# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE118336"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE118336"

# Output paths
out_data_file = "./output/preprocess/1/Sarcoma/GSE118336.csv"
out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE118336.csv"
out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE118336.csv"
json_path = "./output/preprocess/1/Sarcoma/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)
# Step: Dataset Analysis and Clinical Feature Extraction

# 1. Gene Expression Data Availability
#    The Series title indicates "HTA2.0 (human transcriptome array) analysis", which suggests
#    actual gene expression data is available (and not simply miRNA or methylation data).
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

#    2.1 Identify keys in the sample characteristics for each variable:
trait_row = None          # No mention of 'Sarcoma' or a relevant disease key in the dictionary.
age_row = None            # No age information found.
gender_row = None         # No gender information found.

#    2.2 Define data-type conversion functions. Even though data is not available,
#        we provide them as placeholders.

def convert_trait(x: str) -> int:
    # Not used because trait_row is None, but here's a placeholder function.
    # Convert to 'binary' if used, or return None for unknown.
    return None

def convert_age(x: str) -> float:
    # Not used because age_row is None, placeholder function
    return None

def convert_gender(x: str) -> int:
    # Not used because gender_row is None, placeholder function
    return None

# 3. Save Metadata (initial filtering).
#    Trait data availability depends on trait_row. Since trait_row is None, is_trait_available=False.
is_trait_available = False

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. Since trait_row is None, we skip clinical feature extraction and do not call geo_select_clinical_features.
# 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])
requires_gene_mapping = True
# STEP5
# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
if soft_file is None:
    print("No SOFT file found. Skipping gene annotation extraction.")
    gene_annotation = pd.DataFrame()
else:
    try:
        # Attempt to extract gene annotation with the default method
        gene_annotation = get_gene_annotation(soft_file)
    except UnicodeDecodeError:
        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
        import gzip
        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
            content = f.read()
        gene_annotation = filter_content_by_prefix(
            content,
            prefixes_a=['^','!','#'],
            unselect=True,
            source_type='string',
            return_df_a=True
        )[0]

    print("Gene annotation preview:")
    print(preview_df(gene_annotation))
# STEP 6: Gene Identifier Mapping

# We'll attempt to map the probe-level data to gene symbols only if there's a genuine overlap
# between the expression data indices and the annotation IDs.

probe_column_candidates = ["ID", "probeset_id"]
gene_symbol_column_candidates = ["gene_assignment", "mrna_assignment"]

chosen_probe_col = None
chosen_symbol_col = None

# 1. Find a probe column with overlap
for col in probe_column_candidates:
    if col in gene_annotation.columns:
        overlap = set(gene_annotation[col]) & set(gene_data.index)
        if len(overlap) > 0:
            chosen_probe_col = col
            break

# 2. Pick a gene symbol column
for col in gene_symbol_column_candidates:
    if col in gene_annotation.columns:
        chosen_symbol_col = col
        break

# If none found, skip mapping
if not chosen_probe_col or not chosen_symbol_col:
    print("No suitable probe or gene symbol columns found in the annotation. Skipping mapping.")
else:
    # Build a preliminary mapping DataFrame
    mapping_df = get_gene_mapping(
        gene_annotation,
        prob_col=chosen_probe_col,
        gene_col=chosen_symbol_col
    )

    # 3. Check for genuine overlap after dropping invalid entries
    mapped_ids = set(mapping_df["ID"].unique()) & set(gene_data.index)
    if len(mapped_ids) == 0:
        print("No overlapping probe IDs after cleaning. Skipping mapping.")
    else:
        # Proceed with the mapping since there is an actual overlap
        gene_data = apply_gene_mapping(gene_data, mapping_df)
        print("Gene-level mapping performed successfully.")
        print("Mapped gene_data shape:", gene_data.shape)
        print("First few gene symbols after mapping:", gene_data.index[:10].tolist())
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)