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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a5515400",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"Colon_and_Rectal_Cancer\"\n",
"cohort = \"GSE46862\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Colon_and_Rectal_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Colon_and_Rectal_Cancer/GSE46862\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/GSE46862.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE46862.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/clinical_data/GSE46862.csv\"\n",
"json_path = \"../../output/preprocess/Colon_and_Rectal_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "371239d6",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d92dcc2d",
"metadata": {},
"outputs": [],
"source": [
"from tools.preprocess import *\n",
"# 1. Identify the paths to the SOFT file and the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Read the matrix file to obtain background information and sample characteristics data\n",
"background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
"clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
"\n",
"# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n",
"sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
"\n",
"# 4. Explicitly print out all the background information and the sample characteristics dictionary\n",
"print(\"Background Information:\")\n",
"print(background_info)\n",
"print(\"Sample Characteristics Dictionary:\")\n",
"print(sample_characteristics_dict)\n"
]
},
{
"cell_type": "markdown",
"id": "89877816",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "119eae27",
"metadata": {},
"outputs": [],
"source": [
"# Analyzing data availability and creating conversion functions\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this study used Affymetrix GenChip arrays\n",
"# for gene expression profiling, so gene expression data should be available\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# From the sample characteristics dictionary:\n",
"# - trait (chemoradiation therapy response) is in row 0\n",
"# - age is in row 1\n",
"# - gender (Sex) is in row 2\n",
"trait_row = 0\n",
"age_row = 1\n",
"gender_row = 2\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert chemoradiation therapy response to binary (1 for good response, 0 for poor response)\"\"\"\n",
" if not value or ':' not in value:\n",
" return None\n",
" \n",
" response = value.split(':', 1)[1].strip()\n",
" # Based on the description, NT and TO are better responses, MI and MO are worse responses\n",
" if response == 'NT' or response == 'TO':\n",
" return 1 # good response\n",
" elif response == 'MI' or response == 'MO':\n",
" return 0 # poor response\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous numeric value\"\"\"\n",
" if not value or ':' not in value:\n",
" return None\n",
" \n",
" try:\n",
" age = int(value.split(':', 1)[1].strip())\n",
" return age\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary (0 for female, 1 for male)\"\"\"\n",
" if not value or ':' not in value:\n",
" return None\n",
" \n",
" gender = value.split(':', 1)[1].strip().lower()\n",
" if gender == 'male':\n",
" return 1\n",
" elif gender == 'female':\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"# Initial filtering\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" # Sample characteristics dictionary from previous step\n",
" sample_chars = {\n",
" 0: ['chemoradiation therapy response: MO', 'chemoradiation therapy response: TO', 'chemoradiation therapy response: MI', 'chemoradiation therapy response: NT'],\n",
" 1: ['age: 68', 'age: 58', 'age: 66', 'age: 56', 'age: 55', 'age: 50', 'age: 37', 'age: 59', 'age: 46', 'age: 49', 'age: 62', 'age: 65', 'age: 63', 'age: 41', 'age: 33', 'age: 73', 'age: 70', 'age: 69', 'age: 39', 'age: 43', 'age: 48', 'age: 72', 'age: 76', 'age: 40', 'age: 54', 'age: 45', 'age: 71', 'age: 52', 'age: 53', 'age: 67'],\n",
" 2: ['Sex: male', 'Sex: female'],\n",
" 3: ['tumor stage (uicc-7th): IIIB', 'tumor stage (uicc-7th): 0', 'tumor stage (uicc-7th): I', 'tumor stage (uicc-7th): IIA', 'tumor stage (uicc-7th): IIIA', 'tumor stage (uicc-7th): IIIC', 'tumor stage (uicc-7th): IVA', 'tumor stage (uicc-7th): IV', 'tumor stage (uicc-7th): lll', 'tumor stage (uicc-7th): IIB'],\n",
