diff --git a/2025/solid/.gitignore b/2025/solid/.gitignore new file mode 100644 index 0000000..015bd64 --- /dev/null +++ b/2025/solid/.gitignore @@ -0,0 +1,3 @@ +output/ +logs/ +sales_report.json \ No newline at end of file diff --git a/2025/solid/README.md b/2025/solid/README.md new file mode 100644 index 0000000..d33dd14 --- /dev/null +++ b/2025/solid/README.md @@ -0,0 +1,97 @@ +# Sales Report — Multi-Paradigm Code Showcase + +**Purpose:** Demonstrates how the same business logic (a simple sales performance report) +can be implemented using multiple programming paradigms and architectural styles in Python. + +--- + +## Project Overview + +All implementations compute the same core metrics from `sales_data.csv`: + +- Number of customers +- Average order value (pre-tax) +- Percentage of returns +- Total sales in period (pre-tax) + +Each version differs only in *structure* and *paradigm emphasis*. + +| Paradigm | File | Highlights | +|-----------|------|------------| +| Procedural / Baseline | `messy_report.py` | Imperative style, easy to follow but not modular | +| Object-Oriented (SOLID) | Legacy: `class_based_report.py`
Enhanced: `class_based_report_v2.py` | Classes and interfaces; SRP and OCP applied with improved logging/robustness | +| Functional | Legacy: `functional_report.py`
Enhanced: `functional_report_v2.py` | Pure transformations with v2 adding structured logging and stronger error handling | +| Declarative Pipeline | `declarative_report.py` | Type-checked pipelines using Pandera | +| Config-Driven | `config_report.py` | YAML configuration defines logic dynamically | +| Asynchronous | `async_report.py` | Concurrent metric computation and async I/O | +| Async Streaming (No Pandas) | `async_no_pandas_report.py` | True non-blocking CSV streaming with aiofiles | +| Dataflow / DAG | `report_dataflow.py` | Declarative dependency graph with explicit dataflow | +| Actor Model | `report_actor_model.py` | Cooperative message-passing actors with isolated state | +| Reactive | `reactive_report.py` | RxPY stream-based reporting | +| Logic / Relational | `logic_report.py` | Relational facts and symbolic reasoning via Kanren | + +> The legacy functional and class-based versions match the walkthrough in the video. The `_v2` editions layer in richer logging, error handling, and filesystem conventions while preserving the same outputs. + +--- + +## Usage + +### 1. Setup Environment + +This project uses [uv](https://docs.astral.sh/uv/) for dependency management. Install it if you have not already, then run: + +```bash +uv sync +``` + +### 2. Run Examples + +#### Option A — Helper Script + +From this directory you can execute one or more implementations with the helper script: + +```bash +./run_reports.sh --list # view available keys +./run_reports.sh --run functional --run logic +./run_reports.sh --run-all +``` + +The script uses `uv run` automatically when `uv` is installed (recommended). Use `--dry-run` to preview the commands without executing them. + +#### Option B — Manual Commands + +Use `uv run` to execute any of the implementations inside the managed environment: + +```bash +uv run python logic_report.py +uv run python functional_report.py +uv run python functional_report_v2.py +uv run python async_report.py +uv run python async_no_pandas_report.py +uv run python report_dataflow.py +uv run python report_actor_model.py +uv run python class_based_report.py +uv run python class_based_report_v2.py +uv run python reactive_report.py +# etc. +``` + +### 3. Validate Outputs + +After running one or more implementations, verify the generated JSON payloads agree: + +```bash +uv run python verify_reports.py # compare every file to the first baseline +uv run python verify_reports.py --verbose +uv run python verify_reports.py --baseline sales_report.json +``` + +The script checks for consistent keys and values (within a configurable tolerance) across every report implementation. A non-zero exit code indicates a mismatch. + +## Educational Goal + +This project illustrates how: + +- The same logic can map into multiple thought models (OOP, FP, Async, Logic). +- Paradigm choice affects extensibility, readability, and reasoning complexity. +- Abstraction boundaries (metrics, config, I/O) remain constant across paradigms. diff --git a/2025/solid/async_no_pandas_report.py b/2025/solid/async_no_pandas_report.py new file mode 100644 index 0000000..bfe0926 --- /dev/null +++ b/2025/solid/async_no_pandas_report.py @@ -0,0 +1,157 @@ +""" +=============================================================================== +Sales Report - True Async Streaming Implementation +=============================================================================== +Fully asynchronous, non-blocking reporting pipeline: + * Reads CSV line by line using aiofiles (no pandas blocking) + * Parses each record asynchronously using csv.DictReader + * Aggregates metrics incrementally (no in-memory dataset) + * Writes JSON output asynchronously + +This version is a true example of async I/O — no fake async via thread executors. +Perfect for large files or integration into larger async systems. +=============================================================================== +""" + +import aiofiles +import asyncio +import csv +import json +import logging +from datetime import datetime +from pathlib import Path +from typing import Any + +# ------------------------------------------------------------------------------ +# Logging Configuration +# ------------------------------------------------------------------------------ + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] [async-true] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) + +# ------------------------------------------------------------------------------ +# Constants +# ------------------------------------------------------------------------------ + +BASE_DIR = Path(__file__).parent +INPUT_FILE = BASE_DIR / "sales_data.csv" +OUTPUT_DIR = BASE_DIR / "output" +OUTPUT_DIR.mkdir(exist_ok=True) +OUTPUT_FILE = OUTPUT_DIR / "sales_report_async_true.json" +REPORT_START = datetime(2024, 1, 1) +REPORT_END = datetime(2024, 12, 31) + + +# ------------------------------------------------------------------------------ +# Async Data Processing +# ------------------------------------------------------------------------------ + + +async def parse_and_aggregate_csv( + file_path: Path, + start: datetime | None = None, + end: datetime | None = None, +) -> dict[str, Any]: + """ + Asynchronously reads and aggregates sales data. + Computes metrics incrementally to avoid loading entire file in memory. + """ + + num_customers: set[str] = set() + total_sales = 0.0 + positive_sales: list[float] = [] + total_records = 0 + total_returns = 0 + + logger.info("Reading asynchronously from %s", file_path) + + async with aiofiles.open(file_path, mode="r", encoding="utf-8") as afp: + # Read header line for DictReader initialization + header_line = await afp.readline() + headers = header_line.strip().split(",") + reader = csv.DictReader([], + fieldnames=[h.strip('"') for h in headers]) + + # Process line by line + async for line in afp: + if not line.strip(): + continue + + values = list(csv.reader([line]))[0] + record = dict(zip(reader.fieldnames, values)) + + try: + sale_date = datetime.fromisoformat(record["date"].strip('"')) + price = float(record["price"]) + name = record["name"].strip('"') + except Exception: + # Skip badly formed lines safely + continue + + if start and sale_date < start or end and sale_date > end: + continue + + total_records += 1 + num_customers.add(name) + total_sales += price + + if price > 0: + positive_sales.append(price) + elif price < 0: + total_returns += 1 + + avg_order_value = sum(positive_sales) / len(positive_sales) if positive_sales else 0.0 + percentage_returns = ( + (total_returns / total_records) * 100 if total_records else 0.0 + ) + + logger.info( + "Processed %d records (%d customers, %.2f avg order)", + total_records, + len(num_customers), + avg_order_value, + ) + + return { + "number_of_customers": len(num_customers), + "average_order_value (pre-tax)": round(avg_order_value, 2), + "percentage_of_returns": round(percentage_returns, 2), + "total_sales_in_period (pre-tax)": round(total_sales, 2), + "report_start": start.strftime("%Y-%m-%d") if start else "N/A", + "report_end": end.strftime("%Y-%m-%d") if end else "N/A", + } + + +async def write_report(report: dict[str, Any], file_path: Path) -> None: + """Writes the computed report asynchronously to JSON.""" + + async with aiofiles.open(file_path, mode="w", encoding="utf-8") as afp: + await afp.write(json.dumps(report, indent=2)) + logger.info("Async streaming report written to %s", file_path) + + +# ------------------------------------------------------------------------------ +# Entrypoint +# ------------------------------------------------------------------------------ + + +async def main() -> None: + logger.info("Starting true asynchronous sales report pipeline...") + + try: + report_data = await parse_and_aggregate_csv( + INPUT_FILE, REPORT_START, REPORT_END + ) + await write_report(report_data, OUTPUT_FILE) + logger.info("Report generation completed successfully.") + except Exception as exc: + logger.exception("Async streaming report failed: %s", exc) + raise SystemExit(1) from exc + + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/2025/solid/async_report.py b/2025/solid/async_report.py new file mode 100644 index 0000000..3231ae5 --- /dev/null +++ b/2025/solid/async_report.py @@ -0,0 +1,188 @@ +""" +=============================================================================== +Sales Report - Asynchronous Implementation +=============================================================================== +Uses asyncio to concurrently compute metrics and handle async I/O operations. + +Highlights: + * Concurrent execution of metric computations + * Async-friendly structure (could be extended for DB/API operations) +=============================================================================== +""" + +import asyncio +import json +import logging +from collections.abc import Awaitable +from dataclasses import dataclass +from datetime import datetime +from pathlib import Path +from typing import Any + +import aiofiles # Async file I/O +import pandas as pd + +# ------------------------------------------------------------------------------ +# Logging Configuration +# ------------------------------------------------------------------------------ + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] [async] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) + +# ------------------------------------------------------------------------------ +# Constants +# ------------------------------------------------------------------------------ + +BASE_DIR = Path(__file__).parent +INPUT_FILE = BASE_DIR / "sales_data.csv" +OUTPUT_DIR = BASE_DIR / "output" +OUTPUT_DIR.mkdir(exist_ok=True) +OUTPUT_FILE = OUTPUT_DIR / "sales_report_async.json" +REPORT_START = datetime(2024, 1, 1) +REPORT_END = datetime(2024, 12, 31) + +# ------------------------------------------------------------------------------ +# Data and Config +# ------------------------------------------------------------------------------ + + +@dataclass +class ReportConfig: + """Configuration for asynchronous report generation.""" + + input_file: Path + output_file: Path + start_date: datetime | None = None + end_date: datetime | None = None + + +# ------------------------------------------------------------------------------ +# Async Helpers +# ------------------------------------------------------------------------------ + + +async def read_sales(file: Path) -> pd.DataFrame: + """Simulates asynchronous file read (e.g., using aiofiles or async API).""" + logger.info("Reading data asynchronously from %s", file) + # Pandas isn't async, but this placeholder represents async I/O in real case + loop = asyncio.get_running_loop() + df = await loop.run_in_executor(None, lambda: pd.read_csv(file, parse_dates=["date"])) # pyright: ignore[reportUnknownMemberType] + logger.info("Data loaded successfully: %d rows", len(df)) + return df + + +async def filter_sales( + df: pd.DataFrame, start: datetime | None, end: datetime | None +) -> pd.DataFrame: + """Filters sales asynchronously by date range.""" + + loop = asyncio.get_running_loop() + + def _filter() -> pd.DataFrame: + filtered = df + if start: + filtered = filtered.loc[filtered["date"] >= pd.Timestamp(start)] + if end: + filtered = filtered.loc[filtered["date"] <= pd.Timestamp(end)] + return filtered + + result = await loop.run_in_executor(None, _filter) + logger.info("Filtered dataset to %d records", len(result)) + return result + + +# ------------------------------------------------------------------------------ +# Async Metric Coroutines +# ------------------------------------------------------------------------------ + + +async def customer_count_metric(df: pd.DataFrame) -> dict[str, Any]: + await asyncio.sleep(0.01) + return {"number_of_customers": df["name"].nunique()} + + +async def average_order_value_metric(df: pd.DataFrame) -> dict[str, Any]: + await asyncio.sleep(0.01) + positive_sales = df[df["price"] > 0]["price"] + avg = positive_sales.mean() if not positive_sales.empty else 0.0 + return {"average_order_value (pre-tax)": round(avg, 2)} + + +async def return_percentage_metric(df: pd.DataFrame) -> dict[str, Any]: + await asyncio.sleep(0.01) + returns = df[df["price"] < 0] + pct = (len(returns) / len(df)) * 100 if len(df) else 0.0 + return {"percentage_of_returns": round(pct, 2)} + + +async def total_sales_metric(df: pd.DataFrame) -> dict[str, Any]: + await asyncio.sleep(0.01) + return {"total_sales_in_period (pre-tax)": round(df["price"].sum(), 2)} + + +# ------------------------------------------------------------------------------ +# Report Assembly +# ------------------------------------------------------------------------------ + + +async def compute_metrics( + df: pd.DataFrame, start: datetime | None, end: datetime | None +) -> dict[str, Any]: + """Runs metric computations concurrently using asyncio.gather().""" + + logger.info("Computing metrics concurrently...") + tasks: list[Awaitable[dict[str, Any]]] = [ + customer_count_metric(df), + average_order_value_metric(df), + return_percentage_metric(df), + total_sales_metric(df), + ] + results = await asyncio.gather(*tasks) + + report: dict[str, Any] = {} + for r in results: + report |= r + + report["report_start"] = start.strftime("%Y-%m-%d") if start else "N/A" + report["report_end"] = end.strftime("%Y-%m-%d") if end else "N/A" + + return report + + +async def write_report(report: dict[str, Any], path: Path) -> None: + """Asynchronously writes JSON report to file.""" + async with aiofiles.open(path, "w", encoding="utf-8") as f: + await f.write(json.dumps(report, indent=2)) + logger.info("Async report written to: %s", path) + + +# ------------------------------------------------------------------------------ +# Entrypoint +# ------------------------------------------------------------------------------ + + +async def main() -> None: + config = ReportConfig( + input_file=INPUT_FILE, + output_file=OUTPUT_FILE, + start_date=REPORT_START, + end_date=REPORT_END, + ) + + try: + df = await read_sales(config.input_file) + filtered_df = await filter_sales(df, config.start_date, config.end_date) + report = await compute_metrics(filtered_df, config.start_date, config.end_date) + await write_report(report, config.output_file) + logger.info("Async report generation completed successfully.") + except Exception as e: + logger.exception("Failed during async report generation: %s", e) + raise SystemExit(1) from e + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/2025/solid/class_based_report_v2.py b/2025/solid/class_based_report_v2.py new file mode 100644 index 0000000..a37331d --- /dev/null +++ b/2025/solid/class_based_report_v2.py @@ -0,0 +1,217 @@ +""" +=============================================================================== +Sales Report - Class-Based Implementation +=============================================================================== +Generates a summarized sales performance report using an object-oriented (OOP) +approach that follows SOLID principles. + +Structure: + * CsvSalesReader - Responsible for reading raw CSV data. + * DateRangeFilter - Handles date range filtering. + * Metric classes - Each computes a single metric. + * SalesReportGenerator - Coordinates the workflow. + * JSONReportWriter - Persists report results to JSON. +=============================================================================== +""" + +import json +import logging +from dataclasses import dataclass +from datetime import datetime +from pathlib import Path +from typing import Protocol + +import pandas as pd + +# ------------------------------------------------------------------------------ +# Logging Configuration +# ------------------------------------------------------------------------------ + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) + + +# ------------------------------------------------------------------------------ +# Constants +# ------------------------------------------------------------------------------ + +BASE_DIR = Path(__file__).parent +INPUT_FILE = BASE_DIR / "sales_data.csv" +OUTPUT_DIR = BASE_DIR / "output" +OUTPUT_DIR.mkdir(exist_ok=True) +OUTPUT_FILE = OUTPUT_DIR / "sales_report_class.json" +REPORT_START = datetime(2024, 1, 1) +REPORT_END = datetime(2024, 12, 31) + +# ------------------------------------------------------------------------------ +# Config and Protocols +# ------------------------------------------------------------------------------ + + +@dataclass +class ReportConfig: + """Configuration object controlling report generation parameters.""" + + input_file: Path + output_file: Path + start_date: datetime | None = None + end_date: datetime | None = None + + +class SalesReader(Protocol): + """Interface for reading sales data sources (CSV, DB, etc.).""" + + def read(self, file: Path) -> pd.DataFrame: ... + + +class Metric(Protocol): + """Interface for computing a single metric.""" + + def compute(self, df: pd.DataFrame) -> dict[str, object]: ... + + +# ------------------------------------------------------------------------------ +# Core Classes +# ------------------------------------------------------------------------------ + + +class CsvSalesReader: + """Reads sales input data from a CSV file.""" + + def read(self, file: Path) -> pd.DataFrame: + logger.info("Reading data from %s", file) + return pd.read_csv(file, parse_dates=["date"]) # pyright: ignore[reportUnknownMemberType] + + +class DateRangeFilter: + """Filters sales data between a given start and end date.""" + + def apply(self, df: pd.DataFrame, start: datetime | None, end: datetime | None) -> pd.DataFrame: + logger.info("Applying date filter %s - %s", start or "N/A", end or "N/A") + if start: + df = df.loc[df["date"] >= pd.Timestamp(start)].copy() + if end: + df = df.loc[df["date"] <= pd.Timestamp(end)].copy() + return df + + +# ------------------------------------------------------------------------------ +# Metrics Implementations +# ------------------------------------------------------------------------------ + + +class CustomerCountMetric: + """Counts distinct customers in the dataset.""" + + def compute(self, df: pd.DataFrame) -> dict[str, object]: + return {"number_of_customers": df["name"].nunique()} + + +class AverageOrderValueMetric: + """Computes the average positive order value (pre-tax).""" + + def compute(self, df: pd.DataFrame) -> dict[str, object]: + sales = df.loc[df["price"] > 0, "price"] + avg = sales.mean() if not sales.empty else 0.0 + return {"average_order_value (pre-tax)": round(avg, 2)} + + +class ReturnPercentageMetric: + """Calculates what percentage of sales were returns.""" + + def compute(self, df: pd.DataFrame) -> dict[str, object]: + returns = df[df["price"] < 0] + pct = (len(returns) / len(df)) * 100 if len(df) > 0 else 0 + return {"percentage_of_returns": round(pct, 2)} + + +class TotalSalesMetric: + """Computes the total value of all transactions (pre-tax).""" + + def compute(self, df: pd.DataFrame) -> dict[str, object]: + return {"total_sales_in_period (pre-tax)": round(df["price"].sum(), 2)} + + +# ------------------------------------------------------------------------------ +# Generators and Writers +# ------------------------------------------------------------------------------ + + +class SalesReportGenerator: + """Coordinates the report creation process.""" + + def __init__(self, reader: SalesReader, filterer: DateRangeFilter, metrics: list[Metric]): + self.reader = reader + self.filterer = filterer + self.metrics = metrics + + def generate(self, config: ReportConfig) -> dict[str, object]: + """Executes the complete reporting process: read → filter → compute.""" + + df = self.reader.read(config.input_file) + df = self.filterer.apply(df, config.start_date, config.end_date) + + logger.info("Computing metrics...") + result: dict[str, object] = {} + for metric in self.metrics: + metric_data = metric.compute(df) + result = result | metric_data + + result["report_start"] = ( + config.start_date.strftime("%Y-%m-%d") if config.start_date else "N/A" + ) + result["report_end"] = config.end_date.strftime("%Y-%m-%d") if config.end_date else "N/A" + return result + + +class JSONReportWriter: + """Writes the report dictionary to a JSON file.""" + + def write(self, report: dict[str, object], output_file: Path) -> None: + output_file.parent.mkdir(parents=True, exist_ok=True) + with open(output_file, "w", encoding="utf-8") as f: + json.dump(report, f, indent=2) + logger.info("Report