Post meeting notes added

1 files changed, 100 insertions, 9 deletions

diff --git a/admin/meeting-notes/2025-11-3.md b/admin/meeting-notes/2025-11-3.md
index 1190484..2860bc4 100644
--- a/admin/meeting-notes/2025-11-3.md
+++ b/admin/meeting-notes/2025-11-3.md
@@ -14,16 +14,107 @@ and questions
 ---
 ## Discussion 
 
-
 ---
 ## Actions
 
-Module 4: Data Analysis and Processing  
+- Waiting on supplementary data from 338 -> can be used on examples throughout the course
+- 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
+
+
+
+Interpolation problem (C1,C2,C3)
+Assignment/ Problem: Convert pixels to wavelength when calibrating a spectra
+
+put tungsten in spectra (tungsten light)
+theoretical tungsten
+
+R(lambda) - I_measure/ I_True
+Give I_oxygen
+
+Then can calculate dencity of oxygen using equaitons
+
+
+
+
+Schleieren imagery
+Take pressure and dencity data images (txt files) for each image compute dencity and temperature.
+Then compile to make an animation.
+
 
-- 4.1. Introduction to Data Analysis (describe the Panda Toolbox)  -> One Lecture
-- 4.2. We should focus on effective ways of looking at data  (philosophy of making that make sense) -> One Lecture
-- 4.3. I/Ofile handling (different types of data files: ASCII, CSV, TXT, JPEG,...) -> Here they will learn how to read/handle: datasets including images. -> One Lecture
-- 4.4. Statistical Anasis (mean/averaging, standard deviation, variance...) -> **_Reminder: Ciprian to send application from Spectroscopy and CFD -> One Lecture_**
-- 4.5. Data Cleaning (linear regression, least square fitting, extrapolation, moving average) -> One Lecture
-- 4.6. Data Filtering & Signal Processing (filters: Gaussian Filter, Sobel edge detection, Kalman, Butterworth, FFT) -> We need to give a lecture of what is/how it works Digital filter design (lowpass, highpass, bandpass, filter response curves, how does one select a given filter). One lecture all theory so that in the second lecture they learn how to select different filters. -> Two lectures
-- 4.7 Image processing: apply some of the filtering to make images sharper/crispier, teach them how to make videos/gifs out of images. -> One Lecture. Possible application: **_from CFD density data obtain Synthetic Schlieren images (Ciprian will send you this!)_**