    "for sex, group in crossover_fname_middlename_sex.groupby('sex'):\n",
+    "for sex, group in crossover_fname_middlename_sex.groupby(\"sex\"):\n",
     "    print(f\"\\nSEX: {sex}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"  {row['first_name']} {row['middle_name']} — {row['count']}\")"
@@ -2805,7 +2982,12 @@
    "source": [
     "# Crossover 3: Surname vs Middle Name\n",
     "print(\"--- Top 10 Surname vs Middle Name Crossovers ---\")\n",
-    "crossover_surname_middlename = df_simple.groupby(['surname', 'middle_name']).size().sort_values(ascending=False).head(10)\n",
+    "crossover_surname_middlename = (\n",
+    "    df_simple.groupby([\"surname\", \"middle_name\"])\n",
+    "    .size()\n",
+    "    .sort_values(ascending=False)\n",
+    "    .head(10)\n",
+    ")\n",
     "print(crossover_surname_middlename)\n",
     "print(\"\\n\")"
    ]
@@ -2829,20 +3011,22 @@
    ],
    "source": [
     "# frequency of joint appearance\n",
-    "top_surnames = df_simple['surname'].value_counts().head(10).index \n",
-    "top_middlenames = df_simple['middle_name'].value_counts().head(10).index\n",
+    "top_surnames = df_simple[\"surname\"].value_counts().head(10).index\n",
+    "top_middlenames = df_simple[\"middle_name\"].value_counts().head(10).index\n",
     "filtered_df = df_simple[\n",
-    "    df_simple['surname'].isin(top_surnames) &\n",
-    "    df_simple['middle_name'].isin(top_middlenames)\n",
+    "    df_simple[\"surname\"].isin(top_surnames)\n",
+    "    & df_simple[\"middle_name\"].isin(top_middlenames)\n",
     "]\n",
     "\n",
-    "cooccurrence = filtered_df.groupby(['surname', 'middle_name']).size().unstack(fill_value=0)\n",
+    "cooccurrence = (\n",
+    "    filtered_df.groupby([\"surname\", \"middle_name\"]).size().unstack(fill_value=0)\n",
+    ")\n",
     "\n",
     "plt.figure(figsize=(12, 10))\n",
-    "sns.heatmap(cooccurrence, annot=True, fmt='d', cmap='Oranges')\n",
-    "plt.title('Heatmap of Surname vs Middle Name Cooccurrence')\n",
-    "plt.xlabel('Middle Name')\n",
-    "plt.ylabel('Surname')\n",
+    "sns.heatmap(cooccurrence, annot=True, fmt=\"d\", cmap=\"Oranges\")\n",
+    "plt.title(\"Heatmap of Surname vs Middle Name Cooccurrence\")\n",
+    "plt.xlabel(\"Middle Name\")\n",
+    "plt.ylabel(\"Surname\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -2875,11 +3059,17 @@
    ],
    "source": [
     "# Crossover: Surname vs Middle Name vs Sex\n",
-    "crossover_surname_middlename_sex = df_simple.groupby(['sex', 'surname', 'middle_name']).size().reset_index(name='count')\n",
-    "crossover_surname_middlename_sex = crossover_surname_middlename_sex.sort_values('count', ascending=False).head(10)\n",
+    "crossover_surname_middlename_sex = (\n",
+    "    df_simple.groupby([\"sex\", \"surname\", \"middle_name\"])\n",
+    "    .size()\n",
+    "    .reset_index(name=\"count\")\n",
+    ")\n",
+    "crossover_surname_middlename_sex = crossover_surname_middlename_sex.sort_values(\n",
+    "    \"count\", ascending=False\n",
+    ").head(10)\n",
     "\n",
     "print(\"--- Top 10 Surname vs Middle Name Crossovers by Sex ---\")\n",
-    "for sex, group in crossover_surname_middlename_sex.groupby('sex'):\n",
+    "for sex, group in crossover_surname_middlename_sex.groupby(\"sex\"):\n",
     "    print(f\"\\nSEX: {sex}\")\n",
     "    for _, row in group.iterrows():\n",
     "        print(f\"  {row['surname']} {row['middle_name']} — {row['count']}\")"
@@ -2904,29 +3094,29 @@
    ],
    "source": [
     "# Calculate the length of first names, surnames, and middle names\n",
-    "df['firstname_length'] = df_simple['first_name'].str.len()\n",
-    "df['surname_length'] = df_simple['surname'].str.len()\n",
-    "df['middlename_length'] = df_simple['middle_name'].str.len()\n",
+    "df[\"firstname_length\"] = df_simple[\"first_name\"].str.len()\n",
+    "df[\"surname_length\"] = df_simple[\"surname\"].str.len()\n",
+    "df[\"middlename_length\"] = df_simple[\"middle_name\"].str.len()\n",
     "\n",
     "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
     "\n",
     "# Histogram for firstname length\n",
-    "sns.histplot(df['firstname_length'], kde=True, ax=axes[0], color='skyblue')\n",
-    "axes[0].set_title('Distribution of First Name Length')\n",
-    "axes[0].set_xlabel('Number of Letters')\n",
-    "axes[0].set_ylabel('Frequency')\n",
+    "sns.histplot(df[\"firstname_length\"], kde=True, ax=axes[0], color=\"skyblue\")\n",
+    "axes[0].set_title(\"Distribution of First Name Length\")\n",
+    "axes[0].set_xlabel(\"Number of Letters\")\n",
+    "axes[0].set_ylabel(\"Frequency\")\n",
     "\n",
     "# Histogram for surname length\n",
-    "sns.histplot(df['surname_length'], kde=True, ax=axes[1], color='lightgreen')\n",
-    "axes[1].set_title('Distribution of Surname Length')\n",
-    "axes[1].set_xlabel('Number of Letters')\n",
-    "axes[1].set_ylabel('Frequency')\n",
+    "sns.histplot(df[\"surname_length\"], kde=True, ax=axes[1], color=\"lightgreen\")\n",
+    "axes[1].set_title(\"Distribution of Surname Length\")\n",
+    "axes[1].set_xlabel(\"Number of Letters\")\n",
+    "axes[1].set_ylabel(\"Frequency\")\n",
     "\n",
     "# Histogram for middle name length\n",
-    "sns.histplot(df['middlename_length'], kde=True, ax=axes[2], color='salmon')\n",
-    "axes[2].set_title('Distribution of Middle Name Length')\n",
-    "axes[2].set_xlabel('Number of Letters')\n",
-    "axes[2].set_ylabel('Frequency')\n",
+    "sns.histplot(df[\"middlename_length\"], kde=True, ax=axes[2], color=\"salmon\")\n",
+    "axes[2].set_title(\"Distribution of Middle Name Length\")\n",
+    "axes[2].set_xlabel(\"Number of Letters\")\n",
+    "axes[2].set_ylabel(\"Frequency\")\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
diff --git a/notebooks/names.ipynb b/src/notebooks/names.ipynb
similarity index 98%
rename from notebooks/names.ipynb
rename to src/notebooks/names.ipynb
index 146c2bd..519aec1 100644
--- a/notebooks/names.ipynb
+++ b/src/notebooks/names.ipynb
@@ -1,13 +1,16 @@
 {
  "cells": [
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "# Names Analysis & Modeling",
-   "id": "ae0a818eafd9bd24"
+   "id": "ae0a818eafd9bd24",
+   "metadata": {},
+   "source": [
+    "# Names Analysis & Modeling"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 1,
    "id": "initial_id",
    "metadata": {
     "ExecuteTime": {
@@ -15,48 +18,47 @@
      "start_time": "2025-09-28T21:41:26.224407Z"
     }
    },
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import sys \n",
-    "import os\n",
-    "from collections import Counter"
-   ],
-   "outputs": [],
-   "execution_count": 1
+    "import sys\n",
+    "import os"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "584a6fcfcbea71e4",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:41:26.471906Z",
      "start_time": "2025-09-28T21:41:26.234143Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "sys.path.append(os.path.abspath(\"..\"))\n",
     "\n",
-    "from core.utils.data_loader import DataLoader\n",
-    "from core.utils.region_mapper import RegionMapper\n",
-    "from core.config.pipeline_config import PipelineConfig\n",
+    "from ners.core.utils.data_loader import DataLoader\n",
+    "from ners.core.utils.region_mapper import RegionMapper\n",
+    "from ners.core.config.pipeline_config import PipelineConfig\n",
     "\n",
-    "from research.statistics.utils import LETTERS\n",
-    "from research.statistics.utils import build_category_distribution\n",
-    "from research.statistics.utils import build_words_token\n",
-    "from research.statistics.utils import build_transition_probabilities\n",
-    "from research.statistics.utils import build_transition_comparisons\n",
-    "from research.statistics.utils import build_ngrams_count\n",
-    "from research.statistics.plots import plot_transition_matrix\n",
-    "from research.statistics.plots import plot_letter_frequencies"
-   ],
-   "id": "584a6fcfcbea71e4",
-   "outputs": [],
-   "execution_count": 2
+    "from ners.research.statistics.utils import LETTERS\n",
+    "from ners.research.statistics.utils import build_category_distribution\n",
+    "from ners.research.statistics.utils import build_words_token\n",
+    "from ners.research.statistics.utils import build_transition_probabilities\n",
+    "from ners.research.statistics.utils import build_transition_comparisons\n",
+    "from ners.research.statistics.utils import build_ngrams_count\n",
+    "from ners.research.statistics.plots import plot_transition_matrix\n",
+    "from ners.research.statistics.plots import plot_letter_frequencies"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "id": "f1a69290-a9c0-40d0-9fe8-a06d8a466671",
    "metadata": {
     "ExecuteTime": {
@@ -64,6 +66,7 @@
      "start_time": "2025-09-28T21:41:26.478191Z"
     }
    },
+   "outputs": [],
    "source": [
     "config = PipelineConfig(\n",
     "    paths={\n",
@@ -73,29 +76,23 @@
     "        \"outputs_dir\": \"../data/processed\",\n",
     "        \"logs_dir\": \"../logs\",\n",
     "        \"configs_dir\": \"../configs\",\n",
-    "        \"checkpoints_dir\": \"../checkpoints\"\n",
+    "        \"checkpoints_dir\": \"../checkpoints\",\n",
     "    }\n",
     ")\n",
     "\n",
     "loader = DataLoader(config)"
-   ],
-   "outputs": [],
-   "execution_count": 3
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e48c6fd9a213bcd2",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:41:53.362337Z",
      "start_time": "2025-09-28T21:41:26.490902Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")\n",
-    "df['province'] = RegionMapper.clean_province(df['province'])\n",
-    "df.columns"
-   ],
-   "id": "e48c6fd9a213bcd2",
    "outputs": [
     {
      "data": {
@@ -112,47 +109,69 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 4
+   "source": [
+    "df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")\n",
+    "df[\"province\"] = RegionMapper.clean_province(df[\"province\"])\n",
+    "df.columns"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "## Name category distribution",
-   "id": "46429fc98b67a89f"
+   "id": "46429fc98b67a89f",
+   "metadata": {},
+   "source": [
+    "## Name category distribution"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "378147d2abc9ab24",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:42:00.759309Z",
      "start_time": "2025-09-28T21:42:00.246322Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "df_name_categories = build_category_distribution(df)\n",
     "df_name_categories.head(12)\n",
     "\n",
     "# save data\n",
     "df_name_categories.to_csv(\"../assets/identified_category_distribution.csv\", index=False)"
-   ],
-   "id": "378147d2abc9ab24",
-   "outputs": [],
-   "execution_count": 5
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "### Simple vs Compose (all provinces)",
-   "id": "3c99d846cb37c469"
+   "id": "3c99d846cb37c469",
+   "metadata": {},
+   "source": [
+    "### Simple vs Compose (all provinces)"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae30e79a975010d4",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:42:04.015038Z",
      "start_time": "2025-09-28T21:42:00.766470Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(1, 2, figsize=(10, 10))\n",
     "\n",
@@ -160,17 +179,14 @@
     "labels = [\"Simple\", \"Compose\"]\n",
     "values = [\n",
     "    len(df.query(\"identified_category == 'simple'\")),\n",
-    "    len(df.query(\"identified_category == 'compose'\"))\n",
+    "    len(df.query(\"identified_category == 'compose'\")),\n",
     "]\n",
     "axes[0].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
     "axes[0].set_title(\"Distribution by Category\")\n",
     "\n",
     "# Pie 2: Male vs Female\n",
     "labels = [\"Male\", \"Female\"]\n",
-    "values = [\n",
-    "    len(df.query(\"sex == 'm'\")),\n",
-    "    len(df.query(\"sex == 'f'\"))\n",
-    "]\n",
+    "values = [len(df.query(\"sex == 'm'\")), len(df.query(\"sex == 'f'\"))]\n",
     "axes[1].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
     "axes[1].set_title(\"Distribution by Sex\")\n",
     "\n",
@@ -184,46 +200,44 @@
     "plt.savefig(\"../assets/distribution_grid.svg\")\n",
     "\n",
     "plt.show()"
-   ],
-   "id": "ae30e79a975010d4",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 6
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "### Simple vs Compose by Province",
-   "id": "d31d8ae890cdfece"
+   "id": "d31d8ae890cdfece",
+   "metadata": {},
+   "source": [
+    "### Simple vs Compose by Province"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6d5c1abb55b7076a",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:42:04.514885Z",
      "start_time": "2025-09-28T21:42:04.038820Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKEAAAJOCAYAAABvBRRKAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAlKpJREFUeJzs3X98zfX///H72S+/xjRGM7OkzG+bnxFh8ys/SxRqo0Io9Lv5EWF+plIrUagQvUMbNr9/lKhILE1+hIxlfmTRGPtxzvn+4eN8O52NbZzOOXO7Xi7ncrHX6/l6vh7n7PHe+3LuPV+vl8FsNpsFAAAAAAAA2JGbowsAAAAAAABA0UcIBQAAAAAAALsjhAIAAAAAAIDdEUIBAAAAAADA7gihAAAAAAAAYHeEUAAAAAAAALA7QigAAAAAAADYHSEUAAAAAAAA7M7D0QUABWEymZSTkyM3NzcZDAZHlwMAAAAATslsNstkMsnDw0Nubqw/gXMghIJLycnJ0S+//OLoMgAAAADAJdStW1deXl6OLgOQRAgFF3Mtwa9du7Y8PT0dXA2QO5PJpJSUFFWuXJn/6gSnRI/C2dGjcAX0KZxddna29u3bR3/CqRBCwaVcuwTP3d1d7u7uDq4GyJ3BYJCbm5vc3d35P304JXoUzo4ehSugT+HsTCaTJHEbEzgV/lrCJfGHFM7MYDCoXLly9CmcFj0KZ0ePwhXQp3B29CacESuh4JL4gwpnZjAY5O3t7egygDzRo3B29ChcAX0KZ8d3JjgjVkLBJV1bWgo4I5PJpJMnT9KncFr0KJwdPQpXQJ/C2dGbcEashAIAO8jOznZ0CcB10aNwdvQoXAF9Cvy3jEYj/7tzQp6envm+ZzMhFAAAAAAAcFpms1mnTp3S+fPnHV0K8lC2bFndeeedN7wMlBAKAAAAAAA4rWsBVIUKFVSyZEnud+VEzGazMjIydObMGUmSv7//dccTQsEl8UcHzsxgMKhChQr0KZwWPQpnR4/CFdCncHZFpTeNRqMlgCpXrpyjy0EuSpQoIUk6c+aMKlSocN1L8wih4JKKyh9UFE0Gg8HyhxhwRvQonB09CldAn8LZFZXvTNfuAVWyZEkHV4Lrufb7yc7Ovm4IxdPxAACADbPZ7OgSAAAALIpKqFZU5ff3w0oouKQJq/brt7MZji4DAIqk+6uV07Nh1ZSamqqsrCxHlwMAAArBw4Ov+4Vx4cIFffjhh1q/fr3OnTunSpUq6bHHHlNkZKTc3G68juf7779XhQoVVK1atf+gWtdDV8IlJZ/L0IFTFx1dBgAUSXeVu7qcOisrS5mZmQ6uBgAAFIbJZHJ0CS7nr7/+0mOPPaYKFSpo0qRJqly5sn755RdNnDhRJ06c0Ouvv37DOfr3768FCxYQQuWBEAoAAAAAANz23nrrLXl5eWnevHkqVqyYJCkwMFDFixfX0KFD9cQTT6hq1aoOrtK1cU8oAAAAAABwW8vKylJCQoIef/xxSwB1TZs2bfTpp58qICBAhw8f1tNPP63Q0FDVrVtXffv21ZEjRyRJYWFhkqTIyEjFxMRIknbt2qUePXqoXr166tq1q9atW2c196effqqWLVuqQYMGio6OVkREhL766itJUmZmpt588021atVKISEhGjx4sFJTUyVJKSkpCg4O1gcffKDGjRtr1KhRatCggdavX2+ZOzs7W02bNtX3339vnw+tEAihAAAAAADAbe348ePKyMhQ3bp1bfYZDAbdd9998vDw0ODBgxUQEKAVK1boiy++kNFo1JtvvilJWrZsmSQpJiZGTz31lM6ePatnnnlGPXr00KpVqzRgwABFRUVp165dkqSVK1fqvffe06hRo/S///1PKSkp+vHHHy3nHTdunDZs2KBp06bpiy++UE5OjoYOHWp1qeXu3bu1fPlyDRo0SG3btrUKub777jt5eHioSZMmdvnMCoPL8QAAAAAAwG3t77//liSVLl06zzFXrlxR79691bdvX5UsefUemg8//LDmzp0rSfL19ZUk+fj4qFSpUvr444/VvHlzPfHEE5KkoKAg7d+/X5999pkaNWqkxYsXq1+/fnrwwQclSdOmTVOrVq0kXb1B+ooVK/Txxx/rvvvukyTNmDFDrVu31vbt2y2XBfbr109VqlSRJHXu3FkvvPCCMjMzVaxYMa1du1YdO3aUu7v7Lf2sbgYhFAAAAAAAuK2VLVtW0tXwJy8lS5ZUnz59FBcXp6SkJB09elS//vqrypcvn+v4o0ePasuWLQoNDbVsy87OtgRIBw8e1KBBgyz7fHx8LPuOHTsmk8mk+vXrW9VYtWpVHTlyxDIuICDAsv/++++Xl5eXvv32W7Vq1UobN27U7NmzC/hJ2BchFAAAAAAAuK1VqVJFpUuX1r59+1SvXj2b/UOGDNGjjz6q6dOn64477lBYWJi6dOmio0ePav78+bnOmZOTo65du2rw4MFW2z08rkYx7u7uMpvNVvuu/fzv+1JdYzQarS7H++c4Dw8PdejQQevWrZOnp6e8vb3VoEGDfLz7/w73hAIAAAAAALc1Dw8PderUSZ9//rmysrKs9m3evFmbN2/WiRMndObMGS1YsEADBgxQ8+bNdfLkSZsg6ZqqVasqOTlZQUFBltemTZu0atUqSdI999yjffv2WcZfvHhRycnJkq4+lc/Dw0OJiYmW/X/99ZeSk5Ov+4S+rl27auvWrdq8ebM6duwog8FQ2I/ELgihAAAAAADAbW/YsGG6ePGinn76ae3cuVPHjx/X0qVLFRUVpcjISNWtW1cZGRnauHGjUlJStHTpUpvQqmTJkvrtt9+Unp6uvn37KikpSe+8846OHTumVatW6e2331alSpUkSREREVqwYIHWr1+vI0eOaNSoUcrIyJDBYFCpUqXUq1cvTZw4UTt27NCBAwf0yiuv6M4779T999+f53to2LChSpQoodjYWHXu3Nnun1lBcTkeAAAAAAC47fn5+WnJkiWKiYnRyy+/rPPnz6tKlSoaPny4+vTpI3d3dz377LMaP368MjMzFRwcrLFjx2r06NE6ffq0KlasqIiICE2fPl3Hjx/XqFGjNHv2bM2YMUPz5s1TxYoVFRUVpW7dukm6eiPx5ORkjRs3TpmZmXrssccUEBAgT09PSdJrr72madOmafjw4crKylLz5s316aefysvLK8/3YDAY1LFjR23evFl16tT5Tz63gjCY81o3hv9cdna2Zs+erbi4OJ0+fVrly5dXhw4dNGzYMHl7eysqKkqSNHXqVKvjUlJSFB4erk2bNqly5cqKiopSbGysZb+Xl5cCAwP12GOPKTIy8rrL8YKDg7VgwQI1bdrUsm3r1q0aMmSIBg8erGHDhlnqmDJlitq0aaPu3bvrhRdesJnrww8/1KpVq7R69WpFRESoSZMmGjZsmNWYHTt2KDIyUgcPHszXZ2Q0GpWYmKiYRKP2nryYr2MAAAXTsXYFTe5RR8nJycrMzHR0OQAAoBA8PT2VlpamkJAQp3o6WkFduXJFv//+u6pWrarixYs7upxbaufOnQoMDJS/v7+kq/eQuu+++/TBBx9YfScvqJdeeklBQUEaPnz4rSr1hvL7e2IllBOZMWOGvvvuO0VHRyswMFAnTpzQpEmTlJycXOA72j/44IMaPXq0JCkjI0M//PCDpk6dqr///tsmCLqen3/+WSNGjFDfvn1tjjMYDOrUqZPWr1+fawi1Zs0adenSpUB1AwAAAABwO9i4caP27Nmj8ePHq1SpUlqwYIG8vb0VEhJSqPkSExO1b98+bdq0SfHx8be22FuEe0I5kdjYWI0YMULNmjVT5cqV1axZM73xxhvasmWLzpw5U6C5ihcvLj8/P/n5+SkoKEiPPfaYRo0apTlz5uj06dP5muPo0aMaNGiQOnbsqFGjRuU65trTAA4fPmxz7MGDB9W1a9cC1Q0AAAAAwO1g+PDhqlq1qp588kl1795dR48e1dy5c/N8Mt6NfPvtt5oxY4ZeeOEFVa5c+RZXe2sQQjkRg8GgH374wepxi6GhoUpISNAdd9xx0/N369ZNnp6e2rp16w3Hnj59WgMGDFDTpk0VHR2d5yV8tWrV0t13363169dbbV+zZo3q16+vwMDAm64bAAAAAICixtvbW9OnT9eOHTuUmJioTz75RPfcc0+h5xs2bJj27Nmjfv363cIqby1CKCcSGRmphQsXKiwsTOPGjdO6det05coV3XPPPZYbk92MYsWKqXLlyjarlv4tPT1dAwYM0Pnz5zV16tQbXj/cuXNnmxBq7dq1XIoHAAAAAAAsCKGcyLPPPqs333xTd955p7788ksNHz5cLVu21PLly2/ZOby9vXXp0qXrjhk3bpy8vLxkMpk0f/78G87ZtWtX7d+/XydOnJAkHTlyREeOHFGnTp1uSc0AAAAAAMD1EUI5mW7duumLL77Qd999pxkzZujee+/V6NGjlZSUJA8PD6tL9a659oDD/KyWunjxory9vXXy5EmFhoZaXmPHjrWM8fX11fz58zV8+HDNnj1bBw4cuO6cQUFBqlOnjmU11Jo1a9SsWTOVL1/eMiav2k0mkzw8uD8+AAAAAABFHSGUkzhw4ICmTp1q+fmOO+5Q165dtXDhQt1555364YcfVLp0aaWnp9sc+/fff0uSSpcufd1zZGZm6tixY7r33ntVoUIFxcXFWV4jRoywjBs5cqR8fHzUr18/Va9eXSNHjlROTs515+7atas2bNggKfen4uVVe3p6+g3rBgAAAAAAro8QykkYjUZ98skn+vXXX622e3l5qXjx4vL19VVwcLCSkpKUnZ1tNebnn3/WXXfdpZIlS173HKtWrZLBYFCbNm3k4eGhoKAgy6tcuXKWcdfuAeXu7q5Jkybp0KFDmjNnznXnfvDBB/XLL79o165dOnHihNq1a2e1Pzg4WImJiTbH/fzzz6pVq9Z15wYAAAAAAK6PEMpJ1K5dW61bt9bQoUO1atUqpaSkKDExUePGjVNWVpbat2+vdu3ayWAw6NVXX9WBAweUnJysuLg4vfvuu+rfv7/VfFeuXNHZs2d19uxZJScna9GiRZo8ebKGDBkiX1/ffNdVs2ZN9e/fXx9++KEOHjyY57iKFSuqYcOGio6OVuvWreXt7W21/5FHHtGRI0cUHR1tuWfUggULtHjxYqe+cz8AAAAAALg1uBmPE5k5c6Zmz56t999/XydPnlTJkiXVokULLVq0yBLqLFq0SG+++ab69++vjIwMValSRS+99JIeffRRq7nWrFmjNWvWSLp6M/KqVatqzJgx6tGjR4HrGjZsmNavX6+RI0fqyy+/zHNcly5d9Prrr+vZZ5+12XfnnXdq4cKFeuedd9S7d29lZ2erWrVqmj59ulq1alXgmgAAAAAAgGsxmK/d1RpwAUajUYmJiYpJNGrvyYuOLgcAiqSOtStoco86Sk5OVmZmpqPLAQAAheDp6am0tDSFhIRYbrniiq5cuaLff/9dVatWVfHixR1dDvKQ398Tl+MBAAAAAACXYjT9t+tp/uvzFVVcjgcAAAAAAFyKu5tBo2P36fc/L9n9XFXLl9Kkh2sX+Ljk5GRNmDBBu3fvlo+Pj5566ilFRkbqyJEjmjx5svbs2aNSpUrpscce09ChQ+Xm5qaYmBidOHFCpUuX1ldffaU77rhDEyZM0LFjxzRr1iyZTCYNHTpUkZGRkq4+BCw6Olpz5szRuXPnFBYWpgkTJqhUqVKSpD179mj69Onav3+/fH19NXDgQPXp00eSdPLkSY0ZM0Z79uxR8eLF1alTJ0VFRcnT01Nms1mzZs3SkiVLdOXKFTVq1Ehjx45VpUqVbuqzJIQCAAAAAAAu5/c/L+nAKee8TUtmZqaeeuop1a5dW19++aVOnDihl156ST4+Ppo8ebLCwsK0dOlS/f777xozZoy8vb0tDxxbvXq1BgwYoBUrVujtt9/W888/r0aNGmnhwoVau3atpk2bpi5dulgeOvbuu+8qOjpa5cqV06hRozR27Fi99dZbOnLkiPr166f+/ftr0qRJ+vnnnzV+/HiVL19e7dq108SJE1WyZEnFxcXp3LlzGj58uO6++249/vjjWrRokVatWqW33npL5cuX1/z58/XUU09p1apV8vT0LPTnwuV4AAAAAAAAt9C2bduUlpamyZMn695771VYWJjGjBmj8+fPq0SJEpo4caKqVaumtm3basSIEZo7d67l2DvuuEMjRoxQlSpV9PDDDys9PV2jR49WtWrV9PTTTysnJ0fJycmW8QMHDlTr1q1Vt25djR49WmvWrFF6erq+/PJL1apVSy+++KLuvvtuPfzww3riiScs5/rjjz9UunRpVapUSQ0aNNBHH31keXDY3Llz9eqrr6pp06aqVq2aJkyYoAsXLujbb7+9qc+FEAoAAAAAAOAWunaT7mtPupekRx55REePHlXt2rXl4fH/L0wLDQ3V2bNn9ffff0uSKleuLIPBIEmWm3wHBARY/ZyVlWU5vkGDBpZ/16lTR0ajUb///ruOHDmievXqWdUVGhqqI0eOSJIGDBigVatWqVmzZnrxxRd18uRJVa5cWZcuXdKpU6f0wgsvKDQ0VKGhoWrUqJHOnz+vY8eO3dTnwuV4AAAAAAAAt9A/Q6Z/KlasmM02k8kk6erT4PM61s0t7zVE/7w87tpcbm5ueZ7r2nm6deumZs2aaePGjfr66681fPhwDRw4UE8//bSkq5f5Va1a1ep4Hx+fPOvID1ZCAQAAAAAA3EJ33XWXkpOTdfnyZcu2adOmafHixdq3b5+ys7Mt2/fs2SNfX1+VLVu2UOfav3+/5d9JSUny9PRU1apVVbVqVf38889WY/fs2WMJlt555x2dO3dOffr00Zw5c/T8889r/fr1KlOmjMqVK6ezZ88qKChIQUFB8vf315tvvqnff/+9UDVeQwgFAAAAAABwC7Vo0ULly5fX2LFjdeTIEW3atElffPGFZs6cqaysLMv2jRs3KiYmRn369LFcgldQ7733nnbu3Kmff/5Z0dHRevjhh1WqVCn17dtX+/fv19tvv63ff/9dsbGxWrx4sR5//HFJ0tGjRzVhwgQdOHBAv/32m7755hvVqlVLktS/f3/NnDlTmzdv1rFjxzRmzBjt3r1bd9999019LlyOBwAAAAAAXE7V8qWc9jweHh6aNWuWJkyYoIcffljly5fXq6++qrZt26pSpUqaNGmSHnroIfn6+qpfv3565plnCl3fQw89pKioKP3999/q3LmzRo8eLUmqVKmS5syZo+nTp2v+/PmqVKmSoqKi9Mgjj0iS3njjDY0fP14RERHKyclR69atLcc+/fTTunTpksaOHauLFy+qTp06mjdv3k1fjmcwm83mm5oB+A8ZjUYlJiYqJtGovSed81GcAODqOtauoMk96ig5OVmZmZmOLgcAABSCp6en0tLSFBISInd3d0eXU2hXrlyx3OT72k25JcloMsvdrXArhwrjvz5ffgUHB2vBggVq2rSpQ+vI6/f0b1yOBwAAAAAAXMp/HQg5YwDlirgcDy4pqFxJZZkcXQUAFE0BZUtIkry8vBxcCQAAKKy8ns4GOBJdCZc0tmtNl15SCgDOzmw2y9/f39FlAACAQjIajTp16pSjy4CdHTx40NElFAiX48ElmUwsg4LzMplMSk1NpU/htPLTo4V9OgtwK/B3FK6APoWzozfhjAihAMAOsrKyHF0CcF30KJwdPQpXQJ8CQMEQQsEl8V/o4cwMBoN8fHzoUzgtehTOjh6FK6BP4ezoTTgj7gkFl8QfVDgzg8GgsmXLOroMIE/0KJwdPQpXQJ/C2fGdCc6IlVBwSVzfDGdmMpl07tw5+hROix6Fs6NH4QroUzg7ehPOiJVQcElubuSnyJ3ZbHb4f/Vxc3NTuXLlHFoDcD30KJwdPQpXQJ/C2fGdCc6IEAouacKq/frtbIajy4CTub9aOT0bVk2pqancKBQAAAC3NQ8Pvu47ox07digyMlIHDx685XMHBwdrwYIFatq06S2f+1ahK+GSks9l6MCpi44uA07mrnIlJV19Uk1mZqaDqwEAAAAch8vxnFNoaKi2bdvm6DIchvV5AAAAAADApZjNZpc8n5eXl/z8/G7JXK6IlVAAAAAAAMClGAyG/+w2HF5eXvL39y/wcQsWLNAnn3yiP//8U/fee69GjRolo9FouRwvJSVF4eHhmjNnjiZMmKC//vpLjzzyiB599FFFRUXp6NGjatq0qd566y15e3srKipK3t7eSklJ0Xfffae7775bY8eOVYMGDWzOnZWVpenTp2vVqlWSpJYtW2rMmDEOf6onK6EAAAAAAIDLuXYbDnu/ChN0/frrr5o+fbrGjRunNWvWqFGjRnr++edzvUzyo48+0qxZszRx4kQtXLhQzz33nF566SXNmzdPiYmJWrZsmWXsF198oXvuuUexsbFq3LixBg0apLS0NJs53377bSUlJenjjz/WggULdPHiRY0YMaLA7+NWI4QCAAAAAAC4hf744w8ZDAZVqlRJlStX1vPPP68333wz18v6hg4dqho1aqhLly4qV66cOnfurPvvv18NGzZUs2bNdPToUcvYe+65Ry+//LKqVaumkSNHysfHR6tXr7aa7/Lly1q0aJHGjx+vevXqKTg4WNOnT9fOnTvtckP0guByPAAAAAAAgFuoRYsWql69urp27apatWopPDxcvXr10rFjx2zGBgYGWv5dvHhxBQQEWP38z5VY/7z0zs3NTbVq1dKRI0es5jtx4oSys7PVu3dvq+0mk0nHjh1TcHDwzb69QiOEAgAAAAAAuIVKlCihpUuXaufOndqyZYu++uorLVmyRK+99prNWHd3d6uf3dzyvmjNw8M6xjEajTbjjUajJGnx4sUqWbKk1b5y5coV6H3calyOBwAAAAAAcAvt2bNHc+bM0X333aeRI0dq7dq1yszMtAmRCmr//v2WfxuNRh04cMBmZVNgYKDc3d11/vx5BQUFKSgoSN7e3poyZYrOnTt3U+e/WayEAgAAAAAAuIWKFy+uDz74QOXLl1ezZs30448/KiMjQ+fPn7+peXfu3Kn58+erdevWWrRokS5fvqyOHTtajfH29lavXr30xhtvaMKECSpXrpymTJmikydPqnLlyjd1/ptFCAUAAAAAAFyOl5eX056nZs2amjRpkmbNmqUJEyaoUqVKevPNN1W+fPmbqiUsLEw//PCDZs6cqVq1aumTTz5RmTJlbMZFRUVp2rRpGj58uLKzs9W4cWN99NFHNpf+/dcM5txuzQ44KaPRqMTERMUkGrX35EVHlwMn07F2BU3uUUfJycnKzMx0dDkAAACAw3h6eiotLU0hISEODx5uxpUrV/T777+ratWqKl68uGW72WyWwWD4z+r4r8+Xm6ioKEnS1KlTHVpHbvL6Pf0b94SyszNnzuj1119XixYtVK9ePXXu3Fnz5s1TTk6OJCklJUXBwcFWr9q1a6tFixaaOHGi1V3ww8LC9NVXX0mSduzYYXPctVdYWJjV3G+//bZNXTExMYqIiJAkRURE5DlXcHCwUlJSJEkZGRkKCQlR3759beb76quvLOfNTVRUVJ7zx8TEFPLTBQAAAADcjv7rQMjRAVRRweV4dpSamqrevXvr7rvv1syZM1WxYkX98ssvmjFjhn744QfNmTPHMnbp0qXy9/eXJGVmZmrnzp0aN26c7rjjDj333HN5nmPbtm022/6dcs+fP1/du3dXtWrVcp0jJiZG2dnZlrF79uyxCoZ8fX0lSZs3b5afn592796tEydOWD1GMj8efPBBjR492mb7v+/WDwAAAAAAih5CKDuaOHGiAgMDNXfuXEswFBgYqJCQEHXu3FlLlixRq1atJF0Nevz8/CzHVq5cWbt379bGjRuvG0L985i8VKhQQRMmTNBnn32W6/6yZcta/l2yZEl5enrmOm98fLzatm2r7du3Ky4uTsOGDbvhuf+pePHi+aoXAAAAAABYc8bL8AqKy/Hs5M8//9TmzZs1cOBAm5VJlSpVUo8ePfTll19edw4vL69bcu3uyJEj9eOPP2rlypWFnuPChQvatm2bGjVqpDZt2iguLk7cTgwAAAAAAOQXIZSd7Nu3T2azWXXr1s11f8OGDXXgwAGrez5dYzabtWPHDq1atUodOnS46Vpq1qypvn37avr06UpPTy/UHOvXr5e7u7uaN2+u8PBwpaSkaNeuXTddGwAAAAAAuD1wOZ6dXLhwQZJyfVTiP7dfG9elSxfLjc6ysrLk6+uryMhIPf3009c9T2hoqM22Z555RoMHD7ba9vzzz2vt2rV65513NHbs2IK9GUkJCQlq3ry5SpQoobp16+rOO+9UbGysGjdunO85Vq1apXXr1uU6d6VKlQpcEwAAAADg9sCVOM4tv78fQig78fHxkXT1srw777zTZv+ZM2esxn300UeqWLGiTp48qQkTJqhGjRoaPHjwDS/Hi4uLy/Pc/+Tt7a2RI0fq5ZdfVo8ePQr0Xs6ePaudO3dq4sSJkq4+FaBdu3b66quv9Prrr6tEiRL5micsLEwvv/yyzfYKFSoUqB4AAAAAwO3B09NT0tWntef3uyf+exkZGZL+/+8rL4RQdlK3bl25u7srKSkp1xAqKSlJwcHB8vLyknT1PlGVK1dWUFCQ5syZo+7du2vatGkaM2bMdc8TFBSU75o6d+6sZcuW6Y033lDLli3zfdyaNWtkNBr1+uuv6/XXX5d0NeU0mUzasGGDunXrlq95SpUqVaB6AQAAAAC3N3d3d5UtW9aykKNkyZKWq4jgeGazWRkZGTpz5ozKli17w4U0hFB24uvrq7Zt22rWrFlq06aN1S8iNTVVy5Yt06uvvprrsVWqVNGwYcM0ffp0de3aVfXr179ldY0dO1Zdu3ZVWlqaAgIC8nXM6tWr1axZM40aNcpq+7PPPqu4uLh8h1AAAAAAABTUtYUd14IoOJ+yZcvmugDn3wih7Gj06NHq27evBg4cqKFDh6pSpUrat2+fpk+friZNmqhv3746efJkrsdGRkZq+fLlmjBhgpYuXSo3t9zvIX/27Nlct5crVy7X7VWrVtWAAQP04Ycf5iuESklJ0Z49e/Tuu++qevXqVvsee+wxvfXWWzp9+rQk6cqVK9q6davVGB8fH0uIduXKlVzr9fLyyvUSQgAAAAAADAaD/P39VaFCBWVnZzu6HPyLp6fnDVdAXUMIZUcVK1bUl19+qVmzZunll19WWlqaAgMD1bt3b/Xr1y/PYEmSPDw8NGbMGPXv31/Lly9Xr169ch3XokWLXLd/8803ec49ePBgxcfH5+s9rF69WnfccYfCwsJs9vXo0UPvvvuuVqxYofLly+vcuXMaOHCg1ZgGDRpoyZIlkq5e1rdmzRqbeZo1a6ZPP/00X/UAAAAAAG5P7u7u+Q474JwMZm4xDxdiNBqVmJiomESj9p686Ohy4GQ61q6gyT3qKDk5WZmZmY4uBwAAAHAYT09PpaWlKSQkhOAGTiPvpTgAAAAAAADALUIIBQAAAAAAALsjhAIAAAAAAIDdEUIBAAAAAADA7gihAAAAAAAAYHeEUAAAAAAAALA7QigAAAAAAADYHSEUAAAAAAAA7I4QCgAAAAAAAHZHCAUAAAAAAAC7I4QCAAAAAACA3Xk4ugCgMILKlVSWydFVwNkElC0hSfLy8nJwJQAAAIBjeXjwdR/Oh66ESxrbtabc3d0dXQackNlslr+/v6PLAAAAABzKaDTq1KlTji4DsMLleHBJJhPLoJA7g8Hg6BJkMpl07tw5+hROix6Fs6NH4QroUzg7ehPOiJVQcElubuSncF5ubm4qV66co8sA8kSPwtnRo3AF9CmcHd+Z4IzoSrgks9ns6BKAPJnNZp0/f54+hdOiR+Hs6FG4AvoUzo7ehDMihIJL4g8qnJnZbNaFCxfoUzgtehTOjh6FK6BP4ezoTTgjQigAsAOe0AdnR4/C2dGjcAX0KQAUDPeEgkvi+mbnYjabneKG4M7Czc2NJ/TBqdGjcHb0KFwBfQpnx3cmOCNCKLikCav267ezGY4uA5Lur1ZOz4ZVU2pqqrKyshxdDgAAAABJHh583YfzoSvhkpLPZejAqYuOLgOS7ipXUpKUlZWlzMxMB1cDAAAAQJJMJpOjSwBssD4PAAAAAAAAdkcIBQAAAAAAALsjhAIAAAAAAIDdEUIBAAAAAADA7gihAAAAAAAAYHeEUAAAAAAAALA7QigAAAAAAADYHSEUAAAAAAAA7I4QCgAAAAAAAHZHCAUAAAAAAAC7I4Syo+DgYL300ks227/66iuFhYVZbTtz5oxef/11tWjRQvXq1VPnzp01b9485eTkWMakpKQoODjY6lW7dm21aNFCEydOVFZWltW5r71q1Kihhg0b6sknn9TevXuvW3NutZnNZr366qtq1KiR9u/fb5l/x44d+v777xUcHKxjx47lOl+HDh308ccfW2pPSUmxGRMVFaWoqKjr1gUAAAAAAFwbIZSdxcfH6/vvv7/umNTUVPXq1UspKSmaOXOmEhIS9Oyzz+rzzz/XkCFDZDKZrMYvXbpU27Zt07Zt27Ru3Tq9+OKL+vLLL/XRRx9ZjYuJidG2bdv0zTffaOHChapQoYL69eunQ4cOFeg9TJ8+XevXr9ecOXNUs2ZNq31NmjSRn5+f1q9fb3Pcr7/+quTkZHXp0qVA5wMAAAAAAEUPIZSdBQQEaMKECVarlP5t4sSJCgwM1Ny5c9WoUSMFBgaqU6dOWrRokXbt2qUlS5ZYjff19ZWfn5/8/PxUuXJl9ejRQ927d9fGjRutxvn4+MjPz08VK1ZUrVq1NHXqVNWqVUtvv/12vuufN2+eFi5cqPfff18NGza02e/u7q6OHTvmGkKtWbNGDRs2lL+/f77PBwAAAAAAiiZCKDt7/vnndfr0ac2bNy/X/X/++ac2b96sgQMHyt3d3WpfpUqV1KNHD3355Zc3PI+Xl5fN8f9mMBjUq1cvbdu2TVeuXLnhnHFxcXr77bf11ltvqUWLFnmO69q1q5KSkpSammq1fe3atayCAgAAAAAAkgih7K5ixYoaPny4Zs+erRMnTtjs37dvn8xms+rWrZvr8Q0bNtSBAwfyXEllNpu1Y8cOrVq1Sh06dLhhPffcc4+ys7PzvIfTNVu3btWYMWPUvXv3G85bv359Va5c2Wo11LVQqmPHjjesCQAAAAAAFH2EUP+BiIgIBQUFadKkSTb7Lly4IEkqU6ZMrsde237+/HnLti5duig0NFShoaGqU6eOXn75ZUVGRurpp5++YS2lS5eWJF26dCnPMX/99ZdGjBih0NBQxcfH6/fff7/hvJ07d9aGDRssP69Zs0YtWrTQHXfcccNjAQAAAABA0UcI9R9wd3fXG2+8oa+//jrX+zZJVy/Ly82ZM2ckSWXLlrVs++ijjxQXF6dZs2apSpUqatSokQYPHnzDy/Ek6eLFi5Ikb29vzZ492xJmhYaGateuXZKkjIwM9evXT5988omqVaumkSNH2twc/d+6dOmin376SefOnZNkeymep6enpKsrt/7NZDLJw8PjhrUDAAAAAADXRQj1H2nQoIEeeeQRTZo0SZcvX7Zsr1u3rtzd3ZWUlJTrcUlJSQoODpaXl5dlW6VKlRQUFKRmzZppzpw5+vrrrzVt2rR81XHw4EF5enrqrrvuUu/evRUXF2d51alTR5J055136vnnn5eHh4cmTpyovXv36tNPP73uvPfee6/uvfdebdy4UXv37lVaWprCw8Mt+729vSVJ6enpNsf+/ffflhVaAAAAAACgaCKE+g+9/PLLysjIsLpJua+vr9q2batZs2bJaDRajU9NTdWyZcv06KOP5jlnlSpVNGzYMC1atEg///zzDWv46quvFBYWpmLFiqls2bIKCgqyvIoXLy5JViuq6tSpo379+undd9+94WV5Xbp00aZNm7Rx40aFhYWpRIkSln2lSpVSYGCg9uzZY3WM0WjUvn37VKtWrRvWDgAAAAAAXBch1H/ojjvu0Msvv6w//vjDavvo0aN14cIFDRw4ULt27dLJkye1YcMGRUZGqkmTJurbt+91542MjFS1atU0YcIEq8vmLly4oLNnz+r06dPat2+fXnrpJe3bt0/PP/98geoePny4ypcvf8PL8jp37qydO3dq/fr16tq1q83+J554Qu+++64SEhKUkpKin3/+WS+++KK8vLzUrl27AtUEAAAAAABcCzfi+Y/17NlTy5cvt9zrSbr6BL0vv/xSs2bN0ssvv6y0tDQFBgaqd+/e6tevn9zcrp8Venh4aMyYMerfv7+WL1+uXr16SZKGDRsmSXJzc1O5cuXUoEEDffHFF7r77rsLVHOJEiU0fvx4Pf300/rss8/05JNP5jouICBANWrU0O+//67777/fZn///v3l7u6uWbNm6cSJEypZsqTuu+8+LVy40LIKCwAAAAAAFE0Gc253igaclNFoVGJiomISjdp78qKjy4GkjrUraHKPOkpOTlZmZqajywEAAACgqw+HSktLU0hISL4eYgX8F7gcDwAAAAAAAHZHCAUAAAAAAAC7I4QCAAAAAACA3RFCAQAAAAAAwO4IoQAAAAAAAGB3hFAAAAAAAACwO0IoAAAAAAAA2B0hFAAAAAAAAOyOEAoAAAAAAAB2RwgFAAAAAAAAu/NwdAFAYQSVK6ksk6OrgCQFlC0hSfLy8nJwJQAAAACu8fDg6z6cD10JlzS2a025u7s7ugz8H7PZLH9/f0eXAQAAAOD/GI1GnTp1ytFlAFa4HA/ATTMYDI4uAQAAAADg5Aih4JLMZrOjSwDyZDabdfnyZfoUTosehbOjR+EK6FM4O3oTzogQCi6JP6hwZmazWWfOnKFP4bToUTg7ehSugD6Fs6M34YwIoQAAAAAAAGB3hFAAAAAAAACwO0IoALCDkiVLOroE4LroUTg7ehSugD4FgILxcHQBQGG4uZGfwnm5ubnJz8/P0WUAeSqKPWo2m3lSZxFSFHsURQ99CmfHdyY4I0IouKQJq/brt7MZji4DAOAE7q9WTs+GVVNqaqqysrIcXQ4AAE7Bw4Ov+3A+dCVcUvK5DB04ddHRZQAAnMBd5a5eDpOVlaXMzEwHVwMAgHMwmUyOLgGwwfo8AAAAAAAA2B0hFAAAAAAAAOyOEAoAAAAAAAB2RwgFAAAAAAAAuyOEAgAAAAAAgN0RQgEAAAAAAMDuCKEAAAAAAABgd4RQAAAAAAAAsDtCKAAAAAAAANgdIZSTCQ4OVnBwsE6ePGmzb8mSJQoODlZMTIzNvrCwMLVp00Zms9lq+44dOxQcHKz//e9/NsdERUUpKirK6tw7duyQJH311VeWWv79ioiIsJo7PDw81/fywgsvWM15zYkTJxQcHKxXXnnlBp8GAAAAAAAoKjwcXQBseXp6avPmzXriiSestm/cuFEGg8Fm/J49e3TlyhVduXJFO3bs0H333Wcz5u2331a7du3k6+ub7zruvPNOLVu2LNf6/un06dM6dOiQqlevbtmWlZWlb7/9Ntd5V69erSpVqmjjxo26dOmSSpUqle+aAAAAAACAa2IllBNq1KiRNm/ebLXt4sWL2rNnj2rVqmUzPiEhQY0aNVLTpk0VFxeX65ylSpXSm2++WaA63N3d5efnZ/MqW7bsDev9/vvvdc899+Q6b3x8vJ544gl5enpq3bp1BaoJAAAAAAC4JkIoJxQeHq6dO3fq4sWLlm1ff/21GjVqZLNqyGQyae3atWrUqJHatGmjdevWKSMjw2bO0aNHKzY2Vj/99JNd6v13CLVp0ya1bdvWZuzhw4d16NAhNW3aVC1btlRsbOwtrwcAAAAAADgfQignVL16dVWsWFFbt261bNuwYUOuoc6OHTt09uxZtWnTRm3atNGVK1e0fv16m3Hh4eFq06aN3njjDeXk5NzSesPCwvTLL7/ozz//lHQ1GNu8eXOu9cbHxysgIEA1atRQeHi4fvzxR/3xxx+3tB4AAAAAAOB8CKGc1D9XF2VlZWn79u253gA8Pj5ewcHBCgwMlJ+fn0JCQvJcXTRmzBidOHFCn332Wb5qOHnypEJDQ21eK1eutBoXEBCg4OBgbdmyRZKUmJiosmXL6q677rKZc/Xq1QoLC5MktWrVSl5eXnleQggAAAAAAIoOQignFR4erm+//VY5OTn6/vvvVb16dZUrV85qTFZWls0Kqfbt22vHjh25Pl0vICBAQ4cO1fvvv69Tp07dsIYKFSooLi7O5nUtRPp3vZs2bZJ09Qbqua2C2rt3r5KTky37SpUqpebNm2vFihU3rAUAAAAAALg2no7npBo2bChJ+umnn7Rx40a1a9fOZsy3336rCxcu6MMPP9Ts2bMlSWazWWazWStWrNCQIUNsjnnyyScVFxenSZMm3fCpdB4eHgoKCspXveHh4Zo7d64uX76sTZs26e2337YZk5CQIEl66qmnLNtMJpPMZrN++ukny3sGAAAAAABFDyGUk/Lw8FCrVq20efNmbdmyRYMGDbIZs3r1at1999169913rbZPmDBBcXFxuYZQnp6eGjdunCIjIxUQEKAmTZrcknpr1aolX19fff7558rOzlbt2rWt9ptMJq1Zs0bdu3fXgAEDLNuNRqOeeOIJxcXFEUIBAAAAAFCEcTmeEwsPD9fSpUtVrlw5BQYGWu27fPmyNm/erJ49e6p69epWr8cff1zHjh3Tnj17cp23adOm6tat2w1vCG40GnX27Fmb17UbkP9bWFiYZs2aleu9q3bt2qXTp08rIiLCqtaaNWuqW7duWrNmjTIzM/P5yQAAAAAAAFdDCOXEWrRooZycnFzvr7R582ZlZ2froYcestnXtm1b+fn55XmDckl67bXXVKZMmeue/9SpU2rRooXN64EHHsh1fHh4uC5dupTnU/GCg4NVt25dm319+vRRenq6Nm7ceN16AAAAAACA6zKYzWazo4sA8stoNCoxMVExiUbtPXnR0eUAAJxAx9oVNLlHHSUnJ7OqFgCA/+Pp6am0tDSFhITI3d3d0eUAklgJBQAAAAAAgP8AIRQAAAAAAADsjhAKAAAAAAAAdkcIBQAAAAAAALsjhAIAAAAAAIDdEUIBAAAAAADA7gihAAAAAAAAYHeEUAAAAAAAALA7QigAAAAAAADYHSEUAAAAAAAA7I4QCgAAAAAAAHbn4egCgMIIKldSWSZHVwEAcAYBZUtIkry8vBxcCQAAzsPDg6/7cD50JVzS2K415e7u7ugyAABOwmw2y9/f39FlAADgNIxGo06dOuXoMgArXI4Hl2QysQwKzstkMik1NZU+hdMqij1qMBgcXQJuoaLYoyh66FM4O3oTzogQCgDsICsry9ElANdFj8LZ0aNwBfQpABQMIRQAAAAAAADsjhAKAAAAAAAAdkcIBZfEvT/gzAwGgwIDA+lTOC16FM6OHoUroE/h7OhNOCOejgeXdLv/QTWbzbf9Z+DMDAYDvx84NXoUzo4ehSugT+Hs6E84I0IouKQJq/brt7MZji7DIe6vVk7PhlVTamoqN8MEAAAAkCsPD77uw/nQlXBJyecydODURUeX4RB3lSsp6erTWDIzMx1cDQAAAABnZDKZHF0CYIN7QgEAAAAAAMDuCKEAAAAAAABgd4RQAAAAAAAAsDtCKAAAAAAAANgdIRQAAAAAAADsjhAKAAAAAAAAdkcIBQAAAAAAALsjhAIAAAAAAIDdEUIBAAAAAADA7gihAAAAAAAAYHeEUA4SHBysHTt2WG3bunWrateurZiYGElSVFSUoqKiJEkpKSkKDg7W22+/bTNXTEyMIiIiLD9fvHhRkyZN0gMPPKA6deqoffv2ev/995WVlWUZExERYTnPP+3YsUPBwcF5bp85c2au72f79u3q3bu36tevr4YNG2rAgAFKSkrKdWxERIRCQkJ08eLFXPcDAAAAAICihxDKSfz8888aMWKE+vbtq2HDhuU5bv78+Tpy5Mh154qKitL+/fs1c+ZMrV27Vq+++qq+/PJLTZs2rdD1JSQkqEqVKlq5cqXMZrPVvqSkJA0dOlRdu3bVypUrtWTJElWqVEmRkZFKSUmxGnv69Gnt2bNHvr6+WrduXaHrAQAAAAAAroUQygkcPXpUgwYNUseOHTVq1Kjrjq1QoYImTJiQ5/709HRt3LhRo0ePVoMGDVS5cmW1bdtWL730kpYtWyaTyVTg+rKzs7Vu3ToNGTJEqamp2rlzp9X+VatW6f7779fjjz+uoKAgVa9eXePHj5efn59Wr15tNXb16tWqXr26wsLCFBcXV+BaAAAAAACAayKEcrDTp09rwIABatq0qaKjo2UwGK47fuTIkfrxxx+1cuXKXPcbDAYZDAZ99913Vtvbt2+v2NjYG86fm+3btys9PV3h4eGqX7++TXjk5uamgwcP6ty5c1Z1zJ8/X48++qjV2Pj4eDVu3Fht2rTRjz/+aLNSCgAAAAAAFE2EUA6Unp6uAQMG6Pz585o6darc3d1veEzNmjXVt29fTZ8+Xenp6Tb7vb299fDDD2v69Onq2LGjpkyZom+++Ubu7u66++67CxVCJSQkqEGDBvLx8VF4eLjWrl2rjIwMy/6ePXsqLS1Nbdq00ZAhQ7Rw4UIdP35cAQEBKlu2rGXc8ePHlZSUpDZt2qhJkyby9vZmNRQAAAAAALcJQigHGjdunLy8vGQymTR//vx8H/f8889Lkt55551c90dHR2vMmDEqXry4Pv30Uw0aNEht2rTR119/bTVuzpw5Cg0NtXoNHDjQasyVK1e0adMmtW3bVtLVFVUZGRlav369ZUy1atW0dOlStW/fXj/++KOio6PVrl07jRgxQpcvX7aMi4+PV9myZdW4cWN5enqqdevWWrFiRb7fNwAAAAAAcF2EUA7k6+ur+fPna/jw4Zo9e7YOHDiQr+O8vb01cuRILVmyJNcn0Lm5uSkiIkJxcXHaunWroqOj5ePjo+HDh+v06dOWcb1791ZcXJzVKzo62mquLVu26NKlSwoPD5ckyz2f/r2C6Z577tGMGTP0ww8/aNGiRerTp482bNigt956yzImISFBrVu3tqz4at++vY4fP65du3bl630DAAAAAADX5eHoAm5nI0eOlI+Pj/r166f4+HiNHDlSS5culYfHjX8tnTt31rJly/TGG2+oZcuWlu07duzQ7t27NWTIEElSxYoV1atXL7Vr106tWrXS7t279eCDD0qSfHx8FBQUZDXvqVOnrH5OSEiQJHXo0MGyzWQy6fDhw0pNTZW/v7+mTZum7t27q0aNGvLw8FDjxo3VuHFjeXt7a8uWLZKkAwcO6PDhwzp69KhWrVpldY64uDg1atQovx8bAAAAAABwQayEcqBrK4Lc3d01adIkHTp0SHPmzMn38WPHjtWBAwesLmm7cOGCZs2apbNnz1qNLVmypNzd3eXr65vv+S9evKitW7dq0KBBVqulFixYIEmW827btk3Lly+3Ob5MmTKW861evVplypRRbGys1VydO3fWmjVrdOXKlXzXBQAAAAAAXA8hlJOoWbOm+vfvrw8//FAHDx7M1zFVq1bVgAED9Mcff1i2tWnTRtWqVdOTTz6pzZs3KyUlRbt27dJLL72kqlWrqnHjxvmuacOGDTIajYqMjFT16tUtryZNmqhly5aKjY2VJA0dOlSLFi3SjBkzdPDgQR09elTLli3T3Llz1b9/f0lXV1R17dpVNWrUsJqrf//+unjxojZu3Jj/DwsAAAAAALgcQignMmzYMPn7+2vkyJHKycnJ1zGDBw9WYGCg5WdPT099+umnatKkiSZOnKiOHTtqxIgR8vX11bx58+Tmlv9feXx8vB544AH5+fnZ7OvTp4+OHTumxMREPfjgg3r//fe1Z88e9e3bVw899JD+97//afLkyQoPD1diYqJSUlLUs2dPm3nq1aun2rVrWwItAAAAAABQNBnMZrPZ0UUA+WU0GpWYmKiYRKP2nrzo6HIcomPtCprco46Sk5OVmZnp6HIAAAAAOCFPT0+lpaUpJCTEcisYwNFYCQUAAAAAAAC7I4QCAAAAAACA3RFCAQAAAAAAwO4IoQAAAAAAAGB3hFAAAAAAAACwO0IoAAAAAAAA2B0hFAAAAAAAAOyOEAoAAAAAAAB2RwgFAAAAAAAAuyOEAgAAAAAAgN0RQgEAAAAAAMDuPBxdAFAYQeVKKsvk6CocI6BsCUmSl5eXgysBAAAA4Kw8PPi6D+dDV8Ilje1aU+7u7o4uw2HMZrP8/f0dXQYAAAAAJ2U0GnXq1ClHlwFY4XI8uCST6TZdBvV/DAaDo0vAdZhMJqWmpt72fQrnRY/C2dGjcAX0KZwdvQlnRAgFAHaQlZXl6BKA66JH4ezoUbgC+hQACoYQCi6JlUBwZgaDQRUqVKBP4bToUTg7ehSugD6Fs6M34Yy4JxRcEn9Q4cwMBoNKlCjh6DKAPNGjcHb0KFwBfQpnx3cmOCNWQsElcX0znJnJZNKpU6foUzgtehTOjh6FK6BP4ezoTTgjVkLBJbm5kZ/Cfsxm8039lyM3Nzfdeeedt7Ai4NaiR+Hs6FG4AvoUzo7vTHBGhFBwSRNW7ddvZzMcXQaKoPurldOzYdWUmprKzUYBAADgsjw8+LoP50NXwiUln8vQgVMXHV0GiqC7ypWUdPVpN5mZmQ6uBgAAACgcLseDM2J9HgAAAAAAAOyOEAoAAAAAAAB2RwgFAAAAAAAAuyOEAgAAAAAAgN0RQgEAAAAAAMDuCKEAAAAAAABgd4RQAAAAAAAAsDtCKAAAAAAAANgdIRQAAAAAAADsjhAKAAAAAAAAdkcI5WSCg4O1Y8cOq21bt25V7dq1FRMTY7V9x44dCg4O1syZM2225fWKioqyjI2JiVFwcLC+//57mzrCwsLUp08fmc3mXM/5Tzk5OZo3b566deumkJAQNWrUSAMGDNBPP/2U63u83nkBAAAAAEDRRAjl5H7++WeNGDFCffv21bBhw6z2JSQkqEqVKlq5cqUlLAoNDdW2bdssL+lq6HPt59GjR1uOj4+PV5UqVRQXF5fruXfv3q3ly5dftz6TyaRnnnlG8+bN09NPP61Vq1bp888/V/Xq1dW/f3/t2bPH5pgbnRcAAAAAABQ9hQ6h0tPT9fnnnys6OlppaWnasmWLjh8/fitru+0dPXpUgwYNUseOHTVq1CirfdnZ2Vq3bp2GDBmi1NRU7dy5U5Lk5eUlPz8/y0uSfHx8LD+XLl1akrRv3z4dP35cQ4YM0fr163Xp0iWb8wcEBGjGjBk6f/58njUuWbJEP/30k/73v/+pe/fuCgwMVHBwsF599VV169ZNc+bMsRqfn/MCAAAAAICip1Ah1KFDh9S+fXstX75cX3zxhS5duqT169ere/fuljAEN+f06dMaMGCAmjZtqujoaBkMBqv927dvV3p6usLDw1W/fv0CryqKj49XjRo11KFDB2VnZ2v9+vU2Y55++mkVL15cM2bMyHOe5cuXq0ePHgoMDLTZ99JLL9kcm5/zAgAAAACAoqdQIVR0dLT69Omjr776Sp6enpKkKVOmqG/fvpo+ffotLfB2lJ6ergEDBuj8+fOaOnWq3N3dbcYkJCSoQYMG8vHxUXh4uNauXauMjIx8zW82m7VmzRq1adNGpUqVUrNmzRQbG2szrkSJEho9erSWLVuW62V1WVlZ+vXXX9WoUaNcz+Pr6ytvb+8CnxcAAAAAABQ9hQqhfvnlFz300EM223v37q3Dhw/fbE23vXHjxsnLy0smk0nz58+32X/lyhVt2rRJbdu2lSS1b99eGRkZ+V5V9NNPPyk1NdXq+J07d+qPP/6wGduuXTu1atVKb7zxhoxGo9W+8+fPy2w2y8fHx7Lt999/V2hoqNWrMOcFAAAAAABFS6FCKF9fX/3+++8223fv3q1y5crddFG3O19fX82fP1/Dhw/X7NmzdeDAAav9W7Zs0aVLlxQeHi5JCgoKUvXq1fN9SV5CQoICAgJUq1YtSVJ4eLgMBoNWrFiR6/gxY8bo2LFjWrhwodX2a+HT33//bdlWuXJlxcXFKS4uTuPHj7danVXQ8wIAAAAAgKKjUCHUwIEDNWbMGH3++ecym8364Ycf9N5772n8+PF68sknb3WNt52RI0fKx8dH/fr1U/Xq1TVy5Ejl5ORY9ickJEiSOnTooFq1aqlWrVr67bfftGPHDqWmpl53bqPRqLVr1+rkyZOWY1u0aCGTyZRnGBQYGKhnnnlG7733ns6cOWPZXqxYMQUHB1tdqufp6amgoCAFBQWpYsWKN3VeAAAAAABQdHgU5qDevXurQoUKmjdvnooXL67p06eratWqio6OVqdOnW51jbeda/eAcnd316RJk9SzZ0/NmTNHzz77rC5evKitW7dq0KBB6tq1q+WY8+fPq1+/flqxYoUGDx6c59zff/+90tLSFBMTo7vuusuyffv27Zo6dap2796tBg0a2Bw3YMAArVy5Uu+8847V9scee0zvvPOOnnzySfn7+1vtO3369E2fFwAAAAAAFA2FCqEk6YEHHlC9evVUvnx5SdKePXtUu3btW1YYrqpZs6b69++vDz/8UG3bttWvv/4qo9GoyMhI+fn5WY1t2bKlYmNjrxtCJSQk6N5771X79u2ttgcFBWn27NmKi4vLNQzy8vLSuHHj1L9/f6vtffr00XfffafevXvr+eefV4MGDXT58mWtWrVKn332mRo2bHhT5wUAAAAAAEVDoS7H279/v8LDw61umv3yyy+rY8eO+u23325Zcbhq2LBh8vf318iRI7VixQo98MADNgGUdDUQOnbsmBITE3OdJysrSxs2bFDPnj1t9hUrVkw9evTQmjVrlJmZmevxzZo1U5cuXay2ubm56f3339fQoUO1ePFiPfTQQ+rbt68SExM1ceJELVy48KbPCwAAAAAAXJ/BbDabC3pQnz59VLt2bb322mvy9PSUJJlMJk2ePFkHDx60uYE1cKsYjUYlJiYqJtGovScvOrocFEEda1fQ5B51lJycTDAKAAAAl+Xp6am0tDSFhIRYbvkCOFqhV0L169fPEkBJV1fEREZGKikp6ZYVBwAAAAAAgKKhUCGUv7+/vv/+e5vtu3fvttwjCgAAAAAAALimUDcmHzx4sEaPHq09e/aoTp06kqQDBw5o5cqVGjdu3C0tEAAAAAAAAK6vUCFU9+7d5evrqy+//FJLliyRh4eHgoKCNG/ePDVq1OhW1wgAAAAAAAAXV6gQSpJatmypli1b3spaAAAAAAAAUEQVKoTKzs5WXFycfvnlF+Xk5OjfD9ibMmXKLSkOAAAAAAAARUOhbkw+evRoTZo0SX/99ZdNAAUAAAAAAAD8W6FWQm3YsEEffPCB7r///ltdDwAAAAAAAIqgQq2EKl26tCpWrHirawEAAAAAAEARVagQasiQIZo0aZKOHDminJycW10TAAAAAAAAiphCXY738ccf68yZM+rSpUuu+/fv339TRQE3ElSupLJMjq4CRVFA2RKSJC8vLwdXAgAAABSeh0ehvu4DdlWorpw6deqtrgMokLFda8rd3d3RZaCIMpvN8vf3d3QZAAAAQKEZjUadOnXK0WUAVgoVQjVp0uRW1wEUiMlkIoSC3RgMhps63mQy6fTp06pYsaLc3Ap11TNgV/QonB09CldAn8LZmUxcOgLnk+8QKjw8XMuWLdMdd9yhsLCw635J27Rp0y0pDsjLzYYEgD0ZDAaVKFGCPoXTokfh7OhRuAL6FM6O3oQzyncI9dxzz6lUqVKSpGHDhtmtICA/+IMKZ2YwGFS2bFlHlwHkiR6Fs6NH4QroUzg7vjPBGeU7hHr44Yf//0EeHgoPD1fJkiXtUhRwI1yOB2dmMpl09uxZ+fn5sTwfTokehbOjR+EK6FM4Oy7HgzMq1F/LGTNmqHnz5ho+fLjWr1+vzMzMW10XALi0K1euOLoE4LroUTg7ehSugD4FgIIpVAj1zTff6JNPPlFAQICmTZumZs2a6eWXX9bmzZuVnZ19q2sEAJfj5eXl6BKA66JH4ezoUbgC+hQACsZgNpvNNzvJvn37tG7dOn3++efy8PDQjh07bkVtgA2j0ajExESFhITc1pfjmc1mrvEGAAAAkCe+O8EZ5fueULnJyMjQ119/rfXr12vbtm2qWLGiOnXqdKtqA/I0YdV+/XY2w9FlOMT91crp2bBqSk1NVVZWlqPLAQAAAOCEPDxu6us+YBeF6srY2FitX79e3333ncqXL69OnTpp0aJFqlGjxq2uD8hV8rkMHTh10dFlOMRd5a4+ECArK4v7sQEAAADIFTcmhzMqVAj1zjvvqGPHjlqwYIHq169/q2sCAAAAAABAEVOoEOqbb76RwWDQ5cuXdeDAAZlMJlWpUkXe3t63uj4AAAAAAAAUAYUKoXJycvTmm29q8eLFysnJuTqRh4e6du2q8ePH85QIAAAAAAAAWHErzEHTpk3Tli1b9OGHH2rXrl3auXOnPvjgA+3atUvvvPPOra4RAAAAAAAALq5QK6Hi4+P17rvvqmnTppZtrVq1UrFixfTyyy/rtddeu2UFAgAAAAAAwPUVaiWU2WxWuXLlbLb7+vrq0qVLN10UAAAAAAAAipZChVD33XefZsyYoYsXL1q2/f3333r77betVkcBAAAAAAAAUiEvxxs1apQiIyPVsmVLVa1aVZL0+++/KzAwUB9++OEtLRAAAAAAAACur1AhVOnSpRUfH6+tW7fq6NGjKlasmKpWrar7779fbm6FWlwFAAAAAACAIqxQiVGXLl3022+/KTw8XAMHDrSsinJ0ABUcHKwdO3ZYbdu6datq166tmJgYq+07duxQcHCwZs6cmetc27dvV+/evVW/fn01bNhQAwYMUFJSUq5jIyIiFBISYnV54jVhYWH66quvrlt3VlaWZs2apQ4dOqhu3bpq06aNoqOjlZaWZjNXcHCw5VWjRg01adJEQ4YMUWpqqmVcVFSUoqKirD6XvF4pKSmWufv06SOz2Zzr5yRJMTEx153rn+8zr88kJSXF6rwAAAAAAOD2UKjUyM3NTdnZ2be6llvu559/1ogRI9S3b18NGzbMal9CQoKqVKmilStX2gQvSUlJGjp0qLp27aqVK1dqyZIlqlSpkiIjI23Ck9OnT2vPnj3y9fXVunXrClxjTk6OnnnmGcXGxurFF1/U2rVrNWXKFB04cEC9evXS6dOnrcaPGjVK27Zt07Zt2/TNN9/onXfe0W+//XbDJxLGxMRYjvvny9/f3zJm9+7dWr58eZ5zPPXUU5bjroV6/5yrU6dOt+QzAQAAAAAARU+hLsdr3bq1nnzySbVp00YBAQHy8vKy2v/cc8/dkuJuxtGjRzVo0CB17NhRo0aNstqXnZ2tdevW6bXXXtPo0aO1c+dOqxuqr1q1Svfff78ef/xxy7bx48drx44dWr16tQYNGmTZvnr1alWvXl0NGjRQXFycHnnkkQLVuWjRIu3fv1+rVq2Sn5+fJCkgIEANGjRQnz59NGnSJL333nuW8aVLl7aMk6SKFStq+PDheuWVV5Senq7SpUvneh4fHx+r43ITEBCgGTNmqG3btipbtqzN/lKlSqlUqVKW+STlOufNfiYAAAAAAKDoKdRKqIMHD6p27do6c+aM9uzZox07dlheO3fuvNU1Ftjp06c1YMAANW3aVNHR0TIYDFb7t2/frvT0dIWHh6t+/fqKi4uz2u/m5qaDBw/q3Llzlm0Gg0Hz58/Xo48+ajU2Pj5ejRs3Vps2bfTjjz8W+DKzpUuXqkePHjZhjpeXlwYNGqSNGzfqr7/+uu4c10LAm70c8umnn1bx4sU1Y8aMm5rnZj8TAAAAAABQ9BQotVixYoWee+45lS9fXo899pgWLlxo81qwYIG9as2X9PR0DRgwQOfPn9fUqVPl7u5uMyYhIUENGjSQj4+PwsPDtXbtWmVkZFj29+zZU2lpaWrTpo2GDBmihQsX6vjx4woICLBaIXT8+HElJSWpTZs2atKkiby9vW0CrevJyMjQkSNHVLdu3Vz3N2zYUEajUfv27ctzjuPHj+ujjz5Sy5YtLauUCqtEiRIaPXq0li1bpj179hRqjpv9TAAAAAAAQNGU7xDqs88+06hRo3TlyhVdvnxZI0eO1Ntvv23P2gpl3Lhx8vLykslk0vz58232X7lyRZs2bVLbtm0lSe3bt1dGRobWr19vGVOtWjUtXbpU7du3148//qjo6Gi1a9dOI0aM0OXLly3j4uPjVbZsWTVu3Fienp5q3bq1VqxYke9a09PTZTabLZe2/VuZMmUkSefPn7d6f6GhoQoNDVXdunX10EMPqVq1anrzzTeve66BAwdajrv2GjBggM24du3aqVWrVnrjjTdkNBrz/V6uudnPBAAAAAAAFE35vifUF198oUmTJumhhx6SJK1fv14jR47UCy+8YHO5myP5+vpq/vz5Wr58ud5++221bdtWNWrUsOzfsmWLLl26pPDwcElSUFCQqlevrri4OMt7k6R77rlHM2bMUE5Ojvbs2aOEhAR9+eWX8vPz05gxYyRdXVHVunVry2qr9u3ba9WqVdq1a5caNWpkU1vnzp118uRJSVKlSpUsNwE/e/Zsru/lzJkzkmS1+mr48OFq3769Ll26pJiYGP3xxx966aWXdMcdd1z3c4mOjlb9+vWtthUvXjzXsWPGjFGXLl20cOFC1axZ87rz/ltBPxMAAAAAAHB7yHcIdeLECTVr1szyc1hYmC5fvqwzZ86oYsWKdimuMEaOHCkfHx/169dP8fHxGjlypJYuXSoPj6tvNSEhQZLUoUMHyzEmk0mHDx9Wamqq/P39NW3aNHXv3l01atSQh4eHGjdurMaNG8vb21tbtmyRJB04cECHDx/W0aNHtWrVKqsa4uLicg1cPvroI+Xk5EiSPDw8VLx4cVWvXl379u1T9+7dbcYnJSXJ3d1dtWrVsmwrV66cgoKCJEnvvvuuevbsqaFDh+p///ufPD098/xcKlasaDnuRgIDA/XMM8/ovffe0/jx4/N1jFS4zwQAAAAAANwe8n05Xk5OjiXIka6GKMWKFVNWVpZdCiusaytw3N3dNWnSJB06dEhz5syRJF28eFFbt27VoEGDFBcXZ3ldu4/VtcvGtm3bZlml9E9lypSRr6+vpKtPgCtTpoxiY2Ot5urcubPWrFmjK1eu2BwfEBCgoKAgBQUFKSAgQJLUu3dvLV26VKdPn7Yam5OTow8//FBt27a1nPPfvLy8FB0drf379+vTTz8txKeVtwEDBqhChQp655138n1MYT4TAAAAAABwe8j3SihXVLNmTfXv398S5vz6668yGo2KjIy0eRpdy5YtFRsbq8GDB2vo0KF68cUXVaxYMXXt2lWenp7avXu35s6dqylTpki6uqKqa9euVpf6SVL//v2VkJCgjRs3qkuXLjessU+fPvrmm28UERGhV155RbVr19bJkyf1wQcfKD09XaNHj77u8fXq1VPPnj01a9YsdevWLc9VaRcuXMj1sr8yZcqoWLFiNtu9vLw0btw49e/f/4bv4Zr8fCYhISGSpB9//FFHjx61GteyZUunurQTAAAAAADcOgUKodasWSNvb2/LzyaTSRs2bLBZqfPPeys52rBhwyz3rypTpoweeOABmwBKuhoGDR48WImJiXrwwQfl5eWl+fPna8mSJcrOzlZwcLAmT56s8PBwJSYmKiUlRT179rSZp169eqpdu7ZiY2PzFUK5ublp1qxZ+vTTTzVz5kydOHFCvr6+atu2rd566608V0H90wsvvKB169bpzTff1IwZM/L8HHIzffr0XC8FlKRmzZqpS5cuio+Pv2EN+f1MroVQUVFRNuP27dtntdoOAAAAAAAUHQaz2WzOz8CwsLD8TWgwaNOmTTdVFJAXo9GoxMRExSQatffkRUeX4xAda1fQ5B51lJycrMzMTEeXAwAAAMAJeXp6Ki0tTSEhIZbb1gCOlu9lJ5s3b7ZnHQAAAAAAACjC8n1jcgAAAAAAAKCwCKEAAAAAAABgd4RQAAAAAAAAsDtCKAAAAAAAANgdIRQAAAAAAADsjhAKAAAAAAAAdkcIBQAAAAAAALsjhAIAAAAAAIDdEUIBAAAAAADA7gihAAAAAAAAYHceji4AKIygciWVZXJ0FY4RULaEJMnLy8vBlQAAAABwVh4efN2H86Er4ZLGdq0pd3d3R5fhMGazWf7+/o4uAwAAAICTMhqNOnXqlKPLAKxwOR7gggwGg6NLAAAAAACgQAih4JLMZrOjSwDyZDabdf78efoUTosehbOjR+EK6FM4O3oTzogQCi6JP6hwZmazWRcuXKBP4bToUTg7ehSugD6Fs6M34YwIoQAAAAAAAGB3hFAAYAc8vRDOjh6Fs6NH4QroUwAoGJ6OB5fk5kZ+Cufl5uZWpJ5eaDabuRl+EVPUehRFDz0KV0CfwtnxnQnOiBAKLmnCqv367WyGo8sAirz7q5XTs2HVlJqaqqysLEeXAwAAgHzy8ODrPpwPXQmXlHwuQwdOXXR0GUCRd1e5kpKkrKwsZWZmOrgaAAAA5JfJZHJ0CYAN1ucBAAAAAADA7gihAAAAAAAAYHeEUAAAAAAAALA7QigAAAAAAADYHSEUAAAAAAAA7I4QCgAAAAAAAHZHCAUAAAAAAAC7I4QCAAAAAACA3RFCAQAAAAAAwO4IoQAAAAAAAGB3Dg2hgoODtWPHDqttW7duVe3atRUTE2O1fceOHQoODtbMmTNznWv79u3q3bu36tevr4YNG2rAgAFKSkrKdWxERIRCQkJ08eJFm31hYWH66quvrlt3VlaWZs2apQ4dOqhu3bpq06aNoqOjlZaWZjP2woULmjp1qsLCwlS/fn09+OCD+vTTT2UymazGZWZm6v3331eHDh1Ur149tW3bVu+9956uXLliGRMTE6OIiAirWoODgxUcHKwaNWooNDRUvXv31rfffmvzfq+N+/fr2nuNiopS48aNde7cOZv3cO33dO13kNcrKirKqtbg4GB9//33hfqMAQAAAABA0eLh6AL+6eeff9aIESPUt29fDRs2zGpfQkKCqlSpopUrV2rEiBEyGAyWfUlJSRo6dKheffVVTZs2TZmZmVq0aJEiIyO1cuVKVa5c2TL29OnT2rNnjypUqKB169bpkUceKVCNOTk5euaZZ5SSkqKXX35ZderU0YkTJ/T++++rV69eWrx4sSpWrChJ+uuvv/TYY4+pQoUKmjRpkipXrqxffvlFEydO1IkTJ/T6669LuhpqRUZG6vLlyxo5cqSqVaumI0eOaNKkSfr11181e/bsPOsZNWqUOnXqJJPJpAsXLiguLk7PPPOM5s6dq+bNm1vGPfXUU3rqqadsji9durTl33///bemTZum6dOn53qu0NBQbdu2zfJzixYtFBMTo9DQUElS8eLFLfvi4+NVpUoVxcXFqVmzZvn5aAEAAAAAQBHmNJfjHT16VIMGDVLHjh01atQoq33Z2dlat26dhgwZotTUVO3cudNq/6pVq3T//ffr8ccfV1BQkKpXr67x48fLz89Pq1evthq7evVqVa9eXWFhYYqLiytwnYsWLdL+/fu1ePFidejQQQEBAbrvvvs0f/58lS1bVpMmTbKMfeutt+Tl5aV58+apWbNmCgwMVKdOnTRp0iR9/vnn+v333yVJ8+bN04kTJ7RgwQK1bt1agYGBat26tWJiYvT1119r+/btedZTunRp+fn5qWLFiqpevbpeffVVde7cWVOmTLEaV7JkSfn5+dm8/hkcBQQEaMWKFTaf7zVeXl5Wx0qSj4+P5edrgda+fft0/PhxDRkyROvXr9elS5cK/DkDAAAAAICixSlCqNOnT2vAgAFq2rSpoqOjrVY5SVcvtUtPT1d4eLjq169vEx65ubnp4MGDVpeSGQwGzZ8/X48++qjV2Pj4eDVu3Fht2rTRjz/+qJSUlALVunTpUvXo0cMSwlzj5eWlQYMGaePGjfrrr7+UlZWlhIQEPf744ypWrJjV2DZt2ujTTz9VQECAJCk2NlY9evRQ2bJlrcbVqFFDixYtUkhISIFqfOyxx3To0CElJycX6LgmTZqoXbt2Gj9+vLKzswt07D/Fx8erRo0a6tChg7Kzs7V+/fpCzwUAAAAAAIoGh4dQ6enpGjBggM6fP6+pU6fK3d3dZkxCQoIaNGggHx8fhYeHa+3atcrIyLDs79mzp9LS0tSmTRsNGTJECxcu1PHjxxUQEGAV7Bw/flxJSUlq06aNmjRpIm9v7wKthsrIyNCRI0dUt27dXPc3bNhQRqPRshIoIyMj17EGg0H33XefvLy8dPnyZSUnJ+c5Z6NGjVSqVKl81yhJ1apVkyQdPny4QMdJ0ujRo3Xy5El98sknBT5Wksxms9asWaM2bdqoVKlSatasmWJjYws1FwAAAAAAKDocHkKNGzdOXl5eMplMmj9/vs3+K1euaNOmTWrbtq0kqX379srIyLBaXVOtWjUtXbpU7du3148//qjo6Gi1a9dOI0aM0OXLly3j4uPjVbZsWTVu3Fienp5q3bq1VqxYke9a09PTZTab5ePjk+v+MmXKSJLOnz+vv//+W5L1PZdyk99xBXFtrn9eBjdnzhyFhobavP7N399fzz77rGbNmqWTJ08W+Nw//fSTUlNTrX5fO3fu1B9//FHIdwMAAAAAAIoCh4dQvr6+mj9/voYPH67Zs2frwIEDVvu3bNmiS5cuKTw8XJIs93z69wqme+65RzNmzNAPP/ygRYsWqU+fPtqwYYPeeusty5iEhAS1bt3astqqffv2On78uHbt2pVrbZ07d7aENZ07d7aET2fPns11/JkzZyRJZcuWtazAunDhwnXff37HFcS1p/55e3tbtvXu3VtxcXE2r9z0799fgYGBio6OLvC5ExISFBAQoFq1akmSwsPDZTAYChT2AQAAAACAosfhT8cbOXKkfHx81K9fP8XHx2vkyJFaunSpPDyulpaQkCBJ6tChg+UYk8mkw4cPKzU1Vf7+/po2bZq6d++uGjVqyMPDQ40bN1bjxo3l7e2tLVu2SJIOHDigw4cP6+jRo1q1apVVDXFxcWrUqJFNbR999JFycnIkSR4eHipevLiqV6+uffv2qXv37jbjk5KS5O7urlq1aqlMmTIqXbq09u3bp3r16tmMHTJkiCIiItS8eXPde++92rdvnx588EGbcaNGjVLz5s3VpUuX/H6kOnjwoCTp3nvvtWzz8fFRUFBQvo738PDQuHHj9MQTT1g+v/wwGo1au3at/vrrL0sIJV39fa1YsUJDhw7N91wAAAAAAKBocfhKqGurktzd3TVp0iQdOnRIc+bMkXR1Rc/WrVs1aNAgq9U7CxYskCTL6ppt27Zp+fLlNnOXKVNGvr6+kq4+Fa9MmTKKjY21mqtz585as2aNrly5YnN8QECAgoKCFBQUZLmJeO/evbV06VKdPn3aamxOTo4+/PBDtW3bVr6+vvLw8FCnTp30+eefKysry2rs5s2btXnzZlWoUEGS1K1bN3311VeWS/OuOXDggGJjYwt8qd7y5ctVu3ZtBQYGFui4f2rUqJEefvhhTZw4Md/HfP/990pLS9N7771n9RlHRUXp2LFj2r17d6HrAQAAAAAArs3hK6H+qWbNmurfv78lzPn1119lNBoVGRlp8zS6li1bKjY2VoMHD9bQoUP14osvqlixYuratas8PT21e/duzZ07V1OmTJF0dUVV165dVaNGDat5+vfvr4SEBG3cuDFfq4369Omjb775RhEREXrllVdUu3ZtnTx5Uh988IHS09M1evRoy9hhw4apV69eevrppzVs2DDdeeed2rFjh958801FRkbqnnvukSRFRkYqISFBERERevHFF3X33XcrKSlJ06ZNU1hYmB544IE860lPT9fZs2dlNpv1119/admyZVq9erXN/bUyMjJyvYywRIkSVpft/dMrr7yS6+qsvCQkJOjee+9V+/btrbYHBQVp9uzZiouLU4MGDSRJhw4d0tatW63G1a1bV3fccUe+zwcAAAAAAFyHU4VQ0tXgZv369Ro5cqTKlCmjBx54wCaAkq6GQYMHD1ZiYqIefPBBeXl5af78+VqyZImys7MVHBysyZMnKzw8XImJiUpJSVHPnj1t5qlXr55q166t2NjYfIVQbm5umjVrlj799FPNnDlTJ06ckK+vr9q2bau33nrLsvJKkvz8/LRkyRLFxMTo5Zdf1vnz51WlShUNHz5cffr0sYwrXry4PvvsM33wwQcaP368/vzzT/n7+6tnz54aMGCADAZDnvVMnjxZkydPlsFgkK+vr2rVqqVPP/3U5vLC+fPn53rj9549e2rSpEm5zu3r66sXX3xRY8eOveHnkpWVpQ0bNui5556z2VesWDH16NFDy5Yts4R0n3zyic0T+D755BM1b978hucCAAAAAACux2A2m82OLgLIL6PRqMTERMUkGrX35EVHlwMUeR1rV9DkHnWUnJyszMxMR5cDAACAfPL09FRaWppCQkIst8EBHM3h94QCAAAAAABA0UcIBQAAAAAAALsjhAIAAAAAAIDdEUIBAAAAAADA7gihAAAAAAAAYHeEUAAAAAAAALA7QigAAAAAAADYHSEUAAAAAAAA7I4QCgAAAAAAAHZHCAUAAAAAAAC783B0AUBhBJUrqSyTo6sAir6AsiUkSV5eXg6uBAAAAAXh4cHXfTgfuhIuaWzXmnJ3d3d0GcBtwWw2y9/f39FlAAAAoACMRqNOnTrl6DIAK1yOB5dkNpsdXQKQJ7PZrCtXrhSZPjUYDI4uAbdYUetRFD30KFwBfQpnR2/CGRFCwSXxBxXOzGw26/Tp0/QpnBY9CmdHj8IV0KdwdvQmnBEhFAAAAAAAAOyOEAoAAAAAAAB2RwgFAHbg6enp6BKA66JH4ezoUbgC+hQACoan48ElubmRn8J5ubm5qVKlSo4uA8gTPQpnR4/CFdCncHZ8Z4IzoisB3HLcBBEAAAAA8G+shIJLmrBqv347m+HoMpCL+6uV07Nh1ZSamqqsrCxHlwMAAADcljw8+LoP50NXwiUln8vQgVMXHV0GcnFXuZKSpKysLGVmZjq4GgAAAOD2ZDKZHF0CYIPL8QAAAAAAAGB3hFAAAAAAAACwO0IoAAAAAAAA2B0hFAAAAAAAAOyOEAoAAAAAAAB2RwgFAAAAAAAAuyOEAgAAAAAAgN0RQgEAAAAAAMDuCKEAAAAAAABgd4RQAAAAAAAAsDsPRxdQ1IWFhemPP/7Idd+CBQvUtGlT5eTk6NNPP1VsbKxSUlLk6+ur8PBwDR06VL6+vpbxERERatKkiYYNG2Y1z44dOxQZGamDBw/mun3IkCF6/vnnrbbl5eGHH9bUqVMVHByc55hNmzapcuXK+a4nIiJCO3futBpTqlQp1alTR2PGjFH16tXzPBcAAAAAACgaCKH+A6NGjVKnTp1stvv4+MhkMumZZ55RSkqKXn75ZdWqVUsnTpzQ+++/r169emnx4sWqWLFioc6bkJCgKlWqaOXKlRoxYoQMBoNCQ0O1bds2y5gWLVooJiZGoaGhkqTixYtb9v1z+z/9MxjLr6eeekpPPfWUJMlsNuvEiROaNGmSnnvuOa1du1ZubizKAwAAAACgKCOE+g+ULl1afn5+ue77/PPPtW/fPsXHx6t8+fKSpICAADVo0EB9+vTRpEmT9N577xX4nNnZ2Vq3bp1ee+01jR49Wjt37lTTpk3l5eVlU4uPj0+u9eW1vTBKlixpNVeFChU0evRo9e3bV4cOHVKNGjVuyXkAAAAAAIBzYvmJgy1dulQ9e/a0BFDXeHl56ZlnntHGjRv1119/FXje7du3Kz09XeHh4apfv77i4uJuUcW3jpeXlyTJ3d3dwZUAAAAAAAB7I4RyoKysLO3fv1/16tXLdX/Dhg1lNBq1b9++As+dkJCgBg0ayMfHR+Hh4Vq7dq0yMjJutuRb5syZM5o5c6buvfde3X333Y4uBwAAAAAA2BmX4/0Hxo0bp4kTJ1ptq1SpkubPny/p6k26c1OmTBlJKvBKqCtXrmjTpk0aPny4JKl9+/aaMWOG1q9fr4ceeijf8wwcONBmlVLDhg01d+7cAtUjSXPmzLG8X6PRKElq3ry55syZw0ooAAAAAABuA4RQ/4Hhw4erffv2Vts8PDx0xx13SJJOnz6d63Hp6emSrt5T6toxJpPJZpzJZJKHx///VW7ZskWXLl1SeHi4JCkoKEjVq1dXXFxcgUKo6Oho1a9f32rbP29cnt96JKl3796KiIhQVlaWPvvsM3333Xd64YUXFBAQkO96AAAAAACA6yKE+g+UK1dOQUFBue6rUaOGkpKS1KNHD5t9u3fvliTVqlVL0tUw6low9U/p6emWoEq6eimeJHXo0MGyzWQy6fDhw0pNTZW/v3++6q5YsWKedRekHunqTc6vzTVx4kQNHDhQzzzzjFatWmUzFgAAAAAAFD3cE8rBHn30US1fvlypqamSpAMHDqhLly765ptv9NFHH6l169aqUKGCJCk4OFiJiYk2c/z888+WoOrixYvaunWrBg0apLi4OMtrwYIFkqQVK1bcstrzU09uDAaDJkyYoAsXLuitt966ZfUAAAAAAADnRQj1H0hPT9fZs2dtXhkZGerTp4+aNGmiyMhIbdiwQaVKlVKjRo00aNAgJSUlKSoqyjLPI488oiNHjig6OlpHjhzRkSNHtGDBAi1evFj9+vWTJG3YsEFGo1GRkZGqXr265dWkSRO1bNlSsbGx+a77woULudadmZmZ73ryUqlSJT3zzDP63//+p/379xfiUwUAAAAAAK7EYDabzY4uoigLCwvTH3/8keu+ESNGaOjQocrJydEnn3yi2NhYpaSkqGzZsmrZsqVSU1N18uRJTZ48WQ0aNJAkJSUl6Z133tHevXuVnZ2tatWqafDgwWrXrp0k6emnn5aXl5c+/PBDm/Nt2bJFgwcP1v/+9z+FhIRIurqaacGCBWratKnV2ODg4Dzf0/Tp09W9e/d81SNJERERatKkiYYNG2Y1T1ZWljp37iw/Pz8tXrz4Bp/kVUajUYmJiYpJNGrvyYv5Ogb/rY61K2hyjzpKTk62BJYAAAAA/luenp5KS0tTSEgID4OC0yCEcmJms1lr1qzRXXfddd3L224nhFDOjxAKAAAAcDxCKDgjbkzuxAwGgzp16uToMgAAAAAAAG4a94QCAAAAAACA3RFCAQAAAAAAwO4IoQAAAAAAAGB3hFAAAAAAAACwO0IoAAAAAAAA2B0hFAAAAAAAAOyOEAoAAAAAAAB2RwgFAAAAAAAAuyOEAgAAAAAAgN15OLoAoDCCypVUlsnRVSA3AWVLSJK8vLwcXAkAAABw+/Lw4Os+nA9dCZc0tmtNubu7O7oM5MFsNsvf39/RZQAAAAC3LaPRqFOnTjm6DMAKl+PBJZlMLINyZgaDwdElOJTJZFJqaip9CqdFj8LZ0aNwBfQpnB29CWdECAUAdpCVleXoEoDrokfh7OhRuAL6FAAKhhAKAAAAAAAAdkcIBZd0u1/uBedmMBgUGBhIn8Jp0aNwdvQoXAF9CmdHb8IZEUIBgB3k5OQ4ugTguuhRODt6FK6APgWAgiGEgksym82OLgHIk9ls1rlz5+hTOC16FM6OHoUroE/h7OhNOCMPRxcAFIabG/lpQZjNZpbj/ofc3Nzk7+/v6DKAPNGjcHb0KFwBfQpnx3cmOCNCKLikCav267ezGY4uwyXcX62cng2rptTUVJ7gAgAAANwmPDz4ug/nQ1fCJSWfy9CBUxcdXYZLuKtcSUlXHyGcmZnp4GoAAAAA/BdMJpOjSwBssD4PAAAAAAAAdkcIBQAAAAAAALsjhAIAAAAAAIDdEUIBAAAAAADA7gihAAAAAAAAYHeEUAAAAAAAALA7QigAAAAAAADYHSEUAAAAAAAA7I4QCgAAAAAAAHZHCHWTgoODrV733XefxowZo0uXLtmMPXHihIKDg/XKK6/kOldSUpKefvpphYaGKjQ0VI8//ri2b9+erzri4uLUq1cvhYaGqkWLFnrttdeUmppqMy4hIUG9evVS/fr11axZMw0bNkwHDhywGhMREaG2bdsqMzPTantKSoqCg4OVkpJitX3p0qXq1auXGjRoYKl78+bNNuc+c+aMXn/9dbVo0UL16tVT586dNW/ePOXk5OTrPQIAAAAAANdFCHULxMTEaNu2bdq6datmz56tvXv3avr06TbjVq9erSpVqmjjxo02IdWpU6fUr18/hYaGatmyZVq+fLnuu+8+DRo0SD///PN1zz9lyhRNmTJFjz76qOLi4vTBBx/o7NmzeuKJJ5SWlmZV55gxY9SlSxetWrVK8+bN0x133KHevXvr+++/t5rzxIkTmj179g3f++jRozV58mQ99NBDio2N1fLly9WqVSuNGDFCa9eutYxLTU1Vr169lJKSopkzZyohIUHPPvusPv/8cw0ZMkQmk+mG5wIAAAAAAK6LEOoW8PHxkZ+fnypWrKiQkBA988wzWrNmjc24+Ph4PfHEE/L09NS6deus9q1fv16VK1fWc889p2rVqunuu+/WsGHD1LhxYy1fvjzPc+/atUufffaZPvjgA/Xq1UtBQUGqX7++PvjgA+Xk5Oizzz6TJO3bt0+zZs3SBx98oH79+qlKlSqqVauWJkyYoJ49e2rkyJFWK58CAgI0d+5cHTt2LM9zf/PNN1q+fLnmz5+vxx9/XEFBQbr77rs1aNAgDRkyRB988IFl7MSJExUYGKi5c+eqUaNGCgwMVKdOnbRo0SLt2rVLS5Ysye/HDQAAAAAAXBAhlB2UKFHCZtvhw4d16NAhNW3aVC1btlRsbKzVfjc3N/3xxx9KTk622j5t2jQNHz48z3PFxcWpXr16atSokU0NH374oR5//HFJ0rJly1S7dm01b97cZo6hQ4fq9OnT+vbbby3bunfvrurVq2vChAl5nnvZsmVq1aqVQkNDbfZFRkZaArA///xTmzdv1sCBA+Xu7m41rlKlSurRo4e+/PLLPM8DAAAAAABcHyHULZaWlqaFCxeqW7duVtvj4+MVEBCgGjVqKDw8XD/++KP++OMPy/4HH3xQxYsXV6dOnfTUU09p7ty5OnTokCpWrKjy5cvneb4DBw6obt26ue6rVauWKlSoIOnq/abyGufr66u77rpLe/futWxzc3PTG2+8oe+//16rV6/O9bjExEQ1bNgw133e3t7y9fWVdHUVltlszvP8DRs21IEDB5SVlZX7mwQAAAAAAC6PEOoWGDhwoEJDQxUSEqJmzZrp119/VUREhNWY1atXKywsTJLUqlUreXl5KS4uzrK/XLlyWrZsmR555BHt379fb775prp27ap+/frp3LlzeZ47PT1d3t7eN6zxwoULKlOmTJ77fXx8dP78eattdevWVe/evTVlyhRdvHjR5pi//vpLZcuWtfyclZVluan6tdfJkyd14cIFScrz/Ne2//v8AAAAAACg6CCEugWio6MVFxenuLg4ffHFF2rRooX69OljCY/27t2r5ORktW3bVpJUqlQpNW/eXCtWrLCa584779SECRO0fft2LVu2TAMHDlRiYqLGjBkjSRowYIBVwCNJZcuW1d9//33DGn18fPTnn3/muf/MmTNWgdI1L7zwgkwmk959991c5/znuT09PS2fw5w5c5SRkSGTySQfHx9JyvP8Z86csbwXAAAAAABQNHk4uoCioGLFigoKCpIk3XXXXapdu7aaNm2qNWvW6IknnlBCQoIk6amnnrIcYzKZZDab9dNPP6lhw4b66KOPVLduXTVr1kxubm6qW7eu6tatq4CAAE2bNk2SNGnSJF25csXq3LVr11ZSUlKudX322Wf6888/9dJLL6l+/fr66aefch139uxZnTp1KtfL5cqUKaNXX31VI0eOVNOmTa321atXT3v27LH8bDAYLJ/DP+/9VLduXbm7uyspKUl33nmnzTmSkpIUHBwsLy+vXOsDAAAAAACuj5VQduDm5iaz2Syj0SiTyaQ1a9aoe/fullVCcXFxio2Nlbe3t+WSvN27d2vhwoU2c5UpU8Zyb6VrYde1lyR17dpVe/futQmYLl26pM8++0xGo1GS1LNnTx08eFAbN260OceHH36o8uXL64EHHsj1/XTv3l2NGjXS5MmTrbb37t1bX3/9tfbt22dzzOnTpy3/9vX1Vdu2bTVr1ixLPdekpqZq2bJlevTRR3M9NwAAAAAAKBoIoW6BCxcu6OzZszp79qyOHTumCRMmyGg0KiwsTLt27dLp06cVERGh6tWrW141a9ZUt27dtGbNGmVmZmrQoEHaunWrRo8eraSkJCUnJ2v16tV688039eSTT+Z57tDQUPXq1UtDhw7VsmXLdPz4ce3cuVMDBw6Um5ubBg4cKEmqUaOGRowYoVdeeUULFy7UiRMndPDgQUVHR2v58uWaOnWqihUrlud5xo0bZ7ls7ppWrVqpT58+evLJJ7Vw4UIdPXpUR44c0Zw5czRw4EDdc889lkvsRo8erQsXLmjgwIHatWuXTp48qQ0bNigyMlJNmjRR3759b/4XAQAAAAAAnJbBbDabHV2EKwsODrb6uUSJEqpTp46ee+453XfffRo7dqwSExO1cuVKm2MPHTqkrl276u2331bnzp21a9cuffjhh/rll190+fJl3XXXXYqMjFSvXr2uW4PJZNKCBQu0fPlynThxQmXKlNH999+v559/XhUrVrQau2HDBs2fP18HDx6Ul5eXmjRpoqFDh6pGjRqWMREREWrSpImGDRtmdezbb7+tOXPmaNOmTapcubJl+5o1a7R48WIdOHBA2dnZuueee9StWzc99thjVsHWuXPnNGvWLG3atElpaWkKDAxUjx491K9fP3l45O/KUKPRqMTERMUkGrX3pO3N0mGrY+0KmtyjjpKTk5WZmenocgAAAAD8Bzw9PZWWlqaQkBCr26UAjkQIBZdCCFVwhFAAAADA7YcQCs6Iy/EAAAAAAABgd4RQAAAAAAAAsDtCKAAAAAAAANgdIRQAAAAAAADsjhAKAAAAAAAAdkcIBQAAAAAAALsjhAIAAAAAAIDdEUIBAAAAAADA7gihAAAAAAAAYHeEUAAAAAAAALA7QigAAAAAAADYnYejCwAKI6hcSWWZHF2FawgoW0KS5OXl5eBKAAAAAPxXPDz4ug/nQ1fCJY3tWlPu7u6OLsNlmM1m+fv7O7oMAAAAAP8Ro9GoU6dOOboMwAqX48ElmUwsgyoIg8Hg6BJuKyaTSampqfQpnBY9CmdHj8IV0KdwdvQmnBEhFFwSoQqcmcFgUNmyZelTOC16FM6OHoUroE/h7OhNOCMux4NL4g8qnJnBYFCJEiUcXQaQJ3oUzo4ehSugT+Hs+M4EZ8RKKLgklpbCmZlMJp04cYI+hdOiR+Hs6FG4AvoUzo7ehDMihAIAO+BpJHB29CicHT0KV0CfAkDB8FcTLsnNjfwUzsvNzY2nEcKp0aNwdvbuUbPZzGUquGn8LYWz4zsTnBEhFFzShFX79dvZDEeXAQAAXMz91crp2bBqSk1NVVZWlqPLAQC7YaUenBFdCZeUfC5DB05ddHQZAADAxdxVrqQkKSsrS5mZmQ6uBgDsh3tCwRmxPg8AAAAAAAB2RwgFAAAAAAAAuyOEAgAAAAAAgN0RQgEAAAAAAMDuCKEAAAAAAABgd4RQAAAAAAAAsDtCKAAAAAAAANgdIRQAAAAAAADsjhAKAAAAAAAAdkcIBQAAAAAAALsjhLKT4OBgq9d9992nMWPG6NKlSzZjT5w4oeDgYL3yyis2+2JiYlSnTh399ttvNvvCwsL01VdfSZK++uorq/PVrl1bDzzwgKKjo3Xx4kXLMTt27FBwcHCuNUdERCgmJsZqvu3bt1933LX5rr1q1aql5s2b69VXX9Wff/5pOSYlJUXBwcFKSUmxmS8qKkpRUVG51gQAAAAAAIoGQig7iomJ0bZt27R161bNnj1be/fu1fTp023GrV69WlWqVNHGjRtzDamys7M1fvz4G57vzjvv1LZt27Rt2zatX79eEydO1NatWzV06FCZTKZCvYcJEyYoKyvrhuOunXfTpk2aOXOmjh49qn79+ikjI6NQ5wUAAAAAAEULIZQd+fj4yM/PTxUrVlRISIieeeYZrVmzxmZcfHy8nnjiCXl6emrdunU2+ytWrKg9e/YoLi7uuudzd3eXn5+f/Pz8FBAQoFatWmn27Nn66aeftGHDhkLV/+eff+qjjz664dhr5/X391eTJk300Ucf6ezZs/riiy8KfF4AAAAAAFD0EEL9h0qUKGGz7fDhwzp06JCaNm2qli1bKjY21mZMUFCQnnjiCU2fPl1///13gc559913q1GjRoUKoby9vTVixAh99NFHOn78eIGO9fX1Vdu2bQt1XgAAAAAAUPQQQv1H0tLStHDhQnXr1s1qe3x8vAICAlSjRg2Fh4frxx9/1B9//GFz/LBhw+Th4aG33nqrwOe+5557dOTIkULV/fjjj6tatWqaMGHCf3peAAAAAABQtBBC2dHAgQMVGhqqkJAQNWvWTL/++qsiIiKsxqxevVphYWGSpFatWsnLyyvXy+68vb01cuRIffnll9q7d2+B6vD29s71XlP54e7urjfeeEPbt2/X2rVrC3Rs6dKlC31eAAAAAABQtBBC2VF0dLTi4uIUFxenL774Qi1atFCfPn107tw5SdLevXuVnJystm3bSpJKlSql5s2ba8WKFbnO9+CDD6p58+Z64403ZDQa813HpUuX5O3tLUny8PCQpFxvVG4ymSz7/6l+/frq1auXpkyZUqBQ6eLFi5bzenp6SpLMZnO+zwsAAAAAAIoOQig7qlixooKCgnTXXXcpNDRUU6ZM0eXLly03J09ISJAkPfXUU6pVq5Zq1aqlr7/+WsnJyfrpp59ynXPs2LE6fPiwFi9enO86Dh48qHvvvVeSVKZMGUlSenq6zbi///7bsv/fXnrpJWVlZSkmJqZQ570WRuV13tKlS+d7XgAAAAAA4HoIof5Dbm5uMpvNMhqNMplMWrNmjbp3725ZLRUXF6fY2Fh5e3vn+SS8oKAgDRo0SO+++64uXrx4w3MeO3ZMu3btUseOHS3HFy9eXImJiVbj/v77b/3++++qWbNmrvP4+Pjo1Vdf1cKFC/N1k/Lz589r48aNlvOWKlVKgYGB2rNnj9U4o9Goffv2qVatWjecEwAAAAAAuC6ugbKjCxcu6OzZs5KuXhI3f/58GY1GhYWFadeuXTp9+rQiIiJUvXp1q+O6deumVatWacyYMbnOO2jQIK1cuVLJyclW241Go+V82dnZ+vXXXzVt2jQ1a9ZMbdq0kSR5eXnp0Ucf1fjx4zVu3Djdc889OnnypGJiYlSnTh2Fhobm+X4efvhhLVu2TLt27bLZd+28OTk5OnbsmN588035+/urV69eljFPPPGE3n33XZUtW1b169fXuXPnNH/+fHl5ealdu3Y3+jgBAAAAAIALI4Syo2HDhln+XaJECdWpU0cff/yxAgMD9fHHHys4OFh169a1Oa5Pnz5avHixNm7cmOu8Xl5eGjt2rJ5++mmr7adOnVKLFi0kScWKFVOlSpXUtWtXDRgwwGrca6+9Jh8fH0VHR+vUqVPy8fFR69at9corr8hgMFz3Pb3xxht6+OGHbbZfO6+np6fuvPNOtWnTRkOGDFGxYsUsY/r37y93d3fNmjVLJ06cUMmSJXXfffdp4cKFKl68+HXPCwAAAAAAXJvBnNudogEnZTQalZiYqJhEo/aevPHliAAAAP/UsXYFTe5RR8nJycrMzHR0OQBgN56enkpLS1NISIjc3d0dXQ4giXtCAQAAAAAA4D9ACAUAAAAAAAC7I4QCAAAAAADA/2vv3qOirvM/jr8GRy4KmiAaoqLikfKGiGmmpoGZmlZq1mrrJUszb7nVKaGSDNOEMl1NNzK7aaIk4rK0njSt1CxPGpiZLoJLbl6WVEwCQZn5/dHh+2vC+/ZlvhPPxzlzcj7f23vsfeZ85+Xn+/2ajhAKAAAAAAAApiOEAgAAAAAAgOkIoQAAAAAAAGA6QigAAAAAAACYjhAKAAAAAAAApiOEAgAAAAAAgOkIoQAAAAAAAGA6u7sLAK5FWFAdlTvcXQUAAPA0odf5SZK8vb3dXAkAmMtu5+c+rIeuhEeaOfhG1apVy91lAAAAD+R0OhUSEuLuMgDAVBUVFTp27Ji7ywBccDkePJLT6XR3CcBFOZ1OnTlzhj6FZdGjsDqze9Rms5myX9QsfJfC6uhNWBEzoeCROHmEldlsNgUEBLi7DOCi6FFYHT0KT0Cfwur4zQQrYiYUPJLDwQ2hYF0Oh0PHjx+nT2FZ9Cisjh6FJ6BPYXX0JqyIEAoATHD27Fl3lwBcEj0Kq6NH4QnoUwC4OoRQAAAAAAAAMB0hFACYgEd/w+roUVgdPQpPQJ8CwNXhxuTwSF5e5KewLi8vrws++tvpdHKDSFjCxXoUsAp6FJ6APoXV8ZsJVkQIBY/0QuZ3yi0scXcZwBXrER6kyTHhOnr0qMrLy91dDgAAAP7g7HZ+7sN66Ep4pIITJdp/rNjdZQBXrEVQHUlSeXm5ysrK3FwNAAAA/uh4Oh6siPl5AAAAAAAAMB0hFAAAAAAAAExHCAUAAAAAAADTEUIBAAAAAADAdIRQAAAAAAAAMB0hFAAAAAAAAExHCAUAAAAAAADTEUIBAAAAAADAdIRQAAAAAAAAMJ3d3QXg2qWnpysuLk6zZ8/W8OHDjfEZM2ZIkl566SWX9f/zn/8oNjZWH3/8sXbu3Km4uLiL7nvKlCkaMmSIYmNjXcbtdrsaNGigO+64Q08//bS8vb0lSRERERfd18cff6ymTZtKkt59912lpqbq+++/V/369dW7d2/95S9/UXBw8NV9eAAAAAAA4FEIoTxYVlaWmjdvrvXr17uEUFdi4MCB6tWrlyTp6NGjGj58uNLS0hQSEiJJqlOnjk6dOiVJLuNlZWXauXOnEhIS1KBBA02ZMsXY56JFixQVFVXlWIGBgZJ+CaDefPNNJSQkqE2bNvrvf/+rl19+WQ8//LDWrVsnLy8m5gEAAAAA8EdFCOWhTpw4oR07dmjOnDmaMWOGDh8+rGbNml3x9r6+vvL19ZX0S7Ak/RIW/XpGUmUI9dvxpk2bavfu3dq0aZNLCFW/fv1Lzmhat26dHnzwQcXExBj7mT9/vnr37q09e/aoU6dOV1w/AAAAAADwLEw98VAbNmxQQECA7rrrLjVq1Ejr16+v1uN7e3urVq1aV7WNzWbTV199pfLycmPs+uuv14cffqgbbrjh9y4RAAAAAABYCCGUh8rKylKfPn3k5eWlmJgYZWRkyOl0mn5cp9OpL7/8UpmZmbrjjjuuatvRo0dr48aN6t27t+Li4rR+/XoVFRUpPDzcmJUFAAAAAAD+mLgczwMdPXpUu3fv1oMPPihJ6tevn1atWqVdu3apS5cuv/vxBg0aJJvNJkkqLy9XYGCgRo8erYceeshlvfHjx1eZHRUdHa1ly5ZJku655x41aNBA77zzjjIzM5Weni5vb29NmjRJjz766O9eNwAAAAAAsA5CKA+UlZUlHx8f9ezZU5LUtWtX1a9fX+vWrVOXLl1kt9tdLnmrVDlTqnbt2ld1vJSUFDVu3FhHjhzRCy+8oBtuuEETJ06sEjjNnj1bkZGRLmO/neHUu3dv9e7dW8XFxfriiy+UmpqqBQsWqHXr1rr99tuvqi4AAAAAAOA5uBzPA2VlZens2bOKjo5W27Zt1bFjR50+fVobNmzQ2bNnFRAQoDNnzlTZ7qeffpIkBQQEXNXxmjRporCwMHXv3l2vv/66PvnkE82bN6/Keo0bN1ZYWJjLq3HjxpJ+mb2VkJBghGP+/v7q27ev3njjDXXq1Emff/751f41AAAAAAAAD0II5WEOHTqkffv26dlnn1VGRobxevXVV1VcXKyNGzcqIiJCe/fu1blz51y2zcnJUYsWLVSnTp1rPn7z5s01depUrVixQjk5OVe8nbe3t9LS0vTZZ5+5jNtsNvn7+yswMPCaawIAAAAAANZHCOVhsrKydN111+n+++9XmzZtjNfAgQPVunVrZWRk6Pbbb5fNZtNTTz2l/fv3q6CgQBkZGVq4cKHGjh37P9cwevRohYeH64UXXpDD4TDGT58+rcLCwiqvsrIyBQUF6U9/+pPi4+O1atUqff/99/r222+1cOFCffPNNxo2bNj/XBcAAAAAALAu7gnlYbKysjR48GB5e3tXWTZixAi9+OKLKi4u1ooVK5ScnKyxY8eqpKREzZs31xNPPKH77rvvf67Bbrfr2Wef1dixY7V27VoNHz5ckjR16tQLrp+UlKS7775b8fHxCg0N1fvvv6+XXnpJdrtdN910k1asWKEmTZr8z3UBAAAAAADrsjkr71YNeICKigplZ2drUXaF9hwpdnc5wBXr366R5gxtr4KCApWVlbm7HAAAAPzB1a5dWydPnlSnTp2qPFQKcBcuxwMAAAAAAIDpCKEAAAAAAABgOkIoAAAAAAAAmI4QCgAAAAAAAKYjhAIAAAAAAIDpCKEAAAAAAABgOkIoAAAAAAAAmI4QCgAAAAAAAKYjhAIAAAAAAIDpCKEAAAAAAABgOkIoAAAAAAAAmM7u7gKAaxEWVEflDndXAVy50Ov8JEne3t5urgQAAAA1gd3Oz31YD10JjzRz8I2qVauWu8sArorT6VRISIi7ywAAAEANUFFRoWPHjrm7DMAFl+PBIzmdTneXAFyU0+nUmTNnqvSpzWZzU0WAq4v1KGAV9Cg8AX0Kq6M3YUWEUPBIfKHCypxOp06ePEmfwrLoUVgdPQpPQJ/C6uhNWBEhFAAAAAAAAExHCAUAAAAAAADTEULBI3FvHViZzWaTv78/fQrLokdhdfQoPAF9CqujN2FFPB0PHokvVFiZzWZTUFCQu8sALooehdXRo/AE9Cmsjt9MsCJmQsEjcZM9WJnT6dSJEyfoU1gWPQqro0fhCehTWB29CSsihIJH4gsVVuZ0OlVcXEyfwrLoUVgdPQpPQJ/C6uhNWBEhFAAAAAAAAEzHPaHgUSrT/IqKCnl5kaHCmhwOhxwOhyoqKvgXKFgSPQqro0fhCehTWF1FRYUkZkTBWmxOOhIepLy8XN988427ywAAAAAAj9ChQwd5e3u7uwxAEiEUPIzD4dD58+fl5eXF0x4AAAAA4CKcTqccDofsdjtXkcAyCKEAAAAAAABgOuJQAAAAAAAAmI4QCgAAAAAAAKYjhAIAAAAAAIDpCKEAAAAAAABgOkIoAAAAAAAAmI4QCgAAAAAAAKYjhILHKCsrU3x8vLp06aKePXtq+fLl7i4JNdzx48c1bdo0de3aVb169dLcuXNVVlYmSTp8+LDGjh2rTp06aeDAgdq2bZubq0VNN2HCBM2YMcN4v2/fPg0fPlyRkZEaNmyY9u7d68bqUJOVl5dr1qxZuummm3TLLbdo/vz5cjqdkuhTWMfRo0f1yCOPqHPnzoqJidHbb79tLKNP4U7l5eUaNGiQvvzyS2Pscuehn3/+uQYNGqTIyEiNHj1ahw8fru6yUYMRQsFjJCUlae/evXrnnXeUkJCgxYsXa8OGDe4uCzWU0+nUtGnTVFpaqpUrV+rVV1/Vli1btGDBAjmdTk2ePFkNGzbU2rVrdffdd2vKlCk6cuSIu8tGDZWVlaVPP/3UeF9SUqIJEyaoS5cuSk9PV1RUlB555BGVlJS4sUrUVLNnz9bnn3+uN998U6+88orWrFmj1atX06ewlOnTp6tOnTpKT09XfHy8FixYoI0bN9KncKuysjI9/vjjys3NNcYudx565MgRTZ48WUOHDtUHH3ygwMBATZo0yQj/AbPZ3V0AcCVKSkqUlpamN954Q+3atVO7du2Um5urlStXqn///u4uDzVQfn6+srOztX37djVs2FCSNG3aNM2bN0+33nqrDh8+rNTUVNWpU0fh4eHasWOH1q5dq6lTp7q5ctQ0RUVFSkpKUocOHYyxDz/8UD4+Pnrqqadks9n0zDPP6LPPPtOGDRs0dOhQN1aLmqaoqEhr167VW2+9pY4dO0qSxo0bp5ycHNntdvoUlnD69GllZ2crMTFRLVq0UIsWLdSrVy/t2LFDp0+fpk/hFgcPHtQTTzxRJTz64osvLnkempaWpvbt22vcuHGSpLlz56pHjx7auXOnunXr5o6PghqGmVDwCPv379f58+cVFRVljEVHRysnJ0cOh8ONlaGmCg4O1rJly4wAqlJxcbFycnLUtm1b1alTxxiPjo5WdnZ2NVcJSPPmzdPdd9+t1q1bG2M5OTmKjo6WzWaTJNlsNnXu3JkeRbXbtWuX/P391bVrV2NswoQJmjt3Ln0Ky/D19ZWfn5/S09N17tw55efna/fu3brxxhvpU7hNZWi0evVql/HLnYfm5OSoS5cuxjI/Pz+1a9eOnkW1IYSCRygsLFSDBg3k7e1tjDVs2FBlZWUqKipyX2GoserVq6devXoZ7x0Oh1asWKGbb75ZhYWFatSokcv6QUFBOnbsWHWXiRpux44d+uqrrzRp0iSXcXoUVnH48GGFhoYqIyND/fv3V2xsrF577TU5HA76FJbh4+OjmTNnavXq1YqMjNSAAQN06623avjw4fQp3GbkyJGKj4+Xn5+fy/jlepKehbtxOR48QmlpqUsAJcl4X15e7o6SABfJycnat2+fPvjgA7399tsX7Fd6FdWprKxMCQkJmjlzpnx9fV2WXew7lR5FdSspKVFBQYFSU1M1d+5cFRYWaubMmfLz86NPYSl5eXm67bbb9OCDDyo3N1eJiYnq3r07fQrLuVxP0rNwN0IoeAQfH58qX4yV73/74wqobsnJyXrnnXf06quvqk2bNvLx8akyQ6+8vJxeRbVavHix2rdv7zJjr9LFvlPpUVQ3u92u4uJivfLKKwoNDZX0y01zV61apbCwMPoUlrBjxw598MEH+vTTT+Xr66sOHTro+PHjWrp0qZo1a0afwlIudx56sXOAevXqVVeJqOG4HA8eoXHjxjp16pTOnz9vjBUWFsrX15cvTLhVYmKi3nrrLSUnJ+uOO+6Q9Eu//vjjjy7r/fjjj1WmPgNmysrK0qZNmxQVFaWoqChlZmYqMzNTUVFR9CgsIzg4WD4+PkYAJUktW7bU0aNH6VNYxt69exUWFuYSLLVt21ZHjhyhT2E5l+vJiy0PDg6uthpRsxFCwSPceOONstvtLjfM27Vrlzp06CAvL9oY7rF48WKlpqZq/vz5uvPOO43xyMhIffvttzp79qwxtmvXLkVGRrqjTNRQ7733njIzM5WRkaGMjAzFxMQoJiZGGRkZioyM1Ndff208UcfpdGr37t30KKpdZGSkysrKdOjQIWMsPz9foaGh9Ckso1GjRiooKHCZPZKfn6+mTZvSp7Ccy52HRkZGateuXcay0tJS7du3j55FteHXOzyCn5+f7rnnHj3//PPas2ePNm3apOXLl2v06NHuLg01VF5enpYsWaLx48crOjpahYWFxqtr164KCQlRXFyccnNzlZKSoj179ujee+91d9moQUJDQxUWFma86tatq7p16yosLEz9+/fXTz/9pBdffFEHDx7Uiy++qNLSUg0YMMDdZaOGadWqlfr06aO4uDjt379fW7duVUpKikaMGEGfwjJiYmJUu3ZtPfvsszp06JA2b96sv/3tbxo1ahR9Csu53HnosGHDtHv3bqWkpCg3N1dxcXFq2rSpunXr5ubKUVPYnJWxPWBxpaWlev755/XRRx/J399fDz30kMaOHevuslBDpaSk6JVXXrngsgMHDqigoEDPPPOMcnJyFBYWpvj4eN1yyy3VXCXw/2bMmCFJeumllyRJe/bsUUJCgvLy8hQREaFZs2apbdu27iwRNdSZM2eUmJiojRs3ys/PTyNHjtTkyZNls9noU1hGZcC0Z88eBQYG6oEHHtCYMWPoU1hCRESE3n33XSNIutx56Keffqo5c+bo2LFjioqKUmJiopo1a+au8lHDEEIBAAAAAADAdFyOBwAAAAAAANMRQgEAAAAAAMB0hFAAAAAAAAAwHSEUAAAAAAAATEcIBQAAAAAAANMRQgEAAAAAAMB0hFAAAAAAAAAwHSEUAAAAAAAATEcIBQAALCkmJkYRERHGq127durfv7/efvttd5fm4p///KdOnDghSVq0aJFGjRpl2rHKyso0ZMgQnTp1Sg6HQ3FxcercubNGjRpl1CBJ//rXvzR06FA5nU6X7UeNGqWDBw+aVh8AAMClEEIBAADLio+P17Zt27Rt2zZt2rRJjzzyiJKSkpSRkeHu0iRJP/zwg6ZPn67S0lJJ0rhx47Ro0SLTjpeSkqLbbrtNDRo00ObNm7Vz506lpaUpICBAKSkpxnqvvfaaJk2aJJvN5rL95MmTNWvWLNPqAwAAuBRCKAAAYFkBAQEKDg5WcHCwQkJCNGTIEHXv3l0fffSRu0uTpCozjerWravrrrvOlGP9/PPPevfdd3X//fdLkvLz89WpUyeFh4fr1ltvVX5+viQpNzdXhw8fVmxsbJV93Hzzzfrxxx/11VdfmVIjAADApRBCAQAAj2K321W7dm1Jv1xelpiYqNjYWPXp00fFxcU6duyYHnvsMXXt2lXdunXT7NmzVV5eLklKT0/XiBEj9PLLLysqKkp9+vRRWlqasW+Hw6Fly5YpNjZWHTt21KhRo3TgwAFjeUREhBYuXKhu3bpp4sSJRtATGxur9PT0Kpfjff311xoxYoQ6deqkmJgYrVq1ylg2Y8YMzZ07V9OnT1dkZKR69+59yRlemZmZatmypRo3bixJatKkiQ4ePKjy8nLt27dPISEhkqQlS5bo0UcfrTILqtJv6wAAAKguhFAAAMAjnDt3Th999JG2b9/uMssnPT1dycnJWrx4sby9vTVmzBiVlpbqvffe04IFC/TJJ58oKSnJWP+bb77Rd999p9WrV2vKlCmaNWuWtm3bJumXy9iWL1+u+Ph4rVu3TqGhoXr44YdVUlJibL9lyxatWrVKTz75pBFgpaWlaeDAgS715uXlacyYMbrpppuUnp6uqVOnat68edq4caOxzsqVK9WuXTv94x//UL9+/ZSQkKAzZ85c8PNv3bpVt9xyi/G+X79+8vf3V2RkpLZt26bx48crLy9PBQUF6tu370X/Hnv06KFt27ZVmcUFAABgNkIoAABgWQkJCYqKilJUVJQ6duyop59+WmPGjNFdd91lrNOnTx917txZ7du319atW3X8+HElJycrIiJC3bt318yZM7Vq1Sr9/PPPkiSbzaakpCS1adNG9957r+68806tWbNGTqdTK1as0GOPPabY2FiFh4crMTFRtWrV0t///nfjePfff79atWql1q1bKzAwUJIUGBgoX19fl9rXrFmjtm3b6vHHH1erVq00ZMgQ/fnPf9ayZcuMdSIiIjR+/Hg1a9ZMjz32mM6ePavc3NwL/l3s27dP4eHhxntvb2+tXLlSW7du1aZNm9SsWTNjFlROTo4GDx6sAQMGaPv27S77CQ8PV1FRkX744Ydr/L8CAABwbezuLgAAAOBipk2bpn79+kmSfHx8FBwcrFq1armsExoaavw5Ly9PLVq0UP369Y2xzp076/z58/r+++8lSWFhYQoKCjKWt2/fXqmpqTpx4oSKiooUGRlpLKtdu7bat2+vvLy8Cx7vUvLy8tSxY0eXsaioKKWmphrvW7RoYfzZ399fknT+/PkL7u/kyZNq0KBBlfGGDRsax8vPz1ffvn01cOBAPfHEE7r++us1YcIEbdmyRT4+PpJk7OPkyZNq2rTpFX0WAACA3wMhFAAAsKygoCCFhYVdcp3KcOW3f65UUVHh8l+73V5luZeX1wW3rVzucDgueYzL1VXJ4XAYdUgy7m31axe7TM5ms7ls+1tLly7Vo48+qtOnTys/P189e/Y0ZmcdOnRIN9xwg1FD5f4AAACqE5fjAQCAP4yWLVvq3//+t4qKioyx7Oxs2e12NW/eXJJUUFBgXJonSXv37lWbNm0UEBCghg0bKjs721h27tw5ffvtt2rZsuUFj3epIKdly5bKyclxGfv6668vuq/LCQoKcvlcv3bo0CHl5eXp9ttvl5fXL6d3lWFTRUWFS7B16tQpSVJwcPA11QEAAHCtCKEAAMAfRo8ePdSsWTM99dRTOnDggL744gslJiZq0KBBqlevniSppKRECQkJysvL05o1a7RhwwaNHDlSkjR27Fj99a9/1ebNm5WXl6fnnntOZWVlVW46XsnPz0+StH//fpdgS5JGjhyp7777TvPnz9ehQ4e0bt06vf/++3rggQeu6bO1bdvW5Ul9v7Z06VJNnDhRNptN9erVU1hYmNasWaMPP/xQkutlfwcOHFDDhg2Np+wBAABUFy7HAwAAfxi1atXSkiVLlJiYqPvuu09169bV4MGD9fjjjxvrhISEKDg4WPfee6+Cg4OVnJys6OhoSdK4ceNUXFys5557TsXFxYqKitJ7771n3ID8twIDA3XXXXdp+vTpevLJJ12WNWnSRK+//rqSkpK0fPlyNWnSRDNmzNCwYcOu6bP16tVL6enpVcYLCgqUm5urefPmGWOJiYmKi4vT+fPnNWfOHCMsk6Rdu3apZ8+eXI4HAACqnc3J83kBAEANkZ6ersWLF2vz5s3uLuWqFRcXq0+fPlq/fv0V3xz9t5xOp/r27at58+apS5cuv3OFAAAAl8bleAAAAB7A399fDzzwgNasWXPN+9i+fbsaNWpEAAUAANyCEAoAAMBDTJw4UVu2bDFuLn61li5dqueff/73LQoAAOAKcTkeAAAAAAAATMdMKAAAAAAAAJiOEAoAAAAAAACmI4QCAAAAAACA6QihAAAAAAAAYDpCKAAAAAAAAJiOEAoAAAAAAACmI4QCAAAAAACA6QihAAAAAAAAYDpCKAAAAAAAAJju/wDhXkbswhUM9AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "df_name_categories_pct = df_name_categories.div(df_name_categories.sum(axis=1), axis=0) * 100\n",
+    "df_name_categories_pct = (\n",
+    "    df_name_categories.div(df_name_categories.sum(axis=1), axis=0) * 100\n",
+    ")\n",
     "ax = df_name_categories_pct.plot.barh(\n",
-    "    stacked=True,\n",
-    "    figsize=(12, 6),\n",
-    "    colormap=\"tab20c\",\n",
-    "    width=0.85\n",
+    "    stacked=True, figsize=(12, 6), colormap=\"tab20c\", width=0.85\n",
     ")\n",
     "\n",
     "ax.set_xlabel(\"Proportion (%)\")\n",
@@ -239,67 +253,44 @@
     "plt.savefig(\"../assets/identified_category_distribution_by_province.svg\")\n",
     "\n",
     "plt.show()"
-   ],
-   "id": "6d5c1abb55b7076a",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 7
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "## Native Names vs Surnames",
-   "id": "1973b08d18c2258e"
+   "id": "1973b08d18c2258e",
+   "metadata": {},
+   "source": [
+    "## Native Names vs Surnames"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1e7dde234bb3504f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:42:05.285962Z",
      "start_time": "2025-09-28T21:42:04.523383Z"
     }
    },
-   "cell_type": "code",
-   "source": "df_base = df.query(\"identified_category == 'simple'\")",
-   "id": "1e7dde234bb3504f",
    "outputs": [],
-   "execution_count": 8
+   "source": [
+    "df_base = df.query(\"identified_category == 'simple'\")"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "24a4ad40319f441b",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:42:36.611043Z",
      "start_time": "2025-09-28T21:42:05.308895Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "df_names = build_words_token(df_base, 'identified_name', 'name')\n",
-    "df_names = df_names[['name', 'province', 'sex']].reset_index(drop=True)\n",
-    "df_names.describe().T"
-   ],
-   "id": "24a4ad40319f441b",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "             count  unique       top     freq\n",
-       "name      10015595  644336    ilunga    82342\n",
-       "province  10015595      12  KINSHASA  2106077\n",
-       "sex       10015595       2         m  6033856"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -350,6 +341,12 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "             count  unique       top     freq\n",
+       "name      10015595  644336    ilunga    82342\n",
+       "province  10015595      12  KINSHASA  2106077\n",
+       "sex       10015595       2         m  6033856"
       ]
      },
      "execution_count": 9,
@@ -357,31 +354,25 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 9
+   "source": [
+    "df_names = build_words_token(df_base, \"identified_name\", \"name\")\n",
+    "df_names = df_names[[\"name\", \"province\", \"sex\"]].reset_index(drop=True)\n",
+    "df_names.describe().T"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "736b0d787b9a6809",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:42:59.059882Z",
      "start_time": "2025-09-28T21:42:37.124804Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "df_surnames = build_words_token(df_base, 'identified_surname', 'name')\n",
-    "df_surnames = df_surnames[['name', 'province', 'sex']].reset_index(drop=True)\n",
-    "df_surnames.describe().T"
-   ],
-   "id": "736b0d787b9a6809",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "            count  unique       top     freq\n",
-       "name      5007877  253743      jean    89564\n",
-       "province  5007877      12  KINSHASA  1053047\n",
-       "sex       5007877       2         m  3017009"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -432,6 +423,12 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "            count  unique       top     freq\n",
+       "name      5007877  253743      jean    89564\n",
+       "province  5007877      12  KINSHASA  1053047\n",
+       "sex       5007877       2         m  3017009"
       ]
      },
      "execution_count": 10,
@@ -439,35 +436,53 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 10
+   "source": [
+    "df_surnames = build_words_token(df_base, \"identified_surname\", \"name\")\n",
+    "df_surnames = df_surnames[[\"name\", \"province\", \"sex\"]].reset_index(drop=True)\n",
+    "df_surnames.describe().T"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7980efe654809722",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:43:01.097806Z",
      "start_time": "2025-09-28T21:42:59.860815Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "df_names_m = df_names.query(\"sex == 'm'\")\n",
     "df_names_f = df_names.query(\"sex == 'f'\")\n",
     "\n",
     "df_surnames_m = df_surnames.query(\"sex == 'm'\")\n",
     "df_surnames_f = df_surnames.query(\"sex == 'f'\")"
-   ],
-   "id": "7980efe654809722",
-   "outputs": [],
-   "execution_count": 11
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9568492106dd2169",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:43:06.100459Z",
      "start_time": "2025-09-28T21:43:01.376200Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(1, 2, figsize=(10, 10))\n",
     "\n",
@@ -477,11 +492,7 @@
     "names_m = set(df_names_m[\"name\"].str.lower())\n",
     "names_f = set(df_names_f[\"name\"].str.lower())\n",
     "\n",
-    "values = [\n",
-    "    len(names_m - names_f),\n",
-    "    len(names_f - names_m),\n",
-    "    len(names_m & names_f)\n",
-    "]\n",
+    "values = [len(names_m - names_f), len(names_f - names_m), len(names_m & names_f)]\n",
     "axes[0].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
     "axes[0].set_title(\"Native - Distribution by Gender\")\n",
     "\n",
@@ -492,7 +503,7 @@
     "values = [\n",
     "    len(surnames_m - surnames_f),\n",
     "    len(surnames_f - surnames_m),\n",
-    "    len(surnames_m & surnames_f)\n",
+    "    len(surnames_m & surnames_f),\n",
     "]\n",
     "axes[1].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
     "axes[1].set_title(\"Surnames - Distribution by Gender\")\n",
@@ -508,203 +519,197 @@
     "plt.savefig(\"../assets/name_by_gender_distribution_grid.svg\")\n",
     "\n",
     "plt.show()"
-   ],
-   "id": "9568492106dd2169",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 12
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "12a7094d94ad519f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:43:30.859924Z",
      "start_time": "2025-09-28T21:43:07.728723Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "names_transitions = build_transition_probabilities(df_names['name'])\n",
-    "names_transitions_males   = build_transition_probabilities(df_names_m['name'])\n",
-    "names_transitions_females = build_transition_probabilities(df_names_f['name'])\n",
-    "\n",
-    "names_transitions['df_probs'].to_csv(\"../assets/names_transition_probs.csv\", index=False)"
-   ],
-   "id": "12a7094d94ad519f",
    "outputs": [],
-   "execution_count": 13
+   "source": [
+    "names_transitions = build_transition_probabilities(df_names[\"name\"])\n",
+    "names_transitions_males = build_transition_probabilities(df_names_m[\"name\"])\n",
+    "names_transitions_females = build_transition_probabilities(df_names_f[\"name\"])\n",
+    "\n",
+    "names_transitions[\"df_probs\"].to_csv(\n",
+    "    \"../assets/names_transition_probs.csv\", index=False\n",
+    ")"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "684b467b17955d65",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:43:44.217712Z",
      "start_time": "2025-09-28T21:43:30.869697Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "surnames_transitions = build_transition_probabilities(df_surnames['name'])\n",
-    "surnames_transitions_males = build_transition_probabilities(df_surnames_m['name'])\n",
-    "surnames_transitions_females = build_transition_probabilities(df_surnames_f['name'])\n",
-    "\n",
-    "surnames_transitions['df_probs'].to_csv(\"../assets/surnames_transition_probs.csv\", index=False)"
-   ],
-   "id": "684b467b17955d65",
    "outputs": [],
-   "execution_count": 14
+   "source": [
+    "surnames_transitions = build_transition_probabilities(df_surnames[\"name\"])\n",
+    "surnames_transitions_males = build_transition_probabilities(df_surnames_m[\"name\"])\n",
+    "surnames_transitions_females = build_transition_probabilities(df_surnames_f[\"name\"])\n",
+    "\n",
+    "surnames_transitions[\"df_probs\"].to_csv(\n",
+    "    \"../assets/surnames_transition_probs.csv\", index=False\n",
+    ")"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f15ad6eb4df27527",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:43:49.373562Z",
