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Python Automated CAE Injury Reports and Excel Dashboards

30 Chapters
View Course Samples Online
01
Course overview
scope, concepts, and technical context.
technical
02
CAE injury reporting fundamentals
injury metrics, regulatory standards, and data sources.
CAEreporting
03
Python environment setup
installing Python, essential libraries, and configuring a CAE data workflow.
environmentinstalling
04
Reading CAE simulation output
parsing solver result files and extracting injury-relevant time histories.
CAEsimulation
05
Data cleaning and preprocessing
handling missing values, outliers, and unit consistency in injury datasets.
cleaningpreprocessing
06
Automated report generation with Python
templating, document assembly, and batch processing.
Automatedgeneration
07
Excel integration with openpyxl
writing injury metrics and tables to workbooks.
integrationopenpyxl
08
Building dynamic Excel dashboards
charts, pivot tables, and conditional formatting for injury data.
Buildingdashboards
09
Automating data refresh
linking Python scripts to Excel for on-demand dashboard updates.
Automatingon-demand
10
Injury metric calculations
HIC, NIC, chest deflection, and other criteria implemented in Python.
calculationsHIC
11
Case study
frontal impact simulation—extracting and reporting head injury criteria.
simulationextracting
12
Case study
side impact simulation—automating rib fracture risk metrics and dashboard visualization.
simulationautomating
13
Visualizing injury trends
matplotlib and seaborn charts embedded in Excel reports.
Visualizingmatplotlib
14
Interactive dashboards with xlwings
combining Python analytics with Excel user forms.
Interactivedashboards
15
Scheduling and automation
running report generation on a schedule with task scheduler or cron.
Schedulingautomation
16
Error handling and validation
ensuring data integrity in automated injury reports.
handlingvalidation
17
Performance optimization
efficient processing of large CAE datasets for reporting.
Performanceoptimization
18
Customizing report templates
adapting to different regulatory or internal reporting standards.
Customizingtemplates
19
Integrating multiple CAE solvers
handling diverse output formats in a unified pipeline.
Integratingmultiple
20
Dashboard design principles
clarity, interactivity, and effective communication of injury risk.
Dashboardprinciples
21
Automated email distribution
sending Excel dashboards and reports via Python.
Automateddistribution
22
Version control and reproducibility
managing Python scripts and report templates with Git.
reproducibilitymanaging
23
Testing and quality assurance
unit tests for injury metric calculations and report outputs.
assurancecalculations
24
Deployment considerations
packaging Python scripts for use by non-technical engineers.
Deploymentconsiderations
25
Case study
pedestrian protection—automated generation of injury summary dashboards.
pedestrianprotection
26
Advanced Excel features
using Power Query and Power Pivot with Python-generated data.
Advancedfeatures
27
Real-time monitoring
streaming CAE results to Excel dashboards for immediate feedback.
Real-timemonitoring
28
Security and data privacy
handling sensitive injury data in automated workflows.
Securityhandling
29
Extending to web dashboards
exporting Excel data to Plotly Dash or Streamlit for broader access.
Extendingdashboards
30
Capstone project
end-to-end automation of CAE injury reports and Excel dashboards for a vehicle safety scenario.
Capstoneend-to-end