# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rectal_Cancer"
cohort = "GSE109057"

# Input paths
in_trait_dir = "../DATA/GEO/Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE109057"

# Output paths
out_data_file = "./output/preprocess/1/Rectal_Cancer/GSE109057.csv"
out_gene_data_file = "./output/preprocess/1/Rectal_Cancer/gene_data/GSE109057.csv"
out_clinical_data_file = "./output/preprocess/1/Rectal_Cancer/clinical_data/GSE109057.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) Gene Expression Data Availability
is_gene_available = True  # The dataset states "expression microarray experiments for mRNA," so it's gene expression.

# 2) Variable Availability and Data Type Conversion
#    Based on the sample characteristics dictionary:
#    (0) has a single unique value "rectal cancer" => constant feature => trait is not considered available.
trait_row = None  # constant => not useful
age_row = 2       # multiple unique ranges => available
gender_row = 1    # "Sex: M", "Sex: F" => available

import re

def convert_trait(x: str):
    # Not used because trait_row = None, but define it as requested.
    # Return None since trait is effectively unavailable.
    return None

def convert_age(x: str):
    # Extract the substring after the colon, e.g. "35 <= age < 40" => parse numeric range => return midpoint.
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    age_label = parts[1].strip()
    nums = re.findall(r'\d+', age_label)
    if len(nums) >= 2:
        low, high = float(nums[0]), float(nums[1])
        return (low + high) / 2.0
    elif len(nums) == 1:
        return float(nums[0])
    return None

def convert_gender(x: str):
    # Extract the substring after the colon, map "M" -> 1, "F" -> 0
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    gender_val = parts[1].strip().lower()
    if gender_val.startswith('m'):
        return 1
    elif gender_val.startswith('f'):
        return 0
    return None

# 3) Save Metadata with initial filtering
#    Trait data is considered unavailable if trait_row is None.
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, we skip clinical feature extraction for this dataset.
# 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])
# These identifiers (e.g., "11715100_at") are Affymetrix probe IDs, not human gene symbols.

print("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))
# Gene Identifier Mapping

# 1) Identify the columns in 'gene_annotation' that correspond to probe IDs and gene symbols.
#    From the preview, we see:
#       - probe identifier column is "ID"
#       - gene symbol column is "Gene Symbol"

# 2) Create the gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, "ID", "Gene Symbol")

# 3) Convert probe-level measurements in 'gene_data' to gene-level expression data
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)