      "start_time": "2025-09-28T21:43:45.960221Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABlIAAAIOCAYAAADDSpB9AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAfaNJREFUeJzs3Xd4VNX2//HPmUmF0MFIIgKiUgOJdAmiAbkCgoCX+7WB2LBQriKCFJEqotgoChZU1PtTUcTORUCRJiJFREEpSjEWpEhJSMjM+f3BJRJDNDP7DHOYvF888zzJmdlr1rQzi7Oyz7Zs27YFAAAAAAAAAACAQjzhTgAAAAAAAAAAAMCtaKQAAAAAAAAAAAAUgUYKAAAAAAAAAABAEWikAAAAAAAAAAAAFIFGCgAAAAAAAAAAQBFopAAAAAAAAAAAABSBRgoAAAAAAAAAAEARaKQAAAAAAAAAAAAUgUYKEEa2bYc7BQAAcIrwvQ8AANyKOgUA/hqNFKAYevbsqXr16umrr7466fUZGRm69957A4q5efNmXX311QW21a5dW1OmTAk6T6f17NlTtWvX1lVXXVXkbe666y7Vrl074McfzHMGAMCpUFK/9++9917Vrl27yMu8efPCnaIkac6cOapdu7Z27doV7lQAAAgLp2uV06FOAYBwiwp3AsDpwufzaejQoZozZ45iYmKM482bN09r164tsO21117TmWeeaRzbSR6PR+vWrdPPP/9cKLesrCx9/PHHYcoMAIDQKanf+1WqVNHUqVNPel2NGjVObTIAAKBITtYqp0udAgDhxIwUoJjKlCmjzZs3a9q0aSG7j9TUVNcVKvXq1VNsbOxJ/wr1448/Vnx8vBITE8OQGQAAoVNSv/djYmKUmpp60kv58uXDnR4AAPifUNcqbqxTACCcaKQAxVS3bl117dpVzz77rDZs2PCXtz1y5IgeeeQRtW/fXg0aNNAFF1ygG264QRs3bpQkTZkyJf+vPU+cLnv855ycHDVu3FgTJ04sEDcvL08tWrTQuHHj8rfNnj1bnTp1UoMGDXTxxRdrypQp8vl8jj3uUqVKqU2bNidtpHzwwQf6xz/+oaiogpPb9u7dq9GjR+uSSy5RgwYN1KxZM/Xt2/cvT8GRk5Ojhx56SG3atFGDBg3UuXNnffDBB449DgAAAlFSv/eLa8GCBerevbtSUlLUqlUrjRs3TllZWfnXT5kyRZdddpk++ugjXX755UpJSdEVV1yhtWvXat26derRo4caNmyoyy+/XCtWrCgU+5prrlFaWpoaNGigyy67TK+88spf5vPFF1/ouuuuU6NGjdSsWTMNGTJEe/fuDcljBwDADYpbq5TEOgUAQoFGChCAYcOGqUKFCho6dKhyc3OLvN3gwYP15ptvqk+fPpo5c6aGDh2qzZs36+6775Zt2+rRo4f++c9/Sjo2XbZHjx4FxsfGxuof//iHPvzwwwILvi1btkz79u3TFVdcIUmaMWOG7rvvPrVs2VLTp0/Xtddeq2eeeUb33Xefo4+7Y8eO+af3Ou7QoUP69NNPdfnllxe4rW3buvXWW7Vs2TINGjRIzz33nPr166cVK1bo/vvvP2l827bVt29fvfrqq7rhhhv01FNPKS0tTXfddZfmzp3r6GMBAKC4Sur3fl5eXqHLiXm9++676tu3r8455xxNmzZN/fr10zvvvKM77rijwO1+/vlnPfjgg7rtttv0xBNP6MCBAxowYIAGDhyoHj16aNq0abJtW3fddZeOHDkiSfrkk0/Ut29f1a9fX08++aSmTJmiatWqacyYMfryyy9Pmu+qVavUu3dvxcXF6fHHH9ewYcP0+eefq1evXvlxAQCIRMWpVSKtTgGAcGGNFCAA5cqV05gxY3T77bdr2rRpuuuuuwrdJjc3V4cPH9aIESPUsWNHSVKzZs106NAhPfjgg/rtt9905pln5k+RTU1NPel9XXHFFXrzzTe1evVqNWnSRJL0/vvv65xzzlFKSooOHjyoJ598Uv/3f/+nESNGSJLS09NVvnx5jRgxQjfccIPOO+88Rx73xRdfrPj4eM2bN0+9e/eWJH300UeqVKmSGjduXOC2v/76q+Lj4zVkyJD8vJs3b64dO3botddeO2n85cuXa8mSJXrsscfyn7PWrVsrOztbkyZN0uWXX15o1gsAAKFWEr/3f/zxR9WvX7/Q9rvvvlt9+vSRbduaNGmSWrdurUmTJuVfX6NGDfXu3VuLFy/WxRdfLEnKzs7W/fffr4suukiStGXLFj3yyCMaP358/gGbrKwsDRgwQN9//73q1q2rLVu2qFu3bho+fHh+7LS0NDVv3lwrV65Uo0aNCuX2yCOPqGbNmpoxY4a8Xq8kqVGjRurUqZPefPNNXXvttcbPCwAAbvR3tUqk1SkAEE4cmQQClJGRoS5duujZZ59V+/btCx1siImJ0XPPPSdJ+uWXX/T999/rhx9+yF+U/a/+ovVEzZo1U1JSkt5//301adJEOTk5WrBggfr06SNJWrt2rY4cOaKMjAzl5eUVyE869tchJytU/H6//H5//u+WZeUfdChKXFycMjIyCjRS3n//fXXo0EGWZRW4bWJiombNmiXbtrVr1y5t375d27Zt05o1a4p87CtWrJBlWWrTpk2hx/LOO+9o8+bNqlu37l/mCABAKJS07/0qVaroqaeeKrT9+AGWbdu26eeff9att95aII+mTZsqISFBy5Yty2+kSNIFF1yQ/3PlypUlqUAz5Pi6KwcOHJAk3XzzzZKkw4cP6/vvv9eOHTv01VdfSTr5c5mdna0vv/xSN910k2zbzs+pWrVqqlWrlpYtW0YjBQAQ0f6qVnF7nQIApxMaKUAQRowYoRUrVmjo0KF68803C12/ZMkSPfDAA9q2bZtKly6tOnXqqFSpUpJUYCrsX7EsS507d9bs2bM1YsQIffzxx8rKylLnzp0lSfv375ek/MLlz3799deTbh82bJjeeuut/N+Tk5O1aNGiv82nQ4cO6tevn37++WfFxsZqxYoVuvPOO09623feeUePPvqofvrpJ5UvX15169ZVXFxckbH3798v27YLHGz582OhkQIACJeS9L0fExOjlJSUIq8/nsfo0aM1evTov80jISGh0G3i4+OLjL93717df//9WrBggSzLUvXq1fP/8vVkz+WBAwfk9/v1zDPP6Jlnnil0fWxsbJH3BQBApPirWsXNdQoAnE5opABBKFeunEaNGqW+ffvqySefLHDdjh071LdvX7Vr104zZsxQtWrVZFmWXnnlFS1ZsiSg+7niiis0Y8YMrVy5Uh988IGaNm2q5ORkSVLZsmUlSZMmTVKNGjUKjT3+V59/1q9fvwJ/mRkTE1OsXC666CKVLl1a8+bNU6lSpXTWWWepQYMGhW73xRdfaMiQIerZs6duuukmJSYmSpIeeughrV69+qSxy5Qpo1KlSmnWrFknvb569erFyhEAgFAoid/7RTmex+DBg9WsWbNC15crV84o/qBBg7Rt2za98MILSktLU0xMjLKzs/X666+f9PalS5eWZVnq3bu3OnXqVOj6v2raAAAQKYqqVdxepwDA6YTF5oEgtWvXTpdffrmefvpp7d27N3/7hg0blJOToz59+ujss8/OP/XV8SLl+F98eDx///GrVauW6tevr/fff1+LFy9Wly5d8q9r1KiRoqOj9csvvyglJSX/EhUVpUcffVS7du06acyzzjqrwO1r165drMcbExOjdu3a6b///a8+/PDDkx6skI5N6fX7/erfv39+E8Xn82n58uWSVOD0Isc1a9ZMWVlZsm27QG7fffedpk2bVmBqMAAA4VDSvveLcs4556hSpUratWtXgbiJiYl65JFH9M033xjFX716tdq3b6/mzZvnN30+/fRTSSevIRISElSvXj1t27atQD7nnXeepkyZopUrVxrlAwDA6eJktYrb6xQAOJ0wIwUwcN999+mzzz7Tb7/9lr+tfv36ioqK0sMPP6wbb7xRubm5mjNnjj755BNJxxZVlf74i4333ntPjRo1UrVq1U56H1dccYUmTpyoqKgoXXbZZfnbK1SooJtvvllPPPGEDh06pObNm+uXX37RE088IcuyVKdOHccfb8eOHXXrrbfK4/HkLyD3Zw0bNpQkjRkzRldeeaV+//13vfLKK9q0aZOkY4//z6f5aNOmjZo2bao77rhDd9xxh2rVqqX169dr8uTJat26tSpWrOj4YwEAIFAl7Xv/ZLxer+666y6NHDlSXq9Xl1xyiQ4cOKAnn3xSv/zyy0kXqg9Ew4YN9e6776p+/fo688wztWbNGj399NOyLEvZ2dknHTNw4ED16dNHd999t7p06SKfz6eZM2fqyy+/1B133GGUDwAAp5M/1yolrU4BgFBiRgpgoHz58ho1alSBbdWrV9cjjzyiX375RbfffrtGjhwpSXrppZdkWZa++OILSVL79u2VkpKie++9N3/xt5O5/PLLZVmWLrnkEpUpU6bAdXfeeafuvfdeffTRR7rlllv08MMPq3Hjxnr55ZcL3dYJF154ocqWLavzzjtPtWrVOultmjdvrpEjR2rt2rW65ZZb9OCDDyopKUlTp06VpJOe3svj8ejpp59Wp06dNGPGDN1000169dVXdcMNN+ixxx5z/HEAABCMkva9X5QePXrokUce0Zo1a3Tbbbdp1KhROuuss/TSSy8VeeCluB588EE1atRIY8eOVd++fbVw4UKNHj1a6enp+c/ln6Wnp+u5557Tzz//rAEDBmjw4MHyer16/vnnlZqaapQPAACnkz/XKiWxTgGAULHs4q4sBQAAAAAAAAAAUMIwIwUAAAAAAAAAAKAINFIAAAAAAAAAAACKQCMFAAAAAAAAAACgCDRSAAAAAAAAAAAAikAjBQAAAAAAAAAAoAg0UgAAAAAAAAAAAIoQFe4EAAAAAAAAAABAaPj9ftm2HZLYlmXJ44n8+RruaaRk/W42/shh8xziSpvH8B01G++NNs8hRB+KwHLwm8ewHPgAWpbZ+Jws4xTs3CPGMYyfz8OGny9JVtVaxjGMH4cT7wkn3ps52WbjvV7zHKJizWPIBfsK09f0qAOfLyf2uwkVzWOgMNPaQHJkP67YUmbjTWsDKXLqA7/PPIbHcB9qWhtIztQHeblmAfwOfJ9lHzAOYZ1RwyyAG94Tknl94ES958R/Pk3rA0deDydqeNP6IMc8hygH9rulK5jHQGFO1AfZh8zGxyeY5+CG+sANtYEk8/+XOPDd7oL6wHaibnXi/3iHzD5jVtK55jm44ZiSG44dSObHDxw5duAEw/emE8eD3HD8IEzHDvx+v5Z9tEClKlcKSfyoqCilpKREfDPFPY0UAAAAAAAAAADgGNu2VapyJf2nYw/lHnaiafuHmNKldM0Hs0M228VNaKQAAAAAAAAAABDB8g5nyedwIyXP0WjuFtnzbQAAAAAAAAAAAAwwIwUAAAAAAAAAgAjmsSx5nFgT6k8xSwpmpAAAAAAAAAAAABTB8RkpR48eVXR0tNNhAQAAAAAAAABAEDxyflZFSZqlEVQj5bffftOMGTO0ZcsW+Xw+SZJt2zp69Ki2bt2qVatWOZokAAAAAAAAAAAIjsc6dnE6ZkkRVNNo2LBhWrJkiVJSUrRmzRo1atRIFStW1Pr169W/f3+ncwQAAAAAAAAAAAiLoGakrFq1SjNnzlRaWpqWLVumiy++WI0bN9bTTz+tTz/9VL169XI6TwAAAAAAAAAAEARLzp+KqwRNSAnuubNtW4mJiZKkc889V998840kqUOHDvrqq6+cyw4AAAAAAAAAACCMgmqk1KtXT2+//bYkqW7dulq2bJkkadeuXc5lBgAAAAAAAAAAjHksKySXkiKoU3vdfffduu222xQfH68rrrhCzz77rDp37qzMzEx16dLF6RwBAAAAAAAAAADCIqhGSuPGjfXxxx/ryJEjqlChgt58800tWLBA5cuXV4cOHZzOEQAAAAAAAAAABMkj59dIcTqemwXVSJGkhIQEJSQkSJISExN17bXXOpYUAAAAAAAAAACAGwTdSAEAAAAAAAAAAO7nkeRxeEmTkjQjpSQ9VgAAAAAAAAAAgIAwIwUAAAAAAAAAgAjGGilmXNNI8U0bZjTe2/cBhzIx5I02Gm7/tss4BavyWcYxzJPwGoew9/xonkalZLMAsaXMc3AghrGylY1D+Dd+ZhzDU7eFcQxjlgO7+LjS5jEM2T9+ZxzDSj7fLMDRHOMcFB1rNj7KcLwkWQ7Pa4VjfE/fbxzD22e0A5mYJmFWG0iSvTfTOIZVMck4hjGveelp//qD0XjrjBrGOURMfVCmonEI0/rAFbWBZF4fuOH1lGTv3Gg03qpW1zyJvFzzGFExZuNN6wu4mu+JwcYxvP9+yIFMTJNwoD74bafReKtyNeMcnGFWj9t7fzLPoGJV4xim3wWuqA0kqWwVo+H+b5Ybp+Cpd6FxDGORcuwgc4txDCvpXPNEjhrWB058t3P8QJZlyXL4MTgdz81KUtMIAAAAAAAAAAAgIK6ZkQIAAAAAAAAAAJzHqb3MlKTHCgAAAAAAAAAAEBBmpAAAAAAAAAAAEME81rGL0zFLCmakAAAAAAAAAAAAFMF4RsrBgwcVHR2tuLg4J/IBAAAAAAAAAAAOYo0UM0E1Uo4ePaoZM2bo1Vdf1Z49eyRJZ555pnr37q3rr7/e0QQBAAAAAAAAAADCJahGytixY7VkyRINGjRI9erVk9/v1/r16zV58mTt2bNHAwcOdDpPAAAAAAAAAAAQBI9lyWM5u6iJ0/HcLKhGyvvvv68ZM2aoSZMm+dvq1Kmj5ORkDRw4kEYKAAAAAAAAAAAuYcn5U3GVnDZKkM9dQkKCoqIK92DKlClz0u0AAAAAAAAAAACno2J3PTIzM/N/7tWrl4YMGaLhw4crJSVFXq9X3333ncaMGaP+/fuHJFEAAAAAAAAAABA4jyXZDk8h8ZSgKSnFbqRkZGTI+t85z2zbliT16dOn0LbRo0frqquucjpPAAAAAAAAAACAU67YjZSFCxeGMg8AAAAAAAAAABACHkl2CGKWFMVupCQnJ4cyDwAAAAAAAAAAANdhZXgAAAAAAAAAACKYR5ZsObuoicfheG5WkmbfAAAAAAAAAAAABIQZKQAAAAAAAAAARDCPJdkOTyDxlJwJKe5ppHiuvyfcKUi2A8vtWIbvHq9rXhIjvtceNw/i9ZqH+Gd/swC5R4xzcOQ1dcH7wlMrNdwpSLbfPIblgol4fp9xCOvMcxxIxFB0bLgzkDPLpJWgb/3TjOeqfuFO4RjTfY8T+528XPMYLuB768lwpyBvtzvMgziwHzeuO11QG0iS59y0cKcQOfWBL884hJV8vgOJGIqKCXcGkfOewEl5bxkR7hTcIyY+3Bk4wvefSWYBos33O94eA4xjKPuQ2fhYB15Pj/lxFOMU3HDsQHLgu8CB/yeaHiOUjB+HlVjdPAcnuKE+4PgBi80bKkmPFQAAAAAAAAAAICDu+FM2AAAAAAAAAAAQEpzaywwzUgAAAAAAAAAAAIrAjBQAAAAAAAAAACKYR5Zsh9d58Zzm68YEghkpAAAAAAAAAAAARQi6kbJ48WItWbIk//fx48fr008/dSQpAAAAAAAAAADgDI8VmktJEVQj5aWXXtJdd92l3377LX9bVFSU7rzzTr3++uuOJQcAAAAAAAAAABBOQTVSnn/+eT3yyCPq1q1b/rYhQ4bo4Ycf1tNPP+1YcgAAAAAAAAAAwIylY80AJy8laEJKcIvN79u3T2effXah7TVr1iwwSwUAAAAAAAAAAISXx5LjnQ9O7fU3GjdurClTpig7Ozt/W05OjqZPn660tDTHkgMAAAAAAAAAAAinoGakjBw5UjfeeKPS09NVo0YNSdKOHTtUuXJlPfnkk07mBwAAAAAAAAAADHjk/JQUTwk6uVdQjZSzzz5bH3zwgZYsWaIffvhBUVFRqlGjhtLT0+X1ep3OEQAAAAAAAAAAICyCaqRIUkxMjNq2betkLgAAAAAAAAAAwGGskWImqDVSAAAAAAAAAAAASoKgZ6QAAAAAAAAAAAD3C8GElBK0QgozUgAAAAAAAAAAAIrEjBQAAAAAAAAAACIYa6SYcU0jxf/VEqPx3pZdzJOwStArH2Lef/3bOIbvnaeNY9iH9xuNt0qXN84hUti/7TKOYSWdaxjAgUl0tt88hmkeHq9xCvb+X4xjWOUTDZNwwXNZoiaRljz+b1Yax/C2uNw8ESf2Paai48KdgSO8XW83jmFcH2QdMM5Bpcqax4gQ9i8/GI23zqptnoQb6gMncvCa/9fMtD4wrg0kd9QHbthvI2R8X35iHMOR4wdu4M8LdwaO8F59t9F439szjHOwD+0zjmElVDCOEQnsvT8Zx7Cq1jJPxPS7wInvMyf+v2r4OOxDv5mnUO4M4xiSbZqFAzlw/MATgpN7eUrQ80qFCQAAAAAAAAAAUATXzEgBAAAAAAAAAADO49ReZpiRAgAAAAAAAAAAUARmpAAAAAAAAAAAEMFCMaOiJM3SKEmPFQAAAAAAAAAAICDFnpHSs2dPWVbxTno2a9asoBMCAAAAAAAAAADOCcESKY7Hc7NiN1KaN2+e//O+ffv02muvqV27dkpJSVF0dLQ2btyoDz74QNdee21IEgUAAAAAAAAAADjVit1I6devX/7PvXv31rBhw3TNNdcUuE3Tpk312muvOZcdAAAAAAAAAAAwYlmSp5hnnAokZkkR1Bop69atU8uWLQttb9Sokb799lvjpAAAAAAAAAAAgDOsEF1KiqAaKfXq1dPTTz+tnJyc/G2HDh3S5MmTlZqa6lRuAAAAAAAAAAAAYVXsU3udaOzYserTp49atWql6tWry7Zt/fDDD0pKStKMGTOczhEAAAAAAAAAAAQpFLNHStKMlKAaKbVq1dKHH36o5cuXa+vWrZKk8847TxdeeKGiooIKCQAAAAAAAAAA4DpBdz1iYmJ08cUX6+KLL3YwHQAAAAAAAAAA4CRmpJgJao0UAAAAAAAAAACAkoDzcAEAAAAAAAAAEMEsy5IsZ+eQWA7HczNmpAAAAAAAAAAAABTBsm3bDncSkqTD+83G5+Wa5xAdax7D7zMb7/Ga5+CGl9Qt3UjT5+LIIfMUjhw2jmH6fNp7Mo1T8NRKM44h22823nKg93v0iHkMU058RGPiHAhiyIl9jem+4miOeQ5eByZnJlQ0j4HCTGsDyR31gWltIDmz/4sUbqgxsp2oDwxjOPA82Ht/No7hOaeRYRIu+C6RzL9P3FB/S9QHxzlRHzjx/7IylcxjoDA31AduOHYgmb9P3bLvcsN3uxPPheF3uxuOHUiS/duPRuM9515gnIPxsQPJvH7OyTbPweNEDW/43ox2QW0gmX/G3FDvSVJUtNn40hXMcwiCz+fTunXrtOOKf8rOcuC9fQKrVLzOfvsNpaamyut1oH5yMU7tBQAAAAAAAABABPPImb/tPZELWuGnDH/eCAAAAAAAAAAAUARmpAAAAAAAAAAAEMEsS45PIXHD2RlPFWakAAAAAAAAAAAAFIEZKQAAAAAAAAAARDBLzk9JsUrQKinGM1J+//13+f1+2bbTS9UAAAAAAAAAAACEV1CNFNu29dRTT6l58+Zq2bKlfvzxR91zzz0aOXKkcnNznc4RAAAAAAAAAAAEyQrRJVA5OTkaNmyYmjRpovT0dM2cObPI23700Ufq0KGD0tLSdPXVV+vrr78O4h6dEVQjZdq0aXrnnXf04IMPKiYmRpLUrVs3LVu2TA899JCjCQIAAAAAAAAAgNPfQw89pA0bNujFF1/U/fffr6lTp2revHmFbrd582bdfffduvXWW/X222+rbt26uvXWW5WdnR2GrINspLz11lsaM2aMLrnkElnWsb5Tq1atNHHiRH344YeOJggAAAAAAAAAAILnhhkpWVlZmj17toYPH6769evr0ksv1c0336xXXnml0G2XLVumc889V127dtXZZ5+tgQMHavfu3dqyZUvAj90JQTVS9uzZozPOOKPQ9rJlyyorK8s4KQAAAAAAAAAA4AyPFZpLIDZt2qS8vDylpaXlb2vcuLG+/PJL+f3+ArctX768tmzZotWrV8vv92vOnDlKSEjQ2Wef7cTTEbCoYAa1aNFCzz33nMaMGZO/7dChQ3r00UfVvHlzx5IDAAAAAAAAAADudejQIXm93vzfY2Ji8pcEOdHu3btVoUKFAtdVrlxZOTk52r9/vypWrJi/vWPHjlq0aJGuueYaeb1eeTwezZgxQ+XKlQvtgylCUDNSRo0apW+++UatWrVSTk6O7rjjDrVp00Y//vijRowY4XSOAAAAAAAAAAAgSFaI/klSmzZt1Lhx4/zLjBkzTppDdnZ2oQbL8d9zc3MLbN+3b592796tkSNH6vXXX9cVV1yhoUOHas+ePSF4dv5eUDNSzjzzTL3xxhtasWKFtm3bpry8PNWsWVPp6enyeILqzQAAAAAAAAAAgNPM4sWLC81IOZnY2NhCDZPjv8fFxRXYPmnSJJ1//vm69tprJUljx45Vhw4d9Oabb6pPnz5Opl8sQTVSjmvZsqVatmzpVC4AAAAAAAAAAMBhgS4MH0jMhISEAo2UoiQmJmrfvn3Ky8tTVNSx1sTu3bsVFxensmXLFrjt119/rZ49e+b/7vF4VKdOHWVmZjqWfyCYPgIAAAAAAAAAAEKqbt26ioqK0rp16/K3rV69WikpKYXOdHXGGWdo69atBbZ9//33Ouuss05FqoXQSAEAAAAAAAAAIIJZVmgugYiPj1fXrl01atQorV+/XgsWLNDMmTPVq1cvScdmpxw5ckSS9K9//Uuvv/665s6dq+3bt2vSpEnKzMxUt27dnH5qisXo1F4AAAAAAAAAAADFMXToUI0aNUrXX3+9EhIS1L9/f7Vv316SlJ6ergkTJqh79+7q2LGjDh8+rBkzZujnn39W3bp19eKLL6pSpUphyduybdsOyz3/ie+TV43Ge5t1cCiT8LIP7DaOYZWt4kAm4Wf/+J1xDCv5fAcyMeTERyzQ9m4IRMx70+8zj+H5+3M+hppv6lDjGN5+E8wC5OX+/W3+TtTJFx8rNrd8vkqVM4+BQnz/fd44hrd1dwcyCb+I2Qc7wLQ+cEVtIJnvv1xQG0iSfWif0XgroYJDmRiy/WbjLXdM9PdNv89ovPe2seZJUB/8gfogJPyfvWscw9PwIgcyCT/791+NxlvlznAok/CKmGMHEcLe/7NxDKv8mQ5kYsiXZx7DG/6/X/c9Mdg4hvffD5kncjTHbHx0rHkObqgPwlQb+Hw+rVu3Tnu7/0t2Vrajsa1S8ao453WlpqYWa42U05k7Kn4AAAAAAAAAAAAXCn9rFAAAAAAAAAAAhIz1v39ORy0paKQAAAAAAAAAABDBSk7LIzQ4tRcAAAAAAAAAAEARmJECAAAAAAAAAEAEsyw5Py2lBE1zYUYKAAAAAAAAAABAEYrVSFm2bJlyc3NDnQsAAAAAAAAAAHCYFaJLSVGsRkq/fv20d+9eSVLbtm21b9++kCYFAAAAAAAAAADgBsVaI6Vs2bKaNm2aLrjgAv344496//33lZCQcNLbdu3a1cn8AAAAAAAAAACAASskc0hKzpyUYjVSRo4cqSlTpmj58uWyLEvPPvusPJ7Ck1ksy6KRAgAAAAAAAAAAIkaxGilt27ZV27ZtJUkZGRl68803VaFChZAmBgAAAAAAAAAAzHmkkjSBxHHFaqScaNGiRaHIAwAAAAAAAAAAhAA9FDPFWmweAAAAAAAAAACgJAp4RgoAAAAAAAAAADh9MCPFDDNSAAAAAAAAAAAAisCMFAAAAAAAAAAAIpglS87PSyk581yYkQIAAAAAAAAAAFAE98xIWbvCbHzjduY5eKPNY5jy+8OdgTN8eeYxylYyj+EGVmR0Zv3rlxrH8KZ3cyATQx5vuDNwhOeqvuFOwR3vbTfkgND5cpV5jFZXmMdww37DtsOdgTOO5pjHoD5wFf83nxmN9zbr4FAmhqzI+PsyT4/bwp2CO57LCPl84eTszxaaB6nXwmx8VIx5Dk6wI+T4gWmdEym1QYTwr11sHMN7yf85kIlpEu45ZGrCc23/cKdwjIf6wA0sJqQYccG7GAAAAAAAAAAAwJ0io70KAAAAAAAAAABOihkVZmikAAAAAAAAAAAQwUrQWbhCgkYUAAAAAAAAAABAEZiRAgAAAAAAAABABLMs638rzjsa1Nl4LsaMFAAAAAAAAAAAgCIENSMlIyPjWAfrTyzLUnR0tKpUqaIOHTro6quvNk4QAAAAAAAAAAAEr+TMHQmNoBop1113naZOnarrrrtOqampsm1bGzZs0EsvvaQrr7xSZ5xxhp566ikdOnRIt9xyi9M5AwAAAAAAAAAAnBJBNVLmzp2rsWPHqlOnTvnb2rZtq9q1a2v69OmaO3eu6tatqxEjRtBIAQAAAAAAAAAgjJiRYiaoNVJ27NihOnXqFNp+3nnnadu2bZKkGjVqaM+ePWbZAQAAAAAAAAAAhFFQjZTU1FRNmTJFWVlZ+duysrI0bdo0NWzYUJK0ePFiVa9e3ZksAQAAAAAAAABAUCzLCsmlpAjq1F5jx47VbbfdptatW6tGjRqybVvbt29X1apVNWXKFC1dulQPPPCAnnjiCafzBQAAAAAAAAAAAbCsYxcn2ZZkOxvStYJqpFSrVk3vvPOOVqxYoe+++05er1fnnXeeWrZsKcuyVK5cOS1evFgVK1Z0Ol8AAAAAAAAAAIBTJqhGiiR5vV6lp6crPT290HU0UAAAAAAAAAAAcAfLY8nyODwlxWOVmBkpQa2RAgAAAAAAAAAAUBIEPSMFAAAAAAAAAAC4XyjWSFHJWWueGSkAAAAAAAAAAABFcc+MlDopZuO90c7kAWd4zd9a/hnjzNMY9JhxDBzjqdM03CngRLHx4c5A8rjnKwQRql4j8xger3kMN/D7w52BM6JjjUP4n3/AaLy3z2jjHPAHT/U64U4BJ4otFe4MIme/C/eq60B9EBVjHsMNIuXzZvjn0b7HhxunEHXfdOMYOMbT6KJwp4ATxbjg2IEUOfur0xwzUswwIwUAAAAAAAAAAKAI/DkxAAAAAAAAAAARzLIsWU5PSXF8iot70UgBAAAAAAAAACCCcWovM5zaCwAAAAAAAAAAoAjMSAEAAAAAAAAAIIJxai8zQTVSDh48qGeeeUabNm1STk6ObNsucP2sWbMcSQ4AAAAAAAAAACCcgmqkDB48WF9//bU6dOigMmXKOJ0TAAAAAAAAAABwCGukmAmqkbJixQrNmjVLDRs2dDofAAAAAAAAAAAA1wiqkVKlShV5vV6ncwEAAAAAAAAAAA7zhGCNFJs1UgrLzMzM//naa6/ViBEjNHjwYJ111lmFmipJSUnOZQgAAAAAAAAAABAmxW6kZGRk5Hesji8uf8MNNxToYtm2LcuytHHjRofTBAAAAAAAAAAAwWCNFDPFbqQsXLgwlHkAAAAAAAAAAIAQsEJwai/nOzPuVexGSnJycijzAAAAAAAAAAAAcJ2gFpsHAAAAAAAAAACnB8tz7OIop+O5WAl6qAAAAAAAAAAAAIFhRgoAAAAAAAAAABGMNVLMWLZt2+FOQpK0Z5fZeI8DPaHYePMYvjyz8X6feQ5R0eYx5IIPgRs+iFkHjEPYebnmeZjOuzt6xDyF8onGMYxfUyfmHzrwXCjP8HPuxHs7rrR5DDfs/k2fC9N9rhM5SFJCRfMYKMTevcM4hhUda55ITJzZeCfep34HYkTFGAZwwfeyFDn1ge+oWQCP1zgHZR80DmFVMl1H0YHX04n3RE622Xjbb56DE48jtpTZeDfUBhL1Af6a6bEDyXwfavpZk5x5n5rue7xOHDtwATfUBpJxfWA78X9VJ+oDwxrFKlvFPAcnXlPj4yg55jnkGdZ7TohPMI/hhvrAifeEG+qDMNUGPp9P69atU9lbbpCVbVj3/okdH68Dzzyv1NRUeb0O7INcjBkpAAAAAAAAAABEMEvu6T2fjlgjBQAAAAAAAAAAoAjMSAEAAAAAAAAAIIKxRooZGikAAAAAAAAAAEQwywpB36Pk9FE4tRcAAAAAAAAAAEBRmJECAAAAAAAAAEAEsyxLHoenpPhL0Km9mJECAAAAAAAAAABQBGakAAAAAAAAAAAQwUKxRkoJmpBS/EZKZmamqlatKsuylJmZ+Ze3TUpKMk4MAAAAAAAAAAAg3IrdSMnIyNCyZctUqVIlZWRkyDpJu8m2bVmWpY0bNzqaJAAAAAAAAAAACI5lWSc9pm8as6QodiNl4cKFqlixYv7PAAAAAAAAAAAAka7YjZTk5OST/gwAAAAAAAAAANyLNVLMsNg8AAAAAAAAAAARjEaKGU+4EwAAAAAAAAAAAHArZqQAAAAAAAAAABDBLI8ly+PwYvMOx3MzZqQAAAAAAAAAAAAUgRkpAAAAAAAAAABEMNZIMeOaRorvmxVG472N2zuUiSGv2VNqH/jNOAWrwpnGMdzA99C/jWN4Bz9hFqBUWeMcImV/Yu/eYRzDqnK2A5kYio5zIIZ5CFP24d+NY1ilyzmQSZgZ7nPhbvaWtcYxrEYXmydiyoH3qX3QgfqgfGTUB3mjbjEaHzXqGfMkIqU+KF3eOIS972ej8a6pW2Pjw52BM7IOmI134L3tCtQHEc23/lPjGN7mnRzIxDQJB+qDvT8ZjbcqVjXOwQ18j99jHMN758PmiRjuQy1Fxj7Y/vE74xhW8vkOZGIoOtYdMQxx7OAE1AcwxDsIAAAAAAAAAIAI5rEkj+NTUpwN52askQIAAAAAAAAAAFAEZqQAAAAAAAAAABDBWCPFDDNSAAAAAAAAAAAAisCMFAAAAAAAAAAAIphlWbIcnkLidDw3o5ECAAAAAAAAAEAE49ReZji1FwAAAAAAAAAAQBH+dkZKZmamqlatKsuylJmZ+Ze3jY6OVvny5RUdHe1YggAAAAAAAAAAIHgl6dReQ4YMUadOndSqVSt5vV5HYv5tIyUjI0PLli1TpUqVlJGR8ZdPjm3biomJUd++fXXrrbc6kiAAAAAAAAAAAEBxJCQkaPjw4Tp69Kjat2+vjh07qnnz5kaNn79tpCxcuFAVK1bM//mv+Hw+LVmyRI8//jiNFAAAAAAAAAAAXKAkrZFy3333acSIEVq1apXmzZunQYMGSZI6dOigTp06KTU1NeCYf9tISU5OPunPRbn00kv1888/B5wIAAAAAAAAAACAKcuy1KxZMzVr1kwDBw7Us88+q+eff14vv/yykpKS9K9//Uu9e/dWbGxsseL9bSMlUGeccYbuvvtup8MCAAAAAAAAAIAglKQ1UiTp8OHD+vjjjzVv3jwtXbpUiYmJuuGGG9SxY0ft3r1bkyZN0ueff67nnnuuWPEcb6QAAAAAAAAAAAD8WU5OjkaPHq358+crLi5ON954o2688caT3vbbb7/VqFGj9PXXX6t69eoaPny4WrRo8bf3cfvtt2v58uUqW7asOnTooFmzZqlhw4b5159//vk6cOCAhg8fXuy8aaQAAAAAAAAAABDBLEuyPM7HDNRDDz2kDRs26MUXX1RmZqaGDBmipKQkXXbZZQVud/DgQd14443KyMjQgw8+qLffflv9+vXTf//7X1WqVOkv76Ny5cqaMWPGXy4w36RJE82ePbvYeTv81AEAAAAAAAAAADc5fmovpy+ByMrK0uzZszV8+HDVr19fl156qW6++Wa98sorhW771ltvqVSpUho1apSqV6+uAQMGqHr16tqwYcPf3s/27dtVr169Qvnt3btX3bt3lyRVqVJFtWrVKnbuzEgBAAAAAAAAAAAhtWnTJuXl5SktLS1/W+PGjTV9+nT5/X55PH/M+/j888/Vtm1beb3e/G1vvvlmkbE//fRTrV+/XpK0atUqTZ8+XaVKlSpwm+3bt+vHH38MKncaKQAAAAAAAAAARDKPdezidExJhw4dKtDwiImJUUxMTKGb7969WxUqVChwXeXKlZWTk6P9+/erYsWK+dt37typhg0b6r777tOiRYuUnJysIUOGqHHjxidNpWbNmnr22Wdl27Zs29aaNWsUHR2df71lWSpVqpTGjx8f1EN1TSPF/9JzRuO9jds7lEmYRRV+g52WbNs4hOeaOxxIBI6x/eHOACfKyTKPUbqc2XjfUfMcvNF/f5u/4sT70ukThMIxvhemG8fwPHaxeSJu4KU+OM5z410OJALHOPFdAMfYuWb1gVWqrHkSbqgP/D7zHDzev78NwsL//2YZx/A27+RAJi4QHRvuDFzB869bw50CThRluA+XHKkZg1q4IRLlHDaPYXrsQDKvD0xrA4njByHWpk0bZWdn5//er18/9e/fv9DtsrOzCzVYjv+em5tbYHtWVpaefvpp9erVS88884zef/993XTTTfrwww9VtWrVQrGrVaumWbOO1QlDhw7V8OHDlZCQYPzYjnNNIwUAAAAAAAAAAISAZTnfZPxfvMWLFxeakXIysbGxhRomx3+Pi4srsN3r9apu3boaMGCAJKlevXpatmyZ3n77bd12222FYmdmZqpq1aqyLEv9+/fXgQMHdODAgZPmkZSUVMwH+AcaKQAAAAAAAAAAICgJCQkFGilFSUxM1L59+5SXl6eoqGOtid27dysuLk5lyxacHV2lShWdc845BbbVqFFDP/3000ljZ2RkaNmyZapUqZIyMjJkWZbsE2a4Hf/dsixt3Lgx0IdIIwUAAAAAAAAAgIhmWbJCNCOluOrWrauoqCitW7dOTZo0kSStXr1aKSkpBRaal6TU1FStWrWqwLZt27bp8ssvP2nshQsXqkKFCpKkBQsWOP5YaaQAAAAAAAAAAICQio+PV9euXTVq1Cg98MAD+vXXXzVz5kxNmDBB0rHZKWXKlFFcXJyuuuoqvfzyy5oyZYq6dOmiuXPnaufOnbriiitOGjs5OTn/57POOsvx3GmkAAAAAAAAAAAQyTzWsYvTMQM0dOhQjRo1Stdff70SEhLUv39/tW/fXpKUnp6uCRMmqHv37kpOTtazzz6r8ePH6+mnn1atWrX09NNPKzEx8aRx69SpU+xZKKfs1F6bNm1SnTp1Cm0/dOiQnnjiCQ0fPjyYsAAAAAAAAAAAwGkhXGw+EPHx8Zo4caImTpxY6Lpvv/22wO+NGzfWnDlzihX3xRdfdP7UZScIqpHSq1cvTZ8+XRdccEH+trffflsPP/ywYmJiaKQAAAAAAAAAAIBTonnz5iGNH1QjpX///rr55pv1+OOPq2rVqho9erQ2bNigm2++WbfccovTOQIAAAAAAAAAgCBZHkuWw6f2cjqeibZt2+qNN95QhQoVlJGR8ZezUxYuXBhw/KAaKT179lSVKlV05513Kjc3V+3atdMHH3ygpKSkYMIBAAAAAAAAAAAEpV+/fipdurSkYxNBnFbsRkpmZmaB3xs2bKj7779fI0eOVFpaWoHb0FABAAAAAAAAAMAlXLJGSqh069btpD8fPHhQ0dHRiouLM4pf7EZKUdNhbNvWhAkT9OCDD8q2bVmWFdSq9wAAAAAAAAAAACaOHj2qGTNm6NVXX9WePXskSWeeeaZ69+6t66+/PqiYxW6kBHPeMAAAAAAAAAAAEF6RvkbKicaOHaslS5Zo0KBBqlevnvx+v9avX6/Jkydrz549GjhwYMAxi91ISU5ODjg4AAAAAAAAAADAqfL+++9rxowZatKkSf62OnXqKDk5WQMHDgxtIwUAAAAAAAAAAJyGInyNlBMlJCQoKqpw66NMmTIn3V4cNFIAAAAAAAAAAIhkHuvYxemYLpGZmZn/c69evTRkyBANHz5cKSkp8nq9+u677zRmzBj1798/qPg0UgAAAAAAAAAAwGkrIyND1v9myNi2LUnq06dPoW2jR4/WVVddFXB8GikAAAAAAAAAAEQwS1Z+U8HJmG6xcOHCkMZ3TSPFc1mHcKfgCvav241jWGUqOpCJaRIOfIhKlTGPAcfYB/Yax7DOqGGeCCRJ/u3fGMfwVqxqFsDjNc7BnHu+sOE8T9t24U7BNew9mX9/o78RKfWBVaqsA4m4wP/+GipoLjkXsWl9YFWu5lAmkCT/918bjfemnWmehBvqA8sT7gwQQp6Ol4c7Bdewf95mNN4VtYET4kqFOwOcwN7/m3EMK7GmA5lAkvzbNxrH8FZMMk+E+gCnQHJy8t/eJjc3Vxs3bizWbf/MNY0UAAAAAAAAAAAQAhG+RsqJ1qxZo9GjR2vLli3y+/0FrvN6vdqwYUPAMWnFAQAAAAAAAACAiDBu3DglJydr+vTpio+P15QpUzRixAiVL19eDz30UFAxmZECAAAAAAAAAEAksyznTxHsklMO/9nmzZv18MMPq1atWqpfv76io6N17bXXqlKlSnrmmWfUsWPHgGMyIwUAAAAAAAAAAESE+Ph4eb3H1uY555xz9O2330qSGjZsqO+//z6omDRSAAAAAAAAAACIYJYnNBc3atGihR555BH98ssvSktL0wcffKD9+/dr0aJFKlu2bFAxXfpQAQAAAAAAAACAIyz9cXovxy7hflAnN3z4cP3++++aP3++OnXqpISEBLVo0UITJkxQ3759g4rJGikAAAAAAAAAACAiJCYmatasWfm/v/TSS9qyZYvKli2rxMTEoGIWu5GSmZlZ7KBJSUlBJQMAAAAAAAAAAJxleSxZHmenkDgdz0mHDh3S+++/r61btyo6Olq1atVSp06dgo5X7EZKRkaGLOuvnxjbtmVZljZu3Bh0QgAAAAAAAAAAAMFYuXKl+vXrp/Lly6tu3bry+/368MMP9dhjj+mZZ55RnTp1Ao5Z7EbKwoULAw4OAAAAAAAAAADC7Pi6Jk7HdKHx48frn//8pwYPHpw/OcTn82ns2LEaMWKE3njjjYBjFruRkpycHHBwAAAAAAAAAACAU+WHH37QE088UeAMW16vV7169VLXrl2DiulxKDcAAAAAAAAAAOBGHis0Fxdq3bq13nvvvULbP/roIzVv3jyomMWekQIAAAAAAAAAAOA2Q4cOzf/Z4/Fo2rRp+vTTT9WwYUN5PB599913+uKLL4KekUIjBQAAAAAAAACACGZZVoFTXTkV040SEhLUrVs3SVJWVpYkKSkpSV26dJHf7w8qJo0UAAAAAAAAAAAiWShOxeWiU3tNmDAhpPFppAAAAAAAAAAAgIixYMECPfvss9q2bZt8Pp9q1qyp6667jlN7AQAAAAAAAACAk7CsYxenY7rQq6++qokTJ+q6665Tnz595Pf7tWbNGo0ePVpHjx5Vjx49Ao7pnkZKuYpGw30vP2Scgve6wcYxTHnOrmcexLbNY7jgQ2CVrhDuFHACT/J55kFyj5iNj4kzzyFCWHGlzYOY7iuO5prnYPqaumBfhRAqU844hO/Vx4xjeK+6yziGKUf2waafeZd83qyECKkPXPJ8mvJUrWUWwLQ2kKgPTmCVq2wWwIn/RzjxmsaWMhsfIZ8vFMHw2IEk+WaZnfrD22vo39/oFPDUSDELECnHDhLM3xNwjqdabfMg1AeOsbzR5kGc2FfkGR4/iOb1RGCeffZZ3X///QVmn7Rr107nnXeepk+ffpo3UgAAAAAAAAAAgONK0mLze/bsUWpqaqHtaWlp+umnn4KK6THMCQAAAAAAAAAAwBXq1q2ruXPnFtr+1ltv6dxzzw0qJjNSAAAAAAAAAACIZJYleUrGGin33HOPevfurZUrV6pRo0aSpHXr1mnTpk2aPn16UDGDaqRkZWVp9uzZ2rZtm3JzC5/jbsIEs/ONAgAAAAAAAAAABCotLU1z5szR7NmztXXrVsXGxqpp06Z67LHHVLVq1aBiBtVIGThwoNauXasLL7xQcXEs9gMAAAAAAAAAgFuVpDVS7rjjDt1999269957HYsZVCNl5cqVmjlzptLS0hxLBAAAAAAAAAAAhIAnBKf2cjqeQ9asWaOoKGdXNQkq2jnnnKMjR444mggAAAAAAAAAAICJa665RnfddZeuuuoqJSUlKTY2tsD1TZs2DThmUI2UBx98UP369VPnzp2VlJQkj8dT4PquXbsGExYAAAAAAAAAADjNspxfHN6lp/Z68sknJUkjR44sdJ1lWdq4cWPAMYNqpLz++uvavn27/t//+3+FujmWZdFIAQAAAAAAAAAAp9ymTZscjxlUI+WNN97Qo48+qo4dOzqdDwAAAAAAAAAAcJDlsWQ5vKaJ0/GctH//fr3//vvatm2bLMtSnTp1dNlllykhISGoeJ6/v0lhFSpU0LnnnhvUHQIAAAAAAAAAAITC2rVrdemll+r555/X7t279dNPP+nJJ5/UP/7xD3377bdBxQxqRsr999+vMWPGqG/fvjrrrLPk9XoLXJ+UlBRUMgAAAAAAAAAAwGElaI2UsWPHqlu3bho6dKis/+Xo9/s1btw4jR49Wv/5z38CjhlUI+XWW2+VJN1www35iUiSbdtBL9YCAAAAAAAAAABgYuvWrXrkkUcK9C48Ho969uypbt26BRUzqEbKwoULg7ozAAAAAAAAAABwinmsYxenY7pQy5YtNXfuXN11110Fti9evFgtWrQIKmZQjZTk5OSg7gwAAAAAAAAAAJxalmUVmKHhVEw3Ouuss/Tcc89pyZIluuCCCxQVFaWNGzfq888/V0ZGhoYOHZp/2wkTJhQrZlCNFAAAAAAAAAAAALc5fPiwOnfunP+zdGxd965duwYd07Jt23YiOVP27u1G463S5Z1JxJB/42dG4z3nppknER1rHMI3bZjReG/fB4xz0JHD5jFiS5mNzz3iQA7x5jHcwO8zj+Hxmsdwg6OG7wsn9rpR0eYxIuH1yMk2DmHn5RjHsKpUN46BwuxfvjeOYZWp6EAmZvybvzCO4anewDyRmDij4b5nRhmn4L3FPIayD5mNjyttngP1wR9M64NI+C6SHPk+cmShzugYwxw85jm4QU6WcQg7L9c4BvVBaNi7dxjHsEqXcyATM/6vlxnH8NRpZhbAa/5/Ct/j9xjH8N75sFkAR44dOPC9nGv4/wpqgz9ESn1geuxAMj9+wLGDPzhQw9uGr2m4agOfz6d169apwcsPyHvU/BhIgdjRsdpw3TClpqbK642Q90oRIqRSBgAAAAAAAAAAcB6n9gIAAAAAAAAAIJJZljOzoP8cs4RgRgoAAAAAAAAAAEARmJECAAAAAAAAAEAksxSCGSnOhnMzGikAAAAAAAAAACAiHD16VHPnztVXX32lvLw82bZd4PoJEyYEHJNGCgAAAAAAAAAAkawErZEyfPhwzZ8/X61bt1ZCQoIjMYNqpBw4cEAzZ84ssqMza9YsR5IDAAAAAAAAAACGPJ5jF6djutBHH32kadOmqVWrVo7FDKqRMnjwYH311Vfq3LmzYx0dAAAAAAAAAAAAE2XKlFFiYqKjMYNqpCxfvlwvv/yyGjZs6GgyAAAAAAAAAADAaSE4tZdLV5u//fbbNX78eI0YMULVq1dXVJT5CidBRUhMTJTHpdN2AAAAAAAAAABAyfTMM8/o119/1eWXX37S6zdu3BhwzKBP7TVq1CgNGDBA1atXV3R0dIHrk5KSggkLAAAAAAAAAACcVoIWm3/wwQcdjxlUI6V///6SpD59+sg64cmybVuWZQXV0QEAAAAAAAAAADDRrFkzSdIPP/ygrVu3yu/3q2bNmjr33HODjhlUI2XhwoVB3yEAAAAAAAAAADiFStCMlAMHDmjo0KFauHChypUrJ5/Pp8OHD6tp06aaNm2aypQpE3DMoBopycnJwQwDAAAAAAAAAAAImXHjxunnn3/WBx98oHPOOUeStGXLFt17772aMGGCHnjggYBjmi9XDwAAAAAAAAAA3MvjOXZxOqYLLVq0SM8//3x+E0WSzj33XI0cOVK33HJLUDHd+UgBAAAAAAAAAAACFBsbK89JmjyWZcnn8wUVk0YKAAAAAAAAAACR7PgaKU5fXCgjI0OjR4/Wjh078rf98MMPGjdunNq0aRNUTMu2bdupBI1k/W423nfUPAdvtHkM22823nKgt+WSl9QVDD/M9qF95jn488xjBNkpPc7+aZtxCp66LY1jSIbvTUc+H4afUUnKM93fOPAZjY4zj2HKiX2N6RduTpZ5DjHx5jFKlzePgcJMawPJHfWB32wfLknyeM1jREx9EP7vEvvwfuMYxu9NB95X9k8/GMfw1GluHMOYE/95M60PjGsDyZH6ICrWbLwbnkvJ/HOak22eQ4wDtRb1QWi4oT5ww7EDyfyzEjG1gQMc2P/ZB/caBnCgZswzP/5gevzAU+9C4xwc+U50w+cjL9c8hulz4YZjB5L58+lEjXLksHmM2FJm48NUG/h8Pq1bt04N3npCXkfelyfEjorRhm7/VmpqqrxeB/7f6pADBw6ob9+++uKLL1S2bNn8ba1bt9ZDDz2k8uXLBxyTNVIAAAAAAAAAAEBEKFu2rF566SVt2rRJ27ZtU2xsrGrWrFlgzZRA0UgBAAAAAAAAACCSheJUXC46tVdmZqaqVq0qy7KUmZkp6VhDJTU1tcBtJCkpKSng+DRSAAAAAAAAAADAaSsjI0PLli1TpUqVlJGRIeskTR7btmVZljZu3BhwfBopAAAAAAAAAABEMo/n2MXpmC6xcOFCVahQIf9np7nnkQIAAAAAAAAAAAQoOTlZnv81doYOHaoyZcooOTm5wCU+Pl79+/cPKr7RjJTs7Gxt375dfr9fZ599thISEkzCAQAAAAAAAAAAp0X4Gimffvqp1q9fL0latWqVpk+frlKlShW4zfbt2/Xjjz8GFT+oRsrRo0f18MMP6z//+Y/y8vKOBYqKUufOnTV69GjFxMQElQwAAAAAAAAAAEAgatasqWeffVa2bcu2ba1Zs0bR0dH511uWpVKlSmn8+PFBxQ+qkTJx4kQtXrxYTz31lNLS0uT3+7V27VqNGzdOjz32mIYMGRJUMgAAAAAAAAAAwGERPiOlWrVqmjVrlqRjp/YaPny4o2fQCqqR8t577+mJJ55Q8+bN87e1adNGsbGxGjRoEI0UAAAAAAAAAADcIsIbKZmZmapataosy1L//v114MABHThw4KS3TUpKCjh+UI0U27ZVqVKlQtsrVqyow4cPBxMSAAAAAAAAAAAgYBkZGVq2bJkqVaqkjIwMWZYl27YL3c6yLG3cuDHg+EE1Ulq0aKFJkyZp0qRJ+dNjDhw4oEcffbTALBUAAAAAAAAAABBelseS5fE4HtMtFi5cqIoVK+b/7LSgGinDhg1Tr1691Lp1a9WsWVOS9P3336tatWp66qmnHE0QAAAAAAAAAACgKMnJyQV+PnDggGJjYxUbG6tNmzZp6dKlql+/vlq2bBlU/KAaKYmJiXrvvff06aefatu2bYqNjVXNmjXVqlUreRzuagEAAAAAAAAAAAMRvkbKiRYsWKBBgwbpySefVHJysq699lqdeeaZmjZtmu6++25dd911AccMqpEiSdHR0Wrbtq3atm0bbAgAAAAAAAAAAADHPP744xowYIAuvPBCTZo0SVWrVtV7772njz/+WGPHjj21jRQAAAAAAAAAAHAaKEEzUnbs2KEOHTpIOrZeymWXXSZJOu+887R3796gYtJIAQAAAAAAAAAAESEpKUkrV65UYmKivv/+e2VkZEiS3n33XdWoUSOomDRSAAAAAAAAAACIZCVoRsqAAQM0ePBg+Xw+XXzxxUpJSdHEiRP16quvaurUqUHFtGzbth3OMyi+d58yGu9te41DmYSXf/2nxjE8DS9yIBND2QeNQ9h7Mo1jWGfVNo6BY+wfvzOOYSWf70AmkCQ7c4txDCvpXLMAfp9xDvJ4zcY78RXmxJd+qXLmMVCIb9ErxjG8LS53IJPw862ebxzDe8GlZgEc+KzYh383j/HzNqPxnlppxjngD/avPxiNt86o4UgeOMb+aavReKtqLfMkqA/+QH0QEr7ZjxrH8Ha6yYFMws//zXKj8Z56FzqUiaHsQ0bD7QO7jVOwEmsax8Ax9s6NxjGsanUdyASSi47lmNYHprWBJNl+8xiWx2x8mGoDn8+ndevWKeWj5+T1HXU2tjdaX116k1JTU+X1Fu91ysnJ0ejRozV//nzFxcXpxhtv1I033viXY3bt2qXOnTtr+vTpat68ebHuZ+/evfrll19Ut+6xfcq2bdtUtmxZVa5cuVjj/4wZKQAAAAAAAAAAIOQeeughbdiwQS+++KIyMzM1ZMgQJSUl5a9jcjKjRo1SVlZWQPdTsWJF+Xw+/fTTT7JtW3FxccrNzVVmZqaSkpICzptGCgAAAAAAAAAAkcwFp/bKysrS7Nmz9cwzz6h+/fqqX7++Nm/erFdeeaXIRso777yjw4cPB3Q/S5cu1ciRI/XTTz8V2G7btizL0saNgc+eo5ECAAAAAAAAAABCatOmTcrLy1Na2h+nXG7cuLGmT58uv98vj6fg6dP27dunhx9+WDNnztTllxf/1N1jx45Vw4YN9dRTTykhIcGR3GmkAAAAAAAAAAAQyVwwI2X37t2qUKGCYmJi8rdVrlxZOTk52r9/vypWrFjg9g8++KC6deum8847L6D7+fnnn/Xss8+qWrVqAY37KzRSAAAAAAAAAABAUA4dOlRgsfmYmJgCzZLjsrOzC20//ntubm6B7cuXL9fq1av13nvvBZxPkyZNtHr1anc1Uvbt2yePx6Ny5co5kQ8AAAAAAAAAAHBSCGektGnTRtnZ2fmb+/Xrp/79+xe6eWxsbKGGyfHf4+Li8rcdOXJEI0eO1P33319ge3E1bdpUo0eP1ieffKLq1asrOjq6wPX9+vULOGZQjRS/36/Jkydr9uzZ2rt3ryTpjDPO0LXXXqs+ffoEExIAAAAAAAAAAJxmFi9eXGhGyskkJiZq3759ysvLU1TUsdbE7t27FRcXp7Jly+bfbv369dq5c6cGDBhQYPwtt9yirl27asyYMX+Zz7Jly9SgQQPt2bNHe/bsKXCdFWQzKahGyoQJEzR//nzdfffdatCggfx+v7766itNnjxZubm5QXV0AAAAAAAAAABACHg8ku35+9sFGlNSQkJCgUZKUerWrauoqCitW7dOTZo0kSStXr1aKSkpBRaab9iwoebPn19gbPv27TVu3Di1atXqb+/npZdeCuRRFEtQjZS3335bU6dOVbNmzfK31alTR8nJyRo0aBCNFAAAAAAAAAAA3MIFi83Hx8era9euGjVqlB544AH9+uuvmjlzpiZMmCDp2OyUMmXKKC4uTtWrVy80PjExUZUqVSrWfW3cuFGbN2+W3++XJNm2rdzcXH3zzTcaPXp0QHlLQTZS4uLiCp1XTJLKli0b9NQYAAAAAAAAAAAQuYYOHapRo0bp+uuvV0JCgvr376/27dtLktLT0zVhwgR1797d6D6mTp2qqVOnqnLlytqzZ48SExP122+/yefz6dJLLw0qZlCNlMGDB2vYsGEaPHiw0tLSFBUVpU2bNmn8+PG6/vrrlZmZmX/bpKSkoBIDAAAAAAAAAAAOcMGMFOnYrJSJEydq4sSJha779ttvixz3V9f92WuvvabRo0fr//7v/5SRkaEXX3xR5cqV01133aWzzz474JylIBspgwYNkiTdfvvt+TNQbNuWdGzKzGOPPSbbtmVZljZu3BhUYgAAAAAAAAAAAIHYt2+fWrduLenYuixr165Vly5ddNddd2nAgAH5/Y1ABNVIWbhwYTDDAAAAAAAAAADAqRbCxebdJjExUTt37lRSUpJq1aqlb775Rl26dFFCQoL27t0bVMygGinJyclB3RkAAAAAAAAAAECo9OjRQwMHDtQDDzygdu3aqXfv3jrjjDO0fPly1alTJ6iYQTVSAAAAAAAAAADAacIla6ScCrfddpvOPPNMxcfHq2HDhho6dKheffVVlS9fXg888EBQMWmkAAAAAAAAAACAiDBu3Dj16tUrf2H5Hj16qEePHkYx3XkSMwAAAAAAAAAA4IzjM1KcvrjQO++8I4/D67dYtm3bjkYM1uH9ZuNd+qIBQCjcVrqacYzph3c6kAkkSaXKhTuDyJT1e7gzAIDTiml9QG3gMOqD0DA9diBx/ABAicGxA5cJU23g8/m0bt06pax4TV7fUWdje6P1Vcv/U2pqqrxer6OxTTz55JNau3atevfuraSkJMXGxha4PikpKeCYnNoLAAAAAAAAAABEhMmTJ0uSlixZIkmy/vdHFLZty7Isbdy4MeCYNFIAAAAAAAAAAIhkHkuyHV7pw+OeWZ6rVq1SWlqaoqKitHDhQsfj00gBAAAAAAAAAACnrV69emnp0qWqVKmSkpOTHY9PIwUAAAAAAAAAgEgWisXhXbTuWKiXgnd4Lg8AAAAAAAAAAMCpZYWwsRPUjJSePXsWO6lZs2YFcxcAAAAAAAAAAMAJET4jRZKuvPJKeTx/P3ckmDVUgmqktGrVStOnT9dFF12kCy64QNHR0frmm2/0zjvvqFu3bjrjjDOCCQsAAAAAAAAAABCwG264QWXKlAlJ7KAaKcuWLdPAgQPVq1evAtubNm2q119/XWPGjHEkOQAAAAAAAAAAYMjyHLs4HdMlLMtSp06dVKlSpZDED+qRfvXVV0pPTy+0PSUlRV9//bVxUgAAAAAAAAAAwCGWJXkcvrjo1F6uXGy+fv36evrpp5WTk5O/7dChQ3riiSfUpEkTx5IDAAAAAAAAAAD4K926dVNsbGzI4gd1aq8xY8bolltuUatWrVS9enXZtq0ffvhB55xzjqZOnep0jgAAAAAAAAAAIFgRfmqvCRMmhDR+UI2UWrVqad68eVqyZIm+//57xcfHq1atWmrRooXT+QEAAAAAAAAAAIRNUI0USYqJiVHbtm2dzAUAAAAAAAAAADjNCsGaJi5aIyXU3DP3BgAAAAAAAAAAwGWCnpECAAAAAAAAAABOAx6PHJ9X4Sk58zRKziMFAAAAAAAAAAAIkGtmpPg+etlovLd9T/MkbL95DEP+TZ8bx/DUbWGeiG2bjT+aY5yCf8sa4xieehcaxzBm+lxK5ucbdCAH+/B+4xhWQgXjGMbc8Hr4fcYpPLVrlXEMY0ePmMeIjjMb78R+2+JvCtzKN2uCcQxvr6HmiZi+zxx4j/l3fGMcw3N2PeMYxpyoDzavNhrvitpAMv8+csm5iO3DvxuNt0qXcyiTCODAd9pTP37hQCKGHPicKzrWbDz1QUTzrf6vcQxvk8vMAjjxfwoH3qf+9YuNxntSM4xzcOS5yM02Gu7fus44BdfUB25g+Jpy7OAETtRrhscPntq50jwHJ5gePzA9diBRH0iskWLINY0UAAAAAAAAAAAQApbH+WbQ6d5cCkDJeaQAAAAAAAAAAAABYkYKAAAAAAAAAACRjFN7GWFGCgAAAAAAAAAAQBGYkQIAAAAAAAAAQCTzeOT4vApPyZmnUXIeKQAAAAAAAAAAQICYkQIAAAAAAAAAQCRjjRQjQc1IGTdunHbs2OF0LgAAAAAAAAAAAK4SVCPlnXfekVWCuk0AAAAAAAAAAJy2LEuyPA5fSk6PIKhTe/Xu3VtjxoxR7969lZSUpNjY2ALXJyUlOZIcAAAAAAAAAAAw5LEkOdz48NBI+UuTJ0+WJC1ZskSS8men2LYty7K0ceNGh9IDAAAAAAAAAAAIn6AaKQsXLnQ6DwAAAAAAAAAAEAqWR7Js52OWEEE1UpKTk53OAwAAAAAAAAAAwHWCaqQAAAAAAAAAAIDThGU5vzh8CVpsvuTMvQEAAAAAAAAAAAgQM1IAAAAAAAAAAIhkrJFipOQ8UgAAAAAAAAAAgAC5Z0ZK+crhzsAVHTSr0pnhTuEYw/Pb+VbPN07Bc9Z5xjFcwQ3nCnQih5ws8xgJFcxjmHLiufDlmY33OLCviY41j2EqKibcGbhiv40Qqt0w3Bkc44L3mRVXKtwpOML/9TLzIPFlzGO4gRvqAyccOWQ2vnQ5Z/IIN7/PPIYT7wk3fDdHRYc7A0kR8vnCyfkd/kvaYDjxebW85iGq1jTPwzgJ8+fCt/xdo/Gees2Mc8AJTF/TI4fNc+DYwR9Mjx/ExJvn4AQ31Cgu+H9d2FmW5GGNlGDxDgIAAAAAAAAAACiCe2akAAAAAAAAAAAA51mWHJ+5W4JmpNBIAQAAAAAAAAAgklkeSSw2H6yS80gBAAAAAAAAAAACxIwUAAAAAAAAAAAimceSbE7tFSxmpAAAAAAAAAAAABQh6Bkpfr9fHo9Hv/76q1avXq3atWvrnHPOcTI3AAAAAAAAAABgijVSjAT8SFevXq3WrVvr888/16+//qru3btr5MiR6tKliz788MNQ5AgAAAAAAAAAABAWATdSJkyYoI4dO6pRo0Z6/fXXFRsbq2XLlmns2LGaPHlyKHIEAAAAAAAAAADBsqzQXEqIgBsp3333na6//nrFx8dr0aJFat++vWJiYtSsWTNlZmaGIkcAAAAAAAAAAICwCLiRUrlyZW3ZskVbtmzRN998o0suuUSStHz5clWtWtXxBAEAAAAAAAAAgAHLE5pLCRHwYvO9e/dW37595fF4lJKSombNmmn69OmaOnWqJkyYEIocAQAAAAAAAABAsDySbIdPxVVyzuwVeCOlV69eatq0qX788Uelp6dLklq0aKGLL75YderUcTxBAAAAAAAAAACAcAm4kSJJdevWVd26dfN/T01NdSofAAAAAAAAAADgpFCchqsEndqr5DxSAAAAAAAAAACAAAU1IwUAAAAAAAAAAJwmLEuOL2pilZxFUpiRAgAAAAAAAAAAUAT3zEiJckEqtm0ew7QL5/OZ5+AC3qaXGcewsw86kAkcc+RwuDNwDf93XxiN99RtYZyDvXuncQwroYJxDCCkomPCnQEc5qnfyjiGnX3AgUzgmNwj4c7AFfyb1xjH8JzfxDiG/eMWo/FW7abGObhCCfrLyBLJ6w13Bu44diBJeUfNY7iA96LuRuPtLGoDV+FYTj7/ppXGMUzrZ3vvT8Y5OHLswHS/yVe7MzweyYGvsAJYIwUAAAAAAAAAAAAumAYCAAAAAAAAAABChjVSjNBIAQAAAAAAAAAgkoXiNFyc2gsAAAAAAAAAAADMSAEAAAAAAAAAIJJxai8jzEgBAAAAAAAAAAAoAjNSAAAAAAAAAACIZJbH8QkpJWmeRsCPdOjQoTp06FCh7b///rsGDBjgSFIAAAAAAAAAAABuUKwZKWvXrtX27dslSXPnzlX9+vWVkJBQ4Dbbtm3T0qVLnc8QAAAAAAAAAAAELxRrpDg/xcW1itVIiY+P15QpU2Tbtmzb1rPPPiuP54/JLJZlqVSpUho0aFDIEgUAAAAAAAAAADjVitVIqVOnjhYuXChJ6tmzp6ZOnapy5cqFNDEAAAAAAAAAAOAAKxTrmZScNVICXmz+pZdeCkUeAAAAAAAAAAAgFDi1l5GS0zICAAAAAAAAAAAIUMAzUgAAAAAAAAAAwGmEU3sZKTmPFAAAAAAAAAAAIEDMSAEAAAAAAAAAIJJ5pJK0ponTmJECAAAAAAAAAABQBMu2bTvcSUiS9vxoNj7Kgck10XHmMXx5hgEceDk8DjwXtt88hinLiQ6pWQz7wG7zFJz4iPl9ZuON35eSVeVs4xjGz4UT7wnT51KScrLNxjvx+SpV1jyGG3b/pq/p0RzzHDxe8xhlKpnHQGF7M81jeJ2oD2LNxjux33Fiv2FaHzjxODwu+BseB/Z99qF9DuRh+Jr6HHg9/A7UB5WrmQVw4rvIifrAtFbKPWKegxP/D4gvY5iCE7WBE6+p4b7CkfrAgf1VmcrmMVDYnl3mMaKizca74tiBzL9LvIbPw7EkzEP4DR+HI/WF+XeJ/fuvphGMc3CmPjCL4cyxAwdqX9PvEidqXyfqA9M8OHbwBzccPwjTsQOfz6d169YpJet7eZ3Y15wYW5a+KlVTqamp8nodOL7iYi743ywAAAAAAAAAAIA7sUYKAAAAAAAAAACRzJGz/xQKGoKY7kQjBQAAAAAAAACASGZ55MhpBAsGdTiee3FqLwAAAAAAAAAAgCIwIwUAAAAAAAAAgAhmWZbj80ecj+heQTVSevbsKesvzqk2a9asoBMCAAAAAAAAAACRJycnR6NHj9b8+fMVFxenG2+8UTfeeONJb/vJJ5/oscce044dO3TWWWfpzjvvVNu2bU9xxscE1Uhp3rx5gd/z8vK0c+dOLV68WLfffrsjiQEAAAAAAAAAAAe4ZI2Uhx56SBs2bNCLL76ozMxMDRkyRElJSbrssssK3G7Tpk3q16+fBg8erDZt2mjp0qX697//rTfeeEN16tRx6gEUW1CNlH79+p10+5w5czR//nzddNNNRkkBAAAAAAAAAIDIkZWVpdmzZ+uZZ55R/fr1Vb9+fW3evFmvvPJKoUbKe++9pxYtWqhXr16SpOrVq2vRokX68MMPT59GSlGaNm2q0aNHOxkSAAAAAAAAAACYcMGMlE2bNikvL09paWn52xo3bqzp06fL7/fL4/Hkb+/WrZuOHj1aKMbBgweDT9dAUI2UzMzMQtsOHz6s5557TsnJycZJAQAAAAAAAACAyLF7925VqFBBMTEx+dsqV66snJwc7d+/XxUrVszfXqtWrQJjN2/erBUrVuiqq646ZfmeKKhGSkZGRqHF5m3bVtWqVfXAAw84khgAAAAAAAAAAHCAJ/D1TP7esZiHDh2S1+vN3xoTE1OgWXJcdnZ2oe3Hf8/NzS3yXvbu3av+/fvrggsuOL0Wm1+4cGGB3y3LUnR0tCpXrlyowQIAAAAAAAAAAMIohKf2atOmjbKzs/O39uvXT/379y9069jY2EINk+O/x8XFnfQefvvtN91www2ybVuTJ08ucPqvUymoRgqn7wIAAAAAAAAAAIsXLy40I+VkEhMTtW/fPuXl5Skq6lhrYvfu3YqLi1PZsmUL3f6XX37JX2x+1qxZBU79dao5utg8AAAAAAAAAABwmZCcSepYzISEhAKNlKLUrVtXUVFRWrdunZo0aSJJWr16tVJSUgrNNMnKytLNN98sj8ejWbNmqUqVKs6nH4DwzIMBAAAAAAAAAAAlRnx8vLp27apRo0Zp/fr1WrBggWbOnJk/62T37t06cuSIJGnGjBnasWOHJk6cmH/d7t27dfDgwbDkzowUAAAAAAAAAAAiWQjXSAnE0KFDNWrUKF1//fVKSEhQ//791b59e0lSenq6JkyYoO7du+u///2vjhw5oh49ehQY361bNz344IOOZB8Iy7Ztp5+9oPjXzDca76nT3KFMwss+8JtxDKtsZQcyCT/fp28Yx/Be9E8HMoEkKduBbm98GfMYkCT5Zo41juG98T6zAH6fcQ7y/P20T9fnIEmlypnHQCH+b5YZx/DUaOBAJuFnH9htHMMqG95p0E7xzZ1uNN7b9TaHMoEk6chhs/FxpZ3JA5Ik338mGY33XjPIPAnbbx7DMjxxAvVBRPMvf8s4hic1w4FMws8+uMdovFWmkkOZhJf/awdqxvqtHMgEkmQf3m8cwypd3jgGjvE9Odw4hveO8eaJmH43O/G97Ib6IEy1gc/n07p169RQe+R1uJHik6X1qqTU1NRindrrdMaMFAAAAAAAAAAAIpoVzASSv2aHYt0Vd2KNFAAAAAAAAAAAgCIwIwUAAAAAAAAAgEhmWccuTnPFwiGhRyMFAAAAAAAAAIBI5gnFqb0kObD8zOmAU3sBAAAAAAAAAAAUgRkpAAAAAAAAAABEMsvj/IyUEiTgGSn33XefPvvsM9l2CTn5GQAAAAAAAAAAKLECnpGSlZWlvn37Kj4+Xv/4xz/UsWNHNW7cOBS5AQAAAAAAAAAAU1YI1kgpQQJupDzyyCPKzc3V0qVL9dFHH+mOO+5QfHy8OnTooI4dOyolJSUUeQIAAAAAAAAAAJxyQa2REhMTo4yMDGVkZCg3N1cvvPCCpk+frhdeeEEbN250OkcAAAAAAAAAABAs1kgxElQjxefzaeXKlZo/f74WLFggv9+vzp07q1OnTk7nBwAAAAAAAAAAEDYBN1Luvfdeffzxx7JtW23bttWECRN04YUXyuv1hiI/AAAAAAAAAABggjVSjATcSMnNzdX48eN10UUXKSYmJhQ5AQAAAAAAAAAAp3BqLyMBN1IeffTRUOQBAAAAAAAAAADgOkGtkQIAAAAAAAAAAE4TnhDMSLEdjudinnAnAAAAAAAAAAAA4FbMSAEAAAAAAAAAIIJZliXL4RkpJWnJFcu2bVdMwDnUqbnR+ITZ8x3KJLx8S+YYx/C27u5AJuFn7//ZOIZV/kwHMoEkKfeIeYyYOPMYOOZojnmM6FjzGDimVLlwZxCRDl7W1DhGmTkLHMgk/Hwr3zeO4W3eyYFMws/e/4vReKt8okOZQJJ5fUBt4CxeD3ehPgiJQ51bGMdIeO2/DmQSfr5FrxqN92Zc5VAm4cWxA5fh2IG75OWax4iKMY+BY8JUG/h8Pq1bt06NSh+V1+HOh8+WvjwcrdTUVHm9XmeDuwwzUgAAAAAAAAAAiGRWCNZIKUFYIwUAAAAAAAAAAKAIzEgBAAAAAAAAACCSWRYzUgwwIwUAAAAAAAAAAKAIAc9Iueaaa3TRRRepdevWql+/fihyAgAAAAAAAAAATmGNFCMBN1K6du2qZcuW6YUXXpDX61WrVq3UunVrpaenq0KFCqHIEQAAAAAAAAAABMvSsdN7Ocp2OJ57BdxI+de//qV//etfsm1b33zzjVasWKE5c+Zo6NChqlu3rmbPnh2KPAEAAAAAAAAAAE65oBab9/l8+vrrr7VmzRp9+eWX+u677xQTE6OyZcs6nR8AAAAAAAAAADDh8Tg/I8W2JfmdjelSATdSevbsqQ0bNqhs2bJq1KiRGjdurFtvvVX16tWTx8Pa9QAAAAAAAAAAIHIE3EiJioqSZVmqUKGCzjjjDCUmJioxMZEmCgAAAAAAAAAArmSFYEaKs+HcLOBGyvPPP6+8vDx9/fXXWrVqld5++22NHj1aZcqUUZMmTTRhwoRQ5AkAAAAAAAAAAHDKBbVGSlRUlBo1aqTSpUsrPj5eMTEx+vjjj/XZZ585nR8AAAAAAAAAADBhhWCNFMuW5HM2pksF3Eh55ZVX9Pnnn+uLL75QVlaWmjZtqlatWunf//63atWqFYocAQAAAAAAAAAAwiLgRsrrr7+u9PR0XXXVVWrcuLFiYmJCkRcAAAAAAAAAAHCCFYI1UhwO52YBN1LefvvtUOQBAAAAAAAAAABCwlKJ6nw4zBPuBAAAAAAAAAAAANwqqMXmAQAAAAAAAADAaYJTexlxTSMl/t+3hzsFyfabx7DMJvl4m11mnkOE8H+z0jiG98IrHMgEkpzf0ZZktm0ew+ua3bcZ4/2uA+9L3tuuVeq2682DOPF5M32ferzGKXhTM4xjuIIDr4e941uj8Vb5ROMccAK/z2y8E59R9uN/8JrvbyIC76uIFv/vO8KdgiuOHUiS98LO5nlEAP/6pcYxvBf904FMIEnycPIbR7ng/yKu4JL9Lkq2CDkSBwAAAAAAAAAATooZKUZoxQEAAAAAAAAAABSBGSkAAAAAAAAAAEQ0SyVqConDmJECAAAAAAAAAABQBGakAAAAAAAAAAAQyVgjxUjAM1IWLVp00u25ubl67LHHjBMCAAAAAAAAAAAOskJ0KSECbqTceeeduueee7R///78bStXrlTnzp311ltvOZkbAAAAAAAAAABAWAXcSHnrrbeUmZmpyy+/XO+8846GDRumm266SRkZGfrwww9DkSMAAAAAAAAAAAgaU1JMBLxGSq1atfTKK69o0KBBGjx4sKKiojR9+nSlp6eHIj8AAAAAAAAAAICwCXhGys6dO3X77bdrwYIF6tevny6++GL1799fzzzzjPLy8kKRIwAAAAAAAAAACNbxxeadvpQQAc9I6dixo9LS0jR37lzVqFFDkrRgwQKNGzdOc+bM4fReAAAAAAAAAAAgYgTcSLn//vv1z3/+s8C2du3aqUWLFnr88cedygsAAAAAAAAAADghFDNISs6ElMBP7fXnJspxCQkJGjFihHFCAAAAAAAAAAAAbhHwjBQAAAAAAAAAAHA6sVSippA4jEYKAAAAAAAAAACRjFN7GQn41F4AAAAAAAAAAAAlBTNSAAAAAAAAAACIaJzay4R7GikbvzQbn97NPAcr/BN07P2/GMewEms6kEn4WQkVwp0CTmAf3GMcw6qY5EAmEcCJaZRHss1jxCeYxzDlgv0uXOy7b8xjtHfg82Z5zWMYsvf/bBzDFfWBE/u/6BjzGHCMfXi/0XgrrrQzieCYo7lm473RzuQRbk6fsgLu8u1X5jEuvMJsvEtqWHufWX1gVa3lUCbhZZWtGO4UcAL7gBPHDqo6kEmEMN3fHDlonkN8GfMYplyy30XJ5p5GCgAAAAAAAAAAcJ6lEKyRYjsbz8Vo5wEAAAAAAAAAABSBGSkAAAAAAAAAAEQyywrBjJSSc0pVZqQAAAAAAAAAAAAUgRkpAAAAAAAAAABENOt/F6djlgwBz0gZMmSIPv30U/l8vlDkAwAAAAAAAAAAHGRZVkguJUXAM1ISEhI0fPhwHT16VO3bt1fHjh3VvHnzEvWkAQAAAAAAAACAkiHgGSn33XefPv30U02ePFlRUVEaNGiQWrdurfHjx2vdunUhSBEAAAAAAAAAAATt+GLzTl9KiKAWm7csS82aNdPIkSM1b948/fOf/9Trr7+uq6++Wm3bttWMGTOUk5PjdK4AAAAAAAAAAACnVFCLzR8+fFgff/yx5s2bp6VLlyoxMVE33HCDOnbsqN27d2vSpEn6/PPP9dxzzzmdLwAAAAAAAAAACAiLzZsIuJFy++23a/ny5Spbtqw6dOigWbNmqWHDhvnXn3/++Tpw4ICGDx/uaKIAAAAAAAAAAACnWsCNlMqVK2vGjBl/ucB8kyZNNHv2bOPkAAAAAAAAAACAoVCsaVKC1kgJuJEyduzYv71NlSpVVKVKlaASAgAAAAAAAAAAcIug1kgBAAAAAAAAAACnCWakGKGRAgAAAAAAAABARGOxeROecCcAAAAAAAAAAADgVsxIAQAAAAAAAAAgknFqLyPuaaT8/nu4M3AHb3S4M3AN+8jhcKeAE9l2uDPACewc88+HFZ9gmIQD74kS9IWLIPj94c7APaJjw52Ba9j7fgl3CoBr2UcOGo234ko7kAT1AULL/vnncKfgHlEcP5Ak2+8Ldwo4kU0N7yZ2TpZxDCu+jAOJGNYH1AZwAfc0UgAAAAAAAAAAgPNYIsUIa6QAAAAAAAAAAAAUgRkpAAAAAAAAAABENKakmGBGCgAAAAAAAAAAQBGYkQIAAAAAAAAAQESzJIsZKcEKeEbK3LlzdejQoULbDx06pKFDhzqSFAAAAAAAAAAAcIhlheZSQgTcSLn33nt15ZVXatOmTQW2HzlyRHPnznUqLwAAAAAAAAAAgLALao2UVq1a6eqrr9bs2bOdzgcAAAAAAAAAADjKCtGlZAi4kWJZlvr27avHH39ckyZN0pAhQ5STkyOrBE3jAQAAAAAAAAAAJUNQM1IkqU2bNnrzzTe1efNmXXnlldq1a5eTeQEAAAAAAAAAACewRoqRgBsptm3n/3zWWWfp1VdfVaNGjdS7d28n8wIAAAAAAAAAAAi7gBsp/fr1U6lSpfJ/j4mJ0fjx4zVs2DA1adLE0eQAAAAAAAAAAIAhl8xIycnJye8lpKena+bMmUXe9ptvvlGPHj3UqFEjXXnlldqwYYPJM2AkqEZKfHx8oe09evTQSy+95EhSAAAAAAAAAAAgsjz00EPasGGDXnzxRd1///2aOnWq5s2bV+h2WVlZ6tOnj5o0aaI5c+YoLS1Nt956q7KyssKQtcEaKQAAAAAAAAAA4HRghehSfFlZWZo9e7aGDx+u+vXr69JLL9XNN9+sV155pdBtP/jgA8XGxmrw4MGqVauWhg8frtKlS5+06XIq0EgBAAAAAAAAACCSueDUXps2bVJeXp7S0tLytzVu3Fhffvml/H5/gdt++eWXaty4saz/3YdlWbrgggu0bt0646ciGFFhuVcAAAAAAAAAAHBK+P7UqHAy5qFDh+T1evO3x8TEKCYmptDtd+/erQoVKhS4rnLlysrJydH+/ftVsWLFArc999xzC4yvVKmSNm/e7PTDKBbXNFK8I4teVKYksc4uF+4UXMN78VXhTgEnsErx3nQTXg+UBN57p4c7BdfgM/8Hb7te4U4BJ+C96S68HigJosayNutxVvWUcKfgCt70f4Y7BZyA7yJ34fWAdGwmR1RUlL7a8kNI4ufm5qpVq1Y6evRo/rZ+/fqpf//+hW6bnZ1dqMFy/Pfc3Nxi3fbPtztVXNNIAQAAAAAAAAAAzvF4PEpJSZFt2yGJf/ToUX322WcFtp1sNookxcbGFmqEHP89Li6uWLf98+1OFRopAAAAAAAAAABEKI8ndEuln3hKr7+TmJioffv2KS8vT1FRx1oTu3fvVlxcnMqWLVvotr/99luBbb/99pvOOOMM86SDwGLzAAAAAAAAAAAgpOrWrauoqKgCC8avXr1aKSkphZo9jRo10tq1a/Nn0ti2rTVr1qhRo0anMuV8NFIAAAAAAAAAAEBIxcfHq2vXrho1apTWr1+vBQsWaObMmerV69hamLt379aRI0ckSZdddpkOHDig8ePHa8uWLRo/fryys7PVoUOHsORu2aE6ORoAAAAAAAAAAMD/ZGdna9SoUZo/f74SEhJ00003qXfv3pKk2rVra8KECerevbskaf369br//vu1detW1a5dW6NHj1a9evXCkjeNFAAAAAAAAAAAgCJwai8AAAAAAAAAAIAi0EgBAAAAAAAAAAAoAo0UAAAAAAAAAACAIkR8I2XXrl2qXbu2du3aFe5UCpgyZYp69ux5yu934cKFuuiii9SoUSMtWbLklN+/k6+HaSyT8W56HIg8Gzdu1Jo1a8KdBhCx3LrfDVdtIIW3PnDTd2q4xzsdB5GF+gAILbfue6kPTu/6wE2PA5GH2gA4tSK+kYKCJk+erPT0dH3wwQdq2rRpuNMxUrVqVS1dulRVq1YNy3ggVPr27asffvgh3GkAKEEipT6gNkAkoz4AcKpRHzgzHggVagPg1IoKdwI4tQ4ePKjGjRsrOTk53KkY83q9qlKlStjGAwAQKSKlPqA2AADAOdQHzowHAEQG189IWb16ta6++mo1atRIqampuuWWW/Trr78GHGfevHm66KKLdMEFF2jkyJHKzc0t9tjt27frpptuUlpami6++GLNmjUr4PvfsmVL/uPo1auX9u3bF9D4n376SbfddpsaNWqkjIwMTZ06VT6fL6AYGRkZ+vHHHzVs2DBlZGQENPa4nTt3qnfv3mrUqJE6d+6s5557LqhYCxYsULt27dSoUSPddttt+v333wOO4ZbTb0jSSy+9pCZNmmjjxo3Gsf7O8bw/+eQTZWRkKC0tTePGjdN3332n7t27KzU1VbfeeqsOHTpU7Fjz589Xu3btlJKSoltvvVX79+8vdj4///yz/v3vf6tZs2Zq3ry5xo0bF9Dn63gO7777rlq3bq0mTZpo3LhxysvLK3aMLVu26JprrlGjRo103XXX6amnngpq+vvxXKZNm6amTZtqzJgxAY2fNWuWLrnkEqWkpKh79+764osvAs6hZ8+e+vHHHzV06FDde++9AY092Xs60FMB3HXXXRoyZEiBbXfffbeGDx/+t2O7dOmil19+Of/3G264Qdddd13+76+99pquvvrqv40ze/ZsNWjQQNu3b5ckbd26VSkpKVqwYEFxH4ZGjBih2267rcC2sWPH6p577inW+ClTpqh27dqFLm+99Vaxc0DouKE2kMzrA9PaQHJHfUBtULTTsT5wojaQIqc+MK0NpNO/PqA2+AP1gbtRH/yB+qAgN9UHp2NtcGKs0/3YgeSO+iDctYFEfXAc9QECYrvYgQMH7MaNG9tTpkyxd+zYYX/xxRd2+/bt7bFjxxY7xs6dO+3zzz/fvvTSS+0vvvjCXrlypd2mTRt78uTJxRp/5MgROyMjw+7fv7/93Xff2QsXLrRTU1PtRYsWFTuHnJwc+5JLLrHvuecee8uWLfbLL79s16tXz77uuuuKNd7v99vdu3e3hw0bZm/dutX+7LPP7Pbt29tTp04tdg62bdt79uyxL7roIvuFF16w9+zZE9BY27bto0eP2h06dLD79+9vb9682X7nnXfs1NRU+5JLLil2jOOvR5cuXewvv/zSXrdunZ2enm4//PDDAedzPNbOnTsDHms6/sSxH374oZ2ammqvWrXqlORx/PZXX321vXHjRvvdd9/Nf48vXbrU/uKLL+xmzZrZzz//fLFjdevWLf/1aNmypf3oo48WK5ecnBy7ffv29i233GJv2rTJXr58ud22bdugPqPt27e3V61aZa9YscJu3bp1sXM4cuSIffHFF9uDBg2yt2zZYr/wwgt2w4YNi/35OlkuN954o719+3b7+++/L/bYr7/+2q5fv7798ccf2zt37rTHjx9vt2rVyvb5fAHlsG/fvvzP6YEDB4LK/8T30uTJkwN6Lj766CO7adOmdm5urm3bx17jtLQ0e9myZX87duLEiXb//v1t27bt3NxcOzU11W7YsGF+rP79+9vTpk372zh+v9++7rrr7FtvvdX2+/32NddcYw8cOLDYj8G2bXv58uV2gwYN7IMHD9q2bds+n89u1aqV/fHHHxdr/KFDh+xff/01/zJq1Ci7Xbt2Ab8mcJ4bagPbNq8PTGsD23ZHfUBtUPR4k/ogmDycqg9MawPbjqz6wKQ2sO3IqA+oDf5AfeBe1Ad/oD4oOhbHDkr2sQPbdkd94IbawLapD46jPkAgXN1I+fXXX+3nnnvO9vv9+dsmTZpk9+rVq9gxju8YTvwAzJkzx77wwguLNX7BggV2ampq/gfKtm37jTfesD/55JNi5/Dxxx/baWlp9uHDh/O3DRgwoNg7p+XLl9stWrQosFNduHCh3axZs2LncNwll1xiv/nmmwGPs23bXrJkSaHnYtKkSUEVQ0uWLMnfNn78ePumm24KOB83FENvvfWWnZqaGtAO1jSPkz2HLVu2tB9//PH83//973/b9913X7FjnZj/Aw88YN9www3FymXBggV2o0aN7P379+dvW7x4sV2vXj370KFDxYpxPIePPvoof9sbb7xht2jRosBn/69y+PPnq3///kaNlMWLFwc8dv78+XaDBg3sb7/91rZt2z58+LC9fPly++jRowHHCvZz6kQjJScnx27cuHH++2vhwoV2y5Yt7by8vL8du2zZsvzXbe3atXaXLl3s9PR0e926dbbP57ObNWtmr1+/vlh5bNu2zU5JSbEHDhxot2zZMuD/vB0vft5++23btm175cqVdrNmzfILs0B88skndsOGDe2vv/464LFwnhtqA9s2rw9MawPbdkd9QG1w8vGm9YFJI8W0PjCtDWw7suoDk9rAtiOjPqA2ODnqA3ehPvgD9UHRsTh2ULKPHRzPI9z1gRtqA9umPjiO+gCBcPUaKVWqVFHXrl31wgsvaOPGjdqyZYu+/fZbXXDBBQHHatiwYf7P9erV02+//abff/9d5cqV+8tx33//vWrWrKmEhIT8bVdeeWVA971lyxbVqFFDpUqVyt+WkpKixYsXF2v81q1btX//fjVu3Dh/m9/v15EjR7Rv3z5VqFAhoHyC9e233xZ6LlJTU/X+++8HHOvss8/O/7lMmTLKyclxJMdTbeTIkfL5fGFZdK5atWr5P8fFxRU4b21cXFxAU2SrV6+e/3NCQoKOHj1arHFbt25VjRo1CnyOLrjgAuXl5WnHjh2qW7dusXM48XPdoEED7d27V/v27VPFihX/cty2bdsKfb7S0tK0aNGiYt/3nwVzDuD09HSdf/756ty5s+rVq6e2bduqR48eiopy9W62kJiYGLVr107z589Xenq65s+fr3/84x/yer1/O7ZJkybKzs7W5s2btWrVKjVp0kS//vqrVq9eLa/XK4/HowYNGhQrj5o1a6pPnz6aMmWKJk6c+Lfvgz/zeDzq0KGD5s2bpy5duujDDz/UpZdequjo6IDi7Nq1S4MHD9bQoUNVr169gMYiNNxQG0jm9YFpbSC5oz6gNji5SKgPgq0NpMisD4JdHyAS6gNqg8KoD9yH+uAP1AfuFAm1gXR6HzuQ3FEfREJtIFEfnAz1QeRz9Ropv/zyi7p06aLPPvtM9evX17Bhw3TDDTcEFcvj+eOh2rYtScX6UDi1Izt+n8cF8oHMy8vTOeeco7lz5+Zf3nnnHc2fP19lypRxJL/i8Hq9hR7Hn38vrhNfj9PZwIED1bZt26DOl23qz19OJs9pMF8QkhQbG1to2/Fz7wZ6Dt4Tc/D7/ZIky7L+dlx8fHyh92FMTExA9/1nJ3tcxclj9uzZevHFF9WsWTPNmTNH3bt31y+//GKUSyBO9nwFer5YSerYsaMWLlyo3NxcLVq0SB07dizWuJiYGDVp0kSff/65vvjiCzVu3FiNGzfWmjVrtGLFCrVq1apYr+lxmzZtktfr1cqVKwN+DJJ0+eWXa+nSpTp06JA++ugjderUKaDxOTk5GjBggFq3bq2rrroqqBzgPDfUBpIz9YFJbSC5oz6gNji5SKgPgq0NpMisD4KpDY7nEQn1AbXBH6gP3In64A/UB+4UCbWBdHofO5DcUR+4oTaQqA9ORH2A4nL1N9JHH32kcuXKacaMGbr++uvVpEkT7dy5M6gv4O+++y7/5/Xr1+vMM88s0IEuSo0aNbR9+3ZlZ2fnb5s4caLGjRtX7Ps+77zz9MMPP+jgwYP52wJZWKxmzZrKzMxUxYoVVb16dVWvXl27du3S5MmTA9qxmDr+OE5ciOzrr78+ZffvRu3atdOQIUO0YcMGzZ07N9zpnHI1a9bUDz/8UGCBuXXr1ikqKqrAXw4Vx4mfiQ0bNuiMM84o1l9L1apVq9Dn65tvvgnovp2wdu1azZgxQy1atNDQoUM1b9485eTkaPXq1acsh+MF5eHDh/O3BbMg4oUXXiifz6fnn39ecXFxatKkSbHHpqen6/PPP9e6desKFENLly5V69atix1nwYIFWrp0qaZPn653331XK1asCPhxNGrUSImJiXrmmWdk27aaNWsW0PgxY8YoOzs7LP/ZQdHcUBtI5vWBaW0guaM+oDY4OeoD6oPjIqU+oDb4A/WBO1Ef/IH6wJ2oDcJfG0juqA/cUBtI1Acnoj5Acbm6kVK+fHllZmZqxYoV2rlzp55++mnNnz8/oGmHx40dO1Zffvmlli1bpsmTJ6t3797FGpeenq7KlStr5MiR2rp1qxYuXKhXX31V6enpxb7vCy+8UFWrVtXw4cO1detWzZkzRx988EGxx6enpys5OVn33HOPvv32W33xxRe67777FB8fX6wpc05p2bKlqlatqvvuu09bt27VvHnzNGvWrFN2/26VnJysm2++WQ8//HCBL+OSoFWrVqpWrZoGDx6sb7/9Vp999pnGjh2ryy+/XGXLlg0o1vjx4/XVV19p+fLleuKJJ3TttdcWa1yLFi1Us2ZNDRs2TFu2bNHcuXP19ttvB/NwjMTFxWnatGmaPXu2du3apffff19ZWVmqXbt2wLFKlSqlbdu2FSgyi6Ny5cqqWrWqnnvuOe3cuVNz5szRJ598EvD9R0VFqX379po+fbouu+yygP7DlZ6erkWLFikhIUGJiYmqV6+esrOztWrVqmIXQ4cOHdLYsWN1++2366KLLtJ1112n+++/P6hp/B07dtTzzz+vyy67LKD95ezZs/XBBx/ogQceUFZWlnbv3q3du3eXuM+4G7mhNpDM6wPT2uB4DuGuD6gNikZ9QH0gRU59QG1wDPWBe1EfFMyB+sCdqA3CWxtI7qgP3FAbSNQHf0Z9gOJwdSOlQ4cO6tKliwYMGKArr7xSK1eu1JAhQ7R169aAC6Krr75at99+u+68805dccUVuv7/t3P/sMzEcRzHP3ra2Cux2BjYmDUhkQg1cEKDSEz+hsVgEMaLoQNNOSRdpKFmC0sHgsFosEsY2sQg4hKL5xnk8eR5kntyp622j/dr791P0so7+eb3nZz09Lna2lrZtq18Pi/TNGVZlpaXl9XV1eX53cFgUHt7e3p6epJpmspkMr7+0RuGoZ2dHb29vSkWi2lxcVGdnZ1aXV31/IxiCAQCSiaTyuVyGhgYkG3bGhoaKmj1w/9iampKoVBIiUSi3Ef5UoZhyLZtSVIsFivounI0GtXMzIyWlpY0MjKi6elpT5+rqanR1taWXl5eZJqmjo6ONDg46Pv9hWptbZVlWUqlUurr69Pu7q7i8biampp8P2tsbEwHBwe+f+OBQECWZenm5kbRaFSnp6eanZ31/X5J6u/vl+M4vq+0Njc3KxwOf+xkNgxD7e3tamlp8byrdGNjQ3V1dR/rGBYWFuQ4jra3t/39EXr/Xr2+vnq+YvzL8fGxHMfR6OioOjo6FIlEFIlEZFmW7zOguCqhDaTC+6DQNpAqow9og3+jD+iD/6kPvnsbSPRBJaMPfqMPKhttUL42kCqjDyqhDST64G/0Abyo+fHZRZX4dh4fH3V7e/vHZDiVSuns7EzpdLqMJ/ucu7s79fT06Pz8XA0NDeU+zrd1f3+v7u5uZbNZNTY2FuWZyWRS19fXVfm9RHFdXl5qbW1N2Wz2S1chAt8FbYBSoQ9QKrQBUHr0AUqhFG0g0Qd4Rx/Ai4q+kYLKMzc3p8PDQz08POjq6kr7+/vq7e0t97F8y+Vyuri4UDAY9LxLE0D1yOfzOjk5UTwe1/DwMCEElBBtAKAa0AbA16IPAFQD+gB+1Jb7AKge4XBYm5ubSiQSWl9fV319vSYmJjQ+Pl7uo/mWTqeVyWQ0Pz+vUChU7uMAKLLn52etrKyora3t45ovgOKjDQBUC9oA+Dr0AYBqQR/AD1Z7AQAAAAAAAAAAuGC1FwAAAAAAAAAAgAsGKQAAAAAAAAAAAC4YpAAAAAAAAAAAALhgkAIAAAAAAAAAAOCCQQoAAAAAAAAAAIALBikAAAAAAAAAAAAuGKQAAAAAAAAAAAC4YJACAAAAAAAAAADggkEKAAAAAAAAAACAi583bl0z+57mtQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 2000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 2000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(20, 6), sharex=True, sharey=True)\n",
-    "hm1 = plot_transition_matrix(axes[0], names_transitions['df_probs'],   \"Native - Male\")\n",
-    "hm2 = plot_transition_matrix(axes[1], names_transitions_females['df_probs'], \"Native - Female\")\n",
-    "hm3 = plot_transition_matrix(axes[2], names_transitions_males['df_probs'],   \"Native\")\n",
-    "cbar = fig.colorbar(hm3.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02)\n",
+    "hm1 = plot_transition_matrix(axes[0], names_transitions[\"df_probs\"], \"Native - Male\")\n",
+    "hm2 = plot_transition_matrix(\n",
+    "    axes[1], names_transitions_females[\"df_probs\"], \"Native - Female\"\n",
+    ")\n",
+    "hm3 = plot_transition_matrix(axes[2], names_transitions_males[\"df_probs\"], \"Native\")\n",
+    "cbar = fig.colorbar(\n",
+    "    hm3.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02\n",
+    ")\n",
     "cbar.set_label(\"Transition probability\")\n",
     "plt.savefig(\"../assets/names_transition_probabilities.png\")\n",
     "plt.savefig(\"../assets/names_transition_probabilities.svg\")\n",
     "\n",
     "fig, axes = plt.subplots(1, 3, figsize=(20, 6), sharex=True, sharey=True)\n",
-    "hm4 = plot_transition_matrix(axes[0], surnames_transitions['df_probs'],   \"Surnames - Male\")\n",
-    "hm5 = plot_transition_matrix(axes[1], surnames_transitions_females['df_probs'], \"Surnames - Female\")\n",
-    "hm6 = plot_transition_matrix(axes[2], surnames_transitions_males['df_probs'],   \"Surnames\")\n",
-    "cbar = fig.colorbar(hm6.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02)\n",
+    "hm4 = plot_transition_matrix(\n",
+    "    axes[0], surnames_transitions[\"df_probs\"], \"Surnames - Male\"\n",
+    ")\n",
+    "hm5 = plot_transition_matrix(\n",
+    "    axes[1], surnames_transitions_females[\"df_probs\"], \"Surnames - Female\"\n",
+    ")\n",
+    "hm6 = plot_transition_matrix(\n",
+    "    axes[2], surnames_transitions_males[\"df_probs\"], \"Surnames\"\n",
+    ")\n",
+    "cbar = fig.colorbar(\n",
+    "    hm6.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02\n",
+    ")\n",
     "cbar.set_label(\"Transition probability\")\n",
     "plt.savefig(\"../assets/surnames_transition_probabilities.png\")\n",
     "plt.savefig(\"../assets/surnames_transition_probabilities.svg\")\n",
     "plt.show()"
-   ],
-   "id": "f15ad6eb4df27527",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 2000x600 with 4 Axes>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 2000x600 with 4 Axes>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 15
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "### Transition Probability Differences",
-   "id": "1f59136fe68b02a8"
+   "id": "1f59136fe68b02a8",
+   "metadata": {},
+   "source": [
+    "### Transition Probability Differences"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "51ec0792317364fc",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:43:50.242298Z",
      "start_time": "2025-09-28T21:43:49.382953Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "names_transitions_diff = names_transitions_males['df_probs'] - names_transitions_females['df_probs']\n",
-    "surnames_transitions_diff = surnames_transitions_males['df_probs'] - surnames_transitions_females['df_probs']\n",
+    "names_transitions_diff = (\n",
+    "    names_transitions_males[\"df_probs\"] - names_transitions_females[\"df_probs\"]\n",
+    ")\n",
+    "surnames_transitions_diff = (\n",
+    "    surnames_transitions_males[\"df_probs\"] - surnames_transitions_females[\"df_probs\"]\n",
+    ")\n",
     "fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharex=True, sharey=True)\n",
     "\n",
-    "hm1 = sns.heatmap(names_transitions_diff.loc[list(LETTERS), list(LETTERS)], cmap=\"RdBu_r\", center=0, ax=axes[0], cbar=False)\n",
+    "hm1 = sns.heatmap(\n",
+    "    names_transitions_diff.loc[list(LETTERS), list(LETTERS)],\n",
+    "    cmap=\"RdBu_r\",\n",
+    "    center=0,\n",
+    "    ax=axes[0],\n",
+    "    cbar=False,\n",
+    ")\n",
     "axes[0].set_title(\"Names Transition Difference (Male − Female)\")\n",
-    "axes[0].set_xlabel(\"Next letter\"); axes[0].set_ylabel(\"Current letter\")\n",
+    "axes[0].set_xlabel(\"Next letter\")\n",
+    "axes[0].set_ylabel(\"Current letter\")\n",
     "\n",
-    "hm2 = sns.heatmap(surnames_transitions_diff.loc[list(LETTERS), list(LETTERS)], cmap=\"RdBu_r\", center=0, ax=axes[1], cbar=False)\n",
+    "hm2 = sns.heatmap(\n",
+    "    surnames_transitions_diff.loc[list(LETTERS), list(LETTERS)],\n",
+    "    cmap=\"RdBu_r\",\n",
+    "    center=0,\n",
+    "    ax=axes[1],\n",
+    "    cbar=False,\n",
+    ")\n",
     "axes[1].set_title(\"Surnames Transition Difference (Male − Female)\")\n",
-    "axes[1].set_xlabel(\"Next letter\"); axes[1].set_ylabel(\"\")\n",
-    "cbar = fig.colorbar(hm2.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02)\n",
+    "axes[1].set_xlabel(\"Next letter\")\n",
+    "axes[1].set_ylabel(\"\")\n",
+    "cbar = fig.colorbar(\n",
+    "    hm2.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02\n",
+    ")\n",
     "cbar.set_label(\"Difference in transition probability (Male − Female)\")\n",
     "\n",
     "plt.savefig(\"../assets/transition_difference.png\")\n",
     "plt.show()"
-   ],
-   "id": "51ec0792317364fc",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1400x600 with 3 Axes>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 16
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "dae836cd8a6c26e6",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:43:53.422514Z",
      "start_time": "2025-09-28T21:43:51.881491Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "df_comparisons = build_transition_comparisons(\n",
-    "    {\n",
-    "        'm': names_transitions_males,\n",
-    "        'f': names_transitions_females\n",
-    "    },\n",
-    "    {\n",
-    "        'm': surnames_transitions_males,\n",
-    "        'f': surnames_transitions_females\n",
-    "    }\n",
-    ")\n",
-    "df_comparisons.to_csv(\"../assets/transition_comparisons.csv\", index_label=\"category\")\n",
-    "df_comparisons.head(3)"
-   ],
-   "id": "dae836cd8a6c26e6",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "                l2     kl_mf     kl_fm       jsd  permutation_p_value\n",
-       "names     0.318904  0.043201  0.021538  0.032370                0.973\n",
-       "surnames  1.277002  0.293619  0.239895  0.266757                0.003"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -751,6 +756,11 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "                l2     kl_mf     kl_fm       jsd  permutation_p_value\n",
+       "names     0.318904  0.043201  0.021538  0.032370                0.973\n",
+       "surnames  1.277002  0.293619  0.239895  0.266757                0.003"
       ]
      },
      "execution_count": 17,
@@ -758,11 +768,19 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 17
+   "source": [
+    "df_comparisons = build_transition_comparisons(\n",
+    "    {\"m\": names_transitions_males, \"f\": names_transitions_females},\n",
+    "    {\"m\": surnames_transitions_males, \"f\": surnames_transitions_females},\n",
+    ")\n",
+    "df_comparisons.to_csv(\"../assets/transition_comparisons.csv\", index_label=\"category\")\n",
+    "df_comparisons.head(3)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "30078f474d5f456e",
+   "metadata": {},
    "source": [
     "l2 (Euclidean Distance): This metric shows the overall magnitude of the difference between the two matrices. The value for surnames (1.277) is significantly higher than for names (0.3189), indicating that the male and female surname transition matrices are farther apart than those for first names.\n",
     "\n",
@@ -777,92 +795,98 @@
     "surnames (p-value = 0.001): A p-value of 0.001 is very low, well below the significance level of 0.05. This means there's only a 0.1% chance of observing a difference this large or larger if there were no real difference. This provides strong statistical evidence to reject the null hypothesis. The significant difference (JSD = 0.266) is not random; it indicates a genuine, statistically significant difference in the letter transition patterns between male and female surnames.\n",
     "\n",
     "Conclusion: The analysis confirms that male and female surnames have distinct, gender-specific letter transition patterns, while the patterns in first names are not statistically different."
-   ],
-   "id": "30078f474d5f456e"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "### Letters frequency",
-   "id": "c49254df7eee9ae2"
+   "id": "c49254df7eee9ae2",
+   "metadata": {},
+   "source": [
+    "### Letters frequency"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9265e47639c4319d",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:44:08.716709Z",
      "start_time": "2025-09-28T21:43:53.482784Z"
     }
    },
-   "cell_type": "code",
-   "source": "plot_letter_frequencies(df_names_m, df_names_f, title=\"names\")",
-   "id": "9265e47639c4319d",
    "outputs": [
     {
      "data": {
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
-      ],
-      "image/png": "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"
+      ]
      },
      "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 18
+   "source": [
+    "plot_letter_frequencies(df_names_m, df_names_f, title=\"names\")"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "395dace523f1bed4",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:44:18.302146Z",
      "start_time": "2025-09-28T21:44:10.480696Z"
     }
    },
-   "cell_type": "code",
-   "source": "plot_letter_frequencies(df_surnames_m, df_surnames_f, title=\"surnames\")",
-   "id": "395dace523f1bed4",
    "outputs": [
     {
      "data": {
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
-      ],
-      "image/png": "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"
+      ]
      },
      "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 19
+   "source": [
+    "plot_letter_frequencies(df_surnames_m, df_surnames_f, title=\"surnames\")"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "### N-grams analysis",
-   "id": "d3dc057ce0869f93"
-  },
-  {
+   "id": "d3dc057ce0869f93",
    "metadata": {},
-   "cell_type": "markdown",
-   "source": "#### Prefixes and Suffixes",
-   "id": "67994bde86f2f6fc"
+   "source": [
+    "### N-grams analysis"
+   ]
   },
   {
+   "cell_type": "markdown",
+   "id": "67994bde86f2f6fc",
+   "metadata": {},
+   "source": [
+    "#### Prefixes and Suffixes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "a685260c6ab8213d",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:44:19.998062Z",
      "start_time": "2025-09-28T21:44:19.989365Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "import re\n",
-    "import pandas as pd\n",
+    "\n",
     "\n",
     "def _pick_ngram_col(df: pd.DataFrame) -> str:\n",
     "    \"\"\"Detect the n-gram column name like '2-gram', '3-gram', etc.\"\"\"\n",
@@ -871,7 +895,10 @@
     "            return c\n",
     "    raise ValueError(f\"Could not find an n-gram column in: {list(df.columns)}\")\n",
     "\n",
-    "def build_ngram_summary_table(results: dict, top_k: int = 20, sep: str = \", \") -> pd.DataFrame:\n",
+    "\n",
+    "def build_ngram_summary_table(\n",
+    "    results: dict, top_k: int = 20, sep: str = \", \"\n",
+    ") -> pd.DataFrame:\n",
     "    \"\"\"\n",
     "    Convert dict of {subset_key: df} into rows of:\n",
     "      sex | 2-grams | 3-grams | 4-grams | position\n",
@@ -922,19 +949,19 @@
     "            .reset_index(drop=True)\n",
     "        )\n",
     "    return df_out"
-   ],
-   "id": "a685260c6ab8213d",
-   "outputs": [],
-   "execution_count": 20
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3924bde83845366f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:46:12.618810Z",
      "start_time": "2025-09-28T21:44:20.007216Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "names_ngrams = {\n",
     "    # male\n",
@@ -947,7 +974,6 @@
     "    \"male_any_2\": build_ngrams_count(df_names_m, 2, \"any\"),\n",
     "    \"male_any_3\": build_ngrams_count(df_names_m, 3, \"any\"),\n",
     "    \"male_any_4\": build_ngrams_count(df_names_m, 4, \"any\"),\n",
-    "\n",
     "    # female\n",
     "    \"female_prefix_2\": build_ngrams_count(df_names_f, 2, \"prefix\"),\n",
     "    \"female_prefix_3\": build_ngrams_count(df_names_f, 3, \"prefix\"),\n",
@@ -959,54 +985,21 @@
     "    \"female_any_3\": build_ngrams_count(df_names_f, 3, \"any\"),\n",
     "    \"female_any_4\": build_ngrams_count(df_names_f, 4, \"any\"),\n",
     "}"
-   ],
-   "id": "3924bde83845366f",
-   "outputs": [],
-   "execution_count": 21
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9ceaae3cc9040c31",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:46:14.363513Z",
      "start_time": "2025-09-28T21:46:14.334119Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "names_ngrams_summary = build_ngram_summary_table(names_ngrams, top_k=10)\n",
-    "\n",
-    "names_ngrams_summary.to_csv(\"../assets/names_ngrams_summary.csv\", index=False)\n",
-    "names_ngrams_summary"
-   ],
-   "id": "9ceaae3cc9040c31",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "      sex position                                 2-grams  \\\n",
-       "0  female   prefix  ka, ma, mu, mb, ng, ba, ki, lu, ts, bo   \n",
-       "1  female   suffix  ba, ga, la, ka, ma, da, go, ya, bo, na   \n",
-       "2  female      any  ng, ka, mb, an, ba, ma, nd, ga, la, am   \n",
-       "3    male   prefix  ka, mu, ma, ba, mb, ng, ki, lu, ts, bo   \n",
-       "4    male   suffix  ba, ga, la, go, ka, da, bo, le, di, ma   \n",
-       "5    male      any  ng, ka, mb, ba, an, ma, mu, nd, am, al   \n",
-       "\n",
-       "                                            3-grams  \\\n",
-       "0  tsh, kab, ngo, mas, kas, kal, muk, kav, mbu, man   \n",
-       "1  nga, mba, ngo, nda, ala, mbo, ngu, ndo, mbe, mbu   \n",
-       "2  nga, mba, ang, ngo, amb, ong, nda, ala, mbo, eng   \n",
-       "3  kab, tsh, kal, kas, muk, ngo, kam, mut, mul, mbu   \n",
-       "4  nga, mba, ngo, nda, ala, mbo, ngu, mbe, ndo, ele   \n",
-       "5  nga, mba, ngo, amb, ang, ong, ala, nda, shi, mbo   \n",
-       "\n",
-       "                                             4-grams  \n",
-       "0  tshi, kavi, ngoy, kaso, ilun, mbuy, kaba, ntum...  \n",
-       "1  anga, amba, ongo, umba, inga, ombo, unga, enga...  \n",
-       "2  anga, amba, ongo, tshi, umba, inga, ombo, unga...  \n",
-       "3  tshi, ngoy, ilun, kaba, kaso, kamb, muke, kabe...  \n",
-       "4  amba, ongo, anga, umba, unga, ombo, anda, enga...  \n",
-       "5  amba, ongo, anga, tshi, umba, unga, ombo, anda...  "
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1085,6 +1078,31 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "      sex position                                 2-grams  \\\n",
+       "0  female   prefix  ka, ma, mu, mb, ng, ba, ki, lu, ts, bo   \n",
+       "1  female   suffix  ba, ga, la, ka, ma, da, go, ya, bo, na   \n",
+       "2  female      any  ng, ka, mb, an, ba, ma, nd, ga, la, am   \n",
+       "3    male   prefix  ka, mu, ma, ba, mb, ng, ki, lu, ts, bo   \n",
+       "4    male   suffix  ba, ga, la, go, ka, da, bo, le, di, ma   \n",
+       "5    male      any  ng, ka, mb, ba, an, ma, mu, nd, am, al   \n",
+       "\n",
+       "                                            3-grams  \\\n",
+       "0  tsh, kab, ngo, mas, kas, kal, muk, kav, mbu, man   \n",
+       "1  nga, mba, ngo, nda, ala, mbo, ngu, ndo, mbe, mbu   \n",
+       "2  nga, mba, ang, ngo, amb, ong, nda, ala, mbo, eng   \n",
+       "3  kab, tsh, kal, kas, muk, ngo, kam, mut, mul, mbu   \n",
+       "4  nga, mba, ngo, nda, ala, mbo, ngu, mbe, ndo, ele   \n",
+       "5  nga, mba, ngo, amb, ang, ong, ala, nda, shi, mbo   \n",
+       "\n",
+       "                                             4-grams  \n",
+       "0  tshi, kavi, ngoy, kaso, ilun, mbuy, kaba, ntum...  \n",
+       "1  anga, amba, ongo, umba, inga, ombo, unga, enga...  \n",
+       "2  anga, amba, ongo, tshi, umba, inga, ombo, unga...  \n",
+       "3  tshi, ngoy, ilun, kaba, kaso, kamb, muke, kabe...  \n",
+       "4  amba, ongo, anga, umba, unga, ombo, anda, enga...  \n",
+       "5  amba, ongo, anga, tshi, umba, unga, ombo, anda...  "
       ]
      },
      "execution_count": 22,
@@ -1092,16 +1110,24 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 22
+   "source": [
+    "names_ngrams_summary = build_ngram_summary_table(names_ngrams, top_k=10)\n",
+    "\n",
+    "names_ngrams_summary.to_csv(\"../assets/names_ngrams_summary.csv\", index=False)\n",
+    "names_ngrams_summary"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "951e8301584e549c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:47:17.247916Z",
      "start_time": "2025-09-28T21:46:14.408394Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "surnames_ngrams = {\n",
     "    # male\n",
@@ -1114,7 +1140,6 @@
     "    \"male_any_2\": build_ngrams_count(df_surnames_m, 2, \"any\"),\n",
     "    \"male_any_3\": build_ngrams_count(df_surnames_m, 3, \"any\"),\n",
     "    \"male_any_4\": build_ngrams_count(df_surnames_m, 4, \"any\"),\n",
-    "\n",
     "    # female\n",
     "    \"female_prefix_2\": build_ngrams_count(df_surnames_f, 2, \"prefix\"),\n",
     "    \"female_prefix_3\": build_ngrams_count(df_surnames_f, 3, \"prefix\"),\n",
@@ -1126,54 +1151,21 @@
     "    \"female_any_3\": build_ngrams_count(df_surnames_f, 3, \"any\"),\n",
     "    \"female_any_4\": build_ngrams_count(df_surnames_f, 4, \"any\"),\n",
     "}"
-   ],
-   "id": "951e8301584e549c",
-   "outputs": [],
-   "execution_count": 23
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f7ee40432896ef54",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:47:19.856774Z",
      "start_time": "2025-09-28T21:47:19.831696Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "surnames_ngrams_summary = build_ngram_summary_table(surnames_ngrams, top_k=10)\n",
-    "\n",
-    "surnames_ngrams_summary.to_csv(\"../assets/surnames_ngrams_summary.csv\", index=False)\n",
-    "surnames_ngrams_summary"
-   ],
-   "id": "f7ee40432896ef54",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "      sex position                                 2-grams  \\\n",
-       "0  female   prefix  ma, ch, be, na, an, sa, es, jo, ju, me   \n",
-       "1  female   suffix  ne, ie, te, le, ce, ia, se, el, ah, th   \n",
-       "2  female      any  ne, in, el, an, ie, ri, ra, li, ar, er   \n",
-       "3    male   prefix  jo, je, pa, ch, ma, da, al, ju, be, fr   \n",
-       "4    male   suffix  in, el, an, ck, on, re, ce, er, se, is   \n",
-       "5    male      any  an, er, el, ie, in, ri, is, on, en, re   \n",
-       "\n",
-       "                                            3-grams  \\\n",
-       "0  mar, cha, est, chr, gra, dor, sar, rut, ben, mer   \n",
-       "1  ine, tte, lle, rah, nce, ene, nne, her, lie, rie   \n",
-       "2  ine, tte, ett, mar, ari, lle, lin, eli, the, ell   \n",
-       "3  jea, jos, chr, pat, mar, jon, fra, dan, cha, ben   \n",
-       "4  ean, tin, ick, ier, ard, uel, ert, ain, iel, ise   \n",
-       "5  ric, sti, jea, ean, tin, ris, ier, ist, ick, jos   \n",
-       "\n",
-       "                                             4-grams  \n",
-       "0  mari, chri, esth, grac, sara, dorc, ruth, rach...  \n",
-       "1  ette, line, tine, elle, ther, arie, ille, rcas...  \n",
-       "2  ette, line, ther, mari, tine, elle, rist, chri...  \n",
-       "3  jean, chri, jose, jona, patr, fran, mich, emma...  \n",
-       "4  jean, stin, rick, bert, seph, than, oise, avid...  \n",
-       "5  jean, stin, rist, rick, chri, hris, usti, bert...  "
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1252,6 +1244,31 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "      sex position                                 2-grams  \\\n",
+       "0  female   prefix  ma, ch, be, na, an, sa, es, jo, ju, me   \n",
+       "1  female   suffix  ne, ie, te, le, ce, ia, se, el, ah, th   \n",
+       "2  female      any  ne, in, el, an, ie, ri, ra, li, ar, er   \n",
+       "3    male   prefix  jo, je, pa, ch, ma, da, al, ju, be, fr   \n",
+       "4    male   suffix  in, el, an, ck, on, re, ce, er, se, is   \n",
+       "5    male      any  an, er, el, ie, in, ri, is, on, en, re   \n",
+       "\n",
+       "                                            3-grams  \\\n",
+       "0  mar, cha, est, chr, gra, dor, sar, rut, ben, mer   \n",
+       "1  ine, tte, lle, rah, nce, ene, nne, her, lie, rie   \n",
+       "2  ine, tte, ett, mar, ari, lle, lin, eli, the, ell   \n",
+       "3  jea, jos, chr, pat, mar, jon, fra, dan, cha, ben   \n",
+       "4  ean, tin, ick, ier, ard, uel, ert, ain, iel, ise   \n",
+       "5  ric, sti, jea, ean, tin, ris, ier, ist, ick, jos   \n",
+       "\n",
+       "                                             4-grams  \n",
+       "0  mari, chri, esth, grac, sara, dorc, ruth, rach...  \n",
+       "1  ette, line, tine, elle, ther, arie, ille, rcas...  \n",
+       "2  ette, line, ther, mari, tine, elle, rist, chri...  \n",
+       "3  jean, chri, jose, jona, patr, fran, mich, emma...  \n",
+       "4  jean, stin, rick, bert, seph, than, oise, avid...  \n",
+       "5  jean, stin, rist, rick, chri, hris, usti, bert...  "
       ]
      },
      "execution_count": 24,
@@ -1259,7 +1276,12 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 24
+   "source": [
+    "surnames_ngrams_summary = build_ngram_summary_table(surnames_ngrams, top_k=10)\n",
+    "\n",
+    "surnames_ngrams_summary.to_csv(\"../assets/surnames_ngrams_summary.csv\", index=False)\n",
+    "surnames_ngrams_summary"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1271,6 +1293,7 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 25,
    "id": "28fe77f9-f9b0-4267-9dbb-3969e04b83fb",
    "metadata": {
     "ExecuteTime": {
@@ -1278,6 +1301,7 @@
      "start_time": "2025-09-28T21:47:19.901973Z"
     }
    },
+   "outputs": [],
    "source": [
     "# tokens must include '^' (start) and '$' (end)\n",
     "tokens = [\"^\"] + list(LETTERS) + [\"$\"]\n",
@@ -1285,9 +1309,12 @@
     "idx_to_token = np.array(tokens)\n",
     "V = len(tokens)\n",
     "\n",
+    "\n",
     "# Prepare a well-formed probability matrix (rows sum to 1, no NaNs/negatives)\n",
     "def prepare_prob_matrix(df_probs, tokens):\n",
-    "    import pandas as pd, numpy as np\n",
+    "    import pandas as pd\n",
+    "    import numpy as np\n",
+    "\n",
     "    if isinstance(df_probs, pd.DataFrame):\n",
     "        P = df_probs.loc[tokens, tokens].to_numpy(dtype=float, copy=True)\n",
     "    else:\n",
@@ -1304,13 +1331,18 @@
     "    return P\n",
     "\n",
     "\n",
-    "def generate_name(P, token_to_idx, idx_to_token, *,\n",
-    "                  target_len=None,   # exact character length (letters only), if provided\n",
-    "                  min_len=1,         # minimum character length\n",
-    "                  max_len=12,        # hard cap on steps\n",
-    "                  temperature=1.0):\n",
-    "    start = token_to_idx['^']\n",
-    "    end   = token_to_idx['$']\n",
+    "def generate_name(\n",
+    "    P,\n",
+    "    token_to_idx,\n",
+    "    idx_to_token,\n",
+    "    *,\n",
+    "    target_len=None,  # exact character length (letters only), if provided\n",
+    "    min_len=1,  # minimum character length\n",
+    "    max_len=12,  # hard cap on steps\n",
+    "    temperature=1.0,\n",
+    "):\n",
+    "    start = token_to_idx[\"^\"]\n",
+    "    end = token_to_idx[\"$\"]\n",
     "    cur = start\n",
     "    out = []\n",
     "\n",
@@ -1355,42 +1387,21 @@
     "        cur = nxt_idx\n",
     "\n",
     "    return \"\".join(out).capitalize()"
-   ],
-   "outputs": [],
-   "execution_count": 25
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "a01eb547985d5a62",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:48:59.032408Z",
      "start_time": "2025-09-28T21:48:59.020841Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "P = prepare_prob_matrix(names_transitions_males['df_probs'], tokens)\n",
-    "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in range(10)]\n",
-    "\n",
-    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
-    "names"
-   ],
-   "id": "a01eb547985d5a62",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "        name\n",
-       "0      Monga\n",
-       "1  Ngonganga\n",
-       "2      Kanda\n",
-       "3  Mbombonda\n",
-       "4     Mbanga\n",
-       "5     Mbanda\n",
-       "6      Manga\n",
-       "7      Bambi\n",
-       "8      Mbale\n",
-       "9      Mamba"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1457,6 +1468,19 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "        name\n",
+       "0      Monga\n",
+       "1  Ngonganga\n",
+       "2      Kanda\n",
+       "3  Mbombonda\n",
+       "4     Mbanga\n",
+       "5     Mbanda\n",
+       "6      Manga\n",
+       "7      Bambi\n",
+       "8      Mbale\n",
+       "9      Mamba"
       ]
      },
      "execution_count": 33,
@@ -1464,40 +1488,30 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 33
+   "source": [
+    "P = prepare_prob_matrix(names_transitions_males[\"df_probs\"], tokens)\n",
+    "generated_var = [\n",
+    "    generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5)\n",
+    "    for _ in range(10)\n",
+    "]\n",
+    "\n",
+    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
+    "names"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "5d5ec37da189b7a8",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:48:52.059908Z",
      "start_time": "2025-09-28T21:48:52.049436Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "P = prepare_prob_matrix(names_transitions_females['df_probs'], tokens)\n",
-    "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in range(10)]\n",
-    "\n",
-    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
-    "names"
-   ],
-   "id": "5d5ec37da189b7a8",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "          name\n",
-       "0        Mamba\n",
-       "1        Kanga\n",
-       "2  Mbokangango\n",
-       "3      Belango\n",
-       "4       Mbanga\n",
-       "5        Mungo\n",
-       "6        Langa\n",
-       "7        Kambo\n",
-       "8        Konga\n",
-       "9        Mbulu"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1564,6 +1578,19 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "          name\n",
+       "0        Mamba\n",
+       "1        Kanga\n",
+       "2  Mbokangango\n",
+       "3      Belango\n",
+       "4       Mbanga\n",
+       "5        Mungo\n",
+       "6        Langa\n",
+       "7        Kambo\n",
+       "8        Konga\n",
+       "9        Mbulu"
       ]
      },
      "execution_count": 32,
@@ -1571,40 +1598,30 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 32
+   "source": [
+    "P = prepare_prob_matrix(names_transitions_females[\"df_probs\"], tokens)\n",
+    "generated_var = [\n",
+    "    generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5)\n",
+    "    for _ in range(10)\n",
+    "]\n",
+    "\n",
+    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
+    "names"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "746028258a8d8dfe",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:49:58.339225Z",
      "start_time": "2025-09-28T21:49:58.329362Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "P = prepare_prob_matrix(surnames_transitions_females['df_probs'], tokens)\n",
-    "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in range(10)]\n",
-    "\n",
-    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
-    "names"
-   ],
-   "id": "746028258a8d8dfe",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "       name\n",
-       "0    Jomane\n",
-       "1  Maielane\n",
-       "2     Miela\n",
-       "3   Canella\n",
-       "4    Thelie\n",
-       "5     Siele\n",
-       "6   Mielice\n",
-       "7     Juege\n",
-       "8    Anella\n",
-       "9    Sesane"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1671,6 +1688,19 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "       name\n",
+       "0    Jomane\n",
+       "1  Maielane\n",
+       "2     Miela\n",
+       "3   Canella\n",
+       "4    Thelie\n",
+       "5     Siele\n",
+       "6   Mielice\n",
+       "7     Juege\n",
+       "8    Anella\n",
+       "9    Sesane"
       ]
      },
      "execution_count": 35,
@@ -1678,41 +1708,30 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 35
+   "source": [
+    "P = prepare_prob_matrix(surnames_transitions_females[\"df_probs\"], tokens)\n",
+    "generated_var = [\n",
+    "    generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5)\n",
+    "    for _ in range(10)\n",
+    "]\n",
+    "\n",
+    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
+    "names"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "7227ac855ee3688f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-28T21:49:30.676439Z",
      "start_time": "2025-09-28T21:49:30.664938Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "P = prepare_prob_matrix(surnames_transitions_males['df_probs'], tokens)\n",
-    "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in\n",
-    "                 range(10)]\n",
-    "\n",
-    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
-    "names"
-   ],
-   "id": "7227ac855ee3688f",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "           name\n",
-       "0         Jaman\n",
-       "1       Chistis\n",
-       "2       Mannane\n",
-       "3       Celerie\n",
-       "4        Jeusus\n",
-       "5        Drinie\n",
-       "6        Jerien\n",
-       "7  Seristinelel\n",
-       "8   Distugueran\n",
-       "9       Alanche"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1779,6 +1798,19 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "           name\n",
+       "0         Jaman\n",
+       "1       Chistis\n",
+       "2       Mannane\n",
+       "3       Celerie\n",
+       "4        Jeusus\n",
+       "5        Drinie\n",
+       "6        Jerien\n",
+       "7  Seristinelel\n",
+       "8   Distugueran\n",
+       "9       Alanche"
       ]
      },
      "execution_count": 34,
@@ -1786,15 +1818,24 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 34
+   "source": [
+    "P = prepare_prob_matrix(surnames_transitions_males[\"df_probs\"], tokens)\n",
+    "generated_var = [\n",
+    "    generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5)\n",
+    "    for _ in range(10)\n",
+    "]\n",
+    "\n",
+    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
+    "names"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
-   "source": "",
-   "id": "a96630f0d2a9195b"
+   "id": "a96630f0d2a9195b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/overview.ipynb b/src/notebooks/overview.ipynb
similarity index 98%
rename from notebooks/overview.ipynb
rename to src/notebooks/overview.ipynb
index 1f863c5..2cc879d 100644
--- a/notebooks/overview.ipynb
+++ b/src/notebooks/overview.ipynb
@@ -1,21 +1,25 @@
 {
  "cells": [
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "# Overview",
-   "id": "95dcf546dfd256ab"
+   "id": "95dcf546dfd256ab",
+   "metadata": {},
+   "source": [
+    "# Overview"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "initial_id",
    "metadata": {
-    "collapsed": true,
     "ExecuteTime": {
      "end_time": "2025-10-03T09:49:28.626603Z",
      "start_time": "2025-10-03T09:49:28.450965Z"
-    }
+    },
+    "collapsed": true
    },
+   "outputs": [],
    "source": [
     "import sys\n",
     "import os\n",
@@ -24,24 +28,24 @@
     "\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
     "\n",
     "sys.path.append(os.path.abspath(\"..\"))\n",
-    "from core.utils.data_loader import DataLoader\n",
-    "from core.utils.region_mapper import RegionMapper\n",
-    "from core.config.pipeline_config import PipelineConfig"
-   ],
-   "outputs": [],
-   "execution_count": 1
+    "from ners.core.utils.data_loader import DataLoader\n",
+    "from ners.core.utils.region_mapper import RegionMapper\n",
+    "from ners.core.config.pipeline_config import PipelineConfig"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a8fb2b6242a1d3ce",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:49:28.637131Z",
      "start_time": "2025-10-03T09:49:28.633945Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "config = PipelineConfig(\n",
     "    paths={\n",
@@ -51,61 +55,49 @@
     "        \"outputs_dir\": \"../data/processed\",\n",
     "        \"logs_dir\": \"../logs\",\n",
     "        \"configs_dir\": \"../configs\",\n",
-    "        \"checkpoints_dir\": \"../checkpoints\"\n",
+    "        \"checkpoints_dir\": \"../checkpoints\",\n",
     "    }\n",
     ")\n",
     "\n",
     "loader = DataLoader(config)"
-   ],
-   "id": "a8fb2b6242a1d3ce",
-   "outputs": [],
-   "execution_count": 2
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "56b61bdd0628ebe4",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:49:42.520725Z",
      "start_time": "2025-10-03T09:49:28.645041Z"
     }
    },
-   "cell_type": "code",
-   "source": "df = loader.load_csv_complete(config.paths.data_dir / \"names.csv\")",
-   "id": "56b61bdd0628ebe4",
    "outputs": [],
-   "execution_count": 3
+   "source": [
