data_composition_visualization

import csv
import numpy as np
import matplotlib.pyplot as plt

#visualize the data from the composition measurements
"""each spectra contains 27 data entries with uncertainties: three different efficiency
measurements, three different time measurements, and three different peak entries"""

#build a function that reads the csv file

spectraQuantity = np.linspace(1,16,16)
for quan in spectraQuantity:
    quan = int(quan)
    spectra = '/Users/jackiegasca/Documents/radwatch-analysis/NAA_results_with_efficiencies'+str(quan)+'.csv'

    f = open(spectra, 'r')
    reader = csv.reader(f)

    rows = []
    for row in reader:
        rows.append(row)

    contents = rows[len(rows)-1][1]
    fish = rows[len(rows)-1][2]
    elements = contents.split(', ')
    print(elements)

    values = {}
    for i in elements:

        iso_conc = []
        iso_unc = []
        for row in rows:
            for j in row:
                string = 'g of ' + i
                if string in j:
                    num, unc = j.split(' +/- ')
                    unc, extra = unc.split(' g')
                    if num in iso_conc:
                        pass
                    else:
                        iso_conc.append(float(num))
                        iso_unc.append(float(unc))

                else:
                    pass
        values[i] = iso_conc, iso_unc
    lowef_avg_conc = []
    lowef_avg_unc = []
    midef_avg_conc = []
    midef_avg_unc = []
    highef_avg_conc = []
    highef_avg_unc = []
    total_error = []
    for i in elements:
        arr_size =len(values[i][0])
        amount = int(arr_size / 3)
        lowef_avg_conc.append(np.mean(values[i][0][0:amount]))
        lowef_avg_unc.append(np.mean(values[i][1][0:amount]))
        midef_avg_conc.append(np.mean(values[i][0][amount:amount+amount]))
        midef_avg_unc.append(np.mean(values[i][1][amount:amount+amount]))
        highef_avg_conc.append(np.mean(values[i][0][amount+amount:arr_size]))
        highef_avg_unc.append(np.mean(values[i][1][amount+amount:arr_size]))
        total_error.append(np.mean(values[i][0][0:amount])-np.mean(values[i][0][amount+amount:arr_size]))


    x=np.linspace(1,len(elements),len(elements))

    mpl_fig = plt.figure(figsize=(12,6))
    ax = mpl_fig.add_subplot(111)
    width = 0.35
    p3 = plt.errorbar(x, midef_avg_conc, yerr=midef_avg_unc, fmt='.', ecolor='black', capthick=2, capsize=1)
    p1 = ax.bar(x, midef_avg_conc, width, color='white')
    p2 = ax.bar(x, lowef_avg_conc, width, color=(1.0,0.7,0.68),
             bottom=highef_avg_conc)
    plt.xlabel('Isotope')
    plt.ylabel('Concentration in grams')
    plt.title('Isotope concentration of Sample '+str(quan)+': '+fish)
    plt.xticks(x, elements)
    ax.set_yscale('log')
    plt.show()
    print(midef_avg_conc)