In [22]:

    

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt


    

In [23]:

    

stability1=[]
stability2=[]
stability3=[]


    

In [24]:

    

def plot_stability_variations(ax,x,y,title,v=True):
    mean,maxi,mini,std=np.mean(y),np.max(y),np.min(y),np.std(y)
    ax.plot(x,y)
    ax.axhline(mean,color="g")
    ax.axhline(mean+std,color="r")
    ax.axhline(mean-std,color="r")
    ax.set_xlabel("Time (s)",fontsize=15)
    ax.set_ylabel("Ids (Amps)",fontsize=15)
    ax.set_title(title,fontsize=15)
    if v:
        print([mean,std,maxi,mini])
        print("percentage uncertainty",std/mean*100,"%")
    return [mean,std,maxi,mini]


    

In [25]:

    

data1=np.loadtxt("215GT/215GT_pin01-12_IdsT_Vds10mV_Vg0constant_2016-07-11_run1.txt",skiprows=1)
data2=np.loadtxt("215GT/215GT_pin03-10_IdsT_Vds10mV_Vg0constant_2016-07-11_run2.txt",skiprows=1)
data3=np.loadtxt("215GT/215GT_pin05-08_IdsT_Vds10mV_Vg0constant_2016-07-11_run3.txt",skiprows=1)





fig=plt.figure(figsize=(16,12))
ax1=plt.subplot(311)
ax2=plt.subplot(312)
ax3=plt.subplot(313)
stability1.append(plot_stability_variations(ax1,data1[:,0],data1[:,1],title="Device 1"))
stability2.append(plot_stability_variations(ax2,data2[:,0],data2[:,1],title="Device 2"))
stability3.append(plot_stability_variations(ax3,data3[:,0],data3[:,1],title="Device 3"))
fig.suptitle("initial device stabilities",fontsize=20)


    

    




[7.290243333333334e-09, 2.5235708322841977e-10, 8.7920000000000002e-09, 5.9610000000000004e-09]
percentage uncertainty 3.46157284044 %
[5.4555833333333338e-10, 6.8798909370393055e-11, 1.0870000000000001e-09, -1.8e-10]
percentage uncertainty 12.6107338422 %
[2.3953903333333332e-08, 1.0761252749202681e-09, 2.9749999999999998e-08, 1.7242e-08]
percentage uncertainty 4.49248400123 %






    
Out[25]:





<matplotlib.text.Text at 0x7fb364d4db38>






    

In [26]:

    

data1=np.loadtxt("215GT/215GT_pin01-12_IdsT_Vds10mV_Vg0constant_2016-07-12_run1.txt",skiprows=1)
data2=np.loadtxt("215GT/215GT_pin03-10_IdsT_Vds10mV_Vg0constant_2016-07-12_run2.txt",skiprows=1)
data3=np.loadtxt("215GT/215GT_pin05-08_IdsT_Vds10mV_Vg0constant_2016-07-12_run3.txt",skiprows=1)





fig=plt.figure(figsize=(16,12))
ax1=plt.subplot(311)
ax2=plt.subplot(312)
ax3=plt.subplot(313)
stability1.append(plot_stability_variations(ax1,data1[:,0],data1[:,1],title="Device 1"))
stability2.append(plot_stability_variations(ax2,data2[:,0],data2[:,1],title="Device 2"))
stability3.append(plot_stability_variations(ax3,data3[:,0],data3[:,1],title="Device 3"))
fig.suptitle("initial device stabilities",fontsize=20)


    

    




[6.6313666666666663e-09, 1.071955015639908e-10, 7.0049999999999997e-09, 6.4380000000000003e-09]
percentage uncertainty 1.61649184779 %
[9.7327000000000009e-10, 3.8548719390748459e-11, 1.3049999999999999e-09, 7.2299999999999998e-10]
percentage uncertainty 3.96074258846 %
[3.1337370000000003e-08, 2.7149912172970291e-10, 3.3698000000000002e-08, 3.0401000000000002e-08]
percentage uncertainty 0.866374943812 %






    
Out[26]:





<matplotlib.text.Text at 0x7fb366f0a6d8>






    

In [27]:

    

data1=np.loadtxt("215GT/215GT_pin01-12_IdsT_Vds10mV_Vg0constant_2016-07-13_run1.txt",skiprows=1)
data2=np.loadtxt("215GT/215GT_pin03-10_IdsT_Vds10mV_Vg0constant_2016-07-13_run2.txt",skiprows=1)
data3=np.loadtxt("215GT/215GT_pin05-08_IdsT_Vds10mV_Vg0constant_2016-07-13_run3.txt",skiprows=1)





fig=plt.figure(figsize=(16,12))
ax1=plt.subplot(311)
ax2=plt.subplot(312)
ax3=plt.subplot(313)
stability1.append(plot_stability_variations(ax1,data1[:,0],data1[:,1],title="Device 1"))
stability2.append(plot_stability_variations(ax2,data2[:,0],data2[:,1],title="Device 2"))
stability3.append(plot_stability_variations(ax3,data3[:,0],data3[:,1],title="Device 3"))
fig.suptitle("initial device stabilities",fontsize=20)