+    "df = loader.load_csv_complete(config.paths.data_dir / \"names.csv\")"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "## Quantitative",
-   "id": "db9f5c2b2e9f9c0f"
+   "id": "db9f5c2b2e9f9c0f",
+   "metadata": {},
+   "source": [
+    "## Quantitative"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7000df8794ddfaad",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:49:49.680636Z",
      "start_time": "2025-10-03T09:49:48.608824Z"
     }
    },
-   "cell_type": "code",
-   "source": "df.describe().T",
-   "id": "7000df8794ddfaad",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "                count           mean            std     min     25%      50%  \\\n",
-       "id          8069950.0      16.231662      20.139369     0.0     4.0     10.0   \n",
-       "percentage  8069950.0      56.188421       5.403735    10.0    52.0     55.0   \n",
-       "year        8069950.0    2017.599677       4.371243  2008.0  2015.0   2019.0   \n",
-       "line        8069950.0  118021.649797  195762.213691     4.0  6957.0  19192.0   \n",
-       "\n",
-       "                 75%       max  \n",
-       "id              21.0     939.0  \n",
-       "percentage      59.0      96.0  \n",
-       "year          2021.0    2023.0  \n",
-       "line        117146.0  826262.0  "
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -183,6 +175,19 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "                count           mean            std     min     25%      50%  \\\n",
+       "id          8069950.0      16.231662      20.139369     0.0     4.0     10.0   \n",
+       "percentage  8069950.0      56.188421       5.403735    10.0    52.0     55.0   \n",
+       "year        8069950.0    2017.599677       4.371243  2008.0  2015.0   2019.0   \n",
+       "line        8069950.0  118021.649797  195762.213691     4.0  6957.0  19192.0   \n",
+       "\n",
+       "                 75%       max  \n",
+       "id              21.0     939.0  \n",
+       "percentage      59.0      96.0  \n",
+       "year          2021.0    2023.0  \n",
+       "line        117146.0  826262.0  "
       ]
      },
      "execution_count": 4,
@@ -190,54 +195,31 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 4
+   "source": [
+    "df.describe().T"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "## Regions",
-   "id": "572574873e443bda"
+   "id": "572574873e443bda",
+   "metadata": {},
+   "source": [
+    "## Regions"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e02c8023d43b3466",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:49:50.186633Z",
      "start_time": "2025-10-03T09:49:49.727757Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "table = (\n",
-    "    df['region']\n",
-    "    .value_counts()            # counts by frequency\n",
-    "    .rename_axis(\"Value\")      # name the index\n",
-    "    .reset_index(name=\"Count\") # make it a DataFrame\n",
-    "    .sort_values(\"Value\")      # sort alphabetically\n",
-    "    .reset_index(drop=True)    # clean row index\n",
-    ")\n",
-    "\n",
-    "table"
-   ],
-   "id": "e02c8023d43b3466",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "          Value   Count\n",
-       "0      bandundu  165090\n",
-       "1    bandundu-1   37755\n",
-       "2    bandundu-2   75006\n",
-       "3    bandundu-3   27907\n",
-       "4     bas-congo   45604\n",
-       "..          ...     ...\n",
-       "120    tshopo-1   49612\n",
-       "121    tshopo-2   23624\n",
-       "122     tshuapa   13717\n",
-       "123   tshuapa-1    8206\n",
-       "124   tshuapa-2   11220\n",
-       "\n",
-       "[125 rows x 2 columns]"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -321,6 +303,22 @@
        "</table>\n",
        "<p>125 rows × 2 columns</p>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "          Value   Count\n",
+       "0      bandundu  165090\n",
+       "1    bandundu-1   37755\n",
+       "2    bandundu-2   75006\n",
+       "3    bandundu-3   27907\n",
+       "4     bas-congo   45604\n",
+       "..          ...     ...\n",
+       "120    tshopo-1   49612\n",
+       "121    tshopo-2   23624\n",
+       "122     tshuapa   13717\n",
+       "123   tshuapa-1    8206\n",
+       "124   tshuapa-2   11220\n",
+       "\n",
+       "[125 rows x 2 columns]"
       ]
      },
      "execution_count": 5,
@@ -328,24 +326,42 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 5
+   "source": [
+    "table = (\n",
+    "    df[\"region\"]\n",
+    "    .value_counts()  # counts by frequency\n",
+    "    .rename_axis(\"Value\")  # name the index\n",
+    "    .reset_index(name=\"Count\")  # make it a DataFrame\n",
+    "    .sort_values(\"Value\")  # sort alphabetically\n",
+    "    .reset_index(drop=True)  # clean row index\n",
+    ")\n",
+    "\n",
+    "table"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "## Subregions by Parent Region",
-   "id": "2c6b460a94f3ca9d"
+   "id": "2c6b460a94f3ca9d",
+   "metadata": {},
+   "source": [
+    "## Subregions by Parent Region"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8f8a9bb068152311",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:49:50.879683Z",
      "start_time": "2025-10-03T09:49:50.867657Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
-    "def build_subregion_tables(series: pd.Series, mapper: RegionMapper) -> Dict[str, pd.DataFrame]:\n",
+    "def build_subregion_tables(\n",
+    "    series: pd.Series, mapper: RegionMapper\n",
+    ") -> Dict[str, pd.DataFrame]:\n",
     "    \"\"\"\n",
     "    Returns { PARENT -> DataFrame[subregion, count] }.\n",
     "    Parent is the mapped province produced by RegionMapper.map().\n",
@@ -371,14 +387,17 @@
     "    Plot horizontal bar charts of subregion counts for every parent in\n",
     "    RegionMapper.get_provinces(), arranged as a grid with `per_row` columns.\n",
     "    \"\"\"\n",
+    "\n",
     "    def truncate_label(l: str, max_len: int = 15) -> str:\n",
-    "        return l if len(l) <= max_len else l[:max_len - 3] + \"...\"\n",
+    "        return l if len(l) <= max_len else l[: max_len - 3] + \"...\"\n",
     "\n",
     "    # Build subregion tables once\n",
     "    tables = build_subregion_tables(series, mapper)\n",
     "\n",
     "    # Respect the explicit list of parents from RegionMapper\n",
-    "    parents = [p.upper() for p in mapper.get_provinces()]  # mapper.get_provinces() are lowercase\n",
+    "    parents = [\n",
+    "        p.upper() for p in mapper.get_provinces()\n",
+    "    ]  # mapper.get_provinces() are lowercase\n",
     "    n = len(parents)\n",
     "    rows = math.ceil(n / per_row)\n",
     "\n",
@@ -427,61 +446,59 @@
     "\n",
     "    plt.tight_layout()\n",
     "    plt.show()"
-   ],
-   "id": "8f8a9bb068152311",
-   "outputs": [],
-   "execution_count": 6
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4083480dfe1df962",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:50:00.460640Z",
      "start_time": "2025-10-03T09:49:51.239623Z"
     }
    },
-   "cell_type": "code",
-   "source": "plot_all_parents_grid(df[\"region\"], RegionMapper(), per_row=3)",
-   "id": "4083480dfe1df962",
    "outputs": [
     {
      "data": {
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1800x1920 with 12 Axes>"
-      ],
-      "image/png": "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"
+      ]
      },
      "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 7
+   "source": [
+    "plot_all_parents_grid(df[\"region\"], RegionMapper(), per_row=3)"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5242e5b15acb277c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:50:02.908297Z",
      "start_time": "2025-10-03T09:50:00.544028Z"
     }
    },
-   "cell_type": "code",
-   "source": "df['province'] = RegionMapper().map(df['region'])",
-   "id": "5242e5b15acb277c",
    "outputs": [],
-   "execution_count": 8
+   "source": [
+    "df[\"province\"] = RegionMapper().map(df[\"region\"])"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d3e3268c111c5c22",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:50:03.004308Z",
      "start_time": "2025-10-03T09:50:02.998149Z"
     }
    },
-   "cell_type": "code",
-   "source": "df.columns",
-   "id": "d3e3268c111c5c22",
    "outputs": [
     {
      "data": {
@@ -496,42 +513,51 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 9
+   "source": [
+    "df.columns"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4d673e975665827f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:50:03.423521Z",
      "start_time": "2025-10-03T09:50:03.159435Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
-    "import math\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
     "import networkx as nx\n",
     "from matplotlib.patches import Circle\n",
     "\n",
+    "\n",
     "def _province_palette(provinces):\n",
     "    \"\"\"Map each province to a color using matplotlib's default cycle.\"\"\"\n",
-    "    colors = plt.rcParams['axes.prop_cycle'].by_key().get('color', ['C0','C1','C2','C3','C4','C5','C6','C7','C8','C9'])\n",
+    "    colors = (\n",
+    "        plt.rcParams[\"axes.prop_cycle\"]\n",
+    "        .by_key()\n",
+    "        .get(\"color\", [\"C0\", \"C1\", \"C2\", \"C3\", \"C4\", \"C5\", \"C6\", \"C7\", \"C8\", \"C9\"])\n",
+    "    )\n",
     "    return {p: colors[i % len(colors)] for i, p in enumerate(sorted(provinces))}\n",
     "\n",
+    "\n",
     "def plot_region_clusters_by_province(\n",
-    "        df: pd.DataFrame,\n",
-    "        year_col: str = \"year\",\n",
-    "        region_col: str = \"region\",\n",
-    "        province_col: str = \"province\",\n",
-    "        figsize=(14, 9),\n",
-    "        node_size_min: float = 30,\n",
-    "        node_size_max: float = 600,\n",
-    "        province_padding: float = 1.6,   # spacing between province cluster centers\n",
-    "        hull_alpha: float = 0.08,        # alpha for province “enclosing” circles\n",
-    "        label_regions: bool = True,\n",
-    "        label_max_chars: int = 18,\n",
-    "        random_seed: int = 42,\n",
+    "    df: pd.DataFrame,\n",
+    "    year_col: str = \"year\",\n",
+    "    region_col: str = \"region\",\n",
+    "    province_col: str = \"province\",\n",
+    "    figsize=(14, 9),\n",
+    "    node_size_min: float = 30,\n",
+    "    node_size_max: float = 600,\n",
+    "    province_padding: float = 1.6,  # spacing between province cluster centers\n",
+    "    hull_alpha: float = 0.08,  # alpha for province “enclosing” circles\n",
+    "    label_regions: bool = True,\n",
+    "    label_max_chars: int = 18,\n",
+    "    random_seed: int = 42,\n",
     "):\n",
     "    \"\"\"\n",
     "    Visualize how regions cluster inside provinces.\n",
@@ -542,7 +568,7 @@
     "    # --- Prepare aggregated weights per (province, region) ---\n",
     "    dfx = df[[year_col, region_col, province_col]].dropna().copy()\n",
     "    dfx[province_col] = dfx[province_col].astype(str).str.strip()\n",
-    "    dfx[region_col]   = dfx[region_col].astype(str).str.strip()\n",
+    "    dfx[region_col] = dfx[region_col].astype(str).str.strip()\n",
     "    agg = (\n",
     "        dfx.groupby([province_col, region_col], as_index=False)\n",
     "        .size()\n",
@@ -560,7 +586,9 @@
     "    if cmax == cmin:\n",
     "        sizes = np.full(len(agg), (node_size_min + node_size_max) / 2.0)\n",
     "    else:\n",
-    "        sizes = node_size_min + (agg[\"count\"] - cmin) * (node_size_max - node_size_min) / (cmax - cmin)\n",
+    "        sizes = node_size_min + (agg[\"count\"] - cmin) * (\n",
+    "            node_size_max - node_size_min\n",
+    "        ) / (cmax - cmin)\n",
     "    agg[\"_size\"] = sizes\n",
     "\n",
     "    # --- Lay out provinces on a grid; do a small spring layout per province around its center ---\n",
@@ -569,16 +597,19 @@
     "    # Choose grid dimensions close to square\n",
     "    nP = len(provinces)\n",
     "    ncols = math.ceil(math.sqrt(nP))\n",
-    "    nrows = math.ceil(nP / ncols)\n",
+    "    math.ceil(nP / ncols)\n",
     "\n",
     "    # Province centers on grid\n",
     "    centers = {}\n",
     "    for idx, p in enumerate(provinces):\n",
     "        r, c = divmod(idx, ncols)\n",
-    "        centers[p] = (c * province_padding, -r * province_padding)  # y negative to go \"down\"\n",
+    "        centers[p] = (\n",
+    "            c * province_padding,\n",
+    "            -r * province_padding,\n",
+    "        )  # y negative to go \"down\"\n",
     "\n",
     "    # Build positions\n",
-    "    pos = {}   # (province, region) -> (x,y)\n",
+    "    pos = {}  # (province, region) -> (x,y)\n",
     "    hull_radii = {}  # province -> radius used for background circle\n",
     "\n",
     "    for p in provinces:\n",
@@ -607,7 +638,15 @@
     "\n",
     "        # Small local layout\n",
     "        # Note: spring_layout is deterministic with seed\n",
-    "        ploc = nx.spring_layout(G, k=0.25, iterations=60, weight='weight', seed=random_seed, pos=init_pos, fixed=[hub])\n",
+    "        ploc = nx.spring_layout(\n",
+    "            G,\n",
+    "            k=0.25,\n",
+    "            iterations=60,\n",
+    "            weight=\"weight\",\n",
+    "            seed=random_seed,\n",
+    "            pos=init_pos,\n",
+    "            fixed=[hub],\n",
+    "        )\n",
     "\n",
     "        # Save region node positions; track cluster radius\n",
     "        pts = []\n",
@@ -631,7 +670,13 @@
     "    # Province hulls (soft circles)\n",
     "    for p in provinces:\n",
     "        cx, cy = centers[p]\n",
-    "        circ = Circle((cx, cy), radius=hull_radii[p], facecolor=color_map[p], edgecolor=\"none\", alpha=hull_alpha)\n",
+    "        circ = Circle(\n",
+    "            (cx, cy),\n",
+    "            radius=hull_radii[p],\n",
+    "            facecolor=color_map[p],\n",
+    "            edgecolor=\"none\",\n",
+    "            alpha=hull_alpha,\n",
+    "        )\n",
     "        ax.add_patch(circ)\n",
     "\n",
     "    # Region nodes\n",
@@ -643,8 +688,12 @@
     "        for _, row in sub.iterrows():\n",
     "            node = (p, row[region_col])\n",
     "            x, y = pos[node]\n",
-    "            xs.append(x); ys.append(y); ss.append(row[\"_size\"])\n",
-    "        sc = ax.scatter(xs, ys, s=ss, alpha=0.85, edgecolors=\"none\", label=p, color=color_map[p])\n",
+    "            xs.append(x)\n",
+    "            ys.append(y)\n",
+    "            ss.append(row[\"_size\"])\n",
+    "        sc = ax.scatter(\n",
+    "            xs, ys, s=ss, alpha=0.85, edgecolors=\"none\", label=p, color=color_map[p]\n",
+    "        )\n",
     "        handles.append(sc)\n",
     "        labels.append(p)\n",
     "\n",
@@ -655,20 +704,29 @@
     "                x, y = pos[node]\n",
     "                name = row[region_col]\n",
     "                if len(name) > label_max_chars:\n",
-    "                    name = name[:label_max_chars-1] + \"…\"\n",
+    "                    name = name[: label_max_chars - 1] + \"…\"\n",
     "                ax.text(x, y, name, fontsize=7, ha=\"center\", va=\"center\")\n",
     "\n",
     "    # Axes aesthetics\n",
-    "    ax.set_aspect('equal', adjustable='datalim')\n",
+    "    ax.set_aspect(\"equal\", adjustable=\"datalim\")\n",
     "    ax.set_xticks([])\n",
     "    ax.set_yticks([])\n",
     "    ax.set_xlabel(\"\")\n",
     "    ax.set_ylabel(\"\")\n",
-    "    ax.set_title(\"Regions clustered within Provinces (node size = frequency across years)\")\n",
+    "    ax.set_title(\n",
+    "        \"Regions clustered within Provinces (node size = frequency across years)\"\n",
+    "    )\n",
     "\n",
     "    # Province legend\n",
     "    ncols = 4 if len(labels) > 10 else 2\n",
-    "    ax.legend(handles, labels, title=\"Province\", loc=\"upper right\", bbox_to_anchor=(1.0, 1.0), ncol=ncols)\n",
+    "    ax.legend(\n",
+    "        handles,\n",
+    "        labels,\n",
+    "        title=\"Province\",\n",
+    "        loc=\"upper right\",\n",
+    "        bbox_to_anchor=(1.0, 1.0),\n",
+    "        ncol=ncols,\n",
+    "    )\n",
     "\n",
     "    # Expand limits to fit hulls nicely\n",
     "    xs, ys = zip(*centers.values())\n",
@@ -676,33 +734,34 @@
     "    ax.set_ylim(min(ys) - province_padding, max(ys) + province_padding)\n",
     "\n",
     "    plt.tight_layout()\n",
-    "    plt.show()\n"
-   ],
-   "id": "4d673e975665827f",
-   "outputs": [],
-   "execution_count": 10
+    "    plt.show()"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "595487c63277e585",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:58:09.712122Z",
      "start_time": "2025-10-03T09:58:09.686613Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
-    "import math\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import networkx as nx\n",
-    "from matplotlib.patches import Circle\n",
     "import matplotlib.patheffects as path_effects\n",
     "\n",
+    "\n",
     "def _province_palette(provinces):\n",
-    "    colors = plt.rcParams['axes.prop_cycle'].by_key().get('color', ['C0','C1','C2','C3','C4','C5','C6','C7','C8','C9'])\n",
+    "    colors = (\n",
+    "        plt.rcParams[\"axes.prop_cycle\"]\n",
+    "        .by_key()\n",
+    "        .get(\"color\", [\"C0\", \"C1\", \"C2\", \"C3\", \"C4\", \"C5\", \"C6\", \"C7\", \"C8\", \"C9\"])\n",
+    "    )\n",
     "    return {p: colors[i % len(colors)] for i, p in enumerate(sorted(provinces))}\n",
     "\n",
+    "\n",
     "def _separate_points(points, radii, iters=60, step=0.02):\n",
     "    \"\"\"\n",
     "    Simple collision-avoidance: iteratively push circles (points with radii)\n",
@@ -715,7 +774,7 @@
     "    for _ in range(iters):\n",
     "        moved = 0.0\n",
     "        for i in range(N):\n",
-    "            for j in range(i+1, N):\n",
+    "            for j in range(i + 1, N):\n",
     "                d = P[j] - P[i]\n",
     "                dist = np.hypot(d[0], d[1])\n",
     "                min_d = R[i] + R[j]\n",
@@ -734,19 +793,20 @@
     "            break\n",
     "    return P\n",
     "\n",
+    "\n",
     "def plot_region_clusters_by_province_readable(\n",
-    "        df: pd.DataFrame,\n",
-    "        year_col: str = \"year\",\n",
-    "        region_col: str = \"region\",\n",
-    "        province_col: str = \"province\",\n",
-    "        figsize=(18, 11),\n",
-    "        node_size_min: float = 60,\n",
-    "        node_size_max: float = 1100,\n",
-    "        label_top_k: int = 12,        # show labels only for the top-K regions per province\n",
-    "        min_count_for_dot: int = 1,   # drop ultra-rare regions if needed (e.g., 2 or 3)\n",
-    "        base_padding: float = 1.6,    # base spacing; will be auto-scaled per province\n",
-    "        hull_alpha: float = 0.10,\n",
-    "        random_seed: int = 42,\n",
+    "    df: pd.DataFrame,\n",
+    "    year_col: str = \"year\",\n",
+    "    region_col: str = \"region\",\n",
+    "    province_col: str = \"province\",\n",
+    "    figsize=(18, 11),\n",
+    "    node_size_min: float = 60,\n",
+    "    node_size_max: float = 1100,\n",
+    "    label_top_k: int = 12,  # show labels only for the top-K regions per province\n",
+    "    min_count_for_dot: int = 1,  # drop ultra-rare regions if needed (e.g., 2 or 3)\n",
+    "    base_padding: float = 1.6,  # base spacing; will be auto-scaled per province\n",
+    "    hull_alpha: float = 0.10,\n",
+    "    random_seed: int = 42,\n",
     "):\n",
     "    \"\"\"\n",
     "    Readable 'regions clustered within provinces' visualization.\n",
@@ -760,7 +820,7 @@
     "    # --- aggregate ---\n",
     "    dfx = df[[year_col, region_col, province_col]].dropna().copy()\n",
     "    dfx[province_col] = dfx[province_col].astype(str).str.strip()\n",
-    "    dfx[region_col]   = dfx[region_col].astype(str).str.strip()\n",
+    "    dfx[region_col] = dfx[region_col].astype(str).str.strip()\n",
     "\n",
     "    agg = (\n",
     "        dfx.groupby([province_col, region_col], as_index=False)\n",
@@ -781,7 +841,9 @@
     "    if cmax == cmin:\n",
     "        agg[\"_size\"] = (node_size_min + node_size_max) / 2.0\n",
     "    else:\n",
-    "        agg[\"_size\"] = node_size_min + (agg[\"count\"] - cmin) * (node_size_max - node_size_min) / (cmax - cmin)\n",
+    "        agg[\"_size\"] = node_size_min + (agg[\"count\"] - cmin) * (\n",
+    "            node_size_max - node_size_min\n",
+    "        ) / (cmax - cmin)\n",
     "\n",
     "    # radius used for collision (approximate pixel-independent unit)\n",
     "    # convert marker size (points^2) into a rough radius in data coords by picking a scale;\n",
@@ -790,12 +852,16 @@
     "\n",
     "    # --- province centers on grid with adaptive spacing ---\n",
     "    # provinces with many regions need more room\n",
-    "    counts_per_prov = agg.groupby(province_col)[\"region\"].count().reindex(provinces, fill_value=1)\n",
-    "    scale_per_prov = (counts_per_prov / counts_per_prov.median()).clip(0.8, 2.0).to_dict()\n",
+    "    counts_per_prov = (\n",
+    "        agg.groupby(province_col)[\"region\"].count().reindex(provinces, fill_value=1)\n",
+    "    )\n",
+    "    scale_per_prov = (\n",
+    "        (counts_per_prov / counts_per_prov.median()).clip(0.8, 2.0).to_dict()\n",
+    "    )\n",
     "\n",
     "    nP = len(provinces)\n",
     "    ncols = math.ceil(math.sqrt(nP))\n",
-    "    nrows = math.ceil(nP / ncols)\n",
+    "    math.ceil(nP / ncols)\n",
     "\n",
     "    centers = {}\n",
     "    for idx, p in enumerate(provinces):\n",
@@ -832,19 +898,34 @@
     "    # soft province circles\n",
     "    for p in provinces:\n",
     "        cx, cy = centers[p]\n",
-    "        circ = Circle((cx, cy), radius=hull_radii[p], facecolor=color_map[p], edgecolor=\"none\", alpha=hull_alpha)\n",
+    "        circ = Circle(\n",
+    "            (cx, cy),\n",
+    "            radius=hull_radii[p],\n",
+    "            facecolor=color_map[p],\n",
+    "            edgecolor=\"none\",\n",
+    "            alpha=hull_alpha,\n",
+    "        )\n",
     "        ax.add_patch(circ)\n",
     "\n",
     "    # dots + labels (top-K)\n",
     "    handles, labels = [], []\n",
     "    for p in provinces:\n",
-    "        sub = agg[agg[province_col] == p].sort_values(\"count\", ascending=False).reset_index(drop=True)\n",
+    "        sub = (\n",
+    "            agg[agg[province_col] == p]\n",
+    "            .sort_values(\"count\", ascending=False)\n",
+    "            .reset_index(drop=True)\n",
+    "        )\n",
     "        xs, ys, ss = [], [], []\n",
     "        for _, row in sub.iterrows():\n",
     "            x, y = pos[(p, row[region_col])]\n",
-    "            xs.append(x); ys.append(y); ss.append(row[\"_size\"])\n",
-    "        sc = ax.scatter(xs, ys, s=ss, alpha=0.9, edgecolors=\"none\", label=p, color=color_map[p])\n",
-    "        handles.append(sc); labels.append(p)\n",
+    "            xs.append(x)\n",
+    "            ys.append(y)\n",
+    "            ss.append(row[\"_size\"])\n",
+    "        sc = ax.scatter(\n",
+    "            xs, ys, s=ss, alpha=0.9, edgecolors=\"none\", label=p, color=color_map[p]\n",
+    "        )\n",
+    "        handles.append(sc)\n",
+    "        labels.append(p)\n",
     "\n",
     "        # labels only for top-K\n",
     "        top = sub.head(label_top_k)\n",
@@ -855,46 +936,41 @@
     "                txt = txt[:17] + \"…\"\n",
     "            t = ax.text(x, y, txt, fontsize=8, ha=\"center\", va=\"center\", color=\"black\")\n",
     "            # white halo for contrast\n",
-    "            t.set_path_effects([path_effects.Stroke(linewidth=2.5, foreground=\"white\"),\n",
-    "                                path_effects.Normal()])\n",
+    "            t.set_path_effects(\n",
+    "                [\n",
+    "                    path_effects.Stroke(linewidth=2.5, foreground=\"white\"),\n",
+    "                    path_effects.Normal(),\n",
+    "                ]\n",
+    "            )\n",
     "\n",
     "    # legend, axes, limits\n",
     "    ax.legend(handles, labels, title=\"Province\", loc=\"upper left\", ncol=3)\n",
     "\n",
     "    ax.set_aspect(\"equal\", adjustable=\"datalim\")\n",
-    "    ax.set_xticks([]); ax.set_yticks([])\n",
-    "    ax.set_xlabel(\"\"); ax.set_ylabel(\"\")\n",
+    "    ax.set_xticks([])\n",
+    "    ax.set_yticks([])\n",
+    "    ax.set_xlabel(\"\")\n",
+    "    ax.set_ylabel(\"\")\n",
     "\n",
     "    xs, ys = zip(*[centers[p] for p in provinces])\n",
     "    pad = max(base_padding, 1.2)\n",
     "    ax.set_xlim(min(xs) - pad, max(xs) + pad)\n",
-    "    #ax.set_ylim(min(ys) - pad, max(ys) + pad)\n",
+    "    # ax.set_ylim(min(ys) - pad, max(ys) + pad)\n",
     "\n",
     "    plt.savefig(\"../assets/regions-clusters-readable.png\")\n",
-    "    plt.show()\n"
-   ],
-   "id": "595487c63277e585",
-   "outputs": [],
-   "execution_count": 33
+    "    plt.show()"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "20e16282a8eebc97",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:58:17.182810Z",
      "start_time": "2025-10-03T09:58:10.971477Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "plot_region_clusters_by_province_readable(\n",
-    "    df,\n",
-    "    label_top_k=5,\n",
-    "    min_count_for_dot=2,\n",
-    "    base_padding=0.5,\n",
-    "    figsize=(22, 14)\n",
-    ")"
-   ],
-   "id": "20e16282a8eebc97",
    "outputs": [
     {
      "name": "stderr",
@@ -905,69 +981,42 @@
     },
     {
      "data": {
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2200x1400 with 1 Axes>"
-      ],
-      "image/png": "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"
+      ]
      },
      "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 34
+   "source": [
+    "plot_region_clusters_by_province_readable(\n",
+    "    df, label_top_k=5, min_count_for_dot=2, base_padding=0.5, figsize=(22, 14)\n",
+    ")"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "# Year",
-   "id": "530d424405c49287"
+   "id": "530d424405c49287",
+   "metadata": {},
+   "source": [
+    "# Year"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "73eac09aa9dd3d5",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:50:35.543474Z",
      "start_time": "2025-10-03T09:50:35.484794Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "table = (\n",
-    "    df['year']\n",
-    "    .value_counts()            # counts by frequency\n",
-    "    .rename_axis(\"Value\")      # name the index\n",
-    "    .reset_index(name=\"Count\") # make it a DataFrame\n",
-    "    .sort_values(\"Value\")      # sort alphabetically\n",
-    "    .reset_index(drop=True)    # clean row index\n",
-    ")\n",
-    "\n",
-    "table"
-   ],
-   "id": "73eac09aa9dd3d5",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "    Value    Count\n",
-       "0    2008   183051\n",
-       "1    2009   256209\n",
-       "2    2010   280484\n",
-       "3    2011   323867\n",
-       "4    2012   322043\n",
-       "5    2013   270742\n",
-       "6    2014   330042\n",
-       "7    2015   729980\n",
-       "8    2016   387432\n",
-       "9    2017   402320\n",
-       "10   2018   483758\n",
-       "11   2019   519065\n",
-       "12   2020   632869\n",
-       "13   2021   956634\n",
-       "14   2022  1221970\n",
-       "15   2023   769484"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1075,6 +1124,25 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "    Value    Count\n",
+       "0    2008   183051\n",
+       "1    2009   256209\n",
+       "2    2010   280484\n",
+       "3    2011   323867\n",
+       "4    2012   322043\n",
+       "5    2013   270742\n",
+       "6    2014   330042\n",
+       "7    2015   729980\n",
+       "8    2016   387432\n",
+       "9    2017   402320\n",
+       "10   2018   483758\n",
+       "11   2019   519065\n",
+       "12   2020   632869\n",
+       "13   2021   956634\n",
+       "14   2022  1221970\n",
+       "15   2023   769484"
       ]
      },
      "execution_count": 19,
@@ -1082,68 +1150,77 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 19
+   "source": [
+    "table = (\n",
+    "    df[\"year\"]\n",
+    "    .value_counts()  # counts by frequency\n",
+    "    .rename_axis(\"Value\")  # name the index\n",
+    "    .reset_index(name=\"Count\")  # make it a DataFrame\n",
+    "    .sort_values(\"Value\")  # sort alphabetically\n",
+    "    .reset_index(drop=True)  # clean row index\n",
+    ")\n",
+    "\n",
+    "table"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d19d077267fbcff0",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:50:35.915484Z",
      "start_time": "2025-10-03T09:50:35.621736Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+YAAAIzCAYAAACN5AMPAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAVXBJREFUeJzt3Xd4VGXCxuEnmZACoWOigICABgIBIlUNUhREFnYpKqgLiCLSUZAifELogoiC4ErLShClKEUFQRBUVl0QpCuRoAKKkNCkpM+c7w82I5Fi6ryZmd99XV6SM2fmfZ+cSXKeOWfO+FiWZQkAAAAAABjha3oCAAAAAAB4M4o5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAAAAAABhEMQcAwEtYlmV6CgAA4Boo5gAA5KN9+/Zp2LBhat68uerUqaP7779fL774oo4dO2Z0Xm+88YYWLlyYp8cYOXKkWrZsmU8zumzVqlUKCwvTjBkzrnl7YmKiGjVqpK5du8rhcOTr2AAAFBYUcwAA8smSJUvUtWtXnT59WkOHDtX8+fPVu3dvbd++XQ899JAOHjxobG4zZ85UcnKysfGvp2PHjrr33nu1cOHCa35/xo0bp/T0dE2bNk2+vuy2AAA8E3/hAADIBzt37tSkSZP02GOPKSYmRu3bt1fjxo31yCOP6N1331VAQIBGjRplepqF0oQJExQYGKhRo0bJbrc7l3/88cfauHGjhg8frkqVKhmcIQAABYtiDgBAPli4cKGKFy+uIUOGXHVbmTJlNHLkSN13331KSkqSJNntdi1ZskTt27dXnTp11Lx5c02fPl2pqanO+3Xr1k3dunXL8ljbtm1TWFiYtm3bJklauXKlwsPDtWfPHnXp0kURERFq0aJFltPWw8LCJEmzZ892/jslJUXR0dG69957Vbt2bbVp0ybbp7ovW7bMeap+jx499N1330mSzp07p4iIiKtOS09OTlb9+vX1r3/965qPd/PNN2vkyJE6cOCA3nrrLUnS77//rokTJ6pp06Z69NFHJUmpqamaNm2amjVrptq1a6t9+/Zat25dlsdKSUnRK6+8otatW6t27dq688471bNnT33//ffOdUaOHKkePXpo7NixuvPOO9W2bdssLwgAAOBqfqYnAACAu7MsS//5z3/UsmVLBQUFXXOdtm3bZvl6zJgxWrNmjZ5++mk1aNBA3333nebMmaPvv/9eCxYskI+PT7bHdzgcevbZZ/XEE0/o2Wef1Xvvvadp06bpjjvuUNOmTbVs2TJ16dJFDz30kB5++GFJ0uTJk/Wf//xHI0aMULly5fTFF19o2rRpKlWqlDp37nzdsU6cOKHZs2dr6NChCg4O1uzZs9WtWzd9+OGHKl++vO6//359+OGHeu6555wZNm7cqKSkJHXo0OG6j/vwww9r/fr1mj17ttq3b685c+YoPT1dkyZNcn6P+/fvr2+//VaDBg1StWrVtHHjRj333HNKS0tzPvbw4cO1Y8cODRkyRJUqVdKRI0c0c+ZMDR06VGvXrnXOaceOHQoICNCcOXOUlJQkm82W7e83AAD5zWuLeVpamjp16qQXX3xRjRs3ztZ9tm/frkmTJunnn39WWFiYxo8frxo1ahTwTAEAhd3Zs2eVmpqqihUrZmv9+Ph4vffeexo6dKh69+4tSbrnnnsUEhKi4cOH64svvlCzZs2yPb5lWerXr5+zdNevX18bN27UZ599pqZNm6pevXqSLh+Zzvz39u3bdc899+hvf/ubJKlx48YqWrSoypYte8Ox7Ha75syZozp16kiS6tatq/vvv1+LFy/WiBEj1LlzZ61bt07btm1TkyZNJEmrV6/W3XffrVtuueWGjz1hwgS1a9dOzz77rHbt2qWXX35ZoaGhkqSvvvpKW7du1auvvup8kaNp06ZKTk7W9OnT1a5dOzkcDl26dEn/93//51ynUaNGunjxol566SWdOnVKN910kyQpIyND48eP180335zt7zMAAAXFK09lT01N1ZAhQ3To0KFs3+fYsWN6+umn1apVK61Zs0ZhYWHq16+f0tLSCnCmAAB3kHm0NbunQ2/fvl2SnKU409/+9jfZbDbnaeo5ERkZ6fy3v7+/ypQp4zxt/loaN26s5cuX6+mnn9bbb7+tY8eOqX///mrevPkNx7n11ludpVySbrrpJtWrV0/ffPONJOnuu+9W+fLltWbNGkmXj7B//fXX6tix419mKF++vIYNG6adO3eqdevWateunfO2r7/+Wj4+PmrWrJkyMjKc/7Vs2VKJiYk6dOiQ/P39tXDhQrVt21YnT57Uf//7Xy1dulRbtmyRpCx/s0uVKkUpBwAUGl5XzOPj4/XII4/o6NGjObrf22+/rTp16mjAgAGqUqWKRo0aJV9fX/34448FNFMAgLsoWbKkihUrpuPHj193naSkJP3++++S5Px/5tHbTH5+fipdurQuXLiQ4zkEBgZm+drX1/eGn1s+evRoPfvss/rll180YcIE3X///eratetfXjm+XLlyVy0rW7aszp8/7xy3U6dO2rBhg1JSUrRmzRoFBwerVatW2crRtGlTSbrqjIFz587JsizdeeedqlWrlvO/Z599VpKUkJAgSdq6dasefPBB3XvvverXr58++OAD+fv7S8r6Oe7FihXL1nwAAHAFryvm27dvV+PGjbVs2bKrbtuxY4c6deqkOnXqqH379tqwYUOW+7Vu3dr5dVBQkDZt2sSp7AAASVJUVJS2bduW5eJtV1q+fLmaNGmiAwcOqGTJkpIuf0b3ldLT03X27FmVLl3auezPR+FvdBQ8J/z9/dW3b199/PHH2rJli8aMGaNjx45p6NChN7xf5osKV0pMTFSZMmWcX3fq1ElJSUn64osv9PHHH6tt27YKCAjI03yLFy+uokWL6r333rvmf5GRkTp69Kj69++vmjVrauPGjdq5c6feeecdtWjRIk9jAwBQ0LyumD/22GMaNWrUVRfnSUxM1DPPPKNOnTrpww8/VK9evTRy5Ejt2LFD0uVT2QMDAzVo0CDdfffd6t69u+Lj401EAAAUQk8++aTOnTun11577arbEhMTFRMTo+rVq6tWrVpq1KiRJGnt2rVZ1lu7dq3sdrvq168vSQoODtaJEyeyrLNz585cze/KzwBPSUnRAw88oJiYGEmXTyF//PHH9be//e2GR/0l6aeffspy1tlvv/2mXbt2ZbleS4UKFXTXXXcpNjZW33//vTp16pSrOV+pUaNGSkpKkmVZioiIcP73ww8/aM6cOcrIyND+/fuVmpqq3r17q1KlSs4LvW3dulWSbngGAQAAJnntxd/+bMmSJbr77rv1z3/+U5JUuXJlff/991q0aJEaNGigpKQkTZ8+XQMGDNAzzzyj2NhYPfHEE9qwYQOnwwEAVK9ePQ0ePFivvfaaDh8+rA4dOqh06dI6dOiQFi5cqNTUVGdpr169ujp27KhZs2YpOTlZDRs21Pfff6/Zs2ercePGztO5W7Rooc2bN2vKlClq2bKlduzYodWrV+dqfiVKlNC3336rb775Rg0aNFCtWrU0e/ZsFSlSRGFhYfrpp5+0atUqPfDAAzd8nICAAPXt21fPPfec7Ha7Zs6cqVKlSqlHjx5Z1nvooYc0ZMgQVatWTXXr1s3VnK/UrFkzNWzYUP369VO/fv1UrVo17d27V7NmzVLTpk1VpkwZ1apVS35+fnr55Zf15JNPKi0tTStXrtRnn30mKf/ONgAAIL9RzP/nxx9/1JYtW7JcPCc9PV233XabpMsX9mnZsqXz82QnTJig5s2ba/PmzWrfvr2ROQMACpe+ffsqPDxcS5Ys0eTJk/X777/rlltuUfPmzdWnT58sVyWfNGmSKleurPfff1/z589XSEiIunfvrn79+jmPbnfu3FlHjx7VqlWrtHTpUjVs2FCzZs1yfq53TvTp00dvvPGGnn76aa1bt07jx4/Xa6+9ppiYGCUmJqps2bJ66KGHNHjw4Bs+Tnh4uB544AFFR0frwoULuuuuuzRq1Kgsp7JLl4u0j49Pvhwtly4f8Z83b55mzpypuXPn6vTp0woNDVXPnj3Vv39/SZdfVH/llVc0e/Zs9e3bVyVLllS9evW0ePFidevWTTt27HB+jjsAAIWJj+XF53WFhYUpNjZWjRs3Vr9+/VSyZEn16dMnyzp+fn6qUKGCWrZsqa5duzo/1ka6/JmrrVq1yrIMAABI69at0/Dhw/X555//5UewAQDg7Thi/j+33Xabdu3apcqVKzuXxcTEKC0tTX369FG9evUUFxfnvC0tLU3Hjh3L9mfWAgDgDTZt2qR9+/Zp6dKl6tSpE6UcAIBs8LqLv13PY489pv379+vVV1/Vzz//rA8//FAzZsxQ+fLlJUk9evTQhg0b9M477+jnn3/W+PHjFRAQ8Jef9woAgDf55ZdftGjRItWuXVvDhg0zPR0AANwCp7L/71R2Sfrqq680ffp0/fDDD873rWVeDE66fBRg+vTp+vXXX1W7dm2NHz9et99+u6npAwAAAAA8gFcXcwAAAAAATONUdgAAAAAADKKYAwAAAABgkNdcld3hcCgjI0O+vr7y8fExPR0AAAAAgIezLEsOh0N+fn7y9b3+cXGvKeYZGRnat2+f6WkAAAAAALxMRESE/P39r3u71xTzzFcnIiIiZLPZXDKm3W7Xvn37XDqmCeT0LOT0LOT0PN6SlZyehZyehZyexVtySmayZo55o6PlkhcV88zT1202m8ufcCbGNIGcnoWcnoWcnsdbspLTs5DTs5DTs3hLTslM1r96OzUXfwMAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgijkAAAAAAAZRzAEAAAAAMIhiDgAAAACAQRRzAAAAAAAMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAwIsFBQWZnoLXo5gDAAAAgAewO6wc38dmsyk8PFw2m81lY+JqfqYnAAAAAADIO5uvjwYv3aX4hIsuGa96SLBmdo10yViejmIOAAAAAB4iPuGiDhw/b3oayKFCcSp7Wlqa2rVrp23btl13nc8++0z/+Mc/FBkZqfbt2+vTTz914QwBAAAAACgYxot5amqqhgwZokOHDl13nYMHD2rAgAHq3LmzVq9era5du2rw4ME6ePCgC2cKAAAAAED+M3oqe3x8vIYOHSrLuvEFAz766CM1adJE3bt3lyRVrlxZmzdv1scff6waNWq4YqoAAAAAABQIo8V8+/btaty4sZ577jnVq1fvuut17NhR6enpVy2/cOFCAc4OAAAAAICCZ7SYP/bYY9lar1q1alm+PnTokL7++mt17do1x2Pa7fYc3ye3Msdy5ZgmkNOzkNOzkNPzeEtWcnoWcnoWchZeuf3Is7xyl++RiW2a3bF8rL86j9xFwsLCFBsbq8aNG99wvTNnzuixxx5TuXLlFBsbK1/f7L1N3m63a/fu3fkwUwAAAAAoXIKCghQeHq6/zdrqsquy1ypfQmsHNdV3332n5ORkl4zprurVq3fDF07c6uPSTp06pZ49e8qyLM2aNSvbpfxKERERLnslyW63a9++fS4d0wRyehZyehZyeh5vyUpOz0JOz0JO/FlYWJjpKWSLiW2aOeZfcZtifvLkSefF32JjY1WmTJlcPY7NZnP5D5aJMU0gp2chp2chp+fxlqzk9Czk9CzkRCZ3+/4Uxm1q/OPSsiMpKUm9evWSr6+v3n77bYWGhpqeEgAAAAAA+aLQHjFPTExU8eLFFRgYqLlz5+ro0aNavHix8zZJCgwMVPHixU1OEwAAAACAPCm0R8yjoqK0bt06SdKGDRuUkpKihx9+WFFRUc7/Jk2aZHiWAAAAAADkTaE5Yh4XF3fdr9evX+/q6QAAAAAA4BKF9og5AAAAAADegGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgijkAAAAAAAZRzAEAAAAAMIhiDgAAAACAQRRzAAAAAAAMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgijkAAAAAAAZRzAEAAAAAMIhiDgAAAACAQRRzAAAAAAAMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgijkAAAAAAAZRzAEAAAAAMIhiDgAAAACAQRRzAAAAAAAMopgDAAAAAGBQoSjmaWlpateunbZt23bddb777js9/PDDqlu3rjp37qz9+/e7cIYAAAAAABQM48U8NTVVQ4YM0aFDh667TlJSknr37q0GDRpo5cqVioyM1DPPPKOkpCQXzhQAAAAAgPxntJjHx8frkUce0dGjR2+43rp16xQQEKDhw4erWrVqGj16tIoVK6b169e7aKYAAAAAABQMo8V8+/btaty4sZYtW3bD9fbs2aP69evLx8dHkuTj46M777xTu3fvdsEsAQAAAAAoOH4mB3/ssceytV5iYqKqV6+eZVnZsmVvePr79djt9hzfJ7cyx3LlmCaQ07OQ07OQ0/N4S1ZyehZyehZyFl42m83IuO7yPTKxTbM7ltFinl3Jycny9/fPsszf319paWk5fqx9+/bl17QK9ZgmkNOzkNOzkNPzeEtWcnoWcnoWchYuQUFBCg8PNzJ2XFyckpOTjYydG4Vxm7pFMQ8ICLiqhKelpSkwMDDHjxUREeGyV5Lsdrv27dvn0jFNIKdnIadnIafn8Zas5PQs5PQs5MSfhYWFmZ5CtpjYpplj/hW3KOahoaE6depUlmWnTp1SSEhIjh/LZrO5/AfLxJgmkNOzkNOzkNPzeEtWcnoWcnoWciKTu31/CuM2Nf5xadlRt25d7dq1S5ZlSZIsy9K3336runXrGp4ZAAAAAAB5U2iLeWJiolJSUiRJbdq00fnz5zVp0iTFx8dr0qRJSk5O1oMPPmh4lgAAAAAA5E2hLeZRUVFat26dJCk4OFhz587Vzp071alTJ+3Zs0fz5s1T0aJFDc8SAAAAAIC8KTTvMY+Li7vh13Xq1NGqVatcOSUAAAAAAApcoT1iDgAAAACAN6CYAwAAAABgEMUcAAAAAACDKOYAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAANcQFBRkegrwEhRzAAAAAB7N7rByfB+bzabw8HDZbDaXjQnv5Wd6AgAAAABQkGy+Phq8dJfiEy66ZLzqIcGa2TXSJWPBM1DMAQAAAHi8+ISLOnD8vOlpANfEqewAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgijkAAAAAAAZRzAEAAAAAMIhiDgAAAACAQRRzAAAAAAAMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgijkAAAAAAAZRzAEAAAAAMIhiDgAAAACAQRRzAAAAAAAMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAAAAAABhktJinpqZq1KhRatCggaKiohQTE3PddTdu3KgHH3xQkZGRevTRR3XgwAEXzhQAAAAAgIJhtJhPmzZN+/fv16JFizR27FjNnj1b69evv2q9Q4cOaejQoXrmmWe0Zs0a1axZU88884ySk5MNzBoAAAAAgPxjrJgnJSVpxYoVGj16tGrVqqVWrVqpV69eWrJkyVXrfvnll6pevbo6dOigSpUqaciQIUpMTFR8fLyBmQMAAAAAkH+MFfODBw8qIyNDkZGRzmX169fXnj175HA4sqxbqlQpxcfHa+fOnXI4HFq5cqWCg4NVqVIlV08bAAAAAIB85Wdq4MTERJUuXVr+/v7OZeXKlVNqaqrOnTunMmXKOJe3bdtWmzdv1mOPPSabzSZfX1/NnTtXJUuWzPG4drs9X+afk7FcOaYJ5PQs5PQs5PQ83pKVnJ6FnJ7FHXPabDYj47r6e+QtOXPLxHM3u2MZK+bJyclZSrkk59dpaWlZlp89e1aJiYkaM2aM6tatq3fffVcvvPCCVq1apbJly+Zo3H379uVt4rlgYkwTyOlZyOlZyOl5vCUrOT0LOT2Lu+QMCgpSeHi4kbHj4uJcdl0sb8mZHwrjc9dYMQ8ICLiqgGd+HRgYmGX59OnTdccdd+jxxx+XJE2YMEEPPvig3n//ffXu3TtH40ZERLjslSS73a59+/a5dEwTyOlZyOlZyOl5vCUrOT0LOT2Lt+TMD2FhYaan4BLuktPEczdzzL9irJiHhobq7NmzysjIkJ/f5WkkJiYqMDBQJUqUyLLugQMH1K1bN+fXvr6+qlGjho4fP57jcW02m8t/gZgY0wRyehZyehZyeh5vyUpOz0JOz+ItOfPCW74/7pazMD53jV38rWbNmvLz89Pu3budy3bu3KmIiAj5+madVkhIiA4fPpxl2U8//aSKFSu6YqoAAAAAABQYY8U8KChIHTp0UHR0tPbu3atNmzYpJiZG3bt3l3T56HlKSook6ZFHHtHy5cu1evVqHTlyRNOnT9fx48fVsWNHU9MHAAAAACBfGDuVXZJeeOEFRUdHq0ePHgoODtbAgQPVunVrSVJUVJSmTJmiTp06qW3btrp06ZLmzp2rEydOqGbNmlq0aFGOL/wGAAAAAEBhY7SYBwUFaerUqZo6depVt8XFxWX5+uGHH9bDDz/sqqkBAAAAAOASxk5lBwAAAAAAFHMAAAAAAIyimAMAAAAAYBDFHAAAAADgFYKCgkxP4Zoo5gAAAAAAt2F3WLm6n81mU3h4uGw2m8vGzC6jV2UHAAAAACAnbL4+Grx0l+ITLrpkvOohwZrZNbJAx6CYAwAAAADcSnzCRR04ft70NPINp7IDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAALiZoKAg01MAAAD5iGIOAIABdoeVq/vZbDaFh4fLZrO5bEwAAFCw/ExPAAAAb2Tz9dHgpbsUn3DRJeNVDwnWzK6RLhkLAADkDMUcAABD4hMu6sDx86anAQAADONUdgAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYlO/F/MyZM/n9kAAAAAAAeKxcFfOaNWtes4D/+uuvuu+++/I8KQAAAAAAvIVfdldcvXq1Vq5cKUmyLEv9+/dXkSJFsqyTkJCgm266KX9nCAAAAACAB8t2MW/VqpV++eUXSdL27dtVr149FStWLMs6RYsWVatWrfJ3hgAAAAAAeLBsF/NixYppwIABkqQKFSqobdu2CggIKLCJAQAAAADgDbJdzK/UsWNHHTlyRPv371d6evpVt3fo0CGv8wIAAAAAwCvkqpgvWLBA06dPV8mSJa86nd3Hx4diDgAAAABANuWqmMfExGjYsGF66qmn8ns+AAAAAAB4lVx9XFpqaqpat26d33MBAAAAAMDr5KqYt2/fXu+8844sy8rv+QAAAAAA4FVydSr7xYsX9d577+mjjz5SxYoVr/o889jY2HyZHAAAAAAAni5XxbxKlSrq06dPfs8FAAAAAACvk6tinvl55gAAAAAAIG9yVcxfeOGFG94+ZcqUXE0GAAAAAABvk6uLv/1ZRkaGfvrpJ61bt05lypTJj4cEAAAAAMAr5OqI+fWOiC9YsEA//PBDniYEAAAAAIA3yZcj5pnatGmjjRs35udDAgAAAADg0fKtmCclJWn58uUqXbp0fj0kAAAAAAAeL1ensteoUUM+Pj5XLQ8ICNDEiRPzPCkAAAAAALxFrop5bGxslq99fHxUpEgRVa9eXcHBwfkyMQAAAACFU1BQkOkpAB4lV8W8UaNGkqSff/5Zhw8flsPh0G233UYpBwAAANyI3WHJ5nv1mbA3YrPZFB4e7tIxAU+Xq2J+/vx5vfDCC/r0009VsmRJ2e12Xbp0SQ0bNtScOXNUvHjx/J4nAAAAgHxm8/XR4KW7FJ9w0SXjVQ8J1syukS4ZC3AnuSrmEydO1IkTJ7Ru3TpVrVpVkhQfH6+RI0dqypQpmjx5cr5OEgAAAEDBiE+4qAPHz5ueBuDVcnVV9s2bNys6OtpZyiWpevXqGjNmjD799NN8mxwAAAAAAJ4uV8U8ICBAvr5X39XHx0d2uz3PkwIAAAAAwFvkqpi3bNlS48aN09GjR53Lfv75Z02cOFHNmjXLt8kBAAAAAODpcvUe82HDhql///564IEHVKJECUnS77//rnvvvVcvvvhivk4QAAAAAABPluNifuTIEZUvX16LFy9WXFycDh8+rICAAFWpUkXVqlXL0WOlpqZq3Lhx+uSTTxQYGKgnn3xSTz755DXXjYuLU3R0tA4cOKDKlStr9OjRatKkSU6nDwAAAABAoZLtU9kty9LEiRP14IMPateuXZKksLAwtW3bVu+//77atWunl156SZZlZXvwadOmaf/+/Vq0aJHGjh2r2bNna/369Vetd+HCBT355JOqXr26PvzwQ7Vq1UoDBgzQ6dOnsz0WAAAAAACFUbaLeWxsrNatW6c5c+aoUaNGWW574403NGfOHK1atUrvvvtuth4vKSlJK1as0OjRo1WrVi21atVKvXr10pIlS65ad9WqVSpatKiio6NVuXJlDRo0SJUrV9b+/fuzO30AAAAAAAqlbBfz5cuX68UXX1SLFi2ueXvLli31/PPPZ7uYHzx4UBkZGYqMjHQuq1+/vvbs2SOHw5Fl3e3bt+u+++6TzWZzLnv//fe50BwAAAAAwO1l+z3mv/76q+rUqXPDdZo0aaJJkyZl6/ESExNVunRp+fv7O5eVK1dOqampOnfunMqUKeNcfuzYMdWpU0cvvviiNm/erAoVKmjEiBGqX79+dqfv5MqPc8scy9M/Qo6cnoWcnoWchdeVLza7krt8j9xxm+YGOT2LO+b0lt9F5CxY5Mz7fbJdzMuWLatff/1VFSpUuO46J06cUKlSpbL1eMnJyVlKuSTn12lpaVmWJyUlad68eerevbvmz5+vtWvX6qmnntLHH3+sW265JbsRJEn79u3L0fr5wcSYJpDTs5DTs5CzcAkKClJ4eLiRsePi4pScnGxk7Nxwl22aV+T0LO6S01t+F5Gz4JEz77JdzFu1aqXXX39dMTExKlKkyFW3Z2RkaPbs2YqKisrW4wUEBFxVwDO/DgwMzLLcZrOpZs2aGjRokCQpPDxcX375pdasWaM+ffpkN4IkKSIiwmWvsNjtdu3bt8+lY5pATs9CTs9CTvxZWFiY6Slki7dsU3J6Fm/JmR/c5XdRXpHTs+QmZ+bvhb+S7WLer18/PfTQQ+rUqZO6deum2rVrq3jx4vr999914MABvf3227p06ZKmTZuWrccLDQ3V2bNnlZGRIT+/y9NITExUYGCg87PRM910002qWrVqlmVVqlTRb7/9lt3pO9lsNpf/ojQxpgnk9Czk9CzkRCZ3+/54yzYlp2fxlpx54S3fH3J6loLMme1iXqJECS1fvlzTp0/XSy+95DyEb1mWihcvrrZt22rgwIEqV65cth6vZs2a8vPz0+7du9WgQQNJ0s6dOxURESFf36zXpKtXr56++eabLMt+/PFHtWvXLrvTBwAAAACgUMp2MZekUqVKaeLEiRozZoyOHTum8+fPq1SpUqpUqVKOXz0ICgpShw4dFB0drcmTJyshIUExMTGaMmWKpMtHz4sXL67AwEB17dpVb7/9tl5//XX9/e9/1+rVq3Xs2DH94x//yNGYAAAAAAAUNtn+uLQr+fv7q1q1aoqMjNRtt92W60P6L7zwgmrVqqUePXpo3LhxGjhwoFq3bi1JioqK0rp16yRJFSpU0IIFC7Rlyxa1a9dOW7Zs0bx58xQaGpqrcQEAAAAAKCxydMQ8vwUFBWnq1KmaOnXqVbfFxcVl+bp+/fpauXKlq6YGAAAAAIBL5OqIOQAAAAAAyB8UcwAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgijkAAAAAAAZRzAEAAAAAMIhiDgAAAACAQRRzAAAAAAAMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAPJJUFCQ6SkAcEMUcwAAAOBP7A4rx/ex2WwKDw+XzWZz2ZgAPIOf6QkAAAAAhY3N10eDl+5SfMJFl4xXPSRYM7tGumQsAIUPxRwAAAC4hviEizpw/LzpaQDwApzKDgAAAACAQRRzAAAAAAAMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAABS4oKAg01MAgEKLYg4AAIBssTusXN3PZrMpPDxcNpvNZWMCgDvxMz0BAAAAuAebr48GL92l+ISLLhmvekiwZnaNdMlYAGASxRwAAADZFp9wUQeOnzc9DQDwKJzKDgAAAACAQRRzAAAAAAAMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgo8U8NTVVo0aNUoMGDRQVFaWYmJi/vM8vv/yiyMhIbdu2zQUzBAAAAACgYPmZHHzatGnav3+/Fi1apOPHj2vEiBEqX7682rRpc937REdHKykpyYWzBAAAAACg4Bgr5klJSVqxYoXmz5+vWrVqqVatWjp06JCWLFly3WL+wQcf6NKlSy6eKQAAAAAABcfYqewHDx5URkaGIiMjncvq16+vPXv2yOFwXLX+2bNn9fLLL2v8+PGunCYAAECBCgoKMj0FAIBhxo6YJyYmqnTp0vL393cuK1eunFJTU3Xu3DmVKVMmy/ovvfSSOnbsqNtvvz1P49rt9jzdPzdjuXJME8jpWcjpWchZeNlsNiPjusv3yO22qY+vbL4+Ob6bzWZTeHh4roa0OyzJuvpgRkHypuett2QlZ8EiZ8Fwp5zZvY+xYp6cnJyllEtyfp2WlpZl+VdffaWdO3fqo48+yvO4+/bty/NjuMOYJpDTs5DTs5CzcAkKCsp1GcuruLg4JScnGxk7N9xhm2Zuz8FLdyk+4aJLxqweEqyZXSP13Xeu257e9Lz1lqzkLHjkzH+emtNYMQ8ICLiqgGd+HRgY6FyWkpKiMWPGaOzYsVmW51ZERITLXmGx2+3at2+fS8c0gZyehZyehZz4s7CwMNNTyBZ33KbxCRd14Ph5l47pLtszr7wlp+Q9WcnpWch5fZl/z/6KsWIeGhqqs2fPKiMjQ35+l6eRmJiowMBAlShRwrne3r17dezYMQ0aNCjL/Z9++ml16NAhx+85t9lsLv8Db2JME8jpWcjpWciJTO72/WGb3pi3fG+8JafkPVnJ6VnImXfGinnNmjXl5+en3bt3q0GDBpKknTt3KiIiQr6+f1yTrk6dOvrkk0+y3Ld169aaOHGi7rnnHpfOGQAAuA4XRQMAeAtjxTwoKEgdOnRQdHS0Jk+erISEBMXExGjKlCmSLh89L168uAIDA1W5cuWr7h8aGqqyZcu6etoAACAH7A7LyEXRcjMmAACmGCvmkvTCCy8oOjpaPXr0UHBwsAYOHKjWrVtLkqKiojRlyhR16tTJ5BQBAEAe2Hx9jFwUDQAAd2K0mAcFBWnq1KmaOnXqVbfFxcVd9343ug0AABQuJi6KBgCAO/H961UAAAAAAEBBoZgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAPEJQUJDpKQAAAOQKxRwAUKjYHVaO72Oz2RQeHi6bzeayMQEAAPKLn+kJACh8OPIIk2y+Phq8dJfiEy66ZLzqIcGa2TXSJWMBAABcC8Uc8GB2hyWbr0+O7pN55NGVY5rCCxCFV3zCRR04ft70NAAAAFyCYg54MG858sgLEAAAAHBnFHPAw3nDkUdveQECAAAAnoliDsAjeMMLEAAAAPBMXJUdAAAAAACDKOYAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgijmQA0FBQaanAAAAAMDDUMzhdewOK1f3s9lsCg8Pl81mc9mYAAAAADyfn+kJAK5m8/XR4KW7FJ9w0SXjVQ8J1syukS4ZCwAAAID7oZjDK8UnXNSB4+dNTwNwCd6CAQAAULhxKjsAuIncvCUiL2/ByO2YAAAAyBmOmAOAm+BtGAAAAJ6JYg4AboS3YQAAAHgeTmUHAAAAAMAgijkAAAAAAAZRzJEvuOozAAAAAOQOxRxOub36cl6u+swVnwEAAAB4Oy7+Bieu+AwAAAAArkcxRxZc8RkAAAAAXItT2QEAAAAAMIhiDgAAAACAQRRzAAAAAAAMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADKKYAwAAAABgkNFinpqaqlGjRqlBgwaKiopSTEzMddf97LPP9I9//EORkZFq3769Pv30UxfOFAAAAACAgmG0mE+bNk379+/XokWLNHbsWM2ePVvr16+/ar2DBw9qwIAB6ty5s1avXq2uXbtq8ODBOnjwoIFZAwAAAACQf/xMDZyUlKQVK1Zo/vz5qlWrlmrVqqVDhw5pyZIlatOmTZZ1P/roIzVp0kTdu3eXJFWuXFmbN2/Wxx9/rBo1apiYPgAAAAAA+cJYMT948KAyMjIUGRnpXFa/fn29+eabcjgc8vX942B+x44dlZ6eftVjXLhwwSVzBQAAAACgoBgr5omJiSpdurT8/f2dy8qVK6fU1FSdO3dOZcqUcS6vVq1alvseOnRIX3/9tbp27Zrjce12e+4nncuxXDlmXthsNiPjuvr74y05Je/JSs6CRc6CQc6CRc6C4S05Je/JSs6CRc6C4U45s3sfY8U8OTk5SymX5Pw6LS3tuvc7c+aMBg4cqDvvvFP33Xdfjsfdt29fju+TVybGzKmgoCCFh4cbGTsuLk7JyckuGctbckrek5WcBY+c+Y+cBY+c+c9bckrek5WcBY+c+c9Tcxor5gEBAVcV8MyvAwMDr3mfU6dOqWfPnrIsS7Nmzcpyunt2RUREuOwVFrvdrn379rl0THcUFhZmegou4S05Je/JSk7PQk7PQk7P4i05Je/JSk7PQs7ry+yEf8VYMQ8NDdXZs2eVkZEhP7/L00hMTFRgYKBKlChx1fonT550XvwtNjY2y6nuOWGz2Vxekk2M6U685XvjLTkl78lKTs9CTs9CTs/iLTkl78lKTs9Czrwz9nFpNWvWlJ+fn3bv3u1ctnPnTkVERFx1JDwpKUm9evWSr6+v3n77bYWGhrp4tgAAAAAAFAxjxTwoKEgdOnRQdHS09u7dq02bNikmJsZ5VDwxMVEpKSmSpLlz5+ro0aOaOnWq87bExESuyg4AAAAAcHvGTmWXpBdeeEHR0dHq0aOHgoODNXDgQLVu3VqSFBUVpSlTpqhTp07asGGDUlJS9PDDD2e5f8eOHfXSSy+ZmHq2BQUFmZ4CAAAAAKAQM1rMg4KCNHXqVOeR8CvFxcU5/71+/XpXTusqdoclm69Pju9ns9lyfcXA3I4JAAAAAHAvRou5u7D5+mjw0l2KT7jokvGqhwRrZtdIl4wFAAAAADCLYp5N8QkXdeD4edPTAAAAAAB4GGMXfwMAAAAAABRzAAAAAACMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgijkAAAAAAAZRzAEAAAAAMIhiDgAAAACAQRRzAAAAAAAMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADKKYAwAAAABgEMUcAAAAAACDKOYAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgijkAAAAAAAZRzAEAAAAAMIhiDgAAAACAQRRzAAAAAAAMopgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAAAAADCIYg4AAAAAgEEUcwAAAAAADDJazFNTUzVq1Cg1aNBAUVFRiomJue663333nR5++GHVrVtXnTt31v79+104UwAAAAAACobRYj5t2jTt379fixYt0tixYzV79mytX7/+qvWSkpLUu3dvNWjQQCtXrlRkZKSeeeYZJSUlGZg1AAAAAAD5x1gxT0pK0ooVKzR69GjVqlVLrVq1Uq9evbRkyZKr1l23bp0CAgI0fPhwVatWTaNHj1axYsWuWeIBAAAAAHAnxor5wYMHlZGRocjISOey+vXra8+ePXI4HFnW3bNnj+rXry8fHx9Jko+Pj+68807t3r3blVMGAAAAACDf+ZkaODExUaVLl5a/v79zWbly5ZSamqpz586pTJkyWdatXr16lvuXLVtWhw4dyvZ4lmVJktLS0mSz2XI0V5vNppo3F1NAzu6Wa1VvKia73S673e6aAf+HnAXDVE7Je7KSs2CQs2CRs2CQs2B5S07Je7KSs2CQs2C5U87M+2T20evxsf5qjQKyevVqzZw5U1u2bHEuO3bsmO6//359/vnnuvnmm53Le/Toofr162vQoEHOZTNnztSuXbv01ltvZWu8tLQ07du3L9/mDwAAAABAdkRERGQ5KP1nxo6YBwQEKC0tLcuyzK8DAwOzte6f17sRPz8/RUREyNfX13lKPAAAAAAABcWyLDkcDvn53bh6GyvmoaGhOnv2rDIyMpyTTExMVGBgoEqUKHHVuqdOncqy7NSpUwoJCcn2eL6+vjd8hQIAAAAAABOMXfytZs2a8vPzy3IBt507dzqPal+pbt262rVrl/O8fMuy9O2336pu3bqunDIAAAAAAPnOWDEPCgpShw4dFB0drb1792rTpk2KiYlR9+7dJV0+ep6SkiJJatOmjc6fP69JkyYpPj5ekyZNUnJysh588EFT0wcAAAAAIF8Yu/ibJCUnJys6OlqffPKJgoOD9dRTT+mJJ56QJIWFhWnKlCnq1KmTJGnv3r0aO3asDh8+rLCwMI0bN07h4eGmpg4AAAAAQL4wWswBAAAAAPB2xk5lBwAAAAAAFHMAAAAAAIyimAMAAAAAYBDFHAAAAAAAgyjmyHeWZckbrinoLTkzeUvWzJzekteTsQ3hrs6cOWN6Csgn3rSv4E1ZvQX7RK5FMXehPz+pPfVJ7uPjIx8fH9PTcAlvyHnkyBFJl7N66nM2U2pqqi5duiTJs7ftn3eePHW7nj17VpLkcDgMz6RgTZw4UYsXLzY9jQKXnp6ujIwM09MocK+99prGjh1rehoFjn0iz+QNWVNSUiR57nM2E/tErudnbGQvExsbq127dsnPz08NGjRQly5dPPJJ/vbbb+vgwYNKT09XtWrV1Lt3b9NTKhD//ve/tWvXLhUpUkQ1a9ZUr169TE+pQOzYsUMLFy5U9+7ddddddznLuSc+d+fNm6f//ve/On36tOrVq6ehQ4eqRIkSpqeV79566y3t2bNHDodDjRo10uOPP+6R2/PTTz/V2LFjtWTJElWuXFkOh0O+vp73WvRLL72klStX6p133jE9lQI1b948bd++XTabTXXr1lW/fv1MT6lATJ06Vf/+979VpkwZ/fbbbwoNDfXI5y37RJ7HW/aLPvvsMx04cEDdu3dX8eLF2Sdyc4Vtn8jzftsXQrNnz9b8+fNVqVIlBQYGav78+fr8889NTyvfzZ07V2+++aZuueUWhYaGOgvdwYMHPepVxXnz5unNN99UtWrVFBoaqvnz5+upp57yuJySFBgYqK1bt2rJkiX64osvJHnmkfO5c+dq8eLFuu+++/TPf/5TW7Zs0bBhw0xPK9+9/vrrmjdvnqpVq6aSJUsqNjZW27ZtMz2tApGamqpTp07pueee0+HDh+Xr6+txR84nT56sDz74QIsXL1aNGjWcyz3t53PBggX697//rbvuukvVqlXz2DMhJk+erJUrV2rWrFkqUaKETpw44ZGlnH0iz9tX8Kb9om+//VbvvvuuVq5cqd9//519IjdWKPeJLBQYh8NhJSYmWl26dLE2b95sWZZlJSYmWo888oj19ddfW2lpaYZnmD8cDod17tw56/HHH7fee+895/KTJ09azZs3t/75z39aO3futBwOh8FZ5o/k5GSrf//+1vLly53LTp8+bf3jH/+wunfvbu3YscMjcmY6duyYFRkZabVr184aMGCAtXXrVudtnpDT4XBYZ86csf75z39aa9eudS4/dOiQFRERYa1bt87g7PKPw+GwEhISrE6dOllbtmyxLMuyTpw4YXXo0MH66quvrISEBLMTLADnzp2zOnToYHXv3t164IEHrLi4OMuyPON5a1mW9eWXX1phYWHWnj17siw/c+aMdeTIEUOzyn+pqanWoEGDrLfffjvLcofDYZ04ccLQrPLf6NGjrUaNGlnff/+9ZVmW1bdvX+vVV1+1LMuyMjIyDM4s/7BP5Hn7RJblPftFmT+Hq1evtsLCwqzHH3/cWrhwoXXu3DnLsjzjbwv7ROb3iTzvpdhCxMfHR0WLFpUkJSQkSJLKlSunxMRERUdH69FHH9W4ceN04cIFk9PMMx8fHwUGBurixYvOC9bY7XaFhITo0Ucf1TfffKOYmBj99ttvktz7aE5gYKBSUlJ08OBBSZezlClTRosXL1Z6erpef/11/fTTT4ZnmXeZ7+M8ePCgbr31Vk2bNk1JSUlatmyZ/vOf/0jyjCPnmT+jKSkpzqNwknTzzTerUqVKOnr0qMHZ5R8fHx8VKVJEGRkZzveLlS5dWkeOHFF0dLTatGmjZ555Rvv37zc80/xhWZZsNpscDodatGihyMhIDR48WEeOHJGPj49OnTpleop5dvfddysqKkoLFixwLuvXr5+efPJJPfTQQ+rXr5++//57gzPMH/7+/jp//rzi4uIkXT5KPnDgQHXu3FndunXTiy++qPPnzxueZd78+uuv+vbbbxUTE+M886FKlSrOs5RsNpvJ6eUbb9onCgoK8op9Isl79osyfw7j4+PVsGFD1a9fXxs2bPCoI+eZz11v2SdKT08vdPtEFPMC5uPjo/Lly2v16tUaM2aM2rRpo1tuuUU9e/bUo48+qg0bNmjIkCGmp5lnDodDZcuW1c6dO5WWlub8BVa+fHn16NFDhw4d0ksvvSTJfS8gkZGRIcuyVLduXR07dkwnT56Uj4+PMjIyVLx4cc2dO1enTp3StGnTTE811zJ3BP38Ll9+omLFirr11ltVs2ZNDRs2TJcuXfKocm632+Xj4yM/Pz/98ssvSktLk2VZCg4OVsWKFfXLL79IkttfcMput8vf31+1atVScHCwpMunHjZs2FCjRo3S3LlzdfLkSb388su6ePGi4dnmnt1ud/47ODhYzZs3V0hIiHr37q0aNWpowIABeuqpp7Rw4UKlpqYanGnepKenS5L69u2r1NRU/etf/9Lzzz8vPz8/9e3bV2+88YaOHz+uKVOmuHVpzfydW6tWLR0/flwpKSmKjo5WWlqaevXqpWeffVZbtmxx+7+hFSpU0MqVK1WrVi3nc7h79+66dOmS1qxZY3h2+cdut8tms6l8+fJas2aNx+4TZf69KFu2rL799luP3SfK5HA4VK9ePR05csQj94syZe7r2Gw2hYWF6bnnnvPIci5JRYoU0dGjRz12n0i6nDEiIkLFihWTVIj2iUwcpvd0O3bssL755hvnKWlnzpyxRo0aZQ0aNMhq1KiR85RKy7p8alP9+vWvOk3PHfw559GjR61GjRpZzz77rLV+/Xrr66+/tmrXrm198skn1okTJ6x77rnH+s9//mN41jn3+eefZ/k6MTHRatq0qfX88887T11KT0+3LOvy9mzUqJG1atUqV08zz86cOWN16NDBmjFjhnNZSkqKdfbsWefXP/zwg9WzZ0+3P639Wtt0x44dlt1udy7r0aOHNXTo0CzrZZ6y5i7+nPPSpUvOf588edI6efKk8+sLFy5Y99xzjzV//nyXzS+/XJnzym342muvWb1797Ys6/LbMlq1amWFh4dbGzdutCzL/U4R/vP2TE1NtebOnWv17NnT6tKli/XDDz84b7t48aJ1zz33WG+++aarp5lnf8554sQJq0GDBtakSZOsGTNmWNu3b3fedvLkSathw4bWW2+95epp5tmVOf/8XLx48aI1ePBga+TIka6eVr671u/bkSNHWs8995xH7RP9OedPP/1k1a9f3xoyZIhH7RNZlmV98cUX1gcffGBt2rTJSk1Ntc6dO2c1b97cGjJkiEftF2Xm/PTTT63Tp09blmVZCQkJ1uHDh53rvPzyy1aXLl2smJgYtz2tPTNn5t/G33//3dq1a5dzG1qWZ+wTXZnTbrdneftMYdkn4oh5Pnv55Zc1ePBgjRs3To888ogmTpyo06dPa9KkSZo0aZIaNWrkPBqZlpamkJAQNW7c2Hlal7u4Vs7k5GStWrVKv/32m2bMmKHnn39ejz/+uFq1aqXixYurdOnSSkxMND31HDl79qxeffVVvfrqq85l5cqV08KFC7VlyxaNGTNGdrvduU3LlSuniIgIt9ue0uVXDy9evKj333/f+Up+QECA8yqcDodDt99+u0aNGqVLly5pxYoV2rx5syT3esX/etu0fv36WS609OeLLr388suaPHmy0tLSXDbXvLhWzoCAAEmXj7iGhIQoJCRE0uVXv4ODg9WkSROdPn3ayHxz6885fX19na/mt2jRwnmGwL/+9S+lpaXpnnvu0euvv66DBw+61SnC19qe/v7+evTRR5Wenq6EhASVKlVK0uXtWaxYMUVFRXnE79zQ0FC99tprWr16tebOnes89dDhcCgkJERNmjTRiRMnTE05V/6c02azZTnjo1ixYnriiSf04YcfauPGjaammWfX+307ceJEjRkzRk2aNPGIfaJr5axSpYqWL1+uI0eOaObMmR6xTyRJ06dP16hRo7R8+XKNGDFCI0eOVHJyshYsWKDPPvvMY/aLrsw5fPhwTZkyRd99951uuukmVa1a1fl35vnnn1eDBg20YcMGrVq1SufOnXOrfaIrc44cOVJDhw7Vb7/9pnr16jm3oeT++0R/zjl8+HDnW6QK0z4RxTwf7dmzRx999JFee+01vfvuu4qJidEPP/ygGTNm6OOPP1ZwcLB+++035+fN+vv7S/rjvQ6Se7zX6EY5jx07pnfffVfLly/XqlWrNHLkSElS0aJFnU94d3JlWZ06dapz+e2336758+fr448/1vPPP69jx47Jsiz5+vrKz8/PLT/jMjg4WL6+vqpVq5b279+vyZMnS7r8y9hut8vX11eWZal69eoaPXq0jh8/rvXr1yspKcnwzHPmets0c6c4c5sVKVLEWWRnzJihRYsW6fHHH3f+3BZ218qZ+b7rzN83Fy5cUHp6uvOPb1JSkjOfuzx3r5UzM0+FChUUHx+vv//97/rvf/+rmJgYjRgxQqGhoRozZozzND13cL3nbfHixfX6669r0aJFuummmyT9kf/ChQsKDAyU5N7bU5IaNmyoiRMnqkSJElq9erXOnj3r3FG88urs7pzzynKeeXpw7969tXDhQuf7d93N9bZn5vvNjxw54vyoP3fdJ5Ku/cK2JFWtWlWxsbFavHix1qxZ4/b7RF9++aXWrVunOXPmaPHixXr77bd14MABxcbGqlq1apo3b55H7BddK+fevXv18ccfO9fx8/Nz/u55/vnn1bhxYy1btkxr1651m0+MuFbO/fv366OPPpKU9fO93Xmf6Fo59+3bpw0bNkhSodon8rHc4SfETWzdulWTJ0/WmjVrnBvy4MGDWrBggc6dO6fevXvL19dXvXr10n333ac77rhDp0+f1ocffqh33nlHt912m+EE2XOjnGfPntWjjz6q+++/X7/88otWrlwpu92u1NRUrVq1Su+9955uvfVWwwly5oEHHlCVKlV06dIlhYeHa9SoUc7bfvrpJ/Xp00cVK1aU3W5X2bJltWXLFr333nuqWrWqwVlnn/W/z+CMj4/X4MGDNW3aNO3du1dr1qxRnTp1nHkz3x+Yuf6PP/6owMBAlS9f3nCCnLveNr3ys6779u2rokWLqkaNGpo5c6aWLVumWrVqmZx2jl0vp91ul8Ph0MKFC/XNN9+oQoUKstls+uCDD7R8+XJVq1bN8Mxz5no509LSNHr0aB07dkwTJkzQ7bffLkn64YcfVLJkSYWGhpqcdo7d6HeRJO3du1exsbGqXLmyfv/9d61atcrtt2fNmjU1evRoSZe357Zt2/T888+rZs2aKlmypIKDg7Vu3Tq99957bp3zzz+fmWdzfPfdd5o/f74CAwPVv39/VaxY0eSUc+VGz9svv/xSffr00QMPPKDbb7/dLfeJMv3V35WjR49qzZo1ysjIcNt9ovfff19Lly7VihUrnMtiY2M1d+5crVq1SiEhIfr555/1zDPPuPV+0Y1yrl692vkiqJR1v2H27Nnq0KGD2/yc5iSnO+8TZSdnenp6odgn4oh5PrrtttuUmprqvICWJNWoUUN9+/ZViRIl9M4776hYsWJatGiRfvvtN3399dc6ceKEFi1a5FZ/gG6Us1SpUlq7dq3i4uJUqlQpnT17VocPH9bPP/+sRYsWuc0foMzXq+Lj4+Xn56dBgwbpb3/7m/bu3es8kixd/l4sXbpUbdu21W233abQ0FAtX77cLf742O1258XPJKlSpUq6/fbbdcstt6h9+/Zq27ZtlryZR1szL25StWpVtyrl2dmmV54GXbFiRa1du1bz5s3Tu+++6zZ/gLKT02azqUiRIvr73/+uUqVK6cKFC0pLS9PSpUvdptxkJ6e/v7+6d++umTNn6vbbb3fe54477nCbUp7d30XS5bNejh49qt27dyshIUHvvPOO22/Pffv2ZdmeTZs21QcffKB69eqpWLFiCg4OdqsXH7L785n5eyg8PFxNmjRRYmKigoKCjM07p7L7vL3rrrsUGxvrtvtE2f27Ismt94kyhYaG6uzZs85TgCWpcuXKSklJceasUqWKc7+oSpUqbrVflOlGOf98mvqV+w0DBgxwm1Iu5SxnhQoV3HKfSMpeziv3ic6fP29un8gVb2T3ZBkZGc6LtqSkpFjDhw+3hg0bluUiPJZlWfv377c6duzovLBWampqlv8XdtnNeeDAAatjx47WK6+8YlnWH/lSUlJcO+FcujKnZV2e/+DBg63Tp09bFy5csBYtWmR16dLFmjRp0jXvf+WFpwqzN99803ruueeshx56yFqxYoV14MABy7KyXoDoenndJWOm3G7TPXv2WA8++KB18OBBV085V3KbM3N7XnmRl8IsuzknTpzoXMfdLsRjWbnfnpnb0R3/tlhW9nNmblN3+X2Um5xXXpjo999/d+l8cyu32zMzq6c/bzN/Pt1ln8iysmY9evSoNXbsWCsuLs75sxcfH2/VqVPH+uGHHyy73X7VhQzd8Wc0OzndVW5zHjhwwGrXrp3zgs+FXV6ft1f+/nUlTmXPg7lz5youLk7Hjh3TI488oqioKCUlJenZZ5/VXXfdpW7dumV5NXTTpk0aPXq0Vq9erZtvvtl55LGwXyQitznXrFmjkJAQ53uT3Slnly5dFB4ervDw8CynFV68eFErV67UunXrspzmnZGRIT8/P7fIuWDBAi1cuFB9+/ZVfHy8jh8/rrS0ND311FNq1qyZpD/yZOb95JNPdNttt2nChAmGZ58zedmmKSkpzguAFHbe8tzNS84rTzcs7Nief50zPT1dRYoU8Zqc7oDnrWfllP7IevToUXXp0kVRUVG65ZZbnDmky28N6ty5s1atWqVq1arJx8dHa9euVUhIiBo2bOgWWXObc926dQoJCVGDBg0MJ8ie3Ob86KOPVLZsWUVERLjVPlFucoaGhhp93rrHXkohtGDBAr311luqV6+eatasqfXr12v48OFKTU3VlClTtGnTJi1atCjLBVtuvvlmlS9fXgEBAc6NXdh/WeUlp7+/v3NH2B1zvvTSS/r888+df2gzS1qnTp3Utm1bfffdd3rxxRcl/XGxpcKeU5K+//57PfHEE+revbvGjx+vAQMG6I477tDEiRO1adMmSZfz2O12Z957771Xx48f16lTpwzPPvvyuk0DAwPd4g+Qtzx385rTXUo52zN7OTPLqrfkLOx43npWTilr1vDwcK1fv14jRozQ559/7nxxweFw6Pz580pPT1fRokXl4+OjV199VS+88IJuvvlmSYU/a15yjhw50m3eFpWXnKNGjVLFihXdbp8oNzlNP2/9/noVXMuV5UaSdu/erQ8++EADBw7Uq6++qlmzZmnMmDE6ffq07r77bjVo0EDr1q1Tampqof8ldSVvzvnRRx9p4sSJSk9P1/33339VWU1JSdG2bdt06tQplStXznCC7MnIyFBCQkKWPyT16tVTmTJlVKRIEb3yyisKCgrSPffc47zQW3BwsB5//HF16dJFJUuWNDj7nPGWbUpOcpKz8CInOd0xp5S9rJlX0Pfz81NAQIDzEyKWLFniNu+fJyc5C1NOinku/FW5GT58uF577TXNmDFDS5cu1cyZM1WyZEmlpqZq1qxZKl26tMHZZx85PaesHjp0SL6+vipXrpx69Oih6Oho3X///brzzjslXb7w2yOPPKKUlBTFxsbq1ltvVaVKlZxvtyhWrJjhBDnjDdtUIic5yVmYkZOc7phTylnWm266SaVKldKQIUO0c+dOLV26VLVr1zY4++wjJzkLXU4XvZfdI/zwww9WfHy8de7cOWvjxo3WPffcY+3cuTPLOj/++KM1ZswY6+mnn7ZOnTplWZZlnT171jp69Kh15swZE9POMXL+4ccff7Sio6Ot3r17W0eOHHEud6eLSc2YMcNq166d1aJFC6tt27bWxIkTrenTp1sDBw604uLisqy7fft2q0uXLtbWrVsNzTZvvGWbkvMP5CRnYUPOP5DTfXJaVs6z/vTTT9bp06etRo0aWREREW5zYTBy/oGchSsnF3/LpldffVWbN2/WpUuXFBQUpLvvvluBgYE6cuSI8z26mb755htNnz5dAwcOVFRUlMFZ5xw5r53zlVde0YABA9wu57JlyzRnzhzNnj1baWlp+umnnzR9+nQ1b95cKSkpCgwMVK9evZyf7yxJPXr0UOXKlTV+/HiDM885b9mm5CQnOQsvcpLTHXNKecu6ZcsWVaxYMcu+RGFFTnIW5pzucTUcw5YtW6ZVq1Zp0qRJmjZtmp544gl98MEHSkhIkI+PjxYuXKhDhw4512/YsKECAwP1ySefGJx1zpHz+jkDAgLcLqckHT58WC1btlSdOnXUoEEDtWzZUhEREbrlllvUrl07nTt3Tq+++qr27t3rvE/p0qV1yy23GJx1znnLNiUnOclZeJGTnO6YU8p91vXr10uSWrRoUajKzfWQk5yFPSfFPBu8pdyQ03NyZp4Ik5iYmOVK6mXLllWFChX0xRdfqFWrVnr88cdVunRpPfnkk+rfv7+efvppbd26Va1atTI19Vzxhm0qkZOc5CzMyElOd8wp5T5rhQoVDM4658hJzsKek4u/3YD1v8+wS0xMVHp6unP5leXm2WefVUBAgDZs2KAnn3xSjRs3Vlpamr799lsNGDDA4Oyzj5yelVP642Me2rVrp1mzZunkyZMqV66cbDabKlSooB07dsjhcOjee+9VvXr11KxZM33zzTcKCgrSyJEjVa1aNcMJssdbtik5yUnOwouc5HTHnJL3ZCUnOd0lJ0fMb+DKcnPs2DGdPHlSdrtdklShQgWlpqY6y82IESM0efJklS9fXjVr1tTy5ctVvXp1k9PPNnJ6Vs4r3XfffZo3b57Kli3rzG+z2WSz2Zy/zEqUKKGwsDCNHj1aQ4YMcZtSLnnPNiUnOclZeJGTnO6YU/KerOQkp7vk5Ih5Ntx3332qXbv2dctNQECAs9y0bt3a8Gxzj5yelTPTlR8bIUlJSUm6dOmS83T3GTNmaN68efrqq69UqlQp+fq63+t13rJNyUlOd0ROcrojb8kpeU9WcpKzsHO/PXBDQkND5efn5ywt1yo3DzzwgM6cOSOHw2FyqnlCTs/KeaXMbL6+vrLZbAoMDNTrr7+uxYsXa/ny5SpTpoxblvJM3rJNyUlOd0ROcrojb8kpeU9WcpKzMOOIeQ5lvq/hRuXGE5DTs3JeqVSpUipWrJjGjx+vFStW6N1331Xt2rVNTyvfeMs2JSc53RE5yemOvCWn5D1ZyUnOwohinkueXm4ykdNzZJ7mU7t2bU2YMEHx8fFatmyZwsPDDc+sYHjDNpXI6WnI6VnI6Vm8JafkPVnJ6VncPqeFXNm9e7cVFhZm1a5d2zpw4IDp6RQYcnqepKQka8KECdbhw4dNT6VAecs2JadnIadnIadn8ZacluU9WcnpWdw9p49l/e8kfORIcnKyXnnlFT322GOqWrWq6ekUGHJ6poyMDPn5efYJM96yTcnpWcjpWcjpWbwlp+Q9WcnpWdw9J8U8D7yh3EjkhPvylm1KTs9CTs9CTs/iLTkl78lKTs/izjkp5gAAAAAAGOS+n40EAAAAAIAHoJgDAAAAAGAQxRwAAAAAAIMo5gAAAAAAGEQxBwAAAADAIIo5AAAAAAAGUcwBAPAiQ4YMUbNmzZScnHzVbT179lTXrl3FJ6kCAOBaFHMAALzIiBEjdOHCBb355ptZln/yySf65ptvNH78ePn4+BiaHQAA3oliDgCAFwkNDdXAgQP173//W8eOHZMkpaSk6KWXXlLPnj11xx13GJ4hAADeh2IOAICX6datmypXrqyXX35ZkrRgwQL5+vqqf//++u2339SnTx/VrVtXLVu21OzZs2W32533XbFihdq0aaPatWurcePGGjdunPP2kSNHauTIkfr73/+uu+66Sz///LOJeAAAuB0/0xMAAACu5efnpzFjxqhbt27atGmTFi5cqFmzZikgIEADBgxQjRo1tGrVKiUmJmrMmDHy8fFR//79tX37dk2cOFEvv/yywsPDtX//fg0bNkx33XWXWrduLUlas2aN5syZo3LlyqlKlSpmgwIA4CY4Yg4AgBdq2LCh2rdvr8GDB6tFixZq2rSp/vvf/+r48eOaMGGCqlatqsaNG2vEiBGKjY2VJBUtWlSTJk1S69atVbFiRbVp00bh4eE6dOiQ83EjIiLUsmVL1alTx1Q0AADcDkfMAQDwUn369NEHH3yg/v37S5IOHz6sc+fOqX79+s51HA6HUlJSdPbsWdWuXVuBgYGaNWuW4uPjFRcXpyNHjigqKsq5foUKFVyeAwAAd0cxBwDASwUEBGT5f0ZGhqpWrao33njjqnWLFy+urVu3qn///urQoYOaNm2q/v37a9y4cdd8TAAAkH2cyg4AACRJt912m44fP64yZcqocuXKqly5sn755RfNmjVLPj4+WrFihTp37qzx48fr4YcfVrVq1XT06FE+9xwAgDyimAMAAElSVFSUKlSooGHDhikuLk47duzQiy++qKCgINlsNpUqVUq7du1SXFycDh06pJEjRyoxMVFpaWmmpw4AgFujmAMAAEmSzWbTv/71LzkcDj3yyCMaOHCgmjVrpv/7v/+TJA0YMEBly5ZVly5d1LNnTwUEBOjRRx/V999/b3jmAAC4Nx+L888AAAAAADCGI+YAAAAAABhEMQcAAAAAwCCKOQAAAAAABlHMAQAAAAAwiGIOAAAAAIBBFHMAAAAAAAyimAMAAAAAYBDFHAAAAAAAgyjmAAAAAAAYRDEHAAAAAMAgijkAAAAAAAb9P4ta21Gcfd3CAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "table.plot(\n",
-    "    x=\"Value\",\n",
-    "    y=\"Count\",\n",
-    "    kind=\"bar\",\n",
-    "    figsize=(12, 6),\n",
-    "    legend=False\n",
-    ")\n",
+    "table.plot(x=\"Value\", y=\"Count\", kind=\"bar\", figsize=(12, 6), legend=False)\n",
     "\n",
     "plt.xlabel(\"Year\")\n",
     "plt.ylabel(\"Count\")\n",
     "plt.title(\"Counts by Year\")\n",
     "plt.xticks(rotation=45)  # rotate x labels for readability\n",
     "plt.show()"
-   ],
-   "id": "d19d077267fbcff0",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 20
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c3432b78cb51dba9",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:50:36.020895Z",
      "start_time": "2025-10-03T09:50:36.004598Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
-    "def build_parent_year_tables(regions: pd.Series, years: pd.Series, mapper: RegionMapper) -> Dict[str, pd.DataFrame]:\n",
+    "def build_parent_year_tables(\n",
+    "    regions: pd.Series, years: pd.Series, mapper: RegionMapper\n",
+    ") -> Dict[str, pd.DataFrame]:\n",
     "    \"\"\"\n",
     "    Returns { PARENT -> DataFrame[year, count] } where count is the number of rows\n",
     "    in that parent for each year (all subregions summed).\n",
     "    \"\"\"\n",
     "    r = regions.astype(str).str.strip().str.lower()\n",
-    "    y = pd.to_numeric(years, errors=\"coerce\").dropna().astype(int)\n",
+    "    pd.to_numeric(years, errors=\"coerce\").dropna().astype(int)\n",
     "    # align after coercion\n",
     "    df_regions = pd.DataFrame({\"region\": r, \"year\": years}).dropna(subset=[\"year\"])\n",
-    "    df_regions[\"year\"] = pd.to_numeric(df_regions[\"year\"], errors=\"coerce\").astype(\"Int64\")\n",
+    "    df_regions[\"year\"] = pd.to_numeric(df_regions[\"year\"], errors=\"coerce\").astype(\n",
+    "        \"Int64\"\n",
+    "    )\n",
     "    df_regions = df_regions.dropna(subset=[\"year\"])\n",
     "    df_regions[\"year\"] = df_regions[\"year\"].astype(int)\n",
     "\n",
@@ -1160,21 +1237,27 @@
     "    g = df_regions.groupby([\"parent\", \"year\"]).size().rename(\"count\").reset_index()\n",
     "    tables: Dict[str, pd.DataFrame] = {}\n",
     "    for parent, gp in g.groupby(\"parent\"):\n",
-    "        gp = (gp.set_index(\"year\")\n",
-    "        .reindex(full_year_index, fill_value=0)\n",
-    "        .rename_axis(\"year\")\n",
-    "        .reset_index()[[\"year\", \"count\"]])\n",
+    "        gp = (\n",
+    "            gp.set_index(\"year\")\n",
+    "            .reindex(full_year_index, fill_value=0)\n",
+    "            .rename_axis(\"year\")\n",
+    "            .reset_index()[[\"year\", \"count\"]]\n",
+    "        )\n",
     "        tables[parent] = gp.sort_values(\"year\").reset_index(drop=True)\n",
     "    return tables\n",
     "\n",
     "\n",
-    "def plot_all_parents_by_year_grid(regions: pd.Series, years: pd.Series, mapper: RegionMapper, per_row: int = 4):\n",
+    "def plot_all_parents_by_year_grid(\n",
+    "    regions: pd.Series, years: pd.Series, mapper: RegionMapper, per_row: int = 4\n",
+    "):\n",
     "    \"\"\"\n",
     "    Plot horizontal bar charts of yearly counts (summing all subregions) for each parent\n",
     "    from RegionMapper.get_provinces(), arranged as a grid with `per_row` columns.\n",
     "    \"\"\"\n",
     "    tables = build_parent_year_tables(regions, years, mapper)\n",
-    "    parents = [p.upper() for p in mapper.get_provinces()]  # keep your canonical ordering\n",
+    "    parents = [\n",
+    "        p.upper() for p in mapper.get_provinces()\n",
+    "    ]  # keep your canonical ordering\n",
     "    n = len(parents)\n",
     "    rows = math.ceil(n / per_row)\n",
     "\n",
@@ -1218,38 +1301,34 @@
     "        axes[r][c].set_axis_off()\n",
     "\n",
     "    plt.tight_layout()\n",
-    "    plt.show()\n"
-   ],
-   "id": "c3432b78cb51dba9",
-   "outputs": [],
-   "execution_count": 21
+    "    plt.show()"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3e9c97741b7f976f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-10-03T09:50:45.775083Z",
      "start_time": "2025-10-03T09:50:36.076073Z"
     }
    },
-   "cell_type": "code",
-   "source": "plot_all_parents_by_year_grid(df[\"region\"], df[\"year\"], RegionMapper(), per_row=4)",
-   "id": "3e9c97741b7f976f",
    "outputs": [
     {
      "data": {
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2400x1440 with 12 Axes>"
-      ],
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAACVYAAAWVCAYAAADrXxzPAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs/X2c1XWdP/4/YNAEjS4+hURWGCYsSuZGqKVBF2tssubFYmw12a4E28VaBKxazpZpeRWWGUtlRVS4gQ2lWNFnK/vUVmb73TZYZbFy2SSSRqwEIYhhfn/0Y2qGGYQ558z7fc653283b591zplznufxat6vx2fm5TlDurq6ugIAAAAAAAAAAEC3oUUPAAAAAAAAAAAAUDYOVgEAAAAAAAAAAPTiYBUAAAAAAAAAAEAvDlYBAAAAAAAAAAD04mAVAAAAAAAAAABALw5WAQAAAAAAAAAA9OJgFQAAAAAAAAAAQC8OVgEAAAAAAAAAAPTiYBUAAAAAAAAAAEAvDlYBg6a1tTWtra37fX379u254IILcuKJJ+brX/96brrppowfP7779h/84AcZP3583vzmN/f5uKtWrcr48eOzadOm7q/t2bMnn/70p3Puuefmec97Xk4++eSce+65+dSnPpXdu3f3+P7x48fnpptu6vOx9z33D37wgz7nPumkk3LCCSeko6Oj39f9zW9+MxdeeGEmT56cSZMm5S/+4i/yvve9L1u3bu33e2bNmpXx48fna1/7Wr/3AQAaV2tra8aPH9/jn8mTJ+f1r3997r777j6/54Mf/GDGjx+fK6+8st/H/fWvf52rr746L3/5y3PiiSdmypQpufDCC/Ov//qvhzTf97///bz1rW/NGWeckZNOOimveMUrcu211/bbb775zW9m9uzZOeWUU/Lc5z43r3jFK3L11Vfnl7/85X73HT9+fKZNm5bt27fvd9umTZsyfvz4rFq1ar/bvve97+Vtb3tbpk2blhNPPDGnnnpq5s6dm+985zt9zrR79+589rOfzcyZM/Pnf/7n+fM///Puvrhz585DygMAqA+16Fj7fo/16U9/us/bL7300rz0pS/d7/5/+s++fnTttdfmN7/5TZ+P35/+Hr8s8wAAzetg/y74p+bPn5/x48fnU5/61H6P1buz9P7n0ksv7b7/3r17M23atIwfPz7/9V//1ed8L33pS/Pnf/7n2bx5c5+39/f3w7Vr1+ayyy7Ly172sjz3uc/t/v3aHXfc0W8WBzMPUF4OVgGF2r59e2bPnp3//u//zuLFi/Pyl7+83/t+4xvfyO23335Qj9vW1pabbrop06dPz+LFi/PhD384L37xi/OhD30o8+bNq8rsd9xxRx7/+Mdn5MiR+cIXvtDnfb74xS/mzW9+c4499thcf/31ufnmm3PhhRfma1/7Wl796lfnt7/97X7fc//99+dHP/pRjj/++Hz+85+vyqwAQP2ZOHFiVqxYkRUrVuSWW27JNddck8MOOywXXXRRfvKTn/S47969e/OlL30pxx9/fG677bY+Dwb97ne/y2tf+9p861vfypw5c/LJT34y73//+/OUpzwlb33rW7Ns2bKDmusDH/hA/vZv/zbDhg3Lu971rnzsYx/La17zmqxevToXXHDBfoelrrjiirzpTW/KyJEjc+WVV+bjH/94Wltb861vfSuvetWrctddd+33HL/85S9zzTXXHHRWV199df72b/82nZ2dWbhwYT796U/n3e9+d4YMGZLZs2dn6dKlPe6/bdu2vP71r8+iRYsyefLk3Hjjjfnwhz+cqVOnZsmSJbngggvy4IMPHvTzAwD1o9oda58PfvCD+d///d+DnmPfDJ///OezZMmSnHvuuVm5cmVe/epX5+GHHx7w6yvrPAAAyYH/Lrht27Z8/etfz/HHH58VK1akq6ur+7Z3v/vd3X1lxYoVmThxYo9et2LFih5v0PDd7343Dz30UJ797Gcf8G9tjz76aC6//PKDnv/Tn/50Zs2alV/84hd561vf2v37taOPPjoLFizIVVdd1ef3Hew8QDkNK3oAoHntK0/r16/PkiVL8qIXveiA9x85cmTe97735YUvfGGe8pSn9Hu/zZs354tf/GLe+9735oILLuj++hlnnJEnP/nJef/735+1a9fmuc99bkXzr1q1KmeccUYOO+yw3HrrrZk7d26GDu15XnXx4sU566yz8p73vKf7a6eeemomT56cV73qVbn11lsze/bs/R736U9/eubOnZsFCxbkf//3f/OsZz2rolkBgPpz1FFH5XnPe16Pr73whS/MaaedllWrVuWSSy7p/vq//du/5cEHH8wNN9yQ173udbnjjjsyc+bMHt+7Zs2a/OxnP8vXvva1jB07tvvrL3/5y/O73/0uH/7wh/O6170uLS0t/c705S9/OTfffHMuu+yyvOENb+j++qmnnpqpU6fm3HPPzfve97585CMfSZIsX768+w+W5557bo/7n3POOXnjG9+Yt7/97bnjjjt69LuRI0fm1ltvzV/+5V8+Zkf84he/mE9/+tO59NJL87d/+7c9bvvLv/zLXHnllVm0aFGmT5+epz3taUmSd73rXfnJT36Sz3/+85kwYUL3/U8//fS86lWvyt/8zd9kwYIF+exnP5shQ4Yc8PkBgPpS7Y61z+GHH553vvOd+dznPndQ/aH3DC960Yvywhe+MK95zWtyww039PtHuYNVtnkAAB7r74L73vHpXe96Vy688MLcddddOe2005Ikxx13XI/7HnXUUUn27zD7rFq1KieffHLOOOOMLFmyJJdeemn39/ypkSNH5rvf/W5WrlzZ42+KffnBD36Qa665Jq973ev2O4z18pe/PBMmTMi1116bGTNm7DfXwc4DlJN3rAIK8eijj+aNb3xjNmzYkI9//OOP+QezJJk3b1527NjR45BSXx566KF0dXVl7969+932V3/1V3nHO96RkSNHDnT0JMlPf/rT/PjHP860adNy9tln5xe/+EWfHzOzb5beJkyYkMsuuywnnnhij693dnbmS1/6Ul7ykpfk5S9/eUaMGJEVK1ZUNCsA0DiGDx+exz3ucfv9cay9vT3HH398nv/85+eUU07psz889NBDSdJnR5o7d27e/OY37/eRyb19/OMfz3HHHZcLL7xwv9vGjh2bhQsX5uSTT05XV1c6OzuzZMmSnH766T0OVe1z1FFH5aqrrsqvf/3rLF++vMdtr371q3Psscfm8ssv7/MjAf/U4sWL89znPrfHQa8/9Za3vCWnn356fv3rXydJfvKTn+RrX/ta5s6d2+NQ1T7HHnts3va2t+WHP/xhn++mBQA0nko61j6XXnpp/v3f/z2f+cxnBjzHc5/73Jx55pn50pe+VPFHE5dtHgCguR3M3wXb29tz2mmn5dRTT82znvWsAb+z029/+9t8/etfz0te8pLMmDEjO3fuzG233dbnfV/60pdmypQpufbaa/d7F/beFi9enKc97WlZuHBhn7e//vWvz8te9rL9etOhzAOUk4NVwKDbsWNH5syZk3vvvTc333xzTjnllIP6vnHjxuUf/uEf8q//+q8H/JziCRMm5GlPe1quvvrqXHHFFfn2t7/d/Qe5Jz/5yZk7d26Pd2lI/vAHxj179uz3T19/eEz+UO6e+MQn5iUveUkmT56cZz3rWfmXf/mX/e43bdq0fPnLX85b3vKW3HHHHdmyZUv3bW94wxty6qmn9rj/t7/97XR0dOScc87JEUcckb/8y7/MF7/4xcf8IycA0Hi6urq6O8nvf//7dHR0ZNGiRdm9e3fOP//87vv95je/yTe/+c2cc845SZJzzz0369atyz333NPj8c4444wMGzYsF154YT7ykY/kP//zP/P73/8+yR/+aHbRRRdl+PDh/c7T0dGR//7v/860adP6fdeD17zmNbnooosyZMiQrF+/Ph0dHXnpS1/a72OOGzcuEyZMyDe+8Y0eX3/c4x6Xq6++Og8++GCuu+66fr//v//7v/PAAw/krLPO6nemJz/5yfnoRz+aiRMnJkn3YfgDzfXKV74yQ4YM2W8uAKD+Vbtj7XP++efnxS9+cT74wQ/m5z//+YDne9GLXpTf//73Wbdu3YAfo4zzAADN62D+LviTn/wk69at6+5e55xzTr7xjW90/4eCh2L16tXp7OzMX/3VX2XMmDE59dRT+z0gP2TIkLz//e/P3r17D/iRgL/97W/zwx/+MC972cvyuMc9rs/7DBs2LP/8z//c/S5bA5kHKCcHq4BBta88/X//3/+XXbt2ZceOHYf0/RdddFGe+9zn5sorr+y3TB1++OH5+Mc/nmOPPTa33HJL3vjGN+YFL3hB/vqv/zqf/OQn87vf/W6/7/nnf/7nnHDCCfv909c7H+zZsye33357ZsyYkcMPPzzJH3659u1vf3u/0+xXXnllXvGKV+Qb3/hG5s+fnxe/+MX5i7/4i1xzzTU9Dlnts2rVqhx//PGZNGlSkuS8887Lww8/nK997WuHlBMAUP9++MMfdneSE088MaeffnpuvvnmvOlNb8q4ceO677d69ers3bs3r3rVq5IkZ555Zo466qj9/qu+8ePH54Mf/GD27t2bm266Ka9+9aszefLkXHTRRfnqV7/6mPPs6znHHHPMQc2/adOmg7r/s571rPziF7/Y7+snn3xyLrzwwqxYsSLf+973+vzeBx54IEn2OzT/p38w7X1gft9cT3/60/ud6QlPeEKe8IQn9DkXAFDfqt2x/tSVV16ZYcOG5Z3vfGef72B+MPZ9PPJA/ohY9nkAgOZzsH8X3PeGBvv+Q7hzzz03nZ2d+cIXvnDIz7lq1aq8+MUvzlOf+tQkf/hb24YNG/If//Effd7/Gc94Rt7xjnfk3/7t33Lrrbf2eZ9f/OIX2bt3b4499tgeX+/rd1CdnZ0VzQOUj4NVwKD6r//6r/zkJz/J8uXL86xnPSuXXnppOjo6Dvr7W1pacvXVV2fHjh254oor+r3f8ccfny996Uv5whe+kLe//e055ZRT8pOf/CTXXXddzj333Dz88MM97n/BBRfkC1/4wn7/9PUc3/rWt/LQQw/l5S9/eR555JE88sgjeelLX5q9e/fuV7ge//jH58Mf/nC+/vWv55/+6Z/yile8Io888kiWLl2a6dOn50c/+lH3fR9++OHceeed3fd55JFH8pznPCdPf/rTnVwHgCZ0wgkndHeSW2+9NZ/85Cdz4YUX5oMf/GA++MEPdt+vvb09p5xySg4//PA88sgj+f3vf5+XvvSlueOOO/b7GL0zzzwz3/rWt/KJT3wif/d3f5dx48ble9/7Xt7+9rfn4osv7v6DW+9fCHV1dWXYsGFJ+v4owb7se6x939eflpaWfv/Q9/a3vz1jx47t9yMB+5vlC1/4wn4H5t/5znce0lzDhg0b8B8gAYDyqkXH2mf06NG55JJL8sMf/jCf/exnqzJvf+/KeTDKNg8A0HwO5u+Cv//973P77bfn5S9/eX73u9/lkUceyZFHHpnnP//5Wbly5UH/Lir5w7ub33PPPTnzzDO7/9Z26qmnZsSIEQf8W9vrXve6vOAFL8g111yTBx98cL/b+5vh+9///gHftGGg8wDlcuDfJANU2ROe8IQsW7YsEyZMyHXXXZfXvOY1ueSSS/LJT37yoH8xc9xxx+Wtb31rbrjhhnz5y18+4H0nTZqUSZMm5U1velN27tyZT33qU/nwhz+cm2++OZdcckn3/UaNGtX9LlF/qq+T8+3t7UnS57tZfeELX8ib3/zm/f5Qd8wxx+S1r31tXvva12bv3r35+te/nksvvTRXXnllVq1alSS5/fbb8/vf/z433XRTbrrpph7f/4tf/CI/+9nPevyXkwBAYzvyyCP36yenn356duzYkU984hN5/etfny1btmT9+vVJkhe84AX7Pcbtt9+e17zmNT2+dthhh+WMM87IGWeckSTZsmVLrrrqqnzta1/Lt771rTznOc/Jy172sh7fc/XVV+clL3lJhgwZcsB3cfrtb3+bYcOG5cgjj+x+R6jHetenBx54oN93jzriiCPy/ve/P6973ety3XXXZc6cOT1uHzNmTJ/P8bKXvSwTJkzo/vc3velN3f/3n87V+52u9tm+fXsefvjhA76rFQBQn2rVsfaZOXNm1qxZkxtuuCEveclLDnm+fX/IGz16dJJ0f1Tz7t27u985/U/t3r07I0aM6PfxyjYPANBcDubvgt/61reydevW7sPvvX3nO9/J1KlTD+r59n3/ZZddlssuu6zHbV/96lfzzne+M094whP2+759Hwl49tln5/LLL88nPvGJHrfv+x3UvndC3+e5z31uj5nf/e53V2UeoFy8YxUwqMaPH9/9R66TTjopc+fOzXe/+939CspjmT17dk488cRceeWV2bp1a4/brr322kyfPn2/7xk+fHje8pa3ZMKECfnpT386oPkfeuihfPvb385rXvOafOYzn+nxzzve8Y5s2bIld955Z5Lka1/7Wk499dT8z//8T4/HGDp0aM4888ycf/75+dnPftb99fb29px88sn7Pe5HP/rRDB06NP/yL/8yoJkBgMZy4oknZs+ePdm0aVNWrVqVESNG5NOf/vR+HWLs2LE9/su3WbNm7fcLnCQ5+uij8773vS9J8tOf/jSjRo3a7108X/KSl+RJT3pSTjjhhHznO9/p952cPvKRj+TUU0/NQw89lBNPPDGjRo3KmjVr+n0tDzzwQO69997ut3nvy/Of//y0trZm5cqV+f73v9/jthNOOCFHH330fs/x5Cc/ufuA/aRJk3r80W/fcx1orn/913/N3r179ztgBgA0roF2rL5cddVVaWlpGdBH8H3ve9/LiBEjcsIJJyT540fxbdmypc/7P/jgg933qZd5AIDmcTB/F2xvb88znvGM/XrXsmXL8vjHP/6AH8X8p3bv3p3Vq1fnzDPP3O+x3v/+92fXrl354he/2O/3P/OZz8y8efPyne98Z78DXk9+8pNz8skn5+tf/3qPj/o76qijevwO6sgjj6zaPEB5OFgFFOrNb35zJk2alBtvvDFr16496O9raWnJNddck+3bt+djH/tYj9uOPfbY/M///E++8pWv7Pd9jz76aH71q1/l+OOPH9C8t912W/bs2ZMLL7wwp5xySo9/Lrzwwhx11FHdBe85z3lOfvOb32TZsmV9PtbGjRu751i3bl3uu+++nHfeefs97kte8pKceuqpue222/K73/1uQHMDAI1j7dq1aWlpydOf/vSsXr06L33pS3Paaaft1yHOOeec/Pd//3f+8z//M8kf3qlpzZo1eeCBB/Z7zH0HwY8//vgcfvjhPX4hNGnSpDzpSU9Kklx00UW577778rnPfW6/x/jpT3+a9vb2vPCFL8xTnvKUDB06NG9961vz3e9+t88D4r/73e/yzne+M49//OP7fceHfd7xjnfkmc98Zq699toeX9/3HHfffXe/neuXv/xlj4/refazn50ZM2bkYx/7WP7rv/5rv/s/8MAD+cAHPpCTTz45p5566gHnAgAax0A7Vl+e9rSn5ZJLLsndd9+db3zjGwc9w/r16/ONb3wj559/fh73uMcl+cM7Zg0ZMiRf/epX97v/L3/5y6xdu/YxO0vZ5gEAmlfvvwt2dHTkO9/5Ts4666z9etepp56a6dOn5//9v//X76HuP/XNb34zv/nNbzJr1qz9Huv8888/qAPyra2tef7zn59rrrlmv9ve8pa35IEHHsh1113X52H13/72t/nVr35V1XmAcvBRgEChhg0bluuvvz7nnntu3vGOd+RLX/rSQX/vc57znLzlLW/Jhz70oR5fP+ecc7J69er84z/+Y37wgx9k6tSpGTlyZDZu3JjPfOYzOeKII/J3f/d3A5p31apVOeGEE/r82Jgjjjgir3jFK7Jq1ao88MADefazn505c+bkYx/7WDZv3pyzzz47o0ePztatW3Pbbbfl+9//fpYuXZrkD6fxDzvssJx55pl9Pu+rXvWqfO9738tXvvKVnHfeeQOaHQCoL9u3b+/xB7vdu3fnm9/8Ztrb2/PqV786d999d37zm99kxowZfX7/q171qtx44435/Oc/n+c973mZN29efvCDH+Sv//qv8/rXvz4nn3xyhg4dmnXr1uVTn/pUXvziF+fFL37xAWd65Stfme9973u56qqr8uMf/zjTp0/PiBEjsnbt2ixdujRPetKTctVVV3Xf/9WvfnV+9rOf5T3veU9++MMf5pWvfGWe8IQn5P7778+yZcvS0dGRD33oQzn66KMP+Lx/+pGAvV1wwQXZtGlTrr766nz729/OjBkz8vSnPz2//e1v82//9m+57bbbcthhh/X46Jv3vOc9+dWvfpXXve51ec1rXpMXvehFGTp0aH70ox9l2bJlecpTnpIbbrghQ4f6b5EAoNFUu2P154ILLsiaNWvy3e9+NyNHjtzv9n0zdHV15dFHH826devy6U9/OmPHjs3b3va27vuNGTMmr3vd6/KhD30oDz30UM4444wcccQR+elPf5pPfepTefrTn95nRyr7PABAc+r9d8Fzzjkne/bsyVlnndXn/c8555zceuutWblyZf7hH/7hgI/d3t6e//N//k+/h7zPPvvsfPjDH84PfvCDnHLKKX3eZ+jQobn66qtz9tln73fbGWeckba2tlx99dX5z//8z5x77rk59thjs2PHjtx9991pb2/Prl278vrXv75q8wDl4GAVULhjjz02//iP/5grrrgi7373u/s8tNSfN77xjfnXf/3X3HPPPd1fO/zww/PJT34yn/nMZ7JmzZp8+ctfzu9+97uMGjUqL33pS/OmN70p/+f//J9DnvPHP/5xfvrTn+Yf//Ef+73POeeck/b29qxYsSILFizIO97xjvzZn/1Zbr311lx11VXZvn17Ro4cmcmTJ+cLX/hCJkyYkF27duXLX/5yXvSiF+WJT3xin4975pln5oorrsjnP/95B6sAoEnce++9efWrX93974973OO635L8oosuypve9KY84QlPyOmnn97n948ZMyYveMEL8tWvfjWXXXZZjjnmmHzxi1/Mxz72saxevTo333xzurq68qxnPSsXXXRRXv/612fIkCGPOddVV12VU045JStXrsw//dM/5dFHH82YMWMyc+bMXHTRRd3vbrXPO9/5zpxxxhlZvnx53vOe9+SRRx7J0572tEybNi0XXnhhxowZc1B5TJ48Oa973evy2c9+dr/b3vGOd+SlL31pPv/5z+cjH/lIfvWrX+WII47Icccdl7e+9a3567/+6x496/GPf3yWLl2a9vb2fPGLX8ytt96azs7OjB07Nm984xvz2te+NsOHDz+ouQCA+lLtjnUgV111Vb8HtP50hiOOOCLPeMYz8jd/8zeZPXt2jjrqqB73vfzyy/Oc5zynu7vs2rUro0ePzstf/vL8/d///X73r5d5AIDm9Kd/F/z4xz+e5zznOf1+0szzn//8HHPMMbn11lvz5je/OS0tLX3eb8uWLfnud7+bWbNm9XufV73qVbnpppvy+c9//oAHmZ71rGdl3rx5ufrqq/e77bWvfW2mTJmSf/mXf8nSpUvz4IMPpqWlJccee2xe97rX5dWvfnWOPvroqs4DFG9I16F+qDoAAAAAAAAAAECD87kGAAAAAAAAAAAAvThYBQAAAAAAAAAA0IuDVQAAAAAAAAAAAL04WAUAAAAAAAAAANCLg1UAAAAAAAAAAAC9DCt6gCLt3bs3e/bsydChQzNkyJCixwEASqKrqyt79+7NsGHDMnSoc+gHS7cCAPqiWx06vQoA6Itedej0KgCgL4fSq5r6YNWePXuybt26oscAAEpq0qRJOfzww4seo27oVgDAgehWB0+vAgAORK86eHoVAHAgB9Ormvpg1b5TZxMnTlRAe+ns7My6desyadKktLS0FD1Oacilb3Lpn2z6Jpe+yaV/g53NvufzX/4dGt2qfFxXysealI81KR9rUj6Vroludej0qtpynakt+daejGtLvrUl38roVYdOr6qMn9mBk11l5FcZ+Q2c7CpTT/kdSq9q6oNV+97ys6WlpfSLWhTZ9E0ufZNL/2TTN7n0TS79G+xsvD34odGtysualI81KR9rUj7WpHwqXRPd6uDpVYNDvrUl39qTcW3Jt7bkWxm96uDpVdUhv4GTXWXkVxn5DZzsKlNP+R1Mr3KkHQAAAAAAAAAAoBcHqwAAAAAAAAAAAHpxsAoAAAAAAAAAAKAXB6sAAAAAAAAAAAB6cbAKAAAAAAAAAACgFwerAAAAAAAAAAAAenGwCgAAAAAAAAAAoBcHqwAAAAAAAAAAAHpxsAoAAAAAAAAAAKAXB6sAAAAAAAAAAAB6cbAKAAAAAAAAAACgFwerAAAAAAAAAAAAenGwCgAAAAAAAAAAoBcHqwAAAAAAAAAAAHpxsIp+DR8+vOgRSkkufZMLAAAAUC/8HgMA4ODoTQA0u2FFD1AGLS0tRY9QOi0tLZk4cWLRY5SOXPpW61w693alZeiQmj0+ANWlW/2B/QsAqJReVRtF/H5HNwSAYulVAzOQ3qT3ANBoHKxKckn7j7P+wUeLHgPow3GjjsqNs04uegwADoFuZf8CAKpDr2oMuiEAFE+vGhx6DwCNyMGqJPd3PJp7Nj9S9BgAAA1BtwIAqA69CgCgOvQqAGCghhY9AAAAAAAAAAAAQNk4WAUAAAAAAAAAANCLg1UAAAAAAAAAAAC9FHqwaunSpZkxY0ZmzJiRyy67LLt37859992XCy64INOnT8/FF1+cHTt2JEm2b9+eN73pTXnlK1+Z888/Pxs3bux+nPe///155StfmbPOOiurV68u6NUAABRHrwIAqA69CgCgenQrAKDeFXawau3atVm1alVWrlyZ1atXZ8+ePbnllluycOHCLFiwIGvWrMm4ceOyZMmSJMmNN96YE044IV/5yleyYMGCXHrppUmS73//+/nxj3+c1atXZ9myZXnve9+bnTt3FvWyAAAGnV4FAFAdehUAQPXoVgBAIxhW1BOPHDkybW1tGTFiRJJkwoQJ2bBhQ7Zt25YpU6YkSWbOnJnW1tbMnz8/d955Z5YtW5YkOe2003L55Zdn8+bN6ezszO7du7Nnz57s2rUrhx9+eFEvCaihzs7OokcYkH1z1+v8tSKXvsmlf4OdTb2tgV5VXkX/b8l1pXysSflYk/KxJuVT6ZrU01rqVdRSPf0sVMJ1vPZkXFvyrS35VqbectOtmle9/W+1FlzvKiO/yshv4GRXmXrK71BmLOxg1dixYzN27NgkydatW7N8+fLMmjWrx9t6jho1Klu2bEmSbNmyJUcffXSP2x588MGcfvrpWblyZV784hdnx44dWbBgQYYPHz6YLwUYBBs2bKjr/wJl3bp1RY9QSnLpm1z6J5u+6VXlVZb9y89O+ViT8rEm5WNNyqcZ1kSvopbK0g0HSzNcM4om49qSb23JtznoVs2r2XrPgbjeVUZ+lZHfwMmuMo2WX2EHq/bZtGlT5s6dm5kzZ2by5Mm58847e9w+ZMiQJElXV9d+3zt06NCsWLEiLS0t+bd/+7f85je/yetf//qcdNJJed7znjcY4wODZPz48UWPMCCdnZ1Zt25dJk2alJaWlqLHKQ259E0u/RvsbPY9X73Rq8qn6P3LdaV8rEn5WJPysSblU+ma1GO30quohaK74WBxHa89GdeWfGtLvpWpx16V6FbNqFl6z4G43lVGfpWR38DJrjL1lN+h9KpCD1atX78+c+bMyZw5c9La2prNmzeno6Oj+/aOjo6MHj06SXL00Ueno6MjT3va03rc9s///M/5m7/5mxx22GF56lOfmmnTpuXf//3flSloMGW/8D6WlpaWun8NtSCXvsmlf7Lpn15VTmX536ufnfKxJuVjTcrHmpRPs6yJXkWtNMPPz59qlmtGkWRcW/KtLfk2D92qOfn5/iPXu8rIrzLyGzjZVabR8hta1BM//PDDmT17dtra2tLa2pokGTNmTIYPH5677747SdLe3p6pU6cmSaZNm5b29vYkyQ9+8IOMGDEio0ePzoQJE/J//+//TZLs2LEjd911V0444YQCXhEAQDH0KgCA6tCrAACqR7cCABpBYe9YtWzZsmzfvj2LFy/O4sWLk/yhMC1atChtbW3Ztm1bjjnmmCxatChJ8ra3vS3vete7MmPGjBx++OG57rrrkiR///d/nyuuuCLTp0/PsGHDcu655+a0004r6mUBAAw6vQoAoDr0KgCA6tGtAIBGUNjBqnnz5mXevHl93rZixYr9vjZy5MjcdNNN+319xIgRufbaa6s+HwBAvdCrAACqQ68CAKge3QoAaASFfRQgAAAAAAAAAABAWTlYBQAAAAAAAAAA0IuDVQAAAAAAAAAAAL0MK3qAMnj2U4/Mrs6ipwD6ctyoo4oeAYBDpFvZvwCA6tCrGoNuCADF06sGh94DQCNysCrJteeflJaWlqLHAPrRubcrLUOHFD0GAAdJt/oD+xcAUCm9qnHohgBQLL1q8Og9ADQaHwWYpLPTEfXeOjs7c++998qmF7n0rda5KOAA9cU++Qf2LwCgUnpVbRTx+x3dEACKpVcNzEB6k94DQKNxsIp+7dy5s+gRSkkufZMLAAAAUC/8HgMA4ODoTQA0Ower6Nfw4cOLHqGU5NI3uQBAOdmjAQD2pyPVlnwBoHHY1wFodsOKHqAMfKby/lpaWjJx4sSixygdufTtYHLxmdoAzUO3Ko9qdBd7OAAUR6+qDb/fqa3BzFdXBeBg6VUD04y9Sb8AoDcHq5Jc0v7jrH/w0aLHgIZ13KijcuOsk4seA4BBols1Dns4ABRLr4L+6aoAHAq9ioOhXwDQFwerktzf8Wju2fxI0WMAADQE3QoAoDr0KgCA6tCrAICBGlr0AAAAAAAAAAAAAGXjYBUAAAAAAAAAAEAvDlYBAAAAAAAAAAD0UujBqqVLl2bGjBmZMWNGLrvssuzevTv33XdfLrjggkyfPj0XX3xxduzYkSTZvn173vSmN+WVr3xlzj///GzcuDFJsmPHjixcuDCvfOUrc9555+Wb3/xmga8IAKAYehUAQHXoVQAA1aNbAQD1rrCDVWvXrs2qVauycuXKrF69Onv27Mktt9yShQsXZsGCBVmzZk3GjRuXJUuWJEluvPHGnHDCCfnKV76SBQsW5NJLL02SfOxjH8vQoUNzxx135JOf/GSuvfbabNmypaiXBQAw6PQqAIDq0KsAAKpHtwIAGsGwop545MiRaWtry4gRI5IkEyZMyIYNG7Jt27ZMmTIlSTJz5sy0trZm/vz5ufPOO7Ns2bIkyWmnnZbLL788mzdvzvr16/Oa17wmQ4cOzZOe9KRMmDAh3/nOd/LXf/3XRb00oB+dnZ1FjzDo9r3mZnztByKXvsmlf4OdTb2tgV5FrdXbz0SZudaXjzUpH2tSPpWuST2tpV4F9aeerjHVYq+sLfnWlnwrU2+56