Utilities

class AttrDict(dict):
    """Dictionary with attribute-style access and path-based operations."""
    
    def __init__(self, *args, **kwargs):
        """Initialize AttrDict from dict or keyword arguments."""
    
    def get_by_path(self, path: str, default=None):
        """
        Get value using dot-separated path.
        
        Parameters:
        - path: Dot-separated path string (e.g., 'address.0.city')
        - default: Value to return if path not found
        
        Returns:
        Value at path or default
        """

class SearchList(list):
    """List with path-based access capabilities."""
    
    def get_by_path(self, path: str, default=None):
        """
        Get value using dot-separated path.
        
        Parameters:
        - path: Dot-separated path string
        - default: Value to return if path not found
        
        Returns:
        Value at path or default
        """

def chunks(lst: list, n: int) -> Generator:
    """
    Split list into chunks of specified size.
    
    Parameters:
    - lst: List to split
    - n: Chunk size
    
    Yields:
    Sublists of size n (last chunk may be smaller)
    """

def unique_everseen(seq: list) -> list:
    """
    Get unique items from sequence, preserving order.
    
    Parameters:
    - seq: Input sequence
    
    Returns:
    List with unique items in original order
    """

def parse_path(path: str) -> list:
    """
    Parse dot-separated path into list of keys.
    
    Parameters:
    - path: Dot-separated path string
    
    Returns:
    List of path components
    """

def get_by_path(obj, path: list, default=None):
    """
    Get value from nested object using path list.
    
    Parameters:
    - obj: Object to traverse
    - path: List of keys/indices
    - default: Value to return if path not found
    
    Returns:
    Value at path or default
    """

def set_by_path(obj, path: str, value):
    """
    Set value in nested object using dot-separated path.
    
    Parameters:
    - obj: Object to modify
    - path: Dot-separated path string
    - value: Value to set
    """

def convert_values(data, fn):
    """
    Recursively convert data values using provided function.
    
    Parameters:
    - data: Data structure to convert (dict, list, or other)
    - fn: Function that takes a value and returns (converted_value, stop_flag)
    
    Returns:
    Converted data structure with fn applied recursively
    """

def encode_params(params: dict) -> str:
    """
    Encode parameters for URL query string.
    
    Parameters:
    - params: Dictionary of parameters
    
    Returns:
    URL-encoded query string
    """

def format_date_time(date: datetime) -> str:
    """
    Format datetime for FHIR (ISO 8601 format).
    
    Parameters:
    - date: Python datetime object
    
    Returns:
    FHIR-formatted datetime string (YYYY-MM-DDTHH:MM:SSZ)
    """

def format_date(date: date) -> str:
    """
    Format date for FHIR.
    
    Parameters:
    - date: Python date object
    
    Returns:
    FHIR-formatted date string (YYYY-MM-DD)
    """

def transform_param(param: str) -> str:
    """
    Transform parameter name for FHIR (underscore to dash).
    
    Parameters:
    - param: Parameter name with underscores
    
    Returns:
    Parameter name with dashes (preserves leading underscore/dot)
    """

def transform_value(value) -> str:
    """
    Transform value for FHIR search parameters.
    
    Handles:
    - datetime objects → ISO format strings
    - date objects → YYYY-MM-DD strings  
    - bool → 'true'/'false' strings
    - BaseResource/BaseReference → reference strings
    
    Parameters:
    - value: Value to transform
    
    Returns:
    String representation suitable for FHIR
    """

from fhirpy.base.utils import AttrDict, get_by_path, set_by_path, parse_path

def path_access_examples():
    # AttrDict with attribute access
    patient_data = AttrDict({
        'name': [
            {'family': 'Doe', 'given': ['John', 'William']},
            {'family': 'Smith', 'given': ['Johnny'], 'use': 'nickname'}
        ],
        'address': [{
            'line': ['123 Main St', 'Apt 4B'],
            'city': 'Springfield',
            'postalCode': '12345'
        }],
        'telecom': [
            {'system': 'phone', 'value': '555-1234'},
            {'system': 'email', 'value': 'john@example.com'}
        ]
    })
    
    # Access using paths
    family_name = patient_data.get_by_path('name.0.family')      # 'Doe'
    first_given = patient_data.get_by_path('name.0.given.0')     # 'John'
    street = patient_data.get_by_path('address.0.line.0')        # '123 Main St'
    phone = patient_data.get_by_path('telecom.0.value')          # '555-1234'
    
    # Safe access with defaults
    fax = patient_data.get_by_path('telecom.2.value', 'No fax')  # 'No fax'
    
    print(f"Patient: {first_given} {family_name}")
    print(f"Address: {street}, {patient_data.get_by_path('address.0.city')}")
    
    # Attribute-style access
    print(f"Name array: {patient_data.name}")
    print(f"City: {patient_data.address[0]['city']}")

from fhirpy.base.utils import chunks, unique_everseen, set_by_path

def data_manipulation_examples():
    # Split large datasets into manageable chunks
    patient_ids = [f"patient-{i}" for i in range(1, 101)]  # 100 patient IDs
    
    # Process in chunks of 20
    for chunk in chunks(patient_ids, 20):
        print(f"Processing batch of {len(chunk)} patients")
        # Process chunk...
    
