# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE235307"

# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE235307"

# Output paths
out_data_file = "./output/preprocess/3/Cardiovascular_Disease/GSE235307.csv"
out_gene_data_file = "./output/preprocess/3/Cardiovascular_Disease/gene_data/GSE235307.csv"
out_clinical_data_file = "./output/preprocess/3/Cardiovascular_Disease/clinical_data/GSE235307.csv"
json_path = "./output/preprocess/3/Cardiovascular_Disease/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Series title and summary indicate gene expression data

# 2. Variable Availability and Data Type Conversion
# 2.1 Row indices
trait_row = 5  # cardiac rhythm after 1 year follow-up indicates AF status
age_row = 2    # age data available
gender_row = 1 # gender data available

# 2.2 Conversion functions
def convert_trait(x: str) -> int:
    """Convert AF status to binary: 1 for AF, 0 for sinus rhythm"""
    value = x.split(": ")[-1].strip()
    if "Atrial fibrillation" in value:
        return 1
    elif "Sinus rhythm" in value:
        return 0
    return None

def convert_age(x: str) -> float:
    """Convert age to continuous value"""
    try:
        return float(x.split(": ")[-1].strip())
    except:
        return None

def convert_gender(x: str) -> int:
    """Convert gender to binary: 0 for female, 1 for male"""
    value = x.split(": ")[-1].strip().lower()
    if value == "female":
        return 0
    elif value == "male":
        return 1
    return None

# 3. Save metadata for initial filtering
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
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 and save clinical data
print("Preview of extracted clinical features:")
print(preview_df(clinical_features))
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Preview the DataFrame structure
print("DataFrame shape:", genetic_df.shape)
print("\nFirst few rows and columns:")
print(genetic_df.head().iloc[:, :5])

# Print first few lines from the matrix file to inspect format
print("\nRaw file preview:")
with gzip.open(matrix_file, 'rt') as f:
    for i, line in enumerate(f):
        if i > 30 and i < 35:  # Print a few lines around where data starts
            print(line.strip())
# IDs in the gene expression data appear to be numeric indices
# They are non-standard format and need to be mapped to proper human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview column names and first few values
print("Column names and preview of gene annotation data:")
print(preview_df(gene_metadata))
# Get the gene mapping dataframe from annotation
mapping_df = gene_metadata.loc[:, ['SPOT_ID', 'GENE_SYMBOL']]  
mapping_df = mapping_df.rename(columns={'SPOT_ID': 'ID', 'GENE_SYMBOL': 'Gene'})
mapping_df = mapping_df.astype({'ID': 'str'})
mapping_df = mapping_df.dropna()

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

# Preview the gene expression data
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])
# Get the gene mapping dataframe from annotation
# Filter out control probes and extract mapping columns
mapping_df = gene_metadata[gene_metadata['CONTROL_TYPE'] == 'FALSE'].loc[:, ['NAME', 'GENE_SYMBOL']]
mapping_df = mapping_df.rename(columns={'NAME': 'ID', 'GENE_SYMBOL': 'Gene'})
mapping_df = mapping_df.astype({'ID': 'str'})
mapping_df = mapping_df.dropna()

print("Mapping dataframe shape:", mapping_df.shape)

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

# Preview the gene expression data
print("\nGene expression data shape:", gene_data.shape)
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])
# Redo gene mapping with correct ID column
mapping_df = gene_metadata[gene_metadata['CONTROL_TYPE'] == 'FALSE'].loc[:, ['ID', 'GENE_SYMBOL']]
mapping_df = mapping_df.rename(columns={'ID': 'ID', 'GENE_SYMBOL': 'Gene'})
mapping_df = mapping_df.astype({'ID': 'str'})
mapping_df = mapping_df.dropna()

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

# 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_features, gene_data)

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

# 4. Check and handle biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
note = "Gene expression data from blood samples in heart failure patients, measuring Atrial fibrillation status after 1 year follow-up. Contains trait (AF vs Sinus rhythm), age and gender data."
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=note
)

# 6. 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)