VbUq2r8rLneVUZ+lZHfwMmuMvWU36HMWNjBqrFjx2bs2LFJkq1bt2b58uWZNWtW99t6JsmoUaO6T5xv2bIlRx99dI/bHnzwwUycODFr1qzJ6aefnq1bt+Y//uM/8md/9meD+VKAg7Rhw4bs3Lmz6DEKsW7duqJHKCW59E0u/ZNN3/Qqaq2Z9/BacT0rH2tSPtakfJphTfQqqD/N3FWb4bpcJPnWlnybg25Fvapmv3C9q4z8KiO/gZNdZRotv8IOVu2zadOmzJ07NzNnzszkyZNz55139rh9yJAhSZKurq79vnfo0KGZO3durr766px77rk59thjc/rpp+ewww4blNmBQzN+/PiiRxh0nZ2dWbduXSZNmpSWlpaixykNufRNLv0b7Gz2PV+90auolWbcw2vFtb58rEn5WJPyqXRN6rFb6VVQP5qxq9ora0u+tSXfytRjr0p0K+pPNfqF611l5FcZ+Q2c7CpTT/kdSq8q9GDV+vXrM2fOnMyZMyetra3ZvHlzOjo6um/v6OjI6NGjkyRHH310Ojo68rSnPa3Hbdu2bcvb3/72PPnJT06S/P3f/32e+cxnDv6LAR5T2S+etdTS0tLUr78/cumbXPonm/7pVdSSn7vqcz0rH2tSPtakfJplTfQqqC/NcF3qT7Ncl4si39qSb/PQrahH1bw+ud5VRn6Vkd/Aya4yjZbf0KKe+OGHH87s2bPT1taW1tbWJMmYMWMyfPjw3H333UmS9vb2TJ06NUkybdq0tLe3J0l+8IMfZMSIERk9enS+9rWv5UMf+lCS5L//+79zzz335LTTThv8FwQAUBC9CgCgOvQqAIDq0a0AgEZQ2DtWLVu2LNu3b8/ixYuzePHiJH8oTIsWLUpbW1u2bduWY445JosWLUqSvO1tb8u73vWuzJgxI4cffniuu+66JMmrX/3qLFy4MDNmzMiwYcPywQ9+MEcddVRRLwsAYNDpVQAA1aFXAQBUj24FADSCwg5WzZs3L/PmzevzthUrVuz3tZEjR+amm27a7+uHH354brzxxqrPBwBQL/QqAIDq0KsAAKpHtwIAGkFhHwUIAAAAAAAAAABQVg5WAQAAAAAAAAAA9FLYRwGWybOfemR2dRY9BTSu40b5rHOAZqJbNQ57OAAUS6+C/umqABwKvYqDoV8A0BcHq5Jce/5JaWlpKXoMaGide7vSMnRI0WMAMAh0q8ZiDweA4uhVcGC6KgAHS6/iYOkXAPTmowCTdHY6ot5bZ2dn7r33Xtn0Ipe+HUwuSihA87BPlkc1uos9HACKo1fVht/v1NZg5qurAnCw7PsD04y9Sb8AoDcHq+jXzp07ix6hlOTSN7kAQDnZowEA9qcj1ZZ8AaBx2NcBaHYOVgEAAAAAAAAAAPTiYBX9Gj58eNEjlJJc+iYXACgnezQAwP50pNqSLwA0Dvs6AM1uWNEDlEFLS0vRI5ROS0tLJk6cWPQYpSOXvtUql869XT7LGqAO6VblUbbuYm8HgEOjV9VG2TpSo2nUfHVZgPqmVw1Mo+7rh0oPAGhuDlYluaT9x1n/4KNFjwH8ieNGHZUbZ51c9BgADIBuRV/s7QBw6PQqKAddFqD+6VUMlB4AgINVSe7veDT3bH6k6DEAABqCbgUAUB16FQBAdehVAMBADS16AAAAAAAAAAAAgLJxsAoAAAAAAAAAAKAXB6sAAAAAAAAAAAB6KfRg1dKlSzNjxozMmDEjl112WXbv3p377rsvF1xwQaZPn56LL744O3bs6PE97e3tufTSS7v//fe//33e+c535pWvfGXOOuus/PjHPx7slwEAUDi9CgCgenQrAIDq0KsAgHpX2MGqtWvXZtWqVVm5cmVWr16dPXv25JZbbsnChQuzYMGCrFmzJuPGjcuSJUuSJLt27cr111+fq666qsfjLF++PF1dXfnKV76SG2+8Mf/4j/+YPXv2FPGSAAAKoVcBAFSPbgUAUB16FQDQCIYV9cQjR45MW1tbRowYkSSZMGFCNmzYkG3btmXKlClJkpkzZ6a1tTXz58/PXXfdla6urixcuDBr167tfpw777wzb3nLW5Ikxx13XI4++uj86Ec/ygte8ILBf1FA1XV2dhY9QkX2zV/vr6Pa5NI3ufRvsLOptzXQq6gn9fbzVW2u9eVjTcrHmpRPpWtSb2upWwH9Kcv1zF5ZW/KtLflWpt5y06toJIf68+d6Vxn5VUZ+Aye7ytRTfocyY2EHq8aOHZuxY8cmSbZu3Zrly5dn1qxZ2bhxY/d9Ro0alS1btiRJpk6dmqlTp2bVqlU9HmfLli05+uije3zPgw8+WPP5gcGxYcOG7Ny5s+gxKrZu3bqiRyglufRNLv2TTd/0KupJo+ztlXI9Kx9rUj7WpHyaZU10K6A/ZeuyzXJdLop8a0u+zUGvopEMtAe43lVGfpWR38DJrjKNll9hB6v22bRpU+bOnZuZM2dm8uTJufPOO3vcPmTIkAN+f1dX135fGzq0sE84BKps/PjxRY9Qkc7Ozqxbty6TJk1KS0tL0eOUhlz6Jpf+DXY2+56v3uhV1IN639sr5VpfPtakfKxJ+VS6JrrVH+lWUN/K0mXtlbUl39qSb2X0qj/Sqxhsh9oDXO8qI7/KyG/gZFeZesrvUHpVoQer1q9fnzlz5mTOnDlpbW3N5s2b09HR0X17R0dHRo8efcDHOProo9PR0ZFnPetZ3d/zp6fWgfpW9gvuwWppaWmY11JNcumbXPonm/7pVdQLP8N/4HpWPtakfKxJ+TTTmuhWQF/Kdg1sputyEeRbW/JtHnoVjWKg1yzXu8rIrzLyGzjZVabR8ivsOPfDDz+c2bNnp62tLa2trUmSMWPGZPjw4bn77ruTJO3t7Zk6deoBH2fatGlpb29PkvzsZz/Lz3/+8zz3uc+t7fAAACWiVwEAVI9uBQBQHXoVANAICnvHqmXLlmX79u1ZvHhxFi9enOQPxWjRokVpa2vLtm3bcswxx2TRokUHfJzXve51ueKKK3LWWWdlyJAhef/735/DDz98MF4CAEAp6FUAANWjWwEAVIdeBQA0gsIOVs2bNy/z5s3r87YVK1b0+33nnXdezjvvvO5/P/zww/O+972v6vMBANQLvQoAoHp0KwCA6tCrAIBGUNhHAQIAAAAAAAAAAJSVg1UAAAAAAAAAAAC9FPZRgGXy7KcemV2dRU8B/KnjRh1V9AgADJBuRV/s7QBw6PQqKAddFqD+6VUMlB4AgINVSa49/6S0tLQUPQbQS+ferrQMHVL0GAAcIt2K/tjbAeDQ6FVQHrosQH3Tq6iEHgDQ3HwUYJLOTkfUe+vs7My9994rm17k0rda5aKkAtQn+2R5lK272NsB4NCUZQ9vNGXrSI2mUfPVZQHqW6PtS4OlUff1Q6UHADQ3B6vo186dO4seoZTk0je5AEA52aMBAPanI9WWfAGgcdjXAWh2DlYBAAAAAAAAAAD04mAV/Ro+fHjRI5SSXPomFwAoJ3s0AMD+dKTaki8ANA77OgDNbljRA5RBS0tL0SOUTktLSyZOnFj0GKUjl75VO5fOvV0+rxqgjulW5dGo3UVXAKBZ6FW10agdqSzkOzA6LkBt6VUDY1//I3s1QPNysCrJJe0/zvoHHy16DCDJcaOOyo2zTi56DAAqoFtRS7oCAM1Er4LmoOMC1J5eRSXs1QDNzcGqJPd3PJp7Nj9S9BgAAA1BtwIAqA69CgCgOvQqAGCghhY9AAAAAAAAAAAAQNk4WAUAAAAAAAAAANCLg1UAAAAAAAAAAAC9FH6waunSpZkxY0ZmzJiRyy67LLt37859992XCy64INOnT8/FF1+cHTt29Pie9vb2XHrppT2+dt999+Wss84azNEBAEpFrwIAqB7dCgCgOvQqAKCeFXqwau3atVm1alVWrlyZ1atXZ8+ePbnllluycOHCLFiwIGvWrMm4ceOyZMmSJMmuXbty/fXX56qrrurxOKtWrcpFF12UnTt3FvEyAAAKp1cBAFSPbgUAUB16FQBQ74YV+eQjR45MW1tbRowYkSSZMGFCNmzYkG3btmXKlClJkpkzZ6a1tTXz58/PXXfdla6urixcuDBr165NkvzmN7/Jt771rdxwww255JJLCnstQHV1dnYWPUJV7HsdjfJ6qkUufZNL/wY7m3pcA72KZlOPP6eJa30ZWZPysSblU+ma1ONa6lZAEQ72emmvrC351pZ8K1OPuelVNJJD+Rl0vauM/Cojv4GTXWXqKb9DmbHQg1Vjx47N2LFjkyRbt27N8uXLM2vWrGzcuLH7PqNGjcqWLVuSJFOnTs3UqVOzatWq7tuf+MQn5sMf/nA2bdo0mKMDNbZhw4aG+i9P1q1bV/QIpSSXvsmlf7Lpn15Fs6n3ruB6Vj7WpHysSfk005roVkARDrXjNtN1uQjyrS35Ng+9ikYykN9Hud5VRn6Vkd/Aya4yjZZfoQer9tm0aVPmzp2bmTNnZvLkybnzzjt73D5kyJCCJgOKMn78+KJHqIrOzs6sW7cukyZNSktLS9HjlIZc+iaX/g12Nvuerx7pVTSLeu0KrvXlY03Kx5qUT6VrolsBHJyD7bj2ytqSb23JtzJ6FRTrUH4f5XpXGflVRn4DJ7vK1FN+h9KrCj9YtX79+syZMydz5sxJa2trNm/enI6Oju7bOzo6Mnr06AInBIpQ9gvtoWppaWm411QNcumbXPonmwPTq2gm9X4tcD0rH2tSPtakfJptTXQrYLAd6jW22a7Lg02+tSXf5qJX0SgGct1yvauM/Cojv4GTXWUaLb+hRT75ww8/nNmzZ6etrS2tra1JkjFjxmT48OG5++67kyTt7e2ZOnVqkWMCAJSeXgUAUD26FQBAdehVAEC9K/Qdq5YtW5bt27dn8eLFWbx4cZJk2rRpWbRoUdra2rJt27Ycc8wxWbRoUZFjAgCUnl4FAFA9uhUAQHXoVQBAvSv0YNW8efMyb968Pm9bsWJFv9933nnn5bzzzuvxtWOOOSbf/OY3qzofAEC90KsAAKpHtwIAqA69CgCod4V+FCAAAAAAAAAAAEAZOVgFAAAAAAAAAADQS6EfBVgWz37qkdnVWfQUQJIcN+qookcAoEK6FbWkKwDQTPQqaA46LkDt6VVUwl4N0NwcrEpy7fknpaWlpegxgP+/zr1daRk6pOgxABgg3Ypa0xUAaBZ6FTQPHRegtvQqKmWvBmhePgowSWenI+q9dXZ25t5775VNL3LpW7VzUUwB6pt9sjwatbvoCgA0i0bbw8uiUTtSWch3YHRcgNqyLw2Mff2P7NUAzcvBKvq1c+fOokcoJbn0TS4AUE72aACA/elItSVfAGgc9nUAmp2DVQAAAAAAAAAAAL04WEW/hg8fXvQIAABUSKcDANifjgQAcHD0poGTHUBjGFb0AGXQ0tJS9Ail09LSkokTJxY9RiE693b5nGQAqIBuVR616HS6EgAMHr2qNpr5914HoucB0Mj0qoHRmwbuT7PTswDqm4NVSS5p/3HWP/ho0WNQAseNOio3zjq56DEAoK7pVo1LVwKAwaVXMVj0PAAanV5FUfQsgPrnYFWS+zsezT2bHyl6DACAhqBbAQBUh14FAFAdehUAMFBDix4AAAAAAAAAAACgbBysAgAAAAAAAAAA6MXBKgAAAAAAAAAAgF4KPVi1dOnSzJgxIzNmzMhll12W3bt357777ssFF1yQ6dOn5+KLL86OHTt6fE97e3suvfTS7n//9a9/nTe/+c05++yz81d/9Vf58pe/PNgvAwCgcHoVAED16FYAANWhVwEA9a6wg1Vr167NqlWrsnLlyqxevTp79uzJLbfckoULF2bBggVZs2ZNxo0blyVLliRJdu3aleuvvz5XXXVVj8f58Ic/nIkTJ+b222/Ppz71qVx99dV56KGHinhJAACF0KsAAKpHtwIAqA69CgBoBMOKeuKRI0emra0tI0aMSJJMmDAhGzZsyLZt2zJlypQkycyZM9Pa2pr58+fnrrvuSldXVxYuXJi1a9d2P86LX/zinHjiiUmSpz71qXniE5+Yhx56KE95ylMG/0XRMDo7Ow/49f5ub1Zy6Z9s+iaXvsmlf4OdTb2tgV7FYKu3n5Eyca0vH2tSPtakfCpdk3pbS92KZlaNn1fX8dqTcW3Jt7bkW5l6y02vgj+qt5/fotkvKiO/gZNdZeopv0OZsbCDVWPHjs3YsWOTJFu3bs3y5csza9asbNy4sfs+o0aNypYtW5IkU6dOzdSpU7Nq1aoej/OSl7yk+//+8pe/nN27d+e4446r+fw0tg0bNmTnzp393r5u3bpBnKZ+yKV/sumbXPoml/7Jpm96FYPtsboSj831rHysSflYk/JpljXRrWhm1ex5zXLNKJKMa0u+tSXf5qBXwR/5fdrA2C8qI7+Bk11lGi2/wg5W7bNp06bMnTs3M2fOzOTJk3PnnXf2uH3IkCEH9Ti33XZbrr/++nziE5/IsGGFvyzq3Pjx4/v8emdnZ9atW5dJkyalpaVlkKcqL7n0TzZ9k0vf5NK/wc5m3/PVG72KwdJfV+KxudaXjzUpH2tSPpWuiW6lW1E/qtHzXMdrT8a1Jd/akm9l9Cq9ivrl92mHxn5RGfkNnOwqU0/5HUqvKrR1rF+/PnPmzMmcOXPS2tqazZs3p6Ojo/v2jo6OjB49+jEf5+Mf/3g+//nPZ9myZRk3blwtR6ZJPNYPeUtLS+kvBEWQS/9k0ze59E0u/ZNN//QqBpOfw8q5npWPNSkfa1I+zbQmuhXNqpo/4810zSiKjGtLvrUl3+ahV8EfuOYNjP2iMvIbONlVptHyG1rUEz/88MOZPXt22tra0tramiQZM2ZMhg8fnrvvvjtJ0t7enqlTpx7wcVatWpUvfvGLWbFihSIFADQlvQoAoHp0KwCA6tCrAIBGUNg7Vi1btizbt2/P4sWLs3jx4iTJtGnTsmjRorS1tWXbtm055phjsmjRogM+zgc/+MEMGTIks2fP7v7ae9/73px00kk1nR8AoCz0KgCA6tGtAACqQ68CABpBYQer5s2bl3nz5vV524oVK/r9vvPOOy/nnXde979/5zvfqfpsAAD1RK8CAKge3QoAoDr0KgCgERT2UYAAAAAAAAAAAABl5WAVAAAAAAAAAABAL4V9FGCZPPupR2ZXZ9FTUAbHjTqq6BEAoO7pVo1LVwKAwaVXMVj0PAAanV5FUfQsgPrnYFWSa88/KS0tLUWPQUl07u1Ky9AhRY8BAHVLt2psuhIADB69isGk5wHQyPQqiqRnAdQ3HwWYpLPTEfXeOjs7c++99zZlNooNAFSmGftDWdWi0+lKADB49KraaObfex2IngdAI7PvD4zeNHB/mp2eBVDfHKyiXzt37ix6BAAAKqTTAQDsT0cCADg4etPAyQ6gMThYRb+GDx9e9AilJJe+yQUAyskeDQCwPx0JAODg6E0ANLthRQ9QBj5TeX8tLS2ZOHFi0WOUjlz6dqBcfG40QPPRrcrjULqLPRsAykevqg2/36mdzr1dRY8AAH3Sqwam3nuT33cBUA0OViW5pP3HWf/go0WPAQ3nuFFH5cZZJxc9BgCDTLeqP/ZsACgnvYp6sq9TdnYWPQkA7E+vaj5+3wVAtThYleT+jkdzz+ZHih4DAKAh6FYAANWhVwEAVIdeBQAM1NCiBwAAAAAAAAAAACgbB6sAAAAAAAAAAAB6cbAKAAAAAAAAAACgFwerAAAAAAAAAAAAein0YNXSpUszY8aMzJgxI5dddll2796d++67LxdccEGmT5+eiy++ODt27OjxPe3t7bn00ku7/33z5s15/etfn7PPPjszZ87M+vXrB/tlAAAUTq8CAKge3QoAoDr0KgCg3hV2sGrt2rVZtWpVVq5cmdWrV2fPnj255ZZbsnDhwixYsCBr1qzJuHHjsmTJkiTJrl27cv311+eqq67q8TjXXHNN/uqv/iq33357/uEf/iFXXHFFES8HAKAwehUAQPXoVgAA1aFXAQCNYFhRTzxy5Mi0tbVlxIgRSZIJEyZkw4YN2bZtW6ZMmZIkmTlzZlpbWzN//vzcdddd6erqysKFC7N27drux/nQhz7U/X9v2rQpI0eOHNTXATy2zs7OokcozL7X3swZ9EUufZNL/wY7m3pbA72Kaqm3/+3XI9f68rEm5WNNyqfSNam3tdStoDJ79+5NUn8/+/XEXllb8q0t+Vam3nLTqyiDon5uXO8qI7/KyG/gZFeZesrvUGYs7GDV2LFjM3bs2CTJ1q1bs3z58syaNSsbN27svs+oUaOyZcuWJMnUqVMzderUrFq1qsfjDB36hzfdOvPMM7N58+buU+1AeWzYsCE7d+4seoxCrVu3rugRSkkufZNL/2TTN72KarFnDx7Xs/KxJuVjTcqnWdZEt4LK/OQnP0nSPNeMIsm4tuRbW/JtDnoVZVD077tc7yojv8rIb+BkV5lGy6+wg1X7bNq0KXPnzs3MmTMzefLk3HnnnT1uHzJkyEE9zv/9v/8399xzTy666KKsWbMmT3ziE2swLTAQ48ePL3qEwnR2dmbdunWZNGlSWlpaih6nNOTSN7n0b7Cz2fd89UavolLNvGcPFtf68rEm5WNNyqfSNdGtdCuay3Oe85ysXbvWdbyG7JW1Jd/akm9l9Cq9ikNX1O+7XO8qI7/KyG/gZFeZesrvUHpVoQer1q9fnzlz5mTOnDlpbW3N5s2b09HR0X17R0dHRo8efcDH+PrXv57TTz89RxxxRE444YQ8/elPzwMPPKBMQYmU/aI5GFpaWuTQB7n0TS79k03/9Cqqwc/X4HE9Kx9rUj7WpHyaaU10Kxi4fe8q0kzXjKLIuLbkW1vybR56FUUr+lrjelcZ+VVGfgMnu8o0Wn5Di3rihx9+OLNnz05bW1taW1uTJGPGjMnw4cNz9913J0na29szderUAz7Orbfemvb29iTJfffdl61bt2bcuHG1HR4AoET0KgCA6tGtAACqQ68CABpBYe9YtWzZsmzfvj2LFy/O4sWLkyTTpk3LokWL0tbWlm3btuWYY47JokWLDvg47373u/POd74zK1euzOMe97jccMMNGTFixGC8BACAUtCrAACqR7cCAKgOvQoAaASFHayaN29e5s2b1+dtK1as6Pf7zjvvvJx33nnd/z5mzJh8+tOfrvZ4AAB1Q68CAKge3QoAoDr0KgCgERT2UYAAAAAAAAAAAABl5WAVAAAAAAAAAABAL4V9FGCZPPupR2ZXZ9FTQOM5btRRRY8AQAF0q/pjzwaActKrqCc6JQBlplc1H90EgGpxsCrJteeflJaWlqLHgIbUubcrLUOHFD0GAINIt6pP9mwAKB+9inrTuber6BEAoE96VXPy+y4AqsFHASbp7HREvbfOzs7ce++9sulFLn07UC4KK0DzsU+Wx6F0F3s2AJSPXlUbfr9TOzolAGVl3x+Yeu9NugkA1eBgFf3auXNn0SOUklz6JhcAKCd7NADA/nQkAICDozcB0OwcrAIAAAAAAAAAAOjFwSoAAAAAAAAAAIBeHKwCAAAAAAAAAADoxcEqAAAAAAAAAACAXhysAgAAAAAAAAAA6MXBKgAAAAAAAAAAgF4crAIAAAAAAAAAAOil0INVS5cuzYwZMzJjxoxcdtll2b17d+67775ccMEFmT59ei6++OLs2LGjx/e0t7fn0ksv3e+xtm/fnpe//OX5wQ9+MFjjAwCUhl4FAFA9uhUAQHXoVQBAvSvsYNXatWuzatWqrFy5MqtXr86ePXtyyy23ZOHChVmwYEHWrFmTcePGZcmSJUmSXbt25frrr89VV13V5+NdeeWVeeSRRwbzJQAAlIJeBQBQPboVAEB16FUAQCMYVtQTjxw5Mm1tbRkxYkSSZMKECdmwYUO2bduWKVOmJElmzpyZ1tbWzJ8/P3fddVe6urqycOHCrF27tsdjfeUrX8mRRx6Z8ePHD2iWvXv3prOzs7IX1GD25SGXnuTSN7n0TzZ9k0vf5NK/wc6m3tagTL0q0a3KxHWlfKxJ+ViT8rEm5VPpmtTbWpapW+lVteE6U1vyrT0Z15Z8a0u+lam33PSq+udnduBkVxn5VUZ+Aye7ytRTfocyY2EHq8aOHZuxY8cmSbZu3Zrly5dn1qxZ2bhxY/d9Ro0alS1btiRJpk6dmqlTp2bVqlU9Hmfz5s1ZtmxZli1blje+8Y0DmuVnP/tZfve73w3oexvdunXrih6hlOTSN7n0TzZ9k0vf5NI/2fStTL0q0a3KyM9O+ViT8rEm5WNNyqdZ1qRM3Uqvqq1m+d90UeRbezKuLfnWlnybg17VOPzMDpzsKiO/yshv4GRXmUbLr7CDVfts2rQpc+fOzcyZMzN58uTceeedPW4fMmRIv9+7d+/evOtd70pbW1uOOOKIAc8wbty4HHbYYQP+/kbU2dmZdevWZdKkSWlpaSl6nNKQS9/k0j/Z9E0ufZNL/wY7m33PV2/K0KsS3apMXFfKx5qUjzUpH2tSPpWuiW7ld1Zl4zpTW/KtPRnXlnxrS76V0av0qsHmZ3bgZFcZ+VVGfgMnu8rUU36H0qsKPVi1fv36zJkzJ3PmzElra2s2b96cjo6O7ts7OjoyevTofr///vvvz/333593vetdSZKf//znufzyy3PFFVfkhS984UHPMXTo0NIvalFaWlpk0we59E0u/ZNN3+TSN7n0Tzb9K0uvSnSrMvKzUz7WpHysSflYk/JppjUpS7fSq2qrmf43XQT51p6Ma0u+tSXf5qFXNQY/swMnu8rIrzLyGzjZVabR8ivsYNXDDz+c2bNn593vfnfOPPPMJMmYMWMyfPjw3H333ZkyZUra29szderUfh/juOOOy//7f/+v+99bW1vz1re+NaecckrN5wcAKAu9CgCgenQrAIDq0KsAgEZQ2MGqZcuWZfv27Vm8eHEWL16cJJk2bVoWLVqUtra2bNu2Lcccc0wWLVpU1IgAAHVBrwIAqB7dCgCgOvQqAKARFHawat68eZk3b16ft61YsaLf7zvvvPNy3nnn9XnbZz/72arMBgBQT/QqAIDq0a0AAKpDrwIAGsHQogcAAAAAAAAAAAAoGwerAAAAAAAAAAAAenGwCgAAAAAAAAAAoBcHqwAAAAAAAAAAAHpxsAoAAAAAAAAAAKAXB6sAAAAAAAAAAAB6cbAKAAAAAAAAAACgFwer6Nfw4cOLHqGU5NI3uQBAOdmjAQD2pyPVlnwBoHHY1wdOdgCNYVjRA5RBS0tL0SOUTktLSyZOnFj0GKUjl741ay6de7vSMnRI0WMAlI5uVR7NukeX2pChOeyww4qeAoA6oVfVho5UW/WSr9/rADQXvWpg6mVfL6N6zk5PAujJwaokl7T/OOsffLToMYA6ctyoo3LjrJOLHgOglHQr6Nu+/jBsmP9vGAAHR6+C2vB7HYDmo1fBwdGTAPbnN/pJ7u94NPdsfqToMQAAGoJuBQBQHXoVAEB16FUAwEANLXoAAAAAAAAAAACAsnGwCgAAAAAAAAAAoBcHqwAAAAAAAAAAAHoZVuSTL126NO3t7UmSSZMm5YorrsjGjRtz+eWX55FHHsnxxx+fa665JiNGjOj+nvb29vzwhz/MNddckyTZuXNnXvjCF+aZz3xm931WrVqVlpaWwX0xAAAF0qsAAKpHtwIAqA69CgCod4W9Y9XatWuzatWqrFy5MqtXr86ePXtyyy23ZOHChVmwYEHWrFmTcePGZcmSJUmSXbt25frrr89VV13V43HuueeenHrqqbntttu6/1GkAIBmolcBAFSPbgUAUB16FQDQCAp7x6qRI0emra2t+wT6hAkTsmHDhmzbti1TpkxJksycOTOtra2ZP39+7rrrrnR1dWXhwoVZu3Zt9+OsW7cuW7ZsycyZM9PS0pIFCxZk8uTJhbwmoPl0dnYe1O2Pdb9mI5e+yaV/g51Nva2BXgX1p96uM43M/ls+1qR8Kl2TeltL3QqaR71dn/axV9aWfGtLvpWpt9z0KqhfRV9v7BeVkd/Aya4y9ZTfocx4yAerfv7zn/d4q82BGjt2bMaOHZsk2bp1a5YvX55Zs2Zl48aN3fcZNWpUtmzZkiSZOnVqpk6dmlWrVvV4nCFDhmT69Ol54xvfmHvvvTdz587N6tWr86QnPaniGQEey4YNG7Jz587HvN+6desGYZr6I5e+yaV/jZaNXgXNq9GuZ43AmpSPNSmfsq+JbgUcqoP9vU5Zlf26XO/kW1vyLTe9CihLT7JfVEZ+Aye7yjRafod8sOpv/uZv8rGPfSwnnnhiVQbYtGlT5s6dm5kzZ2by5Mm58847e9w+ZMiQA37/G97whu7/+4QTTsikSZPyH//xH3nZy15WlfkADmT8+PEHvL2zszPr1q3LpEmTvDXxn5BL3+TSv8HOZt/z1ZpeBc3Ltb487L/lY03Kp9I10a10Kyirx/q9TlnZK2tLvrUl38roVXoVDJaie5L9ojLyGzjZVaae8juUXnXIB6ue8pSnZOvWrYc8VF/Wr1+fOXPmZM6cOWltbc3mzZvT0dHRfXtHR0dGjx59wMe49dZbc/rpp+dpT3takqSrqyvDhhX2CYdAkznYDaGlpaX0m0cR5NI3ufSv0bLRq6B5Ndr1rBFYk/KxJuVT9jXRrYBDVeZr2sEo+3W53sm3tuRbbnoVUJZrtP2iMvIbONlVptHyO+TWMXHixLz5zW/OpEmT8vSnPz2HH354j9uvvvrqg3qchx9+OLNnz8673/3unHnmmUmSMWPGZPjw4bn77rszZcqUtLe3Z+rUqQd8nHXr1uX+++/PJZdckp/+9Ke599578/znP/9QXxYAwKDTqwAAqke3AgCoDr0KAOCPBnSc++yzz674iZctW5bt27dn8eLFWbx4cZJk2rRpWbRoUdra2rJt27Ycc8wxWbRo0QEfZ968ebnsssty1llnZejQobnuuuty1FFHVTwfAMBg0KsAAKpHtwIAqA69CgDgDw75YNXBnkJ/LPPmzcu8efP6vG3FihX9ft95552X8847r/vfn/SkJ+WjH/1oVWYCABhMehUAQPXoVgAA1aFXAQD80SEfrOrq6so3vvGN/OQnP0lnZ2f313fv3p177703n/jEJ6o6IABAo9KrAACqR7cCAKgOvQoA4I8O+WDVlVdemS984QuZOHFi1q5dm5NPPjk///nP89BDD+Vv/uZvajEjAEBD0qsAAKpHtwIAqA69CgDgjw75YNVXvvKVfOADH8iZZ56Z6dOn5z3veU+OPfbYXHrppfn9739fixlr7tlPPTK7Oh/7fgD7HDfK57cDlWvEXpXoVtAf/QGgthqxW+lVUBt6GcCB6VXQvPQkgP0d8sGq7du358QTT0ySHH/88Vm7dm2e85znZO7cubnooouqPuBguPb8k9LS0lL0GECd6dzblZahQ4oeA6hjjdirEt0KDqRzb1f27NlT9BgADakRu5VeBbXj9zoA/dOroLnpSQA9DT3Ub3jGM56Re++9N0nynOc8J2vXrk3yh89b3rZtW3WnGyR/+vnQ/EFnZ2fuvfde2fQil741ay5KJVCpRuxViW5VJs26R5da1966/a97AcquEbuVPbw2dKTaqpd8/V4HoH96FfvUy75eRvWcnZ4E0NMhv2PV3/3d32XhwoV53/vel1e+8pU577zzMmzYsPzoRz/K85///FrMSEF27txZ9AilJJe+yQXg0OlVDAZ7NADNQrfiUOhItSVfgPqmV/Gn7OsDJzuAxnDIB6tmzpyZsWPHZsSIERk3blw+8pGP5NZbb82JJ56Yf/iHf6jFjAAADUmvAgCoHt0KAKA69CoAgD865INVSfKCF7wgSfLb3/42L3rRi3L66adnyBBvCdhohg8fXvQIpSSXvsmlf7IBDkSvotbsQwA0E92Kg6Uj1ZZ8AeqfXsU+9vWBkx1AYxh6qN/Q1dWVJUuW5JRTTslpp52WX/ziF1m4cGH+6Z/+Kbt3767FjDXX0tJS9Ail09LSkokTJ8qmF7n0TS79a8ZsOvd2FT0C1I1G7FWJblUmzbgPlZX9EaD2GrFb2cNrQ0eqrUbOV6cDmoVexT6NvK/XWjNlpyMBje6Q37Fq8eLF+fKXv5xrrrkm8+bNS5Kce+65+ad/+qdcd911ufzyy6s+ZK1d0v7jrH/w0aLHAKh7x406KjfOOrnoMaBuNGKvSnQr6M3+CDA4GrFb6VVQHjod0Ez0KuBg6UhAMzjkg1Vf/OIXc8011+QFL3hB91t+vuhFL8q1116bt73tbXVZpu7veDT3bH6k6DEAgCbTiL0q0a0AgGI0YrfSqwCAIuhVAAB/dMgfBbh169aMGjVqv6+PHDkyO3bsqMpQAADNQK8CAKge3QoAoDr0KgCAPzrkg1WnnnpqPvnJT/b42vbt23PDDTfklFNOqdpgAACNTq8CAKge3QoAoDr0KgCAPzqog1Wvec1r8rOf/SxJ8p73vCf33ntvXvSiF2XXrl1585vfnKlTp+YXv/hFXb71JwDAYNKrAACqR7cCAKgOvQoAoG/DDuZOj3vc43LOOefk7/7u7/KWt7wlX/jCF/L9738/999/f/bs2ZNjjz02p59+eoYOPbQ3wFq6dGna29uTJJMmTcoVV1yRjRs35vLLL88jjzyS448/Ptdcc01GjBjR/T3t7e354Q9/mGuuuSZJsnv37lx33XX54Q9/mD179uSyyy7L6aeffkhzAAAMFr0KAKB6dCsAgOrQqwAA+nZQ7Wfp0qVZtGhRVq9enRkzZuR73/teTjvttLz2ta/NhRdemBe/+MWHXKTWrl2bVatWZeXKlVm9enX27NmTW265JQsXLsyCBQuyZs2ajBs3LkuWLEmS7Nq1K9dff32uuuqqHo/ziU98Ir/+9a/zpS99KR/60Idy6aWXZu/evYc0CwDAYNGrAACqR7cCAKgOvQoAoG8H9Y5VSXLmmWdm6tSpufnmm/PWt741L33pS/OWt7wlj3vc43rcb8yYMQf1eCNHjkxbW1v3CfQJEyZkw4YN2bZtW6ZMmZIkmTlzZlpbWzN//vzcdddd6erqysKFC7N27drux/nqV7+a66+/PkOGDMlznvOcfPrTn05XV9fBviwAaqCzs/Ogbn+s+zUbufRvsLOp9fPoVdCcOjs7XetLyJqUjzUpn0rXRLfSraCRlGF/slfWlnxrS76V0av0Kiiral+f7BeVkd/Aya4y9ZTfocx40Aerkj+8Dehb3/rWTJgwIW9/+9vz5S9/ufu2rq6uDBkyJOvXrz+oxxo7dmzGjh2bJNm6dWuWL1+eWbNmZePGjd33GTVqVLZs2ZIkmTp1aqZOnZpVq1b1eJz//d//zf/3//1/ede73pWWlpa84x3vyHHHHXcoLwuAKtuwYUN27tz5mPdbt27dIExTf+TSv0bKRq+C5vOn+2MjXc8ahTUpH2tSPmVeE90KGCwH+zuPwVDm63IjkG9tybe89CpgIGrVkewXlZHfwMmuMo2W3yEdrPrFL36R6667Lv/6r/+aGTNmZO7cuTniiCMqGmDTpk2ZO3duZs6cmcmTJ+fOO+/scfuQIUMO+P2dnZ3ZtGlTvvCFL2TDhg2ZPXt2vvrVr+bxj398RXMBMHDjx48/4O2dnZ1Zt25dJk2alJaWlkGaqvzk0r/Bzmbf89WSXgXNZ/z48a71JWRNysealE+la6Jb6VbQSB7rdx6DwV5ZW/KtLflWRq/Sq6Csqt2R7BeVkd/Aya4y9ZTfofSqgzpYtWvXrnz0ox/N0qVL84xnPCOf+cxnMnny5IqGTJL169dnzpw5mTNnTlpbW7N58+Z0dHR0397R0ZHRo0cf8DGe8pSn5C//8i8zZMiQTJgwIaNHj87//M//5LnPfW7F8wEwMAe7Uba0tJR+Uy2CXPrXCNnoVdC8/vT61QjXs0ZjTcrHmpRPGddEtwIGW5mug2W8LjcS+daWfMtHrwIqUatruv2iMvIbONlVptHyO6iDVa94xSuyffv2vP3tb09ra2tVAnj44Ycze/bsvPvd786ZZ56Z5A+fyTx8+PDcfffdmTJlStrb2zN16tQDPs5LXvKSfPWrX81zn/vcbNq0Kb/85S9z7LHHVjwfAEAt6FUAANWjWwEAVIdeBQDQt4M6WPX85z8/l1xySUaNGlW1J162bFm2b9+exYsXZ/HixUmSadOmZdGiRWlra8u2bdtyzDHHZNGiRQd8nAULFuS9731vzjrrrCTJe9/7Xm/9CQCUll4FAFA9uhUAQHXoVQAAfRvS1dXVVfQQRens7Mx//ud/5n137ch/PPBI0eMA1L0TxozMly8+4zHvt+/6+7znPa+h3gayUnLp32BnYy0GRreCvv3p/uj6Uj7WpHysSflUuibW9NDpVVA+B/s7j8Hgulpb8q0t+VZGfodOr4LaqlVHcr2rjPwGTnaVqaf8DmXWoYM0EwAAAAAAAAAAQN1wsAoAAAAAAAAAAKCXYUUPUAbPfuqR2dVZ9BQA9e+4UUcVPQJQAroV9GR/BGCg9CooD50OoL7pVVAbOhLQDBysSnLt+SeV/vMdAepF596utAwdUvQYQIF0K9if/RGAgdCroFx0OoD6pVdB7ehIQKPzUYBJOjsdUe+ts7Mz9957r2x6kUvf5NK/ZsxGeQaa6ZpXds24D5WV/RGAgbCH14aOVFuNnK9OB1C/GnFfGgyNvK/XWjNlpyMBjc7BKvq1c+fOokcoJbn0TS79kw0ARbIPAQDsT0eqLfkCQOOwrw+c7AAag4NVAAAAAAAAAAAAvThYRb+GDx9e9AilJJe+yaV/sgGgSPah8rEmAFA8+3FtyRcAGod9feBkVxn5AWUxrOgByqClpaXoEUqnpaUlEydOLHqM0pFL3+TSv0qy6dzb5XOpgbqkW5WHPbp8qrUmegJAc9CrakNHqi35Hhq9DmBw6FUDY18fONlV5rHy06GAweRgVZJL2n+c9Q8+WvQYAN2OG3VUbpx1ctFjAAyIbgW1pScANA+9ChqbXgcwePQqaBw6FDDYHKxKcn/Ho7ln8yNFjwEA0BB0KwCA6tCrAACqQ68CAAZqaNEDAAAAAAAAAAAAlI2DVQAAAAAAAAAAAL04WAUAAAAAAAAAANDLsCKffOnSpWlvb0+STJo0KVdccUU2btyYyy+/PI888kiOP/74XHPNNRkxYkT397S3t+eHP/xhrrnmmiTJG9/4xvzqV79Kkuzduzf33XdfPv3pT+e0004b/BcEAFAQvQoAoHp0KwCA6tCrAIB6V9g7Vq1duzarVq3KypUrs3r16uzZsye33HJLFi5cmAULFmTNmjUZN25clixZkiTZtWtXrr/++lx11VU9Hufmm2/Obbfdlttuuy1nnXVWZsyYoUgBAE1FrwIAqB7dCgCgOvQqAKARFPaOVSNHjkxbW1v3CfQJEyZkw4YN2bZtW6ZMmZIkmTlzZlpbWzN//vzcdddd6erqysKFC7N27dr9Hu+BBx7ILbfckttuu21QXwdALXV2dhY9Qk3se12N+voGSi79G+xs6m0N9CpoTvV2rSor+2/5WJPyqXRN6m0tdStgsB3qddJeWVvyrS35VqbectOrgFqqt2viYLLfDpzsKlNP+R3KjIUdrBo7dmzGjh2bJNm6dWuWL1+eWbNmZePGjd33GTVqVLZs2ZIkmTp1aqZOnZpVq1b1+XhLlizJG97whjzpSU+q9egAg2bDhg3ZuXNn0WPUzLp164oeoZTk0j/Z9E2vgubU6D1hsNljysealE+zrIluBQy2gfa6ZrkuF0W+tSXf5qBXAbXkd2OPzX47cLKrTKPlV9jBqn02bdqUuXPnZubMmZk8eXLuvPPOHrcPGTLkMR9j+/bt+cY3vpF3vetdtRoToBDjx48veoSa6OzszLp16zJp0qS0tLQUPU5pyKV/g53NvuerN3oVNJdG7QmDzf5bPtakfCpdE91KtwIO7FB7nb2ytuRbW/KtjF6lVwF/5Hdj/bPfDpzsKlNP+R1Kryr0YNX69eszZ86czJkzJ62trdm8eXM6Ojq6b+/o6Mjo0aMf83G+/e1v5/TTT8+RRx5Zy3EBBl3ZN5xKtbS0NPxrHAi59E82/dOroPm4HlaXPaZ8rEn5NNOa6FbAYBrotbWZrstFkG9tybd56FVArdhHHpv9duBkV5lGy29oUU/88MMPZ/bs2Wlra0tra2uSZMyYMRk+fHjuvvvuJEl7e3umTp36mI/1H//xH92fxQwA0Gz0KgCA6tGtAACqQ68CABpBYe9YtWzZsmzfvj2LFy/O4sWLkyTTpk3LokWL0tbWlm3btuWYY47JokWLHvOxfv7zn+fFL35xrUcGACglvQoAoHp0KwCA6tCrAIBGUNjBqnnz5mXevHl93rZixYp+v++8887Leeed1+NrH//4x6s6GwBAPdGrAACqR7cCAKgOvQoAaASFfRQgAAAAAAAAAABAWTlYBQAAAAAAAAAA0EthHwVYJs9+6pHZ1Vn0FAB/dNyoo4oeAWDAdCuoLT0BoHnoVdDY9DqAwaNXQePQoYDB5mBVkmvPPyktLS1FjwHQQ+ferrQMHVL0GACHTLeC2tMTAJqDXgWNT68DGBx6FTQWHQoYTD4KMElnpyPqvXV2dubee++VTS9y6Ztc+ldJNgohUK/sB+Vhjy6faq2JngDQHOzhtaEj1ZZ8D41eBzA47EsDY18fONlV5rHy06GAweRgFf3auXNn0SOUklz6Jpf+yQaAItmHyseaAEDx7Me1JV8AaBz29YGTXWXkB5SFg1X0a/jw4UWPUEpy6Ztc+icbAIpkHyofawIAxbMf15Z8a0/GAAwWe87Aya4y8gPKYljRA5SBz1TeX0tLSyZOnFj0GKUjl77JpX+y+SOfdw3NQ7cqD/tQ+TTrmugBAAOjV9VGs+7Hg0W+tVerjHU2oJHpVQNjXx842VVGfodOl4PacbAqySXtP876Bx8tegyAhnXcqKNy46yTix4DGCS6FfCn9ACAgdOrgMGiswGNTq8CGpkuB7XlYFWS+zsezT2bHyl6DACAhqBbAQBUh14FAFAdehUAMFBDix4AAAAAAAAAAACgbBysAgAAAAAAAAAA6MXBKgAAAAAAAAAAgF4crAIAAAAAAAAAAOil0INVS5cuzYwZMzJjxoxcdtll2b17d+67775ccMEFmT59ei6++OLs2LGjx/e0t7fn0ksv7f733bt3Z/78+ZkxY0bOPvvsfO973xvslwEAUDi9CgCgenQrAIDq0KsAgHpX2MGqtWvXZtWqVVm5cmVWr16dPXv25JZbbsnChQuzYMGCrFmzJuPGjcuSJUuSJLt27cr111+fq666qsfj3HbbbdmzZ0/uuOOOXH/99T2KFgBAM9CrAACqR7cCAKgOvQoAaATDinrikSNHpq2tLSNGjEiSTJgwIRs2bMi2bdsyZcqUJMnMmTPT2tqa+fPn56677kpXV1cWLlyYtWvXdj/O3r17s2vXrnR2dmbXrl054ogjCnk9ADy2zs7OPv9f/kAu/RvsbOptDfQqoB6U9dpq/y0fa1I+la5Jva2lbgU0s3q7ZteCLlJb8q1MveWmVwEMLn+Hq5zsKlNP+R3KjIUdrBo7dmzGjh2bJNm6dWuWL1+eWbNmZePGjd33GTVqVLZs2ZIkmTp1aqZOnZpVq1b1eJxzzz03X/ziF3PGGWfkkUceyQ033DBYLwGAQ7Rhw4bs3Lmz+9/XrVtX4DTlJZf+yaZvehVQD3r3gLKxx5SPNSmfZlkT3QpoZmXvbIOpWfa9osi3OehVAIPL3+GqR3aVabT8CjtYtc+mTZsyd+7czJw5M5MnT86dd97Z4/YhQ4Yc8Ps/8pGP5HnPe17+5V/+JRs3bswb3vCGnHDCCXn6059ey7EBGIDx48cn+cMJ4HXr1mXSpElpaWkpeKrykEv/Bjubfc9Xb/QqoMz29YCysf+WjzUpn0rXRLfSrYD6UdbONph0kdqSb2X0Kr0K4ED8Ha5ysqtMPeV3KL2q0INV69evz5w5czJnzpy0trZm8+bN6ejo6L69o6Mjo0ePPuBjfOMb38gHP/jBDBkyJMcee2xOOumkrF27VpkCKKHeG2hLS0vpN9UiyKV/sumfXgWUXdmv3/aY8rEm5dNMa6JbAc2qWa7zB6OZ9r0iyLd56FUAg8ff4apHdpVptPyGFvXEDz/8cGbPnp22tra0trYmScaMGZPhw4fn7rvvTpK0t7dn6tSpB3ycCRMm5Gtf+1r3Y/7Xf/1XJkyYUNvhAQBKRK8CAKge3QoAoDr0KgCgERT2jlXLli3L9u3bs3jx4ixevDhJMm3atCxatChtbW3Ztm1bjjnmmCxatOiAj3PZZZelra0tr3zlK9PS0pK3ve1tOfbYYwfjJQAAlIJeBQBQPboVAEB16FUAQCMo7GDVvHnzMm/evD5vW7FiRb/fd9555+W8887r/venPOUpWbJkSdXnAwCoF3oVAED16FYAANWhVwEAjaCwjwIEAAAAAAAAAAAoKwerAAAAAAAAAAAAeinsowDL5NlPPTK7OoueAqBxHTfqqKJHAAaRbgX8KT0AYOD0KmCw6GxAo9OrgEamy0FtOViV5NrzT0pLS0vRYwA0tM69XWkZOqToMYBBoFsBvekBAAOjVwGDSWcDGpleBTQ6XQ5qx0cBJunsdES9t87Oztx7772y6UUufZNL/2TzR8ocNA/XvPKwD5VPs66JHgAwMM22XwyWZt2PB4t8a69WGetsQCOzLw2MfX3gZFcZ+R06XQ5qx8Eq+rVz586iRyglufRNLv2TDQBFsg+VjzUBgOLZj2tLvrUnYwAGiz1n4GRXGfkBZeFgFf0aPnx40SOUklz6Jpf+yaZvcgEYHK635WNNAKB49uPakm/tyRiAwWLPGTjZVUZ+lZEfVM+wogcoA5+pvL+WlpZMnDix6DFKRy59k0v/ZNO3auTis6KhvHSr8rAPlY81OTD7O0BPelVt2I9rS761Vw8Z63VA2ehVA1MPe05Zya4y8quMv8NBdTlYleSS9h9n/YOPFj0GAAfpuFFH5cZZJxc9BtAP3QoYCPs7wP70KqAe6XVAGelVAAdPn4OeHKxKcn/Ho7ln8yNFjwEA0BB0KwCA6tCrAACqQ68CAAZqaNEDAAAAAAAAAAAAlI2DVQAAAAAAAAAAAL04WAUAAAAAAAAAANBLoQerli5dmhkzZmTGjBm57LLLsnv37tx333254IILMn369Fx88cXZsWNHj+9pb2/PpZde2v3vDz30UP7+7/8+M2bMyKtf/er86Ec/GuyXAQBQOL0KAKB6dCsAgOrQqwCAelfYwaq1a9dm1apVWblyZVavXp09e/bklltuycKFC7NgwYKsWbMm48aNy5IlS5Iku3btyvXXX5+rrrqqx+Ncc801mTBhQu6444584AMfyMKFC/O73/2uiJcEAFAIvQoAoHp0KwCA6tCrAIBGMKyoJx45cmTa2toyYsSIJMmECROyYcOGbNu2LVOmTEmSzJw5M62trZk/f37uuuuudHV1ZeHChVm7dm3346xfvz5z585NkjzjGc/IE5/4xPzoRz/KaaedNvgvCoBB1dnZWfQIVbXv9TTa66qGwc6m3tZArwIayWBfg+2/5WNNyqfSNam3tdStAKqj3q7/++gitSXfytRbbnoVQH2rt32nGnSVytRTfocyY2EHq8aOHZuxY8cmSbZu3Zrly5dn1qxZ2bhxY/d9Ro0alS1btiRJpk6dmqlTp2bVqlU9HmfixIm544478va3vz0/+clP8tOf/jQPPfTQYL0MAAq0YcOG7Ny5s+gxqm7dunVFj1BasumbXgU0kqL2d3tM+ViT8mmWNdGtAKqj3n9v0yz7XlHk2xz0KoD6Vu99rhK6SmUaLb/CDlbts2nTpsydOzczZ87M5MmTc+edd/a4fciQIQf8/ssuuyxXXnllzj777Jx00kk55ZRTcthhh9VyZABKYvz48UWPUFWdnZ1Zt25dJk2alJaWlqLHKZXBzmbf89UbvQpoBIO9v9t/y8ealE+la6Jb6VZAc6rX39voIrUl38roVXoVwGCq1z5XCV2lMvWU36H0qkIPVq1fvz5z5szJnDlz0trams2bN6ejo6P79o6OjowePfqAj7Fjx45ceeWVOeqoo5Ikf/VXf5VnPvOZNZ0bgHIo+4Y8UC0tLQ372iolm/7pVUCjKOo6b48pH2tSPs20JroVQOXqfc9opn2vCPJtHnoVQP1q5r1aV6lMo+U3tKgnfvjhhzN79uy0tbWltbU1STJmzJgMHz48d999d5Kkvb09U6dOPeDjfO5zn8vy5cuTJP/2b/+W3//+95kwYUJthwcAKBG9CgCgenQrAIDq0KsAgEZQ2DtWLVu2LNu3b8/ixYuzePHiJMm0adOyaNGitLW1Zdu2bTnmmGOyaNGiAz7O3LlzM3/+/KxevTpHHXVUPvKRj2To0MLOiwEADDq9CgCgenQrAIDq0KsAgEYwpKurq6voIYrS2dmZ//zP/8z77tqR/3jgkaLHAeAgnTBmZL588RlFj1F1+/al5z3veQ319pjVMNjZWIuB0a2AShS1v7vml481KZ9K18SaHjq9Cqhn9f57G/tWbcm3MvI7dHoVwKGr9z5XCXttZeopv0OZ1XFuAAAAAAAAAACAXhysAgAAAAAAAAAA6GVY0QOUwbOfemR2dRY9BQAH67hRRxU9AnAAuhUwEPZ3gP3pVUA90uuAMtKrAA6ePgc9OViV5NrzTyr95zsC0FPn3q60DB1S9BhAH3QrYKDs7wA96VVAvdLrgLLRqwAOjT4Hf+SjAJN0djqi3ltnZ2fuvfde2fQil77JpX+y6Vs1clHmoLxc88rDPlQ+1uTA7O8APdkvasN+XFvyrb16yFivA8qmzNfMMquHPaesZFcZ+VXG3+Gguhysol87d+4seoRSkkvf5NI/2fRNLgCDw/W2fKwJABTPflxb8q09GQMwWOw5Aye7ysivMvKD6nGwCgAAAAAAAAAAoBcHqwAAAAAAAAAAAHpxsAoAAAAAAAAAAKAXB6sAAAAAAAAAAAB6cbAKAAAAAAAAAACgFwerAAAAAAAAAAAAenGwCgAAAAAAAAAAoBcHqwAAAAAAAAAAAHop9GDV0qVLM2PGjMyYMSOXXXZZdu/enfvuuy8XXHBBpk+fnosvvjg7duxIkjzwwAO58MILc/bZZ+fcc8/N97///e7HueGGGzJ9+vS84hWvyDe/+c2iXg4AQGH0KgCA6tCrAACqR7cCAOpdYQer1q5dm1WrVmXlypVZvXp19uzZk1tuuSULFy7MggULsmbNmowbNy5LlixJklx55ZU599xzc/vtt+cDH/hA5s+fn87Oznz961/PunXrcscdd+Szn/1s3ve+9+W3v/1tUS8LAGDQ6VUAANWhVwEAVI9uBQA0gmFFPfHIkSPT1taWESNGJEkmTJiQDRs2ZNu2bZkyZUqSZObMmWltbc38+fNz9tln52Uve1mSZOzYsdm9e3d27NiRb37zmzn77LMzbNiwjBo1KlOmTMmdd96Zc84556Bn2bt3bzo7O6v+GuvZvjzk0pNc+iaX/smmb3Lpm1z6N9jZ1NsalKlXJbpVmbiulI81KR9rUj7WpHwqXZN6Wku9qjm4ztSWfGtPxrUl39qSb2XqLbcydSu9amD8zA6c7Cojv8rIb+BkV5l6yu9QZizsYNXYsWMzduzYJMnWrVuzfPnyzJo1Kxs3buy+z6hRo7Jly5YkyYwZM7q//slPfjJ/9md/lsc//vHZsmVLjj766B7f8+CDDx7SLD/72c/yu9/9buAvpoGtW7eu6BFKSS59k0v/ZNM3ufRNLv2TTd/K1KsS3aqM/OyUjzUpH2tSPtakfJphTfSq5tIM/5suknxrT8a1Jd/akm9zKFO30qsq42d24GRXGflVRn4DJ7vKNFp+hR2s2mfTpk2ZO3duZs6cmcmTJ+fOO+/scfuQIUN6/PvHP/7xrFy5Mp/73OeSJF1dXfs95tChh/YJh+PGjcthhx12iJM3ts7Ozqxbty6TJk1KS0tL0eOUhlz6Jpf+yaZvcumbXPo32Nnse756U4ZelehWZeK6Uj7WpHysSflYk/KpdE3qsVvpVY3Ndaa25Ft7Mq4t+daWfCtTj70qKUe30qsGxs/swMmuMvKrjPwGTnaVqaf8DqVXFXqwav369ZkzZ07mzJmT1tbWbN68OR0dHd23d3R0ZPTo0Un+UJquuOKK/OhHP8ott9ySUaNGJUmOPvro/OpXv+rxPc9+9rMPaY6hQ4eWflGL0tLSIps+yKVvcumfbPoml77JpX+y6V9ZelWiW5WRn53ysSblY03Kx5qUT7OsiV7VPJrlf9NFkW/tybi25Ftb8m0eZelWelVl/MwOnOwqI7/KyG/gZFeZRsvv0P9TuSp5+OGHM3v27LS1taW1tTVJMmbMmAwfPjx33313kqS9vT1Tp05Nktx000257777snz58u4ilSTTpk3L7bffnj179uShhx7KXXfdlRe+8IWD/4IAAAqiVwEAVIdeBQBQPboVANAICnvHqmXLlmX79u1ZvHhxFi9enOQPxWjRokVpa2vLtm3bcswxx2TRokV59NFHc/PNN2fUqFF57Wtf2/0YH/3oR3PmmWfmv/7rv/KqV70qnZ2dmTdvXp761KcW9bIAAAadXgUAUB16FQBA9ehWAEAjKOxg1bx58zJv3rw+b1uxYsV+XzvQZxvOnz8/8+fPr9psAAD1RK8CAKgOvQoAoHp0KwCgERT2UYAAAAAAAAAAAABl5WAVAAAAAAAAAABALw5WAQAAAAAAAAAA9OJgFQAAAAAAAAAAQC8OVgEAAAAAAAAAAPTiYBUAAAAAAAAAAEAvDlYBAAAAAAAAAAD04mAV/Ro+fHjRI5SSXPoml/7JBoAi2YfKx5oAQPHsx7UlXwBoHPb1gZNdZeRXGflB9QwreoAyaGlpKXqE0mlpacnEiROLHqN05NI3ufSv3rLp3NuVlqFDih4DqHO6VXnU2z7UDCpZE/s0QPPRq2pDR6qteslXtwJoLnrVwNTLvl5GsquM/CpzKPnpxfDYHKxKckn7j7P+wUeLHgOgUMeNOio3zjq56DGABqBbQfXZpwGak14FtaFbATQfvQpgf3oxHBwHq5Lc3/Fo7tn8SNFjAAA0BN0KAKA69CoAgOrQqwCAgRpa9AAAAAAAAAAAAABl42AVAAAAAAAAAABALw5WAQAAAAAAAAAA9FLowaqlS5dmxowZmTFjRi677LLs3r079913Xy644IJMnz49F198cXbs2JEkeeCBB3LhhRfm7LPPzrnnnpvvf//7PR7rvvvuy1lnnVXEywAAKJxeBQBQPboVAEB16FUAQL0r7GDV2rVrs2rVqqxcuTKrV6/Onj17csstt2ThwoVZsGBB1qxZk3HjxmXJkiVJkiuvvDLnnntubr/99nzgAx/I/Pnz09nZmSRZtWpVLrroouzcubOolwMAUBi9CgCgenQrAIDq0KsAgEYwrKgnHjlyZNra2jJixIgkyYQJE7Jhw4Zs27YtU6ZMSZLMnDkzra2tmT9/fs4+++y87GUvS5KMHTs2u3fvzo4dO9LZ2ZlvfetbueGGG3LJJZcU9XIAGsa+/4/qYDzHYDxXPZFL/wY7m3pbA70Kmke9XZ/qgf23fKxJ+VS6JvW2lroVNI96uz7tY6+sLfnWlnwrU2+56VUA9aHe9pda0lUqU0/5HcqMhR2sGjt2bMaOHZsk2bp1a5YvX55Zs2Zl48aN3fcZNWpUtmzZkiSZMWNG99c/+clP5s/+7M/y+Mc/Pkny4Q9/OJs2bRq02QEa2YYNGwbtv/pZt27doDxPvZFL/2TTN70Kmsdg7tPNxh5TPtakfJplTXQraB713q2a5bpcFPnWlnybg14FUB/qvRfXgq5SmUbLr7CDVfts2rQpc+fOzcyZMzN58uTceeedPW4fMmRIj3//+Mc/npUrV+Zzn/vcYI4J0DTGjx9f8+fo7OzMunXrMmnSpLS0tNT8+eqFXPo32Nnse756o1dB4xuMfbrZ2H/Lx5qUT6VrolsBZVWv3cpeWVvyrS35VkavAqAW6rUX14KuUpl6yu9QelWhB6vWr1+fOXPmZM6cOWltbc3mzZvT0dHRfXtHR0dGjx6dJOnq6soVV1yRH/3oR7nlllsyatSoosYGaGiDucm1tLSUflMtglz6J5v+6VXQHFwDa8ceUz7WpHyaaU10K2gO9X5Na6brchHkW1vybR56FUD52ZP3p6tUptHyG1rUEz/88MOZPXt22tra0tramiQZM2ZMhg8fnrvvvjtJ0t7enqlTpyZJbrrpptx3331Zvny5IgUA8Cf0KgCA6tGtAACqQ68CABpBYe9YtWzZsmzfvj2LFy/O4sWLkyTTpk3LokWL0tbWlm3btuWYY47JokWL8uijj+bmm2/OqFGj8trXvrb7MT760Y/maU97WlEvAQCgFPQqAIDq0a0AAKpDrwIAGkFhB6vmzZuXefPm9XnbihUr9vvaY3224THHHJNvfvObVZkNAKCe6FUAANWjWwEAVIdeBQA0gsI+ChAAAAAAAAAAAKCsHKwCAAAAAAAAAADopbCPAiyTZz/1yOzqLHoKgGIdN+qookcAGoRuBdVnnwZoTnoV1IZuBdB89CqA/enFcHAcrEpy7fknpaWlpegxAArXubcrLUOHFD0GUOd0K6gN+zRA89GroHZ0K4DmolcB9E0vhsfmowCTdHY6ot5bZ2dn7r33Xtn0Ipe+yaV/9ZaN4gRUQ71c85pBve1DzaCSNbFPAzQfe3ht6Ei1VS/56lYAzaXs+1JZ1cu+Xkayq4z8KnMo+enF8NgcrKJfO3fuLHqEUpJL3+TSP9kAUCT7UPlYEwAonv24tuQLAI3Dvj5wsquM/CojP6geB6sAAAAAAAAAAAB6cbCKfg0fPrzoEUpJLn2TS/9k0ze5AAwO19vysSblY03Kx5oAteY6U1vyrT0Z15Z8a0u+UF/8zA6c7Cojv8rIb+BkR2/Dih6gDFpaWooeoXRaWloyceLEoscoHbn0TS79k03fmjWXzr1dPquapqBblUezXm/LzJqUjzUpn8daE52KZqJX1YZrf23Jt/ZkXFvyra2DzVfno9r0qoFxTRw42VVGfpWR38A1YnZ6VeUcrEpySfuPs/7BR4seA4AGdtyoo3LjrJOLHgMGhW4FQK3oVDQbvQqAZqTzUQt6FQDNSK+qDgerktzf8Wju2fxI0WMAADQE3QoAoDr0KgCA6tCrAICBGlr0AAAAAAAAAAAAAGXjYBUAAAAAAAAAAEAvDlYBAAAAAAAAAAD0MqzIJ1+6dGna29uTJJMmTcoVV1yRjRs35vLLL88jjzyS448/Ptdcc01GjBiRBx54IJdffnl+/etfp6WlJf/4j/+Y0047Lbt3784//dM/5Z577snevXszc+bMvOENbyjyZQEADDq9CgCgOvQqAIDq0a0AgHpX2DtWrV27NqtWrcrKlSuzevXq7NmzJ7fccksWLlyYBQsWZM2aNRk3blyWLFmSJLnyyitz7rnn5vbbb88HPvCBzJ8/P52dnfnMZz6ToUOH5vbbb8/KlSuzYsWK3HPPPUW9LACAQadXAQBUh14FAFA9uhUA0AgKe8eqkSNHpq2tLSNGjEiSTJgwIRs2bMi2bdsyZcqUJMnMmTPT2tqa+fPn5+yzz87LXvayJMnYsWOze/fu7NixIyeddFJe/vKXZ8iQITnyyCPzzGc+M7/85S9zwgknFPXSAKBfnZ2dj3nbge7TrAY7m3pbA70KgGZTb3t1vau0i9XTeulVAFAe9dQhBku9ZaJbAUA5DPbf1+qhsxzKjIUdrBo7dmzGjh2bJNm6dWuWL1+eWbNmZePGjd33GTVqVLZs2ZIkmTFjRvfXP/nJT+bP/uzP8vjHPz4veMELur/+7//+71m7dm2uu+66QXkNAHCoNmzYkJ07dx7wPuvWrRukaeqPbPqmVwHQbA6mU1F9zdDF9CoAKA+dr/7pVgBQDoPdqxrtd0iFHazaZ9OmTZk7d25mzpyZyZMn58477+xx+5AhQ3r8+8c//vGsXLkyn/vc53p8/Xvf+14WLlyYRYsW5QlPeELN5waAgRg/fny/t3V2dmbdunWZNGlSWlpaBnGq8hvsbPY9X73RqwBoFgfqVFRfpV2sHruVXgUAxdP59lePvSrRrQCgaIPVq+rpb52H0qsKPVi1fv36zJkzJ3PmzElra2s2b96cjo6O7ts7OjoyevToJElXV1euuOKK/OhHP8ott9ySUaNGdd/vS1/6Uq6//vrceOONmTx58qC/DgA4WAdTIlpaWkpfNooim/7pVQA0E32gGM3SxfQqACiHZugdzUC3AoDiDXavarTfIQ0t6okffvjhzJ49O21tbWltbU2SjBkzJsOHD8/dd9+dJGlvb8/UqVOTJDfddFPuu+++LF++vEeR+v73v59FixblM5/5jCIFADQlvQoAoDr0KgCA6tGtAIBGUNg7Vi1btizbt2/P4sWLs3jx4iTJtGnTsmjRorS1tWXbtm055phjsmjRojz66KO5+eabM2rUqLz2ta/tfoyPfvSjufHGG7Nnz5684x3v6P76W9/61vzFX/zFoL8mAIAi6FUAANWhVwEAVI9uBQA0gsIOVs2bNy/z5s3r87YVK1bs97X+Ptvw85//fFXnAgCoN3oVAEB16FUAANWjWwEAjaCwjwIEAAAAAAAAAAAoKwerAAAAAAAAAAAAeinsowDL5NlPPTK7OoueAoBGdtyoo4oeAQaNbgVArehUNBu9CoBmpPP9/9i7/7gq6/v/4084+AN05FqiqW2UhqaRmoaZFmplptRKh7mKcuVgs7Kckj+SmmVTM1q/yH7YlPXR0oRM+kFzy621pbZKISWszNRII60JShCH6/tHX84EAflxHa5fj/vt5u0W5zrnOu/X6+25rqcX786FYCBXAQC8iFxlDhZWSVo8ob98Pp/VwwAAuJy/ypAvNMTqYQBBR7YCAAQTmQpeQq4CAHgVmQ9mI1cBALyKXNVy3ApQkt/PEvXa/H6/duzYQW9qoS91oy/1ozd182pfCC3wCq99tu3Mq8dbO2NO7Ic5sZ8TzQmZCl7CsSk4OPYHF/0NPnocXPQ3uBrbXzIfzMZnunk4JjYfvWsZ+tcy9K/53Ng7clXLsbAK9SorK7N6CLZEX+pGX+pHb+pGXwCgdXC8tR/mxH6YE/thTgAEG8eZ4KK/wUePg4v+Bhf9BZyFz2zz0buWoX8tQ/+aj96hNhZWoV7h4eFWD8GW6Evd6Ev96E3d6Evd6Ev96A3QPHx27Ic5sR/mxH6YEwDBxnEmuOhv8NHj4KK/wUV/AWfhM9t89K5l6F/L0L/mo3ct48b+hRiGYVg9CKv4/X5t3bpVAwYM4L7KAAA4SLDvB01GaB76BgCAM50oW7X0HE9GaDp6BgCAM3HNyn7oGQAAzmSnXBUWtFE4yKysbSrYf8TqYQAAgEboFdVRj0waaPUw0ACyFQAAzkG2sjdyFQAAzkGusjdyFQAAzmG3XMXCKkm7io9oe9Fhq4cBAADgCmQrAAAAc5CrAAAAzEGuAgAAzRVq9QAAAAAAAAAAAAAAAAAAwG5YWAUAAAAAAAAAAAAAAAAAtbCwCgAAAAAAAAAAAAAAAABqYWEVAAAAAAAAAAAAAAAAANRi6cKq5cuXKyEhQQkJCZozZ44qKiq0c+dOTZw4UWPGjNG0adN09OhRSdLevXt144036sorr9TVV1+td955R5JUUlKi3/72t7riiit01VVX6d///reVJQEAAFiCXAUAAGAeshUAAIA5yFUAAMDpLFtYlZeXp+zsbK1Zs0Y5OTmqrKzUqlWrlJqaqpkzZyo3N1c9e/bU0qVLJUn33Xefrr76aq1fv14PPvigZsyYIb/fryeeeEIxMTHKyclRenq67rzzTqtKAgAAsAS5CgAAwDxkKwAAAHOQqwAAgBuEWfXGkZGRSktLU0REhCSpT58+KiwsVElJieLi4iRJiYmJSkpK0owZM3TllVfq4osvliRFR0eroqJCR48e1Z133im/3y9J2rdvn0466SRrCgIAAK2q+vzvtH0HA7kKAAC0VEP5p3pbczMS2YpsBQCAl3DN6n/IVQAAoCXskqssW1gVHR2t6OhoSdLBgwe1cuVKTZo0Sbt37w48JyoqSgcOHJAkJSQkBB5/9tlnddZZZ+lHP/qRJCksLEw33nij3n33Xd17772tVgMAALBOYWGhysrKrB6GLZCrAABASzUmW+Xn57fSaKxFtgIAAC3BNav/IVcBAICWsEuusmxhVbV9+/YpJSVFiYmJGjx4sDZu3Fhje0hISI2fn376aa1Zs0b/93//V+PxzMxM7du3T5MmTdLAgQPVs2fPoI8dAABYp3fv3kHbt9/vd+QvDslVAACguRrKVtXZKDY2Vj6fr8n7JluRrQAA8BKuWR2PXAUAAJrDLrnK0oVVBQUFSk5OVnJyspKSklRUVKTi4uLA9uLiYnXt2lWSZBiG5s+frw8++ECrVq1SVFSUJOmdd95RTEyMfvKTn6hHjx4699xz9fHHHxOmAABwueb8Us/NyFUAAKAlGpOtfD6fZzIY2QoAADSXV/JSY5GrAABAc9klV4Va9caHDh3SlClTlJaWpqSkJElSt27dFB4eri1btkiSsrKyFB8fL0l67LHHtHPnTq1cuTIQpCRpw4YNevbZZyVJBw4c0IcffqjY2NhWrgYAAMA65CoAAADzkK0AAADMQa4CAABuYNk3VmVmZqq0tFQZGRnKyMiQJI0YMULp6elKS0tTSUmJevToofT0dB05ckTPPPOMoqKidN111wX28eSTT+qOO+7Q3LlzdcUVVygsLEx33XWXunfvblVZAAAArY5cBQAAYB6yFQAAgDnIVQAAwA1CDMMwrB6EVfx+v7Zu3ar7Nx3V+3sPWz0cAADQCP26RerVaRcG9T2qM8KAAQNs8zWjTkC2AgDAeRqTrVqajchWTUeuAgDAebhmZU/kKgAAnMduucqyWwECAAAAAAAAAAAAAAAAgF2xsAoAAAAAAAAAAAAAAAAAagmzegB2cEbnDir3Wz0KAADQGL2iOlo9BJwA2QoAAOcgW9kbuQoAAOcgV9kbuQoAAOewW65iYZWkxRP6cy9qAAAcxF9lyBcaYvUwUA+yFQAAzkK2si9yFQAAzkKusi9yFQAAzmKnXMWtACX5/SxRr83v92vHjh30phb6Ujf6Uj96Uzf6Ujf6Ur/avbFLkELd+DtsHxxX7Ic5sR/mxH6Yk9ZHtrIvPgfBwXEmuOhv8NHj4KK/weX2/pKr7Mutf+eCze2f2WCidy1D/1qG/jUfvWsZM/tnp1zFwirUq6yszOoh2BJ9qRt9qR+9qRt9qRt9qR+9AZqHz479MCf2w5zYD3MCINg4zgQX/Q0+ehxc9De46C/gLHxmm4/etQz9axn613z0rmXc2D8WVgEAAAAAAAAAAAAAAABALSysAgAAAAAAAAAAAAAAAIBaWFgFAAAAAAAAAAAAAAAAALWwsAoAAAAAAAAAAAAAAAAAamFhFQAAAAAAAAAAAAAAAADUwsIqAAAAAAAAAAAAAAAAAKiFhVUAAAAAAAAAAAAAAAAAUAsLqwAAAAAAAAAAAAAAAACgFhZWAQAAAAAAAAAAAAAAAEAtLKwCAAAAAAAAAAAAAAAAgFpYWAUAAAAAAAAAAAAAAAAAtbCwCgAAAAAAAAAAAAAAAABqYWEVAAAAAAAAAAAAAAAAANTCwioAAAAAAAAAAAAAAAAAqCXM6gFYyTAMSZLf75ff77d4NPZS3Q/6UhN9qRt9qR+9qRt9qRt9qV9r96b6faqzAhqHbGU/HFfshzmxH+bEfpgT+2npnJCtmo5cFVwcZ4KL/gYfPQ4u+htc9LdlyFVNR65qGT6zzUfvWob+tQz9az561zJO6l9TclWI4eH0VVFRofz8fKuHAQAAbCo2NlZt27a1ehiOQbYCAAANIVs1HrkKAAA0hFzVeOQqAADQkMbkKk8vrKqqqlJlZaVCQ0MVEhJi9XAAAIBNGIahqqoqhYWFKTSUOyc3FtkKAADUhWzVdOQqAABQF3JV05GrAABAXZqSqzy9sAoAAAAAAAAAAAAAAAAA6sJydgAAAAAAAAAAAAAAAACohYVVAAAAAAAAAAAAAAAAAFALC6sAAAAAAAAAAAAAAAAAoBYWVgEAAAAAAAAAAAAAAABALSysAgAAAAAAAAAAAAAAAIBaWFgFAAAAAAAAAAAAAAAAALWwsAoAAAAAAAAAAAAAAAAAavHswqry8nLNnTtXgwcP1vDhw/WnP/3J6iGZ5sCBA5o2bZri4uJ04YUXauHChSovL5ck7d27V5MnT9aAAQM0duxYvf322zVe++9//1sJCQnq37+/brjhBu3du7fG9hUrVujCCy/UwIEDNXfuXJWVlQW2OamnycnJmj17duDnHTt2KDExUf3799eECRP04Ycf1nj+K6+8oksuuUT9+/fXLbfcokOHDgW2GYahBx98UOeff77i4uL0wAMPqKqqKrD9m2++0W233aaBAwdq1KhRevnll4NfYBNUVFRo/vz5Ou+883TBBRfooYcekmEYkrzdF0n68ssvlZKSonPPPVejRo3SihUrAtu82JuKigolJCRo8+bNgcesPKac6L1bU1292bp1qyZNmqSBAwfqsssu04svvljjNV7oTV19qVZSUqILL7xQ2dnZNR4P5mfnRJ9bNJ+TMoAT2e3462XkbPv5/PPPdfPNN2vgwIEaMWKEli1bFtjGnFiLf3PZx4YNG9S7d+8af6ZNmyaJebErjjEN43zcejiWBwfX4oKL63nBY7d/m9rl+hfszYvn72ORm8xBJmo68k7LkGeah6zSfPyesxEMj7r33nuNK664wvjwww+Nv/zlL8bAgQON119/3ephtVhVVZUxceJEY8qUKcbOnTuNd99917j00kuNRYsWGVVVVcYVV1xhzJgxw/jkk0+MJ5980ujfv7/xxRdfGIZhGF988YUxYMAA49lnnzV27txp3H777UZCQoJRVVVlGIZh5ObmGoMGDTLefPNNY9u2bcbYsWON+fPnB97bKT195ZVXjJiYGGPWrFmGYRjGkSNHjGHDhhmLFi0yPvnkE+O+++4zLrjgAuPIkSOGYRjGtm3bjHPOOcd46aWXjIKCAuP66683kpOTA/t79tlnjfj4eOPdd9813nnnHWP48OHGsmXLAttTUlKMG2+80SgsLDTWrFljnH322ca2bdtat+gGpKWlGaNHjza2bdtm/Pvf/zaGDBliPP/8857vi2EYxsSJE4077rjD+Oyzz4wNGzYY/fv3N/7yl794sjffffedccsttxgxMTHGpk2bDMMwLD2mnOi9W1Ndvfnqq6+MwYMHG+np6cZnn31mvPLKK0ZsbKyxceNGwzC80Zu6+nKstLQ0IyYmxsjKygo8FszPzok+t2gZp2QAJ7Lb8dfLyNn24/f7jdGjRxszZswwPvvsM+Pvf/+7ce655xrr169nTizGv7ns5YknnjBSUlKMr776KvDnv//9L/NiYxxj6sf5uPVwLA8ersUFF9fzgsNu/za1y/Uv2J/Xzt/HIjeZg0zUPOSdliHPNB1Zpfn4PWfjeHJh1ZEjR4zY2Ngav+jNyMgwrr/+egtHZY5PPvnEiImJMYqLiwOP5eTkGMOHDzf+/e9/GwMGDKjxS+Qbb7zRePTRRw3DMIyHH364Rg+OHj1qDBw4MNCna6+9NvBcwzCMd9991zjnnHOMo0ePOqan33zzjXHRRRcZEyZMCISgF1980Rg1alTgg15VVWVceumlgV/2p6amBp5rGIZRVFRk9O7d29izZ49hGIYRHx9fY2HAunXrjJEjRxqGYRiff/65ERMTY+zduzewfe7cuTX2Z6VvvvnG6Nu3r7F58+bAY0899ZQxe/ZsT/fFMAzj22+/NWJiYozCwsLAY7feeqsxf/58z/Xm448/Nq688krjiiuuqHFStfKYcqL3bi319WbVqlXGmDFjajw3LS3N+N3vfmcYhvt7U19fqlX/I37YsGE1PgvB/Oyc6HOL5nNKBnAiOx5/vYycbT8HDhwwbr/9dqOkpCTw2C233GLcc889zImF+DeX/cyYMcNIT08/7nHmxZ44xjSM83Hr4FgePFyLCy6u5wWHHf9taofrX7A/r52/ayM3tRyZqHnIOy1Dnmk6skrz8XvOxvPkrQA/+ugjVVZWauDAgYHHBg0apG3bttX4ujsn6ty5s5YtW6ZTTjmlxuOlpaXatm2b+vbtq4iIiMDjgwYN0tatWyVJ27Zt0+DBgwPbwsPD1a9fP23dulV+v1/5+fk1tg8YMEDff/+9PvroI8f0dPHixfr5z3+uXr16BR7btm2bBg0apJCQEElSSEiIzj333Hr7cuqpp6pbt27atm2bDhw4oC+//FLnnXdeYPugQYP0xRdf6KuvvtK2bdt06qmnqkePHjW2f/DBB0GutHHee+89dezYUXFxcYHHkpOTtXDhQk/3RZLat2+v8PBwZWdn6/vvv9euXbv0/vvv66yzzvJcb7Zs2aIhQ4Zo9erVNR638phyovduLfX1pvprlWsrLS2V5P7e1NcX6YevE01LS9Pdd9+ttm3b1tgWzM/OiT63aD6nZAAnsuPx18vI2fYTFRWlhx9+WB07dpRhGHrvvff07rvvKi4ujjmxEP/msp9PP/1U0dHRxz3OvNgTx5iGcT5uHRzLg4drccHF9bzgsOO/Te1w/Qv257Xzd23kppYjEzUPeadlyDNNR1ZpPn7P2XieXFhVXFysH//4xzV+oXvKKaeovLxc3377rXUDM0FkZKQuvPDCwM9VVVX6v//7P51//vkqLi5WVFRUjef/5Cc/0f79+yWpwe2HDx9WeXl5je1hYWHq1KmT9u/f74ievvPOO/rPf/6jqVOn1nj8RH356quv6t1eXFwsSTW2V4fU6u11vfbAgQPmFNVCe/fuVffu3bVu3TqNGTNGF198sTIyMlRVVeXpvkhSu3btdPfdd2v16tXq37+/Lr/8cl100UVKTEz0XG+uvfZazZ07V+Hh4TUet/KYcqL3bi319aZHjx4aMGBA4OeDBw/q1Vdf1dChQyW5vzf19UWSnnzySfXt21fDhw8/blswPzt26ItbOSEDOJUdj79eRs62t1GjRunaa6/VwIEDddlllzEnFuHfXPZjGIY+++wzvf3227rssst0ySWX6MEHH1RFRQXzYlMcYxrG+Tj4OJYHF9figovrecFhx3+bcp0HjeGl83ddyE0tQyZqPvJOy5Bnmo6s0nz8nrPxwix9d4uUlZUd9y0Z1T9XVFRYMaSgWbJkiXbs2KG1a9dqxYoVddZdXXN9famoqNB3330X+Lmu7YZh2Lqn5eXluueee3T33Xerffv2NbY1VLckfffdd03qy7F1n2jfVjt69Kg+//xzvfDCC1q4cKGKi4t19913Kzw83NN9qfbpp59q5MiR+tWvfqWPP/5Y9913n4YOHUpv/r8TjTWYxxQn9em7777TbbfdplNOOUXXXHONJO/25pNPPtELL7yg9evX17k9mJ8dO/fF6byUq+zCyuMv/oecbS+PPvqovv76a/3+97/XwoUL+ZxYgH9z2VNRUVGgRw8//LD27dunBQsW6LvvvmNebIps1TScj83FsTz4uBYXfFzPaz1cG4TdkatqIjc1HpmoZcg7LUeeMQdZxRz8nvMHnlxY1a5du+MaX/1z7ROkky1ZskSZmZn64x//qJiYGLVr1+641eAVFRWBmuvrS2RkpNq1axf4ufb28PBw+f1+W/f08ccf19lnn11jdX61+uo+UV/Cw8NrfMhr9yg8PPyE+7ZaWFiYSktLlZ6eru7du0v64cL7888/r5/97Gee7Yv0w/+NsHbtWv3jH/9Q+/btFRsbqwMHDmjp0qU67bTTPN2balYeU0703nZx5MgRTZ06Vbt379aqVasCK7692BvDMDRv3jxNmzbtuK+frhbMz46TPltO45VcZSdeznR2Qc62n9jYWEk/XPycOXOmJkyYoLKyshrPYU6Ci39z2VP37t21efNmnXTSSQoJCdFZZ52lqqoqpaamKi4ujnmxIbJV43E+Nh/H8uDjWlxwcT2vdXFtEHZHrvofclPTkIlahrzTMuQZ85BVWo7fc/6PJ28F2KVLF33zzTeqrKwMPFZcXKz27dsrMjLSwpGZ57777tPy5cu1ZMkSXXbZZZJ+qPvrr7+u8byvv/468FVq9W3v3LmzOnXqpHbt2tXYXllZqW+//VadO3e2fU9fffVV/fWvf9XAgQM1cOBA5eTkKCcnRwMHDmxRX7p06SJJga9PPPa/q7fX91o76Ny5s9q1axcINpJ0+umn68svv/R0XyTpww8/1M9+9rMaB+m+ffuqqKjI872pZuUx5UTvbQelpaW6+eab9fHHHyszM1PR0dGBbV7sTVFRkT744AMtXrw4cCwuKirSPffcoylTpkgK7mfHrn1xA7tnADfycqazA3K2fXz99df661//WuOxXr166fvvv1fnzp2Zk1bGv7nsq1OnTgoJCQn83LNnT5WXl7foc8K8BA/HmMbhfBwcHMuDj2txwcX1vNbFtUHYnVfO3ydCbmo6MlHLkHdahjxjHrJKy/B7zpo8ubDqrLPOUlhYmLZu3Rp47L333lNsbKxCQ53fkscff1wvvPCCHnroIY0bNy7weP/+/bV9+/bA169JP9Tdv3//wPb33nsvsK2srEw7duxQ//79FRoaqtjY2Brbt27dqrCwMPXp08f2PX3uueeUk5OjdevWad26dRo1apRGjRqldevWqX///vrggw9kGIakH75V5f3336+3L19++aW+/PJL9e/fX126dFG3bt1qbH/vvffUrVs3RUVFacCAAfriiy9q3PPzvffeq3FPUiv1799f5eXl+uyzzwKP7dq1S927d/d0X6Qf7jH8+eef11gxu2vXLvXo0cPzvalm5THlRO9ttaqqKt16663at2+fnnvuOZ155pk1tnuxN126dNFf/vKXwHF43bp1ioqK0rRp03T//fdLCu5n50SfWzSf3TOAG3k501mNnG0v+/bt06233qoDBw4EHvvwww918skna9CgQcxJK+PfXPb0z3/+U0OGDKnxDW4FBQXq1KmTBg0axLzYEMeYE+N8HDwcy4OPa3HBxfW81sW1QdidV87fDSE3NQ+ZqGXIOy1DnjEPWaX5+D1nHQyPSktLM8aNG2ds27bN2LBhg3Huuecab7zxhtXDarFPPvnEOOuss4w//vGPxldffVXjT2VlpTF27FjjjjvuMHbu3Gk89dRTxoABA4wvvvjCMAzD2Lt3rxEbG2s89dRTxs6dO43bb7/duOKKK4yqqirDMAzjlVdeMc4991xjw4YNxrZt24xx48YZ9913X+C9ndTTWbNmGbNmzTIMwzBKSkqM888/37jvvvuMjz/+2LjvvvuMYcOGGUeOHDEMwzDef/99o1+/fsaaNWuMgoIC4/rrrzdSUlIC+3rqqaeM4cOHG5s2bTI2bdpkDB8+3PjTn/4U2H7TTTcZ119/vVFQUGCsWbPGiI2NNbZt29a6BTcgOTnZuOaaa4yCggLjrbfeMs4//3wjMzPT8305fPiwMWzYMCM1NdXYtWuX8be//c2Ii4sznn/+eU/3JiYmxti0aZNhGIalx5QTvbcVju3N6tWrjT59+hgbN26scRz+5ptvDMPwVm+O7UttI0eONLKysgI/B/Ozc6LPLVrGSRnAqexy/PUycrb9VFZWGuPHjzduuukm4+OPPzb+/ve/GxdccIGxYsUK5sQG+DeXPZSUlBgXXnih8bvf/c749NNPjb///e/G8OHDjaeffpp5sTGOMfXjfNy6OJYHB9figofrecFnl3+b2u36F+zLi+fvauQm85CJmo6803zkmZYhqzQfv+dsmGcXVh09etS48847jQEDBhjDhw83li9fbvWQTPHUU08ZMTExdf4xDMPYvXu3cd111xlnn322MW7cOONf//pXjdf//e9/N0aPHm2cc845xo033mjs2bPnuP0PHTrUGDRokDFnzhzju+++C2xzUk+PDUGGYRjbtm0zrrrqKiM2Ntb4xS9+YWzfvr3G87Oysoz4+HhjwIABxi233GIcOnQosK2ystL4wx/+YAwePNgYMmSIsWTJksBBwzAM4+uvvzZSUlKM2NhYY9SoUUZOTk7wC2yCw4cPG6mpqcaAAQOMoUOHGo899lhg/F7ui2EYxscff2xMnjzZOPfcc41LLrnEWL58ued7U3uRjJXHlBO9d2s7tjc33XRTncfh66+/PvB8r/SmKQurDCO4n50TfW7RfE7KAE5lp+OvV5Gz7Wn//v3GLbfcYpx77rnGsGHDjKVLlwbODcyJtfg3l33s3LnTmDx5sjFgwABj2LBh/JvPATjG1I/zceviWB4cXIsLLq7nBZed/m1qp+tfsC8vnr+rkZvMQyZqOvJOy5Bnmo+s0nz8nrNhIYbx/78rDgAAAAAAAAAAAAAAAAAgSXLGzWwBAAAAAAAAAAAAAAAAoBWxsAoAAAAAAAAAAAAAAAAAamFhFQAAAAAAAAAAAAAAAADUwsIqAAAAAAAAAAAAAAAAAKiFhVUAAAAAAAAAAAAAAAAAUAsLqwAAAAAAAAAAAAAAAACgFhZWAQAAAAAAAAAAAAAAAEAtLKwCAAAAAAAAAAAAAAAAgFpYWAXAVf773/9q0aJFGjVqlPr376/LL79cK1asUFVVVdDfu7S0VOvWrQv6+wAAALQGchUAAIA5yFUAAADmIVsBaG1hVg8AAMzyzTff6JprrlFUVJTuv/9+9ejRQ/n5+brvvvu0d+9epaWlBfX9V6xYoc2bN+uqq64K6vsAAAAEG7kKAADAHOQqAAAA85CtAFiBhVUAXCM9PV1t27bVs88+q3bt2kmSTjvtNLVv315Tp07V9ddfr9NPPz1o728YRtD2DQAA0JrIVQAAAOYgVwEAAJiHbAXACiEGn34ALlBRUaEhQ4bozjvv1C9/+csa2wzD0ObNm3XuueeqrKxMDz74oP72t7+pvLxco0aN0rx583TSSSdp8+bNuuGGG1RYWBh47ezZsyVJixYt0mOPPabdu3erY8eOysnJUbt27XTTTTfp17/+tbKzszVnzpzA647dBwAAgJOQqwAAAMxBrgIAADAP2QqAVUKtHgAAmGHPnj06evSoYmNjj9sWEhKi888/X23bttWtt96qgoICPfnkk1q+fLk+/fTTQGBqjDfeeEPt2rXTSy+9pJtvvlkPPvigPvvsM40dO1Y33XSTBg4cqLffftvM0gAAAFoVuQoAAMAc5CoAAADzkK0AWIVbAQJwhcOHD0uSfvSjH9X7nI8++khbtmxRbm5u4GtAlyxZorFjx2rXrl2Nep9OnTpp1qxZ8vl8mjJlip555hl9+OGHOv300xUREaE2bdqoc+fOLS8IAADAIuQqAAAAc5CrAAAAzEO2AmAVvrEKgCt06tRJkvTf//633ufs2rVLkZGRNe6t3LNnT5100kmNDlM9evSQz+cL/NyhQwdVVlY2b9AAAAA2RK4CAAAwB7kKAADAPGQrAFZhYRUAV/jpT3+qH/3oR9q+fXud23/729+qbdu2dW7z+/3y+/0KCQk5blvtoNSmTZvjnmMYRjNGDAAAYE/kKgAAAHOQqwAAAMxDtgJgFRZWAXCFsLAwjR07VitXrlRFRUWNbW+++abefPNNRUdH6/DhwzVWpH/yyScqLS3V6aefHghKpaWlge379u1r9BjqCmMAAABOQ64CAAAwB7kKAADAPGQrAFZhYRUA17jttttUWlqqm2++WVu2bNGePXv04osvavbs2brhhhvUq1cvXXTRRZo1a5by8vKUl5enWbNm6bzzzlNMTIzOPPNMtW/fXk8++aT27t2rZcuWaceOHY1+//DwcH311VdNCmAAAAB2RK4CAAAwB7kKAADAPGQrAFZgYRUA1+jcubOef/55nXbaaZo5c6YSEhKUmZmpadOmafbs2ZKkxYsX67TTTtPkyZN1880368wzz1RGRoYkqWPHjrrvvvv06quvKiEhQR999JGuu+66Rr//pZdeqqqqKo0bN04HDx4MSo0AAACtgVwFAABgDnIVAACAechWAKwQYnBDUAAAAAAAAAAAAAAAAACogW+sAgAAAAAAAAAAAAAAAIBaWFgFAAAAAAAAAAAAAAAAALWwsAoAAAAAAAAAAAAAAAAAamFhFQAAAAAAAAAAAAAAAADUwsIqAAAAAAAAAAAAAAAAAKiFhVUAAAAAAAAAAAAAAAAAUAsLqwAAAAAAAAAAAAAAAACgFhZWAQAAAAAAAAAAAAAAAEAtLKwCAAAAAAAAAAAAAAAAgFpYWAUAAAAAAAAAAAAAAAAAtbCwCgAAAAAAAAAAAAAAAABqYWEVAAAAAAAAAAAAAAAAANTCwioAAAAAAAAAAAAAAAAAqIWFVQAAAAAAAAAAAAAAAABQCwurAAAAAAAAAAAAAAAAAKAWFlYBAAAAAAAAAAAAAAAAQC0srAIAAAAAAAAAAAAAAACAWlhYBQAAAAAO8d577+m2227TsGHDFBsbq4svvljz5s3Tp59+WuN5o0aNUu/evWv8Oeuss3Teeefp2muv1VtvvVXj+UlJSUpKSgr8nJ2dfdzra/+pfs/q5y5YsKDOMT/22GPq3bu3JGnz5s0n3G/v3r21b9++wOvXrFmj3r176ze/+U2d+69+/2NfU5cTveeDDz7Y4OsBAIA3JCUlqXfv3po0aVK9z5k+fbp69+6t2bNnH7dt0qRJ6t27t954441699+3b1/l5+fXuX3UqFE19ntslpKkffv2nTDXPP/888c9d/Xq1XW+X0lJiWJjY9W7d29t3rz5uO27d+9W7969NWTIEFVUVNTbEwAA4D1NzTVS469tVb++Kde3aj+3b9++GjJkiG666SZt3LixUTXVzl7Hmj9/fo1rSNXXuTZv3qzvvvtOgwYNUnJycr37/vrrr9WvXz898sgj9fandj2PPfZYo8YNILjCrB4AAHebPXu2XnrppXq3n3LKKfrXv/4V+LmwsFCZmZnatGmTvv76a5188sk699xzdeONN6p///7H7XvLli16880369x39S8Hn3vuueO2/fGPf9STTz6p66+/XmlpaY0eryTFxcXpueee02OPPabHH3+8wefm5eWpXbt2gecWFhbW+bzatdS37/DwcHXp0kVjxozRbbfdprAwDuMAAHjF008/rYceekjDhw/X3Llz1blzZ33++ed6/vnndfXVV2vhwoUaN25c4Pnx8fGaOnVq4OfKykrt2bNHTz/9tKZOnaq1a9eqT58+Db7n448/rs6dO9e5rUePHjV+XrlypcaMGaPBgwfXu79+/frV+KXe9u3bde+99+ruu+9Wv379Ao9HRUUF/jsrK0sxMTF666239OWXX+rUU09tcMwN+cUvfqHExMQ6t3Xp0qXZ+wUAAO4SGhqqrVu3av/+/eratWuNbUePHq33F3O7du3SBx98oJiYGL3wwgu67LLL6nye3+/XnDlzlJ2drbZt2zZrjL/97W81YsSIOreddtppNX4ODQ1Vbm6urrnmmuOeu2HDhgYXTGVlZalnz576/PPPlZubqyuvvLJZ4wUAAO7UlFzT1GtbUtOvbx177ef7779XcXGxsrKy9Jvf/EZ33XWXbrjhhmbVee+992rVqlWaOnWqbr/99uO2t2/fXuPGjVNWVpYOHTqkk08++bjn5OTkyO/3a8KECc0aAwDr8Bt5AEHXuXPnehcgtWnTJvDf69atU1pamvr27avbbrtN3bt31/79+7V27Vr98pe/VGpqqn71q1+1eDxVVVVat26dYmJi9PLLL2vmzJkKDw+XJE2dOrXG/5H4xBNPaMeOHTXG37Fjxxr7q+//+JPU7Itj9e37m2++0SuvvKInn3xSlZWVSk1NbdH+AQCAM2zcuFHp6em67bbbdOuttwYej4uL01VXXaUZM2Zo9uzZiomJ0ZlnnilJOvnkkzVgwIAa+xk8eLD69++vsWPHav369SdcWHXWWWcdt4CqPh07dtTcuXO1fv16tW/fvt7nHDum8vJySVKvXr2OG6skffrpp9q6dauWLVum6dOna/Xq1brjjjsaNZ66dO3atc73AQAAOFbfvn31ySefKDc3V5MnT66xbePGjQoPD1dkZORxr8vOzlb37t2VkpKimTNn6vPPP9fPfvaz4573ox/9SB9//LEyMjI0ffr0Zo3xpz/9aaNzzbnnnqvNmzfX+Uu+V199VWeddZYKCgqOe53f79e6det0zTXX6IMPPtALL7zAwioAAFBDY3NNc65tSU2/vlXXtZ+xY8fqtttu0wMPPKBRo0Y1+lpXtQULFmjlypW644479Nvf/rbe5/3iF7/Q6tWr9frrr+u66647bvtLL72koUOHNvn9AViPWwECCLq2bdtqwIABdf6p/maCjz76SPPmzdO4ceMCK9Pj4uJ05ZVX6s9//rOuu+46LV68WP/+979bPJ63335b+/fv1+9//3sdOXJEr7zySmBb9UWp6j8nn3zycePv1atXjf3VV9uAAQMUEhLSorHW3t/IkSOVnp6uAQMGKDs7u0X7BgAAzvH444/rjDPO0C233HLctjZt2ujee++Vz+fTM888c8J9Vf8SsKU5pbZZs2Zpz549euihh0zbZ1ZWlk466SSdf/75uuyyy7R27VpVVlaatn8AAIC6REREKD4+Xrm5ucdte+2113TZZZcd9y3i1YuQRo4cqUsuuUQRERH1/s94Z511lq666iotW7ZMH374YVBqONall16q0NBQbdiwocbj33zzjTZt2nTcN0NUe/vtt/XVV19pxIgRuvLKK/Xee+/pk08+Cfp4AQCAczQ215h5bUtq+vWt6dOn6/vvv9fatWsb9fxq999/v5577jmlpqY2uKhKks455xydeeaZysnJOW5bQUGBCgsL9Ytf/KJJ7w/AHlhYBcAWnnnmGUVEROjuu+9WaOjxh6bU1FSdeuqpysjIaPF7Vd9OZtCgQRoyZEiD3zhlVx07djT9l6EAAMCeDh06pA8//FAjR46s9/zfqVMnXXDBBfrb3/4WeMwwDFVWVgb+lJWV6aOPPtKsWbPUpk0bJSQknPC9q6qqauyj+k9VVdVxzz3//PN1zTXX6LnnntN7773X/IL/v8rKSq1fv14JCQlq06aNrr76ahUXF9d7G+jGqK8eFmsBAIDaxo4dG7gdYLXS0lK99dZbdeaot956S8XFxbrqqqvUvn17XX755XrppZfqvc3e3Llz9eMf/1hz5sxp8FZ89akv1/j9/uOeGxkZqWHDhh23UOyNN95Qt27ddM4559T5HllZWTrzzDN19tlna/To0erQoYNeeOGFJo8VAAC424lyTXOvbUnmXN+SpDPOOEPdunVr0jWrhQsX6s9//rNmz56tKVOmNOo1EyZM0AcffKC9e/fWeHzdunXq1KmTLr300ka/PwD7YGEVgFZR3y+wDMOQ9MNXgA4bNkwRERF1vr5t27a65JJL9N577+mbb75p9ji+/fZbvfnmm7rqqqskSVdffbXy8/O1ffv2Zu+zvtrq+oVjS/ZdUVGhAwcO6JlnntG//vUv/fznP2/x/gEAgP198cUXkqTu3bs3+Lyf/exnKi0t1X//+19JP1yw6devX+DPgAEDNGHCBB05ckTPPvuszjrrrBO+96WXXlpjH9V/6vs/9O68806deuqpmjt3rr777rsmVlpT9S8nx48fL+mHr3mPjo5u0S/znnjiiTrr6devnw4dOtSi8QIAAHcZMWKEwsPDayxG2rBhg37yk59o0KBBxz0/OztbMTExio2NlSSNHz9ehw4d0htvvFHn/k866STde++92rlzZ7P+R8K77rqrzkwzePDgOp9/+eWXB24HWO3VV1/V2LFj63z+N998ozfffDOQxcLDwzV27Fi9/PLLKisra/J4AQCAe50o1zT32pZkzvWtaqeccoq+/vrrRj138eLFyszMlKQmXTP6+c9/rjZt2tT41qrKykrl5OToiiuuUNu2bRu9LwD2EXbipwBAy3zxxReBW/7VdueddwYCUGMClWEYKioq0o9//ONmjSUnJ0dVVVWBRUmjR4/WvffeqxdeeEH33Xdfs/ZZX23XXXed7r777mbts6F9d+vWTbfddpuSk5NbtG8AAOAM1QvR27Rp0+DzfD5fjeePHDky8PXqe/bs0ZIlS9SlSxc9/vjj6ty5c6Pee+nSpXU+t/rr1mvr0KGD7r//fk2ePFl//OMfNWfOnEa9T12ysrJ0+umn66c//akOHz4sSRozZoyeeuop7dmzRz/96U+bvM+JEydq4sSJdW6rryYAAOBN7du316hRo5Sbm6vJkydL+mEh0uWXX37cNy0cOnRIGzdu1G9+85tAbjnzzDPVvXt3rV69WldccUWd7zFq1ChdeeWVWrZsmUaPHl3vNaa63HrrrRoxYsRxj1dnwtouueQSpaWlacOGDbrmmmv01Vdf6T//+Y/uvvvuOn9ZuH79evn9fo0YMSJQ06WXXqoXX3xRr732miZMmNDosQIAAPdrKNc099qWZM71rWPHUZ3j/H5/jfcJCQmpkaNWrFihhQsX6u2339ayZct0wQUXaOjQoSd8j5NPPlkjR45UTk6Opk6dKkn65z//qYMHDzbrNoDcvQawBxZWAQi6zp07a+nSpXVuO/XUUxu9n2PDzrE/N0VWVpaGDBmitm3bBi4KjRo1Sq+88opmzZqljh07Nnmf9d2P+Sc/+Ungv5sbfKr3XVZWphUrVmjz5s2aN2+eLr744mbtDwAAOE/14vPq/7uvPnv37lWHDh3UqVMnST98hXr1NybExsaqd+/emjBhgn79619rzZo1jfo/5GJiYtSjR48mjXfo0KG65ppr9Oc//1mXXXZZk15b7eDBg/rHP/6h77//Xuedd95x21evXq3U1NQm7zcqKirQEwAAgBO5/PLLdeutt2r//v1q166d3nnnHd1xxx3HPW/9+vX6/vvv9dhjj+mxxx6rse2LL77Qp59+qp49e9b5HvPmzdM777yjOXPmKCsrq9Fj6969e5NyTceOHXXRRRcpNzdX11xzjXJzc9WrVy+deeaZ2rx583HPz87OVlVVlS6//PLjtr3wwgssrAIAAMepL9c099qWZM71rWr79+9XTEyMJGny5MnasmVLYFtcXJyee+65wM+LFi3Sz3/+c1188cV67733lJqaqvXr1+vkk08+4ftMmDBBKSkp2r59u/r166d169YpNjZWffr0qfG8iIiIem8JXf14eHh4o+sDEDwsrAIQdG3btj3hhZ4OHTpo3759DT6nent1AAsPD683cEg/hI5jw9eOHTtUUFAgSXX+gm79+vW69tprGxxDXRpzEas6+FRUVNQZ8ioqKuq8DeKx+x48eLAmT56s22+/XStWrKj3q90BAIC7/OQnP9GAAQP0xhtv6Pbbb1do6PF3dC8tLdW//vUvjRo1qt799OrVS9OmTdMDDzygxx9/XL/73e+CNuY777xT//znPzVnzhxdcsklTX79+vXrVVlZqYyMDP3oRz+qse2xxx5Tdna2br/9dr4+HQAABNVFF12kDh06KDc3VxEREerRo4fOPvvs456XlZWlgQMHavr06TUeP3r0qKZOnarnn39e8+bNq/M9TjrpJP3+97/XLbfcoieeeCIodVQbO3asUlNTdejQIb322msaN25cnc/bvn27PvroI02bNu24608bNmzQc889p4KCgibdegcAALhffbnGrGtbUvOvb33yyScqLi7WddddJ0maP3++jhw5EtjeoUOHGs+vvvNNZGSkFi5cqF/96leaPXu2nnrqqRN+mcKFF16oqKgovfLKKzrttNP05ptv6q677jrueaeccoq++uqrOvexf//+wHMAWO/4oxYAWGDkyJH617/+VSPEVFRUqLCwUNIP31L117/+Vf369Qt8E9Qpp5yib7/9tt7FVfv3768ROLKzsxUREaEVK1boz3/+c40/0dHRWr16ddDqqx7HgQMHGjXWuoSGhmrhwoUKCwvT7NmzVV5ebvo4AQCAPd1666367LPP9NBDDx23ze/365577tF3332nKVOmNLifG2+8UTExMfrTn/6k3bt3B2m0P3wjwoIFC7R79+5mZazs7GwNGDBAl1xyiYYMGVLjz8SJE3Xo0CFt2LAhCCMHAAD4n7Zt2+qSSy7RG2+8oddff73OhUj5+fnauXOnxo8ff1xuGTlypM4//3y9/PLL+u677+p9n0suuUQJCQl6+umn67wtn1lGjhyptm3b6v/+7/+0devWehdWZWVlqV27drrxxhuPq+nmm29WaGionn/++aCNEwAAOFd9ucasa1tS865vPfroo2rfvr2uvvpqSdIZZ5yh2NjYwJ8zzjij3tcOHTpUN954o/7xj38oMzPzhO/l8/l09dVX64033tCbb74pn8+nhISE454XFxenbdu21fnFE7m5ufL5fHV+UQSA1sfCKgC2kJKSou+++07z589XVVWVJOnDDz/UVVddpZSUFC1cuFB79uwJ3EdZ+iFwfP/993X+Um3btm3av3+/zj//fEk/LNLKycnRqFGjNHTo0OMuCl111VX66KOPtHXr1qDUd9555ykkJESvv/76cdu+/PJL5eXlBcbakO7du2vq1Knau3evnnnmmWAMFQAA2NCFF16o2bNn69lnn9XNN9+s1157Tf/5z3+0bt06XXvttdqwYYPuv//+475SvLawsDDNnTtX33//vf7whz+c8H0LCgq0devWOv8UFxc3+Nphw4YpMTFRJSUlTao1Ly9PO3furPOCkyRdeuml6tChg1544YUaj2dlZWnFihU1/vz5z3+u8Zz9+/fXW0/1gn4AAIBjjR07Vh988IE2b95c50KkrKwstWnTRqNHj67z9T//+c91+PBhvfbaaw2+T1pamjp16qSysrJGjWvPnj315prPPvusztdEREQoPj5eTz/9tM455xyddtppxz2noqJCr7zyikaMGKGOHTset/3UU09VXFyccnJyVFpa2qixAgAAb6kr15h1bUtq+PrWsdd+/vOf/+jVV1/VlClT9Je//EV33XWXunbt2qyaZsyYoTPPPFMPPvigtm/ffsLnjx8/Xl988YWWLl2qMWPG1Jmrrr/+ep1yyilKSkrSqlWr9O677+qf//ynFi9erEcffVRTpkxRt27dmjVeAObiVoAAgq6ioqLBBUu9e/dWTEyMFi1apDlz5mjPnj2aNGmSevTooTvuuEOPPPKI/H6/hg4dWuMrQAcPHqxRo0Zp7ty52rVrlwYPHqzQ0FDt2LFDy5Yt06BBgzR27FhJ0l//+ld9++239f6C7uc//7keeeQRvfDCCxowYECT6muottNPP10nnXSSunXrpuuvv14PP/ywvv76a1144YVq3769PvnkE/3pT39S9+7ddf311zfq/SZPnqy1a9fqmWee0dVXXx24NSIAAHC3X/3qVxo4cKAyMzO1ePFiHTp0SJ07d9awYcN0//33q1evXo3az9ChQ3XZZZfpjTfe0MaNGzVy5Mh6n3vrrbfWu23OnDmaPHlyg+81e/Zsvf322/ryyy8bNTbph19O+nw+jRkzps7t4eHhuuyyy5Sdna1PP/008Hhdt87x+Xy64YYbAj+vXbtWa9eurXO/ffr00csvv9zocQIAAG+44IILFBkZqVNPPVU9e/assa28vFx/+9vfNGzYMHXq1KnO148ePVrz58/XCy+8oPHjx9f7Pp06ddLvf//7BvPXsZYuXaqlS5fWue3iiy+u97aCY8eOVW5ubuCaWW1//etf9d///rfe7ZJ01VVXadOmTcrJydEvf/nLRo0XAAB4R325xqxrW1L917eOvfYTGhqqTp06qX///lq+fLmGDh3a7Jratm2rJUuWKDExUb/73e+UnZ3d4POjo6N13nnn6d1339X9999f53MiIyO1du1aPfHEE/rTn/6kr776Su3bt9cZZ5yhBQsW6Kqrrmr2eAGYK8QwDMPqQQBwr9mzZ+ull15q8Dnr1q3TWWedJUn6+OOPtWLFCr3zzjsqLi5Wp06dNHDgQMXGxmrZsmXq3r27FixYoL59+0qSvv/+ey1fvlyvvvqq9u7dq6qqKnXv3l1jx47VlClT1K5dO0nSlClTlJeXp3/9619q06ZNneNISkpSXl6e3nrrLZ100kmB8W/ZskVvvvnmcc9/7LHH9PjjjzdYW0ZGhi655JLAz6tXr1ZWVpY+++wzlZeXq2vXrrr44ov1m9/8JvCex+67vm9O+Pvf/66UlBRddtllevTRRxscAwAAAAAAAAAAAAAAaDoWVgFwjEOHDum5557ThAkT1KNHD6uHAwAAAAAAAAAAAAAAXIyFVQAAAAAAAAAAAAAAAABQS6jVAwAAAAAAAAAAAAAAAAAAu2FhFQAAAAAAAAAAAAAAAADUwsIqAAAAAAAAAAAAAAAAAKglzOoBWKmqqkqVlZUKDQ1VSEiI1cMBAAA2YRiGqqqqFBYWptBQ1qE3FtkKAADUhWzVdOQqAABQF3JV05GrAABAXZqSqzy9sKqyslL5+flWDwMAANhUbGys2rZta/UwHINsBQAAGkK2ajxyFQAAaAi5qvHIVQAAoCGNyVWeXlhVveqsb9++BFCT+P1+5efnKzY2Vj6fz+rhOB79NBf9NB89NRf9NF9ze1r9Ov7Pv6YhW9kLxxR7YT7shzmxF+bDfsycE7JV03ktV3npGOClWiXqdTMv1SpRr9s5qV5yVdN5KVc56e9yS1GrO3mpVslb9VKrOzm91qbkKk8vrKr+yk+fz+fIibYzemou+mku+mk+emou+mm+5vaUrwdvGrKVPTEf9sJ82A9zYi/Mh/2YOSdkq8bzaq7yUr1eqlWiXjfzUq0S9bqdk+olVzWeF3MVtboTtbqXl+qlVndyeq2NyVUsaQcAAAAAAAAAAAAAAACAWlhYBQAAAAAAAAAAAAAAAAC1sLAKAAAAAAAAAAAAAAAAAGphYRUAAAAAAAAAAAAAAAAA1MLCKgAAAAAAAAAAAAAAAACohYVVAAAAAAAAAAAAAAAAAFALC6sAAAAAAAAAAAAAAAAAoBYWVgEAAAAAAAAAAAAAAABALSysAgAAAAAAAAAAAAAAAIBaWFgFAAAAAAAAAAAAAAAAALWwsAoAAAAAAAAAAAAAAAAAamFhFQAAAAAAAAAAAAAAAADUwsIqAAAAAAAAAAAAAAAAAKiFhVUAAAAAAAAAAAAAAAAAUAsLqwAAAAAAAABYKjw83OohAAAAOJ6XMpWXagUAWCvM6gHYgc/ns3oIjuGvMuQLDbF6GAAAwMbIVmQmAABgDq/kKp/Pp759+1o9jGYj+wEAYH9eyFVOz1RN0dRayWsAgJZgYZWkWVnbVLD/iNXDsL1eUR31yKSBVg8DAADYnNezFZkJAACYxeu5ygnIfgAAOAO5yrvIawCAlmJhlaRdxUe0veiw1cMAAABwBbIVAACAOchVAAAA5iBXAQCA5gq1egAAAAAAAAAAAAAAAAAAYDcsrAIAAAAAAAAAAAAAAACAWlhYBQAAAAAAAAAAAAAAAAC1WLqwavny5UpISFBCQoLmzJmjiooK7dy5UxMnTtSYMWM0bdo0HT16VJJUWlqq3/72txo7dqwmTJig3bt3B/bzhz/8QWPHjtW4ceOUk5NjUTUAAADWIVcBAACYg1wFAABgHrIVAABwOssWVuXl5Sk7O1tr1qxRTk6OKisrtWrVKqWmpmrmzJnKzc1Vz549tXTpUknSI488on79+um1117TzJkzNXv2bEnSO++8o23btiknJ0eZmZm69957VVZWZlVZAAAArY5cBQAAYA5yFQAAgHnIVgAAwA3CrHrjyMhIpaWlKSIiQpLUp08fFRYWqqSkRHFxcZKkxMREJSUlacaMGdq4caMyMzMlSUOHDtW8efNUVFQkv9+viooKVVZWqry8XG3btrWqJM/w+/0n3NbQc9B49NNc9NN89NRc9NN8ze2p0+aAXGVPVv894phiL8yH/TAn9sJ82I+Zc+KkeSVXobka+/fca8c76nUvL9UqUa/bOaleJ4zxWGQr2InTPj/VnHSMaikv1Sp5q15qdSen19qUcVu2sCo6OlrR0dGSpIMHD2rlypWaNGlSja/1jIqK0oEDByRJBw4cUJcuXWps279/v4YPH641a9booosu0tGjRzVz5kyFh4e3ZimeU1hYeML/EyA/P7+VRuMN9NNc9NN89NRc9NN8bu8pucqeGpOZWoPb//47DfNhP8yJvTAf9uO1OSFXobmamv289tmiXvfyUq0S9bqd1+ptDWQr2IldrtU1l5eOUV6qVfJWvdTqTl6o1bKFVdX27dunlJQUJSYmavDgwdq4cWON7SEhIZIkwzCOe21oaKhWr14tn8+nt99+W99++61uuOEG9e/fXwMGDGiN4XtS7969693m9/uVn5+v2NhY+Xy+VhyVO9FPc9FP89FTc9FP8zW3p9Wvcxpylb00lJlaA8cUe2E+7Ic5sRfmw37MnBMnZityFZqqsdnPa8c76nUvL9UqUa/bOaleJ+YqiWwFe7D6Wl1zOekY1VJeqlXyVr3U6k5Or7UpucrShVUFBQVKTk5WcnKykpKSVFRUpOLi4sD24uJide3aVZLUpUsXFRcX69RTT62x7YknntAvf/lLtWnTRp07d9aIESP0n//8hzAVRI35UPh8Pkd+eOyKfpqLfpqPnpqLfprPCz0lV9mPXf7OeeHvv5MwH/bDnNgL82E/XpwTchWao6mfE699tqjXvbxUq0S9bue1elsL2Qp24fTPt5eOUV6qVfJWvdTqTl6oNdSqNz506JCmTJmitLQ0JSUlSZK6deum8PBwbdmyRZKUlZWl+Ph4SdKIESOUlZUlSdq8ebMiIiLUtWtX9enTR3/5y18kSUePHtWmTZvUr18/CyoCAACwBrkKAADAHOQqAAAA85CtAACAG1j2jVWZmZkqLS1VRkaGMjIyJP0QmNLT05WWlqaSkhL16NFD6enpkqTbb79dd911lxISEtS2bVs98MADkqTf/OY3mj9/vsaMGaOwsDBdffXVGjp0qFVlAQAAtDpyFQAAgDnIVQAAAOYhWwEAADewbGHV9OnTNX369Dq3rV69+rjHIiMj9dhjjx33eEREhBYvXmz6+AAAAJyCXAUAAGAOchUAAIB5yFYAAMANLLsVIAAAAAAAAAAAAAAAAADYFQurAAAAAAAAAAAAAAAAAKAWFlYBAAAAAAAAAAAAAAAAQC1hVg/ADs7o3EHlfqtHYX+9ojpaPQQAAOAAXs9WZCYAAGAWr+cqJyD7AQDgDOQq7yKvAQBaioVVkhZP6C+fz2f1MBzBX2XIFxpi9TAAAICNka3ITAAAwBzkKmcg+wEAYH/kKm8jrwEAWoJbAUry+1mi3liEDgAAcCJkKzITAAAwh1dyld/v144dOxxbL9kPAAD7c2rOaAqnZ6qmaGqt5DUAQEuwsAoAAAAAAACApcrKyqweAgAAgON5KVN5qVYAgLVYWAXThYeHWz0EAAAAiFwGAACcw0u5xUu1StQLAEBr8tJ5iFoBAK0lzOoB2AH3VDaPz+dT3759rR6GabjnMgAATUe2socT5TJyDgAA9ueVXOW260kN8VKtEvVK5G4AsAsv5CovnXep1TnIQgDcgIVVkmZlbVPB/iNWDwM20yuqox6ZNNDqYQAA4DhkK/sj5wAA4AzkKsDZyN0AYB/kKqD1kYUAuAULqyTtKj6i7UWHrR4GAACAK5CtAAAAzEGuAgAAMAe5CgAANFeo1QMAAAAAAAAAAAAAAAAAALthYRUAAAAAAAAAAAAAAAAA1MLCKgAAAAAAAAAAAAAAAACoxdKFVcuXL1dCQoISEhI0Z84cVVRUaOfOnZo4caLGjBmjadOm6ejRo5Kk0tJS/fa3v9XYsWM1YcIE7d69W5J09OhRpaamauzYsRo/frzefPNNCysCAACwBrkKAADAHOQqAAAA85CtAACA01m2sCovL0/Z2dlas2aNcnJyVFlZqVWrVik1NVUzZ85Ubm6uevbsqaVLl0qSHnnkEfXr10+vvfaaZs6cqdmzZ0uSnnrqKYWGhuqVV17Rs88+q8WLF+vAgQNWlQUAANDqyFUAAADmIFcBAACYh2wFAADcIMyqN46MjFRaWpoiIiIkSX369FFhYaFKSkoUFxcnSUpMTFRSUpJmzJihjRs3KjMzU5I0dOhQzZs3T0VFRSooKNC1116r0NBQ/fjHP1afPn30z3/+U7/4xS+sKg0u4/f7LX9vK8fgJvTTfPTUXPTTfM3tqdPmgFyF5nLa33Un4xhvP8yJvTAf9mPmnDhpXslVAILBScfBxvLauZt63c1J9TphjMciWwGQGn/sctLx2Axeqpda3cnptTZl3JYtrIqOjlZ0dLQk6eDBg1q5cqUmTZoU+FpPSYqKigqsOD9w4IC6dOlSY9v+/fvVt29f5ebmavjw4Tp48KDef/99nXXWWa1ZClyusLBQZWVllo4hPz/f0vd3G/ppPnpqLvppPrf3lFyF5rJDzvEatx+PnIg5sRfmw368NifkKgDB4Obc7bXzBPW6m9fqbQ1kKwBS07OQ147HXqqXWt3JC7VatrCq2r59+5SSkqLExEQNHjxYGzdurLE9JCREkmQYxnGvDQ0NVUpKihYuXKirr75ap59+uoYPH642bdq0ytjhDb1797bsvf1+v/Lz8xUbGyufz2fZONyCfpqPnpqLfpqvuT2tfp3TkKvQVFbmHK/hGG8/zIm9MB/2Y+acODFbkasAmMmNudtr527qdTcn1evEXCWRrQCva2wWctLx2Axeqpda3cnptTYlV1m6sKqgoEDJyclKTk5WUlKSioqKVFxcHNheXFysrl27SpK6dOmi4uJinXrqqTW2lZSU6I477tDJJ58sSfrNb36jn/70p61fDFzLDgcBn89ni3G4Bf00Hz01F/00nxd6Sq5Cc7j9c2FHXjgeOQ1zYi/Mh/14cU7IVQDM5ubjqNfOE9Trbl6rt7WQrQA09djqteOxl+qlVnfyQq2hVr3xoUOHNGXKFKWlpSkpKUmS1K1bN4WHh2vLli2SpKysLMXHx0uSRowYoaysLEnS5s2bFRERoa5du+qNN97Qww8/LEn66KOPtH37dg0dOrT1CwIAALAIuQoAAMAc5CoAAADzkK0AAIAbWPaNVZmZmSotLVVGRoYyMjIk/RCY0tPTlZaWppKSEvXo0UPp6emSpNtvv1133XWXEhIS1LZtWz3wwAOSpGuuuUapqalKSEhQWFiY/vjHP6pjx45WlQUAANDqyFUAAADmIFcBAACYh2wFAADcwLKFVdOnT9f06dPr3LZ69erjHouMjNRjjz123ONt27bVI488Yvr4AAAAnIJcBQAAYA5yFQAAgHnIVgAAwA0suxUgAAAAAAAAAAAAAAAAANgVC6sAAAAAAAAAAAAAAAAAoBbLbgVoJ2d07qByv9WjgN30iuL+3AAANAfZyv7IOQAAOAO5CnA2cjcA2Ae5Cmh9ZCEAbsHCKkmLJ/SXz+ezehiwIX+VIV9oiNXDAADAUchWzkDOAQDA/shVgPORuwHAHshVgDXIQgDcgFsBSvL7WaJuFr/frx07drimp5zoAQBoOrfkAKc7US4j5wAAYH9eyVVuu57UEC/VKlGvRO4GALvwwrnIS+ddanUOshAAN2BhFUxXVlZm9RAAAAAgchkAAHAOL+UWL9UqUS8AAK3JS+chagUAtBYWVgEAAAAAAAAAAAAAAABALSysgunCw8OtHgIAAABELgMAAM7hpdzipVol6gUAoDV56TxErQCA1hJm9QDswOfzWT0E1/D5fOrbt6/Vw3AMf5XBvYUBAK5DtrIHq3IZ+QYAAPN4JVd56XqSl2qVqFciHwOAXXghV3npvEutjUMOAQBzsLBK0qysbSrYf8TqYcBjekV11COTBlo9DAAATEe28i7yDQAA5iJXAc5GPgYA+yBXwWvIIQBgHhZWSdpVfETbiw5bPQwAAABXIFsBAACYg1wFAABgDnIVAABorlCrBwAAAAAAAAAAAAAAAAAAdsPCKgAAAAAAAAAAAAAAAACohYVVAAAAAAAAAAAAAAAAAFCLpQurli9froSEBCUkJGjOnDmqqKjQzp07NXHiRI0ZM0bTpk3T0aNHa7wmKytLs2fPDvz8/fffa+7cuRo7dqzGjRunbdu2tXYZAAAAliNXAQAAmIdsBQAAYA5yFQAAcDrLFlbl5eUpOztba9asUU5OjiorK7Vq1SqlpqZq5syZys3NVc+ePbV06VJJUnl5uZYsWaIFCxbU2M/KlStlGIZee+01PfLII7rzzjtVWVlpRUkAAACWIFcBAACYh2wFAABgDnIVAABwgzCr3jgyMlJpaWmKiIiQJPXp00eFhYUqKSlRXFycJCkxMVFJSUmaMWOGNm3aJMMwlJqaqry8vMB+Nm7cqFtuuUWS1KtXL3Xp0kUffPCBzjvvvNYvCmgGv99/wm0NPQeNRz/NR0/NRT/N19yeOm0OyFWwG6d9hloDx3j7YU7shfmwHzPnxGnzSrYCYDanHQcbw2vnbup1NyfV64QxHotcBVjPSccNJx2PzeCleqnVnZxea1PGbdnCqujoaEVHR0uSDh48qJUrV2rSpEnavXt34DlRUVE6cOCAJCk+Pl7x8fHKzs6usZ8DBw6oS5cuNV6zf//+oI8fMEthYaHKysoafE5+fn4rjcYb6Kf56Km56Kf53N5TchXspjH5xqvcfjxyIubEXpgP+/HinJCtAJjNzfnYa+cJ6nU3r9XbGshVgPWcmEO8djz2Ur3U6k5eqNWyhVXV9u3bp5SUFCUmJmrw4MHauHFjje0hISENvt4wjOMeCw217A6HQJP17t273m1+v1/5+fmKjY2Vz+drxVG5E/00Hz01F/00X3N7Wv06pyFXwS4ayjdexTHefpgTe2E+7MfMOSFb/Q/ZCvAmN+Zjr527qdfdnFQvuep/yFVA4zgphzjpeGwGL9VLre7k9FqbkqssXVhVUFCg5ORkJScnKykpSUVFRSouLg5sLy4uVteuXRvcR5cuXVRcXKyf/exngdccu2odsLvGHGR8Pp8jD0Z2RT/NR0/NRT/N54WekqtgJ27/vLWEF45HTsOc2AvzYT9enROyFQAzufk46rXzBPW6m9fqbS3kKsBaTjyuee147KV6qdWdvFCrZcu5Dx06pClTpigtLU1JSUmSpG7duik8PFxbtmyRJGVlZSk+Pr7B/YwYMUJZWVmSpE8//VR79uzROeecE9zBAwAA2Ai5CgAAwDxkKwAAAHOQqwAAgBtY9o1VmZmZKi0tVUZGhjIyMiT9EIzS09OVlpamkpIS9ejRQ+np6Q3u5/rrr9f8+fM1btw4hYSE6A9/+IPatm3bGiUAAADYArkKAADAPGQrAAAAc5CrAACAG1i2sGr69OmaPn16ndtWr15d7+vGjx+v8ePHB35u27at7r//ftPHBwAA4BTkKgAAAPOQrQAAAMxBrgIAAG5g2a0AAQAAAAAAAAAAAAAAAMCuWFgFAAAAAAAAAAAAAAAAALVYditAOzmjcweV+60eBbymV1RHq4cAAEBQkK28i3wDAIC5yFWAs5GPAcA+yFXwGnIIAJiHhVWSFk/oL5/PZ/Uw4EH+KkO+0BCrhwEAgKnIVt5GvgEAwDzkKsD5yMcAYA/kKngROQQAzMGtACX5/SxRN4vf79eOHTvoaSMRZgAAbkQOsAerchn5BgAA83glV3npepKXapWoVyIfA4BdeOFc5KXzLrU2DjkEAMzBwiqYrqyszOohAAAAQOQyAADgHF7KLV6qVaJeAABak5fOQ9QKAGgtLKwCAAAAAAAAAAAAAAAAgFpYWAXThYeHWz0EV6GfAACgucgRAADAKbyUW7xUq0S9AAC0Ji+dh6jVnbxUKwDnCLN6AHbg8/msHoJr+Hw+9e3b1+phuIad+umvMrgXMwCgUchW9mCnHGE1cgwAwKm8kqu8lFu8VKtEvXZENgbgVV7IVU44D5mFWt3JDbWStQB3YmGVpFlZ21Sw/4jVwwBsq1dURz0yaaDVwwAAOATZCnZCjgEAOBm5CoCZyMYAvIxcBSDYyFqAe7GwStKu4iPaXnTY6mEAAAC4AtkKAADAHOQqAAAAc5CrAABAc4VaPQAAAAAAAAAAAAAAAAAAsBsWVgEAAAAAAAAAAAAAAABALSysAgAAAAAAAAAAAAAAAIBaLF9YtXz5ciUkJCghIUFz5sxRRUWFdu7cqYkTJ2rMmDGaNm2ajh49WuM1WVlZmj17do3Hdu7cqXHjxrXm0AEAAGyFXAUAAGAeshUAAIA5yFUAAMDJLF1YlZeXp+zsbK1Zs0Y5OTmqrKzUqlWrlJqaqpkzZyo3N1c9e/bU0qVLJUnl5eVasmSJFixYUGM/2dnZuvnmm1VWVmZFGQAAAJYjVwEAAJiHbAUAAGAOchUAAHC6MCvfPDIyUmlpaYqIiJAk9enTR4WFhSopKVFcXJwkKTExUUlJSZoxY4Y2bdokwzCUmpqqvLw8SdK3336rv//973rooYc0a9Ysy2oBvMDv91s9hBapHr/T67ATemou+mm+5vbUiXNArgIaZvXnmmO8/TAn9sJ82I+Zc+LEeSVbAXAzM4/tTjzGNwf1upuT6nXCGGsjVwHwmsYeq510/mkpanUnp9falHFburAqOjpa0dHRkqSDBw9q5cqVmjRpknbv3h14TlRUlA4cOCBJio+PV3x8vLKzswPbO3XqpEcffVT79u1rzaEDnlRYWOiK/xskPz/f6iG4Dj01F/00nxd6Sq4CGmaXHOOF45HTMCf2wnzYj1fnhGwFwM3MzMZeO09Qr7t5rd7WQq4C4DVNzVpeOv9Qqzt5oVZLF1ZV27dvn1JSUpSYmKjBgwdr48aNNbaHhIRYNDIAx+rdu7fVQ2gRv9+v/Px8xcbGyufzWT0cV6Cn5qKf5mtuT6tf50TkKqBuVucYjvH2w5zYC/NhP2bOCdkKAOzFjGzstXM39bqbk+olVwGA/TU2aznp/NNS1OpOTq+1KbnK8oVVBQUFSk5OVnJyspKSklRUVKTi4uLA9uLiYnXt2tXCEQKo5sQDYl18Pp9rarELemou+mk+r/SUXAXUzy7HAK8cj5yEObEX5sN+vDwnZCsAbmXmcd1r5wnqdTev1duayFUAvKSp5xIvnX+o1Z28UGuolW9+6NAhTZkyRWlpaUpKSpIkdevWTeHh4dqyZYskKSsrS/Hx8VYOEwAAwPbIVQAAAOYhWwEAAJiDXAUAAJzO0m+syszMVGlpqTIyMpSRkSFJGjFihNLT05WWlqaSkhL16NFD6enpVg4TAADA9shVAAAA5iFbAQAAmINcBQAAnM7ShVXTp0/X9OnT69y2evXqel83fvx4jR8/vsZjPXr00Jtvvmnq+AAAAJyCXAUAAGAeshUAAIA5yFUAAMDpLL0VIAAAAAAAAAAAAAAAAADYEQurAAAAAAAAAAAAAAAAAKAWS28FaBdndO6gcr/VowDsq1dUR6uHAABwELIV7IQcAwBwMnIVADORjQF4GbkKQLCRtQD3YmGVpMUT+svn81k9DMDW/FWGfKEhVg8DAOAAZCvYDTkGAOBU5CoAZiMbA/AqchWA1kDWAtyJWwFK8vtZom4Wv9+vHTt20FOT2KmfhAAAQGPZ4bwFe+UIq5FjAABO5ZXzuJdyi5dqlajXjsjGALzKzsdmszjhPGQWanUnN9RK1gLciYVVMF1ZWZnVQ3AV+gkAAJqLHAEAAJzCS7nFS7VK1AsAQGvy0nmIWt3JS7UCcA4WVgEAAAAAAAAAAAAAAABALSysgunCw8OtHgIAAABELgMAAM7hpdzipVol6gUAoDV56TxEre7kpVoBOEeY1QOwA5/PZ/UQXMPn86lv375WD6NV+KsM7pMLAEAdyFb24KVcZiYyHgDATrySq7yUW7xUq0S9ZiGjAkDLeSFXeem8S63udGyt5B8AdsLCKkmzsrapYP8Rq4cBB+kV1VGPTBpo9TAAALAlshWciowHALAbchUAMioAmINcBTgH+QeA3bCwStKu4iPaXnTY6mEAAAC4AtkKAADAHOQqAAAAc5CrAABAc4VaPQAAAAAAAAAAAAAAAAAAsBsWVgEAAAAAAAAAAAAAAABALSysAgAAAAAAAAAAAAAAAIBaLF1YtXz5ciUkJCghIUFz5sxRRUWFdu7cqYkTJ2rMmDGaNm2ajh49WuM1WVlZmj17duDnb775RlOnTtWVV16pK664Qq+++mprlwEAAGA5chUAAIB5yFYAAADmIFcBAACns2xhVV5enrKzs7VmzRrl5OSosrJSq1atUmpqqmbOnKnc3Fz17NlTS5culSSVl5dryZIlWrBgQY39PProo+rbt6/Wr1+vP/3pT1q4cKG+/vprK0oCAACwBLkKAADAPGQrAAAAc5CrAACAG4RZ9caRkZFKS0tTRESEJKlPnz4qLCxUSUmJ4uLiJEmJiYlKSkrSjBkztGnTJhmGodTUVOXl5QX2c9FFF+nss8+WJHXu3FmdOnXS119/rVNOOaX1i4Ln+P3+Vtl/sN/HK+in+eipuein+ZrbU6fNAbkKMFcwjgEc4+2HObEX5sN+zJwTp80r2QqAHdntWOq1czf1upuT6nXCGI9FrgLQEk475jWVk84/LUWt7uT0WpsybssWVkVHRys6OlqSdPDgQa1cuVKTJk3S7t27A8+JiorSgQMHJEnx8fGKj49XdnZ2jf2MHDky8N+vvvqqKioq1KtXr6CPH5CkwsJClZWVBf198vPzg/4eXkI/zUdPzUU/zef2npKrAHMFM+O5/XjkRMyJvTAf9uPFOSFbAbCj1roO2VReO09Qr7t5rd7WQK4C0BJ2zT9m89L5h1rdyQu1Wrawqtq+ffuUkpKixMREDR48WBs3bqyxPSQkpFH7efnll7VkyRItW7ZMYWGWlwWP6N27d1D37/f7lZ+fr9jYWPl8vqC+lxfQT/PRU3PRT/M1t6fVr3MachVgjmBkPI7x9sOc2AvzYT9mzgnZimwFoOWCfR2yqbx27qZed3NSveQqchXgJXbLP2Zz0vmnpajVnZxea1NylaWpo6CgQMnJyUpOTlZSUpKKiopUXFwc2F5cXKyuXbuecD9PP/20XnjhBWVmZqpnz57BHDJQQ2sdIHw+nyMPRnZFP81HT81FP83nhZ6SqwDzBPN44YXjkdMwJ/bCfNiPV+eEbAXAbux6LPbaeYJ63c1r9bYWchWA5vLKMdlL5x9qdScv1Bpq1RsfOnRIU6ZMUVpampKSkiRJ3bp1U3h4uLZs2SJJysrKUnx8fIP7yc7O1ksvvaTVq1cTpAAAgCeRqwAAAMxDtgIAADAHuQoAALiBZd9YlZmZqdLSUmVkZCgjI0OSNGLECKWnpystLU0lJSXq0aOH0tPTG9zPH//4R4WEhGjKlCmBx+699171798/qOMHAACwC3IVAACAechWAAAA5iBXAQAAN7BsYdX06dM1ffr0OretXr263teNHz9e48ePD/z8z3/+0/SxAQAAOAm5CgAAwDxkKwAAAHOQqwAAgBtYditAAAAAAAAAAAAAAAAAALArFlYBAAAAAAAAAAAAAAAAQC2W3QrQTs7o3EHlfqtHASfpFdXR6iEAAGBbZCs4FRkPAGA35CoAZFQAMAe5CnAO8g8Au2FhlaTFE/rL5/NZPQw4jL/KkC80xOphAABgO2QrOBkZDwBgJ+QqABIZFQDMQK4CnIX8A8BOuBWgJL+fJepm8fv92rFjhyd6yskcAIC6eSEHOIGXcpmZyHgAADvxynncS7nFS7VK1GsWMioAtJwXzkVeOu9SqzsdWyv5B4CdsLAKpisrK7N6CAAAABC5DAAAOIeXcouXapWoFwCA1uSl8xC1upOXagXgHCysgunCw8OtHgIAAABELgMAAM7hpdzipVoBAEDr8lLOoFZ38lKtAJwjzOoB2AH3VDaPz+dT3759rR6GrXFPYACA25Gt7IFcVhMZDADgRF7JVV7KLXarlYwEAPAKL+Qqu+WMYKJWd7JrrWRmACyskjQra5sK9h+xehjwgF5RHfXIpIFWDwMAgKAiW8FuyGAAAKciVyGYyEgAAC8hVwFoDjIzAImFVZKkXcVHtL3osNXDAAAAcAWyFQAAgDnIVQAAAOYgVwEAgOYKtXoAAAAAAAAAAAAAAAAAAGA3LKwCAAAAAAAAAAAAAAAAgFpYWAUAAAAAAAAAAAAAAAAAtbCwCgAAAAAAAAAAAAAAAABqsXRh1fLly5WQkKCEhATNmTNHFRUV2rlzpyZOnKgxY8Zo2rRpOnr0aI3XZGVlafbs2YGfi4qKdMMNN+jKK69UYmKiCgoKWrsMAAAAy5GrAAAAzEO2AgAAMAe5CgAAOJ1lC6vy8vKUnZ2tNWvWKCcnR5WVlVq1apVSU1M1c+ZM5ebmqmfPnlq6dKkkqby8XEuWLNGCBQtq7GfRokW64oortH79et12222aP3++FeUAAABYhlwFAABgHrIVAACAOchVAADADcKseuPIyEilpaUpIiJCktSnTx8VFhaqpKREcXFxkqTExEQlJSVpxowZ2rRpkwzDUGpqqvLy8gL7efjhhwP/vW/fPkVGRrZqHUBz+P3+Jj2vsc9Hw+in+eipuein+ZrbU6fNAbkKaBwrP9sc4+2HObEX5sN+zJwTp80r2QpeE8zPqNeO716q10u1StTrdk6q1wljPBa5CoBbBOP466TzT0tRqzs5vdamjNuyhVXR0dGKjo6WJB08eFArV67UpEmTtHv37sBzoqKidODAAUlSfHy84uPjlZ2dXWM/oaE/fOnW6NGjVVRUFFjVDthZYWGhysrKGv38/Pz8II7Ge+in+eipuein+dzeU3IV0DhNzWDB4PbjkRMxJ/bCfNiPF+eEbAWvaY2M5LVjiZfq9VKtEvW6ndfqbQ3kKgBuEczM7KXzD7W6kxdqtWxhVbV9+/YpJSVFiYmJGjx4sDZu3Fhje0hISKP285e//EXbt2/XzTffrNzcXHXq1CkIowXM0bt370Y9z+/3Kz8/X7GxsfL5fEEelfvRT/PRU3PRT/M1t6fVr3MachXQsMZmsGDgGG8/zIm9MB/2Y+ackK3IVrC3YGYkrx3fvVSvl2qVqNftnFQvuYpcBcAawcjMTjr/tBS1upPTa21KrrJ0YVVBQYGSk5OVnJyspKQkFRUVqbi4OLC9uLhYXbt2bXAff/3rXzV8+HC1b99e/fr1U/fu3bV3717CFGytqQcWn8/nyIORXdFP89FTc9FP83mhp+Qq4MTscBzwwvHIaZgTe2E+7Merc0K2gpe0xmfca8cSL9XrpVol6nU7r9XbWshVANwgmOcHL51/qNWdvFBrqFVvfOjQIU2ZMkVpaWlKSkqSJHXr1k3h4eHasmWLJCkrK0vx8fEN7ufFF19UVlaWJGnnzp06ePCgevbsGdzBAwAA2Ai5CgAAwDxkKwAAAHOQqwAAgBtY9o1VmZmZKi0tVUZGhjIyMiRJI0aMUHp6utLS0lRSUqIePXooPT29wf3cc889mjt3rtasWaN27drpoYceUkRERGuUAAAAYAvkKgAAAPOQrQAAAMxBrgIAAG5g2cKq6dOna/r06XVuW716db2vGz9+vMaPHx/4uVu3blqxYoXZwwMAAHAMchUAAIB5yFYAAADmIFcBAAA3sOxWgAAAAAAAAAAAAAAAAABgVyysAgAAAAAAAAAAAAAAAIBaLLsVoJ2c0bmDyv1WjwJe0Cuqo9VDAAAg6MhWsBsyGADAqchVCCYyEgDAS8hVAJqDzAxAYmGVJGnxhP7y+XxWDwMe4a8y5AsNsXoYAAAEDdkKdkQGAwA4EbkKwUZGAgB4BbkKQHORmQFwK0BJfj9L1M3i9/u1Y8cOetoATrwAALcjB9gDuawmMhgAwIm8ch73Um6xW61kJACAV9jl3BtMdssZwUSt7mTXWsnMAFhYBdOVlZVZPQQAAACIXAYAAJzDS7nFS7UCAIDW5aWcQa3u5KVaATgHC6sAAAAAAAAAAAAAAAAAoBYWVgEAAAAAAAAAAAAAAABALSysAgAAAAAAAAAAAAAAAIBaWFgFAAAAAAAAAAAAAAAAALWwsAoAAAAAAAAAAAAAAAAAamFhFQAAAAAAAAAAAAAAAADUwsIqAAAAAAAAAAAAAAAAAKjF0oVVy5cvV0JCghISEjRnzhxVVFRo586dmjhxosaMGaNp06bp6NGjNV6TlZWl2bNnH7ev0tJSXXLJJdq8eXNrDR8AAMA2yFUAAADmIVsBAACYg1wFAACczrKFVXl5ecrOztaaNWuUk5OjyspKrVq1SqmpqZo5c6Zyc3PVs2dPLV26VJJUXl6uJUuWaMGCBXXu77777tPhw4dbswQAAABbIFcBAACYh2wFAABgDnIVAABwgzCr3jgyMlJpaWmKiIiQJPXp00eFhYUqKSlRXFycJCkxMVFJSUmaMWOGNm3aJMMwlJqaqry8vBr7eu2119ShQwf17t27WWOpqqqS3+9vWUGQpEAf6ac56Ke56Kf56Km56Kf5mttTp82BnXKVRLayC44p9sJ82A9zYi/Mh/2YOSdOm1c7ZSuv5CovHQO8VKtEvW7mpVol6nU7J9XrhDEei1zVupz0d7mlqNWdvFSr5K16qdWdnF5rU8Zt2cKq6OhoRUdHS5IOHjyolStXatKkSdq9e3fgOVFRUTpw4IAkKT4+XvHx8crOzq6xn6KiImVmZiozM1O//vWvmzWWTz/9VN99912zXou65efnWz0EV6Gf5qKf5qOn5qKf5nN7T+2UqySyld24/e+/0zAf9sOc2AvzYT9enBM7ZSuv5Sov/X3zUq0S9bqZl2qVqNftvFZvayBXWcNLf5ep1Z28VKvkrXqp1Z28UKtlC6uq7du3TykpKUpMTNTgwYO1cePGGttDQkLqfW1VVZXuuusupaWlqX379s0eQ8+ePdWmTZtmvx7/4/f7lZ+fr9jYWPl8PquH43j001z003z01Fz003zN7Wn165zGDrlKIlvZBccUe2E+7Ic5sRfmw37MnBOyFdesTsRLxwAv1SpRr5t5qVaJet3OSfWSq8hVDXHS3+WWolZ38lKtkrfqpVZ3cnqtTclVli6sKigoUHJyspKTk5WUlKSioiIVFxcHthcXF6tr1671vn7Xrl3atWuX7rrrLknSnj17NG/ePM2fP18XXHBBo8cRGhrqyIm2M5/PR09NRD/NRT/NR0/NRT/N54We2iVXSWQru/HC338nYT7shzmxF+bDfrw6J3bJVl7LVV76++alWiXqdTMv1SpRr9t5rd7WQq5qfV76u0yt7uSlWiVv1Uut7uSFWi1bWHXo0CFNmTJF99xzj0aPHi1J6tatm8LDw7VlyxbFxcUpKytL8fHx9e6jV69e+sc//hH4OSkpSbfeequGDBkS9PEDAADYBbkKAADAPGQrAAAAc5CrAACAG1i2sCozM1OlpaXKyMhQRkaGJGnEiBFKT09XWlqaSkpK1KNHD6Wnp1s1RAAAAEcgVwEAAJiHbAUAAGAOchUAAHADyxZWTZ8+XdOnT69z2+rVq+t93fjx4zV+/Pg6tz333HOmjA0AAMBJyFUAAADmIVsBAACYg1wFAADcINTqAQAAAAAAAAAAAAAAAACA3bCwCgAAAAAAAAAAAAAAAABqYWEVAAAAAAAAAAAAAAAAANTCwioAAAAAAAAAAAAAAAAAqIWFVQAAAAAAAAAAAAAAAABQCwurAAAAAAAAAAAAAAAAAKAWFlYBAAAAAAAAAAAAAAAAQC0srILpwsPDrR6Cq9BPAADQXOQIe2E+AACon5fOk16qVaJeAABak5fOQ9TqTl6qVfJWvV6qFe4TZvUA7MDn81k9BNfw+Xzq27ev1cNwDbv3019lyBcaYvUwAAA2Q7ayB7vnCK+xej7IbQDgTF7JVVafJ1uTl2qVqNdOyIMAvM4LucrO5yGzUas7ealWyVv1BrNWci5aAwurJM3K2qaC/UesHgbgKL2iOuqRSQOtHgYAwIbIVoC9kNsAwLnIVQDMQB4EAHIVALgRORethYVVknYVH9H2osNWDwMAAMAVyFYAAADmIFcBAACYg1wFAACaK9TqAQAAAAAAAAAAAAAAAACA3bCwCgAAAAAAAAAAAAAAAABqYWEVAAAAAAAAAAAAAAAAANQSZuWbL1++XFlZWZKk2NhYzZ8/X7t379a8efN0+PBhxcTEaNGiRYqIiAi8JisrS++++64WLVokSSorK9MFF1ygn/70p4HnZGdny+fztW4xAAAAFiJXAQAAmIdsBQAAYA5yFQAAcDrLvrEqLy9P2dnZWrNmjXJyclRZWalVq1YpNTVVM2fOVG5urnr27KmlS5dKksrLy7VkyRItWLCgxn62b9+u888/Xy+//HLgD0EKAAB4CbkKAADAPGQrAAAAc5CrAACAG1j2jVWRkZFKS0sLrEDv06ePCgsLVVJSori4OElSYmKikpKSNGPGDG3atEmGYSg1NVV5eXmB/eTn5+vAgQNKTEyUz+fTzJkzNXjwYEtqArzI7/dbPYRGqx6rk8Zsd/TUXPTTfM3tqdPmgFwFoDGcdmwLNs679sJ82I+Zc+K0eSVbAXArM4/HXjt3U6+7OaleJ4zxWOQqAEBrsNP50Um5oqWcXmtTxt3khVV79uyp8VWbzRUdHa3o6GhJ0sGDB7Vy5UpNmjRJu3fvDjwnKipKBw4ckCTFx8crPj5e2dnZNfYTEhKiMWPG6Ne//rV27NihlJQU5eTk6Mc//nGLxwjgxAoLC1VWVmb1MJokPz/f6iG4Dj01F/00n117Sq4C0JqcmNtag13PEV7FfNiPk+aEbAUADQtGHnTSecIM1OtuXqu3IeQqAICT2PG6p5dyhRdqbfLCql/+8pd66qmndPbZZ5sygH379iklJUWJiYkaPHiwNm7cWGN7SEhIg6+fPHly4L/79eun2NhYvf/++7r44otNGR+AhvXu3dvqITSa3+9Xfn6+YmNj+Zpgk9BTc9FP8zW3p9WvCzZyFYDW5KTc1ho479oL82E/Zs4J2YpsBcAezMyDXjt3U6+7OalechW5CgBwPDtd93RSrmgpp9falFzV5IVVp5xyig4ePNjkQdWloKBAycnJSk5OVlJSkoqKilRcXBzYXlxcrK5duza4jxdffFHDhw/XqaeeKkkyDENhYZbd4RDwHCceJH0+nyPHbWf01Fz003x27Sm5CkBrsuNx0A7seo7wKubDfpw0J2QrAGhYMI7nTjpPmIF63c1r9TaEXAUAcBI7nr+9lCu8UGuTU0ffvn01depUxcbGqnv37mrbtm2N7QsXLmzUfg4dOqQpU6bonnvu0ejRoyVJ3bp1U3h4uLZs2aK4uDhlZWUpPj6+wf3k5+dr165dmjVrlj755BPt2LFDgwYNampZAAAArY5cBQAAYB6yFQAAgDnIVQAAAP/TrOXcV155ZYvfODMzU6WlpcrIyFBGRoYkacSIEUpPT1daWppKSkrUo0cPpaenN7if6dOna86cORo3bpxCQ0P1wAMPqGPHji0eHwAAQGsgVwEAAJiHbAUAAGAOchUAAMAPQgzDMKwehFX8fr+2bt2q+zcd1ft7D1s9HMBR+nWL1KvTLrR6GE1S/ZkfMGCA67+OsLXQU3PRT/M1t6fMRfOQrQB7cmJuaw0c6+2F+bAfM+eE+W06chUAMwUjD3rt2E697uakep00VrsgVwGAe9nxuqeXztVOr7Up42/yN1YZhqG//e1v+vjjj+X3+wOPV1RUaMeOHVq2bFnTRwwAAOBB5CoAAADzkK0AAADMQa4CAAD4nyYvrLrvvvu0du1a9e3bV3l5eRo4cKD27Nmjr7/+Wr/85S+DMUYAAABXIlcBAACYh2wFAABgDnIVAADA/zR5YdVrr72mBx98UKNHj9aYMWP0+9//Xqeffrpmz56t77//PhhjDLozOndQuf/EzwPwP72iuH85ALSUG3OVRLYC7IbcBsAr3JityFUAzEAeBNBU5CoAgBOQc9FamrywqrS0VGeffbYkKSYmRnl5eTrzzDOVkpKim2++2fQBtobFE/o78p6PgNX8VYZ8oSFWDwMAHMuNuUoiWwF2RG4D4AVuzFbkKgBmIQ8CaApyFQDAKci5aA2hTX3Baaedph07dkiSzjzzTOXl5Un64X7LJSUl5o6ulRx7f2i0jN/v144dO+ipSezeT05SANAybsxVEtnKLuyeI7zG6vkgtwHwAjdmK6+cx60+T7YmL9UqUa+dkAcBNAW5ypnsfB4yG7W6k5dqlbxVbzBrJeeiNTT5G6tuuukmpaam6v7779fYsWM1fvx4hYWF6YMPPtCgQYOCMUY4TFlZmdVDcBX6CQDuRa5CsJEj7IX5AIDgIls5m5fOk16qVaJeAHAicpVzeek8RK3u5KVaJW/V66Va4T5NXliVmJio6OhoRUREqGfPnnr88cf14osv6uyzz9Ztt90WjDECAAC4ErkKAADAPGQrAAAAc5CrAAAA/qfJC6sk6bzzzpMk/fe//9WwYcM0fPhwhYTwFWv4QXh4uNVDcBX6CQDuRq5CMJEj7IX5AIDgI1s5l5fOk16qVaJeAHAqcpUzeek8RK3u5KVaJW/V66Va4T6hTX2BYRhaunSphgwZoqFDh+qLL75Qamqq7r77blVUVARjjEHn8/msHoJr+Hw+9e3bl56axMn99FcZVg8BAGzPjblKIlvZhZNzhBsxH+YiawKoixuzlVfOG146T3qpVol6zUL2AdDayFXO5KXzLrW6k5dqlbxVr9tqJZ97T5O/sSojI0OvvvqqFi1apOnTp0uSrr76at1999164IEHNG/ePNMHGWyzsrapYP8Rq4cBuEavqI56ZNJAq4cBALbnxlwlka0ABBdZE0B93JityFUAyD4ArECuAgCgbuRzb2rywqqXXnpJixYt0nnnnRf4ys9hw4Zp8eLFuv322x0ZpnYVH9H2osNWDwMAAHiMG3OVRLYCAADWcGO2IlcBAAArkKsAAAD+p8m3Ajx48KCioqKOezwyMlJHjx41ZVAAAABeQK4CAAAwD9kKAADAHOQqAACA/2nywqrzzz9fzz77bI3HSktL9dBDD2nIkCGmDQwAAMDtyFUAAADmIVsBAACYg1wFAADwP41aWHXttdfq008/lST9/ve/144dOzRs2DCVl5dr6tSpio+P1xdffOHIr/4EAABoTeQqAAAA85CtAAAAzEGuAgAAqFtYY57Url07XXXVVbrpppt0yy23aO3atXrnnXe0a9cuVVZW6vTTT9fw4cMVGtq0L8Bavny5srKyJEmxsbGaP3++du/erXnz5unw4cOKiYnRokWLFBEREXhNVlaW3n33XS1atEiSVFFRoQceeEDvvvuuKisrNWfOHA0fPrxJ4wAAAGgt5CoAAADzkK0AAADMQa4CAACoW6PSz/Lly5Wenq6cnBwlJCTo3//+t4YOHarrrrtON954oy666KImB6m8vDxlZ2drzZo1ysnJUWVlpVatWqXU1FTNnDlTubm56tmzp5YuXSpJKi8v15IlS7RgwYIa+1m2bJm++eYbrVu3Tg8//LBmz56tqqqqJo0FAACgtZCrAAAAzEO2AgAAMAe5CgAAoG6N+sYqSRo9erTi4+P1zDPP6NZbb9WoUaN0yy23qF27djWe161bt0btLzIyUmlpaYEV6H369FFhYaFKSkoUFxcnSUpMTFRSUpJmzJihTZs2yTAMpaamKi8vL7Cf119/XUuWLFFISIjOPPNMrVixQoZhNLYsAEHk9/utHkIN1eOx27icjJ6ai36ar7k9DfYckKsAoOVaeqzmvGsvzIf9mDknZCuyFYCWs9s50mvnbup1NyfVS64iVwEA7KG+c7KTckVLOb3Wpoy70QurpB++BvTWW29Vnz59dMcdd+jVV18NbDMMQyEhISooKGjUvqKjoxUdHS1JOnjwoFauXKlJkyZp9+7dgedERUXpwIEDkqT4+HjFx8crOzu7xn4+//xzvffee7rrrrvk8/n0u9/9Tr169WpKWQCCpLCwUGVlZVYP4zj5+flWD8F16Km56Kf57NhTchUAtIxZWdOO5wgvYz7sxylzQrYC4HZcZ7MH6nU3r9VbH3IVAAAndqJ87qVc4YVam7Sw6osvvtADDzygDRs2KCEhQSkpKWrfvn2LBrBv3z6lpKQoMTFRgwcP1saNG2tsDwkJafD1fr9f+/bt09q1a1VYWKgpU6bo9ddf149+9KMWjQtAy/Xu3dvqIdTg9/uVn5+v2NhY+Xw+q4fjCvTUXPTTfM3tafXrgolcBQAt09KsyXnXXpgP+zFzTshWZCsALcd1NmtRr7s5qV5yFbkKAGAP9eVzJ+WKlnJ6rU3JVY1aWFVeXq4nn3xSy5cv12mnnaY///nPGjx4cIsGKUkFBQVKTk5WcnKykpKSVFRUpOLi4sD24uJide3atcF9nHLKKbr88ssVEhKiPn36qGvXrvrss890zjnntHh8AFrGrgdQn89n27E5FT01F/00n516Sq4CAHOYdVy30zkCzIcd2X1OyFYAvMKux2K7nyfMRr3u5rV6ayNXAQDQeCfKDF7KFV6otVELqy677DKVlpbqjjvuUFJSkilNOXTokKZMmaJ77rlHo0ePlvTDPZnDw8O1ZcsWxcXFKSsrS/Hx8Q3uZ+TIkXr99dd1zjnnaN++ffryyy91+umnt3h8AAAAwUCuAgAAMA/ZCgAAwBzkKgAAgLo1amHVoEGDNGvWLEVFRZn2xpmZmSotLVVGRoYyMjIkSSNGjFB6errS0tJUUlKiHj16KD09vcH9zJw5U/fee6/GjRsnSbr33nv56k8AAGBb5CoAAADzkK0AAADMQa4CAACoW4hhGIbVg7CK3+/X1q1bdf+mo3p/72GrhwO4Rr9ukXp12oVWD+M41Z/5AQMGuP7rCFsLPTUX/TRfc3vKXDQP2QpAazAra3Kstxfmw37MnBPmt+nIVQCqcZ3NHqjX3ZxUr5PGahfkKgCAmU6Uz710rnZ6rU0Zf2grjQkAAAAAAAAAAAAAAAAAHIOFVQAAAAAAAAAAAAAAAABQS5jVA7CDMzp3ULnf6lEA7tErqqPVQwAAWIhsBSCYyJoAvIRcBYDsAwDmIFcBAMxAPvcmFlZJWjyhvyPv+QjYmb/KkC80xOphAAAsQLYCEGxkTQBeQa4CIJF9AMAM5CoAgFnI597DrQAl+f0sUTeL3+/Xjh076KlJnNxPTiYA4F1OPG+5kZNzhBsxH+YiawLwCq+cN7x0nvRSrRL1moXsAwAt54VzkZfOu9TqTl6qVfJWvW6rlXzuPSysgunKysqsHoKr0E8AANBc5Ah7YT4AAKifl86TXqpVol4AAFqTl85D1OpOXqpV8la9XqoV7sPCKgAAAAAAAAAAAAAAAACohYVVMF14eLjVQ3AV+gkAAJqLHGEvzIf9MCcAYB9eOiZ7qVaJegEAaE1eOg9Rqzt5qVbJW/VSK5wszOoB2IHP57N6CK7h8/nUt29fq4fhGl7vp7/K4B61AOBAZCt78HqOsBvmw36aOidkUwBW8Equ8tJ50ku1StTrZGQfAG7jhVzlpvPQiVCrO3mpVslb9VJrw8je9sfCKkmzsrapYP8Rq4cB4Bi9ojrqkUkDrR4GAKAZyFYA3IZsCsAq5CoAViD7AHAjchUAwI7I3s7AwipJu4qPaHvRYauHAQAA4ApkKwAAAHOQqwAAAMxBrgIAAM0VavUAAAAAAAAAAAAAAAAAAMBuWFgFAAAAAAAAAAAAAAAAALWwsAoAAAAAAAAAAAAAAAAAagmz8s2XL1+urKwsSVJsbKzmz5+v3bt3a968eTp8+LBiYmK0aNEiRUREBF6TlZWld999V4sWLZIk/frXv9ZXX30lSaqqqtLOnTu1YsUKDR06tPULAgAAsAi5CgAAwDxkKwAAAHOQqwAAgNNZ9o1VeXl5ys7O1po1a5STk6PKykqtWrVKqampmjlzpnJzc9WzZ08tXbpUklReXq4lS5ZowYIFNfbzzDPP6OWXX9bLL7+scePGKSEhgSAFAAA8hVwFAABgHrIVAACAOchVAADADSz7xqrIyEilpaUFVqD36dNHhYWFKikpUVxcnCQpMTFRSUlJmjFjhjZt2iTDMJSamqq8vLzj9rd3716tWrVKL7/8cqvWASC4/H6/6fsyc59eR0/NRT/N19yeOm0OyFUA0Dqcdn5wEnKQ/Zg5J06bV7IVAPygoeO3187d1OtuTqrXCWM8FrkKAIDGcdo5XnJWhqpLU8Zt2cKq6OhoRUdHS5IOHjyolStXatKkSdq9e3fgOVFRUTpw4IAkKT4+XvHx8crOzq5zf0uXLtXkyZP14x//ONhDB9CKCgsLVVZWZuo+8/PzTd0f6KnZ6Kf53N5TchUAtI5gZFPU5PZzthN5cU7IVgDwg8ZkH6+dJ6jX3bxWb2sgVwEA0DhOvu7ohQxl2cKqavv27VNKSooSExM1ePBgbdy4scb2kJCQE+6jtLRUf/vb33TXXXcFa5gALNK7d2/T9uX3+5Wfn6/Y2Fj5fD7T9utl9NRc9NN8ze1p9euchlwFAMFlZjZFTeQg+zFzTshWZCsAztRQ9vHauZt63c1J9ZKryFUAAHdy4nVHJ2WoujQlV1m6sKqgoEDJyclKTk5WUlKSioqKVFxcHNheXFysrl27nnA/b731loYPH64OHToEc7gALBCMg7DP53Pkwd3O6Km56Kf5vNBTchUABJ/bzyV24IVzttN4dU7IVgDQuOzjtfME9bqb1+ptLeQqAABOzMkZxAsZKtSqNz506JCmTJmitLQ0JSUlSZK6deum8PBwbdmyRZKUlZWl+Pj4E+7r/fffD9yLGQAAwGvIVQAAAOYhWwEAAJiDXAUAANzAsm+syszMVGlpqTIyMpSRkSFJGjFihNLT05WWlqaSkhL16NFD6enpJ9zXnj17dNFFFwV7yAAAALZErgIAADAP2QoAAMAc5CoAAOAGli2smj59uqZPn17nttWrV9f7uvHjx2v8+PE1Hnv66adNHRsAAICTkKsAAADMQ7YCAAAwB7kKAAC4gWW3AgQAAAAAAAAAAAAAAAAAu2JhFQAAAAAAAAAAAAAAAADUYtmtAO3kjM4dVO63ehQAjtUrqqPVQwAANBPZCoDbkE0BWIVcBcAKZB8AbkSuAgDYEdnbGVhYJWnxhP7y+XxWDwNALf4qQ77QEKuHAQBoIrIVADcimwKwArkKgFXIPgDchlwFALArsrf9cStASX4/S9TN4vf7tWPHDnpqEq/3kxMIADiTV89bduP1HGE3zIf9NHVOyKYArOCV84aXzpNeqlWiXicj+wBwGzccm0/ETeehE6FWd/JSrZK36qXWhpG97Y+FVTBdWVmZ1UNwFfoJAACaixxhL8yH/TAnAGAfXjome6lWiXoBAGhNXjoPUas7ealWyVv1UiucjIVVMF14eLjVQ3AV+mku+gkA8BLOe/bCfNgPcwIA9uGlY7KXapWo1828VKsktWnTxuohAMAJeenYTK3u5KVaJW/VS61wsjCrB2AH3FPZPD6fT3379rV6GK5BP81lZT+5Ny4ALyFb2QM5wl6YD/ux85yQHQFU80qusvMx2WxeqlWiXjfzUq3SD/We1bef1cMA0AJeyFVeOjZTqzt5qVbJW/VSqzNwTbJ+LKySNCtrmwr2H7F6GABcqldURz0yaaDVwwCAVkO2AoDmIzsCOBa5CgDsoTqj+f1+q4cCoJnIVQAA1I9rkg1jYZWkXcVHtL3osNXDAAAAcAWyFQAAgDnIVQAAAOYgVwEAgOYKtXoAAAAAAAAAAAAAAAAAAGA3LKwCAAAAAAAAAAAAAAAAgFpYWAUAAAAAAAAAAAAAAPD/2Lv38CjK+/3jd7IBDSJiKwEBNQgSBGNAEKWCQUSlGKlIg3yViK00eEQRIocSW8UzRoua4hlpBSWaiEZbrFZsRQU8QhQERKkgEgNoCSQQspnfH/6yJUs4ZJjsM4f367q4NDuzs8/nM9mZO5MnOwAQhYlVAAAAAAAAAAAAAAAAABDF6MSqWbNmKSMjQxkZGZo8ebKqqqq0evVqDR8+XIMGDdLYsWNVUVFR5zmFhYWaNGlS5OuqqiqNHz9eGRkZGjJkiN57771YlwEAAGAcuQoAAMA5ZCsAAABnkKsAAIDXGZtYtXz5chUVFamgoEDFxcWqrq7W3LlzlZOTowkTJmjBggXq2LGjZs6cKUnatWuXpk+frjvuuKPOdl5++WVVV1fr1Vdf1fTp0+sELQAAgCAgVwEAADiHbAUAAOAMchUAAPCDBFMv3KJFC+Xm5qpZs2aSpC5dumjVqlUqLy9X7969JUmZmZnKysrS+PHjtXjxYlmWpZycHC1fvjyynZqaGu3atUvhcFi7du3S4YcfbqQeADiQcDhsegiOq63Jj7WZQD+dZ7enXtsH5CoA8B+vnYsOFTnIfZzcJ17br2QrAMC+1NTUmB5CTAQtm3mpXi+McU/kKgAAvKUhWcNLGao+DRm3sYlVycnJSk5OliRt2bJFc+bM0YgRI7Ru3brIOklJSSotLZUkpaenKz09XUVFRXW2M3ToUL300kvq16+ftm3bpgceeCBWJQBAg6xatUqVlZWmh9EoSkpKTA/BV+in8/zeU3IVAPiPn7Pj/vj9nO1FQdwnZCsAwL6sWbMmUBktaDkgaPXGArkKAABvsXNNMggZytjEqlobNmzQmDFjlJmZqV69emnhwoV1lsfFxe33+Y888oi6d++u5557TuvWrdOVV16pbt26qV27do05bABosJSUFNNDcFw4HFZJSYlSU1MVCoVMD8fz6Kfz7Pa09nleQ64CAP/wY3bcH3KQ+zi5T8hWZCsA8IuTTjpJ8fHxpofR6IKWzbxUL7mKXAUAQGNqyDVJL2Wo+jQkVxmdWLVy5UplZ2crOztbWVlZ2rhxo8rKyiLLy8rK1KZNm/1u45///KcefPBBxcXFqUOHDkpLS9Py5csJUwBcx4snlIMVCoV8XV+s0U/nBaGn5CoA8Be/n7f2JQjnbK8J6j4hWwEA6hMfHx+o82LQckDQ6o0VchUAAN5hJwsFIUMZ+9OKrVu3avTo0crNzVVWVpYkqW3btkpMTNTSpUslSYWFhUpPT9/vdrp06aLXX389ss3PPvtMXbp0adzBAwAAuAi5CgAAwDlkKwAAAGeQqwAAgB8Y+8Sq2bNna/v27crPz1d+fr4kqX///srLy1Nubq7Ky8vVvn175eXl7Xc7kydPVm5urgYPHqxQKKQbb7xRHTp0iEUJAAAArkCuAgAAcA7ZCgAAwBnkKgAA4AdxlmVZpgdhSjgc1qeffqo7F1fo4/XbTA8HgE91a9tCr43tZ3oYjaL2ONq9e3fff8RjLNBP59ntKfvCHrIVABw6P2fH/eHc6z5O7hP2b8ORqwDAXWozWjgcDsS5LGjnbi/V66WxugW5CgCAA7NzTdLruaQh4zd2K0AAAAAAAAAAAAAAAAAAcCsmVgEAAAAAAAAAAAAAAABAlATTA3CDE1sdoV1h06MA4FedkpqbHgIAxBTZCgDsIzsC2BO5CgDcgYwGeB+5CgCAfSPv7h8TqyTdOyzNk/d8BOAd4RpLofg408MAgJggWwHAoSE7AqhFrgIA99hdHRYRDfAuchUAAPvHNcl941aAksJhpqg7JRwOa8WKFfTUIfTTWSb7yUkIQJBw3nIHcoS7sD/cx837hOwIoJYbj1GNwc3HZKcFqVaJev0sSLVKP9W7csXnpocB4BAE4XgVpGMztfpTkGqVglUvtXoD1yT3jYlVcFxlZaXpIfgK/XQW/QQABAnnPXdhf7gP+wQA3CNIx+Qg1SpRr58FqVZJ2r17t+khAMABBenYTK3+FKRapWDVS63wMiZWwXGJiYmmh+Ar9NNZ9BMAECSc99yF/eE+7BMAcI8gHZODVKtEvX4WpFoBwCuCdGymVn8KUq1SsOqlVn8KSq0JpgfgBtxT2TmhUEhdu3Y1PQzfoJ/OclM/uUctAD8jW7mDm857YH+4UUP3CfkNgAlByVVBOk8GqVaJev3Mr7WS+QD/CkKu8uuxuT7U6k9BqlUKVr3U6k+NXaubsjkTqyRNLFymlZt2mB4GgIDolNRcM0b0MD0MAGg0ZCsAfkN+A2AKuQoAYofMB/gbuQoAAO9wWzZnYpWkr8p26PON20wPA1/lts8AANSWSURBVAAAwBfIVgAAAM4gVwEAADiDXAUAAOyKNz0AAAAAAAAAAAAAAAAAAHAbJlYBAAAAAAAAAAAAAAAAQBQmVgEAAAAAAAAAAAAAAABAFKMTq2bNmqWMjAxlZGRo8uTJqqqq0urVqzV8+HANGjRIY8eOVUVFRZ3nFBYWatKkSZGvN2/erKuvvloZGRm69NJL9cknn8S6DAAAAOPIVQAAAM4hWwEAADiDXAUAALzO2MSq5cuXq6ioSAUFBSouLlZ1dbXmzp2rnJwcTZgwQQsWLFDHjh01c+ZMSdKuXbs0ffp03XHHHXW2c88996hLly569dVXdf/99ysnJ0c7d+40URIAAIAR5CoAAADnkK0AAACcQa4CAAB+kGDqhVu0aKHc3Fw1a9ZMktSlSxetWrVK5eXl6t27tyQpMzNTWVlZGj9+vBYvXizLspSTk6Ply5dHtrNy5UqNGTNGknTcccepZcuW+uSTT9SnT5/YFwUADRAOh00P4ZDV1