# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atherosclerosis"
cohort = "GSE125771"

# Input paths
in_trait_dir = "../DATA/GEO/Atherosclerosis"
in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE125771"

# Output paths
out_data_file = "./output/preprocess/1/Atherosclerosis/GSE125771.csv"
out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE125771.csv"
out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE125771.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)
import pandas as pd
from typing import Optional, Callable

# 1. Determine gene expression availability
is_gene_available = True  # Based on the background info ("RNA expression data")

# 2. Determine variable availability and define converter functions
#    Inspecting the dictionary:
#      0 -> ['tissue: carotid-atherosclerotic-plaque']  (only one unique value, not useful for association)
#      1 -> ['ID: ...']                                (sample IDs, not needed)
#      2 -> ['Sex: Male', 'Sex: Female']               (gender info)
#      3 -> ['age: 73', 'age: 60', ...]                (age info)

trait_row = None  # Only a single unique value in row 0, so treat trait as not available
age_row = 3       # Multiple unique values
gender_row = 2    # Contains both "Male" and "Female"

# Data type conversion functions
def convert_trait(value: str) -> Optional[int]:
    """No trait data in this dataset (None). Function provided for completeness."""
    return None

def convert_age(value: str) -> Optional[float]:
    """Extract numeric age from the string after 'age: '. Unknown/invalid -> None."""
    try:
        val_str = value.split(':', 1)[1].strip()
        return float(val_str)
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    """
    Convert gender to binary.
    - "Female" -> 0
    - "Male" -> 1
    Unknown -> None
    """
    try:
        val_str = value.split(':', 1)[1].strip().lower()
        if val_str == 'male':
            return 1
        elif val_str == 'female':
            return 0
        else:
            return None
    except:
        return None

# 3. Conduct initial filtering on usability
#    Trait data is unavailable since 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 trait_row is not None. Here, it is None, so we skip.
# 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])
# Based on the listed identifiers (e.g., "TC01000001.hg.1"), these are not recognized human gene symbols.
# They appear to be proprietary or custom probe identifiers that likely require mapping to standard gene symbols.
print("These gene identifiers are not standard human gene symbols.\nrequires_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. Identify the matching column for the probe identifiers ("ID") and the column containing gene symbol information ("gene_assignment").
# 2. Obtain the mapping dataframe.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="gene_assignment")

# 3. Apply mapping to convert the probe-level expression to gene-level expression.
gene_data = apply_gene_mapping(gene_data, mapping_df)
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, header=0)
    # By design, each row in this CSV might represent a clinical feature (e.g., trait, age, gender).
    # Since trait_row was None, we typically wouldn't have a valid trait row, but let's proceed safely:
    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,  # We do have a clinical file now
        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.")