# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"
cohort = "GSE37837"

# Input paths
in_trait_dir = "../DATA/GEO/Endometriosis"
in_cohort_dir = "../DATA/GEO/Endometriosis/GSE37837"

# Output paths
out_data_file = "./output/preprocess/3/Endometriosis/GSE37837.csv"
out_gene_data_file = "./output/preprocess/3/Endometriosis/gene_data/GSE37837.csv"
out_clinical_data_file = "./output/preprocess/3/Endometriosis/clinical_data/GSE37837.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
# Based on the background info mentioning "genome-wide expression analysis" using "whole human genome oligo microarray"
is_gene_available = True

# 2.1 Data availability
# Trait can be inferred from tissue info in Feature 2 - comparing eutopic vs ectopic
trait_row = 2
# Age is in Feature 0
age_row = 0
# Gender is in Feature 1 but only has one value (female) so not useful
gender_row = None

# 2.2 Data type conversion functions
def convert_trait(x):
    if x is None:
        return None
    # Extract value after colon and strip whitespace
    value = x.split(':', 1)[1].strip() if ':' in x else x.strip()
    # Convert tissue type to binary - eutopic (0) vs ectopic (1)  
    if 'eutopic' in value.lower():
        return 0
    elif 'ectopic' in value.lower():
        return 1
    return None

def convert_age(x):
    if x is None:
        return None
    # Extract numeric age value
    try:
        return int(x.split(':', 1)[1].strip())
    except:
        return None

def convert_gender(x):
    # Not used since gender is constant
    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. Extract clinical features
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
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    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 identifiers starting with "A_23_P" in the data, 
# these appear to be Agilent probe IDs rather than direct gene symbols.
# They need to be mapped to official HGNC 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))
# Map probe IDs to gene symbols using ID and GENE_SYMBOL columns
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Preview results
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows:")
print(gene_data.head())
print("\nFirst 20 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 so features become columns
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)
# 1. Gene expression data availability 
is_gene_available = False # Conservative assumption without data

# 2. Variable availability and conversion functions 
def convert_trait(x: str) -> Optional[int]:
    if pd.isna(x):
        return None
    value = str(x).lower()
    if 'normal' in value or 'control' in value:
        return 0
    elif 'endometrios' in value:
        return 1
    return None

def convert_age(x: str) -> Optional[float]:
    if pd.isna(x):
        return None
    if ':' in str(x):
        value = x.split(':')[1].strip()
        try:
            return float(value)
        except:
            return None
    return None

def convert_gender(x: str) -> Optional[int]:
    if pd.isna(x):
        return None
    value = str(x).lower()
    if 'female' in value or 'f' in value:
        return 0
    elif 'male' in value or 'm' in value:
        return 1
    return None

# Without data we can't identify rows
trait_row = None
age_row = None
gender_row = None

# 3. Save metadata
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. Clinical feature extraction
if trait_row is not None:
    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
    )
    
    print("Preview of processed clinical data:")
    print(preview_df(selected_clinical_df))
    
    selected_clinical_df.to_csv(out_clinical_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 check
# Based on background info mentioning "genome-wide expression analysis" and "whole human genome oligo microarray"
is_gene_available = True

# 2.1 Data availability
# Trait: tissue type indicates eutopic vs ectopic endometrium, which can be used to identify endometriosis samples
trait_row = 2
# Age: available in Feature 0
age_row = 0  
# Gender: constant "female" in Feature 1, so not useful for association study
gender_row = None

# 2.2 Data type conversion functions
def convert_trait(value: str) -> int:
    """Convert tissue type to binary endometriosis indicator"""
    if not value:
        return None
    value = value.split(": ")[1].lower()
    if "endometrioma_ectopic" in value:
        return 1
    elif "autologous_eutopic" in value:
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age string to float"""
    if not value:
        return None
    try:
        return float(value.split(": ")[1])
    except:
        return None

# 3. Save metadata
is_usable = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=True
)

# 4. Extract clinical features
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=None,
    convert_gender=None
)

# Preview the extracted features
print("Preview of extracted clinical features:")
print(preview_df(selected_clinical_df))

# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# Re-extract clinical features
selected_clinical_df = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=2,
    convert_trait=lambda x: 1 if "endometrioma_ectopic" in str(x).lower() else 0 if "autologous_eutopic" in str(x).lower() else None,
    age_row=0,
    convert_age=lambda x: float(x.split(": ")[1]) if x and ":" in x else None
)

# 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(selected_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)