uCHWtyAfjrPbk+9tg/IVQAQG147P3gJOch9nNwnXtuvZCsACLb6zltByyrU615eGOOeyFUAAOBQNGb2aci2jU2sSk5OVnJysiRpy5YtmjNnjkaMGKF169ZF1klKSlJpaakkKT09Xenp6SoqKqqzna5du+rVV1/VTTfdpDVr1ujLL7/U5s2bY1UGANi2atUqVVZWmh6GI0pKSkwPwVfop/P83lNyFQDEhp/ym1v5/ZztRUHcJ2QrAAi2/WW+oJ0XqReHilwFAAAOhVuuxxqbWFVrw4YNGjNmjDIzM9WrVy8tXLiwzvK4uLj9Pn/y5MmaNm2ahgwZorS0NJ1xxhlq0qRJYw4ZAByRkpJiegiHLBwOq6SkRKmpqQqFQqaH43n003l2e1r7PK8hVwFA4/JDfnMrcpD7OLlPyFZkKwDwkvoyX9CyCvW6F7mKXAUAQJA05vXYhuQqoxOrVq5cqezsbGVnZysrK0sbN25UWVlZZHlZWZnatGmz321UVFRo2rRpat68uSTpoosu0vHHH9+o4wYAJ7j9h/SGCIVCvqrHNPrpvCD0lFwFAI3P7+cSNwjCOdtrgrpPyFYAEFz7O+8F7bxIvXACuQoAANjllmwWb+qFt27dqtGjRys3N1dZWVmSpLZt2yoxMVFLly6VJBUWFio9PX2/23n22Wc1Z84cSdKiRYu0e/dudenSpXEHDwAA4CLkKgAAAOeQrQAAAJxBrgIAAH5g7BOrZs+ere3btys/P1/5+fmSpP79+ysvL0+5ubkqLy9X+/btlZeXt9/tjBkzRuPHj1dxcbGaN2+uRx55RPHxxuaLAQAAxBy5CgAAwDlkKwAAAGeQqwAAgB/EWZZlmR6EKeFwWJ9++qnuXFyhj9dvMz0cAAHRrW0LvTa2n+lhOKL2ONq9e3fXfBSjl9FP59ntKfvCHrIVAL/yU35zK8697uPkPmH/Nhy5CgBib3+ZL2jnMup1Ly+N1S3IVQAAeE8srsc2JFcxnRsAAAAAAAAAAAAAAAAAojCxCgAAAAAAAAAAAAAAAACiJJgegBuc2OoI7QqbHgWAoOiU1Nz0EACgUZGtAPgN+Q2AKeQqAIgdMh/gb+QqAAC8w23ZnIlVku4dlsa9qAHEVLjGUig+zvQwAKBRkK0A+BH5DYAJ5CoAiC0yH+Bf5CoAALzFTdmcWwFKCoeZou6UcDisFStW0FOH0E9nuamfbjkJAEBjcMNxFu4674H94UYN3SfkNwAmBOW8EaTzZJBqlajXz/xaK5kP8C+/Ha/q49djc32o1Z+CVKsUrHqp1Z8au1Y3ZXMmVsFxlZWVpofgK/TTWfQTABAknPfchf3hPuwTAHCPIB2Tg1SrRL1+FqRaAcArgnRsplZ/ClKtUrDqpVZ/CkqtTKwCAAAAAAAAAAAAAAAAgChMrAIAAAAAAAAAAAAAAACAKEysAgAAAAAAAAAAAAAAAIAoTKwCAAAAAAAAAAAAAAAAgChMrAIAAAAAAAAAAAAAAACAKEysAgAAAAAAAAAAAAAAAIAoTKwCAAAAAAAAAAAAAAAAgChMrAIAAAAAAAAAAAAAAACAKEYnVs2aNUsZGRnKyMjQ5MmTVVVVpdWrV2v48OEaNGiQxo4dq4qKCknS+vXrNWrUKA0ZMkRDhw7V+++/H9nOAw88oEGDBumCCy7QW2+9ZaocAAAAY8hVAAAAziBXAQAAOIdsBQAAvM7YxKrly5erqKhIBQUFKi4uVnV1tebOnaucnBxNmDBBCxYsUMeOHTVz5kxJ0rRp0zR06FC98soruv/++zV+/HiFw2G9+eabKikp0auvvqq//vWvuvPOO/Xf//7XVFkAAAAxR64CAABwBrkKAADAOWQrAADgBwmmXrhFixbKzc1Vs2bNJEldunTRqlWrVF5ert69e0uSMjMzlZWVpfHjx2vIkCE699xzJUnJycmqqqpSRUWF3nrrLQ0ZMkQJCQlKSkpS7969tXDhQl188cUHPZaamhqFw2HHawyi2j7ST2fQT2fRT+fRU2fRT+fZ7anX9oGbcpVEtnILjinuwv5wH/aJu7A/3MfJfeKl/UquMiNIx4Ag1SpRr58FqVaJev3OS/V6YYx7clO2CkKu8tL38qGiVn8KUq1SsOqlVn/yeq0NGbexiVXJyclKTk6WJG3ZskVz5szRiBEjtG7dusg6SUlJKi0tlSRlZGREHn/qqad08skn68gjj1Rpaalat25d5zmbNm1q0FjWrl2rnTt32i8GeykpKTE9BF+hn86in86jp86in87ze0/dlKskspXb+P3732vYH+7DPnEX9of7BG2fkKvMCtL3W5BqlajXz4JUq0S9fhe0emPBTdkqSLkqSN/L1OpPQapVCla91OpPQajV2MSqWhs2bNCYMWOUmZmpXr16aeHChXWWx8XF1fn68ccfV0FBgZ599llJkmVZe20zPr5hdzjs2LGjmjRp0sCRoz7hcFglJSVKTU1VKBQyPRzPo5/Oop/Oo6fOop/Os9vT2ud5jRtylUS2cguOKe7C/nAf9om7sD/cx8l94sVsRa6KrSAdA4JUq0S9fhakWiXq9Tsv1evFXCW5I1sFIVd56Xv5UFGrPwWpVilY9VKrP3m91obkKqMTq1auXKns7GxlZ2crKytLGzduVFlZWWR5WVmZ2rRpI+mn0HTbbbfpk08+0dy5c5WUlCRJat26tb7//vs6zznxxBMbNI74+HhP7mg3C4VC9NRB9NNZ9NN59NRZ9NN5QeipW3KVRLZymyB8/3sJ+8N92Cfuwv5wnyDuE3KVOUH6fgtSrRL1+lmQapWo1++CVm+suCVbBSlXBel7mVr9KUi1SsGql1r9KQi1NvxP5RyydetWjR49Wrm5ucrKypIktW3bVomJiVq6dKkkqbCwUOnp6ZKkhx9+WKtXr9acOXMiQUqS+vfvr1deeUXV1dXavHmzFi9erF/84hexLwgAAMAQchUAAIAzyFUAAADOIVsBAAA/MPaJVbNnz9b27duVn5+v/Px8ST8Fo7y8POXm5qq8vFzt27dXXl6eduzYoSeeeEJJSUm6/PLLI9t49NFHdf755+uzzz7Tr371K4XDYY0bN06tWrUyVRYAAEDMkasAAACcQa4CAABwDtkKAAD4gbGJVePGjdO4cePqXTZv3ry9HtvfvQ3Hjx+v8ePHOzY2AAAALyFXAQAAOINcBQAA4ByyFQAA8ANjtwIEAAAAAAAAAAAAAAAAALdiYhUAAAAAAAAAAAAAAAAARGFiFQAAAAAAAAAAAAAAAABEYWIVAAAAAAAAAAAAAAAAAERhYhUAAAAAAAAAAAAAAAAARGFiFQAAAAAAAAAAAAAAAABEYWIVAAAAAAAAAAAAAAAAAERhYhUcl5iYaHoIvkI/nUU/AQBBwnnPXdgfAADsW5DOk0GqVaJePwtSrQDgFUE6NlOrPwWpVilY9QapVvhPgukBuEEoFDI9BN8IhULq2rWr6WH4Bv10lpv7Ga6xFIqPMz0MAHAE2cod3HzeCyL2h7PITgCCIii5KkjnySDVKlGvnzlRK5kOQCwFIVdxHvInavWvINV7qLWSG2EaE6skTSxcppWbdpgeBoCA6pTUXDNG9DA9DABwDNkKQGMiOwEIEnIVAL8i0wGINXIVAHgTuRFuwMQqSV+V7dDnG7eZHgYAAIAvkK0AAACcQa4CAABwBrkKAADYFW96AAAAAAAAAAAAAAAAAADgNkysAgAAAAAAAAAAAAAAAIAoTKwCAAAAAAAAAAAAAAAAgChGJ1bNmjVLGRkZysjI0OTJk1VVVaXVq1dr+PDhGjRokMaOHauKigpJ0vr16zVq1CgNGTJEQ4cO1fvvv19nW6tXr9aFF15oogwAAADjyFUAAADOIVsBAAA4g1wFAAC8ztjEquXLl6uoqEgFBQUqLi5WdXW15s6dq5ycHE2YMEELFixQx44dNXPmTEnStGnTNHToUL3yyiu6//77NX78eIXDYUlSUVGRrrrqKlVWVpoqBwAAwBhyFQAAgHPIVgAAAM4gVwEAAD9IMPXCLVq0UG5urpo1ayZJ6tKli1atWqXy8nL17t1bkpSZmamsrCyNHz9eQ4YM0bnnnitJSk5OVlVVlSoqKhQOh/X222/rgQce0MSJE02VAwCHrPYHRC+pHbMXx+5G9NN5dnvqtX1ArgIQRId6rOa86y7sD/dxcp94bb+SrQAgdrxyjghaVqFe9/LCGPdErgIAOMUr50Av5YpD5fVaGzJuYxOrkpOTlZycLEnasmWL5syZoxEjRmjdunWRdZKSklRaWipJysjIiDz+1FNP6eSTT9aRRx4pSXrooYe0YcOGmI0dABrDqlWrPPvXNiUlJaaH4Cv003l+7ym5CkAQOZWd/H6O8Br2h/sEcZ+QrQAgdrx2PSxo50XqxaEiVwEAnEJudK8g1GpsYlWtDRs2aMyYMcrMzFSvXr20cOHCOsvj4uLqfP3444+roKBAzz77bCyHCQCNLiUlxfQQGiwcDqukpESpqakKhUKmh+N59NN5dnta+zyvIVcBCJJDzU6cd92F/eE+Tu4TshUAYF+8cj0saFmFet2LXAUACCpyo/t4vdaG5CqjE6tWrlyp7OxsZWdnKysrSxs3blRZWVlkeVlZmdq0aSNJsixLt912mz755BPNnTtXSUlJpoYNAI3CiyecWqFQyNPjdxv66bwg9JRcBSBonDquB+Ec4SXsD/cJ6j4hWwFAbHjtHBO08yL1wgnkKgCAE7x2jg5SrghCrfGmXnjr1q0aPXq0cnNzlZWVJUlq27atEhMTtXTpUklSYWGh0tPTJUkPP/ywVq9erTlz5hCkAAAA9kCuAgAAcA7ZCgAAwBnkKgAA4AfGPrFq9uzZ2r59u/Lz85Wfny9J6t+/v/Ly8pSbm6vy8nK1b99eeXl52rFjh5544gklJSXp8ssvj2zj0Ucf1bHHHmuqBAAAAFcgVwEAADiHbAUAAOAMchUAAPADYxOrxo0bp3HjxtW7bN68eXs9dqB7G7Zv315vvfWWI2MDAADwEnIVAACAc8hWAAAAziBXAQAAPzB2K0AAAAAAAAAAAAAAAAAAcCsmVgEAAAAAAAAAAAAAAABAFGO3AnSTE1sdoV1h06MAEFSdkpqbHgIAOIpsBaAxkZ0ABAm5CoBfkekAxBq5CgC8idwIN2BilaR7h6UpFAqZHgaAAAvXWArFx5keBgA4gmwFoLGRnQAEBbkKgJ+R6QDEErkKALyL3AjTuBWgpHCYKepOCYfDWrFiBT11CP10lpv7SRgA4CduPM4GkZvPe0HE/nAW2QlAUATlvBGk82SQapWo18+cqJVMByCWODb7C7X6U5BqlYJV76HWSm6EaUysguMqKytND8FX6Kez6CcAIEg477kL+wMAgH0L0nkySLVK1OtnQaoVALwiSMdmavWnINUqBaveINUK/2FiFQAAAAAAAAAAAAAAAABEYWIVHJeYmGh6CL5CP51FPwEAQcJ5z13YH+7DPgEA9wjSMTlItUrUCwBALAXpPESt/hSkWqXg1Qt4VYLpAbhBKBQyPQTfCIVC6tq1q+lh+Ab9dFaQ+hmusbjfMABjyFbuEKTznhewP9wnKPuEXAh4W1ByVVCOyVKwapWotzFxjgeAhglCrgrSeZda/SlItUrerZcciiBiYpWkiYXLtHLTDtPDAABHdEpqrhkjepgeBoAAI1sBgDuQCwHvI1cBqA/neABoOHIVABw6ciiCiolVkr4q26HPN24zPQwAAABfIFsBAAA4g1wFAADgDHIVAACwK970AAAAAAAAAAAAAAAAAADAbZhYBQAAAAAAAAAAAAAAAABRmFgFAAAAAAAAAAAAAAAAAFESTL74rFmzVFhYKElKTU3VbbfdpnXr1mnq1Knatm2bOnfurHvuuUfNmjXT+vXrNXXqVP3www8KhUK65ZZb1KdPH1VVVenWW2/V559/rpqaGmVmZurKK680WRYAAEDMkasAAACcQa4CAABwDtkKAAB4nbFPrFq+fLmKiopUUFCg4uJiVVdXa+7cucrJydGECRO0YMECdezYUTNnzpQkTZs2TUOHDtUrr7yi+++/X+PHj1c4HNZf/vIXxcfH65VXXlFBQYHmzZunzz//3FRZAAAAMUeuAgAAcAa5CgAAwDlkKwAA4AfGPrGqRYsWys3NVbNmzSRJXbp00apVq1ReXq7evXtLkjIzM5WVlaXx48dryJAhOvfccyVJycnJqqqqUkVFhdLS0jRw4EDFxcXpiCOO0PHHH6/vvvtO3bp1M1UaALhCOByO2WvE4rWCgH46z25PvbYPyFUAgP3xwnmNHOQ+Tu4TL+1XchUALzF5fA3auZt6/c1L9XphjHsiWwGAP9n9nYvXzmN2UKt3NGTcxiZWJScnKzk5WZK0ZcsWzZkzRyNGjNC6desi6yQlJam0tFSSlJGREXn8qaee0sknn6wjjzxSp59+euTxDz/8UMuXL9d9990XkxoAwM1WrVqlysrKmLxWSUlJTF4nKOin8/zeU3IVAGB/YpkLD5Xfz9leFLR9Qq4C4CVuOMcH7TxBvf4WtHpjgWwFAP5kN4cG6VxLrf5ibGJVrQ0bNmjMmDHKzMxUr169tHDhwjrL4+Li6nz9+OOPq6CgQM8++2ydx9977z3l5OQoLy9PRx11VKOPGwDcLiUlpdFfIxwOq6SkRKmpqQqFQo3+en5HP51nt6e1z/MachUAoD6xyIWHihzkPk7uEy9mK3IVAC8weY4P2rmbev3NS/V6MVdJZCsA8JuG5lAvnWsPFbV6R0NyldGJVStXrlR2drays7OVlZWljRs3qqysLLK8rKxMbdq0kSRZlqXbbrtNn3zyiebOnaukpKTIevPnz9f06dM1Y8YM9erVK+Z1AIAbxfIEFgqFPHnCdCv66bwg9JRcBQDYFy+dA4NwzvaaIO4TchUAr3DD8Tlo5wnq9beg1RsrZCsA8B+758sgnWup1V/iTb3w1q1bNXr0aOXm5iorK0uS1LZtWyUmJmrp0qWSpMLCQqWnp0uSHn74Ya1evVpz5sypE6Tef/995eXl6S9/+QtBCgAABBK5CgAAwBnkKgAAAOeQrQAAgB8Y+8Sq2bNna/v27crPz1d+fr4kqX///srLy1Nubq7Ky8vVvn175eXlaceOHXriiSeUlJSkyy+/PLKNRx99VDNmzFB1dbVuvvnmyOPXX3+9zjvvvJjXBAAAYAK5CgAAwBnkKgAAAOeQrQAAgB8Ym1g1btw4jRs3rt5l8+bN2+uxfd3b8Pnnn3d0XAAAAF5DrgIAAHAGuQoAAMA5ZCsAAOAHxm4FCAAAAAAAAAAAAAAAAABuxcQqAAAAAAAAAAAAAAAAAIhi7FaAbnJiqyO0K2x6FADgjE5JzU0PAUDAka0AwB3IhYD3kasA1IdzPAA0HLkKAA4dORRBxcQqSfcOS1MoFDI9DABwTLjGUig+zvQwAAQU2QoA3INcCHgbuQrAvnCOB4CGIVcBgDPIoQgibgUoKRxmirpTwuGwVqxYQU8dQj+dFaR+EmgAmBSE46wXBOm85wXsD/cJyj4hFwLe5vdjVK2gHJOlYNUqUW9j4hwPAA0ThHNRkM671OpPQapV8m695FAEEROr4LjKykrTQ/AV+uks+gkACBLOe+7C/nAf9gkAuEeQjslBqlWiXgAAYilI5yFq9acg1SoFr17Aq5hYBcclJiaaHoKv0E9n0U/n0VNn0U8ATuKY4i7sD/dhn7gL+wMItiAdA4JUq0S9fhakWiXq9bug1Qv/CtL3MrX6U5BqlYJVL7XCyxJMD8ANuKeyc0KhkLp27Wp6GL5BP51FP51HT51FPw8e9/B2N7KVO3BMcRf2h/uwT9yF/WEW2cq9gpKrgnQMCFKtEvX6WZBqlajX75ysl1zlXkHIVUF671KrPwWpVilY9VKr/4RrLNNDiCkmVkmaWLhMKzftMD0MAABwEDolNdeMET1MDwP7QbYCAMA7yFbuRq4CAMA7yFXuRq4CAMAZtZknHDY9kthhYpWkr8p26PON20wPAwAAwBfIVgAAAM4gVwEAADiDXAUAAOyKNz0AAAAAAAAAAAAAAAAAAHAbJlYBAAAAAAAAAAAAAAAAQBQmVgEAAAAAAAAAAAAAAABAFCZWAQAAAAAAAAAAAAAAAEAUoxOrZs2apYyMDGVkZGjy5MmqqqrS6tWrNXz4cA0aNEhjx45VRUWFJGn9+vUaNWqUhgwZoqFDh+r999+XJJWXl+uaa67RRRddpIsvvljvvfeeyZIAAACMIFcBAAA4h2wFAADgDHIVAADwOmMTq5YvX66ioiIVFBSouLhY1dXVmjt3rnJycjRhwgQtWLBAHTt21MyZMyVJ06ZN09ChQ/XKK6/o/vvv1/jx4xUOh/XnP/9ZnTt3VnFxsfLy8nTLLbeYKgkAAMAIchUAAIBzyFYAAADOIFcBAAA/SDD1wi1atFBubq6aNWsmSerSpYtWrVql8vJy9e7dW5KUmZmprKwsjR8/XkOGDNG5554rSUpOTlZVVZUqKip0yy23KBwOS5I2bNigo446ykxBAAAgpmrP/wezzsGs29Btuwm5CgAAHKro/GM3Rx3Mtt2ObAUAAA5FY2YfchW5CgAAt6ipqZHkvXxSqyHjNjaxKjk5WcnJyZKkLVu2aM6cORoxYoTWrVsXWScpKUmlpaWSpIyMjMjjTz31lE4++WQdeeSRkqSEhASNGjVKH3zwgW6//faY1QAAAMxZtWqVKisrD2rdkpKSRh6NWeQqAABwqPaVrfyeo+pDtgIAAIeiIdes/I5cBQCAf61Zs0ZSMK4dGZtYVWvDhg0aM2aMMjMz1atXLy1cuLDO8ri4uDpfP/744yooKNCzzz5b5/HZs2drw4YNGjFihHr06KGOHTs2+tgBAIA5KSkpB1wnHA6rpKREqampCoVCB73t2ud5DbkKAADYFZ2t7Oao+pCtyFYAAATJwVyzsotcRa4CAMAtTjrpJC1fvtyRa0cmNCRXGZ1YtXLlSmVnZys7O1tZWVnauHGjysrKIsvLysrUpk0bSZJlWbrtttv0ySefaO7cuUpKSpIkvf/+++rcubN+/vOfq3379jrttNO0Zs0awhQAAD7XkJAWCoU8GeoaglwFAAAOxb6yUhByVH3IVgAAwK4gZqf9IVcBAOBP8fHxkoJx7Sje1Atv3bpVo0ePVm5urrKysiRJbdu2VWJiopYuXSpJKiwsVHp6uiTp4Ycf1urVqzVnzpxIkJKkN954Q0899ZQkqbS0VJ999plSU1NjXA0AAIA55CoAAADnkK0AAACcQa4CAAB+YOwTq2bPnq3t27crPz9f+fn5kqT+/fsrLy9Pubm5Ki8vV/v27ZWXl6cdO3boiSeeUFJSki6//PLINh599FHddNNNmjJlii666CIlJCTo97//vdq1a2eqLAAAgJgjVwEAADiHbAUAAOAMchUAAPCDOMuyLNODMCUcDuvTTz/VnYsr9PH6baaHAwAADkK3ti302th+B7Vu7bm+e/fuDfoYUrvPCzqyFQAA3rOvbOVkHiJbNRy5CgAA72nINSu7yFUNR64CAMBZtZnH67mkIeM3ditAAAAAAAAAAAAAAAAAAHArJlYBAAAAAAAAAAAAAAAAQJQE0wNwgxNbHaFdYdOjAAAAB6NTUnPTQ8ABkK0AAPAOspW7kasAAPAOcpW7kasAAHBGEDMPE6sk3TsszZP3fAQAIKjCNZZC8XGmh4F9IFsBAOAtZCv3IlcBAOAt5Cr3IlcBAOCccI1leggxxa0AJYXDTFF3Sjgc1ooVK+ipQ+ins+in8+ips+jnweMClbvxPewOHFPchf3hPuwTd2F/mEW2cq+gvCeCdAwIUq0S9fpZkGqVqNfvnKyXXOVeQfh+DtJ7l1r9KUi1SsGql1r9J2iZh4lVcFxlZaXpIfgK/XQW/XQePXUW/QTgJI4p7sL+cB/2ibuwP4BgC9IxIEi1StTrZ0GqVaJevwtavfCvIH0vU6s/BalWKVj1Uiu8jIlVAAAAAAAAAAAAAAAAABCFiVUAAAAAAAAAAAAAAAAAEIWJVQAAAAAAAAAAAAAAAAAQhYlVAAAAAAAAAAAAAAAAABCFiVUAAAAAAAAAAAAAAAAAEIWJVQAAAAAAAAAAAAAAAAAQhYlVAAAAAAAAAAAAAAAAABCFiVUAAAAAAAAAAAAAAAAAEIWJVQAAAAAAAAAAAAAAAAAQhYlVAAAAAAAAAAAAAAAAABCFiVUAAAAAAAAAAAAAAAAAEIWJVQAAAAAAAAAAAAAAAAAQhYlVAAAAAAAAAAAAAAAAABCFiVUAAAAAAAAAAAAAAAAAECXB9ABMsixLkhQOhxUOhw2Pxh9q+0g/nUE/nUU/nUdPnUU/nWe3p7Xr12YFHByylbtwTHEX9of7sE/chf3hPk7uE7JVwwUtVwXpGBCkWiXq9bMg1SpRr995qV5yVcMFKVd56Xv5UFGrPwWpVilY9VKrP3m91obkqjgrwOmrqqpKJSUlpocBAABcKjU1VU2bNjU9DM8gWwEAgP0hWx08chUAANgfctXBI1cBAID9OZhcFeiJVTU1NaqurlZ8fLzi4uJMDwcAALiEZVmqqalRQkKC4uO5c/LBIlsBAID6kK0ajlwFAADqQ65qOHIVAACoT0NyVaAnVgEAAAAAAAAAAAAAAABAfZjODgAAAAAAAAAAAAAAAABRmFgFAAAAAAAAAAAAAAAAAFGYWAUAAAAAAAAAAAAAAAAAUZhYBQAAAAAAAAAAAAAAAABRmFgFAAAAAAAAAAAAAAAAAFGYWAUAAAAAAAAAAAAAAAAAUZhYBQAAAAAAAAAAAAAAAABRAjuxateuXZoyZYp69eqlvn376umnnzY9JNeoqqpSRkaGlixZEnls/fr1uvLKK9W9e3cNHjxYixYtqvOc9957TxkZGUpLS9MVV1yh9evX11n+zDPPqF+/furRo4emTJmiysrKyDK/7ovS0lKNHTtWvXv3Vr9+/XT33Xdr165dkuinXf/5z3901VVXqUePHurfv7+efPLJyDJ6emiys7M1adKkyNcrVqxQZmam0tLSNGzYMH322Wd11n/11Vc1cOBApaWl6brrrtPWrVsjyyzL0v33368zzzxTvXv31n333aeamprI8h9++EE33HCDevTooQEDBujll19u/AJj5I033lBKSkqdf2PHjpVET+2oqqrSbbfdptNPP12/+MUv9MADD8iyLEn0022CcJw0hVzmDuQ69yEXuhe50h3Ipd7m5WONqXOmG8Ty+GeKqZ/RTPnuu+80ZswYnXbaaRowYICeeeaZyDI/1Ruknznqq/XTTz/ViBEj1KNHD11wwQV64YUX6jzHq7VK9ddbq7y8XP369VNRUVGdx72cA+qrd+PGjfrd736ntLQ0nXfeefrb3/5W5zlerhcHx/T78FCYyvSxFKRzkFR/vXfcccde+/nZZ5+NLPfacSpI19D2V6vf9muQrsPtr1a/7dc9cT3vIFgBdfvtt1sXXXSR9dlnn1n/+Mc/rB49elh///vfTQ/LuJ07d1rXXXed1blzZ2vx4sWWZVlWTU2NddFFF1njx4+3vvzyS+vRRx+10tLSrG+//dayLMv69ttvre7du1tPPfWUtXr1auvGG2+0MjIyrJqaGsuyLGvBggVWz549rbfeestatmyZNXjwYOu2226LvKYf90VNTY01fPhwa/To0dbq1autDz74wDrvvPOse+65h37aFA6HrfPPP98aP3689fXXX1tvv/22ddppp1mvvPIKPT1Er776qtW5c2dr4sSJlmVZ1o4dO6yzzjrLuueee6wvv/zSmjZtmvWLX/zC2rFjh2VZlrVs2TLr1FNPtV566SVr5cqV1siRI63s7OzI9p566ikrPT3d+uCDD6z333/f6tu3r/Xkk09Glo8ZM8YaNWqUtWrVKqugoMA65ZRTrGXLlsW26Eby5z//2RozZoz1/fffR/7997//pac25ebmWueff761bNky67333rPOOOMM67nnnqOfLuT346Qp5DJ3INe5D7nQvciV7kEu9TavHmtMnjNNi/XxzxRTP6OZMnz4cOumm26yvv76a+uNN96w0tLSrH/84x++qjdIP3PUV+v3339v9erVy8rLy7O+/vpr69VXX7VSU1OthQsXerrWfdW7p9zcXKtz585WYWFh5DEv54D66t29e7eVkZFhXX311dbatWut5557zurWrZu1atUqz9eLg+fVXGVZ5jJ9rATpHLSvei3Lsq688krrscceq7OfKyoqLMvy3nEqSNfQ9lerZflrvwbpOtz+arUsf+3XPXE97+AEcmLVjh07rNTU1Donrvz8fGvkyJEGR2XemjVrrCFDhlgXXXRRnRP7e++9Z3Xv3j3yZrEsyxo1apT10EMPWZZlWX/605/q9K6iosLq0aNH5PmXXXZZZF3LsqwPPvjAOvXUU62Kigrf7osvv/zS6ty5s1VWVhZ5rLi42Orbty/9tKm0tNS68cYbrfLy8shj1113nfWHP/yBnh6CH374wTr77LOtYcOGRU6YL7zwgjVgwIBIsKmpqbHOO++8yIWWnJycyLqWZVkbN260UlJSrG+++cayLMtKT0+vc1Fm/vz51jnnnGNZlmX95z//sTp37mytX78+snzKlCl1tudl48ePt/Ly8vZ6nJ423A8//GB17drVWrJkSeSxxx57zJo0aRL9dBm/HydNIZe5B7nOfciF7kSudBdyqXd5+Vhj6pxpWqyPf6aY+hnNlB9//NHq3LlzZAKGZVnW9ddfb912222+qTdIP3Psq9a5c+dagwYNqrNubm6udfPNN1uW5c1aLWvf9e45zvPOO88666yz6nwvejUH7KveN9980+rZs2ednxuuueYa6/nnn7csy7v14uB5OVdZlplMHytBOgdZ1v6Py/369bPeeeedep/nteNUkK6h7a9Wy/LXfg3Sdbj91WpZ/tqvtbied/ACeSvAL774QtXV1erRo0fksZ49e2rZsmWu+JhpU5YuXaozzjhD8+bNq/P4smXL1LVrVzVr1izyWM+ePfXpp59Glvfq1SuyLDExUd26ddOnn36qcDiskpKSOsu7d++u3bt364svvvDtvmjVqpWefPJJHXPMMXUe3759O/20KSkpSX/605/UvHlzWZaljz76SB988IF69+5NTw/Bvffeq1/96lfq1KlT5LFly5apZ8+eiouLkyTFxcXptNNO22c/jz32WLVt21bLli1TaWmpvvvuO51++umR5T179tS3336r77//XsuWLdOxxx6r9u3b11n+ySefNHKlsbF27VolJyfv9Tg9bbiPPvpIzZs3V+/evSOPZWdn6+6776afLuP346Qp5DL3INe5D7nQnciV7kIu9S4vH2tMnTNNi/XxzxRTP6OZcvjhhysxMVFFRUXavXu3vvrqK3388cc6+eSTfVNvkH7m2FettbftibZ9+3ZJ3qxV2ne90k+3ocrNzdWtt96qpk2b1lnm1Rywr3qXLl2qPn36qHnz5pHH/vznP+vSSy+V5N16cfC8nKskM5k+VoJ0DpL2Xe/27dtVWlpa736WvHecCtI1tP3V6rf9GqTrcPur1W/7tRbX8w5eICdWlZWV6eijj67zg8MxxxyjXbt26ccffzQ3MMMuu+wyTZkyRYmJiXUeLysrU1JSUp3Hfv7zn2vTpk0HXL5t2zbt2rWrzvKEhAS1bNlSmzZt8u2+aNGihfr16xf5uqamRs8++6zOPPNM+umAAQMG6LLLLlOPHj10wQUX0FOb3n//fX344Ye69tpr6zx+oH5+//33+1xeVlYmSXWW1wbL2uX1Pbe0tNSZogyyLEtff/21Fi1apAsuuEADBw7U/fffr6qqKnpqw/r169WuXTvNnz9fgwYN0rnnnqv8/HzV1NTQT5fx83HSJHKZe5Dr3I1c6A7kSnchl3qbl481ps6ZJpk4/pli6mc0Uw477DDdeuutmjdvntLS0vTLX/5SZ599tjIzM31Tb5B+5thXre3bt1f37t0jX2/ZskWvvfaa+vTpI8mbtUr7rleSHn30UXXt2lV9+/bda5lXc8C+6l2/fr3atGmj+++/X/369dOQIUP05ptvRpZ7tV4cPC/nKlOZPlaCdA6S9l3v2rVrFRcXp0cffVRnn322hgwZopdeeimy3GvHqSBdQ9tfrX7br3sK0nW46Fr9uF+5ntcwCaYHYEJlZeVef41R+3VVVZWJIbnavvpV26v9Ld+5c2fk6/qWW5YViH0xffp0rVixQi+++KKeeeYZ+nmIHnroIW3evFl//OMfdffdd/M9asOuXbv0hz/8QbfeeqsOP/zwOssO1M+dO3c2qJ979utA2/ayjRs3Rur705/+pA0bNuiOO+7Qzp076akNFRUV+s9//qPnn39ed999t8rKynTrrbcqMTGRfroMuSq2OOeZR65zF3KheeRK9yGXepufslWszpmmmDr+mWLqZzST1q5dq3POOUe/+c1vtGbNGk2bNk19+vTxbb21gpqndu7cqRtuuEHHHHNM5BON/Fbrl19+qeeff16vvPJKvcv9lgMqKir00ksvafDgwXr00Ue1ZMkSjR07VvPmzVNqaqrv6sXevJyrTGV604J2Dvrqq68UFxenE088USNHjtQHH3yg3NxcNW/eXOedd57nj1NBuoa2Z62ff/65b/drkK7DRdfarVs3X+1Xruc1XCAnVh122GF77Zzar6O/cfBTv6JnflZVVUV6ta9+tmjRQocddljk6+jliYmJCofDvt8X06dP1+zZs/Xggw+qc+fO9NMBqampkn466E+YMEHDhg1TZWVlnXXo6f498sgjOuWUU+rMqK+1r34dqJ+JiYl1To7RvU1MTDzgtr2sXbt2WrJkiY466ijFxcXp5JNPVk1NjXJyctS7d2962kAJCQnavn278vLy1K5dO0k/XVB47rnndMIJJ9BPFyFXxRY5wixynfuQC80jV7oPudTb/JKtYnnONMXU8c8UUz+jmfL+++/rxRdf1L/+9S8dfvjhSk1NVWlpqWbOnKnjjjvOd/XuKYgZd8eOHbr22mu1bt06zZ07N7Iv/FSrZVmaOnWqxo4du9dti2r5LQeEQiG1bNlSf/zjHxUfH69u3brpww8/VEFBgVJTU31XL/bm5VxlKtObFrRz0MUXX6xzzjlHLVu2lCR16dJF69at03PPPafzzjvP08epIF1Di671pJNO8u1+DdJ1uOhaP/74Y1/tV67nNVwgbwXYunVr/fDDD6quro48VlZWpsMPP1wtWrQwODJ3at26tTZv3lznsc2bN0c+pm1fy1u1aqWWLVvqsMMOq7O8urpaP/74o1q1auX7fTFt2jTNmjVL06dP1wUXXCCJftq1efPmOh/VLEmdOnXS7t271apVK3raQK+99prefPNN9ejRQz169FBxcbGKi4vVo0ePQ/oebd26tSRFPupxz/+vXb6v5/pBy5YtI/cclqSOHTtq165dh/Q9GtSetmrVSocddljkgr0kdejQQd999x3foy7j1+OkW5EjzCHXuQe50F3Ile5ELvUuPxxrYn3ONMXU8c8UUz+jmfLZZ5/phBNOqHOBv2vXrtq4caMv691T0DLu9u3bddVVV2nNmjWaPXu2kpOTI8v8VOvGjRv1ySef6N57740ctzZu3Kg//OEPGj16tCT/5YCkpCQlJycrPv5/vwarPW5J/qsXe3Pb+7ChTGR604J2DoqLi4tM0qh14oknRm6J5dXjVJCuodVXq9/2a5Cuw+2v1u3bt/tqv3I9r+ECObHq5JNPVkJCgj799NPIYx999JFSU1PrhGz8JC0tTZ9//nnko9ukn/qVlpYWWf7RRx9FllVWVmrFihVKS0tTfHy8UlNT6yz/9NNPlZCQoC5duvh6XzzyyCN6/vnn9cADD+jCCy+MPE4/7dmwYYOuv/76OvdY/eyzz/Szn/1MPXv2pKcN9Ne//lXFxcWaP3++5s+frwEDBmjAgAGaP3++0tLS9Mknn8iyLEk//UXbxx9/vM9+fvfdd/ruu++Ulpam1q1bq23btnWWf/TRR2rbtq2SkpLUvXt3ffvtt3Xu1/7RRx+pe/fusSm8Eb3zzjs644wz6szOX7lypVq2bKmePXvS0wZKS0vTrl279PXXX0ce++qrr9SuXTu+R13Gr8dJtyJHmEGucxdyobuQK92HXOptXj/WmDhnmmLq+GeKqZ/RTElKStJ//vOfOn9N/dVXX6l9+/a+rHdPQcq4NTU1uv7667Vhwwb99a9/1UknnVRnuZ9qbd26tf7xj39Ejlnz589XUlKSxo4dqzvvvFOS/3JAWlqa1qxZo3A4HHls7dq1kQmifqsXe3Pb+7AhTGV604J0DpKkGTNm6Morr6zz2BdffKETTzxRkjePU0G6hravWv22X4N0HW5/tf71r3/11X7lep4NVkDl5uZaF154obVs2TLrjTfesE477TTr9ddfNz0s1+jcubO1ePFiy7Isq7q62ho8eLB10003WatXr7Yee+wxq3v37ta3335rWZZlrV+/3kpNTbUee+wxa/Xq1daNN95oXXTRRVZNTY1lWZb16quvWqeddpr1xhtvWMuWLbMuvPBCa9q0aZHX8uO++PLLL62TTz7ZevDBB63vv/++zj/6aU91dbV1ySWXWL/97W+tNWvWWG+//bb1i1/8wnrmmWfoqQMmTpxoTZw40bIsyyovL7fOPPNMa9q0adaaNWusadOmWWeddZa1Y8cOy7Is6+OPP7a6detmFRQUWCtXrrRGjhxpjRkzJrKtxx57zOrbt6+1ePFia/HixVbfvn2tp59+OrL8t7/9rTVy5Ehr5cqVVkFBgZWammotW7YstgU3gvLycqtfv37WzTffbK1du9Z6++23rb59+1qPP/44PbUpOzvbuvTSS62VK1da//73v60zzzzTmj17Nv10oSAcJ00il5lFrnMfcqG7kSvNI5d6n1ePNSbPmW4Qy+OfKaZ+RjNh27Zt1llnnWXl5ORYX331lfXPf/7T6t27t/Xcc8/5st4g/cyxZ63z5s2zunTpYi1cuLDOMeuHH37wRa3R9UY755xzrMLCwsjXfsgBe9ZbXl5u9e3b18rNzbXWrVtnPfvss1bXrl2tzz77zDf14sDc8D60w2Smj7UgnYOi6122bJnVtWtX68knn7T+85//WHPmzLFOOeUU6+OPP7Ysy3vHqSBdQ9tfrX7br0G6Dre/Wv22X6NxPe/AAjuxqqKiwrrlllus7t27W3379rVmzZplekiuEv0D17p166zLL7/cOuWUU6wLL7zQevfdd+us//bbb1vnn3++deqpp1qjRo2yvvnmmzrLH3vsMatPnz5Wz549rcmTJ1s7d+6MLPPjvnjssceszp071/vPsuinXZs2bbKuu+4667TTTrPOOussa+bMmZGTLz09NHueMC3rp0B/8cUXW6mpqdavf/1r6/PPP6+zfmFhoZWenm51797duu6666ytW7dGllVXV1t33XWX1atXL+uMM86wpk+fHtlPlmVZmzdvtsaMGWOlpqZaAwYMsIqLixu/wBhZvXq1deWVV1rdu3e3zjrrLOvhhx+O1E5PG27btm1WTk6O1b17d6tPnz7008WCcJw0iVxmFrnOnciF7kWudAdyqbd59Vhj8pzpBrE8/pli6mc0U9asWWNdeeWV1mmnnWYNHDjQmjVrlm/rDdLPHHvW+tvf/rbeY9bIkSMj63u5Vstq2MQqy/J+Doiud82aNZHv5fPPP3+vX8Z6vV4cmBveh3aZyvSxFqRzkGXtXe8bb7xhXXTRRVZqaqo1aNAgTx+ngnQN7UC1+mm/WlawrsPtr1a/7dc9cT3vwOIs6/9/hhcAAAAAAAAAAAAAAAAAQJLk7psIAwAAAAAAAAAAAAAAAIABTKwCAAAAAAAAAAAAAAAAgChMrAIAAAAAAAAAAAAAAACAKEysAgAAAAAAAAAAAAAAAIAoTKwCAAAAAAAAAAAAAAAAgChMrAIAAAAAAAAAAAAAAACAKEysAgAAAAAAAAAAAAAAAIAoTKwCAAAAAAAAAAAAAAAAgChMrALgK//97391zz33aMCAAUpLS9Mvf/lLPfPMM6qpqWn0196+fbvmz5/f6K8DAAAQC+QqAAAAZ5CrAAAAnEO2AhBrCaYHAABO+eGHH3TppZcqKSlJd955p9q3b6+SkhJNmzZN69evV25ubqO+/jPPPKMlS5bo4osvbtTXAQAAaGzkKgAAAGeQqwAAAJxDtgJgAhOrAPhGXl6emjZtqqeeekqHHXaYJOm4447T4YcfrmuvvVYjR45Uhw4dGu31LctqtG0DAADEErkKAADAGeQqAAAA55CtAJgQZ/HuB+ADVVVVOuOMM3TLLbfo//7v/+ossyxLS5Ys0WmnnabKykrdf//9+uc//6ldu3ZpwIABmjp1qo466igtWbJEV1xxhVatWhV57qRJkyRJ99xzjx5++GGtW7dOzZs3V3FxsQ477DD99re/1e9+9zsVFRVp8uTJkeftuQ0AAAAvIVcBAAA4g1wFAADgHLIVAFPiTQ8AAJzwzTffqKKiQqmpqXsti4uL05lnnqmmTZvq+uuv18qVK/Xoo49q1qxZWrt2bSQwHYzXX39dhx12mF566SVdddVVuv/++/X1119r8ODB+u1vf6sePXpo0aJFTpYGAAAQU+QqAAAAZ5CrAAAAnEO2AmAKtwIE4Avbtm2TJB155JH7XOeLL77Q0qVLtWDBgsjHgE6fPl2DBw/WV199dVCv07JlS02cOFGhUEijR4/WE088oc8++0wdOnRQs2bN1KRJE7Vq1erQCwIAADCEXAUAAOAMchUAAIBzyFYATOETqwD4QsuWLSVJ//3vf/e5zldffaUWLVrUubdyx44dddRRRx10mGrfvr1CoVDk6yOOOELV1dX2Bg0AAOBC5CoAAABnkKsAAACcQ7YCYAoTqwD4wvHHH68jjzxSn3/+eb3Lr7nmGjVt2rTeZeFwWOFwWHFxcXstiw5KTZo02Wsdy7JsjBgAAMCdyFUAAADOIFcBAAA4h2wFwBQmVgHwhYSEBA0ePFhz5sxRVVVVnWVvvfWW3nrrLSUnJ2vbtm11ZqR/+eWX2r59uzp06BAJStu3b48s37Bhw0GPob4wBgAA4DXkKgAAAGeQqwAAAJxDtgJgChOrAPjGDTfcoO3bt+uqq67S0qVL9c033+iFF17QpEmTdMUVV6hTp046++yzNXHiRC1fvlzLly/XxIkTdfrpp6tz58466aSTdPjhh+vRRx/V+vXr9eSTT2rFihUH/fqJiYn6/vvvGxTAAAAA3IhcBQAA4AxyFQAAgHPIVgBMYGIVAN9o1aqVnnvuOR133HGaMGGCMjIyNHv2bI0dO1aTJk2SJN1777067rjjdOWVV+qqq67SSSedpPz8fElS8+bNNW3aNL322mvKyMjQF198ocsvv/ygX/+8885TTU2NLrzwQm3ZsqVRagQAAIgFchUAAIAzyFUAAADOIVsBMCHO4oagAAAAAAAAAAAAAAAAAFAHn1gFAAAAAAAAAAAAAAAAAFGYWAUAAAAAAAAAAAAAAAAAUZhYBQAAAAAAAAAAAAAAAABRmFgFAAAAAAAAAAAAAAAAAFGYWAUAAAAAAAAAAAAAAAAAUZhYBQAAAAAAAAAAAAAAAABRmFgFAAAAAAAAAAAAAAAAAFGYWAUAAAAAAAAAAAAAAAAAUZhYBQAAAAAAAAAAAAAAAABRmFgFAAAAAAAAAAAAAAAAAFGYWAUAAAAAAAAAAAAAAAAAUZhYBQAAAAAAAAAAAAAAAABRmFgFAAAAAAAAAAAAAAAAAFGYWAUAAAAAAAAAAAAAAAAAUZhYBQAAAAAAAAAAAAAAAABRmFgFAAAAAAAAAAAAAAAAAFGYWAUAAAAAAAAAAAAAAAAAUZhYBcCY1atXa9y4cTrrrLN0yimnqG/fvrrpppv0xRdfRNaZNGmSBgwYsM9tZGVlKSsrq876KSkpkX9dunRR9+7dddFFF+mRRx7Rzp07DziuDRs2KCUlRUVFRXstW7x4cWR7W7ZskSQNGDBAkyZNkiRNnTpVXbt2VVlZ2T63f/XVV2vAgAGqqanRww8/rJSUlH2ue6D6AQBA44rOGrW2b9+u4cOH65RTTtGbb75ZZ9n48eOVkpKip59+ep/b/fbbb/X73/9e6enpOuWUU3TmmWfq6quv1tKlS/f5nIKCAqWkpOjqq6+ud/n+Mkx9tm7dqvvuu0+DBg3Sqaeeqj59+mjUqFH629/+ts/nlJaWRp6Tlpamvn376uqrr9aHH354SOsvWbJEKSkpWrJkSeSxPTNdSkqKunbtqjPOOEO//e1vtXDhwr1ea8CAAXs9Z89/48aNq7Puaaedpo0bN9Y77pSUFD388MOSfvoe2N92U1JSIllQkmpqatS/f3+lpKTos88+q3f7e+ZHAACwb2Sxn9ReP4r+1717d11wwQV68MEHVV1dHVm/qKhIKSkp2rBhQ+SxA+WZ+++/v866/fv31/bt2w9Y54Ey2J65Svpp36Wlpalbt277vH4W/RwAAOBf+8tuDfkd4b7y0p7/arfV0Gx1MNvetWtXZP0ffvhBd999twYOHKhTTjlFvXv31qhRo/TGG2841TYAMZZgegAAgmnNmjW69NJL1b17d02dOlU///nPtWnTJj377LMaPny4/vKXv6h79+62tt2qVSs98sgjkn76xVZ5ebk+/PBDPfbYY1q0aJFmz56tww47rMHbXbp0qa6++mp16NBBTz/9tI4++ui91hk2bJheeOEFvfbaa7ryyiv3Wr5lyxa98847uuaaaxQfz9xWAAC8aPv27Ro9erS++OIL5efnKz09PbKsvLxcb775pjp37qx58+bpN7/5jeLi4uo8v6ysTJdeeqlat26tm2++Wccee6y2bt2qF154QaNGjdKMGTN0/vnn7/W6hYWF6ty5s/7973/ru+++07HHHmu7hi+++EKjR49WQkKCrrjiCnXr1k3l5eX65z//qfHjx+v111/X/fffryZNmkSe89FHH+m6667T0UcfrSuuuEIdOnTQjz/+qHnz5ikrK0t33323Lr74Ytvr1+fXv/61MjMzJUm7d+9WWVmZCgsLdfXVV+v3v/+9rrjiijrrp6en69prr613W9HZbceOHZo6dep+f+kqSX/4wx/q/FLxtttuizxe62c/+1nk/999911t3rxZJ554op5//nndcccd+90+AABomKBmMUmaN29ena9/+OEHvfrqq3r00UdVXV2tnJyc/b7untkqWuvWret8/d133+mee+45YJZ55JFHVFVVFfn6+uuvV9euXetksjZt2kT+/9VXX9WRRx6pcDisF198Uddcc81+tw8AAPzrYLLbwcrMzFS/fv0iX7/wwgt68cUX6+Snpk2b1nlOQ7NV9Pp7qt32zp07dfnllyscDis7O1snnHCCysvL9fe//13XX3+9pkyZolGjRtmqEYA5TKwCYMSsWbN09NFH64knnlBCwv8ORQMHDtSgQYP05z//WY8//ritbTdt2nSvSVnp6elKS0vTddddp6effrrBF20++OADjRkzRp06ddLTTz+tFi1a1Ltejx491LFjRxUXF9c7saq4uFg1NTW65JJLGvT6AADAHWp/kbdy5UrNnDlTZ511Vp3lr776qiTp97//vUaNGqXFixerT58+ddYpKCjQtm3btGDBAjVv3jzy+HnnnafMzMx6f5m3du1affrpp3ryySc1btw4zZs3TzfddJOtGiorK3XttdeqVatWmj17dp1cM3DgQJ1zzjm64YYb1KFDh8hr/Pjjj7rpppuUnJysWbNmKTExMfKcCy64QNnZ2br11lvVt29fHXPMMQ1ef1/atGmzV64bPHiwbrjhBt13330aMGCA2rdvH1n2s5/97KAn57do0ULvvvuuCgoKNHz48H2u16lTpzpf1+6zfb1OUVGRevTooX79+mnmzJmaNGlSnf0MAADsC2oWq1Vf/jjnnHO0YcMGFRUVHXBiVX3Zal9atGihF154Qb/85S/36vOeunbtWufrpk2b7jeTFRUVqV+/fmrSpIleeOEFjRkzhj8+BAAgoA4mux2sNm3a1JnM/c4770ja9/WbfS3bX7Y6mBy1YMECrV27Vq+//rqSk5Mjjw8cOFA7d+7UQw89pJEjRyoUCh1wWwDcg59YABixefNmWZalmpqaOo83a9ZMU6ZM0S9/+UvHX3PgwIHq3r27nn/++QY978MPP1R2drZSUlL0zDPP7HNSVa1hw4bps88+09dff73Xspdeekm/+MUv1LZt2waNAQAAmLdjxw797ne/06pVq/T444/X+wumwsJC9enTR2eeeaZOOOGEenPH5s2bFRcXp3A4XOfxUCik8ePH69JLL613u0cddZTOPPNMXXDBBXrxxRfrfCR5QxQVFenbb7/VH/7wh3pzzfnnn6/BgwfrmWee0Y4dOyRJ8+fP1/fff68pU6bUmSQlSfHx8ZowYYIuv/zyyCc7NXT9hho3bpx2796tF1980dbzpZ9uW9O7d2/de++9+u6772xvZ0///e9/9eabb+qcc85RRkaGKisr9fLLLzuybQAAgi7IWexAmjdvbvvTHfbl0ksvVYcOHTR16lTbmS3al19+qWXLlql///4aMmSIvv3228gvPQEAQPAcTHYz4VCy1ebNmyVpr99/StKYMWN07bXX1vm0TwDewMQqAEb0799fGzdu1IgRIzRnzhytXbtWlmVJkgYNGqShQ4c2yuueddZZ2rRpk7799tuDWv+jjz7S7373O6WkpOipp546qE8b+NWvfqWEhAQVFxfXefyLL77QF198sc+PXAcAAO5VUVGh7OxsrVixQk888YTOOOOMvdZZs2aNSkpKIre3u/jii/XPf/4zckGlVv/+/bVz504NHz5cTz31lFasWBH5xd5ZZ5211+3tqqur9corrygjI0NNmjTR0KFDVVZWprfeestWLe+8884BP9npwgsvVGVlpd57773Ic4455hideuqp9a7fpUsXTZw4MfKXeA1dv6FOPPFEtW3bVh999FGdxy3LUnV1db3/osXFxemuu+5STU2Npk6damsc0YqLixUOh3XRRRepbdu2OvPMM/f7MfEAAODgBD2L7TmW2n9VVVUqLS3VE088oXfffVe/+tWvDvjaNTU1B52VDjvsMN19993atGmT7rvvvgbXWZ/CwkK1bNlS55xzjnr16qUTTjhBzz33nCPbBgAA3nKw2a0xNTRb7StH7TmJql+/fkpISNCoUaP0yCOP6NNPP9Xu3bslSaeeeqquuuqqvf4IEYD7MbEKgBGXXXaZrr32Wn355Ze6/fbbNXjwYPXp00cTJkzQ8uXLG+11a281czDB7NNPP9Xvfvc7VVZW6ocffmjQa/Tv3z/yEaa15s+fr6OPPloDBgxo2KABAIBRtb/I++ijj7Rr1y5VVFTUu17tL4pqz/VDhw5VOBze61OV0tPTdeutt2rz5s267777NHToUPXu3VvXX3+93n333b22++9//1tlZWWRWwn36tVLycnJtv+Kb8OGDWrXrt1+1zn++OMlKTIZfdOmTQd8zp4aur4dxxxzzF6Zbv78+erWrVu9/0pKSvbaxnHHHaebb75ZixYt0gsvvHDIYyoqKtLZZ5+tVq1aSZIuueQSrVq1Sh9//PEhbxsAgKAii/3PntkmNTVVZ599tubOnasbbrhBN9988wFf+89//vM+s9LWrVv3Wr9Hjx4aNWqU5s2bt9ckr4bac4Ja06ZNJf20j/7973879umhAADAOw42uzWmhmarfeWoO+64I7JOSkqKHnzwQdXU1Ojhhx/WpZdeql69eumqq67S3//+95jVBsBZCaYHACC4brzxRl155ZV655139P7772vJkiUqLi7Wq6++qilTpuiKK65w/GPMaz8VKy4uTpZl7fWx7wkJ/zsszps3T+np6br00kt13XXX6fbbb9e99957UK8zbNgwXXPNNVq2bJnS0tIUDodVXFysX/3qV5GLR7XjAAAA7vbZZ5+pZcuWmjNnjqZMmaJJkybp5ZdfjkyekaTdu3frlVde0cCBA7Vz507t3LlTRxxxhHr27KmCggJlZ2crPv5/f9dy+eWX65JLLtGiRYv0/vvva+nSpXrjjTf0xhtv6De/+Y0mTZoUWbewsFAdOnTQ8ccfr23btkn66RM+H3vsMX3zzTeRX7xFq6mpqfMXc3FxcQqFQrIsq07mqU8oFJL0v+wUCoX2yk0Hen5D1rfDsqy9stQ555yj6667rt71O3XqVO/jI0eO1Ouvv6577rlH/fr1U5s2bWyN54svvtDnn3+ukSNHRvbTmWeeqWbNmmnevHk67bTTbG0XAICgI4v9T+0vGisrK/XMM89oyZIlmjp1qs4999yDaaWGDx+u4cOH17usvtsSStJNN92khQsXaurUqXrllVcO6nXq8/bbb2vz5s0aOHBgpI8DBgzQjBkz9MILL2js2LG2tw0AALzlYLNbY/8OraHZal+Tvn7+85/X+fr888/XOeeco8WLF+u9997TkiVL9N5772nRokX6+9//rhkzZvD7QcBjmFgFwKijjjpKGRkZysjIkCStWLFCOTk5mj59ui666CIlJibu917DVVVVatmy5UG/XmlpqSSpdevWeumllzR58uQ6y//5z39G/j89PV2PPPKImjZtqssvv1zPPvuszjrrLA0ZMuSAr1P7SQXFxcVKS0vTokWLtHnz5r1uA1j7cZ9VVVV1JlztWV+zZs0Ouj4AAOC8o446SrNnz1aXLl1033336bLLLtPEiRP11FNPRS6CvP3229qyZYtefPHFei+yvPPOO0pPT6/zWGJios477zydd955kqT//Oc/mjJlimbNmqVLLrlEnTt31pYtW/Svf/1Lu3fv1umnn77XdufNm6ecnJx6xz1lyhS99NJLka/btWunt956S+3atdPKlSv3W/OGDRskSW3bto3890CfKvrdd9/p2GOPtbW+HZs2bVLnzp3rPNayZUulpqY2aDu1twQcMmSIpk6dqieffNLWeGr3++TJk/fKmH//+981ZcoUHXXUUba2DQBAkJHF/mfPnNOrVy9deeWVuvHGG/XMM8+oV69e+92mJCUlJTU4Kx1++OG66667NHLkSN13333Kzs5u0PNrFRYWSpKuvPLKvZa9+OKLuvbaaw844QwAAPjDwWY3p39HGK2h2aohOapJkybq16+f+vXrJ+mn30/ecccdev311/X222/rnHPOsT1uALHHrQABxFxpaan69u1b7+1WunbtqnHjxqmqqkrr16/XMcccox9//HGfwWnTpk2R2/sdjPfee08nnHCCWrdurXPOOScS2mr/JSUlRdYdNGhQZLJTTk6OTjzxRP3xj3/UN998c8DXSUhI0MUXX6y//e1vCofDmj9/vrp3777XJyXUjr12wteh1gcAAJyXkpKiLl26SJLS0tI0ZswYvfvuu3Um4BQWFuq4447TX/7ylzr/Zs+erSOPPDJyq5hwOKwBAwbooYce2ut1TjjhBE2dOlWS9OWXX0qSXnnlFVVXVys/P3+vbZ9++ukqKiraZ066/vrr6+ScmTNnSvrpkwG+//77/d6ebsGCBTr88MN11llnSZL69eunLVu21Hs7PUlauXKl+vfvr2eeecbW+g315ZdfqqysrN5fcNpx/PHHa9y4cXrnnXdsfeR8VVWViouLdf755++1n+666y7t2rWrzi9WAQDAwSOL1S8+Pl533323EhISNGnSJO3atWt/bTwkPXv2VFZWlgoKCvT+++83+PmbN2/Wv//9b1122WV79fHmm29WaWmpFi5c2AgjBwAAbnSw2c3p3xHuj1PZasSIEXv9wZ300wc+3HnnnZL+lzUBeAcTqwDE3DHHHKOEhATNnTu33mDy1Vdf6bDDDtMJJ5yg3r17a/fu3XrjjTf2Wm/ZsmXatGmTzjzzzIN63bffflslJSX6v//7P0nS0UcfrdTU1Dr/6vvUKOmnv86bPn26qqqqNG7cOO3evfuArzds2DBt2bJFixYt0ttvv61f//rXe61z+umnKy4urt77Kn/33Xdavnz5QdcHAABi49prr1VqaqpmzJih5cuXq6ysTO+8844uvPBCnXHGGXX+nXnmmRo0aJD+9a9/qbS0VKFQSElJSSosLNQPP/yw17a//vprSYp8ElNRUZG6d++ugQMH7rXt4cOHa+vWrfXmJElq3759nZyTkpIiSRoyZIhOOOEE3XrrrfWOYeHChZo/f76ysrLUvHnzyHNatWqlu+++Wzt37qyzfjgc1v33368mTZrol7/8pa31G+qhhx7S4YcfrqFDh9p6fn2ysrLUs2dP3XPPPQ1+7ltvvaUff/xRI0aM2Gs/DRs2TMnJyZo3b55jYwUAIMiCmMX2pV27drr22mu1fv16PfHEEw3qY0PdfPPNOv7443Xvvfc2+Lkvv/yyqqurNWrUqL36OGrUKDVv3jzyC1QAAOBvDcluTv6O8GA4ka3atWunBQsWaP369Xsti86aALyDz9YFEHOhUEh//OMfdd1112nYsGG6/PLL1bFjR1VWVurdd9/VnDlzdOONN+qoo45Sr169NGDAAE2ZMkVfffWVevXqpfj4eK1YsUJPPvmkevbsqcGDB9fZflVVlT799FNJkmVZ2rZtmz788EP95S9/0RlnnKGRI0faGvcpp5yia6+9VjNmzNADDzygiRMn7nf9Dh066LTTTtNdd90lSXuNU/rpI91HjhypP/3pT9q8ebP69eunww8/XF9++aWefvpptWvXzvZ4AQBA40hISND06dM1dOhQ3Xzzzbr44otVXV2tCy+8sN71L774Yr3wwgsqKCjQDTfcoKlTpyorK0uXXHKJrrjiCp188smqqanRBx98oGeeeUYjRoxQp06dtHz5cq1evVq5ubn1bve8887TEUccoeeff36fr12fZs2a6eGHH9aYMWN08cUX6ze/+Y26du2qyspKvfXWW3rxxRd17rnn6sYbb4w858gjj9Q999yj66+/XpmZmRo5cqSSk5O1adMmzZkzR8uXL1deXp5at25ta/192bRpUyTXVVdXq7S0VC+99JIWLVqk22+/XW3atKmz/tatWyPrRwuFQvv9yPbav0w8mNs+RyssLNTPf/7zfV7MGzJkiB566CEtWbJEZ5xxhqSf/jqxvk/sOu2003Tqqac2eAwAAARFELPY/lx55ZV68cUX9cQTT2jo0KFq167dPtfdM1tFS0xMjEz+qs+etwRsqKKiInXr1k3Jycn1bveCCy5QUVGR1q9fr+OOO06S9Omnn9ablfr166eOHTs2eAwAAMAd5s+f36Ds1tDfER6q/WWrfeUo6affCR511FEaN26clixZol//+te64oor1KNHD8XHx6ukpERPP/20zj77bJ199tmOjhlA42NiFQAj+vfvr4KCAj311FN69NFHtXXrVjVt2lRdu3bVgw8+qPPPPz+y7kMPPaRZs2bptdde06xZs1RTU6N27drpsssu0+jRoxUKhepsu6ysTJdeemnk62bNmqlDhw4aO3assrKy1KRJE9vjHjNmjP79739r1qxZ+sUvfhG5N/K+/PrXv9aUKVM0bNgwHXHEEfWuM3XqVJ100kkqLCzUSy+9pF27dqlNmzYaOHCgrr766gP+dSIAAIi9Dh066JZbbtFtt92mxx9/XCeddNI+/9qsZ8+eat++vV544QVde+21OuWUUzR//nw99thjevbZZ1VWVqZQKKROnTppypQpkU+5LCwsVCgU0qBBg+rdbmJiYuSXUGvXrtVhhx120ONPSUlRUVGRnn32Wb344ovasGGDDj/8cHXp0kX33XdfvRe3am/l/PTTT+uxxx7T5s2b1bJlS51yyimaN2+e0tLSDmn9+tTeOkf6aeJTy5YtlZaWplmzZqlPnz57rf+vf/1L//rXv+rd1pFHHqkPP/xwv693wgknaNy4cbr77rsPOLZapaWlevfddzVixIi9cmmtX/3qV3r44Yf1/PPPRyZWlZSU1HurxBtvvJGJVQAAHEAQs9i+NG3aVFOmTNGYMWN077331nubw1p7ZqtoXbp00csvv7zf1+rVq5dGjhypv/71rwc9vmXLlunLL7/ULbfcss91Lr74YhUWFmrevHmaMGGCJGnRokVatGjRXuseffTRTKwCAMDDioqKGpTdGvo7wkO1v2y15+8eo+Xn52vgwIFq3769XnrpJT322GMqLi7WE088IcuydMIJJ+iqq67SFVdcobi4OEfHDKDxxVmWZZkeBAAAAAAAAAAAAAAAAAC4SbzpAQAAAAAAAAAAAAAAAACA2zCxCgAAAAAAAAAAAAAAAACiMLEKAAAAAAAAAAAAAAAAAKIwsQoAAAAAAAAAAAAAAAAAojCxCgAAAAAAAAAAAAAAAACiJJgegEk1NTWqrq5WfHy84uLiTA8HAAC4hGVZqqmpUUJCguLjmYd+sMhWAACgPmSrhiNXAQCA+pCrGo5cBQAA6tOQXBXoiVXV1dUqKSkxPQwAAOBSqampatq0qelheAbZCgAA7A/Z6uCRqwAAwP6Qqw4euQoAAOzPweSqQE+sqp111rVrVwKoAeFwWCUlJUpNTVUoFDI9nMCh/2bRf3PovVle6X/tOPnLv4YhW9njlfeFG9E7e+ibffTOPnpnj1/6RrZqOHKVfX5535hA7+yjd/bRO/vonX1e7h25quHIVfZ4+X1iGr2zh77ZR+/soW/2+KlvDclVgZ5YVfuRn6FQyPM73cvov1n03yz6bw69N8sr/efjwRuGbHVo6Jt99M4e+mYfvbOP3tnjl76RrQ4euerQ0Tv76J199M4+emcfvbPPy70jVx08ctWhoW/20Tt76Jt99M4e+maPn/p2MLmKKe0AAAAAAAAAAAAAAAAAEIWJVQAAAAAAAAAAAAAAAAAQhYlVAAAAAAAAAAAAAAAAABCFiVUAAAAAAAAAAAAAAAAAEIWJVQAAAAAAAAAAAAAAAAAQhYlVAAAAAAAAAAAAAAAAABCFiVUAAAAAAAAAAAAAAAAAEIWJVQAAAAAAAAAAAAAAAAAQhYlVAAAAAAAAAAAAAAAAABCFiVUAAAAAAAAAAAAAAAAAEIWJVQAAAAAAAAAAAAAAAAAQhYlVAAAAAAAAAAAAAAAAABCFiVUAAAAAAAAAAAAAAAAAEIWJVQAAAAAAAAAAAAAAAAAQhYlVMCoxMdH0EAAAAAAAAOAjXG8CAAAAAABOYWKVpFAoZHoIgRQKhdS1a9f99j9cY8VwRAAAwAl+ylZkEQAAYJKfclWsHMz1poYiEwIAAAAAEFwJpgfgBhMLl2nlph2mh4EonZKaa8aIHqaHAQAAGsgv2YosAgAATPNLrvIyMiEAAAAAAMHGxCpJX5Xt0Ocbt5keBgAAgC+QrQAAAJxBrgIAAAAAADCLWwECAAAAAAAAAAAAAAAAQBQmVgEAAAAAAAAAAAAAAABAFCZWAQAAAAAAAAAAAAAAAEAUoxOrZs2apYyMDGVkZGjy5MmqqqrS6tWrNXz4cA0aNEhjx45VRUWFJGn79u265pprNHjwYA0bNkzr1q2LbOeuu+7S4MGDdeGFF6q4uNhQNQAAAOaQqwAAAJxBrgIAAHAO2QoAAHidsYlVy5cvV1FRkQoKClRcXKzq6mrNnTtXOTk5mjBhghYsWKCOHTtq5syZkqQZM2aoW7du+tvf/qYJEyZo0qRJkqT3339fy5YtU3FxsWbPnq3bb79dlZWVpsoCAACIOXIVAACAM8hVAAAAziFbAQAAP0gw9cItWrRQbm6umjVrJknq0qWLVq1apfLycvXu3VuSlJmZqaysLI0fP14LFy7U7NmzJUl9+vTR1KlTtXHjRoXDYVVVVam6ulq7du1S06ZNTZWERhIOh00PwZdq+0p/zaD/5tB7s7zSf7ePLxq5qnE19veDV94XbkTv7KFv9tE7++idPX7pm5fGT67Cvnjp+9guvxxzTKB39tE7++idfV7undfG7KZsVVNT47n+meTl94lp9M4e+mYfvbOHvtnjp741pAZjE6uSk5OVnJwsSdqyZYvmzJmjESNG1PlYz6SkJJWWlkqSSktL1bp16zrLNm3apL59+6qgoEBnn322KioqNGHCBCUmJsayFDSyVatW8ZcHjaikpMT0EAKN/ptD782i/84iVzWuWGUR3hf20Tt76Jt99M4+emcPfYsdchX2JUjXpzjm2Efv7KN39tE7++hd43NTtlq7dq127tx5yDUFDe8T++idPfTNPnpnD32zJ2h9MzaxqtaGDRs0ZswYZWZmqlevXlq4cGGd5XFxcZIky7L2em58fLzmzZunUCikRYsW6ccff9QVV1yhtLQ0de/ePRbDRwykpKSYHoIvhcNhlZSUKDU1VaFQyPRwAof+m0PvzfJK/2vH6TXkqsbR2FnEK+8LN6J39tA3++idffTOHr/0zYvZilyFaEG4PuWXY44J9M4+emcfvbPPy73zYq6S3JGtOnbsqCZNmhxSHUHi5feJafTOHvpmH72zh77Z46e+NSRXGZ1YtXLlSmVnZys7O1tZWVnauHGjysrKIsvLysrUpk0bSVLr1q1VVlamY489ts6yP//5z/q///s/NWnSRK1atVL//v314YcfcqHKR7z+hnS7UChEjw2i/+bQe7Pov/PIVY0nVt+rvC/so3f20Df76J199M4e+hZb5CrUJ0jvQY459tE7++idffTOPnoXG27JVvHx8exvG3if2Efv7KFv9tE7e+ibPUHrW7ypF966datGjx6t3NxcZWVlSZLatm2rxMRELV26VJJUWFio9PR0SVL//v1VWFgoSVqyZImaNWumNm3aqEuXLvrHP/4hSaqoqNDixYvVrVs3AxUBAACYQa4CAABwBrkKAADAOWQrAADgB8Y+sWr27Nnavn278vPzlZ+fL+mnwJSXl6fc3FyVl5erffv2ysvLkyTdeOON+v3vf6+MjAw1bdpU9913nyTp6quv1m233aZBgwYpISFBQ4cOVZ8+fUyVBQAAEHPkKgAAAGeQqwAAAJxDtgIAAH5gbGLVuHHjNG7cuHqXzZs3b6/HWrRooYcffnivx5s1a6Z7773X8fEBAAB4BbkKAADAGeQqAAAA55CtAACAHxi7FSAAAAAAAAAAAAAAAAAAuBUTqwAAAAAAAAAAAAAAAAAgChOrAAAAAAAAAAAAAAAAACBKgukBuMGJrY7QrrDpUSBap6TmpocAAABs8Eu2IosAAADT/JKrvIxMCAAAAABAsDGxStK9w9IUCoVMDwP1CNdYCsXHmR4GAABoAD9lK7IIAAAwyU+5ysvIhAAAAAAABBe3ApQUDvOnfyaEw2GtWLFiv/3nohUAAN7jp2xFFgEAACb5KVfFysFcb2ooMiEAAAAAAMHFxCoYVVlZaXoIAAAAAAAA8BGuNwEAAAAAAKcwsQpGJSYmmh4CAACAcWQiAAAA55CtAAAAAACAUxJMD8ANQqGQ6SEEUigUUteuXWP6muEai49vBwCgkZGtGsZEJoo1MhgAAPaQqxouCNkqGlkLAAAAAIDGw8QqSRMLl2nlph2mh4FG1impuWaM6GF6GAAA+B7ZCnsigwEAYB+5CgdC1gIAAAAAoHExsUrSV2U79PnGbaaHAQAA4AtkKwAAAGeQqwAAAAAAAMyKNz0AAAAAAAAAAAAAAAAAAHAbJlYBAAAAAAAAAAAAAAAAQBQmVgEAAAAAAAAAAAAAAABAFKMTq2bNmqWMjAxlZGRo8uTJqqqq0urVqzV8+HANGjRIY8eOVUVFhSRp+/btuuaaazR48GANGzZM69atkyRVVFQoJydHgwcP1iWXXKK33nrLYEUAAABmkKsAAACcQa4CAABwDtkKAAB4nbGJVcuXL1dRUZEKCgpUXFys6upqzZ07Vzk5OZowYYIWLFigjh07aubMmZKkGTNmqFu3bvrb3/6mCRMmaNKkSZKkxx57TPHx8Xr11Vf11FNP6d5771VpaampsgAAAGKOXAUAAOAMchUAAIBzyFYAAMAPEky9cIsWLZSbm6tmzZpJkrp06aJVq1apvLxcvXv3liRlZmYqKytL48eP18KFCzV79mxJUp8+fTR16lRt3LhRK1eu1GWXXab4+HgdffTR6tKli9555x39+te/NlUaXC4cDpsegivU9oF+mEH/zaH3Znml/24fXzRyFbygMd5XXjmmuA19s4/e2Ufv7PFL37w0fnIVvMqJ95lfjjkm0Dv76J199M4+L/fOa2N2U7aqqanxXP9M8vL7xDR6Zw99s4/e2UPf7PFT3xpSg7GJVcnJyUpOTpYkbdmyRXPmzNGIESMiH+spSUlJSZEZ56WlpWrdunWdZZs2bVLXrl21YMEC9e3bV1u2bNHHH3+sk08+OZalwGNWrVqlyspK08NwjZKSEtNDCDT6bw69N4v+O4tcBS9ozAzGMcUe+mYfvbOP3tlD32KHXAWvcjJrccyxj97ZR+/so3f20bvG56ZstXbtWu3cufOQawoa3if20Tt76Jt99M4e+mZP0PpmbGJVrQ0bNmjMmDHKzMxUr169tHDhwjrL4+LiJEmWZe313Pj4eI0ZM0Z33323hg4dqg4dOqhv375q0qRJTMYOb0pJSTE9BFcIh8MqKSlRamqqQqGQ6eEEDv03h96b5ZX+147Ta8hVcLPGyGBeOaa4DX2zj97ZR+/s8UvfvJityFXwGieyll+OOSbQO/vonX30zj4v986LuUpyR7bq2LEjeawBvPw+MY3e2UPf7KN39tA3e/zUt4bkKqMTq1auXKns7GxlZ2crKytLGzduVFlZWWR5WVmZ2rRpI0lq3bq1ysrKdOyxx9ZZVl5erptuukk/+9nPJElXX321jj/++NgXA8/w+hvcaaFQiJ4YRP/Nofdm0X/nkavgdo35nueYYg99s4/e2Ufv7KFvsUWughc5eYzgmGMfvbOP3tlH7+yjd7HhlmwVHx/P/raB94l99M4e+mYfvbOHvtkTtL7Fm3rhrVu3avTo0crNzVVWVpYkqW3btkpMTNTSpUslSYWFhUpPT5ck9e/fX4WFhZKkJUuWqFmzZmrTpo1ef/11/elPf5IkffHFF/r888/Vp0+f2BcEAABgCLkKAADAGeQqAAAA55CtAACAHxj7xKrZs2dr+/btys/PV35+vqSfAlNeXp5yc3NVXl6u9u3bKy8vT5J044036ve//70yMjLUtGlT3XfffZKkSy+9VDk5OcrIyFBCQoIefPBBNW/e3FRZAAAAMUeuAgAAcAa5CgAAwDlkKwAA4AfGJlaNGzdO48aNq3fZvHnz9nqsRYsWevjhh/d6vGnTppoxY4bj4wMAAPAKchUAAIAzyFUAAADOIVsBAAA/MHYrQAAAAAAAAAAAAAAAAABwKyZWAQAAAAAAAAAAAAAAAEAUY7cCdJMTWx2hXWHTo0Bj65TE/bYBAIgFshX2RAYDAMA+chUOhKwFAAAAAEDjYmKVpHuHpSkUCpkeBmIgXGMpFB9nehgAAPga2QrRyGAAANhDrsLBIGsBAAAAANB4uBWgpHCYP/0zIRwOa8WKFTHtPxeZAABofGSrhjGRiWKNDAYAgD1+zgeNJQjZKhpZCwAAAACAxsPEKhhVWVlpeggAAADGkYkAAACcQ7YCAAAAAABOYWIVAAAAAAAAAAAAAAAAAERhYhWMSkxMND0EAAAA48hE9tE7AAAQjXxgH70DAAAAAKCuBNMDcINQKGR6CIEUCoXUtWtX08OICNdYCsXHmR4GAACeR7ZqGLdlIi+p7V24xjI9FAAAGgW5quHIVvY1Zu+47gYAAAAA8ComVkmaWLhMKzftMD0MGNQpqblmjOhhehgAAPgC2QqxRI4DAPgZuQp+QF4DAAAAAHgZE6skfVW2Q59v3GZ6GAAAAL5AtgIAAHAGuQoAAAAAAMCseNMDAAAAAAAAAAAAAAAAAAC3YWIVAAAAAAAAAAAAAAAAAERhYhUAAAAAAAAAAAAAAAAARDE6sWrWrFnKyMhQRkaGJk+erKqqKq1evVrDhw/XoEGDNHbsWFVUVNR5TmFhoSZNmhT5evfu3ZoyZYoGDx6sCy+8UMuWLYt1GQAAAMaRqwAAAJxDtgIAAHAGuQoAAHidsYlVy5cvV1FRkQoKClRcXKzq6mrNnTtXOTk5mjBhghYsWKCOHTtq5syZkqRdu3Zp+vTpuuOOO+psZ86cObIsS3/72980Y8YM3XLLLaqurjZREgAAgBHkKgAAAOeQrQAAAJxBrgIAAH6QYOqFW7RoodzcXDVr1kyS1KVLF61atUrl5eXq3bu3JCkzM1NZWVkaP368Fi9eLMuylJOTo+XLl0e2s3DhQl133XWSpE6dOql169b65JNPdPrpp8e+KHheOBw2PYSYqa01SDW7Cf03h96b5ZX+u3180chVgPfetyZ55VjsRvTOPnpnj1/65rXxk62AxuG1Y0FD+OV4bQK9s4/e2efl3nltzG7KVTU1NZ7rn0lefp+YRu/soW/20Tt76Js9fupbQ2owNrEqOTlZycnJkqQtW7Zozpw5GjFihNatWxdZJykpSaWlpZKk9PR0paenq6ioqM52SktL1bp16zrP2bRpU6OPH/60atUqVVZWmh5GTJWUlJgeQqDRf3PovVn031nkKiCYOe5QcSy2j97ZR+/soW+xRbYCGkcQ8hrHa/vonX30zj561/jclKvWrl2rnTt32iskwHif2Efv7KFv9tE7e+ibPUHrm7GJVbU2bNigMWPGKDMzU7169dLChQvrLI+Li9vv8y3L2uux+HhjdziEx6WkpJgeQsyEw2GVlJQoNTVVoVDI9HACh/6bQ+/N8kr/a8fpNeQqBFmQctyh8sqx2I3onX30zh6/9I1s9T9kKwSZn/OaX47XJtA7++idfV7uHbnqfxqaqzp27KgmTZo06DlB5uX3iWn0zh76Zh+9s4e+2eOnvjUkVxmdWLVy5UplZ2crOztbWVlZ2rhxo8rKyiLLy8rK1KZNm/1uo3Xr1iorK9MJJ5wQec6es9aBhvD6m9+OUCgUyLrdgv6bQ+/Nov/OI1ch6DimNBzHYvvonX30zh76FntkK8B5QTiOcby2j97ZR+/so3ex4ZZcFR8fz/62gfeJffTOHvpmH72zh77ZE7S+Gfszua1bt2r06NHKzc1VVlaWJKlt27ZKTEzU0qVLJUmFhYVKT0/f73b69++vwsJCST99jOc333yjU089tXEHDwAA4CLkKgAAAOeQrQAAAJxBrgIAAH5g7BOrZs+ere3btys/P1/5+fmSfgpGeXl5ys3NVXl5udq3b6+8vLz9bmfkyJG67bbbdOGFFyouLk533XWXmjZtGosSAAAAXIFcBQAA4ByyFQAAgDPIVQAAwA+MTawaN26cxo0bV++yefPm7fN5l1xyiS655JLI102bNtWdd97p+PgAAAC8glwFAADgHLIVAACAM8hVAADAD4zdChAAAAAAAAAAAAAAAAAA3IqJVQAAAAAAAAAAAAAAAAAQxditAN3kxFZHaFfY9ChgUqek5qaHAACAb5CtEEvkOACAn5Gr4AfkNQAAAACAlzGxStK9w9IUCoVMDwOGhWssheLjTA8DAADPI1sh1shxAAC/IlfBL8hrAAAAAACv4laAksJh/vTPhHA4rBUrVrim/1zcAQDAGW45t3uF2zKRl9T2TlaN6aEAANAoyAcNR7ayrzF7x3U3AAAAAIBXMbEKRlVWVpoeAgAAgHFkIvvoHQAAiEY+sI/eAQAAAABQFxOrAAAAAAAAAAAAAAAAACAKE6tgVGJioukhBBr9BwDAHTgn20fvAABANPKBffQOAAAAAIC6EkwPwA1CoZDpIQRSKBRS165dTQ8jsNzS/3CNpVB8nOlhAAAcRLZqGLeck73IRO/ILgCAWCJXNRzZyj639I68BQBoDLW5ivMMAABoKCZWSZpYuEwrN+0wPQwgcDolNdeMET1MDwMA4DCyFfyK7AIAiDVyFYKGvAUAaCwTC5dpd00c5xkAANBgTKyS9FXZDn2+cZvpYQAAAPgC2QoAAMAZ5CoAAABnfFW2Q7vCpkcBAAC8KN70AAAAAAAAAAAAAAAAAADAbZhYBQAAAAAAAAAAAAAAAABRmFgFAAAAAAAAAAAAAAAAAFGMT6yaNWuWMjIylJGRocmTJ6uqqkqrV6/W8OHDNWjQII0dO1YVFRV1nlNYWKhJkybVeWz16tW68MILYzl0AAAAVyFXAQAAOIdsBQAA4AxyFQAA8DKjE6uWL1+uoqIiFRQUqLi4WNXV1Zo7d65ycnI0YcIELViwQB07dtTMmTMlSbt27dL06dN1xx131NlOUVGRrrrqKlVWVpooAwAAwDhyFQAAgHPIVgAAAM4gVwEAAK9LMPniLVq0UG5urpo1ayZJ6tKli1atWqXy8nL17t1bkpSZmamsrCyNHz9eixcvlmVZysnJ0fLlyyVJP/74o95++2098MADmjhxorFaANgXDodNDyHmamsOYu2m0XuzvNJ/t4+vPuQqIHa8eIzYk1eOxW5E7+yjd/b4pW9eHD/ZCjDLa8cNvxyvTaB39tE7+7zcOy+O2Y25yot9jDUvv09Mo3f20Df76J099M0eP/WtITUYnViVnJys5ORkSdKWLVs0Z84cjRgxQuvWrYusk5SUpNLSUklSenq60tPTVVRUFFnesmVLPfTQQ9qwYUMshw7AQatWrQrsX5mUlJSYHkJg0Xuz6L/zyFVA7Pglu3Asto/e2Ufv7KFvsUe2Aszyat7ieG0fvbOP3tlH72LDjbnKq+cZE3if2Efv7KFv9tE7e+ibPUHrm9GJVbU2bNigMWPGKDMzU7169dLChQvrLI+LizM0MgCxkJKSYnoIMRcOh1VSUqLU1FSFQiHTwwkUem+WV/pfO04vIlcBjc/r2cUrx2I3onf20Tt7/NI3shWAhvJa3vLL8doEemcfvbPPy70jVznDa+cZE7z8PjGN3tlD3+yjd/bQN3v81LeG5CrjE6tWrlyp7OxsZWdnKysrSxs3blRZWVlkeVlZmdq0aWNwhAAam9cPuociFAoFun6T6L1Z9L9xkKuA2PDL8YtjsX30zj56Zw99M4NsBZjj1WMex2v76J199M4+ehc7bstV7PeDx/vEPnpnD32zj97ZQ9/sCVrf4k2++NatWzV69Gjl5uYqKytLktS2bVslJiZq6dKlkqTCwkKlp6ebHCYAAIDrkasAAACcQ7YCAABwBrkKAAB4ndFPrJo9e7a2b9+u/Px85efnS5L69++vvLw85ebmqry8XO3bt1deXp7JYQIAALgeuQoAAMA5ZCsAAABnkKsAAIDXGZ1YNW7cOI0bN67eZfPmzdvn8y655BJdcskldR5r37693nrrLUfHBwAA4BXkKgAAAOeQrQAAAJxBrgIAAF5n9FaAAAAAAAAAAAAAAAAAAOBGTKwCAAAAAAAAAAAAAAAAgChGbwXoFie2OkK7wqZHAQRPp6TmpocAAGgEZCv4FdkFABBr5CoEDXkLANBYTmx1hHbXxJkeBgAA8CAmVkm6d1iaQqGQ6WEAgRSusRSK54cZAPATshX8jOwCAIglchWCiLwFAGgMtbmK8wwAAGgobgUoKRzmT/9MCIfDWrFiBf03xC395wcYAPAf0+cWr3HLOdmLTPSO7AIAiCXyQcORrexzS+/IWwCAxlB7fuM8AwAAGoqJVTCqsrLS9BACjf4DAOAOnJPto3cAACAa+cA+egcAAAAAQF1MrAIAAAAAAAAAAAAAAACAKEysglGJiYmmhxBo9B8AAHfgnGwfvQMAANHIB/bROwAAAAAA6kowPQA3CIVCpocQSKFQSF27djU9jMBya//DNRb3OAcAjyNbNYxbz8le4PXekXsAAAdCrmo4r+cDk5zsHTkHAOA2Xs1VnFMBADCPiVWSJhYu08pNO0wPAwi8TknNNWNED9PDAAAcIrIVcGDkHgDAwSBXwYvIOQAAN/JiruKcCgCAOzCxStJXZTv0+cZtpocBAADgC2QrAAAAZ5CrAAAAnEGuAgAAdsWbHgAAAAAAAAAAAAAAAAAAuA0TqwAAAAAAAAAAAAAAAAAgChOrAAAAAAAAAAAAAAAAACCK0YlVs2bNUkZGhjIyMjR58mRVVVVp9erVGj58uAYNGqSxY8eqoqKiznMKCws1adKkyNc//PCDrr32Wg0ZMkQXXXSRXnvttViXAQAAYBy5CgAAwDlkKwAAAGeQqwAAgNcZm1i1fPlyFRUVqaCgQMXFxaqurtbcuXOVk5OjCRMmaMGCBerYsaNmzpwpSdq1a5emT5+uO+64o852HnroIXXt2lWvvPKKnn76ad19993avHmziZIAAACMIFcBAAA4h2wFAADgDHIVAADwgwRTL9yiRQvl5uaqWbNmkqQuXbpo1apVKi8vV+/evSVJmZmZysrK0vjx47V48WJZlqWcnBwtX748sp2zzz5bp5xyiiSpVatWatmypTZv3qxjjjkm9kUBcEQ4HDY9hEZXW2MQanUbem+WV/rv9vFFI1cB3mXieOOVY7Eb0Tv76J09fumb18ZPtgKc4bX3/qHwy/HaBHpnH72zz8u989qYyVXO4NqBt9A7e+ibffTOHvpmj5/61pAajE2sSk5OVnJysiRpy5YtmjNnjkaMGKF169ZF1klKSlJpaakkKT09Xenp6SoqKqqznXPOOSfy/6+99pqqqqrUqVOnRh8/gMazatUqVVZWmh5GTJSUlJgeQmDRe7Pov7PIVYB3mcw9HIvto3f20Tt76Ftska0AZwTp+k4tjtf20Tv76J199K7xkaucwbUDb6J39tA3++idPfTNnqD1zdjEqlobNmzQmDFjlJmZqV69emnhwoV1lsfFxR3Udl5++WVNnz5dTz75pBISjJcF4BCkpKSYHkKjC4fDKikpUWpqqkKhkOnhBAq9N8sr/a8dp9eQqwDvMZF7vHIsdiN6Zx+9s8cvfSNbka0QTEG4vlPLL8drE+idffTOPi/3jlwVzFzFtQNvoXf20Df76J099M0eP/WtIbnKaOpYuXKlsrOzlZ2draysLG3cuFFlZWWR5WVlZWrTps0Bt/P444/r+eef1+zZs9WxY8fGHDKAGPD6QbghQqFQoOp1E3pvFv13HrkK8CaTx0KOxfbRO/vonT30LfbIVsChC+Jxi+O1ffTOPnpnH72LDXLVoePagTfRO3vom330zh76Zk/Q+hZv6oW3bt2q0aNHKzc3V1lZWZKktm3bKjExUUuXLpUkFRYWKj09fb/bKSoq0ksvvaR58+YFLkgBAABI5CoAAAAnka0AAACcQa4CAAB+YOwTq2bPnq3t27crPz9f+fn5kqT+/fsrLy9Pubm5Ki8vV/v27ZWXl7ff7Tz44IOKi4vT6NGjI4/dfvvtSktLa9TxAwAAuAW5CgAAwDlkKwAAAGeQqwAAgB8Ym1g1btw4jRs3rt5l8+bN2+fzLrnkEl1yySWRr9955x3HxwYAAOAl5CoAAADnkK0AAACcQa4CAAB+YOxWgAAAAAAAAAAAAAAAAADgVkysAgAAAAAAAAAAAAAAAIAoxm4F6CYntjpCu8KmRwGgU1Jz00MAADiAbAUcGLkHAHAwyFXwInIOAMCNvJirOKcCAOAOTKySdO+wNIVCIdPDACApXGMpFB9nehgAgENAtgIODrkHAHAg5Cp4FTkHAOA2Xs1VnFMBADCPWwFKCoc9NkXdJ8LhsFasWEH/DXFr//kBAQC8z23nFrdz6znZC7zeO3IPAOBAvHqOM8nr+cAkJ3tHzgEAuI1XswHnVAAAzGNiFYyqrKw0PYRAo/8AALgD52T76B0AAIhGPrCP3gEAAAAAUBcTq2BUYmKi6SEAAAAYRyayj94BAIBo5AMAAAAAAOCUBNMDcAMv3lPZD0KhkLp27Wp6GL7H/bcBALFGtmoYMpF9B9M7shAAwMvIVQ0X9GxF9gEAoH575irOlwAAoCGYWCVpYuEyrdy0w/QwAMd1SmquGSN6mB4GACBgyFZwC7IQAMDryFVoCLIPAAD7VpurOF8CAICGYmKVpK/KdujzjdtMDwMAAMAXyFYAAADOIFcBAAA4g1wFAADsijc9AAAAAPy/9u4/zuq6zhf4eziYDiLZ3ZwMqTuGIlITooSa1rhW5uroJt1x2WzSRxLczbJYILWcWsO9kThubbFY5iK7QSs1ZGIbu/2wu7frD1wzGIXAH5EiiSO6McAIceZ7//AyOSMI58uZ+Z4fz+fj0ePRnO+c73l/X8fz/b7mPD6cAwAAAAAAAJQaC6sAAAAAAAAAAAD6sbAKAAAAAAAAAACgHwurAAAAAAAAAAAA+sl0YdXChQujqakpmpqa4pprroldu3bF+vXr4+KLL45zzz03rrzyytixY0ef+7S3t8fVV1/d+/OmTZviIx/5SFx44YXR3Nwca9euHezDAADInF4FAFA8uhUAQHHoVQBAuctsYdXq1atj2bJlsXTp0li+fHns3r07lixZErNnz45Zs2bFihUrYvTo0bFgwYKIiNi5c2fMmzcvrr/++j77mTt3blxwwQVx5513xic/+cm47rrrsjgcAIDM6FUAAMWjWwEAFIdeBQBUgqFZPfCIESOitbU1hg0bFhERY8eOjXXr1kVXV1dMmjQpIiKam5ujpaUlZs6cGffdd18kSRKzZ8+O1atX9+7nK1/5Su//37hxY4wYMWJQjwPKQT6f3+dte9vGwJN/dmSfrXLJv9Tn60+vgldXbq/pgVYu5+JSJLv0ZJdOpeRWbvPrVpS7tK+5SjnnZEF26ckuPdmlV87ZldvMpdyryi3LwVbOr5OsyS4duaUnu3Tklk4l5VbIMWS2sKq+vj7q6+sjImLLli2xePHimDJlSmzYsKH3d+rq6mLz5s0REdHY2BiNjY2xbNmyPvsZMuSlD90655xzYtOmTb2r2oE/WrduXXR3d+91W0dHxyBPw8vJPzuyz5b8i0uvglf3al2omjkXpye79GSXjtwGl25FuTvY7uOck57s0pNderJLT3YDr5R7lfcKDozXSXqyS0du6ckuHbmlU225Zbawao+NGzfG9OnTo7m5OSZOnBh33313n+01NTUHtJ9///d/j0ceeSQuv/zyWLFiRRx55JEDMC2UpxNOOOEVt+Xz+ejo6IiGhobI5XIZTFXd5J8d2WerXPLfM2e50atg7/bWhapZuZyLS5Hs0pNdOpWSm26lWzG40nafSjnnZEF26ckuPdmlV87Z6VXF61XeK3h15fw6yZrs0pFberJLR27pVFJuhfSqTBdWrV27NqZNmxbTpk2LlpaW2LRpU3R2dvZu7+zsjKOPPvpV9/GTn/wkzjzzzDjssMPirW99axxzzDHx1FNPeZMKXubVTmq5XK7sT3rlTP7ZkX225F98ehXsm/PN3jkXpye79GSXjtwGn25FOTvY84VzTnqyS0926ckuPdkNjlLtVZ77A+N1kp7s0pFberJLR27pVFtuQ7J64Oeffz6mTp0ara2t0dLSEhERI0eOjNra2li5cmVERLS3t0djY+Or7ue73/1utLe3R0TE+vXrY8uWLTF69OiBHR4AoIToVQAAxaNbAQAUh14FAFSCzD6xatGiRbFt27aYP39+zJ8/PyIizjrrrGhra4vW1tbo6uqKUaNGRVtb26vu5wtf+EJ89rOfjaVLl8ahhx4aN910UwwbNmwwDgEAoCToVQAAxaNbAQAUh14FAFSCzBZWzZgxI2bMmLHXbbfffvs+7zd58uSYPHly788jR46M2267rdjjAQCUDb0KAKB4dCsAgOLQqwCASpDZVwECAAAAAAAAAACUKgurAAAAAAAAAAAA+snsqwBLyVuOOjx25rOeAorvuLrhWY8AQBXSrSgVuhAA5U6vohC6DwDs255e5XoJABTKwqqI+PIHx0cul8t6DBgQ+Z4kckNqsh4DgCqiW1FKdCEAypleRaF0HwDYu5f3KtdLAKAQvgowIvJ5//QvC/l8PtasWSP/AeaPAwAGm2t7YXSi9A4kO10IgHKmHxSu2ruV7gMAe/fybuB6CQAUwsIqMtXd3Z31CAAAmdOJ0pMdANCffgAAAAAUi4VVAAAAAAAAAPSqra3NeoSyJbt05AaUqqFZDwAAAAAAAAAwUHK5XNYjlJVcLhfjxo3LeoyyJLt0BiO3fE/iq0CBVCysAgAAAAAAACrWVe2rYu0z27MeA8jIcXXD46tTJmQ9BlCmLKwCAAAAAAAAKtYTndvjkU1bsx4DAChDQ7IeAAAAAAAAAAAAoNRYWAUAAAAAAAAAANCPhVUAAAAAAAAAAAD9ZLqwauHChdHU1BRNTU1xzTXXxK5du2L9+vVx8cUXx7nnnhtXXnll7Nixo8992tvb4+qrr37FvrZt2xbvfe974/777x+s8QEASoZeBQBQPLoVAEBx6FUAQLnLbGHV6tWrY9myZbF06dJYvnx57N69O5YsWRKzZ8+OWbNmxYoVK2L06NGxYMGCiIjYuXNnzJs3L66//vq97m/OnDmxdevWwTwEAICSoFcBABSPbgUAUBx6FQBQCYZm9cAjRoyI1tbWGDZsWEREjB07NtatWxddXV0xadKkiIhobm6OlpaWmDlzZtx3332RJEnMnj07Vq9e3Wdf//qv/xqHH354nHDCCalm6enpiXw+f3AHRMH2ZC77bMg/W/LPjuyzVS75l/p8/ZVSr4rQrQpVLq+LUiS7dOSWnuzSk106lZJbuc1fSt1KrypcpbxusiC79GSXnuzSk1165Zxduc1cSr0KIKL8zqMHopyva1mSWzqVlFshx5DZwqr6+vqor6+PiIgtW7bE4sWLY8qUKbFhw4be36mrq4vNmzdHRERjY2M0NjbGsmXL+uxn06ZNsWjRoli0aFF87GMfSzXL448/Hi+++GKq+3LwOjo6sh6hqsk/W/LPjuyzJf/iKqVeFaFbpeV1kZ7s0pFberJLT3bpyG1wlVK30qvS87pJT3bpyS492aUnu/RkN/BKqVcBRESsW7cuuru7sx5jQLiupSO3dKott8wWVu2xcePGmD59ejQ3N8fEiRPj7rvv7rO9pqZmn/ft6emJz33uc9Ha2hqHHXZY6hlGjx4dhxxySOr7k04+n4+Ojo5oaGiIXC6X9ThVR/7Zkn92ZJ+tcsl/z5zlphR6VYRuVahyeV2UItmlI7f0ZJee7NKplNx0K+9ZDaZKed1kQXbpyS492aUnu/TKOTu96uDeswKoxE+9K+frWpbklk4l5VZIr8p0YdXatWtj2rRpMW3atGhpaYlNmzZFZ2dn7/bOzs44+uij93n/J554Ip544on43Oc+FxERTz75ZFx77bVx3XXXxTvf+c4DnmPIkCFl/6SXs1wuJ/8MyT9b8s+O7LMl/+IrlV4VoVul5XWRnuzSkVt6sktPdunIbfCVSrfSq9LzuklPdunJLj3ZpSe79GQ3OEqlVwFEREWf913X0pFbOtWWW2YLq55//vmYOnVqfOELX4hzzjknIiJGjhwZtbW1sXLlypg0aVK0t7dHY2PjPvdx3HHHxf/+3/+79+eWlpb4xCc+EaeeeuqAzw8AUCr0KgCA4tGtAACKQ68CACpBZgurFi1aFNu2bYv58+fH/PnzIyLirLPOira2tmhtbY2urq4YNWpUtLW1ZTUiAEBZ0KsAAIpHtwIAKA69CgCoBJktrJoxY0bMmDFjr9tuv/32fd5v8uTJMXny5L1u++d//ueizAYAUE70KgCA4tGtAACKQ68CACrBkKwHAAAAAAAAAAAAKDUWVgEAAAAAAAAAAPST2VcBAgAAAAAAAAy0txx1eOzMZz0FkJXj6oZnPQJQxiysAgAAAAAAACrWlz84PnK5XNZjABnK9ySRG1KT9RhAGfJVgAAAAAAAAEDFyud9XFUh8vl8rFmzRm4pyC6dwcjNoiogLQurAAAAAAAAAOjV3d2d9QhlS3bpyA0oVRZWAQAAAAAAAAAA9GNhFZmqra3NeoSqJn8AKA2uyenJDgDoTz9IT3YAwB56QXqyS0duQKkamvUApSCXy2U9QlXK5XIxbty4rMeoWvJ/pXxP4vuVAYpAtyqMa3J6A5GdPgBAKdGrCqdbpVfJ2el4AOhVhankXjDQZJdOpeamh0JlsLAqIq5qXxVrn9me9RhAho6rGx5fnTIh6zEAKoJuRbnSBwAoNXoVHDwdD4AIvQoYfHooVA4LqyLiic7t8cimrVmPAQBQEXQrAIDi0KsAAIpDrwIA0hqS9QAAAAAAAAAAAAClxsIqAAAAAAAAAACAfiysAgAAAAAAAAAA6Gdolg++cOHCaG9vj4iIhoaGuO6662LDhg1x7bXXxtatW2PMmDExd+7cGDZsWO992tvb44EHHoi5c+dGRER3d3e8853vjDe/+c29v7Ns2bLI5XKDezAAABnSqwAAike3AgAoDr0KACh3mX1i1erVq2PZsmWxdOnSWL58eezevTuWLFkSs2fPjlmzZsWKFSti9OjRsWDBgoiI2LlzZ8ybNy+uv/76Pvt55JFH4rTTTosf/OAHvf9TpACAaqJXAQAUj24FAFAcehUAUAky+8SqESNGRGtra+8K9LFjx8a6deuiq6srJk2aFBERzc3N0dLSEjNnzoz77rsvkiSJ2bNnx+rVq3v309HREZs3b47m5ubI5XIxa9asmDhxYibHBJS/fD4/qI8zWI/HH8k+W+WSf6nP159eBcVVbueAQpXLubgUyS492aVTKbmV2/y6FVSmgTwXVcr5OguyS0926ZVzduU2s14FMPDn7nK+rmVJbulUUm6FHEPBC6uefPLJPh+1mVZ9fX3U19dHRMSWLVti8eLFMWXKlNiwYUPv79TV1cXmzZsjIqKxsTEaGxtj2bJlffZTU1MT5557bnzsYx+LNWvWxPTp02P58uXxute97qBnBKrPunXroru7e9Aer6OjY9Aei75kny35v0SvgtI02H0gK87F6ckuPdmlI7cDo1sBr2YwOp7zdXqyS0926clu3/QqgOIZrPcaXdfSkVs61ZZbwQur/vIv/zK+8Y1vxNve9raiDLBx48aYPn16NDc3x8SJE+Puu+/us72mpuZV73/ZZZf1/v+3vvWt0dDQEL/85S/jPe95T1HmA6rLCSecMCiPk8/no6OjIxoaGnxk8SCTfbbKJf89cw40vQpK02D1gayUy7m4FMkuPdmlUym56Va6FZSCgex4lXK+zoLs0pNdeuWcnV6lVwHlZ6Dfayzn61qW5JZOJeVWSK8qeGHV61//+tiyZUvBQ+3N2rVrY9q0aTFt2rRoaWmJTZs2RWdnZ+/2zs7OOProo191H9/97nfjzDPPjDe+8Y0REZEkSQwdmtk3HAJlbrAvALlcruwvOuVK9tmS/0v0KihN1XJ+ci5OT3bpyS4duR0Y3Qp4NYNxHnW+Tk926ckuPdntm14FUDyDda1xXUtHbulUW24Ft45x48bFxz/+8WhoaIhjjjkmXvOa1/TZ/qUvfemA9vP888/H1KlT4wtf+EKcc845ERExcuTIqK2tjZUrV8akSZOivb09GhsbX3U/HR0d8cQTT8RVV10Vjz32WKxZsyZOOeWUQg8LAGDQ6VUAAMWjWwEAFIdeBQDwR6mWc1944YUH/cCLFi2Kbdu2xfz582P+/PkREXHWWWdFW1tbtLa2RldXV4waNSra2tpedT8zZsyIa665Js4///wYMmRI3HDDDTF8+PCDng8AYDDoVQAAxaNbAQAUh14FAPCSmiRJkqyHyEo+n49f/epX8bf37YhfPrU163GADL115Ij44ZXvGrTH23P+Oemkk6rqYxJLgeyzVS75l8ucpUa3otwNdh/IinNcerJLT3bpVEpulXIcg0mvguIZjI7nPJee7NKTXXrlnF05z54VvQrIymC91+jakI7c0qmk3Ao5loI/sSpJkvjpT38ajz76aOTz+d7bd+3aFWvWrIlvfetbhU8MAFCF9CoAgOLRrQAAikOvAgD4o4IXVs2ZMye+973vxbhx42L16tUxYcKEePLJJ+O5556Lv/zLvxyIGQEAKpJeBQBQPLoVAEBx6FUAAH9U8MKqf/3Xf40bb7wxzjnnnDj33HPjb/7mb+LYY4+Nq6++Ov7whz8MxIwD7i1HHR478/v/PaByHVfn+9iBwVeJvSpCt6J86QMA5a0Su5VeBQdPxwMonF4FcPD0UKgcBS+s2rZtW7ztbW+LiIgxY8bE6tWr4/jjj4/p06fH5ZdfXvQBB8OXPzi+7L//ETh4+Z4kckNqsh4DqCKV2KsidCvKmz4AUL4qsVvpVVAcOh5AYfQqgOLQQ6EyDCn0Dm9605tizZo1ERFx/PHHx+rVqyPipe9b7urqKu50g+Tl3w/N4Mnn87FmzRr5Z0T+r6TYAIOtEntVhG5VKNfk9AYiO30AoHxVYrfSDwqnW6VXydnpeACF0auo5F4w0GSXTqXmpodCZSj4E6s++tGPxuzZs+Nv//Zv47zzzovJkyfH0KFD46GHHopTTjllIGakgnV3d2c9QlWTP0C29Cr2cE1OT3YA7KFbsYd+kJ7sAIjQq3iJXpCe7NKRG1CqCl5Y1dzcHPX19TFs2LAYPXp0fP3rX4/vfve78ba3vS0++clPDsSMAAAVSa8CACge3QoAoDj0KgCAPyp4YVVExDve8Y6IiPj9738fZ5xxRpx55plRU+Nj7ChcbW1t1iNUNfkDZE+vIsI1GQCKRbciQrc6GLIDYA+9CgDgJUMKvUOSJLFgwYI49dRT4/TTT4+nn346Zs+eHZ///Odj165dAzHjgMvlclmPUJVyuVyMGzdO/hmR/77le5KsRwCqRCX2qgjdqlDFvia7jgFQrSqxW+lVhfN+R3qDlZ2+ClD6KrlXuQ4BAIUq+BOr5s+fHz/84Q9j7ty5MWPGjIiIuOiii+Lzn/983HDDDXHttdcWfciBdlX7qlj7zPasxwBKwHF1w+OrUyZkPQZQJSqxV0XoVllyHQOgmlVit9KrqDT6KkB5qNRe9YeeGtchAKBgBS+s+v73vx9z586Nd7zjHb0f+XnGGWfEl7/85fjUpz5VlmXqic7t8cimrVmPAQBUmUrsVRG6FQCQjUrsVnoVAJCFSu1VO/NZTwEAlKOCvwpwy5YtUVdX94rbR4wYETt27CjKUAAA1UCvAgAoHt0KAKA49CoAgD8qeGHVaaedFrfeemuf27Zt2xY33XRTnHrqqUUbDACg0ulVAADFo1sBABSHXgUA8EcHtLDqQx/6UDz++OMREfE3f/M3sWbNmjjjjDNi586d8fGPfzwaGxvj6aefLsuP/gQAGEx6FQBA8ehWAADFoVcBAOzd0AP5pUMPPTQ+8IEPxEc/+tG44oor4nvf+17ce++98cQTT8Tu3bvj2GOPjTPPPDOGDCnsA7AWLlwY7e3tERHR0NAQ1113XWzYsCGuvfba2Lp1a4wZMybmzp0bw4YN671Pe3t7PPDAAzF37tyIiNi1a1fccMMN8cADD8Tu3bvjmmuuiTPPPLOgOQAABoteBQBQPLoVAEBx6FUAAHt3QO1n4cKF0dbWFsuXL4+mpqa455574vTTT49LLrkkLr300nj3u99dcJFavXp1LFu2LJYuXRrLly+P3bt3x5IlS2L27Nkxa9asWLFiRYwePToWLFgQERE7d+6MefPmxfXXX99nP9/61rfihRdeiDvuuCO+8pWvxNVXXx09PT0FzQIAMFj0KgCA4tGtAACKQ68CANi7A/rEqoiIc845JxobG+OWW26JT3ziE3H22WfHFVdcEYceemif3xs5cuQB7W/EiBHR2trauwJ97NixsW7duujq6opJkyZFRERzc3O0tLTEzJkz47777oskSWL27NmxevXq3v386Ec/innz5kVNTU0cf/zxcdttt0WSJAd6WAB7lc/nB2X/A/04vJLss1Uu+Q/0fHoVA63UX2PFUi7nlFIjt/Rkl57s0qmU3HQr3Qr6K/fzWn+Vcr7OguzSk1165ZydXlWcXlWOz/1gK+fXSdZkl47c0pNdOnJLp5JyK+QYDnhhVcRLHwP6iU98IsaOHRuf/vSn44c//GHvtiRJoqamJtauXXtA+6qvr4/6+vqIiNiyZUssXrw4pkyZEhs2bOj9nbq6uti8eXNERDQ2NkZjY2MsW7asz35++9vfxoMPPhif+9znIpfLxV//9V/HcccdV8hhAbzCunXroru7e8Afp6OjY8Afg72Tfbbkr1cxsAbrOlYqnFPSkVt6sktPdunIbf90KygvldpXna/Tk116sktPdntXDb2qUq9DA8HrJD3ZpSO39GSXjtzSqbbcClpY9fTTT8cNN9wQP/7xj6OpqSmmT58ehx122EENsHHjxpg+fXo0NzfHxIkT4+677+6zvaam5lXvn8/nY+PGjfG9730v1q1bF1OnTo0f/ehHccQRRxzUXEB1O+GEEwZ0//l8Pjo6OqKhoSFyudyAPhZ9yT5b5ZL/njkHkl7FQBro61ipKJdzSqmRW3qyS0926VRKbrqVbgX9VVpfrZTzdRZkl57s0ivn7PSq4vSqSrsODYRyfp1kTXbpyC092aUjt3QqKbdCetUBLazauXNn3HzzzbFw4cJ405veFP/0T/8UEydOPKghIyLWrl0b06ZNi2nTpkVLS0ts2rQpOjs7e7d3dnbG0Ucf/ar7eP3rXx9/9md/FjU1NTF27Ng4+uij4ze/+U28/e1vP+j5gOo1WBeCXC5X9hedciX7bFVz/noVg6HaXl/VfE45GHJLT3bpyS4due2bbgXlqVLPac7X6ckuPdmlJ7u+qqlXed4PnNdJerJLR27pyS4duaVTbbkd0MKq97///bFt27b49Kc/HS0tLUUJ6Pnnn4+pU6fGF77whTjnnHMi4qXvZK6trY2VK1fGpEmTor29PRobG191P3/6p38aP/rRj+Ltb397bNy4MX73u9/Fsccee9DzAQAMBL0KAKB4dCsAgOLQqwAA9u6AFladcsopcdVVV0VdXV3RHnjRokWxbdu2mD9/fsyfPz8iIs4666xoa2uL1tbW6OrqilGjRkVbW9ur7mfWrFnxxS9+Mc4///yIiPjiF7/oI9UBgJKlVwEAFI9uBQBQHHoVAMDeHdDCqv0VmjRmzJgRM2bM2Ou222+/fZ/3mzx5ckyePLn35+HDh8cNN9xQ9PkAAAaCXgUAUDy6FQBAcehVAAB7NyTrAQAAAAAAAAAAAEqNhVUAAAAAAAAAAAD9HNBXAVa6txx1eOzMZz0FUAqOqxue9QgAZU+3yo7rGABUFr2KSqOvApCVtxx1ePyhpybrMQCAMmRhVUR8+YPjI5fLZT0GUCLyPUnkhvgDCyAt3SpbrmMAUDn0KiqRvgpAFvb0KtchAKBQvgowIvJ5//QvC/l8PtasWSP/jMh/3/xRBXBwXFsKU+xrsusYAFQOvapw3u9Ib7Cy01cByMKe65vrEABQKAuryFR3d3fWI1Q1+QNAaXBNBgAoHt0qPdkBAABAXxZWAQAAAAAAAAAA9GNhFZmqra3NeoSqJv9syR+APQ455JCsRwAAqBj+3k5PdunJDgAAoDINzXqAUpDL5bIeoSrlcrkYN25c1mNULflna1/553sS3/EOlD3dqjC5XC5OHPfWrMcAAEqQXlU473ekJ7v0Bio77xMBAABkz8KqiLiqfVWsfWZ71mMAVe64uuHx1SkTsh4D4KDpVoXZc/7P5/NZjwIAlBi9CqqX94kAAABKg4VVEfFE5/Z4ZNPWrMcAAKgIuhUAQHHoVQAAAADZGpL1AAAAAAAAAAAAAKXGwioAAAAAAAAAAIB+LKwCAAAAAAAAAADoZ2iWD75w4cJob2+PiIiGhoa47rrrYsOGDXHttdfG1q1bY8yYMTF37twYNmxY733a29vjgQceiLlz50ZExMc+9rF49tlnIyKip6cn1q9fH7fddlucfvrpg39AAAAZ0asAAIpHtwIAKA69CgAod5l9YtXq1atj2bJlsXTp0li+fHns3r07lixZErNnz45Zs2bFihUrYvTo0bFgwYKIiNi5c2fMmzcvrr/++j77ueWWW+IHP/hB/OAHP4jzzz8/mpqaFCkAoKroVQAAxaNbAQAUh14FAFSCzD6xasSIEdHa2tq7An3s2LGxbt266OrqikmTJkVERHNzc7S0tMTMmTPjvvvuiyRJYvbs2bF69epX7O+pp56KJUuWxA9+8INBPQ6AYsvn81mPUNH25CvnbJRL/qU+X396VWXo6enJeoSyUy7nlFIjt/Rkl57s0qmU3Mptft0KKCXldg4tVKVc67Igu/TKObtym7mUelVPT0/Z5Zelcn6dZE126cgtPdmlI7d0Kim3Qo4hs4VV9fX1UV9fHxERW7ZsicWLF8eUKVNiw4YNvb9TV1cXmzdvjoiIxsbGaGxsjGXLlu11fwsWLIjLLrssXve61w306AADat26ddHd3Z31GBWvo6Mj6xGqmvyLS6+qDI8++qjzf0rOKenILT3ZpSe7dOQ2uHQroJRUy/tErnXpyS492Q28UupVjz/+eLz44osF36/aeZ2kJ7t05Jae7NKRWzrVlltmC6v22LhxY0yfPj2am5tj4sSJcffdd/fZXlNTs999bNu2LX7605/G5z73uYEaE2DQnHDCCVmPUNHy+Xx0dHREQ0ND5HK5rMepOuWS/545y41eVd6OP/74GDIks2/qLkvlck4pNXJLT3bpyS6dSslNt9KtgPQq/X2iSrnWZUF26ZVzdnpV+l41evToOOSQQ1LdtxqV8+ska7JLR27pyS4duaVTSbkV0qsyXVi1du3amDZtWkybNi1aWlpi06ZN0dnZ2bu9s7Mzjj766P3u5z/+4z/izDPPjMMPP3wgxwUYFOV+ESoXuVxO1hmSf/HpVeVvyJAhXhcpOaekI7f0ZJee7NKR2+DTrYBSUS3nf9e69GSXnuwGR6n0Ku+7pON1kp7s0pFberJLR27pVFtumf2T+Oeffz6mTp0ara2t0dLSEhERI0eOjNra2li5cmVERLS3t0djY+N+9/XLX/6y97uYAQCqjV4FAFA8uhUAQHHoVQBAJcjsE6sWLVoU27Zti/nz58f8+fMjIuKss86Ktra2aG1tja6urhg1alS0tbXtd19PPvlkvPvd7x7okQEASpJeBQBQPLoVAEBx6FUAQCXIbGHVjBkzYsaMGXvddvvtt+/zfpMnT47Jkyf3ue2b3/xmUWcDACgnehUAQPHoVgAAxaFXAQCVILOvAgQAAAAAAAAAAChVFlYBAAAAAAAAAAD0k9lXAZaStxx1eOzMZz0FUO2Oqxue9QgARaFbFcb5HwDYF70Kqpe/EwAAAEqDhVUR8eUPjo9cLpf1GACR70kiN6Qm6zEADopuVbg/7M6H0z8A0J9eBdXN+0QAAADZ81WAEZHP+6d/Wcjn87FmzRr5Z0T+2dpX/t4sAyqBa0th8vl8rF3zSNZjAAAlSK8qnPc70pNdegOVnfeJAAAAsmdhFZnq7u7OeoSqJv9syR+APf7whz9kPQIAQMXw93Z6sktPdgAAAJXJwioyVVtbm/UIVU3+2ZI/AHu4JgAAFI9ulZ7s0pNderIDAABK2dCsBygFuVwu6xGqUi6Xi3HjxmU9RtWSf7bk31e+J/Hx9lBBdKvCVMM1wXkeANLRqwpXDd1qoMguPdmlV8zs/N0BAAAMBAurIuKq9lWx9pntWY8BUJWOqxseX50yIesxgCLSrXg553kASE+vAjgw/u4AAAAGioVVEfFE5/Z4ZNPWrMcAAKgIuhUAQHHoVQAAAADZGpL1AAAAAAAAAAAAAKXGwioAAAAAAAAAAIB+LKwCAAAAAAAAAADox8IqAAAAAAAAAACAfjJdWLVw4cJoamqKpqamuOaaa2LXrl2xfv36uPjii+Pcc8+NK6+8Mnbs2NHnPu3t7XH11Vf3/rxr166YOXNmNDU1xYUXXhj33HPPYB8GAEDm9CoAgOLRrQAAikOvAgDKXWYLq1avXh3Lli2LpUuXxvLly2P37t2xZMmSmD17dsyaNStWrFgRo0ePjgULFkRExM6dO2PevHlx/fXX99nPD37wg9i9e3fcddddMW/evD5FCwCgGuhVAADFo1sBABSHXgUAVIKhWT3wiBEjorW1NYYNGxYREWPHjo1169ZFV1dXTJo0KSIimpubo6WlJWbOnBn33XdfJEkSs2fPjtWrV/fup6enJ3bu3Bn5fD527twZhx12WCbHA8DByefzg/o4g/V49FUu+Zf6fP3pVZSDgXhdlcs5pdTILT3ZpSe7dColt3KbX7cCKF/lds05GJXSE7JQztmV28yl1Kt6enrKLr8slfPrJGuyS0du6ckuHbmlU0m5FXIMmS2sqq+vj/r6+oiI2LJlSyxevDimTJkSGzZs6P2durq62Lx5c0RENDY2RmNjYyxbtqzPfi666KL4/ve/H+9617ti69atcdNNNw3WIQBQROvWrYvu7u5Be7yOjo5BeyxeSf7FpVdRDgbyPO+cko7c0pNderJLR26DS7cCKF+D/f5SKdAT0pPdwCulXvX444/Hiy++mPpYqpXXSXqyS0du6ckuHbmlU225Zbawao+NGzfG9OnTo7m5OSZOnBh33313n+01NTWvev+vf/3rcdJJJ8V3vvOd2LBhQ1x22WXx1re+NY455piBHBuAIjvhhBMG5XHy+Xx0dHREQ0ND5HK5QXlM/qhc8t8zZ7nRqyhlA3GeL5dzSqmRW3qyS0926VRKbrqVbgUwWAbr/aVSUCk9IQvlnJ1elb5XjR49Og455JBU81ejcn6dZE126cgtPdmlI7d0Kim3QnpVpgur1q5dG9OmTYtp06ZFS0tLbNq0KTo7O3u3d3Z2xtFHH/2q+/jpT38af/d3fxc1NTVx7LHHxvjx42P16tXepAIoM4N98c3lcmV/wS9n8i8+vYpSN5CveeeUdOSWnuzSk106cht8uhVAearG66WekJ7sBkep9KohQ4Z4vlPwOklPdunILT3ZpSO3dKottyFZPfDzzz8fU6dOjdbW1mhpaYmIiJEjR0ZtbW2sXLkyIiLa29ujsbHxVfczduzY+Ld/+7fefT788MMxduzYgR0eAKCE6FUAAMWjWwEAFIdeBQBUgsw+sWrRokWxbdu2mD9/fsyfPz8iIs4666xoa2uL1tbW6OrqilGjRkVbW9ur7ueaa66J1tbWOO+88yKXy8WnPvWpOPbYYwfjEAAASoJeBQBQPLoVAEBx6FUAQCXIbGHVjBkzYsaMGXvddvvtt+/zfpMnT47Jkyf3/vz6178+FixYUPT5AADKhV4FAFA8uhUAQHHoVQBAJcjsqwABAAAAAAAAAABKlYVVAAAAAAAAAAAA/WT2VYCl5C1HHR4781lPAVCdjqsbnvUIQJHpVryc8zwApKdXARwYf3cAAAADxcKqiPjyB8dHLpfLegyAqpXvSSI3pCbrMYAi0a3oz3keANLRqwAOnL87AACAgeCrACMin/dP/7KQz+djzZo18s+I/LMl/7686QWVxbmtMNVwTXCeB4B0KrkfDJRq6FYDRXbpyS69Ymbn7w4AAGAgWFhFprq7u7MeoarJP1vyB2AP1wQAgOLRrdKTXXqyS092AABAKbOwikzV1tZmPUJVk3+25A/AHq4J6ckuHbkBUMlc59KTXXqyS092AABAKRua9QClIJfLZT1CVcrlcjFu3Lisx6ha8s9W1vnnexIfjw4MGN2qMFlfE8qZ7NKpltz0HaAS6FWFq5br3ECQXXqyS+/l2elvwEB6ea9yvgEACmFhVURc1b4q1j6zPesxAAbFcXXD46tTJmQ9BlDBdCsga/oOUCn0KqBa6G/AQNvTq5xvAIBCWVgVEU90bo9HNm3NegwAgIqgWwEAFIdeBQBQHHoVAJDWkKwHAAAAAAAAAAAAKDUWVgEAAAAAAAAAAPRjYRUAAAAAAAAAAEA/mS6sWrhwYTQ1NUVTU1Ncc801sWvXrli/fn1cfPHFce6558aVV14ZO3bs6HOf9vb2uPrqq3t/fu655+J//s//GU1NTfEXf/EX8dBDDw32YQAAZE6vAgAoHt0KAKA49CoAoNxltrBq9erVsWzZsli6dGksX748du/eHUuWLInZs2fHrFmzYsWKFTF69OhYsGBBRETs3Lkz5s2bF9dff32f/cydOzfGjh0bd911V9x4440xe/bsePHFF7M4JACATOhVAADFo1sBABSHXgUAVIKhWT3wiBEjorW1NYYNGxYREWPHjo1169ZFV1dXTJo0KSIimpubo6WlJWbOnBn33XdfJEkSs2fPjtWrV/fuZ+3atTF9+vSIiHjTm94URx55ZDz00ENx+umnD/5BAZSRfD6f9QiZ2HPc1Xr8WSuX/Et9vv70KoC9K/b5vFyuY6VIdulUSm7lNr9uBZCdcrtmZKlSekIWyjm7cpu5lHtVuWU52Mr5dZI12aUjt/Rkl47c0qmk3Ao5hswWVtXX10d9fX1ERGzZsiUWL14cU6ZMiQ0bNvT+Tl1dXWzevDkiIhobG6OxsTGWLVvWZz/jxo2Lu+66Kz796U/Ho48+Go899lg899xzg3UYAGVr3bp10d3dnfUYmeno6Mh6hKom/+LSqwD2bqD6jutYerJLR26DS7cCyE61v1+Vhp6QnuwGXin3KuebA+N1kp7s0pFberJLR27pVFtumS2s2mPjxo0xffr0aG5ujokTJ8bdd9/dZ3tNTc2r3v+aa66JOXPmxIUXXhjjx4+PU089NQ455JCBHBmgIpxwwglZj5CJfD4fHR0d0dDQELlcLutxqk655L9nznKjVwH0Vey+Uy7XsVIku3QqJTfdSrcCOFDV+n5VGpXSE7JQztnpVcXrVc43r66cXydZk106cktPdunILZ1Kyq2QXpXpwqq1a9fGtGnTYtq0adHS0hKbNm2Kzs7O3u2dnZ1x9NFHv+o+duzYEXPmzInhw4dHRMQFF1wQb37zmwd0boBKUO4Xu4OVy+WqPoMsyb/49CqAVxqoa43rWHqyS0dug0+3AsiG613h9IT0ZDc4SrVXee4PjNdJerJLR27pyS4duaVTbbkNyeqBn3/++Zg6dWq0trZGS0tLRESMHDkyamtrY+XKlRER0d7eHo2Nja+6n29/+9uxePHiiIj4xS9+EX/4wx9i7NixAzs8AEAJ0asAAIpHtwIAKA69CgCoBJl9YtWiRYti27ZtMX/+/Jg/f35ERJx11lnR1tYWra2t0dXVFaNGjYq2trZX3c/06dNj5syZsXz58hg+fHh8/etfjyFDMlsvBgAw6PQqAIDi0a0AAIpDrwIAKkFmC6tmzJgRM2bM2Ou222+/fZ/3mzx5ckyePLn359e97nXxj//4j0WfDwCgXOhVAADFo1sBABSHXgUAVALLuQEAAAAAAAAAAPqxsAoAAAAAAAAAAKCfzL4KsJS85ajDY2c+6ykABsdxdcOzHgGocLoVkDV9B6gUehVQLfQ3YKDt6VXONwBAoSysiogvf3B85HK5rMcAGDT5niRyQ2qyHgOoULoVUAr0HaAS6FVANdHfgIH08l7lfAMAFMJXAUZEPu+f/mUhn8/HmjVr5J8R+Wcr6/z90QgMJNeWwmR9TShnskunWnLTd4BKUOnn6oFQLde5gSC79GSX3suz09+AgfTyc7TzDQBQCAuryFR3d3fWI1Q1+WdL/gDs4ZqQnuzSkRsAlcx1Lj3ZpSe79GQHAACUMgurAAAAAAAAAAAA+rGwCgAAAAAAAIBetbW1WY9QtmSXjtzSk106ckunGnMbmvUAAAAAAAAAAAMll8tlPUJZyeVyMW7cuKzHKEuyS0du6ckuHbmlM5i55XuSyA2pGZTH2h8LqwAAAAAAAICKdVX7qlj7zPasxwAADsBxdcPjq1MmZD1GLwurAAAAAAAAgIr1ROf2eGTT1qzHAADK0JCsBwAAAAAAAAAAACg1FlYBAAAAAAAAAAD0Y2EVAAAAAAAAAABAP5kurFq4cGE0NTVFU1NTXHPNNbFr165Yv359XHzxxXHuuefGlVdeGTt27IiIiKeeeiouvfTSuPDCC+Oiiy6Ke++9t3c/N910U5x77rnx/ve/P372s59ldTgAAJnRqwAAikOvAgAoHt0KACh3mS2sWr16dSxbtiyWLl0ay5cvj927d8eSJUti9uzZMWvWrFixYkWMHj06FixYEBERc+bMiYsuuijuvPPOuPHGG2PmzJmRz+fjJz/5SXR0dMRdd90V//zP/xx/+7d/G7///e+zOiwAgEGnVwEAFIdeBQBQPLoVAFAJhmb1wCNGjIjW1tYYNmxYRESMHTs21q1bF11dXTFp0qSIiGhubo6WlpaYOXNmXHjhhfGe97wnIiLq6+tj165dsWPHjvjZz34WF154YQwdOjTq6upi0qRJcffdd8cHPvCBA56lp6cn8vl80Y+RV7cnc9lnQ/7Zkn92ZJ+tcsm/1Ofrr5R6VYRuVahyeV2UItmlI7f0ZJee7NKplNzKaX69qvxVyusmC7JLT3bpyS492aVXztmV28yl1q0AgPIykN2nkH1ntrCqvr4+6uvrIyJiy5YtsXjx4pgyZUps2LCh93fq6upi8+bNERHR1NTUe/utt94aJ554YhxxxBGxefPmeMMb3tDnPs8880xBszz++OPx4osvpj8YDkpHR0fWI1Q1+WdL/tmRfbbkX1yl1KsidKu0vC7Sk106cktPdunJLh25DR69qnJ43aQnu/Rkl57s0pNderIbeKXWrQCA8rJu3bro7u7OeozsFlbtsXHjxpg+fXo0NzfHxIkT4+677+6zvaamps/P3/zmN2Pp0qXx7W9/OyIikiR5xT6HDCnsGw5Hjx4dhxxySIGTc7Dy+Xx0dHREQ0ND5HK5rMepOvLPlvyzI/tslUv+e+YsN6XQqyJ0q0KVy+uiFMkuHbmlJ7v0ZJdOpeRWjt1KrypflfK6yYLs0pNderJLT3bplXN25dirIkqnWwEA5eWEE04YsH0X0qsyXVi1du3amDZtWkybNi1aWlpi06ZN0dnZ2bu9s7Mzjj766Ih4qTRdd9118dBDD8WSJUuirq4uIiLe8IY3xLPPPtvnPm95y1sKmmPIkCFlV54rSS6Xk3+G5J8t+WdH9tmSf/GVSq+K0K3S8rpIT3bpyC092aUnu3TkNrj0qsrgdZOe7NKTXXqyS0926clucJRStwIAykupdLXMlnM///zzMXXq1GhtbY2WlpaIiBg5cmTU1tbGypUrIyKivb09GhsbIyLia1/7Wqxfvz4WL17cW6QiIs4666y48847Y/fu3fHcc8/FfffdF+985zsH/4AAADKiVwEAFIdeBQBQPLoVAFAJMvvEqkWLFsW2bdti/vz5MX/+/Ih4qRi1tbVFa2trdHV1xahRo6KtrS22b98et9xyS9TV1cUll1zSu4+bb745zjnnnHj44Yfjz//8zyOfz8eMGTPiqKOOyuqwAAAGnV4FAFAcehUAQPHoVgBAJchsYdWMGTNixowZe912++23v+K2V/tuw5kzZ8bMmTOLNhsAQDnRqwAAikOvAgAoHt0KAKgEmX0VIAAAAAAAAAAAQKmysAoAAAAAAAAAAKCfzL4KEAAAAAAAAGCgveWow2NnPuspAIADcVzd8KxH6MPCKgAAAAAAAKBiffmD4yOXy2U9BgBwgPI9SeSG1GQ9RkT4KkAAAAAAAACgguXzPq6qEPl8PtasWSO3FGSXjtzSk106cktnMHMrlUVVERZWAQAAAAAAAPAy3d3dWY9QtmSXjtzSk106ckunGnOzsAoAAAAAAAAAAKAfC6vIVG1tbdYjVDX5A0BpcE1OT3bpyC092QGUPufq9GSXnuzSkx1AaXJ+Tk926cgtPdnBwBqa9QClIJfLZT1CVcrlcjFu3Lisx6ha8s9OvifJegSAAaVbFcY1OT3ZpSO39IqdXb4nidyQmqLtD6g8elXhXOfSk116sktvILLTsYC90asK49qWnuzSkVt6sts//ZCDZWFVRFzVvirWPrM96zGAKnBc3fD46pQJkc9nPQnAwNGtAPZvTy8EeDV6FUBhdCxgX/QqgOqkH1IMFlZFxBOd2+ORTVuzHgMAoCLoVgAAxaFXAQAUh14FAKQ1JOsBAAAAAAAAAAAASo2FVQAAAAAAAAAAAP1YWAUAAAAAAAAAANBPpgurFi5cGE1NTdHU1BTXXHNN7Nq1K9avXx8XX3xxnHvuuXHllVfGjh07IiLiqaeeiksvvTQuvPDCuOiii+Lee+/ts6/169fH+eefn8VhAABkTq8CACge3QoAoDj0KgCg3GW2sGr16tWxbNmyWLp0aSxfvjx2794dS5YsidmzZ8esWbNixYoVMXr06FiwYEFERMyZMycuuuiiuPPOO+PGG2+MmTNnRj6fj4iIZcuWxeWXXx7d3d1ZHQ4AQGb0KgCA4tGtAACKQ68CACrB0KweeMSIEdHa2hrDhg2LiIixY8fGunXroqurKyZNmhQREc3NzdHS0hIzZ86MCy+8MN7znvdERER9fX3s2rUrduzYEfl8Pn7+85/HTTfdFFdddVVWhwNQkJ6enoiI3j8KGTx7Mpd9Nsol/1Kfrz+9CqB8lds1J61y6QClplJyK7f5dSuA8ldu1540KqUnZKGcsyu3mfUqAEpF/2toOfeBLFVSboUcQ2YLq+rr66O+vj4iIrZs2RKLFy+OKVOmxIYNG3p/p66uLjZv3hwREU1NTb2333rrrXHiiSfGEUccERERf//3fx8bN24ctNkBDtajjz4aEREdHR0ZT1K9ZJ8t+ReXXgVQvtatW1dV/+JaB0hHboNLtwIof9XUsfSE9GQ38PQqAErFvvqhPpBOteWW2cKqPTZu3BjTp0+P5ubmmDhxYtx99919ttfU1PT5+Zvf/GYsXbo0vv3tbw/mmABFdfzxx8fq1aujoaEhcrlc1uNUlXw+Hx0dHbLPSLnkv2fOcqNXAZSfE044IesRBkW5dIBSUym56VYADLZq6FiV0hOyUM7Z6VUAkE7/fljOfSBLlZRbIb0q04VVa9eujWnTpsW0adOipaUlNm3aFJ2dnb3bOzs74+ijj46IiCRJ4rrrrouHHnoolixZEnV1dVmNDXDQhgwZEhERuVyu7C865Ur22ZJ/8elVAOWp2q6HOkA6cht8uhVAeaum66aekJ7sBodeBUAp2Nc1Xx9Ip9pyG5LVAz///PMxderUaG1tjZaWloiIGDlyZNTW1sbKlSsjIqK9vT0aGxsjIuJrX/tarF+/PhYvXqxIAQC8jF4FAFA8uhUAQHHoVQBAJcjsE6sWLVoU27Zti/nz58f8+fMjIuKss86Ktra2aG1tja6urhg1alS0tbXF9u3b45Zbbom6urq45JJLevdx8803xxvf+MasDgEAoCToVQAAxaNbAQAUh14FAFSCzBZWzZgxI2bMmLHXbbfffvsrbtvfdxuOGjUqfvaznxVlNgCAcqJXAQAUj24FAFAcehUAUAky+ypAAAAAAAAAAACAUmVhFQAAAAAAAAAAQD+ZfRVgKXnLUYfHznzWUwDV4Li64VmPADDgdCuA/dMLgQOhVwEURscC9kWvAqhO+iHFYGFVRHz5g+Mjl8tlPQZQJfI9SdYjAAwo3QrgwOR7ksgNqcl6DKCE6VUAhdOxgL3RqwCql37IwfJVgBGRz1uinoV8Ph9r1qyRf0bknx0XbqDSubYUxjU5PdmlI7f0ip2dXgjsj3N14Vzn0pNderJLbyCy07GAvXGOLoxrW3qyS0du6clu//RDDpaFVWSqu7s76xGqmvwBoDS4Jqcnu3Tklp7sAEqfc3V6sktPdunJDqA0OT+nJ7t05Jae7GBgWVgFAAAAAAAAAADQj4VVZKq2tjbrEQAAMqcTpSe7dOSWnuzSkx0wWJxv0pNderJLT3YApcn5OT3ZpSO39GSXjtzSqcbchmY9QCnI5XJZj1CVcrlcjBs3LusxBl2+J/E9rgBUNN2qMNXaiYpBdunILT3ZpTfQ2fk7i0qlVxXOuTo92aUnu/Rkt396DhSHXlUY5+f0ZJeO3NKTXTpyS6cYuZVjv7WwKiKual8Va5/ZnvUYVIHj6obHV6dMyHoMABhQuhUAg8nfWVQyvQqguuk5UDx6FQBkr1z7rYVVEfFE5/Z4ZNPWrMcAAKgIuhUAQHHoVQAAxaFXAQBpDcl6AAAAAAAAAAAAgFJjYRUAAAAAAAAAAEA/FlYBAAAAAAAAAAD0MzTLB1+4cGG0t7dHRERDQ0Ncd911sWHDhrj22mtj69atMWbMmJg7d24MGzYsnnrqqbj22mvjhRdeiFwuF5/5zGfi9NNPj127dsXnP//5eOSRR6Knpyeam5vjsssuy/KwAAAGnV4FAFAcehUAQPHoVgBAucvsE6tWr14dy5Yti6VLl8by5ctj9+7dsWTJkpg9e3bMmjUrVqxYEaNHj44FCxZERMScOXPioosuijvvvDNuvPHGmDlzZuTz+finf/qnGDJkSNx5552xdOnSuP322+ORRx7J6rAAAAadXgUAUBx6FQBA8ehWAEAlyOwTq0aMGBGtra0xbNiwiIgYO3ZsrFu3Lrq6umLSpEkREdHc3BwtLS0xc+bMuPDCC+M973lPRETU19fHrl27YseOHTF+/Ph473vfGzU1NXH44YfHm9/85vjd734Xb33rW7M6NNivfD6f9Qi9M5TCLNVI/tmRfbbKJf9Sn68/vQoAyu/6fSDKpTvtTznNr1cBUIr2di2tlJ6QhXLOrtxm1q0AgL0phU5TyAyZLayqr6+P+vr6iIjYsmVLLF68OKZMmRIbNmzo/Z26urrYvHlzREQ0NTX13n7rrbfGiSeeGEcccUS84x3v6L39P//zP2P16tVxww03DMoxQFrr1q2L7u7urMeIiIiOjo6sR6hq8s+O7LMl/+LSqwCgtP7OKjbdafDoVQCUolfrOXpCerIbeLoVALA35fY+XmYLq/bYuHFjTJ8+PZqbm2PixIlx991399leU1PT5+dvfvObsXTp0vj2t7/d5/Z77rknZs+eHW1tbfHa1752wOeGg3HCCSdkPULk8/no6OiIhoaGyOVyWY9TdeSfHdlnq1zy3zNnudGrAKhmpfB3VrGVS3fan3LsVnoVAKVkbz2nUnpCFso5u3LsVRG6FQDQVym8j1dIr8p0YdXatWtj2rRpMW3atGhpaYlNmzZFZ2dn7/bOzs44+uijIyIiSZK47rrr4qGHHoolS5ZEXV1d7+/dcccdMW/evPjqV78aEydOHPTjgEKV0h9ruVyupOapNvLPjuyzJf/i06sAqHaV3C10p8GlVwFQal6tB+gJ6clucOhWAEB/5dbBhmT1wM8//3xMnTo1Wltbo6WlJSIiRo4cGbW1tbFy5cqIiGhvb4/GxsaIiPja174W69evj8WLF/cpUvfee2+0tbXFP/3TPylSAEBV0qsAAIpDrwIAKB7dCgCoBJl9YtWiRYti27ZtMX/+/Jg/f35ERJx11lnR1tYWra2t0dXVFaNGjYq2trbYvn173HLLLVFXVxeXXHJJ7z5uvvnm+OpXvxq7d++Ov/7rv+69/ROf+ES8733vG/RjAgDIgl4FAFAcehUAQPHoVgBAJchsYdWMGTNixowZe912++23v+K2fX234b/8y78UdS4AgHKjVwEAFIdeBQBQPLoVAFAJMvsqQAAAAAAAAAAAgFJlYRUAAAAAAAAAAEA/mX0VYCl5y1GHx8581lNQDY6rG571CAAw4HQrAAaTv7OoZHoVQHXTc6B49CoAyF659lsLqyLiyx8cH7lcLusxqBL5niRyQ2qyHgMABoxuBcBg83cWlUqvAkDPgeLQqwCgNJRjv/VVgBGRz1uinoV8Ph9r1qypuvzL7SQBAIWqtmv7warWTlQMsktHbunJLr2Bzs7fWVQq55vCOVenJ7v0ZJee7PZPz4HicJ4pjPNzerJLR27pyS4duaVTjNzKsd9aWEWmuru7sx4BACBzOlF6sktHbunJLj3ZAYPF+SY92aUnu/RkB1CanJ/Tk106cktPdunILZ1qzM3CKjJVW1ub9QhVTf7Zkn92ZJ8t+cMreV2kJ7t05Jae7NKTXTpyg8J53aQnu/Rkl57s0pNderIDAOBADc16gFLgO5WzkcvlYty4cVmPUbXkny35Z0f22SpW/uX4/cvVRLcqjPNSerJLR27pyS492aUzWLnpVqVLryqc8016sktPdunJLj3ZpTeQ2elVAACVx8KqiLiqfVWsfWZ71mMAAAfguLrh8dUpE7Ieg1ehWwFA+dCtSpteBQDlQ68CAKhMFlZFxBOd2+ORTVuzHgMAoCLoVgAAxaFXAQAAAGRrSNYDAAAAAAAAAAAAlBoLqwAAAAAAAAAAAPqxsAoAAAAAAAAAAKAfC6sAAAAAAAAAAAD6yXRh1cKFC6OpqSmamprimmuuiV27dsX69evj4osvjnPPPTeuvPLK2LFjR0REPPXUU3HppZfGhRdeGBdddFHce++9ERHR1dUVf/VXfxUXXHBBfOADH4h77rkny0MCAMiEXgUAUDy6FQBAcehVAEC5y2xh1erVq2PZsmWxdOnSWL58eezevTuWLFkSs2fPjlmzZsWKFSti9OjRsWDBgoiImDNnTlx00UVx5513xo033hgzZ86MfD4f//AP/xBjxoyJ5cuXR1tbW3zmM5/J6pAAADKhVwEAFI9uBQBQHHoVAFAJhmb1wCNGjIjW1tYYNmxYRESMHTs21q1bF11dXTFp0qSIiGhubo6WlpaYOXNmXHjhhfGe97wnIiLq6+tj165dsWPHjvjMZz4T+Xw+IiI2btwYr33ta7M5IABgUO25/pfbvgeCXgUAHCzd6o90KwDgYOhVf1RKvaqnp6fs8svSnqxkVjjZpSO39GSXjtzSqaTcCjmGzBZW1dfXR319fUREbNmyJRYvXhxTpkyJDRs29P5OXV1dbN68OSIimpqaem+/9dZb48QTT4wjjjgiIiKGDh0al156aTzwwAPxxS9+cdCOAQDIzrp166K7uzvrMUqCXgUAHCzd6o90KwDgYOhVf1RKverxxx+PF198Mf3BVKmOjo6sRyhbsktHbunJLh25pVNtuWW2sGqPjRs3xvTp06O5uTkmTpwYd999d5/tNTU1fX7+5je/GUuXLo1vf/vbfW5ftGhRbNy4MaZMmRITJkyI0aNHD/jsAEB2TjjhhAHbdz6fL8tSqFcBAGnpVq+kWwEAaehVr1QKvWr06NFxyCGHpD+IKrPnv7WGhobI5XJZj1NWZJeO3NKTXTpyS6eSciukV2W6sGrt2rUxbdq0mDZtWrS0tMSmTZuis7Ozd3tnZ2ccffTRERGRJElcd9118dBDD8WSJUuirq4uIiLuvffeGDNmTPzJn/xJjBo1Kk4++eR49NFHvUkFABWu3AtbselVAMDB0K360q0AgLT0qr5KpVcNGTLEc5NCLpeTW0qyS0du6ckuHbmlU225DcnqgZ9//vmYOnVqtLa2RktLS0REjBw5Mmpra2PlypUREdHe3h6NjY0REfG1r30t1q9fH4sXL+4tUhERP/7xj+PWW2+NiIjNmzfHww8/HA0NDYN8NAAA2dGrAACKR7cCACgOvQoAqASZfWLVokWLYtu2bTF//vyYP39+REScddZZ0dbWFq2trdHV1RWjRo2Ktra22L59e9xyyy1RV1cXl1xySe8+br755vj0pz8dn/3sZ+OCCy6IoUOHxuc+97k45phjsjosAIBBp1cBABSPbgUAUBx6FQBQCTJbWDVjxoyYMWPGXrfdfvvtr7jt1b7b8Otf/3rR5gIAKDd6FQBA8ehWAADFoVcBAJUgs68CBAAAAAAAAAAAKFUWVgEAAAAAAAAAAPST2VcBlpK3HHV47MxnPQUAcCCOqxue9Qjsh24FAOVDtyptehUAlA+9CgCgMllYFRFf/uD4yOVyWY8BABygfE8SuSE1WY/BPuhWAFBedKvSpVcBQHnRqwAAKo+vAoyIfN4//ctCPp+PNWvWyD8j8s+W/LMj+2wVK39vUJU2r6/COC+lJ7t05Jae7NKTXTqDlZtuVbq8ZgrnfJOe7NKTXXqyS0926Q1kdnoVAEDlsbCKTHV3d2c9QlWTf7bknx3ZZ0v+8EpeF+nJLh25pSe79GSXjtygcF436ckuPdmlJ7v0ZJee7AAAOFAWVgEAAAAAAAAAAPRjYRUAAAAAAAAAAEA/FlYBAAAAAAAAAAD0Y2EVAAAAAAAAAABAPxZWAQAAAAAAAAAA9GNhFQAAAAAAAAAAQD8WVgEAAAAAAAAAAPRjYRUAAAAAAAAAAEA/FlYBAAAAAAAAAAD0Y2EVAAAAAAAAAABAPxZWAQAAAAAAAAAA9GNhFQAAAAAAAAAAQD8WVgEAAAAAAAAAAPRjYRUAAAAAAAAAAEA/Q7MeIEtJkkRERD6fj3w+n/E01WdP5rLPhvyzJf/syD5b5ZL/nvn2dAUOjG6VTrm8LkqR7NKRW3qyS0926VRKbrpV4fSq9CrldZMF2aUnu/Rkl57s0ivn7PSqwulV6ZTz6yRrsktHbunJLh25pVNJuRXSq2qSKm5fu3btio6OjqzHAABKVENDQ7zmNa/JeoyyoVsBAK9GtzpwehUA8Gr0qgOnVwEAr+ZAelVVL6zq6emJ3bt3x5AhQ6KmpibrcQCAEpEkSfT09MTQoUNjyBDfnHygdCsAYG90q8LpVQDA3uhVhdOrAIC9KaRXVfXCKgAAAAAAAAAAgL2xnB0AAAAAAAAAAKAfC6sAAAAAAAAAAAD6sbAKAAAAAAAAAACgHwurAAAAAAAAAAAA+rGwCgAAAAAAAAAAoB8LqwAAAAAAAAAAAPqxsAoAAAAAAAAAAKCfql1YtXPnzvjsZz8bEydOjDPPPDP+8R//MeuRytKuXbuiqakp7r///t7bnnrqqbjsssvipJNOivPOOy9+8Ytf9LnPPffcE01NTTF+/Pj4yEc+Ek899VSf7bfddlu8613vigkTJsRnP/vZ6O7u7t3meXvJ5s2b48orr4xJkybFu971rvjSl74UO3fujAj5D4bf/va3cfnll8eECRPirLPOim9961u92+Q/eKZNmxZXX311789r1qyJ5ubmGD9+fHzwgx+Mhx9+uM/v33XXXfHe9743xo8fH1dccUU8//zzvduSJIkbb7wxTjvttJg0aVLccMMN0dPT07v9hRdeiE9+8pMxYcKEOPvss+MHP/jBwB9gifrxj38cJ5xwQp//XXnllRHhOahmzk86URr6THq6yMHTIwrj+p/erl274rrrrot3vOMd8c53vjNuuummSJIkImTHvlXS+TYt3apwulV6ulVx6FeF0a/S068oRKWdaw9EqfWo/T121kq1Q5V6bhGl26HKIbs9Sqk/7e+xs1aq3anUc4so3e5UDtn1kVSpL37xi8kFF1yQPPzww8m///u/JxMmTEh+9KMfZT1WWXnxxReTK664IhkzZkxy3333JUmSJD09PckFF1yQzJw5M3nssceSm2++ORk/fnzy9NNPJ0mSJE8//XRy0kknJbfeemuyfv365FOf+lTS1NSU9PT0JEmSJCtWrEhOOeWU5Gc/+1myatWq5Lzzzkuuu+663sf0vL2U8cUXX5xMnTo1Wb9+ffLAAw8k73vf+5K5c+fKfxDk8/nknHPOSWbOnJn85je/SX7+858nJ598cnLnnXfKfxDdddddyZgxY5KrrroqSZIk2b59e3LGGWckc+fOTR577LFkzpw5yTvf+c5k+/btSZIkyapVq5K3v/3tyfe///1k7dq1yYc//OFk2rRpvfu79dZbk8bGxuSBBx5I7r333uTMM89MvvWtb/Vunz59enLppZcm69atS5YuXZq87W1vS1atWjW4B10i/uEf/iGZPn168uyzz/b+7/e//73noMpV+/lJJyqcPpOeLnLw9IjCuf6n19rampxzzjnJqlWrknvuuSc59dRTk+985zuy41VVyvk2Ld2qcLpVerpVcehXhdOv0tOvKEQlnWsPRKn1qP09dtZKtUOVem5JUrodqhyy26OU+tP+HrsUlGJ3KofckqQ0u1O5ZPdyVbmwavv27UlDQ0NvqUiSJJk/f37y4Q9/OMOpysujjz6aXHjhhckFF1zQp6Ddc889yUknndTnP/pLL700+fu///skSZLkK1/5Sp+cd+zYkUyYMKH3/h/60Id6fzdJkuSBBx5I3v72tyc7duzwvP1/jz32WDJmzJiks7Oz97bly5cnZ555pvwHwebNm5NPfepTSVdXV+9tV1xxRfKFL3xB/oPkhRdeSN797ncnH/zgB3sL53e/+93k7LPP7i3ePT09yfve976kvb09SZIkmT17du/vJkmSbNq0KTnhhBOSJ598MkmSJGlsbOz93SRJkjvuuCP50z/90yRJkuS3v/1tMmbMmOSpp57q3f7Zz362z/6qycyZM5O2trZX3O45qF7Vfn7SidLRZ9LTRQ6OHpGO6386L7zwQjJu3Ljk/vvv773tG9/4RnL11VfLjn2qlPNtWrpVOrpVerrVwdOv0tGv0tGvKEQlnWsPRCn2qP09dtZKtUOVem5JUrodqhyyS5LS60/7e+xSUIrdqRxyK9XuVA7Z9VeVXwX461//Onbv3h0TJkzove2UU06JVatW9fl4MvZt5cqVceqpp8btt9/e5/ZVq1bFuHHjYtiwYb23nXLKKfGrX/2qd/vEiRN7t9XW1sZb3/rW+NWvfhX5fD46Ojr6bD/ppJPiD3/4Q/z617/2vP1/Rx11VHzrW9+K17/+9X1u37Ztm/wHQV1dXXzlK1+J4cOHR5Ik8eCDD8YDDzwQkyZNkv8g+fKXvxx//ud/Hscdd1zvbatWrYpTTjklampqIiKipqYmTj755H1m/8Y3vjFGjhwZq1atis2bN8fvfve7eMc73tG7/ZRTTomnn346nn322Vi1alW88Y1vjFGjRvXZ/tBDDw3wkZamxx9/POrr619xu+egelX7+UknSkefSU8XOTh6RDqu/+k8+OCDMXz48Jg0aVLvbdOmTYsvfelLsmOfKuV8m5ZulY5ulZ5udfD0q3T0q3T0KwpRSefaA1GKPWp/j521Uu1QpZ5bROl2qHLILqL0+tP+HrsUlGJ3KofcSrU7lUN2/VXlwqrOzs543eteF695zWt6b3v9618fO3fujP/6r//KbrAy8qEPfSg++9nPRm1tbZ/bOzs7o66urs9tf/InfxLPPPPMfrdv3bo1du7c2Wf70KFD48gjj4xnnnnG8/b/jRgxIt71rnf1/tzT0xPf/va347TTTpP/IDv77LPjQx/6UEyYMCHe//73y38Q3HvvvfGf//mf8fGPf7zP7fvL/tlnn93n9s7OzoiIPtv3/CG1Z/ve7rt58+biHFQZSZIkfvOb38QvfvGLeP/73x/vfe9748Ybb4xdu3Z5DqpYtZ+fdKJ09Jni0EUKo0ek4/qf3lNPPRXHHHNM3HHHHXHuuefGe97znpg/f3709PTIjn2qhPPtwdCt0tGtikO3Kpx+lY5+lZ5+RSEq5Vx7oEqxR+3vsbNWqh2q1HPrr5Q6VDlkV4r9qdRzK9XuVOq5RZRudyqH7PobmvUAWeju7u5zwo2I3p937dqVxUgVY1/Z7sn11ba/+OKLvT/vbXuSJJ63vZg3b16sWbMmvve978Vtt90m/0H093//9/Hcc8/F3/zN38SXvvQl//0PsJ07d8YXvvCF+PznPx+HHXZYn237y/7FF18sKPuXZ7u/fVeTTZs29ebxla98JTZu3BjXX399vPjii56DKqZX7Z1rQmH0mXR0kQOnR6Tn+p/ejh074re//W38y7/8S3zpS1+Kzs7O+PznPx+1tbWyY590q71zjSuMbpWOblUY/So9/So9/YpC6FUvyfJ6Vm6vnVLpUOWWWyl1qFLPrlT7U6nnVqrdqdRziyjd7lQO2fVXlQurDj300Fc8KXt+7n8SozCHHnroK1b679q1qzfXfWU/YsSIOPTQQ3t/7r+9trY28vm8562fefPmxaJFi+Lv/u7vYsyYMfIfZA0NDRHxUhGaNWtWfPCDH4zu7u4+vyP/4vn6178eb3vb2/r8C5I99pXt/rKvra3tc6Hv/zzU1tbud9/V5Jhjjon7778/Xvva10ZNTU2ceOKJ0dPTE7Nnz45JkyZ5DqqUXrV3rskHTp9JTxc5cHpEeq7/6Q0dOjS2bdsWbW1tccwxx0TES28Gfuc734n//t//u+zYK91q7/SDA6dbpadbFUa/Sk+/Sk+/ohB61Uuy7AL7e+xSUkodqpxyiyitDlXq2ZVqfyr161ypdqdSzy2idLtTOWTXX1V+FeAb3vCGeOGFF2L37t29t3V2dsZhhx0WI0aMyHCy8veGN7whnnvuuT63Pffcc70f5bav7UcddVQceeSRceihh/bZvnv37viv//qvOOqoozxv/cyZMycWLlwY8+bNi/e///0RIf/B8Nxzz8VPfvKTPrcdd9xx8Yc//CGOOuoo+Q+gH/7wh/GTn/wkJkyYEBMmTIjly5fH8uXLY8KECQf13/4b3vCGiIjej618+f/fs31f961GRx55ZO93HkdEjB49Onbu3HlQ//17Dsqb89PeuSYfGH2mcLpIOnrEwXH9T+eoo46KQw89tPeNq4iIY489Nn73u9/57459Kvfz7UDRDw6MblU43So9/erg6Ffp6FcUohLOtcWQZRfY32OXilLrUOWQW6l2qFLPrlT7U6nnFlGa3akccivV7lQO2fVXlQurTjzxxBg6dGj86le/6r3twQcfjIaGhhgypCojKZrx48fHI4880vvxbxEvZTt+/Pje7Q8++GDvtu7u7lizZk2MHz8+hgwZEg0NDX22/+pXv4qhQ4fG2LFjPW8v8/Wvfz3+5V/+JW666aY4//zze2+X/8DbuHFjfOITn+j9DtiIiIcffjj+23/7b3HKKafIfwD98z//cyxfvjzuuOOOuOOOO+Lss8+Os88+O+64444YP358PPTQQ5EkSUS89H3Lv/zlL/eZ/e9+97v43e9+F+PHj483vOENMXLkyD7bH3zwwRg5cmTU1dXFSSedFE8//XSf7/V98MEH46STThqcAy8h/+f//J849dRT+/xrk7Vr18aRRx4Zp5xyiuegSjk/7Z1r8v7pM+noIunoEem5/qc3fvz42LlzZ/zmN7/pve2JJ56IY445xn937FO5n28Hin6wf7pVOrpVevpVevpVevoVhaiEc20xZNkF9vfYpaAUO1Q55FaqHarUsyvV/rS/x85aqXanUs8tonS7Uzlk9wpJlWptbU3OP//8ZNWqVcmPf/zj5OSTT07+7d/+LeuxytKYMWOS++67L0mSJNm9e3dy3nnnJZ/+9KeT9evXJ9/4xjeSk046KXn66aeTJEmSp556KmloaEi+8Y1vJOvXr08+9alPJRdccEHS09OTJEmS3HXXXcnJJ5+c/PjHP05WrVqVnH/++cmcOXN6H8vzliSPPfZYcuKJJyZ/93d/lzz77LN9/if/gbd79+5k8uTJyUc/+tHk0UcfTX7+858n73znO5PbbrtN/oPsqquuSq666qokSZKkq6srOe2005I5c+Ykjz76aDJnzpzkjDPOSLZv354kSZL88pe/TN761rcmS5cuTdauXZt8+MMfTqZPn967r2984xvJmWeemdx3333Jfffdl5x55pnJP/7jP/Zu/+hHP5p8+MMfTtauXZssXbo0aWhoSFatWjW4B1wCurq6kne9613JX//1XyePP/548vOf/zw588wzk29+85uegyrn/PQSnejA6TPp6SLFoUccONf/gzNt2rTkL/7iL5K1a9cm//Ef/5GcdtppyaJFi2THq6qk8+3B0K0OnG6Vnm5VPPrVgdOvDo5+RSEq7Vx7oEqlR+3vsbNWqh2q1HNLktLtUOWQ3cuVSn/a32NnrVS7U6nntkcpdqdyye7lqnZh1Y4dO5LPfOYzyUknnZSceeaZycKFC7MeqWy9vKAlSZJs2LAhueSSS5K3ve1tyfnnn5/83//7f/v8/s9//vPknHPOSd7+9rcnl156afLkk0/22f6Nb3wjOf3005NTTjklueaaa5IXX3yxd5vn7aV8xowZs9f/JYn8B8MzzzyTXHHFFcnJJ5+cnHHGGcmCBQt6C5/8B8/LC2eSJMmqVauSD3zgA0lDQ0PyP/7H/0geeeSRPr/f3t6eNDY2JieddFJyxRVXJM8//3zvtt27dyf/63/9r2TixInJqaeemsybN6/3OU2SJHnuueeS6dOnJw0NDcnZZ5+dLF++fOAPsEStX78+ueyyy5KTTjopOeOMM5Kvfe1rvVl5DqqX89NLdKIDp88cHF3k4OkRhXH9T2/r1q3J7Nmzk5NOOik5/fTTZccBqaTz7cHQrQ6cbnVwdKvi0K8Ko1+lp19RiEo71x6oUupR+3vsLJVyhyrl3PYo1Q5VDtntUUr9aX+PnbVS7U6lnluSlG53KofsXq4mSf7/52sBAAAAAAAAAAAQERHV8yXCAAAAAAAAAAAAB8jCKgAAAAAAAAAAgH4srAIAAAAAAAAAAOjHwioAAAAAAAAAAIB+LKwCAAAAAAAAAADox8IqAAAAAAAAAACAfiysAgAAAAAAAAAA6MfCKgAAAAAAAAAAgH4srAIqyu9///uYO3dunH322TF+/Pj4sz/7s7jtttuip6dnwB9727Ztcccddwz44wAADAa9CgCgOPQqAIDi0a2AwTY06wEAiuWFF16Iv/iLv4i6urr427/92xg1alR0dHTEnDlz4qmnnorW1tYBffzbbrst7r///vjABz4woI8DADDQ9CoAgOLQqwAAike3ArJgYRVQMdra2uI1r3lN3HrrrXHooYdGRMSb3vSmOOyww+LjH/94fPjDH45jjz12wB4/SZIB2zcAwGDSqwAAikOvAgAoHt0KyEJN4tUPVIBdu3bFqaeeGp/5zGfiL//yL/tsS5Ik7r///jj55JOju7s7brzxxvjpT38aO3fujLPPPjuuvfbaeO1rXxv3339/fOQjH4l169b13vfqq6+OiIi5c+fG1772tdiwYUMMHz48li9fHoceemh89KMfjY997GOxbNmyuOaaa3rv9/J9AACUE70KAKA49CoAgOLRrYCsDMl6AIBiePLJJ2PHjh3R0NDwim01NTVx2mmnxWte85r4xCc+EWvXro2bb745Fi5cGI8//nhvYToQ//Zv/xaHHnpofP/734/LL788brzxxvjNb34T5513Xnz0ox+NCRMmxC9+8YtiHhoAwKDSqwAAikOvAgAoHt0KyIqvAgQqwtatWyMi4ogjjtjn7/z617+OlStXxooVK3o/BnTevHlx3nnnxRNPPHFAj3PkkUfGVVddFblcLqZOnRq33HJLPPzww3HsscfGsGHD4pBDDomjjjrq4A8IACAjehUAQHHoVQAAxaNbAVnxiVVARTjyyCMjIuL3v//9Pn/niSeeiBEjRvT5buXRo0fHa1/72gMuU6NGjYpcLtf78+GHHx67d+9ONzQAQAnSqwAAikOvAgAoHt0KyIqFVUBFePOb3xxHHHFEPPLII3vd/ld/9Vfxmte8Zq/b8vl85PP5qKmpecW2/kXpkEMOecXvJEmSYmIAgNKkVwEAFIdeBQBQPLoVkBULq4CKMHTo0DjvvPNi8eLFsWvXrj7bfvazn8XPfvazqK+vj61bt/ZZkf7YY4/Ftm3b4thjj+0tStu2bevdvnHjxgOeYW9lDACg3OhVAADFoVcBABSPbgVkxcIqoGJ88pOfjG3btsXll18eK1eujCeffDK++93vxtVXXx0f+chH4rjjjot3v/vdcdVVV8Xq1atj9erVcdVVV8U73vGOGDNmTBx//PFx2GGHxc033xxPPfVUfOtb34o1a9Yc8OPX1tbGs88+W1ABAwAoRXoVAEBx6FUAAMWjWwFZsLAKqBhHHXVUfOc734k3velNMWvWrGhqaopFixbFlVdeGVdffXVERHz5y1+ON73pTXHZZZfF5ZdfHscff3zMnz8/IiKGDx8ec+bMiR/+8IfR1NQUv/71r+OSSy454Md/3/veFz09PXH++efHli1bBuQYAQAGg14FAFAcehUAQPHoVkAWahJfCAoAAAAAAAAAANCHT6wCAAAAAAAAAADox8IqAAAAAAAAAACAfiysAgAAAAAAAAAA6MfCKgAAAAAAAAAAgH4srAIAAAAAAAAAAOjHwioAAAAAAAAAAIB+LKwCAAAAAAAAAADox8IqAAAAAAAAAACAfiysAgAAAAAAAAAA6MfCKgAAAAAAAAAAgH4srAIAAAAAAAAAAOjn/wFU8/d5hkx0ZQAAAABJRU5ErkJggg=="
+      ]
      },
      "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 22
+   "source": [
+    "plot_all_parents_by_year_grid(df[\"region\"], df[\"year\"], RegionMapper(), per_row=4)"
+   ]
   }
  ],
  "metadata": {
diff --git a/train.py b/train.py
deleted file mode 100755
index 7087662..0000000
--- a/train.py
+++ /dev/null
@@ -1,58 +0,0 @@
-#!.venv/bin/python3
-import argparse
-import logging
-import sys
-import traceback
-
-from core.config import setup_config
-from research.experiment.experiment_builder import ExperimentBuilder
-from research.model_trainer import ModelTrainer
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Train DRC Names Models using Research Templates")
-    parser.add_argument("--name", type=str, required=True, help="Model name to train")
-    parser.add_argument("--type", type=str, required=True, help="Experiment type")
-    parser.add_argument("--config", type=str, help="Path to configuration file")
-    parser.add_argument("--env", type=str, default="development", help="Environment name")
-    parser.add_argument("--templates", type=str, default="research_templates.yaml")
-    args = parser.parse_args()
-
-    try:
-        # Setup pipeline configuration
-        config = setup_config(config_path=args.config, env=args.env)
-        experiment_builder = ExperimentBuilder(config)
-
-        # Load research templates
-        logging.info(f"Loading research templates from: {args.templates}")
-        templates = experiment_builder.load_templates(args.templates)
-
-        # Find the specific experiment configuration
-        logging.info(f"Looking for experiment: name='{args.name}', type='{args.type}'")
-        experiment_config = experiment_builder.find_template(templates, args.name, args.type)
-
-        logging.info(f"Found experiment: {experiment_config.get('name')}")
-        logging.info(f"Description: {experiment_config.get('description')}")
-        logging.info(f"Features: {experiment_config.get('features')}")
-
-        # Train the model using template configuration
-        trainer = ModelTrainer(config)
-        trainer.train_single_model(
-            model_name=experiment_config.get("name"),
-            model_type=experiment_config.get("model_type"),
-            features=experiment_config.get("features"),
-            model_params=experiment_config.get("model_params", {}),
-            tags=experiment_config.get("tags", []),
-        )
-
-        logging.info("Training completed successfully!")
