# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bone_Density"
cohort = "GSE56815"

# Input paths
in_trait_dir = "../DATA/GEO/Bone_Density"
in_cohort_dir = "../DATA/GEO/Bone_Density/GSE56815"

# Output paths
out_data_file = "./output/preprocess/1/Bone_Density/GSE56815.csv"
out_gene_data_file = "./output/preprocess/1/Bone_Density/gene_data/GSE56815.csv"
out_clinical_data_file = "./output/preprocess/1/Bone_Density/clinical_data/GSE56815.csv"
json_path = "./output/preprocess/1/Bone_Density/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)
# 1. Gene Expression Data Availability
is_gene_available = True  # based on "Gene expression study" in the background info

# 2. Variable Availability and Data Type Conversion

# From the sample characteristics dictionary:
# 0 -> ['gender: Female']
# 1 -> ['bone mineral density: high BMD', 'bone mineral density: low BMD']
# 2 -> ['state: postmenopausal', 'state: premenopausal']
# 3 -> ['cell type: monocytes']
#
# Trait of interest in this context is "Bone_Density". We see it's recorded under key=1 as "bone mineral density: high BMD" or "bone mineral density: low BMD".
trait_row = 1  # multiple unique values ("high BMD", "low BMD"), so it's available

# There's no numeric age data, so age is not available
age_row = None

# Gender is always female under key=0, which means it's constant and not useful for further analysis
gender_row = None

def convert_trait(value: str) -> int:
    # Typical format: "bone mineral density: high BMD" or "bone mineral density: low BMD"
    # Extract the portion after the colon
    parts = value.split(':', 1)
    val = parts[1].strip() if len(parts) > 1 else value.strip()
    if val.lower() == 'high bmd':
        return 1
    elif val.lower() == 'low bmd':
        return 0
    else:
        return None

def convert_age(value: str):
    return None  # No age data available

def convert_gender(value: str):
    return None  # Not used, as gender is constant in this dataset

# 3. Save Metadata with initial filtering
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 if trait_row is not None -> data is available)
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,            # assumed to be available in the environment
        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
# 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 gene identifiers (e.g., "1007_s_at", "1053_at") are Affymetrix probe set IDs,
# which are not standard human gene symbols. Therefore, they 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))
# STEP6: Gene Identifier Mapping

# 1. From our observation, the 'ID' column in gene_annotation matches the probe identifiers in the gene_data,
#    and the 'Gene Symbol' column contains the actual gene symbols we need.
probe_col = "ID"
symbol_col = "Gene Symbol"

# 2. Extract the gene mapping dataframe from the annotation:
mapping_df = get_gene_mapping(gene_annotation, probe_col, symbol_col)

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

# Optional: Print the shape of the mapped gene expression data for verification
print("Gene expression data after mapping. Shape:", gene_data.shape)
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, "Gender"]
    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.