# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Eczema"
cohort = "GSE57225"

# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE57225"

# Output paths
out_data_file = "./output/preprocess/1/Eczema/GSE57225.csv"
out_gene_data_file = "./output/preprocess/1/Eczema/gene_data/GSE57225.csv"
out_clinical_data_file = "./output/preprocess/1/Eczema/clinical_data/GSE57225.csv"
json_path = "./output/preprocess/1/Eczema/cohort_info.json"

# STEP1
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
# From the background, "whole genome expression arrays" are used, so we set:
is_gene_available = True

# 2. Variable Availability
# Based on the sample characteristics dictionary, we identify:
#   - trait_row=1 for "disease state: eczema / psoriasis / control"
#   - age_row=4 for "age: xx"
#   - gender_row=3 for "gender: male/female"
trait_row = 1
age_row = 4
gender_row = 3

# 2.2 Data Type Conversion Functions
def convert_trait(raw_value: str) -> int:
    """
    Convert raw_value (e.g., 'disease state: eczema') to binary.
    1 if eczema, else 0.
    Unknown -> None
    """
    parts = raw_value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if "eczema" in val:
        return 1
    elif "psoriasis" in val or "control" in val:
        return 0
    return None

def convert_age(raw_value: str) -> float:
    """
    Convert raw_value (e.g., 'age: 48y') to continuous.
    Unknown -> None
    """
    parts = raw_value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    val = val.replace('y', '').strip()  # Remove 'y'
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(raw_value: str) -> int:
    """
    Convert raw_value (e.g., 'gender: male') to binary.
    male -> 1, female -> 0, unknown -> None
    """
    parts = raw_value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == "male":
        return 1
    elif val == "female":
        return 0
    return None

# Determine if trait data is available
is_trait_available = (trait_row is not None)

# 3. Save Metadata (initial filtering)
is_usable = 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 (only if trait_row is not None)
if trait_row is not None:
    selected_clinical_df = 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 and save
    print("Preview of selected clinical features:", preview_df(selected_clinical_df))
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# STEP3
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])
# These identifiers (e.g., A_19_P00315452) appear to be microarray probe IDs rather than human gene symbols.
# Therefore, they would require mapping to standard gene symbols.
print("requires_gene_mapping = True")
# STEP5
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP: Gene Identifier Mapping

# 1. Based on the preview, it appears 'ID' contains the microarray probe identifiers matching those in gene_data (e.g., "A_19_P..."),
#    and 'GENE_SYMBOL' is intended to store the gene symbols (though many rows may currently appear empty or be non-informative).

# 2. Get a gene mapping dataframe from the annotation.
mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col="ID",
    gene_col="GENE_SYMBOL"
)

# 3. Convert probe-level measurements to gene-level expression.
gene_data = apply_gene_mapping(
    expression_df=gene_data,
    mapping_df=mapping_df
)

# Print some info to verify the result.
print("Mapped gene_data shape:", gene_data.shape)
print("First 20 gene symbols in mapped data:", gene_data.index[:20].tolist())
import pandas as pd

# STEP7

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

# 2) Read the clinical data saved in Step 2 and reassign its row labels so geo_link_clinical_genetic_data
#    can recognize Eczema, Age, and Gender as row names.
selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
# We know from earlier steps that the output file has 3 rows: Eczema, Age, Gender
selected_clinical_df.index = ["Eczema", "Age", "Gender"]

# Link the clinical and genetic data on sample IDs
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3) Handle missing values using the known trait column
final_data = handle_missing_values(linked_data, trait_col=trait)

# 4) Evaluate whether the trait and demographics are biased; drop biased demographics
trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

# 5) Final validation. Since we do have trait data, set is_trait_available=True
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=final_data,
    note="Both gene and trait data processed successfully."
)

# 6) Save final data only if usable
if is_usable:
    final_data.to_csv(out_data_file)