" 4: ['description (operation): LAR', 'description (operation): ULAR', 'description (operation): LAR, TAH', 'description (operation): LAR, ileostomy', \"description (operation): Hartmann's procedure\", 'description (operation): APR', 'description (operation): APR,TAH,LSO,S6 segmentectomy', 'description (operation): TEM', 'description (operation): L-LAR, Rt PLND, ileostomy', 'description (operation): ISR, colonic pouch, ileostomy', 'description (operation): ISR, coloanal, ileostomy', 'description (operation): R-LAR, ileostomy', 'description (operation): ISR, colonic pouch, Ileostomy', 'description (operation): ISR, colonic pouch, ileostomy, Rt PLND', 'description (operation): ISR, Coloanal, ileostomy', 'description (operation): ELAR', 'description (operation): ISR']\n",
" }\n",
" \n",
" # Create a clinical data DataFrame with the structure expected by geo_select_clinical_features\n",
" # Where rows are characteristics and each column would represent a sample\n",
" clinical_data = pd.DataFrame()\n",
" for key, values in sample_chars.items():\n",
" clinical_data.loc[key] = values\n",
" \n",
" # Extract clinical features\n",
" selected_features = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the extracted features\n",
" preview = preview_df(selected_features)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_features.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ab64f083",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6eacdd2",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"import gzip\n",
"from typing import Optional, Callable, Dict, Any, List\n",
"import numpy as np\n",
"import re\n",
"\n",
"# Load the clinical data \n",
"clinical_data_file = os.path.join(in_cohort_dir, \"GSE46862_series_matrix.txt.gz\")\n",
"\n",
"try:\n",
" # Read the clinical data\n",
" with gzip.open(clinical_data_file, 'rt') as file:\n",
" lines = file.readlines()\n",
" \n",
" # Extract sample characteristics\n",
" sample_char_lines = [line for line in lines if line.startswith('!Sample_characteristics_ch1')]\n",
" \n",
" # Extract sample IDs\n",
" sample_id_line = next((line for line in lines if line.startswith('!Sample_geo_accession')), None)\n",
" if sample_id_line:\n",
" sample_ids = sample_id_line.strip().split('\\t')[1:]\n",
" else:\n",
" sample_ids = []\n",
" \n",
" # Create a dictionary to organize sample characteristics\n",
" sample_char_dict = {}\n",
" \n",
" for line in sample_char_lines:\n",
" values = line.strip().split('\\t')[1:]\n",
" sample_char_dict[len(sample_char_dict)] = values\n",
" \n",
" # Create a DataFrame from the dictionary\n",
" clinical_data = pd.DataFrame(sample_char_dict, index=sample_ids).T\n",
" \n",
" # Print some sample data to analyze\n",
" print(\"Sample characteristics dictionary:\")\n",
" for key, values in sample_char_dict.items():\n",
" unique_values = set(values)\n",
" print(f\"Key {key}: {list(unique_values)[:5]}{' ...' if len(unique_values) > 5 else ''} (Unique values: {len(unique_values)})\")\n",
" \n",
" # Extract title and description for background information\n",
" title_line = next((line for line in lines if line.startswith('!Series_title')), None)\n",
" title = title_line.strip().split('!Series_title\\t')[1] if title_line else \"No title found\"\n",
" \n",
" description_lines = [line for line in lines if line.startswith('!Series_summary')]\n",
" description = ' '.join([line.strip().split('!Series_summary\\t')[1] for line in description_lines]) if description_lines else \"No description found\"\n",
" \n",
" print(\"\\nDataset Title:\")\n",
" print(title)\n",
" print(\"\\nDataset Description:\")\n",
" print(description)\n",
" \n",
" # Check if this is a gene expression dataset\n",
" platform_line = next((line for line in lines if line.startswith('!Series_platform_id')), None)\n",
" platform_id = platform_line.strip().split('!Series_platform_id\\t')[1] if platform_line else \"Unknown\"\n",
" \n",
" print(\"\\nPlatform ID:\")\n",
" print(platform_id)\n",
" \n",
"except Exception as e:\n",
" print(f\"Error reading clinical data: {e}\")\n",
" clinical_data = pd.DataFrame()\n",
" sample_char_dict = {}\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on platform info (GPL6244) and description, determine if gene expression data is likely available\n",
"is_gene_available = True # The platform GPL6244 is for gene expression\n",
