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diff --git a/admin/meeting-notes/2025-11-17.md b/admin/meeting-notes/2025-11-17.md new file mode 100644 index 0000000..a1fe4d7 --- /dev/null +++ b/admin/meeting-notes/2025-11-17.md @@ -0,0 +1,98 @@ +## Updates +- https://pythonnumericalmethods.studentorg.berkeley.edu/notebooks/Index.html +- Numerical Methods for Engineers Steven C Chapra + +--- +## Topics +and questions + +- [ ] Decide on textbook +- [ ] Spectroscopy problem +- [ ] Module 4: Revise lecture order for 4.2 plotting philosophy +- [ ] Module 5: Go over outline + +--- +## Discussion + +- Slight changes to module 4 outline + +> Module 4: Outline +>1. Introduction to Data and Scientific Datasets +> a. What is scientific data +> b. Data Processing flow work +> c. Intro to Pandas +> d. Manipulating data frames +> e. Problem 1: Create a dataframe from Numpy arrays +> f. Problem 2: Selecting data from a dataframe to calculate work done. +> +> 2. Interpreting Data +> a. Understanding your data +> b. Purpose +> c. Composition +> d. Color +> e. Problem 1: Composing or fixing a plot. Apply PCC +> f. Data don't lie +> g. Problem 2: Misleading plots by changing axis limits or omitting context. Explain *why* it's misleading. +> +> 3. Importing, Exporting and Managing Data +> a. File types +> b. Importing spreadsheets with pandas +> c. Handling header, units and metadata +> d. Writing and editing data in pandas +> e. Problem: Importing time stamped data pressure and temperature data. Convert timestaps to datetime and plot timperature vs. time +> f. Problem: [TBD] +> +> 4. Statistical Analysis I +> a. Engineering Models +> b. Statistics Review +> c. Statistics function in python (Numpy and Pandas describe) +> d. Statistical Distributions +> e. Spectrocopy (basics) +> f. Problem: Statistical tools in Spectroscopy readings (intensity vs wavelangth) to compute mean, variance and detect outliers. +> g. Problem 2: Fit a Gaussian distribution to the same data and overlay it on the histogram. +> +> 5. Statistical Analysis II: Regression and Smoothing +> a. Linear Square Regression and Line of Best Fit +> b. Linear, Exponential and Power functions +> d. Polynomial +> e. Using scipy +> f. How well did we do? (R and R^2) +> g. Extrapolation and limitations +> h. Moving averages +> i. Problem 1: Fit a linear and polynomial model to stress-strain data. Compute R^2 and discuss which model fits better. +> j. Problem 2: Apply a moving average to noisy temperature data and compare raw vs. smoothed signals. +> +> 6. Data Filtering and Signal Processing +> a. What is it and why it matters - noise vs. signal +> b. Moving average and window functions +> c. Frequency domain basics (sampling rate, Nyquist frequency) +> d. Fourier transform overiew (numpy.fft, scipy.fft) +> e. Low-pass and high-pass filters (scipy.singla.butter, filtfilt) +> f. Example: Removing high-frequency noise from a displacement signal +> g. Example: Removing noise from an image to help for further analysis (PIV) +> h. Problem 1: Generate a synthetic signal (sum of two sine waves+random noise). Apply a moving average and FFT to show frequency components.) +> i. Problem 2: Design a Butterworkth low-pass filter to isolate the funcamental frequency of a vibration signal (e.g. roating machinery). Plot before and after. +> +> 7. Data Visualization and Presentation +> a. Review of PCC framework +> b. Plotting with Pandas and Matplotlib +> c. Subplots, twin axes, and annotations +> d. Colomaps and figure aesthetics +> e. Exporitn gplots for reports (DPI, figure size) +> f. Creating dashboards or summary figures +> g. Problem 1: Using pandas to plot spectroscopy data from raw data. Add labels, units, title, and annotations for peaks +> h. Problem 2: Create a multi-panel figure showing raw data, fitted curve, and residuals. Format with consistent style, legend and color scheme for publication-ready quality. +> + + +Module 5: Outline + +Lectures +1. Supervised learning: Classification +2. Supervised learning: Regression +3. Unsupervised learning: Clustering +4. Unsupervised learning: Dimensional reduction + +--- +## Action + |