# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Chronic_kidney_disease"
cohort = "GSE45980"

# Input paths
in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE45980"

# Output paths
out_data_file = "./output/preprocess/1/Chronic_kidney_disease/GSE45980.csv"
out_gene_data_file = "./output/preprocess/1/Chronic_kidney_disease/gene_data/GSE45980.csv"
out_clinical_data_file = "./output/preprocess/1/Chronic_kidney_disease/clinical_data/GSE45980.csv"
json_path = "./output/preprocess/1/Chronic_kidney_disease/cohort_info.json"

# STEP1
from tools.preprocess import *

# 1. Attempt to identify the paths to the SOFT file and the matrix file
try:
    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
except AssertionError:
    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
    soft_file, matrix_file = None, None

if soft_file is None or matrix_file is None:
    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
else:
    # 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("\nSample Characteristics Dictionary:")
    print(sample_characteristics_dict)
# Step 1: Determine if gene expression data is available
# From the background info, mRNA expression profiling was performed, so we set:
is_gene_available = True

# Step 2: Variable Availability and Data Type Conversion

# 2.1 The dataset trait is chronic kidney disease (CKD). However, all subjects are CKD patients,
# so there's no variation (everyone has the trait). Hence, it's effectively not available for our analysis:
trait_row = None

# For age, row 1 contains varied age values "age (yrs): <number>".
age_row = 1

# For gender, row 0 contains "gender: male" or "gender: female", so it is available and non-constant.
gender_row = 0

# 2.2 Define conversion functions for each variable.

def convert_trait(value: str) -> int:
    # The trait is not actually variable in this dataset, so we won't use this.
    # But we define a no-op function for completeness.
    return None

def convert_age(value: str) -> float:
    try:
        # Example: "age (yrs): 72"
        # Split at the first colon and parse the right side
        right_side = value.split(':', 1)[1].strip()
        return float(right_side)
    except:
        return None

def convert_gender(value: str) -> int:
    try:
        # Example: "gender: male"
        right_side = value.split(':', 1)[1].strip().lower()
        if right_side == 'male':
            return 1
        elif right_side == 'female':
            return 0
        else:
            return None
    except:
        return None

# 3. Save Metadata (initial filtering)
# Trait availability depends on whether trait_row is None.
is_trait_available = (trait_row is not None)

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 proceed if the trait is available, i.e., trait_row is not None.
if trait_row is not None:
    # Suppose we have a DataFrame called clinical_data already loaded.
    # (In practice, it would be passed from previous steps.)
    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 = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview)

    # Save to CSV
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# STEP3
# Attempt to read gene expression data; if the library function yields an empty DataFrame,
# try re-reading without ignoring lines that start with '!' (because sometimes GEO data may
# place actual expression rows under lines that begin with '!').

gene_data = get_genetic_data(matrix_file)
if gene_data.empty:
    print("[WARNING] The gene_data is empty. Attempting alternative loading without treating '!' as comments.")
    import gzip

    # Locate the marker line first
    skip_rows = 0
    with gzip.open(matrix_file, 'rt') as file:
        for i, line in enumerate(file):
            if "!series_matrix_table_begin" in line:
                skip_rows = i + 1
                break

    # Read the data again, this time not treating '!' as comment
    gene_data = pd.read_csv(
        matrix_file,
        compression="gzip",
        skiprows=skip_rows,
        delimiter="\t",
        on_bad_lines="skip"
    )
    gene_data = gene_data.rename(columns={"ID_REF": "ID"}).astype({"ID": "str"})
    gene_data.set_index("ID", inplace=True)

# Print the first 20 row IDs to confirm data structure
print(gene_data.index[:20])
# Based on the probe-like format of the identifiers (e.g., 'A_23_P100001'),
# they do not appear to be standard human gene symbols. They likely require mapping.
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. Decide that the column "ID" in the annotation dataframe corresponds to the probe IDs,
#    and "GENE_SYMBOL" corresponds to the gene symbol.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")

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

# Show a quick preview of the resulting gene-level data.
print("Mapped gene_data shape:", gene_data.shape)
print(gene_data.head())
import os
import pandas as pd

# STEP7: Data Normalization and Linking

# 1) Normalize the gene symbols in the previously obtained gene_data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2) Load clinical data only if it exists and is non-empty
if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
    # Read the file
    clinical_temp = pd.read_csv(out_clinical_data_file)

    # Adjust row index to label the trait, age, and gender properly
    if clinical_temp.shape[0] == 3:
        clinical_temp.index = [trait, "Age", "Gender"]
    elif clinical_temp.shape[0] == 2:
        clinical_temp.index = [trait, "Age"]
    elif clinical_temp.shape[0] == 1:
        clinical_temp.index = [trait]

    # 2) Link the clinical and normalized genetic data
    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)

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

    # 4) Check for severe bias in the trait; remove biased demographic features if present
    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5) Final quality validation and save metadata
    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=f"Final check on {cohort} with {trait}."
    )

    # 6) If the linked data is usable, save it
    if is_usable:
        linked_data.to_csv(out_data_file)
else:
    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
    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,  # Force a fallback so that it's flagged as unusable
        df=pd.DataFrame(),
        note=f"No trait data found for {cohort}, final metadata recorded."
    )
    # Per instructions, do not save a final linked data file when trait data is absent.