In [2]:

    

from scipy import stats
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
import auswertung as au


    

In [3]:

    

au.import_experiment_data('../Daten/2017-05-08-1414-Aufgabe 4')


    

In [4]:

    

DruckT1 = au.messungen[0]
p0 = au.messungen[0][0]
V = 10.1
Zeit = au.messungen[1]
print(au.messgroessen)


    

    




[['Druck IM', 'Bar', 'IM', -5, 0.0], ['Zeit', 's', 'uhr', 0, 0.0]]

In [4]:

    

# calculate the neccesary data
logpdurchp0 = [np.log(elem/p0) for elem in DruckT1]
Sp = [-V*elem*(1/Zeit[i]) for i,elem in enumerate(logpdurchp0)]


    

    




/usr/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: divide by zero encountered in double_scalars
  app.launch_new_instance()
/usr/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in double_scalars
  app.launch_new_instance()

In [5]:

    

# create LaTeX table
tabellendaten = au.pivot_table(au.messungen)
au.Create_Messdaten_tabellen('../Daten/Aufgabe4Tabelle',au.messgroessen, tabellendaten)


    

In [6]:

    

plt.rc('text', usetex=True)
plt.rc('font', family='serif')


    

In [7]:

    

# render figure
figure = plt.figure()
axis_1 = figure.add_subplot(111)
xlabel_1 = axis_1.set_xlabel(r'Druck [$10^{-5}$ Bar]')
ylabel_1 = axis_1.set_ylabel('S(p) [l/s]')
T1kurve = axis_1.plot(DruckT1, Sp, 'bx', label='S(p)')
plt.legend()

plt.savefig('../Daten/grap_aufgabe4.pdf')

plt.show()


    

In [8]:

    

figure = plt.figure()
axis_1= figure.add_subplot(111)
xlabel_1 = axis_1.set_xlabel('ln(Druck)')
ylabel_1 = axis_1.set_ylabel('S(p) [l/s]')
SpLogp = axis_1.plot(np.log10(DruckT1), Sp, color='cyan', marker='x', linestyle ='', label='S(p)')
plt.legend()

plt.savefig('../Daten/grap_aufgabe_ln_4.pdf')

plt.show()


    

In [9]:

    

mean_range = np.array([ x for x in zip(Sp,DruckT1) if 1.5 > x[0] > 1])
print(mean_range)


    

    




[[ 1.31657251  0.59      ]
 [ 1.386953    0.13      ]
 [ 1.28148139  0.05      ]
 [ 1.12837189  0.03      ]]

In [10]:

    

mean_performance = np.mean(au.pivot_table(mean_range)[0])
print(mean_performance)
print('Mean Performance of Pump = '+str(mean_performance*3.6)+' m^3/h')


    

    




1.27834469657
Mean Performance of Pump = 4.60204090766 m^3/h