User Guide

This guide shows you how to use DaSPi's three flagship workflows for process analysis, from installation through real-world applications.

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🎯 Start Here: Three Essential Workflows

These workflows cover 90% of process analysis tasks. Each produces visual + interpretation in under 20 lines.

📊 1. Process Capability Analysis

When to use: Evaluate if your process meets specifications (Cp, Cpk analysis).

import daspi as dsp

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

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="Comparison of two methods",
    info=True)

Output: Distribution analysis, Cp/Cpk/Pp/Ppk indices, capability interpretation.

📖 Complete Capability Guide →

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🔍 2. Root Cause Analysis

When to use: Identify which factors significantly impact your process.

import daspi as dsp

df = dsp.load_dataset("painkillers-dissolution")

model = dsp.LinearModel(
    source=df,
    target="dissolution",
    factors=["employee", "brand", "catalyst"],
    covariates=["temperature"]
)
model.recursive_elimination()

dsp.ResidualsCharts(model).plot().stripes().label(info=True)
dsp.ParameterRelevanceCharts(model).plot().stripes().label(info=True)

Output: ANOVA tables, parameter effects, residual diagnostics, significance tests.

📖 Complete Root Cause Guide →

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📈 3. Statistical Process Control (SPC)

When to use: Monitor process stability and detect out-of-control conditions.

import daspi as dsp

df = dsp.load_dataset("grnr_spc")

chart = dsp.SingleChart(
    source=df,
    target="result",
    feature="measurement_order"
).plot(
    dsp.Scatter
).stripes(
    mean=True,
    control_limits=True,
    spec_limits=dsp.SpecLimits(lower=2.0, upper=4.5),
    agreement=3
).label(
    fig_title="SPC Chart: Process Monitoring",
    info=True
)

Output: Control chart with mean, UCL/LCL, specification limits, trend analysis.

📖 Complete SPC Guide →

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🚀 Getting Started

Step 1: Install DaSPi

pip install daspi

Step 2: Choose Your Workflow

• Need to verify process capability? → Start with Workflow 1
• Need to find root causes? → Start with Workflow 2
• Need to monitor stability? → Start with Workflow 3

Step 3: Explore Advanced Topics

Once you master the three workflows, explore: - Design of Experiments (DOE) for systematic testing - Gage R&R Analysis for measurement system validation - 3S Methodology for structured problem-solving

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🚀 Getting Started

Guide	What You'll Learn
Installing	Install DaSPi from PyPI and verify your setup
Configuration	Configure language, username, and plotting styles
Plotting	Create professional charts for process data visualization

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📊 Process Analysis Workflows

Guide	What You'll Learn
ANOVA	Fit linear models, run ANOVA, and identify key factors automatically
DOE	Design efficient experiments (full & fractional factorial)
Hypothesis Testing	Test normality, variance, location, and proportions
Gage Analysis	Evaluate measurement systems (MSA Type 1, Gage R&R)

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🧭 3S Methodology

The 3S Methodology is a streamlined, three-phase problem-solving framework for process improvement that combines best practices from 8D and Six Sigma DMAIC.

Phase	Focus
Overview	Introduction, comparison with DMAIC / 8D
Specify	Define & contain — team, charter, SIPOC, containment
Scrutinize	Investigate & analyze — root cause, DOE, hypothesis tests
Stabilize	Implement & control — solution validation, SPC, knowledge transfer

Phase guides coming soon

Detailed step-by-step guidance for the Specify, Scrutinize, and Stabilize phases is in preparation.

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🔄 Typical Analysis Workflow

A process analysis with DaSPi typically follows these steps:

1. Load Data

df = dsp.load_dataset("drop_card")  # or read your own CSV/Excel

2. Explore Visually

Use SingleChart or MultivariateChart with plotters like Scatter, GaussianKDE, or QuantileBoxes to understand your data.

3. Check Assumptions

Run anderson_darling_test and variance_test to verify statistical prerequisites.

4. Test Hypotheses

Apply position_test or proportions_test to compare groups or conditions.

5. Fit a Model

Build a LinearModel with optional backward elimination to identify significant factors.

6. Validate Residuals

Use ResidualsCharts(model).plot() to check model assumptions.

7. Interpret Results

Analyze with ParameterRelevanceCharts, model.anova(), and model.gof_metrics().

8. Assess Capability

Evaluate process performance with ProcessCapabilityAnalysisCharts and SpecLimits.

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🎨 Data Visualization Architecture

DaSPi's plotting system is built in layers for maximum flexibility:

AxesFacets          ← subplot grid (rows × cols or mosaic layout)
  └─ Chart          ← data wiring (source, target, hue, shape, size)
       ├─ Plotter   ← mark drawing (Scatter, Line, GaussianKDE, …)
       └─ Facets    ← labels, legend, reference stripes

This layered approach lets you create simple charts quickly while maintaining the flexibility to customize every detail when needed.

See the Plotting Guide for examples at every layer.