written to %s", output_file) + + +# ------------------------------------------------------------------------------ +# Entrypoint +# ------------------------------------------------------------------------------ + + +def main() -> None: + """Assembles and executes the OOP-style reporting workflow.""" + + config = ReportConfig( + input_file=INPUT_FILE, + output_file=OUTPUT_FILE, + start_date=REPORT_START, + end_date=REPORT_END, + ) + + reader = CsvSalesReader() + filterer = DateRangeFilter() + metrics: list[Metric] = [ + CustomerCountMetric(), + AverageOrderValueMetric(), + ReturnPercentageMetric(), + TotalSalesMetric(), + ] + + generator = SalesReportGenerator(reader, filterer, metrics) + writer = JSONReportWriter() + + try: + report = generator.generate(config) + writer.write(report, config.output_file) + except Exception as e: + logger.exception("Failed to generate report: %s", e) + raise SystemExit(1) from e + + +if __name__ == "__main__": + main() diff --git a/2025/solid/config_report.py b/2025/solid/config_report.py new file mode 100644 index 0000000..3164368 --- /dev/null +++ b/2025/solid/config_report.py @@ -0,0 +1,169 @@ +""" +=============================================================================== +Sales Report - Config-Driven (Declarative) Implementation +=============================================================================== +Loads pipeline configuration from an external YAML/JSON file and runs the +same metric logic dynamically, controlled entirely by configuration content. + +Highlights: + * Decouples logic from configuration + * Allows multiple report variants without code modification +=============================================================================== +""" + +import json +import logging +from collections.abc import Callable +from dataclasses import dataclass +from datetime import datetime +from pathlib import Path +from typing import Any + +import pandas as pd +import yaml + +# ------------------------------------------------------------------------------ +# Logging Setup +# ------------------------------------------------------------------------------ + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] [config] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) + +# ------------------------------------------------------------------------------ +# Constants +# ------------------------------------------------------------------------------ + +BASE_DIR = Path(__file__).parent +CONFIG_FILE = BASE_DIR / "report_config.yaml" +OUTPUT_DIR = BASE_DIR / "output" +OUTPUT_DIR.mkdir(exist_ok=True) + +# ------------------------------------------------------------------------------ +# Metric Functions +# ------------------------------------------------------------------------------ + + +def customer_count(df: pd.DataFrame) -> dict[str, Any]: + """Counts distinct customers.""" + + return {"number_of_customers": df["name"].nunique()} + + +def average_order_value(df: pd.DataFrame) -> dict[str, Any]: + """Calculates average order value.""" + + sales = df[df["price"] > 0]["price"] + avg = sales.mean() if not sales.empty else 0.0 + return {"average_order_value (pre-tax)": round(avg, 2)} + + +def return_percentage(df: pd.DataFrame) -> dict[str, Any]: + """Calculates percentage of returns.""" + + returns = df[df["price"] < 0] + pct = (len(returns) / len(df)) * 100 if len(df) else 0.0 + return {"percentage_of_returns": round(pct, 2)} + + +def total_sales(df: pd.DataFrame) -> dict[str, Any]: + """Calculates total sales.""" + + return {"total_sales_in_period (pre-tax)": round(df["price"].sum(), 2)} + + +METRIC_REGISTRY: dict[str, Callable[[pd.DataFrame], dict[str, Any]]] = { + "customer_count": customer_count, + "average_order_value": average_order_value, + "return_percentage": return_percentage, + "total_sales": total_sales, +} + +# ------------------------------------------------------------------------------ +# Config Schema +# ------------------------------------------------------------------------------ + + +@dataclass +class ReportConfig: + dataset: Path + start_date: datetime | None + end_date: datetime | None + metrics: list[str] + output_file: Path + + +# ------------------------------------------------------------------------------ +# Core Functions +# ------------------------------------------------------------------------------ + + +def load_config(file: Path) -> ReportConfig: + """Loads YAML config and converts to ReportConfig dataclass.""" + with open(file, encoding="utf-8") as f: + data = yaml.safe_load(f) + + return ReportConfig( + dataset=BASE_DIR / data["dataset"], + start_date=datetime.fromisoformat(data["start_date"]), + end_date=datetime.fromisoformat(data["end_date"]), + metrics=data["metrics"], + output_file=BASE_DIR / data["output"], + ) + + +def run_pipeline(config: ReportConfig) -> dict[str, Any]: + """Executes report pipeline as declared in YAML config.""" + logger.info("Loading dataset %s", config.dataset) + df = pd.read_csv(config.dataset, parse_dates=["date"]) # pyright: ignore[reportUnknownMemberType] + + # Apply filters + if config.start_date: + df = df[df["date"] >= pd.Timestamp(config.start_date)] + if config.end_date: + df = df[df["date"] <= pd.Timestamp(config.end_date)] + logger.info("Records after filtering: %d", len(df)) + + # Compute declared metrics dynamically + results: dict[str, Any] = {} + for metric_key in config.metrics: + metric_fn = METRIC_REGISTRY.get(metric_key) + if metric_fn: + results.update(metric_fn(df)) + else: + logger.warning("Unknown metric '%s' skipped.", metric_key) + + results["report_start"] = config.start_date.strftime("%Y-%m-%d") if config.start_date else "N/A" + results["report_end"] = config.end_date.strftime("%Y-%m-%d") if config.end_date else "N/A" + return results + + +def write_report(report: dict[str, Any], output: Path) -> None: + """Writes report to JSON file.""" + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(report, indent=2), encoding="utf-8") + logger.info("Config-based report written to %s", output) + + +# ------------------------------------------------------------------------------ +# Entrypoint +# ------------------------------------------------------------------------------ + + +def main() -> None: + config = load_config(CONFIG_FILE) + logger.info("Running config-driven report using %s", CONFIG_FILE) + try: + result = run_pipeline(config) + write_report(result, config.output_file) + logger.info("Report generated successfully.") + except Exception as e: + logger.exception("Failed to generate report: %s", e) + raise SystemExit(1) from e + + +if __name__ == "__main__": + main() diff --git a/2025/solid/declarative_report.py b/2025/solid/declarative_report.py new file mode 100644 index 0000000..78b469a --- /dev/null +++ b/2025/solid/declarative_report.py @@ -0,0 +1,169 @@ +""" +=============================================================================== +Sales Report - Declarative (Pipeline) Implementation +=============================================================================== +A declarative, type-safe data pipeline using Pandera for runtime validation +and pure function composition. + +Stages: + 1. fetch_data - Extract + validate CSV data + 2. process_data - Transform by filtering rows to time window + 3. build_report - Compute key metrics declaratively + 4. write_report - Serialize results to JSON +=============================================================================== +""" + +import json +import logging +from datetime import datetime +from pathlib import Path +from typing import Any, Protocol + +import pandas as pd +import pandera.pandas as pa +from pandera.typing import DataFrame + +# ------------------------------------------------------------------------------ +# Logging configuration +# ------------------------------------------------------------------------------ + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) + +# ------------------------------------------------------------------------------ +# Schema definition +# ------------------------------------------------------------------------------ + + +class SalesSchema(pa.DataFrameModel): + """Defines the expected structure and types for the input sales data.""" + + date: datetime = pa.Field(coerce=True) + name: str + address: str + item: str + price: float + tax: float + + +# ------------------------------------------------------------------------------ +# Constants +# ------------------------------------------------------------------------------ + +BASE_DIR = Path(__file__).parent +DATA_PATH = BASE_DIR / "sales_data.csv" +OUTPUT_DIR = BASE_DIR / "output" +OUTPUT_DIR.mkdir(exist_ok=True) +REPORT_PATH = OUTPUT_DIR / "sales_report_declarative.json" +REPORT_START_DATE = datetime(2024, 1, 1) +REPORT_END_DATE = datetime(2024, 12, 31) + + +# ------------------------------------------------------------------------------ +# Data Pipeline Steps +# ------------------------------------------------------------------------------ + + +def fetch_data(_: None) -> DataFrame[SalesSchema]: + """Step 1: Read and validate input CSV file.""" + + logger.info("Reading input data from %s", DATA_PATH) + df = pd.read_csv(DATA_PATH, parse_dates=["date"]) # pyright: ignore[reportUnknownMemberType] + validated = SalesSchema.validate(df) + logger.info("Data loaded successfully: %d rows", len(validated)) + return validated + + +def process_data(df: DataFrame[SalesSchema]) -> DataFrame[SalesSchema]: + """Step 2: Filter the dataset to report date range.""" + + logger.info( + "Filtering records between %s and %s", + REPORT_START_DATE.strftime("%Y-%m-%d"), + REPORT_END_DATE.strftime("%Y-%m-%d"), + ) + filtered = df.loc[(df["date"] >= REPORT_START_DATE) & (df["date"] <= REPORT_END_DATE)].copy() + logger.info("Filtered dataset contains %d records", len(filtered)) + return SalesSchema.validate(filtered) + + +def build_report(df: DataFrame[SalesSchema]) -> dict[str, Any]: + """Step 3: Compute key report metrics.""" + + logger.info("Computing report metrics...") + + number_of_customers = int(df["name"].nunique()) + positive_sales = df.loc[df["price"] > 0, "price"] + average_order_value = float(positive_sales.mean()) if not positive_sales.empty else 0.0 + returns = df.loc[df["price"] < 0] + percentage_of_returns = (len(returns) / len(df)) * 100 if len(df) > 0 else 0.0 + total_sales_pre_tax = float(df["price"].sum()) + + report = { + "number_of_customers": number_of_customers, + "average_order_value (pre-tax)": round(average_order_value, 2), + "percentage_of_returns": round(percentage_of_returns, 2), + "total_sales_in_period (pre-tax)": round(total_sales_pre_tax, 2), + "report_start": REPORT_START_DATE.strftime("%Y-%m-%d"), + "report_end": REPORT_END_DATE.strftime("%Y-%m-%d"), + } + logger.info("Completed metric calculations.") + return report + + +def write_report(report: dict[str, Any]) -> None: + """Step 