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AutoPlot_mob_and_Vth_inTextFile.py
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AutoPlot_mob_and_Vth_inTextFile.py
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import matplotlib.pyplot as plt
import numpy as np
#print(__file__)
#regression intercept, slope, r_coef and y_pred for x=V_g and y=I_ds
def Regression_Regimes(V_g, I_ds):
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression
x = np.array(V_g).reshape((-1, 1))
y = np.array(I_ds)
model = LinearRegression().fit(x, y)
r_coef = float(model.score(x, y))
intercept = float(model.intercept_)
slope = float(model.coef_)
y_pred = model.predict(x)
#plt.plot(x,y_pred, color='coral', lw=2, linestyle='dashed', zorder=1)
#plt.scatter(x,y, color= 'teal', lw=0.3, zorder=3)
return (intercept, slope, r_coef, y_pred) #delete tuple if it does not work
#keep trying regressions with different data and return the best regression
def Regression_optimization_Regimes(V_g, I_ds, addPlot = False):
minimum_data = int(0.15 * len(V_g))
maximum_data = int(0.4 * len(V_g))
amount_of_data = 0
optimal_V_g = 0
optimal_I_ds= 0
intercept = 0
slope = 0
r = 0
y_pred = 0
amount_of_data_2 = 0
optimal_V_g_2 = 0
optimal_I_ds_2 = 0
intercept_2 = 0
slope_2 = 0
y_pred_2 = 0
r_2 = 0
for i in range(minimum_data, maximum_data): #rang de nums que agafo
for j in range(int(len(V_g)/2)-i): #check, on començo a agafar números, shift
data = Regression_Regimes(V_g[0+j:i+j+1], I_ds[0+j:i+j+1])
new_r = data[2] #check
if new_r > r:
amount_of_data = i
r = new_r
optimal_V_g = V_g[0+j:i+j+1]
optimal_I_ds= I_ds[0+j:i+j+1]
intercept = data[0]
slope = data[1]
y_pred = data[3]
data2 = Regression_Regimes(V_g[int(len(V_g)/2)+j:int(len(V_g)/2) + i+j+1], I_ds[int(len(V_g)/2)+j:int(len(V_g)/2) + i+j+1])
new_r_2 = data2[2]
if new_r_2 > r_2:
amount_of_data_2 = i
r_2 = new_r_2
optimal_V_g_2 = V_g[int(len(V_g)/2) + j : int(len(V_g)/2) + i + j +1]
optimal_I_ds_2= I_ds[int(len(V_g)/2) + j : int(len(V_g)/2) + i +j +1]
intercept_2 = data2[0]
slope_2 = data2[1]
y_pred_2 = data2[3]
print('First half: '+str(amount_of_data)+'/'+str(int(len(V_g)/2)), '\n R=' + str(r))
print('Second half: '+str(amount_of_data_2)+'/'+str(int(len(V_g)/2)), '\n R=' + str(r_2))
if addPlot:
if r>=r_2:
plt.plot(optimal_V_g,y_pred, color='coral', lw=2, linestyle=(0, (5, 4)), zorder=1)
plt.scatter(optimal_V_g,optimal_I_ds, color= 'teal', lw=0.3, zorder=3)
else:
plt.plot(optimal_V_g_2,y_pred_2, color='coral', lw=2, linestyle=(0, (5, 4)), zorder=1)
plt.scatter(optimal_V_g_2,optimal_I_ds_2, color= 'teal', lw=0.3, zorder=3)
if r>=r_2: return (intercept, slope, r) #check tuples created
else: return (intercept_2, slope_2, r_2)
#returns touple (v_th, mobility) for linear regime
def LinearRegime_params(V_g, I_ds, L, W, V_ds):
data = Regression_optimization_Regimes(V_g, I_ds, addPlot = False)
V_th = - data[0]/data[1]
mobility = data[1]/V_ds * L/W * 1/0.00000001726
print('Linear_Regime_params calculated')
return (V_th, mobility)
#returns touple (v_th, mobility) for saturation regime
def SaturationRegime_params(V_g, I_ds, L, W):
import numpy as np
sqrtI_ds = [np.sqrt(i) for i in I_ds]
data = Regression_optimization_Regimes(V_g, sqrtI_ds, addPlot = False)
V_th = - data[0]/data[1]
mobility = data[1]**2 * 2*L/W * 1/0.00000001726
print('Saturation_Regime_params calculated')
return (V_th, mobility)
#function to plot output data, it takes 2 params:
