# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anxiety_disorder"
cohort = "GSE60190"

# Input paths
in_trait_dir = "../DATA/GEO/Anxiety_disorder"
in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE60190"

# Output paths
out_data_file = "./output/preprocess/3/Anxiety_disorder/GSE60190.csv"
out_gene_data_file = "./output/preprocess/3/Anxiety_disorder/gene_data/GSE60190.csv"
out_clinical_data_file = "./output/preprocess/3/Anxiety_disorder/clinical_data/GSE60190.csv"
json_path = "./output/preprocess/3/Anxiety_disorder/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
```python
# 1. Gene Expression Data Availability
# Based on Series_summary, this dataset uses Illumina HumanHT-12 v3 microarray for gene expression measurement
is_gene_available = True

# 2.1 Data Availability & 2.2 Data Type Conversion
# trait (anxiety) can be inferred from dx field
trait_row = 3  

def convert_trait(value):
    if not isinstance(value, str):
        return None
    val = value.split(": ")[-1]
    # Anxiety disorder can be comorbid with OCD, so consider OCD cases as anxiety cases
    if val in ["OCD", "Tics"]:
        return 1
    elif val == "Control":
        return 0
    return None

# age is available
age_row = 5

def convert_age(value):
    if not isinstance(value, str):
        return None
    try:
        return float(value.split(": ")[-1])
    except:
        return None

# gender is available  
gender_row = 7

def convert_gender(value):
    if not isinstance(value, str):
        return None
    val = value.split(": ")[-1]
    if val == "F":
        return 0
    elif val == "M":
        return 1
    return None

# 3. Save Metadata
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=True)

# 4. Clinical Feature Extraction
sample_characteristics = {
    '0': ['rin: 7.4', 'rin: 8.6', 'rin: 7.8', 'rin: 8.2', 'rin: 8.5', 'rin: 8.3', 'rin: 8.1', 'rin: 8.8', 'rin: 8.7', 'rin: 7.5', 'rin: 9', 'rin: 7.1', 'rin: 7.2', 'rin: 7.7', 'rin: 8.9', 'rin: 6.7', 'rin: 6', 'rin: 8.4', 'rin: 7.3', 'rin: 8', 'rin: 9.1', 'rin: 7.9', 'rin: 9.7', 'rin: 9.2', 'rin: 6.5', 'rin: 7', 'rin: 7.6', 'rin: 6.6', 'rin: 5.4', 'rin: 5.6'],
    '1': ['ocd: ED', 'ocd: Control', 'ocd: OCD'],
    '2': ['rinmatched: 1', 'rinmatched: 0'],
    '3': ['dx: Bipolar', 'dx: Control', 'dx: MDD', 'dx: Tics', 'dx: OCD', 'dx: ED'],
    '4': ['ph: 6.18', 'ph: 6.59', 'ph: 6.37', 'ph: 6.6', 'ph: 6.38', 'ph: 6.02', 'ph: 6.87', 'ph: 6.95', 'ph: 6.82', 'ph: 6.27', 'ph: 6.53', 'ph: 6.55', 'ph: 6', 'ph: 6.13', 'ph: 6.08', 'ph: 6.29', 'ph: 6.98', 'ph: 5.91', 'ph: 6.06', 'ph: 6.9', 'ph: 6.83', 'ph: 6.36', 'ph: 6.84', 'ph: 6.74', 'ph: 6.28', 'ph: 6.49', 'ph: 6.7', 'ph: 6.63', 'ph: 6.48', 'ph: 6.62'],
    '5': ['age: 50.421917', 'age: 27.49863', 'age: 30.627397', 'age: 61.167123', 'age: 32.69589', 'age: 39.213698', 'age: 58.605479', 'age: 49.2', 'age: 41.041095', 'age: 51.750684', 'age: 50.89863', 'age: 26.745205', 'age: 29.104109', 'age: 39.301369', 'age: 48.978082', 'age: 57.884931', 'age: 28.364383', 'age: 24.041095', 'age: 19.268493', 'age: 27.230136', 'age: 46.605479', 'age: 23.443835', 'age: 51.038356', 'age: 39.663013', 'age: 46.109589', 'age: 77.989041', 'age: 46.967123', 'age: 63.241095', 'age: 62.306849', 'age: 83.641095'],
    '6': ['pmi: 27', 'pmi: 19.5', 'pmi: 71.5', 'pmi: 22.5', 'pmi: 64', 'pmi: 28', 'pmi: 18', 'pmi: 29', 'pmi: 49', 'pmi: 13', 'pmi: 26.5', 'pmi: 16.5', 'pmi: 35', 'pmi: 19', 'pmi: 20.5', 'pmi: 9.5', 'pmi: 65.5', 'pmi: 68', 'pmi: 17.5', 'pmi: 44', 'pmi: 34', 'pmi: 21.5', 'pmi: 67.5', 'pmi: 26', 'pmi: 46.5', 'pmi: 33.5', 'pmi: 24.5', 'pmi: 30.5', 'pmi: 29.5', 'pmi: 51.5'],
    '7': ['Sex: F', 'Sex: M'],
    '8': ['race: CAUC'],
    '9': ['batch1: 16', 'batch1: 18', 'batch1: 19', 'batch1: 20', 'batch1: 21', 'batch1: 9', 'batch1: 10', 'batch1: 12', 'batch1: 14', 'batch1: 23', 'batch1: 24', 'batch1: 25', 'batch1: 26', 'batch1: 27', 'batch1: 29', 'batch1: 33', 'batch1: 32', 'batch1: 31', 'batch1: 36', 'batch1: 37', 'batch1: 38', 'batch1: 39', 'batch1: 40', 'batch1: 41', 'batch1: 42', 'batch1: 44', 'batch1: 45', 'batch1: 48', 'batch1: 53', 'batch1: 59']
}

clinical_data = pd.DataFrame(sample_characteristics)

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_
print("Step 3 cannot be implemented without the output from the previous step that contains:")
print("1. Sample characteristics dictionary")
print("2. Background information about the dataset")
print("3. Preview of the clinical data")
print("\nPlease provide this information to proceed with proper data analysis.")