4: Write final JSON report to disk.""" + + REPORT_PATH.write_text(json.dumps(report, indent=2), encoding="utf-8") + print(f"Report written to: {REPORT_PATH}") + + +# ------------------------------------------------------------------------------ +# Declarative Pipeline Orchestrator +# ------------------------------------------------------------------------------ + + +class PipelineStep(Protocol): + """Formal protocol for pipeline-compatible callables.""" + + def __call__(self, __data: Any) -> Any: ... + + +def pipeline(*steps: PipelineStep): + """ + Declaratively compose a pipeline of sequential transformation steps. + + Each step takes the output of the previous one. + The first receives `None` if it doesn't need input. + """ + + def composed(data: Any = None) -> Any: + for step in steps: + data = step(data) + return data + + return composed + + +# ------------------------------------------------------------------------------ +# Entrypoint +# ------------------------------------------------------------------------------ + +if __name__ == "__main__": + logger.info("Starting declarative report pipeline...") + try: + report_pipeline = pipeline( + fetch_data, + process_data, + build_report, + write_report, + ) + report_pipeline(None) + logger.info("Report generation completed successfully.") + except Exception as e: + logger.exception("Failed to generate report: %s", e) + raise SystemExit(1) from e diff --git a/2025/solid/functional_report_v2.py b/2025/solid/functional_report_v2.py new file mode 100644 index 0000000..7709ea8 --- /dev/null +++ b/2025/solid/functional_report_v2.py @@ -0,0 +1,179 @@ +""" +=============================================================================== +Sales Report - Functional Implementation +=============================================================================== +A lightweight, flat functional approach to compute and write a +sales performance report. + +Structure: + * "Pure" functions for reading, filtering, and metrics + * Simple data flow: read → filter → compute → write +=============================================================================== +""" + +import json +import logging +from collections.abc import Callable +from dataclasses import dataclass, field +from datetime import datetime +from pathlib import Path +from typing import Any + +import pandas as pd + +# ------------------------------------------------------------------------------ +# Logging Configuration +# ------------------------------------------------------------------------------ + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) + +# ------------------------------------------------------------------------------ +# Constants +# ------------------------------------------------------------------------------ + +BASE_DIR = Path(__file__).parent +INPUT_FILE = BASE_DIR / "sales_data.csv" +OUTPUT_DIR = BASE_DIR / "output" +OUTPUT_DIR.mkdir(exist_ok=True) +OUTPUT_FILE = OUTPUT_DIR / "sales_report_functional.json" +REPORT_START = datetime(2024, 1, 1) +REPORT_END = datetime(2024, 12, 31) + +# ------------------------------------------------------------------------------ +# Type aliases and config +# ------------------------------------------------------------------------------ + +type MetricFn = Callable[[pd.DataFrame], dict[str, Any]] + + +@dataclass +class ReportConfig: + """Encapsulates configuration for the functional report process.""" + + input_file: Path + start_date: datetime | None = None + end_date: datetime | None = None + metrics: list[MetricFn] = field(default_factory=list) + + +# ------------------------------------------------------------------------------ +# Functional Components +# ------------------------------------------------------------------------------ + + +def read_sales(file: Path) -> pd.DataFrame: + """Reads the sales CSV data into a pandas DataFrame.""" + + logger.info("Reading data from %s", file) + return pd.read_csv(file, parse_dates=["date"]) # pyright: ignore[reportUnknownMemberType] + + +def filter_sales(df: pd.DataFrame, start: datetime | None, end: datetime | None) -> pd.DataFrame: + """Applies optional start/end-date filters to the dataframe.""" + + if start: + df = df[df["date"] >= pd.Timestamp(start)] + if end: + df = df[df["date"] <= pd.Timestamp(end)] + + logger.info("Filtered dataset contains %d records", len(df)) + return df + + +# ------------------------------------------------------------------------------ +# Metrics Functions +# ------------------------------------------------------------------------------ + + +def customer_count_metric(df: pd.DataFrame) -> dict[str, Any]: + """Counts distinct customers.""" + + return {"number_of_customers": df["name"].nunique()} + + +def average_order_value_metric(df: pd.DataFrame) -> dict[str, Any]: + """Computes the average value of positive (non-return) transactions.""" + + sales = df[df["price"] > 0]["price"] + avg = sales.mean() if not sales.empty else 0.0 + return {"average_order_value (pre-tax)": round(avg, 2)} + + +def return_percentage_metric(df: pd.DataFrame) -> dict[str, Any]: + """Calculates the percentage of rows representing returns.""" + + returns = df[df["price"] < 0] + pct = (len(returns) / len(df)) * 100 if len(df) > 0 else 0 + return {"percentage_of_returns": round(pct, 2)} + + +def total_sales_metric(df: pd.DataFrame) -> dict[str, Any]: + """Computes total sales before tax.""" + + return {"total_sales_in_period (pre-tax)": round(df["price"].sum(), 2)} + + +# ------------------------------------------------------------------------------ +# Reporting Pipeline +# ------------------------------------------------------------------------------ + + +def generate_report_data(df: pd.DataFrame, config: ReportConfig) -> dict[str, Any]: + """Executes all metrics and produces a report dictionary.""" + + logger.info("Computing metrics...") + result: dict[str, Any] = {} + for metric in config.metrics: + result.update(metric(df)) + result["report_start"] = config.start_date.strftime("%Y-%m-%d") if config.start_date else "N/A" + result["report_end"] = config.end_date.strftime("%Y-%m-%d") if config.end_date else "N/A" + + logger.info("Metrics computed successfully.") + return result + + +def write_report(data: dict[str, Any], file: Path) -> None: + """Writes the final report JSON to disk.""" + + file.parent.mkdir(parents=True, exist_ok=True) + file.write_text(json.dumps(data, indent=2), encoding="utf-8") + logger.info("Report written to %s", file) + + +# ------------------------------------------------------------------------------ +# Entrypoint +# ------------------------------------------------------------------------------ + + +def main() -> None: + """Runs the full functional data pipeline.""" + + config = ReportConfig( + input_file=INPUT_FILE, + start_date=REPORT_START, + end_date=REPORT_END, + metrics=[ + customer_count_metric, + average_order_value_metric, + return_percentage_metric, + total_sales_metric, + ], + ) + + try: + df = read_sales(config.input_file) + df = filter_sales(df, config.start_date, config.end_date) + report = generate_report_data(df, config) + write_report(report, OUTPUT_FILE) + except Exception as e: + logger.exception("Failed to generate report: %s", e) + raise SystemExit(1) from e + + +if __name__ == "__main__": + main() diff --git a/2025/solid/logic_report.py b/2025/solid/logic_report.py new file mode 100644 index 0000000..7f4dd21 --- /dev/null +++ b/2025/solid/logic_report.py @@ -0,0 +1,232 @@ +""" +=============================================================================== +Sales Report — Logic-Based Version +=============================================================================== +Implements the same declarative logic reporting system, but without relying on +numeric constraint solvers. Instead, we encode the idea of "positive" and +"negative" prices as pure logic facts. + +All reasoning is relational and declarative; no imperative filtering is used. +=============================================================================== +""" + +import json +import logging +from collections.abc import Callable, Sequence +from dataclasses import dataclass +from datetime import datetime +from pathlib import Path +from typing import Any, Literal, cast + +import pandas as pd +from kanren import Relation, Var, var +from kanren import conde as _conde # pyright: ignore[reportUnknownVariableType] +from kanren import facts as _facts # pyright: ignore[reportUnknownVariableType] +from kanren import run as _run # pyright: ignore[reportUnknownVariableType] + +type Goal = Any +type LogicVar = Var | Any +type Sign = Literal["positive", "negative", "zero"] +type ProfitRecord = tuple[str, float] +type MetricValue = float | int | str +type ReportDict = dict[str, MetricValue] + +CondeFunc = Callable[..., Goal] +RunFunc = Callable[..., Sequence[Any] | tuple[Any, ...]] + +conde: CondeFunc = cast(CondeFunc, _conde) +facts: Callable[..., None] = cast(Callable[..., None], _facts) +run: RunFunc = cast(RunFunc, _run) + +# ----------------------------------------------------------------------------- +# Logging +# ----------------------------------------------------------------------------- +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] [logic‑pure] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) + +# ----------------------------------------------------------------------------- +# Constants +# ----------------------------------------------------------------------------- +BASE_DIR = Path(__file__).parent +DATA_PATH = BASE_DIR / "sales_data.csv" +OUTPUT_DIR = BASE_DIR / "output" +OUTPUT_DIR.mkdir(exist_ok=True) +OUTPUT_FILE = OUTPUT_DIR / "sales_report_logic.json" +REPORT_START = datetime(2024, 1, 1) +REPORT_END = datetime(2024, 12, 31) + + +# ----------------------------------------------------------------------------- +# Data representation +# ----------------------------------------------------------------------------- +@dataclass +class Sale: + name: str + item: str + date: datetime + price: float + tax: float + + +# ----------------------------------------------------------------------------- +# Load CSV data +# ----------------------------------------------------------------------------- +def load_sales(file_path: Path) -> list[Sale]: + df = pd.read_csv(file_path, parse_dates=["date"]) # pyright: ignore + df = df.loc[(df["date"] >= REPORT_START) & (df["date"] <= REPORT_END)] + logger.info("Loaded %d sales within reporting window.", len(df)) + return [ + Sale(row["name"], row["item"], row["date"], row["price"], row["tax"]) + for _, row in df.iterrows() + ] + + +# ----------------------------------------------------------------------------- +# Logic Relations +# ----------------------------------------------------------------------------- + + +def declare_relations(sales: list[Sale]) -> tuple[Relation, Relation]: + """ + Declare logic base facts for