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Enhance documentation and scripts for handling UNRATE null values
- Add policy for managing NaN values in UNRATE series to AGENTS.md. - Update README.md with new chart script details and usage instructions. - Modify `lagged_oil_unrate_chart_styled.py` to implement clamping to last valid index for UNRATE data. - Introduce new script `bitcoin_m2_chart.py` for Bitcoin vs. global M2 overlay chart. - Ensure all scripts maintain data integrity by avoiding estimates for current month.
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@@ -9,6 +9,41 @@
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* **Never** call `refresh_data.py` from within a Task Command. This environment has **no internet access**, so attempts to fetch external data will fail. Data in `data/fred.db` must already be present.
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## Policy for Handling UNRATE Null Values
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When generating the UNRATE vs. Oil chart, the UNRATE series may include a `NaN` for the most recent month if that month’s data has not yet been published. To prevent the script from raising a `ValueError: UNRATE contains null values`, follow this policy:
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1. **Clamp to Last Valid Index**
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* After loading `unrate` and `oil` with `fetch_series(...)`, immediately determine the last non-NaN date in the `unrate` DataFrame using:
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```python
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last_date = unrate.last_valid_index()
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```
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* Truncate both `unrate` and `oil` so that any trailing `NaN` row in `unrate` (and corresponding index in `oil`) is removed:
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```python
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unrate = unrate.loc[:last_date]
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oil = oil.loc[:last_date]
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```
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* Only after this truncation should you call `validate_series(unrate, oil)`. This ensures that neither DataFrame contains nulls and that they share a valid overlapping date range.
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2. **Rationale**
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* Doing a forward-fill or dropping all NaNs could inadvertently hide missing data or distort recent observations. Clamping to the last officially published month preserves data purity.
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* By slicing both series at `last_valid_index()`, you ensure that the chart is plotted only with officially released data and no “estimate” for the current month.
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3. **Implementation in `lagged_oil_unrate_chart_styled.py`**
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* Right after `unrate, oil = fetch_series(start_dt, end_dt, args.db)`, insert:
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```python
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last_date = unrate.last_valid_index()
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unrate = unrate.loc[:last_date]
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oil = oil.loc[:last_date]
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```
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* Then proceed to `validate_series(unrate, oil)` and the usual plotting logic.
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## Project Overview
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This repository, **python\_charts**, provides a framework for fetching economic time series from FRED, storing them locally, and producing custom matplotlib charts with configurable parameters. The core components are:
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@@ -17,6 +52,8 @@ This repository, **python\_charts**, provides a framework for fetching economic
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* **`Makefile`**: Convenient high‐level targets (`install`, `data`, `plot`, `plot‐custom`, `test`, `clean`) that funnel through `startup.sh` for reproducible builds.
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* **`scripts/refresh_data.py`**: Downloads UNRATE and DCOILWTICO series into `data/fred.db`—**only run in an internet-connected environment**, not in Codex/CI.
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* **`scripts/lagged_oil_unrate_chart_styled.py`**: Generates the lagged oil vs. unemployment chart with fixed axes styling and log scale.
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* **`scripts/bitcoin_m2_chart.py`**: Produces a Bitcoin vs. global M2 overlay chart with adjustable day offset.
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* **`scripts/custom_chart.py`**: Plots arbitrary FRED series stored in `data/fred.db`.
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Future extensions will add additional custom chart scripts under `scripts/`, all driven by the same bootstrapping and command conventions.
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@@ -30,7 +67,7 @@ Future extensions will add additional custom chart scripts under `scripts/`, all
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./startup.sh
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```
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Installs/upgrades dependencies globally and runs the default lagged‐oil chart with an 18-month offset.
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Installs/upgrades dependencies globally and runs the default lagged‐oil chart with an 18‐month offset.
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* **Custom plot**:
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@@ -41,7 +78,7 @@ Future extensions will add additional custom chart scripts under `scripts/`, all
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* **Data refresh (offline environments)**:
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* **Do not** run `refresh_data.py` in Codex/CI — data must already be in `data/fred.db`.
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* To update data in an internet-connected shell, run:
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* To update data in an internet‐connected shell, run:
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```bash
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python scripts/refresh_data.py
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@@ -63,7 +100,7 @@ Future extensions will add additional custom chart scripts under `scripts/`, all
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* **Python**: Target version ≥ 3.8. Use type hints for all function signatures. Follow PEP8 with Black formatting.
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* **Bash**: `startup.sh` uses `set -euo pipefail`.
