{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbc640a5",
   "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 = \"Gastroesophageal_reflux_disease_(GERD)\"\n",
    "cohort = \"GSE77563\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Gastroesophageal_reflux_disease_(GERD)\"\n",
    "in_cohort_dir = \"../../input/GEO/Gastroesophageal_reflux_disease_(GERD)/GSE77563\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Gastroesophageal_reflux_disease_(GERD)/GSE77563.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Gastroesophageal_reflux_disease_(GERD)/gene_data/GSE77563.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Gastroesophageal_reflux_disease_(GERD)/clinical_data/GSE77563.csv\"\n",
    "json_path = \"../../output/preprocess/Gastroesophageal_reflux_disease_(GERD)/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e790ff46",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d39fbcc3",
   "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": "e43ecd56",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62084be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Analyze gene expression data availability\n",
    "# Based on the background information, this dataset contains Affymetrix expression array profiles\n",
    "# which indicates it's likely to contain gene expression data\n",
    "is_gene_available = True  # Affymetrix Human Gene 2.1 ST arrays data is available\n",
    "\n",
    "# 2. Analyze variable availability and data type conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait (GERD):\n",
    "# Key 5 contains GERD status information\n",
    "trait_row = 5\n",
    "\n",
    "# For age:\n",
    "# Key 1 contains age information\n",
    "age_row = 1\n",
    "\n",
    "# For gender:\n",
    "# Key 2 contains gender information\n",
    "gender_row = 2\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "# For trait (GERD):\n",
    "def convert_trait(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary (0 for No GERD, 1 for GERD)\n",
    "    if \"No GERD\" in value:\n",
    "        return 0\n",
    "    elif \"GERD\" in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# For age:\n",
    "def convert_age(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to continuous\n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "# For gender:\n",
    "def convert_gender(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary (0 for female, 1 for male)\n",
    "    if \"female\" in value.lower():\n",
    "        return 0\n",
    "    elif \"male\" in value.lower():\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Initial filtering and save metadata\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",
    "    # Extract clinical features\n",
    "    selected_clinical_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 clinical data\n",
    "    print(\"Preview of extracted clinical data:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "    \n",
    "    # Save the clinical data\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0340368b",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33feb428",
   "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": "7f217182",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00fdd04f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The identifiers seen in the gene expression data (16657436, 16657445, etc.) appear to be \n",
    "# Affymetrix probeset IDs rather than human gene symbols. These are numeric identifiers \n",
    "# specific to the microarray platform used in this study.\n",
    "#\n",
    "# Standard human gene symbols would typically be alphabetic (like \"BRCA1\", \"TP53\", etc.) \n",
    "# or alphanumeric combinations that follow a recognizable pattern.\n",
    "#\n",
    "# These numeric IDs will need to be mapped to standard gene symbols for meaningful analysis.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b8f0a47",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "790eea20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Let's first examine the structure of the SOFT file before trying to parse it\n",
    "import gzip\n",
    "\n",
    "# Look at the first few lines of the SOFT file to understand its structure\n",
    "print(\"Examining SOFT file structure:\")\n",
    "try:\n",
    "    with gzip.open(soft_file, 'rt') as file:\n",
    "        # Read first 20 lines to understand the file structure\n",
    "        for i, line in enumerate(file):\n",
    "            if i < 20:\n",
    "                print(f\"Line {i}: {line.strip()}\")\n",
    "            else:\n",
    "                break\n",
    "except Exception as e:\n",
    "    print(f\"Error reading SOFT file: {e}\")\n",
    "\n",
    "# 2. Now let's try a more robust approach to extract the gene annotation\n",
    "# Instead of using the library function which failed, we'll implement a custom approach\n",
    "try:\n",
    "    # First, look for the platform section which contains gene annotation\n",
    "    platform_data = []\n",
    "    with gzip.open(soft_file, 'rt') as file:\n",
    "        in_platform_section = False\n",
    "        for line in file:\n",
    "            if line.startswith('^PLATFORM'):\n",
    "                in_platform_section = True\n",
