Metadata-Version: 2.1
Name: DaSPi
Version: 2.1.0
Summary: Visualize and analyze your data with DaSPi. This package is designed for users who want to find relevant influencing factors in processes and validate improvements.
Author-Email: =?utf-8?q?Reto_J=C3=A4ggli?= <reto@jaeggli.email>
License: MIT
Project-URL: Repository, https://github.com/j4ggr/DaSPi
Project-URL: Documentation, https://j4ggr.github.io/DaSPi/
Project-URL: Changelog, https://j4ggr.github.io/DaSPi/CHANGELOG
Requires-Python: >=3.13
Requires-Dist: matplotlib>=3.11.0
Requires-Dist: pandas>=3.0.3
Requires-Dist: statsmodels>=0.14.6
Requires-Dist: scipy>=1.18.0
Requires-Dist: numpy>=2.5.0
Description-Content-Type: text/markdown

![pypi](https://badge.fury.io/py/daspi.svg)
![licence](https://img.shields.io/github/license/j4ggr/daspi.svg)
![GitHub last commit](https://img.shields.io/github/last-commit/j4ggr/daspi)

![logo](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/logo.svg)

# DaSPi — Process Analytics & Six Sigma in Python

DaSPi helps engineers analyze and improve processes using statistical workflows.

## 🎯 The Problem

Process analysis in practice is fragmented:

- Excel is error-prone and hard to scale  
- Minitab / JMP are expensive and closed  
- Python tools (pandas, scipy, statsmodels) are powerful but disconnected  

👉 Engineers spend more time combining tools than improving processes.

## ✅ The Solution

DaSPi provides **integrated workflows for process analytics**:

- Capability analysis (Cp, Cpk)  
- Root cause analysis (ANOVA, regression)  
- Statistical process control (SPC)  
- Professional visualization  

All in one consistent and intuitive interface.

## 🚀 Four Flagship Workflows

DaSPi provides **four ready-to-use workflows** that cover the complete quality cycle. Each workflow produces **visual output + interpretation** in under 20 lines of code.

---

### 📏 Workflow 1: Gage R&R Analysis

**Verify your measurement system is capable before analyzing process data.**

```python
import daspi as dsp

# Load data
df = dsp.load_dataset("grnr_layer_thickness")

# Step 1: Evaluate the gage itself (MSA Type 1)
gage = dsp.GageStudyModel(
    source=df,
    target="result_gage",
    reference="reference",
    u_cal=df["U_cal"][0],
    tolerance=df["tolerance"][0],
    resolution=df["resolution"][0]
)

# Step 2: Evaluate repeatability & reproducibility (MSA Type 2)
model = dsp.GageRnRModel(
    source=df,
    target="result_rnr",
    part="part",
    gage=gage,
    u_av="operator"  # Operator variation
)

# Step 3: Visualize complete analysis
chart_gage = dsp.GageStudyCharts(
        gage,
        stretch_figsize=1.5
    ).plot(
    ).stripes(
    ).label(
        fig_title='Gage Study Layer Thickness Measurement System',
        sub_title='Measurement System Analysis (MSA Type 1)',
        info=True)

chart_rnr = dsp.GageRnRCharts(
        model,
        spread_accepted_limit=0.1,   # 10% threshold for acceptance
        spread_rejected_limit=0.3,   # 30% threshold for rejection
        u_accepted_limit=0.15,       # 15% uncertainty threshold
        stretch_figsize=1.5
    ).plot(
    ).stripes(  # Adds acceptance zones
    ).label(
        fig_title='Gage R&R Layer Thickness Measurement System',
        sub_title='Measurement System Analysis (MSA Type 2)',
        info=True)
```

**Output:** Comprehensive measurement system evaluation with repeatability (EV), reproducibility (AV), variance components, ANOVA tables, and capability indices (Cg, Cgk).