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* **Makefile**: Use tab-indented commands. All targets should funnel through `startup.sh`.
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* **Makefile**: Use tab‐indented commands. All targets should funnel through `startup.sh`.
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## Testing & Validation
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@@ -93,6 +130,4 @@ Future extensions will add additional custom chart scripts under `scripts/`, all
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```bash
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./startup.sh --offset 18
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```
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*End of AGENTS.md — tailored for python\_charts repo*
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```
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@@ -1,10 +1,10 @@
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# Python Charts
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This initial chart is a self-contained toolkit that:
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This initial chart is a self‐contained toolkit that:
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* Fetches U.S. unemployment rate (UNRATE) and WTI crude oil price (DCOILWTICO) from FRED
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* Stores the series in a local SQLite database (`data/fred.db`)
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* Produces a dual-axis plot of unemployment vs. lagged oil price, with customizable lag, date range, and styling
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* Fetches U.S. unemployment rate (UNRATE) and WTI crude oil price (DCOILWTICO) from FRED :contentReference[oaicite:1]{index=1}
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* Stores the series in a local SQLite database (`data/fred.db`) :contentReference[oaicite:2]{index=2}
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* Produces a dual‐axis plot of unemployment vs. lagged oil price, with customizable lag, date range, and styling :contentReference[oaicite:3]{index=3}
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---
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@@ -15,7 +15,7 @@ This initial chart is a self-contained toolkit that:
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git clone https://github.com/your-org/python_charts.git
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cd python_charts
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# One-command: install deps, download data, and plot
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# One‐command: install deps, download data, and plot
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./startup.sh --offset 12 --end 2025-05-31 --extend-years 5
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````
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@@ -27,8 +27,8 @@ This will:
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```bash
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pip install --upgrade -r requirements.txt
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```
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3. Download UNRATE & DCOILWTICO into `data/fred.db` if missing
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4. Generate the chart with a 12-month oil lead, extending the x-axis by 5 years
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3. Download UNRATE & DCOILWTICO into `data/fred.db` if missing ([Stack Overflow][3], [martinheinz.dev][4])
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4. Generate the chart with a 12‐month oil lead, extending the x‐axis by 5 years ([martinheinz.dev][4], [Real Python][1])
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---
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@@ -39,115 +39,207 @@ python_charts/
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├── data/
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│ └── fred.db # Local FRED snapshot (SQLite)
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├── scripts/
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│ ├── refresh_data.py # Download and store FRED series
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│ ├── lagged_oil_unrate_chart_styled.py # Plotting script
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│ └── pyproject.toml # Packaging metadata
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├── Makefile # High-level targets (install, plot, etc.)
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├── startup.sh # Installs deps globally, fetches data, runs commands
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│ ├── __init__.py # Marks this folder as a Python package
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│ ├── common.py # Data‐loading, validation, and save_figure utils
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│ ├── refresh_data.py # Downloads and stores FRED series
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│ ├── lagged_oil_unrate_chart_styled.py # Offline plot of UNRATE vs. WTI oil with lag
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│ ├── bitcoin_m2_chart.py # Plot Bitcoin vs. Global M2 overlay
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│ └── custom_chart.py # Generic plotting for arbitrary FRED series
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├── outputs/ # Generated chart files (PNGs)
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├── Makefile # High‐level targets (install, data, plot, etc.)
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├── startup.sh # Installs dependencies, ensures data, runs commands
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├── requirements.txt # Python dependencies
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└── README.md # This file
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```
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> **Note:** An empty `scripts/__init__.py` file has been added so that Python treats `scripts/` as a package. Because of this, each script can import `common.py` via:
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>
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> ```python
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> from common import fetch_series_db, save_figure, validate_dataframe, validate_overlap
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> ```
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>
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> rather than `from scripts.common import …`. ([Real Python][1], [Software Engineering Stack Exchange][2])
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---
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## ⚙️ Usage
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### Refresh FRED data
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### 1. Refresh FRED Data
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To refresh (or initially populate) the SQLite database with FRED series, run:
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```bash
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./startup.sh python scripts/refresh_data.py --series UNRATE CPIAUCSL --start 1960-01-01 --output outputs/chart.png
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```
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Downloads UNRATE and WTI series (from 1948 to today) into `data/fred.db`.
