# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriasis"
cohort = "GSE252029"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE252029"

# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE252029.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE252029.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE252029.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")
# First create clinical_data DataFrame from the sample characteristics
sample_chars = {
    0: ['study id: CNTO1959PSO3001'],
    1: ['subject id: 10521', 'subject id: 10563', 'subject id: 10294', 'subject id: 10461', 'subject id: 10079', 'subject id: 10062', 'subject id: 10115', 'subject id: 10205', 'subject id: 10193', 'subject id: 10252', 'subject id: 10798', 'subject id: 10332', 'subject id: 10063', 'subject id: 10118', 'subject id: 10500', 'subject id: 10263', 'subject id: 10265', 'subject id: 10334', 'subject id: 10932', 'subject id: 10933', 'subject id: 10982', 'subject id: 10401', 'subject id: 10512', 'subject id: 10110', 'subject id: 10027', 'subject id: 10566', 'subject id: 10989', 'subject id: 10227', 'subject id: 10380', 'subject id: 10286'],
    2: ['treatment: Placebo to Guselkumab', 'treatment: Guselkumab', 'treatment: Adalimumab'],
    3: ['time point: WK_0', 'time point: WK_4', 'time point: WK_24', 'time point: WK_48'],
    4: ['skin: LS', 'skin: NL']
}
clinical_data = pd.DataFrame(sample_chars).transpose()

# 1. Gene Expression Data Availability
# Yes - Dataset uses GeneChip HT HG-U133+ PM Array for transcriptomics
is_gene_available = True

# 2.1 Data Row Indices 
# Trait can be inferred from skin type (LS=lesional skin, NL=nonlesional skin)
trait_row = 4  
# Age and gender not available
age_row = None
gender_row = None

# 2.2 Conversion Functions
def convert_trait(value: str) -> float:
    """Convert skin type to binary trait value
    LS (lesional) = 1, NL (nonlesional) = 0"""
    if pd.isna(value) or not isinstance(value, str):
        return None
    value = value.split(": ")[-1].strip().upper()
    if value == "LS":
        return 1.0
    elif value == "NL": 
        return 0.0
    return None

def convert_age(value: str) -> float:
    return None

def convert_gender(value: str) -> float:
    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
selected_clinical = geo_select_clinical_features(
    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 clinical data
preview_df(selected_clinical)

# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Cannot proceed with analysis as the output from previous step (sample characteristics and background info) is not provided
print("Error: Missing prerequisite data - sample characteristics and background information needed for analysis.")
raise ValueError("Output from previous step containing sample characteristics and dataset background information is required to analyze variables and extract clinical features.")
# 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)
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Extract ID and Gene Symbol columns for mapping
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')

# Apply gene mapping to get gene expression
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Save gene data to file
gene_data.to_csv(out_gene_data_file)
# Get file paths 
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get gene expression data
gene_data = get_genetic_data(matrix_file)

# Get gene mapping
gene_metadata = get_gene_annotation(soft_file)
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')
gene_data = apply_gene_mapping(gene_data, mapping_data)

# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Get clinical data
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']
)

# Extract trait row from skin type
trait_row = 4
age_row = None 
gender_row = None

def convert_trait(value: str) -> float:
    """Convert skin type to binary trait value
    LS (lesional) = 1, NL (nonlesional) = 0"""
    if pd.isna(value) or not isinstance(value, str):
        return None
    value = value.split(": ")[-1].strip().upper()
    if value == "LS":
        return 1.0
    elif value == "NL": 
        return 0.0
    return None

def convert_age(value: str) -> float:
    return None

def convert_gender(value: str) -> float:
    return None

# Extract clinical features
selected_clinical = geo_select_clinical_features(
    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
)

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

# Handle missing values
linked_data = handle_missing_values(linked_data, trait_col=trait)

# Check for trait bias and remove biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Save validation 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=trait_biased,
    df=linked_data,
    note="Contains gene expression and trait data (skin type: lesional vs nonlesional)."
)

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