diff --git a/.claude/skills/pdf/SKILL.md b/.claude/skills/pdf/SKILL.md new file mode 100644 index 0000000..5e341c9 --- /dev/null +++ b/.claude/skills/pdf/SKILL.md @@ -0,0 +1,223 @@ +--- +name: pdf +description: Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill. +--- + +# PDF Processing + +## Quick Start + +```python +from pypdf import PdfReader, PdfWriter + +reader = PdfReader("document.pdf") +print(f"Pages: {len(reader.pages)}") + +text = "" +for page in reader.pages: + text += page.extract_text() +``` + +## Python Libraries + +### pypdf — Basic Operations + +**Merge PDFs:** +```python +from pypdf import PdfWriter, PdfReader + +writer = PdfWriter() +for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]: + reader = PdfReader(pdf_file) + for page in reader.pages: + writer.add_page(page) + +with open("merged.pdf", "wb") as output: + writer.write(output) +``` + +**Split PDF:** +```python +reader = PdfReader("input.pdf") +for i, page in enumerate(reader.pages): + writer = PdfWriter() + writer.add_page(page) + with open(f"page_{i+1}.pdf", "wb") as output: + writer.write(output) +``` + +**Extract Metadata:** +```python +reader = PdfReader("document.pdf") +meta = reader.metadata +print(f"Title: {meta.title}") +print(f"Author: {meta.author}") +``` + +**Rotate Pages:** +```python +reader = PdfReader("input.pdf") +writer = PdfWriter() +page = reader.pages[0] +page.rotate(90) +writer.add_page(page) +with open("rotated.pdf", "wb") as output: + writer.write(output) +``` + +### pdfplumber — Text and Table Extraction + +**Extract Text with Layout:** +```python +import pdfplumber + +with pdfplumber.open("document.pdf") as pdf: + for page in pdf.pages: + text = page.extract_text() + print(text) +``` + +**Extract Tables:** +```python +with pdfplumber.open("document.pdf") as pdf: + for i, page in enumerate(pdf.pages): + tables = page.extract_tables() + for j, table in enumerate(tables): + print(f"Table {j+1} on page {i+1}:") + for row in table: + print(row) +``` + +**Extract Tables to DataFrame:** +```python +import pandas as pd +import pdfplumber + +with pdfplumber.open("document.pdf") as pdf: + all_tables = [] + for page in pdf.pages: + tables = page.extract_tables() + for table in tables: + if table: + df = pd.DataFrame(table[1:], columns=table[0]) + all_tables.append(df) + +if all_tables: + combined_df = pd.concat(all_tables, ignore_index=True) + combined_df.to_excel("extracted_tables.xlsx", index=False) +``` + +### reportlab — Create PDFs + +**Basic PDF Creation:** +```python +from reportlab.lib.pagesizes import letter +from reportlab.pdfgen import canvas + +c = canvas.Canvas("hello.pdf", pagesize=letter) +width, height = letter +c.drawString(100, height - 100, "Hello World!") +c.save() +``` + +**Multi-page PDF:** +```python +from reportlab.lib.pagesizes import letter +from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak +from reportlab.lib.styles import getSampleStyleSheet + +doc = SimpleDocTemplate("report.pdf", pagesize=letter) +styles = getSampleStyleSheet() +story = [] + +story.append(Paragraph("Report Title", styles['Title'])) +story.append(Spacer(1, 12)) +story.append(Paragraph("Body content here.", styles['Normal'])) +story.append(PageBreak()) +story.append(Paragraph("Page 2", styles['Heading1'])) +doc.build(story) +``` + +⚠️ **IMPORTANT**: Never use Unicode subscript/superscript characters (₀₁₂₃, ⁰¹²³) in ReportLab — use `` and `` tags instead. + +## Command-Line Tools + +**pdftotext (poppler-utils):** +```bash +pdftotext input.pdf output.txt +pdftotext -layout input.pdf output.txt # preserve layout +pdftotext -f 1 -l 5 input.pdf output.txt # pages 1-5 +``` + +**qpdf:** +```bash +qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf +qpdf input.pdf --pages . 1-5 -- pages1-5.pdf +qpdf input.pdf output.pdf --rotate=+90:1 +qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf +``` + +## Common Tasks + +**OCR Scanned PDFs:** +```python +import pytesseract +from pdf2image import convert_from_path + +images = convert_from_path('scanned.pdf') +text = "" +for i, image in enumerate(images): + text += f"Page {i+1}:\n" + text += pytesseract.image_to_string(image) + text += "\n\n" +``` + +**Add Watermark:** +```python +from pypdf import PdfReader, PdfWriter + +watermark = PdfReader("watermark.pdf").pages[0] +reader = PdfReader("document.pdf") +writer = PdfWriter() +for page in reader.pages: + page.merge_page(watermark) + writer.add_page(page) +with open("watermarked.pdf", "wb") as output: + writer.write(output) +``` + +**Password Protection:** +```python +from pypdf import PdfReader, PdfWriter + +reader = PdfReader("input.pdf") +writer = PdfWriter() +for page in reader.pages: + writer.add_page(page) +writer.encrypt("userpassword", "ownerpassword") +with open("encrypted.pdf", "wb") as output: + writer.write(output) +``` + +**Extract Images (CLI):** +```bash +pdfimages -j input.pdf output_prefix +``` + +## Quick Reference + +| Task | Best Tool | +|------|-----------| +| Merge PDFs | pypdf | +| Split PDFs | pypdf | +| Extract text | pdfplumber | +| Extract tables | pdfplumber | +| Create PDFs | reportlab | +| CLI merge | qpdf | +| OCR scanned PDFs | pytesseract + pdf2image | +| Fill PDF forms | scripts/ (see forms.md) | + +## References + +- **Form filling**: See [references/forms.md](references/forms.md) — use bundled scripts in `scripts/` +- **Advanced usage** (pypdfium2, pdf-lib JS, batch processing, troubleshooting): See [references/reference.md](references/reference.md) diff --git a/.claude/skills/pdf/references/forms.md b/.claude/skills/pdf/references/forms.md new file mode 100644 index 0000000..d2612b0 --- /dev/null +++ b/.claude/skills/pdf/references/forms.md @@ -0,0 +1,104 @@ +# PDF Form Filling + +## Initial Assessment + +First check if the PDF has fillable form fields: +```bash +python scripts/check_fillable_fields.py file.pdf +``` + +--- + +## For Fillable Forms + +**Step 1 — Extract field info:** +```bash +python scripts/extract_form_field_info.py input.pdf fields.json +``` +Outputs JSON cataloging all fields (IDs, locations, types: text/checkbox/radio/choice). + +**Step 2 — Visually verify (optional):** +```bash +mkdir pages && python scripts/convert_pdf_to_images.py input.pdf pages/ +``` + +**Step 3 — Create field values JSON:** +```json +[ + {"field_id": "FirstName", "page": 1, "value": "John"}, + {"field_id": "Agree", "page": 1, "value": "/Yes"}, + {"field_id": "Gender", "page": 1, "value": "/Male"} +] +``` +- Checkboxes: use the `checked_value` shown in the field info JSON +- Radio groups: use one of the `radio_options[].value` values +- Choice fields: use one of the `choice_options[].value` values + +**Step 4 — Fill the form:** +```bash +python scripts/fill_fillable_fields.py input.pdf field_values.json output.pdf +``` + +--- + +## For Non-Fillable Forms (Text Annotations) + +### Approach A — Preferred (digitally-created PDFs) + +Extract structural coordinates: +```bash +python scripts/extract_form_structure.py input.pdf structure.json +``` + +Build a `fields.json` with `form_fields` array using PDF coordinates: +```json +{ + "pages": [{"page_number": 1, "pdf_width": 612, "pdf_height": 792}], + "form_fields": [ + { + "description": "Name field", + "page_number": 1, + "label_bounding_box": [72, 100, 150, 115], + "entry_bounding_box": [155, 98, 400, 116], + "entry_text": {"text": "John Smith", "font": "Helvetica", "font_size": 11} + } + ] +} +``` + +### Approach B — Fallback (scanned PDFs) + +Convert to images, determine pixel coordinates visually, then use image-based coordinates: +```json +{ + "pages": [{"page_number": 1, "image_width": 1000, "image_height": 1294}], + "form_fields": [...] +} +``` + +### Hybrid + +Use Approach A for most fields and Approach B for anything extract_form_structure misses. + +--- + +## Validation + +Before generating output, validate bounding boxes: +```bash +python scripts/check_bounding_boxes.py fields.json +``` + +Visually verify a page: +```bash +python scripts/create_validation_image.py 1 fields.json pages/page_1.png validation_page_1.png +``` + +Red boxes = entry areas, Blue boxes = label areas. + +**Fill with annotations:** +```bash +python scripts/fill_pdf_form_with_annotations.py input.pdf fields.json output.pdf +``` + +Then convert output to images to verify text placement. diff --git a/.claude/skills/pdf/references/reference.md b/.claude/skills/pdf/references/reference.md new file mode 100644 index 0000000..36a14b0 --- /dev/null +++ b/.claude/skills/pdf/references/reference.md @@ -0,0 +1,174 @@ +# PDF Advanced Reference + +## pypdfium2 — High-Performance Rendering + +```python +import pypdfium2 as pdfium + +pdf = pdfium.PdfDocument("document.pdf") + +# Render page to image +page = pdf[0] +bitmap = page.render(scale=2) # 2x scale = 144 DPI +pil_image = bitmap.to_pil() +pil_image.save("page_1.png") + +# Extract text with coordinates +textpage = page.get_textpage() +for i in range(textpage.count_chars()): + char = textpage.get_charbox(i) + print(f"Char: {textpage.get_text_range(i, 1)}, Box: {char}") + +pdf.close() +``` + +**When to prefer pypdfium2**: High-quality image rendering, pixel-accurate text extraction, processing large batches. + +--- + +## pdf-lib (JavaScript) + +```javascript +import { PDFDocument, rgb, StandardFonts } from 'pdf-lib'; +import fs from 'fs'; + +// Load and modify +const pdfBytes = fs.readFileSync('input.pdf'); +const pdfDoc = await PDFDocument.load(pdfBytes); + +const page = pdfDoc.getPages()[0]; +const font = await pdfDoc.embedFont(StandardFonts.Helvetica); +page.drawText('Hello World', { x: 50, y: 700, size: 20, font, color: rgb(0, 0, 0) }); + +const output = await pdfDoc.save(); +fs.writeFileSync('output.pdf', output); +``` + +**When to use**: Node.js environments, browser-side PDF generation. + +--- + +## Batch Processing + +```python +import os +from pathlib import Path +from pypdf import PdfReader +import logging + +logging.basicConfig(level=logging.INFO) + +def process_pdfs(input_dir: str, output_dir: str): + Path(output_dir).mkdir(exist_ok=True) + errors = [] + + for pdf_file in Path(input_dir).glob("*.pdf"): + try: + reader = PdfReader(str(pdf_file)) + text = "\n".join(page.extract_text() or "" for page in reader.pages) + + out_path = Path(output_dir) / (pdf_file.stem + ".txt") + out_path.write_text(text, encoding="utf-8") + logging.info(f"Processed: {pdf_file.name}") + except Exception as e: + logging.error(f"Failed: {pdf_file.name} — {e}") + errors.append((str(pdf_file), str(e))) + + return errors +``` + +--- + +## Memory-Efficient Large File Processing + +```python +import pdfplumber + +def stream_text(pdf_path: str): + """Yield text page by page to avoid loading entire PDF into memory.""" + with pdfplumber.open(pdf_path) as pdf: + for i, page in enumerate(pdf.pages): + yield i + 1, page.extract_text() or "" + +for page_num, text in stream_text("large_document.pdf"): + print(f"--- Page {page_num} ---") + print(text) +``` + +--- + +## Handling Encrypted / Corrupted PDFs + +```python +from pypdf import PdfReader + +# Encrypted PDF +reader = PdfReader("encrypted.pdf") +if reader.is_encrypted: + reader.decrypt("password") + +# Corrupted PDF — try qpdf first +import subprocess +result = subprocess.run( + ["qpdf", "--recover", "corrupted.pdf", "repaired.pdf"], + capture_output=True, text=True +) +if result.returncode != 0: + print("qpdf repair failed:", result.stderr) +``` + +--- + +## OCR Fallback for Mixed PDFs + +```python +import pdfplumber +import pytesseract +from pdf2image import convert_from_path + +def extract_with_ocr_fallback(pdf_path: str) -> list[str]: + """Use pdfplumber first; fall back to OCR if page has no text.""" + pages_text = [] + images = None # lazy-load + + with pdfplumber.open(pdf_path) as pdf: + for i, page in enumerate(pdf.pages): + text = page.extract_text() + if text and text.strip(): + pages_text.append(text) + else: + if images is None: + images = convert_from_path(pdf_path, dpi=200) + pages_text.append(pytesseract.image_to_string(images[i])) + + return pages_text +``` + +--- + +## Tool Selection Guide + +| Use case | Recommended tool | +|----------|-----------------| +| Simple text extraction | pdfplumber | +| Tables | pdfplumber | +| High-quality image rendering | pypdfium2 | +| Merge / split / rotate | pypdf | +| Create new PDFs | reportlab | +| JavaScript / browser | pdf-lib | +| CLI text extraction | pdftotext (poppler) | +| CLI manipulation | qpdf | +| OCR | pytesseract + pdf2image | +| Repair corrupted | qpdf --recover | + +## Library Licenses + +| Library | License | +|---------|---------| +| pypdf | BSD | +| pdfplumber | MIT | +| reportlab | BSD | +| pypdfium2 | Apache 2.0 | +| pdf-lib | MIT | +| poppler-utils | GPL-2 | +| qpdf | Apache 2.0 | diff --git a/.claude/skills/pdf/scripts/check_bounding_boxes.py b/.claude/skills/pdf/scripts/check_bounding_boxes.py new file mode 100644 index 0000000..2fc9876 --- /dev/null +++ b/.claude/skills/pdf/scripts/check_bounding_boxes.py @@ -0,0 +1,64 @@ +from dataclasses import dataclass +import json +import sys + + +@dataclass +class RectAndField: + rect: list[float] + rect_type: str + field: dict + + +def get_bounding_box_messages(fields_json_stream) -> list[str]: + messages = [] + fields = json.load(fields_json_stream) + messages.append(f"Read {len(fields['form_fields'])} fields") + + def rects_intersect(r1, r2): + disjoint_horizontal = r1[0] >= r2[2] or r1[2] <= r2[0] + disjoint_vertical = r1[1] >= r2[3] or r1[3] <= r2[1] + return not (disjoint_horizontal or disjoint_vertical) + + rects_and_fields = [] + for f in fields["form_fields"]: + rects_and_fields.append(RectAndField(f["label_bounding_box"], "label", f)) + rects_and_fields.append(RectAndField(f["entry_bounding_box"], "entry", f)) + + has_error = False + for i, ri in enumerate(rects_and_fields): + for j in range(i + 1, len(rects_and_fields)): + rj = rects_and_fields[j] + if ri.field["page_number"] == rj.field["page_number"] and rects_intersect(ri.rect, rj.rect): + has_error = True + if ri.field is rj.field: + messages.append(f"FAILURE: intersection between label and entry bounding boxes for `{ri.field['description']}` ({ri.rect}, {rj.rect})") + else: + messages.append(f"FAILURE: intersection between {ri.rect_type} bounding box for `{ri.field['description']}` ({ri.rect}) and {rj.rect_type} bounding box for `{rj.field['description']}` ({rj.rect})") + if len(messages) >= 20: + messages.append("Aborting further checks; fix bounding boxes and try again") + return messages + if ri.rect_type == "entry": + if "entry_text" in ri.field: + font_size = ri.field["entry_text"].get("font_size", 14) + entry_height = ri.rect[3] - ri.rect[1] + if entry_height < font_size: + has_error = True + messages.append(f"FAILURE: entry bounding box height ({entry_height}) for `{ri.field['description']}` is too short for the text content (font size: {font_size}). Increase the box height or decrease the font size.") + if len(messages) >= 20: + messages.append("Aborting further checks; fix bounding boxes and try again") + return messages + + if not has_error: + messages.append("SUCCESS: All bounding boxes are valid") + return messages + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: check_bounding_boxes.py [fields.json]") + sys.exit(1) + with open(sys.argv[1]) as f: + messages = get_bounding_box_messages(f) + for msg in messages: + print(msg) diff --git a/.claude/skills/pdf/scripts/check_fillable_fields.py b/.claude/skills/pdf/scripts/check_fillable_fields.py new file mode 100644 index 0000000..ddeff88 --- /dev/null +++ b/.claude/skills/pdf/scripts/check_fillable_fields.py @@ -0,0 +1,9 @@ +import sys +from pypdf import PdfReader + + +reader = PdfReader(sys.argv[1]) +if reader.get_fields(): + print("This PDF has fillable form fields") +else: + print("This PDF does not have fillable form fields; you will need to visually determine where to enter data") diff --git a/.claude/skills/pdf/scripts/convert_pdf_to_images.py b/.claude/skills/pdf/scripts/convert_pdf_to_images.py new file mode 100644 index 0000000..c35a760 --- /dev/null +++ b/.claude/skills/pdf/scripts/convert_pdf_to_images.py @@ -0,0 +1,31 @@ +import os +import sys + +from pdf2image import convert_from_path + + +def convert(pdf_path, output_dir, max_dim=1000): + images = convert_from_path(pdf_path, dpi=200) + + for i, image in enumerate(images): + width, height = image.size + if width > max_dim or height > max_dim: + scale_factor = min(max_dim / width, max_dim / height) + new_width = int(width * scale_factor) + new_height = int(height * scale_factor) + image = image.resize((new_width, new_height)) + + image_path = os.path.join(output_dir, f"page_{i+1}.png") + image.save(image_path) + print(f"Saved page {i+1} as {image_path} (size: {image.size})") + + print(f"Converted {len(images)} pages to PNG images") + + +if __name__ == "__main__": + if len(sys.argv) != 3: + print("Usage: convert_pdf_to_images.py [input pdf] [output directory]") + sys.exit(1) + pdf_path = sys.argv[1] + output_directory = sys.argv[2] + convert(pdf_path, output_directory) diff --git a/.claude/skills/pdf/scripts/create_validation_image.py b/.claude/skills/pdf/scripts/create_validation_image.py new file mode 100644 index 0000000..8a41eac --- /dev/null +++ b/.claude/skills/pdf/scripts/create_validation_image.py @@ -0,0 +1,35 @@ +import json +import sys + +from PIL import Image, ImageDraw + + +def create_validation_image(page_number, fields_json_path, input_path, output_path): + with open(fields_json_path, 'r') as f: + data = json.load(f) + + img = Image.open(input_path) + draw = ImageDraw.Draw(img) + num_boxes = 0 + + for field in data["form_fields"]: + if field["page_number"] == page_number: + entry_box = field['entry_bounding_box'] + label_box = field['label_bounding_box'] + draw.rectangle(entry_box, outline='red', width=2) + draw.rectangle(label_box, outline='blue', width=2) + num_boxes += 2 + + img.save(output_path) + print(f"Created validation image at {output_path} with {num_boxes} bounding boxes") + + +if __name__ == "__main__": + if len(sys.argv) != 5: + print("Usage: create_validation_image.py [page number] [fields.json file] [input image path] [output image path]") + sys.exit(1) + page_number = int(sys.argv[1]) + fields_json_path = sys.argv[2] + input_image_path = sys.argv[3] + output_image_path = sys.argv[4] + create_validation_image(page_number, fields_json_path, input_image_path, output_image_path) diff --git a/.claude/skills/pdf/scripts/extract_form_field_info.py b/.claude/skills/pdf/scripts/extract_form_field_info.py new file mode 100644 index 0000000..59b7bb6 --- /dev/null +++ b/.claude/skills/pdf/scripts/extract_form_field_info.py @@ -0,0 +1,119 @@ +import json +import sys + +from pypdf import PdfReader + + +def get_full_annotation_field_id(annotation): + components = [] + while annotation: + field_name = annotation.get('/T') + if field_name: + components.append(field_name) + annotation = annotation.get('/Parent') + return ".".join(reversed(components)) if components else None + + +def make_field_dict(field, field_id): + field_dict = {"field_id": field_id} + ft = field.get('/FT') + if ft == "/Tx": + field_dict["type"] = "text" + elif ft == "/Btn": + field_dict["type"] = "checkbox" + states = field.get("/_States_", []) + if len(states) == 2: + if "/Off" in states: + field_dict["checked_value"] = states[0] if states[0] != "/Off" else states[1] + field_dict["unchecked_value"] = "/Off" + else: + print(f"Unexpected state values for checkbox `${field_id}`. Its checked and unchecked values may not be correct; if you're trying to check it, visually verify the results.") + field_dict["checked_value"] = states[0] + field_dict["unchecked_value"] = states[1] + elif ft == "/Ch": + field_dict["type"] = "choice" + states = field.get("/_States_", []) + field_dict["choice_options"] = [{ + "value": state[0], + "text": state[1], + } for state in states] + else: + field_dict["type"] = f"unknown ({ft})" + return field_dict + + +def get_field_info(reader: PdfReader): + fields = reader.get_fields() + + field_info_by_id = {} + possible_radio_names = set() + + for field_id, field in fields.items(): + if field.get("/Kids"): + if field.get("/FT") == "/Btn": + possible_radio_names.add(field_id) + continue + field_info_by_id[field_id] = make_field_dict(field, field_id) + + radio_fields_by_id = {} + + for page_index, page in enumerate(reader.pages): + annotations = page.get('/Annots', []) + for ann in annotations: + field_id = get_full_annotation_field_id(ann) + if field_id in field_info_by_id: + field_info_by_id[field_id]["page"] = page_index + 1 + field_info_by_id[field_id]["rect"] = ann.get('/Rect') + elif field_id in possible_radio_names: + try: + on_values = [v for v in ann["/AP"]["/N"] if v != "/Off"] + except KeyError: + continue + if len(on_values) == 1: + rect = ann.get("/Rect") + if field_id not in radio_fields_by_id: + radio_fields_by_id[field_id] = { + "field_id": field_id, + "type": "radio_group", + "page": page_index + 1, + "radio_options": [], + } + radio_fields_by_id[field_id]["radio_options"].append({ + "value": on_values[0], + "rect": rect, + }) + + fields_with_location = [] + for field_info in field_info_by_id.values(): + if "page" in field_info: + fields_with_location.append(field_info) + else: + print(f"Unable to determine location for field id: {field_info.get('field_id')}, ignoring") + + def sort_key(f): + if "radio_options" in f: + rect = f["radio_options"][0]["rect"] or [0, 0, 0, 0] + else: + rect = f.get("rect") or [0, 0, 0, 0] + adjusted_position = [-rect[1], rect[0]] + return [f.get("page"), adjusted_position] + + sorted_fields = fields_with_location + list(radio_fields_by_id.values()) + sorted_fields.sort(key=sort_key) + + return sorted_fields + + +def write_field_info(pdf_path: str, json_output_path: str): + reader = PdfReader(pdf_path) + field_info = get_field_info(reader) + with open(json_output_path, "w") as f: + json.dump(field_info, f, indent=2) + print(f"Wrote {len(field_info)} fields to {json_output_path}") + + +if __name__ == "__main__": + if len(sys.argv) != 3: + print("Usage: extract_form_field_info.py [input pdf] [output json]") + sys.exit(1) + write_field_info(sys.argv[1], sys.argv[2]) diff --git a/.claude/skills/pdf/scripts/extract_form_structure.py b/.claude/skills/pdf/scripts/extract_form_structure.py new file mode 100644 index 0000000..5e175d9 --- /dev/null +++ b/.claude/skills/pdf/scripts/extract_form_structure.py @@ -0,0 +1,101 @@ +import json +import sys +import pdfplumber + + +def extract_form_structure(pdf_path): + structure = { + "pages": [], + "labels": [], + "lines": [], + "checkboxes": [], + "row_boundaries": [] + } + + with pdfplumber.open(pdf_path) as pdf: + for page_num, page in enumerate(pdf.pages, 1): + structure["pages"].append({ + "page_number": page_num, + "width": float(page.width), + "height": float(page.height) + }) + + words = page.extract_words() + for word in words: + structure["labels"].append({ + "page": page_num, + "text": word["text"], + "x0": round(float(word["x0"]), 1), + "top": round(float(word["top"]), 1), + "x1": round(float(word["x1"]), 1), + "bottom": round(float(word["bottom"]), 1) + }) + + for line in page.lines: + if abs(float(line["x1"]) - float(line["x0"])) > page.width * 0.5: + structure["lines"].append({ + "page": page_num, + "y": round(float(line["top"]), 1), + "x0": round(float(line["x0"]), 1), + "x1": round(float(line["x1"]), 1) + }) + + for rect in page.rects: + width = float(rect["x1"]) - float(rect["x0"]) + height = float(rect["bottom"]) - float(rect["top"]) + if 5 <= width <= 15 and 5 <= height <= 15 and abs(width - height) < 2: + structure["checkboxes"].append({ + "page": page_num, + "x0": round(float(rect["x0"]), 1), + "top": round(float(rect["top"]), 1), + "x1": round(float(rect["x1"]), 1), + "bottom": round(float(rect["bottom"]), 1), + "center_x": round((float(rect["x0"]) + float(rect["x1"])) / 2, 1), + "center_y": round((float(rect["top"]) + float(rect["bottom"])) / 2, 1) + }) + + lines_by_page = {} + for line in structure["lines"]: + page = line["page"] + if page not in lines_by_page: + lines_by_page[page] = [] + lines_by_page[page].append(line["y"]) + + for page, y_coords in lines_by_page.items(): + y_coords = sorted(set(y_coords)) + for i in range(len(y_coords) - 1): + structure["row_boundaries"].append({ + "page": page, + "row_top": y_coords[i], + "row_bottom": y_coords[i + 1], + "row_height": round(y_coords[i + 1] - y_coords[i], 1) + }) + + return structure + + +def main(): + if len(sys.argv) != 3: + print("Usage: extract_form_structure.py ") + sys.exit(1) + + pdf_path = sys.argv[1] + output_path = sys.argv[2] + + print(f"Extracting structure from {pdf_path}...") + structure = extract_form_structure(pdf_path) + + with open(output_path, "w") as f: + json.dump(structure, f, indent=2) + + print(f"Found:") + print(f" - {len(structure['pages'])} pages") + print(f" - {len(structure['labels'])} text labels") + print(f" - {len(structure['lines'])} horizontal lines") + print(f" - {len(structure['checkboxes'])} checkboxes") + print(f" - {len(structure['row_boundaries'])} row boundaries") + print(f"Saved to {output_path}") + + +if __name__ == "__main__": + main() diff --git a/.claude/skills/pdf/scripts/fill_fillable_fields.py b/.claude/skills/pdf/scripts/fill_fillable_fields.py new file mode 100644 index 0000000..a706cd2 --- /dev/null +++ b/.claude/skills/pdf/scripts/fill_fillable_fields.py @@ -0,0 +1,96 @@ +import json +import sys + +from pypdf import PdfReader, PdfWriter + +from extract_form_field_info import get_field_info + + +def fill_pdf_fields(input_pdf_path: str, fields_json_path: str, output_pdf_path: str): + with open(fields_json_path) as f: + fields = json.load(f) + fields_by_page = {} + for field in fields: + if "value" in field: + field_id = field["field_id"] + page = field["page"] + if page not in fields_by_page: + fields_by_page[page] = {} + fields_by_page[page][field_id] = field["value"] + + reader = PdfReader(input_pdf_path) + + has_error = False + field_info = get_field_info(reader) + fields_by_ids = {f["field_id"]: f for f in field_info} + for field in fields: + existing_field = fields_by_ids.get(field["field_id"]) + if not existing_field: + has_error = True + print(f"ERROR: `{field['field_id']}` is not a valid field ID") + elif field["page"] != existing_field["page"]: + has_error = True + print(f"ERROR: Incorrect page number for `{field['field_id']}` (got {field['page']}, expected {existing_field['page']})") + else: + if "value" in field: + err = validation_error_for_field_value(existing_field, field["value"]) + if err: + print(err) + has_error = True + if has_error: + sys.exit(1) + + writer = PdfWriter(clone_from=reader) + for page, field_values in fields_by_page.items(): + writer.update_page_form_field_values(writer.pages[page - 1], field_values, auto_regenerate=False) + + writer.set_need_appearances_writer(True) + + with open(output_pdf_path, "wb") as f: + writer.write(f) + + +def validation_error_for_field_value(field_info, field_value): + field_type = field_info["type"] + field_id = field_info["field_id"] + if field_type == "checkbox": + checked_val = field_info["checked_value"] + unchecked_val = field_info["unchecked_value"] + if field_value != checked_val and field_value != unchecked_val: + return f'ERROR: Invalid value "{field_value}" for checkbox field "{field_id}". The checked value is "{checked_val}" and the unchecked value is "{unchecked_val}"' + elif field_type == "radio_group": + option_values = [opt["value"] for opt in field_info["radio_options"]] + if field_value not in option_values: + return f'ERROR: Invalid value "{field_value}" for radio group field "{field_id}". Valid values are: {option_values}' + elif field_type == "choice": + choice_values = [opt["value"] for opt in field_info["choice_options"]] + if field_value not in choice_values: + return f'ERROR: Invalid value "{field_value}" for choice field "{field_id}". Valid values are: {choice_values}' + return None + + +def monkeypatch_pypdf_method(): + from pypdf.generic import DictionaryObject + from pypdf.constants import FieldDictionaryAttributes + + original_get_inherited = DictionaryObject.get_inherited + + def patched_get_inherited(self, key: str, default=None): + result = original_get_inherited(self, key, default) + if key == FieldDictionaryAttributes.Opt: + if isinstance(result, list) and all(isinstance(v, list) and len(v) == 2 for v in result): + result = [r[0] for r in result] + return result + + DictionaryObject.get_inherited = patched_get_inherited + + +if __name__ == "__main__": + if len(sys.argv) != 4: + print("Usage: fill_fillable_fields.py [input pdf] [field_values.json] [output pdf]") + sys.exit(1) + monkeypatch_pypdf_method() + input_pdf = sys.argv[1] + fields_json = sys.argv[2] + output_pdf = sys.argv[3] + fill_pdf_fields(input_pdf, fields_json, output_pdf) diff --git a/.claude/skills/pdf/scripts/fill_pdf_form_with_annotations.py b/.claude/skills/pdf/scripts/fill_pdf_form_with_annotations.py new file mode 100644 index 0000000..a2ac423 --- /dev/null +++ b/.claude/skills/pdf/scripts/fill_pdf_form_with_annotations.py @@ -0,0 +1,104 @@ +import json +import sys + +from pypdf import PdfReader, PdfWriter +from pypdf.annotations import FreeText + + +def transform_from_image_coords(bbox, image_width, image_height, pdf_width, pdf_height): + x_scale = pdf_width / image_width + y_scale = pdf_height / image_height + + left = bbox[0] * x_scale + right = bbox[2] * x_scale + + top = pdf_height - (bbox[1] * y_scale) + bottom = pdf_height - (bbox[3] * y_scale) + + return left, bottom, right, top + + +def transform_from_pdf_coords(bbox, pdf_height): + left = bbox[0] + right = bbox[2] + + pypdf_top = pdf_height - bbox[1] + pypdf_bottom = pdf_height - bbox[3] + + return left, pypdf_bottom, right, pypdf_top + + +def fill_pdf_form(input_pdf_path, fields_json_path, output_pdf_path): + with open(fields_json_path, "r") as f: + fields_data = json.load(f) + + reader = PdfReader(input_pdf_path) + writer = PdfWriter() + + writer.append(reader) + + pdf_dimensions = {} + for i, page in enumerate(reader.pages): + mediabox = page.mediabox + pdf_dimensions[i + 1] = [mediabox.width, mediabox.height] + + annotations = [] + for field in fields_data["form_fields"]: + page_num = field["page_number"] + + page_info = next(p for p in fields_data["pages"] if p["page_number"] == page_num) + pdf_width, pdf_height = pdf_dimensions[page_num] + + if "pdf_width" in page_info: + transformed_entry_box = transform_from_pdf_coords( + field["entry_bounding_box"], + float(pdf_height) + ) + else: + image_width = page_info["image_width"] + image_height = page_info["image_height"] + transformed_entry_box = transform_from_image_coords( + field["entry_bounding_box"], + image_width, image_height, + float(pdf_width), float(pdf_height) + ) + + if "entry_text" not in field or "text" not in field["entry_text"]: + continue + entry_text = field["entry_text"] + text = entry_text["text"] + if not text: + continue + + font_name = entry_text.get("font", "Arial") + font_size = str(entry_text.get("font_size", 14)) + "pt" + font_color = entry_text.get("font_color", "000000") + + annotation = FreeText( + text=text, + rect=transformed_entry_box, + font=font_name, + font_size=font_size, + font_color=font_color, + border_color=None, + background_color=None, + ) + annotations.append(annotation) + writer.add_annotation(page_number=page_num - 1, annotation=annotation) + + with open(output_pdf_path, "wb") as output: + writer.write(output) + + print(f"Successfully filled PDF form and saved to {output_pdf_path}") + print(f"Added {len(annotations)} text annotations") + + +if __name__ == "__main__": + if len(sys.argv) != 4: + print("Usage: fill_pdf_form_with_annotations.py [input pdf] [fields.json] [output pdf]") + sys.exit(1) + input_pdf = sys.argv[1] + fields_json = sys.argv[2] + output_pdf = sys.argv[3] + + fill_pdf_form(input_pdf, fields_json, output_pdf) diff --git a/book/myst.yml b/book/myst.yml index 617cab2..63e0e4f 100644 --- a/book/myst.yml +++ b/book/myst.yml @@ -23,6 +23,10 @@ project: file: why_causal_inference/why_causal_inference_en.ipynb - file: why_causal_inference/why_causal_inference_ko.ipynb hidden: true + - title: Who Should Be Treated? + file: who_should_be_treated/who_should_be_treated_en.ipynb + - file: who_should_be_treated/who_should_be_treated_ko.ipynb + hidden: true site: template: book-theme diff --git a/book/who_should_be_treated/assets/AJStyles&Undertaker.jpg b/book/who_should_be_treated/assets/AJStyles&Undertaker.jpg new file mode 100644 index 0000000..4782de9 Binary files /dev/null and b/book/who_should_be_treated/assets/AJStyles&Undertaker.jpg differ diff --git a/book/who_should_be_treated/data/multi_attribution_sample.csv b/book/who_should_be_treated/data/multi_attribution_sample.csv new file mode 100644 index 0000000..a629466 --- /dev/null +++ b/book/who_should_be_treated/data/multi_attribution_sample.csv @@ -0,0 +1,2001 @@ +Global Flag,Major Flag,SMC Flag,Commercial Flag,IT Spend,Employee Count,PC Count,Size,Tech Support,Discount,Revenue +1,0,1,0,45537,26,26,152205,0,1,17688.363 +0,0,1,1,20842,107,70,159038,0,1,14981.43559 +0,0,0,1,82171,10,7,264935,1,1,32917.13894 +0,0,0,0,30288,40,39,77522,1,1,14773.76855 +0,0,1,0,25930,37,43,91446,1,1,17098.69823 +0,0,1,0,34597,44,51,218703,1,0,17280.70918 +1,0,0,0,40199,21,14,126342,0,0,9153.974376 +0,0,1,1,30454,11,8,96784,0,0,5760.075096 +0,0,0,1,23428,50,55,77298,1,1,17265.11146 +0,0,1,1,7970,131,153,77888,1,1,21953.37179 +0,0,0,1,151143,84,58,417289,1,1,50875.56035 +1,0,1,1,30759,169,136,116016,1,0,22153.82215 +0,0,1,1,19899,279,195,66555,0,0,11972.18818 +0,0,1,1,8420,29,26,36973,1,0,10063.85753 +0,0,1,1,34577,20,15,91689,1,0,10521.9988 +0,0,0,1,1294,18,15,11292,0,1,2296.739654 +1,0,1,1,80629,13,9,466694,0,1,41743.72206 +1,0,1,0,144013,119,124,413240,1,1,56307.34264 +1,0,1,1,17143,24,24,49673,0,1,9673.221778 +0,0,1,1,22729,110,110,117138,0,1,15397.70575 +0,0,1,1,28856,34,34,136831,1,1,21267.51393 +0,0,0,0,14057,24,18,67434,0,1,6321.956387 +0,0,1,0,14682,13,16,45908,0,0,2694.216001 +0,0,0,0,8321,82,50,26999,0,0,2531.466231 +0,0,1,0,22051,170,140,56512,0,0,9116.230185 +0,0,1,0,15052,156,154,41571,0,1,12387.81871 +1,0,0,0,21432,106,122,148865,1,1,27860.93773 +0,0,0,0,21232,221,146,84567,0,0,9072.07955 +0,0,1,1,41352,15,11,126934,1,1,19867.56935 +0,0,1,1,12744,10,10,54468,0,0,2868.606553 +0,0,1,0,13980,46,32,58046,0,1,7126.30626 +1,1,0,0,35303,108,136,120173,0,1,20237.79168 +1,0,1,1,10923,26,21,74202,0,1,11610.05857 +0,0,0,1,16662,11,10,110110,1,1,18647.91666 +0,0,0,1,88422,34,27,295436,1,1,37025.88584 +0,0,0,0,3971,41,30,29867,0,0,1038.615975 +0,0,1,0,22692,17,15,133197,1,1,19821.22882 +0,0,1,1,31488,17,10,112661,0,1,13903.71762 +1,1,0,0,45422,45,56,193432,0,0,15182.77493 +0,0,0,1,62739,108,131,250338,1,0,25477.56325 +1,0,1,0,2168,48,47,11009,1,1,13173.24059 +0,1,0,1,20901,85,80,115902,1,0,15864.39188 +1,0,1,1,4857,44,48,16491,0,1,8488.73425 +0,1,0,0,25002,34,22,99114,1,0,13303.01519 +0,0,0,1,23904,32,20,176769,1,0,15658.03599 +0,0,1,0,5972,28,26,16210,0,1,1866.574021 +0,0,0,1,19486,22,22,116783,0,1,11262.22719 +0,0,1,0,16858,63,45,99214,1,1,18338.09893 +0,0,1,1,37432,98,76,272286,1,1,36192.4073 +0,1,0,1,32794,76,47,101073,0,0,8797.224912 +0,0,0,1,21666,13,14,59452,0,0,3346.04696 +0,0,0,1,2272,33,34,13700,0,0,2984.716846 +0,0,0,1,23677,58,65,93466,1,1,18338.7401 +1,0,1,0,7813,15,15,27740,0,0,5350.908641 +0,0,1,1,117098,13,10,535638,1,1,60479.55167 +0,0,0,1,6590,34,24,19900,0,0,3312.640665 +0,0,0,1,17049,61,62,60369,1,0,12021.49536 +0,1,0,0,88501,127,152,417564,1,1,54047.43047 +1,0,1,1,2937,57,68,10629,0,0,8835.047329 +1,1,0,0,28651,30,28,106147,1,1,23904.30481 +0,1,0,1,7587,50,32,69779,1,1,15366.9466 +0,1,0,1,80706,141,175,244713,0,0,18149.13843 +0,0,0,1,4672,33,24,11762,0,1,3087.227865 +0,0,1,0,41677,26,32,205525,0,1,19301.61921 +0,1,0,0,61286,31,36,179022,1,0,18214.01619 +0,0,1,0,157655,15,18,429946,1,0,28356.19404 +0,1,0,1,24376,20,12,165690,0,1,17103.73349 +0,1,0,1,78105,31,28,429628,1,1,52180.4918 +1,1,0,0,10468,13,14,54499,1,1,17418.02118 +0,0,0,1,14156,59,63,40721,0,1,6480.237874 +1,0,1,0,11328,37,37,56684,1,0,14732.56658 +0,0,1,0,9291,27,33,39421,0,1,6212.661339 +0,0,1,0,47794,51,63,310950,1,1,37274.32798 +0,0,1,1,25131,34,35,129251,0,1,13546.97187 +0,0,0,0,17376,78,60,72051,0,1,6934.633026 +0,0,0,1,45245,34,23,242353,0,1,22886.89951 +0,0,1,1,50999,48,59,128288,1,0,16289.16582 +0,0,0,0,2867,20,16,12868,0,0,427.6482735 +0,0,1,1,67890,15,13,406365,1,1,47587.29878 +1,1,0,1,160244,98,124,454291,1,1,63162.5783 +1,0,0,1,7278,103,93,43359,0,0,8583.56821 +0,0,1,0,61684,14,11,315315,1,1,37902.30016 +0,0,1,1,23501,101,74,59631,0,0,5172.847451 +0,0,1,1,8771,317,268,27460,1,0,20175.58992 +0,0,1,0,10076,27,29,84398,1,1,13690.74117 +0,1,0,1,6669,33,25,34115,1,1,11546.04042 +0,0,1,1,43708,10,7,126892,1,0,13310.68181 +0,0,1,1,24160,32,25,181818,1,0,15695.07674 +0,0,1,1,22691,57,52,149726,1,1,22908.60641 +0,0,1,1,150289,53,56,377721,1,0,28330.76253 +0,1,0,1,24836,120,92,145100,1,0,17501.09664 +0,0,1,0,66191,84,58,176803,1,1,26121.34126 +1,0,1,0,24416,17,18,102243,1,1,18963.73396 +1,0,0,1,6203,38,47,31973,0,1,8998.929994 +0,0,1,1,3176,17,13,21184,0,1,4208.953344 +1,0,0,0,20620,61,68,64891,0,1,11729.14754 +0,0,1,1,10130,42,45,35605,0,1,5698.071425 +1,0,1,0,2082,23,24,11269,1,0,10750.36634 +1,1,0,1,32247,21,24,86154,0,0,10332.25678 +0,0,0,0,4531,83,84,12688,0,0,5205.635952 +0,0,1,1,54076,64,76,211136,1,1,29938.98853 +0,0,1,1,8030,51,50,22089,0,0,3892.89282 +0,1,0,0,22105,58,48,92998,0,1,10775.65117 +0,0,1,0,2446,70,87,12772,1,1,8732.876532 +0,0,0,1,68678,17,18,235152,1,1,30804.12152 +0,0,1,1,28402,143,164,110430,0,1,18359.92468 +0,0,1,1,33525,86,65,98371,1,1,19446.96031 +1,0,1,1,32296,60,51,134989,0,0,10618.8936 +0,1,0,1,25297,27,24,227115,1,0,20154.21729 +0,0,1,1,10143,31,24,81870,1,0,11441.82429 +0,0,0,0,2517,27,17,21453,1,1,8533.242525 +0,0,1,1,8405,73,52,26417,0,0,3883.200484 +1,0,0,1,71141,28,24,210928,0,0,13785.74983 +0,0,1,1,75907,50,42,208770,0,1,20463.75335 +0,0,0,0,2337,159,183,17870,0,0,7309.382264 +1,1,0,1,16756,35,31,70823,1,0,16577.22067 +0,0,0,1,4359,55,48,30794,1,1,11868.75457 +0,1,0,1,22628,14,16,99704,0,1,11800.72088 +0,1,0,1,52238,14,10,150085,1,0,16800.52139 +0,0,0,1,31620,125,146,108026,0,0,11742.24034 +1,0,1,1,20553,174,178,58863,1,0,20339.32765 +0,0,1,1,46828,21,14,121378,1,1,19690.81724 +0,1,0,1,10302,103,98,36116,0,0,8061.36729 +0,0,0,1,11896,20,12,58290,0,1,7112.372385 +0,0,1,0,27461,88,86,77141,1,1,16303.77806 +1,1,0,1,138371,154,95,391156,1,1,55238.15934 +0,1,0,0,17406,14,8,80062,0,1,8856.093234 +0,0,0,1,6672,175,205,17437,0,0,10980.00749 +0,0,0,1,5733,24,20,15552,0,0,1263.065792 +0,1,0,1,4927,71,68,37194,1,1,14424.33442 +0,0,0,0,7712,113,143,35022,0,1,7429.766914 +0,0,0,1,23011,95,68,61522,1,0,10737.71349 +0,0,1,1,37491,17,18,272112,0,1,23602.45932 +0,0,1,0,6444,22,27,46013,1,1,9932.041465 +0,0,1,1,3984,16,10,15835,0,1,4004.123217 +0,1,0,1,94564,59,69,288104,1,0,24503.26207 +0,0,1,0,43904,23,21,199942,0,1,17650.28582 +1,0,1,1,18953,71,53,68308,0,0,9877.285199 +0,0,1,1,22096,30,34,79186,1,1,15465.93262 +0,0,1,1,8555,31,39,31220,1,0,10514.12972 +0,0,0,0,34368,78,52,118289,1,0,14608.44238 +0,0,1,1,15294,120,75,40569,0,0,5958.755283 +0,1,0,0,34126,69,61,187211,0,1,20432.53813 +0,0,1,0,16562,77,66,85917,1,0,12823.25034 +0,1,0,1,9700,109,141,24838,0,1,12434.5908 +1,0,0,0,70265,34,27,330589,1,1,43556.70504 +0,0,0,1,2040,57,68,12825,1,0,10518.12191 +0,0,0,1,8710,105,68,23341,0,1,7931.258914 +0,1,0,1,12776,90,62,41423,1,1,15312.82009 +0,0,0,1,156713,14,13,677214,1,0,40166.67407 +1,1,0,1,3954,20,12,21408,0,0,8699.634473 +0,0,1,1,38797,19,17,326943,0,0,9399.076282 +0,0,1,1,9867,18,11,27353,0,0,2588.025236 +0,1,0,1,11356,11,13,75715,1,1,16257.93618 +0,0,0,1,18931,145,164,50727,0,1,12850.98872 +1,0,0,1,7671,57,35,21975,1,0,11839.8829 +1,0,1,1,17200,150,110,114140,0,1,18577.31238 +1,0,1,1,60359,20,24,459345,1,1,55748.82324 +0,1,0,1,25135,55,45,131549,0,0,8435.037353 +0,0,1,0,2298,19,12,11375,1,0,8415.235112 +0,0,0,0,48536,37,33,335333,1,1,38288.29422 +0,0,0,1,19084,18,19,55362,0,1,6461.177574 +0,0,1,1,12220,66,57,63430,0,0,6489.251169 +0,0,1,0,33933,60,70,157628,0,0,10567.21174 +0,0,1,1,14702,17,16,123893,0,0,5898.268951 +0,0,0,1,27897,35,25,73351,0,0,3352.079607 +1,0,1,1,52982,22,19,159086,0,1,19184.64834 +0,0,1,1,13605,206,156,62175,1,1,19216.95099 +0,0,1,1,5764,53,43,19948,0,1,5020.076872 +0,1,0,1,2244,27,17,15691,0,0,5593.089558 +1,0,0,1,5038,46,32,17721,0,0,6922.064446 +0,0,0,0,68096,13,11,244989,1,1,28475.95454 +1,1,0,1,4849,50,59,14330,0,1,11182.43115 +1,0,0,1,46944,60,72,177787,0,1,22913.23627 +0,0,0,0,24935,80,103,81077,0,1,10502.37863 +0,0,1,1,13657,21,16,44931,1,0,9730.116239 +1,0,1,1,2161,30,37,13652,1,0,12702.69822 +1,0,0,0,12526,89,112,72595,0,1,15586.68329 +1,0,1,0,68732,237,183,224090,1,1,42906.04591 +0,1,0,1,23802,89,70,126710,1,0,14178.61795 +0,0,1,0,117322,41,38,315544,1,1,38604.23011 +0,1,0,1,10745,37,35,28092,1,0,10258.59208 +1,0,1,1,4068,18,13,38388,1,0,13535.50829 +0,0,1,1,3937,61,71,33293,1,0,9309.372487 +0,0,1,1,27937,77,56,155237,0,1,15265.72861 +0,0,1,1,118244,70,71,315199,1,0,24561.74379 +0,0,1,0,22615,14,16,212549,1,0,16571.64054 +0,0,0,1,21515,48,53,75989,0,1,10118.85221 +0,0,0,1,5980,15,14,19643,0,1,2838.325206 +0,0,1,1,3639,56,52,14509,0,0,4098.491438 +0,1,0,0,88237,118,150,229779,1,0,25590.53569 +0,0,1,0,17314,19,18,106862,1,1,18383.28659 +0,1,0,1,8387,175,197,45477,1,0,20337.33835 +0,0,0,1,27267,96,76,104636,0,0,6272.606914 +0,0,1,0,20605,33,33,72497,0,1,5924.766859 +0,0,1,1,12890,29,35,101666,0,0,6657.532861 +0,0,1,1,57910,20,24,268205,1,0,21539.93028 +0,0,0,0,5402,125,149,25775,0,0,7405.369309 +0,0,1,1,69176,48,61,199564,1,1,29080.77548 +0,0,1,1,26470,36,32,94398,1,1,18038.439 +1,0,1,1,10428,18,17,34956,1,1,12826.48382 +0,0,0,0,8917,21,24,24997,0,0,740.2041154 +0,0,1,1,91861,38,23,232176,1,1,30069.56041 +1,0,1,1,5560,14,17,47658,1,1,14984.83452 +0,0,1,1,20889,36,22,64873,0,1,9138.845406 +0,0,0,1,62209,79,97,180677,0,1,21423.36985 +0,0,0,1,40967,64,74,232979,1,1,33710.50043 +0,0,1,1,17317,94,88,77496,0,0,5267.233703 +0,0,0,0,32250,21,22,188642,1,1,25062.01954 +0,1,0,1,5259,27,28,26085,1,0,9904.940439 +1,0,1,1,17184,95,65,120086,1,1,24304.85393 +0,0,1,1,21320,63,57,82308,0,0,6406.755284 +0,0,0,1,2905,41,47,26025,0,1,5116.346422 +0,0,1,1,26657,64,58,161527,0,0,9800.742942 +0,1,0,1,4178,135,107,19275,0,1,9646.977542 +0,0,1,0,1854,33,23,14154,0,0,2925.00813 +0,0,0,1,4586,38,45,21698,1,1,8043.890063 +0,0,1,1,33402,17,13,89124,0,1,10193.08059 +0,0,0,1,15479,90,84,56697,1,0,14903.83914 +0,0,0,0,17982,73,51,52065,1,0,9625.035979 +0,1,0,0,49673,23,28,239833,1,0,19319.62226 +0,0,1,1,18700,30,34,78490,0,1,8096.444424 +0,0,0,1,21059,42,52,115642,1,1,20582.73153 +0,0,0,0,19225,79,75,71811,1,0,11738.91702 +0,0,1,1,76818,13,14,246340,1,0,18364.88524 +0,1,0,1,12616,80,62,57031,0,0,8819.660959 +0,1,0,0,35916,17,21,132169,1,1,22732.21829 +0,0,1,1,33999,95,99,245949,1,1,35096.32977 +0,0,1,0,38213,42,29,338043,0,1,27467.93103 +0,0,1,1,35147,19,18,106306,0,1,10597.60992 +0,0,1,1,6744,12,9,26371,0,1,3115.42718 +0,0,1,1,16928,10,10,85080,1,1,15044.39546 +0,0,1,1,62920,27,30,216075,1,1,29230.15641 +0,0,0,0,4594,76,79,25081,0,1,6347.182107 +0,0,1,1,29370,23,27,143831,1,0,14397.39567 +0,0,1,0,21590,34,21,77756,0,1,6808.504874 +0,0,1,1,18171,33,34,128257,1,1,21128.28362 +0,0,1,1,11026,142,165,54051,0,1,12749.13256 +0,1,0,1,27952,13,16,107788,1,0,14240.70685 +1,1,0,1,21526,11,12,58430,0,1,13134.84644 +0,0,1,1,15825,11,13,59235,0,1,7629.090292 +0,1,0,0,24708,68,64,179604,0,0,9458.421442 +0,0,1,0,6608,35,34,28620,1,0,7657.136936 +0,1,0,1,36287,16,19,112196,1,1,19856.81149 +0,0,0,1,33094,66,55,100932,0,0,5185.256598 +1,0,1,1,4383,83,96,30708,1,1,17481.04576 +0,0,0,1,3176,38,22,13208,1,1,7630.788895 +1,0,1,1,7792,48,49,65744,1,1,21119.4915 +0,0,1,0,5292,15,13,21678,0,0,1678.447341 +0,0,0,1,9937,56,50,55617,0,1,6276.100271 +0,1,0,0,24133,40,50,93204,0,0,6579.419624 +0,1,0,1,26263,20,25,70868,1,1,15935.48222 +1,0,1,0,38842,16,12,126003,1,1,23245.99828 +1,0,0,1,5995,46,57,41312,0,1,11022.39004 +0,0,1,1,20999,84,64,65775,0,1,8633.419379 +0,0,1,0,7930,47,37,41473,1,0,8780.115203 +0,0,1,0,32635,19,20,107181,0,1,11260.84271 +0,1,0,1,24506,14,9,82940,0,0,6434.636636 +0,0,0,1,23291,27,19,84287,1,0,10648.04763 +0,0,1,1,8595,126,113,23124,0,1,8794.347788 +0,0,1,1,11123,51,32,41222,1,1,12292.45826 +0,0,0,1,21949,56,62,59551,1,0,11770.64662 +0,0,1,1,65556,143,119,181708,1,0,20077.4196 +0,0,0,1,3153,34,36,21218,0,0,1691.743608 +0,0,1,0,40992,43,29,120675,0,0,5962.412434 +0,0,1,1,14999,38,41,117742,0,1,14147.67247 +0,0,1,1,7797,31,26,20878,0,0,1364.936583 +0,0,0,0,4365,170,203,22037,1,0,16981.36868 +0,0,0,1,3718,24,20,21031,1,0,6464.915122 +1,0,1,0,47892,41,45,208753,0,1,22672.5671 +1,0,1,1,41496,19,16,188159,1,1,32056.56452 +0,0,1,1,11782,42,40,36314,0,1,5456.59679 +0,0,1,1,15526,73,67,46628,0,0,4740.708498 +0,0,1,1,9601,10,12,27481,0,1,2577.495205 +0,0,1,1,22798,41,25,66720,1,1,13955.58479 +0,0,1,1,5365,22,27,26066,0,1,4995.809424 +0,1,0,1,3045,131,101,21883,0,0,8470.103969 +1,0,0,1,142325,47,49,396317,1,1,51419.71823 +1,1,0,1,9976,18,20,51091,0,1,11592.21297 +0,0,1,0,15861,17,21,104855,0,0,4686.019904 +0,0,1,0,18104,36,27,164427,1,0,15162.89897 +0,0,0,1,106731,103,84,387171,1,0,28861.64052 +0,0,1,0,17323,52,46,65568,0,1,7747.679224 +1,0,0,1,15095,105,124,54340,0,1,15752.30457 +0,0,1,0,19925,19,15,156070,0,0,4339.93969 +0,1,0,1,28974,92,72,110336,1,0,17146.50535 +0,1,0,1,6453,156,187,17014,1,0,15899.73864 +0,0,0,0,1647,29,34,14242,0,1,2675.457403 +0,0,1,1,29419,19,13,146931,0,0,6868.147956 +0,1,0,1,5531,38,40,14398,0,1,7639.256995 +0,0,1,1,55573,31,29,165848,1,1,26572.05176 +0,0,1,1,6886,129,106,17220,0,1,6543.552993 +0,0,0,1,21029,128,93,91299,0,1,12639.67366 +0,1,0,1,2810,535,407,15503,0,0,21445.05937 +0,0,1,1,10824,25,23,42483,0,0,2736.140569 +0,0,1,1,89014,109,140,269738,0,1,29168.27255 +1,0,0,1,132855,77,59,335574,1,0,28664.11676 +0,0,1,0,6807,32,27,25433,1,1,9820.295287 +0,1,0,1,23915,38,30,158725,1,1,23856.34284 +0,1,0,1,13930,131,112,65562,1,0,16241.99888 +0,0,1,1,107773,40,42,346802,1,0,25373.13155 +0,0,0,1,36663,32,33,112622,1,1,17939.21897 +0,1,0,1,8902,104,130,43286,1,1,18184.09885 +0,0,1,0,97203,39,42,288126,1,1,36123.32499 +0,0,1,1,55789,95,60,362323,1,1,43607.018 +1,0,0,1,6370,68,63,51541,1,0,14975.50384 +0,1,0,1,34325,69,69,116687,1,0,18127.12363 +1,1,0,0,5632,34,40,30658,1,0,13068.91705 +1,0,1,1,47377,34,42,129626,1,0,18042.88613 +0,0,0,1,21329,12,12,54067,0,1,4810.106068 +0,0,0,1,43377,106,67,166282,1,0,16043.1834 +0,0,1,0,6150,75,54,47342,0,0,4809.24011 +1,0,1,0,6274,25,31,48622,0,1,9585.262451 +0,1,0,1,12858,18,11,119528,1,1,20842.74918 +0,0,0,1,27159,17,15,134303,0,0,4998.686659 +0,0,0,1,61431,16,14,241590,1,1,30281.73375 +1,0,0,1,12073,32,40,67400,0,0,9195.50682 +0,0,1,0,21109,25,26,147722,0,1,12990.02835 +0,0,1,1,5826,231,270,22805,1,1,18310.04886 +0,0,0,1,4372,43,46,23236,1,0,8068.479679 +0,0,1,0,4162,62,66,25960,0,0,2139.823082 +0,0,0,0,16024,13,8,52378,1,1,10904.29261 +0,1,0,0,4397,55,36,34740,0,1,5827.637873 +0,1,0,1,55877,40,35,246428,1,0,20483.00682 +0,1,0,0,74493,27,21,299245,1,1,37353.16765 +0,0,1,1,20592,20,15,144860,1,0,13779.91158 +0,0,1,1,17654,37,27,161052,0,1,14308.17281 +0,1,0,1,18912,49,53,89464,1,0,15859.34335 +0,0,1,0,28673,113,82,242562,1,1,32562.24665 +0,1,0,0,51176,62,52,140044,1,0,16471.73165 +1,1,0,1,59946,74,74,244004,1,1,40497.96124 +0,0,1,1,13579,180,211,38702,1,0,16523.06401 +1,0,1,1,28764,71,66,88237,0,0,10473.37958 +0,0,1,1,7113,51,45,24939,0,1,5621.495511 +0,0,0,0,44746,82,86,143547,0,1,16116.64649 +1,0,1,1,13932,20,17,80535,1,1,18253.09563 +0,1,0,1,13314,79,82,82226,0,1,12864.58506 +0,0,1,1,2698,10,11,22466,1,0,9765.044709 +0,0,1,1,6696,18,15,16756,0,0,1610.197453 +0,0,1,1,18693,77,91,85722,0,0,8291.163787 +0,1,0,0,79104,150,98,277467,1,0,26538.01097 +0,0,1,1,3610,171,207,28817,0,1,12939.36505 +0,0,1,0,56810,78,69,250355,1,0,20643.79915 +0,0,0,1,13651,35,43,92141,0,1,10595.26695 +0,0,0,0,181671,14,13,456529,1,1,51924.83565 +0,0,1,1,67999,16,16,251152,1,1,31213.60214 +0,1,0,0,25672,16,14,93188,1,0,12642.9538 +0,0,1,1,36456,40,48,117070,0,0,6927.236082 +0,0,1,0,14293,26,31,57400,0,1,5814.425667 +0,0,1,1,44035,12,8,180498,0,0,7340.06039 +0,0,1,1,18056,44,36,70973,1,0,12523.29614 +1,0,1,0,7446,57,54,62155,0,0,8267.670198 +0,0,0,1,60483,67,65,311253,1,1,40723.91115 +0,0,1,1,7166,39,47,20130,1,1,9371.622527 +0,0,1,1,41540,35,36,119009,0,1,14220.32201 +0,0,1,1,5214,27,31,20180,0,0,2267.005255 +0,1,0,1,28456,111,77,84648,1,0,14936.89896 +0,0,0,1,63025,244,184,244034,0,1,29132.85827 +0,0,1,1,26422,42,50,78664,0,1,9850.210724 +0,0,1,1,7722,106,69,31439,1,1,13160.67895 +0,0,1,1,6694,47,33,17661,0,1,5902.711888 +0,0,1,0,16144,49,63,130026,1,0,14066.81751 +0,0,1,0,47156,125,108,203387,1,1,30241.19053 +0,0,1,1,8336,25,22,26640,0,0,3632.90825 +0,0,0,1,17564,121,75,136194,0,0,8156.040297 +0,1,0,1,15949,33,23,105290,0,0,7177.14562 +1,0,1,1,10062,35,33,52179,1,0,14583.99348 +0,1,0,1,9108,92,77,34421,0,0,8403.453102 +0,0,1,1,26039,66,71,81208,0,1,11195.85353 +0,0,1,0,9249,43,51,46714,1,0,11168.71204 +1,0,0,1,17134,83,59,80182,1,1,20948.36407 +0,0,1,1,180249,53,45,523072,1,1,59799.16955 +0,0,1,1,34393,11,10,127796,0,0,5156.378765 +0,0,0,1,34886,36,40,267997,0,1,23392.26768 +0,0,0,1,29790,64,76,102901,1,0,14147.29512 +1,0,1,1,10440,10,11,30476,0,0,6164.337526 +0,1,0,0,5732,101,111,19706,0,1,9219.297591 +0,0,0,0,13071,103,113,41131,0,0,5684.753484 +0,0,0,1,10572,16,15,53019,1,0,9111.931717 +0,0,1,0,8155,63,56,35718,1,0,10277.30621 +0,0,0,0,37613,79,65,111708,1,0,13816.50717 +0,0,0,1,21993,144,98,71301,1,1,17817.12326 +1,0,1,1,2708,92,92,12849,1,0,16609.08389 +0,1,0,1,26275,34,30,67361,0,0,5067.939335 +0,1,0,1,1805,40,49,10101,0,0,4233.988521 +1,0,0,0,4465,23,25,39431,1,0,12541.37395 +0,0,1,1,19104,185,148,63023,0,0,8468.373517 +0,0,1,1,15114,14,16,118715,0,1,10159.10407 +0,0,1,1,5579,131,85,40869,1,0,11627.39836 +1,0,0,0,7080,25,32,35339,1,0,12927.24394 +0,0,0,1,24857,45,38,124626,0,0,7304.186619 +0,1,0,1,62020,13,15,186338,1,0,19648.6002 +1,0,1,1,36893,15,16,285473,1,1,41386.39792 +0,0,1,1,39103,82,97,139197,1,1,25824.9784 +0,0,0,1,45474,58,73,150931,1,0,16145.67606 +0,0,1,1,2976,66,80,10166,0,1,5360.752999 +0,0,1,0,1556,10,11,11288,0,1,3080.489274 +0,0,0,1,11294,30,33,112288,1,0,11639.04193 +0,0,1,1,88673,59,68,354861,0,1,32723.36488 +0,0,1,0,24787,30,20,116506,0,1,11147.73518 +1,0,0,1,31224,160,200,144355,1,1,32341.84295 +0,0,1,1,70994,84,73,278699,1,1,38448.55502 +0,0,1,1,17925,11,7,73279,0,1,7830.856999 +0,0,0,1,11086,47,39,39742,1,0,7762.919592 +0,1,0,1,22586,37,47,67962,0,0,6274.782362 +0,0,1,1,9337,49,36,24599,0,1,6195.65709 +0,0,1,1,4106,14,15,13447,0,1,3287.837466 +0,0,0,1,2820,11,13,11436,0,1,3141.438092 +0,1,0,0,3473,28,19,15797,0,0,2367.974069 +0,0,1,1,62198,18,12,162238,0,0,8267.460899 +0,1,0,1,12529,136,129,100303,1,1,23611.31726 +0,0,1,1,17934,213,223,48109,0,0,13133.45761 +1,0,0,0,26517,27,33,68723,0,0,9594.085757 +1,0,1,1,84183,47,55,211681,1,0,21632.58762 +1,0,0,1,6616,23,14,30771,0,0,4529.899662 +0,0,0,0,19834,57,43,71494,1,1,15139.6409 +0,1,0,0,2920,16,13,10914,1,0,7605.428215 +0,1,0,1,71548,95,66,246262,1,1,34952.13563 +0,0,1,1,7136,30,29,29883,0,1,5157.582288 +0,0,0,1,30996,28,25,77894,0,1,9597.463865 +1,0,1,1,4381,16,15,23762,0,1,7237.045021 +0,0,1,0,48086,13,11,134061,1,1,20640.76421 +1,0,1,1,13047,43,44,50236,0,0,9437.606288 +1,0,0,0,20779,30,29,58572,0,0,6832.944493 +0,0,0,1,3701,100,82,14786,0,0,5994.344433 +1,0,1,0,105657,23,16,365657,1,0,27892.79878 +0,0,1,1,51015,186,172,130256,1,1,26391.25123 +0,1,0,0,9247,92,114,75889,0,1,13929.84848 +0,1,0,1,16894,58,46,98446,0,0,7199.232341 +0,0,0,1,77983,106,105,240548,1,1,34452.27461 +0,0,1,1,43922,34,24,317542,1,1,38424.33029 +0,0,0,1,101715,58,52,441327,1,1,52058.55769 +0,1,0,1,65186,66,54,181351,1,0,21550.5332 +0,0,1,1,34484,12,13,133730,1,1,22621.54606 +1,0,1,0,2263,10,6,22627,0,0,5749.223418 +0,0,1,1,36360,81,78,162718,1,0,17983.01013 +0,0,0,1,42258,83,59,226327,1,1,32179.11067 +0,0,1,1,23536,36,28,107698,0,0,4986.290485 +0,0,0,0,4772,93,74,31472,1,1,12786.3002 +0,0,0,1,8391,107,77,21257,1,0,11037.04497 +0,0,1,1,19917,69,48,111687,0,0,5879.647635 +0,0,1,1,53873,39,37,164836,1,0,17266.07837 +0,0,1,0,2276,10,10,11649,1,0,4619.491361 +1,0,1,0,7709,53,61,37189,0,1,9916.962461 +0,0,1,1,2692,14,16,14203,0,1,2161.660276 +0,0,1,1,90742,97,107,579829,1,1,70405.60593 +0,1,0,1,14130,45,51,59812,0,1,10011.99584 +0,1,0,0,119865,52,31,450829,1,1,54908.52665 +1,0,1,1,24101,35,42,77767,0,1,13610.06466 +0,0,1,1,6533,65,56,32288,0,1,5449.408473 +0,0,0,0,103019,32,37,336370,1,1,39979.63889 +0,0,1,1,84538,50,50,261351,0,1,23226.09424 +0,0,0,0,20640,14,10,55757,0,1,4643.135404 +1,0,1,1,11230,14,11,104708,0,0,8611.12893 +0,0,0,0,16160,10,9,89259,0,0,3531.240563 +0,0,0,1,19215,18,13,148376,1,1,21817.54653 +0,0,1,1,4843,25,26,31145,0,1,5595.403592 +0,0,1,1,20410,29,27,133682,1,1,21018.41974 +0,0,1,1,49747,11,10,150840,0,0,7639.384949 +0,0,1,1,14819,15,12,47163,1,0,9710.63789 +0,0,1,1,21792,211,136,131719,0,1,16740.27343 +0,0,0,0,35220,27,21,184873,0,0,6516.061032 +0,0,1,1,6192,41,43,17203,1,1,10457.70207 +0,0,1,0,27843,184,160,77525,0,0,10263.97187 +0,0,0,1,19419,103,100,65629,0,0,8372.933098 +0,1,0,0,4127,10,8,12863,0,1,5745.206084 +0,0,1,1,47825,23,15,174200,1,0,16693.90473 +1,0,1,0,7680,111,89,61748,1,0,16859.57223 +0,0,1,1,95504,82,60,303089,1,1,39591.00952 +0,0,0,0,17615,16,9,102904,1,1,15570.84483 +0,1,0,1,12249,97,107,39792,0,0,10507.14384 +0,1,0,1,14642,57,63,74178,0,0,9045.136014 +1,0,0,1,9785,14,9,26468,0,0,4921.146726 +0,0,0,0,5737,16,14,46649,0,1,3290.437078 +0,0,1,1,23504,67,40,83551,1,0,11067.72266 +0,0,1,1,20753,46,28,53095,0,0,4100.066835 +0,0,1,1,8840,29,36,72334,0,0,4716.348468 +0,0,1,1,39863,58,67,210389,1,1,31346.13915 +0,0,0,0,9049,68,63,35945,0,0,2342.322272 +0,1,0,1,17463,66,47,140023,1,0,16650.33293 +1,0,1,0,8275,19,16,21935,0,0,6043.553795 +0,0,1,0,37609,76,93,186672,1,0,18216.06353 +0,0,1,1,10049,100,68,53619,1,1,15951.4408 +0,0,1,1,10942,125,154,33710,1,0,15178.07679 +0,0,0,0,72978,46,39,189662,1,1,25200.70828 +0,0,1,0,12363,27,22,31227,1,1,9139.886332 +1,0,1,0,27289,36,31,121945,1,0,16779.91344 +0,0,1,1,7667,44,47,46684,0,1,6629.553622 +0,0,1,0,8679,18,11,28879,0,0,2725.168602 +1,0,1,1,22840,11,6,126504,0,0,9502.755081 +0,1,0,1,9708,89,59,45745,0,0,5006.419232 +1,0,0,0,26739,81,87,120447,0,0,10819.49251 +0,0,0,1,56503,53,48,146965,1,1,21664.35844 +0,0,1,1,1995,112,104,15256,0,1,6100.597739 +0,0,0,1,35938,19,20,95950,0,0,4656.715179 +1,0,0,1,4312,38,41,30816,0,0,8202.154684 +1,0,1,1,11240,13,14,61523,1,0,14287.6097 +1,0,1,1,12051,95,114,32497,1,0,17091.29017 +0,1,0,1,14072,25,16,134313,1,0,13281.92333 +1,1,0,0,10772,30,20,43592,0,0,8978.875867 +0,0,1,0,29398,71,72,103500,0,1,12218.00156 +0,0,1,1,2009,82,83,13299,0,1,6398.359921 +0,0,1,0,55019,75,65,151204,0,1,15393.43583 +0,0,1,1,8024,117,117,24001,1,0,12216.88022 +0,0,1,1,3163,16,10,29186,0,0,3243.766223 +0,0,0,0,9121,17,18,84477,0,0,3859.295896 +0,0,1,1,4566,56,40,13178,0,0,4549.874946 +0,0,1,1,49437,46,44,228672,1,0,18526.7485 +0,0,1,1,52486,15,10,140954,0,0,5858.523325 +0,0,1,1,69370,114,103,184808,0,1,20807.66651 +0,0,0,1,9789,156,197,28010,0,1,13530.65248 +0,0,1,1,8656,80,95,43736,1,0,12710.58625 +0,0,1,1,21175,48,48,102315,1,0,12091.04998 +0,1,0,1,7702,292,312,26266,1,0,22859.58487 +1,0,1,0,60376,124,104,157177,1,1,31904.76107 +0,0,0,0,58435,60,41,191337,0,1,17286.22888 +1,0,1,1,3496,125,161,16201,0,0,12469.58864 +0,0,1,1,7388,40,48,20111,0,1,5416.301687 +0,0,1,0,10081,104,76,25349,1,0,7577.97488 +0,1,0,1,59263,45,33,159316,1,1,26015.1458 +0,0,1,1,39850,117,126,266732,1,1,38457.41874 +0,0,1,1,124422,30,24,426440,0,1,37135.08325 +1,0,1,1,23914,30,35,130900,0,1,16918.90171 +1,0,1,1,7699,27,25,33783,1,0,14934.28956 +1,0,1,1,78624,80,97,213882,1,1,36116.57596 +0,0,1,0,17187,53,60,96134,0,1,10323.35981 +0,1,0,1,54043,18,22,213215,1,1,31239.19889 +0,0,0,0,26325,47,33,152157,1,1,21319.23994 +1,0,1,1,42241,21,25,253465,1,1,36720.15919 +0,0,0,1,5858,15,10,17547,0,0,866.5027041 +1,0,0,1,6384,102,72,35227,1,0,14702.80333 +0,0,1,1,2786,21,17,12121,0,1,2258.131151 +0,0,0,1,82116,31,37,456050,1,1,51880.29928 +0,0,1,1,24232,142,88,157885,1,0,18343.63045 +1,0,1,1,2620,45,30,26001,0,0,5512.498855 +0,0,1,1,3921,31,24,14241,0,0,3313.33739 +1,0,0,1,118363,143,147,304508,1,1,47852.08547 +0,1,0,1,21732,11,12,66918,0,1,8795.355976 +0,0,1,0,8770,50,42,82729,1,1,15661.76372 +1,0,1,1,3432,28,20,17932,1,0,11402.19819 +1,0,0,1,6756,45,34,34062,0,0,8351.665632 +0,0,1,1,30137,74,73,117713,0,0,7571.36749 +0,0,0,0,48923,25,27,131711,0,1,12597.96129 +0,0,0,1,10364,36,33,35162,0,0,2499.167204 +0,0,1,0,15403,11,8,47092,1,1,11240.48073 +0,0,1,1,5730,213,267,47487,0,0,12059.02577 +0,0,0,0,27791,50,47,117492,0,0,5134.198316 +1,0,1,1,11571,23,21,53433,0,1,10629.10275 +0,0,1,1,6523,43,36,18816,1,0,9464.520369 +0,1,0,0,37292,42,42,96920,0,0,7979.394504 +0,0,0,1,91266,90,57,287031,1,1,36334.68281 +0,0,1,1,25181,241,297,70897,1,1,27973.15607 +0,0,1,1,15019,50,55,38278,0,1,6338.094915 +0,1,0,1,5511,12,14,48863,1,1,11006.59102 +1,0,1,1,29488,58,47,125085,1,1,26038.79802 +0,0,1,1,32206,38,36,284796,1,1,36645.63815 +0,0,0,1,124635,35,41,368727,1,1,45373.57403 +0,0,1,0,12544,20,20,91212,0,1,8654.983774 +0,1,0,1,12907,12,13,71272,0,0,5779.293269 +0,1,0,1,20808,156,121,58423,0,1,11890.29075 +0,0,0,0,15182,96,94,91407,1,1,19539.92132 +0,1,0,1,4417,18,20,15004,0,0,3937.913319 +0,0,1,1,27261,39,35,89449,0,0,5573.486266 +0,0,1,0,94970,138,145,270429,0,1,28266.38094 +0,0,1,1,4418,21,16,13274,0,0,3095.427966 +1,0,0,1,8841,37,22,33257,0,0,5563.593837 +0,0,0,0,6372,51,36,25215,1,0,8733.23786 +1,1,0,0,2144,43,38,14802,0,0,8901.799611 +0,0,0,1,4515,23,23,38466,1,1,12135.37366 +0,1,0,0,2454,39,33,16107,0,1,3831.361007 +1,0,0,0,14352,102,88,45394,0,1,11339.28766 +0,1,0,1,19537,106,102,87840,0,1,14775.65201 +0,0,1,0,5145,45,50,25495,0,1,4170.850008 +1,1,0,1,19614,69,56,57081,1,0,17970.53412 +0,0,0,1,8633,49,62,74423,0,0,7596.227578 +0,0,1,1,11846,78,90,54722,1,1,15920.2787 +0,0,1,1,10003,78,67,26486,0,1,4266.673288 +0,1,0,1,9625,29,27,31766,0,1,6432.659745 +0,0,1,1,16926,13,12,99886,1,0,12412.85596 +0,0,1,1,10846,12,13,62702,1,0,8862.558253 +0,1,0,1,30862,11,10,141853,1,1,22100.80708 +0,0,0,1,37431,17,14,100785,1,1,16670.66491 +0,1,0,1,23443,63,61,150607,0,0,10265.69667 +0,1,0,1,5254,45,39,16314,0,0,3674.955232 +0,0,0,1,1484,74,83,10356,0,1,3749.068364 +0,1,0,0,32211,61,61,169634,1,0,17965.99838 +1,0,0,1,24262,18,22,114054,1,0,14901.1801 +0,0,1,1,23411,52,34,61017,1,1,13705.96307 +0,0,1,1,14557,31,29,48400,1,0,10045.32635 +0,0,1,1,3612,31,39,15993,0,1,4451.110194 +0,0,1,0,6048,74,91,26755,0,0,4823.024566 +0,0,0,0,41713,38,31,127759,0,0,3579.628507 +0,0,0,1,3755,110,81,31828,0,0,5080.331732 +0,0,0,1,58730,11,13,156161,1,0,16838.31593 +1,0,1,1,9946,23,18,25537,0,1,8450.373565 +0,0,1,1,62638,29,34,201956,1,0,16353.64152 +0,0,1,0,24886,35,31,140824,1,1,21353.98798 +0,0,0,0,59675,79,49,172544,1,1,23784.014 +1,0,1,1,22479,149,152,57507,0,0,14855.23627 +0,0,1,1,6765,64,55,36235,0,0,3924.85708 +0,0,1,1,4370,163,113,17863,1,0,13067.51613 +1,1,0,0,32908,63,69,182030,1,1,31603.40384 +1,0,0,0,44768,72,78,288131,1,1,41805.45738 +0,0,1,0,51501,19,14,160081,0,1,13253.74293 +0,1,0,1,37973,12,9,118758,0,0,9247.633942 +0,0,0,1,41362,21,26,126915,0,1,12458.48419 +0,0,1,1,100158,397,323,274719,0,1,37514.29356 +0,0,1,1,13531,68,68,41268,0,0,7088.813884 +0,1,0,1,10693,39,38,52841,1,1,15146.54356 +0,0,1,0,9508,55,39,66334,0,1,5978.721145 +0,0,0,1,6476,64,80,23459,0,1,7550.86325 +0,0,1,0,11469,29,35,54717,0,0,3341.823071 +0,0,1,1,50873,17,12,193027,1,1,26327.42469 +0,0,0,1,19255,198,216,48928,0,0,9853.256899 +0,0,0,0,68221,157,133,184800,1,0,18208.87536 +0,0,0,0,6760,42,49,52894,1,1,10408.71595 +1,0,1,1,10816,85,53,27988,0,1,9306.015641 +0,0,1,1,13410,17,19,60339,0,0,2884.160011 +0,1,0,1,20812,47,43,65449,0,1,9734.050914 +0,0,1,0,10132,12,14,75928,0,0,2883.081314 +0,0,0,0,59470,41,27,292631,1,1,36257.64003 +0,0,1,1,112782,38,39,315258,0,1,27193.64001 +0,0,1,1,20229,84,53,58026,0,0,5205.690894 +0,1,0,1,17655,142,98,96204,1,0,15345.31194 +0,0,0,0,18655,23,17,48163,1,1,10107.28003 +0,0,1,1,55725,25,23,166714,1,0,17594.85795 +0,0,0,1,8346,30,32,30487,0,0,3175.214083 +1,0,0,1,24125,92,77,102457,0,0,11334.54085 +0,0,1,0,6244,90,75,51526,0,0,4258.853967 +1,0,1,0,13683,44,56,67320,0,0,8328.713237 +1,0,0,1,6983,18,12,55021,1,1,14420.92728 +1,0,0,1,24811,15,18,102213,0,1,14348.56177 +0,0,1,0,44913,63,44,417163,1,1,47465.3412 +0,0,1,0,4186,12,10,15928,1,1,8648.999344 +0,0,1,1,3885,96,86,17580,1,1,10898.43799 +1,0,1,0,10182,18,20,74347,1,0,12159.90168 +0,0,0,1,11623,16,19,87554,0,1,7233.63393 +0,0,0,1,37849,24,19,108781,1,1,17732.64553 +0,0,1,0,26267,19,24,111482,0,0,5042.940727 +0,0,0,0,36834,84,99,100638,1,0,15057.14152 +0,0,1,1,11158,34,22,32180,0,0,4486.460688 +0,0,1,1,41565,13,9,108630,1,1,17150.95795 +0,0,1,1,8298,73,93,69506,1,0,11789.74401 +0,0,1,1,10833,115,107,33468,0,0,5959.569767 +0,0,0,0,13728,28,26,52748,0,0,2417.57566 +0,0,1,0,43005,16,15,243471,0,1,19579.10068 +0,0,0,1,23209,51,59,64592,0,0,6723.133299 +1,0,1,1,24414,54,36,89895,1,0,16628.32527 +0,0,0,1,12277,22,26,34947,0,0,4656.094392 +1,0,1,0,38269,12,13,107397,1,0,15324.51276 +0,0,1,1,20574,39,40,78478,1,0,9484.149855 +0,1,0,1,8841,10,9,34104,0,0,5098.284531 +0,0,1,0,9260,41,34,87200,0,0,3820.739798 +0,0,1,1,21805,59,54,106506,0,1,11924.61423 +0,1,0,0,27969,36,24,155298,0,1,15583.72657 +0,0,0,1,16583,92,65,58958,1,0,12828.23697 +1,0,1,1,72348,51,35,241828,0,1,26127.9543 +1,0,0,1,14403,27,20,55228,0,0,6713.853191 +0,0,0,1,22024,49,46,118816,1,0,14502.08795 +1,1,0,1,62831,102,115,180594,1,1,35018.90603 +0,1,0,0,82642,25,22,271759,1,0,20509.34615 +0,0,1,1,37274,35,36,140684,0,0,7800.921691 +0,1,0,0,52187,12,9,147369,1,1,20957.4076 +1,0,0,1,2582,97,120,14821,1,0,15673.91672 +0,0,1,1,11106,102,80,49998,0,0,5621.031559 +0,0,0,1,11587,125,114,69017,0,0,9156.330752 +0,1,0,1,41123,62,71,225071,1,0,21751.17177 +0,0,1,1,4741,100,74,39316,1,0,11367.81931 +0,0,1,1,2983,24,18,12027,1,0,7367.999154 +0,0,1,1,67185,19,23,592031,1,1,66294.40273 +0,0,1,1,13886,111,107,58209,1,1,15133.085 +0,0,1,1,4282,70,47,10741,0,1,5618.644444 +0,0,1,1,14278,49,47,50781,1,1,13386.88583 +0,1,0,0,33723,16,20,230303,1,1,31614.4348 +1,0,0,1,108490,49,36,285019,1,1,40717.37495 +1,1,0,1,12425,101,107,31355,1,0,18790.94691 +1,0,1,1,37843,13,10,159448,0,0,10230.59352 +0,0,1,1,4886,22,23,18308,0,0,3440.503781 +0,0,0,1,24118,22,23,85655,0,1,9020.869609 +0,0,0,1,41197,71,85,106057,1,1,19384.67619 +0,0,0,1,36730,86,57,118542,0,1,12495.46075 +1,0,1,1,6270,39,38,35737,0,0,8370.509775 +0,0,0,1,48090,39,41,194165,1,0,17499.98951 +0,0,1,1,4196,46,37,28286,1,1,9859.146847 +0,1,0,0,7210,35,44,49912,0,0,5967.841137 +0,0,1,1,4610,18,22,24152,1,0,7881.626501 +0,0,1,1,82500,149,125,207800,1,0,21643.22548 +0,0,1,1,3152,68,82,15647,0,0,5102.757612 +0,0,1,0,3001,113,104,12047,0,1,4998.714087 +0,0,0,1,36290,11,13,189870,0,1,18641.16406 +0,0,1,1,13968,257,311,66876,0,1,19960.93409 +0,0,0,1,7859,46,43,39429,0,1,7089.908942 +0,1,0,1,51940,178,174,292856,0,1,31379.32219 +1,0,1,1,40464,26,24,185851,1,0,19645.23798 +1,0,0,1,5855,22,15,41749,1,1,13650.70235 +1,0,0,0,20478,70,55,73767,0,0,7603.842548 +0,0,0,0,12950,20,14,63673,1,1,11390.60223 +0,0,1,1,50706,185,191,259323,0,1,29831.33311 +0,0,1,0,12336,66,47,65794,0,0,5295.01662 +0,0,1,1,23842,37,33,92404,1,0,12156.37941 +0,0,0,0,5618,19,22,14347,1,1,5903.90688 +0,0,0,1,28132,93,117,74672,0,0,6671.775593 +1,0,1,1,12270,17,22,31254,0,1,8959.680159 +0,0,0,1,35356,124,107,100331,0,1,12764.66444 +0,1,0,1,39636,23,23,139884,0,1,13465.12537 +0,1,0,1,14221,143,151,51248,1,0,19010.06087 +1,0,0,0,4411,17,17,14701,1,0,10443.93574 +0,0,1,1,35540,19,23,111631,1,0,14659.38464 +0,0,0,1,2436,78,53,11520,0,0,3432.258329 +0,0,1,1,10278,119,122,64211,0,0,9013.157714 +0,0,1,1,23473,30,28,60043,1,1,13186.55818 +0,1,0,1,32605,62,53,130204,1,0,17086.88052 +1,0,1,0,5443,51,53,29779,1,1,12891.60928 +0,1,0,1,12287,14,11,32485,1,0,10804.32922 +1,1,0,1,16551,14,13,72707,0,0,10831.10914 +0,0,0,0,112485,37,33,294377,1,0,20969.14879 +1,1,0,0,43867,15,15,178062,1,1,29001.08064 +0,0,1,0,74769,18,20,211482,1,1,27140.0278 +1,0,1,1,41708,229,147,125470,0,1,21229.99095 +0,1,0,1,14132,32,23,73589,0,1,9436.539748 +0,0,1,1,1645,42,29,10561,1,0,7917.72614 +0,0,1,0,12549,76,57,38509,0,0,4186.247555 +0,0,1,1,41952,132,123,159724,1,0,19038.30905 +0,1,0,1,57496,28,30,180825,1,0,18473.04575 +0,0,0,0,57708,43,55,188921,0,0,8200.044385 +0,0,1,1,26207,63,72,105094,1,1,19052.52686 +0,0,1,0,87395,14,11,265685,1,1,31667.2133 +0,0,0,0,31288,62,66,128180,0,0,7876.537537 +0,0,1,1,22486,28,32,186502,0,1,17617.60425 +0,1,0,1,62431,23,29,170763,1,1,26957.69292 +0,0,1,1,16166,22,28,47458,0,0,4414.542295 +1,0,0,0,10481,66,84,34409,0,1,9644.67258 +0,0,1,1,11046,68,54,39023,0,0,4836.424456 +1,0,1,0,5716,33,27,18758,1,0,10680.46847 +0,0,1,1,31073,177,135,120287,0,0,10224.81206 +0,0,0,1,18683,128,151,93074,1,0,18355.78078 +0,1,0,1,36027,50,32,153666,0,1,17033.61914 +1,0,0,1,57356,59,69,270515,1,1,38720.5649 +1,0,1,1,17026,72,65,68146,0,1,12469.49981 +0,0,0,1,10509,25,27,35371,0,0,3850.495537 +0,0,1,1,71571,18,20,243258,1,1,32101.21364 +0,0,1,1,18663,33,38,111100,0,0,7038.626683 +0,0,0,1,3083,69,54,16495,1,0,9018.927033 +0,0,0,0,8970,14,11,84745,1,1,13071.80872 +0,0,0,1,31243,68,54,136917,1,1,23749.7002 +0,0,1,1,17250,29,24,60243,1,1,13029.4313 +0,0,1,1,4554,18,17,13039,0,0,4193.71714 +0,0,0,0,13831,69,76,90289,0,0,4992.113273 +0,1,0,0,44217,42,51,130825,1,0,16737.69932 +0,0,1,0,10513,29,34,27477,1,1,10363.02502 +0,0,0,0,15967,39,35,89785,1,0,10216.49236 +0,0,1,0,3019,13,8,15849,0,1,1918.472293 +0,0,1,1,7839,127,112,66145,1,1,17110.84006 +1,0,0,1,46790,28,29,202190,0,1,23053.75214 +0,0,0,1,25059,84,95,101515,0,0,8605.208523 +0,0,0,1,60204,67,83,290129,1,1,38259.78067 +0,0,0,0,5553,37,31,24958,0,1,3215.696532 +1,1,0,0,2635,176,170,10540,1,0,18151.19212 +0,1,0,1,11919,73,94,116933,0,1,16588.51885 +0,0,1,1,44548,78,49,139936,1,0,16430.5856 +0,1,0,1,12196,72,58,54559,0,1,10381.80894 +0,0,1,0,30518,58,48,85608,1,1,14940.5702 +1,1,0,1,11235,15,16,33761,0,1,8618.757569 +0,0,1,0,16918,24,20,49967,0,0,2205.347793 +0,0,1,1,51551,19,23,228340,0,1,19487.9771 +0,0,0,1,1161,137,126,10110,0,0,6438.150315 +0,0,1,1,23339,10,6,121433,1,0,11475.91936 +0,0,1,1,11747,13,14,30759,0,0,3952.418228 +0,0,0,1,38292,17,19,276460,1,1,32961.26813 +0,0,0,0,9385,14,12,24120,0,1,2150.764164 +1,1,0,1,22095,90,88,186400,0,1,24938.68313 +0,0,1,0,4524,52,57,11477,1,1,8481.162147 +0,0,1,0,3590,84,65,17743,0,0,3901.467895 +0,0,0,0,57173,69,81,194397,1,0,18470.39534 +0,0,1,1,11691,12,10,30587,0,0,2445.971539 +1,0,0,1,43341,132,82,119059,0,0,12205.08601 +1,0,1,1,39893,27,23,118939,0,1,15217.28724 +0,0,0,1,43130,25,25,155603,1,1,22927.8039 +0,0,1,1,2432,20,22,10707,0,1,3579.826911 +0,0,0,1,4616,99,94,14943,1,0,11813.63627 +0,0,0,1,43744,20,19,123561,0,1,12900.7674 +0,0,1,0,19166,84,96,164619,1,1,24497.47256 +0,0,0,1,12244,106,114,47774,0,1,9585.584733 +0,0,1,1,48686,60,76,335624,1,1,41627.81473 +0,0,0,1,14376,59,75,39382,0,1,6177.235708 +0,0,0,1,23822,21,17,70596,0,1,7828.339295 +1,1,0,1,5162,132,119,51026,1,1,22679.74804 +1,0,1,1,97206,70,85,288765,1,0,28145.2671 +0,0,0,1,7972,56,71,20187,0,0,4358.590607 +0,0,1,0,119282,111,73,381223,1,1,48825.15791 +1,1,0,1,48188,36,24,166317,0,0,13475.01085 +1,0,1,1,21309,75,93,96090,0,0,12753.54049 +0,1,0,1,59161,10,9,177549,0,1,17060.57066 +0,0,1,0,18220,34,28,95970,0,1,8836.230462 +0,0,0,1,58011,81,84,164011,0,1,19483.50593 +0,0,1,1,14562,27,31,50186,0,0,4372.063859 +0,0,0,1,30014,276,295,205223,0,1,29953.98746 +0,0,1,0,25956,43,36,207341,1,0,17356.96793 +0,1,0,1,23191,55,62,115530,1,0,17237.24459 +0,0,0,1,63723,18,17,277788,1,1,33984.33364 +0,0,1,1,34485,23,26,94661,1,0,12052.66583 +0,0,0,1,10832,89,80,36405,1,0,11457.6029 +1,1,0,1,3425,11,7,20511,1,1,14623.94369 +0,0,1,1,48257,25,16,138281,1,1,21028.4986 +0,0,1,1,13363,39,44,44636,0,0,6198.280838 +0,0,1,1,11948,48,45,78276,0,0,8591.515014 +0,0,1,1,24625,80,81,154873,0,1,17730.1473 +0,0,0,0,20477,22,24,150152,0,1,12872.69707 +0,0,1,1,54552,52,58,178123,0,0,9448.094145 +0,0,1,1,12774,91,103,38037,0,0,6335.50074 +0,0,1,1,4302,23,18,37911,1,1,11050.47634 +0,0,1,1,25692,110,93,107027,1,0,15720.36657 +0,0,0,1,12288,91,66,85794,1,1,17085.61706 +1,0,0,0,31423,61,69,149848,1,1,26623.66442 +0,1,0,0,47336,48,50,199343,1,0,20241.82221 +0,1,0,1,56979,15,13,223319,1,1,30246.84776 +0,0,1,0,42895,11,8,409816,1,0,27934.82979 +0,0,1,1,27340,60,45,199973,1,1,28643.98619 +0,1,0,1,18050,96,73,46960,0,0,7386.644608 +0,0,1,1,17590,117,111,64199,1,1,16801.29799 +0,0,1,1,73270,51,35,210451,1,0,17615.61941 +0,0,1,0,19700,60,40,52203,0,0,3702.353016 +1,0,0,1,62574,15,17,211830,0,0,10518.47624 +0,0,1,0,14416,124,114,61017,0,1,10537.92488 +0,0,0,1,3721,61,71,18761,0,0,4274.035841 +0,1,0,0,32014,39,34,168779,1,1,22833.47745 +0,0,1,1,3085,10,11,17143,0,0,1725.572935 +0,0,1,1,7698,67,67,21786,0,1,4493.399538 +1,0,0,1,25514,16,18,79330,0,0,8614.599349 +0,0,0,0,80766,13,8,409216,0,1,32385.27363 +0,0,1,1,9472,23,15,70972,0,1,7159.863106 +0,0,1,1,14786,173,120,51263,0,1,9207.940664 +0,0,1,1,12143,16,10,84849,1,1,15010.17268 +0,0,1,1,34725,35,31,151778,0,1,15802.91663 +0,1,0,1,38681,25,17,234451,1,1,30319.0011 +1,0,0,1,89769,32,22,267633,1,1,38036.22259 +0,0,1,0,25641,13,11,69995,1,0,11522.14837 +1,1,0,1,44697,79,56,237676,1,0,25230.62232 +1,0,1,1,32141,13,7,126438,1,0,16778.87418 +1,1,0,1,22923,268,312,194055,1,1,43769.61503 +0,0,0,0,2684,77,58,17673,0,0,3511.944554 +0,0,0,1,9460,68,52,68510,0,0,5695.145297 +0,0,0,1,12360,14,14,67594,0,0,3685.758896 +1,1,0,1,53037,21,15,211044,1,0,24571.00018 +0,0,0,1,34956,153,147,142812,0,1,19931.75744 +1,0,1,1,20072,111,98,108150,0,1,19529.78425 +0,1,0,1,8014,109,120,52152,1,1,19136.5617 +0,0,1,1,18900,11,7,74524,0,0,4241.815778 +1,0,0,1,41220,50,44,275648,1,1,39463.25931 +0,0,0,1,12064,114,141,58648,0,0,7356.327876 +0,0,0,1,2348,66,61,11965,0,0,3747.519848 +0,0,1,1,50879,130,115,278253,1,0,25501.66844 +0,0,1,1,106257,60,42,273111,1,1,34219.61616 +0,0,1,1,19632,77,82,130469,1,0,15473.29918 +0,1,0,0,11690,19,15,111676,0,0,6369.082518 +0,1,0,1,32570,45,34,110579,1,0,14653.6345 +0,0,1,1,8791,34,27,22367,1,1,9900.519725 +0,1,0,0,16224,40,27,40810,1,0,10836.31695 +1,0,1,1,70877,37,38,182144,0,0,10354.31599 +0,1,0,0,43168,11,12,164172,1,0,17868.72587 +0,0,0,1,5976,33,30,22015,0,0,2909.544956 +0,1,0,0,37102,21,20,102313,0,1,13090.73802 +0,1,0,1,23036,43,49,80175,1,1,17671.33507 +1,1,0,1,23049,99,122,63075,1,1,23058.83592 +1,0,0,1,4835,60,44,21741,0,1,8089.685727 +1,0,1,1,12372,18,20,73617,0,0,7871.632576 +0,0,0,0,167714,32,19,532991,1,1,59494.49477 +0,0,0,0,23597,18,20,111714,0,1,10550.17236 +0,0,0,1,3226,66,47,29406,0,0,3692.980006 +0,0,1,0,32170,156,162,206448,0,0,13529.61913 +1,0,0,1,95361,130,121,325754,1,1,48460.80509 +0,0,0,0,64591,109,98,284399,1,0,23513.40276 +0,1,0,0,4407,72,49,20032,1,0,11115.66682 +1,1,0,1,18638,97,73,114884,1,1,26747.56274 +0,1,0,1,82656,32,27,226035,1,1,31697.45921 +0,0,1,0,9507,26,15,92186,1,0,11675.94125 +0,0,1,0,32765,53,56,91646,1,0,12127.16554 +0,1,0,1,9417,41,38,24161,1,1,13055.80096 +0,0,1,1,9982,115,131,96959,0,1,13698.22229 +0,0,1,0,38505,33,27,134791,0,0,4399.388273 +0,0,0,0,63185,43,28,213695,0,1,17689.83084 +0,1,0,0,16589,56,34,73505,1,0,12810.15442 +0,0,0,1,27012,20,24,73207,0,0,4864.281094 +0,0,1,1,9818,33,38,77656,0,0,6199.903732 +0,0,0,1,14712,31,35,40750,0,0,2730.122799 +0,0,1,1,21903,40,46,81450,1,0,13859.8355 +0,0,1,0,7400,69,60,28434,1,0,9762.730488 +1,1,0,0,11898,10,12,92882,1,0,15522.97777 +0,0,1,1,12334,115,91,84071,0,1,11690.24518 +1,0,1,1,25406,64,56,66919,0,1,14219.43138 +1,0,1,1,7389,14,11,53856,1,0,12486.7371 +1,1,0,0,86368,62,43,224519,1,1,35715.53248 +0,0,0,0,117187,20,19,324495,0,1,25494.5016 +0,1,0,0,33745,72,47,173644,1,0,18047.44692 +0,0,1,1,18768,104,108,78648,1,1,15877.81596 +0,1,0,0,2701,43,44,17103,1,0,10857.14235 +1,1,0,1,12999,20,23,98378,0,0,11892.40261 +0,1,0,1,19686,179,231,156726,1,1,33337.30767 +0,0,0,0,43841,77,49,161258,0,1,14596.43392 +0,0,0,1,69194,208,216,178354,1,0,23894.58049 +0,0,0,1,2474,33,34,24532,0,0,2858.632027 +0,1,0,1,14448,34,21,86961,0,0,5878.004417 +0,0,1,1,26173,42,25,99311,0,1,9894.79667 +0,0,1,0,2137,161,157,16364,1,0,11303.72537 +0,0,0,1,26067,135,131,155046,1,1,28146.75714 +1,0,1,1,11034,10,12,46612,0,1,8520.844793 +0,0,0,1,13843,132,135,92344,1,0,15489.22167 +0,0,1,0,13819,34,25,35881,1,1,8913.420934 +0,0,1,0,24105,23,27,65307,0,1,5669.662516 +0,0,1,0,54221,63,55,222019,1,1,28354.79066 +1,0,0,1,84700,200,190,377756,1,1,53778.42969 +0,1,0,0,2368,105,81,21188,0,1,9076.91555 +0,0,0,1,2363,27,20,10165,1,0,6417.365157 +0,1,0,1,70745,72,73,199052,1,1,30427.48377 +0,1,0,1,23133,17,17,116491,1,0,13790.5221 +0,1,0,1,8661,55,60,67170,0,0,8687.583676 +0,0,1,1,21920,55,51,82313,1,0,12226.4228 +0,0,1,1,7778,97,114,61796,1,0,14462.92373 +0,0,1,0,18925,52,41,105305,0,1,10454.9325 +0,0,1,1,4115,68,73,16836,0,0,5430.179286 +1,0,1,1,23519,71,43,126031,1,1,23700.03716 +0,0,1,1,63336,67,83,268036,1,1,37758.74083 +0,0,1,1,19677,50,34,102561,0,0,5443.590796 +1,1,0,1,16814,61,49,63214,1,1,20121.49264 +0,0,0,1,33617,21,22,135596,0,0,5190.790215 +0,0,1,1,42113,127,76,212139,1,1,27746.21581 +1,0,1,1,4631,317,250,25033,1,1,22180.12798 +1,0,1,1,86415,34,39,315105,1,1,44069.65938 +1,0,0,1,5281,86,104,33488,1,1,16338.14719 +1,0,0,0,3797,10,8,10273,0,0,5414.094946 +0,0,1,1,5677,17,15,23129,0,0,4098.004662 +0,1,0,1,7971,178,123,25390,0,0,8156.575502 +0,0,1,1,3632,69,56,24876,0,1,5957.777274 +0,0,1,0,27567,51,50,118976,1,0,15984.9995 +0,0,1,0,55529,35,35,175141,1,0,14314.20721 +1,0,1,0,27218,46,38,74856,1,0,13268.24686 +1,1,0,0,5909,127,115,21842,1,0,16346.18441 +1,0,1,1,23480,26,16,98484,1,0,16978.09629 +0,1,0,0,41426,120,110,150732,1,0,20274.3054 +1,0,1,1,3100,28,33,21758,0,0,9822.09884 +0,0,0,0,4836,50,53,19446,0,1,3234.454299 +0,0,0,0,1829,33,37,11692,0,0,374.4919543 +0,1,0,1,97609,16,16,251115,1,1,34324.18839 +0,0,1,1,57828,62,55,253448,1,1,34590.33593 +0,1,0,1,14845,42,28,109708,0,0,6290.856127 +0,0,1,0,24978,27,28,93186,0,1,8775.30422 +0,0,0,0,11430,96,72,83583,1,0,14556.84843 +0,0,1,0,8300,111,75,32700,0,1,5379.372413 +0,0,0,0,6110,29,22,48991,1,1,11711.37175 +0,1,0,0,12882,35,37,96742,0,1,11139.01919 +0,1,0,0,13966,132,136,47141,1,1,16030.57305 +1,0,1,1,47068,72,53,179251,0,1,20728.17565 +0,0,1,1,15723,11,8,61958,0,1,6943.252728 +1,0,1,1,49000,69,64,132441,0,0,11586.90069 +0,0,1,1,7533,68,73,31732,0,0,5844.905558 +0,0,1,0,36929,41,33,187615,1,0,15022.90035 +0,0,1,0,32258,129,104,101546,1,1,19915.63252 +0,0,0,0,30929,47,40,79995,0,1,7886.598072 +0,0,1,1,19383,144,183,95841,0,1,17049.23992 +0,0,1,1,44333,11,10,186906,0,0,6885.897147 +0,0,1,0,4121,24,23,26389,1,0,6905.691011 +0,0,1,0,10302,93,112,86224,1,1,17494.19729 +0,0,1,1,23366,117,111,126904,0,0,8984.496102 +1,0,0,1,5885,72,51,33055,0,0,8370.312256 +0,0,0,0,3411,61,56,17393,0,0,927.7440511 +0,0,1,1,37285,142,146,180782,1,1,29339.67577 +1,0,1,1,55002,22,15,319537,1,1,42297.84965 +1,0,1,0,26594,17,19,140264,1,1,24339.567 +0,0,1,0,3672,100,120,16333,1,0,10969.97368 +0,1,0,1,33596,23,23,104648,0,0,6289.555494 +0,0,1,1,7898,15,18,26614,0,1,3894.353505 +0,0,1,0,120053,16,20,348026,1,0,25019.12665 +1,0,0,1,26374,155,101,156262,1,0,21733.72894 +0,0,1,1,11169,74,59,39394,0,0,4933.718505 +0,0,1,0,10343,117,101,29140,0,0,4516.709131 +0,0,0,0,119811,74,79,338780,1,1,41893.04823 +0,0,1,1,55155,41,28,209808,1,1,28148.2893 +0,0,1,0,25486,43,37,200461,0,1,18717.08297 +0,0,0,1,17133,115,114,43800,1,0,13719.34886 +0,0,1,0,10986,22,25,55203,0,0,3877.036058 +0,0,1,1,3720,120,118,29719,1,1,14819.01866 +0,0,0,0,37833,220,204,101318,1,0,19501.87294 +1,1,0,0,47449,11,12,251510,1,1,34882.6487 +1,0,1,1,8760,131,84,81243,1,0,19114.5469 +1,0,1,0,35447,135,124,110438,0,0,12688.82126 +0,0,1,1,13150,56,43,70848,0,1,8498.405233 +0,0,0,1,47170,32,24,163505,1,1,23199.82368 +0,0,1,1,16848,30,24,131324,1,0,13032.75978 +1,0,0,1,27695,11,7,237921,0,1,25148.51695 +0,0,1,0,77625,74,94,512799,1,1,61842.53886 +0,0,0,1,134562,15,18,343625,1,1,40063.98894 +0,0,1,1,47989,152,179,255746,1,1,38496.75612 +0,0,1,1,4747,14,11,13521,0,0,2955.875518 +1,0,1,1,29087,61,45,97119,1,0,17603.9525 +1,0,1,0,59544,54,45,218979,0,1,25110.63315 +0,0,1,1,8135,85,74,58753,0,0,8419.202725 +0,0,0,1,85468,22,25,314297,1,1,38741.92019 +0,0,1,1,12137,62,59,101757,1,1,19272.74509 +0,0,1,0,33471,17,17,205233,1,1,27122.56479 +0,1,0,1,4448,22,24,13398,0,0,3398.11952 +0,0,0,1,41467,31,29,264171,1,1,32723.72263 +0,0,0,1,12207,99,59,60497,0,0,4879.220052 +1,0,0,1,63941,32,29,235279,1,1,34810.55066 +1,1,0,1,11578,41,33,32790,0,1,11304.1594 +1,0,1,0,169712,22,23,426030,0,0,18095.77891 +0,0,1,1,20110,49,42,105859,1,0,13418.95409 +0,0,0,1,84131,83,59,247807,0,0,11316.10913 +0,0,1,0,2335,79,102,15524,0,1,6788.539469 +0,0,1,1,22064,106,64,161425,1,1,26358.09252 +1,0,0,0,64113,46,48,204894,1,0,20667.64091 +0,0,1,0,50248,171,140,208390,1,1,32069.98303 +1,0,1,1,8338,63,79,48841,1,1,16647.35755 +0,0,0,1,19899,38,45,73329,0,0,5064.05926 +1,1,0,1,110002,264,226,275491,1,1,50307.33259 +0,0,1,1,95362,33,25,256642,0,1,23432.22518 +0,0,0,0,11397,26,31,30444,0,1,5145.141759 +1,0,0,1,43817,213,271,379290,1,1,60203.89043 +0,0,1,0,16580,88,56,99898,0,0,4183.562699 +0,0,0,0,12933,51,47,53317,1,1,11588.65023 +0,0,1,1,5449,20,19,15266,0,0,3658.278053 +1,1,0,1,100167,48,51,358940,1,1,52019.16027 +1,0,1,0,74555,22,17,226384,1,1,32896.6055 +0,0,0,0,47194,152,155,126111,1,0,17573.86443 +0,0,1,0,3926,55,54,10454,0,1,3504.503079 +1,1,0,1,104055,18,13,305435,1,1,43612.60606 +1,0,0,0,16056,28,28,49332,1,1,15206.90912 +0,0,1,1,54424,67,60,253921,1,0,21686.95205 +0,0,1,1,18775,26,27,124740,1,1,18199.69751 +0,0,1,1,14302,43,42,84235,0,0,6303.212166 +0,1,0,1,16525,50,41,91894,0,1,13907.25597 +0,1,0,0,9501,53,50,38192,1,0,10861.04743 +1,0,1,0,28947,146,134,89687,1,1,23753.70897 +0,0,0,0,38258,338,327,227586,1,0,30043.81505 +1,1,0,1,5411,110,101,30244,0,0,10369.50698 +0,0,0,1,26835,243,187,99838,1,0,19400.48493 +0,1,0,1,12232,75,49,38683,0,1,8939.53286 +1,0,1,0,3209,120,145,15965,0,0,11643.57355 +0,0,0,0,31661,127,157,117083,1,1,24038.8003 +0,0,0,1,11598,19,12,33497,1,1,10677.20017 +0,0,0,1,83791,18,22,273756,0,1,25331.78145 +1,0,0,0,50206,12,15,149350,1,0,17083.90622 +1,0,0,1,45543,10,10,140943,0,0,10211.61067 +0,0,0,1,27402,54,44,79984,0,0,3774.022142 +0,0,0,1,67216,13,13,369854,1,1,42810.83364 +0,0,1,1,35233,30,18,90039,1,0,10666.32347 +0,1,0,1,10080,21,18,79275,0,0,6123.187067 +1,0,0,0,15824,138,84,86526,1,0,17595.12246 +0,0,1,1,7156,44,55,24417,1,0,10982.72842 +0,0,1,1,22383,30,33,133443,1,0,13957.71172 +0,1,0,0,31957,21,26,151588,1,0,14916.32438 +0,0,1,1,7142,76,90,38019,0,1,8745.555866 +1,0,0,0,25718,135,137,69886,0,0,12375.25549 +0,0,1,1,25393,70,61,81306,0,1,9984.021432 +0,0,1,1,4272,244,278,18389,0,0,12912.11771 +1,0,0,0,14482,30,21,89187,1,1,19112.70034 +1,1,0,1,36515,27,31,91536,0,1,15703.48185 +0,0,1,1,83954,22,13,414028,1,1,48411.73584 +1,0,1,1,5359,24,18,17539,1,0,12403.82279 +0,0,0,0,25396,93,81,169520,1,1,24682.17914 +0,0,1,1,8218,93,103,21545,0,1,8013.702893 +1,0,1,1,20775,24,14,71600,0,0,8119.431132 +0,0,0,1,17762,243,237,70104,1,1,25916.50473 +0,0,0,0,49517,11,11,189560,1,1,23457.60884 +0,0,1,1,15628,25,15,71621,1,0,10537.24554 +0,0,1,1,13660,38,24,46070,0,0,5594.256812 +0,1,0,1,5732,36,46,27540,1,1,12872.95036 +1,1,0,1,10033,74,65,56750,1,0,16952.31794 +0,1,0,1,3970,12,10,21244,0,1,7616.587121 +1,0,0,1,54925,102,85,171154,1,0,22461.02533 +0,0,0,1,21284,37,22,103826,0,1,11795.08376 +0,0,0,1,12314,37,43,33933,0,0,4730.494451 +0,0,1,1,26324,170,133,74431,1,1,18602.41447 +0,1,0,0,77559,71,57,290795,1,1,37418.4234 +1,0,1,1,3976,42,25,14692,1,1,12010.44682 +0,0,1,1,65118,14,9,214925,1,1,29424.86975 +0,0,1,0,73434,11,10,254975,1,0,19171.56902 +0,0,1,1,52217,21,18,171420,1,1,24924.6775 +0,0,1,1,20795,15,14,61856,1,0,10738.01935 +0,0,1,1,10102,247,306,55864,0,0,16188.5893 +0,0,1,1,53540,99,80,184557,1,0,19937.30604 +0,0,1,1,3516,134,132,26005,1,0,12624.47883 +0,0,0,1,31130,75,52,138529,1,1,22232.06116 +0,0,1,1,49152,98,126,161032,1,1,29057.13155 +1,0,1,1,6429,34,42,47778,1,0,15575.59092 +0,0,0,1,24143,19,17,150038,1,1,22069.98835 +0,0,1,0,10347,177,154,29299,1,0,12110.95703 +0,0,1,1,25178,31,20,90695,0,1,10792.43108 +0,0,1,0,14148,33,23,48945,0,0,3046.58905 +0,1,0,0,113561,59,67,291874,1,1,38101.29937 +0,0,1,1,17240,17,16,62576,1,0,10924.6916 +0,0,1,1,3641,74,56,15051,1,0,12652.01364 +0,0,1,0,12569,47,60,61120,1,0,11758.11622 +0,0,1,1,74494,28,22,224557,1,0,21570.27881 +1,0,1,0,75145,13,13,436643,1,1,51481.47076 +1,0,1,1,31558,25,28,128626,1,1,25264.31538 +0,0,0,1,18540,30,27,72997,0,1,9055.466622 +0,1,0,0,34325,41,40,120679,0,0,8536.313565 +0,0,1,1,5720,57,61,26848,0,0,6769.439448 +0,1,0,1,12020,203,234,30600,0,0,12671.6971 +0,0,0,1,20576,66,50,152654,1,0,16145.62882 +0,1,0,1,16597,34,33,73011,0,0,6803.452299 +0,0,0,0,25021,39,33,159410,0,0,5687.746612 +1,0,1,1,41644,146,93,244406,1,1,37938.17821 +0,1,0,0,12911,49,62,39582,1,1,14715.84123 +0,0,1,0,24486,84,108,91734,1,0,13786.72061 +0,0,1,1,24412,80,51,124649,1,0,13916.59201 +0,0,1,1,35015,40,30,89385,1,1,17680.56379 +0,0,1,1,81286,11,13,308051,1,1,36980.83711 +0,1,0,1,36647,155,108,196940,0,1,23306.00006 +0,0,1,1,26933,101,76,123624,1,0,16293.66391 +0,0,0,0,32174,45,31,240885,1,1,31262.08172 +1,0,1,1,41344,41,49,115047,0,0,9719.121829 +0,0,1,1,4077,13,12,19584,1,0,9121.758337 +0,0,0,0,2000,27,24,13828,0,0,1950.003882 +0,0,0,1,19511,42,47,147592,1,1,21766.20787 +0,1,0,1,15166,203,125,42304,0,0,9369.386154 +1,0,1,0,33239,13,13,99772,1,1,19402.91401 +0,1,0,1,68209,50,59,180279,0,1,20803.41705 +0,0,1,0,4131,103,83,20340,1,1,10261.71063 +0,0,1,1,20910,134,170,143777,0,1,19449.43461 +0,1,0,1,4243,16,13,21518,1,0,12566.93159 +0,1,0,1,3108,110,70,17153,0,0,6432.405158 +0,1,0,0,31160,20,21,146114,0,0,6955.52316 +0,0,1,1,2924,71,56,21425,1,0,11287.25064 +0,0,1,1,6267,130,160,22855,0,0,8529.479435 +0,0,1,1,2398,49,34,23887,0,0,3459.079997 +0,0,1,1,38047,15,15,180869,1,0,14293.74411 +0,0,1,1,125515,34,42,621723,1,1,69502.99747 +1,1,0,1,47333,118,137,363714,1,0,35334.1679 +0,0,1,0,32360,50,50,178707,1,0,15949.85578 +1,1,0,0,49230,19,11,185925,0,0,13743.69156 +0,0,0,0,86318,27,24,415986,1,1,47684.93267 +0,0,0,1,23830,330,232,83006,0,1,17790.00111 +0,1,0,1,14024,49,31,111457,1,1,19360.29741 +0,0,1,0,45845,159,110,157802,1,0,18662.28483 +1,0,1,0,19093,29,27,189251,0,1,21412.61012 +0,0,1,1,4333,16,17,11813,1,0,7057.716994 +0,0,0,0,8334,113,110,59012,0,0,5815.313267 +0,0,1,0,7503,77,86,39893,0,1,6041.691655 +0,0,1,1,3344,37,35,20632,0,0,2487.28966 +0,0,1,1,8425,43,39,25626,0,1,4957.736644 +0,1,0,0,19158,18,14,80909,1,0,12110.20072 +0,0,1,0,19452,52,65,56500,1,0,9610.462044 +1,0,1,0,21621,213,133,68031,1,1,22110.9188 +0,0,1,1,27904,18,13,160297,0,0,8022.861962 +1,0,1,0,19168,31,30,146996,1,1,23680.3172 +0,0,0,1,19530,51,52,133215,0,1,12813.65007 +0,0,0,0,2295,87,85,11652,1,1,12866.98374 +0,0,0,1,10431,111,109,43425,1,1,14834.34432 +0,1,0,0,1914,130,129,18960,1,0,14030.81194 +0,1,0,1,11769,10,12,101790,1,0,13709.73817 +0,1,0,1,40599,11,13,391334,1,1,47007.10273 +0,0,1,1,3673,93,66,29982,0,1,5465.561091 +0,0,1,0,43813,300,291,117866,0,1,22034.16676 +0,0,0,1,24922,28,36,238370,1,1,30232.00281 +0,1,0,1,29650,75,58,129825,1,1,26170.72392 +0,0,1,1,57779,117,100,155279,0,1,16874.24697 +0,0,1,1,8710,18,19,37352,0,0,4343.695185 +0,0,1,0,12372,31,37,87831,1,0,10074.31003 +1,0,1,1,25210,152,93,82789,0,1,16246.96505 +1,0,0,1,35232,95,93,88361,0,0,11747.05758 +0,0,1,1,2910,28,22,19665,1,0,7535.963856 +0,0,0,1,4697,11,13,19975,0,0,3153.999581 +1,0,1,0,22028,139,142,60867,1,0,18548.92683 +1,0,1,1,20085,42,48,55217,0,0,8837.617126 +0,1,0,1,25176,16,11,75388,1,1,15671.0671 +0,0,0,1,55601,128,152,364693,1,1,48422.49158 +0,0,1,0,60042,34,42,180787,0,0,7457.534234 +0,0,0,1,43814,44,28,139225,0,1,14923.19864 +1,0,1,1,8175,175,174,57797,1,0,20795.30641 +1,0,1,1,20911,23,27,157897,1,0,18476.45679 +1,0,1,1,7020,92,57,36048,1,1,16736.85308 +0,1,0,0,24809,16,13,82969,1,1,18157.45591 +0,0,1,1,5037,27,22,44426,0,1,5106.006203 +1,1,0,1,8911,37,25,36052,1,0,15058.3902 +0,0,0,1,6184,66,52,34816,0,1,6351.385349 +0,0,1,0,54124,26,24,360237,1,1,41718.57662 +0,0,1,0,6964,12,8,25839,0,1,2562.555572 +0,0,1,1,37921,11,6,112185,0,1,9782.389138 +0,0,1,1,36046,94,91,123950,0,1,14914.87993 +1,0,1,0,20439,34,32,53451,1,0,13411.55529 +0,0,0,1,34471,70,77,112570,0,0,8116.201911 +0,0,1,1,26216,80,75,83602,1,0,14465.35069 +0,0,1,1,72410,113,98,213094,0,1,23650.45983 +0,0,0,1,24612,11,6,83804,1,1,14458.94926 +0,0,0,1,20156,50,44,105410,0,0,5360.756553 +0,0,0,1,65471,131,86,312961,1,1,41559.2825 +0,0,1,1,5085,52,64,19455,0,1,5690.371584 +0,0,0,1,26726,35,35,80233,1,1,15856.81648 +0,0,0,1,28145,100,91,75364,0,0,8782.850133 +1,0,0,1,6326,88,106,19067,0,0,11346.83188 +1,1,0,1,5595,200,168,32218,0,0,16131.02992 +0,0,1,1,8670,175,151,63222,0,1,13452.202 +0,0,0,1,48104,110,136,121870,0,1,16826.19827 +0,0,0,1,2715,50,52,18984,0,0,3276.441955 +0,0,1,1,7555,81,62,25721,0,0,5182.933638 +0,0,0,0,8202,52,50,37711,1,0,10393.92094 +1,0,1,0,60229,16,10,169389,0,1,17528.55139 +1,0,1,1,76500,16,11,303731,1,1,39653.45191 +0,0,1,1,187785,125,132,486587,1,1,60619.11284 +0,0,1,1,18389,15,14,84984,0,0,4554.40667 +0,1,0,1,6423,16,14,61674,0,0,7472.361427 +0,0,0,1,2961,42,34,10950,0,0,3248.018934 +0,0,1,0,44693,189,159,138383,0,1,20930.42881 +1,0,1,0,25553,146,118,72761,1,1,22416.93314 +0,0,1,0,12754,29,31,47655,0,1,5814.054048 +0,0,1,1,5905,15,16,26206,1,0,8830.422136 +0,0,1,1,16602,179,219,78992,0,0,12512.88604 +0,1,0,1,9423,39,35,64041,0,1,9039.266551 +0,0,0,1,18287,55,61,76185,1,1,15442.85173 +0,1,0,0,7816,18,15,25347,1,0,9855.571089 +1,1,0,1,2889,103,73,23204,0,0,10374.1152 +1,0,0,0,2238,31,25,17128,0,1,6439.1138 +0,0,1,0,17184,13,9,45179,0,0,3240.677761 +0,1,0,1,71207,22,15,324583,1,1,41789.21678 +1,1,0,1,95864,126,100,313896,1,1,48722.19318 +0,0,0,0,61565,88,82,155731,1,1,23979.2456 +0,0,1,0,52056,84,78,249586,0,1,24583.70491 +0,0,1,0,42209,287,368,132812,0,1,26763.89677 +0,0,1,1,30881,350,346,78447,0,0,19044.33861 +0,0,1,1,32098,76,47,94511,0,1,10933.44821 +0,0,0,0,125643,17,15,353921,1,1,40255.44235 +0,0,0,1,15553,40,39,102679,0,0,6468.444106 +0,1,0,1,27005,129,130,116103,1,0,18377.2377 +0,1,0,0,42988,167,172,206798,0,0,14047.61075 +0,0,0,0,7672,32,39,33964,0,1,3792.469079 +0,0,0,0,8166,36,33,35400,0,1,4509.107352 +1,0,1,0,31533,158,166,160557,1,1,31782.88533 +0,0,1,1,12678,82,51,51160,0,1,6702.151585 +0,1,0,1,10525,28,27,90436,0,1,11455.40518 +0,1,0,1,14493,111,83,47701,0,0,8361.661382 +0,0,1,0,9689,23,20,30535,1,0,7362.893813 +0,1,0,1,42165,35,43,182719,0,1,20467.30031 +0,0,1,1,37048,86,64,128094,0,0,8159.382761 +0,0,1,1,148792,28,31,387643,1,1,46328.9537 +0,1,0,1,22286,30,26,64435,1,1,17184.19603 +0,0,0,1,17740,58,55,60088,1,1,14089.96524 +0,0,0,1,9428,26,16,35205,1,1,11627.70748 +0,0,1,1,62294,142,179,236867,1,1,36651.53787 +0,0,1,1,26526,151,195,85307,1,0,16851.32486 +0,1,0,0,9974,36,35,26139,0,1,6011.221209 +0,0,1,1,16751,12,8,60287,0,0,2161.067162 +0,0,1,0,2017,16,11,10767,0,1,1075.306142 +0,0,1,0,29482,40,51,287594,0,1,26263.23327 +1,0,0,1,66115,14,11,175283,0,1,20758.60175 +0,0,0,0,18516,54,56,108860,1,0,12850.2833 +0,0,0,1,10060,158,145,35440,0,0,8283.110327 +0,0,1,1,3280,71,54,16764,0,0,5008.528603 +0,0,1,1,20957,178,196,70511,1,1,23263.80294 +1,0,1,1,11821,48,35,58965,0,0,8584.94953 +0,0,1,1,14031,12,14,62941,1,0,10127.11694 +0,0,1,1,52153,41,27,204358,0,0,9614.969945 +0,0,1,1,40238,184,122,355100,1,1,46075.22695 +1,0,1,1,5342,44,44,18610,0,1,8958.329522 +1,1,0,0,49513,35,34,273202,1,0,26057.50382 +0,0,1,1,36169,154,184,171836,0,1,22141.74528 +0,0,0,1,5712,10,12,30801,1,1,9849.800909 +0,0,1,1,14457,51,63,101818,0,0,7633.370285 +1,0,0,1,11786,24,28,69099,0,0,8669.574323 +0,0,1,0,9982,117,95,27981,0,1,7712.577363 +0,0,1,1,28616,32,36,111547,1,1,18512.21463 +0,0,0,1,8003,149,89,33333,0,1,5532.672742 +0,0,1,1,13539,24,22,65113,0,1,5200.339559 +0,0,1,1,21443,13,9,93531,0,0,4438.57358 +1,0,1,0,55566,71,51,380317,1,1,50858.10683 +0,0,1,0,2780,74,45,14419,0,0,2272.127562 +0,0,0,1,37114,36,43,157622,1,1,23926.54642 +0,0,1,0,6722,67,82,29573,1,1,11457.61484 +0,0,1,1,6531,56,47,36447,1,1,12740.23798 +0,0,1,0,11264,36,32,28892,0,1,4429.202694 +0,0,1,1,43847,19,19,157285,0,0,8283.264431 +0,0,1,1,5582,59,62,17240,0,0,6302.147101 +1,0,1,0,4154,37,26,14490,0,1,7030.601816 +0,0,0,1,85025,13,12,227981,1,1,28421.08444 +0,0,1,1,12019,58,68,44029,1,0,12317.583 +0,0,1,1,27506,188,135,219199,0,1,25131.74074 +0,0,1,1,64011,155,108,191203,1,1,31791.75098 +0,0,1,0,10387,130,123,60886,0,1,11196.20622 +0,0,1,1,54804,104,90,230590,1,0,22851.47202 +0,1,0,1,3739,136,100,10517,0,1,7861.118021 +0,0,1,0,43218,90,59,240968,1,1,31185.63017 +1,0,0,1,133727,58,57,343435,1,0,29186.81641 +0,0,0,1,2145,80,74,19592,0,1,7154.496475 +0,0,1,0,18340,19,19,46709,0,0,1788.257783 +0,0,1,1,40388,23,28,163068,1,1,22514.99273 +0,0,1,0,27683,14,8,79956,0,1,8201.242114 +1,0,0,0,37653,10,10,169230,0,0,9536.348658 +1,0,1,1,43133,88,89,126433,1,0,22077.31349 +0,0,0,0,4783,32,23,14964,0,1,2205.170846 +0,1,0,1,10860,46,53,31509,0,1,8237.521679 +0,0,1,1,14286,73,47,39297,0,0,2805.656965 +0,0,1,1,7035,24,28,27539,0,0,3451.888589 +0,0,1,1,185820,45,36,561249,1,0,38349.62419 +0,0,0,1,65137,122,147,237129,1,0,24103.33576 +0,0,0,0,11679,50,41,99386,1,1,16866.80194 +0,0,1,0,6399,25,21,16146,1,1,7812.085994 +0,0,1,1,72852,71,83,227646,1,1,32424.35195 +0,1,0,1,5047,24,29,28839,1,0,10910.47314 +0,0,0,1,6566,21,17,19778,0,0,1526.97737 +0,0,1,1,63185,282,292,163772,1,1,33763.74732 +0,0,1,1,1923,15,18,17196,0,0,3599.09488 +1,0,0,0,21061,23,24,134540,0,1,14828.91803 +0,0,0,1,6541,20,15,39541,0,0,2689.880991 +0,0,1,0,24886,34,35,98687,1,0,12058.38907 +0,0,0,1,7147,22,14,39237,0,0,3793.687727 +0,0,0,1,20846,64,56,112843,0,1,12762.26916 +1,0,1,1,23796,48,49,60288,1,0,14779.28191 +0,0,1,1,17231,58,48,74124,1,1,15862.94307 +0,1,0,1,17530,28,20,115727,0,1,13850.06656 +0,0,1,1,6434,20,20,36841,0,0,2466.731959 +0,0,1,0,12337,75,45,53062,1,0,10506.62169 +0,0,1,1,15110,22,15,44091,0,1,5042.730342 +0,0,1,0,14587,82,83,46146,0,1,8148.839687 +0,0,1,1,9466,51,64,44456,0,1,6819.859368 +0,0,0,1,10197,105,108,37298,0,1,8699.677047 +0,0,1,1,88324,20,21,248728,0,0,10088.00987 +0,0,0,1,12919,16,17,89955,0,1,9269.945628 +0,1,0,1,20893,47,50,66235,1,1,15915.97157 +0,0,0,1,34937,27,18,204745,1,1,27843.71872 +1,0,0,1,21028,64,55,118109,1,0,16707.76303 +1,0,1,1,35716,176,145,258150,1,1,43287.31095 +0,1,0,1,26653,10,12,196556,1,0,17609.17247 +0,0,1,1,21425,81,69,93682,0,0,5730.848545 +0,1,0,1,132165,128,117,369686,1,1,50130.92151 +0,1,0,1,10282,12,8,93054,1,0,12674.01825 +0,0,1,0,7932,68,61,64418,0,0,4660.333545 +0,0,1,1,41186,31,31,133654,0,1,13759.22737 +1,0,1,0,3082,33,27,30297,1,0,12782.91284 +0,1,0,0,44056,23,23,152713,1,1,24418.0332 +0,0,0,1,34057,38,27,119427,1,1,19079.72898 +0,0,1,1,3888,54,43,29769,0,1,6113.80667 +0,0,0,1,13275,80,54,41946,0,1,6875.8485 +0,0,1,0,73103,39,42,224032,1,1,28756.2661 +0,0,1,0,8505,62,55,29014,1,0,7046.27083 +0,0,1,1,18993,82,96,153617,1,0,17746.91304 +1,0,0,1,25259,50,47,105468,0,1,15388.45275 +1,0,0,1,5900,67,83,25413,0,0,9931.152373 +0,0,1,1,4799,15,13,25828,0,0,1808.608641 +1,0,1,0,6233,93,97,55279,0,0,8637.988017 +1,0,0,1,1855,82,76,13145,1,0,14090.36477 +0,0,0,1,16861,55,34,153278,0,0,6254.98617 +0,0,0,1,43118,32,35,127226,0,0,6983.619267 +0,0,1,0,23597,264,220,73249,1,1,22659.50419 +0,0,0,0,22245,12,10,96594,0,1,8108.028387 +0,0,0,0,12420,90,78,37446,0,0,4271.401033 +0,0,1,1,78109,13,12,232974,1,0,18953.51594 +0,0,1,1,24163,24,17,162345,1,1,21509.95807 +0,1,0,0,17457,23,19,57875,0,0,4471.087288 +1,0,1,1,16434,37,25,82730,1,0,16182.27909 +0,0,1,0,5115,19,12,17494,0,1,2799.743004 +0,0,0,0,13959,40,49,101399,0,0,4744.473057 +1,0,0,1,9149,75,71,53781,1,1,18086.028 +1,0,1,1,5180,105,90,13802,1,0,13681.09963 +0,0,1,1,14921,136,132,47549,0,0,9496.221711 +0,0,0,0,19206,22,22,67538,1,1,12374.67423 +0,0,1,1,4865,120,118,32082,0,0,7445.272025 +1,0,0,1,7157,57,68,17917,0,1,9655.977756 +0,0,1,1,55763,72,70,274217,1,1,36554.17464 +0,0,0,1,45926,32,25,121688,1,0,13102.90052 +0,0,0,0,12303,53,59,83824,1,1,15267.41038 +0,0,1,1,12864,15,17,64482,1,1,14042.46102 +0,0,0,0,15689,21,15,41964,0,0,1113.172781 +0,0,0,1,49295,85,58,131982,1,0,15754.24544 +1,0,0,1,10933,56,38,57713,0,1,10785.29754 +0,0,0,1,42488,74,55,143637,1,1,24193.11289 +0,0,1,1,17395,105,117,48496,0,1,9994.092275 +0,0,1,1,50126,34,43,125504,1,1,20932.81831 +1,0,0,1,19693,73,65,75585,1,0,16648.60247 +0,0,0,1,44522,88,104,216508,1,1,30794.48969 +0,0,1,1,71691,12,11,329923,1,1,38545.90173 +1,0,0,0,27011,34,29,96403,1,1,19490.37588 +1,0,1,1,41441,29,20,105035,0,0,10722.2157 +0,1,0,1,32052,51,44,101540,0,1,13513.02957 +0,0,1,0,10763,39,26,29301,0,1,4918.077477 +1,0,1,1,17253,62,72,48319,1,1,18683.07929 +0,0,1,1,48633,37,34,126911,0,0,5982.366767 +0,1,0,1,4028,46,31,22089,1,1,11764.24383 +0,1,0,1,30229,92,113,248850,1,1,37524.42834 +0,0,1,1,2029,13,12,18641,0,1,5965.460187 +1,0,1,0,19544,14,17,60230,1,0,12723.99488 +1,0,1,0,3438,110,108,31135,1,1,17993.10381 +0,0,0,1,38621,99,107,293510,1,1,39756.50128 +1,1,0,1,38283,73,48,121796,0,1,20599.22185 +1,0,1,1,4328,125,119,16090,0,0,9928.621027 +0,0,1,1,11517,33,22,55005,0,0,3714.651945 +0,1,0,1,2778,80,65,13682,0,0,5325.555068 +0,0,0,1,72346,14,10,194938,0,0,7866.984969 +0,0,1,1,37139,227,221,134594,1,1,29605.25558 +0,0,0,1,34975,16,15,140578,0,1,12305.625 +0,0,0,1,25050,34,36,70904,1,1,15090.02796 +1,0,0,1,17714,16,11,48556,0,0,6507.064194 +0,0,1,1,16104,16,12,47197,0,0,4627.528321 +0,0,1,1,26560,46,49,87562,0,0,6624.822918 +1,0,1,1,13485,16,11,69563,1,0,15812.20706 +0,1,0,1,71203,21,26,245123,0,1,25313.38013 +1,1,0,1,8016,61,46,25613,0,1,10625.00185 +0,1,0,1,7958,38,42,77649,1,1,17474.30546 +0,0,1,1,9141,65,40,59573,1,0,11450.96151 +0,0,1,0,73924,58,65,316663,0,0,13905.29862 +1,0,1,1,3798,11,11,15615,0,0,6929.75123 +0,0,0,1,47051,55,61,198478,1,1,28593.84247 +0,0,0,1,30457,39,39,77466,1,1,17704.23462 +1,0,1,1,12036,22,18,47870,1,0,12267.90934 +0,1,0,1,6063,45,31,17352,0,0,4157.376807 +0,1,0,0,16212,170,140,63835,0,0,11058.92666 +0,0,0,1,25627,68,64,73479,0,0,4728.372297 +0,0,1,1,2595,134,169,12752,0,1,9363.243188 +1,1,0,0,10994,19,21,44245,1,1,15004.20719 +0,0,1,1,11023,93,92,100300,1,0,12888.18501 +1,0,0,0,23399,82,87,63825,0,1,10876.15546 +0,1,0,1,66950,78,72,229300,0,1,26308.99261 +0,0,0,0,9442,41,45,35149,0,0,3897.27888 +1,0,1,1,7350,59,43,56526,0,1,11354.20289 +1,1,0,1,22699,21,25,89220,1,1,21847.66321 +0,0,0,1,15888,53,40,134756,1,1,20548.47139 +0,1,0,0,90030,71,56,227082,1,0,21740.35477 +1,0,0,1,15860,29,28,65533,1,0,15546.20046 +1,0,1,1,9837,87,99,49832,1,1,18958.14154 +0,0,0,1,16449,122,96,52638,1,1,16276.81724 +0,0,1,1,52276,23,15,175190,1,0,15262.2085 +0,0,0,1,30669,24,17,125355,1,1,20924.00185 +0,0,1,1,49274,40,41,146268,1,1,24373.30679 +0,1,0,0,9733,70,60,34800,1,0,11246.90037 +0,0,1,1,9333,71,65,29377,0,1,5236.862906 +0,0,1,1,91066,20,12,239951,1,1,31190.52552 +0,0,1,0,6268,210,222,25450,0,0,9725.253749 +0,1,0,1,31784,31,27,118649,1,1,23041.97953 +0,0,1,1,12565,49,59,101747,1,1,18578.82007 +0,0,0,1,5898,39,37,22433,0,1,4665.17618 +0,0,0,1,12432,21,17,68448,1,1,13605.75904 +0,0,1,1,24080,15,14,99400,1,0,13929.53007 +0,0,1,0,50785,23,16,170686,1,1,21878.81944 +1,0,1,1,46151,40,24,228243,1,1,34461.63062 +0,0,1,1,29833,19,16,200222,1,1,25888.90355 +0,0,0,1,6223,141,155,47986,1,0,15543.72503 +1,0,0,1,5389,21,22,46579,1,0,13139.07023 +0,1,0,1,9687,10,12,73454,0,0,5929.673647 +0,0,0,1,13982,181,186,68710,0,1,13725.05944 +0,0,1,0,28634,27,19,86259,1,1,14586.45649 +0,0,0,1,19698,223,194,68434,0,0,12146.46301 +1,1,0,1,48400,46,52,152151,1,1,29080.33788 +1,0,0,0,36848,95,89,154038,1,1,29094.56132 +1,1,0,1,18391,25,19,48419,0,0,9525.496591 +0,1,0,1,11356,26,25,33958,0,1,6615.064745 +0,0,0,0,4571,28,22,12955,0,1,889.9756528 +0,0,1,1,12171,12,13,42237,0,0,1408.456289 +0,0,1,1,3613,35,30,30750,0,1,6131.616399 +0,0,0,1,20584,41,49,52063,1,0,10898.83007 +0,0,1,0,5651,22,15,14223,0,0,1608.868027 +0,0,0,1,7610,14,16,19559,0,0,2836.787536 +0,0,1,1,19834,21,22,141636,1,1,21384.87729 +0,0,1,0,32146,78,61,88939,1,1,17804.1785 +0,1,0,1,12634,25,15,89284,1,1,18148.86182 +0,0,1,1,216847,58,59,576931,1,0,39189.79226 +1,0,1,1,8892,18,13,79262,0,0,5216.817245 +1,0,0,1,36354,47,34,334533,0,1,34620.77186 +0,0,0,0,15505,236,154,78785,0,0,10416.76761 +0,0,1,1,43928,14,9,142408,0,0,6604.038947 +0,0,1,0,5047,34,38,21189,0,0,1496.571961 +0,0,1,0,33099,32,21,159814,1,1,20189.74348 +0,0,0,0,9390,27,19,51588,0,1,5638.55887 +1,0,0,1,26849,82,89,100878,1,0,18010.79571 +0,0,1,0,5514,39,31,53677,0,1,7323.304046 +0,0,1,1,9182,25,26,25319,1,1,7832.328389 +0,1,0,1,90715,215,134,252573,0,1,30418.1706 +0,0,1,0,29291,22,25,219386,1,1,28408.8204 +1,0,1,1,2769,22,20,12621,0,1,7731.261504 +1,0,0,1,24130,39,39,238170,0,1,23439.61324 +0,0,1,0,22121,206,242,94992,1,0,21355.66519 +0,1,0,0,15892,79,49,48896,0,0,5184.767479 +0,0,0,0,14824,23,18,49678,1,0,5816.155015 +1,1,0,1,55185,114,146,195651,1,0,25214.32095 +0,1,0,1,4890,32,27,19932,0,0,4906.737437 +0,0,0,0,4574,45,56,11464,1,0,7700.722688 +0,0,1,0,32713,42,36,89221,0,1,6824.258074 +0,0,1,0,84172,83,82,289225,1,1,37057.45653 +0,1,0,1,18823,22,25,144786,1,0,16952.15638 +0,0,1,1,26311,26,15,106429,0,1,11563.57478 +0,0,1,1,14319,49,62,49910,0,0,4846.826271 +0,1,0,0,8243,53,67,20861,0,0,6473.5128 +0,0,1,1,2457,24,17,12147,0,0,1816.05151 +0,0,0,0,32519,23,14,121554,0,0,3944.826915 +0,0,1,1,14614,53,34,46570,1,0,9455.624525 +0,1,0,1,31770,123,117,79885,1,0,17573.13979 +0,0,1,1,26622,65,61,76943,0,1,10611.93383 +0,0,0,0,43574,26,25,122852,0,0,5382.789214 +0,0,1,1,20046,72,55,107275,1,0,12982.07293 +0,0,1,1,22681,56,44,71702,0,1,9683.228815 +0,0,1,1,33502,48,42,100847,0,1,12161.26982 +0,0,1,0,7014,12,7,48094,1,0,8736.408395 +0,0,1,1,31031,68,55,87512,0,0,5908.813014 +0,0,0,1,55835,47,31,339298,1,1,39957.12295 +0,1,0,1,26338,56,34,91201,1,0,13443.9427 +0,0,0,1,54085,18,15,164706,1,1,23309.57527 +0,0,1,0,4195,78,51,11390,1,0,7885.634278 +0,0,0,1,7331,11,11,21523,0,1,3581.785919 +1,1,0,1,7216,39,33,45321,1,0,15447.03202 +0,0,1,0,23982,64,47,60092,1,1,12370.46019 +0,0,1,1,6898,77,86,58994,0,1,10328.88638 +0,0,1,1,10665,55,69,64120,1,0,11766.00932 +0,0,1,0,63286,38,42,160582,0,1,16046.82812 +0,0,0,1,14194,34,26,54669,0,1,7219.732899 +0,0,0,1,12785,77,98,38887,1,1,14129.6421 +1,1,0,1,13980,26,18,38892,0,0,9816.213152 +0,0,1,1,36150,12,12,115412,0,1,10856.31406 +0,0,1,1,17826,63,52,50404,1,0,12470.55267 +1,0,1,0,16838,59,56,64514,0,0,9496.053381 +0,0,1,0,18778,35,31,55095,1,1,12093.38079 +0,0,1,0,4459,55,63,25729,1,0,9576.578823 +0,0,0,0,135692,178,213,618047,1,1,77528.81537 +0,0,1,1,3675,26,17,25096,1,1,9942.700669 +0,0,1,0,42914,14,15,254051,1,1,31201.35727 +0,0,0,0,108334,218,192,291538,1,1,43629.75392 +0,0,0,0,2444,27,28,15688,1,0,7456.745373 +0,1,0,1,24130,26,31,86989,0,0,7454.659858 +1,0,0,1,42664,41,35,134974,1,0,18862.37313 +0,0,0,0,9113,59,61,52406,0,0,5423.927958 +0,0,0,1,11283,55,50,70135,0,0,6679.766799 +0,0,1,1,30243,169,203,247768,1,0,28086.67604 +0,1,0,1,15694,24,30,84643,1,0,12468.53816 +0,0,1,1,31333,42,36,94666,0,0,5333.651027 +1,0,1,1,11357,59,55,75101,0,0,10147.14027 +1,0,0,0,10117,20,22,31883,1,0,11395.4986 +0,0,1,1,11262,17,18,39822,0,1,6271.528516 +1,0,1,0,33583,85,77,161560,1,1,29537.65233 +0,0,0,1,8268,121,142,29476,1,0,13640.71751 +0,0,0,1,9502,15,17,92715,1,1,15155.16726 +1,1,0,1,41557,114,98,332003,1,1,48728.22763 +0,0,0,1,3933,24,27,10768,1,0,8371.741955 +0,0,1,0,18772,52,43,51145,1,0,10488.15887 +0,0,0,1,50921,20,21,188000,0,1,15941.99873 +0,1,0,1,8786,40,37,68130,0,0,5894.35398 +0,0,0,0,59529,24,15,181704,1,0,14601.54169 +0,0,1,0,6277,20,12,30417,0,0,1835.451631 +0,0,0,1,60133,129,160,172913,1,1,32519.54847 +0,1,0,1,34596,42,42,105828,0,1,13968.27771 +0,0,0,1,12412,26,26,44954,0,0,4081.900584 +0,0,1,0,22916,77,58,62639,0,0,4171.2487 +1,1,0,1,32039,11,12,166808,1,0,21418.21206 +0,1,0,1,7566,103,74,64992,1,1,16998.38856 +0,0,1,1,4319,24,16,22989,1,1,7655.750618 +0,0,1,1,31472,51,38,125245,1,1,21036.03817 +0,1,0,0,41368,70,79,188113,0,0,10174.99537 +0,0,1,1,59887,24,14,176089,1,1,24340.71476 +0,0,0,1,11400,36,27,96026,0,0,5364.934704 +1,1,0,0,5190,18,22,23923,0,0,7416.942714 +0,0,1,0,21753,12,10,57036,0,1,5541.2102 +0,1,0,1,31244,33,28,161287,1,0,17158.78318 +0,1,0,1,14349,57,42,70116,1,1,16646.46136 +0,0,1,0,11225,72,57,45233,0,1,6225.88462 +0,0,0,0,58172,32,22,256127,1,1,32063.72955 +1,1,0,1,8119,71,75,47949,0,0,10124.27551 +0,0,1,1,8935,75,84,34192,0,0,6418.173838 +0,0,1,0,5955,11,13,56655,1,0,9543.824782 +0,1,0,1,21769,25,25,115008,0,0,7619.890571 +0,1,0,0,54710,57,37,145035,1,1,23480.37139 +1,0,1,0,20201,15,17,62075,0,0,5279.11638 +0,1,0,1,13168,24,16,70591,1,1,14673.046 +0,0,1,1,40516,43,45,142076,1,0,15228.48508 +1,0,1,0,10601,125,95,30445,1,0,15968.83095 +0,1,0,1,13643,63,44,89773,1,1,19043.65555 +0,0,0,1,17353,14,8,46146,0,1,6127.903977 +0,0,0,1,35304,15,9,123038,0,1,11212.04662 +0,1,0,1,29430,12,7,77690,0,0,5465.409643 +0,0,1,1,3181,66,52,23386,0,1,4155.17803 +0,0,0,0,58779,87,54,298788,1,1,39230.68034 +0,0,1,0,13745,29,32,52031,1,0,8031.738977 +1,0,1,1,12862,30,21,45223,0,1,10476.25359 +1,0,0,0,33526,15,17,112292,0,1,14707.84229 +0,0,0,1,22332,68,66,70057,1,0,13970.97992 +0,1,0,1,10233,51,65,26333,0,0,5677.481518 +1,0,1,0,92769,37,35,266060,1,0,22936.35093 +0,0,1,0,16204,56,68,44187,1,1,13024.00882 +0,0,0,1,7781,67,53,20585,1,0,10420.06826 +0,0,0,1,77157,66,47,260624,0,0,11088.22641 +0,0,1,1,2020,114,125,11821,0,0,7004.24437 +1,0,1,0,4208,23,14,25922,0,0,6203.790479 +0,0,1,1,38290,121,105,193140,1,0,20883.98191 +1,0,1,1,33872,26,17,85308,0,0,8423.4853 +0,0,0,1,72987,54,48,257270,1,1,33902.75033 +0,0,1,0,6755,42,35,48614,1,0,9096.476076 +0,1,0,1,22843,14,16,127307,1,0,15132.06439 +0,0,0,0,24694,49,53,63961,0,0,4710.660484 +0,0,0,1,7651,38,46,55372,0,1,7548.081392 +0,0,1,1,19961,146,128,87993,0,0,10474.25199 +0,0,1,1,19379,127,84,68897,1,1,17654.38141 +1,0,1,0,92517,107,89,525030,1,1,65050.49043 +0,0,1,1,24728,210,247,143155,1,1,32194.46318 +0,0,1,0,43604,20,15,193262,0,0,6513.837374 +0,0,1,1,3718,87,69,12453,1,0,10546.46439 +0,0,1,1,2656,79,100,15633,0,0,6646.573074 +0,0,1,1,13651,65,59,43059,0,0,4597.490173 +0,0,0,1,17527,20,20,115576,0,1,11554.45132 +0,1,0,0,62843,110,133,313708,0,1,34085.18712 +1,1,0,1,35866,28,32,125668,1,1,25430.41466 +0,0,1,0,47793,35,28,149714,0,1,13584.77301 +0,0,1,1,11016,58,55,35876,1,1,11025.45128 +0,0,1,0,4853,73,46,19814,1,0,9097.568257 +0,0,0,1,56856,25,16,143099,1,0,15055.79704 +1,0,1,0,22924,18,19,140933,1,0,17543.84502 +0,0,0,1,16492,57,52,105671,0,1,13203.11481 +0,1,0,1,3837,14,17,14864,0,1,4378.016683 +0,0,1,0,5218,15,10,42069,0,0,1439.392273 +0,0,1,1,2236,13,14,15777,0,0,1983.398267 +0,0,1,1,14765,57,65,62142,0,1,9307.400678 +0,0,0,0,16950,101,93,131327,1,0,15252.16749 +0,0,1,1,19100,134,97,80943,0,0,5675.826269 +0,0,1,1,110805,38,25,478518,0,1,41818.39213 +0,1,0,1,2410,27,23,19804,0,0,3395.495012 +1,0,0,1,43058,83,94,175626,1,1,32215.16098 +1,0,0,1,10751,17,10,37642,0,0,5915.114035 +0,0,1,0,15375,25,32,58423,1,0,9731.674473 +1,0,0,1,9136,39,24,77650,1,1,18608.01124 +0,0,0,1,45636,190,214,138040,0,1,21710.83319 +0,0,0,0,17132,22,13,56766,1,0,7688.134548 +0,0,1,0,4451,19,22,15751,0,0,2342.172593 +0,1,0,1,21171,24,18,68592,1,1,16261.05254 +0,0,0,1,53513,35,21,171389,1,0,15341.2549 +0,0,0,1,65868,20,17,202041,1,1,27864.98293 +0,0,0,1,12421,100,81,94980,1,1,17726.35317 +0,0,1,1,44950,183,233,120480,0,1,19661.73817 +0,0,1,0,23056,34,29,191204,1,0,17610.66107 +0,0,0,1,12015,104,131,74728,0,1,12023.65435 +0,1,0,1,39637,17,21,309604,1,1,37891.172 +0,1,0,0,32529,49,37,90631,0,0,6647.296616 +0,0,0,1,77350,15,12,303470,1,1,35467.78765 +0,0,1,1,61039,66,43,196930,1,1,28597.51471 +0,0,0,1,16226,90,72,54075,0,0,7220.87191 +0,0,1,0,12813,158,188,97736,1,1,22883.8854 +0,0,1,1,52575,153,155,143587,0,1,21617.33461 +1,0,0,1,9349,34,20,39847,0,1,9099.395522 +0,1,0,1,32920,10,11,192001,1,1,27934.92302 +0,0,1,1,7853,94,92,52369,1,1,14899.93712 +0,0,0,1,2740,44,55,10867,1,0,9150.068397 +0,0,0,0,115796,13,8,418788,1,1,46668.59688 +0,0,1,1,40637,85,100,243944,1,1,33519.77889 +0,0,1,1,2964,43,42,26090,0,1,6237.467069 +0,0,1,1,29107,13,12,90923,1,0,12351.10675 +0,1,0,1,8140,16,11,70111,0,0,6673.257513 +0,0,0,0,18938,35,29,54633,0,1,5766.333262 +0,0,1,1,1755,51,65,12679,1,0,8678.13295 +0,0,0,1,86106,52,35,267114,0,0,8834.50154 +0,0,1,0,94521,72,92,301844,0,1,28800.33804 +0,1,0,1,5344,147,137,17498,0,0,9284.071213 +1,0,1,1,11215,33,22,37339,0,1,9379.400485 +0,0,0,0,8397,76,86,78647,1,1,16291.33259 +0,0,1,1,51343,32,36,159946,0,0,7940.947136 +0,1,0,0,13017,118,84,54033,1,1,15732.86598 +0,0,1,0,13512,16,16,34524,0,0,1657.60029 +0,0,0,0,5153,86,53,24055,0,0,1768.879168 +1,1,0,1,32540,14,12,200125,0,1,21874.94337 +0,0,1,1,87157,33,36,236683,1,0,20887.12542 +0,1,0,1,95183,29,33,281831,0,0,15365.64843 +0,0,1,0,35818,55,70,286403,1,1,35776.06122 +0,0,0,1,40342,53,60,124441,0,1,14019.02331 +0,0,1,1,7565,17,17,21557,0,0,2572.29145 +0,0,1,1,39299,30,25,121658,1,0,13440.23615 +0,1,0,0,3695,52,57,27252,0,0,6427.628148 +0,0,1,1,34997,76,82,124451,0,0,6864.032046 +1,0,1,1,2228,10,10,22187,0,0,6828.300545 +0,0,1,0,34935,37,24,312042,1,1,37676.723 +0,0,1,0,11982,52,40,71221,0,1,8315.512754 +1,0,1,1,15945,114,109,52711,0,1,14559.37835 +0,0,0,1,22829,19,21,122986,0,0,5520.08532 +0,0,0,1,41241,53,61,218244,0,0,10153.0231 +0,0,0,1,93644,18,20,369755,1,1,45167.61109 +0,0,1,1,26096,31,40,112556,0,0,7420.574514 +0,0,1,1,33283,37,47,113427,0,0,8036.636786 +0,0,0,0,12263,64,53,76953,0,1,9011.419677 +0,0,1,1,44908,38,49,171011,1,1,25780.19642 +0,0,1,1,63404,35,25,186296,1,1,26795.14017 +0,1,0,1,29286,40,37,80408,1,1,17264.45423 +0,0,1,0,50350,17,17,170110,1,1,22058.75821 +1,1,0,1,18843,35,41,112058,1,0,18925.58012 +0,0,1,0,41753,21,22,174845,1,0,16353.77671 +0,0,1,1,12170,26,16,34745,1,0,9797.931115 +0,0,1,1,8425,21,24,69801,1,0,11471.60871 +0,0,1,1,89351,42,49,235865,1,0,20195.00107 +0,0,1,1,40103,34,41,262309,1,0,19585.5975 +0,0,1,1,3153,57,43,10245,0,0,4387.135137 +0,1,0,1,11462,204,204,35991,0,0,13619.82974 +0,0,1,1,10851,72,44,28921,1,1,12724.2847 +0,1,0,1,3446,77,52,31509,0,0,5947.278689 +0,0,0,1,5816,25,31,25646,1,0,9107.229617 +0,0,1,1,3757,72,62,21188,0,0,6550.153588 +1,0,1,1,12841,158,149,86687,0,0,14162.78827 +0,1,0,0,26798,30,24,98948,0,0,7180.914803 +0,0,1,1,66632,74,76,193061,0,1,20889.74858 +1,0,0,1,7182,27,23,23792,1,1,13954.74776 +0,0,0,1,4857,240,196,27719,1,1,17028.09659 +1,0,1,1,26045,92,95,72144,1,0,17047.86739 +0,1,0,0,13861,35,23,34800,1,0,9965.380853 +1,0,1,0,8576,84,94,27701,1,0,16472.20159 +0,0,0,0,11033,26,30,60672,0,1,5748.774662 +1,0,1,0,35919,24,29,97257,1,1,21762.75624 +0,0,1,0,3726,173,106,31027,0,1,7139.476089 +0,0,1,1,14114,29,30,56959,0,1,7991.801988 +0,0,0,1,4919,51,44,38379,0,0,3672.918732 +0,0,0,1,30702,153,145,95179,1,1,21795.06614 +0,0,0,1,16510,184,219,46455,0,1,13878.99218 +0,0,1,1,7157,16,14,69576,1,1,14189.7407 +0,0,1,1,2284,122,158,12521,0,1,10381.90741 +0,0,1,0,47012,38,39,143772,0,0,6887.731045 +0,0,1,0,73855,141,139,188319,1,1,30681.06136 +0,1,0,1,9381,83,85,42495,1,0,13716.10988 +0,0,0,1,38673,28,30,142276,1,1,21574.47709 +0,0,0,0,15243,26,32,38690,1,1,10110.00069 +0,1,0,1,8698,22,23,46078,1,1,14498.91261 +0,0,1,1,7373,33,40,24073,0,0,2680.364289 +0,0,1,1,4251,33,29,38335,0,0,5480.749596 +0,1,0,1,34672,129,160,126165,1,0,19823.71177 +1,0,0,1,103613,74,52,259317,0,1,29737.78514 +0,0,0,1,23857,30,23,73164,1,1,14328.8149 +0,0,0,1,8915,38,27,59774,0,0,5781.273289 +0,0,1,1,19374,39,34,66436,1,0,10420.16974 +0,0,1,1,18630,266,325,100278,0,1,23166.06052 +0,0,1,1,4127,32,31,15569,0,1,2921.20266 +0,1,0,1,8279,38,30,52969,0,1,10113.77289 +1,0,1,1,6508,33,39,38371,0,1,10330.24255 +0,1,0,0,43287,26,33,207662,1,0,19666.70339 +0,0,0,0,65671,13,14,171025,1,0,15897.04316 +0,0,1,0,62591,57,59,205256,1,1,27906.4413 +0,1,0,1,21605,70,52,59139,0,0,5977.569192 +0,0,1,1,4809,21,26,34871,0,1,5239.444732 +0,0,1,1,75639,30,19,210431,1,0,19023.10679 +0,1,0,1,20232,29,27,92846,0,0,6904.359815 +0,1,0,1,3315,67,66,11681,0,0,5434.443462 +0,1,0,0,42932,23,15,125437,1,0,15123.5866 +0,0,0,0,1640,15,11,11267,0,0,-616.5724513 +0,0,0,1,56765,54,53,160784,1,1,23112.51345 +0,0,1,1,4644,56,49,45005,0,0,5500.479797 +0,0,0,1,6217,56,41,36316,1,0,7328.944312 +0,1,0,1,10443,60,68,99629,1,1,19656.17731 +0,0,1,1,17254,49,62,46979,0,1,8632.529171 +0,0,1,1,10801,80,98,32666,1,0,12627.10734 +0,0,1,1,19955,30,26,60593,0,1,7854.669318 +1,0,0,1,7690,20,20,22101,1,0,13912.19876 +0,0,0,0,35600,66,76,171952,0,1,17500.87214 +1,0,0,0,63483,142,92,204411,1,1,33250.97377 +1,1,0,1,17853,94,94,84566,0,1,17705.53082 +0,0,1,0,4721,24,14,17807,1,0,6322.44198 +0,0,1,0,11592,59,65,77087,0,0,4565.842911 +0,1,0,1,133052,36,40,399228,1,0,28104.52976 +0,0,0,0,49286,22,14,150010,1,1,20132.08548 +1,0,1,1,29684,21,15,160862,1,1,26783.82687 +0,0,1,1,8922,92,65,62289,1,1,15612.3162 +0,0,1,1,64836,112,127,197133,1,1,31799.31029 +1,1,0,1,46758,91,96,170928,1,1,33713.93212 +0,0,1,1,50599,107,67,229859,1,1,33989.91842 +0,0,0,1,5394,13,12,30169,1,0,7777.676957 +0,0,1,0,2115,16,17,11709,0,1,2117.546395 +0,0,1,0,19769,197,231,55240,1,0,17419.95201 +0,0,0,0,54806,123,145,283472,0,1,28592.42148 +0,0,1,0,59798,178,203,558513,1,1,70110.41759 +1,1,0,0,24172,70,72,81282,1,1,22154.12055 +0,0,1,1,10226,10,7,92253,0,1,8899.070061 +0,1,0,1,3121,16,16,14226,1,1,11642.06011 +0,0,0,1,4568,21,14,14755,1,0,5933.833852 +0,1,0,1,15788,42,44,97815,0,1,13636.95272 +0,1,0,1,13014,45,37,93611,0,1,12127.64035 +0,0,1,1,6018,27,20,19427,1,0,8389.650104 +0,0,0,1,84149,71,88,310718,1,1,38438.5399 +0,0,1,0,32655,36,44,135278,1,1,21340.90633 +1,0,1,0,18844,70,90,55184,0,1,13191.2218 +0,1,0,1,58594,55,62,490270,1,1,58714.84971 +0,0,0,1,10630,23,27,55771,0,0,3980.851169 +0,0,0,1,8220,19,15,72357,1,0,10492.48497 +0,0,1,1,9317,211,254,39983,0,0,14542.64742 +1,0,1,0,28734,48,38,269252,1,1,37507.32502 +1,0,0,0,12037,42,42,61609,0,0,6937.60052 +0,0,0,1,49427,28,33,288358,1,1,35979.81746 +1,0,0,1,5752,37,46,54873,0,0,6987.138356 +1,0,0,1,7096,91,86,65357,0,0,11234.4588 +0,1,0,1,13359,18,12,77553,1,0,11368.25645 +1,0,0,0,21722,59,48,136034,0,1,15218.78818 +0,0,1,0,24113,27,27,160121,1,0,13816.97789 +0,0,1,1,17293,29,37,80727,0,0,7133.681372 +0,0,0,0,12688,11,10,42969,0,1,2549.01298 +0,0,1,0,28207,125,95,76872,1,1,18576.01452 +0,0,1,1,71943,18,14,248566,0,1,20998.0432 +1,0,0,0,84320,54,65,225946,0,1,23284.55072 +0,0,1,1,22042,14,14,68408,1,0,9544.957874 +0,1,0,0,18815,80,81,61704,0,0,7972.853529 +0,0,1,0,10897,24,28,74510,0,1,7692.030915 +0,0,1,0,8332,21,13,48657,1,0,7872.254385 +1,0,1,0,60860,227,214,165733,1,0,26875.78401 +0,0,0,0,23524,92,111,216948,1,1,30042.24231 +0,0,1,0,1501,22,18,12243,0,0,3118.114431 +0,0,1,1,12010,31,30,42780,0,1,7446.547831 +0,0,0,1,59331,323,338,260230,1,1,46530.42113 +0,0,1,0,31294,17,14,100087,1,0,9617.575449 +0,0,0,1,43525,79,101,201749,1,1,32638.07335 +1,0,1,0,21105,34,21,146090,1,1,25425.5671 +0,1,0,1,42867,48,56,191941,1,1,29134.66939 +0,1,0,0,12838,68,41,48972,1,0,14081.51429 +0,1,0,0,21339,20,25,98120,1,0,12868.98743 +0,1,0,1,36471,16,13,101443,1,1,18642.82558 +0,1,0,1,38793,139,165,192679,1,1,34058.60358 +0,0,0,1,112566,89,97,306079,1,1,38693.56767 +0,1,0,1,1727,65,42,14266,0,1,4852.886305 +0,0,1,1,15814,32,35,79801,0,0,6464.14138 +0,1,0,1,54198,21,22,276950,1,0,24003.60504 +1,0,1,0,6176,77,88,24603,1,1,16244.06651 +0,0,1,1,75071,71,89,369362,1,1,49636.20396 +1,1,0,1,17200,29,24,163971,1,0,21486.33935 +1,0,0,1,8656,28,22,43930,1,0,13369.71389 +0,1,0,0,1883,30,34,11621,1,1,10157.94009 +0,0,0,1,45309,39,43,137828,1,1,20308.17965 +0,0,0,1,15369,26,16,61966,0,1,7223.827695 +0,0,0,1,107765,163,155,357482,0,1,36079.39157 +0,0,1,1,28942,109,125,146101,0,0,9826.859963 +0,0,0,1,4176,76,72,24504,0,0,4875.567785 +0,0,1,1,11363,49,52,105082,0,1,11544.48893 +0,0,1,1,12749,22,26,75798,0,0,4021.204818 +1,0,1,1,37759,20,15,136223,0,1,16741.71293 +1,1,0,1,45201,74,78,158032,1,1,31342.41506 +1,0,1,1,23587,152,104,103485,0,0,13710.65014 +1,1,0,1,12588,234,271,41540,0,0,21326.34189 +0,0,0,0,76824,88,99,556966,1,1,64641.33112 +1,0,1,1,8236,14,15,26162,0,1,8053.329576 +0,0,0,0,4874,22,15,32888,0,1,3085.171282 +0,0,1,1,16975,17,13,104309,1,0,10897.16033 +0,0,1,1,27236,10,7,90537,1,0,11655.52611 +0,0,1,0,12979,14,16,43358,0,1,3744.097328 +0,0,0,1,6638,21,21,29166,0,1,3814.303434 +0,0,1,0,38213,40,28,100002,0,1,9744.287477 +1,0,1,1,52165,78,53,316043,1,1,43156.33691 +0,1,0,1,4837,74,80,45004,1,1,16722.52173 +0,1,0,1,46825,37,35,239934,1,0,20709.49016 +0,1,0,1,62909,15,19,185341,0,0,11098.19468 +0,0,0,0,34929,25,23,98476,0,1,9266.628165 +0,0,1,0,49604,18,14,216230,1,1,28216.15072 +0,0,0,1,9678,73,47,24449,0,0,3398.862946 +0,0,1,0,38592,226,198,115829,1,0,19909.6354 +0,0,0,1,60156,30,18,273062,0,1,23779.99628 +0,0,0,1,10476,24,23,47249,0,1,7022.488822 +0,0,0,1,86093,65,75,285143,1,1,38203.11784 +0,0,0,1,7414,32,33,34819,1,1,9752.831101 +0,0,1,1,45546,41,50,128179,0,0,10259.69591 +0,0,1,1,16846,84,87,49484,0,1,10842.52317 +0,0,1,1,11467,100,121,31288,0,0,7444.036432 +0,0,1,1,11169,11,9,42955,1,0,8119.356813 +0,0,0,1,14193,38,35,75949,0,0,5396.306639 +0,0,0,1,20864,54,66,61250,0,0,5077.933824 +0,1,0,0,53006,26,33,262468,1,1,34569.82493 +0,0,0,0,19280,59,59,56606,0,0,4302.232812 +0,0,1,1,15826,148,97,45361,1,1,16062.76019 +1,0,1,1,4897,55,37,12435,0,0,8105.305773 +0,0,0,1,17992,57,36,68079,0,0,3393.358658 +0,0,0,1,55622,57,73,278418,1,1,36875.60652 +0,0,1,0,43553,36,26,114094,1,1,16561.71637 +0,0,0,0,1959,43,40,14795,1,0,7434.790067 +0,0,1,1,28795,23,27,162999,0,1,16569.3359 +0,0,1,1,21700,51,56,182905,0,0,6948.191324 +0,0,1,1,15220,283,188,43339,1,0,16128.40668 +0,1,0,1,6288,12,7,35941,1,0,9764.999051 +0,0,0,1,4131,60,69,28178,0,0,4651.985014 +0,1,0,1,12338,14,16,60982,1,1,14309.01002 +0,0,1,1,9858,13,9,65645,1,1,11808.9937 +0,1,0,0,49639,34,39,236428,1,0,22720.78752 +0,0,0,1,46007,38,34,151678,0,1,15213.31743 +0,1,0,1,8458,56,63,29407,0,1,8268.107679 +1,0,1,0,31635,88,100,96045,0,0,11546.25512 +0,0,1,0,20130,35,34,180710,1,0,17637.81626 +0,0,0,1,22448,108,65,85971,1,0,12121.85281 +0,0,1,0,9400,214,225,38115,0,1,13017.22591 +1,0,1,1,125862,30,35,349699,0,1,34921.74153 +0,0,1,0,3029,75,69,13965,1,0,9034.125983 +0,0,1,0,3899,141,150,14010,1,0,10357.27798 +0,0,1,0,7801,76,50,51789,1,1,12466.48763 +0,0,1,1,8357,34,26,24866,1,0,8358.920998 +0,0,1,0,12245,16,16,70499,1,1,13222.14717 +0,1,0,1,37314,44,56,189686,0,0,13143.71507 +0,0,0,1,6231,10,9,16430,0,0,1299.102236 +0,0,1,0,17341,17,17,47528,0,0,3814.032543 +0,0,1,1,7188,53,34,26282,0,1,4400.598975 +1,0,1,1,2161,109,75,15424,0,0,7953.444885 +1,0,1,1,17351,16,12,50651,0,1,10928.05785 +1,0,1,1,50693,75,74,221486,1,1,36120.18731 +0,0,1,1,57128,150,99,158373,1,1,28399.87285 +0,0,0,1,27799,45,41,97169,1,1,18611.64529 +0,1,0,1,15352,50,60,136254,0,0,9350.540121 +0,0,0,1,1903,39,30,13700,0,1,5861.649012 +1,1,0,0,34179,21,18,143612,0,1,17312.76386 +0,0,1,1,11925,99,83,58553,0,1,9550.739313 +0,1,0,0,15724,20,17,39611,1,0,9581.700883 +0,1,0,0,73832,60,38,287082,1,1,37591.60621 +0,0,0,0,53577,16,14,152576,0,0,4783.476068 +0,0,1,1,18461,25,28,52829,1,0,9443.426405 +0,0,1,1,20166,62,70,59649,1,0,12419.66799 +0,0,1,1,94161,41,24,276626,1,0,21897.14378 +0,1,0,1,259808,58,38,766485,1,1,86006.92445 +0,0,0,1,28400,58,52,82686,1,0,10331.16381 +0,0,1,1,8755,47,43,47511,0,0,3946.520963 +0,0,1,0,3319,90,106,21859,0,0,5554.865993 +0,1,0,1,12435,37,42,108507,0,1,13941.19144 +1,0,0,1,8712,74,60,41868,1,0,14081.63434 +0,1,0,1,19731,10,6,60657,0,1,7952.221689 +0,0,1,0,5900,186,170,23567,1,1,14249.44051 +0,0,1,0,7499,13,9,21854,0,0,1880.998166 +0,0,1,0,3231,12,11,23226,0,0,869.5679452 +0,0,1,1,23285,101,125,82213,0,0,9610.946665 +1,0,0,0,11851,20,13,94601,1,1,18687.84542 +1,0,0,1,44676,44,49,140724,1,1,26882.98389 +0,0,0,1,18777,65,62,60298,1,0,10622.92178 +1,0,1,0,27383,25,29,127925,0,1,15879.43813 +0,0,0,1,60902,16,19,161836,1,0,14719.98451 +0,0,0,1,13097,30,20,47851,0,0,1680.262467 +0,0,0,1,75199,93,100,193057,1,0,21270.02348 +0,0,1,1,47956,71,87,150646,0,1,17077.46962 +0,1,0,1,30668,12,14,76685,0,1,11054.46492 +0,1,0,1,24828,32,34,66207,0,1,9464.745128 +0,0,1,0,9588,24,23,64208,0,0,1576.904992 +0,0,1,1,15014,25,18,46293,1,1,13356.46203 +0,0,0,1,55478,32,31,150647,1,1,21544.90395 +0,0,0,0,26110,61,65,88044,1,1,16860.49338 +0,0,0,0,58097,14,11,200479,0,1,15998.36376 +0,0,1,1,1981,87,105,13886,0,1,6807.419547 +1,1,0,0,19786,105,80,61173,1,1,21710.26054 +0,1,0,1,4494,15,12,41218,0,1,6891.681471 +0,1,0,0,11548,194,178,32745,1,0,15415.53276 +1,1,0,1,33526,21,26,98231,0,0,12487.33731 +0,0,1,1,6034,74,79,17817,1,0,11138.53115 +0,0,0,1,37957,30,36,109245,0,1,12192.21526 +1,0,0,1,19390,29,37,74918,0,1,12960.35675 +1,0,1,0,8551,45,53,40703,0,0,8123.101703 +0,1,0,1,13988,49,33,50219,0,0,8672.851562 +1,0,1,1,42616,107,94,122328,1,1,28184.29894 +0,1,0,1,78554,56,71,228506,1,0,23682.07355 +0,1,0,1,52583,106,88,137381,0,1,20940.13031 +1,0,1,1,42105,38,41,229669,1,1,34305.23927 +0,0,1,1,18524,65,62,137759,1,1,23509.18852 +1,0,0,1,92410,41,35,422327,0,1,40789.41791 +0,0,0,1,69328,11,10,209910,0,1,18452.77644 +1,0,1,1,5864,71,86,41264,0,0,11360.06959 +0,0,0,1,34784,26,25,221889,0,1,18096.93978 +0,0,1,1,36554,184,127,138290,1,0,20023.38286 +0,0,1,1,34041,87,56,201086,1,1,28382.05576 +0,0,0,1,4290,60,69,21262,0,1,5242.13016 +0,0,0,1,34241,84,76,130518,1,1,22971.45448 +0,0,1,0,18514,91,58,57277,0,0,5125.167803 +1,0,1,0,76835,68,63,244594,1,0,24399.42021 +0,0,0,1,7216,11,10,33670,1,0,6363.877963 +0,0,0,1,19414,59,76,137979,1,1,23419.19662 +0,1,0,0,43270,119,99,117234,1,1,23452.52164 +0,0,0,0,32414,48,52,107806,1,0,11894.77167 +0,0,1,1,7683,37,24,20041,1,0,6912.385715 +0,0,1,1,36966,10,11,156425,0,0,6046.072857 +0,0,1,0,7598,41,26,22896,0,0,3173.607208 +0,0,0,0,11723,79,87,43580,0,1,6342.437748 +1,0,0,1,16234,202,183,77772,0,1,18668.96923 +0,1,0,1,34980,23,20,130407,1,1,20730.45615 +0,0,0,1,74432,12,8,278883,1,0,20267.32063 +0,0,0,1,15286,21,13,68312,0,1,7316.069738 +0,1,0,1,1864,15,9,15110,0,0,3960.120835 +0,0,1,0,4402,18,13,14696,1,0,5994.52877 +0,0,1,1,44773,15,14,125674,1,1,19955.65869 +1,0,0,0,73881,49,43,190863,1,0,22145.54476 +1,0,0,0,54554,248,312,470588,1,1,68429.09507 +0,1,0,1,11124,70,71,43253,1,0,14168.48187 +0,0,0,1,16223,61,59,57955,0,1,9284.721324 +1,0,1,1,27346,59,55,151249,1,1,27932.65921 +0,0,1,1,30256,54,67,85065,0,0,5937.476304 +1,0,1,1,21063,62,40,65840,0,1,12962.64014 +0,0,0,1,23500,11,7,91670,1,0,13413.1494 +1,0,0,1,7939,16,11,65674,0,0,7322.936783 +0,0,0,1,73202,24,16,252970,0,0,6949.114958 +0,1,0,1,13139,14,16,73949,0,1,10114.24582 +1,0,1,1,20919,28,23,160371,0,0,11384.83831 +0,0,0,0,47645,104,76,163568,0,0,7956.801304 +0,0,1,1,41860,28,16,106287,0,1,8605.681492 +0,0,0,1,103125,55,45,318677,1,1,39303.1489 +0,0,0,1,59409,50,58,211780,0,1,19458.78897 +0,1,0,1,38481,30,34,104335,1,0,15149.97842 +1,0,1,0,30814,70,54,88879,1,1,19835.77323 +0,0,0,1,11045,47,50,38137,0,0,6500.022999 +1,0,1,1,69117,11,10,215342,1,1,33756.74093 +0,0,1,1,3348,20,18,30282,1,0,8886.536678 +0,0,1,1,2327,66,70,15085,0,0,7924.059399 +0,0,1,0,2982,68,43,16262,0,0,2613.223994 +0,0,1,1,10988,106,102,109237,0,1,13443.79982 +0,0,1,0,57973,69,63,153445,0,0,9036.567702 +0,0,1,1,14793,32,40,42943,0,1,8199.699674 +0,0,1,0,36920,204,143,96848,0,0,8235.537343 +0,0,1,1,9682,224,286,84905,0,1,21328.54713 +0,0,0,1,4440,64,47,20368,0,0,2290.876869 +0,0,0,0,2346,45,52,17580,1,0,7918.8815 +1,0,0,1,14772,63,74,54855,0,0,8930.495022 +0,0,0,1,3504,321,334,15802,1,0,21081.75578 +0,0,0,1,25058,161,139,127963,1,0,18045.75122 +1,0,1,1,105538,97,117,267677,0,1,32454.81798 +0,1,0,1,20893,70,47,112880,1,0,15249.06291 +0,0,1,1,4716,27,32,18150,0,0,4607.795027 +0,0,0,1,17504,28,30,124231,1,0,13802.46645 +1,0,1,1,22440,33,29,99953,1,0,17706.83971 +0,0,1,1,46186,74,48,141579,0,1,13930.12862 +0,0,1,0,39683,12,13,111848,0,0,4753.072214 +0,1,0,0,4195,14,17,11924,0,0,2161.745939 +1,0,0,1,10664,68,47,40037,1,1,17694.82079 +0,1,0,1,21618,30,20,152869,0,0,9412.468409 diff --git a/book/who_should_be_treated/who_should_be_treated_en.ipynb b/book/who_should_be_treated/who_should_be_treated_en.ipynb new file mode 100644 index 0000000..619ef09 --- /dev/null +++ b/book/who_should_be_treated/who_should_be_treated_en.ipynb @@ -0,0 +1,2308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ae5a6c4", + "metadata": {}, + "source": [ + "**🌐 Language:** **English** | [한국어 →](/who-should-be-treated-ko)\n", + "\n", + "# Who Should Receive Which Promotion?\n", + "\n", + "Many problems in causal inference focus on the Average Treatment Effect (ATE). For example, the question \"should we run a promotion or not?\" compares a policy that offers the same promotion to all customers against one that offers it to no one.\n", + "\n", + "But if the budget is limited and you need to maximize impact within it, the story changes. If promotion effects vary significantly across customers, a one-size-fits-all strategy may not be optimal.\n", + "\n", + "- You waste money on always-converters who would have purchased anyway,\n", + "- sleeping dogs who react negatively to marketing actually cause losses, and\n", + "- you miss potential revenue by failing to prioritize high-responders.\n", + "\n", + "The core question is therefore:\n", + "\n", + "_\"Which customers, when targeted, yield the greatest net profit?\"_\n", + "\n", + "Policy learning explores possible treatment rules and learns to maximize the overall average net profit. In other words, it answers the question: \"Who should be treated?\"" + ] + }, + { + "cell_type": "markdown", + "id": "2b06ptb53eu", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fc2741f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:46.211163Z", + "iopub.status.busy": "2026-05-09T16:49:46.211076Z", + "iopub.status.idle": "2026-05-09T16:49:48.058022Z", + "shell.execute_reply": "2026-05-09T16:49:48.057674Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import tempfile\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "os.environ.setdefault(\"MPLCONFIGDIR\", str(Path(tempfile.gettempdir()) / \"matplotlib\"))\n", + "os.environ.setdefault(\"MPLBACKEND\", \"Agg\")\n", + "os.environ.setdefault(\"LOKY_MAX_CPU_COUNT\", \"8\")\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from scipy import stats\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from econml.dr import DRLearner\n", + "from econml.policy import DRPolicyForest, DRPolicyTree\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "SEED = 1\n", + "rng = np.random.default_rng(SEED)\n", + "np.random.seed(SEED)\n", + "sns.set_theme(style='whitegrid')" + ] + }, + { + "cell_type": "markdown", + "id": "8b741c26", + "metadata": {}, + "source": [ + "## Problem Setup\n", + "\n", + "A software sales company wants to know whether incentives such as discounts or technical support actually increase software purchases, and which customers respond most effectively.\n", + "\n", + "Ideally, one would run a randomized controlled trial (RCT) assigning different incentives randomly to each customer. In practice, however, factors such as cost, sales strategy, and the risk of losing major clients make randomized experiments difficult. This data is therefore observational, not from a randomized experiment.\n", + "\n", + "The dataset contains information on approximately 2,000 customers, described by the following variables.\n", + "\n", + "- Customer characteristics: detailed information on each customer's industry, size, revenue, and technology profile.\n", + "- Intervention: information on the incentive provided to each customer.\n", + "- Outcome: software purchase revenue over the year following the incentive.\n", + "\n", + "| Variable | Type | Description |\n", + "| --------------- | ---- | ---------------------------------------------------------------------------- |\n", + "| Global Flag | X | Whether the customer has a global office (overseas branch) |\n", + "| Major Flag | X | Whether the customer is a large consumer in its industry (vs. SMC or SMB) |\n", + "| SMC Flag | X | Whether the customer is a small-medium corporation (vs. Major or SMB) |\n", + "| Commercial Flag | X | Whether the customer's business type is commercial (vs. public sector) |\n", + "| IT Spend | X | Amount spent on IT-related purchases |\n", + "| Employee Count | X | Number of employees |\n", + "| PC Count | X | Number of PCs used by the customer |\n", + "| Size | X | Customer size measured by annual total revenue |\n", + "| Tech Support | T | Whether the customer received technical support (binary) |\n", + "| Discount | T | Whether the customer received a discount (binary) |\n", + "| Revenue | Y | Customer revenue from software purchases |\n", + "\n", + "\n", + "Since both `Tech Support` and `Discount` are interventions, we combine them to define four treatments:\n", + "\n", + "$$\n", + "A_i \\in \\{0, 1, 2, 3\\}\n", + "$$\n", + "\n", + "Each treatment means:\n", + "- $A_i = 0$: no intervention\n", + "- $A_i = 1$: technical support only\n", + "- $A_i = 2$: discount only\n", + "- $A_i = 3$: both technical support and discount" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "477aed7a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.059813Z", + "iopub.status.busy": "2026-05-09T16:49:48.059518Z", + "iopub.status.idle": "2026-05-09T16:49:48.071045Z", + "shell.execute_reply": "2026-05-09T16:49:48.070709Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2000, 13)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Global FlagMajor FlagSMC FlagCommercial FlagIT SpendEmployee CountPC CountSizeTech SupportDiscountRevenuetreatmenttreatment_name
010104553726261522050117688.363002discount_only
1001120842107701590380114981.435592discount_only
20001821711072649351132917.138943discount_plus_support
30000302884039775221114773.768553discount_plus_support
40010259303743914461117098.698233discount_plus_support
\n", + "