---

### 📊 Workflow 2: Process Capability Analysis

**Evaluate if your process meets specifications.**

```python
import daspi as dsp

# Load data
df = dsp.load_dataset("drop_card")
spec_limits = dsp.SpecLimits(0, float(df.loc[0, "usl"]))

# Analyze capability
chart = dsp.ProcessCapabilityAnalysisCharts(
    source=df,
    target="distance",
    spec_limits=spec_limits,
    hue="method"
).plot().stripes().label(
    fig_title="Process Capability Analysis of Drop Card Data",
    sub_title="Drop Card Distance Comparison between Two Methods",
    target_label="Distance s (cm)",
    info=True)

chart.show()
```

**Output:** 5-panel analysis with Cp, Cpk, Pp, Ppk, distribution plots, and statistical interpretation.

---

### 🔍 Workflow 3: Root Cause Analysis

**Identify which factors significantly impact your process.**

```python
import daspi as dsp

# Load data
df = dsp.load_dataset("painkillers-dissolution")

# Fit model with automatic factor selection
model = dsp.LinearModel(
    source=df,
    target="dissolution",
    factors=["employee", "brand", "water", "catalyst"],
    covariates=["temperature"],
    order=2,
)

# Perform recursive elimination to select significant factors
df_gof = pd.concat(model.recursive_elimination())

# Visualize results
chart_res = dsp.ResidualsCharts(model).plot().stripes().label(info=True)
chart_param = dsp.ParameterRelevanceCharts(model).plot().stripes().label(info=True)
```

**Output:** Residual diagnostics + parameter effects with ANOVA tables and significance tests.

---

### 📈 Workflow 4: Statistical Process Control (SPC)

**Monitor process stability and detect out-of-control conditions.**

```python
import daspi as dsp

# Load process data
df = dsp.load_dataset("grnr_spc")

# Create control chart
chart = dsp.JointChart(
        source=df,
        target='result',
        feature=('measurement_order', ''),
        nrows=1,
        ncols=2,
        width_ratios=[4, 1],
        sharey=True,
    ).plot(
        dsp.Scatter
    ).plot(
        dsp.Line,
        on_last_axes=True,
    ).plot(
        dsp.GaussianKDE,
        hide_axis='feature',
        visible_spines='target',
    ).stripes(
        mean=True,
        control_limits=True,    # UCL/LCL at 3-sigma
        agreement=3,            # 3-sigma agreement lines
        strategy='norm',        # Use normal distribution for control limits
    ).label(
        fig_title='SPC Chart: Layer Thickness',
        sub_title='Control limits at ±3σ',
        target_label='Layer Thickness (µm)',
        feature_label=('Measurement Order', ''),
        info=True
    )

chart.show()
```

**Output:** Control chart with mean, control limits (UCL/LCL), specification limits, and trend analysis.

## 🏭 Use Cases

- **Manufacturing:** Monitor tolerances and reduce defects  
- **Quality Engineering:** Automate Six Sigma DMAIC workflows  
- **Process Optimization:** Identify key drivers of variation  
- **Data Analysts:** Unify statistics and visualization in one tool  

### 📊 Example Outputs

#### MSA1: Gage Study (Single Reference)

[![Gage R&R](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/gage_study_single_reference.png)](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/gage_study_single_reference.png)

#### MSA2: Gage R&R (Repeatability & Reproducibility)

[![Gage R&R](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/gage_rnr_layer_thickness.png)](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/gage_rnr_layer_thickness.png)

#### Process Capability Analysis

[![Process Capability](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/cpk-analysis_drop-card.png)](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/cpk-analysis_drop-card.png)

#### Root Cause Analysis (ANOVA)

[![ANOVA Residuals](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/anova_dissolution_residues.png)](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/anova_dissolution_residues.png)
[![ANOVA Parameters](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/anova_dissolution_params.png)](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/anova_dissolution_params.png)

**formula:**

dissolution ~ 26.8292 + 2.3750*employee[T.B] + 0.8375*employee[T.C] - 10.7500*brand[T.ZapPain] - 9.5167*water[T.tap] + 5.7167*brand[T.ZapPain]:water[T.tap]

**Model Summary:**

|     | hierarchical | least_parameter | p_least  | s        | aic        | r2       | r2_adj   | r2_pred  |
| --- | ------------ | --------------- | -------- | -------- | ---------- | -------- | -------- | -------- |
| 0   | True         | employee        | 0.023298 | 2.374693 | 224.835935 | 0.857379 | 0.840400 | 0.813719 |

