# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Underweight"
cohort = "GSE84954"

# Input paths
in_trait_dir = "../DATA/GEO/Underweight"
in_cohort_dir = "../DATA/GEO/Underweight/GSE84954"

# Output paths
out_data_file = "./output/preprocess/3/Underweight/GSE84954.csv"
out_gene_data_file = "./output/preprocess/3/Underweight/gene_data/GSE84954.csv"
out_clinical_data_file = "./output/preprocess/3/Underweight/clinical_data/GSE84954.csv"
json_path = "./output/preprocess/3/Underweight/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Print shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# This is microarray data studying molecular pathways in tissues, so it should contain gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# Trait - use chronic liver disease as trait indicator (disease status)
trait_row = 1  # Disease information in row 1

def convert_trait(value: str) -> Optional[int]:
    """Convert disease status to binary (0 for control, 1 for liver disease)"""
    if not value or ':' not in value:
        return None
    value = value.split(':', 1)[1].strip()
    if 'Crigler-Najjar' in value:  # Control group
        return 0
    elif 'chronic liver disease' in value or 'Alagille' in value:  # Disease group
        return 1
    return None

# Age - not available in sample characteristics
age_row = None  
convert_age = None

# Gender - not available in sample characteristics  
gender_row = None
convert_gender = None

# 3. Save metadata
# Trait data is available since trait_row is not None
is_trait_available = trait_row is not None

validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)

# 4. Extract clinical features since trait data is available
selected_clinical_df = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    age_row=age_row,
    convert_age=convert_age,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# Preview the clinical data
print("Clinical Data Preview:")
print(preview_df(selected_clinical_df))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs 
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# Analyzing gene identifiers
# The identifiers appear to be numeric probe IDs (16650001, 16650003 etc)
# These are not standard human gene symbols which are typically alphanumeric like 'BRCA1'
# They seem to be probe IDs from a microarray platform that need mapping to gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure
print("Gene annotation preview:")
print(preview_df(gene_annotation))

print("\nAll columns in annotation data:")
print(list(gene_annotation.columns))
# 1. Get metadata from SOFT file with correct pattern matching
metadata_pattern = r'!platform_table_begin\n(.*?)\n!platform_table_end'
with gzip.open(soft_file_path, 'rt') as f:
    content = f.read()
    
matches = re.findall(metadata_pattern, content, re.DOTALL)
if matches:
    platform_data = pd.read_csv(io.StringIO(matches[0]), sep='\t')
    
    # Create mapping using platform data
    mapping_data = platform_data[['ID', 'Gene Symbol']].copy()
    mapping_data = mapping_data.dropna()
    mapping_data = mapping_data.rename(columns={'Gene Symbol': 'Gene'})

    # 3. Apply mapping to get gene expression data
    gene_data = apply_gene_mapping(genetic_data, mapping_data)

    # Preview the mapped gene data
    print("\nFirst 10 rows of mapped gene expression data:")
    print(preview_df(gene_data.head(10)))

    # Save gene data
    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
    gene_data.to_csv(out_gene_data_file)
else:
    print("Could not find platform table in SOFT file")
# 1. Get metadata from SOFT file with correct pattern matching
# Need to look for lines with gene symbol information
platform_pattern = r'#ID = (.*?)\n(.*?)!platform_table_begin'
gene_pattern = r'#Gene_Symbol = (.*?)\n'

with gzip.open(soft_file_path, 'rt') as f:
    content = f.read()
    
# Find and extract the platform GPL information section which contains annotation details
platform_matches = re.search(platform_pattern, content, re.DOTALL)
if platform_matches:
    platform_section = platform_matches.group(2)
    gene_matches = re.search(gene_pattern, platform_section)
    if gene_matches:
        # Create mapping dataframe with the proper gene symbol column
        platform_data = pd.read_csv(io.StringIO(platform_matches.group(2)), sep='\t')
        gene_col = gene_matches.group(1).strip()
        
        mapping_data = platform_data[['ID', gene_col]].copy()
        mapping_data = mapping_data.dropna()
        mapping_data = mapping_data.rename(columns={gene_col: 'Gene'}) 

        # Apply mapping to get gene expression data
        gene_data = apply_gene_mapping(genetic_data, mapping_data)

        # Preview the mapped gene expression data
        print("\nFirst 10 rows of mapped gene expression data:")
        print(preview_df(gene_data.head(10)))

        # Save gene data
        os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
        gene_data.to_csv(out_gene_data_file)
    else:
        print("Could not find gene symbol column information in platform metadata")
else:
    print("Could not find platform metadata section in SOFT file")
# Let's examine the SOFT file structure more carefully to find gene symbols
with gzip.open(soft_file_path, 'rt') as f:
    platform_section = False
    gene_mapping_lines = []
    for line in f:
        if line.startswith('!Platform_table_begin'):
            platform_section = True
            continue
        elif line.startswith('!Platform_table_end'):
            platform_section = False
            continue
        if platform_section:
            gene_mapping_lines.append(line)

# Create mapping dataframe
mapping_data = pd.read_csv(io.StringIO(''.join(gene_mapping_lines)), sep='\t')
# Filter rows where gene symbol exists and is not empty
mapping_data = mapping_data[['ID', 'Symbol']].copy()
mapping_data = mapping_data.dropna(subset=['Symbol'])
mapping_data = mapping_data[mapping_data['Symbol'].str.strip() != '']
mapping_data = mapping_data.rename(columns={'Symbol': 'Gene'})

# Apply mapping to get gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Normalize gene symbols using NCBI data
gene_data = normalize_gene_symbols_in_index(gene_data)
print("\nGene data shape (after normalization):", gene_data.shape)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# Link clinical and genetic data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

# Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# Check for bias in features
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate and save dataset metadata
note = "Dataset contains gene expression data from liver disease patients and controls, with proper mapping to standardized gene symbols."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_trait_biased,
    df=linked_data,
    note=note
)

# Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
# Get gene annotation data using the library function
gene_annotation = get_gene_annotation(soft_file_path)

# Create mapping using ID and Symbol columns
mapping_data = gene_annotation[['ID', 'Symbol']].copy()
mapping_data = mapping_data.dropna()
mapping_data = mapping_data.rename(columns={'Symbol': 'Gene'})

# Apply mapping to get gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview the mapped gene expression data
print("\nFirst few rows of mapped gene expression data:")
print(preview_df(gene_data.head()))
# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Print shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)