# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE57793"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE57793"

# Output paths
out_data_file = "./output/preprocess/1/Essential_Thrombocythemia/GSE57793.csv"
out_gene_data_file = "./output/preprocess/1/Essential_Thrombocythemia/gene_data/GSE57793.csv"
out_clinical_data_file = "./output/preprocess/1/Essential_Thrombocythemia/clinical_data/GSE57793.csv"
json_path = "./output/preprocess/1/Essential_Thrombocythemia/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
# Based on the background info ("Microarrays were used to assess gene expression..."),
# we conclude that gene expression data is available.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# From the Sample Characteristics Dictionary:
# {0: ['disease state: ET', 'disease state: PMF', 'disease state: PV'],
#  1: ['treatment: untreated', 'treatment: IFN-alpha2'],
#  2: ['tissue: Whole blood']}
#
# We see that key=0 has multiple values (ET, PMF, PV).
# We can map "ET" vs. "non-ET" for our trait "Essential_Thrombocythemia".
# There are no age or gender data in the dictionary.

trait_row = 0   # because we can obtain "ET" from key=0
age_row = None
gender_row = None

def convert_trait(value: str) -> Optional[int]:
    """
    Convert disease state to a binary representation:
      ET -> 1
      PMF/PV -> 0
      otherwise -> None
    """
    # Split on colon and take the last part
    val = value.split(':')[-1].strip()
    if val == "ET":
        return 1
    elif val in ["PMF", "PV"]:
        return 0
    else:
        return None

# Since age and gender are not available, define no-op converters returning None.
def convert_age(value: str) -> Optional[float]:
    return None

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

# 3. Save Metadata (initial filtering, not final)
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 perform if trait_row is available
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data, 
        trait=trait,  # will name the column as "Essential_Thrombocythemia"
        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 features
    preview = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview)

    # Save extracted clinical data 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])
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. Identify the columns to use for the probe IDs (the same as the gene expression data) and the gene symbols
probe_col = 'ID'           # the column in annotation that matches gene_data's index
gene_symbol_col = 'Gene Symbol'  # the column storing gene symbols

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

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

# Print basic information about the resulting gene_data
print("Gene-level data shape:", gene_data.shape)
print("Preview of gene-level data:")
print(gene_data.head(5))
import os
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)

# Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2
if os.path.exists(out_clinical_data_file):
    # 2) Link the clinical and gene expression data
    # Load the single-row clinical CSV without forcing an index column
    tmp_df = pd.read_csv(out_clinical_data_file, header=0)
    # Rename the single row to the trait. Now columns = sample IDs, index = [trait].
    tmp_df.index = [trait]
    selected_clinical_df = tmp_df

    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

    # 4) Evaluate bias in the trait (and remove biased demographics if any)
    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

    # 5) Final validation
    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; row renamed to trait for linking."
    )

    # 6) If the dataset is usable, save
    if is_usable:
        final_data.to_csv(out_data_file)
else:
    # If the clinical file does not exist, the trait is unavailable
    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="No trait data was found; linking and final dataset output are skipped."
    )