Exemplos de load_average_data em Python

Exemplo n.º 1

Arquivo: plot_CFD_FN_backup.py Projeto: cfinch/Dissertation

baseNames = ['/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/FN/Results/FN_250ngml',
    '/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/FN/Results/FN_500ngml',
    '/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/FN/Results/FN_1ugml',
    '/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/FN/Results/FN_5ugml',
    '/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/FN/Results/FN_10ugml']
expFileName = '/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/Experimental Data Analysis/Fn/Kerry/Fn_DETA_experiments.h5'

# GO
#baseNames = ['/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/GO/Results/GO_glass_10ugml_adsorption_RESONATORSENSINGAREA',
#        '/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/GO/Results/GO_glass_100ugml_adsorption_RESONATORSENSINGAREA']
#expFileName = '/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/Experimental Data Analysis/GO/Data/GO_glass_150mlhr.h5'

molar_mass = 160e3
f = molar_mass * 1e5

exp_C, exp_t, exp_mean, std, labels = load_average_data(expFileName)

plt.figure(facecolor='white', figsize=(width, width/1.618))
plt.subplots_adjust(left=0.16, top=0.88, right=0.97, bottom=0.19)

plt.figure(facecolor='white', figsize=(width, width/1.618))
plt.subplots_adjust(left=0.16, top=0.88, right=0.97, bottom=0.19)

for i in range(len(exp_C)):
    # Near-surface concentration
    fileName = baseNames[i] + '_Near_Surf_Conc.DAT'
    t, c = read_ACE_near_surface_concentration(fileName)
    plt.figure(1)
#    plt.title('Near-Surface Concentration')
    plt.plot(t, c * 1000 * molar_mass, color='k', dashes=dashes[i], 
            label=r"$" + str(exp_C[i]) + "\, \mu g/ml$")  # mol/L to micrograms/ml

Exemplo n.º 2

Arquivo: plot_Fn_experimental_results_combined.py Projeto: cfinch/Dissertation

mpl.rc('font', size=8.0, family='serif')    # Set default font to 8pt

if not separate_plots:
    width = 190.0/25.4
    plt.figure(figsize=(width,width/1.618), facecolor='w')
    plt.subplots_adjust(left=0.08, right=0.96, top=0.93, bottom=0.09, 
            wspace=0.15, hspace=0.4)
else:
    width = 90.0/25.4

# Fn on 13F
experimentalFileName = "/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/Experimental Data Analysis/Fn/Kerry/Fn_13F_experiments.h5"

(concentrations, times, mean_surf_conc, std_surf_conc, exp_labels) = \
        load_average_data(experimentalFileName)

if separate_plots:
    plt.figure(figsize=(width,width/1.618), facecolor='w')
    plt.subplots_adjust(left=0.16, bottom=0.2, top=0.87)
else:
    plt.subplot(2,2,1)
    plt.title("FN on 13F")

for i in range(len(mean_surf_conc)):
    plt.plot(times[i], mean_surf_conc[i], color=colors[exp_labels[i]], 
            dashes=dashes[exp_labels[i]], antialiased=True, label=labels[exp_labels[i]])
    plt.plot(times[i], mean_surf_conc[i]+std_surf_conc[i], 
            color=colors[exp_labels[i]], linestyle=':', antialiased=True)
    plt.plot(times[i], mean_surf_conc[i]-std_surf_conc[i], 
            color=colors[exp_labels[i]], linestyle=':', antialiased=True)

Exemplo n.º 3

Arquivo: plot_CFD_FN_results.py Projeto: cfinch/Dissertation

mpl.rc('font', size=8.0, family='serif')    # Set default font to 8pt

width = 3.0

baseNames = ['/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/FN/Results/FN_250ngml',
    '/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/FN/Results/FN_500ngml',
    '/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/FN/Results/FN_1ugml',
    '/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/FN/Results/FN_5ugml',
    '/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/CFD/FN/Results/FN_10ugml']

# Fn on 13F
experimentalFileName = "/home/cfinch/cfinch/Microfluidics/Whispering Gallery Mode Sensor/Experimental Data Analysis/Fn/Kerry/Fn_13F_experiments.h5"

(concentrations, times, mean_surf_conc, std_surf_conc, exp_labels) = \
        load_average_data(experimentalFileName)

plt.figure(figsize=(width,width/1.618), facecolor='w')
plt.subplots_adjust(left=0.16, bottom=0.2, top=0.87)

plt.figure(figsize=(width,width/1.618), facecolor='w')
plt.subplots_adjust(left=0.16, bottom=0.2, top=0.87)

for i in range(len(mean_surf_conc)):
    plt.figure(1)
    plt.plot(times[i], mean_surf_conc[i], color=colors[exp_labels[i]], 
            dashes=dashes[exp_labels[i]], antialiased=True, label=labels[exp_labels[i]])
    plt.plot(times[i], mean_surf_conc[i]+std_surf_conc[i], 
            color=colors[exp_labels[i]], linestyle=':', antialiased=True)
    plt.plot(times[i], mean_surf_conc[i]-std_surf_conc[i], 
            color=colors[exp_labels[i]], linestyle=':', antialiased=True)