# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE197147"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE197147"

# Output paths
out_data_file = "./output/preprocess/1/Sarcoma/GSE197147.csv"
out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE197147.csv"
out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE197147.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 the dataset contains gene expression data
is_gene_available = True  # The series description explicitly mentions "Gene expression profiling"

# 2. Identify variable availability and define row indices
#    Only one key (0) is present with multiple histotypes ("HB","NB","RMS","WT").
#    We'll map "RMS" to the trait of interest (Sarcoma=1) and others to 0.
trait_row = 0  # We can infer the Sarcoma trait from 'histotype: RMS' vs others
age_row = None  # No age information in the sample dictionary
gender_row = None  # No gender information in the sample dictionary

# 2.2 Define conversion functions
def convert_trait(value: str):
    # Extract the part after the colon.
    val = value.split(':')[-1].strip().lower()
    # Map RMS => 1 (Sarcoma), others => 0
    if val == 'rms':
        return 1
    elif val in ['hb', 'nb', 'wt']:
        return 0
    return None  # For any unexpected values

def convert_age(value: str):
    # No rows found for age, so no real conversion logic needed
    return None

def convert_gender(value: str):
    # No rows found for gender, so no real conversion logic needed
    return None

# 2.1 Check if the trait data is available
is_trait_available = (trait_row is not None)

# 3. Save metadata with initial filtering
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. If trait data is available (trait_row != None), extract clinical features
if trait_row is not None:
    selected_clinical = geo_select_clinical_features(
        clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    # Preview the extracted clinical features
    print(preview_df(selected_clinical, n=5))
    # Save to CSV
    selected_clinical.to_csv(out_clinical_data_file, index=False)
# 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 provided gene expression data index (e.g., 'TC0100006437.hg.1'), 
# these appear to be probe or platform-specific identifiers rather than standard human gene symbols.
# Therefore, we conclude that these IDs must be mapped to standard gene symbols for proper analysis.

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 which annotation columns store the gene IDs and gene symbol references.
#    From the preview, it appears the column "ID" matches the row index of our gene expression data,
#    and the column "SPOT_ID.1" contains gene symbol references.
probe_id_col = "ID"
gene_symbol_col = "SPOT_ID.1"

# 2. Get a gene mapping dataframe from the annotation dataframe.
mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col=probe_id_col,
    gene_col=gene_symbol_col
)

# 3. Convert probe-level measurements into gene-level expression.
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Print a small preview to confirm the result
print("Mapped gene expression data preview:")
print(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)