"\n",
"# 2. Clinical Feature Availability and Data Type Conversion\n",
"# Analyze sample characteristics to identify trait, age, and gender information\n",
"\n",
"# 2.1 Trait Availability (Chemoradiation therapy response)\n",
"trait_row = 0 # Chemoradiation therapy response is in row 0\n",
"\n",
"# Function to convert trait values\n",
"def convert_trait(value):\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" \n",
" # Remove quotes and extract value after colon\n",
" value = str(value).strip('\"')\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert therapy responses to binary\n",
" value_lower = str(value).lower()\n",
" if 'nt' in value_lower or 'mo' in value_lower: # Non-responders\n",
" return 0\n",
" elif 'mi' in value_lower or 'to' in value_lower: # Responders\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 2.2 Age Availability\n",
"age_row = 1 # Age information is in row 1\n",
"\n",
"# Function to convert age values\n",
"def convert_age(value):\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" \n",
" # Remove quotes and extract value after colon\n",
" value = str(value).strip('\"')\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Try to extract numeric age\n",
" try:\n",
" age_match = re.search(r'(\\d+)', str(value))\n",
" if age_match:\n",
" return float(age_match.group(1))\n",
" except:\n",
" pass\n",
" \n",
" return None\n",
"\n",
"# 2.3 Gender Availability\n",
"gender_row = 2 # Sex information is in row 2\n",
"\n",
"# Function to convert gender values\n",
"def convert_gender(value):\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" \n",
" # Remove quotes and extract value after colon\n",
" value = str(value).strip('\"')\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary: Female = 0, Male = 1\n",
" value_lower = str(value).lower()\n",
" if 'female' in value_lower or 'f' == value_lower:\n",
" return 0\n",
" elif 'male' in value_lower or 'm' == value_lower:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata - Initial Filtering\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction (if trait_row is not None)\n",
"if trait_row is not None:\n",
" # Extract clinical features\n",
" clinical_features_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the extracted features\n",
" print(\"\\nExtracted Clinical Features Preview:\")\n",
" print(preview_df(clinical_features_df))\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save to CSV\n",
" clinical_features_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "4a804951",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c18dad67",
"metadata": {},
"outputs": [],
"source": [
"# 1. Get the file paths for the SOFT file and matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. First, let's examine the structure of the matrix file to understand its format\n",
"import gzip\n",
"\n",
"# Peek at the first few lines of the file to understand its structure\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" # Read first 100 lines to find the header structure\n",
" for i, line in enumerate(file):\n",
" if '!series_matrix_table_begin' in line:\n",
" print(f\"Found data marker at line {i}\")\n",
" # Read the next line which should be the header\n",
" header_line = next(file)\n",
" print(f\"Header line: {header_line.strip()}\")\n",
" # And the first data line\n",
" first_data_line = next(file)\n",
" print(f\"First data line: {first_data_line.strip()}\")\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Matrix table marker not found in first 100 lines\")\n",
" break\n",
"\n",
"# 3. Now try to get the genetic data with better error handling\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(gene_data.index[:20])\n",
"except KeyError as e:\n",
" print(f\"KeyError: {e}\")\n",
" \n",
" # Alternative approach: manually extract the data\n",
" print(\"\\nTrying alternative approach to read the gene data:\")\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Find the start of the data\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" \n",
" # Read the headers and data\n",
" import pandas as pd\n",
" df = pd.read_csv(file, sep='\\t', index_col=0)\n",
" print(f\"Column names: {df.columns[:5]}\")\n",
" print(f\"First 20 row IDs: {df.index[:20]}\")\n",
" gene_data = df\n"
]
},
{
"cell_type": "markdown",