    

    




[5.6253016666666671e-09, 1.9349975193064777e-10, 8.4610000000000005e-09, 4.962e-09]
percentage uncertainty 3.43981111408 %
[9.4608000000000011e-10, 3.1182745228731864e-11, 1.219e-09, 8.0000000000000003e-10]
percentage uncertainty 3.29599454895 %
[3.3110036666666673e-08, 2.3953051716964074e-10, 3.4689e-08, 3.2484000000000002e-08]
percentage uncertainty 0.723437788913 %






    
Out[27]:





<matplotlib.text.Text at 0x7fb364f30b38>






    

In [31]:

    

stability1=np.array(stability1)
stability2=np.array(stability2)
stability3=np.array(stability3)
fig=plt.figure(figsize=(8,21))

ax1=plt.subplot(311)
ax1.errorbar(range(len(stability1)),stability1[:,0],yerr=stability1[:,1],fmt='r.',markersize=10)
ax1.axis([-0.1,len(stability1)-0.9,min(stability1[:,0]*0.9),max(stability1[:,0]*1.1)])

mean,maxi,mini,std=np.mean(stability1[:,0]),np.max(stability1[:,0]),np.min(stability1[:,0]),np.std(stability1[:,0])
ax1.axhline(mean,color='blue')
# ax1.axhline(mean+std,color='orange')
# ax1.axhline(mean-std,color='orange')
# ax1.axhline(maxi,color='red')
# ax1.axhline(mini,color='red')


ax2=plt.subplot(312)
ax2.errorbar(range(len(stability2)),stability2[:,0],yerr=stability2[:,1],fmt='r.',markersize=10)
ax2.axis([-0.1,len(stability2)-0.9,min(stability2[:,0]*0.9),max(stability2[:,0]*1.1)])

mean,maxi,mini,std=np.mean(stability2[:,0]),np.max(stability2[:,0]),np.min(stability2[:,0]),np.std(stability2[:,0])
ax2.axhline(mean,color='blue')
# ax2.axhline(mean+std,color='orange')
# ax2.axhline(mean-std,color='orange')
# ax2.axhline(maxi,color='red')
# ax2.axhline(mini,color='red')
ax2.set_xlabel("Relaxation time (h)",fontsize=15)
ax2.set_ylabel("Current stability (Amps)",fontsize=15)
ax2.set_title("device 2 current stability",fontsize=15)

ax3=plt.subplot(313)
ax3.errorbar(range(len(stability3)),stability3[:,0],yerr=stability3[:,1],fmt='r.',markersize=10)
ax3.axis([-0.1,len(stability3)-0.9,min(stability3[:,0]*0.9),max(stability3[:,0]*1.1)])

mean,maxi,mini,std=np.mean(stability3[:,0]),np.max(stability3[:,0]),np.min(stability3[:,0]),np.std(stability3[:,0])
ax3.axhline(mean,color='blue')
# ax3.axhline(mean+std,color='orange')
# ax3.axhline(mean-std,color='orange')
# ax3.axhline(maxi,color='red')
# ax3.axhline(mini,color='red')
ax3.set_xlabel("Relaxation time (h)",fontsize=15)
ax3.set_ylabel("Current stability (Amps)",fontsize=15)
ax3.set_title("device 2 current stability",fontsize=15)


    

    
Out[31]:





<matplotlib.text.Text at 0x7fb364a6e7b8>






    

In [32]:

    

# print(stability1,stability2,stability3)


    

    




[[  7.29024333e-09   2.52357083e-10   8.79200000e-09   5.96100000e-09]
 [  6.63136667e-09   1.07195502e-10   7.00500000e-09   6.43800000e-09]
 [  5.62530167e-09   1.93499752e-10   8.46100000e-09   4.96200000e-09]] [[  5.45558333e-10   6.87989094e-11   1.08700000e-09  -1.80000000e-10]
 [  9.73270000e-10   3.85487194e-11   1.30500000e-09   7.23000000e-10]
 [  9.46080000e-10   3.11827452e-11   1.21900000e-09   8.00000000e-10]] [[  2.39539033e-08   1.07612527e-09   2.97500000e-08   1.72420000e-08]
 [  3.13373700e-08   2.71499122e-10   3.36980000e-08   3.04010000e-08]
 [  3.31100367e-08   2.39530517e-10   3.46890000e-08   3.24840000e-08]]

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