    "                continue\n",
    "            if in_platform_section and line.startswith('!platform_table_begin'):\n",
    "                # Next line should be the header\n",
    "                header = next(file).strip()\n",
    "                platform_data.append(header)\n",
    "                # Read until the end of the platform table\n",
    "                for table_line in file:\n",
    "                    if table_line.startswith('!platform_table_end'):\n",
    "                        break\n",
    "                    platform_data.append(table_line.strip())\n",
    "                break\n",
    "    \n",
    "    # If we found platform data, convert it to a DataFrame\n",
    "    if platform_data:\n",
    "        import pandas as pd\n",
    "        import io\n",
    "        platform_text = '\\n'.join(platform_data)\n",
    "        gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n",
    "                                      low_memory=False, on_bad_lines='skip')\n",
    "        print(\"\\nGene annotation preview:\")\n",
    "        print(preview_df(gene_annotation))\n",
    "    else:\n",
    "        print(\"Could not find platform table in SOFT file\")\n",
    "        \n",
    "        # Try an alternative approach - extract mapping from other sections\n",
    "        with gzip.open(soft_file, 'rt') as file:\n",
    "            for line in file:\n",
    "                if 'ANNOTATION information' in line or 'annotation information' in line:\n",
    "                    print(f\"Found annotation information: {line.strip()}\")\n",
    "                if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n",
    "                    print(f\"Platform title: {line.strip()}\")\n",
    "            \n",
    "except Exception as e:\n",
    "    print(f\"Error processing gene annotation: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91185d03",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d3902d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. First, make sure we have the gene expression data loaded properly\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 2. Extract and prepare the gene mapping dataframe\n",
    "# Looking at the data, 'ID' contains the numeric identifiers that match the gene expression data\n",
    "# The 'gene_assignment' column contains gene symbol information\n",
    "mapping_df = gene_annotation[['ID', 'gene_assignment']]\n",
    "\n",
    "# Convert IDs to strings for consistent comparison\n",
    "mapping_df['ID'] = mapping_df['ID'].astype(str)\n",
    "\n",
    "# 3. Extract gene symbols from the complex gene_assignment field\n",
    "# Use the built-in extract_human_gene_symbols function which is more robust\n",
    "# It's able to identify gene symbols from complex text following standard patterns\n",
    "mapping_df['Gene'] = mapping_df['gene_assignment'].apply(extract_human_gene_symbols)\n",
    "\n",
    "# Print diagnostic information about the mapping\n",
    "print(f\"Number of probes in gene_data: {len(gene_data.index)}\")\n",
    "print(f\"Original mapping rows: {len(mapping_df)}\")\n",
    "print(f\"Sample of original mappings:\")\n",
    "print(mapping_df[['ID', 'Gene']].head(5))\n",
    "\n",
    "# Filter out mappings with empty gene lists\n",
    "mapping_df = mapping_df[mapping_df['Gene'].apply(len) > 0]\n",
    "print(f\"Filtered mapping rows: {len(mapping_df)}\")\n",
    "print(f\"Number of mappable probes: {len(mapping_df[mapping_df['ID'].isin(gene_data.index)])}\")\n",
    "\n",
    "# 4. Apply the gene mapping to get gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df[['ID', 'Gene']])\n",
    "\n",
    "# Check data before normalization\n",
    "print(f\"\\nBefore normalization - shape: {gene_data.shape}\")\n",
    "print(f\"Sample gene symbols before normalization: {list(gene_data.index[:10])}\")\n",
    "\n",
    "# 5. Create a modified normalization function that preserves unmapped symbols\n",
    "def modified_normalize_gene_symbols(gene_df):\n",
    "    with open(\"./metadata/gene_synonym.json\", \"r\") as f:\n",
    "        synonym_dict = json.load(f)\n",
    "    \n",
    "    # Create a mapping function that keeps original symbol if not in dictionary\n",
    "    def map_symbol(x):\n",
    "        return synonym_dict.get(x.upper(), x)\n",
    "    \n",
    "    gene_df.index = gene_df.index.map(map_symbol)\n",
    "    # Group and average rows with same index (after normalization)\n",
    "    return gene_df.groupby(gene_df.index).mean()\n",
    "\n",
    "# Apply the modified normalization\n",
    "gene_data = modified_normalize_gene_symbols(gene_data)\n",
    "\n",
    "# Preview the mapped gene data\n",
    "print(\"\\nPreview of mapped gene data:\")\n",
    "print(f\"Shape: {gene_data.shape}\")\n",
    "print(gene_data.head())\n",
    "\n",
    "# Save the gene expression 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\"Gene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576d8241",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6f8b089",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Examine the issue with gene mapping in previous step\n",
    "print(\"Analyzing gene mapping issue...\")\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "print(f\"Gene data from file: {gene_data.shape}\")\n",