"id": "a1898c17",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abd87d55",
"metadata": {},
"outputs": [],
"source": [
"# Looking at the identifiers in the gene expression data\n",
"# The identifiers appear to be numeric codes (like 7892501, 7892502, etc.)\n",
"# These are not standard human gene symbols (which would be like BRCA1, TP53, etc.)\n",
"# These are likely probe IDs from a microarray platform that need to be mapped to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "22ca76e6",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b9c95ba",
"metadata": {},
"outputs": [],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "21fc70fd",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08403493",
"metadata": {},
"outputs": [],
"source": [
"# Analyze the format of gene identifiers in both datasets\n",
"print(\"\\nIdentifying mapping columns:\")\n",
"print(f\"Gene expression data has identifiers like: {gene_data.index[:5]}\")\n",
"print(f\"Gene annotation data columns: {gene_annotation.columns}\")\n",
"\n",
"# The 'ID' column in gene_annotation matches the index in gene_data\n",
"# The 'gene_assignment' column contains gene symbols\n",
"\n",
"# Identifying gene symbols in the gene_assignment column\n",
"print(\"\\nChecking gene_assignment format:\")\n",
"sample_gene = gene_annotation['gene_assignment'].iloc[2]\n",
"print(f\"Sample gene assignment: {sample_gene[:200]}...\")\n",
"\n",
"# Extract the mapping between probe IDs and gene symbols\n",
"gene_mapping = get_gene_mapping(\n",
" annotation=gene_annotation, \n",
" prob_col='ID', \n",
" gene_col='gene_assignment'\n",
")\n",
"\n",
"print(\"\\nMapped genes preview:\")\n",
"print(gene_mapping.head())\n",
"\n",
"# Apply the gene mapping to convert probe-based expression to gene expression\n",
"gene_data = apply_gene_mapping(\n",
" expression_df=gene_data,\n",
" mapping_df=gene_mapping\n",
")\n",
"\n",
"# Preview the gene expression data\n",
"print(\"\\nGene expression data after mapping:\")\n",
"print(f\"Shape: {gene_data.shape}\")\n",
"print(f\"First 5 genes: {gene_data.index[:5]}\")\n",
"print(f\"Sample columns: {gene_data.columns[:5]}\")\n",
"\n",
"# Save the gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "c3daa7cf",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d00aab4f",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Retrieve the original clinical data directly from step 2\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"background_info, clinical_df = get_background_and_clinical_data(matrix_file)\n",
"\n",
"# Extract clinical features using the geo_select_clinical_features function as in step 3\n",
"clinical_features_df = geo_select_clinical_features(\n",
" clinical_df=clinical_df,\n",
" trait=trait,\n",
" trait_row=0, # chemoradiation therapy response\n",
" convert_trait=lambda x: 1 if any(resp in str(x).upper() for resp in [\"NT\", \"TO\"]) else 0, # better response = 1\n",
" age_row=1, # age information\n",
" convert_age=lambda x: int(str(x).split(\":\", 1)[1].strip()) if \":\" in str(x) else None,\n",
" gender_row=2, # gender information\n",
" convert_gender=lambda x: 0 if \"female\" in str(x).lower() else 1 if \"male\" in str(x).lower() else None\n",
")\n",
"\n",
"# Fix column names in gene data to match clinical data format\n",
"normalized_gene_data.columns = [col.strip('\"') for col in normalized_gene_data.columns]\n",
"\n",
"# 3. Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n",
"print(f\"Initial linked data shape: {linked_data.shape}\")\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Data after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 5. Determine whether the trait and demographic features are severely biased\n",
"is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 6. Conduct final quality validation and save cohort information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=True, \n",
" is_biased=is_trait_biased, \n",
" df=linked_data,\n",
" note=\"Dataset contains gene expression data from rectal cancer patients with variable responses to chemoradiation therapy.\"\n",
")\n",
"\n",
"# 7. If the linked data is usable, save it as a CSV file\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Data was determined to be unusable and was not saved\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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