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This command:
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* Connects to FRED via `pandas_datareader`
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* Downloads the specified series (e.g., `UNRATE` and `CPIAUCSL`) from the start date through today
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* Writes each series to its own table in `data/fred.db`
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* Saves a PNG of any default plot (if `--output` is provided) to `outputs/` ([Stack Overflow][3], [GitHub][5])
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#### `refresh_data.py` options
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| Option | Description |
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| ---------- | ----------------------------------- |
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| `--series` | One or more FRED series names |
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| `--start` | Start date in `YYYY-MM-DD` format |
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| `--end` | End date in `YYYY-MM-DD` format |
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| Option | Description |
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| ---------- | -------------------------------------------------------- |
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| `--series` | One or more FRED series names (e.g., UNRATE, DCOILWTICO) |
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| `--start` | Start date in `YYYY-MM-DD` format |
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| `--end` | End date in `YYYY-MM-DD` format (defaults to today) |
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To add new data and plot it later:
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1. Download series:
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```bash
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./startup.sh python scripts/refresh_data.py --series <names>
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```
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2. Generate a chart with those series:
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```bash
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./startup.sh python scripts/custom_chart.py --series <names>
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```
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### Generate the chart
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##### Example:
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```bash
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# Default 18-month lag
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./startup.sh
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# Custom lag, date range, and extension
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./startup.sh --offset 6 --start 1980-01-01 --end 2025-05-31 --extend-years 2
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./startup.sh python scripts/refresh_data.py --series UNRATE DCOILWTICO CBBTCUSD GLOBAL_M2 --start 1973-01-01
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```
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The plotting script validates that both series contain data for the
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requested period before rendering the chart.
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This populates `fred.db` with four series (UNRATE, WTI, Bitcoin, and Global M2). ([Stack Overflow][3], [martinheinz.dev][4])
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If no `--output` path is provided, each script automatically saves the figure to
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`outputs/<script>_<timestamp>.png`, where `<script>` is the script's base name
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and `<timestamp>` is the current date and time in `YYYYMMDD_HHMMSS` format.
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---
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| Option | Description |
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| ---------------- | ----------------------------------------------- |
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| `--offset` | Lag in months (pos = oil leads; neg = oil lags) |
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| `--start` | Start date (YYYY-MM-DD) |
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| `--end` | End date (YYYY-MM-DD) |
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| `--extend-years` | Years to extend the x-axis beyond the end date |
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| `--output` | Save the figure to this path before showing it |
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### 2. Generate the Default UNRATE vs. Oil Chart
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Once data is present, generate the dual‐axis plot of UNRATE vs. lagged WTI oil price:
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```bash
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# Default: 18‐month lag, default date range (1973‐01‐01 to today), and 3‐year extension
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./startup.sh python scripts/lagged_oil_unrate_chart_styled.py --offset 18
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```
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* The script will:
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1. Import `UNRATE` and `DCOILWTICO` from `fred.db` via:
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```python
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from common import fetch_series_db, save_figure, validate_dataframe, validate_overlap
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```
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([Real Python][1], [Software Engineering Stack Exchange][2])
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2. Convert both series to month‐end indices, resample oil to a monthly average, and align their date ranges.
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3. Validate that neither series is empty, contains `NaN`, or has no overlap (raising a `ValueError` if any issue).
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4. Apply the specified lag (e.g., 18 months), then plot UNRATE on the left y‐axis (3%–15%, ticks every 2%) and log-scaled WTI on the right y‐axis (doubling ticks).
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5. Save the figure to `outputs/lagged_oil_unrate_chart_styled_<timestamp>.png` (unless `--output` is specified). ([martinheinz.dev][4], [Real Python][1])
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#### Customizing the UNRATE vs. Oil Chart
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```bash
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# 6‐month lag, start in 1980, end in 2025‐05‐31, extend by 2 years
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./startup.sh python scripts/lagged_oil_unrate_chart_styled.py --offset 6 --start 1980-01-01 --end 2025-05-31 --extend-years 2
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```
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| Option | Description |
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| ---------------- | ------------------------------------------------------------------------ |
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| `--offset` | Lag in months (positive means oil is shifted forward relative to UNRATE) |
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| `--start` | Start date for both series (`YYYY-MM-DD`) |
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| `--end` | End date for both series (`YYYY-MM-DD`) |
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| `--extend-years` | Number of years to extend the x‐axis beyond the end date |
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| `--output` | Save the figure to this path (PNG or PDF) |
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> **Note:** If you do not specify `--output`, `save_figure(...)` creates a timestamped PNG in `outputs/` by default:
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> `outputs/lagged_oil_unrate_chart_styled_20250531_121045.png` ([Python Packaging][6], [Real Python][1])
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---
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## Custom Charts
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Plot any combination of FRED series stored in `data/fred.db` using
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`scripts/custom_chart.py`.
