# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE26927"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE26927"

# Output paths
out_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/GSE26927.csv"
out_gene_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/gene_data/GSE26927.csv"
out_clinical_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE26927.csv"
json_path = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/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 background info: "Gene expression analysis was performed... using the Illumina whole genome HumanRef8 v2 BeadChip"
is_gene_available = True

# 2.1 Data Availability
# Trait data is in Feature 0: "disease: [disease name]" 
trait_row = 0

# Age data is in Feature 2: "age at death (in years): [value]"
age_row = 2

# Gender data is in Feature 1: "gender: [M/F]" 
gender_row = 1

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    # Extract value after colon
    value = x.split(': ')[-1].strip().lower()
    # Convert to binary: 1 for ALS, 0 for other diseases
    return 1 if 'amyotrophic lateral sclerosis' in value else 0

def convert_age(x):
    if not isinstance(x, str):
        return None
    try:
        # Extract numeric value after colon
        age = float(x.split(': ')[-1].strip())
        return age  # Return as continuous value
    except:
        return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    # Extract value after colon
    value = x.split(': ')[-1].strip().upper()
    # Convert to binary: 0 for F, 1 for M
    if value == 'F':
        return 0
    elif value == 'M':
        return 1
    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_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
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# 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 have prefix "ILMN_" indicating they are Illumina probe IDs
# These need to be mapped to human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation from SOFT file and get meaningful data 
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))

print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers") 
print("'Symbol' column: Gene name mapping")
# 1. Identify relevant columns for mapping:
# From gene expression data, we see "ILMN_" identifiers
# From annotation preview, 'ID' column contains same ILMN identifiers
# 'SYMBOL' column contains gene symbols

# 2. Extract mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SYMBOL')

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

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 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)

# 2. Link clinical and genetic data
try:
    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

    # 4. Determine if features are biased
    is_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=is_trait_biased,
        df=linked_data,
        note="Gene expression data successfully mapped and linked with clinical features"
    )

    # 6. Save linked data only if usable AND trait is not biased
    if is_usable and not is_trait_biased:
        linked_data.to_csv(out_data_file)

except Exception as e:
    print(f"Error in data linking and processing: {str(e)}")
    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=True,
        df=pd.DataFrame(),
        note=f"Data processing failed: {str(e)}"
    )