# Path Configuration
from tools.preprocess import *

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

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

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

# STEP1
from tools.preprocess import *
# 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, 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("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Decide if the dataset contains gene expression data
is_gene_available = True  # Based on the description, it appears to be gene expression (not miRNA or methylation).

# 2. Determine availability (row indices) and define data type conversions for trait, age, and gender
trait_row = 1   # The row with "primary diagnosis: ATOPIC_ECZEMA" among multiple diagnoses
age_row = 3     # The row containing multiple entries of "age: XX"
gender_row = 2  # The row that includes "Sex: Male" or "Sex: Female"

def convert_trait(x: str) -> Optional[int]:
    """
    Convert trait-related string to binary (0 or 1).
    We consider 'ATOPIC_ECZEMA' (case-insensitive) as 1, all other entries as 0.
    """
    # Split by colon to isolate the actual value
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if 'eczema' in val:
        return 1
    return 0

def convert_age(x: str) -> Optional[float]:
    """
    Convert age-related string to a floating/continuous value.
    """
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(x: str) -> Optional[int]:
    """
    Convert gender string to binary (Female=0, Male=1). 
    """
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'male':
        return 1
    elif val == 'female':
        return 0
    return None

# 3. Initial filtering and saving 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 if trait data is available
if trait_row is not None:
    # Assume 'clinical_data' DataFrame is loaded from a previous step
    selected_clinical = 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 the selected clinical features
    preview_output = preview_df(selected_clinical, n=5, max_items=200)
    print("Preview of extracted clinical features:", preview_output)

    # Save clinical features to CSV
    selected_clinical.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 provided gene identifiers, they appear to be numeric rather than standard human gene symbols.
# Therefore, gene symbol 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 6: Gene Identifier Mapping
# We'll use Entrez IDs as our gene identifiers without discarding numeric IDs.
# The key fix is to carefully join the expression DataFrame with the mapping DataFrame on their shared ID index.

prob_col = 'ID'
gene_col = 'ENTREZ_GENE_ID'

# 1. Obtain a mapping DataFrame (probe -> Entrez ID).
mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col).copy()

def apply_entrez_id_mapping(expression_df: pd.DataFrame, map_df: pd.DataFrame) -> pd.DataFrame:
    # Keep only probes present in expression_df
    map_df = map_df[map_df['ID'].isin(expression_df.index)].copy()
    map_df.rename(columns={gene_col: 'Gene'}, inplace=True)

    # Each probe maps to exactly one Entrez ID in this dataset
    map_df['num_genes'] = 1
    map_df.set_index('ID', inplace=True)

    # Join on the expression_df index (probe IDs)
    merged_df = expression_df.join(map_df, how='inner')

    # Expression columns are the sample columns
    expr_cols = expression_df.columns

    # Divide each probe's expression by the number of mapped genes
    merged_df[expr_cols] = merged_df[expr_cols].div(merged_df['num_genes'].replace(0, 1), axis=0)

    # Sum contributions for each gene
    gene_expression_df = merged_df.groupby('Gene')[expr_cols].sum()
    return gene_expression_df

# 2. Apply our custom mapping function to get gene-level expression data
gene_data = apply_entrez_id_mapping(gene_data, mapping_df)

# 3. Inspect the final shape and a sample of the gene index
print("Mapped gene_data shape:", gene_data.shape)
print("Sample of mapped gene_data index:", list(gene_data.index[:10]))
import pandas as pd

# STEP7

# 1) Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2) Reconstruct the clinical DataFrame so that it has row indices for "Eczema", "Age", "Gender", 
#    and sample IDs as columns, matching the library's expected format.
#    Since in Step 2 we used 'index=False' when saving the CSV, we must parse the file accordingly.
tmp_clin = pd.read_csv(out_clinical_data_file, header=None)
# The first row in tmp_clin should contain the sample IDs. Make them column headers.
tmp_clin.columns = tmp_clin.iloc[0]
# Remove that row from the DataFrame
tmp_clin = tmp_clin.iloc[1:].copy()
# Now tmp_clin has shape (3, number_of_samples),
# so assign the index to [trait, "Age", "Gender"]:
tmp_clin.index = [trait, "Age", "Gender"]

selected_clinical_df = tmp_clin

# 3) Link the clinical and gene expression data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 4) Handle missing values using the trait column
final_data = handle_missing_values(linked_data, trait_col=trait)

# 5) Evaluate bias in the trait, and remove biased demographic features if any
trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

# 6) Final validation
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=final_data,
    note="Trait data successfully extracted and reformatted."
)

# 7) If usable, save final linked data
if is_usable:
    final_data.to_csv(out_data_file)