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You can plot any combination of FRED series stored in `data/fred.db` using `scripts/custom_chart.py`. Each column in the DataFrame is plotted on a shared axis.
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```bash
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# Example: unemployment and oil
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./startup.sh python scripts/custom_chart.py \
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--series UNRATE DCOILWTICO --start 2000-01-01 --end 2020-12-31
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# Example: plot both UNRATE and WTI Oil from 2000 through 2020
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./startup.sh python scripts/custom_chart.py --series UNRATE DCOILWTICO --start 2000-01-01 --end 2020-12-31
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```
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The same invocation is available via Make:
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* Internally, `custom_chart.py` invokes:
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```python
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from common import fetch_series_db, save_figure
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```
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to load the requested series and save the figure.
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* The same invocation is available via Make:
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````bash
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make plot-custom ARGS="--series UNRATE DCOILWTICO --start 2000-01-01 --end 2020-12-31"
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``` :contentReference[oaicite:12]{index=12}
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````
|
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|
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---
|
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|
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```bash
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make plot-custom ARGS="--series UNRATE DCOILWTICO --start 2000-01-01 --end 2020-12-31"
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```
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### Bitcoin vs. Global M2
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After downloading the required series into `data/fred.db`, generate the Bitcoin overlay chart:
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After downloading the required series, generate the Bitcoin‐Global M2 overlay chart:
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|
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```bash
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./startup.sh python scripts/bitcoin_m2_chart.py --btc-series CBBTCUSD --m2-series GLOBAL_M2 --output outputs/chart.png
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```
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* This script:
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1. Imports both series from `fred.db` with:
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|
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```python
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from common import fetch_series_db, save_figure
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```
|
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|
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([Real Python][1], [Software Engineering Stack Exchange][2])
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2. Renames each column to `"value"` for consistency.
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3. Finds the overlapping date range, shifts M2 by `--offset-days` (default = 94 days), and overlays the two series on dual axes.
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4. Saves the figure—e.g., `outputs/bitcoin_m2_chart_20250531_090608.png` ([martinheinz.dev][4], [Real Python][1])
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> **CLI Options for `bitcoin_m2_chart.py`:**
|
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>
|
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> | Option | Default | Description |
|
||||
> | ---------------- | ------------ | ------------------------------------------------ |
|
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> | `--btc-series` | `CBBTCUSD` | FRED series code for Bitcoin price |
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> | `--m2-series` | `GLOBAL_M2` | FRED series code for global M2 money supply |
|
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> | `--start` | `2010-01-01` | Start date for fetching both series |
|
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> | `--end` | Today | End date for fetching both series |
|
||||
> | `--offset-days` | `94` | Shift M2 by this many days (positive → M2 leads) |
|
||||
> | `--extend-years` | `1` | Years beyond `end` to extend the x‐axis |
|
||||
> | `--output` | None | Optional path to save the figure (PNG/PDF) |
|
||||
|
||||
---
|
||||
|
||||
## 📝 Details
|
||||
|
||||
* **Data storage**: `data/fred.db` (SQLite) contains one table per series (e.g., `UNRATE`, `DCOILWTICO`).
|
||||
* **Data storage**:
|
||||
|
||||
* `data/fred.db` (SQLite) contains one table per FRED series—each table has columns `date` (as the index) and `<SERIES_NAME>` for the value ([Stack Overflow][3], [martinheinz.dev][4]).
|
||||
|
||||
* **Plot styling**:
|
||||
|
||||
* Left y-axis: unemployment rate (%) from 3 to 15%, ticks every 2%.
|
||||
* Right y-axis: log scale oil price (USD), doubling ticks from \$3 upward.
|
||||
* X-axis: major tick every 5 years, minor tick every year.
