# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rectal_Cancer"
cohort = "GSE170999"

# Input paths
in_trait_dir = "../DATA/GEO/Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE170999"

# Output paths
out_data_file = "./output/preprocess/1/Rectal_Cancer/GSE170999.csv"
out_gene_data_file = "./output/preprocess/1/Rectal_Cancer/gene_data/GSE170999.csv"
out_clinical_data_file = "./output/preprocess/1/Rectal_Cancer/clinical_data/GSE170999.csv"
json_path = "./output/preprocess/1/Rectal_Cancer/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. Determine gene expression data availability
# Based on the background info, it's an Affymetrix U133 platform gene expression study,
# so we set is_gene_available = True.
is_gene_available = True

# 2. Identify availability of trait, age, and gender data:
#    From the sample characteristics dictionary, we only see KRAS mutation status entries.
#    There's no direct or variable trait data about Rectal_Cancer (all samples are rectal cancer patients),
#    so it's effectively constant and not useful for association analysis.
#    Similarly, there's no age or gender data.
trait_row = None
age_row = None
gender_row = None

# 2.2 Define data type conversion functions.
#     Even though data rows are not available, we still define them as placeholders.

def convert_trait(value: str):
    # No actual data is present. Return None for demonstration.
    return None

def convert_age(value: str):
    # No age data provided. Return None.
    return None

def convert_gender(value: str):
    # No gender data provided. Return None.
    return None

# 3. Save metadata with initial filtering (is_final=False).
#    Trait data is not available because trait_row is None, hence is_trait_available=False.
is_trait_available = (trait_row is not None)
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. Since trait_row is None, we do NOT extract or save clinical features.
#    (Skipping geo_select_clinical_features step as per instructions.)
# STEP3
import gzip
import pandas as pd

try:
    # 1. Attempt to extract gene expression data using the library function
    gene_data = get_genetic_data(matrix_file)
except KeyError:
    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
    # and rename the first column to "ID".
    marker = "!series_matrix_table_begin"
    skip_rows = None

    # Determine how many rows to skip before the matrix data begins
    with gzip.open(matrix_file, 'rt') as f:
        for i, line in enumerate(f):
            if marker in line:
                skip_rows = i + 1
                break
        else:
            raise ValueError(f"Marker '{marker}' not found in the file.")

    # Read the data from the determined position
    gene_data = pd.read_csv(
        matrix_file,
        compression='gzip',
        skiprows=skip_rows,
        comment='!',
        delimiter='\t',
        on_bad_lines='skip'
    )

    # If a different column name is used instead of 'ID_REF', rename appropriately
    if 'ID_REF' in gene_data.columns:
        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
    else:
        first_col = gene_data.columns[0]
        gene_data.rename(columns={first_col: 'ID'}, inplace=True)

    gene_data['ID'] = gene_data['ID'].astype(str)
    gene_data.set_index('ID', inplace=True)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])
# The identifiers resemble Affymetrix probe set IDs and are not human gene symbols.
# Therefore, gene symbol mapping is required.
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. Determine which columns in the gene_annotation DataFrame correspond to
#    the probe identifiers (matching those in gene_data.index) and to the gene symbols.
id_col = "ID"
symbol_col = "Gene Symbol"

# 2. Extract the gene mapping DataFrame with two columns: the probe ID and the gene symbol.
mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=symbol_col)

# 3. Convert probe-level measurements to gene-level expression data by applying
#    the mapping rules (splitting probe expression among multiple genes, etc.).
gene_data = apply_gene_mapping(gene_data, mapping_df)
import os
import pandas as pd

# STEP 7: Data Normalization and Linking

# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
if not os.path.exists(out_clinical_data_file):
    # No trait data file => dataset is not usable for trait analysis
    df_null = pd.DataFrame()
    is_biased = True  # Arbitrary boolean to satisfy function requirement
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=is_biased,
        df=df_null,
        note="No trait data file found; dataset not usable for trait analysis."
    )

else:
    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
    normalized_gene_data.to_csv(out_gene_data_file)

    # 2. Load the previously extracted clinical CSV.
    selected_clinical_df = pd.read_csv(out_clinical_data_file)
    # If we had a single-row trait, rename row 0 to the trait name (example usage).
    selected_clinical_df = selected_clinical_df.rename(index={0: trait})

    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
    combined_clinical_df = selected_clinical_df

    # Link the clinical and genetic data by matching sample IDs in columns.
    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)

    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
    processed_data = handle_missing_values(linked_data, trait)

    # 4. Check trait bias and remove any biased demographic features (if any).
    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)

    # 5. Final validation and metadata saving.
    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=processed_data,
        note="Completed trait-based preprocessing."
    )

    # 6. If final dataset is usable, save. Otherwise, skip.
    if is_usable:
        processed_data.to_csv(out_data_file)