# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE65986"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE65986"

# Output paths
out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE65986.csv"
out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE65986.csv"
out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE65986.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. Determine if gene expression data is available
#    From the background info: "Gene expression ... was analyzed by Affymetrix U133plus2 array."
#    Hence, it is gene expression rather than miRNA or methylation data.
is_gene_available = True

# 2. Identify data availability for trait, age, gender, and define conversion functions

# Observed keys in the sample characteristics dictionary:
#   0 -> histology: [Clear, Endometrioid, Serous]
#   1 -> age: [64, 57, ...]
#   2 -> Stage: ...
#   3 -> pfs: ...
#   4 -> prognosis: ...
# There is no key containing gender data, and it is likely all female since this is an ovarian cancer study.

trait_row = 0  # row 0 has multiple values including "Endometrioid", so it is not constant and is relevant to our trait.
age_row = 1    # row 1 has multiple age values, so it is valid.
gender_row = None  # no gender info is available or it is constant (all female), so treat as not available.

def convert_trait(value: str):
    """Convert the 'histology' entries to a binary trait.
       'Endometrioid' -> 1, others -> 0."""
    val = value.split(':')[-1].strip().lower()
    if val == 'endometrioid':
        return 1
    elif val in ['clear', 'serous']:
        return 0
    return None  # unknown or unexpected text

def convert_age(value: str):
    """Convert 'age' entries to a continuous numeric type."""
    val = value.split(':')[-1].strip()
    try:
        return float(val)
    except:
        return None

def convert_gender(value: str):
    """Convert 'gender' entries to binary: female->0, male->1.
       Although not used here, define for completeness."""
    val = value.split(':')[-1].strip().lower()
    if val == 'female':
        return 0
    elif val == 'male':
        return 1
    return None

# 3. Save metadata after 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 if trait data is available
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,           # clinical_data is assumed to be already loaded from a previous step
        trait,
        trait_row,
        convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    # Preview and save
    print("Preview of selected clinical features:\n", preview_df(selected_clinical_df))
    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])
# These IDs are Affymetrix probe set identifiers, not standard gene symbols.
# They require mapping to gene symbols.

print("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))
# STEP: Gene Identifier Mapping

# 1. Identify the corresponding columns in the annotation dataframe for probe IDs and gene symbols
probe_col = "ID"
gene_symbol_col = "Gene Symbol"

# 2. Get the gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

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

# (Optional) Print shape and preview if desired
print("Mapped gene_data dimensions:", gene_data.shape)
print("Preview of mapped gene_data:\n", gene_data.iloc[:5, :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)

# 2) Read the clinical dataframe with header=0 to ensure the first row is recognized as column headers,
#    leaving two rows of data to be indexed as [trait, "Age"].
selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
selected_clinical_df.index = [trait, "Age"]

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

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

# 5) 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)

# 6) 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 and Age data in the first two rows of the clinical CSV."
)

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