Скачать или смотреть Numerical Integration of NMR Spectra with Python

This video is an introductory tutorial aimed at teaching chemistry majors about Pandas DataFrames and numerical integration using the left endpoint rectangle method.

0:00 – Introduction
0:05 – Overview of the lesson and goals
0:28 – Loading and examining the simulated ethanol NMR data (CSV)
1:14 – Importing required Python libraries (pandas, os, matplotlib)
2:05 – Setting up the data path with os.path.join
3:44 – Why using os.path makes code portable across systems
4:17 – Reading CSV data into a pandas DataFrame
5:22 – Inspecting the DataFrame and correcting the header issue
6:21 – Assigning column names (Chemical Shift, Intensity)
7:28 – Plotting the spectrum using pandas’ .plot() method
8:57 – Setting x and y axes for the plot
9:11 – Removing the legend (legend=False)
9:38 – Adjusting x-axis limits (0–5 ppm)
10:05 – Understanding descending x-axis in NMR spectra
10:58 – Cutting DataFrames with logical conditions
11:50 – Example: Selecting rows where ChemicalShift less than or equal to 5
13:01 – How Boolean indexing works in pandas
14:25 – Data types and boolean masks explained
15:26 – Creating a trimmed DataFrame for plotting
16:03 – Inverting the x-axis using plt.gca().invert_xaxis()
17:45 – Finished plot and comparison with target spectrum
17:58 – Introduction to numerical integration concepts
18:45 – Rectangle rule vs. trapezoid rule
19:39 – Factoring out constant box width in integration
20:31 – Determining Δx (box width) from spectral data
21:15 – Using .iloc to access rows and columns
22:48 – Assigning the width variable
23:33 – Checking for constant spacing between data points
24:11 – Integrating the entire spectrum
25:26 – Summing intensities and multiplying by width
25:51 – Selecting and integrating only the quartet region
27:22 – Combining multiple conditions with parentheses and &
28:46 – Verifying correct subset by plotting the quartet
29:39 – Calculating the quartet’s integral
30:00 – Comparing full spectrum vs. quartet integration results
30:36 – Scaling and performance of numerical integration in Python
30:42 – Closing remarks