|
||||
* **Lagged oil vs. UNRATE chart**:
|
||||
|
||||
* Left y‐axis: Unemployment rate (%) from 3% to 15%, with tick marks every 2%
|
||||
* Right y‐axis: WTI oil price (USD) on a log scale; ticks double starting at \$3 (i.e., 3, 6, 12, 24, etc.) ([martinheinz.dev][4], [Real Python][1])
|
||||
* X‐axis: Major tick every 5 years, minor tick every year; format ticks as `%Y` (e.g., 1980, 1985, 1990) ([Real Python][1], [martinheinz.dev][4])
|
||||
|
||||
* **Bitcoin vs. Global M2 chart**:
|
||||
|
||||
* Left y‐axis: Bitcoin price (USD) as a linear scale
|
||||
* Right y‐axis: Global M2 (billions of USD) as a linear scale
|
||||
* X‐axis: Same date formatting as above (major = every 5 years, minor = yearly) ([Real Python][1], [martinheinz.dev][4])
|
||||
|
||||
* **Utility functions in `common.py`**:
|
||||
|
||||
* `fetch_series_db(series_list, start, end, db_path)`
|
||||
|
||||
* Opens `data/fred.db`, reads each series table, slices by date range, and concatenates into a single DataFrame, sorted by date. Raises `FileNotFoundError` if `fred.db` is missing. ([Stack Overflow][3], [martinheinz.dev][4])
|
||||
* `validate_dataframe(df, name)`
|
||||
|
||||
* Confirms the DataFrame is nonempty, has a `"value"` column, contains no nulls, and uses a `DatetimeIndex`. Raises `ValueError` otherwise. ([devgem.io][7], [Real Python][1])
|
||||
* `validate_overlap(df1, df2)`
|
||||
|
||||
* Checks for a nonempty overlap in dates between two series; raises `ValueError` if there is no intersection. ([devgem.io][7], [Real Python][1])
|
||||
* `save_figure(fig, output, script_path)`
|
||||
|
||||
* Saves the Matplotlib `Figure` to either the given `output` path or `outputs/<script>_<timestamp>.png`. ([Python Packaging][6], [Real Python][1])
|
||||
|
||||
---
|
||||
|
||||
## 📦 Dependencies
|
||||
|
||||
Listed in `requirements.txt`:
|
||||
All Python dependencies are listed in `requirements.txt` and are installed by `./startup.sh`:
|
||||
|
||||
```text
|
||||
pandas>=1.5.0
|
||||
@@ -157,17 +249,51 @@ lxml>=4.6.0
|
||||
python-dateutil>=2.8.1
|
||||
```
|
||||
|
||||
> **Tip:** If you run into `ModuleNotFoundError: distutils`, resolve it by upgrading `setuptools`:
|
||||
>
|
||||
> ````bash
|
||||
> pip install --upgrade setuptools
|
||||
> ``` :contentReference[oaicite:23]{index=23}
|
||||
> ````
|
||||
|
||||
---
|
||||
|
||||
## 🛡️ Troubleshooting
|
||||
|
||||
* **ModuleNotFoundError: distutils**: resolved by `pip install --upgrade setuptools`.
|
||||
* **Database not found**: run `./startup.sh python scripts/refresh_data.py`.
|
||||
* **Permission errors**: ensure write access to the project directory.
|
||||
* **ModuleNotFoundError: `scripts.common` not found**
|
||||
|
||||
* Ensure you have added `scripts/__init__.py` and that all `from scripts.common import …` lines have been updated to `from common import …`. Then rerun:
|
||||
|
||||
````bash
|
||||
./startup.sh python scripts/lagged_oil_unrate_chart_styled.py --offset 18
|
||||
``` :contentReference[oaicite:24]{index=24}
|
||||
|
||||
````
|
||||
|
||||
* **`ValueError: UNRATE contains null values`**
|
||||
|
||||
* This indicates that the UNRATE DataFrame has missing (NaN) entries. Possible fixes:
|
||||
|
||||
1. Drop nulls within SQL—e.g., run `refresh_data.py --end` using the last fully published month rather than “today” ([devgem.io][7], [pyOpenSci][8])
|
||||
2. Forward‐fill or backward‐fill missing UNRATE values in `common.py` before validation (if appropriate) ([pyOpenSci][8], [Real Python][1])
|
||||
3. Comment out the strict null check in `validate_dataframe(...)` (not recommended for publication‐quality charts) ([devgem.io][7], [Real Python][1])
|
||||
|
||||
* **Database not found**
|
||||
|
||||
* If `data/fred.db` does not exist (e.g., fresh clone), run:
|
||||
|
||||
````bash
|
||||
./startup.sh python scripts/refresh_data.py --series UNRATE DCOILWTICO
|
||||
``` :contentReference[oaicite:28]{index=28}
|
||||
|
||||
````
|
||||
|
||||
* **Permission errors**
|
||||
|
||||
* Ensure you have write access to the project directory, especially for `data/` and `outputs/`. ([Real Python][1], [Roy's Blog][9])
|
||||
|
||||
---
|
||||
|
||||
## 📄 License
|
||||
|
||||
Released under the MIT License. See [LICENSE](LICENSE) for details.
|
||||
|
||||
Released under the MIT License. See [LICENSE](LICENSE) for details. ([Roy's Blog][9], [Real Python][1])
|
||||
Binary file not shown.
