Scientific computing tools and programming methods for environments, arrays, tables, curve fitting, visualization and network analysis.
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Represent nodes, edges, directions, and weights in NetworkX, then calculate degree, centrality, clustering, and shortest paths. Compare graph models and community methods while keeping the meaning of each network metric explicit.
Load experimental measurements into arrays and work with slicing, Boolean masks, broadcasting, and summary statistics. Use these operations to align observations, center columns, fit simple relationships, and save processed numerical results.
Organize experimental records with DataFrames, clean missing or inconsistent values, and summarize repeated measurements by condition. Merge related tables by sample identity and export results while retaining the labels needed for later analysis.
Connect NumPy, SciPy, pandas, plotting libraries, atomistic tools, and image analysis to common materials-research tasks. Follow examples of force-curve processing, grouped statistics, microscopy segmentation, and predictive modeling.
Build readable line plots, distributions, heatmaps, and multi-panel figures with Matplotlib and Seaborn. Match visual encodings to the data, keep units and uncertainty visible, and export figures at the dimensions needed for a manuscript.
Fit linear trends, stress relaxation, and indentation models with SciPy while tracking parameter units and initial guesses. Interpret residuals, covariance matrices, and confidence intervals to turn a fitted curve into an inspectable physical calculation.
Set up isolated Python environments, install the scientific stack, and connect the right interpreter to JupyterLab and VS Code. Learn how to diagnose package conflicts and reproduce a working setup.