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Python Automated CAE Injury Reports and Excel Dashboards

30 Chapters
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01
Course overview
scope, concepts, and technical context of automated CAE injury reporting and Excel dashboards.
technicalautomated
02
CAE injury data fundamentals
injury criteria (HIC, NIC, chest deflection, etc.), dummy/ATD channels, and simulation output formats.
CAEcriteria
03
Python environment setup
installing and configuring Python, pandas, openpyxl, xlsxwriter, and CAE post-processing libraries.
environmentinstalling
04
Reading CAE simulation results
parsing common formats (CSV, JSON, HDF5, binary) and extracting injury-relevant time histories.
CAEsimulation
05
Data cleaning and validation
handling missing values, unit consistency, outlier detection, and quality checks for injury metrics.
cleaningvalidation
06
Computing injury criteria programmatically
implementing HIC, 3ms clip, chest acceleration, and other metrics from time-series data.
Computingcriteria
07
Automating report generation
templating with Jinja2 and generating structured text/HTML sections for injury reports.
Automatinggeneration
08
Creating Excel workbooks with openpyxl
writing data, formatting cells, and structuring sheets for injury results.
Creatingworkbooks
09
Building dynamic Excel dashboards
charts, pivot tables, conditional formatting, and slicers for injury data exploration.
Buildingdashboards
10
Integrating Python with Excel
using xlsxwriter for advanced charts and formulas, and embedding Python-generated visuals.
Integratingxlsxwriter
11
Batch processing multiple CAE runs
iterating over load cases, extracting key injury metrics, and aggregating results.
processingmultiple
12
Automated chart generation
creating comparison plots (bar, line, scatter) for injury metrics across configurations.
Automatedgeneration
13
Report assembly pipeline
combining computed metrics, charts, and narrative into a final Excel report with multiple sheets.
assemblypipeline
14
Error handling and logging
robust automation with try/except, logging, and validation of input data.
handlingautomation
15
Performance optimization
efficient data processing with vectorized operations and parallel execution for large datasets.
Performanceoptimization
16
Case study
frontal impact injury report automation from CAE output to Excel dashboard.
automationCAE
17
Case study
side impact injury assessment and dashboard creation with occupant kinematics.
assessmentdashboard
18
Case study
pedestrian protection injury metrics and automated reporting.
pedestrianprotection
19
Customizing dashboards for different stakeholders
engineering, management, and regulatory views.
Customizingdashboards
20
Adding interactivity
using Excel form controls and Python-generated macros (VBA) for user-driven filtering.
interactivitycontrols
21
Data visualization best practices for injury metrics
color scales, thresholds, and highlighting critical values.
visualizationpractices
22
Automating email distribution
sending reports via SMTP with attachments and summary highlights.
Automatingdistribution
23
Version control and reproducibility
managing scripts, templates, and data with Git and configuration files.
reproducibilitymanaging
24
Testing and validation of automated reports
unit tests for injury calculations and comparison with manual results.
validationautomated
25
Deploying as a command-line tool
packaging scripts with argparse and creating executable workflows.
Deployingcommand-line
26
Integrating with CAE software APIs
extracting data directly from solvers (e.g., LS-DYNA, Radioss) via Python.
IntegratingCAE
27
Cloud and batch processing
running automation on HPC clusters and handling large-scale CAE datasets.
processingautomation
28
Advanced Excel features
using Power Query, Power Pivot, and dynamic arrays for injury data analysis.
Advancedfeatures
29
Regulatory and standardization considerations
ensuring reports comply with ISO, SAE, and FMVSS requirements.
Regulatorystandardization
30
Capstone project
end-to-end automation of injury reports and interactive Excel dashboards for a realistic CAE dataset.
Capstoneend-to-end