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# The IDs start with "ILMN_" which indicates these are Illumina probe IDs
# These need to be mapped to official human gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# 1. Identify mapping columns:
# 'ID' in annotation matches the probe IDs like 'ILMN_1343291' in gene expression data
# 'Symbol' contains the gene symbols we want to map to

# 2. Extract mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

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

# Inspect the result
print("Mapped gene expression data shape:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
print("\nFirst 20 mapped gene symbols:")
print(gene_data.index[:20])
# Re-run gene mapping to restore gene_data
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
gene_data = apply_gene_mapping(expression_df, mapping_df) 

# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save normalized gene data 
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# Get clinical features from sample characteristics
trait_row = 3  # From 'dx' field

def convert_trait(value):
    if not isinstance(value, str):
        return None
    val = value.split(": ")[-1]
    # Anxiety disorder can be comorbid with OCD, so consider OCD cases as anxiety cases
    if val in ["OCD", "Tics"]:
        return 1
    elif val == "Control":
        return 0
    return None

age_row = 5

def convert_age(value):
    if not isinstance(value, str):
        return None
    try:
        return float(value.split(": ")[-1])
    except:
        return None

gender_row = 7

def convert_gender(value):
    if not isinstance(value, str):
        return None
    val = value.split(": ")[-1]
    if val == "F":
        return 0
    elif val == "M":
        return 1
    return None

# Extract clinical features and save
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)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

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

# 4. Determine if features are biased
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort 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=linked_data,
    note="Gene expression data successfully mapped and linked with clinical features"
)

# 6. Save linked data only if usable AND trait is not biased
if is_usable and not is_trait_biased:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get genetic data and map to genes
gene_data = get_genetic_data(matrix_file)
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
gene_data = apply_gene_mapping(gene_data, mapping_df)

# 1. Normalize gene symbols and save
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
try:
    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

    # 4. Determine if features are biased
    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Validate and save cohort 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=linked_data,
        note="Gene expression data successfully mapped and linked with clinical features"
    )

    # 6. Save linked data if usable
    if is_usable and not is_trait_biased:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)

except Exception as e:
    print(f"Error in data linking and processing: {str(e)}")
    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=True,
        df=pd.DataFrame(),
        note=f"Data processing failed: {str(e)}"
    )
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Yes, based on the background info showing Illumina HumanHT-12 microarray data
is_gene_available = True

# 2.1 Data Availability & 2.2 Data Type Conversion
# Trait: Available in feature 1 'ocd' with values like 'ED', 'Control', 'OCD'
trait_row = 1
def convert_trait(value):
    # Since we're looking for anxiety disorder, OCD patients are the cases
    if not value or ':' not in value:
        return None
    val = value.split(':')[1].strip()
    if val == 'OCD':
        return 1
    elif val == 'Control':
        return 0
    return None  # Other values like 'ED' are not relevant

# Age: Available in feature 5
age_row = 5
def convert_age(value):
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(':')[1].strip())
    except:
        return None

# Gender: Available in feature 7 'Sex'
gender_row = 7
def convert_gender(value):
    if not value or ':' not in value:
        return None
    val = value.split(':')[1].strip()
    if val == 'F':
        return 0
    elif val == 'M':
        return 1
    return None

# 3. 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. Clinical Feature Extraction
selected_clinical = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                               age_row, convert_age,
                                               gender_row, convert_gender)

print("Preview of selected clinical features:")
print(preview_df(selected_clinical))

selected_clinical.to_csv(out_clinical_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save normalized gene data
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
try:
    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

    # 4. Determine if features are biased
    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Validate and save cohort 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=linked_data,
        note="Gene expression data successfully mapped and linked with clinical features"
    )

    # 6. Save linked data only if usable AND trait is not biased
    if is_usable and not is_trait_biased:
        linked_data.to_csv(out_data_file)

except Exception as e:
    print(f"Error in data linking and processing: {str(e)}")
    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=True,
        df=pd.DataFrame(),
        note=f"Data processing failed: {str(e)}"
    )