# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Eczema"
cohort = "GSE182740"

# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE182740"

# Output paths
out_data_file = "./output/preprocess/1/Eczema/GSE182740.csv"
out_gene_data_file = "./output/preprocess/1/Eczema/gene_data/GSE182740.csv"
out_clinical_data_file = "./output/preprocess/1/Eczema/clinical_data/GSE182740.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)
import pandas as pd
from typing import Optional, Any

# 1. Gene Expression Data Availability
# From the background info: "Global mRNA expression ... microarray analysis"
# => This indicates RNA expression data is present.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
# Observing the sample characteristics dictionary:
#   Row 1 => "disease: Psoriasis", "disease: Atopic_dermatitis", "disease: Mixed", "disease: Normal_skin"
# For the trait "Eczema", we interpret "Atopic_dermatitis" or "Mixed" as having eczema, and the others as not.
trait_row = 1  # disease row
age_row = None  # no age info found
gender_row = None  # no gender info found

# 2.2 Data Type Conversion
def convert_trait(value: str) -> Optional[int]:
    """
    Convert 'disease: X' to a binary indicator:
       Eczema(Atopic_dermatitis) or Mixed => 1
       otherwise => 0
    Unknown => None
    """
    # Split with ':', take the part after the first colon (if any).
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    disease_str = parts[1].strip().lower()
    
    if 'atopic_dermatitis' in disease_str or 'mixed' in disease_str:
        return 1
    elif 'psoriasis' in disease_str or 'normal_skin' in disease_str:
        return 0
    else:
        return None

# We have no age or gender data, but define no-op converters for completeness
def convert_age(value: str) -> Optional[float]:
    return None

def convert_gender(value: str) -> Optional[int]:
    return None

# 3. Save Metadata (initial filtering)
# Trait data availability depends on whether trait_row is None. Here trait_row=1 => True
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_row is not None
if trait_row is not None:
    # Suppose the clinical_data DataFrame was already created in a previous step
    # We'll assume 'clinical_data' is in the environment
    selected_features = 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 output
    preview = preview_df(selected_features)
    print("Preview of selected clinical features:", preview)

    # Save the selected features
    selected_features.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 identifiers (e.g., "1007_s_at", "1053_at") appear to be Affymetrix probe IDs rather than 
# standard human gene symbols, hence they require gene symbol mapping.
print("\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. Decide the columns for probe ID and gene symbol. 
#    From the annotation preview, "ID" matches the expression data's row indices (e.g., '1007_s_at'), 
#    and "Gene Symbol" stores the gene symbols (e.g., 'DDR1 /// MIR4640').
probe_id_col = "ID"
gene_symbol_col = "Gene Symbol"

# 2. Get a gene mapping dataframe using the get_gene_mapping function.
mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)

# 3. Convert probe-level data to gene-level data using the apply_gene_mapping function.
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# (Optional) Check the result by printing first five rows of the newly mapped gene_data.
print(gene_data.head())
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)

# Read back the clinical dataframe saved in Step 2. 
# According to Step 2, we saved 1 row (the trait) × N columns (the samples) without the row index.
selected_clinical_df = pd.read_csv(out_clinical_data_file)  # shape: (1, number_of_samples)
# Rename the row index to the trait (e.g., "Eczema")
selected_clinical_df.index = [trait]

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

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

# 4) Evaluate bias in the trait (and remove biased demographic features if they existed)
trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

# 5) Final validation. Since we do have trait data, set is_trait_available=True
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 from Step 2."
)

# 6) If the dataset is deemed usable, save final linked data
if is_usable:
    final_data.to_csv(out_data_file)