Updated meeting notes, fixed typo in scpectroscopy problem and changed integration method to simpson.

Ammended meeting note commit.

4 files changed, 117 insertions, 14 deletions

diff --git a/admin/meeting-notes/2025-03-14.md b/admin/meeting-notes/2025-03-14.md
index 9834fca..4b50f7d 100644
--- a/admin/meeting-notes/2025-03-14.md
+++ b/admin/meeting-notes/2025-03-14.md
@@ -1,4 +1,4 @@
-## Updates
+l## Updates
 
 - Finishing off Module 1
 - Slow progress
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
+
diff --git a/tutorials/module_4/Spectroscopy problem.md b/tutorials/module_4/Spectroscopy problem.md
index 5252d46..29d26f5 100644
--- a/tutorials/module_4/Spectroscopy problem.md
+++ b/tutorials/module_4/Spectroscopy problem.md
@@ -99,7 +99,7 @@ Calibrate wavelength by converting the length dimension from pixels to nm.
 $$
 \lambda_p=I+C_1p+C_2p^2+C_3p^3
 $$
-- Solve for C1 C2 and C3, use $I=195.54$
+- Solve for C1, C2 and C3, use $I=195.54$
 
 
 Well-known/defined Hg lines:
@@ -154,5 +154,3 @@ $$
 $$
 n_{1,2}=\frac{\int_{\lambda_0-\Delta\lambda}^{\lambda_0+\Delta\lambda} I(\lambda)d\lambda}{\frac{1}{4\pi}hv_{1,2}A_{1,2}l}\tag{1}
 $$
-
-|
\ No newline at end of file
diff --git a/tutorials/module_4/spectroscopy_problem/spectroscopy.py b/tutorials/module_4/spectroscopy_problem/spectroscopy.py
index 404e5ca..1ef6579 100644
--- a/tutorials/module_4/spectroscopy_problem/spectroscopy.py
+++ b/tutorials/module_4/spectroscopy_problem/spectroscopy.py
@@ -15,7 +15,7 @@ Problem:
 - Interpolate and convert x-axis from pixels to nm (true wavelength) using Hg lamp data (using data in file: **Lampa_Calibrare_Mercur.xlsx**)
 - Find response function of the spectrometer using the tungsten lamp data from file: "**Calibrare Intensitate Oxigen.xlsx**)": $R=\frac{I_{measured}}{I_{true}}$ (where True is computed by Planck's law of radiation (see notes in the pptx above)
 - Convert y-axis from Intensity [a.u.] into Intensity in [W/(cm^2*sr*nm)] by dividing the measured Oxygen spectrum with the response function: $I_{oxygen, true}=\frac{I_{oxygen, measured}}{R}$
-- Once the spectra is in real units: compute the density of one of the oxygen lines by integrating underneath one of the peaks (see equation from Slide 39 - bottom). We will give all of the constants that are in this equation (see the "I**ntensity_Calibration_Oxygen_Discharge_Solution.xlsx**")
+- Once the spectra is in real units: compute the density of one of the oxygen lines by integrating underneath one of the peaks (see equation from Slide 39 - bottom). We will give all of the constants that are in this equation (see the "Intensity_Calibration_Oxygen_Discharge_Solution.xlsx")
 """
 
 import numpy as np
@@ -118,7 +118,7 @@ I_Ox_measured = df_Ox['I_Plasma Oxygen [a.u.]']-df_Ox['I_Background [a.u.]']
 I_Ox_true = R * I_Ox_measured
 
 
-# Plot Hg intensity-wavelength plot
+# Plot Hg spectra [a.u.] plot
 plt.figure(figsize=(8,5))
 plt.plot(wavelength, I_Ox_measured, linestyle='-')
 plt.xlabel('Wavelength [$nm$]')
@@ -128,7 +128,7 @@ plt.title('Wavelength-Intensity ($Ox_{measured}$)')
 plt.grid(True)
 plt.show()
 
-# Plot Hg intensity-wavelength plot
+# Plot Hg spectra real units plot
 plt.figure(figsize=(8,5))
 plt.plot(wavelength, I_Ox_true, linestyle='-')
 plt.xlabel('Wavelength [$nm$]')
@@ -141,17 +141,24 @@ plt.show()
 
 # %% Calculate Dencity of Ox at upper state
 
-from scipy.interpolate import interp1d
-from scipy.integrate import quad
+from scipy.integrate import simpson
 
-# Create an interpolated callable function
-I_Ox_true = interp1d(wavelength, I_Ox_true, kind='linear',
-                     fill_value=0, bounds_error=False)
+I_Ox_true = I_Ox_true.to_numpy()
 
-# Now integrate
+# Integrate numerator
 delta = 1
 peak = 846.5
 bound_lower = peak - delta
 bound_upper = peak + delta
 
-numerator, err = quad(I_Ox_true, bound_lower, bound_upper)
+mask = (wavelength >= bound_lower) & (wavelength <= bound_upper)
+wavelength_trimmed = wavelength[mask]
+I_Ox_trimmed = I_Ox_true[mask]
+numerator = simpson(I_Ox_trimmed, wavelength_trimmed)
+
+v = c/(peak*10**(-9))
+A = 3.6e6 # 1/s
+l = 0.015 # m
+
+n = numerator / (1/(4*np.pi)*h*v*A*l)
+