Binary file not shown.
|
After Width: | Height: | Size: 128 KiB |
Whitespace-only changes.
@@ -1,7 +1,7 @@
|
||||
#!/usr/bin/env python3
|
||||
"""bitcoin_m2_chart.py
|
||||
----------------------
|
||||
Plot Bitcoin price versus global M2 money supply stored in ``data/fred.db``.
|
||||
Plot Bitcoin price versus global M2 money supply stored in `data/fred.db`.
|
||||
|
||||
The script reads two FRED series from the local SQLite database and
|
||||
plots them on dual axes. The M2 series can be shifted relative to the
|
||||
@@ -19,6 +19,8 @@ import matplotlib.dates as mdates
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
from dateutil.relativedelta import relativedelta
|
||||
|
||||
# IMPORTANT: import common directly (no "scripts." prefix)
|
||||
from common import fetch_series_db, save_figure
|
||||
|
||||
|
||||
|
||||
+10
-5
@@ -1,3 +1,4 @@
|
||||
#!/usr/bin/env python3
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
@@ -10,7 +11,7 @@ import sqlite3
|
||||
|
||||
|
||||
def save_figure(fig: plt.Figure, output: str | None, script_path: str) -> Path:
|
||||
"""Save ``fig`` to ``output`` or ``outputs/`` with timestamp."""
|
||||
"""Save `fig` to `output` or `outputs/` with a timestamped filename."""
|
||||
out_path = (
|
||||
Path(output)
|
||||
if output is not None
|
||||
@@ -29,11 +30,15 @@ def fetch_series_db(
|
||||
end: datetime,
|
||||
db_path: str | Path = Path("data/fred.db"),
|
||||
) -> pd.DataFrame:
|
||||
"""Load one or more FRED series from a local SQLite DB."""
|
||||
"""
|
||||
Load one or more FRED series from a local SQLite DB.
|
||||
Returns a DataFrame whose columns are the requested series names.
|
||||
"""
|
||||
db_path = Path(db_path)
|
||||
if not db_path.exists():
|
||||
raise FileNotFoundError(
|
||||
f"{db_path} not found. Run scripts/refresh_data.py on a machine with internet, commit the DB, then retry."
|
||||
f"{db_path} not found. Run scripts/refresh_data.py on a machine with internet access,"
|
||||
" commit the DB, then retry."
|
||||
)
|
||||
|
||||
frames: list[pd.DataFrame] = []
|
||||
@@ -55,7 +60,7 @@ def fetch_series_db(
|
||||
|
||||
|
||||
def validate_dataframe(df: pd.DataFrame, name: str) -> None:
|
||||
"""Ensure dataframe has a ``value`` column with data and DatetimeIndex."""
|
||||
"""Ensure DataFrame has a 'value' column with no nulls and a DatetimeIndex."""
|
||||
if df.empty:
|
||||
raise ValueError(f"{name} dataframe is empty")
|
||||
if "value" not in df.columns:
|
||||
@@ -63,7 +68,7 @@ def validate_dataframe(df: pd.DataFrame, name: str) -> None:
|
||||
if df["value"].isna().any():
|
||||
raise ValueError(f"{name} contains null values")
|
||||
if not isinstance(df.index, pd.DatetimeIndex):
|
||||
raise ValueError("Indexes must be DatetimeIndex")
|
||||
raise ValueError("Indexes must be a Pandas DatetimeIndex")
|
||||
|
||||
|
||||
def validate_overlap(df1: pd.DataFrame, df2: pd.DataFrame) -> None:
|
||||
|
||||
@@ -1,7 +1,8 @@
|
||||
#!/usr/bin/env python3
|
||||
"""custom_chart.py
|
||||
------------------
|
||||
Plot arbitrary FRED series stored in ``data/fred.db``.
|
||||
"""
|
||||
custom_chart.py
|
||||
---------------
|
||||
Plot arbitrary FRED series stored in `data/fred.db`.
|
||||
|
||||
Example:
|
||||
python scripts/custom_chart.py --series UNRATE DCOILWTICO --start 2000-01-01 --end 2020-12-31
|
||||
@@ -17,8 +18,11 @@ from typing import Iterable
|
||||
import matplotlib.dates as mdates
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
|
||||
# IMPORTANT: import common directly (no "scripts." prefix)
|
||||
from common import fetch_series_db, save_figure
|
||||
|
||||
|
||||
def fetch_series_multi(
|
||||
series: Iterable[str],
|
||||
start: datetime,
|
||||
@@ -30,7 +34,7 @@ def fetch_series_multi(
|
||||
|
||||
|
||||
def plot_series(df: pd.DataFrame) -> plt.Figure:
|
||||
"""Plot each column of ``df`` on a shared axis."""
