# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bone_Density"
cohort = "GSE198934"

# Input paths
in_trait_dir = "../DATA/GEO/Bone_Density"
in_cohort_dir = "../DATA/GEO/Bone_Density/GSE198934"

# Output paths
out_data_file = "./output/preprocess/1/Bone_Density/GSE198934.csv"
out_gene_data_file = "./output/preprocess/1/Bone_Density/gene_data/GSE198934.csv"
out_clinical_data_file = "./output/preprocess/1/Bone_Density/clinical_data/GSE198934.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  # Transcriptional profiling using Affymetrix suggests gene expression data is available.

# 2.1 Data Availability (rows):
trait_row = None  # No key found in sample characteristics for Bone_Density (BMD).
age_row = 0       # Key found at row=0 for age data.
gender_row = None # All subjects are women (constant), so gender feature is effectively not available.

# 2.2 Data Type Conversion
def convert_trait(x: str):
    """
    Convert raw trait data to a continuous type.
    Data not actually available in the dictionary for this cohort, but function is defined for completeness.
    """
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val_str = parts[1].strip().lower()
    try:
        return float(val_str)
    except ValueError:
        return None

def convert_age(x: str):
    """
    Convert raw age data to a continuous type.
    Expected format: 'age (years): <numeric_value>'
    """
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val_str = parts[1].strip().lower()
    try:
        return float(val_str)
    except ValueError:
        return None

def convert_gender(x: str):
    """
    Convert raw gender data to binary type: female -> 0, male -> 1.
    """
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val_str = parts[1].strip().lower()
    if 'female' in val_str:
        return 0
    elif 'male' in val_str:
        return 1
    else:
        return None

# 3. 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 - skip if trait_row is None
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,  # assumed to be available from a previous step
        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)
    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])
# Based on the observed numeric probe-like identifiers, they 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))
# STEP: Gene Identifier Mapping

# 1. The gene expression data has numeric probe IDs matching the "ID" column in the annotation.
#    The column "gene_assignment" in the annotation dataframe appears to store the gene symbol information.

# 2. Get the gene mapping dataframe by extracting these two columns from the gene annotation dataframe.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="gene_assignment")

# 3. Convert probe-level measurements to gene expression data by applying the mapping.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# For observation, optionally inspect a small preview of the resulting gene_data
print("Mapped gene_data shape:", gene_data.shape)
print("First 5 genes in mapped data:", gene_data.index[:5].tolist())
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.