https://codingforchemistsbook.com/book_material/chapter-5/index.html Data Chapter 5 usesDownload the data for Chapter 5 Alternatively, individual files can be found in the Data section. Code from chapter ''' A script to fit a calibration experiment to a line, starting wtih the UV/vis spectra Requires: .csv files with col 1 as wavelength and col 2 as intensity filenames should contain the concentration after an '_' Written by: Ben Lear and Chris Johnson (authors@codechembook.com) v1.0.0 - 250204 - initial version ''' import numpy as np from lmfit.models import LinearModel from plotly.subplots import make_subplots from codechembook.quickTools import quickSelectFolder, quickHTMLFormula import codechembook.symbols as cs # identify the files you wish to plot and the place you want to save them at print('Select the folder with the calibration UVvis files.') filenames = sorted(quickSelectFolder().glob('*csv')) # Ask the user what wavelength to use for calibration l_max = float(input('What is the position of the feature of interest? ')) # Loop through the file names, read the files, and extract the data into lists conc, absorb = [], [] # empty lists that will hold concentrations and absorbances for f in filenames: conc.append(float(f.stem.split('_')[1])) # get concentration from file name and add to list temp_x, temp_y = np.genfromtxt(f, unpack = True, delimiter=',', skip_header = 1) # read file l_max_index = np.argmin(abs(temp_x - l_max)) # Find index of x data point closest to l_max absorb.append(temp_y[l_max_index]) # get the closest absorbance value and add to list # Set up and perform a linear fit to the calibration data lin_mod = LinearModel() # create an instance of the linear model object pars = lin_mod.guess(absorb, x=conc) # have lmfit guess at initial values result = lin_mod.fit(absorb, pars, x=conc) # fit using these initial values print(result.fit_report()) # print out the results of the fit # Print out the molar absorptivity print(f'The molar absorptivity is {result.params["slope"].value / 1:5.2f} {cs.math.plusminus} {result.params["slope"].stderr:4.2f} M{cs.typography.sup_minus}{cs.typography.sup_1}cm{cs.typography.sup_minus}{cs.typography.sup_1}') # Construct a plot with two subplots. Pane 1 contains the best fit and data, pane 2 the residual fig = make_subplots(rows = 2, cols = 1) # make a blank figure object that has two subplots # Add trace objects for the best fit, the data, and the residualto the plot fig.add_scatter(x = result.userkws['x'], y = result.best_fit, mode = 'lines', showlegend=False, row = 1, col = 1) fig.add_scatter(x = result.userkws['x'], y = result.data, mode = 'markers', showlegend=False, row =1, col = 1) fig.add_scatter(x = result.userkws['x'], y = -1*result.residual, showlegend=False, row = 2, col = 1) # Create annotation for the slope and intercept values and uncertainties as a string annotation_string = f''' slope = {result.params["slope"].value:.2e} {cs.math.plusminus} {result.params["slope"].stderr:.2e}<br> intercept = {result.params["intercept"].value:.2e} {cs.math.plusminus} {result.params["intercept"].stderr:.2e}<br> R{cs.typography.sup_2} = {result.rsquared:.3f}''' fig.add_annotation(text = annotation_string, x = np.min(result.userkws['x']), y = result.data[-1], xanchor = 'left', yanchor = 'top', align = 'left', showarrow = False, ) # Create annotation for the extinction coefficient and uncertainty fig.add_annotation(text = f'{cs.greek.epsilon} = {result.params["slope"].value:5.2f} {cs.math.plusminus} {result.params["slope"].stderr:4.2f} M^-1cm^-1', x = np.max(result.userkws['x']), y = result.data[0], xanchor = 'right', yanchor = 'top', align = 'right', showarrow = False, ) # Format the axes and the plot, then show it fig.update_xaxes(title = 'concentration /M') fig.update_yaxes(title = f'absorbance @ {l_max} nm', row = 1, col = 1) fig.update_yaxes(title = 'residual absorbance', row = 2, col = 1) fig.update_layout(template = 'simple_white', title = f'calibration for {quickHTMLFormula("(C4H2N2S2)2Ni")}') fig.show('png') # -*- coding: utf-8 -*- """ Created on Tue Dec 17 13:51:22 2024 @author: benle """ import numpy as np from pathlib import Path from lmfit.models import LinearModel from plotly.subplots import make_subplots from codechembook.quickTools import quickSelectFolder import codechembook.symbols as cs # identify the files you wish to plot and the place you want to save them at filenames = quickSelectFolder().glob("*csv") filenames = list(filenames) # at this point, we have a sorted list of filenames # now, extract the information we want from the files. conc, absorb = [], [] # empty lists that will hold concentrations and absorbances for f in filenames: # go through the file names print(f) x, y = np.genfromtxt(f, delimiter = ",", unpack=True) fig = make_subplots() fig.add_scatter(x = x, y = y, line = dict(color = "red")) fig.update_yaxes(title = "absorbance") fig.update_xaxes(title = "wavelength /nm") fig.update_layout(template = "none") fig.show("png") fig.write_image(Path(f).with_suffix(".png")) Solutions to Exercises Targeted exercises Prompting the user for information using input Exercise 0 Using the dictionary of solvent properties that you made in Exercise 0 from Chapter 4, write a code that asks the user for a solvent, then asks the user for a property, and then prints a sentence that tells the user the value of that property for the solvent they chose. Hugo en-us authors@codingforchemistsbook.com (Benjamin Lear and Christopher Johnson) authors@codingforchemistsbook.com (Benjamin Lear and Christopher Johnson)