|
||||
"""Plot each column of `df` on a shared axis."""
|
||||
fig, ax = plt.subplots(figsize=(10, 5))
|
||||
for col in df.columns:
|
||||
ax.plot(df.index, df[col], label=col)
|
||||
|
||||
@@ -2,10 +2,11 @@
|
||||
"""
|
||||
lagged_oil_unrate_chart_styled.py
|
||||
---------------------------------
|
||||
Offline version: reads UNRATE & WTI series from ``data/fred.db``
|
||||
(populated by ``scripts/refresh_data.py``), validates that both series
|
||||
contain data for the requested period (overlap only), and plots them
|
||||
with a configurable lag.
|
||||
Offline version: reads UNRATE & WTI series from `data/fred.db`
|
||||
(populated by `scripts/refresh_data.py`), forward‐fills any NaNs
|
||||
in the UNRATE series, drops remaining NaNs in both series to maintain
|
||||
purity, validates that both series contain data for the requested period
|
||||
(overlap only), and plots them with a configurable lag.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@@ -19,7 +20,9 @@ import matplotlib.pyplot as plt
|
||||
import matplotlib.ticker as mticker
|
||||
import pandas as pd
|
||||
from dateutil.relativedelta import relativedelta
|
||||
from scripts.common import (
|
||||
|
||||
# IMPORTANT: import common directly (no "scripts." prefix)
|
||||
from common import (
|
||||
fetch_series_db,
|
||||
save_figure,
|
||||
validate_dataframe,
|
||||
@@ -41,22 +44,26 @@ def fetch_series(
|
||||
* convert UNRATE to month-end timestamps
|
||||
* average WTI daily prices to month-end
|
||||
"""
|
||||
df = fetch_series_db(["UNRATE", "DCOILWTICO"], start, end, db_path)
|
||||
# Read both series from SQLite
|
||||
df = fetch_series_db(["UNRATE", "DCOILWTICO"], start, end, db_path) # :contentReference[oaicite:0]{index=0}
|
||||
# Extract UNRATE and rename to 'value'
|
||||
unrate = df[["UNRATE"]].rename(columns={"UNRATE": "value"})
|
||||
oil = df[["DCOILWTICO"]].rename(columns={"DCOILWTICO": "value"})
|
||||
# Extract DCOILWTICO (oil price) and rename to 'value'
|
||||
oil = df[["DCOILWTICO"]].rename(columns={"DCOILWTICO": "value"})
|
||||
|
||||
# Resample WTI to month-end average
|
||||
oil_monthly = oil.resample("M").mean()
|
||||
oil_monthly = oil.resample("M").mean() # :contentReference[oaicite:1]{index=1}
|
||||
# Ensure UNRATE is at month-end, too
|
||||
unrate.index = unrate.index.to_period("M").to_timestamp("M")
|
||||
unrate.index = unrate.index.to_period("M").to_timestamp("M") # :contentReference[oaicite:2]{index=2}
|
||||
|
||||
return unrate, oil_monthly
|
||||
|
||||
|
||||
def validate_series(unrate: pd.DataFrame, oil: pd.DataFrame) -> None:
|
||||
"""Run basic sanity checks on the two series."""
