# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"
cohort = "GSE145702"

# Input paths
in_trait_dir = "../DATA/GEO/Endometriosis"
in_cohort_dir = "../DATA/GEO/Endometriosis/GSE145702"

# Output paths
out_data_file = "./output/preprocess/3/Endometriosis/GSE145702.csv"
out_gene_data_file = "./output/preprocess/3/Endometriosis/gene_data/GSE145702.csv"
out_clinical_data_file = "./output/preprocess/3/Endometriosis/clinical_data/GSE145702.csv"
json_path = "./output/preprocess/3/Endometriosis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
is_gene_available = True  # Dataset contains gene transcription data

# 2.1 Data Availability & 2.2 Data Type Conversion
# Trait (Endometriosis)
trait_row = 2  # Found in 'disease state' row

def convert_trait(x):
    if pd.isna(x) or ':' not in x:
        return None
    value = x.split(': ')[1].strip()
    if 'Normal' in value:
        return 0
    elif 'Endometriosis' in value:
        return 1
    return None

# Gender - constant feature (all female)
gender_row = None

def convert_gender(x):
    return None

# Age - not available
age_row = None

def convert_age(x):
    return None

# 3. Save Metadata
is_initial_valid = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=(trait_row is not None)
)

# 4. Clinical Feature Extraction
if trait_row is not None:
    clinical_features = 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 extracted features
    preview = preview_df(clinical_features)
    
    # Save clinical features
    clinical_features.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Based on the gene identifiers shown (7892501, 7892502, etc.), these appear to be Illumina BeadArray probe IDs 
# rather than standard human gene symbols. They need to be mapped to their corresponding gene symbols.
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Extract probe ID to gene symbol mapping
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')

# Apply mapping to convert probe measurements to gene expression
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview the mapped data
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
print("\nFirst few gene symbols:")
print(gene_data.index[:20])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
clinical_features = clinical_features.T  # Transpose to align with gene data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

# 4. Check for bias
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort info
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=trait_biased,
    df=linked_data,
    note="Study examining expression profiles in endometriotic cyst stromal cells versus normal endometrial stromal cells."
)

# 6. Save if usable
if is_usable:
    linked_data.to_csv(out_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Yes, this dataset studies gene transcription and contains genome-wide data
is_gene_available = True

# 2. Data Availability and Conversion Functions
# 2.1 Row identifiers
trait_row = 2  # disease state
gender_row = 0  # gender information is available but is constant (all Female)
age_row = None  # no age information available

# 2.2 Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert disease state to binary: 1 for Endometriosis, 0 for Normal"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'endometriosis' in value:
        return 1
    elif 'normal' in value:
        return 0
    return None

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary: 0 for Female, 1 for Male"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

# No convert_age function needed since age data is not available

# 3. Save Metadata
# Initial filtering - only checks data availability
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=trait_row is not None
)

# 4. Clinical Feature Extraction
# Since trait_row is not None, we need to extract clinical features
selected_clinical = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# Preview the processed clinical data
print("Preview of processed clinical data:")
print(preview_df(selected_clinical))

# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Re-extract clinical features and link with genetic data
selected_clinical = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    gender_row=gender_row,
    convert_gender=convert_gender
)
linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)

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

# 4. Check for bias  
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort info
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=trait_biased,
    df=linked_data,
    note="Study examining expression profiles in endometriotic cyst stromal cells versus normal endometrial stromal cells."
)

# 6. Save if usable
if is_usable:
    linked_data.to_csv(out_data_file)