# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE162785"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE162785"

# Output paths
out_data_file = "./output/preprocess/1/Sarcoma/GSE162785.csv"
out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE162785.csv"
out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE162785.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)
# 1) Determine gene expression data availability
is_gene_available = True  # Microarray analysis suggests gene expression data is available

# 2) Variable availability and data type conversion

# From the sample characteristics dictionary:
# {0: ['cell line: A673', 'cell line: CHLA-10', 'cell line: EW7', 'cell line: SK-N-MC']}
# There is only one key (0), whose values all refer to Ewing sarcoma cell lines. For our trait of interest ("Sarcoma"),
# this dataset effectively has just one value for all samples (i.e., all are Ewing Sarcoma), so it is considered constant
# and therefore not available for association analysis.
trait_row = None   # No variability in the trait
age_row = None     # No age data available
gender_row = None  # No gender data available

# Although the rows are None, we still define the conversion functions as requested.
def convert_trait(value: str):
    # Not used here because trait_row is None
    # Typically, we would parse the string after the colon and convert, but there's no relevant data to parse.
    return None

def convert_age(value: str):
    # Not used here because age_row is None
    return None

def convert_gender(value: str):
    # Not used here because gender_row is None
    return None

# 3) Save metadata (initial filtering)
# Trait data is considered unavailable since trait_row is None.
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) Clinical feature extraction is skipped because trait_row is None (no clinical data for trait).
# 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])
print("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: Gene Identifier Mapping

# 1. Identify the columns in the annotation dataframe that match our probe IDs and contain gene symbols.
#    From the preview, the "ID" column matches the numerical probe identifiers, and "gene_assignment" contains gene symbols.
prob_col = "ID"
gene_col = "gene_assignment"

# 2. Create a mapping dataframe with these two columns.
mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)

# 3. Convert the probe-level expression data to gene-level expression data using this mapping.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# (Optional) Print shape and a small preview of the resulting mapped data
print("Mapped gene_data shape:", gene_data.shape)
print("Mapped gene_data preview:\n", gene_data.head())
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)