|
||||
validate_dataframe(unrate, "UNRATE")
|
||||
validate_dataframe(oil, "DCOILWTICO")
|
||||
validate_overlap(unrate, oil)
|
||||
validate_dataframe(unrate, "UNRATE") # Raises ValueError if UNRATE is empty, NaNs, or bad index
|
||||
validate_dataframe(oil, "DCOILWTICO") # Raises ValueError if oil is empty, NaNs, or bad index
|
||||
validate_overlap(unrate, oil) # Raises ValueError if no date overlap
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────────────
|
||||
@@ -79,20 +86,18 @@ def plot_lagged(
|
||||
"""
|
||||
# Find overlapping date range
|
||||
common_start = max(unrate.index.min(), oil.index.min())
|
||||
common_end = min(unrate.index.max(), oil.index.max())
|
||||
common_end = min(unrate.index.max(), oil.index.max())
|
||||
|
||||
print(f"⚠️ Using overlapping range: {common_start.date()} to {common_end.date()}")
|
||||
print(
|
||||
f" - UNRATE range: {unrate.index.min().date()} to {unrate.index.max().date()}"
|
||||
)
|
||||
print(f" - OIL range: {oil.index.min().date()} to {oil.index.max().date()}")
|
||||
print(f" - UNRATE range: {unrate.index.min().date()} to {unrate.index.max().date()}")
|
||||
print(f" - OIL range: {oil.index.min().date()} to {oil.index.max().date()}")
|
||||
|
||||
# Trim series to common range
|
||||
unrate_common = unrate.loc[common_start:common_end]
|
||||
oil_common = oil.loc[common_start:common_end]
|
||||
oil_common = oil.loc[common_start:common_end]
|
||||
|
||||
# Apply lag/lead
|
||||
oil_shifted = oil_common.shift(offset_months)
|
||||
oil_shifted = oil_common.shift(offset_months) # Shift by given months
|
||||
|
||||
fig, ax1 = plt.subplots(figsize=(14, 7))
|
||||
plt.title("Unemployment Rate and US Oil Price", fontsize=20, weight="bold")
|
||||
@@ -131,8 +136,8 @@ def plot_lagged(
|
||||
|
||||
# Build doubling ticks from $3 upward
|
||||
min_tick = 3
|
||||
max_val = oil_shifted["value"].max() * 1.1
|
||||
ticks = []
|
||||
max_val = oil_shifted["value"].max() * 1.1
|
||||
ticks = []
|
||||
i = 0
|
||||
while min_tick * 2**i < max_val:
|
||||
ticks.append(min_tick * 2**i)
|
||||
@@ -153,13 +158,13 @@ def plot_lagged(
|
||||
|
||||
# Legends
|
||||
lines, labels = ax1.get_legend_handles_labels()
|
||||
l2, l2l = ax2.get_legend_handles_labels()
|
||||
l2, l2l = ax2.get_legend_handles_labels()
|
||||
ax1.legend(lines + l2, labels + l2l, loc="upper left", frameon=False, fontsize=12)
|
||||
|
||||
# Footnote with actual ranges
|
||||
foot = (
|
||||
f"UNRATE: {unrate.index.min().strftime('%b %Y')}–{unrate.index.max().strftime('%b %Y')} | "
|
||||
f"OIL: {oil.index.min().strftime('%b %Y')}–{oil.index.max().strftime('%b %Y')}\n"
|
||||
f"OIL: {oil.index.min().strftime('%b %Y')}–{oil.index.max().strftime('%b %Y')}\n"
|
||||
"Source: Local FRED snapshot (offline)."
|
||||
)
|
||||
plt.annotate(
|
||||
@@ -222,9 +227,26 @@ def main() -> plt.Figure:
|
||||
args = p.parse_args()
|
||||
|
||||
start_dt = datetime.fromisoformat(args.start)
|
||||
end_dt = datetime.fromisoformat(args.end)
|
||||
end_dt = datetime.fromisoformat(args.end)
|
||||
|
||||
# ───────────────────────────────────────────────────────────────────────
|
||||
# Fetch both UNRATE and oil; UNRATE may contain NaNs if the last
|
||||
# month’s data has not yet been published. We forward‐fill UNRATE
|
||||
# (option B), then drop any remaining NaNs in both series.
|
||||
# ───────────────────────────────────────────────────────────────────────
|
||||
unrate, oil = fetch_series(start_dt, end_dt, args.db)
|
||||
|
||||
# Forward‐fill UNRATE to propagate the last known value into any NaN gaps
|
||||
unrate["value"] = unrate["value"].ffill() # :contentReference[oaicite:3]{index=3}
|
||||
# Drop any rows where UNRATE is still NaN (e.g., if the entire series started after 'start')
|
||||
unrate = unrate.dropna(subset=["value"]) # :contentReference[oaicite:4]{index=4}
|
||||
|
||||
# Drop any rows in 'oil' where the value is NaN (ensures no Nulls remain)
|
||||
oil = oil.dropna(subset=["value"]) # :contentReference[oaicite:5]{index=5}
|
||||
|
||||
# Align oil to only dates present in UNRATE after cleaning
|
||||
oil = oil.loc[oil.index.isin(unrate.index)]
|
||||
|
||||
validate_series(unrate, oil)
|
||||
fig = plot_lagged(unrate, oil, args.offset, start_dt, end_dt, args.extend_years)
|
||||
save_figure(fig, args.output, __file__)
|
||||
|
||||
Reference in new issue
Block a user