# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE68600"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE68600"

# Output paths
out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE68600.csv"
out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE68600.csv"
out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE68600.csv"
json_path = "./output/preprocess/1/Endometrioid_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  # "Assay Type: Gene Expression" indicates gene expression data is likely present.

# 2. Variable Availability and Data Type Conversion
# Observing the sample characteristics dictionary:
#  - trait ("Endometrioid_Cancer") can be inferred from row 4 (histology).
#  - age is not found => age_row = None.
#  - gender appears to be uniformly female => no variability => gender_row = None.

trait_row = 4
age_row = None
gender_row = None

# Define data-conversion functions:
def convert_trait(sample_value: str):
    """
    Convert sample_value into a binary representation:
    1 if 'endometrioid' is found in the histology,
    0 if it is any other histology,
    None if it can't be parsed.
    """
    parts = sample_value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    # Mark samples with 'endometrioid' as 1, all others as 0
    if 'endometrioid' in val:
        return 1
    else:
        return 0

def convert_age(sample_value: str):
    # Age data is not available in this dataset, so return None
    return None

def convert_gender(sample_value: str):
    # Gender is uniformly female, so it's not useful for analysis. Return None.
    return None

# Determine whether trait data is available
is_trait_available = (trait_row is not None)

# 3. Save Metadata (Initial Filtering)
dataset_passed_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. Clinical Feature Extraction (only if trait data is available)
if trait_row is not None:
    # 'clinical_data' is assumed to be the DataFrame containing the sample characteristics
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=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 data
    print(preview_df(selected_clinical_df))

    # Save the extracted clinical features to CSV
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# STEP3
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])
# Based on the identifiers, these appear to be Affymetrix probe IDs rather than standard gene symbols.
# Therefore, they require mapping to gene symbols.
requires_gene_mapping = True
# STEP5
import pandas as pd
import io

# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
annotation_text, _ = filter_content_by_prefix(
    source=soft_file,
    prefixes_a=['^', '!', '#'],
    unselect=True,
    source_type='file',
    return_df_a=False,
    return_df_b=False
)

# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
gene_annotation = pd.read_csv(
    io.StringIO(annotation_text),
    delimiter='\t',
    on_bad_lines='skip',
    engine='python'
)

print("Gene annotation preview:")
print(preview_df(gene_annotation))
# Gene Identifier Mapping
prob_col = 'ID'
gene_col = 'Gene Symbol'

# 1 & 2. Identify the columns for the probe IDs and gene symbols, then retrieve the mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)

# 3. Convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_df)

# (Optional) Print a brief preview of the mapped gene_data
print("Mapped gene expression data shape:", gene_data.shape)
print("First 5 genes:\n", gene_data.head(5))
import pandas as pd

# STEP7

# 1) Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Read back the clinical dataframe saved in Step 2. 
# According to Step 2, we saved 1 row (the trait) × N columns (the samples) without the row index.
selected_clinical_df = pd.read_csv(out_clinical_data_file)  # shape: (1, number_of_samples)
# Rename the row index to the trait (e.g., "Eczema")
selected_clinical_df.index = [trait]

# 2) Link the clinical and gene expression data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3) Handle missing values using the trait column
final_data = handle_missing_values(linked_data, trait_col=trait)

# 4) Evaluate bias in the trait (and remove biased demographic features if they existed)
trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

# 5) Final validation. Since we do have trait data, set is_trait_available=True
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=final_data,
    note="Trait data successfully extracted from Step 2."
)

# 6) If the dataset is deemed usable, save final linked data
if is_usable:
    final_data.to_csv(out_data_file)