# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE159848"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE159848"

# Output paths
out_data_file = "./output/preprocess/1/Sarcoma/GSE159848.csv"
out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE159848.csv"
out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE159848.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 if gene expression data is available
is_gene_available = True  # The dataset is described as "Expression data from 50 mixoid liposarcomas"

# 2. Identify availability of variables and define their data-conversion functions
# According to the dictionary, row 0 has sex: M/F, and row 1 has age
# For the trait "Sarcoma," the dataset is entirely mixoid liposarcomas (no variation), so we consider it unavailable.
trait_row = None
age_row = 1
gender_row = 0

def convert_trait(value: str):
    """
    Since trait is not available/variable in this dataset, always return None.
    """
    return None

def convert_age(value: str):
    """
    Extract the age value after the colon, converting it to float if possible.
    Return None for invalid or unknown values.
    """
    val = value.split(':')[-1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(value: str):
    """
    Extract the gender value (M or F) after the colon and convert to binary:
      M or male -> 1
      F or female -> 0
    Others -> None
    """
    val = value.split(':')[-1].strip().lower()
    if val in ['m', 'male']:
        return 1
    elif val in ['f', 'female']:
        return 0
    else:
        return None

# 3. Save metadata with initial filtering
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. Since trait_row is None, skip extraction of 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])
# Based on the observed probe IDs (e.g., "A_23_P100001"), these are not standard human gene symbols.
# Thus, gene-to-symbol mapping is required.

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))
# STEP6: Gene Identifier Mapping

# 1. Identify the matching columns in the gene annotation dataframe.
#    From the preview, the probe identifiers are under "ID" and the gene symbols are under "GENE_SYMBOL".
probe_col = "ID"
symbol_col = "GENE_SYMBOL"

# 2. Create the mapping dataframe from probe to gene symbol.
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)

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

# (Optional) Print the shape and a small index preview to verify successful mapping
print("Mapped gene_data shape:", gene_data.shape)
print("First 20 gene symbols in mapped gene_data:", list(gene_data.index[:20]))
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)