" + ], + "text/plain": [ + " Global Flag Major Flag SMC Flag Commercial Flag IT Spend \\\n", + "0 1 0 1 0 45537 \n", + "1 0 0 1 1 20842 \n", + "2 0 0 0 1 82171 \n", + "3 0 0 0 0 30288 \n", + "4 0 0 1 0 25930 \n", + "\n", + " Employee Count PC Count Size Tech Support Discount Revenue \\\n", + "0 26 26 152205 0 1 17688.36300 \n", + "1 107 70 159038 0 1 14981.43559 \n", + "2 10 7 264935 1 1 32917.13894 \n", + "3 40 39 77522 1 1 14773.76855 \n", + "4 37 43 91446 1 1 17098.69823 \n", + "\n", + " treatment treatment_name \n", + "0 2 discount_only \n", + "1 2 discount_only \n", + "2 3 discount_plus_support \n", + "3 3 discount_plus_support \n", + "4 3 discount_plus_support " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('./data/multi_attribution_sample.csv')\n", + "data.columns = data.columns.str.strip()\n", + "\n", + "covariates = [\n", + " 'Global Flag',\n", + " 'Major Flag',\n", + " 'SMC Flag',\n", + " 'Commercial Flag',\n", + " 'IT Spend',\n", + " 'Employee Count',\n", + " 'PC Count',\n", + " 'Size',\n", + "]\n", + "outcome = 'Revenue'\n", + "\n", + "TREATMENT_NAMES = {\n", + " 0: 'none',\n", + " 1: 'tech_support_only',\n", + " 2: 'discount_only',\n", + " 3: 'discount_plus_support',\n", + "}\n", + "TREATMENT_LABELS = [TREATMENT_NAMES[i] for i in range(4)]\n", + "\n", + "required_columns = covariates + ['Tech Support', 'Discount', outcome]\n", + "for col in required_columns:\n", + " data[col] = pd.to_numeric(data[col], errors='coerce')\n", + "\n", + "policy_df = data[required_columns].dropna().copy()\n", + "policy_df['treatment'] = (\n", + " 2 * policy_df['Discount'].astype(int)\n", + " + policy_df['Tech Support'].astype(int)\n", + ").astype(int)\n", + "policy_df['treatment_name'] = policy_df['treatment'].map(TREATMENT_NAMES)\n", + "\n", + "n = len(policy_df)\n", + "X = policy_df[covariates].to_numpy()\n", + "Y = policy_df[outcome].to_numpy(dtype=float)\n", + "A = policy_df['treatment'].to_numpy(dtype=int)\n", + "\n", + "print(policy_df.shape)\n", + "policy_df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8bfa27b6", + "metadata": {}, + "source": [ + "### Cost Setup\n", + "\n", + "In real-world operations, costs must be considered alongside incentive effects. Furthermore, the cost of the same incentive can vary across customers depending on their characteristics.\n", + "\n", + "For example, technical support may require more support hours for larger companies, while discounts may need to be deeper for larger customers.\n", + "\n", + "Since this dataset does not include actual cost information, we simulate costs that vary with customer characteristics.\n", + "\n", + "Technical support cost increases with employee count, and discount cost increases with customer size.\n", + "\n", + "We use the log-normal distribution so that costs are always positive and can exhibit the heavy-tailed behavior seen in practice.\n", + "\n", + "$$\n", + "C_i(\\text{tech}) \\sim \\text{LogNormal}(\\log C_{\\text{tech}} + 0.5 \\cdot \\tilde{e}_i,\\ 0.3)\n", + "$$\n", + "\n", + "$$\n", + "C_i(\\text{disc}) \\sim \\text{LogNormal}(\\log C_{\\text{disc}} + 0.4 \\cdot \\tilde{s}_i,\\ 0.3)\n", + "$$\n", + "\n", + "where $\\tilde{e}_i$ is the standardized employee count and $\\tilde{s}_i$ is the standardized company size.\n", + "\n", + "With per-customer costs defined this way, the net outcome is:\n", + "\n", + "$$\n", + "Y_i^{net} = Y_i - C_i(A_i)\n", + "$$\n", + "\n", + "Using $Y_i^{net}$ as the outcome when computing AIPW scores means $\\hat\\Gamma_{i,a}$ naturally estimates $E[Y(a) - C(a) \\mid X_i]$. Policy learning and evaluation based on AIPW thus automatically incorporate costs." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5174ddd2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.572223Z", + "iopub.status.busy": "2026-05-09T16:49:48.572150Z", + "iopub.status.idle": "2026-05-09T16:49:48.576620Z", + "shell.execute_reply": "2026-05-09T16:49:48.576207Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmentmean_coststd_costmean_revenuemean_net_revenue
0none0.0000000.0000006585.8917926585.891792
1tech_support_only5037.8158944917.64135815104.11153410066.295640
2discount_only5181.5703343043.34317212247.9359537066.365619
3discount_plus_support12665.2832967375.49012526784.12464914118.841353
\n", + "
" + ], + "text/plain": [ + " treatment mean_cost std_cost mean_revenue \\\n", + "0 none 0.000000 0.000000 6585.891792 \n", + "1 tech_support_only 5037.815894 4917.641358 15104.111534 \n", + "2 discount_only 5181.570334 3043.343172 12247.935953 \n", + "3 discount_plus_support 12665.283296 7375.490125 26784.124649 \n", + "\n", + " mean_net_revenue \n", + "0 6585.891792 \n", + "1 10066.295640 \n", + "2 7066.365619 \n", + "3 14118.841353 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "COST_TECH_SUPPORT = 4_000.0 # baseline cost for tech support\n", + "COST_DISCOUNT = 5_000.0 # baseline cost for discount\n", + "\n", + "\n", + "rng_c = np.random.default_rng(SEED + 99)\n", + "emp_idx = covariates.index('Employee Count')\n", + "size_idx = covariates.index('Size')\n", + "\n", + "emp_z = (X[:, emp_idx] - X[:, emp_idx].mean()) / (X[:, emp_idx].std() + 1e-8)\n", + "size_z = (X[:, size_idx] - X[:, size_idx].mean()) / (X[:, size_idx].std() + 1e-8)\n", + "\n", + "# Tech support: higher employee count → higher support cost\n", + "c_tech = COST_TECH_SUPPORT * np.exp(0.5 * emp_z + 0.3 * rng_c.standard_normal(len(policy_df)))\n", + "c_tech = np.clip(c_tech, 200.0, 40_000.0)\n", + "\n", + "# Discount: larger company → higher discount cost\n", + "c_disc = COST_DISCOUNT * np.exp(0.4 * size_z + 0.3 * rng_c.standard_normal(len(policy_df)))\n", + "c_disc = np.clip(c_disc, 200.0, 30_000.0)\n", + "\n", + "C_obs = np.select(\n", + " [A == 1, A == 2, A == 3],\n", + " [c_tech, c_disc, c_tech + c_disc],\n", + " default=0.0,\n", + ")\n", + "\n", + "Y_net = Y - C_obs\n", + "\n", + "pd.DataFrame({\n", + " 'treatment': TREATMENT_LABELS,\n", + " 'mean_cost': [C_obs[A == a].mean() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'std_cost': [C_obs[A == a].std() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'mean_revenue': [Y[A == a].mean() for a in range(4)],\n", + " 'mean_net_revenue': [Y_net[A == a].mean() for a in range(4)],\n", + "})" + ] + }, + { + "cell_type": "markdown", + "id": "pl2exzwbceb", + "metadata": {}, + "source": [ + "### Data Split\n", + "\n", + "Using the same data for policy learning and evaluation leads to overestimation. To prevent this, we split the data into train and test sets. Policies are learned on the train set and evaluated using AIPW scores computed on the test set.\n", + "\n", + "The same split is used for propensity estimation in the positivity check." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "n7ziuiv612q", + "metadata": {}, + "outputs": [], + "source": [ + "idx = np.arange(len(policy_df))\n", + "train_idx, test_idx = train_test_split(\n", + " idx,\n", + " test_size=0.5,\n", + " stratify=A,\n", + " random_state=SEED,\n", + ")\n", + "\n", + "X_train = X[train_idx]\n", + "X_test = X[test_idx]\n", + "A_train = A[train_idx]\n", + "A_test = A[test_idx]\n", + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n" + ] + }, + { + "cell_type": "markdown", + "id": "024e5803", + "metadata": {}, + "source": [ + "### Exploratory Data Analysis and Diagnostics\n", + "\n", + "Before entering policy learning, we first examine the data structure across the four treatments ($A \\in \\{0,1,2,3\\}$).\n", + "\n", + "1. Sample size per treatment: are there enough observations in each treatment?\n", + "2. Mean customer characteristics per treatment: which customer segments tend to receive which treatment?\n", + "3. Revenue distribution per treatment: how do Revenue scale, variance, and outlier patterns differ across treatments?\n", + "4. Positivity check: are all treatments sufficiently co-observed across the range of customer characteristics?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3d0f329e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countshare
treatment
none5170.2585
tech_support_only4620.2310
discount_only4770.2385
discount_plus_support5440.2720
\n", + "
" + ], + "text/plain": [ + " count share\n", + "treatment \n", + "none 517 0.2585\n", + "tech_support_only 462 0.2310\n", + "discount_only 477 0.2385\n", + "discount_plus_support 544 0.2720" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_counts = policy_df['treatment_name'].value_counts().reindex(TREATMENT_LABELS).rename_axis('treatment').to_frame('count')\n", + "treatment_counts['share'] = treatment_counts['count'] / len(policy_df)\n", + "\n", + "display(treatment_counts)" + ] + }, + { + "cell_type": "markdown", + "id": "8e911f32", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.072480Z", + "iopub.status.busy": "2026-05-09T16:49:48.072405Z", + "iopub.status.idle": "2026-05-09T16:49:48.076539Z", + "shell.execute_reply": "2026-05-09T16:49:48.076070Z" + } + }, + "source": [ + "The actual sample sizes are `none` 517, `tech_support_only` 462, `discount_only` 477, and `discount_plus_support` 544. Each treatment's share ranges from 23.1% to 27.2%, indicating a reasonably balanced distribution with no treatment severely underrepresented.\n", + "\n", + "The sample distribution is therefore stable for policy learning with four treatments." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d6488c62", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_covariate_means = (\n", + " policy_df\n", + " .groupby('treatment_name')[covariates]\n", + " .mean()\n", + " .reindex(TREATMENT_LABELS)\n", + ")\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "sns.heatmap(treatment_covariate_means.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", + "plt.xlabel('Treatments')\n", + "plt.ylabel('Covariate')\n", + "plt.title('Mean customer characteristics by treatments')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "c49e9856", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.078246Z", + "iopub.status.busy": "2026-05-09T16:49:48.078155Z", + "iopub.status.idle": "2026-05-09T16:49:48.194453Z", + "shell.execute_reply": "2026-05-09T16:49:48.193850Z" + } + }, + "source": [ + "Customer characteristics differ across treatments. The mean `Size` ranges from approximately 70,943 in the `none` group to approximately 171,467 in the `discount_plus_support` group, and `IT Spend` also ranges from roughly 18,056 to 41,371. In other words, larger customers tend to receive both technical support and a discount.\n", + "\n", + "This indicates that treatment assignment is not independent of customer characteristics. Policy learning and evaluation will therefore use AIPW to correct for these differences." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c925a3d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmenttreatment_namecountmeanmedianstdminmax
00none5176585.8917926123.1870673363.163462-616.57245121445.05937
11tech_support_only46215104.11153414483.7193205400.3198584619.49136140166.67407
22discount_only47712247.93595310454.9325007472.178476889.97565341818.39213
33discount_plus_support54426784.12464923560.25289013124.9680835903.90688086006.92445
\n", + "
" + ], + "text/plain": [ + " treatment treatment_name count mean median \\\n", + "0 0 none 517 6585.891792 6123.187067 \n", + "1 1 tech_support_only 462 15104.111534 14483.719320 \n", + "2 2 discount_only 477 12247.935953 10454.932500 \n", + "3 3 discount_plus_support 544 26784.124649 23560.252890 \n", + "\n", + " std min max \n", + "0 3363.163462 -616.572451 21445.05937 \n", + "1 5400.319858 4619.491361 40166.67407 \n", + "2 7472.178476 889.975653 41818.39213 \n", + "3 13124.968083 5903.906880 86006.92445 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_revenue = (\n", + " policy_df\n", + " .groupby(['treatment', 'treatment_name'])[outcome]\n", + " .agg(['count', 'mean', 'median', 'std', 'min', 'max'])\n", + " .reset_index()\n", + ")\n", + "display(treatment_revenue)\n", + "\n", + "plt.figure(figsize=(8, 4))\n", + "\n", + "sns.histplot(\n", + " data=policy_df,\n", + " x=outcome,\n", + " hue='treatment_name',\n", + " bins=40,\n", + " element='step',\n", + " stat='density',\n", + " common_norm=False\n", + ")\n", + "\n", + "plt.title('Revenue density by treatment')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "639d2437", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.195732Z", + "iopub.status.busy": "2026-05-09T16:49:48.195645Z", + "iopub.status.idle": "2026-05-09T16:49:48.262584Z", + "shell.execute_reply": "2026-05-09T16:49:48.262041Z" + } + }, + "source": [ + "Mean Revenue is approximately 6,586 for `none`, 12,248 for `discount_only`, 15,104 for `tech_support_only`, and 26,784 for `discount_plus_support`.\n", + "\n", + "As noted above, larger customers tend to receive stronger interventions, so the raw mean differences confound customer characteristics with actual treatment effects. Evaluating policy based on unadjusted means would therefore be misleading.\n", + "\n", + "We should instead rely on AIPW-based policy evaluation for proper comparison." + ] + }, + { + "cell_type": "markdown", + "id": "0b51rgb0z128", + "metadata": {}, + "source": [ + "### Identification Assumptions\n", + "\n", + "For AIPW estimation from observational data to be causally valid, two assumptions are required.\n", + "\n", + "1. Unconfoundedness\n", + "\n", + " $$\\{Y_i(0), Y_i(1), Y_i(2), Y_i(3)\\} \\perp A_i \\mid X_i$$\n", + "\n", + " Conditional on observed covariates $X_i$, treatment assignment must be independent of potential outcomes. That is, we assume variables such as customer size, IT spend, and employee count sufficiently explain treatment assignment.\n", + "\n", + "2. Positivity\n", + "\n", + " $$P(A_i = a \\mid X_i = x) > 0 \\quad \\forall a \\in \\{0,1,2,3\\},\\ \\forall x$$\n", + "\n", + " Each treatment must be observed with positive probability across all ranges of customer characteristics." + ] + }, + { + "cell_type": "markdown", + "id": "61b7329f", + "metadata": {}, + "source": [ + "Positivity is verified directly by estimating propensity scores:\n", + "\n", + "$$\n", + "e_a(x) = P(A_i = a \\mid X_i=x)\n", + "$$\n", + "\n", + "We also check whether propensity scores are severely skewed. If $\\hat{e}_a(x)$ is very close to zero for some customers under some treatment, those observations receive excessively large weights, which can destabilize estimation." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9be31313", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmentmean_propensitymin_propensityp01_propensitypropensity_below_0_05_rate
0none0.2639410.0576000.0668310.0
1tech_support_only0.2298630.1265820.1562830.0
2discount_only0.2379340.1256380.1413220.0
3discount_plus_support0.2682630.0872990.1013040.0
\n", + "
" + ], + "text/plain": [ + " treatment mean_propensity min_propensity p01_propensity \\\n", + "0 none 0.263941 0.057600 0.066831 \n", + "1 tech_support_only 0.229863 0.126582 0.156283 \n", + "2 discount_only 0.237934 0.125638 0.141322 \n", + "3 discount_plus_support 0.268263 0.087299 0.101304 \n", + "\n", + " propensity_below_0_05_rate \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_propensity = RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "multi_propensity.fit(X_train, A_train)\n", + "e_hat_raw = multi_propensity.predict_proba(X_test)\n", + "\n", + "propensity_summary = pd.DataFrame({\n", + " 'treatment': TREATMENT_LABELS,\n", + " 'mean_propensity': e_hat_raw.mean(axis=0),\n", + " 'min_propensity': e_hat_raw.min(axis=0),\n", + " 'p01_propensity': np.quantile(e_hat_raw, 0.01, axis=0),\n", + " 'propensity_below_0_05_rate': (e_hat_raw < 0.05).mean(axis=0),\n", + "})\n", + "display(propensity_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fl52yekx049", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prop_df = pd.DataFrame(e_hat_raw, columns=TREATMENT_LABELS)\n", + "prop_long = prop_df.melt(var_name='treatment', value_name='propensity')\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "sns.histplot(\n", + " data=prop_long, x='propensity', hue='treatment',\n", + " bins=30, element='step', stat='density', common_norm=False, ax=ax,\n", + ")\n", + "\n", + "ax.axvline(0.05, color='tomato', lw=1.5, ls='--', alpha=0.8, label='threshold = 0.05')\n", + "ax.legend(title='Treatment', fontsize=8)\n", + "ax.set_xlabel('Propensity score')\n", + "ax.set_ylabel('Density')\n", + "ax.set_title('Propensity score distribution by treatment')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0b42eca5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.264007Z", + "iopub.status.busy": "2026-05-09T16:49:48.263913Z", + "iopub.status.idle": "2026-05-09T16:49:48.571081Z", + "shell.execute_reply": "2026-05-09T16:49:48.570638Z" + } + }, + "source": [ + "In this dataset the minimum value of $\\hat e_a(X_i)$ is approximately 0.058, and no observations have `e_hat < 0.05`. Excessively large weights from extremely small propensity scores are therefore not a major concern here.\n", + "\n", + "The propensity distributions for all four treatments overlap well in the graph, and no clear positivity violations are observed." + ] + }, + { + "cell_type": "markdown", + "id": "f4edd2d8", + "metadata": {}, + "source": [ + "## Policy Learning Methods\n", + "\n", + "We compare three policy learning approaches.\n", + "\n", + "- Plug-in Policy (DRLearner)\n", + "\n", + " First estimates per-customer CATE, then selects the treatment with the highest expected net benefit.\n", + "\n", + " This is a flexible approach with no constraints on policy form, but final performance depends heavily on the quality of CATE estimation.\n", + "\n", + "- DRPolicyTree\n", + "\n", + " DRPolicyTree learns a policy by directly maximizing the AIPW score.\n", + "\n", + " The policy structure is constrained to a shallow decision tree, making results interpretable as decision rules. This is useful when policy interpretability and explainability matter.\n", + "\n", + "- DRPolicyForest\n", + "\n", + " DRPolicyForest also directly maximizes the AIPW score but uses a forest rather than a single tree.\n", + "\n", + " A single tree has a simple structure but can exhibit high variance. A forest averages across many trees, reducing variance and learning heterogeneous treatment effects more stably.\n", + "\n", + " However, the resulting policy is harder to interpret as a single clear decision rule set.\n", + "\n", + "All three policies are learned on the train set and compared using the same test set. It is important to use separate data for learning and evaluation, as using the training data for evaluation leads to overestimation." + ] + }, + { + "cell_type": "markdown", + "id": "3c967473", + "metadata": {}, + "source": [ + "### Plug-in Policy\n", + "\n", + "A plug-in policy first estimates per-customer treatment effects (CATE) and then uses those estimates to construct the policy.\n", + "\n", + "For binary treatment, the standard rule is to treat if $\\hat\\tau(x) > 0$, or if costs are present, treat when $\\hat\\tau(x) > c(x)$.\n", + "\n", + "With multiple treatments, we compare relative effects against a baseline. Here we set `none` (no treatment) as the baseline and use `DRLearner` to estimate:\n", + "\n", + "$$\n", + "\\hat\\tau_a(x) = \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "\\qquad a \\in \\{1,2,3\\}\n", + "$$\n", + "\n", + "Since the relative effect of the baseline treatment is 0, we compare the following four values per customer:\n", + "\n", + "$$\n", + "[0, \\hat\\tau_1(x), \\hat\\tau_2(x), \\hat\\tau_3(x)]\n", + "$$\n", + "\n", + "and select the treatment with the highest value:\n", + "\n", + "$$\n", + "\\hat\\pi_{plugin}(x) = \\arg\\max_{a \\in \\{0,1,2,3\\}} \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "$$\n", + "\n", + "This approach is intuitive and flexible, but it depends heavily on the quality of CATE estimation. The learned policy is therefore evaluated on a separate test set using AIPW value.\n", + "\n", + "> **Note** One might ask \"why not just select the treatment with the highest $\\hat\\Gamma_{i,a}$ for each customer?\" However, AIPW scores have very high variance at the individual observation level, so a policy built by pointwise argmax overfits to noise. AIPW scores must always be used in aggregated form — for example, summed across observations for policy evaluation (value estimation, cost curves, etc.), averaged within nodes for DRPolicyTree/Forest, or used as pseudo-outcomes for regression in DRLearner." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "debd92a9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:49.484723Z", + "iopub.status.busy": "2026-05-09T16:49:49.484610Z", + "iopub.status.idle": "2026-05-09T16:49:52.385416Z", + "shell.execute_reply": "2026-05-09T16:49:52.384824Z" + } + }, + "outputs": [], + "source": [ + "dr_cate = DRLearner(\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_final=RandomForestRegressor(\n", + " n_estimators=300,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_cate.fit(Y_net_train, A_train, X=X_train)\n", + "\n", + "# returns E[Y_net(a) - Y_net(0) | X] for each treatment relative to none (baseline)\n", + "cate_vs_none = dr_cate.const_marginal_effect(X_test)\n", + "plugin_arm_values = np.column_stack([\n", + " np.zeros(len(X_test)), # none: baseline, relative effect = 0\n", + " cate_vs_none,\n", + "])\n", + "pi_plugin = np.argmax(plugin_arm_values, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "33437c2c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:52.389559Z", + "iopub.status.busy": "2026-05-09T16:49:52.389447Z", + "iopub.status.idle": "2026-05-09T16:49:52.392977Z", + "shell.execute_reply": "2026-05-09T16:49:52.392636Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.092\n", + "tech_support_only 0.500\n", + "discount_only 0.035\n", + "discount_plus_support 0.373\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(pi_plugin).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "33ktqy8zrqm", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SizeEmployee CountIT Spendassigned_treatmentτ_techτ_discτ_both
09620414217655none-6140-1419-1719
1253901787971none-14556-4252-7232
26655527919899none-17629-2896-16582
3708682026263tech_support_only4265-404155
424152184610tech_support_only4174-2316-771
5548556314772tech_support_only2042-698553
639922836133052discount_plus_support7849-128111819
71817042459529discount_plus_support9158521610248
829437737112485discount_plus_support71348589048
\n", + "
" + ], + "text/plain": [ + " Size Employee Count IT Spend assigned_treatment τ_tech τ_disc \\\n", + "0 96204 142 17655 none -6140 -1419 \n", + "1 25390 178 7971 none -14556 -4252 \n", + "2 66555 279 19899 none -17629 -2896 \n", + "3 70868 20 26263 tech_support_only 4265 -40 \n", + "4 24152 18 4610 tech_support_only 4174 -2316 \n", + "5 54855 63 14772 tech_support_only 2042 -698 \n", + "6 399228 36 133052 discount_plus_support 7849 -1281 \n", + "7 181704 24 59529 discount_plus_support 9158 5216 \n", + "8 294377 37 112485 discount_plus_support 7134 858 \n", + "\n", + " τ_both \n", + "0 -1719 \n", + "1 -7232 \n", + "2 -16582 \n", + "3 4155 \n", + "4 -771 \n", + "5 553 \n", + "6 11819 \n", + "7 10248 \n", + "8 9048 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "none_idx = np.where(pi_plugin == 0)[0][:3]\n", + "tech_idx = np.where(pi_plugin == 1)[0][:3]\n", + "both_idx = np.where(pi_plugin == 3)[0][:3]\n", + "sample_ids = np.concatenate([none_idx, tech_idx, both_idx])\n", + "\n", + "df_plugin_sample = pd.DataFrame(X_test[sample_ids], columns=covariates)[['Size', 'Employee Count', 'IT Spend']]\n", + "df_plugin_sample['assigned_treatment'] = pd.Series(pi_plugin[sample_ids]).map(TREATMENT_NAMES).values\n", + "df_plugin_sample[['τ_tech', 'τ_disc', 'τ_both']] = np.round(cate_vs_none[sample_ids], 0).astype(int)\n", + "df_plugin_sample.index = pd.RangeIndex(len(df_plugin_sample))\n", + "df_plugin_sample" + ] + }, + { + "cell_type": "markdown", + "id": "05b0ee18", + "metadata": {}, + "source": [ + "The plug-in policy assigns `tech_support_only` to approximately 50% of customers, `discount_plus_support` to 37%, and `none` to about 9%.\n", + "\n", + "After accounting for costs, some customers are expected to have negative net profit from treatment, so the policy chooses not to treat them." + ] + }, + { + "cell_type": "markdown", + "id": "97d958cd", + "metadata": {}, + "source": [ + "### Policy Tree\n", + "\n", + "In practice, policy interpretability is often as important as performance. Shallow decision tree-based policies are frequently used in operations for the following reasons:\n", + "\n", + "- **Stakeholder communication**: the intuitive decision rule branching structure makes policies easy to explain and share.\n", + "- **Fairness review**: the explicit structure of who receives which treatment makes it easy to audit for bias.\n", + "- **Operational stability**: simple rule-based policies are more manageable in production than complex models.\n", + "\n", + "Rather than searching for the optimal policy across all possible functions, we restrict the search to a limited policy class $\\Pi$:\n", + "\n", + "$$\n", + "\\hat\\pi = \\arg\\max_{\\pi \\in \\Pi}\n", + "\\frac{1}{n}\\sum_{i=1}^{n} \\widehat\\Gamma_{i,\\pi(X_i)}\n", + "$$\n", + "\n", + "Policy Tree restricts $\\Pi$ to shallow decision trees. With a small depth, the resulting policy is human-readable. We use `DRPolicyTree` from `econml`.\n", + "\n", + "To prevent unstable value estimation in small leaves under multi-treatment settings, `min_samples_leaf` is used to ensure leaves are large enough." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6c987a8b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:53.651642Z", + "iopub.status.busy": "2026-05-09T16:49:53.651575Z", + "iopub.status.idle": "2026-05-09T16:49:55.354506Z", + "shell.execute_reply": "2026-05-09T16:49:55.354139Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.199\n", + "tech_support_only 0.475\n", + "discount_only 0.000\n", + "discount_plus_support 0.326\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_tree = DRPolicyTree(\n", + " max_depth=2,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_policy_tree.fit(Y_net_train, A_train, X=X_train)\n", + "pi_tree = dr_policy_tree.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_tree).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "64529830", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:55.355867Z", + "iopub.status.busy": "2026-05-09T16:49:55.355774Z", + "iopub.status.idle": "2026-05-09T16:49:55.574472Z", + "shell.execute_reply": "2026-05-09T16:49:55.574068Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(13, 7))\n", + "dr_policy_tree.plot(feature_names=covariates, treatment_names=TREATMENT_LABELS, ax=ax)\n", + "ax.set_title('4-way DRPolicyTree')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2a3ffb5d", + "metadata": {}, + "source": [ + "This tree policy primarily splits customers by `Size`. Notably, it assigns `none` to approximately 20% of customers — after accounting for costs, the expected net profit from treatment is low enough for some customers that not treating them is the better choice.\n", + "\n", + "Overall, the policy assigns `discount_plus_support` to large customers and `tech_support_only` to the rest, while choosing `none` for customers with low expected net profit." + ] + }, + { + "cell_type": "markdown", + "id": "35dd95b1", + "metadata": {}, + "source": [ + "### Policy Forest\n", + "\n", + "Policy Forest learns policies more stably by aggregating many trees.\n", + "\n", + "Like DRPolicyTree, DRPolicyForest directly maximizes the AIPW score, but it averages across many trees instead of using a single one. This reduces variance and allows more stable learning of complex heterogeneous treatment effects.\n", + "\n", + "We use `DRPolicyForest` from `econml`.\n", + "\n", + "Unlike tree-based policies, the resulting policy cannot be easily expressed as a single clear decision rule. After fitting, we compare policy values using AIPW scores from the same test set." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d7939447", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:55.582919Z", + "iopub.status.busy": "2026-05-09T16:49:55.582816Z", + "iopub.status.idle": "2026-05-09T16:49:57.495404Z", + "shell.execute_reply": "2026-05-09T16:49:57.494962Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.113\n", + "tech_support_only 0.471\n", + "discount_only 0.000\n", + "discount_plus_support 0.416\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_forest = DRPolicyForest(\n", + " n_estimators=400,\n", + " max_depth=5,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "dr_policy_forest.fit(Y_net_train, A_train, X=X_train)\n", + "pi_forest = dr_policy_forest.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_forest).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "15d7d727", + "metadata": {}, + "source": [ + "DRPolicyForest shows a treatment assignment pattern broadly similar to the plug-in policy. It assigns `none` to 11%, `tech_support_only` to 47%, and `discount_plus_support` to 42% — a slightly higher share of `discount_plus_support` compared to the plug-in policy." + ] + }, + { + "cell_type": "markdown", + "id": "99bac91c", + "metadata": {}, + "source": [ + "## Policy Evaluation\n", + "\n", + "We now compare the learned policies using AIPW scores from the same test set.\n", + "\n", + "As benchmarks, we also include constant policies that apply the same treatment to every customer:\n", + "\n", + "- `all_none`\n", + "- `all_tech_support_only`\n", + "- `all_discount_only`\n", + "- `all_discount_plus_support`\n", + "\n", + "For example, `all_discount_plus_support` represents the average net profit when both technical support and a discount are provided to every customer.\n", + "\n", + "By contrast, the learned policies assign different treatments depending on each customer's characteristics $X_i$.\n", + "\n", + "If a learned policy's value exceeds the best constant policy, it means that customer-level targeting is more effective than applying the same treatment to everyone." + ] + }, + { + "cell_type": "markdown", + "id": "5261c54f", + "metadata": {}, + "source": [ + "The AIPW (Augmented Inverse Probability Weighting) score is defined as:\n", + "\n", + "$$\n", + "\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n", + "$$\n", + "\n", + "- $\\hat\\mu_a(X_i)$: predicted $E[Y^{net}(a)\\mid X_i]$ from the outcome model\n", + "- $\\hat e_a(X_i)$: predicted $P(A_i=a\\mid X_i)$ from the propensity model\n", + "- The second term corrects the difference between observed and predicted values via IPW.\n", + "\n", + "This structure ensures unbiasedness holds as long as either the outcome model or the propensity model is correctly specified. This property is called doubly robust.\n", + "\n", + "- If the outcome model is correct: the expected value of the IPW correction term approaches zero, so $\\hat\\mu_a(X_i)$ contributes directly.\n", + "- If the propensity model is correct: the IPW correction term removes the bias of the outcome model.\n", + "\n", + "This score is used to estimate the value of each learned policy." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c51d8c52", + "metadata": {}, + "outputs": [], + "source": [ + "e_hat_net = np.clip(e_hat_raw, 0.02, 0.98)\n", + "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", + "\n", + "gamma_net = np.zeros((len(X_test), 4))\n", + "mu_hat_net = np.zeros((len(X_test), 4))\n", + "\n", + "for treatment_id in range(4):\n", + " outcome_model = RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " )\n", + " outcome_model.fit(X_train[A_train == treatment_id], Y_net_train[A_train == treatment_id])\n", + " mu_a = outcome_model.predict(X_test)\n", + " observed_a = (A_test == treatment_id).astype(float)\n", + "\n", + " gamma_net[:, treatment_id] = mu_a + observed_a / e_hat_net[:, treatment_id] * (Y_net_test - mu_a)\n", + " mu_hat_net[:, treatment_id] = mu_a" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0349d9ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
samplee_hatmu_hatgamma
sample_idactual_armobserved_net_outcomenonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_support
00discount_plus_support9254.5585410.2486330.2673380.2723660.2116625370.0992819559.7777634547.4845526984.7316095370.0992819559.7777634547.48455217708.540116
11tech_support_only7702.8616620.2110870.3072040.2433940.23831510468.9425226728.9884198373.31663311121.82660910468.9425229899.1085088373.31663311121.826609
22tech_support_only5062.1844200.3765700.2419010.2562610.1252682879.7417865449.409859937.5151032959.5143472879.7417863848.652052937.5151032959.514347
33none8930.4950220.3396530.1947010.2465050.2191428458.7792178254.3720696054.5989736560.0280699847.5980528254.3720696054.5989736560.028069
44none8156.5755020.3122650.2500210.3121230.12559010137.4698213378.7480355743.8029392988.2173143793.8450543378.7480355743.8029392988.217314
\n", + "
" + ], + "text/plain": [ + " sample e_hat \\\n", + " sample_id actual_arm observed_net_outcome none \n", + "0 0 discount_plus_support 9254.558541 0.248633 \n", + "1 1 tech_support_only 7702.861662 0.211087 \n", + "2 2 tech_support_only 5062.184420 0.376570 \n", + "3 3 none 8930.495022 0.339653 \n", + "4 4 none 8156.575502 0.312265 \n", + "\n", + " mu_hat \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 0.267338 0.272366 0.211662 5370.099281 \n", + "1 0.307204 0.243394 0.238315 10468.942522 \n", + "2 0.241901 0.256261 0.125268 2879.741786 \n", + "3 0.194701 0.246505 0.219142 8458.779217 \n", + "4 0.250021 0.312123 0.125590 10137.469821 \n", + "\n", + " gamma \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 9559.777763 4547.484552 6984.731609 5370.099281 \n", + "1 6728.988419 8373.316633 11121.826609 10468.942522 \n", + "2 5449.409859 937.515103 2959.514347 2879.741786 \n", + "3 8254.372069 6054.598973 6560.028069 9847.598052 \n", + "4 3378.748035 5743.802939 2988.217314 3793.845054 \n", + "\n", + " \n", + " tech_support_only discount_only discount_plus_support \n", + "0 9559.777763 4547.484552 17708.540116 \n", + "1 9899.108508 8373.316633 11121.826609 \n", + "2 3848.652052 937.515103 2959.514347 \n", + "3 8254.372069 6054.598973 6560.028069 \n", + "4 3378.748035 5743.802939 2988.217314 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_ids = np.arange(5)\n", + "\n", + "sample_prediction_table = pd.concat(\n", + " {\n", + " 'sample': pd.DataFrame({\n", + " 'sample_id': sample_ids,\n", + " 'actual_arm': pd.Series(A_test[sample_ids]).map(TREATMENT_NAMES).to_numpy(),\n", + " 'observed_net_outcome': Y_net_test[sample_ids],\n", + " }),\n", + " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=TREATMENT_LABELS),\n", + " },\n", + " axis=1,\n", + ")\n", + "\n", + "sample_prediction_table" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "14b616ad", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:57.502590Z", + "iopub.status.busy": "2026-05-09T16:49:57.502507Z", + "iopub.status.idle": "2026-05-09T16:49:57.508438Z", + "shell.execute_reply": "2026-05-09T16:49:57.508058Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
policyvalueci_lowerci_upperrate_nonerate_tech_support_onlyrate_discount_onlyrate_discount_plus_support
4plugin_drlearner_4treatment11810.37771211283.56156312337.1938610.0920.5000.0350.373
6dr_policy_forest_4treatment11625.62754011102.02258912149.2324910.1130.4710.0000.416
5dr_policy_tree_4treatment11148.46236910700.60923011596.3155080.1990.4750.0000.326
1all_tech_support_only10526.5238769833.03475111220.0130010.0001.0000.0000.000
3all_discount_plus_support10136.7088829439.44594110833.9718240.0000.0000.0001.000
2all_discount_only7545.0792056993.5366218096.6217880.0000.0001.0000.000
0all_none7249.3454676955.4474327543.2435021.0000.0000.0000.000
\n", + "
" + ], + "text/plain": [ + " policy value ci_lower ci_upper \\\n", + "4 plugin_drlearner_4treatment 11810.377712 11283.561563 12337.193861 \n", + "6 dr_policy_forest_4treatment 11625.627540 11102.022589 12149.232491 \n", + "5 dr_policy_tree_4treatment 11148.462369 10700.609230 11596.315508 \n", + "1 all_tech_support_only 10526.523876 9833.034751 11220.013001 \n", + "3 all_discount_plus_support 10136.708882 9439.445941 10833.971824 \n", + "2 all_discount_only 7545.079205 6993.536621 8096.621788 \n", + "0 all_none 7249.345467 6955.447432 7543.243502 \n", + "\n", + " rate_none rate_tech_support_only rate_discount_only \\\n", + "4 0.092 0.500 0.035 \n", + "6 0.113 0.471 0.000 \n", + "5 0.199 0.475 0.000 \n", + "1 0.000 1.000 0.000 \n", + "3 0.000 0.000 0.000 \n", + "2 0.000 0.000 1.000 \n", + "0 1.000 0.000 0.000 \n", + "\n", + " rate_discount_plus_support \n", + "4 0.373 \n", + "6 0.416 \n", + "5 0.326 \n", + "1 0.000 \n", + "3 1.000 \n", + "2 0.000 \n", + "0 0.000 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "policy_assignments = {\n", + " **{f'all_{treatment_name}': np.full(len(X_test), treatment_id) for treatment_id, treatment_name in TREATMENT_NAMES.items()},\n", + " 'plugin_drlearner_4treatment': pi_plugin,\n", + " 'dr_policy_tree_4treatment': pi_tree,\n", + " 'dr_policy_forest_4treatment': pi_forest,\n", + "}\n", + "\n", + "eval_rows = []\n", + "for policy_name, policy_assignment in policy_assignments.items():\n", + " pi = np.asarray(policy_assignment).astype(int).ravel()\n", + " scores = gamma_net[np.arange(len(pi)), pi]\n", + "\n", + " row = {\n", + " 'policy': policy_name,\n", + " 'value': scores.mean(),\n", + " 'value_se': scores.std(ddof=1) / np.sqrt(len(scores)),\n", + " }\n", + " for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " row[f'rate_{treatment_name}'] = np.mean(pi == treatment_id)\n", + " eval_rows.append(row)\n", + "\n", + "policy_eval = pd.DataFrame(eval_rows)\n", + "policy_eval['ci_lower'] = policy_eval['value'] - 1.96 * policy_eval['value_se']\n", + "policy_eval['ci_upper'] = policy_eval['value'] + 1.96 * policy_eval['value_se']\n", + "\n", + "display_cols = ['policy', 'value', 'ci_lower', 'ci_upper'] + [f'rate_{n}' for n in TREATMENT_LABELS]\n", + "policy_eval.sort_values('value', ascending=False)[display_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4814d1ed", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n", + "plot_df = policy_eval.sort_values('value', ascending=False)\n", + "sns.barplot(data=plot_df, x='value', y='policy', ax=axes[0], color='seagreen')\n", + "axes[0].set_title('4-way net policy value')\n", + "axes[0].set_xlabel('AIPW-estimated net value')\n", + "axes[0].set_ylabel('')\n", + "\n", + "treatment_rate_cols = [f'rate_{name}' for name in TREATMENT_LABELS]\n", + "rate_df = policy_eval.set_index('policy')[treatment_rate_cols]\n", + "rate_df.columns = TREATMENT_LABELS\n", + "rate_df.loc[['plugin_drlearner_4treatment', 'dr_policy_tree_4treatment', 'dr_policy_forest_4treatment']].plot(kind='barh', stacked=True, ax=axes[1], colormap='tab20')\n", + "axes[1].set_title('Assigned treatment share')\n", + "axes[1].set_xlabel('Share')\n", + "axes[1].set_ylabel('')\n", + "axes[1].legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "dec4e9f6", + "metadata": {}, + "source": [ + "All three learned policies achieve higher value than any constant policy, confirming that assigning different treatments by customer is more effective than treating everyone the same.\n", + "\n", + "Among the learned policies, DRLearner plug-in (11,810) ranked first, followed by DRPolicyForest (11,626) and DRPolicyTree (11,148). However, since the plug-in and forest confidence intervals overlap substantially, the difference between them is not statistically clear. In theory, DR policies that directly optimize the AIPW score should have an advantage, but in practice the outcome depends on sample size and data characteristics. The tree policy trades some performance for an interpretable structure.\n", + "\n", + "Among constant policies, `all_tech_support_only` (10,527) performed best. `all_discount_plus_support` (10,137) falls short because discount costs scale with customer size, so for some customers the discount benefit does not sufficiently offset the cost." + ] + }, + { + "cell_type": "markdown", + "id": "9bh57tga2aq", + "metadata": {}, + "source": [ + "## Policy Comparison under Budget Constraints\n", + "\n", + "So far we have compared policies by their overall AIPW value. In practice, however, budgets are limited. Cost curves are useful for comparing which policy is more efficient within the same budget." + ] + }, + { + "cell_type": "markdown", + "id": "qdxockulyq9", + "metadata": {}, + "source": [ + "### Estimating Per-Customer Expected Costs\n", + "\n", + "To construct cost curves, we first estimate $E[C(a) \\mid X_i]$ per customer. Since actual costs are observed only for the treatment the customer received, we fit a separate cost regression model on the training data for each treatment and apply it to the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a1z6z7eabyj", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated E[C(a)|X] — test set (mean ± std):\n", + " tech_support_only : 4,892 ± 3,466\n", + " discount_only : 5,454 ± 2,330\n", + " discount_plus_support : 10,687 ± 4,139\n" + ] + } + ], + "source": [ + "# Estimate E[C(a) | X] per treatment\n", + "# c_hat_test[i, a] = predicted cost for customer i under treatment a\n", + "c_true_by_treatment = {1: c_tech, 2: c_disc, 3: c_tech + c_disc}\n", + "\n", + "c_hat_test = np.zeros((len(X_test), 4)) # treatment 0 → cost = 0\n", + "for treatment_id in [1, 2, 3]:\n", + " mask_train = (A_train == treatment_id)\n", + " mdl = RandomForestRegressor(n_estimators=300, min_samples_leaf=20,\n", + " random_state=SEED, n_jobs=1)\n", + " mdl.fit(X_train[mask_train], c_true_by_treatment[treatment_id][train_idx[mask_train]])\n", + " c_hat_test[:, treatment_id] = np.maximum(mdl.predict(X_test), 200.0)\n", + "\n", + "print(\"Estimated E[C(a)|X] — test set (mean ± std):\")\n", + "for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " if treatment_id > 0:\n", + " print(f\" {treatment_name:25s}: {c_hat_test[:, treatment_id].mean():8,.0f} ± {c_hat_test[:, treatment_id].std():6,.0f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "dgt4sg335xc", + "metadata": {}, + "source": [ + "### Cost Curves and Budget-Constrained Comparison\n", + "\n", + "The two axes of a cost curve are:\n", + "\n", + "- **x-axis**: cumulative average treatment cost per customer\n", + "- **y-axis**: cumulative average net profit per customer — the increment relative to baseline(`none`), with **costs already deducted**\n", + "\n", + "Within each policy, customers are treated in descending order of their benefit-to-cost ratio $\\hat\\rho(x) = \\hat\\tau(x) / \\hat\\gamma(x)$. Drawing a vertical line at $x = B$ gives the budget-$B$ policy comparison. A higher $y$ value at the same budget means a more efficient policy.