# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE139038"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE139038"

# Output paths
out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE139038.csv"
out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE139038.csv"
out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE139038.csv"
json_path = "./output/preprocess/1/Cystic_Fibrosis/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. Gene Expression Data Availability
is_gene_available = True  # Based on the series info indicating a gene expression study

# 2. Variable Availability and Data Type Conversion

# Looking at the sample characteristics dictionary,
# - For "trait" (Cystic_Fibrosis), no matching or inferable data was found. So trait_row = None.
# - For "age", it appears in row 0 and has multiple unique values. So age_row = 0.
# - For "gender", row 1 has only "Female" (constant). Hence, it's not useful for association. gender_row = None.

trait_row = None
age_row = 0
gender_row = None

# Define the data conversion functions.
def convert_trait(val: str):
    """
    Convert trait data to binary (1/0). Return None if unknown.
    """
    parts = val.split(':', 1)
    if len(parts) < 2:
        return None
    raw = parts[1].strip().lower()
    # Example placeholder logic:
    # If the variable explicitly indicated "cystic fibrosis," return 1; 
    # if it indicated "normal"/"control," return 0; else None.
    if raw == "cystic fibrosis":
        return 1
    elif raw in ["normal", "control", "no"]:
        return 0
    return None

def convert_age(val: str):
    """
    Convert age data to continuous (float). Return None if unknown.
    """
    parts = val.split(':', 1)
    if len(parts) < 2:
        return None
    raw = parts[1].strip()
    try:
        return float(raw)
    except ValueError:
        return None

def convert_gender(val: str):
    """
    Convert gender data to binary (female=0, male=1). Return None if unknown.
    """
    parts = val.split(':', 1)
    if len(parts) < 2:
        return None
    raw = parts[1].strip().lower()
    if raw == "female":
        return 0
    elif raw == "male":
        return 1
    return None

# 3. Save Metadata - Initial Filtering
# Trait data availability depends on whether 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
# Since trait_row is None, we skip extracting clinical features.
# 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., "10_10_1", "10_10_10") are not standard human gene symbols.
# They appear to be platform-specific probe references, so a mapping to human gene symbols is needed.

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. We have determined that the gene annotation column "ID" matches the identifier in the gene expression data index,
#    and "Gene_Symbol" provides the corresponding gene symbols.

# 2. Create a gene mapping dataframe from the annotation dataframe using the appropriate columns.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene_Symbol')

# 3. Apply the mapping to convert probe-level data to gene-level data.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Optionally print shape or a small preview to verify results
print("Mapped gene_data shape:", gene_data.shape)
print(gene_data.head())
import pandas as pd

# STEP7

# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Check if trait data was actually available from previous steps
# (In previous steps, we set is_trait_available = (trait_row is not None).)
# We'll assume here it's accessible in the environment, or re-derive it:
is_trait_available = False  # Reflecting the outcome from prior steps

if not is_trait_available:
    # 2-4: Skip linking, missing value handling, and bias checks because trait is unavailable.
    # 5. Conduct final validation with an empty DataFrame, forcing the dataset to be marked not usable.
    empty_df = pd.DataFrame()
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,    # The expression data exists
        is_trait_available=False,  # Trait data is not available
        is_biased=True,            # Force as biased so the dataset is not usable
        df=empty_df,
        note="Trait data not available; skipping further steps."
    )
else:
    # 2. Define a placeholder for selected_clinical_data (if we actually had trait data).
    #    In this dataset, trait_row was None, so this part won't run.
    selected_clinical_data = pd.DataFrame()  # Placeholder if trait were available
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

    # 3. Handle missing values in the linked data
    linked_data = handle_missing_values(linked_data, trait)

    # 4. Determine whether the trait and demographic features are severely biased
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Conduct final quality validation and save the cohort 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,
        is_biased=is_trait_biased,
        df=unbiased_linked_data
    )

    # 6. If the dataset is usable, save it as CSV
    if is_usable:
        unbiased_linked_data.to_csv(out_data_file)