-        return 0
-
-    except Exception as e:
-        logging.error(f"Training failed: {e}")
-        traceback.print_exc()
-        return 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..cf26eff
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,2442 @@
+version = 1
+revision = 3
+requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version < '3.12'",
+]
+
+[[package]]
+name = "altair"
+version = "5.5.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jinja2" },
+    { name = "jsonschema" },
+    { name = "narwhals" },
+    { name = "packaging" },
+    { name = "typing-extensions", marker = "python_full_version < '3.14'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/16/b1/f2969c7bdb8ad8bbdda031687defdce2c19afba2aa2c8e1d2a17f78376d8/altair-5.5.0.tar.gz", hash = "sha256:d960ebe6178c56de3855a68c47b516be38640b73fb3b5111c2a9ca90546dd73d", size = 705305, upload-time = "2024-11-23T23:39:58.542Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/aa/f3/0b6ced594e51cc95d8c1fc1640d3623770d01e4969d29c0bd09945fafefa/altair-5.5.0-py3-none-any.whl", hash = "sha256:91a310b926508d560fe0148d02a194f38b824122641ef528113d029fcd129f8c", size = 731200, upload-time = "2024-11-23T23:39:56.4Z" },
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
+]
+
+[[package]]
+name = "anyio"
+version = "4.11.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "sniffio" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094, upload-time = "2025-09-23T09:19:12.58Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097, upload-time = "2025-09-23T09:19:10.601Z" },
+]
+
+[[package]]
+name = "attrs"
+version = "25.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5a/b0/1367933a8532ee6ff8d63537de4f1177af4bff9f3e829baf7331f595bb24/attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b", size = 812032, upload-time = "2025-03-13T11:10:22.779Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815, upload-time = "2025-03-13T11:10:21.14Z" },
+]
+
+[[package]]
+name = "blinker"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/21/28/9b3f50ce0e048515135495f198351908d99540d69bfdc8c1d15b73dc55ce/blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf", size = 22460, upload-time = "2024-11-08T17:25:47.436Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/10/cb/f2ad4230dc2eb1a74edf38f1a38b9b52277f75bef262d8908e60d957e13c/blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc", size = 8458, upload-time = "2024-11-08T17:25:46.184Z" },
+]
+
+[[package]]
+name = "blis"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/96/f3/7c5a47a0d5ec0362bab29fd4f497b4b1975473bf30b7a02bc9c0b0e84f7a/blis-1.3.0.tar.gz", hash = "sha256:1695a87e3fc4c20d9b9140f5238cac0514c411b750e8cdcec5d8320c71f62e99", size = 2510328, upload-time = "2025-04-03T15:09:47.767Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/64/a1/ea38adca95fbea0835fd09fd7e1a5fd4d15e723645108360fce8e860e961/blis-1.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:456833a6006dce2165d68e1ab0aa7678608a9a99a18aa37af7aa0437c972f7f6", size = 6976242, upload-time = "2025-04-03T15:08:53.473Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/13/a3b66fd57c75343a5b2e6323cd8f73bdd2e9b328deba7cf676ec334ec754/blis-1.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8072fbb03505444c818810536ad77616a18d97bbde06e8ec69755d917abb7f31", size = 1281504, upload-time = "2025-04-03T15:08:54.934Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/a1/22d728aac953c1293d9d9ba119f467233c8991cb4ecb00689970bf6c2449/blis-1.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:594c2332bcb1a0fdacb5e857a1afaf338d52c05ba24710515cddbf25862787ac", size = 3101280, upload-time = "2025-04-03T15:08:56.35Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/8b/40301bfa2dab268c4a52735d830939a26ef2e1d6d5ce5add4d3c4a9ba276/blis-1.3.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2cf336a810bd0e6ab52e8ba5455c42ff02f6216acb196ffc831cd30ab084127e", size = 3316521, upload-time = "2025-04-03T15:08:59.852Z" },
+    { url = "https://files.pythonhosted.org/packages/da/77/6fbd4d9b923f3914c589d38a19dfc8fd45f54296aef75aba908a7d176871/blis-1.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cad91ae2c8a11286b32e80ac7e579d7028f8c0a22afa1e817edddc18051f05b2", size = 11650028, upload-time = "2025-04-03T15:09:02.009Z" },
+    { url = "https://files.pythonhosted.org/packages/d7/24/336d40ed5b4ca33f098eb6e753814526279837069b7770db7bd25fcba9a7/blis-1.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1bf4267616fb97a3b869cc8d278383faa86882dc8330067421f9bf9c06e6b80c", size = 3115887, upload-time = "2025-04-03T15:09:03.987Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/ee/a69b3322b0659705c5e2aeec3bbbd474eb37d028fd58fd32795cfc5cbf84/blis-1.3.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:45c6f6e801c712592f487f4021c9a85079d6ff8fc487f3d8202212edd4900f8e", size = 4348881, upload-time = "2025-04-03T15:09:05.976Z" },
+    { url = "https://files.pythonhosted.org/packages/95/c9/774812eac52a11be854f0d41afdade2ac1ce1be0b749aec63c3816b57b7d/blis-1.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:570113bc81bce8890fa2c067a30f6e6caa82bb3be7de0926d659e986e40f5509", size = 14840892, upload-time = "2025-04-03T15:09:08.439Z" },
+    { url = "https://files.pythonhosted.org/packages/35/3a/f9414cf9b2c43aad87e8687ad2cdb0e66e996c20288584621a12725e83dd/blis-1.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:75ecaa548589cba2ba75e621e2a8b89888e3f326ef1a27e7a9b1713114467ff2", size = 6232289, upload-time = "2025-04-03T15:09:11.029Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/3f/67140d6588e600577f92d2c938e9492a8cd0706bab770978ee84ecb86e70/blis-1.3.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:ef188f1f914d52acbbd75993ba25554e381ec9099758b340cd0da41af94ae8ae", size = 6988854, upload-time = "2025-04-03T15:09:13.203Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/05/30587d1b168fa27d1bf6869a1be4bcb3f10493f836381a033aa9c7a10ab8/blis-1.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:626f84522faa51d5a52f9820551a84a5e02490bf6d1abdfc8d27934a0ff939de", size = 1282465, upload-time = "2025-04-03T15:09:15.081Z" },
+    { url = "https://files.pythonhosted.org/packages/35/13/60d2dd0443a7a56a0a160d873444e4b9189bb2939d93457864432ee18c90/blis-1.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f56e0454ce44bc08797383ce427ee5e2b044aab1eafb450eab82e86f8bfac853", size = 3061088, upload-time = "2025-04-03T15:09:16.535Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/30/4909baf57c3cd48414c284e4fced42157c4768f83bf6c95b0bb446192b45/blis-1.3.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c9bb5770efe233374d73a567af5cdef24f48bead83d118bdb9bd5c2187b0f010", size = 3259127, upload-time = "2025-04-03T15:09:18.528Z" },
+    { url = "https://files.pythonhosted.org/packages/bb/bf/625121119107d3beafe96eb776b00a472f0210c07d07b1ed160ab7db292a/blis-1.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d52ce33a1895d82f2f39f7689d5e70b06ebba6bc6f610046ecd81db88d650aac", size = 11619003, upload-time = "2025-04-03T15:09:20.139Z" },
+    { url = "https://files.pythonhosted.org/packages/81/92/0bad7a4c29c7a1ab10db27b04babec7ca4a3f504543ef2d1f985fb84c41a/blis-1.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6c78e8dd420e0e695df0ceecf950f3cf823e0a1b8c2871a7e35117c744d45861", size = 3062135, upload-time = "2025-04-03T15:09:22.142Z" },
+    { url = "https://files.pythonhosted.org/packages/35/b5/ea9b4f6b75c9dce24ce0d6fa15d5eaab54b115a57967d504e460db901c59/blis-1.3.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:7a060700ee98ea44a1b9833b16d3dd1375aaa9d3230222bfc5f13c4664e5710e", size = 4298755, upload-time = "2025-04-03T15:09:24.064Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/c5/9b7383752cdc4ca92359c161b1086bd158b4f3cda5813a390ff9c8c1b892/blis-1.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:250f0b0aeca0fdde7117751a54ae6d6b6818a446a619f3c0c63f3deb77f700a8", size = 14785385, upload-time = "2025-04-03T15:09:25.74Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/92/6bb1940a491ce9d3ec52372bc35988bec779b16ace7e87287d981df31eeb/blis-1.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:2e6f468467a18a7c2ac2e411643f5cfa45a435701e2c04ad4aa46bb02fc3aa5c", size = 6260208, upload-time = "2025-04-03T15:09:28.207Z" },
+    { url = "https://files.pythonhosted.org/packages/91/ec/2b1e366e7b4e3cdb052a4eeba33cc6a3e25fe20566f3062dbe59a8dd7f78/blis-1.3.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:4d6a91c8726d0bc3345a8e0c8b7b8e800bee0b9acc4c2a0dbeb782b8b651f824", size = 6985730, upload-time = "2025-04-03T15:09:29.884Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/8b/a3374a970e1ae6138b2ec6bffeb1018068c5f0dbf2b12dd8ab16a47ae4a0/blis-1.3.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e3c20bc3d7143383195cc472373fb301d3bafbacd8ab8f3bffc27c68bef45d81", size = 1280751, upload-time = "2025-04-03T15:09:32.007Z" },
+    { url = "https://files.pythonhosted.org/packages/53/97/83cc91c451709c85650714df3464024bf37ef791be1e0fae0d2a0f945da6/blis-1.3.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:778c4b84c6eccab223d8afe20727820f6c7dd7a010c3bfb262104cc83b0a8e4c", size = 3047726, upload-time = "2025-04-03T15:09:33.521Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/21/fbf9b45d6af91c5ce32df4007886c0332b977558cba34b0bc00b98ebc188/blis-1.3.0-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:69584589977366366cd99cc7cb23a76a814df8bcae8b777fde4a94e8684c1fb8", size = 3249935, upload-time = "2025-04-03T15:09:36.264Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/b1/5716b8cd784c0a0d08f9b3773c8eb4c37f5f9ed3a9f6ef961373e123b1cf/blis-1.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3b2adc4549e610b59e8db5a57ab7206e4ac1502ac5b261ed0e6de42d3fb311d5", size = 11614296, upload-time = "2025-04-03T15:09:38.342Z" },
+    { url = "https://files.pythonhosted.org/packages/36/0f/e2ed2642cf41dcae3431cfbcd94543646adba46eaa2736ac27647216e4f7/blis-1.3.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:9aaa84df638e0bb7909a35e3c220168df2b90f267967b3004a88f57b49fbe4ec", size = 3063082, upload-time = "2025-04-03T15:09:40.329Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/f0/627a36b99a9cd9af73be7bb451d6884d5b4aece297eb29b9fc13e70c1f2b/blis-1.3.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:0da7b54331bed31aa55839da2d0e5451447e1f5e8a9367cce7ff1fb27498a22a", size = 4290919, upload-time = "2025-04-03T15:09:41.845Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/f9/a415707185a82082b96ab857e5c3b7a59b0ad73ed04ace1cbb64835c3432/blis-1.3.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:682175bf2d047129b3715e3f1305c6b23a45e2ce24c4b1d0fa2eb03eb877edd4", size = 14795975, upload-time = "2025-04-03T15:09:43.611Z" },
+    { url = "https://files.pythonhosted.org/packages/16/f1/8cc8118946dbb9cbd74f406d30d31ee8d2f723f6fb4c8245e2bc67175fd4/blis-1.3.0-cp313-cp313-win_amd64.whl", hash = "sha256:91de2baf03da3a173cf62771f1d6b9236a27a8cbd0e0033be198f06ef6224986", size = 6258624, upload-time = "2025-04-03T15:09:46.056Z" },
+]
+
+[[package]]
+name = "cachetools"
+version = "6.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9d/61/e4fad8155db4a04bfb4734c7c8ff0882f078f24294d42798b3568eb63bff/cachetools-6.2.0.tar.gz", hash = "sha256:38b328c0889450f05f5e120f56ab68c8abaf424e1275522b138ffc93253f7e32", size = 30988, upload-time = "2025-08-25T18:57:30.924Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6c/56/3124f61d37a7a4e7cc96afc5492c78ba0cb551151e530b54669ddd1436ef/cachetools-6.2.0-py3-none-any.whl", hash = "sha256:1c76a8960c0041fcc21097e357f882197c79da0dbff766e7317890a65d7d8ba6", size = 11276, upload-time = "2025-08-25T18:57:29.684Z" },
+]
+
+[[package]]
+name = "catalogue"
+version = "2.0.10"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/38/b4/244d58127e1cdf04cf2dc7d9566f0d24ef01d5ce21811bab088ecc62b5ea/catalogue-2.0.10.tar.gz", hash = "sha256:4f56daa940913d3f09d589c191c74e5a6d51762b3a9e37dd53b7437afd6cda15", size = 19561, upload-time = "2023-09-25T06:29:24.962Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/96/d32b941a501ab566a16358d68b6eb4e4acc373fab3c3c4d7d9e649f7b4bb/catalogue-2.0.10-py3-none-any.whl", hash = "sha256:58c2de0020aa90f4a2da7dfad161bf7b3b054c86a5f09fcedc0b2b740c109a9f", size = 17325, upload-time = "2023-09-25T06:29:23.337Z" },
+]
+
+[[package]]
+name = "certifi"
+version = "2025.10.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4c/5b/b6ce21586237c77ce67d01dc5507039d444b630dd76611bbca2d8e5dcd91/certifi-2025.10.5.tar.gz", hash = "sha256:47c09d31ccf2acf0be3f701ea53595ee7e0b8fa08801c6624be771df09ae7b43", size = 164519, upload-time = "2025-10-05T04:12:15.808Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e4/37/af0d2ef3967ac0d6113837b44a4f0bfe1328c2b9763bd5b1744520e5cfed/certifi-2025.10.5-py3-none-any.whl", hash = "sha256:0f212c2744a9bb6de0c56639a6f68afe01ecd92d91f14ae897c4fe7bbeeef0de", size = 163286, upload-time = "2025-10-05T04:12:14.03Z" },
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/83/2d/5fd176ceb9b2fc619e63405525573493ca23441330fcdaee6bef9460e924/charset_normalizer-3.4.3.tar.gz", hash = "sha256:6fce4b8500244f6fcb71465d4a4930d132ba9ab8e71a7859e6a5d59851068d14", size = 122371, upload-time = "2025-08-09T07:57:28.46Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7f/b5/991245018615474a60965a7c9cd2b4efbaabd16d582a5547c47ee1c7730b/charset_normalizer-3.4.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:b256ee2e749283ef3ddcff51a675ff43798d92d746d1a6e4631bf8c707d22d0b", size = 204483, upload-time = "2025-08-09T07:55:53.12Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/2a/ae245c41c06299ec18262825c1569c5d3298fc920e4ddf56ab011b417efd/charset_normalizer-3.4.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:13faeacfe61784e2559e690fc53fa4c5ae97c6fcedb8eb6fb8d0a15b475d2c64", size = 145520, upload-time = "2025-08-09T07:55:54.712Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/a4/b3b6c76e7a635748c4421d2b92c7b8f90a432f98bda5082049af37ffc8e3/charset_normalizer-3.4.3-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:00237675befef519d9af72169d8604a067d92755e84fe76492fef5441db05b91", size = 158876, upload-time = "2025-08-09T07:55:56.024Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/e6/63bb0e10f90a8243c5def74b5b105b3bbbfb3e7bb753915fe333fb0c11ea/charset_normalizer-3.4.3-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:585f3b2a80fbd26b048a0be90c5aae8f06605d3c92615911c3a2b03a8a3b796f", size = 156083, upload-time = "2025-08-09T07:55:57.582Z" },
+    { url = "https://files.pythonhosted.org/packages/87/df/b7737ff046c974b183ea9aa111b74185ac8c3a326c6262d413bd5a1b8c69/charset_normalizer-3.4.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0e78314bdc32fa80696f72fa16dc61168fda4d6a0c014e0380f9d02f0e5d8a07", size = 150295, upload-time = "2025-08-09T07:55:59.147Z" },
+    { url = "https://files.pythonhosted.org/packages/61/f1/190d9977e0084d3f1dc169acd060d479bbbc71b90bf3e7bf7b9927dec3eb/charset_normalizer-3.4.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:96b2b3d1a83ad55310de8c7b4a2d04d9277d5591f40761274856635acc5fcb30", size = 148379, upload-time = "2025-08-09T07:56:00.364Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/92/27dbe365d34c68cfe0ca76f1edd70e8705d82b378cb54ebbaeabc2e3029d/charset_normalizer-3.4.3-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:939578d9d8fd4299220161fdd76e86c6a251987476f5243e8864a7844476ba14", size = 160018, upload-time = "2025-08-09T07:56:01.678Z" },
+    { url = "https://files.pythonhosted.org/packages/99/04/baae2a1ea1893a01635d475b9261c889a18fd48393634b6270827869fa34/charset_normalizer-3.4.3-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:fd10de089bcdcd1be95a2f73dbe6254798ec1bda9f450d5828c96f93e2536b9c", size = 157430, upload-time = "2025-08-09T07:56:02.87Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/36/77da9c6a328c54d17b960c89eccacfab8271fdaaa228305330915b88afa9/charset_normalizer-3.4.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:1e8ac75d72fa3775e0b7cb7e4629cec13b7514d928d15ef8ea06bca03ef01cae", size = 151600, upload-time = "2025-08-09T07:56:04.089Z" },
+    { url = "https://files.pythonhosted.org/packages/64/d4/9eb4ff2c167edbbf08cdd28e19078bf195762e9bd63371689cab5ecd3d0d/charset_normalizer-3.4.3-cp311-cp311-win32.whl", hash = "sha256:6cf8fd4c04756b6b60146d98cd8a77d0cdae0e1ca20329da2ac85eed779b6849", size = 99616, upload-time = "2025-08-09T07:56:05.658Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/9c/996a4a028222e7761a96634d1820de8a744ff4327a00ada9c8942033089b/charset_normalizer-3.4.3-cp311-cp311-win_amd64.whl", hash = "sha256:31a9a6f775f9bcd865d88ee350f0ffb0e25936a7f930ca98995c05abf1faf21c", size = 107108, upload-time = "2025-08-09T07:56:07.176Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/5e/14c94999e418d9b87682734589404a25854d5f5d0408df68bc15b6ff54bb/charset_normalizer-3.4.3-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:e28e334d3ff134e88989d90ba04b47d84382a828c061d0d1027b1b12a62b39b1", size = 205655, upload-time = "2025-08-09T07:56:08.475Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/a8/c6ec5d389672521f644505a257f50544c074cf5fc292d5390331cd6fc9c3/charset_normalizer-3.4.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0cacf8f7297b0c4fcb74227692ca46b4a5852f8f4f24b3c766dd94a1075c4884", size = 146223, upload-time = "2025-08-09T07:56:09.708Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/eb/a2ffb08547f4e1e5415fb69eb7db25932c52a52bed371429648db4d84fb1/charset_normalizer-3.4.3-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c6fd51128a41297f5409deab284fecbe5305ebd7e5a1f959bee1c054622b7018", size = 159366, upload-time = "2025-08-09T07:56:11.326Z" },
+    { url = "https://files.pythonhosted.org/packages/82/10/0fd19f20c624b278dddaf83b8464dcddc2456cb4b02bb902a6da126b87a1/charset_normalizer-3.4.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3cfb2aad70f2c6debfbcb717f23b7eb55febc0bb23dcffc0f076009da10c6392", size = 157104, upload-time = "2025-08-09T07:56:13.014Z" },
+    { url = "https://files.pythonhosted.org/packages/16/ab/0233c3231af734f5dfcf0844aa9582d5a1466c985bbed6cedab85af9bfe3/charset_normalizer-3.4.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1606f4a55c0fd363d754049cdf400175ee96c992b1f8018b993941f221221c5f", size = 151830, upload-time = "2025-08-09T07:56:14.428Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/02/e29e22b4e02839a0e4a06557b1999d0a47db3567e82989b5bb21f3fbbd9f/charset_normalizer-3.4.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:027b776c26d38b7f15b26a5da1044f376455fb3766df8fc38563b4efbc515154", size = 148854, upload-time = "2025-08-09T07:56:16.051Z" },
+    { url = "https://files.pythonhosted.org/packages/05/6b/e2539a0a4be302b481e8cafb5af8792da8093b486885a1ae4d15d452bcec/charset_normalizer-3.4.3-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:42e5088973e56e31e4fa58eb6bd709e42fc03799c11c42929592889a2e54c491", size = 160670, upload-time = "2025-08-09T07:56:17.314Z" },
+    { url = "https://files.pythonhosted.org/packages/31/e7/883ee5676a2ef217a40ce0bffcc3d0dfbf9e64cbcfbdf822c52981c3304b/charset_normalizer-3.4.3-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:cc34f233c9e71701040d772aa7490318673aa7164a0efe3172b2981218c26d93", size = 158501, upload-time = "2025-08-09T07:56:18.641Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/35/6525b21aa0db614cf8b5792d232021dca3df7f90a1944db934efa5d20bb1/charset_normalizer-3.4.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:320e8e66157cc4e247d9ddca8e21f427efc7a04bbd0ac8a9faf56583fa543f9f", size = 153173, upload-time = "2025-08-09T07:56:20.289Z" },
+    { url = "https://files.pythonhosted.org/packages/50/ee/f4704bad8201de513fdc8aac1cabc87e38c5818c93857140e06e772b5892/charset_normalizer-3.4.3-cp312-cp312-win32.whl", hash = "sha256:fb6fecfd65564f208cbf0fba07f107fb661bcd1a7c389edbced3f7a493f70e37", size = 99822, upload-time = "2025-08-09T07:56:21.551Z" },
+    { url = "https://files.pythonhosted.org/packages/39/f5/3b3836ca6064d0992c58c7561c6b6eee1b3892e9665d650c803bd5614522/charset_normalizer-3.4.3-cp312-cp312-win_amd64.whl", hash = "sha256:86df271bf921c2ee3818f0522e9a5b8092ca2ad8b065ece5d7d9d0e9f4849bcc", size = 107543, upload-time = "2025-08-09T07:56:23.115Z" },
+    { url = "https://files.pythonhosted.org/packages/65/ca/2135ac97709b400c7654b4b764daf5c5567c2da45a30cdd20f9eefe2d658/charset_normalizer-3.4.3-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:14c2a87c65b351109f6abfc424cab3927b3bdece6f706e4d12faaf3d52ee5efe", size = 205326, upload-time = "2025-08-09T07:56:24.721Z" },
+    { url = "https://files.pythonhosted.org/packages/71/11/98a04c3c97dd34e49c7d247083af03645ca3730809a5509443f3c37f7c99/charset_normalizer-3.4.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:41d1fc408ff5fdfb910200ec0e74abc40387bccb3252f3f27c0676731df2b2c8", size = 146008, upload-time = "2025-08-09T07:56:26.004Z" },
+    { url = "https://files.pythonhosted.org/packages/60/f5/4659a4cb3c4ec146bec80c32d8bb16033752574c20b1252ee842a95d1a1e/charset_normalizer-3.4.3-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1bb60174149316da1c35fa5233681f7c0f9f514509b8e399ab70fea5f17e45c9", size = 159196, upload-time = "2025-08-09T07:56:27.25Z" },
+    { url = "https://files.pythonhosted.org/packages/86/9e/f552f7a00611f168b9a5865a1414179b2c6de8235a4fa40189f6f79a1753/charset_normalizer-3.4.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:30d006f98569de3459c2fc1f2acde170b7b2bd265dc1943e87e1a4efe1b67c31", size = 156819, upload-time = "2025-08-09T07:56:28.515Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/95/42aa2156235cbc8fa61208aded06ef46111c4d3f0de233107b3f38631803/charset_normalizer-3.4.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:416175faf02e4b0810f1f38bcb54682878a4af94059a1cd63b8747244420801f", size = 151350, upload-time = "2025-08-09T07:56:29.716Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/a9/3865b02c56f300a6f94fc631ef54f0a8a29da74fb45a773dfd3dcd380af7/charset_normalizer-3.4.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6aab0f181c486f973bc7262a97f5aca3ee7e1437011ef0c2ec04b5a11d16c927", size = 148644, upload-time = "2025-08-09T07:56:30.984Z" },
+    { url = "https://files.pythonhosted.org/packages/77/d9/cbcf1a2a5c7d7856f11e7ac2d782aec12bdfea60d104e60e0aa1c97849dc/charset_normalizer-3.4.3-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:fdabf8315679312cfa71302f9bd509ded4f2f263fb5b765cf1433b39106c3cc9", size = 160468, upload-time = "2025-08-09T07:56:32.252Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/42/6f45efee8697b89fda4d50580f292b8f7f9306cb2971d4b53f8914e4d890/charset_normalizer-3.4.3-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:bd28b817ea8c70215401f657edef3a8aa83c29d447fb0b622c35403780ba11d5", size = 158187, upload-time = "2025-08-09T07:56:33.481Z" },
+    { url = "https://files.pythonhosted.org/packages/70/99/f1c3bdcfaa9c45b3ce96f70b14f070411366fa19549c1d4832c935d8e2c3/charset_normalizer-3.4.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:18343b2d246dc6761a249ba1fb13f9ee9a2bcd95decc767319506056ea4ad4dc", size = 152699, upload-time = "2025-08-09T07:56:34.739Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/ad/b0081f2f99a4b194bcbb1934ef3b12aa4d9702ced80a37026b7607c72e58/charset_normalizer-3.4.3-cp313-cp313-win32.whl", hash = "sha256:6fb70de56f1859a3f71261cbe41005f56a7842cc348d3aeb26237560bfa5e0ce", size = 99580, upload-time = "2025-08-09T07:56:35.981Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/8f/ae790790c7b64f925e5c953b924aaa42a243fb778fed9e41f147b2a5715a/charset_normalizer-3.4.3-cp313-cp313-win_amd64.whl", hash = "sha256:cf1ebb7d78e1ad8ec2a8c4732c7be2e736f6e5123a4146c5b89c9d1f585f8cef", size = 107366, upload-time = "2025-08-09T07:56:37.339Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/91/b5a06ad970ddc7a0e513112d40113e834638f4ca1120eb727a249fb2715e/charset_normalizer-3.4.3-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:3cd35b7e8aedeb9e34c41385fda4f73ba609e561faedfae0a9e75e44ac558a15", size = 204342, upload-time = "2025-08-09T07:56:38.687Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/ec/1edc30a377f0a02689342f214455c3f6c2fbedd896a1d2f856c002fc3062/charset_normalizer-3.4.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b89bc04de1d83006373429975f8ef9e7932534b8cc9ca582e4db7d20d91816db", size = 145995, upload-time = "2025-08-09T07:56:40.048Z" },
+    { url = "https://files.pythonhosted.org/packages/17/e5/5e67ab85e6d22b04641acb5399c8684f4d37caf7558a53859f0283a650e9/charset_normalizer-3.4.3-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2001a39612b241dae17b4687898843f254f8748b796a2e16f1051a17078d991d", size = 158640, upload-time = "2025-08-09T07:56:41.311Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/e5/38421987f6c697ee3722981289d554957c4be652f963d71c5e46a262e135/charset_normalizer-3.4.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:8dcfc373f888e4fb39a7bc57e93e3b845e7f462dacc008d9749568b1c4ece096", size = 156636, upload-time = "2025-08-09T07:56:43.195Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/e4/5a075de8daa3ec0745a9a3b54467e0c2967daaaf2cec04c845f73493e9a1/charset_normalizer-3.4.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:18b97b8404387b96cdbd30ad660f6407799126d26a39ca65729162fd810a99aa", size = 150939, upload-time = "2025-08-09T07:56:44.819Z" },
+    { url = "https://files.pythonhosted.org/packages/02/f7/3611b32318b30974131db62b4043f335861d4d9b49adc6d57c1149cc49d4/charset_normalizer-3.4.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ccf600859c183d70eb47e05a44cd80a4ce77394d1ac0f79dbd2dd90a69a3a049", size = 148580, upload-time = "2025-08-09T07:56:46.684Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/61/19b36f4bd67f2793ab6a99b979b4e4f3d8fc754cbdffb805335df4337126/charset_normalizer-3.4.3-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:53cd68b185d98dde4ad8990e56a58dea83a4162161b1ea9272e5c9182ce415e0", size = 159870, upload-time = "2025-08-09T07:56:47.941Z" },
+    { url = "https://files.pythonhosted.org/packages/06/57/84722eefdd338c04cf3030ada66889298eaedf3e7a30a624201e0cbe424a/charset_normalizer-3.4.3-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:30a96e1e1f865f78b030d65241c1ee850cdf422d869e9028e2fc1d5e4db73b92", size = 157797, upload-time = "2025-08-09T07:56:49.756Z" },
+    { url = "https://files.pythonhosted.org/packages/72/2a/aff5dd112b2f14bcc3462c312dce5445806bfc8ab3a7328555da95330e4b/charset_normalizer-3.4.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d716a916938e03231e86e43782ca7878fb602a125a91e7acb8b5112e2e96ac16", size = 152224, upload-time = "2025-08-09T07:56:51.369Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/8c/9839225320046ed279c6e839d51f028342eb77c91c89b8ef2549f951f3ec/charset_normalizer-3.4.3-cp314-cp314-win32.whl", hash = "sha256:c6dbd0ccdda3a2ba7c2ecd9d77b37f3b5831687d8dc1b6ca5f56a4880cc7b7ce", size = 100086, upload-time = "2025-08-09T07:56:52.722Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/7a/36fbcf646e41f710ce0a563c1c9a343c6edf9be80786edeb15b6f62e17db/charset_normalizer-3.4.3-cp314-cp314-win_amd64.whl", hash = "sha256:73dc19b562516fc9bcf6e5d6e596df0b4eb98d87e4f79f3ae71840e6ed21361c", size = 107400, upload-time = "2025-08-09T07:56:55.172Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/1f/f041989e93b001bc4e44bb1669ccdcf54d3f00e628229a85b08d330615c5/charset_normalizer-3.4.3-py3-none-any.whl", hash = "sha256:ce571ab16d890d23b5c278547ba694193a45011ff86a9162a71307ed9f86759a", size = 53175, upload-time = "2025-08-09T07:57:26.864Z" },
+]
+
+[[package]]
+name = "click"
+version = "8.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/46/61/de6cd827efad202d7057d93e0fed9294b96952e188f7384832791c7b2254/click-8.3.0.tar.gz", hash = "sha256:e7b8232224eba16f4ebe410c25ced9f7875cb5f3263ffc93cc3e8da705e229c4", size = 276943, upload-time = "2025-09-18T17:32:23.696Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/db/d3/9dcc0f5797f070ec8edf30fbadfb200e71d9db6b84d211e3b2085a7589a0/click-8.3.0-py3-none-any.whl", hash = "sha256:9b9f285302c6e3064f4330c05f05b81945b2a39544279343e6e7c5f27a9baddc", size = 107295, upload-time = "2025-09-18T17:32:22.42Z" },
+]
+
+[[package]]
+name = "cloudpathlib"
+version = "0.22.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/05/bc/d7345595a4467144b9e0b32e5eda9e4633ea6e4982262b0696935adb2229/cloudpathlib-0.22.0.tar.gz", hash = "sha256:6c0cb0ceab4f66a3a05a84055f9318fb8316cae5e096819f3f8e4be64feab6e9", size = 52304, upload-time = "2025-08-30T05:20:04.6Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f1/72/e8e53d8232e801e040f4b557ff3a453cecbb630d53ae107bd5e66a206bb9/cloudpathlib-0.22.0-py3-none-any.whl", hash = "sha256:2fdfaf5c4f85810ae8374d336d04dee371914d0e41a984695ae67308d7a5a009", size = 61520, upload-time = "2025-08-30T05:20:03.232Z" },
+]
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
+]
+
+[[package]]
+name = "confection"
+version = "0.1.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pydantic" },
+    { name = "srsly" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/51/d3/57c6631159a1b48d273b40865c315cf51f89df7a9d1101094ef12e3a37c2/confection-0.1.5.tar.gz", hash = "sha256:8e72dd3ca6bd4f48913cd220f10b8275978e740411654b6e8ca6d7008c590f0e", size = 38924, upload-time = "2024-05-31T16:17:01.559Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0c/00/3106b1854b45bd0474ced037dfe6b73b90fe68a68968cef47c23de3d43d2/confection-0.1.5-py3-none-any.whl", hash = "sha256:e29d3c3f8eac06b3f77eb9dfb4bf2fc6bcc9622a98ca00a698e3d019c6430b14", size = 35451, upload-time = "2024-05-31T16:16:59.075Z" },
+]
+
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/91/2e/c4390a31919d8a78b90e8ecf87cd4b4c4f05a5b48d05ec17db8e5404c6f4/contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1", size = 288773, upload-time = "2025-07-26T12:01:02.277Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/44/c4b0b6095fef4dc9c420e041799591e3b63e9619e3044f7f4f6c21c0ab24/contourpy-1.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381", size = 270149, upload-time = "2025-07-26T12:01:04.072Z" },
+    { url = "https://files.pythonhosted.org/packages/30/2e/dd4ced42fefac8470661d7cb7e264808425e6c5d56d175291e93890cce09/contourpy-1.3.3-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7", size = 329222, upload-time = "2025-07-26T12:01:05.688Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/74/cc6ec2548e3d276c71389ea4802a774b7aa3558223b7bade3f25787fafc2/contourpy-1.3.3-cp311-cp311-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1", size = 377234, upload-time = "2025-07-26T12:01:07.054Z" },
+    { url = "https://files.pythonhosted.org/packages/03/b3/64ef723029f917410f75c09da54254c5f9ea90ef89b143ccadb09df14c15/contourpy-1.3.3-cp311-cp311-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a", size = 380555, upload-time = "2025-07-26T12:01:08.801Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/4b/6157f24ca425b89fe2eb7e7be642375711ab671135be21e6faa100f7448c/contourpy-1.3.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db", size = 355238, upload-time = "2025-07-26T12:01:10.319Z" },
+    { url = "https://files.pythonhosted.org/packages/98/56/f914f0dd678480708a04cfd2206e7c382533249bc5001eb9f58aa693e200/contourpy-1.3.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620", size = 1326218, upload-time = "2025-07-26T12:01:12.659Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/d7/4a972334a0c971acd5172389671113ae82aa7527073980c38d5868ff1161/contourpy-1.3.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f", size = 1392867, upload-time = "2025-07-26T12:01:15.533Z" },
+    { url = "https://files.pythonhosted.org/packages/75/3e/f2cc6cd56dc8cff46b1a56232eabc6feea52720083ea71ab15523daab796/contourpy-1.3.3-cp311-cp311-win32.whl", hash = "sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff", size = 183677, upload-time = "2025-07-26T12:01:17.088Z" },
+    { url = "https://files.pythonhosted.org/packages/98/4b/9bd370b004b5c9d8045c6c33cf65bae018b27aca550a3f657cdc99acdbd8/contourpy-1.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42", size = 225234, upload-time = "2025-07-26T12:01:18.256Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/b6/71771e02c2e004450c12b1120a5f488cad2e4d5b590b1af8bad060360fe4/contourpy-1.3.3-cp311-cp311-win_arm64.whl", hash = "sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470", size = 193123, upload-time = "2025-07-26T12:01:19.848Z" },
+    { url = "https://files.pythonhosted.org/packages/be/45/adfee365d9ea3d853550b2e735f9d66366701c65db7855cd07621732ccfc/contourpy-1.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb", size = 293419, upload-time = "2025-07-26T12:01:21.16Z" },
+    { url = "https://files.pythonhosted.org/packages/53/3e/405b59cfa13021a56bba395a6b3aca8cec012b45bf177b0eaf7a202cde2c/contourpy-1.3.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6", size = 273979, upload-time = "2025-07-26T12:01:22.448Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/1c/a12359b9b2ca3a845e8f7f9ac08bdf776114eb931392fcad91743e2ea17b/contourpy-1.3.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7", size = 332653, upload-time = "2025-07-26T12:01:24.155Z" },
+    { url = "https://files.pythonhosted.org/packages/63/12/897aeebfb475b7748ea67b61e045accdfcf0d971f8a588b67108ed7f5512/contourpy-1.3.3-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8", size = 379536, upload-time = "2025-07-26T12:01:25.91Z" },
+    { url = "https://files.pythonhosted.org/packages/43/8a/a8c584b82deb248930ce069e71576fc09bd7174bbd35183b7943fb1064fd/contourpy-1.3.3-cp312-cp312-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea", size = 384397, upload-time = "2025-07-26T12:01:27.152Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/8f/ec6289987824b29529d0dfda0d74a07cec60e54b9c92f3c9da4c0ac732de/contourpy-1.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1", size = 362601, upload-time = "2025-07-26T12:01:28.808Z" },
+    { url = "https://files.pythonhosted.org/packages/05/0a/a3fe3be3ee2dceb3e615ebb4df97ae6f3828aa915d3e10549ce016302bd1/contourpy-1.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7", size = 1331288, upload-time = "2025-07-26T12:01:31.198Z" },
+    { url = "https://files.pythonhosted.org/packages/33/1d/acad9bd4e97f13f3e2b18a3977fe1b4a37ecf3d38d815333980c6c72e963/contourpy-1.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411", size = 1403386, upload-time = "2025-07-26T12:01:33.947Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/8f/5847f44a7fddf859704217a99a23a4f6417b10e5ab1256a179264561540e/contourpy-1.3.3-cp312-cp312-win32.whl", hash = "sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69", size = 185018, upload-time = "2025-07-26T12:01:35.64Z" },
+    { url = "https://files.pythonhosted.org/packages/19/e8/6026ed58a64563186a9ee3f29f41261fd1828f527dd93d33b60feca63352/contourpy-1.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b", size = 226567, upload-time = "2025-07-26T12:01:36.804Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/e2/f05240d2c39a1ed228d8328a78b6f44cd695f7ef47beb3e684cf93604f86/contourpy-1.3.3-cp312-cp312-win_arm64.whl", hash = "sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc", size = 193655, upload-time = "2025-07-26T12:01:37.999Z" },
+    { url = "https://files.pythonhosted.org/packages/68/35/0167aad910bbdb9599272bd96d01a9ec6852f36b9455cf2ca67bd4cc2d23/contourpy-1.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5", size = 293257, upload-time = "2025-07-26T12:01:39.367Z" },
+    { url = "https://files.pythonhosted.org/packages/96/e4/7adcd9c8362745b2210728f209bfbcf7d91ba868a2c5f40d8b58f54c509b/contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1", size = 274034, upload-time = "2025-07-26T12:01:40.645Z" },
+    { url = "https://files.pythonhosted.org/packages/73/23/90e31ceeed1de63058a02cb04b12f2de4b40e3bef5e082a7c18d9c8ae281/contourpy-1.3.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286", size = 334672, upload-time = "2025-07-26T12:01:41.942Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/93/b43d8acbe67392e659e1d984700e79eb67e2acb2bd7f62012b583a7f1b55/contourpy-1.3.3-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5", size = 381234, upload-time = "2025-07-26T12:01:43.499Z" },
+    { url = "https://files.pythonhosted.org/packages/46/3b/bec82a3ea06f66711520f75a40c8fc0b113b2a75edb36aa633eb11c4f50f/contourpy-1.3.3-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67", size = 385169, upload-time = "2025-07-26T12:01:45.219Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/32/e0f13a1c5b0f8572d0ec6ae2f6c677b7991fafd95da523159c19eff0696a/contourpy-1.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9", size = 362859, upload-time = "2025-07-26T12:01:46.519Z" },
+    { url = "https://files.pythonhosted.org/packages/33/71/e2a7945b7de4e58af42d708a219f3b2f4cff7386e6b6ab0a0fa0033c49a9/contourpy-1.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659", size = 1332062, upload-time = "2025-07-26T12:01:48.964Z" },
+    { url = "https://files.pythonhosted.org/packages/12/fc/4e87ac754220ccc0e807284f88e943d6d43b43843614f0a8afa469801db0/contourpy-1.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7", size = 1403932, upload-time = "2025-07-26T12:01:51.979Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/2e/adc197a37443f934594112222ac1aa7dc9a98faf9c3842884df9a9d8751d/contourpy-1.3.3-cp313-cp313-win32.whl", hash = "sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d", size = 185024, upload-time = "2025-07-26T12:01:53.245Z" },
+    { url = "https://files.pythonhosted.org/packages/18/0b/0098c214843213759692cc638fce7de5c289200a830e5035d1791d7a2338/contourpy-1.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263", size = 226578, upload-time = "2025-07-26T12:01:54.422Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/9a/2f6024a0c5995243cd63afdeb3651c984f0d2bc727fd98066d40e141ad73/contourpy-1.3.3-cp313-cp313-win_arm64.whl", hash = "sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9", size = 193524, upload-time = "2025-07-26T12:01:55.73Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/b3/f8a1a86bd3298513f500e5b1f5fd92b69896449f6cab6a146a5d52715479/contourpy-1.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d", size = 306730, upload-time = "2025-07-26T12:01:57.051Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/11/4780db94ae62fc0c2053909b65dc3246bd7cecfc4f8a20d957ad43aa4ad8/contourpy-1.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216", size = 287897, upload-time = "2025-07-26T12:01:58.663Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/15/e59f5f3ffdd6f3d4daa3e47114c53daabcb18574a26c21f03dc9e4e42ff0/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae", size = 326751, upload-time = "2025-07-26T12:02:00.343Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/81/03b45cfad088e4770b1dcf72ea78d3802d04200009fb364d18a493857210/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20", size = 375486, upload-time = "2025-07-26T12:02:02.128Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/ba/49923366492ffbdd4486e970d421b289a670ae8cf539c1ea9a09822b371a/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99", size = 388106, upload-time = "2025-07-26T12:02:03.615Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/52/5b00ea89525f8f143651f9f03a0df371d3cbd2fccd21ca9b768c7a6500c2/contourpy-1.3.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b", size = 352548, upload-time = "2025-07-26T12:02:05.165Z" },
+    { url = "https://files.pythonhosted.org/packages/32/1d/a209ec1a3a3452d490f6b14dd92e72280c99ae3d1e73da74f8277d4ee08f/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a", size = 1322297, upload-time = "2025-07-26T12:02:07.379Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/9e/46f0e8ebdd884ca0e8877e46a3f4e633f6c9c8c4f3f6e72be3fe075994aa/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e", size = 1391023, upload-time = "2025-07-26T12:02:10.171Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/70/f308384a3ae9cd2209e0849f33c913f658d3326900d0ff5d378d6a1422d2/contourpy-1.3.3-cp313-cp313t-win32.whl", hash = "sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3", size = 196157, upload-time = "2025-07-26T12:02:11.488Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/dd/880f890a6663b84d9e34a6f88cded89d78f0091e0045a284427cb6b18521/contourpy-1.3.3-cp313-cp313t-win_amd64.whl", hash = "sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8", size = 240570, upload-time = "2025-07-26T12:02:12.754Z" },
+    { url = "https://files.pythonhosted.org/packages/80/99/2adc7d8ffead633234817ef8e9a87115c8a11927a94478f6bb3d3f4d4f7d/contourpy-1.3.3-cp313-cp313t-win_arm64.whl", hash = "sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301", size = 199713, upload-time = "2025-07-26T12:02:14.4Z" },
+    { url = "https://files.pythonhosted.org/packages/72/8b/4546f3ab60f78c514ffb7d01a0bd743f90de36f0019d1be84d0a708a580a/contourpy-1.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a", size = 292189, upload-time = "2025-07-26T12:02:16.095Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/e1/3542a9cb596cadd76fcef413f19c79216e002623158befe6daa03dbfa88c/contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77", size = 273251, upload-time = "2025-07-26T12:02:17.524Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/71/f93e1e9471d189f79d0ce2497007731c1e6bf9ef6d1d61b911430c3db4e5/contourpy-1.3.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5", size = 335810, upload-time = "2025-07-26T12:02:18.9Z" },
+    { url = "https://files.pythonhosted.org/packages/91/f9/e35f4c1c93f9275d4e38681a80506b5510e9327350c51f8d4a5a724d178c/contourpy-1.3.3-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4", size = 382871, upload-time = "2025-07-26T12:02:20.418Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/71/47b512f936f66a0a900d81c396a7e60d73419868fba959c61efed7a8ab46/contourpy-1.3.3-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36", size = 386264, upload-time = "2025-07-26T12:02:21.916Z" },
+    { url = "https://files.pythonhosted.org/packages/04/5f/9ff93450ba96b09c7c2b3f81c94de31c89f92292f1380261bd7195bea4ea/contourpy-1.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3", size = 363819, upload-time = "2025-07-26T12:02:23.759Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/a6/0b185d4cc480ee494945cde102cb0149ae830b5fa17bf855b95f2e70ad13/contourpy-1.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b", size = 1333650, upload-time = "2025-07-26T12:02:26.181Z" },
+    { url = "https://files.pythonhosted.org/packages/43/d7/afdc95580ca56f30fbcd3060250f66cedbde69b4547028863abd8aa3b47e/contourpy-1.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36", size = 1404833, upload-time = "2025-07-26T12:02:28.782Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/e2/366af18a6d386f41132a48f033cbd2102e9b0cf6345d35ff0826cd984566/contourpy-1.3.3-cp314-cp314-win32.whl", hash = "sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d", size = 189692, upload-time = "2025-07-26T12:02:30.128Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/c2/57f54b03d0f22d4044b8afb9ca0e184f8b1afd57b4f735c2fa70883dc601/contourpy-1.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd", size = 232424, upload-time = "2025-07-26T12:02:31.395Z" },
+    { url = "https://files.pythonhosted.org/packages/18/79/a9416650df9b525737ab521aa181ccc42d56016d2123ddcb7b58e926a42c/contourpy-1.3.3-cp314-cp314-win_arm64.whl", hash = "sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339", size = 198300, upload-time = "2025-07-26T12:02:32.956Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/42/38c159a7d0f2b7b9c04c64ab317042bb6952b713ba875c1681529a2932fe/contourpy-1.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772", size = 306769, upload-time = "2025-07-26T12:02:34.2Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/6c/26a8205f24bca10974e77460de68d3d7c63e282e23782f1239f226fcae6f/contourpy-1.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77", size = 287892, upload-time = "2025-07-26T12:02:35.807Z" },
+    { url = "https://files.pythonhosted.org/packages/66/06/8a475c8ab718ebfd7925661747dbb3c3ee9c82ac834ccb3570be49d129f4/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13", size = 326748, upload-time = "2025-07-26T12:02:37.193Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/a3/c5ca9f010a44c223f098fccd8b158bb1cb287378a31ac141f04730dc49be/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe", size = 375554, upload-time = "2025-07-26T12:02:38.894Z" },
+    { url = "https://files.pythonhosted.org/packages/80/5b/68bd33ae63fac658a4145088c1e894405e07584a316738710b636c6d0333/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f", size = 388118, upload-time = "2025-07-26T12:02:40.642Z" },
+    { url = "https://files.pythonhosted.org/packages/40/52/4c285a6435940ae25d7410a6c36bda5145839bc3f0beb20c707cda18b9d2/contourpy-1.3.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0", size = 352555, upload-time = "2025-07-26T12:02:42.25Z" },
+    { url = "https://files.pythonhosted.org/packages/24/ee/3e81e1dd174f5c7fefe50e85d0892de05ca4e26ef1c9a59c2a57e43b865a/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4", size = 1322295, upload-time = "2025-07-26T12:02:44.668Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/b2/6d913d4d04e14379de429057cd169e5e00f6c2af3bb13e1710bcbdb5da12/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f", size = 1391027, upload-time = "2025-07-26T12:02:47.09Z" },
+    { url = "https://files.pythonhosted.org/packages/93/8a/68a4ec5c55a2971213d29a9374913f7e9f18581945a7a31d1a39b5d2dfe5/contourpy-1.3.3-cp314-cp314t-win32.whl", hash = "sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae", size = 202428, upload-time = "2025-07-26T12:02:48.691Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/96/fd9f641ffedc4fa3ace923af73b9d07e869496c9cc7a459103e6e978992f/contourpy-1.3.3-cp314-cp314t-win_amd64.whl", hash = "sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc", size = 250331, upload-time = "2025-07-26T12:02:50.137Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/8c/469afb6465b853afff216f9528ffda78a915ff880ed58813ba4faf4ba0b6/contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b", size = 203831, upload-time = "2025-07-26T12:02:51.449Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/29/8dcfe16f0107943fa92388c23f6e05cff0ba58058c4c95b00280d4c75a14/contourpy-1.3.3-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497", size = 278809, upload-time = "2025-07-26T12:02:52.74Z" },
+    { url = "https://files.pythonhosted.org/packages/85/a9/8b37ef4f7dafeb335daee3c8254645ef5725be4d9c6aa70b50ec46ef2f7e/contourpy-1.3.3-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8", size = 261593, upload-time = "2025-07-26T12:02:54.037Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/59/ebfb8c677c75605cc27f7122c90313fd2f375ff3c8d19a1694bda74aaa63/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e", size = 302202, upload-time = "2025-07-26T12:02:55.947Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/37/21972a15834d90bfbfb009b9d004779bd5a07a0ec0234e5ba8f64d5736f4/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989", size = 329207, upload-time = "2025-07-26T12:02:57.468Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/58/bd257695f39d05594ca4ad60df5bcb7e32247f9951fd09a9b8edb82d1daa/contourpy-1.3.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77", size = 225315, upload-time = "2025-07-26T12:02:58.801Z" },
+]
+
+[[package]]
+name = "cycler"
+version = "0.12.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615, upload-time = "2023-10-07T05:32:18.335Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" },
+]
+
+[[package]]
+name = "cymem"
+version = "2.0.11"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f2/4a/1acd761fb6ac4c560e823ce40536a62f886f2d59b2763b5c3fc7e9d92101/cymem-2.0.11.tar.gz", hash = "sha256:efe49a349d4a518be6b6c6b255d4a80f740a341544bde1a807707c058b88d0bd", size = 10346, upload-time = "2025-01-16T21:50:41.045Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/03/e3/d98e3976f4ffa99cddebc1ce379d4d62e3eb1da22285267f902c99cc3395/cymem-2.0.11-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3ee54039aad3ef65de82d66c40516bf54586287b46d32c91ea0530c34e8a2745", size = 42005, upload-time = "2025-01-16T21:49:34.977Z" },
+    { url = "https://files.pythonhosted.org/packages/41/b4/7546faf2ab63e59befc95972316d62276cec153f7d4d60e7b0d5e08f0602/cymem-2.0.11-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4c05ef75b5db217be820604e43a47ccbbafea98ab6659d07cea92fa3c864ea58", size = 41747, upload-time = "2025-01-16T21:49:36.108Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/4e/042f372e5b3eb7f5f3dd7677161771d301de2b6fa3f7c74e1cebcd502552/cymem-2.0.11-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a8d5381e5793ce531bac0dbc00829c8381f18605bb67e4b61d34f8850463da40", size = 217647, upload-time = "2025-01-16T21:49:37.433Z" },
+    { url = "https://files.pythonhosted.org/packages/48/cb/2207679e4b92701f78cf141e1ab4f81f55247dbe154eb426b842a0a993de/cymem-2.0.11-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f2b9d3f42d7249ac81802135cad51d707def058001a32f73fc7fbf3de7045ac7", size = 218857, upload-time = "2025-01-16T21:49:40.09Z" },
+    { url = "https://files.pythonhosted.org/packages/31/7a/76ae3b7a39ab2531029d281e43fcfcaad728c2341b150a81a3a1f5587cf3/cymem-2.0.11-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:39b78f2195d20b75c2d465732f6b8e8721c5d4eb012777c2cb89bdb45a043185", size = 206148, upload-time = "2025-01-16T21:49:41.383Z" },
+    { url = "https://files.pythonhosted.org/packages/25/f9/d0fc0191ac79f15638ddb59237aa76f234691374d7d7950e10f384bd8a25/cymem-2.0.11-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:2203bd6525a80d8fd0c94654a263af21c0387ae1d5062cceaebb652bf9bad7bc", size = 207112, upload-time = "2025-01-16T21:49:43.986Z" },
+    { url = "https://files.pythonhosted.org/packages/56/c8/75f75889401b20f4c3a7c5965dda09df42913e904ddc2ffe7ef3bdf25061/cymem-2.0.11-cp311-cp311-win_amd64.whl", hash = "sha256:aa54af7314de400634448da1f935b61323da80a49484074688d344fb2036681b", size = 39360, upload-time = "2025-01-16T21:49:45.479Z" },
+    { url = "https://files.pythonhosted.org/packages/71/67/0d74f7e9d79f934368a78fb1d1466b94bebdbff14f8ae94dd3e4ea8738bb/cymem-2.0.11-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:a0fbe19ce653cd688842d81e5819dc63f911a26e192ef30b0b89f0ab2b192ff2", size = 42621, upload-time = "2025-01-16T21:49:46.585Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/d6/f7a19c63b48efc3f00a3ee8d69070ac90202e1e378f6cf81b8671f0cf762/cymem-2.0.11-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:de72101dc0e6326f6a2f73e05a438d1f3c6110d41044236d0fbe62925091267d", size = 42249, upload-time = "2025-01-16T21:49:48.973Z" },
+    { url = "https://files.pythonhosted.org/packages/d7/60/cdc434239813eef547fb99b6d0bafe31178501702df9b77c4108c9a216f6/cymem-2.0.11-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bee4395917f6588b8ac1699499128842768b391fe8896e8626950b4da5f9a406", size = 224758, upload-time = "2025-01-16T21:49:51.382Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/68/8fa6efae17cd3b2ba9a2f83b824867c5b65b06f7aec3f8a0d0cabdeffb9b/cymem-2.0.11-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5b02f2b17d760dc3fe5812737b1ce4f684641cdd751d67761d333a3b5ea97b83", size = 227995, upload-time = "2025-01-16T21:49:54.538Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/f3/ceda70bf6447880140602285b7c6fa171cb7c78b623d35345cc32505cd06/cymem-2.0.11-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:04ee6b4041ddec24512d6e969ed6445e57917f01e73b9dabbe17b7e6b27fef05", size = 215325, upload-time = "2025-01-16T21:49:57.229Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/47/6915eaa521e1ce7a0ba480eecb6870cb4f681bcd64ced88c2f0ed7a744b4/cymem-2.0.11-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e1048dae7e627ee25f22c87bb670b13e06bc0aecc114b89b959a798d487d1bf4", size = 216447, upload-time = "2025-01-16T21:50:00.432Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/be/8e02bdd31e557f642741a06c8e886782ef78f0b00daffd681922dc9bbc88/cymem-2.0.11-cp312-cp312-win_amd64.whl", hash = "sha256:0c269c7a867d74adeb9db65fa1d226342aacf44d64b7931282f0b0eb22eb6275", size = 39283, upload-time = "2025-01-16T21:50:03.384Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/90/b064e2677e27a35cf3605146abc3285d4f599cc1b6c18fc445ae876dd1e3/cymem-2.0.11-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f4a311c82f743275c84f708df89ac5bf60ddefe4713d532000c887931e22941f", size = 42389, upload-time = "2025-01-16T21:50:05.925Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/60/7aa0561a6c1f0d42643b02c4fdeb2a16181b0ff4e85d73d2d80c6689e92a/cymem-2.0.11-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:02ed92bead896cca36abad00502b14fa651bdf5d8319461126a2d5ac8c9674c5", size = 41948, upload-time = "2025-01-16T21:50:08.375Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/4e/88a29cc5575374982e527b4ebcab3781bdc826ce693c6418a0f836544246/cymem-2.0.11-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:44ddd3588379f8f376116384af99e3fb5f90091d90f520c341942618bf22f05e", size = 219382, upload-time = "2025-01-16T21:50:13.089Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/3a/8f96e167e93b7f7ec105ed7b25c77bbf215d15bcbf4a24082cdc12234cd6/cymem-2.0.11-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:87ec985623624bbd298762d8163fc194a096cb13282731a017e09ff8a60bb8b1", size = 222974, upload-time = "2025-01-16T21:50:17.969Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/fc/ce016bb0c66a4776345fac7508fddec3b739b9dd4363094ac89cce048832/cymem-2.0.11-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:e3385a47285435848e0ed66cfd29b35f3ed8703218e2b17bd7a0c053822f26bf", size = 213426, upload-time = "2025-01-16T21:50:19.349Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/c8/accf7cc768f751447a5050b14a195af46798bc22767ac25f49b02861b1eb/cymem-2.0.11-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5461e65340d6572eb64deadce79242a446a1d39cb7bf70fe7b7e007eb0d799b0", size = 219195, upload-time = "2025-01-16T21:50:21.407Z" },
+    { url = "https://files.pythonhosted.org/packages/74/65/c162fbac63e867a055240b6600b92ef96c0eb7a1895312ac53c4be93d056/cymem-2.0.11-cp313-cp313-win_amd64.whl", hash = "sha256:25da111adf425c29af0cfd9fecfec1c71c8d82e2244a85166830a0817a66ada7", size = 39090, upload-time = "2025-01-16T21:50:24.239Z" },
+]
+
+[[package]]
+name = "fonttools"
+version = "4.60.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4b/42/97a13e47a1e51a5a7142475bbcf5107fe3a68fc34aef331c897d5fb98ad0/fonttools-4.60.1.tar.gz", hash = "sha256:ef00af0439ebfee806b25f24c8f92109157ff3fac5731dc7867957812e87b8d9", size = 3559823, upload-time = "2025-09-29T21:13:27.129Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ea/85/639aa9bface1537e0fb0f643690672dde0695a5bbbc90736bc571b0b1941/fonttools-4.60.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7b4c32e232a71f63a5d00259ca3d88345ce2a43295bb049d21061f338124246f", size = 2831872, upload-time = "2025-09-29T21:11:20.329Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/47/3c63158459c95093be9618794acb1067b3f4d30dcc5c3e8114b70e67a092/fonttools-4.60.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3630e86c484263eaac71d117085d509cbcf7b18f677906824e4bace598fb70d2", size = 2356990, upload-time = "2025-09-29T21:11:22.754Z" },
+    { url = "https://files.pythonhosted.org/packages/94/dd/1934b537c86fcf99f9761823f1fc37a98fbd54568e8e613f29a90fed95a9/fonttools-4.60.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5c1015318e4fec75dd4943ad5f6a206d9727adf97410d58b7e32ab644a807914", size = 5042189, upload-time = "2025-09-29T21:11:25.061Z" },
+    { url = "https://files.pythonhosted.org/packages/d2/d2/9f4e4c4374dd1daa8367784e1bd910f18ba886db1d6b825b12edf6db3edc/fonttools-4.60.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e6c58beb17380f7c2ea181ea11e7db8c0ceb474c9dd45f48e71e2cb577d146a1", size = 4978683, upload-time = "2025-09-29T21:11:27.693Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/c4/0fb2dfd1ecbe9a07954cc13414713ed1eab17b1c0214ef07fc93df234a47/fonttools-4.60.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ec3681a0cb34c255d76dd9d865a55f260164adb9fa02628415cdc2d43ee2c05d", size = 5021372, upload-time = "2025-09-29T21:11:30.257Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/d5/495fc7ae2fab20223cc87179a8f50f40f9a6f821f271ba8301ae12bb580f/fonttools-4.60.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f4b5c37a5f40e4d733d3bbaaef082149bee5a5ea3156a785ff64d949bd1353fa", size = 5132562, upload-time = "2025-09-29T21:11:32.737Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/fa/021dab618526323c744e0206b3f5c8596a2e7ae9aa38db5948a131123e83/fonttools-4.60.1-cp311-cp311-win32.whl", hash = "sha256:398447f3d8c0c786cbf1209711e79080a40761eb44b27cdafffb48f52bcec258", size = 2230288, upload-time = "2025-09-29T21:11:35.015Z" },
+    { url = "https://files.pythonhosted.org/packages/bb/78/0e1a6d22b427579ea5c8273e1c07def2f325b977faaf60bb7ddc01456cb1/fonttools-4.60.1-cp311-cp311-win_amd64.whl", hash = "sha256:d066ea419f719ed87bc2c99a4a4bfd77c2e5949cb724588b9dd58f3fd90b92bf", size = 2278184, upload-time = "2025-09-29T21:11:37.434Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/f7/a10b101b7a6f8836a5adb47f2791f2075d044a6ca123f35985c42edc82d8/fonttools-4.60.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:7b0c6d57ab00dae9529f3faf187f2254ea0aa1e04215cf2f1a8ec277c96661bc", size = 2832953, upload-time = "2025-09-29T21:11:39.616Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/fe/7bd094b59c926acf2304d2151354ddbeb74b94812f3dc943c231db09cb41/fonttools-4.60.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:839565cbf14645952d933853e8ade66a463684ed6ed6c9345d0faf1f0e868877", size = 2352706, upload-time = "2025-09-29T21:11:41.826Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/ca/4bb48a26ed95a1e7eba175535fe5805887682140ee0a0d10a88e1de84208/fonttools-4.60.1-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:8177ec9676ea6e1793c8a084a90b65a9f778771998eb919d05db6d4b1c0b114c", size = 4923716, upload-time = "2025-09-29T21:11:43.893Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/9f/2cb82999f686c1d1ddf06f6ae1a9117a880adbec113611cc9d22b2fdd465/fonttools-4.60.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:996a4d1834524adbb423385d5a629b868ef9d774670856c63c9a0408a3063401", size = 4968175, upload-time = "2025-09-29T21:11:46.439Z" },
+    { url = "https://files.pythonhosted.org/packages/18/79/be569699e37d166b78e6218f2cde8c550204f2505038cdd83b42edc469b9/fonttools-4.60.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a46b2f450bc79e06ef3b6394f0c68660529ed51692606ad7f953fc2e448bc903", size = 4911031, upload-time = "2025-09-29T21:11:48.977Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/9f/89411cc116effaec5260ad519162f64f9c150e5522a27cbb05eb62d0c05b/fonttools-4.60.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6ec722ee589e89a89f5b7574f5c45604030aa6ae24cb2c751e2707193b466fed", size = 5062966, upload-time = "2025-09-29T21:11:54.344Z" },
+    { url = "https://files.pythonhosted.org/packages/62/a1/f888221934b5731d46cb9991c7a71f30cb1f97c0ef5fcf37f8da8fce6c8e/fonttools-4.60.1-cp312-cp312-win32.whl", hash = "sha256:b2cf105cee600d2de04ca3cfa1f74f1127f8455b71dbad02b9da6ec266e116d6", size = 2218750, upload-time = "2025-09-29T21:11:56.601Z" },
+    { url = "https://files.pythonhosted.org/packages/88/8f/a55b5550cd33cd1028601df41acd057d4be20efa5c958f417b0c0613924d/fonttools-4.60.1-cp312-cp312-win_amd64.whl", hash = "sha256:992775c9fbe2cf794786fa0ffca7f09f564ba3499b8fe9f2f80bd7197db60383", size = 2267026, upload-time = "2025-09-29T21:11:58.852Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/5b/cdd2c612277b7ac7ec8c0c9bc41812c43dc7b2d5f2b0897e15fdf5a1f915/fonttools-4.60.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:6f68576bb4bbf6060c7ab047b1574a1ebe5c50a17de62830079967b211059ebb", size = 2825777, upload-time = "2025-09-29T21:12:01.22Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/8a/de9cc0540f542963ba5e8f3a1f6ad48fa211badc3177783b9d5cadf79b5d/fonttools-4.60.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:eedacb5c5d22b7097482fa834bda0dafa3d914a4e829ec83cdea2a01f8c813c4", size = 2348080, upload-time = "2025-09-29T21:12:03.785Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/8b/371ab3cec97ee3fe1126b3406b7abd60c8fec8975fd79a3c75cdea0c3d83/fonttools-4.60.1-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b33a7884fabd72bdf5f910d0cf46be50dce86a0362a65cfc746a4168c67eb96c", size = 4903082, upload-time = "2025-09-29T21:12:06.382Z" },
+    { url = "https://files.pythonhosted.org/packages/04/05/06b1455e4bc653fcb2117ac3ef5fa3a8a14919b93c60742d04440605d058/fonttools-4.60.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2409d5fb7b55fd70f715e6d34e7a6e4f7511b8ad29a49d6df225ee76da76dd77", size = 4960125, upload-time = "2025-09-29T21:12:09.314Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/37/f3b840fcb2666f6cb97038793606bdd83488dca2d0b0fc542ccc20afa668/fonttools-4.60.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c8651e0d4b3bdeda6602b85fdc2abbefc1b41e573ecb37b6779c4ca50753a199", size = 4901454, upload-time = "2025-09-29T21:12:11.931Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/9e/eb76f77e82f8d4a46420aadff12cec6237751b0fb9ef1de373186dcffb5f/fonttools-4.60.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:145daa14bf24824b677b9357c5e44fd8895c2a8f53596e1b9ea3496081dc692c", size = 5044495, upload-time = "2025-09-29T21:12:15.241Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/b3/cede8f8235d42ff7ae891bae8d619d02c8ac9fd0cfc450c5927a6200c70d/fonttools-4.60.1-cp313-cp313-win32.whl", hash = "sha256:2299df884c11162617a66b7c316957d74a18e3758c0274762d2cc87df7bc0272", size = 2217028, upload-time = "2025-09-29T21:12:17.96Z" },
+    { url = "https://files.pythonhosted.org/packages/75/4d/b022c1577807ce8b31ffe055306ec13a866f2337ecee96e75b24b9b753ea/fonttools-4.60.1-cp313-cp313-win_amd64.whl", hash = "sha256:a3db56f153bd4c5c2b619ab02c5db5192e222150ce5a1bc10f16164714bc39ac", size = 2266200, upload-time = "2025-09-29T21:12:20.14Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/83/752ca11c1aa9a899b793a130f2e466b79ea0cf7279c8d79c178fc954a07b/fonttools-4.60.1-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:a884aef09d45ba1206712c7dbda5829562d3fea7726935d3289d343232ecb0d3", size = 2822830, upload-time = "2025-09-29T21:12:24.406Z" },
+    { url = "https://files.pythonhosted.org/packages/57/17/bbeab391100331950a96ce55cfbbff27d781c1b85ebafb4167eae50d9fe3/fonttools-4.60.1-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8a44788d9d91df72d1a5eac49b31aeb887a5f4aab761b4cffc4196c74907ea85", size = 2345524, upload-time = "2025-09-29T21:12:26.819Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/2e/d4831caa96d85a84dd0da1d9f90d81cec081f551e0ea216df684092c6c97/fonttools-4.60.1-cp314-cp314-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:e852d9dda9f93ad3651ae1e3bb770eac544ec93c3807888798eccddf84596537", size = 4843490, upload-time = "2025-09-29T21:12:29.123Z" },
+    { url = "https://files.pythonhosted.org/packages/49/13/5e2ea7c7a101b6fc3941be65307ef8df92cbbfa6ec4804032baf1893b434/fonttools-4.60.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:154cb6ee417e417bf5f7c42fe25858c9140c26f647c7347c06f0cc2d47eff003", size = 4944184, upload-time = "2025-09-29T21:12:31.414Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/2b/cf9603551c525b73fc47c52ee0b82a891579a93d9651ed694e4e2cd08bb8/fonttools-4.60.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:5664fd1a9ea7f244487ac8f10340c4e37664675e8667d6fee420766e0fb3cf08", size = 4890218, upload-time = "2025-09-29T21:12:33.936Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/2f/933d2352422e25f2376aae74f79eaa882a50fb3bfef3c0d4f50501267101/fonttools-4.60.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:583b7f8e3c49486e4d489ad1deacfb8d5be54a8ef34d6df824f6a171f8511d99", size = 4999324, upload-time = "2025-09-29T21:12:36.637Z" },
+    { url = "https://files.pythonhosted.org/packages/38/99/234594c0391221f66216bc2c886923513b3399a148defaccf81dc3be6560/fonttools-4.60.1-cp314-cp314-win32.whl", hash = "sha256:66929e2ea2810c6533a5184f938502cfdaea4bc3efb7130d8cc02e1c1b4108d6", size = 2220861, upload-time = "2025-09-29T21:12:39.108Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/1d/edb5b23726dde50fc4068e1493e4fc7658eeefcaf75d4c5ffce067d07ae5/fonttools-4.60.1-cp314-cp314-win_amd64.whl", hash = "sha256:f3d5be054c461d6a2268831f04091dc82753176f6ea06dc6047a5e168265a987", size = 2270934, upload-time = "2025-09-29T21:12:41.339Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/da/1392aaa2170adc7071fe7f9cfd181a5684a7afcde605aebddf1fb4d76df5/fonttools-4.60.1-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:b6379e7546ba4ae4b18f8ae2b9bc5960936007a1c0e30b342f662577e8bc3299", size = 2894340, upload-time = "2025-09-29T21:12:43.774Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/a7/3b9f16e010d536ce567058b931a20b590d8f3177b2eda09edd92e392375d/fonttools-4.60.1-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9d0ced62b59e0430b3690dbc5373df1c2aa7585e9a8ce38eff87f0fd993c5b01", size = 2375073, upload-time = "2025-09-29T21:12:46.437Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/b5/e9bcf51980f98e59bb5bb7c382a63c6f6cac0eec5f67de6d8f2322382065/fonttools-4.60.1-cp314-cp314t-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:875cb7764708b3132637f6c5fb385b16eeba0f7ac9fa45a69d35e09b47045801", size = 4849758, upload-time = "2025-09-29T21:12:48.694Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/dc/1d2cf7d1cba82264b2f8385db3f5960e3d8ce756b4dc65b700d2c496f7e9/fonttools-4.60.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a184b2ea57b13680ab6d5fbde99ccef152c95c06746cb7718c583abd8f945ccc", size = 5085598, upload-time = "2025-09-29T21:12:51.081Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/4d/279e28ba87fb20e0c69baf72b60bbf1c4d873af1476806a7b5f2b7fac1ff/fonttools-4.60.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:026290e4ec76583881763fac284aca67365e0be9f13a7fb137257096114cb3bc", size = 4957603, upload-time = "2025-09-29T21:12:53.423Z" },
+    { url = "https://files.pythonhosted.org/packages/78/d4/ff19976305e0c05aa3340c805475abb00224c954d3c65e82c0a69633d55d/fonttools-4.60.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f0e8817c7d1a0c2eedebf57ef9a9896f3ea23324769a9a2061a80fe8852705ed", size = 4974184, upload-time = "2025-09-29T21:12:55.962Z" },
+    { url = "https://files.pythonhosted.org/packages/63/22/8553ff6166f5cd21cfaa115aaacaa0dc73b91c079a8cfd54a482cbc0f4f5/fonttools-4.60.1-cp314-cp314t-win32.whl", hash = "sha256:1410155d0e764a4615774e5c2c6fc516259fe3eca5882f034eb9bfdbee056259", size = 2282241, upload-time = "2025-09-29T21:12:58.179Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/cb/fa7b4d148e11d5a72761a22e595344133e83a9507a4c231df972e657579b/fonttools-4.60.1-cp314-cp314t-win_amd64.whl", hash = "sha256:022beaea4b73a70295b688f817ddc24ed3e3418b5036ffcd5658141184ef0d0c", size = 2345760, upload-time = "2025-09-29T21:13:00.375Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/93/0dd45cd283c32dea1545151d8c3637b4b8c53cdb3a625aeb2885b184d74d/fonttools-4.60.1-py3-none-any.whl", hash = "sha256:906306ac7afe2156fcf0042173d6ebbb05416af70f6b370967b47f8f00103bbb", size = 1143175, upload-time = "2025-09-29T21:13:24.134Z" },
+]
+
+[[package]]
+name = "geopandas"
+version = "1.1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pandas" },
+    { name = "pyogrio" },
+    { name = "pyproj" },
+    { name = "shapely" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8c/76/e1960ba846f153ab109575242abf89dc98f8e057faa32f3decf4cce9247a/geopandas-1.1.1.tar.gz", hash = "sha256:1745713f64d095c43e72e08e753dbd271678254b24f2e01db8cdb8debe1d293d", size = 332655, upload-time = "2025-06-26T21:04:56.57Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0b/70/d5cd0696eff08e62fdbdebe5b46527facb4e7220eabe0ac6225efab50168/geopandas-1.1.1-py3-none-any.whl", hash = "sha256:589e61aaf39b19828843df16cb90234e72897e2579be236f10eee0d052ad98e8", size = 338365, upload-time = "2025-06-26T21:04:55.139Z" },
+]
+
+[[package]]
+name = "gitdb"
+version = "4.0.12"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "smmap" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/72/94/63b0fc47eb32792c7ba1fe1b694daec9a63620db1e313033d18140c2320a/gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571", size = 394684, upload-time = "2025-01-02T07:20:46.413Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a0/61/5c78b91c3143ed5c14207f463aecfc8f9dbb5092fb2869baf37c273b2705/gitdb-4.0.12-py3-none-any.whl", hash = "sha256:67073e15955400952c6565cc3e707c554a4eea2e428946f7a4c162fab9bd9bcf", size = 62794, upload-time = "2025-01-02T07:20:43.624Z" },
+]
+
+[[package]]
+name = "gitpython"
+version = "3.1.45"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "gitdb" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9a/c8/dd58967d119baab745caec2f9d853297cec1989ec1d63f677d3880632b88/gitpython-3.1.45.tar.gz", hash = "sha256:85b0ee964ceddf211c41b9f27a49086010a190fd8132a24e21f362a4b36a791c", size = 215076, upload-time = "2025-07-24T03:45:54.871Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/01/61/d4b89fec821f72385526e1b9d9a3a0385dda4a72b206d28049e2c7cd39b8/gitpython-3.1.45-py3-none-any.whl", hash = "sha256:8908cb2e02fb3b93b7eb0f2827125cb699869470432cc885f019b8fd0fccff77", size = 208168, upload-time = "2025-07-24T03:45:52.517Z" },
+]
+
+[[package]]
+name = "h11"
+version = "0.16.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250, upload-time = "2025-04-24T03:35:25.427Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" },
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.9"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "h11" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484, upload-time = "2025-04-24T22:06:22.219Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784, upload-time = "2025-04-24T22:06:20.566Z" },
+]
+
+[[package]]
+name = "httpx"
+version = "0.28.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "certifi" },
+    { name = "httpcore" },
+    { name = "idna" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406, upload-time = "2024-12-06T15:37:23.222Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517, upload-time = "2024-12-06T15:37:21.509Z" },
+]
+
+[[package]]
+name = "idna"
+version = "3.10"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f1/70/7703c29685631f5a7590aa73f1f1d3fa9a380e654b86af429e0934a32f7d/idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9", size = 190490, upload-time = "2024-09-15T18:07:39.745Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442, upload-time = "2024-09-15T18:07:37.964Z" },
+]
+
+[[package]]
+name = "jinja2"
+version = "3.1.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markupsafe" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" },
+]
+
+[[package]]
+name = "joblib"
+version = "1.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e8/5d/447af5ea094b9e4c4054f82e223ada074c552335b9b4b2d14bd9b35a67c4/joblib-1.5.2.tar.gz", hash = "sha256:3faa5c39054b2f03ca547da9b2f52fde67c06240c31853f306aea97f13647b55", size = 331077, upload-time = "2025-08-27T12:15:46.575Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/e8/685f47e0d754320684db4425a0967f7d3fa70126bffd76110b7009a0090f/joblib-1.5.2-py3-none-any.whl", hash = "sha256:4e1f0bdbb987e6d843c70cf43714cb276623def372df3c22fe5266b2670bc241", size = 308396, upload-time = "2025-08-27T12:15:45.188Z" },
+]
+
+[[package]]
+name = "jsonschema"
+version = "4.25.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "jsonschema-specifications" },
+    { name = "referencing" },
+    { name = "rpds-py" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/74/69/f7185de793a29082a9f3c7728268ffb31cb5095131a9c139a74078e27336/jsonschema-4.25.1.tar.gz", hash = "sha256:e4a9655ce0da0c0b67a085847e00a3a51449e1157f4f75e9fb5aa545e122eb85", size = 357342, upload-time = "2025-08-18T17:03:50.038Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bf/9c/8c95d856233c1f82500c2450b8c68576b4cf1c871db3afac5c34ff84e6fd/jsonschema-4.25.1-py3-none-any.whl", hash = "sha256:3fba0169e345c7175110351d456342c364814cfcf3b964ba4587f22915230a63", size = 90040, upload-time = "2025-08-18T17:03:48.373Z" },
+]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2025.9.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "referencing" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/19/74/a633ee74eb36c44aa6d1095e7cc5569bebf04342ee146178e2d36600708b/jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d", size = 32855, upload-time = "2025-09-08T01:34:59.186Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437, upload-time = "2025-09-08T01:34:57.871Z" },
+]
+
+[[package]]
+name = "kiwisolver"
+version = "1.4.9"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5c/3c/85844f1b0feb11ee581ac23fe5fce65cd049a200c1446708cc1b7f922875/kiwisolver-1.4.9.tar.gz", hash = "sha256:c3b22c26c6fd6811b0ae8363b95ca8ce4ea3c202d3d0975b2914310ceb1bcc4d", size = 97564, upload-time = "2025-08-10T21:27:49.279Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6f/ab/c80b0d5a9d8a1a65f4f815f2afff9798b12c3b9f31f1d304dd233dd920e2/kiwisolver-1.4.9-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:eb14a5da6dc7642b0f3a18f13654847cd8b7a2550e2645a5bda677862b03ba16", size = 124167, upload-time = "2025-08-10T21:25:53.403Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/c0/27fe1a68a39cf62472a300e2879ffc13c0538546c359b86f149cc19f6ac3/kiwisolver-1.4.9-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:39a219e1c81ae3b103643d2aedb90f1ef22650deb266ff12a19e7773f3e5f089", size = 66579, upload-time = "2025-08-10T21:25:54.79Z" },
+    { url = "https://files.pythonhosted.org/packages/31/a2/a12a503ac1fd4943c50f9822678e8015a790a13b5490354c68afb8489814/kiwisolver-1.4.9-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2405a7d98604b87f3fc28b1716783534b1b4b8510d8142adca34ee0bc3c87543", size = 65309, upload-time = "2025-08-10T21:25:55.76Z" },
+    { url = "https://files.pythonhosted.org/packages/66/e1/e533435c0be77c3f64040d68d7a657771194a63c279f55573188161e81ca/kiwisolver-1.4.9-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:dc1ae486f9abcef254b5618dfb4113dd49f94c68e3e027d03cf0143f3f772b61", size = 1435596, upload-time = "2025-08-10T21:25:56.861Z" },
+    { url = "https://files.pythonhosted.org/packages/67/1e/51b73c7347f9aabdc7215aa79e8b15299097dc2f8e67dee2b095faca9cb0/kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a1f570ce4d62d718dce3f179ee78dac3b545ac16c0c04bb363b7607a949c0d1", size = 1246548, upload-time = "2025-08-10T21:25:58.246Z" },
+    { url = "https://files.pythonhosted.org/packages/21/aa/72a1c5d1e430294f2d32adb9542719cfb441b5da368d09d268c7757af46c/kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:cb27e7b78d716c591e88e0a09a2139c6577865d7f2e152488c2cc6257f460872", size = 1263618, upload-time = "2025-08-10T21:25:59.857Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/af/db1509a9e79dbf4c260ce0cfa3903ea8945f6240e9e59d1e4deb731b1a40/kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:15163165efc2f627eb9687ea5f3a28137217d217ac4024893d753f46bce9de26", size = 1317437, upload-time = "2025-08-10T21:26:01.105Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/f2/3ea5ee5d52abacdd12013a94130436e19969fa183faa1e7c7fbc89e9a42f/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:bdee92c56a71d2b24c33a7d4c2856bd6419d017e08caa7802d2963870e315028", size = 2195742, upload-time = "2025-08-10T21:26:02.675Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/9b/1efdd3013c2d9a2566aa6a337e9923a00590c516add9a1e89a768a3eb2fc/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:412f287c55a6f54b0650bd9b6dce5aceddb95864a1a90c87af16979d37c89771", size = 2290810, upload-time = "2025-08-10T21:26:04.009Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/e5/cfdc36109ae4e67361f9bc5b41323648cb24a01b9ade18784657e022e65f/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:2c93f00dcba2eea70af2be5f11a830a742fe6b579a1d4e00f47760ef13be247a", size = 2461579, upload-time = "2025-08-10T21:26:05.317Z" },
+    { url = "https://files.pythonhosted.org/packages/62/86/b589e5e86c7610842213994cdea5add00960076bef4ae290c5fa68589cac/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f117e1a089d9411663a3207ba874f31be9ac8eaa5b533787024dc07aeb74f464", size = 2268071, upload-time = "2025-08-10T21:26:06.686Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/c6/f8df8509fd1eee6c622febe54384a96cfaf4d43bf2ccec7a0cc17e4715c9/kiwisolver-1.4.9-cp311-cp311-win_amd64.whl", hash = "sha256:be6a04e6c79819c9a8c2373317d19a96048e5a3f90bec587787e86a1153883c2", size = 73840, upload-time = "2025-08-10T21:26:07.94Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/2d/16e0581daafd147bc11ac53f032a2b45eabac897f42a338d0a13c1e5c436/kiwisolver-1.4.9-cp311-cp311-win_arm64.whl", hash = "sha256:0ae37737256ba2de764ddc12aed4956460277f00c4996d51a197e72f62f5eec7", size = 65159, upload-time = "2025-08-10T21:26:09.048Z" },
+    { url = "https://files.pythonhosted.org/packages/86/c9/13573a747838aeb1c76e3267620daa054f4152444d1f3d1a2324b78255b5/kiwisolver-1.4.9-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:ac5a486ac389dddcc5bef4f365b6ae3ffff2c433324fb38dd35e3fab7c957999", size = 123686, upload-time = "2025-08-10T21:26:10.034Z" },
+    { url = "https://files.pythonhosted.org/packages/51/ea/2ecf727927f103ffd1739271ca19c424d0e65ea473fbaeea1c014aea93f6/kiwisolver-1.4.9-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f2ba92255faa7309d06fe44c3a4a97efe1c8d640c2a79a5ef728b685762a6fd2", size = 66460, upload-time = "2025-08-10T21:26:11.083Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/5a/51f5464373ce2aeb5194508298a508b6f21d3867f499556263c64c621914/kiwisolver-1.4.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4a2899935e724dd1074cb568ce7ac0dce28b2cd6ab539c8e001a8578eb106d14", size = 64952, upload-time = "2025-08-10T21:26:12.058Z" },
+    { url = "https://files.pythonhosted.org/packages/70/90/6d240beb0f24b74371762873e9b7f499f1e02166a2d9c5801f4dbf8fa12e/kiwisolver-1.4.9-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f6008a4919fdbc0b0097089f67a1eb55d950ed7e90ce2cc3e640abadd2757a04", size = 1474756, upload-time = "2025-08-10T21:26:13.096Z" },
+    { url = "https://files.pythonhosted.org/packages/12/42/f36816eaf465220f683fb711efdd1bbf7a7005a2473d0e4ed421389bd26c/kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:67bb8b474b4181770f926f7b7d2f8c0248cbcb78b660fdd41a47054b28d2a752", size = 1276404, upload-time = "2025-08-10T21:26:14.457Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/64/bc2de94800adc830c476dce44e9b40fd0809cddeef1fde9fcf0f73da301f/kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2327a4a30d3ee07d2fbe2e7933e8a37c591663b96ce42a00bc67461a87d7df77", size = 1294410, upload-time = "2025-08-10T21:26:15.73Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/42/2dc82330a70aa8e55b6d395b11018045e58d0bb00834502bf11509f79091/kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7a08b491ec91b1d5053ac177afe5290adacf1f0f6307d771ccac5de30592d198", size = 1343631, upload-time = "2025-08-10T21:26:17.045Z" },
+    { url = "https://files.pythonhosted.org/packages/22/fd/f4c67a6ed1aab149ec5a8a401c323cee7a1cbe364381bb6c9c0d564e0e20/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d8fc5c867c22b828001b6a38d2eaeb88160bf5783c6cb4a5e440efc981ce286d", size = 2224963, upload-time = "2025-08-10T21:26:18.737Z" },
+    { url = "https://files.pythonhosted.org/packages/45/aa/76720bd4cb3713314677d9ec94dcc21ced3f1baf4830adde5bb9b2430a5f/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:3b3115b2581ea35bb6d1f24a4c90af37e5d9b49dcff267eeed14c3893c5b86ab", size = 2321295, upload-time = "2025-08-10T21:26:20.11Z" },
+    { url = "https://files.pythonhosted.org/packages/80/19/d3ec0d9ab711242f56ae0dc2fc5d70e298bb4a1f9dfab44c027668c673a1/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:858e4c22fb075920b96a291928cb7dea5644e94c0ee4fcd5af7e865655e4ccf2", size = 2487987, upload-time = "2025-08-10T21:26:21.49Z" },
+    { url = "https://files.pythonhosted.org/packages/39/e9/61e4813b2c97e86b6fdbd4dd824bf72d28bcd8d4849b8084a357bc0dd64d/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:ed0fecd28cc62c54b262e3736f8bb2512d8dcfdc2bcf08be5f47f96bf405b145", size = 2291817, upload-time = "2025-08-10T21:26:22.812Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/41/85d82b0291db7504da3c2defe35c9a8a5c9803a730f297bd823d11d5fb77/kiwisolver-1.4.9-cp312-cp312-win_amd64.whl", hash = "sha256:f68208a520c3d86ea51acf688a3e3002615a7f0238002cccc17affecc86a8a54", size = 73895, upload-time = "2025-08-10T21:26:24.37Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/92/5f3068cf15ee5cb624a0c7596e67e2a0bb2adee33f71c379054a491d07da/kiwisolver-1.4.9-cp312-cp312-win_arm64.whl", hash = "sha256:2c1a4f57df73965f3f14df20b80ee29e6a7930a57d2d9e8491a25f676e197c60", size = 64992, upload-time = "2025-08-10T21:26:25.732Z" },
+    { url = "https://files.pythonhosted.org/packages/31/c1/c2686cda909742ab66c7388e9a1a8521a59eb89f8bcfbee28fc980d07e24/kiwisolver-1.4.9-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a5d0432ccf1c7ab14f9949eec60c5d1f924f17c037e9f8b33352fa05799359b8", size = 123681, upload-time = "2025-08-10T21:26:26.725Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/f0/f44f50c9f5b1a1860261092e3bc91ecdc9acda848a8b8c6abfda4a24dd5c/kiwisolver-1.4.9-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:efb3a45b35622bb6c16dbfab491a8f5a391fe0e9d45ef32f4df85658232ca0e2", size = 66464, upload-time = "2025-08-10T21:26:27.733Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/7a/9d90a151f558e29c3936b8a47ac770235f436f2120aca41a6d5f3d62ae8d/kiwisolver-1.4.9-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1a12cf6398e8a0a001a059747a1cbf24705e18fe413bc22de7b3d15c67cffe3f", size = 64961, upload-time = "2025-08-10T21:26:28.729Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/e9/f218a2cb3a9ffbe324ca29a9e399fa2d2866d7f348ec3a88df87fc248fc5/kiwisolver-1.4.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b67e6efbf68e077dd71d1a6b37e43e1a99d0bff1a3d51867d45ee8908b931098", size = 1474607, upload-time = "2025-08-10T21:26:29.798Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/28/aac26d4c882f14de59041636292bc838db8961373825df23b8eeb807e198/kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5656aa670507437af0207645273ccdfee4f14bacd7f7c67a4306d0dcaeaf6eed", size = 1276546, upload-time = "2025-08-10T21:26:31.401Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/ad/8bfc1c93d4cc565e5069162f610ba2f48ff39b7de4b5b8d93f69f30c4bed/kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:bfc08add558155345129c7803b3671cf195e6a56e7a12f3dde7c57d9b417f525", size = 1294482, upload-time = "2025-08-10T21:26:32.721Z" },
+    { url = "https://files.pythonhosted.org/packages/da/f1/6aca55ff798901d8ce403206d00e033191f63d82dd708a186e0ed2067e9c/kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:40092754720b174e6ccf9e845d0d8c7d8e12c3d71e7fc35f55f3813e96376f78", size = 1343720, upload-time = "2025-08-10T21:26:34.032Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/91/eed031876c595c81d90d0f6fc681ece250e14bf6998c3d7c419466b523b7/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:497d05f29a1300d14e02e6441cf0f5ee81c1ff5a304b0d9fb77423974684e08b", size = 2224907, upload-time = "2025-08-10T21:26:35.824Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/ec/4d1925f2e49617b9cca9c34bfa11adefad49d00db038e692a559454dfb2e/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:bdd1a81a1860476eb41ac4bc1e07b3f07259e6d55bbf739b79c8aaedcf512799", size = 2321334, upload-time = "2025-08-10T21:26:37.534Z" },
+    { url = "https://files.pythonhosted.org/packages/43/cb/450cd4499356f68802750c6ddc18647b8ea01ffa28f50d20598e0befe6e9/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:e6b93f13371d341afee3be9f7c5964e3fe61d5fa30f6a30eb49856935dfe4fc3", size = 2488313, upload-time = "2025-08-10T21:26:39.191Z" },
+    { url = "https://files.pythonhosted.org/packages/71/67/fc76242bd99f885651128a5d4fa6083e5524694b7c88b489b1b55fdc491d/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d75aa530ccfaa593da12834b86a0724f58bff12706659baa9227c2ccaa06264c", size = 2291970, upload-time = "2025-08-10T21:26:40.828Z" },
+    { url = "https://files.pythonhosted.org/packages/75/bd/f1a5d894000941739f2ae1b65a32892349423ad49c2e6d0771d0bad3fae4/kiwisolver-1.4.9-cp313-cp313-win_amd64.whl", hash = "sha256:dd0a578400839256df88c16abddf9ba14813ec5f21362e1fe65022e00c883d4d", size = 73894, upload-time = "2025-08-10T21:26:42.33Z" },
+    { url = "https://files.pythonhosted.org/packages/95/38/dce480814d25b99a391abbddadc78f7c117c6da34be68ca8b02d5848b424/kiwisolver-1.4.9-cp313-cp313-win_arm64.whl", hash = "sha256:d4188e73af84ca82468f09cadc5ac4db578109e52acb4518d8154698d3a87ca2", size = 64995, upload-time = "2025-08-10T21:26:43.889Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/37/7d218ce5d92dadc5ebdd9070d903e0c7cf7edfe03f179433ac4d13ce659c/kiwisolver-1.4.9-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:5a0f2724dfd4e3b3ac5a82436a8e6fd16baa7d507117e4279b660fe8ca38a3a1", size = 126510, upload-time = "2025-08-10T21:26:44.915Z" },
+    { url = "https://files.pythonhosted.org/packages/23/b0/e85a2b48233daef4b648fb657ebbb6f8367696a2d9548a00b4ee0eb67803/kiwisolver-1.4.9-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:1b11d6a633e4ed84fc0ddafd4ebfd8ea49b3f25082c04ad12b8315c11d504dc1", size = 67903, upload-time = "2025-08-10T21:26:45.934Z" },
+    { url = "https://files.pythonhosted.org/packages/44/98/f2425bc0113ad7de24da6bb4dae1343476e95e1d738be7c04d31a5d037fd/kiwisolver-1.4.9-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:61874cdb0a36016354853593cffc38e56fc9ca5aa97d2c05d3dcf6922cd55a11", size = 66402, upload-time = "2025-08-10T21:26:47.101Z" },
+    { url = "https://files.pythonhosted.org/packages/98/d8/594657886df9f34c4177cc353cc28ca7e6e5eb562d37ccc233bff43bbe2a/kiwisolver-1.4.9-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:60c439763a969a6af93b4881db0eed8fadf93ee98e18cbc35bc8da868d0c4f0c", size = 1582135, upload-time = "2025-08-10T21:26:48.665Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/c6/38a115b7170f8b306fc929e166340c24958347308ea3012c2b44e7e295db/kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92a2f997387a1b79a75e7803aa7ded2cfbe2823852ccf1ba3bcf613b62ae3197", size = 1389409, upload-time = "2025-08-10T21:26:50.335Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/3b/e04883dace81f24a568bcee6eb3001da4ba05114afa622ec9b6fafdc1f5e/kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a31d512c812daea6d8b3be3b2bfcbeb091dbb09177706569bcfc6240dcf8b41c", size = 1401763, upload-time = "2025-08-10T21:26:51.867Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/80/20ace48e33408947af49d7d15c341eaee69e4e0304aab4b7660e234d6288/kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:52a15b0f35dad39862d376df10c5230155243a2c1a436e39eb55623ccbd68185", size = 1453643, upload-time = "2025-08-10T21:26:53.592Z" },
+    { url = "https://files.pythonhosted.org/packages/64/31/6ce4380a4cd1f515bdda976a1e90e547ccd47b67a1546d63884463c92ca9/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a30fd6fdef1430fd9e1ba7b3398b5ee4e2887783917a687d86ba69985fb08748", size = 2330818, upload-time = "2025-08-10T21:26:55.051Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/e9/3f3fcba3bcc7432c795b82646306e822f3fd74df0ee81f0fa067a1f95668/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:cc9617b46837c6468197b5945e196ee9ca43057bb7d9d1ae688101e4e1dddf64", size = 2419963, upload-time = "2025-08-10T21:26:56.421Z" },
+    { url = "https://files.pythonhosted.org/packages/99/43/7320c50e4133575c66e9f7dadead35ab22d7c012a3b09bb35647792b2a6d/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:0ab74e19f6a2b027ea4f845a78827969af45ce790e6cb3e1ebab71bdf9f215ff", size = 2594639, upload-time = "2025-08-10T21:26:57.882Z" },
+    { url = "https://files.pythonhosted.org/packages/65/d6/17ae4a270d4a987ef8a385b906d2bdfc9fce502d6dc0d3aea865b47f548c/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dba5ee5d3981160c28d5490f0d1b7ed730c22470ff7f6cc26cfcfaacb9896a07", size = 2391741, upload-time = "2025-08-10T21:26:59.237Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/8f/8f6f491d595a9e5912971f3f863d81baddccc8a4d0c3749d6a0dd9ffc9df/kiwisolver-1.4.9-cp313-cp313t-win_arm64.whl", hash = "sha256:0749fd8f4218ad2e851e11cc4dc05c7cbc0cbc4267bdfdb31782e65aace4ee9c", size = 68646, upload-time = "2025-08-10T21:27:00.52Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/32/6cc0fbc9c54d06c2969faa9c1d29f5751a2e51809dd55c69055e62d9b426/kiwisolver-1.4.9-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:9928fe1eb816d11ae170885a74d074f57af3a0d65777ca47e9aeb854a1fba386", size = 123806, upload-time = "2025-08-10T21:27:01.537Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/dd/2bfb1d4a4823d92e8cbb420fe024b8d2167f72079b3bb941207c42570bdf/kiwisolver-1.4.9-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:d0005b053977e7b43388ddec89fa567f43d4f6d5c2c0affe57de5ebf290dc552", size = 66605, upload-time = "2025-08-10T21:27:03.335Z" },
+    { url = "https://files.pythonhosted.org/packages/f7/69/00aafdb4e4509c2ca6064646cba9cd4b37933898f426756adb2cb92ebbed/kiwisolver-1.4.9-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2635d352d67458b66fd0667c14cb1d4145e9560d503219034a18a87e971ce4f3", size = 64925, upload-time = "2025-08-10T21:27:04.339Z" },
+    { url = "https://files.pythonhosted.org/packages/43/dc/51acc6791aa14e5cb6d8a2e28cefb0dc2886d8862795449d021334c0df20/kiwisolver-1.4.9-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:767c23ad1c58c9e827b649a9ab7809fd5fd9db266a9cf02b0e926ddc2c680d58", size = 1472414, upload-time = "2025-08-10T21:27:05.437Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/bb/93fa64a81db304ac8a246f834d5094fae4b13baf53c839d6bb6e81177129/kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72d0eb9fba308b8311685c2268cf7d0a0639a6cd027d8128659f72bdd8a024b4", size = 1281272, upload-time = "2025-08-10T21:27:07.063Z" },
+    { url = "https://files.pythonhosted.org/packages/70/e6/6df102916960fb8d05069d4bd92d6d9a8202d5a3e2444494e7cd50f65b7a/kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f68e4f3eeca8fb22cc3d731f9715a13b652795ef657a13df1ad0c7dc0e9731df", size = 1298578, upload-time = "2025-08-10T21:27:08.452Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/47/e142aaa612f5343736b087864dbaebc53ea8831453fb47e7521fa8658f30/kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d84cd4061ae292d8ac367b2c3fa3aad11cb8625a95d135fe93f286f914f3f5a6", size = 1345607, upload-time = "2025-08-10T21:27:10.125Z" },
+    { url = "https://files.pythonhosted.org/packages/54/89/d641a746194a0f4d1a3670fb900d0dbaa786fb98341056814bc3f058fa52/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:a60ea74330b91bd22a29638940d115df9dc00af5035a9a2a6ad9399ffb4ceca5", size = 2230150, upload-time = "2025-08-10T21:27:11.484Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/6b/5ee1207198febdf16ac11f78c5ae40861b809cbe0e6d2a8d5b0b3044b199/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:ce6a3a4e106cf35c2d9c4fa17c05ce0b180db622736845d4315519397a77beaf", size = 2325979, upload-time = "2025-08-10T21:27:12.917Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/ff/b269eefd90f4ae14dcc74973d5a0f6d28d3b9bb1afd8c0340513afe6b39a/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:77937e5e2a38a7b48eef0585114fe7930346993a88060d0bf886086d2aa49ef5", size = 2491456, upload-time = "2025-08-10T21:27:14.353Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/d4/10303190bd4d30de547534601e259a4fbf014eed94aae3e5521129215086/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:24c175051354f4a28c5d6a31c93906dc653e2bf234e8a4bbfb964892078898ce", size = 2294621, upload-time = "2025-08-10T21:27:15.808Z" },
+    { url = "https://files.pythonhosted.org/packages/28/e0/a9a90416fce5c0be25742729c2ea52105d62eda6c4be4d803c2a7be1fa50/kiwisolver-1.4.9-cp314-cp314-win_amd64.whl", hash = "sha256:0763515d4df10edf6d06a3c19734e2566368980d21ebec439f33f9eb936c07b7", size = 75417, upload-time = "2025-08-10T21:27:17.436Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/10/6949958215b7a9a264299a7db195564e87900f709db9245e4ebdd3c70779/kiwisolver-1.4.9-cp314-cp314-win_arm64.whl", hash = "sha256:0e4e2bf29574a6a7b7f6cb5fa69293b9f96c928949ac4a53ba3f525dffb87f9c", size = 66582, upload-time = "2025-08-10T21:27:18.436Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/79/60e53067903d3bc5469b369fe0dfc6b3482e2133e85dae9daa9527535991/kiwisolver-1.4.9-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:d976bbb382b202f71c67f77b0ac11244021cfa3f7dfd9e562eefcea2df711548", size = 126514, upload-time = "2025-08-10T21:27:19.465Z" },
+    { url = "https://files.pythonhosted.org/packages/25/d1/4843d3e8d46b072c12a38c97c57fab4608d36e13fe47d47ee96b4d61ba6f/kiwisolver-1.4.9-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:2489e4e5d7ef9a1c300a5e0196e43d9c739f066ef23270607d45aba368b91f2d", size = 67905, upload-time = "2025-08-10T21:27:20.51Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/ae/29ffcbd239aea8b93108de1278271ae764dfc0d803a5693914975f200596/kiwisolver-1.4.9-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:e2ea9f7ab7fbf18fffb1b5434ce7c69a07582f7acc7717720f1d69f3e806f90c", size = 66399, upload-time = "2025-08-10T21:27:21.496Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/ae/d7ba902aa604152c2ceba5d352d7b62106bedbccc8e95c3934d94472bfa3/kiwisolver-1.4.9-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b34e51affded8faee0dfdb705416153819d8ea9250bbbf7ea1b249bdeb5f1122", size = 1582197, upload-time = "2025-08-10T21:27:22.604Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/41/27c70d427eddb8bc7e4f16420a20fefc6f480312122a59a959fdfe0445ad/kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d8aacd3d4b33b772542b2e01beb50187536967b514b00003bdda7589722d2a64", size = 1390125, upload-time = "2025-08-10T21:27:24.036Z" },
+    { url = "https://files.pythonhosted.org/packages/41/42/b3799a12bafc76d962ad69083f8b43b12bf4fe78b097b12e105d75c9b8f1/kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7cf974dd4e35fa315563ac99d6287a1024e4dc2077b8a7d7cd3d2fb65d283134", size = 1402612, upload-time = "2025-08-10T21:27:25.773Z" },
+    { url = "https://files.pythonhosted.org/packages/d2/b5/a210ea073ea1cfaca1bb5c55a62307d8252f531beb364e18aa1e0888b5a0/kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:85bd218b5ecfbee8c8a82e121802dcb519a86044c9c3b2e4aef02fa05c6da370", size = 1453990, upload-time = "2025-08-10T21:27:27.089Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/ce/a829eb8c033e977d7ea03ed32fb3c1781b4fa0433fbadfff29e39c676f32/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:0856e241c2d3df4efef7c04a1e46b1936b6120c9bcf36dd216e3acd84bc4fb21", size = 2331601, upload-time = "2025-08-10T21:27:29.343Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/4b/b5e97eb142eb9cd0072dacfcdcd31b1c66dc7352b0f7c7255d339c0edf00/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:9af39d6551f97d31a4deebeac6f45b156f9755ddc59c07b402c148f5dbb6482a", size = 2422041, upload-time = "2025-08-10T21:27:30.754Z" },
+    { url = "https://files.pythonhosted.org/packages/40/be/8eb4cd53e1b85ba4edc3a9321666f12b83113a178845593307a3e7891f44/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:bb4ae2b57fc1d8cbd1cf7b1d9913803681ffa903e7488012be5b76dedf49297f", size = 2594897, upload-time = "2025-08-10T21:27:32.803Z" },
+    { url = "https://files.pythonhosted.org/packages/99/dd/841e9a66c4715477ea0abc78da039832fbb09dac5c35c58dc4c41a407b8a/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:aedff62918805fb62d43a4aa2ecd4482c380dc76cd31bd7c8878588a61bd0369", size = 2391835, upload-time = "2025-08-10T21:27:34.23Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/28/4b2e5c47a0da96896fdfdb006340ade064afa1e63675d01ea5ac222b6d52/kiwisolver-1.4.9-cp314-cp314t-win_amd64.whl", hash = "sha256:1fa333e8b2ce4d9660f2cda9c0e1b6bafcfb2457a9d259faa82289e73ec24891", size = 79988, upload-time = "2025-08-10T21:27:35.587Z" },
+    { url = "https://files.pythonhosted.org/packages/80/be/3578e8afd18c88cdf9cb4cffde75a96d2be38c5a903f1ed0ceec061bd09e/kiwisolver-1.4.9-cp314-cp314t-win_arm64.whl", hash = "sha256:4a48a2ce79d65d363597ef7b567ce3d14d68783d2b2263d98db3d9477805ba32", size = 70260, upload-time = "2025-08-10T21:27:36.606Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/0f/36d89194b5a32c054ce93e586d4049b6c2c22887b0eb229c61c68afd3078/kiwisolver-1.4.9-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:720e05574713db64c356e86732c0f3c5252818d05f9df320f0ad8380641acea5", size = 60104, upload-time = "2025-08-10T21:27:43.287Z" },
+    { url = "https://files.pythonhosted.org/packages/52/ba/4ed75f59e4658fd21fe7dde1fee0ac397c678ec3befba3fe6482d987af87/kiwisolver-1.4.9-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:17680d737d5335b552994a2008fab4c851bcd7de33094a82067ef3a576ff02fa", size = 58592, upload-time = "2025-08-10T21:27:44.314Z" },
+    { url = "https://files.pythonhosted.org/packages/33/01/a8ea7c5ea32a9b45ceeaee051a04c8ed4320f5add3c51bfa20879b765b70/kiwisolver-1.4.9-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:85b5352f94e490c028926ea567fc569c52ec79ce131dadb968d3853e809518c2", size = 80281, upload-time = "2025-08-10T21:27:45.369Z" },
+    { url = "https://files.pythonhosted.org/packages/da/e3/dbd2ecdce306f1d07a1aaf324817ee993aab7aee9db47ceac757deabafbe/kiwisolver-1.4.9-pp311-pypy311_pp73-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:464415881e4801295659462c49461a24fb107c140de781d55518c4b80cb6790f", size = 78009, upload-time = "2025-08-10T21:27:46.376Z" },
+    { url = "https://files.pythonhosted.org/packages/da/e9/0d4add7873a73e462aeb45c036a2dead2562b825aa46ba326727b3f31016/kiwisolver-1.4.9-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:fb940820c63a9590d31d88b815e7a3aa5915cad3ce735ab45f0c730b39547de1", size = 73929, upload-time = "2025-08-10T21:27:48.236Z" },
+]
+
+[[package]]
+name = "langcodes"
+version = "3.5.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "language-data" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3a/7a/5a97e327063409a5caa21541e6d08ae4a0f2da328447e9f2c7b39e179226/langcodes-3.5.0.tar.gz", hash = "sha256:1eef8168d07e51e131a2497ffecad4b663f6208e7c3ae3b8dc15c51734a6f801", size = 191030, upload-time = "2024-11-19T10:23:45.546Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c3/6b/068c2ea7a712bf805c62445bd9e9c06d7340358ef2824150eceac027444b/langcodes-3.5.0-py3-none-any.whl", hash = "sha256:853c69d1a35e0e13da2f427bb68fb2fa4a8f4fb899e0c62ad8df8d073dcfed33", size = 182974, upload-time = "2024-11-19T10:23:42.824Z" },
+]
+
+[[package]]
+name = "language-data"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "marisa-trie" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/dd/ce/3f144716a9f2cbf42aa86ebc8b085a184be25c80aa453eea17c294d239c1/language_data-1.3.0.tar.gz", hash = "sha256:7600ef8aa39555145d06c89f0c324bf7dab834ea0b0a439d8243762e3ebad7ec", size = 5129310, upload-time = "2024-11-19T10:21:37.912Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5d/e9/5a5ffd9b286db82be70d677d0a91e4d58f7912bb8dd026ddeeb4abe70679/language_data-1.3.0-py3-none-any.whl", hash = "sha256:e2ee943551b5ae5f89cd0e801d1fc3835bb0ef5b7e9c3a4e8e17b2b214548fbf", size = 5385760, upload-time = "2024-11-19T10:21:36.005Z" },
+]
+
+[[package]]
+name = "lightgbm"
+version = "4.6.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "scipy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/68/0b/a2e9f5c5da7ef047cc60cef37f86185088845e8433e54d2e7ed439cce8a3/lightgbm-4.6.0.tar.gz", hash = "sha256:cb1c59720eb569389c0ba74d14f52351b573af489f230032a1c9f314f8bab7fe", size = 1703705, upload-time = "2025-02-15T04:03:03.111Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f2/75/cffc9962cca296bc5536896b7e65b4a7cdeb8db208e71b9c0133c08f8f7e/lightgbm-4.6.0-py3-none-macosx_10_15_x86_64.whl", hash = "sha256:b7a393de8a334d5c8e490df91270f0763f83f959574d504c7ccb9eee4aef70ed", size = 2010151, upload-time = "2025-02-15T04:02:50.961Z" },
+    { url = "https://files.pythonhosted.org/packages/21/1b/550ee378512b78847930f5d74228ca1fdba2a7fbdeaac9aeccc085b0e257/lightgbm-4.6.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:2dafd98d4e02b844ceb0b61450a660681076b1ea6c7adb8c566dfd66832aafad", size = 1592172, upload-time = "2025-02-15T04:02:53.937Z" },
+    { url = "https://files.pythonhosted.org/packages/64/41/4fbde2c3d29e25ee7c41d87df2f2e5eda65b431ee154d4d462c31041846c/lightgbm-4.6.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4d68712bbd2b57a0b14390cbf9376c1d5ed773fa2e71e099cac588703b590336", size = 3454567, upload-time = "2025-02-15T04:02:56.443Z" },
+    { url = "https://files.pythonhosted.org/packages/42/86/dabda8fbcb1b00bcfb0003c3776e8ade1aa7b413dff0a2c08f457dace22f/lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:cb19b5afea55b5b61cbb2131095f50538bd608a00655f23ad5d25ae3e3bf1c8d", size = 3569831, upload-time = "2025-02-15T04:02:58.925Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/23/f8b28ca248bb629b9e08f877dd2965d1994e1674a03d67cd10c5246da248/lightgbm-4.6.0-py3-none-win_amd64.whl", hash = "sha256:37089ee95664b6550a7189d887dbf098e3eadab03537e411f52c63c121e3ba4b", size = 1451509, upload-time = "2025-02-15T04:03:01.515Z" },
+]
+
+[[package]]
+name = "marisa-trie"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/c5/e3/c9066e74076b90f9701ccd23d6a0b8c1d583feefdec576dc3e1bb093c50d/marisa_trie-1.3.1.tar.gz", hash = "sha256:97107fd12f30e4f8fea97790343a2d2d9a79d93697fe14e1b6f6363c984ff85b", size = 212454, upload-time = "2025-08-26T15:13:18.401Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/bf/2f1fe6c9fcd2b509c6dfaaf26e35128947d6d3718d0b39510903c55b7bed/marisa_trie-1.3.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5ef045f694ef66079b4e00c4c9063a00183d6af7d1ff643de6ea5c3b0d9af01b", size = 174027, upload-time = "2025-08-26T15:12:01.434Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/5a/de7936d58ed0de847180cee2b95143d420223c5ade0c093d55113f628237/marisa_trie-1.3.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:cbd28f95d5f30d9a7af6130869568e75bfd7ef2e0adfb1480f1f44480f5d3603", size = 158478, upload-time = "2025-08-26T15:12:02.429Z" },
+    { url = "https://files.pythonhosted.org/packages/48/cc/80611aadefcd0bcf8cd1795cb4643bb27213319a221ba04fe071da0b75cd/marisa_trie-1.3.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b173ec46d521308f7c97d96d6e05cf2088e0548f82544ec9a8656af65593304d", size = 1257535, upload-time = "2025-08-26T15:12:04.271Z" },
+    { url = "https://files.pythonhosted.org/packages/36/89/c4eeefb956318047036e6bdc572b6112b2059d595e85961267a90aa40458/marisa_trie-1.3.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:954fef9185f8a79441b4e433695116636bf66402945cfee404f8983bafa59788", size = 1275566, upload-time = "2025-08-26T15:12:05.874Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/63/d775a2fdfc4b555120381cd2aa6dff1845576bc14fb13796ae1b1e8dbaf7/marisa_trie-1.3.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ca644534f15f85bba14c412afc17de07531e79a766ce85b8dbf3f8b6e7758f20", size = 2199831, upload-time = "2025-08-26T15:12:07.175Z" },
+    { url = "https://files.pythonhosted.org/packages/50/aa/e5053927dc3cac77acc9b27f6f87e75c880f5d3d5eac9111fe13b1d8bf6f/marisa_trie-1.3.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:3834304fdeaa1c9b73596ad5a6c01a44fc19c13c115194704b85f7fbdf0a7b8e", size = 2283830, upload-time = "2025-08-26T15:12:08.319Z" },
+    { url = "https://files.pythonhosted.org/packages/71/3e/e314906d0de5b1a44780a23c79bb62a9aafd876e2a4e80fb34f58c721da4/marisa_trie-1.3.1-cp311-cp311-win32.whl", hash = "sha256:70b4c96f9119cfeb4dc6a0cf4afc9f92f0b002cde225bcd910915d976c78e66a", size = 117335, upload-time = "2025-08-26T15:12:09.776Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/2b/85623566621135de3d57497811f94679b4fb2a8f16148ef67133c2abab7a/marisa_trie-1.3.1-cp311-cp311-win_amd64.whl", hash = "sha256:986eaf35a7f63c878280609ecd37edf8a074f7601c199acfec81d03f1ee9a39a", size = 143985, upload-time = "2025-08-26T15:12:10.988Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/40/ee7ea61b88d62d2189b5c4a27bc0fc8d9c32f8b8dc6daf1c93a7b7ad34ac/marisa_trie-1.3.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:5b7c1e7fa6c3b855e8cfbabf38454d7decbaba1c567d0cd58880d033c6b363bd", size = 173454, upload-time = "2025-08-26T15:12:12.13Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/fc/58635811586898041004b2197a085253706ede211324a53ec01612a50e20/marisa_trie-1.3.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c12b44c190deb0d67655021da1f2d0a7d61a257bf844101cf982e68ed344f28d", size = 155305, upload-time = "2025-08-26T15:12:13.374Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/98/88ca0c98d37034a3237acaf461d210cbcfeb6687929e5ba0e354971fa3ed/marisa_trie-1.3.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9688c7b45f744366a4ef661e399f24636ebe440d315ab35d768676c59c613186", size = 1244834, upload-time = "2025-08-26T15:12:14.795Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/5f/93b3e3607ccd693a768eafee60829cd14ea1810b75aa48e8b20e27b332c4/marisa_trie-1.3.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:99a00cab4cf9643a87977c87a5c8961aa44fff8d5dd46e00250135f686e7dedf", size = 1265148, upload-time = "2025-08-26T15:12:16.229Z" },
+    { url = "https://files.pythonhosted.org/packages/db/6e/051d7d25c7fb2b3df605c8bd782513ebbb33fddf3bae6cf46cf268cca89f/marisa_trie-1.3.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:83efc045fc58ca04c91a96c9b894d8a19ac6553677a76f96df01ff9f0405f53d", size = 2172726, upload-time = "2025-08-26T15:12:18.467Z" },
+    { url = "https://files.pythonhosted.org/packages/58/da/244d9d4e414ce6c73124cba4cc293dd140bf3b04ca18dec64c2775cca951/marisa_trie-1.3.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:0b9816ab993001a7854b02a7daec228892f35bd5ab0ac493bacbd1b80baec9f1", size = 2256104, upload-time = "2025-08-26T15:12:20.168Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/f1/1a36ecd7da6668685a7753522af89a19928ffc80f1cc1dbc301af216f011/marisa_trie-1.3.1-cp312-cp312-win32.whl", hash = "sha256:c785fd6dae9daa6825734b7b494cdac972f958be1f9cb3fb1f32be8598d2b936", size = 115624, upload-time = "2025-08-26T15:12:21.233Z" },
+    { url = "https://files.pythonhosted.org/packages/35/b2/aabd1c9f1c102aa31d66633ed5328c447be166e0a703f9723e682478fd83/marisa_trie-1.3.1-cp312-cp312-win_amd64.whl", hash = "sha256:9868b7a8e0f648d09ffe25ac29511e6e208cc5fb0d156c295385f9d5dc2a138e", size = 138562, upload-time = "2025-08-26T15:12:22.632Z" },
+    { url = "https://files.pythonhosted.org/packages/46/a2/8331b995c1b3eee83aa745f4a6502d737ec523d5955a48f167d4177db105/marisa_trie-1.3.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:9de573d933db4753a50af891bcb3ffbfe14e200406214c223aa5dfe2163f316d", size = 172272, upload-time = "2025-08-26T15:12:24.016Z" },
+    { url = "https://files.pythonhosted.org/packages/97/b8/7b9681b5c0ea1bb950f907a4e3919eb7f7b7b3febafaae346f3b3f199f6f/marisa_trie-1.3.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f4bae4f920f2a1082eaf766c1883df7da84abdf333bafa15b8717c10416a615e", size = 154671, upload-time = "2025-08-26T15:12:25.013Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/16/929c1f83fdcff13f8d08500f434aaa18c21c8168d16cf81585d69085e980/marisa_trie-1.3.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bf9f2b97fcfd5e2dbb0090d0664023872dcde990df0b545eca8d0ce95795a409", size = 1238754, upload-time = "2025-08-26T15:12:26.217Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/0a/b0e04d3ef91a87d4c7ea0b66c004fdfc6e65c9ed83edaebecfb482dfe0ed/marisa_trie-1.3.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ecdb19d33b26738a32602ef432b06cc6deeca4b498ce67ba8e5e39c8a7c19745", size = 1262653, upload-time = "2025-08-26T15:12:27.422Z" },
+    { url = "https://files.pythonhosted.org/packages/de/1f/0ecf610ddc9a209ee63116baabb47584d5b8ecd01610091a593d9429537e/marisa_trie-1.3.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a7416f1a084eb889c5792c57317875aeaa86abfe0bdc6f167712cebcec1d36ee", size = 2172399, upload-time = "2025-08-26T15:12:28.926Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/74/6b47deff3b3920449c135b9187c80f0d656adcdc5d41463745a61b012ea1/marisa_trie-1.3.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ee428575377e29c636f2b4b3b0488875dcea310c6c5b3412ec4ef997f7bb37cc", size = 2255138, upload-time = "2025-08-26T15:12:30.271Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/fa/3dbcbe93dfaa626a5b3e741e7bcf3d7389aa5777175213bd8d9a9d3c992d/marisa_trie-1.3.1-cp313-cp313-win32.whl", hash = "sha256:d0f87bdf660f01e88ab3a507955697b2e3284065afa0b94fc9e77d6ad153ed5e", size = 115391, upload-time = "2025-08-26T15:12:31.465Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/ce/ddfab303646b21aef07ff9dbc83fba92e5d493f49d3bc03d899ffd45c86f/marisa_trie-1.3.1-cp313-cp313-win_amd64.whl", hash = "sha256:a83f5f7ae3494e0cc25211296252b1b86901c788ed82c83adda19d0c98f828d6", size = 139130, upload-time = "2025-08-26T15:12:32.4Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/1e/734b618048ad05c50cb1673ce2c6e836dc38ddeeeb011ed1804af07327a4/marisa_trie-1.3.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a850b151bd1e3a5d9afef113adc22727d696603659d575d7e84f994bd8d04bf1", size = 175131, upload-time = "2025-08-26T15:12:33.728Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/78/c7051147cc918cb8ff4a2920e11a9b17d9dcb4d8fc122122694b486e2bfe/marisa_trie-1.3.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:9dc61fb8f8993589544f6df268229c6cf0a56ad4ed3e8585a9cd23c5ad79527b", size = 163094, upload-time = "2025-08-26T15:12:35.312Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/b8/3b904178d7878319aacaabae5131c1f281519aaac0f8c68c8ed312912ccf/marisa_trie-1.3.1-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d4bd41a6e73c0d0adafe4de449b6d35530a4ce6a836a6ee839baf117785ecfd7", size = 1279812, upload-time = "2025-08-26T15:12:36.831Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/bf/e77a1284247b980560b4104bbdd5d06ed2c2ae3d56ab954f97293b6dbbcd/marisa_trie-1.3.1-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8c8b2386d2d22c57880ed20a913ceca86363765623175671137484a7d223f07a", size = 1285690, upload-time = "2025-08-26T15:12:38.754Z" },
+    { url = "https://files.pythonhosted.org/packages/48/82/f6f10db5ec72de2642499f3a6e4e8607bbd2cfb28269ea08d0d8ddac3313/marisa_trie-1.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9c56001badaf1779afae5c24b7ab85938644ab8ef3c5fd438ab5d49621b84482", size = 2197943, upload-time = "2025-08-26T15:12:40.584Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/d0/74b6c3011b1ebf4a8131430156b14c3af694082cf34c392fff766096fd4b/marisa_trie-1.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:83a3748088d117a9b15d8981c947df9e4f56eb2e4b5456ae34fe1f83666c9185", size = 2280132, upload-time = "2025-08-26T15:12:42.059Z" },
+    { url = "https://files.pythonhosted.org/packages/28/b2/b8b0cb738fa3ab07309ed92025c6e1b278f84c7255e976921a52b30d8d1b/marisa_trie-1.3.1-cp313-cp313t-win32.whl", hash = "sha256:137010598d8cebc53dbfb7caf59bde96c33a6af555e3e1bdbf30269b6a157e1e", size = 126446, upload-time = "2025-08-26T15:12:43.339Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/c6/2381648d0c946556ef51c673397cea40712d945444ceed0a0a0b51a174d2/marisa_trie-1.3.1-cp313-cp313t-win_amd64.whl", hash = "sha256:ec633e108f277f2b7f4671d933a909f39bba549910bf103e2940b87a14da2783", size = 153885, upload-time = "2025-08-26T15:12:44.309Z" },
+    { url = "https://files.pythonhosted.org/packages/40/8a/590f25a281e08879791aabec7b8584c7934ff3d5f9d52859197d587246ec/marisa_trie-1.3.1-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:389721481c14a92fa042e4b91ae065bff13e2bc567c85a10aa9d9de80aaa8622", size = 172803, upload-time = "2025-08-26T15:12:45.342Z" },
+    { url = "https://files.pythonhosted.org/packages/20/7f/fd19a4aa57ad169d08e518a6ee2438e7e77bfba7786c59f65891db69d202/marisa_trie-1.3.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0e6f3b45def6ff23e254eeaa9079267004f0069d0a34eba30a620780caa4f2cb", size = 155506, upload-time = "2025-08-26T15:12:46.701Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/05/857832b8fe6b2ec441de1154eadc66dee067ce5fb6673c3ee0b8616108ee/marisa_trie-1.3.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a96ef3e461ecc85ec7d2233ddc449ff5a3fbdc520caea752bc5bc8faa975231", size = 1239979, upload-time = "2025-08-26T15:12:47.943Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/08/f9ea8b720a627d54e8e19f19a0ec1cc2011e01aa2b4f40d078e7f5e9e21f/marisa_trie-1.3.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5370f9ef6c008e502537cc1ff518c80ddf749367ce90179efa0e7f6275903a76", size = 1255705, upload-time = "2025-08-26T15:12:49.24Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/c3/42360fb38cdfde5db1783e2d7cfeb8b91eea837f89ef678f308ee026d794/marisa_trie-1.3.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:0dcd42774e367ceb423c211a4fc8e7ce586acfaf0929c9c06d98002112075239", size = 2175092, upload-time = "2025-08-26T15:12:50.602Z" },
+    { url = "https://files.pythonhosted.org/packages/09/ba/215b0d821fd37cdc600e834a75708aa2e117124dcf495c9a6c6dc7fdcb6b/marisa_trie-1.3.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:3e2a0e1be95237981bd375a388f44b33d69ea5669a2f79fea038e45fff326595", size = 2250454, upload-time = "2025-08-26T15:12:52.435Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/a3/292ab31a12ec1cb356e6bc8b9cc8aaec920aa892a805757c011d77e8cd93/marisa_trie-1.3.1-cp314-cp314-win32.whl", hash = "sha256:c7a33506d0451112911c69f38d55da3e0e050f2be0ea4e5176865cf03baf26a9", size = 119101, upload-time = "2025-08-26T15:12:53.615Z" },
+    { url = "https://files.pythonhosted.org/packages/95/83/0ea5de53209993cf301dd9d18d4cb22c20c84c753b4357b66660a8b9eb48/marisa_trie-1.3.1-cp314-cp314-win_amd64.whl", hash = "sha256:68678816818efcd4a1787b557af81f215b989ec88680a86c85c34c914d413690", size = 142886, upload-time = "2025-08-26T15:12:54.835Z" },
+    { url = "https://files.pythonhosted.org/packages/37/00/c7e063867988067992a9d9d2aceaede0be7787ca6d77ef34f2eca9d2708e/marisa_trie-1.3.1-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9e467e13971c64db6aed8afe4c2a131c3f73f048bec3f788a6141216acda598d", size = 175163, upload-time = "2025-08-26T15:12:55.908Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/64/eaf49d10c8506ecd717bbbeda907e474842c298354a444b875741ef4a0d9/marisa_trie-1.3.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:076731f79f8603cb3216cb6e5bbbc56536c89f63f175ad47014219ecb01e5996", size = 163119, upload-time = "2025-08-26T15:12:58.054Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/26/f24dd9c98ce6fc8c8d554b556e1c43f326c5df414b79aba33bd7d2d2fbfd/marisa_trie-1.3.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:82de2de90488d0fbbf74cf9f20e1afd62e320693b88f5e9565fc80b28f5bbad3", size = 1277783, upload-time = "2025-08-26T15:12:59.225Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/1a/efd63e75d1374e08f8ebe2e15ff1b1ed5f6d5cf57614a5b0884bd9c882ee/marisa_trie-1.3.1-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0c2bc6bee737f4d47fce48c5b03a7bd3214ef2d83eb5c9f84210091370a5f195", size = 1282309, upload-time = "2025-08-26T15:13:00.797Z" },
+    { url = "https://files.pythonhosted.org/packages/33/4c/0cefa1eceec7858766af5939979857ac079c6c5251e00c6991c1a26bb1b7/marisa_trie-1.3.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:56043cf908ddf3d7364498085dbc2855d4ea8969aff3bf2439a79482a79e68e2", size = 2196594, upload-time = "2025-08-26T15:13:02.158Z" },
+    { url = "https://files.pythonhosted.org/packages/bb/64/900f4132fc345be4b40073e66284707afa4cc203d8d0f1fe78c6b111cd47/marisa_trie-1.3.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:9651daa1fdc471df5a5fa6a4833d3b01e76ac512eea141a5995681aebac5555f", size = 2277730, upload-time = "2025-08-26T15:13:03.528Z" },
+    { url = "https://files.pythonhosted.org/packages/62/ab/6d6cf25a5c8835589a601a9a916ec5cdee740e277fed8ee620df546834bb/marisa_trie-1.3.1-cp314-cp314t-win32.whl", hash = "sha256:c6571462417cda2239b1ade86ceaf3852da9b52c6286046e87d404afc6da20a7", size = 131409, upload-time = "2025-08-26T15:13:05.106Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/61/c4efc044141429e67e8fd5536be86d76303f250179c7f92b2cc0c72e8d0b/marisa_trie-1.3.1-cp314-cp314t-win_amd64.whl", hash = "sha256:9e6496bbad3068e3bbbb934b1e1307bf1a9cb4609f9ec47b57e8ea37f1b5ee40", size = 162564, upload-time = "2025-08-26T15:13:06.112Z" },
+]
+
+[[package]]
+name = "markdown-it-py"
+version = "4.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mdurl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5b/f5/4ec618ed16cc4f8fb3b701563655a69816155e79e24a17b651541804721d/markdown_it_py-4.0.0.tar.gz", hash = "sha256:cb0a2b4aa34f932c007117b194e945bd74e0ec24133ceb5bac59009cda1cb9f3", size = 73070, upload-time = "2025-08-11T12:57:52.854Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/94/54/e7d793b573f298e1c9013b8c4dade17d481164aa517d1d7148619c2cedbf/markdown_it_py-4.0.0-py3-none-any.whl", hash = "sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147", size = 87321, upload-time = "2025-08-11T12:57:51.923Z" },
+]
+
+[[package]]
+name = "markupsafe"
+version = "3.0.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313, upload-time = "2025-09-27T18:37:40.426Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/08/db/fefacb2136439fc8dd20e797950e749aa1f4997ed584c62cfb8ef7c2be0e/markupsafe-3.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad", size = 11631, upload-time = "2025-09-27T18:36:18.185Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/2e/5898933336b61975ce9dc04decbc0a7f2fee78c30353c5efba7f2d6ff27a/markupsafe-3.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a", size = 12058, upload-time = "2025-09-27T18:36:19.444Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/09/adf2df3699d87d1d8184038df46a9c80d78c0148492323f4693df54e17bb/markupsafe-3.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50", size = 24287, upload-time = "2025-09-27T18:36:20.768Z" },
+    { url = "https://files.pythonhosted.org/packages/30/ac/0273f6fcb5f42e314c6d8cd99effae6a5354604d461b8d392b5ec9530a54/markupsafe-3.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf", size = 22940, upload-time = "2025-09-27T18:36:22.249Z" },
+    { url = "https://files.pythonhosted.org/packages/19/ae/31c1be199ef767124c042c6c3e904da327a2f7f0cd63a0337e1eca2967a8/markupsafe-3.0.3-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f", size = 21887, upload-time = "2025-09-27T18:36:23.535Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/76/7edcab99d5349a4532a459e1fe64f0b0467a3365056ae550d3bcf3f79e1e/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a", size = 23692, upload-time = "2025-09-27T18:36:24.823Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/28/6e74cdd26d7514849143d69f0bf2399f929c37dc2b31e6829fd2045b2765/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115", size = 21471, upload-time = "2025-09-27T18:36:25.95Z" },
+    { url = "https://files.pythonhosted.org/packages/62/7e/a145f36a5c2945673e590850a6f8014318d5577ed7e5920a4b3448e0865d/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a", size = 22923, upload-time = "2025-09-27T18:36:27.109Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/62/d9c46a7f5c9adbeeeda52f5b8d802e1094e9717705a645efc71b0913a0a8/markupsafe-3.0.3-cp311-cp311-win32.whl", hash = "sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19", size = 14572, upload-time = "2025-09-27T18:36:28.045Z" },
+    { url = "https://files.pythonhosted.org/packages/83/8a/4414c03d3f891739326e1783338e48fb49781cc915b2e0ee052aa490d586/markupsafe-3.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01", size = 15077, upload-time = "2025-09-27T18:36:29.025Z" },
+    { url = "https://files.pythonhosted.org/packages/35/73/893072b42e6862f319b5207adc9ae06070f095b358655f077f69a35601f0/markupsafe-3.0.3-cp311-cp311-win_arm64.whl", hash = "sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c", size = 13876, upload-time = "2025-09-27T18:36:29.954Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/72/147da192e38635ada20e0a2e1a51cf8823d2119ce8883f7053879c2199b5/markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e", size = 11615, upload-time = "2025-09-27T18:36:30.854Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/81/7e4e08678a1f98521201c3079f77db69fb552acd56067661f8c2f534a718/markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce", size = 12020, upload-time = "2025-09-27T18:36:31.971Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/2c/799f4742efc39633a1b54a92eec4082e4f815314869865d876824c257c1e/markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d", size = 24332, upload-time = "2025-09-27T18:36:32.813Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/2e/8d0c2ab90a8c1d9a24f0399058ab8519a3279d1bd4289511d74e909f060e/markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d", size = 22947, upload-time = "2025-09-27T18:36:33.86Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/54/887f3092a85238093a0b2154bd629c89444f395618842e8b0c41783898ea/markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a", size = 21962, upload-time = "2025-09-27T18:36:35.099Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/2f/336b8c7b6f4a4d95e91119dc8521402461b74a485558d8f238a68312f11c/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b", size = 23760, upload-time = "2025-09-27T18:36:36.001Z" },
+    { url = "https://files.pythonhosted.org/packages/32/43/67935f2b7e4982ffb50a4d169b724d74b62a3964bc1a9a527f5ac4f1ee2b/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f", size = 21529, upload-time = "2025-09-27T18:36:36.906Z" },
+    { url = "https://files.pythonhosted.org/packages/89/e0/4486f11e51bbba8b0c041098859e869e304d1c261e59244baa3d295d47b7/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b", size = 23015, upload-time = "2025-09-27T18:36:37.868Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/e1/78ee7a023dac597a5825441ebd17170785a9dab23de95d2c7508ade94e0e/markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d", size = 14540, upload-time = "2025-09-27T18:36:38.761Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/5b/bec5aa9bbbb2c946ca2733ef9c4ca91c91b6a24580193e891b5f7dbe8e1e/markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c", size = 15105, upload-time = "2025-09-27T18:36:39.701Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/f1/216fc1bbfd74011693a4fd837e7026152e89c4bcf3e77b6692fba9923123/markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f", size = 13906, upload-time = "2025-09-27T18:36:40.689Z" },
+    { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622, upload-time = "2025-09-27T18:36:41.777Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029, upload-time = "2025-09-27T18:36:43.257Z" },
+    { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374, upload-time = "2025-09-27T18:36:44.508Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980, upload-time = "2025-09-27T18:36:45.385Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990, upload-time = "2025-09-27T18:36:46.916Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784, upload-time = "2025-09-27T18:36:47.884Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588, upload-time = "2025-09-27T18:36:48.82Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041, upload-time = "2025-09-27T18:36:49.797Z" },
+    { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543, upload-time = "2025-09-27T18:36:51.584Z" },
+    { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113, upload-time = "2025-09-27T18:36:52.537Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911, upload-time = "2025-09-27T18:36:53.513Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658, upload-time = "2025-09-27T18:36:54.819Z" },
+    { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066, upload-time = "2025-09-27T18:36:55.714Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639, upload-time = "2025-09-27T18:36:56.908Z" },
+    { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569, upload-time = "2025-09-27T18:36:57.913Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284, upload-time = "2025-09-27T18:36:58.833Z" },
+    { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801, upload-time = "2025-09-27T18:36:59.739Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769, upload-time = "2025-09-27T18:37:00.719Z" },
+    { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642, upload-time = "2025-09-27T18:37:01.673Z" },
+    { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612, upload-time = "2025-09-27T18:37:02.639Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200, upload-time = "2025-09-27T18:37:03.582Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973, upload-time = "2025-09-27T18:37:04.929Z" },
+    { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619, upload-time = "2025-09-27T18:37:06.342Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029, upload-time = "2025-09-27T18:37:07.213Z" },
+    { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408, upload-time = "2025-09-27T18:37:09.572Z" },
+    { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005, upload-time = "2025-09-27T18:37:10.58Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048, upload-time = "2025-09-27T18:37:11.547Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821, upload-time = "2025-09-27T18:37:12.48Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606, upload-time = "2025-09-27T18:37:13.485Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043, upload-time = "2025-09-27T18:37:14.408Z" },
+    { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747, upload-time = "2025-09-27T18:37:15.36Z" },
+    { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341, upload-time = "2025-09-27T18:37:16.496Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073, upload-time = "2025-09-27T18:37:17.476Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661, upload-time = "2025-09-27T18:37:18.453Z" },
+    { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069, upload-time = "2025-09-27T18:37:19.332Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670, upload-time = "2025-09-27T18:37:20.245Z" },
+    { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598, upload-time = "2025-09-27T18:37:21.177Z" },
+    { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261, upload-time = "2025-09-27T18:37:22.167Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835, upload-time = "2025-09-27T18:37:23.296Z" },
+    { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733, upload-time = "2025-09-27T18:37:24.237Z" },
+    { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672, upload-time = "2025-09-27T18:37:25.271Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819, upload-time = "2025-09-27T18:37:26.285Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426, upload-time = "2025-09-27T18:37:27.316Z" },
+    { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146, upload-time = "2025-09-27T18:37:28.327Z" },
+]
+
+[[package]]
+name = "matplotlib"
+version = "3.10.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "contourpy" },
+    { name = "cycler" },
+    { name = "fonttools" },
+    { name = "kiwisolver" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pillow" },
+    { name = "pyparsing" },
+    { name = "python-dateutil" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a0/59/c3e6453a9676ffba145309a73c462bb407f4400de7de3f2b41af70720a3c/matplotlib-3.10.6.tar.gz", hash = "sha256:ec01b645840dd1996df21ee37f208cd8ba57644779fa20464010638013d3203c", size = 34804264, upload-time = "2025-08-30T00:14:25.137Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/80/d6/5d3665aa44c49005aaacaa68ddea6fcb27345961cd538a98bb0177934ede/matplotlib-3.10.6-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:905b60d1cb0ee604ce65b297b61cf8be9f4e6cfecf95a3fe1c388b5266bc8f4f", size = 8257527, upload-time = "2025-08-30T00:12:45.31Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/af/30ddefe19ca67eebd70047dabf50f899eaff6f3c5e6a1a7edaecaf63f794/matplotlib-3.10.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7bac38d816637343e53d7185d0c66677ff30ffb131044a81898b5792c956ba76", size = 8119583, upload-time = "2025-08-30T00:12:47.236Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/29/4a8650a3dcae97fa4f375d46efcb25920d67b512186f8a6788b896062a81/matplotlib-3.10.6-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:942a8de2b5bfff1de31d95722f702e2966b8a7e31f4e68f7cd963c7cd8861cf6", size = 8692682, upload-time = "2025-08-30T00:12:48.781Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/d3/b793b9cb061cfd5d42ff0f69d1822f8d5dbc94e004618e48a97a8373179a/matplotlib-3.10.6-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a3276c85370bc0dfca051ec65c5817d1e0f8f5ce1b7787528ec8ed2d524bbc2f", size = 9521065, upload-time = "2025-08-30T00:12:50.602Z" },
+    { url = "https://files.pythonhosted.org/packages/f7/c5/53de5629f223c1c66668d46ac2621961970d21916a4bc3862b174eb2a88f/matplotlib-3.10.6-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:9df5851b219225731f564e4b9e7f2ac1e13c9e6481f941b5631a0f8e2d9387ce", size = 9576888, upload-time = "2025-08-30T00:12:52.92Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/8e/0a18d6d7d2d0a2e66585032a760d13662e5250c784d53ad50434e9560991/matplotlib-3.10.6-cp311-cp311-win_amd64.whl", hash = "sha256:abb5d9478625dd9c9eb51a06d39aae71eda749ae9b3138afb23eb38824026c7e", size = 8115158, upload-time = "2025-08-30T00:12:54.863Z" },
+    { url = "https://files.pythonhosted.org/packages/07/b3/1a5107bb66c261e23b9338070702597a2d374e5aa7004b7adfc754fbed02/matplotlib-3.10.6-cp311-cp311-win_arm64.whl", hash = "sha256:886f989ccfae63659183173bb3fced7fd65e9eb793c3cc21c273add368536951", size = 7992444, upload-time = "2025-08-30T00:12:57.067Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/1a/7042f7430055d567cc3257ac409fcf608599ab27459457f13772c2d9778b/matplotlib-3.10.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:31ca662df6a80bd426f871105fdd69db7543e28e73a9f2afe80de7e531eb2347", size = 8272404, upload-time = "2025-08-30T00:12:59.112Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/5d/1d5f33f5b43f4f9e69e6a5fe1fb9090936ae7bc8e2ff6158e7a76542633b/matplotlib-3.10.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1678bb61d897bb4ac4757b5ecfb02bfb3fddf7f808000fb81e09c510712fda75", size = 8128262, upload-time = "2025-08-30T00:13:01.141Z" },
+    { url = "https://files.pythonhosted.org/packages/67/c3/135fdbbbf84e0979712df58e5e22b4f257b3f5e52a3c4aacf1b8abec0d09/matplotlib-3.10.6-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:56cd2d20842f58c03d2d6e6c1f1cf5548ad6f66b91e1e48f814e4fb5abd1cb95", size = 8697008, upload-time = "2025-08-30T00:13:03.24Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/be/c443ea428fb2488a3ea7608714b1bd85a82738c45da21b447dc49e2f8e5d/matplotlib-3.10.6-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:662df55604a2f9a45435566d6e2660e41efe83cd94f4288dfbf1e6d1eae4b0bb", size = 9530166, upload-time = "2025-08-30T00:13:05.951Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/35/48441422b044d74034aea2a3e0d1a49023f12150ebc58f16600132b9bbaf/matplotlib-3.10.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:08f141d55148cd1fc870c3387d70ca4df16dee10e909b3b038782bd4bda6ea07", size = 9593105, upload-time = "2025-08-30T00:13:08.356Z" },
+    { url = "https://files.pythonhosted.org/packages/45/c3/994ef20eb4154ab84cc08d033834555319e4af970165e6c8894050af0b3c/matplotlib-3.10.6-cp312-cp312-win_amd64.whl", hash = "sha256:590f5925c2d650b5c9d813c5b3b5fc53f2929c3f8ef463e4ecfa7e052044fb2b", size = 8122784, upload-time = "2025-08-30T00:13:10.367Z" },
+    { url = "https://files.pythonhosted.org/packages/57/b8/5c85d9ae0e40f04e71bedb053aada5d6bab1f9b5399a0937afb5d6b02d98/matplotlib-3.10.6-cp312-cp312-win_arm64.whl", hash = "sha256:f44c8d264a71609c79a78d50349e724f5d5fc3684ead7c2a473665ee63d868aa", size = 7992823, upload-time = "2025-08-30T00:13:12.24Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/db/18380e788bb837e724358287b08e223b32bc8dccb3b0c12fa8ca20bc7f3b/matplotlib-3.10.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:819e409653c1106c8deaf62e6de6b8611449c2cd9939acb0d7d4e57a3d95cc7a", size = 8273231, upload-time = "2025-08-30T00:13:13.881Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/0f/38dd49445b297e0d4f12a322c30779df0d43cb5873c7847df8a82e82ec67/matplotlib-3.10.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:59c8ac8382fefb9cb71308dde16a7c487432f5255d8f1fd32473523abecfecdf", size = 8128730, upload-time = "2025-08-30T00:13:15.556Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/b8/9eea6630198cb303d131d95d285a024b3b8645b1763a2916fddb44ca8760/matplotlib-3.10.6-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:84e82d9e0fd70c70bc55739defbd8055c54300750cbacf4740c9673a24d6933a", size = 8698539, upload-time = "2025-08-30T00:13:17.297Z" },
+    { url = "https://files.pythonhosted.org/packages/71/34/44c7b1f075e1ea398f88aeabcc2907c01b9cc99e2afd560c1d49845a1227/matplotlib-3.10.6-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:25f7a3eb42d6c1c56e89eacd495661fc815ffc08d9da750bca766771c0fd9110", size = 9529702, upload-time = "2025-08-30T00:13:19.248Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/7f/e5c2dc9950c7facaf8b461858d1b92c09dd0cf174fe14e21953b3dda06f7/matplotlib-3.10.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f9c862d91ec0b7842920a4cfdaaec29662195301914ea54c33e01f1a28d014b2", size = 9593742, upload-time = "2025-08-30T00:13:21.181Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/1d/70c28528794f6410ee2856cd729fa1f1756498b8d3126443b0a94e1a8695/matplotlib-3.10.6-cp313-cp313-win_amd64.whl", hash = "sha256:1b53bd6337eba483e2e7d29c5ab10eee644bc3a2491ec67cc55f7b44583ffb18", size = 8122753, upload-time = "2025-08-30T00:13:23.44Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/74/0e1670501fc7d02d981564caf7c4df42974464625935424ca9654040077c/matplotlib-3.10.6-cp313-cp313-win_arm64.whl", hash = "sha256:cbd5eb50b7058b2892ce45c2f4e92557f395c9991f5c886d1bb74a1582e70fd6", size = 7992973, upload-time = "2025-08-30T00:13:26.632Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/4e/60780e631d73b6b02bd7239f89c451a72970e5e7ec34f621eda55cd9a445/matplotlib-3.10.6-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:acc86dd6e0e695c095001a7fccff158c49e45e0758fdf5dcdbb0103318b59c9f", size = 8316869, upload-time = "2025-08-30T00:13:28.262Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/15/baa662374a579413210fc2115d40c503b7360a08e9cc254aa0d97d34b0c1/matplotlib-3.10.6-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e228cd2ffb8f88b7d0b29e37f68ca9aaf83e33821f24a5ccc4f082dd8396bc27", size = 8178240, upload-time = "2025-08-30T00:13:30.007Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/3f/3c38e78d2aafdb8829fcd0857d25aaf9e7dd2dfcf7ec742765b585774931/matplotlib-3.10.6-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:658bc91894adeab669cf4bb4a186d049948262987e80f0857216387d7435d833", size = 8711719, upload-time = "2025-08-30T00:13:31.72Z" },
+    { url = "https://files.pythonhosted.org/packages/96/4b/2ec2bbf8cefaa53207cc56118d1fa8a0f9b80642713ea9390235d331ede4/matplotlib-3.10.6-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8913b7474f6dd83ac444c9459c91f7f0f2859e839f41d642691b104e0af056aa", size = 9541422, upload-time = "2025-08-30T00:13:33.611Z" },
+    { url = "https://files.pythonhosted.org/packages/83/7d/40255e89b3ef11c7871020563b2dd85f6cb1b4eff17c0f62b6eb14c8fa80/matplotlib-3.10.6-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:091cea22e059b89f6d7d1a18e2c33a7376c26eee60e401d92a4d6726c4e12706", size = 9594068, upload-time = "2025-08-30T00:13:35.833Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/a9/0213748d69dc842537a113493e1c27daf9f96bd7cc316f933dc8ec4de985/matplotlib-3.10.6-cp313-cp313t-win_amd64.whl", hash = "sha256:491e25e02a23d7207629d942c666924a6b61e007a48177fdd231a0097b7f507e", size = 8200100, upload-time = "2025-08-30T00:13:37.668Z" },
+    { url = "https://files.pythonhosted.org/packages/be/15/79f9988066ce40b8a6f1759a934ea0cde8dc4adc2262255ee1bc98de6ad0/matplotlib-3.10.6-cp313-cp313t-win_arm64.whl", hash = "sha256:3d80d60d4e54cda462e2cd9a086d85cd9f20943ead92f575ce86885a43a565d5", size = 8042142, upload-time = "2025-08-30T00:13:39.426Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/58/e7b6d292beae6fb4283ca6fb7fa47d7c944a68062d6238c07b497dd35493/matplotlib-3.10.6-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:70aaf890ce1d0efd482df969b28a5b30ea0b891224bb315810a3940f67182899", size = 8273802, upload-time = "2025-08-30T00:13:41.006Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/f6/7882d05aba16a8cdd594fb9a03a9d3cca751dbb6816adf7b102945522ee9/matplotlib-3.10.6-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:1565aae810ab79cb72e402b22facfa6501365e73ebab70a0fdfb98488d2c3c0c", size = 8131365, upload-time = "2025-08-30T00:13:42.664Z" },
+    { url = "https://files.pythonhosted.org/packages/94/bf/ff32f6ed76e78514e98775a53715eca4804b12bdcf35902cdd1cf759d324/matplotlib-3.10.6-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f3b23315a01981689aa4e1a179dbf6ef9fbd17143c3eea77548c2ecfb0499438", size = 9533961, upload-time = "2025-08-30T00:13:44.372Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/c3/6bf88c2fc2da7708a2ff8d2eeb5d68943130f50e636d5d3dcf9d4252e971/matplotlib-3.10.6-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:30fdd37edf41a4e6785f9b37969de57aea770696cb637d9946eb37470c94a453", size = 9804262, upload-time = "2025-08-30T00:13:46.614Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/7a/e05e6d9446d2d577b459427ad060cd2de5742d0e435db3191fea4fcc7e8b/matplotlib-3.10.6-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:bc31e693da1c08012c764b053e702c1855378e04102238e6a5ee6a7117c53a47", size = 9595508, upload-time = "2025-08-30T00:13:48.731Z" },
+    { url = "https://files.pythonhosted.org/packages/39/fb/af09c463ced80b801629fd73b96f726c9f6124c3603aa2e480a061d6705b/matplotlib-3.10.6-cp314-cp314-win_amd64.whl", hash = "sha256:05be9bdaa8b242bc6ff96330d18c52f1fc59c6fb3a4dd411d953d67e7e1baf98", size = 8252742, upload-time = "2025-08-30T00:13:50.539Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/f9/b682f6db9396d9ab8f050c0a3bfbb5f14fb0f6518f08507c04cc02f8f229/matplotlib-3.10.6-cp314-cp314-win_arm64.whl", hash = "sha256:f56a0d1ab05d34c628592435781d185cd99630bdfd76822cd686fb5a0aecd43a", size = 8124237, upload-time = "2025-08-30T00:13:54.3Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/d2/b69b4a0923a3c05ab90527c60fdec899ee21ca23ede7f0fb818e6620d6f2/matplotlib-3.10.6-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:94f0b4cacb23763b64b5dace50d5b7bfe98710fed5f0cef5c08135a03399d98b", size = 8316956, upload-time = "2025-08-30T00:13:55.932Z" },
+    { url = "https://files.pythonhosted.org/packages/28/e9/dc427b6f16457ffaeecb2fc4abf91e5adb8827861b869c7a7a6d1836fa73/matplotlib-3.10.6-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:cc332891306b9fb39462673d8225d1b824c89783fee82840a709f96714f17a5c", size = 8178260, upload-time = "2025-08-30T00:14:00.942Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/89/1fbd5ad611802c34d1c7ad04607e64a1350b7fb9c567c4ec2c19e066ed35/matplotlib-3.10.6-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee1d607b3fb1590deb04b69f02ea1d53ed0b0bf75b2b1a5745f269afcbd3cdd3", size = 9541422, upload-time = "2025-08-30T00:14:02.664Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/3b/65fec8716025b22c1d72d5a82ea079934c76a547696eaa55be6866bc89b1/matplotlib-3.10.6-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:376a624a218116461696b27b2bbf7a8945053e6d799f6502fc03226d077807bf", size = 9803678, upload-time = "2025-08-30T00:14:04.741Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/b0/40fb2b3a1ab9381bb39a952e8390357c8be3bdadcf6d5055d9c31e1b35ae/matplotlib-3.10.6-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:83847b47f6524c34b4f2d3ce726bb0541c48c8e7692729865c3df75bfa0f495a", size = 9594077, upload-time = "2025-08-30T00:14:07.012Z" },
+    { url = "https://files.pythonhosted.org/packages/76/34/c4b71b69edf5b06e635eee1ed10bfc73cf8df058b66e63e30e6a55e231d5/matplotlib-3.10.6-cp314-cp314t-win_amd64.whl", hash = "sha256:c7e0518e0d223683532a07f4b512e2e0729b62674f1b3a1a69869f98e6b1c7e3", size = 8342822, upload-time = "2025-08-30T00:14:09.041Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/62/aeabeef1a842b6226a30d49dd13e8a7a1e81e9ec98212c0b5169f0a12d83/matplotlib-3.10.6-cp314-cp314t-win_arm64.whl", hash = "sha256:4dd83e029f5b4801eeb87c64efd80e732452781c16a9cf7415b7b63ec8f374d7", size = 8172588, upload-time = "2025-08-30T00:14:11.166Z" },
+    { url = "https://files.pythonhosted.org/packages/12/bb/02c35a51484aae5f49bd29f091286e7af5f3f677a9736c58a92b3c78baeb/matplotlib-3.10.6-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:f2d684c3204fa62421bbf770ddfebc6b50130f9cad65531eeba19236d73bb488", size = 8252296, upload-time = "2025-08-30T00:14:19.49Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/85/41701e3092005aee9a2445f5ee3904d9dbd4a7df7a45905ffef29b7ef098/matplotlib-3.10.6-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:6f4a69196e663a41d12a728fab8751177215357906436804217d6d9cf0d4d6cf", size = 8116749, upload-time = "2025-08-30T00:14:21.344Z" },
+    { url = "https://files.pythonhosted.org/packages/16/53/8d8fa0ea32a8c8239e04d022f6c059ee5e1b77517769feccd50f1df43d6d/matplotlib-3.10.6-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d6ca6ef03dfd269f4ead566ec6f3fb9becf8dab146fb999022ed85ee9f6b3eb", size = 8693933, upload-time = "2025-08-30T00:14:22.942Z" },
+]
+
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d6/54/cfe61301667036ec958cb99bd3efefba235e65cdeb9c84d24a8293ba1d90/mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba", size = 8729, upload-time = "2022-08-14T12:40:10.846Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979, upload-time = "2022-08-14T12:40:09.779Z" },
+]
+
+[[package]]
+name = "murmurhash"
+version = "1.0.13"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/54/e9/02efbc6dfc2dd2085da3daacf9a8c17e8356019eceaedbfa21555e32d2af/murmurhash-1.0.13.tar.gz", hash = "sha256:737246d41ee00ff74b07b0bd1f0888be304d203ce668e642c86aa64ede30f8b7", size = 13258, upload-time = "2025-05-22T12:35:57.019Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2c/d1/9d13a02d9c8bfff10b1f68d19df206eaf2a8011defeccf7eb05ea0b8c54e/murmurhash-1.0.13-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b20d168370bc3ce82920121b78ab35ae244070a9b18798f4a2e8678fa03bd7e0", size = 26410, upload-time = "2025-05-22T12:35:20.786Z" },
+    { url = "https://files.pythonhosted.org/packages/14/b0/3ee762e98cf9a8c2df9c8b377c326f3dd4495066d4eace9066fca46eba7a/murmurhash-1.0.13-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:cef667d2e83bdceea3bc20c586c491fa442662ace1aea66ff5e3a18bb38268d8", size = 26679, upload-time = "2025-05-22T12:35:21.808Z" },
+    { url = "https://files.pythonhosted.org/packages/39/06/24618f79cd5aac48490932e50263bddfd1ea90f7123d49bfe806a5982675/murmurhash-1.0.13-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:507148e50929ba1fce36898808573b9f81c763d5676f3fc6e4e832ff56b66992", size = 125970, upload-time = "2025-05-22T12:35:23.222Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/09/0e7afce0a422692506c85474a26fb3a03c1971b2b5f7e7745276c4b3de7f/murmurhash-1.0.13-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:64d50f6173d266ad165beb8bca6101d824217fc9279f9e9981f4c0245c1e7ee6", size = 123390, upload-time = "2025-05-22T12:35:24.303Z" },
+    { url = "https://files.pythonhosted.org/packages/22/4c/c98f579b1a951b2bcc722a35270a2eec105c1e21585c9b314a02079e3c4d/murmurhash-1.0.13-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:0f272e15a84a8ae5f8b4bc0a68f9f47be38518ddffc72405791178058e9d019a", size = 124007, upload-time = "2025-05-22T12:35:25.446Z" },
+    { url = "https://files.pythonhosted.org/packages/df/f8/1b0dcebc8df8e091341617102b5b3b97deb6435f345b84f75382c290ec2c/murmurhash-1.0.13-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f9423e0b0964ed1013a06c970199538c7ef9ca28c0be54798c0f1473a6591761", size = 123705, upload-time = "2025-05-22T12:35:26.709Z" },
+    { url = "https://files.pythonhosted.org/packages/79/17/f2a38558e150a0669d843f75e128afb83c1a67af41885ea2acb940e18e2a/murmurhash-1.0.13-cp311-cp311-win_amd64.whl", hash = "sha256:83b81e7084b696df3d853f2c78e0c9bda6b285d643f923f1a6fa9ab145d705c5", size = 24572, upload-time = "2025-05-22T12:35:30.38Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/53/56ce2d8d4b9ab89557cb1d00ffce346b80a2eb2d8c7944015e5c83eacdec/murmurhash-1.0.13-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:bbe882e46cb3f86e092d8a1dd7a5a1c992da1ae3b39f7dd4507b6ce33dae7f92", size = 26859, upload-time = "2025-05-22T12:35:31.815Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/85/3a0ad54a61257c31496545ae6861515d640316f93681d1dd917e7be06634/murmurhash-1.0.13-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:52a33a12ecedc432493692c207c784b06b6427ffaa897fc90b7a76e65846478d", size = 26900, upload-time = "2025-05-22T12:35:34.267Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/cd/6651de26744b50ff11c79f0c0d41244db039625de53c0467a7a52876b2d8/murmurhash-1.0.13-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:950403a7f0dc2d9c8d0710f07c296f2daab66299d9677d6c65d6b6fa2cb30aaa", size = 131367, upload-time = "2025-05-22T12:35:35.258Z" },
+    { url = "https://files.pythonhosted.org/packages/50/6c/01ded95ddce33811c9766cae4ce32e0a54288da1d909ee2bcaa6ed13b9f1/murmurhash-1.0.13-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fde9fb5d2c106d86ff3ef2e4a9a69c2a8d23ba46e28c6b30034dc58421bc107b", size = 128943, upload-time = "2025-05-22T12:35:36.358Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/27/e539a9622d7bea3ae22706c1eb80d4af80f9dddd93b54d151955c2ae4011/murmurhash-1.0.13-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3aa55d62773745616e1ab19345dece122f6e6d09224f7be939cc5b4c513c8473", size = 129108, upload-time = "2025-05-22T12:35:37.864Z" },
+    { url = "https://files.pythonhosted.org/packages/7a/84/18af5662e07d06839ad4db18ce026e6f8ef850d7b0ba92817b28dad28ba6/murmurhash-1.0.13-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:060dfef1b405cf02c450f182fb629f76ebe7f79657cced2db5054bc29b34938b", size = 129175, upload-time = "2025-05-22T12:35:38.928Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/8d/b01d3ee1f1cf3957250223b7c6ce35454f38fbf4abe236bf04a3f769341d/murmurhash-1.0.13-cp312-cp312-win_amd64.whl", hash = "sha256:a8e79627d44a6e20a6487effc30bfe1c74754c13d179106e68cc6d07941b022c", size = 24869, upload-time = "2025-05-22T12:35:40.035Z" },
+    { url = "https://files.pythonhosted.org/packages/00/b4/8919dfdc4a131ad38a57b2c5de69f4bd74538bf546637ee59ebaebe6e5a4/murmurhash-1.0.13-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b8a7f8befd901379b6dc57a9e49c5188454113747ad6aa8cdd951a6048e10790", size = 26852, upload-time = "2025-05-22T12:35:41.061Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/32/ce78bef5d6101568bcb12f5bb5103fabcbe23723ec52e76ff66132d5dbb7/murmurhash-1.0.13-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f741aab86007510199193eee4f87c5ece92bc5a6ca7d0fe0d27335c1203dface", size = 26900, upload-time = "2025-05-22T12:35:42.097Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/4c/0f47c0b4f6b31a1de84d65f9573832c78cd47b4b8ce25ab5596a8238d150/murmurhash-1.0.13-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:82614f18fa6d9d83da6bb0918f3789a3e1555d0ce12c2548153e97f79b29cfc9", size = 130033, upload-time = "2025-05-22T12:35:43.113Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/cb/e47233e32fb792dcc9fb18a2cf65f795d47179b29c2b4a2034689f14c707/murmurhash-1.0.13-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:91f22a48b9454712e0690aa0b76cf0156a5d5a083d23ec7e209cfaeef28f56ff", size = 130619, upload-time = "2025-05-22T12:35:44.229Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/f1/f89911bf304ba5d385ccd346cc7fbb1c1450a24f093b592c3bfe87768467/murmurhash-1.0.13-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c4bc7938627b8fcb3d598fe6657cc96d1e31f4eba6a871b523c1512ab6dacb3e", size = 127643, upload-time = "2025-05-22T12:35:45.369Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/24/262229221f6840c1a04a46051075e99675e591571abcca6b9a8b6aa1602b/murmurhash-1.0.13-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:58a61f1fc840f9ef704e638c39b8517bab1d21f1a9dbb6ba3ec53e41360e44ec", size = 127981, upload-time = "2025-05-22T12:35:46.503Z" },
+    { url = "https://files.pythonhosted.org/packages/18/25/addbc1d28f83252732ac3e57334d42f093890b4c2cce483ba01a42bc607c/murmurhash-1.0.13-cp313-cp313-win_amd64.whl", hash = "sha256:c451a22f14c2f40e7abaea521ee24fa0e46fbec480c4304c25c946cdb6e81883", size = 24880, upload-time = "2025-05-22T12:35:47.625Z" },
+]
+
+[[package]]
+name = "narwhals"
+version = "2.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/00/dd/40ff412dabf90ef6b99266b0b74f217bb88733541733849e0153a108c750/narwhals-2.6.0.tar.gz", hash = "sha256:5c9e2ba923e6a0051017e146184e49fb793548936f978ce130c9f55a9a81240e", size = 561649, upload-time = "2025-09-29T09:08:56.482Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/50/3b/0e2c535c3e6970cfc5763b67f6cc31accaab35a7aa3e322fb6a12830450f/narwhals-2.6.0-py3-none-any.whl", hash = "sha256:3215ea42afb452c6c8527e79cefbe542b674aa08d7e2e99d46b2c9708870e0d4", size = 408435, upload-time = "2025-09-29T09:08:54.503Z" },
+]
+
+[[package]]
+name = "ners"
+version = "0.1.0"
+source = { editable = "." }
+dependencies = [
+    { name = "geopandas" },
+    { name = "joblib" },
+    { name = "lightgbm" },
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "ollama" },
+    { name = "pandas" },
+    { name = "plotly" },