\n", + "\n", + "The endpoint (\\u25cf) of each curve shows the position when all targeted customers have been treated. Customers assigned `none` incur no treatment cost and therefore do not appear in the curve." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ttwi6dbqcfo", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAAJICAYAAADxUwLTAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnQd4FFUXhr/03kMJvfcqvSqIKCAooqCI2EAUOz92VETsCooNC1IsoAiIIkgTpEiv0muoSUjvbbP7P9/ZzGay7KaHtPvm2SdbZmfu3Htn9px7moPJZDJBoVAoFAqFQqFQKAqAY0E2UigUCoVCoVAoFAqlQCgUCoVCoVAoFIpCoSwQCoVCoVAoFAqFosAoBUKhUCgUCoVCoVAUGKVAKBQKhUKhUCgUigKjFAiFQqFQKBQKhUJRYJQCoVAoFAqFQqFQKAqMUiAUCoVCoVAoFApFgVEKhEKhUCgUCoVCoSgwSoFQKOzQv39/NG/e3PJo0aIFrrvuOowZMwa7du0qcL8tXbpUvq/f76effloh+/3kyZPYuHFjWTejSpGSkoIff/yxWPuIjY3F4sWLUZrcd999ua6X1q1bo1+/fvjwww+RkZGR53e//PJLdO3aFR07dsR///1XIu3ZsWOHtOPixYslsr/S3G9JjHFFuf6t74elBY/BY1V2rlV/KhTWKAVCociDhx56CFu2bJHHpk2bsGjRInh7e2PcuHG4fPlykfru119/lf1WRCZMmFBiAp6iYHz33XeYM2dOsbrr/fffx++//17qXT5o0CDL9bJmzRq8/vrr+O233zBz5ky730lMTMQnn3yC0aNHY8WKFaKoVzVKYowryvU/ePBgmR+KkkH1p6KsUAqEQpEHnp6eqFatmjyqV6+OZs2a4Y033kBaWhrWrl1bpL4LDAyEl5eX6ndFgTCZTOViHwXB3d3dcr3Url0bN9xwg1gm8loJTkhIkPZ1795dvuPi4oKqxrUan/KANkcUqj8VFRulQCgUhcTZ2Vn+u7q6yn8qEx9//DFuvPFGtG3bFrfddhtWr15t9/vWLkybN2/GqFGj0L59e/Tt21dWa7OysjB//nxx6UhNTbVsazQaZRt77g7Jycl488030bt3b/ku3a0OHTpk+Xzfvn0YO3YsOnXqhG7duuGll14S9xaNgwcPykowv9ulSxc8+eSTFksL233p0iV89tlnIhTaE4TY7ptvvhnt2rXDkCFDZFWZ0OWDpna6gGhYv/fiiy/iqaeeEgsN3cW+/vprWZH+559/ch2H7b7nnnvkOd1jPvjgA/Tp00faPXLkyFwrnOxLfn799dejTZs2uOWWW7Bw4UJcKzIzM2WFne48HOM77rgDW7dutesOo3+P84T9zX7Py22G7klDhw6VPu/QoYOMobZSzD5dtmwZdu7cadPVge0bPny4PAwGg7x3/PhxmctcGSd61yTrh3487QmM9uB3Oa/I/fffb5lXYWFhmDx5Mnr16iXn8/DDD+PYsWOW71nPk2+++cbuMf7++28MGDBAzof71++Hr7kvPdbv7d69G3fddZf07bBhw3J9X5tfvGZ5zbGtbNdbb72V6xo5ffo0xo8fL/OT2/3vf/9DZGSkfFbQMc7rXlGQ+xCVuJtuusnyn9cC5+KePXuKfP2zvbNmzZK5zfMKDQ2V7Z999ln06NFD3NjYTl5/vHdp7dDPQz6nVfaBBx6QPuZ+eAw9GzZskLbyc7ad56l3iwsPD8djjz0m7ebx/vjjD+THuXPn5DvavXDSpEmIjo62fE7LGcebx+S5f/HFF5a+1u5bf/75J26//Xbpb7aP4/z555+jZ8+e4pLHxSZNOeQ4857Fz3m8zp07y30sKSnJcswTJ06IlYd9z/HhWGrXoLYP3tPZv5z3vNdb9yfvlWwL5wjHgHM5Pj7e8jnb+Oijj0obeO6crxxXDY4t3Q5ffvllaSOPw/mqb6dCIZgUCoVN+vXrZ5o1a1au98LDw01PPfWUqUOHDqZLly7Je4899pjp+uuvN23YsMF05swZ+U7z5s1Na9eulc+XLFliatasmc397t2719SiRQvTe++9Zzp16pTpn3/+MXXt2lU+j46ONrVu3dr022+/Wb67efNmU5s2bUxxcXE22zxu3DjTgAEDTJs2bTKFhoaaXnzxRVOXLl1k+wMHDsj+pk2bJsfatm2badCgQabhw4ebDAaDPLp3726aMWOG6fz586ZDhw6Z7rjjDtP9998v+2Z7+vbta3r33XdNsbGxNo//9ddfm9q3b2/65ZdfTOfOnTMtWLDA1LJlSznWhQsXpB+2b99u2d76vRdeeEFef/PNN9KXly9fNo0ZM8Y0efJky3fS0tJM1113nRyDTJo0yXTbbbfJPs6ePWv67rvv5Dw5HoRt6N+/v2nPnj2mixcvmr7//ns5xq5du67JzH/99delX1etWiV9wv7lGJ4+fVrazLawHzT07yUlJUl/s9+vXLkiY2TNmjVrZH+cJzy/ffv2ybgNGzZMPk9ISDA9/fTTplGjRsk+bHHy5ElT27ZtTbNnzzalp6ebbr31VtPDDz9sMhqN8jm/Z+/B7QnHieOnh+d48803mz744AObx+V3OS95vqtXr5Z5lZiYKNcT98fPjh49apo4caKpU6dOcn725ok1Wj9yX7wejh8/bpowYYKpV69eppSUFLtt1r/H64D98uqrr8o189dff8n1qR8zjk+3bt1kHLgNry9e/9yPds/gd9588035/L///jM98sgjch9ITk4u0Bjnd68o6H2I18Vdd90lc+TEiROm0aNHy/2C41yU65/9wHM/ePCg7JNw3nHucNy4n7lz58p29u6HfN65c2eZv9z+yy+/lPd27twpn/M827VrZ1q4cKFcP7wHDhw4UO7DJDMz0zRkyBCZ32wz+4n3A+6Dx7JFfHy8zIOHHnpIxuPw4cOmESNGWMaMbeY19cMPP8g9hW3jPWf69Om57ls33nijaceOHXKufM57Le9VHJ+ffvpJtlm/fr18h+PB/r/77rulnZyf/A77inBOsk3PP/+8fJ/Hff/992UfR44cseyDr9kO9hW30fcnx0hrN6+V3bt3y73v5Zdfls/5Hq+jJ598Utq8f/9+ac8NN9wg1502/9nOjz76SPa/bt06uad/+umnNvtSUXVRCoRCYQf+wPNGSmWBD96YeaOm0L1x40bZhjd6vvf333/n+i4FHv4g5adAPPvss/LDp4dCyo8//ijPeaPnj5wGhWXth9MaCms8Dn9g9cL222+/LT8EFCIpEOjhjwi/w/OhkkGBgz8+WVlZ8jl/pDTBwLrt1lAI4Q8gf3islQq2qaAKBH+E9SxdulT6XxP6Vq5cKT9o/MGjkqT/gdXgj7AmDPDHlgJxRESE5fOtW7eaoqKiTKUN28g5tGjRolzvs48oHOenQBD2N/vdHhS0li9fnus9Ci8UNjXYr1p/2INCE4VlzrGePXsWun+4/1atWlmuF543z4MCqr7vrbGeA5z7FBgpDGmkpqbK3KLwbG+eWKP1IwUgveDItmnKZ34KxIcffih9rxfqNYGY7eac1IRb/XVApVzr75kzZ1qUOQ3te5qAm98Y53evKMx9SH+tUKjnexyfolz//C7vL/pxmjNnzlUKHefTZ599lqsd+n1ogrkGFQoqs+See+656nMuSGhjQOWQz6lcaPAc81IgOF6cB/qFGN4LOd5UatleKkp65s2bJ3OaCrk2Z7X7NOH2/Fy7T5EePXpYzoP9xt8QKpQaVI64H967Od+/+uorUSj1929+vmzZMss++Jpt0ND3p3be+nlARZHnRqiQ9OnTx6L0EyqtvO457oTzlgqY9TzS/w4pFMTsi6FQKGxy9913W8z1jo6O8Pf3h4+Pj+VzunoQmoL10AQ9Y8aMfHuVJmu6aeih+4/GiBEjxMx+5coVicdYt26duAzY2xehG4WGm5ubmMntHYvuQTwfngddfBgcTrM4j0GfdL7HwNiCQFcoumXQdK6HrhukoFlr6tevf1V/TJs2DevXr8ett94qwcB0SWEwO106CN0urN1yfH195fm9994r/cZzadmypfQBXauCgoKuOjb3zcDfolCrVi1xadBz9uxZaYt1n9BdguTn/lMQONc014kzZ86IawbHU3MZKSh0IWIf0+WM+9L3D11D7EH3Ibo6ELp60PWI0B2KriWzZ88WFyC6hAQEBOTbDs7TBg0aSKyQ3g2KriTaHLc1T+yhvzY5J7hv/X7ya0urVq3g5ORkeY8uHRrsd7oO6a85BwcHOabm6nTkyBHJXmTdh+np6fJ9a+gCxPmph66Hed0rVq5cedW52rsPNW7c2PJcu5dxjjLGqyjXv34cOE50sfnrr7/EHUqbi1FRUXnOR32btHaxTVr/cV90c9LQ3ILYf6dOnYKfnx/q1atn+ZzXeV6uc9oc4/f090I+6MbE9lr3JV2S2CZeY9q1oT933p+Dg4Ph4eGRqz/0rlY8Zo0aNa6aS2wPXSu1RAI85/Pnz1vmkL7veGz9b5AenjfvkXRRYpwJ5wvjkOj2pR2HrlGa+y3hdg0bNsx1TTRq1CjXfnk8xiopFHqUAqFQ5AF/YAoqqOjhD5wWK5EX+W1Df2D+KPFHhcoLBSC+V5R92QvU5Pta4CqFP/6I0Y9227ZtIkx8++23Ivzpf3RsUZTgV82nWI/1Dz9/mPnjSr9mnjuVBsZG6M+JMSHWgelU+LQfbWYEYgwAYw+YhpJC7zvvvCN+/3ooAFsL+wXFVv+XVJ/kBfuFfs6MgaBAQqWXwgCVrsJAAYFCC8+DMSRU0jQ4/vbQC0QcA/31QsGwSZMm4pdOIZfKXH7Ym6cUovR9nJeAqEcv/Gv9m9dc1uJANGXAWvDVt0F7nlcQNL9PYdyWYmpLEKQgb6u/C3I/Kch9yNa5a+0vyvWvHwemo6UCQaWK1yyvLyp++Y17Xm1i/1Gxsb5WNeGXSoQt5SSv/srrs7zmn/V3rfej3XPsYX0/0K51zlEuvjC+hYoz70O81zG2gkqcnvzm/UcffYTHH39csgb++++/eO6550QZYmxaXuemb1t+93qFgqggaoWiGGjBa/pARC3wkoJTflDAsk6LyBs9V2y1HxYG6THjEwMiGRhpLRDp90X0+6MwxB8jrgiyrdbt5AoXg+P4Xa6sUcjhCheD/bgKSeGBP9DWgaO2oDBE4cf6fBikR2Fd+4HSB+Mx6LIg0BJD4Z+CDBUqCmSkadOm8p8/vhRctQcDC7XMPwsWLBAFgqtxzz//vAjcDC7UVm310Kqh309hHswgZA3f53lb9wkDvefNm1egPqEQmxdUpu688068++67Iqhx1fnChQvymSYw5LcPwoBPrp7S+sB0xRRA9Odh75GfQKMXBAsC5yn7QB/QytV6JgMoyDVljT6JQExMjOxbmzfsf33fs41a3xGuSPP7+lVk/f6089+/f3+uYx44cMDynMfiNRQSEmLpMy5MvP3225ZVX/34UCi17uP87hXFvQ+R4l7/hIrn4cOH5Zrjdc8Uo7ymOJZFzTTF/qMlT98ftGwxNTGTRnDVnamAaeXR4BjnFfTLPuE2/J4G2837Au+ZvMfY6kvOF72lo7DwPPTHpGWJ0MrFRaK4uDhJ8DBx4kSxGmjBzwXtO847zitaEBiUznsDX2/fvl3GgPOEc0g/n2ltoaXI2gqkUOSHUiAUimLAmy4zkFD44so2fyCYQYSuIAWp9cCVNQofzNLDHzSu/DHbB83OGsyowR8GribZWoXToBl64MCB0hb+YLAtr776qghfNL8/+OCD4k7AVUUKBXSf4Yojf7z4w0n3ErrgvPbaa/I5v8/sPRR2NJM2V5jZTv7o2OKRRx4RoWb58uWymk1Bgn3BbCJULihk83Punz/QPO+CCLd0kaEARqGGSpS20kfhgv1PwYfZdij80brw1VdfWX7oKTRqLlDMNkILxtGjR/N0yykpKJBzRZbnyeOzT+hSQsGRq/JMC0wLC3/o+RnbNnfu3Fz74OcUJDR3KGvYL3v37hUBiPugYvLDDz/IZ5qgwH3QDU4vHOuh8LJq1SoRNjj3qJC88sorItAUBq48U5nTHhS2uR8en3OzINCSQmvbM888I64rFF45T7m6zRXawsL5zNV0jjmz17C/KNgSuh5RMaWyRCGK14beVYOCNLOgMSMN5yyzAekzqHF86eLIeUk3OY7Re++9l0uB4Io+hUaeA8+FD7aDghzHvyBjnN+9orj3IVIS13/NmjUtroC81ih0UxjmOeVXTNAedIHk4gnPh23iWNItk31KCwSzCdFqyMUB9g/7lc/zsgZwjvG8uDrP8eA85T2E48FzYNYvXkM//fSTzAsuOvD4nH/23IcKAucw28brn/dz3pc4F3lf5HE517jYQzc2KmOaq2NB+47KGtvMrFdsN4/DhRJaYTm+nM9UurTz5vX19NNPy2fWbnMKRX4oBUKhKCYUCOnuQUGJaf80IYMm/Pzg6hlXfPmjT99VCgBMs8q4Bw3e/PkDSUE/v1UiCoBcgeaPAhUPpsNkgSqaxbkPrijyx5JWDQpoFKIpsHJljT8iFL75w88VciorjFvg5/xhIhSW2FZ7QgmFZQoMFHL4g8T0okw1SQWGigJXDbkySCWAggp/IPMz+2uwPfzx43np4f4pnHJ//DGmlYJpNDVl64knnhCBePr06eIzzu34Q8p0idcCniPPlwIKBRcqblQYKJSxX/ljT59ntp399sILL+T6Ps+NghLnFrezhkoiV0zZ91yN5vxjPxNtxZrjTeGEcywiIiLX9/maggy/r/lksw0cr8LGg1AJoesFH0yrq80TziG9q1NeUECj8EZ3Pa6iUgCnYsKV2bp166KwcD5S4KTwR9cMXgOaiwbbR+WW1wvnPAV5vSDFNlPh5Yo35xOtPPprk/C7HJspU6ZIP/Oa4z61Y7DNPB/OXc479jOvNyrXWpxHfmNckHtFce5DpCSuf7orsa95boyd4HPej9jeohagY/t5jVNB4/VD4Vef6pX3Dy4Y8Hpiu3hdcwz1MTTWUPHjfZHWBrr8UTmjVYLpYQn3w2uAY8998bqkIkNFsjhQeeU40lLI+wLnCeeUdp5UXPiafcd7Oe9b7L+C9h1/HzjmXEDiXOR8o8Wa48p+qlOnjsxFKsm8Hng8zjteW1rMmEJRUBwYSV3grRUKxTWHlygFAwbGaa5NCoWifED3QvqY6wVWCqBcUaYQqFAQCva06NBSqlBUBlQQtUJRTqHZnz82XE2i6VuZmBWK8gdXsuk2QtcUrtTTbYjXrL4AmEKhUFQ2lAKhUJRT6OZAtxtCNxe6VygUivIFq/bS7YTuVnS1oisMXV60QH+FQqGojCgXJoVCoVAoFAqFQlFgVBC1QqFQKBQKhUKhKDBKgVAoFAqFQqFQKBQFRikQCoVCoVAoFAqFosCoIOoiwOqRTK1pXZZeoVAoFAqFQqGoqNkfWQOoIIVWlQWiCFB5KMvyGUajscyOrShd1NhWTqrSuJb1/fFaU5XGtiqhxrXyosa2ZO7fygJRBDTLQ9u2bXGtycrKQmJiolRrZYVJReVBjW3lRI1r5UWNbeVEjWvlRY1t3hSmYryyQCgUCoVCoVAoFIoCoywQCoVCoSgx14ADBw7I8/bt28PRUa1RKRQKRWVE3d0VCoVCUWIKxLFjx+Sh/IwVCoWi8qIsECUMg08MBoP42ZUG3G9GRgbS0tJUDEQlQ41t5aQqjSvvfVqMGM/X2blgPzHsF27L7B8KhUKhKP8oBaIEoZAQFhaGlJQUlHaEfFRUlPqxrWSosa2cVKVx5Xk2aNBAnp8/f75Q5+vp6YmQkBC4urqWYgsVCoVCURIoBaKEoLn+7NmzspJWq1Yt+REsDWGBP9Bc0eRxKrswUtVQY1s5qUrjynONi4uT5/7+/gU6X36Hiy+RkZFyD23atKmKnVAoFIpyjlIgSgj+AFKJqFu3rqyklRZVSRipaqixrZxUpXHluWoWBHd39wKfr4eHh7g+nTt3Tu6l/K5CoVAoyi8qiLqkO1RlHVEoFAp171QoFIpKjFIgKjEXL15EmzZtcNttt8lj0KBBeOSRR3DhwgXLNs2bN7d8PmzYMPTr1w+vvfaaJQicn1dUli5dihdffLFE9vXJJ59g/fr1Rf7+8ePH8c4776CkmDVrFnbv3p3vdn///TfuuOMO3HLLLZg+fbrNbc6cOYP77rtPxv/hhx9GfHx8rs+PHDki80hf6v7ll1/G4MGDMWTIEEvazpLk559/xooVK656/9NPP5VHacLzvfHGG3HPPffg448/tow7+6gwwcSjRo2SOWiLjz76CP3797dcez/++KO8f+XKFblG+d7dd98t17CeJUuW5JrTc+bMwT///FPEM1UoFAqFomgoF6ZKTvXq1bF8+XLL6++//16ERApnmquB/vOkpCTceuut2LJlC66//voyaXN55Omnny7W9998800R+kuKXbt2oVu3bnluQ0Xx9ddfx+LFixEUFIT7778fGzduxA033JDL5eSxxx7DK6+8gr59++LDDz/E7Nmz8cILL8jnqampmDZtmigNGhR2+b2VK1fi1KlTePzxx/Hnn38WOONOQdi3bx+6du2KsoBKF5Wj//3vf7ne37lzZ4H38fnnnyM0NNTu5wcPHsRnn32GVq1a5Xr/+eefx8033yzKy8KFC/H+++/LvElPT5f/P/30k3yuQaVm9OjR6NGjR7kJPvbz8yvrJigUCoWilFEKRBWDAgeFkM2bN8sqqzWxsbEiNDIAMi8OHTqEt99+W7b18fERQbVx48YiZM2YMUMEHq5kUwjjKjVXTblvZmahME7BiKusW7dulaDLV199FX369EFMTIxYQC5fvizHeeKJJ2SllqvO+/fvR3h4OO666y488MADlrZw3/S1PnHiBBITE0UgHj58eK72ch8LFixAnTp1ZFV37NixIihGRESI0Mbj1q5dW17zWNxOD49BgZaPiRMnonXr1jh8+LD4anM1mbEv9ti+fTsCAgIQGBgoryl4U8Bkmxkw+u6770r8zJQpUyR/Pt3gqOTdfvvt4hNOYZ5+4fShf+mllyTQlP3P7SlUsr+5cq3nqaeeku9SEK5Zs6a8N3PmzKuETJ4DY3aoPJBHH300lwWCbWNfU6DX2LBhgygNpEmTJqhRo4Z83qVLF5vnz/6212e25hHHnmPDfqPiY63IUvgeOXKkKLv8r80Frsb/8ccf0pdsC/uK86Uwx2ZbKbgTKkTMqsYx/++//+Q9WnO+/PJLsRJY8/XXX0tf7NmzRyxOtObZgsrX0aNHZQ7weqAiyDnI8+H4z507V7YbMWKEKAbaHOL3nnvuOTl/DY5np06dZBGA10VZwzldkoqkQqFQKMon6k5fivx5MAwz1h5HcnrJ1oQwwQRvN2f8b2BzDG4bUujvN2vWTNxWNAWCgjxdlphmsmHDhrIazSqy9tBcWL744gsRtCkwUbChu8YPP/wgq+0UjCn0vPXWW6JAEAppq1atkudUIPj6l19+wdq1a0W4pQLB7elKM3DgQBHq6QaitYWCHle6bUGhc9GiRYiOjhbBu2fPngXqCx6Pgh6FUCooXPnNDyoq/F7btm3FLYgr8nm5StEFRltNp6DP7/z666+SrYvKEvuE+/T19RVBMCEhQYTBFi1aSJ+y/6j47dixQ/p6/Pjx+O2330S5atmyJb755hubx6VATAGTygiPSyXqmWeeybUNBdhq1aqJsE2BmnODypzWbubyp/uTHipZFJT1Vi4K6oXtMyqX9uYR28o+s2UF47lQyKeiQIGeAjjHnePHfqWixX7lfKC1pbDHpusQefLJJy3jyr6k4q25JOmtdnqoBFDpopJBa44tqEh37NhR+pzKHRVEbs+2ck7w+9u2bZOUpjwPwn7gw5ZLFPuJ510eFAiFQqFQVA2UAlGKfL3pNE5HJpfKviOQjq82nSmSAkH0WU40YWjevHniY613cbEFV8ApeGqr0ITCPlfJP/jgA1mhXrNmjfjGJyfnnD+FJj2acEhBWUv9SNepkydPyuqs5kt++vRped6hQwe7baLFgVlcKJB17tw514p5XvB4XIXW9l+QmA+uilMYJRTg84tFoCVAczdiu9gPFBQJ3YMIBWEtRoHWCip3tOZQqaJgS+GY/UXLiTVUKGxZIKgU0tJEgdnLy0tW4pctWybH0mD/UljlNu3atROffwqwFLAp1HJOWMOV8MImD7DVZ3nNo7xgLI+W6YzKH/uJCgxX5rlqT2gBoyLBuVySx9YUKHsWCCoNtOIEBwfb/T4tUV999ZXlNRU8Ku29e/cWKwnHicoFXc+owNDtMC9oOeO5lAc4N6joa5mVKnvWKYVCoaiqKAWiFJlwfWN8tKb0LBAT+jYq0vfpJqGtsurhKjwFTgqQmlBtCy1draZ4UGigUMXVbgpwXBHt3r27uF9MnjzZ8j0KFHrc3Nzkv17I4L7paqS5UFEwpsDF1Xfr7+vRu01wH7Yq/mqCr96fn9txe2so0HFFnlgHH2vt1tpuS6C2Fq616rzWqTypOLE91vvQKppTgaDLE2MX+J8KgObiomHPAkG3G44BBWhCpYRCtl6BoPWhXr16ojwQxr9Q+eDx2LZ7773Xsi0tVRRmaX1gzv769evL+3yut0jYwlaf5TWP8utPfT9x7Kkscf4++OCD8j5d2XgcnkNJHpvwXG1ZIGh9oDJGiwddy+j+RCscjzl06FDLdoyNoHLN/tTPV44FFaMBAwZYxsJe4Lsenn95yv6mVyAUCoVCUTlRCkQpQutAUS0EpZVTngIghSQK+LbgiidX88eMGXNVgKdGo0aNxE+egbz0Naff+XfffSer1RSO6OpBoY2xBFo2p4LCdvH7XIXlvujjrrk95cVff/0l/v4U2iicUfCiEKzBVX36pVNo5LYavXr1EqGcAcbMvkPhj/1Kl5eSgoK2lk2HgjqtDhS6KTAyfoGVe3neXHGm4kIXl3Xr1knmJ7rOMAMS3VO4jRbbwfHPr2+5Ok/feo6Vt7e3WFvoGqSH1hB+znPneDOjD//zeHqXGFpmNKGZq/q0VNHSQ+sQV781BaQw2JtHdM/K6/xo3aKyQEGV1i4GfdPdh0I75wuta88++6woX7bifPI7tj3YJip19nz8tT62jpuhS53+XKhMUkmnVYrKCN3+brrpJlHkeB6M/+A4aWORH5xbmjKnUCgUCsW1QCkQlRyu4OtXOhnjwBVre8oHYxco8DDlqOY6oXc9oiBOAYfCLa0U9JHnqildN2g1oNBJn30KU4xd4Od6N6b8YGAwhWau2FJZoiCvraDnBVfx77zzTnFdeeONNywByxr0/WdsBl2j9FlsKLBT0KNvOZULCvUlXcSKwiCFRK7mM16AMQbjxo2T8aD7FpU1us5MnTpVxorv00WGQjkz2tBHni5GXGXmNpoQz37iOFGQtwX7n+5NPC77h9YIzfrA92lpoGsP+4S+9hTI2T66oeUF28s+5jhzHnEeUCnVXHvsxQdYw+/YmkeE7jxsB12vqBhau+wwVoXt5fEYvM8HFUQqEBTWKbjzvO3FZuR1bHtQyGd8DuMNilIsktcV3ZzYfroo0XWJCgmDoDXLCTMzcVypWPLcqWjkB60c9hQlhUKhUChKAwdTfv4XiqvQMrJoftWEQgj9qimgl2YV1apU1bagaCu9etecgkLBngI4BXkKwBQ6ucJd0i4hTLXJFfK8fOOv5djSWkTrCxXGkoSByXm5v1VFtHFl4gAqbVSuSwoqnnRHZMB4eUjjynNlLAmhEl+YeXyt7qElCceV7nJMCGHLbVJRMVHjWnlRY1t4+dYeygKhqNLQ9YMr/BR0KADQSlEa/uS0rDBwlhaP8gAtSUzBWpKkpKTYTV1a1aFgTVe0klQeNEWQ2bjKg/KgUCgUiqqDskAUAWWBUJQGyrpUOalK46osEIrKgFqlrryosc0bZYFQKBQKRaUkw2BEREIariSmIzndgJSMLKRkGJCckSWfaVBVo77GjHUBnq4I8HJFdR83hPi5w9mp/GStUigUioqIcmFSKBQKRYnBwP+SICY5AwcuxOFERCJOXknCqStJuBCTgujk/Gt15IWTo4MoEXUCPNAw2AutQnzRMsQXLUJ8RdlQKBQKRf6ou6VCoVAoSgS6aNlLc5sfhiyjWBLeWXkUG0/H4Vx0SqmMSpbRhIuxqfLYfsYc8K3RoqYPejcJRu+mwejaMBCeruonUqFQKGyh7o4KhUKhKBPSDVlISDUgMS0TScmpiEnOxLqjV3Ap0XYNkJq+7gjxd5f/NXzd4evuDE83Z3i6Oomw7+rsKK5LWmpBo9Ek+45NyRSLRnh8Gi7GpeBCTCriU3MKSmocC0+Ux7dbzsLVyRF9m1XDsA61MKBldaVMKBQKhQ6lQCgUCoWixIKomY6VMBWrraDxtMwsxKVkIiEtU55bvmsR+wE3Z0e0re2HDnX90bq2L5pU80Gjal7wKkEXIx7/ZEQijoQl4sjlBPx3KQ6HLydAS2yekWXEuqMR8qCCcsd1tfFgr4ZoXK1kM2kpFApFRUQpEJUYVqi95ZZbpMiWljOexdJYyIz/tQrDrIGg/fgzpzkr+LKYFbPG6D9ngTNW0dXqLtiDFajJk08+KYXRClpYTINFzlgAj2lBo6KipEIvYbpKFvMqSZg/nz7bgwYNKrHaEj///LMUAbv11luLtJ/Dhw9LJW2ev4eHhxRt4xjMmTNHUq9ef/31+e5j06ZNOHnypBQrKwlY+Iz9zyJoeTFq1CgpHKjlxGfVbRa0s+5zFkzTCgSyKB4rR2uwyjUrRFsXUUtKSpJibCwuyCrOJYl+zOzN5dKCVcC5fxbx43kx/zYLw913332WYo724PXKauO81tnnfN6zZ8+rtuM1yCJ2pG/fvpK6mLDatVZAr1mzZjJe7AdWgWfqYVbrZoFIvs/6DKziHh0dLfVS7MF5S/S1HKgocMU/PiUTaQbb1gUXJ0d4uzvjw7vao1OjGmJNKE183V3QqX6gPDTiUjKw7XQ0Np+KwvqjEYhISDefU0YWfth+Xh79mlfDhOsbo3uj/AtcKhQKRWVFKRCVHAolegGeAgmFyhUrVlhyx+s/p5BGIWrLli0WQVX/+caNG/H000+LgEplIj8KqzwQVsomO3bsEEEzPyGqqLDgFfuBxeRKkn379uWpYOUHhTtWnO7SpQu2bt0qQuHvv/8uAiUL0rGidF55/6koUvBl9eqSgmPx+OOP57kNq11funRJ5kZetTQOHjwoCqq1Msgq4iy299NPP+WqFq7BGh0JCQkoDYo7ZsWBld1Zbft///tfrvd37tyZ73e/+OILEfy//PJLnD59Gvfff79cu3pYsZtK16pVq0RZZhXvf//9V+pSUMGdP3++7IPX3cyZM0VxoMJIpZeV5ffv3y+V3Hktc0GClcj5P78K8Yw1SEjLELckxjbYgm5Hvh7OIsybDBkITXJBw3oBpa482MPf0xWD2obII+u2Nth5NgbL91/C7wcuixJBNhyPlEfPxkF45sYmaBaofkYVCkXVQ935qhgUQimgbd68WVY5rYmNjRWBg6uOtujevbsI3lz5pEWChdEuX74sgZNcRebqph5aMI4fPy6CHwWTEydOiOIxefJk2Q8Vkk8++US25Qp7XFzcVYKUHq7QtmvXDuHh4fj1119F+P/jjz+kLRS4KfiwLRR2KBgx5zNX7bmKb13Ei9/VBFVaX7jivWHDBtSoUUO+pwmU9vbFzykEM28yLQVcyeXKLQXC7du3WwQsCtQLFy4UawoFsokTJ8prrsTroUBHwfrBBx+UcyEtW7ZEWFiYPKfS0KlTJ2kP92MP9ge305SMBQsWyJhTqOfqNMeMq8i2xo7CIq0f7A83Nzd5vnr1arEIPfLII6LMPfTQQ1cdk9tx/9wXP+fYskIyFR5r2F/sCyo5tKxwXvj6+kqf8bjPPfecKBl6Vq5cKSvjnE/5wdV49nHr1q3FmsOV8I8++kisbocOHZJK2ZzjrB7M/mYf6MfM2sLDtowcOVKUa/5/4IEHLPPVeu5xXto6dkhIiBz7nXfeyXVstpVzgbDvONacV1oubgrxVA7Y99bQokDlkvNSO29bGZD4OdtI1yLOW77m2HKu1qpVS5QHwiKA48aNk/E4evQohgwZIu936NBBxv/ChQvShwMHDpQ5pbca6TGaTMgwmBAenogszR9Ih5erM/w8XeDn4SJWB420rPJVI4PZmno0DpLHS4Na4ufd5zH/33O4FJcqn/97OloevRsHYOqwNmhSw7esm6xQKBTXDpOi0Bw8eFAeelJTU01HjhyR/xYOLTWZPu1sMn3YosQeRnk0Nxm530PL8mznhQsXTP369bvq/aeeesr09ddfy/NmzZqZhg0bZhoyZIipW7duprvvvtu0fPlyy7b8XM+PP/5ouvnmmy37+eabb+T5+fPnTb169TJFRkaaZs2aJQ/99998803TW2+9Jc9DQ0NNQ4cONSUnJ5t69Ohhio+Pl/dvvfVW09mzZy3H2r59u2nMmDG5js/9bd26VZ5v3rzZ9MQTT5gyMzNNRqPRNGXKFNP3339vOnXqlJyHNhZffPGF6d13372qH3jex44dk+erVq0y3XvvvaaMjAxTdHS0nMuSJUvy3BfbsnjxYnm+YMEC0/jx4+X5Cy+8IN/Vnj/yyCOmrKwsU1RUlKldu3amxMREm+PFc9DORePVV181vfjii5bX69atMz366KOmvHjsscdM69evl+f//fef6cYbbzTFxcVJGyZMmCD9am/sJk6caPnun3/+aVq6dKk85zzifMqLbdu2SVvZV+xDzql///031zZsA/vp0KFDcp4ffPCB6bnnnsu1DfuO/aZx6dIl08iRI2W/nA9sf16wnc2bN7dco5x777zzjowt5512Hrt37zYNHz78qjHTw3nMecK5ynG76aab5Dq3N/dsHfvtt982paSk2D22/nrRt8P62suLsWPHmlq2bGmZj9ZwfrZt29bUtWtX0+OPPy5t5nXXs2dP09GjRy3taN26tTy/5557TD///LM85xi2aNHCtGfPHnnN7XkuegxZRlN0UprpZHiC6ezFcHkcvBBrOpD9OB6eYLqSkGZKz8yyew4276HljExDlmnJngumvu//bar/wgrLo+nLf5re/+uoKTk9s6ybqCgBDAaDKTY2Vv4rKhdqbAsv39pDWSBKk62zgKgTJbpLyxpdYhjw7yyg9e1F2o/eP1lzM5o3b574n9MnXQ/jGDQXFfrAaxYDrtjSrYRwZZK+7gcOHLDrAvPBBx/I8/r164tLDhkwYIC4VrRq1UpWZRs0aJBv2zt27Cj/6d7D1eERI0ZYXGA03/tz586JPz4xGAyWmA893IYrsJq7CFdWaR0JDAy0WFK2bdtmd1/cVottGD58OGbMmGGzvTxHrs5zdZv7pl85V65tWSBo3SBcJeZKOVetaf3QYP+fP38+z/6xPq/+/ftbVqZnz54t/5966imbY8dtuQLN1Wg+bLkSafPB2gJB6xQf2vy68847xcJElysN9oPmi6/Fu+QV16JZuRi3o5+z+cG+ZiyBZsXZvXs3zp49K32nd8WipYQuX3nB+BhPT095zj5hn9LSYGvu8dqxPjbjObjaX5Rjk4iICLsWCFrLCOcILRC0+vD60OKeyLFjx+S6pnWN1xitf7Se0Nrw3nvvSd+yn2ld0dwSaY3j/KCVjtcCLUXaZ/o5yMJt0cnpkuGILku8P/lk36QcHRzE0hDo5QoPl8pRhZsF6O64rg6Gtq+FZXsvYca6E5LZKSPLhM83nJb33ry9DW5saR4XhUKhqKwoBaI06fU0sOEtID2pxHZpdggwAW4+cOj5VJH2QYGCgoY1dM2gaxOFBwqv+cUx0N3E+jUFbFtogr3GmTNnJDiaAhgVC/pvFzTQmO5CmpDNNtPlh9CtikIKhSX6lFMQ1oI6bQlq+pz1fK4/H+19HsPevvgdTSiiAGbP719/7tpx6IfOhx6+z+Nx/3QP4bHoKqJ3vWK78oovIJorkXZsveAWGRkpgqC9seN4UOCn4E+Fkv+pHOixNx/oV0+3qc6dO1v6xHrc6TrFQFwtCNfWNtbzhA8qEYSCK8eCipatYGENuuhY9zmPRWVJaz/fo3CeVzwJ0fc3v8O+tTf36IJn69jcvijHJlQS7PU5FVy6IFFpqVOnDq677joJntcrEIyJ4JhqLnW8zujSxrZz34sXL5b36XKlKcecC59//rm0j/32yy+/yP71c/B8dIoERuuzJ5n7ywGuTg6oE+RTbis+p2elIyI5AldSrsgjKjUKSZlJSMxIlP9JGUlIzkxGlikLRpNRHs6OzvBy8bI8/Nz88NSw6th61IC1h4zISPPD5fg0PDx/N4Z3rI3Xh7aSmAqFQqGojCgFojShdaCIFgK7ZAsjInQVYUWPPuwUCrSVYmsYVMnVdAZK0iqQF9wHBQuuZNI/es+ePeLXzTgHa+jXTT92rmRypXTs2LGyIsqVb8ZHMBsMg7MLA4/PoFuunHJ1mkI3M0gxTuK7777Do48+iuDgYPE7p9Lx8ssv5/o+rR0M+mVcAwUsBpFSqKevOIUu+n7ntS8K+uvWrZMV9KVLl6JXr16yX46N5pdeFLgiTCGf7bEOVGff0YKTF/xcOy/65jOmhFl+uIrOdtMyYG/saBGgYMzYhaZNm8r5FvScuKLOFWsqPeybZcuWSfYePWwDYx8o6HJ1nvMxLwsEz4FzQx/Dw2xQRcnC1KhRI7H+0CLAfqEViGP722+/5Xl+a9askT5h7ALnLK04jGmwNfdsxRXld2x7sE0U5PMqzLZ27VpR+hkLQYWEFistw5IGrzlaGthv7H/GezBGg8oN41WYgYrnw/ZQWSYMpqb1iQkVqGDQOsa4KNZU2HXwBKqH1EFcao5Szn35e7ggyMsFqUlZlhiCsiYlMwVHY47iZOxJhCaE4mz8WYTGhyIsOewqxac4uDUE3E3OMKRXgzEtBCvOheCfzxvgjZsHY2g7cxY5hUKhqEwoBaKSw+BHzeWEK4lMxUjB1J47AYVGpsqk4Jhf9iOuCr/22muW1VG6PGguFdZQgOW2w4YNk2NToNGEY2Z0oRDLINnCQHcbBmhTiNOCnrmyTYGLwhJXWHnOXI2lYmTr+1zBpZBKNyMGrlJgoqJAgU8TvvLa1/r160WQrFatmiXtaO/evcWqUtjz0dyP2J8cJwr6GlRQKFDSbcyekGp9XgwGphJIZY2KEdtPdxQKhlSObI0dz5Wr+wwIZz9q58pj0o2GbjP2FBgG3XIVm/ONx+JYaO5mVEzoNkXXHgYVc790+2E/cy4UBc21p6CZvqg40/2O1jUqiRSmtRSm+jHThGgNuuyw/6hA8HicA3zYmnt0bbJ37I8//liuK+tj24OKFa8XJgvQXKisYXYkKoVDhw6V8eI1yfbq+4bnxlSxtDywHVQG+BmtCLQuTZgwQRQ+KpVa2l8qJAxmZwA3M7m9MvVNnLqShNTMLOzauQNdepld/JwdHcVFKcjbVQKiaVlx8S2bYOJMYyZOxJ7A4ajD+C/qPxyKOoQz8WfEenAtMDkY4OQeJg/e2Vim7qU9X+H9/U0xqs0A3Nl8GGp61bwmbVEoFIrSxoGBEKV+lEqGliFF83MmFAroY03BrzC+2oVFc4ewdk2piPBcGFfBVXAKQlyVvpZwxZw+6VoWnMKiZZi6VmNLIY+uZ4ydyC+Na0G2u5bQHYoWGiqoJQmFZ727XXmkrK7Z4vYN200XpSsJ6blqN0x5ejymvPURmtStAX8PV3FZKgkKew/NMmbhQOQBbAvbhr0Re3Ew8iDSssxF7PLC28UbDXwboLZPbVT3rI4anjUQ7BEMX1df+Lj6yOfert7ipkS3JUcHVrd2gMFoQIohxeLeFJ0WjUuJlxAaE4orGVfM1o2EUHF7soUDHNGnTm/c2+Je9KjVo8LfvyszvF7plsiYobxcLBUVDzW2hZdv7aEsEIoyg/74XO2lxeNaKw+EAc20ONC1ynrVuTxCIZwWgvyUAn7O1X4G1nLlvzwQEBAglp6ShDEiDGpWlHzfJKUbJDjYun7Drk3rMXTIEHRpXrdMBGC6JP17+V9suLABmy9uRmx6rN1tnRyc0DSgKVoHtUaroFZo6NdQHkHuQUVqu6uTKzxdPEXZsCeMMLbidNxpHI85jt+ObcHeK7sB5xjZ1gQjNl3cJI9mAc3wQOsHcEuDW+DilH89HYVCoShvKAtEEVAWCEVpUJmsS4qKOa6sGE3FISGNDjg5eLo6o7qPG3zcnfM8B54rXdMIg8kLc772LBCxabFYf369PHaE7RBXJVuEeIWgY/WOaBvcFm2C26BFYAu4O5eeNbggq5mX41LxyMLVOJH8D1z8d8HRJT7X53V96uJ/nf6H/vX6l/u5UZVQq9SVFzW2eaMsEAqFQqEoMJlZRkQkpCE2OSNXaLG7ixNq+rrnqzjoSU5Olv/6bFSFJT49Hn+f/xurQ1dje9h2m25BHs4e6F27N/rW6YtuNbshxDsE5Y1a/h5YOn4Y3l3VDN9t7Q9nn8NwDdoEJ48L8vmFxAt4ZuMz6FKzC17p9goa++dkz1IoFIryjHJhUigUiioKLQbRSRmISEyTOg4aDIiu4euOAE+Xa7YyzmDnNEMa3tz2JlZeXCkxB9YwXuGGujfIg0K3m1PRlZRrhauzI14b2gqd6gdg8mIXpIS2gZNHKHxrrYfB9ZRssyt8F+764y6Mbzce49qMU25NCoWi3KMUCIVCoaiCJKVlSt0Cui1pODk4oJqPG4K93UosODo/MrIyxE0pJjEGMWkxIkzrlQe6Jt3c4GaJF2AsQ0V19RnSLgT1Aj3x0PxdiExsiNjTDyOo2gkE1luD8JRL4pr1xf4vsCZ0Dd7o+QbaVWtX1k1WKBQKuygFQqFQKKoQrB4dFp8qGZb0BHi6oqafu1gfroW1gUXbqDgwo5H2ngazIw2sPxC3NLwF7YLbVVilwZq2dfzw2+O98NDcXTgekYjoyOZwSGuBO/oewvKzP4mr1qm4UxizcgzGthqLp657SoK3FQqForyhFAiFQqGoAhhNJkQlpuNKYro81/B0dUItPw94upX+z0FmVqZYGZg9iWlY9TBVKoOep/acim51u8HJsXKmz6zt74GFj3THmG934EhYAqISTVizpRM+HT0Qnx58Wwrfscjd/CPzJf7jvb7vqdgIhUJR7lAKRCWGVYtZpI1Fr7T6AHXr1pVKx/yv1TJgsTTNH5oZRVhRl1WJmVVE/zmLg7H4G4uAsXCWPVhpWCsex6JiBS30pbFjxw6p/FyvXu4Krixs51tKRapY4Ti/wnlk06ZNOHnypKXgVnF56aWXJDUri3/lBatGs14F03OywJyt9KwsNscigSwoxkrNHCc+Z3E3Forjd1lFm4XiOKbcBwsNamPLCuJMFcuq3CUFqxyzOBvT5dqbI6UFi6dx/yyExv5gXmsWxCvoWBNWgmaBuFGjRkkhNmuYKpfnSFi4jwXYWA16ypQplm3i4uLk/8aNG5GUlCTX1qlTp2RV/a233pKq0HPmzJE0t9xHaZCcbsDF2FSk6+o5sAhcTT83sTyU5go/7yusnxCTGoOEjISrPmca0wC3ALjDHRfjLqJhjYaVVnnQYPG9n8Z3w+hvzEpEWHwapi9NwE+PzMfyswvx2b7PxKXpeOxxjFoxCi92fRF3NsspLKlQKBRljVIgKjkUnvQCPAUnCr8rVqyw1BPQf04Bh8Leli1bLMKM/nMKQU8//bQI0lol6bworPKgwWq5BRXySoKdO3fmuw0VMAq+P/74Y4kdl8oSi9nlxe7du/Hdd99h8eLFUj14+PDhMjbNmjWzbHPmzBnMnDlTqhazovTUqVOl/1hBm0ItX3fp0gVbt26V17///rsoGxqzZ8+W/ZWk8kD27duXp7JZmvz9999S3+N///tfocda4/PPP0doaKjNz6hI/vTTT/jtt9/kWqKiwf5l5Wdt3jOl6V133YVJkybJa1aiDgkJkWrcvIZYDXzJkiWi1IwePVr6vySL/2UZjQiPT0d0sjm1qrbSz8rR1X3dRIkoTcUhLj1OCq6lG3KOr7XBx81HFAcWbKMCwzSuVQl/T1fMe6gLRnz5Ly7EpIpL01MLD2D+gw+gV61eeGHTCzgdf1pqS7yx7Q2prP1yt5eVS5NCoSgXKAWiikFBhULP5s2bZTXWmtjYWKSmpsLf39/m97t37y4VnGmp4Kr1K6+8gsuXL8tK97PPPou+ffvarNackJAgq7Jc5abiMXnyZNkPFZJPPvlEtuUqLFdrKYDZIyoqyuYxKdjv378f4eHhIrD1799fhGaeDwWyF154QYrVrV+/Hp999pkILDzHDz74QF4TrjBzFd8ef/zxBzp16mQR8BYsWCB9SaG+Z8+e0q7o6Gib7WPbpk+fLkIV01vy+erVq8UC8Mgjj4iw/9BDD8nn+tVgbrdq1SoRLplrnlCZsB4f9nHHjh1FeSAsIvb111/j/vvvFyWCygNp2bIlwsLCcn33woULch75KXu0aE2cOFFWzGnVYK5+CsK0ZnHVnVWPOXfYTq6ysw8oxG/fvh1BQUFXra4fPHgQI0eOFKWV/x944AHLPGBfc36x3bTScFwLc2y2VaswznHgOVOR0XJcc6y//PJL6Xtr2G/sxz179ki/2ivIxqraVMQ5nznPeB7WFjKOFdvMOc2xXbNmjcxBwnlRs2ZNec45xbnFMeD8LQkY48A6BEzRquHh6oQ6/p7yv7Tw8vaS+IaTcSevyqTEys4B7gGiOKgCakB1H3cseKibKBExyRnYeioa768+jpcHt8SiWxfho90fYdHxRdJ3S04ukT79rP9n0ocKhUJRligFohRhDvPP939uCRIsMUyAl6sXnujwBAY2GFjor3OlmSvWmgJBNyMWV6FwziJOFIDbt29v87sUsLkNqzjTEkEBb9y4cSKE3nPPPbIaa4tZs2aJsMT/586dE9eSRYsWiQBP5YKCF79LRSAiIkKEQrZL4+6775b90xXH3jEpQP7555/ynO+//PLL4rZy/vx5EU4pvHH/7733nig2VAB4HAqcFKDzUh4IBT+6DxF+j9/n6jGFVgq3XNnmfmy1j6v93IaKDStfHzhwAI899phYFSiwUsDmdrYKjlHB8vPzE+UvPj5e2jB27NhcbaNL0rvvvitCO4Xfv/76S8aTyo3WZvLxxx9jwIABub5LQZr9w2rR+UEFkG437FcqN7TGcIWfff3FF1+gTp06Ing/99xz0p88XwrutlxzqDxRyKeiQIGebkZUwKhs0ZLCfuAKPefJDTfcUOhjc84QzjW6cxHrsbanNFEZYH+ybz788EO7/UHlgfvjNrxmNHc/rR7CDz/8IHOE8NyoKHB7jg/nDa81DfYTz7u4CoQhy4hLcbmDpB0dHCQta7B36bkrUVlgfANdlazrNni4eEgFaB9XHzg6lH6QdkWiYbAXvrqvE+75ejsMRhO+3nQGbWr7YVj7Wnil+yuSjYkWCFoiDkYexIN/PYivB34tgeYKhUJRVigFohSZd2gezsafLZ2dpwLzDs8rkgJB9JVeNSGK/u8Udiis6dEE+czMTPHV1ywGXFmmQE+4EkwBioKxPVcdKgukfv364kJDKMxyhb1Vq1YiUDVo0EAUCHsuTHkds0OHDhbBjSvNej90+rJzFZrHmzBhgihPFG579epV4D6j4lOrVi15TmWB36dgr7kAkaeeespm+7gt28PVbD5uvvnmq/Z/++2327RAUKng8b766is5jzFjxogSSGuQBpU6CtNUUji2jH3RVtsJ98FVeio+9NvXC8pUjPSCbF5Q0aEAr1kz6F7F6sFU0vSuWLQu0eUrLwYNGgRPT095zj7hOdLSQMvEiBEjLC5AVCQ4J0vy2ITzzJ4FggoB43CCg4Pz3Q+tQ7Sg0MpFa5bmrkTrBOOJqNCx/zUlnedMZZHuTmy3ZpHgtcVzKQ4JqZkS62Aw5lgdfNxdUNvfHa7OTqWmOESlRonywPmrhwpDsEcwPF3M46ywTZcGgXh9aCu8uvywvH7h14NoU8sXjap5Y2jjoRJE/cT6JxCZGiluTfevuh/fDPwGdXzqqC5VKBRlglIgSpEH2zyIz/Z/VmoWiAdam10+CsuxY8csq7N6uApN1yauvFLY1LC3SmstLPA1BVxbUAjUQwsIg6QpKFKxOH36tM0g1cIckwHChCvaXOnVt5vCYrVq1SRgmX7x//zzjxyXwiotAQWBq/l0h9HORy/oR0ZGymq0vfbxPOnfTpctKmr8T+VAjz0LBIVYWkwYjEwolFI50CsQFLQpXGvWGFpbtEB5CtPPPPOMBFHTauLt7W35Hv3w6V6j7Ts/9NWF2UaeH/ubx9L6m++xv/Pz5Wd/6vuJfcvz5zyk2xWhqxyPQ9e2kjw2oWBva25Tqdq2bZtYPGgxo+JJxZXtpZKnQTcpKgRUXNn2oUOHWtymyLp168SFTIMWHm6nBZRTeeWY0DJB5Yif6fuksLEOl+PSEJuSozg5OTpIJWR/j9IpBscsSoxviE6NzpWC1cXkInENQd5BklVJUTDGdK+PAxfj8euei0jNzMKzP+/Hr4/1lLS6rH8xf9B8jF8zHpeSLuFi0kWMWzMOPwz+QRS0vDBlZcGUlgZjWpr5f3o6TJmZMPG+SUWTnxuNltfc3vKfn8lrE81YcOB93NEx+78THJjyl/+dneDg5g5HTw84enjA0dNT/juUYDyPQqEoXygFohShdaCoFgJ7UECyJWQWFK7qU7jSC5966OrBIF2uctMqkBfcB7MDae46dB+hiwgFL2vonkHXHbp4UPCiC86GDRtkhZ4uTBTo6RKVHwU5pmbJoHBI6wlXqrlvHo9KCoVCCqgU6LTVX/YnBX1NQbAFLSeXLl2SbDl0U+KKP91juKJMNxq6CtlrHzMeUTDmajV95xlMqx2X45kXXJ2n4M8+4/jTmmMdGEz3LX5Oaw7Hl+PMVXHCrFtUbuhGZR34vnfv3mIHOTdq1Ehcq3bt2iX9wvgF+v5Tmcnr/KjksE/Ydo4NrTgMMOb4sO20pDCGhAqTrXid/I5tj/zGmgoWkwhoaFnH9MoDYdwD3aWWLVsmc4B937lzZ/mM40TlVHtNOC6MlaGbHd3R+DmVXs11jNcF51hRCsLR6pChi3XwpdUhwKNUajrw3Ght4Gq4PhUr70cS25DuAocshwpRJbo8wf5787Y22HsuFmeikkWZ+HT9STzdsw6yYqIRFBWNr93GYe7eT5EaFwXPtPP4feVtuDGwOxyT02BMSEAWY9OSk2FMp7KQLkoDMnPX+7imUCnOViicfH3g5OcPpwB/OPkHwMmf//3hXC0YLiEhcA6pBZca1eFQgOQcCoWi7FEKRCWHfuaaCxJXa+nqQkHSnvJB4ZaCEgXc/LIg0e2FPuraKi5dd7QgXmsoaHPbYcOGybEZh6AJs3S3obBdkFXwgh6T1gUGUX/77bciMNLtigIcBW8qEzw2BVRuQ2666SZpG33QNbcaa+iGxJVp+vNTuaLAzhgH9isDYumWxNVoW+2j5YPpU+kaQ8FV88mnYEw3GrrNWKet1aDFhK4tHBcK41zppiBKOLZa0C+FbaYbpXDMVW5uxwxCFKY57vpYCMYAsF+4X+vAd821p6AZtNiv7F9arZhJh/2nxQ3QusGx4NjyPPTQZYf9RwWCx2O6YT4YuEwFgudKwZ3ZjejaVNhj26MgY50XWp/T4sM5wD6n5YBt1QLBqVxwnDnH9FYpxnBwfjD1K/ufgeCa1YFWDnuKki2MRhPCE9IQlZSeq5J0iL8HAjxLx+qQlJGE8ORw8cfXkIQEbv6o5lFNgqRj0mNK/LiVFQr4mZcvI/PS5ez/lzDrTCiOHziBoJQ4+P+ehBNWgejmZQGNGKRgJcotBgOMVGoSE2GIiMh/ewcHOFerJgqFa4P6cG3YCK6NGsKtUSO41KsHR2XRUCjKDQ4ma58LRb5ovuWaPzah8EJ/bApq+viCkqa4FojyBM+FcRVcraeLDbMklWfoCkTXLwb1lmSqzeKMLQVnxl3o3ZJKAlpU9G5siqL3eUHGtbBzKyXDIKk/9XUdvNycUTfAo1RiHZiGNTwlXBQIPX5ufhLMq1VLFutEjFmBYKKFwtyjrtU9tCThuNLNjlZPazdNDboHGcLCkH42FBlnz5ofoaFIDz0Lw+XcGdGKC1fvHb294eDhDkc395z/7m65X3Pxhm5HrLfh5Hj1fydnq/cd5TyQZYSJVifdfxizYDJkma0eKakwpmqPlJzXKSliHTGlpBT95Bwd4VqvHtxbtYRby5Zwb9VKHs4FSP5QGuOqqJiosS28fGsPZYFQlBmMG+CqNFfWy7vyQCjYUXBkELKtQm7XGgprDDYvaeWBfvn2UpdWdUqrzxkXQytVfsoDj0+LA2s7sFqxlmGppp87grxKPsMSYxsiUyIl1kG/1sSsSjU9a6rgaOv+SktD+smTSDt6FOnHjiHtyFGknThRJME52d0bV1y9Ee/qjTqNaqNpi3pwDgyCk78fnHx9EWqMwpuHZiDe1YAUd2Dy9a/h9ta57RPlDcZfZMXFISs21vw/JgaGyEhkXg5DZng4MsPCRNnie1d/2SiKFx9YucrytnNIiCgSHu3awaNjB3i0bStuUwqFopJbIOguYe1CQehCQ3/1o0ePitmf2WO4okUXAX0KS7qPMPMJ02FyxYB+0HQR0AJISX77KCzKAqEoDSqTdUlR8uPKeg6MdUhMy/Fp92RdhwBPuLuU/CppQnqCuCuxIrIGXZRqeNWAn6ufzXOpShYICsMphw4hftcumE6dQsbx40g/c1YCjwuCo58f3Bo0gEvdunCpVcv8qJ39PyQE/0Vn4LbPt8q2vu7O2PhcP6lgrWfZyWV47d/X5LmLowvm3jIX7avZTsFd0RSxjHPnkXH2DNLPnEHGmbPIOHMG6adPw5SeuyjhVTg5wb1FC3h07CgKhWfHjtKnhUGtUlde1NhWIgsEMwIxuwozluh/bGg6pB8xg13pe07/ceaH53/6U2tpHpn/nXnVmTmIdQbob02XGAZTcjWvIPtQKBSK8gyVBros6dOzVvdxQ3Vfd7FAlCQZWRmiOLAYnAbvzazjwIw/TnRrqWKIu+X580g9eBCp+w/I/7RjxwoUoOxSpw7cmjWDa8MGcGvYEK7ZD6eAgDwVrPaenhhxXR0s2XsRCWkGzFx7Am/e3ibXNsObDsfRmKNYeGyhKHrPbngWvwz9Jd/MTOUdR3d3uDdvJg89zBRFhSLtyBGkHz2KtMNHxNrDwHELWVlIO3xYHrE//CBvUUnz6t4dXj26w7N7dzgHBl7rU1IoKh1lrkAwew4z5lSvfnVRHLqKMNh12rRpEpDIAEvm4mcAI4V/+g0z4wqrGmu1C2bOnClZW5jhhYGkzIiT1z4UCoWivGJkWtqENEQm5qy6Ojs5ol6AB7zdSzZbDd2VmJKV2ZX0hmmmZA3xCoGbc9XJqkTrAhWFlN27zMrCgYPicpMnLi5wa9JEVr/dW/LREm7Nm4u7UVF5/pbmWHUoDCkZWfhxxzlJ9dq8prkivcZzXZ7DidgT2BOxR8bu9X9fl2rVldGK6cDEBM2ayQPZWdEYm5EReg6p+/cjdd8++Z9+6hS1Psv3Mi9cQBwfixfLa7cWLXIUis6d4VjANNYKhaIcKRDMuEKh3hZMv8nMJvp0i0yTyYJazL/OqrssGsb8+hqsaMwMOUzrSAUiv30UpFCUQqFQXGsYIH0hJkWER31ROAZKU4koSVINqVJfgMHSenelml414evqWyhhtKTjQ66pwrBzpzxSDxyAKZ9ihK6NGsG9XTugWVP4d+kCz+bNS7zuAauHP96vCT5YfVxKMcz6+yQ+H507XoyuSx9e/yFG/D5C0utuurgJvxz/BaNajEJVgAHebpKpqSH87xgu72UlJCD1wEFRKFJ27RKlgrUvNBifwkfMvHmSapbxE149e8K7T2+4t8lt5VEoFOXYAsE86EzVSP9X5kFnYS/GRTB1Iyvu6tEsFSzupKV2ZO546220z/LbR3EUCH1+ez7nqp32KC20favkWZUPNbaVk6KMq1ZROiv7OxTga/q6IcjLjZkuS+z6p9WBVaT50BPoHohqntXg5GB2VyrM8fSB4IX5nnbv1Kp2lyY8Dn3qUzZvQfKWzUjdszdPhcHR3x8e7dvBvW07uLdrC/e2bcWyoPlTu/j4wMhsPaXQ7vu718OcLWcRk5yBlf+F4WR4vFSo1hPgGoA3eryBJzc8Ka8/3P0hOlXvhIZ+DVEl8fKCR88e8qCzEjNBpe7di5TtO5CyYzvSjxzNsVAYDPIZH1GffSaxKZ49esChUye43tgfbnZSkysqJtr9pbTvMVWBMlUgmK+eFYlZmIt58blyxSJLzAk/d+5cCaqzzkqiVaNl5V3mjye2tmFxKZLfPooKg7f5w6FBd6pr9ePHYxcHFkNjIPnatWslXScDz1k8riDs3LlT4k6YNYbB6szXz6w0ZQGLj9EdTcu9X1hat26Nw4cPozxR3LHVCpLRwsYaFPnx8ccfS3xQO66kFhNeD5xPn376aYG2Z00N1iShG6K+PYyH4rVE10Rb2aAYx8Q6IrQ+cnGAaVV5jR85ckTqejA1MBcVGBdFi6S+wjk/40IF7zNFKdpmDyZyoCW0U6dOud7Xri+tFkt+UKS5kpiB6OQcYdbVyRF1/N3h7uIIo65wmwarxzOui0FvjB3jNclzy28s0rLScDzsOD6e/jEiwiJgMppw94N3Y9Rto+Du5A4wayfMx2NMGceK0EWUBfRIYfucMW8Msmb9D2u0RRiOK79T0jCVaMbuPUjftk0eWXZqixCn2rXhel1HuHbsCNd27eS1ZoXhFSo5lVjfwGiU3xh+VtQq4gXhvi4h+GTjOZF5P//7BKYObnrVNh18O2B4g+FYFrpMxvaFTS/gq75fiSVJAaAdlb92cH9kPIzx8Ujfuw8Zu3YhffduZJ0/b+kifpb011/AX38h8a234NysKdy6d5cH5wLdqBQVl2t1zVbk/ilov5TplUC3IlbVZXYSLesGhdGTJ09izpw58h4Fcz2a0M8CUNp3uI0+awe3YYVXkt8+igo7mD/WGpyQdIniuZRm3mhtRY/HL6qPqzY5tKwwfF3QNuu/y8xWZQmza2ltKSrlKcd3SYytZnVjYb78zo3WPxaS69ixI0qCpKQkERDzOy4FRSqgjEOiAqBtT8WBCh2FVQqZrNpN4dvPzy/XMVgzhAUCmzdvjkmTJklRPFowKbwyzS4L/fE5Y6i4rb4gHqtyDxgwQCpYlyR0laRrpPW5a9eX9shrXJlliYHSyRk5hcP8PVxQy98DTo72v8d4rwkTJkgdCb0SaW8sJDVraqTEOyz4agEaNWuEl957CQ7JDhg/ejyG9RsGr+Acn3Au1DArHivJcyw4Lrxvs5ihdZ9zXB9//HHJisfYM56vvs/5YJX7QYMGISgoKFe7tPsRE1yUVBYmQ1QUkjZsRNL69Ujdvj2XG4sepgL1pD98ly7w6NwFLrVyW7XtoSk9XPwqzXvJg32a4rvtl5CYZsDKI1F4+dY2krbXmue7P4/9MftxNuEsTsSfwIrLK3Bfy/tKrV0VFv5216kDDBsqL5k+NuXff5G8eQtStm2DMSmn3onhxEl5JC/4XmIlPLp3g1fv3vIobHYnRdlzra7ZikphlKoyV6VtVR9mNeQtW7ZIViVWUtajvWblXVowtPf0VXz5msIFyW8fxUE/+bQfP+1R2hTkOOwfrg5SUIyOjpZgda6U6ttZkDZzLChA0HLDFIva9vfdd5/krud4scKzZvWZOHGiCAwUYGiloADCcX7//fdlnGbPno3ff/9d+qxXr16ymkl3MqbW/fvvv2UfFApp7aBQwhVyruBu3boVcXFxIpDQDY3VfAlXPu+66y6bbaegw0rFFEgYM0OrA1esNSsUz0NbpWW1bJLfedmDK+PTp0+XmxP3z+cUmDgXGeujtYdjwCrfPA77k+mF2Ue0wrE/+DlXbGkpoiDNFWVa5Yi9vnv44YdFIOP5sI94rq+//rokD7AHV/5Zg4PfoZJNiwX7nIo998dVZJ4TFUUqyEzLyf1xdZvJCX744QfZtk6dOjK2/D5re9AFkULklClTrjomhflTp05JhWz2D/tBm3u0KLFCNAVPXpuMXdq4caO0UePff/+Vat8tWrSQ15wLnOfcB1dOWMOCz7lI4O/vn2te79mzRx5sQ17znWP05ZdfygLB6dOn5R4yY8YM2R+vBVpJ+CPENnLln+9R8WFbZs2ahZYtW1r2xePwHH788UfpY84h1j7h91kxm9Wn2f7+Awbi5rvux4G9u7Dkx7nw9PJGxMVzqBVSUxJD2Dv2pk2bsH79erHmEAr5nLtcgLE1FlQemK2Hc3TG/Bm4rvt1aNWmFRr5NYJ7kLsch/eKatWqWc6B/coH+5T/eXxeT/b6nEoe4XzZu3fvVX3Oiu0cd1ZO16PdV4q7CJNx8RIS161F4tp14paiD6a1HMvFBR6dO8G7T1949+0D18aNi3zf1tpbmsKIv5cT7u5SF99sPosMgxG/7L6IJ/pfbYXwdvLGW73fwr0r75U6IbMPzsbgRoOl2J8ijzGsUwfuI0cicORIUTKT9+1H7N9/I3PXTqQfPmLZjtmektf/LQ/C7FreN/aHT/8b4d6mdaUMXK+MXItrtipQpgoELQ2jRo2SH+tu3bpZ3qdARbcm/hCzMquWQ53wB5dCF4Ul/sBTi+QPvqZAJCQkiFmdq1yEK5h57aM0SfjrL0TO+jR3irkSwQRHL29Ue+op+N5ys92t9u3bJ9okhT0KDBTQKXBQiC4oFHpeeOEFcSnjavErr7xy1TZcMaaQTAGYAhePR0Gbwu3TTz8tz3/77TdZNb7xxhtlpXnJkiUiKFJo5/hwBTMvONbcL92uKFBRwdBWXO0pD/q8xjw+hXm2h8ILU/3mh73zsgddPCggUuGhIHfgwIF8V7opdLEvqGw99NBDIgwSKn1UkDhudC/j6jZTEtvrOwrkXNGncK8pKXkpD9wvBXYKvYQCLhUltpvXEFf0b7rpJhHy2N8U2letWiUr/jz+Rx99JG3huPBzKgVUFjnHqOQQ/cq/Hgr/VB7YTuuaMHqlnkoilSPNMsMfZ7qsURnlufOc6TL00ksvyeccJ6ZspoJICyTHSw+Fe1okClLpmWNHl5tatWqJEM65MGTIEEkTTcsGBWUqclSuuF/OLwrueuVBg8ohU01TsbvzzjvlnkRFmW467Mvw+GRMeupJ+NdqCHcPD5w8cghLlv+OJg3qFejYFNCpbFHh4/gR67FYumwprqRckSBbDfbn4JsGS8pPPuf58nrnvVcP77G8bmg1YL+y/VrhR+s+1+ZsXn3Otj7//PNXKRDFgak9eb+l0sD0nrZwrlkT3jdcD+++feHVrVuFy7wztkcDiYVgMPX3289hwvWN4WIjmL5ttba4s9mdWHxiMZIzkzFzz0y80+edMmlzRUSUy07XwdCsKXyemwxTXBySt25FksTLbJEieBrpJ07II/rL2XCuUQPe/fuJMuHVvZu52rdCUYkpUwWC2ZcoYFHQ4Woag6n5o89VT/6wUsCn0EmhlQLfwYMHxUTObQl/lKgocCWPq121a9eWH1muGA4cOFC2YarWvPZRmkTP+U4C9UqHK4j+7rs8FQj+0FPYoHDIWBMKXFwtLAxcOacgpwWiU5jlir4eKn9csabbBIPfKdxR2KUrjSZwcxWZD/quMzuW5mLG8aHwlZ8CoX1O4ZOCWGGgsKNl+qIlg3OsIAqErfPKCyoOXOml3z4fXGnNDyo/FN4oeHLeUnEgXKXWstlQ6WJWMbrI2es7zv/C+PRzfDQzLqEwT+GWSjavQwqibAv92ak8EAqQFEwZ68Bz5fZsG681uh6yn/RKmz0LBJF4IYMRxiwjkuPTYcwyITPdgIToNERdTITJCKTEZ8CU4YTI8+ZYI/ZTYmwKNm7YiG+/nC/X+1vvTcPnn36Bhx8aJz7/8+bOQ/sO7UXhpeJLpYrQosP2afeF/KD1icoD4dhQuaJSQcvO/fffXyhfUVpVaKmhcsS+5H5oTeNCx9btO5BlNCE9LRXnz55C+/Yd0KxZU1EeSuLYZNe+XaIo6gV7RwdHmTtaalaOC++dvFfqM9YRKre8H1PhpMLI2BRaOHjv5X2VSg1jaNjnVChorcyrzzludJ0rLnQ7SVi5EvF//mkOirWTKclnwAD43HRThV8hrhvoiQEta2DNkQhEJKRj1aFwDGtv24Xm6euexppzaxCfHo8VZ1ZgTMsxaB1c8IUjRQ7OQUHwGzZMHkwZy9oTSZs3IemffyS9r4YhIgJxCxfJw8nPD94DboTvLYOUMqGotJSpAsEfQK6QcTWTq1Rc+WQKVm21m/AHjSttFFxpVufKlT7gl/63dAGgsEI3CwrN/HHjCi3RlJC89lFaBD38MCJnzSo1C0TQQw/luRVXs+nywCBjrk5qQmNh4A+u/jvWwoUm1K9evVqsGxQy6BpDH2j9jzVXWylM2AoS1lxQ9MexDqLUuxwVFn2beQxrwUtzxbA+tq3z4iq8vTZQoGcgLV1WqKTyP1fatePye9bnpTehsg3aa32btfft9R3RlIqCwrboj2FdJZkCnq3j8Tx4TAqJFIDZN7Q0cfVdH0DMgF69smDINCIzLQvxkakwZGYhK9OIxOg0eT85zhyTFBRYTdwLqUyQ6JgodGyfs0/uJ8AvCC2at0aN4FowpJvQt2d/LF3+C/bvPgRnR1eEBDREzOUkDB5wGz7++BMYMrLg5OIo1hKu4hd0/mjzTesrLUECz1Fb1edqPe9Z+aEfY+6H96ZMgwH3PfIkOvY0K8aJ8fGoHeyL8LPHc/n/F+fY/F5EcgQ863rio3kfWfZHdxYWhdP6gkoWLVlUBGyl1KbrFOe1ZrHlvYQWFVoS2E9aAD6tyfrFBd5/bPU5511RgxcNsbFIWLUKCX+uROqePTa3YRpOs9IwAG52UoRXVB7s1VAUCPL9tlC7CoSfmx8ea/8Y3t35rrx+f9f7mHfLvAqtQJWXlLEebdvIo9rEici8ckVibBL/Xo+Ubdstmbyy4uMRv2SpPESZuGlAjjKhgrAVlYQyj4FgGlVtxcoW/HHSfN3t/ThTgNGyghRlH6UFrQN5WQiKgiZMWAt8tqBfNH/AKdjSPYSr2PqaGQWBLjz0iaaPN12f6OZgDQUQWjaoBHI1nKvvbCdXcCl89O7dWwTxFStWiJDx+eefy38KUlzZ1AJlaVmgAElBhYGhej9sW7APCpJJi37YdIXhCjBXXdkePVxx54qwtmqqxSvYOi+uvnNV3hbjx48XZY1BplzB1uY1989VXK4m/8XsHjrYnwxi5oo9z5/fo7JCgZeuIXR/obsLM19xBdpW39nqF02xsAfbpGW8oUsQhUEqR7QsUDBlfAaFRK5+0yLIlXNaJWgloYBLyxItOY8++qgciwHttNhoxzUaTchINcgjPdUgGX7yo0fX3vj9z6UYOOBmJCYmYM/+XZg44Qm4uDmJGzvb261rD3w7fzbCI8JQs0YItu/cimZNWqBOrboIvxKG0PNn0aBeQ6zfsB7Nm7RATFgyHJ0csGPbLoy+Z7RYPZyciya8tm/fXhYqOEfoAsmFCrpbckzY5/ayr7HfuArPmBaO80uvTkWjVh2x8vdlaNu1F0xZWXjnxSfxzNNP5UrMUNBj24LZk9Iz03OlZ/Vw8UBt79pwc8pRjugKuGzZMrk/2rveqEjTckglkYknOB95L6DFi3OS7n1UPLT3NehapbmS6uFCQmGsZVz1Td62DXG//oqkdettBkIzrarv4MHwvXlgpQ5s7d4oEE2re+PklSTsCo3F6cgkNLZK6aoxsvlILDq2CKEJodh7ZS/Wn1+PAfXtu2AqCo9L9eoIGDVSHlwoTNqyFYlr1iBxwwaYsq39okz8ukQeTsHB8BsyBH7Db5eCgwpFRabMFQhF6UH3GLobUGiluxcFVb2bSUGgoMoAUrom8LktH2+6svA4Q4cOldVFuvpQyKZLBIO4+Z+vKVAz4JbCJr9DYZOZXOirzX1TGGVcA5XKzp07SwBxXlBgpeJIYTivVK4UeuknTyWCChQFcD1Usrhaesstt4hLnbaSbu+87EEBi65xdKnj9uwzQlcaBrLSzYiuUPTj16BiR2sYV/vZz5pVgEI9FREqLFRMqMjxYavvuA899GFnICvjFbhPe7B6O12XqDQwrSbd3OhuQ0GdweN0NWF8A613VGQo3NKiRWsHx4rCIZUJKn8MdqfiVyukFkbddTc+/egbm8GrggNXoR1FMaAw7xvsASdnB9wxaijOh5/CwxPHiDA++bn/oUnr+hbXMyp0ra9rgrffeQtTpj8nq/BNmzbDc89PhpuLB954dTreePsVqd7s7+ePlya/Jt+lRePCxfPwcQ9E9KUkOa6bpwtOnjmGzz7/1JKeND8oYNPXn777bB/Pl8HjWl8yaJ1znHNXD8eS1yLH7LmXX0NslituvHU4Ll08jxcevQ8OJiNuHTJElDLruJCCHFsPxy4hPQGerp6oFlINrzz2Ct7+8m2p6aDFOujh+PI9vUsfXUp5zTBwn1YkKty0NtHywPsI3dX4GecBFQu2icfl/GYbNZgJzLpGjxaDRte3/MgMD0fcUvMqbualS1d9zsBn3yGDRSBzLcGUvOUZjtWoLnUx/U+zy9Yvuy/gpUFX35O1AnOTOk3CUxuektcz9sxA3zp94epUssXuFGYYU0MFlg9jWhqSNm1C4l+rkbhxY44yERWFmPnz5cFq2H633Qa/obfCWRW0VVRAHEyqIlmh4Sqi5qahQfcpbXWwpFIQFtcCocid9ai8oWV70hIIaGNLawPHNr+Yi+LCGAcGubJ/igOtDWnJmUhNyJAVfmt4Lq4eTnBxcxbh3dm18GlqKZjSXbEgVY5p7aCbVGZ6FjLSssR1ytZtju99OOsdTHtjGty8XOCYR6rU4iAuXFlZuJKUieikHIuZt5sz6gV6llhV6cysTKkmzcBZDVobavvUhodz4VzcCGNK9ApBQc9VU/ypUNgaZyqrtJzwcz1yDz1zBtWvXEHywoWSUhNWbnROgYFmoeu2YXBj5ecyvgdqheSoXF+rjC6cQ93eXg+D0YRgbzdse6m/zWBqbTzGrRmHneE75fXkzpNxf2tzHI3i2owri9glbdqMhBUrkERlwtqC5uQEn/79ETD6Hnh2717mc7qyUxbXbEWXb+2hLBAK+eG2XpXXYBAxV6XLM3QRYeE0WzB7TEnCFVemErWGiiNX5ysSjDOiK4nmolRYqCykJmYgNSnzKhclB0cHuHu6wM3TGS7uxVN2KQRx1bsgyoN2bLOy4gxPX/P3zcqEAekpBom/IFciIzCg381IjElDUmy6tNXd20WUnJL8EWeQ9Hmp75Dj4kTBr6afOxxL6DgMlr2cdFnStGoEegSihmcNCZguLHTds1XEryDkNU68Vmnxs1YeTAYDMmNixKc8/I1pcAwLy/mQtSH69Ib/iDvh0+8GOBQgi1ZlJsjbTYKp/zocjqikdGw8HombWtlOSc55TKVh1IpRktb12/++xV3N7oKnS9FrICkKh6OHh8UykRUXJzE8cb/9lhOATYF2LdMOr4Vrw4YIuOce+N0xHE4FvN8pFGWFskAUAWWBUFRl6xIVh5T4dFEcrKGy4OHtCjcPZxHkyyMMrKYikZaSaVEm9DDomufg7uUMx2JaB9IysxAanSy5+wnHtba/OwK9cuIQikOWMQvhKeGIS8vJTMbKw4x18Hb1Lvfz3ZSaCkNMjPiJp2dl4XxkJJymvyUKBGMZ/EbcAf/hw8ttXENZrWZuOHYFD87bJc+pTHx7f263OWte3Pwi/jxjjl975rpn8HDbh69JOysq12Jc08+cRfzy5YhfuhSGyMhcnzl6e8P/zjsRMGYMXOvULpXjV1WUBSJvlAVCoVCUOFlZVBzMFgfr+AZ3Lxd4+LrCxbX8m4SdXelG5QRPP1cYMoxIS8oUZUKzolCpSIpNQ3KcA9y8nEWZoFWisCSlZeJcTIpYIOS4jo6oH+QJL7eSMfymGlJxMfEiMrLMmV+07Ds1vWqKElGeFQdjQoJUiaZ7hzUeXTqj+pAhUunXQbkY2KRvs2qo6euO8IQ0bDh+RSwRtGrZ45F2j2DlmZVihVhwZAHua3WfioUoY9waNUT1Z59BtSceR+L69Yj98Sek7DIrhayEHTNvHmIWLIDPwIEIvH8sPDt2LOsmKxS5KBnnW4VCUWlhrQYK1DGXksVlSVMeuJru6euKoNreEghdEZQHPWw/FQOfIHcE8xyCPHIpChR0qVzEhidLNqfUpAyJ9ygIMcnpOBuVozy4OzuhSXWvElEe2K7o1GicjT9rUR7opkSrQx2fOmWqPLBtDG7nwzruhNmmDNHRSD9xEhkXLuRSHqgoOAUEwLl6ddSaNg3e11+vlIc8cHJ0wO0dzSvTnGNrs1O72oOVxgc2MNfjYDHB1aGrizPMihKEBed8b7kF9b9fgIa/L4c/awNpbnpGIxL/+gvn7hmN0FF3S7FEuvspFOUBpUAoFAqbcEWeRd6iLyUjJSFHIMxRHLzgHeBe5LSo5QmJ2fB2QUBNLwSGeMHDxzWXGxndnli3glmcqEzZChYn7KOw+FRcjE2V1V7i6+6CBlROSiBY2mA04ELiBYQnh1vGw93ZHY38G8Hf3R/lAbp+8KFhzMxEZkSEVOxl8TdTZo7FxNHNHS61a0tAtEu1aipHfiEY1Kam5fnqw+H5bj+6xWjLc6Z3VZQ/3Js1Q8ib09Bk4wYEP/UknLJrr5DUAwdw6ZlncXrgzYj+bi6ydNeYQlEWVPxffoVCUeKkp2TKqjuLvFlWkh0ggnVgLbPiUNz4gPIK3Zt8At0RVMdb/vO1XqmiMkVFIiEqVRQLDVonzsekIDIxJ9MS3UqYaakkwkGYXel03GkkZuQIDkEeQWjo1zBXbYfygjE9HRmXLoniQB9vWiD0Pt6u9RvAtUljOAcESIEuReFoV8cPIX7mjH//nopGQtrVMUl6OlbviOYBzeX5waiDOBx1WHV5OcU5MFAK1TXZ8DdC3n4bbtmFdUnm5cu48v77ONWvP67M/FhiiBSKskDdtRUKhQWmP42LSJGK0fpVdq7OB9UyC9SVweJQEJjWlQpTQE1PeTDOg0qUBlPXUsmKu5KC1JRMnIlKQnyqWYjjZrX8PeRR3Hh4zWUpND5ULBDEydEJ9XzrSbxDUbIslSpGI0xpacg4fRpZsbE58TIODnDy94dbkyZwa9AATj7e5TpZQHmHfTcwO/tSRpZRAqvz2/6eFvdYXi88trDU26goHo6urvC/YzgaLv8N9b6bA6/r+1o+Y5xE9Fdf4dSNAxDxzjti5VMoriXl7JdHcS1gMTkWDyMsdsZqtHnBWgG7d+8u1nEqGgcPHpQCeKVR+8FesbCCpNZk3Qh7FY8LCysHz50717J6nhSThpjLyZLuVIMxAQEhXhIfsHnLJrvjSZ93Fp+79dZbJe2vluqWBfJYiI5F+lgMbvHixZbv/PHHH1Kkjw8W2mPV65KEbjQTJ060+RmL8hUuVsJZ4jyoRNF9S59hitW2EyNT4Z5qgqvJQVKz1g/2woy335AK1Kx+zsJ71n2eF0x3yn4ZPHgwnpr8FC7EXbB85uXihcZ+jREXEYf7779f+pvFCFl5nrzyyivS19qD1aHZ7wsXLsz1PquYsxAj5xP7n/OrOLB4VsbFi1KRV3Lday5vjk5SKIurqK516sCxFOvkVDVubp3jxrTmcP4C5OBGg+Hjaq52vursKsSmxZZq+xQlA+9BXj17ot5XX6HRyj/hf9edrPIqnzGTWcz8BTg94CaEvT5VrkGF4lpQflN1KMoNu3btshQ7qyqcOnUK0dHRKE+w+BYFxZJKK3j48GFzoHBypvj1s2KzBt2TvAPcpDYCf7yioqKkBoY9WLGYlZZXrFiB48ePS/XsTZs2YcmSJTh58iR+//13USbuvfdeqWbOyspU0ChcsyYAKyJ/+umnmDJlCkoKKiTHjh1DSULrC923PP3ckJaUgeT4DEv2JoY9Bhod4OTsBHc4iGJORZRV1qkMaH1ekHazgNviZYuR7JKMt19+GxtWbsDA2wZKNenqntVlTN58802ZD7fffjtOnz4tyunmzZtFYdMraYsWLZLtWOWcRdwIi16yxgsrrXM+jRgxQvqfVdMLizEjA4aICEnFKmj5652c4BwUDOegQBUQXUp0bRgIf08XxKVkYuPxK5I22N3F/v2BBQWHNxkumZgyjBn4/fTvqrBcBcOtUSOEvPkmgidOlFiIuMWLxeJHpT3u558R9+uv8Lv1VgQ/PhGu9eqVdXMVlRilQFRiKNBNnTpVKg5TGG7QoEGhqw5TADx06JAIdrREeHl5yT5jY2Ph6uoqAsd1112HsLAwvPTSSyJo8n1uQ8EwPT1dVqbZBgow3EfdunXtHm/9+vXSRgpI/v7+ImSmpqZi7NixsnpLKJjt3LkT7777rqyI80ELCYXh6dOnSwVFWla4Dx6XK9GPPfaYCFEUYimccYXc0dFRVm8feeQRsQq8//77sg+2mwoEV2TZFq7659U/XFHmsbjS++qrr0of9ejRQ4RGtiszMxPvvPOOFEPTYF/xNQVqMmHCBHnes2dPm8dJSkrCmjVrMGnSJHlNwfi1116TvuHx2PZ69eph9uzZIqxTKOzVq5esMPP4zz//PM6fPy/tZNHAjh07YuHCRXK+Xq7+iLgSjq3bNonzjaOT+cF+1QrxcfzZDx999JHN9rFfOdZczeZ/rRr7kSNHcNNNN8mcIFREOcYUZKdNm2YpKEalgsJuXnBMmZed++R843mw33jMDz/8ENu3b5c5T2sHV9S5/ytXrsjqP/vFGvbfgQMH4OfnJ3OpVq1a0ke25retY9815kFcMmZi0Tef49C+XdKOfn1uxANjxuHRCY9K39555514/fXXRVD/8ssvRZgnNWvWxOXLly1zWkPr89//+h0R6RFISUxBfGw8fHx9UNenLnzdfC3bcu7eeOON8pzXNq1AnLNsJ+G8Z7/MmzdPrj097BtW9q5Rw+wC07dvX1E8aLHRvp8fzAbD2AbxwdZnXHJwhIOrC9xobbA6rqJkYRVz1oH4dc9FKVS49VQUbmxpu6icxp3N7hQFgigFouLiEhKCmq+8jOBHJyBm3nzE/vSTWP9YmE7qS/z5J/zvuAPBEx+DS80cS5VCUVKou3spcmrPFez84wwy0krG5SQHE1zcndFtaCM06VTd7lb79u0TIfmXX34RYYZCOFeFKegWFK5McpWYwiOFPAp+L7/8sgjpFLYeeOABEWzfeOMNqVxLlwoK91QUKIhRcRkzZowIrBSif/rppzxXObkKypVuupgsWLBAlJfGjRvn2UYKPGwjhTEKen/+aS6YRAGNAhvbwFVaCufr1q3DpUuXRMimYM1VW1Zk9vDwkFXZDRs2iECpKSl5KQ9caadQyP6lIMw+oMLB84uJiUGnTp2kryjAsVI2z03frxT6qTRERkaKwkKB3zr1pQaF4xYtWlisD1QMKGgOGDBAzv3bb78VYZLnR6WGq95PPvmknD+FRO6X21EwpgI1dNBwDBs8nFMJQwfdLvuc+OgT8GFWJZfcno0ch1atWqF9+/Z2+4LK2bJly9CnTx8kJCRgxowZ8j6/t2rVKtxxxx3S31u3bkW7du1QvXp1eRAqQV9//bWMRX5w7L7//ntRVG+++WaZj9w/980xowJBIZjjSAWBc96W8kA4PhSkf/zxRxGeaeHh2Nma3/pjc7x47HbXD8a/G9chzZCBT+f9hCBnBzz19JPYun0z3p02A30GdsG3n/6AuCRzkSjO6bvvvlue33XXXTYrpXOcIpIjEJUWhV1bduHTtz5FcLVg3D7g9lzKA6G7mMacOXPk+tQL/xw3zg9WSddDpZb9RzcmDSoY/D7nGRW+vDAZjZKONYuB0UZj7lSswdUkdkR7rbg2bkxUIAirUuenQDDovl21djgYeRAnYk/gWMwxtAhsoYaqguIcFITq/5uEoHEPI+bHHxE7f4HZGmgwIO6XXxD/228IuOduBI0fL66ECkVJoRSIUmTfmnOIDS+eX7F9MrBv7fk8FQj6OHMVnwLSmTNnEBoaWiw/5+TkZKlSqHczocDGFVmu4GsxA127dpUHYyAoJFJ5IBTU84uloMDDVWUKw1yNpVDN/eTFyJEj5T+3pwJB4V0TailIc7W3c+fOolCxnRTeKTDxQSsBfcf53UaNGonyUBjXLipN2io6V6VpWdC44YYb5D8Ff66662F7KGhTaaHiw3ZQ2bMX38CxCwkJkedUAsLDw6WvCJUjPqh4UaikMqRX/mgVoYBMl5Xevfpgwv1PSHal7Cyj4q60YNE3+GfzxlzHZJ8MGjRIBGgqQTymPag4dejQQfzs2VYK3lRU2YZz587JGFGRoRJHC4UGz4XWAioaHK/8oILCfuK84tzmKjuVEloGOB6aQkLLU9OmTe3uh9YFruATCtIff/xxnvNbOzYtOAY3X3j5+CElOQkHdu/AhTMn8cy4ey3HvhRxDo5OOcGOLLxH9zC6ilFB0AKHP/nkk1wWCKZ97dyrM0Y8NEJed+ndBcvWL8Oi2YvwzvR37Fp/qHxRif3hhx9y9mUyXfWeBsfowQcfvCqAmRYYjlWeBeASE7NTseoy/jg4wjk4yCycODrCSze+itKnR+MgODs6wGA0YevpqAJ957bGt4kCQZafWo4WXZUCUdFx8vOTzE2BY8eKRSJm7lxzPFJGhsRIxP6yGIH33Yeghx+SbRWK4qIUiFKk48D6pWqB6HhT3v6NFFopGFGY4wowhTV7K9wFgW4qFLzo764REREh/uwUxvUCCf3eKcjqXSf4eX7H54o/A0f/+ecfUUjoQ05BT/89rjbr0ccEsI3aa/2xtff5Xw/3SyGRaIJ3YfrD3r6Im5s5taa9TDMcE7rtcLWcwmReUGjWzse6r9kfVLKs20PYHgruf/65En+v+QebN28Sa8WCb34278vNSdKyTpr8rDysoSWJK+5UBHgcugRxFV1zxdHPNcYxsF1c8aa1gmPn6ekplgW68BC6mNHVSlvRp1JDZVH7PD+0PtXPJypddNFiwDLhPKcLVV4xLOxPrQ+5D/ZpXvOb8DOmaWWmJe3YzPD64gvPi0VCf2w3t+xAYd04sY5EeooBrh7OFuuDZoFIN6TjfOJ5KQxHl6Vzp8/hputvQpB7kChWzz579djw+LR6UTGmZU+z6BAqQlQ4rd0FOYZ03+P3rGEf2IuvYYB0Zni4ZH7RoxV/c8wO6CSa+5ri2uDt5oz2df2x51wszkQmIzw+DTWz07va4+YGN+O9ne9JHMTKsysxqfMkuDjmjKGi4uLk7S3VrQPuHY2YOXMQ88OP5hiJ1FREf/21WCWqPfO0uWCdshIqioHKwlSK0Dowemp3PPBurxJ73P9OT4yZ3h2jX++Wp/WBcGV9yJAhIvwFBwfLCm1RMvhQqOD36B5BX2tNwKI1gUIwhVRaHDTXIQo0mq9+YdHcMrhCSsWHK8u0CsTFxYnwynZoLiX6jDVk7dq1qF+/vsWK8Ndff4lQSEGVvu50Wenevbu4+LDNXC2mAE9Lja1z1isDtuA5U3DWLB5c8bW1L3tQMKSFwNvb2yJU24PnxfMgHAeuFm/ZskVer169WqwPPDcGMfO82HaeJ9uzdMlvePmFKejasSeefnyyKErRcZHw9veAk4s5Xak96CfP/XPMudJNIdVaedCsLNyOsD/oesb3OBfoEkRhlxYMjhEtJ7RCUHmgMkJ3rOKk8+R5s+8pHPPcOW9olaBAbG8M09LSpC2E/UTLSF7zm+2PTEq3pGklIX4euL53L8lwZH1srU9ZlM7VXbdOY3RAUlwqUhJzCvMlZSThTPwZS1VpBmXPemMWshKypF94XdFiZQ1d4mhpoYVRrzyQPXv2yPy0httzLnHOWUMl1HoesnYDLQ7pp0/nUh4cvbwkHatr7dq5lAdF2dCzcU7BsW1n8rdC+Ln54Ya6N1gqU2+9tLVU26e49rC+SvXJk9Fk7RoEjBkjFa9JVlwcwqe+gbMj7kRKttVWoSgKygJRiaGfNVd2KUhz9ZSuRPm5A9mCrjgMBGUMA60CjG3gKjaFbK6cc990k6HrB1dC+TqvjD15wYBrrsrS9YgrmTwWhR0GwlLYpCJEYUoT2glXuunCw9VpxhVoUKhjECuFVa640tWIbkZ0seH2/JwKC4NurVOr0h2HPvE8D3sxGxSQGZzNuA/ui244tlZ27cGVbQpsel90ezAom7ELVKDY79o48L+vr6+MTZ06dXD06FE5Zwq9/M7tg+9CSlI61q5ei/vGj4SLswtuGnATuvRsDyf3LBHeAwICRPAtLHSFoVLH8aLrFucArUdsH9+jJYICORUdTTFkilG2kwI/YwwYN8EHYf/xPDinKBBrGYPyg/OCrjdUyDgOVJqppLCvateujdGjR8u81MPA840bN4oQzmMxiJrYmt8OTs5ISDMgPdNs4WGaVrqM+Hm42D22hrOLI7wD3STtKwPTO3bojOnvvgZfHz+MGX0fjD5pCE/NcQ1zc3ZDlyZdpC/HjRsnCgRdsbR5pfUNrXLffPONPNcC8QnjPWh5YN/SZdAavq+5wulhXzFDlHbdimUnLk6yKzFYWoNCCAMyHX19bSp9eiuctaVMUXr0aBSET/8+Jc93no3F8I518v3O0MZDseaceTFm3bl1FoVCUblwrlYNNae8gqAHH8CVjz9BQnayivRjx3DuvrHwHTwY1Z+bLEHZCkVhcDAVx6elikL3AMJAS/2KJv3ZKTSVpglfc9mgcKN+nM1++gwWpVCqh7EQXIHlCnJ5hOPIQFYKtwzoplUgv7GlcEvFJr8gV62SdGJM7tSsdJ0pyUJwVOIYvEslpCSh1YmWC71gXFYwLWZoVLIU6iJUHBoEecHTreBrL9q4Ojg4SuxJWlKOFcMII5LcYpDunCr5+Wt715Yicde6b2iN2b9/v4yluCtdvgyjPl6KShNdFYOD86wazXPVlHsq7IW5R12re2hJwnFlHA6tVyWVXrkopGZkoe3U1RIH0aS6N9ZNuj7f76RnpaPPoj5INaTC380fG0ZugLOjWlMsT+NaGqTs2YPwt95C+pGjlvccPDwQ/NhjCHrgfjhkZ8yrrFTmsS0t+dYe6m6hkB9urszbgm4mWrBpSUErA7MOWcMUn3R3KU/QjYX59m1Bn38GXhcFuvvQqsMV+YLGXtDaQV9/Kk32bnx0f0mMTcslpHL1m9mV3LxKdkWYtQdKQ8inVUOfYaisSMkwiPJAoYy4OjmiQbBXnnn284JuTSzI5+LhiPjoVDgaHcE/3/RgmBwMqObtL7EZ17pv+INKNy4qqKxma4iKypWW1cnXF860OlRywaIi4+HqhDa1/bD/QhxOXUlCTHIGAr3yHi83Jzf0rt0ba8+tRVx6HPZf2Y/ONa92lVNULjw7dULDxYsRt2QJImd+LNXiGR8ROWMGEv74HTWnTpVtFIr8UBaIIqAsEIryaF0yZGYhITJN/peW1aGqkJRuwLmoZGRlC9JUGhoGe8HFybFY48pVXwZLGwxZ8MkIgJvBM1fVb1a7LouxykpKEqsDM7ZocCXSpVYtCcosKMoCUXa8vfIovt50Rp5/dV+nXFWq7fHH6T/w8hbzos3YVmPxXJeStSZWVKrKKjXTvUZ++pnUkIAuCYffnSNQ/X//kziKykZVGdtrYYFQUoVCUQlgitDYsBSL8kAFhIqDX7WyEUgrMgmpmWJ50JQHLzdnNK5WNOVBD4Olz8afRWZWJkwORqR4xMPN38mSqSkzPQux4cnITM87eL8kYXxDxsWLyAgNzVEest2VGCRdGOVBUbZ0rp8j7O06mxMjlhd96/SFk4NZiPr7/N/FytKnqHgwnSvjIxr88gvcdYVO439dgjODhyBu2W9qTijsoiQLhaICwx/8pLh0JESlWm70LAQXUNMTHj6uKk6mkMSlZOBcdAqM2X3p4+6ChkFecMrHtSjf/WbEieXBaDKv8rk7u6ORH+uOeCGghqfU4iCMWYmNSJUYltJEgp3j4pB+8qQES2s4enrCrXFjuNSokWesg6L80aWBuR4N2Xs+tkDfYTamzjXMbksXky7iVNzVrqWKyo9Hm9Zo8PMi1JgyRTKsEbo2hb30Es6PvV+ysCkU1qhfCIWigmI0mkRxSInPKdzl7uWCgJpecGaBAkWhoN/4hZgUKehGmGWpfpBnnmlu80PS1yaHIyIlwvIeK0qzGrALc+hmuy4FhHjK/+wvIT4yFalJOe5EJYkxMxOZ588j8+JFSdNKmA+e7kquDRvCsYIEMCtyE+DlirqB5niq4+GJcn8oCP3q9bM833Bhg+rWKgrvAYFj7kWjlSvhO3iQ5X2mej1z+3Bc+fhjSbCgUGgoBUKhqIBkGYyIi0iRwmQa3gFu8AlyL5bAW1WJTEzHxVgqD2YCPV1RL9BTUrYWlSxjFi4kXpA8+xrBHsGo410Hjg65b71OTo7wr+EpCqC+8FxyfHqJuRCI1SE2FhmnTiErMTHn2L5+4q7kXMisSYryR/MavvI/OSMLF2NTC/Sd6+vkZGzaGb6z1NqmqBi41KiO2jNmoO43X8NFK0SZmYno2V/hzLDbkLRF1QxRmFEKhKJcw5SQzNXPWgssjqXI8ZU3ZOTEOzDWwdPXTQmARRCqIxLSEBafI2wFe7uhdoBHsfqScQ6hCaFIzMgR1EO8QlDDq4bd/UrcSpC7uJ5pMO0rH8VVImh1yKDV4dKlHKuDszNc69aFa726liJTJQGrj/OhuPa0DPGxPN95NlrSEOcHUwfX8Kwhzw9GHkSmsXTd5xQVA+8+fdDoj98R9OgEIPv+QMvlhXHjcGnSJGReuVLWTVSUMUqBqMSwaFybNm2kUBlTsbLIFwtUsbBaYWDa0KIUoCMsMKZVUC4sGRkZuO+++6QKNYtSMUf9P//8I59t3rxZ0r7y3PjgcWwxY8YMKRR388034++//7a8v2rVKin6NXDgQHz22We5KvWOHDlSvsMqzCm6XPj8znfffYeS4MKFC0VKWctgaVoetPoOjs7meAc3T1UNuEjuRfFpokBo1PB1R4ife7GUhzRDmgRL87+MkYMj6nrXRYB7/hlNeFxakrz83SzvpSRkFFmJEKtDTAwyTp6EUW918PM3B0lnV20vKdh+piXmQ1kzrj2sU6Ix+deDaPXaX5jw/W7sDrUfVM1xuq7GdfKcNSGOxxy/Jm1VlH/ozlj9mWfQaNlSeHbOSfGbsHIVzgy5FXG//qqCrKswSoGo5LBS7fLly6VY2Z9//inFy3799ddrdnxWeC7q6ilrRbC69IMPPigF1FiJWVN+WH2adRF4bnywgrE169atk5RkK1aswPfff4+33noL8UxbFxkpFav5HvuEtR6okBAqKVr17saNG+PLL7+07G/Tpk24/vr8CzQVhMuXL4sSURgoSOqDpekzH1jTU8U7FAH24aW4VEQm5SjTIX4eokAUR/C1ZFrKXsV1cXSReAdP54KvyPP4Xn5ukkWrOEqEMSMDGefOmdOzZqdoFKtD7ZpwDfaGQ1o0EHceiD4FRB4HIo4A4YeAsIPmR/h/5tcRh4HIY0D0afP2SRFAWjxgSM9VL0JRtny//RxmrjuZ6z2GQaw7egV3zd6GH7afs/vd66qbFQiyN2JvqbZTUfHgYkO97xcg5O234eTvL+9xQSJsyqu48PDDkslNUfVQheRKGYPBkKegoM9DXJhti7qiTwE8ODhYXnN1/4knnpCVfNK8eXMcP35cVvwpSIeFhaFJkyYWoT0zM1OKqlEpCAgIkP3QOsFqzxTi58+fLzmW+Z033nhDBHQWvnrkkUfkeVBQkKUt48ePl8/0cMX/xhtvtLyuWbOmWC9CQ0Pldffu3S2fUTFgAbzffvsNISEhUpSN2+uhxYGWF1ovqEixMvWGDRss+2KlXHL77bdj5cqV0m7mh+Z25K677pI+YuE7Cm6skkulgv1CiweVMVdXV0ydOlX2NXbsWIuVY+nSpdi5cyfeffddsXCwcBzbwdzK06ZNkweViNdee02ez549W5Q8FhLr1auXFIzjcagksTLvkcNHUbd2PfTs1gd/rVuBhMQEfP75Zwio2QyHDh0S5So1NVVyW7Mv2E623dfXVwq+sR1UwhTZykNsKmJScoKU6wR4INArZ9W/KDDWISwpzPLaw9kDdX3rwtnBWa6LwkJXJornSTFpFiWCKVa9/PLOriV1J2JjYQgPtygOxMndES4eaXBIvQAUxD1erxtI8218iZWLXb0Bd1/A3R8mB0fLuRa1nomi8OwKjcFrvx2y+VlWdjD1q78dQouaPuisy9akoVkgyN4rezG29Vg1DIpc8Fr2v2M4vPvdgCvvvof45cvl/eR/t0lsRPVnn0XAvaNV9rYqhFIgSpnFixfb/YyC7w033GB5TaHTnqBRrVo1DBgwoNDHp5BOFx8KFRRIa9eujT59+uT5nVmzZqFFixb45ptvZHWerjvk559/RmxsrAjbXMkfMWKEKBAUUBctWoSffvoJ7u7usmr/+eef44UXXpDz//rrr3MpD4T7zg8K5Vq1ZgojLi4ueOihh0Ro9/Pzw+jRo9G3b185LoX8H3/8Mdf3IyIiUKOG2beXUIkIDw8XIZ3P9e9zW1vb8z1y+PBhtGzZUp5TOerXr5/EZVBJYH9RibAFFRK2jxYO3oD5XY4DFQcqFlQe6JZFawktQ2zbs88+K/1Jawddqqa8MBX1azfG6IdGoGGDxpg/90fM/f5r/LrkV1H06Ar1xRdfoE6dOhInwvc4lwiVIo6FwgyvAwaXxmYrDw5wkMw1/p6uxdpnZGokIlMiLe/5uvqitk9tcV8qTvyCZ3Y8hEWJiE+XshG0UNjNsHTxIozJyZb3HBxNcPHMgpNrPu1gPQAGd1sCvE3ZFgYTYLSzuMH30+LMD4dLgEcA4g3usg9NQVeUPt9uPiPJEzRlwRb8/NstZ20qEE38m8DH1UdidvZd2SdzVil/CluwuFyt996F75DBCHt9KgxhYTClpCDirbeQsGoVQqZPh1ujhqrzqgBKgagiLkyaBYIr57Qi2HL50aBQ/OGHH8rzzp07o252JoYtW7bIqjyFeQoHN910k7y/bds2nDt3DqNGjbJYUrTv2KMgFgjCuI127dpJIPW+ffvw3nvvSRwH/2tQkWCsg1ZdUsOW4EYB3ahbldXgj6W99wmFfM19iRaYDz74QJ7TWsGHvRgRtofWgDvvvFOUjjFjxojieP78ecs227dvx6233ip+41QgqZjRskLlKDgoGA3rNpXtqgVXR+++veDt7yaKIMeJVhHu6/HHH88VeM6xJtddl7OyWNXhfLgQmyq1HjTloV6gB/yKqTyEJYchNi0n736QR5AEpZaUAGatRNCVicKgPtialoas6HAYrsTk8ipycjPCxSMrRydwdgPoTuXiDji5ml/zPy0JebWXO6WykJVhdl1ifEdmCpCRzINnb5MFpEQB8DfvMysTcC563yoKBgOl1x6JEHelvKByseZwuGzPyup6qOh2rN4Rmy5uEksaEwDQ9U6hsId3374SZH3lww8Rt+hneS91716cvf12BD/xBIIeelBcJhWVFzW6pQwFbntYCxh0BSrotkWBK/eDBg3CzJkzrxKyNYHT1rHoekOoONgSyin0UtCfMmWKvGbgsX5/tiiIBYIxEHSpat++vayujxs3Tlbn6VI1b948TJgwIdd5aO3UoDVBr6Qw9qFRo0ayLYVvDW5D9yc+uI1+e80tikrDww8/bOkPfR+dPHlSss7o+4buXhoLFizA3r17xQrBfWjKmYa14sL98PuJ0elwds4OjnYAnF0c4eXjftV3qaxpSqJkFYqIkLEmtAgpspWHmBTEpWbmKA9BnlLroaiwKNzFxIu5Mi3V9KopCkRJQyXCZDSJ8kASY9Lg4OgAd1cDTIlRyIyKQ1aGQ26rg1cWnFhbwi0QcPcD3HwAxyK6QXK+s24FH67mQlMC5zyViJRoIJUF6bLnMhUNxk341AS8gnVWDUVJk5hmyFd50OB23N5agSCaAkFohVAKhCI/WKk+ZOpU+A4ajLBXX5UsTaxoH8kFvbVrxVLh1qiR6shKirqrlzIUNu09rGMaCrNtUeFqd+vWreU54xiOHTsmzxk0rNGjRw8sW7ZMntO/ntYFQt98+ulTaE1ISBB/fwrSjKFYu3atxAQQWjfoUkPY7qL4f2uKCPdFdymydetWNG3aVAKr6aKjxRssWbJE/Pu5gq+H7mFsLy0ibBvPvWfPnnJ+fM73KKhzG25bq1Yt2YemXHC/tDrQbYvva8I4LQ4Mvia0ikyaNElcqhg7QmWE57tmzRr5nJYJxmGwz5955hnpQypF7Bct5oXxGAz0ZgwD3+O5tW3ZEZnphhzf0+o51Yr1UCFi/+zatUte//HHH3j00UeL1N+VFVaVPq9XHhwcpEBccZQH1ng4l3DOojxwn3V86pSK8qDh6etqsUaQhKgUpF86h/Sw3MqDkxvgVisATiFNgRptgID6gId/0ZWH/BQLN2/zMWq2BrzpAuiQY5FIuAREngAycrKZKUoWH3dnFLT0C7fj9rboVKOT5fmeCJUyW1FwvLp1RaPlvyHwwQdp5pf30v77D2eH34GY73/IFYulqDwoC0QlR4uBIFy5b9CgAaZPny6vuYLPOAUqCxSqGWehuRK9+OKLkuaUAqrmjkRrClfbKRAzOJer8xTmGS/BYGxmS6JyQZcdfp/QJYlB1IyDqF+/fqHaTtelu+++WwRiCvoM8mb2JEKXJcYdfPTRRxJfobk0rV+/XhQLZlxiilYqQDx/CvW0XmjnyDgBtpeWEsZxaO5Y3B9dpOgORasHXzPwunfv3pZ28XNaWxjbwJV+Htvb21vayfYyuJyuX3Ql4j5onRk+fLgoIVRS+JyKQlJSkigfPJejR49K//L9Th264I6hIxEZZbae+FX3gKudH30e/5NPPpEgagaV0xJibeFAVVceolOQkKZTHgI94VsM5YE1Hs4lnkM6XXm0NK0+deHNYOJSxMFogJdzLIyOQJqRVgAHJKAmPBEORxjEIuFSPQhOQTXydkcqLegG5RMCZEabXZy0dQNDKhB1wqxc+LBtat2qJKE14aZWNSTbUl4xEE6ODrKdLesDaR3UGq6OrsgwZogFQqEoDI4eHqjxwvPwveVmXH7hRWSEhsKUni6xEYl/r0ett9+GS0iI6tRKhIOppMqcViGYAYgwo44GhTf6ozNjTmm6jUiGlaysMslwwjgACtwUtinQ33vvvSKo0ypQmnAVn0rOk08+me+2bB+FaXtBzeUZKl9xV1JhSNcViMtDeVAUXnloEOQJH/eiKw9UGmh50NK0Ojk6ob5vfcm4VGrXbGYakHwFSGEufxOyDA6IN9SCwcl8n3E0ZsLbIQmutWvBsQQLwhUFnisVZxLo7QaH+AtmZULD2R0IaAC4XN1f1+oeWpJwXLXYq5KyEhc1C9PI2dtyJc6yhjNv8aM9bAZRa9y/6n7JwkQ2jNwgldOrIuVlXCsqxtRUXPnwI8TqEps4+vig5qtT4Dt0aJkG6KuxLbx8aw+1FKQoMLQsMPaAK/qM16CForSVB8JV/IIoD+TMmTNiBahoUPBKiErLpTz411DKQ1ExGk04p1MeHEtAeUjJTMHZBF2NBydzjYe8lIdiwdiCmDNA5FGJMZCicGmOyEhwhntqFBwZpMxzdXRBmme18hewyFiJas0Bb8YRZQsMVCbo0iTKkKKk6NIgEG/e3kZ6mZYGPXzNd/h5XsoDaRPcxvL8RMwJNUCKIlsjqCzUnfMtnLMzG7JuxOXnX0DYiy/myhKnqLiUs18cRXmGgrx1qtTyBt2pKqrykJGqi3mo4QEXN3V5FtXycC4mBYlWyoN3MZQHxjowYJqB08Td2R31fOtJobgSh/ECiWFAeoLlLR42M9kZWZlm4dDBZISXMR5JztVk/nDupMRn5KpgXVbkikWiu5JvCODhB8SeN7szMdA67pw5i5NvLeXSVEKM6V5f6jwwVSuzLdGbiboE3ZbG9W6Yr/JAmgc2tzw/HnscPWv3LKnmKaog3r16odHvyxE+/S0k/PGHvBe//Hek/ncItT+eCfdmzcq6iYpioCQUhaIMofCXFJOO9JTsrE0OgG81d6U8FNNtKZfyEOwF72IoY3HpcbiceBmmbAcRLxcviXmg+1KJkplqVhxY5VlHlsEFmcmOMGXlBCI6BwXJyp5TOt3ezAHKyfHpcHZzgptH2d3WqfwyDucqXDyB4GZAwoUc60NyJGDIMLs0ZQdeKooHlQQ+mKqV2ZYYMG0v5sEWzQJyBLoTscoCoSg+Tn5+qP3B+/C+/nqEv/YajMzSeOYMQkeOQs1XX4XfHcNVzZEKirprKxRlCNNypiblpLz1CXRTMQ8lFPNQEsoDc+JfSrxkUR583XzF8lCiygNdkWLPmdOe6pQHk6MLMrP8kZEAi/Lg4OQM1/r1JRjRwdERrh7OuawOCVGpyDKU04wnVBL86wN+TMqQ7WaTHm9209JqSShKBCoN1XzcCqU8kEZ+jaRyumaBUChKCr9bh6DBkl/hlu0lYEpLQ9grryiXpqqsQDDFpZZmU3F1Tn+Fwh6sKpySoFMegtzh4q4C9oqqPLDOQ+6Yh+IpD1GpUQhLCrO8DnAPQB3vOpJ1qUSg0JwUAVw5AqTqYgIcXWD0DEFGkjsM8Tm+wo7e3nBr0hhOumKJWnpXKhLmXZqQGJ1WrOrXxUHiNAwGedhtA+tCBDXOcV1iKtzYczAWMd2zouRwdXJFA78G8vxs3FlksJ6HQlFCuDVsiAaLFsI/u+is5tJ09q6RSDuhLF4VjUL/ujL15C+//CLpMg8ePGjJZc90kky7ybSdDLBlms+qBM+fVY4vX74sqUL5ujQyDZRlFiZFyZGenImk7KJgxNPfFXDKQlpahhrbolSDjktFoq5uRoi/O5xhQFqaoUj7o+WBD40AtwAEOAVIKuQSuWbTE4HECMCoF9CcAK8gZGW5wnDxSk7udAcHOAcHA/7+SKeQbUPQZrxyakomjFkmZNKi5WyAu9e1rwLNc2U9FOLv75/HPcoF8KoDxF2AyWRERnosIlPi4OjsaSmCqCgbGAdxKu4UDCYDzsafzRUXoVAUF0d3d4S8MRWeXbsg/NXcLk0hb74Jv6G3qk6ubAoEV9ZZPZj5/JnLnoW3Ro0ahcDAQPlxZOq+w4cPS/Gtzz//XHLss85AVUmBRuWB6QfDwsJEiSjNH2g++MOsFIiKiSHTiNTEDGbkFFw9neGW4azGtghwkTs2JQMpGVr2KiDIyxVXUop+34lPj0dyZs7Kv4+rD5xdnZGIxOJfs0YDkBZnjnfQ4+oNk7svjJcuyA+qBScnOAcGwoFCebZgbg9DZhZSE7JjacIhrk2OBa0wVkLwPFkAUrNO53uPyjSaYyGMBnhG7kO9WnXg6KgE1rKkeUBz/Ik/LQXlmKaYSQMUipLEb8gQuLdqhUvPTkL6sWPi0nT5ueeQdug/VJ88GQ5lnJJaUYJ1IFjkqkmTJnjooYfyTd3JPLLz58+XnN5UKKpSnlzNhF/U6sv5wf0mJyfDy8uryihnlYnYiGSsnXMYmenm1eWmXWqgy5AGImipsS0cLJr1wepjWHskQl47Ozpg2m1t0K1RUJGrS3954EusCTVXESfj2o7DsCbDirS/XNdsQhy8Ty6D4+7vclsdQjoAfSYjI9MHEe++i4xTpy0fed9wPYKfeBJOXjaCku2w7bfTOLMvUp436hCMHsOb4FrCe59W1f6WW26Bc0FSyx75HU5/TYZzRgIcGFsydjnQIKdwY3mmMuaUn394Pj7cnVOMki57/er2w/2t70fH6h1RFaiM41peMaanI3zaNMQvWWp5z7NzZ8nSJJbXEkaNbcnVgSiwAsFKuS1btkRhOHLkCFq1aoWq3MEljZr8FZek2HQseX+3/CcN2gZh0KNt4ehk9gVXY1u4Og8vLj2IX3ZftCgPX47pJCkri4LBaMCUrVPw5xnzyisz50/tORV3NL0DxSXr7Fbgj6fhFHMy502fWsDAN4E2I5CwejXCXpliyY3u4OaGGq+8DP+77iq0lZExNT9N3Y70FLPr1h2Tr0NIE39cSwVi8eLFlkWnAikQZM0U4N9Pzc9ZN+Kxf8Wdq7xT2a7Zn4/9jOk7pl/1vpODk6QwntJ9CkY2H4nKTmUb1/KOuD7+shjh06cDmWYrqnP16qgz6xN4dOhQosdSY1sGheQKqzyQyqg8KBRFISPNgBWfH7AoD9Xq+WDguDYW5UFROOXhld8OWZQHFsr6bPR1RVYeGCj6v43/sygPzELzXt/3iq88pMaaFYf5g3OUBwYO93gCeGIXTC1vR8T7H+DSM89alAdXBhn+8jMCRo4skosiA6q7DWtkef3PohMw6tK/llsGvAE06md+nhQO/PGU2T9Ncc3YG7EXb+14y+ZnWaYsyUQ2fft07LuyT42KokThvS5g1Eg0+H6BpfCc4coVhN43FrGLfi6zpBCKvCmU9LJy5UocO3bM8jo1NRXvvfcehg8fLjEPq1atKszuFIoqI/CumXMY0ReTLNmWhjzeDi5uamWrsPCH5PXfD2PhzvMW5WHW3R1xSxtWOy48qYZUPPn3k/j7wt/ymoXhZtwwA4MaDkKxOLkW+KIHsGdeTttDOgKPbARufguG5Aycf3gcYubOtXzuO3QoGv66GO7NixcD0LpvbQTX9ZbnnHOHN5deTFaJQdel4bMBz2yrw7EVwL7vy7pVVYoFRxbkm2GMny84vOCatUlRtaC1oeGSX8WFScjMRPjUqQibMkVcnRQVVIF48cUX8dxzz4krk8YTTzyB3377Dd26dUNAQIB8XhljHhSK4rBj+Rmc+y9anrt5OmPok+3h5Vf2FYMrovLwxh9H8P32c/Ka8cEzRrbHkHYhRdpfSmYKnlj/BP69/K+89nD2wOc3fo5+9bJXwosC6zgsfwL48U5zUTi229UbKddPhfGhNUBIe6T+9x/OjrgTKTt2mL/j7Iwar72KWu+/B0cvLxQXBk5ff0+OErLrz7OWKuflGp+awLBsNyay6kUgOicmRFF6pBnSsOHCBrE05AU/p7LN7RWK0oBxD/XmfofA+8da3mN8xLmxY2GINMd3KSqQAnH69Gn8+eef+Omnn8TaQE6dOoV///0Xs2bNEuVixowZeOmllzBnzpzSbrNCUWE4viMce1ebBV4HRwfcPL4NAmoWX0isisrDu6uOYd6/ofKa3j0fjWyP2zrULrLyMHH9ROwM3ymvvV288dVNX6FHrR5Fb+TpDcAXPXOvnDfuD+Oj/yKj44Oyyh736684N/peGMLMyoVTtWDUXzAfgaNHl2hWtZqN/NC0c3V5npqYib1rzHPwWmSja9GihTz4vNC0GAJcd7/5OTNh/f6kcmW6BiRlJkmMQ0HgdtxeoSgtmIGpxksvodaHH8LB3ZwBLO3AQZwdOQppR46oji8nFCjCbdq0afD09MSiRYvkQS5evCjBRUuXLpUHYXagc+fOiSIxYMAAqQmhUFRVIkITsOH7HJe/3nc1Qd2WgWXaporKrPWn8NWmM/Kccvb7I9pheMc6RdoXU7ROXDcRe6/sldc+Lj6iPLStVsSkCOlJwNpXAWZY0nD1FlclEYaNRpiioxHxxhuI/8UcYEw8rrtOMo24VDcL+iVNt9sa4/S+SKkNcWDdBbTpWwfeAaVr+aLS0LFjMTP13Pw2cPYfIDYUOLcVOPgL0D6n8JSi5KECTfekgigR3I7bKxTXono1i2deeGyiLLrwEXrvGNR67134DhyoBqCMKdAS0YgRI+Dh4YHHH39c3Jb4SEtLw+233255zceQIUOkCBCfd+3atfRbr1CUU5Lj0rHqy4PIMph/kFv1CkHbG4om8FZ1vtl0BjPX5VQpnX57G9zVuW6R9pWUkYRH1z6aozy4+uCbgd8UXXk4tw34smdu5aFhX2DiNqDTA6LtZEZEIHri47mUh4B770X9eXNLTXkgftU80Pb6OpbaIztXmBWwco+bNzD4w9wZmtISyrJFlR7WeWCqVmZbygt+3r9uf1UXQnHNcG/RAg1/+Rke7dvLa1NqKi499TSiZs9WwdUVQYGgJYEB05999hnOnDmDhQsXStG4MWPGoHbt2lJ5mZaHmTNnolevXvIe058pFFWRrCwjVn9zCMnx5nz/IU380Pee5qrwXxH4Yfs5vLUyJ+5qypCWuLdb/SKNS2JGIiasm4D9kfvlta+rL74d+C1aB7cu/M6yDMDfbwHzBgNx2e5BLp5mwfe+5YB/PXmL8Q7nR92NzEOHLClaQ955BzVfnQKHa1BxufPgBnD1MBuaj/0bhuhLSaXuakZLNB/FypzS9CagRXZF2uQrwJaZJdZGhW3GthqbrwWCn49tneObrlBcC5yrVUO9BfPhO2yo5b3Ijz/B5eeeV8HV5V2BYNEyVpfes2cPxo8fjx9//BEvv/yy+LmSdevWSYE5WileffXV0m6zQlGu2fHbGYSdjpfndBm55ZG2cHJW6VoLy9K9F/HqcrPgTSbd1Azj+uSkKC0MCRkJmLB2Ag5GHpTX/m7+mHPzHLQKKkKq6ZizwNxbgE3vA5rAVa8H8NhWoOt4+vGYj7lyJc6NuQ9Z2YF/zrVqof5PP8J/+O24Vrh7u6DTLWaFi/L89uWla4VgjvXff/9dHsUupkkXMKdsJWv7F0DchRJpo8I219W4Tuo8sAaKtSWCr/k+P68qxeQU5QtHNzfUeu89VJs0yezHynvsihU4//DDyEpQFsqyoMBSTefOnbF27Vps374dO3fuFOuDRs+ePcUqsWzZMtTIzuGrUFRFzh6IxL615hSjjk4OuPmRNpKbX1E4Vv0XhsmLD1hKAUy4vhGe7F+0qsrx6fF4ZM0j+C/KXCAnwC1ALA8tAs0LIIXiwM/A7D7AxV3m1xS0+k0BHvgTCDQrNyajEZGzPsWlSf+DKTv1oGuHDqj38yJ4tC6CtaOYtOtXB17+5tiH0INRCD9rVm7LPQENgG4TzM+Z9Wf9tLJuUaWHReLmD5ov7kxaSletEjXfrwpF5BTlFyaaCH5kPOp8OgsOnp7yXuruPTh37xhxFVWUUwWCVgbi7+8PFxeXXJ/xPVuBc2vWrCmJNioUFYKEqFSsn5/jbtPzjiao2dCvTNtUEdlw/AqeWrQPxmzlYWyP+njxlhZFcgGj8jB+zXgcjj4srwPdA8Xy0DywkLUW0hOBJeOAZY8AGYk5Au5Dq4HrnzPXMaCLB/1zn52EqC++sHzVd/jtCPx0FpwDyyaA3tnVSVyZNHb+XkFiIUifyYBHdr/99wtwaU9Zt6jSQwvDzH4zsWP0DmwYuUH+87WyPCjKCz4DBqD+/Plwyr6npp88idC770H6aZX2uVwqELQujB07Fps2bYLBYD+nOM3WVBzuueceS3YmhaKyk5VpjntITzFfG406VkO7/ipourBsOx2NR7/fg8wss/ZwZ6c6mDq0dZGUh9i0WIxbMw5HY8xKXZB7EL67+Ts0DWhauB1FHAG+7gf8lxMEjfb3ABM2A3W7WN7iChhdlhJXrza/4eCA6s8/jxpvvilpCcuSlj1D4BtsTod44WgsLp2IRYXAwx+44cWc12tfL8vWVLnA6mCPYBUwrSiXeLRtgwYLf4JLXXNCDWZoYorslH2qUnq5SuNKGAOxYsUKvPnmm0hISBC3pWbNmiEoKEiUhpiYGBw5cgS7d++WAGpmYmKWJoWiKrB16SlcOWdemfat5oH+Y1uqoOlCsu98LMbN34X07MxVLBD33oh2UhitsMSkxYjycDL2pLyu5lEN3978LRr5FTKG4sAi4I9nAEOq+bWbL3DrTKDtnbk2Y7D0xYmPWwodOXp6otZHH8KnX7/ixwKUAIzB6XJrQ6yfd9RS3HD45Osqxhzt/BCw4ysg5jQQutlshajdqaxbpVAoyhjX+vXR4KcfcX7CBKQfOYqs+Hicf/Ah1Jn1Cbz79i3r5lV6ChXZeeutt2L16tWYOnWqpQbEO++8gw8++ECC5hhsTQXjr7/+UsqDospwZn8k/ttw0SKo3TK+DdyyM98oCsaJiEQ8MHcXkjPMwvaNLapj5sgOcCqC8hCXFiduS5ryUN2julgeCqU8ZKaZFYdlE3KUhxptgUc2XqU8JKxaJZYHTXlwqV0b9RctFOWhPNGsa00E1DT7DTPIP+xUHCoETi5Ar6dzXm/LcQ9TKBRVG2Zoqr9gATx7dJfXprQ0XHz8CSRu3FjWTav0OBelUNCgQYPkoVBUdVjvIVexuJFNUa2eSmFcGC7EpOC+OTsQn5opr3s2DsLn914H1yJkrpKA6bWP4ESsuW5EdU+z8lDftxCpX1nA7JexQNiBnPc63gcM/gBw8bC8xTSl0d98i8gZMyzveXTuhDqzyi7eIS9oyek0qAHWzTVXct235jxqNQ1AhaDdSHMQdUoUcHgZMPBNwLdWWbdKoVCUA5y8vVHvq69w6fkXkPjXXzBlZuLik0+hziefwKd/+VrIqUyo3JIKRRExGU1YN+8I0pLNgm+jDtXQuo8SagpDVFI6xn63ExEJ5mxF7ev44euxneHukndBq7xStWoxD3RbKrTycGod8FXfHOXB2R247XPgts9yKw+ZmQh/7fVcyoPf8OGo/9135VJ50GjSubqlGnXof9GIuZxcovunS1STJk3kUaLuUez7zg+an5uyzK5lCoVCod17XF1R+8MP4Dt4sPkNKhFPP43Ev/9WfVRKKAVCoSgi+9adx8Vj5mBULz9X9BtTtExBVZXEtEw8MHcnzkaZhdhG1bww98Gu8HZzLlKF6cfWPmbJtsSAaWZbKrDywHyxW2cBP94FpGWnOWVa1nHrgY45KatJVlISLjw2EXGLc4Kqqz3zNELefuuaFIcrDk5Ojmh/Y91cc7hk9++ELl26yIPPSxT9OOz/0TxmCoVCkY2DszNqvf8efIcM0SkRzyAxO4uoomRRCoRCUQQizydKIKrgAAx4sJUU7VIUjLTMLIxfsBuHLpkLAIX4ueP7h7sh0KvwAnhKZgomrp+Ig1EHc6VqbejXsGA7yEw1xzqsfTWnMFzzIeZ4h5ptcm+anekjecsWec3sSrU+/BDBjz5aYZTHVr1rwc3TrKSd2BGOpFiz9afcw7S5DfqYn0efAi7sLOsWKRSK8qhEvPcufG+9NUeJeOZZZYkoBZQCoVAUksz0LKyZcxjG7FSj1w2shzotyq/bSnnDkGXE04v2YfuZGHnt7+mC7x/uitr+OS5ChVUe9l3ZZ6kw/c3Ab9DYv3HBdpBwGZg7CDj4c857178AjPoBcM9dwyPtyBGEjrob6SfM8RVOfn6oN/c7+N2avdpVQXB1d0abvrXlOefwwQ0lV+GZcSFpaWny4PMSp8O9Oc/3/1Dy+1coFJVHiRg21PyGwYBLzzyLlF3ZBUAVZadA3HnnnTh2LCdwVKGoSmz59STiIlLkefX6Pug6tJCpQaswFCpfWXYIqw+bq4Z6ujph7gNd0KR64QPPUw2pePLvJ7EnwlxczNfVV5SHZgHNCrYDrmB/fQNwOTtvuIsXMHIB0O9lRhzn2jTpn38QykxLV66YN61XTzIteXbujIpI23514Ohstpgc3nwZmdnZr4oLU9ayZhAfpZK+ttUwwNXb/PzQMiDDfB0qFAqFHgcnJ9R65x2LO5MpI0NcT9OOKtm1TBWICxcuwDO7jLhCUZU4dzgaRzZflufObk646aHWkrpVUTDeX30cP+82r3i7ODlg9phO6Fiv8JmA0rPS8fTfT2NnuNmNxcfFB18P/BotAlsUbAf7fgDmDQGSzIoM/OsBD68BWt121aaxCxfKD48pxSysenTogAaLFsKtYQFdpMohXn5uaNalhjzPSDXg9B6zYlTucfUCWmfXF2JF8FNry7pFCoWiXCsRb8Orr9n10ZiUhEsTJsBw2fwbrigeRZJ8xo0bh1deeQUbN27EmTNncPny5VwPhaIywmxLGxaYM/yQXiOawL+GUqQLyrebz+DLjaflOcMFZozsgL7NqhV6HDKyMvD0hqexLWybvPZy8cLsm2ajdVDr/L9sNAJrXwOWPw5kZZjfo1/9+KvjHWgtufLxxwh/Y5r5e1RUbrkF9ebNLdeZlgpK6z5mNybNClFhaD085/nRFWXZEoVCUc5hYos6H38sCz8kKzoasf+bjKxEc+FXRdEpUrWrjz/+WMzTu3btyhU4yB9cvj56NEfIUigqC5sWnUByvFnorNcqUKVsLQRL9lzE9D9z7gvThrXG0PaFT3mbmZWJSRsnYeulrfLa09kTswfMRrtq7fL/Mt1dlj0CHP0j570u44Fb3jEXK9NhMhgQNnUq4n9dYnkvaNzDqDZpEhys3JsqKjUa+iKotheiLyUj/Ew8oi8lIah2tntQeaZBX8DND0iPB06sBgwZgHP5zn6lUCjKDkdPT9Sd/SVCR9+LjDNnYDh7FmGTn0O92V9KvISiaBSp5+bOnVvEwykUFZNTe67g5C6zuwsz2PS7r2WFybpT1mw4fgXPLzFnSCLPDGiK+3o0KPR+Mo2ZeG7Tc/jn4j/y2sPZA18M+AIdqptXlvIkMRxYeHdOvIODEzDoPaDr+Ks2Naal4dL/JiNp/frsbR1Q4+WXEXhf7nSuFR3O31a9a2Pzz+ag8MNbLqPvqALGj5QlVBaaDQT+W2xWIs5tARr3L+tWKRSKcoyTvz/qfvkFzo4cBWN8PFK2bEHEu++h5pRXyrppVUuB6Nq1q+V5RkYGXMt57nOFojikJGTgn5+OW173GdXMUoxLkTcHLsRh4g97kWU0Z+QZ26M+nr6xaaG7zWA04MVNL2L9ebNQ7+7kjs/6f4ZONTrl/+Xw/4Cf7gYSLppfu/oAd80Dmg64atOs+HhcmPg4UveYA7Ph4oLazOahFSeqZDTvVgPblp6CIdOIkzsj0OvOJlIrotzT4lazAqG5MSkFQqFQ5INr/fqo9cnHuDhuvGRmiv3hB7g2aojA0aNV3xWBIv9SLFy4EP3790eHDh0kqPr111/HF198UdTdKRTlErrlbfjhWE616Y7V0KyrOfhUkTehUcl4aN4upGaas/EMblsTrw9tXWjLjdFkxOv/vo4159bIa1dHV8zqPwtdQ3IWMuxyYg3w3S05yoNfXXOwtA3lITPiCs6Nuc+iPNDsXe+r2ZVWeSBuni5o0D5YnnOOXzxqLoxY7mkyAHDKVuKP/WmJUVEoFIq88OzSBX4vPG95HfHW2yq967VUIP744w989NFHGD58OFxczL7DjRs3xuzZs/Hdd98VtS0KRbnj+I5whB6MkucePi64YXRz5bpUACIT0zH2u52ITjbHjHRtGChB006ODoVW4N7Z8Q5+P/27vHZ2dMbH/T5Gj1o98v/yjq+AhaOAjCTz69qdzJWla7S6atP0s2dx7p57kH7ypLx2CgxEvQUL4NWzJyo7TTvnKMSam15RoXLYsGFDeZSqi5+bN9C4n/l5UjhwKdtipFAoFPngOXQoAh58wPwiKwsXJ01CZnaKbkUpKxBUEpiF6cknn4RjdkDh2LFj8dprr+Hnn3UFmRSKCu66tOUXs0BJbri3BTx8lLtefiSlG/DgvJ04H2NOe9q8hg++GdsZ7i5OhVYePt77MRYdXySvHR0c8X7f99GnTnY1YntkGYCVzwGrns+pLN3qduCBPwGfq61Hqf/9J9WlM7MzyLnUqYMGP/0IjzYFyOpUCajfOshSmfrM/kgYilETwsnJCd27d5cHn5cqLXQF/E7/XbrHUigUlYrgZ5+FZ4/u8jwrMgqXnp0EU6bZ00BRigrE2bNn0dlGAaVu3bohLCysKLtUKModm385gfQUgzxv2qUGGnUofMrRqkaGwYjHftiDQ5cS5HWInzvmPdQFfh65sxwVhG/++wbfHcqxaE7vNR031b8pnwYkA4tGAzu/znmvz/+AO+cCLldXuk7ashXn7n8AWbFm1x23Fi1Q/6cf4dqg8EHeFRUnF0fL3GaV9dD/olEhaHRDzvPQzWXZEoVCUQFrRNT+6CM416wpr+m6euXDj8q6WZVfgQgODhYlwpp9+/ahevXqRW4M99mxY0csXbrU8h5Two4ZM0ZiLRhzsWDBglzfMRqNmDVrFvr06SPbjB8/XmIy9OS3D4XCmtD/onBqt9mk6e7lgj4jCx/4W9UwGk14YclBbD5pdvnydXfG/Ie6IsTvasE9P3448gM+3fep5fWUblMwtPHQvL+UHA3MHwacXG1+7egC3PYFcONrV1WWJvF/rMCFRx+1FIijb2z97xfApRj3sErhxrS76G5MtBoZDAZ58HmpwuJ//vXNzy/uMqdzVSgUigLCej51PvlYkmWQmPnzkbhuneq/0lQgRo0ahWnTpmF9dppDFpNjUPVbb72FO+64oyi7RGZmJiZPnoyU7B9zEhsbiwcffBD16tXDkiVL8Pjjj+PDDz+U5xoM3P7pp5/w5ptvYtGiRaJQsNAds0MVdB8KhZ6MNEOurEu97mqiXJcKwHurj2HZvkvy3M3ZEXMe6IJmNXwKPbmWnVyG93a9Z3k9qdMkjGoxKu8vxZ4DvrsZuLTb/NrNF7hvKdDxXpubxyxYgMvPPSeZOIjPTTeh7rffwMmn8O2tDNRu7i8xPuT8oWhkFtGNifWBFi9eLA8+L3XqZcfCGNKA8JxUwQqFQlEQPNq3R82XX7K8DntlCjLDw1XnlZYCwVX+wYMHY9KkSUhNTcWECRNEeRg6dCgeffTRouwSn376Kby9cxcx+uWXXyRIm8oKg7RHjBiBBx54AF9/bXZPoJLAeIynnnoKN9xwA1q0aIGZM2ciPDwca9asKdA+FAprdiw/g6TYdHlet2UAmnczmzgV9pm79Sy++ueMPGec9Cd3d0SXBoWv1vzX2b8k45LGhHYT8GCbB/NP0zpnIBCdHa/iXRN4cBXQsO9Vm0p16ZkfI+Ltdyzv+Y8cidofz4SjW9VNzevo5IiG7c1uTEzpeuFIDCoEdXWZuM5vL8uWKBSKCor/3XfDZ+BASyrvy889D9O1WACpqmlcqTxs375dVpoopPP5lClTLEHVhYEVrRl8/e677+Z6f/fu3VJzwllXKZDBeaGhoYiKisKxY8eQnJyMHj1yMrL4+vqiVatWss+C7EOh0BNxNgEHN5pTfjq7OOL60S1U1qV8WHHwMqatOGJ5Pe22NrilTeGVro0XNuKlzS/BBLPry5iWY/B4h8fz/tLZzcDcweZMPCSoKTBuLVCzzVWbmoxGSdkX/dVXlveCJ05EzTemij9sVadhdjpXcvZAJCoE9cxBkMKFHWXZEoVCUUFhxriQN6fBOSREXqfs2oVotchcegpEfHw8Tpw4gbS0NKSnp0ucAYV2TXAvKAkJCXj++edF+QjJHjwNWhJqZge4aGgxFgzW5ufE+nvcRvssv30oFBpZWUZs+OEosuVXdLm1IfyqFd5/vyrx7+koTPr5ADR39yf7N8GY7tl+6YVge9h2/G/j/2AwmV2KRjQdgee7PJ+38nZ4GfDDHUC6OWAbdbqYazzQN94KriaFvfqqFA7SqPHqFFR76kmlIGZTp0UAnN3MilTowWgYsypAbYVqLc3uapoCUdpxFwqFolLi5OeH2h+8b4mXi/zsc6Ts3VvWzap8lagZP/DGG29I3IJ1oBx/8KlMFJSpU6dK4DTdn6yhcmJd5dot282ASgvdp4itbajgFGQfxeGa+PjaOKb2UJQse1efR/SlZHkeVMcbbfvVvqb9XNHG9lh4Ih5ZsAcZ2YLmXZ1q4+n+jQvd/oORB/HU308hw2iOWxrUYBBe7vKyxDPZw2HXN3D460U4ZGt7pqYDYbxjDuDqJXm99TA1X/hLLyNx1SrzG46OqDn9Tfjedts16euKMq4OjkC9lgE4sz9KispdOhmLWk39C7UPnqP2m8DnpVoLIhvHOp3hwDSuSRHIij4DBFy7DFoVZWwVhUONa9UcW7eOHRH02KOI/vwL+R259L/JqL90CZx8sxcpFMVXIJj16LbbbpNYAnd3dxSV3377TVyMWJjOFty3FgytoQn9np6elmNzG307uI2Hh0eB9lFUKNwkJiYW+fvFOS6VIv4wF8VdTGGbxOh07FkZKs8p83S5vQ6SU7ILkF0jKtLYXo5PwwPf/yc1H0ifxgF44cb6SEoqXJ+dTTiLx7c8jlSDeTGgd83eeL7t80hJzkmmkAuTCe7bZ8J9xyeWt9Jb3YXUG98B0o1Aeu5r0pSejtgpryJ9c3aaTycn+E97Aw79+1+z67cijWvN5l6iQJCTe8LgU7Nwrl1aBibC/tW7jpYWbtXawyO7DkTa2R3IdA7CtaIija2i4Khxrbpj63LPPXDZshWZBw7AEBaGS6+/joBp01CV+sexgPeyIt3d6Xb08MMPo0Exc6XTkhEdHS0B0Hpef/11rFy5UlyPrlhVB9Re16hRw/JDxfeYZUm/TfPmzeV5fvsoKuxgnzLI2KKt8DHgvNQLNVUR2J+bvw9FlsG8ckrLQ4OWRZ8blX1sY1My8OSv+xGZZFbM29fxwxdjOsHTtXC3k7DkMEzeMRmJmWZBvlvNbvio30dwc7ITzGwywmHNK3DcmRPDYOz9Pzjf8DJ8bKx0G1NTcXnSJKT/u01eO7i6ImTmDHhb3W9Km4oyrqTZde7495dz4sZ35Uxyoe9xvCdrSgO/ey0UCNRuZ3nqkXwB7tfwvlyRxlZRcNS4Vu2x9fjwQ5wbMQLGhASkrVkLh1tvhfeNN6Iq4FiIhZAi3d0HDBiAf/75p9gKBNOpUhPUM3DgQMmqNGzYMCxfvlxSs3LAtYFmsHbDhg0RFBQkP1CcBDt27LAoEFRujhw5InUfSJcuXfLcR3Eoqx8MHld7KIoPq++eP2zOOuPl54puwxqrsbVDWmYWJvywD6cjza5eDYO98N0DXeDjUbgMRrFpsXhs/WO4kmJW5lsFtcIn/T+Bp4un/erSK54G9v+Y894t78Kx+2O2N09KwqVHH0Xq7j3y2sHDA3W/+BxeuoQL15KKcs16+TmhWl0fRJ5PFHe+9OQsePoWrvq6di+m8nBNzrd6C8tTR2bkoiucjaKBVX1sFYVDjWvVHVununVQc8oruPz8C/I64s034d21K5z8C+fSWdkpkgLx3HPPSczC6tWr5cfC2s/1nXdyUiTmhT0LAAV7fsaUq99++y1eeeUVqe1w8OBBzJs3T+IvCGMbqChQEQkMDETt2rXxwQcfiNWBigjJbx+Kqg0r727++YTlde+RzeDqfg1WTSsghiwjnvhpH/acM1dtrubjhgUPdUWQd+GUh5TMFExcNxGhCWaXsQa+DfDlgC/h5eJl58DpwJJxwNHfc5z1h30KdDQvEliTFReH8+PGI+3QIXnt6O2Nul9/Bc/rritUO6sqTF1MBYJcPB6DZl0KnlGLP8i9e/fGNSVZlzHq2B/A27WA5kOAnk/kztKkUCgUBcR36FAkrFyFpI0bkRUZhYh33kWt93JnCq3qFMlpc/r06ZI+lbEFly5dwsWLF3M9SgoqEhT+WaF6+PDh+OyzzyRjE59r0Fpx5513Shane+65R37A5syZI7UfCroPRdVl98rQXDUfGl9nzoWvyA1Nvq8uP4x1R81Vir3dnDH3gS6oG1i4OKLMrEw8s+EZHIo2C/fVPapj9k2zEehup2ZERgqw8J4c5YHVpe+ca1d5MERF4dzY+y3KAzNr1Js3TykPhaBOi5yxuHjUrCyWW3Z9Cyy4Lfd7JiNwYhXw3S3Arjll1TKFQlGB4cI4U3w7ZrtExi9fjqStW8u6WeUKB5N1GqUC0KFDByn81qdPH1RF/vvvP/nftm3ba35sumIxOJHuW8pkXjxiwpLx85s7YTSa4OjsgHte7Qb/GkUPrK/MY/vJupOYuc5sqXFxcsDcB7qid9OcugEFwWgy4sVNL2JVqDkbko+rD+bfMh9NA5ra/kJ6EvDTSOBc9k3b2QMY9QPQdIDNzTMjruD8Aw8g4+xZee0UHIx6382Be7NmKEvK87jawpCZhW8nbUZWphHeAW4Y+3bP8pnq9tw2YO4gagx5bOQAPPRXqVkiKtrYKgqGGtfKS2HHNnbxYoS/+po8d6lfD42WL4djMZIHVSb5tkgWiICAANSqVasoX1UoygXUm7f8ckKUB3LdwPplqjyUZ37edd6iPJAP72pfaOWB/f3uznctygMDpT+/8fM8lIdE4IcROcoDc/3ft9S+8hAejvNjx1qUBxYEavDD92WuPFREnF2cENLYT57TOhcXYScjlp0g6oULF8pDS3JRamz7nCW0896Gn3M7hUKhKAL+I0bAo1MneZ557jyidIVIqzpFUiAeffRRvPXWW+IWpPJfKyoiof9F40K2e4ZPoDs63VL44mdVgX9OROLlZWZ3IPLK4Ja4rUPtQu/n64NfY+GxhfLcycEJH13/ETpW72h747QE4Ps7gAvbza/d/YCxy4H6PW1unhkWJm5LGefOyWuX2rVR//vv4VrMJA9Vmbotc9yYwk6Za+qUKzJTgeN/AsZ8lBR+fmyFeXuFQqEoJA6OjgiZ+jqzQsjr6G/nIP30adWPRQ2iZozB5cuXMXjwYJufF6aQnEJxrckyGLH115OW1z1HNIGzq3I/sObI5QQ8/uNeZGVbaR7s1QDj+zYqdH//cvwXfLb/M8vrab2m4fq619veOC3erDxc2m1+7REA3PcbUKuDzc0zL13CufsfQGZ27JVL3bqoP38eXJSFtFhoFggSdioOrXqXM4szLVSMdSgI3I7bX8PMTAqFovLg1rQpgh5+GNG0PmRmIuz111F/wQJRLqoyRVIgHnvMdupEhaIicHDDRcRfMa9IhjTxU4HTNgiLT8VD83ZZCsXd3LoGpgxpVei+XhO6BtO3T7e8ntx5MoY1HmZ749Q44PvhwOW95tcegcD9vwM1bftiZly8iPNj70fm5csW/9T68+fDpWbBswYpbFOtvo/EBRkNJoSdLocWCLf/t3cf4FFUXxvA3+ym916A0HvvRaUjIIgUpagU/QNiQwUFUUCqFUQFRfRTxAIIIhYERUBQFEG6SO+EkJDe27bvuXeym4QsZBM2ZXff3/Ms3NmdnczspMyZe8+5Pko1LkuCCLGeWJ+IqIyCn3gcaVu2QBMVJcuDp373nRze5MjKFECwghHZqqy0PBzYfNGUX9l1RMOqmSBaidJzNHj0s/2ITVPmaGkd6Y93R7aBWlW6z2lfzD7M2D0Dhvwk10ebP4pxzcaZXzkrSQkeYo4oy55BwFgRPDQ3u3relSu4/Mgj0F6LkctiuFJNETyEhZZqH+nmeRChNX0ReyEVqfHZ8uemtPNBlCvRmyBKtYpqS7caxqRyBhoNYO8DEd0WkTgdPmcOoiZMkMtxby2Cd8+ecA68SQVBB1Dm/pcdO3ZgxIgRsiJT+/btMWrUKGzbts26e0dkZfs2XUBejk62m9wRgZCavDNZmEanx5OrD+FUrDIPQM1AT3wyrj08SjnE61TSKTy781lo9Bq5PKT+EExpO+XmwYMoxWkKHoKBcT/dPHi4fFnmPJiCh7p1UfMLBg/lOYwptir2QnR5CtArP8s3JV4X6xER3Sbvu+6E78CBsq1LTUXcm2859GdapgDi119/xdNPP43Q0FBMmTJFtsV8C88++6wMLIiqooSr6Tj5Z/5wF3c1Og+uV9m7VPXmevj+P+w+myCX/T1d8NmjHRBcyoniojOi8cT2J5CpUWar7lGjB+Z0mWO+pyczEfj8PiD2X2XZKxR4ZDMQZn64VO7Fi7g8Ziy0sbFy2bV+PdQSwUMoex6sLbxwHsT5FFQ5tboAA99WuhJFT0NhctlJeZ2TyRGRlYTNeLHI3BCZe/OLfTigMg1hWr58OZ566ikZOBg98sgjcpK2FStWoHfv3tbcRyIrlW09C+OsJ+3vqV21hmRUAct3ncfX+6Nk21Wtwsdj2qNeiHeptpGSk4LHtz2OhGwlCGkV0gpvdX8Lzjde4AmZCUrwEHdcWfYOB8ZtAkLMl17NvXABl8eNk7OCCm4NG6LmZyvhHBRUyiOl0vZAXL+UZtF7RJAYERFhape7DuOBsGZKqVZRbUnkRIicBzFsSfQ8MHggIityDglB6PPPI3buXLl8/dVXUee77+CUX6XJkZTpiC9cuIBBgwYVe/7ee+/F//3f/1ljv4is6uLRBESfUe6i+oZ4oFWvSH7ChfxwJBqLtp42Lb89ohU61ind2M5sbTae+u0pXEq7JJdr+9bG+73eh4eYAO5GGXFK8BCfX7HNJ0IZthRc3+y2c8+dw+VHHoUuIT94aNxYCR4CAngey4mHj6ucSE7MBZF4NUMG4SUFBWJiph49elTsORFBgniIUq2i2pJImGbFJSIqJ/4jhiPl22+Rc+wYcs+eQ8qGDQgYNcrhPu8yDWESQ5cu59dcL0w8J2b3I6pK9Do9/v6uoG7znffXh9rFscuvFbb3QiKmfZM/hAjAi/0bY1Cr0pXt1Oq1mP77dPwbr2wn2CMYK+5eAX93/+IrZ8QDnw8qCB58qyvDlm4VPIx7pCB4aNqEwUMFCa6h9ECJvKH0RCWpvsoSQYN3KIMHIipXonxr2EsvmZbj31sKXZplvbT2pExXUaKnYe7cufj999+RkZEhH6I9b968m84NQVRZTvwVY5pNV5RtrdOqdLMo27Nzcel47IsDyNMp5TAf7FgTj3cv3VwP4s60KNW66+ouuezl4oUP+3yI6t7Vzec8iITp+FPKsm8N4JGfgKB6Nx+2JHoeEhPlsnuzZqi1kj0PFSU4suCGUEJURoV9XSKiqsyzbRv4DrhHtnXJyUj8+GM4mjLPA3HmzBlMmjTJ1KUtLiJE17VIqiaqKjS5Ouz/6WLBpHHD6rNsa7749Fw88tl+pOUoZTB7NArBgsHNSv35rDi6At+e/Va2Ra7Dez3fQ+PAxjcp1Tq4IOdB9jz8BATWuWnC9JVCPQ8ieKi58lOo/QrG5lP5CqruXaQIQd02IbdcX6vVYuPGjbI9bNgwODvguGAicgyhzz+P9O07YMjLQ9KXXyFgzBi4hIVV9m5VmDL9dndzc5OJ1OfPn5eBhAgeGjVqhHr16sk2UVVxZPsVWcNeEBc/4XV58Slk5Wkx4fP9uJqsTKjXrJov3n+oLZzVpeuU3HBmA5YfXW5afu2u19ApotPNJ4mLPVYo52HTTYMHUapVBA/a+Hi57NakCWp++gmDhwoWHFk4gLCsB0KnK6G0KhGRHXCpXh0BDz2EpFWrYMjNRcLyDxExT0mudgRlGsIkqiylpKTIgOGee+6Rw5ZE+/r16+jcubP195KoDETgcPjXK7LtpHJC58GlG5pjr3R6A55ZewRHryq1/av5uWPlIx3g7Va6+wm7onZhwd4FpuVp7afhnjpKl24ROanAV8MK5nnwDlOCh5sMW8qLipI5D9q4OLns1qiR0vPgbyafgsqVX7AHXNyUOUA4hImIqKigSY9B5eUl2yKxWtz8chQWXzFs2bIFu3fvlu3o6GjMnz9f9kQUJp7nrL5UVRzYckkOYRKa3lUNAeHKD7mjW/DTCWw/eV22fdycsfLRDgjzdS/VNo7GH8W036dBL8pmAhjXdBzGNhtbfEVRFeerB4DogwWTxIkZpoMbmN1u3tVoWarVOM+DW4MGTJiuRCLwDqzmhesX05CelANNng4upZxUkIjIXjkHBCDw0UeR8P77Ygwn4pe9j+qLF8ERWNwD0aZNGxkgXL16VS5fu3ZNto0P8ZqnpyfefPPN8txfIoukxGXh+B/Rsu3spkaHgbX5yQH4fM8lrNqjlFl1Vjnhw9Ht0Djct1SfzYXUC3hqx1PI0SlVeQbUGYCp7acWXzEvE1g9Arj6j7LsEaj0PIQ2Np+vcu0arowrNMN0/XqoueozOAeWrpwsWZd/mKepnRqnFCMgIiJF4COPQJ1fUjxt82bknC4oiW7PLO6BEJMDffHFF7I9ZswYOWmcH5MZqYra98MF6PVKPk7rPpHw8ivdbMr2aNfpOMzbdLwgX2FYC9zVoHQVqeKy4vDEtieQmqsMfxL5DgvvXAiVmLyrsLwsYM1I4MoeZVmUcx37w01nmNbExsphS5poJehzrVsXtVat4iRxVYB/aEEAkRybheAaLNVNRGSk9vaSQ5ni3nhTVBRC/DvvInLFh7B3ZcqB+PLLL4sED0lJSfjll19MvRNElSnuchrOHVTGz3v4uKDN3TUd/oScuZ6OyWsOIz+mwhM96mFE+9JNppeel44ntz+Ja5nX5LKotPRuj3fhonYpuqKY0OvrB4FLypBHuPkBY78HIlqa3a7mepwctqSJyp8Fu3ZtpechmOV2q1oPhLEcMhERFQh48EE4h4fLdsauXcg+XnCzzl6VKYAQlZf69euH/fv3Iy0tDffddx+ee+45mUy9d+9e6+8lUSns/eGCqd1hYB24ujt2KcnEjFz8b9V+pOcq5Vr7NQvDtL6NSrWNPF0epuycgtPJStesmONhee/l8HYtqNIjaXKArx8GLihzQsDNFxjzHVCtjdntauLi5LAlzWUl2d2lVk3U/HwVXEJDy3CkVB4Cwi0PIEQOXEhIiHwwH46IHIXKzQ3Bkx4zLSd+ZP/zQpQpgBB5DrVq1ULdunXx008/ydrfYiK58ePH491337X+XhJZ6NrZZESdSJJtnyB3mTztyHI0Ojz25cEi5VrfGdkaKpXlcz2IROlZf87Cvth9ctnfzV9OFBfiecOcANpcYP1Y4PwOZVkEF6O/BWq0M7tdbUICrjzyKPIuKTkZLpGRqPX55w5VR9sW+IV4AE6WBRBqtRp9+vSRD9EmInIUfsOGQR2i9Jynb9uG3PPnYc/KFEAcPnwYL774IoKCgmRlpu7duyMsLExOHHTqVP4Ms0QVTMxBUrj3oeO9daB2LtO3uN18Hi9tPIaDl5PlcpivGz4d1wGerqXrkXn7wNv4+dLPsu2udsf7vd9HHb8b5m/QaYBvHgXOblWWXTyBh78BIjua3aY2ORlXHv0f8i5cMNXTriV6HvK7gKnqcHZVwydQqdKVfD2Lc/0QEd2kFyLokUeVBYPB7menLtPVlUqlgqurq+x5+Oeff9ClSxf5fGZmJtzdS1cOksharpxIQsy5VNOwi4adHPtidPmu8/jusJKU7O6iwidjOyDcr3Q/n58f/xxfnFCKJ4hE6UXdF6FVSKuiK+l1wMbHgNOblWVnD+Ch9UCtO8xuU5eWhqjxE5B79qyyerUI1BQ9D9Ucu7eoKgvIz4PQ5OhMEzMSEVFRAaNGmiY8Tf1pMzQxSlVBe1SmAKJ169b46KOPsHTpUuTm5qJbt25yErklS5bI14gq4267qLxk1HFQ3VIN07E3W47FYNHWglJy745sjRY1SjcL99ZLW7H4wGLT8iudX0GPyB5FV9LrgU3PAMc3KstqN+DBtUCdrma3qcvIRNTEx5Bz4oRcdg4NlcOWXGtUL9W+USUmUsfefBiTuKm0ceNG+RBtIiJHovLyQsDDDykLOh2S16yFvSpTADF79mycOHECa9euxcsvv4zAwEB8/PHHOH/+PKZPn279vSQqwYUj8Yi/ki7bwZHeqNfmhvH5DuRoVAqmrs+f9RnA9P6N0L95RKm2cTjuMF7e/bJp+clWT+L+hvcXXclgALa+BBz+SllWOQMjvwTq9TS7TX12Nq4++SSyjx6Vy+rAQFltyTWydNWgqJIDiBLmghA3lcSDiMgR+Y8aBbgo1QlT1q+Xf/vsUZnK04gEanGHqbCnnnpKBhNMnKOKJuZ72PfjRdNyp/vqyhl0HVFMajYmfHEAORplhuhhbavjie71SrWNy2mX8cxvzyBPrwxVGVxvMB5v9XjxFX9bCOxbobTFPBD3fwI07Gd2m/q8PFyd/Ayy/lEmlVP5+ckZpt3q1i3lEVJl8BWJ1PnSE5UJBImIqDhRRdD3nv5I+3ETdKmpSN20CQEjRsDelCmAELNQm5OTo/xhqcaxzFSBzu6/juSYTNkOr+uLWs2DHLbi0qQvDyI+Xbn726F2AF4f1qJU5TSTcpLwxPYnkJKbIpc7R3TGnDvmFN/G7iXA7oLhTbjvfaDZULPbNGg0iJ46FZl//imXVd7eqPnJJ3BvVLpSslR5fIMKcmfSGEAQEd1S4JixMoAQkr/8Ev7Dh9tdaesyBRC9evW65Qdx8uTJ29knIovpdHr8s6kg96HT4Hp290NamopL/15VkshrBHhgxeh2cHO2vJRmjjYHk3+bjKh0ZUK3BgENsKTHEriobpgobt/HwI55BcsDFgNtHja/Xzodrr04AxnbldKuTh4eiPz4I3i0aF6Go6TKYqzCJLAHgojo1sTfOI82bZB9+DByz55D1t9/w+sO84VFHCqA+OILpSqLkU6nw8WLF7Fq1SrMmDHDWvtGVKJTe2KQlqD0fNVoHIAajQIc8lP7ZPdFU8UlDxc1/m9sewR5u5VqroeX/3wZ/8b/K5dDPULlRHE+rj5FVzy8Gvh5WsFy7zlAx4lmt2nQ6xEzazbStmyRy06urohc/gE827YtwxFSZZdyFbO6Z6drkJ5on+N5iYisKXDsGEQfPizbSV98yQBC6NixeG13Uco1MjISy5Ytkz0UROVNq9HhwBZlEjKh02DHHE//+5l4vP5zQa/fkhGt0CTCt9RzPWy7vE22PZ098UGfDxDudUMZ3OPfAT8+XbDc9QWg69Sb9ohcX7gQqd99pzzh4oIay5bCK7/kM9kenyAPGUBkpuVBp9FD7eK4c6wQEZXE5+674RwRAW1MDDJ+/12WdHWJKF1Bk6rMqn8BateuzYnkqMKc/CsGGcnKeP/aLYMRXqd0ZUrtwcWETExecwh6g7L8TK/6uKdF6X5BrTm5xjTXg9pJjbd7vI3GgY2LrnRmK/DtBNGtoCx3ehzoNeumwUPcosUF5etUKlRfvBje3buX+vioCuZBGID0ZPOJ1GL4oKjKJx6OOJSQiMjIydkZ/vfnVy80GJD6ww+wJ1ZLos7IyJBzQ9SoUcMa+0V0S+IO6MFfLheZddrRpOdoMPGLA0jLUert92kShuf6NCzVNnZe2Yk3979pWp7VeRbuqn5X0ZUu/w2sHwvo8+v6txkN9HtdXC2a3WbC+x8gaeVKZcHJCdXeeB2+/fqW7uCoyudB+IcWlHY1ElX4+vUzX4mLiMjR+A8dgoQPPpABRMq3GxH02GNwUtlH763VkqjFXUdPT08sWrTIWvtGdFMn/rqGzBSl96FOq2CE1LxhrL4DlK6dsu4IzsVlyOUGod54Z2SrUk2e91/Cf5j+x3SZ/yBMaDEBDzR8oOhKsf8Ba0YC2vw7zs2GAYOWyl4FcxI//VT5ZZkvfN5c+N13XxmOkKoan0KVmNKTWMqViKgkLtWrw6tLZ2Tu+RuaqChkHTgALzNpAA6bRC24uLigYcOG8PLyssZ+Ed2y9+HQ1oLehw4DHa/34Z3tZ7D9ZJxs+7o7y6RpH/cbqiXdwtX0q3hqx1PI0SkXgvfUuQeT20wuulLyJeCr+4FcpbIT6vUChn4EqMxXdkrZsEEOXTIKe/llu6x97aiKBBAs5UpEZBG/YffLAEJI/Xaj3QQQqrImUYeGhsoeB9EWj3///RcJCQnW30OiG5z8u2jug6P1Pmz+NwbLfjsn26LD4f2H2qJ2sOWBe2puKp7c8aSc80FoF9YOC+9cCJWYDM4oIx74chiQEassV28HjPhSlOMxu820bdsQ88oc03LIc8/KChRknwFE2k0qMWm1Wvzwww/yIdpERI7Op09vqHyVwiZpW7dCl54Ohw0g9uzZg8GDB2PbNqVqi7B582YMGTIEBw4csOb+ERXPffi5oPJSh4G1HeoTOnEtDS98c9S0/PKAJujWMMTi9+fp8vDczudwMVWZubu2b2281/M9uKoLBQY5acDq+4Gk88pycEPgoW8AN2+z28zcuw/Xpj4vxlXJ5cBx4xA0aVIZj5BsfS6IrKws+SAiIkDl7g6/e++VH4UhJwdpW3523ADi7bffxiOPPIIpU6aYnlu/fj3GjBmDxYsLzU5LVJ69Dy2CEFqrdOVKbVlSZp5Mms7W6OTysDbVMf4uy4dviTylV/a8ggPXlSA/0D0Qy/ssh59boepV2lxg3cNATH6Q4lsdGL0R8DI/u3f2f8dx9amn5GzTgt/gwQh9cTor8NghV3dnuHsrw+Q4hImIyHJ+w4aZ2sa5kRwygDh//jweeOCGZEsAw4cPx+nTp62xX0TF6LSi8lKh3gcHqryk0enx5OqDiE5Rho60quGH14a1KNWF+op/V2Dzhc2y7a52x/u93kekT2TBCnodsHEicPEPZdkjQAke/AutU0juhYuImjgR+sxMuezdsyciFi6wmwoTdPNeCFHAQMwCT0REJXNv1hQuNWvKdtb+/dAmJ8PWlekvvajxferUqWLPnz17Fj4+jjUenSrOKdH7kKT0PtRq7li9D69uPom9F5SchRAfN3w0pj3cXcwnM5vz88WfsfzIctl2ghPe6PoGWoS0KFjBYAA2Pw+cyK9T7eKpDFsKvWE+iHya2FhcmTAeuvxfgh7t26H6O0vg5GJ5IjfZHu8AN9O3S3aa0utERES3Jm72+fa9W1nQ65Hx229wyCpMIv9h7ty5SElJQatWreRzx44dw7vvvivzIIisTdztPPizY1Ze+u7wVazao/S8uKpVWDG6HcL9Csajl+RI3BHM+rNg0rcp7aagd63eRVfa9Tpw8DOlrXJWEqYjO5jdnrhzcmX8BGivxchlt8aNEfnhh3KcJ9k3D5+CXJns9DxTQEFERCXPTJ34yaeynb7jt4JJ5hwpgHjqqaeQnJyM+fPny0obYmy1s7OzzIF49tlnrb+X5PDO7Ltuqj1fs1kQwuo4Ru/D8WupeGnjMdPyvMHN0K5WgMXvj86IxrM7n0WePk8uD2swDI80e6ToSvs+Bn4vmEwOQ1YADfqY3Z4+KwtRjz+OvPNKgrXokq35fx9DzZ5Hh+Dh41IkgCAiIsu4t2gBtb8/dCkpyDp4EAa93qaH/JYpgBDBguiBmDZtGi5evCiXa9euDXfegaRymjSt8LwP7Qc4RuWllKw8TPryIHI0yljzUR0i8WBHZQylJdLz0vH0jqdN5Vo7hnfErE6ziuZN/Pct8PP0guX+bwIth5vdnkiUvjplCnKO/iuXnUNCUHPlp/J/csAeiAzzQ5h888sVEhFRAREseLRrh4wdO6BPTUXuuXNwb9gQDhVAGIlJ45o3b269vSEy4+KReKRcV8pCVmvgj4h6haoG2Smd3oDJaw/janJ+0nSkv+x9sJRWr8W0P6bhXMo5U7nWJT2WwEVdKEfhwi5goyi3alCWu74AdH7c7PZEL6OY5yHzdyXBWuXjg8hPPoFrjRq3cZRkazxvGMJ0I3EzaeDAgRW8V0REtsGzbVsZQAjZhw7ZdABhu30n5BDEhevBXwp6H9r1rwVHsGTbaew+q0zMGOTlig8fbgs3Z8uTpt/a/xb+iv5LtkWZ1g96f1C0XGvMv8DXowF9/l3ktmOBXgV5EjeKf+89pH73nWyLROkaH7wP90a2+4uPysa9yBAmJlETEZWGZ/t2pnbWgYOwZQwgqEqLOpGE+CvKrI1ixunIpoGwd1uPX8cHO5UcA7XKSc40Xc3fw+L3rzm5BmtPrZVtZ5Uz3unxDmr6Fhr6lHwZWP0AkJc/G2ajAcDAd0SZCLPbS1q9GokrPlIWnJxQbdEieHXsWPYDJLvtgSAioptzb9oUTvnD/bMOMYAgKjc39j6UZt4DW3QxMQvTv1VyDISX7mmMLvXMT+Jmzu6ru/Hm/oKE6Dld5qBDeKFqSllJwFf3AxnXleUaHYH7PwXU5kczpm39FdcXvmpaDps5E779+5XyqMheqzDdSBTV2Lx5s3yINhERFRA9+B751UtFJUPNtWtwqB4IMYmcuXkgiKwp5lwKrp1NkW3/ME/UbW3fybrpOVpM3XgKGbnKTNODWlUr1UzTZ5PPyrwHvUFJuh7ffDyG1C9UVjkvC1gzAkg8qywHNQAeWge4eprdnpjs5tq0aUrRf7H6Y48hcPTDt3GEZOvcvZxLTKJOS0uTDyIiKs6zXaFhTAcPwaECiKioKHh6mr/oILKWg4UqL7XtVwtOKie7zvV4ceMxXExUkqYbh/vgzfstn2k6OScZk3+bjEyNMiv03bXuxjNtnylYQacFNvwPuLpfWfYOA0Z/C3iaHxKWc/oMop58CoY85S6z35AhCJny3G0eJdk6lVoFdy8lD4JDmIiISs+jTWtTO+fECThUFaYJEyZg5syZGD9+PGrWrFmsfGu1atWstX/koBKupuPysUTZ9g50Q8NOYbBnK/+6JHMfBF93Z3w0ph08XS378dToNXj+9+flnA9C06CmePWuV6FyUhWaZXoqcOZnZdnVB3h4AxBgPiFdExODqMcegz5dyZHw6tYVEQvm2/3wMbJ8LoicTA2ymERNRFRq7o0bm9o5p07CoQIIMeO0TqfD/v37i1xUiLuoYvnkSdv9QKhqOFQo96HN3TWhVttvvv+hK8l4fUvBz8zbw1uiVpCXxe9/8583sT9W6VkI9gjG0p5L4eFcKOlaTBJ36HOlrXIBRq0GIlqa3ZaY4ObKhInQXr9umvimxjvvyHGbRMY8iOTYLGhzdcjOyIOHd0FeBBER3ZqYO0kdEgxdfAJyT5w0XTs7RADx2WefWX9PiPKJOR/OHYwz3e1scqf99mglZ+bh6dWHoNUreQaPdq6OXo1DLX7/N2e+wbrT62TbReWCd3u+izCvQr01Bz4Ddr1esDx0BVC3u9lt6XNy5LAl0yzTtWoi8qMVUHlZHsyQfbt2LgWp8cqcLMJn0/5EnVbBaN2nJiLq+1fqvhER2Qr3xk2QGb8butRUaGNj4RIRAYcIIDoWKuGYl5cHV1fegSLrOfzrZWPeLlr1joSLq+XzH9jaDNtT1h/BtdQcudyhdgCe6mb5PBcHrx/Ea3tfMy2/0uUVtApRqjtIp7YoQ5eM+r4KtHjA7LYMOh2iX3hBTmwjqIODUfOTT+AcaP9lc8ky//1+Fb+vPQMUulEmfk4v/puIC0cS0P2hRmh8h30PNSQistYwpszdu2U75+QpmwwgyjwuZO3atejVqxdat24tk6rnzJmD5cuXW3fvyOFkpuTi1N5Y2XZ1V6N5d/ud6fjD389j1+l402Rx741sBWcLE8WvZVzD1F1ToTUopTJHNxldtOJS1D9K0nR+RSZ0eRq442mz2xLdp7HzFyBjuzI7psrTU/Y8uEZG3uYRkj31PMjgQcgP7o0M+b1nv685jdjzqbLABotsEBHdnHsT28+DKFMAsWnTJrz99tsYOnQoXPLHRterVw8rVqzAypUrrb2P5ED+3RkFvU65IGnevTrcPMrUSVbl/X0+EW//elq2xdDH90a1QZhv0WIEN5OlycKzO59FUk6SXO4S0QXPt3++YIX4M0q5Vq1S0QnNHwDuXnDT7SV8+CFS1inDoODsjOrLlsKjWbOyHxzZnaPbr5RYBU28fmzXNQwePFg+nJ3t82eXiOh2uTVuYmrnnnSgAEIECaIK0+TJk6FSKZsYO3YsXnnlFawzXogQlVJethb//a5UElI5O6FlL/u8Ax6XnoNnvj6M/Bu3eK53Q9zVINii94regtl/zcapJGUelkifSCzqvkjOOC2lxSgTxWUnK8t1ugFDlgP5P6c3Sv7mGyQsXWZarvb6a/C+887bO0CyK9o8HS4eTTD1NNyMeP3ikXi5PhER3ZxrrZpwyp8OQQxhcpgA4uLFi2jfvn2x5zt16oSYmBhr7Bc5oON/XkNejnLx0ahjOLz83GBvdHoDnl17BPHpuXK5a4NgPN2rvsXv/79j/4dfL/8q214uXljWaxn83PyUF3NSgdXDgdQrynJYC2DkasDZ/OeY/ttOxM6Za1oOnT4dfoMG3cbRkT0SP5PGnKSSiPWMP8NERGSek1oN94YNZVtz9Sp0Njj5ZpkCiODgYBlE3Ojw4cMIDbW8ggyRkU6nx7+/RZmWW99d0y4/nPe2n8HfF5T5LcJ83fDOyNZQW5j3sPPKTiw7rPQWOMEJb3R9A/X86ykvanOBdaOB68eUZb+awOgNgLuv2W1lHT6M6KlTRSa3XA4cNw5B/3vUCkdI9kbkIllaYVCs9/ufO7F161ZZ6puIiMxzK5QHkXtaGdJs9wHEyJEjMX/+fOzYoSRdXrhwQSZVv/rqqxg2bJi195EcwLn915GRrNyVr90yGIER9lc6dPfZeCzbeU62RdDw/kNtEextWS/LueRzmLF7hml5cpvJ6BHZQ1kQQcD3TwAX/1CWPQKAMRsBn3Cz28q9cAFXH38Chhyl+pPvgAEIfXH6bR4d2StnV7Us1WpJDkTtVsFISU1CUlKSHG5HREQ3L+VqZIvDmMqU5TZx4kSkp6dj6tSpyM3NxaRJk2TC3KhRo/D4449bfy/JrokLjcPbrhSZOM7eiCFLU9YdNQ0FmdavETrUtqxEampuKp7Z+QyytEr9/f61+2NCiwkFK2ybDfz3rdIWE8g9tB4IbmB2W5rrcYiaMFHWnhY8u3RGxBuvw+kmORJEQqs+NWWp1pJyIFr2rI7dh47zQyMiKk0lJhtMpC5zmQwRPDzxxBM4d+6cvACsW7cuvL29rbt35BCunEhCYnSmbIfV8UVE/fwx/XY038PU9UeQkKH0sPRoFILHuta16L1avRYv/P4CotKV4V1NAptg/p3zC2at3PcR8Pf7SttJBTywEogsmKelMF1GJqIefxyaa9fksluTJqixbBlUnMeFSlCtvr+c50GUahU9DYUTqo3L4vXwen6AMpUIERHdgpvIgRA37/R65JxykB4IISMjA1u2bMGZM2dkJaZmzZqhf//+cHOzv8RXKl+Hfy3U+9C3pk1O6X4r/7f7AnafVe7ehvi4YfHwVlBZmPfw7sF3sTdmr2wHugfivZ7vwUP0MhgnivulYFgTBi4BGg8wux2DRoPo554zlYtzqVZNzvWgZtBPFmrerTqCqnnhyI4oWW1J9KaJH1U5E3XvSDkTtVarzEtCRES3pnJ3h2vdOsg7dx654mZ8Xh6cbOiGXpkCiPPnz2PcuHHIzMxEnTp1ZLLc+vXr5URyn3/+OcLDzY+9JrpRwtV0RJ9WSo76hXigTqsQu/qQjkSlYNHWgvke3h3Z2uK8h58v/ozPT3wu285OzljSYwkivPNnq4w+BHw7vmCiuLumAO0fvcVEcfOR+eefclnl54fI//sYLix4QKUkggQZKOTpZLUlkWAtciSIiKhseRAigIBGI/MTxQzVtqJMA58XLlyIJk2aYNeuXdi4cSN++OEH/Pbbb6hWrZp8jchSx3Yp8z4IrXpHWnxn3hak52jwzNrD0OYP93iiez3cWd+y+R5OJ53GnD1zTMvTO05Hu7B2ykLKFWDNSECj5ESg2TCg1ys33VbiRx8h5ZsNsu3k4oLI95fBrV5+9SaiMhBBg6evK4MHIiJr5UGcsK08iDIFEEeOHMF0UTPer2CsemBgIF588UXs2bPHmvtHdiwnU4Mz+2Jl28VdjUad7afnStz1n/ndf7iSpFzkt6npjyl3KzWfS5KWm4bndj6H7PyZpAfXG4xRjUYpL2anKHM9ZMYpyzW7AEM+vOlEcak//oj4d98zLYuEac8OHW7z6IhuTgxj5VBWIqKSuTcpVInplG0FEM5lnQciNjYWDRo0KJYX4e/vb619Izt36u8YaDXKEJzGnSPg6l7mlJwq55uDV/HjUSVZ2cfNGUtHtYGLuuR4XWfQYeZfM3E146pcbhrUFLO7zFbyQrR5wPoxQHx+slVgPWDUGhF9md1W5t69uDZzlmk59IXn4TdwoHUOkMgMUY2PpbyJiCzjVmjIUq6NlXItUw+E6H2YN28etm/fjrS0NGRlZWHfvn145ZVXMHbsWFy7ds30IDJHVG059nvB8KUWParbzQd1Li4Dc34oKGX5+v0tEBmoTFlfks9OfYa/rv0l2wFuAXi3x7twU7spU/xuerZgrgfPIODhbwBP86Vgc8+exdXJz8hxlYL/g6MQOH787R8cERERWYVzYCCcw8JkW1RisqX5c8p0y3fy5Mny/6effrpIxRxx4G+++aZ8iLZ47aQN1ral8nflZBLS4pUhOjUaByAg3D4mjsvR6DB57WFka5RZeEd1iMS9LatZ9N7fon7D52eUpGmVkwqLui8qSJr+/S3g6BqlLQKKB78GgurddK6HK49Ngj49XS57d++O8Jkz7a66FRERka1zb9wYGdevy7/ZmuhouNaoAbsNIL744gvr7wk5lGO7lCE6QosetvHDYok3fj6FkzFpsl0/1BtzBjWz6H0XUi9g9p7ZpuWp7aaiU0QnZeHIWmDXawUrD/v41nM9PPE4tDExctm9WTNUX/I2nJztZ3gYVV2iIt/OnTtlu2fPnlCrWaGJiOhW3Jo0Rsbvv5smlLPrAKJjR/MXL0SWSI3PxuX/EmXbO8ANtVsE2cUHt/NUHFbtuSTbrs4qvP9QG3hYUOIyIy9DJk1napTJ9PrX6o+xTccqL4ohSz8qPX7S3QuAZkPMbseg1SJ6yhTknig018OKD6Hyso/eHar6RM9zfHy8qU1ERLfmLiaUy5d3/jxw992w2xwIotvx3x/RQP61RfPu1aGyILm4qkvMyMW0Df+almcOaILG4b4lvk9v0GPWX7NwMfWiXK7rWxdzusxRhhvFnQK+Hg3olTwGtB8P3FEomLhxrod585C5e7dcVvn6yrkenEPsa14NIiIie+Jar76pnXv+AmyF7V+5kU0RE1Cd/EtJrlc5O6HpnZblB1Rl4uJ9xsZjSMjIlcs9GoVgbJdaFr3302OfYseVHbLt4+qD1zq+psw0nX5dKdeam6qs2KAvcM9bymx0ZiR+9HHRuR4+eJ9zPRAREVVxrnVqm0qx554/B1vBAIIq1PnD8cjN0sp2/Xah8PCxnWnbb2bd/ihsO3FdtgO9XPHWAy0tSlj+K/ovLDu8TLad4ITX73wd1b2qA3mZwNqRQOoVZcXwlsADnwFq51vM9fCuaTnidc71QEREZAtUrq5wrVlTtvPOX4BBpxRhscsAYv/+/dBqlYvAwnJzc7F161Zr7BfZqRN/FpT2bXaX7ZduvZiQiXmbTpiW3xjWAqE+5udlKCw2MxYv7n4RhvyxXE+1fgp3Vb9L1LeF6vvHgWuHlRV9awAPrQfcvM1uJ2v//iJzPYQ8PxV+93KuByIiIlvhWl+pqmjIzYXGRqZAKFMAIeZ6EPM/3OjcuXOYNm1aqbaVmJgo39O5c2e0adMGjz32GM6LJJJ8ogzs6NGj0bp1a/Tq1atYBSi9Xo+lS5eia9eucp2JEyciKiqqyDolbYMqRsr1LFw7myLbAeGeiKhfMJO5LdLo9Hhu3RFTydYHO0aib7OSZ9PW6DR4/vfnkZo/PKlHjR6Y2HKibLv/9RacTm9WVnT1AR5eD/jml3K9Qd6lS7j69OSCuR5GjkTQhAnWOjwiIiKqAG6F8yDOnbOvAGLVqlVo0qSJfIgx33feeadp2fh44IEH5P+l8dRTT+Hy5cv4+OOPsWHDBri7u+ORRx5BdnY2kpOT8eijj6JmzZr49ttv5bqLFy+WbaPly5djzZo1WLBgAb7++msZUEyYMAF5eXnydUu2QRXf+9Dkzmo2Py/Bst/O4WiUEhDVCfbC7HubWvS+dw69g3/jlYTr6t7VsfCuhXLeB6cjq+F+4ENlJScVMGIVEGa+DKw2ORlRkx6HLlUJQry6dkX47Fk2/5mS7ROlW1m+lYjIcm75PRCmSkz2VMZV3MH39/eXF+gvv/wyXnrpJfj4+JheFxcunp6esifBUqmpqahevTomTZqEhvllrJ588kkMHjwYZ8+exd9//w0XFxfMnz8fzs7OqFevninYuP/++2WQsHLlSrzwwgvo0aOHfP8777wjeyN+/fVX3HvvvVi/fv0tt0EVQ6fV49ReZW4CldoJjTuXfKe+Kjt4ORnv/3ZWttUqJ7wzsjU8XUv+cdp+eTu+PPGlbLuoXPB297fh5+YHXPoTTpunFqwoEqbr9zG7DUNeHqInP4O8y5flsluDBqj+zhLO9UCVTvyOHTFiRGXvBhGRTXGrVxBA5J6zswBC/GEYMmSIKVgYOHAgXF1vLwHWz88Pb7/9tmk5KSlJ9nSEh4ejfv36WLZsmZxzQnxtIxGgfPTRR0hISMC1a9eQmZmJLl26mF739fVF06ZNZZ6GCCAOHDhwy20EBwff1jGQZS79m4DsdGWoTZ1WITadPJ2Rq8WUdUegzy9F+2zvBmgd6V/i+66kXcHsvwomi5veYTqaBTcDEs8D60bDKb9cq77DRKg6KkOabiR6/2Jmv4KsAwfksjo4WM71oPY2nyNBREREVZtrnTqmtvHmoN0EEN9//z0GDBgggwYRQGzZsuWm6xoDjdKYPXu27C0Q2//www9lb0ZsbKypZ8IoNDRU/h8TEyNfFyIiIoqtY3ytpG3cTgAhZl2taOJrGh+25PjuaFO78R1hNrf/hc378T9cScqS7TY1/TGpa+0SjydXl4upu6YiQ5NhmizugfoPQJeRCNWakXDKTpbP59XsBvSeD/VNtpf40UdI/eEH2XZyc0O1ZcugCg+36c/T3tnqzyyVjOfWPvG82q8qe25dXeX8Tfq0NGgTEqre/t1OADFjxgw5NCgoKEi2b0YEF2UJIMaNG4eRI0di9erVMk9B5DXk5OQU6+Vwc3MzVXwSeRKCuXXE8CihpG2UlRjKlZ6eXub3387XFcckPmdVft3gqi4jKRdRp5QLZO9AV/hVc66Uz84afjudiG8OKsGQp6sK8++pi+wsZQbpW1l0ZBFOJ5+W7UivSExpNgUZqcnw+n4c1InKUChtYAMk9FwE96wcqFRKDk9h2du2I2WpUvZV8JvzCrR1atvsZ+kobPFntqzEHz3R+yt06NDB7nMhHOncOhKeV/tVlc+tU4A/IAKIxMRK+7suPh9LPxeLA4j3338f3vnDJE6dOgVrE0OWhFdffRVHjx7FV199JROqjcnQRsaLftFDIV4XxDrGtnEdDw8P2S5pG2UlPuDCOSAV+QdaDGMR58JW/jif/iPRNPO0SJ729St5huaqSEwUt3BrwdjEOfc2RdOaSm/WrWy+sBk/Xv5Rtt3V7ljSYwnC/EPhtOV5qKL+ks8bPIOgH7UWbupAs+c2+8gRpC5YYFoOnjIFgYMHW/HoqLzY4s9sWYny3mIoqiCOt/DQUXvkSOfWkfC82q+qfG5TgkOQffkKDFlZ8HJxgarQdW1FKU1QZfFvd5Go/Msvv8j8hN69e8sqRiKp+naIPzQiUbpfv36mPzRi50UwERcXJ7+W+L8w43JYWJhpLgrxnKiyVHidRo0ayXZJ27gdlfXNZ6xyUtW++c0RP6hn/lEmWYMT0PSOajax3+aOY+6mk0jKUvIU+jULw4gONUusenQ+5TwW/rPQtDyz80w0Dm4M7P0QOLRKeVLtCqdRa6AOqgt1enqxc5t39SquTX5GJk8LfvcPQ/BjE1lxyYbY0s/s7f6cGH8mHOF4HencOhqeV/tVVc+tc1BQwUJqKtReXqjKLA4gRLQmkprbt2+P6Oho/PTTT6YeiRtZOoRJJDFPnToVn3zyiRweJWg0Gpw4cULO1yDyE0RpVhExGk/03r17UadOHTmUSvQAiH3Yt2+fKYAQ81OI94uqUcZu9Fttg8pX7PlUpCXkyHaNRgHwDqj4iNoaNv0bg5//izXNNv3q0BYlXsBnabLw/K7nka1VhtoNrT8UQ+oPAc5sBba+XLDife8DNTuLWyPFtqFLS1PKtebf1fXs1AkRc+YweCAiIrIj6qBAU1ubmASXatVgFwHElClT8NZbb8meB3HhtHBhwV3VsuZAiOTmbt26yW2Jh6jKJKojiSBAzAUhchVEcDFz5kw5t8O///4rqzTNmzdPvl/kNohAQczrEBgYKEvCLlq0SPY69O3bV64jSrXeahtUvk7tUy66BVst3RqXnoNXfvjPtLxgcHMEeyt5NLfy1v63cD5VGfLUIKABXur0EhB/GtgwXs44LXV9AWg10uz7DRoNop+bYqoJLao01Fj6Hpxus/oZERERVS3OgQU3tTWxMfBo0Rx2EUCIC3HjvAmNGzfGn3/+aZUSqEuWLJGlXEWAIpJGRA+HSKSulh95iYt/kRcxdOhQhISEYPr06bJt9Mwzz8ihTLNmzZKJMaLH4dNPP5VzPwiil6GkbVD50Gp0OH9QGS7m7KZGndYhNjkk4+WNx5CSP3RpYMsI+SjJ1ktb8e1ZZbJCD2cPLO6+GB6aHGDtg0BefnJUk/uAnjNvuo3rr7+BzD17ZFvt74/Ij1ZA7Wfbs3cTERFRUVkHDyJ9xw7TcvQzzyKtT28EPvooPNu2RVXkZBBXSKUkhjCJC3zR2yASlG93Pghbc+zYMfl/ixYtKvxri6FYItASw7eq2vi9G507GIet/6fcuW/UORx9HrFspuaq5NuDV/H8N0dlO9jbFb9O6S6HMN1KdEY0hv84HOkaJVCYf8d8DK13H7B6OHA+/xdEWHNg/K+Aq5fZc5v2zQbEzp0rn3dycUHNVZ/Bs1278jtQKje29DN7u8TNnG+++Ua2hw8f7hBJ1I5ybh0Jz6v9qornNnntWsTOXyCG8IgySAUviP3T6xE+5xUEjBpV5a5vy1TDSgwVEnkFIk+hdevWiIqKwpw5c7B8+fKybI7s1OlCw5cadbK94UuxqTmYu+m4aXnhkBYlBg8avQYv/vGiKXi4p/Y9St7D9rkFwYNHIDBqTZHgoTAxSVxsoSGC4XPnMnggIiKyw56HWBE8iHv5hYMHQeRFGgyInTcfWYcOoaopUwCxadMmOexIDAMyDhWqV68eVqxYgZUrV1p7H8kGZaXl4cp/ibLt5e+G6o0CYEtEx9yMjf8iPUep9DWkdTX0b15yEPThkQ9xNF7psajuXR2zu8yG07ENwJ6lygpOamDEF0BALbPv18bEIOa5KeJWrlwOHDcW/vcPs96BEZUj0ePw4IMPyoe99z4QEd2upFWrRPnRW6+kUinr2UMAIYIEkZQ8efJkU83YsWPH4pVXXsG6deusvY9kg84euA69Xhkd16hTGFSqW1csqmrWH4jCrtPxsh3q44a59zUr8T37Yvbhk2OfyLazkzPe6vYWfBLOAT8+XbDSPW8CdZSKYzfSZ2Yhedp06JKVSfe87rgDodOmWeeAiIiIqMrQ5+QgfcdvZiswFiGGXW3fIde3+QDi4sWLMtn5Rp06dUJMTIw19ots3Om9BcOXGtrY8KXolGws+OmkafmN+1vA3/PWQ5eScpLw0u6XYMifMe/pNk+jpUc48PXDgDb/h77tWKDDBLPvN+j1iJ05E9pz5+SyS62aqP7OEjjxLi4REZHd0WdkFB+2dNOV9cr6th5AiOpLIoi40eHDhxEaWvLMvGTfEq9lIP6KkgMQUtMHQdXMzxdSVYcuvbTxGDJylSFEw9vVQK/GYSW+Z/ZfsxGfrfRYdInogkcbPwysGwOkRSsrRXYCBixWkqTMSFj+ITK2bZNtlbc3IpcvZ8UlsskERVGhTzxEm4iIzBN/60scvmRaWaWsb+sBxMiRIzF//nzsyC85deHCBaxdu1aWSh02jOO1Hd2ZffkzT+dXX7Il3x6Kxh9nlEAg3Ncds+4tuXLU6pOr8cfVP2Q70D0Qr931KlQ/Twei9ior+FQDRnwpatmafX/ar78i4f33lQUnJ4Qvegtu9epZ7ZiIKooIpkVRDfEoQ4E/IiKHoXJ3h0/vXkq1pVtRq+HTp7dcvyopU5bbxIkTZRksMYt0bm4uJk2aJBPmRo0ahccff9z6e0k2Q1w0iPwHwUnlhAbtb333viqJS8vB/EJVl14d2hx+HkqRgJs5mXgSSw4uKXjPXa8i+PgPwKHPlSec3YFRqwEf859DzqlTuPbiDNOyz5NPwrtbt9s/GCIiIqrSAh95ROY33JJeL9eraspcJkMED0888QTOnTsnLxrr1q0L7yrWvUIV7/qlNKQnKmP+azQOgKevbcwRIoch/fAf0vKrLg1uXQ29m9w6+MnSZGH6H9Nl6VZhXNNxuEs0f36xYKVBS4Hq5ieB0SYl4eqTT8GQnS2Xfe4dCK/RD1vvoIiIiKjK8mzXTs7zIEq1yuFMhYd+FpoHoipOJlfmACI7OxtnzpyBRqORF18nTxYknYrZoMkxnduvzDwt1G9nO/kwW47FYutxpeckyMsVcwaVXHXpzf1v4lLaJdluGtQUz9YbCnxyN6BXghDcMRloNdLsew15eXKmSc21a3LZvUULhM2bh0yNEowQERGR/QsYNQpuDRvKUq2yN0IkVqtUcniT6HmoisFDmQMIkfswY8YMZGRkFBvnKmanLhxMkOMw6A04d1C5CFepnVC3dQhsQXJmHub8qMyYLcwb3KzECeN2XN6BjWc3yraHswfe6jIfLt88CmQpc1+gXi+gz7ybvj/2tdfkhHGCc0gIary/TBnfyACCiIjIoXi2bSsfolSrqLYkEqarWs6DVQKIxYsXo0uXLnjyySfldOBEQsz5FGSm5sl2zWZBcPe6df5AVTH/pxNIyFD2u2/TMAxsEXHL9eOy4jD377mm5Zc6zECt3e8BMcoEcgioAzywUkRRN522PuVrZb4UJ1dXGTy4hIWxag0REZEDU7m7V/nA4bYCiKtXr+Kjjz5CzZo1rb9HZLPOFhq+1KC9bQxf2nkqDt8dVkqt+ro7Y+GQ5rIX7Wb0Br0s2ZqSmyKX+9TsgyEpScCR1coKLp7AyK8AD/Mzb4teh9hXXzMth8+fB49Wrax7UERERERVLYCoXbs2YmNjGUCQiV6nx/nDSgChdlGhdsvgKv/ppOVo8PJ3x0zLs+9tilDfW0f+a0+txZ5re2Q7xCMEc2oNgtNXIwpWuG8ZEN7c7Hs1sbG4+uxzgFbJkQh89FH4DxlinYMhqgLUajWGDx9uahMRkX0qUwDxwgsvYMGCBZgyZYqsvuTqWnS8eLVq1ay1f2Qjok+nIDtdSQCu3SIIru5lzs+vMG/8fAoxqUrFqG4NQ/BAuxq3XP9s8lksOVBQsnVh2xfgv/EJIL8KEzo/BbR4wOx79Xl5uPrss9AlKjkSXnd0QejzU613MERVgOi9EyW9iYjIvpXpN73IfRCzjIr/Cw/3EAnVTKJ2TMa5HwRbmPvhwKUkrNl3Rba9XNV4beithy7l6fIwY/cM5OmVXInRjR/CHX8sBTJilRVqdwXunn/T919fsBA5R/+VbZdq1VDt7bfhxAstIiIicpQA4rPPPrP+npDN0mn1uHBEmb3ZxU2NWs2DUJXlafVFhi5N69cINQI8b/mepYeW4kzyGdmu718fz4mehCt/F8w0/cBngNr8j1Py+vVI+eYb2XZyc5NJ084B5nMkiGyZuLG0f/9+UzlvDmMiIrJPZQogOnbsaGrn5eUVG8JEjiXqZBJys5Rx/XVaBcPZtWqPff7kzws4cz1DtlvW8MOYLrVvuf7emL34/IQys7SLygVvhPWE2y+zlRfVrkrStLf5krXZR47I3gejiPnz4N60qfUOhqgKEb3QFy9elO327dtX9u4QEVE5UZX1jWvXrkWvXr3QunVrREVFYc6cOVi+fLl1945swoXDSu+DUK9t1a6+dCUxC0t3nJVtlRPw2tAWUIvGTaTmpmLmnzNNy881fBCNthdUUcKAxUCNdmbfq42Px9VnnoUhf26HgDFj4Dd4sPUOhoiIiMhWAohNmzbh7bffxtChQ+HiotT6r1evHlasWIGVK1daex+pildfung0Qbad3dSo2TQQVfnu6Owf/kOORi+XH7mjDppX97vl+vP/ni/nfRA6h3XA6P3rAG22skLbsUC7cebfq9Hg6pQp0MYp7/Vs3x5h06dZ/6CIiIiIbCGAEEHCzJkzMXnyZKhUyibGjh2LV155BevWKRNkkWO4djYFOZn51ZeaB1Xp4Uubj8Xg9zNKb0mEnzum9m14y/V/ufQLfr38q2z7ufphYboOqgSl9wLhLYB7Ft30vdfffAvZBw7KtnNYGKq/+w6c8oNtIiIiIocLIMQYV3PjWzt16oSYmBhr7BfZiPOFhi/VbWM+D6AqSM3WYN6mE6blOYOawdvt5ilACdkJeHXfq6blWWFdEXb8e2XB1QcY/jngYn7OiJTvv0fyV1/Jtggaaix9D87BVX9eDCIiIqJyCyCCg4NNiXKFHT58GKGhVXsMPFmPQW8wVV9SO6uqdPWlxVtPIz49V7b7NAlFv2Zhtxy6NG/PPJn/IPQL74z+ewoNzbtvKRBUz+x7s48fR+ycuablsFdmc6ZpIiIisitlCiBGjhyJ+fPnY8eOHXL5woULMqn61VdfxbBhw6y9j1RFxV5IRVaqMi9CZNPAKjt53NGoFHy177Jse7ioMfe+Zrec8+HH8z9i19Vdsh3oFoCZZw8CWmXCOXSYADQ3/z2uTU7G1cmTYchVAhX/ESMQkD8rLxEREZG9KNMV38SJE5Geno6pU6ciNzcXkyZNkrOPjho1Co8//rj195Kq/PClem2r5vAlvd6AV374DwaDsjzl7ga3nPMhNjMWb/7zpml5DoIRkHBUWYhoBfQtGNZUmEGnw7Xnn4f2mjKEz6NVK4TNKqjeROQIxLwPoriGsU1ERPapzLeMRfDwxBNP4Ny5c3LIR926deHt7W3dvaMqS5xzY/lWlcoJtVtUzTH+3xyMwtGrylCkBqHeePTOOrc8pjl75iBdky6XB/k1Qa8jW5UX3XyB4atumveQ8MEHyNyjTCynDg5G9aXvQcX5UcjBiJ49d3fzPyNERGQ/yhxAiJ6HzZs34+zZs3IiuYYNG+Kee+6RPRFk/+KvpCM9SRnWU71xANy9ql6FoZSsPLz5y2nT8rzBzeCivvmovQ1nN2DPtT2yHeoWgBf/+73gxfuWAYF1zb4vY/efSPhwhbKgVqPGO0vgEnbzHAsiIiIiW1amq/1Tp05hwoQJyMzMRJ06daDT6fDFF1/IieT+7//+DzVq1LD+nlLVHb5URasvLdl2BkmZSo7GvS0jcEe9m/eSRGdEY/H+xableUmp8DPmPXR8DGg2xOz7NDExuDZtmui+kMshzz0Lzw4drHsgRDZC/C04dOiQbLdt25bDmIiI7FSZkqhFsnTz5s3xxx9/YOPGjfjhhx+wc+dOWYFp4cKF1t9LqnIu5ldfErnIdVpVvQDi+LVUfLW3IHF65sAmN11Xb9Bj9l+zkaXNksv3OwfjrrhLyosRrYG+C286WVz01OehS0mRy949eiBo/HjrHwyRjRDDAMWwVuPQViIisk9l6oE4evSoDBx8fHxMzwUGBmLGjBl48MEHrbl/VAWlXM9CcqxysR1ezw+evq6oSmQuww/Hoc+/fpncuz4i/Dxuuv660+uwP3a/bEe4+OKFs4eVF9z8lLwHZzez74t7ewmyDyvrulSrhmpvvA6n/IkViYiIiOxVma52wsLCEBcXV+z51NRUBAQEWGO/qAq7+G+CqV2nZdXrffjucDQOXE6W7brBXhh/V51bDl165+A7puX516LgbbxzOuhdIND8e9O2bUPSqlXKgouLnGla7e9v1eMgIiIisukA4tq1a6bH2LFjMWvWLOzatQtpaWnIyMjAvn37MGfOHDz77LPlu8dU6S4VDiBaVa3qS+k5Gry25ZRpec59zeDmrL7lhHHZ2my5PFzrhs4ZSsUmtBl90/ke8q5cQcxLL5uWw158ER4tW1r3QIiIiIhsfQhTr169iky+JS6+xJwPNz730ksvYcgQ8wmnZPtyMjSIOaeM+fcP85SPquTd7WeRkKFM5Na3aRi6N7x5D8n3577H3zFK6dUwlQemXj2jvBBUH7jnLbPv0efl4epzz0GfkSGXfe7pj4CHH7L+gRARERHZegAhqiwRXf4vwTQpW52WVav34cz1dKzaoyQ/uzmrMPvepjddNy4rDov2LzItv3LtsjJ0SeUC3P8p4Opl/n2LFiP3xEnZdq1dGxELFt5yVmsiIiIihw0gOnbsWL57QjaX/1C7Cg1fMiZO6/Izp5/sUR+RgZ43XXfB3gUFE8bl6NAtO79ka585QLXWZt+X/ttOJH/5pWw7uboqeQ/e5gMNIiIiInvFWd/IYjqNHleOJ8m2mDguvK5flfn0th6/jr8vJMp2ZKAHJnU3P+mb8MulX7ArapdsB0KN6devKi/U6wV0fsrsezTXryPm5YK8h9AXp8O9cWPrHgSRjVOr1bjvvvtMbSIisk8MIMhi0WeSocnVyXbtFkFQqarG0J1crQ6vbVGGFQkzBzSFu4v5i5eknCS8vu/1gnWvx8Jfrwc8g4EhKwAzZVgNOh2uTZteMN9Dn94IeIh5D0Q3EsP5vLzYK0dEZO9YtJ5sfvjS53su4UqSMi9F57qB6Ncs7KbrvvHPG0jOVUq83p2Vg75ZSgUmDPkQ8DH/vsSPP0bWP//ItnN4OKotZN4DEREROa4yBRD79++HVqst9nxubi62bt1qjf2iKkbkDRjLt6qcnRDZJBBVgai4tGzHOdkWucwicfpmSc1i2NLPF3+WbV8D8HJCfkDU6QmgYV+z78k6dAjx73+gLKhUqL54Eed7ILoJvV6Pw4cPy4doExGRfSpTACHmgRDzP9zo3LlzmDZtmjX2i6qYhKgMZCQr5VFrNAqEq3vVGP32zrYzSM9VgtkR7SLRrJr5vIwsTRZe3feqaXlGQgKCdXogrAVw9zyz79GlpiL6hRcAnTJsK/jJJ+HZvn25HAeRPRBBw6lTp+SDAQQRkf2y+Cpw1apVePPNN013o++8806z67XkhFr2P/t0FRm+dCo2DWv/uSLbXq5qPN+v4U3X/eDIB4jNjJXtLtnZuDcjC3D2AB74FHB2K7a++B6PmTUb2msxclkEDsFPPF5ux0JERERkdwHE6NGj4e/vL+8qvfzyy3LCOB8fH9PrYtiIp6cnOnfuXF77SlVk9mmRQF3ZxAX+wp9OIr9qK57sWR+hPu5m1z2VdAqrT66WbTeDAbMTkiEHOfVbCIQ0MvuelHXrkL5tm2yr/fxQbfEiOLGqDBEREZHlAYSzs7NphmkRLAwcOBCurq78CB1ARnIO4q8ocyaE1PSBd4D5C/WK9NupOPx5Tglqqvt7YPxddcyup9PrMP/v+dAZlGFIjyWnIlLk79S/G2g/3ux7cs6cwfXX3zAtR7z2KlzCw8vlOIiIiIgcIgdi6NChSElJwfvvv4/nn38eiYmJ+OWXX3DhwgXr7yFVukvHlPkVhNpVYPZpjU6PVzcXlG19aUDjm5Zt/ebMNziWcEy26+Zp8GhqGuARCAx+X8m6voE+OxvRU6fCkKvke4hyrT69e5fbsRARERE5RABx+fJlDBo0CN99952supSVlYUtW7bg/vvvx9GjR62/l1SprhxPrFLDl77aexkXEjJlu32tAAxsEWF2vfiseLx36D3T8uzEJLiIxqD3AB/zPQrX33wTeefOy7Zbo0ZywjgiIiIius0A4o033kCfPn2wfft2uLjISzIsWbIEvXr1wuLFi8uySaqidFo9rp5S5k3w8HFBSGRB3ktlSM3WYOmOs6blW5VtfWv/W8jQZMj2kPQMtM/JBVo/DDRVZsq9UfpvO5Hy9TrZdnJ3R/Ulb0PlVjzBmoiIiMiRlakW56FDh7B69eoiF24iR+LJJ5/EiBEjrLl/VMlizqeaZp+ObBoIp0qefXrF7+eRnOB2p54AAFeiSURBVKWR7SGtq6FVpL/Z9f6K/gu/XPpFtv11OkxNSgH8agL9C3IbCtMmJCBm1izTctiMGXCrV69cjoHIXqnVagwYMMDUJiIi+1SmAEJUYjJX4zszM5N/NOx4+FKtZpU7fCkmNRsr/7wo265qFZ7va76CUo42Bwv3LjQtP5+UggBRrmnYR4C7r9mKTtdmzoQuKUkue/fqBf+RDISJSkvcVPLzMz8XCxEROfgQprvuugsfffRRkSBCJFUvWrSIZVztNYBwUnogKtOSX88gV6t8z427oxYiAz3Nrvfxvx/jasZV2W6fnYPBGZnAHZOBWneYXT95zRpk/v6HbKuDgxGxcMFNh0UREREROboy9UDMmDFDzkYtAonc3Fw88cQTiI6OlvNEiPwIsp/yrYnRSrJyaC1feHi7VuqkcRsOKUGBr7sznupZ3+x6F1Mv4rPjn8m2s5jzITEJTkENgJ4zza6fe+4c4t5aZFqu9tqrcA6s3ECJyFaJm0rHjx+X7WbNmkGlKtM9KiIisscAIiwsDN9//z1++uknnDx5Uv7RePDBBzF48GB4e3tbfy+pUlw5oQzpEWo2q9yL6jd+PgVD/qRxT/eqD39PV7NDkd78501o9Vq5LEq21tXolJKtLsXnrtDn5SH6hWkFJVsffhje3bqV96EQ2S3xt+C///6T7SZNmjCAICKyU2UKIAQPDw8MHz7cuntDVcqV/6pG/sNf5xKw63S8adK4sV1qm11vZ9RO/HXtL9mO0GoxMSUN6DQJqGl+dvT4d99D7qlTsu1avx5Cp71QbsdARERE5NABRHx8PN59911ZjUmj0cg7v4Xt2LHDWvtHlUSn0yPqpNID4ebljNDaxZOPK4Jeb8DrPxdMGvdCv4ZmJ40TidOibKtpvcRkeIiqS71fMbvdzL17kfSZMtQJLi6ovmgRVO6VP8M2ERERkV0GELNnz5bd1AMHDoSPT+XOC0Dl4/rFNOTlKOVbazYJhKqSyrf+/F8s/otOk+2mEb4Y3Kq62fVE3kN0RrRsd8rOwd1Z2cADSwFXr2Lr6lJScO3FGWLMk1wOfe45uDdpUq7HQUREROTQAcTevXvxySefoH379tbfI6oSrub3PgiRTStn+JJWp8eSbadNy9P7NzIbyIjA4dNjn5oSp18SidNtxwF1e5jdbuz8BdBevy7bnp07I/DRR8rtGIiIiIjsTZlKZHh6eiIoqHLnBKDyZZx9WqjROKBSPu7vDkfjfLxSBapj7UB0bxhidr3F+xcjV6ckQj+Ylo567qFA3wVm1037ZSvStmyRbZWvL6q98TqcWCmGiIiIqHwDCFFtSfRA6HTKEBeyL3nZWsReVIYN+Yd5wiew4nMD8rR6vLfjrGn5hX6NzM7NsOfaHmy/sl22g7Q6PJGcCgx6F3AvPpmVNjERsfPmmZbDZ8+CS3h4uR0DERERkT0q0xAmMWmcKOG6a9cuREZGwtW1aEnNL774wlr7R5Xg2tkUGMTMzWL4UiX1PqzbfwVXk7Nlu1vDEHSsU7yMrEanwRv/FMw7MiU5BT7N7gca9iu2rkj0j507D7pkpWfF5+4+8L333nI9BiJHo1ar0bdvX1ObiIjsU5nLuN7Liy+7Zay+JNRoUvHzP2Tn6bDst3Om5Rf6NjS73uqTq+XEcUKrnFwM0joD/V4zu27aT5uRvm2bbKv9/RE+Zw5nmyayMtFLyOGtRET2r0wBxOuvv279PaEqIyo//0GMGKreqOJ7IL7cewlx6UpOQ79mYWhZw7/YOvFZ8fjw6IfKfhoMeDkxCaq+bwE+YcXW1VyPQ+zChabl8Llz4BwcXK7HQERERGSvytwDQfYpMyUXyTFK4rKY+8HNo2K/RdJzNFi+67wpgHm+byOz67176F1kabNk+/70DDQNbQO0fcT80KU5c6BPTZXLvgPugW///uV6DESOPBP16dNK5bRGjUTVtDKl2RERURXHAIKKuHqqUPnWShi+9OmfF5GSpZHtIa2ro2FY8XlGjiccx4/nf5RtX50Oz6RkAMPfAcxcrKR+9z0ydu2SbXVwMMJmzy73YyBy5ADiyJEjst2gQQMGEEREdoq3h8js8CUhsknFDl9KzszDJ7uVnAZnlROe69PAbI/CW/vfNC0/kZKGgE5PAOHNi62riYnB9dcKciIi5s2Fc0DlJIUTERER2QsGEFTk4tw4/4OzqwphdYqXQi1PK/44j4xcrWwPbx+JWkHFZ5HednkbDsUdlu1aGg1GOgUAPWaYPZaYV+ZAn5Ehl/0G3wef3r3L/RiIiIiI7N1tBRD79+/H119/jYyMDJw7dw5arXLxR7YpNT5b5kAIEfX9oXauuPgyLj0Hn++5JNuuzio807t+sXXydHlYcmCxafn5pBS4DFwMuBYPNFK//wGZu3fLtnNoKMJefrlc95+IiIjIUZQpB0IEDBMmTJBjXUXZvjvvvBOLFy9GVFQUVq5cibCw4pVwqOq7dibF1K7esHjlo/K0YtcF5Gj0sj26Uy1E+HmYLdsanRkj252yc9CjZh+zcz5o4uJwvVClsPB5c6H2q9jeFCIiIiJ7VaZbzEuWLJH/b9u2De7uyizF06ZNkxPKvfXWW9bdQ6ow0WcL8h+qN6y4XIG4tBys3ndZtt1dVHi8R91i6yRmJ+LjIwVlW6el5cFpwFvmqy7Nnw99mjKTtpgszqdnz3I/BiIiIiJHUaYAYufOnZg+fbqchdqoXr16eOWVV/D3339bc/+ogogLb2MPhMh/CKlVvPpRefnw9/PI1Rb0PoT6KEFpYcuPfIAMnTIz9bCMTDTqNgPwrVZsvfRffkHG9h2yrQ4MRNhMDl0iIiIiqvQhTElJSQgJCSn2vK+vL7KylNr8ZFvSErKRkZyf/1DPD2p1xeQ/XJe9D1dMvQ+Tutcrts7Z5LPYcGaDbHvq9XhaHQZ0mFhsPW1yMmIXFJowbvYsVl0iqkBqtRq9evUytYmIyD6V6SqxRYsW+Pnnn4s9v3r1ajRt2tQa+0UVLLpQ/kO1Chy+9OGu88jL730Y26U2QnzcivWMLN73OvQwyOUJKWkIvudtQF089r3+2uvQJSnzWHj36Q0fThhHVKFETpzIgRMP0SYiIvtUph6IqVOn4n//+x/+/fdfWXnpww8/xPnz53H8+HF8+umn1t9LqtgE6gYVk0Adm5qDNf8ovQ8eLmo81q147sOf0X9iz/X9sh2h1WJM5N1A7TuLrZe+cyfSNm2SbZWvL8JfeYUXMERERERVpQeibdu2snyrp6cnatWqJasxhYeHyx6ITp06WX8vqVyJu/zRZ/Lnf3BRIbS2b4V84h/uOleo96EWgr2L9j5o9Bos3lswJGlKWg7c+75abDu6tDTEzplrWg6bMQMuoaHluu9EZH4m6jNnzsiHaBMRkX0qUw+ESJTu0qWLVSoupaSkyKpOu3btkuVhGzVqhOeffx7t27c3fa1FixbJHo6IiAhMnjwZAwcONL0/NzcXb7zxBn755Rfk5OTI8bczZ85EYGBgkf291TYcXXpijin/IVzkP1TA/A8xqdlY+0+Uqfdhopnehw2nN+BC5jXZbpWTi/4dngV8I4qtF/f2Emjj4mTb66674Dd0SLnvPxEVJ4KGgwcPynbdunWhUnGuUiIie1Sm3+5i+JK4UF+6dKmc++F2iOFQhw8flkHEt99+iyZNmmD8+PG4cOGCvOCfNGkSunbtio0bN2L48OGy+lPhSk9z587Fn3/+iWXLluHzzz+X73vmmWdMr1uyDUdn7H2oyPkfZO6DLr/34Y7ivQ8ZeRn48OC7puXpOm84dX6y2HayDh1Gyrp1sq3y9ETE/HkcukRERERU1XogduzYgR9//BE//fSTzH8QQ5qGDh2Ke+65B15exWcFvpnLly/jr7/+wpo1a9CuXTv53OzZs7F7925s2rQJiYmJskdiypQpplKxJ06cwCeffCJ7QK5fv47vv/8eK1asMPVYiECkf//+Mihp06aNDCputQ0qmv9QrUFAhVRe+jq/98HTVY3HuhbvfVh5dAWSdUpFr3syMtGy3/+J+rJF1jFoNIidM8e0HPLcs3CpVry0KxERERFVcg9EtWrV8Pjjj8sAQvQatGzZEh988AHuuusuvPjiixZvJyAgAB9//LGs6mQkKneIR1paGg4cOFDsIr9z586yi1yM2zd2lYvnjOrUqSMrgOzfryTelrQNAq6dUwIItYsKYRWQ//B/f1ww9T6M6VwLQTf0PsRlxeHLE1/JtrPBgMmB7YD6vYttJ3HVKuSePSvb7k2bIuDhh3k6iYiIiMrZbQ9QFWVb7733XvkQ411F74SlxLwR3bt3lzNYG23dulX2TIghR7GxsTI5u7DQ0FBkZ2cjOTlZ9kCIIMTNza3YOuK9QknbcHSZqblIS8iR7dBaPjKIKE/JmXmmeR/cnFWYYKb3Yfm+N5EDnWyPSs9CZP/FxdbJi4pCwgfLlQWVCuHz58OJdeeJiIiIquYQJkHkPohhRuIhLvhF9SUxE3W/fv3KvDOHDh3CSy+9hL59+6JHjx4yKbpwcCEYl/Py8mQQcOPrgggoRHK1UNI2bodOp1zkViTxNY0Pa4jJ730Qwur4lvsxrfzzArI1ytcY0a4GAj2di3zN8ynn8d2VX2XbW6/HhAYjofOtIQ7ctI7oOYqZNx+GHCXw8X/oIbg2aVwp56Mqn1uqGhzpvIpjNPbsira9zwXhSOfWkfC82i+e20oOIEaMGIFjx46hRo0aGDJkiMx/EMOabsf27dvxwgsvyHyKxYsXmwKBGy/yjcseHh5wd3c3GwSI4EG8bsk2bqfaSHp6epnffztfVwRF4g+zNSqcXDkdb2r7RbiU6zFl5Gqxas8l2XZWOeHBtiHFvt47u2fDWPzxf5k6qNs+VWyd7O3bkfXnn7KtCgmB26OPVMq5qOrnlqoGRzqvYl4g8RDEz6Szc5nvUdkERzq3joTn1X7x3Jb8+Vj6u6xMv91FIvK0adPQoUMHWMNXX32FV199VSY/v/nmm6YeAlFyNS6/PKeRWBbzT/j4+MihSaIMrAgICvcyiHVEHoQl2ygr8QHfzvtv9w6ft7c31FYYspN0VbmLL9RuGgYPn+I9Otay5vAFpOcqd+oGt66GRjVCirx+IHY//kw9IduhWi0e7jgVbkERxeZ8iHv3PdNy2KyZ8LlhiJqtsva5parBkc6r+OPTs2dP2fbz87P7i2pHOreOhOfVfvHc3lppfmeXKYB4/fXXYS2iAtOCBQswZswYOX9D4S5vUVnpn3/+KbL+3r17ZS+FOEhRuclYd9yYKH3x4kWZG2EMbkraxu2orD8Y4usaH7dDp9Ej/opy594v1APe/mXvkSlJjkaHlX9dlm1xip/sWb/I/os/wu/tKaio9LTOG57txwOqoscYt3QpdAkJsu3dqxf8+va1q2ES1jq3VLU4ynkVxxcZGQlH4ijn1tHwvNovnlvrsDiAEPMziPkWgoKC0Lhx41tetJ08edKibYqL/ddeew133323nKshIf/CUBDDk0RQIYZHiSFN4v/ff/9dThgnSrAKopdBTAg3a9YsuR0xJGnOnDno2LEjWrduLdcpaRuOLD4qHXqtMl45oq5fuX6t9QeikJCh5KUMaB6BeiHeRV7/7eLPOJalTBpXPy8P9/V+r1jwkH3sGFK+VuZ8cPL0RPisogEnEREREVWhAEJcoBuH7FirB0JUXNJoNNi2bZt8FCYu9sUM08uXL5ezSIv5HETOhWgXLssqei/Evj399NNyuVu3bjKgMGrQoEGJ23BUMedTTW0xA3V50ej0+Oj3C6blJ3vWK/K6Tq/D+3tfMy0/41EP6nq9iqxj0OsRO3+B6KqQyyGTJ3POB6IqRvQIX7qk5DnVrl3b7ocwERE5KosDCHFBbyTu+g4YMKBYdaOsrCysX7/e4i8u5pIQj1sRAYF43IzIZVi4cKF8lHUbjir2QqEAohx7IL4/HI3olGzZ7tkoBM2qFf1av5xYi3MaZV9a5Oahx9CCHAej1I0bkXPsmGy7NaiPwNGc84GoKgYQ+/btk+2aNWsygCAicvQAIikpSVabEESpVXFnX8zBcOPQJTET9COPPGL9PSWrEjkHsfk9EK7uagRGWD6DeGno9AZ8+Pt50/LTveoXeV2j12D5oYKAYXJYVziFNCi6jdRUxL29xLQcNnMWnFxcymV/iYiIiMhKAcQff/yBGTNmyN4HcfH5wAMPFFtHPC8mhqOqLz0pB1lpeab5H5xU5ZNLsPV4LC7EZ8p2pzqBaFcrsMjrPx5YhisGJTDtkKtF5z5vFttG/HtLocuf9M93wD3w6typXPaViIiIiKwYQIj5HqpXry67qMeNG4elS5fKMn1GIrAQw4kaNmxo6SapEsVdKpg3IaxO+QxfEgHlBzvP3bT3IU+bixUnvgDyY5dnGoyAk1dQkXVyTp5E8tdfmxKnQ6dPL5d9JSIiIqJyKONqLI36xRdfyDKo9j5JkD2Lu5xmaofWKp/5LP48l4Dj15Sv07KGH+6qH1zk9W/+eAWxTsq8EF01TmjdrSD53TTMasFCMbBaLgc/8Thc7GTOByIiIiJbVaYIQJRJPXXqFM6cOSN7JIwXe2JCNzFD9a0SmqlqiLtc0AMRWsu3XL5G4cpLT3SvV6TkalZuGj6+vAXIL9Iyue0zgLpoXkPapk3IPnRItl1r10bguHHlsp9EREREVM4BxGeffSZnjBaMORHGtpi4jao2g96A+PweCC8/V3j5u1n9axy7mip7IITaQZ7o26xoz8GaHdOQlB889DV4okmb8UVe12Vk4PqiRablsJkzobqh6hcRERERVbwyFelevXo1Jk6ciKNHj8pKTGJyth9++AH16tVD7969rb+XZFWp8dnIy1GGDoXWLqfehz8KKi9N7FYX6kJJ2mnp1/DZ9b9kW2Uw4Kk7ZinTUxeS8P4H0MXnzzjdpze8u95VLvtJRNYj5n2488475YNzQBAR2a8yBRCxsbEYPnw43Nzc5KzUYthSo0aNZJWmDRs2WH8vyaquXyqc/2D9AOJKYha2HIuR7WBvV9zftkaR17/YPhVp+QHFvc7BqNtwUJHXc8+eRdKXX8q2k5sbwma8ZPV9JCLrE0GDmP+Bc0AQEdm3MgUQotqSTqfcwRZ/KM6dUyrtiB6I6Oho6+4h2VwC9f/tvgC9MqoNj9xRG+4uatNryQmn8WWKMiGcs8GAJ7oXzEBtSpx+7TUg//sr6LGJcK1R3er7SEREREQVGECICkwff/wxsrOz0bRpU/z2228ymfrgwYPw8iqfCcnIeuLLMYE6MSMX6w9EybanqxpjOtcu8vqqHc8jS6V8293vUQs1Iu8o8nrGzp3I+nuvbLvUqIGg8UVzI4io6hJ/B65cuSIfxgIbRERkf8oUQEydOhW7d++WuRADBw5EQkKCrMz04osvYtiwYdbfS7IavU6P+CtKAOEb7A53b+vO6Pz535eRq1UuHB7sWBN+ngXbT7q6H2uzLsq2q8GAib0XF3mvIS8PcW++ZVoOnTYNKnd3q+4fEZUfETT89ddf8sEAgojIfpWpCpOYLG779u3IysqSPQ7r16/Hpk2bEBERgf79+1t/L8lqkmOzoNXoy6X3IStPiy/+viTbzion/O+uOkVeX7XrRWTn9z484NsEYcFNiu7b2rXIu3xZtj3bt4dP37utun9EREREdPvKPBOcu7u7fAjBwcF49NFHrbA7ZMsJ1Ov2RyElSyPb97Wqhur+HqbXEs9vx9ea6yLLUvY+jO9VUKJV0KWkIH75hwX7NmNGkXkjiIiIiMjGAohevXpZfEG3Y8eO29knqrD8B+slUGt1enyyWxmeJDzWvW6R11f9OcfU+zA8qC1C/YvmRsR/sBz61FTZ9hs8GB7Nm1lt34iIiIioEgKIoUOH8o6wHYiPKgggQmpaL4D49cR1RKdky3aPRiFoHF7Qu5FwahO+1qfI3gc3A/C/Hm8UeW/uhYty+JLg5OGBkKlTrLZfRERERFRJAcTkyZOt/KWpoun1BiRGZ8i2b4gHXD3KPIKtmJV/FvQ+jC+c+2AwYNWehchR5/c+hHZEqE+1Iu+Ne+stQKuVbVF1ySUszGr7RURERETWVaYryO+///6Wrw8ZMqSs+0PlKOV6FrR5SgJ1SKS31bZ77GoqDlxOlu0God64q36w6bWE4xuwzkkELfm9D91eLfLezD17kLFrl2w7h4Uh6H/MpSEiIiKyuwBCzDhtjpiZOjw8nAFEFZVQaPhScKT1hi999ldB78Ojd9YpGOqm12PlvjeR45zf+xB+B0K8w03rGnQ6XH/jTdNy6NQpUHl6Wm2/iKjiZ6Lu1KmTqU1ERPapTAHEqVOniiyLWakvXbqEuXPnYuTIkdbaN7Ky+Chl+JIQYqUAIi4tB5v+vSbb/p4uGNqmYNbo+KOrsV6Vbep9GN9tYZH3pmz4Frlnzsi2e/Pm8B00yCr7RESVQwQNdesWLaBARET2xyq3iNRqNerVq4eXXnoJ7733njU2SeXeA2GdIUxf7bsCjc5gmjjOw1WtvKDXYeWBt5GbfxdyRLXuCPYMMb1Pl5GB+KVLTcthL82AE+9YEhEREVV5zta++xQXF2fNTZKVGAwGUwUmT19XePm53fY2czQ6rNmnTPymVjlhTOdaptcSD63CBmcxJ4QK7gYxqdycIu9N/Ohj6BITZdunf394tmt32/tDRJVLzD4dExMj22JiUQ5jIiKyT1ZLos7IyJAzUrds2dIa+0VWlpGci9xMrVXzHzYdvYaEjDzZvqd5OKoZJ47TabD64LvIcc+fdbpGjyK9D3lXo5H0+eey7eTigtAXnrfK/hBR5QcQf/zxh2wPHz6cAQQRkZ2yWhK1s7Mz2rRpI/MgqOqJv1Jo/gcrDF8SPRqf/XWpSPK0UdqhVVjrKqo9qeQ32LguLxfdl/fegyFPCTwCHxkH1xo1bnt/iIiIiMiGkqjJ8Sow/XMxCSdi0mS7VQ0/tK3pr7yg02DdwWXI8FB6HwZHdEW4V0HlpZwTJ5C2aZNsq/39EfTYY7e9L0RERERUcVhnzwErMFkjgXplodKt/7uroHRr1pGv8KWr1vTN9b/ORXur4ha/XbAfTz4BtY/1yskSERERURXtgfj3338xb948nD17FhqNSJQt6uTJk9bYNyqHHggXdzX8gvNzFcroWko2tp24LtuhPm64p3mE8oJOi43/vINkT6USU7/QjqjpW9P0voy//pITx8n9qF4d/qNG3dZ+EBEREZGNBBCzZs2Sk8aJsq3u7u7W3yuyqpwMjUyiFoJreMNJlT/RWxmt2XcFeqVyKx7qVBOu+RPF5R1dg89cNaZvqwmdXzS9x6DXI+7tgt6HkOeeg8rV9bb2g4iIiIhsJIC4fPkyNmzYgAYNGlh/j8jqjOVbrTGBXK5Wh6/3X5FtZ5WTnPtB0mmxad/biPNUvqV6BLVCw4CGpvelbd6C3BNKz5R706bwHTjgtvaDiIiIiGwogGjevDmio6MZQNhgAHG7CdS//BdrKt3ar1k4wnyVHijtsfX41EX0crjI5Ymdppveo8/LQ/y775qWRdlWThpHZH/EvA/t8ud04RwQRET2q0wBxPz58/Hkk0/i6NGjiIyMLPaHYsiQIdbaP7KChEIJ1CE1by+B+su/lYnjhDFd8ieO02nx699vIcpTCR46+TdCy5CC+UBS1q6FJjpatr3uuEM+iMj+iL8FDRsW9DwSEZF9KlMAsWXLFjmM6cMPPyz2mqjGwwCiaiZQq5ydEBDhVebtnLiWhgOXk2W7YZg3OtUJlG39sQ34P+dsAEpOw8SO00zv0aWnI+HDFaZlThpHRERE5IABxFdffYXnnnsO48aNg4fH7VX0ofKlydUh+XqWbAdV84ZaXfbKvV/uLZg4bkznWkrpVr0Ov//9Js55KsFDS5866Bje0bRe4sqV0KWkyLbvoEEy/4GI7JOYYDIuLk62Q0NDTeWdiYjIvpTpalKv12PgwIEMHmxAYnQGYLj9+R9SszX4/vA12fZ2c8bQtsrs0YYTP+L/VJmm9SZ2mGq6aNAmJyP58y+UF1xcEPLsM7dxJERU1el0Ovz222/yIdpERGSfyhRADB48GGvWrLH+3pDVxV+xTgWmbw9eRbZGuSAY1ra6DCJgMGD/32/hmLubfL6hZzV0r9Hd9J6klZ9Bn6X0fvjfPwyuNZSgg4iIiIgcbAhTeno6Nm/ejJ9++kkmUTs7F93MF1/k33WmKpP/cDsVmPR6A77aW5A8PbpzfvL0+R1YqY0HXJVhbP9rO7mg9yExEUmrV8u2k4sLgidNup3DICIiIiJbDiBEpY1BgwZZf2/I6hKu5ldgcgKCqpctgXrP+URcSFCGKXWuG4iGYUogcnr3G/jLUwkeqrn6o1+d/qb3JH66EgZj78OIEXCJyJ+tmoiIiIgcL4B4/fXXrb8nZHWi5yDpmnLh7xfiAVd359tOnh7bpbbSuLIXq7IuAN5KUDK21SQ4q5Tta+PjkZw/xM3J1RVBjz12u4dCRERERFVEma4o9+/ff8vXO3ToUNb9IStKi8+GVqOX7eDqZUugjkvPwY6TSlWVEB833N00TLav/fEmfvbylG0/tTuGNhhmek/iJ5/AkJMj2wEPjoJLWOhtHwsRERER2XAAMWbMGDnWXZTsMxLL4iGGN/3333/W3Ee63eFLooRrjbIFEBsPRUOrV87z8HY14CLKwMYew5dJh6Dz85XPP9h0DDxdlGBCcz0OyWu/lm0nd3cETZzI80dERETk6AHEjh07iiyLcn0XL17Ee++9hxdeeMFa+0bWKOGaT8wBUVoiQFy/P8q0PKJ9pPw/dfcifOujbM/NSY0Hm44u+JoffwxDXp5sBzz8EJyDg2/rGIjIdogbSK1btza1iYjIPpUpgKhevXqx52rWrAlvb2/MnTsXmzZtssa+kTUDiBqlT6DefynZlDzdpW4Qagd7AYnnsS56F7ID/OTzQ+oPQaC7MiO1JiYGKevXy7aTpyeCxo/nOSRyICJoaNKkSWXvBhERlTOr3iIKCAjA5csF5T6pagQQzm5q+AaVfsbwr/dfMbVHdVR6H3L+fBurfZXeBxWcMK5FQZCQsOIjGDQa2Q4cPRrOgUpgQURERET2w2pJ1BkZGfj888/RoEEDa+wX3aa8bC3SEpRE5qBqXnBSKfMzlGbm6S3HYmTbz8MF/ZqFA2nX8OP5n5AUpPQ+9I3shUgfJbDIuxqNlG+/lW2VlxcCH32E55DIwYhhj0lJSbIdGBhomheGiIjsi9WSqI1DmxYtWmStfaPbkJhfvrWsCdQ/Hr2GnPwKTkPbVIe7ixq63z7A575KsrTwSKuCBOnEj1YAWq1sB44bC+eAAJ4/Igcj8uF+/fVX2R4+fHixSUaJiMg+WCWJWnBxcUFoKMt1VsX8h7KUcP3mQEHy9MgOkUBOKn47vgZXgpRtdQppi2ZBzWRbc+0aUr7/QbZV3t4IHDfOCkdARERERHaTAyF6GqKjo2W+g2iLx2effYYDBw5Yfw+pTBILl3AtZQBxLi4D/15Nle0W1f3QJMIXOLgKq7xcTOv8r9Wkgq/16UogP/chYMxoqP2UIU5EREREZH/KFEBs3rwZ//vf/3Dy5EnTcyKgeOSRR7B9+3Zr7h9ZoQcisJpXqYcvGQ1pUx3Q5uHfAyvwr7ubfK6hTy10qdbFNOt0yoYNsu3k4YHAsWN5zoiIiIjsWJkCiBUrVmDGjBkYX6hM59KlSzF9+nQsW7bMmvtHZSByU4wBhHeAG9wL9RxY8t4fj0TLtsh/HNQyAvhvA1Y755rWGd1ivCk5MnHVKhhyldcCRo1i7gMRERGRnStTAHHlyhV079692PM9e/bEpUuXrLFfdBvSk3KQl6MrUwK1GLp0KTFLtu+oF4RQHzfE//0efvVSkqf9nb1wT517ZFubnFww67SrKysvERERETmAMgUQERERZku5Hj58GCEhIdbYL7oNidGZZc5/+OFIwfClwa2qA+d24JvcGGjzexzubzwK7s7usp385ZcwZCnBhv8DD8CFSfREREREdq9MVZgefPBBLFiwQPZEtGrVSj537NgxOQ/Ek08+ae19pFJKjikIIAIjLM9/0OkN2PSvEkC4qlXo1zwcmvVPYL2PEoSoocLIRiOVddPTkfTVauWNzs4ImsBZp4kcnZiJunnz5qY2ERHZpzIFEOPGjUNeXh6++OILmQ8hiBKuU6ZMwejRo629j1RKybFlCyD2XkhEfLqSz9CzcQj8ko/jp7iDSAwNls/1qtkLEd4RytdYsxb6tDTZ9ht8H1yqVeN5InJwImho0aJFZe8GERGVszLP8jNx4kT5SE5OlnNAeHuXfq4BKh8p15VhRYJfqIfF79tUqPrSfWL40p6ZWOvrY3ruoSYPy//1WVlIWrVKeVKlQvDEggnliIiIiMi+3XYfc0BAAIOHKkRUUUqOzTJVYHJ1tyxG1Or02Ho8VrY9XNToVV2HY+e2FJRu9a+PdmHtZDvl243QJSfLtu8998C1du1yOhoisrXfP6mpqfIh2kREZJ84SNXOZKdrkJulle2AcKVykiX2XUxCcpYyGVyvJqHw+PdLrPEpeP/DTcfI0q0GrRZJn31mej7oMfY+EJFCp9Nhy5Yt8iHaRERknxhA2JmU6wX5D/7hluc/bDkWY2oPbBqMhEOr8Et+6VY/Fx8MqDNAttN+2QrNNWWok1fXrnBv1MiKe09EREREVR0DCDtjHL4kBIR5Wlx9yTh8yd1FhV74B9+oswpKtzYaLku3ygnqPv3U9L6gCROsvv9EREREVLUxgLDnAMLCIUz/XExCQkaebPdoGAr14U+x3kdJnlbByVS6NfOvPcg9eVK23Vu0gGfHDuVwBERERERkd1WYGjduLMfDmyMqMoWHh2Pw4MFyToibrUcVEUBYNoTp5/8Khi+NqJmGbf8cQUKh0q3VvJUSrYmffmJaL2j8eJ5bIiIiIgdUpgDi5Zdfxttvvy0nlGvfvr1pFurVq1fL5/z8/OQcEa6urrLUK1V8DoSLuxqefq4lrq/XG/Dzf8rwJVdnFbqmfI9HzZRuzf7vOLL+3qtsu2ZN+Nzdp5yOgIiIiIjsLoDYvHmzDCJGjlSGtgh9+vRB3bp1sWHDBqxduxYNGjTAW2+9xQCiAmnzdEhLzDHlP1jS+3PwSrJp8rj+9T1w4fRGHA3zl8v1/eqifZgSICatLJT78L9H4aRWl9NREBEREZHd5UCcPHkSnTt3Lva86I04fvy4bDdt2hQxMQVDY6j8pcRlA4bSDV8qXH1pvM8+fONREBiMavyQDELyoqJk9SVBHRQEvyFDrL3rRGQnM1GLIa7iIdpERGSfyvQbvkaNGti5c2ex58VzIv9BuHLlCgIDA29/D8liybGFS7h6WjR86Zf84UsuaqBOzLfY5K0EHh5qNwysO1C25bwPer1sB44ZDZW7O88KERUjgoY2bdrIBwMIIiL7VaYhTE888QRmzJiBY8eOyT8Uer0eR48exS+//IL58+fj4sWLeOmll9C3b1/r7zHdVMr10lVgOhyVgphUZcjTuJqJ2JpxFVk+QXJ5QN174e3qDV1KClI2fiefc/L0RMCoUTwDRERERA6sTAHEoEGD4O3tjZUrV2LJkiVwdnZGo0aNsGLFCnTt2hX79++X6zz99NPW32OyqAKTvwVzQPxcaPjSgy678FJ+6VZheKPh8v+UDRtgyFGCDP9hw6D2V/IjiIhuJOaKycpSfg95elqWh0VERA4SQERFRaFnz57yYU6HDh3kgypWalx+AOEE+Id4lviH/tcT12XbR5WLrIRtOJmfPN0ssAmaBTWDQatF0po1pvcEjlYqMhERmaPT6fDjjz/K9vDhw+XNJSIisj9lyoG4++678fDDD+Pbb7813W2iypeakC3/9w5wg9rl1qf29PV0XElSzt3TYcewwbPgD/2IxsowpfTffoP2mtJL4dW9G1xr1y7HvSciIiIiuw0gvvzyS9SrV0+Wab3zzjsxffp0/P3339bfO7JYTqYGuZla2fYrofdB2HZc6X0Q+mA7fvZS3uOt9kD/2v1lO/nLr0zrBI4ew7NBRERERGULIMTwJJEs/eeff8ogIicnB48//jh69eqFpUuX8mOtBKnxSu+D4BfqUeL6xuFL9ZyisV97Adn5JRfvrT8Yni6eyDl1Cln798vnXOvUgdedd5TbvhMRERGR7bitQt0uLi5yONPcuXPx7LPPIjU1FR999JH19o4slhpfMJTML+TWAcS1lGwci06V7cf99uF7n4I5Ix5o+ID8P+mrgt6HgDGj4cSa7kRERERU1iRqQeQ+bNu2DZs2bcLevXtRvXp1jB8/HkOHDuUHWwnSCvdAlBBAbMvvfRCzzjVQ/4Xjbm5yqYl/AzQKbARtcjLSNv0kn1N5e8N/8OBy3HMiIiIisvsAYsqUKdi1a5cs0de/f3+sWrVKzkItiOFMVPFSxSzU+UrKgfj1hDJ5XBunc9jhKt7nIpcHN7xf/p/yzQYYcnNl2//++6HysmxWayIiIiKyf2UawpSQkIA5c+bgr7/+wmuvvSaDh3PnzmHhwoXo1q1bmXdGDH8aM6Zosu7JkycxevRotG7dWuZYfPHFF0VeF5PYibwLMf+EWGfixImyzGxptmF3ORC36IFIzdJg34Uk2R7l9Q9+yp952tlJhYF1BsrSrcnG0q1OTgh4+KHy3nUishPiplL9+vXlg3NAEBHZrzJXYRoyZAjUarWs+f3QQw/JiePWrFmDzp07l2lHVq9ejXfffbfIc8nJyXj00UdRs2ZNWTL2qaeewuLFi2XbaPny5fLrLliwAF9//bUMKCZMmIC8vDyLt2FPAYSnrytc3NQ3XW/n6Tho9QaooEeY+34kOCvrdqt2J/zd/ZG+fQe0sUoPhXePHnCtWbOCjoCIbJ34m2CcB0i0iYjIPpVpCNPly5flxfp3332HlJQUeadp2LBhshJTZGRkqbZ1/fp12Zuxb98+1L5hnoH169fLRG1R8UlMSCRKx4qv/fHHH+P++++XQYKYDfuFF15Ajx495Hveeecd2Rvx66+/4t577y1xG/YgL0eLrLQ8iyowbT+p5D90cDqN3W4aAO5yuX+9QfL/pK++NK0bOGZ0Oe41EREREdl1D4SYYfTnn3/GI488IvMeRC+EGBK0aNEieadJ3OUvbfAgHD9+XF7gi56MVq1aFXntwIED6NixY5HZTEUPx6VLl+QwqlOnTiEzMxNdunQxve7r64umTZtif34J0pK2YQ/S8ieQK2n4klanx+6zyjEPcduL7flzP7g7OaN7je7IOXEC2QcOyudc69WDZ6HPlYioJGKGe5EHJx6iTUREDt4D0b17d6Snp8uLbzFcSJRv9fPzk6/NmDGjzDsgchLEw5zY2Fg0bNiwyHOhoaHy/5iYGPm6EBERUWwd42slbSM4OLjM+y6CqoomvqbxYZQcm2lq+wS733S/Dl5ORmq2BmroEOp1CElqJf/hrup3wU3lhtjV+bkPInn64YfkcDCq3HNLts+RzqtWq8XGjRtlW/TwFr5xY48c6dw6Ep5X+8Vzaz0W/3YXwUNQUBCqVasGf39/eHiUPFnZ7RJ3sVxdXYs855ZfcjQ3NxfZ2cqdd3PriDkpLNlGWYmLa/GZVDTxdcUxiWFjqvy5GeKiU0yvu3or58qcbf9Fy//vUB3Hbo+Cu4PdInohNSYG6Zs3y2UnLy849ehRKcfnyMydW7J9jnReRQAhHoL4/WHvAYQjnVtHwvNqv3huS/58LP1dZvFvd1FxacuWLTL5eO3atfDy8kLv3r0xYMCAcqu24e7ubkqGNjJe9Ht6esrXBbGOsW1cxxjglLSNshIfsI+PDyojehZDA7y9vU1JirlpMabXwyIDb7pff19Mk//fo/obyz2Vz8dD5Yq7692N3PXfw5Bfgtf3vvvgFxZWAUdDJZ1bsn2OdF5F8GAMGsTvIXsPIBzp3DoSnlf7xXN7a6W5EWLxb3fxC3LEiBHycf78eWzYsEFOIidyF0QAIeaCECVUa9WqBWsJDw9HXFxckeeMy2FhYaY7XeI5UWWp8DqNGjWyaBu3o7L+YIiva3zcmAMREOZldr/i0nJwPCYNztAixPcoUtW+8vkekT3g5eqFuPXrTesGjhrFP4aV5MZzS/bBUc6ruJg23lByhON1pHPraHhe7RfPrXWUqc9VVDJ68cUX8fvvv+ODDz6QPRHff/897rnnHllC1VpEKcCDBw8WGV8qZr2uU6eOHE7VuHFjGdiICk5GaWlpOHHihHyvJduwp0nk3Lyc4e6lTAp3o11n4uX/nVUn8btnwWnvV3cAsg8fRu7Zs3LZo21buDcqmjNCRERERGSkut0oTgQP77//Pv744w9MmzZNlmW1FpGEl5GRgZkzZ8qJ6kRynujpmDRpknxd5DaICeLEvA47duyQVZnELNmi16Fv374WbcPWaTU6ZKTkljgD9a7TSq9Lb/U+/JY/dMtL5SYTqJO//tq0XsCokeW+z0RERERku6w2QDUwMFCWchUPaxE9BJ988gleffVVDB06FCEhIZg+fbpsGz3zzDNyKNOsWbNkMpvocfj0009laVhLt2HL0hJyAMOtS7hq8su3OkGPAO9/ka5WAoiekT2hTstC+i9b5bLazw8+/fpV3M4TERERkc2pUhlub7zxRrHnWrZsiXXr1t2yF0T0fIjHzZS0DVuWlj8D9a0CiEOXk5Geo0VrpwvY511Qfal/vXuR+t33MOQnmfsNHQpVfoUqIqLSEvkPYniosU1ERPapSgUQVHppiUrlJME3uKASVWE7Tyv5D32c92F1fvUlH5UbOod3wtV1r5vW8x8xgqeAiMpM3NARcwUREZF9Y+FqG5eeWNAD4RPkccv8h1Cvw8jIL9HVo0Y3aA8cRt7ly3LZs3NnuNVV7hwSEREREd0MAwgbl164ByKoeA9ETGo2TsWmo55TNE54Fazbu+5AJK8rKN3K5GkiskYZV+NkcqJNRET2iQGEjUtPUoICMdzYK6B4/sLv+cOX7lb9g51e+ZPrOTmjk3czZOzYIZfVgYHw6dWrQvebiOyPKJf9zTffyEfh0tlERGRfGEDYSQ6El78b1Orip1NUXxLqeB1AUv5ER3eEtYfm150waDRy2W/QIDi5ulbofhMRERGRbWIAYcM0uTrkZChBgI+Z4Ut6vQF7zicgHIm44p1ser53/UFI+f5707Lf0CEVtMdEREREZOsYQNhN/kPxBOqTsWlIztKgj/oAduRPHqeGE+7Q1ELO0X/lslvjxnBv3LgC95qIiIiIbBkDCBuWVqQCU/EeiD3nEuX/TdwPINpFqdjbPrAp9JuV3AfBb8jgCtlXIiIiIrIPDCDspAfCXADx1/kEeCAHiT7XTM/1rnsfUjdtUhbUavjde2/F7CwRERER2QUGEHYaQORp9dh3IQldVCfwu2dBdaauScHQxsbKtnfXrnAODq7APSYiIiIiW8eZqO2ghKvgE1g0gDgSlYJsjQ5t3ffjIzelwlILr0iof/vbtI7vwIEVuLdEZO+cnJwQGRlpahMRkX1iAGHDMpJzTW3vG+aA+OucKN9qgLP3aQBKANG9Vj+kL1Emj3Nyc4N3z54VvMdEZM/UajXuuuuuyt4NIiIqZxzCZMMyUpQeCHdvFzi7KHM8GInyrfWdovGfV8FkTl0TQ6FLSpJt7x49oPb2quA9JiIiIiJbxwDCRhn0BmSl5JntfcjR6OQQpm7qQ/jHXXktWO0J/93HTev43nNPBe8xEREREdkDBhA2KjtdIyeKE7z9iwYQx6JTodEZUM3zELJVyinuFtIR6du3y7aTpye8u3erhL0mInum1Wqxdu1a+RBtIiKyTwwgbFRGSkH+g1dA0QTqg5eT4Y0sxHmLPAhF75Q60KemyrZPz55QeRSfeI6IiIiIqCQMIGxUZqEAwttfSZI2OnQ5GXeq/sOe/PKtYvbpWgeiTa/7DhxQgXtKRERERPaEAYSNyixUgcnLv6AHwmAw4NCVZLRy24/LLi7yuXaedZDz+5+yrfLygherpBARERFRGTGAsFGZqeZLuF5JykJCRi6cvM+anhuQ1RT6tDRl3R49oHIt2mNBRERERGQpBhA2KjNVqcAkePq5Fsl/aOAUjaOeSoK10OxEtqnt07dvBe4lEREREdkbBhA2XIXJyNOnIIA4fCUFndVHsN9dGdYUofKC6s8Dsu3k7g7vrpzkiYiIiIjKjgGEjcrJUAIIJyfAzUvJdRD+jU5FiNdR5Kmc5PKQzEbQJSbKtnfXrlB5elbSHhORvXNyckJERIR8iDYREdkn58reASqb7Iw80yzUqvxgQaPT42xMEjqExorUavlch4sF5Vp9+t7Nj5uIyo1arUaPHj34CRMR2Tn2QNionPwhTB6Fhi+dvZ6BZrozOOih9EioDQb4/n1CedHFRSZQExERERHdDgYQNkibp4NWo5dtD++C4Uv/RaeipcsRnMuvsnR3UhD0cfGy7XVHF6h9fCppj4mIiIjIXjCAsEE5GVpTu3APxL/RKfDwPmVa7nUlyNT2ZfUlIipnWq0W69evlw/RJiIi+8QAwgblZBYKIAr1QBy/mow4z2RlwWBAzX+V5Gmo1fDu1avC95OIHI9Op5MPIiKyXwwgbL0HwtfVlEDtFHsUh/LzH+omqaC6Fifbnu3bwzkgoJL2loiIiIjsCQMIG5RrpgdCJFA3Uh/GdWelsFb/qIJ8ByZPExEREZG1MICw4TkgCudAiARqL6/TpudbXSoY2uTdvVsF7yERERER2SvOA2HrPRA+SqBwLDoVOR4i58EF7rkG+J9NkM+71KgB1zp1Km1fiYiIiMi+MICwkypMUVEXkZA/Z1zrSwY4aZUyr97dunFGWCIiIiKyGgYQNl+FyVUmUKsSDuByXaU3osclEVTkyDaHLxFRRXFyckJISIipTURE9okBhA3Ky1ICCPH32c3TGaeupyPQ9T/lRYMBTc4rvQ9Obm7w7NixMneViByIWq1Gnz59Kns3iIionDGJ2gZpcpUa6y7uznBSOeFkTBqcPa7K52rGAx6pebItggeVR/64JiIiIiIiK2AAYYM0uUoPg6u7Wv5/8loqUt1TZbvNeYNpPZH/QERERERkTRzCZMs9EG5KABEbfQln3ZTxxu2KBBBdK2kPicgRabVa/Pjjj7J93333wTl/XhoiIrIv7IGwMQaDAdo8vSmAEMv6+L/lBHKeOQY0vKoEEK61a8O1Vq1K3lsicjS5ubnyQURE9osBhI3RiuFL+Z0MIgciPj0X7molgbrlRQNU+a+x+hIRERERlQcGEDY6fMmYA3EyNh1O7tfkcpsLBcOXvJj/QERERETlgAGEjcnLLZgDwkUEEDFpSHZLl+VbW1wyFJRvbd++EveSiIiIiOwVAwgbo8kp1APh5ozLV6/ikhsQkQQEpynPi+BB5eZWeTtJRERERHaLAYQND2ESSdRZMfsR5+yMlvm9D4LXHV0qae+IiIiIyN6xxp6N0WmVCkyCykUFXd5h2TYOXxK8ujCAIKKK5+TkhMDAQFObiIjsEwMIGw4gsrQ6uLhehUpvQLPLSgChDgiAW+PGlbiHROSo1Go1+vXrV9m7QURE5YxDmGyMTlPQ05CSq0WOexLqxgBe+WXXvbp0hpOKp5WIiIiIygevNG24ByIlOwexrpoi+Q+eHL5EREREROWIAYQNBxDZaTG45OqMFpcKnvPqckcl7RkROTqtVosffvhBPkSbiIjsE3MgbDiASM86BycfoGG0suwSGQnXGtUrb+eIyOFlZWU5/GdARGTv2ANhY3SaggAiU3sV9a8BLvmVXT07dqi8HSMiIiIih8AAwsbotAX5DjmqRDSJKpT/0J4BBBERERGVLwYQtpwD4ZKOJlcKBRAd2lfSXhERERGRo2AAYcNDmNJUOWgUrQQQzuHhcKnO/AciIiIiKl8MIGy4B8I5Qwd3jdL2bNeOM78SERERUbljFSYbzoGIjCsok8jhS0RUFfj6+lb2LhARUTljAGFj9IV6IOrEaAolUDP/gYgql7OzMwYOHMjTQERk5ziEycZoC+VA1L6uBBA6L1e41qtXiXtFRERERI6CAYQN90D4ZSlDmFT1azH/gYiIiIgqBIcw2XAStUqvBBC+rTh8iYgqn1arxdatW2W7X79+ckgTERHZH/52t+EkamMA4dOiTSXuERFRgbS0NH4cRER2jkOYbHgeCJVeyYFwb9KkEveIiIiIiBwJAwgbHsLkZNBB6wy41q5dqftERERERI6DAYSNBhCi98EJQGqYO5w4zpiIiIiIKggDCBvNgXDKz3/Q1gyp5D0iIiIiIkfCAMLG6HX5PRAGnfzfo3HDSt4jIiIiInIkrMJkYwzGIkwGJZAIa9+1UveHiKgwT09PfiBERHaOAYSNMejzhzAZ9NA7AREd+1X2LhERSWLeh8GDB/PTICKycxzCZKNJ1E4GA64HAm4+/pW9S0RERETkQBhA2BitJi+/ZUBisLqS94aIiIiIHA2HMNkYvUZMHucqhzDl+rlX9u4QEZnodDps375dtvv06QO1mjc5iIjsEQMIG6PXG4cw6eHk51vZu0NEZGIwGJCUlGRqExGRfVI50oX30qVL0bVrV7Ru3RoTJ05EVFQUbE7+32Qn6OESHFbZe0NEREREDsZhAojly5djzZo1WLBgAb7++msZUEyYMAF5ecacAhthcDIlUftUr1fZe0NEREREDsYhAggRJKxcuRLPPPMMevTogcaNG+Odd95BbGwsfv31V9hiACHmgQhv0Lqy94aIiIiIHIxDBBCnTp1CZmYmunTpYnrO19cXTZs2xf79+2FLDDD2QOhRrxUnkSMiIiKiiuUQAYToaRAiIiKKPB8aGmp6zWY4KadMr9LD1585EERERERUsRyiClN2drb839XVtcjzbm5uSE1Nva2ShRVN9DwYnNTQO2kr5etT+RHn0/gg++FI51Uco4uLi6nt5JQ/5NJOOdK5dSQ8r/aL59Z6HCKAcHd3N+VCGNtCbm4uPDw8yrRNkYSdnp6Oiuam3QONqgPUwecr5etT+RHfUzk5OfKiS6VyiM5Bh+Bo51XM/1D4xo09c7Rz6yh4Xu0Xz23Jn4+lv8scIoAwDl2Ki4tDzZo1Tc+L5UaNGpVpm+ID9vHxQUUbs2ImEhPiERTcl5M02eGdEVE739vbm+fWjvC82i+eW/vE82q/eG5vrTQ3QhwigBBVl8RF2b59+0wBRFpaGk6cOIHRo0eXebuVNcuqh6eX/Nqc5dX+GM8rz6194Xm1Xzy39onn1X7x3FqHQwQQIvdBBAqLFy9GYGAgqlevjkWLFiE8PBx9+/at7N0jIrKbu3s7d+6U7Z49ezIQJiKyUw4RQAhiDgitVotZs2bJMasdOnTAp59+akr4IyKi2yOG4MXHx5vaRERkn5wdqctq2rRp8kFERERERGXDshFERERERGQxBhBERERERGQxBhBERERERGQxBhBERERERGQxh0miJiKi8sc5TIiI7B8DCCIiss4fFGdnjBgxgp8mEZGd4xAmIiIiIiKyGAMIIiIiIiKyGIcwERGRVeh0OuzevVu2u3btynwIIiI7xQCCiIiswmAwICYmxtQmIiL7xCFMRERERERkMQYQRERERERkMQYQRERERERkMQYQRERERERkMQYQRERERERkMVZhKgONRiMrjBw7dgyVQa/XQ6Vi7GePeG7tkyOd19q1a8v/T548CUfgSOfWkfC82i+e25vLy8uDk5MTLMEAogws/XDLC/9Y2S+eW/vkSOfV29sbjsSRzq0j4Xm1Xzy3t76+tfQa18nAYt1ERERERGQh3johIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYCwIXq9HkuXLkXXrl3RunVrTJw4EVFRUZW9W1RISkoKXnnlFXTr1g1t27bFgw8+iAMHDphe//vvvzFs2DC0atUK/fv3x+bNm4t8frm5uZg3bx66dOmCNm3a4Pnnn0dSUlKRdUraBpW/ixcvyvOzceNG03MnT57E6NGj5c9mr1698MUXX5T657ekbVD5+P777zFgwAC0aNECAwcOxM8//2x67erVq5g0aZL8eb7rrrvw7rvvQqfTFXn/6tWr0bt3b7Rs2RIPPfQQTpw4UeR1S7ZB1qfVavHee++hZ8+e8uf14YcfxpEjR0yv82fW9nz00UcYM2ZMkecq4jzy+ssMA9mMZcuWGTp16mTYuXOn4eTJk4b//e9/hr59+xpyc3Mre9co36OPPmq49957Dfv37zdcuHDBMG/ePEPLli0N58+fN5w7d87QokULw5IlS2T7k08+MTRt2tSwZ88e0+c3Y8YMQ58+feT7jx49ahgyZIjh4YcfNr1uyTaofOXl5RmGDRtmaNiwoeHbb7+VzyUlJcmfzZdeekmelw0bNsjzJP639OfXkm2Q9X3//ffyZ+irr74yXL582bB8+XJD48aNDYcOHZLnWpyjxx57zHD69GnDtm3bDB07djS89957pvdv3LhR/oz/8MMPhrNnzxqmTZsm10lMTDR9v5S0DSofS5cuNdx5552G3bt3Gy5dumSYOXOmoV27dobr16/zZ9YGiZ9R8bM5evRo03MV9buX11/FMYCwEeIbvU2bNobVq1ebnktNTZV/uDZt2lSp+0YK8QdKXFQeOHDA9JHo9XoZELz77ruG2bNnGx544IEiH9fUqVPlLzMhNjZW/nLctWuX6XURhIhtiosZoaRtUPl7++23DWPHji0SQKxYscJw1113GTQaTZH1xB8pS39+S9oGWZ/4+ezZs6fhjTfeKPK8+HkS50Ocm+bNmxtSUlJMr3399deGtm3bmi4+xPl56623TK+L89e9e3f5fsGSbVD5uO+++wyvv/66aTk9PV3+3G7dupU/szZE/G2cNGmSoXXr1ob+/fsXCSAq4ncvr7/M4xAmG3Hq1ClkZmbKoS1Gvr6+aNq0Kfbv31+p+0aKgIAAfPzxx3IYhJGTk5N8pKWlyaFMhc+f0LlzZxw8eFAE8vJ/43NGderUQVhYmOkcl7QNKl/iPKxbtw5vvPFGkefFeenYsSOcnZ2LnJdLly4hISHBop/fkrZB5TMULTo6GoMGDSry/KeffiqHHIlz0qxZM/j5+RU5JxkZGXLIQ2Jiojw/hc+rOH/t27cvcl5vtQ0qP0FBQdi5c6ccQiaGjImfXVdXVzRu3Jg/szbk+PHjcHFxwY8//iiH7lb0715ef5nHAMJGxMbGyv8jIiKKPB8aGmp6jSqX+KXUvXt3+QfKaOvWrbh8+bIceynOU3h4eLHzl52djeTkZFy/fl0GIW5ubjc9xyVtg8qPCAKnT5+OWbNmFfs5vNl5EWJiYiz6+S1pG1Q+AYSQlZWF8ePHy4uM4cOH47fffuN5tQMzZ86UF54iP0Xc2HnnnXfkWPiaNWvyZ9aGiJyEZcuWITIysthrFfG7l9df5jGAsBHiAlEofHEqiItNkXhLVc+hQ4fw0ksvoW/fvujRowdycnKKnT/jcl5enjzHN75+4zkuaRtUfubOnSsTMW+8W32z82IMBMW5s+Tnt6RtkPWJXgDhxRdfxL333ouVK1fizjvvxJNPPimLFfC82rZz587Bx8cHH3zwgex9EMUnXnjhBdnzw3NrHyriPPL6y7yC/hqq0tzd3U0Xica28Zvbw8OjEveMzNm+fbv8QyWqrixevNj0C+nGi3zjsjiH4ryaCwIKn+OStkHlV6VHdHNv2rTJ7Ovmzp3xj5Onp6dFP78lbYOsT9ydFkTvw9ChQ2W7SZMmsorSZ599VqrzeuM6PK+VS9w5FlXsVq1aJYeUCaIXQgQV4m42f2btQ0WcR15/mcceCBth7H6Li4sr8rxYFmPkqer46quvMHnyZFk6cMWKFaY7GeIcmjt/4heUuEsmulBFGdgbf5EVPsclbYPKx7fffivHu4ueJNELIR7CnDlzMGHCBHnuzJ0XQZw7S35+S9oGWZ/xc23YsGGR5+vXry/HzfO82q6jR49Co9EUyUkTxBh6MayU59Y+VMR55PWXeQwgbIRI+vL29sa+ffuKjMkWd8o6dOhQqftGBdasWYMFCxbIeuNLliwp0i0q7oL9888/RT6uvXv3yl4KlUqFdu3ayVrTxmRq4xhtkRthPMclbYPKh+hF2rJli+yJMD6EZ555Bq+++qo8P+K8Fa7tL86LSIIXiZyW/PyWtA2yPpHc7OXlJS82Cztz5owcJy/OiThHxqFOxnMi3iPOqTgv4vwUPq9i7gHRW1X4vN5qG1Q+jGPaT58+Xezc1q5dmz+zdqIifvfy+usmblKdiaogUftf1A/fvn17kVrGos44VT5RcrVZs2aGp556yhAXF1fkkZaWZjhz5ox8fdGiRbLW9KefflpsDgdRkrVXr16GvXv3muaBKFyyzpJtUMUoXMY1ISHB0KFDB8OLL74o5wIQz4s64mKOAEt/fi3ZBlnfBx98IMs8ipKOheeBED+DOTk5sgzz+PHj5TkzzuEgasIbrVu3TpaEFOfJOA+EqClvnAfCkm2Q9el0OsODDz4oy37+/fffhosXLxreeecdQ5MmTQxHjhzhz6yNEr8fC/9NrKjfvbz+Ko4BhA3RarWy3njnzp1lPeSJEycaoqKiKnu3KN+HH34oLyrNPcQvJuH333+XE82JuvDiD9vmzZuLfH6ZmZlysqP27dvLhwgoxCQ3hZW0Dar4AEIQAd+IESPkeRFzC3z55Zel/vktaRtUPlauXCkDdxGci7kDxEV+4fldxASR4oJC1IoXc7qIi9PCxISO3bp1k4HEQw89ZDhx4kSR1y3ZBlmfmHtj7ty5hh49esggceTIkYZ9+/aZXufPrO0HEBV1Hnn9VZyT+OdmvRNERERERESFcdA0ERERERFZjAEEERERERFZjAEEERERERFZjAEEERERERFZjAEEERERERFZjAEEERERERFZjAEEERERERFZjAEEERERERFZjAEEERERlbukpCT83//9H6ZOnYqoqCh+4kQ2jAEEEdmsjIwMtGrVCnfccQc0Gg0czbJly9CrVy+L1zcYDPjuu++QmJgolzdu3IhGjRrBVmVlZWH16tVwBGfPnsWuXbtgK7RaLR544AH8999/cvmXX36R36tffvkl9uzZg379+uGdd94xrb99+3Y8+eSTlbjHRFQaDCCIyGZt3rwZQUFBSE9Px7Zt2yp7d6q8/fv3Y8aMGcjOzpbLAwYMwJ9//glbtXLlSnz66adwBJMmTcKxY8dgK8R5qV+/Ppo3by6/31566SWMGzcOixYtwsMPP4zFixdjxYoVOHLkiFy/T58+8ud406ZNlb3rRGQBBhBEZLO+/fZbdO3aFZ07d8bXX39d2btT5YkeiMLc3d0REhICezkeqhpEIPDxxx9j/Pjxcvn8+fOyt+i+++4zrXPPPfdg1qxZ8Pf3Nz33v//9D++++y50Ol2l7DcRWY4BBBHZJHFRcvToUdx5553o27cv9u3bh4sXL8rXxowZg+eee67Y3XcxXOfy5ctyWdzpFBcxLVq0wPDhw/HFF1/ccjiPuFj9/PPP5dCLli1bYuDAgfjpp5/ka1evXpXvFftgdONz4s7/9OnTsXDhQrRv3x4dO3bE0qVL5XE89NBDcpuDBg2Sx2Qk3i+GGRVm7jmjM2fOyDvVHTp0kHd+e/fuLe/SC2I/xo4dK9viebGNwkOYxP6Jz6Gw6OhoNG7cWA45EQ4dOiTvHot97dGjB+bNmyeHkd3Kjz/+KC8cxXvE1xWfoVFKSorcRvfu3eXro0aNKvIZijvXM2fOlOdYnKchQ4bg119/NQ3fev/99+U+imMQn/eNxPF169YN69evx1133YU2bdrgqaeewvXr103r5OXlybviIhAVr48YMaJIr4zYxt133y3PW7t27W46zCYzMxMLFiwwfZ3Ro0ebhu+YGyp243P//vuv/D4Q7xXnb/Lkybh27Zp8TQz9Eccpjld8b1vy2YnP55FHHpHvEUP8xHZfeeUVxMTEyO8RMfRPHFfhYVHW+izWrVuH8PBwNGjQQC4HBwfDyckJe/fuNa0jlsWx1K5d2/Sc+OxE8GE8x0RUdTGAICKbtGHDBnh6esoLRHFR4+LiYuqFGDZsGHbu3Fnk4lZcyLZt2xa1atWSr7344otyjLZ4XqwvhlTcyieffCLHbE+YMEEGDuKCTQQEhS+KSrJlyxao1Wp5ISYu7j744AM8/vjj8k7tN998Azc3N3lRWBbiYlvcwRV3dMXnIPaxf//+ePPNN3Hy5El5QSguKgXxtcTwpcLEZyAuYq9cuWJ6TgRZ4kJQ9PCcOnUKjz76qLy4FJ+Z+LyOHz8uv+bNegLE8YrPefDgwfI9InlWvE8cv7jLLN574MABedEqnmvYsKH8LMR+CO+99x5Onz4t72aLbYlzPWXKFBksiPeKh9g/cZEbERFx08RdEbSIO9vif3EBLc6hGKMviKE1f/31l9wvkR8igkpxTgpfWIvPJC4uDt9//738+uaIgPWPP/7A66+/LteLjIyU+5eamlriuROfhTHwE5/TqlWrZPDw8ssvm77XxXGK7YlzaMlnJ4jXRVAt8kTE3X5xYS++58UxivfUq1dPBo7G82etz0LkM4jAxkjsu/jeEYGH+H4U+RzmkqjFz7AIFnfs2FHiZ0ZElcxARGRjNBqN4Y477jBMnTrV9NykSZMMHTt2NOTk5BgyMzMNrVu3Nnz33XfytdzcXEOHDh0M69evl8sPP/ywYcqUKUW2+dprrxkaNmxo9uvp9XrDnXfeaXj77beLPP/xxx8bdu/ebYiKipLv3bt3r+m1G5978cUX5TZ0Op1cFvsoXl+yZInpPatXrzY0a9bMtCxe//bbb4t8zcLPLV261NCzZ0/ZTkxMNHz00UeGjIwM07risxDrGz8HsS9iWeybILZjPGZxjL179zYsW7bM9P4BAwaY9u+FF14wPPHEE0X25cqVK8WOu7ARI0YUOUfCunXrDJs3bzbs2rVLvvf06dNFPuchQ4YYnnnmGbksvt7YsWMNqampclmr1Rr++OMPQ1paWrHjN8d4fMeOHTM9d+7cOfmc2M6lS5dk+8SJE0XeN336dMPo0aOLbOPkyZM3/Trnz5+X64jvhcKfvfieunjxYpHP+cZ9E1JSUgyNGjUyfPXVV6bvD/HZHj582LS+OE5xvIIln51Yt0mTJob09HTTOp06dSpyPozbuX79utU+C7H/TZs2NX3PFfbPP/8YnnzySfk9Lrbz/PPPy3Na2AcffCC/D4moanOu7ACGiKi0fv/9dyQkJMhhREaiLXoWfv75ZznURdx9F3fQRVusL4ZniDuqgrhzLoY9FSbu/oo7v+YkJycjPj5eDvsobOLEifJ/c8NnzKlRowZUKqXjV/SeCOJOdeGchLJWkwoMDJRDYETPw4kTJ+SdYtFrIOj1+hLfL4aUiM9KfGZPP/203Ma5c+ewfPly+bpYFsO/RE/GjcQwrE6dOpkdUlX4HAliWIwgynn6+PjIO+eF90EM7zIOmxGfr7gD3qVLFzlMR9ydFsO8xPss5eXlJYdzGYm77n5+fnLfjD1U4nMrTJwDX1/fIs8VHmpj7jiF1q1bm54TvUnijr5x6NetiP0RvSJiCJQY1iZ6fMQdfOP3q7mvV9JnJ4gCA97e3qZl8T1Xs2bNIt9vgvjZEOfXGp+FGFolenfE176R+BkT34ti6JYYGvfCCy/InynjsCzj97H42Saiqo0BBBHZHGMOgLjQvZEYviMuhMWQHFH1RVyMiItiUeXFeDHl7Oxs0UV14aEVpWUuEdTcdowBhSWMw27MEQHOyJEj5QWYGDMvxpOLvIHCQ0lKMnToUDlmXlT7EUOGjEO+BPF5iYt3cUF/I/E1zRGf883cbNiTeN74PhGsiOBPDKv5+++/5bCZDz/8UA4nE0GFJcx95uLciKFkxn0QQ3xEoHGr82K82C7tcVr6/SEupsXFuzhecawimBDHKY7Z1dW11J9dab/frPVZiEBGKPzzJXJODh48aBo2J9YRAbwYkiaOtXAAIT4X4zaIqOpiDgQR2RQxh4G4yBIBgri4Kvy4//77cfjwYXmHVtyNrV69On744Qc5hlusbyTufhZOVhbE+25G3O0NDQ0tVkbzmWeekWPejRdqhXMuLl26dNvHKrZbeJvGBHBzRM+DuPu7du1amdwq8kKM4++NF4clXZiJz0v0JGzdulX25BT+zERCrOiREAGF8SECGnH8Iq/AHHG3/8bPTKwvPjdxF1okzBrv3hv3U1xoivKfgrgbL5ZF8rUYwy/2S/TYiP8tOR5BfCaFx9uL8ffiM23atKkpyVcEX4WPy5hgbilxnELhYxWfjQjkxPwHJX1/XLhwAXPmzJF37R988EF53CJ4ED07xl6kwiz57ErLWp9FQECAPF6Re2L0zz//yHwJY5EDIxHcF67CJIj3iZ81IqraGEAQkU0RSabi4kwMbxFDOAo/xN1xcbdU9EIYh+SIRGVxh1wMCzES7xUXdp999pm8kBPlYL/66qtbft3HHntMJuGKgEQMDxJVm0Syp7i4FRc84uJbvC4u+sSFnEgAvt07qWJIjEh4FknQYojJ3Llzi92NLpyoKhKpxXGJBFwxlEUkLRuHqBQeNiUuSkXVoJv1QqxZs0ZeeBceQiOSdsU+iCRvcYwi4Hr++efl53ezIS3iMxM9GWLyMPGZiZ4gEeAYe0iaNGkityEuMMU258+fLy+KRc+RIC78xYW1uEstqhCJwEEcm3EYlTgeESSJC9NbDf2aNm2arIgk5hwQie/GSkfiorlnz57ya/z222/y64mhVR999FGRoT4lqVOnjryjLj4bkVQv9mf27NnIzc2V1bbEeRTfCyIBWgx3E8GZSFIufNEt5jQRVZLE5yDeL14XQ5vq1q0r1xG9AuKzFhfdlnx2pWWtz0IQw83EMEEjcb5FuWCRsC2OTXxviaR2EcSLpO7CxPtuHCpIRFVQZSdhEBGVxr333mt49NFHb/r6008/bWjXrp1MUo6OjjY0bty4SKKykUioFsmaIqFz5MiRhtdff71IAvONRJKqSJoWyazNmzeX+7F161bT6/v37zcMHTpUbkMkH4uEWpHEWjiJ2piMerMk6RuTbc+ePSsTvsXX69Wrl+GHH34w9OnTx2wStdi/RYsWyeTyli1bGu655x7DypUr5ftfeuklUzL5xIkT5T5++umnZpN7s7KyDG3atJEJrjfas2eP/KxatGghE3Kfe+45Q0xMjOFWNmzYYOjXr5/8mn379jWsWbPG9JpI/BZJuiLBvVWrVnJfRaKtkUgAnjVrlkw+N77/888/N70uksHFtsXnc+TIkWJf23h8Ijm5S5cu8vti2rRpMmm58PG++uqr8muI7YhzJ/b5ZufkZkRit9hXkcgvEvjHjRtXJNn466+/Nn3viNdEknHh7R46dEgef9u2beVnIdb577//iiSfi9cGDRpk0WdnLsG8cCK2uaR6a30W4ntL/HwUJhK+x4wZI8+jeNx3332G7du3F1knLy9Pfn6//PJLiV+DiCqXk/insoMYIqKKJO7aitr0xru7gpgVV5TLFCUoyT6IoTcikVmUgqWKI3oYRK+D6JETeTiFiR4aMSeLmOfiRqL3TJSlLTzsi4iqJg5hIiKHI4b3iJr54mJGDIkRQ5HExY6Yr4CIbo/IaxBD3sxVNROVyMSwLnPEz6AojMDggajqYxUmInI44iIlKytLjocXSZtiEjIxsZsopUlEt0/kv4jJFsXEdiInonAAIR432rZtmywXK3JwiKjq4xAmIiIiIiKyGIcwERERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERERLPX/1EIjCfxOALcAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def policy_cost_curve(pi_eval, gamma_matrix, c_hat_matrix):\n", + " \"\"\"\n", + " Generate cost curves using per-treatment estimated customer costs.\n", + " c_hat_matrix[i, a] = Ê[C(a) | X_i]\n", + "\n", + " x = cumulative average treatment cost per customer (gross)\n", + " y = cumulative average net profit per customer (costs already deducted from Y_net)\n", + "\n", + " Treated customers are sorted in descending order of net profit / estimated cost ratio.\n", + " \"\"\"\n", + " pi = np.asarray(pi_eval).ravel().astype(int)\n", + " n = len(pi)\n", + " treated = pi > 0\n", + " if treated.sum() == 0:\n", + " return np.array([0.0]), np.array([0.0])\n", + "\n", + " benefit = gamma_matrix[np.arange(n), pi] - gamma_matrix[:, 0]\n", + " cost = c_hat_matrix[np.arange(n), pi].astype(float)\n", + "\n", + " b_t, c_t = benefit[treated], cost[treated]\n", + " ratio = np.where(c_t > 0, b_t / c_t, b_t)\n", + " order = np.argsort(-ratio)\n", + "\n", + " cum_cost = np.r_[0, np.cumsum(c_t[order])]\n", + " cum_benefit = np.r_[0, np.cumsum(b_t[order])]\n", + " return cum_cost / n, cum_benefit / n\n", + "\n", + "\n", + "n_test = len(X_test)\n", + "policies_curve = [\n", + " ('DRLearner plug-in', pi_plugin),\n", + " ('DRPolicyTree (depth=2)', pi_tree),\n", + " ('DRPolicyForest', pi_forest),\n", + " ('all_discount_plus_support', np.full(n_test, 3)),\n", + " ('all_tech_support_only', np.full(n_test, 1)),\n", + "]\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple']\n", + "\n", + "for (name, pi_eval), color in zip(policies_curve, colors):\n", + " cc, cg = policy_cost_curve(pi_eval, gamma_net, c_hat_test)\n", + " ax.plot(cc, cg, lw=2, color=color,\n", + " label=f'{name} (cost={cc[-1]:,.0f}, net benefit={cg[-1]:,.0f})')\n", + " ax.scatter([cc[-1]], [cg[-1]], s=50, color=color, zorder=5)\n", + "\n", + "budget_example = 5_000\n", + "ax.axvline(budget_example, color='gray', lw=1.5, ls='--', alpha=0.7,\n", + " label=f'Budget = ${budget_example:,}/customer')\n", + "\n", + "ax.set_xlabel('Avg cumulative cost per customer ($)', fontsize=11)\n", + "ax.set_ylabel('Avg cumulative net benefit per customer ($)', fontsize=11)\n", + "ax.set_title('Policy cost curves — cut at x=B for budget-constrained comparison', fontsize=11)\n", + "ax.legend(fontsize=8, loc='upper left')\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "qoq9ch9m2fg", + "metadata": {}, + "source": [ + "The most effective policy varies with the available budget. At low budgets of \\$1,000\\u2013\\$3,000, `all_tech_support_only` is the most efficient. Its lower average cost means that even a small budget can reach many high benefit-to-cost customers quickly.\n", + "\n", + "As the budget grows to \\$4,000\\u2013\\$6,000, the learned policies (plug-in, forest) pull ahead. Customer-level targeting starts paying off, delivering higher net profit for the same spend.\n", + "\n", + "Even when budgets are fully exhausted, the learned policies remain most efficient. Plug-in spends approximately \\$6,600 to yield \\$4,561 in net profit, and forest spends around \\$6,900 for \\$4,376. By contrast, `all_discount_plus_support` exhausts \\$10,700 and still achieves only \\$2,887 in net profit, making it the least efficient in terms of budget utilization." + ] + }, + { + "cell_type": "markdown", + "id": "b40f71c0", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "- [Stanford ML+CI Tutorial — Policy Learning I (Binary Treatment)](https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/policy-learning-i---binary-treatment.html)\n", + "- [Athey and Wager (2021, Econometrica) — Policy Learning with Observational Data](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732)\n", + "- [Sun, Du, Wager et al. (2021) — Treatment Allocation under Uncertain Costs](https://arxiv.org/abs/2103.11066)\n", + "- [Imai and Li (2019) — Experimental Evaluation of Individualized Treatment Rules](https://arxiv.org/pdf/1905.05389.pdf)\n", + "- [arXiv 2604.06123](https://arxiv.org/html/2604.06123v1)" + ] + } + ], + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/who_should_be_treated/who_should_be_treated_ko.ipynb b/book/who_should_be_treated/who_should_be_treated_ko.ipynb new file mode 100644 index 0000000..1d986f9 --- /dev/null +++ b/book/who_should_be_treated/who_should_be_treated_ko.ipynb @@ -0,0 +1,2317 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ae5a6c4", + "metadata": {}, + "source": [ + "**🌐 언어:** [← English](/who-should-be-treated-en) | **한국어**\n", + "\n", + "# 누구에게 어떤 프로모션을 제공해야 할까요?\n", + "\n", + "인과추론의 많은 문제는 평균 처치 효과(ATE)에 집중합니다. 예를 들어 “프로모션을 진행할 것인가, 하지 않을 것인가?”라는 질문은 모든 고객에게 동일한 프로모션을 제공하는 정책과 아무에게도 제공하지 않는 정책을 비교하는 문제입니다.\n", + "\n", + "하지만 예산이 제한되어 있고 그 안에서 최대 효과를 내야 한다면 이야기가 달라집니다. 프로모션 효과가 고객마다 다르다면, 모두에게 동일하게 제공하는 전략이 최선이 아닐 수 있습니다.\n", + "\n", + "- 이미 구매할 고객(always-converters)에게 불필요한 비용을 쓰게 되고,\n", + "- 마케팅에 부정적으로 반응하는 고객(sleeping dogs) 때문에 오히려 손실이 발생하며,\n", + "- 효과가 클 고객(high-responders)을 제대로 우선순위에 두지 못해 잠재 매출을 놓칩니다.\n", + "\n", + "따라서 핵심 질문은 다음과 같습니다.\n", + "\n", + "_\"어떤 고객을 대상으로 해야 순이익이 가장 커질까?\"_\n", + "\n", + "이번에는 가능한 처치 규칙들을 탐색해 전체 평균 순수익을 근사적으로 최대화하는 방법을 배웁니다. 다시 말해, “누구를 처치해야 하는가?”라는 질문에 답하는 문제를 다룹니다. 우리는 이를 정책학습(policy learning)이라고 부릅니다." + ] + }, + { + "cell_type": "markdown", + "id": "2b06ptb53eu", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "fc2741f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:46.211163Z", + "iopub.status.busy": "2026-05-09T16:49:46.211076Z", + "iopub.status.idle": "2026-05-09T16:49:48.058022Z", + "shell.execute_reply": "2026-05-09T16:49:48.057674Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import tempfile\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "os.environ.setdefault(\"MPLCONFIGDIR\", str(Path(tempfile.gettempdir()) / \"matplotlib\"))\n", + "os.environ.setdefault(\"MPLBACKEND\", \"Agg\")\n", + "os.environ.setdefault(\"LOKY_MAX_CPU_COUNT\", \"8\")\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from econml.dr import DRLearner\n", + "from econml.policy import DRPolicyForest, DRPolicyTree\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "SEED = 1\n", + "rng = np.random.default_rng(SEED)\n", + "np.random.seed(SEED)\n", + "sns.set_theme(style='whitegrid')" + ] + }, + { + "cell_type": "markdown", + "id": "8b741c26", + "metadata": {}, + "source": [ + "## 정책학습 문제 정의\n", + "\n", + "한 소프트웨어 판매 회사는 할인이나 기술지원 같은 프로모션이 고객의 소프트웨어 구매를 실제로 늘리는지, 그리고 어떤 고객에게 더 효과적인지 알고 싶어합니다.\n", + "\n", + "고객마다 서로 다른 프로모션을 무작위로 배정하는 실험(RCT)을 수행하는 것이 가장 이상적이지만 실제 비즈니스 환경에서는 비용 문제, 영업 전략, 대형 고객을 놓칠 위험 등으로 인해 무작위 실험을 수행하기 어렵습니다. 따라서 이번 데이터는 무작위 실험 데이터가 아닌 관찰 데이터입니다.\n", + "\n", + "데이터에는 약 2,000명의 고객 정보가 포함되어 있으며, 다음과 같은 변수들로 구성됩니다.\n", + "\n", + "- 고객 특성: 각 고객의 산업 분야, 규모, 매출, 기술 프로필에 대한 세부 정보.\n", + "- 개입: 고객에게 제공된 프로모션에 대한 정보.\n", + "- 결과: 프로모션 제공 이후 1년 동안의 소프트웨어 구매 금액\n", + "\n", + "| 변수명 | 타입 | 설명 |\n", + "| --------------- | -- | -------------------------------------------------------------- |\n", + "| Global Flag | X | 고객이 글로벌 오피스(해외 지사)를 보유하고 있는지 여부 |\n", + "| Major Flag | X | 고객이 해당 산업에서 대규모 소비 고객인지 여부 (SMC 또는 SMB와 구분) |\n", + "| SMC Flag | X | 고객이 중소기업(SMC, Small Medium Corporation)인지 여부 (Major 및 SMB와 구분) |\n", + "| Commercial Flag | X | 고객의 사업 유형이 상업용(Commercial)인지 여부 (공공 부문과 구분) |\n", + "| IT Spend | X | IT 관련 구매에 사용한 금액 |\n", + "| Employee Count | X | 직원 수 |\n", + "| PC Count | X | 고객이 사용하는 PC 수 |\n", + "| Size | X | 연간 총매출 기준 고객 규모 |\n", + "| Tech Support | T | 고객이 기술 지원(Tech Support)을 받았는지 여부 (이진값) |\n", + "| Discount | T | 고객이 할인 혜택을 받았는지 여부 (이진값) |\n", + "| Revenue | Y | 고객의 소프트웨어 구매 금액 기준 매출(Revenue) |\n", + "\n", + "\n", + "`Tech Support`와 `Discount`는 모두 개입(intervention)이므로, 이를 조합해 다음과 같이 네 가지 처치로 정의합니다.\n", + "\n", + "$$\n", + "A_i \\in \\{0, 1, 2, 3\\}\n", + "$$\n", + "\n", + "각 처치는 다음을 의미합니다.\n", + "- $A_i = 0$: 아무 개입도 제공하지 않음\n", + "- $A_i = 1$: 기술지원만 제공\n", + "- $A_i = 2$: 할인만 제공\n", + "- $A_i = 3$: 기술지원과 할인을 모두 제공" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "477aed7a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.059813Z", + "iopub.status.busy": "2026-05-09T16:49:48.059518Z", + "iopub.status.idle": "2026-05-09T16:49:48.071045Z", + "shell.execute_reply": "2026-05-09T16:49:48.070709Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2000, 13)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Global FlagMajor FlagSMC FlagCommercial FlagIT SpendEmployee CountPC CountSizeTech SupportDiscountRevenuetreatmenttreatment_name