# path is the adress, the location of the file in the system entered as a string
# file_name is the name of the text file to plot including it's extension, probably .txt, also entered as a string
def output_plot(path, file_name):
I_ds = []
V_ds = []
V_g = []
file = open(path+file_name)
content = file.readlines()
row_count = 0
for x in content:
row = x.split()
if row_count==0:
I_d_index = row.index('ID')
I_s_index = row.index('IS')
V_d_index = row.index('VD')
V_s_index = row.index('VS')
V_g_index = row.index('VG')
row_count += 1
else:
V_ds.append(float(row[V_d_index]) - float(row[V_s_index]))
#row_count += 1
if V_ds[-1]>0: I_ds.append(-abs(float(row[I_d_index])))
else: I_ds.append(abs(float(row[I_d_index])))
V_g.append(float(row[V_g_index]))
V_g_values = []
contadors = []
contadors.append(0)
contador=0
for i in V_g:
if i not in V_g_values and contador==0:
V_g_values.append(i)
contador = 1
elif i not in V_g_values:
V_g_values.append(i)
contadors.append(contador)
else:
contador+=1
contadors.append(contador)
for i in range(len(V_g_values)):
plt.plot(V_ds[contadors[i]: contadors[i+1]], I_ds[contadors[i]: contadors[i+1]], label=r'V$_G$' + f'={int(V_g_values[i])} V')
plt.xlabel(r'$V_{DS}$ (V)')
plt.ylabel(r'|$I_{DS}$| (A)')
plt.tick_params(axis='both', which='both', direction='in')
plt.ticklabel_format(axis='y', style='sci', scilimits=(0,0), useOffset = False, useMathText=True)
plt.legend()
plt.xlim(max(V_ds),min(V_ds))
plt.savefig('Plot_'+file_name[:-4] + '.png', dpi=500) #change to plt.savefig(file_name[:-4] + '.jpg', dpi=1000) to make it an HD image file
plt.close()
#function to plot transfer data, it takes 3 params:
# path is the adress, the location of the file in the system entered as a string
# file_name is the name of the text file to plot including it's extension, probably .txt, also entered as a string
# style is set to linear so it's not necessary to specify, only if 'log' is entered the plot will be logarithic instead of linear
#returns a list with the different touples of V_th and mobility for linear and another one for saturation
def transfer_plot(path, file_name, style='linear', **calculate_params):
I_ds = []
V_ds = []
V_g = []
file = open(path + file_name) #Output Characteristics p-type [ph-btbtc10,pristine,150um,before annealing,4(1) ; 21_07_06 3_40_40 PM]
content = file.readlines()
row_count = 0
for x in content:
row = x.split()
if row_count==0:
I_d_index = row.index('ID')
I_s_index = row.index('IS')
V_d_index = row.index('VD')
V_s_index = row.index('VS')
V_g_index = row.index('VG')
absID_index = row.index('AbsID')
row_count += 1
else:
row_count += 1
V_ds.append(float(row[V_d_index]) - float(row[V_s_index]))
V_g.append(float(row[V_g_index]))
I_ds.append(float(row[absID_index]))
if row_count == 2:
V_ds_lin = float(row[V_d_index]) - float(row[V_s_index])
if row_count == len(content) - 1:
V_ds_sat = float(row[V_d_index]) - float(row[V_s_index])
V_ds_values = []
contadors = []
contadors.append(0)
contador=0
for i in V_ds:
if i not in V_ds_values and contador==0:
V_ds_values.append(i)
contador = 1
elif i not in V_ds_values:
V_ds_values.append(i)
contadors.append(contador)
else:
contador+=1
contadors.append(contador)
if calculate_params['params'] == True:
linear_r_params = 0
saturation_r_params = 0
for i in range(len(V_ds_values)):
#uncomment to specify linear and saturation voltage values
'''
if V_ds_values[i] == calculate_params['Linear_V_ds']:
linear_r_params.append(LinearRegime_params(V_g[contadors[i]: contadors[i+1]], I_ds[contadors[i]: contadors[i+1]], calculate_params['L'], calculate_params['W'], V_ds_values[i]))