+    { name = "psutil" },
+    { name = "pydantic" },
+    { name = "pyyaml" },
+    { name = "scikit-learn" },
+    { name = "seaborn" },
+    { name = "spacy" },
+    { name = "streamlit" },
+    { name = "tqdm" },
+    { name = "typer" },
+    { name = "xgboost" },
+]
+
+[package.dev-dependencies]
+dev = [
+    { name = "ruff" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "geopandas", specifier = ">=1.1.1" },
+    { name = "joblib", specifier = ">=1.5.2" },
+    { name = "lightgbm", specifier = ">=4.6.0" },
+    { name = "matplotlib", specifier = ">=3.10.6" },
+    { name = "numpy", specifier = ">=2.3.3" },
+    { name = "ollama", specifier = ">=0.6.0" },
+    { name = "pandas", specifier = ">=2.3.3" },
+    { name = "plotly", specifier = ">=6.3.1" },
+    { name = "psutil", specifier = ">=7.1.0" },
+    { name = "pydantic", specifier = ">=2.11.10" },
+    { name = "pyyaml", specifier = ">=6.0.3" },
+    { name = "scikit-learn", specifier = ">=1.7.2" },
+    { name = "seaborn", specifier = ">=0.13.2" },
+    { name = "spacy", specifier = ">=3.8.7" },
+    { name = "streamlit", specifier = ">=1.50.0" },
+    { name = "tqdm", specifier = ">=4.67.1" },
+    { name = "typer", specifier = ">=0.19.2" },
+    { name = "xgboost", specifier = ">=3.0.5" },
+]
+
+[package.metadata.requires-dev]
+dev = [{ name = "ruff", specifier = ">=0.13.3" }]
+
+[[package]]
+name = "numpy"
+version = "2.3.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d0/19/95b3d357407220ed24c139018d2518fab0a61a948e68286a25f1a4d049ff/numpy-2.3.3.tar.gz", hash = "sha256:ddc7c39727ba62b80dfdbedf400d1c10ddfa8eefbd7ec8dcb118be8b56d31029", size = 20576648, upload-time = "2025-09-09T16:54:12.543Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7a/45/e80d203ef6b267aa29b22714fb558930b27960a0c5ce3c19c999232bb3eb/numpy-2.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0ffc4f5caba7dfcbe944ed674b7eef683c7e94874046454bb79ed7ee0236f59d", size = 21259253, upload-time = "2025-09-09T15:56:02.094Z" },
+    { url = "https://files.pythonhosted.org/packages/52/18/cf2c648fccf339e59302e00e5f2bc87725a3ce1992f30f3f78c9044d7c43/numpy-2.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e7e946c7170858a0295f79a60214424caac2ffdb0063d4d79cb681f9aa0aa569", size = 14450980, upload-time = "2025-09-09T15:56:05.926Z" },
+    { url = "https://files.pythonhosted.org/packages/93/fb/9af1082bec870188c42a1c239839915b74a5099c392389ff04215dcee812/numpy-2.3.3-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:cd4260f64bc794c3390a63bf0728220dd1a68170c169088a1e0dfa2fde1be12f", size = 5379709, upload-time = "2025-09-09T15:56:07.95Z" },
+    { url = "https://files.pythonhosted.org/packages/75/0f/bfd7abca52bcbf9a4a65abc83fe18ef01ccdeb37bfb28bbd6ad613447c79/numpy-2.3.3-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:f0ddb4b96a87b6728df9362135e764eac3cfa674499943ebc44ce96c478ab125", size = 6913923, upload-time = "2025-09-09T15:56:09.443Z" },
+    { url = "https://files.pythonhosted.org/packages/79/55/d69adad255e87ab7afda1caf93ca997859092afeb697703e2f010f7c2e55/numpy-2.3.3-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:afd07d377f478344ec6ca2b8d4ca08ae8bd44706763d1efb56397de606393f48", size = 14589591, upload-time = "2025-09-09T15:56:11.234Z" },
+    { url = "https://files.pythonhosted.org/packages/10/a2/010b0e27ddeacab7839957d7a8f00e91206e0c2c47abbb5f35a2630e5387/numpy-2.3.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bc92a5dedcc53857249ca51ef29f5e5f2f8c513e22cfb90faeb20343b8c6f7a6", size = 16938714, upload-time = "2025-09-09T15:56:14.637Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/6b/12ce8ede632c7126eb2762b9e15e18e204b81725b81f35176eac14dc5b82/numpy-2.3.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:7af05ed4dc19f308e1d9fc759f36f21921eb7bbfc82843eeec6b2a2863a0aefa", size = 16370592, upload-time = "2025-09-09T15:56:17.285Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/35/aba8568b2593067bb6a8fe4c52babb23b4c3b9c80e1b49dff03a09925e4a/numpy-2.3.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:433bf137e338677cebdd5beac0199ac84712ad9d630b74eceeb759eaa45ddf30", size = 18884474, upload-time = "2025-09-09T15:56:20.943Z" },
+    { url = "https://files.pythonhosted.org/packages/45/fa/7f43ba10c77575e8be7b0138d107e4f44ca4a1ef322cd16980ea3e8b8222/numpy-2.3.3-cp311-cp311-win32.whl", hash = "sha256:eb63d443d7b4ffd1e873f8155260d7f58e7e4b095961b01c91062935c2491e57", size = 6599794, upload-time = "2025-09-09T15:56:23.258Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/a2/a4f78cb2241fe5664a22a10332f2be886dcdea8784c9f6a01c272da9b426/numpy-2.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:ec9d249840f6a565f58d8f913bccac2444235025bbb13e9a4681783572ee3caa", size = 13088104, upload-time = "2025-09-09T15:56:25.476Z" },
+    { url = "https://files.pythonhosted.org/packages/79/64/e424e975adbd38282ebcd4891661965b78783de893b381cbc4832fb9beb2/numpy-2.3.3-cp311-cp311-win_arm64.whl", hash = "sha256:74c2a948d02f88c11a3c075d9733f1ae67d97c6bdb97f2bb542f980458b257e7", size = 10460772, upload-time = "2025-09-09T15:56:27.679Z" },
+    { url = "https://files.pythonhosted.org/packages/51/5d/bb7fc075b762c96329147799e1bcc9176ab07ca6375ea976c475482ad5b3/numpy-2.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:cfdd09f9c84a1a934cde1eec2267f0a43a7cd44b2cca4ff95b7c0d14d144b0bf", size = 20957014, upload-time = "2025-09-09T15:56:29.966Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/0e/c6211bb92af26517acd52125a237a92afe9c3124c6a68d3b9f81b62a0568/numpy-2.3.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:cb32e3cf0f762aee47ad1ddc6672988f7f27045b0783c887190545baba73aa25", size = 14185220, upload-time = "2025-09-09T15:56:32.175Z" },
+    { url = "https://files.pythonhosted.org/packages/22/f2/07bb754eb2ede9073f4054f7c0286b0d9d2e23982e090a80d478b26d35ca/numpy-2.3.3-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:396b254daeb0a57b1fe0ecb5e3cff6fa79a380fa97c8f7781a6d08cd429418fe", size = 5113918, upload-time = "2025-09-09T15:56:34.175Z" },
+    { url = "https://files.pythonhosted.org/packages/81/0a/afa51697e9fb74642f231ea36aca80fa17c8fb89f7a82abd5174023c3960/numpy-2.3.3-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:067e3d7159a5d8f8a0b46ee11148fc35ca9b21f61e3c49fbd0a027450e65a33b", size = 6647922, upload-time = "2025-09-09T15:56:36.149Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/f5/122d9cdb3f51c520d150fef6e87df9279e33d19a9611a87c0d2cf78a89f4/numpy-2.3.3-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1c02d0629d25d426585fb2e45a66154081b9fa677bc92a881ff1d216bc9919a8", size = 14281991, upload-time = "2025-09-09T15:56:40.548Z" },
+    { url = "https://files.pythonhosted.org/packages/51/64/7de3c91e821a2debf77c92962ea3fe6ac2bc45d0778c1cbe15d4fce2fd94/numpy-2.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d9192da52b9745f7f0766531dcfa978b7763916f158bb63bdb8a1eca0068ab20", size = 16641643, upload-time = "2025-09-09T15:56:43.343Z" },
+    { url = "https://files.pythonhosted.org/packages/30/e4/961a5fa681502cd0d68907818b69f67542695b74e3ceaa513918103b7e80/numpy-2.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:cd7de500a5b66319db419dc3c345244404a164beae0d0937283b907d8152e6ea", size = 16056787, upload-time = "2025-09-09T15:56:46.141Z" },
+    { url = "https://files.pythonhosted.org/packages/99/26/92c912b966e47fbbdf2ad556cb17e3a3088e2e1292b9833be1dfa5361a1a/numpy-2.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:93d4962d8f82af58f0b2eb85daaf1b3ca23fe0a85d0be8f1f2b7bb46034e56d7", size = 18579598, upload-time = "2025-09-09T15:56:49.844Z" },
+    { url = "https://files.pythonhosted.org/packages/17/b6/fc8f82cb3520768718834f310c37d96380d9dc61bfdaf05fe5c0b7653e01/numpy-2.3.3-cp312-cp312-win32.whl", hash = "sha256:5534ed6b92f9b7dca6c0a19d6df12d41c68b991cef051d108f6dbff3babc4ebf", size = 6320800, upload-time = "2025-09-09T15:56:52.499Z" },
+    { url = "https://files.pythonhosted.org/packages/32/ee/de999f2625b80d043d6d2d628c07d0d5555a677a3cf78fdf868d409b8766/numpy-2.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:497d7cad08e7092dba36e3d296fe4c97708c93daf26643a1ae4b03f6294d30eb", size = 12786615, upload-time = "2025-09-09T15:56:54.422Z" },
+    { url = "https://files.pythonhosted.org/packages/49/6e/b479032f8a43559c383acb20816644f5f91c88f633d9271ee84f3b3a996c/numpy-2.3.3-cp312-cp312-win_arm64.whl", hash = "sha256:ca0309a18d4dfea6fc6262a66d06c26cfe4640c3926ceec90e57791a82b6eee5", size = 10195936, upload-time = "2025-09-09T15:56:56.541Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/b9/984c2b1ee61a8b803bf63582b4ac4242cf76e2dbd663efeafcb620cc0ccb/numpy-2.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f5415fb78995644253370985342cd03572ef8620b934da27d77377a2285955bf", size = 20949588, upload-time = "2025-09-09T15:56:59.087Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/e4/07970e3bed0b1384d22af1e9912527ecbeb47d3b26e9b6a3bced068b3bea/numpy-2.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d00de139a3324e26ed5b95870ce63be7ec7352171bc69a4cf1f157a48e3eb6b7", size = 14177802, upload-time = "2025-09-09T15:57:01.73Z" },
+    { url = "https://files.pythonhosted.org/packages/35/c7/477a83887f9de61f1203bad89cf208b7c19cc9fef0cebef65d5a1a0619f2/numpy-2.3.3-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:9dc13c6a5829610cc07422bc74d3ac083bd8323f14e2827d992f9e52e22cd6a6", size = 5106537, upload-time = "2025-09-09T15:57:03.765Z" },
+    { url = "https://files.pythonhosted.org/packages/52/47/93b953bd5866a6f6986344d045a207d3f1cfbad99db29f534ea9cee5108c/numpy-2.3.3-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:d79715d95f1894771eb4e60fb23f065663b2298f7d22945d66877aadf33d00c7", size = 6640743, upload-time = "2025-09-09T15:57:07.921Z" },
+    { url = "https://files.pythonhosted.org/packages/23/83/377f84aaeb800b64c0ef4de58b08769e782edcefa4fea712910b6f0afd3c/numpy-2.3.3-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:952cfd0748514ea7c3afc729a0fc639e61655ce4c55ab9acfab14bda4f402b4c", size = 14278881, upload-time = "2025-09-09T15:57:11.349Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/a5/bf3db6e66c4b160d6ea10b534c381a1955dfab34cb1017ea93aa33c70ed3/numpy-2.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5b83648633d46f77039c29078751f80da65aa64d5622a3cd62aaef9d835b6c93", size = 16636301, upload-time = "2025-09-09T15:57:14.245Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/59/1287924242eb4fa3f9b3a2c30400f2e17eb2707020d1c5e3086fe7330717/numpy-2.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:b001bae8cea1c7dfdb2ae2b017ed0a6f2102d7a70059df1e338e307a4c78a8ae", size = 16053645, upload-time = "2025-09-09T15:57:16.534Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/93/b3d47ed882027c35e94ac2320c37e452a549f582a5e801f2d34b56973c97/numpy-2.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8e9aced64054739037d42fb84c54dd38b81ee238816c948c8f3ed134665dcd86", size = 18578179, upload-time = "2025-09-09T15:57:18.883Z" },
+    { url = "https://files.pythonhosted.org/packages/20/d9/487a2bccbf7cc9d4bfc5f0f197761a5ef27ba870f1e3bbb9afc4bbe3fcc2/numpy-2.3.3-cp313-cp313-win32.whl", hash = "sha256:9591e1221db3f37751e6442850429b3aabf7026d3b05542d102944ca7f00c8a8", size = 6312250, upload-time = "2025-09-09T15:57:21.296Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/b5/263ebbbbcede85028f30047eab3d58028d7ebe389d6493fc95ae66c636ab/numpy-2.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:f0dadeb302887f07431910f67a14d57209ed91130be0adea2f9793f1a4f817cf", size = 12783269, upload-time = "2025-09-09T15:57:23.034Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/75/67b8ca554bbeaaeb3fac2e8bce46967a5a06544c9108ec0cf5cece559b6c/numpy-2.3.3-cp313-cp313-win_arm64.whl", hash = "sha256:3c7cf302ac6e0b76a64c4aecf1a09e51abd9b01fc7feee80f6c43e3ab1b1dbc5", size = 10195314, upload-time = "2025-09-09T15:57:25.045Z" },
+    { url = "https://files.pythonhosted.org/packages/11/d0/0d1ddec56b162042ddfafeeb293bac672de9b0cfd688383590090963720a/numpy-2.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:eda59e44957d272846bb407aad19f89dc6f58fecf3504bd144f4c5cf81a7eacc", size = 21048025, upload-time = "2025-09-09T15:57:27.257Z" },
+    { url = "https://files.pythonhosted.org/packages/36/9e/1996ca6b6d00415b6acbdd3c42f7f03ea256e2c3f158f80bd7436a8a19f3/numpy-2.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:823d04112bc85ef5c4fda73ba24e6096c8f869931405a80aa8b0e604510a26bc", size = 14301053, upload-time = "2025-09-09T15:57:30.077Z" },
+    { url = "https://files.pythonhosted.org/packages/05/24/43da09aa764c68694b76e84b3d3f0c44cb7c18cdc1ba80e48b0ac1d2cd39/numpy-2.3.3-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:40051003e03db4041aa325da2a0971ba41cf65714e65d296397cc0e32de6018b", size = 5229444, upload-time = "2025-09-09T15:57:32.733Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/14/50ffb0f22f7218ef8af28dd089f79f68289a7a05a208db9a2c5dcbe123c1/numpy-2.3.3-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:6ee9086235dd6ab7ae75aba5662f582a81ced49f0f1c6de4260a78d8f2d91a19", size = 6738039, upload-time = "2025-09-09T15:57:34.328Z" },
+    { url = "https://files.pythonhosted.org/packages/55/52/af46ac0795e09657d45a7f4db961917314377edecf66db0e39fa7ab5c3d3/numpy-2.3.3-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:94fcaa68757c3e2e668ddadeaa86ab05499a70725811e582b6a9858dd472fb30", size = 14352314, upload-time = "2025-09-09T15:57:36.255Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/b1/dc226b4c90eb9f07a3fff95c2f0db3268e2e54e5cce97c4ac91518aee71b/numpy-2.3.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:da1a74b90e7483d6ce5244053399a614b1d6b7bc30a60d2f570e5071f8959d3e", size = 16701722, upload-time = "2025-09-09T15:57:38.622Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/9d/9d8d358f2eb5eced14dba99f110d83b5cd9a4460895230f3b396ad19a323/numpy-2.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:2990adf06d1ecee3b3dcbb4977dfab6e9f09807598d647f04d385d29e7a3c3d3", size = 16132755, upload-time = "2025-09-09T15:57:41.16Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/27/b3922660c45513f9377b3fb42240bec63f203c71416093476ec9aa0719dc/numpy-2.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ed635ff692483b8e3f0fcaa8e7eb8a75ee71aa6d975388224f70821421800cea", size = 18651560, upload-time = "2025-09-09T15:57:43.459Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/8e/3ab61a730bdbbc201bb245a71102aa609f0008b9ed15255500a99cd7f780/numpy-2.3.3-cp313-cp313t-win32.whl", hash = "sha256:a333b4ed33d8dc2b373cc955ca57babc00cd6f9009991d9edc5ddbc1bac36bcd", size = 6442776, upload-time = "2025-09-09T15:57:45.793Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/3a/e22b766b11f6030dc2decdeff5c2fb1610768055603f9f3be88b6d192fb2/numpy-2.3.3-cp313-cp313t-win_amd64.whl", hash = "sha256:4384a169c4d8f97195980815d6fcad04933a7e1ab3b530921c3fef7a1c63426d", size = 12927281, upload-time = "2025-09-09T15:57:47.492Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/42/c2e2bc48c5e9b2a83423f99733950fbefd86f165b468a3d85d52b30bf782/numpy-2.3.3-cp313-cp313t-win_arm64.whl", hash = "sha256:75370986cc0bc66f4ce5110ad35aae6d182cc4ce6433c40ad151f53690130bf1", size = 10265275, upload-time = "2025-09-09T15:57:49.647Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/01/342ad585ad82419b99bcf7cebe99e61da6bedb89e213c5fd71acc467faee/numpy-2.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:cd052f1fa6a78dee696b58a914b7229ecfa41f0a6d96dc663c1220a55e137593", size = 20951527, upload-time = "2025-09-09T15:57:52.006Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/d8/204e0d73fc1b7a9ee80ab1fe1983dd33a4d64a4e30a05364b0208e9a241a/numpy-2.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:414a97499480067d305fcac9716c29cf4d0d76db6ebf0bf3cbce666677f12652", size = 14186159, upload-time = "2025-09-09T15:57:54.407Z" },
+    { url = "https://files.pythonhosted.org/packages/22/af/f11c916d08f3a18fb8ba81ab72b5b74a6e42ead4c2846d270eb19845bf74/numpy-2.3.3-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:50a5fe69f135f88a2be9b6ca0481a68a136f6febe1916e4920e12f1a34e708a7", size = 5114624, upload-time = "2025-09-09T15:57:56.5Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/11/0ed919c8381ac9d2ffacd63fd1f0c34d27e99cab650f0eb6f110e6ae4858/numpy-2.3.3-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:b912f2ed2b67a129e6a601e9d93d4fa37bef67e54cac442a2f588a54afe5c67a", size = 6642627, upload-time = "2025-09-09T15:57:58.206Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/83/deb5f77cb0f7ba6cb52b91ed388b47f8f3c2e9930d4665c600408d9b90b9/numpy-2.3.3-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9e318ee0596d76d4cb3d78535dc005fa60e5ea348cd131a51e99d0bdbe0b54fe", size = 14296926, upload-time = "2025-09-09T15:58:00.035Z" },
+    { url = "https://files.pythonhosted.org/packages/77/cc/70e59dcb84f2b005d4f306310ff0a892518cc0c8000a33d0e6faf7ca8d80/numpy-2.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ce020080e4a52426202bdb6f7691c65bb55e49f261f31a8f506c9f6bc7450421", size = 16638958, upload-time = "2025-09-09T15:58:02.738Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/5a/b2ab6c18b4257e099587d5b7f903317bd7115333ad8d4ec4874278eafa61/numpy-2.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e6687dc183aa55dae4a705b35f9c0f8cb178bcaa2f029b241ac5356221d5c021", size = 16071920, upload-time = "2025-09-09T15:58:05.029Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/f1/8b3fdc44324a259298520dd82147ff648979bed085feeacc1250ef1656c0/numpy-2.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d8f3b1080782469fdc1718c4ed1d22549b5fb12af0d57d35e992158a772a37cf", size = 18577076, upload-time = "2025-09-09T15:58:07.745Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/a1/b87a284fb15a42e9274e7fcea0dad259d12ddbf07c1595b26883151ca3b4/numpy-2.3.3-cp314-cp314-win32.whl", hash = "sha256:cb248499b0bc3be66ebd6578b83e5acacf1d6cb2a77f2248ce0e40fbec5a76d0", size = 6366952, upload-time = "2025-09-09T15:58:10.096Z" },
+    { url = "https://files.pythonhosted.org/packages/70/5f/1816f4d08f3b8f66576d8433a66f8fa35a5acfb3bbd0bf6c31183b003f3d/numpy-2.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:691808c2b26b0f002a032c73255d0bd89751425f379f7bcd22d140db593a96e8", size = 12919322, upload-time = "2025-09-09T15:58:12.138Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/de/072420342e46a8ea41c324a555fa90fcc11637583fb8df722936aed1736d/numpy-2.3.3-cp314-cp314-win_arm64.whl", hash = "sha256:9ad12e976ca7b10f1774b03615a2a4bab8addce37ecc77394d8e986927dc0dfe", size = 10478630, upload-time = "2025-09-09T15:58:14.64Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/df/ee2f1c0a9de7347f14da5dd3cd3c3b034d1b8607ccb6883d7dd5c035d631/numpy-2.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9cc48e09feb11e1db00b320e9d30a4151f7369afb96bd0e48d942d09da3a0d00", size = 21047987, upload-time = "2025-09-09T15:58:16.889Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/92/9453bdc5a4e9e69cf4358463f25e8260e2ffc126d52e10038b9077815989/numpy-2.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:901bf6123879b7f251d3631967fd574690734236075082078e0571977c6a8e6a", size = 14301076, upload-time = "2025-09-09T15:58:20.343Z" },
+    { url = "https://files.pythonhosted.org/packages/13/77/1447b9eb500f028bb44253105bd67534af60499588a5149a94f18f2ca917/numpy-2.3.3-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:7f025652034199c301049296b59fa7d52c7e625017cae4c75d8662e377bf487d", size = 5229491, upload-time = "2025-09-09T15:58:22.481Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/f9/d72221b6ca205f9736cb4b2ce3b002f6e45cd67cd6a6d1c8af11a2f0b649/numpy-2.3.3-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:533ca5f6d325c80b6007d4d7fb1984c303553534191024ec6a524a4c92a5935a", size = 6737913, upload-time = "2025-09-09T15:58:24.569Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/5f/d12834711962ad9c46af72f79bb31e73e416ee49d17f4c797f72c96b6ca5/numpy-2.3.3-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0edd58682a399824633b66885d699d7de982800053acf20be1eaa46d92009c54", size = 14352811, upload-time = "2025-09-09T15:58:26.416Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/0d/fdbec6629d97fd1bebed56cd742884e4eead593611bbe1abc3eb40d304b2/numpy-2.3.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:367ad5d8fbec5d9296d18478804a530f1191e24ab4d75ab408346ae88045d25e", size = 16702689, upload-time = "2025-09-09T15:58:28.831Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/09/0a35196dc5575adde1eb97ddfbc3e1687a814f905377621d18ca9bc2b7dd/numpy-2.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8f6ac61a217437946a1fa48d24c47c91a0c4f725237871117dea264982128097", size = 16133855, upload-time = "2025-09-09T15:58:31.349Z" },
+    { url = "https://files.pythonhosted.org/packages/7a/ca/c9de3ea397d576f1b6753eaa906d4cdef1bf97589a6d9825a349b4729cc2/numpy-2.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:179a42101b845a816d464b6fe9a845dfaf308fdfc7925387195570789bb2c970", size = 18652520, upload-time = "2025-09-09T15:58:33.762Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/c2/e5ed830e08cd0196351db55db82f65bc0ab05da6ef2b72a836dcf1936d2f/numpy-2.3.3-cp314-cp314t-win32.whl", hash = "sha256:1250c5d3d2562ec4174bce2e3a1523041595f9b651065e4a4473f5f48a6bc8a5", size = 6515371, upload-time = "2025-09-09T15:58:36.04Z" },
+    { url = "https://files.pythonhosted.org/packages/47/c7/b0f6b5b67f6788a0725f744496badbb604d226bf233ba716683ebb47b570/numpy-2.3.3-cp314-cp314t-win_amd64.whl", hash = "sha256:b37a0b2e5935409daebe82c1e42274d30d9dd355852529eab91dab8dcca7419f", size = 13112576, upload-time = "2025-09-09T15:58:37.927Z" },
+    { url = "https://files.pythonhosted.org/packages/06/b9/33bba5ff6fb679aa0b1f8a07e853f002a6b04b9394db3069a1270a7784ca/numpy-2.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:78c9f6560dc7e6b3990e32df7ea1a50bbd0e2a111e05209963f5ddcab7073b0b", size = 10545953, upload-time = "2025-09-09T15:58:40.576Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/f2/7e0a37cfced2644c9563c529f29fa28acbd0960dde32ece683aafa6f4949/numpy-2.3.3-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:1e02c7159791cd481e1e6d5ddd766b62a4d5acf8df4d4d1afe35ee9c5c33a41e", size = 21131019, upload-time = "2025-09-09T15:58:42.838Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/7e/3291f505297ed63831135a6cc0f474da0c868a1f31b0dd9a9f03a7a0d2ed/numpy-2.3.3-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:dca2d0fc80b3893ae72197b39f69d55a3cd8b17ea1b50aa4c62de82419936150", size = 14376288, upload-time = "2025-09-09T15:58:45.425Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/4b/ae02e985bdeee73d7b5abdefeb98aef1207e96d4c0621ee0cf228ddfac3c/numpy-2.3.3-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:99683cbe0658f8271b333a1b1b4bb3173750ad59c0c61f5bbdc5b318918fffe3", size = 5305425, upload-time = "2025-09-09T15:58:48.6Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/eb/9df215d6d7250db32007941500dc51c48190be25f2401d5b2b564e467247/numpy-2.3.3-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:d9d537a39cc9de668e5cd0e25affb17aec17b577c6b3ae8a3d866b479fbe88d0", size = 6819053, upload-time = "2025-09-09T15:58:50.401Z" },
+    { url = "https://files.pythonhosted.org/packages/57/62/208293d7d6b2a8998a4a1f23ac758648c3c32182d4ce4346062018362e29/numpy-2.3.3-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8596ba2f8af5f93b01d97563832686d20206d303024777f6dfc2e7c7c3f1850e", size = 14420354, upload-time = "2025-09-09T15:58:52.704Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/0c/8e86e0ff7072e14a71b4c6af63175e40d1e7e933ce9b9e9f765a95b4e0c3/numpy-2.3.3-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e1ec5615b05369925bd1125f27df33f3b6c8bc10d788d5999ecd8769a1fa04db", size = 16760413, upload-time = "2025-09-09T15:58:55.027Z" },
+    { url = "https://files.pythonhosted.org/packages/af/11/0cc63f9f321ccf63886ac203336777140011fb669e739da36d8db3c53b98/numpy-2.3.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:2e267c7da5bf7309670523896df97f93f6e469fb931161f483cd6882b3b1a5dc", size = 12971844, upload-time = "2025-09-09T15:58:57.359Z" },
+]
+
+[[package]]
+name = "nvidia-nccl-cu12"
+version = "2.28.3"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/99/76/a87f70dc4544f76abf528c335a5c107130cca5c345725b1d2ad03bf2c2a2/nvidia_nccl_cu12-2.28.3-py3-none-manylinux_2_18_aarch64.whl", hash = "sha256:85144f2197e81148e18f3ffd28a30d78b5046844877630d2710a1b22669a6e46", size = 295887310, upload-time = "2025-09-06T00:31:43.228Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/24/11df42593d1a6d10b3ffef049cec064832f108e77bc5cac12726e4ec1cb2/nvidia_nccl_cu12-2.28.3-py3-none-manylinux_2_18_x86_64.whl", hash = "sha256:79cf0412094e4a552889e5cb7757d92c010ead557ec722c5eebe6a94b1d8681c", size = 295901337, upload-time = "2025-09-06T00:32:01.348Z" },
+]
+
+[[package]]
+name = "ollama"
+version = "0.6.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "httpx" },
+    { name = "pydantic" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d6/47/f9ee32467fe92744474a8c72e138113f3b529fc266eea76abfdec9a33f3b/ollama-0.6.0.tar.gz", hash = "sha256:da2b2d846b5944cfbcee1ca1e6ee0585f6c9d45a2fe9467cbcd096a37383da2f", size = 50811, upload-time = "2025-09-24T22:46:02.417Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b5/c1/edc9f41b425ca40b26b7c104c5f6841a4537bb2552bfa6ca66e81405bb95/ollama-0.6.0-py3-none-any.whl", hash = "sha256:534511b3ccea2dff419ae06c3b58d7f217c55be7897c8ce5868dfb6b219cf7a0", size = 14130, upload-time = "2025-09-24T22:46:01.19Z" },
+]
+
+[[package]]
+name = "packaging"
+version = "25.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a1/d4/1fc4078c65507b51b96ca8f8c3ba19e6a61c8253c72794544580a7b6c24d/packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f", size = 165727, upload-time = "2025-04-19T11:48:59.673Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484", size = 66469, upload-time = "2025-04-19T11:48:57.875Z" },
+]
+
+[[package]]
+name = "pandas"
+version = "2.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "python-dateutil" },
+    { name = "pytz" },
+    { name = "tzdata" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/33/01/d40b85317f86cf08d853a4f495195c73815fdf205eef3993821720274518/pandas-2.3.3.tar.gz", hash = "sha256:e05e1af93b977f7eafa636d043f9f94c7ee3ac81af99c13508215942e64c993b", size = 4495223, upload-time = "2025-09-29T23:34:51.853Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c1/fa/7ac648108144a095b4fb6aa3de1954689f7af60a14cf25583f4960ecb878/pandas-2.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:602b8615ebcc4a0c1751e71840428ddebeb142ec02c786e8ad6b1ce3c8dec523", size = 11578790, upload-time = "2025-09-29T23:18:30.065Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/35/74442388c6cf008882d4d4bdfc4109be87e9b8b7ccd097ad1e7f006e2e95/pandas-2.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8fe25fc7b623b0ef6b5009149627e34d2a4657e880948ec3c840e9402e5c1b45", size = 10833831, upload-time = "2025-09-29T23:38:56.071Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/e4/de154cbfeee13383ad58d23017da99390b91d73f8c11856f2095e813201b/pandas-2.3.3-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b468d3dad6ff947df92dcb32ede5b7bd41a9b3cceef0a30ed925f6d01fb8fa66", size = 12199267, upload-time = "2025-09-29T23:18:41.627Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/c9/63f8d545568d9ab91476b1818b4741f521646cbdd151c6efebf40d6de6f7/pandas-2.3.3-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b98560e98cb334799c0b07ca7967ac361a47326e9b4e5a7dfb5ab2b1c9d35a1b", size = 12789281, upload-time = "2025-09-29T23:18:56.834Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/00/a5ac8c7a0e67fd1a6059e40aa08fa1c52cc00709077d2300e210c3ce0322/pandas-2.3.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1d37b5848ba49824e5c30bedb9c830ab9b7751fd049bc7914533e01c65f79791", size = 13240453, upload-time = "2025-09-29T23:19:09.247Z" },
+    { url = "https://files.pythonhosted.org/packages/27/4d/5c23a5bc7bd209231618dd9e606ce076272c9bc4f12023a70e03a86b4067/pandas-2.3.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:db4301b2d1f926ae677a751eb2bd0e8c5f5319c9cb3f88b0becbbb0b07b34151", size = 13890361, upload-time = "2025-09-29T23:19:25.342Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/59/712db1d7040520de7a4965df15b774348980e6df45c129b8c64d0dbe74ef/pandas-2.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:f086f6fe114e19d92014a1966f43a3e62285109afe874f067f5abbdcbb10e59c", size = 11348702, upload-time = "2025-09-29T23:19:38.296Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/fb/231d89e8637c808b997d172b18e9d4a4bc7bf31296196c260526055d1ea0/pandas-2.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6d21f6d74eb1725c2efaa71a2bfc661a0689579b58e9c0ca58a739ff0b002b53", size = 11597846, upload-time = "2025-09-29T23:19:48.856Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/bd/bf8064d9cfa214294356c2d6702b716d3cf3bb24be59287a6a21e24cae6b/pandas-2.3.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:3fd2f887589c7aa868e02632612ba39acb0b8948faf5cc58f0850e165bd46f35", size = 10729618, upload-time = "2025-09-29T23:39:08.659Z" },
+    { url = "https://files.pythonhosted.org/packages/57/56/cf2dbe1a3f5271370669475ead12ce77c61726ffd19a35546e31aa8edf4e/pandas-2.3.3-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ecaf1e12bdc03c86ad4a7ea848d66c685cb6851d807a26aa245ca3d2017a1908", size = 11737212, upload-time = "2025-09-29T23:19:59.765Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/63/cd7d615331b328e287d8233ba9fdf191a9c2d11b6af0c7a59cfcec23de68/pandas-2.3.3-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b3d11d2fda7eb164ef27ffc14b4fcab16a80e1ce67e9f57e19ec0afaf715ba89", size = 12362693, upload-time = "2025-09-29T23:20:14.098Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/de/8b1895b107277d52f2b42d3a6806e69cfef0d5cf1d0ba343470b9d8e0a04/pandas-2.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a68e15f780eddf2b07d242e17a04aa187a7ee12b40b930bfdd78070556550e98", size = 12771002, upload-time = "2025-09-29T23:20:26.76Z" },
+    { url = "https://files.pythonhosted.org/packages/87/21/84072af3187a677c5893b170ba2c8fbe450a6ff911234916da889b698220/pandas-2.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:371a4ab48e950033bcf52b6527eccb564f52dc826c02afd9a1bc0ab731bba084", size = 13450971, upload-time = "2025-09-29T23:20:41.344Z" },
+    { url = "https://files.pythonhosted.org/packages/86/41/585a168330ff063014880a80d744219dbf1dd7a1c706e75ab3425a987384/pandas-2.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:a16dcec078a01eeef8ee61bf64074b4e524a2a3f4b3be9326420cabe59c4778b", size = 10992722, upload-time = "2025-09-29T23:20:54.139Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/4b/18b035ee18f97c1040d94debd8f2e737000ad70ccc8f5513f4eefad75f4b/pandas-2.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:56851a737e3470de7fa88e6131f41281ed440d29a9268dcbf0002da5ac366713", size = 11544671, upload-time = "2025-09-29T23:21:05.024Z" },
+    { url = "https://files.pythonhosted.org/packages/31/94/72fac03573102779920099bcac1c3b05975c2cb5f01eac609faf34bed1ca/pandas-2.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:bdcd9d1167f4885211e401b3036c0c8d9e274eee67ea8d0758a256d60704cfe8", size = 10680807, upload-time = "2025-09-29T23:21:15.979Z" },
+    { url = "https://files.pythonhosted.org/packages/16/87/9472cf4a487d848476865321de18cc8c920b8cab98453ab79dbbc98db63a/pandas-2.3.3-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e32e7cc9af0f1cc15548288a51a3b681cc2a219faa838e995f7dc53dbab1062d", size = 11709872, upload-time = "2025-09-29T23:21:27.165Z" },
+    { url = "https://files.pythonhosted.org/packages/15/07/284f757f63f8a8d69ed4472bfd85122bd086e637bf4ed09de572d575a693/pandas-2.3.3-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:318d77e0e42a628c04dc56bcef4b40de67918f7041c2b061af1da41dcff670ac", size = 12306371, upload-time = "2025-09-29T23:21:40.532Z" },
+    { url = "https://files.pythonhosted.org/packages/33/81/a3afc88fca4aa925804a27d2676d22dcd2031c2ebe08aabd0ae55b9ff282/pandas-2.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:4e0a175408804d566144e170d0476b15d78458795bb18f1304fb94160cabf40c", size = 12765333, upload-time = "2025-09-29T23:21:55.77Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/0f/b4d4ae743a83742f1153464cf1a8ecfafc3ac59722a0b5c8602310cb7158/pandas-2.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:93c2d9ab0fc11822b5eece72ec9587e172f63cff87c00b062f6e37448ced4493", size = 13418120, upload-time = "2025-09-29T23:22:10.109Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/c7/e54682c96a895d0c808453269e0b5928a07a127a15704fedb643e9b0a4c8/pandas-2.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:f8bfc0e12dc78f777f323f55c58649591b2cd0c43534e8355c51d3fede5f4dee", size = 10993991, upload-time = "2025-09-29T23:25:04.889Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/ca/3f8d4f49740799189e1395812f3bf23b5e8fc7c190827d55a610da72ce55/pandas-2.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:75ea25f9529fdec2d2e93a42c523962261e567d250b0013b16210e1d40d7c2e5", size = 12048227, upload-time = "2025-09-29T23:22:24.343Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/5a/f43efec3e8c0cc92c4663ccad372dbdff72b60bdb56b2749f04aa1d07d7e/pandas-2.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:74ecdf1d301e812db96a465a525952f4dde225fdb6d8e5a521d47e1f42041e21", size = 11411056, upload-time = "2025-09-29T23:22:37.762Z" },
+    { url = "https://files.pythonhosted.org/packages/46/b1/85331edfc591208c9d1a63a06baa67b21d332e63b7a591a5ba42a10bb507/pandas-2.3.3-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6435cb949cb34ec11cc9860246ccb2fdc9ecd742c12d3304989017d53f039a78", size = 11645189, upload-time = "2025-09-29T23:22:51.688Z" },
+    { url = "https://files.pythonhosted.org/packages/44/23/78d645adc35d94d1ac4f2a3c4112ab6f5b8999f4898b8cdf01252f8df4a9/pandas-2.3.3-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:900f47d8f20860de523a1ac881c4c36d65efcb2eb850e6948140fa781736e110", size = 12121912, upload-time = "2025-09-29T23:23:05.042Z" },
+    { url = "https://files.pythonhosted.org/packages/53/da/d10013df5e6aaef6b425aa0c32e1fc1f3e431e4bcabd420517dceadce354/pandas-2.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a45c765238e2ed7d7c608fc5bc4a6f88b642f2f01e70c0c23d2224dd21829d86", size = 12712160, upload-time = "2025-09-29T23:23:28.57Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/17/e756653095a083d8a37cbd816cb87148debcfcd920129b25f99dd8d04271/pandas-2.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:c4fc4c21971a1a9f4bdb4c73978c7f7256caa3e62b323f70d6cb80db583350bc", size = 13199233, upload-time = "2025-09-29T23:24:24.876Z" },
+    { url = "https://files.pythonhosted.org/packages/04/fd/74903979833db8390b73b3a8a7d30d146d710bd32703724dd9083950386f/pandas-2.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:ee15f284898e7b246df8087fc82b87b01686f98ee67d85a17b7ab44143a3a9a0", size = 11540635, upload-time = "2025-09-29T23:25:52.486Z" },
+    { url = "https://files.pythonhosted.org/packages/21/00/266d6b357ad5e6d3ad55093a7e8efc7dd245f5a842b584db9f30b0f0a287/pandas-2.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:1611aedd912e1ff81ff41c745822980c49ce4a7907537be8692c8dbc31924593", size = 10759079, upload-time = "2025-09-29T23:26:33.204Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/05/d01ef80a7a3a12b2f8bbf16daba1e17c98a2f039cbc8e2f77a2c5a63d382/pandas-2.3.3-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6d2cefc361461662ac48810cb14365a365ce864afe85ef1f447ff5a1e99ea81c", size = 11814049, upload-time = "2025-09-29T23:27:15.384Z" },
+    { url = "https://files.pythonhosted.org/packages/15/b2/0e62f78c0c5ba7e3d2c5945a82456f4fac76c480940f805e0b97fcbc2f65/pandas-2.3.3-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ee67acbbf05014ea6c763beb097e03cd629961c8a632075eeb34247120abcb4b", size = 12332638, upload-time = "2025-09-29T23:27:51.625Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/33/dd70400631b62b9b29c3c93d2feee1d0964dc2bae2e5ad7a6c73a7f25325/pandas-2.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c46467899aaa4da076d5abc11084634e2d197e9460643dd455ac3db5856b24d6", size = 12886834, upload-time = "2025-09-29T23:28:21.289Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/18/b5d48f55821228d0d2692b34fd5034bb185e854bdb592e9c640f6290e012/pandas-2.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6253c72c6a1d990a410bc7de641d34053364ef8bcd3126f7e7450125887dffe3", size = 13409925, upload-time = "2025-09-29T23:28:58.261Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/3d/124ac75fcd0ecc09b8fdccb0246ef65e35b012030defb0e0eba2cbbbe948/pandas-2.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:1b07204a219b3b7350abaae088f451860223a52cfb8a6c53358e7948735158e5", size = 11109071, upload-time = "2025-09-29T23:32:27.484Z" },
+    { url = "https://files.pythonhosted.org/packages/89/9c/0e21c895c38a157e0faa1fb64587a9226d6dd46452cac4532d80c3c4a244/pandas-2.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:2462b1a365b6109d275250baaae7b760fd25c726aaca0054649286bcfbb3e8ec", size = 12048504, upload-time = "2025-09-29T23:29:31.47Z" },
+    { url = "https://files.pythonhosted.org/packages/d7/82/b69a1c95df796858777b68fbe6a81d37443a33319761d7c652ce77797475/pandas-2.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:0242fe9a49aa8b4d78a4fa03acb397a58833ef6199e9aa40a95f027bb3a1b6e7", size = 11410702, upload-time = "2025-09-29T23:29:54.591Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/88/702bde3ba0a94b8c73a0181e05144b10f13f29ebfc2150c3a79062a8195d/pandas-2.3.3-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a21d830e78df0a515db2b3d2f5570610f5e6bd2e27749770e8bb7b524b89b450", size = 11634535, upload-time = "2025-09-29T23:30:21.003Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/1e/1bac1a839d12e6a82ec6cb40cda2edde64a2013a66963293696bbf31fbbb/pandas-2.3.3-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2e3ebdb170b5ef78f19bfb71b0dc5dc58775032361fa188e814959b74d726dd5", size = 12121582, upload-time = "2025-09-29T23:30:43.391Z" },
+    { url = "https://files.pythonhosted.org/packages/44/91/483de934193e12a3b1d6ae7c8645d083ff88dec75f46e827562f1e4b4da6/pandas-2.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:d051c0e065b94b7a3cea50eb1ec32e912cd96dba41647eb24104b6c6c14c5788", size = 12699963, upload-time = "2025-09-29T23:31:10.009Z" },
+    { url = "https://files.pythonhosted.org/packages/70/44/5191d2e4026f86a2a109053e194d3ba7a31a2d10a9c2348368c63ed4e85a/pandas-2.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3869faf4bd07b3b66a9f462417d0ca3a9df29a9f6abd5d0d0dbab15dac7abe87", size = 13202175, upload-time = "2025-09-29T23:31:59.173Z" },
+]
+
+[[package]]
+name = "pillow"
+version = "11.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f3/0d/d0d6dea55cd152ce3d6767bb38a8fc10e33796ba4ba210cbab9354b6d238/pillow-11.3.0.tar.gz", hash = "sha256:3828ee7586cd0b2091b6209e5ad53e20d0649bbe87164a459d0676e035e8f523", size = 47113069, upload-time = "2025-07-01T09:16:30.666Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/db/26/77f8ed17ca4ffd60e1dcd220a6ec6d71210ba398cfa33a13a1cd614c5613/pillow-11.3.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:1cd110edf822773368b396281a2293aeb91c90a2db00d78ea43e7e861631b722", size = 5316531, upload-time = "2025-07-01T09:13:59.203Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/39/ee475903197ce709322a17a866892efb560f57900d9af2e55f86db51b0a5/pillow-11.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9c412fddd1b77a75aa904615ebaa6001f169b26fd467b4be93aded278266b288", size = 4686560, upload-time = "2025-07-01T09:14:01.101Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/90/442068a160fd179938ba55ec8c97050a612426fae5ec0a764e345839f76d/pillow-11.3.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7d1aa4de119a0ecac0a34a9c8bde33f34022e2e8f99104e47a3ca392fd60e37d", size = 5870978, upload-time = "2025-07-03T13:09:55.638Z" },
+    { url = "https://files.pythonhosted.org/packages/13/92/dcdd147ab02daf405387f0218dcf792dc6dd5b14d2573d40b4caeef01059/pillow-11.3.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:91da1d88226663594e3f6b4b8c3c8d85bd504117d043740a8e0ec449087cc494", size = 7641168, upload-time = "2025-07-03T13:10:00.37Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/db/839d6ba7fd38b51af641aa904e2960e7a5644d60ec754c046b7d2aee00e5/pillow-11.3.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:643f189248837533073c405ec2f0bb250ba54598cf80e8c1e043381a60632f58", size = 5973053, upload-time = "2025-07-01T09:14:04.491Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/2f/d7675ecae6c43e9f12aa8d58b6012683b20b6edfbdac7abcb4e6af7a3784/pillow-11.3.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:106064daa23a745510dabce1d84f29137a37224831d88eb4ce94bb187b1d7e5f", size = 6640273, upload-time = "2025-07-01T09:14:06.235Z" },
+    { url = "https://files.pythonhosted.org/packages/45/ad/931694675ede172e15b2ff03c8144a0ddaea1d87adb72bb07655eaffb654/pillow-11.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:cd8ff254faf15591e724dc7c4ddb6bf4793efcbe13802a4ae3e863cd300b493e", size = 6082043, upload-time = "2025-07-01T09:14:07.978Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/04/ba8f2b11fc80d2dd462d7abec16351b45ec99cbbaea4387648a44190351a/pillow-11.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:932c754c2d51ad2b2271fd01c3d121daaa35e27efae2a616f77bf164bc0b3e94", size = 6715516, upload-time = "2025-07-01T09:14:10.233Z" },
+    { url = "https://files.pythonhosted.org/packages/48/59/8cd06d7f3944cc7d892e8533c56b0acb68399f640786313275faec1e3b6f/pillow-11.3.0-cp311-cp311-win32.whl", hash = "sha256:b4b8f3efc8d530a1544e5962bd6b403d5f7fe8b9e08227c6b255f98ad82b4ba0", size = 6274768, upload-time = "2025-07-01T09:14:11.921Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/cc/29c0f5d64ab8eae20f3232da8f8571660aa0ab4b8f1331da5c2f5f9a938e/pillow-11.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:1a992e86b0dd7aeb1f053cd506508c0999d710a8f07b4c791c63843fc6a807ac", size = 6986055, upload-time = "2025-07-01T09:14:13.623Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/df/90bd886fabd544c25addd63e5ca6932c86f2b701d5da6c7839387a076b4a/pillow-11.3.0-cp311-cp311-win_arm64.whl", hash = "sha256:30807c931ff7c095620fe04448e2c2fc673fcbb1ffe2a7da3fb39613489b1ddd", size = 2423079, upload-time = "2025-07-01T09:14:15.268Z" },
+    { url = "https://files.pythonhosted.org/packages/40/fe/1bc9b3ee13f68487a99ac9529968035cca2f0a51ec36892060edcc51d06a/pillow-11.3.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:fdae223722da47b024b867c1ea0be64e0df702c5e0a60e27daad39bf960dd1e4", size = 5278800, upload-time = "2025-07-01T09:14:17.648Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/32/7e2ac19b5713657384cec55f89065fb306b06af008cfd87e572035b27119/pillow-11.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:921bd305b10e82b4d1f5e802b6850677f965d8394203d182f078873851dada69", size = 4686296, upload-time = "2025-07-01T09:14:19.828Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/1e/b9e12bbe6e4c2220effebc09ea0923a07a6da1e1f1bfbc8d7d29a01ce32b/pillow-11.3.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:eb76541cba2f958032d79d143b98a3a6b3ea87f0959bbe256c0b5e416599fd5d", size = 5871726, upload-time = "2025-07-03T13:10:04.448Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/33/e9200d2bd7ba00dc3ddb78df1198a6e80d7669cce6c2bdbeb2530a74ec58/pillow-11.3.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:67172f2944ebba3d4a7b54f2e95c786a3a50c21b88456329314caaa28cda70f6", size = 7644652, upload-time = "2025-07-03T13:10:10.391Z" },
+    { url = "https://files.pythonhosted.org/packages/41/f1/6f2427a26fc683e00d985bc391bdd76d8dd4e92fac33d841127eb8fb2313/pillow-11.3.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:97f07ed9f56a3b9b5f49d3661dc9607484e85c67e27f3e8be2c7d28ca032fec7", size = 5977787, upload-time = "2025-07-01T09:14:21.63Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/c9/06dd4a38974e24f932ff5f98ea3c546ce3f8c995d3f0985f8e5ba48bba19/pillow-11.3.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:676b2815362456b5b3216b4fd5bd89d362100dc6f4945154ff172e206a22c024", size = 6645236, upload-time = "2025-07-01T09:14:23.321Z" },
+    { url = "https://files.pythonhosted.org/packages/40/e7/848f69fb79843b3d91241bad658e9c14f39a32f71a301bcd1d139416d1be/pillow-11.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3e184b2f26ff146363dd07bde8b711833d7b0202e27d13540bfe2e35a323a809", size = 6086950, upload-time = "2025-07-01T09:14:25.237Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/1a/7cff92e695a2a29ac1958c2a0fe4c0b2393b60aac13b04a4fe2735cad52d/pillow-11.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6be31e3fc9a621e071bc17bb7de63b85cbe0bfae91bb0363c893cbe67247780d", size = 6723358, upload-time = "2025-07-01T09:14:27.053Z" },
+    { url = "https://files.pythonhosted.org/packages/26/7d/73699ad77895f69edff76b0f332acc3d497f22f5d75e5360f78cbcaff248/pillow-11.3.0-cp312-cp312-win32.whl", hash = "sha256:7b161756381f0918e05e7cb8a371fff367e807770f8fe92ecb20d905d0e1c149", size = 6275079, upload-time = "2025-07-01T09:14:30.104Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/ce/e7dfc873bdd9828f3b6e5c2bbb74e47a98ec23cc5c74fc4e54462f0d9204/pillow-11.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:a6444696fce635783440b7f7a9fc24b3ad10a9ea3f0ab66c5905be1c19ccf17d", size = 6986324, upload-time = "2025-07-01T09:14:31.899Z" },
+    { url = "https://files.pythonhosted.org/packages/16/8f/b13447d1bf0b1f7467ce7d86f6e6edf66c0ad7cf44cf5c87a37f9bed9936/pillow-11.3.0-cp312-cp312-win_arm64.whl", hash = "sha256:2aceea54f957dd4448264f9bf40875da0415c83eb85f55069d89c0ed436e3542", size = 2423067, upload-time = "2025-07-01T09:14:33.709Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/93/0952f2ed8db3a5a4c7a11f91965d6184ebc8cd7cbb7941a260d5f018cd2d/pillow-11.3.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:1c627742b539bba4309df89171356fcb3cc5a9178355b2727d1b74a6cf155fbd", size = 2128328, upload-time = "2025-07-01T09:14:35.276Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/e8/100c3d114b1a0bf4042f27e0f87d2f25e857e838034e98ca98fe7b8c0a9c/pillow-11.3.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:30b7c02f3899d10f13d7a48163c8969e4e653f8b43416d23d13d1bbfdc93b9f8", size = 2170652, upload-time = "2025-07-01T09:14:37.203Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/86/3f758a28a6e381758545f7cdb4942e1cb79abd271bea932998fc0db93cb6/pillow-11.3.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:7859a4cc7c9295f5838015d8cc0a9c215b77e43d07a25e460f35cf516df8626f", size = 2227443, upload-time = "2025-07-01T09:14:39.344Z" },
+    { url = "https://files.pythonhosted.org/packages/01/f4/91d5b3ffa718df2f53b0dc109877993e511f4fd055d7e9508682e8aba092/pillow-11.3.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ec1ee50470b0d050984394423d96325b744d55c701a439d2bd66089bff963d3c", size = 5278474, upload-time = "2025-07-01T09:14:41.843Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/0e/37d7d3eca6c879fbd9dba21268427dffda1ab00d4eb05b32923d4fbe3b12/pillow-11.3.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7db51d222548ccfd274e4572fdbf3e810a5e66b00608862f947b163e613b67dd", size = 4686038, upload-time = "2025-07-01T09:14:44.008Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/b0/3426e5c7f6565e752d81221af9d3676fdbb4f352317ceafd42899aaf5d8a/pillow-11.3.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2d6fcc902a24ac74495df63faad1884282239265c6839a0a6416d33faedfae7e", size = 5864407, upload-time = "2025-07-03T13:10:15.628Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/c1/c6c423134229f2a221ee53f838d4be9d82bab86f7e2f8e75e47b6bf6cd77/pillow-11.3.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f0f5d8f4a08090c6d6d578351a2b91acf519a54986c055af27e7a93feae6d3f1", size = 7639094, upload-time = "2025-07-03T13:10:21.857Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/c9/09e6746630fe6372c67c648ff9deae52a2bc20897d51fa293571977ceb5d/pillow-11.3.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c37d8ba9411d6003bba9e518db0db0c58a680ab9fe5179f040b0463644bc9805", size = 5973503, upload-time = "2025-07-01T09:14:45.698Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/1c/a2a29649c0b1983d3ef57ee87a66487fdeb45132df66ab30dd37f7dbe162/pillow-11.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:13f87d581e71d9189ab21fe0efb5a23e9f28552d5be6979e84001d3b8505abe8", size = 6642574, upload-time = "2025-07-01T09:14:47.415Z" },
+    { url = "https://files.pythonhosted.org/packages/36/de/d5cc31cc4b055b6c6fd990e3e7f0f8aaf36229a2698501bcb0cdf67c7146/pillow-11.3.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:023f6d2d11784a465f09fd09a34b150ea4672e85fb3d05931d89f373ab14abb2", size = 6084060, upload-time = "2025-07-01T09:14:49.636Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/ea/502d938cbaeec836ac28a9b730193716f0114c41325db428e6b280513f09/pillow-11.3.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:45dfc51ac5975b938e9809451c51734124e73b04d0f0ac621649821a63852e7b", size = 6721407, upload-time = "2025-07-01T09:14:51.962Z" },
+    { url = "https://files.pythonhosted.org/packages/45/9c/9c5e2a73f125f6cbc59cc7087c8f2d649a7ae453f83bd0362ff7c9e2aee2/pillow-11.3.0-cp313-cp313-win32.whl", hash = "sha256:a4d336baed65d50d37b88ca5b60c0fa9d81e3a87d4a7930d3880d1624d5b31f3", size = 6273841, upload-time = "2025-07-01T09:14:54.142Z" },
+    { url = "https://files.pythonhosted.org/packages/23/85/397c73524e0cd212067e0c969aa245b01d50183439550d24d9f55781b776/pillow-11.3.0-cp313-cp313-win_amd64.whl", hash = "sha256:0bce5c4fd0921f99d2e858dc4d4d64193407e1b99478bc5cacecba2311abde51", size = 6978450, upload-time = "2025-07-01T09:14:56.436Z" },
+    { url = "https://files.pythonhosted.org/packages/17/d2/622f4547f69cd173955194b78e4d19ca4935a1b0f03a302d655c9f6aae65/pillow-11.3.0-cp313-cp313-win_arm64.whl", hash = "sha256:1904e1264881f682f02b7f8167935cce37bc97db457f8e7849dc3a6a52b99580", size = 2423055, upload-time = "2025-07-01T09:14:58.072Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/80/a8a2ac21dda2e82480852978416cfacd439a4b490a501a288ecf4fe2532d/pillow-11.3.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:4c834a3921375c48ee6b9624061076bc0a32a60b5532b322cc0ea64e639dd50e", size = 5281110, upload-time = "2025-07-01T09:14:59.79Z" },
+    { url = "https://files.pythonhosted.org/packages/44/d6/b79754ca790f315918732e18f82a8146d33bcd7f4494380457ea89eb883d/pillow-11.3.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5e05688ccef30ea69b9317a9ead994b93975104a677a36a8ed8106be9260aa6d", size = 4689547, upload-time = "2025-07-01T09:15:01.648Z" },
+    { url = "https://files.pythonhosted.org/packages/49/20/716b8717d331150cb00f7fdd78169c01e8e0c219732a78b0e59b6bdb2fd6/pillow-11.3.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1019b04af07fc0163e2810167918cb5add8d74674b6267616021ab558dc98ced", size = 5901554, upload-time = "2025-07-03T13:10:27.018Z" },
+    { url = "https://files.pythonhosted.org/packages/74/cf/a9f3a2514a65bb071075063a96f0a5cf949c2f2fce683c15ccc83b1c1cab/pillow-11.3.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f944255db153ebb2b19c51fe85dd99ef0ce494123f21b9db4877ffdfc5590c7c", size = 7669132, upload-time = "2025-07-03T13:10:33.01Z" },
+    { url = "https://files.pythonhosted.org/packages/98/3c/da78805cbdbee9cb43efe8261dd7cc0b4b93f2ac79b676c03159e9db2187/pillow-11.3.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1f85acb69adf2aaee8b7da124efebbdb959a104db34d3a2cb0f3793dbae422a8", size = 6005001, upload-time = "2025-07-01T09:15:03.365Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/fa/ce044b91faecf30e635321351bba32bab5a7e034c60187fe9698191aef4f/pillow-11.3.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:05f6ecbeff5005399bb48d198f098a9b4b6bdf27b8487c7f38ca16eeb070cd59", size = 6668814, upload-time = "2025-07-01T09:15:05.655Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/51/90f9291406d09bf93686434f9183aba27b831c10c87746ff49f127ee80cb/pillow-11.3.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a7bc6e6fd0395bc052f16b1a8670859964dbd7003bd0af2ff08342eb6e442cfe", size = 6113124, upload-time = "2025-07-01T09:15:07.358Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/5a/6fec59b1dfb619234f7636d4157d11fb4e196caeee220232a8d2ec48488d/pillow-11.3.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:83e1b0161c9d148125083a35c1c5a89db5b7054834fd4387499e06552035236c", size = 6747186, upload-time = "2025-07-01T09:15:09.317Z" },
+    { url = "https://files.pythonhosted.org/packages/49/6b/00187a044f98255225f172de653941e61da37104a9ea60e4f6887717e2b5/pillow-11.3.0-cp313-cp313t-win32.whl", hash = "sha256:2a3117c06b8fb646639dce83694f2f9eac405472713fcb1ae887469c0d4f6788", size = 6277546, upload-time = "2025-07-01T09:15:11.311Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/5c/6caaba7e261c0d75bab23be79f1d06b5ad2a2ae49f028ccec801b0e853d6/pillow-11.3.0-cp313-cp313t-win_amd64.whl", hash = "sha256:857844335c95bea93fb39e0fa2726b4d9d758850b34075a7e3ff4f4fa3aa3b31", size = 6985102, upload-time = "2025-07-01T09:15:13.164Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/7e/b623008460c09a0cb38263c93b828c666493caee2eb34ff67f778b87e58c/pillow-11.3.0-cp313-cp313t-win_arm64.whl", hash = "sha256:8797edc41f3e8536ae4b10897ee2f637235c94f27404cac7297f7b607dd0716e", size = 2424803, upload-time = "2025-07-01T09:15:15.695Z" },
+    { url = "https://files.pythonhosted.org/packages/73/f4/04905af42837292ed86cb1b1dabe03dce1edc008ef14c473c5c7e1443c5d/pillow-11.3.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:d9da3df5f9ea2a89b81bb6087177fb1f4d1c7146d583a3fe5c672c0d94e55e12", size = 5278520, upload-time = "2025-07-01T09:15:17.429Z" },
+    { url = "https://files.pythonhosted.org/packages/41/b0/33d79e377a336247df6348a54e6d2a2b85d644ca202555e3faa0cf811ecc/pillow-11.3.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0b275ff9b04df7b640c59ec5a3cb113eefd3795a8df80bac69646ef699c6981a", size = 4686116, upload-time = "2025-07-01T09:15:19.423Z" },
+    { url = "https://files.pythonhosted.org/packages/49/2d/ed8bc0ab219ae8768f529597d9509d184fe8a6c4741a6864fea334d25f3f/pillow-11.3.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0743841cabd3dba6a83f38a92672cccbd69af56e3e91777b0ee7f4dba4385632", size = 5864597, upload-time = "2025-07-03T13:10:38.404Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/3d/b932bb4225c80b58dfadaca9d42d08d0b7064d2d1791b6a237f87f661834/pillow-11.3.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2465a69cf967b8b49ee1b96d76718cd98c4e925414ead59fdf75cf0fd07df673", size = 7638246, upload-time = "2025-07-03T13:10:44.987Z" },
+    { url = "https://files.pythonhosted.org/packages/09/b5/0487044b7c096f1b48f0d7ad416472c02e0e4bf6919541b111efd3cae690/pillow-11.3.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:41742638139424703b4d01665b807c6468e23e699e8e90cffefe291c5832b027", size = 5973336, upload-time = "2025-07-01T09:15:21.237Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/2d/524f9318f6cbfcc79fbc004801ea6b607ec3f843977652fdee4857a7568b/pillow-11.3.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:93efb0b4de7e340d99057415c749175e24c8864302369e05914682ba642e5d77", size = 6642699, upload-time = "2025-07-01T09:15:23.186Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/d2/a9a4f280c6aefedce1e8f615baaa5474e0701d86dd6f1dede66726462bbd/pillow-11.3.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7966e38dcd0fa11ca390aed7c6f20454443581d758242023cf36fcb319b1a874", size = 6083789, upload-time = "2025-07-01T09:15:25.1Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/54/86b0cd9dbb683a9d5e960b66c7379e821a19be4ac5810e2e5a715c09a0c0/pillow-11.3.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:98a9afa7b9007c67ed84c57c9e0ad86a6000da96eaa638e4f8abe5b65ff83f0a", size = 6720386, upload-time = "2025-07-01T09:15:27.378Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/95/88efcaf384c3588e24259c4203b909cbe3e3c2d887af9e938c2022c9dd48/pillow-11.3.0-cp314-cp314-win32.whl", hash = "sha256:02a723e6bf909e7cea0dac1b0e0310be9d7650cd66222a5f1c571455c0a45214", size = 6370911, upload-time = "2025-07-01T09:15:29.294Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/cc/934e5820850ec5eb107e7b1a72dd278140731c669f396110ebc326f2a503/pillow-11.3.0-cp314-cp314-win_amd64.whl", hash = "sha256:a418486160228f64dd9e9efcd132679b7a02a5f22c982c78b6fc7dab3fefb635", size = 7117383, upload-time = "2025-07-01T09:15:31.128Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/e9/9c0a616a71da2a5d163aa37405e8aced9a906d574b4a214bede134e731bc/pillow-11.3.0-cp314-cp314-win_arm64.whl", hash = "sha256:155658efb5e044669c08896c0c44231c5e9abcaadbc5cd3648df2f7c0b96b9a6", size = 2511385, upload-time = "2025-07-01T09:15:33.328Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/33/c88376898aff369658b225262cd4f2659b13e8178e7534df9e6e1fa289f6/pillow-11.3.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:59a03cdf019efbfeeed910bf79c7c93255c3d54bc45898ac2a4140071b02b4ae", size = 5281129, upload-time = "2025-07-01T09:15:35.194Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/70/d376247fb36f1844b42910911c83a02d5544ebd2a8bad9efcc0f707ea774/pillow-11.3.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:f8a5827f84d973d8636e9dc5764af4f0cf2318d26744b3d902931701b0d46653", size = 4689580, upload-time = "2025-07-01T09:15:37.114Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/1c/537e930496149fbac69efd2fc4329035bbe2e5475b4165439e3be9cb183b/pillow-11.3.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ee92f2fd10f4adc4b43d07ec5e779932b4eb3dbfbc34790ada5a6669bc095aa6", size = 5902860, upload-time = "2025-07-03T13:10:50.248Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/57/80f53264954dcefeebcf9dae6e3eb1daea1b488f0be8b8fef12f79a3eb10/pillow-11.3.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c96d333dcf42d01f47b37e0979b6bd73ec91eae18614864622d9b87bbd5bbf36", size = 7670694, upload-time = "2025-07-03T13:10:56.432Z" },
+    { url = "https://files.pythonhosted.org/packages/70/ff/4727d3b71a8578b4587d9c276e90efad2d6fe0335fd76742a6da08132e8c/pillow-11.3.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4c96f993ab8c98460cd0c001447bff6194403e8b1d7e149ade5f00594918128b", size = 6005888, upload-time = "2025-07-01T09:15:39.436Z" },
+    { url = "https://files.pythonhosted.org/packages/05/ae/716592277934f85d3be51d7256f3636672d7b1abfafdc42cf3f8cbd4b4c8/pillow-11.3.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:41342b64afeba938edb034d122b2dda5db2139b9a4af999729ba8818e0056477", size = 6670330, upload-time = "2025-07-01T09:15:41.269Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/bb/7fe6cddcc8827b01b1a9766f5fdeb7418680744f9082035bdbabecf1d57f/pillow-11.3.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:068d9c39a2d1b358eb9f245ce7ab1b5c3246c7c8c7d9ba58cfa5b43146c06e50", size = 6114089, upload-time = "2025-07-01T09:15:43.13Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/f5/06bfaa444c8e80f1a8e4bff98da9c83b37b5be3b1deaa43d27a0db37ef84/pillow-11.3.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a1bc6ba083b145187f648b667e05a2534ecc4b9f2784c2cbe3089e44868f2b9b", size = 6748206, upload-time = "2025-07-01T09:15:44.937Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/77/bc6f92a3e8e6e46c0ca78abfffec0037845800ea38c73483760362804c41/pillow-11.3.0-cp314-cp314t-win32.whl", hash = "sha256:118ca10c0d60b06d006be10a501fd6bbdfef559251ed31b794668ed569c87e12", size = 6377370, upload-time = "2025-07-01T09:15:46.673Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/82/3a721f7d69dca802befb8af08b7c79ebcab461007ce1c18bd91a5d5896f9/pillow-11.3.0-cp314-cp314t-win_amd64.whl", hash = "sha256:8924748b688aa210d79883357d102cd64690e56b923a186f35a82cbc10f997db", size = 7121500, upload-time = "2025-07-01T09:15:48.512Z" },
+    { url = "https://files.pythonhosted.org/packages/89/c7/5572fa4a3f45740eaab6ae86fcdf7195b55beac1371ac8c619d880cfe948/pillow-11.3.0-cp314-cp314t-win_arm64.whl", hash = "sha256:79ea0d14d3ebad43ec77ad5272e6ff9bba5b679ef73375ea760261207fa8e0aa", size = 2512835, upload-time = "2025-07-01T09:15:50.399Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/e3/6fa84033758276fb31da12e5fb66ad747ae83b93c67af17f8c6ff4cc8f34/pillow-11.3.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:7c8ec7a017ad1bd562f93dbd8505763e688d388cde6e4a010ae1486916e713e6", size = 5270566, upload-time = "2025-07-01T09:16:19.801Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/ee/e8d2e1ab4892970b561e1ba96cbd59c0d28cf66737fc44abb2aec3795a4e/pillow-11.3.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:9ab6ae226de48019caa8074894544af5b53a117ccb9d3b3dcb2871464c829438", size = 4654618, upload-time = "2025-07-01T09:16:21.818Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/6d/17f80f4e1f0761f02160fc433abd4109fa1548dcfdca46cfdadaf9efa565/pillow-11.3.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fe27fb049cdcca11f11a7bfda64043c37b30e6b91f10cb5bab275806c32f6ab3", size = 4874248, upload-time = "2025-07-03T13:11:20.738Z" },
+    { url = "https://files.pythonhosted.org/packages/de/5f/c22340acd61cef960130585bbe2120e2fd8434c214802f07e8c03596b17e/pillow-11.3.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:465b9e8844e3c3519a983d58b80be3f668e2a7a5db97f2784e7079fbc9f9822c", size = 6583963, upload-time = "2025-07-03T13:11:26.283Z" },
+    { url = "https://files.pythonhosted.org/packages/31/5e/03966aedfbfcbb4d5f8aa042452d3361f325b963ebbadddac05b122e47dd/pillow-11.3.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5418b53c0d59b3824d05e029669efa023bbef0f3e92e75ec8428f3799487f361", size = 4957170, upload-time = "2025-07-01T09:16:23.762Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/2d/e082982aacc927fc2cab48e1e731bdb1643a1406acace8bed0900a61464e/pillow-11.3.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:504b6f59505f08ae014f724b6207ff6222662aab5cc9542577fb084ed0676ac7", size = 5581505, upload-time = "2025-07-01T09:16:25.593Z" },
+    { url = "https://files.pythonhosted.org/packages/34/e7/ae39f538fd6844e982063c3a5e4598b8ced43b9633baa3a85ef33af8c05c/pillow-11.3.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:c84d689db21a1c397d001aa08241044aa2069e7587b398c8cc63020390b1c1b8", size = 6984598, upload-time = "2025-07-01T09:16:27.732Z" },
+]
+
+[[package]]
+name = "plotly"
+version = "6.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "narwhals" },
+    { name = "packaging" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0c/63/961d47c9ffd592a575495891cdcf7875dc0903ebb33ac238935714213789/plotly-6.3.1.tar.gz", hash = "sha256:dd896e3d940e653a7ce0470087e82c2bd903969a55e30d1b01bb389319461bb0", size = 6956460, upload-time = "2025-10-02T16:10:34.16Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3f/93/023955c26b0ce614342d11cc0652f1e45e32393b6ab9d11a664a60e9b7b7/plotly-6.3.1-py3-none-any.whl", hash = "sha256:8b4420d1dcf2b040f5983eed433f95732ed24930e496d36eb70d211923532e64", size = 9833698, upload-time = "2025-10-02T16:10:22.584Z" },
+]
+
+[[package]]
+name = "preshed"
+version = "3.0.10"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cymem" },
+    { name = "murmurhash" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4d/3a/db814f67a05b6d7f9c15d38edef5ec9b21415710705b393883de92aee5ef/preshed-3.0.10.tar.gz", hash = "sha256:5a5c8e685e941f4ffec97f1fbf32694b8107858891a4bc34107fac981d8296ff", size = 15039, upload-time = "2025-05-26T15:18:33.612Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/08/99/c3709638f687da339504d1daeca48604cadb338bf3556a1484d1f0cd95e6/preshed-3.0.10-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d96c4fe2b41c1cdcc8c4fc1fdb10f922a6095c0430a3ebe361fe62c78902d068", size = 131486, upload-time = "2025-05-26T15:17:52.231Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/27/0fd36b63caa8bbf57b31a121d9565d385bbd7521771d4eb93e17d326873d/preshed-3.0.10-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:cb01ea930b96f3301526a2ab26f41347d07555e4378c4144c6b7645074f2ebb0", size = 127938, upload-time = "2025-05-26T15:17:54.19Z" },
+    { url = "https://files.pythonhosted.org/packages/90/54/6a876d9cc8d401a9c1fb6bb8ca5a31b3664d0bcb888a9016258a1ae17344/preshed-3.0.10-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9dd1f0a7b7d150e229d073fd4fe94f72610cae992e907cee74687c4695873a98", size = 842263, upload-time = "2025-05-26T15:17:55.398Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/7d/ff19f74d15ee587905bafa3582883cfe2f72b574e6d691ee64dc690dc276/preshed-3.0.10-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9fd7b350c280137f324cd447afbf6ba9a849af0e8898850046ac6f34010e08bd", size = 842913, upload-time = "2025-05-26T15:17:56.687Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/3a/1c345a26463345557705b61965e1e0a732cc0e9c6dfd4787845dbfa50b4a/preshed-3.0.10-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:cf6a5fdc89ad06079aa6ee63621e417d4f4cf2a3d8b63c72728baad35a9ff641", size = 820548, upload-time = "2025-05-26T15:17:58.057Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/6b/71f25e2b7a23dba168f43edfae0bb508552dbef89114ce65c73f2ea7172f/preshed-3.0.10-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:b4c29a7bd66985808ad181c9ad05205a6aa7400cd0f98426acd7bc86588b93f8", size = 840379, upload-time = "2025-05-26T15:17:59.565Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/86/d8f32b0b31a36ee8770a9b1a95321430e364cd0ba4bfebb7348aed2f198d/preshed-3.0.10-cp311-cp311-win_amd64.whl", hash = "sha256:1367c1fd6f44296305315d4e1c3fe3171787d4d01c1008a76bc9466bd79c3249", size = 117655, upload-time = "2025-05-26T15:18:00.836Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/14/322a4f58bc25991a87f216acb1351800739b0794185d27508ee86c35f382/preshed-3.0.10-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6e9c46933d55c8898c8f7a6019a8062cd87ef257b075ada2dd5d1e57810189ea", size = 131367, upload-time = "2025-05-26T15:18:02.408Z" },
+    { url = "https://files.pythonhosted.org/packages/38/80/67507653c35620cace913f617df6d6f658b87e8da83087b851557d65dd86/preshed-3.0.10-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:5c4ebc4f8ef0114d55f2ffdce4965378129c7453d0203664aeeb03055572d9e4", size = 126535, upload-time = "2025-05-26T15:18:03.589Z" },
+    { url = "https://files.pythonhosted.org/packages/db/b1/ab4f811aeaf20af0fa47148c1c54b62d7e8120d59025bd0a3f773bb67725/preshed-3.0.10-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6ab5ab4c6dfd3746fb4328e7fbeb2a0544416b872db02903bfac18e6f5cd412f", size = 864907, upload-time = "2025-05-26T15:18:04.794Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/db/fe37c1f99cfb26805dd89381ddd54901307feceb267332eaaca228e9f9c1/preshed-3.0.10-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:40586fd96ae3974c552a7cd78781b6844ecb1559ee7556586f487058cf13dd96", size = 869329, upload-time = "2025-05-26T15:18:06.353Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/fd/efb6a6233d1cd969966f3f65bdd8e662579c3d83114e5c356cec1927b1f7/preshed-3.0.10-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a606c24cda931306b98e0edfafed3309bffcf8d6ecfe07804db26024c4f03cd6", size = 846829, upload-time = "2025-05-26T15:18:07.716Z" },
+    { url = "https://files.pythonhosted.org/packages/14/49/0e4ce5db3bf86b081abb08a404fb37b7c2dbfd7a73ec6c0bc71b650307eb/preshed-3.0.10-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:394015566f9354738be903447039e8dbc6d93ba5adf091af694eb03c4e726b1e", size = 874008, upload-time = "2025-05-26T15:18:09.364Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/17/76d6593fc2d055d4e413b68a8c87b70aa9b7697d4972cb8062559edcf6e9/preshed-3.0.10-cp312-cp312-win_amd64.whl", hash = "sha256:fd7e38225937e580420c84d1996dde9b4f726aacd9405093455c3a2fa60fede5", size = 116701, upload-time = "2025-05-26T15:18:11.905Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/5e/87671bc58c4f6c8cf0a5601ccd74b8bb50281ff28aa4ab3e3cad5cd9d06a/preshed-3.0.10-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:23e6e0581a517597f3f76bc24a4cdb0ba5509933d4f61c34fca49649dd71edf9", size = 129184, upload-time = "2025-05-26T15:18:13.331Z" },
+    { url = "https://files.pythonhosted.org/packages/92/69/b3969a3c95778def5bf5126484a1f7d2ad324d1040077f55f56e027d8ea4/preshed-3.0.10-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:574e6d6056981540310ff181b47a2912f4bddc91bcace3c7a9c6726eafda24ca", size = 124258, upload-time = "2025-05-26T15:18:14.497Z" },
+    { url = "https://files.pythonhosted.org/packages/32/df/6e828ec4565bf33bd4803a3eb3b1102830b739143e5d6c132bf7181a58ec/preshed-3.0.10-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2bd658dd73e853d1bb5597976a407feafa681b9d6155bc9bc7b4c2acc2a6ee96", size = 825445, upload-time = "2025-05-26T15:18:15.71Z" },
+    { url = "https://files.pythonhosted.org/packages/05/3d/478b585f304920e51f328c9231e22f30dc64baa68e079e08a46ab72be738/preshed-3.0.10-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5b95396046328ffb461a68859ce2141aca4815b8624167832d28ced70d541626", size = 831690, upload-time = "2025-05-26T15:18:17.08Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/65/938f21f77227e8d398d46fb10b9d1b3467be859468ce8db138fc3d50589c/preshed-3.0.10-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:3e6728b2028bbe79565eb6cf676b5bae5ce1f9cc56e4bf99bb28ce576f88054d", size = 808593, upload-time = "2025-05-26T15:18:18.535Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/1c/2a3961fc88bc72300ff7e4ca54689bda90d2d77cc994167cc09a310480b6/preshed-3.0.10-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:c4ef96cb28bf5f08de9c070143113e168efccbb68fd4961e7d445f734c051a97", size = 837333, upload-time = "2025-05-26T15:18:19.937Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/8c/d3e30f80b2ef21f267f09f0b7d18995adccc928ede5b73ea3fe54e1303f4/preshed-3.0.10-cp313-cp313-win_amd64.whl", hash = "sha256:97e0e2edfd25a7dfba799b49b3c5cc248ad0318a76edd9d5fd2c82aa3d5c64ed", size = 115769, upload-time = "2025-05-26T15:18:21.842Z" },
+]
+
+[[package]]
+name = "protobuf"
+version = "6.32.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fa/a4/cc17347aa2897568beece2e674674359f911d6fe21b0b8d6268cd42727ac/protobuf-6.32.1.tar.gz", hash = "sha256:ee2469e4a021474ab9baafea6cd070e5bf27c7d29433504ddea1a4ee5850f68d", size = 440635, upload-time = "2025-09-11T21:38:42.935Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c0/98/645183ea03ab3995d29086b8bf4f7562ebd3d10c9a4b14ee3f20d47cfe50/protobuf-6.32.1-cp310-abi3-win32.whl", hash = "sha256:a8a32a84bc9f2aad712041b8b366190f71dde248926da517bde9e832e4412085", size = 424411, upload-time = "2025-09-11T21:38:27.427Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/f3/6f58f841f6ebafe076cebeae33fc336e900619d34b1c93e4b5c97a81fdfa/protobuf-6.32.1-cp310-abi3-win_amd64.whl", hash = "sha256:b00a7d8c25fa471f16bc8153d0e53d6c9e827f0953f3c09aaa4331c718cae5e1", size = 435738, upload-time = "2025-09-11T21:38:30.959Z" },
+    { url = "https://files.pythonhosted.org/packages/10/56/a8a3f4e7190837139e68c7002ec749190a163af3e330f65d90309145a210/protobuf-6.32.1-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:d8c7e6eb619ffdf105ee4ab76af5a68b60a9d0f66da3ea12d1640e6d8dab7281", size = 426454, upload-time = "2025-09-11T21:38:34.076Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/be/8dd0a927c559b37d7a6c8ab79034fd167dcc1f851595f2e641ad62be8643/protobuf-6.32.1-cp39-abi3-manylinux2014_aarch64.whl", hash = "sha256:2f5b80a49e1eb7b86d85fcd23fe92df154b9730a725c3b38c4e43b9d77018bf4", size = 322874, upload-time = "2025-09-11T21:38:35.509Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/f6/88d77011b605ef979aace37b7703e4eefad066f7e84d935e5a696515c2dd/protobuf-6.32.1-cp39-abi3-manylinux2014_x86_64.whl", hash = "sha256:b1864818300c297265c83a4982fd3169f97122c299f56a56e2445c3698d34710", size = 322013, upload-time = "2025-09-11T21:38:37.017Z" },
+    { url = "https://files.pythonhosted.org/packages/97/b7/15cc7d93443d6c6a84626ae3258a91f4c6ac8c0edd5df35ea7658f71b79c/protobuf-6.32.1-py3-none-any.whl", hash = "sha256:2601b779fc7d32a866c6b4404f9d42a3f67c5b9f3f15b4db3cccabe06b95c346", size = 169289, upload-time = "2025-09-11T21:38:41.234Z" },
+]
+
+[[package]]
+name = "psutil"
+version = "7.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b3/31/4723d756b59344b643542936e37a31d1d3204bcdc42a7daa8ee9eb06fb50/psutil-7.1.0.tar.gz", hash = "sha256:655708b3c069387c8b77b072fc429a57d0e214221d01c0a772df7dfedcb3bcd2", size = 497660, upload-time = "2025-09-17T20:14:52.902Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/46/62/ce4051019ee20ce0ed74432dd73a5bb087a6704284a470bb8adff69a0932/psutil-7.1.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:76168cef4397494250e9f4e73eb3752b146de1dd950040b29186d0cce1d5ca13", size = 245242, upload-time = "2025-09-17T20:14:56.126Z" },
+    { url = "https://files.pythonhosted.org/packages/38/61/f76959fba841bf5b61123fbf4b650886dc4094c6858008b5bf73d9057216/psutil-7.1.0-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:5d007560c8c372efdff9e4579c2846d71de737e4605f611437255e81efcca2c5", size = 246682, upload-time = "2025-09-17T20:14:58.25Z" },
+    { url = "https://files.pythonhosted.org/packages/88/7a/37c99d2e77ec30d63398ffa6a660450b8a62517cabe44b3e9bae97696e8d/psutil-7.1.0-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:22e4454970b32472ce7deaa45d045b34d3648ce478e26a04c7e858a0a6e75ff3", size = 287994, upload-time = "2025-09-17T20:14:59.901Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/de/04c8c61232f7244aa0a4b9a9fbd63a89d5aeaf94b2fc9d1d16e2faa5cbb0/psutil-7.1.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8c70e113920d51e89f212dd7be06219a9b88014e63a4cec69b684c327bc474e3", size = 291163, upload-time = "2025-09-17T20:15:01.481Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/58/c4f976234bf6d4737bc8c02a81192f045c307b72cf39c9e5c5a2d78927f6/psutil-7.1.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7d4a113425c037300de3ac8b331637293da9be9713855c4fc9d2d97436d7259d", size = 293625, upload-time = "2025-09-17T20:15:04.492Z" },
+    { url = "https://files.pythonhosted.org/packages/79/87/157c8e7959ec39ced1b11cc93c730c4fb7f9d408569a6c59dbd92ceb35db/psutil-7.1.0-cp37-abi3-win32.whl", hash = "sha256:09ad740870c8d219ed8daae0ad3b726d3bf9a028a198e7f3080f6a1888b99bca", size = 244812, upload-time = "2025-09-17T20:15:07.462Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/e9/b44c4f697276a7a95b8e94d0e320a7bf7f3318521b23de69035540b39838/psutil-7.1.0-cp37-abi3-win_amd64.whl", hash = "sha256:57f5e987c36d3146c0dd2528cd42151cf96cd359b9d67cfff836995cc5df9a3d", size = 247965, upload-time = "2025-09-17T20:15:09.673Z" },
+    { url = "https://files.pythonhosted.org/packages/26/65/1070a6e3c036f39142c2820c4b52e9243246fcfc3f96239ac84472ba361e/psutil-7.1.0-cp37-abi3-win_arm64.whl", hash = "sha256:6937cb68133e7c97b6cc9649a570c9a18ba0efebed46d8c5dae4c07fa1b67a07", size = 244971, upload-time = "2025-09-17T20:15:12.262Z" },
+]
+
+[[package]]
+name = "pyarrow"
+version = "21.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ef/c2/ea068b8f00905c06329a3dfcd40d0fcc2b7d0f2e355bdb25b65e0a0e4cd4/pyarrow-21.0.0.tar.gz", hash = "sha256:5051f2dccf0e283ff56335760cbc8622cf52264d67e359d5569541ac11b6d5bc", size = 1133487, upload-time = "2025-07-18T00:57:31.761Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/94/dc/80564a3071a57c20b7c32575e4a0120e8a330ef487c319b122942d665960/pyarrow-21.0.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:c077f48aab61738c237802836fc3844f85409a46015635198761b0d6a688f87b", size = 31243234, upload-time = "2025-07-18T00:55:03.812Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/cc/3b51cb2db26fe535d14f74cab4c79b191ed9a8cd4cbba45e2379b5ca2746/pyarrow-21.0.0-cp311-cp311-macosx_12_0_x86_64.whl", hash = "sha256:689f448066781856237eca8d1975b98cace19b8dd2ab6145bf49475478bcaa10", size = 32714370, upload-time = "2025-07-18T00:55:07.495Z" },
+    { url = "https://files.pythonhosted.org/packages/24/11/a4431f36d5ad7d83b87146f515c063e4d07ef0b7240876ddb885e6b44f2e/pyarrow-21.0.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:479ee41399fcddc46159a551705b89c05f11e8b8cb8e968f7fec64f62d91985e", size = 41135424, upload-time = "2025-07-18T00:55:11.461Z" },
+    { url = "https://files.pythonhosted.org/packages/74/dc/035d54638fc5d2971cbf1e987ccd45f1091c83bcf747281cf6cc25e72c88/pyarrow-21.0.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:40ebfcb54a4f11bcde86bc586cbd0272bac0d516cfa539c799c2453768477569", size = 42823810, upload-time = "2025-07-18T00:55:16.301Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/3b/89fced102448a9e3e0d4dded1f37fa3ce4700f02cdb8665457fcc8015f5b/pyarrow-21.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8d58d8497814274d3d20214fbb24abcad2f7e351474357d552a8d53bce70c70e", size = 43391538, upload-time = "2025-07-18T00:55:23.82Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/bb/ea7f1bd08978d39debd3b23611c293f64a642557e8141c80635d501e6d53/pyarrow-21.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:585e7224f21124dd57836b1530ac8f2df2afc43c861d7bf3d58a4870c42ae36c", size = 45120056, upload-time = "2025-07-18T00:55:28.231Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/0b/77ea0600009842b30ceebc3337639a7380cd946061b620ac1a2f3cb541e2/pyarrow-21.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:555ca6935b2cbca2c0e932bedd853e9bc523098c39636de9ad4693b5b1df86d6", size = 26220568, upload-time = "2025-07-18T00:55:32.122Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/d4/d4f817b21aacc30195cf6a46ba041dd1be827efa4a623cc8bf39a1c2a0c0/pyarrow-21.0.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:3a302f0e0963db37e0a24a70c56cf91a4faa0bca51c23812279ca2e23481fccd", size = 31160305, upload-time = "2025-07-18T00:55:35.373Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/9c/dcd38ce6e4b4d9a19e1d36914cb8e2b1da4e6003dd075474c4cfcdfe0601/pyarrow-21.0.0-cp312-cp312-macosx_12_0_x86_64.whl", hash = "sha256:b6b27cf01e243871390474a211a7922bfbe3bda21e39bc9160daf0da3fe48876", size = 32684264, upload-time = "2025-07-18T00:55:39.303Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/74/2a2d9f8d7a59b639523454bec12dba35ae3d0a07d8ab529dc0809f74b23c/pyarrow-21.0.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:e72a8ec6b868e258a2cd2672d91f2860ad532d590ce94cdf7d5e7ec674ccf03d", size = 41108099, upload-time = "2025-07-18T00:55:42.889Z" },
+    { url = "https://files.pythonhosted.org/packages/ad/90/2660332eeb31303c13b653ea566a9918484b6e4d6b9d2d46879a33ab0622/pyarrow-21.0.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:b7ae0bbdc8c6674259b25bef5d2a1d6af5d39d7200c819cf99e07f7dfef1c51e", size = 42829529, upload-time = "2025-07-18T00:55:47.069Z" },
+    { url = "https://files.pythonhosted.org/packages/33/27/1a93a25c92717f6aa0fca06eb4700860577d016cd3ae51aad0e0488ac899/pyarrow-21.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:58c30a1729f82d201627c173d91bd431db88ea74dcaa3885855bc6203e433b82", size = 43367883, upload-time = "2025-07-18T00:55:53.069Z" },
+    { url = "https://files.pythonhosted.org/packages/05/d9/4d09d919f35d599bc05c6950095e358c3e15148ead26292dfca1fb659b0c/pyarrow-21.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:072116f65604b822a7f22945a7a6e581cfa28e3454fdcc6939d4ff6090126623", size = 45133802, upload-time = "2025-07-18T00:55:57.714Z" },
+    { url = "https://files.pythonhosted.org/packages/71/30/f3795b6e192c3ab881325ffe172e526499eb3780e306a15103a2764916a2/pyarrow-21.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:cf56ec8b0a5c8c9d7021d6fd754e688104f9ebebf1bf4449613c9531f5346a18", size = 26203175, upload-time = "2025-07-18T00:56:01.364Z" },
+    { url = "https://files.pythonhosted.org/packages/16/ca/c7eaa8e62db8fb37ce942b1ea0c6d7abfe3786ca193957afa25e71b81b66/pyarrow-21.0.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:e99310a4ebd4479bcd1964dff9e14af33746300cb014aa4a3781738ac63baf4a", size = 31154306, upload-time = "2025-07-18T00:56:04.42Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/e8/e87d9e3b2489302b3a1aea709aaca4b781c5252fcb812a17ab6275a9a484/pyarrow-21.0.0-cp313-cp313-macosx_12_0_x86_64.whl", hash = "sha256:d2fe8e7f3ce329a71b7ddd7498b3cfac0eeb200c2789bd840234f0dc271a8efe", size = 32680622, upload-time = "2025-07-18T00:56:07.505Z" },
+    { url = "https://files.pythonhosted.org/packages/84/52/79095d73a742aa0aba370c7942b1b655f598069489ab387fe47261a849e1/pyarrow-21.0.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:f522e5709379d72fb3da7785aa489ff0bb87448a9dc5a75f45763a795a089ebd", size = 41104094, upload-time = "2025-07-18T00:56:10.994Z" },
+    { url = "https://files.pythonhosted.org/packages/89/4b/7782438b551dbb0468892a276b8c789b8bbdb25ea5c5eb27faadd753e037/pyarrow-21.0.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:69cbbdf0631396e9925e048cfa5bce4e8c3d3b41562bbd70c685a8eb53a91e61", size = 42825576, upload-time = "2025-07-18T00:56:15.569Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/62/0f29de6e0a1e33518dec92c65be0351d32d7ca351e51ec5f4f837a9aab91/pyarrow-21.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:731c7022587006b755d0bdb27626a1a3bb004bb56b11fb30d98b6c1b4718579d", size = 43368342, upload-time = "2025-07-18T00:56:19.531Z" },
+    { url = "https://files.pythonhosted.org/packages/90/c7/0fa1f3f29cf75f339768cc698c8ad4ddd2481c1742e9741459911c9ac477/pyarrow-21.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:dc56bc708f2d8ac71bd1dcb927e458c93cec10b98eb4120206a4091db7b67b99", size = 45131218, upload-time = "2025-07-18T00:56:23.347Z" },
+    { url = "https://files.pythonhosted.org/packages/01/63/581f2076465e67b23bc5a37d4a2abff8362d389d29d8105832e82c9c811c/pyarrow-21.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:186aa00bca62139f75b7de8420f745f2af12941595bbbfa7ed3870ff63e25636", size = 26087551, upload-time = "2025-07-18T00:56:26.758Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/ab/357d0d9648bb8241ee7348e564f2479d206ebe6e1c47ac5027c2e31ecd39/pyarrow-21.0.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:a7a102574faa3f421141a64c10216e078df467ab9576684d5cd696952546e2da", size = 31290064, upload-time = "2025-07-18T00:56:30.214Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/8a/5685d62a990e4cac2043fc76b4661bf38d06efed55cf45a334b455bd2759/pyarrow-21.0.0-cp313-cp313t-macosx_12_0_x86_64.whl", hash = "sha256:1e005378c4a2c6db3ada3ad4c217b381f6c886f0a80d6a316fe586b90f77efd7", size = 32727837, upload-time = "2025-07-18T00:56:33.935Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/de/c0828ee09525c2bafefd3e736a248ebe764d07d0fd762d4f0929dbc516c9/pyarrow-21.0.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:65f8e85f79031449ec8706b74504a316805217b35b6099155dd7e227eef0d4b6", size = 41014158, upload-time = "2025-07-18T00:56:37.528Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/26/a2865c420c50b7a3748320b614f3484bfcde8347b2639b2b903b21ce6a72/pyarrow-21.0.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:3a81486adc665c7eb1a2bde0224cfca6ceaba344a82a971ef059678417880eb8", size = 42667885, upload-time = "2025-07-18T00:56:41.483Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/f9/4ee798dc902533159250fb4321267730bc0a107d8c6889e07c3add4fe3a5/pyarrow-21.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:fc0d2f88b81dcf3ccf9a6ae17f89183762c8a94a5bdcfa09e05cfe413acf0503", size = 43276625, upload-time = "2025-07-18T00:56:48.002Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/da/e02544d6997037a4b0d22d8e5f66bc9315c3671371a8b18c79ade1cefe14/pyarrow-21.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6299449adf89df38537837487a4f8d3bd91ec94354fdd2a7d30bc11c48ef6e79", size = 44951890, upload-time = "2025-07-18T00:56:52.568Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/4e/519c1bc1876625fe6b71e9a28287c43ec2f20f73c658b9ae1d485c0c206e/pyarrow-21.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:222c39e2c70113543982c6b34f3077962b44fca38c0bd9e68bb6781534425c10", size = 26371006, upload-time = "2025-07-18T00:56:56.379Z" },
+]
+
+[[package]]
+name = "pydantic"
+version = "2.11.10"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "annotated-types" },
+    { name = "pydantic-core" },
+    { name = "typing-extensions" },
+    { name = "typing-inspection" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ae/54/ecab642b3bed45f7d5f59b38443dcb36ef50f85af192e6ece103dbfe9587/pydantic-2.11.10.tar.gz", hash = "sha256:dc280f0982fbda6c38fada4e476dc0a4f3aeaf9c6ad4c28df68a666ec3c61423", size = 788494, upload-time = "2025-10-04T10:40:41.338Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bd/1f/73c53fcbfb0b5a78f91176df41945ca466e71e9d9d836e5c522abda39ee7/pydantic-2.11.10-py3-none-any.whl", hash = "sha256:802a655709d49bd004c31e865ef37da30b540786a46bfce02333e0e24b5fe29a", size = 444823, upload-time = "2025-10-04T10:40:39.055Z" },
+]
+
+[[package]]
+name = "pydantic-core"
+version = "2.33.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ad/88/5f2260bdfae97aabf98f1778d43f69574390ad787afb646292a638c923d4/pydantic_core-2.33.2.tar.gz", hash = "sha256:7cb8bc3605c29176e1b105350d2e6474142d7c1bd1d9327c4a9bdb46bf827acc", size = 435195, upload-time = "2025-04-23T18:33:52.104Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3f/8d/71db63483d518cbbf290261a1fc2839d17ff89fce7089e08cad07ccfce67/pydantic_core-2.33.2-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:4c5b0a576fb381edd6d27f0a85915c6daf2f8138dc5c267a57c08a62900758c7", size = 2028584, upload-time = "2025-04-23T18:31:03.106Z" },
+    { url = "https://files.pythonhosted.org/packages/24/2f/3cfa7244ae292dd850989f328722d2aef313f74ffc471184dc509e1e4e5a/pydantic_core-2.33.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e799c050df38a639db758c617ec771fd8fb7a5f8eaaa4b27b101f266b216a246", size = 1855071, upload-time = "2025-04-23T18:31:04.621Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/d3/4ae42d33f5e3f50dd467761304be2fa0a9417fbf09735bc2cce003480f2a/pydantic_core-2.33.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dc46a01bf8d62f227d5ecee74178ffc448ff4e5197c756331f71efcc66dc980f", size = 1897823, upload-time = "2025-04-23T18:31:06.377Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/f3/aa5976e8352b7695ff808599794b1fba2a9ae2ee954a3426855935799488/pydantic_core-2.33.2-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:a144d4f717285c6d9234a66778059f33a89096dfb9b39117663fd8413d582dcc", size = 1983792, upload-time = "2025-04-23T18:31:07.93Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/7a/cda9b5a23c552037717f2b2a5257e9b2bfe45e687386df9591eff7b46d28/pydantic_core-2.33.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:73cf6373c21bc80b2e0dc88444f41ae60b2f070ed02095754eb5a01df12256de", size = 2136338, upload-time = "2025-04-23T18:31:09.283Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/9f/b8f9ec8dd1417eb9da784e91e1667d58a2a4a7b7b34cf4af765ef663a7e5/pydantic_core-2.33.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3dc625f4aa79713512d1976fe9f0bc99f706a9dee21dfd1810b4bbbf228d0e8a", size = 2730998, upload-time = "2025-04-23T18:31:11.7Z" },
+    { url = "https://files.pythonhosted.org/packages/47/bc/cd720e078576bdb8255d5032c5d63ee5c0bf4b7173dd955185a1d658c456/pydantic_core-2.33.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:881b21b5549499972441da4758d662aeea93f1923f953e9cbaff14b8b9565aef", size = 2003200, upload-time = "2025-04-23T18:31:13.536Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/22/3602b895ee2cd29d11a2b349372446ae9727c32e78a94b3d588a40fdf187/pydantic_core-2.33.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:bdc25f3681f7b78572699569514036afe3c243bc3059d3942624e936ec93450e", size = 2113890, upload-time = "2025-04-23T18:31:15.011Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/e6/e3c5908c03cf00d629eb38393a98fccc38ee0ce8ecce32f69fc7d7b558a7/pydantic_core-2.33.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:fe5b32187cbc0c862ee201ad66c30cf218e5ed468ec8dc1cf49dec66e160cc4d", size = 2073359, upload-time = "2025-04-23T18:31:16.393Z" },
+    { url = "https://files.pythonhosted.org/packages/12/e7/6a36a07c59ebefc8777d1ffdaf5ae71b06b21952582e4b07eba88a421c79/pydantic_core-2.33.2-cp311-cp311-musllinux_1_1_armv7l.whl", hash = "sha256:bc7aee6f634a6f4a95676fcb5d6559a2c2a390330098dba5e5a5f28a2e4ada30", size = 2245883, upload-time = "2025-04-23T18:31:17.892Z" },
+    { url = "https://files.pythonhosted.org/packages/16/3f/59b3187aaa6cc0c1e6616e8045b284de2b6a87b027cce2ffcea073adf1d2/pydantic_core-2.33.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:235f45e5dbcccf6bd99f9f472858849f73d11120d76ea8707115415f8e5ebebf", size = 2241074, upload-time = "2025-04-23T18:31:19.205Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/ed/55532bb88f674d5d8f67ab121a2a13c385df382de2a1677f30ad385f7438/pydantic_core-2.33.2-cp311-cp311-win32.whl", hash = "sha256:6368900c2d3ef09b69cb0b913f9f8263b03786e5b2a387706c5afb66800efd51", size = 1910538, upload-time = "2025-04-23T18:31:20.541Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/1b/25b7cccd4519c0b23c2dd636ad39d381abf113085ce4f7bec2b0dc755eb1/pydantic_core-2.33.2-cp311-cp311-win_amd64.whl", hash = "sha256:1e063337ef9e9820c77acc768546325ebe04ee38b08703244c1309cccc4f1bab", size = 1952909, upload-time = "2025-04-23T18:31:22.371Z" },
+    { url = "https://files.pythonhosted.org/packages/49/a9/d809358e49126438055884c4366a1f6227f0f84f635a9014e2deb9b9de54/pydantic_core-2.33.2-cp311-cp311-win_arm64.whl", hash = "sha256:6b99022f1d19bc32a4c2a0d544fc9a76e3be90f0b3f4af413f87d38749300e65", size = 1897786, upload-time = "2025-04-23T18:31:24.161Z" },
+    { url = "https://files.pythonhosted.org/packages/18/8a/2b41c97f554ec8c71f2a8a5f85cb56a8b0956addfe8b0efb5b3d77e8bdc3/pydantic_core-2.33.2-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:a7ec89dc587667f22b6a0b6579c249fca9026ce7c333fc142ba42411fa243cdc", size = 2009000, upload-time = "2025-04-23T18:31:25.863Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/02/6224312aacb3c8ecbaa959897af57181fb6cf3a3d7917fd44d0f2917e6f2/pydantic_core-2.33.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:3c6db6e52c6d70aa0d00d45cdb9b40f0433b96380071ea80b09277dba021ddf7", size = 1847996, upload-time = "2025-04-23T18:31:27.341Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/46/6dcdf084a523dbe0a0be59d054734b86a981726f221f4562aed313dbcb49/pydantic_core-2.33.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4e61206137cbc65e6d5256e1166f88331d3b6238e082d9f74613b9b765fb9025", size = 1880957, upload-time = "2025-04-23T18:31:28.956Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/6b/1ec2c03837ac00886ba8160ce041ce4e325b41d06a034adbef11339ae422/pydantic_core-2.33.2-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:eb8c529b2819c37140eb51b914153063d27ed88e3bdc31b71198a198e921e011", size = 1964199, upload-time = "2025-04-23T18:31:31.025Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/1d/6bf34d6adb9debd9136bd197ca72642203ce9aaaa85cfcbfcf20f9696e83/pydantic_core-2.33.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c52b02ad8b4e2cf14ca7b3d918f3eb0ee91e63b3167c32591e57c4317e134f8f", size = 2120296, upload-time = "2025-04-23T18:31:32.514Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/94/2bd0aaf5a591e974b32a9f7123f16637776c304471a0ab33cf263cf5591a/pydantic_core-2.33.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:96081f1605125ba0855dfda83f6f3df5ec90c61195421ba72223de35ccfb2f88", size = 2676109, upload-time = "2025-04-23T18:31:33.958Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/41/4b043778cf9c4285d59742281a769eac371b9e47e35f98ad321349cc5d61/pydantic_core-2.33.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f57a69461af2a5fa6e6bbd7a5f60d3b7e6cebb687f55106933188e79ad155c1", size = 2002028, upload-time = "2025-04-23T18:31:39.095Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/d5/7bb781bf2748ce3d03af04d5c969fa1308880e1dca35a9bd94e1a96a922e/pydantic_core-2.33.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:572c7e6c8bb4774d2ac88929e3d1f12bc45714ae5ee6d9a788a9fb35e60bb04b", size = 2100044, upload-time = "2025-04-23T18:31:41.034Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/36/def5e53e1eb0ad896785702a5bbfd25eed546cdcf4087ad285021a90ed53/pydantic_core-2.33.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:db4b41f9bd95fbe5acd76d89920336ba96f03e149097365afe1cb092fceb89a1", size = 2058881, upload-time = "2025-04-23T18:31:42.757Z" },
+    { url = "https://files.pythonhosted.org/packages/01/6c/57f8d70b2ee57fc3dc8b9610315949837fa8c11d86927b9bb044f8705419/pydantic_core-2.33.2-cp312-cp312-musllinux_1_1_armv7l.whl", hash = "sha256:fa854f5cf7e33842a892e5c73f45327760bc7bc516339fda888c75ae60edaeb6", size = 2227034, upload-time = "2025-04-23T18:31:44.304Z" },
+    { url = "https://files.pythonhosted.org/packages/27/b9/9c17f0396a82b3d5cbea4c24d742083422639e7bb1d5bf600e12cb176a13/pydantic_core-2.33.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:5f483cfb75ff703095c59e365360cb73e00185e01aaea067cd19acffd2ab20ea", size = 2234187, upload-time = "2025-04-23T18:31:45.891Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/6a/adf5734ffd52bf86d865093ad70b2ce543415e0e356f6cacabbc0d9ad910/pydantic_core-2.33.2-cp312-cp312-win32.whl", hash = "sha256:9cb1da0f5a471435a7bc7e439b8a728e8b61e59784b2af70d7c169f8dd8ae290", size = 1892628, upload-time = "2025-04-23T18:31:47.819Z" },
+    { url = "https://files.pythonhosted.org/packages/43/e4/5479fecb3606c1368d496a825d8411e126133c41224c1e7238be58b87d7e/pydantic_core-2.33.2-cp312-cp312-win_amd64.whl", hash = "sha256:f941635f2a3d96b2973e867144fde513665c87f13fe0e193c158ac51bfaaa7b2", size = 1955866, upload-time = "2025-04-23T18:31:49.635Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/24/8b11e8b3e2be9dd82df4b11408a67c61bb4dc4f8e11b5b0fc888b38118b5/pydantic_core-2.33.2-cp312-cp312-win_arm64.whl", hash = "sha256:cca3868ddfaccfbc4bfb1d608e2ccaaebe0ae628e1416aeb9c4d88c001bb45ab", size = 1888894, upload-time = "2025-04-23T18:31:51.609Z" },
+    { url = "https://files.pythonhosted.org/packages/46/8c/99040727b41f56616573a28771b1bfa08a3d3fe74d3d513f01251f79f172/pydantic_core-2.33.2-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:1082dd3e2d7109ad8b7da48e1d4710c8d06c253cbc4a27c1cff4fbcaa97a9e3f", size = 2015688, upload-time = "2025-04-23T18:31:53.175Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/cc/5999d1eb705a6cefc31f0b4a90e9f7fc400539b1a1030529700cc1b51838/pydantic_core-2.33.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f517ca031dfc037a9c07e748cefd8d96235088b83b4f4ba8939105d20fa1dcd6", size = 1844808, upload-time = "2025-04-23T18:31:54.79Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/5e/a0a7b8885c98889a18b6e376f344da1ef323d270b44edf8174d6bce4d622/pydantic_core-2.33.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a9f2c9dd19656823cb8250b0724ee9c60a82f3cdf68a080979d13092a3b0fef", size = 1885580, upload-time = "2025-04-23T18:31:57.393Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/2a/953581f343c7d11a304581156618c3f592435523dd9d79865903272c256a/pydantic_core-2.33.2-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2b0a451c263b01acebe51895bfb0e1cc842a5c666efe06cdf13846c7418caa9a", size = 1973859, upload-time = "2025-04-23T18:31:59.065Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/55/f1a813904771c03a3f97f676c62cca0c0a4138654107c1b61f19c644868b/pydantic_core-2.33.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1ea40a64d23faa25e62a70ad163571c0b342b8bf66d5fa612ac0dec4f069d916", size = 2120810, upload-time = "2025-04-23T18:32:00.78Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/c3/053389835a996e18853ba107a63caae0b9deb4a276c6b472931ea9ae6e48/pydantic_core-2.33.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0fb2d542b4d66f9470e8065c5469ec676978d625a8b7a363f07d9a501a9cb36a", size = 2676498, upload-time = "2025-04-23T18:32:02.418Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/3c/f4abd740877a35abade05e437245b192f9d0ffb48bbbbd708df33d3cda37/pydantic_core-2.33.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9fdac5d6ffa1b5a83bca06ffe7583f5576555e6c8b3a91fbd25ea7780f825f7d", size = 2000611, upload-time = "2025-04-23T18:32:04.152Z" },
+    { url = "https://files.pythonhosted.org/packages/59/a7/63ef2fed1837d1121a894d0ce88439fe3e3b3e48c7543b2a4479eb99c2bd/pydantic_core-2.33.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:04a1a413977ab517154eebb2d326da71638271477d6ad87a769102f7c2488c56", size = 2107924, upload-time = "2025-04-23T18:32:06.129Z" },
+    { url = "https://files.pythonhosted.org/packages/04/8f/2551964ef045669801675f1cfc3b0d74147f4901c3ffa42be2ddb1f0efc4/pydantic_core-2.33.2-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:c8e7af2f4e0194c22b5b37205bfb293d166a7344a5b0d0eaccebc376546d77d5", size = 2063196, upload-time = "2025-04-23T18:32:08.178Z" },
+    { url = "https://files.pythonhosted.org/packages/26/bd/d9602777e77fc6dbb0c7db9ad356e9a985825547dce5ad1d30ee04903918/pydantic_core-2.33.2-cp313-cp313-musllinux_1_1_armv7l.whl", hash = "sha256:5c92edd15cd58b3c2d34873597a1e20f13094f59cf88068adb18947df5455b4e", size = 2236389, upload-time = "2025-04-23T18:32:10.242Z" },
+    { url = "https://files.pythonhosted.org/packages/42/db/0e950daa7e2230423ab342ae918a794964b053bec24ba8af013fc7c94846/pydantic_core-2.33.2-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:65132b7b4a1c0beded5e057324b7e16e10910c106d43675d9bd87d4f38dde162", size = 2239223, upload-time = "2025-04-23T18:32:12.382Z" },
+    { url = "https://files.pythonhosted.org/packages/58/4d/4f937099c545a8a17eb52cb67fe0447fd9a373b348ccfa9a87f141eeb00f/pydantic_core-2.33.2-cp313-cp313-win32.whl", hash = "sha256:52fb90784e0a242bb96ec53f42196a17278855b0f31ac7c3cc6f5c1ec4811849", size = 1900473, upload-time = "2025-04-23T18:32:14.034Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/75/4a0a9bac998d78d889def5e4ef2b065acba8cae8c93696906c3a91f310ca/pydantic_core-2.33.2-cp313-cp313-win_amd64.whl", hash = "sha256:c083a3bdd5a93dfe480f1125926afcdbf2917ae714bdb80b36d34318b2bec5d9", size = 1955269, upload-time = "2025-04-23T18:32:15.783Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/86/1beda0576969592f1497b4ce8e7bc8cbdf614c352426271b1b10d5f0aa64/pydantic_core-2.33.2-cp313-cp313-win_arm64.whl", hash = "sha256:e80b087132752f6b3d714f041ccf74403799d3b23a72722ea2e6ba2e892555b9", size = 1893921, upload-time = "2025-04-23T18:32:18.473Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/7d/e09391c2eebeab681df2b74bfe6c43422fffede8dc74187b2b0bf6fd7571/pydantic_core-2.33.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:61c18fba8e5e9db3ab908620af374db0ac1baa69f0f32df4f61ae23f15e586ac", size = 1806162, upload-time = "2025-04-23T18:32:20.188Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/3d/847b6b1fed9f8ed3bb95a9ad04fbd0b212e832d4f0f50ff4d9ee5a9f15cf/pydantic_core-2.33.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95237e53bb015f67b63c91af7518a62a8660376a6a0db19b89acc77a4d6199f5", size = 1981560, upload-time = "2025-04-23T18:32:22.354Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/9a/e73262f6c6656262b5fdd723ad90f518f579b7bc8622e43a942eec53c938/pydantic_core-2.33.2-cp313-cp313t-win_amd64.whl", hash = "sha256:c2fc0a768ef76c15ab9238afa6da7f69895bb5d1ee83aeea2e3509af4472d0b9", size = 1935777, upload-time = "2025-04-23T18:32:25.088Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/27/d4ae6487d73948d6f20dddcd94be4ea43e74349b56eba82e9bdee2d7494c/pydantic_core-2.33.2-pp311-pypy311_pp73-macosx_10_12_x86_64.whl", hash = "sha256:dd14041875d09cc0f9308e37a6f8b65f5585cf2598a53aa0123df8b129d481f8", size = 2025200, upload-time = "2025-04-23T18:33:14.199Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/b8/b3cb95375f05d33801024079b9392a5ab45267a63400bf1866e7ce0f0de4/pydantic_core-2.33.2-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:d87c561733f66531dced0da6e864f44ebf89a8fba55f31407b00c2f7f9449593", size = 1859123, upload-time = "2025-04-23T18:33:16.555Z" },
+    { url = "https://files.pythonhosted.org/packages/05/bc/0d0b5adeda59a261cd30a1235a445bf55c7e46ae44aea28f7bd6ed46e091/pydantic_core-2.33.2-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2f82865531efd18d6e07a04a17331af02cb7a651583c418df8266f17a63c6612", size = 1892852, upload-time = "2025-04-23T18:33:18.513Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/11/d37bdebbda2e449cb3f519f6ce950927b56d62f0b84fd9cb9e372a26a3d5/pydantic_core-2.33.2-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2bfb5112df54209d820d7bf9317c7a6c9025ea52e49f46b6a2060104bba37de7", size = 2067484, upload-time = "2025-04-23T18:33:20.475Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/55/1f95f0a05ce72ecb02a8a8a1c3be0579bbc29b1d5ab68f1378b7bebc5057/pydantic_core-2.33.2-pp311-pypy311_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:64632ff9d614e5eecfb495796ad51b0ed98c453e447a76bcbeeb69615079fc7e", size = 2108896, upload-time = "2025-04-23T18:33:22.501Z" },
+    { url = "https://files.pythonhosted.org/packages/53/89/2b2de6c81fa131f423246a9109d7b2a375e83968ad0800d6e57d0574629b/pydantic_core-2.33.2-pp311-pypy311_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:f889f7a40498cc077332c7ab6b4608d296d852182211787d4f3ee377aaae66e8", size = 2069475, upload-time = "2025-04-23T18:33:24.528Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/e9/1f7efbe20d0b2b10f6718944b5d8ece9152390904f29a78e68d4e7961159/pydantic_core-2.33.2-pp311-pypy311_pp73-musllinux_1_1_armv7l.whl", hash = "sha256:de4b83bb311557e439b9e186f733f6c645b9417c84e2eb8203f3f820a4b988bf", size = 2239013, upload-time = "2025-04-23T18:33:26.621Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/b2/5309c905a93811524a49b4e031e9851a6b00ff0fb668794472ea7746b448/pydantic_core-2.33.2-pp311-pypy311_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:82f68293f055f51b51ea42fafc74b6aad03e70e191799430b90c13d643059ebb", size = 2238715, upload-time = "2025-04-23T18:33:28.656Z" },
+    { url = "https://files.pythonhosted.org/packages/32/56/8a7ca5d2cd2cda1d245d34b1c9a942920a718082ae8e54e5f3e5a58b7add/pydantic_core-2.33.2-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:329467cecfb529c925cf2bbd4d60d2c509bc2fb52a20c1045bf09bb70971a9c1", size = 2066757, upload-time = "2025-04-23T18:33:30.645Z" },
+]
+
+[[package]]
+name = "pydeck"
+version = "0.9.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jinja2" },
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a1/ca/40e14e196864a0f61a92abb14d09b3d3da98f94ccb03b49cf51688140dab/pydeck-0.9.1.tar.gz", hash = "sha256:f74475ae637951d63f2ee58326757f8d4f9cd9f2a457cf42950715003e2cb605", size = 3832240, upload-time = "2024-05-10T15:36:21.153Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ab/4c/b888e6cf58bd9db9c93f40d1c6be8283ff49d88919231afe93a6bcf61626/pydeck-0.9.1-py2.py3-none-any.whl", hash = "sha256:b3f75ba0d273fc917094fa61224f3f6076ca8752b93d46faf3bcfd9f9d59b038", size = 6900403, upload-time = "2024-05-10T15:36:17.36Z" },
+]
+
+[[package]]
+name = "pygments"
+version = "2.19.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b0/77/a5b8c569bf593b0140bde72ea885a803b82086995367bf2037de0159d924/pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887", size = 4968631, upload-time = "2025-06-21T13:39:12.283Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" },
+]
+
+[[package]]
+name = "pyogrio"
+version = "0.11.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "numpy" },
+    { name = "packaging" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/bf/1d/ae0340237207664e2da1b77f2cdbcb5f81fd0fc9f3200a48ca993a5e12ef/pyogrio-0.11.1.tar.gz", hash = "sha256:e1441dc9c866f10d8e6ae7ea9249a10c1f57ea921b1f19a5b0977ab91ef8082c", size = 287267, upload-time = "2025-08-02T20:19:20.167Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d0/81/50441f029609bcb883ee2738bdee3f81a998a11e4052b6ad0ef0ae4c0ae5/pyogrio-0.11.1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:d36162ddc1a309bb941a3cfb550b8f88c862c67ef2f52df6460100e5e958bbc6", size = 19459279, upload-time = "2025-08-02T20:18:18.955Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/4a/a3a2fae13e42ee98574b18591ddb66bca88bc1fa812c017437b42c85569f/pyogrio-0.11.1-cp311-cp311-macosx_12_0_x86_64.whl", hash = "sha256:845c78d5e7c9ec1c7d00250c07e144e5fe504fdb4ccdc141d9413f85b8c55c91", size = 20646364, upload-time = "2025-08-02T20:18:21.171Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/f2/1dd5795f8cccf8f97d5ac7f28fee31fc1afc5f6bce9fab2ac4486ed3af44/pyogrio-0.11.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:50aa869509f189fa1bff4d90d2d4c7860b963e693af85f2957646306e882b631", size = 26999659, upload-time = "2025-08-02T20:18:25.482Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/ef/4f8d61afb6798edde8bd6d2721032e868ff78a25395d9512f7e5e50b23c4/pyogrio-0.11.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:dd0f44dd2d849d32aea3f73647c74083996917e446479645bf93de6656160f2d", size = 26523036, upload-time = "2025-08-02T20:18:28.761Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/99/81d9a441ac7709407750f359813889b9a3f6076999cb9ae8893d5ba7c707/pyogrio-0.11.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:36b910d4037694b2935b5b1c1eb757dcc2906dca05cb2992cbdaf1291b54ff97", size = 27678041, upload-time = "2025-08-02T20:18:31.103Z" },
+    { url = "https://files.pythonhosted.org/packages/74/4e/a5d00c30e5ca3f4133a425fe41531b219139ad4451ea8edc3520f221f9dd/pyogrio-0.11.1-cp311-cp311-win_amd64.whl", hash = "sha256:cb744097f302f19dcc5c93ee5e9cfd707b864c9a418e399f0908406a60003728", size = 19226619, upload-time = "2025-08-02T20:18:34.261Z" },
+    { url = "https://files.pythonhosted.org/packages/72/d3/2ba967ca4255cdfa130a6d8b437826488567b4bc1bb417c442bb43d62611/pyogrio-0.11.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:f186456ebe5d5f61e7bd883bad25a59d43d6304178d4f0d3e03273f42b40a4cc", size = 19450110, upload-time = "2025-08-02T20:18:36.643Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/e1/3bc29ae71d24a91cf91f7413541e50acb7de2ce609587168ce2f4b405d3b/pyogrio-0.11.1-cp312-cp312-macosx_12_0_x86_64.whl", hash = "sha256:b8a199bc0e421eac444af96942b7553268e43d0cadf30d0d6d41017de05b7e9e", size = 20635348, upload-time = "2025-08-02T20:18:38.714Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/b2/ec453e544370a90b4e8b2c6afa72501963ddc33afe883f0e5ba34af6a80f/pyogrio-0.11.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:afce80b4b32f043fcf76a50e8572e3ad8d9d3e6abbbfa6137f0975ba55c4eeb8", size = 26980190, upload-time = "2025-08-02T20:18:41.197Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/f6/337f122b58f697f807bf9093b606b33b3ef52fe06a21e88d8a9230844cc3/pyogrio-0.11.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:0cfd79caf0b8cb7bbf30b419dff7f21509169efcf4d431172c61b44fe1029dba", size = 26474852, upload-time = "2025-08-02T20:18:43.74Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/0f/8193a4a879f1284d693793e59a2e185c8fd3c47cb562b0e5daf7289997ea/pyogrio-0.11.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:ab3aa6dbf2441d2407ce052233f2966324a3cff752bd43d99e4c779ea54e0a16", size = 27659721, upload-time = "2025-08-02T20:18:46.398Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/7d/3e818625a435fcc196ea441a6ca8495f87dd1f1eebeb95760eb401ea425d/pyogrio-0.11.1-cp312-cp312-win_amd64.whl", hash = "sha256:cd10035eb3b5e5a43bdafbd777339d2274e9b75972658364f0ce31c4d3400d1e", size = 19219350, upload-time = "2025-08-02T20:18:48.866Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/68/86328e36d010ee565ce0c65cdf9b830afcb1fb5972f537fe1cc561a49247/pyogrio-0.11.1-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:3b368c597357ff262f3b46591ded86409462ee594ef42556708b090d121f873c", size = 19445347, upload-time = "2025-08-02T20:18:51.088Z" },
+    { url = "https://files.pythonhosted.org/packages/20/bc/34bd87641fc2ecc6d842d6d758bbaa8d58aea4d36aa6a1111cbc9d450e74/pyogrio-0.11.1-cp313-cp313-macosx_12_0_x86_64.whl", hash = "sha256:1cb82cfd3493f32396e9c3f9255e17885610f62a323870947f4e04dd59bc3595", size = 20630594, upload-time = "2025-08-02T20:18:53.176Z" },
+    { url = "https://files.pythonhosted.org/packages/68/9a/41b72ffa3e21354eb9afbbae855c86b94dbf06b22e89c16a807cc8b22bd2/pyogrio-0.11.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5d61aae22e67030fd354f03e21c6462537bf56160134dd8663709335a5a46b28", size = 26929440, upload-time = "2025-08-02T20:18:55.614Z" },
+    { url = "https://files.pythonhosted.org/packages/42/dd/c968c49a2e9b7c219eac0cc504241c21ef789f1f1b34d33780508cea9764/pyogrio-0.11.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:76150a3cd787c31628191c7abc6f8c796660125852fb65ae15dd7be1e9196816", size = 26433178, upload-time = "2025-08-02T20:18:58.274Z" },
+    { url = "https://files.pythonhosted.org/packages/89/a9/79eca15094f7806a3adcf0bb976ab4346b0fb1bd87956c1933df44546c14/pyogrio-0.11.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:e929452f6988c0365dd32ff2485d9488160a709fee28743abbbc18d663169ed0", size = 27616835, upload-time = "2025-08-02T20:19:01.112Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/f3/7722bc81e9eee39b528c1cbc6289a26d2d3b1b187491ed8493457d6a3a0e/pyogrio-0.11.1-cp313-cp313-win_amd64.whl", hash = "sha256:d6d56862b89a05fccd7211171c88806b6ec9b5effb79bf807cce0a57c1f2a606", size = 19219088, upload-time = "2025-08-02T20:19:03.732Z" },
+]
+
+[[package]]
+name = "pyparsing"
+version = "3.2.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f2/a5/181488fc2b9d093e3972d2a472855aae8a03f000592dbfce716a512b3359/pyparsing-3.2.5.tar.gz", hash = "sha256:2df8d5b7b2802ef88e8d016a2eb9c7aeaa923529cd251ed0fe4608275d4105b6", size = 1099274, upload-time = "2025-09-21T04:11:06.277Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/10/5e/1aa9a93198c6b64513c9d7752de7422c06402de6600a8767da1524f9570b/pyparsing-3.2.5-py3-none-any.whl", hash = "sha256:e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e", size = 113890, upload-time = "2025-09-21T04:11:04.117Z" },
+]
+
+[[package]]
+name = "pyproj"
+version = "3.7.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/04/90/67bd7260b4ea9b8b20b4f58afef6c223ecb3abf368eb4ec5bc2cdef81b49/pyproj-3.7.2.tar.gz", hash = "sha256:39a0cf1ecc7e282d1d30f36594ebd55c9fae1fda8a2622cee5d100430628f88c", size = 226279, upload-time = "2025-08-14T12:05:42.18Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a6/bd/f205552cd1713b08f93b09e39a3ec99edef0b3ebbbca67b486fdf1abe2de/pyproj-3.7.2-cp311-cp311-macosx_13_0_x86_64.whl", hash = "sha256:2514d61f24c4e0bb9913e2c51487ecdaeca5f8748d8313c933693416ca41d4d5", size = 6227022, upload-time = "2025-08-14T12:03:51.474Z" },
+    { url = "https://files.pythonhosted.org/packages/75/4c/9a937e659b8b418ab573c6d340d27e68716928953273e0837e7922fcac34/pyproj-3.7.2-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:8693ca3892d82e70de077701ee76dd13d7bca4ae1c9d1e739d72004df015923a", size = 4625810, upload-time = "2025-08-14T12:03:53.808Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/7d/a9f41e814dc4d1dc54e95b2ccaf0b3ebe3eb18b1740df05fe334724c3d89/pyproj-3.7.2-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:5e26484d80fea56273ed1555abaea161e9661d81a6c07815d54b8e883d4ceb25", size = 9638694, upload-time = "2025-08-14T12:03:55.669Z" },
+    { url = "https://files.pythonhosted.org/packages/ad/ab/9bdb4a6216b712a1f9aab1c0fcbee5d3726f34a366f29c3e8c08a78d6b70/pyproj-3.7.2-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:281cb92847814e8018010c48b4069ff858a30236638631c1a91dd7bfa68f8a8a", size = 9493977, upload-time = "2025-08-14T12:03:57.937Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/db/2db75b1b6190f1137b1c4e8ef6a22e1c338e46320f6329bfac819143e063/pyproj-3.7.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:9c8577f0b7bb09118ec2e57e3babdc977127dd66326d6c5d755c76b063e6d9dc", size = 10841151, upload-time = "2025-08-14T12:04:00.271Z" },
+    { url = "https://files.pythonhosted.org/packages/89/f7/989643394ba23a286e9b7b3f09981496172f9e0d4512457ffea7dc47ffc7/pyproj-3.7.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a23f59904fac3a5e7364b3aa44d288234af267ca041adb2c2b14a903cd5d3ac5", size = 10751585, upload-time = "2025-08-14T12:04:02.228Z" },
+    { url = "https://files.pythonhosted.org/packages/53/6d/ad928fe975a6c14a093c92e6a319ca18f479f3336bb353a740bdba335681/pyproj-3.7.2-cp311-cp311-win32.whl", hash = "sha256:f2af4ed34b2cf3e031a2d85b067a3ecbd38df073c567e04b52fa7a0202afde8a", size = 5908533, upload-time = "2025-08-14T12:04:04.821Z" },
+    { url = "https://files.pythonhosted.org/packages/79/e0/b95584605cec9ed50b7ebaf7975d1c4ddeec5a86b7a20554ed8b60042bd7/pyproj-3.7.2-cp311-cp311-win_amd64.whl", hash = "sha256:0b7cb633565129677b2a183c4d807c727d1c736fcb0568a12299383056e67433", size = 6320742, upload-time = "2025-08-14T12:04:06.357Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/4d/536e8f93bca808175c2d0a5ac9fdf69b960d8ab6b14f25030dccb07464d7/pyproj-3.7.2-cp311-cp311-win_arm64.whl", hash = "sha256:38b08d85e3a38e455625b80e9eb9f78027c8e2649a21dec4df1f9c3525460c71", size = 6245772, upload-time = "2025-08-14T12:04:08.365Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/ab/9893ea9fb066be70ed9074ae543914a618c131ed8dff2da1e08b3a4df4db/pyproj-3.7.2-cp312-cp312-macosx_13_0_x86_64.whl", hash = "sha256:0a9bb26a6356fb5b033433a6d1b4542158fb71e3c51de49b4c318a1dff3aeaab", size = 6219832, upload-time = "2025-08-14T12:04:10.264Z" },
+    { url = "https://files.pythonhosted.org/packages/53/78/4c64199146eed7184eb0e85bedec60a4aa8853b6ffe1ab1f3a8b962e70a0/pyproj-3.7.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:567caa03021178861fad27fabde87500ec6d2ee173dd32f3e2d9871e40eebd68", size = 4620650, upload-time = "2025-08-14T12:04:11.978Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/ac/14a78d17943898a93ef4f8c6a9d4169911c994e3161e54a7cedeba9d8dde/pyproj-3.7.2-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:c203101d1dc3c038a56cff0447acc515dd29d6e14811406ac539c21eed422b2a", size = 9667087, upload-time = "2025-08-14T12:04:13.964Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/be/212882c450bba74fc8d7d35cbd57e4af84792f0a56194819d98106b075af/pyproj-3.7.2-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:1edc34266c0c23ced85f95a1ee8b47c9035eae6aca5b6b340327250e8e281630", size = 9552797, upload-time = "2025-08-14T12:04:16.624Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/c0/c0f25c87b5d2a8686341c53c1792a222a480d6c9caf60311fec12c99ec26/pyproj-3.7.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:aa9f26c21bc0e2dc3d224cb1eb4020cf23e76af179a7c66fea49b828611e4260", size = 10837036, upload-time = "2025-08-14T12:04:18.733Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/37/5cbd6772addde2090c91113332623a86e8c7d583eccb2ad02ea634c4a89f/pyproj-3.7.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f9428b318530625cb389b9ddc9c51251e172808a4af79b82809376daaeabe5e9", size = 10775952, upload-time = "2025-08-14T12:04:20.709Z" },
+    { url = "https://files.pythonhosted.org/packages/69/a1/dc250e3cf83eb4b3b9a2cf86fdb5e25288bd40037ae449695550f9e96b2f/pyproj-3.7.2-cp312-cp312-win32.whl", hash = "sha256:b3d99ed57d319da042f175f4554fc7038aa4bcecc4ac89e217e350346b742c9d", size = 5898872, upload-time = "2025-08-14T12:04:22.485Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/a6/6fe724b72b70f2b00152d77282e14964d60ab092ec225e67c196c9b463e5/pyproj-3.7.2-cp312-cp312-win_amd64.whl", hash = "sha256:11614a054cd86a2ed968a657d00987a86eeb91fdcbd9ad3310478685dc14a128", size = 6312176, upload-time = "2025-08-14T12:04:24.736Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/68/915cc32c02a91e76d02c8f55d5a138d6ef9e47a0d96d259df98f4842e558/pyproj-3.7.2-cp312-cp312-win_arm64.whl", hash = "sha256:509a146d1398bafe4f53273398c3bb0b4732535065fa995270e52a9d3676bca3", size = 6233452, upload-time = "2025-08-14T12:04:27.287Z" },
+    { url = "https://files.pythonhosted.org/packages/be/14/faf1b90d267cea68d7e70662e7f88cefdb1bc890bd596c74b959e0517a72/pyproj-3.7.2-cp313-cp313-macosx_13_0_x86_64.whl", hash = "sha256:19466e529b1b15eeefdf8ff26b06fa745856c044f2f77bf0edbae94078c1dfa1", size = 6214580, upload-time = "2025-08-14T12:04:28.804Z" },
+    { url = "https://files.pythonhosted.org/packages/35/48/da9a45b184d375f62667f62eba0ca68569b0bd980a0bb7ffcc1d50440520/pyproj-3.7.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:c79b9b84c4a626c5dc324c0d666be0bfcebd99f7538d66e8898c2444221b3da7", size = 4615388, upload-time = "2025-08-14T12:04:30.553Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/e7/d2b459a4a64bca328b712c1b544e109df88e5c800f7c143cfbc404d39bfb/pyproj-3.7.2-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:ceecf374cacca317bc09e165db38ac548ee3cad07c3609442bd70311c59c21aa", size = 9628455, upload-time = "2025-08-14T12:04:32.435Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/85/c2b1706e51942de19076eff082f8495e57d5151364e78b5bef4af4a1d94a/pyproj-3.7.2-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:5141a538ffdbe4bfd157421828bb2e07123a90a7a2d6f30fa1462abcfb5ce681", size = 9514269, upload-time = "2025-08-14T12:04:34.599Z" },
+    { url = "https://files.pythonhosted.org/packages/34/38/07a9b89ae7467872f9a476883a5bad9e4f4d1219d31060f0f2b282276cbe/pyproj-3.7.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f000841e98ea99acbb7b8ca168d67773b0191de95187228a16110245c5d954d5", size = 10808437, upload-time = "2025-08-14T12:04:36.485Z" },
+    { url = "https://files.pythonhosted.org/packages/12/56/fda1daeabbd39dec5b07f67233d09f31facb762587b498e6fc4572be9837/pyproj-3.7.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8115faf2597f281a42ab608ceac346b4eb1383d3b45ab474fd37341c4bf82a67", size = 10745540, upload-time = "2025-08-14T12:04:38.568Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/90/c793182cbba65a39a11db2ac6b479fe76c59e6509ae75e5744c344a0da9d/pyproj-3.7.2-cp313-cp313-win32.whl", hash = "sha256:f18c0579dd6be00b970cb1a6719197fceecc407515bab37da0066f0184aafdf3", size = 5896506, upload-time = "2025-08-14T12:04:41.059Z" },
+    { url = "https://files.pythonhosted.org/packages/be/0f/747974129cf0d800906f81cd25efd098c96509026e454d4b66868779ab04/pyproj-3.7.2-cp313-cp313-win_amd64.whl", hash = "sha256:bb41c29d5f60854b1075853fe80c58950b398d4ebb404eb532536ac8d2834ed7", size = 6310195, upload-time = "2025-08-14T12:04:42.974Z" },
+    { url = "https://files.pythonhosted.org/packages/82/64/fc7598a53172c4931ec6edf5228280663063150625d3f6423b4c20f9daff/pyproj-3.7.2-cp313-cp313-win_arm64.whl", hash = "sha256:2b617d573be4118c11cd96b8891a0b7f65778fa7733ed8ecdb297a447d439100", size = 6230748, upload-time = "2025-08-14T12:04:44.491Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/f0/611dd5cddb0d277f94b7af12981f56e1441bf8d22695065d4f0df5218498/pyproj-3.7.2-cp313-cp313t-macosx_13_0_x86_64.whl", hash = "sha256:d27b48f0e81beeaa2b4d60c516c3a1cfbb0c7ff6ef71256d8e9c07792f735279", size = 6241729, upload-time = "2025-08-14T12:04:46.274Z" },
+    { url = "https://files.pythonhosted.org/packages/15/93/40bd4a6c523ff9965e480870611aed7eda5aa2c6128c6537345a2b77b542/pyproj-3.7.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:55a3610d75023c7b1c6e583e48ef8f62918e85a2ae81300569d9f104d6684bb6", size = 4652497, upload-time = "2025-08-14T12:04:48.203Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/ae/7150ead53c117880b35e0d37960d3138fe640a235feb9605cb9386f50bb0/pyproj-3.7.2-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:8d7349182fa622696787cc9e195508d2a41a64765da9b8a6bee846702b9e6220", size = 9942610, upload-time = "2025-08-14T12:04:49.652Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/17/7a4a7eafecf2b46ab64e5c08176c20ceb5844b503eaa551bf12ccac77322/pyproj-3.7.2-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:d230b186eb876ed4f29a7c5ee310144c3a0e44e89e55f65fb3607e13f6db337c", size = 9692390, upload-time = "2025-08-14T12:04:51.731Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/55/ae18f040f6410f0ea547a21ada7ef3e26e6c82befa125b303b02759c0e9d/pyproj-3.7.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:237499c7862c578d0369e2b8ac56eec550e391a025ff70e2af8417139dabb41c", size = 11047596, upload-time = "2025-08-14T12:04:53.748Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/2e/d3fff4d2909473f26ae799f9dda04caa322c417a51ff3b25763f7d03b233/pyproj-3.7.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8c225f5978abd506fd9a78eaaf794435e823c9156091cabaab5374efb29d7f69", size = 10896975, upload-time = "2025-08-14T12:04:55.875Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/bc/8fc7d3963d87057b7b51ebe68c1e7c51c23129eee5072ba6b86558544a46/pyproj-3.7.2-cp313-cp313t-win32.whl", hash = "sha256:2da731876d27639ff9d2d81c151f6ab90a1546455fabd93368e753047be344a2", size = 5953057, upload-time = "2025-08-14T12:04:58.466Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/27/ea9809966cc47d2d51e6d5ae631ea895f7c7c7b9b3c29718f900a8f7d197/pyproj-3.7.2-cp313-cp313t-win_amd64.whl", hash = "sha256:f54d91ae18dd23b6c0ab48126d446820e725419da10617d86a1b69ada6d881d3", size = 6375414, upload-time = "2025-08-14T12:04:59.861Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/f8/1ef0129fba9a555c658e22af68989f35e7ba7b9136f25758809efec0cd6e/pyproj-3.7.2-cp313-cp313t-win_arm64.whl", hash = "sha256:fc52ba896cfc3214dc9f9ca3c0677a623e8fdd096b257c14a31e719d21ff3fdd", size = 6262501, upload-time = "2025-08-14T12:05:01.39Z" },
+    { url = "https://files.pythonhosted.org/packages/42/17/c2b050d3f5b71b6edd0d96ae16c990fdc42a5f1366464a5c2772146de33a/pyproj-3.7.2-cp314-cp314-macosx_13_0_x86_64.whl", hash = "sha256:2aaa328605ace41db050d06bac1adc11f01b71fe95c18661497763116c3a0f02", size = 6214541, upload-time = "2025-08-14T12:05:03.166Z" },
+    { url = "https://files.pythonhosted.org/packages/03/68/68ada9c8aea96ded09a66cfd9bf87aa6db8c2edebe93f5bf9b66b0143fbc/pyproj-3.7.2-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:35dccbce8201313c596a970fde90e33605248b66272595c061b511c8100ccc08", size = 4617456, upload-time = "2025-08-14T12:05:04.563Z" },
+    { url = "https://files.pythonhosted.org/packages/81/e4/4c50ceca7d0e937977866b02cb64e6ccf4df979a5871e521f9e255df6073/pyproj-3.7.2-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:25b0b7cb0042444c29a164b993c45c1b8013d6c48baa61dc1160d834a277e83b", size = 9615590, upload-time = "2025-08-14T12:05:06.094Z" },
+    { url = "https://files.pythonhosted.org/packages/05/1e/ada6fb15a1d75b5bd9b554355a69a798c55a7dcc93b8d41596265c1772e3/pyproj-3.7.2-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:85def3a6388e9ba51f964619aa002a9d2098e77c6454ff47773bb68871024281", size = 9474960, upload-time = "2025-08-14T12:05:07.973Z" },
+    { url = "https://files.pythonhosted.org/packages/51/07/9d48ad0a8db36e16f842f2c8a694c1d9d7dcf9137264846bef77585a71f3/pyproj-3.7.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:b1bccefec3875ab81eabf49059e2b2ea77362c178b66fd3528c3e4df242f1516", size = 10799478, upload-time = "2025-08-14T12:05:14.102Z" },
+    { url = "https://files.pythonhosted.org/packages/85/cf/2f812b529079f72f51ff2d6456b7fef06c01735e5cfd62d54ffb2b548028/pyproj-3.7.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d5371ca114d6990b675247355a801925814eca53e6c4b2f1b5c0a956336ee36e", size = 10710030, upload-time = "2025-08-14T12:05:16.317Z" },
+    { url = "https://files.pythonhosted.org/packages/99/9b/4626a19e1f03eba4c0e77b91a6cf0f73aa9cb5d51a22ee385c22812bcc2c/pyproj-3.7.2-cp314-cp314-win32.whl", hash = "sha256:77f066626030f41be543274f5ac79f2a511fe89860ecd0914f22131b40a0ec25", size = 5991181, upload-time = "2025-08-14T12:05:19.492Z" },
+    { url = "https://files.pythonhosted.org/packages/04/b2/5a6610554306a83a563080c2cf2c57565563eadd280e15388efa00fb5b33/pyproj-3.7.2-cp314-cp314-win_amd64.whl", hash = "sha256:5a964da1696b8522806f4276ab04ccfff8f9eb95133a92a25900697609d40112", size = 6434721, upload-time = "2025-08-14T12:05:21.022Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/ce/6c910ea2e1c74ef673c5d48c482564b8a7824a44c4e35cca2e765b68cfcc/pyproj-3.7.2-cp314-cp314-win_arm64.whl", hash = "sha256:e258ab4dbd3cf627809067c0ba8f9884ea76c8e5999d039fb37a1619c6c3e1f6", size = 6363821, upload-time = "2025-08-14T12:05:22.627Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/e4/5532f6f7491812ba782a2177fe9de73fd8e2912b59f46a1d056b84b9b8f2/pyproj-3.7.2-cp314-cp314t-macosx_13_0_x86_64.whl", hash = "sha256:bbbac2f930c6d266f70ec75df35ef851d96fdb3701c674f42fd23a9314573b37", size = 6241773, upload-time = "2025-08-14T12:05:24.577Z" },
+    { url = "https://files.pythonhosted.org/packages/20/1f/0938c3f2bbbef1789132d1726d9b0e662f10cfc22522743937f421ad664e/pyproj-3.7.2-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:b7544e0a3d6339dc9151e9c8f3ea62a936ab7cc446a806ec448bbe86aebb979b", size = 4652537, upload-time = "2025-08-14T12:05:26.391Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/a8/488b1ed47d25972f33874f91f09ca8f2227902f05f63a2b80dc73e7b1c97/pyproj-3.7.2-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:f7f5133dca4c703e8acadf6f30bc567d39a42c6af321e7f81975c2518f3ed357", size = 9940864, upload-time = "2025-08-14T12:05:27.985Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/cc/7f4c895d0cb98e47b6a85a6d79eaca03eb266129eed2f845125c09cf31ff/pyproj-3.7.2-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:5aff3343038d7426aa5076f07feb88065f50e0502d1b0d7c22ddfdd2c75a3f81", size = 9688868, upload-time = "2025-08-14T12:05:30.425Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/b7/c7e306b8bb0f071d9825b753ee4920f066c40fbfcce9372c4f3cfb2fc4ed/pyproj-3.7.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:b0552178c61f2ac1c820d087e8ba6e62b29442debddbb09d51c4bf8acc84d888", size = 11045910, upload-time = "2025-08-14T12:05:32.507Z" },
+    { url = "https://files.pythonhosted.org/packages/42/fb/538a4d2df695980e2dde5c04d965fbdd1fe8c20a3194dc4aaa3952a4d1be/pyproj-3.7.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:47d87db2d2c436c5fd0409b34d70bb6cdb875cca2ebe7a9d1c442367b0ab8d59", size = 10895724, upload-time = "2025-08-14T12:05:35.465Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/8b/a3f0618b03957de9db5489a04558a8826f43906628bb0b766033aa3b5548/pyproj-3.7.2-cp314-cp314t-win32.whl", hash = "sha256:c9b6f1d8ad3e80a0ee0903a778b6ece7dca1d1d40f6d114ae01bc8ddbad971aa", size = 6056848, upload-time = "2025-08-14T12:05:37.553Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/56/413240dd5149dd3291eda55aa55a659da4431244a2fd1319d0ae89407cfb/pyproj-3.7.2-cp314-cp314t-win_amd64.whl", hash = "sha256:1914e29e27933ba6f9822663ee0600f169014a2859f851c054c88cf5ea8a333c", size = 6517676, upload-time = "2025-08-14T12:05:39.126Z" },
+    { url = "https://files.pythonhosted.org/packages/15/73/a7141a1a0559bf1a7aa42a11c879ceb19f02f5c6c371c6d57fd86cefd4d1/pyproj-3.7.2-cp314-cp314t-win_arm64.whl", hash = "sha256:d9d25bae416a24397e0d85739f84d323b55f6511e45a522dd7d7eae70d10c7e4", size = 6391844, upload-time = "2025-08-14T12:05:40.745Z" },
+]
+
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" },
+]
+
+[[package]]
+name = "pytz"
+version = "2025.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f8/bf/abbd3cdfb8fbc7fb3d4d38d320f2441b1e7cbe29be4f23797b4a2b5d8aac/pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3", size = 320884, upload-time = "2025-03-25T02:25:00.538Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00", size = 509225, upload-time = "2025-03-25T02:24:58.468Z" },
+]
+
+[[package]]
+name = "pyyaml"
+version = "6.0.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960, upload-time = "2025-09-25T21:33:16.546Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6d/16/a95b6757765b7b031c9374925bb718d55e0a9ba8a1b6a12d25962ea44347/pyyaml-6.0.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e", size = 185826, upload-time = "2025-09-25T21:31:58.655Z" },
+    { url = "https://files.pythonhosted.org/packages/16/19/13de8e4377ed53079ee996e1ab0a9c33ec2faf808a4647b7b4c0d46dd239/pyyaml-6.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824", size = 175577, upload-time = "2025-09-25T21:32:00.088Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/62/d2eb46264d4b157dae1275b573017abec435397aa59cbcdab6fc978a8af4/pyyaml-6.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c", size = 775556, upload-time = "2025-09-25T21:32:01.31Z" },
+    { url = "https://files.pythonhosted.org/packages/10/cb/16c3f2cf3266edd25aaa00d6c4350381c8b012ed6f5276675b9eba8d9ff4/pyyaml-6.0.3-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00", size = 882114, upload-time = "2025-09-25T21:32:03.376Z" },
+    { url = "https://files.pythonhosted.org/packages/71/60/917329f640924b18ff085ab889a11c763e0b573da888e8404ff486657602/pyyaml-6.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d", size = 806638, upload-time = "2025-09-25T21:32:04.553Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/6f/529b0f316a9fd167281a6c3826b5583e6192dba792dd55e3203d3f8e655a/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a", size = 767463, upload-time = "2025-09-25T21:32:06.152Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/6a/b627b4e0c1dd03718543519ffb2f1deea4a1e6d42fbab8021936a4d22589/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4", size = 794986, upload-time = "2025-09-25T21:32:07.367Z" },
+    { url = "https://files.pythonhosted.org/packages/45/91/47a6e1c42d9ee337c4839208f30d9f09caa9f720ec7582917b264defc875/pyyaml-6.0.3-cp311-cp311-win32.whl", hash = "sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b", size = 142543, upload-time = "2025-09-25T21:32:08.95Z" },
+    { url = "https://files.pythonhosted.org/packages/da/e3/ea007450a105ae919a72393cb06f122f288ef60bba2dc64b26e2646fa315/pyyaml-6.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf", size = 158763, upload-time = "2025-09-25T21:32:09.96Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/33/422b98d2195232ca1826284a76852ad5a86fe23e31b009c9886b2d0fb8b2/pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196", size = 182063, upload-time = "2025-09-25T21:32:11.445Z" },
+    { url = "https://files.pythonhosted.org/packages/89/a0/6cf41a19a1f2f3feab0e9c0b74134aa2ce6849093d5517a0c550fe37a648/pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0", size = 173973, upload-time = "2025-09-25T21:32:12.492Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/23/7a778b6bd0b9a8039df8b1b1d80e2e2ad78aa04171592c8a5c43a56a6af4/pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28", size = 775116, upload-time = "2025-09-25T21:32:13.652Z" },
+    { url = "https://files.pythonhosted.org/packages/65/30/d7353c338e12baef4ecc1b09e877c1970bd3382789c159b4f89d6a70dc09/pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c", size = 844011, upload-time = "2025-09-25T21:32:15.21Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/9d/b3589d3877982d4f2329302ef98a8026e7f4443c765c46cfecc8858c6b4b/pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc", size = 807870, upload-time = "2025-09-25T21:32:16.431Z" },
+    { url = "https://files.pythonhosted.org/packages/05/c0/b3be26a015601b822b97d9149ff8cb5ead58c66f981e04fedf4e762f4bd4/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e", size = 761089, upload-time = "2025-09-25T21:32:17.56Z" },
+    { url = "https://files.pythonhosted.org/packages/be/8e/98435a21d1d4b46590d5459a22d88128103f8da4c2d4cb8f14f2a96504e1/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea", size = 790181, upload-time = "2025-09-25T21:32:18.834Z" },
+    { url = "https://files.pythonhosted.org/packages/74/93/7baea19427dcfbe1e5a372d81473250b379f04b1bd3c4c5ff825e2327202/pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5", size = 137658, upload-time = "2025-09-25T21:32:20.209Z" },
+    { url = "https://files.pythonhosted.org/packages/86/bf/899e81e4cce32febab4fb42bb97dcdf66bc135272882d1987881a4b519e9/pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b", size = 154003, upload-time = "2025-09-25T21:32:21.167Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/08/67bd04656199bbb51dbed1439b7f27601dfb576fb864099c7ef0c3e55531/pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd", size = 140344, upload-time = "2025-09-25T21:32:22.617Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669, upload-time = "2025-09-25T21:32:23.673Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252, upload-time = "2025-09-25T21:32:25.149Z" },
+    { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081, upload-time = "2025-09-25T21:32:26.575Z" },
+    { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159, upload-time = "2025-09-25T21:32:27.727Z" },
+    { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626, upload-time = "2025-09-25T21:32:28.878Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613, upload-time = "2025-09-25T21:32:30.178Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115, upload-time = "2025-09-25T21:32:31.353Z" },
+    { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427, upload-time = "2025-09-25T21:32:32.58Z" },
+    { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090, upload-time = "2025-09-25T21:32:33.659Z" },
+    { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246, upload-time = "2025-09-25T21:32:34.663Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814, upload-time = "2025-09-25T21:32:35.712Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809, upload-time = "2025-09-25T21:32:36.789Z" },
+    { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454, upload-time = "2025-09-25T21:32:37.966Z" },
+    { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355, upload-time = "2025-09-25T21:32:39.178Z" },
+    { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175, upload-time = "2025-09-25T21:32:40.865Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228, upload-time = "2025-09-25T21:32:42.084Z" },
+    { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194, upload-time = "2025-09-25T21:32:43.362Z" },
+    { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429, upload-time = "2025-09-25T21:32:57.844Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912, upload-time = "2025-09-25T21:32:59.247Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108, upload-time = "2025-09-25T21:32:44.377Z" },
+    { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641, upload-time = "2025-09-25T21:32:45.407Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901, upload-time = "2025-09-25T21:32:48.83Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132, upload-time = "2025-09-25T21:32:50.149Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261, upload-time = "2025-09-25T21:32:51.808Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272, upload-time = "2025-09-25T21:32:52.941Z" },
+    { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923, upload-time = "2025-09-25T21:32:54.537Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062, upload-time = "2025-09-25T21:32:55.767Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341, upload-time = "2025-09-25T21:32:56.828Z" },
+]
+
+[[package]]
+name = "referencing"
+version = "0.36.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "rpds-py" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2f/db/98b5c277be99dd18bfd91dd04e1b759cad18d1a338188c936e92f921c7e2/referencing-0.36.2.tar.gz", hash = "sha256:df2e89862cd09deabbdba16944cc3f10feb6b3e6f18e902f7cc25609a34775aa", size = 74744, upload-time = "2025-01-25T08:48:16.138Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c1/b1/3baf80dc6d2b7bc27a95a67752d0208e410351e3feb4eb78de5f77454d8d/referencing-0.36.2-py3-none-any.whl", hash = "sha256:e8699adbbf8b5c7de96d8ffa0eb5c158b3beafce084968e2ea8bb08c6794dcd0", size = 26775, upload-time = "2025-01-25T08:48:14.241Z" },
+]
+
+[[package]]
+name = "requests"
+version = "2.32.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "charset-normalizer" },
+    { name = "idna" },
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c9/74/b3ff8e6c8446842c3f5c837e9c3dfcfe2018ea6ecef224c710c85ef728f4/requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf", size = 134517, upload-time = "2025-08-18T20:46:02.573Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6", size = 64738, upload-time = "2025-08-18T20:46:00.542Z" },
+]
+
+[[package]]
+name = "rich"
+version = "14.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py" },
+    { name = "pygments" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fe/75/af448d8e52bf1d8fa6a9d089ca6c07ff4453d86c65c145d0a300bb073b9b/rich-14.1.0.tar.gz", hash = "sha256:e497a48b844b0320d45007cdebfeaeed8db2a4f4bcf49f15e455cfc4af11eaa8", size = 224441, upload-time = "2025-07-25T07:32:58.125Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e3/30/3c4d035596d3cf444529e0b2953ad0466f6049528a879d27534700580395/rich-14.1.0-py3-none-any.whl", hash = "sha256:536f5f1785986d6dbdea3c75205c473f970777b4a0d6c6dd1b696aa05a3fa04f", size = 243368, upload-time = "2025-07-25T07:32:56.73Z" },
+]
+
+[[package]]
+name = "rpds-py"
+version = "0.27.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e9/dd/2c0cbe774744272b0ae725f44032c77bdcab6e8bcf544bffa3b6e70c8dba/rpds_py-0.27.1.tar.gz", hash = "sha256:26a1c73171d10b7acccbded82bf6a586ab8203601e565badc74bbbf8bc5a10f8", size = 27479, upload-time = "2025-08-27T12:16:36.024Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b5/c1/7907329fbef97cbd49db6f7303893bd1dd5a4a3eae415839ffdfb0762cae/rpds_py-0.27.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:be898f271f851f68b318872ce6ebebbc62f303b654e43bf72683dbdc25b7c881", size = 371063, upload-time = "2025-08-27T12:12:47.856Z" },
+    { url = "https://files.pythonhosted.org/packages/11/94/2aab4bc86228bcf7c48760990273653a4900de89c7537ffe1b0d6097ed39/rpds_py-0.27.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:62ac3d4e3e07b58ee0ddecd71d6ce3b1637de2d373501412df395a0ec5f9beb5", size = 353210, upload-time = "2025-08-27T12:12:49.187Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/57/f5eb3ecf434342f4f1a46009530e93fd201a0b5b83379034ebdb1d7c1a58/rpds_py-0.27.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4708c5c0ceb2d034f9991623631d3d23cb16e65c83736ea020cdbe28d57c0a0e", size = 381636, upload-time = "2025-08-27T12:12:50.492Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/f4/ef95c5945e2ceb5119571b184dd5a1cc4b8541bbdf67461998cfeac9cb1e/rpds_py-0.27.1-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:abfa1171a9952d2e0002aba2ad3780820b00cc3d9c98c6630f2e93271501f66c", size = 394341, upload-time = "2025-08-27T12:12:52.024Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/7e/4bd610754bf492d398b61725eb9598ddd5eb86b07d7d9483dbcd810e20bc/rpds_py-0.27.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4b507d19f817ebaca79574b16eb2ae412e5c0835542c93fe9983f1e432aca195", size = 523428, upload-time = "2025-08-27T12:12:53.779Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/e5/059b9f65a8c9149361a8b75094864ab83b94718344db511fd6117936ed2a/rpds_py-0.27.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:168b025f8fd8d8d10957405f3fdcef3dc20f5982d398f90851f4abc58c566c52", size = 402923, upload-time = "2025-08-27T12:12:55.15Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/48/64cabb7daced2968dd08e8a1b7988bf358d7bd5bcd5dc89a652f4668543c/rpds_py-0.27.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb56c6210ef77caa58e16e8c17d35c63fe3f5b60fd9ba9d424470c3400bcf9ed", size = 384094, upload-time = "2025-08-27T12:12:57.194Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/e1/dc9094d6ff566bff87add8a510c89b9e158ad2ecd97ee26e677da29a9e1b/rpds_py-0.27.1-cp311-cp311-manylinux_2_31_riscv64.whl", hash = "sha256:d252f2d8ca0195faa707f8eb9368955760880b2b42a8ee16d382bf5dd807f89a", size = 401093, upload-time = "2025-08-27T12:12:58.985Z" },
+    { url = "https://files.pythonhosted.org/packages/37/8e/ac8577e3ecdd5593e283d46907d7011618994e1d7ab992711ae0f78b9937/rpds_py-0.27.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6e5e54da1e74b91dbc7996b56640f79b195d5925c2b78efaa8c5d53e1d88edde", size = 417969, upload-time = "2025-08-27T12:13:00.367Z" },
+    { url = "https://files.pythonhosted.org/packages/66/6d/87507430a8f74a93556fe55c6485ba9c259949a853ce407b1e23fea5ba31/rpds_py-0.27.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ffce0481cc6e95e5b3f0a47ee17ffbd234399e6d532f394c8dce320c3b089c21", size = 558302, upload-time = "2025-08-27T12:13:01.737Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/bb/1db4781ce1dda3eecc735e3152659a27b90a02ca62bfeea17aee45cc0fbc/rpds_py-0.27.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:a205fdfe55c90c2cd8e540ca9ceba65cbe6629b443bc05db1f590a3db8189ff9", size = 589259, upload-time = "2025-08-27T12:13:03.127Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/0e/ae1c8943d11a814d01b482e1f8da903f88047a962dff9bbdadf3bd6e6fd1/rpds_py-0.27.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:689fb5200a749db0415b092972e8eba85847c23885c8543a8b0f5c009b1a5948", size = 554983, upload-time = "2025-08-27T12:13:04.516Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/d5/0b2a55415931db4f112bdab072443ff76131b5ac4f4dc98d10d2d357eb03/rpds_py-0.27.1-cp311-cp311-win32.whl", hash = "sha256:3182af66048c00a075010bc7f4860f33913528a4b6fc09094a6e7598e462fe39", size = 217154, upload-time = "2025-08-27T12:13:06.278Z" },
+    { url = "https://files.pythonhosted.org/packages/24/75/3b7ffe0d50dc86a6a964af0d1cc3a4a2cdf437cb7b099a4747bbb96d1819/rpds_py-0.27.1-cp311-cp311-win_amd64.whl", hash = "sha256:b4938466c6b257b2f5c4ff98acd8128ec36b5059e5c8f8372d79316b1c36bb15", size = 228627, upload-time = "2025-08-27T12:13:07.625Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/3f/4fd04c32abc02c710f09a72a30c9a55ea3cc154ef8099078fd50a0596f8e/rpds_py-0.27.1-cp311-cp311-win_arm64.whl", hash = "sha256:2f57af9b4d0793e53266ee4325535a31ba48e2f875da81a9177c9926dfa60746", size = 220998, upload-time = "2025-08-27T12:13:08.972Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/fe/38de28dee5df58b8198c743fe2bea0c785c6d40941b9950bac4cdb71a014/rpds_py-0.27.1-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:ae2775c1973e3c30316892737b91f9283f9908e3cc7625b9331271eaaed7dc90", size = 361887, upload-time = "2025-08-27T12:13:10.233Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/9a/4b6c7eedc7dd90986bf0fab6ea2a091ec11c01b15f8ba0a14d3f80450468/rpds_py-0.27.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2643400120f55c8a96f7c9d858f7be0c88d383cd4653ae2cf0d0c88f668073e5", size = 345795, upload-time = "2025-08-27T12:13:11.65Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/0e/e650e1b81922847a09cca820237b0edee69416a01268b7754d506ade11ad/rpds_py-0.27.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:16323f674c089b0360674a4abd28d5042947d54ba620f72514d69be4ff64845e", size = 385121, upload-time = "2025-08-27T12:13:13.008Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/ea/b306067a712988e2bff00dcc7c8f31d26c29b6d5931b461aa4b60a013e33/rpds_py-0.27.1-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9a1f4814b65eacac94a00fc9a526e3fdafd78e439469644032032d0d63de4881", size = 398976, upload-time = "2025-08-27T12:13:14.368Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/0a/26dc43c8840cb8fe239fe12dbc8d8de40f2365e838f3d395835dde72f0e5/rpds_py-0.27.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:7ba32c16b064267b22f1850a34051121d423b6f7338a12b9459550eb2096e7ec", size = 525953, upload-time = "2025-08-27T12:13:15.774Z" },
+    { url = "https://files.pythonhosted.org/packages/22/14/c85e8127b573aaf3a0cbd7fbb8c9c99e735a4a02180c84da2a463b766e9e/rpds_py-0.27.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e5c20f33fd10485b80f65e800bbe5f6785af510b9f4056c5a3c612ebc83ba6cb", size = 407915, upload-time = "2025-08-27T12:13:17.379Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/7b/8f4fee9ba1fb5ec856eb22d725a4efa3deb47f769597c809e03578b0f9d9/rpds_py-0.27.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:466bfe65bd932da36ff279ddd92de56b042f2266d752719beb97b08526268ec5", size = 386883, upload-time = "2025-08-27T12:13:18.704Z" },
+    { url = "https://files.pythonhosted.org/packages/86/47/28fa6d60f8b74fcdceba81b272f8d9836ac0340570f68f5df6b41838547b/rpds_py-0.27.1-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:41e532bbdcb57c92ba3be62c42e9f096431b4cf478da9bc3bc6ce5c38ab7ba7a", size = 405699, upload-time = "2025-08-27T12:13:20.089Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/fd/c5987b5e054548df56953a21fe2ebed51fc1ec7c8f24fd41c067b68c4a0a/rpds_py-0.27.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f149826d742b406579466283769a8ea448eed82a789af0ed17b0cd5770433444", size = 423713, upload-time = "2025-08-27T12:13:21.436Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/ba/3c4978b54a73ed19a7d74531be37a8bcc542d917c770e14d372b8daea186/rpds_py-0.27.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:80c60cfb5310677bd67cb1e85a1e8eb52e12529545441b43e6f14d90b878775a", size = 562324, upload-time = "2025-08-27T12:13:22.789Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/6c/6943a91768fec16db09a42b08644b960cff540c66aab89b74be6d4a144ba/rpds_py-0.27.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:7ee6521b9baf06085f62ba9c7a3e5becffbc32480d2f1b351559c001c38ce4c1", size = 593646, upload-time = "2025-08-27T12:13:24.122Z" },
+    { url = "https://files.pythonhosted.org/packages/11/73/9d7a8f4be5f4396f011a6bb7a19fe26303a0dac9064462f5651ced2f572f/rpds_py-0.27.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:a512c8263249a9d68cac08b05dd59d2b3f2061d99b322813cbcc14c3c7421998", size = 558137, upload-time = "2025-08-27T12:13:25.557Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/96/6772cbfa0e2485bcceef8071de7821f81aeac8bb45fbfd5542a3e8108165/rpds_py-0.27.1-cp312-cp312-win32.whl", hash = "sha256:819064fa048ba01b6dadc5116f3ac48610435ac9a0058bbde98e569f9e785c39", size = 221343, upload-time = "2025-08-27T12:13:26.967Z" },
+    { url = "https://files.pythonhosted.org/packages/67/b6/c82f0faa9af1c6a64669f73a17ee0eeef25aff30bb9a1c318509efe45d84/rpds_py-0.27.1-cp312-cp312-win_amd64.whl", hash = "sha256:d9199717881f13c32c4046a15f024971a3b78ad4ea029e8da6b86e5aa9cf4594", size = 232497, upload-time = "2025-08-27T12:13:28.326Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/96/2817b44bd2ed11aebacc9251da03689d56109b9aba5e311297b6902136e2/rpds_py-0.27.1-cp312-cp312-win_arm64.whl", hash = "sha256:33aa65b97826a0e885ef6e278fbd934e98cdcfed80b63946025f01e2f5b29502", size = 222790, upload-time = "2025-08-27T12:13:29.71Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/77/610aeee8d41e39080c7e14afa5387138e3c9fa9756ab893d09d99e7d8e98/rpds_py-0.27.1-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:e4b9fcfbc021633863a37e92571d6f91851fa656f0180246e84cbd8b3f6b329b", size = 361741, upload-time = "2025-08-27T12:13:31.039Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/fc/c43765f201c6a1c60be2043cbdb664013def52460a4c7adace89d6682bf4/rpds_py-0.27.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1441811a96eadca93c517d08df75de45e5ffe68aa3089924f963c782c4b898cf", size = 345574, upload-time = "2025-08-27T12:13:32.902Z" },
+    { url = "https://files.pythonhosted.org/packages/20/42/ee2b2ca114294cd9847d0ef9c26d2b0851b2e7e00bf14cc4c0b581df0fc3/rpds_py-0.27.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:55266dafa22e672f5a4f65019015f90336ed31c6383bd53f5e7826d21a0e0b83", size = 385051, upload-time = "2025-08-27T12:13:34.228Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/e8/1e430fe311e4799e02e2d1af7c765f024e95e17d651612425b226705f910/rpds_py-0.27.1-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:d78827d7ac08627ea2c8e02c9e5b41180ea5ea1f747e9db0915e3adf36b62dcf", size = 398395, upload-time = "2025-08-27T12:13:36.132Z" },
+    { url = "https://files.pythonhosted.org/packages/82/95/9dc227d441ff2670651c27a739acb2535ccaf8b351a88d78c088965e5996/rpds_py-0.27.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ae92443798a40a92dc5f0b01d8a7c93adde0c4dc965310a29ae7c64d72b9fad2", size = 524334, upload-time = "2025-08-27T12:13:37.562Z" },
+    { url = "https://files.pythonhosted.org/packages/87/01/a670c232f401d9ad461d9a332aa4080cd3cb1d1df18213dbd0d2a6a7ab51/rpds_py-0.27.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c46c9dd2403b66a2a3b9720ec4b74d4ab49d4fabf9f03dfdce2d42af913fe8d0", size = 407691, upload-time = "2025-08-27T12:13:38.94Z" },
+    { url = "https://files.pythonhosted.org/packages/03/36/0a14aebbaa26fe7fab4780c76f2239e76cc95a0090bdb25e31d95c492fcd/rpds_py-0.27.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2efe4eb1d01b7f5f1939f4ef30ecea6c6b3521eec451fb93191bf84b2a522418", size = 386868, upload-time = "2025-08-27T12:13:40.192Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/03/8c897fb8b5347ff6c1cc31239b9611c5bf79d78c984430887a353e1409a1/rpds_py-0.27.1-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:15d3b4d83582d10c601f481eca29c3f138d44c92187d197aff663a269197c02d", size = 405469, upload-time = "2025-08-27T12:13:41.496Z" },
+    { url = "https://files.pythonhosted.org/packages/da/07/88c60edc2df74850d496d78a1fdcdc7b54360a7f610a4d50008309d41b94/rpds_py-0.27.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:4ed2e16abbc982a169d30d1a420274a709949e2cbdef119fe2ec9d870b42f274", size = 422125, upload-time = "2025-08-27T12:13:42.802Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/86/5f4c707603e41b05f191a749984f390dabcbc467cf833769b47bf14ba04f/rpds_py-0.27.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a75f305c9b013289121ec0f1181931975df78738cdf650093e6b86d74aa7d8dd", size = 562341, upload-time = "2025-08-27T12:13:44.472Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/92/3c0cb2492094e3cd9baf9e49bbb7befeceb584ea0c1a8b5939dca4da12e5/rpds_py-0.27.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:67ce7620704745881a3d4b0ada80ab4d99df390838839921f99e63c474f82cf2", size = 592511, upload-time = "2025-08-27T12:13:45.898Z" },
+    { url = "https://files.pythonhosted.org/packages/10/bb/82e64fbb0047c46a168faa28d0d45a7851cd0582f850b966811d30f67ad8/rpds_py-0.27.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9d992ac10eb86d9b6f369647b6a3f412fc0075cfd5d799530e84d335e440a002", size = 557736, upload-time = "2025-08-27T12:13:47.408Z" },
