NatComm2020/in-vitro/PlotStatistics.py

# -*- coding: utf-8 -*-
"""
Created on Wed Jul 24 11:17:18 2019

@author: rgarcia1
"""

import deepdish as dd
import numpy as np
import matplotlib.pyplot as plt
import math

def set_box_color(bp, color):
    plt.setp(bp['boxes'], color=color)
    plt.setp(bp['whiskers'], color=color)
    plt.setp(bp['caps'], color=color)

#%% Load Data from 10 probes, one of them is  discarded because of conection problems during characterization
plt.close('all')
GmAll, IrmsAll, UrmsAll, Ids = dd.io.load('GmIrmsUrmsIds10Probe_2.h5')

#%%
#plot histogram
Gm = np.array([])

BestProbes = ['B12784O18-T3',] #select 3 best probes for specific analysis
for keyProbe in GmAll.keys():
    if keyProbe in BestProbes:
        for keyTrt in GmAll[keyProbe].keys():

            Gm = np.append(Gm,np.max(GmAll[keyProbe][keyTrt][:-1]))
           

hist, vect = np.histogram(Gm*1000/0.1, bins=22 , range = (1.3,3.3))        
plt.figure(1)
plt.hist(Gm*1000/0.1, bins =22, range = (1.3,3.3), color = 'firebrick', label = '192 g-SGFETs from 3 probes')
xbin = np.linspace(1.2,3.5,100)
Gm = np.sort(Gm)[1:]

plt.plot(xbin, 14*np.exp(-(xbin-np.mean(Gm)*10000)**2/(2*(np.std(Gm)*10000)**2)),'k',label = 'Gaussian, $\mu$ = '+'{} - '.format(round(np.mean(Gm)*10000),1)+'$\sigma$ = '+'{}'.format(round(np.std(Gm)*10000),1) + '[mS/V]')
plt.xlabel('G$_m$ (mS/V)')
plt.ylabel('# counts')
plt.legend()

Urms = np.array([])
for keyProbe in UrmsAll.keys():
    for keyTrt in UrmsAll[keyProbe].keys():
        if keyProbe in BestProbes:
            Urms = np.append(Urms,np.min(UrmsAll[keyProbe][keyTrt][:-1]))
hist, vect = np.histogram(np.log10(Urms*1e6), bins=50 , range = (0,2))  
xbin = np.linspace(0,3.5,1000)      
plt.figure(2)
plt.hist(np.log10(Urms*1e6), bins =50, range = (0,2),color= 'blue',label = '192 g-SGFETs from 3 probes')
Urms = np.sort(Urms)[:-8]
plt.plot(xbin, 15*np.exp(-(xbin-np.mean(np.log10(Urms*1e6)))**2/(2*(np.std(np.log10(Urms*1e6)))**2)),'k',label = 'Gaussian, $\mu$ = '+'{} - '.format(round(np.mean(Gm)*10000),1)+'$\sigma$ = '+'{}'.format(round(np.std(Gm)*10000),1) + '[mS/V]')

plt.xlabel('log(U$_{gs-rms}$)')
plt.ylabel('# counts')
plt.legend()



#%% plot boxplots
GM = []
for keyProbe in GmAll.keys():
    if keyProbe == 'B12142O37-T4': #probe discarded due to wrong characterization
        continue
    
    Gm = np.array([]) 

    for keyTrt in GmAll[keyProbe].keys():

        Gm = np.append(Gm,np.max(GmAll[keyProbe][keyTrt][:-1]))
    Gm = Gm*10000
    Gm = np.ndarray.tolist(Gm)
    GM.append(Gm) 
    
URMS = []
for keyProbe in UrmsAll.keys():
    if keyProbe == 'B12142O37-T4':
        continue
    Urms = np.array([]) 
    
    for keyTrt in UrmsAll[keyProbe].keys():

        va = np.min(UrmsAll[keyProbe][keyTrt][:-1])
        if math.isnan(va):
            Urms = 1e-3  #sets Nan to an extremely bad sensitivity
        else:
            Urms = np.append(Urms,va)
    Urms = Urms*1e6
    Urms = np.ndarray.tolist(Urms)
    URMS.append(Urms)    
    
Fig, ax = plt.subplots()
bpr = ax.boxplot(GM,flierprops={'markeredgecolor': 'firebrick'})
ax.set_ylabel('G$_m$ (mS/V)')
set_box_color(bpr, 'firebrick')
plt.xlabel('Device number')

Fig, ax2 = plt.subplots()
bpr2 = ax2.boxplot(URMS,positions=[2,6,3,5,1,4,7,8,9],flierprops={'markeredgecolor': 'blue'})
set_box_color(bpr2, 'blue')
ax2.set_ylabel('U$_{gs-rms}$ ($\mu$V)')
ax2.set_yscale('log')
plt.xlabel('Device number')