# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE100521"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE100521"

# Output paths
out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE100521.csv"
out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE100521.csv"
out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE100521.csv"
json_path = "./output/preprocess/1/Cystic_Fibrosis/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)
import re
import pandas as pd

# 1. Gene Expression Data Availability
is_gene_available = True  # Based on the background info, this dataset has Illumina HumanHT-12 v4 microarray data

# 2. Variable Availability and Data Type Conversion

# 2.1 Find rows for trait, age, and gender
trait_row = 0   # row 0 contains CF vs Non CF info
age_row = 1     # row 1 contains age info
gender_row = 2  # row 2 contains gender info

# 2.2 Define data conversion functions
def convert_trait(value: str):
    """
    Convert a string describing the subject's CF status to a binary value:
    0 for Non-CF subject, 1 for CF patient.
    Unknown values => None
    """
    # Extract the part after the colon
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if 'non cf subject' in val:
        return 0
    elif 'cf patient' in val:
        return 1
    else:
        return None

def convert_age(value: str):
    """
    Convert a string describing the age to a continuous (float) value.
    Unknown values => None
    """
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    # Attempt to convert to float
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(value: str):
    """
    Convert a string describing gender to a binary value:
    female => 0, male => 1
    Unknown values => None
    """
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'female':
        return 0
    elif val == 'male':
        return 1
    else:
        return None

# We assume the variable "clinical_data" is available in this environment,
# containing the sample characteristics as a DataFrame.

# 3. Save Metadata (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. Clinical Feature Extraction (only if trait_row is not None)
if trait_row is not None:
    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
    preview_output = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview_output)

    # Save to CSV
    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
# 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 biomedical expertise, 'ILMN_xxxxx' identifiers are Illumina probe IDs and not human gene symbols.
# Therefore, they require mapping to 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))
# STEP: Gene Identifier Mapping

# 1. Identify the columns in the gene_annotation dataframe that match the probe identifiers in gene_data (ILMN_xxx) 
#    and those that represent gene symbols. From the annotation preview, 'ID' matches 'ILMN_xxx' and 'Symbol' is the gene symbol.

# 2. Create a gene mapping dataframe using the identified columns
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

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

# (Optional) Quick check - display the shape or a small preview
print("Mapped gene_data shape:", gene_data.shape)
print("Mapped gene_data head:\n", gene_data.head(5))
import pandas as pd

# STEP7

# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Check trait availability
is_trait_available = True

if not is_trait_available:
    # If trait is unavailable, skip further processing
    empty_df = pd.DataFrame()
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=True,
        df=empty_df,
        note="Trait data not available; skipping further steps."
    )
else:
    # Read the previously saved clinical data with index_col=0
    selected_clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)

    # 3. Link the clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

    # 4. Handle missing values in the linked data
    linked_data = handle_missing_values(linked_data, trait)

    # 5. Determine whether the trait and demographic features are severely biased
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 6. Final quality check and record the dataset info
    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=is_trait_biased,
        df=unbiased_linked_data,
        note="Final check after linking and missing-value handling."
    )

    # 7. If usable, save the final linked data
    if is_usable:
        unbiased_linked_data.to_csv(out_data_file)