# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anxiety_disorder"
cohort = "GSE119995"

# Input paths
in_trait_dir = "../DATA/GEO/Anxiety_disorder"
in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE119995"

# Output paths
out_data_file = "./output/preprocess/3/Anxiety_disorder/GSE119995.csv"
out_gene_data_file = "./output/preprocess/3/Anxiety_disorder/gene_data/GSE119995.csv"
out_clinical_data_file = "./output/preprocess/3/Anxiety_disorder/clinical_data/GSE119995.csv"
json_path = "./output/preprocess/3/Anxiety_disorder/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
# From series title and summary, this dataset contains mRNA expression data from blood plasma
is_gene_available = True

# 2.1 Data Availability
# Trait: all samples have panic disorder (Feature 0), so not useful for case-control study
trait_row = None 

# Age: not available in sample characteristics
age_row = None

# Gender: available in Feature 2
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Not used since trait_row is None
    return None

def convert_age(x):
    # Not used since age_row is None
    return None

def convert_gender(x):
    if pd.isna(x):
        return None
    val = x.split(': ')[1].lower()
    if val == 'female':
        return 0
    elif val == 'male':
        return 1
    return 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
# Skip since trait_row is None
# 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)
# The gene IDs start with "ILMN_" which indicates these are Illumina probe IDs
# They need to be mapped to human gene symbols for analysis
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# 1. Observe the gene identifiers in both gene expression data and annotation:
# Gene expression data uses 'ILMN_' probe IDs which is stored as 'ID' column in annotation
# Gene symbols are stored in 'Symbol' column in annotation

# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_data)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save normalized gene data
gene_data.to_csv(out_gene_data_file)

# Since we already determined in Step 2 that there's no valid trait variation
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,  # All samples have same trait value
    is_biased=True,
    df=pd.DataFrame(),
    note="Dataset lacks trait variation - all samples have panic disorder"
)