plt.close()
elif V_ds_values[i] == calculate_params['Saturation_V_ds']:
saturation_r_params.append(SaturationRegime_params(V_g[contadors[i]: contadors[i+1]], I_ds[contadors[i]: contadors[i+1]], calculate_params['L'], calculate_params['W']))
plt.close()
'''
#if only 2 V_ds are used and the first is linear and the second is saturated
if V_ds_values[i] == V_ds_lin:
linear_r_params = LinearRegime_params(V_g[contadors[i]: contadors[i+1]], I_ds[contadors[i]: contadors[i+1]], calculate_params['L'], calculate_params['W'], V_ds_values[i])
plt.close()
elif V_ds_values[i] == V_ds_sat:
saturation_r_params = SaturationRegime_params(V_g[contadors[i]: contadors[i+1]], I_ds[contadors[i]: contadors[i+1]], calculate_params['L'], calculate_params['W'])
plt.close()
plt.close()
for i in range(len(V_ds_values)):
plt.plot(V_g[contadors[i]: contadors[i+1]], I_ds[contadors[i]: contadors[i+1]], label=r'V$_{DS}$'+f'={int(V_ds_values[i])} V')
plt.xlabel(r'V$_{G}$ (V)')
plt.ylabel(r'|I$_{D}$| (A)')
plt.tick_params(axis='both', which='both', direction='in')
#plt.ticklabel_format(axis='y', style='sci', scilimits=(0,0), useOffset = False, useMathText=True)
plt.legend()
plt.xlim(max(V_g),min(V_g))
if style=='log':
plt.yscale('log')
if style=='linear':
plt.ticklabel_format(axis='y', style='sci', scilimits=(0,0), useOffset = False, useMathText=True)
plt.savefig('Plot_'+file_name[:-4] +' '+ style + '.png', dpi=500)
plt.show()
plt.close()
print(file_name, str(style), 'finished')
if calculate_params['params'] == True: return linear_r_params, saturation_r_params
import os
path = os.getcwd()+'/'
print(path)
files = os.listdir(path)
#print(files)
total_params_lin = []
total_params_sat = []
for i in files:
if i[0:6]=='Output' and i[-4:]=='.txt':
output_plot(path, i)
elif i[0:8]=='Transfer' and i[-4:]=='.txt':
t_lin, t_sat = transfer_plot(path, i, 'linear', params = True, L = 1, W = 100, Saturation_V_ds = -20.0, Linear_V_ds = -2.0)
total_params_lin.append(t_lin)
total_params_sat.append(t_sat)
tl = transfer_plot(path, i, 'log', params = False)
print(total_params_lin)
print(total_params_sat)
print('Linear Regimes params:\n', 'Average V_th=', np.mean([i[0] for i in total_params_lin]),'\n', 'St. dev. (V_th)=', np.std([i[0] for i in total_params_lin]),
'\n', 'Average mobility=', np.mean([i[1] for i in total_params_lin]), '\n', 'St. dev. (mobility)=', np.std([i[1] for i in total_params_lin]))
print('Saturation Regimes params:\n', 'Average V_th=', np.mean([i[0] for i in total_params_sat]),'\n', 'St. dev. (V_th)=', np.std([i[0] for i in total_params_sat]),
'\n', 'Average mobility=', np.mean([i[1] for i in total_params_sat]), '\n', 'St. dev. (mobility)=', np.std([i[1] for i in total_params_sat]))
path_names = path.split('\\')
data_analysis_file_name = path_names[-1][:-1]
with open('analysis '+data_analysis_file_name+'.txt', 'w') as f:
f.write('Linear Regimes params:\n Average V_th='+str(np.mean([i[0] for i in total_params_lin]))+'\n St. dev. (V_th)='+ str(np.std([i[0] for i in total_params_lin]))+
'\n Average mobility=' + str(np.mean([i[1] for i in total_params_lin])) + '\n St. dev. (mobility)=' + str(np.std([i[1] for i in total_params_lin])))
f.write(5*'\n')
f.write('Saturation Regimes params:\n Average V_th='+str(np.mean([i[0] for i in total_params_sat]))+'\n St. dev. (V_th)='+ str(np.std([i[0] for i in total_params_sat]))+
'\n Average mobility=' + str(np.mean([i[1] for i in total_params_sat])) + '\n St. dev. (mobility)=' + str(np.std([i[1] for i in total_params_sat])))