    # Remove duplicates while preserving order
    search_params = ['name', 'birthdate', 'gender', 'name', 'active', 'gender']
    unique_params = unique_everseen(search_params)
    print(f"Unique parameters: {unique_params}")  # ['name', 'birthdate', 'gender', 'active']
    
    # Modify nested data structures
    patient_data = {'name': [{'family': 'Doe'}]}
    set_by_path(patient_data, 'name.0.given', ['John'])
    set_by_path(patient_data, 'active', True)
    set_by_path(patient_data, 'address.0.city', 'Springfield')  # Creates nested structure
    
    print(f"Modified patient: {patient_data}")

import datetime
from fhirpy.base.utils import transform_param, transform_value, format_date_time, format_date

def parameter_transformation_examples():
    # Parameter name transformation
    fhir_param = transform_param('general_practitioner')  # 'general-practitioner'
    preserved = transform_param('_id')                    # '_id' (unchanged)
    dot_param = transform_param('.effectiveDate')         # '.effectiveDate' (unchanged)
    
    print(f"Transformed: {fhir_param}")
    
    # Value transformation for search parameters
    now = datetime.datetime(2024, 1, 15, 10, 30, 0, tzinfo=datetime.timezone.utc)
    today = datetime.date(2024, 1, 15)
    
    datetime_str = transform_value(now)      # '2024-01-15T10:30:00Z'
    date_str = transform_value(today)        # '2024-01-15'
    bool_str = transform_value(True)         # 'true'
    false_str = transform_value(False)       # 'false'
    regular_str = transform_value('text')    # 'text'
    
    print(f"DateTime: {datetime_str}")
    print(f"Date: {date_str}")
    print(f"Boolean: {bool_str}")
    
    # Direct date formatting
    fhir_datetime = format_date_time(now)    # '2024-01-15T10:30:00Z'
    fhir_date = format_date(today)           # '2024-01-15'

from fhirpy.base.utils import encode_params

def parameter_encoding_examples():
    # Simple parameters
    params1 = {'name': ['Smith'], 'active': ['true']}
    query1 = encode_params(params1)  # 'name=Smith&active=true'
    
    # Multiple values for same parameter
    params2 = {'status:not': ['active', 'entered-in-error']}
    query2 = encode_params(params2)  # 'status:not=active&status:not=entered-in-error'
    
    # Complex parameters with modifiers
    params3 = {
        'name:contains': ['John'],
        'birthdate:ge': ['1990-01-01'],
        'address-city': ['Springfield', 'Boston']
    }
    query3 = encode_params(params3)
    
    print(f"Simple: {query1}")
    print(f"Multiple: {query2}")
    print(f"Complex: {query3}")

from fhirpy.base.utils import AttrDict, SearchList
import json

async def search_results_processing():
    # Simulate FHIR Bundle response
    bundle_data = {
        'resourceType': 'Bundle',
        'total': 3,
        'entry': [
            {
                'resource': {
                    'resourceType': 'Patient',
                    'id': '1',
                    'name': [{'family': 'Doe', 'given': ['John']}],
                    'address': [{'city': 'Springfield'}]
                }
            },
            {
                'resource': {
                    'resourceType': 'Patient', 
                    'id': '2',
                    'name': [{'family': 'Smith', 'given': ['Jane']}],
                    'address': [{'city': 'Boston'}]
                }
            }
        ]
    }
    
    # Convert to AttrDict for easier access
    bundle = AttrDict(bundle_data)
    
    # Extract patient data with path operations
    patients = SearchList()
    for entry in bundle.entry:
        patient = AttrDict(entry.resource)
        patients.append(patient)
    
    # Process patients using path-based access
    for patient in patients:
        name = patient.get_by_path('name.0.family')
        given = patient.get_by_path('name.0.given.0')
        city = patient.get_by_path('address.0.city', 'Unknown')
        
        print(f"Patient {patient.id}: {given} {name} from {city}")
    
    # Aggregate data
    cities = [p.get_by_path('address.0.city') for p in patients if p.get_by_path('address.0.city')]
    unique_cities = unique_everseen(cities)
    print(f"Cities represented: {unique_cities}")

from fhirpy import AsyncFHIRClient
from fhirpy.base.utils import chunks, transform_value
import datetime

async def utility_integration_example():
    client = AsyncFHIRClient('https://hapi.fhir.org/baseR4')
    
    # Batch process large number of patients
    patient_ids = [f"patient-{i}" for i in range(1, 101)]
    
    all_patients = []
    for id_chunk in chunks(patient_ids, 10):  # Process 10 at a time
        # Build search for this chunk
        id_query = '|'.join(id_chunk)  # FHIR OR syntax
        
        patients = await (client.resources('Patient')
                         .search(_id=id_query)
                         .fetch())
        all_patients.extend(patients)
    
    print(f"Processed {len(all_patients)} patients")
    
    # Use date transformation for search
    cutoff_date = datetime.date(2020, 1, 1)
    recent_patients = await (client.resources('Patient')
                            .search(birthdate__ge=transform_value(cutoff_date))
                            .fetch())
    
    print(f"Found {len(recent_patients)} patients born after {cutoff_date}")
    
    # Process results with path operations
    for patient in recent_patients:
        birth_date = patient.get_by_path('birthDate')
        name = patient.get_by_path('name.0.family', 'Unknown')
        print(f"{name}: {birth_date}")