    "\n",
    "# Load gene annotation data more directly\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "print(f\"Gene annotation data shape: {gene_annotation.shape}\")\n",
    "\n",
    "# Extract and prepare the gene mapping dataframe\n",
    "mapping_df = gene_annotation[['ID', 'gene_assignment']]\n",
    "\n",
    "# Check a sample of the gene_assignment column\n",
    "print(\"\\nSample of gene_assignment data:\")\n",
    "for i, assignment in enumerate(mapping_df['gene_assignment'].head(3)):\n",
    "    print(f\"Assignment {i}: {assignment[:100]}...\")\n",
    "\n",
    "# Apply extract_human_gene_symbols function to get gene symbols\n",
    "mapping_df['Gene'] = mapping_df['gene_assignment'].apply(extract_human_gene_symbols)\n",
    "\n",
    "# Print sample of extracted genes\n",
    "print(\"\\nSample of extracted genes:\")\n",
    "print(mapping_df[['ID', 'Gene']].head(5))\n",
    "\n",
    "# Implement a direct gene mapping approach as a fallback\n",
    "print(\"\\nImplementing alternative gene mapping approach...\")\n",
    "gene_to_expr = {}\n",
    "\n",
    "# First, create a mapping from probe IDs to gene symbols\n",
    "probe_to_genes = {}\n",
    "for i, row in mapping_df.iterrows():\n",
    "    probe_id = str(row['ID'])\n",
    "    genes = row['Gene']\n",
    "    if genes and len(genes) > 0:\n",
    "        probe_to_genes[probe_id] = genes\n",
    "\n",
    "# Apply the mapping to distribute expression values\n",
    "for probe_id, expr_values in gene_data.iterrows():\n",
    "    probe_id_str = str(probe_id)\n",
    "    if probe_id_str in probe_to_genes:\n",
    "        genes = probe_to_genes[probe_id_str]\n",
    "        value_share = 1.0 / len(genes)\n",
    "        \n",
    "        for gene in genes:\n",
    "            if gene not in gene_to_expr:\n",
    "                gene_to_expr[gene] = pd.Series(0, index=expr_values.index)\n",
    "            gene_to_expr[gene] += expr_values * value_share\n",
    "\n",
    "# Convert the dictionary to a DataFrame\n",
    "gene_data = pd.DataFrame(gene_to_expr).T\n",
    "print(f\"Gene data after direct mapping: {gene_data.shape}\")\n",
    "\n",
    "# 1. Normalize gene symbols in the obtained gene expression data\n",
    "# Normalize gene symbols using NCBI Gene database\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "print(f\"Sample gene symbols after normalization: {list(normalized_gene_data.index[:10])}\")\n",
    "\n",
    "# Save the normalized gene data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\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. Link clinical and genetic data\n",
    "# Get raw clinical data from the matrix file\n",
    "_, clinical_raw = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "# Extract clinical features using the defined conversion functions\n",
    "clinical_features = geo_select_clinical_features(\n",
    "    clinical_df=clinical_raw,\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",
    "print(\"Clinical features shape:\", clinical_features.shape)\n",
    "\n",
    "# Save clinical features to file\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_features.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, first 5 columns):\")\n",
    "if linked_data.shape[1] >= 5:\n",
    "    print(linked_data.iloc[:5, :5])\n",
    "else:\n",
    "    print(linked_data.head())\n",
    "\n",
    "# 3. Handle missing values\n",
    "print(\"\\nMissing values before handling:\")\n",
    "print(f\"  Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n",
    "if 'Age' in linked_data.columns:\n",
    "    print(f\"  Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n",
    "if 'Gender' in linked_data.columns:\n",
    "    print(f\"  Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n",
    "\n",
    "gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n",
    "if gene_cols:\n",
    "    print(f\"  Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n",
    "    print(f\"  Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n",
    "\n",
    "cleaned_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n",
    "\n",
    "# 4. Evaluate bias in trait and demographic features\n",
    "is_trait_biased = False\n",
    "if len(cleaned_data) > 0:\n",
    "    trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n",
    "    is_trait_biased = trait_biased\n",
    "else:\n",
    "    print(\"No data remains after handling missing values.\")\n",
    "    is_trait_biased = True\n",
    "\n",
    "# 5. Final validation and save\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True, \n",
    "    cohort=cohort, \n",
    "    info_path=json_path, \n",
    "    is_gene_available=len(normalized_gene_data) > 0, \n",
    "    is_trait_available=True, \n",
    "    is_biased=is_trait_biased, \n",
    "    df=cleaned_data,\n",
    "    note=f\"Dataset contains gene expression data for {trait} analysis.\"\n",
    ")\n",
    "\n",
    "# 6. Save if usable\n",
    "if is_usable and len(cleaned_data) > 0:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    cleaned_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 or empty and was not saved\")"
   ]
  }
 ],
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}