# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Stroke"
cohort = "GSE37587"

# Input paths
in_trait_dir = "../DATA/GEO/Stroke"
in_cohort_dir = "../DATA/GEO/Stroke/GSE37587"

# Output paths
out_data_file = "./output/preprocess/3/Stroke/GSE37587.csv"
out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE37587.csv"
out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE37587.csv"
json_path = "./output/preprocess/3/Stroke/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
# Yes, this dataset contains gene expression data from peripheral blood samples
is_gene_available = True

# 2.1 Data Availability
# Trait data is in Feature 6 "disease state"
trait_row = 6
# Age data is in Feature 0
age_row = 0
# Gender data is in Feature 4 
gender_row = 4

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Binary: 1 for stroke, 0 for control
    # But all samples are stroke cases, so this is not useful
    return None

def convert_age(x):
    # Continuous
    try:
        return int(x.split(': ')[1])
    except:
        pass
    return None

def convert_gender(x):
    # Binary: 0 for female, 1 for male
    try:
        gender = x.split(': ')[1].lower()
        if gender == 'female':
            return 0
        elif gender == 'male':
            return 1
    except:
        pass
    return None

# 3. Save Metadata 
# Initial filtering - trait data not usable since all samples have stroke
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False
)

# 4. Clinical Feature Extraction skipped since trait data not usable
# 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 identifiers starting with ILMN_ indicate these are Illumina probe IDs
# rather than standard human gene symbols. These need to be mapped.
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))
# Extract mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# Apply gene mapping to get gene expression data 
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Create a dummy DataFrame with the same size as gene expression data
# All samples are stroke cases (value 1)
dummy_clinical = pd.DataFrame({'Stroke': [1]*gene_data.shape[1], 
                             'Age': gene_data.iloc[0].values,  # Use first row to match size 
                             'Gender': gene_data.iloc[1].values}, # Use second row to match size
                             index=gene_data.columns)
dummy_data = geo_link_clinical_genetic_data(dummy_clinical, gene_data)

# Evaluate bias - will be biased since all samples are stroke cases
is_biased, dummy_data = judge_and_remove_biased_features(dummy_data, 'Stroke')

# Save cohort info indicating severe bias
is_usable = validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True, 
    df=dummy_data,
    note="Study examining transcriptome profiles from peripheral blood of stroke patients. Not usable for trait analysis since all samples are stroke cases."
)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2-6. Record dataset as not usable for trait analysis 
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path, 
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,
    df=gene_data,
    note="Study examining transcriptome profiles from peripheral blood of stroke patients. Not usable for trait analysis since all samples are stroke cases."
)