Metadata-Version: 2.4
Name: signal-scope
Version: 0.1.0
Summary: A tiny (<200 line) Python tool for analyzing time-series signals: trends, volatility, drift, regime shifts, anomalies.
Project-URL: Homepage, https://github.com/rjsabouhi/signal-scope
Author-email: Ryan Sabouhi <your-email@example.com>
License: MIT License
        
        Copyright (c) 2026 Ryan Sabouhi
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Keywords: analysis,anomaly detection,signal processing,timeseries,volatility
Requires-Python: >=3.8
Requires-Dist: hatchling>=1.27.0
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Description-Content-Type: text/markdown

# signal-scope

A tiny (<200 lines) Python tool for analyzing time-series data:

- Trend strength
- Volatility
- Drift detection
- Regime shifts
- Anomaly scoring
- Optional matplotlib visualizations

Perfect for finance, forecasting, sensor data, and fast exploratory analysis.

---

## Install

```bash
pip install signal-scope
```

---

## Usage

```python
from signal_scope.core import analyze_ts

result = analyze_ts("my_data.csv", col="price", plot=True)
print(result)
```

Expected output:

```json
{
  "trend": 0.87,
  "volatility": 0.032,
  "drift": -0.12,
  "turning_points": 5,
  "anomaly_score": 0.014
}
```

---

## Input format

CSV must contain a numeric column, e.g.:

```csv
timestamp,price
2024-01-01,120.3
2024-01-02,121.0
...
```

---

## Programmatic usage

```python
import pandas as pd
from signal_scope.metrics import volatility

df = pd.read_csv("my_data.csv")
print(volatility(df["price"]))
```

---

## Plotting

Enable visual output:

```python
analyze_ts("my_data.csv", col="price", plot=True)
```

---

MIT License.