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319 lines
13 KiB
Markdown
319 lines
13 KiB
Markdown
# Python Charts [](LICENSE)
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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 :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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## Table of Contents
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- [Quickstart](#quickstart)
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- [Repository Structure](#repository-structure)
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- [Usage](#usage)
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- [Custom Charts](#custom-charts)
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- [Details](#details)
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- [Dependencies](#dependencies)
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- [Troubleshooting](#troubleshooting)
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- [License](#license)
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---
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## 🚀 Quickstart
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```bash
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# Clone the repo
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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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./startup.sh --offset 12 --end 2025-05-31 --extend-years 5
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````
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This will:
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1. Upgrade `pip` & `setuptools` globally
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2. Install required packages from `requirements.txt` via
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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 ([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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## 📁 Repository Structure
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```
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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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│ ├── __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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### 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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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 (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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##### Example:
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```bash
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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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This populates `fred.db` with four series (UNRATE, WTI, Bitcoin, and Global M2). ([Stack Overflow][3], [martinheinz.dev][4])
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---
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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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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: 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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* 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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### Bitcoin vs. Global M2
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Before running `bitcoin_m2_chart.py`, make sure the Bitcoin price
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(`CBBTCUSD`) and global M2 (`GLOBAL_M2`) series exist in `data/fred.db`.
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If the database is missing these tables, fetch them with `refresh_data.py`:
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```bash
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./startup.sh python scripts/refresh_data.py --series CBBTCUSD GLOBAL_M2 --start 2010-01-01
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```
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Once the data is present, generate the Bitcoin‐Global M2 overlay chart:
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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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```python
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from common import fetch_series_db, save_figure
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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` (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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> | ---------------- | ------------ | ------------------------------------------------ |
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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 |
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> | `--offset` | `94` | Shift M2 by this many days (positive → M2 leads) |
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> | `--extend-years` | `1` | Years beyond `end` to extend the x‐axis |
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> | `--output` | None | Optional path to save the figure (PNG/PDF) |
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---
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## 📝 Details
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* **Data storage**:
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* `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]).
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* **Plot styling**:
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* **Lagged oil vs. UNRATE chart**:
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* Left y‐axis: Unemployment rate (%) from 3% to 15%, with tick marks every 2%
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* 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])
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* 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])
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* **Bitcoin vs. Global M2 chart**:
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* Left y‐axis: Bitcoin price (USD) as a linear scale
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* Right y‐axis: Global M2 (billions of USD) as a linear scale
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* X‐axis: Same date formatting as above (major = every 5 years, minor = yearly) ([Real Python][1], [martinheinz.dev][4])
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* **Utility functions in `common.py`**:
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* `fetch_series_db(series_list, start, end, db_path)`
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* 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])
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* `validate_dataframe(df, name)`
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* 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])
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* `validate_overlap(df1, df2)`
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* Checks for a nonempty overlap in dates between two series; raises `ValueError` if there is no intersection. ([devgem.io][7], [Real Python][1])
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* `save_figure(fig, output, script_path)`
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* Saves the Matplotlib `Figure` to either the given `output` path or `outputs/<script>_<timestamp>.png`. ([Python Packaging][6], [Real Python][1])
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---
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## 📦 Dependencies
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All Python dependencies are listed in `requirements.txt` and are installed by `./startup.sh`:
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```text
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pandas>=1.5.0
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pandas_datareader>=0.10.0
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matplotlib>=3.5.0
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lxml>=4.6.0
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python-dateutil>=2.8.1
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```
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> **Tip:** If you run into `ModuleNotFoundError: distutils`, resolve it by upgrading `setuptools`:
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>
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> ````bash
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> pip install --upgrade setuptools
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> ``` :contentReference[oaicite:23]{index=23}
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> ````
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---
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## 🛡️ Troubleshooting
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* **ModuleNotFoundError: `scripts.common` not found**
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* Ensure you have added `scripts/__init__.py` and that all `from scripts.common import …` lines have been updated to `from common import …`. Then rerun:
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````bash
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./startup.sh python scripts/lagged_oil_unrate_chart_styled.py --offset 18
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``` :contentReference[oaicite:24]{index=24}
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````
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* **`ValueError: UNRATE contains null values`**
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* This indicates that the UNRATE DataFrame has missing (NaN) entries. Possible fixes:
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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])
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2. Forward‐fill or backward‐fill missing UNRATE values in `common.py` before validation (if appropriate) ([pyOpenSci][8], [Real Python][1])
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3. Comment out the strict null check in `validate_dataframe(...)` (not recommended for publication‐quality charts) ([devgem.io][7], [Real Python][1])
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* **Database not found**
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* If `data/fred.db` does not exist (e.g., fresh clone), run:
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````bash
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./startup.sh python scripts/refresh_data.py --series UNRATE DCOILWTICO
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``` :contentReference[oaicite:28]{index=28}
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````
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* **Permission errors**
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* Ensure you have write access to the project directory, especially for `data/` and `outputs/`. ([Real Python][1], [Roy's Blog][9])
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---
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## 📄 License
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Released under the MIT License. See [LICENSE](LICENSE) for details. ([Roy's Blog][9], [Real Python][1])
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