# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"
cohort = "GSE75427"

# Input paths
in_trait_dir = "../DATA/GEO/Endometriosis"
in_cohort_dir = "../DATA/GEO/Endometriosis/GSE75427"

# Output paths
out_data_file = "./output/preprocess/3/Endometriosis/GSE75427.csv"
out_gene_data_file = "./output/preprocess/3/Endometriosis/gene_data/GSE75427.csv"
out_clinical_data_file = "./output/preprocess/3/Endometriosis/clinical_data/GSE75427.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
# Title indicates expression profiles, so gene expression data is likely available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Trait: Can be inferred from cell type field (Feature 0)
trait_row = 0  

# Age: Available in Feature 2
age_row = 2

# Gender: Available in Feature 1 but constant (all Female)
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    # Extract text after colon and strip whitespace
    value = value.split(':')[1].strip().lower()
    # If contains "normal endometrium", code as 0 (control)
    # Otherwise code as 1 (endometriosis)
    return 0 if 'normal endometrium' in value else 1

def convert_age(value: str) -> float:
    try:
        # Extract number from strings like "age: 37y"
        return float(value.split(':')[1].strip().replace('y',''))
    except:
        return None

# 3. Save Metadata
_ = 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_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=None,
        convert_gender=None
    )
    
    # Preview the extracted features
    preview = preview_df(clinical_df)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_df.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)
# These identifiers (A_19_P*) appear to be Agilent array probe IDs which need mapping to HGNC 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))
# ID and GENE_SYMBOL are the relevant columns for mapping
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')

# Apply the mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview the results to verify the mapping
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# 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 
linked_data = geo_link_clinical_genetic_data(clinical_df, 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)