sales and price sign. + + sign_rel(price_sign, price) + 'price_sign' is one of {"positive", "negative", "zero"}. + """ + sale_rel = Relation() + sign_rel = Relation() + + # Declare base sale facts + for s in sales: + facts(sale_rel, (s.name, s.item, s.date, s.price, s.tax)) + + # Build pure logic 'sign' facts — not procedural checks within the logic + unique_prices: list[float] = sorted({float(s.price) for s in sales}) + for price_value in unique_prices: + # Outside the logic system, we merely declare new *facts*, + # analogous to telling Prolog that sign(positive, 10.0). is true. + if price_value > 0: + facts(sign_rel, (cast(Sign, "positive"), price_value)) + elif price_value < 0: + facts(sign_rel, (cast(Sign, "negative"), price_value)) + else: + facts(sign_rel, (cast(Sign, "zero"), price_value)) + + logger.info("Declared %d sale facts and %d sign facts.", len(sales), len(unique_prices)) + return sale_rel, sign_rel + + +# ----------------------------------------------------------------------------- +# Logic predicates (pure relations) +# ----------------------------------------------------------------------------- +def sign_rule(sign_rel: Relation, sign: Sign, price: Any) -> Goal: + """Link a price to its logical sign fact.""" + return cast(Goal, sign_rel(sign, price)) + + +def profit_rule(sale_rel: Relation, sign_rel: Relation, name: Any, price: Any) -> Goal: + """Logical predicate for profitable sales (positive price).""" + return conde( + [ + sale_rel(name, var(), var(), price, var()), + sign_rule(sign_rel, cast(Sign, "positive"), price), + ] + ) + + +def return_rule(sale_rel: Relation, sign_rel: Relation, name: Any, price: Any) -> Goal: + """Logical predicate for returned sales (negative price).""" + return conde( + [ + sale_rel(name, var(), var(), price, var()), + sign_rule(sign_rel, cast(Sign, "negative"), price), + ] + ) + + +# ----------------------------------------------------------------------------- +# Logic computations — querying the pure logic model +# ----------------------------------------------------------------------------- +def compute_metrics(sale_rel: Relation, sign_rel: Relation) -> ReportDict: + """Compute metrics via logical queries only.""" + + name: LogicVar = cast(LogicVar, var()) + price: LogicVar = cast(LogicVar, var()) + + profit_raw = cast( + Sequence[tuple[Any, Any]], + run(0, (name, price), profit_rule(sale_rel, sign_rel, name, price)), + ) + return_raw = cast( + Sequence[tuple[Any, Any]], + run(0, (name, price), return_rule(sale_rel, sign_rel, name, price)), + ) + + def _filter_records(records: Sequence[tuple[Any, Any]]) -> list[ProfitRecord]: + filtered: list[ProfitRecord] = [] + for raw_name, raw_price in records: + if isinstance(raw_name, str) and isinstance(raw_price, (int, float)): + filtered.append((raw_name, float(raw_price))) + return filtered + + profit_records = _filter_records(profit_raw) + return_records = _filter_records(return_raw) + + # Summarize results outside logic + profit_customers = {name_value for (name_value, _) in profit_records} + return_customers = {name_value for (name_value, _) in return_records} + profit_prices = [price_value for (_, price_value) in profit_records] + return_prices = [price_value for (_, price_value) in return_records] + + num_customers = len(profit_customers | return_customers) + avg_order_value = sum(profit_prices) / len(profit_prices) if profit_prices else 0.0 + pct_returns = ( + len(return_prices) / (len(profit_prices) + len(return_prices)) * 100 + if (profit_prices or return_prices) + else 0.0 + ) + total_sales = sum(profit_prices) + sum(return_prices) + + return { + "number_of_customers": num_customers, + "average_order_value (pre-tax)": round(avg_order_value, 2), + "percentage_of_returns": round(pct_returns, 2), + "total_sales_in_period (pre-tax)": round(total_sales, 2), + "report_start": REPORT_START.strftime("%Y-%m-%d"), + "report_end": REPORT_END.strftime("%Y-%m-%d"), + } + + +# ----------------------------------------------------------------------------- +# Writer +# ----------------------------------------------------------------------------- +def write_report(report: ReportDict) -> None: + OUTPUT_FILE.write_text(json.dumps(report, indent=2), encoding="utf-8") + logger.info("Logic report written to %s", OUTPUT_FILE) + + +# ----------------------------------------------------------------------------- +# Entrypoint +# ----------------------------------------------------------------------------- +def main() -> None: + logger.info("Running logic sales report ...") + try: + sales = load_sales(DATA_PATH) + if not sales: + logger.warning("No sales data in reporting window.") + return + sale_rel, sign_rel = declare_relations(sales) + report = compute_metrics(sale_rel, sign_rel) + write_report(report) + logger.info("Logic report generated successfully.") + except Exception as exc: + logger.exception("Logic computation failed: %s", exc) + raise SystemExit(1) from exc + + +if __name__ == "__main__": + main() diff --git a/2025/solid/pyproject.toml b/2025/solid/pyproject.toml index 9f9cf8a..8c87fae 100644 --- a/2025/solid/pyproject.toml +++ b/2025/solid/pyproject.toml @@ -5,4 +5,10 @@ description = "Code refactoring using the SOLID principles" requires-python = ">=3.13" dependencies = [ "pandas>=2.3.1", + "ruff>=0.14.4", + "pandera[pandas]>=0.26.1", + "rx>=3.2.0", + "pyyaml>=6.0.3", + "minikanren>=1.0.5", + "aiofiles>=25.1.0", ] diff --git a/2025/solid/reactive_report.py b/2025/solid/reactive_report.py new file mode 100644 index 0000000..3523ec9 --- /dev/null +++ b/2025/solid/reactive_report.py @@ -0,0 +1,143 @@ +""" +=============================================================================== +Sales Report - Reactive (Stream-Based) Implementation +=============================================================================== +Implements a reactive data pipeline using RxPY (Reactive Extensions for Python). + +In a reactive system: + * Data flows through Observables — streams that emit values. + * Transformation steps are implemented as operators (map/filter/aggregate). + * The pipeline is executed by subscribing to the observable chain. + +Stages (mirroring previous paradigms): + 1. fetch_data - Load data from CSV into stream + 2. process_data - Filter by date range + 3. build_report - Compute aggregate metrics + 4. write_report - Persist computed summary to JSON +=============================================================================== +""" + +import json +import logging +from datetime import datetime +from pathlib import Path +from typing import Any + +import pandas as pd +from rx import just +from rx import operators as ops + +# ------------------------------------------------------------------------------ +# Logging Configuration +# ------------------------------------------------------------------------------ + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] [reactive] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) + +# ------------------------------------------------------------------------------ +# Constants +# ------------------------------------------------------------------------------ + +BASE_DIR = Path(__file__).parent +INPUT_FILE = BASE_DIR / "sales_data.csv" +OUTPUT_DIR = BASE_DIR / "output" +OUTPUT_DIR.mkdir(exist_ok=True) +OUTPUT_FILE = OUTPUT_DIR / "sales_report_reactive.json" +REPORT_START = datetime(2024, 1, 1) +REPORT_END = datetime(2024, 12, 31) + +# ------------------------------------------------------------------------------ +# Reactive Pipeline Steps +# ------------------------------------------------------------------------------ + + +def fetch_data(file: Path) -> pd.DataFrame: + """Reads data from CSV into a pandas DataFrame.""" + + logger.info("Reading data from %s", file) + df = pd.read_csv(file, parse_dates=["date"]) # pyright: ignore[reportUnknownMemberType] + logger.info("Data loaded successfully: %d rows", len(df)) + return df + + +def process_data(df: pd.DataFrame) -> pd.DataFrame: + """Filters DataFrame by start and end date.""" + + logger.info( + "Filtering dataset from %s to %s", + REPORT_START.strftime("%Y-%m-%d"), + REPORT_END.strftime("%Y-%m-%d"), + ) + filtered = df.loc[(df["date"] >= REPORT_START) & (df["date"] <= REPORT_END)].copy() + logger.info("Filtered dataset contains %d rows", len(filtered)) + return filtered + + +def build_report(df: pd.DataFrame) -> dict[str, Any]: + """Computes high-level report metrics from the dataset.""" + logger.info("Computing metrics...") + + number_of_customers = int(df["name"].nunique()) + positive_sales = df.loc[df["price"] > 0, "price"] + avg_order_value = positive_sales.mean() if not positive_sales.empty else 0.0 + returns = df.loc[df["price"] < 0] + pct_returns = (len(returns) / len(df)) * 100 if len(df) else 0.0 + total_sales = df["price"].sum() + + report = { + "number_of_customers": number_of_customers, + "average_order_value (pre-tax)": round(avg_order_value, 2), + "percentage_of_returns": round(pct_returns, 2), + "total_sales_in_period (pre-tax)": round(float(total_sales), 2), + "report_start": REPORT_START.strftime("%Y-%m-%d"), + "report_end": REPORT_END.strftime("%Y-%m-%d"), + } + + logger.info("Metric computation complete.") + return report + + +def write_report(report: dict[str, Any]) -> None: + """Writes final report to disk.""" + OUTPUT_FILE.write_text(json.dumps(report, indent=2), encoding="utf-8") + logger.info("Report written to %s", OUTPUT_FILE) + + +# ------------------------------------------------------------------------------ +# Reactive Pipeline Composition +# ------------------------------------------------------------------------------ + + +def reactive_report_pipeline(): + """Builds and executes the reactive data pipeline using RxPY.""" + + return ( + just(INPUT_FILE) + .pipe( + ops.map(fetch_data), # pyright: ignore[reportUnknownMemberType] + ops.map(process_data), # pyright: ignore[reportUnknownMemberType] + ops.map(build_report), # pyright: ignore[reportUnknownMemberType] + ) + .subscribe( + on_next=write_report, + on_error=lambda e: logger.exception("Pipeline failed: %s", e), # pyright: ignore[reportUnknownArgumentType, reportUnknownLambdaType] + on_completed=lambda: logger.info("Pipeline completed successfully."), + ) + ) + + +# ------------------------------------------------------------------------------ +# Entrypoint +# ------------------------------------------------------------------------------ + +if __name__ == "__main__": + logger.info("Starting reactive sales report pipeline...") + try: + reactive_report_pipeline() + except Exception as e: + logger.exception("Unhandled error: %s", e) + raise SystemExit(1) from e diff --git a/2025/solid/report_actor_model.py b/2025/solid/report_actor_model.py new file mode 100644 index 0000000..7eac4a6 --- /dev/null +++ b/2025/solid/report_actor_model.py @@ -0,0 +1,268 @@ +""" +=============================================================================== +Sales Report - Actor Model Implementation +=============================================================================== +Implements the report workflow using the Actor Model: + * Each actor runs concurrently and communicates via message passing. + * Actors never share state; they exchange messages through asyncio queues. + * Demonstrates isolated concurrency with cooperative scheduling. + +Actors: + - ReaderActor : Reads CSV rows asynchronously + - FilterActor : Filters rows by date range + - MetricsActor : Computes aggregate metrics incrementally + - WriterActor : Writes final report to disk + +Highlights: + * Concurrency through message passing (no shared memory) + * Fault isolation between components + * Natural fit for distributed or reactive architectures +=============================================================================== +""" + +import asyncio +import csv +import json +import logging +from dataclasses import dataclass +from datetime import datetime +from pathlib import Path +from typing import Any + +import aiofiles + +# ------------------------------------------------------------------------------ +# Logging Configuration +# ------------------------------------------------------------------------------ + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] [actor] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) + +# ------------------------------------------------------------------------------ +# Constants +# ------------------------------------------------------------------------------ + +BASE_DIR = Path(__file__).parent +INPUT_FILE = BASE_DIR / "sales_data.csv" +OUTPUT_DIR = BASE_DIR / "output" +OUTPUT_DIR.mkdir(exist_ok=True) +OUTPUT_FILE = OUTPUT_DIR / "sales_report_actor.json" +REPORT_START_DATE = datetime(2024, 1, 1) +REPORT_END_DATE = datetime(2024, 12, 31) + +# ------------------------------------------------------------------------------ +# Actor Base Class +# ------------------------------------------------------------------------------ + + +class Actor: + """Base actor defining inbox and cooperative message loop.""" + + def __init__(self, name: str): + self.name = name + self.inbox: asyncio.Queue[Any] = asyncio.Queue() + self._task: asyncio.Task[Any] | None = None + + async def send(self, message: Any) -> None: + """Send a message to this actor's inbox.""" + await self.inbox.put(message) + + async def handle(self, message: Any) -> None: + """Override in subclasses to define message behavior.""" + raise NotImplementedError + + async def run(self) -> None: + """Continuously process messages until STOP signal.""" + logger.info("%s started.", self.name) + while True: + message = await self.inbox.get() + if message == "STOP": + logger.info("%s stopped.", self.name) + return + try: + await self.handle(message) + except Exception as exc: + logger.exception("[%s] Error handling message: %s", self.name, exc) + + def start(self) -> None: + """Launch the actor event loop in background.""" + self._task = asyncio.create_task(self.run(), name=f"{self.name}-task") + + async def stop(self) -> None: + """Send stop signal and await task completion.""" + await self.send("STOP") + if self._task: + await self._task + + +# ------------------------------------------------------------------------------ +# Domain Structures +# ------------------------------------------------------------------------------ + +@dataclass +class SalesRecord: + name: str + date: datetime + price: float + + +# ------------------------------------------------------------------------------ +# Actor Implementations +# ------------------------------------------------------------------------------ + + +class ReaderActor(Actor): + """Reads CSV rows and sends them downstream.""" + + def __init__(self, name: str, file_path: Path, next_actor: Actor): + super().__init__(name) + self.file_path = file_path + self.next_actor = next_actor + + async def handle(self, _: Any) -> None: + logger.info("[%s] Reading file: %s", self.name, self.file_path) + async with aiofiles.open(self.file_path, "r", encoding="utf-8") as afp: + header_line = await afp.readline() + headers = [h.strip('"') for h in header_line.strip().split(",")] + reader = csv.DictReader([], fieldnames=headers) + + async for line in afp: + values = next(csv.reader([line])) + row = dict(zip(reader.fieldnames, values)) + try: + record = SalesRecord( + name=row["name"].strip('"'), + date=datetime.fromisoformat(row["date"].strip('"')), + price=float(row["price"]), + ) + await self.next_actor.send(record) + except Exception as exc: + logger.warning("[%s] Skipping malformed row: %s (%s)", self.name, row, exc) + + await self.next_actor.send("EOF") + + +class FilterActor(Actor): + """Filters records that fall outside a date range.""" + + def __init__(self, name: str, start_date: datetime, end_date: datetime, next_actor: Actor): + super().__init__(name) + self.start_date = start_date + self.end_date = end_date + self.next_actor = next_actor + + async def handle(self, message: Any) -> None: + if message == "EOF": + await self.next_actor.send("EOF") + return + record: SalesRecord = message + if self.start_date <= record.date <= self.end_date: + await self.next_actor.send(record) + + +class MetricsActor(Actor): + """Accumulates sales metrics incrementally.""" + + def __init__(self, name: str, next_actor: Actor): + super().__init__(name) + self.next_actor = next_actor + self.num_customers: set[str] = set() + self.positive_prices: list[float] = [] + self.returns_count: int = 0 + self.total_records: int = 0 + self.total_sales: float = 0.0 + + async def handle(self, message: Any) -> None: + if message == "EOF": + await self._finalize() + await self.next_actor.send("EOF") + return + + record: SalesRecord = message + self.total_records += 1 + self.num_customers.add(record.name) + self.total_sales += record.price + + if record.price > 0: + self.positive_prices.append(record.price) + elif record.price < 0: + self.returns_count += 1 + + async def _finalize(self) -> None: + avg_order_value = ( + sum(self.positive_prices) / len(self.positive_prices) + if self.positive_prices + else 0.0 + ) + pct_returns = ( + (self.returns_count / self.total_records) * 100 if self.total_records else 0.0 + ) + report = { + "number_of_customers": len(self.num_customers), + "average_order_value (pre-tax)": round(avg_order_value, 2), + "percentage_of_returns": round(pct_returns, 2), + "total_sales_in_period (pre-tax)": round(self.total_sales, 2), + "report_start": REPORT_START_DATE.strftime("%Y-%m-%d"), + "report_end": REPORT_END_DATE.strftime("%Y-%m-%d"), + } + logger.info("[%s] Metrics computed. Forwarding to writer...", self.name) + await self.next_actor.send(report) + + +class WriterActor(Actor): + """Receives final report and writes it to disk.""" + + def __init__(self, name: str, output_file: Path): + super().__init__(name) + self.output_file = output_file + + async def handle(self, message: Any) -> None: + if message == "EOF": + # Writer receives EOF last—it can stop silently + return + report: dict[str, Any] = message + await asyncio.to_thread(self._write_json, report) + logger.info("[%s] Report successfully written to %s", self.name, self.output_file) + + def _write_json(self, report: dict[str, Any]) -> None: + self.output_file.write_text(json.dumps(report, indent=2), encoding="utf-8") + + +# ------------------------------------------------------------------------------ +# Entrypoint +# ------------------------------------------------------------------------------ + + +async def main() -> None: + """Assemble and execute the concurrent actor system.""" + + logger.info("Starting Actor Model Sales Report...") + + # Instantiate the actor chain + writer = WriterActor("WriterActor", OUTPUT_FILE) + metrics = MetricsActor("MetricsActor", next_actor=writer) + filterer = FilterActor("FilterActor", REPORT_START_DATE, REPORT_END_DATE, next_actor=metrics) + reader = ReaderActor("ReaderActor", INPUT_FILE, next_actor=filterer) + + actors = [reader, filterer, metrics, writer] + + # Start all actors + for a in actors: + a.start() + + # Kick off processing + await reader.send("START") + + # Wait for all to gracefully shut down + for a in actors: + await a.stop() + + logger.info("Actor Model Sales Report completed successfully.") + + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/2025/solid/report_config.yaml b/2025/solid/report_config.yaml new file mode 100644 index 0000000..51a7a0f --- /dev/null +++ b/2025/solid/report_config.yaml @@ -0,0 +1,9 @@ +dataset: "sales_data.csv" +start_date: "2024-01-01" +end_date: "2024-12-31" +metrics: + - customer_count + - average_order_value + - return_percentage + - total_sales +output: "output/sales_report_config.json" \ No newline at end of file diff --git a/2025/solid/report_dataflow.py b/2025/solid/report_dataflow.py new file mode 100644 index 0000000..e51282c --- /dev/null +++ b/2025/solid/report_dataflow.py @@ -0,0 +1,189 @@ +""" +=============================================================================== +Sales Report - Dataflow (DAG) Implementation +=============================================================================== +Implements the report workflow as a declarative Dataflow Graph (Directed +Acyclic Graph). Each node represents a data transformation or side effect. + +Execution is topologically ordered based on declared dependencies — +the flow of data, not the order of code, determines execution. + +Highlights: + * Explicit dependency graph (no implicit control flow) + * Reusable dataflow engine + * Great for illustrating data-oriented design patterns +=============================================================================== +""" + +import json +import logging +from datetime import datetime +from pathlib import Path +from typing import Any, Callable + +import pandas as pd + +# ------------------------------------------------------------------------------ +# Logging +# ------------------------------------------------------------------------------ + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] [dataflow] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) + +# ------------------------------------------------------------------------------ +# Constants +# ------------------------------------------------------------------------------ + +BASE_DIR = Path(__file__).parent +INPUT_FILE = BASE_DIR / "sales_data.csv" +OUTPUT_DIR = BASE_DIR / "output" +OUTPUT_DIR.mkdir(exist_ok=True) +OUTPUT_FILE = OUTPUT_DIR / "sales_report_dataflow.json" +REPORT_START = datetime(2024, 1, 1) +REPORT_END = datetime(2024, 12, 31) + +# ------------------------------------------------------------------------------ +# Dataflow Engine +# ------------------------------------------------------------------------------ + + +class Node: + """Represents a single transformation node in the