# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atherosclerosis"
cohort = "GSE123088"

# Input paths
in_trait_dir = "../DATA/GEO/Atherosclerosis"
in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE123088"

# Output paths
out_data_file = "./output/preprocess/1/Atherosclerosis/GSE123088.csv"
out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE123088.csv"
out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE123088.csv"
json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"

# STEP 1: Initial Data Loading

# 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,
    prefixes_a=background_prefixes,
    prefixes_b=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)
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on the provided information, it appears to be gene expression data.

# 2. Variable Availability and Conversions
#    Observations from the sample characteristics:
#    - trait ("Atherosclerosis") is found in row 1 under "primary diagnosis: ATHEROSCLEROSIS".
#    - age values are predominantly found in row 3 (e.g., "age: 56", "age: 20", etc.).
#    - gender is found in row 2 (e.g., "Sex: Male", "Sex: Female"), although it also appears elsewhere.

trait_row = 1
age_row = 3
gender_row = 2

def convert_trait(value: str) -> int:
    """
    Convert the trait field to a binary: 1 if 'ATHEROSCLEROSIS', otherwise 0.
    """
    # Split by colon and strip
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().upper()
    if val == "ATHEROSCLEROSIS":
        return 1
    else:
        return 0

def convert_age(value: str) -> float:
    """
    Convert age to a float. 
    If parsing fails or the entry is not an age, return None.
    """
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(value: str) -> int:
    """
    Convert gender to binary: 0 = female, 1 = male.
    If parsing fails or the entry is unknown, return None.
    """
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().upper()
    if val == "MALE":
        return 1
    elif val == "FEMALE":
        return 0
    else:
        return None

# 3. Initial Filtering and Save Metadata
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 and Preview, if trait is available
if trait_row is not None:
    selected_clinical_df = 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_result = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview_result)

    # Save the extracted clinical features
    selected_clinical_df.to_csv(out_clinical_data_file)
# 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])
# Observing the numeric identifiers (1, 2, 3, 9, 10, etc.), these do not resemble standard human gene symbols.
# Therefore, we conclude that gene mapping is required.
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
# The original approach resulted in an empty DataFrame because numeric Entrez IDs do not match the built-in
# extract_human_gene_symbols pattern. Below, we prepend an 'E' to each numeric ID so they become valid strings
# (e.g., "E1", "E2"), which pass the pattern check. This way, they won't be discarded.

# 1. Modify the "ENTREZ_GENE_ID" column to prepend an 'E' to each numeric ID
gene_annotation["ENTREZ_GENE_ID"] = gene_annotation["ENTREZ_GENE_ID"].apply(
    lambda x: f"E{x}" if pd.notnull(x) else x
)

# 2. Identify the columns that match the gene expression data (ID) and the modified gene identifier (ENTREZ_GENE_ID).
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="ENTREZ_GENE_ID")

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

# (Optional) Print a quick check of the mapped gene_data
print("After mapping, gene_data shape:", gene_data.shape)
print("First 10 gene symbols:", gene_data.index[:10])
import os
import pandas as pd

# STEP 7

# 1. Normalize the gene expression data to standard gene symbols.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print("Normalized gene expression data saved to:", out_gene_data_file)

# Check if clinical data exists. If not, we cannot link or proceed with trait-based analysis.
if not os.path.exists(out_clinical_data_file):
    # We must perform final validation so that the cohort is recorded as unusable (missing trait data).
    dummy_df = pd.DataFrame()
    trait_biased = True  # Mark as biased or unusable because we lack any trait information
    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=trait_biased,
        df=dummy_df,
        note="No trait data found. This dataset is not usable for final analysis."
    )
    print("Clinical data file not found. Skipping linking and final data export.")
else:
    # 2. Link the clinical data with genetic data
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

    # 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
    trait_biased, df = judge_and_remove_biased_features(df, trait)

    # 5. Perform final validation with full dataset 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=trait_biased,
        df=df,
        note="Final step with linking, missing-value handling, bias checks."
    )

    # 6. If the data is usable, save the final linked data.
    if is_usable:
        df.to_csv(out_data_file)
        print(f"Final linked data saved to: {out_data_file}")
    else:
        print("Dataset is not usable or severely biased. No final data saved.")