+    { url = "https://files.pythonhosted.org/packages/00/95/3c863973d409210da7fb41958172c6b7dbe7fc34e04d3cc1f10bb85e979f/rpds_py-0.27.1-cp313-cp313-win32.whl", hash = "sha256:4f75e4bd8ab8db624e02c8e2fc4063021b58becdbe6df793a8111d9343aec1e3", size = 221462, upload-time = "2025-08-27T12:13:48.742Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/2c/5867b14a81dc217b56d95a9f2a40fdbc56a1ab0181b80132beeecbd4b2d6/rpds_py-0.27.1-cp313-cp313-win_amd64.whl", hash = "sha256:f9025faafc62ed0b75a53e541895ca272815bec18abe2249ff6501c8f2e12b83", size = 232034, upload-time = "2025-08-27T12:13:50.11Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/78/3958f3f018c01923823f1e47f1cc338e398814b92d83cd278364446fac66/rpds_py-0.27.1-cp313-cp313-win_arm64.whl", hash = "sha256:ed10dc32829e7d222b7d3b93136d25a406ba9788f6a7ebf6809092da1f4d279d", size = 222392, upload-time = "2025-08-27T12:13:52.587Z" },
+    { url = "https://files.pythonhosted.org/packages/01/76/1cdf1f91aed5c3a7bf2eba1f1c4e4d6f57832d73003919a20118870ea659/rpds_py-0.27.1-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:92022bbbad0d4426e616815b16bc4127f83c9a74940e1ccf3cfe0b387aba0228", size = 358355, upload-time = "2025-08-27T12:13:54.012Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/6f/bf142541229374287604caf3bb2a4ae17f0a580798fd72d3b009b532db4e/rpds_py-0.27.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:47162fdab9407ec3f160805ac3e154df042e577dd53341745fc7fb3f625e6d92", size = 342138, upload-time = "2025-08-27T12:13:55.791Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/77/355b1c041d6be40886c44ff5e798b4e2769e497b790f0f7fd1e78d17e9a8/rpds_py-0.27.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fb89bec23fddc489e5d78b550a7b773557c9ab58b7946154a10a6f7a214a48b2", size = 380247, upload-time = "2025-08-27T12:13:57.683Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/a4/d9cef5c3946ea271ce2243c51481971cd6e34f21925af2783dd17b26e815/rpds_py-0.27.1-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e48af21883ded2b3e9eb48cb7880ad8598b31ab752ff3be6457001d78f416723", size = 390699, upload-time = "2025-08-27T12:13:59.137Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/06/005106a7b8c6c1a7e91b73169e49870f4af5256119d34a361ae5240a0c1d/rpds_py-0.27.1-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6f5b7bd8e219ed50299e58551a410b64daafb5017d54bbe822e003856f06a802", size = 521852, upload-time = "2025-08-27T12:14:00.583Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/3e/50fb1dac0948e17a02eb05c24510a8fe12d5ce8561c6b7b7d1339ab7ab9c/rpds_py-0.27.1-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:08f1e20bccf73b08d12d804d6e1c22ca5530e71659e6673bce31a6bb71c1e73f", size = 402582, upload-time = "2025-08-27T12:14:02.034Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/b0/f4e224090dc5b0ec15f31a02d746ab24101dd430847c4d99123798661bfc/rpds_py-0.27.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0dc5dceeaefcc96dc192e3a80bbe1d6c410c469e97bdd47494a7d930987f18b2", size = 384126, upload-time = "2025-08-27T12:14:03.437Z" },
+    { url = "https://files.pythonhosted.org/packages/54/77/ac339d5f82b6afff1df8f0fe0d2145cc827992cb5f8eeb90fc9f31ef7a63/rpds_py-0.27.1-cp313-cp313t-manylinux_2_31_riscv64.whl", hash = "sha256:d76f9cc8665acdc0c9177043746775aa7babbf479b5520b78ae4002d889f5c21", size = 399486, upload-time = "2025-08-27T12:14:05.443Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/29/3e1c255eee6ac358c056a57d6d6869baa00a62fa32eea5ee0632039c50a3/rpds_py-0.27.1-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:134fae0e36022edad8290a6661edf40c023562964efea0cc0ec7f5d392d2aaef", size = 414832, upload-time = "2025-08-27T12:14:06.902Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/db/6d498b844342deb3fa1d030598db93937a9964fcf5cb4da4feb5f17be34b/rpds_py-0.27.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:eb11a4f1b2b63337cfd3b4d110af778a59aae51c81d195768e353d8b52f88081", size = 557249, upload-time = "2025-08-27T12:14:08.37Z" },
+    { url = "https://files.pythonhosted.org/packages/60/f3/690dd38e2310b6f68858a331399b4d6dbb9132c3e8ef8b4333b96caf403d/rpds_py-0.27.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:13e608ac9f50a0ed4faec0e90ece76ae33b34c0e8656e3dceb9a7db994c692cd", size = 587356, upload-time = "2025-08-27T12:14:10.034Z" },
+    { url = "https://files.pythonhosted.org/packages/86/e3/84507781cccd0145f35b1dc32c72675200c5ce8d5b30f813e49424ef68fc/rpds_py-0.27.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dd2135527aa40f061350c3f8f89da2644de26cd73e4de458e79606384f4f68e7", size = 555300, upload-time = "2025-08-27T12:14:11.783Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/ee/375469849e6b429b3516206b4580a79e9ef3eb12920ddbd4492b56eaacbe/rpds_py-0.27.1-cp313-cp313t-win32.whl", hash = "sha256:3020724ade63fe320a972e2ffd93b5623227e684315adce194941167fee02688", size = 216714, upload-time = "2025-08-27T12:14:13.629Z" },
+    { url = "https://files.pythonhosted.org/packages/21/87/3fc94e47c9bd0742660e84706c311a860dcae4374cf4a03c477e23ce605a/rpds_py-0.27.1-cp313-cp313t-win_amd64.whl", hash = "sha256:8ee50c3e41739886606388ba3ab3ee2aae9f35fb23f833091833255a31740797", size = 228943, upload-time = "2025-08-27T12:14:14.937Z" },
+    { url = "https://files.pythonhosted.org/packages/70/36/b6e6066520a07cf029d385de869729a895917b411e777ab1cde878100a1d/rpds_py-0.27.1-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:acb9aafccaae278f449d9c713b64a9e68662e7799dbd5859e2c6b3c67b56d334", size = 362472, upload-time = "2025-08-27T12:14:16.333Z" },
+    { url = "https://files.pythonhosted.org/packages/af/07/b4646032e0dcec0df9c73a3bd52f63bc6c5f9cda992f06bd0e73fe3fbebd/rpds_py-0.27.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:b7fb801aa7f845ddf601c49630deeeccde7ce10065561d92729bfe81bd21fb33", size = 345676, upload-time = "2025-08-27T12:14:17.764Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/16/2f1003ee5d0af4bcb13c0cf894957984c32a6751ed7206db2aee7379a55e/rpds_py-0.27.1-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fe0dd05afb46597b9a2e11c351e5e4283c741237e7f617ffb3252780cca9336a", size = 385313, upload-time = "2025-08-27T12:14:19.829Z" },
+    { url = "https://files.pythonhosted.org/packages/05/cd/7eb6dd7b232e7f2654d03fa07f1414d7dfc980e82ba71e40a7c46fd95484/rpds_py-0.27.1-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:b6dfb0e058adb12d8b1d1b25f686e94ffa65d9995a5157afe99743bf7369d62b", size = 399080, upload-time = "2025-08-27T12:14:21.531Z" },
+    { url = "https://files.pythonhosted.org/packages/20/51/5829afd5000ec1cb60f304711f02572d619040aa3ec033d8226817d1e571/rpds_py-0.27.1-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ed090ccd235f6fa8bb5861684567f0a83e04f52dfc2e5c05f2e4b1309fcf85e7", size = 523868, upload-time = "2025-08-27T12:14:23.485Z" },
+    { url = "https://files.pythonhosted.org/packages/05/2c/30eebca20d5db95720ab4d2faec1b5e4c1025c473f703738c371241476a2/rpds_py-0.27.1-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bf876e79763eecf3e7356f157540d6a093cef395b65514f17a356f62af6cc136", size = 408750, upload-time = "2025-08-27T12:14:24.924Z" },
+    { url = "https://files.pythonhosted.org/packages/90/1a/cdb5083f043597c4d4276eae4e4c70c55ab5accec078da8611f24575a367/rpds_py-0.27.1-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:12ed005216a51b1d6e2b02a7bd31885fe317e45897de81d86dcce7d74618ffff", size = 387688, upload-time = "2025-08-27T12:14:27.537Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/92/cf786a15320e173f945d205ab31585cc43969743bb1a48b6888f7a2b0a2d/rpds_py-0.27.1-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:ee4308f409a40e50593c7e3bb8cbe0b4d4c66d1674a316324f0c2f5383b486f9", size = 407225, upload-time = "2025-08-27T12:14:28.981Z" },
+    { url = "https://files.pythonhosted.org/packages/33/5c/85ee16df5b65063ef26017bef33096557a4c83fbe56218ac7cd8c235f16d/rpds_py-0.27.1-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:0b08d152555acf1f455154d498ca855618c1378ec810646fcd7c76416ac6dc60", size = 423361, upload-time = "2025-08-27T12:14:30.469Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/8e/1c2741307fcabd1a334ecf008e92c4f47bb6f848712cf15c923becfe82bb/rpds_py-0.27.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:dce51c828941973a5684d458214d3a36fcd28da3e1875d659388f4f9f12cc33e", size = 562493, upload-time = "2025-08-27T12:14:31.987Z" },
+    { url = "https://files.pythonhosted.org/packages/04/03/5159321baae9b2222442a70c1f988cbbd66b9be0675dd3936461269be360/rpds_py-0.27.1-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:c1476d6f29eb81aa4151c9a31219b03f1f798dc43d8af1250a870735516a1212", size = 592623, upload-time = "2025-08-27T12:14:33.543Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/39/c09fd1ad28b85bc1d4554a8710233c9f4cefd03d7717a1b8fbfd171d1167/rpds_py-0.27.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:3ce0cac322b0d69b63c9cdb895ee1b65805ec9ffad37639f291dd79467bee675", size = 558800, upload-time = "2025-08-27T12:14:35.436Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/d6/99228e6bbcf4baa764b18258f519a9035131d91b538d4e0e294313462a98/rpds_py-0.27.1-cp314-cp314-win32.whl", hash = "sha256:dfbfac137d2a3d0725758cd141f878bf4329ba25e34979797c89474a89a8a3a3", size = 221943, upload-time = "2025-08-27T12:14:36.898Z" },
+    { url = "https://files.pythonhosted.org/packages/be/07/c802bc6b8e95be83b79bdf23d1aa61d68324cb1006e245d6c58e959e314d/rpds_py-0.27.1-cp314-cp314-win_amd64.whl", hash = "sha256:a6e57b0abfe7cc513450fcf529eb486b6e4d3f8aee83e92eb5f1ef848218d456", size = 233739, upload-time = "2025-08-27T12:14:38.386Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/89/3e1b1c16d4c2d547c5717377a8df99aee8099ff050f87c45cb4d5fa70891/rpds_py-0.27.1-cp314-cp314-win_arm64.whl", hash = "sha256:faf8d146f3d476abfee026c4ae3bdd9ca14236ae4e4c310cbd1cf75ba33d24a3", size = 223120, upload-time = "2025-08-27T12:14:39.82Z" },
+    { url = "https://files.pythonhosted.org/packages/62/7e/dc7931dc2fa4a6e46b2a4fa744a9fe5c548efd70e0ba74f40b39fa4a8c10/rpds_py-0.27.1-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:ba81d2b56b6d4911ce735aad0a1d4495e808b8ee4dc58715998741a26874e7c2", size = 358944, upload-time = "2025-08-27T12:14:41.199Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/22/4af76ac4e9f336bfb1a5f240d18a33c6b2fcaadb7472ac7680576512b49a/rpds_py-0.27.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:84f7d509870098de0e864cad0102711c1e24e9b1a50ee713b65928adb22269e4", size = 342283, upload-time = "2025-08-27T12:14:42.699Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/15/2a7c619b3c2272ea9feb9ade67a45c40b3eeb500d503ad4c28c395dc51b4/rpds_py-0.27.1-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a9e960fc78fecd1100539f14132425e1d5fe44ecb9239f8f27f079962021523e", size = 380320, upload-time = "2025-08-27T12:14:44.157Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/7d/4c6d243ba4a3057e994bb5bedd01b5c963c12fe38dde707a52acdb3849e7/rpds_py-0.27.1-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:62f85b665cedab1a503747617393573995dac4600ff51869d69ad2f39eb5e817", size = 391760, upload-time = "2025-08-27T12:14:45.845Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/71/b19401a909b83bcd67f90221330bc1ef11bc486fe4e04c24388d28a618ae/rpds_py-0.27.1-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fed467af29776f6556250c9ed85ea5a4dd121ab56a5f8b206e3e7a4c551e48ec", size = 522476, upload-time = "2025-08-27T12:14:47.364Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/44/1a3b9715c0455d2e2f0f6df5ee6d6f5afdc423d0773a8a682ed2b43c566c/rpds_py-0.27.1-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f2729615f9d430af0ae6b36cf042cb55c0936408d543fb691e1a9e36648fd35a", size = 403418, upload-time = "2025-08-27T12:14:49.991Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/4b/fb6c4f14984eb56673bc868a66536f53417ddb13ed44b391998100a06a96/rpds_py-0.27.1-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1b207d881a9aef7ba753d69c123a35d96ca7cb808056998f6b9e8747321f03b8", size = 384771, upload-time = "2025-08-27T12:14:52.159Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/56/d5265d2d28b7420d7b4d4d85cad8ef891760f5135102e60d5c970b976e41/rpds_py-0.27.1-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:639fd5efec029f99b79ae47e5d7e00ad8a773da899b6309f6786ecaf22948c48", size = 400022, upload-time = "2025-08-27T12:14:53.859Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/e9/9f5fc70164a569bdd6ed9046486c3568d6926e3a49bdefeeccfb18655875/rpds_py-0.27.1-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fecc80cb2a90e28af8a9b366edacf33d7a91cbfe4c2c4544ea1246e949cfebeb", size = 416787, upload-time = "2025-08-27T12:14:55.673Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/64/56dd03430ba491db943a81dcdef115a985aac5f44f565cd39a00c766d45c/rpds_py-0.27.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:42a89282d711711d0a62d6f57d81aa43a1368686c45bc1c46b7f079d55692734", size = 557538, upload-time = "2025-08-27T12:14:57.245Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/36/92cc885a3129993b1d963a2a42ecf64e6a8e129d2c7cc980dbeba84e55fb/rpds_py-0.27.1-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:cf9931f14223de59551ab9d38ed18d92f14f055a5f78c1d8ad6493f735021bbb", size = 588512, upload-time = "2025-08-27T12:14:58.728Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/10/6b283707780a81919f71625351182b4f98932ac89a09023cb61865136244/rpds_py-0.27.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f39f58a27cc6e59f432b568ed8429c7e1641324fbe38131de852cd77b2d534b0", size = 555813, upload-time = "2025-08-27T12:15:00.334Z" },
+    { url = "https://files.pythonhosted.org/packages/04/2e/30b5ea18c01379da6272a92825dd7e53dc9d15c88a19e97932d35d430ef7/rpds_py-0.27.1-cp314-cp314t-win32.whl", hash = "sha256:d5fa0ee122dc09e23607a28e6d7b150da16c662e66409bbe85230e4c85bb528a", size = 217385, upload-time = "2025-08-27T12:15:01.937Z" },
+    { url = "https://files.pythonhosted.org/packages/32/7d/97119da51cb1dd3f2f3c0805f155a3aa4a95fa44fe7d78ae15e69edf4f34/rpds_py-0.27.1-cp314-cp314t-win_amd64.whl", hash = "sha256:6567d2bb951e21232c2f660c24cf3470bb96de56cdcb3f071a83feeaff8a2772", size = 230097, upload-time = "2025-08-27T12:15:03.961Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/ed/e1fba02de17f4f76318b834425257c8ea297e415e12c68b4361f63e8ae92/rpds_py-0.27.1-pp311-pypy311_pp73-macosx_10_12_x86_64.whl", hash = "sha256:cdfe4bb2f9fe7458b7453ad3c33e726d6d1c7c0a72960bcc23800d77384e42df", size = 371402, upload-time = "2025-08-27T12:15:51.561Z" },
+    { url = "https://files.pythonhosted.org/packages/af/7c/e16b959b316048b55585a697e94add55a4ae0d984434d279ea83442e460d/rpds_py-0.27.1-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:8fabb8fd848a5f75a2324e4a84501ee3a5e3c78d8603f83475441866e60b94a3", size = 354084, upload-time = "2025-08-27T12:15:53.219Z" },
+    { url = "https://files.pythonhosted.org/packages/de/c1/ade645f55de76799fdd08682d51ae6724cb46f318573f18be49b1e040428/rpds_py-0.27.1-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eda8719d598f2f7f3e0f885cba8646644b55a187762bec091fa14a2b819746a9", size = 383090, upload-time = "2025-08-27T12:15:55.158Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/27/89070ca9b856e52960da1472efcb6c20ba27cfe902f4f23ed095b9cfc61d/rpds_py-0.27.1-pp311-pypy311_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3c64d07e95606ec402a0a1c511fe003873fa6af630bda59bac77fac8b4318ebc", size = 394519, upload-time = "2025-08-27T12:15:57.238Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/28/be120586874ef906aa5aeeae95ae8df4184bc757e5b6bd1c729ccff45ed5/rpds_py-0.27.1-pp311-pypy311_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:93a2ed40de81bcff59aabebb626562d48332f3d028ca2036f1d23cbb52750be4", size = 523817, upload-time = "2025-08-27T12:15:59.237Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/ef/70cc197bc11cfcde02a86f36ac1eed15c56667c2ebddbdb76a47e90306da/rpds_py-0.27.1-pp311-pypy311_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:387ce8c44ae94e0ec50532d9cb0edce17311024c9794eb196b90e1058aadeb66", size = 403240, upload-time = "2025-08-27T12:16:00.923Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/35/46936cca449f7f518f2f4996e0e8344db4b57e2081e752441154089d2a5f/rpds_py-0.27.1-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aaf94f812c95b5e60ebaf8bfb1898a7d7cb9c1af5744d4a67fa47796e0465d4e", size = 385194, upload-time = "2025-08-27T12:16:02.802Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/62/29c0d3e5125c3270b51415af7cbff1ec587379c84f55a5761cc9efa8cd06/rpds_py-0.27.1-pp311-pypy311_pp73-manylinux_2_31_riscv64.whl", hash = "sha256:4848ca84d6ded9b58e474dfdbad4b8bfb450344c0551ddc8d958bf4b36aa837c", size = 402086, upload-time = "2025-08-27T12:16:04.806Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/66/03e1087679227785474466fdd04157fb793b3b76e3fcf01cbf4c693c1949/rpds_py-0.27.1-pp311-pypy311_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2bde09cbcf2248b73c7c323be49b280180ff39fadcfe04e7b6f54a678d02a7cf", size = 419272, upload-time = "2025-08-27T12:16:06.471Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/24/e3e72d265121e00b063aef3e3501e5b2473cf1b23511d56e529531acf01e/rpds_py-0.27.1-pp311-pypy311_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:94c44ee01fd21c9058f124d2d4f0c9dc7634bec93cd4b38eefc385dabe71acbf", size = 560003, upload-time = "2025-08-27T12:16:08.06Z" },
+    { url = "https://files.pythonhosted.org/packages/26/ca/f5a344c534214cc2d41118c0699fffbdc2c1bc7046f2a2b9609765ab9c92/rpds_py-0.27.1-pp311-pypy311_pp73-musllinux_1_2_i686.whl", hash = "sha256:df8b74962e35c9249425d90144e721eed198e6555a0e22a563d29fe4486b51f6", size = 590482, upload-time = "2025-08-27T12:16:10.137Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/08/4349bdd5c64d9d193c360aa9db89adeee6f6682ab8825dca0a3f535f434f/rpds_py-0.27.1-pp311-pypy311_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:dc23e6820e3b40847e2f4a7726462ba0cf53089512abe9ee16318c366494c17a", size = 556523, upload-time = "2025-08-27T12:16:12.188Z" },
+]
+
+[[package]]
+name = "ruff"
+version = "0.13.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/c7/8e/f9f9ca747fea8e3ac954e3690d4698c9737c23b51731d02df999c150b1c9/ruff-0.13.3.tar.gz", hash = "sha256:5b0ba0db740eefdfbcce4299f49e9eaefc643d4d007749d77d047c2bab19908e", size = 5438533, upload-time = "2025-10-02T19:29:31.582Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d2/33/8f7163553481466a92656d35dea9331095122bb84cf98210bef597dd2ecd/ruff-0.13.3-py3-none-linux_armv6l.whl", hash = "sha256:311860a4c5e19189c89d035638f500c1e191d283d0cc2f1600c8c80d6dcd430c", size = 12484040, upload-time = "2025-10-02T19:28:49.199Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/b5/4a21a4922e5dd6845e91896b0d9ef493574cbe061ef7d00a73c61db531af/ruff-0.13.3-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:2bdad6512fb666b40fcadb65e33add2b040fc18a24997d2e47fee7d66f7fcae2", size = 13122975, upload-time = "2025-10-02T19:28:52.446Z" },
+    { url = "https://files.pythonhosted.org/packages/40/90/15649af836d88c9f154e5be87e64ae7d2b1baa5a3ef317cb0c8fafcd882d/ruff-0.13.3-py3-none-macosx_11_0_arm64.whl", hash = "sha256:fc6fa4637284708d6ed4e5e970d52fc3b76a557d7b4e85a53013d9d201d93286", size = 12346621, upload-time = "2025-10-02T19:28:54.712Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/42/bcbccb8141305f9a6d3f72549dd82d1134299177cc7eaf832599700f95a7/ruff-0.13.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1c9e6469864f94a98f412f20ea143d547e4c652f45e44f369d7b74ee78185838", size = 12574408, upload-time = "2025-10-02T19:28:56.679Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/19/0f3681c941cdcfa2d110ce4515624c07a964dc315d3100d889fcad3bfc9e/ruff-0.13.3-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5bf62b705f319476c78891e0e97e965b21db468b3c999086de8ffb0d40fd2822", size = 12285330, upload-time = "2025-10-02T19:28:58.79Z" },
+    { url = "https://files.pythonhosted.org/packages/10/f8/387976bf00d126b907bbd7725219257feea58650e6b055b29b224d8cb731/ruff-0.13.3-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:78cc1abed87ce40cb07ee0667ce99dbc766c9f519eabfd948ed87295d8737c60", size = 13980815, upload-time = "2025-10-02T19:29:01.577Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/a6/7c8ec09d62d5a406e2b17d159e4817b63c945a8b9188a771193b7e1cc0b5/ruff-0.13.3-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:4fb75e7c402d504f7a9a259e0442b96403fa4a7310ffe3588d11d7e170d2b1e3", size = 14987733, upload-time = "2025-10-02T19:29:04.036Z" },
+    { url = "https://files.pythonhosted.org/packages/97/e5/f403a60a12258e0fd0c2195341cfa170726f254c788673495d86ab5a9a9d/ruff-0.13.3-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:17b951f9d9afb39330b2bdd2dd144ce1c1335881c277837ac1b50bfd99985ed3", size = 14439848, upload-time = "2025-10-02T19:29:06.684Z" },
+    { url = "https://files.pythonhosted.org/packages/39/49/3de381343e89364c2334c9f3268b0349dc734fc18b2d99a302d0935c8345/ruff-0.13.3-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6052f8088728898e0a449f0dde8fafc7ed47e4d878168b211977e3e7e854f662", size = 13421890, upload-time = "2025-10-02T19:29:08.767Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/b5/c0feca27d45ae74185a6bacc399f5d8920ab82df2d732a17213fb86a2c4c/ruff-0.13.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dc742c50f4ba72ce2a3be362bd359aef7d0d302bf7637a6f942eaa763bd292af", size = 13444870, upload-time = "2025-10-02T19:29:11.234Z" },
+    { url = "https://files.pythonhosted.org/packages/50/a1/b655298a1f3fda4fdc7340c3f671a4b260b009068fbeb3e4e151e9e3e1bf/ruff-0.13.3-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:8e5640349493b378431637019366bbd73c927e515c9c1babfea3e932f5e68e1d", size = 13691599, upload-time = "2025-10-02T19:29:13.353Z" },
+    { url = "https://files.pythonhosted.org/packages/32/b0/a8705065b2dafae007bcae21354e6e2e832e03eb077bb6c8e523c2becb92/ruff-0.13.3-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:6b139f638a80eae7073c691a5dd8d581e0ba319540be97c343d60fb12949c8d0", size = 12421893, upload-time = "2025-10-02T19:29:15.668Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/1e/cbe7082588d025cddbb2f23e6dfef08b1a2ef6d6f8328584ad3015b5cebd/ruff-0.13.3-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:6b547def0a40054825de7cfa341039ebdfa51f3d4bfa6a0772940ed351d2746c", size = 12267220, upload-time = "2025-10-02T19:29:17.583Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/99/4086f9c43f85e0755996d09bdcb334b6fee9b1eabdf34e7d8b877fadf964/ruff-0.13.3-py3-none-musllinux_1_2_i686.whl", hash = "sha256:9cc48a3564423915c93573f1981d57d101e617839bef38504f85f3677b3a0a3e", size = 13177818, upload-time = "2025-10-02T19:29:19.943Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/de/7b5db7e39947d9dc1c5f9f17b838ad6e680527d45288eeb568e860467010/ruff-0.13.3-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:1a993b17ec03719c502881cb2d5f91771e8742f2ca6de740034433a97c561989", size = 13618715, upload-time = "2025-10-02T19:29:22.527Z" },
+    { url = "https://files.pythonhosted.org/packages/28/d3/bb25ee567ce2f61ac52430cf99f446b0e6d49bdfa4188699ad005fdd16aa/ruff-0.13.3-py3-none-win32.whl", hash = "sha256:f14e0d1fe6460f07814d03c6e32e815bff411505178a1f539a38f6097d3e8ee3", size = 12334488, upload-time = "2025-10-02T19:29:24.782Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/49/12f5955818a1139eed288753479ba9d996f6ea0b101784bb1fe6977ec128/ruff-0.13.3-py3-none-win_amd64.whl", hash = "sha256:621e2e5812b691d4f244638d693e640f188bacbb9bc793ddd46837cea0503dd2", size = 13455262, upload-time = "2025-10-02T19:29:26.882Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/72/7b83242b26627a00e3af70d0394d68f8f02750d642567af12983031777fc/ruff-0.13.3-py3-none-win_arm64.whl", hash = "sha256:9e9e9d699841eaf4c2c798fa783df2fabc680b72059a02ca0ed81c460bc58330", size = 12538484, upload-time = "2025-10-02T19:29:28.951Z" },
+]
+
+[[package]]
+name = "scikit-learn"
+version = "1.7.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "joblib" },
+    { name = "numpy" },
+    { name = "scipy" },
+    { name = "threadpoolctl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/98/c2/a7855e41c9d285dfe86dc50b250978105dce513d6e459ea66a6aeb0e1e0c/scikit_learn-1.7.2.tar.gz", hash = "sha256:20e9e49ecd130598f1ca38a1d85090e1a600147b9c02fa6f15d69cb53d968fda", size = 7193136, upload-time = "2025-09-09T08:21:29.075Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/43/83/564e141eef908a5863a54da8ca342a137f45a0bfb71d1d79704c9894c9d1/scikit_learn-1.7.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c7509693451651cd7361d30ce4e86a1347493554f172b1c72a39300fa2aea79e", size = 9331967, upload-time = "2025-09-09T08:20:32.421Z" },
+    { url = "https://files.pythonhosted.org/packages/18/d6/ba863a4171ac9d7314c4d3fc251f015704a2caeee41ced89f321c049ed83/scikit_learn-1.7.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:0486c8f827c2e7b64837c731c8feff72c0bd2b998067a8a9cbc10643c31f0fe1", size = 8648645, upload-time = "2025-09-09T08:20:34.436Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/0e/97dbca66347b8cf0ea8b529e6bb9367e337ba2e8be0ef5c1a545232abfde/scikit_learn-1.7.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:89877e19a80c7b11a2891a27c21c4894fb18e2c2e077815bcade10d34287b20d", size = 9715424, upload-time = "2025-09-09T08:20:36.776Z" },
+    { url = "https://files.pythonhosted.org/packages/f7/32/1f3b22e3207e1d2c883a7e09abb956362e7d1bd2f14458c7de258a26ac15/scikit_learn-1.7.2-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8da8bf89d4d79aaec192d2bda62f9b56ae4e5b4ef93b6a56b5de4977e375c1f1", size = 9509234, upload-time = "2025-09-09T08:20:38.957Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/71/34ddbd21f1da67c7a768146968b4d0220ee6831e4bcbad3e03dd3eae88b6/scikit_learn-1.7.2-cp311-cp311-win_amd64.whl", hash = "sha256:9b7ed8d58725030568523e937c43e56bc01cadb478fc43c042a9aca1dacb3ba1", size = 8894244, upload-time = "2025-09-09T08:20:41.166Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/aa/3996e2196075689afb9fce0410ebdb4a09099d7964d061d7213700204409/scikit_learn-1.7.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:8d91a97fa2b706943822398ab943cde71858a50245e31bc71dba62aab1d60a96", size = 9259818, upload-time = "2025-09-09T08:20:43.19Z" },
+    { url = "https://files.pythonhosted.org/packages/43/5d/779320063e88af9c4a7c2cf463ff11c21ac9c8bd730c4a294b0000b666c9/scikit_learn-1.7.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:acbc0f5fd2edd3432a22c69bed78e837c70cf896cd7993d71d51ba6708507476", size = 8636997, upload-time = "2025-09-09T08:20:45.468Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/d0/0c577d9325b05594fdd33aa970bf53fb673f051a45496842caee13cfd7fe/scikit_learn-1.7.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e5bf3d930aee75a65478df91ac1225ff89cd28e9ac7bd1196853a9229b6adb0b", size = 9478381, upload-time = "2025-09-09T08:20:47.982Z" },
+    { url = "https://files.pythonhosted.org/packages/82/70/8bf44b933837ba8494ca0fc9a9ab60f1c13b062ad0197f60a56e2fc4c43e/scikit_learn-1.7.2-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b4d6e9deed1a47aca9fe2f267ab8e8fe82ee20b4526b2c0cd9e135cea10feb44", size = 9300296, upload-time = "2025-09-09T08:20:50.366Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/99/ed35197a158f1fdc2fe7c3680e9c70d0128f662e1fee4ed495f4b5e13db0/scikit_learn-1.7.2-cp312-cp312-win_amd64.whl", hash = "sha256:6088aa475f0785e01bcf8529f55280a3d7d298679f50c0bb70a2364a82d0b290", size = 8731256, upload-time = "2025-09-09T08:20:52.627Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/93/a3038cb0293037fd335f77f31fe053b89c72f17b1c8908c576c29d953e84/scikit_learn-1.7.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0b7dacaa05e5d76759fb071558a8b5130f4845166d88654a0f9bdf3eb57851b7", size = 9212382, upload-time = "2025-09-09T08:20:54.731Z" },
+    { url = "https://files.pythonhosted.org/packages/40/dd/9a88879b0c1104259136146e4742026b52df8540c39fec21a6383f8292c7/scikit_learn-1.7.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:abebbd61ad9e1deed54cca45caea8ad5f79e1b93173dece40bb8e0c658dbe6fe", size = 8592042, upload-time = "2025-09-09T08:20:57.313Z" },
+    { url = "https://files.pythonhosted.org/packages/46/af/c5e286471b7d10871b811b72ae794ac5fe2989c0a2df07f0ec723030f5f5/scikit_learn-1.7.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:502c18e39849c0ea1a5d681af1dbcf15f6cce601aebb657aabbfe84133c1907f", size = 9434180, upload-time = "2025-09-09T08:20:59.671Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/fd/df59faa53312d585023b2da27e866524ffb8faf87a68516c23896c718320/scikit_learn-1.7.2-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7a4c328a71785382fe3fe676a9ecf2c86189249beff90bf85e22bdb7efaf9ae0", size = 9283660, upload-time = "2025-09-09T08:21:01.71Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/c7/03000262759d7b6f38c836ff9d512f438a70d8a8ddae68ee80de72dcfb63/scikit_learn-1.7.2-cp313-cp313-win_amd64.whl", hash = "sha256:63a9afd6f7b229aad94618c01c252ce9e6fa97918c5ca19c9a17a087d819440c", size = 8702057, upload-time = "2025-09-09T08:21:04.234Z" },
+    { url = "https://files.pythonhosted.org/packages/55/87/ef5eb1f267084532c8e4aef98a28b6ffe7425acbfd64b5e2f2e066bc29b3/scikit_learn-1.7.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:9acb6c5e867447b4e1390930e3944a005e2cb115922e693c08a323421a6966e8", size = 9558731, upload-time = "2025-09-09T08:21:06.381Z" },
+    { url = "https://files.pythonhosted.org/packages/93/f8/6c1e3fc14b10118068d7938878a9f3f4e6d7b74a8ddb1e5bed65159ccda8/scikit_learn-1.7.2-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:2a41e2a0ef45063e654152ec9d8bcfc39f7afce35b08902bfe290c2498a67a6a", size = 9038852, upload-time = "2025-09-09T08:21:08.628Z" },
+    { url = "https://files.pythonhosted.org/packages/83/87/066cafc896ee540c34becf95d30375fe5cbe93c3b75a0ee9aa852cd60021/scikit_learn-1.7.2-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:98335fb98509b73385b3ab2bd0639b1f610541d3988ee675c670371d6a87aa7c", size = 9527094, upload-time = "2025-09-09T08:21:11.486Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/2b/4903e1ccafa1f6453b1ab78413938c8800633988c838aa0be386cbb33072/scikit_learn-1.7.2-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:191e5550980d45449126e23ed1d5e9e24b2c68329ee1f691a3987476e115e09c", size = 9367436, upload-time = "2025-09-09T08:21:13.602Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/aa/8444be3cfb10451617ff9d177b3c190288f4563e6c50ff02728be67ad094/scikit_learn-1.7.2-cp313-cp313t-win_amd64.whl", hash = "sha256:57dc4deb1d3762c75d685507fbd0bc17160144b2f2ba4ccea5dc285ab0d0e973", size = 9275749, upload-time = "2025-09-09T08:21:15.96Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/82/dee5acf66837852e8e68df6d8d3a6cb22d3df997b733b032f513d95205b7/scikit_learn-1.7.2-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fa8f63940e29c82d1e67a45d5297bdebbcb585f5a5a50c4914cc2e852ab77f33", size = 9208906, upload-time = "2025-09-09T08:21:18.557Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/30/9029e54e17b87cb7d50d51a5926429c683d5b4c1732f0507a6c3bed9bf65/scikit_learn-1.7.2-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:f95dc55b7902b91331fa4e5845dd5bde0580c9cd9612b1b2791b7e80c3d32615", size = 8627836, upload-time = "2025-09-09T08:21:20.695Z" },
+    { url = "https://files.pythonhosted.org/packages/60/18/4a52c635c71b536879f4b971c2cedf32c35ee78f48367885ed8025d1f7ee/scikit_learn-1.7.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9656e4a53e54578ad10a434dc1f993330568cfee176dff07112b8785fb413106", size = 9426236, upload-time = "2025-09-09T08:21:22.645Z" },
+    { url = "https://files.pythonhosted.org/packages/99/7e/290362f6ab582128c53445458a5befd471ed1ea37953d5bcf80604619250/scikit_learn-1.7.2-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:96dc05a854add0e50d3f47a1ef21a10a595016da5b007c7d9cd9d0bffd1fcc61", size = 9312593, upload-time = "2025-09-09T08:21:24.65Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/87/24f541b6d62b1794939ae6422f8023703bbf6900378b2b34e0b4384dfefd/scikit_learn-1.7.2-cp314-cp314-win_amd64.whl", hash = "sha256:bb24510ed3f9f61476181e4db51ce801e2ba37541def12dc9333b946fc7a9cf8", size = 8820007, upload-time = "2025-09-09T08:21:26.713Z" },
+]
+
+[[package]]
+name = "scipy"
+version = "1.16.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4c/3b/546a6f0bfe791bbb7f8d591613454d15097e53f906308ec6f7c1ce588e8e/scipy-1.16.2.tar.gz", hash = "sha256:af029b153d243a80afb6eabe40b0a07f8e35c9adc269c019f364ad747f826a6b", size = 30580599, upload-time = "2025-09-11T17:48:08.271Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0b/ef/37ed4b213d64b48422df92560af7300e10fe30b5d665dd79932baebee0c6/scipy-1.16.2-cp311-cp311-macosx_10_14_x86_64.whl", hash = "sha256:6ab88ea43a57da1af33292ebd04b417e8e2eaf9d5aa05700be8d6e1b6501cd92", size = 36619956, upload-time = "2025-09-11T17:39:20.5Z" },
+    { url = "https://files.pythonhosted.org/packages/85/ab/5c2eba89b9416961a982346a4d6a647d78c91ec96ab94ed522b3b6baf444/scipy-1.16.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:c95e96c7305c96ede73a7389f46ccd6c659c4da5ef1b2789466baeaed3622b6e", size = 28931117, upload-time = "2025-09-11T17:39:29.06Z" },
+    { url = "https://files.pythonhosted.org/packages/80/d1/eed51ab64d227fe60229a2d57fb60ca5898cfa50ba27d4f573e9e5f0b430/scipy-1.16.2-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:87eb178db04ece7c698220d523c170125dbffebb7af0345e66c3554f6f60c173", size = 20921997, upload-time = "2025-09-11T17:39:34.892Z" },
+    { url = "https://files.pythonhosted.org/packages/be/7c/33ea3e23bbadde96726edba6bf9111fb1969d14d9d477ffa202c67bec9da/scipy-1.16.2-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:4e409eac067dcee96a57fbcf424c13f428037827ec7ee3cb671ff525ca4fc34d", size = 23523374, upload-time = "2025-09-11T17:39:40.846Z" },
+    { url = "https://files.pythonhosted.org/packages/96/0b/7399dc96e1e3f9a05e258c98d716196a34f528eef2ec55aad651ed136d03/scipy-1.16.2-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e574be127bb760f0dad24ff6e217c80213d153058372362ccb9555a10fc5e8d2", size = 33583702, upload-time = "2025-09-11T17:39:49.011Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/bc/a5c75095089b96ea72c1bd37a4497c24b581ec73db4ef58ebee142ad2d14/scipy-1.16.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f5db5ba6188d698ba7abab982ad6973265b74bb40a1efe1821b58c87f73892b9", size = 35883427, upload-time = "2025-09-11T17:39:57.406Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/66/e25705ca3d2b87b97fe0a278a24b7f477b4023a926847935a1a71488a6a6/scipy-1.16.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ec6e74c4e884104ae006d34110677bfe0098203a3fec2f3faf349f4cb05165e3", size = 36212940, upload-time = "2025-09-11T17:40:06.013Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/fd/0bb911585e12f3abdd603d721d83fc1c7492835e1401a0e6d498d7822b4b/scipy-1.16.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:912f46667d2d3834bc3d57361f854226475f695eb08c08a904aadb1c936b6a88", size = 38865092, upload-time = "2025-09-11T17:40:15.143Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/73/c449a7d56ba6e6f874183759f8483cde21f900a8be117d67ffbb670c2958/scipy-1.16.2-cp311-cp311-win_amd64.whl", hash = "sha256:91e9e8a37befa5a69e9cacbe0bcb79ae5afb4a0b130fd6db6ee6cc0d491695fa", size = 38687626, upload-time = "2025-09-11T17:40:24.041Z" },
+    { url = "https://files.pythonhosted.org/packages/68/72/02f37316adf95307f5d9e579023c6899f89ff3a051fa079dbd6faafc48e5/scipy-1.16.2-cp311-cp311-win_arm64.whl", hash = "sha256:f3bf75a6dcecab62afde4d1f973f1692be013110cad5338007927db8da73249c", size = 25503506, upload-time = "2025-09-11T17:40:30.703Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/8d/6396e00db1282279a4ddd507c5f5e11f606812b608ee58517ce8abbf883f/scipy-1.16.2-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:89d6c100fa5c48472047632e06f0876b3c4931aac1f4291afc81a3644316bb0d", size = 36646259, upload-time = "2025-09-11T17:40:39.329Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/93/ea9edd7e193fceb8eef149804491890bde73fb169c896b61aa3e2d1e4e77/scipy-1.16.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:ca748936cd579d3f01928b30a17dc474550b01272d8046e3e1ee593f23620371", size = 28888976, upload-time = "2025-09-11T17:40:46.82Z" },
+    { url = "https://files.pythonhosted.org/packages/91/4d/281fddc3d80fd738ba86fd3aed9202331180b01e2c78eaae0642f22f7e83/scipy-1.16.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:fac4f8ce2ddb40e2e3d0f7ec36d2a1e7f92559a2471e59aec37bd8d9de01fec0", size = 20879905, upload-time = "2025-09-11T17:40:52.545Z" },
+    { url = "https://files.pythonhosted.org/packages/69/40/b33b74c84606fd301b2915f0062e45733c6ff5708d121dd0deaa8871e2d0/scipy-1.16.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:033570f1dcefd79547a88e18bccacff025c8c647a330381064f561d43b821232", size = 23553066, upload-time = "2025-09-11T17:40:59.014Z" },
+    { url = "https://files.pythonhosted.org/packages/55/a7/22c739e2f21a42cc8f16bc76b47cff4ed54fbe0962832c589591c2abec34/scipy-1.16.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ea3421209bf00c8a5ef2227de496601087d8f638a2363ee09af059bd70976dc1", size = 33336407, upload-time = "2025-09-11T17:41:06.796Z" },
+    { url = "https://files.pythonhosted.org/packages/53/11/a0160990b82999b45874dc60c0c183d3a3a969a563fffc476d5a9995c407/scipy-1.16.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f66bd07ba6f84cd4a380b41d1bf3c59ea488b590a2ff96744845163309ee8e2f", size = 35673281, upload-time = "2025-09-11T17:41:15.055Z" },
+    { url = "https://files.pythonhosted.org/packages/96/53/7ef48a4cfcf243c3d0f1643f5887c81f29fdf76911c4e49331828e19fc0a/scipy-1.16.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:5e9feab931bd2aea4a23388c962df6468af3d808ddf2d40f94a81c5dc38f32ef", size = 36004222, upload-time = "2025-09-11T17:41:23.868Z" },
+    { url = "https://files.pythonhosted.org/packages/49/7f/71a69e0afd460049d41c65c630c919c537815277dfea214031005f474d78/scipy-1.16.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:03dfc75e52f72cf23ec2ced468645321407faad8f0fe7b1f5b49264adbc29cb1", size = 38664586, upload-time = "2025-09-11T17:41:31.021Z" },
+    { url = "https://files.pythonhosted.org/packages/34/95/20e02ca66fb495a95fba0642fd48e0c390d0ece9b9b14c6e931a60a12dea/scipy-1.16.2-cp312-cp312-win_amd64.whl", hash = "sha256:0ce54e07bbb394b417457409a64fd015be623f36e330ac49306433ffe04bc97e", size = 38550641, upload-time = "2025-09-11T17:41:36.61Z" },
+    { url = "https://files.pythonhosted.org/packages/92/ad/13646b9beb0a95528ca46d52b7babafbe115017814a611f2065ee4e61d20/scipy-1.16.2-cp312-cp312-win_arm64.whl", hash = "sha256:2a8ffaa4ac0df81a0b94577b18ee079f13fecdb924df3328fc44a7dc5ac46851", size = 25456070, upload-time = "2025-09-11T17:41:41.3Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/27/c5b52f1ee81727a9fc457f5ac1e9bf3d6eab311805ea615c83c27ba06400/scipy-1.16.2-cp313-cp313-macosx_10_14_x86_64.whl", hash = "sha256:84f7bf944b43e20b8a894f5fe593976926744f6c185bacfcbdfbb62736b5cc70", size = 36604856, upload-time = "2025-09-11T17:41:47.695Z" },
+    { url = "https://files.pythonhosted.org/packages/32/a9/15c20d08e950b540184caa8ced675ba1128accb0e09c653780ba023a4110/scipy-1.16.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:5c39026d12edc826a1ef2ad35ad1e6d7f087f934bb868fc43fa3049c8b8508f9", size = 28864626, upload-time = "2025-09-11T17:41:52.642Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/fc/ea36098df653cca26062a627c1a94b0de659e97127c8491e18713ca0e3b9/scipy-1.16.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:e52729ffd45b68777c5319560014d6fd251294200625d9d70fd8626516fc49f5", size = 20855689, upload-time = "2025-09-11T17:41:57.886Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/6f/d0b53be55727f3e6d7c72687ec18ea6d0047cf95f1f77488b99a2bafaee1/scipy-1.16.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:024dd4a118cccec09ca3209b7e8e614931a6ffb804b2a601839499cb88bdf925", size = 23512151, upload-time = "2025-09-11T17:42:02.303Z" },
+    { url = "https://files.pythonhosted.org/packages/11/85/bf7dab56e5c4b1d3d8eef92ca8ede788418ad38a7dc3ff50262f00808760/scipy-1.16.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7a5dc7ee9c33019973a470556081b0fd3c9f4c44019191039f9769183141a4d9", size = 33329824, upload-time = "2025-09-11T17:42:07.549Z" },
+    { url = "https://files.pythonhosted.org/packages/da/6a/1a927b14ddc7714111ea51f4e568203b2bb6ed59bdd036d62127c1a360c8/scipy-1.16.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c2275ff105e508942f99d4e3bc56b6ef5e4b3c0af970386ca56b777608ce95b7", size = 35681881, upload-time = "2025-09-11T17:42:13.255Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/5f/331148ea5780b4fcc7007a4a6a6ee0a0c1507a796365cc642d4d226e1c3a/scipy-1.16.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:af80196eaa84f033e48444d2e0786ec47d328ba00c71e4299b602235ffef9acb", size = 36006219, upload-time = "2025-09-11T17:42:18.765Z" },
+    { url = "https://files.pythonhosted.org/packages/46/3a/e991aa9d2aec723b4a8dcfbfc8365edec5d5e5f9f133888067f1cbb7dfc1/scipy-1.16.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9fb1eb735fe3d6ed1f89918224e3385fbf6f9e23757cacc35f9c78d3b712dd6e", size = 38682147, upload-time = "2025-09-11T17:42:25.177Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/57/0f38e396ad19e41b4c5db66130167eef8ee620a49bc7d0512e3bb67e0cab/scipy-1.16.2-cp313-cp313-win_amd64.whl", hash = "sha256:fda714cf45ba43c9d3bae8f2585c777f64e3f89a2e073b668b32ede412d8f52c", size = 38520766, upload-time = "2025-09-11T17:43:25.342Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/a5/85d3e867b6822d331e26c862a91375bb7746a0b458db5effa093d34cdb89/scipy-1.16.2-cp313-cp313-win_arm64.whl", hash = "sha256:2f5350da923ccfd0b00e07c3e5cfb316c1c0d6c1d864c07a72d092e9f20db104", size = 25451169, upload-time = "2025-09-11T17:43:30.198Z" },
+    { url = "https://files.pythonhosted.org/packages/09/d9/60679189bcebda55992d1a45498de6d080dcaf21ce0c8f24f888117e0c2d/scipy-1.16.2-cp313-cp313t-macosx_10_14_x86_64.whl", hash = "sha256:53d8d2ee29b925344c13bda64ab51785f016b1b9617849dac10897f0701b20c1", size = 37012682, upload-time = "2025-09-11T17:42:30.677Z" },
+    { url = "https://files.pythonhosted.org/packages/83/be/a99d13ee4d3b7887a96f8c71361b9659ba4ef34da0338f14891e102a127f/scipy-1.16.2-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:9e05e33657efb4c6a9d23bd8300101536abd99c85cca82da0bffff8d8764d08a", size = 29389926, upload-time = "2025-09-11T17:42:35.845Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/0a/130164a4881cec6ca8c00faf3b57926f28ed429cd6001a673f83c7c2a579/scipy-1.16.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:7fe65b36036357003b3ef9d37547abeefaa353b237e989c21027b8ed62b12d4f", size = 21381152, upload-time = "2025-09-11T17:42:40.07Z" },
+    { url = "https://files.pythonhosted.org/packages/47/a6/503ffb0310ae77fba874e10cddfc4a1280bdcca1d13c3751b8c3c2996cf8/scipy-1.16.2-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:6406d2ac6d40b861cccf57f49592f9779071655e9f75cd4f977fa0bdd09cb2e4", size = 23914410, upload-time = "2025-09-11T17:42:44.313Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/c7/1147774bcea50d00c02600aadaa919facbd8537997a62496270133536ed6/scipy-1.16.2-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ff4dc42bd321991fbf611c23fc35912d690f731c9914bf3af8f417e64aca0f21", size = 33481880, upload-time = "2025-09-11T17:42:49.325Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/74/99d5415e4c3e46b2586f30cdbecb95e101c7192628a484a40dd0d163811a/scipy-1.16.2-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:654324826654d4d9133e10675325708fb954bc84dae6e9ad0a52e75c6b1a01d7", size = 35791425, upload-time = "2025-09-11T17:42:54.711Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/ee/a6559de7c1cc710e938c0355d9d4fbcd732dac4d0d131959d1f3b63eb29c/scipy-1.16.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:63870a84cd15c44e65220eaed2dac0e8f8b26bbb991456a033c1d9abfe8a94f8", size = 36178622, upload-time = "2025-09-11T17:43:00.375Z" },
+    { url = "https://files.pythonhosted.org/packages/4e/7b/f127a5795d5ba8ece4e0dce7d4a9fb7cb9e4f4757137757d7a69ab7d4f1a/scipy-1.16.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:fa01f0f6a3050fa6a9771a95d5faccc8e2f5a92b4a2e5440a0fa7264a2398472", size = 38783985, upload-time = "2025-09-11T17:43:06.661Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/9f/bc81c1d1e033951eb5912cd3750cc005943afa3e65a725d2443a3b3c4347/scipy-1.16.2-cp313-cp313t-win_amd64.whl", hash = "sha256:116296e89fba96f76353a8579820c2512f6e55835d3fad7780fece04367de351", size = 38631367, upload-time = "2025-09-11T17:43:14.44Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/5e/2cc7555fd81d01814271412a1d59a289d25f8b63208a0a16c21069d55d3e/scipy-1.16.2-cp313-cp313t-win_arm64.whl", hash = "sha256:98e22834650be81d42982360382b43b17f7ba95e0e6993e2a4f5b9ad9283a94d", size = 25787992, upload-time = "2025-09-11T17:43:19.745Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/ac/ad8951250516db71619f0bd3b2eb2448db04b720a003dd98619b78b692c0/scipy-1.16.2-cp314-cp314-macosx_10_14_x86_64.whl", hash = "sha256:567e77755019bb7461513c87f02bb73fb65b11f049aaaa8ca17cfaa5a5c45d77", size = 36595109, upload-time = "2025-09-11T17:43:35.713Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/f6/5779049ed119c5b503b0f3dc6d6f3f68eefc3a9190d4ad4c276f854f051b/scipy-1.16.2-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:17d9bb346194e8967296621208fcdfd39b55498ef7d2f376884d5ac47cec1a70", size = 28859110, upload-time = "2025-09-11T17:43:40.814Z" },
+    { url = "https://files.pythonhosted.org/packages/82/09/9986e410ae38bf0a0c737ff8189ac81a93b8e42349aac009891c054403d7/scipy-1.16.2-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:0a17541827a9b78b777d33b623a6dcfe2ef4a25806204d08ead0768f4e529a88", size = 20850110, upload-time = "2025-09-11T17:43:44.981Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/ad/485cdef2d9215e2a7df6d61b81d2ac073dfacf6ae24b9ae87274c4e936ae/scipy-1.16.2-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:d7d4c6ba016ffc0f9568d012f5f1eb77ddd99412aea121e6fa8b4c3b7cbad91f", size = 23497014, upload-time = "2025-09-11T17:43:49.074Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/74/f6a852e5d581122b8f0f831f1d1e32fb8987776ed3658e95c377d308ed86/scipy-1.16.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9702c4c023227785c779cba2e1d6f7635dbb5b2e0936cdd3a4ecb98d78fd41eb", size = 33401155, upload-time = "2025-09-11T17:43:54.661Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/f5/61d243bbc7c6e5e4e13dde9887e84a5cbe9e0f75fd09843044af1590844e/scipy-1.16.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d1cdf0ac28948d225decdefcc45ad7dd91716c29ab56ef32f8e0d50657dffcc7", size = 35691174, upload-time = "2025-09-11T17:44:00.101Z" },
+    { url = "https://files.pythonhosted.org/packages/03/99/59933956331f8cc57e406cdb7a483906c74706b156998f322913e789c7e1/scipy-1.16.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:70327d6aa572a17c2941cdfb20673f82e536e91850a2e4cb0c5b858b690e1548", size = 36070752, upload-time = "2025-09-11T17:44:05.619Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/7d/00f825cfb47ee19ef74ecf01244b43e95eae74e7e0ff796026ea7cd98456/scipy-1.16.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5221c0b2a4b58aa7c4ed0387d360fd90ee9086d383bb34d9f2789fafddc8a936", size = 38701010, upload-time = "2025-09-11T17:44:11.322Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/9f/b62587029980378304ba5a8563d376c96f40b1e133daacee76efdcae32de/scipy-1.16.2-cp314-cp314-win_amd64.whl", hash = "sha256:f5a85d7b2b708025af08f060a496dd261055b617d776fc05a1a1cc69e09fe9ff", size = 39360061, upload-time = "2025-09-11T17:45:09.814Z" },
+    { url = "https://files.pythonhosted.org/packages/82/04/7a2f1609921352c7fbee0815811b5050582f67f19983096c4769867ca45f/scipy-1.16.2-cp314-cp314-win_arm64.whl", hash = "sha256:2cc73a33305b4b24556957d5857d6253ce1e2dcd67fa0ff46d87d1670b3e1e1d", size = 26126914, upload-time = "2025-09-11T17:45:14.73Z" },
+    { url = "https://files.pythonhosted.org/packages/51/b9/60929ce350c16b221928725d2d1d7f86cf96b8bc07415547057d1196dc92/scipy-1.16.2-cp314-cp314t-macosx_10_14_x86_64.whl", hash = "sha256:9ea2a3fed83065d77367775d689401a703d0f697420719ee10c0780bcab594d8", size = 37013193, upload-time = "2025-09-11T17:44:16.757Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/41/ed80e67782d4bc5fc85a966bc356c601afddd175856ba7c7bb6d9490607e/scipy-1.16.2-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:7280d926f11ca945c3ef92ba960fa924e1465f8d07ce3a9923080363390624c4", size = 29390172, upload-time = "2025-09-11T17:44:21.783Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/a3/2f673ace4090452696ccded5f5f8efffb353b8f3628f823a110e0170b605/scipy-1.16.2-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:8afae1756f6a1fe04636407ef7dbece33d826a5d462b74f3d0eb82deabefd831", size = 21381326, upload-time = "2025-09-11T17:44:25.982Z" },
+    { url = "https://files.pythonhosted.org/packages/42/bf/59df61c5d51395066c35836b78136accf506197617c8662e60ea209881e1/scipy-1.16.2-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:5c66511f29aa8d233388e7416a3f20d5cae7a2744d5cee2ecd38c081f4e861b3", size = 23915036, upload-time = "2025-09-11T17:44:30.527Z" },
+    { url = "https://files.pythonhosted.org/packages/91/c3/edc7b300dc16847ad3672f1a6f3f7c5d13522b21b84b81c265f4f2760d4a/scipy-1.16.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:efe6305aeaa0e96b0ccca5ff647a43737d9a092064a3894e46c414db84bc54ac", size = 33484341, upload-time = "2025-09-11T17:44:35.981Z" },
+    { url = "https://files.pythonhosted.org/packages/26/c7/24d1524e72f06ff141e8d04b833c20db3021020563272ccb1b83860082a9/scipy-1.16.2-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7f3a337d9ae06a1e8d655ee9d8ecb835ea5ddcdcbd8d23012afa055ab014f374", size = 35790840, upload-time = "2025-09-11T17:44:41.76Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/b7/5aaad984eeedd56858dc33d75efa59e8ce798d918e1033ef62d2708f2c3d/scipy-1.16.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:bab3605795d269067d8ce78a910220262711b753de8913d3deeaedb5dded3bb6", size = 36174716, upload-time = "2025-09-11T17:44:47.316Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/c2/e276a237acb09824822b0ada11b028ed4067fdc367a946730979feacb870/scipy-1.16.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:b0348d8ddb55be2a844c518cd8cc8deeeb8aeba707cf834db5758fc89b476a2c", size = 38790088, upload-time = "2025-09-11T17:44:53.011Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/b4/5c18a766e8353015439f3780f5fc473f36f9762edc1a2e45da3ff5a31b21/scipy-1.16.2-cp314-cp314t-win_amd64.whl", hash = "sha256:26284797e38b8a75e14ea6631d29bda11e76ceaa6ddb6fdebbfe4c4d90faf2f9", size = 39457455, upload-time = "2025-09-11T17:44:58.899Z" },
+    { url = "https://files.pythonhosted.org/packages/97/30/2f9a5243008f76dfc5dee9a53dfb939d9b31e16ce4bd4f2e628bfc5d89d2/scipy-1.16.2-cp314-cp314t-win_arm64.whl", hash = "sha256:d2a4472c231328d4de38d5f1f68fdd6d28a615138f842580a8a321b5845cf779", size = 26448374, upload-time = "2025-09-11T17:45:03.45Z" },
+]
+
+[[package]]
+name = "seaborn"
+version = "0.13.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "pandas" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/86/59/a451d7420a77ab0b98f7affa3a1d78a313d2f7281a57afb1a34bae8ab412/seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7", size = 1457696, upload-time = "2024-01-25T13:21:52.551Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987", size = 294914, upload-time = "2024-01-25T13:21:49.598Z" },
+]
+
+[[package]]
+name = "setuptools"
+version = "80.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/18/5d/3bf57dcd21979b887f014ea83c24ae194cfcd12b9e0fda66b957c69d1fca/setuptools-80.9.0.tar.gz", hash = "sha256:f36b47402ecde768dbfafc46e8e4207b4360c654f1f3bb84475f0a28628fb19c", size = 1319958, upload-time = "2025-05-27T00:56:51.443Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a3/dc/17031897dae0efacfea57dfd3a82fdd2a2aeb58e0ff71b77b87e44edc772/setuptools-80.9.0-py3-none-any.whl", hash = "sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922", size = 1201486, upload-time = "2025-05-27T00:56:49.664Z" },
+]
+
+[[package]]
+name = "shapely"
+version = "2.1.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4d/bc/0989043118a27cccb4e906a46b7565ce36ca7b57f5a18b78f4f1b0f72d9d/shapely-2.1.2.tar.gz", hash = "sha256:2ed4ecb28320a433db18a5bf029986aa8afcfd740745e78847e330d5d94922a9", size = 315489, upload-time = "2025-09-24T13:51:41.432Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/8d/1ff672dea9ec6a7b5d422eb6d095ed886e2e523733329f75fdcb14ee1149/shapely-2.1.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:91121757b0a36c9aac3427a651a7e6567110a4a67c97edf04f8d55d4765f6618", size = 1820038, upload-time = "2025-09-24T13:50:15.628Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/ce/28fab8c772ce5db23a0d86bf0adaee0c4c79d5ad1db766055fa3dab442e2/shapely-2.1.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:16a9c722ba774cf50b5d4541242b4cce05aafd44a015290c82ba8a16931ff63d", size = 1626039, upload-time = "2025-09-24T13:50:16.881Z" },
+    { url = "https://files.pythonhosted.org/packages/70/8b/868b7e3f4982f5006e9395c1e12343c66a8155c0374fdc07c0e6a1ab547d/shapely-2.1.2-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cc4f7397459b12c0b196c9efe1f9d7e92463cbba142632b4cc6d8bbbbd3e2b09", size = 3001519, upload-time = "2025-09-24T13:50:18.606Z" },
+    { url = "https://files.pythonhosted.org/packages/13/02/58b0b8d9c17c93ab6340edd8b7308c0c5a5b81f94ce65705819b7416dba5/shapely-2.1.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:136ab87b17e733e22f0961504d05e77e7be8c9b5a8184f685b4a91a84efe3c26", size = 3110842, upload-time = "2025-09-24T13:50:21.77Z" },
+    { url = "https://files.pythonhosted.org/packages/af/61/8e389c97994d5f331dcffb25e2fa761aeedfb52b3ad9bcdd7b8671f4810a/shapely-2.1.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:16c5d0fc45d3aa0a69074979f4f1928ca2734fb2e0dde8af9611e134e46774e7", size = 4021316, upload-time = "2025-09-24T13:50:23.626Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/d4/9b2a9fe6039f9e42ccf2cb3e84f219fd8364b0c3b8e7bbc857b5fbe9c14c/shapely-2.1.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6ddc759f72b5b2b0f54a7e7cde44acef680a55019eb52ac63a7af2cf17cb9cd2", size = 4178586, upload-time = "2025-09-24T13:50:25.443Z" },
+    { url = "https://files.pythonhosted.org/packages/16/f6/9840f6963ed4decf76b08fd6d7fed14f8779fb7a62cb45c5617fa8ac6eab/shapely-2.1.2-cp311-cp311-win32.whl", hash = "sha256:2fa78b49485391224755a856ed3b3bd91c8455f6121fee0db0e71cefb07d0ef6", size = 1543961, upload-time = "2025-09-24T13:50:26.968Z" },
+    { url = "https://files.pythonhosted.org/packages/38/1e/3f8ea46353c2a33c1669eb7327f9665103aa3a8dfe7f2e4ef714c210b2c2/shapely-2.1.2-cp311-cp311-win_amd64.whl", hash = "sha256:c64d5c97b2f47e3cd9b712eaced3b061f2b71234b3fc263e0fcf7d889c6559dc", size = 1722856, upload-time = "2025-09-24T13:50:28.497Z" },
+    { url = "https://files.pythonhosted.org/packages/24/c0/f3b6453cf2dfa99adc0ba6675f9aaff9e526d2224cbd7ff9c1a879238693/shapely-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:fe2533caae6a91a543dec62e8360fe86ffcdc42a7c55f9dfd0128a977a896b94", size = 1833550, upload-time = "2025-09-24T13:50:30.019Z" },
+    { url = "https://files.pythonhosted.org/packages/86/07/59dee0bc4b913b7ab59ab1086225baca5b8f19865e6101db9ebb7243e132/shapely-2.1.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ba4d1333cc0bc94381d6d4308d2e4e008e0bd128bdcff5573199742ee3634359", size = 1643556, upload-time = "2025-09-24T13:50:32.291Z" },
+    { url = "https://files.pythonhosted.org/packages/26/29/a5397e75b435b9895cd53e165083faed5d12fd9626eadec15a83a2411f0f/shapely-2.1.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0bd308103340030feef6c111d3eb98d50dc13feea33affc8a6f9fa549e9458a3", size = 2988308, upload-time = "2025-09-24T13:50:33.862Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/37/e781683abac55dde9771e086b790e554811a71ed0b2b8a1e789b7430dd44/shapely-2.1.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1e7d4d7ad262a48bb44277ca12c7c78cb1b0f56b32c10734ec9a1d30c0b0c54b", size = 3099844, upload-time = "2025-09-24T13:50:35.459Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/f3/9876b64d4a5a321b9dc482c92bb6f061f2fa42131cba643c699f39317cb9/shapely-2.1.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e9eddfe513096a71896441a7c37db72da0687b34752c4e193577a145c71736fc", size = 3988842, upload-time = "2025-09-24T13:50:37.478Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/a0/704c7292f7014c7e74ec84eddb7b109e1fbae74a16deae9c1504b1d15565/shapely-2.1.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:980c777c612514c0cf99bc8a9de6d286f5e186dcaf9091252fcd444e5638193d", size = 4152714, upload-time = "2025-09-24T13:50:39.9Z" },
+    { url = "https://files.pythonhosted.org/packages/53/46/319c9dc788884ad0785242543cdffac0e6530e4d0deb6c4862bc4143dcf3/shapely-2.1.2-cp312-cp312-win32.whl", hash = "sha256:9111274b88e4d7b54a95218e243282709b330ef52b7b86bc6aaf4f805306f454", size = 1542745, upload-time = "2025-09-24T13:50:41.414Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/bf/cb6c1c505cb31e818e900b9312d514f381fbfa5c4363edfce0fcc4f8c1a4/shapely-2.1.2-cp312-cp312-win_amd64.whl", hash = "sha256:743044b4cfb34f9a67205cee9279feaf60ba7d02e69febc2afc609047cb49179", size = 1722861, upload-time = "2025-09-24T13:50:43.35Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/90/98ef257c23c46425dc4d1d31005ad7c8d649fe423a38b917db02c30f1f5a/shapely-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b510dda1a3672d6879beb319bc7c5fd302c6c354584690973c838f46ec3e0fa8", size = 1832644, upload-time = "2025-09-24T13:50:44.886Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/ab/0bee5a830d209adcd3a01f2d4b70e587cdd9fd7380d5198c064091005af8/shapely-2.1.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:8cff473e81017594d20ec55d86b54bc635544897e13a7cfc12e36909c5309a2a", size = 1642887, upload-time = "2025-09-24T13:50:46.735Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/5e/7d7f54ba960c13302584c73704d8c4d15404a51024631adb60b126a4ae88/shapely-2.1.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fe7b77dc63d707c09726b7908f575fc04ff1d1ad0f3fb92aec212396bc6cfe5e", size = 2970931, upload-time = "2025-09-24T13:50:48.374Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/a2/83fc37e2a58090e3d2ff79175a95493c664bcd0b653dd75cb9134645a4e5/shapely-2.1.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7ed1a5bbfb386ee8332713bf7508bc24e32d24b74fc9a7b9f8529a55db9f4ee6", size = 3082855, upload-time = "2025-09-24T13:50:50.037Z" },
+    { url = "https://files.pythonhosted.org/packages/44/2b/578faf235a5b09f16b5f02833c53822294d7f21b242f8e2d0cf03fb64321/shapely-2.1.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a84e0582858d841d54355246ddfcbd1fce3179f185da7470f41ce39d001ee1af", size = 3979960, upload-time = "2025-09-24T13:50:51.74Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/04/167f096386120f692cc4ca02f75a17b961858997a95e67a3cb6a7bbd6b53/shapely-2.1.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:dc3487447a43d42adcdf52d7ac73804f2312cbfa5d433a7d2c506dcab0033dfd", size = 4142851, upload-time = "2025-09-24T13:50:53.49Z" },
+    { url = "https://files.pythonhosted.org/packages/48/74/fb402c5a6235d1c65a97348b48cdedb75fb19eca2b1d66d04969fc1c6091/shapely-2.1.2-cp313-cp313-win32.whl", hash = "sha256:9c3a3c648aedc9f99c09263b39f2d8252f199cb3ac154fadc173283d7d111350", size = 1541890, upload-time = "2025-09-24T13:50:55.337Z" },
+    { url = "https://files.pythonhosted.org/packages/41/47/3647fe7ad990af60ad98b889657a976042c9988c2807cf322a9d6685f462/shapely-2.1.2-cp313-cp313-win_amd64.whl", hash = "sha256:ca2591bff6645c216695bdf1614fca9c82ea1144d4a7591a466fef64f28f0715", size = 1722151, upload-time = "2025-09-24T13:50:57.153Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/49/63953754faa51ffe7d8189bfbe9ca34def29f8c0e34c67cbe2a2795f269d/shapely-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:2d93d23bdd2ed9dc157b46bc2f19b7da143ca8714464249bef6771c679d5ff40", size = 1834130, upload-time = "2025-09-24T13:50:58.49Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/ee/dce001c1984052970ff60eb4727164892fb2d08052c575042a47f5a9e88f/shapely-2.1.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:01d0d304b25634d60bd7cf291828119ab55a3bab87dc4af1e44b07fb225f188b", size = 1642802, upload-time = "2025-09-24T13:50:59.871Z" },
+    { url = "https://files.pythonhosted.org/packages/da/e7/fc4e9a19929522877fa602f705706b96e78376afb7fad09cad5b9af1553c/shapely-2.1.2-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8d8382dd120d64b03698b7298b89611a6ea6f55ada9d39942838b79c9bc89801", size = 3018460, upload-time = "2025-09-24T13:51:02.08Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/18/7519a25db21847b525696883ddc8e6a0ecaa36159ea88e0fef11466384d0/shapely-2.1.2-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:19efa3611eef966e776183e338b2d7ea43569ae99ab34f8d17c2c054d3205cc0", size = 3095223, upload-time = "2025-09-24T13:51:04.472Z" },
+    { url = "https://files.pythonhosted.org/packages/48/de/b59a620b1f3a129c3fecc2737104a0a7e04e79335bd3b0a1f1609744cf17/shapely-2.1.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:346ec0c1a0fcd32f57f00e4134d1200e14bf3f5ae12af87ba83ca275c502498c", size = 4030760, upload-time = "2025-09-24T13:51:06.455Z" },
+    { url = "https://files.pythonhosted.org/packages/96/b3/c6655ee7232b417562bae192ae0d3ceaadb1cc0ffc2088a2ddf415456cc2/shapely-2.1.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6305993a35989391bd3476ee538a5c9a845861462327efe00dd11a5c8c709a99", size = 4170078, upload-time = "2025-09-24T13:51:08.584Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/8e/605c76808d73503c9333af8f6cbe7e1354d2d238bda5f88eea36bfe0f42a/shapely-2.1.2-cp313-cp313t-win32.whl", hash = "sha256:c8876673449f3401f278c86eb33224c5764582f72b653a415d0e6672fde887bf", size = 1559178, upload-time = "2025-09-24T13:51:10.73Z" },
+    { url = "https://files.pythonhosted.org/packages/36/f7/d317eb232352a1f1444d11002d477e54514a4a6045536d49d0c59783c0da/shapely-2.1.2-cp313-cp313t-win_amd64.whl", hash = "sha256:4a44bc62a10d84c11a7a3d7c1c4fe857f7477c3506e24c9062da0db0ae0c449c", size = 1739756, upload-time = "2025-09-24T13:51:12.105Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/c4/3ce4c2d9b6aabd27d26ec988f08cb877ba9e6e96086eff81bfea93e688c7/shapely-2.1.2-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:9a522f460d28e2bf4e12396240a5fc1518788b2fcd73535166d748399ef0c223", size = 1831290, upload-time = "2025-09-24T13:51:13.56Z" },
+    { url = "https://files.pythonhosted.org/packages/17/b9/f6ab8918fc15429f79cb04afa9f9913546212d7fb5e5196132a2af46676b/shapely-2.1.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:1ff629e00818033b8d71139565527ced7d776c269a49bd78c9df84e8f852190c", size = 1641463, upload-time = "2025-09-24T13:51:14.972Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/57/91d59ae525ca641e7ac5551c04c9503aee6f29b92b392f31790fcb1a4358/shapely-2.1.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f67b34271dedc3c653eba4e3d7111aa421d5be9b4c4c7d38d30907f796cb30df", size = 2970145, upload-time = "2025-09-24T13:51:16.961Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/cb/4948be52ee1da6927831ab59e10d4c29baa2a714f599f1f0d1bc747f5777/shapely-2.1.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:21952dc00df38a2c28375659b07a3979d22641aeb104751e769c3ee825aadecf", size = 3073806, upload-time = "2025-09-24T13:51:18.712Z" },
+    { url = "https://files.pythonhosted.org/packages/03/83/f768a54af775eb41ef2e7bec8a0a0dbe7d2431c3e78c0a8bdba7ab17e446/shapely-2.1.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1f2f33f486777456586948e333a56ae21f35ae273be99255a191f5c1fa302eb4", size = 3980803, upload-time = "2025-09-24T13:51:20.37Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/cb/559c7c195807c91c79d38a1f6901384a2878a76fbdf3f1048893a9b7534d/shapely-2.1.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:cf831a13e0d5a7eb519e96f58ec26e049b1fad411fc6fc23b162a7ce04d9cffc", size = 4133301, upload-time = "2025-09-24T13:51:21.887Z" },
+    { url = "https://files.pythonhosted.org/packages/80/cd/60d5ae203241c53ef3abd2ef27c6800e21afd6c94e39db5315ea0cbafb4a/shapely-2.1.2-cp314-cp314-win32.whl", hash = "sha256:61edcd8d0d17dd99075d320a1dd39c0cb9616f7572f10ef91b4b5b00c4aeb566", size = 1583247, upload-time = "2025-09-24T13:51:23.401Z" },
+    { url = "https://files.pythonhosted.org/packages/74/d4/135684f342e909330e50d31d441ace06bf83c7dc0777e11043f99167b123/shapely-2.1.2-cp314-cp314-win_amd64.whl", hash = "sha256:a444e7afccdb0999e203b976adb37ea633725333e5b119ad40b1ca291ecf311c", size = 1773019, upload-time = "2025-09-24T13:51:24.873Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/05/a44f3f9f695fa3ada22786dc9da33c933da1cbc4bfe876fe3a100bafe263/shapely-2.1.2-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:5ebe3f84c6112ad3d4632b1fd2290665aa75d4cef5f6c5d77c4c95b324527c6a", size = 1834137, upload-time = "2025-09-24T13:51:26.665Z" },
+    { url = "https://files.pythonhosted.org/packages/52/7e/4d57db45bf314573427b0a70dfca15d912d108e6023f623947fa69f39b72/shapely-2.1.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:5860eb9f00a1d49ebb14e881f5caf6c2cf472c7fd38bd7f253bbd34f934eb076", size = 1642884, upload-time = "2025-09-24T13:51:28.029Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/27/4e29c0a55d6d14ad7422bf86995d7ff3f54af0eba59617eb95caf84b9680/shapely-2.1.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b705c99c76695702656327b819c9660768ec33f5ce01fa32b2af62b56ba400a1", size = 3018320, upload-time = "2025-09-24T13:51:29.903Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/bb/992e6a3c463f4d29d4cd6ab8963b75b1b1040199edbd72beada4af46bde5/shapely-2.1.2-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a1fd0ea855b2cf7c9cddaf25543e914dd75af9de08785f20ca3085f2c9ca60b0", size = 3094931, upload-time = "2025-09-24T13:51:32.699Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/16/82e65e21070e473f0ed6451224ed9fa0be85033d17e0c6e7213a12f59d12/shapely-2.1.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:df90e2db118c3671a0754f38e36802db75fe0920d211a27481daf50a711fdf26", size = 4030406, upload-time = "2025-09-24T13:51:34.189Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/75/c24ed871c576d7e2b64b04b1fe3d075157f6eb54e59670d3f5ffb36e25c7/shapely-2.1.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:361b6d45030b4ac64ddd0a26046906c8202eb60d0f9f53085f5179f1d23021a0", size = 4169511, upload-time = "2025-09-24T13:51:36.297Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/f7/b3d1d6d18ebf55236eec1c681ce5e665742aab3c0b7b232720a7d43df7b6/shapely-2.1.2-cp314-cp314t-win32.whl", hash = "sha256:b54df60f1fbdecc8ebc2c5b11870461a6417b3d617f555e5033f1505d36e5735", size = 1602607, upload-time = "2025-09-24T13:51:37.757Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/f6/f09272a71976dfc138129b8faf435d064a811ae2f708cb147dccdf7aacdb/shapely-2.1.2-cp314-cp314t-win_amd64.whl", hash = "sha256:0036ac886e0923417932c2e6369b6c52e38e0ff5d9120b90eef5cd9a5fc5cae9", size = 1796682, upload-time = "2025-09-24T13:51:39.233Z" },
+]
+
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/58/15/8b3609fd3830ef7b27b655beb4b4e9c62313a4e8da8c676e142cc210d58e/shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de", size = 10310, upload-time = "2023-10-24T04:13:40.426Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686", size = 9755, upload-time = "2023-10-24T04:13:38.866Z" },
+]
+
+[[package]]
+name = "six"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031, upload-time = "2024-12-04T17:35:28.174Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" },
+]
+
+[[package]]
+name = "smart-open"
+version = "7.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wrapt" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/16/be/bf2d60280a9d7fac98ece2150a22538fa4332cda67d04d9618c8406f791e/smart_open-7.3.1.tar.gz", hash = "sha256:b33fee8dffd206f189d5e704106a8723afb4210d2ff47e0e1f7fbe436187a990", size = 51405, upload-time = "2025-09-08T10:03:53.726Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e5/d9/460cf1d58945dd771c228c29d5664f431dfc4060d3d092fed40546b11472/smart_open-7.3.1-py3-none-any.whl", hash = "sha256:e243b2e7f69d6c0c96dd763d6fbbedbb4e0e4fc6d74aa007acc5b018d523858c", size = 61722, upload-time = "2025-09-08T10:03:52.02Z" },
+]
+
+[[package]]
+name = "smmap"
+version = "5.0.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/44/cd/a040c4b3119bbe532e5b0732286f805445375489fceaec1f48306068ee3b/smmap-5.0.2.tar.gz", hash = "sha256:26ea65a03958fa0c8a1c7e8c7a58fdc77221b8910f6be2131affade476898ad5", size = 22329, upload-time = "2025-01-02T07:14:40.909Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/04/be/d09147ad1ec7934636ad912901c5fd7667e1c858e19d355237db0d0cd5e4/smmap-5.0.2-py3-none-any.whl", hash = "sha256:b30115f0def7d7531d22a0fb6502488d879e75b260a9db4d0819cfb25403af5e", size = 24303, upload-time = "2025-01-02T07:14:38.724Z" },
+]
+
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a2/87/a6771e1546d97e7e041b6ae58d80074f81b7d5121207425c964ddf5cfdbd/sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc", size = 20372, upload-time = "2024-02-25T23:20:04.057Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235, upload-time = "2024-02-25T23:20:01.196Z" },
+]
+
+[[package]]
+name = "spacy"
+version = "3.8.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "catalogue" },
+    { name = "cymem" },
+    { name = "jinja2" },
+    { name = "langcodes" },
+    { name = "murmurhash" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "preshed" },
+    { name = "pydantic" },
+    { name = "requests" },
+    { name = "setuptools" },
+    { name = "spacy-legacy" },
+    { name = "spacy-loggers" },
+    { name = "srsly" },
+    { name = "thinc" },
+    { name = "tqdm" },
+    { name = "typer" },
+    { name = "wasabi" },
+    { name = "weasel" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/1e/9e/fb4e1cefe3fbd51ea6a243e5a3d2bc629baa9a28930bf4be6fe5672fa1ca/spacy-3.8.7.tar.gz", hash = "sha256:700fd174c6c552276be142c48e70bb53cae24c4dd86003c4432af9cb93e4c908", size = 1316143, upload-time = "2025-05-23T08:55:39.538Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/29/c5/5fbb3a4e694d4855a5bab87af9664377c48b89691f180ad3cde4faeaf35c/spacy-3.8.7-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:bdff8b9b556468a6dd527af17f0ddf9fb0b0bee92ee7703339ddf542361cff98", size = 6746140, upload-time = "2025-05-23T08:54:23.483Z" },
+    { url = "https://files.pythonhosted.org/packages/03/2a/43afac516eb82409ca47d7206f982beaf265d2ba06a72ca07cf06b290c20/spacy-3.8.7-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9194b7cf015ed9b4450ffb162da49c8a9305e76b468de036b0948abdfc748a37", size = 6392440, upload-time = "2025-05-23T08:54:25.12Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/83/2ea68c18e2b1b9a6f6b30ef63eb9d07e979626b9595acfdb5394f18923c4/spacy-3.8.7-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7dc38b78d48b9c2a80a3eea95f776304993f63fc307f07cdd104441442f92f1e", size = 32699126, upload-time = "2025-05-23T08:54:27.385Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/0a/bb90e9aa0b3c527876627567d82517aabab08006ccf63796c33b0242254d/spacy-3.8.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2e43bd70772751b8fc7a14f338d087a3d297195d43d171832923ef66204b23ab", size = 33008865, upload-time = "2025-05-23T08:54:30.248Z" },
+    { url = "https://files.pythonhosted.org/packages/39/dd/8e906ba378457107ab0394976ea9f7b12fdb2cad682ef1a2ccf473d61e5f/spacy-3.8.7-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c402bf5dcf345fd96d202378c54bc345219681e3531f911d99567d569328c45f", size = 31933169, upload-time = "2025-05-23T08:54:33.199Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/b5/42df07eb837a923fbb42509864d5c7c2072d010de933dccdfb3c655b3a76/spacy-3.8.7-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:4234189861e486d86f1269e50542d87e8a6391a1ee190652479cf1a793db115f", size = 32776322, upload-time = "2025-05-23T08:54:36.891Z" },
+    { url = "https://files.pythonhosted.org/packages/92/e7/8176484801c67dcd814f141991fe0a3c9b5b4a3583ea30c2062e93d1aa6b/spacy-3.8.7-cp311-cp311-win_amd64.whl", hash = "sha256:e9d12e2eb7f36bc11dd9edae011032fe49ea100d63e83177290d3cbd80eaa650", size = 14938936, upload-time = "2025-05-23T08:54:40.322Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/10/89852f40f926e0902c11c34454493ba0d15530b322711e754b89a6d7dfe6/spacy-3.8.7-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:88b397e37793cea51df298e6c651a763e49877a25bead5ba349761531a456687", size = 6265335, upload-time = "2025-05-23T08:54:42.876Z" },
+    { url = "https://files.pythonhosted.org/packages/16/fb/b5d54522969a632c06f4af354763467553b66d5bf0671ac39f3cceb3fd54/spacy-3.8.7-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f70b676955fa6959347ca86ed6edd8ff0d6eb2ba20561fdfec76924bd3e540f9", size = 5906035, upload-time = "2025-05-23T08:54:44.824Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/03/70f06753fd65081404ade30408535eb69f627a36ffce2107116d1aa16239/spacy-3.8.7-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6c4b5a624797ade30c25b5b69daa35a93ee24bcc56bd79b0884b2565f76f35d6", size = 33420084, upload-time = "2025-05-23T08:54:46.889Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/19/b60e1ebf4985ee2b33d85705b89a5024942b65dad04dbdc3fb46f168b410/spacy-3.8.7-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d9d83e006df66decccefa3872fa958b3756228fb216d83783595444cf42ca10c", size = 33922188, upload-time = "2025-05-23T08:54:49.781Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/a3/1fb1a49dc6d982d96fffc30c3a31bb431526008eea72ac3773f6518720a6/spacy-3.8.7-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0dca25deba54f3eb5dcfbf63bf16e613e6c601da56f91c4a902d38533c098941", size = 31939285, upload-time = "2025-05-23T08:54:53.162Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/55/6cf1aff8e5c01ee683e828f3ccd9282d2aff7ca1143a9349ee3d0c1291ff/spacy-3.8.7-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:5eef3f805a1c118d9b709a23e2d378f5f20da5a0d6258c9cfdc87c4cb234b4fc", size = 32988845, upload-time = "2025-05-23T08:54:57.776Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/47/c17ee61b51aa8497d8af0999224b4b62485111a55ec105a06886685b2c68/spacy-3.8.7-cp312-cp312-win_amd64.whl", hash = "sha256:25d7a68e445200c9e9dc0044f8b7278ec0ef01ccc7cb5a95d1de2bd8e3ed6be2", size = 13918682, upload-time = "2025-05-23T08:55:00.387Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/95/7125bea6d432c601478bf922f7a568762c8be425bbde5b66698260ab0358/spacy-3.8.7-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:dda7d57f42ec57c19fbef348095a9c82504e4777bca7b8db4b0d8318ba280fc7", size = 6235950, upload-time = "2025-05-23T08:55:02.92Z" },
+    { url = "https://files.pythonhosted.org/packages/96/c3/d2362846154d4d341136774831605df02d61f49ac637524a15f4f2794874/spacy-3.8.7-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:de0e0bddb810ed05bce44bcb91460eabe52bc56323da398d2ca74288a906da35", size = 5878106, upload-time = "2025-05-23T08:55:04.496Z" },
+    { url = "https://files.pythonhosted.org/packages/50/b6/b2943acfbfc4fc12642dac9feb571e712dd1569ab481db8f3daedee045fe/spacy-3.8.7-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5a2e58f92b684465777a7c1a65d5578b1dc36fe55c48d9964fb6d46cc9449768", size = 33085866, upload-time = "2025-05-23T08:55:06.65Z" },
+    { url = "https://files.pythonhosted.org/packages/65/98/c4415cbb217ac0b502dbb3372136015c699dd16a0c47cd6d338cd15f4bed/spacy-3.8.7-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:46330da2eb357d6979f40ea8fc16ee5776ee75cd0c70aac2a4ea10c80364b8f3", size = 33398424, upload-time = "2025-05-23T08:55:10.477Z" },
+    { url = "https://files.pythonhosted.org/packages/12/45/12a198858f1f11c21844876e039ba90df59d550527c72996d418c1faf78d/spacy-3.8.7-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:86b6a6ad23ca5440ef9d29c2b1e3125e28722c927db612ae99e564d49202861c", size = 31530066, upload-time = "2025-05-23T08:55:13.329Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/df/80524f99822eb96c9649200042ec5912357eec100cf0cd678a2e9ef0ecb3/spacy-3.8.7-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ccfe468cbb370888153df145ce3693af8e54dae551940df49057258081b2112f", size = 32613343, upload-time = "2025-05-23T08:55:16.711Z" },
+    { url = "https://files.pythonhosted.org/packages/02/99/881f6f24c279a5a70b8d69aaf8266fd411a0a58fd1c8848112aaa348f6f6/spacy-3.8.7-cp313-cp313-win_amd64.whl", hash = "sha256:ca81e416ff35209769e8b5dd5d13acc52e4f57dd9d028364bccbbe157c2ae86b", size = 13911250, upload-time = "2025-05-23T08:55:19.606Z" },
+]
+
+[[package]]
+name = "spacy-legacy"
+version = "3.0.12"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d9/79/91f9d7cc8db5642acad830dcc4b49ba65a7790152832c4eceb305e46d681/spacy-legacy-3.0.12.tar.gz", hash = "sha256:b37d6e0c9b6e1d7ca1cf5bc7152ab64a4c4671f59c85adaf7a3fcb870357a774", size = 23806, upload-time = "2023-01-23T09:04:15.104Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c3/55/12e842c70ff8828e34e543a2c7176dac4da006ca6901c9e8b43efab8bc6b/spacy_legacy-3.0.12-py2.py3-none-any.whl", hash = "sha256:476e3bd0d05f8c339ed60f40986c07387c0a71479245d6d0f4298dbd52cda55f", size = 29971, upload-time = "2023-01-23T09:04:13.45Z" },
+]
+
+[[package]]
+name = "spacy-loggers"
+version = "1.0.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/67/3d/926db774c9c98acf66cb4ed7faf6c377746f3e00b84b700d0868b95d0712/spacy-loggers-1.0.5.tar.gz", hash = "sha256:d60b0bdbf915a60e516cc2e653baeff946f0cfc461b452d11a4d5458c6fe5f24", size = 20811, upload-time = "2023-09-11T12:26:52.323Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/33/78/d1a1a026ef3af911159398c939b1509d5c36fe524c7b644f34a5146c4e16/spacy_loggers-1.0.5-py3-none-any.whl", hash = "sha256:196284c9c446cc0cdb944005384270d775fdeaf4f494d8e269466cfa497ef645", size = 22343, upload-time = "2023-09-11T12:26:50.586Z" },
+]
+
+[[package]]
+name = "srsly"
+version = "2.5.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "catalogue" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b7/e8/eb51b1349f50bac0222398af0942613fdc9d1453ae67cbe4bf9936a1a54b/srsly-2.5.1.tar.gz", hash = "sha256:ab1b4bf6cf3e29da23dae0493dd1517fb787075206512351421b89b4fc27c77e", size = 466464, upload-time = "2025-01-17T09:26:26.919Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/df/9c/a248bb49de499fe0990e3cb0fb341c2373d8863ef9a8b5799353cade5731/srsly-2.5.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:58f0736794ce00a71d62a39cbba1d62ea8d5be4751df956e802d147da20ecad7", size = 635917, upload-time = "2025-01-17T09:25:25.109Z" },
+    { url = "https://files.pythonhosted.org/packages/41/47/1bdaad84502df973ecb8ca658117234cf7fb20e1dec60da71dce82de993f/srsly-2.5.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7a8269c40859806d71920396d185f4f38dc985cdb6a28d3a326a701e29a5f629", size = 634374, upload-time = "2025-01-17T09:25:26.609Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/2a/d73c71989fcf2a6d1fa518d75322aff4db01a8763f167f8c5e00aac11097/srsly-2.5.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:889905900401fefc1032e22b73aecbed8b4251aa363f632b2d1f86fc16f1ad8e", size = 1108390, upload-time = "2025-01-17T09:25:29.32Z" },
+    { url = "https://files.pythonhosted.org/packages/35/a3/9eda9997a8bd011caed18fdaa5ce606714eb06d8dab587ed0522b3e92ab1/srsly-2.5.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bf454755f22589df49c25dc799d8af7b47dce3d861dded35baf0f0b6ceab4422", size = 1110712, upload-time = "2025-01-17T09:25:31.051Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/ef/4b50bc05d06349f905b27f824cc23b652098efd4be19aead3af4981df647/srsly-2.5.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:cc0607c8a59013a51dde5c1b4e465558728e9e0a35dcfa73c7cbefa91a0aad50", size = 1081244, upload-time = "2025-01-17T09:25:32.611Z" },
+    { url = "https://files.pythonhosted.org/packages/90/af/d4a2512d9a5048d2b18efead39d4c4404bddd4972935bbc68211292a736c/srsly-2.5.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:d5421ba3ab3c790e8b41939c51a1d0f44326bfc052d7a0508860fb79a47aee7f", size = 1091692, upload-time = "2025-01-17T09:25:34.15Z" },
+    { url = "https://files.pythonhosted.org/packages/bb/da/657a685f63028dcb00ccdc4ac125ed347c8bff6fa0dab6a9eb3dc45f3223/srsly-2.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:b96ea5a9a0d0379a79c46d255464a372fb14c30f59a8bc113e4316d131a530ab", size = 632627, upload-time = "2025-01-17T09:25:37.36Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/f6/bebc20d75bd02121fc0f65ad8c92a5dd2570e870005e940faa55a263e61a/srsly-2.5.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:683b54ed63d7dfee03bc2abc4b4a5f2152f81ec217bbadbac01ef1aaf2a75790", size = 636717, upload-time = "2025-01-17T09:25:40.236Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/e8/9372317a4742c70b87b413335adfcdfb2bee4f88f3faba89fabb9e6abf21/srsly-2.5.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:459d987130e57e83ce9e160899afbeb871d975f811e6958158763dd9a8a20f23", size = 634697, upload-time = "2025-01-17T09:25:43.605Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/00/c6a7b99ab27b051a27bd26fe1a8c1885225bb8980282bf9cb99f70610368/srsly-2.5.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:184e3c98389aab68ff04aab9095bd5f1a8e5a72cc5edcba9d733bac928f5cf9f", size = 1134655, upload-time = "2025-01-17T09:25:45.238Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/e6/861459e8241ec3b78c111081bd5efa414ef85867e17c45b6882954468d6e/srsly-2.5.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:00c2a3e4856e63b7efd47591d049aaee8e5a250e098917f50d93ea68853fab78", size = 1143544, upload-time = "2025-01-17T09:25:47.485Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/85/8448fe874dd2042a4eceea5315cfff3af03ac77ff5073812071852c4e7e2/srsly-2.5.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:366b4708933cd8d6025c13c2cea3331f079c7bb5c25ec76fca392b6fc09818a0", size = 1098330, upload-time = "2025-01-17T09:25:52.55Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/7e/04d0e1417da140b2ac4053a3d4fcfc86cd59bf4829f69d370bb899f74d5d/srsly-2.5.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c8a0b03c64eb6e150d772c5149befbadd981cc734ab13184b0561c17c8cef9b1", size = 1110670, upload-time = "2025-01-17T09:25:54.02Z" },
+    { url = "https://files.pythonhosted.org/packages/96/1a/a8cd627eaa81a91feb6ceab50155f4ceff3eef6107916cb87ef796958427/srsly-2.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:7952538f6bba91b9d8bf31a642ac9e8b9ccc0ccbb309feb88518bfb84bb0dc0d", size = 632598, upload-time = "2025-01-17T09:25:55.499Z" },
+    { url = "https://files.pythonhosted.org/packages/42/94/cab36845aad6e2c22ecee1178accaa365657296ff87305b805648fd41118/srsly-2.5.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:84b372f7ef1604b4a5b3cee1571993931f845a5b58652ac01bcb32c52586d2a8", size = 634883, upload-time = "2025-01-17T09:25:58.363Z" },
+    { url = "https://files.pythonhosted.org/packages/67/8b/501f51f4eaee7e1fd7327764799cb0a42f5d0de042a97916d30dbff770fc/srsly-2.5.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:6ac3944c112acb3347a39bfdc2ebfc9e2d4bace20fe1c0b764374ac5b83519f2", size = 632842, upload-time = "2025-01-17T09:25:59.777Z" },
+    { url = "https://files.pythonhosted.org/packages/07/be/5b8fce4829661e070a7d3e262d2e533f0e297b11b8993d57240da67d7330/srsly-2.5.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6118f9c4b221cde0a990d06a42c8a4845218d55b425d8550746fe790acf267e9", size = 1118516, upload-time = "2025-01-17T09:26:01.234Z" },
+    { url = "https://files.pythonhosted.org/packages/91/60/a34e97564eac352c0e916c98f44b6f566b7eb6a9fb60bcd60ffa98530762/srsly-2.5.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7481460110d9986781d9e4ac0f5f991f1d6839284a80ad268625f9a23f686950", size = 1127974, upload-time = "2025-01-17T09:26:04.007Z" },
+    { url = "https://files.pythonhosted.org/packages/70/a2/f642334db0cabd187fa86b8773257ee6993c6009338a6831d4804e2c5b3c/srsly-2.5.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6e57b8138082f09e35db60f99757e16652489e9e3692471d8e0c39aa95180688", size = 1086098, upload-time = "2025-01-17T09:26:05.612Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/9b/be48e185c5a010e71b5135e4cdf317ff56b8ac4bc08f394bbf882ac13b05/srsly-2.5.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:bab90b85a63a1fe0bbc74d373c8bb9bb0499ddfa89075e0ebe8d670f12d04691", size = 1100354, upload-time = "2025-01-17T09:26:07.215Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/e2/745aeba88a8513017fbac2fd2f9f07b8a36065e51695f818541eb795ec0c/srsly-2.5.1-cp313-cp313-win_amd64.whl", hash = "sha256:e73712be1634b5e1de6f81c273a7d47fe091ad3c79dc779c03d3416a5c117cee", size = 630634, upload-time = "2025-01-17T09:26:10.018Z" },
+]
+
+[[package]]
+name = "streamlit"
+version = "1.50.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "altair" },
+    { name = "blinker" },
+    { name = "cachetools" },
+    { name = "click" },
+    { name = "gitpython" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pandas" },
+    { name = "pillow" },
+    { name = "protobuf" },
+    { name = "pyarrow" },
+    { name = "pydeck" },
+    { name = "requests" },
+    { name = "tenacity" },
+    { name = "toml" },
+    { name = "tornado" },
+    { name = "typing-extensions" },
+    { name = "watchdog", marker = "sys_platform != 'darwin'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d6/f6/f7d3a0146577c1918439d3163707040f7111a7d2e7e2c73fa7adeb169c06/streamlit-1.50.0.tar.gz", hash = "sha256:87221d568aac585274a05ef18a378b03df332b93e08103fffcf3cd84d852af46", size = 9664808, upload-time = "2025-09-23T19:24:00.31Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2a/38/991bbf9fa3ed3d9c8e69265fc449bdaade8131c7f0f750dbd388c3c477dc/streamlit-1.50.0-py3-none-any.whl", hash = "sha256:9403b8f94c0a89f80cf679c2fcc803d9a6951e0fba542e7611995de3f67b4bb3", size = 10068477, upload-time = "2025-09-23T19:23:57.245Z" },
+]
+
+[[package]]
+name = "tenacity"
+version = "9.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0a/d4/2b0cd0fe285e14b36db076e78c93766ff1d529d70408bd1d2a5a84f1d929/tenacity-9.1.2.tar.gz", hash = "sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb", size = 48036, upload-time = "2025-04-02T08:25:09.966Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e5/30/643397144bfbfec6f6ef821f36f33e57d35946c44a2352d3c9f0ae847619/tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138", size = 28248, upload-time = "2025-04-02T08:25:07.678Z" },
+]
+
+[[package]]
+name = "thinc"
+version = "8.3.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "blis" },
+    { name = "catalogue" },
+    { name = "confection" },
+    { name = "cymem" },
+    { name = "murmurhash" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "preshed" },
+    { name = "pydantic" },
+    { name = "setuptools" },
+    { name = "srsly" },
+    { name = "wasabi" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/01/f4/7607f76c2e156a34b1961a941eb8407b84da4f515cc0903b44d44edf4f45/thinc-8.3.6.tar.gz", hash = "sha256:49983f9b7ddc4343a9532694a9118dd216d7a600520a21849a43b6c268ec6cad", size = 194218, upload-time = "2025-04-04T11:50:45.751Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/23/b4/b4ed217679327849ad796dc8ced307447a05e9f607bb12f290b2ec99fb35/thinc-8.3.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:7c7c44f8736f27d1cced216246c00e219fb5734e6bc3b8a78c09157c011aae59", size = 895694, upload-time = "2025-04-04T11:49:56.102Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/53/5f9eeb725c2ca94adef76a2cd0289bc530728b0a035eed815c766a9291ef/thinc-8.3.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:92b3c38bdfdf81d0485685a6261b8a6ea40e03120b08ced418c8400f5e186b2d", size = 845256, upload-time = "2025-04-04T11:49:57.401Z" },
+    { url = "https://files.pythonhosted.org/packages/89/8e/fae78ba63b1b0fb9017fd51d0aeffdc22e837807a93d55100c92e6d30570/thinc-8.3.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:853eb187b1f77057adada1a72e7f6ea3f38643930363681cfd5de285dab4b09b", size = 4402142, upload-time = "2025-04-04T11:49:59.144Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/7e/6b1bb6eba9c25a5911e1624c0da33ca007d7697b41ee11c059f3d23d8bbc/thinc-8.3.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1c12bf75a375b3b1f7c32a26cbd69255b177daa693c986a27faaf2027439c7ef", size = 4463797, upload-time = "2025-04-04T11:50:00.647Z" },
+    { url = "https://files.pythonhosted.org/packages/42/39/c5f48785f76cd97a79e83b1acd3dab7d0835e4b560c9d7add846b47ea984/thinc-8.3.6-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:5bf1708c22fb54e7846e8e743a9e6a43a22cbe24cab0081ba4e6362b4437a53f", size = 5313270, upload-time = "2025-04-04T11:50:02.241Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/32/ce8e7827ed0a1f86eab864e6b0b8ae79118e4e5c40ffd8b21938f8961e23/thinc-8.3.6-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:169d7c5779f6f1a78fa91b2bc3a6485f7bbe4341bd8064576f8e067b67b6a0b5", size = 5508338, upload-time = "2025-04-04T11:50:04.18Z" },
+    { url = "https://files.pythonhosted.org/packages/94/40/7e5e840ac2e835fbf5d87e3ab94df7d678d846aaf28b12d46538ed36bf7f/thinc-8.3.6-cp311-cp311-win_amd64.whl", hash = "sha256:59c244ce11a3359b9a33b4c3bbc9ba94f7174214356ed88c16a41e39f31fe372", size = 1775998, upload-time = "2025-04-04T11:50:05.744Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/c8/a9250944fb9a0a4c65b5d456f3a87ee6c249b53962757d77c28df8fadb46/thinc-8.3.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c54705e45a710e49758192592a3e0a80482edfdf5c61fc99f5d27ae822f652c5", size = 890177, upload-time = "2025-04-04T11:50:07.543Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/89/1ac54b18d4de79872c633302a10825695a36cd2e552cb8d4fea820b7a357/thinc-8.3.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:91acdbf3041c0ac1775ede570535a779cdf1312c317cd054d7b9d200da685c23", size = 839410, upload-time = "2025-04-04T11:50:09.26Z" },
+    { url = "https://files.pythonhosted.org/packages/37/76/e1a76ab42e4637c4b8988d59784cdc1169a532d3043c36d2faf1a8d95228/thinc-8.3.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f5a1db861614f91ff127feecce681c2213777b2d3d1ee6644bcc8a886acf0595", size = 4195748, upload-time = "2025-04-04T11:50:10.92Z" },
+    { url = "https://files.pythonhosted.org/packages/00/a9/c59ac3260e7aff6b9dc80f495f1846a80b490595db06d040b05205d1f7f8/thinc-8.3.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:512e461989df8a30558367061d63ae6f1a6b4abe3c016a3360ee827e824254e0", size = 4261270, upload-time = "2025-04-04T11:50:12.953Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/8e/e86c5cbc6ebe238aa747ef9e20a969f6faba9ebbe1cbce059119f9614dd6/thinc-8.3.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a087aea2a63e6b9ccde61163d5922553b58908e96f8ad49cd0fd2edeb43e063f", size = 5067567, upload-time = "2025-04-04T11:50:18.317Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/8a/16670e4de36231aab5b052c734ad716be29aab2c0d2f3d8dd9c8dd27fafc/thinc-8.3.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b1d85dd5d94bb75006864c7d99fd5b75d05b1602d571e7fcdb42d4521f962048", size = 5309405, upload-time = "2025-04-04T11:50:20.075Z" },
+    { url = "https://files.pythonhosted.org/packages/58/08/5439dd15b661610d8a3b919f18065ebf0d664b6a54a3794206622a74c910/thinc-8.3.6-cp312-cp312-win_amd64.whl", hash = "sha256:1170d85294366127d97a27dd5896f4abe90e2a5ea2b7988de9a5bb8e1128d222", size = 1749275, upload-time = "2025-04-04T11:50:21.769Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/03/0ba9bec3057f4a9c0b7ba53839aebcbbbc28de3b91330cb8de74a885b8f6/thinc-8.3.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:d8743ee8ad2d59fda018b57e5da102d6098bbeb0f70476f3fd8ceb9d215d88b9", size = 883375, upload-time = "2025-04-04T11:50:23.273Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/79/ac31cd25d1d973b824de10ebbc56788688aecdd8f56800daf8edfff45097/thinc-8.3.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:89dbeb2ca94f1033e90999a70e2bc9dd5390d5341dc1a3a4b8793d03855265c3", size = 832654, upload-time = "2025-04-04T11:50:24.871Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/0d/fb5e8e49dfb53cc02ce907f81002031c6f4fe7e7aa44b1004ea695630017/thinc-8.3.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:89a5460695067aa6e4182515cfd2018263db77cc17b7031d50ed696e990797a8", size = 4158592, upload-time = "2025-04-04T11:50:26.403Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/42/c87990ca214b9910f33b110d3b1ac213407388d35376bc955ad45e5de764/thinc-8.3.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0aa8e32f49234569fd10c35b562ee2f9c0d51225365a6e604a5a67396a49f2c1", size = 4236211, upload-time = "2025-04-04T11:50:27.943Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/10/9975bcee4dd4634bfb87df0447d7fa86d6c9b2d9228e56d4adb98cc19cbc/thinc-8.3.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f432158b80cf75a096980470b790b51d81daf9c2822598adebfc3cb58588fd6c", size = 5049197, upload-time = "2025-04-04T11:50:29.583Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/34/e1b384009eb8ad2192770157961cd0c2e2712fedf49e1dfd902e3d9b9973/thinc-8.3.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:61fb33a22aba40366fa9018ab34580f74fc40be821ab8af77ac1fdbeac17243b", size = 5278543, upload-time = "2025-04-04T11:50:31.524Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/26/f77ef4bd174bfeac491237a4ca3f74ba2ee2f672004f76cff90f8407a489/thinc-8.3.6-cp313-cp313-win_amd64.whl", hash = "sha256:ddd7041946a427f6a9b0b49419353d02ad7eb43fe16724bfcc3bdeb9562040b1", size = 1746883, upload-time = "2025-04-04T11:50:33.038Z" },
+]
+
+[[package]]
+name = "threadpoolctl"
+version = "3.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274, upload-time = "2025-03-13T13:49:23.031Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638, upload-time = "2025-03-13T13:49:21.846Z" },
+]
+
+[[package]]
+name = "toml"
+version = "0.10.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/be/ba/1f744cdc819428fc6b5084ec34d9b30660f6f9daaf70eead706e3203ec3c/toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f", size = 22253, upload-time = "2020-11-01T01:40:22.204Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/44/6f/7120676b6d73228c96e17f1f794d8ab046fc910d781c8d151120c3f1569e/toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b", size = 16588, upload-time = "2020-11-01T01:40:20.672Z" },
+]
+
+[[package]]
+name = "tornado"
+version = "6.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/09/ce/1eb500eae19f4648281bb2186927bb062d2438c2e5093d1360391afd2f90/tornado-6.5.2.tar.gz", hash = "sha256:ab53c8f9a0fa351e2c0741284e06c7a45da86afb544133201c5cc8578eb076a0", size = 510821, upload-time = "2025-08-08T18:27:00.78Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f6/48/6a7529df2c9cc12efd2e8f5dd219516184d703b34c06786809670df5b3bd/tornado-6.5.2-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:2436822940d37cde62771cff8774f4f00b3c8024fe482e16ca8387b8a2724db6", size = 442563, upload-time = "2025-08-08T18:26:42.945Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/b5/9b575a0ed3e50b00c40b08cbce82eb618229091d09f6d14bce80fc01cb0b/tornado-6.5.2-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:583a52c7aa94ee046854ba81d9ebb6c81ec0fd30386d96f7640c96dad45a03ef", size = 440729, upload-time = "2025-08-08T18:26:44.473Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/4e/619174f52b120efcf23633c817fd3fed867c30bff785e2cd5a53a70e483c/tornado-6.5.2-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b0fe179f28d597deab2842b86ed4060deec7388f1fd9c1b4a41adf8af058907e", size = 444295, upload-time = "2025-08-08T18:26:46.021Z" },
+    { url = "https://files.pythonhosted.org/packages/95/fa/87b41709552bbd393c85dd18e4e3499dcd8983f66e7972926db8d96aa065/tornado-6.5.2-cp39-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b186e85d1e3536d69583d2298423744740986018e393d0321df7340e71898882", size = 443644, upload-time = "2025-08-08T18:26:47.625Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/41/fb15f06e33d7430ca89420283a8762a4e6b8025b800ea51796ab5e6d9559/tornado-6.5.2-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e792706668c87709709c18b353da1f7662317b563ff69f00bab83595940c7108", size = 443878, upload-time = "2025-08-08T18:26:50.599Z" },
+    { url = "https://files.pythonhosted.org/packages/11/92/fe6d57da897776ad2e01e279170ea8ae726755b045fe5ac73b75357a5a3f/tornado-6.5.2-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:06ceb1300fd70cb20e43b1ad8aaee0266e69e7ced38fa910ad2e03285009ce7c", size = 444549, upload-time = "2025-08-08T18:26:51.864Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/02/c8f4f6c9204526daf3d760f4aa555a7a33ad0e60843eac025ccfd6ff4a93/tornado-6.5.2-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:74db443e0f5251be86cbf37929f84d8c20c27a355dd452a5cfa2aada0d001ec4", size = 443973, upload-time = "2025-08-08T18:26:53.625Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/2d/f5f5707b655ce2317190183868cd0f6822a1121b4baeae509ceb9590d0bd/tornado-6.5.2-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:b5e735ab2889d7ed33b32a459cac490eda71a1ba6857b0118de476ab6c366c04", size = 443954, upload-time = "2025-08-08T18:26:55.072Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/59/593bd0f40f7355806bf6573b47b8c22f8e1374c9b6fd03114bd6b7a3dcfd/tornado-6.5.2-cp39-abi3-win32.whl", hash = "sha256:c6f29e94d9b37a95013bb669616352ddb82e3bfe8326fccee50583caebc8a5f0", size = 445023, upload-time = "2025-08-08T18:26:56.677Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/2a/f609b420c2f564a748a2d80ebfb2ee02a73ca80223af712fca591386cafb/tornado-6.5.2-cp39-abi3-win_amd64.whl", hash = "sha256:e56a5af51cc30dd2cae649429af65ca2f6571da29504a07995175df14c18f35f", size = 445427, upload-time = "2025-08-08T18:26:57.91Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/4f/e1f65e8f8c76d73658b33d33b81eed4322fb5085350e4328d5c956f0c8f9/tornado-6.5.2-cp39-abi3-win_arm64.whl", hash = "sha256:d6c33dc3672e3a1f3618eb63b7ef4683a7688e7b9e6e8f0d9aa5726360a004af", size = 444456, upload-time = "2025-08-08T18:26:59.207Z" },
+]
+
+[[package]]
+name = "tqdm"
+version = "4.67.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737, upload-time = "2024-11-24T20:12:22.481Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2", size = 78540, upload-time = "2024-11-24T20:12:19.698Z" },
+]
+
+[[package]]
+name = "typer"
+version = "0.19.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "rich" },
+    { name = "shellingham" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/21/ca/950278884e2ca20547ff3eb109478c6baf6b8cf219318e6bc4f666fad8e8/typer-0.19.2.tar.gz", hash = "sha256:9ad824308ded0ad06cc716434705f691d4ee0bfd0fb081839d2e426860e7fdca", size = 104755, upload-time = "2025-09-23T09:47:48.256Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/00/22/35617eee79080a5d071d0f14ad698d325ee6b3bf824fc0467c03b30e7fa8/typer-0.19.2-py3-none-any.whl", hash = "sha256:755e7e19670ffad8283db353267cb81ef252f595aa6834a0d1ca9312d9326cb9", size = 46748, upload-time = "2025-09-23T09:47:46.777Z" },
+]
+
+[[package]]
+name = "typing-extensions"
+version = "4.15.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391, upload-time = "2025-08-25T13:49:26.313Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614, upload-time = "2025-08-25T13:49:24.86Z" },
+]
+
+[[package]]
+name = "typing-inspection"
+version = "0.4.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/55/e3/70399cb7dd41c10ac53367ae42139cf4b1ca5f36bb3dc6c9d33acdb43655/typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464", size = 75949, upload-time = "2025-10-01T02:14:41.687Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7", size = 14611, upload-time = "2025-10-01T02:14:40.154Z" },
+]
+
+[[package]]
+name = "tzdata"
+version = "2025.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380, upload-time = "2025-03-23T13:54:43.652Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839, upload-time = "2025-03-23T13:54:41.845Z" },
+]
+
+[[package]]
+name = "urllib3"
+version = "2.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/15/22/9ee70a2574a4f4599c47dd506532914ce044817c7752a79b6a51286319bc/urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760", size = 393185, upload-time = "2025-06-18T14:07:41.644Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc", size = 129795, upload-time = "2025-06-18T14:07:40.39Z" },
+]
+
+[[package]]
+name = "wasabi"
+version = "1.1.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ac/f9/054e6e2f1071e963b5e746b48d1e3727470b2a490834d18ad92364929db3/wasabi-1.1.3.tar.gz", hash = "sha256:4bb3008f003809db0c3e28b4daf20906ea871a2bb43f9914197d540f4f2e0878", size = 30391, upload-time = "2024-05-31T16:56:18.99Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/06/7c/34330a89da55610daa5f245ddce5aab81244321101614751e7537f125133/wasabi-1.1.3-py3-none-any.whl", hash = "sha256:f76e16e8f7e79f8c4c8be49b4024ac725713ab10cd7f19350ad18a8e3f71728c", size = 27880, upload-time = "2024-05-31T16:56:16.699Z" },
+]
+
+[[package]]
+name = "watchdog"
+version = "6.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/db/7d/7f3d619e951c88ed75c6037b246ddcf2d322812ee8ea189be89511721d54/watchdog-6.0.0.tar.gz", hash = "sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282", size = 131220, upload-time = "2024-11-01T14:07:13.037Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a9/c7/ca4bf3e518cb57a686b2feb4f55a1892fd9a3dd13f470fca14e00f80ea36/watchdog-6.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7607498efa04a3542ae3e05e64da8202e58159aa1fa4acddf7678d34a35d4f13", size = 79079, upload-time = "2024-11-01T14:06:59.472Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/51/d46dc9332f9a647593c947b4b88e2381c8dfc0942d15b8edc0310fa4abb1/watchdog-6.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:9041567ee8953024c83343288ccc458fd0a2d811d6a0fd68c4c22609e3490379", size = 79078, upload-time = "2024-11-01T14:07:01.431Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/57/04edbf5e169cd318d5f07b4766fee38e825d64b6913ca157ca32d1a42267/watchdog-6.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:82dc3e3143c7e38ec49d61af98d6558288c415eac98486a5c581726e0737c00e", size = 79076, upload-time = "2024-11-01T14:07:02.568Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/cc/da8422b300e13cb187d2203f20b9253e91058aaf7db65b74142013478e66/watchdog-6.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:212ac9b8bf1161dc91bd09c048048a95ca3a4c4f5e5d4a7d1b1a7d5752a7f96f", size = 79077, upload-time = "2024-11-01T14:07:03.893Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/3b/b8964e04ae1a025c44ba8e4291f86e97fac443bca31de8bd98d3263d2fcf/watchdog-6.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:e3df4cbb9a450c6d49318f6d14f4bbc80d763fa587ba46ec86f99f9e6876bb26", size = 79078, upload-time = "2024-11-01T14:07:05.189Z" },
+    { url = "https://files.pythonhosted.org/packages/62/ae/a696eb424bedff7407801c257d4b1afda455fe40821a2be430e173660e81/watchdog-6.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:2cce7cfc2008eb51feb6aab51251fd79b85d9894e98ba847408f662b3395ca3c", size = 79077, upload-time = "2024-11-01T14:07:06.376Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/e8/dbf020b4d98251a9860752a094d09a65e1b436ad181faf929983f697048f/watchdog-6.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:20ffe5b202af80ab4266dcd3e91aae72bf2da48c0d33bdb15c66658e685e94e2", size = 79078, upload-time = "2024-11-01T14:07:07.547Z" },
+    { url = "https://files.pythonhosted.org/packages/07/f6/d0e5b343768e8bcb4cda79f0f2f55051bf26177ecd5651f84c07567461cf/watchdog-6.0.0-py3-none-win32.whl", hash = "sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a", size = 79065, upload-time = "2024-11-01T14:07:09.525Z" },
+    { url = "https://files.pythonhosted.org/packages/db/d9/c495884c6e548fce18a8f40568ff120bc3a4b7b99813081c8ac0c936fa64/watchdog-6.0.0-py3-none-win_amd64.whl", hash = "sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680", size = 79070, upload-time = "2024-11-01T14:07:10.686Z" },
+    { url = "https://files.pythonhosted.org/packages/33/e8/e40370e6d74ddba47f002a32919d91310d6074130fe4e17dabcafc15cbf1/watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f", size = 79067, upload-time = "2024-11-01T14:07:11.845Z" },
+]
+
+[[package]]
+name = "weasel"
+version = "0.4.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cloudpathlib" },
+    { name = "confection" },
+    { name = "packaging" },
+    { name = "pydantic" },
+    { name = "requests" },
+    { name = "smart-open" },
+    { name = "srsly" },
+    { name = "typer" },
+    { name = "wasabi" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a7/1a/9c522dd61b52939c217925d3e55c95f9348b73a66a956f52608e1e59a2c0/weasel-0.4.1.tar.gz", hash = "sha256:aabc210f072e13f6744e5c3a28037f93702433405cd35673f7c6279147085aa9", size = 38417, upload-time = "2024-05-15T08:52:54.765Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2a/87/abd57374044e1f627f0a905ac33c1a7daab35a3a815abfea4e1bafd3fdb1/weasel-0.4.1-py3-none-any.whl", hash = "sha256:24140a090ea1ac512a2b2f479cc64192fd1d527a7f3627671268d08ed5ac418c", size = 50270, upload-time = "2024-05-15T08:52:52.977Z" },
+]
+
+[[package]]
+name = "wrapt"
+version = "1.17.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/95/8f/aeb76c5b46e273670962298c23e7ddde79916cb74db802131d49a85e4b7d/wrapt-1.17.3.tar.gz", hash = "sha256:f66eb08feaa410fe4eebd17f2a2c8e2e46d3476e9f8c783daa8e09e0faa666d0", size = 55547, upload-time = "2025-08-12T05:53:21.714Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/52/db/00e2a219213856074a213503fdac0511203dceefff26e1daa15250cc01a0/wrapt-1.17.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:273a736c4645e63ac582c60a56b0acb529ef07f78e08dc6bfadf6a46b19c0da7", size = 53482, upload-time = "2025-08-12T05:51:45.79Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/30/ca3c4a5eba478408572096fe9ce36e6e915994dd26a4e9e98b4f729c06d9/wrapt-1.17.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5531d911795e3f935a9c23eb1c8c03c211661a5060aab167065896bbf62a5f85", size = 38674, upload-time = "2025-08-12T05:51:34.629Z" },
+    { url = "https://files.pythonhosted.org/packages/31/25/3e8cc2c46b5329c5957cec959cb76a10718e1a513309c31399a4dad07eb3/wrapt-1.17.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:0610b46293c59a3adbae3dee552b648b984176f8562ee0dba099a56cfbe4df1f", size = 38959, upload-time = "2025-08-12T05:51:56.074Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/8f/a32a99fc03e4b37e31b57cb9cefc65050ea08147a8ce12f288616b05ef54/wrapt-1.17.3-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b32888aad8b6e68f83a8fdccbf3165f5469702a7544472bdf41f582970ed3311", size = 82376, upload-time = "2025-08-12T05:52:32.134Z" },
+    { url = "https://files.pythonhosted.org/packages/31/57/4930cb8d9d70d59c27ee1332a318c20291749b4fba31f113c2f8ac49a72e/wrapt-1.17.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8cccf4f81371f257440c88faed6b74f1053eef90807b77e31ca057b2db74edb1", size = 83604, upload-time = "2025-08-12T05:52:11.663Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/f3/1afd48de81d63dd66e01b263a6fbb86e1b5053b419b9b33d13e1f6d0f7d0/wrapt-1.17.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d8a210b158a34164de8bb68b0e7780041a903d7b00c87e906fb69928bf7890d5", size = 82782, upload-time = "2025-08-12T05:52:12.626Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/d7/4ad5327612173b144998232f98a85bb24b60c352afb73bc48e3e0d2bdc4e/wrapt-1.17.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:79573c24a46ce11aab457b472efd8d125e5a51da2d1d24387666cd85f54c05b2", size = 82076, upload-time = "2025-08-12T05:52:33.168Z" },
+    { url = "https://files.pythonhosted.org/packages/bb/59/e0adfc831674a65694f18ea6dc821f9fcb9ec82c2ce7e3d73a88ba2e8718/wrapt-1.17.3-cp311-cp311-win32.whl", hash = "sha256:c31eebe420a9a5d2887b13000b043ff6ca27c452a9a22fa71f35f118e8d4bf89", size = 36457, upload-time = "2025-08-12T05:53:03.936Z" },
+    { url = "https://files.pythonhosted.org/packages/83/88/16b7231ba49861b6f75fc309b11012ede4d6b0a9c90969d9e0db8d991aeb/wrapt-1.17.3-cp311-cp311-win_amd64.whl", hash = "sha256:0b1831115c97f0663cb77aa27d381237e73ad4f721391a9bfb2fe8bc25fa6e77", size = 38745, upload-time = "2025-08-12T05:53:02.885Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/1e/c4d4f3398ec073012c51d1c8d87f715f56765444e1a4b11e5180577b7e6e/wrapt-1.17.3-cp311-cp311-win_arm64.whl", hash = "sha256:5a7b3c1ee8265eb4c8f1b7d29943f195c00673f5ab60c192eba2d4a7eae5f46a", size = 36806, upload-time = "2025-08-12T05:52:53.368Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/41/cad1aba93e752f1f9268c77270da3c469883d56e2798e7df6240dcb2287b/wrapt-1.17.3-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:ab232e7fdb44cdfbf55fc3afa31bcdb0d8980b9b95c38b6405df2acb672af0e0", size = 53998, upload-time = "2025-08-12T05:51:47.138Z" },
+    { url = "https://files.pythonhosted.org/packages/60/f8/096a7cc13097a1869fe44efe68dace40d2a16ecb853141394047f0780b96/wrapt-1.17.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:9baa544e6acc91130e926e8c802a17f3b16fbea0fd441b5a60f5cf2cc5c3deba", size = 39020, upload-time = "2025-08-12T05:51:35.906Z" },
+    { url = "https://files.pythonhosted.org/packages/33/df/bdf864b8997aab4febb96a9ae5c124f700a5abd9b5e13d2a3214ec4be705/wrapt-1.17.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6b538e31eca1a7ea4605e44f81a48aa24c4632a277431a6ed3f328835901f4fd", size = 39098, upload-time = "2025-08-12T05:51:57.474Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/81/5d931d78d0eb732b95dc3ddaeeb71c8bb572fb01356e9133916cd729ecdd/wrapt-1.17.3-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:042ec3bb8f319c147b1301f2393bc19dba6e176b7da446853406d041c36c7828", size = 88036, upload-time = "2025-08-12T05:52:34.784Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/38/2e1785df03b3d72d34fc6252d91d9d12dc27a5c89caef3335a1bbb8908ca/wrapt-1.17.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3af60380ba0b7b5aeb329bc4e402acd25bd877e98b3727b0135cb5c2efdaefe9", size = 88156, upload-time = "2025-08-12T05:52:13.599Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/8b/48cdb60fe0603e34e05cffda0b2a4adab81fd43718e11111a4b0100fd7c1/wrapt-1.17.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0b02e424deef65c9f7326d8c19220a2c9040c51dc165cddb732f16198c168396", size = 87102, upload-time = "2025-08-12T05:52:14.56Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/51/d81abca783b58f40a154f1b2c56db1d2d9e0d04fa2d4224e357529f57a57/wrapt-1.17.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:74afa28374a3c3a11b3b5e5fca0ae03bef8450d6aa3ab3a1e2c30e3a75d023dc", size = 87732, upload-time = "2025-08-12T05:52:36.165Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/b1/43b286ca1392a006d5336412d41663eeef1ad57485f3e52c767376ba7e5a/wrapt-1.17.3-cp312-cp312-win32.whl", hash = "sha256:4da9f45279fff3543c371d5ababc57a0384f70be244de7759c85a7f989cb4ebe", size = 36705, upload-time = "2025-08-12T05:53:07.123Z" },
+    { url = "https://files.pythonhosted.org/packages/28/de/49493f962bd3c586ab4b88066e967aa2e0703d6ef2c43aa28cb83bf7b507/wrapt-1.17.3-cp312-cp312-win_amd64.whl", hash = "sha256:e71d5c6ebac14875668a1e90baf2ea0ef5b7ac7918355850c0908ae82bcb297c", size = 38877, upload-time = "2025-08-12T05:53:05.436Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/48/0f7102fe9cb1e8a5a77f80d4f0956d62d97034bbe88d33e94699f99d181d/wrapt-1.17.3-cp312-cp312-win_arm64.whl", hash = "sha256:604d076c55e2fdd4c1c03d06dc1a31b95130010517b5019db15365ec4a405fc6", size = 36885, upload-time = "2025-08-12T05:52:54.367Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/f6/759ece88472157acb55fc195e5b116e06730f1b651b5b314c66291729193/wrapt-1.17.3-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a47681378a0439215912ef542c45a783484d4dd82bac412b71e59cf9c0e1cea0", size = 54003, upload-time = "2025-08-12T05:51:48.627Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/a9/49940b9dc6d47027dc850c116d79b4155f15c08547d04db0f07121499347/wrapt-1.17.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:54a30837587c6ee3cd1a4d1c2ec5d24e77984d44e2f34547e2323ddb4e22eb77", size = 39025, upload-time = "2025-08-12T05:51:37.156Z" },
+    { url = "https://files.pythonhosted.org/packages/45/35/6a08de0f2c96dcdd7fe464d7420ddb9a7655a6561150e5fc4da9356aeaab/wrapt-1.17.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:16ecf15d6af39246fe33e507105d67e4b81d8f8d2c6598ff7e3ca1b8a37213f7", size = 39108, upload-time = "2025-08-12T05:51:58.425Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/37/6faf15cfa41bf1f3dba80cd3f5ccc6622dfccb660ab26ed79f0178c7497f/wrapt-1.17.3-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:6fd1ad24dc235e4ab88cda009e19bf347aabb975e44fd5c2fb22a3f6e4141277", size = 88072, upload-time = "2025-08-12T05:52:37.53Z" },
+    { url = "https://files.pythonhosted.org/packages/78/f2/efe19ada4a38e4e15b6dff39c3e3f3f73f5decf901f66e6f72fe79623a06/wrapt-1.17.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0ed61b7c2d49cee3c027372df5809a59d60cf1b6c2f81ee980a091f3afed6a2d", size = 88214, upload-time = "2025-08-12T05:52:15.886Z" },
+    { url = "https://files.pythonhosted.org/packages/40/90/ca86701e9de1622b16e09689fc24b76f69b06bb0150990f6f4e8b0eeb576/wrapt-1.17.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:423ed5420ad5f5529db9ce89eac09c8a2f97da18eb1c870237e84c5a5c2d60aa", size = 87105, upload-time = "2025-08-12T05:52:17.914Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/e0/d10bd257c9a3e15cbf5523025252cc14d77468e8ed644aafb2d6f54cb95d/wrapt-1.17.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e01375f275f010fcbf7f643b4279896d04e571889b8a5b3f848423d91bf07050", size = 87766, upload-time = "2025-08-12T05:52:39.243Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/cf/7d848740203c7b4b27eb55dbfede11aca974a51c3d894f6cc4b865f42f58/wrapt-1.17.3-cp313-cp313-win32.whl", hash = "sha256:53e5e39ff71b3fc484df8a522c933ea2b7cdd0d5d15ae82e5b23fde87d44cbd8", size = 36711, upload-time = "2025-08-12T05:53:10.074Z" },
+    { url = "https://files.pythonhosted.org/packages/57/54/35a84d0a4d23ea675994104e667ceff49227ce473ba6a59ba2c84f250b74/wrapt-1.17.3-cp313-cp313-win_amd64.whl", hash = "sha256:1f0b2f40cf341ee8cc1a97d51ff50dddb9fcc73241b9143ec74b30fc4f44f6cb", size = 38885, upload-time = "2025-08-12T05:53:08.695Z" },
+    { url = "https://files.pythonhosted.org/packages/01/77/66e54407c59d7b02a3c4e0af3783168fff8e5d61def52cda8728439d86bc/wrapt-1.17.3-cp313-cp313-win_arm64.whl", hash = "sha256:7425ac3c54430f5fc5e7b6f41d41e704db073309acfc09305816bc6a0b26bb16", size = 36896, upload-time = "2025-08-12T05:52:55.34Z" },
+    { url = "https://files.pythonhosted.org/packages/02/a2/cd864b2a14f20d14f4c496fab97802001560f9f41554eef6df201cd7f76c/wrapt-1.17.3-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:cf30f6e3c077c8e6a9a7809c94551203c8843e74ba0c960f4a98cd80d4665d39", size = 54132, upload-time = "2025-08-12T05:51:49.864Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/46/d011725b0c89e853dc44cceb738a307cde5d240d023d6d40a82d1b4e1182/wrapt-1.17.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:e228514a06843cae89621384cfe3a80418f3c04aadf8a3b14e46a7be704e4235", size = 39091, upload-time = "2025-08-12T05:51:38.935Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/9e/3ad852d77c35aae7ddebdbc3b6d35ec8013af7d7dddad0ad911f3d891dae/wrapt-1.17.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:5ea5eb3c0c071862997d6f3e02af1d055f381b1d25b286b9d6644b79db77657c", size = 39172, upload-time = "2025-08-12T05:51:59.365Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/f7/c983d2762bcce2326c317c26a6a1e7016f7eb039c27cdf5c4e30f4160f31/wrapt-1.17.3-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:281262213373b6d5e4bb4353bc36d1ba4084e6d6b5d242863721ef2bf2c2930b", size = 87163, upload-time = "2025-08-12T05:52:40.965Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/0f/f673f75d489c7f22d17fe0193e84b41540d962f75fce579cf6873167c29b/wrapt-1.17.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dc4a8d2b25efb6681ecacad42fca8859f88092d8732b170de6a5dddd80a1c8fa", size = 87963, upload-time = "2025-08-12T05:52:20.326Z" },
+    { url = "https://files.pythonhosted.org/packages/df/61/515ad6caca68995da2fac7a6af97faab8f78ebe3bf4f761e1b77efbc47b5/wrapt-1.17.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:373342dd05b1d07d752cecbec0c41817231f29f3a89aa8b8843f7b95992ed0c7", size = 86945, upload-time = "2025-08-12T05:52:21.581Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/bd/4e70162ce398462a467bc09e768bee112f1412e563620adc353de9055d33/wrapt-1.17.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d40770d7c0fd5cbed9d84b2c3f2e156431a12c9a37dc6284060fb4bec0b7ffd4", size = 86857, upload-time = "2025-08-12T05:52:43.043Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/b8/da8560695e9284810b8d3df8a19396a6e40e7518059584a1a394a2b35e0a/wrapt-1.17.3-cp314-cp314-win32.whl", hash = "sha256:fbd3c8319de8e1dc79d346929cd71d523622da527cca14e0c1d257e31c2b8b10", size = 37178, upload-time = "2025-08-12T05:53:12.605Z" },
+    { url = "https://files.pythonhosted.org/packages/db/c8/b71eeb192c440d67a5a0449aaee2310a1a1e8eca41676046f99ed2487e9f/wrapt-1.17.3-cp314-cp314-win_amd64.whl", hash = "sha256:e1a4120ae5705f673727d3253de3ed0e016f7cd78dc463db1b31e2463e1f3cf6", size = 39310, upload-time = "2025-08-12T05:53:11.106Z" },
+    { url = "https://files.pythonhosted.org/packages/45/20/2cda20fd4865fa40f86f6c46ed37a2a8356a7a2fde0773269311f2af56c7/wrapt-1.17.3-cp314-cp314-win_arm64.whl", hash = "sha256:507553480670cab08a800b9463bdb881b2edeed77dc677b0a5915e6106e91a58", size = 37266, upload-time = "2025-08-12T05:52:56.531Z" },
+    { url = "https://files.pythonhosted.org/packages/77/ed/dd5cf21aec36c80443c6f900449260b80e2a65cf963668eaef3b9accce36/wrapt-1.17.3-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:ed7c635ae45cfbc1a7371f708727bf74690daedc49b4dba310590ca0bd28aa8a", size = 56544, upload-time = "2025-08-12T05:51:51.109Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/96/450c651cc753877ad100c7949ab4d2e2ecc4d97157e00fa8f45df682456a/wrapt-1.17.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:249f88ed15503f6492a71f01442abddd73856a0032ae860de6d75ca62eed8067", size = 40283, upload-time = "2025-08-12T05:51:39.912Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/86/2fcad95994d9b572db57632acb6f900695a648c3e063f2cd344b3f5c5a37/wrapt-1.17.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:5a03a38adec8066d5a37bea22f2ba6bbf39fcdefbe2d91419ab864c3fb515454", size = 40366, upload-time = "2025-08-12T05:52:00.693Z" },
+    { url = "https://files.pythonhosted.org/packages/64/0e/f4472f2fdde2d4617975144311f8800ef73677a159be7fe61fa50997d6c0/wrapt-1.17.3-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:5d4478d72eb61c36e5b446e375bbc49ed002430d17cdec3cecb36993398e1a9e", size = 108571, upload-time = "2025-08-12T05:52:44.521Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/01/9b85a99996b0a97c8a17484684f206cbb6ba73c1ce6890ac668bcf3838fb/wrapt-1.17.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:223db574bb38637e8230eb14b185565023ab624474df94d2af18f1cdb625216f", size = 113094, upload-time = "2025-08-12T05:52:22.618Z" },
+    { url = "https://files.pythonhosted.org/packages/25/02/78926c1efddcc7b3aa0bc3d6b33a822f7d898059f7cd9ace8c8318e559ef/wrapt-1.17.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e405adefb53a435f01efa7ccdec012c016b5a1d3f35459990afc39b6be4d5056", size = 110659, upload-time = "2025-08-12T05:52:24.057Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/ee/c414501ad518ac3e6fe184753632fe5e5ecacdcf0effc23f31c1e4f7bfcf/wrapt-1.17.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:88547535b787a6c9ce4086917b6e1d291aa8ed914fdd3a838b3539dc95c12804", size = 106946, upload-time = "2025-08-12T05:52:45.976Z" },
+    { url = "https://files.pythonhosted.org/packages/be/44/a1bd64b723d13bb151d6cc91b986146a1952385e0392a78567e12149c7b4/wrapt-1.17.3-cp314-cp314t-win32.whl", hash = "sha256:41b1d2bc74c2cac6f9074df52b2efbef2b30bdfe5f40cb78f8ca22963bc62977", size = 38717, upload-time = "2025-08-12T05:53:15.214Z" },
+    { url = "https://files.pythonhosted.org/packages/79/d9/7cfd5a312760ac4dd8bf0184a6ee9e43c33e47f3dadc303032ce012b8fa3/wrapt-1.17.3-cp314-cp314t-win_amd64.whl", hash = "sha256:73d496de46cd2cdbdbcce4ae4bcdb4afb6a11234a1df9c085249d55166b95116", size = 41334, upload-time = "2025-08-12T05:53:14.178Z" },
+    { url = "https://files.pythonhosted.org/packages/46/78/10ad9781128ed2f99dbc474f43283b13fea8ba58723e98844367531c18e9/wrapt-1.17.3-cp314-cp314t-win_arm64.whl", hash = "sha256:f38e60678850c42461d4202739f9bf1e3a737c7ad283638251e79cc49effb6b6", size = 38471, upload-time = "2025-08-12T05:52:57.784Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/f6/a933bd70f98e9cf3e08167fc5cd7aaaca49147e48411c0bd5ae701bb2194/wrapt-1.17.3-py3-none-any.whl", hash = "sha256:7171ae35d2c33d326ac19dd8facb1e82e5fd04ef8c6c0e394d7af55a55051c22", size = 23591, upload-time = "2025-08-12T05:53:20.674Z" },
+]
+
+[[package]]
+name = "xgboost"
+version = "3.0.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "nvidia-nccl-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" },
+    { name = "scipy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7d/c0/561b88dabe82f45555dd2b68abd5d79f787d3e383436a6a54453d5deeb3f/xgboost-3.0.5.tar.gz", hash = "sha256:1a57a0d64a06b596992b664fe17dd1f9782138c6e35ad8fb6355e19a359fa50f", size = 1159729, upload-time = "2025-09-05T09:18:59.3Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ea/df/bf2416bfc138b1d2fa261ecb7f612ca00545aae126aeb1dc314df3291d73/xgboost-3.0.5-py3-none-macosx_10_15_x86_64.whl", hash = "sha256:f974028c7a0c3ae51ef87e2900405436ddafff46452f6f334f5cc295e921429b", size = 2249197, upload-time = "2025-09-05T09:19:35.726Z" },
+    { url = "https://files.pythonhosted.org/packages/28/c0/2c3e8a141180171aae913825bf5a200f37d1f7c598268cd8b6a219fb0eab/xgboost-3.0.5-py3-none-macosx_12_0_arm64.whl", hash = "sha256:40c324c329bf74f44571cdafb7aa7435366309d76ec14b3e16874862aeb14351", size = 2025998, upload-time = "2025-09-05T09:19:59.21Z" },
+    { url = "https://files.pythonhosted.org/packages/66/68/e0e8285282ba81858d74d699cfee9e562a2a3cc7975f0fbd068b83aac559/xgboost-3.0.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:c580c82500ad566d927581381550561300492c0bc9c143b2e5be208d16f093d1", size = 4841604, upload-time = "2025-09-05T09:25:51.836Z" },
+    { url = "https://files.pythonhosted.org/packages/69/32/eb7e862179194c6440eab63f834a3de064d6340a8b873b5520ac035891db/xgboost-3.0.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:d7f57a04629b52bae91a80e6721b9cdd009b605827a9eca67953675292b4487e", size = 4906211, upload-time = "2025-09-05T09:26:40.229Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/d5/6c60111482f41fd680eb0e81e016498bea313cc00a6a4a41b38c8b45bd5c/xgboost-3.0.5-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:d0fe44aaca76e9c4598d3be98ae94661aa53e4c4ceb161dc7183258f0e6fc138", size = 4602832, upload-time = "2025-09-05T09:27:29.547Z" },
+    { url = "https://files.pythonhosted.org/packages/64/ad/61a86228e981b15361ff963e84648b1a29ab43debd95f7c2b3ef9d94dca1/xgboost-3.0.5-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:a03210a3e54c9e543f480db9636fee57247cfcd1ae850b353aeac59eea5ca350", size = 94872848, upload-time = "2025-09-05T09:40:26.786Z" },
+    { url = "https://files.pythonhosted.org/packages/00/5a/f43bad68b31269a72bdd66102732ea4473e98f421ee9f71379e35dcb56f5/xgboost-3.0.5-py3-none-win_amd64.whl", hash = "sha256:660774249d28a729ba8d22dd3d2c048c56e58f65a683b25ef3252e3383fe956f", size = 56826727, upload-time = "2025-09-05T09:23:55.462Z" },
+]
diff --git a/web/__init__.py b/web/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/web/interfaces/data_overview.py b/web/interfaces/data_overview.py
deleted file mode 100644
index 74a3acc..0000000
--- a/web/interfaces/data_overview.py
+++ /dev/null
@@ -1,155 +0,0 @@
-from datetime import datetime
-
-import pandas as pd
-import plotly.express as px
-import streamlit as st
-
-from core.utils.data_loader import OPTIMIZED_DTYPES
-
-
-@st.cache_data
-def load_dataset(file_path: str) -> pd.DataFrame:
-    try:
-        return pd.read_csv(file_path, dtype=OPTIMIZED_DTYPES)
-    except Exception as e:
-        st.error(f"Error loading dataset: {e}")
-        return pd.DataFrame()
-
-
-class DataOverview:
-    def __init__(self, config):
-        self.config = config
-
-    def index(self):
-        st.title("Data Overview")
-        data_files = {
-            "Names": self.config.data.input_file,
-            "Featured Dataset": self.config.data.output_files["featured"],
-            "Evaluation Dataset": self.config.data.output_files["evaluation"],
-            "Male Names": self.config.data.output_files["males"],
-            "Female Names": self.config.data.output_files["females"],
-        }
-
-        selected_file = st.selectbox("Select Dataset", list(data_files.keys()))
-        file_path = self.config.paths.get_data_path(data_files[selected_file])
-
-        if not file_path.exists():
-            st.warning(f"Dataset not found: {file_path}")
-            st.warning("Please run data processing first to generate datasets.")
-            return
-
-        # Load and display data
-        df = load_dataset(str(file_path))
-
-        if df.empty:
-            st.error("Failed to load dataset")
-            return
-
-        # Basic statistics
-        col1, col2, col3, col4 = st.columns(4)
-
-        with col1:
-            st.metric("Total Records", f"{len(df):,}")
-
-        with col2:
-            if "annotated" in df.columns:
-                annotated_pct = (df["annotated"] == 1).mean() * 100
-                st.metric("Annotated", f"{annotated_pct:.1f}%")
-
-        with col3:
-            if "words" in df.columns:
-                avg_words = df["words"].mean()
-                st.metric("Avg Words", f"{avg_words:.1f}")
-
-        with col4:
-            if "length" in df.columns:
-                avg_length = df["length"].mean()
-                st.metric("Avg Length", f"{avg_length:.0f}")
-
-        # Data quality analysis
-        st.subheader("Data Quality Analysis")
-
-        col1, col2 = st.columns(2)
-
-        with col1:
-            # Missing values
-            missing_data = df.isnull().sum()
-            if missing_data.sum() > 0:
-                fig = px.bar(
-                    x=missing_data.index, y=missing_data.values, title="Missing Values by Column"
-                )
-                fig.update_layout(height=400)
-                st.plotly_chart(fig, use_container_width=True)
-            else:
-                st.success("No missing values found")
-
-        with col2:
-            # Gender distribution
-            if "sex" in df.columns:
-                gender_counts = df["sex"].value_counts()
-                fig = px.pie(
-                    values=gender_counts.values,
-                    names=gender_counts.index,
-                    title="Gender Distribution",
-                )
-                fig.update_layout(height=400)
-                st.plotly_chart(fig, use_container_width=True)
-
-        # Word count distribution
-        if "words" in df.columns:
-            st.subheader("Name Structure Analysis")
-
-            col1, col2 = st.columns(2)
-
-            with col1:
-                word_dist = df["words"].value_counts().sort_index()
-                fig = px.bar(
-                    x=word_dist.index,
-                    y=word_dist.values,
-                    title="Distribution of Word Count in Names",
-                )
-                fig.update_layout(height=400)
-                st.plotly_chart(fig, use_container_width=True)
-
-            with col2:
-                # Province distribution
-                if "province" in df.columns:
-                    province_counts = df["province"].value_counts().head(10)
-                    fig = px.bar(
-                        x=province_counts.values,
-                        y=province_counts.index,
-                        orientation="h",
-                        title="Top 10 Provinces by Name Count",
-                    )
-                    fig.update_layout(height=400)
-                    st.plotly_chart(fig, use_container_width=True)
-
-        # Sample data
-        st.subheader("Sample Data")
-
-        # Display columns selector
-        if not df.empty:
-            columns_to_show = st.multiselect(
-                "Select columns to display",
-                df.columns.tolist(),
-                default=(
-                    ["name", "sex", "province", "words"]
-                    if all(col in df.columns for col in ["name", "sex", "province", "words"])
-                    else df.columns[:5].tolist()
-                ),
-            )
-
-            if columns_to_show:
-                sample_size = st.slider("Number of rows to display", 10, min(1000, len(df)), 50)
-                st.dataframe(df[columns_to_show].head(sample_size), use_container_width=True)
-
-        # Data export
-        st.subheader("Export Data")
-        if st.button("Download as CSV"):
-            csv = df.to_csv(index=False)
-            st.download_button(
-                label="Download CSV",
-                data=csv,
-                file_name=f"{selected_file.lower().replace(' ', '_')}_{datetime.now().strftime('%Y%m%d')}.csv",
-                mime="text/csv",
-            )
diff --git a/web/interfaces/log_reader.py b/web/interfaces/log_reader.py
deleted file mode 100644
index 3305316..0000000
--- a/web/interfaces/log_reader.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import re
-from dataclasses import dataclass
-from datetime import datetime
-from pathlib import Path
-from typing import List, Dict, Optional
-
-
-@dataclass
-class LogEntry:
-    """Represents a single log entry."""
-
-    timestamp: datetime
-    logger: str
-    level: str
-    message: str
-    raw_line: str
-
-
-class LogReader:
-    """Utility class for reading and parsing log files."""
-
-    def __init__(self, log_file_path: Path):
-        """Initialize the log reader with a log file path."""
-        self.log_file_path = Path(log_file_path)
-        # Pattern to match Python logging format: timestamp - logger - level - message
-        self.log_pattern = re.compile(
-            r"(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2},\d{3}) - (.+?) - (\w+) - (.+)"
-        )
-
-    def read_last_entries(self, count: int = 10) -> List[LogEntry]:
-        """Read the last N entries from the log file."""
-        if not self.log_file_path.exists():
-            return []
-
-        try:
-            with open(self.log_file_path, "r", encoding="utf-8") as file:
-                lines = file.readlines()
-
-            # Parse log entries from the end
-            entries = []
-            for line in reversed(lines[-count * 2 :]):  # Read more lines in case some don't match
-                entry = self._parse_log_line(line.strip())
-                if entry:
-                    entries.append(entry)
-                if len(entries) >= count:
-                    break
-
-            # Return entries in chronological order (oldest first of the last N)
-            return list(reversed(entries))
-
-        except Exception as e:
-            print(f"Error reading log file: {e}")
-            return []
-
-    def read_entries_by_level(self, level: str, count: int = 50) -> List[LogEntry]:
-        """Read entries filtered by log level."""
-        if not self.log_file_path.exists():
-            return []
-
-        try:
-            with open(self.log_file_path, "r", encoding="utf-8") as file:
-                lines = file.readlines()
-
-            entries = []
-            for line in reversed(lines):
-                entry = self._parse_log_line(line.strip())
-                if entry and entry.level.upper() == level.upper():
-                    entries.append(entry)
-                if len(entries) >= count:
-                    break
-
-            return list(reversed(entries))
-
-        except Exception as e:
-            print(f"Error reading log file: {e}")
-            return []
-
-    def read_entries_since(self, since: datetime, count: int = 100) -> List[LogEntry]:
-        """Read entries since a specific datetime."""
-        if not self.log_file_path.exists():
-            return []
-
-        try:
-            with open(self.log_file_path, "r", encoding="utf-8") as file:
-                lines = file.readlines()
-
-            entries = []
-            for line in reversed(lines):
-                entry = self._parse_log_line(line.strip())
-                if entry:
-                    if entry.timestamp >= since:
-                        entries.append(entry)
-                    else:
-                        # Stop reading if we've gone past the since time
-                        break
-                if len(entries) >= count:
-                    break
-
-            return list(reversed(entries))
-
-        except Exception as e:
-            print(f"Error reading log file: {e}")
-            return []
-
-    def get_log_stats(self) -> Dict[str, int]:
-        """Get statistics about the log file."""
-        if not self.log_file_path.exists():
-            return {}
-
-        try:
-            with open(self.log_file_path, "r", encoding="utf-8") as file:
-                lines = file.readlines()
-
-            stats = {
-                "total_lines": len(lines),
-                "INFO": 0,
-                "WARNING": 0,
-                "ERROR": 0,
-                "DEBUG": 0,
-                "CRITICAL": 0,
-            }
-
-            for line in lines:
-                entry = self._parse_log_line(line.strip())
-                if entry:
-                    level = entry.level.upper()
-                    if level in stats:
-                        stats[level] += 1
-
-            return stats
-
-        except Exception as e:
-            print(f"Error reading log file: {e}")
-            return {}
-
-    def _parse_log_line(self, line: str) -> Optional[LogEntry]:
-        """Parse a single log line into a LogEntry object."""
-        if not line:
-            return None
-
-        match = self.log_pattern.match(line)
-        if not match:
-            return None
-
-        try:
-            timestamp_str, logger, level, message = match.groups()
-            timestamp = datetime.strptime(timestamp_str, "%Y-%m-%d %H:%M:%S,%f")
-
-            return LogEntry(
-                timestamp=timestamp, logger=logger, level=level, message=message, raw_line=line
-            )
-        except ValueError:
-            return None
-
-
-class MultiLogReader:
-    """Reader for multiple log files."""
-
-    def __init__(self, log_directory: Path):
-        """Initialize with a directory containing log files."""
-        self.log_directory = Path(log_directory)
-
-    def get_available_log_files(self) -> List[Path]:
-        """Get list of available log files."""
-        if not self.log_directory.exists():
-            return []
-
-        return list(self.log_directory.glob("*.log"))
-
-    def read_from_all_files(self, count: int = 10) -> List[LogEntry]:
-        """Read entries from all log files and merge them chronologically."""
-        all_entries = []
-
-        for log_file in self.get_available_log_files():
-            reader = LogReader(log_file)
-            entries = reader.read_last_entries(count)
-            all_entries.extend(entries)
-
-        # Sort by timestamp
-        all_entries.sort(key=lambda x: x.timestamp, reverse=True)
-
-        return all_entries[:count]
diff --git a/web/interfaces/predictions.py b/web/interfaces/predictions.py
deleted file mode 100644
index 7bd2813..0000000
--- a/web/interfaces/predictions.py
+++ /dev/null
@@ -1,362 +0,0 @@
-from datetime import datetime
-from typing import Optional
-
-import numpy as np
-import pandas as pd
-import plotly.express as px
-import streamlit as st
-
-from core.utils.data_loader import OPTIMIZED_DTYPES
-from research.experiment.experiment_runner import ExperimentRunner
-from research.experiment.experiment_tracker import ExperimentTracker
-
-
-class Predictions:
-    def __init__(
-        self, config, experiment_tracker: ExperimentTracker, experiment_runner: ExperimentRunner
-    ):
-        self.config = config
-        self.experiment_tracker = experiment_tracker
-        self.experiment_runner = experiment_runner
-
-    def index(self):
-        st.title("Predictions")
-
-        # Load available models
-        experiments = self.experiment_tracker.list_experiments()
-        completed_experiments = [
-            e for e in experiments if e.status.value == "completed" and e.model_path
-        ]
-
-        if not completed_experiments:
-            st.warning("No trained models available. Please run some experiments first.")
-            return
-
-        # Model selection
-        model_options = {
-            f"{exp.config.name} (Acc: {exp.test_metrics.get('accuracy', 0):.3f})": exp
-            for exp in completed_experiments
-            if exp.test_metrics
-        }
-
-        selected_model_name = st.selectbox("Select Model", list(model_options.keys()))
-
-        if not selected_model_name:
-            return
-
-        selected_experiment = model_options[selected_model_name]
-
-        # Prediction modes
-        prediction_mode = st.radio(
-            "Prediction Mode", ["Single Name", "Batch Upload", "Dataset Prediction"]
-        )
-
-        if prediction_mode == "Single Name":
-            self.show_single_prediction(selected_experiment)
-        elif prediction_mode == "Batch Upload":
-            self.show_batch_prediction(selected_experiment)
-        elif prediction_mode == "Dataset Prediction":
-            self.show_dataset_prediction(selected_experiment)
-
-    def show_single_prediction(self, experiment):
-        """Show single name prediction interface"""
-        name_input = st.text_input("Enter a name:", placeholder="e.g., Jean Baptiste Mukendi")
-        if name_input and st.button("Predict Gender"):
-            try:
-                # Load the model
-                model = self.experiment_runner.load_experiment_model(experiment.experiment_id)
-
-                if model is None:
-                    st.error("Failed to load model")
-                    return
-
-                # Create a DataFrame with the input
-                input_df = self._prepare_single_input(name_input)
-
-                # Make prediction
-                prediction = model.predict(input_df)[0]
-
-                # Get prediction probability if available
-                confidence = self._get_prediction_confidence(model, input_df)
-
-                # Display results
-                self._display_single_prediction_results(
-                    prediction, confidence, experiment, name_input
-                )
-
-            except Exception as e:
-                st.error(f"Error making prediction: {e}")
-
-    def _prepare_single_input(self, name_input: str) -> pd.DataFrame:
-        """Prepare single name input for prediction"""
-        return pd.DataFrame(
-            {
-                "name": [name_input],
-                "words": [len(name_input.split())],
-                "length": [len(name_input.replace(" ", ""))],
-                "province": ["unknown"],  # Default values
-                "identified_name": [None],
-                "identified_surname": [None],
-                "probable_native": [None],
-                "probable_surname": [None],
-            }
-        )
-
-    def _get_prediction_confidence(self, model, input_df: pd.DataFrame) -> Optional[float]:
-        """Get prediction confidence if available"""
-        try:
-            probabilities = model.predict_proba(input_df)[0]
-            return max(probabilities)
-        except:
-            return None
-
-    def _display_single_prediction_results(
-        self, prediction: str, confidence: Optional[float], experiment, name_input: str
-    ):
-        """Display single prediction results"""
-        col1, col2 = st.columns(2)
-
-        with col1:
-            gender_label = "Female" if prediction == "f" else "Male"
-            st.success(f"**Predicted Gender:** {gender_label}")
-
-        with col2:
-            if confidence:
-                st.metric("Confidence", f"{confidence:.2%}")
-
-        # Additional info
-        st.info(f"Model used: {experiment.config.name}")
-        st.info(f"Features used: {', '.join([f.value for f in experiment.config.features])}")
-
-    def show_batch_prediction(self, experiment):
-        uploaded_file = st.file_uploader("Upload CSV file with names", type="csv")
-        if uploaded_file is not None:
-            try:
-                df = pd.read_csv(uploaded_file, dtype=OPTIMIZED_DTYPES)
-
-                st.write("**Uploaded Data Preview:**")
-                st.dataframe(df.head(), use_container_width=True)
-
-                # Column selection
-                df = self._prepare_batch_data(df)
-
-                if st.button("Run Batch Prediction"):
-                    self._run_batch_prediction(df, experiment)
-
-            except Exception as e:
-                st.error(f"Error processing file: {e}")
-
-    def _prepare_batch_data(self, df: pd.DataFrame) -> pd.DataFrame:
-        """Prepare batch data for prediction"""
-        # Column selection
-        if "name" not in df.columns:
-            name_column = st.selectbox("Select the name column:", df.columns)
-            df = df.rename(columns={name_column: "name"})
-
-        # Add missing columns with defaults
-        required_columns = [
-            "words",
-            "length",
-            "province",
-            "identified_name",
-            "identified_surname",
-            "probable_native",
-            "probable_surname",
-        ]
-
-        for col in required_columns:
-            if col not in df.columns:
-                if col == "words":
-                    df[col] = df["name"].str.split().str.len()
-                elif col == "length":
-                    df[col] = df["name"].str.replace(" ", "").str.len()
-                else:
-                    df[col] = None
-
-        return df
-
-    def _run_batch_prediction(self, df: pd.DataFrame, experiment):
-        """Run batch prediction and display results"""
-        with st.spinner("Making predictions..."):
-            # Load model
-            model = self.experiment_runner.load_experiment_model(experiment.experiment_id)
-
-            if model is None:
-                st.error("Failed to load model")
-                return
-
-            # Make predictions
-            predictions = model.predict(df)
-            df["predicted_gender"] = predictions
-            df["gender_label"] = df["predicted_gender"].map({"f": "Female", "m": "Male"})
-
-            # Try to get probabilities
-            try:
-                probabilities = model.predict_proba(df)
-                df["confidence"] = np.max(probabilities, axis=1)
-            except:
-                df["confidence"] = None
-
-        st.success("Predictions completed!")
-
-        # Show results
-        self._display_batch_results(df)
-
-    def _display_batch_results(self, df: pd.DataFrame):
-        """Display batch prediction results"""
-        result_columns = ["name", "gender_label", "predicted_gender"]
-        if "confidence" in df.columns:
-            result_columns.append("confidence")
-
-        st.dataframe(df[result_columns], use_container_width=True)
-
-        # Download results
-        csv = df.to_csv(index=False)
-        st.download_button(
-            label="Download Predictions",
-            data=csv,
-            file_name=f"predictions_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
-            mime="text/csv",
-        )
-
-        # Summary statistics
-        self._display_batch_summary(df)
-
-    def _display_batch_summary(self, df: pd.DataFrame):
-        """Display batch prediction summary"""
-        st.subheader("Prediction Summary")
-        gender_counts = df["gender_label"].value_counts()
-
-        col1, col2, col3 = st.columns(3)
-        with col1:
-            st.metric("Total Predictions", len(df))
-        with col2:
-            st.metric("Female", gender_counts.get("Female", 0))
-        with col3:
-            st.metric("Male", gender_counts.get("Male", 0))
-
-        # Gender distribution chart
-        fig = px.pie(
-            values=gender_counts.values,
-            names=gender_counts.index,
-            title="Predicted Gender Distribution",
-        )
-        st.plotly_chart(fig, use_container_width=True)
-
-    def show_dataset_prediction(self, experiment):
-        dataset_options = {
-            "Featured Dataset": self.config.data.output_files["featured"],
-            "Evaluation Dataset": self.config.data.output_files["evaluation"],
-        }
-
-        selected_dataset = st.selectbox("Select Dataset", list(dataset_options.keys()))
-        file_path = self.config.paths.get_data_path(dataset_options[selected_dataset])
-
-        if not file_path.exists():
-            st.warning(f"Dataset not found: {file_path}")
-            return
-
-        # Load and show dataset info
-        df = self._load_dataset(str(file_path))
-        if df.empty:
-            return
-
-        st.write(f"Dataset contains {len(df):,} records")
-
-        # Prediction options
-        col1, col2 = st.columns(2)
-
-        with col1:
-            sample_size = st.number_input(
-                "Sample size (0 = all data)", 0, len(df), min(1000, len(df))
-            )
-
-        with col2:
-            compare_with_actual = False
-            if "sex" in df.columns:
-                compare_with_actual = st.checkbox("Compare with actual labels", value=True)
-
-        if st.button("Run Dataset Prediction"):
-            self._run_dataset_prediction(df, experiment, sample_size, compare_with_actual)
-
-    def _load_dataset(self, file_path: str) -> pd.DataFrame:
-        """Load dataset with error handling"""
-        try:
-            return pd.read_csv(file_path, dtype=OPTIMIZED_DTYPES)
-        except Exception as e:
-            st.error(f"Error loading dataset: {e}")
-            return pd.DataFrame()
-
-    def _run_dataset_prediction(
-        self, df: pd.DataFrame, experiment, sample_size: int, compare_with_actual: bool
-    ):
-        """Run dataset prediction and display results"""
-        with st.spinner("Running predictions..."):
-            # Sample data if requested
-            if sample_size > 0:
-                df_sample = df.sample(n=sample_size, random_state=42)
-            else:
-                df_sample = df
-
-            # Load model and make predictions
-            model = self.experiment_runner.load_experiment_model(experiment.experiment_id)
-
-            if model is None:
-                st.error("Failed to load model")
-                return
-
-            predictions = model.predict(df_sample)
-            df_sample["predicted_gender"] = predictions
-
-            # Show results
-            if compare_with_actual and "sex" in df_sample.columns:
-                self._display_dataset_comparison(df_sample)
-            else:
-                self._display_dataset_predictions(df_sample)
-
-    def _display_dataset_comparison(self, df_sample: pd.DataFrame):
-        """Display dataset predictions with actual comparison"""
-        # Calculate accuracy
-        accuracy = (df_sample["sex"] == df_sample["predicted_gender"]).mean()
-        st.metric("Accuracy on Selected Data", f"{accuracy:.4f}")
-
-        # Confusion matrix
-        from sklearn.metrics import confusion_matrix
-
-        cm = confusion_matrix(df_sample["sex"], df_sample["predicted_gender"])
-
-        fig = px.imshow(cm, text_auto=True, aspect="auto", title="Confusion Matrix")
-        st.plotly_chart(fig, use_container_width=True)
-
-        # Sample of correct and incorrect predictions
-        correct_mask = df_sample["sex"] == df_sample["predicted_gender"]
-
-        col1, col2 = st.columns(2)
-
-        with col1:
-            st.write("**Sample Correct Predictions**")
-            correct_sample = df_sample[correct_mask][["name", "sex", "predicted_gender"]].head(10)
-            st.dataframe(correct_sample, use_container_width=True)
-
-        with col2:
-            st.write("**Sample Incorrect Predictions**")
-            incorrect_sample = df_sample[~correct_mask][["name", "sex", "predicted_gender"]].head(
-                10
-            )
-            st.dataframe(incorrect_sample, use_container_width=True)
-
-    def _display_dataset_predictions(self, df_sample: pd.DataFrame):
-        """Display dataset predictions without comparison"""
-        # Just show predictions
-        st.write("**Sample Predictions**")
-        sample_results = df_sample[["name", "predicted_gender"]].head(20)
-        st.dataframe(sample_results, use_container_width=True)
-
-        # Gender distribution
-        gender_counts = df_sample["predicted_gender"].value_counts()
-        fig = px.pie(
-            values=gender_counts.values,
-            names=gender_counts.index,
-            title="Predicted Gender Distribution",
-        )
-        st.plotly_chart(fig, use_container_width=True)
diff --git a/web/pages/3_⚙️_Data_Processing.py b/web/pages/3_⚙️_Data_Processing.py
deleted file mode 100644
index 78c71b7..0000000
--- a/web/pages/3_⚙️_Data_Processing.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import sys
-from pathlib import Path
-
-import streamlit as st
-
-# Add parent directory to Python path to access core modules
-parent_dir = Path(__file__).parent.parent.parent
-sys.path.insert(0, str(parent_dir))
-
-from web.interfaces.data_processing import DataProcessing
-
-st.set_page_config(page_title="Data Processing", page_icon="⚙️", layout="wide")
-
-if "config" in st.session_state:
-    data_processing = DataProcessing(st.session_state.config, st.session_state.pipeline_monitor)
-    data_processing.index()
-else:
-    st.error("Please run the main app first to initialize the configuration.")
-    st.markdown("Go back to the [main page](/) to start the application.")