010104553726261522050117688.363002discount_only
1001120842107701590380114981.435592discount_only
20001821711072649351132917.138943discount_plus_support
30000302884039775221114773.768553discount_plus_support
40010259303743914461117098.698233discount_plus_support
\n", + "
" + ], + "text/plain": [ + " Global Flag Major Flag SMC Flag Commercial Flag IT Spend \\\n", + "0 1 0 1 0 45537 \n", + "1 0 0 1 1 20842 \n", + "2 0 0 0 1 82171 \n", + "3 0 0 0 0 30288 \n", + "4 0 0 1 0 25930 \n", + "\n", + " Employee Count PC Count Size Tech Support Discount Revenue \\\n", + "0 26 26 152205 0 1 17688.36300 \n", + "1 107 70 159038 0 1 14981.43559 \n", + "2 10 7 264935 1 1 32917.13894 \n", + "3 40 39 77522 1 1 14773.76855 \n", + "4 37 43 91446 1 1 17098.69823 \n", + "\n", + " treatment treatment_name \n", + "0 2 discount_only \n", + "1 2 discount_only \n", + "2 3 discount_plus_support \n", + "3 3 discount_plus_support \n", + "4 3 discount_plus_support " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('./data/multi_attribution_sample.csv')\n", + "data.columns = data.columns.str.strip()\n", + "\n", + "covariates = [\n", + " 'Global Flag',\n", + " 'Major Flag',\n", + " 'SMC Flag',\n", + " 'Commercial Flag',\n", + " 'IT Spend',\n", + " 'Employee Count',\n", + " 'PC Count',\n", + " 'Size',\n", + "]\n", + "outcome = 'Revenue'\n", + "\n", + "TREATMENT_NAMES = {\n", + " 0: 'none',\n", + " 1: 'tech_support_only',\n", + " 2: 'discount_only',\n", + " 3: 'discount_plus_support',\n", + "}\n", + "TREATMENT_LABELS = [TREATMENT_NAMES[i] for i in range(4)]\n", + "\n", + "required_columns = covariates + ['Tech Support', 'Discount', outcome]\n", + "for col in required_columns:\n", + " data[col] = pd.to_numeric(data[col], errors='coerce')\n", + "\n", + "policy_df = data[required_columns].dropna().copy()\n", + "policy_df['treatment'] = (\n", + " 2 * policy_df['Discount'].astype(int)\n", + " + policy_df['Tech Support'].astype(int)\n", + ").astype(int)\n", + "policy_df['treatment_name'] = policy_df['treatment'].map(TREATMENT_NAMES)\n", + "\n", + "n = len(policy_df)\n", + "X = policy_df[covariates].to_numpy()\n", + "Y = policy_df[outcome].to_numpy(dtype=float)\n", + "A = policy_df['treatment'].to_numpy(dtype=int)\n", + "\n", + "print(policy_df.shape)\n", + "policy_df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8bfa27b6", + "metadata": {}, + "source": [ + "### 비용 설정\n", + "\n", + "수익 극대화를 위해서는 매출과 함께 비용도 고려해야 합니다. 또한 동일한 프로모션이라도 고객 특성에 따라 비용이 달라질 수 있습니다.\n", + "\n", + "예를 들어 기술지원은 직원 수가 많은 기업일수록 더 많은 지원 시간이 필요할 수 있고, 할인 역시 규모가 큰 고객일수록 더 큰 할인 폭을 요구할 수 있습니다.\n", + "\n", + "하지만 이 데이터에는 실제 비용 정보가 포함되어 있지 않기 때문에 고객 특성에 따라 달라지는 비용을 시뮬레이션해 사용합니다.\n", + "\n", + "기술지원 비용은 직원 수가 많을수록 증가하도록 설정하고, 할인 비용은 고객 규모가 클수록 증가하도록 설정합니다.\n", + "\n", + "이때 로그정규분포를 사용해 비용은 항상 양수이고, 일부 고객에서 큰 비용이 발생할 수 있는 현실적인 분포를 반영합니다.\n", + "\n", + "$$\n", + "C_i(\\text{tech}) \\sim \\text{LogNormal}(\\log C_{\\text{tech}} + 0.5 \\cdot \\tilde{e}_i,\\ 0.3)\n", + "$$\n", + "\n", + "$$\n", + "C_i(\\text{disc}) \\sim \\text{LogNormal}(\\log C_{\\text{disc}} + 0.4 \\cdot \\tilde{s}_i,\\ 0.3)\n", + "$$\n", + "\n", + "여기서 $\\tilde{e}_i$는 표준화된 직원 수, $\\tilde{s}_i$는 표준화된 회사 규모입니다. \n", + "\n", + "이렇게 정의한 고객별 비용을 반영한 최종 순이익(net outcome)은 다음과 같습니다.\n", + "\n", + "$$\n", + "Y_i^{net} = Y_i - C_i(A_i)\n", + "$$\n", + "\n", + "결과적으로 AIPW score가 순이익을 기준으로 계산되므로, 비용을 고려한 정책 최적화와 평가가 가능해집니다." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "5174ddd2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.572223Z", + "iopub.status.busy": "2026-05-09T16:49:48.572150Z", + "iopub.status.idle": "2026-05-09T16:49:48.576620Z", + "shell.execute_reply": "2026-05-09T16:49:48.576207Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmentmean_coststd_costmean_revenuemean_net_revenue
0none0.0000000.0000006585.8917926585.891792
1tech_support_only5037.8158944917.64135815104.11153410066.295640
2discount_only5181.5703343043.34317212247.9359537066.365619
3discount_plus_support12665.2832967375.49012526784.12464914118.841353
\n", + "
" + ], + "text/plain": [ + " treatment mean_cost std_cost mean_revenue \\\n", + "0 none 0.000000 0.000000 6585.891792 \n", + "1 tech_support_only 5037.815894 4917.641358 15104.111534 \n", + "2 discount_only 5181.570334 3043.343172 12247.935953 \n", + "3 discount_plus_support 12665.283296 7375.490125 26784.124649 \n", + "\n", + " mean_net_revenue \n", + "0 6585.891792 \n", + "1 10066.295640 \n", + "2 7066.365619 \n", + "3 14118.841353 " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "COST_TECH_SUPPORT = 4_000.0 # baseline cost for tech support\n", + "COST_DISCOUNT = 5_000.0 # baseline cost for discount\n", + "\n", + "\n", + "rng_c = np.random.default_rng(SEED + 99)\n", + "emp_idx = covariates.index('Employee Count')\n", + "size_idx = covariates.index('Size')\n", + "\n", + "emp_z = (X[:, emp_idx] - X[:, emp_idx].mean()) / (X[:, emp_idx].std() + 1e-8)\n", + "size_z = (X[:, size_idx] - X[:, size_idx].mean()) / (X[:, size_idx].std() + 1e-8)\n", + "\n", + "# Tech support: 직원 수가 많을수록 더 많은 지원 비용 발생\n", + "c_tech = COST_TECH_SUPPORT * np.exp(0.5 * emp_z + 0.3 * rng_c.standard_normal(len(policy_df)))\n", + "c_tech = np.clip(c_tech, 200.0, 40_000.0)\n", + "\n", + "# Discount: 규모가 큰 기업일수록 더 큰 할인 비용 발생\n", + "c_disc = COST_DISCOUNT * np.exp(0.4 * size_z + 0.3 * rng_c.standard_normal(len(policy_df)))\n", + "c_disc = np.clip(c_disc, 200.0, 30_000.0)\n", + "\n", + "C_obs = np.select(\n", + " [A == 1, A == 2, A == 3],\n", + " [c_tech, c_disc, c_tech + c_disc],\n", + " default=0.0,\n", + ")\n", + "\n", + "Y_net = Y - C_obs\n", + "\n", + "pd.DataFrame({\n", + " 'treatment': TREATMENT_LABELS,\n", + " 'mean_cost': [C_obs[A == a].mean() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'std_cost': [C_obs[A == a].std() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'mean_revenue': [Y[A == a].mean() for a in range(4)],\n", + " 'mean_net_revenue': [Y_net[A == a].mean() for a in range(4)],\n", + "})" + ] + }, + { + "cell_type": "markdown", + "id": "dc34dc79", + "metadata": {}, + "source": [ + "### 데이터 분할\n", + "\n", + "정책학습과 평가를 같은 데이터로 수행하면 과대추정이 발생할 수 있습니다. 따라서 데이터를 train/test로 나눠 학습과 평가를 분리합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "1272f0ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train: 1000 \n", + "Test: 1000\n" + ] + } + ], + "source": [ + "idx = np.arange(len(policy_df))\n", + "train_idx, test_idx = train_test_split(\n", + " idx,\n", + " test_size=0.5,\n", + " stratify=A,\n", + " random_state=SEED,\n", + ")\n", + "\n", + "X_train = X[train_idx]\n", + "X_test = X[test_idx]\n", + "A_train = A[train_idx]\n", + "A_test = A[test_idx]\n", + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n", + "\n", + "print(f\"Train: {len(train_idx)} \\nTest: {len(test_idx)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "024e5803", + "metadata": {}, + "source": [ + "### 데이터 탐색 및 진단\n", + "\n", + "정책학습에 들어가기 전에, 먼저 네 가지 처치 조합($A \\in {0,1,2,3}$)에 대해 데이터의 구조를 점검합니다.\n", + "\n", + "1. 처치별 표본 수: 각 처치에 충분한 관측치가 존재하는가?\n", + "2. 처치별 고객 특성 평균: 어떤 고객군이 특정 처치를 받는 경향이 있는가?\n", + "3. 처치별 Revenue 분포: Revenue의 규모, 분산, 이상치 분포가 처치마다 어떻게 다른가?\n", + "4. Positivity Check: 고객 특성 전반에서 각 처치가 충분히 함께 관측되는가?" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "3d0f329e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countshare
treatment
none5170.2585
tech_support_only4620.2310
discount_only4770.2385
discount_plus_support5440.2720
\n", + "
" + ], + "text/plain": [ + " count share\n", + "treatment \n", + "none 517 0.2585\n", + "tech_support_only 462 0.2310\n", + "discount_only 477 0.2385\n", + "discount_plus_support 544 0.2720" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_counts = policy_df['treatment_name'].value_counts().reindex(TREATMENT_LABELS).rename_axis('treatment').to_frame('count')\n", + "treatment_counts['share'] = treatment_counts['count'] / len(policy_df)\n", + "\n", + "display(treatment_counts)" + ] + }, + { + "cell_type": "markdown", + "id": "8e911f32", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.072480Z", + "iopub.status.busy": "2026-05-09T16:49:48.072405Z", + "iopub.status.idle": "2026-05-09T16:49:48.076539Z", + "shell.execute_reply": "2026-05-09T16:49:48.076070Z" + } + }, + "source": [ + "실제 표본 수는 `none` 517명, `tech_support_only` 462명, `discount_only` 477명, `discount_plus_support` 544명입니다. 각 처치의 비중은 23.1%에서 27.2% 사이로 비교적 고르게 분포되어 있으며, 특정 처치에 표본이 극단적으로 부족한 문제는 보이지 않습니다.\n", + "\n", + "따라서 네 가지 처치를 비교하는 정책학습을 진행하기에 표본 분포는 안정적인 편입니다." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "d6488c62", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_covariate_means = (\n", + " policy_df\n", + " .groupby('treatment_name')[covariates]\n", + " .mean()\n", + " .reindex(TREATMENT_LABELS)\n", + ")\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "sns.heatmap(treatment_covariate_means.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", + "plt.xlabel('Treatments')\n", + "plt.ylabel('Covariate')\n", + "plt.title('Mean customer characteristics by treatments')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "c49e9856", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.078246Z", + "iopub.status.busy": "2026-05-09T16:49:48.078155Z", + "iopub.status.idle": "2026-05-09T16:49:48.194453Z", + "shell.execute_reply": "2026-05-09T16:49:48.193850Z" + } + }, + "source": [ + "처치별 고객 특성은 완전히 동일하지 않습니다. `Size` 평균은 `none` 그룹의 약 70,943에서 `discount_plus_support` 그룹의 약 171,467까지 큰 차이를 보이며, `IT Spend` 역시 약 18,056에서 41,371까지 차이가 납니다. 즉, 규모가 큰 고객일수록 기술지원과 할인을 함께 제공받는 경향이 있습니다.\n", + "\n", + "이는 처치 배정이 고객 특성과 독립적이지 않음을 의미합니다. 따라서 이후 정책학습과 평가에서는 AIPW를 사용해 이러한 차이를 보정합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "c925a3d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmenttreatment_namecountmeanmedianstdminmax
00none5176585.8917926123.1870673363.163462-616.57245121445.05937
11tech_support_only46215104.11153414483.7193205400.3198584619.49136140166.67407
22discount_only47712247.93595310454.9325007472.178476889.97565341818.39213
33discount_plus_support54426784.12464923560.25289013124.9680835903.90688086006.92445
\n", + "
" + ], + "text/plain": [ + " treatment treatment_name count mean median \\\n", + "0 0 none 517 6585.891792 6123.187067 \n", + "1 1 tech_support_only 462 15104.111534 14483.719320 \n", + "2 2 discount_only 477 12247.935953 10454.932500 \n", + "3 3 discount_plus_support 544 26784.124649 23560.252890 \n", + "\n", + " std min max \n", + "0 3363.163462 -616.572451 21445.05937 \n", + "1 5400.319858 4619.491361 40166.67407 \n", + "2 7472.178476 889.975653 41818.39213 \n", + "3 13124.968083 5903.906880 86006.92445 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_revenue = (\n", + " policy_df\n", + " .groupby(['treatment', 'treatment_name'])[outcome]\n", + " .agg(['count', 'mean', 'median', 'std', 'min', 'max'])\n", + " .reset_index()\n", + ")\n", + "display(treatment_revenue)\n", + "\n", + "plt.figure(figsize=(8, 4))\n", + "\n", + "sns.histplot(\n", + " data=policy_df,\n", + " x=outcome,\n", + " hue='treatment_name',\n", + " bins=40,\n", + " element='step',\n", + " stat='density',\n", + " common_norm=False\n", + ")\n", + "\n", + "plt.title('Revenue density by treatment')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "639d2437", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.195732Z", + "iopub.status.busy": "2026-05-09T16:49:48.195645Z", + "iopub.status.idle": "2026-05-09T16:49:48.262584Z", + "shell.execute_reply": "2026-05-09T16:49:48.262041Z" + } + }, + "source": [ + "Revenue 평균은 `none` 약 6,586, `discount_only` 약 12,248, `tech_support_only` 약 15,104, `discount_plus_support` 약 26,784로 나타납니다.\n", + "\n", + "앞서 확인했듯이 규모가 큰 고객일수록 더 강한 개입을 받는 경향이 있기 때문에 평균 차이를 그대로 정책을 평가하면 안 됩니다. 즉, Revenue 차이에는 고객 특성의 영향과 실제 처치 효과가 함께 섞여 있습니다." + ] + }, + { + "cell_type": "markdown", + "id": "0b51rgb0z128", + "metadata": {}, + "source": [ + "### 식별 가정\n", + "\n", + "관찰 데이터에서 AIPW 추정이 인과적으로 유효하려면 두 가지 가정이 필요합니다.\n", + "\n", + "1. Unconfoundedness\n", + "\n", + " $$\\{Y_i(0), Y_i(1), Y_i(2), Y_i(3)\\} \\perp A_i \\mid X_i$$\n", + "\n", + " 관측된 공변량 $X_i$를 조건부로 했을 때, 처치 배정은 잠재 결과와 독립이어야 합니다. 즉, 고객 규모, IT 지출, 직원 수 등의 변수들이 처치 배정을 충분히 설명한다고 가정합니다.\n", + "\n", + "2. Positivity\n", + "\n", + " $$P(A_i = a \\mid X_i = x) > 0 \\quad \\forall a \\in \\{0,1,2,3\\},\\ \\forall x$$\n", + "\n", + " 모든 고객 특성 범위에서 각 처치가 어느 정도 관측되어야 합니다." + ] + }, + { + "cell_type": "markdown", + "id": "61b7329f", + "metadata": {}, + "source": [ + "이 중 Positivity는 propensity score를 추정해 직접 확인합니다.\n", + "\n", + "$$\n", + "e_a(x) = P(A_i = a \\mid X_i=x)\n", + "$$\n", + "\n", + "추가로 propensity score가 극단적으로 치우쳐 있지 않은지도 확인합니다. 만약 특정 고객군에서 어떤 처치의 $e_a(x)$ 값이 0에 매우 가까우면, 일부 관측치에 지나치게 큰 가중치가 부여되어 추정이 불안정해질 수 있습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "9be31313", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmentmean_propensitymin_propensityp01_propensitypropensity_below_0_05_rate
0none0.2639410.0576000.0668310.0
1tech_support_only0.2298630.1265820.1562830.0
2discount_only0.2379340.1256380.1413220.0
3discount_plus_support0.2682630.0872990.1013040.0
\n", + "
" + ], + "text/plain": [ + " treatment mean_propensity min_propensity p01_propensity \\\n", + "0 none 0.263941 0.057600 0.066831 \n", + "1 tech_support_only 0.229863 0.126582 0.156283 \n", + "2 discount_only 0.237934 0.125638 0.141322 \n", + "3 discount_plus_support 0.268263 0.087299 0.101304 \n", + "\n", + " propensity_below_0_05_rate \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_propensity = RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "multi_propensity.fit(X_train, A_train)\n", + "e_hat_raw = multi_propensity.predict_proba(X_test)\n", + "\n", + "propensity_summary = pd.DataFrame({\n", + " 'treatment': TREATMENT_LABELS,\n", + " 'mean_propensity': e_hat_raw.mean(axis=0),\n", + " 'min_propensity': e_hat_raw.min(axis=0),\n", + " 'p01_propensity': np.quantile(e_hat_raw, 0.01, axis=0),\n", + " 'propensity_below_0_05_rate': (e_hat_raw < 0.05).mean(axis=0),\n", + "})\n", + "display(propensity_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "fl52yekx049", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prop_df = pd.DataFrame(e_hat_raw, columns=TREATMENT_LABELS)\n", + "prop_long = prop_df.melt(var_name='treatment', value_name='propensity')\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "sns.histplot(\n", + " data=prop_long, x='propensity', hue='treatment',\n", + " bins=30, element='step', stat='density', common_norm=False, ax=ax,\n", + ")\n", + "\n", + "ax.axvline(0.05, color='tomato', lw=1.5, ls='--', alpha=0.8, label='threshold = 0.05')\n", + "ax.legend(title='Treatment', fontsize=8)\n", + "ax.set_xlabel('Propensity score')\n", + "ax.set_ylabel('Density')\n", + "ax.set_title('Propensity score distribution by treatment')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0b42eca5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.264007Z", + "iopub.status.busy": "2026-05-09T16:49:48.263913Z", + "iopub.status.idle": "2026-05-09T16:49:48.571081Z", + "shell.execute_reply": "2026-05-09T16:49:48.570638Z" + } + }, + "source": [ + "이 데이터에서는 $\\hat e_a(X_i)$의 최솟값이 약 0.058이며, `e_hat < 0.05`인 경우도 관측되지 않았습니다. 즉, 극단적으로 작은 propensity score로 인해 일부 관측치에 과도한 가중치가 부여되는 문제는 크지 않아 보입니다.\n", + "\n", + "또한 그래프에서도 네 가지 처치의 propensity 분포가 전반적으로 잘 겹쳐 나타나므로, 뚜렷한 positivity 위반은 관찰되지 않습니다." + ] + }, + { + "cell_type": "markdown", + "id": "f4edd2d8", + "metadata": {}, + "source": [ + "## 정책학습 방법론\n", + "\n", + "세 가지 정책학습 방법을 비교합니다.\n", + "\n", + "- Plug-in Policy (DRLearner)\n", + "\n", + " 먼저 고객별 CATE를 추정한 뒤, 기대 순이익(net benefit)이 가장 큰 처치를 선택합니다.\n", + "\n", + " 정책 형태에 제약이 없는 유연한 접근이지만, 최종 성능은 CATE 추정 품질에 크게 영향을 받습니다.\n", + "\n", + "- DRPolicyTree\n", + "\n", + " DRPolicyTree는 AIPW score를 직접 최대화하도록 정책을 학습합니다.\n", + "\n", + " 정책 구조를 얕은 decision tree로 제한하기 때문에, 결과를 조건 분기 규칙으로 해석할 수 있습니다. 따라서 정책 해석과 설명이 중요한 상황에서 유용합니다.\n", + "\n", + "- DRPolicyForest\n", + "\n", + " DRPolicyForest 역시 AIPW score를 직접 최대화하지만, 단일 트리 대신 forest 구조를 사용합니다.\n", + "\n", + " 단일 tree는 구조가 단순하지만 분산이 커질 수 있습니다. 반면 forest는 여러 트리의 결과를 평균적으로 활용하기 때문에 분산을 줄이면서 더 안정적으로 이질적인 처치 효과를 학습할 수 있습니다.\n", + "\n", + " 대신 최종 정책을 하나의 명확한 조건 분기 규칙 형태로 해석하기는 더 어렵습니다.\n", + "\n", + "세 정책은 모두 train set에서 학습하고, 동일한 test set에서 정책 가치를 비교합니다. 학습에 사용한 데이터로 정책을 평가하면 과대추정이 발생하므로, 학습과 평가에 서로 다른 데이터를 사용하는 것이 중요합니다." + ] + }, + { + "cell_type": "markdown", + "id": "3c967473", + "metadata": {}, + "source": [ + "### Plug-in Policy\n", + "\n", + "Plug-in policy는 먼저 고객별 처치 효과(CATE)를 추정한 뒤, 그 값을 이용해 정책을 만드는 방식입니다.\n", + "\n", + "Binary treatment에서는 일반적으로 $\\hat\\tau(x) > 0$이면 처치하고, 비용이 있으면 $\\hat\\tau(x) > c(x)$일 때 처치합니다.\n", + "\n", + "여러 처치가 있는 경우에는 baseline 처치와의 상대효과를 비교합니다. 여기서는 `none`(처치 없음)을 baseline으로 두고, `DRLearner`로 다음 값을 추정합니다.\n", + "\n", + "$$\n", + "\\hat\\tau_a(x) = \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "\\qquad a \\in \\{1,2,3\\}\n", + "$$\n", + "\n", + "baseline 처치의 상대효과는 0이므로, 고객별로 다음 네 값을 비교합니다.\n", + "\n", + "$$\n", + "[0, \\hat\\tau_1(x), \\hat\\tau_2(x), \\hat\\tau_3(x)]\n", + "$$\n", + "\n", + "그리고 가장 큰 값을 갖는 처치를 선택합니다.\n", + "\n", + "$$\n", + "\\hat\\pi_{plugin}(x) = \\arg\\max_{a \\in \\{0,1,2,3\\}} \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "$$\n", + "\n", + "이 방식은 직관적이고 유연하지만, CATE 추정 품질에 크게 영향을 받습니다. 따라서 학습된 정책은 별도의 test set에서 AIPW value로 평가합니다.\n", + "\n", + "> **NOTE** \"각 고객의 $\\hat\\Gamma_{i,a}$ 중 가장 큰 처치를 선택하면 되지 않나?\"라고 생각할 수 있습니다. 그러나 AIPW score는 개별 관측치 수준에서 분산이 매우 크기 때문에, pointwise argmax로 만든 정책은 노이즈에 과적합됩니다. AIPW score는 반드시 집계된 형태로 사용해야 합니다. 정책 평가(value 추정, 비용 곡선 등)처럼 여러 관측치를 합산하거나, DRPolicyTree/Forest처럼 노드 단위 평균을 최적화하거나, DRLearner처럼 pseudo-outcome으로 회귀하는 방식이 그 예입니다." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "debd92a9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:49.484723Z", + "iopub.status.busy": "2026-05-09T16:49:49.484610Z", + "iopub.status.idle": "2026-05-09T16:49:52.385416Z", + "shell.execute_reply": "2026-05-09T16:49:52.384824Z" + } + }, + "outputs": [], + "source": [ + "dr_cate = DRLearner(\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_final=RandomForestRegressor(\n", + " n_estimators=300,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_cate.fit(Y_net_train, A_train, X=X_train)\n", + "\n", + "# none을 baseline으로, 나머지 처치별 E[Y_net(a) - Y_net(0) | X] 반환\n", + "cate_vs_none = dr_cate.const_marginal_effect(X_test)\n", + "plugin_treatment_values = np.column_stack([\n", + " np.zeros(len(X_test)), # none: baseline, 상대효과 = 0\n", + " cate_vs_none,\n", + "])\n", + "pi_plugin = np.argmax(plugin_treatment_values, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "33437c2c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:52.389559Z", + "iopub.status.busy": "2026-05-09T16:49:52.389447Z", + "iopub.status.idle": "2026-05-09T16:49:52.392977Z", + "shell.execute_reply": "2026-05-09T16:49:52.392636Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.092\n", + "tech_support_only 0.500\n", + "discount_only 0.035\n", + "discount_plus_support 0.373\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(pi_plugin).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "33ktqy8zrqm", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SizeEmployee CountIT Spendassigned_treatmentτ_techτ_discτ_both
09620414217655none-6140-1419-1719
1253901787971none-14556-4252-7232
26655527919899none-17629-2896-16582
3708682026263tech_support_only4265-404155
424152184610tech_support_only4174-2316-771
5548556314772tech_support_only2042-698553
639922836133052discount_plus_support7849-128111819
71817042459529discount_plus_support9158521610248
829437737112485discount_plus_support71348589048
\n", + "
" + ], + "text/plain": [ + " Size Employee Count IT Spend assigned_treatment τ_tech τ_disc \\\n", + "0 96204 142 17655 none -6140 -1419 \n", + "1 25390 178 7971 none -14556 -4252 \n", + "2 66555 279 19899 none -17629 -2896 \n", + "3 70868 20 26263 tech_support_only 4265 -40 \n", + "4 24152 18 4610 tech_support_only 4174 -2316 \n", + "5 54855 63 14772 tech_support_only 2042 -698 \n", + "6 399228 36 133052 discount_plus_support 7849 -1281 \n", + "7 181704 24 59529 discount_plus_support 9158 5216 \n", + "8 294377 37 112485 discount_plus_support 7134 858 \n", + "\n", + " τ_both \n", + "0 -1719 \n", + "1 -7232 \n", + "2 -16582 \n", + "3 4155 \n", + "4 -771 \n", + "5 553 \n", + "6 11819 \n", + "7 10248 \n", + "8 9048 " + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "none_idx = np.where(pi_plugin == 0)[0][:3]\n", + "tech_idx = np.where(pi_plugin == 1)[0][:3]\n", + "both_idx = np.where(pi_plugin == 3)[0][:3]\n", + "sample_ids = np.concatenate([none_idx, tech_idx, both_idx])\n", + "\n", + "df_plugin_sample = pd.DataFrame(X_test[sample_ids], columns=covariates)[['Size', 'Employee Count', 'IT Spend']]\n", + "df_plugin_sample['assigned_treatment'] = pd.Series(pi_plugin[sample_ids]).map(TREATMENT_NAMES).values\n", + "df_plugin_sample[['τ_tech', 'τ_disc', 'τ_both']] = np.round(cate_vs_none[sample_ids], 0).astype(int)\n", + "df_plugin_sample.index = pd.RangeIndex(len(df_plugin_sample))\n", + "df_plugin_sample" + ] + }, + { + "cell_type": "markdown", + "id": "05b0ee18", + "metadata": {}, + "source": [ + "plug-in 정책은 약 50% 고객에게 `tech_support_only`, 37% 고객에게 `discount_plus_support`, 그리고 약 9% 고객에게 `none`을 배정합니다.\n", + "\n", + "비용을 함께 고려하면서, 기대 순이익이 음수로 예상되는 일부 고객에게는 처치를 하지 않는 선택이 나타납니다." + ] + }, + { + "cell_type": "markdown", + "id": "97d958cd", + "metadata": {}, + "source": [ + "### Policy Tree\n", + "\n", + "현업에서는 성능뿐 아니라 정책의 설명 가능성도 중요한 경우가 많습니다. 얕은 decision tree 기반 정책은 다음과 같은 이유로 실무에서 자주 사용됩니다.\n", + "\n", + "- **이해관계자 설명**: 직관적인 분기 규칙 형태라 정책을 쉽게 설명하고 공유할 수 있습니다.\n", + "- **공정성 검토**: 어떤 고객이 어떤 처치를 받는지 구조가 명확해 편향 여부를 점검하기 쉽습니다.\n", + "- **운영 안정성**: 복잡한 모델보다 단순한 규칙 기반 정책이 실제 운영과 관리 측면에서 안정적입니다.\n", + "\n", + "이 경우 정책을 모든 가능한 함수에서 찾는 대신, 제한된 정책 클래스 $\\Pi$ 안에서 탐색합니다.\n", + "\n", + "$$\n", + "\\hat\\pi = \\arg\\max_{\\pi \\in \\Pi}\n", + "\\frac{1}{n}\\sum_{i=1}^{n} \\widehat\\Gamma_{i,\\pi(X_i)}\n", + "$$\n", + "\n", + "Policy Tree는 $\\Pi$를 얕은 decision tree로 제한합니다. 따라서 depth를 작게 두면 사람이 읽을 수 있는 형태로 정책을 해석할 수 있습니다. 여기서는 `econml`의 `DRPolicyTree`를 사용합니다.\n", + "\n", + "또한 leaf가 지나치게 작아지면 다중 처치 환경에서 value 추정이 불안정해질 수 있으므로, `min_samples_leaf`를 사용해 너무 작은 leaf를 방지합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "6c987a8b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:53.651642Z", + "iopub.status.busy": "2026-05-09T16:49:53.651575Z", + "iopub.status.idle": "2026-05-09T16:49:55.354506Z", + "shell.execute_reply": "2026-05-09T16:49:55.354139Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.199\n", + "tech_support_only 0.475\n", + "discount_only 0.000\n", + "discount_plus_support 0.326\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_tree = DRPolicyTree(\n", + " max_depth=2,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_policy_tree.fit(Y_net_train, A_train, X=X_train)\n", + "pi_tree = dr_policy_tree.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_tree).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "64529830", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:55.355867Z", + "iopub.status.busy": "2026-05-09T16:49:55.355774Z", + "iopub.status.idle": "2026-05-09T16:49:55.574472Z", + "shell.execute_reply": "2026-05-09T16:49:55.574068Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(13, 7))\n", + "dr_policy_tree.plot(feature_names=covariates, treatment_names=TREATMENT_LABELS, ax=ax)\n", + "ax.set_title('4-way DRPolicyTree')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2a3ffb5d", + "metadata": {}, + "source": [ + "이 트리 정책은 주로 `Size`를 기준으로 고객을 나눕니다. 특징적으로 약 20%의 고객에게는 `none`을 배정합니다. 비용을 반영한 결과, 일부 고객은 처치했을 때 기대 순이익이 낮아 처치하지 않는 편이 더 낫다고 판단한 것입니다.\n", + "\n", + "전체적으로는 규모가 큰 고객에게 `discount_plus_support`를, 그 외 고객에게는 주로 `tech_support_only`를 배정합니다. 기대 순이익이 낮은 고객에게는 `none`을 선택합니다." + ] + }, + { + "cell_type": "markdown", + "id": "35dd95b1", + "metadata": {}, + "source": [ + "### Policy Forest\n", + "\n", + "Policy Forest는 여러 개의 tree를 활용해 보다 안정적으로 정책을 학습합니다.\n", + "\n", + "Policy Forest 역시 AIPW score를 직접 최대화하지만, 단일 tree 대신 여러 tree의 결과를 평균적으로 활용하기 때문에 분산이 더 작고 복잡한 이질성을 더 안정적으로 학습할 수 있습니다.\n", + "\n", + "여기서는 `econml`의 `DRPolicyForest`를 사용합니다.\n", + "\n", + "다만 tree 기반 정책처럼 하나의 명확한 의사결정 규칙 형태로 해석하기는 어렵습니다. 학습 이후에는 동일한 test set의 AIPW score를 사용해 정책 가치를 비교합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "d7939447", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:55.582919Z", + "iopub.status.busy": "2026-05-09T16:49:55.582816Z", + "iopub.status.idle": "2026-05-09T16:49:57.495404Z", + "shell.execute_reply": "2026-05-09T16:49:57.494962Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.113\n", + "tech_support_only 0.471\n", + "discount_only 0.000\n", + "discount_plus_support 0.416\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_forest = DRPolicyForest(\n", + " n_estimators=400,\n", + " max_depth=5,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "dr_policy_forest.fit(Y_net_train, A_train, X=X_train)\n", + "pi_forest = dr_policy_forest.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_forest).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "15d7d727", + "metadata": {}, + "source": [ + "DRPolicyForest는 전반적으로 plug-in 정책과 유사한 배정을 보입니다. `none` 11%, `tech_support_only` 47%, `discount_plus_support` 42%로, plug-in 정책보다 `discount_plus_support` 비중이 조금 더 높게 나타납니다." + ] + }, + { + "cell_type": "markdown", + "id": "99bac91c", + "metadata": {}, + "source": [ + "## 정책 평가\n", + "\n", + "이제 학습된 정책들을 동일한 test set의 AIPW score 기준으로 비교합니다.\n", + "\n", + "비교를 위해 모든 고객에게 동일한 처치를 적용하는 baseline 정책도 함께 사용합니다.\n", + "\n", + "- `all_none`\n", + "- `all_tech_support_only`\n", + "- `all_discount_only`\n", + "- `all_discount_plus_support`\n", + "\n", + "예를 들어 `all_discount_plus_support`는 모든 고객에게 기술지원과 할인을 함께 제공했을 때의 평균 순이익을 의미합니다.\n", + "\n", + "반면 학습된 정책은 고객 특성 $X_i$에 따라 서로 다른 처치를 배정합니다.\n", + "\n", + "만약 학습된 정책의 value가 가장 좋은 baseline 정책보다 높다면, 모든 고객에게 동일한 처치를 적용하는 것보다 고객별 targeting이 더 효과적이라는 의미입니다." + ] + }, + { + "cell_type": "markdown", + "id": "1e2f1760", + "metadata": {}, + "source": [ + "AIPW(Augmented Inverse Probability Weighting) score는 다음과 같이 정의합니다.\n", + "\n", + "$$\n", + "\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n", + "$$\n", + "\n", + "- $\\hat\\mu_a(X_i)$: outcome model이 예측한 $E[Y^{net}(a)\\mid X_i]$\n", + "- $\\hat e_a(X_i)$: propensity model이 예측한 $P(A_i=a\\mid X_i)$\n", + "- 두 번째 항은 실제 관측값과 예측값의 차이를 IPW로 보정하는 역할을 합니다.\n", + "\n", + "이 구조 덕분에 outcome model과 propensity model 중 하나만 올바르게 추정되어도 불편성이 유지됩니다. 이를 doubly robust(이중 견고성)라고 부릅니다.\n", + "\n", + "- outcome model이 정확하면, IPW 보정항의 기댓값이 0에 가까워져 $\\hat\\mu_a(X_i)$가 직접 기여합니다.\n", + "- propensity model이 정확하면, IPW 보정항이 outcome model의 편향을 제거합니다.\n", + "\n", + "이 score를 사용해 학습된 정책의 가치를 추정합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "9c3bfe27", + "metadata": {}, + "outputs": [], + "source": [ + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n", + "\n", + "e_hat_net = np.clip(e_hat_raw, 0.02, 0.98)\n", + "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", + "\n", + "gamma_net = np.zeros((len(X_test), 4))\n", + "mu_hat_net = np.zeros((len(X_test), 4))\n", + "\n", + "for treatment_id in range(4):\n", + " outcome_model = RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " )\n", + " outcome_model.fit(X_train[A_train == treatment_id], Y_net_train[A_train == treatment_id])\n", + " mu_a = outcome_model.predict(X_test)\n", + " observed_a = (A_test == treatment_id).astype(float)\n", + "\n", + " gamma_net[:, treatment_id] = mu_a + observed_a / e_hat_net[:, treatment_id] * (Y_net_test - mu_a)\n", + " mu_hat_net[:, treatment_id] = mu_a" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "0d014d17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
samplee_hatmu_hatgamma
sample_idactual_treatmentobserved_net_outcomenonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_support
00discount_plus_support9254.5585410.2486330.2673380.2723660.2116625370.0992819559.7777634547.4845526984.7316095370.0992819559.7777634547.48455217708.540116
11tech_support_only7702.8616620.2110870.3072040.2433940.23831510468.9425226728.9884198373.31663311121.82660910468.9425229899.1085088373.31663311121.826609
22tech_support_only5062.1844200.3765700.2419010.2562610.1252682879.7417865449.409859937.5151032959.5143472879.7417863848.652052937.5151032959.514347
33none8930.4950220.3396530.1947010.2465050.2191428458.7792178254.3720696054.5989736560.0280699847.5980528254.3720696054.5989736560.028069
44none8156.5755020.3122650.2500210.3121230.12559010137.4698213378.7480355743.8029392988.2173143793.8450543378.7480355743.8029392988.217314
\n", + "
" + ], + "text/plain": [ + " sample e_hat \\\n", + " sample_id actual_treatment observed_net_outcome none \n", + "0 0 discount_plus_support 9254.558541 0.248633 \n", + "1 1 tech_support_only 7702.861662 0.211087 \n", + "2 2 tech_support_only 5062.184420 0.376570 \n", + "3 3 none 8930.495022 0.339653 \n", + "4 4 none 8156.575502 0.312265 \n", + "\n", + " mu_hat \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 0.267338 0.272366 0.211662 5370.099281 \n", + "1 0.307204 0.243394 0.238315 10468.942522 \n", + "2 0.241901 0.256261 0.125268 2879.741786 \n", + "3 0.194701 0.246505 0.219142 8458.779217 \n", + "4 0.250021 0.312123 0.125590 10137.469821 \n", + "\n", + " gamma \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 9559.777763 4547.484552 6984.731609 5370.099281 \n", + "1 6728.988419 8373.316633 11121.826609 10468.942522 \n", + "2 5449.409859 937.515103 2959.514347 2879.741786 \n", + "3 8254.372069 6054.598973 6560.028069 9847.598052 \n", + "4 3378.748035 5743.802939 2988.217314 3793.845054 \n", + "\n", + " \n", + " tech_support_only discount_only discount_plus_support \n", + "0 9559.777763 4547.484552 17708.540116 \n", + "1 9899.108508 8373.316633 11121.826609 \n", + "2 3848.652052 937.515103 2959.514347 \n", + "3 8254.372069 6054.598973 6560.028069 \n", + "4 3378.748035 5743.802939 2988.217314 " + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_ids = np.arange(5)\n", + "\n", + "sample_prediction_table = pd.concat(\n", + " {\n", + " 'sample': pd.DataFrame({\n", + " 'sample_id': sample_ids,\n", + " 'actual_treatment': pd.Series(A_test[sample_ids]).map(TREATMENT_NAMES).to_numpy(),\n", + " 'observed_net_outcome': Y_net_test[sample_ids],\n", + " }),\n", + " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=TREATMENT_LABELS),\n", + " },\n", + " axis=1,\n", + ")\n", + "\n", + "sample_prediction_table" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "14b616ad", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:57.502590Z", + "iopub.status.busy": "2026-05-09T16:49:57.502507Z", + "iopub.status.idle": "2026-05-09T16:49:57.508438Z", + "shell.execute_reply": "2026-05-09T16:49:57.508058Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
policyvalueci_lowerci_upperrate_nonerate_tech_support_onlyrate_discount_onlyrate_discount_plus_support
4plugin_drlearner_4treatment11810.37771211283.56156312337.1938610.0920.5000.0350.373
6dr_policy_forest_4treatment11625.62754011102.02258912149.2324910.1130.4710.0000.416
5dr_policy_tree_4treatment11148.46236910700.60923011596.3155080.1990.4750.0000.326
1all_tech_support_only10526.5238769833.03475111220.0130010.0001.0000.0000.000
3all_discount_plus_support10136.7088829439.44594110833.9718240.0000.0000.0001.000
2all_discount_only7545.0792056993.5366218096.6217880.0000.0001.0000.000
0all_none7249.3454676955.4474327543.2435021.0000.0000.0000.000
\n", + "
" + ], + "text/plain": [ + " policy value ci_lower ci_upper \\\n", + "4 plugin_drlearner_4treatment 11810.377712 11283.561563 12337.193861 \n", + "6 dr_policy_forest_4treatment 11625.627540 11102.022589 12149.232491 \n", + "5 dr_policy_tree_4treatment 11148.462369 10700.609230 11596.315508 \n", + "1 all_tech_support_only 10526.523876 9833.034751 11220.013001 \n", + "3 all_discount_plus_support 10136.708882 9439.445941 10833.971824 \n", + "2 all_discount_only 7545.079205 6993.536621 8096.621788 \n", + "0 all_none 7249.345467 6955.447432 7543.243502 \n", + "\n", + " rate_none rate_tech_support_only rate_discount_only \\\n", + "4 0.092 0.500 0.035 \n", + "6 0.113 0.471 0.000 \n", + "5 0.199 0.475 0.000 \n", + "1 0.000 1.000 0.000 \n", + "3 0.000 0.000 0.000 \n", + "2 0.000 0.000 1.000 \n", + "0 1.000 0.000 0.000 \n", + "\n", + " rate_discount_plus_support \n", + "4 0.373 \n", + "6 0.416 \n", + "5 0.326 \n", + "1 0.000 \n", + "3 1.000 \n", + "2 0.000 \n", + "0 0.000 " + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "policy_assignments = {\n", + " **{f'all_{treatment_name}': np.full(len(X_test), treatment_id) for treatment_id, treatment_name in TREATMENT_NAMES.items()},\n", + " 'plugin_drlearner_4treatment': pi_plugin,\n", + " 'dr_policy_tree_4treatment': pi_tree,\n", + " 'dr_policy_forest_4treatment': pi_forest,\n", + "}\n", + "\n", + "eval_rows = []\n", + "for policy_name, policy_assignment in policy_assignments.items():\n", + " pi = np.asarray(policy_assignment).astype(int).ravel()\n", + " scores = gamma_net[np.arange(len(pi)), pi]\n", + "\n", + " row = {\n", + " 'policy': policy_name,\n", + " 'value': scores.mean(),\n", + " 'value_se': scores.std(ddof=1) / np.sqrt(len(scores)),\n", + " }\n", + " for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " row[f'rate_{treatment_name}'] = np.mean(pi == treatment_id)\n", + " eval_rows.append(row)\n", + "\n", + "policy_eval = pd.DataFrame(eval_rows)\n", + "policy_eval['ci_lower'] = policy_eval['value'] - 1.96 * policy_eval['value_se']\n", + "policy_eval['ci_upper'] = policy_eval['value'] + 1.96 * policy_eval['value_se']\n", + "\n", + "display_cols = ['policy', 'value', 'ci_lower', 'ci_upper'] + [f'rate_{n}' for n in TREATMENT_LABELS]\n", + "policy_eval.sort_values('value', ascending=False)[display_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "4814d1ed", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n", + "plot_df = policy_eval.sort_values('value', ascending=False)\n", + "sns.barplot(data=plot_df, x='value', y='policy', ax=axes[0], color='seagreen')\n", + "axes[0].set_title('4-way net policy value')\n", + "axes[0].set_xlabel('AIPW-estimated net value')\n", + "axes[0].set_ylabel('')\n", + "\n", + "treatment_rate_cols = [f'rate_{name}' for name in TREATMENT_LABELS]\n", + "rate_df = policy_eval.set_index('policy')[treatment_rate_cols]\n", + "rate_df.columns = TREATMENT_LABELS\n", + "rate_df.loc[['plugin_drlearner_4treatment', 'dr_policy_tree_4treatment', 'dr_policy_forest_4treatment']].plot(kind='barh', stacked=True, ax=axes[1], colormap='tab20')\n", + "axes[1].set_title('Assigned treatment share')\n", + "axes[1].set_xlabel('Share')\n", + "axes[1].set_ylabel('')\n", + "axes[1].legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "dec4e9f6", + "metadata": {}, + "source": [ + "세 학습 정책 모두 단일 처치 정책보다 높은 가치를 가집니다. 고객별로 처치를 다르게 배정하는 것이 모두에게 같은 처치를 적용하는 것보다 효과적이라는 의미입니다.\n", + "\n", + "학습 정책 중에서는 DRLearner plug-in(11,810)이 가장 높았고, DRPolicyForest(11,626)와 DRPolicyTree(11,148)가 뒤를 이었습니다. 다만 plug-in과 forest는 신뢰구간이 상당 부분 겹쳐 실질적인 차이가 있다고 보기는 어렵습니다. 이론적으로는 AIPW score를 직접 최적화하는 DR 정책이 더 유리할 수 있지만, 실제로는 표본 크기나 데이터 특성에 따라 결과가 달라집니다. tree 정책은 해석 가능한 구조를 유지하는 대신 성능을 일부 내준 모습입니다.\n", + "\n", + "단일 처치 정책에서는 `all_tech_support_only`(10,527)가 가장 좋았습니다. `all_discount_plus_support`(10,137)는 할인 비용이 고객 규모에 따라 커지도록 설정했기 때문에, 일부 고객에서는 할인 편익이 비용을 충분히 상쇄하지 못한 것으로 보입니다." + ] + }, + { + "cell_type": "markdown", + "id": "9bh57tga2aq", + "metadata": {}, + "source": [ + "## 예산 제약 하의 정책 비교\n", + "\n", + "지금까지는 어떤 정책이 전체적으로 더 높은 AIPW value를 갖는지 비교했습니다. 그러나 실제 환경에서는 예산이 제한되어 있을 수 있습니다. 같은 예산 안에서 어떤 정책이 더 효율적인지 비교하려면 비용 곡선(cost curve)이 유용합니다." + ] + }, + { + "cell_type": "markdown", + "id": "qdxockulyq9", + "metadata": {}, + "source": [ + "### 고객별 기대 비용 추정\n", + "\n", + "이를 위해 먼저 고객별 $E[C(a) \\mid X_i]$를 추정합니다. 실제 비용은 해당 고객이 받은 처치에서만 관측되므로, 처치별로 훈련 데이터에서 비용 회귀 모델을 학습해 test set에 적용합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "a1z6z7eabyj", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated E[C(a)|X] — test set (mean ± std):\n", + " tech_support_only : 4,892 ± 3,466\n", + " discount_only : 5,454 ± 2,330\n", + " discount_plus_support : 10,687 ± 4,139\n" + ] + } + ], + "source": [ + "# 처치별 E[C(a) | X] 추정\n", + "# c_hat_test[i, a] = 고객 i가 처치 a를 받을 때의 예측 비용\n", + "c_true_by_treatment = {1: c_tech, 2: c_disc, 3: c_tech + c_disc}\n", + "\n", + "c_hat_test = np.zeros((len(X_test), 4)) # 처치 0 → 비용 = 0\n", + "for treatment_id in [1, 2, 3]:\n", + " mask_train = (A_train == treatment_id)\n", + " mdl = RandomForestRegressor(n_estimators=300, min_samples_leaf=20,\n", + " random_state=SEED, n_jobs=1)\n", + " mdl.fit(X_train[mask_train], c_true_by_treatment[treatment_id][train_idx[mask_train]])\n", + " c_hat_test[:, treatment_id] = np.maximum(mdl.predict(X_test), 200.0)\n", + "\n", + "print(\"Estimated E[C(a)|X] — test set (mean ± std):\")\n", + "for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " if treatment_id > 0:\n", + " print(f\" {treatment_name:25s}: {c_hat_test[:, treatment_id].mean():8,.0f} ± {c_hat_test[:, treatment_id].std():6,.0f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "dgt4sg335xc", + "metadata": {}, + "source": [ + "### 비용 곡선과 예산 제약 하 비교\n", + "\n", + "비용 곡선의 두 축은 다음과 같습니다.\n", + "\n", + "- **x축**: 고객 1인당 누적 평균 처치 비용\n", + "- **y축**: 고객 1인당 누적 평균 순수익 — baseline(`none`) 대비 증분이며, **비용이 이미 차감된 값**입니다.\n", + "\n", + "각 정책에서 편익/비용 비율 $\\hat\\rho(x) = \\hat\\tau(x) / \\hat\\gamma(x)$이 높은 고객부터 순서대로 처치합니다. x = B에서 세로선을 그으면 예산 B 하의 정책 비교가 됩니다. 같은 예산에서 y값이 높을수록 더 효율적인 정책입니다.\n", + "\n", + "그래프의 종점(●)은 각 정책의 처치 대상 전원을 처치했을 때의 위치입니다. `none`으로 배정된 고객은 처치 비용이 없으므로 곡선에 포함되지 않습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "ttwi6dbqcfo", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def policy_cost_curve(pi_eval, gamma_matrix, c_hat_matrix):\n", + " \"\"\"\n", + " 처치별 고객 비용 추정값을 사용해 비용 곡선을 생성합니다.\n", + " c_hat_matrix[i, a] = Ê[C(a) | X_i]\n", + "\n", + " x = 고객 1인당 누적 평균 처치 비용 (gross)\n", + " y = 고객 1인당 누적 평균 순이익 (Y_net에 비용이 이미 차감된 값)\n", + "\n", + " 처치 고객은 순이익 / 추정 비용 비율(내림차순)로 정렬합니다.\n", + " \"\"\"\n", + " pi = np.asarray(pi_eval).ravel().astype(int)\n", + " n = len(pi)\n", + " treated = pi > 0\n", + " if treated.sum() == 0:\n", + " return np.array([0.0]), np.array([0.0])\n", + "\n", + " benefit = gamma_matrix[np.arange(n), pi] - gamma_matrix[:, 0]\n", + " cost = c_hat_matrix[np.arange(n), pi].astype(float)\n", + "\n", + " b_t, c_t = benefit[treated], cost[treated]\n", + " ratio = np.where(c_t > 0, b_t / c_t, b_t)\n", + " order = np.argsort(-ratio)\n", + "\n", + " cum_cost = np.r_[0, np.cumsum(c_t[order])]\n", + " cum_benefit = np.r_[0, np.cumsum(b_t[order])]\n", + " return cum_cost / n, cum_benefit / n\n", + "\n", + "\n", + "n_test = len(X_test)\n", + "policies_curve = [\n", + " ('DRLearner plug-in', pi_plugin),\n", + " ('DRPolicyTree (depth=2)', pi_tree),\n", + " ('DRPolicyForest', pi_forest),\n", + " ('all_discount_plus_support', np.full(n_test, 3)),\n", + " ('all_tech_support_only', np.full(n_test, 1)),\n", + "]\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple']\n", + "\n", + "for (name, pi_eval), color in zip(policies_curve, colors):\n", + " cc, cg = policy_cost_curve(pi_eval, gamma_net, c_hat_test)\n", + " ax.plot(cc, cg, lw=2, color=color,\n", + " label=f'{name} (cost={cc[-1]:,.0f}, net benefit={cg[-1]:,.0f})')\n", + " ax.scatter([cc[-1]], [cg[-1]], s=50, color=color, zorder=5)\n", + "\n", + "budget_example = 5_000\n", + "ax.axvline(budget_example, color='gray', lw=1.5, ls='--', alpha=0.7,\n", + " label=f'Budget = ${budget_example:,}/customer')\n", + "\n", + "ax.set_xlabel('Avg cumulative cost per customer ($)', fontsize=11)\n", + "ax.set_ylabel('Avg cumulative net benefit per customer ($)', fontsize=11)\n", + "ax.set_title('Policy cost curves — cut at x=B for budget-constrained comparison', fontsize=11)\n", + "ax.legend(fontsize=8, loc='upper left')\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "qoq9ch9m2fg", + "metadata": {}, + "source": [ + "예산 규모에 따라 효과적인 정책이 다릅니다. 예산이 $1,000~$3,000 수준으로 적을 때는 `all_tech_support_only`가 가장 효율적입니다. 기술지원 평균 비용이 낮아 소규모 예산으로도 편익/비용 비율이 높은 고객부터 빠르게 처치할 수 있기 때문입니다.\n", + "\n", + "예산이 $4,000~$6,000 수준으로 늘어나면 학습 정책(plug-in, forest)이 앞서기 시작합니다. 고객별 최적 처치를 배정하는 타겟팅 효과가 발휘되며, 같은 비용으로 더 높은 순이익을 냅니다.\n", + "\n", + "예산을 전부 소진하는 상황에서도 학습 정책이 가장 효율적입니다. plug-in은 약 $6,600을 써서 $4,561, forest는 약 $6,900을 써서 $4,376의 순이익을 냅니다. 반면 `all_discount_plus_support`는 $10,700을 전부 소진해도 순이익이 $2,887에 그쳐 예산 대비 효율이 가장 낮습니다." + ] + }, + { + "cell_type": "markdown", + "id": "b40f71c0", + "metadata": {}, + "source": [ + "## 참고 자료\n", + "\n", + "- [Stanford ML+CI Tutorial — Policy Learning I (Binary Treatment)](https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/policy-learning-i---binary-treatment.html)\n", + "- [Athey and Wager (2021, Econometrica) — Policy Learning with Observational Data](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732)\n", + "- [Sun, Du, Wager et al. (2021) — Treatment Allocation under Uncertain Costs](https://arxiv.org/abs/2103.11066)\n", + "- [Imai and Li (2019) — Experimental Evaluation of Individualized Treatment Rules](https://arxiv.org/pdf/1905.05389.pdf)\n", + "- [arXiv 2604.06123](https://arxiv.org/html/2604.06123v1)" + ] + } + ], + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements-book.txt b/requirements-book.txt index 898be45..0548471 100644 --- a/requirements-book.txt +++ b/requirements-book.txt @@ -5,6 +5,9 @@ pandas scipy matplotlib seaborn -scikit-learn +scikit-learn<1.7 statsmodels graphviz +econml +scikit-uplift +kagglehub diff --git a/requirements.txt b/requirements.txt index d010b0b..c75646f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,6 @@ # Jupyter Book (2.x - MyST-MD 기반) jupyter-book>=2.0.0 +jupyterlab # Video Production manim>=0.18.0 @@ -9,6 +10,18 @@ numpy pandas matplotlib seaborn -scikit-learn +scikit-learn<1.7 +scikit-uplift statsmodels graphviz +econml +kagglehub +ipywidgets + +# PDF Processing +pypdf +pdfplumber +reportlab +pdf2image +pytesseract +Pillow