DAG.""" + + def __init__( + self, + name: str, + func: Callable[..., Any], + dependencies: list[str] | None = None, + ): + self.name = name + self.func = func + self.dependencies = dependencies or [] + self.result: Any = None + + +class DAG: + """Lightweight dataflow DAG executor.""" + + def __init__(self): + self.nodes: dict[str, Node] = {} + + def add(self, name: str, func: Callable[..., Any], deps: list[str] | None = None): + if name in self.nodes: + raise ValueError(f"Node '{name}' already exists.") + self.nodes[name] = Node(name, func, deps) + + def _resolve_order(self) -> list[Node]: + """Topologically sort nodes based on declared dependencies.""" + visited: set[str] = set() + order: list[Node] = [] + + def visit(n: str): + if n in visited: + return + node = self.nodes.get(n) + if not node: + raise ValueError(f"Node '{n}' not found in DAG.") + for dep in node.dependencies: + visit(dep) + visited.add(n) + order.append(node) + + for name in self.nodes: + visit(name) + + return order + + def run(self): + """Execute nodes in topological order, passing results between them.""" + logger.info("Executing dataflow pipeline...") + order = self._resolve_order() + results: dict[str, Any] = {} + + for node in order: + kwargs = {dep: results[dep] for dep in node.dependencies} + logger.info("→ Running %s ...", node.name) + node.result = node.func(**kwargs) + results[node.name] = node.result + + logger.info("Dataflow pipeline completed successfully.") + return results + + +# ------------------------------------------------------------------------------ +# Step Functions (pure transformations) +# ------------------------------------------------------------------------------ + + +def read_data() -> pd.DataFrame: + logger.info("Reading CSV from %s", INPUT_FILE) + df = pd.read_csv(INPUT_FILE, parse_dates=["date"]) + logger.info("Loaded %d records.", len(df)) + return df + + +def filter_data(read_data: pd.DataFrame) -> pd.DataFrame: + logger.info("Filtering dates between %s and %s", REPORT_START, REPORT_END) + df = read_data[ + (read_data["date"] >= pd.Timestamp(REPORT_START)) + & (read_data["date"] <= pd.Timestamp(REPORT_END)) + ].copy() + logger.info("Filtered %d records.", len(df)) + return df + + +def compute_metrics(filter_data: pd.DataFrame) -> dict[str, Any]: + logger.info("Computing metrics declaratively...") + + positive_sales = filter_data.loc[filter_data["price"] > 0, "price"] + returns = filter_data.loc[filter_data["price"] < 0, "price"] + + num_customers = filter_data["name"].nunique() + avg_order_value = positive_sales.mean() if not positive_sales.empty else 0.0 + pct_returns = (len(returns) / len(filter_data)) * 100 if len(filter_data) else 0.0 + total_sales = filter_data["price"].sum() + + report = { + "number_of_customers": int(num_customers), + "average_order_value (pre-tax)": round(avg_order_value, 2), + "percentage_of_returns": round(pct_returns, 2), + "total_sales_in_period (pre-tax)": round(total_sales, 2), + "report_start": REPORT_START.strftime("%Y-%m-%d"), + "report_end": REPORT_END.strftime("%Y-%m-%d"), + } + return report + + +def write_report(compute_metrics: dict[str, Any]) -> None: + OUTPUT_FILE.write_text(json.dumps(compute_metrics, indent=2), encoding="utf-8") + logger.info("Report written to %s", OUTPUT_FILE) + + +# ------------------------------------------------------------------------------ +# Entrypoint +# ------------------------------------------------------------------------------ + + +def main() -> None: + """Constructs and runs the dataflow DAG.""" + logger.info("Starting Dataflow (DAG) Sales Report...") + + dag = DAG() + dag.add("read_data", read_data) + dag.add("filter_data", filter_data, deps=["read_data"]) + dag.add("compute_metrics", compute_metrics, deps=["filter_data"]) + dag.add("write_report", write_report, deps=["compute_metrics"]) + + try: + dag.run() + except Exception as e: + logger.exception("Dataflow pipeline failed: %s", e) + raise SystemExit(1) from e + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/2025/solid/run_reports.sh b/2025/solid/run_reports.sh new file mode 100755 index 0000000..05f90e5 --- /dev/null +++ b/2025/solid/run_reports.sh @@ -0,0 +1,551 @@ +#!/usr/bin/env bash + +set -euo pipefail + +# ============================================================================ +# Configuration +# ============================================================================ + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +cd "$SCRIPT_DIR" + +# Logging configuration +LOG_DIR="${LOG_DIR:-$SCRIPT_DIR/logs}" +LOG_FILE="${LOG_FILE:-$LOG_DIR/run_reports_$(date +%Y%m%d_%H%M%S).log}" +VERBOSE="${VERBOSE:-false}" +QUIET="${QUIET:-false}" + +# Execution configuration +CONTINUE_ON_ERROR="${CONTINUE_ON_ERROR:-false}" +TIMEOUT="${TIMEOUT:-0}" # 0 = no timeout +MAX_JOBS="${MAX_JOBS:-1}" # 1 = sequential + +# Report mappings (can be moved to config file) +declare -A REPORTS=( + ["messy"]="messy_report.py" + ["class-based"]="class_based_report.py" + ["class-based-v2"]="class_based_report_v2.py" + ["functional"]="functional_report.py" + ["functional-v2"]="functional_report_v2.py" + ["declarative"]="declarative_report.py" + ["config"]="config_report.py" + ["async"]="async_report.py" + ["async-no-pandas"]="async_no_pandas_report.py" + ["dataflow"]="report_dataflow.py" + ["actor"]="report_actor_model.py" + ["reactive"]="reactive_report.py" + ["logic"]="logic_report.py" +) + +# Execution tracking +declare -A REPORT_START_TIMES +declare -A REPORT_END_TIMES +declare -A REPORT_EXIT_CODES +SUCCESS_COUNT=0 +FAILURE_COUNT=0 +START_TIME=0 +END_TIME=0 + +# ============================================================================ +# Logging Functions +# ============================================================================ + +init_logging() { + mkdir -p "$LOG_DIR" + if [ ! -w "$LOG_DIR" ]; then + echo "Error: Cannot write to log directory: $LOG_DIR" >&2 + exit 1 + fi +} + +log() { + local level="$1" + shift + local message="$*" + local timestamp + timestamp=$(date +'%Y-%m-%d %H:%M:%S') + local log_entry="[$timestamp] [$level] $message" + + # Only show errors and warnings on terminal (since detailed logs go to file) + if [ "$QUIET" = false ]; then + case "$level" in + ERROR) + echo "$log_entry" >&2 + ;; + WARN) + echo "$log_entry" >&2 + ;; + # INFO and DEBUG only go to log file, not terminal + esac + fi + + # Always write to log file + echo "$log_entry" >> "$LOG_FILE" +} + +log_info() { log "INFO" "$@"; } +log_warn() { log "WARN" "$@"; } +log_error() { log "ERROR" "$@"; } +log_debug() { + if [ "$VERBOSE" = true ]; then + log "DEBUG" "$@" + fi +} + +# ============================================================================ +# Signal Handling +# ============================================================================ + +cleanup() { + local exit_code="${1:-130}" + log_warn "Received interrupt signal. Cleaning up..." + + # Kill any running background jobs + jobs -p | xargs -r kill 2>/dev/null || true + + # Print partial summary + if [ $START_TIME -gt 0 ]; then + END_TIME=$(date +%s) + print_summary + fi + + exit "$exit_code" +} + +trap 'cleanup 130' SIGINT SIGTERM + +# ============================================================================ +# Validation Functions +# ============================================================================ + +validate_report_key() { + local key="$1" + + # Check key format (alphanumeric and hyphens only) + if [[ ! "$key" =~ ^[a-z0-9-]+$ ]]; then + log_error "Invalid report key format: '$key' (must be lowercase alphanumeric with hyphens)" + return 1 + fi + + # Check key exists in REPORTS array + if [ -z "${REPORTS[$key]:-}" ]; then + log_error "Unknown report key: '$key'" + return 1 + fi + + local script="${REPORTS[$key]}" + + # Check script file exists + if [ ! -f "$script" ]; then + log_error "Report script not found: '$script' for key '$key'" + return 1 + fi + + # Check script is executable or readable + if [ ! -r "$script" ]; then + log_error "Report script is not readable: '$script'" + return 1 + fi + + return 0 +} + +validate_environment() { + log_debug "Validating environment..." + + # Check Python availability + if ! command -v python >/dev/null 2>&1; then + log_error "Python interpreter not found" + return 1 + fi + + # Check Python version (example: require 3.8+) + local python_version + python_version=$(python --version 2>&1 | awk '{print $2}') + log_debug "Python version: $python_version" + + # Check uv availability if uv.lock exists + if [ -f "uv.lock" ] && ! command -v uv >/dev/null 2>&1; then + log_warn "uv.lock found but uv not available. Using system Python." + fi + + return 0 +} + +validate_all_report_keys() { + local invalid_keys=() + + for key in "$@"; do + if ! validate_report_key "$key"; then + invalid_keys+=("$key") + fi + done + + if [ ${#invalid_keys[@]} -gt 0 ]; then + log_error "Invalid report keys: ${invalid_keys[*]}" + return 1 + fi + + return 0 +} + +# ============================================================================ +# Execution Functions +# ============================================================================ + +determine_runner() { + RUNNER=(python) + if command -v uv >/dev/null 2>&1 && [ -f "$SCRIPT_DIR/uv.lock" ]; then + RUNNER=(uv run python) + log_debug "Using uv runner" + else + log_debug "Using system Python" + fi +} + +run_report_with_timeout() { + local key="$1" + local script="${REPORTS[$key]}" + local cmd=("${RUNNER[@]}" "$script") + local pid + local exit_code=0 + + REPORT_START_TIMES[$key]=$(date +%s) + log_info "Starting report: $key ($script)" + log_debug "Command: ${cmd[*]}" + + # Show minimal progress on terminal (only if not quiet) + if [ "$QUIET" = false ] && [ "$DRY_RUN" = false ]; then + echo -n "Running $key... " >&2 + fi + + if [ "$DRY_RUN" = true ]; then + log_info "[DRY RUN] Would execute: ${cmd[*]}" + if [ "$QUIET" = false ]; then + echo "[DRY RUN]" >&2 + fi + REPORT_EXIT_CODES[$key]=0 + REPORT_END_TIMES[$key]=$(date +%s) + return 0 + fi + + # Add separator in log file for this report's output + { + echo "" + echo "--- Report: $key ($script) ---" + echo "Started at: $(date +'%Y-%m-%d %H:%M:%S')" + echo "" + } >> "$LOG_FILE" + + # Run with timeout if specified + # By default, Python output only goes to log file (not terminal) to reduce clutter + # Use --verbose to see Python output on terminal + if [ "$TIMEOUT" -gt 0 ]; then + if [ "$VERBOSE" = true ] && [ "$QUIET" = false ]; then + # Show output on terminal and append to log file (verbose mode) + timeout "$TIMEOUT" "${cmd[@]}" 2>&1 | tee -a "$LOG_FILE" + exit_code=${PIPESTATUS[0]} + else + # Only append to log file (default behavior) + timeout "$TIMEOUT" "${cmd[@]}" >> "$LOG_FILE" 2>&1 || exit_code=$? + fi + + if [ $exit_code -eq 124 ]; then + log_error "Report '$key' timed out after ${TIMEOUT}s" + exit_code=124 + fi + else + if [ "$VERBOSE" = true ] && [ "$QUIET" = false ]; then + # Show output on terminal and append to log file (verbose mode) + "${cmd[@]}" 2>&1 | tee -a "$LOG_FILE" + exit_code=${PIPESTATUS[0]} + else + # Only append to log file (default behavior) + "${cmd[@]}" >> "$LOG_FILE" 2>&1 || exit_code=$? + fi + fi + + # Add separator in log file for end of report output + { + echo "" + echo "--- End of report: $key (exit code: $exit_code) ---" + echo "" + } >> "$LOG_FILE" + + REPORT_END_TIMES[$key]=$(date +%s) + REPORT_EXIT_CODES[$key]=$exit_code + + local duration=$((REPORT_END_TIMES[$key] - REPORT_START_TIMES[$key])) + + # Show minimal status on terminal + if [ "$QUIET" = false ]; then + if [ $exit_code -eq 0 ]; then + echo "✓" >&2 + else + echo "✗" >&2 + fi + fi + + if [ $exit_code -eq 0 ]; then + log_info "Completed report: $key (duration: ${duration}s)" + ((SUCCESS_COUNT++)) || true + return 0 + else + log_error "Failed report: $key (exit code: $exit_code, duration: ${duration}s)" + ((FAILURE_COUNT++)) || true + return $exit_code + fi +} + +run_report() { + local key="$1" + + if ! validate_report_key "$key"; then + return 1 + fi + + if ! run_report_with_timeout "$key"; then + if [ "$CONTINUE_ON_ERROR" = false ]; then + log_error "Stopping execution due to failure (use --continue-on-error to continue)" + return 1 + fi + return 0 # Continue despite error + fi + + return 0 +} + +# ============================================================================ +# Summary & Reporting +# ============================================================================ + +print_summary() { + local total=$((SUCCESS_COUNT + FAILURE_COUNT)) + local duration=0 + + if [ $START_TIME -gt 0 ] && [ $END_TIME -gt 0 ]; then + duration=$((END_TIME - START_TIME)) + fi + + # Build summary text + local summary="" + summary+=$'\n' + summary+="========================================"$'\n' + summary+="Execution Summary"$'\n' + summary+="========================================"$'\n' + summary+="Total reports: $total"$'\n' + summary+="Successful: $SUCCESS_COUNT"$'\n' + summary+="Failed: $FAILURE_COUNT"$'\n' + + if [ $duration -gt 0 ]; then + summary+="Total duration: ${duration}s"$'\n' + fi + + if [ $FAILURE_COUNT -gt 0 ]; then + summary+=$'\n' + summary+="Failed reports:"$'\n' + for key in "${!REPORT_EXIT_CODES[@]}"; do + if [ "${REPORT_EXIT_CODES[$key]}" -ne 0 ]; then + local duration_key=$((REPORT_END_TIMES[$key] - REPORT_START_TIMES[$key])) + summary+=" - $key (exit code: ${REPORT_EXIT_CODES[$key]}, duration: ${duration_key}s)"$'\n' + fi + done + fi + + summary+="========================================"$'\n' + summary+="Log file: $LOG_FILE"$'\n' + summary+="========================================"$'\n' + + # Write to log file + echo "$summary" >> "$LOG_FILE" + + # Also show on terminal (unless quiet) + if [ "$QUIET" = false ]; then + echo "$summary" + fi +} + +# ============================================================================ +# Usage & Help +# ============================================================================ + +usage() { + cat <<'EOF' +Usage: ./run_reports.sh [OPTIONS] + +Run one or more sales-report implementations from this directory. + +Options: + --list Show available report keys. + --run [--run ] Run one or more reports by key. + --run-all Run every report sequentially. + --dry-run Print the commands without executing them. + --continue-on-error Continue execution even if a report fails. + --timeout Set timeout per report (0 = no timeout). + --max-jobs Run up to N reports in parallel (default: 1). + --verbose Enable verbose/debug logging. + --quiet Suppress stdout (errors still shown). + --log-dir Specify log directory (default: ./logs). + -h, --help Show this help message. + +Environment Variables: + CONTINUE_ON_ERROR Continue on error (true/false) + TIMEOUT Timeout per report in seconds + MAX_JOBS Maximum parallel jobs + VERBOSE Enable verbose logging + QUIET Suppress stdout + LOG_DIR Log directory path + +Examples: + ./run_reports.sh --list + ./run_reports.sh --run functional --run logic + ./run_reports.sh --run-all + ./run_reports.sh --run-all --continue-on-error --timeout 300 + ./run_reports.sh --run-all --max-jobs 4 --verbose + +Reports execute inside a uv environment when available; otherwise the +system python interpreter is used. +EOF +} + +list_reports() { + printf "Available reports:\n" + for key in "${!REPORTS[@]}"; do + local script="${REPORTS[$key]}" + local status="" + if [ ! -f "$script" ]; then + status=" [MISSING]" + fi + printf " %-20s %s%s\n" "$key" "$script" "$status" + done | sort +} + +# ============================================================================ +# Main +# ============================================================================ + +main() { + # Initialize + init_logging + log_info "Starting report execution" + log_debug "Script directory: $SCRIPT_DIR" + log_debug "Log file: $LOG_FILE" + + # Parse arguments + RUN_ALL=false + DRY_RUN=false + REQUESTED_KEYS=() + + while (($# > 0)); do + case "$1" in + --list) + list_reports + exit 0 + ;; + --run) + shift || { log_error "--run requires a report key"; exit 1; } + REQUESTED_KEYS+=("$1") + ;; + --run-all) + RUN_ALL=true + ;; + --dry-run) + DRY_RUN=true + ;; + --continue-on-error) + CONTINUE_ON_ERROR=true + ;; + --timeout) + shift || { log_error "--timeout requires a value"; exit 1; } + TIMEOUT="$1" + if ! [[ "$TIMEOUT" =~ ^[0-9]+$ ]]; then + log_error "Invalid timeout value: $TIMEOUT (must be a number)" + exit 1 + fi + ;; + --max-jobs) + shift || { log_error "--max-jobs requires a value"; exit 1; } + MAX_JOBS="$1" + if ! [[ "$MAX_JOBS" =~ ^[0-9]+$ ]] || [ "$MAX_JOBS" -lt 1 ]; then + log_error "Invalid max-jobs value: $MAX_JOBS (must be >= 1)" + exit 1 + fi + ;; + --verbose) + VERBOSE=true + ;; + --quiet) + QUIET=true + ;; + --log-dir) + shift || { log_error "--log-dir requires a path"; exit 1; } + LOG_DIR="$1" + ;; + -h|--help) + usage + exit 0 + ;; + *) + log_error "Unknown option: $1" + echo "" + usage + exit 1 + ;; + esac + shift + done + + # Validate inputs + if [ "$RUN_ALL" = false ] && [ "${#REQUESTED_KEYS[@]}" -eq 0 ]; then + log_error "No reports specified." + echo "" + usage + exit 1 + fi + + # Setup + if [ "$RUN_ALL" = true ]; then + mapfile -t REQUESTED_KEYS < <(printf "%s\n" "${!REPORTS[@]}" | sort) + fi + + # Validate environment and report keys + if ! validate_environment; then + exit 1 + fi + + if ! validate_all_report_keys "${REQUESTED_KEYS[@]}"; then + exit 1 + fi + + # Determine runner + determine_runner + + # Execute reports + START_TIME=$(date +%s) + STATUS=0 + + for key in "${REQUESTED_KEYS[@]}"; do + if ! run_report "$key"; then + STATUS=1 + if [ "$CONTINUE_ON_ERROR" = false ]; then + break + fi + fi + done + + END_TIME=$(date +%s) + + # Print summary + print_summary + + # Exit with appropriate code + if [ $FAILURE_COUNT -gt 0 ]; then + exit 1 + fi + + exit "$STATUS" +} + +# Run main function +main "$@" + diff --git a/2025/solid/uv.lock b/2025/solid/uv.lock index 37b43f4..e48f078 100644 --- a/2025/solid/uv.lock +++ b/2025/solid/uv.lock @@ -1,57 +1,157 @@ version = 1 -revision = 2 +revision = 1 requires-python = ">=3.13" +[[package]] +name = "aiofiles" +version = "25.1.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354 } +wheels = [ + { url = 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argparse.ArgumentParser( + description="Compare JSON outputs from the sales-report implementations." + ) + parser.add_argument( + "--outputs-dir", + type=Path, + default=Path(__file__).parent / "output", + help="Directory containing the JSON outputs.", + ) + parser.add_argument( + "--baseline", + type=Path, + default=None, + help=( + "Specific baseline JSON file to compare against. " + "Defaults to the first file found in outputs-dir." + ), + ) + parser.add_argument( + "--tolerance", + type=float, + default=1e-6, + help="Absolute tolerance for comparing numeric values.", + ) + parser.add_argument( + "--verbose", + action="store_true", + help="Print per-file comparison details even when all metrics match.", + ) + return parser.parse_args(list(argv)) + + +def load_metrics(path: Path) -> MetricMap: + with path.open("r", encoding="utf-8") as fh: + data = json.load(fh) + + if not isinstance(data, dict): + raise TypeError(f"Expected JSON object in {path}, found {type(data).__name__}") + + if not all(isinstance(key, str) for key in data): + raise TypeError(f"Expected string keys in {path}") + + return cast(MetricMap, data) + + +def is_close(a: Any, b: Any, tolerance: float) -> bool: + if isinstance(a, (int, float)) and isinstance(b, (int, float)): + return math.isclose(float(a), float(b), rel_tol=0.0, abs_tol=tolerance) + return a == b + + +def compare_metrics( + baseline_metrics: MetricMap, + candidate_metrics: MetricMap, + tolerance: float, +) -> tuple[bool, str]: + baseline_keys = set(baseline_metrics) + candidate_keys = set(candidate_metrics) + + missing = baseline_keys - candidate_keys + extra = candidate_keys - baseline_keys + + if missing or extra: + parts = [] + if missing: + parts.append(f"missing keys: {sorted(missing)}") + if extra: + parts.append(f"unexpected keys: {sorted(extra)}") + return False, "; ".join(parts) + + mismatches = [] + for key in sorted(baseline_keys): + baseline_value = baseline_metrics[key] + candidate_value = candidate_metrics[key] + if not is_close(baseline_value, candidate_value, tolerance): + mismatches.append( + f"{key!r}: {candidate_value!r} != {baseline_value!r}" + ) + + if mismatches: + return False, "; ".join(mismatches) + return True, "" + + +def main(argv: Iterable[str]) -> int: + args = parse_args(argv) + + outputs_dir: Path = args.outputs_dir + if not outputs_dir.exists() or not outputs_dir.is_dir(): + print(f"Outputs directory does not exist: {outputs_dir}", file=sys.stderr) + return 2 + + json_files = sorted(outputs_dir.glob("*.json")) + if not json_files: + print(f"No JSON files found in {outputs_dir}", file=sys.stderr) + return 2 + + baseline_arg = args.baseline + if baseline_arg is not None: + if not baseline_arg.exists(): + print(f"Baseline file not found: {baseline_arg}", file=sys.stderr) + return 2 + baseline_path = baseline_arg + else: + baseline_path = json_files[0] + + baseline_metrics = load_metrics(baseline_path) + print(f"Baseline: {baseline_path.name}") + + failures: list[tuple[Path, str]] = [] + for path in json_files: + if path == baseline_path: + if args.verbose: + print(f"✔ {path.name} (baseline)") + continue + + metrics = load_metrics(path) + ok, message = compare_metrics(baseline_metrics, metrics, args.tolerance) + if ok: + if args.verbose: + print(f"✔ {path.name}") + else: + print(f"✖ {path.name}: {message}") + failures.append((path, message)) + + if failures: + print(f"\n{len(failures)} file(s) differ from baseline.", file=sys.stderr) + return 1 + + print("\nAll report outputs match.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv[1:])) +