# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriasis"
cohort = "GSE123086"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE123086"

# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE123086.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE123086.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE123086.csv"
json_path = "./output/preprocess/3/Psoriasis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# 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
is_gene_available = True  # RNA microarray data confirmed in Series_overall_design

# 2. Variable Availability and Data Type Conversion
# 2.1 Row identifiers
trait_row = 1  # 'primary diagnosis' contains trait status
age_row = 3    # Age data starts in row 3 and continues in row 4
gender_row = 2 # Gender data in row 2 (some continue in row 3)

# 2.2 Conversion functions
def convert_trait(value: str) -> int:
    # Convert to binary: 1 for psoriasis, 0 for control
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1]
    if value == 'PSORIASIS':
        return 1
    elif value == 'HEALTHY_CONTROL':
        return 0
    return None

def convert_age(value: str) -> float:
    # Convert age to continuous numeric value
    if not isinstance(value, str):
        return None
    try:
        return float(value.split(': ')[-1])
    except:
        return None

def convert_gender(value: str) -> int:
    # Convert to binary: 0 for female, 1 for male
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1]
    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
selected_clinical = 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_df(selected_clinical))

# Save clinical data
selected_clinical.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)
# The identifiers appear to be integers (1,2,3,9,10 etc)
# These are not human gene symbols and require mapping
requires_gene_mapping = True
# Extract gene annotation data, more inclusive prefix filtering
gene_metadata = get_gene_annotation(soft_file, prefixes=['^', '!', '#', 'Platform'])

# Remove rows where all values are NaN or empty
gene_metadata = gene_metadata.dropna(how='all')

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist()) 
print("\nFirst few rows preview:")
print(preview_df(gene_metadata, n=10))

# Inspect raw file content to help identify relevant sections
import gzip
print("\nFirst few lines from SOFT file:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    head = [next(f) for _ in range(10)]
    print('\n'.join(head))
# Map probes to Entrez Gene IDs first
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='ENTREZ_GENE_ID')
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Then normalize Entrez IDs to gene symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Use original probe-level expression data from step 3
gene_data = get_genetic_data(matrix_file)
print("Gene data shape:", gene_data.shape)
print("Gene data head:")
print(gene_data.head())

# Load and verify clinical data 
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0).T
print("\nClinical data shape:", selected_clinical_df.shape)
print("Clinical data head:")
print(selected_clinical_df.head())

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

# Handle missing values
linked_data = handle_missing_values(linked_data, "Psoriasis")

# Evaluate bias in features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, "Psoriasis")

# Record cohort information
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_biased,
    df=linked_data,
    note="Contains numerical probe-level expression data (gene mapping not implemented) and clinical data."
)

# Save data if usable
if is_usable:
    linked_data.to_csv(out_data_file)