# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE40785"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE40785"

# Output paths
out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE40785.csv"
out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE40785.csv"
out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE40785.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 the dataset contains gene expression data
#    Based on the series description, there are gene probes and expression profiling.
#    So we conclude it likely has suitable gene expression data (not just miRNA or methylation).
is_gene_available = True

# 2. Determine availability of trait, age, and gender
#    From the dictionary, we see various "histology:" entries at key=1, including "histology: Endometrioid".
#    This indicates trait data is present (key=1). No mention of age or gender data was found in the dictionary.
trait_row = 1
age_row = None
gender_row = None

# 2.2 Data Type Conversion
#    We'll define functions that convert the raw strings to appropriate data types.
#    Trait: We'll treat "Endometrioid" or values containing "Endometrioid" as 1, and anything else as 0.
def convert_trait(value: str) -> Optional[int]:
    if not isinstance(value, str):
        return None
    # Split on colon if present
    parts = value.split(':', 1)
    val = parts[-1].strip().lower()  # value after colon, or the entire string if no colon
    if 'endometrioid' in val:
        return 1
    elif 'histology' in val or 'mucinous' in val or 'clear cell' in val or 'serous' in val:
        return 0
    return None  # for unknown cases

#    Since we don't have age_row or gender_row, we only define simple converters returning None if called.
def convert_age(value: str) -> Optional[float]:
    return None

def convert_gender(value: str) -> Optional[int]:
    return None

# 3. Conduct initial filtering on dataset usability and save metadata
is_trait_available = (trait_row is not None)
is_usable_flag = 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:
    # Assume "clinical_data" is the DataFrame obtained previously
    selected_clinical_df = 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 selected clinical features
    clinical_preview = preview_df(selected_clinical_df)
    print("Preview of Clinical Features:", clinical_preview)

    # 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 given gene expression data indices (e.g., "ILMN_1343291"), these are Illumina probe IDs,
# not standard human gene symbols. Hence, gene mapping is required.
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 & 2. Determine columns for probe IDs ("ID") and gene symbols ("Symbol") from our annotation.
#    Then extract a gene mapping DataFrame with two columns: [ID, Gene].
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

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

# (Optional) Let's preview and then save the resulting gene-level expression data.
print("Preview of gene-level expression data:", preview_df(gene_data))
gene_data.to_csv(out_gene_data_file)
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)