def get_file_names(extension, sample='All'): directory = get_xlsx(extension) results = pandas.read_excel(directory[0], 0, index_col='Date') results = results.dropna(subset=['Time']) # generate list with file names of available results files = [] results['Sample'] = results['Sample'] + '.csv' if sample.upper() == 'ALL': for file_name in results['Sample']: files.append( set_filename('Wimpie Data/Metrastat Results/Unfiltered/' + file_name)) elif sample.upper() == 'CA': for file_name in results['Sample']: if (file_name[0:2] == 'Ca') or (file_name[0:1] == 'N'): files.append( set_filename('Wimpie Data/Metrastat Results/Unfiltered/' + file_name)) else: for file_name in results['Sample']: if file_name[0:2] == 'Mg' or (file_name[0:1] == 'N'): files.append( set_filename('Wimpie Data/Metrastat Results/Unfiltered/' + file_name)) return files
def get_csv(extension): "file type csv" # access database with names of available results fname = set_filename(extension) directory = glob.glob(os.path.join(fname, '*.csv')) return directory
def get_file_names(extension, sample='All'): directory = get_xlsx(extension) results = pandas.read_excel(directory[0], 0, index_col='Date') results = results.dropna(subset=['Time']) # generate list with file names of available results files = [] results['Sample'] = results['Sample'] + '.csv' if sample.upper() == 'ALL': for file_name in results['Sample']: files.append(set_filename('Wimpie Data/Metrastat Results/Unfiltered/' + file_name)) elif sample.upper() == 'CA': for file_name in results['Sample']: if (file_name[0:2] == 'Ca') or (file_name[0:1] == 'N'): files.append(set_filename('Wimpie Data/Metrastat Results/Unfiltered/' + file_name)) else: for file_name in results['Sample']: if file_name[0:2] == 'Mg' or (file_name[0:1] == 'N'): files.append(set_filename('Wimpie Data/Metrastat Results/Unfiltered/' + file_name)) return files
YI, time = refine_YI(sample) constants = all_constants[i] initial = all_initial[i] conc = odeint(metrastat, initial, time, args=(k12, constants)) conc_mat = array(conc, dtype=float) fit = Mg[0]*conc_mat[:,1] + \ Mg[1]*conc_mat[:,5] + \ Mg[2]*conc_mat[:,6] + \ Mg[3]*conc_mat[:,5]**2 + \ Mg[4] # plot concentration profile _, fname = data.rsplit('/', 1) sample_name, _ = fname.split('.') plt.figure(i) plt.title('Fit for sample ' + sample_name) plt.xlabel('Time /min') plt.ylabel('Concentration') plt.plot(time, YI) plt.plot(time, fit) plt.legend(['Experimental', 'Fit'], loc=0) # save plot fname = set_filename('YI Profiles/' + sample_name + '.svg') plt.savefig(fname) plt.show()
k12 = 0 for i, data in enumerate(exp_data): # simulate the metrastat sample = pd.read_csv(data, index_col='Time') sample.dropna() YI, time = refine_YI(sample) constants = all_constants[i] initial = all_initial[i] conc = odeint(metrastat, initial, time, args=(k12, constants)) conc_mat = array(conc, dtype=float) # plot concentration profile _, fname = data.rsplit('/', 1) sample_name, _ = fname.split('.') plt.figure(i) plt.title('Fit for sample ' + sample_name) plt.xlabel('Time /min') plt.ylabel('Concentration') plt.plot(time, conc_mat) plt.legend(['HCl', 'LDH', 'Active sites', 'Radical', 'Primary Stabiliser', 'Double bonds', 'Cross-link'], loc=0) # save plot fname = set_filename('Concentration Profiles/' + sample_name + '.svg') plt.savefig(fname) plt.show()
def get_xlsx(extension): "file type xlsl" fname = set_filename(extension) directory = glob.glob(os.path.join(fname, '*.xlsx')) return directory