**Parameter statistics:**

|                               |       coef |  std err |          t |        p |     ci_low |    ci_upp |
| ----------------------------: | ---------: | -------: | ---------: | -------: | ---------: | --------: |
|                     Intercept |  26.829167 | 0.839581 |  31.955433 | 0.000000 |  25.134824 | 28.523509 |
|                 employee[T.B] |   2.375000 | 0.839581 |   2.828793 | 0.007133 |   0.680657 |  4.069343 |
|                 employee[T.C] |   0.837500 | 0.839581 |   0.997522 | 0.324224 |  -0.856843 |  2.531843 |
|              brand[T.ZapPain] | -10.750000 | 0.969464 | -11.088598 | 0.000000 | -12.706458 | -8.793542 |
|                  water[T.tap] |  -9.516667 | 0.969464 |  -9.816417 | 0.000000 | -11.473125 | -7.560208 |
| brand[T.ZapPain]:water[T.tap] |   5.716667 | 1.371030 |   4.169616 | 0.000149 |   2.949817 |  8.483516 |

**Analysis of variance:**

| Typ-I       |   DF |         SS |         MS |          F |        p |       n2 |
| ----------- | ---: | ---------: | ---------: | ---------: | -------: | -------: |
| employee    |    2 |  46.431667 |  23.215833 |   4.116891 | 0.023298 | 0.027960 |
| brand       |    1 | 747.340833 | 747.340833 | 132.526821 | 0.000000 | 0.450027 |
| water       |    1 | 532.000833 | 532.000833 |  94.340328 | 0.000000 | 0.320355 |
| brand:water |    1 |  98.040833 |  98.040833 |  17.385695 | 0.000149 | 0.059037 |
| Residual    |   42 | 236.845000 |   5.639167 |        nan |      nan | 0.142621 |

**Variance inflation factor:**

|             |   DF |      VIF |     GVIF | Threshold | Collinear |              Method |
| ----------- | ---: | -------: | -------: | --------: | --------: | ------------------: |
| Intercept   |    1 | 5.000000 | 2.236068 |  2.236068 |      True |           R_squared |
| employee    |    2 | 1.000000 | 1.000000 |  1.495349 |     False |         generalized |
| brand       |    1 | 1.000000 | 1.000000 |  2.236068 |     False |           R_squared |
| water       |    1 | 1.000000 | 1.000000 |  2.236068 |     False |           R_squared |
| brand:water |    1 | 1.000000 | 1.000000 |  2.236068 |     False | single_order-2_term |

#### SPC: Control Chart with Mean, UCL/LCL, Specification Limits

[![SPC Chart](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/spc_chart_layer_thickness.png)](https://raw.githubusercontent.com/j4ggr/DaSPi/main/docs/img/spc_chart_layer_thickness.png)

## 🚀 Installation

```bash
pip install daspi
```

## 📚 Documentation

- **[User Guide](https://j4ggr.github.io/DaSPi/guides/)** — Complete tutorials for each workflow  
- **[API Reference](https://j4ggr.github.io/DaSPi/anova/)** — Detailed documentation  
- **[3S Methodology](https://j4ggr.github.io/DaSPi/guides/3s-methodology/)** — Structured problem-solving

## 🔧 Technical Features

- **Centralized configuration** — Manage language, username, and styles globally  
- **Multivariate visualization** — Explore complex relationships  
- **Linear models & ANOVA** — Statistical inference made simple  
- **Hypothesis testing** — Confidence intervals and p-values  
- **Monte Carlo simulation** — Assess uncertainty  
- **Process capability** — Cp, Cpk, Pp, Ppk calculations

## ⚙️ Built on Proven Libraries

DaSPi leverages the Python scientific stack:

- **pandas** — Data manipulation  
- **numpy** — Numerical computing  
- **matplotlib** — Visualization  
- **scipy** — Statistical functions  
- **statsmodels** — Advanced statistics  

## 👤 About

DaSPi is created and maintained by **Reto Jäggli**, Data Scientist at Festo Microtechnology AG.

The project is driven by a passion to make **process analytics and Six Sigma workflows more accessible in Python**.

## ⚠️ Disclaimer

DaSPi is under active development and may contain bugs.  
Results should be validated with trusted statistical tools when required.

## 🤝 Feedback & Contributions

**If you use DaSPi in real-world process analysis:**  
👉 I would love to hear your use case.

Feedback, ideas, and contributions are very welcome.

---