# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE73637"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73637"

# Output paths
out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE73637.csv"
out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE73637.csv"
out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE73637.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)
import pandas as pd

# 1. Determine if gene expression data is available
#    Based on background info, this dataset uses gene expression for GECA analysis.
is_gene_available = True

# 2. Identify data availability and define row indices
#    From the sample characteristics dictionary, histopathology is stored in row 3,
#    which includes “Endometrioid” among other values (i.e., multiple categories).
#    There's no mention of age or gender, so age_row and gender_row are None.

trait_row = 3     # row containing histopathology with "Endometrioid"
age_row = None
gender_row = None

# 2.2 Define data type conversion functions
def convert_trait(value: str):
    """
    Convert histopathology values to binary, indicating whether
    'Endometrioid' is present (1) or not (0). Unknown/unexpected
    values become None.
    """
    parts = value.split(':', 1)
    if len(parts) == 2:
        val_str = parts[1].strip().lower()
    else:
        val_str = parts[0].strip().lower()

    # Heuristic: Any mention of 'endometrioid' or 'endometroid' is mapped to 1
    # Otherwise, map known terms to 0, else None.
    if 'endometrioid' in val_str or 'endometroid' in val_str:
        return 1
    elif any(keyword in val_str for keyword in ['serous', 'carcinoma', 'clear cell', 'adenocarcinoma']):
        return 0
    else:
        return None

def convert_age(value: str):
    # No age data available; return None
    return None

def convert_gender(value: str):
    # No gender data available; return None
    return None

# 3. Perform initial filtering and save metadata
is_trait_available = trait_row is not None
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. If trait_row is available, extract clinical data and save
if trait_row is not None:
    # Suppose 'clinical_data' is the DataFrame that holds the sample characteristics
    # in the same format as shown in the "Sample Characteristics Dictionary".
    data_dict = {
        0: ['cell type: ovarian cells'],
        1: [
            'cell line: COV504',
            'cell line: COV362',
            'cell line: UWB1.289+BRCA1',
            'cell line: OV56',
            'cell line: UWB1.289',
            'cell line: COV318',
            'cell line: NCI/ADR-RES',
            'cell line: OVCAR3',
            'cell line: OVCAR4',
            'cell line: OVCAR8',
            'cell line: IGR-OV1',
            'cell line: SK-OV-3',
            'cell line: OVCAR5',
            'cell line: ES-2',
            'cell line: TOV-21G',
            'cell line: TOV-112D',
            'cell line: PEO1',
            'cell line: PEO4'
        ],
        2: ['tumor site of origin: Ovarian'],
        3: [
            'histopathology: Serous',
            'histopathology: Endometrioid',
            'histopathology: Poorly differentiated serous',
            'histopathology: Undifferentiated carcinoma',
            'histopathology: Poorly differentiated carcinoma',
            'histopathology: Moderately differentiated carcinoma',
            'histopathology: Endometroid with serous/clear cell',
            'histopathology: Well-differentiated adenocarcinoma',
            'histopathology: Poorly differentiated clear cell',
            'histopathology: Clear Cell'
        ]
    }
    # Convert the dictionary to a DataFrame similar to how GEO data often appear
    clinical_data = pd.DataFrame.from_dict(data_dict, orient='index').fillna('')

    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 DataFrame
    preview_result = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview_result)

    # Save 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])
# Based on the output, the gene identifiers are numeric (e.g., '1', '2', '3', etc.),
# which indicates they are not human gene symbols and likely require mapping.
# Therefore:

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. Decide which columns correspond to the gene expression ID and the gene symbol
#    From the previews, "ID" matches the numeric identifiers in gene_data, 
#    and "GeneSymbol" stores the actual gene symbols.

# 2. Get a gene mapping DataFrame
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GeneSymbol")

# 3. Convert probe-level measurements to gene-level expression data
gene_data = apply_gene_mapping(gene_data, mapping_df)
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 back the clinical DataFrame we saved in Step 2.
#    Since we saved a single row with no header or index, we read with header=None to keep that row as data.
selected_clinical_df = pd.read_csv(out_clinical_data_file, header=None)

#    If the number of columns aligns with the gene expression DataFrame's columns (i.e., same samples),
#    rename the clinical DataFrame columns accordingly to achieve correct sample alignment.
if selected_clinical_df.shape[1] == normalized_gene_data.shape[1]:
    selected_clinical_df.columns = normalized_gene_data.columns

#    Set the row index to the trait name (e.g., "Endometrioid_Cancer")
selected_clinical_df.index = [trait]

# 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 present)
trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

# 6) Final validation. We do have trait data, so 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="Aligned columns in clinical DataFrame to match gene expression samples."
)

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

# STEP8
# 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 back the clinical DataFrame saved in Step 2 (one or more rows × number_of_samples columns, no header).
selected_clinical_df = pd.read_csv(out_clinical_data_file, header=None)

# In case there are empty columns (e.g., trailing commas), drop them
selected_clinical_df = selected_clinical_df.dropna(axis=1, how='all')

# If the number of columns matches the number of samples (columns) in the gene data, rename to align sample IDs
if selected_clinical_df.shape[1] == normalized_gene_data.shape[1]:
    selected_clinical_df.columns = normalized_gene_data.columns
else:
    print(f"Warning: Mismatch in shape. Clinical data has {selected_clinical_df.shape[1]} columns, "
          f"while gene data has {normalized_gene_data.shape[1]} columns. Linking may fail.")

# Set the row index to the trait, so we can keep track of it
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 present)
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="Ensured clinical and gene sample columns were aligned if possible."
)

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